From 89b25800178e4cc41f744160704dd3fdd2dd3cdf Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Sat, 18 Apr 2026 22:07:13 +0200
Subject: [PATCH 1/9] Fix small formatting issues in live script tutorials

---
 tutorials/dynamically_loaded_filters.mlx      | Bin 251432 -> 251968 bytes
 tutorials/ecephys.mlx                         | Bin 360796 -> 353337 bytes
 tutorials/images.mlx                          | Bin 9697 -> 9703 bytes
 tutorials/ophys.mlx                           | Bin 174223 -> 174228 bytes
 .../mcode/dynamically_loaded_filters.m        |   4 ++--
 tutorials/private/mcode/ecephys.m             |   2 +-
 tutorials/private/mcode/images.m              |   6 +++---
 tutorials/private/mcode/ophys.m               |   6 +++---
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/tutorials/dynamically_loaded_filters.mlx b/tutorials/dynamically_loaded_filters.mlx
index 1978b616aa698a6ff9afce350b769e7baf98f259..318191860b94deddfc49d75ce3b258f877583d62 100644
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diff --git a/tutorials/ecephys.mlx b/tutorials/ecephys.mlx
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diff --git a/tutorials/private/mcode/dynamically_loaded_filters.m b/tutorials/private/mcode/dynamically_loaded_filters.m
index 25b633d47..f3bea3a9e 100644
--- a/tutorials/private/mcode/dynamically_loaded_filters.m
+++ b/tutorials/private/mcode/dynamically_loaded_filters.m
@@ -37,13 +37,13 @@
 % 
 % |python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)|
 % 
-% 2. Set the environment variable |HDF5_PLUGIN_PATH| to point to the local installation 
+% 3. Set the environment variable |HDF5_PLUGIN_PATH| to point to the local installation 
 % of the plugins (from |hdf5plugin.PLUGINS_PATH|) through _System Properties > 
 % Advanced > Environment Variables:_
 % 
 % 
 % 
-% 3. Restart MATLAB.
+% 4. Restart MATLAB.
 % 
 % That's it! Now you can read datasets that use the following filters:
 %% 
diff --git a/tutorials/private/mcode/ecephys.m b/tutorials/private/mcode/ecephys.m
index 5879aeb67..c683daf7b 100644
--- a/tutorials/private/mcode/ecephys.m
+++ b/tutorials/private/mcode/ecephys.m
@@ -330,7 +330,7 @@
 % Define spike snippets: 20 events, 3 channels, 40 samples per event. 
 spike_snippets = rand(20, 3, 40);
 % Permute spike snippets (See dimensionMapNoDataPipes tutorial)
-spike_snippets = permute(spike_snippets, [3,2,1]) 
+spike_snippets = permute(spike_snippets, [3,2,1]); 
 
 % Create electrode table region referencing electrodes 0, 1, and 2
 shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
diff --git a/tutorials/private/mcode/images.m b/tutorials/private/mcode/images.m
index d64ac3bbb..b444fd934 100644
--- a/tutorials/private/mcode/images.m
+++ b/tutorials/private/mcode/images.m
@@ -219,9 +219,9 @@
 % is shown at each time point. |timestamps| define when each indexed image was 
 % presented.
 % 
-% For example, if <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html 
-% |*IndexSeries*|>|.data| is |[0, 1, 0, 1]|, then the first and third presentations 
-% use the first image in <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html 
+% For example, if the |data| property of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html 
+% |*IndexSeries*|> is |[0, 1, 0, 1]|, then the first and third presentations use 
+% the first image in <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html 
 % |*ImageReferences*|>, and the second and fourth presentations use the second 
 % image:
 
diff --git a/tutorials/private/mcode/ophys.m b/tutorials/private/mcode/ophys.m
index 26bcbb810..3d906700e 100644
--- a/tutorials/private/mcode/ophys.m
+++ b/tutorials/private/mcode/ophys.m
@@ -69,7 +69,7 @@
 % |manufacturer|, |model_number|, |model_name|, and |serial_number| are optional, 
 % but recommended. Then create an <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html 
 % |*OpticalChannel*|> and add both of these to the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html 
-% |*ImagingPlane*|>|*.*|
+% |*ImagingPlane*|>.
 
 device = types.core.Device( ...
     'description', 'My two-photon microscope', ...
@@ -102,7 +102,7 @@
 % |*TwoPhotonSeries*|>, like <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html 
 % |*SpatialSeries*|>, inherits from <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html 
 % |*TimeSeries*|> and is similar in behavior to <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html 
-% |*OnePhotonSeries*|>|.|
+% |*OnePhotonSeries*|>.
 % 
 % 
 
@@ -272,7 +272,7 @@
 img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation);
 %% 
 % Add the |img_seg| object to the "ophys" <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html 
-% |*ProcessingModule*|> we created before, naming it "|ImageSegmentation|"|.|
+% |*ProcessingModule*|> we created before, naming it "|ImageSegmentation|".
 
 ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);
 % Storing fluorescence of ROIs over time

From 0e3ec28607d44b66d5f73c7840a87610df2ba610 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Sun, 19 Apr 2026 17:22:52 +0200
Subject: [PATCH 2/9] Add generation of rst output for each tutorial

---
 docs/source/_static/css/custom.css            |  11 +
 .../_static/html/tutorials/basicUsage.html    |  43 +-
 .../_static/html/tutorials/behavior.html      |   6 +-
 .../tutorials/dimensionMapWithDataPipes.html  |  18 +-
 .../html/tutorials/dynamic_tables.html        |  20 +-
 .../tutorials/dynamically_loaded_filters.html |  66 +-
 .../_static/html/tutorials/ecephys.html       | 998 ++----------------
 .../_static/html/tutorials/icephys.html       |   4 +-
 .../source/_static/html/tutorials/images.html |  10 +-
 docs/source/_static/html/tutorials/intro.html |  18 +-
 docs/source/_static/html/tutorials/ogen.html  |   8 +-
 docs/source/_static/html/tutorials/ophys.html |  16 +-
 .../_static/html/tutorials/read_demo.html     |  14 +-
 .../html/tutorials/read_demo_dandihub.html    |  42 +-
 .../_static/html/tutorials/remote_read.html   |   2 +-
 .../_static/html/tutorials/scratch.html       |   6 +-
 .../tutorials/media/basicUsage/figure_0.png   | Bin 0 -> 94934 bytes
 .../media/dimensionMapNoDataPipes/image_0.png | Bin 0 -> 283314 bytes
 .../media/dimensionMapNoDataPipes/image_1.png | Bin 0 -> 285395 bytes
 .../dimensionMapWithDataPipes/image_0.png     | Bin 0 -> 285361 bytes
 .../dimensionMapWithDataPipes/image_1.png     | Bin 0 -> 286142 bytes
 .../dimensionMapWithDataPipes/image_2.png     | Bin 0 -> 286463 bytes
 .../dimensionMapWithDataPipes/image_3.png     | Bin 0 -> 288485 bytes
 .../dynamically_loaded_filters/image_0.png    | Bin 0 -> 37655 bytes
 .../dynamically_loaded_filters/image_1.png    | Bin 0 -> 213992 bytes
 .../tutorials/media/ecephys/image_0.png       | Bin 0 -> 64015 bytes
 .../tutorials/media/ecephys/image_1.png       | Bin 0 -> 102074 bytes
 .../tutorials/media/ecephys/image_2.png       | Bin 0 -> 140390 bytes
 .../tutorials/media/ecephys/image_3.png       | Bin 0 -> 62655 bytes
 .../tutorials/media/icephys/image_0.png       | Bin 0 -> 450506 bytes
 .../tutorials/media/icephys/image_1.png       | Bin 0 -> 291613 bytes
 .../_static/tutorials/media/intro/image_0.png | Bin 0 -> 7894 bytes
 .../_static/tutorials/media/intro/image_1.png | Bin 0 -> 98745 bytes
 .../_static/tutorials/media/intro/image_2.png | Bin 0 -> 23302 bytes
 .../_static/tutorials/media/intro/image_3.png | Bin 0 -> 22211 bytes
 .../_static/tutorials/media/intro/image_4.png | Bin 0 -> 27270 bytes
 .../_static/tutorials/media/intro/image_5.png | Bin 0 -> 68026 bytes
 .../tutorials/media/ophys/figure_0.png        | Bin 0 -> 2865 bytes
 .../_static/tutorials/media/ophys/image_0.png | Bin 0 -> 25033 bytes
 .../_static/tutorials/media/ophys/image_1.png | Bin 0 -> 45686 bytes
 .../_static/tutorials/media/ophys/image_2.png | Bin 0 -> 50708 bytes
 .../_static/tutorials/media/ophys/image_3.png | Bin 0 -> 48884 bytes
 .../tutorials/media/read_demo/figure_0.png    | Bin 0 -> 46024 bytes
 .../tutorials/media/read_demo/figure_1.png    | Bin 0 -> 357605 bytes
 .../tutorials/media/read_demo/figure_2.png    | Bin 0 -> 61588 bytes
 .../tutorials/media/read_demo/figure_3.png    | Bin 0 -> 55479 bytes
 .../tutorials/media/read_demo/image_0.png     | Bin 0 -> 288178 bytes
 .../tutorials/media/read_demo/image_1.png     | Bin 0 -> 98247 bytes
 .../tutorials/media/read_demo/image_2.png     | Bin 0 -> 109132 bytes
 .../tutorials/media/remote_read/image_0.png   | Bin 0 -> 1054 bytes
 .../tutorials/media/remote_read/image_1.png   | Bin 0 -> 360 bytes
 .../tutorials/media/remote_read/image_2.png   | Bin 0 -> 18624 bytes
 .../tutorials/media/scratch/image_0.png       | Bin 0 -> 26763 bytes
 docs/source/pages/tutorials/basicUsage.rst    | 461 +++++++-
 docs/source/pages/tutorials/behavior.rst      | 363 ++++++-
 docs/source/pages/tutorials/convertTrials.rst | 592 ++++++++++-
 docs/source/pages/tutorials/dataPipe.rst      | 203 +++-
 .../tutorials/dimensionMapNoDataPipes.rst     |  88 +-
 .../tutorials/dimensionMapWithDataPipes.rst   | 117 +-
 .../source/pages/tutorials/dynamic_tables.rst | 638 ++++++++++-
 .../tutorials/dynamically_loaded_filters.rst  | 143 ++-
 docs/source/pages/tutorials/ecephys.rst       | 729 ++++++++++++-
 docs/source/pages/tutorials/icephys.rst       | 548 +++++++++-
 docs/source/pages/tutorials/images.rst        | 367 ++++++-
 docs/source/pages/tutorials/intro.rst         | 477 ++++++++-
 docs/source/pages/tutorials/ogen.rst          | 203 +++-
 docs/source/pages/tutorials/ophys.rst         | 519 ++++++++-
 docs/source/pages/tutorials/read_demo.rst     | 357 ++++++-
 .../pages/tutorials/read_demo_dandihub.rst    | 173 ++-
 docs/source/pages/tutorials/remote_read.rst   | 113 +-
 docs/source/pages/tutorials/scratch.rst       | 156 ++-
 .../_rst_templates/tutorial.rst.template      |  15 +-
 tools/documentation/matnwb_exportTutorials.m  |   4 +
 .../private/convertTutorialMCodeToRst.m       | 412 ++++++++
 .../private/convertTutorialMarkdownToRst.m    | 477 +++++++++
 .../private/generateRstForTutorials.m         | 163 ++-
 tools/githooks/pre-commit-linux               |  12 +-
 tools/githooks/pre-commit-mac                 |  14 +-
 78 files changed, 7406 insertions(+), 1220 deletions(-)
 create mode 100644 docs/source/_static/tutorials/media/basicUsage/figure_0.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapNoDataPipes/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapNoDataPipes/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapWithDataPipes/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapWithDataPipes/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapWithDataPipes/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/dimensionMapWithDataPipes/image_3.png
 create mode 100644 docs/source/_static/tutorials/media/dynamically_loaded_filters/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/dynamically_loaded_filters/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/ecephys/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/ecephys/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/ecephys/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/ecephys/image_3.png
 create mode 100644 docs/source/_static/tutorials/media/icephys/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/icephys/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_3.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_4.png
 create mode 100644 docs/source/_static/tutorials/media/intro/image_5.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/figure_0.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/image_3.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/figure_0.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/figure_1.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/figure_2.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/figure_3.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/read_demo/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/remote_read/image_0.png
 create mode 100644 docs/source/_static/tutorials/media/remote_read/image_1.png
 create mode 100644 docs/source/_static/tutorials/media/remote_read/image_2.png
 create mode 100644 docs/source/_static/tutorials/media/scratch/image_0.png
 create mode 100644 tools/documentation/private/convertTutorialMCodeToRst.m
 create mode 100644 tools/documentation/private/convertTutorialMarkdownToRst.m

diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css
index 680bd1afe..153d9846d 100644
--- a/docs/source/_static/css/custom.css
+++ b/docs/source/_static/css/custom.css
@@ -72,3 +72,14 @@ button.copybtn {
     border: none;
     background: none;
 }
+
+.rst-content img.tutorial-media {
+    max-width: 100% !important;
+    height: auto;
+    display: block;
+    margin: 1rem auto;
+}
+
+.rst-content .highlight-text button.copybtn {
+    display: none !important;
+}
diff --git a/docs/source/_static/html/tutorials/basicUsage.html b/docs/source/_static/html/tutorials/basicUsage.html
index 478e73e4c..340b864bb 100644
--- a/docs/source/_static/html/tutorials/basicUsage.html
+++ b/docs/source/_static/html/tutorials/basicUsage.html
@@ -1,19 +1,21 @@
 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
-<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge,IE=9,chrome=1"><meta name="generator" content="MATLAB 2023b"><title>Using NWB Data</title><style type="text/css">.rtcContent { padding: 30px; } .S0 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }
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-.S13 { color: rgb(33, 33, 33); padding: 10px 0px 6px 17px; background: rgb(255, 255, 255) none repeat scroll 0% 0% / auto padding-box border-box; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; overflow-x: hidden; line-height: 17.234px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Using NWB Data</span></h1><div  class = 'S1'><span style=' font-style: italic;'>last updated: January 1st, 2023</span></div><div  class = 'S1'><span>In this tutorial, we demonstrate the reading and usage of the NWB file produced in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/tutorials/convertTrials.html"><span>File Conversion Tutorial</span></a><span>. The output is a near-reproduction of Figure 1e from the </span><a href = "https://www.ncbi.nlm.nih.gov/pubmed/27074502"><span>Li et al</span></a><span> publication, showing raster and peristimulus time histogram (PSTH) plots for neural recordings from anterior lateral motor cortex (ALM). This figure illustrates the main finding of the publication, showing the robustness of motor planning behavior and neural dynamics following short unilateral network silencing via optogenetic inhibition. </span></div><h2  class = 'S2'><span>Reading NWB Files</span></h2><div  class = 'S1'><span>NWB files can be read in using the </span><span style=' font-style: italic;'>nwbRead()</span><span> function.  This function returns a </span><span style=' font-style: italic;'>nwbfile</span><span> object which is the in-memory representation of the NWB file structure.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = nwbRead(</span><span style="color: #a709f5;">'out\ANM255201_20141124.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><h2  class = 'S4'><span style=' font-weight: bold;'>Constrained Sets</span></h2><div  class = 'S1'><span>Analyzed data in NWB is placed under the </span><span style=' font-style: italic;'>analysis</span><span> property, which is a </span><span style=' font-weight: bold;'>Constrained Set</span><span>.  A constrained set consists of an arbitrary amount of key-value pairs similar to </span><a href = "https://www.mathworks.com/help/matlab/map-containers.html"><span>Map containers</span></a><span> in MATLAB or a dictionary in Python.  However, constrained sets also have the ability to validate their own properties closer to how a typed Object would.</span></div><div  class = 'S1'><span>You can get/set values in constrained sets using their respective </span><span style=' font-style: italic;'>.get()/.set()</span><span> methods and retrieve all Set properties using the </span><span style=' font-style: italic;'>keys() </span><span>method, like in a </span><span style=' font-style: italic;'>containers.Map.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >unit_names = keys(nwb.analysis);</span></span></div></div></div><h2  class = 'S2'><span>Dictionaries with MATLAB R2022b</span></h2><div  class = 'S1'><span>As noted above, constrained sets uses Map container objects, a custom class that allows user to retrieve values using a corresponding key.  For users working with MATLAB version R2022b, a notable addition of the </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta"><span>dictionary</span></a><span> object offers significantly faster functionality.</span></div><div  class = 'S1'><span>You can initialize a dictionary and configure its contents from relevant analysed NWB data with the same key/values noted above. </span></div><div  class = 'S1'><span>Number of entries in a dictionary can be done with the new </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.numentries.html"><span>numEntries()</span></a><span> function. One can also return all the keys and values of a dictionary as arrays using </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.keys.html"><span>keys(dict_name)</span></a><span> and </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.values.html"><span>values(dict_name)</span></a><span> functions. </span></div><h2  class = 'S2'><span>Dynamic Tables</span></h2><div  class = 'S1'><span style=' font-style: italic;'>nwb.intervals_trials</span><span> returns a unique type of table called a </span><span style=' font-weight: bold;'>Dynamic Table</span><span>.  Dynamic tables inherit from the NWB type </span><span style=' font-style: italic;'>types.hdmf_common.DynamicTable</span><span> and allow for a table-like interface in NWB.  In the case below, we grab the special column </span><span style=' font-style: italic;'>start_time</span><span>.  Dynamic Tables allow adding your own vectors using the </span><span style=' font-style: italic;'>vectordata</span><span> property, which are Constrained Sets.  All columns are represented by either a </span><span style=' font-style: italic;'>types.hdmf_common.VectorData </span><span>or a </span><span style=' font-style: italic;'>types.hdmf_common.VectorIndex</span><span> type.</span></div><h2  class = 'S2'><span>Data Stubs</span></h2><div  class = 'S1'><span>The </span><span style=' font-style: italic;'>data</span><span> property of the column </span><span style=' font-style: italic;'>id</span><span> in </span><span style=' font-style: italic;'>nwb.units</span><span> is a </span><span style=' font-style: italic;'>types.untyped.DataStub.</span><span>  This object is a representation of a dataset that is not loaded in memory, and is what allows MatNWB to lazily load its file data.  To load the data into memory, use the </span><span style=' font-style: italic;'>.load() </span><span>method which extracts all data from the NWB file. Alternatively, you can index into the DataStub directly using conventional MATLAB syntax.</span></div><h2  class = 'S2'><span>Jagged Arrays in Dynamic Tables</span></h2><div  class = 'S1'><span>With the new addition of </span><span style=' font-style: italic;'>addRow</span><span> and </span><span style=' font-style: italic;'>getRow</span><span> to Dynamic Tables, the concept of jagged arrays can be worked around and no longer require full understanding outside of specific data format concerns or low-level nwb tool development. The below paragraph is retained in its entirety from its original form as purely informational.</span></div><div  class = 'S1'><span>All data in a Dynamic Table must be aligned by row and column, but not all data fits into this paradigm neatly.  In order to represent variable amounts of data that is localised to each row and column, NWB uses a concept called </span><span style=' font-weight: bold;'>Jagged Arrays</span><span>.  These arrays consist of two column types: the familiar </span><span style=' font-style: italic;'>types.core.VectorData</span><span>, and the new </span><span style=' font-style: italic;'>types.core.VectorIndex</span><span>.  A Vector Index holds no data, instead holding a reference to another Vector Data and a vector of indices that align to the Dynamic Table dimensions.  The indices represent the last index boundary in the Vector Data object for the Vector Index row.  As an example, an index of three in the first row of the Vector Index column points to the first three values in the referenced Vector Data column.  Subsequently, if the next index were a five, it would indicate the fourth and fifth elements in the referenced Vector Data column.</span></div><div  class = 'S1'><span>The jagged arrays serve to represent multiple trials and spike times associated to each unit by id. A convenient way to represent these in MATLAB is to use </span><a href = "https://www.mathworks.com/help/matlab/map-containers.html"><span>Map containers</span></a><span> where each unit's data is indexed directly by its unit id. Below, we utilize </span><span style=' font-style: italic;'>getRow</span><span> in order to build the same Map.</span></div><div  class = 'S1'><span>Once again, for users with MATLAB R2022b versions or newer, we can utilize MATLAB  </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta"><span>dictionary</span></a><span> objects instead of map containers to build the Map. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >unit_ids = nwb.units.id.data.load(); </span><span style="color: #008013;">% array of unit ids represented within this</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize trials &amp; times Map containers indexed by unit_ids</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >unit_trials = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >unit_times = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div></div><h4  class = 'S8'><span>MATLAB R2022b Versions</span></h4><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #008013;">% unit_trials = dictionary();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% unit_times  = dictionary();</span></span></div></div></div><div  class = 'S9'><span>Adding key-value pairs, one at a time:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >last_idx = 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(unit_ids)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_id = unit_ids(i);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    row = nwb.units.getRow(unit_id, </span><span style="color: #a709f5;">'useId'</span><span >, true, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >, </span><span style="color: #a709f5;">'trials'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_trials(unit_id) = row.trials{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_times(unit_id) = row.spike_times{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S4'><span>Process Units</span></h2><div  class = 'S1'><span>We now do the following for each Unit:</span></div><ul  class = 'S10'><li  class = 'S11'><span>Filter out invalid trials</span></li><li  class = 'S11'><span>Separate datasets based on resulting mouse behavior (right/left licks).</span></li><li  class = 'S11'><span>Derive "sample", "delay", and "response" times for this analyzed neuron.</span></li><li  class = 'S11'><span>Compose a peristimulus time histogram from the data.</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >sorted_ids = sort(unit_ids);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >Photostim = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ind'</span><span >, true,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % mask into xs and ys for this photostim</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'period'</span><span >, </span><span style="color: #a709f5;">'none'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'duration'</span><span >, 0,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % in seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ramp_offset'</span><span >, 0); </span><span style="color: #008013;">% in seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize Map container of plotting data for each unit, stored as structure</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >Unit = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div></div><h4  class = 'S8'><span>MATLAB R2022b Versions</span></h4><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Unit = dictionary();</span></span></div></div></div><div  class = 'S9'><span>Initializing unit structure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >unit_struct = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xs'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ys'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xlim'</span><span >, [-Inf Inf],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sample'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'delay'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'left_scatter'</span><span >, false,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'right_scatter'</span><span >, false,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'photostim'</span><span >, Photostim); </span><span style="color: #008013;">% can have multiple photostim</span></span></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >unit_id = unit_ids'</span></span></div></div></div><div  class = 'S9'><span>We first extract trial IDs from the Unit IDs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >    unit_trial_id = unit_trials(unit_id);</span></span></div></div></div><div  class = 'S9'><span>Then filter out outliers from the Sample, Delay, and Response time points with which we derive a "good enough" estimate.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    trial = nwb.intervals_trials.getRow(unit_trial_id, </span><span style="color: #a709f5;">'useId'</span><span >, true,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'PoleInTime'</span><span >, </span><span style="color: #a709f5;">'PoleOutTime'</span><span >, </span><span style="color: #a709f5;">'CueTime'</span><span >, </span><span style="color: #a709f5;">'GoodTrials'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_sample = trial.PoleInTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_delay = trial.PoleOutTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_response = trial.CueTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_good_trials = trial.GoodTrials;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Subjective parameters</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    delay_threshold = 0.064;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    response_threshold = 0.43;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_delay_offset = 1.3; </span><span style="color: #008013;">% determined from figure 1a</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_response_offset = 1.3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_delay = unit_sample + expected_delay_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_response = unit_delay + expected_response_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    good_delay = (unit_delay &gt; expected_delay - delay_threshold) &amp;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        (unit_delay &lt; expected_delay + delay_threshold);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    good_response = (unit_response &gt; expected_response - response_threshold) &amp;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        (unit_response &lt; expected_response + response_threshold);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    avg_sample = mean(unit_sample(good_delay &amp; good_response));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    avg_delay = mean(unit_delay(good_delay &amp; good_response));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    avg_response = mean(unit_response(good_delay &amp; good_response));</span></span></div></div></div><div  class = 'S9'><span>Filter the rest of the data by "good" trials.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    unit_good_trials = unit_good_trials &amp; good_delay &amp; good_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_trial_id = unit_trial_id(unit_good_trials);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_spike_time = unit_times(unit_id);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    unit_spike_time = unit_spike_time(unit_good_trials);</span></span></div></div></div><div  class = 'S9'><span>Retrieve good trial data and organize by stimulation type.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    trial = nwb.intervals_trials.getRow(unit_trial_id, </span><span style="color: #a709f5;">'useId'</span><span >, true,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'HitR'</span><span >, </span><span style="color: #a709f5;">'HitL'</span><span >, </span><span style="color: #a709f5;">'StimTrials'</span><span >, </span><span style="color: #a709f5;">'PhotostimulationType'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_is_photostim = logical(trial.StimTrials);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_stim_type = trial.PhotostimulationType;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_no_stim = ~unit_is_photostim &amp; 0 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_sample_stim = unit_is_photostim &amp; 1 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_early_stim = unit_is_photostim &amp; 2 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    unit_middle_stim = unit_is_photostim &amp; 3 == unit_stim_type;</span></span></div></div></div><div  class = 'S9'><span>Compose Scatter Plots and the Peristimulus Time Histogram zeroed on the Response time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    xs = unit_spike_time - trial.start_time - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ys = unit_trial_id;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit = unit_struct;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.xs = xs;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.ys = ys;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.left_scatter = logical(trial.HitL);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.right_scatter = logical(trial.HitR);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.sample = avg_sample - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.delay = avg_delay - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.response = 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Photostim periods</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.photostim.ind = unit_no_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Sample</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_sample_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.ind = unit_sample_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.period = </span><span style="color: #a709f5;">'Sample'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        curr_unit.photostim(end+1) = SampleStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Early Delay</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_early_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        early_stim_types = unique(unit_stim_type(unit_early_stim));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">for </span><span >i_early_types=1:length(early_stim_types)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            early_type = early_stim_types(i_early_types);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim.period = </span><span style="color: #a709f5;">'Early Delay'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim.ind = early_type == unit_stim_type &amp; unit_early_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">if </span><span >early_type == 2</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">else</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.duration = 0.8;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.ramp_offset = 0.2;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            curr_unit.photostim(end+1) = EarlyStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Middle Delay</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_middle_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.ind = unit_middle_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.period = </span><span style="color: #a709f5;">'Middle Delay'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        curr_unit.photostim(end+1) = MiddleStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    Unit(unit_id) = curr_unit;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S4'><span>Plot Example Neurons</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >neuron_labels = [2, 3]; </span><span style="color: #008013;">% neuron labels from Figure 1e</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >neuron_ids = [11, 2]; </span><span style="color: #008013;">% neuron unit IDs corresponding to the Fig 1e labels</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >num_conditions = 4; </span><span style="color: #008013;">% photostim conditions: nostim, sample, early, middle if applicable</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >num_neurons = length(neuron_ids);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize data structures for each summary plot of categorized neural spike data at specified stimulus condition</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >RasterPlot = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xs'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ys'</span><span >, 0);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ConditionPlot = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'label'</span><span >, </span><span style="color: #a709f5;">''</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xlim'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sample'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'delay'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'right_scatter'</span><span >, RasterPlot,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'left_scatter'</span><span >, RasterPlot,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'psth_bin_window'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stim_type'</span><span >, </span><span style="color: #a709f5;">''</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >fig = figure;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Plot neural spike data for each neuron and stimulus condition in a subplot array: num_neurons (rows) x num_conditions (columns)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >nn=1:num_neurons</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    Neuron = Unit(neuron_ids(nn));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Initialize structure with neural + stimulus condition data</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot = ConditionPlot;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.xlim = [min(Neuron.xs) max(Neuron.xs)];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.sample = Neuron.sample;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.delay = Neuron.delay;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.response = Neuron.response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Plot each neuron/condition</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    plot_row = (nn - 1) * num_conditions;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >cc=1:num_conditions</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        ax = subplot(num_neurons, num_conditions, plot_row + cc, </span><span style="color: #a709f5;">'Parent'</span><span >, fig);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        Stim = Neuron.photostim(cc);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.stim_type = Stim.period;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">if </span><span >strcmp(Stim.period, </span><span style="color: #a709f5;">'none'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.label = sprintf(</span><span style="color: #a709f5;">'Neuron %d'</span><span >, neuron_labels(nn));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.psth_bin_window = 9;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">else</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.label = Stim.period;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.psth_bin_window = 2;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_left_scatter_ind = Stim.ind &amp; Neuron.left_scatter;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_left_scatter_trials = Neuron.ys(stim_left_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.left_scatter.xs = Neuron.xs(stim_left_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        [~,CurrPlot.left_scatter.ys] = ismember(stim_left_scatter_trials,unique(stim_left_scatter_trials));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_right_scatter_ind = Stim.ind &amp; Neuron.right_scatter;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_right_scatter_trials = Neuron.ys(stim_right_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.right_scatter.xs = Neuron.xs(stim_right_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        [~,CurrPlot.right_scatter.ys] = ismember(stim_right_scatter_trials,unique(stim_right_scatter_trials));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        plot_condition(ax, CurrPlot);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div><div  class = 'S13'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="6506FCBC" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent"><img class="figureImage figureContainingNode" 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" style="width: 560px; padding-bottom: 0px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport"></div><div class="outputLayer activeOutputDecorationLayer doNotExport"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport"></div><div class="outputLayer navigationFocusLayer doNotExport" tabindex="-1"></div></div></div></div></div><h2  class = 'S4'><span>Helper Functions</span></h2><div  class = 'S1'><span>PSTH helper function</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >[psth_xs, psth_ys] = calculate_psth(xs, bin_window, bin_width)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[bin_counts, edges]  = histcounts(xs, </span><span style="color: #a709f5;">'BinWidth'</span><span >, bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_xs = edges(1:end-1) + (bin_width / 2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >moving_avg_b = (1/bin_window) * ones(1,bin_window);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_ys = filter(moving_avg_b, 1, bin_counts);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><div  class = 'S9'><span>Plotter function for each stimulus condition</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >plot_condition(ax, ConditionPlot)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_cdata = [1 0 0]; </span><span style="color: #008013;">% red</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_cdata = [0 0 1]; </span><span style="color: #008013;">% blue</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hist_margin = 50;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter_margin = 10;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Calculate PSTH values</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% moving average over 200 ms as per figure 1e</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hist_bin_width = 0.2 / ConditionPlot.psth_bin_window;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[left_psth_xs, left_psth_ys] =</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    calculate_psth(ConditionPlot.left_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[right_psth_xs, right_psth_ys] =</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    calculate_psth(ConditionPlot.right_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_scatter_offset = min(ConditionPlot.right_scatter.ys);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_scatter_height = max(ConditionPlot.right_scatter.ys) - right_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_scatter_offset = min(ConditionPlot.left_scatter.ys);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_scatter_height = max(ConditionPlot.left_scatter.ys) - left_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_height = max([left_psth_ys right_psth_ys]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_y_offset = hist_margin</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + psth_height</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    - left_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_y_offset = scatter_margin</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_y_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_scatter_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_scatter_height</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    - right_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subplot_height = right_y_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + right_scatter_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + right_scatter_height;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hold(ax, </span><span style="color: #a709f5;">'on'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% PSTH</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >plot(ax, left_psth_xs, left_psth_ys, </span><span style="color: #a709f5;">'Color'</span><span >, left_cdata);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >plot(ax, right_psth_xs, right_psth_ys, </span><span style="color: #a709f5;">'Color'</span><span >, right_cdata);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Scatter Plot</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ConditionPlot.left_scatter.xs,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    left_y_offset + ConditionPlot.left_scatter.ys,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Marker'</span><span >, </span><span style="color: #a709f5;">'.'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'CData'</span><span >, left_cdata,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'SizeData'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ConditionPlot.right_scatter.xs,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    right_y_offset + ConditionPlot.right_scatter.ys,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Marker'</span><span >, </span><span style="color: #a709f5;">'.'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'CData'</span><span >, right_cdata,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'SizeData'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% sample, delay, response lines</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.sample, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.delay, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.response, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% blue bar for photoinhibition period</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~strcmp(ConditionPlot.stim_type, </span><span style="color: #a709f5;">'none'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_height = subplot_height;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_width = 0.5; </span><span style="color: #008013;">% seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% end time relative to 'go' cue as described in the paper.</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">switch </span><span >ConditionPlot.stim_type</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Sample'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 1.6;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Early Delay'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 0.8;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Middle Delay'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 0.3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">otherwise</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            error(</span><span style="color: #a709f5;">'Invalid photostim period `%s`'</span><span >, ConditionPlot.stim_type);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_offset = ConditionPlot.response - stim_width - end_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    patch_vertices = [</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset, 0;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset, stim_height;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset+stim_width, stim_height;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset+stim_width, 0];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    patch(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Faces'</span><span >, 1:4,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Vertices'</span><span >, patch_vertices,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'FaceColor'</span><span >, </span><span style="color: #a709f5;">'#B3D3EC'</span><span >,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % light blue shading</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'EdgeColor'</span><span >, </span><span style="color: #a709f5;">'none'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'FaceAlpha'</span><span >, 0.8);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >title(ax, ConditionPlot.label);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >xlabel(ax, </span><span style="color: #a709f5;">'Time (Seconds)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ylabel(ax, </span><span style="color: #a709f5;">'Spikes s^{-1}'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >xticks(ax, [-2 0 2]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >yticks(ax, [0 max(10, round(psth_height, -1))]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%     legend(ax, [scatter_left_plot, scatter_right_plot], {'Left Lick', 'Right Lick'},...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%         'location', 'northwestoutside');</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.TickDir = </span><span style="color: #a709f5;">'out'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.XLim = ConditionPlot.xlim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.YLim = [0 subplot_height];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hold(ax, </span><span style="color: #a709f5;">'off'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div>
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+.S12 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S13 { color: rgb(33, 33, 33); padding: 10px 0px 6px 17px; background: rgb(255, 255, 255) none repeat scroll 0% 0% / auto padding-box border-box; font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px; overflow-x: hidden; line-height: 17.234px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Using NWB Data</span></h1><div  class = 'S1'><span style=' font-style: italic;'>last updated: January 1st, 2023</span></div><div  class = 'S1'><span>In this tutorial, we demonstrate the reading and usage of the NWB file produced in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/tutorials/convertTrials.html"><span>File Conversion Tutorial</span></a><span>. The output is a near-reproduction of Figure 1e from the </span><a href = "https://www.ncbi.nlm.nih.gov/pubmed/27074502"><span>Li et al</span></a><span> publication, showing raster and peristimulus time histogram (PSTH) plots for neural recordings from anterior lateral motor cortex (ALM). This figure illustrates the main finding of the publication, showing the robustness of motor planning behavior and neural dynamics following short unilateral network silencing via optogenetic inhibition. </span></div><h2  class = 'S2'><span>Reading NWB Files</span></h2><div  class = 'S1'><span>NWB files can be read in using the </span><span style=' font-style: italic;'>nwbRead()</span><span> function.  This function returns a </span><span style=' font-style: italic;'>nwbfile</span><span> object which is the in-memory representation of the NWB file structure.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = nwbRead(</span><span style="color: #a709f5;">'out\ANM255201_20141124.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><h2  class = 'S4'><span style=' font-weight: bold;'>Constrained Sets</span></h2><div  class = 'S1'><span>Analyzed data in NWB is placed under the </span><span style=' font-style: italic;'>analysis</span><span> property, which is a </span><span style=' font-weight: bold;'>Constrained Set</span><span>.  A constrained set consists of an arbitrary amount of key-value pairs similar to </span><a href = "https://www.mathworks.com/help/matlab/map-containers.html"><span>Map containers</span></a><span> in MATLAB or a dictionary in Python.  However, constrained sets also have the ability to validate their own properties closer to how a typed Object would.</span></div><div  class = 'S1'><span>You can get/set values in constrained sets using their respective </span><span style=' font-style: italic;'>.get()/.set()</span><span> methods and retrieve all Set properties using the </span><span style=' font-style: italic;'>keys() </span><span>method, like in a </span><span style=' font-style: italic;'>containers.Map.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >unit_names = keys(nwb.analysis);</span></span></div></div></div><h2  class = 'S2'><span>Dictionaries with MATLAB R2022b</span></h2><div  class = 'S1'><span>As noted above, constrained sets uses Map container objects, a custom class that allows user to retrieve values using a corresponding key.  For users working with MATLAB version R2022b, a notable addition of the </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta"><span>dictionary</span></a><span> object offers significantly faster functionality.</span></div><div  class = 'S1'><span>You can initialize a dictionary and configure its contents from relevant analysed NWB data with the same key/values noted above. </span></div><div  class = 'S1'><span>Number of entries in a dictionary can be done with the new </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.numentries.html"><span>numEntries()</span></a><span> function. One can also return all the keys and values of a dictionary as arrays using </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.keys.html"><span>keys(dict_name)</span></a><span> and </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.values.html"><span>values(dict_name)</span></a><span> functions. </span></div><h2  class = 'S2'><span>Dynamic Tables</span></h2><div  class = 'S1'><span style=' font-style: italic;'>nwb.intervals_trials</span><span> returns a unique type of table called a </span><span style=' font-weight: bold;'>Dynamic Table</span><span>.  Dynamic tables inherit from the NWB type </span><span style=' font-style: italic;'>types.hdmf_common.DynamicTable</span><span> and allow for a table-like interface in NWB.  In the case below, we grab the special column </span><span style=' font-style: italic;'>start_time</span><span>.  Dynamic Tables allow adding your own vectors using the </span><span style=' font-style: italic;'>vectordata</span><span> property, which are Constrained Sets.  All columns are represented by either a </span><span style=' font-style: italic;'>types.hdmf_common.VectorData </span><span>or a </span><span style=' font-style: italic;'>types.hdmf_common.VectorIndex</span><span> type.</span></div><h2  class = 'S2'><span>Data Stubs</span></h2><div  class = 'S1'><span>The </span><span style=' font-style: italic;'>data</span><span> property of the column </span><span style=' font-style: italic;'>id</span><span> in </span><span style=' font-style: italic;'>nwb.units</span><span> is a </span><span style=' font-style: italic;'>types.untyped.DataStub.</span><span>  This object is a representation of a dataset that is not loaded in memory, and is what allows MatNWB to lazily load its file data.  To load the data into memory, use the </span><span style=' font-style: italic;'>.load() </span><span>method which extracts all data from the NWB file. Alternatively, you can index into the DataStub directly using conventional MATLAB syntax.</span></div><h2  class = 'S2'><span>Jagged Arrays in Dynamic Tables</span></h2><div  class = 'S1'><span>With the new addition of </span><span style=' font-style: italic;'>addRow</span><span> and </span><span style=' font-style: italic;'>getRow</span><span> to Dynamic Tables, the concept of jagged arrays can be worked around and no longer require full understanding outside of specific data format concerns or low-level nwb tool development. The below paragraph is retained in its entirety from its original form as purely informational.</span></div><div  class = 'S1'><span>All data in a Dynamic Table must be aligned by row and column, but not all data fits into this paradigm neatly.  In order to represent variable amounts of data that is localised to each row and column, NWB uses a concept called </span><span style=' font-weight: bold;'>Jagged Arrays</span><span>.  These arrays consist of two column types: the familiar </span><span style=' font-style: italic;'>types.core.VectorData</span><span>, and the new </span><span style=' font-style: italic;'>types.core.VectorIndex</span><span>.  A Vector Index holds no data, instead holding a reference to another Vector Data and a vector of indices that align to the Dynamic Table dimensions.  The indices represent the last index boundary in the Vector Data object for the Vector Index row.  As an example, an index of three in the first row of the Vector Index column points to the first three values in the referenced Vector Data column.  Subsequently, if the next index were a five, it would indicate the fourth and fifth elements in the referenced Vector Data column.</span></div><div  class = 'S1'><span>The jagged arrays serve to represent multiple trials and spike times associated to each unit by id. A convenient way to represent these in MATLAB is to use </span><a href = "https://www.mathworks.com/help/matlab/map-containers.html"><span>Map containers</span></a><span> where each unit's data is indexed directly by its unit id. Below, we utilize </span><span style=' font-style: italic;'>getRow</span><span> in order to build the same Map.</span></div><div  class = 'S1'><span>Once again, for users with MATLAB R2022b versions or newer, we can utilize MATLAB  </span><a href = "https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta"><span>dictionary</span></a><span> objects instead of map containers to build the Map. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >unit_ids = nwb.units.id.data.load(); </span><span style="color: #008013;">% array of unit ids represented within this</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize trials &amp; times Map containers indexed by unit_ids</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >unit_trials = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >unit_times = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div></div><h4  class = 'S8'><span>MATLAB R2022b Versions</span></h4><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #008013;">% unit_trials = dictionary();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% unit_times  = dictionary();</span></span></div></div></div><div  class = 'S9'><span>Adding key-value pairs, one at a time:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >last_idx = 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(unit_ids)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_id = unit_ids(i);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    row = nwb.units.getRow(unit_id, </span><span style="color: #a709f5;">'useId'</span><span >, true, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >, </span><span style="color: #a709f5;">'trials'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_trials(unit_id) = row.trials{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_times(unit_id) = row.spike_times{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S4'><span>Process Units</span></h2><div  class = 'S1'><span>We now do the following for each Unit:</span></div><ul  class = 'S10'><li  class = 'S11'><span>Filter out invalid trials</span></li><li  class = 'S11'><span>Separate datasets based on resulting mouse behavior (right/left licks).</span></li><li  class = 'S11'><span>Derive "sample", "delay", and "response" times for this analyzed neuron.</span></li><li  class = 'S11'><span>Compose a peristimulus time histogram from the data.</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >sorted_ids = sort(unit_ids);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >Photostim = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ind'</span><span >, true,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % mask into xs and ys for this photostim</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'period'</span><span >, </span><span style="color: #a709f5;">'none'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'duration'</span><span >, 0,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % in seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ramp_offset'</span><span >, 0); </span><span style="color: #008013;">% in seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize Map container of plotting data for each unit, stored as structure</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >Unit = containers.Map(</span><span style="color: #a709f5;">'KeyType'</span><span >,class(unit_ids),</span><span style="color: #a709f5;">'ValueType'</span><span >,</span><span style="color: #a709f5;">'any'</span><span >);</span></span></div></div></div><h4  class = 'S8'><span>MATLAB R2022b Versions</span></h4><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Unit = dictionary();</span></span></div></div></div><div  class = 'S9'><span>Initializing unit structure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >unit_struct = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xs'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ys'</span><span >, [],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xlim'</span><span >, [-Inf Inf],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sample'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'delay'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'left_scatter'</span><span >, false,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'right_scatter'</span><span >, false,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'photostim'</span><span >, Photostim); </span><span style="color: #008013;">% can have multiple photostim</span></span></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >unit_id = unit_ids'</span></span></div></div></div><div  class = 'S9'><span>We first extract trial IDs from the Unit IDs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >    unit_trial_id = unit_trials(unit_id);</span></span></div></div></div><div  class = 'S9'><span>Then filter out outliers from the Sample, Delay, and Response time points with which we derive a "good enough" estimate.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    trial = nwb.intervals_trials.getRow(unit_trial_id, </span><span style="color: #a709f5;">'useId'</span><span >, true,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'PoleInTime'</span><span >, </span><span style="color: #a709f5;">'PoleOutTime'</span><span >, </span><span style="color: #a709f5;">'CueTime'</span><span >, </span><span style="color: #a709f5;">'GoodTrials'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_sample = trial.PoleInTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_delay = trial.PoleOutTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_response = trial.CueTime;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_good_trials = trial.GoodTrials;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Subjective parameters</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    delay_threshold = 0.064;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    response_threshold = 0.43;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_delay_offset = 1.3; </span><span style="color: #008013;">% determined from figure 1a</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_response_offset = 1.3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_delay = unit_sample + expected_delay_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    expected_response = unit_delay + expected_response_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    good_delay = (unit_delay &gt; expected_delay - delay_threshold) &amp;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        (unit_delay &lt; expected_delay + delay_threshold);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    good_response = (unit_response &gt; expected_response - response_threshold) &amp;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        (unit_response &lt; expected_response + response_threshold);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    avg_sample = mean(unit_sample(good_delay &amp; good_response));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    avg_delay = mean(unit_delay(good_delay &amp; good_response));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    avg_response = mean(unit_response(good_delay &amp; good_response));</span></span></div></div></div><div  class = 'S9'><span>Filter the rest of the data by "good" trials.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    unit_good_trials = unit_good_trials &amp; good_delay &amp; good_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_trial_id = unit_trial_id(unit_good_trials);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_spike_time = unit_times(unit_id);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    unit_spike_time = unit_spike_time(unit_good_trials);</span></span></div></div></div><div  class = 'S9'><span>Retrieve good trial data and organize by stimulation type.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    trial = nwb.intervals_trials.getRow(unit_trial_id, </span><span style="color: #a709f5;">'useId'</span><span >, true,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'HitR'</span><span >, </span><span style="color: #a709f5;">'HitL'</span><span >, </span><span style="color: #a709f5;">'StimTrials'</span><span >, </span><span style="color: #a709f5;">'PhotostimulationType'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_is_photostim = logical(trial.StimTrials);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_stim_type = trial.PhotostimulationType;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_no_stim = ~unit_is_photostim &amp; 0 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_sample_stim = unit_is_photostim &amp; 1 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    unit_early_stim = unit_is_photostim &amp; 2 == unit_stim_type;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    unit_middle_stim = unit_is_photostim &amp; 3 == unit_stim_type;</span></span></div></div></div><div  class = 'S9'><span>Compose Scatter Plots and the Peristimulus Time Histogram zeroed on the Response time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >    xs = unit_spike_time - trial.start_time - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ys = unit_trial_id;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit = unit_struct;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.xs = xs;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.ys = ys;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.left_scatter = logical(trial.HitL);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.right_scatter = logical(trial.HitR);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.sample = avg_sample - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.delay = avg_delay - avg_response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.response = 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Photostim periods</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    curr_unit.photostim.ind = unit_no_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Sample</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_sample_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.ind = unit_sample_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.period = </span><span style="color: #a709f5;">'Sample'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        SampleStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        curr_unit.photostim(end+1) = SampleStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Early Delay</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_early_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        early_stim_types = unique(unit_stim_type(unit_early_stim));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">for </span><span >i_early_types=1:length(early_stim_types)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            early_type = early_stim_types(i_early_types);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim.period = </span><span style="color: #a709f5;">'Early Delay'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            EarlyStim.ind = early_type == unit_stim_type &amp; unit_early_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">if </span><span >early_type == 2</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">else</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.duration = 0.8;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >                EarlyStim.ramp_offset = 0.2;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            curr_unit.photostim(end+1) = EarlyStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Middle Delay</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >any(unit_middle_stim)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim = Photostim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.ind = unit_middle_stim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.period = </span><span style="color: #a709f5;">'Middle Delay'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.duration = 0.5;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        MiddleStim.ramp_offset = 0.1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        curr_unit.photostim(end+1) = MiddleStim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    Unit(unit_id) = curr_unit;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S4'><span>Plot Example Neurons</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >neuron_labels = [2, 3]; </span><span style="color: #008013;">% neuron labels from Figure 1e</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >neuron_ids = [11, 2]; </span><span style="color: #008013;">% neuron unit IDs corresponding to the Fig 1e labels</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >num_conditions = 4; </span><span style="color: #008013;">% photostim conditions: nostim, sample, early, middle if applicable</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >num_neurons = length(neuron_ids);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Initialize data structures for each summary plot of categorized neural spike data at specified stimulus condition</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >RasterPlot = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xs'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'ys'</span><span >, 0);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ConditionPlot = struct(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'label'</span><span >, </span><span style="color: #a709f5;">''</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xlim'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sample'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'delay'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'right_scatter'</span><span >, RasterPlot,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'left_scatter'</span><span >, RasterPlot,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'psth_bin_window'</span><span >, 0,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stim_type'</span><span >, </span><span style="color: #a709f5;">''</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >fig = figure;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Plot neural spike data for each neuron and stimulus condition in a subplot array: num_neurons (rows) x num_conditions (columns)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >nn=1:num_neurons</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    Neuron = Unit(neuron_ids(nn));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Initialize structure with neural + stimulus condition data</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot = ConditionPlot;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.xlim = [min(Neuron.xs) max(Neuron.xs)];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.sample = Neuron.sample;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.delay = Neuron.delay;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    CurrPlot.response = Neuron.response;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Plot each neuron/condition</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    plot_row = (nn - 1) * num_conditions;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >cc=1:num_conditions</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        ax = subplot(num_neurons, num_conditions, plot_row + cc, </span><span style="color: #a709f5;">'Parent'</span><span >, fig);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        Stim = Neuron.photostim(cc);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.stim_type = Stim.period;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">if </span><span >strcmp(Stim.period, </span><span style="color: #a709f5;">'none'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.label = sprintf(</span><span style="color: #a709f5;">'Neuron %d'</span><span >, neuron_labels(nn));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.psth_bin_window = 9;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">else</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.label = Stim.period;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            CurrPlot.psth_bin_window = 2;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_left_scatter_ind = Stim.ind &amp; Neuron.left_scatter;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_left_scatter_trials = Neuron.ys(stim_left_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.left_scatter.xs = Neuron.xs(stim_left_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        [~,CurrPlot.left_scatter.ys] = ismember(stim_left_scatter_trials,unique(stim_left_scatter_trials));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_right_scatter_ind = Stim.ind &amp; Neuron.right_scatter;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_right_scatter_trials = Neuron.ys(stim_right_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        CurrPlot.right_scatter.xs = Neuron.xs(stim_right_scatter_ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        [~,CurrPlot.right_scatter.ys] = ismember(stim_right_scatter_trials,unique(stim_right_scatter_trials));</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        plot_condition(ax, CurrPlot);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div><div  class = 'S13'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling 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KHh8ssvZ3h4mIYlS5YwY8YMHn/8cY4dO8b/F1/72tf47ne/y9e+9jX+7d/+jUWLFnGipUuX0vDUU0/R2dnJxo0bqVQqXHLJJbzV7dq1i02bNrFp0yY2bdrEpk2b2LVrF8PDwzQsWbKEGTNm8Pjjj3Ps2DEmlUolTicIAo4fP86XvvQlPM9j1qxZnM6uXbvYtGkTGzZswPM8Dh06xOc+9zmWLl1Kw1NPPUVnZycbN26kUqlwySWXcKLh4WEalixZwowZM3j88cc5duwYk4Ig4Pjx43zpS1/C8zxmzZrFuebDH/4wx48f5/jx43zwgx/kdD7wgQ/Q0NPTw/DwMJ/97GeZ9IEPfICGnp4ehoeH+exnP8vpLF26lIannnqKzs5ONm7cSKVS4ZJLLuHldu3axaZNm9iwYQOe53Ho0CE+97nP0bB06VIannrqKTo7O9m4cSOVSoUZM2ZwouHhYRqWLFnCjBkzePzxxzl27BgNQRBw/PhxvvSlL+F5HrNmzeJcV6JwRmptbeXv/u7vmNTZ2cnatWvp6elhxowZPPPMM6xatYpp06bxyU9+ksHBQWbNmsVv//Zv82re+973cv/996Oq/MEf/AFJkvCnf/qnfP7zn+eNuOeee2g4duwY99xzD/fccw/33HMPExMTFE52xRVX8Pd///e89NJLLF++nI9//OO88MIL9Pb2Mm/ePG6//XZmzpzJggULuPrqq5k/fz7j4+OMj4/zRhljWLZsGZ/61Ke45ppruPPOOzlRZ2cna9eupaenhxkzZvDMM8+watUqCvCDH/yA2267jdtuu43bbruN2267jR/+8IfcfvvtzJw5kwULFnD11Vczf/58xsfHGR8f57W8613voqOjg+PHj3PLLbfwan7wgx9w22238elPf5qxsTG++c1vcvPNN9PZ2cnatWvp6elhxowZPPPMM6xatYqXu/3225k5cyYLFizg6quvZv78+YyPj3PgwAEa3vWud9HR0cHx48e55ZZbOBfNnz+fqVOncuGFFzJ//nxOZ968eaxfv56HHnqIhQsXMm3aNC644AIa5s2bx/r163nooYdYuHAh06ZN44ILLuBUOjs7Wbt2LT09PcyYMYNnnnmGVatWcSo/+MEPuO222/j0pz/N2NgY3/zmN7n55ptp6OzsZO3atfT09DBjxgyeeeYZVq1axcvdfvvtzJw5kwULFnD11Vczf/58xsfHOXDgAO9617vo6Ojg+PHj3HLLLbwVlCicEVpbW6nX6/T09DDp+uuvp16v88ADD9Cwbt06qtUqY2Nj7Nmzh+uuu46GTZs2ceTIEQ4fPsydd95JvV7n1ltvpaGzs5N6vc6KFSuYdOONN/L8889z6NAhjh49yt13303D9OnTqdfr9PT00NDa2kq9Xqenp4eX+/rXv069Xqder1Ov16nX69TrdVpaWii80ooVK9i7dy+HDh3i0KFD/Md//Ae33norDXPmzGF0dJSxsTFGR0fp7++nVqvx8Y9/nHq9zk033UTDypUrqdfrtLa20vDQQw+xd+9eTtTZ2cm+ffs4dOgQWZZx8cUX09raSr1ep6enh4Z169ZRrVYZGxtjz549XHfddbyVtba2Uq/Xqdfr1Ot16vU69Xqder3OihUrmDNnDqOjo4yNjTE6Okp/fz+1Wo3FixdTr9dZsWIFk1pbW6nX6/T09DCpVqsxdepUbr75Zk6ltbWVer1OvV6nXq9Tq9V4+umnCcOQSevWraNarTI2NsaePXu47rrraGhtbaVer9PT08OcOXMYHR1lbGyM0dFR+vv7qdVqXHzxxUyq1WpMnTqVm2++mXPF9OnTqdfr3HrrrTQcPnyY8fFxGm699Vbq9Tqtra20trZSr9fp6emhYc2aNVSrVY4cOcLXv/51qtUq3/jGN2hYs2YN1WqVI0eO8PWvf51qtco3vvENWltbqdfr9PT0MGndunVUq1XGxsbYs2cP1113HSdqbW2lXq9Tr9ep1+vUajWefvppwjDkROvWraNarTI2NsaePXu47rrraG1tpV6v09PTQ8OcOXMYHR1lbGyM0dFR+vv7qdVqXHzxxTTUajWmTp3KzTffzFtBicJZpVQq0draysu1tLTQ0tLCGzFt2jRKpRKF/zrTpk1j2rRpnEpraytvhlKpxLRp03g1pVKJ1tZWCq9fa2srb0R/fz/z5s3jkUceYfXq1bS0tPCfUSqVaG1t5bW0trbycv39/cybN49HHnmE1atX09LSQgHOP/98WlpaOJXzzz+flpYWXo9SqURrayv/WaVSidbWVl5La2srJ+rv72fevHk88sgjrF69mpaWFt4KShQKhbPee9/7XrZs2UJ7ezuFM8MVV1yBMYb777+fu+66i/9OV1xxBcYY7r//fu666y4K55YrrrgCYwz3338/d911F28VJQqFwlnvkksu4aabbuKyyy6jcGZ497vfzd13383SpUv57/bud7+bu+++m6VLl1I497z73e/m7rvvZunSpbyVlCj8/0IVVEEVrAVrIY4hjiGOwVpQBVVQBVWIY7AWVDlJHEMYgnOgCqqgCnEMcQxxDHEMcQyqnEQVVCGOIQzBOVAFayEMob0d2tth9mwIQ7AWrIUwhDAEVZpUIY7BOXKqYC1YC6oQxxDH5FQhDCEMwTnOGXEMYQhxDHEM1oIqxDGEIVgLzkEcg3O8JmshjnlVzkEcg7UQx+AcOAdxDHEMYQhxTM45iGOYPRvCEFR5BVWIY3CON0wV4hicI+ccxDGocsaLY7AWnIM4hjiGMIT2dghDiGNwjiZrIY7BWk7JWrCWJucgjkGVV6UKcQzt7RCGoEqTtRCG0N4Os2fD7NlgLa/gHMQxOAdxDGEI1pJzDtrbIQyhvR3CkKY4hjgGVXAO4hjiGMIQwhDiGOIYnOOMZi2EIcQxxDFYS5NzEMcQx2Atr2AtWMsrOAdxDKo0qUIcg7XQ3g5hCM7xulkLcQzt7RCGMHs2hCGo8p9mLcQxxDGo0mQtWEvOWohjUOWcVKLwplKF7u4fkSQQhhCGEIYQhpAkkKaQppCmEIbQ3g5JAqqQphCGkCSgCs6RyzJwDpyDJAFryaUpJAkkCSQJJAkkCThHzloIQ2hvhzQF58A5sBbCEKwF58A5UAXnQBWSBJwDVXLOgXNgLWQZTdZCkkCWQRhCksDICKjS5ByEIVjLSZwDVc54X/3qV3EOrIU4hiwDVRgZIZemYC2MjIC1oAppCv+wfYL/mHaQR587yKPPHeTR5w7y6HMHefS5g/RlY8QxxDGokrMWrAVrQRWshTiG9nZIU0gSyDJIEkhTcA7SFJIEnIM0hTgG5yBNQZUma8E5sBbiGJyDJCHnHFhLkypYC3EM1kIcg7XknANrIU0hTSEMyaUpZzxV+F//a5RJIyOQJGAtqIJzkCS8YVkG1kIcQ5pClsFzvz7Io88d5NHnDvLocwd59LmDPPrcQR597iDWgiqMjIBz4BxYC86Bc5BloAqqIEIuSSCOQZUm5yBJIE0hy0AVsgycIycC1oIqWAuqYC2MjECW0ZSmkCScJE05KzzwwP8hSchlGYQhOAfOkcsyyDKwFqwFa8E5mpwDayGOwTlQ5bRUQYRckkAYgio8+txBHn3uII8+d5BHnzvIo88d5NHnDtKXjRHHkCSQpiACzoEqOWvBOV6VteAcTc6BtRDHNKUpJAlYC9aCtTRZC1kGScI5q0ThTWUt7NixAGvBORChyRhQBVVQBedAlZwquSAAVQhDSBJwDkTIJQmo0qRKzhgwBkTAWghDsBbCEJwjpwqqkGWQJOREyImAMaAKSQK+D74Pvg/OQRhCkpCLIjCGJmM4iXPgHFgLUQS+D8aACE1xDGEISQLWcsZShS9/+Uc4B2kKaQqeB6rQ1gYioAojI9DWBkEASQKqMGcOvO1tnNKRo5BlkKYgAsZAkkCaQpaBtZCmkCSgCs6Rcw5EwDlIUxABEVAFVUhTyDJyItDWRi5NwTnIMsgySFMwBkRAFbIMVGlKEnJZBlkGSQLWQppCmoIIqIIqOEfOOXCOM5YqnH/+KKpgDPg+iIAI+D5EEQQBOVVQhSCAOIYgAOfAOU4SBOB5kCSQpmAtiHBaR49ClkGagu+DCBgDSQLt7RCGoAoiYAxEEUQRuTQF58ipQpKQcw58H3wfnIM0BWPA90EERMAYcA6ShFylAiKgSs4YaGuDKAJjQARUOaOJwJQpLxAEYAyogrWQJDAyAiLg+6AKWQaqkKagCkEAQUAuTSFJwDlyIiBCzjlyQQAiEEXgeeAcOAfWclpHjkKWgQiIgOeBCIiQS1NQ5STOgXPkVCHLwDlwjpxzEIY0iYAIGENOFbIMREAEsgysBWM4Z5UovKlEOIlzEATkVMEYcsbA0BBUKuB5YAw5VVAFVRABY8D3IYpABFRhZATCEEQgCKBSgUoFoghEQARUwRgIAjAGRCAIQASCAIIAjAFjQBVUQQSMIScCWQZpSk4VREAEnANVGBkB58A5UIUgAFVIEhgZIRcE4Ps0WQtJAsZwxnMOXnhhM1kGvg8ikCSgCkkCSQIi4Hk01etQqUAUQ0sLp/SeOeD7oApJAmEIxoAItLVBloHnQRCA70MUgQioQhRBFIEqOAeqIEJOFZwDERCBkRFyIiACUQRDQ+D7NAUBRBE5VVAlJwKeR1OSgHPkRCCKwPchSUCEXJKAc5yRjIGLLnqALAPnwDkwBnyfXBCA70OawuzZYC1NzkEYQpKAKicJAhgagigCEVCFffs4pZYWiCJoa4P2dlAl97OfQaXCSZyDMIQsA98HETCGnHPkRCCKwBgIAogicA6shTQF3wffB1VylQo550AV0hR8HyoVcrNng3NQqUAQcEZzjpzngQj4PhgDngeeB2EIYQiqoArGQKUCQUDOORAB3wdjYGQEkgSyDJwDVUgSsBZUIQwhSSAMyalCksD+/ZzSe+ZApQK+D74PIuD74PtgLaiCCDlVUIU0BefAOXJRRC5NaTIGgoCcMVCpQKUCxoAx4HmQJJCm0NYGlQpEEahyTipReFOJ8AptbRAEEEUgQs45mtIUkoScc+RUoa0NVEEVVEEVVME5cA5UyamCc5CmIEIuy8A5sBacA1VQBVXwPPA8sBZUaVIFEcgyEIEoAt8HVYgiCAJwDpKEXFsbGAPGgO+D75MzBqIIREAEVCFNQRVUIQhAFTwPgoAznipkGUQRBAEYA5UKRBFEEQQBGANRRE4ERHhVxkAQkFMl5xykKfg+xDH4PgQBqIIqORFQJWcMuSiCIKDJOXCOnHPgHIiACDkRiCIQoSlJwFowBkQgSSBNwfNgaAiGhqBSAd8H5yAMIctgaAiiCIIAogiM4Yw1deqPcI6cMeD7YAwYQ84Y8H0YGgJjaFIlpwphCM5xEhEwBowh97a3cVqqkKYgAiKgCtZClpFTBec4ycgIiIAIOAdpClEEvg+qoErOGIgiUAXnIMtAlZwqGAO+D2kKzoHvQxCACIyMQBCAMSDCGc8YmDJllDSFJIEkAVVIU0hTqFTAGFAlJwIiNKUpzJ4NYQieB74PxoC1EIYQhqAKSQJpSs5amkR4TaqQppAk0N4OSQJpSk4ERMhZC9ZCFIExkKaQJCACxkAUkQsCqFRAhCYREAHnoL0dkoSc74MIpCkkCaQpqHLOKVF4U4nA9OlfxRhyqpBl0NYGqqBKU5qCCIiAteREyIlAlkGSQBhCmoIxNBlDzlpIU0hTEAFVciIgwklUwTlIEkgSMAaiCETIBQFEEUQRGAOqkGUQBCACzkGWgQi5IIAogkoFjAERqFTA90GEJlWwFsIQ0pRcFIExnBV8H1RBFXwfPA+SBLKMnLXgHG+ICPg+VCoQRaAKxkAUgTHkjAERcsaACKQppCmIgCo5VXAORGgyBlQhy8ipklMF50CVnLXQ3g6eB8aQ831yqjAyAqogQm5kBHwfogh8H4wB58BaMIYz2rFjsxAhZwwYA8aAMTQZA6qgykl8H4wBEZqsBWvJqdJ0wQWclnMgAqqgCs5BkoBz5HRSKysAACAASURBVFRBFVRBhCbfJ+ccTSKcRASCAETAGPA8MAZEYGQErAVjaAoCECHX1gaqIMJZwTk4ePBGVME5UAVVcqqQpqAKIqAK1nIS34ehIRgagiAA58BaqFQgiiCKoFKBoSGIIhgagqEhCAJyIhAE8La38apUQQSMARFO4hw4B2kKIiACxkBbG6iCc5CmoEpOBEQ4JREQgSiCKAJjwBiIIogi8H0Q4ZxTovCmEiHnHLkoAt+HLIM0JVepgDHgeZAkoAoi5FRBBFTBOXAORMipgjFgDDljyDkHzoG1oAqqYC2okjMGjAFjwBhQBRFwDsIQVEGEnHOgCs6BKngeOAdJAkkC1oK1MHs2tLdDkkAcQ5JAGEKaQpKAKjnnIElABERoMgZEOKOJwP/4H/+TIADfBxEwBtIURKCtDURABEQgSSAMQZXX5Bw4B87ByAg4R04VwhDCEFQhjiFNQRWMAedAFURAlZwIqIIquSgCEVAlZwyIgCpYC0kCSQLt7RCG5IIAjCFnDDnfB2shTWlqa4MggCAAEQhDSBJIElDljPbrXy/A98EYTss5UAVVcqogAnEMlQr4PjlVUAVVcqqgCqowNsarUgURXkGVk6iCc9DWRs45GBkBz4MsIxfHYAwnEYFKBeKYnO+DtZBloAqq5FTBOXLGgCpYC86BKmc0Y2DatGGiCHwfggCCAFQ5pTSFMIQwhDgmZwwYAyIgApUKGANBAMaAMWAMiJBzDlRBhJznQUsLpyUCUQSeB6qgSk4ERCDLyEURGEOTMaAKzoHngQivSYScCBgD1kKSgHPkjOGcVKLwpnIOXnrpRkTAGDAG0pQm50KyzDJJFVRBBEQgCMD3QQQqFfB98H2IIlClSTVGJCaKQAREaBIBY8iJgCo5VRABEXKVCkSRBUImJQmkKYQhhCFkGTlVEIEoAmNAhJwqpCmogio4B85BkkAYQpKACPg+ORFoa+OsIgIi5KyFKCKXZSFJYjmRc2At/O//zWvKMkgScC5GJEYV0hScA2vJpSk5VZpEyImQUwUR/h8WCBEBzwMRUIW2NlAFVRgZISdCLgjA98k5B9aCCPg+jIyACE3GQBCAc2AtOEeTMZzxpk/vwRhyqqBKUxiGhKElSUAEgoCctZCmoErOOQhDUIUggCAgFwTgeaAac+TILzidIIChIRAhJwIioErOGKhUoFKxqIaoQppCkoBz4PswMgIiEAS8gnOQJGAtOAdJAqoQBOAcWAsiIALWQpqCKoiA70OaQns7OMcZzRiYPr2HIABjoK2NnAiognMhIhbfBxFQBVWwFpIE0pSctWAtBAE558BamuI4ZvbsmDSFJAFVEAFVEOFVOQdpCiMjoGpRDREBVRAhJ0LOObAW4hicIydCzjlwDpyDOAZVTmItOAfGQJpCHEOSgHOQppCmoMo5qUThTTd16o9oUIU0BedAhCZrQRWyDFTJeR74PrS1gSoYA8aACAQBqEIQgOeBtaAKzoEx5ETAGDAGREAVjKHJOXCOnCqoQpJAmpJzDqwFEfB9iCIQAecgisAYciLg+xBFYAw4x0mMIeccqIIqOWPIqUKWgbXgHDjHGUsVDh68EWshTaG9HVQhCMDzwDlwDsIQsgwqFYgiSFPY/QtelQh4HjlVMIYmEYgiUCUnAiLgHKjSJALGQJqCCIiQyzJIU/A8qFRABLIMRMD3IQggimBoCKIIjCGnCkkCzoExNHkeOAfOgSpkGaQpqILvgzHQ1gYinNEOH15AmkIYQhhCGIIqTZ4HUQRBACLkggB8H0TIGQOVCoiACIjQFAQQBHD0KKclAiLgeSACUQS+D0EAQ0PgeZCmkKZgDAwNQaUCUQTGgDHgeaAK1vIKxoDvQ5aBKkQRiIBzEEUQxxBFYAwEAfg+qEKS0CQCqpwVVME5SBJwDlTBGHIiIAJRRE4VggCCAFQhjkEV0pScKiQJpxUEoAqeByKQpjA2ximpQpKAc2AtTSIQBOAc+D6IQJZBmkKWQZpCloExoAqqkCSgCqowMsJJrIU0BRHwfXAO0hRUyYmQU+WcVKLwpjIGpk//Kr4Pvg+eB8ZAWxtNQQAi5ERABEZGQASMgTQF34ckgTQF5yDLwPNABCoVMAZEyEUReB6ogipNzoEqqJIzBqwFEXKqoAoiIELOOQhDSBJQBVUIQ3AOrIUwhCSBLANViCIwBlRBFZwjpwrOgSqogipEETlVyDIIQ0hTUOWM9etfLyAMQRUqFQgCckEAQUCTKjgHSQJRBH7Aq1KFLIMogigiV6nA0BBUKhAEYAxUKhBFIALGwNAQVCpQqUClAr5PThWMARFyIpBl4BwYA1FETgSiCERAFZyDJAFVECHnHCQJTSLgHCQJOd+HSgWCAIwB34csA1XOeJ5HTgQ8j1cwhpOIgDE0GQPGgAivIAJtbdDSwmsyBqIIjAFjwPPAGDAGREAERMAYMAaMAWPIBQH4PohwSkEAngciYAwYA1EEIuSMIScCxoAIeB4YA74Pvg/GcFZQhSShKQjA88hZC2kKzoEIqEJbG1QqUKlAloEIDA2BcxAE8LOfQRBwEt+HKALfB2Mgy0AEVGFsjNMyhlNSBWPIqYIqeB74PqiCCFQq5NIUVMmJgOeRcw5UIcvA9yEIwBgYGgLfp0kEfB+M4ZxUovCmcg6OHZtFEIAxIAK+D0lCU1sbtLWBKqiCCKhCmoIqiIBz0NYGngci4HmQZZAkkKbknIM0BVUwBkRoMgZEQIRXUCUnQpMqBAEYAyKgCiIQBBBF4PsQRVCpgO+D74PngTHgeTA0BEEAImAMGAPGgAiIQJJAkoAxoAqq5NraQIQzkipMmTKKMeD7EAQgQk6EnO/D0BD4PojQJMJp/duzkCTgeeSSBKwF50AVkgRUwTlIElAF34e2NmhvB+doUgVVUCWnCs6BKkQRuTAEVbAWkgREwDlIEhCBKAIREAFjyEURRBFUKmAMGEMuDCFNaVKFJAEREOGMNm3ajwgCqFSgUoE4BhH+WxgDQQAiYAwEATljoFIBz+NVBQEYw2kFARhDTgSCAIzhlEQgCMAYiGOIYzCGs4IqiJAzBqII4hiCAETA8yCOwfchCCCOyYlApQJBAM5BkoBznJYIGAOVCngeWEtuxgxOSQSiCIaGIAjAGHLWgnPgHDgH1oIqiIAqTaoQBBBFYAy5NIUsA2shSchFERhDkwgEAfzsZzA0BFEExnDOKlF4UzkHv/71AlShvR2SBEQgikCE3MgIOd8HY8DzwPPAOUgSUAUREAFjIEkgTcE5EAFVcA6CAJyDMIT2dnAOVMFaTsk5mkRAlZwqOWtBFVR5BRFIEghDSFNIEkgSaG+HLIM0BVUwBpwDzwMRUAVVmqIIggBUQQREOGMZA5de+j/xfQgCctZCeztYS06EXJaBKhgDWQaqnNZ75oDvQxBAmoIIBAEYAyKgCmkKaQqeByKQppCmEATkZs+G9nZIUzAGggCcI+f7oEpOBJwD5yDLwDlwDkSgrQ2cA1VwDsIQVCHLwDmwFpyDOAZVECHneeAcJAk5Y0AVnKNQ+C9lDFQqMDQEUQQiNPk+BAE5Y8D3OYkIOWMgisAYXpMITSLQ0sJpiYAx4HmgCiLkoggqFTAGggAqFf4ve3CMM0t2Hgj24EEsgYUhZagBCSojv9/pJajNuDk7kDYQNxYgX17kdRry22snIjYgLSFvmOIS5NSXBgdqoMsQ2SChKgIa8gJKzI9XVSTFJ85j4T9HKZTC/c48E0EEtbKulMI8M8/UyroSQQQRXokggggifKe98+aDiuBnP/tvWqMUeieTUjztO+dJpuE4OE8yDaUQwbJwHESwrmwb28a6UiuXC+tqiPBUCpmGTEoxRBBhyPRKBLVSCqVQq6fzpDXDtlEKEdzvbBvTxDyTSSbrSq1ME7USwTwzz4Z1ZdtYV0rx0crkF7/4zK/cbtxuHAeZRHjlcjHMM9NEhG9VKxHMMxGGCHongt7Zd0Pv9E4EmRyHIZNSmCYyvVIKx0HvrKthnimF4zDcbkQYIohgmphnIoggggjO01AKpRDB5cK+G6aJCG/e/F5FUAqlEOGVCCIMpVCKb1QKEX4jpbBtzLPfSK2UQilsG7VSCqUQQSlEEEEplOKVUoigFEohglK8+aV33nxQEfz8538pgmkigvMkk0wiDBHcbswzmWQSQSlcLmQSQSaXi6F3euc46N1QK7WS6WmayCTTsO+GTDKJ8J5Meqd3eieTTC4XQ4RXMsnkPDlPjsPQO+dJa2R6ZVl4eaE1Mg0RPmpffPE3loXHg9aIMBwHvZNJJhGUQiYRfmMRRJBJJudpiDC0xuNBhKdMQwTzTAQRRBDB48E8M02cJ7Vyu1Er68rlYsg0lEIE80wEtRJhyCSCaeJyYV2JoBRq5fEw1EqEN2++80qhVkrxG5smQ61EePOBvPPmg4rg+9//kX3nOMgkk+MgwlMmt5shkwhD75wnx0GE4ThYFo6DZfF0HNxubBu1EmE4T+8pxVMmmUQQ4SmTCLaNdWXbiDDMM9tGKUyT4Ti4XJhn5pl1pRQymSYeDzINrZFJrew7y0LvPnpfffWZCHqnFEMm+04mx8H1Sia90xq9+430znnSO5lkMs/0zjQRQSlkGnpnmjzNM5kcB/PMPBvmmVKIMOw7mYYIbjdKIZPjINPQO5n0Tu+0xuNBJrVyuxHhKYJ5JpPevXnz5s3v1TtvPqhMvve9H4vw1Du9k2kohXmmNTKJoHdKIYJMMolgmlhXaiWTCOaZeTacJ/vONJFp6N1ThCGTCE8RlEKmVyKIMERQK9tGrZRCJrUyTfTO42Hondbo3dM0Mc/MMxGG3imFzz+nVh+1UvjhD//Br5RCJvtuiCCCeebzz6mV4yCTx4NM36p3WmOamGdKoRQyiaAU7nfWlW3z1BoRlMJ5cp6sKxGeIgylsK6cJ717TylsG6UYaqVWeuc4mGfmmVJ8owjWlVq9efPmze/VO28+qN75+c//UgSZRFCKIZNMw3kaWqMUaqV3evfUO62xLOw7EWRyHDweRNA7rXEclEIpRBgiyCSCCEMEpbBtTJP37DvXK8fBsnC7se/sO63RGr1zHIZ953qlNTKJIJPz5DhojdbIJIJMMtl3Mn3UIvjhD//etrGubBv3O/c720YEEUQQwTzz+efMMxG+VSmsK7UaLhdPmYYIMnl5IZNaud+531lX5plMrleuV5aFTPbdK72T6WtFeIogglLYNkNrLAv77j2ZtEbv3rx58+b37p03H1QEX331mQhK8VSKIWITUWUaIpgmto15plbWlXU1lEIE28a2cb8zTX7pZp5vajVkksk0UYoh01PvZJJp6J1MIio2EYZaiSCTUng8iDBcLqyroRRqNdRKrYZ55n5nmgwRXqmVCM6T3sn0Ufv00x8phQhKoRRKoRTu902t1b8rhQhK8RspxXC73dxuN79SCttGhCGCbeN+Z54phQhKoVbmmW1j27jfq/t9U4qnCLaNWv3GSqEUSmGemWcivCeCaaIUf/C2bVNr9bu63W7+4i/+wu+q1mrbNm/+Y7ZtU2v1u7rdbm63m99VrdW2bd58WO+8+aBK4Xvf+7FMMolgmpgmQya9E+Ep03C7sa6UQqahdyIohVIohVLIJJNp8kpr7DuZnjK90jutcRyUQilksq5kEkGEp1qplduNUiiFdWVd2TYuF0MEEZRCrawr00SEIYJ5JoIIjoPevfn/iKBWIgylUCulUIr31EqtlEIplEIpniKolQi/tVKolVopxdeqlVK8efPmze/dO28+qAj+6I9+LIJSiKBWSmHbWFci6J1a2TZq9RRB77RGBKUwz4Zl4XYjwivbxjyTSQQRXonwnghD7/ROKdTKujJNZNI7mdxuZJJJJr0TQSa1GvadUiiFTPad3olg27jfWVdDJpnevHnz5s2b/7B33nxQvfOLX3wmk97Zd5aF4+A4OE8yKYVpohQiyGTfySSCdWWaWFdDpuE46J1MjoPjIJPHw5BJJhG+UQSZRHjqndbI5DzpnXUlguNg32mN65XWeHmhNXo31MrlYmiN1jgOjoPeDZlkkmnI5Dzp3Zs3/+k+++orMsn0SiaZZJLplUwyPWWSSaanTP8hmZ4yySSTTDLJJNNTplcyDZlkkunXyvSeTDLJ9ObNH5J33nxQEXz/+z/SO5mGTDLJpHcimGdq9ZTJcdAavRNhaI3WOA5PEYZMMjlPeveNMr2nFEOmIYJMMpkmaiWTTCI4DjLJpHciiOA4OA72nfOkd/adTDLJ5DhojdY4DjLJpFZDpjdv/nPtu7/7539mWWiN3g2ZtMb1yvXKvnu63WiN1th39p3WaI19N9xuXK/07reSSWvsO72zLLy88PLC9crLC9cry0Lv7Dv77mnf2XcyWRauV/bdt8qkNfbdK/vO9UprZHrz5g/FO28+uB/+8O/VSgQRht7JNERQiqfeaY1pMjweZPJ4eJomQymGeWbbmGdDKYYIQ6ZvlEnvZBoiyKR3w3GQSWueSiGTUiiFeWaeyaQUSqF3WqMUSqFW1pV1ZZ5ZV0MEEcwz80yt3rz5zxXh7//kT4gw9M7tRoSneSbCK70bztPQO5cLEYbHgwgyud3Yd98qk9uN1th3WqM1eveU6SmCCCKIMPTOcfB4sO9ME/PMebLv9M7txu3mad/pnWkik9uNTHqnNTLpnd653cj0sfvLn/2M3sk09E7vZJJJ796TSe9k0ju90zuZ9E4mvZNJ7556J5NMeieT3v1amfRO7/Tud9a7p97JpHd6N/ROJpn0Tu++y95580H1zhdf/I11ZZ7JJJNaWVciFpfL7lcyud3IZJoohcuF3qmVaWKamGdqZdvYNkrxSzfnefMr88w0sW1EEEGEbxRhyPRLOxalUArHQSa9UyvzTCaXC+vKPDNNtMb1SiaZ9E4plMK2Mc/sO5mGTDKZJjLpnevVH7RlWez77nd1u93cbje/q33fLcvizdeIMKyrpwjDNFmwR1CrpwjmmcuFTDKJMEQYpsmQSWturfl/Mn2jTI6D3g2Z9E4EEf7djiXT0DulEEHvhgguF0+lEEEmmZwnEew7mRwHmdRKKZ4yqZVSyCST8/TR693ffPEFy0ImvdMay8KyWF5e7K2Racgkk965XrleuV65Xrle6Z3joDVa43rlONxuN7dl4Th4eeHlheuV65Vl4YsvfKt9pzX7slhaI9OQSabfSu+0Ru/0TmssC9crx8G+0xq90zvLQmv0TqbvonfefFCl8Kd/+j9kUgrrSq3MM4+H9xwHx8F50jsRlELvLAvHweNBpvf0zuNB75wnx0HvvtG6sm1eiSCTaWKayCSCWllXIojgPCnF8HgQQa2U4pXeaY3zNJwny2KolduNdTXUSinevPnPl+mv/+VfaI1MSqEUMjlPMskk05BJrdTK40EmrdE7rdG7oRQiaM2QySef+FpffslxkEmmVzLJ9J59J5NMMjkOIpgmzpNaDderp+PgfqcUjoN9J4IIQynUaiiFdWVdKYXWiCDCRy3CP376KZn0Tia9E0GmoXd6N/ROa0Rwv1OKp1ophXlmnllXto15Nuy74X7nfqdWIojgX//VtyqFdWWe6Z3eDfvOvvutZBoiiPAUweVCrawrmZwn62o4DpaF3n3XvPPmg/vFLz5zvdKaYZroncvF077TuyGTTEOtrCulsG3Ms6F3ejdk8ngQwTQRwb4b1pVMMg0RlEIpvlYmpXCenCcRZLLvhlKYZ+bZEEHvRHC5MM+GCO9ZV6aJUoggk32nFLaNy8WbN78fpfjHTz8lk3mmd65XrlcuF0qhNfadTJaFfWffyWTb2DZK4X6nVkME68q6evryS1/rk09YV2r1GyvFsCyGdSWCUlhXMjkOto119ZRJ7/ROa0wTpRgyaY1lYVm4Xg3ryrr6gxDhH374Q2r1yjRRCqVQK6V42neOg1K4XDxdLrRGa2TSGq3RGsdh6J3joDV6p3d6962uV5aF65XjMBwH1yvHwXGwLFyvvLyw7/TOsrAsvLzQO/vO9UprTBMRRDDPzLPhPFkWjoMIpolaiaB3IojwXfPOmw/uX/7lr0XQO62xLJwnjweZHAfHwXGQSSbzzO1miCCCWimFdWVZyDRk0jvTRCmcp6fzNJRiyCTTcBwsiyHCEGHond49RZBpqJVayeQ4DL1znmTSu2GaDJmGCEphXSmF1jgOMjlPSvHmze/NP/zwh0SQyXl6qtWwrpRC70RQCqUQQe8cB5m0Rmv0zr6zLByHp08+8bV++lNao3ci/FoRTBO1Ms+UQoQhglLonVIohQhP+855UivbRq1EGCLIZJqIMByHoRTm2Ucv01/95CfsO8fhPZmGZWFZiCCC3g0RRFArjwe9GzJ9rUwyvefLL32jCEMpZBoyySSTTEPvZNIamVwuhnk2ZNI7mZwntxu9GzLJJNPTeZLJ7cblYrhciPBd886bDyqTTz/9RxHUSq2G3g0RhnlmnomgVmr1Su/sO62x75RCpqEU5tnT5UKtZBoiiKBWQyaZzDPryjxTCrVSCr17ivDKvrPv9G64XIgggghKoVYyvXK5kMlxEEEE8+xp3zkOevdR++yrr8j0SiaZhkwy/Ydkkml4PAyZZJJJJplkkkkmmWSSSSaZZJJpyCSTTDI9ZZJpyPSeTDLJNGSSSSaZZBoyyfSUSaaPXu/+8uc/Z5o4DjINmew7EURQimGaiKAUpskQQQQR9E7vnCeZZBJh+PJL36h3Msn0a2VSK/vOcbDv3nMchkxDBJkcB/vO5UKEV3onk8eDeaYUT6VQij8Ef/2TnxBBJsdheDzIJJN9J5N9p3fm2VMEmVwuhnlmXXk8WFfud+535pkIQwTryrqyroZPPvGNpoltY56JoBQiDBHUyroaIiiFUrjd2DZuNyKIMJRCJsfBcdAajwcRZLKuzDP7zuPBcXCelMLt5rvonTcfVCZffPE3eudyYZpYV8O+GzLJ5Dw9tcbtRu9k0hrLQu+cJ9NkyPRKBLcbl4shwtA7mUR4Ok8eD0+Z9G6IIIIISmGeDefJstAamdTKuhLB5UIE02R4PCjFcBwcB5n0zu1GJr2zLIZ95zh81P7qJz/hemXfDb2zLCwL+855cr2y735rvfPyQmv0Tu/sOy8vvLzw8sL1yrLw8sLLC9crLy+8vPDywssLLy+8vNAaEew71yutkWnYd/ad3lkW9t1T7ywLLy+0Rib7zr6z77y8sO+GfWffPe07++6jl+lvvviCCEoxZBoeD0MmvVMKEWTSOxHUyrqyrswz80xrhlKIIIIIPvnE1/rBDyiFCCKI8EqEV0oxnCeZHAf7TqahdyLIpHd6J4IIQ62G3r1yHEQYemfb2DZKoXd/EDJ99tVXzLOhd8PlwjQZSiGCWjlPjoMIejeUwuNB7zwehnk29E7vPB5EGPad42BZDBG+USat0TvLQgS9k8k88/nnzDOZhnVl24jw1LuhNUphntk25pneyWSemWdKoXcyDZmG3rlcfFe98+aDqpWXl//btnGeZHK7sW2sKxFE8Hgwz9zvzDP7znHQO73TO6UYeufxMLRGpvfUyv3OujLPZNK7IcKQaThP9p3ePWWSaZhnQ2vMM59/zjSRSWv0zrpSCr1znobLhWkigkwiiDA8HjwelMK6cr+zrj5u++6vf/ITw3lyvdI700SEIZNMWuPlhduNfed//k/fKpPjoFZKIZPeOU9fK8KQ6SnCK5nsO8dh6N1TKZRCBPNMJsvCstA7EURwudA7x8FxcByUwnlyvXIcnCf7zu1GBOfJsrDvPlqZfvxHf2TonQgivNIax0HvHAf7zvXKstAaLy8sC8vCcRBh2HdPmXz5pa/15ZdkkkkmmV7JpFYiDL3TGtNEBKWwLPRO7yyLYV15PLheyWSeDb3zeFCrp97pnWlimjhPejf0Tmv07qPXux99//tEGNaVbaM1joMIQ+/0TqanZeF6JZN5Zl3JpDWuV65XrleuV/ad3g0R9G5ozfDll77W//kpmbRm6N1QCqWQSWssC7VSK5lkGnqnNfadbTO0xvXK48E8E0EEpTDPtMZxeIowPB6+q95588F973s/lkkmjwe9e8pknrlc6J3WaI0IImiN82RdWVfWlVLoneMwRHglk9bYd1rjODxlkmnIZN/pnQhKIdNTrfROa5ynIYJMQymG86Q1emdZ2HciOE8yWVe2jW1j26iVbWOemSZqpRRK4XLx8TpPQyaZ9E5rhnmmFDINmZRChGHf+elPfa1/+idao3evtEbvXsmkdzK9J9PXyiSTTDI9ZRpqNWRyufB4ME3MM7USQSaZZNI700QmmUwT50lrnKdh3zlPH61S/P2f/AnHwTwzz4ZtY54N68o8UwrTxHlSq6d19Z7eDZn0TgSffOJrffIJ80yEIcIQ4al3Mj31blhX5pn7nVIohW1jXYmgd9aVUiiFbWPbDPvuqRS2jVophXWlFEMprCsRPnq1+h9/+qfUyv1OrYYI1pVSmCa2jW1j29g21pVt435nXYmgFNaVbeN+535n27jfqZVSWFdDpqdMv7XeWRYyWVe2jXU17Dv7biiFaeI4uF7JNNTK5UJrZLLvtGaIoHfDNBFh6J1M30XvvPmgMvnZz/7SeRLB5ULvnCfHQSaPB48HjweZhkwiKMXweLAsPB6sK/NMKYbbzfB40DuZZHKe9O6VCCLI9J7ePUWQyTwzz2wb22bI5Dg4DuaZefY0z9zvzDPTRASZlEIEvZPp6fGgd3onk8eD3n2cpslT70QQwXHQu6EUIlhXto1SyDT89Ke+USYR7Du9GyJ8owi/VoQhgggi6J3eOQ5aY98NtxvbRgSXC8dhiDDUSinUSq2GUqiVCPbdK6UYMn2UIvy3n/2M3nk8yDRkksm+G0ohglqZZ0+XC6VQChGUQilkGkqhFMOXX/pGtTLPhkxDpqdM73k8yKQUSiGC3smkFMO6UgoRRFAKpTBNRNA7txuZlEIEEZTitVi+MgAAIABJREFUKZPeyfTRi/CjTz81lEIEmWRSiuE8OU9D7/RuOA565zxpjd4phd7pnUzOk0wyyeTxIJMIQymGTz7xWymFTEPv9E6EoRRKMdxutOYpkwh65zgohQjOkwjO07CurCuPB70TQSaZvoveefNBZfK//tffmSamiQjOk8uFCK9kMk1ME6UwTcwz8+ypd1qjFLbN8Hhwnuw7vVMK9zv3O6WQ6SmTTEohglIMmV7JJJNSqJXe2XeWheMwXC5EcJ7sO63RGr0bWuM4WBZaY99pjd4NpbBtnCfXK8vi41arv/2zP6MUQ4QhguMg0yuZ7DutGb780tf6r/+VbWOe/cYy/VqZnkohk9Zojd4phfMk09A7rRmmidbYd0phXbnf2TbW1dM00RoRRDDPrCvbxroS4aOU6bOvvqIUMmmNTB4PztMQ4ZVamSbmmduNUtg27ne2jW1jXSmFbeN+Z5755BPfqlYiiKAUIgwRRBBhiDDsO5leyeQ8ySSCWinFe2qlVjJ5PPxaj4c/WBFEGPadTPad4/B0HOw7jwf7zuViuN1ojeMgk945T3o37LshwhDBuvKDH/han/yx90SQSa1EEEGE4XbjODxFsG2UYoggglJYV9aVCDINvZPpqXcyKYV1JcJ30TtvPqgI/uzP/tbtRgTHQQSPBxHUuqHKNByHYZo4T0/ryrpSiqE19p1pIpOIm1JuIrynFGolglKolQhKMUQYSvFLVcTmV0qhNVrjOHg8mGfWlW2jFE8RrCv3O7Ua1pVMT8fBPFMKmWSSyb5TCvc700QpPlqf/eIXZBJhiCCTdbXNs9q74TxpjdaIMHzyiW+UyXkS4YabX8qkVv9RFZtfyjR8/jnrSu+UwrqybUQYauV+p1ZKoVbOk0wiDJlkchxME7Uyz0RwvxtaI5MIH61S/O2f/znryv3Ov/0b//ZvbBvravv8c7VW76mV2803ut3YNiL8yu128xd/8Re+VQT3O/c728b9zuefc79zv3O/q59/brvf+fxz/u3fuN28UivrSoTfSK2sKxG+UQTrSik+evvur37yE/adZWHfOU9DazbUTEPvPB6GfTfsOxGcJ5m0RimUwnEY9t0Nt0xP02TYd8Mf/7FvFOHfVWyZ9G6IMESw77RGpqdaqZXLhQjmmWli26iVUphn1pVp8vR48HiQSa1cLkSQ6bvonTcfVASffvojv1IK08Q8s22sK9vGunK/s22sKxHUamiN1lgWjoN9N/TOcRimiWny1Dv7Tu9MExH0TqYhk0wyySSTCNaVUsgkgkx6p3e2jXWlVkMmrdEa08T9TgSZtMZ5GkqhFDK90hr7zrIYpolMjoPefbQ+++orSmFdDZmeHg9qZdtYV+aZWimF//7f+S//xTfqnd7JJMIQQe+GCO+J8BuplUx6pxTud+aZCCK8EkEEpTDPhn039E5rZLJt1Oopk0wiWFcifOx+/L3vEeE9EUT4D4vwW4sggggiiCCCCCKIIMK3ivBbifBrRfiDcJ7+7p//mWWhd46DfTf0TgQRhggyOU9KIYIIQ+8ch2GamGdKIZMIIgwRRHAcns6Tf/1XX+vLf/WNMrndOA56pzXWlfudCPadfWffiaAUIgyZnmolgghDpmGaqJV1pRTO03fVO28+uK+++kwmvdOaVzKJMERQK6UQwTwTwTyzrqwr9zvbRq1ME7Vyu1Er80wEmSwLy0JrXC6UYuid3smkdzK9EkEEEfRuKMVT77RGJvNMpqfzZFnIZJ6plW1jXQ2ZtEbvTBMRhlq53SiFdaUUH60ff+97ZBLBNFGKIZNMQ+/0Tu+GTMMnn/hGjwcRRBBhyCTTU4SnCDL9Vs6TCHr3a2XSO5k8HmTSO5cLpZDJsnC9cp6G3umd46A1Mr1583sxTZ4iyDRkEkEmmUSQyTSxbWwb9zv3O+tqyDQsC8tC70Qwz6wr20YEmUSwrkQwTfzxH/ta/9cPiKAUSvFK77RmKIV1pVZ6J5PWOE+Og0wuF5aF4yDTUybHwbKQadh3MplnIiiFdaUU30XvvPmgMvnii79xvdKaIZNMWmPf6d3TvrMs9E4pbBu1UgqlUAqZ3pPJeZJJKdzvzLPhPMkkwhBBhCGCUgytcbmwrkQQwboyTfTOsnAchlKIIJPzJJN5ZtvYNmolwpBJBJlksiwsC8fBNBn2nQhK8fHL5DgMmYbzpHcuFx4PQ2uGCN/qn/7JkEkmvXtPJpmeMv1GIuidTO/J5HYj09O+s+/0zuNBBPPsqRQiPGUaImjNmzf/vyjF3/75nxvmmXUlgnUlk23j88+535lnjsMQQQQRhghKIcKQSSaZHAfLwrLQu6F3WiOT1vjf/9vX+uILeqd3evfKulIKEbRGa/TOcZDJtrGuTBOtcRxsG/NMKWQaeieTbeN+Z9uI4DjonUzfde+8+aAi+PTTf1QK88y2EUEmvfN40BqZZHKeZBJhiCCTTEMmrbHvhn1nWdh3LhdDhKE1Iuid3j1FMM9eKYVMWmNZ2HciOA6Og9bIpHcyyaR3Q++0xnFwHOy7Yd/Zd46DTLaNdaUUIsjkPMnkPP1B+OyrrwyZPB6eMongOJhnSqEU9p11pRTfqndqJYIIQ4T3RPhaEb5WJhFkMs/0bsikd46DTDLZd46D1jhPponLhQh653ajFHongmli27jf2TZDBNvGPJPpzZvfiwg/+v73ud8phfMkk8fDU4ThPA2ZXimFbWNdmWciqJUIIgylEEEEtbKuRBDBuvKDH/haf/wJtVIr60qEIYLzZJ65XMg0RDDPlEIpRFAr9zvbZjhPeqc19p1SWFdKIZPjMERwniwLtxutkem76J03H9wPf/gPLhdDKZRCJplcLp4yDaV46p1l4f9lD45C7KjzRI9/aTebsROCOBSm0w/9C8QVNxwSG50cokv9+kECA81oXoQVU1UEI8HxIRAfBJuqUiEZGFnMhNkH6a0q5yWYcG/CWdq4Kudf2HRgRMYgrMw22/kdwiQMkw2hb+xu71w7d7rYHKYn6qjdZj1JfT5pSkUEREAVRMCMSllSKUtwDpwDVfB9CEOWMYM0pcs5GBoCVRChIkIljsH3IQig3YYsgywDVeh0qKhCEIAZiIAIXWYQBOD7YAaqYAZBAFkGcQxBAENDYEZvMAMRCAIQATMQoSICIqAKQQCqYMZX+ru/gyyjSwTabQgCbmBGlwgVEQgCEOEGqhAEoArOQZpCmkIUQRSBCIiAGaQplSyDIIA0hTSFKIKiAOfADIoCigLSFIoCzMAMVOlyDpyjVrupnIMoAucgy8D3IQjADPIcRMD3qZjRZQZ5DkUBRQGdDpiBc2BGlwiYgRk4B0UBZmAGnQ783//LF/rbteAcOAdFAWYgAqpgBmUJaUqXCKiCcxBFkCRURMA5MAPfB1XwfRCh4hzkOYiACMQxBAH4Pvg+qILvc6vqo/adCEPodMA5KiKQZdDpRJjl5DlEEeQ5+D6IsMzQEBUzKkEAYQhJAkND4FxCWSb4PjgHRQFmUBTgHBUzEAEzMAMRutIUnAOzHIgwAxEIQ0gSSBIQAVVQBRHwfTAD3wcRKkEAYUglDCFJQBVEQAREQISKKqhCGEKSgAjfe/97wwY4dw7iGFQhjqHdhiwjEiH3fRChEoYQx6DKX6UKcQztNonvkzgHYQjtNpw7B+02tNtw7hy029BuQ5ZBuw1ZBkkCWQbtNrTb0G6TxzERfxKGEMeQJJBl0G7DuXNw7hxkGYiAKmQZxDGogipkGbTbkGUQx6AKIhDHEMeQZRDHIAKqEMegSiUMIQzpVVEUkec5K5UkCRcuXGCl8jwniiJqX4PvgwgVM+h0iIqCnP+WJBDHYAZJAlEEeU7FOchzKmFIJY4hCECVxDkS/psZmNGV5/B//g9fygzMwIwciMyoqIJzoAoiEAR0mUGeQ6cDzoFzVERAFZIERMAM0hSKAkRAFbIMwpCKKqiCc6AKItyK+qh9J0QgCECViiqEIZUggDCELIN2G1TpUoU4hjCkIgJBAKp0qUIcg+9DGEIYQpZBlkGWQZZBuw3tNrTb0G5Duw1ZBlkG7Ta025BlEMegCu02BAFfSRXabVAFVYhjUOULhSGEIYhAHEMY0pN+t2YNiIAIFRFQBREQARGWUeVrEwERukRAFURAFVRBBFRBFVRBFVSpqIIqqIIqiIAIFVUqqqAKIiACInSpgipdqqAKIqBKlwiIgCqI0KVKlwiIUKvdLL9bswaSBJKEihmUJZiBGcuEIahCWVIRgTCEIABVGBqCLINz5yBJIEkgyyAIQBXCEOIYwhBE6Prbv+VLiYAIiIAIFTMIAlCFIIB2G5IEROhShTimYgZhCGEIZtDpgBmYgRkEAajSZQZmYEal0wEzblV91L4zqiDCDURABFRBFURYRhVE6FIFEbpUWUYEVEEVVEEVVEEVREAVVEEVVEEVVEEVREAEVEGVryQCqiBCRZWvRRVEqNVqte9Guw1JAlkG7TaEITdQhSyDLIMwBBFIEmi3IUn4UiKQZZAkkGXQbsO1a3DuHGzaxBf6u7+DdhvabTh3DuIYwhDabVCFOIYw5AZJAlkGIhCGkCQgQkUV4hjCEJIE2m1IEpYRgSSBMARViGNQ5VbVR61Wq9VqtwIRvpII39jQEMuIUBHhK4mACF9IhC8lwpcS4RsR4VbWR61Wq9VqtVqP6aNWq9VqtVqtx/RR+26ZgXNUnKNiBs5RcQ7MqDgHzoEZOAfOgRldZuAclU4HOh0w40s5B2bgHJiBGTgHztFlBmbgHF3OgRk4B86Bc+AcOMeXMgPnwAycA+eoOAfOgRk4B85RcQ7MqNVqtVrt2+ijtrrMeHx2FqIInAPnIE0hSSCKIM+hLGFkBKII0hScA+egKGBkBNIURkYgiiCKwIyKc5CmkCRgBnkOeU7FDPIckgTMwAyiCNIUogicgzSFkREYGYEogiSBNAXnIIogisA5KAqIIogiGBmBKIKREUhTiCJwjooZ5DnkOaQpjIzA5s0wMgJpClEEUQRRBFEEIyOQpuAcpCk4R61Wq9Vq30YftVX33H/9F+Q5OAciVNIURCAMwQxEIM/BDMzAOXAOVME5KmbgHKQpbN4MRQHOQVmCc1TKEpyDPIcogjSFKIIoopLnVNIUnKMrz6Eo6DKDPIc0BefAOSqqYAaq4Bw4B2kKZmAGZQllCXkOqtBuQxiCc2AGqhAEVETADIqCSllSq9Vqtdq30cctanp6munpaaanp5menmZ6eprp6Wluhl/feSeIQJpCFIFzVJyDPAczMKNiBmlKJY7BOTCDOAZVEIE8B1UqImAGIlScgyiCNKXLOXAOzKiYgRmYsYwZN3AOzEAERMA5UAXfp2IGzoEZqEIcQxyDCDgHUQR5DnEMQQBm0OmAGZiBGZiBc3xd09PTTE9PMz09zfT0NNPT00xPT1PrPdPT00xPTzM9Pc309DTT09NMT09T603T09NMT08zPT3N9PQ009PTTE9PU6vdDH3coh577DHm5uZYWFhgYWGBhYUFFhYW+M6ZMfjHP0IQQByDCKhCHEOWkWUZIX8iAqqQZaAKZQlmIAKq0OlQMaMyNARmYAZmJECiCqpgxrcVAhl/EoYQhhCGkGUQxyACcQxxDGEI7TZkGWQZy+Q5mFERgXYbVMEMzMA5KiIQhhAEIAJDQ2DGX/PYY48xNzfHwsICCwsLLCwssLCwwP+0LMsIw5CVSpKEJElYqTAMybKM77PHHnuMubk5FhYWWFhYYGFhgYWFBf6nZVlGGIasVJIkbNq0iZUKw5Asy/i+e+yxx5ibm2NhYYGFhQUWFhZYWFjgf1qWZYRhyEolSUKSJKxUGIZkWUZtdfVxizpz5gxHjhyh0WjQaDRoNBo0Gg1uhh/Nz0NRQFmCc2AGnQ6UJZUwBBEwAzNwDsygLMEMzMA5cA5EQATCEIIARCAMIY4hyyCOQRVEuIEIX9vQEMQxBAGUJZWhIRABVRABVQhDMIM0BTNwDsoSREAEzECEriCAIIAsg3Yb4piKCBQF5Dl/zZkzZzhy5AiNRoNGo0Gj0aDRaFDrPWfOnOHIkSM0Gg0ajQaNRoNGo0GtN505c4YjR47QaDRoNBo0Gg0ajQa12s3Qxy1qw4YNjI+Pc9OZUTED51jGOUhTlilLvpAZXWYQRdDpgBnkORQF5Dk4B2ZgBiIsY8bXVpbgHKiC71MJQwhDyHPIc0gSiCIoCioiUJZUggDiGOIYRMCMiiokCaiCGeQ5XSIgwl+zYcMGxsfHqfW+DRs2MD4+Tu3WsGHDBsbHx6nV/if0cRuIoojPP/+cm+V3a9ZQEaHLOTCj4hw4ByJgBqp0qYIZiNAlAmbgHIiACDgHRQFpSpcZy4jwtZlBUYAZmEFRQJ6DGZhBFEFZghmIgAg4B2bgHHQ6UJZgBmZgBkUBzoFz4ByYQZqCGfg+BAGEId9EFEV8/vnn1HpfFEV8/vnn1G4NURTx+eefU6vdLH3cBs6fP89No8rv/uZvQATMqJiBGRUzUKXiHIiA74MqmIHvQ5ZBEIAImIEZiFAxo5JlEARURLiBCJjxtZmB74MIFREqaQpFAXEMQQBBAM6Bc+AclTgG3wffh6KAKIKiADMoCogiiCIwA1UoS+h0qCQJOMfXdf78eWq3hvPnz1O7dZw/f55a7Wbq4zawefNmbqbB//f/6BIBVVAFVSpDQxDHEIbgHBQFOMcynQ6YgQiIgO9TEQEziCIoSyrOgQiIUBEBM76xTgfMoCzBOeh0qJhRSVMwgzAEEShLKp0OmEFZggiYUQlDiGMIAggC6HSomIFzkKaQpmDG17V582Zqt4bNmzdTu3Vs3ryZWu1m6uM28Prrr3PHHXdwUzjH4B//CGagSsU5EAERIiAvChChkmVw7hy029BuUykKMAMRutIUzMCMJQmQOEeXGZhRMePryoGI/5bnkKZgBmbgHJhBGIIIiIAIxDEEAfg+mIEZpCk4B86BGYiA74MqlCWUJeQ5OAdmYEYlDEGVr+v111/njjvu4PsgiiLyPGelkiQhSRJWKs9zoiiiV7z++uvccccdfB9EUUSe56xUkiRcuHCBlcrznCiK6CWvv/46d9xxB98HURSR5zkrlSQJSZKwUnmeE0URtdXVx22qLEuyLGPVmfHrO+8EVXCOiiqYgXNUVMEMhoYgDKmIQJ5DUYAZXWZgBiJ858IQ8pwuEQgCyDIqZlCW4ByVoqBiRkUEREAVzMAMzMA5cA7imIoIhCGIQBCACCtRliVZllHrfWVZkmUZtVtDWZZkWUat9l3o4zbSaDQoy5KZmRn27dvHxMQEo6OjrCoRfjQ/D2YgQsU5cA7MqJhRCUMqZuAclCWIUHGOLhEwoyLCd8aMiggVEUhTcA7MQJVKFFHJMohjUKXiHIiAGYiACDgHIqBKRRVUIY4hy0CVb6PRaFCWJTMzM+zbt4+JiQlGR0ep9Z5Go0FZlszMzLBv3z4mJiYYHR2l1psajQZlWTIzM8O+ffuYmJhgdHSUWm219XGbmJmZYXh4GN/3OXDgAGNjYxw/fpzZ2Vnm5+dZNar84oc/BBG6RKiIUHEOOh0QATOIIhgZATOWMQMRKiIQxyACYQgiIAIirBrnWMY5UKVSluD7EAQQx1CWYAZhCHEMIlR8H0TAOShLKAoQATPodMAM8hzSlG9rZmaG4eFhfN/nwIEDjI2Ncfz4cWZnZ5mfn6fWO2ZmZhgeHsb3fQ4cOMDY2BjHjx9ndnaW+fl5ar1lZmaG4eFhfN/nwIEDjI2Ncfz4cWZnZ5mfn6dWW0193EY2btzIkk6nw65du1iyuLjItWvXWDXOsXt2FnwfVOkSATMqIpDnkCSQpmBGRYQuERABMxABEShLcA6cAzMwY9WJ0BUEEMcgQpcqFTMqZmAGQQAiUJYgAqpUVME5KnlOV55DnoNzfBsbN25kSafTYdeuXSxZXFzk2rVr1HrLxo0bWdLpdNi1axdLFhcXuXbtGrXes3HjRpZ0Oh127drFksXFRa5du0attpr6uE0MDg5y8uRJdu/eTX9/P57ncfDgQdatW0d/fz+rRoRKmoIZmIEIxDGEIRUzEIGigDxnGefAjC4RcI6Kc1TM6DJj1YiAKjgHItDpgHOQplREqHQ64BxEEaQpRBGUJQQB+D4MDYEIOAd5TsUMVOlShU4HnOObGhwc5OTJk+zevZv+/n48z+PgwYOsW7eO/v5+ar1jcHCQkydPsnv3bvr7+/E8j4MHD7Ju3Tr6+/up9ZbBwUFOnjzJ7t276e/vx/M8Dh48yLp16+jv76dWW0193CbWrl3LmTNnGBsbY2pqiiVbt27l9OnTfGecA1UqRQF5TpcZmIEqxDGEIV0iEAR0iYBzdIlQEQERvpIIX5sI+D5kGYhQKUvwfYhjUAUzGBoCEYhjCAIIAhABVVAFEfB9CAIQAREqQQDtNrTbEATg+9DpQJ7zTaxdu5YzZ84wNjbG1NQUS7Zu3crp06ep9Za1a9dy5swZxsbGmJqaYsnWrVs5ffo0td6zdu1azpw5w9jYGFNTUyzZunUrp0+fplZbbX3cRu6++24eeOABrouiiFUnwjLOge+DCIiwjAg4B2UJcQxxDKqgCmUJImAGZiDCF1LlK5nxjaiCKohQCQIIQ1BlmSAAETCDsoQgABFIUyphCKogQiWOQRVEQBXMoCzBjG/j7rvv5oEHHuC6KIqo9aa7776bBx54gOuiKKLWu+6++24eeOABrouiiFrtu9BHbXU5R5cqxDGowtAQqIIqXSIQhmAGeQ5m4Pt0OUeXGV1mVMwgz1kVIuAc5DmVIIAgAFWWEYEkAVUoSygKusyolCUkCThHRQQ6HchzMAMzKs5Rq9Vqtdq30UftuyECzkFRwMgIpCnkOThHlxk4B2aQphBFkKaQ5+AcXSIgQkWELhEQoUuEG4jwtQQBqEKaQpqCKqhScQ7M6DIDMyoiIAIjI1AUEAQQBNDpQFmCCDgHeQ5pCnkOmzdDp0PF96nVarVa7Zvq4zZVliVZlrHqzBj84x/BDESoqIIIhCFZGBLyJ6oQBKAKZiBClyqosowqiEAQQByTqJKYgRkVETDjBmZ8lRDIRFjGOXAOzMA5SFMwA+cgSSDPoSggjiHLwPchy8AM0hTMwPdBBJyDMIQwBFUqcQxmIAJhCGHISpRlSZZl3GxZlhGGISuVJAlJkrBSYRiSZRm9rCxLsizjZsuyjDAMWakkSdi0aRMrFYYhWZbR68qyJMsybrYsywjDkJVKkoQkSVipMAzJsoza6urjNtJoNCjLkpmZGfbt28fExASjo6OsKhEqIhAEkGUQBJBlEMdURMD3IQzB96HdhiCAOIYsA9+HOAZVEAEzcA7MoCig06EiAiJUzEAERPjGzKAoQATCEMwgTWFkBMwgjunqdEAE4hhEqIQhhCFkGWQZiEBZghmoUhkaAjMQAVUQAREQ4dtoNBqUZcnMzAz79u1jYmKC0dFRar2n0WhQliUzMzPs27ePiYkJRkdHqfWmRqNBWZbMzMywb98+JiYmGB0dpVZbbX3cJmZmZhgeHsb3fQ4cOMDY2BjHjx9ndnaW+fl5Vo0q/3vDBhABVSpFQcU5yHMIAlAFMygKcA6KAsoSzKAoYGQEzECVihkVM8hzcA7MwIwuMzDjW8tzMIMsgyAAESgKcA6iCIoC4hjCEFRZxgycA+fAjIoZ+D6YQacDImAGaQpDQ+D7fBszMzMMDw/j+z4HDhxgbGyM48ePMzs7y/z8PLXeMTMzw/DwML7vc+DAAcbGxjh+/Dizs7PMz89T6y0zMzMMDw/j+z4HDhxgbGyM48ePMzs7y/z8PLXaaurjNrJx40aWdDoddu3axZLFxUWuXbvGqhHhf23YAL5PlxkUBZiBCBQFmIEZxDGEIQQBlU4HVEEVVMEMVEEEROgSAREqIqyYGYiA70MYggj4PpU0BVVwDsyoOAdRBM5RcQ7SFIoCzMD3QQTMqJiBc9DpQBxDkkAY8m1t3LiRJZ1Oh127drFkcXGRa9euUestGzduZEmn02HXrl0sWVxc5Nq1a9R6z8aNG1nS6XTYtWsXSxYXF7l27Rq12mrq4zYxODjIyZMn2b17N/39/Xiex8GDB1m3bh39/f2sGud47r/+C9IUogicAxHIcwhDCAIIAjCDoqAiAqrg+xAEVJwD58A5cA5EQAREQISKGRUzVoUZlCVEERQFpCk4ByIwNESlKMAMRMA5KnkORQEiVIoCypJKpwNBAL4PquAcXc5BnvNNDQ4OcvLkSXbv3k1/fz+e53Hw4EHWrVtHf38/td4xODjIyZMn2b17N/39/Xiex8GDB1m3bh39/f3Uesvg4CAnT55k9+7d9Pf343keBw8eZN26dfT391OrraY+bhNr167lzJkzjI2NMTExwZKtW7dy+vRpVpVz/Gh+HkQgjkGEiirkORVVEGEZMyhLcI6KKsQxqIIq+D44ByIgAiJUVEEERKiIUBHhGxGhYkbFDFRBhEqnQyXPwTkwAzMoCkhTcA58H8yo5Dk4B2ZQFFCW4PtgBmZgBmZQlmDGN7F27VrOnDnD2NgYExMTLNm6dSunT5+m1lvWrl3LmTNnGBsbY2JigiVbt27l9OnT1HrP2rVrOXPmDGNjY0xMTLBk69atnD59mlpttfVxG3n//fd58skn2bFjB7Ozs7z22mtMTk6yqsKQX/zwhyBCxQyCAIIARIjSlDyKIE1BhIpzUJZgBmkKeU5FFXwfzEAE4hhEwDkS50hEwDkwAzNQBTMqZnwdORDxJ6p0OQdm4PsQx6AKQ0MQBFSKApyDMIQ8B1VQBREIQzADVYhj8H0QgSwDEVCFsoQoAlWIYxDhm3r//fd58skn2bFjB7Ozs7z22mtMTk5yM0VRRJ7nrFSSJCRJwkrleU4URfSa999/nyeffJIdO3YwOzvLa6+9xuTkJDdTFEXkec5KJUnChQsXWKk8z4miiF70/vvv8+STT7Jjxw5mZ2eloAeDAAAgAElEQVR57bXXmJyc5GaKoog8z1mpJElIkoSVyvOcKIqora4+bhNXrlwhjmM+/PBDNm/ezJITJ06wf/9+VpUIXUUBnQ6oQppCFFFRBTPIcxgZgZERyHMQAVUqZhBFUJZUyhKKgkoYggiYgQiEIYiA71MR4QYi3ECELudAhC5VSBIIQ/B9SFMoCghDMANViGNQhaEhCAIwo5JlEAQQhqAKQQAiYAa+D0NDVMxAhG/qypUrxHHMhx9+yObNm1ly4sQJ9u/fT623XLlyhTiO+fDDD9m8eTNLTpw4wf79+6n1nitXrhDHMR9++CGbN29myYkTJ9i/fz+12mrr4zZx6dIlnnjiCe68806u27JlC/fccw9Xr15lNR394Q/B9yEIqIyMgBmIUMlzEIEwhCyDMARVKs5BGIIq+D4EAYiAGZiBGeQ5mFExgzwHM0hTKmYgwjJmVEToMgMRulRBBOIYzCDPwQxUIY6p+D6IUBGBIICyBBEQgaEhUKWSpmAGqpDnUBRUkgTimIoZ39SlS5d44oknuPPOO7luy5Yt3HPPPVy9epVa77h06RJPPPEEd955J9dt2bKFe+65h6tXr1LrLZcuXeKJJ57gzjvv5LotW7Zwzz33cPXqVWq11dTHbWLdunW89957/KULFy6wfv16VtPgH/8InQ6kKTgHIpBlkGWgSkUE4hhUWUYEhoYgCCBJQJUuVfB9EKEiAiJ8ITO+kBnLmFExA+fo8n0oCnAOREAVVKEoII5BlRuEISQJiIAIlSiCKIKyBDMoS8hzcA6iCPKcb2rdunW89957/KULFy6wfv16ar1j3bp1vPfee/ylCxcusH79emq9Zd26dbz33nv8pQsXLrB+/XpqtdXUx21iYGCAoaEhfN/n3LlzPP3002zbto3nnnuOVWXG47OzYAaqEARUypKKCBXn6BoagiwD3wcRKEsoCogiuszAOShLuszAjC8kwjcmAnEMYQhhCL4PqlRUIcsgjkGELjPwfVBlGTMYGgJVMAPfhyyDIAARKEtQBVW+qYGBAYaGhvB9n3PnzvH000+zbds2nnvuOWq9ZWBggKGhIXzf59y5czz99NNs27aN5557jlrvGRgYYGhoCN/3OXfuHE8//TTbtm3jueeeo1ZbbX3cRsbHxxkfH+fRRx/l3nvv5a233mL//v2sqjxn9+wsOAd5DkUBzkGeQ1GAGYiACOQ5pCmUJZiBCPg+iECeg3NgBmZgRsU5lhHhC5nxjZmBGRXnoCggz8GMLlUqeQ5mkCQQhpDnYEaXCHQ6IAJxDGEIZQllCWYQxxDHoMq3MT4+zvj4OI8++ij33nsvb731Fvv376fWe8bHxxkfH+fRRx/l3nvv5a233mL//v3UetP4+Djj4+M8+uij3Hvvvbz11lvs37+fWm219XGbuHjxIku2bNnC0aNHeeWVV9i0aRPPP/883ykzuvIcnAMzMINOh4pzUBSQptDpQJ5TUaUiAiKgCiIgQkUEzOgS4UuFIV9JhEpZQhRBWYIqlCU4B0kCzlExg6IAM8hzSFMoCjCjSxWCAJwDMypBAENDkKZURPg2Ll68yJItW7Zw9OhRXnnlFTZt2sTzzz9PrbdcvHiRJVu2bOHo0aO88sorbNq0ieeff55a77l48SJLtmzZwtGjR3nllVfYtGkTzz//PLXaauvjNrJ9+3auO3v2LI1Gg/n5eVZVkvCLH/6QigjEMZw7B+02xDGVMIRz5yCOIY4hy1im3YY4Bt8HVRChYgZm4BwVM5Yx40vlOV9JFeKYrqEhiGMIAigKKEtwDsxAFbKMihkMDUGWgSrLmFExo0uE1bB9+3auO3v2LI1Gg/n5eWq9Z/v27Vx39uxZGo0G8/Pz1HrT9u3bue7s2bM0Gg3m5+ep1VZbH7eJgYEBXn75ZXbu3MkLL7zAnj17KIqCo0ePstp+9zd/Q8UMOh1wDlRBhMrQEF0iEIYQxxAE4Bw4B2EIYcgNwhDCkK4whDgGVb6SCF8pzyGKqAwN0WUGZiACZQlmVJyDNAURSBIQYZkkgaIAMyhLSBIoCihLKs5BnvNtDAwM8PLLL7Nz505eeOEF9uzZQ1EUHD16lFpvGRgY4OWXX2bnzp288MIL7Nmzh6IoOHr0KLXeMzAwwMsvv8zOnTt54YUX2LNnD0VRcPToUWq11dbHbWR0dJQXX3yRt99+m7NnzzI8PMx3RhVUIc/BDMwgiqiUJUQRpCk4B85RSRLIMip5TiXPIc/BjIoZy+Q5pCk4ByIgAiLcwIxlRFhGBETADESgLMEMyhLMwPchjkGVLt8HVXCOG3Q6YAYiVEQgCMD3IQig0wEzvq3R0VFefPFF3n77bc6ePcvw8DC13jQ6OsqLL77I22+/zdmzZxkeHqbWu0ZHR3nxxRd5++23OXv2LMPDw9Rq34U+bnF79+6l2WzSbDZpNpu89NJLfPrppzz00EM0m0127tzJavt1fz/4PjhHpSjAOQhDMlVCETCjYgZpCs5RUQUREKEiAnEMWQaq4BzkOYkICX/BDMzAjBuIsIwZS0Ig409U4dw5aLchDKHdBhEwgziGMARVusIQkgTMIE3BOZYJAioiEAQQhqAKYQhJAr4PZQlmfF179+6l2WzSbDZpNpu89NJLfPrppzz00EM0m0127tzJzZRlGWEYslJJkpAkCSsVhiFZltEL9u7dS7PZpNls0mw2eemll/j000956KGHaDab7Ny5k5spyzLCMGSlkiRh06ZNrFQYhmRZRq/Yu3cvzWaTZrNJs9nkpZde4tNPP+Whhx6i2Wyyc+dObqYsywjDkJVKkoQkSVipMAzJsoza6urjFnf48GFarRatVotWq0Wr1WJycpKJiQlarRanTp3iOxGG0G5Duw1ZBqoQxxDHkGWQZRDHEIYQx6BKVxhCGFJRhSSBMIQ4hnYb2m3IMmi3od2GdhvabWi3IcsgDCHLIMsgjiGOIcug3YZ2G9ptaLeh3YZ2G9ptCAJuIAJZBmHIl1KFOAZVllGFLIM4BlVuoApxDCJ8XYcPH6bVatFqtWi1WrRaLSYnJ5mYmKDVanHq1ClqveHw4cO0Wi1arRatVotWq8Xk5CQTExO0Wi1OnTpFrXccPnyYVqtFq9Wi1WrRarWYnJxkYmKCVqvFqVOnqNVWWx+3OM/z8DwPz/PwPA/P8/A8D8/z8DwPz/P4ToiAKqiCKoiACKhSUQURKqogwl8lAqqgCqqgCqqgCqqgCmEIcQxhCGEIYQhJAqqgCqqgCqqgCqqgCqp8IVUQ4Sup8oVUQRVEuIEIqPJNeJ6H53l4nofneXieh+d5eJ6H53l4nketN3ieh+d5eJ6H53l4nofneXieh+d5eJ5HrXd4nofneXieh+d5eJ6H53l4nofneXieR6222vq4xe3du5e9e/dy8eJFms0mzWaTZrNJs9mk2Wyyc+dObjkidIlwq9i7dy979+7l4sWLNJtNms0mzWaTZrNJs9lk586d1HrD3r172bt3LxcvXqTZbNJsNmk2mzSbTZrNJjt37qTWO/bu3cvevXu5ePEizWaTZrNJs9mk2WzSbDbZuXMntdpq6+MWd/jwYQ4fPszAwACtVotWq8XU1BRTU1O0Wi1OnTpFrTccPnyYw4cPMzAwQKvVotVqMTU1xdTUFK1Wi1OnTlHrDYcPH+bw4cMMDAzQarVotVpMTU0xNTVFq9Xi1KlT1HrH4cOHOXz4MAMDA7RaLVqtFlNTU0xNTdFqtTh16hS12mrr4xbneR6e57HkxIkTqCrDw8Ns27aN0dFRFhYWqPUGz/PwPI8lJ06cQFUZHh5m27ZtjI6OsrCwQK03eJ6H53ksOXHiBKrK8PAw27ZtY3R0lIWFBWq9w/M8PM9jyYkTJ1BVhoeH2bZtG6OjoywsLFCrrbY+bhOXL19mfHycDz74gI8++oiPP/6Yo0eP8vjjj3MzRVFEnuesVJIkJEnCSuV5ThRF9JLLly8zPj7OBx98wEcffcTHH3/M0aNHefzxx7mZoigiz3NWKkkSkiRhpfI8J4oiesnly5cZHx/ngw8+4KOPPuLjjz/m6NGjPP7449xMURSR5zkrlSQJFy5cYKXyPCeKInrN5cuXGR8f54MPPuCjjz7i448/5ujRozz++OPcTFEUkec5K5UkCUmSsFJ5nhNFEbXV1cdt4vLly8RxTH9/P9c9+OCD3HXXXVy9epVa77h8+TJxHNPf3891Dz74IHfddRdXr16l1jsuX75MHMf09/dz3YMPPshdd93F1atXqfWWy5cvE8cx/f39XPfggw9y1113cfXqVWq11dTHbWJwcJBDhw7x5y5dusTc3Bzr16+n1jsGBwc5dOgQf+7SpUvMzc2xfv16ar1jcHCQQ4cO8ecuXbrE3Nwc69ev58v88pe/5Oc//znXtdttfvKTn/DjH/+Y06dPU/ufMTg4yKFDh/hzly5dYm5ujvXr11OrraY+bhNXrlxhbm6O++67j5GREXzf5+GHH2ZxcZFms0mz2eSTTz6h9v135coV5ubmuO+++xgZGcH3fR5++GEWFxdpNps0m00++eQTat9/V65cYW5ujvvuu4+RkRF83+fhhx9mcXGRZrNJs9nkk08+4c/95je/Icsy5ubmWPL73/+eQ4cO8atf/Ypjx45x5MgRzp8/T+3mu3LlCnNzc9x3332MjIzg+z4PP/wwi4uLNJtNms0mn3zyCbXaaujjNjEwMMA777zD5OQkb775JidOnGBycpJWq0Wr1aLVanH//fdT+/4bGBjgnXfeYXJykjfffJMTJ04wOTlJq9Wi1WrRarW4//77qX3/DQwM8M477zA5Ocmbb77JiRMnmJycpNVq0Wq1aLVa3H///Vw3NzfHz372M376059y3a9//Wt+9KMfsWHDBjZs2MCuXbtot9vUbr6BgQHeeecdJicnefPNNzlx4gSTk5O0Wi1arRatVov777+fWm019HGb+Oyzz/A8D8/zWL9+PZcvX2bNmjV4nofneXieR603fPbZZ3ieh+d5rF+/nsuXL7NmzRo8z8PzPDzPo9YbPvvsMzzPw/M81q9fz+XLl1mzZg2e5+F5Hp7n8edefvllnn32We666y6u+/d//3cajQbXiQj/8R//Qe3m++yzz/A8D8/zWL9+PZcvX2bNmjV4nofneXieR622Wvq4DURRxCOPPMKSS5cusX37dp599ll27NhBq9Wi1juiKOKRRx5hyaVLl9i+fTvPPvssO3bsoNVq8VV++ctf8vOf/5zr2u02P/nJT/jxj3/M6dOnqd1cURTxyCOPsOTSpUts376dZ599lh07dtBqtfhL//Zv/8add97JP/zDP/DnPvvsM+644w6+zC9+8Qvuu+8+at+tKIp45JFHWHLp0iW2b9/Os88+y44dO2i1WtRqq62PW9wbb7zBlStXmJqaYsmePXt46qmnePfdd2m324yNjVHrDW+88QZXrlxhamqKJXv27OGpp57i3Xffpd1uMzY2xpf5zW9+Q5ZlzM3NseT3v/89hw4d4le/+hXHjh3jyJEjnD9/ntrN8cYbb3DlyhWmpqZYsmfPHp566ineffdd2u02Y2Nj/KXDhw/zn//5nzzzzDP8y7/8C8453njjDf7+7/+e3/72t1x36dIlBgcHue65557jt7/9LbXvzhtvvMGVK1eYmppiyZ49e3jqqad49913abfbjI2N8VV++ctf8vOf/5zr2u02P/nJT/jxj3/M6dOnqdW+SB+3uGPHjvHqq6+yZs0alpw/f55nnnmGJZs2baKvr4/5+Xlq33/Hjh3j1VdfZc2aNSw5f/48zzzzDEs2bdpEX18f8/Pz/KW5uTl+9rOf8dOf/pTrfv3rX/OjH/2IDRs2sGHDBnbt2kW73aZ2cxw7doxXX32VNWvWsOT8+fM888wzLNm0aRN9fX3Mz8/z5/71X/+Vf/7nf+af/umf+Md//EceeeQRnnjiCRqNBh9++CHXvffeezzyyCPUbp5jx47x6quvsmbNGpacP3+eZ555hiWbNm2ir6+P+fl5vshvfvMbsixjbm6OJb///e85dOgQv/rVrzh27BhHjhzh/Pnz1Gp/qY9b3OLiIj/4wQ9Y8oc//IHFxUU8z+O6zz//nL6+Pmrff4uLi/zgBz9gyR/+8AcWFxfxvP/PHhyG1nWmCZ7/+zqyYpOIYk1BKDfolaH6mVW4s9gDHpNROO/tDy1jbMSwmDKxwz1nmSWB0sXMB8erAee+h2JwGRpqIzldn+Jzbsm4hD9oISmMKNt9zv0ww+JF7irUNjymU/cVpkujRHaTdW8yUirxItXIiWPLkW3JsZTz+/2QRV988QWlUolv+tnPfsZPf/pTfvCDH7Do2rVrlMtlFhljuH79OouGhoYQEQqr48svv+T5559n3scff8yXX37JD3/4QxZ98cUXlEolvm7Lli1s2bKFLVu28Pzzz/Pcc8/R3t6OiBAEAQcOHODgwYOUy2XK5TJrlvesNV9++SXPP/888z7++GO+/PJLfvjDH7Loiy++oFQq8U2ffvopJ0+epL+/n0WXL19m165ddHR00NHRQW9vL1mWUSh8U4l1bt++fZw6dYp57777Ljt37mRRlmVs2rSJ9vZ2npYkSQjDkCflnMM5x5MKw5AkSVgL9u3bx6lTp5j37rvvsnPnThZlWcamTZtob2/n637729+yefNmXn31Vb5udnaWjRs3spRarYaq8jBJkhCGIU/KOYdzjicVhiFJkrAW7Nu3j1OnTjHv3XffZefOnSzKsoxNmzbR3t7OUvr6+nj77bdZdOTIEc6ePcvw8DADAwM8qiRJCMOQJ+Wc40c/+hFPxHvCKCJJEtaKffv2cerUKea9++677Ny5k0VZlrFp0yba29v5pp/97Gf89Kc/5Qc/+AGLrl27RrlcZpExhuvXr7NoaGgIEeFhkiQhDEOelHMO5xxPKgxDkiShsLJKrHP9/f1MTEzQ3d3N+++/z+nTp5n3+uuv8+abb/LOO+9QWBv6+/uZmJigu7ub999/n9OnTzPv9ddf58033+Sdd97hm37+85/z4Ycf8sYbb3D69GnyPOdXv/oV3d3dqCqLZmZm2LZtG2tSnoP3rCX9/f1MTEzQ3d3N+++/z+nTp5n3+uuv8+abb/LOO+/wqNra2mhra2PNy3MWpClrRX9/PxMTE3R3d/P+++9z+vRp5r3++uu8+eabvPPOO3zTb3/7WzZv3syrr77K183OzrJx40aWUqvVUFXWFO8prLwS3wMffPAB4+PjXLlyhba2Nua99dZbjI+P88orr1BYOz744APGx8e5cuUKbW1tzHvrrbcYHx/nlVde4Zt+85vf8Mtf/pJf/OIXvPbaa/T09PCTn/yEcrnM+Pg4iy5dukRPTw9rUhxDHLPWfPDBB4yPj3PlyhXa2tqY99ZbbzE+Ps4rr7zC91azCWEIzSZryQcffMD4+DhXrlyhra2NeW+99Rbj4+O88sorfNPPf/5zPvzwQ9544w1Onz5Nnuf86le/oru7G1Vl0czMDNu2bWPN8h6iCDZsgDynsHJKfE9s3ryZryuXy7zwwgsU1p7NmzfzdeVymRdeeIEH2bJlC1u2bGHLli08//zzPPfcc7S3tyMiBEHAgQMHOHjwIOVymXK5zJqT5+A9pClr0ebNm/m6crnMCy+8wPeW95DnUK1CmrLWbN68ma8rl8u88MILPMhvfvMbfvnLX/KLX/yC1157jZ6eHn7yk59QLpcZHx9n0aVLl+jp6WHN8p4FYQjeU1g5JQqF74m+vj7efvttFh05coSzZ88yPDzMwMAAa1IcQ70OxoD3FNYBY8BaMAa8Z73asmULW7ZsYcuWLTz//PM899xztLe3IyIEQcCBAwc4ePAg5XKZcrnMmuU9GENh5ZUoFL7H2traaGtrY01yjgVhCNZCnlNY4+IYrGWBMZDnfB/09fXx9ttvs+jIkSOcPXuW4eFhBgYGWNO8h85O6OwE7ymsnBKFpyqKItI05Uk553DO8aTSNCWKIgqPLooi0jTlSTnncM6xbN6Dc9BoQJKwIAhIo4goiig8uiiKSNOUJ+Wc449//COPxTnIc6jXSdOUKM/5Pmtra6OtrY3HEUURaZrypJxzOOd4IpOTpM0mURzD5CSFlVOiUCisLWkKk5PQaoExLLCWwhrmHDQakGVgDIV1xHswhsLKK1EoFNYO76HRgCThHsZQWKOcg0YDsgyM4R7eU1jjvIfOTjAGvKewckoUCoW1I47BWh7IGAprTJpCowGtFhhDYR3yHowBY8B7CiunRKFQWBu8hzyHep0HspbCGtNoQJKwpMlJCmuY92AMC4wB7ymsnBKFQmFt8B6MAWMorBPegzEU1pg8B+f4Vt6DMRRWR4lCobA25DkEAUsKAgprjPdgDIU1Js+h0eBbec89jAHvKayMEoVCYW1oNsFaCuuE92AMSzIGvKfwDGo2wXu+lfcQBBRWR4lCofDs8x7yHKzlofKcwhrhPRhDYY3xHrznsRgD3lNYGSUKhcLaYAzfynsKa4T3PJQx4D2FZ4z3YAwYA3NzPNTkJBjDXcaA9xRWRonCQ/3t3/4tf/M3f8OiLMvo6+tj7969jI2N8aiSJCEMQ56Ucw7nHE8qDEOSJKHw6JIkIQxDnpRzDucc9/EenGNBmkK1ysOEYUhiDHhP4dEkSUIYhjwp5xw/+tGPWBbvIQh4kDAMSbIMvKfw6JIkIQxDnpRzDucc94hjCAKWxXswhjAMSZKEwsoqUVjS3//935MkCZ9++inzpqenOXHiBMPDw4yMjDA4OMiNGzcoFFZFFEGjAWkKjQZYy7J4T2ENmJwEYyisId6D9+AcGAOzszySzk7wnsLKKFF4oE8//ZSTJ0/S39/PosuXL7Nr1y46Ojro6Oigt7eXLMsoFFZcmoL3kCQQRWAtWMu3sha8p7AGeA/GUFhD8hyMYYExMDfHQ3kPxlBYHSUKD/Szn/2Mn/70p/zgBz9g0bVr1yiXyywyxnD9+nUKhRXXaECSgLWQZZAkLEtnJ3hPYZ0wBryn8IxoNKBeZ9m8B2O4yxiYnKSwMkoU7vPb3/6WzZs38+qrr/J1s7OzbNy4kaUMDQ0hIjzz8hzyHCoVqFQgTSk8Y/IcjGGBtSybMTA5SWENyHOwlsIakabgPVjLgs5OmJ1lSd6DMRRWT4nCfX7+85/z4Ycf8sYbb3D69GnyPOdXv/oV3d3dqCqLZmZm2LZtG4tqtRqqyjPLexbEMTQaEARQrUIcg/cUnhHegzFgDI/MGPCewjPOezCGwhrhPcQxZBl3GQNzczwSayHPKayMEoX7/OY3v+GXv/wlv/jFL3jttdfo6enhJz/5CeVymfHxcRZdunSJnp4eHkUURaRpypNyzuGcY9m8hyiCSoUFSQLOkQKR95DnFB5NFEWkacqTcs7hnOMu78EYHlWapkSNBuQ5hUcTRRFpmvKknHP88Y9/5EmlaUoURWAMeE/h0URRRJqmPCnnHM45SFOoVsEYls17MIZ5aZoSRREYA95TWBklCvfZsmULW7ZsYcuWLTz//PM899xztLe3IyIEQcCBAwc4ePAg5XKZcrnMM897iGMIAkgSyDLu02xSeEbkOQQBhXXMezCGb2UMeE/hO9ZsgrWsCGPAewpPrkThofr6+nj77bdZdOTIEc6ePcvw8DADAwOsCXnOAufAGO5jDHhP4RnRbIK1PDZjwHsKz7A8B2MorBHegzHcwxiYm2NJ3oMx3MdayHMKT65E4ZG1tbXR1tbGmtFoQLXKkqyFPKfwDPAevAdreWzWQp5TeMZ1dlJYAyYnWWAM9zAGZmdZkvfQ2Ulh9ZQorG/eg/dgLQ9lDHhP4TvmPRhDYZ2bnARj+FadneA9he+Q92AM9zEG5uZ4ZEEAzSaFJ1eisH7lOVQqUK3yrayFPKfwHZmcZEEcQ73Oo0pTaDbBe8gJoNmk8AzzHoyhsAbkOQQBj2xyEoyhsHpKFNanNIU4hiQB5yg84/IcurrAe7CW5XIOurqg2eSuShqSE1B4iryHPGfZvAdj+FbGwOQkhWVKU9iwAdKUFWUtD7RpE8zN8UDegzF8XZ5DSghpSuHJlSisP95DHEO1CtayLEEAzSaF74gxUK1Cvc5y5Tk0GtBqQZJAEIAxkCTQSCk8TXEMcQxxzLJ4D8ZQWGFxDFkG1rJijAFrWQl5DlEEHkPhyZUorD95DtZCGFJYA5pNCAJwDsKQ5YpjSBLuE4aQElJ4iryHep1l8R6MYVmMAe8pLIP3LLAWjIFGg2/lPXjPPbyHSgU2bIA4hmqVJbW3w+wsy9VsgjGQUoU0pfBkShTWlzyHOIZqlUdiLeQ5hafMe8hzvo33kKbgHAvyHLwHa3kgY8B7Ck+D9+A9WAvG8K28B2NYFmshzyksg/dgDHflOeQ5DxXH0NUF3rPAe4hjqFah1YJ6nYfatAnm5ngg78EYFnkP3kOSQIMQmk0KT6ZE4alKkoQwDHlSzjmcc9wjzyGOIUnAWpYjDEOSJAFjwHsKy5ckCWEY8kS8x1mLc46HyXNoNKDZBOcgjqFe5x5hGJIkCfOshTynsExJkhCGIY/FezCGec5aftTezpMKw5B6PSFNKSxXnkMQMC9JEsIkgUaDJXkPeQ5hCHnOAu/BewhDMAbnHM45Hov3YAzzwjCkXk8wBqwFjMGnOYUnU6KwPngPUQRZBtbyWIwB7yk8nPfgPSsjzyEI+DaNBtTrkCQQxywIQx7KewpPQ55DELAgCOD2bR4qzyEIeBjvoVKBRgMiEvCewrdoNsFa7gpDyHPwngeKIqjXIQig2WRBHEO9zrJt2gSzs9zHezCGr8tzCAIWGAMeA96zqvIcvGe9KlFY+7yHNIV6ncLqi2Po6gLneHLNJjkW51iS9+A9WAvGQJZBkvBQQQCTkxSehslJMIa75uZYriiCPOc+eQ7GQJZBSgh5TuEhvMfnHqzlHvU6xDH38R68hzCEMIQ0Be/Be+b7bFQAACAASURBVLCWh4kicI4/a2+HuTkexntIU2g2IQxZYAx4W4UoYkV4D3kO3nOX91CpQByzXpUorH3eQ6MBYcjjynOISCDPKSzNe8hzuHMH4pgnlntD3LQ0GpCmPFCaQrXKXdaCMTyUNZ40pfA0eA/GsCAM4fZtHqrZBGtJU0hTaDS4K03BOWg0oF5ngTHgMRQewHvm5Tl00cI57hWGkKbgPfeIIqjXuSsMoasLrOVhvIc8hzgG71na3BwYwzzvodGALANjWNDZCT4IIQigqwvSlCXlOVQqEEUsKc8hiqCrCyoVcA7iGMIQ0pT1qkRh7fIe8hwaDajXeZA0hSiCNOWh8hy8seTeUPiGPMdXInyaE8dQrbLAGPCex+c9ue8kCKBeh2aT+3gPzSZYyyMx1jDPex5bnkOlAmnK+uM95Dk4B97z2LwH78Fa7nrxRZibY0ne4z00GpBlkOfgPXgPccwCY8BaFlgLeZPCg0QRVCo0opwwhEYD8px7hSHEMQu8B+fAewhD7koSuHMHkoSHSVOwFup1iGNg0yaYm+Nh4hiqVe5hDExOAs5BlkGjAWnKA0URBAF4D85xD+/Be4hjyDK4cweCAIyBzk5IErAW0pT1qERh7cpziGPo7IQw5JvSFBoNqNchjsF7Hsg5mJyEahUak5bCvdIop+ITKpEhz8FOppDnWAt56sF7Hov3NLFYC2EIaQreQxRBpQLeg/cssJZHFpqcPOexxTEEATQa4D3rS55DFLGgUoE851vlOXR1QaXCXd6DMdxj0ya4fZsH8h68xxuL92AtWAtxDFEE9To4B0nCXUEATSyFe237/HPwHm8CUkKqVajXodFggfeQpuDrCeQ55DmkKTSb0GrxOJpNqFbBOUhTljY3C8bgPXgPYcjSjIFqFZpN7uM9C5yDJIE4BucgTaFSgUoFKhWo18EYFjgHYQjOsaBahWaT9ahE4amKoog0TXlSLopwcQz1OjjHgzSbUK+DMVCvQxRBpQLOQaUCzkGapsRxRLUKoclJUwpf8/nn24hxtFrQshGtqsPmMVQqkHfRbMTQ1QXO8cjynHlx7HDOYS10dUFnJ1Sr0NUFUQT1OsuSpilRFLEoqFviGLznkXkP3oNzYAzkOffyHrxnkfesDXkOaQpxDEkCzkGSEEURaaXCkryHSgXqdahWoasLvIc4hmqVRc45/njzJg9lDHkO1rKgXofOTqjXIQxZkKYpURQxzxjIcwrfsO3zz8EYfNVhDFgLYQhpCpVKRBSlNJtQqQBJAlEEjQZkGcvlnMM5xzzvwXuwlgXGwBztMDvLfWbnoLMT78EYSNOUKIpYZC3kOV8xBtKU+3gPxrDAGGi1YHIS4hiqVWi1oNWCMGRJYQidnaxHJQprj3OQ51CtgrU8SJ5DnoO1LAhDSBLIMghDyDIWRBGEIVgLWIvBk+cU/ofPP9+GwbOgWoU4hnodWi2wltQHuPoduuKQNOWR+OYk3liCgAX1OlgLzkEYQpKAMWAtjyUMWVCpwIYN4D3LludgDAuCAJpNvuI9RBF0dUGeU6lApcIzb9vnn0MUQaMB1oK1LLAWrIU8hzTlgfIcrIUwhDCEahWiCPIcwpB7bNoEt2/zQN6DMTSbUK2ywBhwDqzlgawF78F7Cl+z67PPIAhoNKBa5a4whDwHYyBJwBhwuYUkgSzjcaUpVKvcZS3cntsEc3MsJc8hCLiPMeA9X7EWjAHvuUccQxBwlzGQJNBqQRiybM6xHpUorB7vwXvu4z3kOcvmPQu8hyiCyUmoVnkQ7yFNIY6hXucexrDAGBY4B0nCPazxNBoU/oebN2sExrMgDCHLIAzBGBZNTkKSQCPK6eqCSgUaKd/K5x5juMtayDLuCkPIMp5IlnjqPqJuc+KYBXkOXV2Q59wrivAbuogqnjiGJGFBaD1pCqQpVCqkUY7zVcgy8qiB92AMpCnPtG2ffw7GQJZBknAfY6DZ5D7eQ6MB9Tp3OccCa3kkeQ5BgPdgDMsWWk+es7Q0Be9ZiveQ56wr//bTT/HGkqZgLXdVq2AtBAELsgyaTeiKLBjD42o2wVru6uyE2VkebG4WjKHZBGt5IGPAe75iLeQ597GWx+E9RBFEEetWicLq8B6iCOIY31WBNOWuZhMaDejqYkneg/eQ5xBFUKlApQJBAEnCUioV8B7qdQhDHlnVenzuKfzZZ5/twgZ8xVq+LkkgScCGhizxtOiinlf4T/+Jh/OenIAgYFUZawizKo6YNIXUeeIYrIVGg694D3mOD+v43NPKPMYAaQqVCgaPb3pSUyf2IVRDNlQsEQn1qidJeObVbt6EIGBJxnAf7yGKIAjAWu6RJJBlLMpzaDaBF1+E27dZip9kgTEsW2dgaEYpeM99nIMogijiLudwGxyVLo9zUKlApQJ5zgN5D5UKbNgAzvHs855dn32GNxZjwFrushaM4R5ZBtUqRBFL8h7SFLznPt6D92AtdxkDc3PApk0wN8c95ubwGPIcrGVJ3vOVIIA45i7vwXuwlkflPaQpC/Ic8px1qURhdaQpLg+o+IQun1GJDK4rJU2h4QNcZwLWgnPcxzmoVCCKoNGAahWyDFotCEOW4hxYC86BtTwWm4TgPWnKY/Ee8pz75DlrjvfwH9r+M9ayPGEIrRa2lWDJuX2bpeU5TSzWsvqshSyjlXniGIyBJIE8hyiCSgVcDBhD7EPqpgFxDGkKcQxZhg0NkXfEuSXLwDlotSCr54RxFwZPGPLs8p5dn31GjsU5SFOoVGDDBogi/qxahTTlLu+hUoEgAOe4jzF8XZ5DnsO/zG2CuTkeqNkkx2IMj8QY/iyOmZemEEWQRjk0GtBqsSDPIc+JYsNk6KibFNNMSRLIMqhUIE35ivfgHFHFE5BzJ0lpNCDPebYZw+XNm2k0oFplWZyDNOUe3rPAe4giaDahqwu85x55DtZyjzCEmzd5KGNYkrXgPV8JQ7ytEnXl5BUHlQrU6yyH91CpQFcXdHVBpcKCJIEkAe9Zl0oUVoVvTtIwjiyDO3cgqFuohlgL1bqh0QDXmeAbTZyDKAKfe6hU8I0mtFqQZZAkpIREEVQqEEWQptzHe2g0IEl4YvXE0IxSKhXIo5RvSlNwDpyDNAXnIIqgUoEogiiCDRsgTcF7qHR54sjT1QWp8+SpJ00hTXmmGQP/+3P/mUdmDNUQbt5kSb7RxBuLtTw1xhpaYUzS6ZiXJBB0ehJfoZl6KmR4D7aVQJpCHEOSgDHU61CtQpaBMSwwBkxoIQyhUoE855llDL/o+NdEDcvkJDQaUK3CnTuQpuA9f2YMeM+CKIJqFZzj23gPcQzWwu3bcJsXYW6O+3hPIzfU6zySMISUENIUn3viyBPkjii15EkLjIF6HSoVfCUiNyFJAjZzhHmENR5rIcsgjiGKAOegUiFtgMfgiKHZJEkgz3nmHXxpN3kO1rJs1kKassA5qFRgwwaoVMAYSBIIQ8hz7pqchDiGep37bNoEc7TD7Cz3mJ3DYzCGJVWrEMeQ59yVdjq8sVRyR5608DakqwucA+8himDDBkhT7kpTqFSgWoUsgyyDVgucY4G1EIasSyUKjyTLMvr6+ti7dy9jY2M8yLbPPyfOA6zlLufAOTCGBVkGzSZ0+QwaKVXviCLo8hkVMioV2LABNmyAZpMFSQLVKjSb0GhAswlRBM5BpQJJwoqwoSG5E1IPcippyIYNEEUQRVCpQKPBAmPAezAGAp+S+AqJr9Cqp7TqKcSOqOIJLGQmIjGOOIYoNvimpxHlVCp8Z7Iso6+vj7179zI2NsaD7PrsM7CWR/XXfw03b7KknABrefqSBOIYvMf6lLBRwVQDspYhCKBe58+yDOp1sJZ5xkAYgjHcL0kgy8BavitZltHX18fevXsZGxvjQRyOahWSBLIMwpAFSQJ5DnEMFTLS2OPTHLwH55jnPTgHlQo4x328B2shCFhwk61w+zb3mJsj9waPwVoemTGQm5A0yrHkhHaSLIMogjQFbyy+dYc8aWEtX0kSqFQgTbHG08o85DldcUhqE2IcWQZkGSQJ1oJzfKeyLKOvr4+9e/cyNjbGg9y8WcNasJZlq9eh0YAogkYDkgTu3IFWC5KEBUEAzSZ3pSnU62AM93nxRbg9twnm5rjH3Cy5NwQBS7IW6nWIItiwAbq6oNGAJIFWCyoVqFSgWoVGAyoV6OyEJIFGgwXeQxxDkkAYgjFgDN8bJQrLNj09zYkTJxgeHmZkZITBwUFu3LjBN+38f/8nchOSJCzJGMgyaLXAtUJsEpK1DK0WtFqQJHDnDrRakCSQJGAMWAtJAtUqGAPVKhgDWQbWsqKss9y5A60WVDtzqnlEYhwZFdxkROgdbjIibFQIaWCSOqaVgfcYA2FiyVoGlxjIMmzmaN0xtFrgEkPWMlRNDt7ztE1PT3PixAmGh4cZGRlhcHCQGzducA/v+ae2Nh6Ltbz4Ity8yf2uX6eRG6pVvhv1OlQqEMeQZeAcGINzEIb8mbUQhiybMXxXpqenOXHiBMPDw4yMjDA4OMiNGzf4ps8+20UYcp8whDCEahXqicF76IoszmY4B1EElQoL6nVoNsE5FngPzkEUQb3OghdfhJts5T4zM8TUqdd5LNUqRL5Og5Bq3UCSYC0kCTQaUKlApQJxDNUqXwlDqNchjqFSgUqFpO6pJ4YmlnodjOGZMT09zYkTJxgeHmZkZITBwUFu3LjBgyQJj8RaSBIIAmi1wFruE4aQppDn0GxCGEIY8kAvvgi3eZF7zM2BMTSbYC0PFYbQasGdO5Ak0GqBMWAMtFqQZeAcZBkkCTgHYQjeg/eQpmAtWMv3UonCsl2+fJldu3bR0dFBR0cHvb29ZFnGPbxn/ObfkCQsizH8mTF8nTEsMIYldXaCtRCGYAyrxhiwzmJbCaYeQpZBEIAxUK1ClkGWgbUscA7CEKzloYwhTCwYw9N2+fJldu3aRUdHBx0dHfT29pJlGfcwhtf/4i94XFu3wk1/G+bm+LqJqa14Y7GW74ZzUK9DloExrHWXL19m165ddHR00NHRQW9vL1mW8U1/8RevYwxLMgasBZcYWpmHTsO8ahVaLXAOrIUkgTiGSgUqFZichCQBa1nwwguwaRPM3bzN193+F/DGEoY8FucgaxmSBKyzLLIWsgxaLWi1oNUCa7lXGEKrBa0WtFoQhoQhJAmEIc+Uy5cvs2vXLjo6Oujo6KC3t5csy/iml176P3gcxkAY8lD1OkQReA+dnSxp61a4yVbmbt7m69S34z1Yy7JZyz2MAWNYYAxYy13WQhRBswn1Ot9bJQrLdu3aNcrlMouMMVy/fp1FQ0NDbP+r/43/0vZj3nhDEBFEBBFBRBARRkdHGRgYQEQQEUQEEUFEEBFEBBFBRBARRAQRQUQQEUQEEeHUqVOcOnUKEUFEEBFEBBFBRBARRAQRQUQQEUQEEUFEEBFEhIGBAUZHRxERRAQRQUQQEUQEEUFEEBFEBOntRUSQEyeQEyeQN95ARBARRAQRQUQQEUQEEUFEEBFEBBFBRBARRIShoSG+C9euXaNcLrPIGMP169dZNDQ0hIjwT21tiAgigoggIogIIsLo6CgDAwOICCKCiCAiiAiv9rzKf5v6B/6/UonJf7jJ73/3Eb//3Uf8/ncf8U///b/zpz+9joggIpw6dYpTp04hIogIIoKIICKICCKCiCAiiAgigoggIogIIoKIMDAwwOjoKCKCiCAiiAgigoggIogIIoKcOIH09iIiiAgigoggIogIIoKIICKICCKCiCAiiAgigoggIogIIsLQ0BDfhWvXrlEul1lkjOH69essGhoaQkRoa/snRAQRQUQQEUQEEWF0dJSBgQFEBOntpfeNXn79a+HXvxbeeEMQEUQEEaG3V/jLvxT+8R9fZ9Mm4b/+V+GNNwQR4dSpU3z80Ud88cU/88fbL/KPf//f+P3vPuL3v/uIyX/p4E9/eh0RQUQQEUQEEUFEEBFEBBFhYGCA0dFRRAQRQUQQEXp7hTfeEEQEEUFEEBFEBBFBRBARRAQRQUQQEUQEEUFEEBFEBBFBRBARRAQRQUQQEYaGhviuXLt2jXK5zCJjDNevX2fR0NAQIsI8EUFEEBFEBBFBRBgdHWVgYAARQUQQEUQEEUFEEBFEBBFBRBARRAQRQUT49a+FDRv+itu3T3Hq1ClEBBFBRBARRAQR4eo//AMbN/4zf7z9Itd+N83vf/cR1679M//U9hx/+tPriAgiwsDAAKOjo4gIIoKIICKICCKCiCAiiAgigoggIogIIoKIICKICCLCxYuvc+XK/8Xk5F/R2yuICCKCiCAiiAgigoggIgwNDbEelSgs2+zsLBs3bmQptVqNP/zh75ib24aqoqqoKqqKqqKqfPLJJ0xNTaGqqCqqiqqiqqgqqso8VUVVUVVUFVVFVVFVVJWZmRlmZmZQVVQVVUVVUVVUFVVFVVFVVBVVRVVRVVQVVUVVmZqa4qWXXkJVUVVUFVVFVVFVVBVVRVVRVVQVVUVVUVVUFVVFVVFVVBVVRVVRVVQVVUVVUVXmqSqqiqpSq9X4LszOzrJx40aWUqvVUFVUFVVFVVFVVBVVRVX55JNPmJqaQlVRVVQVVUVV+X/+7//Cm33/jv/zrcP849V/xczEj5mZ+DEzEz/m9b/+V9y4MYyqoqrMzMwwMzODqqKqqCqqiqqiqqgqqoqqoqqoKqqKqqKqqCqqytTUFC+99BKqiqqiqqgqqoqqoqqoKqqKqqKqqCqqiqqiqqgqqoqqoqqoKqqKqqKqqCqqiqoyT1VRVVSVWq3Gd2F2dpaNGzeylFqthqqiqqgqqoqqoqqoKqrKJ598wtTUFKqKqqKqqCqqiqqiqsxTVVQVVeXGjWFUFVVFVVFVZmZm6P9fA/b/G8OH17Yz+Q/CzMSPmZn4Mf/zD7dx48YwqoqqoqqoKqqKqqKqqCqqytTUFC+99BKqiqqiqqgqqoqqoqqoKqqKqqKqqCqqiqqiqqgqqoqqoqqoKqqKqqKqqCqqiqoyT1VRVVSVWq3Gd2V2dpaNGzeylFqthqqiqqgqqoqqoqqoKqrKJ598wtTUFKqKqqKqqCqqiqqiqsxTVVQVVUVVUVVUFVXlD3/4O2ZmZpiZmUFVUVVUFVVFVVFV3uz7d+z/N4YPr21nauIvmZn4MVMTf8m//cv/hRs3hlFVVJWpqSleeuklVBVVRVVRVVQVVUVVUVVUFVVFVVFVVBVVRVVRVVQVVeXGjWE++eTf84c//B2qiqqiqqgqqso8VUVVUVVqtRrrUYnCsnV3d6OqLJqZmWHbtm2stP7+fp6m/v5+npb+/n6eBd3d3agqi2ZmZti2bRsr7T/+x3+Pc+AcOAfOgTGsmv7+fp6W/v5+ngXd3d2oKotmZmbYtm0bK62/v5/lMgacA+fAOXAOrOWR9Pf387T09/fzrOju7kZVWTQzM8O2bdtYaf39/awEY8A5cA6cA+fAWu7T39/P09Lf38/3QYnCspXLZcbHx1l06dIlenp6WGm1Wo2nqVar8bTUajWeBeVymfHxcRZdunSJnp4eVlqtVuNpqtVqPC21Wo1nQblcZnx8nEWXLl2ip6eHlVar1XiaarUaT0utVuNZUS6XGR8fZ9GlS5fo6elhpdVqNZ6mWq3G01Kr1fg+KFFYNhEhCAIOHDjAwYMHKZfLlMtlCmuPiBAEAQcOHODgwYOUy2XK5TKFtUdECIKAAwcOcPDgQcrlMuVymcLaJCIEQcCBAwc4ePAg5XKZcrlMofBNJQqP5MiRI5w9e5bh4WEGBgYorF1Hjhzh7NmzDA8PMzAwQGHtOnLkCGfPnmV4eJiBgQEKa9uRI0c4e/Ysw8PDDAwMUCg8SInCI2tra6OtrY3C2tfW1kZbWxuFta+trY22tjYK60NbWxttbW0UCkspUSgUCoVCobDGlCg8c86cOUNfXx979uxhcHCQ1ZJlGX19fezdu5exsTFW25kzZ+jr62PPnj0MDg7yfXDmzBn6+vrYs2cPg4ODrJYsy+jr62Pv3r2MjY2x2s6cOUNfXx979uxhcHCQ74MzZ87Q19fHnj17GBwcZLVkWUZfXx979+5lbGyM1XbmzBn6+vrYs2cPg4ODfF+cOXOGvr4+9uzZw+DgIKslyzL6+vrYu3cvY2NjrLYzZ87Q19fHnj17GBwcZD0rUXimXLlyhdHRUc6dO8f58+cZHx9nbGyMlTY9Pc2JEycYHh5mZGSEwcFBbty4wWq5cuUKo6OjnDt3jvPnzzM+Ps7Y2Bjr2ZUrVxgdHeXcuXOcP3+e8fFxxsbGWGnT09OcOHGC4eFhRkZGGBwc5MaNG6yWK1euMDo6yrlz5zh//jzj4+OMjY2xnl25coXR0VHOnTvH+fPnGR8fZ2xsjJU2PT3NiRMnGB4eZmRkhMHBQW7cuMFquXLlCqOjo5w7d47z588zPj7O2NgY692VK1cYHR3l3LlznD9/nvHxccbGxlhp09PTnDhxguHhYUZGRhgcHOTGjRuslitXrjA6Osq5c+c4f/484+PjjI2NsV6VKDxTtm7dyvHjx2lvb6dUKrF7925UlZV2+fJldu3aRUdHBx0dHfT29pJlGatl69atHD9+nPb2dkqlErt370ZVWc+2bt3K8ePHaW9vp1QqsXv3blSVlXb58mV27dpFR0cHHR0d9Pb2kmUZq2Xr1q0cP36c9vZ2SqUSu3fvRlVZz7Zu3crx48dpb2+nVCqxe/duVJWVdvnyZXbt2kVHRwcdHR309vaSZRmrZevWrRw/fpz29nZKpRK7d+9GVVnvtm7dyvHjx2lvb6dUKrF7925UlZV2+fJldu3aRUdHBx0dHfT29pJlGatl69atHD9+nPb2dkqlErt370ZVWa9KFJ4pnZ2d7Nixg3m3bt1iZGSE/fv3s9KuXbtGuVxmkTGG69evs1o6OzvZsWMH827dusXIyAj79+9nPevs7GTHjh3Mu3XrFiMjI+zfv5+Vdu3aNcrlMouMMVy/fp3V0tnZyY4dO5h369YtRkZG2L9/P+tZZ2cnO3bsYN6tW7cYGRlh//79rLRr165RLpdZZIzh+vXrrJbOzk527NjBvFu3bjEyMsL+/ftZ7zo7O9mxYwfzbt26xcjICPv372elXbt2jXK5zCJjDNevX2e1dHZ2smPHDubdunWLkZER9u/fz3pVovCdmpiY4OLFi1y8eJGPP/6YRdPT0xw+fJijR4+yfft2Vtrs7CwbN27kaZuenubw4cMcPXqU7du3s55MTExw8eJFLl68yMcff8yi6elpDh8+zNGjR9m+fTsrbXZ2lo0bN/K0TU9Pc/jwYY4ePcr27dtZTyYmJrh48SIXL17k448/ZtH09DSHDx/m6NGjbN++nZU2OzvLxo0bedqmp6c5fPgwR48eZfv27aw3ExMTXLx4kYsXL/Lxxx+zaHp6msOHD3P06FG2b9/OSpudnWXjxo08bdPT0xw+fJijR4+yfft21qsShe/UxMQEFy5c4MKFC3z00UfMu3r1KocOHeLYsWPs27eP1dDd3Y2qsmhmZoZt27axmq5evcqhQ4c4duwY+/btY72ZmJjgwoULXLhwgY8++oh5V69e5dChQxw7dox9+/axGrq7u1FVFs3MzLBt2zZW09WrVzl06BDHjh1j3759rDcTExNcuHCBCxcu8NFHHzHv6tWrHDp0iGPHjrFv3z5WQ3d3N6rKopmZGbZt28Zqunr1KocOHeLYsWPs27eP9WhiYoILFy5w4cIFPvroI+ZdvXqVQ4cOcezYMfbt28dq6O7uRlVZNDMzw7Zt21hNV69e5dChQxw7dox9+/axnpUofKdee+01Tp48ycmTJ3n55ZeZmpqiVqsxNDREEASslnK5zPj4OIsuXbpET08Pq2VqaoparcbQ0BBBELAevfbaa5w8eZKTJ0/y8ssvMzU1Ra1WY2hoiCAIWC3lcpnx8XEWXbp0iZ6eHlbL1NQUtVqNoaEhgiBgPXrttdc4efIkJ0+e5OWXX2ZqaoparcbQ0BBBELBayuUy4+PjLLp06RI9PT2slqmpKWq1GkNDQwRBwHr12muvcfLkSU6ePMnLL7/M1NQUtVqNoaEhgiBgtZTLZcbHx1l06dIlenp6WC1TU1PUajWGhoYIgoD1rkThmfLee+8xPT3NoUOH2LlzJzt37uTkyZOsNBEhCAIOHDjAwYMHKZfLlMtlVst7773H9PQ0hw4dYufOnezcuZOTJ0+ynr333ntMT09z6NAhdu7cyc6dOzl58iQrTUQIgoADBw5w8OBByuUy5XKZ1fLee+8xPT3NoUOH2LlzJzt37uTkyZOsZ++99x7/P3vwAhZlgSj+/+vrwjBIG22wGh1XLB/vVxCy2TUuD6TiBc1FhTSZpSnlzMG8nCWeBhxj1MxVu4ywtRiDKGk+eKgl8jJNM44il8MYm2QY6bjuoLNiIreZZBj+z+t5Zv+saZffqbNq8/nY7XaefPJJwsLCCAsLY9OmTfzQRowYQVRUFElJSSxatIhx48Yxbtw4fiw7duzAbrfz5JNPEhYWRlhYGJs2beJut2PHDux2O08++SRhYWGEhYWxadMmfmgjRowgKiqKpKQkFi1axLhx4xg3bhw/lh07dmC323nyyScJCwsjLCyMTZs2cbcS8LqtqFQqGhoasFgsWCwWLBYLmZmZ/BhWrFhBSUkJxcXFZGVl8WNSqVQ0NDRgsViwWCxYLBYyMzO5m6lUKhoaGrBYLFgsFiwWC5mZmfwYVqxYQUlJCcXFxWRlZfFjUqlUNDQ0YLFYsFgsWCwWMjMzuZupVCoaGhqwWCxYLBYsFguZmZn8GFasWEFJSQnFxcVkZWXxY1KpVDQ0NGCxWLBYLFgsFjIzM7nbqVQqGhoasFgsWCwWLBYLmZmZ/BhWrFhBSUkJxcXFZGVl8WNSqVQ0NDRgsViwWCxYLBYyMzO5Wwl4/aT5+Pjg4+OD153Px8cHHx8fvO58Pj4+jSZ+PgAAIABJREFU+Pj44HV38PHxwcfHB68floCXl5eXl5eX1x1GwMvLy8vLy8vrDiPg5eXl5eXl5XWHEfDy8vLy8vLyusMIeP0gSkpKUKlUqFQqVCoVKpUKlUqFSqVCtHHjRn4sBoMBi8WCqKuri7y8PDIyMlixYgU7d+6kp6eHf5UzZ85QWFjIrTgcDrRaLbeTkpISVCoVKpUKlUqFSqVCpVKhUqkQbdy4kR+LwWDAYrEg6urqIi8vj4yMDFasWMHOnTvp6enhX+XMmTMUFhZyKw6HA61Wy+2kpKQElUqFSqVCpVKhUqlQqVSoVCpEGzdu5MdiMBiwWCyIurq6yMvLIyMjgxUrVrBz5056enr4Vzlz5gyFhYXcisPhQKvVcrspKSlBpVKhUqlQqVSoVCpUKhUqlQrRxo0b+bEYDAYsFguirq4u8vLyyMjIYMWKFezcuZOenh7+Vc6cOUNhYSG34nA40Gq13G0EvH4Qw4YNIyIigoiICCoqKrjnnnuIiIggIiKC06dP093dzY+hra2NN998k7CwMETPPPMM58+fZ968ecyePZsPP/yQVatW8a9y8eJFjhw5wq1IpVLa29uprq7mdjFs2DAiIiKIiIigoqKCe+65h4iICCIiIjh9+jTd3d38GNra2njzzTcJCwtD9Mwzz3D+/HnmzZvH7Nmz+fDDD1m1ahX/KhcvXuTIkSPcilQqpb29nerqam4Xw4YNIyIigoiICCoqKrjnnnuIiIggIiKC06dP093dzY+hra2NN998k7CwMETPPPMM58+fZ968ecyePZsPP/yQVatW8a9y8eJFjhw5wq1IpVLa29uprq7mdjJs2DAiIiKIiIigoqKCe+65h4iICCIiIjh9+jTd3d38GNra2njzzTcJCwtD9Mwzz3D+/HnmzZvH7Nmz+fDDD1m1ahX/KhcvXuTIkSPcilQqpb29nerqau4mAl4/iMjISBITE0lMTMTX15cpU6aQmJhIYmIigwYNIiEhgba2NhobG2lpaUGv19PU1ISosbERg8FAa2srfVksFvR6Pc3NzdyKTqcjMTERkcPhwGKxsHHjRmJiYoiLiyM/P5/q6mrcbjceFosFvV5Pc3MzfVksFvR6PXa7nb5qamrQ6/U0Nzcjamtro7GxkdbWVgwGAydOnOBGZrMZk8lEX19++SUGgwGTyUR3dzceS5cuZdOmTdwuIiMjSUxMJDExEV9fX6ZMmUJiYiKJiYkMGjSIhIQE2traaGxspKWlBb1eT1NTE6LGxkYMBgOtra30ZbFY0Ov1NDc3cys6nY7ExEREDocDi8XCxo0biYmJIS4ujvz8fKqrq3G73XhYLBb0ej3Nzc30ZbFY0Ov12O12+qqpqUGv19Pc3Iyora2NxsZGWltbMRgMnDhxghuZzWZMJhN9ffnllxgMBkwmE93d3XgsXbqUTZs2cbuIjIwkMTGRxMREfH19mTJlComJiSQmJjJo0CASEhJoa2ujsbGRlpYW9Ho9TU1NiBobGzEYDLS2ttKXxWJBr9fT3NzMreh0OhITExE5HA4sFgsbN24kJiaGuLg48vPzqa6uxu1242GxWNDr9TQ3N9OXxWJBr9djt9vpq6amBr1eT3NzM6K2tjYaGxtpbW3FYDBw4sQJbmQ2mzGZTPT15ZdfYjAYMJlMdHd347F06VI2bdrE7SQyMpLExEQSExPx9fVlypQpJCYmkpiYyKBBg0hISKCtrY3GxkZaWlrQ6/U0NTUhamxsxGAw0NraSl8WiwW9Xk9zczO3otPpSExMRORwOLBYLGzcuJGYmBji4uLIz8+nuroat9uNh8ViQa/X09zcTF8WiwW9Xo/dbqevmpoa9Ho9zc3NiNra2mhsbKS1tRWDwcCJEye4kdlsxmQy0deXX36JwWDAZDLR3d2Nx9KlS9m0aRN3EwGvH93JkydRKpWcPHmS1atXs3LlSgwGA8nJyeTk5PD666+zf/9+Zs+ejdvtRqRUKtm2bRu1tbWkpqZSUVHBzezdu5e4uDhEEomEQYMG8eKLL9LQ0IDI39+fqqoqBEFApFQq2bZtG7W1taSmplJRUYHo2Wef5dVXX6W2tpZFixZx9OhR3G43crmc7du3c/ToUZKTkyktLeXkyZOsXr2a9PR0DAYDq1evJj8/H1FPTw+LFi2ipKSEgwcPkpOTg+jMmTMsWLCA48ePU15ezowZM3A4HIhCQkLo6emhqamJ293JkydRKpWcPHmS1atXs3LlSgwGA8nJyeTk5PD666+zf/9+Zs+ejdvtRqRUKtm2bRu1tbWkpqZSUVHBzezdu5e4uDhEEomEQYMG8eKLL9LQ0IDI39+fqqoqBEFApFQq2bZtG7W1taSmplJRUYHo2Wef5dVXX6W2tpZFixZx9OhR3G43crmc7du3c/ToUZKTkyktLeXkyZOsXr2a9PR0DAYDq1evJj8/H1FPTw+LFi2ipKSEgwcPkpOTg+jMmTMsWLCA48ePU15ezowZM3A4HIhCQkLo6emhqamJ293JkydRKpWcPHmS1atXs3LlSgwGA8nJyeTk5PD666+zf/9+Zs+ejdvtRqRUKtm2bRu1tbWkpqZSUVHBzezdu5e4uDhEEomEQYMG8eKLL9LQ0IDI39+fqqoqBEFApFQq2bZtG7W1taSmplJRUYHo2Wef5dVXX6W2tpZFixZx9OhR3G43crmc7du3c/ToUZKTkyktLeXkyZOsXr2a9PR0DAYDq1evJj8/H1FPTw+LFi2ipKSEgwcPkpOTg+jMmTMsWLCA48ePU15ezowZM3A4HIhCQkLo6emhqamJO8HJkydRKpWcPHmS1atXs3LlSgwGA8nJyeTk5PD666+zf/9+Zs+ejdvtRqRUKtm2bRu1tbWkpqZSUVHBzezdu5e4uDhEEomEQYMG8eKLL9LQ0IDI39+fqqoqBEFApFQq2bZtG7W1taSmplJRUYHo2Wef5dVXX6W2tpZFixZx9OhR3G43crmc7du3c/ToUZKTkyktLeXkyZOsXr2a9PR0DAYDq1evJj8/H1FPTw+LFi2ipKSEgwcPkpOTg+jMmTMsWLCA48ePU15ezowZM3A4HIhCQkLo6emhqamJu4WA1/+ps2fP8sYbb7BhwwamTZtGS0sLWq0WrVaLj48PH3/8MSaTCbvdTnFxMVlZWRQXF7N27Vrcbjd9XbhwAYfDQXBwMCJBENi1axcOh4OUlBQmTJjAmjVraGpqQmQymbDb7RQXF5OVlUVxcTFr167FYDDQ2tpKUVERWVlZrF+/nsbGRg4cOIDD4aCoqAi1Ws2uXbtQq9WMHTuWs2fPUlBQgEajITs7m0OHDiE6ePAgEomE/Px8NmzYQGpqKqL6+npGjhzJCy+8wObNm3n++efp6OjAY+TIkXzyySfcSc6ePcsbb7zBhg0bmDZtGi0tLWi1WrRaLT4+Pnz88ceYTCbsdjvFxcVkZWVRXFzM2rVrcbvd9HXhwgUcDgfBwcGIBEFg165dOBwOUlJSmDBhAmvWrKGpqQmRyWTCbrdTXFxMVlYWxcXFrF27FoPBQGtrK0VFRWRlZbF+/XoaGxs5cOAADoeDoqIi1Go1u3btQq1WM3bsWM6ePUtBQQEajYbs7GwOHTqE6ODBg0gkEvLz89mwYQOpqamI6uvrGTlyJC+88AKbN2/m+eefp6OjA4+RI0fyySefcCc5e/Ysb7zxBhs2bGDatGm0tLSg1WrRarX4+Pjw8ccfYzKZsNvtFBcXk5WVRXFxMWvXrsXtdtPXhQsXcDgcBAcHIxIEgV27duFwOEhJSWHChAmsWbOGpqYmRCaTCbvdTnFxMVlZWRQXF7N27VoMBgOtra0UFRWRlZXF+vXraWxs5MCBAzgcDoqKilCr1ezatQu1Ws3YsWM5e/YsBQUFaDQasrOzOXToEKKDBw8ikUjIz89nw4YNpKamIqqvr2fkyJG88MILbN68meeff56Ojg48Ro4cySeffMKd5uzZs7zxxhts2LCBadOm0dLSglarRavV4uPjw8cff4zJZMJut1NcXExWVhbFxcWsXbsWt9tNXxcuXMDhcBAcHIxIEAR27dqFw+EgJSWFCRMmsGbNGpqamhCZTCbsdjvFxcVkZWVRXFzM2rVrMRgMtLa2UlRURFZWFuvXr6exsZEDBw7gcDgoKipCrVaza9cu1Go1Y8eO5ezZsxQUFKDRaMjOzubQoUOIDh48iEQiIT8/nw0bNpCamoqovr6ekSNH8sILL7B582aef/55Ojo68Bg5ciSffPIJdwsBr/9TgYGB+Pv7I/L19WXQoEF4DBkyBKfTSWVlJS0tLSiVSpRKJbm5uXR2dmK32+mrqamJ4cOH01dISAgbN27kxIkT7Ny5k+DgYBYsWIDdbqeyspKWlhaUSiVKpZLc3Fw6Ozs5deoUI0aMwEMmk5GWlkZVVRUymQyPwYMH4+fnh9FoJDAwEH9/f0QSiYSWlhZEVVVVPPzww3hEREQgmjp1KmfOnGHKlCmsWbMGPz8/goOD8RgwYAD19fXcSQIDA/H390fk6+vLoEGD8BgyZAhOp5PKykpaWlpQKpUolUpyc3Pp7OzEbrfTV1NTE8OHD6evkJAQNm7cyIkTJ9i5cyfBwcEsWLAAu91OZWUlLS0tKJVKlEolubm5dHZ2curUKUaMGIGHTCYjLS2NqqoqZDIZHoMHD8bPzw+j0UhgYCD+/v6IJBIJLS0tiKqqqnj44YfxiIiIQDR16lTOnDnDlClTWLNmDX5+fgQHB+MxYMAA6uvruZMEBgbi7++PyNfXl0GDBuExZMgQnE4nlZWVtLS0oFQqUSqV5Obm0tnZid1up6+mpiaGDx9OXyEhIWzcuJETJ06wc+dOgoODWbBgAXa7ncrKSlpaWlAqlSiVSnJzc+ns7OTUqVOMGDECD5lMRlpaGlVVVchkMjwGDx6Mn58fRqORwMBA/P39EUkkElpaWhBVVVXx8MMP4xEREYFo6tSpnDlzhilTprBmzRr8/PwIDg7GY8CAAdTX13OnCQwMxN/fH5Gvry+DBg3CY8iQITidTiorK2lpaUGpVKJUKsnNzaWzsxO73U5fTU1NDB8+nL5CQkLYuHEjJ06cYOfOnQQHB7NgwQLsdjuVlZW0tLSgVCpRKpXk5ubS2dnJqVOnGDFiBB4ymYy0tDSqqqqQyWR4DB48GD8/P4xGI4GBgfj7+yOSSCS0tLQgqqqq4uGHH8YjIiIC0dSpUzlz5gxTpkxhzZo1+Pn5ERwcjMeAAQOor6/nbiHg9X9KEAS+jZ+fH5GRkWg0GjQaDRqNhsrKSoKCguirX79+dHR04FFTU8PmzZsRCYLAhAkTyMzMZNKkSdTX1+Pn50dkZCQajQaNRoNGo6GyspLg4GDcbjceDoeDU6dOIZFI6OzspK+enh4CAgIQBIGbkUqluFwuPK5du4YoKCiIiooKCgsLGTVqFJmZmZSXl+PhdruRSCTcSQRB4Nv4+fkRGRmJRqNBo9Gg0WiorKwkKCiIvvr160dHRwceNTU1bN68GZEgCEyYMIHMzEwmTZpEfX09fn5+REZGotFo0Gg0aDQaKisrCQ4Oxu124+FwODh16hQSiYTOzk766unpISAgAEEQuBmpVIrL5cLj2rVriIKCgqioqKCwsJBRo0aRmZlJeXk5Hm63G4lEwp1EEAS+jZ+fH5GRkWg0GjQaDRqNhsrKSoKCguirX79+dHR04FFTU8PmzZsRCYLAhAkTyMzMZNKkSdTX1+Pn50dkZCQajQaNRoNGo6GyspLg4GDcbjceDoeDU6dOIZFI6OzspK+enh4CAgIQBIGbkUqluFwuPK5du4YoKCiIiooKCgsLGTVqFJmZmZSXl+PhdruRSCTcaQRB4Nv4+fkRGRmJRqNBo9Gg0WiorKwkKCiIvvr160dHRwceNTU1bN68GZEgCEyYMIHMzEwmTZpEfX09fn5+REZGotFo0Gg0aDQaKisrCQ4Oxu124+FwODh16hQSiYTOzk766unpISAgAEEQuBmpVIrL5cLj2rVriIKCgqioqKCwsJBRo0aRmZlJeXk5Hm63G4lEwt1CwOu2M3XqVGpqahAEgcDAQGw2GzNnzkQQBPoKCwvj3LlzeDzwwAPs3LmTQ4cO4fH555/T0NDA8OHDmTp1KjU1NQiCQGBgIDabjZkzZ7JgwQJqamro6upCVFZWxvbt24mLi8NkMtHV1YWouroaqVSKRCLhVuLj46mpqaGrqwuRXq9HlJ+fz/r16xk1ahRpaWk8+uij2Gw2PNra2pgwYQJ3m6lTp1JTU4MgCAQGBmKz2Zg5cyaCINBXWFgY586dw+OBBx5g586dHDp0CI/PP/+choYGhg8fztSpU6mpqUEQBAIDA7HZbMycOZMFCxZQU1NDV1cXorKyMrZv305cXBwmk4muri5E1dXVSKVSJBIJtxIfH09NTQ1dXV2I9Ho9ovz8fNavX8+oUaNIS0vj0UcfxWaz4dHW1saECRO420ydOpWamhoEQSAwMBCbzcbMmTMRBIG+wsLCOHfuHB4PPPAAO3fu5NChQ3h8/vnnNDQ0MHz4cKZOnUpNTQ2CIBAYGIjNZmPmzJksWLCAmpoaurq6EJWVlbF9+3bi4uIwmUx0dXUhqq6uRiqVIpFIuJX4+Hhqamro6upCpNfrEeXn57N+/XpGjRpFWloajz76KDabDY+2tjYmTJjA3Wjq1KnU1NQgCAKBgYHYbDZmzpyJIAj0FRYWxrlz5/B44IEH2LlzJ4cOHcLj888/p6GhgeHDhzN16lRqamoQBIHAwEBsNhszZ85kwYIF1NTU0NXVhaisrIzt27cTFxeHyWSiq6sLUXV1NVKpFIlEwq3Ex8dTU1NDV1cXIr1ejyg/P5/169czatQo0tLSePTRR7HZbHi0tbUxYcIE7hYCXredyZMnk5KSwowZM1i+fDnLli1Do9HQv39/+vL392fYsGE0NTUhGjx4MFqtlpdffpmJEycSERFBamoqWVlZhIaGMnnyZFJSUpgxYwbLly9n2bJlaDQaRElJScyaNYu0tDR2795NdnY2jzzyCAkJCUybNg25XE5WVhZarZZ+/fpxK5MnT2bOnDnMmjWLlJQU6urqECUnJ1NXV0dKSgqLFy+mubmZhQsX4lFXV0d4eDh3m8mTJ5OSksKMGTNYvnw5y5YtQ6PR0L9/f/ry9/dn2LBhNDU1IRo8eDBarZaXX36ZiRMnEhERQWpqKllZWYSGhjJ58mRSUlKYMWMGy5cvZ9myZWg0GkRJSUnMmjWLtLQ0du/eTXZ2No888ggJCQlMmzYNuVxOVlYWWq2Wfv36cSuTJ09mzpw5zJo1i5SUFOrq6hAlJydTV1dHSkoKixcvprm5mYULF+JRV1dHeHg4d5vJkyeTkpLCjBkzWL58OcuWLUOj0dC/f3/68vf3Z9iwYTQ1NSEaPHgwWq2Wl19+mYkTJxIREUFqaipZWVmEhoYyefJkUlJSmDFjBsuXL2fZsmVoNBpESUlJzJo1i7S0NHbv3k12djaPPPIICQkJTJs2DblcTlZWFlqtln79+nErkydPZs6cOcyaNYuUlBTq6uoQJScnU1dXR0pKCosXL6a5uZmFCxfiUVdXR3h4OHejyZMnk5KSwowZM1i+fDnLli1Do9HQv39/+vL392fYsGE0NTUhGjx4MFqtlpdffpmJEycSERFBamoqWVlZhIaGMnnyZFJSUpgxYwbLly9n2bJlaDQaRElJScyaNYu0tDR2795NdnY2jzzyCAkJCUybNg25XE5WVhZarZZ+/fpxK5MnT2bOnDnMmjWLlJQU6urqECUnJ1NXV0dKSgqLFy+mubmZhQsX4lFXV0d4eDh3CwGvH1xVVRVRUVF4yGQyqqqqkMlkmM1mPHJycsjJycGjsLAQmUyGSKFQYDab2bp1K2azmZiYGG5m0aJFvP/++3hERUWh1+upra3lyJEjHDt2jMTERDwUCgVms5mtW7diNpuJiYlBpFAo0Ov1aLVaysvLGThwIKKMjAxMJhN5eXkYDAYmTZqETCbDbDbjIZPJMJvNeKSnp3P48GGKiorYtWsXhYWFBAYGsn//fgoLCyksLKSkpITAwEBE9fX1jBs3joEDB3K7qaqqIioqCg+ZTEZVVRUymQyz2YxHTk4OOTk5eBQWFiKTyRApFArMZjNbt27FbDYTExPDzSxatIj3338fj6ioKPR6PbW1tRw5coRjx46RmJiIh0KhwGw2s3XrVsxmMzExMYgUCgV6vR6tVkt5eTkDBw5ElJGRgclkIi8vD4PBwKRJk5DJZJjNZjxkMhlmsxmP9PR0Dh8+TFFREbt27aKwsJDAwED2799PYWEhhYWFlJSUEBgYiKi+vp5x48YxcOBAbjdVVVVERUXhIZPJqKqqQiaTYTab8cjJySEnJwePwsJCZDIZIoVCgdlsZuvWrZjNZmJiYriZRYsW8f777+MRFRWFXq+ntraWI0eOcOzYMRITE/FQKBSYzWa2bt2K2WwmJiYGkUKhQK/Xo9VqKS8vZ+DAgYgyMjIwmUzk5eVhMBiYNGkSMpkMs9mMh0wmw2w245Gens7hw4cpKipi165dFBYWEhgYyP79+yksLKSwsJCSkhICAwMR1dfXM27cOAYOHMjtqKqqiqioKDxkMhlVVVXIZDLMZjMeOTk55OTk4FFYWIhMJkOkUCgwm81s3boVs9lMTEwMN7No0SLef/99PKKiotDr9dTW1nLkyBGOHTtGYmIiHgqFArPZzNatWzGbzcTExCBSKBTo9Xq0Wi3l5eUMHDgQUUZGBiaTiby8PAwGA5MmTUImk2E2m/GQyWSYzWY80tPTOXz4MEVFRezatYvCwkICAwPZv38/hYWFFBYWUlJSQmBgIKL6+nrGjRvHwIEDuVsIeN22BEFAKpXyTZKSkjh+/DgOh4O+fHx8kEql3IwgCEilUm4kCAJSqZQbCYKAVCrl++jfvz8+Pj7cSCKR4OPjQ19vvfUWzz33HHczQRCQSqV8k6SkJI4fP47D4aAvHx8fpFIpNyMIAlKplBsJgoBUKuVGgiAglUr5Pvr374+Pjw83kkgk+Pj40Ndbb73Fc889x91MEASkUinfJCkpiePHj+NwOOjLx8cHqVTKzQiCgFQq5UaCICCVSrmRIAhIpVK+j/79++Pj48ONJBIJPj4+9PXWW2/x3HPPcbcTBAGpVMo3SUpK4vjx4zgcDvry8fFBKpVyM4IgIJVKuZEgCEilUm4kCAJSqZTvo3///vj4+HAjiUSCj48Pfb311ls899xz3E0EvO5ogiCQnZ3NqVOnuBO1trYSGxvLsGHD+KkTBIHs7GxOnTrFnai1tZXY2FiGDRvGT50gCGRnZ3Pq1CnuRK2trcTGxjJs2DC8QBAEsrOzOXXqFHei1tZWYmNjGTZsGHcTAa873pgxYwgLC+NOFBgYSGJiIl7/Y8yYMYSFhXEnCgwMJDExEa//MWbMGMLCwrgTBQYGkpiYiNf/b8yYMYSFhXEnCgwMJDExkbuNgJeXl5eXl5fXHUbAy8vLy8vLy+sOI+Dl5eXl5eXldYcR8PLy8vLy8vK6wwh4eXl5eXl5ed1hBLy8vLy8vLy87jACXl5eXl5eXl53GAEvLy8vLy8vrzuMgNdtwel0olAoeOWVV+grIyODvLw8bkc2m40XXngBuVxOeXk5Xrdmt9vJzs7mt7/9Lb/97W/5wx/+gNPp5P+C0+lEoVDw9ttv4/V1TqcThUKBQqFAoVCgUChQKBQoFApOnjzJ9+F0OlEoFLz99tt8V06nE4VCgUKhQKFQoFAoeOWVV7h8+TLfxul0olAoePvtt/mp6ujoQKFQoFAo2L9/Px779+9HoVCgUChwOp04nU4UCgVvv/02N3K5XCgUCoqLi7mRy+VCoVBQXFyM0+lEoVDw9ttv8105nU4UCgUKhQKFQoFCoeCVV17h8uXLfBun04lCoeDtt9/G6+sEvG4LLpeLgoICVq5cyfHjx/HQ6XTo9XpuNx0dHYSFhaHT6fjkk0+YPXs2b775Jl5f53a7mTp1Kps2bcLlcnHhwgX+8z//kyeeeIL/Cy6Xi4KCAo4dO4bX17lcLgoKCiguLqasrIyysjLKysooKyvj4sWLfB8ul4uCggKOHTvGd+VyuSgoKKC4uJiysjJKS0tZuXIlo0eP5vz583wTl8tFQUEBx44d46fqq6++oqCggIKCAt566y08CgoKKCgooKCgAJfLhcvlYu/evdTV1XEjt9tNQUEBR44c4UZut5uCggKOHDmCy+WioKCAY8eO8V25XC4KCgooLi6mrKyM0tJSVq5cyejRozl//jzfxOVyUVBQwLFjx/D6OgGv245CocDtdnMzJpOJsrIybDYbora2NvR6PTabDdG1a9fQ6/XYbDZEV65cQa/Xc+nSJSoqKvj0008RnT59mrKyMj766CM8rl27hl6vx263c/r0acrKyjh37hw388knnzBp0iQOHz7Mf//3f3PPPfdQUVGB19eZTCY+//xzXn75ZcrKyjh27Bjz58+nqqqKtrY2RC6Xi4qKCsrKyqiurkZ0+fJl9Ho9ly9f5tixY7z33ns4nU6uXbvGe++9x0cffYRHW1sber2eS5cuUVdXx3vvvcfly5e5FZPJRFlZGTabDa//sXDhQi5dusSlS5e4dOkSly5dIi4uDpHL5aKiooKysjKqq6sRXblyBb1ez6VLl6ioqODTTz+lL5PJxLFjx/C4cuUKer0em83GzSxcuJBLly7x5Zdf8v777/P3v/8dlUqFh8lkoqysDJvNxq24XC4qKiooKyujuroaD5PJxLFjx/C4cuUKer0em83G3WLMmDEcOnQIt9uN2+1Gr9czatQoPPz8/Ni/fz/PPPMMHnq9nvfeew+3282N9Ho97733Hm63m29jMpkoKyvDZrNxKwsXLuTSpUt8+eWXvP/++/z9739HpVLRl8lkoqysDJvNxq24XC4qKiooKyujurqdS432AAAgAElEQVQakclk4tixY3hcuXIFvV6PzWbjbibgdVv55S9/SUNDA5s3b6Yvt9tNQkIC0dHRpKSkMHToUPbt20d9fT3x8fEcOHAAUXt7O/Hx8Rw4cABRXV0d8fHxxMTEMHPmTLZs2UJ2djYjRoxg3rx5xMbG8sgjj3D58mXa29uJj48nJSWFiRMnsmDBAkaMGMFf/vIXbvToo49y4MABBg4cyI4dO+jq6uLRRx/F6+vGjx+PRCLhxRdfZNWqVZSXl7Nnzx6+/PJLfv7zn9PR0cHo0aOZO3cuixcvZsqUKaxatYrq6mri4+OJj48nPj6exMREpkyZQlhYGIsWLSI2NpYXXngBUX19PfHx8fz2t7/l17/+NU888QSjR4/m9OnT9OV2u0lISCA6OpqUlBSGDh3Kvn378ILm5mb0ej16vR69Xo/JZELU0dHB6NGjmTt3LosXL2bKlCmsWrWKuro64uPjiYmJYebMmWzZsoW+dDodUVFRXLlyBVFxcTHx8fFcvHiRb5OQkMCkSZPYu3cvbrebhIQEoqOjSUlJYejQoezbt48bdXR0MHr0aObOncvixYuZMmUKq1atQqTT6YiKiuLKlSuIiouLiY+P5+LFi9wtHnvsMbq7uzEYDBgMBrq7u5k6dSoeTqeT+Ph4XnvtNUTTp08nPj6elJQUJk+eTF/Tp08nPj6elJQUJk+ezK243W4SEhKIjo4mJSWFoUOHsm/fPr5NQkICkyZNYu/evYjcbjcJCQlER0eTkpLC0KFD2bdvHzfq6Ohg9OjRzJ07l8WLFzNlyhRWrVqFTqcjKiqKK1euICouLiY+Pp6LFy9yNxPwuq0kJSUxadIkcnNzsdlseOzevZsPPviA0tJSurq6mDNnDunp6XxXAwYM4G9/+xtPPPEEGo2G3/3ud/T09HD48GFqamrIzs7Gw+l00tHRQXl5OV999RUffPABt6LX63n66acJDg5m3rx5eH3d/fffz549exgwYADbtm1j9uzZBAYG8sorryC6fPkyjz/+OBaLhY6ODmJjY9HpdHiMGjWKrq4uUlNTqa+vJz09na6uLsaNG8euXbvo69q1a7S1tfHxxx9z+fJlVCoVfe3evZsPPviA0tJSurq6mDNnDunp6XiBXq8nPj6e+Ph44uPjmT17NqLLly/z+OOPY7FY6OjoIDY2Fp1Oh8eAAQP429/+hkajoa/U1FR6enrYt28fon379jFmzBjCw8P5LkJDQ/nqq6/YtWsXH3zwAaWlpXR1dTFnzhzS09O50eXLl3n88cexWCx0dHQQGxuLTqdDlJqaSk9PD/v27UO0b98+xowZQ3h4OHeL8PBwpFIpJpOJjz76CKlUSmRkJDdTUVHBwYMHycnJoaOjg/T0dDwqKio4ePAgOTk5dHR0kJ6ezq3s3r2bDz74gNLSUrq6upgzZw7p6el8F6GhoXz11Ve43W52797NBx98QGlpKV1dXcyZM4f09HRudPnyZR5//HEsFgsdHR3Exsai0+lITU2lp6eHffv2Idq3bx9jxowhPDycu5mA121Hp9PhdDr593//dzwMBgOi0tJSli5dyoULF2hpacFms/FdzJ07lwcffJC//OUviBYvXowgCMTFxREeHs67776Lx6RJkxAEAZlMhuj8+fPcSlJSEg6Hg1/96lfExMTgdrvx+rq5c+dy/vx5jh49yksvvcT999/PypUrqa6uZsiQIcyfP58tW7YwceJEDAYDbrcbj1mzZiEaPnw4ounTpyP61a9+RVdXF30tWbIEX19fxo4dS3h4OMeOHaMvg8GAqLS0lKVLl3LhwgVaWlpoamripy4uLo7Dhw9z+PBhDh8+zJ///GdEQ4YMYf78+WzZsoWJEydiMBhwu914zJ07lwcffJAHHniAvqKionjooYfYu3cvFy5c4OjRoyxZsoTvyuVy0b9/fz766CNEpaWlLF26lAsXLtDS0kJTUxN9DRkyhPnz57NlyxYmTpyIwWDA7XYjioqK4qGHHmLv3r1cuHCBo0ePsmTJEu42CQkJGAwGjhw5QkJCArfy17/+FVFsbCyip556Co+//vWviGJjYxE99dRT3IrBYEBUWlrK0qVLuXDhAi0tLTQ1NfFtXC4X/fv3RxAEDAYDotLSUpYuXcqFCxdoaWnhzJkz9DVkyBDmz5/Pli1bmDhxIgaDAbfbTVRUFA899BB79+7lwoULHD16lCVLlnC3E/C67YwfP57nnnuOd999l/b2dkS+vr6Ipk+fTkJCAhkZGezZs4df/OIXiDo7OxGdPn2amwkJCUF0//33I+ru7sbjypUr3HfffXwfn376Kbt378btduPn50d4eDjNzc10dHTg9c+Kior4xS9+gclk4te//jWZmZn84Q9/QHTmzBn27t1LbGwsAwcOZMeOHcybNw8fHx88BEFAdO+99yL65S9/ya1cunQJj6tXr3LvvffSl6+vL6Lp06eTkJBARkYGe/bs4d577+WnLiQkhLi4OOLi4oiLiyMqKgrR3r17iY2NZeDAgezYsYN58+bh4+ODR0hICLeSmpqKyWQiLy+P/v37s3jxYr4Lp9OJwWBg9OjR+Pr6Ipo+fToJCQlkZGSwZ88e7r33Xvrau3cvsbGxDBw4kB07djBv3jx8fHzwSE1NxWQykZeXR//+/Vm8eDF3m9/85jdUV1dz/PhxHnvsMW7Fx8cHUXd3N6L29nY8fHx8EHV3dyNqb2/nVnx9fRFNnz6dhIQEMjIy2LNnD/feey/fxOl0YjAYGD16NCJfX19E06dPJyEhgYyMDPbs2cPPf/5z+tq7dy+xsbEMHDiQHTt2MG/ePHx8fBClpqZiMpnIy8ujf//+LF68mLudgNdtSa1WExISgkdSUhKiEydOEBcXR15eHoWFhfzmN79B9O6772K329HpdNyMIAiIHnvsMfr3709ubi6HDh0iNzeXM2fOkJyczPfx17/+lcWLF/Pcc89RUVHBn//8Z371q1/x85//HK9/Fh4ezpUrV/jd737H7t272bdvH+vWrUM0ZswYqqurEc2fP5+goCBqa2vp7u7m/8Uf//hHDhw4wB//+EcaGxuZPn06fSUlJSE6ceIEcXFx5OXlUVhYSHBwMD91TU1N7Nixgx07drBjxw527NhBU1MT1dXViObPn09QUBC1tbV0d3fjIQgCt5KamkpPTw8bN24kKiqKBx98kFtpampix44dvPLKK0RFRdHZ2UlWVhZJSUmITpw4QVxcHHl5eRQWFhIcHExf1dXViObPn09QUBC1tbV0d3fjkZqaSk9PDxs3biQqKooHH3yQu83UqVPp6emhp6eHX//619xKREQEIq1WS3V1Nb///e/xiIiIQKTVaqmurub3v/89t5KUlIToxIkTxMXFkZeXR2FhIcHBwdyoqamJHTt28MorrxAVFUVnZydZWVmIkpKSEJ04cYK4uDjy8vIoLCwkKCiIvqqrqxHNnz+foKAgamtr6e7uRpSamkpPTw8bN24kKiqKBx98kLudgNdtKSAggPz8fDzi4uJQqVRotVqCgoL47LPPUCqV+Pv787vf/Q6DwcCDDz7Iv/3bv/FNRo4cyTvvvIPVamXatGmsW7eO//iP/+CFF17g+5g+fToqlYo//vGPzJw5E19fX9599128vm7s2LHs3LmTtrY2Fi9ezIIFC/jb3/5GQUEB48ePZ9myZYSEhBAZGYlMJiM8PJyOjg46Ojr4vqKjo0lJSWH58uU89thjZGdn01dcXBwqlQqtVktQUBCfffYZSqUSL6isrOTpp5/m6aef5umnn+bpp5/m+PHjLFu2jJCQECIjI5HJZISHh9PR0UFHRwffZvDgwcTGxtLT08NTTz3FN6msrOTpp59mzZo1tLe389Zbb5GcnExcXBwqlQqtVktQUBCfffYZSqWSGy1btoyQkBAiIyORyWSEh4fT0dHBlStXEA0ePJjY2Fh6enp46qmnuBuFh4cjlUoZMGAA4eHh3Mr48ePZsmUL5eXlTJkyBX9/fyQSCaLx48ezZcsWysvLmTJlCv7+/kgkEm4mLi4OlUqFVqslKCiIzz77DKVSyc1UVlby9NNPs2bNGtrb23nrrbdITk5GFBcXh0qlQqvVEhQUxGeffYZSqeRGy5YtIyQkhMjISGQyGeHh4XR0dHDlyhUGDx5MbGwsPT09PPXUU/wUCHjdFgICAujt7UWr1eIxZ84cent72b9/P6Lc3FycTift7e3Y7XZmzZqFaMeOHXz11Vd0dXWRnZ1Nb28vaWlpiOLi4ujt7WXJkiV4PPHEE5w/f57Ozk6uXbvGa6+9huj++++nt7cXrVaLKCAggN7eXrRaLTeTm5uL0+mks7OTL774gokTJ+J1c0uWLOHSpUt0dnbS2dlJa2sraWlpiIYPH47NZqO9vR2bzUZZWRkul4sFCxbQ29vLwoULEaWnp9Pb20tAQACi8vJyLl26RF9xcXG0tLTQ2dmJyWTivvvuIyAggN7eXrRaLaLc3FycTift7e3Y7XZmzZrFT1lAQAC9vb309vbS29tLb28vvb299Pb2smTJEoYPH47NZqO9vR2bzUZZWRkul4u5c+fS29vLkiVL8AgICKC3txetVouHy+VCKpWSnJzMzQQEBNDb20tvby+9vb24XC4+/fRT5HI5Hrm5uTidTtrb27Hb7cyaNQtRQEAAvb29aLVahg8fjs1mo729HZvNRllZGS6Xi/vuuw8Pl8uFVColOTmZu8X9999Pb28vaWlpiLq6uujo6ECUlpZGb28vAQEBBAQE0Nvbi1arRbRq1SqcTidfffUVb7zxBk6nkz/96U+IVq1ahdPp5KuvvuKNN97A6XTypz/9iYCAAHp7e9FqtXjk5ubidDppb2/Hbrcza9Ys+goICKC3t5fe3l56e3txuVx8+umnyOVy+srNzcXpdNLe3o7dbmfWrFkEBATQ29uLVqtFNHz4cGw2G+3t7dhsNsrKynC5XNx3332IXC4XUqmU5ORkfgoEvO4ogiAQEBDAjXx9ffH19eX78Pf3RxAE/jd+9rOf4e/vj9d34+/vj7+/PzcTEBDAD0EQBPz9/fkmgiAQEBCA13cXEBDA91FWVsb48eM5cuQIGRkZ+Pr68r8hCAIBAQF8m4CAAG5UVlbG+PHjOXLkCBkZGfj6+uIFP/vZz/D19eVmfvazn+Hr68t3IQgCAQEB/G8JgkBAQADfJiAggL7KysoYP348R44cISMjA19fX34KBLy8vO54I0eOZM+ePcTExOB1exg7dizR0dG88847vPTSS/wrjR07lujoaN555x1eeuklvO4uY8eOJTo6mnfeeYeXXnqJnwoBLy+vO15wcDALFy7koYcewuv2MGzYMF577TWSkpL4Vxs2bBivvfYaSUlJeN19hg0bxmuvvUZSUhI/JQJePyidDtRqUKtBpwOrFXQ60On4B6sV1GqQy0EuB6OR66xWUKtBpwOdDnQ6rtPpQKfjnxiNoFZDTAwMHQpyOcjlEBMDQ4eCXA4xMSCXg9EIRiMYjVxnNIJaDTodPxidDtRqrjMaQa0GqxWsVlCrQa0GtRqsVu4oRiOo1aDTgVoNVivfymgEtRqsVr6R1QpyOcjlEBMDMTEQEwMxMSCXQ0wMDB0Kcjn/YDSCWg1yOcjlIJeDXA5qNajVIJeD0YjXtzAaQa0GuRzkclCrQS4HnQ7UatDpQK0GtRrUahg6FIYOhZgYUKtBLge1GtRqiIkBuRyMRlCrwWjkGxmNEBMDcjmo1aBWg1zOdVYrqNWgVoNaDUYj/ytGI6jVYLXyT3Q60On4B7UadDowGkGtBquVO4pOB2o1/6DTgVoNVivfm04HOh3I5TB0KMjlEBMDcjkMHQpDh4JcDnI5yOXclNEIajWo1aDT8aOwWkGtBqORW9LpQK0Gq5W7koDXD8pqhQ0bbBQVgckEcjmsWwdFRaDTgU4H69bBunVgNILRCOvWgVoNOh2sWwcmE/9gNILJBOvWgU4HRiMYjVBUBOvWcV1oKFitoNOB1QpWK+h0YDSCTgfr1kFMDMjlIJeD1QpFRbBuHcjloNOB1cr/itUKJhP/RKcDo5Hrzp3jjvRf//Vf/L/4yuXmC8dVzF9cxfzFVcxfXMX8xVXMX1yl9lw7Hjod6HRgtYLRCEYjGI2g04HRCKGhoNOBXA5qNRQVwbp1YDSCTgc6HVitYDLBunV4fYPXX38doxGMRq4rKgKrFaxWWLcOrFb+yblzYDKByQRWK4SGcp3JxHXnzoHJBEYj11mtXNcVeBXzF1cxf3EV8xdXMX9xFfMXVzF/cRWR0ch1RiOcOwdFRWA0gk4HRiOYTFxXVMT3YjSC0QhWK+h0oFZzXVERGI1gtYJOBzodWK1gMoFOBzodmExgtYLVCiYTGI3c9hIT/wudDtRqKCqCdevAagW1GoqKYN06sFpBLoehQyEmBmJiYOhQkMtBp4OYGJDLYehQGDoUhg4FuRzkctDp+CdGI9eFhoLVCkYjxC6+ivmLq5i/uIr5i6uYv7iK+YurdAVe5dw5OHeOH4zRCGo1WK2gVoNcDuvWgdUKajUYjfzkCHj9oEJDuS40FIxGsFrBaoXQUJDLQS4Ho5HrQkMhOhqsVli3DoqKuM5qBasVTCYwGsFohNBQkMth3TqQy0Gn4zqrFYxGsFq5zmrluuhoiI7muqVLITqa63Q6sFrBauU6oxHWrQOrlZuyWsFo5GuMRv5JdDRERYHVCtHREB0NJhNYrZCaCmvXQnQ01xmN3DF27/4boaFgtUJ0NFitoNOBTgc6HRiNoNOBTgdGI+h0YDTCAyHg68st6XSg00FoKNdZrVwXHc11oaEQHc110dH8g04H0dEQGgqhoRAdzT+kpv5/7MG9riXpeRjqBw2zBREYOphEkA6wXya+BClc30qcyzdQVflxbkdVX6bMgZRXFZxLl1DvCq1LcLLfBUgEDZiBSYECZwIdseCzcPbp7vkhm2bPcD8PlwutefUe/+W//J1MeieCCIaBeaY1hoHWGEdaYxioIoII5pl1ZRioYhgYBiIYBlpjHPnsMx9Uxe1GFVVsG1VUMU1ME5ncbkQQQZVTlQ+qYt/JpIre6Z19d+qd3umd3rndyKR39p0Ip2kikyqfvEx6Z9+d1tXD5cI8e4hwiiCCKqcIqogggghaozVaI4JMqpyqqPK1/uiPnKqoYpqYJpaFZaGKTKaJTC9UeahiWdg2p97pnX2nitZozamKZWGaqHJqjWUhwvfSG68+qiovVBFBJq0R4YUqD1VOmfTOttE7VWQSQSZVHqqcqrwQweXi4XKhNafbjdaYZyKIoDWq2Da2zUMmvVPFtpFJJr2T6SGC+53eqWLfne53Mtk29p3e2XffCVV8+eX/ZZq43aiiimlimpgmemeamCauV6aJ3rklX3zhK/VOa16ochoGLhequFy4XIhwGgaGgXnmcmGeGQaGgQiqyPSQSSaZ3pFJplMmmR6qyKSKTDLJJJMqMqmiikyftEx++tO/cr9TxbaR6dQa8+w0TUwT1yvXKxEeeufHP2aayOR6ZZqo4nrleuV65Wc/80Hb5r3mmeOgNeaZdWWenXpnmpgmMr1XBPNMa+w7w8BxMM+sK8fBPLOuHAfryrrSGhFcLuw768rzM+Pok/ejH/2teWaeGQbGkQiWhWVhWWiNdeU4OA7mmXVlXWmNy4XLhXlmXYlwulxYVyKooooqpyoyqfJBb986ZXK7sW1kcr+z7x6enrywbfROFVX0Tu/0zr4zz05VVHG50DsRVLHvZNI700TvZPreeuPV79S6Ms+0xjAQ4dQamUQwDEQwz4wj48i6Ms/MM8fBcbCurCut0ZpThPfaNnp3miZ6Z9ucMslkmsjkcmGamCb23cO2Oc2zUxVVtMY805qHCJ6enLaNYWBdGQZaYxx5euJy4emJbfPJi+Dzz//a8zPDQGu0xrpyHKwr68q68vzM8zPPz6wrP/mJrxXBtnmhymnf6Z3WnKaJaSLC6XZjmuidaWKa6J3e2TZ6p4pMpolpItMLVfROplMm++4hk97pnWmid/adfWfb6J1MMtl3qnyyIvi3//ZvDQPzTGusK605tUYEw8AwMM+sK8PAMLCuDAPryjwzjn4jrTHPzDOtEcE8c7+T6XS7MU1kOl0uDAPDQIQPiqA1LhfGkdaIoDUiiKA1IoigNYaBYaA15pnWiCDCJ++HP/x7rTGOtOahiiqqqHKqYtvonW3jeuV6ZZrond65Xtk2Mtl3polMX+mLL7zXL37hIdNDJhFE0BoRZFLlFMHl4hTB5cI8MwxcLoyjUwSt0RpPT+w7vTtVUUUmmew7Vb6X3nj1UbXG55//tQginG43qpgmMolwas1p351uN7aNTPad+53bzUMEEVwufiMR3hHhlEkmEYwjVdxuTq0RQWtEsCxOmWwb20aVh9ZojQgi2DaquN243Zz2nSrfGfvO9Urv3G5kcrsxTfROlVMEEfzn/8zbtz4ognlmXZln76hy2jZ691DFNLFtHiKcMqmiNeaZCFpjXVlXxtELVWQS4RRBFVVOrTEMTq0xDAwDlwutMQxE0BrD4JMWwY9+9Hd+LcIL20YmVUQQQYQXqqgigtacWiOCCCKooopf/coHtUZrjCPzzDAwjlwuTpcLlwsRHsaR1hhHInytcSTCN9IarRHBOBLhO+PnP/9LvbNt9M62UcW2MU30zjSxbfRO7063G1VUecikyimCKjKp8pXevvVef/RHHiI8VJHJtjlFEEGEU2uMIxFO48iysCyMo9PlwjwzDLTGsjAMrCvDwLoyDMwzw8DlQoTvpTdefVSt8cd//PeGgQh6Z9vIJILWJlWbTDLZNqqcqpyqqCKTTK5Xrld+/GOuV3onc8Giyjtac2rNQ2u0xrpyHKwr40jEZt8nrTGOHiKYZ1pzyqR3Mp0y6Z0qp23jdmOeac3DtnG7OV0uZNI7lwsRPmmZ/PSnf+V6JYJ5Zhic9p1tm7D5td65Xtk2pone+cUvvFcV+87txr7T+4JFhA8aR46DcaQ1WmMcmWcy/W+bqkkVrXlojdaI8EIEx8E4OrXGPBPhFME4MgwMA1XsO70zTUwTmUQQQe9sm09aJtcr08S+s230zvU6mabN9co0cb0yTUwT1yvTRO9ME9crmU7DwDBQ5X9b/NM//cRXqaKK1hhHp9YYR5aFceRy2dxukwivPuDf/Jt/9Gu3m1MV08S+kzmp2mQyTWRS5RThIcIpwkOV0zjS2oLFh7x9673evmUYGEeq/KtN1WQcmWcinFpjHH1jEYwj48g4ehhHxpFlYRwZR8aRZWEcfW+98eqj2jZ+9rP/aN/JJMIpwinTaZ4ZRy9UEeFU5SHCOyK8I8KpyqnKKcKpin2nd6qcqmiNeWYYGAaqyKSKTDKdLhciWBbGkXlmWYjgfqeKTDLJpMrpciGC1hgG1pXWyCTTJyuCH/zgH1U57bvTMNCaUxVVZDr1TgRVPiiCKraNKg+tMY5OrXkhk30nkyoyyWTfnVpzao0I30gErXmIoDXvaI3WaI3LhdYYBtaV1pwiuFyI8EkbR56fOQ7mmWVhXRlH5pnnZ46D52eOg+Pg+Znj4Dg4DtaVKjK53dh37/jiC+9VRe9ksm1cr0wTP/4xP/4xy0Lv9E6EV1/hhz/8e8NAlVPvVHkYBtaVeabKadvYNg8RTlVeiGDbyPQbi+DpyQtPT7TmlEkmVVSRSaZTFVUeMsl0yiSTKjLJdMokkyqq/EF449VHFcEPf/jfVDlV0RoRRNAaw0AE20aEd0QwjlQRQZV3VHlHlVOVU5VTFVVkkkkmETw9EeGUyfXKNDFNTBPTRO9ME72z71RR5RRBFZlkMgzcblyvZJLJvnO/U8U0se/sO72z7/ROlU9SlVNrZDrtu1OmUxURjKOHCKcvvvBBmURQ5RRBlQ+qYtuoosqpiiqnKiKIIIJlYZrI9NG0xjiyrh5ao4plIYLWfJIy+dnP/m+/FkEVmVTRmlMEEUQQQWu0RgStEcG2Oc0z88zlwjAwjkQ4vX3L27feq4pM9p19d6qiNSK43ahyyiSTZWGa2DaWhW3zjkymiWliWahiWcj0jW0by+I7pTWGgcuFdWUYmGdacxpHloXWvFcmEd5R5StF0BqffeaDto1990LvTBO9M01cr1yv9E7vTBPbRu/0TibbxjQxTWwbvXO90ju9M00sC/vONHG90juZZPpee+PVR9UaP/rR3zkO1pV1ZRic5pkIIpzmmXlmXalyqqKKy4XWqPIbifAQwTx7iGDf6Z1MqujdQyZVzDPDwDAQwTA4TRPXK9cr08T9TmtOmawrEfROa069e7hcPFT5pP3gB/8ggmFgGHh6choGpwhaYxhozenpiQhfaRyJ8FDltG1Omb6VCKqooor73UdTxbKQ6TurygtVvlYmmWSSSRW3G1W0Rmu0RmtcLr6RKiKocopwqiLC6XKhNaqocsr0QZlkOlVxv5PJ7UaVd2SS6SGTKqq436nynfDLX/65KiJojQjGkQgiqKKKTC4X5pl5Zhy9UOVbq6LKV4ogwjuqyCSC1ohg28h0ut3IZNuYJnp3GgZa43LxMM+0xr5zudAaEU69M01k+t5649XvTGu0RgTDQARVThEsC+NIBPPMPDOOzDOtcbkwjkTQmm+lykMEvTuNIxFUEUEEmUQQQWuMo1MEy8KysK60xrIQQQRVZFJFFb07VTlFkMm2kUmV0/3O5UKVU5VPUmt8/vnfWFeWhXFkWWiNZWEcmWeGgSqGgSrud6r41a980OXC5eKFTN9IBBHeEUEm+87lwjDQmlMV20YVVWwbVU5VbBvLQibbRibbxrJQxb6T6ZTpYVmoYllozSdrHPn8878R4dQay0KEhyoPVew708T1yjQxTWTSO9cr1yvXK9PENFHl9PatD2rNqYoqpypaYxjIZN+dWqM1xpHnZ8aRZWEcvbDv3O+sK8fBujpF0JpTlYd9J9Opin2nd/adYdbxVC0AACAASURBVPCdUMXPf/4fbBu9s230Tu9cr2TSO9PEvnO/0zu9s22+kQhfqYpf/MIHjSOXi1NrRLCuzDPHwboyz8wzx8FxMAxcLqwrx8G6sq6sK+NIBOPIcTAMtMY8s66MI/PMPLOuzDPrSmu+t9549Tu1bVQRwTSRyb7TO9PEsjBN3O9e2Dbud6p8YxFeiHDKpMpp28gkwkMEVVSxbWR6Rya9k8nl4qE1p0zGkdbond690JpTFZkeWvPJyySTTDLJpIoqqshk39l3p21z+uILH3S70buHCA8RvlJrDAMRRDhleqhi36nyUMW+U0UV+06VUxW9c7uRyb6Tyb6z7+w7w+CUyb6z7/TO7UaV75RMeifTB0UwDAwD48i6chw8PzOOjCPHwXGwrhwHx8E48sUXPiiCYSDCKdNp25gmIqiiyqmK3sn0QZcLVWR6aI1hIIIqemfbnOaZcXTaNqenJ1qjit6p8kmL4Ic//G/udzLZd6cqxpHWaI15Zhi4XGjNqTVa87WqfK1/+idfKYIIMqli3+mdTKqIoDVaozXud243WqM1WiOCKjKdIrwQQWseqpxaozXfa2+8+p3JpIp9Z9+p8sK20TtVbBu9s230Tu9sG5lUkelrVdEardEaVU7zzHEwz0QQQaZTlYfWGEencaSKbWPbqGKenSKcIohwao3LhUwiqGIYnCKcxpFxdLrdnIaB1nyyvvzyz+w71yu9M030ThUR3O9EUEUVEb6Rpyen1hhHqjxUea8IWmPb2HeqqCKCdWUYaI1hYBhozUNrrCut0RrrSgRVtMZxsK60xrqyLKwrw0AVEbTGvpPpNAwMAxFkUuU7IYLLhQgPVd7RGvc7VWQyTfROlVMm+04mmWRSxdu3vtI4Mgy0RgTzzLqyrswz80yEUwSXCxE+qDWGgQgPEbTm4XIhwimCCKfWuFyIYBiI4HIhwietyunpiXGkNS4XLhfmmSoiqCKCCC4XjoPLxak1WiPCKcI3EuEba41h8JBJBPc7+06mFy4XqqjykMk0MU1kOu07md5Rxb6TybaR6XvtjVcfVRW//OWfWxb2nd7JdGqNCKcqIoggwkcTQYT3iqCKKqcqpyqqyGTbqGLb6J1pYprYdzLZd6aJTKdtI5NMpokqqmjNqYoqMqni6YkIIhgGInzSfvCDfzQMHAfryroyz7TmtG3cbkSQSZVvpDUiyGTbfCNVZDpVOY0jVVRxv1NF7/ROphciPESwbWybhyp6J5Mqp3FkXali36miigj2nd6ZJqaJTJ+8TKoYR6dMMj1kkkkmmTw9Oe07VWQ6VdE7mfTO/c6+U+VrVdEaw0CEUwQRtEZrPD05RTCORPigKiKI8F4RjCOteUdrjCPjSGu0xjj65FXx05/+lduNbWPbuN3onUwP00Tv7Du3G5n0TiaZZFJFa0Q4RfhKVR5+9Su++MIHVRHhFME4Ms/MM/NMa15ojXUlgiq2jQjGkXEkgm3j6ckpk20jk22jNSKc9p0q32tvvPqoto3/8T/+yv1OlVNrTttGlVOEUxVVHiK80JpvZdvYNjI99M71yjT5WhGMo4fWOA6GgX13iiCCKlojgggvVLHvRBDhlEnvZHK5MI5E+KR9+eWfaY0Ip9aI8DDPDAPDwDw7jSMRvH3rg6qo8q2No1MErZHpdL+zbVQR4RThvarIpHciqKJ3MrlcuN2YJjKJIJN9p4oqp0yqiKA15pnWfPIyyXTK5HrleiWT3rlemSamieuV3smkiiqqyCTTqcpp26iiytfK5HrleiWT3rleuV7pnWmidyJ8I5nsuz8orfEnf/KfVNEarVHl1DuZVDltG5lOt5tTa7TmFEEmmURQ5aE1pwjv9dlnvH3rvarYd6bJKcJDBBFEeCGCCKcq9p0qnp64XKhimrjdnKq43cjkdqOKpycyWVda8732xquPKoIf/ehvPT1RxThyufD0xDg6RXghgginKiKcIsgkgggiiCCCCO8V4b0iiCCCCKcIDxG0xjAwzxwHrVHFvtMaw8DTExFO40iEh3kmgtacqpxaozUiiGAciSCTKp+sZeEf/uG/2jamiWliWZgmrlcy6Z1p4nbjfnfK5N/9Oz77zHtV0bvfSKZTlVMV88zl4iGCKnpnWbxQxbaRybrSGlVsGxG05pTptG3sOxFUEUFrjCMRXC4MA1Vsm0/eODKOTq1xHBwH48g8cxwcB8fBcXAcHAfHwXFwHBwHx8FxcBwcB8fBcTCOvlZrHAfHwXFwHDw/cxzMM+vKPPvGxpFh8Afnj//47x0H68q6chzMM8PAODIMPD/z/MxxsK7MM61R5aHKQ5UXMp2qvKM13r71QVVkesikinGkimWhim1j2zxsG9tGa6wrrXG/O0UwjgwD48g4Ms8sC8PAthHBOBJBhO+1N159dL/85V+43ZyquN+538mktRWjTKcIqrxQ5YUqqqjyULVg8f9X5b2qqKKKKqcq/2rE6tfWlWUhgtZYV+aZpyencSTCaV0ZBqcqImiN1qjyXlVEOGUyTWybT9b9zuef/7UIIrhciGAYnIZhNc+j1nh6oooIIvjv/50vvvBeEbTm/2PB4tvK9LDvRIxY/b8iuN+ZJpaFKiKIoDXGkQgvtMYwMM+0RgTDwLoyDFwuDAPryjzTGq0RQYRPXgQRThG0Rmus62pZRq0RQQSt0Rqt0Rqt0Rqt0Rqt0Rqt0Rqtsa6LP/3TP/VVImiN1miN1oigNSJojWUZrevqm4igNX9w/vmf/1wm28a2eYhgXVetjbaNKiKoIpPLhSoinFojggjvsWDxIZ995oNaY54ZRyJGEatfWxYyud/ZNvadfWfbWBb2nX1n28hk23h6Yhw9VJHJtpHJtpHJ7cbtRhVVLAvLwrb5Xnrj1UfVGp9//tfmmeNgnrlceHriOJhn5pnj4DhYV46DdeU4mGfmmeNgXTkOjoPjYJ5pjXlmHGmNdWWeifCtRXio8pDJtjm1RoRTBFU8PbHv9E6m0zDQmocIqqgik0wP28b9TgS3m0/WuvKjH/2d1lhXloVxZBw5DpaFZWFdWRaGgXlmXfn3/563b33QMDCOtOa31juZVDk9PTHPXC5cLl7YNqfWPEQQ4bRtVLEsTplO20ZrRDhVUeVhHBlHr179H/Pll39m37nfud+pYhwZRw/3uxfud6dxZBiYZy4XjoN5pjUiiCCC1mjNKYLWnCKI8LXGkWGgNSK4XLjfiWCeac0pgtuN+50IMqmiin13qqLK6XZzqqKKKiKYZ6cqqrjfud2o8r30xqvfmSoy2XduN6aJ3pkm9p1pIpPeySSTfad3Mumd65V9p3f2nW2jdzLJZN+53ajyrVU5RdAaVU6Z3G5UUcW+U0UV+87txrZRRRURRFBFplOVhyoiiPCQSRURvjfGkXEkgnnhs898UGusK8NAa0QQQWuMI60xjrRGa0QQ4RRBBBG0RgTjSGuMI62xLIwj48i6sixEUOW9hoEIqrzjduN+d6pyquJ+9+rV783nn/+N42BdmWdaI8JDBPNMa06tMc8sC/NMaywLy0IE48i6chwcB8fBurKu/Mu/cBysK8/PHAfzzGef+Vqtsa6sK8vCPNMaEbTGurKurCvzzLryL//CsrAsPD+zLETQGvPMcTCOLAvLwrIwjrTGPDOOtMY8cxwsi++lN159VNvGP/zDf3W9cr3SO5lUUUUVVWyb0+1GJvtO71Q59U6V07aRSZVTFVVOmWT6rURwuZDJshDBPBNBa6wrrRHBMDi1RgTzzDCw70wTEcwzrRFBBOPIPFNFBONIFa3x9OQP2jhyHDw/8/zMcbCuHAfrynFwHDw/8/zM8zPPzzw/8/zMcfD8zLpyHKwrrfmgZWEcvRDBstAay8I4OkWwLKwr60prLAvjSGusK6159er3LsJ7RXghwinCe0UQQQQRRBDhFEEEEUQQ4VuJcIrwQoSHCF8rwleK8BDhe+2NVx/dH//x32uNcWQcmWeGgXVlGJhn5pl15XJhXVlXxpEIxpEIL0T41iK8EOGFCKcIp9653YggwkOEh2XhOJhn1pVlYRydLhfmmXFkGBgGWmOeiWAcuVwYBsaRYWBZvHr16tWrV9/aG68+un/+5z+XydMTw8D97pRJ7/TO7Ubv9M7tRu9sG1VkUkWVhyoPEb6RKi9UObVGBBEeWmMcGQZa85WqyCSTKqdhoDUiWBYyieDpiW1zmmenTC4XWvPq1atXr179Rt549VGNI59//tdac6oik/ud3v2rSdUmk21z2jYyPVT5SlX+1YLFb6KKKqeIzbZNrlcyifCOKraNKqdt43ajd6aJ3pkmeieT+519Z9+53dh3pokqbjd6Z99ZFrbNd9Y0TbZt89talsWyLH5b27aZpsmrb2+aJtu2+W0ty+InP/mJ39a2baZp8uo3M02Tbdv8tpZlsSyL39a2baZp8urjeuPVRxXB55//jeNgWWiN1rhcmGenCFojgggiaM0pgginCKcIHxRBhHdE+KAqpyqqnCKcqqjyQhW9k0kmEVwuRBDBMNCa0+1GptPlwjDQGhFkMgyMIxHc71R59erVq1evvrU3Xv1OZVLFvnO7EcE8MwxeyPRQ5VTlVOWDqqgiwgtVThFOEd5R5YXW2Hd6J9NDBK1xuzFNVLHvRJBJFZlOVbTmoXe2jUx6p4oqto1MxtGrV69evXr1rb3x6neqNeaZeWZdiaCK1lhXhoEIIohwao3WaM03VuW9qjxEEOG9Inh6IoJMqrxQRSZV7DtVbBtV9E6VUwTbRhX7ThXjSGtEME1UOQ0DEV69evXqN5Ppvaqo8kGZTpm+tUyqqOKLL3ytKqqocsqkyqvf3huvfie2jWWhyimCTKqcMsl0GgZa80IEVUT4KKo8jCPj6CHCqXeqnG43MqmidzIZBlqjyimCCFrzUOU0jk4RTpleiKA1r169evUb+cuf/5ze2TYyyaSKbaOKfWdZyCSTKjLJpHemid5ZFqdMMqnycL9zv3vIZN+5Xvnxj/mf/9PX2jZ6d6qidzK9+u298eqj+/LLP/Nr9zvTRO9cr0wTVfTONNE7vdM720YVVWSybVRR5RtpjXH0jggPVVSRSaaHKjKdMqmiin13enpiHFkWhoEIIpyq2DanTDIZR6cqqnh6ojWqiKCKKvbdq1e/X8vCtvmDVMWysG2+c6r8xS9/SQS3G9crvdM7vVNFFb1zvdI7vXO9su9EsG1Use8sC71zvZLpVOWFKnrn6Yl15Tj47DNfqzXGkSqqmGda8+q398arjyqTn/3sP+qdKlqjigginFojgnEkwqk1IohwivCtZLJt3lHlhXGkyjta89CaF1pzynSKIMIpwimCCFqjiirGkdaI4HKhNVojgtbIpMqrr5JJJplkkkkmmWRSRSaZXn2FTH/585+zbSwL28a2cbux71Rxu7FtbBvbxraxLGwb28aykOm0bWwby8KycL2yLNxuvtK2sW1sm9+7TKcI3zkR/uEHP+DpiUxaI5NtozVaY545Do6DeWaeGUeqeHqiNQ+3m9M4EkEmVU6ZbBtVXC5EePijP/KNPD1R5dQaEV799t549VFF8OWXf+bXMskkggiqiPBwuVBFFZlUUeVU5aOLINOpygtVTq0RwboyDEQQwdMTEYwjw+DUGq3RGhFUkUmV09MTlwvTxL6TybZRRZXvhyofxf1OFVVUOVXRO9cr1yvTRO/0zjSx72wb1yu9c7t5qKLK70QVVb5TqvyH//W/uN243bjduN08ZFLFvnO7se/0zu1G7/TOvlNFFVX0zr5zu1HF7UYVX3zhg6qYJqqoItPvRRW3GxG05lRFle+EKn/xy19yv7OurCvHwXEwz0QQQWu0RmtEMM/MM8vCPLOurCvryjBQRRWZ7DtPT1Sx7/RO7+w708T1yq9+5YOq2DamiX2nNSK8+njeePVRRfCDH/yjeeY4mGciyHSqItNp350i/B9RRZX3qnIaBp6euF7pnUy2zSmCKvadKraNTKrI9FDl1Du3GxHeUUUVVT5dVf7syy+poooqqryw72RS5aHKN1bF7UYV1ys//jHTRBWZZHqIIJPLhXVlGBhHjoNh4HLxsG1sm9+JbaN3pyrfCePoP/3JnzDPHAfzzDxzHAwDEV6ocsqkymkYGEe2jfud42AYqCKCeWYYnL74wnuNI8/PjCPbxr77vYhgnhlHD9vGtvlOiPCPP/gBmUQQQf0/7MHPqx73vSDop5WWRY47XsQYRDRwPlqbs4gX8fL9ajZeGTLaZvFWbQwNcxcGL+5GVNVihtkmd1YdQlXRS196vBKmN++3SLCYQP8FB6TPITehN4bbumAJ+8aeqJj70gf/jo4VyXqfJ8kkk0yWhUyrTKaJWsmkVjKtMpkmqwgyieD4mLMzIqwiiCCTTF8rk2EgglKsIlyoWqnVOZlME5lME7X6vrrk4EJlWmUSQQTTRKZzIpwT4W8mgnFkHCmFCCKIIIJSKMU5pVhlWkUQYVUKETQN2y3bLdstEVYRVk1DhKfXNPnP//RPtC3DwDAwDNTKNFErmcwzw8A0USvDQK2+Vq0MA7VSK5lEkMkw0LaUYm+7ZRxpGkqhFCKsloVlsVcKpfhOlMJ2S60MA7V66k2T/+u//3eGgevXGQaGgbYlgkwi6Dq6jnFkHNnt6DpKIYK2ZZ7ZbIiwiqDrKIWzM6sXXvCFMhkGhoEItlt/MxHOKYVSPCv+6fJlSiGCTOaZYaBtqZVaybSqlWGgbRkGhoG2pW1pW4aBtmWamGfalmFgmsikVqaJTCJoGiK4csWXKoVxpOusInytTKaJWpkmamWa6HsyqZVpYproe+aZYaBtmSamiVppW4aBYWCefV9dcnChIjg6+n/NMzducOOGvQh/MaJRK7USQSaZvqUevb9WhP9fI2KUSSbTRARdZ5VJKZRiVSubDdstpRBBKWy3lEIpbDZkcnzMsrAsRJBJ0xBBKZ5+EX7/wx9SK5lkMk0MA8tizNSUQq0cH7MsDAO1+lqZ1Mo06SP0/ieZ1EoEmw1dRwTLQgQRzolgs9F0nXEcrUqhFN+JUiiFCKtayfS0++Ply/ZqJZNpolZjKRp/kUmtZJLJPDPP1MqyUCuZLAvTxDxbZZKp32z85Cc/8aUyqZVMMinFF2maxrjZUKsnphRK8az44+XLHB8zTdRK17Hd0nXGe/c0ux0RTBOlsNtx7x6lkEkpZFpF0DQ0DZmU4pE+Qt91NI1VBLUyTWTypz/5SqUQocG42fhamcwztTLP1MowsCxME/PMPDPPzDPTRCbTxDwzDAyDVa1WmWT6Prrk4EJl8skn15RCJk1D0xBBJhFEUAoRlEKECxVBhL0IXymCszOGgWGwivCFloVhIJPNhgh740jXEUEmZ2fUSoRVBJsNEURYZXp6leKfLl+madhsiKBprGq1l2mvVjJ9rVoZBqtMe5lWmfaahlKYJqtM+p5a6XumiUyGgb6n78n0rU0TfW9vmpgmaqXv6XumyarvqZXjY5bFU69p/P3Vq2y3dJ1VBKWwLGw2ZDLPDANtyzBQK5lWtdqbJoaBTGplWchkGPiXf/GlMq0imGemiVrJJJNMq0yWhUxqdaEyqdX3wtkZmWQSwdmZVQQRZLIsZFpNE5lEUCsRRFAK08Q0WWVaZTIM1GoVQdPY+/hjX+jjj5kmaiWCrqNpfK1SGEf6nnGk79ntGEcirLqO7ZZSGEd2O+7dYxzZ7djtuHeP3Y7djnEkwvfRJQcXqlY+/PDvTJPVNDFNZFplkkmtZFIrmS5UJplEWGX6nEx7tTJNRDhnu7XX9/Q900QpZDIMbDZWy0Lfk8k8W02T1TQxDPamiVrJpBRPr0w379+3OjujVjYbNhsiKIVaKYWzM99aBKX4nAirTCLItOo6IqyWhXkmgnnm7IxMzs5cmEyrTOfUyjzbi6BWT7trn3zCPLMs9iKszs7IpFarCDLJtMokkwh72629aaJWSvGVSqEUq1JoW27c4Pp1rl9nGMgkgq6zGgZqdWFqZRjI9Myq1bVPPmG7pe9pGmrl+JimYZqYJkqh6yiFTIbBarslgggiqNVehFUEpVhlEmFVq70XXvClloVMqwjfWIRVhFUEETQNXUcpNA1dR9MQQQQRRBBBBBFEEOH76pKDC1WKVYQvFeGJyHROhC+USYRVKUQwTbQtw0DbMs8MA+NI11EKpXB2xmZDBMPAMJBJhFUmEURYzbNVBJlME9Pk6VSra598QiaZRDDPzDObDV1HBJnUyjQRYfVf/ysffugrZbLZWEVYRViVYrUsDAO1WmUSwXbL8TFNw717jCO7HeNI3xPhW2sa+t5e39M0NA19T9/TNGRSCqXQ9xwfk+lp98fLlzk+JpNxZLMhk65ju2W7pRS2WyLoOkqh69jtGEc2G0phHGkadjt2O3Y7mobtlhde8KVKYbMh02q3Y7djt2O3Y7slwiqCUug6SnFhSqHriPDMyvR3H35oL8JerbQty0KtRDBNDIO9TEoh0yqCUogggggiqNUqglKIINPXeuEFuo6mcaEi7EV43l1ycKEiePnlfxBB01hFEEEEEVZNQ4RVBBFWEb4zmb5QBBFWEURQCl1H11llMo6UQilkUiuZZFIrTcN2a5VJBBFEEEEmtdI0jCNdR9uS6ekU4Y+XLxNBrVa1EsHZmdV2SyaZVplW08y//IsvVQqlMM9WmfYyqdVqmqjVahiolVqJoBRqJZNa7dVKrWQ6p1ZqpVYyrWqlVmqlVmqlVjKplVqplVrtNQ1dR4RV39P3nnY/++gjzs6IoBTmmQgy7WVSCpmcnVGrz6mVTKaJeaZWMskk09eKINOqFEqhFEqhFOdEUIoLFUEpnmml+IeXX3ZO09D3Vk1jlWm1LFalkMkwUCsRVrVaZVIrmWRaRbDdstnYK8XXinDw3brk4MLdv39TrUyTVSaZZJJJJtNkL5NMIsj0N1Gr1fGxvUzmmVppGkohgloZBqtaGQYyqZWzMzLJJJNMNhumyd40MQxWTUMpnm6ZVplEkMk0MQzMs1WEVQSlUDZ8/LGvFEGmczJ9oUy6zqptaVvmmRs3uHGDeaZWMqmVGzeo1V4mbcuNG9y4wTRZ1cqNG8wzw8Aw0LZME23LjRu0LbU6J5NarTKp1dPuZw8eME1WmVbHx8wzbcuyWNVqNU32aqVtGQareWYYqJVlYVloW9qWDz/0lWol0yrTwV8hwv/98suU4nNKYRwZR5rGquuolQi2WyKcUwoRRFAKmWQSQSZnZwwD0+ScH/3Il8rkxg3alkwHF++SgwuVyQ9/+HsRRFiVYlWKv2gxeSST7ZZSKMUqwjfUo/f4JpmtpiHTXibDQCYRbLdE2CvFqhQiiLCq1SqCUohgWexFWE0Ty0ImmZ5Opfj9D39IJhFWmWR6pK3VlGmVaZVJrdSF//AffKFMhoFp8kiP3v+k6+g6nzPPzDNdR9ex2Vh1HdutqVbtMBDBbkcp9iIYR3Y7djuaxqpp2O3Ybuk6xpFxpGkYR3Y7xpGmsVcrw0DbUiu1Mgxkemplev2jj6wyuXGDTIaBWrWZplrtdZ29ZbEaRyIohe2WriOCzYZpIlOPP/kaTcO9e3QdEb7INE3atnXwLWSS6ZG2bU3TZJVJpr2zM0qhFGq1yqRWSrEXoc/U+4tpItNerVb/8i++VAQRHplq1batby3T15om+t7z6JKD78R2SwSlMI40DRGUQgSl0HVEkEkEmWR64krh+JgIq1opha6zN8/0PTduMAwcHxNhVQqlUAoRVl3HdksmmfYyrSLIJNPTK8J/eeklxpFxZBzZ7djtGEeahqZht2O3YxzZ7RhH/s//gx/9yBeKYLulaWgamsY5EUTYaxp2O8aRrqMUloVMxpFSKMUqk6ahFCKcUwqlUAoRVhGUQimUQgSlEEEplEIpRNiLYLNhuyWCUthuifDUqtW1f/1XIpwTQYRVKUSwLAwDpVjVytkZw2CVyTwzz2SyLJRC0/hGIoggwrc2TfQ9tdL31Oob6XumyffJ//7hh/y7f0ffM020LdNE2zJNZFIrw8A8W223TBPTxDRZ1WoVQa3UapXpC0UQYfWTn/hK48g4+qvUyjBQq68V4Xl0ycGFqpVPPrlmWajVaprIZJqolUwiaBqWxWqaiPCFIly4CHu1EkEpnJ0xDGTSNEQQ4XPm2apWjo+tMtls6DoyWRarTOeMI13HONJ1nmq/PzqiFEqhFEqhFJrGarOhFEqhaSiFTPKMH/3IlyqFzYbjY2q1F2EVYRXB8TGZ1ErTWG02lELTUCt9bxXhW5km+p5Me5n0PdNkL5O2pVYi6Hv6nggiaBpPtWWxV6tzMq0yqdVquyXTKoJaySSTTKtMq0xqpVYHT0im1z/6yGpZWBYyWRY2G0phGJhnq82GcSSCrqMUmoamoWnoOjYbtluahlJ8oVIoxarreOEF31imbyWC42MifKWmoWl8TiaZZPq+uuTgwv3rv15Tq1Umy0KtzqmVYSCTTN9IhG8swheKoBSfM89WtbLZUAq1WmWy2dD37HaMIxFk0jScnbHdstsRwdkZZ2dWTUMplGLVdWQyDGTSNDSNZ1cmfU+tVn3P2Rlx7Gs1DaWQSYS9ZWGeKcUqwirCKoKmoRSrCKsI31oEEUQ4ZxjIpO/JtDo+9szqOr//4Q/Zbmkatlu2WzLtlULTsN3SNERYRVhF2MskgkwyrTKJ4IUXfGeahr6nFPqeUnwjfU/T+N6I8MfLl+k6uo7jYyKolQgirGq1KoWmoVaWhVrJpFamibZlWVgW50Q4p1amiUzOzvjwQ99YBJm+sQj6ngjfWq0MA23LNPm+uuTgQpXCD3/4exH2MimFpqEUmoZSyLSKIMJeBKUQQdNYRVhFOCeCCJ+T6ZwImoauY7u1KoVSKIXtls2G7ZZSrCLYbNhsiLAXwXbLOLLdstnYi2CzoesYR8aR3Y6uYxxpGiKIINOzJZNpolZfalmsmsY3kkmEVQSlsNlQK5lk0jT2psnnouS9TAAAIABJREFUlELf+1K1Mk1kWtXKNJFJJhE+p+ucE0HfU4pnUoR/ePll+p7tlrMzq3v3uHePprHqOkohgnHk3j3Gkd2OcaRpaBru3WO349497t3j3j3u3WO75eOPHXzHanXtk0+YZ9qWZWGzoRSGgVrtTZNVJvNMrURQK5mUQinUajVN1GqVaS/COdPECy/4RjLJJMITEcFmw3ZLKb6vLjm4UBFcvfr3xpF799jtGEe6jnG0Oj5mHOk6djvGkXFkt2O3YxzZ7RhHxpHdjt2OcWS3Y7ej62gaxpHdjt2O3Y7djt2O3Y7djt2O3Y5xpOtoGpqGcWS3Y7slgqahaeh7SrEqhaah7ynFOU1D01AKTUMpVqXQNEQ4pxSahgiahq6jFE+3Wv3so4+YJmplmphn5plMq7MzmoZSqJUIq//0n3wjTcN2S6bV8TGZZJJJKdTKsjAMLAvTRK2+sUyWxV4mmWSyLGQ6J4K+p+/peyJ8H/zx8mWrUthsKIUIIsjk+JgIIqwiiKBW2pZ5plZqZRi4cYNhoG0ZBoaBeeaFFxx8xyLsZVrNM5nUSqZVKVa1MgzUSgSZVhFEUKtVrfYirCKsSqEUq1JoGt9aJrVSK5lWtZJJJplkkkmtVrWSSa3UapVJJrVaZZJplWnVNDQNpfi+uuTgO1EKEURQCqVYRRBhVYpVKZRCBKVQilUpVhFEUAoRlGJ1fEwpRFAKpVAKpVAKpVAKpVAKEfZK8TdVChGebrX6uw8/ZJ6ZZ87O2GzIJNOqVjKtMq2Oj/ngDh9/7FvJZJ4ZBqsItlurWsm0WhYyfWNNQ9cRYdU0NA2l0HU0jefBzz76iFqtSiGTTKsIIqiVWpkmaqVW5tk5EUwTmdRqNU3USiYff+wrZTJNZHoiaqVW3ysR/uHll+k6xpHNhu2WUug6Imgauo5xpGnYbtnt6DrGka6jFGolwudkWmVaTRO1WmVSKx9/7GtlsizUStsyDLQttVIrbcswMAwMA8PA9evMM9NE29K23LjBMFAr08SNGwwDmUwT02Q1DEyT58ElBwcHX6xp/P3Vq4wj40jX0feMI6VYbbeUYtU0dB19T9/xwgu+sabh3j12O+7d4949djuahlLY7bh3j66j62ga30qEcyKsIjwvfvbgAfNMJpkMA7WSaZVJrcwzy0Kt1GrVdWw2RNB1lGIvkwirCF54wVeqlWGgVjKtMskk04WrlVp93/z+6IimoWnoe/qecaTvKYXNhgiaxqoUSqFpaBr6nu2WcWQc2e3Y7RhHdju6jqahaeg6djvGkd2OcaTr+NGPfK0INhtKoevoOrqOUiiFcWSzYbtlu2W7Zbdju6UUxpHtlt2OriOCUhhHuo4ISqEUq+2WUjwPLjk4OPhiEf54+TIRVhFWEfYinBNhFeFbOT4mgggiiCDCXgQRRBDh4FvK9L988gnTxI0btC0RZDIMZDLPnJ3RdRwfM8/Ms9WNG7QttVKrVdNQCpn2Mvn4Y1+padhuaVumiUzaluvXmSYXrmloGgdfoBRKoRRKoRSahlLoe46POT6m7ymFpqEUSqFpeOEF30gEEZRCKTQNEVal0DSUQimUQimUQgSl0DSUQilEUAqlUIpVKZRiVQqleB5c8j11enrq9PTU6emp09NTp6enTk9PPQnXPvmETF8ok0xqJdPn1EqtZFIrtfrGaqVWn1MrmfZqJdOz4vT01OnpqdPTU6enp05PT52enjp49pyenjo9PXV6eur09NTp6anT01PfuVpd++QTuo4Iuo7t1qpWqwgyrUphHOk6NhsiKIUIloVaqZVaKYUIey+84CtNE8vCbkcpRLDdsttRigsXQYTvwunpqdPTU6enp05PT52enjo9PXVw8CRc8j3185//3EcffeThw4cePnzo4cOHHj586En43+7fZ5p8TqYxQuMvhoFayaRWpom+ZxhoW9qWGzeYZ2q1yvRv+r7Xl8I0USu1Mgy0LW3LNDFNTBPDwDBQK9NE29K2TJOmaYzj6Gn285//3EcffeThw4cePnzo4cOHHj586G9tHEdN03hcfd/r+97japrGOI6eZj//+c999NFHHj586OHDhx4+fOjhw4e+c5lWEWSSaVUKEcbtVrPd2iuFUmgamobtlggiiKBpiKAUNhs2G0rRl+InV674WpsNpVCKVdNQCqV4pGka4zh62v385z/30UcfefjwoYcPH3r48KGHDx/6WxvHUdM0Hlff9/q+97iapjGOo4OLdcn31J07d/zqV79ycnLi5OTEycmJk5MT37lMe7VSq70Itlsi2GzIpG0ZBtqWebbKJMIqk3mmbRkGrl+nbZkm5pm2ZRgYBjLJZJoYBtqWtiWTaaJW2pYIMlkWMj3t7ty541e/+pWTkxMnJydOTk6cnJw4ePbcuXPHr371KycnJ05OTpycnDg5OfGdi/CzBw9oWyKslsUqgkwy2WyIcE4Efc84stsxjowjux3jSN/T94wjux0vv+wrNQ197/vgzp07fvWrXzk5OXFycuLk5MTJyYmDgyfhku+pl156yW9+8xtPXK3+7sMPmWfalhs3aFvalr5nnlkWhoF5JpNaiWC7Zbul6zg+9jnHx2QyTQwD00SEVQSlEEEplELX0XVE0HWUwjiy3bLdst16Frz00kt+85vfOHj2vfTSS37zm9944krxv16/zr17jCNNQ9dRCl1H09A0NI2Db+6ll17ym9/8xsHB38Ilz4G2bf35z3/2RET4h5dfZrul62gae2dn1GrVdValWEVQCsvCsjDPdB1dx2bD8TFNQ9NQChE0jVWtHB+z2ZBJJplEcHZGJmdnDAPzTCbLQtty4wZ971nRtq0///nPDp59bdv685//7ImI8MfLl4kggggirCKIIIIIB3+dtm39+c9/dnDwpFzyHPjDH/7giSnF73/4Q87OKIXtluNjNhurCLZb+p5xZLtlHOk6q+2WrqPrrGolwiqTcWS3Yxytuo5xJIJSGEerWmlbpolMpolaqZVlIZNMMj1L/vCHPzj4fvjDH/7g4PvjD3/4g4ODJ+mS58D169c9MbW6ef8+08QwMM8Mg9U0UavVNJFJJpnMM21LJrUSwbKwLERwdsY8M01kksk0sSxWpTBNVqVYdR3jSATjyG7HvXuMI+PIbse9e5TiWXH9+nUH3w/Xr1938P1x/fp1BwdP0iXPgV//+td+8IMfeCIyXfvkEyKYJqaJCIbBI22EqVbmmXmmbZlnaiWTeWYYGAYyrabJqlbmmWHQD4PeX9RK29K2DAPDQK1Ww8AwkEkm80yt1MowUKupVm3bUqvHUiu1+q79+te/9oMf/MDToG1b0zR5XH3f6/ve45qmSdu2nhW//vWv/eAHP/A0aNvWNE0eV9/3/vSnP3lc0zRp29az5Ne//rUf/OAHngZt25qmyePq+17f9x7XNE3atnVwsS55Ti3LYhxH34U/Xr5sFWEv0yqTZSGTTKtMIoigVqvNhlKolbMzpskqk0yrCCLoOmp1TtNQCplEWG02RBDBdkuEVQSZZPqr1co8+1tYlsU4jg6efcuyGMfRwffDsizGcXRw8F245DlycnJiWRZ379711ltvuX37tjfffNNF+9mDB2SSaZXpnEwyyXROJhH2arWaJqtMMsmkVjKplWFwTikcH7PZOKdpKIVSaBqaxt48U6uvlMk0kelzmoau841lehwnJyeWZXH37l1vvfWW27dve/PNNx08e05OTizL4u7du9566y23b9/25ptvOng2nZycWJbF3bt3vfXWW27fvu3NN990cHDRLnlO3L1712uvvWaz2Xj77bfdunXLu+++6/79+x48eOAiXfvkE6umIcIqggjnRFAKEdRqL4JhIIIIexFWmc6JIIKmsTo+5uyMebaKYFloW18ogq4jglqtaqVWamWarDJZFjKdk0kmEb5QJrWSaTVNTBOZ/hp379712muv2Ww23n77bbdu3fLuu++6f/++Bw8eOHh23L1712uvvWaz2Xj77bfdunXLu+++6/79+x48eODg2XL37l2vvfaazWbj7bffduvWLe+++6779+978OCBg4OLdMlz5OrVqx45OzvzxhtveOTTTz/12WefuUh/vHzZKpNMe6UQQYS9CKtSiCCTCEqxirBqGnul2Isg02qayGQYmCYyrTKplVrJpO9pW2plWcikFDKZZzKplWGgbRkGMimFrqMU59TKPJPpnEyrTIaBWskkwmqaqNVf4+rVqx45OzvzxhtveOTTTz/12WefOXi2XL161SNnZ2feeOMNj3z66ac+++wzB8+eq1eveuTs7Mwbb7zhkU8//dRnn33m4OAiXfKcuHbtmvfee8/NmzcdHR155ZVXvPPOO1588UVHR0cuTKa9WokggkxqJdM500QmEUQQQa1kkmlvsyHC52QSQaYvlUnTUIrVPJNpdXxsL8Je09B1RJBJhFWEz4mwqpVamSamibal7602G+aZtmUYWBaGgUzf1rVr17z33ntu3rzp6OjIK6+84p133vHiiy86Ojpy8Oy4du2a9957z82bNx0dHXnllVe88847XnzxRUdHRw6eLdeuXfPee++5efOmo6Mjr7zyinfeeceLL77o6OjIwcFFuuQ5ceXKFXfu3HHr1i0ffPCBR1599VXvv/++CxXh2iefOCeTUijFarOhadhuaRqraaJWMq0yyaRWq2Vhs7HKdE4pNA0R9iKIoBQi2G7ZbmlbSqHrqJVlIcJeJplEWGVa9T1970tlMgy0LfNsFcE8c+MG80yt1EomEYwjTePbunLlijt37rh165YPPvjAI6+++qr333/fwbPlypUr7ty549atWz744AOPvPrqq95//30Hz54rV664c+eOW7du+eCDDzzy6quvev/99x0cXLRLniM//vGP/fSnP/Vv2rb1Xfjj5ctWTWPVNFbHx1bzTCYRbLfcu8c4+krTxDAQQaZzaiWTTHsRlEKtlEKtDIPVZkOEVQSZVqUwjpRiVQpdRwRnZ0T4QqUwjux27HaMI01D17Hbce8eux337nHvHuNI19E0/lo//vGP/fSnP/Vv2rZ18Gz68Y9/7Kc//al/07atg2fXj3/8Yz/96U/9m7ZtHRx8Fy45uFiZ/vjv/z27HZsNpdB1dJ297ZbNhmFgnsm0iqBpiCCCCHsRRJBJKc7JJNNehL2uYxzpezYbq1KIoO/ZbIiwF+GcpmEcGUeaxpeKIIIIIqwiiCCCCCKolXl2cHBwcHDwOC45uHA/e/CAeSaTWpkmamVZrJaFZaEUq0zalkxqtcok0yqCTDKJINNeBBFk2sskk1qJoFb63mq7ZZqYJnuZ1OpLleLCRLDZkOng4ODg4OCvdclzalkW4zi6cBH+n5deYpoYBjIZBpaFzcbYNJpMq82GTJbFXgSlUIpVBKVQilWmR3r0EVaZPieTTNqWGzcYBpaFZbHK9EhTinGzcWGmiVp9qVKIYBio1UVYlsU4jp60cRw1TeNx9X2v73uPq2ka4zh6li3LYhxHT9o4jpqm8bj6vveTn/zE42qaxjiOnnXLshjH0ZM2jqOmaTyuvu/1fe9xNU1jHEcHF+uS58jJyYllWdy9e9dbb73l9u3b3nzzTReqFP/lpZfoOiIohQgiKIVxZLtlsyGCCKuuI4JMaiWTCKtMMinFKtMqk0xfK8KqVmrl7IyzM/requ8pxWPLZFnIdE6t1GqvFLZbIvy1Tk5OLMvi7t273nrrLbdv3/bmm286ePacnJxYlsXdu3e99dZbbt++7c0333TwbDo5ObEsi7t373rrrbfcvn3bm2++6eDgol3ynLh7967XXnvNZrPx9ttvu3Xrlnfffdf9+/c9ePDAhanVf/6nf2KeySSTTKaJYWCaGAbmmRs3mCamiWEgk0wyySSTTGol06oU31omEVaZ1Eom88w0+VqZvpEItluaxiqTTOaZWq0yyaQUIvw17t6967XXXrPZbLz99ttu3brl3Xffdf/+fQ8ePHDw7Lh7967XXnvNZrPx9ttvu3Xrlnfffdf9+/c9ePDAwbPl7t27XnvtNZvNxttvv+3WrVveffdd9+/f9+DBAwcHF+mS58jVq1c9cnZ25o033vDIp59+6rPPPnOR/nj5MpmU4pwIloUIImga30qt1OpLRfhCTUOEVQRdx27HONI0vlKttC21+lq1MgzUajVNTBPbLU1jVSvXrzNNHsfVq1c9cnZ25o033vDIp59+6rPPPnPwbLl69apHzs7OvPHGGx759NNPffbZZw6ePVevXvXI2dmZN954wyOffvqpzz77zMHBRbrkOXHt2jXvvfeemzdvOjo68sorr3jnnXe8+OKLjo6OPBGZTBOZ1EqtRFCKvQgXphSrWtlsrCKIIJNamSaraaLvyXROBNstEb5WBNstEVYRlEIpRFhFMI5E+Gtdu3bNe++95+bNm46OjrzyyiveeecdL774oqOjIwfPjmvXrnnvvffcvHnT0dGRV155xTvvvOPFF190dHTk4Nly7do17733nps3bzo6OvLKK6945513vPjii46OjhwcXKRLnhNXrlxx584dt27dcvv2bY+8+uqr3n//fReqFP/w8stEkEkEpbDbsd0SwTjSNESw3ZJJ11llWkVQinMifKVM59RKKWQyDESQSdsyDCyLvQirCOdE0DRE+FoRNA0RVk1DKVaZVqXQNDSNv9aVK1fcuXPHrVu33L592yOvvvqq999/38Gz5cqVK+7cuePWrVtu377tkVdffdX777/v4Nlz5coVd+7ccevWLbdv3/bIq6++6v3333dwcNEueY789re/9Ytf/MLrr7/u/v37fvnLX/rd737nQkVYbbdEWGUyz2RqMS0LXcdmY2+erSKIsMokggjnROjR+4sIIqwiiCDCORFEEGHVdWy3dJ0pQtu2lELf+07UyjBQq4vy29/+1i9+8Quvv/66+/fv++Uvf+l3v/udJ6ltW9M0eVx93+v73uOapknbtp41v/3tb/3iF7/w+uuvu3//vl/+8pd+97vfeZLatjVNk8fV970//elPHtc0Tdq29Sz67W9/6xe/+IXXX3/d/fv3/fKXv/S73/3Ok9S2rWmaPK6+7/V973FN06RtWwcX65LnxD//8z/rus5/+2//zfXr1z3yj//4j/7jf/yPLlStbv6P/8HZmdV2y3ZrNc9Wmw0RVstCKWy3NA0RVhFEkMn/xx78xlhV5Qm///axqxAsiZm+e0zBPKl1JhgikwpMxT8nJZ299iQ+doiE+MLodA+ctUOMMkrMfaF9mWjvtdWE6qRNRyy75w3udWSuwxjST/B0FFvJXifD8EJStFzSGrzp1K9CHgixpkIaLKxixJs6fasGqAIKqvhTsD8frcEY0BqUAq1BKSaI0CQCIiACStEkwiS1GqQpeE+TCDgHIlwVSkEYglJMEAFrwXsu1/Hjx0mShL6+PsrlMmN27NjBhg0bKMwtx48fJ0kS+vr6KJfLjNmxYwcbNmygMPccP36cJEno6+ujXC4zZseOHWzYsIFCYbaVuEUMDg7yxBNPMH/+fMYtWbKEu+++m5MnTzKb/ndLC01hyATvQSmaGg1wDgYGQCmaajVwDrynyXvwnibnwDlwDkRoUoomESYoxQQRmkRAKRBhgvcgAgMDNHkPaQppCiLMOqXAGFCKCSKQplyJwcFBnnjiCebPn8+4JUuWcPfdd3Py5EkKc8fg4CBPPPEE8+fPZ9ySJUu4++67OXnyJIW5ZXBwkCeeeIL58+czbsmSJdx9992cPHmSQmE2lbhF3HHHHezevZvzHTlyhLa2NmaNCA+cOgXeQ6MBcQxpSpP3NDkHcQzOgXPgHIiAUjSJMIlSoBRNzoH3TCICSoFSTFAKlAKtIUkgy8AY0BrCkCatIcsgDEEpLosIeA8i4D2IgPfgPU3eg/dgLcQxWAtxTJNSIMLluuOOO9i9ezfnO3LkCG1tbRTmjjvuuIPdu3dzviNHjtDW1kZhbrnjjjvYvXs35zty5AhtbW0UCrOpxC2ivb2djo4OwjCkv7+fp556iuXLl7Nx40ZmlTH8ZuFCEAGlIEkgSUBrUApEQCnIMlAKtAalmEQpziECIlySCIhwjo4OmkTAe/AenINGA2o1UAq0BmO4bM5BrQYikKaQphDHkKbgPXgPtRo0GjQpBSIQx6A1KMXlam9vp6OjgzAM6e/v56mnnmL58uVs3LiRwtzS3t5OR0cHYRjS39/PU089xfLly9m4cSOFuae9vZ2Ojg7CMKS/v5+nnnqK5cuXs3HjRgqF2VbiFrJ161a2bt3Kww8/zD333MOHH37Ihg0bmFUiNCkFIjAwAGkKIqA1KAVK0aQ1ZBnkOeQ5VKugFE3VKigFSjGJUkyiNSjFOZSiKU3Be2g0YGAAlAKtwTkmiIBzIMJlMQaqVVAKwhBEIEkgSUBr0BqqVUgSSBIwBpIEsgySBLTmSmzdupWtW7fy8MMPc8899/Dhhx+yYcMGCnPP1q1b2bp1Kw8//DD33HMPH374IRs2bKAwN23dupWtW7fy8MMPc8899/Dhhx+yYcMGCoXZVuIWcfToUcYsWbKE3t5eXnvtNRYtWsQLL7zArPKepmoVlIKODkgSUAqqVdAaRECEpjgG50AEajVQCpSCgQEQoUkpUAqUgiRhEq1BKVCKc4jQpBQoRVMYQpZBtQpKMUEEajUQYRLvQYQpiYBSNCkF1SpoDUqB96AUTVqDUjRpDVqDUlyJo0ePMmbJkiX09vby2muvsWjRIl544QUKc8vRo0cZs2TJEnp7e3nttddYtGgRL7zwAoW55+jRo4xZsmQJvb29vPbaayxatIgXXniBQmG2lbiFrFixgnEHDhygs7OTU6dOMdv+6vRpaDRAhKaBARABEc7R0UHTwACI0CQCStGkFE0ioBSIgNZMUIqmMKTJe86hFIiAUiACHR3QaID30GjQJEKT1pAkoDXnEIE0Be+ZRATSFLwH50AEjAHnIIogTSFNoVYDEWbTihUrGHfgwAE6Ozs5deoUhblnxYoVjDtw4ACdnZ2cOnWKwty0YsUKxh04cIDOzk5OnTpFoTDbStwi2tvbefXVV+nu7mbTpk2sW7eOWq1Gb28vs23x6dMgQlOjAR0doBTUauAcTQMDMDBAk/eQpjSJgPfgHIiACE0iNMUxiNAkQlOaggiTiECSQBiC1mAMhCE0GjQpRZMIeM+UlIIkAa2ZRClIEtAatAatadIakgSSBJIEqlVQCrwH75mp9vZ2Xn31Vbq7u9m0aRPr1q2jVqvR29tLYW5pb2/n1Vdfpbu7m02bNrFu3TpqtRq9vb0U5p729nZeffVVuru72bRpE+vWraNWq9Hb20uhMNtK3EJWr17NSy+9xEcffcSBAwfo6urialj8X/9Fkwh4D7UaeM8EEXAOnAPvQYQmES5IhCYRpuQ9E5SiyRgYGKApDME5mpIEqlXIMqhWaUpTqNU4h/dgLSgFSjElrUEpUAq8B+9BKRABEZq8B+fAe0hTcA5EQAREQITLtXr1al566SU++ugjDhw4QFdXF4W5afXq1bz00kt89NFHHDhwgK6uLgpz1+rVq3nppZf46KOPOHDgAF1dXRQKV0OJm9z69eupVCpUKhUqlQqvvPIKX3/9Nffffz+VSoXu7m5m2+LTp0EpEAFjoFoFERAhUwqjFE1KcQ6taVKKc2hNk9aMs0phuQilwDnwniatodGANAXnIE1BBGMMWZKAUuAceM+M1Go0DQxArQbOQZpCmjIhjqFcBucgTcE5pmP9+vVUKhUqlQqVSoVXXnmFr7/+mvvvv59KpUJ3dzfXUpZlGGOYKWst1lpmyhhDlmXMBevXr6dSqVCpVKhUKrzyyit8/fXX3H///VQqFbq7u7mWsizDGMNMWWtZtGgRM2WMIcsy5or169dTqVSoVCpUKhVeeeUVvv76a+6//34qlQrd3d1cS1mWYYxhpqy1WGuZKWMMWZZRmF0lbnI9PT3U63Xq9Tr1ep16vc6ePXv44IMPqNfr7Ny5k1mlFE1KgVJQrYJSYAwoBVpDtQrG0GQMJAkYA9UqGAPVKmjNJCKgFGgNSjFBa9CaSZSiqVajKQxpUgq8B6XAe/AekgSMAaWYoDVYC0pxQd7TpBQYQ5P3EIY0DQxAkkC1CkrRlCSQZaA1hCFozXT09PRQr9ep1+vU63Xq9Tp79uzhgw8+oF6vs3PnTgpzQ09PD/V6nXq9Tr1ep16vs2fPHj744APq9To7d+6kMHf09PRQr9ep1+vU63Xq9Tp79uzhgw8+oF6vs3PnTgqF2VbiJhcEAUEQEAQBQRAQBAFBEBAEAUEQEAQBs+1/LVwIIiACItBogHPgPTgHjQYTqlWwFjo6oFYDEUhTEGGCCE1a0+Q9eM8E72nSmiYREAGtadIalAKlQCkQAWOYoBQ4R5NSTJv3kKbgPXgP3kO1CsbQFIaQJGAtWAvGQJKAtaA1aA1ag9ZMRxAEBEFAEAQEQUAQBARBQBAEBEFAEAQU5oYgCAiCgCAICIKAIAgIgoAgCAiCgCAIKMwdQRAQBAFBEBAEAUEQEAQBQRAQBAFBEFAozLYSN7n169ezfv16jh49SqVSoVKpUKlUqFQqVCoVuru7mVUiPPanP4EIGANxDN7TpBQYAyLgPU1pCtZCrUaT96AUiIBSTFAKOjpAKVCKcygF3oP3oBQoBUpBtQpZBlkGStEkAkpBRwekKTQaoBRoDdUqFyQCznEOpSBJQAS8hzQFEfAeRGBgAESY4D2kKVgLcQxxDGkK3jMd69evZ/369Rw9epRKpUKlUqFSqVCpVKhUKnR3d1OYG9avX8/69es5evQolUqFSqVCpVKhUqlQqVTo7u6mMHesX7+e9evXc/ToUSqVCpVKhUqlQqVSoVKp0N3dzVQ+/fRTnnzySf7u7/6ON954g3F5nrNmzRpWrVrFrl27KBSmUuIm19PTQ09PD+3t7dTrder1Onv37mXv3r3U63V27tzOP2oTAAAgAElEQVTJrFKK/7VwIShFkzEgAkoxQYRzpClozQQRmkRoEgERSFOaRDiHCBNEQAREQASUokkEajXQGowBpSAMIQxBKdAatOaCvIdaDUSYoBQoBbUa1GqgNYiA92AthCHUaiBCkwiIQK0GSkEYQhiCUkxHT08PPT09tLe3U6/Xqdfr7N27l71791Kv19m5cyeFuaGnp4eenh7a29up1+vU63X27t3L3r17qdfr7Ny5k8Lc0dPTQ09PD+3t7dTrder1Onv37mXv3r3U63V27tzJ+YaGhvinf/on3njjDT755BP27dvHv//7v3Ps2DE2b97Mtm3b2L59O1u2bOHw4cMUCucrcZMLgoAgCBizY8cOtNZ0dXWxfPlyVq9ezTfffMOs0po3f/ADSBJIEqhWwRioVqFaBREmaA1hCMZARwdUq5AkYAwTlOIcIqAUF6QUTUpBowFxDCKQpjSFIU3GgDFgDCjFJWkNSQJKcQ6lIEkgzyFJwBgwhiatIUlAKZq0hjyHPIckAWPAGFCK6QiCgCAIGLNjxw601nR1dbF8+XJWr17NN998Q2FuCIKAIAgYs2PHDrTWdHV1sXz5clavXs0333xDYe4IgoAgCBizY8cOtNZ0dXWxfPlyVq9ezTfffMP5/uM//oMf/ehHBEHAd999R5ZlPPTQQ3z66ac88MADLFy4kIULF/LII4+Q5zmFwvlK3CKGhobYunUr+/bt47PPPuPgwYP09vby2GOPMau8Z+N//iekKUQRpClNjQbUasTe4/j/iUCjASLQaECaQq0GznEOpWhSCkRABAtYpqA1KMUEEYhj8B6qVWg0II4Z55wjjmMuSSnQmilpDUqBUqAUKEWTUqAUE5QCpUApUIorNTQ0xNatW9m3bx+fffYZBw8epLe3l8cee4xrKY5jnHPMlLUWay0z5ZwjjmPmkqGhIbZu3cq+ffv47LPPOHjwIL29vTz22GNcS3Ec45xjpqy1HDlyhJlyzhHHMXPN0NAQW7duZd++fXz22WccPHiQ3t5eHnvsMc63b98+jh8/zt///d/z+OOP84//+I+cPn2azz//nM7OTsYppfjyyy+5HHEc45xjpqy1WGuZKecccRxTmF0lbhFDQ0MkScKCBQsYd99993HXXXdx8uRJZtNjf/oTeA9KQRiC9yACIpxDBLwH78F7EGESERChSQSUAqWYRCmanAMREAERUArCEKpVUAq8Z0a8B2tBhOtlaGiIJElYsGAB4+677z7uuusuTp48SWHuGBoaIkkSFixYwLj77ruPu+66i5MnT1KYW4aGhkiShAULFjDuvvvu46677uLkyZOc7cyZMxw6dIh//dd/5Te/+Q1/8Rd/wb/9278xMjLCbbfdxoW8+eabLF26lEKhxC1i8eLFbN68mbMNDg4yPDxMW1sbs+nT+fNBawhDsBaSBLQGrUEpUIompUApMAaUAqWgWoUkAa1Ba1AKjAFjIEkgzyHLwBjQGpIEjIEkgTyHPIc8h/5+yDJIEjAGrAWtIcsgSZjLFi9ezObNmznb4OAgw8PDtLW1UZg7Fi9ezObNmznb4OAgw8PDtLW1UZhbFi9ezObNmznb4OAgw8PDtLW1cba//Mu/5G/+5m8olUqMWblyJV9++SXLli3j0KFDjBscHGTx4sWM27hxI4cOHaJQKHGLOH78OMPDwyxdupQoigjDkIceeogzZ85QqVSoVCp88cUXzJjWrP0f/wPyHKylyRjIMshz0BqSBPr7Ic+hvx+yDPr7ob8frAVrIc8hz6G/H7IMsgysBaVAa+jogDAEayHLwBjQGrQGrUEpUAq0BqWYoDUoxRXTGqwFpbhejh8/zvDwMEuXLiWKIsIw5KGHHuLMmTNUKhUqlQpffPEFhRvf8ePHGR4eZunSpURRRBiGPPTQQ5w5c4ZKpUKlUuGLL76gMDccP36c4eFhli5dShRFhGHIQw89xJkzZ6hUKlQqFb744gvGrFy5kj/84Q+M27dvH52dnXR2dtLX18e43bt3s3LlSgqF85W4RbS3t/Pxxx+zZ88e3nvvPXbs2MGePXuo1+vU63Xq9Tr33nsvhRtfe3s7H3/8MXv27OG9995jx44d7Nmzh3q9Tr1ep16vc++991K48bW3t/Pxxx+zZ88e3nvvPXbs2MGePXuo1+vU63Xq9Tr33nsvhbmhvb2djz/+mD179vDee++xY8cO9uzZQ71ep16vU6/XuffeexnT1dXFypUreeyxx4jjmMHBQR5//HGWLl1KGIY8/vjjPPnkk3R2dtLZ2UmhcL4St4iRkRGCICAIAtra2hgaGqKlpYUgCAiCgCAIKMwNIyMjBEFAEAS0tbUxNDRES0sLQRAQBAFBEFCYG0ZGRgiCgCAIaGtrY2hoiJaWFoIgIAgCgiCgMHeMjIwQBAFBENDW1sbQ0BAtLS0EQUAQBARBwNmef/55duzYwT//8z/zq1/9ilKpxJjnn3+ed999l23btrFp0yZuBN6DcxRuICVuAXEcs3LlSsYMDg6yYsUKnn32WR588EHq9TqFuSOOY1auXMmYwcFBVqxYwbPPPsuDDz5IvV6nMHfEcczKlSsZMzg4yIoVK3j22Wd58MEHqdfrFOaWOI5ZuXIlYwYHB1mxYgXPPvssDz74IPV6nQu57bbbmDdvHudraWmhpaWFG4H3EMeQpuAchRtEiZvcO++8w/Hjx9m7dy9j1q1bx9q1a/nkk0/I85yXX36ZwtzwzjvvcPz4cfbu3cuYdevWsXbtWj755BPyPOfll1+mMDe88847HD9+nL179zJm3bp1rF27lk8++YQ8z3n55ZcpzB3vvPMOx48fZ+/evYxZt24da9eu5ZNPPiHPc15++WXmMu+hWoU8hzimcIMocZPbvn07r7/+Oi0tLYw5fPgwTz/9NGMWLVpEqVTi1KlTFG5827dv5/XXX6elpYUxhw8f5umnn2bMokWLKJVKnDp1igv51a9+xS9+8QvG5XnOmjVrWLVqFbt27aJw7Wzfvp3XX3+dlpYWxhw+fJinn36aMYsWLaJUKnHq1CkKc8P27dt5/fXXaWlpYczhw4d5+umnGbNo0SJKpRKnTp1irhoYAKVAKUgSiCIKN4ASN7kzZ85w++23M+arr77izJkzBEHAuG+//ZZSqUThxnfmzBluv/12xnz11VecOXOGIAgY9+2331IqlZjK73//e7IsY3h4mDHHjh1j8+bNbNu2je3bt7NlyxYOHz5M4do4c+YMt99+O2O++uorzpw5QxAEjPv2228plUoU5oYzZ85w++23M+arr77izJkzBEHAuG+//ZZSqcRcJQJK0WQtTd5TuM5K3OQeffRRent7GfPWW2/R1dXFuDzPaW1tZd68eVwrWZZhjGGmrLVYa5kpYwxZljEXPProo/T29jLmrbfeoquri3F5ntPa2sq8efM43/DwMD//+c957rnnGPfpp5/ywAMPsHDhQhYuXMgjjzxCnudcjizLMMYwU9ZarLXMlDGGLMuYCx599FF6e3sZ89Zbb9HV1cW4PM9pbW1l3rx5XCtZlmGMYaastSxatIiZMsaQZRlzxaOPPkpvby9j3nrrLbq6uhiX5zmtra3MmzePayXLMowxzJS1Fmst56tWoVZj2owxZFlGYXaVuMk999xzHDx4kGXLlvH+++/z9ttvM2bt2rU888wzvPHGGxTmhueee46DBw+ybNky3n//fd5++23GrF27lmeeeYY33niDqbz66qs8++yz3HXXXYz7/PPP6ezsZJxSii+//JLCtfHcc89x8OBBli1bxvvvv8/bb7/NmLVr1/LMM8/wxhtvUJg7nnvuOQ4ePMiyZct4//33efvttxmzdu1annnmGd544w3mMhFQiglag/cUrrMSt4B6vU5fXx/79++npaWFMS+++CJ9fX10d3dTmDvq9Tp9fX3s37+flpYWxrz44ov09fXR3d3N+X73u98xf/58fvjDH3K2kZERbrvtNi7kzTffZOnSpRSunnq9Tl9fH/v376elpYUxL774In19fXR3d1OYW+r1On19fezfv5+WlhbGvPjii/T19dHd3c1cJgJKMUEpEKFwnZW4RcyfP5+zdXZ20tbWRmHumT9/Pmfr7Oykra2NqfT09PDHP/6Rp59+mrfffhvvPe+88w7Lli3j0KFDjBscHGTx4sWM27hxI4cOHaJwdc2fP5+zdXZ20tbWRmFumj9/Pmfr7Oykra2NuUwElKJwAypRKNzEfvvb3/LrX/+aX/7yl/z4xz9m5cqVPPHEE3R2dtLX18e43bt3s3LlSgqFQqEwN5QoFG5iCxYsYMGCBSxYsIDbb7+d73//+8ybN4+lS5cShiGPP/44Tz75JJ2dnXR2dnK9xDE4R6FQuMGIgFJMohSMjlK4jkoUrqk4jnHOMVPWWqy1zJRzjjiOuRWsWbOGn/3sZ4x7/vnneffdd9m2bRubNm3icsVxjHOOmbLW4pyl0WBGnHPEcUzh8sVxjHOOmbLWcuTIEWbKOUccxxSuTBzHOOeYKWstcWxRihlxzhHHMYXZVaJQuIW1tLTQ0tJCoVAoTEUEOjqYRCkYGaFwHZUoFArXXZKAcxQKhTlCKRgdpXAdlSgUCtdVo0GT1uAciEC5DHFMoVC4AWhN4QZUolAoXDci4D1NYQhpClEESQLeg7VQLoP3FAqFaySKII4p3OBKFAqF60aECcZAlkF/PxgDWQaNBmQZRBF4T6FQuMpEwHvwnialQGsm6eiAkREK11GJQqFwQ1AKtGaC1pDnoDVkGdRqFAqFqyxNIUloGhgApSjcoEoUCoXrRoRpMQaco1AoXEUi4D0YA1qDcxCGTEkpGB2lcB2VKBQK140I06YUiFAoFK4SEVAKlIIwpHCDK1EoFOYErcF7CoXCVeI9hCFNxoDWXJBSMDpK4ToqUbimsizDGMNMWWux1jJTxhiyLKNw+bIswxjDTAwMQJZZrLVcShhCo8EFGWPIsozC5cuyDGMMM2WtZdGiRcyUMYYsyyhcmSzLMMZwuRoN0JoJeW6x1jJTxhiyLKMwu0oUCoXrRgSUYlqMAecgisB7bkn/8i//wpo1a/jRj37Eli1bGJfnOWvWrGHVqlXs2rWLQuFyiYAIaM20KAUjIxSuoxKFQuHG4BxYy8X090OSQBSB99xS9u/fz29+8xvee+89PvjgA/r6+ti1axfHjh1j8+bNbNu2je3bt7NlyxYOHz5MoXA5REApLsvoKIXrqEThon71q1/xi1/8gnF5nrNmzRpWrVrFrl27KBRmQgSUAkQgTSFNuRilQGvo74co4pbygx/8gJdffpl58+ZRKpWoVCocOnSITz/9lAceeICFCxeycOFCHnnkEfI8p1C4HGkK1SrTphSF66xE4YJ+//vfk2UZw8PDjDl27BibN29m27ZtbN++nS1btnD48GEKhSslAkoB3oPWYAw4x6UoBcaAc9wyOjo6+Nu//VvGDA0NsX37dlavXs3nn39OZ2cn45RSfPnll8yqOAbvKdycREAEjKEwh5QoTGl4eJif//znPPfcc4z79NNPeeCBB1i4cCELFy7kkUceIc9zCoUrIQJK8We1GlSrEIbQaDAdYQi1GrecY8eO8Q//8A+88MIL/PVf/zUjIyPcdtttXMibb77J0qVLuWIi4D3EMYWbkwgoxdREQISptLbC6CiF66REYUqvvvoqzz77LHfddRfjPv/8czo7OxmnlOLLL7/kcsRxjHOOmbLWYq1ljAhXzDlHHMcULl8cxzjnmClrLVYEtAZjwDkQ4VKMARHwnibnHHEcczP7wx/+wE9+8hN++tOf8uijjzJm2bJlHDp0iHGDg4MsXryYcRs3buTQoUNcTBzHOOeYkvegFCgF5TJYC95zNhGIY4giy5EjR5gp5xxxHFO4MnEc45xjutIUwpBJrLXYKIJyGeKYK+WcI45jCrOrRGGS3/3ud8yfP58f/vCHnG1kZITbbruNC3nzzTdZunQp15IIlMsQReA9hTlEhP+mFBOMAe+ZjiSBWo1bwtGjR9m4cSNvvvkmYRgyrrOzk76+Psbt3r2blStXMmtqNQhDyHPIMhgYgDgG5xgj1hGVhQ7xeE9hjhEBEbCWC/vuOxAB7znbvHkwMkLhOilRmKSnp4c//vGPPP3007z99tt473nnnXdYtmwZhw4dYtzg4CCLFy9m3MaNGzl06BDXUhyDMRCGUKtBFIEIhTlCKf4sDJnQ0QGNBtNhDDgHItz0tm7dyrFjx/jJT35CV1cXXV1d/PznP2fp0qWEYcjjjz/Ok08+SWdnJ52dncwqrWnSGrIMkgQaDfAelwraKKzEKAYozC3eg9ZMrdGAapWmJIE4pnDjKFGY5Le//S2//vWv+eUvf8mPf/xjVq5cyRNPPEFnZyd9fX2M2717NytXruR6GRigKcvAWsgyUAqiCLyncJ2IgAiXJAJKAY0G51CKy2EMeM9N76WXXuIPf/gD+/fvZ//+/ezfv5+f/vSnjHn++ed599132bZtG5s2bWLWiIAIaM05jAHn8B5qypJlQJKgEApzhwjUalCtMpkIeM8ErUFrKJcZ19oKo6MUrpMShUkWLFjAggULWLBgAbfffjvf//73mTdvHkuXLiUMQx5//HGefPJJOjs76ezs5HpxDqpVzpFlkCRQq1G4TpyDSHNJItDRAXjPOYwB50CE6ahWIY655bW0tNDS0sKsEgGlmIpoQ1pTZBl/ZgwKoTA3iEAcg1KgNZM5xyRZBkkC3/seWEvh+ipRuKg1a9bws5/9jHHPP/887777Ltu2bWPTpk1cT0qBMUxiDHgPIhSug4EBGB1legaEKWkNUQTf+x6IcDFag9ZQq1GYbd5DGHI+EYgkI8SjNRM6FJdNBJyjcI3FMYQhZBmTiUCagtZMYgx89x2kKa2tMDLCRYlAo0HhKihRuGwtLS20tLRwvWnNBWkN3lO4DkRoGh3logYGQCFMKcsgz8EY8J5LSRLwnsJsazRAa84mAnEM1SrYfsP5Rke5LGkKcQzWUrhGREAErOW/OQdRBN6Dc6A1hCEXpBSMjnIpIuAceE9hlpUozDneQ60GHR1cUJJArQZRBCIUriHvoWrgxAkuSgSUeKakFCgF1SrEMZeiNWQZOAciFGaDCIiA1pzNOZqsZTKtGR1l2kTAe8hzqNVAhMI14D0oxX8TgTSFMIQ0hUYD8pyLUop5jDA6ykV5D0qBCDhHYRaVKMwZ3kMUQZqC1lyUUpBlEIaQphSuERFQCnQIJ04wc1qD1uA90+UchQsRgSiCOOaSREApziYCtRpkGVPr6ODECabNe1AKtAatwXsK10CtBklVwFoolyGKoFoFayHPIc+5JKVgdJRLaTRAa5oaDQqzqEThhicCUQS1GiQJ5Dl0dHBJSoG1IALOUbgGREApmkZHuSgRUNIArbmoahVqNaZrYIDChUQRhCGIQBSBCBfkPYQh40QgiiBJQCkuanSUSxKBWg2ShKYwhEaDwlUmAiKgxUGjAXkO/f1gLZclDGkdOcHoKNNiDDgHIhRmSYnCNZVlGcYYpksEnAOlIMtAa5qstVhrmY4sg1oN4phJjDFkWUbh8mVZhjGGs3kPYQj/8xE4cYKLEgGlwCYJ1louyBhwDkS4GGMM332X4RyFKSw+fZomayHPoVqFchmsJcsyjNZMEIFGA7RmnHOgNRjDBVmtaeX/YDq8p0lrmowB52gyxpBlGYUrk2UZxhim4j0oBNIUkgSU4kKstVhruVIiIAJZZsiyDGPAewqzpEThhpamNCUJV0wpyHNwDqIIvKdwlTQaoAccmJjWVhgdZUoioBTgPWjNJRkDzjEdSoH3FC7FGOjvh0YDymWIYyiXwVrwniatGSMCtRokCRenNXdyghMnuKRaDZKEcygFIhSuoloNkqqA1qA1V8wY5o2eYGSECxIBpZgQhtBoUJglJQo3JBFwDkTAWlCKGfvuO0gSSFPwnsIsEwHvAVeD3/2OMSMjTEkEEAGlmJYsg1oNvOdStAbvKZxn8enToBTnUAqyDLIM8hzyHAYGoNGALGNcmoLWoBSX1trKpYiAeEFrzqE1eE/hKhEBEdCNFKpVZqq1FUZHuSDvIQyZoDU4R2GWlCjccEQgjqHRgCzj4qyFKAIRpkNrSBKIY4giiCLwnsIsEAGNRysB1cGd80YZHeWCFAJKMW1ZBlHEpYQhDAxQOM8Dp05BGDKJUqA1TUpBlkGWgVKMsRZEIMuYltZ5MDLCRTkHCgERzhaGUKtRuEpEQCEgAsZwrSlFkwiFWVCicEMRgTSFahWyDJQCRJiSCNRqUK1CHDNdWkN/PyQJhCGkKYVZ4D2EeEgS0BpOnOBCREApLo/WoBSIcDHGgHMUZoH3UKtBljFt81phdJSLajQgUTXwnrMZQ5O1FK6CNIWqpJAkzIo77+RiGg3QmnMoRWGWlCjcMEQgjqFaBWP4MxGIYyiXwXsmiEAcQ7UKxkAYQhyDtWAtOMelaA3Wggh4T+FKiEC5DFFEowFaDYAxoBStjDAywpREoEM8hCGXRWvwnktRCkQonOXB4WHQmsuRppBloBTT1zqP0VEuzHvwHnQIjQbnyzKo1cA5CrNIBMQLRnkwhlnR2koro4yOMm1ag/cUZkGJwjUVxzHOOc4nAnEM1VDQ3oL34BzEMSQJZBnEMXgPzmGjCKsUWEuTtZAkoBRoDWkKznEpzjlEYmo1ChfhHMSRgHOMi+OYOEqJJSHyCXiPTkKaOhTzGGV0lCkNDIBigDHWWqy1TEsYQqPBVJxzxHHMGK3Be64KEXAOnOOmEccxzjnOJgIioDXTZq3lxH8KIyNckHhBlEaHgAjnUwqSxBHHMd5TmC4RcA6cI45jnLWczTnQeKhWmS5rLdZaLmjePC7Ge9AanHPEccyYMIRGg8IsKFG4IXgPiGBqEU21GqQpZBloDVpDloH3NGkNHR2cQykwBrSG/n6IY/CeSzEGvKdwAeI8aQpeFFGsoFxGnMd7msLMkOSa3NTAGJoe+Z9cjAgoBLTmshgDznEpHR3QaDDrRCBNIU2hVoMoYs544NQp0JoLEeEczoHWXLZWRhgd5YKkISgFGAPeczG1GoWLEUGiGPe9GKIIGg2o1cB7aDSgXAYRRKBWg0zXQGtm0zxGGBlhEhFQikmUAu+ZERHwnlteicJ15z3UUiEjhiwDayHLoL8flGKC1mAtGAMdHVxSfz/EMYhwKVqDcxTOs/j0aXxcQyno7we0xuuENBZEIExCjAGtgSzjbHe2jnLiBBenNZdNKRDhYpTiqnCOpv5+yHOanOPGJ8L/bmnhfCLgPYhAmkK5TJMI1GpQrXL57ryTi/FowpA/UwpEmIox4ByFi0lTYl+lYTLK9GM7Mlw1x6OhWoUkQeKUKIKkKiACWjNr7ryTVkYZHWUSEVCKSbQGEWbEe4giKJe5pZUoXDMiNDUaEEUQx2AtpCkkyqG0Aq2ZNUpBkkAUcSlJAmlK4TyLT5+mRpUkoSlJIPYGrwxGCxfTemcro6NMSQSU4spkGUQROAfeMxVjwDlmjQhYC40GZBkTkgTimBufUvxfd9/N2UQgTSFNQQSyDKpViCLwHpQCrbl8ra20MsroKJOJ0PCgNX+mNTjHhWgNznFNeA8izBkPDA8jziNKk2WQ5zQ1GqAUpCnEDUPkE6pVMDjQmlnV2sqFiHBBWoNzXLFaDfIctIY4ZoJzIMIto0Th6okiiCK89cQxxJHgHIhAEnpCbxlIHVno0FKDJGHWGQPVKpTL4D0XohRUqxBFzDoR8B7iGOIYymWIIuaErj/9BaI0WtOkNSQJ5DmgFFdCBBBBKa6M1pBl0GhAmkK5DN5zPqXAe6ZNBJxjEhGIIprynHNoDcaAc9zwPl2wgHEiEEXQ0QF5DlrTZC1NcQxJwhWbxwgjI0zJo9GaP8sySFMQYSrVKjQaXFUiYC3EMZTLYC1zwgOnTpGSoDVNSoG1kGWgFFSrEIbQb1IsFmo1qFaZda2tTEUEwpApJQnEMYgwiQjnEIFyGaIIvAcREAGtIcvAOfAerIU0hSiCKOKWUKJwVYh1WB9S9hlpQ1MNhazqMTiqPkbXYkwVMtNADTQgSUAprgprIcsgisA5EGEq1kKSQBSBCJckAlEEcQzWQhRBFEG5DOUylMsQx5CmEMdQrUKWQZ5DGEK5DCLc0P6fP/2fVKucwxhQiku7805aGWV0lHOMjIJCIAy5YlpDlkGeQ55DFEGjwdmSBGo1psVaiCJIU4giEKFJBOIYqlWwlillGcQxeM+c4BxEEVSrYC2TZBlkGWjNFWtthdFRJhEBpTiXMRDHYC3nMwa8B++5LCIgwiWJgHNQq0F/P3z3HaQpiHDDu3v4L/HKUK0yJaXAGCAMIU1Ba9Ca2dY6D0ZGmGRgAJQCRCBNwTmIIsZoDcZAuQzf+x5YC96DtRBFEEXgPVgLUQTVKlSrEMcQx6A1E5IEoghqNchz6O+HMIRyGaIIvOemVaJwWfI8Z82aNaxatYpdu3YxlcWnT+PTBg1tyXJFnoM2CmUNGANZBv39YC1kGWQZGMNVpTX094MIRBFYy1S0BqUgjrkoEYhjSBKoVkFrSBJIEshz6O+HPIdqFbIM+vtBa5qUAmshy6BcBhGuizzPWbNmDatWrWLXrl2cT7zwf7c8jDFcmR/8gHmMMDLCOU6egBAPWjMrlII8B+doEmGMMeAcFyUC1kItFfIc+vshDKFchnIZ4rKnKharPecQARGaRMhzEOG6yfOcNWvWsGrVKnbt2sVUTp9ejLWQptDfD9YyJaXAGGbmzjsZGWES8YJSnCvLoFqFRgPimPMlCaQplyQCIiACUQTlMjjHBYlAHMPAAPT3M8EYSFOuqzzPWbNmDatWrWLXrl1M5f/9r4dQCrTm4oyBLIMs42qY1wqjo0wiAgqBOAalwBiaoogxWQb9/dDfT1Oa0tTfD9UqpCkMDECeg7VgDOQ5JAlkGSACUSDVhmoAACAASURBVITF8l3u6e8HpWiyFvIckqoQReA9N6UShWk7duwYmzdvZtu2bWzfvp0tW7Zw+PBhzrf6P++kphKSBLTmxqEUWAt5TlMUQa3G+bKMpigCa2nyHpwD58BaiGNIEtAatAatQWvQGpSiSSnQmgvSGrKM6+LYsWNs3ryZbdu2sX37drZs2cLhw4c5m9KKv/qrtSjFFWtthdFRznHiJGgazCqtIcvAOYgi+N73wFqSBMpliGOwFrz1+NjhnRDHEJUFlcb0U0ZFZXAOOxDznYnJichzMJmGKALvQQSshSiCKIJyGaIIrQRjuC6OHTvG5s2b2bZtG9u3b2fLli0cPnyY8/3pT48xMAD9/Vx1d3KC0VEm8Q0IQyYzBvIcvAfvOZsxNMUxUxKBchmiCKII4hiqVejvhzQFa8FaKJchisBaiCKIIghDyDL+v/bgBibqO2H0/dc/ZxyGtpREiVSvEa3rz5fOtVChlNUAjX3wBTrHnGAR3AjXa33JjLRVQ+d2QLaO8lBPNJVR0qZ2xKGsNadsn3ssQR0HcSziEMYSRPOjBOjjBTp21oNUmaqFvZl9MuewvfV5dnOlFfb/+fwVux1SUvjV+Hw+SktLcTgcnDhxgkOHDnHjxg1+au9/2sTu3fxt8vIYK09Nvs+f/sRfuX8fenogtecYf7FhA39RX89flJQQFBsLsbFQUgL19VBSAvT0kJfaQ/3u89h39xB7/hjk50NaGrHHSkhNBUpKIC0NUlL4i/x8mD0bSkogPx/S0ohNm03q79Oot/fQ08OEpKD6m3k8HhITE4mMjCQyMpL09HTq6+v5Kz09/N+D75OyIZbUVB5PsbFQUgJ2O8TGwrFjkJ8P+flw/jz09FBfD7t38xdps3v4fX4PDQ1QWclf1NdDair/S08P9PTAsWNw7BicPw89PXD+PBw7BufPw7FjcOwYHDsGaWmQlkZeTwmxsfziPB4PiYmJREZGEhkZSXp6OvX19fyURtPL/y9PPcVo9+/DlO97SE0FUlN55PLyoLsburuhoYGSb/Kx7+5hA8f4pvI8v29I5fc9eVT+vodZ54/RHZtG3u5Z8Oc/w+7d0NMDGzbAhg3EdtdDaiqkpkJ9PaSlQVoafPMNdHdDdzd0d0N3N8TG8mvxeDwkJiYSGRlJZGQk6enp1NfX81MPHvxv7N7NL2LK5O/505/g/n3+p/v3obInldRUHs5uh7Q0/qKnB0pKIC0Ne8oxgmbPhrQ0KCkBenro6YG0NNiwAbq7obsb6uuhpARiY6G+Hr5p6KHy9z10p+azgWPwTQ8bekroTs2npHI2lJTA+fOMlpfHr8bj8ZCYmEhkZCSRkZGkp6dTX1/PT2k0vaSm8qub/NRknuJ7/vQn/qe+PkiN7YHKSrDb+St2O1RWQkkJ9PRASQnk50NaGuTnw+zZkJYG+fmQnw+VlZCSAnY7fzF7NlRWQn09lJRASQl0d0N9PcTGwqxZYLdDdzd0d5OaF0teHhOSgupvdu3aNfR6PSGxsbF0dHQQUl5ezssvv8x/0vTyhz8IhBAIIRBCIIRACEFNTQ1msxkhBEIIhBAIIRBCIIRACIEQAiEEQgiEEAghEEIghEAIgRACm82GzWZDCIEQAiEEQgiEEAghEEIghEAIgRACIQRCCIQQCCEQQiDS0zHX1VETGcnvnE7K//t/x7NqFcyeTfnUqfwPw9Osei+Cqt55VPXOY+unEVR9OZnFB5/mj08/TfnUqfzx6afxRETQO28enoUL+WNBAX8sKMCzahWehQv5o8HAHwsK8KxaxR8LCijfuZO3zWZ+19nJ7zo78dy8ya/h2rVr6PV6QmJjY+no6CCkvLwcIQRBQgiEEAghEEIghEAIQU1NDWazGSEEQgiEEAghEEKwbOky2q9eRXO7lb6e+7R+dZPWr25y7dr/4DvNCL/r7EQIgRACm82GzWZDCIEQAiEEQgiEEAghEEIghEAIgRACIQRCCIQQCCEQQiCEwGw2U1NTgxACkZ6O6OvjjzU1/Ob1eUR8uo1vJv0f9PUJ+voEjZM3s/hWAW//8APiD39ACIEoLUX84Q+IzZsRmzcjhEAIgRACsXkzL8+eze9+/BHR2IgQAiEEQgiEEAghKC8v59dw7do19Ho9IbGxsXR0dBBSXl6OEIKYmLdJTxcIIRBCIIRACIEQgpqaGsxmM0IIhBAIIRBCIIRACIEQAiEEQgiEEAghEEIghEAIgRACIQQ2m43vbt6k409/4hn6aG+/S+tXN2n96iZXr97n++/L2bxZIIRACIEQAiEEQgiEEIjNmymfMgXy8/l/vvyScpuN8rY2NPtex/rJZKp652H9ch4Nvz/P7NmQNruHDT0lbNo3mfKpUymfOpXyqVMpnzqV8qlTubL4aaxfzuNA5GLerqvjaW8Bmz6Zx9O3DvI7p5OXJ02i3Gaj95/+iT8+/TRCCIQQlJeX82u5du0aer2ekNjYWDo6OggpLy9HCEFMzNsIIRBCIIRACIEQAiEENTU1mM1mhBAIIRBCIIRACIEQAiEEQgiEEAghEEIghEAIgRACIQRCCGw2GzabDSEEQgiEEAghEEIghKD96lXav/mG4bDb/Ou/3qX1q5u0fnWT7/90n//zy3/idz/+iEhPx2w2U1NTgxACkZ7Oy5Mm8ceDB+mdN49ym43fOZ2Ut7XxO6eTl2fPRkyejJg8GdHXh+jrQ5SWItLTEX/4A2LyZMTkyYj0dIQQCCEQQiDS0xGlpYg//AGRno4QAiEEQgjKy8uZiBRUf7N79+4RFhbGw5hMJlxdXZy9n4uUEiklUkqklEgpkVJy+/Zt+vv7kVIipURKiZQSKSVSSqSUBEkpkVIipURKiZQSKSVSSqSU+P1+/H4/UkqklEgpkVIipURKiZQSKSVSSqSUSCmRUiKlREqJlBIpJf39/cTExOC4cQOT30/i0BD8+c+Y/tt/Y82//AuJtbXMuH+fGffvkzg0xIwzZ1jzL//Cmvffx/Rf/ytr3n+fxNpaZpw5Q+LQEGtu32bN7dskDg2RODTEmtu3WXP7NolDQ6y5fRvblCn8c38/jhs3cNy4QeKRI/wa7t27R1hYGA9jMpmQUiKlREqJlBIpJVJKpJRIKbl9+zb9/f1IKZFSIqVESomUkuamL9li+C27zZtYlRiNv+03+Nt+w5/aYin8p6dx3LiBlBIpJX6/H7/fj5QSKSVSSqSUSCmRUiKlREqJlBIpJVJKpJRIKZFSIqVESkl/fz8xMTFIKZFSIqVkze3bzLh/n8ShIVxdXUgpkVIipWTN7dv8c38/UkqklEgpkVIipURKiZQSKSVSSqSUuLq6cNy4gZQSKSVBUkqklEgpMZlM/Bru3btHWFgYD2MymZBSIqVESomUEiklUkqklEgpuX37Nv39/UgpkVIipURKiZQSKSVSSoKklEgpkVIipURKiZQSKSVSSvx+P8b/ksLG/7yU1xJ1RN6Zjr/tN/jbfsPi6Gj8fhNSSqSUSCmRUiKlREqJlBIpJSa/n7zubv519mxMfj8mv58Z9+8zo6OD3374f/Hb+x3U/zmV+u5Y6rtjKflzCTM6OjAZjZiMRkxGIyajEZPRyJr332dGRwdrbt/mn/v7WXP7NjPu32fN7ds4btzA1dWFye/n5dmzWXP7NlJKpJSYTCZ+Lffu3SMsLIyHMZlMSCmRUiKlREqJlBIpJVJKpJTcvn2b/v5+pJRIKZFSIqVESomUEiklQVJKpJRIKZFSIqVESomUEiklfr8fv9+PlBIpJVJKpJRIKZFSssXwW7YYfsvr/2UR//ud+/jbfoO/7TcsHLrLbzvO4LhxAykl/f39xMTEIKVESomrq4s1t28z4/59TH4/jhs3MPn9OG7cwNXVhZQSKSVSSqSUSCmRUiKlREqJlBIpJVJKpJRIKZFSIqVESkmQlBIpJVJKTCYTE5GC6m+2cOFCpJSE+P1+ZsyYwaNmNBr5JRmNRv4/UlMhNRVSU/krqamQmgp5eZCXB3l5kJoKqan8LYxGI4+DhQsXIqUkxO/3M2PGDB41o9FIbCyUlEBJCZSUALGxjBWj0cgvxWg08jhYuHAhUkpC/H4/M2bM4FEzGo38XWJjycuDkhIoKYHUVP52sbEYjUb+Smws5OUREhsLsbH8m9hYKCmBkhIoKYGSEigpgbw8iI3lP2I0GnlcLFy4ECklIX6/nxkzZvCoGY1GHqXUvFhKSqCkBFLzYiE2ltGMRiO/FKPRyD8CBdXfTK/X09LSQsi5c+dYunQpj5rJZOKXZDKZ+KWYTCYeB3q9npaWFkLOnTvH0qVLedRMJhO/JJPJxC/FZDLxONDr9bS0tBBy7tw5li5dyqNmMpn4JZlMJn4pJpOJx4Ver6elpYWQc+fOsXTpUh41k8nEL8lkMvFLMZlM/CNQUP3NhBCkpKSQlZVFdnY2er0evV6PavwRQpCSkkJWVhbZ2dno9Xr0ej2q8UcIQUpKCllZWWRnZ6PX69Hr9ajGJyEEKSkpZGVlkZ2djV6vR6/Xo1L9lILq71JQUEB1dTUOhwOz2Yxq/CooKKC6uhqHw4HZbEY1fhUUFFBdXY3D4cBsNqMa3woKCqiursbhcGA2m1Gpfo6C6u+m0WjQaDSoxj+NRoNGo0E1/mk0GjQaDaqJQaPRoNFoUKkeRkGlUqlUKpVqnFFQPXaqqqowGAysWLGCQ4cOMVbq6+sxGAysWrWKuro6xlpVVRUGg4EVK1Zw6NAh/hFUVVVhMBhYsWIFhw4dYqzU19djMBhYtWoVdXV1jLWqqioMBgMrVqzg0KFD/COoqqrCYDCwYsUKDh06xFipr6/HYDCwatUq6urqGGtVVVUYDAZWrFjBoUOH+EdRVVWFwWBgxYoVHDp0iLFSX1+PwWBg1apV1NXVMdaqqqowGAysWLGCQ4cOMZEpqB4rXq+XmpoaTp48SW1tLS0tLdTV1fGo+Xw+SktLcTgcnDhxgkOHDnHjxg3GitfrpaamhpMnT1JbW0tLSwt1dXVMZF6vl5qaGk6ePEltbS0tLS3U1dXxqPl8PkpLS3E4HJw4cYJDhw5x48YNxorX66WmpoaTJ09SW1tLS0sLdXV1TGRer5eamhpOnjxJbW0tLS0t1NXV8aj5fD5KS0txOBycOHGCQ4cOcePGDcaK1+ulpqaGkydPUltbS0tLC3V1dUx0Xq+XmpoaTp48SW1tLS0tLdTV1fGo+Xw+SktLcTgcnDhxgkOHDnHjxg3GitfrpaamhpMnT1JbW0tLSwt1dXVMVAqqx8qUKVMoKipCq9WiKApJSUlIKXnUPB4PiYmJREZGEhkZSXp6OvX19YyVKVOmUFRUhFarRVEUkpKSkFIykU2ZMoWioiK0Wi2KopCUlISUkkfN4/GQmJhIZGQkkZGRpKenU19fz1iZMmUKRUVFaLVaFEUhKSkJKSUT2ZQpUygqKkKr1aIoCklJSUgpedQ8Hg+JiYlERkYSGRlJeno69fX1jJUpU6ZQVFSEVqtFURSSkpKQUjLRTZkyhaKiIrRaLYqikJSUhJSSR83j8ZCYmEhkZCSRkZGkp6dTX1/PWJkyZQpFRUVotVoURSEpKQkpJROVguqxMmvWLOLi4gi6desWJ06cIDMzk0ft2rVr6PV6QmJjY+no6GCszJo1i7i4OIJu3brFiRMnyMzMZCKbNWsWcXFxBN26dYsTJ06QmZnJo3bt2jX0ej0hsbGxdHR0MFZmzZpFXFwcQbdu3eLEiRNkZmYykc2aNYu4uDiCbt26xYkTJ8jMzORRu3btGnq9npDY2Fg6OjoYK7NmzSIuLo6gW7duceLECTIzM5noZs2aRVxcHEG3bt3ixIkTZGZm8qhdu3YNvV5PSGxsLB0dHYyVWbNmERcXR9CtW7c4ceIEmZmZTFQKql9VW1sbTqcTp9PJd999R4jP52P9+vXs2rWLOXPm8Kjdu3ePsLAwfmk+n4/169eza9cu5syZw0TS1taG0+nE6XTy3XffEeLz+Vi/fj27du1izpw5PGr37t0jLCyMX5rP52P9+vXs2rWLOXPmMJG0tbXhdDpxOp189913hPh8PtavX8+uXbuYM2cOj9q9e/cICwvjl+bz+Vi/fj27du1izpw5TDRtbW04nU6cTiffffcdIT6fj/Xr17Nr1y7mzJnDo3bv3j3CwsL4pfl8PtavX8+uXbuYM2cOE5WC6lfV1tbG2bNnOXv2LDdv3iSovb2d3NxcCgsLycjIYCwsXLgQKSUhfr+fGTNmMJba29vJzc2lsLCQjIwMJpq2tjbOnj3L2bNnuXnzJkHt7e3k5uZSWFhIRkYGY2HhwoVIKQnx+/3MmDGDsdTe3k5ubi6FhYVkZGQw0bS1tXH27FnOnj3LzZs3CWpvbyc3N5fCwkIyMjIYCwsXLkRKSYjf72fGjBmMpfb2dnJzcyksLCQjI4OJqK2tjbNnz3L27Flu3rxJUHt7O7m5uRQWFpKRkcFYWLhwIVJKQvx+PzNmzGAstbe3k5ubS2FhIRkZGUxkCqpfVU5ODmVlZZSVlbFo0SL6+/sxmUyUl5eTkpLCWNHr9bS0tBBy7tw5li5dyljp7+/HZDJRXl5OSkoKE1FOTg5lZWWUlZWxaNEi+vv7MZlMlJeXk5KSwljR6/W0tLQQcu7cOZYuXcpY6e/vx2QyUV5eTkpKChNRTk4OZWVllJWVsWjRIvr7+zGZTJSXl5OSksJY0ev1tLS0EHLu3DmWLl3KWOnv78dkMlFeXk5KSgoTVU5ODmVlZZSVlbFo0SL6+/sxmUyUl5eTkpLCWNHr9bS0tBBy7tw5li5dyljp7+/HZDJRXl5OSkoKE52C6rFy9OhRfD4fubm5xMfHEx8fT1lZGY+aEIKUlBSysrLIzs5Gr9ej1+sZK0ePHsXn85Gbm0t8fDzx8fGUlZUxkR09ehSfz0dubi7x8fHEx8dTVlbGoyaEICUlhaysLLKzs9Hr9ej1esbK0aNH8fl85ObmEh8fT3x8PGVlZUxkR48exefzkZubS3x8PPHx8ZSVlfGoCSFISUkhKyuL7Oxs9Ho9er2esXL06FF8Ph+5ubnEx8cTHx9PWVkZE93Ro0fx+Xzk5uYSHx9PfHw8ZWVlPGpCCFJSUsjKyiI7Oxu9Xo9er2esHD16FJ/PR25uLvHx8cTHx1NWVsZEpaB6rFgsFtrb2/F6vXi9XrxeL4WFhYyFgoICqqurcTgcmM1mxpLFYqG9vR2v14vX68Xr9VJYWMhEZrFYaG9vx+v14vV68Xq9FBYWMhYKCgqorq7G4XBgNpsZSxaLhfb2drxeL16vF6/XS2FhIROZxWKhvb0dr9eL1+vF6/VSWFjIWCgoKKC6uhqHw4HZbGYsWSwW2tvb8Xq9eL1evF4vhYWFTHQWi4X29na8Xi9erxev10thYSFjoaCggOrqahwOB2azmbFksVhob2/H6/Xi9Xrxer0UFhYyUSmo/qFpNBo0Gg2q8U+j0aDRaFCNfxqNBo1Gg2pi0Gg0aDQaVI+WgkqlUqlUKtU4o6BSqVQqlUo1ziioVCqVSqVSjTMKKpVKpVKpVOOMguqRqK6uxmKxYLFYsFgsWCwWLBYLFouFoNLSUsaKy+XC6/USNDQ0xJEjR9i+fTsFBQUcP36c4eFhfi1dXV3Y7XYeJhAIYLPZeJxUV1djsViwWCxYLBYsFgsWiwWLxUJQaWkpY8XlcuH1egkaGhriyJEjbN++nYKCAo4fP87w8DC/lq6uLux2Ow8TCASw2Ww8Tqqrq7FYLFgsFiwWCxaLBYvFgsViIai0tJSx4nK58Hq9BA0NDXHkyBG2b99OQUEBx48fZ3h4mF9LV1cXdrudhwkEAthsNh431dXVWCwWLBYLFosFi8WCxWLBYrEQVFpaylhxuVx4vV6ChoaGOHLkCNu3b6egoIDjx48zPDzMr6Wrqwu73c7DBAIBbDYbE42C6pGYO3cuCQkJJCQkUFtby1NPPUVCQgIJCQl0dHTw4MEDxsLg4CAffvgh8fHxBL3++uvcuHGDNWvWkJmZyblz53jrrbf4tXz77bdcuHCBh9HpdHz//fdcvnyZx8XcuXNJSEggISGB2tpannrqKRISEkhISKCjo4MHDx4wFgYHB/nwww+Jj48n6PXXX+fGjRusWbOGzMxMzp07x1tvvcWv5dtvv+XChQs8jE6n4/vvv+fy5cs8LubOnUtCQgIJCQnU1tby1FNPkZCQQEJCAh0dHTx48ICxMDg4yIcffkh8fDxBr7/+Ojdu3GDNmjVkZmZy7tw53nrrLX4t3377LRcuXOBhdDod33//PZcvX+ZxMnfuXBISEkhISKC2tpannnqKhIQEEhIS6Ojo4MGDB4yFwcFBPvzwQ+Lj4wl6/fXXuXHjBmvWrCEzM5Nz587x1ltv8Wv59ttvuXDhAg+j0+n4/vvvuXz5MhOJguqRSExMxGAwYDAYmDx5MklJSRgMBgwGAzExMaxatYrBwUGklPj9fpxOJ52dnQRJKXG5XAwMDDCa1+vF6XTS19fHwxw7dgyDwUBQIBDA6/VSWlpKWloay5cvp6KigsuXLzMyMkKI1+vF6XTS19fHaF6vF6fTic/nYzSPx4PT6aSvr4+gwcFBpJQMDAzgcrm4cuUKP+V2u2loaGC0W7du4XK5aGho4MGDB4Rs2LCBsrIyHheJiYkYDAYMBgOTJ08mKSkJg8GAwWAgJiaGVatWMTg4iJQSv9+P0+mks7OTICklLpeLgYEBRvN6vTidTvr6+niYY8eOYTAYCAoEAni9XkpLS0lLS2P58uVUVFRw+fJlRkZGCPF6vTidTvr6+hjN6/XidDrx+XyM5vF4cDqd9PX1ETQ4OIiUkoGBAVwuF1euXOGn3G43DQ0NjHbr1i1cLhcNDQ08ePCAkA0bNlBWVsbjIjExEYPBgMFgYPLkySQlJWEwGDAYDMTExLBq1SoGBweRUuL3+3E6nXR2dhIkpcTlcjEwMMBoXq8Xp9NJX18fD3Ps2DEMBgNBgUAAr9dLaWkpaWlpLF++nIqKCi5fvszIyAghXq8Xp9NJX18fo3m9XpxOJz6fj9E8Hg9Op5O+vj6CBgcHkVIyMDCAy+XiypUr/JTb7aahoYHRbt26hcvloqGhgQcPHhCyYcMGysrKeJwkJiZiMBgwGAxMnjyZpKQkDAYDBoOBmJgYVq1axeDgIFJK/H4/TqeTzs5OgqSUuFwuBgYGGM3r9eJ0Ounr6+Nhjh07hsFgICgQCOD1eiktLSUtLY3ly5dTUVHB5cuXGRkZIcTr9eJ0Ounr62M0r9eL0+nE5/Mxmsfjwel00tfXR9Dg4CBSSgYGBnC5XFy5coWfcrvdNDQ0MNqtW7dwuVw0NDTw4MEDQjZs2EBZWRkTiYJqzF29ehWj0cjVq1fZsWMHb775Ji6Xi3Xr1lFcXEx5eTk1NTVkZmYyMjJCkNFo5ODBgzQ3N5OXl0dtbS0/59NPP2X58uUEabVaYmJiePfdd2lvbycoIiKCpqYmFEUhyGg0cvDgQZqbm8nLy6O2tpagzZs38/7779Pc3Ex2djYXL15kZGSE/Px8Dh8+zMWLF1m3bh2fffYZV69eZceOHWzbtg2Xy8WOHTuoqKggaHh4mOzsbKqrqzl9+jTFxcUEdXV1sXbtWi5dusSpU6dYuXIlgUCAoOnTpzM8PExnZyePu6tXr2I0Grl69So7duzgzTffxOVysW7dOoqLiykvL6empobMzExGRkYIMhqNHDx4kObmZvLy8qitreXnfPrppyxfvpwgrVZLTEwM7777Lu3t7QRFRETQ1NSEoigEGY1GDh48SHNzM3l5edTW1hK0efNm3n//fZqbm8nOzubixYuMjIyQn5/P4cOHuXjxIuvWreOzzz7j6tWr7Nixg23btuFyudixYwcVFRUEDQ8Pk52dTXV1NadPn6a4uJigrq4u1q5dy6VLlzh16hQrV64kEAgQNH36dIaHh+ns7ORxd/XqVYxGI1evXmXHjh28+eabuFwu1q1bR3FxMeXl5dTU1JCZmcnIyAhBRqORgwcP0tzcTF5eHrW1tfycTz/9lOXLlxOk1WqJiYnh3Xffpb29naCIiAiamppQFIUgo9HIwYMHaW5uJi8vj9raWoI2b97M+++/T3NzM9nZ2Vy8eJGRkRHy8/M5fPgwFy9eZN26dXz22WdcvXqVHTt2sG3bNlwuFzt27KCiooKg4eFhsrOzqa6u5vTp0xQXFxPU1dXF2rVruXTpEqdOnWLlypUEAgGCpk+fzvDwMJ2dnYwHV69exWg0cvXqVXbs2MGbb76Jy+Vi3bp1FBcXU15eTk1NDZmZmYyMjBBkNBo5ePAgzc3N5OXlUVtby8/59NNPWb58OUFarZaYmBjeffdd2tvbCYqIiKCpqQlFUQgyGo0cPHiQ5uZm8vLyqK2tJWjz5s28//77NDc3k52dzcWLFxkZGSE/P5/Dhw9z8eJF1q1bx2effcbVq1fZsWMH27Ztw+VysWPHDioqKggaHh4mOzub6upqTp8+TXFxMUFdXV2sXbuWS5cucerUKVauXEkgECBo+vTpDA8P09nZyUShoPpFdXd388EHH7Bv3z7S09Px+/3YbDZsNhsajYavvvqKhoYGfD4fDocDs9mMw+Fg9+7djIyMMFp/fz+BQIDo6GiCFEWhqqqKQCBATk4OixcvZufOnXR2dhLU0NCAz+fD4XBgNptxOBzs3r0bl8vFwMAAlZWVmM1m9u7di5SSuro6AoEAlZWVlJSUUFVVRUlJCc899xzd3d189NFHWK1WioqKOHPmDEGnT59Gq9VSUVHBvn37yMvLI6i1tZX58+fzzjvvsH//ft5++23u3LlDyPz582lra2M86e7u5oMPPmDfvn2kp6fj9/ux2WzYbDY0Gg1fffUVDQ0N+Hw+HA4HZrMZh8PB7t27GRkZYbT+/n4CgQDR0dEEKYpCVVUVgUCAnJwcFi9ezM6dO+ns7CSooaEBn8+HHpc1EAAAEhlJREFUw+HAbDbjcDjYvXs3LpeLgYEBKisrMZvN7N27FykldXV1BAIBKisrKSkpoaqqipKSEp577jm6u7v56KOPsFqtFBUVcebMGYJOnz6NVquloqKCffv2kZeXR1Brayvz58/nnXfeYf/+/bz99tvcuXOHkPnz59PW1sZ40t3dzQcffMC+fftIT0/H7/djs9mw2WxoNBq++uorGhoa8Pl8OBwOzGYzDoeD3bt3MzIywmj9/f0EAgGio6MJUhSFqqoqAoEAOTk5LF68mJ07d9LZ2UlQQ0MDPp8Ph8OB2WzG4XCwe/duXC4XAwMDVFZWYjab2bt3L1JK6urqCAQCVFZWUlJSQlVVFSUlJTz33HN0d3fz0UcfYbVaKSoq4syZMwSdPn0arVZLRUUF+/btIy8vj6DW1lbmz5/PO++8w/79+3n77be5c+cOIfPnz6etrY3xpru7mw8++IB9+/aRnp6O3+/HZrNhs9nQaDR89dVXNDQ04PP5cDgcmM1mHA4Hu3fvZmRkhNH6+/sJBAJER0cTpCgKVVVVBAIBcnJyWLx4MTt37qSzs5OghoYGfD4fDocDs9mMw+Fg9+7duFwuBgYGqKysxGw2s3fvXqSU1NXVEQgEqKyspKSkhKqqKkpKSnjuuefo7u7mo48+wmq1UlRUxJkzZwg6ffo0Wq2WiooK9u3bR15eHkGtra3Mnz+fd955h/379/P2229z584dQubPn09bWxsThYLqFxUVFUVERARBkydPJiYmhpBZs2bxww8/0NjYiN/vx2g0YjQa2bNnD3fv3sXn8zFaZ2cn8+bNY7Tp06dTWlrKlStXOH78ONHR0axduxafz0djYyN+vx+j0YjRaGTPnj3cvXuX69evI4QgJDk5mY0bN9LU1ERycjIhM2fOJDw8nPPnzxMVFUVERARBWq0Wv99PUFNTE88++ywhCQkJBC1btoyuri6SkpLYuXMn4eHhREdHE/LEE0/Q2trKeBIVFUVERARBkydPJiYmhpBZs2bxww8/0NjYiN/vx2g0YjQa2bNnD3fv3sXn8zFaZ2cn8+bNY7Tp06dTWlrKlStXOH78ONHR0axduxafz0djYyN+vx+j0YjRaGTPnj3cvXuX69evI4QgJDk5mY0bN9LU1ERycjIhM2fOJDw8nPPnzxMVFUVERARBWq0Wv99PUFNTE88++ywhCQkJBC1btoyuri6SkpLYuXMn4eHhREdHE/LEE0/Q2trKeBIVFUVERARBkydPJiYmhpBZs2bxww8/0NjYiN/vx2g0YjQa2bNnD3fv3sXn8zFaZ2cn8+bNY7Tp06dTWlrKlStXOH78ONHR0axduxafz0djYyN+vx+j0YjRaGTPnj3cvXuX69evI4QgJDk5mY0bN9LU1ERycjIhM2fOJDw8nPPnzxMVFUVERARBWq0Wv99PUFNTE88++ywhCQkJBC1btoyuri6SkpLYuXMn4eHhREdHE/LEE0/Q2trKeBMVFUVERARBkydPJiYmhpBZs2bxww8/0NjYiN/vx2g0YjQa2bNnD3fv3sXn8zFaZ2cn8+bNY7Tp06dTWlrKlStXOH78ONHR0axduxafz0djYyN+vx+j0YjRaGTPnj3cvXuX69evI4QgJDk5mY0bN9LU1ERycjIhM2fOJDw8nPPnzxMVFUVERARBWq0Wv99PUFNTE88++ywhCQkJBC1btoyuri6SkpLYuXMn4eHhREdHE/LEE0/Q2trKRKGg+kUpisJ/JDw8nMTERKxWK1arFavVSmNjI1OnTmW0SZMmcefOHUI8Hg/79+8nSFEUFi9eTGFhIXFxcbS2thIeHk5iYiJWqxWr1YrVaqWxsZHo6GhGRkYICQQCXL9+Ha1Wy927dxlteHiYJ598EkVR+Dk6nY4ff/yRkPv37xM0depUamtrsdvtLFiwgMLCQk6dOkXIyMgIWq2W8URRFP4j4eHhJCYmYrVasVqtWK1WGhsbmTp1KqNNmjSJO3fuEOLxeNi/fz9BiqKwePFiCgsLiYuLo7W1lfDwcBITE7FarVitVqxWK42NjURHRzMyMkJIIBDg+vXraLVa7t69y2jDw8M8+eSTKIrCz9HpdPz444+E3L9/n6CpU6dSW1uL3W5nwYIFFBYWcurUKUJGRkbQarWMJ4qi8B8JDw8nMTERq9WK1WrFarXS2NjI1KlTGW3SpEncuXOHEI/Hw/79+wlSFIXFixdTWFhIXFwcra2thIeHk5iYiNVqxWq1YrVaaWxsJDo6mpGREUICgQDXr19Hq9Vy9+5dRhseHubJJ59EURR+jk6n48cffyTk/v37BE2dOpXa2lrsdjsLFiygsLCQU6dOETIyMoJWq2W8URSF/0h4eDiJiYlYrVasVitWq5XGxkamTp3KaJMmTeLOnTuEeDwe9u/fT5CiKCxevJjCwkLi4uJobW0lPDycxMRErFYrVqsVq9VKY2Mj0dHRjIyMEBIIBLh+/TparZa7d+8y2vDwME8++SSKovBzdDodP/74IyH3798naOrUqdTW1mK321mwYAGFhYWcOnWKkJGREbRaLROFguqxs2zZMjweD4qiEBUVRW9vL6tXr0ZRFEaLj4/nm2++IeSZZ57h+PHjnDlzhpCvv/6a9vZ25s2bx7Jly/B4PCiKQlRUFL29vaxevZq1a9fi8XgYGhoi6PPPP+fw4cMsX76choYGhoaGCLp8+TI6nQ6tVsvDvPLKK3g8HoaGhghyOp0EVVRUsHfvXhYsWMDGjRt56aWX6O3tJWRwcJDFixcz0SxbtgyPx4OiKERFRdHb28vq1atRFIXR4uPj+eabbwh55plnOH78OGfOnCHk66+/pr29nXnz5rFs2TI8Hg+KohAVFUVvby+rV69m7dq1eDwehoaGCPr88885fPgwy5cvp6GhgaGhIYIuX76MTqdDq9XyMK+88goej4ehoSGCnE4nQRUVFezdu5cFCxawceNGXnrpJXp7ewkZHBxk8eLFTDTLli3D4/GgKApRUVH09vayevVqFEVhtPj4eL755htCnnnmGY4fP86ZM2cI+frrr2lvb2fevHksW7YMj8eDoihERUXR29vL6tWrWbt2LR6Ph6GhIYI+//xzDh8+zPLly2loaGBoaIigy5cvo9Pp0Gq1PMwrr7yCx+NhaGiIIKfTSVBFRQV79+5lwYIFbNy4kZdeeone3l5CBgcHWbx4MRPRsmXL8Hg8KIpCVFQUvb29rF69GkVRGC0+Pp5vvvmGkGeeeYbjx49z5swZQr7++mva29uZN28ey5Ytw+PxoCgKUVFR9Pb2snr1atauXYvH42FoaIigzz//nMOHD7N8+XIaGhoYGhoi6PLly+h0OrRaLQ/zyiuv4PF4GBoaIsjpdBJUUVHB3r17WbBgARs3buSll16it7eXkMHBQRYvXsxEoaB67CxZsoScnBxWrlzJ1q1b2bJlC1arlbCwMEaLiIhg7ty5dHZ2EjRz5kxsNhvvvfcezz//PAkJCeTl5WE2m4mNjWXJkiXk5OSwcuVKtm7dypYtW7BarQRlZWWRkZHBxo0b+eSTTygqKuLFF19k1apVpKenk5+fj9lsxmazMWnSJB5myZIlvPrqq2RkZJCTk0NLSwtB69ato6WlhZycHNavX09fXx+vvfYaIS0tLbzwwgtMNEuWLCEnJ4eVK1eydetWtmzZgtVqJSwsjNEiIiKYO3cunZ2dBM2cORObzcZ7773H888/T0JCAnl5eZjNZmJjY1myZAk5OTmsXLmSrVu3smXLFqxWK0FZWVlkZGSwceNGPvnkE4qKinjxxRdZtWoV6enp5OfnYzabsdlsTJo0iYdZsmQJr776KhkZGeTk5NDS0kLQunXraGlpIScnh/Xr19PX18drr71GSEtLCy+88AITzZIlS8jJyWHlypVs3bqVLVu2YLVaCQsLY7SIiAjmzp1LZ2cnQTNnzsRms/Hee+/x/PPPk5CQQF5eHmazmdjYWJYsWUJOTg4rV65k69atbNmyBavVSlBWVhYZGRls3LiRTz75hKKiIl588UVWrVpFeno6+fn5mM1mbDYbkyZN4mGWLFnCq6++SkZGBjk5ObS0tBC0bt06WlpayMnJYf369fT19fHaa68R0tLSwgsvvMBEtGTJEnJycli5ciVbt25ly5YtWK1WwsLCGC0iIoK5c+fS2dlJ0MyZM7HZbLz33ns8//zzJCQkkJeXh9lsJjY2liVLlpCTk8PKlSvZunUrW7ZswWq1EpSVlUVGRgYbN27kk08+oaioiBdffJFVq1aRnp5Ofn4+ZrMZm83GpEmTeJglS5bw6quvkpGRQU5ODi0tLQStW7eOlpYWcnJyWL9+PX19fbz22muEtLS08MILLzBRKKgeuaamJlJSUghJTk6mqamJ5ORk3G43IcXFxRQXFxNit9tJTk4maNOmTbjdbg4cOIDb7SYtLY2fk52dzRdffEFISkoKTqeT5uZmLly4wJdffonBYCBk06ZNuN1uDhw4gNvtJi0tjaBNmzbhdDqx2WycOnWKadOmEbR9+3YaGho4cuQILpeLuLg4kpOTcbvdhCQnJ+N2uwnZtm0bZ8+epbKykqqqKux2O1FRUdTU1GC327Hb7VRXVxMVFUVQa2srer2eadOm8bhpamoiJSWFkOTkZJqamkhOTsbtdhNSXFxMcXExIXa7neTkZII2bdqE2+3mwIEDuN1u0tLS+DnZ2dl88cUXhKSkpOB0OmlububChQt8+eWXGAwGQjZt2oTb7ebAgQO43W7S0tII2rRpE06nE5vNxqlTp5g2bRpB27dvp6GhgSNHjuByuYiLiyM5ORm3201IcnIybrebkG3btnH27FkqKyupqqrCbrcTFRVFTU0Ndrsdu91OdXU1UVFRBLW2tqLX65k2bRqPm6amJlJSUghJTk6mqamJ5ORk3G43IcXFxRQXFxNit9tJTk4maNOmTbjdbg4cOIDb7SYtLY2fk52dzRdffEFISkoKTqeT5uZmLly4wJdffonBYCBk06ZNuN1uDhw4gNvtJi0tjaBNmzbhdDqx2WycOnWKadOmEbR9+3YaGho4cuQILpeLuLg4kpOTcbvdhCQnJ+N2uwnZtm0bZ8+epbKykqqqKux2O1FRUdTU1GC327Hb7VRXVxMVFUVQa2srer2eadOm8ThqamoiJSWFkOTkZJqamkhOTsbtdhNSXFxMcXExIXa7neTkZII2bdqE2+3mwIEDuN1u0tLS+DnZ2dl88cUXhKSkpOB0OmlububChQt8+eWXGAwGQjZt2oTb7ebAgQO43W7S0tII2rRpE06nE5vNxqlTp5g2bRpB27dvp6GhgSNHjuByuYiLiyM5ORm3201IcnIybrebkG3btnH27FkqKyupqqrCbrcTFRVFTU0Ndrsdu91OdXU1UVFRBLW2tqLX65k2bRoThYLqsaUoCjqdjn9PVlYWly5dIhAIMJpGo0Gn0/FzFEVBp9PxU4qioNPp+ClFUdDpdPw9wsLC0Gg0/JRWq0Wj0TDaxx9/zBtvvMFEpigKOp2Of09WVhaXLl0iEAgwmkajQafT8XMURUGn0/FTiqKg0+n4KUVR0Ol0/D3CwsLQaDT8lFarRaPRMNrHH3/MG2+8wUSmKAo6nY5/T1ZWFpcuXSIQCDCaRqNBp9PxcxRFQafT8VOKoqDT6fgpRVHQ6XT8PcLCwtBoNPyUVqtFo9Ew2scff8wbb7zBRKcoCjqdjn9PVlYWly5dIhAIMJpGo0Gn0/FzFEVBp9PxU4qioNPp+ClFUdDpdPw9wsLC0Gg0/JRWq0Wj0TDaxx9/zBtvvMFEoqAa1xRFoaioiOvXrzMeDQwM8PLLLzN37lz+0SmKQlFREdevX2c8GhgY4OWXX2bu3Ln8o1MUhaKiIq5fv854NDAwwMsvv8zcuXNRgaIoFBUVcf36dcajgYEBXn75ZebOnctEoqAa9xYtWkR8fDzjUVRUFAaDAdW/WbRoEfHx8YxHUVFRGAwGVP9m0aJFxMfHMx5FRUVhMBhQ/S+LFi0iPj6e8SgqKgqDwcBEo6BSqVQqlUo1ziioVCqVSqVSjTMKKpVKpVKpVOOMgkqlUqlUKtU4o6BSqVQqlUo1ziioVCqVSqVSjTMKKpVKpVKpVOOMgkqlUqlUKtU4o6BSqVQqlUo1ziioVCqVSqVSjTMKKpVKpVKpVOOMgkqlUqlUKtU48/8CcOhf2ZK14eoAAAAASUVORK5CYII=" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h2  class = 'S4'><span>Helper Functions</span></h2><div  class = 'S1'><span>PSTH helper function</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >[psth_xs, psth_ys] = calculate_psth(xs, bin_window, bin_width)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[bin_counts, edges]  = histcounts(xs, </span><span style="color: #a709f5;">'BinWidth'</span><span >, bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_xs = edges(1:end-1) + (bin_width / 2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >moving_avg_b = (1/bin_window) * ones(1,bin_window);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_ys = filter(moving_avg_b, 1, bin_counts);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><div  class = 'S9'><span>Plotter function for each stimulus condition</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >plot_condition(ax, ConditionPlot)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_cdata = [1 0 0]; </span><span style="color: #008013;">% red</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_cdata = [0 0 1]; </span><span style="color: #008013;">% blue</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hist_margin = 50;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter_margin = 10;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Calculate PSTH values</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% moving average over 200 ms as per figure 1e</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hist_bin_width = 0.2 / ConditionPlot.psth_bin_window;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[left_psth_xs, left_psth_ys] =</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    calculate_psth(ConditionPlot.left_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[right_psth_xs, right_psth_ys] =</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    calculate_psth(ConditionPlot.right_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_scatter_offset = min(ConditionPlot.right_scatter.ys);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_scatter_height = max(ConditionPlot.right_scatter.ys) - right_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_scatter_offset = min(ConditionPlot.left_scatter.ys);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_scatter_height = max(ConditionPlot.left_scatter.ys) - left_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >psth_height = max([left_psth_ys right_psth_ys]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >left_y_offset = hist_margin</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + psth_height</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    - left_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >right_y_offset = scatter_margin</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_y_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_scatter_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + left_scatter_height</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    - right_scatter_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subplot_height = right_y_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + right_scatter_offset</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    + right_scatter_height;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hold(ax, </span><span style="color: #a709f5;">'on'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% PSTH</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >plot(ax, left_psth_xs, left_psth_ys, </span><span style="color: #a709f5;">'Color'</span><span >, left_cdata);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >plot(ax, right_psth_xs, right_psth_ys, </span><span style="color: #a709f5;">'Color'</span><span >, right_cdata);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Scatter Plot</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ConditionPlot.left_scatter.xs,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    left_y_offset + ConditionPlot.left_scatter.ys,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Marker'</span><span >, </span><span style="color: #a709f5;">'.'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'CData'</span><span >, left_cdata,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'SizeData'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >scatter(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    ConditionPlot.right_scatter.xs,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    right_y_offset + ConditionPlot.right_scatter.ys,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Marker'</span><span >, </span><span style="color: #a709f5;">'.'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'CData'</span><span >, right_cdata,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'SizeData'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% sample, delay, response lines</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.sample, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.delay, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >line(ax, repmat(ConditionPlot.response, 1, 2), [0 subplot_height],</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Color'</span><span >, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'LineStyle'</span><span >, </span><span style="color: #a709f5;">'--'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% blue bar for photoinhibition period</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~strcmp(ConditionPlot.stim_type, </span><span style="color: #a709f5;">'none'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_height = subplot_height;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_width = 0.5; </span><span style="color: #008013;">% seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% end time relative to 'go' cue as described in the paper.</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">switch </span><span >ConditionPlot.stim_type</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Sample'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 1.6;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Early Delay'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 0.8;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">'Middle Delay'</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            end_offset = 0.3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #0e00ff;">otherwise</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >            error(</span><span style="color: #a709f5;">'Invalid photostim period `%s`'</span><span >, ConditionPlot.stim_type);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    stim_offset = ConditionPlot.response - stim_width - end_offset;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    patch_vertices = [</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset, 0;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset, stim_height;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset+stim_width, stim_height;</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        stim_offset+stim_width, 0];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >    patch(ax,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Faces'</span><span >, 1:4,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Vertices'</span><span >, patch_vertices,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'FaceColor'</span><span >, </span><span style="color: #a709f5;">'#B3D3EC'</span><span >,</span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % light blue shading</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'EdgeColor'</span><span >, </span><span style="color: #a709f5;">'none'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'FaceAlpha'</span><span >, 0.8);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >title(ax, ConditionPlot.label);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >xlabel(ax, </span><span style="color: #a709f5;">'Time (Seconds)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ylabel(ax, </span><span style="color: #a709f5;">'Spikes s^{-1}'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >xticks(ax, [-2 0 2]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >yticks(ax, [0 max(10, round(psth_height, -1))]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%     legend(ax, [scatter_left_plot, scatter_right_plot], {'Left Lick', 'Right Lick'},...</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%         'location', 'northwestoutside');</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.TickDir = </span><span style="color: #a709f5;">'out'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.XLim = ConditionPlot.xlim;</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ax.YLim = [0 subplot_height];</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >hold(ax, </span><span style="color: #a709f5;">'off'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div>
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diff --git a/docs/source/_static/html/tutorials/behavior.html b/docs/source/_static/html/tutorials/behavior.html
index 1b1fff997..3983ee832 100644
--- a/docs/source/_static/html/tutorials/behavior.html
+++ b/docs/source/_static/html/tutorials/behavior.html
@@ -43,7 +43,7 @@
 .S7 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left;  }
 .S8 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
 .S9 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 25px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }
-.S10 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Behavior Data</span></h1><div  class = 'S1'><span>This tutorial will guide you in writing behavioral data to NWB.</span></div><h2  class = 'S2'><span>Creating an NWB File</span></h2><div  class = 'S1'><span>Create an NWBFile object with the required fields </span><span>(</span><span style=' font-family: monospace;'>session_description</span><span>,</span><span> </span><span style=' font-family: monospace;'>identifier</span><span>, and</span><span> </span><span style=' font-family: monospace;'>session_start_time</span><span>) and additional metadata.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'My Name'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="C0B9DD53" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S10 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Behavior Data</span></h1><div  class = 'S1'><span>This tutorial will guide you in writing behavioral data to NWB.</span></div><h2  class = 'S2'><span>Creating an NWB File</span></h2><div  class = 'S1'><span>Create an NWBFile object with the required fields </span><span>(</span><span style=' font-family: monospace;'>session_description</span><span>,</span><span> </span><span style=' font-family: monospace;'>identifier</span><span>, and</span><span> </span><span style=' font-family: monospace;'>session_start_time</span><span>) and additional metadata.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'My Name'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="512F524B" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -96,7 +96,7 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'>&nbsp;</div></div></div><h2  class = 'S9'><span>SpatialSeries: Storing continuous spatial data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> is a subclass of</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-family: monospace;'>TimeSeries</span></a><span> that represents data in space, such as the spatial direction e.g., of gaze or travel or position of an animal over time.</span></div><div  class = 'S1'><span>Create data that corresponds to x, y position over time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >position_data = [linspace(0, 10, 50); linspace(0, 8, 50)]; </span><span style="color: #008013;">% 2 x nT array</span></span></div></div></div><div  class = 'S1'><span>In</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> data, the first dimension is always time (in seconds), the second dimension represents the x, y position. However, as described in the</span><span> </span><a href = "./dimensionMapNoDataPipes.html"><span>dimensionMapNoDataPipes</span></a><span> tutorial, when a MATLAB array is exported to HDF5, the array is transposed. Therefore, in order to correctly export the data, in MATLAB the last dimension of an array should be time.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> data should be stored as one continuous stream as it is acquired, not by trials as is often reshaped for analysis. Data can be trial-aligned on-the-fly using the trials table. See the trials tutorial for further information.</span></div><div  class = 'S1'><span>For position data</span><span> </span><span style=' font-family: monospace;'>reference_frame</span><span> indicates the zero-position, e.g. the 0,0 point might be the bottom-left corner of an enclosure, as viewed from the tracking camera.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >timestamps = linspace(0, 50, 50)/ 200;</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >position_spatial_series = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Postion (x, y) in an open field.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is the bottom left corner.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >    )</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="C5C6D8A0" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="300" data-hashorizontaloverflow="true" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 311px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">position_spatial_series = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  SpatialSeries with properties:
+</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'>&nbsp;</div></div></div><h2  class = 'S9'><span>SpatialSeries: Storing continuous spatial data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> is a subclass of</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-family: monospace;'>TimeSeries</span></a><span> that represents data in space, such as the spatial direction e.g., of gaze or travel or position of an animal over time.</span></div><div  class = 'S1'><span>Create data that corresponds to x, y position over time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >position_data = [linspace(0, 10, 50); linspace(0, 8, 50)]; </span><span style="color: #008013;">% 2 x nT array</span></span></div></div></div><div  class = 'S1'><span>In</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> data, the first dimension is always time (in seconds), the second dimension represents the x, y position. However, as described in the</span><span> </span><a href = "./dimensionMapNoDataPipes.html"><span>dimensionMapNoDataPipes</span></a><span> tutorial, when a MATLAB array is exported to HDF5, the array is transposed. Therefore, in order to correctly export the data, in MATLAB the last dimension of an array should be time.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> data should be stored as one continuous stream as it is acquired, not by trials as is often reshaped for analysis. Data can be trial-aligned on-the-fly using the trials table. See the trials tutorial for further information.</span></div><div  class = 'S1'><span>For position data</span><span> </span><span style=' font-family: monospace;'>reference_frame</span><span> indicates the zero-position, e.g. the 0,0 point might be the bottom-left corner of an enclosure, as viewed from the tracking camera.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >timestamps = linspace(0, 50, 50)/ 200;</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >position_spatial_series = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Postion (x, y) in an open field.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is the bottom left corner.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >    )</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="2F6263E2" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="300" data-hashorizontaloverflow="true" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 311px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">position_spatial_series = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  SpatialSeries with properties:
 
         reference_frame: '(0,0) is the bottom left corner.'
      starting_time_unit: 'seconds'
@@ -115,7 +115,7 @@
           starting_time: []
      starting_time_rate: []
              timestamps: [0 0.0051 0.0102 0.0153 0.0204 0.0255 0.0306 0.0357 0.0408 0.0459 0.0510 0.0561 0.0612 0.0663 0.0714 0.0765 0.0816 0.0867 0.0918 0.0969 0.1020 0.1071 0.1122 0.1173 0.1224 0.1276 0.1327 0.1378 0.1429 0.1480 0.1531 … ] (1×50 double)
-</div></div></div></div></div></div><h2  class = 'S9'><span>Position: Storing position measured over time</span></h2><div  class = 'S1'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object represents the position of the subject, store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object inside a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-family: monospace;'>Position</span></a><span> object, which can hold one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >position = types.core.Position();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >position.spatialseries.set(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >, position_spatial_series);</span></span></div></div></div><h2  class = 'S9'><span>Create a Behavior Processing Module</span></h2><div  class = 'S1'><span>Create a processing module called "behavior" for storing behavioral data in the NWBFile, then add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-family: monospace;'>Position</span></a><span> object to the processing module.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >behavior_processing_module = types.core.ProcessingModule(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'stores behavioral data.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">"Position"</span><span >, position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">"behavior"</span><span >, behavior_processing_module);</span></span></div></div></div><h2  class = 'S9'><span>CompassDirection: Storing view angle measured over time</span></h2><div  class = 'S1'><span>Analogous to how position can be stored, we can create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object for representing the view angle of the subject.</span></div><div  class = 'S1'><span>For direction data</span><span> </span><span style=' font-family: monospace;'>reference_frame</span><span> indicates the zero direction, for instance in this case "straight ahead" is 0 radians.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >view_angle_data = linspace(0, 4, 50);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >direction_spatial_series = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'View angle of the subject measured in radians.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, view_angle_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'straight ahead'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'radians' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >direction = types.core.CompassDirection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >direction.spatialseries.set(</span><span style="color: #a709f5;">'spatial_series'</span><span >, direction_spatial_series);</span></span></div></div></div><div  class = 'S1'><span>We can add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CompassDirection.html"><span style=' font-family: monospace;'>CompassDirection</span></a><span> object to the behavior processing module the same way we have added the position data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'CompassDirection'</span><span >, direction);</span></span></div></div></div><h2  class = 'S9'><span>BehaviorTimeSeries: Storing continuous behavior data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralTimeSeries.html"><span style=' font-family: monospace;'>BehavioralTimeSeries</span></a><span> is an interface for storing continuous behavior data, such as the speed of a subject.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >speed_data = linspace(0, 0.4, 50);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >speed_time_series = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, speed_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'he speed of the subject measured over time.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m/s' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_time_series = types.core.BehavioralTimeSeries();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_time_series.timeseries.set(</span><span style="color: #a709f5;">'speed'</span><span >, speed_time_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_time_series to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralTimeSeries'</span><span >, behavioral_time_series);</span></span></div></div></div><h2  class = 'S9'><span>BehavioralEvents: Storing behavioral events</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEvents.html"><span style=' font-family: monospace;'>BehavioralEvents</span></a><span> is an interface for storing behavioral events. We can use it for storing the timing and amount of rewards (e.g. water amount) or lever press times.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >reward_amount = [1.0, 1.5, 1.0, 1.5];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >event_timestamps = [1.0, 2.0, 5.0, 6.0];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >time_series = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reward_amount, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, event_timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The water amount the subject received as a reward.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'ml' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_events = types.core.BehavioralEvents();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_events.timeseries.set(</span><span style="color: #a709f5;">'lever_presses'</span><span >, time_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_events to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralEvents'</span><span >, behavioral_events);</span></span></div></div></div><div  class = 'S1'><span>Storing only the timestamps of the events is possible with the ndx-events NWB extension. You can also add labels associated with the events with this extension. You can find information about installation and example usage</span><span> </span><a href = "https://github.com/nwb-extensions/ndx-events-record"><span>here</span></a><span>.</span></div><h2  class = 'S2'><span>BehavioralEpochs: Storing intervals of behavior data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> is for storing intervals of behavior data.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> uses</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> to represent the time intervals. Create an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> object that represents the time intervals when the animal was running. IntervalSeries uses 1 to indicate the beginning of an interval and -1 to indicate the end.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >run_intervals = types.core.IntervalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was running.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, -1, 1, -1, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [0.5, 1.5, 3.5, 4.0, 7.0, 7.3] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_epochs = types.core.BehavioralEpochs();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavioral_epochs.intervalseries.set(</span><span style="color: #a709f5;">'running'</span><span >, run_intervals);</span></span></div></div></div><div  class = 'S1'><span>You can add more than one</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> to a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >sleep_intervals = types.core.IntervalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was sleeping'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, -1, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [15.0, 30.0, 60.0, 95.0] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_epochs.intervalseries.set(</span><span style="color: #a709f5;">'sleeping'</span><span >, sleep_intervals);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_epochs to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralEpochs'</span><span >, behavioral_epochs);</span></span></div></div></div><h3  class = 'S7'><span>Another approach: TimeIntervals</span></h3><div  class = 'S1'><span>Using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-family: monospace;'>TimeIntervals</span></a><span> to represent time intervals is often preferred over </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span>. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-family: monospace;'>TimeIntervals</span></a><span> is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-family: monospace;'>DynamicTable</span></a><span>, which offers flexibility for tabular data by allowing the addition of optional columns which are not defined in the standard DynamicTable class.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >sleep_intervals = types.core.TimeIntervals( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was sleeping.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'stage'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.3, </span><span style="color: #a709f5;">'stop_time'</span><span >, 0.35, </span><span style="color: #a709f5;">'stage'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.7, </span><span style="color: #a709f5;">'stop_time'</span><span >, 0.9, </span><span style="color: #a709f5;">'stage'</span><span >, 2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 1.3, </span><span style="color: #a709f5;">'stop_time'</span><span >, 3.0, </span><span style="color: #a709f5;">'stage'</span><span >, 3);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.intervals.set(</span><span style="color: #a709f5;">'sleep_intervals'</span><span >, sleep_intervals);</span></span></div></div></div><h2  class = 'S9'><span>EyeTracking: Storing continuous eye-tracking data of gaze direction</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html"><span style=' font-family: monospace;'>EyeTracking</span></a><span> is for storing eye-tracking data which represents direction of gaze as measured by an eye tracking algorithm. An</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html"><span style=' font-family: monospace;'>EyeTracking</span></a><span> object holds one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> objects that represent the gaze direction over time extracted from a video.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >eye_position_data = [linspace(-20, 30, 50); linspace(30, -20, 50)];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >right_eye_position = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The position of the right eye measured in degrees.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, eye_position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 50.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is middle'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'degrees' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >left_eye_position = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The position of the right eye measured in degrees.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, eye_position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 50.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is middle'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'degrees' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking = types.core.EyeTracking();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking.spatialseries.set(</span><span style="color: #a709f5;">'right_eye_position'</span><span >, right_eye_position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking.spatialseries.set(</span><span style="color: #a709f5;">'left_eye_position'</span><span >, left_eye_position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'EyeTracking'</span><span >, eye_tracking);</span></span></div></div></div><h2  class = 'S9'><span>PupilTracking: Storing continuous eye-tracking data of pupil size</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html"><span style=' font-family: monospace;'>PupilTracking</span></a><span> is for storing eye-tracking data which represents pupil size.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html"><span style=' font-family: monospace;'>PupilTracking</span></a><span> holds one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-family: monospace;'>TimeSeries</span></a><span> objects that can represent different features such as the dilation of the pupil measured over time by a pupil tracking algorithm.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >pupil_diameter = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Pupil diameter extracted from the video of the right eye.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0.001, 0.002, 50), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'meters' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >pupil_tracking = types.core.PupilTracking();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >pupil_tracking.timeseries.set(</span><span style="color: #a709f5;">'pupil_diameter'</span><span >, pupil_diameter);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'PupilTracking'</span><span >, pupil_tracking);</span></span></div></div></div><h2  class = 'S9'><span>Writing the behavior data to an NWB file</span></h2><div  class = 'S1'><span>All of the above commands build an NWBFile object in-memory. To write this file, use</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/nwbExport.html"><span style=' font-family: monospace;'>nwbExport</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Save to tutorials/tutorial_nwb_files folder</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwbFilePath = misc.getTutorialNwbFilePath(</span><span style="color: #a709f5;">'behavior_tutorial.nwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwbExport(nwb, nwbFilePath);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >fprintf(</span><span style="color: #a709f5;">'Exported NWB file to "%s"\n'</span><span >, </span><span style="color: #a709f5;">'behavior_tutorial.nwb'</span><span >)</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextElement" uid="71BDDA92" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class=" textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="17" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="text output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">Exported NWB file to "behavior_tutorial.nwb"</div></div></div></div></div>
+</div></div></div></div></div></div><h2  class = 'S9'><span>Position: Storing position measured over time</span></h2><div  class = 'S1'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object represents the position of the subject, store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object inside a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-family: monospace;'>Position</span></a><span> object, which can hold one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >position = types.core.Position();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >position.spatialseries.set(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >, position_spatial_series);</span></span></div></div></div><h2  class = 'S9'><span>Create a Behavior Processing Module</span></h2><div  class = 'S1'><span>Create a processing module called "behavior" for storing behavioral data in the NWBFile, then add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-family: monospace;'>Position</span></a><span> object to the processing module.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >behavior_processing_module = types.core.ProcessingModule(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'stores behavioral data.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">"Position"</span><span >, position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">"behavior"</span><span >, behavior_processing_module);</span></span></div></div></div><h2  class = 'S9'><span>CompassDirection: Storing view angle measured over time</span></h2><div  class = 'S1'><span>Analogous to how position can be stored, we can create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> object for representing the view angle of the subject.</span></div><div  class = 'S1'><span>For direction data</span><span> </span><span style=' font-family: monospace;'>reference_frame</span><span> indicates the zero direction, for instance in this case "straight ahead" is 0 radians.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >view_angle_data = linspace(0, 4, 50);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >direction_spatial_series = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'View angle of the subject measured in radians.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, view_angle_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'straight ahead'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'radians' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >direction = types.core.CompassDirection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >direction.spatialseries.set(</span><span style="color: #a709f5;">'spatial_series'</span><span >, direction_spatial_series);</span></span></div></div></div><div  class = 'S1'><span>We can add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CompassDirection.html"><span style=' font-family: monospace;'>CompassDirection</span></a><span> object to the behavior processing module the same way we have added the position data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'CompassDirection'</span><span >, direction);</span></span></div></div></div><h2  class = 'S9'><span>BehaviorTimeSeries: Storing continuous behavior data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralTimeSeries.html"><span style=' font-family: monospace;'>BehavioralTimeSeries</span></a><span> is an interface for storing continuous behavior data, such as the speed of a subject.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >speed_data = linspace(0, 0.4, 50);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >speed_time_series = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, speed_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'he speed of the subject measured over time.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m/s' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_time_series = types.core.BehavioralTimeSeries();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_time_series.timeseries.set(</span><span style="color: #a709f5;">'speed'</span><span >, speed_time_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_time_series to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralTimeSeries'</span><span >, behavioral_time_series);</span></span></div></div></div><h2  class = 'S9'><span>BehavioralEvents: Storing behavioral events</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEvents.html"><span style=' font-family: monospace;'>BehavioralEvents</span></a><span> is an interface for storing behavioral events. We can use it for storing the timing and amount of rewards (e.g. water amount) or lever press times.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >reward_amount = [1.0, 1.5, 1.0, 1.5];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >event_timestamps = [1.0, 2.0, 5.0, 6.0];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >time_series = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reward_amount, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, event_timestamps, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The water amount the subject received as a reward.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'ml' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_events = types.core.BehavioralEvents();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_events.timeseries.set(</span><span style="color: #a709f5;">'lever_presses'</span><span >, time_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_events to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralEvents'</span><span >, behavioral_events);</span></span></div></div></div><div  class = 'S1'><span>Storing only the timestamps of the events is possible with the ndx-events NWB extension. You can also add labels associated with the events with this extension. You can find information about installation and example usage</span><span> </span><a href = "https://github.com/nwb-extensions/ndx-events-record"><span>here</span></a><span>.</span></div><h2  class = 'S2'><span>BehavioralEpochs: Storing intervals of behavior data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> is for storing intervals of behavior data.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> uses</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> to represent the time intervals. Create an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> object that represents the time intervals when the animal was running. IntervalSeries uses 1 to indicate the beginning of an interval and -1 to indicate the end.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >run_intervals = types.core.IntervalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was running.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, -1, 1, -1, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [0.5, 1.5, 3.5, 4.0, 7.0, 7.3] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_epochs = types.core.BehavioralEpochs();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavioral_epochs.intervalseries.set(</span><span style="color: #a709f5;">'running'</span><span >, run_intervals);</span></span></div></div></div><div  class = 'S1'><span>You can add more than one</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span> to a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >sleep_intervals = types.core.IntervalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was sleeping'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, -1, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [15.0, 30.0, 60.0, 95.0] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >behavioral_epochs.intervalseries.set(</span><span style="color: #a709f5;">'sleeping'</span><span >, sleep_intervals);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Add behavioral_epochs to the processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'BehavioralEpochs'</span><span >, behavioral_epochs);</span></span></div></div></div><h3  class = 'S7'><span>Another approach: TimeIntervals</span></h3><div  class = 'S1'><span>Using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-family: monospace;'>TimeIntervals</span></a><span> to represent time intervals is often preferred over </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html"><span style=' font-family: monospace;'>BehavioralEpochs</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html"><span style=' font-family: monospace;'>IntervalSeries</span></a><span>. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-family: monospace;'>TimeIntervals</span></a><span> is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-family: monospace;'>DynamicTable</span></a><span>, which offers flexibility for tabular data by allowing the addition of optional columns which are not defined in the standard DynamicTable class.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >sleep_intervals = types.core.TimeIntervals( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Intervals when the animal was sleeping.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'stage'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.3, </span><span style="color: #a709f5;">'stop_time'</span><span >, 0.35, </span><span style="color: #a709f5;">'stage'</span><span >, 1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.7, </span><span style="color: #a709f5;">'stop_time'</span><span >, 0.9, </span><span style="color: #a709f5;">'stage'</span><span >, 2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >sleep_intervals.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 1.3, </span><span style="color: #a709f5;">'stop_time'</span><span >, 3.0, </span><span style="color: #a709f5;">'stage'</span><span >, 3);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.intervals.set(</span><span style="color: #a709f5;">'sleep_intervals'</span><span >, sleep_intervals);</span></span></div></div></div><h2  class = 'S9'><span>EyeTracking: Storing continuous eye-tracking data of gaze direction</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html"><span style=' font-family: monospace;'>EyeTracking</span></a><span> is for storing eye-tracking data which represents direction of gaze as measured by an eye tracking algorithm. An</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html"><span style=' font-family: monospace;'>EyeTracking</span></a><span> object holds one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-family: monospace;'>SpatialSeries</span></a><span> objects that represent the gaze direction over time extracted from a video.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >eye_position_data = [linspace(-20, 30, 50); linspace(30, -20, 50)];</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >right_eye_position = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The position of the right eye measured in degrees.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, eye_position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 50.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is middle'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'degrees' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >left_eye_position = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The position of the right eye measured in degrees.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, eye_position_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 50.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is middle'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'degrees' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking = types.core.EyeTracking();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking.spatialseries.set(</span><span style="color: #a709f5;">'right_eye_position'</span><span >, right_eye_position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >eye_tracking.spatialseries.set(</span><span style="color: #a709f5;">'left_eye_position'</span><span >, left_eye_position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'EyeTracking'</span><span >, eye_tracking);</span></span></div></div></div><h2  class = 'S9'><span>PupilTracking: Storing continuous eye-tracking data of pupil size</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html"><span style=' font-family: monospace;'>PupilTracking</span></a><span> is for storing eye-tracking data which represents pupil size.</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html"><span style=' font-family: monospace;'>PupilTracking</span></a><span> holds one or more</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-family: monospace;'>TimeSeries</span></a><span> objects that can represent different features such as the dilation of the pupil measured over time by a pupil tracking algorithm.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >pupil_diameter = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Pupil diameter extracted from the video of the right eye.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0.001, 0.002, 50), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NB: Important to set starting_time when using starting_time_rate</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'meters' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >pupil_tracking = types.core.PupilTracking();</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >pupil_tracking.timeseries.set(</span><span style="color: #a709f5;">'pupil_diameter'</span><span >, pupil_diameter);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >behavior_processing_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'PupilTracking'</span><span >, pupil_tracking);</span></span></div></div></div><h2  class = 'S9'><span>Writing the behavior data to an NWB file</span></h2><div  class = 'S1'><span>All of the above commands build an NWBFile object in-memory. To write this file, use</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/nwbExport.html"><span style=' font-family: monospace;'>nwbExport</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Save to tutorials/tutorial_nwb_files folder</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwbFilePath = misc.getTutorialNwbFilePath(</span><span style="color: #a709f5;">'behavior_tutorial.nwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwbExport(nwb, nwbFilePath);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >fprintf(</span><span style="color: #a709f5;">'Exported NWB file to "%s"\n'</span><span >, </span><span style="color: #a709f5;">'behavior_tutorial.nwb'</span><span >)</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextElement" uid="300E8381" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class=" textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="17" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="text output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">Exported NWB file to "behavior_tutorial.nwb"</div></div></div></div></div>
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-.S6 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></h1><div  class = 'S1'><span>This tutorial is easier to follow if you have already looked at the </span><a href = "dimensionMapNoDataPipes.html"><span style=' font-weight: bold;'>dimensionMapNoDataPipes</span></a><span> tutorial or if you compare these side by side.</span></div><div  class = 'S1'><span>The key difference when using </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> instead of </span><span style=' font-weight: bold; font-family: monospace;'>VectorData</span><span> is that 1D data can be represented in HDF5 as 2D, thus allowing you to write either row or column vectors. This is made possible because of the </span><span style=' font-family: monospace;'>maxSize</span><span> property of the </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> class, which lets you specify a max size for each dimension. By setting the </span><span style=' font-family: monospace;'>maxSize</span><span> to [1, N] or [N, 1], vectors in HDF5 are represented as 2D arrays, just like in MATLAB. The flipping of the dimension order still applies, so a row vector in MATLAB becomes a column vector in HDF5 and vice versa.</span></div><div  class = 'S1'><span style=' font-weight: bold;'>Please note</span><span>: The following tutorial mixes row and column vectors and does not produce a valid dynamic table. The tutorial is only meant to showcase how data maps onto HDF5 datasets when using </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> objects.</span></div><h2  class = 'S2'><span>Create Table</span></h2><div  class = 'S1'><span>First, create an expandable </span><span style=' font-family: monospace;'>TimeIntervals</span><span> table of height 10.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% 1D column </span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >start_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'start times column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, 1:10, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, )</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, Inf </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column </span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >stop_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'stop times column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, 1:10, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, 1)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, [1 Inf], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'axis'</span><span >, 2 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >cond_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'condition column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, randi(2,10,1), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (1, 10)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, [Inf, 1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'axis'</span><span >, 1 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 4D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >randomval_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'randomvalues column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'data'</span><span >, rand(5,2,3,10), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> # maps onto HDF5 dataset of size (10, 3, 2, 5)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'maxSize'</span><span >, [5, 2, 3, Inf], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'axis'</span><span >, 4 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >ids_col = types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(0:9), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, )</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, Inf </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Create table</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >trials_table = types.core.TimeIntervals(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                 </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'test dynamic table column'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'randomvalues'</span><span >, </span><span style="color: #a709f5;">'conditions'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'start_time'</span><span >, start_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'stop_time'</span><span >, stop_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'conditions'</span><span >, cond_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'randomvalues'</span><span >, randomval_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'id'</span><span >, ids_col </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">     </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S2'><span>Export Table</span></h2><div  class = 'S1'><span>Create NWB file with expandable </span><span style=' font-family: monospace;'>TimeIntervals</span><span> table and export.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Create NwbFile object with required arguments</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >file = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(</span><span style="color: #a709f5;">'2022-01-01 00:00:00'</span><span >, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'ident1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'test file' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Assign to intervals_trials</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >file.intervals_trials = trials_table;</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Export</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwbExport(file, </span><span style="color: #a709f5;">'testFileWithDataPipes.nwb'</span><span >);</span></span></div></div></div><div  class = 'S6'><span>You can examine the dimensions of the datasets on file using </span><a href = "https://www.hdfgroup.org/downloads/hdfview/"><span>HDFView</span></a><span>. Screenshots for this file are below.</span></div><div  class = 'S1'><img class = "imageNode" src = 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CC17BFOPJVJ4nFjlrhcuh5XtAlbvLOGtwVI+CmVMgcOhHhcesABixs6RFzVERKwjD21YcvIQRhpw3Fh4zTirU0nwdfgsW0w6t5R1y5fsnLB5j+3iE1/bBHr124UG9dtFhvWblLzVNuym7aZjY2NjY2NjY2Nje2AsQPv/X/zn1sVt3FNcp2li5YuHDVq1LWSA9UPJDM4m8S5tMwQzsJMSwph5yMOJaUSZZpjKUKIb4DkWgoXwyH3K2lyMGDPoGzz4GI9dsSSpQt7cNNIUHQM6cXTLImj8EhFblpaWqMJH018ecv6rYrM+hNaJrhsbGxsbGxsbGxsTHAP3Pd/ILlAejf+sVn8d9JnL8VisaY63teWgZnmMaJxuTGSWTlKPLjYKRmxJBGmyYQDJGY3sL8S3KDloAM+iaZClmDlIAlkjgZSazFFiHY8PZBavzaDZEGGoOsGH/9nwitwsbes33bIdXA2NjY2NjY2NjY2tkPz/R/4z6SPP3lFx+fmohBOnLcog8iX0xEHi1kMc7YQicWlZYMCRMZsi9EN7o/eW7/1tE5SxHJyIQvBDRP9N/beppFMX5nEtW6ILrjiazx474PXgpseRjAO9Q7OxsbGxsbGxsbGxnbovP+bkMwHRz18LYrJpZ7cDJJ8ivKtGCkZhL23YYss2cb//Jyk+yXBpcwca7KDlqzK+ISjxM0dIYHLZh3O8mXc5hlk5MHUqzVWEdzxSxctW2AuLBNcNjY2NjY2NjY2NrZD6f0fEmQBJ9JS5Yqoti4mu2kku3IGybIcRt5bW5WbCInPjfgofMMk23LQss1+FX8b9DnICDqhCGH8EURoY8hjGyU68DRSyzYTEVxc78nE3lZ98N4Hrwa3/PpCJrhsbGxsbGxsbGxsbIfe+z9wIYjLHTVq1NWSI1VDBNc4BrNQ8inDs2IWJ2OahdRGLGWDghb+F/KRKNsyMe8Xcbe25ZClri3NqIwbIEq8ujHirc2wFCvOQvMcvT5HFz+uN/GjSW/AxeQOzsbGxsa2J18cuB3Y2NjYmODuz+//wIk+/WTy65IjHa7JbA7iT1mEV6URz246CRfFOZOCJMlUlKh6A0TZG/Thj8H9IaMyTR4VJGmiaY2jEEkshTNx4bJA6T41b80IQiZKMJWNRh0MsS2n541n58/5UWUQK+nlJPGnmP/6y+Lby/qIyZ2OEZM7HiumyeUFr78qv1u/0x17feGfYsm0MSL/zc7i26ePUJY/trNY+u0YsUF+t7Md8k/5m/e+GyNufLub6PdCE9H3uTy53FW8K9f9WbieH0RsbGyHjPllwqfrzeeSMufT7fD2pcH67JPPRfdu3cWH7/5nr5x/fFmB+O3XWWJtwR+71V7U/li9XsycPlusWlrA/YyNjY0J7kHq4JLP+R8lR2qkMyqXR57bXJKoN4vIldOJojYdkdl0iycXh6jakk6FSFxuhBDd/Sa5VMgSZxsm8whyQeP42wgJYE5D+m6sA8eNjrN/ZWtim4uIbvOli5Yv31RCcql1C5eIbwafK77tfqKY1a+TmD+wu5g7sJuYdWFHte6bwb3EWrlNaTv1H6sWi19fO0PMfqW+WP5+C7F6YhtprcWKD1qI2a82UN/9sXJRqTvicrm/YeM6iIvebCou+/hocdVnJ4qrpcFy/7FNxXXjzxLLdmJ/bGxsbAeyde/aQ3Q6u7NY9vsKz/oLzrtAdOrYSSyY87v63KNbT9HxrI5y206i49kdxeCB/xAPP/CImPPbPPc3X30+VW7TSW3X8ezkHLad+9t8/dtOYs6Mue5vfp+3WOHD+vyffxNv/nusWn7un8/v0fOG871uyPXqeAG/S6cu4pEHR4t1CTvRLSxYJ0Y/9Kh7/nPReVMCPObpZ0W3Lt3d87/ysivFvFkLuL+xsbExwT3ICO7ShcuXS47UQnOlXM2fyvl4c9NJWKixMFHcxixOTBqWGrAknKJ1c0P7QxxuiByQbe6XLjpMkkmlWUhuOnKPx9ByJvHe5qCLYy5WJWktS8qcvGHNevG1JLA/nHOqmDe4h5g/qIeYJwnuPHfeU353ivhm0Lliw+qSPbkbCteLX/99hpj7eiOxZlI7sXpSG01w24rVE+R8Ulv5XUNFcjesKXn0/c+16xW5HTC+hbh68smS2J4k5ydKknuSIrrXyHX95XdDx3aQ2/7JDyQ2NraDn+B266FIHiW450uCC+vnz16oCW4PRdiuHzpcDL1mqOjapZsib31793O3+XLyFLVNr3PPEzffcItrD416WH1/6cWXqt+8/sobLs7bY99ROAP7D3IJIpDm0tdX3zUbft0N6lhuu+V28cqLr4r+F/ZXn98e927Ktr/PXSwGDRjkElYguXN8CO57b70vOnXsLAbJ83nlX6+KW268VZ3f9cOGc39jY2NjgnuQEVxQtgJH0lypgoXgYmVsejGZlXEoacySOJhmWA5ZYnDDPvLkYDFZl/eK9xYHENuK+GI3tS1TsmH82GMbsySWyrKQWyxLdjMn64sF85YlvXAseP0V8V23kySx7emQ2kHdxdxB3dUcbP7AHor4gid34Ruvltihl373rJj9an1FZIHYFnzcViQmSPu4jZy3USR3jfxu1isN1LYldcL3v39OXDS2qSS1mth+epK48lOH3Dp2kvruorHNxHvfj+EHEhsb26FBcM/uCKPQKR5cIHPzZy1EHlwgws52q1cWimuvulatG3HH3ZrgfqW8oUMkAbZhvfrSa2qfw4Zc56676YabFf5z/3zBeU6/86Ekk4OVJ9cQ3ufHvCjJ8WXi3J69FGH++ftf1XdXX3G12ja+LKE+A/kGb/EvP0xXnx956FH5ebD4YdpP3sHONRvEXfKYR937gEuk3x3/niKujz7yeMpxf/PVt+K8c88XH384UfS5oI86h7kz51vP8TVJ3u+6fYT47ZeZWgKdUNuf3+sC7m9sbGxMcA9OgttW86eK2jFYHpFcHIubQ6TKGYibZSJel4ayKwcRvwsRwhuyJJ8qrhZueF/Kk22Zk8MkIxaVK4dR3G3YUkg4jbjFM4kePAeRXCNNNgYEtwpcvM1/Ft/Jvr2sr5h14dnKWwvS5HkDwYvbPenJHeiQ3ln9Ooppl/YpsUNPf7OzWPlBC0VkE8Ym6vnH2os7sa1Y8UFzMf2NTiV2whvf6ioum3C0kiQrYvvpidpOSs4lyYVtbnyrGz+Q2NjYDhmJ8rjXx4uP3p8g/vP+x3L+sejZ4xxF+Ix31iHCSYIL9uO3PyuvZt8+/ZIeXLnN1Vdeo4gdxLcWLE+IdYk/XU+okjl37KxiU9esWqukvIA/O9+RLf/75dfVPp595jn1+cnHnlK/gX3Ccs/u5yiiC9LpO2+7S30H0uglC5Y5smhpL7/4ivptb0lGu3Xppsi43/l/8dmX4r23P1AeZCDxP3/3i0WCtkwsX7RSLRuCW5LkGOJ6J0hCDGQXtgeSzv2NjY2NCe7BRXA3rNsEBLe1JrcVkQIWe3JzCdfKRKQWOyPTkOcWe3LDlkRTIZKvKWCRJActCuC9mlwqQNzHQUtwcJiQ3TBi4zitdJSQWpylKwONGmSiMkDZlqRSldBoRGVp7UoiuP89s01SkjxIk9xByc9Gqgye3E87tCuxQ0976nBFYAvAazvR8dyuVgS3LSK7bZWHd9pT9UrshOePqe96bpW3FuJvtQf3Sk1wr4b55yeK3s8ewQ8kNja2Q0aijGNmXZOfMcHtRAjuyiVxLdntrMjqF5995cbZJvcBhPNV9zfXgNdXrp/4n0ni8/9+oZYv+celyAPqENwxTz+nvKudO3VVn7+b+r2Y/mO+GDniXvV5rCTk7771vlr+1/MviY8/mCDOlsfbr8+FSn68aN5i7S2+vvi/C1qKrUi1xCxYsbrY7ftc0LdYD66xxx553D3/odcME7NR3DEbGxsbE9yDg+BCJmXJkdpoZ2AlRHQraGKLQ0AziUc3HVWyiaHqNlHE7WIWghsgZDdoCWENWYjuPom9pUHAYfI9rW8UQQ2AywGlWUoCxVADpiM3eSaKuc0hnlujJa+kL1o7qIFb3EX+tENb13PrkNoeiNQ665VkWS5/1vHYkgnuk3UdebLrwW2b9OR+7JjjxW0tpj1dMiG9YMwRirwqafJ/T1Kk9kqrF/dE0ffZPH4gsbGxHfTWQ3tmJ330iZLign395TRxTo9z1PpkkinjwU3G6v4EHly57sK+/+dKlOFzv74XiheefVHZi8/9S3z39Q/ub4CYwjb3SKL64KiHFCGGWFUs8QViDYmaFi9Yqry9Z7vJrRyDzw/c96DjEZafQeb84P0PiYEXDXQTPH0kCS989++XXys+8eCiFeJXSZxH3Hm3IuoPyP0UT3D7KHxImlXcdgXLVysv71tvvq2O/fzzeqtEVdzn2NjYmOAeRGVC/9gMBLedroVbSVsu8ebmkiS+NPEUdUbSONwQyaocJbLkEMmwbOORez3ZlK2eESW/EeKKxsu4PlIaWhcjowEZZOQAS5Jz0EhDeW0VtOcW7DCH4BZfIujby/uKmf06JeNtkSx5vonFletmXdhJfFsKiXL+G53F8g+aO4mltCzZJbraswvfQUZlkDOXWqI8WXtqP9Ue3E91DO6nJyYlym+zRJmNje3QILiQLRl7Zt0YXPDg6hhckDJjiTKU1YFYWtjGE4Mr9+UXg6szTorOkrT2OqeX6H1+b0X+FmoSTT24UA8Xjg+2B8IIBBHmIEdOrFijtocYXIiPvejC/irL8TdfTVP7vPzSK3Rm5hkpxzBv5gJxx613iicefRLJrX9yYmXPu8CN/TWxvZTgduzo9eCuWpYsBQQJtWDfS1F79u3teH2/nfo99zk2NjYmuAcRwQXnn+RIR2u+hL235Qm3ojLlbER0MwjJpaWEIiTuNoycnQGSv4lyzCCRNO+TBFM0MBhLk3Eq6KheFyWJpqiOOwPN00kZINPAuWheDl0cMwpRVdtRG9cVn2RK1b7tdqITczso6a2Fz3MN4R3c09lGbltikqlvx4hZrxwhVn/SVmVQTqBYXMdz62RSng1Jpr4tOSkUJI6CBFImyZQ3DvdEnVX5JJWI6r3vn+UHEhsb2yEhUQaPZGqZoN5OkqnZXoJ7w/U3iuHDhoue3Z2kU5BFeeGcRUkPrlw35OqhxWJeN/R61xt71RVXe777t05EBXJh+HzfyPvV5xuH36TihK+47EpFeD987yP1PZBU4939dNJkRbxNhmc4B1tyREgy1a9PPyeL8s23K6/vgIsGqs8j77rHGRCVeJ07dVEebUpwAd8QXJU1Wf7uMZ2c6vHRT6g2GDRwsIojhqRYsF+IaTaxyGxsbGxMcA8WgquSTB2jPbhVNdGtgpL05hKVbA4KE8VxuJTkphF1bgQlm4qQ8kARS36mgKXc7F4huWFCYIMkDjdskSWHiRcX10dKQ5/TUWPRmrcZqABxDhpVwF7bivoiVUEe3GM2lRCDuz7xhyoB5JYJGuhYMg63h/hRfjd14DmlKxO05k/x67/bi7mvNRJrJrbV8beI4EpyO++NRnKb00tVJuiPtX+KYWM7iAFvtVBEVpUHIuQWSggNefNM8Uchv4iwsbEdOhLlZYtS6+AqifLs3z1Jpkyc7sWDL1FZirEn86vPpyhyh7MkWwcbIXZWx72++do4z3evvfy6IozPPePUwQVSeN/I+xRBhO27d+0uXnj2X15MXboHElfBuhugBJA8TiDHfscAxw31aUECDb/v2rmruPvOkW4M7k2a4E6b8l1qDC5IlPV5v//2h+r3huACwYb6wJDcyhDvfwy6WOT/NIP7GxsbGxPcgzPJVDvNm6pp7oQJbkVUoaYc4l/lSFWbLEJ4canXqCV7chiFqNLqO9h5GrLke9qjJNeW4SpgkSiHiSyZxtxinTb14KaRwsLGNY6Xc1FSqQrIc2u8t0BuawLB3bJhW4kXet3CJeKbwecqL+3MCzuKeQOc+NuZ/TqK73qcKAlwL7F24dJSd+p1KxaJX19rL2a9eoRY8X4LXQfXkSXPerWBIrd/rFxc6o64bOUicd24s0T/sU3FZROOkcT2BHHV5BPEpXL5/95sIobJ75avWswPIzY2Nrb9yMDrCvLmsq6PC5JnIKt/qEFX9NIicbD0uDhbsWSV9Xhhv/HlCb5+bGxsTHAP7iRTx0qrTry4VYkqtgIhtrhEK04CHLPUxw0jR2aUJBe21cYNEF4Z3ptxuCELqcUe2whxMWOiGyXZtHAsrqmjlE4SS2EvbjlUAqgciruthMoCVUMjETXg4kEgdWku9vrVf4p5//6X+OaSC8SnHY8Wn0n75pLeYv5rL8nv1u90x95QuF4smfaMmP7GWWLaUw3Et88cIZc7qHUbdsHTCt5ZkCsPH9dJ9H2+sUooNfytzuLd7/7Jnls2NjY2NjY2NjY2JriljcEFiXJtzZmqIhVsBWSYb5VH4aKZxBmZiRJNYcdlBHl2w8Q7iz25IUJoKc8M7a16uCGf7MnYBR0ksbdYokz12pj9ZyIvLk5RXQ59xo1vpMmVEMGtrgnucRvWbeIOzsbGxsbGxsbGxsbGHtw/lQf3eGm1kAe3GqpCUwVlVjZENxvNsywxudh7a5JNhVEMbphIlyMk5DXsQ3ZDezO5VMiS5cqWYRmT3RjJlhwhOu0MNAqQSfTduaQIMU4oVQWNOlTR8uRqelTiuJJicJngsrGxsbGxsbGxsbEdQhLl47Uz0NhhiE+Z2riVkUMxB/ExnPg3gzgr01EoKq2WEyU5mgI+ZJfmftrjJDfok80qTPTSYSJFDpEaSDF08lS/nUFqLeUgy7V4byuii1JdG8Tf1oGLt5E9uGxsbGxsbGxsbGxs/P5vCO6JmisZx+BhSKpcCZFcLFEuR2TKOchBmUGyKqch7hcjCafwcsBSkSdgWQ7tjSRTVKYcIkw8TOJyoxa3dBTpsjMs8bfZSJacTRoY17utjC5MNU1ua+n58aWNwWWCy8bGxsbGxsbGxsZ2UL//S24EKldpdTVfqqEdhDifUWWUyLcC8t6ajMo46VQ6icOlyYNjllq4OHw1ZKnCE/QJi92jHtyQT+xtiJQFilpkylFS7xYT3CzkvTXENocEOWN5skkoVV0T3OqI3B4u7eTN67dyB2djY2NjY2NjY2NjYw9uMgb3cM2ZamsedRiRKldGBLcCKh2Uq7kaLRuUjjy5McQDY4T4Yq6I5yHiyaVJqfZagqmgT7YrfOARUh4oimJw0xDjTyfSZOP+zkXkNpeUBKqCYnDNRamlXe5gJ29iDy4bGxsbGxsbGxsbG7//G4J7qrR6yDFYEzkMK5F43PJIrlwOSZUxsTXLMZRwKg05M6Mk4XAMEVsc5hq05Hvao8mmQhZdNGbVlH1HyDxMCgBjt3UG0XBnIe+tLXNyZeJGN8S2hp7DSER9aSdxkik2NjY2NjY2NjY2Nn7/dwnuiZor1dHcqTpKNlUNkdsqJBY3F8mUs1G1G+zBpTG42LkZJZ7bsE8S45Icq2WeXCpkWR8mRXvDyIsbRSeUjj5nIHkyJbc5qAEroJGDishdXpmUBDLEtrbWlCuCyzG4bGxsbGxsbGxsbGz8/u/WwT1Vc6XDNYeqhby51VFMbhWknMUEN5fkSsok8mTswU0ncuUQIrcRwicjRA28xyTKNuYcJtmTAyRIOEySS0VQQV9c9BfXTspCZYGy0agAHjEwJBfH3hoPrtGQ19EEt4G006ZMmSI+n/w/8fn/Pheff560yWj58/9N1nNpkz9PLn/utf+lLP/PXWc+e743+2J8xmd8xmd8xmd8xmd8xmd8xt/H+FOnTgWCe5qWKB+uuVNtRG5xVmXjVCxPauPSRFO0zGsGireNkZq4OE8TTTQVtFTtsfHPMq19S93INt10iGwbQScVQ7LkNKTRNiyfpp7ORnpvHHNbmQRD45GHuojgnrxm9WpRUFAgEtKS87j72VgiDvOEZ1tl8YT8HEefne3wujiZq+VEgQWX8Rmf8Rmf8Rmf8Rmf8Rmf8Rl/3+CvltwIeXBra5JbWytiayKFbBVUOgjXxM1FSYCzUf4k6sGlIalRlJ8pQpIWhyy8szj+WSbyZFtNIlwGKIKIbpR4cXFJIEx0cf3bLGK5yGtbHjVoJURwq6KsybURuTXu9iNgdGL16oTqFAn3wieSnUpZQnYMuS5BOog7h+8Sns6Y/H1CdRb3cyKe2kHhe8ZnfMZnfMZnfMZnfMZnfMZn/H2Mv8YhuKdIa4R4kwnxrG2JxcXJpsqTrMo4ZxKuhYvNFouLlb00OXGoFPyzTAhuxBKLi4HCZNswqXsbJeQWZ1HOIK7tHFQD15QHqoySS1UlWZMPQ271OtrdDhersbT2hYWF7qhFcgREL8MoB3SKBB5BcUY3EokCT0c0oyvuPKHNLOv1cbN9XC8nEozP+IzP+IzP+IzP+IzP+IzP+Pscf03hGiC4pyOCW5fE4poY3JqI4JpkU9iDm2upfmOSTqWRkFSaRTlCkkyFLTyzJP652wTXVgYobMmiHLIU7jXu6ZCl7m06isFN14Q2E40E5BLvbSXkua2BrJa+MEaeDN7bhuB+T3YMM8phRk1SJQJqdEN3mng8gTpNwh0dcTtJIqE7i8USWILA+IzP+IzP+IzP+IzP+IzP+Iy/7/G1RLm95kuH67BOk8eoJonFrYaq1xgVbUXkiMxF5DYbxd6aOFyaewlnUzbcMkrkx6FS8M8yzaAc8MmgHPQpF4QZOtVgm89ZiNRmEWKbS8htZWRmZKEWqn1rLlI9TXBPX61jcL2jJtBJdCdIWHTvqlPFdWewdaJ4qjzA6OhVx/OOmuTn5+9TfL/z/23GjH2K73f+06dP3+P4+RJjX7c/4zM+4zM+4zM+4zM+4zP+3sQvXKM8uGdqD25DlGzKOAuxE7GapR6u8eSWIyVdqUQ5DRHeGOKFOPFU1JKkOFgK/llmHtywpQgvzqQcLiaDsjmZNFQiiHpxsxHJzUUZlI3Guyqpe4vlySZA2siT4WI1kdZhdWI10quTYGzQvbsdTevdcUB2wquJT6DfKV17IjlKEk/phHqd3G6GJJL7Et/v/PNn5O9TfL/zn6EGBPYsvhp02Mftz/iMz/iMz/iMz/iMz/iMvzfxtUQZPLh5mjM1sMiUDbmtgTIqVyYOyHKkJm42SjqVQTy3EUJ0w8QRikNeQyQBVbgsk0v5MeWghWnTzMkhcuBRJFWOEtd1BmqIHJSVq6TatzVRYinjva2v7QgTg6uyKLuu/zi52GhUA32X8GQsswV1J1wpgNvREia7mZEQJEdWZvz22z7F9zv//Pzp+xTf7/wNwd2T+Cle9f3o/Bmf8Rmf8Rmf8Rmf8Rmf8fcEPiTglRzpDO0MbKB5Uz0S7lkTZVQ22ZSrIE5WEdXCLUfyKaVbkk1FCNnFSYojxXBLG/8sMw9ukOwwaGHXFDiEMmaFEcnNQG5r0wCZxItrGssQXJM9uRqKwTUNXwNdkHroQoHbvUNCXkR3lMR0EB3YrTpEXHcUPeIRL/AGbyfTcTupuRMFpPMUaC18PI5+4w0Gn27ksPsI3+/88/Nn7BH8K5rfKvrUuFKcfPLJxeL7nT94lvf0+TsY+7b9GZ/xGZ/xGZ/xGZ/xGZ/x9yb+mjWFhuDmId5UH9XErYM4lvHgVkFhohWRMzLHUirIkF1cJigdKXuxBzdo8dLays8WFza72xJlWn8Ip3XGZYJwKmiznG75nI5ILc6cbKwC0nmbEQST2QvLk+uh+NtG2nvbVNpZq1ev0SMa8WTa7TgJvk54R07czuCOiiQ738y2lcXMNlWkVRa/tauMRmC8coI42s+M/Bllhm9qY+FRmpLw/c4/Pz+/zPGH9xgpru1xq7i09s2iS5W+on5mE3H6sR126vxVbPAePv8ZM37b5+3P+IzP+IzP+IzP+IzP+Iy/N/HXODG4Z0lrhuJwG6ByQXVQNuWaKES0iia1OB4XJ5sqh2TKGciRmUZKx0ZQVZ0wKRVkkyhT/lkmHlzKnAPEjUzjb8Pks/HgGvd0OnFfZyLmXw7Vv62ISgOZ4OZqSA9eixBc471tiEjumQmTZCqB3Pq0ThSSA8QTCU9gdxxtA6QWB3X/BmRXmpMBDWUuM6m/5e+S8Z67jw/bzNSYYICfzMDmj+93/h6ZbinxPanLEwUe/Bt63COGdL9V9K97rRhY6wYxuPY94tK6j4sOlc8XLcsdK/5x2cBSnb8rUUb4aioSIhqNuhaJRd31RfCfOxW5c1iynb9L7hG++WUMY0Siyf1hCPxRLuxK++/K9S+u/Rmf8Rmf8Rmf8Rmf8Rmf8YvD11mUO2iC25Akm6qvCW5tzbWqoRhcLFMuj+JxyyFnZQaymOZ5UeTFjSKSG0UJiqOkKk+4GP5ZZgQ3QIBClizKIRIkHCW1j3AG5Qx0opnIk1sOJZkqj+JvK6IGrobkybWRVtwkl6qvXe5w0c7ydJR4gSd9ttGnO2m4vUHdtBPNalvZmpIb1sdJum81woK09UCmdhff/M7xIDsE16wvCd/v/Gdo4r0z+KnFpp1939BjpBjS41YxqOZ14tLat4o+1a8W91zZWEx5vYV44MqWYkDN4eKMSj1Fy5xjRLvj2hZ7/jOUB9eLD1M0EhV0AjJaRFknWWE7fyXPJvgKI5qK4a4rEtbJJdE72f67cv392p/xGZ/xGZ/xGZ/xGZ/xGb8k/MLCQkNw87Q1QuQWy5SrI09uVZRoylS3yUWWSZJMZVk8uLb42zDijyHiRPXjn8GyTDIVtLiGaUrnMHE3R5FHNw1psQ2xNVptTG5zkMsbx9/iLMpGF16blAY6QgdMN9YE92y4iDTwOqUTxeOWoO5ESlYymyTA8aIabbvtd05JmrLCV57bdkkPbtyjrbfj+50/EO/dPX9FbruD5/YWMaDWMDGw1nXiorpDxPwfG4n1248QYoecb2wg5v/QUFxV70ZJdK+T5PcKRXTrNKxpxZ9hJMoI3xBN47l1l6MROY9J8hvxrFemCbHt/I0HF+MDU424v4+5+/B8tuALRXB3vv3Lov/tzvVnfMZnfMZnfMZnfMZn/EMLP5FIGILbXId0HoFUsMZxaFSyNTTBNbmQKiKCiyXK5S3klpYJihFpchTVww2RerihYvhnmRLcgMVjGyaJqGyMnNa9TUfJprJRIHI2qoFrvLflEcHFtW9rIO9tLZRBuREaiVAxuI5rPuEJwE5mI/PWkcIjK/EC/5ET5UFto2NxlVy4ivLkKrkylgHEUUkaH3xDlIwstiR8VyYNcmWNX0D1+wTf7/zdLMo7ef5m34l4QlzR4hYxpMdtYpAktgNrOgT3sjo3i8sbDRCXt28nzqt5h/jm3aaS6DYUN15VXVxce4S4rPYtatu6Vepb8WcYjzdaD22zZUl9sVnaFtcaiC1L64vKjdeJKtLUd0vB6jlz+Tmi25Sev3Pu3uvvYDj73CytSuO1ar+V5RysSp6cS6P4QH53pf13tf/h9k/g0UDGZ3zGZ3zGZ3zGZ3zGZ/xi8MFjDE5AzZUaa97USJPc+igOt7ZWz5pkU1iinIvCSo1MORNZFpIqp1kyKUct5YJCPqWCQpaExmVCcMOWrMoBS7wtDhSmQcQ0kxbWaOegOkq5SNeNSwNVtmRQrovib4/QIw+NtRe3hZNkarWnU+ARESoJKEjoAsrxArvm3R35iLse1Fl6PtP15GqJgCngnEAEl+DHYlEULer8H5NkqTT4dom0Hd/v/GfMyN/l81cJpbqPFFf1vF4MkGR1gCa34KEdWPMG0b9xDzG6Wz/x7K1NlSd3/pJ64oozW4l/1Llb9KlxhagYrubb/vnTU9sLyOfmpUkCC8tAaDEB3by0gbPNkgZqWZHPWMR6/pBkiuKDN3YL/G6JQ3CBzMJ+Mw/f7uLAHOMDhkOid779d6f/2SUqjM/4jM/4jM/4jM/4jM/4/viFhW6SqRaWRFOGV2GZcjVSC9fkRqqA4nBzUblXXAI2HfG/GMqkHEEeXFup2VAx/LNMCG6AaKCDljjcKALGpYFCiKVHkauaktsM1DDlEcEtj0juYbqRzShCHSRRNhelgSa3TfUFOzNOspWldpqEkAcNF9o1N7A74S2QbFz8cSMXbmcSPlWyB47rbbEHF+PHlMQ15k1qpC0K0ltJgGORGJLNJrcp0LWvHGIdLxbf7/xNtuIEkTWU5vxNzO1A7LmtfbMit5efeoyYMaeu8tpOnVFXvPdYc3HxcSeLi4HcVr9C1K/SSFSrURWNPnnx89VxefGVd1WTys2GgCpiu86dG6+t68EF8inbz3b+rjwb4RsSvVmT6CpNwGu7zkNmMb4xuFa70v5+51/a/peUnMQZn/EZn/EZn/EZn/EZn/FLxC90sih30pypGfLgHmFJMlVbOxUPQyGjlVCeJMzfyiF5cjYht1iqbOJxg/pzyFL7NlgM/yxTYmvTQduyWuFsWPgkYmiOSwQZV7ZJLW1GAYwH1wQ1H4bc5NXRyEIdfTGMBzdPX7CWcPEgFTbWnic8nSGuCC2QGXChAjmCuTwrRHqD6nOAkGDT8WZ6kk856bwTbkfSGXuRp9TgR5W3sJ5Lpqjs1chkN5vPS4zX0Nk26noNC0rE9zt/V6KsC0ab0Z9EAo8MeYtEw/yG7pLcdr9NktnrFLG9FCTHQHRr3SD615Kkt3EP8c6HNcR9w2uL8xv0En1qXCMG1b5XkdvDq9YXD49+xKmR6x6fFx88yxQf2gufP3halVxYS5QdgtvAQ0ShXaNmMICcvyeDtMbHEmWwjMO3S4x11vY3nl5Hohzdpfb3O/+S2t9bLHzXrz/jMz7jMz7jMz7jMz7jH3r4WqLcTDsFqUT5cOTFrUVq4VYl3tvyKItyDsmmnIlIboyErUaIk5RKkCMlxOGWiUQ56FNgN2RJMBVCMmUbwY2iJFOZKP62HCK22IwrvAqSJ9fSVteSYCpPX6zmiuCuXqNd+jizWMLtEEBWYQLyApMiuepzAyUZdtYXueuB0AD5VQWU3c4WF/EEkgmYOlcJU3N1egq+IVObtdfR9Ry6y/CdJsCYVOltwLtbWny/88e1YM1IT8Ijd0jeVCbjW/+6Q0SlCpVFpYqVxRmVerieWyC2F7Y6WXw7pb649/raok/t/uL/al3hxNzWuVmS3CtF3SoNxB0jbxMzZ84WRx3b1hMQj/Ed8unFdzy4yfOv3GStS3AhVhY+O8S0gfawOjJlSBJlO/8kRhLfjfPV7W88w1V0/G0V5CV2ya6cxyT53pX29zv/4trfPPx2pv8xPuMzPuMzPuMzPuMzPuM7ZYLWGIJrJMomQa+pRoMTTdVAZkJGK6OauCZvkiG3WRaCm47CVtNQGGuElAyKWDIm2/hnmSSZCllicUNEooylyiFy0GHE2g2xNW7qLFT/FsfhmgRTlVEwczXkIjcJpuqgtNZH6ItjCC5ctI6QRTklOBuNhCgProfUOiTWS3b1XBJSqKzqeHCTHSWR0jm9y/morqvBVx5ckxTJlb0i7+0Ss75BUg5rll0Pbunw/c7fk0kY18oiEgr35oknROeqfUT9vMPFI6MfFq1aHSlys8uLDpUvEL2rXyMubnWW+F4S3PturimJ73AlV4aEUuC5rRquIe64+zbx5ZdfiG9/+lqcduyZIhKIWfG9x+XgK/mwPv/KimyudcgsENw87cF1k0wl2zDF0x3HJJoksopFPb+tQiTQlfOcZTfRlcaPRWK71P4p25ai/dXDMF6wU/2P8Rmf8Rmf8Rmf8Rmf8RnfSTKlHHwdpbXSnKmJ5lF5pFxQLWS4XGsl5MU1zknsvcUlgtJJsimcgDhGEhRHSY4nP/4ZKCuC6+cejvjIk/HBR0gcbjqpf5uDsm3loFTTlZBE2ZQHqo4yKNdGgdAN9IUx2ZPBjgQProfgJhJI3+5kVwb5cZEisfU0ia3n9eh65o5HN2gIboLq3XXRZdLJkjG4SXwn3tNk7HW8tQ6hrecSOZesYSK8FMliS4nvd/5QC9YZCUokR4nicVcW4QlQ1yNHXWr2EUcd11YsWbxYTP7iM/Gvf70o6jSspeJq21fuKfocdo24qu4oFZd7Td27xTnVBom6VR3P7RdTvhD/kwT34RGPiV51/yFOPvkUK745LoyvyOcSk914neu1TYnDXZL0hKt2ikSt559vShEh/Jgi0d72hwRTuP2Tiabk52XO9TIkemfbn+KXpv3Nfnem/zE+4zM+4zM+4zM+4zM+48NyYeFaILhdtNq1McqkfIS2eprgmmo1NVGoaBVEcisiiXIu4nFZyINLw1TTUPnYkMXCZG7jn+E9QXJpHdwASfMcQp7bKIm7jaITzkAENxvVUcLlgUytpWooyVR1FPBsGh/H3zbWFww8uJ0SaFTEoz/Xmncgq5t17C145BTZlZ+DnrjboDO3xOK63+nvE1g3n6A1V5P4QLwcT2C9FI+gJ47UeHaXoLhcEveZehPQIHT7+UO92YQrc9A3gCksnXBGilz5RNxZ17lqX9Hy2CYCTz//9JN47InRokHjeiKvYgvRIvsYcfXhd4tzqw9SpYCGDr9WTJ0yVXz55f/EqLseEl1rXyhOUTG4BVZ8lQCK4EN7mfNXZFaX7DFk19gW7Ole5vXgeuKPp+en4LtJpoppf0OmTbIpuGZukqmdbH+/8y+u/W1xF7t6/Rmf8Rmf8Rmf8Rmf8Rn/0MIH558muC01Z2qCvLhHaH5VB8mUayIuZsq3VkBe3PKE3GKZchbigWmoBm4aUftGSTmg4vhnmdXBDZAAYL9AYJw92ebFjaFMWulEo41jcHNRdq4qKIuyKQ9kkkvh+rdHoAtkkkx1VmWCaGFkk43MqQPllScvcTy0m3Um3iT5rO8hPx7Pqo77TEqXk6MpNMmUwXfL3rjENhlf6pCq4vGpRDmOtfoE3+/8p+vjSqDRpAQZ+Yl7RosKVGKp8+oOFk9c/4J4afzzYtPmjW6Zo8WLFykye/xpx4qakcPF4TrmdupUSW6/+kKMuvMh0Uv+9uRTTnbb34bvZHf24oMH16/9q7iksx6Kj62fTMZlOX8g0RTfeImLa3+QKRsibdaD53dX2t/v/Itrf9vDbVevP+MzPuMzPuMzPuMzPuMfevg6Brc5IriNkSK2AZEo10Yq2qqoTFBlFIObi4htNvLeZvokmaJO0QjilmFLiCyelwnBDVsYs9l5hBDdIGLhMXTQ6UienEGKAGeTJFO5aESgMpInV0UZlE0Mbn1SA7cRSjDVGgiuI1FOkA6RvOAB5cHVCaWApAYDOpNuAzeJEM7Oq77T6926q0vrK0+uGxDuSgdMTOmMFHy37M1SI4mt55afKQ0+kDF74Hoqvt/5m0zCcRzQnvCmLk8kyCiRXK4WriHa5Z4kLqozRNx+zd3iuTHPIX9ukYBBhcGXDZDk9nbxxZdfqLjb+yW57VlrgJI3J0hKc4oP7UXxjQcXt39ltzatSQa1ziWmTobqBp6BAHz+M1wSncRXXnWN4da9bWLibx2czMN3pFx/L0bp29/v/Etqf5NCfnevP+MzPuMzPuMzPuMzPuMfWvjwnq49uK1Qkqk8RHLrozjcmkSiXLUYiXIukSjTWNw0FLaK42/DJKNyiCScovwzXJYe3BApuhsiIDQDVoQQ3XRycpjs5qISQRWITNl4cE0G5doog3JddCHyUP3bpvqidXbd+iRQuyCRTDJlPLeK7C5Nxrl64mDdjMapmY9VVuW4lhjEE6Tj6WROBD8ai6DSQPVTJMgl4ePYUiU9KAbf7/xxMidbUHqc3oj4RpJ4EHt7RqWe4oI6F4thg28S4155R6zZmHCJbmJ1QsXpjlLktr8kt21S2t+Gb44L45syQbj9kwS30CGgTdY5ZZWWNHAHC7B8GJ//jN/yU/C9dXCT8b5OfK9jtusPWZR3pf39zr+07b+715/xGZ/xGZ/xGZ/xGZ/xDy18LVGGOrhtUO4iQ3Lroxjc2qhUUC3kcMRZlCsi3paNkkyZOriG7MaIJDmC4nDDiOzirMk2/hksy/hbWneIBv5G0DxsicHFiabSLA1QDmm3KyAvblXUkNWR1UEEtx6RKJsMykqibCt+nMxMlnBJLXhgU5I5ubGYDbzZeZFEFn5nZARODao4ChwvSK25qvEjQNjUfusl693uBL6JLTXyhOLw/c7fKRNEbhxcMyte4DvyZAYOTj+2g2h3XFtxZuUeolfjQWJoj1uUHBlKAf2xuVDcd8tDolvtC8VRx7axtr8N3yXeCB/OF6TAMIe2i+pl53NUkUwzd9bF3O9t5+/Is734avtYcr+xiFmOlYi/K+3vd/470/67c/0Zn/EZn/EZn/EZn/EZ/9DCX7NmjfHgHok8uE2QItYkmjIEtwbKhWR4mZEo4/DSXMTvclBSYSNPzkCq3pgltDWIyG7YklAqVJYEN0BibgMkoVSYEF7sZsZe3XTkuc1ErmtjtAZuRdR41VEMbg0Uf4vlyQ1JFmWQKHcCN7wa1UgkZQCmEHJCe3BVqSCVxVjHwqIsuk5243qeuE4TL6tibuPJ9N4JGvTtEtzfUvCjkQgirA12Gt/xTMZLhe93/jPcUjkJNQrk3CwJZ5QnUZASEF8Qp4HtSfzBlw0UebWaina5J4t+za8Qw7uPFGNffltlXXayJdvb34Y/Q7XXzuHv7PlDkik//F05/11pf8ZnfMZnfMZnfMZnfMZn/L2Jv8aRKHfWzsAWKA7XOAzrI55lKtcchvhYFRSHiyXKxmmZieTJWRZ5chQllYoirmhzoIYs3twySzJFC+3StM1YlhwmBx9DHtx0FHCcgWrgltONkkOKBtMyQbhEECa5efqiYA9ua0einEhJs606VDzpwfWzoMmcHExmUA6i75IdLpna2+m4cUJw81PwgeCaGM7NbiwukiMv8ZYFSiZWcr4Db2Jp8f3O3/Hg2kZ44skRpESBJyObKiptbiYf/AaZjUWLnGOULNkklPJrfxv+jBn5u4VfmvP/LX+GL/7unn9p25/xGZ/xGZ/xGZ/xGZ/xGX9v4q9erTy4XTVXakpKBTVEMuW6SJ5cE8XgVkHJgMsTD245pNTFNXBxRZ0oIrvUQRry4Ztl7r0NWtzCQYvXFsuUo2iZZk6OoRPORhmUDfsvr+eVkUS5CsreVVM39OGoGHEjfWHy9CiEGZFQWZQTRqeeSHizkiUcF38inkzT7RZhNnWo4skaVXGsi0+gFN+JZEa0OA4G19ID8JRSfJDCRmIge40psmvksUoGqz87y1HlrY1pKWwk6nyOKlls6fD9zh+yO+/J8x98+cBi8f3aPz9/epm2vw1fZbbeS9d/X/c/xmd8xmd8xmd8xmd8xmd89fvVCROD21rzJSNTbohIbj0Sg1sdlQqqilS2FTWHM/wNJxHOQjG4WJYcQw7RCFIDY04ZIiriMpcpU+aMaxTR2Nsw8eKmWWTK6UibnYUIbg4aATAZlCsjD2515ME1ZYJwgqk8fXGaoyzK3QoL1+iLnvAEZtNRkziVA1CLJzObJfeV8HyfzG4Wd/cFnW96fv4+xfc7f0Uk9yG+3/nn5+fvcXwaF70/nT/jMz7jMz7jMz7jMz7jM/6ewNdZlA3Bba5JbmOkiG2APLh1kAfXxODSerjlSS3cckita2Jv05A3N2rJ2UTDXkOEc4YI6S2TJFMhn6K7IYuHFx9oDHlw00n25HQUe0uTTOHiwdWI7rs2SjJlPLgN9AXJ06MQLbV1BTe8c7HjJFDbWyA5pVCyDuBOyWaGRkWS6xKkeLNXOqDI1D7E9zt/l0juI3y/8zdllfYkfrJ00/53/ozP+IzP+IzP+IzP+IzP+HsCXxPc7ojgmvxFedp7eziSKddBCtrqSF1bCZFbXCbIODGzkSMTx+BGUHUdmmQq7JPvyaYkLrMp5FMyKISChGkcLvbexgh7zyIe3HKI7OIEUyaQuQaqxVQHNX4DfTFMkeJmuA4uXERvJ9IjGSTldmox5eSISMJ18ycsnS21k8L+4om420E9ZYL2Ab7f+bsxuPsI3+/88/Pz9zh+vptFef87f8ZnfMZnfMZnfMZnfMZn/D2Br2NwqUQ5DxHcBppn1UFxuDWQTLmaJZNyLnJWZqHkUumWUrFRwhUjPnG4VKIcJEmPyyyDctAS8Bsm9YsipHBvDMXdYhafrYlujrZcpOGuhIjtYSjBVE2UZKouyqJsYnBNHdwWOIuyOxoS946GJOLx1KxjiQJLMWbTseIpHTEZ7B1XtaXiqNZU3LMd4zM+4zM+4zM+4zM+4zM+4zP+vsNfu8aTZKolqoNrZMpHIJKLE00dRvIjVUaeXKzIzUR5lkxMbhry3IYtdXHDhD9GLBLlQFl6cG0u4QiJwQ1YDi6KmDrOpBxDRDcLZdvKRUmmKiIvbjVkpkyQafAGyItrYnANwYXixd1cfXqimCLKibhn9CNldKQgtdMlCrxSAZqi25PljPEZn/EZn/EZn/EZn/EZn/EZfx/ja4lyN10HtyVKMpWHygSZUkG1UJKpmiQG15R1LY8IbjlCdHH25HTCEf0STQWJPDlIYm/LLAY3QCTJtizKVEONPbhR5J42EmWcZcvotMsjPXcl3WjVkES5NrJ6JItynkWi3GmN68HFHSFORkgKPGm7EynB3XFSk8q7Lm7ptHEyEsP4jM/4jM/4jM/4jM/4jM/4jL8v8TXB7aIJblNUB7chyqBcH0mU6yAPri2LcnkkUc5BHC8DJZtKR47OGPLcBslyyEJm/WJxy7QOLi0VFCF1cGNofZTE3kZJiSATg5tLPLg49rYqSjRVE40k1CXJpRqhGriG4HZZvTrhKaocT3hHOdziygnSQdx5IhnEnTKikvBkQksk4hZ5QZzxGZ/xGZ/xGZ/xGZ/xGZ/xGX+f469JZlFuizy4jS1e3MO1U9FkUa6GeFk1lEW5op7nkARTxmJI1ZuOuGIMeXBDZJnWwQ1anK+7TXAjaDlUTAAwPrg0Uu8oihi8YfFGo41d2hVRFmVTJqgGIri1UZKpeqhMUCN9YVpoa+eUCSpM1pbCenW3FpUO5I4nA7sTurZUgdX9n0C/9WY2Mx3TyAPUMq5txfiMz/iMz/iMz/iMz/iMz/iMv4/w1xS6MbhtUJKpZqRMkAkBrU1kyobYGp5WHlk5VC7IxN+mIf6H699GiTw5YqnUE7WUqy2zDMo0/jZEgoDxQYTJchgR2giqg4RZfQ5aNkHKJga3CgpmNnVwaQblBsh7a6yVdrt3pkHciYQlG5mWCKjRDVMwWQVqJ9xRFDM64naSRMK/ZlUibk8HzviMz/iMz/iMz/iMz/iMz/iMv4/wUQxuK1RetbG2hsiDi+XJuBYuzaJcASUMNsmljCeX1sHFMmVDWmNIGRy0EN2ApU5umRBcm945RAKCQ5ZivRHivcUa7AxUIigHMf8KKItyZRTMXJ2UCjoclQhqqEcdsET5SEeivNpbVNmMfMRNRjKL7l11qrjuDLZOFE+VBxgdvep4ZNSE8fc4/rKlS8WCefPFnLlzxexZc8Sc2bPF7NmzxKxZjs1Wn6XNmS1myfWz5TrYZhaYXp6tl5XJbebIbc16r83S285M7hvmM6XNmcP4jM/4jM/4jM/4jH9Q4c+R+1+wYL5831rG758HOH6hk0XZ1ME9UpNcnEm5AamDWwvVwK2GpMpGdWvyJ2UTeXIGmqcTz20aCW0Nk5JBIR8yGyxLgkuDfG3y5IhP7G0MnUyG/oxr4GYh1m+ClE1mriooFTWWJ5ug58MRwW2CAqVbaV1599WJ1Z6iyZ5gbNC9x5Opt+MkyBvLBOK4E+psZ8nvU0dk3HXxAsbfg/irVq0SixYtFouXLBEbNmwQ27dvF2Yqcv/DU5FnVVFRkXd7n6mohBVFlpWMz/iMz/iMz/iMz/j7Lb7zT+wocqzIzIX3M6z4++/tYuOmjWLJ4iVi8eJF6v2L338PTHwtUe6iCW4r7Rw0MbhHIA9uHVIqyJDcyojg4jJBuSibchbx3GJPbpRU3YmSkNgQIrERktg4UJYSZVp7KEzYdohkTsYsHNfBxSeYjbIom1JB5VGwcmU0QnAY8uLiGrhGntwIZVDGdXC7qCzKrus/Ti42GtVA37kB2aviqgPCTQwGn1etWilNr4vL5ZWrnHVx+T3IDPR2MALj/GaV6lDwvfNbsx/nM+zf2VYvw281nrPvVQclvsKKryq2/akEIzlqlXClGL8v/F3d6MLnj0bxfyaKivsTYv26yPOnpMj+M8ZnfMZnfMZnfMZn/P0UH7YD4rp9hySu24vEtr+F2PaXtG1CbMX2l7P+b/n93zuKXCKcWF3gvn+ZOFC3PA3O4rsT79/qt+ad+yB+/y0L/HiBs25X2x8S8KIY3OaaNzVFdXAbIQ9ubaSeNTmRjBe3EgorLYeSTGFHZibifzjJVBSpfqPFcMugTx6oMsuiHPIpGRQgnl3s6Y0hF7Q5KXyytFQQJrgVUfxtNd2oRgNeByWYqqcvBq2DCyMS3RLyIrqjJIYg6cBu1SHipg6VM+JhOuPy5cvF4iWLxcIF88W8efOkzRfz582VJpfnzxNz584V88Dk+nlz56lltU5+N19vP09vC7IO53tYD9vM1fvQv5s3R20P8to56vNc9Vs1nzvH2e9BhD9/wTyxYOEC5XldsWy5elDg9sfB88l06E5qdFzoGmTJS5YsdZ7bRf5/ILwP/CLvg77IO/5ZVOyoaJH/3w3GZ3zGZ3zGZ3zGZ/z9HB/+B2K7OC7EyPE7RO+Hdoju928X3e5z5t3VfIfocR8sO597P7xdbbs4UaR+CzuB9y8lV47H0TubNxlSad6/gRwuXbZMLFq8WL0bzp8//6B9/y0LfPh+4YIFYvHiJWI5vEOvXLnT7b9mTSEuE9RcW1MUg9sQZVE2cbg1tKPxMOS9rYzkybmI3Jr423QkT84kMuWIpcRsmJSiDVnq4ZZpmSBcjyhEiG2QMG1c3wgHEuMMWmmI6JZD7uwKxHCSqZooyVQdPaJgkkw1RjG4LfUFa6MkyqvX6BGNeDLtdpwEXyeSnkMYGVkibzIgX2vXrRXb/96e+vApMoNnRf7Pm2IeYilPspIFKQcdPkhd1q1bJ9t5kXyoLXJGomjWOHdUCpFfU7haXsP5C+aLP//4g9uf8Rmf8Rmf8Rmf8Rm/FPhFkqD+Lslt74d3OKT2Xse63SvJrDJYv92zvpte1/uhIvH7qiKxQ+4I3r/gPayAlLiJ4/e4Et6/waGkJM/yXXDtWnjn/puvfynw//r7L+cdWr4/L5acxZGLl7791yRjcE2ZIOPBzfORKOMsylVQkqkqiLNVRPmUypFQ1HRLZR3DESMkUbFNokzr35aJBzdEmHOAuJFtkmVaFzdC5MlZxHtr3NoVkLu7CmpEk7GrJqqBWw8R3DxNck0Mbkvtwe2SMEmmEklZKw3YNtIIFc+5ZIlYsXw5knF49CH2bpjS2YuEV1AirN95um2Rj4jlEMBfsWKFfLg5N2g8kfAE1sdTUpwnSTAkTPgLtDPc/ozP+IzP+IzP+IzP+MXiwz5BknzXmztE13skcZXW9Z4djgGJNcv3ON931d93G+kQXVge8WaRAN/Ptq1/ibmz58j3NpS515S+KYijeFP7+zdIboGcLdfv3Hz9dw1/2YplyjGnSG4p219nUcZlgkyCXpNJ+QjNr+pp3lVbOxlrkCzKlVGiqfKI2GYhYpuF+F8GcYRGSdkgKk8OWyr5hMqS4AZKkCQH0UEFSaKpdDQ3y1iinI08uRVQLdxKiOBWReS2FkowVR+50k0MbhNNcOGidfMQpXiBJ3220ac7abgL1E22cMHCnRlYSV1dZFvhF/DvJ0U59PAXLfpdtb835pbEEhAD6Qe3P+MzPuMzPuMzPuMzfsn4QJogrrbXqB2iiySrnUfucElsl5HG5GdpXe4xy868i9wOvj9X/hb2AfuETMy43I1S2JHYWr/37+UrVogFC3/n618G+L///rsK+Stt+xcWFhqC2xJ5cI2zsBEiuMaDWwcR3MOQurYishzkvc0htXAxF4xaEktFffhlABHeQFnXwrUV1w2TWFzMttNIgimznI4sA3lvs1BhYDOvQiTK1bWZIGejB6+PyG0eqoHbUieZ6gYXkQZep5CouJP0aNHvi8Qauf3O9LMiNNRSKtWBLS9AUZH/d4cIPsQDQPvH43Fr/a6kZDl5HSFtvQ1/6dKl4tdff91vzn/b1q3is08/EztAF7QP23/rtq3iUziOHUX71fWfMmWKWLZsWZngT5ky1dnXbvT/rXC9PpvsXK/98P77+Zdf3HP0w//hhx9UMoq93f+3bt0mPvr4I/HXX3+p8I7PPvtM/AVZS8oIH/qw2b8N/xfUNvvr8y+lj+7Hz/9ffk5tT/77x/iMf+Diw5//TVuF6DRiu+g8Yoead5JzZ1nO79br797hzs16taxsu9i41dnX7NlzdGyn7b2twPf923nn/l1JZfn67z4+ZEX+3fMOXXz7JxIJE4PbRod1NkV5jLBEuR4KCzXlWqsiwxLlSoTc0thbUyY2DXHFKKqHa3I3BSzxt1SaXKYElyaXoq7jEDpYXBM3jXhwM1AMbg4KRDbktiIKXK6ECG415L2tgyTK9VAd3GakDm5XxzWf8ARgJ7Px4uLJcRXMvQ3SxvkNlvioC3w3LK7TFxUvxN8X+EuWLBHffDNNLYNs+Mcff9xr+EACof3jepQpXuDvuTVy5Tmz5lh3feedd4j77r9PDLn2WvHoo495YXfsEI0bNxZzlfd377T/t99+K04++eR9fv2noeNo2qSJbIc81RatW7cWA/r3VwMD+6L/derUSTw7Zoxanjlzlhg/btwu48O+xjw7ZrfuP7heJ9HrtRvn/+GHH4qffvqpzK7/wAEDxauvvFrMiO4Occwxx0hy8rNsz5li3Ljxu/X82SYJ6pNPPpmMd5dfDhs6TDTOS/ahJtJuvfVWpapo0bK5WLBggVi/fr1o2KiRkkL54cMfY/g9yNTMdNddd4nT27f3bH7GGWeIt996W8yRL1MtmrcQC+YvVMfx4YcfeNp24EDZNq++Yr3+w4YNU1hOnz9S9Jd9/vXXXi9V+3vaoITr/99PPxGN5Hk3zssTeRLrtNNPF48/8bi3j4755373/LdBQXu+4rbnwf33j/EZ/1DAB+gNm4tEx7u2S3PIK1jHEcnPHRXplXaXWQ/LyTms37jZ2RcQXE/+lEQypCxOEh7h92+ooqHeubdt263zL9Lvq8kSkc4XK+W6H+g77EF8/eEdGpJWQXbl0rQ/qFbBCai5UkvNm5ohh2F9RHBrEZJrOFlFJE8282xi2IObYVH34tDWCOGWIctysKyzKId9PLphSwasKCK6EURw0wjJzURxuOVQBq4KSKJsRghqoFjc2qgObgNNbhuhOrgtkES5y2oTg5tSdibhqTMF2vVZs2emdqRiuh/erIkiDE3UC82RR7YW/QcMkIRhmaAZ84qK6bMpWvudwMfThx98KH786efUb+Xif//7X3WMefJYmzRprJbbtmuntnryySdEj+491HbjJMno07vPLuHbJBqlOX9of0g25V4nEnObHHmKux5cG36Xzl3kS+an4pprrhGPjH7EAwZeuUZ5jdRDeU+1Pz3/p556SgwdOnSvXX+/9n/qSX0ccoLr/803X4u169aJ6fn56mW/R88eexTf7/y379ju7hFIjOqDu4iv/sjtYv8z/7vtVEbnP2jQIPHss8+W2fUfOGiAJB2v+m4G5K9Fy1bi77//Fm+9NV706NFjt54/UF86r1GeGgAzXwwZMkSMuPtuUbimUI3Ag23atFlfA8fzvWG9/J2816AWeXH4Z3boIN5++x0XsMOZHeQ9mieWLnEGXGCQEvDNAMyOHcnEf4MHDxZjoG31NAAIGZB/y8UYcq1zzDDSPW/+fPHaa/+W7dRSPP3M0yW2P7QBkFbnmIq//pMmfSKOOvpolQAEXiTeffdd0apVK9W3TX/fF/f/rlx/D8HdB/j7+vwZn/EPNnzwum7YIkTHO3eIs+7YLs66U9odO8QNb2wXk/KF6HGfXK+/g+zJE/OLxGMTi8TZcpuz7wTbrubrNzvxvEmJciKZBFTHfOJ3bPr+DQR31qxZu3z+8Lf+zjvuEEcddZR6/24p/+bdLZ/vf//lJKmCd9i+ffvssfZ/9NFH1cBunh40Ne//MPC7fMXKnb7+M2f9JsbDYPRuXH8Y0F61Ml6q9i9M1sE1HtxmKItyI5RFub4muEZBWx2VCKqquVol5MXFCaayUUgq9uDGCFeMkVxOIcItaVWfMksyFSC66CAit0FUv8h8FyPZsNIJY88kVk7PDcGtiAgujsOtiQKda+tRhXrajd4EacdNgim4YJ3iJFtyKmnSWXrjcfHbb7/5d/ZiR1eKVAf/5uuvxbq160R+/nRFGHpKwlBUJOw3jf/udwkfT4MH6xdqpH4wm8DL1ymnnKJeSAvVi2mhWFu41v3tjh071Kbjxo0VfeDhsAv4+PPOnD+0/8qVK5OJpRLeAtVGYhHXcuVZs2el4IGHBbxKcH7XXHO1ePiR0Z5N4PzyGjVyyfEsSXR7ndtLPhxbSoLXU5E90/7jJTno3LGzOP6E49WDs0hLVi+77HJx/6j7xZlnnqkJY5F4/fXXxemnnybatmkjX/6vVS/E5vwHDBggX6YdTxF4oOAzDIJA//jhx6T3adz48aJjp04ab4QraYZjPffcc51j7OEcI0ybt2wWt9x6qzhavkyfcMIJ4oEHRrkjg5dffpm4//77xRlndHDJ2oCBA1yPVeO8xrK9Z7gt9+WXX0oykqfkubBu/PhxolPnTuKE409UpADaDaaxY8eJYcOuE8OuGyZaHdlKXNC7t0cODsfav/9Fyivct29f8f0PP7rtD2102mmnizaqjYaIjbKNAOuaq64WH330kZgwcaJod1Q70aRpE9lHTxYLFsxXvxsr+yIMWsB5DhkyVKxf77QtrL/m2mvlS/gA0bx5C7XuarWv/7het1tuuVUce+yxomvXrkraak4Y2uH0006Tx9JWDJX7NNcLJhicev3111QfO/XkU8VTTz8l/4gerTxvP/30s/x+oGjZqqXod2E/lfURdgqEqU+f3qKV/CPbsePZ4n//+5/a1/DhN6hjg3OG7QH/8ssvF489+pjo0qWzvG4nikdHj1alHEbLP5iPwLIAqe8W0b796WLKV1Ndr/L555/vkriR94wUvc7rJdu5jbjyyivEn3/+mexHsl0uvPBCMXHiBNGu3VGiaZOm4pSTT3Hbc7zsZ506ymsr+9mIu0eKIi1Z37R5s7j++uvVgBe0/0svvyS2bN4iTjrpJEXuTjzxBPHCCy+q4xsydIh48KEHU+6/tYWF6rcbN27UHtw8h+DKTcC7O6D/ANUWgwYNlmRxifrlnbffoby/RjnSvHlz0Uf2q3GyD8IGE+R5nCavFUyF8ll1snx+bdy0UQy/Ybhu29aiX79+rnd7xIi7RPfu3RXOddddr4g+7AeO+aEHH/Ic8gcffiiaNW3mehHefudt5T0+UvZtuF/g2OE+gzYAsg7XC9rAbNsetpXkFe5pqCsI0yefTJLPgtM9zz/o7+BBBtyrr3b6u3qWyL7w9FNPqeNt27atulc3bd7kHuBb495yngnHn6CeQRBeAN9Agr1zeznPhJ49esi/OfnW5y88qx577DHRuXNnef1OFKMfcfrao6MfdfqdGvnfJtqf3l58NWWKq/Q4//zz1PKgAYPE3SPvFr169VLtfOWVV4o//vhzjzz/y+rvH+MzPuP74zseXCHOum276HC7JLK3SZPz4a87f+PzlxWpxFOQNXmGFnXBd2fdLreX2yqTyxu2FGmJ8ix74lbzrubz/l2g3rln7vL533vfveK4444TP/74g3o/gffu0049VTzyyMMuwe3dp+8ea394TkNII7xrduvWXbz44gvqnbpwzWr3fWlnrv/4t94S3eWzfHeuP7xDQ+Ku0rR/YTKLcktEcJshgmu8t3VRDG5NHX9bHSUCrqqJreFvuZrYZhCZcjqJxTUK3yDx5NKSQLhST5mTWxzwS+Nwgz7S5YglFXQaYu8Z6ORNpi1aKsiQ28OQ7rumJre1EMGtry9GY6Qfd5NMQefD2vMEliW7habjqqiyIrhFfh2p+GEVILjwe5cwfPGleiECwqA6L3qpvNslDEXqxerW224Rxx17nLo5//nPMUmyAC+E8oWtTds2YvCgQdqDUqQ6KZBUILHHH3+8JD+9xOTJk9Vvrh8+XLRo0Vy0kS++/fr1TTlO5+WrPdE4OENL777zjrhOkhd1vODB7ZMc/QLC47xoabKnX4od8uUQxJ6KIE5HzVRUCp1FEl/dnCsd6URSpuwtUo2LVZskU3j6/PPPxdlnn632BwT3kUceIcqQ7fJlPU89lGGj8847T9w14k71sj1y5EhxwfkXqO2+++475d157733xbRp0xQZevqZZ9Rv4GUPrteEjyeoa7JixUpFDgEbXjx79OguX4JfUNvCKCOQPTjWzZs2yxf1UxXp+eWXnyXe3eqldot8gf5OvlRCG773rsaTL8hPP/20Opbz5MsmyDYBC142zTECSQOS/f33P8jr+olo3qy5mPr11+o7IO3HHnuMmDBhgqrn/Pf2vxWpnjNntu6veYrgmvZ/4oknxNlnnaXaDc69lTyWd997T0z7Zpo4TRL3Z/SxwHnBud51510q3nG47G8ge94sydGmTZvEKaeeIm6+6SZFeu+/7z5FSkFFAcTFaaPJqi26d+8mXlRtJESfvpLMSOIMgxNPPPGkJJIdlXRpq7w3pk79WjRr3ky8Je8fiCs9T5K6K6+4wnMscOxGTt9b9tnxmhjBIEHXLl1k+3wvXpPkukWLFnK/c8SKlSvVvQnHMpscC8SNgnwVJOxwzNBXhg4b6g5awUjxv158Ufz6y6/qGv3zn/9Uv7tIfgfkMH9GvrhPnjeMKsOACGRkv+CCCxQhhdJjTv85Tz0Hvp46Vfav90SzZk3FbzN+Ex9L0tOzR0+HZHzzjSKV9957n/r8+OOPy/0PV78HrxocB/wBh2vVtWs3ce21Q9y76oYbblDS/D///EO1TUfZnnPkecOz5rvvdD97713Zz75RAw7PPPO0+iGcV0f5jFq4cIH4ZNInisRD+Yaff/5ZHQuQdiM3hkEBGECB55sxIF8Q1wNtC+R2g7RGWqIM/QOeO7fffpv4RfYNIHzdunZV+wIirvqenN555135zOsvr8eLahv1InPvveKWm29Wy2r/cp/gHV6xfIXTtiPvUfUTHfI/QBwt2x6OFUjkkUceKfvxuy7JfMhDyosUqYd7YUb+DPVcObLVkWqAAGTPMKh0+223q4Gmn+Vnpw0+F2u0KsjZdpzse3rb229Xe4V70SG4yedfb0nY4bkJ6/rIly74nXmWNG/RTHzw/gfqmoP3+vY7bncGNeBatZDX6v331HdwrdxnwnnOM8F9bl1wgfevk37+wv5h8Gsq6msz5HMW2qanVmx8M+1rdW73yXaG3z3xxOPqvi7SAwZOXxsrjwf6WhcV+lGitG4Xnv8Wh0ep//4yPuMzfunwd2iJ8pm3bhdn3LpDzc+8xbG73i7SJFeIN6Y5JO2ud3ao72DbM24x2+5QJLlID8C7XlnjNYzj92v7+zcQMceptPPnv2P7DvX3HBSJnvc/+dy/6aYbtQpxrPcddtx49W5x/AknqHeDIk1C58+fpwZUQWUDf/+++MIZnIZzgYFhGCBsJ9/T4Lnu1/7nyOc/vMOYb2HQ8NZbbnEG17t1dQc0x8tjAkUVvPPBvuBd4YYbh4uJEyaqdwY1GC3f7RfMX7BL1/+3mTOVZ7w07Q/faw9uK21NUZmgRigO15Dc2pqDVUMEtxKqfFMe8bhyKMFUFiK5hgtG9ecIcZKGSZZkWhu3zAku3mmQkN0QypwctrBwG8GNoiRTWaggcC4ittiqapJrvLh1kES5PnKlN9by5GZoRKLbmtVrtKQVZxZLpCQyWiU7hPHglrZTFaHhmbwmecnfw0vCk0+KszTRcl4qW6kXjGnfTnNeKp9+Rm16p3xJOavDWeJrSSQ++PADuV0LlZhl85Yt6iUJXpp+lURiKLwQduumfrNSEiqQ2V522WWKZNwhXxpPb3+66wE5/3x4odYvfeRBN0kSXMB3Xkq3qTm81MMWIEUDb5gz+jXefTgo8qVetN4X3yqyd7oj6ytyCOIIeQ6Ll6IXraLSPGhTWxraL65reblFqfWNaDJeG/JLPbgG4aGHHhK3yAcLfL4aJMoPP+LBVxJlRXAdotdBvljfdtttSqqLpxEj7laEZQu0k7wWQCaBwKuXS/nS+Nzzz7vXf64kjY3kS/KkSRNdb5FBhBfno45qK3GLFBlp266t2KYlNCDlhAcfSBkV3vDrFdYWwHv9DVcyfFYH+dIrj3EdOUa3/8lz+nPDetFBbvfKK68o8F7yujwvj9G0/wxJoNq1a+cmToIBmX79LhSXX36F6CJJYLPmzVX/dM59hCJTcBxw/q+/9ppLvGCU8hRJaGE/gA+kqbUkET/89IMkoN8rjL/k+Zk/32e0P0O2ywRJaucqMjpRttFff2/3nAf0s7E6ThSIrCuplXsYIf8Q3XTjjZ4RSvC4QQ04ILjwhwVf/z59+7p/aEAmOlneS+ANhfP4vwv/Tzz/wvOS1DrHMmniJOd6CXy98p122uGUr4L7DGJ5YIJY2lNPPdU9FiAs0NcwPgxowB8nkNhC34UvHEXFc+7vgOAaYgwTeLrHjHlWSWchNnrz5k1i9OhHZf+9Wv5x7Kb2Dx5CkLoqL6UkuDfdfJN7/eGeBG+6+QzPDeONe2v8W7Lfdk/267vRtd2yVXnVoV/DLx997FHRqWNHVYweP/c2yL4FpBLHaIM3FEgRGHzXULYTJAsDMgvr1m/YoMwhuAkVCwWEEEa9ARcGHuE+hCyakAgQpF3gDR8u77nnn39O9RcYoIFzOkceH4RdwAT7B7nyenlM8B1IlJ97downZvTBBx9yP196yaXigQfudwlu0uucnGDQ7uMJEzzr4BgffXS0vueLHJk2yKaXLkm5/+CeHf3YaPf5MGnSJHX9J06cKN5/732l6GjarJkaaHH6uyG4QhHQe+Sz2hzvp59+qgipex9Kogn734qulfPc6iBuve1W/UwoSlElms8w0OXE++q+1k/3tTVr1MsUEPzRo0erZyW8iKlBAtnX3lF9zRlMwfcfKAngOhapciM+5TB28flfPBko/u8v4zM+45cOf4f24J5xMxBWh7S2l/P2QGJv3i7ueGuH++M73t6h1uFtYRm2NRLl2bMkqUqgZKCmzm3CWfZ7/46jd+6dPf8FCxeovy2Ocsl+/qCIc95hnXcvNbD7/nvim2nfKkWQeQe/6KKLxHVmcPree/XgtHD//g+Sz0AY6Pd9/5L7P+fccxShNvigjOrStav4XuK+8cZrkoy3VIP28N7SsVNnRZpnzZotWkpSDdmP/5DnAYPR8PfXiUv+a5euP3jEwVlXmvZfvdqVKLfWBNfkL2qCPLj1UQ3c2qgOrpEnVyN5kwzBzfYhuJgP4iTEOKtyxKcWbpB4ccskyVTIEosbJhJlLFUOk4OOoBjcDBJwbIKQsfc2VzdUBdSAVVH8bU1UB9dIlE2SqeaI4MJF6wovTzQ5UYLIKBI6BhdGP4q9zXS9qyKLIgQIw4V9+yn5IXTs5vKFxksYrlcvlfCy8oZ8UTHkBSQW//v8c3dX4KWaKR8YMFcvhBs3y5fzLUpK3KghvBAud7xh8uY2iVlARufEhy3REmV46XvW+vD8RL585akX04buC+qt6iVdqLg+eKFx4xc0wVUvxcOHq5c+OJbXEOEBgnj7rbcraXap06j73LTQ/qtcgpuUkLsPz3jywajq4Oo4WvwcuOCC810icM3V10jC+7A3RlOSK0ei7JDjqVOmig5nnqGuH5AjaHeYQCYJ2+XlNVbkFV7Gof3h+oPH+j2NYXBBctqmdWvlMQLp5Lp1juz75ZdeFhdffLFafvPNN912o+cPUkt4Kc7TZogDTFOmTlGe2ry8Jko2/r0+xoULF6oHOLyogsH2L730L5eEm3YApJdffsk5jiLhenBhYGLMmDHiLLnvm266KXksLZonk+TA+ec1UucP/f4FSXDBW4mnbl27qfjCsfL8zunZ0/MdYD751FNq+bFHH1VtBB5S8BCZPxjQz8wL/1tvvYUIrhD9+w8Qzz6XlNuDXBQSF8G5v/jii+KSSy7xXP8+el/gvXQJmI6RgeVbbnb6OpA5dSxHthY3oGN5+eXk9VqpPbjQ52GaMGGivLe7uPc/DKbcBJ5FUD/Itj6jfXsHM8+5dtCXlcxzUDJkALZ1rs077jkOv+469QcPJpBSgwf/vPPOF/nTp8vnw/EiIQkiSJFXyj9cRZrsPv/c8+7vzf1vssGDx23Txg0uwe2O2rNFM9OvG6X0s0JJMOHZAeuByINHF6b1GzeodliyeKn7/IMBtzvuuENl2F2+dLmaQ5KL1fKPKpw/eG/Xaw8ueF3Hjh2rsfJ0v3KuDXgWVR/q1l18+tmnSkYLcVnwx/uE408QP//0i5Kswz4AF9qiYSNn/6pt4Vk35jm3bWGADp5jZrrrrhHi7pEjkx5cI1F2yfsGdTygKtj21zblFT1GEmt1HzbS/V5vB/f/Eklw4ThgYEdte8wxonGjxp52nPTJJ2oZZPVgl156mauwgZ05/X2sS3DhvjETvOxA+23cuEm0aN7cee401s8EiQ/fAf7UKc4zAfp13z76uWV5/vZCzwGl8LnuevG46mtFSt0Afe2C888T06GvHX+cGkBo3aa1ChVxJN+DxHPPP+fef6b9oa+V1rlQ2ud/SS//xf39ZXzGZ/zS4SuJ8pYi0f7G7eL0myRZvcmZYxv6yg4xTNrpN8Jn/b1ePk3+rr1chjheIMtzVBZlkielmOSg5v0b/j7O9HnnLun8IaQKPK7Fnf84rUIsQu/garAQ3mFffw29i6HB6QUL1DMW3jHN3/+ly5eV2P7nnAMe3LHuqqOPPkpM/myy884sMSFkyDgcwCkF6igIS3nmmWfc39B3n125/o5EeVWp2l9nUYYyQW1RmaDG2otreFUD4lSsjurgGolyReSczEXe22zE+TLQcjrKzxRFRNeUDIoSnmlLNlVmHtzi5MkR4k4OEVIbJZmUY6RMUAYJRs7Vbu4KmuRWQe7w6igOt64240JvpAluE5Rkqm0KwU0kUHxnwpW/xnXA+0wlUS7ayXG2Itcjdpe8iZ795xhxZoczHZkEIgzwYgcv2o30SyXMYdQGbiZIRlWEi5QJJ77QvIQqa+y8cE39eqp6+YBl58Xb2b5ps6ZKbgkfB6EYXDpBDC7Ek8EL6dJl8sVU3rzwYgv4kJl14ADtwR2fDNAHsgfH29gcS6O8JPmSL1pn6Bet3nL7H7//QRSVmB2uyCLWKVLt7xBc57p4AuQTBZ5MfYrgKrltEgu8dDCwsFgT/QcffFC9XOIJ2shIKDH+vPnzxLChQ8XRRx2tLgF4yEZBTKvl6IGgJAluEh8ktV99+ZXyroDnHaYrr7zKvRZT5Mv8sccd5zn/latWir/+/lvhPTBqVLH9b/68eSpG75ijj1HbQZztpZdeqiS3SgYp2/+lf72k2rJXLy8Jv+oqeRzPPeciw/WaoUdPJ06cJK9xMzXyB/u9WZK2+1PO3fkdENyzzzrbs+5o+ZIP/fLLr74SJ55wouf6A3ExElG3jb76UpwFnvPb70AerbEWD64Qt9xys7hn5D0u1qLFi9SxgxwajuWSSy72XJ2+vcEbPE4RJCCw8OLu1/9glPSrKfJ6dYBjuc1ppyuvdL2tK1euUH0F/kCpdpowQRFc4aoFHlQDAxvlscD99/Irr0hiuUn1VbjH44bgQiKkMc+6yL16navk36aVhkmCC4TbyHGh3x53/PFK4gxxmyCzB++8OW4YhAKvnzkjuI4g44Z+9PF/PpKEtjsKLfD+0bzpppvFqFH3W+8/8/yBONd/v/ZvRZQhEZkid3l5qpC8mYa6ct8iT9sC2TYE1JEo50lStFq9kJx62qm+9/99998rLrn4EnG8vD/MLXDDDcPVYEPHjh3dLRPag+sS3EGD1CCN2Q9IanFSpDvvvEspSxyv81Dl3cX4Y8e+qQaXQML/wQcfqPt/+vR8lQgKsjHDgJkiwhs3OIOKS5w2UNseA9v+KrfdoTDP1+oV8OC21zG4tuefGoSR/Vz1hfN6iccee9xtf5Aig4IAJrgPR40aVezfn3n6mQDHssN5A/Lcf4rgovsP4ucfM33tvvvcvgaJu1Rfe/hh3deSHnF8/0Ff+3/2vgQ8iiprO93pdDYCIew7JBDCFvZNAXVGRAVxHJcZQXAZRZ1xHZfP7R/Rb1xRB3RGBRXcPncFRB0XFERBRIWQjRCSlqUTOg2EoLJD3f+eU/dWnbpdnQUbiXL7ST1dXVVdb9WtTtV973nPeyAVQiof1Ouv4jfk/k+ffw19/mp8ja/x64cPz8af+CPt5FsPI1mF95Nv5ST2Fv75Fk5gxedTHBMnt2KdnH5CgmtYEuWQqIMbtQKG0v+GMnZAcI/k/DfyfgD0na0SQy7nb6bZTXIEELLl4GpPM2gBr3femY9+Fz0xiGEOam7lfEB9/tfW/hDBhX4HfKwRg+syQAL9Fdj37UJZCBv9jffHBvD+CQyqyvNHgnv22T/r+hcVFPI+5dZ6tT8850UObn8SHJR+Rj1JHVxaJqg9Mf1tS2TKzQl3a0rycGUEl9bApSWCEl3KAblV63HLv40/GiRXrYMbp7hfxZNIbgI5sWTynkJYfVMiUU4njdWSTDQHtwPRg8sQek8y8tCHmEydFQ6H3fXnIecy6IwWkBxa+r9Wt8rEsHJwLcLASS2QF+yo3CY7KoqjGgcYPHgQW7nya2vVzpqdSL6ABJx00smu+EBo4B+I/uP16d3bykkFAxerVIpynBDBxRxcdSSRv7344ouYw2Z2jG0XZYhSPfjAg7WeP5Kvv9+IEQ3DqMc/qAs+tH9laKvl/hYKkUT5kEJ4+SQd+OQ+Vq36GiNA8gWRT3CKXrBgAdbNhGsNI2kQgYWvgNveny64AGWBZsmRd7GjCCN5C/l3gEjCzfsg/wwRWuhwu0VFVvBOKZjcmFEmA0mtJEwjR46w8kN37dyJObdAdiDaBRH+fpyYwCAMSBmHjxiBjoTQeYQoC0Q5D/Ljho7zJx9/YnasyTGCtAYikod453z5cjNX9fl588xjPN/ZsQWlwHfffmN9RpOpAjP/BY75bE5EwegHWhLaC+ShIKE5xI/lX+JY4AUGO/Dbg4gcyNvnzHkGI7IgL63eWY3GPnM5yYZ6pQsWLmB9YMBh4yZso4smT8I2Avnv3/76N5SGmzmJF+LDCF7gfg37gAcX/I7gOgwaPAQfhEAyoDMP7W8eCxDcKx2/K5D7yrI4V151JbvyymlsV80utm37NnY5J5pwjfF6TZqMedwG79iDcc7/u8u8XiPQtOJbIfcX/2eQR2+YuaITRN6oOYAyA0lIdfUOM794yWd4ve6//wF8+MJDEl6Q1w7GQgf277N+P3SABNZDdBteny1dgjnbEG00RF78QN4e0+++20E6oI1AjgS5qFfx85QmSyCbtgmJKXmFbeH8AQuu7fDhI9g6vLaHkOzgteVYj3AiDbnC8PuD9h/M2x3yhIE4g5HSggXzbYkyyH2VaKiUEGcLCTFMKFHmpHcHbyOQsoPMFiTsIPmCaHX1DrODsnTJEux0gExfvhYsWIjfB7mXhAhLCbQguGAide2111gmUZcpLsp383aTBPfGG67HkXxQw0CONfyW+/bpbcn6QWExasxojObDecDxwf0BoOFeAcZTkKYBr1deeRUHCmtqalCCDfnEMg8WBhFlyojb/Q8HdF57XQyWnYt5WmUbNuC+pkydgoNW8IL8f7ifghEevVZwfSANBe9bzCyVNHzYMCSpcF+EvO0fRQTfea/ivzUYTIHSafxQIE95EP/fvR5yaoUKYcCggdhm8vkHA54DBw3GwUeQql951VXWb60ugWVD7//qnhry/NX4Gl/j1w8f9rubdx3H3WES2pOBuN4M74fZSfCZz58C8zcfxvmTbjHfT77F3AamcXccwn04cnDDtuoOpbBq3VWl/w1qoyLpA9LA8wcPG0j7g2cEXQlKlgcfeMgK0sg0O3hOPyQGdinQ7t0/sd687/zC88/joLk1OM2f3erzv7Z2PfePf8SglDxuUMFYfjQKKKT6gBkpPD9efulFa9WbaDJ19s+6/maQKFSv9od+pyC4AwnJ7UsIbpaQJ9McXMnFWhEH5ZYkituMkFtVpuwnUmU/Ibg+xVHZq3g8xbt4PsW0Dm6c4pzsc0kE9ir6aR+J2voU5k5NplKIyVQ6SVZuoRQTbkcclLuKKVOMMuQQibKc4KJNxDJB5EKHqBtv2C54jKNJxUVRxtrq/qFBdLagoMDSxk88mxMGIYdEwsA7KtCpRLIkCIOBEb6/IemCTtWmTRtRlgdyT/jxwQgPuO9C5wqkZNDhglEXGFkyCe4+C7937z5WXilEhK65hneo9+2P+D8A+ZysMameyouYg2tLlEEOKwkPyPZM8iWO/9ZbTIL4pwvYYt7RMkRUAzpakFvq2lZGlCLV8p8TcjkUeUWoys65VeUWkKd34IBdtxgMWK6/7jrHtXqJ30AgIgIDAEBCIDc1VBWytoEONxAAcNMdwcno+++/bx3dQw89xMnVYBy8OP2M0005jWEavFCCC/lx0N5wo4SoIRBoMITaGPgeo9/7xTHCd5evWI4RfjiWIUOHWjJ2eIELMhBgIJ9glFUEUk3rGAficUJnWB7j8uUrsL0BF35vkJ83zyK457P5b5vH+P334jj27bfanEZw4fXpp5/hYICM0j0ojgVGIM/gx1JcbI60AikAN1/oOAORgQEFIN/yv+WLZcuwZin8Pk8ec5JlrgDSIHA4hlxEIGxgyrNR5DNCh1mSUnDtBVknfB9yrGG/ICuFa5jTqxfmr8LvH17PPTcXCSz9b508ycxvhM87+f8K/G8B8YfzhbJRe0ReMZAiPBZ+veBcNm3azNspgLLQA0iWDFRKwHHsEwNJ4PI8YYJNcGcgwTWl3Q8++BBGPOF3djv/v0dpLn+I4HXi1xxMyk4bOxaPC0zD4PcjrwXcCyCKBy9wHwa5OToI89dW3gmAkWAwh5O/aYhaAtGHewXgwODEli1BXAlEC45TbgtmFuMnjMd9FIvf00MPP2Re2xzzdyYHiqC9IToP+fYDOPGBAQgZqYPfA7S/rCsN+Lbc125/lCgDwYUI7i4zdzcs3IXBeA9ch6Gd4Lf80ssvW9/H8+7dC+81co/bIE+X/598IoxEACMs5NhAtsz/geWYczx2rBnhvuwyk+DK87+XE1oZ7YZjRrk9n4Cwg9T+lVdesY4BBmngt90rpxe6ecOgxAXC0A1eDzz4IK6D+5+1Lf+fgZwt+P+XLtcfffSxeY+Ncv+DPFj5G4XfHkjnYNABVDqgDjHLKhnW7wrvCb3EPaHIvFZwTwDZOrQjGN69h/cEA58TkGtWWrrBvlfJgS7DNCGcOWuWiEqbvzWI8sML7ov4W/vwQ6IWuAyVLWCKBu0OioxgMFhrZCrqs7KO+3/te6z9+avxNb7Grx8+uvTzR9z1T3ESe9MhJLIn3QSE9pD5zqcxNx1yvMM6nP+7+Z0b+HdhHxA4WF+y3ooMhmm/LVQVYXJE+98QzIj0van/+UPQYSi/T0N+LfQ5of97Gu9b/a/wNHjjNTvNzhzYNfuwhzBg8S92M+/Dwn0c+gaffbYE+5JgjCnTi9Tnf23tDxLl14hEGQber+ITpEpZg+u8fwxfh+cO5PrKwWyZygQpOkB8wTfoSK+/VEHWp/1DZh3c8cRkSkZxs5UyQVSi3IE4KbcmMmUpUU4nxDaNRG9TCP+jJlPxCnf0kZTXeCUXN17hoTEhuPEujNmrGEzRCK/qoOxTorjJhNSmkoaQtXDTyYgAdVFuQxq3syC4MoSeTaK4fcXFIhJlp6lUWLE0lxJlahJV+y0ocjnUwLIc4SAS89mnvPPTG6NXdkdlCP4znUE6KiBnmDr1Yox0wXTr/9zKicgBs0P4+VJOFuwO4cuiQ1gpkt8pwe3Tpy8mpwM+yAqhZM1YIjWTRwxOcKcKgivXyH8OiOBeyjvPOJrEOz0QDbPIl+xo8fPE4xeDAVBOxSRfgwX5es+1jWoTzUh8NJmqClmlm+w6XmFH/S5ZQmjDhjLTyEe8IH/wRTkipljlA4H5gZSCofiQR1JTs9Mll8PAqMiuXT/Ua8QUokhQvkRu8+abb1kjiOo37H3a5w9/IHf8gayzz8Ewj7HW5arwyXzByKCU6tTW/upX4UHwww/OkiDgbitlwTtraqL+b8gIm3pU0EZ7fvqpTvz9+/Y7zgFGbHeTtq17NJNI1/fsdfyv0OsF+5T48Jt3lsaq//8/knhOnM0SL+6/f1vWWb/2rwsfIuG0nWEeSJLpdGxEHBt9QYfA/G4kPkSFsZ6wso+DBw8gZkPbX/0EbtmHDxs/+/zp60CEZLbh9x+5Efz/uZ2/2QaHLPdN8/94FyooGnL+9AVy9bffmY/3GetaGup1PuwoA0UjMc7/fXnPOvyzzt+t/WGfP/7wwxH9/8UCv/4RK42v8TV+Xfjg87h6A2On3XaIjeakdczfD5kTkFogsWIZkNkxdD2fH8e/s3qDgfuA/hcYPtl9tJDos4VczFyd/W8zB7fwiM8f7n//uPsfOPgNAR4Y6AeVHTj2Y5lH0ofFgd2HzD5sLxzYPUP0wQ324EMP4+AqfB9kxDBAC8dnVn7Iduk7RB4XBDWkM745WLrDHFzn/XLoM1933fU4uP7222+xE084wSpLeBWmmV2F34PjHj9hgmmCWrzuiK4/VMZAF+V6tD/0EyAIKIKBucRgqrcgt9RFuSOJ4LYTvKyt4GotSPQ2XYniJov3JML/kpS0VR+ZvCQXl7ooq8FWT6wkyiqpVc2lJAh1wvKSz4nEOTnZJYqbSlh/OqmBK2XKMoLbnuTfdiKh8x6kFm5f4gQ20JQoK6MYobBVGogmxKNEuaCglhE2I2r9bPrPaRjRbzROsuTcO3TCYTTMDR86N/DdhuKbHWojyhCiUee/jnr+kJdmkS/HhofZzp01dZ5/XfjQ/tRkKqQORFCyG6rCPGLTFMosxwODAEWFxUeMX9f5N7T9IT9T1mQ7FviO43js0ZjgmwR32hH9/o/V+dcX37xejx4z/J97/ks/hwjp2F9t+x+P+GquvG5/ja/xNf7RxgeN3R7O21YUGeyqmYfZqbccZqNvPCQm9/nf33qIb2uw5UXmd2GMEvpfwS2bzZQ/yPEMhRXH3lDU/jcauyoR3CM5/8O87xcIfI/+HnWdP5QXMvvgzr1DyhgQzFi3P5hiOnJ463H95WD0keAXFBSZEuV6tL+QKIOL8hCifJV5uDJ4mEmitzIPty3Jv22tVL9pTiK4TQjJTSX8L0GJ2CYSzign6pocp+TkxkyeHKeEiNWoLi3C61Hck+OVWkdJxE1Znngqid6mkULBMorbhrgotyd5uF0Iwc0ihYn7kAgu5uC6FZ+2nXnDFnGCfE+Q6lly0jrlANHGzIwoX432c63FEuA4wj+w/wC2f4i6W4eqokbe5cAFlOEpEhLLrejsebjRnD/Izs3RxGPb/ngcu3fHBB/yVHYqUaPfyu8fnK/37Nn9q/3/g9/azuod+v7zK8Kv5p2MPXv36PbX+Bpf4/+i+Jzrsb37DBbexVh5JWPFmwxWxKfCjeZU9D28M1b4PaxjLLCVsW01/DmzH1QiDPtr0P+SaWRmDdyQZdxapQQo1P637HPv3b9PX/8Y4IMirWSd2YeuT/uDz4aI4A4iJVb7EEVsNnFR7kJKBLUnBFeaAacrtXBpDq6UKEv+l0L4oJ/wxXil7KxPqdxDOWnMCG6cErWNU5ytVJcrn4ubso+EppOIwVQqIbrNSD2lDJK83IrYUndUygRlkhJBVKLcV1y0CRCGx1GlsF1XVRaiDlPZa2gr1q9ER1o3X3W3URM1qSLa79Rw+SkbLhI95qazPz7woeg3tD/UI8ZRpnBVhCFYlUJ4cXCicivW2IRcPChdccBFBqrbX+NrfI2v8TW+xtf4Gt9eCClOBw9DigfUtOeE94CZnwtZcuZk8M8Gzu/HdQexjwbVM6DuPfS/qsJhR1lH1SslWv8b+mvgAl+JOaj6+v9c/K2cu4D3Aph31af9t5sS5bNIDm6uILg5hNx2U/JwaZkgqa5tqUiUm5DqODIHN5XwvyQljdWrlAtyC6B6XWrjxsxkyqOEjNWyQT4lxJyglAjyEVlyokJwm5LCwLIWbnNCcFuTkHgHJeFZNr40maIR3EEOF+Wwk9CGQ+o/YwiNgb777jv7R2e4j59EqgxUd7wG/OrJT9tQMY4j/O+++xbr+tLRJrxG4SqHI1wYR6fEtQzZ1w4cf5d+vpS9/977mMy/UEzgmovz7y7E5fD53XcXsUULzXcwHzC3WUC+A+98/QI+v+hd/C5OfN0i8f4uX79wwULnd981l2t8ja/xNb7G1/gaX+P/lvDff+89tmTpUlYCzskhGh0UQYmQ4tbrUuaR9r+hz7d69erjvv8bC/zvVn/HNm3cVO/237Ztu3RRHiR4U28x0ehtplDLSoPfjsQTSZYIkiZTNILblERxk5QUVUpwEwnJVUsFufHNmEdvPS5hYY/CsmloOYEkCftccm5pLVxZJ6kpMZlqrkiUaQ5uB5LsLMltFqmBmyNC7XI0AiO4YWmRLepAWa68eNHNiw3LYCSpuKiIrVmz2vUHa7Doqft11Nq2fpiuGv+olZuPD/y8NWuw3SstB+WQXQc3FLbc+UI0Lzds30ixpFBYzduVUeBwva8/rNf4Gl/ja3yNr/E1vsbX+HDNn34AAIAASURBVEcXf2sl73MXFrG8vLzjtv8bC/zVeWuwygcoUOvb/lDdQLgoDyImU70ViTLNwe1E5MntSPRWGk01I5HcFEWhm0r4n58EP+OVfNx4hVN6FRVxzGXKKnP2KuWCPOTA1Bq4akJxMglXSwvpFJKDm0G03C1JEnNbQnBlY3chidA5pA5uPzHBRTt7x47t4p8uTEacInXp8h+zIljBCe4atnTpUhYIBGz3UaM+QnjlZ25EH+hxH8Vh9TdO+5XjgyFAoLwc2xnae0swqMiQlSlkOyvb1zLsWC9vzNZ1FbVzG3L9Nb7G1/gaX+NrfI2v8TX+0ceHsmOreR8Q6nIHynmf2y0nV/e/Iz7v27eflfP2ghryeWvW8nbc0qD2Fy7K40kObi4JFPYU/EpGcLuQCG57EXxsS3haBlHfppGgZQohtslKNNevcEa/S9qrm7mx1yUv92eZTHld3JQ9Lss9SrFeP4ngUv01JbrNXEymZA5uhmjIdmSiEmUZwe3uUiaov1kHd7u42CHFqCjsCN/TMD7Yg0PJnVVfr2Iff/wxSjgWLFyAco4FQsohZSCwDOQb+L5AvottF8xHqQfOz59vSkAWLBDfWyi2WWDJP2Df74p15rZiG7Hut4K/aNEi9hFvV6hfVlJSwioqKyILhVdVWTfUkCK1kKNS9rKw4+YcrlKkMw28/hpf42t8ja/xNb7G1/ga/+jjQ5+7ZH0J73N/bfW5f6v931jgL1r0LrbTypUr2frSUhwkaGj7C4L7BxeTKZmD243k4XYmCtp2RF3bkhBbWiYolTgppxJjqSSStuoncuV4xVjKze/JTUkcs5c3SskgL0kSpnWLVJMpv8LeU5UIblPSOBlE293KRaIs6+BKcpstore0VJBlMuX8JxYjGUrJmbBSEBmstisqgmxLcAvbvGkznzbh+0b+DnkDkK+7afMmfMfPcn7TRnO7zeKdr7O2x+9sRp08bAe1WXHZxk24f7o/qJ+7aZO5398i/uYtm3n7VpplgVzan9a7DVsyi7DLzTbyJg37C4VDZMSx4ddf42t8ja/xNb7G1/gaX+Mfffytoa1IdDdv3vyb7//GAh+4CRBb7EMfQfuLHFxVopxDCG53Qm6pRFnWwaV5uC0Ff0snEdxUF3MpWirW7yJR9rjk4aoSZY9iehwzB2WPS8IvZd7qFE9OhDppJZL6SGkketuMGEy1UgymqES5E4ng9iA5uNJkKlfUwR0vCW5YhOjpP2g4FIp0fQtXRZSkqbL+sUMRNwLb7CiEtaVCpNZUyLGdxtf4Gl/ja3yNr/E1vsbX+Bpf4x87/Gq7TNBAoXjtQwKFOYJbdXeRKLdV/JFaEsVtOlHkphCfpRTC/2j5WL9SWjZe4Y8+F4lyXCwjuG4hYZ+SgxvncnAJRJrsV2rhphDCK922mhGTqQySh9uGNGgHMXVVcnC7kxxcSXAxB9fKDwgr1uWOH1XIMfoRMTpVFfmjk1KOsPI58oen8TW+xtf4Gl/ja3yNr/E1vsbX+MceX0iUwUV5gCC40mRKktssMXUl5r7tBAejObjNSQ5uOlHkUqLrJ/yPklvVaCqekFmPIk/2KLm3McvBjVMkyW4uyvGKRNlHEocTFHlysuKyJXXazUkjtRBTGyJR7kQiuKqLco4iUcYI7nYrgkt/CCFlhKTKUTYoHJFcH1JqsjqXhVx+tCFlJEbja3yNr/E1vsbX+Bpf42t8ja/xjyX+NrsO7gARve1HXJSzCb+ixr4do7goZygS5TTC8ZKJ2VQyUfX6SeTWo8x7XchstFzcmNbBVUsF+ZQ6uH6ynNY6cisRJHNwm5EIbjpx5mrlUgdXhsm7iMbPVurg5hKCe9a2bWE7xwAvvHOUw8wrMEc4Qi4jLOa6sKuzXLgq7HCiC4dDLvKCkMbX+Bpf42t8ja/xNb7G1/gaX+Mfc/zttovyYBLB7UWiuN1d6uDK6C2dWinK2zTFYEpOfpKimkS4op9EcL3KvFoH1+MSfP3ZBNdH5r21JADTg0skBy7fkxUnLanRluHsZiTULROXW4tGbUtycKXJlIziyppNvYjd9RDQl+/YscOu7UX16lYtLpFIH7IT68OitleVa/g/TL7rdJaTP0wpD8B5WltM42t8ja/xNb7G1/gaX+NrfI2v8Y8R/vYd22UEV5pMSRdlyaeySBS3EwkuSpMpN4Op5sQwOI3k3yYS/kfNpRIUebLPpVJPgku52pg5KKv5t16XorxeEl72KgnDSURzLcPTlNWnkfkMpVSQjODKUkHtCbnNJBpx6aIsp/4i7D5eTeIOh13c4IREAEc3ZMFqTNQOW6MocnTE+pGEw9FrhoVD7nboGl/ja3yNr/E1vsbX+Bpf42t8jX+M8IVEeSIxmMolLso9SAS3i+BdHcXUgdTCbUlIbnMSqJTmUjKS6yf8T5UpS9LqJ8pgjwvRjXOpkxsTguumd/YqCcFewsq9hI3T6G0yid4mkxJBaaREEHVRlhHcdoTgUqOpLHEhpJNybxHB7SsIrl0myDFqYtaFCpMaXqomPgQjKeHI5VWWhl2RB0gdPf7wlFETja/xNb7G1/gaX+NrfI2v8TW+xj/G+DtMF+WzRTrnABcXZRnB7UYIbjuSNir9kTKIf1JzxVcplZSFTSYuygnEiJimtsYrJYO8UcisJ5YEV03ydZMn+6Lk3vrJycg8XFoDN5WUCZLFgluI+VbEiroDMZmSSc9dCcHtRVyU+wtd+dnbwtscRasdydigew/Z1tshJcmbygRC9Eco3M7s9ZEjMtayUJXG1/gaX+NrfI2v8TW+xtf4Gl/jH3N8IVGeQMoE9SNmvd1JBLczqVwjSW4bEoDMIJwtnXgqNSXElvowyc8JStWdBCUl1ktIrE8xNo6LpURZrT0Ur7Btr+KcTFm4XzGX8ismU01IqaDmJFm5JRkhaEecu2QN3G5EniyNpvoQNzC4aBPQRdkK/YeUi01GNci6sMOxzC2pO2zbe8sfWpjYdoeUelYaX+NrfI2v8TW+xtf4Gl/ja3yNf4zxwYCX5OD2JS7KOSQPV0ZwOxH1bHtCctuQGrgyxTRNST1NIS7KfsVkKoGofhNq4ZaeKD5QMXNR9kYpGRSnRHZppNdPQtDypOjJqqWC1Dq4rUk4vD3RgHcmBlPdxMXoKUYeJMGFEYmJYX4RrVES+QMRid34gwjJOlTmiEeoypm8bdtxm9bc4YhizEILHwqR7ziTwTW+xtf4Gl/ja3yNr/E1vsbX+Br/WONv375DRnAHCIKr1sHtQVyUZR5ue5Iy2poEI2n0No0oc5uQ4Kbkf0ku5lJelzq4Kq/01pE2+7MlymrYmMqTvcTy2U/01X4StU0ipDeF5ODKcHZzZVIlyu2JRLkTMZnKITm4ueKCDUKJ8rbtYkQjZCVsr81by/LzC9jatWvNKZ9/XpvPCvLz8bP5nm8u59vBujy+PE9uzz/nW+vNd2tfYjuY8h3b5uG2eTCv8TX+rww/RKU1obBjlJAWEafHgbhiys+Xx5bvPAeCn299b62F79zOXgbnYJ23PN61azW+xtf4Gl/ja3yNr/E1PsG3yg4Jx+btdg7uYFJetbfgUm4S5Y5EotyKyJRbKerbdFIZh6aiJrlU1pEc0acYFbtJlNX6tzGJ4HoV5hynhJHdJMtqXVyfIk9OVaK30miqOQl3tyKNKMPhHUgN3G6E4PYUJFfm4OaKCO6EsDSZCtthfbj4jBlkinizXoYRfaVBZgzHSsN6V/dH12l8jd/Y8YHUypsjlcw4RhLRAdAcJYSbaUVFkAUrK1glvAcrWAVMFXIK4nsQ1sF80JxgO5zEdvJ7wWBQbCv3Y8+b3yfzdBuNr/E1vsbX+Bpf42v84xi/Cvtw+basWfThhIuylCj3Iwa9OaQObpbgWh1FULG9IlFuKaYMEsVtqlTKSSJ1cJMJF6QS5Xhl3qcogtVKPt5YEty4OiTJHnJQHsVoKom8y3kqUW5CIrnSZjqDOCm3IRHcjoTgylJBMpQuc3B7CYILF22iI7FaFDw2Ca7a5Y+kAlG5QW2L+YcEn58lJCSIyWdvU+v3Y4cfuUDja/wjwwcSC/8v7m5+5v8TNTjIz1tr3pyVG3kwGHTeoK2btLypB53LYHu5zFonlgXJw4Le3OX3Nb7G1/gaX+NrfI2v8Y9zfKmsk/xHSp537NghCW4uieDKYGE2IbgygtuZENy2RF2bQaY0Er1NU2rhUi6Y4GIslRCFX8YRwhsX61q4bsV145VcXMq2ExWDKTmfRKZkEr1NJYWB5XsrRaIsNd8yyVnqwTMJue1JauDmCpOpiXAR1cRrleCKMJmIhDWMZxhkHzAPpFZ9+WGZwaJjxBC/zi9pfI3fAHzr5lgVdpUkhxy5HWYEl44sVlgjkkFrZNIapZQ34WBQ3NTJjV1uZ93wK6yRzCB9cMhtxUintV+Nr/E1vsbX+Bpf42v84xhfBvVov810aQ7LHNxBIq2zN/ExohLlbiQtVJZrbU0mKlFuoZBbNfdWlolNJFwxgdTDld5NcS75t6o0OaYEVzWXUkPHXnKwtCZuohLBTSY5uGkkEVmS2wySuNyCENw2JHrbmUiUu5E6uH2UOrhnmdLksCMBG7TpZqfeaFiwLIq6k75k1BbffT7msyK5fPKb736xTm4XS3yn3rQW0kPO/6OPPmLfBwI4v2LFCpyPJf5HH33MAmL/Kv6mTZvYF198cUzPv672LysrY4sXLz5m+A05f7M9l8UMP0zlLXSC0UCr3prt/AdyZnP0UEpv7FFIS5IjRyOFNMch3wnSmz/5blDKeSrIOxndpCOcGl/ja3yNr/E1vsbX+Mc5PvTPIF9X8h/pxgzuyxAEFFwpV/CmPiRgmEkIbkeF5EpOlkHkyRlKHdwmRKGbQvifqu6lqa0+hVt6XeY9sXZRjo8S0Y13ccBKIETXRwhuokJyU0geblPiwNWcSJTlCEF7kovbidTBzRLkNpvUwe1HJMoTtskcXJJDSCO4hptM06hdEqqKOg2L3PrZns2ZbO+mLP6exVrl7GStelWzljnV5nxPcx62gfV78T0TSS7d48svvcy+/PLLBuPT1avXrGFz5syx1r7zzjvM6/Eyj9fLvDjFs+bN03Ft79692X3334/bnjb2NDZr5qxaz3/UqBPY/Xx7uXrxp5/iPr/55lvre7fdfjs744wz8APsH7aH5d+tXi2Oy3zN5vOjRo1ybf+3335bHKuX+Xw+NmDAAHbNNdewXbt21nn+2I4v83Zc/oVjuRGFyPHfCk7xvF2aNW3GJk6ciDcGeE2fPh2xG9L+rh9qwY/F7w9e0LajThwdM3z5/xJ2jPyFokiUw7itOepYGXHTrlBGIW0Jjz1Cqcp06IgnfRAEHaOiQXsU1Pq+xtf4Gl/ja3yNr/E1/vGLb+Xghpx1cnfYdXBlBLcPcVHOJi7KmYLgSgUtLRHUWnC1FiSKSw2mmpCUVBrB9Stc0a94OXkVbqlW9YmZyVScoov2EHLrIfWL5Dq/4oaVpDD2FGVqKt4lwc0gBJfm4XYgic6dxKhCNxFG70W049JgCi7YmSFS4FgWSEaC62Kqs3XrVtfO/t69e3GKHl0zrOjt3k2c4G7uhuS1FSezLXvuZCldD/H3avG52iS2uJ1JdP1KFPe0cePYAw/cH5XkGHUuMdizzz7LBg4caKlP33rzLdapcyf7R87ft4XD+M3Dhw5Z3zx17Fg2c9as6K5D/HX7HXewM848w/p8xx23Izl88MEHLfwTTzyRf34A8Q/B/sV+nn3GPC75efacZ9joUaNd6dtbb7/FOnfsjAQKIpNvvfUW69+/PxsxciT7afdPtZ4/vMaNO40T6wfdSaMRSXA//PBDtm37dvbJ4sVs2LChbNzp46wNDh8+3KD2V9W/deFHpa8NvP5IcEedGDP8sCCtVJocknJlS/JiE15wAHSOPNpmCbYJgp0nEqTLqPmCNQpZKXJY7O8Fg06JTwUZJa0IBjW+xtf4Gv+o4pudyi1syxaYNtvvm52fN28O4vzmzWJb5X3zFr6fLXIeps34jvvg+w/y72+29ie32YLr5DKN3zjw4fcQ3KL//zR+48KvohJlQnJ32C7KuYTg9iEEV0Zvu5Ac3A4i/7YdMQJuLYhtM1ILt4kgtWkKuU1R3JQTCW+MV8oGUULrOxoOymrCr5qH64kiXfa5WEEnEvaeTE5eOm2ppYIkuW1LdN8dBLntSAhuprgYOUQ/bplMgRU21Z5DLSmni7LZmV9XvI5Nv/tutmb1GkdHH0jvA5yoLZg/v66wGhJci7huEgQ3p9qK1O4R5BejvEiCu4kIrs/a7ZSLprLEpETWrGlTNnrMGFwG8oJTT/09a8qXAWkDyax8/ec//2Fdu3bFdX/6059Yza5d7M033mDN05szny+ederUiRUVFyE57Natm4vOlbFzz/0je/WVV3F+7Klj2azHzQju3n372F/+cjlr1boVy83tx1559VX8Dsh109PTLdI3fPhw9rvf/Y6N5eQY9rh//z6W6E9kq1Z9be7/vHPZq/y7b7z+Bstono7R2E6dOrKiokIkZCNGDGeTLpzE0tLS2KCBg1jJ+hL83pt4zJmOdt+6tZK1aNGCvfbaa/i5sLAQyXRKSgrr1asXW/Tee6Idp7DERD+2y5iTxljbjjrxBLFtDnvvvUXWviG6/e2339rRY46dmJTEDh48xOY9P49NmnShSchnP83nJ7ELL7yQpaamshNOOIGtXLnSuh5Qnmfs2FNZ0zTntfrpp5/YRRddxK9LM7wmjz32qGti7Oyn57A/X/hn9sdzzmFNmzXDaxr4PsBWrFiO5F5uePnll2M02ySxBp57Xt5ajIifcMJIdtmll7EmTVLZ0KFD2WdLPqtF56yEdp0BXxzpyy/IJyRWLVbuHBWE86+kJglklNFpshB0zlMJDs1RCdryG2vU0vVhYee6aHyNT/FhvqSkBEsr5K1ZzdasWcOn1WLi83mr8b6P86vz+Oc1fLs1qIJZs8b8vMbxPVifJz7TKQ+/kwfbrybfybO30fi/bvzvvvuOfbNqFfvqq6/Yl198yZYtW8Y+/xymz/F9KX9ftuxz8VlM5POyZUvJ9nL9UrYM1ol9wPeXkX1+7tif3NZcL7+j8Y8tPvwOQHW38uuVrLCggG3etFnffzV+o8CXRqGS/9j9tZCM4PYXU29SJiib5OFKkttJcLA2hOC2IJVv0gmPa0oMplIJyZVcMEF89ilB0njFJVmtjRtzgkt36lHIrpc4J8e7sHA3gptATKZSSUHgZoTY0qm1ILkyituZSJQzSSg9R8iT+5ARiYnbt20Xkko7DxeMcNyiVvMXzDdJ7hqT5O7du4c99fTT7MEHHrCiu840Rmc5Fozgbs5CIguR2ZSuh1GWbBJZuRyIbpYZ6UUpM5UoM7Zx40Y2cuRIdt1117PS0vVs9+7dnOR1ZdOmTeMP1ZXsz5zw9M/Ntbb1eDxs0bsLWR4nwSCjnTHjEbZjRzX7x93TWa/evTHfeP/+vZzgvo1EeO8+iEbvw4g0kDdTcjwKiRESXE5SZ80yCe411/yN9evXjy1dugSJdFJyMivgZGcf/25SchL+09TU1DA/J+jffPcNS+br9+3bjw/+ZpxgHwICzE9s9OgTOZF9hlVXV7O7p9/NyVhvlEzAtkBwIXo6/Z57OM5SPPczzzzTjDq/9aZCys3XWRMmsFv/51ZsfyDWF02exFZ9u4rdcMMNLCOjBTMOG+z7jZvYSE4+r7v2OrZhw3r8Hmw7+aLJbNU339rbimsI0m0kuOI1c+Ys1qZNa5yfNWsmtgu8Hpkxg5NhD7v6r1djvjKQ1k4dO7Afd/8krlUmm3bFNLaCX6s//fnPLLdfLh7/I488iiS0eF0xJ+5vI8kuLw9E0M4ZfP9wTWH7Tz9djNf01ltvwX37ExNZoLwcw7ItW7Vk7du3x+8UFRaw1LQm7MCBA9jOnjgPmzL1YvYVJ95XTruStWrVAot6N9yg2bDyasOi1FY4bJcICllkN8RC0sCNX9cgdQekI4pSoiPzR1QjBUlILBlP0N4HzVVxOA1WWN+pIBb7Gl/jw7SJ3wfWrl2D9ZxhgGtdyTq2bl0Jn+i7PV/i+Fws5otxOZDkkhKxDX8vFutL+HtxCfleMawvMadi+F6xjaXxf7X4xcXF2DcoLChkUH5w//797uXYDGcJt/pYBxp1OQO62igYGr8R4cPzNxzehoMv8Oz9nvfP9P1f4x9rfOA/a60yQVUWF9q2zZIoDxQEV/oX9SIR3ExSA7cTqYMr5cltFN8kSXCbRCG4lA9SE2LqquyLUgvXo0RxY2Iy5XXJxY1XJMpUqhyvHLSP5OAmKwnHMgmZRm+biYZqThqwNcm/7UDq4EqJsjSZ6ksILly0s8BFWTXHybciuJEviAzePX06Jyhfsaefeoo98MCDrDK01ZJ7wq3MiKJI9fsSMForp5YYwd0p3oU82ZImd7PmVYnyuHHj2P2cVMNr2bIvWEqTVPbjjz9ykrMXSQUQQiC3QNSBDL35xlt4c6Wn9JyQKMvDgwiuzDP1iPe//OUvuH40J7hzXAhui4wMtnDhu2w3J/p79uxhJ510Cnvo4Yfw/E855RT25JNP8vUL2dDBQ3H7nj2y2ZIlS1Cq/Iezz7aO5USyf5AoD8LjMixJbTdOvOXrhRdeYJ07d7aiqEAYDSK3hfa/5ppr2Xnnnedo/4MHD7J1RcV4XvBPLtvxAd6O6sPvEN+2uLjIsS3MA2l+/fXX2c0334xR4hs5CUay+/gsMzrNv/vwjIdZhw4drej1vn37WFpqGhplffH5MozqwrXay9sLfm+wX3jQ3XXXXUhwC3hHu7aHJhBcM1Jrvh5//Ak2YJB5HceMOZm98OILbPXqNUh8s7KysNM35+k55qCAYbYnRNcPiM4XyMNbt2nD3hORbeXJX+cLo7JWHVzzxhiy7ObDjmiudOtzk+cElRFKarhQGWG2QEY8g5F5KkHHfuyHRW3yII1/HOJvCbK8vDxWUFBgkhf+v7K+ZD0rXb8eVSLr+TtMpeKdTiUlcnkp31YuK8Xv4X7o9nw57qcEPpeIfdrblFjbrdf4v1J8ILygkCkPlEd9/te7jFsD7r+O3dfR/9D4jQe/rGwDKrs2b96s7/8a/5ji25UwqkipoJB0UYYyQYNJmaAcEcWVvCpLCSq2I3VwpUQ5gwQnm5HobRPC+ZLJfBLxZ0ogRFeWDEpQeKab2VTMIri1yZN9SjjZq5DaBMVJ2a+UCUpWkpGbiTB3c0FyW5FweDuSh9tFTDKEni0Ibi9iMjU4guDyCxoiJlNOsab5CYjJU089jZFGyCF1Rm6NWm+fPpGDaxpN2Tm40mSqZa+dSHhNeXKWZTTlS/DbY5D87okE98EHhCR2tklKvR7m9cQhoQWC+vHHH+P6u+66E6W9TZo0YRdNmcIgYm2IHNxBguAyQXA7dOzIH9ABFuAP6UDgexbm25oEdDSbPWe2kCifygnu42xHdTXjVxix0egp3ou4l112GW73z3/+E6Oh1113Lbvjjjtw2d+uuYbddeddbMJZZ3Fi9jgxpbIJ7jNIvAdYLTfnmTmIL89/yWefsbZt25nH/LaUVTvb+nTePnfeeSfOP//8PJaZmWmRd5ggZ0Zu98D9D1jffp6T527Wth6cIJfGEAQXzq9vnz4oeb73f+9lhw4fEhFcTnBPG4v4QEBP/f3vHceUm5vLnnvuOfa0uFZx4jpBpDdOXCu4mYw7/XS+zsO6dOnMHnnkEdff34yHH2bjx0+w9r5gwQLWs2dPs835MV111VXs0UcfZbfeeitGkZ988j/s4osvxmXYnrPnsMFDhjh+oaNHj2aPPfZYRH6tG7769Kb5G2FiLBW2RgNDjpJBMHgUdEhzqKGCWqPNvhHb1vZqbTen86DDcKGSmiuoBdA1/vGOvx5kyWvzMWqHxKVUkp0Sm/iIZRbhKXGus0hPqf29aMtKBIkqsUiWSZTo9hr/14lfWFTICvLza33+G1b0sC53+vrff+uOM2r8xooPMndQjuj7v8Y/lvhVonKM5D9mSccwKj1FDm5/EhyUfkY9SR1cWiaoPTH9bUtkys0Jd2tK8nBlBJfWwKUlghJdygG5Vetxy7+NPxokV62DG6e4X8WTSG4CObFk8p5CWH1TIlFOJ43Vkkw0B7cD0YPLEHpPMvLQh5hMnRUOhx35t5aLsjqSR95Avguy3MrKrfVUuQiTKX+CyL81pcd7tmSiwZSM1O7d1E3k5e5kRYNasqLBLXFZAqmTa8jII7gU8w8ffPAB69S5S634+/btZf/9739Zds9sduW0aWak9DlOJAc5CW5mt66uI5lIQGfPsUymZgmTqSZN0tjXX3/tev4gz+3SpQvr06cvthW83l30LhsxfDhKf6FDEElwbfMrub9nOLGG9fLzZ0uWsDZt2pg5uG+aBJfiQ7khyKsFh2WIlIJz9cyZ/8L5yq2VSFK3bA6aEdzTx5nuzfwF60FKPXPmTHPbikokn1sEGZY5uIZLediZnPBDbjK8HuYEVxJO+WrRIgNJ7Af//YB1EdHnaL8T+D0+8cQT6Nb8ySefRPz+YP/jx4+38IHg9sjuietXfLUCyTSsh+++8/Y77Lzzzmc9enTHSJXZnnNYu3btHagwAPDmm29GJthG+f3T8zdlxwXmyF+4yjXvlpq45VOJcmXQkUdSKUYYgw6re/K5goxQyodBMOiQ+gTJgyJIC547Rj81vsavQO8CkCXLiB+N6q23iE+J43MJieqVlEjCJNeXOKKBJnkqdZCsUhlRLI0kYBr/14m/jk9AVuD+1xCBa63b1fP+25D+h8ZvXPigtlu58ithPKXv/xr/2OCbfbi1zvxb3p+D4J8guAMJye1LCG6WkCfTHFzJxVoRB+WWJIrbjJBbVabsJ1JlPyG4PsVR2at4PMW7eD7FtA5unOKc7HNJBPYq+mkfidr6FOZOTaZSiMlUOklWbqEUE25HHJS7iilTjDLkEImynOCiTcQyQaRjjlGmgvx63BaNyBteHTIYMIvau4kYSllyZNt4CqXKvXYiuYXl8A4kjb4mT5qExk8gfwUCAdHZ/zz5H7b/wAH26eJPkeQAUQKzpzFjxrCtlZWY73rOOX9g0664wiwLNH8+a5bWlIVCIYvgduvaLfJ8kICOxtxNKVF+XBDcCRMmsPF8gtxZuFlDBBJKGMELJMEQOQZJ7sEDB3FfP/74A9b3BYJK22rUaEFwDfO4mjYzj8uwXH9tF2WQOLdFgmvgMXfihBGu26bNm9DkqldOL8ylhfwnuLYQKX1v0XtsH/984403mAR3ixmVnXwhtOO5fN0+c9s4Dyfhi3g7wrY3CoK7xSS4Sg4uvf6PiwiugRHWGfg9iKzDQAhE+dP49YGcLOgAwTzkK8O1+oxcq9tuuw3zgQ/wdoO2BBnxRyIKP336PThgICXKE5DgmvjzOcHNzs7GefhuerN01rJlS8SG/GfYT+tWrdhhUdf2GZHTDER53569KCMH0zJwoVbLBNXn9y+jslW1kFuQKMuRQTOCW2HnmRBXwCAdgQxGOghWVDhlOW7mDPYIaNBRB84qkk6Kpmv84xs/b/UalCZTUkMjcxZZWl/qIDufffoZKiJAZQH3WBohXLBgIbv44qkkklgiJK/rkUjPmzsX7zlFRUVCMru+wfg0CkkjkCUOYmbjv//e++zpp2c7pLsllmS34fjXX3c9Kphg/n//95/oO1AbPj3/WODL84f7I6TA1HX+gA8y9L///e9Y3g3u8bHAt9qfn9c336xC08XaZbBG7X2FI7j/NqT/ofEbF/7+vfvQhCoY1Pd/jX/s8DHwICTKIVoNw6yDO56YTMkobrZSJohKlDsQJ+XWRKYsJcrphNimkehtCuF/1GQqXuGOPpLyGq/k4sYrPDQmBDfehTF7FYMpGuFVHZR9ShQ3mZDaVNIQshZuOhkRoC7KbUjjdhYEV4bQs0kUt6+4WESiHHZ0yPNJHVxbUOLmJGs0aMQOXZRFiSBqLIVmUsJ4ypQrV5vRWyS4LVCiTPE/+XgxklGIzMHrgw/+i1G4+Ph41rRpM/bvfz+By/dwAgOdKYgcp3EyC465kPsBr927f2J9+/ZDsgNOkG+9/TbfR5bruYwaPYYTzdmIf9o4qINryou3bQuzk08+ifn9iUgAzz//fL7fPdb5g5QWSDB9nXTSGOE4bBCJ7BjLxArMkvr17SuOaw175pk5iC/Pf8nSJaxN67aiDu5bnMCacmLAB7II+be7dtVY+4Z8WV+CDwcJQD4N+62srMD9ffLJYjTW6tEjG4/m5ptvQQdnyHmW28INwECC62Hfkjq+dAbyYKE+sElAH0HX52HDh+FxgdET1BiWrw8+eJ9lZcG18opr9W9cDo6cPXvmoBEXkOArrrgCBwkOHTrI2rZrz+67758IB53qCePPsvAXLgSCa0eMJ0w4i5122ljrM7g4g9OyXSZoNhs0aCA75ZST8fhgsOGll16uVbRV2+8/LEzZnKQ25DAroPMwUuis1SbzUCocjoHU6t6yzFdv+iSnxLqxB9XRTOpG6JT2aPzjGx/ueybBLXUQxVIaCcR5MwoIBO3iSy7BvH9QlYwZM5rfM7vx+9lkiyjPmzuP31f7cJJUokQPTZIEjuvnn38eElybONUP3yJvIjIJ9/bXX3/N3E8JJWMlFj7Ir+FeBMcM6okSQfhgGyCnkD5C8c8771z2xmuv1Yp/w/U3YO1vWHbZZZeyv/71b1Hx1fOn+OY5nMdef+31Izr/66+/DtuytvOX+A899DC/Ln3ZzFkz0eVY4v+c9kfyXGrm38oByLqpkXtC6JHefxsesdT4jQn/008/FSWE9P1f4x8b/Cos9ZhPAhJVwmRqG/SBJ4pgYC4xmOotyC11Ue5IIrjtBC9rK7haCxK9TVeiuMniPYnwvyQlbdVHJi/JxaUuymqw1RMribJKalVzKQlCnbC85HMicU5OdoniphLWn05q4EqZsozgtif5t51I6LwHqYXblziBDTQlylWOGp5glLO2FpOp6CN8RhSzAvvmmOD3C4fkLDSUMs2ldlgmU0BsU7ocQrJbNKSFkCibLspuu4UoIMXYUb2DHT4cqaXZv28v+/GHH5UUEsNygq7FXrBe5w+kdM+e3XWevxt+xB7Fx7379sSs/ffxc/zpx5+i4h+w2tFge/fsxZI9R3r9geCihJh/gMGTaOdfDdfKOByxx5qdu5DY0uWHSB3in/P7U89/586d/PPhn3X9bYMC6ZQctiQuDul/yMxxX5uXb0tvlBs4HaF0zCvmC8GKCsdN275hByNzTCoqnA8Msk+Nf3zjQ3mXdSXFJDpXYhkNmQTJGeG7++67Wffu3dnrb7xhyWFh8Co7uwe7/fbbkLyZBLevQ9JaKvdbKogUSGStaGZJvfGdUcUS1rtPb1S42ESsxDZdEvhgipfTMxsHRMEDgOJDvv5FUyY78Pv07sNmz55TL3yYv/TSyzjB/WtU/LrOH84B1C5Hcv7XXX89pmDUdv4S/9prr8XSbbFsfzkPgxvLly+P6fPvaPQ/NH7jw1/86WIkuPr+r/GPFb4MPFCj0KpwSEqUwUV5CFG+yjxcGTzMJNFbmYfbluTftlaq3zQnEdwmhOSmEv6XoERsEwlnlBN1TY5TcnJjJk+OU0LEalSXFuH1KO7J8UqtoyTipixPPJVEb9NIoWAZxW1DXJTbkzzcLoTgZpHCxH1IBBdzcGnNJzkPif8Gi2IxX4+BumgmemgyJSK40kAKzKXMyTSa2iMky4WDWyAZxgiuPyEm+EYUaU3tt+vYnf/xgA8S4jNFjuzxcP4mwc13jPzhfLjKIU8OS/l/fp5S663CUYycjjQGVekNrV2qGCVE3Ohd5D32iKfG1/gVqBApWacSnfVWpFFG6UpElG/okCHszjvvsCN9gkRBCgE4m5sR3LmsT98+6M4OXgKDBg1i1153PZaqgf08//xcdvoZp4uo4Hr20osvsTPPHI91t//4xz+im7nEBzJ2++23sxNGnYheBhecfz774otlGP2F2uKZ3TLZgP4D2Dnn/NHl2M15UIJMmTqV3XD9dRjxlXmmV1/9V3Rtz8nJwX19/PFHbMSIEagEAgf2c/54Dp7ztGlXIJE84/QzkdyDuzwQ2n/84x94/pdeeim75OJL0cAQ9gXpIVA6T+JPnjyZPfLoo1ZU9M03X2ejR4/i51CMfgwSD+p6rxcy5ttvv4ONPGEkb79B6BkBJSysaCsxdrrhuuvRLR8IK3g3QOrGrbfcwnL757Lf//737PFZj+N2f7n8ctabnyt4I4wYPgxVNNC2kBYChoFQ6g5URVCntLbrr+LL6w8R3OUkgqufvxq/vviQUoYEV9//Nf4xwne4KAsVHgQrtm/fLiO4g0iJ1T5EEZtNXJS7kBJB7QnBlWbA6UotXJqDKyXKkv+lED7oJ3wxXik761Mq91BOGjOCG6dEbeMUZyvV5crn4qbsI6HpJGIwlUqIbjNSTymDJC+3IrbUHZUyQZmkRBCVKPcVF20ChOFDMGoRDlskF+p6sogaakaUPAtDcRtgDu89ugwisTD5xLv12ZeAElpTSpuAkmIwmfLjeh8nu61igh/1Pm243MqPwvkfD/hgUAXuc8fL+VsOfI6obdgaNAqLyK1dJijfWevNcgmscNrbB51SmgprdJLUh1Nrw9HacY4Rzch6chpf4wPBNcsDyXIvVIZqS2mB4IAZDBBKqBduRvjsSN67Cxei2d3nny/DCC7IgSdMGM/efudtNvfZ51hfTqD+h5Mp2P7Jp55iQ4YOwX1+8P4HSLruvns6e+/99zGiOoiTum+//QbxwXEe5M7PPvcce+ftt9g5fziH/Y4TN8D/ZPEnSCj/ed8/2dIlS5R8XJOYrV2bx3rn9GJz581DM7qszCy2YsVXiP3l8uXsoskXIamGVI3CwiI0psvpmcPuu+8+3Cec/wUXnM96ZGezR2Y8zBa9a+auTrpwMrvhxhsQCwhuZtdu7BZOLMHr4PbbbudEuAdb+O5CPIYzzziT3XPvvRYpfOnll1k2P2cgsoCbk9OT/fOf97ElSz/H9gfiDC76UA7ubd7WkNJy6WWXWVFgKzrL2//66yXBLUFjPohSX3PNNWwRP46bbr4JvQlWf7caz/WSSy5h5517HmJCPu7td9zBiW0umzt3Lps/fwEn9H9ko8eMRuLtdv3d8K1Irojg6uefxm8oPtSy37x5i77/a/xjhg/8x6yDG7KDE7zftt2UKJ9FcnBzBcHNIeS2m5KHS8sESXVtS0Wi3IRUx5E5uKmE/yUpaaxepVyQWwDV61IbN2YmUx4lZKyWDfIpIeYEpUSQj8iSExWC25QUBpa1cJsTgtuahMQ7KAnPsvGlyRSN4A5yuCiTMicQwaV28EbE7c+oxVDAcFG5GPVzInC762p8jd+I8eH/BuUtYVuOLCO2NuENmaQ3bJJhWovNMbJIiphv/P577LBDZ7SgsJBP/L2gEF1vCwvEfEEhriukU4G9XYFYD/uwti0wt8Gp0JwK5HxBgYVXKHE1/m8WXxJc1ZgIo6slJVaU1TSPmo8k9osvv7CjekB4ONFZ+fVK1i2zG3vjjTcwggvbLV261NovlEyDnF2Yf+rJpzASDPMgaz75lJPZqlWr0KTo65Ur8bvPv/Ai4p944ih0eZela1at+gYJWVFRIS5DOTFIlEsj80eBnIGZHZBrOFdYN3LkSHYfJ5MyinvVVVeyi6Zc5Dj/3r17m/sU53/BBRewy6+4wiHJhWjnDTfciN+79LJL0WCP4o8bdzq74/bbcf0ZZ3KCe890az2YEIJkWpLDPpZE2VwPPhH/j5NcyJNd9c03eA7QJutEBJzKg8Hs6jzIweXn/8S/n0DCv664CNdBvnHP7J7YXnAu4M0w9eKLRZ3bEozcPvTgg5Ys2RzA6MbJ8SLX678+Ii/Xvv6Q52zn4Ornj8avPz5IlOGZqO//Gj/W+KBO+n7j9xZxtvN3nf0vGdQLkwguBCa2bdsuXZQHCd7UW0w0epsp1LLS4Lcj8USSJYKkyRSN4DYlUdwkJUWVEtxEQnLVUkFufDPm0VuPS1jYo7BsGlpOIEnCPpecW1oLV9ZJakpMpporEmWag9uBJDtLcptFauDmiFC7HI3ACG5Y5gmKOlD5Sh3c6JW7nTcsoxbrAqOeZgauOR4aX+M3UnwzZz3PkiCHSEmgMBkRDAkCvBYJLjFVcLgJmsvKNmzAaM6GsjIWKA+wQFk51mbGGs3wubzcqtdczj+Xl4v1uFxOAfs7clm5uX0AvxMQy8z9yX3CskA52YfG/83iYw7uOrtEUEmp090XPpcKsrN69Wqskf3aq69a+ZulguRAiS0wm1r59dcYLc0WBE6SzZdeepFlZmWy4uJijOACiYMo68mnnILR3n59+7F+/foiGQUyB8QLSCzgvfJ/r9i1V0slpvnZJIdzHHVbaR3X4cOG4v6AkF7IJ5jvyvFklPdKyMG9aIrj/DGvVxBO+PynC/6Esl+KjwT3xhtxHnJw//KXvzjwr7jicjZlyhTcJxDce++5xyKHEMGFqLXMle3NSTrgwbaFRUV4jEA0++XyNsnNxag5RIi//PKLiPO/wcrBLWH/fuIJlFrT8x86dAi2N+z7b9dew6ZOvRjXg8QbDBX/75X/cxBzaH8g1G7X36395fWHQZIVK5br54/GbzD+4sWf8uddmb7/a/yY44OhLChl4D1YoUigSf8rbFXCsNPJsP+2LSxdlAcRk6neikSZ5uB2IvLkdiR6K42mmpFIboqi0E0l/M9Pgp/xSj5uvMIpvYqKOOYyZZU5e5VyQR5yYGoNXDWhOJmEq6WFdArJwc0gWu6WJIm5LSG4srG7kEToHFIHt5+Y4KKdvWPHdtEpD1sRp3xZsN0wWG2DcfVKvHAduYtSW82oaxRR42v8xoVv5W9wohumbuQhpQZuyEwBMF2U3SU68BlGHOGmbD4Mys2HgePhQB8W4mEg1wXsm79cZj9AxMMjUB7xYCjDdQHnPjT+bx5/zeo1/Le2zlE71Vk2xlnWBqJ+066YRiJ55gQ5qcOGDkfSZkZwM1GyKtfPePhhNnL4SCReTyHBNSO4N990M7oAR8MfOWIEflfiw3MJSv5AxNCKtsroJxgnEdfgL7/4Eo8DSN1jjz3GHnv0X+yGG67HSPP775t5vlddCQR3sgO/T58+ZJ8lGMG99dZbHNtMmjTJkihfduml7ExOYin+hLPO4qT4VlwPru5Qcs1cX8KefvppTnCzCV5vNL+Sn4GkPv74467t7yjls96UKJ8PEmU+/8Tj/8YcYlrKZ8iQodje8Pmav11LyjeVcJwRWOoJzKRgf9999x1KnEHKHe36q/jrHSZTK/TzR+M3GB8kyhs4wdX3f41/tPChP7UxsNERuXVIlNFkKt9hDEpclMeTHNxcEijsKfiVjOB2IRHc9iL42JbwtAyivk0jQcsUQmyTlWiuX+GMfpe0VzdzY69LXu7PMpnyurgpe1yWe5RivX4SwaX6a0p0m7mYTMkc3AzRkO3IRCXKMoLb3aVMUH+zDu520QEPWXmC0JFQR9yMWlQthmPrWtz3jFruioZRizGBczWUd4ESNh7x/kvjH+vz1/iNBz/CoEA4KVPDKZmDCzdQB8GtsN39pIHCejHiWG7d6ANkdJLewOloJrnpB5Tv4EMkQB4W9sin9aDB/ZZZo6cBiqXxf7P4a9astiK4FpkpcRIaWRcV5PLznn+eZWVmslv/53+wvMeSz5agCRSQxmeffQaJ07x5JsH985/+xJYuXcLmz5+PRlPXXHst7vtJQnDfeON1NG6aOXMmW/X11+x5vn+QL8N3APemv/8dDZgglxfye6dOncpGjxrFCa55rKNHj2bTpk1jX6/62ukEzOfvvfceTvCGmBLsUjt3FHJ4bxTk9I47bkeiB7V81xWbuaewfzCW+hpL6axn50uCW7LeilpCWSSQKJcKgtuVnz+YaoFM9+GHH8Ko65voNG2SfyhVBtJfaC+oo25GcM3jhRrncA5w/maN3euQ5C5YuJB9zZfddNNN7He/O8Uqw1RKrs0NguCC2gNycIePGG6df6mM4D75JJ4/5OZeLCK4Jjm+gQ0ePIgtmL+At+3nbOqUKWzosKGWnNvt+qv4peK9uIQT3BXL9fNH4zcYHyO4qFTS93+Nf3Tw4fcFzwxHfi7pf5l9uDzFKDQsCe4fXEymZA5uN5KH25koaNsRdW1LQmxpmaBU4qScSoylkkjaqp/IleMVYyk3vyc3JXHMXt4oJYO8JEmY1i1STab8CntPVSK4TUnjZBBtdysXibKsgyvJbbaI3tJSQZbJlKNMEO+MQ05E1NE4o7ZhunpldwjvgVrvmFE/A6Hd+dVMPs0y31fORKJbw+d/CfyGnv9HH32E/+C14cM/IOSiuO1k79697NVXXmUH9u9vFO2v8Z0rZF5tmDgmO8mucx5yPWihcmlhL+XJkDsib9SB8oBzpJLcxM11Zda8NUpKRzXJSKk9QkpHUwPObQMBe/RT4//m8fOsOrjUNKhUMRFaL/I2zWUQXYT6t0Biu3XrisZEM/810yI8c+fOQ8kx1N8Gh2CosQ0Rz3xBnJ566klOYoeKerCl7F+PPcaGDRuGJBlcje8FQyaBD5LmKzjZhEgtyHbHnHQSGlPJGq4QgezVuxc/njER5Oskvu3fOTlUy9xAPvDAAQMxCgwOrpCXC7LgDz/6ENc/9ti/WK8cvs+TzH1CBBcMpOT5w7FNnjyJ3cgJLkQGLvvLZWwyJ7xnnH46Etv+/Qcg2ZVtumzZMjw+eX5AnmH/8vwhugzLJR7Ihy+79DKUSkMbQ9QczKYovjx/NJk634zgQj3xE/i50OjqkKF2BBfKBF0MObjiehYVF7HLwV0Z2pZfJ6jv/vHHH9d5/Sk+nD+SX6iDCyZT+vmj8RuI/+nixdj/0fd/jX808aFfRWvl0v6Xo0xQWKjvwlYOripRziEEtzsht1SiLOvg0jzcloK/pZMIbqqLuRQtFet3kSh7XPJwVYmyRzE9jpmDsscl4Zcyb3WKJydCnbQSSX2kNBK9bUYMplopBlNUotyJRHB7kBxcaTKVK+rgjpcENyxkldBBz8vLq3vYzjUVw2DRhwENppoTRGRuqKZ7yv6A3N4ztT8S2xokuUBwZ7F7pgxg1StnieWzkPTCPGwfS/wjOf8+vANx/33314o/ffrd2OmCBXv37eOfp7Pq6mrcH+RsJiUlYWfvWLe/xo/El3m1IemarBBb22wqhLXVINpbEXQ6+ll13vgEpglyFNMeyQxE3OQDNFeFyHickh7lIUG2CZTbDxP6cCgjo6ga/7eNvzpPmEwJshUhPy0pscvDKBLilV99xb5ascIhWy0huZooKea/dZgsUlRagg7FY0aPcZgXwfTNqm/YuuJ1rvhw7wM5NcWX+Z+wDhyQ3fBtR2gbX60RC98BIxN6/rBPyAGu7fxLlf3D97799lvcnxs+mGgVCQMoZ5S8ROAVOdq/mH/+ju+vIfhHcv5Q9mgtf9439PpTfLNM0HL9/NH4DcYH9QR4Tuj7v8Y/mvhwj3dIk0n/y6qEEapyBCiq7TJBA4XitQ8JFOYIbtXdRaLcVvFHakkUt+lEkZtCfJZSCP+j5WP9SmnZeIU/+lwkynGxjOC6hYR9Sg5unMvBJRBpsl+phZtCCK9022pGTKYySB5uG9KgHcTUVcnB7U5ycCXBxRxcKz+QdMqpRJlKUIx6D9E15K7ovk83fN6SbOeKmUho75nKpyn9cdopyG4NklxzAgIM21kkNwb4R3L+hw4dqhf+YeMwzu7atQuPGQyG5Ovg4YONov01fi05uGod3CqnVFl+hpFC2/Y+SOzrTTdlcAYMiNHOcmskssw2aiizZT3WqCeRAgXIiCY1eLAfKrbUh8p9yiMkRRr/t44PpNF0US5F8uN0UibzpTZxisjTrcckXY0feOBB1qNHd/aPu++2ys5Y+bO/AD7N0dX4scMHYv3VV1+xffv26eePxq83/v79+9mnn33GysrK9f1f4x9VfOhXUTNP2v9Co9D8tY7825CdgwsuygMEwZUmU5LcZompKzH3bSc4GM3BbU5ycNOJIpcSXT/hf5TcqkZT8YTMehR5skfJvY1ZDm6cIkl2c1GOVyTKPpI4nKDIk5MVly2p025OGqmFmNoQiXInEsFVXZRzFIkyRnC3WxHcKlKrM69u2YpjGNBQ5CfOZUYdSpd6CV0MM/fWitLKCO6KWSKaa0dubYLbH4vYS5Ir9wqlE7plZbLU1FR26qmnssqtW3E5PKCvuPxy1rp1K9a1SxeMNsjXj7t+YOeffwFr2rQp5olBjtfZZ5+NOz3l5JNQ3iVBLr7kEjb76afw47nnnsteefUVnF+Tl4cOosnJyWzg4EFYHgOPZ948NnnSJLZ7927WsUMHPN727dqxGTNmMCjj1KlTJ8SHF0TXT/3971laWhPMQVv2+RcICnIL2A7KarRu04YNGzqELVy4MKbtf6yvf2PEh5tjvsjfsGvhhi0H5SrirBwSZBgJbaV7IXK4EVs3+UA5GeEkEhx6U0fZTxmR5KhmDCT/xfHQcI6kBgJO0waN/9vHB4kyRA7XETOhUuLC6zScWu++DSdHpWR7jGASB+X1JLL43w8/ZEuXfm6TJRcydTTxnaRN48cKf33JOvbNN9/w+9eW4+7+r/GPHH/L5i3siy++YGWO6J6+/2v82OOjRJlUraD9L5k6ZvMgU4m3za6DO0BEb/sRF+Vswq+osW/HKC7KGYpEOY1wvGRiNpVMVL1+Ern1KPNeFzIbLRc3pnVw1VJBPqUOrp8sp7WO3EoEyRzcZiSCm06cuVq51MGVYfIuovGzlTq4uYTgnrVtmylNlgQ3FA5jJ7w2w4E6Bv+i3N6i78Ewas/hMBz5tya5rRaE1nyf5czLFe/3ThmA5SfsKK5JBIHYzp79NPvyyy/RiOOKK6bh2quvvpr16NkTZTMvv/wyS+FEdOGChYh/yaWXsJ45OWiu8sILL7DEpCR0rYR1PXvmsPnz37GOd/z48ezhGQ/j/KhRJ7I5c+bg/PARw9B0BHJOrr32Gsz/gmOaOXMWO23sWHb48GH25YoVeLzvvfceGn+FtoaQ2NfsqmE//Pgj69y5M7uEE+ivVq5Ed86MFi3YVr7Npk0b8XsTJ05EsxPIXYOcsFi2/7G+/o0RP1Qli4RXRUymq7JNdMNSokxzb4NBq6h50JIoB5xSnnJitU8MF8qJSYPjxu54qDhlPdZIJ9nO8dBwSII0/m8ZH6S98GAHaXCpRXxKHVG/Usy1XG/LWxUnXVoblb4DqSolEUPLAKokcvuSkvUa/1eOD/e1L79cftzd/zX+kePDYNe3336j7/8a/6jjQx1dGbVV+1/Af/JFHy4UtitfbLddlAeTCG4vEsXt7lIHV0Zv6dRKUd6mKQZTcvKTFNUkwhX9JILrVebVOrgel+Drzya4PjLvrSUBmB5cIjlw+Z6sOGlJjbYMZzcjoW6ZuNxaNGpbkoMrTaZkFFfWbOpF7K6HgL58x44d1qiFjEBBp8d9pK6WW5pRP6sBI2Kkr44RQbKcRnBrrGjtTDuCa02m+RTk62IE1xPnir97zx521113ssGDBuGC1q1bs0XvLrK2WvbFMrZ6zXc43zKjBXv//Q+sdUBQgeDCK4eT4vnvzHcQXIi+mgR3FJvzjElws7Oz2eVXXMG2b9/uOP/HZ81iY8eeakqUa0yJcpmQKG8NVeLx79pZg06i6enN2MEDB0SKjMGysrLYG2+8wTZt3ITfKy3dgMe4detW/F4ZkTr/3PY/1te/MeLD/0wB3hxJiSCZhxt2OitH1sE1b7aVFVKiXCEiuGVKTTdlBNMyaxCTw3afPgwCJF+FuBKS0VRze6cVv8Y/PvAhcgK1cGF0e926YjPSV0LIT6nM3RQRvJLSyHUl6xXipG4jzYlK3cvNlKoRQY3/a8QvLl6HA6urVn1zXN3/Nf6R4a/8aiVb/OmnbAPk3+r7v8Y/yvimRNmuWkH7X2FaCSNkc6HtO7bLCK40mZIuypJPZZEobicSXJQmU24GU82JYXAayb9NJPyPmkslKPJkn0ulngSXcrUxc1BW82+9LkV5vSS87FUShpOI5lqGpymrTyPzGUqpIBnBlaWC2hNym0k04tJFWU79Rdh9vKNWJ0aZzIRrN0WKEcV/wKg1t8M4Iq89N/w4j8eK1lYTMluNhPdfJIIrzaf6KxFcU4Z89V+vYq1atmQej8cqOQSmTvAO/zAq/vYdO5gHSCdxQ37yyactgpvNCe6CBSSCeyYnuI/MwJ1ABHe2iOCCozKUxfDGe7AcBThswmsWEtyxOP/Drl3oCi1zcJGoxpkE96nZs9ngIYMd7Q81GKffcw/bvNmM4O7hpF0efIIvAeXmsWr/Y339GyM+/M/kgYtyOOSUKYfDEXXVZL6uXXSc1MAV8hkgGwFig1/uMFgIOOzzHcXSUaZDXQmpo6BzJLVcNWgoJ7b7gXKNfxzhw4DYmjVrcAS7CMyaoC7uuhLMzS3h0zo6zwkwuA/D53XFJda64nUQBSy2tysW264T266j87CuWOybrhM4Gv9Xiw9GLlAa6oMP/ovExczJNfTzR+Nbfa/SDaVs0XuL2Ecffsh/N8X6/q/xfxH8AiFRxj6X0v+SgQfJf2Q1DCFRnkgMpnKJi3IPEsHtInhXRzF1ILVwWxKS25wEKqW5lIzk+gn/U2XKkrT6iTLY40J041zq5MaE4Lrpnb1KQrCXsHIvYeM0eptMorfJpERQGikRRF2UZQS3HSG41GgqS1wI6aTcW0Rw+wqCa5cJkhHcsGmZXZeDXq0289EELAaLOhoYsT8XfFoiqBpNpWg+rh25lVHee6bkOnJwAeKll19iLTJaYI3BQ4cOIrkEqTCYPDVr1owtWbLEwq/mxLaGE06IlDZp0sQipPC64847+PdMggtlHubNm2etO+mkMWzGjEcQECO4guDKUywsKGJ//vOfWYsWGbhvOIZTZQQXTKY8cWxDaamI4AqCW1PD/vvf/7J27do52qt///5YP3LTpk02wRXrEhJ8Trn5z2z/Y339GyN+WK2Da+VwKPJkmEJmQXGr9i0tFSTMDwoLCxyOgIGA0/qeFjIPUCv9gCIHosuV+nLlZQGH26C57zL7gaHxjyv88rIyVlhUiB4BQHYhqrtm9WqWh/PmMpwn0+o1fH1e5HJzWo35vfAul8F2eavF/lbnie+tduzP+r7G/9XiQy7u0qVLse7v/738MvpLPPfcc2wuTM8+y56d+xx79tnncNlzzz1rvsPy52ASy+fyeb5srtjOWo7bPyuWie/C/LP2vGMdWa7xjy3+vLlz8fcA5HbF8q8w8q/vvxr/l8KH+t7SVErtf9E6uJbyLhRiO0wX5bNFOucAFxdlGcHtRghuO5I2Kv2RMoh/UnPFVymVlIVNJi7KCcSImKa2xislg7xRyKwnlgRXTfJ1kyf7ouTe+snJyDxcWgM3lZQJksWCW4j5VsSKugMxmZJJz10Jwe1FXJT7C1352dvCpEyQjDLl55N+vlGvcT+jjgWGy8Joxb+dI4UGIbgeJLA10il5pR2xrV5JXJRXmoT3XilR5t+T+FAXsEOHDgyk2WAuldsvV+TCMnbOOecgOd3Gf9gwAt22XVu8ecPr9DNOZ78/dSx+D/5ZWrRoYRHcCRMmsPF82rlzJ0pv/Il+NuNhIlGe8wzbf+AAbj9/villhhxfyJ89dPAQEtzTxp6Gyw8c2M9SUlPZiy++iJ8rKysxegw5uOHwdpaWlsYefeRRrI/70ksvMr/fj8e6kRNcj0VwzXb1+xMsghuL9j/W178x4suobIi6KIfNKG44bBcND1kR3HwygmgbHVhlgvhvq5zmnZAC6fIBYBdAd9rnBxzbBIgZAzF3KA8o1vrUeZBur/E1vsbX+A3HB/URSqBLSrA0UlGRWQYJShJhSSTyDutgvrjInGA7axmuLzKXYykl8d2iYmubIvm9YvtzkdwfwdD4xx5f1twuK9ug//80/i+KL8sEycgt7X9ZZYIUU1AhUZ5AygT1I2a93UkEtzOpXCNJbhsSgMwgnC2deCo1JcSW+jDJzwlK1Z0EJSXWS0isTzE2joulRFmtPRSvsG2v4pxMWbhfMZfyKyZTTUipoOYkWbklGSFoR5y7ZA3cbkSeLI2m+hA3MLhoE9BFOSzLBIWsi10frz33VA3DSSjcvmYY9aQpkQAg371XlAe65yLxPtV8B1MpiNpiGSEhT+7Xtx/zkBxcON/hw4ezeF88a968ObocjxAEt7p6B/vd737HEv2JSBynTp1ilT4Ah8jBgwczr8fLMuT3hpsEF3KPWrdqzeK98axHdg/cbsYjZgR39KjR7BmRg/uf/zzJmnKCCiS1VetW7LXXXjNzcJ94gp027jTrHG++6WYWHx+P+cEyl7amZqcpc/7wI9Y9KwujtZ06dmKvvvoqLoccXGibPXt2W60FBNe+lrFp/2N9/RsbvrNMkIzayjzckE10RR4u1AWVplJyFFFKlOHGi2WCXB8ItIacjLyVu49aupk7OOQ9dt5KQCmwHlBq12l8ja/xNb7G1/gaX+P/WvFNiXKlyMN19r+A/5hGoSFHsA8MeEkObl/iopxD8nBlBLcTUc+2JyS3DamBK1NM05TU0xTiouxXTKYSiOo3oRZu6YniAxUzF2VvlJJBcUpkl0Z6/SQELU+KnqxaKkitg9uahMPbEw14Z2Iw1U1cjJ5i5EESXBiRmBjmFzEUCll1OuEir81f6xrmMly7/EbduR1GFMmn4UY4DJd8Dic+ED6IzPblUy6QWP7eT5k3P5vRWyC5Kv7OnTXs4IGDrvi79/zE9h/Y74pfvXMnO3T4EJYaGo4RXPu71Tu213n+QMAgClzX+R84eIAdPmxEbf+amppj1v7H+vo3JnwoB2QSXLM0ULjKNpey/qdCpuRF2tHbUdugVR5IFiG3JMryAVBmj3DSHJZyYq9vj2iWiRyW8ghrfvpgccuLCaj2+hpf42t8ja/xNb7G1/i/cnxQXVrEVul/Af/JF304JLkQmOB9uO3bd8gI7gBBcNU6uD2Ii7LMw21PUkZbk2Akjd6mEWVuExLclPwvycVcyutSB1flld460mZ/tkRZDRtTebKXWD77ib7aT6K2SYT0ppAcXBnObq5MqkS5PZEodyImUzkkBzdXXLBBKFHetl2Y4oREeD7M1uatrcOHwN22wFA+RMuTNFQhqEIiIpiEyx7QHIoTXXj3CKMoexKfBRE+Gvhz585jI4RE+Vic/7Fuf40vJcphjJKHiLQFauOa/1PURZmaTFWQUcRKuwC5KBPktMinNePKnaOcLgXUrZt/mWqpT0dF1QeALQcqi7Do1/gaX+NrfI2v8TW+xv914kO/SsqS1f6XrBwj+Y9pDhrGaiciB3cwKa/aW3ApN4lyRyJRbkVkyq0U9W06qYxDU1GTXCrrSI7oU4yK3STKav3bmERwvQpzjlPCyG6SZbUurk+RJ6cq0VtpNNWchLtbkUaU4fAOpAZuN0JwewqSK3Nwc0UEd0JYmkyFbVmlmbdpOHv6RhQRpxF9pdOSPkoNtloNghov/u7du9Fl7Xg9f41vvkKkSDh1TXZEckNQXy0kDKnyTTJbCeWBhG29ZXpgRnADEQ8C8pnIfFwLpweIFT+x1y8LlCs15QKOUVNZm84eEdX4Gl/ja3yNr/E1vsb/deObdXAr7GAC6X+Zyrp8O61M9OGEi7KUKPcjBr05pA5uluBaHUVQsb0iUW4ppgwSxW2qVMpJInVwkwkXpBLleGXepyiC1Uo+3lgS3Lg6JMkeclAexWgqibzLeSpRbkIiudJmOoM4KbchEdyOhODKUkEylC5zcHsJggsXbaIjsTpUZbnC1mXQU8/AmnuJNCPK3mv9vsbX+I0PH13H+f9LuCqyJJAkuiHyOT9vrXDxI1b1UqYsJcriRl4RtEccbcdlOgIZjHBjNouZE5dAUu/N+n4t+DQnWONrfI2v8TW+xtf4Gv/XiC+jwIWE4Kr4lo9KiBiFVoUxlVAQ3FwSwZXBwmxCcGUEtzMhuG2JujaDTGkkepum1MKlXDDBxVgqIQq/jCOENy7WtXDdiuvGK7m4lG0nKgZTcj6JTMkkeptKCgPL91aKRFlqvmWSs9SDZxJy25PUwM0VJlMT4SKGrdqdtozStWSKUS+a4Z4VaRj1U326YWh8jd9I8W2TqbCrJDlEcttNMixclJXIrZTMFBQUWqOcltsfsbcPkpt40HHDr7BKDdF6bxIrGHTWf4uGT+vyanyNr/E1vsbX+Bpf4/8a8aVkGVyUo+FbpR7DSnAiHJY5uINEWmdv4mNEJcrdSFqoLNfamkxUotxCIbdq7q0sE5tIuGICqYcrvZviXPJvVWlyTAmuai6lho695GBpTdxEJYKbTHJw00gisiS3GSRxuQUhuG1I9LYzkSh3I3Vw+yh1cM8ypclhq2Meli7KRu2JiEZd5Udr/bpRN+nQ+Br/V4AfpvIWOsFoYChsGU3JkUE0maqQN2nq6BcULsoFYtSxTKyvcDj+ff/9Rra+pAQlN4U4FeI83MDhu/Id3JhxEtvgdgXyO2KS2xea2xUU2t+191dof79Q7FfOa3yNr/E1vsbX+Bpf4x8jfChH9v3335sR3GAFea+w5NCwnRXhVfpf0D8DY13Jf2Q1DKh+AUFAwZVyBW/qQwKGmYTgdlRIruRkGUSenKHUwW1CFLophP+p6l6a2upTuKXXZd4Taxfl+CgR3XgXB6wEQnR9hOAmKiQ3heThNiUOXM2JRFmOELQnubidSB3cLEFus0kd3H5Eojxhm8zBJTmENIJruMk0jboKkxq1b2ZE+WBEL9RC9+g0lIr7xfGP9flr/MaDL/9fwo6Rv1AUiXIYtzVHHSsdMh4pnymwXJQDRMJjjlBuKNuA9SXLNpQ5TRusGnAilyWgGjmoOSvSgZDUmgsoDoYBWkeuPNLsQeNrfI2v8TW+xtf4Gv8Y4kPdbSC5Zfydll4E8irxzQgujfra/S8rBzdEgxQgUd6uRnD7EBflbOKinCkIrlTQ0hJBrQVXa0GiuNRgqglJSaURXL/CFf2Kl5NX4ZZqVZ+YmUzFKbpoDyG3HlK/SK7zK25YSQpjT1GmpuJdEtwMQnBpHm4HkujcSYwqdBNh9F5EOy4NpuCCnRmS0VsprQwJgmtE5xBGba47UT1u3T8bBnMnLVF2D4S2+quZbCdMK2eymq9m4TL4/Evgq+d/6OAhtmDBAqtebkPxN2woZ4sXLz5i/F+6/TW+Ez8sSCuVJoekXNmSvNiEF+zoae6INJmSkVysgysKllv5KHz9xu834k2cGi+o9vnSYMGqHRcgDwJp4lBe7ngAlVMr/wAtoE5NIMQy8lDR+Bpf42t8ja/xNb7Gbwz4ZiR3o6V6MyXKMge30Iraqv2vKipRJiR3h+2inEsIbh9CcGX0tgvJwe0g8m/bESPg1oLYNiO1cJsIUpumkNsUxU05kfDGeKVsECW0vqPhoKwm/Kp5uJ4o0mWfixV0ImHvyeTkpdOWWipIktu2RPfdQZDbjoTgZoqLkUP045bJFFhhU+25rOvp1HgaruEwo5ZYV53SUqN2+uCGD0T2nikDLGJrEt1Z7J6p/U3Cy+d3CvIriW8s8d3Ov6ZmF5Yj2rq18ojOf/r06WzAwAFHjP9Ltr/Gj8THMkEF+YTEOouFO6cwy+P/W5XUJCFo55lYJlPipmwbLgRZyToYodxg5ec6iporteVosXT5sDAfMHRZuVJ0XSm8TmrT2W6E5Rpf42t8ja/xNb7G1/iNCt+M5K6z+lSmRNnEL5Rlgvg6tf8ljUIl/7H7ayEZwe0vpt6kTFA2ycOVJLeT4GBtCMFtQSrfpBMe15QYTKUSkiu5YIL47FOCpPGKS7JaGzfmBJfu1KOQXS9xTo53YeFuBDeBmEylkoLAzQixpVNrQXJlFLczkShnklB6jpAn9yEjEhO3b9suJJV2Hi4Y4dTPoLZ2UmEYUcqxGPUhGpFIMlJ7z5T+SHTvmZLrJLcrZ7EaQX6BBE/n25kkNzb4bktramoQY+vWrUd8/oZx6Ijxf8n21/iRS2VebViU2gqH7RJBIYvshlhIGrjl51tGCkHq/EclyuLmTY0UsD6uw0LfbWTUrSi6Lf0JKCOq5cSa316uSInK6fYaX+NrfI2v8TW+xtf4jQ/fzLW1nZbleqtMEDGykv0v4D9rrTJBVRYX2rbNkigPFARX+hf1IhHcTFIDtxOpgyvlyW0U3yRJcJtEIbiUD1ITYuqq7ItSC9ejRHFjYjLldcnFjVckylSqHK8ctI/k4CYrCccyCZlGb5uJhmpOGrA1yb/tQOrgSomyNJnqSwguXLSzwEVZNcfJtyK49XsZRO4JVMIwGsRDoqRERm4MkVJJZM0orUlsq1eK9xUmsa0W6+6ZOoD169ePeeI8jl3OnTuXZWVmstTUVHbqqaeySkFOJ06cyO668y7Wr29f1q5dW3bHHXdYJAn+cU4cdSJLSUlhvXr1Yu+99x4utyO4W9n06feySy+71Dr/DRs2sC5durAd26tZXl4eGzZkKEtOTmaDBg1i36xahdvMmzePXThpEs7/9NNP7KLJF7Hm6emsU6eO7LHHHmtU7a/xXcoEQVTWqoNr3hhDlt182BHNlW59bvJkM4IbFBJl80YvRyIr+Tu6K8u8lEB5xAinQxpEbvi0oHqA1pILKA8QRfpDHyYBJW9G42t8ja/xNb7G1/gavzHhA5G1Sw5VWPjQf3KTJ1dQiTIplWqaTIVlmaDBpExQjojiSl6VpQQV25E6uFKinEGCk81I9LYJ4XzJZD6J+DMlEKIrSwYlKDzTzWwqZhHc2uTJPiWc7FVIbYLipOxXygQlK8nIzUSYu7kgua1IOLwdycPtIiYZQs8WBLcXMZkaHEFw+QUNEZMpp1jTUJIW6xvnMpgaLDPqdKc1XPE9EMFdIaO1MzFau3OlzMedJQiviOAiwe3PCW5fS6oM+4EfdEpqCps9ezZb/uWXbOjQoWzatGm4ftjw4axN27bs448/Yi+88ALzJfjZt999i9875XencPI5GYnpDTfcyDJaZOAxWRHc0Fa27POlSJr37d2L+5vxyAw2evQonB/O9331X/+KBkHXXHstGzlyJC6fNWsWGzt2LM4/8ugjLIeT5+LiIvbWW2+x5JRU/OdtLO2v8SPxaf5GmBhLha3RwJCjZBAMHgUd0mRqiy/LBJkPhyC5EcONGx8UZUSWEyh3NWmwjRjKHaOftEh6gDx4yq1RV5oPE3A+POQ2Gl/ja3yNr/E1vsbX+I0Mv0BIkXGqDFr4SHwtgyln/6tKVI6R/Mcs6QgmU9UyB7c/CQ5KP6OepA4uLRPUnpj+tiUy5eaEuzUlebgygktr4NISQYku5YDcqvW45d/GHw2Sq9bBjVPcr+JJJDeBnFgyeU8hrL4pkSink8ZqSSaag9uB6MFlCL0nGXnoQ0ymzgqHw478W8tFWY1kuZjuGEZ9VZ5GA2NxzBUfDaYEoa1ZKSK2X0HEdpZJfFc683BBytyvX24Up2XG9uzdw+68806MqCIJHTaU3XfffRbe6NGj2f333+c4jAMHD3ICWsw8njj85zAJrodtrdyKJKhjx45s0aJFuO2oUaPYk08+hV/Ozs5mV0y7AhPX6YsS3P93150YHQbHt8bY/ho/Et+UHReYI3/hKte8W2rilk8lypV2nTgZqS0UEuWAkCjLUkFYPoiOXlIjhoDTpKFccTG08lcCyoOD5L5E5LlY72XOEVmNr/E1vsbX+Bpf42v8RoYPBNeumVth4UP/KUKiLPpfZh9urTP/lvfnIPgnCO5AQnL7EoKbJeTJNAdXcrFWxEG5JYniNiPkVpUp+4lU2U8Irk9xVPYqHk/xLp5PMa2DG6c4J/tcEoG9in7aR6K2PoW5U5OpFGIylU6SlVsoxYTbEQflrmLKFKMMOUSiLCe4aBOxTBDpmGOUqSC/3nY9hrrEqN/WRhRzW6MWWiIlyjJCC1FbKUe2IrkOgjsAI7gQ+ZX4+/ftY1dffTVr0bIl83JiiqWGPCYBHjZ0GJv3/PMW3qTJk9ldd92F88/z5ZlZmbi9R5Qn2rJlC9tJIriAcdPfb2KXX345yhsSExOR9MDrw48+Yt27Z7F4rxeJ7+fLluEJIsE9bazlyHvauHHM4/WgtHnGI480qvbX+JF7lFHZqlrILVxXOTJoRnArLHmMrH9rRmqDItfWvGHLEUe4KcPygOMh4DRjUI0aLEdCOlJKZT2wrixSDlRO3QqtHJkAMXfQ+Bpf42t8ja/xNb7Gb1z4KEW26txWWPiwXKaBqf0vDDwIiXKIVsMw6+COJyZTMoqbrZQJohLlDsRJuTWRKUuJcjohtmkkeptC+B81mYpXuKOPpLzGK7m48QoPjQnBjXdhzF7FYIpGeFUHZZ8SxU0mpDaVNISshZtORgSoi3Ib0ridBcGVIfRsEsXtKy4WkSiHHR3yfFIH1xZnujnJGkcQMTNq9cB1217io8kU5t/aJLdGidpa8yttgisJLLxeeulF1rJlC/b11yvZoUOHkWCOHHkCYgwbNhyJrEVwJ03iBPdO9sOPP7GEBB+bOXMm+/GnH1llRSXm9W7Zspnt2mVHcOH17bffsTZtWrNnnn2GjRt3esTZQITuwgv/zFqAxFkS3LGnOc5/GyfHTzzxBIuPj2effPJxo2l/jR+JHxambE5SG3KYFdB5GCl01mqTebjmqGIhSpHLRZkgUQfXMp8qt9wIA+qoKZH9BBSzBof9foDWqgtYNvwyx4U6IgYc+S3lGl/ja3yNr/E1vsbX+I0Sv5Dk2gZlDm5AEl8hT1b6X1VY6jGfBCSqhMnUNuAcE0UwMJcYTPUW5Ja6KHckEdx2gpe1FVytBYnepitR3GTxnkT4X5KStuojk5fk4lIXZTXY6omVRFkltaq5lAShTlhe8jmROCcnu0RxUwnrTyc1cKVMWUZw25P8204kdN6D1MLtS5zABpoS5SpHDU8wyllbi8lU9AiXEcWsx0kODKM2omHUbjIlXJSlyVS1ErGtpmR3hczB7YcRV7m7J598mnXo0BGLOIMxVG5uPysfdtiwoez5ec9b+CbBvQt/6B6PB6XH+/bvZzfeeCMey+YtW4iLcqV1xN27d8dlkiwfPLCfjRwxks1fsADP/+WX/w8J7qHDhzjBnWlJlG+77TZ23bXXsYMHD7JQ1VaWnp7OPv7440bT/ho/Et82KJBOyWFL4uKQ/ofMHPe1efm29JgQWDnqWIg5uAFhMlVhbVsocnPpyGW5Kgki9vmBctVuX4yEltl2/Q6ZUMA5ukpzaQJqwXaNr/E1vsbX+Bpf42v8RoQP/aT/z951wEdVpd/MpBNq6FVJKKGFroAgrHQE1BXURdG/oLgWlCbuKqyIBRQLoCiguKtrASzAohQRURdBJEFMIihkZgWTMAyhSQttvv/77rv3vu/deTMZJJiIN7/fy/R35r157dzzfefQHluBn81LlPPINZW4/hLCAzUK3ev3iRJldFHuQCpfRR+uEA9TiHor+nBrkf7bGkr6TRWi4JYnJDeJ8L9YRbGNJ5xRTNQ1OUrpyS2x8uQoRSJWVV0awutS3JOjlayjBOKmLBY8iai3FUhQsFBxaxIX5TqkD/cSQnBTSTBxC6Lgsh5cmvkk7mdlZRO6EMaSJxDZ04EwBj7F0xU7PqqxZkxQa5jKb1lk0K1tYOqtra3XOLnFiZpMFe7zQ6dOncAdHQNVqlSB66+/nhPcADOCEqQUUW+5ZThMnjyJfW7ChAchJiYGYuNiYcSIEWyeBbQHl8QEPWKQYnz98OHDEvflOXOgfIUKUKliBahevTq8u3ARe2327NnQp08f9q5vv/0WmjZtypyWk8onwZ13jjLI7qkytf41vv0jJsHNso38sfv+vbbyZL8o/8/aasu3lYYI/ICcLXJwjYN1Xp410ogjkGpIus0y32uNatqy5ozbXDliSowZvF4y4uq197h4rZFTOhqr8SPDxxaPjIwMNmVmZMJmdpth3mZuZsZ1m43nxXvMaTO5b3wG38fes9m4n8HvZ7DPynlvtt6fwTEylEnja3yNr/E1vsb/PeNHev2Tw8yk7Dm4ueJ56aJsv/6yuSjzKjwUKwoLC4WC245ErLYgFbFNiIvyJSQiqA4huMIMuLKShUt7cEWJsuB/5QgfjCN8MVqJnY1RknsoJy0xghulqLZRirOV6nIV4+CmHEOk6QRiMJVEiG4lkqeUTJqXqxNb6npKTFAKiQiiJcot+Y82ENVJH45a+P2S5GKuJwRliAZC9C4GFLcfsHnPRuLvY308EPykAz4SylbprTiBbclifdj9luK5dPN57qCMz6l/Bw8eZErpueAXnSiCI0ePFrv8I0feATfddFPQ8mPkEI4KFbf8WPZ8+tSpMrv+Nb7dRTlLlCj7VGMpH/i5cmvFBGVZqm2e5ZIscttkTBCaTJFSmhzhouy1SnfUwHRrdNRr9bbkOgen24LVPV7yHkIGadYccTrU+OHx8cSs9z+Nr/E1vsbX+Br//PEzDaIbyfnXVHAtbxNx/mc5uDL/1n79hfzHzMH1WeKEcd1WaJYoDyI9uOmc4KYRcttQ6cOlMUGiuraaUqJcnqTjiB7cJML/EpQ2VrcSF+QkoLodsnFLzGTKpUjGamxQjCIxxyoRQTGkLDleIbgVSTCwyMKtQghuDSKJ11UansXKFyZTVMFtZ3NRJjEnqODSOJRA0OYXCGPoE3Co8gxE5gTktNWHwJdmTy4yEQMocWuS25LHd5rNyZMnoWWL5szA6oft2y/o8pf2+tf4XMH18/IWv1WOLBRbi/D6TNLrN8kwzWIT5lL5vKwGS2mEuphHCK7Vg4sH81x7j4rXUh+99DWFuNks/EnenFQ2PWo4u9ceqq7xI8LHkWq9/2l8ja/xNb7G1/jnj49KbiTnX3RLzsuz3JLFOVwS37z8oOsvIer5iYKLwsS+fYXCRbkd503N+UTV2xReLSsMfusRTyQRESRMpqiCW5GouAlKiyoluPGE5KpRQU58s8TVW5eDLOxSWDaVlmNJk3CMQ88tzcIVOUkViclUFaVEmfbg1iXNzoLcppIM3DQutYvRCKbg+kWfIM+BylJycIOzR5032ACEtu4JhK8qlTuGY4/l7wT/rPG+Ld9+C0eOHPlDLv8fEd/sWd8qS5B9JBLIT0YEfZwAf8cILsm/tbkpi7xb86BtEtwCsweXuyt7iTW+N8jIgfS1iNe9XhuRs5M/6nTosZs8eImhQy4t69X4xeFnCIKr9z+Nr/E1vsbX+Br/vPCxxDmS86+Zg2uVIIv3MQU3n19zKddffpmEYbWTseu3fX7hotyOmEw1V0qUaQ9ufVKeXJuot8JoqhJRcsspFbpJhP/FEfEzWunHjVY4pVupIi7xMmWVObuVuCAX+WJqBq7aUJxI5GphIV2O9OAmk1ruaqSJuRYhuGJlX0IaodNIDm4rPuGPdg2aLflEWaVPKLhZIUZxIHLjWsfHymYeKGbeGl/jl3F82b9hEF0/dSP3KRm4PrMFwHRRdi5RxsdIZIWSaNram+9hyq4aoK70oXgdlUdi1W8LR7dODLkkZN1Lwtip8unxeDR+hPisRFnvfxpf42t8ja/xNf5542NVVCTn3xxiJmW6KHstd2XqoExLlJnJVJbNGJS4KF9NenDTiVDYlPMroeBeQhTcOlx8rEV4WjKpvq1ARMtyhNgmKmpunMIZ4xzaXp3Mjd0OfbnnZTLldnBTdjk871LCeuOIgkvrrynRreRgMiV6cJP5iqxNJlqiLBTcRg4xQa3NHNxCfgHuk32CSHDVEZdAmKqCgO3dYdxnA2H2ioDz2I/G1/hlGT/IoIA7KVPDKdGDiwdQG8GVt/nS2h4P0IKkCac/SXBtBM5rt+SnCibpI7UZLkkzhuD+U1baS3PmvCRs3etUuqvxQ+GbPbh6/9P4Gl/ja3yNr/HPFz9DKrjhz7+mmZRVfizO/8xFWQgJyvWXeQ23VTEK9QuCe62DyZTowW1I+nAbkAra2qS6thohtjQmKIk4KScRY6kE0rYaR8qVoxVjKSe/J6dK4hL7c4eIDHKTJmGaW6SaTMUp7D1JUXArkpWTTGq7qzuUKIscXEFum3D1lkYFSZMpW0yQcTGOfYIhR2MC4YZpIqqu573nYfcYx8crxneGFWONaRxOXYz7ndj9lWO7wEq8NZ776JGrYPX3s2EVTtvMW3xcEvilvfwav+zhi75aP3FMtpNd+33s9TAPtHmS2EqTqXxTwRVEycrBFSXKqh1/rmKnbzddaNS4MUyZMoVY63th8eJFcPlll1t5dLS31Baybrfmt42cRohvy7CzhbV7L2p8q0S5bG7/u3fthq82btD7v8bX+Bpf42v8Mo9vligXf/5lebf8+koouCbxzbGIr3L9ZYsJ8vPqO7/swVVLlNMIwW1EyC0tURY5uLQPtxrnb5WJgpvkYC5Fo2LjHEqUXQ59uGqJsksxPS4xB2WXQ8MvZd7qFE0WhDppxZN8pApEva1EDKaqKwZTtES5PlFwG5MeXGEylc5zcK8WBNfPyyrxAn3r1q3FD9uoAzBO7w+owzf25vSgyn3VdE2ZH5LbUPhIdH/etBxWTOoJH0+6yiS4nNyuYgT3/PFLe/k1ftnDF321PuGarBBby2zKx7LVUO0VfbfC0S9flCLn5bMeEqEiiuggnHK4TX6QKZKSBUdLalMapkBKSgp88ska+ZmFCxdCxw4dpZrpUXLmcomK6SXB6irJiwTfo+TXUXfiixkfYxAi3f7+859lkJmZeUG3/++/3waLFi2S+C+9+BJce821ev/X+Bpf42t8jV/m8XHQOJLzLyOyBaLlK0+eu80cXFKaTK6/ZBKGb69NoDhgxQS15RWvLYhQmMa5VSOHEuVaij9SNVJxW5lU5JYjPkvlCP+j8bFxSrRstMIfYxxKlKNKUsF1koRjlB7cKIcvF0tKk+OULNxyhPAKt61KxGQqmfTh1iQrtC6fLlV6cBuRHlxBcFkPruwPJBfltESZliBEaoB2bnuF8zyd8JHEhsJnr/G/jx+5ik2o4K7eZiq56py//OILeG/xe+z+6lWfgPd/XhYbtHTpUigqKiqTy38h8c+cPcOWHZ2gSwO/tJf/1+LTEmVbDu5ee6myeIwjhTKDTZQg5xGllsUEmWpjHgkjlwpuLom18dJ+U65IkhHN1NRGcN2110LvPn1g546d7PmFiwyC27Eje32bQXzuufdeaNO6DbTv0B4eeughNn+cz7Cbb4aHH34YrrzySkhv3YrlOP/j0X+wKK4uXbrAosWLJf5LL70If/rTn6BDhw4wduxYyM3NZfPfuvU7mD59Gmzbvp2QSppBaymrnqCSYupcnGsZRZ3D8lODqd8SP7yCa9/+Ro4cCfPmzb+g2/97771nbAfX2eYcOHv2otj/NL7G1/gaX+Nf3PgZQsEt5vyL10mUwIrzdg5Xdm0ZuPz6ihmFZn1n67/1WT246KLchhNcYTIlyG0qny4l5r61OQejPbhVSA9uZVKRS4luHOF/lNyqRlPRhMy6lPJkl9J7W2I9uFFKSbKTi3K0UqIcQxqHY5Xy5ETFZUvUaVchK6kqn2qSEuX6RMFVXZTTlBJlpuAWSgV3L8nq3Fp82YBtGCaglB/YnwsUU+kQUaGD8RSWIOOfiADCSeBLgms8/iXvFKyY9Cei5M6yzfW5556D5GpVYfTo0exxs+bN4amnnoJDhw6xee/ZU1Aml1/8vf3WW7B+/X9LFP+wsey4PvcUFITFR/L/2JQpcODA/lJb/tJe//QJPDhm8f4NKwvXLx2U9xJnZR8nw+zgWyBMDmhfiElwxcHaymvjxFcaI3ltCqXXm0tKci0zpNTUVFi+fDlc0bUrTJ06lT2/cOEiRnDxvTNfmAndunaDJUuXwptvvAFNmjSB9w0yhPMYMGAAdOrUCZYuXQaPP/44NDbI8rBhw2DlypVw112joFevXux9Sz5cAs2apcHcuXPhgw8+gM6dOxtYjzP8t4ztFBVk/Ix0LvbYy4zsfbEeEuFDVVG7yhrp8tMS5d8S38rBtbaUd95+B/501VXQvn17Nghw5OgRmDhxIosxa2c8d8stN7P3njp5Eh5++O9wxRVXQN++feHVV1+VM7n77nvgpZdeYoMWHXA+48bC8WPHw27/uO7x927RvAV079EDdu7cCR9++CGMnzCevY7K7hjj+4wadSe0adMGxo4ZA199tR569+4NHTp2gGeeeUbO94cffoT/u/12ho3EfNeuXX/4/V/ja3yNr/E1/oXFz9ycEdH5F5VamoNrK1EmqRX0+ku0jlk8yKzE22fl4Lbh6m0r4qLchPArauxbL4SLcrJSolyBcLxEYjaVSKp644hy61Luux3IbKhe3BLNwVWjgmKUHNw48jzNOnKKCBI9uJWIgluZOHNVd8jBFTL5JXzlN1FycNMJwR20b59ZmiwIrs/vZxfhISsNih98CbF5h55DIBB215LPfUxUWhWfKrjieSTEqw1yaym45ruvu+46mDTpEfnc6dNn2D0kuFEul0Fw95TJ5Re3eOH71LRpJYovCG6+suwq/uHDh9n7dubmltryl/b6p/d9e0VI+N6gyXRVtoiuX5Qo097bPJGDazr/mSXK5kFZHoyZu3K2YmbkJeU5Xjux459vZJDSTz5ZzZT5pk2bwtq1n8KihZaCK96HiiueGLp16wYznnuO4fc3CO4zTz/N7v/w4w6DqDaEj1d8zDBxfi1atmT3x4wZC3eOGgXbt28zpu0GaX4B+hjbp/g+69ats5UVSaVTyZeVJy1bSbAV6SMV1HNYflme/Bvj42gz3f4KCgqgaVpT+OyztQZJ/AGuNQjqggWvsd/2xhtvhCcefwJ++ukn9u7Jjz7KBhc2btwIKz7+mKnr//nPcvbakKFDGCFetmwZbNiwwVjPfWDy5H+E3f5xv37xxReN37M//PjjD6xC480334QRBlHFv9eM75GWlsZK19ev/8ogtR2Zao/zX7pkCaQ1TYP//e8nOHHiOFzVsydM/sdk+PbbbxkpHjx48B9+/9f4Gl/ja3yNf2Hxse0nkvMvM5nKz5fXVJL45mRL1Va9/kL+k8Wv4Xx+K/mi0HJRbk8U3GZExW3kkIMr1Fs6VVcqbysoBlNiiiMtqgmEK8YRBdet3FdzcF0O4ut5E9wYct8dpgGYfrl48sXFbaLipCVqtIWcXYlI3aJxuQZfqbVID64wmRIqrshsakbsrjtgffn+/fvlqIVQoHA0w3mkJswmXUzzedArgRCZWYEQO0IAe3C7sDuosrpcXMEVBJeYT60cJ+53Zj24q0mJ8g3GBWViQgJUqliJXcyxC8frr4d3333HJLhEwS06WQQj7xgB1WtUh9at0uHddxYWu/xFRSfhjjtGQo0aNeCSSy+BJ598kj2fn1cA9evXh0cmTYIqlSvDB+9/wD7y2muvQovmzaFWrZpw3333wdkzZ9nMcMTpiq5XQLmkRGjWvBl8vPwjNp/hw2+BhPh4qFSpIrsQxb+TRcb3HDESqlevDunprdmyRLL+n37mGbjkkgZQq2ZNeP7559iyF+SbCu7rr78OKakpkFS+HPTq3Qv2FOyBY8eOQ926ddj6r1OnNsyYMYPNCjPEevXsBRUrVjTIdz+DMO28IL9/aW9/TvPGfSabHRxJRJDow/XbnZWDc3DNg22BNJPK5wquOSKZx0ch86S7MiFetFfUFntjkbHURkhwP2GPx44dB/0M4vnW22+bCi6Sz88/h2uuGQxNGjeGRo0bsZ5dVOwQv3///vDK3FfkSQVf+/zzLxjGqpUroXmL5gwbiTOS35SUVDI1tJUP2cqGaQ6t/L72KB6x/B5CKu0mUpEtf2nhqyXKPxjEEoniqlWr4NTp07bt74477oD58+fL7apL5y7Gel4nP/vsszPg/gfuZy8PHTIUnnjicfnap2vWQNeuXYvd/t973ypRxleQ4N5++wh2f8HrC+CGoUPlZ+7+693w9NPT5WfxGPTlf7+EjM2boU2btnDs6FEoOlEEGC+Hvz1eKJTm/lfa+7/G1/gaX+Nr/AuLb1Nww5x/c3JypINyHie4XlmibKVW0OsvP03C8FlcqHB/oVBwhcmUcFEWfCqVqLj1ibgoTKacDKaqEMPgCqT/Np7wP2ouFauUJ8c4JPXEOsTVlpiDstp/63YI5XUTedmtNAwnkJprIU9TVl+B3E9WooKEgiuiguoQcptCasSFi7KYWnPZ/WpbVidTmcyGa6eKBNtmFzTqEqq2PvCrvNac8FfwEuVI8ZH0MgWXuCjjBeiV3bvBuLFjmZqCf3iRiBeZVomyj80CCScqJp8bRODll+ZAfGIiZGVnhcW/++67DcLQBD5duxbe+vdbkJiYwEpAsZwPCWSfPn1g7Wefwv7CQqZuJSaWgzfeeAPWGu+/9NJL4YnHzQtYLGe8+eabYbNxYTlmzBioWiUZkPr+tOsnVgY6evQDsGPHDvbee+8bbRDblux7zpkzxyDACcb3zA67/lEdiomJgVdffY19D1xORnALCoydvsAg1kkwb948WL9+PSNEd905Cs4YC/3Vhq/Y+5Yv/xh8Pp9Beo/BpQ0bwl133clUJ1Sk0lulX5Dfv7S3Pyd83Ge2oouy32cvU/b7g3LVRL+uOLDm0QxcmoPLe0CFesvclXlMkMemPnpJLE4uUTBNcoclyqjg4vPff/89653F0tOOl3Vg78Pta9iwm43f7Wv2HlTkZsx4huH37z8A5r4yV544UozfeN0XX7D5Ytlrs2Yt2Gu4vU+c+JAjvlXya1dSPapBhIfY/nutHlgbEfV4znn5Sws/Y3NG0PY3c+ZMaNeuHbRt2xYmPvQgHDp4kL1l5Mg7ZA/u4UOHGWn8+ec8ua198MGHMHDgQDavIQYRfXfhQrn94ffE9x89eizs9o89uNded518+c1/vwEjDIJrKrgLYNRdd8nXHjDI9MtzXpazuMo4Dn1h/O6o/DdJS2Nqb1paUwO3GcNe/9X6Ut3/Snv/1/gaX+NrfI1/YfGR4EZy/hUxQXmcxIr3ZvMSZSEY0OsvITwI/iPSMHiJ8mBiMJVOXJQbEwX3Es676vGpLsnCrUZIbhUiVApzKaHkxhH+p5YpC9IaRyqDXQ5EN8ohJ7dECK5TvbNbaQh2E1buJmycqreJRL1NJBFBFUhEEHVRFgpubUJwqdFUKv8hhJNyc67gtuQE14oJEgqu37TMLs5BLazNd6gChgCEHA0Kmp8DvihDFv23QsWlE50vxgit3kYUXD7LQQMH8d6ygCS48+bPsxRc3x6GXzU5GZb95z9w/PhxNvXo0cP43NNhlx+V2+XL/yO/x5dffglbtmyB3QbBRfKMZlbio/feew/ccsstbN7Hjh9j5BQvgunynz5zGrZt22aWDxs7Jf6ZJcpPScxk/J7LlpHv2d34njPCrv+//vWvcNttt8qX1n62VhJcin/8xAl45JFH5PeySpRNlRaXL8kgw0eOHGHYuA3h6z/9tKvEf//S3v6c8P1qDq7s4VDKk3HymYHiMvuWRgXxEUdZiixKlKnJlM0YyVImKaGjbr4mwf1EEroP3/8AGqamSBflP1//Z7jnnntg544dBglazHpwnzEILr4fFVwkuKJ0F3tpcQAF58MIbvNmDB8HQbB3c82aNWzAZfLkyTBq1Cg2/2++2QQTJjzIyp9tpk65XpvbsXkSyrVOWNTt2GuP/jmX5aelTL8lfkbGZsft71TRSaaG9u3TD/7BSosDpsnU3LlsewoY2xz26DIXZr79YW/zqLtGsftDhw6BWbNmyu3v6683ssGn4rZ/02TqWvn0m2+8yQluABYYBPcu4/cS2zOqxXNeniPxsSwZCS6a8uHxr6ztfxpf42t8ja/xL258FhMUwfk3mxFc0fKVJ8//OSwHN08SX3r9RXNwZeWdz8dEKG4y1Zb04VIXZaHgNiQEtzZpGxX+SMnEP6mK4quURGJhE4mLciwxIqatrdFKZJA7BJl1lSTBVZt8ncqTY0L03saRhRF9uDQDN4nEBImw4Kr8fnViRV2XmEyJpudLCcFtRlyUW/O68mv2+UlMkFCZsrLIdhaIaNwlUMwTAYcnQ4U/20eKrEdmiXLk+CvHd+YmUy/a8AcNMgmuwEeC++r8VyXBLdizBw4cOABRnEBHu93gjnYb910wYsSIkPjsM8b7cYdSv9BPXMFFEij+4uLjOVF3gdvlliQdP/qvf/2LlQiz1/F547vs/tksB+zbpy9M4z24B/YfkJ9j39OY8L1CoQm1/nsaF67Tpk+XT+EObRLcfDhRVMSUuWrVq9nwRQ8uPs7lPbhIcBg+WwYXWxZ87yerPynx37+0tz8nfKHK+qiLst9Ucf1+KzTcJxXcLDKCaBkdyJggVGr56KQwnhLxQYKAyVJbrz2+xkscgfHA34iXKNNe0/vvv1+WKCPxwR7Pxo0bQ5/efaBf3368RNkLAwZcbZCrV2xk+YsvvmTzZiXKzZtL/L899BC0bNWK4XXv3gNWf7KafebDDz+AtGZpsPazzyxzJ49XsfanzsdeUibstcX6eGzLFtnye710fr8dvklQrW1kw4aNTC3H7eHs2QDce9+9MHnSZPaW8ePHw+j7R8Opk6d4BchfWfkwHot++ul/7Nj0+oLX2WtDhgyFTp07Qe7OnXDQeB0HqHCgqrjtf82aT6Fd+3ZQWLjPJLhvvmGVKL9mEFyi4OL28dKcl+VjdMdGcrv/wH5o07YdvP3O23D61GlWrYHl7djiUpr7X2nv/xpf42t8ja/xLyw+68GN4PyLObj0ukqcz0VMkBAM6PWXjAlSTEF5ifJAEhPUipj1NiIKbgOSXCNIbk0iQCYTzlaZeCpVJMSW+jCJx7FK6k6s0hLrJiQ2RjE2jirJEmU1eyhaYdtuxTmZsvA4xVwqTjGZKk+igqqQZuVqZISgNnHuEhm4DUl5sjCaakHcwPBHG8hclP0iJsgnf+zgrbRYL2/b5hlwqmtw2GnC7yZ2AKHguqKClVuTiLls+CvGdTKzcG0xQcBK/qQ7KFdw57+KJcoH2Xx8WKJsfMcKFcrDpk2bIl9+Y6pYqSIr+RXfYn/hfoMUHoJdu3cxgnzCILhi+W+//XZ2gavO8OiRoxAbGwszZ77AlNGCPQXse/3888/sdeylfOqpJ83Vy75nBdj09aZzWv+3/d9t0kWajZBtyZQKLpZWV61a1Vj2r+HMmTMwa/Ys6GxcWOPfL4d/Yesf3VhZ2fiKFdCgQYPf5Pcv7e3P6e32mCCh2oo+XJ9FdHkfLvYr55F8W8u2niu1xC1ZqLqyRNlDe0mFZb6DaulkrmQr76F9K7mw1fhOTpmwdkLoCYuP2wMeN1R8fD4cPn3tfPB/7fJfCHx0UabbX1HRCUZi0Zirfbt2cP3118Pu3bs4+f2KqaRozIVvPnjwINw6/FY2gNC2bRuYZBDhswGzL3/o0KHw4IQHmbs1lgkj4cUByuK2fzSIGjhoIDRtmgbbt21jPbgjR5oDYK+/bhBcTpLxzQ/cb5Yoi8c9e/ViqjPeRyW3t/G4ebPm0N4gzHicKO39T+NrfI2v8TX+xY2PVVGRnH9NIitU2jx5rjdLlAt4H679+gv5j2kU6rOJfWjAS3pwWxIX5TTShysU3PqkerYOIbk1SQauaDGtoLSeliMuynGKyVQsqfqNDcMtXSF8oErMRdkdIjIoSlF2qdIbRyRosVB0YdWoIDUHtwaRw+uQGvAGxGCqIf8xmvKRB0FwcURisN/4EbGX0i8NcnxmibLDMEvAcZsuZieQHwy9g9g3eAWF4CPBPRd8NJtC9RaNpij+YKbgzpBvx0iOV1kP7mFeolzAifAgGHT1QDhwcD9bR3379YN/v/VWWHx0aO7eozsUFhYypaVWrdpMJdn100+mgnvCUnCxVBBVUnQlPXP6NLuYRROpfYV+cLldsHz5R6yscey4MYwc7zYILqINu3kYu0g+cbKIYSJhx+ngAfN79uvfj0W0hFv/Cxa8zoy2thjEFpXngVdfzTBQwX1l7stQt15d2L//APgK9rAc1M6du7DZnDx1CpLKJ7GLZHy8zyBu5cuXhznGRfHpUydZL3F663SD1PlL/Pcv7e3PCR/jgEyCa0YD+fda5lJyn/KZJS/Cjt5SbfMslVaYTBG3ZKnscuJLe0g9JN7GGtHM5T2knqBoHErsnPpSvWq8jiCAuZbCqfEjw2cKrsP2hw7G2LPutP2dFuZTfCtDUoqDS3T7QxflDz9cwgjvL78ckVvlwoXvsmoMzByezm/Z42nT2bFF4GPEV0ls/4cOH2JKdFnY/zS+xtf4Gl/jX9z4OGgcyflXmEwJdVaaT2Vbpcvq9RdeM2fxazhGclGYMK7hCgv3CwW3DSe4ag5uY+KiLPpw65CW0RpEjKTqbQVSmVueiJuC/yU4mEu5HXJwVV7pLqZt9rxLlFXZmJYnu4nlcxypr44jqm0CIb3lSA+ukLOrKJNaolyHlCjXJyZTaaQHN53/YO1YifK+Qm6K4+PyvB++2/pdMX3gzm3jAeVBqDr9gFqIoOxEQXsSIayWguviJcQuptzaenD551eMD3ZRZsTVILhP815a/MMs0Pmvih5cF3MMxs9jf3L37t0hLj4Oot3RTEExL1BDLz+Sxav+1BPiYuMgNj4Wbr31VubGvIuUKNPlxx5FJJruaBczcMncYl6QThg/gZlAoZI7YuQItoyiBxfdUy+9tCE0btKYPS40fsMrje8ZGxfHSpSHDrkBjh09Fnb940Uy9kqiyRUu2/0PjDb7fI1lR1Wo0+WdIDo6GqpUqQLXD7meubuKvwnjxxmvxcCkyZPM32XFSmiYkmIsQzRzUn7pxTkX5Pcv7e3PCd9UZbNYWYtPmkxxUmtzUaYmU/lkFLHACiCXpcgmoRJlNKaCm2PrA/XSUlwPCT0XB/9c1VKfqpIqAbTKcXODInJoZqzGjwQ/Y3PmBdn+8PiDGbZBLsmL34MZzz7LJnRdnjHjOXh2hnH73IygyLeLbf/T+Bpf42t8jX9x42caBDeS82+2MJnKEzm44vkc2/UUvf4SyTGC/5jmoH4mUvEe3PYkXrU551JOJcr1SIlydVKmXF2pvq1MknFoK2qCQ7KO4IgxilGxU4mymn9bIgquW2HOUYqM7FSyrObixijlyUmKeiuMpqoQubs6WYlCDq9LMnAbEoLblJNc0YObzhXcgX5hMuW3yirNi6KAfUsLhCgiCIR+0W4JHiIDK+TOE4yvKrjF4cuYIGM6H3wktcePmcrrhq+/hvHjxsE4PmH8yrhxY9l9NOURf8eNz5w8dTKi5UelBnvq1NdQxTl69EjI9X/q1Cnb8iOpFT2+WAo9djz/nsZ3HDt+LPneY7mSHGD9tjZVieAfPHgATp8+5YiP2GfPnrXhYz8wPnehfv/S3v6c8H0kJJy6JtuUXB/mq/m4IVWWSWYLMB6I29ZL0wNTwRUH8jyR22ZM2dk5JKfVE9xb6rXs8z3koC9OBrlej5Lp6rWpliIb1hoRpUSQPNb4xeKLmKCS3v6w3xVN38rS9q/xNb7G1/gaX+NfSHxMJojk/CtycPN5DJA4/5s5uFZFHL3+Mivrsqy2Mn4Nx12URYlyK2LQm0ZycFM516rHRcU6SolyNT4lExW3opKUk0BycBMJF6QlytHK/RilIlhN8nGXJMGNKqYk2UW+lEsxmkogt+I+LVEuT5RcYTOdTJyUaxIFtx4huCIqSEjpoge3GSe4+KMNNqMIJAAAgABJREFUtjVW+/ZKV1gIW4oQ8cBO8NMBcMzMChT7+YBUcCPFX8kJLo0JOh98/MOe3IceeohPE+V9NNqZz+M+LtTyn8v6P2FcCIvvNVF+X+PxRON24t+I2dWFwS/t5f+t8JnruLG/+PcGRwIJousjj7O2fif7Q/KJbb0wk2IlyvxAnp9HFFwkvl5PcFmt16u4BpN8OFt2K59yaXmu6hrsIRhey1xJlAJp/Ijw1Rzci3n71/gaX+NrfI2v8S8kProoR3L+xeukfOKSbHteEF/l+kv6qPiIUehePxtQ5gQ3nSi4QixsQgiuUHAbEIJbi1TXJpOpAlFvKyhZuJQLxjoYS8WG4JdRhPBGlXQWrlO4brTSi0vZdrxiMCXuJ5Apkai3SSQYWNxWV0qURc23aHIW9eAphNw2JRm46dxkajD+iH6Z3WmVUToM04TqGQ+7QQfIPCKqenDCCIjYn85MlcVp5bgu5q14bmwX+RqdVm2bZZDcF0sEv7SXX+OXPXzLZMrvWJLsI73tJhnmLsqKcitKZkyl1iRU0u2PKrhktNK6JZlwohzXa+8ltfeuWCOg3qDsWAcH4RCjpRrfGT/LOJkuXLRQTosWLoKFC/ljvM9uzefNiT9etMh8HZ8Tn13EPyvmsZDMc9EieSvnu4g/1vgaX+NrfI2v8S8C/Gxuslnc+VfNuxXnfPw8zb+l118y6tGviBN+v+jBbcfbOpsTHyNaotyQtIWKuNYaZKIlylUVcqv23oqY2HjCFWNJHq7wbopy6L9VS5NLlOCq5lKqdOwmX5Zm4sYrCm4i6cGtQBqRBblNJo3LVQnBrUnU2wakRLkhycFtoeTgDjJLk/3ywtwvXJQD4QvxA8WQhPB1/IHiSYfG1/i/A3w/LW+hE44G+vzSaEqMDDKTqXzT3MDu6JfHXZSzOanK5a+L0uUcEmuj5MB5PUFZraI3RZbjks8I4mcjdl6PraTXzIUlMTrUeEnja3yNr/E1vsbX+Bq/DOFn87xbQWAFPjOf4qZS6vUXXp+hsa7gPyINA9MvUATkXCmd86YWRDBMIQS3nkJyBSdLJuXJyUoObnlSoVuO8D+1upe2tsYo3NLtcN9V0i7K0SEU3WgHB6xYQnRjCMGNV0huOdKHW5E4cFUhJcpihKAO6cWtT3JwUzm5bUJycFuREuWB+0QPLukhpApuwKlMIRC+JEEtaggUW7MQWj3T+Bq/LOOL/cVvG/nzhShR9rP3mqXHBVbYOMnCzZYuyl5SwsyVXWp25FUURXnQt9RHj1PkjYc6EBKl0qs4CHtpjqwn2GxJ42t8ja/xNb7G1/gav4zgUxdlJK8C31Rw80l/rnX9JXtwfVSkwBLlQlXBbUFclJsQF+UUTnBFBS2NCKrBuVpVouJSg6nypCWVKrhxCleMU7yc3Aq3VFN9SsxkKkqpi3YRcusi+UXitTjFDStBYezllKkivxUEN5kQXNqHW5c0OtfnowoNuYzejNSOC4Mp/MEG+IR6K0orfZzgBkJziEC4rnOAMI3jwY8DAXAmLRpf4/8O8P2ctNLSZJ8oV5YlLxbhRTt60f+RT0ymhJLL3JJ5yQ21uzcVXK/N+ChoBFOW43ptJwpqzmDrM/WQ99uyYb1WZI4cifVqfI2v8TW+xtf4Gl/jl0l8FAJE1ZtZomwnvpaKa11/7aUlyoTk7rdclNMJwW1BCK5Qby8hPbh1ef9tbWIEXIMT20okC7c8J7UVFHJbTnFTjie8MVqJDaKENuZCOCirDb9qH64rROlyjIMVdDxh74lk4YXTlhoVJMhtLVL3XZeT23qE4KbwHyON1I9Lkym0wqa15yLX017jGXCUwwJhtK5iS0sD4elDOHyMB3LxWCCM9fmt8Ut7+TV+2cFnMUHZWYTE2sPC7ZMfthr7VoHsuRUH3Hy7yRQ/KEuCK0uXScmObTTUnu1qM1eS/Sle5Tl7NiwtB/LYXvfajJs0vsbX+Bpf42t8ja/xyxq+LFHOFyXKJn6OiAkyXlOvv4RRqOA/1vWaTyi4rfnUnMQENSF9uILk1uccrCYhuFVJ8k1lwuMqEoOpJEJyBReM5Y9jFJE0WnFJVrNxS5zg0pm6FLLrJs7J0Q4s3IngxhKTqSQSCFyJEFs61eAkV6i4DUiJcgqR0tN4eXILMiIxGDNUzZJKqw8XjXAiM0gLTyoCAWc78HWfrYNt23+AYu3THJCQ1B7cONOYZpm3X8/iz82KGN9WinqO+CWx/KHwT58+DUuXLYWiopOlgl/ay/97xBd9tX4eteX3WxFBPkl2feATBm5ZWWZOmyyXISOKokSZH7yFi3K+yMe1Rdg4jYx6g0Y1PaT0x6uMqHpINI71vFJKpDgOa3yNr/E1vsbX+Bpf45c1fLPX1sq6Fa/LmKD8/KDrL+Q/38mYoL2SC+3bJ0uU23KCK/yLmhEFN4Vk4NYnObiiPLmm4pskCG75EASX8kFqQkxdlWNCZOG6FBW3REym3A69uNFKiTItVY5WvnQM6cFNVBqORRMyVW8r8RVVhazAGqT/ti7JwRUlysJkqiUhuPijDUIXZdUcJ0squJH9BUi5J1KJQPhGRli6dClMmfIofPvtt2FaIoPxUbl97NbWjNQiuT3Eie4UfM64PbDBeP7rmXCAv4bENxJxL1L8klr+UJ8/dOgQ+86+PXtKBb+0l//3iM9UWZmDax4YfdJu3m9Tc4Vbn1N5ch7PuzVLlM0DvRiJLBAuyqIvxWbq4CUnBa8SeUMt9O1uwbZyIiXz1auYQHiVvhmNHx4f+3kw1iAjI5NFBmVu5rfsuYyg+7Zps3htc/B7MzNt7xUYmzPNx5s5zubNGRpf42t8ja/xNf5Fgc9clCM4/yKRFdVweO0kzv94/eRUnpxPS5RJVKppMuUXMUHtSUxQGldxBa9KVUTF2iQHV5QoJxNxshJRb8sTzpdI7icQf6ZYQnRFZFCswjOdzKZKTMENV54co8jJboXUxipOynFKTFCi0oxcicvcVTjJrU7k8NqkD/cSPgkJvQknuM2IyVT7IIJr/KA+YjJlL9YMKE2LkepcAVDFMpzX0iVLDJI7BbZs+TZkf6OKj2XJBzfMhMeGt2FEd6oxMcLLlVyT8CK5NQkwviZJroIfiT3RhVx+p79Dhw+z77tnT0Gp4Jf28v8e8Wn/hp8YS/nlaKDPFhmEg0d5ttJkPpLIRxRFHBAeyPPIgRgP3Iyo5ZKyHJLNSk0aLCMG1aXQG+R06HGy27eVFnns79H4xeJn2nJw9f6n8TW+xtf4Gl/j/1p8JLyRnH+zeSkymwry5PmfEV9pMGW//trLk2ME/zEjHdFk6oDowW1NxEHhZ9SU5ODSmKA6xPS3FilTrkK4W0XShysUXJqBSyOC4h3igJzSepz6b6MvBMlVc3CjFPeraKLkxpIFSyS35Qirr0hKlCuTlVWNTLQHty6pBxcSelMy8tCCmEwN8vv9tv5b6aKsKlkOpjuBQKRVns4vLlm6BB591FJynRssLRAkfwe+5iT2a67YbkTFdhYjvge/JqXLSHCHt4ZWrdJtJBf/Pv/8C7i80+WQVD4JbrnlFrjxxhvh9ddfZyC47L169YKKFSpC3759jR1qJ/sM7hj169eHp6ZNg5o1a8Jll3WEpUuXyW/43Vbjc72Nz1U0P5ebm2t+Lr/A+Fw9mDRpElSuUhk+/OADyMn5Hq7oegWUSyoHzZo1g48++oi99/Chg5zg7vlN1n/Y92n8iPDNsuNsc+TPv9ex75aauGXREuWCPCuTjSu1ObxE2ctLlEVUEOvBpaOX1IjBazdp8CguhrJ/xasQN9L7EtTnIm9z7Yqoxi8WP8NGcPX+p/E1vsbX+Bpf4/9afBw0juT8K3Nw88xJnP/x+imoRJlff5nXcN/Z+2+N6zkU/zjBbUtIbktCcFN5eTLtwRVcrDpxUK5GVNxKhNyqZcpxpFQ5jhDcGMVR2a14PEU7eD6VaA5ulOKcHOPQCOxW6qdjiGobozB3ajJVjphMVSbNylWVMOHaxEH5Uj6l8FGGNFKiLCb80QazmCByYc5UpuysCDbLQPAGX2wZqH00p+j4CZg+fRpMMyaQozuhdwssUUYCe4j034py5IOc+NoJbhuD4LZkyq/AP3jgAFSrVg3uHDUKNm38Gu655x4231mzZsGxo8fg0oYNYdRdo2Djxo1wg0F809PT2Sd37drF3jd48GDYuGEDjBp1JzQ03ovzPX7sGDRMacjmuXHDRkaY6ecQv3efPrB27afMme2qq66CYTffDBnfbIYxY8ZActVk1i8qSpT3+Pb8JuvfyVw4EPFhSePjn1Bl94Yht1iiLEYGTQU3X5bHiPxbU6nN47225gFbjDjiQRmf99pImN2MQTVqkI6EVKmkZT34Wm5wOa6HuhXKHhkvMXfQ+MXhBxNcvf9pfI2v8TW+xtf4vwYfy50jOf+yUmSZc5svz/+muzLPvlWuv5jwwEuUfTQNw8zBvZqYTAkVt4kSE0RLlOsSJ+UapExZlChXJsS2AlFvyxH+R02mohXuGENaXqOVXtxohYeWCMGNdmDMbsVgiiq8qoNyjKLiJhJSm0RWhMjCrUxGBKiLck2ychtwgisk9CZExW3JfyxSouy3XZBnkRxcq6DAyUk28CtGbMzXDh48AK/MnQvTpk2HPXt8YYsWBD4zk2L9txbJPaSotpb5lEVwkZiKvxUrPoYaNarDmTOn+ShUAC655FKYaRDcL7/8LyQlJcHRI0fg+PHjbF0g5v9+2mUQ1Z/Y/R07d7BF8O1hDmsskFp87gj73AnTfc3A/Il9ziTGXu//lHUTgNOnz8C2bduYEzTudIcOHSY9uBd2/YfyIA63/jV+ML6fm7LZSa3PZlZA7+NIoewBIQdaMaqYk2O5JcscXGk+5ZFuhF5VtbQFrNvNGmz2+16aVeeVNvyix5Q6Enpt/S0ejR8hPpZT6f1P42t8ja/xNb7GP398PKdGcv7NIb22eaIH1yuILy9PVq6/9rKoxywiSOzlJlP78Hp8MBcD04nBVHNObqmLcj2i4NbmvKwW52pViXpbWVFxE/ltAuF/CUrbagyZ3KQXl7ooq2Krq6RKlFVSq5pLCRDqhOUmj+OJc3Kig4qbRFh/ZZKBK8qUhYJbh/Tf1ifSeWOShduSOIG1NUuU99oyPNEo57swJlOhR3gCIcx67DsHljUgoZ02zVRuRb9p8BCOg8mUcFD+WpQm28ntAUp2N5g9uK1atWIKqpjd3LnzoW3bdrYd7corr4RZs2fBvHnzGIbL5QKX27zFx5988olJVI37xwziK/5iYmPZuhKfi8LPudxBn0P848eOy+X/17/egJSUhoz4RvG4o59//tlScMOYTJXE+g99oAkUe/DS+Pb7lkGBcEr2yxIXW+m/z+xx/25rllV6TAisGHXMYT24Xm4ylS/fm8N7c+nIpUctySX2+V6ParfPlchcy67fVqbrtaubtJf1o+XLYcNXX5UavlcNjC/j+EzB1fufxtf4Gl/ja3yNf974WKIcyfkXr5Noj604/2fzEuU8ck0lrr+E8ECNQvf6faJEGV2UO5DKV9GHK8TDFKLeij7cWqT/toaSflOFKLjlCclNIvwvVlFs4wlnFBN1TY5SenJLrDw5SpGIVVWXhvC6FPfkaCXrKIG4KYsFTyLqbQUSFCxU3JrERbkO6cO9hBDcVBJM3IIouKwHl2Y+iftZWdlkcw3TEh6I7OmAUqYwd+4rBrmdzolccbtLwB4RJEuRLRdlNqHJFCW+xn2zB7eV7MHF+fz3yy+Y2rqvcD97DklllSpVYNasmbBixQpo0KCBI/6uXbvZfI6fsAhuXIxJcFesWCk/py6JIMYnDGKMzx89+gvEGsT4hZmzmOJbUFDACe5uG8G9kOvf+aPFr3+NH/wRk+Bm2Ub+2H3/Xlt5sl+U/2dtteXbSkMEfkDOFjm4xsE6L88aacQRSDUk3WaZ77VGNQWZS0lJMafURpDeqiXcfPPN8O2WTGnLbymeXluP6fxXX4Vl/1kmS3yHDB0Kzz773Dnji9tcqZgSY4gw+NIUSlFjfy/4aomy2H52795tbCtbYf369bD757wyt/2fOXMGPv30Uzh58mSx+EVFRbB2zadw9uzZYvFPFJ2Ejz/+CE6eOhUWH4+FYt0Vh8/W5dat+vin8TW+xtf4Fzk+K1GO4Pybw8yk7Dm4ueJ56aJsv/6yuSjzKjwUKwoLC4WC245ErLYgFbFNiIvyJSQiqA4huMIMuLKShUt7cEWJsuB/5QgfjCN8MVqJnY1RknsoJy0xghulqLZRirOV6nIV4+CmHEOk6QRiMJVEiG4lkqeUTJqXqxNb6npKTFAKiQiiJcot+Y82EGV4H45a+P2S5GKuJwRliJL/QaMzAedRG7Wonv8VGATu4MGD1jsDDruSAz4zmWKGUmYckFBymcEUu51lEV7uotyqZSumrNILsw4dOkDbtm3hscceY/crVKhoENzZTMUuX748vPzyHDhlXOR9tnYtpKe3YutmNy9RZgou/2qxTMHdysgLfm7OS8bnjIu4tWs/Y59DkrPrp10M/zhXfnF9o8K7fPlydiE5duxYTnDz4KAwmcIe3Au4/oMOLRGuf40fjC8d+GyqrV8OGvm5cmvFBGVZqm2e5ZIscttkTBCaTJFSmhzhouy1SmfVwHRLnTQJHVYJLF64CL41iMD7778PA/r3hxtuuMFmrmQZM1mROjfeeAM8+cSTUrVEgvvcc8+eMz57T65zcHs4fElGadYdcTou6/h4Mnba/qY8+igzqRtw9QCYO3desdvftu+3waLFi36z7f+XX36Bpk2bsmNUcdv/15u+hu7du7MH/1m2DDIzM0Pi//Djj8yTwMON90LhL1q0CIb9ZVhE+9+jUx5lVUD6+KfxNb7G1/gXN36mcFEu5vxrKriWt4k4/7McXJl/a7/+Qv5j5uD6LHHCuG4rNEuUB5Ee3HROcNMIuW2o9OHSmCBRXVtNKVEuT9JxRA9uEuF/CUobq1uJC3ISUN0O2bglZjLlUiRjNTYoRpGYY5WIoBhSlhyvENyKJBhYZOFWIQS3BpHE6yoNz2LlC5MpquC2s7kok5gTVHCpHXggaPMLhGkoDzhUGajuaOew1Sv4SAAfG57O1Nmpw82YIDmx59JhCn8NyS1VcMXMfHsK4KmnnoLhw4fDu+++y1yTsUQZ/1DFTUlJhWi3GypWrAQvvfQSV3BNgmsSVfO7xMfGSUOuFStXMLUsJtoNFSpWNMjuSwxLKLjYmyvwH5wwHmJiYhhBHjFiBHs9vyAfDksF11dm17/Gt+PjfsPKW/xWObJQbC3C6zNJr98kwzSLTZhL5fOyGpn3JkymhIIre3A9rO/b1iPqtdRHL3ktNbURrFq1SpK1f73xL0hrmgY7d+yAzz77DAYPGgTNmjeDHj16GPvBOwxz1KhRBslJg1YtWsI1114rCe6YMQ9A7969oWXLlnDnnXfAjz/uYPP86OOPYMGCBY74KnG0RfiQvFmprHrUcHivPdT9HJe/tPA3CwVX2f4GDhoIaz5ZA2fPnIlo+3vvvffhWuM3+K22/yOc4Pr3FRa7/b/00hwYN3YcezTyjpEwf/78sPhnz5wtFn+xQXD/MuwvEe1/AwcOYutSH/80vsbX+Br/4sa3enDDn3/RLTkvz3JLFudwSXzz8oOuv4So5ycKLgoT+/YVChfldpw3NecTVW9TeLWsMPitRzyRRESQMJmiCm5FouImKC2qlODGE5KrRgU58c0SV29dDrKwS2HZVFqOJU3CMQ49tzQLV+QkVSQmU1WUEmXag1uXNDsLcptKMnDTuNQuRiOYgusXfYI8BypLycENzr5y3mADELp1PBC+qkHuGI41/go+kkAkrunGZJLYlux+Sz7hY/P1luy9eCvmgFmzGA2UmbmFPcYL9fj4ePh0zSc2/AMHDsCZwNlzXv79xuessr3Qy3/iRBEcPXb0Vy1/aa9/jW89NnvWt8oSZB+JBPKTEUEfJ8DfMYJL8m9tbsoi79Y8aJsEt8DsweXuyl5ije8NMlIifaXG641SU2GlQXAFkXv22WehXfv2bB5//vP1cMedd8LKlavgwQkToE3rdMg1Pr9h40YYePVAGD9+HHz53y/Ze4cOGQKtjdffefddRmZbNG/ODOJwvnfeOQq6dOniiO+13RLV1GPPmPXSnlmvFekjlzeXlhVHvvylhW+VKFvbyaHDh1gkWGHhfrh/9Gj46KOP2fN3330PzJo5EwYNHAhdu3WD559/nm1vK1ethI6XXWas6xbQo3t3yOVxZe8tXgxXDxgAV3TtCo8/PtUgjuZWi+rn/Q88ALePuJ0d/9B1u3fvXnCiqEh+ExzQW/7RR7Bz5074y1+GQes2rWFA/wHw+bp17B2/HDkCTVDB9e9jn3n7nbeh51U9oX27dqzS5MjRo3K5/u/22+Ed4/WJD05keO2N7QpL4ANsme5mLSh9+vSGsePGGcfS/dC9R3c4anweX//ggw+gV89e0KZtG7j9/27neMYyGMv2l2Gmgnv8+DGYMPFB6NixI1v+f/3zn3Jd4kAgrsv9+wv18U/ja3yNr/EvcnzWgxvB+dfMwbVKkMX7mIKbz6+5lOsvv0zCsNrJ2PXbPr9wUW5HTKaaKyXKtAe3PilPrk3UW2E0VYkoueWUCt0kwv/iiPgZrfTjRiuc0q1UEZd4mbLKnN1KXJCLfDE1A1dtKE4kcrWwkC5HenCTSS13NdLEXIsQXLGyLyGN0GkkB7cVn/BHuwYvEnyirNInFNysEKM4ELlxmuNjZTMPFDPvMPjCnMmaXOCKcsn74haJroo/0bhwio6Ohjq164Db7WIXa+hofC74pb38Gr9s4Mv+DYPo+qkbuU/JwPWZLQCmi7JziTI+RiIrlETT1t58D1N2bT2hwX0oXkV5xIqCv//t7yzj+aGJf4M2BqGZ8OAE24lh584d8Nm6z9l7v/nmGzYPLGN+8sknpfI5dOgQ+JsxH4GPKtvEiRPNE4pxrNi0aZMjvuVSTBRUryfoxJQrlVYyD5JjayenkS9/aeGzEmVlO1n32efQr19fdn+Ysf4WLVrM3jP0hiHQ5YqusP6rr2DJkiXQokULtg1gP/7s2bNhgEFmf/zxRyg6eZKt59bprVl+OMaYYdzYyy+/zOa54LUFkJbWDF40PpORmckU065du8HatWvZ67t37YbmzZuxvqLbbrsVJkyYYPx22fDUtCcNIn0529RpiTJeBKQZ91Hp/+GHH+Caa65lgxv4nU+fOc1aPPB5fB/Goj3x+JPMNR7/hg4dCp07dWbeBNiisc+4cGjaJM2Y/2G2L7Q1iO3ChYuMi5ZMGDL0epg0+R/sc7hOhnGCu2DB66ykHsuaV69eBa1bt2Z9t4j/2brPjHXZTx9/NL7G1/ga/w+Av5mZTBV//s0hZlKmi7LXclemDsq0RJmZTGXZjEGJi/LVpAc3nQiFTTm/EgruJUTBrcPFx1qEpyWT6tsKRLQsR4htoqLmximcMc6h7dXJ3Njt0Jd7XiZTbgc3ZZfD8y4lrDeOKLi0/poS3UoOJlOiBzeZr8jaZKIlykLBbeQQE9TazMEt5BfgPtkniARXHXEJhKkqCNjeHcZ9LRBmrwg4j/1cKHxUaL/9dgu7kCwN/NJefo1fMvhBBgXcSZkaTokeXDyA2giuvM2X1vZ4gBYkTTj9SYJrI3BeeyQOVTBlD24K68O9qmdPGDRoEEx9bCpT7/C1V16ZC1dc0cUyomrYkBEonJ9JcJ+SPatIcFH9FfgPjBkD48aNKxY/yPBJmkEE97+y0mKacyeJJpnXOS5/aeGbPbj2v2dmzIBHHn6EE9xhJsE1tq6hQ4bC3Llz5QZ2s/HavHlz2Xb33nvvwbXXXSe3v6lTp8LEBx9kqixWgLzz9jvwZ+N13P4XLHgNrr/+z7ZtGtswHn74YXb/n//8J1NL6R9GpeXmehipxeM/Ety0Jkhw/Yy8pqWlsQoAJLR0+88ytsUOHTpC4Kz55Ig7RsK8+fPk/ofL9Nqrr8r344UCKsNHfjliwy8qOgEvvDATrvvzn9kCYr/xsL+YJcozZ85i6vKPO3YE7X+4Lh9+5BF9/NH4Gl/ja/w/AH6GVHDDn39NMymr/Fic/5mLshASlOsv8xpuq2IU6hcE91oHkynRg9uQ9OE2IBW0tUl1bTVCbGlMUBJxUk4ixlIJpG01jpQrRyvGUk5+T06VxCX25w4RGeQmTcI0t0g1mYpT2HuSouBWJCsnmdR2V3coURY5uILcNuHqLY0KkiZTtpgg42Ic+wRDjsYEwg3TRFRdz3vPw+4xGl/j/y7wRV+tnzgm28mu/T72epgH2jxJbKXJVL6p4AqiZOXgihJlNQ4nV4mzsZsupDZKlT24lkLpge9zvofGTRozZ+Tvjflu3rwZUlJTTCUWFdyhN8ATTz5hmUwNGQLPGe8V+A888IBBcMcXi2/LkCX41CTC9l6v1xYBQKN5bCO3ES5/aeGrLsr4d+NNN8GHHy4xCe5fhjEyx9ROY92K5/FvwoQHYaZB+oAR3PcNgnut3OZapbeCpgYBTUtrytTapk2bQFPjvql4LoC7Rt1l2zy3GidtLB/Hlolbhg9nRmP4t8TA69mrJ+vHRnKLExrb/XLkF9Z/jYqrSTJnQrt27aBtm7bw0MSJzAQPWMzZv2DUnaPkznDHyDtg/rx5cvdAgvvhhx8SgutnGL8YBBeN9aY8OgUu73Q5XwYTnym4i7mCG8BWj/0w0pgvkuw/9fgTq0IQAKgYL2HrTB9/NL7G1/ga/2LHN0uUiz//srxbfn0lFFyT+OZYxFe5/rLFBPl59Z1f9uCqJcpphOA2IuSWliiLHFzah1uN87fKRMFNcjCXolGxcQ4lyi6HPly1RNmlmB6XmIOyy6HhlzJvdYomC0KdtOJJPlIFot5WIgZT1RWDKVqiXJ8ouI1JD64wmUrnObhXC4Lr52WVeIG+VcQvBALFbqj2EZoAhB6GCYDanB5Uua+arml8jV/G8UVfrU+4JivE1jKb8rFsNVR7Rd+tcPTLF6XIefmsh0SoiCI6CKccHhMUZIqkZMHRktrUVEJwSZ/Kt1u+hdSUVHj7nXfgxx9/gAcfnMAeb/p6E+Qa77njjjvgtttugx9/+IERPiw5fY7HBOG8keCOZwquBxYuXAgzZjzriO9R8mOpO7HXZgBlvS+XqKheEuyuksxIlr+08DMy7SZTJ06cgJYtW8Du3bvYU0hwFy9abJLBoUMMsvahfP+4CeMZscS/9w3CJxRc/Pv73/8O05+e7rj9v2YQXDQIU7dPNNBDV3cs8cUS4aPHjkGL5i3hjTfeYPexPxyJsm+PqeDSHlzc/k+ePAVffvkl9O3bF/4xeTJ7+r7R98F8VGw5PhLRuUhw+f4y5AaD4C5ZIl/3G/NDDCTQS5ctg8sv68T2gzMG8X7zzTfgJoOw4p9pMjXMtv/t338A3nrrLWjerDnLYj5RdIIZneG61Mcfja/xNb7Gv/jxcdA4kvMvI7IFouUrT567zRxcUppMrr9kEoZvr02gOGDFBLXlFa8tiFCYxrlVI4cS5VqKP1I1UnFbmVTkliM+S+UI/6PxsXFKtGy0wh9jHEqUo0pSwXWShGOUHtwohy8XS0qT45Qs3HKE8Aq3rUrEZCqZ9OHWJCu0Lp8uVXpwG5EeXEFwWQ+u7A8kF+W0RJmWIERqgHZue4XzPJ3wV4zrDCvG8mlc8LTSmD6edBWs/n42m1ZtM263mbclgV/ay6/xyx4+LVG25eDutZcqi8c4Uigz2EQJch5RallMkKk25pEwcqng5pJYGy/tN+WKJBnRlC7KXkrqzM/87aGHoHGjRtC4SRO45+57oWFqQ9aDi/NBpa9z5y7QrVs39hj7RJ977jmJP2bMWBg/fjx7PGbsWOau7IRPDZ5UfJuxk0pKxXNSXc21jKLOYflLC19VcDcZ67Vb165ya0IStxgVXGNbQodqquDiekWCi5vZmjVrmHnTvkKTcC5dthQ6Xd4Jtm/fDqfPnGHve3DiREvBveuuoO34hReeZyXq99x9N3uMrRlN09Jg3brP4JRBXjG2CPtjmYJLenC/2rCBmUahy/7ZwBm47777YNLkSew747aBfb70O48ePZpFqwFXcJeQZfIb80vjJcro1t3tyivh0KGDTCkePHgw3HjDTaaCizFBnOA+a2xvTzzxBFtOXH40m1r/1Xr4ZtM30FWuS3380fgaX+Nr/IsdP0MouMWcf/E6iRJYcd7O4cquLQOXX18xo9Cs72z9tz6rBxddlNtwgitMpgS5TeXTpcTctzbnYLQHtwrpwa1MKnIp0Y0j/I+SW9VoKpqQWZdSnuxSem9LrAc3SilJdnJRjlZKlGNI43CsUp6cqLhsiTrtKmQlVeVTTVKiXJ8ouKqLcppSoswU3EKp4O4lWZ1biy8bsA3DBJTyA/tzgWIqHSIqdDCeWjmuS0h8JLg/f7McPn7kKoPk9jQI7ixGbFch0f1+Vongl/bya/yyh48Hxyzev2Fl4fqlg/Je4qzs42SYHXwLhMkB7QsxCa44WFt5bZz4SmMkr02h9HpzSUmuaoZE+k+VuJsftv/AslatvlQPidDxwo87fiQKpyckvid356/Cp0qqE76HPBcO/9cu/4XCt3Jwzb85c+Yw1Vs8c/Owm2WJMvY7M7WT/2GP7axZpoJ7/MRxGDhwICOd27dtY889/fTT0LFDB2hmkNT+/fuz3w9YD+7rcNdf7wra/nfs2AFNmjaBjz/+WOLjPJq3aA4tWrRkPbqo2uL2yXpwWUyQn+WFj75/NDO9wjLlIdcPYUZV//vf/5hj/cmTRXI32LBhPTO86tO3DzfOugGWLLFKlJEkixJlJNg3Gq83M/Av63gZc5TG8m2m4C5eDLcYpBpnsn3bdmYklZ7emhlaTZ40Gc4YZBdNtcYY61IffzS+xtf4Gv+PgZ+5OSOi8y8qtTQH11aiTFIr6PWXaB2zeJBZibfPysFtw9XbVsRFuQnhV9TYt14IF+VkpUS5AuF4icRsKpFU9cYR5dal3Hc7kNlQvbglmoOrRgXFKDm4ceR5mnXkFBEkenArEQW3MnHmqu6Qgytk8kv4ym+i5OCmE4I7CPuj8KJcEFyf388uwkNWGhQ/+BJi8w49h0Ag7K4ln/vYILGh8FeQ15DgItFd/f2LkuiKd2O5IGbfygs0go8XUEuWLWN9YiW5/KtXr2Y7pXgWjX4WLnwX9uzZw+I/1qz5NKLlL+31r/GDn/PtFSHhe4Mm01XZIrp+UaJMe2/zRA6u6fxnliibB2V5MGbuytmKmZGXlOd47cTORursZT1SaVTyXeVJw1aSa0XqSAVT4xeLj6PNdPsbOXIkvPnvN3/19ldUdNL2Gh6nDhtk9Hy2fySwx44dLxYfVd6jR4/J19D4CkusnfaJ06dPR4z/y+HDpnN9Mfio+prvM1/DdfnvN9/Uxx+Nr/E1vsb/g+Bj208k519mMpWfL6+pJPHNyZaqrXr9hfwni1/D+fxW8kWh5aLcnii4zYiK28ghB1eot3SqrlTeVlAMpsQUR1pUEwhXjCMKrlu5r+bguhzE1/MmuDHkvjtMAzD9cvHki4vbRMVJS9RoCzm7EpG6ReNyDb5Sa5EeXGEyJVRckdnUjNhdd8D68v3798tRC6FA4WiG80hNmE26mObzoFcCITKzAiF2BONmxdgu7I6xIsHlMuOBxB+WLcuLoryTrFRZliuTEuWtBsFITEhgyoeKj47KOM+CgvzzWv633vo3rF+/Xj5GteSpJ6ex++gKivioiGzbvh0ee2wKtG3TJqLlL+31r/GD5437TDY7OJKIINGH67c7Kwfn4JoH2wJpJpXPFVxzRDKPj0LmSXdlQrxor6gt9oaSMS/pVyGuwETNNN9vj8IR+B5C6uwmTho/HD4tUca4nnbt23Gl9fe//f/tbw/B8889Xyr4bF22ayvXpT7+aHyNr/E1/sWPb1Nww5x/c3JypINyHie4XlmibKVW0OsvP03C8FlcqHB/oVBwhcmUcFEWfCqVqLj1ibgoTKacDKaqEMPgCqT/Np7wP2ouFauUJ8c4JPXEOsTVlpiDstp/63YI5XUTedmtNAwnkJprIU9TVl+B3E9WooKEgiuiguoQcptCasSFi7KYWnPZ/WpbVidTmcyGa6eKBNtmFzTqEqq2PvCrvNac8FeQEmUVf+X4TkEfRtLLFNzvZ9tmdebsaUdMJLguRnD3nNfy9+3TF6ZNnyaXH/vIxB+qx03TmthmjS6nkSx/aa9/jR+Mj/vMVnRR9vvsZcp+f1CumujXFQfWPJqBS3NweQ+oUG+ZuzKPCfLY1EcvicXJJQqmx2ag5FGUTI9q0OAhtvteqwfVRgQ9Ho0fIX7G5gzb9ufz+YztJXBRbP/opHz82LFSw/ftNdelPv5ofI2v8TX+HwMfCW4k518RE5THSax4bzYvURaCAb3+EsKD4D8iDYOXKA8mBlPpxEW5MVFwL+G8qx6f6pIs3GqE5FYhQqUwlxJKbhzhf2qZsiCtcaQy2OVAdKMccnJLhOA61Tu7lYZgN2HlbsLGqXqbSNTbRBIRVIFEBFEXZaHg1iYElxpNpfIfQjgpN+cKbktOcK2YIKHg+k3L7OIc1MLafIcqYAhAyNGgoPk54IsyZFRZo7iKS1+T09hOrF8Xb1dvIwpuAFh/Wf169eEIK/ELwDPPPAMNGjSAGjVrwHPPP28quHsKTAfRoiIYecdIqF69OqSnpzNyin+4c9SrXw+mGyS2Zs2azABl2bJl7LXhw4dDfHwcVKxYCa68sht77vrrr4eFxmffevPfkFw1GWKiY6B+/fqQkbGZ5VOisYpY/ldffZUpvrWM+Y6+7z44GzDJ77x585ibLRr6xMfHl8r6L+3fvyzi+9UcXNnDoZQn4+QzA8Vl9i2NCuIjjrIUWZQoU5MpmzGSpUxSQmdzEyalPDZTpVyvzW3YPAnkWicM6jbstUfvaPzi8XG/1vufxtf4Gl/ja3yNf/74LCYogvNvNiO4ouUrT57/c1gObp4kvvT6i+bgyso7nw/2my7K1/B2zjYOLspCwW1ICG5t0jYq/JGSiX9SFcVXKYnEwiYSF+VYYkRMW1ujlcggdwgy6ypJgqs2+TqVJ8eE6L2NIwsj+nBpBm4SiQkSYcFV+f3qxIq6LjGZEk3PlxKC24y4KLfmdeXXYCSE1S/IVaasLLKdBSIadwkU80TA4clQ4c/2kSLrkVmiHDn+yvGducnUixIf+16RxB46dBg++vhjRjaRVH6+7nNo1aoVey2/oIC9G+MwMJLii88/hzkvz4H4hERWq7971y72vmuuGQwbNmxg8RyXNmzIPvPTTz9B5y6d4f7R98POnTvYc127doN58+fD/v2F8OSTTzLjlqysbDh2/DjMmjmLkVb8W7duHSQmJsC/3ngD1q5da8zzUpg6dSp7bcaMGRDlcsOjjz4K//3yv6Wy/kv79y+L+EKV9VEXZb+p4vr9Vmi4Tyq4WWQE0TI6kDFBqNTy0UlhPCXigwQBk6W2Xnt8jZc4AlNyZjNX8ngVa33qPEzf77XF6nhs89b44fDxJLto4SLmCowxSgsXmreL2GTcX8Sf47f4Pvb+hdb72XPsdfGZRfIz5vvE/Bex+S5cZD0W79H4Gl/ja3yNr/F/7/jyuqiY8y/m4NLrKnE+FzFBQjCg118yJkgxBeUlygNJTFArYtbbiCi4DUhyjSC5NYkAmUw4W2XiqVSREFvqwyQexyqpO7FKS6ybkNgYxdg4qiRLlNXsoWiFbbsV52TKwuMUc6k4xWSqPIkKqkKalauREYLaxLlLZOA2JOXJwmiqBXEDwx9tIHNR9ouYIJ/8sYMv6Yv18rbRg4BTXYMDaQhPU+wAQsF1cQVXnVxRLhv+inGdGMGlPbg+QXAPH4K//vWvLO9T/H322Vr22p6CPexxcnIVpsyim+nx48egR48eMOOZGZLg7vjRJLCCNKOFOeJjZiTGb4jl79atK8x/dT57LxqkYPSH+Js9axb04gT33nvvgVtuuQVOHD9h4B2HOXNeYg6mJsF9Bjp06FCq67+0f/+yiG+PCRKqrejD9VlEl/fhZmGJMsm3tWzruVJL3JKFqitLlD20l1RY5juolk7mSrbyHqtvhb6mZsLaCaFH42t8ja/xNb7G1/gav0zim0RWqLR5EsssUS7gfbj26y/kP6ZRqM8m9qEBL+nBbUlclNNIH65QcOuT6tk6hOTWJBm4osW0gtJ6Wo64KMcpJlOxpOo3Ngy3dIXwgSoxF2V3iMigKEXZpUpvHJGgxULRhVWjgtQc3BpEDq9DasAbEIOphvzHaMpHHgTBxRGJwViyiz1ifmmQ4zNLlB1kroDjJX8EvWWBECUPASfCEXCo57dycM8FH3NxVzEn5dkSH/MeXVzB7XlVT5g+fbrEx/Vglijnw4GDB4z7LvZed7QL3O5o9trtI0bArt0mwT124rjEjI2NZXEx+Ne3X1+YNm2a/B5drzAI7rx57D66qXYURNX46KzZBsHt1Ys9jI9PMIm6GyeXxMc3IrEecPXVpbr+S/v3L4v4GAdkElwzGsi/1zKXkvuUzyx5EXb0lmqbZ6m0wmSKuCVLZZcTX9pD6iHxNtaIZi7vIfUERePQE4tTX6pXjdcRJ6BcS+HU+Bpf42t8ja/xNb7GL4v4wmRKqLPSfCrbKl1Wr7/wuj+LX8MxkovChHENV1i4Xyi4bTjBVXNwGxMXZdGHW4e0jNYgYiRVbyuQytzyRNwU/C/BwVzK7ZCDq/JKdzFts+ddoqzKxrQ82U0sn+NIfXUcUW0TCOktR3pwhZxdRZnUEuU6pES5PjGZSiM9uOn8B2vHSpT3FXJTHB+X5/3w3dbviukDd24bDygPQtXpB9RCUIVEBDEJQlgtBdcggK4oTgJdUsWl+CvGdw5yURZq68FDh+D/brsNRo++X4JmZmay+RXkF0DgbAAqVKgAmzZtCvoeu3f9xN53nBFc8y/GILhbebxS3z794Kknn5LL2BUV3PmmgvsmKrgGwRWvzZo1G3r36s3wb7/9dhg3foLj8j8741kYaBDc0lz/pf37l0V8U5XNYmUtPmkyxUmtzUWZmkzlk1HEAiuAXJYimwd0UUZjKrg5tj5QLy3F9ZDQc3Hwz1Ut9emoqHoCsspxc4MicmhmrMbX+Bpf42t8ja/xNX7Zw88WJlN5IgdXPJ9ju56i118iOUbwH9Mc1G8QXNmD257EqzbnXMqpRLkeKVGuTsqUqyvVt5VJMg5tRU1wSNYRHDFGMSp2KlFW829LRMF1K8w5SpGRnUqW1VzcGKU8OUlRb4XRVBUid1cnK1HI4XVJBm5DQnCbcpIrenDTuYI70C9MpvxWWaWZgxuwX+kHQhRxBkK/aLcED5GBFbZB3Y6vKrjF4eP7GcE1JoHECK7LLFF+fcFrULFSJdiyZQsjvFcbBBJfK2A9uAEYOPBqNh04cAB8e3zQr18/+Pfbb8sSZSwjFvio4LKBAeMPTaPQWKqIZ+12u6IrzHt1niS4HTq2l8s4c5bVg4uvVateFbZu3QqnT5+CSZMegeHDbzFLlJ+Zwb5faa7/0v79yyK+j4SEU9dkm5Lrw3w1HzekyjLJbAHGA3Hbeml6YCq44kCeJ3LbjCk7O4fktHqCe0u9ln2+hxz0xckg1+tRMl29tlFTkQ1rjYjSExF5rPE1vsbX+Bpf42t8jV/G8EUObj6PARL4Zg6uVRFHr7/Myrosq62MX8NxF2VRotyKGPSmkRzcVM616nFRsY5SolyNT8lExa2oJOUkkBzcRMIFaYlytHI/RqkIVpN83CVJcKOKKUl2kS/lUoymEsituE9LlMsTJVfYTCcTJ+WaRMGtRwiuiAoSUrrowW3GCS7+aINtjdW+vdIVtjiDngiFteCnA+CYmRUo9vMBqeDaem9dov/WnosrFN/VzGSK9OAW7GHqK0YCYWnqqFF3QmJiIrijo5kxlBkTZJpM4QbevUcPiI2Lg2i3C264YSgcO3YMdhGCK/7ijfeI3uU1a9bApZdeCo0bN+YKbjep4KKTcof2Ha0e3NmzoE+fPnL5H5wwASoZpBtLopulpcGWb79lrz377AwYNGhgqa7/0v79yyI+cx039hf/3uBIIEF0feRx1tbvZH9IPrGtF2ZSrESZH8jz84iCi8TX6wku6/F6Fddgkg9ny27lUy4tD1Jdgz0Ew2uZK4lSII2v8TW+xtf4Gl/ja/wyiI/XSfnEJdn2vCC+yvWX9FHxEaPQvX7Yv3+/ILjpRMEVYmETQnCFgtuAENxapLo2mUwViHpbQcnCpVww1sFYKjYEv4wihDeqpLNwncJ1o5VeXMq24xWDKXE/gUyJRL1NIsHA4ra6UqIsar5Fk7OoB08h5LYpycBN5yZTg/FH9MvsTquM0tGyOxARzXDuigwEIqv6dMLg+Jhrey74qOCu2jbLILkvhsXHOKBjR4+FxD929KhJZs9x+U+dOvWrlh9zcw8ePFjm1r/GD37NMpnyO5Yk+0hvu0mGuYuyotyKkhlTqTUP6NLtjyq4ZLTSuiWZcKIcyGvvZbH3rlgjoN6g7FgHB+EQo6Ua3xk/yziZZmRksGkzu81k0UGbM8X9DNuEEQiYnZuRmRHitc3m481ifk7zybQ+T+aj8TW+xtf4Gl/j/57xs7nJZnHnXzXvVpzz8fM0/5Zef8moR78iTvj9oge3HW/rbE58jGiJckPSFiriWmuQiZYoV1XIrdp7K2Ji4wlXjCV5uMK7Kcqh/1YtTS5RgquaS6nSsZt8WZqJG68ouImkB7cCaUQW5DaZNC5XJQS3JlFvG5AS5YYkB7eFkoM7yCxN9ssLc79wUQ6Eb0QMFBd/FfbjgeJJhwO+cFGOFH/l2C6wGgku6cE9H/zSXn6NX/bw/bS8hU44GujzS6MpMTLITKbyTXMDu6NfHndRzuakKpe/LkqXc0isjZID5/UEZbWK3hRZDkQ+I4ifjdh5PbaSIjMXlsToUOMHjR8WH0/Wev/T+Bpf42t8ja/xzx+f5eBGcP7N5nm3gsCK8z8zn+KmUur1F16fobGu4D8iDQPTL1AE5FwpnfOmFkQwTCEEt55CcgUnSyblyclKDm55UqFbjvA/tbqXtrbGKNzS7XDfVdIuytEhFN1oBwesWEJ0YwjBjVdIbjnSh1uROHBVISXKYoSgDunFrU9ycFM5uW1CcnBbkRLlgftEDy7pIaQKbsCpTDMQviRULeoMFFszGlo9o/PA2J+V47oYxLUzI7srxK0xrSS3K433idfVEuXzwS/t5df4ZQ9f7C9+28ifL0SJsp+91yw9LrDCxkkWbrZ0UfaSEmau7FKzBa+iKMqDvqU+epws9z3UgZAolV7FwdBLc2Q9wWYPGj8kfmbmZr3/aXyNr/E1vsbX+CWAn8EJbnHnX+qijORVnP9NBTef9Oda11+yB9dHRQosUS5UFdwWxEW5CXFRTuEEV1TQ0oigGpyrVSUqLjWYKk9aUqmCG6dwxTjFy8mtcEs11afETKailLpoFyG3LpJfJF6LU9ywEhTGXk6ZKvJbQXCTCcGlfbh1SaNzfT6q0JDL6M1I7bgwmMIfbIBPqLeitNLHCW4g9DbsuLGHHd0JnS8aCIDzTqPxNf7vAN/PSSstTfaJcmVZ8mIRXrSjF/0f+cRkSii5zC2Zl9xQu3tTwfXajBeCRjBlOa7XRtSoOYOtz8VD3m/LpvNalv1SCfVq/AjxN2dk6v1P42t8ja/xNb7GLwF8k+AWf/5FIUBUvZklynbia6m41vXXXlqiTEjufstFOZ0Q3BaE4Ar19hLSg1uX99/WJkbANTixrUSycMtzUltBIbflFDfleMIbo5XYIEpoYy6Eg7La8Kv24bpClC7HOFhBxxP2nkgWXjhtqVFBgtzWInXfdTm5rUcIbgr/MdJI/bg0mUIrbFp7LnI97TUGAcfhmECYsZ5iSxsC4Xcfja/xfw/4LCYoO4uQWHtYuH3ysyipAtlzKw64+XaTKX5QlgRXli6TklmbGmnPlrOZO8j+FK/ynEcJXVeC10k2HTWO0PjF4+PJWO9/Gl/ja3yNr/E1/vnjY9tPJOdfWaKcL0qUzfN/jogJMl5Tr7+EUajgP9b1mk8ouK351JzEBDUhfbiC5NbnHKwmIbhVSfJNZcLjKhKDqSRCcgUXjOWPYxSRNFpxSVazcUuc4NKZuhSy6ybOydEOLNyJ4MYSk6kkEghciRBbOtXgJFeouA1IiXIKkdLTeHlyCzIiMbhwXyEvqbT6cL/jbsDhR2WK36gDgRBxLIFINnSNr/HLPr7oq/XzqC2/34oI8kmy6wOfMHDLyjJz2mS5DBlRFCXK/OAtXJTzRT6uzULfSZl0CkW3Sm+9iqLpIdb81vNKKa/ieKjxi8fP5ARX738aX+NrfI2v8TX++eFvliXK4c+/Zq+tlXUrXpcxQfn5QddfyH++kzFBeyUX2rdPlii35QRX+Bc1IwpuCsnArU9ycEV5ck3FN0kQ3PIhCC7lg9SEmLoqx4TIwnUpKm6JmEy5HXpxo5USZVqqHK186RjSg5uoNByLJmSq3lbiK6oKWYE1SP9tXZKDK0qUhclUS0Jw8UcbhC7KqjlOllRwI/sLkHID3JQDgXPaD0KU5IfHt6KBXCwq6LfGL+3l1/hlA5+psjIH1zww+qTdvN+m5gq3Pqfy5Dyed2uWKJtES4xEFggXZdEXajM1IuHqhNhNnjwZUhs1gtTUVGiUireNjNtU9njjxo32cl4lc86rmDB5lb7VSPC9NqWUlvx6pO3/xYq/eXOG3v80vsbX+Bpf42v8EsAXJcrFnX+RyIpqOLx2Eud/vH5yKk/OpyXKJCrVNJnyi5ig9iQmKI2ruIJXpSqiYm2SgytKlJOJOFmJqLflCedLJPcTiD9TLCG6IjIoVuGZTmZTJabghitPjlHkZLdCamMVJ+U4JSYoUWlGrsRl7iqc5FYncnht0od7CZ+EhN6EE9xmxGSqfRDBNX5QHzGZshcLWM5n5zbOEwB1sCZQrDtbICw+ktqDX8+EQ8aEtwc3zmQkV2ThupSMXJGPi3M4cfwEvPPuO3Dy5MlfjX+hln+DQTxwJy4t/NJe/t8jPu3f8BNjKb8cDfTZIoNw8CjPVprMRxL5iKKIA8IDeR45EOOBmxG1XFKWQ7LhqEkSvm/b999D5pZMyMzMgF69e8P06dMhw3i8JSMTdubuJGW8Dnb7ttJej/09EeJ7SV6d5VLsDXI6vhjxM20lyub20rxFc8gxfhO6/X3x+Rdw5ZXd2f01n6yBZs2aQVpaM2jevDlce+21MPXxx+GXI7/I7W/8uPEsG7uZ8Z60Zmns/ZMmPWLO64sv2GtpxnPN+PTolEcdt3+cP5uP8R7EHzN2LGzdujWi7f97YxkWLVp8wfa/IuO4/OJLL8Lhw4f18Ufja3yNr/E1PosZiuT8m81LkdlUkCfP/4z4SoMp+/XXXp4cI/iPGemIJlMHRA9uayIOCj+jpiQHl8YE1SGmv7VImXIVwt0qkj5coeDSDFwaERTvEAfklNbj1H8bfSFIrpqDG6W4X0UTJTeWLFgiuS1HWH1FUqJcmaysamSiPbh1ST24kNCbkpGHFsRkapDf77f130oXZXUkxaGnPBCItMog0tGgQOiRnICl3CKhPfT1LDiwYRa7f2CjcbtBkF3zOWuaJQku/iHBSEhIhG3bt4XFLzpRBFOmTIEDB/b/Zsvfp3cvmDVrVtjlL+31r/Ht+GbZcbY58uff69h3S03csmiJckGelcnGldocXqLs5SXKIiqI9eDS0UtqhOS1myR5FBfhfv36wezZs6XbYP9+/eGNN9+Q75sxYwaMGDGCzevmm282tvvHjG2xN7Rq2QpGGs8jqRGKKJKPHj3+BB06dICxBjHK3bmT4X+0/CNYsOA1R3zZv+pViCPpfQ3qc5W3uXZF9lcs/2+NH9yDG2BkEsun6N+6z9dBt27d2P3Vq1dDj+7dmbFFfn4BrF61Cq4ZfA3cdOMNcPyYmcGN63vq41MBfRNwYBJvMaMbt8kP3v8A7rprFHteTMfwcw7bP5Lo9evXw77CffDNN9/Aww8/DO3atYPsrO+K3f4Xv/8eI98Xav87cuQING3aFHbv2qWPPxpf42t8ja/x2aBxJOdfmYObZ07i/I/XT0Elyvz6y7yG+87ef2tcz+E5lBPctoTktiQEN5WXJ9MeXMHFqhMH5WpExa1EyK1aphxHSpXjCMGNURyV3YrHU7SD51OJ5uBGKc7JMQ6NwG6lfjqGqLYxCnOnJlPliMlUZdKsXFUJE65NHJQv5VMKH2VIIyXKYsIfbTCLCSIX5kxlys6KYLMMBG/wxZQh7NlT4PjeE0Un4MSJE0E24U6zQrJ6SJBYJLlIdjdaSq6d4Jr3UdGl3/bMmTPO35Xgo4KAWLm5uSW2/E6jWfSvd6/eMFMQ3Auw/ovDj2T9a3z7q0KV3RuG3GKJshgZNBXcfFkeI/JvTaU2j/famgdsMeKIB2V83msjYXYzJNUoSToCG88hoWUEl7937NhxBqEdCd5c8339B/SH559/nr3ef8AAaJrWFObPnwfvffA+I2CjR9/P5vXhkg+Zejhv7lx433itc5cuMHXqVDbPUaPuhC7GYyd8L3UqJr2tAj84k1bprxEns1+5/L81vpPJFJJKNMCgW8/n6yjBXQU9r+pp2+b8xrH58ssvhxUrVrDHY8eOYYMRTlv0/Ffnw6NTprAnzp49E3b7R5UXKwWsi5YA3HvvvXDffffJ+X3wwQfQs1cvaNOmDdx+++2A54mVq1bCZR0vgxbNm0P3Ht1hp3FsPH78ODzyyCNweafL4YorroCnn35G9l5t3/4DDBlyPbRu3Rquu+461qsu/hYvXgxXD7gauna9gpH2wNmzbF7drrySbX9du3aF1xYs0Mcfja/xNb7G/4PjZ2RkRnT+ZaXIMuc2X57/TXdlnn2rXH8x4YGXKPtoGoaZg3s1MZkSKm4TJSaIlijXJU7KNUiZsihRrkyIbQWi3pYj/I+aTEUr3DGGtLxGK7240QoPLRGCG+3AmN2KwRRVeFUH5RhFxU0kpDaJrAiRhVuZjAhQF+WaZOU24ARXSOhNiIrbkv9YpETZb7sgzyI5uFZBgZOTWiDiERu82EFFdMu3W2zv2bNnD0yfNh2WLFkacl4C/7HhreGxW41peBuYakz4eCp/zF4bLl4z3ycIbqtWrayLRmOjbdCgPvzyy2GYN28+3HTTTcbF15+hYqVKcOONN7GL2ePHjkHdunUZwa1duxY8M+NZho/Kdq/evaBihUrQr28/g/zuZPNExaV+/fowadIkqFy5inFx+D50vKwjLF26TOLOMOYxbNgwOHbsKNxxx0ioWjUZatWqDRMmTOC9CwbB7d1HKrhYMtixY0dITEyAtu3awzffbDqv9R/8WiDCT5TM73+x4vu5KZud1PpsZgX0Po4Uyh4QcqAVo4o5OZZbsszBleZTHukG7FVVS1J261XMkvr1RwX3RTkCunz5cmjZsgX8uGMHU/AaN2rMTiA47wEG2Z0wbpyMx3nz329C+w4d2XzGjBkDd44aBdu3bTf25+3wwgsvQN++fdh7UZnetOlrR3xb/I6XZtV6JY7ocaWOiF5bf43nVy//b42P5VRBBNcglYsWLYJNxn6M6xz353nz50uCu2rVaujZs2fQ5+666y55/MH1P236dCgqOgknjamoqMg4dpxl75v21DS4dfhw6H5lD2hjEMp777sHjhw56rg9m2pytu1ZPP4KfCyvb9OmLfu+WOJ+/ZChMPkfk+HQoUMw+8UXYUD/AfDjjzvg5MkieOedt9lxC5fpk09WG9tVK9jw1VdsPkOH3gCPPTYFdu/eBY8//gTceNONDP+bTZsM0pvOMLEf/CqD2M+Z8zKcNQ6EW7ZsgbSmTWHdus+ZQq2PPxpf42t8jf/HxsdzaiTn3xzSa5snenC9gvjy8mTl+msvi3rMIoLEXm4ytQ85wGAuBqYTg6nmnNxSF+V6RMGtzXlZLc7VqhL1trKi4iby2wTC/xKUttUYMrlJLy51UVbFVldJlSirpFY1lxIg1AnLTR7HE+fkRAcVN4mw/sokA1eUKQsFtw7pv61PpPPGJAu3JXECa2uWKO+1ZXiiUc53YUymQo/wBELmN4udY+nSpfDoo1Pg2y1b2XOo3L4y9xWYNm0aV3cDYXceSWJvNQntlOGU3KbDFEp2bxEkdyakt2opZ1dQ4GM9vHixhmqIyxUFzz77LKxdu9a4qGsNEx+cCGfPnoWvNqxnBPejjz4Cn89nENNj0LBhQ3aBjxdlN954I7RKT2fz3LVrF3tv7z59jPl8xi7M7rv3PrjlllvksrRr2w5efuUVeOmll6GRQSi++OJzppLExcXCmjVrTAUXS5RnmgT38k6XwT333MMU5NGjR0Pnzp3Pe/3TshLnA02g2IOXxrfftwwKhFOyX5a42Er/fWaP+3dbs6zSY0JgxahjDuvB9XKTqXz53hzem0tHLj1qSS6xz6elO/379YXZs2abJ4Vcs4+lc6dOTEWbNXsWDBkyRBI7JC/4XjEv7O1MSUmB73O+Z6WjDY37KcysKsW8b0zF4VtxPCa+tPinZcJeu7pKe2lp7M+vWf7fGp8puMr2hwpuu/bt4bKOl8Pll10Ol112GbRt19YguF0tBbfnVUHb3xOPPw4P3H8/V3DHQhPjN2jKJ7z/3//+l702fvw46Nu/H2za9A3DH3j1QEaInbZ/VOFzpJpsvrDZuIBo1iwNzp45a3seWzVmvvAC/Pm669jj9957D67l9+3lamdZv3CfPn3hjTfeYC/27duXDfodOnjAhj/1sanw4IT/Z+9LwKOo0q6TzgIh7BD2RQKEACFA2FUcFxZnWFxmc0UJgoq7M784M26fo+IuKDiK4wCjfqKjAzrqiAuiqDgCGpIACkl9okloGlDckaXuX/fWfW+de7s6CRq1ZW4/TyW9VNfpqq6uqnPP+57ze79yx5seeeQRdtLJJzG/RPkLsW5bP9xqjz8W3+JbfItv8UWJcn3Ov/w6CXts6fxfJkuUq+Caiq6/SHhAo9DtsSiVKHMX5aFQ+Up9uCQe5oJ6S324HaD/tp2RftMKFNymQHKzgf9lGIptI+CMNKFrcorRk9tg5ckphkRsqroYwptquCenGVlHjcFNmVY8G9TbZhAUTCpue3BR7gR9uN2B4PaEYOL+oOCKHlzMfKL7paVlsLvW0hLu1u9p3JWXLHmUXXfdtezN1W+x+zzCx9VbruIm/rkE9wIF15+uB9WWk97r8bUpNM8gViAVXL6cqEekRR/v7k8FweXlc4TDyzgHDx4sHu3+dLcgwlyl5e977bVVLDs7m33+xeeilI5vJ176/MEHH3gE90OxzP8TBlE+zhtvvMFatWrF9u3b712sfcgyMzIEwaH1POD94yS7d+/ebM6cOeL5sWPHKgW3d15vNn36DI8s72jQ7R9WWlL74cq1+LXg+wS3VBv5E/dj27Xy5BiV/5eWaPm2yhBBHpDLKAfXO1hXVQUjjXwEUi+FNSzznWBUU8t69f6PP/54ds89d6vRTD5d+Yc/sEsuuUQM1MyfP1+V4vJy5WuuuUadPHg1Ah/I4fdnnn8+mzXrioPGr1CKKRhDOA4oro7e4+oEyimqsd92/X9ofLNEme8/nDyWiR7cYP/TSpSff15TcGmvmzZtGrtzzl0+wfUI67XXXsM++ugjMVV9+JFQcfntQ+8x+QXwd65e/SYb4hFq94Abt//7n2WD9hmfeOIfoiqF37gBHzeoGjlylEfM+0hSne8TXG++k2QPLl8gX99TTz2d9evfn/X3Jk5OFy5cKF5+fdXrHuEdJ9Tr0087la1Zs0a8iVfU5HnLy8/3J5+w54n3UA9uQHDt8cfiW3yLb/H/m/FFiXI9zr/lwkxKz8GtoOeVi7J+/aW5KMsqPC5WcKFKKrhFELHaHypi88BFuTtEBHUCgktmwC2NLFzswaUSZeJ/TYAPZgJfTDNiZ9ON5B7kpA1GcFMM1TbFcLYyXa7SQ9yU00GabgwGU9lAdFtAnlJraF7OAVvqLkZMUC5EBGGJcoH80iZyGT7KRy1iMUVyRa9UXIYV/I0bnXHDR23MonrvxnttuWrLlVwit64b8lNKgH+dUGgH+UR3Cqi1+F+97hFc7zksUeZ4oo9XKrgTJkxQUEuXLRUXV/z2qUeAU0UPrk9wF9x/n3RmTvWej7DUSKp4zMvytnokl9//Spm6+IpG9+7d2IqXV7D5994r1Ax+46WdvL8sPT1dTPx9d9xxp3htDJhMLX9+OevdqxeLeDhHePOveu3VBtn+oQekg9j+Fl/HUQ58mmobU4NGMancBjFBpYFqWxW4JFNum4oJ4iZTUEpTTi7KTlA66xgKZaBOOkFvaQUvUfZ7cNFW/4UXXhDqK5/4Z6JRUa7gFhUNZitWrGDrS9azk08+iZ12+uliebykf9DgwcLxd/OWzSKKaMaMGWK5jy55VJThh+HrSqqjmTsFxlBBpI8io5h1B07HB7v+PzQ+Pxmb+x8nefy7xdvKV1YKgst3teeXL2fHcgUX9r8qj7QOGVLElnvfFV/QZZdexm7lPbgh+/8TTzzB3tv0nnqWl4wXFg5g+/bui9vf8xXZ9m8HPBI8tbiYXXqZr/g+/fRTQmXm+zXv513897+zU0R5MWOP/+MJj+CepN57/vnnsfPPPY9V11SLj8LbMBYuXKSt/+YtW4T6PHLECLGuf/jDH4Srd9jvTxBcj1TzgUN7/LH4Ft/iW3yLv45clOs4//oKbuBtQud/kYOr8m/16y/Of/wc3GggTnjXbTv9EuVJ0INbKAluPpDbHkYfLsYEUXVtW6NEuSmk41APbjbwv8ZGG2vEiAsKE1AjIdm4DWYylWpIxmZsULohMWcYEUHpUJbcyCC4zSEYmLJwWwHBbQeSeGej4Zk2PplMoYJbpLkoQ8wJV3DRDtyN2/3cWhrK3ZAqA90dbc/XX7FXXnlFKrd17PUGPpUjKwV3CkziuUJRuqzUXe/5AQUFAcGt2SaIq09wbxUEl25PLVvG8vLyFMHlCu6WLT7Bfe65Z1nXbt1C15+rDTySiCu7uP6zrpglygS5I+2iRYvEc5MnTfamiaKsmd842eUGP/yGCi6tP3de/e0pp4ie3QPmQedbbv+DOup8D9//oYTPfzeivCUWlCOTYhsQ3qhPemM+GcYsNjKXqpZlNbyUhtTFKiC4QQ8uP5hX6D2iTqA+OviaPCH8XCi48/QIHW/Kze3BJnn7olI2eTmzR4bPmT6DDR40SJQi88droDxo1qxZbMCAApGry11/l7+wXCyXExi+/4bhK7Jo4DtQIuwYRk9YYqz3wB78+v/Q+GtIwYX9j5cFC1IJ+9/KlbrJ1DFHHy1GjatratizzzwjDL/OOuss9s3eveI9PM7HN5mK3/+5v4Ho8f/ic6HqXnzRxeysKWeF7v9cweUuyrw6hLdbXH755Wz48GHCEZvf/vfRJewo73N94h0j+QDopMmThdLPl8LbKTjp3rFjp1jWlLPPYn/80x+FcR+vWhlQ0F8ouPv27ROeBi++9KIAffrpp4URFS+BfmrZUjZy5Egx2Ld//z5RwXLFFVcI7L379opBlKfAk8Eefyy+xbf4Fv+/Fz/owa39/Mtbb6qqArdkOocr4ltVHXf9RaJeDBRcLkzwc5x0US6SvKmfnFC9zZXVsmTw2wU8kSgiiEymUMFtDipuY6NFFQluIyC5ZlRQGN9scPU2NUQWTjVYNkrLGdAknB7Sc4tZuJST1BxMploZJcrYg9sZmp2J3PaEDNx8KbXTaIRQcGPUJyhzoEqNHNzEyc36DuuyxK3jdWQ9qx9GaI2/gU99uKIM+YzAbIoT2+uA8HKyy//zHlxaQo1ScD9lt93OFdyJCm3ZU0+x3pLg8gutJk2z2UN/f0jg84GAps2asnvnz2d7934jenYHFhaKEtStWz8QpPlLj+DiJy159x1hVtWieXO2+7NPxXPHHHusiGThpcv84i8zs5EkuC4bM853Ud7nXdCOOnwU+6d3IchvDz/0kEdw27D9yh31x93+Fj947Pesl6gS5ChEAsVgRDAqCfB6QXAh/1ZzU6a8W/+g7RPcGr8HV7orO2CN78QZKUFfKb3uOBqRoxPDhg0b2dBhQ9nixX+HGB2/RJm3DvDeb+6m7mhuw/57t2zZLEY8TXzuqltffMyYRXzVCwt4Jn5DrP/3iR+UKLuaasq/Q9yT/Jigo8T9F5YvZ3myHJiT4ckeqeQ5uF9woyi5/13mEdHbJcE19/9du3ayU089jfX3CCYfgODvj3OtZ1Qu3VeUHHOl9KijjmKXXnIpW19Sovb/Tz7+mP3mt79hffv1E67JF194ETvlt79VFTgTJ04UlS6bNm30SO2bbMSI4ax//37shBNOZCf98mS2cNFCnyg/8rCIHxrqEeKRo0ayZ599Vn0O7rY8ZOhQsa58EGXjxg0K/5abb/aw+7K75s6xxx+Lb/EtvsX/L8cXPbj1OP/6ObhBCTLNJxTcannNZVx/xVQSRtBOJq7fdsTIRbkITKb6GSXK2IPbFcqTO4J6S0ZTLUDJbWJU6GYD/8sE8TPN6MdNMzhlxKgibvAyZZM5R4y4oFT4YGYGrtlQnAVyNVlIN4Ee3NZQy90Wmpg7AMGljd0dGqHzIQd3gJz4l3YCvzCKUllllBTc0gSjOKz+xmmhj43d3K1j2bXgB67J4J5MJcv8/xn+c7w8WZQoS3wsUb79ttuFgkXLXrY0UHD57XfeBWVaWpowSvFV3OdYbo9c8Vxzj7TOmzfPV3CFyVQq+/qrr+PWn/eYnXzyyQqfKyBcjeXlyYMGDmJDhw5hd0gFdxy4KPO+yGZNmwqcnJwc9uiSJUm1/S2+JLjUv+ER3Ri6kUeNDNyo3wLguyiHlyjzx5wEkZLo29r78whlV+sJje9DcUKVR4jKkf2n3B136NBhYr+sdCpYhVI6uYvyLzyCe5+WI6uTw++Ob56YEF8t4yeML0qUf6T9n5dV8f2uIfB5VBpXZsPmod5f/2O6bPcnu0M/j3ht9yeh+Pv3HxBu9mH4e/ft0+KO7PHH4lt8i2/x/zvx/Sqyus+/5WAm5bsoO4G7MjooY4myMJkq1YxBwUV5AvTgFoJQ2EfyK1Jwu4OC20mKjx2Ap7WG6ttmIFo2AWKbZai5mQZnzAxpew0zN46E9OV+J5OpSIibcmrI86lGWG8mKLhYf41Et0WIyRT14LaWG7IjTFiiTApur5CYoIF+Du5OeQEeVX2CnOCaIy5uLVUFuo1JLe5rbi2/Cjd87CcRPiephZK8FgwokES2QE4D1Gt84uXD3xaflwcecA9oL+/6+GPhtPxt159f9HGHtrrw+Xw7d+1Kyu1v8Q2Cu327FgmEhlPUg8sPoBrBVf+rlbU9P0ATSSOnP0VwNQLn6JE4qGBCH6lmuCSJHFfrVq5cKZRYZdBQ6auv76x7h23cuDHAwmU1EL7jxPe/Ej6qqT9VfL8H1/7+LL7Ft/gW3+Jb/O+Kv1YpuLWff30zqaD8mM7/wkWZhATj+su/hisxjEJjRHBPDDGZoh7cHtCH2w0qaDtCdW1bILYYE5QNTsrZYCzVGNpWM6FcOc0wlgrzewqrJG6wWyRBZFAEmoQxt8g0mco02Hu2oeA2h43TGmq7c0JKlCkHl8htnlRvMSpImUxpMUHexTjvE0w4GuPWNkxTr+p62Xte6y+mXvi+6VMKS03xDZ/ov5pS/f/fF/6Pvf4W/8fHp77aGDgm62RXv897PfwDbZUitspkqtpXcIkoBTm4VKJsxuFUGHE2hukCZrg6gfkSmjRo8zqOZsGP0TjayKnFrxXfdFG2vz+Lb/EtvsW3+Bb/2+H7Jcp1n39F3q28viIF1ye+5QHxNa6/tJigmKy+i6keXLNEOR8Ibi8gt1iiTDm42IfbVvK3lqDgZoeYS2FUbGZIiXJqSB+uWaKcapgeN5iDcmpIwy8yb3NKgxVBJ61GkI/UDNTbFmAwlWMYTGGJcldQcHtDDy6ZTBXKHNwJRHBjsqySX6CXlJTUPWxjDsCEze+awzd6c3pc5b5pumbxLX6S41NfbZRckw1iG5hNRUW2Gld7qe+WHP2qqRS5qlr0kJCKSNFBfCqXMUFxpkhGFpxeUmuQNJjH0QyYgvkqQMV0IFjdJHkWPzH+2nVr7e/P4lt8i2/xLb7FbwB8Pmhcn/OvILI11PJVpc7dfg4ulCbD9ZdKwohu1wSKj4OYoMGy4rU/CIX5klv1CilR7mD4I7WFituWUJHbBHyWmgD/w/jYTCNaNs3gj+khJcopDanghknC6UYPbkrIh8uA0uRMIwu3CRBecttqASZTraEPtz1s0M5yOszowe0FPbhEcEUPruoPhItyLFHGEoT6GqAd3K8ifJkW3+L/FPCxRFnLwd2ulyrTYz5SqDLYqAS5CpRaERPkq41VEEauFNwKiLVxsN9UKpLoFAwGS47mOmgYK5mkkJ5T6mZFYNRk8evE5yfTx5Y8xpY89hh77LEl/v8lj8nnlqjnH1vi319Czy/xn3uMnuOvy9fEc3KeJXLZtKxgmXJSWBbf4lt8i2/xLf5PG99v0ar7/Muvk5DA0nm7XCq7WgauvL4SRqGl67X+22jQg8tdlAdJgksmU0Rue8rpMDD37Sg5GPbgtoIe3JZQkYtENxP4H5Jb02gqDchsqlGenGr03jZYD26KUZIc5qKcZpQop0PjcIZRnpxluGxRnXYr2Eht5NQeSpS7goJruijnGyXKQsHdqRTc7ZDVWVJ32YA2DOMa5Qf6c24dlQ71KnSw+BY/CfH5wbFU9m8EWbgx5aC8HZyVo5IMi4NvDZkcYF+IT3DpYB3ktUniq4yRHE2hdJwKKMk1zZCg/9SIu0ElM1A3geTBc1oJsMW3+Bbf4lt8i2/xLX4S4XMijDm4WokypFbg9Re1jgU8yK/E2xHk4A6S6u0AcFHOA36Fxr5dErgotzZKlJsBx8sCs6ksqOrNBOU21bgfCSGziXpxGzQH14wKSjdycDPhecw6CosIoh7cFqDgtgRnrpyQHFySybvLjZ9n5OAWAsGdtGOHX5pMBDcai4mL8ISVBnWLXwnoReIluG7tNfwW3+InK350O4WEb4+bfFflgOjGqEQZe2+rKAfXd/7zS5T9g7I6GAt35TLDzMiB8hxHP7FoJxW9rEcpjUa+qzppaCW5QaSOUjAtvsW3+Bbf4lt8i2/xkwxfmExVV6trKkV8y8uUamtef3H+Uyqv4aKxIPliZ+CiPAQU3L6g4vYKycEl9RanHKPytplhMEVTJrSoNgaumAkKbsS4b+bgpoaIr9+Z4KbD/UgtDcD44RrBB6f/WYaTFtVok5zdAqRualxuJzdqB+jBJZMpUnEps6kv2F0P5fXl3M2XRi1IgeKjGeFKWS2Uwq1fq7kbp7TVoch9T/hkRJUqTKpSfnD8H3v9LX7D4PPfTJk4OEJEEPXhxnRn5fgcXP9gW6PMpKqlguuPSFbJUcgq5a4MB37sFdVib/Bk4EC/CrgCw2iqP78ehUP4lXBS0U2cLL7Ft/gW3+JbfItv8ZMHv7y8XDkoV0mC66gS5SC1Aq+/YpiEEQ240M5dO0nBJZMpclEmPtUTVNyuIC6SyVSYwVQrMAxuBv23jYD/oblUhlGenB6S1JMRElfbYA7KZv9tJCSUNwLycsRoGG4MNdckTyOrbwb3WxtRQaTgUlRQJyC3uVAjTi7KNA2UsvsELatTqEx+w3VYRaaboP/crbW30f1WXmvfJz53V969ei77ZPUcb5orJkV4w9yYOQE+CPyKLRXspZdfrnP933zzTe/H938/+Pr/2Nv/UMLnv5kS7qIci+plyrFYXK4a9evSgbUKM3AxB1f2gJJ6K9yVZUxQpTb66UAsTgWMoFZqBkqVxkhqpWnQUAm2+07Qg6qdiCorLb7Ft/gW3+JbfItv8ZMSn2KCqiSJpXnLZIkyCQZ4/UXCA/EfSsOQJcqTwWCqEFyUe4OC213yri5y6gxZuG2B5LYCoZLMpUjJzQT+Z5YpE2nNhMrg1BCimxKSk9sgBDes3jliNARHgJVHgI2jepsF6m0WRAQ1g4ggdFEmBbcjEFw0muopvwhyUu4nFdwCSXCDmCBScGO+ZXZdDmq12nwnKiB1WUI1LG553xM+J7efvDkXCK4/fbz6Lp/svjXHn7znxDxvzY2LGnr44YfZqtdXJUS/7rpr2aBBg+tc/7Fjx7C5c+f+oOv/Y2//Qw0/Zubgqh4OozyZT1E/UFxl32JUkBxxVKXIVKKMJlOaMVIwMoonFM1NGEp5NFOlCkdzG/ZPAhXBCQPdhh09esfiW3yLb/EtvsW3+BY/2fDLBMGllq8qhV8ucnCrFPHF6y/MwVWVd9Eo2+W7KJ8g2zkHhbgok4LbAwhuR2gbJX+k1uCf1MrwVcqGWNgscFHOACNibG1NMyKDIgnIbGpDElyzyTesPDk9Qe9tJqwM9eFiBm42xARRWHAbeT8HrKg7g8kUNT0fBgS3L7goD5R15SfsiEFMEKlMpaVwne/WS/dy63jCDXkyUfizrpQFj2q2RUOxv/76a/aVN9UHn5NVUm4/JgX3LSK8cyXZnavILp8nVRJcWuT4cePZ7Nmza13/A/sP1Ln+Y8aOZXPnzK33+v/Y29/ix99IlY2ii3LMV3FjsSA0PKoU3FIYQQyMDlRMEFdq5egkGU9RfBCdAFSpj6PH1zjgCIwnB83codIxrPXReRjnd7RYnUpt2Ra/Nnx+kl27Zi1bt26tiDdYu3YdW7OW7hvPrfEfr/Me02tqXu/9a9asERmA4r6cZ518PVi+/5518D6Lb/EtvsW3+Bb/UMBX10V1nH95Di5eV9H5nGKCSDDA6y8VE2SYgsoS5YkQEzQAzHp7gYLbDZJriOS2BwGyNXC2luCp1ByILfow0eMMI3Unw2iJjQCJTTeMjVMaskTZzB5KM9h2xHBORhaeaZhLZRomU00hKqgVNCu3hRGCjuDcRRm4PaA8mYym+oMbGP/SJgoX5RjFBEXVl10fr9nwVkVXJxRhb3PdetKU4PGmTRvZdddey0reLdFe3rZtG5t982y2dOnSOvH/Z8ogdv2ZA9n/8GkK/y8fe8+LSb52/Zn+fSLCAwoHqCVOOXMKa9yoEWvevDk7avRR4rnJkyezyy+7nPXu1Zudcspv2cKFC9mpp50m3vPlF1+yc86Zxtq0bcM6dOjI/t/vf88OHDggFdyx7G6p4JasL2HDhg1lWVlZbEjREPb2228n1fa3+OH4ekwQqbbUhxsNiK7swy3lJcqQbxvY1kulFtySSdVVJcqV2MtClvkho6Zh5g5aeU/Qt4KvmZmwOiGstPj1xOcnb/v7s/gW3+JbfItv8b87/tq1a+p1/vWJLKm0Vepc75co18g+XP36i/Mf3yg0qol93IAXenALwEU5H/pwScHtCtWznYDktocMXGoxbWa0njYBF+VMw2QqA6p+M2rhlqkJfKAazEU5kiAyKMVQdlHpzQQJmlYKV9aMCjJzcNuBHN4JasC7gcFUD/ll9JEjD0Rw+YjE5Jj3JUajUZXTyb/k9Wgy5er6l1urKlaLAZCb+Aei7/BuSD+l/3jpsqXsWo/kvlvyjnj81Z6v2X33/YXdPHu2ILp14ROx9UnuIPX4ujML4blCn/xKArz7TU5wC9Ri/u+DD9iow0exiy++mL2/ZbN4dvjw4SwnJ4c9/tgStmXLFjb37rmCvPLb/Pn3sl49e7FXV65kTz75JMtslMleevFFn+COGSvm5es/csQINnPmBayiYgu76OILBUaybX+LH4/G44B8gutHA8W2B+ZS6jcV9UteyI4+UG2rApWWTKbALVkpu5L4Yg9LJdjrByOaFbKHpTLOmh+JXVhfjGPa+xMBrAgUTotfP3w+8mx/fxbf4lt8i2/xLf53x+eDxvU5/5LJFKmzynyqLChdNq+/OP8plddwguRyYcK7htu5cxcpuIMkwTVzcHuDizL14XaCltF2IEaietsMKnObgrhJ/K9xiLlUJCQH1+SVkTraZr9zibIpG2N5cgQsnzOhvjoTVNvGQHqbQA8uydmtjMksUe4EJcpdwWQqH3pwC+UXViRKlHfslKY4USnPx9j6kvV1+PCE2/a4xoNEfZKuWQhq/Ijifklwe/TRR4WSu3r1W+wvgtzezKLbovXCJ4X2Ojn5Si0R3UL5Gim5gaJbMGCAtpzjx49jsz1cuo0YPoLdfMstCm3unLsFeUV8/iPevXs36927N7trzhzxGifBc+feLT5rXp/ebPr06WyH96Oqbf1/7O1v8XV8X5UtFWUtUWUyJUmt5qKMJlPVMIpYEwSQq1Jkn1BRGY2v4JZrfSgOluJWQug5HfwrTEt9VCVNAhiU41bEWfRjZp3Frw/+2jXr7O/P4lt8i2/xLb7FbwB8XsJcn/NvGZlMVVEOLj1frl1P4fUXJccQ//HNQWMewVU9uEMgXrWf5FJhJcpdoEQ5B8qUc4zq25aQjIOtqI1DknWII6YbRsVhJcpm/m2DKLgRgzmnGDJyWMmymYubbpQnZxvqLRlNtQK5Owc2IsnhnSEDtwcQ3D6S5FIPbqFUcCfGyGQqFpRV+jm4rr6nuQmKCNzEL+qRLAkySBP+eMLxv9qzh933l78IJZeT25qabQeFr8isLFe+jsqSpwyC0uWBbMOQtpLgFrIBnOACxPHjgx5cvv7Dhw9jixYtUvNw4yhOXvnDTZs2sdFHHMnSMtJYRnq66AG+8847xHzjxo5hc2SJ8vIXXmC9evVikbRUduSRR7LXXns1Kbe/xddvUQgJR9dkTcmN8ny1qDSkKvXJbA2PB5K29cr0wFdw6UBeRblt3lRWVg45cZXxvaVOYJ9fCQd9OhlUOJVGppyjqZaUTReMiCIRhMcWv0583j9kf38W3+JbfItv8S3+d8fnvbr1Of9SDm61jAGi87+fgxtUxOH1l19ZVxq0lclrOOmiTCXKA8CgNx9ycHtKrtVFioqdjBLltnJqDSpucyMppzHk4GYBF8QS5TTjfrpREWwm+UQakuCm1FGSnAofKtUwmmoM/+k+lig3BSWXbKZbg5Nye1BwuwDBpaggktKpB7evJLj8S5usNVZHtytXWFZrKUK9B3bin3ZZaGapW+f7gxe5qdQrr7zCtm2rOWj868/Qy5NpCoiuT243DMnxSa5HfH0FN1jKeI/g3nTTTeoxL1FeHEJw+e2EyZPZxEmT2NatW8VjTnbvuOMucd9XcOdo67+hvJydcsoprE2btqqsI9m2v8UPbsJ13Pu9xLbHRwIR0Y3C49KS9ao/pBps68lMSpQoywN5dRUouJz4OpXxZbVOfKi6yoeD59966y22ZfNm5lQEJ4bNW7awhx5+iL33/nvByCiono8/9jh7c/XqoBToO+CrxxVYHmy6Jur4ytwpAf6qVa+xB//2N9Gf82PgJ1p/Irj292fxLb7Ft/gW3+J/N3xuNFWf8y+/TqoGl2TteSK+xvWX8lGJglHo9hjbtWsXEdxCUHBJLMwDgksKbjcguB2gurY1TM1AvW1mZOEiF8wIMZbKSMAvU4DwpjR0Fm5YuG6a0YuLbLuRYTBF9xvDlAXqbTYEA9P/HKNEmWq+qcmZ6sFzgdz2gQzcQmkyNZl/iTGV3RmUUYYM0yTqGa91h3ZhGfWqegjDaGB8Umn/R5Yo68ZTA1m5ILhtxX9exjygoEDDP+2009gvf/krtmfPHkVwFy1arDCxB/eYY49hxcXT2L69e9kLL73IMjMz2Z133ukTXN6D65Fh/tqokaN8oywP4KGHH/YIbhu2f//+Q3L7H0r4gclULLQkOQq97T4Zli7KhnJLJTO+UusTKuX2hwoujFYG/yETjspxHV5uW8H+3xX/jw0dMpT16tWT9SvoJ/ZdbmjGl1NaWsZyc3MFIQus9gOV86ifHSUqFRKNltaFz6cFDyxgTz/9L6N3JhiB5SX7vXr2ZD29qX///t7v6pfs9dffMPpo4/Ffe20V65Ofz84+62y2cuXKhPiVjtlvq+M7cdl9IQ7KB7n+2IP7Y+3/n3/+uXCBDMP/5ptvWE11jd77ZH//Ft/iW3yLb/GTEF9do9Rx/jXzbumcz82nMP8Wr79U1GPMECdiMerBLZJtnf3AxwhLlHtAWyjFtbaDCUuU2xjk1uy9pZjYRsAVMyAPl7ybUkL6b83S5AYluKa5lCkdR+DDYiZuI0PBzYIe3GbQiEzktjU0LrcBgtse1NtuUKLcA3Jw+xs5uJP80uSYujCPkYuyW3shvlvHTlp7Hb9b907/PeMTmb1OuSj7BHeDJLccf8OQNuL+AKMHl5tEdT/sMHFxzm8jRozQSpTvnns3Gzd2nHj8ojcvV2N5efKgQYPYkCFDoER5rCpRnj9/PmvWvJlwZ26Xk8OWPProIb39DxX8GJa34MRHA6MxZTRFI4PCZKraNzfQHf2qpItymSRVFfJ1Kl0uh1gbIwfOqYzLiuPP/+kPf2IDBxayf/zjH2zz+++zl156iU2Y8As2efIk9r73mH/uHkRwnUqtpJfjc8M0FaODxkv1xOfTb37zG3bjjTdp70EsTmz/8cQT7N2Sd9lz/36O/cojuLxCws/Fq0yI/9cH/8p+5hHwuvBVOXICfFPJxfWvDb+29eflVOb+9+nu3eyiiy5iffv2ZR9+9KF4jhvP5ef3Fc/17ZvP8r3/3F/A3P947jaf58Ybb1TLO+C9vnjxYu/7HcgWLFigdsrPPvuMTT37bFY4oJANKChkv/71r9i2aFQu0mU33zzbO54ViOPQ4aNGiUgG+/u3+Bbf4lt8i5+s+OvoGqWO82+ZzLslAkvnf2E+JU2lzOsvfn3GjXWJ/1AaBk+/4CKg5EqFkjf1B8EwFwhuF4PkEidrDeXJrY0c3KZQodsE+J9Z3YutrekGt4yE3E9taBfltASKblqIA1YGEN10ILiNDJLbBPpwm4MDVysoUaYRgk7Qi9sVcnB7SnKbBzm4A6BEeeIO6sGFHkJUcN2wMgW39pIEs6jBrbNmIfHozfeFn5KaKogrd0gW//lUMECotv6UwzYO9e/zvtkw/L3f7K0XPr8Y5Up5XevvHnDZTpzvEN7+hwo+/V5i2shfNEGJckzM65ce1wRh45CFW6ZclB0oYZbKLpodOYaiqA76PiGrqKgQpGjh3/6mRd7w+Kk+efns6X89w8qEgtuD3XLLLWzosGFsxPDh7Po/X6/wp0yZwv4m3//ee++xmRfMZEWDi9iY48Z4BPNBtVxe4nzhhReyoqIiNsojTTfc8Gfx/IwZM1h+nz6sf0EBO+GEE0AFdRTp5Mry888/r3pgH12yhOV6pHeTh8efu2fePezoY47xCNlQdtnllwrzqAX3LxDEjves88El/v71JSVCbeY9745jGkmZPTsBvlJqHcNB2cEc3cp4s6latv+6dWu03cT5P4eNPmo0mz59BsvztseHH34knueu6ueeO0McG3Z97E07d7Evv/pS2/8+/OgjsV1PPPEk9mdvu9KCz5k+nY0ZM0YMBty/4H41/+9+9zs2derZjHsrfPnll+ycadPY5ZdfLl5btep1sb22bv1QfLYbbriRTZo00f7+Lb7Ft/gW3+InLT4Nwtd1/kUXZU5e6fzvK7jV0J8bXH+pHtwoihS8RHmnqeD2BxflPHBRzpUElypoMSKoneRqbUDFRYOpptCSigpupsEVMw0vp4jBLc1UnwYzmUox6qJTgdymQn4RvZZpuGE1Nhh7E2NqLv8TwW0NBBf7cDtDo3NXOarQQ8rofaF2nAym+Bf2iyipt1RaGZUE1028D4fu7LWO7iTOt3JdFv6j+QHwOXH1vhL5P4Vt8AjthiJfweX/NxLR9aZDcf0t/nfHj0nSiqXJUSpXViUvAeHldvTU/1ENJlOk5Aq3ZFlyg3b3voLraMZHcSOYqhzXYStfXSnKj0tK1mvmDHwaO3YMu+uuu3wFt0cu+9nPjmZPPPEku//++1heXp5QBvkyJk2cyObNny8weM7zsccdJ8ro+Xvz8/uwl19+Sbx2ySWXsKOOOoo98eQTbMGCB4QqyXt731z9JpvoLeN3HsHiJcWO2ecqFNxegqDS5+fmcT/72c8E/tJ//lMs6/777hdkcNThh7Prr7/eW6cSQaKPPfYY9vJLL7ONmzYKlZOvr78sRyOq5vojfiVGCWkjxGhCJZ+r5/Zfs3adtr+s9rbD//7v/7LPP/uM9cnjBNfvx+fK67XX/o+4H7Qj6PvftGnnsD9f/2exXf58ww3qpT/96Y/s8y8+Z9OKpymC+803e8T22rKlQth37Nu/j+3du5d9/vln4nXeh/2k9z3Twt9443U2YECh/f1bfItv8S2+xU9afJ/g1n3+5UIAVb35Jco68Q1U3OD6azuWKAPJ3RW4KBcCwe0PBJfU2+7Qg9tZ9t92BCPgdpLYtoAs3KaS1DYzyG0Tw025EfDGNCM2CAlt+vfhoGw2/Jp9uKkJSpfTQ6ygGwF7z4KVJ6ctMyqIyG0HqPvuLMltFyC4ufLLyIf6cWUyxa2wsfaccj31GgM3dDjGrWWsp87SBrf2n8+Pge+T2zYavt+P2+a/Yv0t/sHji5igslIgsXpYuD7FWIn326pRPbd0wK3WTabkQVkRXFW6DCWzmhqpZ7vy+0uWLGH9+vZTZE2ZJnmvnX766WzWrFmszCO4nBQ+uuRRtaxLL7mYnXveeeIxV/fmzZsvlj+wcKAgkZs2bhIq6cknnST7cx1WNKSI/e8jjyj8p5YtYy8sf0Hc90uUb9TwldopS5RPPPFEdsYZZ7Djjj2O9enTm93nEVr++mWXXSpUz00egeW4czxiPX7ceLH+C+6/n40//viAzFZUCOO5StNcKmT9Eb9SI7xBSTNm46JxVX22/9oEZb+ff/6FUHA/4iXK3s4ze/ZN7MwzzxSEnivSF8y8gH0hySifYcWKl9mIkSPZZ599yq695lp2w5//HLf/FSuC6wrTsMGDB7H7/nKfUNoHDx7MrrjiCkFyw/b/P/7xDx6BnmZ//xbf4lt8i2/xkxaft/3U5/yrSpSrqUTZP/+XU0yQ95p5/UVGocR/guu1KCm4A+XUD2KC8qAPl0huV8nB2gPBbQPJNy2BxzUHg6lsILnEBTPk43RDJE0zXJLNbNwGJ7i40FSD7EbAOTkthIWHEdwMMJnKhkDgFkBscWonSS6puN2gRDkXpPR8WZ7cH0YkJu/csVOWVAZ9uNwIp+5Rmbp3atdNYAfu1mdHt/gWP/nxqa82JqO2YrEgIiiqyG6URcnAzSOVVeCOrI0oUomyPHiTi3I15eNqETZhymRw8F/1+uusR48ebN26dar0lsjb0Ucfw+6+Z55HzMtYbo9ctnr1G4rU3XHnHWzCxEni/kTvP1dw+cGf9+r2yO2hDKFyc3uymTPPF6/lejhveHiIT6OqnODedOMNGj5m0/LlXXbZJewGjwSPHj2anXf+eWrd+vTJFwS8p4fFy5Y5Psfir91//wJ2/PHjE65/pWPE+iTAV4quYxhRGeou9enWZ/uvo5ggY//jxk98nT780C8R/t3vf8+OH388e/vtt9ha73viavelHqnnt3379rHx3mu8ZJvfrhEK7p/j9r9ij6Byss9v3GyLl4TzcvB3S0rYm2++Kbbp7Jtuitv/n/7Xv9jAQYNEb7b9/Vt8i2/xLb7FT1b8NapEufbzr99rG2Td0usqJqi6Ou76i/Of9SomaLviQjt2qBLlwZLgkn9RX1BwcyEDtyvk4FJ5cnvDN4kIbtMEBBf5IJoQo6tyeoIs3FRDxW0Qk6lISC9umlGijKXKacaHToce3Cyj4ZiakFG9bSE3VCvYgO2g/7Yz5OBSiTKZTBUAweVf2iTe/2Wa45QqBbd+NxfKDfiu7LoH9TtIUJJv8S1+8uMLVVbl4PoHxqiym49pai659YWVJ1fJvFu/RNknWjQSWUMuytQXqpkaOUDKgNhVVApX4vmyxJhKbl999VVhjvbcc8+KEmVOcJ966ilV+vPHP/yBFU+dKpbDS5TF+ysqvGUVeO95Lh6/wmEFAwrYP/+5VOFzcsXVYT6vUHBvuBFKfitV7A5/jpPlf8sS5UULF4oS3v+89ZZYzvnnn89mXTEL1jNY//s9UvdzVHCN9Xc0pdZJiK+5I2MJs6ObVpn4tW3/NWvWhu5gXwiC20eZTHGiy4+/tKdxQjqkaAhzDxxgixcvEtv/wP4D4jUsUcZbMZQo83L0vD55bPXqt9Trjz/+OPv5+J9rn+Pdd98V5mPP//t5+/u3+Bbf4lt8i5/U+FSiXNf5lxNZqobj1050/ufXT2HlydVYogxRqb7JVIxigoZATFC+VHGJV/U0RMWOkINLJcqtQZxsAeptU+B8WXC/MfgzZQDRpcigDINnhplNNZiCW1t5crohJ0cMUpthOClnGjFBWUYzcgspc7eSJDcH5PCO0IfbXU4koedJgtsXTKaGxBFc7wuNgsmUXiwQOJ8d3DiPy8zBGrdOdzY3KfGLx3VhxWM7i/9Tx3v/+TROTuO7iP9Tx/mv8/uH2vpb/Hh87N+IgbFUTI0GRrXIID54VKWVJsuRRDmiSHFA/EBeBQdifuAWRK0CynIgmxVNksiIgSt+nORyFZAbQS1/4QURTfXrX/9aOCSXyR5cHs3DydGKV1YIVe+uu+4Uy+P5zfPnzxN4p552Kjvt1NOEmdPatevYb377W/bAAwsEPi+zPfGkE1mJR5xWrVrFhg4dyu6d75c2n3PONDblrLP8rF0IaacyYurBpZKjMWPHsgsumCnwuUrLS25fePFFtnnLZnbN1VezGdNniM/DSd3Pf368Wv+317zN/t/vfy9KlAIjCtMlOR6/MixuQCttrtTnqcf2X6eVKAf7y2dffC4I/NYPfYL75BNPCPMuunFFfUBhIdu3b6+33c5hffv1Y4UeEeXP9cnvw/Lz89mZZ5yp7X/TphULss9vvGeIE2j+vdLt6X89zY455hj1ST7a+iEbOWqkMB+zv3+Lb/EtvsW3+MmOr7Lu6zj/lslSZDHVVKnzvyC+ymBKv/7aLpNjiP/4kY7cZOpj6sEdCOIg+Rn1gRxcjAnqBKa/HaBMuRVwt+bQh0sKLmbgYkRQo5A4oLC0nrD+27Tvg+SaObgphvtVGii5GbBiWfC/CbD65lCi3BI2VluYsAe3M9SDk4TeB0Ye+oPJ1KRYLKb13yoXZXMkJaSn3HXrW2VQ39EgN/FIThLgcxLLiS3icyLrqteD16aO63zIrb/Fj8f3y47L/JG/2PbQvls0cSvFEuWaqiCTTSq15bJE2ZElyhQVJHpwcfQSjZAc3SSpEkyUeA9m0eDBQinlUTSnnXqqyL/l8/DeYe6izF128/J6s/w+eey8884TDsyOLFGeP2+eWM6777zjkdiTWG9vHr6sqVOnsg0bNor5eF/xySef7JGrPGFSde655wrixt/3xD+eEHE0vFQ2cDsMTKD4soQxlFRMH3nkEeGOvOq1VWK+K2ddIRzOeSnzz44+2iPpy8VyH1iwQJTw0vpz8yS+fi+vWKGtP5FdR3NbBBOqRH226n+FrgjXY/vH9+D6ew03hcoHF+Xr/uc6dtppp7IvvviC7flmD7v4oovYWWed5au7W7eK7btxwwbv/wZ20YUXiu+JOzJrx59ij+DymCC5Y06fMUN8h1999TX75ONP2EknnsT+cOWV4rVPP93NjjjiCDbz/AtERq6YPFJ84MAB+/u3+Bbf4lt8i5+U+HzQuD7nX5WDW+VPdP7n109xJcry+su/hluv999613Nc/JMEdzCQ3AIguD1leTL24BIXywEH5bag4rYAcmuWKWdCqXImENx0w1E5Yng8pYV4PjVoDm6K4ZycHtIIHDHqp9NBtU03mDuaTDUBk6mW0KzcxggT7ggOyofJKVeOMuRDiTJN/EubLGKC4MJcqExlpfXYLd34Hb7OMgS39nndBCHRSYJPBFYptZzwKgXXf26qVG+LDSX3h1x/7si6bNky9s0337Cvv/5aZOru8e6H4S9fvjy4aE7y7Z+M+KTKbq+F3PISZRoZ9BXcalUeQ/m3vlJbJXtt/QM2jTjygzJ/3tFImG6GZBolKUdgOS93MX7vvU1ghuS9VhGU43LjKD5RbyonuYd7ZGjRwkXBycSbb+NG3/ApDH+D99r7770fh89PMu+/v1kvKzLw9Uxavb+Gf5b1ouS59vWPy+0FfDTgOlh8XP/6bP8wkym/B/czRXD54107dnoE9zTWv6C/IPGTJ01mNdu2he5/13CTKa0H13+V9+A+8MADgULrLfvkk08Sy+vXr7+Ievr8s8/Faw8++KDA5yovN7vKz/Pv29+/xbf4Ft/iW/xkxedVY/U5/4pSZJVzW63O/767ssy+Na6/hPAgS5SjmIbh5+BOAJMpUnHzjJggLFHuDE7K7aBMmUqUWwKxbQbqbRPgf2gylWZwx3RoeU0zenHTDB7aIAQ3LYQxRwyDKVR4TQfldEPFzQJSmw0bgrJwW8KIALoot4eN200SXJLQ80DFLZBfFpQox7QL8lLIwQ0KCsKc1NxvMWITXpBfW9FCsuCTKsuJq6nS4n0ivPFK7g+z/rt3f8K8PY3V1GwTanzjrMYeMdkkXnv44YdEPAi9t1+/fuymm2b/JLZ/MuLHpCmbTmqjmlkB3ucjhaoHBA60NKpYXh64JascXGU+VancgB1TtYWyW8cwS9Lib5xKyH91VAwO9ZgS2RszZqyID+Lqodan+gPhq+c106efBj4vpzqY/Y8b/fETa0Pu/3z/+vjjTw7535/Ft/gW3+Jb/EMbn59T63P+LYde2yrqwXWI+MryZOP6a7uIeiwFQWK7NJnawQnuZCkGFoLBVD9JbtFFuQsouB0lL+sguVobUG9bGipulvzfGPhfY6NtNR2mCPTioouyKbamNlSJsklqTXMpAkEnrAg8bgTOyVkhKm42sP6WkIFLZcqk4HaC/tuuIJ33hizcAnACG+yXKG/XMjy5Uc76WkymEo/wuAnzm/HH4bq17ehunT+eHxOflNupQHCLlWLbye/LHSdV3bFw33vth1z/3bt3i1zfmpoa8fjA/n1q5nHjjmezZ9+s3r2fv/YT2f7JiB8YFJBTckyVuGil/1G/x319SWlQegwElkYdy0UPriNNpqrVvOWyNxdHLivNkmSwz3cqzbgbqURWBHE5Wpmuo6ub1PPiGLE7PxQ+9hL/1PCFgvtfsv9bfItv8S2+xbf43yc+L1Guz/mXXydhjy2d/8tkiXIVXFPR9RcJD2gUuj0WpRJl7qI8FCpfqQ+XxMNcUG+pD7cD9N+2M9JvWoGC2xRIbjbwvwxDsW0EnJEmdE1OMXpyG6w8OcWQiE1VF0N4Uw335DQj66gxuCnTimeDetsMgoJJxW0PLsqdoA+3OxDcnhBM3B8UXNGDi5lPdJ/36LksgcV3PYZdajdRcxO8NdHPJXnwi8cZJcqo2o7voi0JHxMhJmT+YzpuzFjWtGlTVlRUJIx56FVuGFNQUMBatW7FfvvbU4Qa64rnF7BTTjmFnXTySaxFixbea79l/1cZ9OPdeuutrHu3bqx9+/Yi6oUILu+x7tK1K/v8s8/YGWeeyRo3ymTNmzdno4/6mXgfNxh69NFHBf6ePXtETx9fRu+83uyWW25V6z958gnsqquuEo65HTt2ZH/84x895ux/Zm5QNGzYMNYkK0usz3/e/s8h+f2HvcUnuKXayJ+4H9uulSfHqPy/tETLt1WGCPKAXEY5uN7BuqoqGGnkI5B6Kaxhme8GE8EOAACAAElEQVQEo5pa1isvN1aKJRgzOA4ono7eY+oEyiWqoRa/fvhmifKhvP9bfItv8S2+xbf43ye+KFGux/m3XJhJ6Tm4FfS8clHWr780F2VZhcfFCu5PIRXcIohY7Q8VsXngotwdIoI6AcElM+CWRhYu9uBSiTLxvybABzOBL6YZsbPpRnIPctIGI7gphmqbYjhbmS5X6SFuyukgTTcGg6lsILotIE+pNTQv54AtdRcjJigXIoKwRLlAfmkTuQwf5aMWsZgiuTzXk8VlWMHfuNEZN3zUxiyqT/Q7cUN+SkmIzxVZQV7HkotyF4UvSpFdXe0l/KlAdrmhTPfu3YXzLI8FueiiizzC2YJ9+eVXwkAnMzOTPfjgX9lrr77KRo4YwU488QSx2Ntvu5Wlpqay226/nb380go2aPAgYSDEAZ595hmWnp7OHljwAHvllVfYgAGFkuBWs20121iK9z6u6n7wwQds1KhR7OKLL2abN28Wn+fII48U5JnfZpw7g/Xt25etWPGy6NvNbtKEPfLIwwJ/xPBhrEOHDsKJd/Giv7OMzAx5Ie96rw1nM2fOZBVbKsT6jBp1+CH5/YfhKwc+TbWNqUGjmFRug5ig0kC1rQpckim3TcUEcZMpKKUpJxdlJyiddQyFMlAnnaC3tMJUMh3NXCkwpgoidRQZxKw5cBq2+LXj85Pxf8v+b/EtvsW3+Bbf4n+f+OvIRbmO86+v4AbeJnT+Fzm4Kv9Wv/7i/MfPwY0G4oR33bbTL1GeBD24hZLg5gO57WH04WJMEFXXtjVKlJtCOg714GYD/2tstLFGjLigMAE1EpKN22AmU6mGZGzGBqUbEnOGERGUDmXJjQyC2xyCgSkLtxUQ3HYgiXc2Gp5p45PJFCq4RZqLMsSccAUX7cDduN3PraWh3A2pMjDd0Q5ir08ifGUoNa6TJLqo4HbW8EWfrksKbjDfyldWspYtmgsDKH7jhlBcQeWjRRdecAE7++yzFT6/UM7IyBAGUbd5BLdw4ECFMffuu1nRoMHi0Xnnn8fOOvss9Rp3kk3lBLe6hm3btk2Q3d27PxWvjh83nt08+yb1eTjBXbDAJ7jt27Vjzz77rMKfNetK9qtf/UqsxnCP4N5w440K48jRo9lNN/rL4dmb02dMF+twKH//Yfj8dyPKW2JBOTIptgHhjfqkN+aTYcxiI3OpallWw0tpSF2sAoIb9ODyg3mF3iPqBOqjg68ZxA3Lbish71Upm1qfKZQCaeW5Fr8ufB7389hjj7ElS7xJ/F/CHpP3+f/HvMfiuce8/48F9+l5f74l6r3+cmi+4LnH6Hk+P030msW3+Bbf4lt8i38I4JeVldbr/MvdkquqArdkOocr4ltVHXf9RaJeDBRcLkzs2LGTXJSLJG/qJydUb3NltSwZ/HYBTySKCCKTKVRwm4OK29hoUUWC2whIrhkVFMY3G1y9TQ2RhVMNlo3ScgY0CaeH9NxiFi7lJDUHk6lWRoky9uB2hmZnIrc9IQM3X0rtNBohFNwY9QnKHKhSIwc3cXKzThhclrh1vI6sZ0UaQmv8kwSflFhVqsydk8fKPlvlouzf3zCkrSxl7iIe07L+ct99oow3DP/YMcex2TffHKi9X33pEdVUtmnjRo/g3sYmTJyg5l321DLhiMoXfNxxx7JbvPfR549GfVK7jSu4HsHlZPeTT3eLd44bP57Nnj0bCO5oQXA/+XiXMKbiBwS6/W3RQjagYIBY/+HDh7NFixcp/NPPOJ1dfdVV0on5eda7Vy+WGomwIzzC/Nprrx2S33/Ycv2e9RJVghyFSKAYjAhGJQFeLwgu5N9qbsqUd+sftH2CW+P34Ep3ZQes8Z04IyXoK6XXHUcjcjr5Q6fhSt1kyQFDhwos67X4Ft/iW3yLb/EtvsVPLnw/BzcoQab5hIJbLa+5jOuvmErCCNrJxPXbjhi5KBeByVQ/o0QZe3C7QnlyR1BvyWiqBSi5TYwK3Wzgf5kgfqYZ/bhpBqeMGFXEDV6mbDLniBEXlAofzMzANRuKs0CuJgvpJtCD2xpqudtCE3MHILi0sbtDI3Q+5OAOkBP/0k7YtWunvCiPKcWJZ3WGq1is/sZpoY8NmuHWsewkwy+WZchTx4f04I4NypA3DMlhG4raiv8M3sf/8Fiedu1yNLytH37I9u7dx4qnFovyYbptfv99FvFI4+eff85u5QR3wgT1Hh4DxDNH+Y1nZ150YfC+devWyRLlbYGC+8mnAnC8R3BvuulGhT969JEiR5OvavNmzWU/sH+78YYbfEzvNowT3EWLFD6PN+E9ubiBNpRvEH3Cbdq0Ze4B95D7/sPmUf0bHtGNoRt51MjAjfotAL6LcniJMn/MiSwpib6tvT+PUHa1ntD4PhQnVHmEqBwtHD04MVQopROWATmy+snJ4lt8i2/xLb7Ft/gWP7nwy8FMyndRdgJ3ZXRQxhJlYTJVqhmDgovyBOjBLQShsI/kV6TgdgcFt5MUHzsAT2sN1bfNQLRsAsQ2y1BzMw3OmBnS9hpmbhwJ6cv9TiZTkRA35dSQ51ONsN5MUHCx/hqJbosQkynqwW0tN2RHmLBEmRTcXiExQQP9HNyd8gI8qvoEOcE1FS+3lqpOV5u7Fvc1txZW4rq1NKYnBz4ZR00V7shdVFwQx/dLlH18Tmw3DmkrVFxf6e2i8Hfu2sVatmjhkcyb2Ndff8UWL17MGjVqJLb7Qw89JF5755132KeffirIIieW/Hbrrajg+gS3T14fcf+vD/5VGE+988469sknH3ukdKJfolwjS5RTU9gnu3cLfE5MubEUN5QSpcZUouyt4OTJk9m4MWOFe9z7mzezTp06iZ5fQXCHoYLrL+fqq672iPleNnLUSLZ06VKxPB5DxAnugf37D7nvP+wWZ1AgnZTRcIp6cPkBVCO46n+1srbnB2g6SZDTnyK42gnE0SNxcAQV+kg1wyVlxhDffypKezFnTp1oYFkW3+JbfItv8S2+xbf4SYjvm0kF5ceEL1yUSUgwrr/8a7gSwyg0RgT3xBCTKerB7QF9uN2ggrYjVNe2BWKLMUHZ4KScDcZSjaFtNRPKldMMY6kwv6ewSuIGu0USRAZFoEkYc4tMk6lMg71nGwpuc9g4raG2OyekRJlycInc5kn1FqOClMmUFhPkXYzzPsGEaphbu41aPbobZe95rYwlKfH9GKBAuZ0K0UDFMjKoHIjthqFtVaky3l584UXWs3dPFkmNsFatWgmSS7crrpjlkdyWLC09TZQFb926VeDf4RHNiZMmgoL7FMvL660+34wZM1iTrCYskpYmVGCh4G6DHlxZovziiy+yw7ofxnrl9RKPR48erXpweQ/t0Ucfw9LT0lnTZk3Z9OnT2YEDB8TyueGVILgS/4wzzmRXX+0ruPfOv5c1a9aMtWjegrXNyRF9E4fi9x+GT321MXBM1smufp/3evgH2ipFbJXJVLWv4NKJIsjBpRJlMw6nwoizMUwXMMPVCcyX0KRBm9dxNAt+jMbRRk4tvsW3+Bbf4lt8i2/xkwhf5N3K6ytScH3iWx4QX+P6S4sJisnqu5jqwTVLlPOB4PYCcoslypSDi324bSV/awkKbnaIuRRGxWaGlCinhvThmiXKqYbpcYM5KKeGNPwi8zanNFgRdNJqBPlIzUC9bQEGUzmGwRSWKHcFBbc39OCSyVShzMGdQAQ3Jssq+QV6SUlJ3bJZaCukW4s/uBtnDhTXOWmariUp/lSIC9Kec11BaMs9YiuIbpFPcKeO7RyK//HuT0LxOankZckHu/579nzNvvryy3qtP1deE63/l94y9u3bd1D4B7x5fZOpQ//7x+VTX22UXJMNYhuYTUVFthpXe6nvlhz9qqkUuapa9JDQKCZFB/GpXMYExZkiGVlwekmPcZKAeRzNgCmYrwJGUR0IVjdPMhbf4lt8i2/xLb7Ft/jJgi+IbA21fFWp9/o5uFCaDNdfKgkjul0TKD4OYoIGy4rX/iAU5ktu1SukRLmD4Y/UFipuW0JFbhPwWWoC/A/jYzONaNk0gz+mh5QopzSkghsmCacbPbgpIR8uA0qTM40s3CZAeMltqwWYTLWGPtz2sEE7y+kwowe3F/TgEsEVPbiqPxAuyrFEOc4ovF4S2cGwkvBlJjO+KFEe30VzR+Zkl8qSgynHJ7xS0T1U1t/ih5coazm42/VSZXrMRwpVBhuVIFeBUitigvzRzioII1cKbgXE2jjY7yJHRNEpGAweHM110DB2ME9K9JwaXa0IjCIsvsW3+Bbf4lt8i2/xkwyfXychgaX3lUtlV8vAlddXwii0dL3WfxsNenC5i/IgSXDJZIrIbU85HQbmvh0lB8Me3FbQg9sSKnKR6GYC/0NyaxpNpQGZTTXKk1ON3tsG68FNMUqSw1yU04wS5XRoHM4wypOzDJctqtNuBRupjZzaQ4lyV1BwTRflfKNEWSi4O5WCux2yOkvqLtvUZDDXKP+s28HWPdhC0yTEF0ZTY7uwabxUeZzvnFxe1FbDp+cOxfW3+L6Lcqns3wiycGPKQXk7OCtHJRkWB98aMjnAvhCf4NLBOshrk8RXGTM42gip41RASZBpxgD9L0bcDY6kBqOrcJKB57QSJItv8S2+xbf4Ft/iW/wkwudKLebgaiXKkFqB11/UOhbwIL8Sb0eQgztIqrcDwEU5D/gVGvt2SeCi3NooUW4GHC8LzKayoKo3E5TbVON+JITMJurFbdAcXDMqKN3Iwc2E5zHrKCwiiHpwW4CC2xKcuXJCcnBJJu8uN36ekYNbCAR30o4dfmkyEdxoLCYuwmsz3KlP7IpbJ5WAIGe39h5Ki2/xkxU/up1CwrfHTb6rckB0Y1SijL23VZSD6zv/+SXK/kFZHYyFu3KZYebgQHmOo59YtJOKXtajRlqNfFd10tBKggJLfzWCavEtvsW3+Bbf4lt8i59k+MJkqrpaXVMp4lteplRb8/qL859SeQ0XjQXJFzsDF+UhoOD2BRW3V0gOLqm3OOUYlbfNDIMpmjKhRbUxcMVMUHAjxn0zBzc1RHz9zgQ3He5HamkAxg/XCD44/c8ynLSoRpvk7BYgdVPjcju5UTtADy6ZTJGKS5lNfcHueiivL+eOuTRqQQoUH80IV8pqoRRu/ax+3DilrQ5FzuJb/CTG57+ZMnFwhIgg6sON6c7K8Tm4/sG2RplJVUsF1x+RrJKjkFXKXRkO/Nirotnu48nAgX4VcCWE0VR/ft2Kn/Ar4aSim0hYfItv8S2+xbf4Ft/iJw9+eXm5clCukgTXUSXKQWoFXn/FMAkjGnChnbt2koJLJlPkokx8qieouF1BXCSTqTCDqVZgGNwM+m8bAf9Dc6kMozw5PSSpJyMkrrbBHJTN/ttISChvBOTliNEw3BhqrkmeRlbfDO63NqKCSMGlqKBOQG5zoUacXJRpGihl9wlaVqdQmfyGazfUeTbcf8ettbfR/VZet8mIL/pux/uOyVPHdpFxQZ39yKDxnZSbMjeV4vMdautv8ePx+W+mhLsox6J6mXIsFperRv26dGCtwgxczMGVPSik3gp3ZRkTVKmNfkJYuijTQVdCdDTUR1IrTYOGSrDdd4IeGO1EVFlp8S2+xbf4Ft/iW3yLn5T4FBNUJUkszVsmS5RJMMDrLxIeiP9QGoYsUZ4MBlOF4KLcGxTc7pJ3dZFTZ8jCbQsktxUIlWQuRUpuJvA/s0yZSGsmVAanhhDdlJCc3AYhuGH1zhGjITgCrDwCbBzV2yxQb7MgIqgZRAShizIpuB2B4KLRVE/5RZCTcj+p4BZIghvEBJGCG/Mts+tykK01ZiVRAanLEqphcctLQnyMCKL5KQtXd1V25fOH1vpb/Hj8mJmDq3o4jPJkPkX9QHGVfYtRQXLEUZUiU4kymkxpxgzByCieUDQ3Qyjl0UwdKhzN7dA/CVQEJwx0O3R063+Lb/EtvsW3+Bbf4lv8ZMMvEwSXWr6qFH65yMGtUsQXr78wB1dV3kWjbJfvonyCbOccFOKiTApuDyC4HaFtlPyRWoN/UivDVykbYmGzwEU5A4yIsbU1zYgMiiQgs6kNSXDNJt+w8uT0BL23mbAy1IeLGbjZEBNEYcFt5P0csKLuDCZT1PR8GBDcvuCiPFDWlZ+wIwYxQaQylZbCdb5bL93LreMJN+RJN8EbdaUsefCnSjIrVFpOdqWTsno8Tqq6lJcLTsuE//XXX7NHH32UffPN3jj8/fv3s2XLlrE933xjt/9PBJ9U2Si6KMd8FTcWC0LDo0rBLYURxMDoQMUEcaVWjk6S8RTFB9EJIAhA1+3zHXAkxJODZu5Q6RjW+uh8iPM7mq1/pbZsi2/xLb7Ft/gW3+Jb/OTB5zm4eF1Fy6OYIBIM8PpLxQQZpqCyRHkixAQNALPeXqDgdoPkGiK57UGAbA2crSV4KjUHYos+TPQ4w0jdyTBaYiNAYtMNY+OUhixRNrOH0gy2HTGck5GFZxrmUpmGyVRTiApqBc3KbWGEoCM4d1EGbg8oTyajqf7gBsa/tInCRTlGMUFR9WXXx2s2vFXR1QlF2Ntct540Jbnwi438Wz/flshvoOwWjyNi62pRQvxW6pGhRo0asY2bNsah7979Cf9BsW012+z2/4ng6zFBpNpSH240ILqyD5d//1WQbxvY1kulFtySSdVVJcqV2MtClvkho6Zh5g5aeU/Qt+IYAeuOkV1XGZJrZ/EtvsW3+Bbf4lt8i59M+D6RJZW2SmH5Jco1sg9Xv/7i/Mc3Co1qYh834IUe3AJwUc6HPlxScLtC9WwnILntIQOXWkybGa2nTcBFOdMwmcqAqt+MWrhlagIfqAZzUY4kiAxKMZRdVHozQYKmlcKVNaOCzBzcdiCHd4Ia8G5gMNVDfhl95MgDEVw+IjE55n2J0WhU5XTyL3k9mky5uv7l1qqK1WIA5CYmKDrhcEP6KZMDf6pUarlCKx6TUqv14XLlNlB0xcSJMGAc2L8/FH/37k8Fwa3ZVm23/08En8cB+QTXjwaKbQ/MpdRvKuqXvJAdfaDaVgUqLZlMgVuyUnYl8cUelkqw1w9GNCtkD0tlnDU/nljC+mIc096fTkAVwQirxbf4Ft/iW3yLb/EtfjLik8kUqbPKfKosKF02r784/ymV13CC5HJhwruG27lzFym4gyTBNXNwe4OLMvXhdoKW0XYgRqJ62wwqc5uCuEn8r3GIuVQkJAfX5JWROtpmv3OJsikbY3lyBCyfM6G+OhNU28ZAeptADy7J2a2MySxR7gQlyl3BZCofenAL5RdWJEqUd+yUpjhRKc/H2PqS9XX48ITb9rjGg0R9kq5ZCGqQiDgmkST4qOAWy1JkVHARn6u7qk93fNCnG4vFWJeuXdmnn30mHt96622sW9durH2HduzOO+/0flCprKa6xm7/nwi+r8qWirKWqDKZkqRWc1FGk6lqGEWsCQLIVSmyf0CnMhpfwS3X+lAcLAWCAHV18K8wLfVxVNQ8AQXlQBVxFv2YWWfxLb7Ft/gW3+JbfIuffPhlZDJVRTm49Hy5dj2F11+UHEP8xzcHjXkEV/XgDoF41X6SS4WVKHeBEuUcKFPOMapvW0IyDraiNg5J1iGOmG4YFYeVKJv5tw2i4EYM5pxiyMhhJctmLm66UZ6cbai3ZDTVCuTuHNiIJId3hgzcHkBw+0iSSz24hVLBnRgjk6lYUFbp5+C6+pW+m6CI0038oh7JkiCDtFaDoOTCJxJLqi0nuYQvSpFdJMNd1MPicQHZ3bZtG/P2CLb7093suWefYWnp6eyBBx5gr6xcyQYMGCBeq6mpsdv/J4IfhZBwdE3WlNwoz1eLSkOqUp/M1vB4IGlbr0wPfAWXDuRVlNvmTWVl5ZATVxnf2+IE9vmVcNCnk0GFU2lkyjnaqCll0wUjongigscW3+JbfItv8S2+xbf4SYZPObjVMgaI8P0c3KAiDq+//Mq60qCtTF7DSRdlKlEeAAa9+ZCD21NyrS5SVOxklCi3lVNrUHGbG0k5jSEHNwu4IJYopxn3042KYDPJJ9KQBDeljpLkVPhQqYbRVGP4T/exRLkpKLlkM90anJTbg4LbBQguRQWRlE49uH0lweVf2mStsTq6XbnC1mXQU09hLTyi1E2w9Frf/+PjF48PDKV012TppuyyEEdlpvXhRmu2CZV29+7d7Lzzz2NnnTVFYax4eQVLTSGCa7f/TwFfuI57v5fY9vhIICK6UXhcWrJe9YdUg209mUmJEmV5IK+uAgWXE1+nMr6sx4kPVVf5cFp2nJwqsDzIdC2sBAwnMHegUiCLb/EtvsW3+Bbf4lv8JMTn10nV4JKsPU/E17j+Uj4qUTAK3R5ju3btIoJbCAouiYV5QHBJwe0GBLcDVNe2hqkZqLfNjCxc5IIZIcZSGQn4ZQoQ3pSGzsINC9dNM3pxkW03Mgym6H5jmLJAvc2GYGD6n2OUKFPNNzU5Uz14LpDbPpCBWyhNpibzLzGmsjuDMsrQyBS3XjQjvCvSdetX9RmGkST4xaDgYg/u1PGdlXsyTRuGtJXPd5bk18cXCq5HYjnBPe6449jNN9+scHkvAH+t2iC4dvsnL35gMhULLUmOQm+7T4ali7Kh3FLJjK/U+gd05faHCi6MVgb/IROOyoEcvZdF710JRkCduOy6EAfDBKOlFt/iW3yLb/EtvsW3+MmAb+bdEiY3n8L8W7z+UlGPMUOciMWoB7dItnX2Ax8jLFHuAW2hFNfaDiYsUW5jkFuz95ZiYhsBV8yAPFzybkoJ6b81S5MblOCa5lKmdByBD4uZuI0MBTcLenCbQSMykdvW0LjcBghue1Bvu0GJcg/Iwe1v5OBO8kuTY+rCPEYuym7tjYhuXfGjtb7drZt0JCE+9uBOhUxcpdLK2TcUtZVTTlwPripR9gjuWVOmsAsvuki9tu6ddb7JFCe4dvv/JPBjWN6CEx8NjMaU0RSNDAqTqWrf3EB39KuSLspl8qBeIV+n0uVysNU3cuCcyrisOAeC0/0RUAccDSFs3RhJDUZdK4KTkAPvs/gW3+JbfItv8S2+xU8y/DKZd0sElvCF+ZQ0lTKvv/j1GTfWJf5DaRg8/YKLgJIrFUre1B8Ew1wguF0MkkucrDWUJ7c2cnCbQoVuE+B/ZnUvtramG9wyEnI/taFdlNMSKLppIQ5YGUB004HgNjJIbhPow20ODlytoESZRgg6QS9uV8jB7SnJbR7k4A6AEuWJO6gHF3oIUcF1w8o03bqCSd3aZ3MTPHATB7UkA/7U8Z2A4HbRYoKKxwUkdsPQNkLBLfcms0S5ZpssUf70U/bggw+yFi2bs3feeYd98sknbMIvJgQE127/nwQ+/V5i2shfNEGJckzM65ce1wRh45CFW6ZclB0oYZbKLpotOMaIpjroB6OflWGW+5XoQAgjpY7hYOhgjl1lvNmDxbf4Ft/iW3yLb/EtfpLgo4syJ6+E7yu41dCfG1x/qR7cKIoUvER5p6ng9gcX5TxwUc6VBJcqaDEiqJ3kam1AxUWDqabQkooKbqbBFTMNL6eIwS3NVJ8GM5lKMeqiU4HcpkJ+Eb2WabhhNTYYexNjai7/E8FtDQQX+3A7Q6NzVzmq0EPK6H2hdpwMpvgX9osoqbdUWhmVBNdNzCHc2lx3Enrchj92XRZOWpIQX8UEyYxbvye3i3quWJYmE7HdMLSteIxK77aaoESZx9NMn34uy8rKYmmRNHbxJReDgmu3/08BPyZJK5YmR6lcWZW8BISX29FT/0c1mEyRkivckmXJDdrd+wquoxkvxI1gOkFIOp4o0JxB63OBUHU9m84JLPvVSKxj8S2+xbf4Ft/iW3yLn5T4XAigqje/RFknvoGKG1x/bccSZSC5uwIX5UIguP2B4JJ62x16cDvL/tuOYATcThLbFpCF21SS2mYGuW1iuCk3At6YZsQGIaFN/z4clM2GX7MPNzVB6XJ6iBV0I2DvWbDy5LRlRgURue0Add+dJbntAgQ3V34Z+VA/rkymuBU21p5Trqde4+mGymFuLVpXnaWlbu30IZnxiykHVzOZ8u9vGOKrtxuG5PhlyrwXF9TdMPw93+xhX3755U9m/S1+cFfEBJWVAonVw8L1KcZKvN9Wjeq5pQNutW4yJQ/KiuCq0mUo2dFGQ/VsOc3cQfWnOMZzlUbouhG8Dtl0aBxh8S2+xbf4Ft/iW3yLn2z4qkS5mkqUffxyignyXjOvv8golPhPcL0WJQV3oJz6QUxQHvThEsntKjlYeyC4bSD5piXwuOZgMJUNJJe4YIZ8nG6IpGmGS7KZjdvgBBcXmmqQ3Qg4J6eFsPAwgpsBJlPZEAjcAogtTu0kySUVtxuUKOeClJ4vy5P7w4jE5J07dsqSyqAPlxvh1M+gtnZS4boJ4ljc+hCN5MVXJcqK4LriOU5qNw7xSS1Xb8uLfLK70SO9h9L6W/zgWeqrjcmorVgsiAiKKrIbZVEycCst9XPaVLkMjChSibI8eJOLcjXl42oW+mEjo2Gh6EHpj2OMqFaCNX/wvFFKZDgeWnyLb/EtvsW3+Bbf4icbvt9rG2Td0usqJqi6Ou76i/Of9SomaLviQjt2qBLlwZLgkn9RX1BwcyEDtyvk4FJ5cnvDN4kIbtMEBBf5IJoQo6tyeoIs3FRDxW0Qk6lISC9umlGijKXKacaHToce3Cyj4ZiakFG9bSE3VCvYgO2g/7Yz5OBSiTKZTBUAweVf2iTuomya45QqBbd+NxfKPTmVcN2D4iEJWiKTH5+ycMlFubwoR5QnI74gu9Js6lBbf4vvK7glKgfXPzBGld18TFNzya0vrDy5Subd+iXK/oGeRiJryEWZ+lI0UwcIVzcMFxzNQl93K9TKiYzMOccwgXCMvhmLb/EtvsW3+Bbf4lv8ZMLnRJaq4fi1E+Hz66ew8uRqLFGGqFTfZCpGMUFDICYoX6q4xKt6GqJiR8jBpRLl1iBOtgD1tilwviy43xj8mTKA6FJkUIbBM8PMphpMwa2tPDndkJMjBqnNMJyUM42YoCyjGbmFlLlbSZKbA3J4R+jD7S4nktDzJMHtCyZTQ+IIrveFRsFkSi/WdI2mxfrqXC4zxTK3Tnda1+Jb/J8EPvZvxMBYKqZGA6NaZBAfPKrSSpPlSKIcUaQ4IH4gr4IDMT9wixNFBZTlQDYcmjQERgymS6ET53RYGWa3r5UWVerzWHyLb/EtvsW3+Bbf4icZfpksRRZTTZXCF8RXGUzp11/bZXIM8R8/0pGbTH1MPbgDQRwkP6M+kIOLMUGdwPS3A5QptwLu1hz6cEnBxQxcjAhqFBIHFJbWE9Z/m/Z9kFwzBzfFcL9KAyU3A1YsC/43AVbfHEqUW8LGagsT9uB2hnpwktD7wMhDfzCZmhSLxbT+W+WibCpZIaY7rlvfKk/3ILU4ZvEt/k8C3y87LvNH/mLbQ/tu0cStFEuUa6qCTDap1JbLEmVHlihTVJDowcXRSzRicHSThkrDxVD1rzjGiQN6X+L6XNT/Cn1E1uJbfItv8S2+xbf4Fj/J8FUObpU/ET6/foorUZbXX/413Hq9/9a7nuPinyS4g4HkFgDB7SnLk7EHl7hYDjgotwUVtwWQW7NMORNKlTOB4KYbjsoRw+MpLcTzqUFzcFMM5+T0kEbgiFE/nQ6qbbrB3NFkqgmYTLWEZuU2RphwR3BQPkxOuXKUIR9KlGniX9pkERMEF+ZCZSorrbddj2s+49ZvbjeBua1bb1ryw+P7rsmdgxzc8V1UHm7xeHJY7qLmOdTW3+LHL5FU2e21kFteokwjg76CW63KYyj/1ldqq2SvrX/AphFHflDmzzvaSUA3YzCNGpQjIY6UYlkPf60ivhyoEt0KVY+MA+YOFt/iW3yLb/EtvsW3+MmFL0qRVc5ttcL33ZVl9q1x/SWEB1miHMU0DD8HdwKYTJGKm2fEBGGJcmdwUm4HZcpUotwSiG0zUG+bAP9Dk6k0gzumQ8trmtGLm2bw0AYhuGkhjDliGEyhwms6KKcbKm4WkNps2BCUhdsSRgTQRbk9bNxukuCShJ4HKm6B/LKgRDmmXZCXQg5uUJwZ5iTrfgvFzK3VAzds/mTBFwSWOygDPjkqo5Oy+fyhsv4WPx4/Jk3ZdFIb1cwK8D4fKVQ9IHCgpVHF8vLALVnl4CrzqUrlRuiYo6ZawLpu1qDZ7zuYVecoG37qcUFHQkfrb6m0+Bbf4lt8i2/xLb7FT0r8cui1raIeXIeIryxPNq6/touox1IQJLZLk6kdnOBOlmJgIRhM9ZPkFl2Uu4CC21Hysg6Sq7UB9baloeJmyf+Ngf81NtpW02GKQC8uuiibYmtqQ5Uom6TWNJciEHTCisDjRuCcnBWi4mYD628JGbhUpkwKbifov+0K0nlvyMItACewwX6J8nYtw5Mb5ayvxWQqscLlJjDr0cmB69ZGNNw6ycuPic8JrCvigboEKq5SbDsJ8ku5uMVjg/s/1Pq/8eab3o/5/37U7b9164ds1apVh+T3H4YfGBSQU3JMlbhopf9Rv8d9fUlpUHoMBJZGHctFD64jTaaq1bzlsjcXRy4rzZIgsM93Kk27fTkSWhHY9WtlQo4+uoq9NI4Z2G7xLb7Ft/gW3+JbfIufRPj8Ogl7bAm/TJYoV8E1FV1/kfCARqHbY1EqUeYuykOh8pX6cEk8zAX1lvpwO0D/bTsj/aYVKLhNgeRmA//LMBTbRsAZaULX5BSjJ7fBypNTDInYVHUxhDfVcE9OM7KOGoObMq14Nqi3zSAomFTc9uCi3An6cLsDwe0JwcT9QcEVPbiY+UT3S0vLgC7UYsnj1u9ptxYDn7rpSvLgk0Jr/vfLl4OsWypf9ufp8oOt//hx49jcuXN/1O2/YMECdsSRRx6S33/YW3yCW6qN/In7se1aeXKMyv9LS7R8W2WIIA/IZZSD6x2sq6qCkUY+AmmGpGuW+U4wqqllzXn/K9SIKRgzOA6MuDp6j4sTjJziaKzFP7Txn3nmGfbGG2/Y7W/xLb7Ft/gW/yeHXy7MpPQc3Ap6Xrko69dfmouyrMLjYsXOnTtJwS2CiNX+UBGbBy7K3SEiqBMQXDIDbmlk4WIPLpUoE/9rAnwwE/himhE7m24k9yAnbTCCm2KotimGs5XpcpUe4qacDtJ0YzCYygai2wLylFpD83IO2FJ3MWKCciEiCEuUC+SXNpHL8FE+ahGLKZLLcz1ZXIaom6B30TXcfpjmPVsff5/g7W78k0mE7yuyXVjx2C6K1JJaK8qXx3YJ5iGFl78G5Pf7XP9xY32C+2Nuf05wjzxy9CH5/YfhKwc+TbWNqUGjmFRug5ig0kC1rQpckim3TcUEcZMpKKUpJxdlJyjdMQPTg9FRJ+htqQgPTteC1eVye/XuxXr27MV69erJhg8fzqYVFwvSU6nl24XjL1++nN1z9z3fCV+ZUjiVEC/gv/+FF5azeffMS4gvPnuu99m9zz98xAhWXDxVfPb64C9f/gK7hy+7Fvy61r8htv+Pjf/rX/+a3XH7HaH4vXv727Znr15sxPARbOq0Yvbss8/WC3/5C3z73lOv9X/B+y7mzbvnv3L7W3yLb/EtvsX/9vi+ght4mxC+yMFV+bf69RfnP34ObjQQJ7zrtp1+ifIk6MEtlAQ3H8htD6MPF2OCqLq2rVGi3BTScagHNxv4X2OjjTVixAWFCaiRkGzcBjOZSjUkYzM2KN2QmDOMiKB0KEtuZBDc5hAMTFm4rYDgtgNJvLPR8Ewbn0ymUMEt0lyUIeaEK7gYh+LGXf67tRj6uCFVnm79nIDCWEcS4U9Vym0nSXQ7awZUiM+JLsFMBaWX/6i6de3CrrrqKtayZUv25JNPiHDqI448gmU3acL69u3rXZg/K/Crq2tY165d2U2zZ7P27duxocOGsaeeekphvLHqdTZy1EiW3TSbnX7G6ezoo49md8+dI177Zs8eVjy12Htfe5aX15vdcsstamVOmDyJXXnlld7zeaxVq1bswQcfZNdcew1r2aIF69GjB3v++eUCf9iw4WzZsmVq/W+97TZ22mmnifsP/u1vrGePXIE9ZuwYti26Tcz1wIL7lYLLL5YvuugitQWffPJJNnnSZPX4gQceYP369WMdOnRgF150IXMPHBDPl5SsZ8OGDmVZWU1YUVER+8/bbyft/sd/N6K8JRaUI5NiGxDeqE96Yz4Zxiw2MpeqlmU1vJSGRjergOAGPbj8YF6h96g4weing68ZJw7Nwh/y5uh+r5492eOPP87WrlvHli1dys6feQHr378/e+65f0N5UDj+/Pnz2bhx474TvpaTh6VI3nvmzZ/HxnrLT4TfyyNe/LOvW7uWLV26jF0wc6b32QvYc//+d5348+bNk589MX5d698Q2//Hxv+NR3Bvv/32UPye3r7xGN++fN9YxveNmaxAbN/n6sSn7Vuf9efzjh03/r9y+1t8i2/xLb7F//b43C25qipwS6ZlKOJbVR13/UWiXgwUXC5M7Nixk1yUiyRv6icnVG9zZbUsGfx2AU8kiggikylUcJuDitvYaFFFgtsISK4ZFRTGNxtcvU0NkYVTDZaN0nIGNAmnh/TcYhYu5SQ1B5OpVkaJMvbgdoZmZyK3PSEDN19K7TQaIRTcGPUJyhyoUiMHNz57NJwwuLVY97j1NPMJ7bFMEvypquzYL0EuVoZTvqkUvt+f11Xkl57/YOtW/qPxLvjGs5dXvMx27tzFjj32WHbG6aext9euYZdceilr07q1+DQffLiVpaakeqRwEnvzzTfZjHNnsFyPgPLlfvrZp6xTx47srClnsdWrvdemTxfLnSMV3HPPPZf165vPXn75ZfbYkkdZlkeeH3nkEfE5uDrXrVt39tqq19gNN9zI0tLS2KSJEwT+GWeeyQYNHCiWcYFHOs8840y1/kVDithf7r1XEDFObBfcfz973SPZw4YNZefOmCGWfb9QcH2Ce/VVf5KE2N8SixcvFiSd3155ZaX3mbK85/4uPmOPw7qz62/4s5hv5IgRbOYFM9kW70B28UUXs1EjRybt/uf3rJeoEuQoRALFYEQwKgnwekFwIf9Wc1OmvFv/oO0T3Bq/B1e6Kztgje/EGTlAXwu97jjaiUQ/+aDTYaVQ5/79/PMKv8L7P+XMKewsb+LL2LhpI7vgggvE/jFk6FB25ZWzWEVFJfvbwr+xgQMLPZLZW6inr7yygm3cyOedyQYOGsSGDh3CrvDmJfw5d81ho0YdzgoGFLDi4mI/Asl77b333/e+9wtY0eDBbMyY49hfH3xQfNaFf5PL791b7BuvrFgRt/5cdeafndaff/YzzzyDTZnif/YN8rP7n2comzXrSjHPwoUL2cDCQqFQjvD2sxXeZ9+kfXZvPa+YpU68L734Ivv58cez/L592fjx4xX5v+eeu9m0c87xpmmsX7++bKL3m33u2UD9nv+Xe9kRRxzB+vbrx371q1+xNWvWqu3PlenjvGNAQeEA8XnXl5SI1957/z3vc1zIigbx7TFGbQ/z+9+4cYO/bgMHie9lFv9evHnWvP0fNsLbXjfddKP47f785z9nDz38sPr+n/7X02zChIlikGn69HPYiSefxG6/4/agLIz2twp/8OPfy5/XLlDO9I4VU848S9zn22zmzAvU9z1LfN8V3r6xUH53vcRnWbHiFbFv0LxDhnjbd5acd9FCVsi/C28/GjHSm/eVV9gvfv4L9vdFi9W+fPttt7HiqVPZ22vWsJHePHfecScb7O0vhx8+SgzA0ffP9yX+HQ4uKpL70l/r3P81kxNj/R18/D39/iy+xbf4Ft/if3t8Pwc3KEGm+YSCWy2vuYzrr5hKwgjaycT1244YuSgXgclUP6NEGXtwu0J5ckdQb8loqgUouU2MCt1s4H+ZIH6mGf24aQanjBhVxA1epmwy54gRF5QKH8zMwDUbirNAriYL6SbQg9saarnbQhNzByC4tLG7QyN0PuTgDpAT/9JO2LVrp7wojynFiWd1hqtYB2FcG/rYrT1b1K1LRftx8YtVyXFID+5YvQx56ljDXVnib5UEl484mfj79u4TF3/8df4j3PrBB+L+5s2bxTzbtm0TjysqKrwLxRVCAd67d698737WsUNHVaLcLqcde/bZ59RnmHXFld6F9S/F/eHDhwmlhi90795vxDL/85//iMevvfYaa9asmZjvjddfFwrvvn372NYPP2QZGRmipxRvX331FfvTn/7kkZIhqgdXlCh7y7rqqqsVweWPFy9e5JHhYeIBvyA/4/Qz2Ndff8W+9JYxf/693jKKxKxcWZ7uEXZeIpLs+5/q3/CIbgzdyKNGBm7UbwHwXZTDS5T5Y05kiUD4tvb+PELZNQPUjT4UJ3TkE6z6tXD04MRQIUdauUr3PBFcOd1/330eeThc3L9rzhw2+sgj2dJly8RgBf+envjHP8T6X3vNdeyYo49hL730kiAXd911l9gPlsl5+/Tuw/7xj8fZ6rdWezi57OFHHmEvvvgSGzduLJt9880C/7LLLmPHHXccW7p0qXh/fp8+7GVveWL5114rKhT85b8Xt/68tJpXHuD633ff/eKz8+c4qT7yyCPk51nkfXb+ebzPXlrCrrn2Om/ZwWf35z1SqNhiPfvkiXn5Npvwi1+IQahV3m/j8ssvZxMnThRYN8++RXyGSy65hD3z7LNi/x0+fATbsGEje/vtt1m/vv3Z3XfPZU8//bRHNI8XlQ38fbyCo3fvPHb3Pfd49/8pSOg5HlHmr1122aX69sjPF5/R/P7vEp+Xb+un5Of112316rdYbm4uO/3009kz/3rGw7yQHX7E4dJVskyQ9/POP5f965ln2YUXXsRye+SyO+64Q/v+ab/R9g2Jfx/uG95nGD16tNxmf2d5vfk2eyL47n7Gt++L4rubM0fuG0uXsb//fTHrzb+LJ7x5S7x5r4Pv+b1N7HJvn5g2rVjtz7/wts8dd97prdtqsW5cGebb5+677xHbUXxPfF+6lO9LY8Rrc8S+1Ffg17b/a78hY/0rKyu/99+fxbf4Ft/iW/xvj18OZlK+i7ITuCujgzKWKAuTqVLNGBRclCdAD24hCIV9JL8iBbc7KLidpPjYAXhaa6i+bQaiZRMgtlmGmptpcMbMkLbXMHPjSEhf7ncymYqEuCmnhjyfaoT1ZoKCi/XXSHRbhJhMUQ9ua7khO8KEJcqk4PYKiQka6Ofg7pQX4FHVJ8gJrql4uS6rM0glTEOrvTLUDSUTYYpbMuCTcRQvTyYDqWIZHUQ5uP7/LmzjkLZaXi7hk4LLiSEtfdGiRUKZ5c97O4j4/+FHHyky/JVHAunjZXokk6uA/OJy6JCh2vqPHn0km+tdRH/88cdiOYJEyxtXUnhJIb+NGD5cYBI+x+AKDL+tfXsNy87O9rekB9qte3ehas2bN5+NP368mGfPnm/Y+eefz3La5rDU1FTxfj7x5S1YcL/swWWiDFtTcBctlgSXsUaNGvnr671fXwYT/ZxcTYxEIqLcmSvNybr/xRkUSCdlNJyiHlx+ANUIrvpfraztSc3kB3xy+lMEVzuBOLolP46gQh+LZvigzBji+194aZEgMf9+Phgh9f4ve+opoY5u2bJFPcfVQd7GcNToo2RJa6UoIeZk1cTngzH8sx/pkZ/bb7udvegRF05wFy5cxDZvfl/DHzhwIHvkoYfZpk2b2EZvOumkk9js2bPFsrQy4pD179mLE7B/a/hPPbVMrNOWigq1/vyzl3nHN07Gbr/9NjGvKK8eO1YPmxeOj5XiWDj6qNHsttv80l2+zpwMvvtuiYZ/s0fSeWVERcUWscz333uP9e/fT7QU4Pbn63X11VeJCg7+GS/1iBivthClXd7j1W++yR5+6CGBxbcHV1w3eURv40Z/e9zsbY9E33+FtwyeYT5afC+3SRLYw/v9rBL4a9esZT08Uvj666sEmS4sHMDef3+zWN4W77sYMnQIu+P227Xvn7alTnB9fL5uXNndzNdZ7W8Vct+g7euXr48V351jjLz784rvQqqv81U5s4//9L/+JZR+/jnfXvO2UHd5CT2Rdz5gQOt/zjnT2O9+d7lYNq8yeJhvu00bxf50oth2N9e6/5vfv1Np/Ja+59+fxbf4Ft/iW/xvj++bSQXlx4QvXJRJSDCuv/xruBLDKDRGBPfEEJMp6sHtAX243aCCtiNU17YFYosxQdngpJwNxlKNoW01E8qV0wxjqTC/p7BK4ga7RRJEBkWgSRhzi0yTqUyDvWcbCm5z2DitobY7J6REmXJwidzmSfUWo4KUyZQWE+RdjPM+wfpls7h1CmmJ2IbL3LqjSpMMnzJwi8dRBFCnuAxcPusGj9yKaWjbOKWXSOuXX38pHn/++edCGZ0zd464X1Pjq7QfVQHB/fIr9f6M9AxBqLg626FDe+1j9sztKUqUOTFt3rw5W+VdyNLtxhtvZBMmTPAV3GHD2cLFC9VrguB6F9H8xksns5tkB8rvrFns0ksuFQ7NixYvEiv40EN/Z23atGVv/ecttv/Afnb33LvZqFGjxPxYoszVGK5I0Y2TFEFwvWWcXXw2+/3vfl/r9udq5imnnMLatG5Ti5HUj7v/UV9tDByTdbKr3+e9Hv6BtkoRW2UyVe0ruHSiCHJwqUTZtOOvMOz0DdMFzJDTwtIhjw7m9Unicg2DkxNO6vh8K19dKXqoeTlv7969Rb/2rbfd6s/nfbd8HyH8lSu9eU+YLObj83Mycuuttwn8q6+6mvUv6C/6e2fMmOGRxXcF8efz5Hr7MCdT/LPwxzNnzgxI0vhxCdefE63lvEQZ1n/+vd5nP+oocV98nsnys3skqYdH/G7jn0f0fXoEd/w4FTK/8hX87L2FsnnbrbcKrMcfe9wj60cKtXbSpElsmUeiOf4tHsH91S9/peEfN2aM+NzveWT3kksvYYMGDRbkni+P4/Pt/8tf/pLddNNN4B7pf1/B9sgVv2u+fj289848f2bc+r+68lW5br1FGbe/rW8Vajm/zwkeff+8H/+lF18SA2HHH3+89v2fcMIJ7PY77oiLfBAl4JzgLn/e2Dfmie3L7/Pte4L3GTg+377iM3j7RqXcNwRplfsf376TJk8Siqs2r0ODDUG/7v9n7zrgo6i2N6kQeleakgRCaCE0KzaU8qQo+B5Kb9anoD7b3/fU57Oi2LGBigooKFakKFZUehIhJFmFZEVMeJslIEhH2POfc+eeO2fuziaLxkfEm99vftkyO9/O7O7M/e75zvfhhMRpp50O899803rPT9nH2HpulUVw8fuHx4m+//fecy8Mu/RScX5Uxy7V/j7Rd6m87z8//vr+uyoHv9Pvz+AbfINv8A3+r8cXebdyfEUVXJv45jnEVxt/uWKCglJ9F1Q9uLpEOZ0R3DaM3HKJMuXg8j7cxpK/1WcV3Foe5lI8KjbRQ6Ic49GHq0uUYzTT40pzUI7xaPjlzFtf4tiOcCet6iwfqQ6r3tZjBlNNNIMpLlFuxSq4bVkPLplMZcgc3AFEcINSVokD9HXr1lVcNvVshQyVk88SCjMHCuuc1E1vqxg+EVU7/7aFi7gKIym5fp5FbPOQ4HZrzOTKNo5dwY0RFVx8ZNu2IMTEVBMSwoMHDsINN95oE9wff4QtQqIcI6u9kuAmJkhJbABqJtUUksL9+w/ANGsQia97SkqUccDb54I+Istr43cboXnzZtbgdardg9vzVFHBpf3H133r+1a8Rextwwou7T9Wqlq0aCEI865du8TDzz33HLS0Httetl3IpjMyusBpSHCt9V9AgnuWTXBnvToLGtSvD74CH+zYsR16ntLD7sG1cGbNskhyk8bWd+0b+OWXQ4L0YL/vwUOHBFlGaSFub86c1ywy3QgOHz5cJb9/1FcbINdkjdg6ZlMBka2Gnx313ZKjXwlJkYtLRA8JzWJSdBAueTImKMyUQcuCc0t6tIsEW8fvMoCwH0MiQD24olK7aZNw1sU+WVwPpa4jRo4QlUHcFpJdIjFPIwHt00fhi3VHjBBEBF872CI09ro2FpK+eW+8YRGks4R0Fh9Hwrto8WKWfef03TzztEN8vPYfCfGSJUsUPlZt//bXv7re+0h679Y6+PsQPZtFRZKc97Mrzn5n3VXWuogv1n34YdfxxxYBdJnOzMwUfcgosyayR+tkdskUktnp02dY63WBxYsXwUbrmKL5mpA2W9vGXvMbLPJLn5PPOi4oacaLdydh8LVYyxYM3/+RI0dZn8tItW+DLrLfL8l4sfpLr0ESjAR37ty5ol+Xf/5nnnGm6sHVjz9V99V3y3V8/cLkTn3e1jqD1DHz2xMIffuo79/IEaOsxXq/q5zvBk424O2nlVmZg3/77bcL6felwy4VpBrXW4XkPTkFPvn0E/X9nzx5Mky67jrxGQpztEWLjur7T5+/1/6Xd/wr6/dn8A2+wTf4Bv/X4wsiu5VavorVa+0cXCZNZuMvlYQRKHUVKHY4MUFdpeK1IysUpktu1cZDonyi5o/UmClu6zNFbk3ms1ST8T8eH5uoRcvGafwx3kOiXK0yK7heJeF4rQe3msebS2DS5EQtC7cmI7zktlWPmUw1ZH24J7AD2kIurbUe3DasB5cIrujBVf2BbFDOJcphQSlRlciOhpV6b7Mq4gspcp8WSqI8niTItFjPU/UWRCW3ibg9vh+v4Np9tfuRtEqQm2+6BeIT4kUlFweM+Dz++PRqb0hIlBNVjzS6xjZr1kysg71waMBDPbiY4YV9hfHx8VC7dm1hQnXkiE3m0Ozl1VdeUfgx1WLhu43fyQquI1Gmv3bt28HQoUPV8cdJEXRvRnMq7NHFKhRVcNEZGSWH+Pfzz7vFoB8lyHXr1LEGshcpibK93zdDvXr1IDYmDtLbp0NOTo54HKs4ta31kVQ3adIE5r4xt8p+/7hE2ZWDW+qWKtN9nClUGWwkQS5mlVoRE2TPdhazMHJVwS1ktvp+3u8iZ0S5UyEzePC7XAc1YwfWZ/mmdMp96+23YeLEicL0ByXquJ2hlwwVFcSNGzeJnknswSXSOmv2bOjcuZN4LeJcYn1fsGK2adNGa9350M5aF0nMW9brLrr4YsiyvmeFmwph9JgxcN21kwT+iBHDYcTwEeJ4ZmVlw6WXXip6uov8hTDb2j5KVbOysjz3H9/7fPne0a37cnzvXboIwytc75Khlwhjo42u92NXDWfN4tsuEt9ndAnG3nckqGnt2sJUaz+/s+4PHDBQrI+vm/HCDEFwUb6NEmUkk0899ZSYLMKqbAeLZGF1GlUV2BaA7uBZWWtFZXfgwAHi+L/44ovQsQMS+0VCXoW//2HDhol9RMKIy7rcdWK/htHx0PZ/qDrWm+z3K/cNCaQguD6f+vyxgosy8ezsLOjQvj08ZK3ns97vY48/JuTMKDmn75/jXFkoKrhvzH9THCM8vmioZX83PhfrDGWfN/ZltxPfDbtiL74bnazvhvWZFonv0SVw7d+vgU0b7c8irW079T2aPWuWWBc/f8L/6OOPxH6gvBonk/C90b6NGjVKnA9xcqOT9brp06eL/cTvER478V2y9vUy69i9MOOFcr//bkmde/+VUcrv+Psz+Abf4Bt8g//r8XGcxAksvS5PVnZdGbhyfCWMQnPXu/pvA04PLrooZ0qCSyZTRG5T5dKamfs2kxyM9+A2YD249ZkilxPdRMb/OLnVjabiGJmN0eTJMVrvbaX14FbTJMleLspxmkQ5njUOJ2jy5CTNZYt02g3YQWoklxOYRLkVq+DqLsrpmkRZVHDLVAW3lGV1rqtYtukqg4U0+WfFDrahoxWaViH8iX1bOLFBIXeEUF43u3pb0L2RXcVFgsvky5HwD+zfD3v27Dnq/Q8dAdj5046I+79371449Msvv8vx/+mnn4S5VUXHf+euXcIMywsTK7M7rO2Ea4hDovpc1b9/eHLMlf0bThZuUDkolzJn5YAkw+Lku5VMDnhfiE1w6WTt5LVJ4lvkd/W0OLlxhUwSFB6Q7sykuu32+Uwq9VmmCmlnsugnHT9+vMg6pYvRW/PfEoQurW1bUa1FF2Eh87Xw0Ryt9/m9hWx06dKlguSgSy5WDC+w1u3fr7+QKKNcduzYsdDWIkDYE44SdjRsQvxvcnJgiEV+kYRh5uq48ePEdhHbl29tv/f5gtR8bG1f33+SoSJ+T+u9T7Beu1DktNr7jO+d3o947/3le7eez7cw0MWYto1mWLifbeS6/cV+2tXIx594XBApXNC9d+ZLMwU+ElxsAcD9QSJ2Ss8e8KpF1hAfJ27wOewrR1KI+z9g0EB1/P91xx2iHxb3oY9Ffpct+0J8xjkWOb5YHI80QTDHjxtvHw/t83/rrTedfbsAP5f+NsFVFdwC9fnjtsioCh2kMY4L3+8g6/1ceOGForqsvmN+Rx7WRh5fXE51fTfs44/EWhwzax/79OkrjhlWcIvU8T1f4CwVx9f6bnS1329f8Vn0FxMI5Mbcu/d5Amep/JwRH6u1gwYOUu+N9u0fN/4D2rZJhfT09nDDDTeo7/833+TIY9cOUuSxw/dR3vefy+v0/XdJ8H6n35/BN/gG3+Ab/F+Pj5VanoPrkiiz1Ao+/qLWMYcH2Uq8bU4Obqas3nZmLsppjF9xY9+WEVyUG2oS5TqM4yUxs6kkpupNZJXbGO12rAeZjdSLW6k5uHpUULyWg5vIHudZR14RQdSDW49VcOszZ64mHjm4VCY/WR78NC0HN4MR3EEokcVBORHcQDAoBuHlGe5EE7sSqpBKOFsIhcrvoaxq+BNY5TbEJMqq99ZakOgWiApuI1XRPV723+A7twOlFBJeGrbYrsoO0Q2SRJn33hZTDq7t/GdLlO2TsjoZC3flDZqZg5/Jc/zuC4vrouKW9aiZVi1fTl00XJKg8OB2fHydiLHxxkfnW2e7frluOD5KlPPy8j3xkZDxqiPHRwnvr91/lP3i+4m0/1jJpP3H3k+M6wnbf78tz/1G7ZeNjwR3+PDhYrtYqfXCx4soVoW98IukGZfX8c8v8AmzpPI+/0Kxb+uP+vNHEzD8Tkb9+Zf7/ZPfjQj49ndDftaFheo4eX3/8HNW+59fAD169oBXXn1VbUtVpwtsQzKf/N7p+48RSqIHubK+/8f692fwDb7BN/gG3xNfmEyVlKgxlSK+eRtU1VYffyH/yZVjuEDQSb4oc1yUu7MKbntWxW3jkYNL1Vu+NNGUt3U0gylaElmLag3GFRNZBTdWu63n4MZ4FF9/M8GNZ7djy2kA5m+uOnvj9D9Jc9IijTaVs+uxUjc1LjeVB/VE1oNLJlNUxaXMpvbM7roH6suxSkazFlSBwoGYd6WsHEoRis7qJxRWaaugIleF8ScweTLKlm1y28S1DjebOt723+DbEuUN4uTIIoKoDzfodlYOz8G1T7ZblZlUiazg2jOSxXIWsli5K7MTP+9Vcdnu84uBn/WrMFdCNptqr++24if8InZRcZtIGHwdf8qUh2yCa45/peK/8eY8QW5R8m7L0+xtiR7clGTR32++fwbf4Bt8g2/w8/LylINysSS4fiVRdlIr+PgryJMwAg4XKtteRhVcMpkiF2XiU6msituKFRfJZMrLYKoBMwyuw/pvqzP+x82lEjR5crxHUk+CR1xtpTko6/23sR6hvLGsvByrNQzXYJprKk9zVl+H3W6oRQVRBZeigpozcpvCNOLkokxLF1l2H+DK6hRVJrvhOuTpPOvtvxMqt7cx9Ku8bg2+wf8j4ONvZh26KAcDbplyMBiWq0b9unRiLeYZuDwHV/agUPVWuCvL6l6Ra/aThaULmQ53JeSOhlolUzdoKGK2+36nB8Z1ISoqMvjl4Ofn59nVYXP8KxUfq7zo0FwoY6oIf9OmIli9eo35/hl8g2/wDb7Bdyq4ZCBl/ad1N0iJMhUM+PiLCg/EfygNQ0qUBzODqQzmotyWVXBPlryrpVxasCzcxozkNmCFSjKXokpuIuN/ukyZSGsiUwbHeBDdah45uZVCcL30zrFaQ3AsY+WxjI3z6m0Sq94msYigOiwiiLsoUwW3GSO43GgqVX4Q5KTcQVZwO0mC68QEUQU3aFtmV+QgW27MSiQBaQgiVsPCtmfwDf4fAD+o5+CqHg5NnoxLwA4UV9m3PCpIzjgqKTJJlLnJlMuYwZkZ5RcUl5uhfBzlwLnWe8zJyobsnGxh9oOmRWg0ZP/PFkY8WfJ2dk6WMBPKybbXV4+z1+D6OTn8cX2dHNf2cVsG3+AbfINv8A2+wTf4XvjIPb797ltWqXVHD1U0/tkgCC61fBUrKXOeyMEtVsSXj794Dq5S3gUCsN12Ub5ItnNmergoUwU3mRHcZqxtlPyRGjL/pAaar1ItFgubxFyUE5gRMW9tjdMig2IjkNmYyiS4epOvlzw5PkLvbSLbGerD5Rm4tVhMEIUFN5K3mzAr6hbMZIqanlszgtueuSh3kbryi7YFWUwQVZlyc9k4PxRV3StUwQMhjwdDEV7orpRVHXyUIQsnZerB7ddS9eKOV9m4zjrH2/4b/PA/qsoGuIty0K7iBoNOaHhAVXBz2QyiY3SgYoKwUitnJ8l4iuKDaMbSCUB32+f7mSMhXRzy8vMFJhJk0dsqehbd/7FHEaWeuKh1ZN8n3vfJPkd6ja/AeZ39fIH9uGsd5/kCtV2Db/ANvsE3+Abf4Bv8cHyhhLLGK/nWuMXvih7yRzX+wRxcPq4i2TPFBFHBgI+/VEyQZgoqJcoDWUxQZ2bW24ZVcE9iyTVEck9gBciGjLPVZ55KdRmx5T5MdD9BS91J0FpiYxmJjdeMjatVpkRZzx6K09h2rOaczFl4omYulaiZTNVmUUENWLNyYzZD0Iw5d1EGbjKTJ5PRVEfmBoYf2kDhohykmKCA+rCj8Zr1blUMuQmF18tCoShpStXCnyBJLH9aEVlxu6UTKSSdlo+n/Tf44U+7Y4Koakt9uAGH6Mo+XKykFrN8W8e2XlZqmVsyVXWVRLmI97IUqZ7EsKqt/I8zofjexIXKJ8145IXGpy4uznN0MbMXfkFyLlDu5+TCL4TsgqYukAbf4Bt8g2/wDb7BN/hR4OO4RRlWarm65Y1/bCJLVdpiVem1JcpbZR+ue/yF/Mc2Cg24in1owMt6cDsxF+V01odLFdxWTD3bnJHcE1gGLrWY1tFaT2syF+VEzWQqgal+E8rhljERfKAqzUU5NkJkUDWtsssrvYmsBE07xXdWjwrSc3CbsnJ4c6YBP4kZTCXLD6OdnHkggoszEoOD1ocYCARUTid+yOu5yVTIXf8KlVsVK8cAKBSZoLgJR8ijn7Jq4I+XrslYoeWVWjKZmtCvucrDpYquIMR/gP1f+tFH1ong+yp9/KsiPsYB2QTXjgYKljrmUuo3FbAlL2RH71Rti50qLZlMMbdkVdmVxJf3sBQxe31nRrNQ9rBI1145E6pmWQsKnNlSumipi5O84NBjdAGSFyGfmnEtYDOw9BxdrPLt+64Lns/gG3yDb/ANvsE3+AY/Kvz8gjyhPPMXOtm60Yx/yGSKqrPKfGqDI13Wx1/If3LlGE6QXCxMWGO4srLtVMHNlARXz8Fty1yUqQ+3OWsZbcqKkbx6W4cpc2uz4ibxvxoe5lKxHjm4Oq+MraBt9jdLlPWyMZcnxzLL50Smr05kVdsajPTWZD24VM5uoC26RLk5kyi3YiZT6awHN0N+YN2ERHlbmTTFCcjyfBDWr1tfgQ+Pt21PSLsTqU8ypAtBNRIRxiSqCD5Va+k/ElxgMUEhLmXu00Jg6FLl8vDnzJkDX329/Fft//4DB+Dfd98NO3bsiGr/Z8+ZDV9//bV6oEPH9vDAAw9U6eNfFfHtqmyukLUElMmUJLUuF2VuMlXCZhG3OgHkSopsn9BJRmNXcPNcfSh+LkVmAerq5F9YBFk5WSpyp8BDLuRcgBw5kj6TShchPnPrK3AueGo9NRPLMPg2Db7BN/gG3+AbfINv8KPAx95cd2ZuxeOfDWQyVUw5uPR4nms8xcdflBxD/Mc2Bw1aBFf14HZn8aodJJfykii3ZBLlJkym3ERT39ZnyTi8FbWGR7IOccR4zajYS6Ks599WSgU3VmPO1bQyspdkWc/FjdfkybW06i0ZTTVg5e4m7CBSObwFy8BNZgS3nSS51IObISu4A4NkMhV0ZJV2Dm7IPdIPRRBxhiI/6Y5kiZBBWq5BUNXCRxI7vl9LRnTtHlxaJvRp6VRztQpvNPj9+vWDB6c88Kv2f9euXfhjFLmW0ex/v/79YMqDD6hHDx85UuWPf1XED7CQcO6a7KrkBjBfLSANqXJtMrsV44Gkbb0yPbAruHQiL6bcNmvZsCGP5cQVuaTIrouA37HiR9MG18XI58zIOnIh+z9/Tpcgue5zmRHvrVF9N7SO069j8A2+wTf4Bt/gG3yDHy0+mlBxg6loxj+Ug1siY4CIDNs5uI4ijo+/bGVdrtNWJsdw0kWZJMqdmUFvOsvBTZVcq6UsKjbXJMqN5dKQVXHrakk5NVgObhLjglyiHKfdjtcUwXqST2xlEtxqFUiSY9ibitGMpmqw/3SbS5Rrs0ou2Uw3ZE7KJ7AKbktGcCkqiErp1IPbXhJc/NAGuxqrA6XKFbYig54oC2veEaWhCFsv9/XHHn+CrNgqQsuqs0qKrFV7nX5cZ2PPPP0stG7dGurWrQuXXjpMkNNRo0dD9cTqUM967Oyzzxar5uflwZm9ekFSzZrQoX06LFq0UDyOP9RWrVrBHXfcAfUb1IfXX38dWrZoIQhus2bNYOrDU8vd/9GjxkD16tUF/tlnnyUeHnrJJTD39bni9uDBg+H//u92aJeWBg0aNoQXX3oJ/n3XnVC/fj1ITk6GDz/8UG0O5ewXXHCBeN/9+vZzEezj7fP3+hOu49bvJVgaHglERDfA7ueuW6/6Q0qYbT2ZSQmJsjyRlxSzCi4SX7/ec8Lt+P0uC35csgXB9bFZV0cKRHIhR44kn/fxC5nP6Z3x8efYhc8141vALmrs4mbwDb7BN/gG3+AbfIMfJb6o4PqZFDmK8Q+Ok0qYS7LrcSK+2vhL+agEmFFoaRC2b99OBDeDVXCpWJjGCC5VcE9iBPdEpq5tyJY6rHpbR8vC5VwwwcNYKiECv6zGCG+1ys7C9QrXjdN6cTnbrq4ZTNHtGmxJYtXbWiwYmP430STKpPmmJmfSg6cwctuOZeBmSJOpwfghBlV2pyOj9IxMCUVFM7y7IkOh6FSfXhhVBH+ClCGT2ZRbotzStSkitSFFfm38H374AWJiYmDBggXiOGdmZsIjU6fC5u83w+mnnw6TJ02G7zZuFK/t3bs3jBo5EtauWQM33HADNGzQEI5Y28FtWF816Nu3L3z66acQDAZh+fIVguAuXLgQ/lsaKHf/N2+WWJMnw0aJ1evMXjBjxgzxgp6nnAInnXQSfPnll3D/ffdBXGwsDBw40HofawUR79Kli3jN3r17BeG98sorYeXKVRZZvxQyMjKO28/f6znHZCroKUkOsN52mwxLF2WtckuSGbtSa5/Qldsfr+Cy2UrnP8uEkxcEvI0XiHDJEOul0QwllAuimmUNN47gs7XOf8c9UcmRfO5eHoNv8A2+wTf4Bt/gG/xo8FGBphtnVjT+0fNuqWULzad4/i0ff6mox6BWnAgGqQe3m2zr7MB8jLhEOZm1hVJca1O2cIlyI43c6r23FBNbnXHFBJaHS95N1Tz6b3VpcqUSXN1cSi8dx7I3yzNxq2sV3CTWg1uHNSITuW3IGpcbMYJ7AqvensQkysksB7ejloM7yJYmB9XAPEguyqHyGxFDFcWPlvvyUMWkowriT1DGUQ7RdVVpGT7Klek+xgnRHzazx8RWg/nz58Ohg4fcEuX+/eDBBx8Mw//ll1/ESQAJLP4gtwiCW03YotOfS6Icxf4LOfQDjkS5Vy+L4L4wQ9w+1SK4SLpxvYMHD4rtrlm9WjyHpLdunTridXi7Vq1asHv3bti3b5+YJMF1kawfj5+/F1SQy1v4grOBgaAymqKZQWEyVWKbG7gd/Yqli/IGeVIvlM+TdDmPhZprOXB8RpP1qGRn5ThyIB9zNvQVMLv/AtcFyZl1zXcuQj72ugL3trj5hN6j45IjGXyDb/ANvsE3+Abf4EeBb0uUWfU2ivHPBpl3SwTWLyf9hfmUNJXSx184PkMlIvEfSsPA9AssAkqulCF5U0dWMExhBLelRnKJkzVk8uSGWg5ubabQrcn4n67u5a2t8Rq3jPW4HVPZLspxESq6cR4OWAmM6MYzgltdI7k1WR9uXebA1YBJlGmGoDnrxW3FcnBTJblNYzm4nZlEeeA26sFlPYS8ghvykmmGKgomDZW/WijCnVDkoJaqgD++X3O3yVQfdwauIsD9WkB+98YsG9dtNHXHv+6EOnXrQu3atWH0mFGimV0Q3L794AEiuNbfq6++AskpKYI00lL8YzH8sNkmuPv27vMmuFHsf7/+/SWZDjkEd/oMce8Ui+C+8sqr6rW4XTwJ4b2stVlQq2Yt8dz056eL57AiTQve//jjj47Lz98Ln34vQdfMXyCCRDko1rWlx1udsHGWhbtBuSj7mYRZVna52YJfm9FUJ32/kvJgeLq7T8bnsv73+VjWnU9zUPTxHLuCcLMJ17ac2Ve1TZ9Xn47BN/gG3+AbfINv8A1++fg5OdnOGEc3m4ow/uEuykheqQJsV3BLWH+uM/5SPbgBXqRAiXKZXsHtyFyU05iLcookuKSg5RFBTSVXa8SquNxgqjZrSeUV3ESNKyZqXk6xGrfUU30qzWSqmqaLjmHkNoblF9FziZobVg2NsdfUlrryPxHchozg8j7cFqzRuZWcVUiWZfT2TDtOBlP4gV0YoOotSSsDkuCGInOIUHmuOxE9br3vh0LgTVqqID4RVeq/FT25ofA+2zyL3Ob3sBe9H5e2euDAAViyZAm0bdtOSHxt0mkR3PttkymsiiYmJMATTzwJe3bvga1btwryuOXHH2GzrODu279P4RPB3bRpU1T7jxXcBx64X+2/kCi/YEuUBcF9+RX1SutLK05A+Ldm7VqoWauWeGbxksVw8kkn/2k+fy/8oCStXJocILmykrw4hBcr+NT/UcJMpqiSK9ySpeSG293bFVw/y4PzmMEkB2X5WHZOtnNRYZb+Ps2a351b53MuVlxG5NPz8DxmcH1se+xCZfANvsE3+Abf4Bt8gx8tfo5wUfZL80x/VOMfLASQ6s2WKLuJr1PFdcZfpVyizEjudsdFOYMR3I6M4FL19mTWg9tC9t82Y0bATSWxrceycGtLUltHI7c1NTfl6ow3xmmxQZzQxv8eDsp6w6/ehxsTQboc72EFXZ2x9yS28+S0pUcFEbk9kem+W0hy25IR3BT5YaQz/bgymcLqIdeeU66nW+MZ8iyHhcqpdVUoLQ2VTx+qIv4EKUOe0C+8MuuQ2JCo3uZ3bwL53RqH9ed+/MkncM45Z8PWkq1w5MhhGDJkCFxx5RXiuREjRsBfLxkqyC+GTMfEVIMPPvgADh48ADfecKMgsD9u+RF+2PKDIJ179zkV3EOHDgq58KxZr0a1/yOGj4BLJJaq4GIPrrWGXcF9Wa2NuHgiwz/sw61Vu5Y0WApC7dp14JlnnxH42A+ckdFZ9C4cj5+/F76ICdqQy0isOyzcvQRhnfXb2qp6bumEW+I2mZInZUVwlXS5yJHs8N4UlgXnlihnO30tPsf2X8mFXKHr7uB1bgjh8xWE39dncLl5BDeXMPh/CvysrCxYvmIFLF++HFbgYt1esXyF+L9c/l8hn19J661w1rVft0I8tnyF/d91m7bLt7WC3V++3OAbfINv8A1+FccX3iBRXH9IosyNMysa/yiJcglJlO0J/zyKCbKe08dfZBRK/McZrwWogttFLh1YTFAa68MlkttKcrATGMFtxJJv6jMeV5cZTNViJJe4YIK8H68VSeM0l2Q9G7fSCS7faIxGdmOZc3KcBwv3IrgJzGSqFgsErseILV+aSpJLVdyTmEQ5hZXS06U8uSObkRhctq1MSiqdPlw0wonOoLZ8UhEKRYhjCUVDNKoevqrc9uVuyihNbi4rui2hQJBbm9jmdWskbk9gGbn79u2HS4ZeAvEJiUKm3LNHT+sHuMkmvx9/DK2TW0Pbtmni/s033wLx8fGQkJAA4ydMUD24ZDK1b99e1/7fdNPNEB8XB3fecUeF+7/0408srGRo26ateOCss86WBBfg1FNPswjuK2p1lB5/+913NsFdu1YQadrQ4sWLISUlGeIs3Lr16sEzzzx93H7+Xo9SX21QRm0Fg05EUECR3QAEyMAtN9fOaVNyGTajSBJlefImF+USysf1F4Vb5EvbfD+TJ9OsJpKOAib9cRETckdUfTGaEYQ2u0p9Mk5kgM8VG+Bj8iR3KLzB/zPg42DmeD//G3yDb/ANvsH/bfhIeKO5/mTLCi6Nc6IZ/9i9tk7WLT2vYoJKSsLGX8h/1quYoFLFhbZtUxLlrpLgkn9Re1bBTWEZuK1YDi7Jk0/QfJOI4NaOQHA5H+QmxNxVOT5CFm6MVsWtFJOpWI9e3DhNosylynHam45nPbhJWsMxNSHz6m09eaAasAPYlPXftmA5uCRRJpOpTozg4oc2CF2UdXOcXFXBje4vxOSe+FUOhY7qdxChJbLq4js5uC0Y+W0pcFGenNetsVPFFQS3ZdgmDx44IGTIXvi/HDqkHt2/fz/s3bMn6v0/eOigtd098I9//EMtN914k/xv39/nqvweqpTjv2PHDjh8OPSn+PzdLspBWKdycO0TY0DZzQdd1Vxy6/OSJxfLvFtbomz3ldBM5FZyUfaTVIebSvmV+UKRZrjgzJRyyZEjF3IRGE16xA0ffFrfTIEuMdKlSWo7PoP/J8HH2fs/y/nf4Bt8g2/wDf6vw8dKbjTXH5vg8nFOxeMfJLKkhsOxE0UI4fjJS55cwiXKLCrVNpkKUkxQdxYTlC6ruMSrUrWiYjOWg0sS5YasOFmPVW9rM86XxG7XYP5MCYzoUmRQgsYzvcymKq2CW548OV4rJ8dqpDZBc1JO1GKCkrRm5HqyzN1AktwmrBzejPXhniwXKqGnSYLbnplMdQ8juNYHGmAmU26xZkhrWox2nicE+mRRqEJ32lCVxqeIoAksJgirt/ndG7sX7MPt3khVdP9X+3/AIsW33XYb3PZ/1nLbrfZta7kV/996m0va/Ec8/lUJn/dvBJmxVFDNBgZckUE4eVTskibLmUQ5o0hxQHgiL2YnYjxxi8psIZPlsGw4PwWgMyMG0YPrcin0uZ0Omb2/02PDM+oYEaLKYD43lijwNIngmXgG/8+Bj4OWP+Pv3+AbfINv8A1+9PgobY7m+pOVneOQV1KmVTD+2SClyGLZWqyqvIL4KoMp9/irVCbHEP+xIx3RZGoH9eB2YcVB8jNqx3JweUxQc2b6eyKTKTdg3K0u68OlCi7PwOURQdU94oC80nq8+m/jfg+Sq+fgVtPcr+JYJTeB7VgS+1+Tsfq6TKJcnx2sxmzhPbgtmB6cSujt2MxDR2YyNSgYDLr6b5WLsj6T42G6EwpFq3KIdjYoFHkmqQriOy7KLWFCH9s5OU+SWfqzq7iNjsv9N/iS4OZusGf+gqWefbfcxC2XS5S3FjuZbLJSmyclyn4pUaaoINGDy2cvZW+JK+zcX+Sq4uIMqK/Abe7g82kmEFofp2MsQf/z3YTI586sc4W9h+XcFRj8Pwk+9l2Z84/BN/gG3+Ab/PLwbYlyxdefbJGDW8hMpSoe/6gc3GJ7ocl+HD+FSZTl+Msew613999a4zks/kmC25WR3E6M4KZKeTLvwSUu1oQ5KDdmVdx6jNzqMuVEJlVOZAQ3XnNUjtU8nuI8PJ8qNQe3muacHO/RCByr6afjWdU2XmPu3GSqJjOZqs+alRtpYcLNmINya7mkyFmGdCZRpgU/tMEiJogNzEWVaUNuFD+LUPgXvkIZRKj8dUMe2zP4Br8K41NVtrQccosSZZoZtCu4JUoeQ/m3dqW2WPba2idsmnHEkzI+7neZLbATe5Ezk8lz42yCywPU3c6Fvny3HFXvzXR6ZHzMbMjn9HgysyLdKEI5IkaJn5W1Fl6e+TK8+957vwr/tdfmwGeff/6r8Stj/9Fk7fXX51YK/ufWvnywcGG5+O+++y589dXXlXL8f+v+oxGJOf8YfINv8A2+wS9vbWxnieb6kyUILiOzUYx/hBRZ5dyWKMNN211ZZt9q4y9ReJAS5QBPw7BzcAcwkymq4qZpMUFcotyCOSk3ZTJlkijXZ8S2Dqve1mT8j5tMxWncMZ61vMZpvbhxGg+tFIIb58GYYzWDKV7h1R2U47UqbhIjtbXYgaAs3PpsRoC7KJ/ADu5JkuBSCT2NVXE7yQ+LSZSDrgE5ftiZIzZBV2vJHLFR3Lb/W8tI+3F6ruvIjc7tERvlc/bjmWydTNdr7HXs5+nxTXLbG9VzBt/gV3X8oDRlc5PagMusgN/GmULVA8JOtDSrmJfnuCWrHFxlPkWmCu6wc909maTK2dk54fb7sornGDv4VI8m3m/TJlWYlPH1Z895DXr27MFMhbTgds2916eZRUSD37//X+D88y+AZ599hrn12uuPHTdeGJmlpKRAh44dYMjFQ+CRqY+4tn3O2efAf+6++1fj0/5zR2KnYqmbNnnv/x3WccN9+K34+Xl5cMEFfWDu3Lnl4j/33LPCCb0yjv9v3f/lLpMpHsMVgq1bS+DgwUPac6EoawwhZ3AUilBdOKqKhcE3+Abf4Bv8Y4WvenAruP6IHlw2volm/JPHem2LqQfXT8RXypO18VepiHrMZQWJUmkytQ0J7mBZDMxgBlMdJLnlLsotWQW3meRlJ0qu1ohVb+trVdwk+b8G4381tLbVeLbEsl5c7qKsF1tjKkuirJNa3VyKQLgTViy7X505Jyd5VHFrMdZfn2XgkkyZKrjNWf9tK1Y6b8uycDsxJ7CutkS51JXhiUY5KFHOHLlRDeQ5cchUg/+NklQwUsDJxnBGMPC54TaxyBzOiMNIuSiigq8jslH18G05Mjknt5Cuyi2ls3Jzuy9XxgehZJn/qI+H/Tf44fiOQQE5JQeVxMUl/Q/YPe7r1+U60mNGYGnWMU/04PqlyVSJWjdP9ubymcsiTZJcxOzz3SZTTFaa78TFuGRC8sKS3DpZLIsWL1TVujmzZ0OP7t3DA9vDyA+LpylwV/jKw9+Qu0EQ2MWLF7nMkgh/7NixMHnyZFi5ciUs+fBDeOjhh6Fdu3Zw5113qXVxYuDX4vP998L3RQqs99j//PyC34w/Y/oMESVWEX5efh5069YN3nzzzd90/Ctj/1UFl41xvv76azj11FOhR4/u0LFDR5g2bRpr4fIaaIUqHDxFrjCEIpiluLdr8A2+wTf4Bv/Y4aPjfjTXn2yq4BbxWKDyxz84TuI9tjQW2iAlysVsTEXjLyo8cKPQ0mCAJMrootyDKV+pD5eKhymsekt9uCey/tumWvpNA1bBrc1Ibi3G/xK0im11xhlp4a7J1bSe3EqTJ1fTSsR6VZeH8MZo7slxWtZRDeamTDtei1Vv67CgYKrinsBclJuzPtyTGcFNZcHEHVkFV/Tg8swnup1rDTgzR7KBPLtNRMCpmMnnRm5ilTGnStZVVckYkeDb00iEqphVQXw7CqiF63c7gWfh9nNcldF0iv/Wj4f9N/jh+DbBzXXN/InbwVKXPDlI8v/cda58W2WIIE/IGygH1zpZFxc7M404A+mWImuW+X5nVpN6VIRE2SIj+cosiBkD8XgZJmXFKungwYPh/AvOF5j4+Kw5s6F79x5KCvvYY4/CueeeCxmdOwvyuXZtlnLYdZEp+Z/wH330MTjvvHNEVvKYseNEjNGaNWugZ8+ekJzc2sLoDnfceadjiiTf1xgL45abb3VJcZ955hlo26aNmFzA9UaNGgXPP/+8IFU9T+kJd1nkN6NzhkUUz4G33n4bhg4dIkjx4EEDYdXqVWr/n3j8CbFOt25dBYnOz88Xz102/DK4/fZ/wnnWfnaz3tdtt/2fRQ7zBf6iRYugb9++kG5t74ILLoD33ntfvGbaU0/B+PHj1X4/hvt77nnQOSMDxo0dI44THb+x48bByBEjoFPHTuIYfv75Z6piOnHiRLjp5pvU/i8UeP0EXp8+F8D777+niOvll18O//jHTer4v/vuO/Dc8895Hv9oPn8lPWbHv8Ald3Y7XpIs2jaZcs53R44cEZ/r/Pnzxf21a9dAmvX+/f7CCMOnSMOlcixRQtE9HPKQyRl8g2/wDb7B/9/j42RoNNcfVKD5GZGNZvyTJ8yk3Dm4hfS4clF2j79cLspShYfFirKyMqrgdmMRqx2ZIjaNuSifzCKCmjOCS2bA9bUsXN6DSxJl4n81GR9MZHwxToudjdeSezgnrTSCW02r2lbTnK10l6t4DzfleFaarsEMpmoxoluP5Sk1ZM3LTZgtdUstJiiFRQRxiXIn+aENxDJ8AGctgkFFcjHXM9NVtdLIwnBZ4RrJq2UbWXWMEYrhtqwzk5GRTK36RvJQ9djIjVUSH6uzgrz2IRdlp1orKrt9WsoKLqvwykpuVd3/kXdsgdue+u8f4vhXRXzlwOeq2gbVpFFQVm6dmKBcp2pb7LgkU26biglCkykmpckjF2Up0bFdBP3uE32hfE72q2BQOva5uCt5Ppe5g2NMZK+DBPedd96GM844A+668y7x2OzZc0QVDi9Er732ukUs0+CJJ58Q5KV//34wYvhwe3v5jtxI9dZI/Ndeew3S2raFJ554At4Ur+tvvW6EIJTvv78AWienwKxZs2D518sdMiarhkhwb73lZtn7aWPkfPMNpKamWq99X+zXgIEDLEL5OCxbtswiyykwbtxYeM8igihnTrWI8H333QfvvvMunHba6XDXv/8ttvvGG28I0jtt2tMwd948UW1EYoz4/fr1g+7duon3Pe3pp6Ft2zbwwYIPBH7/fn+B6yZdB5999pkgxRf+5S9ie1MenGIRaVsyjK/D1yCBns/2F5+79757xXu8/777Yd68udC79/lw8y23qn0+++yzBIGn/e9nvXYS4n1q4V1/vYV3oZIaP/DA/TBo4EB1/CdOmCj2UT/+0X7+vgJf2PEvcOXrOj24PrZddMbkIxuMDXvkkanwy6Ff1GM9uveAT61jFjYgCnkMpcIyFEMR+rxCmtsJH1aFovJXMfgG3+AbfIP/v8HHa0U0158cGRNkk9eiqMY/dgXX8TbxFzouyiofVxt/If+xc3ADTnHCGreV2RLlQawHN0MS3HRGbpO1PlweE0Tq2saaRLk2S8ehHtxajP/V0NpYY7W4IK8CaqxHNm6lmUzFaCVjPTYoXisxJ2gRQfFMllxdI7h1WTAwZeE2YAS3KSuJt9Aanungk8kUr+B2c7kos5gTrOByiaebFOD/77SBPq+YbXT3NY5wiINYd7iznrO+uzJmb7vq4Y9X+bfNJdENr97iL5qky7iS/ZpQVPj3vVgK1zxY/Lvu/xX3FsNDr5Sqdb/I3g2//BKCnmM2VfnjXxXx8Xcj5C1BR45MFVuH8AZs0hu0yTDPYiNzqRIpq0EpDYWTFzOC6/Tg4sm8UM5oSnmO33EY9LPnsrOyXRcOV4QMy5tTlT1JcBcuXATz33gT2qalweLFiy2CO1tUV3G9SZMmwRVXXK56ZxZ8sADaWEQOjelc/Zu8r4Zed+UVCn/BggWCAOLr1q1bbxG+ZPhCmEQVuEyOiODeYhFcRxabL7bdLbMrTJ8xXaw30CJ5jz/+GHy57Ato3TrZ+r9MrPPMs8/CKaf0VPt//eTr4aqrrlTvacLECRb+Oli3fh1MnTpVVGRtEtsP/n333epCjNu/G+9b2+jV60y4+pprYPWq1U78jrVMmTIFLhk6RKw/WRynK9Q+LLDIsThO1ud/n0Vwe/furY7/lCkPWbjnq8+lS5dMeGPuPPXaM3v1gmuuuRpWrV7t2n/cJ9z/s1HOTLPeWWvhyy+/Cjv+0X7+Xsdfl2FzfNp/p4Ib8lC5heCrr76CjC5drEFDGRzVqIsNrUJ6fSFUXs9YhF6xii0/Db7BN/gG3+D/TvgrJMGt6PqDFVxVoS0qimr8g27JxcWOWzLJmRXxLS4JG39RUS/IKrhYmNi2rYxclLtJ3tRBLrx6myLVsmTw25J5IlFEEJlM8QpuXVbFraG1qHKCW52RXD0qyItvVnr1NsajLByjsWxeWk5gTcLxHj23PAuXcpLqMpOpBppEmffgtmDNzkRuU1kGbrostdNshKjgBqlPUOZA5coeXGWoM5x6HB05p7g90q6aOUY+DlnIZEY+XUdSf6RGPlxSUa3SVgXxx/ejftsWitRi1Xa8lC6Pp6ot3u7X0okQsu5Hg+/fegje/GTn77r/09/ZDjt3H1b4fa/9Hi65ZfMf4vhXRXy7Z32dkiAHWCRQkM0IBiQBXi8ILsu/dbkpU96tfdK2Ce5WuwdXuiv7mTW+y2CB9aHYix9ysrOcLFNetWN9Lkp6Ki8sSHAXLVwonps0eTL07dMXXnn5ZbsH13p+yJAhcM9//qO2+80366B1cmtYunSpu6+GGRnh/4uHXAz33HOPwrdflyxet14Q3Nbw+RdfOCSZEcexY8ZYBPcWlwwaHRaxb3fRosUCdyBWcB97DL4QFdzWgrQi7vQZM6D3eeeq/b/Z2s6VV14p3gcSeOw3Tkm2l9bW9vA9+UTVtB88NW2awpswYQLc/s9/ivuvzZkDZ5x5JqSmpsCAAQNh/vw3BdaDFsEdghVcsb/WcbqHHad13wgS/5G1v1hNHjbsUnX8X545E846q5eSZqW1TYP3yE1a4p1p4aWkpgqiPR97buV2X3n5VSEF9nEC6nH8o/38vY6/kiZzGTrPyJU5uCHwti4JBP5rHa8zYIb1WVRkZuLZ4xUKRRbBhdzVhUjWKaHyVXUG3+AbfINv8P8H+Cuki3JF15+snGx7vFMoxzdRjH/sHFxHgkzriQpuiRxzaeOvoErCcNrJxPhtW5BclLsxk6kOmkSZ9+C2YvLkZqx6S0ZT9Vglt6am0K3F+F8iK37Gaf24cRqnjNVUxJUuU9aZc6wWFxTD3piegas3FCexcjVZSNdkPbgNmZa7MWtiPpERXDrYJ7NG6HSWg9tZLvihXbR9e5kclAdVxQmzOsNlnBvVYD+Tk4kRjjmPS/I53CETzvO8UrZJGv84braZw90OtVUNXxBbrMr2I4MpVsHt09L10x7Pq7vWeoTfbdQmeOn97RD86TDs238EVuXthX6TvocVuXvFtg/+EoLNFtFFvPOuKoLP1uyBXXsOw9Ztv8BrS36CbiNtYrahcD98uOJn2BI4BPsPhGD5Oms7131f7v4/MLMU9uw7Ik4z/y37Ba5+oBieemMbrC3YJ9Z99s0y2LTlIORu2g8HD4VgjfXeHnw5CNt++gX2Wu918fKfoedoG//qB4th/cZ9Yr2cb/fBuH//eNx//l74qn/DIrpB7kYe0DJwA3YLgO2i7C1RxvtIZElyY9va2+uIyi5zSPbqQ6Gwc5q1tF2UWa+Lr8AVIUMzp/kFTsi6XcFdaEuEcnLg9NNOg/PPP19WcH1w1ZVXwfU3XK/6MD/55BPxmhwLy+dV+ZP4V115NVx//WSFL16XbL3um2xWwf1CbsPnynHFHtWbb77F1RP66KOPCsmzMJdCgjsAK7gkUU6GdRaBFoZNFqk6zyK4tP+3CIJ7hXjuyiuuhJtu/odr/wkfJcrYU0tVynHjx8Ptt9/OelIL4MMlS4QUOiMjAwryC+ChKQ+JXl/c1pV4nK6/Qb3nTz7+VBByPKZYwR02bJg6/i/NfBl69eql8Lt36y4kzrzyKvA+/BDGWccio0uG9Z7tx5599lmLwPdm7937+Ef7+Xsdfzc5LnDn7MrbK1wuys6IZu/evXDxRRfBP278R/gwKxR5ot+7igDRG3d63jf4Bt/gG3yDfyzx7Qpuxdef7Kwsp0pbVBTV+CePmUnZLsp+x12ZOyhzibIwmcp1GYMyF+UBrAc3gxUK20l+RRXck1kFt7ksPp7IeFpDpr6tw4qWNRmxTdKquYkaZ0z0aHv1MjeO9ejL/U0mU7EebsoxHo/HaGG9iayCy/XXnOjW8zCZoh7chvJANmMLlyhTBbeNR0xQFzsHt0wOwAOqT9AmuFoFLEK8SlcPN1pXVWwE75Xc5N4uq6h1Hf5duHFQFcMn46jxqs+2pZr7siXKzhyW23yqpcK/4r5i8di8j3bClFdKYZ9FTt/8eCdMergYfvr5MHxjkcX/m/Zfgbs2fx/sPxiC598ug/mf7BSvm/H2dvFc2c7D4v6CZbvg+XfK4IBFNLMK9pa7/5f93xZYuWGvINZ3PheAc68qhFkLf4LN/z0k9n/uRz+JbX68eje8tGCHOL/h3iAhX/jVz+K5a6cUQ//J38Nuiyh/+/1BeOL1bfD9fw/C9l2Hodflhcf15++FH2ZQIJ2UueEU9eDiCdRFcNX/EmVtjydoIqnk9KcIrovA+l2RQNxJ0OnBzXYbPlEPpc/d/2JfVPIFcUklgivJEkbVJFvErLus4D77zDPQsVMn+OCDhWL7SLj69u8nXvv111/BjTfeAFlZ2S7JEW7/aet1aKiE2a7YGzx27Djo17+vwMfjl9y6NXzxxeeu2Vz6P9YikdddN8l2UV68GO697z5oa5FbJJREuAbICu6yZV+Iqux6rOBaz82YMd0iuOep/ecV3KefngZdunQRplF43JHAXj5hoqzg9heuv3S8xo2zCO4/bxeOz3+58EJ46aWZYnsYa9TZIpzoaIwS5aFSovys2N+O1nH6QBwnJOn9+/YX27MruMPU8Z85cyb0OvNMNXuNEwqPWvuCt3Ot93XhhX+x8F5Sxz8jo4tF7PMFPvYMD71kqCKds2bPggcfnBJ2/KP9/L2Ov8/nJV32uSKJcNCiz+EfOXJYyLRHjxkDBw4e9BgchTwHU14Vh1A5qrqQa+1y3D8NvsE3+Abf4B9TfCK4FV1/RExQkZ+p0yoe/9hmUo78mAw3hYsyFRK08Zc9hlunGYUGieBe7GEyRT24yawP9ySmoG3G1LWNGbHlMUG1mJNyLWYsVYO1rSYyuXKcZizl5ffkpSSutL/YCJFBsaxJmOcW6SZTiRp7r6VVcOuyg9OQabubeEiUKQeXyG2arN7yqCBlMuWKCbIG49gnxiNXMocz2SbLG81k0Sz8MdXL6HKf3aTIQldXDIu27shNYZEvVQXfjgFyKrfj+zZ3VWn5CWF8X62CK/FvfmKrWOmVhdthwPWbRZX23CuLBN73JQctIrtLYCFZxJPKk3O3Kfyv1u+xSOUBcRsJ7pr8vep9Pje/TGz39AmF5e7/jHe3w86fj6jXzVq4w64YW/fnWqR7154j0F0SxsIfD8IX2XsE/hnjbfdT7BP+9/SAuH31Az/C0Fs2w7+fLxX3r7q/5Lj+/L3wqa82yByT3WTXfRt7PewTbbEitspkqsSu4BJRdXJwSaKsxwEVqtvcSp9mNbNzstVMqTNDWuAyCXL6XqREOTVVElzH1fDv1/xdSpTtdbEftlOnzqJyi1Je7HfF7cybO0845X645EN3jIzEx+pp586dLBKdKkjxl8u+FK8TBNci0Sgv5hE0KgfXIockIW6f3h4uvvgilYNLfaVotPT4Y4/bEmWL4KJEGZ+zK7jnqf0XFdwrrlTbv/mmm6CjRURTUlPgrLPOUtXr/n8hgmvv80SUKP/rn+L21KkPQ4cOHa2lA2RmZsL056eL/XjoIazgXqKOKZLpTp06CTMsJMxYXcbjj+ZSwy4dpo6/ILhKouyDG2+4Aa6+5mq1/1MfngodLawOnTpCZpdMeH7682qWe/iI4fCfu/+jqrTXTZokCLLX8Y/m8/c6/k7sUHgcFN1esXx52MT9f+65B9LT02Hz5u+hbPt2KCvbDvv375PeI+WOmCLfD5W3YlTdZQbf4Bt8g2/wjxG+iAmK4vqTTSZTNKaJYvwj8m7l+IoquDbxzXOIrzb+csUEBaX6Lqh6cHWJcjojuG0YueUSZcrB5X24jSV/q88quLU8zKV4VGyih0Q5xqMPV5cox2imx5XmoBzj0fDLmbe+xLEd4U5a1Vk+Uh1Wva3HDKaaaAZTXKLcilVw27IeXDKZypA5uAOI4AalrBIH6Dg47MqyRPWs0cwwkx63pNMtISW5qNMnSXJS3vPoloJurJL4DrFtoYgr9dmOZ7m4SITzujeWz7cUcmXCP90iisuy9qjq6MYfDsCYu7YIjO+LD6oeXHQ3xpX+PqVE4b/43nZRqcVtbbcI7uxFP6n9n/RIiVj/0tt/KHf/RQ+uRXBp/2ctQoJ7UNyf++FPsPHHg2r/8zbth3c//1nhh44A3D+zVEilvVox7n0xcFx//l741FcbINdkjdg6ZlMBka2GZI76bsnRr4SkyMUlooeEst8oOgiXPBkTRCd521UwPAuOS3rQZMolN2XuuD6XAZFDWPLZLKpTyXNX62zH3nwhSybiRNvKz8t3S1g1/Dz5ut+KryqUPm8zpUj4Xvufbx3zLIw6Opr9t5Y1wvSpfHx1nI7i+L/zzjvQ1SLO+L6Ua3E+4Tn7gttt36E9LF36sQs/X5hARb//lXH8l6PJFJvm37p1K7RLayeijfB/u3Zp0C49DZ597jlNGhdyaeVCESRyFQ3EQl7rh/TygdscxeAbfINv8A3+/xYfCW4015+crCzHNVkjuZHGP4LIbqWWr2L1WjsHl0mT2fhLJWEESl0Fih1OTFBXqXjtyAqF6ZJbtfGQKJ+o+SM1Zorb+kyRW5P5LNVk/I/HxyZq0bJxGn+M95AoV6vMCq5XSThe68Gt5vHmEpg0OVHLwq3JCC+5bdVjJlMNWR/uCeyAtpBLa60Htw3rwSWCK3pwVX8gG5STRDlTVbe+cwb3w92xKlwOyrNHXTLPke68UbeE1O59DK+OVT18JK62M3JLYSKlV2npL98it/bSxCbE1usUvoXTfdQm6Hut3yKEpfDT7sOQ49sv8P0lB+HtT3eJ9c672i9ee//MoMJfsvJn8FskWEiUdx2Gzy2iTPvy8KygWP+cK4vK3f8ZFsH9ed8Rtf+zLJKsKrgWwcUeXNp/7PN99/NdCv+Ite/3W+8Z+3Lxb+AN38O5VxWJKvRfJn8Pp4w5vj9/L3wuUXbl4Ja6pcp0H2cKVQYbSZCLWaVWxATZEpxiFkauKriFTu4bmUnx7Fs/m9G0c3ALmHuu43rrMnbQSSE9xmZXeTVPETof7/eUM7I+ToZ8Bv9X4o8YOVJIrsvDR3nytddeWyX2n+fgHt2oyHvaP+QhgYvWANTgG3yDb/ANftXEx8nQaK4/qoLrL5QmUxWPf3CcxAksvS5PVnZdGbhyfCWMQnPXu/pvA04PLrooZ0qCSyZTRG5T5dKamfs2kxyM9+A2YD249ZkilxPdRMb/OLnVjabiGJmN0eTJMVrvbaX14FbTJMleLspxmkQ5njUOJ2jy5CTNZYt02g3YQWoklxOYRLkVq+DqLsrpmkRZVHDLVAW3lGV1rtOMdjZKJ1kWuTKCV8O+c8jE8I2uHFJXTMsIt0utMvQZ6a6ehWFUEXzquZ2oKrct1K9ekV3rPlZvC7o3Ev+p4kv4D70ShEO/hOCel0ph4j3FwjwK+2IRY913++DHwCG4/F7bsGnTjwdhh0VkURL82JxtYoJs3tKd4rmynXa+5LR5ZXDPjADs3H1ESIor2n8iwoh/3lV+m+BiD+4Im+AWbjmo1ieCS/uP+Pdbrxv2fz/AYYvtfr1uD1w7pQTeWGpXdEffteW4/vy98PHkmCv7N5ws3KByUC5lzsoBSYbFyXcrmRzwvhCb4NLJ2slrk8RXOQayk7roUSlk/beOGUNWdjYzc/Ap0yEuLXX3ZTozqbo81ac/xyqE7p5O3YzI4P8afOwhXrN2Tbn4q1atEjPPVWH/l5PJVMiVmBhxHBU6WqFbpP6xUPigzeAbfINv8A1+1cRXEuUKrj9EcP1qqXj8g5VanoPrkiiz1Ao+/qLWMYcH2Uq8bU4Obqas3nZmLsppjF9xY9+WEVyUG2oS5TqM4yUxs6kkpupNZJXbGO12rAeZjdSLW6k5uHpUULyWg5vIHudZR14RQdSDW49VcOszZ64mHjm4VCY/WR78NC0HN4MR3EHbttnSZCK4gWBQDMIdB9lNrqgVng3qmPCEP6Y70PKs0a7aepnDGQEZuclFJKoSPu/BDcmqrR0D1FLJk6l6i88XdG9i3W4k1iP83lcVCfOokMUWkTB+v/UgDP/nDwLnX88EYNvOw8J4CtcffMNmKPAfsMik7a78adZuOG1coXgOe3CRmO7db582sPp7ya0/VLj/f5n0vSDVvxwOwfWPbIWZC7bDt5sPiHVnL/4JNvgPqP3Pswjue1jBlfu/98ARuPP5gHjurunWd2bHL6Kqi07K2Nt7vH/+XviBUgoJLw1bbFdlh+gGSaLMe2+LKQfXdv6zJcr2SVmdjIW78gbNTMrP5Dl+t8ugfL0tBXbLSlWlr6DAVaXzMamtusiwSBkfi58p0GNimGsv366XrNXgH5/4y1csjzAeqmhYFXIp20Ll1BxCEFXxweAbfINv8A1+FcVfISu4FV1/crKz5VjI71RwKxj/CJOpkhI1plLEN2+Dqtrq4y/kP7lyDBcIOskXZY6LcndWwW3PqrhtPHJwqXrLlyaa8raOZjBFSyJrUa3BuGIiq+DGarf1HNwYj+Lrbya48ex2bDkNwPzNVWdvnP4naU5apNGmcnY9VuqmxuWm8qCeyHpwyWSKqriU2dSe2V33QH359u3b1awFVaBwNiOTu9OOdCpYiiCM9OqDZP2KLIu060hdNsqqaSO9Ylm+q5L49COlSi6XJY+Xj+ULUislyj3s/xOURNnBR6J62vhCT/weoze68E8dtwlOHbPJtf/bd/0Cz84vE7E9KBGm/V+6cg/k+/fL5QDkFx1Q/x+cWSrWwaihnqMLK+X4nzWx0Hq/m/4Un78XPv5mNoiTI4sIoj7coNtZOTwH1z7ZblVmUiWygmvPSBbLWchi5a7sZ7OWbGbTlQfnXAxsk6kCt2yV56AyWWtYFIwrU1Wr4CmzogInI7WA2fxrZMjgH//4y10xQaGKBXKhCJmJoQhDqYpyKiowPzH4Bt/gG3yDf+zxRQU3iusPjl+I1LpNNCOPf/Ly8pSDcrEkuH4lUXZSK/j4K8iTMAIOFyrbXkYVXDKZIhdl4lOprIrbihUXyWTKy2CqATMMrsP6b6sz/sfNpRI0eXK8R1JPgkdcbaU5KOv9t7EeobyxrLwcqzUM12CaaypPc1Zfh91uqEUFUQWXooKaM3KbwjTi5KJMSxdZdh/gyuoUVSa74TqMCIywCYOScw7f5K5miT5FJhMduclxn+X9jEoeyiWkjJyMdKppVQ2ffraictunpSK1oprbr7lDbLshuW1i/5dS5cre/20/HYZn5m8P2/9HZgdh5vs7YOYHO8T/lxdsh5kL8P8OmHDPj3/o418V8fE3sw5dlIMBt0w5GAzLVaN+XTqxFvMMXJ6DK3twqXor3JVlTJCyypczm04sUCGr4NoznLbJVIHT4+JyweUSVhb74nNmU11EqMBxOfSxjFUV1u7Ld8tkmYGEwT/+8bOz18Ls2bPtZZa1zJlj/5/tscyZBXPU/Tmu5+bMsRbrsVl4mz8+W25zDmHMUa+dZf2fY23T4Bt8g2/wDX7Vxs+yrhXRXH8oJsgxmKp4/EMxQcWSxNK6G6REmQoGfPxFhQfiP5SGISXKg5nBVAZzUW7LKrgnS97VUi4tWBZuY0ZyG7BCJZlLUSU3kfE/XUxYw/wAAIAASURBVKZMpDWRKYNjPIhuNY+c3EohuF5651itITiWsfJYxsZ59TaJVW+TWERQHRYRxF2UqYLbjBFcbjSVKj8IclLuICu4nSTBdWKCqIIbtC2zVTXL5TTrJgxdR3r3JWaO2KjJPJkr7Ujb1Ed3rc2kPsqRzO22iuGD1ng/oZ90SdbkyVzAQY9V9v6fd3UR9JpY+Kc6/lURP6jn4KoeDk2ejEvADhRX2bc8KkjOOCopMkmUuckUP9ErJ0G/I9nhLoPW45g3S2ZALlOHfB/rHSUSlK8Ii8tt1+fTHHh5ZZD1YzJ7f5d7r8E3+Abf4Bt8g2/wDf5R4AuJst8dfVjR+GeDILjU8lWspMx5Ige3WBFfPv7iObhKeRcIwHbbRfki2c6Z6eGiTBXcZEZwm7G2UfJHasj8kxpovkq1WCxsEnNRTmBGxLy1NU6LDIqNQGZjKpPg6k2+XvLk+Ai9t4lsZ6gPl2fg1mIxQRQW3EjebsKsqFswkylqem7NCG575qLcRerKL9oWZDFBVGWyBuFUzVL9iSOpR3FjWO+j25iHZYyy3kg9P9TJGN3EHG55f6PBN/hVH5+qsgHuohy0q7jBoBMaHlAV3Fw2g+gYHaiYIKzUytlJMp6i+CCasaRZSdfJ31+kKr+UDZcjJcouR1xWdStwBa37WKSMz1UNdCz9nYtVAb/4caddtY6PmREZfINv8A2+wTf4Bt/gR4cvJMoq+tAf1fgHc3D5uIp6cykmiAoGfPylYoI0U1ApUR7IYoI6M7PeNqyCexJLriGSewIrQDZknK0+81Sqy4gt92Gi+wla6k6C1hIby0hsvGZsXK0yJcp69lCcxrZjNedkzsITNXOpRM1kqjaLCmrAmpUbsxmCZsy5izJwk5k8mYymOjI3MPzQBgoX5SDFBAXUhx2N15q3VJ97qIW8XxaquDXd4Bv8PwK+OyaIqrbUhxtwiK7sw81FiTLLt3Vs62WllrklU1VXSZSLnAggceIuLAqr2hYxkylRwaXZV5e8tMDtvOtz57j6eB+my8SoQHuuwHvWtkCb9TX4Bt/gG3yDb/ANvsGPEh/HLzzjNprxj01kqUpbrCq9tkR5q+zDdY+/kP/YRqEBV7EPDXhZD24n5qKczvpwqYLbiqlnmzOSewLLwKUW0zpa62lN5qKcqJlMJTDVb0I53DImgg9Upbkox0aIDKqmVXZ5pTeRlaBpp/jO6lFBeg5uU1YOb8404Ccxg6lk+WG0kzMPRHBxRmJw0PoQA4GAyunEDxklyu5xfVgktJtEQBQN6KHIBMVNOELgkSBt8A1+lcTHOCCb4NrRQMFSx1xK/aYCtuSF7Oidqm2xU6Ulkynmlqwqu5L4Og6Cjnug3zWjaefF+eV6OTwmiGel8tlVl0yIue76Ctx9MTyrjrnn+lwzuvlStsQveAbf4Bt8g2/wDb7BN/jR42dn54C/0O9IkaMY/5DJFFVnlfnUBke6rI+/kP/kyjGcILlYmLDGcGVl26mCmykJrp6D25a5KFMfbnPWMtqUFSN59bYOU+bWZsVN4n81PMylYj1ycHVeGVtB2+xvlijrZWMuT45lls+JTF+dyKq2NRjprcl6cKmc3UBbdIlycyZRbsVMptJZD26G/MC6CYnytjJpihOQ5fkgrF+3voJAZ+/krJB2JxRhIyHQntNIRBiT0P6sA2p98WLAOrLi//8a/1jvv8GvOvh2VTZXyFoCymRKklqXizI3mSphs4hbnQByJUW2T+gko7EruHmuPhQ/kyKrkz+3zi8sgqycLNYHEy4X8nGDhzBzIiYZ8rlnbpWjrivLzum34VmrBt/gG3yDb/ANvsE3+EeDTzm4zjin4vHPBjKZKqYcXHo8zzWe4uMvSo4h/mObgwYtgqt6cLuzeNUOkkt5SZRbMolyEyZTbqKpb+uzZBzeilrDI1mHOGK8ZlTsJVHW828rpYIbqzHnaloZ2UuyrOfixmvy5Fpa9ZaMphqwcncTdhCpHN6CZeAmM4LbTpJc6sHNkBXcgUEymQo6skochDuj/JC3W7iSe0Z+0m1JHiGDq9zUrXD8ajExsHPlE/CTWJ6EHdZ/az/E/f8F/rHY/8LCQvjk40+qxPE3+O6/AAsJ567JrkpuAPPVAtKQKtcms1sxHkja1ivTA7uCSyfyYspts5YNG/LUyVpJdIqcwHN1sidXQWvJys52X4x8zNzB53PNmvLndAmS6z6XGfHeGl+Btm2fO17G4B/X+FlZWSL+YcWK5bB8xQpxG7NxxWN0Wz4u1lm5Albif/G8fG6F/Rytb7/eXnA9sdC2abvLl8t1Vxh8g2/wDb7Br+L4ZH5Z0fXHligXsfFOxeMfysEtkTFARIbtHFxHEcfHX7ayLtdpK5NjOOmiTBLlzsygN53l4KZKrtVSFhWbaxLlxnJpyKq4dbWknBosBzeJcUEuUY7TbsdrimA9ySe2MglutQokyTHsTcVoRlM12H+6zSXKtVkll2ymGzIn5RNYBbclI7gUFUSldOrBbS8JLn5og12N1YFS5QoL5UpBoy6seUdkhSJsvdzXhyAmphr8Z3QXSW7lsupJ8dhOi+za9x3yi8S3MvGP1f7/++67oWtml2N+/A1++J9wHbd+L8HS8EggIroBdj933XrVH1LCbOvJTEpIlOWJvKSYVXCR+Pr1nhPmKFjkZyd6e8kWBNfHZl0LXAHrKn+OyYT4TKoylyApkq8gXFbkmvEtcDkmurNTDf7xjI+DF3P+MfgG3+AbfINfHv5ykYNb8fVHVHD9TIocxfgHx0klzCXZ9TgRX238pXxUAswotDQI27dvJ4KbwSq4VCxMYwSXKrgnMYJ7IlPXNmRLHVa9raNl4XIumOBhLJUQgV9WY4S3WmVn4XqF68ZpvbicbVfXDKbodg22JLHqbS0WDEz/m2gSZdJ8U5Mz6cFTGLltxzJwM6TJ1GD8EIMqu9ORUXqUySJ59kTj/SO2EZXq0wtD4iNhxYrtf8Z0sUhtpvh/DxLeVU8KQivILd5eZZNdfJ5IbmXgH8v9P3LkyDE//gY//DnHZCroKUkOsN52mwxLF2WtckuSGbtSa5/Qldsfr+Cy2UrnP8uEkxcEvI0XiHDJkB6q7nP1x7gkQ75w4wheLXT+s0xUkiP53L08Bv/4xsdBizn/GHyDb/ANvsEvDx+rudFcf1CBphtnVjT+0fNuqWULzad4/i0ff6mox6BWnAgGqQe3m2zr7MB8jLhEOZm1hVJca1O2cIlyI43c6r23FBNbnXHFBJaHS95N1Tz6b3VpcqUSXN1cSi8dx7I3yzNxq2sV3CTWg1uHNSITuW3IGpcbMYJ7AqvensQkysksB7ejloM7yJYmB9XAPEguyqHyGxFDFfxIyu9jDFX8o/PAt6XIT8LOVbY0GQmtqtyGLU9a5DcTOnfupPp08Q+/0K1OagnTpk2DJo0bw8mtW8Pjjz9uP7fVeq5VK7jzjjugfr168Pbbb4nHp0+fDp07dYKGDRvCpZdeCrt27lTv7IUXXoBO1nP1rPWHDhkqZn3wyQP7D8DECZdDkyZNICOjC8ybO1e9h3Xrv4GePXtCUo0a0LVbN1izZo18fJ14vGZSEnTr2tV6fLV4/OWXX4aRI0ao9zL8ssvg4iFDBCa+H7RHp8P6wAMPwMknnQxNmzSFp596WuwP/lAr4/gf68+/KuIHubyFLzgbGAgqoymaGRQmUyW2uYHb0a9YuihvkCf1Qvk8SZfzWKi5lgPHZzRZj0p2Vo4jR+V5dr4CZvdf4CJETtUv37kI+djrtGw8bj6h9+i45LAG/7jGR6nan/H3b/ANvsE3+AY/enyUMUdz/SEXZVW9jWL8s0Hm3RKB9ctJf2E+JU2l9PEXjs/QWJf4D6VhYPoFFgElV8qQvKkjKximMILbUiO5xMkaMnlyQy0HtzZT6NZk/E9X9/LW1niNW8Z63I6pbBfluAgV3TgPB6wERnTjGcGtrpHcmqwPty5z4GrAJMo0Q9Cc9eK2Yjm4qZLcprEc3M5MojxwG/Xgsh5CXsENeckUQuVLEnRRQ6hCzUTk2SO+DSS4DqG1/q+Q/+m+XHausu+jdDmjc2enimv9/fDDD4LwIrH84osvYPqM5yE+Ph4+/vhj+GHzD2Ldvn37wqeffmaR1TL4aOlSSExMhBdefBG+/HIZnHbqaXDxRReLbWFfbEJCgiCdn332mSCnIwQRDcF1110HnSzsZRbG0888AzUsMpubu0G87tRTToO/X3ut6K2dNPk6OP300+Xjp8I1f/+7/fikyXDGGaeL/X7yySfhgj59xHYfmfoIxFjvf+ojj8Cnn3wKmZmZcOutt4rXv/322+K9vvrqy2LfcB/RkKtk69ZKOf7H+vOvivj0ewm6Zv4CESTKQbGuLT3e6oSNsyzcDcpF2c8kzLKyy80W/NqMpjrp+5WUJ8u6QLj7NH0u63+fj2Xd+TQHRR/PsSsIN5twbcuZfVXb9Hn1iRr84xVfSJT/hL9/g2/wDb7BN/jR42MfbjTXnxyZgxvmM1LO+Ie7KCN5pQqwXcEtYf25zvhL9eAGeJEiKMb/WgW3I3NRTmMuyimS4JKClkcENZVcrRGr4nKDqdqsJZVXcBM1rpioeTnFatxST/WpNJOpapouOoaR2xiWX0TPJWpuWDU0xl5TW+rK/0RwGzKCy/twW7BG51ZyViFZltHbM+04GUzhB3ZhgKq3JK0MSIIbivwd9vyylzu7EzlfNBQC7x+Nx+bJTGqH6r/VKriryHjKliujRLmzJ8GtBl9//bXCR1I62SKUP2yxnxMVUQn892uvg/HjxqnXr83KgniL1B48eACuve5aGDt6rHpu8+bN8N5774mXNmzUEN5f8D7s27fPWvbCueeeCw8//JBYLy2tLVxxxRVQVrbNtf9p7dLgisuvEO5tfP+fevIp6CMILsDDU6dCRpcu6shMe2oadO3aVdweP348XHXVVeqYrVq9SuzP1q0llXL8j/XnXxXxg5K0cmlygOTKSvLiEF60o6f+jxJmMkWVXOGWLCU33O7eruD6WR6cxwwmOSjLxzAoXRERZunv06z53bl1PocscRmRlL5++umn8PrcuWo7rgqij22PXagqEx/XQ6XD888/B7kbcisdf0PuBvj888/FBY/jv/baa/CZ9Tg3Xvo1+//2W2/ZM9gR8Jd9sQwWLFhQ7v7/FvzKOP5e+JEkysf779/gG3yDb/ANfvT45V3/+PUnR7go+6V5pj+q8Q8WAkj1ZkuU3cTXqeI6469SLlFmJHe746KcwQhuR0ZwqXp7MuvBbSH7b5sxI+CmktjWY1m4tSWpraOR25qam3J1xhvjtNggTmjjfw8HZb3hV+/DjYkgXY73sIKuzth7Ett5ctrSo4KI3J7IdN8tJLltyQhuivww0pl+XJlMIZni2nPK9XRrDEKe0zGhcuZ6KpQ2hMr/+Xjhk0SZiO0OqtwyYykiuztlBVcnuJstgotmVTu271Dbv/e+e6F///6C/GLFE0kp4Z9//vkwZcqD6q3s3bNPxBN9a/0Ie4vnpoS97x07dghMXGJjY8WCtydMmCDWWrr0I2iT2sZ6PAZ69eoFX371pXjlRx99BG3bpIr1e/U6U1SM8e/Jp56APhfYBHfqw1NhwMABCvG9996Hdunt1Ht94IH71b7i52kT3K2VcvyP9edfFfFFTNCGXEZi3WHh7iVokbP1sFX13NIJt8RtMiVPyorgKulykSPZ4b0pLAvOLVHOdvpafI7tv5KYukLX7YtLG+v7l5KCSwr07NkDxowdC++++44yKLrjX3fA+Recz6p7PiY38rljBHhfTZT43OHXxyWy7P7ixYuhXbt24vdSWfh4AbzhhhshrW0bSLO23aZNG5g8eZK4YOLz55x9Dvzn7ruZZPfo9//DDz+EU087DdatWxdx/1evWgk9evaEr778MuL+/1r8yjr+XvjotHk0v7/9+/fDzp0/mfOPwTf4Bt/g/4nwV65YEdX1hyTK3DizovGPkiiXkETZnvDPo5gg6zl9/EVGocR/nPFagCq4XeTSgcUEpbE+XCK5rSQHO4ER3EYs+aY+43F1mcFULUZyiQsmyPvxWpE0TnNJ1rNxK53g8o3GaGQ3ljknx3mwcC+Cm8BMpmqxQOB6jNjypakkuVTFPYlJlFNYKT1dypM7shmJwWXbyqSk0unDRSOcimdlKv5Sh0IR4lhC0fzQwpGogusis6uYRHmVLU/eKUnv3ZLgImkNqQruFrGd9VIujE9cc81VcMUVV9rV3RgkuPsVLpLSyZMnq9dv3LhRENDdu/fAxIkT4O9//7t6//sPHBA/HpwWq1O3Dqxatarc/c/Lz4fLLr0MGjdu6Myk4eN51uOXXQaNGjUWx9CRKFsEd6pFcAcMUBvCijFWhPFvkvU+Bw0cqLYzf/58sa//3VpSKcf/WH/+VRGf+mqDMmorGHQiggKK7AYgQAZuubl2TpuSy7AZRZIoy5M3uSiXUD6uvyjcIl/a5vuZPJlmNTG6pYBJf1zEhNxxVV+m/XhKagrMmfOakJy++eZ8uPKqq6BDhw7w3vvvKyKVn5/PjIt8Ts+nzysU/ujwXaZIbHaX+nToooi/kcrEf/nlV6Bjx47wwcIPBObChQtF7/xzzz3n4LkidI5+/8eMGQ3/uuOOCvf/uusmwaRJk8rd/2N9/HX8FctXhP06fvrpJ7Ef7dPT4Ycft6jH7733Xsjs2tU6vhkwccJEi+zu+02//50WzmTEad8efmQ4f4bzj8E3+Abf4P+R8JdLiXJF159sWcGlcU404x+719bJuqXnVUxQSUnY+Av5z3oVE1SquNC2bUqi3FUSXPIvas8quCksA7cVy8ElefIJmm8SEdzaEQgu54PchJi7KsdHyMKN0aq4lWIyFevRixunSZS5VDlOe9PxrAc3SWs4piZkXr2tJw9UA3YAm7L+2xYsB5ckymQy1YkRXPzQBqEpkm6Ok6squNH9hZjcAb/KodBR/Q4itASEr4zkk4isXaWVkmSSJq+wie0O+dx/xmRKghujNkkS5Ysvugh27twJa9euhdp16sCsWbPUc/tVBRdg9uzZwnAqJycHdv38syCep5xyinhu7uuvQ+3aFpFdsxp+tp4bNuwy6Nuvr9j/gRbRxAWruYH/looK8Wuz58ChQ4dEz+17774rtjFnzhxo2KiRqGjg4+++Q4/PtghuIzh8+DA8ZRHcPp4E167gtm2XJm5nZ38DNZJqwIgRI+H222+H5ORku4Jb8t9KOf7H+vOviviiKqtycO0TY0DZzQdd1Vxy6/OSJxfLvFtbomz3ldBM5FZyUfaTVIebSvmV+UKRZrggXJRZoDo3dXDJWVmfZWpyCnyAEllGZkaOGA4jR44Sr3vqqaeEFB4ff/zRx2DsmHHwt2F/g3SLXIwePRZee/018T1GU7abb75F4S9etBiGDhlikcgOcMlf/wqfffapwH4MtzF2rGgTQII5dsxY+AKlwBb+N9ZvDieYcFs9e54CDzz4gCBoK1etEv3uOWL/LDK6aJEwXWvfviMMHDRQEHN879iGgOv95+57hIy/X9++FpGd6bn/99xzD5x33nnsglsASxYvFpJsfGzUqFHw/PPPiz73ntbvH7d7irXg+5pw+USxHZwYvOrqq6BLly7Q+7zeYn3af5Q+d+zQAd5//z2Fj79l7Onv0MHa77FjxIQEYs169VXrHNNTEdc1q1dbpPA+Uf1X0Qpc4qVLk5WE2HfUnz/ffx+PH2J9u174mH3If3/f+7+Hs88+W7RitEtLhy1btoifFE4cdO/eHYoKC8W58eKLh8B99933q39/3/s3w1lnnyVw0hDHIrh/pvOPwTf4Bt/g/5HwcfI8muuPTXD5OKfi8Q8SWVLD4diJIoRw/OQlTy7hEmUWlWqbTAUpJqg7iwlKl1Vc4lWpWlGxGcvBJYlyQ1acrMeqt7UZ50tit2swf6YERnQpMihB45leZlOVVsEtT54cr5WTYzVSm6A5KSdqMUFJWjNyPVnmbiBJbhNWDm/G+nBPlguV0NMkwW3PTKa6hxFc6wMNMJMpt1jBcV47unmeEOiTRaEK3dlCEfGRsKmIIIwLEpFBXVRkEN5G92S83Tmjs3RRdvJwyUgKTaDi4+IgKSlJVmGt57bY1V3smeX4t9x6G9SrX0+sj4NaJML0989//hPq168PsXGx0MXC+/bbb8XjaN51zrnnQGJigqj4/u1vf4M9e/eKjaLpVB2LVNerW1e4LM+bN0+85plnnxVku27dOtbjTWHuvDfE408+NQ369LUJ7iPWoHjgwEGM4GIFN00dqZUrV8IN118PV199NXy9/GuXRLkyjv+x/vyrGj7v3wgyY6mgmg0MuCKDcPKo2CVNljOJckaR4oDwRF7MTsR44haV2UImy2HZcH4KQGdGDKIH1+VS6HM77bJ4GeqxTE5JgQUL3lf38WIzbdrTcNrppwqyM+XBB2Ho0KHiNUhMUMr86KOPWsT2dUFEe/ToAXPnzoWnn55mbSsZPvnkY/hm3Trr9acJAzWcwBljkbneSCat7SNxw/Xuv/9+6/s+T8j+b7nlZvHcfdZjZ59zNnz00VJ44YUZkJ6eDp99+pmYAW6dnCwIIU484YTTFVdeISaNbrzxRut32EVIZpd9ucxarzVcNnw4vPPOOwL/dItQeu0/Eq+21u/ob8OGwYsvvgBr1q51EUFsC3j8sccF3uLFS2DxksVCut2lS4b4TeK2sIqIJPnNN98U5BV76lFOjdtZsmQJJKemwLpvbHnysmXLxLHDyvEiax1sL7jv/vsE1ldffSkmp1ZYv2XEn/nyy2I/xMRDPjeWKvA06eCZuEf7+bszchkRpspwBHzbZMr5bayy3vvrc18XE38oJxfE03r8umuvg4enPqzW+/jjT+DMXmeG/f7wnHX+BRcIH4EQ2H1bf+nfH3agSz37w/Pd69Z3b/fu3ZBuHW9BpP9E5x+Db/ANvsH/I+FjTFA015+s7ByHvJIyrYLxzwYpRRbL1mJV5RXEVxlMucdfpTI5hviPHemIJlM7qAe3CysOkp9RO5aDy2OCmjPT3xOZTLkB4251WR8uVXB5Bi6PCKruEQfkldbj1X8b93uQXD0Ht5rmfhXHKrkJbMeS2P+ajNXXZRLl+uxgNWYL78FtwfTgVEJvx2YeOjKTqUHBYNDVf6tclPWZHI+e8lAoWpVDtLNBocgzSewfkjZ0R+7UqbOo0KJbcWe8ndHJvs0WdExGkkt/W6SL8r79+2CvRWSxchoN/pHDR2D3nj2e+3/4SAh27dzluUd79+5ThNm9pyHYvmN7+KPWRilq6GiP/8cWmbjmmmtEDzFuB6tjKHM+cuRwpR7/Y/35VyV8W3a8wZ75C5Z69t1yE7dcLlHeWuxksslKbZ6UKPulRJmigkQPLp+9lL0lrrBzf5GrioszoL4Ct7mQz6eZEGl9nCmC4C5gBkP5Quqeaj2OkvoHH5oiCC6R0379+il50fDhl8FNN9+sttu1azeYPXuO9fo3hXwUyeF6i+yuXLFSELgvln1hkeR7oXfv3gr/oYcessjeBeI2qhDOPvssQQ59LBgeI2lSrNevXZslyCSeB7BCivuXb613xhlnwIzpMwSJTE5OESQbX48XVsRFh3Sv/Z9nEaUhQ4ZAamoqtG1jG8HlrrcJ6cABA0ScWAEjlJdffoWI76L9z8joLMgo9tjiMnjwYHGM8H3hJFaXzC7q+COpRWI/Y8YM0TdUwPpcUQ6N+/fhkg/F8UdSufSjj9zVU24E5XObdDj7VXDUn7/zuTufv4sQR8B3enDdf9jK0Y4Rz8GDBsG7777nVGA3fy8I8F6c/NN+f4899ph13C+EAwcOiOP/4osvRvz97/55t/AiCCe4x/f5x+AbfINv8P9I+LZEueLrT7bIwS1kplIVj39UDm6xvdBkP46fwiTKcvxlj+HWu/tvrfEcjsMlwe3KSG4nRnBTpTyZ9+ASF2vCHJQbsypuPUZudZlyIpMqJzKCG685KsdqHk9xHp5PlZqDW01zTo73aASO1fTT8axqG68xd24yVZOZTNVnzcqNtDDhZsxBubVcUuQsQzqTKNOCH9pgERPEBuaiyrQhN4qfRSj8C1+hDCJU/rqhCCHRHn9k4qSWmGrhj1kLkVvC3yJlyHtRhvwb8I/1/nvhbysrg1N6niKq0ihvrlmzJsyd+/r/DP9Y7/+xwKeqbGk55BYlyjQzaFdwS5Q8hvJv7Uptsey1tU/YNOOIJ2V83O8yW2An9iJnJpPnxtkElweou50LffluOWoBEdwPFijigv+xBxzN0HDdh6ZMEXnPVMEdNmyYIk+jRo2GO++8S1X2UH6L0n8kKq1bJwtyicQNSSfef33Oa/Y2Lh2m8GfOnAlnnXWWuI9S5L9ecol4T6eeeircf/99grAhmcKKJlZwH3n0Uejbp4/LqGLYsL9Z7+MOSXBbW6R6vdr/tm3aCHIZaf9xn7HyjeZx6R3awx133CGO34ABAy2C+5jKl3322WeFVHn16tXitWvXrhFV5WRrv/D94oK3r7rqSvF6rNSS7JiO/z//73bR39zewpk4cSKsXrVa9Sa1S0uDd9571/X5+XxOvi13SPZ5GHUoR8qj/Px5b5TTI1UxPlZYvX5TdmWViGcITjvtNPjii2Vq3V27dgqCu9kiuvrvb//BA2ICBbO+Ucrsnqhz/6J379ktzMG2bPnxT3X+MfgG3+Ab/D8S/nJhMlXx9SdLEFxGZqMY/wgpssq5LVGGm7a7ssy+1cZfovAgJcoBnoZh5+AOYCZTVMVN02KCuES5BXNSbspkyiRRrs+IbR1Wva3J+B83mYrTuGM8a3mN03px4zQeWikEN86DMcdqBlO8wqs7KMdrVdwkRmprsQNBWbj12YwAd1E+gR3ckyTBpRJ6GqvidpIfFpMoB10D8lyWg+uIE7yc1EK/YsbIuyGgPNFEZeLjIAm/1McK//fef5xVw8FiXn4eHDr0S5U7/scbflCasrlJbcBlVsBv40yh6gFhJ1qaVczLc9ySVQ6uMp8iUwV32LnunkxS5ezsnHD7fVnFc4wdfKpHE+8jMfuAYmqs+/nW92jI0EtEjyhua8pDNsG1K7j3WuRjmCJPo0ePgruQ4Ep5LDoGI8GdPXuWRe5O9cTHCuewYZcqfEFwzzzT9X5XrlwFDz38EKSmpArZKxoaJYsK7lrRq449ndw9+PzevWHa009ZBPdLQXDXiSqsvb02bdsI2bC+/y+9NBPenD/f5UiMpHP06NFKooxEHV+DMunOnTNEZVpVTa2LtTDjeu99Jg92Kqvz33oLOnXqFLb/+N3BKveZZ/YSEmp8Pi83V5BlzNku0KuyPhbb44oG0npmNfxoP3++/07FtmL85ctXeP6akOC2Y8Tz4iEXwztvv63Wwe9tert02L//gOevD02/0qznP/54abm/fzfOn+f8Y/ANvsE3+H8kfNWDW8H1R/TgsvFNNOOfPNZrW0w9uH4ivlKerI2/SkXUYy4rSJRKk6ltSHAHy2JgBjOY6iDJLXdRbskquM0kLztRcrVGrHpbX6viJsn/NRj/q6G1rcazJZb14nIXZb3YGlNZEmWd1OrmUgTCnbBi2f3qzDk5yaOKW4ux/vosA5dkylTBbc76b1ux0nlbloXbiTmBdbUlyqWuDE80yllfjslU5BmeUMT8aP7jCIXK+6GFKvzxGHyDX5XwHYMCckoOKomLS/ofsHvc16/LdaTHjMDSrGOe6MH1S5OpErVunuzN5TOXRZokuYjZ57tNppisNN+Ji3HJhJREORVeQxfllcth3tx5MG7cOCH1/+jDj2yCO8WWKOPte++T5FRuY5QkuIRJBBf7Mdunt4epjzwCubkbxHaxB3flqpUiogurwIQ/86WXoZdF9vD+bbfeBtdff72QF+FFEQki5tHiDDASV8ykXrVqtSCWWN1FIvv0089A27Zt4dNPPhUVXCSK69d9o/a/TWpbYXil7z++j9Ot97tk8RJxcf1o6YeiaowVZp+QKA8UBDfXei99+/WDf/3rX2HkDyvRl156GaxZs0aQ8L/+9a/wzDPPiNd/Yr0fJOVrVq8Vxx8N6gYNGiT65NGVesTIEXDNNVeL7WFfKu5f1tossS6aZd14w422K3aBh+Ok6zNkldqwOKCKP39uVkXVWh47FAlfVXC1H4rqjf3Blg5PnnyDdUzvVysggcUIJs/fn/VjHTVylPiujB41WrRdRPr97979s7uCa85/Bt/gG3yDX+Xw8doYzfUnmyq4RTwWqPzxD46TeI8tjYU2SIlyMRtT0fiLCg/cKLQ0GCCJMroo92DKV+rDpeJhCqveUh/uiaz/tqmWftOAVXBrM5Jbi/G/BK1iW51xRlq4a3I1rSe30uTJ1bQSsV7V5SG8MZp7cpyWdVSDuSnTjtdi1ds6LCiYqrgnMBfl5qwP92RGcFNZMHFHVsEVPbg884lu4yA0BBEsxqOYqCnfRC0UqfYY4edi8A1+1cW3CW6ua+ZP3A6WuuTJQZL/565z5dsqQwR5Qt5AObjWybq42JlpxBlItxRZs8z3O7Oa1KMiJMpYhVVmQcwYiMfLMCmrkNZaRAwlwD169BTRNiiVJbkr9sheIgnu/Rb5Q/kozb5itfOuO+9UZArdlOfMniPwsYp7mnW/TWqKcEt++OGpYnv3W2QHt0H4WMFFObRt/LQIzrJup6W3g/btO8A1V18tKtzYg4vvEQkgYs2e/Zrou8XH0ATuueefE9uyJcrJwuSK9j8trQ0sWbI4bP/RsRkrtth/i0QJ45LGjh0nHsf3gsZuj1sEd968uYJ84nFqY62Lebl//evfxPZWr14FgwcPgjYWwU61Xo/HA02l6Pjj8Xz99Tni9jfr1gtnZuz1RYKODs+ffWb3BqPZXO/ze6tjPtfCbNcuHT78cAmTImuRPdLh2EVm5f+j+fyV9JhJnAtccmdvfNtkKvyXtGf3HnE8f5QmU599+gl07ZoJGzdtgu1lZYLko0mZ1+9v7uvzoE+fC6Bse5mocL8hzfi8gFCiTFJoc/4z+Abf4Bv8qomPk6HRXH9QgeZnRDaa8U+eMJNy5+AW0uPKRdk9/nK5KEsVHhYrysrKqILbjUWsdmSK2DTmonwyiwhqzggumQHX17JweQ8uSZSJ/9VkfDCR8cU4LXY2Xkvu4Zy00ghuNa1qW01zttJdruI93JTjWWm6BjOYqsWIbj2Wp9SQNS83YbbULbWYoBQWEcQlyp3khzYQy/D/z951gElRZV0mEockIKAoM8AwpCGDIKILioFsJBgxYFZ0Rf8VFV0F40oQJbpKEIFdBRQQI4KgwAwgzHS7OswaZrBpMkiWvv+7t957det1dRgdHVyr+YrpWLeruqv6nXfOPSeAsxbBoAa5mOsJYRlW7P+w2ZmQ+6yNKeqPdJyEXA4lr75X/w9QXzvwOVjboJ40Ckrm1o4J2myztkW2S7LKbdMxQWgyxaQ0ecpFWUp0LBfBQueJvkA+JvtVMCgd+1ycTJ7fYe5gGxP5HaZEPs7aOfJV7R5Mv8EQ2uyg3+6tMeqvW7de5ujGXx+ZWtz+WPXXS8YzWv1Y24/mUO+//z4B01+6/Rs3WiZTZv37RoyA22+/w1Efz7UkJWfbf/XVQ2H06NGO+igVL439/1t9/mjg5Xb8/UTmT1kO8ydkxTFjGBl5zBI/cuRo2PGHx0qH9u1hxYoVdO/iRQvF7Q7kM+B2/JNEOVMAad2D653/vPpefa++V/9kq4+/FfH8/myQMUEWeN0a1/jHYnBtb5PCAttFWefjGuMvxD9WDm7AJifEuG2nJVHuy3pwsyXAzWLgNt3ow+UxQUpdW8uQKFdh6TiqB7cyw38VjDbWRCMuyI1ATXTJxi01k6kEgzI2Y4OSDYo5xYgISmay5PIGwK3KgoFVFm4NBnDrMEr8NKPhWe18ZTLFGdx2DhdlFnOCDC63Aw+Fff1DURrKQy4qB9OdrQRHnUt9ZSCVIM2lfu/6Zb39Xv2Tpz4eNyRvCdpyZMXY2oA3YIHeoAWGeRabMpcqlrIalNKocPIiBnDtHlw8mRfIGU0pzym0HQYL2WO5ObmOHw5HhAzLm9PMHs9J5VJUQwbrI2ddp9mQ3+zf5H01Xn26jZE3HTt0hFUrV0asv+y9ZcR0b9q48Q+1/TaDG9/x99NPP8GuXTu9849X36vv1ffq/4nqr5EAN9bvD078aoZ269a4xj/YzlRUZLslKzmzBr5FxWHjL0XqBRmDi8TEjh07lYtyO4mbmsuFs7cZUi2rDH5PZ55IKiJImUxxBrcqY3ErGC2qHOCWZyDXjApyw5ulzt4muNDCCQbK5tRyCmsSTnbpueVZuConqSozmaphSJR5D+5prNlZgdtGLAM3S1LtajaCGNyg6hOUOVCbjRzcyMnNzgMmBJFb12NkTesDw1Xjz+ojqN3z+TjY88V42PuF+LtmPIFdvP571C/N7T/+83EypcEYjLKoX9bb/79Q3+pZ36QlyAEWCRRkM4IBCYC/JIDL8m8dbsoq79Y6aVsAd5vVgyvdlQuZNb7DYIH1oVhLIWzIzbGzTDlrx/pctPSU5aVqWSqTvfqN246MVC5d9fPX+rz6Rv2169aRHCpS/Y0C2OasW/+H234EuN75x6vv1ffqe/W9+tHqr5EuyrF+f3I25FrjnQI5volj/GPl4NoSZPU8YnCL5ZjLGH8FdRKG3U5G47cdQeWi3I6ZTDU3JMq8B7cBkyfXY+ytMpqqxpjcSoZCtzLDf6mM/Ewy+nGTDEyZaKiIS12mbCLnRCMuKIG9MTMD12worsjoamUhXYn14NZkWu5arIm5LgO4amefyRqhs1gObiu54IfWH2fRA0pWGVAM7uYIszgQv3Ga623jMAvFWLdRH4Hs49e0FuB2nAVy5UL3rUHAO16D3z1rxlHebWnWj3f7Z8+eA5+t+izm9u/dsxcSEsrBth9/LNX6v9X+9+qHP0f3bwigG+Ru5AEjAzdgtQBYLsruEmW8jUBWSW4sW3vrOcTsModktz4UFXauZi0tF2XW6+L3OSJkFDDK99kh636/32Eo5DclrT6fM2fVIXd1Yf68+n+K+mscLsre+cer79X36nv1vfrh9S0GN/bvT25Ojs3Sbt0a1/gnj5lJWS7Khba7MndQ5hJlMpna7DAGZS7KvVkPbjYjCptKfKUY3DMZg1tfko91GU6rydS3aYy0rMSAbUWDzU01MGOqS9urm7lxoktf7q8ymUp0cVNOcLk/wQjrTWUMLtdfc6BbzcVkSvXg1pQ7sh5buERZMbiNXWKCWls5uDvlADyg+wQR4JozPqEoqoaQ49lR3NdCUY6KkPvcD6+PcuS9BGjbEKh9Qv7djYBXgV76O14+rzWB4tKqH+/2Y3bj02PHxtz+vXv30Pv7EQFuKdb/rfa/Vz/8EmZQIJ2UueGU6sHFE6gD4Oq/xdraHk/QCqQqpz8NcB0AttARCcSdBO0e3Fyn4ZA2I3L2v1g/KvnOsPUwps6UrvqdkTR+nxFJ4ws3PPLq/8/Wx0GLd/7x6nv1vfpefa9+tPoK4Mb6/aGYoK2FTJ0We/xjmUnZ8mNluEkuyopIMMZf1hhuk2EUGlQAd4CLyZTqwU1nfbhnMAVtPaaurcWALY8JqsyclCszY6kKrG01lcmVkwxjKTe/JzclcaldEiNEBiWyJmGeW2SaTKUa6L2yweBWZTunJtN213aRKKscXAVuMyV7y6OCtMmUIyZIDMaxTzDibEwo2jRNXOp+2Xse9YhxvY0A12Zux2tAu1cCWvy7mzO717amiBMFctVl0kuT4Mz0M6Fa1Wrk4Lpv3z66H4HK+edfAFUqV4F27dvBqlWr6P4pk6fAlVdcARf0ugDKp5an+/Ly8uHss7tCpcqVoHmz5vDuu+/S/Vdfcw2UL18eqlatCt3P7U73oQT5pptugjp16sCZZ55JzrG4/fv27qP39sQTT0D90+pDgwYNYNq06dFnw8pw/3v1w+urvtogc0x2gl3ndez1sE60RRrYapOpYovBVUDVzsFVEmUzDqhAX+dW+mpWM3dDrp4ptWdIfQ6TILvvxQ5Z17b9fpu9czB3fp8jasaMknGs06v/p6iPs+3omD1z1izxdxbMwmW2/Muvy78zZ8+k54c/Lu6bqW7Lx2fKdfL10WMz6T5cz+xZs7z6Xn2vvlffq3+S17e8QWL//uQqkyk1polj/EN5t3J8pRhcC/jm2cDXGH85YoKCUn0X1D24pkQ5iwHcxgzccomyysHlfbi1JH6rzhjcyi7mUjwqNtVFopzg0odrSpQTDNPjUnNQTnBp+OXI21yS2IZwJ63yLB8pjbG31ZjBVG3DYIpLlBswBrcJ68FVJlPZMge3twK4QSmrxAE6OoDGnLZxleKHIPI0UAjM5vQw5b5pumasD8GgYmr3KiBLsmQBbKU8GUGueg4yuDbAtep8+913kFAuERYvXgybxJe6TZvW8Nxzz8LBnw7CmWecSQAVewTuuusuqC5A6sGDh8Tjz0FCQgI89thjsOozC/T27NEDhg4ZCuvXr4d77x0BNWrWhNCJE/Ddt99Cl65d4O6774av//M1Pfe2W2+DzMwm8OFHH9AJpGKFSrBw4ULYs28vvbeePXvA52s+p6zLxKQk2L9//0m5/7364fVVX21AuSYbwNY2mwpQthpOoqi+W+XoV6ykyEXF1EOist9UdBAueTImSJ3kLVfB8Cw4LunBHxKH3JS54/odBkQ2YMlns6g2k2ewhdwUwu9uZuSQuHr1vfpefa++V9+r79X36sdZf0NOju2abIDcSOMfArLbVMtXkX6tlYPLpMls/KWTMALbHQTFbjsmqK1UvLZgRGGWxFaNXSTKdQ1/pFpMcVudKXIrMZ+lSgz/8fjYVCNaNsnAj8kuEuVypcngulHCyUYPbjmXN5fCpMmpRhZuJQZ4ldtWNWYyVZP14Z7Kduhpcmlo9OA2Zj24CuBSD67uD2SDci5R5hKEUNwUWUlQifs63eoTg7tGsreq31YxuQhuJdi1JcptwhhcBBgIVv+1YAEcO3pMF1vx6QqoVq0aHD1qRVYc//lnmDt3LmVhPfv8s9ChQwfX7T9+/DgdmFhjmzhg8IIS5bFMoozM7bvvvKO3ZuXKlZSviT24+Lr163LoEQS2ycnJsHbt2pNy/3v1IXIPrpmDu90pVVa3caZQZ7ApCXIRY2opJsiS4BSxMHLN4BbYuW/KTIpn3xayGU0rB5ezfMx112f+QPG+Sr/N/LmwefoHzc/7PeWMrJ//GPq9+l59r75X36vv1ffqe/VLVF8zuIUF0mQq9vgHx0kcwKrX5Ulm15GBK8dXZBS6+UtH/23A7sFFF+U2EuAqkykFbhvJpSEz960nMRjvwa3BenCrM0UuB7qpDP9xcGsaTSUxMJtgyJMTjN7bUuvBLWdIkt1clJMMiXIyaxxOMeTJFQ2XLaXTrsF20ilyOZVJlBswBtd0Uc4yJMrE4O7UDO52ltW5KbZs00GDhQz5p/O+UAylZ1xCU+zBLZdgs7SKwV0zXrK545n5lN2DywGuWuuoUaOgStU0qFKlClxzzTWUrThl8mRo166da31kcPtc0ttx32uv/RMyMjLoPSXI2KIffviBXtqLenDH0PN2795Nj+EBaG7z3r0WwNUmU+JSu1YtAYA/PSn3v1c//A48OW6W/Rt2Fm5QOyhvZ87KAQmG6eS7TZkc8L4QC+Cqk7Wd1yaBr3YMZCd16lEpYP23thlDTm4uM3Pwa9Mhp7TV7+iz9PPnM3mq33yMzdA6ezpNMyKvvlffq+/V9+p79b36Xv346yuAW6iX2OMfZGp5Dq5DosxSK/j4S7WO2TjIUuLtsHNw20j2thVzUc5k+Iob+54ewUW5piFRTmMYryIzm6rIVL2pjLlNMK4nuoDZSL24pZqDa0YFJRs5uKnsfp515BYRpHpwqzEGtzpz5qrtkoOraPIz5c7PNHJwsxnA7btjhyVNVgA3EAzSIDxaw3kM8isCvIi8hlAoeg8lz79VjO1uCWh3u7K51t8niMFtyRhce+1HjhyGZcuWkXT45ptvgfeXLyemldf/4fvv4fix4/CsALi9+/TWjx04cACSU1Jg3LhxsP/AT7Bt2482wJUM7lNjnpIq1xPEDH/yySe6/q5duwjcaoC7zQa4p9SuTQzvybj/vfrh9wW2q5Dw7WGL5apsA92gkijz3tsilYNrOf9ZEmXrpKxPxuSuvMUwkypk8pxCp8ugfP2G3A1hsiI90+rzOWZp/UxqZPdy2pEyfhY/4zNjYphrL1+vm6zJq+/V9+p79b36Xn2vvlc/Wv0NublyLFRoM7gxxj9kMlVcrMdUGvjmbdGsrTn+QvyzWY7hAkE7+WKn7aLcnjG4zRiL29glB1ext3ypbShv0wyDKbWkshbVCgwrpjIGN9G4bubgJriQr78a4Caz64lRGoD5myvP3rj6W9Fw0lIabUVnV2NUt2pcriN3al3Wg6tMphSLqzKbmjG76w6oL0ewpWYtFAOFsxnuTFkUSBGKz+onFMa0xWDk2P2cwd3LooI0g6sXy3xKm0wl2BLlDz78EM7t3h22FW+Dn0+cgEsvHQi33HwzYFwSAtExT42Bw4cPweuvvw7lK5SH4I7t8NzznMEN0Rc/MTEB3nnnHZI03zdihAPgDhkyBC677DI4cvQI3R4oapx77nkkd/76m2+gXt26MGP6DNiz14oJChCDa21nrVNMBvfk2f9e/fB14zGzhU6OLCJI9eEGnc7K4Tm41sl2mzaTKpYMrjUjWSRnIYu0u3Ihm7VkM5uOPDj7x8AymfI5ZUM8B5XJisKiYByZqsYMLnPp9ft47IyUKBk/hl59r75X36vv1ffqe/W9+vHWx/GLArVOE83I45+8vDztoFwkAW6hlijbqRV8/BXkSRgBGwvt3LVTMbjKZEq5KCs81YixuA0YuahMptwMpmoww+A01n9bnuE/bi6VYsiTk12SelJc4mpLzUHZ7L9NdAnlTWT0cqLRMFyBaa4VPc1RfRq7XtOIClIMrooKqs/AbQbTiCsXZbW0lrR7b0dWJ7FMVsN1yNV51t1/JxS1tzH0i7xuI9VHsIrSYysqyIoJGi3+PnFttrxfLte2gVYtWxkMLsChQ4fgsssvhdSUFEhLS4OOHTtBQUGBBX4/+ACaNG4MiQmJUL1GDQK5JFF+/nno27uP4z3df/8D1C+bItZzw7BhBFSLZQ/uB+9/AA3T08W6mtAb37V7N/To0QNSU1Npufa6a+DokSOSwU2wYoJUv27t2rBq1Wcn7f736jvr4zGzCV2UgwGnTDkYDMtVU/266sRaxDNweQ6u7MFV7C25K8uYIG2VL2c27VigAsbgWjOclsmUz+5xcbjgcgkRi33x27Opjh8in+1y6GcZqzqs3Z/vlCn5uaOvV/9/vX5u7nrKwsUIiNXi7+o1a+jvmtWrret6WU3LGnVdPhdvW6+3Fuu1a+zH2Xroun7Muo51vfpefa++V9+rf3LXzxG/FfH8/qiYINtgKvb4R8UEFUkQq567RUqUFWHAx1+KeFD4R6VhSIlyP2Ywlc1clJswBvdMibtOl8tpLAu3FgO5NRhRqcylFJObyvCfKVNWoDWVKYMTXIBuOZec3FIBuG5650SjITiRofJEhsY5e1uRsbcVWURQGosI4i7KisGtxwAuN5pqJD8I5aTcXDK4LSXAtWOCFIMbtCyzYznIRo1ZiSQgDUFENixsfVHqI2BtKYBrdnYrYmjDl5ZiydbgFu8z62N0z4ED+13r79m7O67tR5nzwZ9+irj9x44dddx76KeDcBTv+5XbX9b736tvPz9o5uDqHg5DnoxLwAoU19m3PCpIzjhqKbKSKHOTKX6i106ChbZkh7sMivtzc3O0GYTD1CHfz3pnFAjK14DF4Xbo9xsOiHxmlvXjMHt/h3uiV/9PUR8HRd75x6vv1ffqe/W9+tHqI/CN5/eHJMqFzujDWOOfLQRwVctXkZYy51EObpEGvnz8xXNwtfIuEIBdlotyf9nO2cbFRVkxuOkM4NZjbaPKH6km80+qYfgqVWaxsBWZi3IKMyLmra1JRmRQYgQwm1CaANds8nWTJydH6L1NZRuj+nB5Bm5lFhOkwoJPkddrMyvq05jJlGp6bsgAbjPmotxa6sr77wiymCDFMm3ezL7nobh4r1CMO0Iud0YKn3YyZe71Ebgia1qunL0os6dy8n58XIHb0q5f1tvv1T856itWNsBdlIMWixsM2qHhAc3gbmYziLbRgY4JQqZWzk4q4ykVH6RmLNWspOPkX7hVM78qG26DlCg7HBEZ6+ZzBK37maW/38EG2pb+NljycfDDnRb1c/zMjMKr/79ef7UD4HrnH6++V9+r79X36ofXRxY3nt8fkijr6MPCuMY/mIPLx1WqN1fFBCnCgI+/dEyQYQoqJcp9WExQK2bW25gxuGew5BoFck9lBGRNhtmqM0+lqgzYch8mdTvFSN1JMVpiExmITTaMjcuVpkTZzB5KMtB2ouGczFF4qmEulWqYTFVhUUE1WLNyLTZDUI85d6kM3HQmT1ZGUy2YGxh+aH3IRTmoYoIC+sMO/5bGzFJxHB4h90bHsIMm+mHi1ffqn9z1nTFBirVVfbgBG+jKPtzNKFFm+ba2bb1kaplbsmJ1tUR5qx0BRCfugq1hrO1WZjJFDK5i3xzyUp/T+dDvzLHz8z4ch4mEz3jM584a+gzWz6v/P19/9erV3vnHq+/V9+p79b36Uevjb0U8vz84fuEZt/GMfywgq1jaIs30WhLlbbIP1zn+QvxjGYUGHGQfGvCyHtyWzEU5i/XhKga3AVPP1mcg91SWgataTNOM1tNKzEU51TCZSmGq35Qo2DIhgg9UqbkoJ0aIDCpnMLuc6U1lFLTaKL6xZlSQmYNbh9Hh9ZkG/AxmMJUuP4ymcuZBAVyckegXFB9iIBDQOZ34IX/JTaZCzvmXUNRZoSgGQKHIB4jzgDOqePW9+idxfYwDsgCuFQ0U3G6bS+ljKmBJXpQdvc3aFtksrTKZYm7JmtmVwNd2ELTdAwsdM5pWXlyhfN4GHhPEs+o4u+eQCTHXQ7/P2RfDs+qYe6LfwejlS9kqB1xe/T9DfYvB9c4/Xn2vvlffq+/Vj1wfe3Xj+f3Jzd0AhQWFthQ5jvGPMplS7Kw2n9piS5fN8Rfin81yDEcgF4kJMYbbuXOXYnDbSIBr5uA2YS7Kqg+3PmsZrcPISM7epjFlbhVGbir8V8HFXCrRJQfXxJWJMdpmf7VE2aSNuTw5kVk+pzJ9dSpjbSsw0FuJ9eAqOruGsZgS5fpMotyAmUxlsR7cbPmBtSOJ8o6d0hQnIOn5IHy56csYPjzutj0h40YknX7IFEIYB1HYkeTV9+qfpPUtVnYzyVoC2mRKglqHizI3mSpms4jb7AByLUW2TuhKRmMxuHmOPpRCJkXWJ39unV+wFXI25LA+zHC5qp8bPISZEzHJkN/J3GlHRZcAd5595+Pr/AX158yZQ/FaZVUfF/y8Xpk8GbZs3lLq9dEEbOXKVY76aFg2+ZXJsJnq/fL9j9/J115/LWr9Tz/9FBYtWlwqnz/Kzrzzj1ffq+/V9+p79aPVx8nQeH5/VQ6uPc6JPf7ZokymilQOrro/zzGe4uMvlRyj8I9lDhqk1BPZg9uexas2l1jKTaJ8OpMo12Yy5dqG+rY6S8bhragVXJJ1FEZMNoyK3STKZv5tqTC4iQZyLmfQyG6SZTMXN9mQJ1c22FtlNFWD0d212U5UdPhpLAM3nQHcphLkqh7cbMng9gkqk6mgLau0cnBDzq9tKIKIIRT5QWckS4QM0ogHT3j9pfd1gSX3dYVl4u/SEV3otvX3LLq+TFxfMqoHvOebAMvzJ+i/uJRG/bLefq/+yVc/wELCuWuyg8kNYL5aQBpSbbbA7DaMB5K29dr0wGJw1Ym8SOW2iWXLljx9stYSna124Lk+2StXQbHk5OY6wYifmQv5/Y5ZU/VYenpDGHHvCIcElRi61avFY+nw4EMPMaDjd8hUnev2O+MFjPozXp0BjTIyIF0sjRs1hp7nnw9333035OSs1+vvfm53GD36Macklstsf0X9SNtvM6LWsnTJEmiamQnLly8vtfqrVq2Eiy+5hLa/SZNMaJ2dDa+++io9tnSpUc/n+0Xb/9hjj8Ftt98WdfvXfvEFdOjQEVatXBlx++OtTwxuKR5/xeL4yMnJodvff/edNrGK9/g/euQovLvkXTh27NivPv7x+LTei3f+8+p79b36Xv1fUx+dluP5/bUkylvZeCf2+Efl4BbLGCAFhq0cXFsRx8dflrJus91WJsdw0kVZSZRbMYPeLJaD20hirdMlqVjfkCjXkktNxuJWNZJyKrAc3IoMC3KJcpJxPdlQBJtJPomlCXDLxZAkJ7A3lWAYTVVgf9V1LlGuwphcZTNdkzkpn8oY3NMZwFVRQYpKVz24zSTAxQ+tn6OxOrBdu8JCVClC3BM74XeHwDWzNBTz9SECtpEu+NgPa98hgLvk4R4a2BLQFUtp1C/r7ffqn3z1yXVcHC/B7eGRQAroBtjtzZu+1P0hxcy2XplJkURZnsiLixiDi8C30Ow5YY6CWwvZid5acgng+hnr5nMGsfud8TH4GIJYXLAnRZsLieXRRx+BDHH/aAGcfG6Oukxe5HfUc68/bdo06NSpE6z5fA0xiVPFbYzSuuSSi2HDxo203vy8fLt3lElvS6N+pO3nM8lq+7fk5Zdq/RtuGAYDBwyAdevXQV5+Hox9eiw0zcqCXAGisAbKrHj9km7/RrH/ssT6li9/L+b233nXnXDXXXdF3f546q9xAbjNmjWDr/xfOe5etmQpXHTRJTGPv3nz51OeOF4mvvQSDBjQv0TH/1dffQXZ2dmkZMC7Fy9eTMfDLzn+581fAIMHD/pV558vN22CXr16wRWXXe6df736Xn2v/p+2Proox/P7SwxuIZMixzH+wXFSMXNJdtyvgK8x/tI+KgFmFLo9CLt27VIAN5sxuIoszGQAVzG4ZzCAW5epa2uyJY2xt2lGFi7HgikuxlIpEfBlOQZ4y5V2Fq5buG6S0YvL0XZ5w2BKXa/AloqMva3MgoHV39qGRFlpvlWTs9KDZzBw25Rl4GZLk6l++CEGdXanLaN0maaJ1DMe9QsdYuuIS/XgVkPWR7Y20jqWjuiqrxPIlUzue77xAuROLJX6Jdl+ZGDQ1a00tz9a/TVrPqd6v+X+V48dPnwY5s59E44ePVrizx8zhz/88MPffP//ltvvALjaZCroKkkOsN52CwxLF2WDuVWSGYuptU7o2u2PM7hsttL+yzLh5A8CXscfiHDJrBmq7nf0Z6anZ1CG8+TJkzUriI916dIF0humEzOI97/33ntwSe/ekNU0C7p37w6vvz6T7v/Hiy8SG7tx00a6fe99I+CmG28Oq48A96yzujjqIwuIoGTy5Cn0Qzf06qthCr4P8digQYNh5MiRcHa3bhT/NX7cePjbww9DyxYt4awuZ8Hs2bO1TPfFcS9C93PPhbbt2sFdd98F+fnW+l948QW47rrrYcjQIdCiRQu47trrSAKN9Tds2ADDbhwGLVu2pGzsMWPG0Pq++OILcbsj9QPhOt5dsgQGDBwAzZu3gD59+8ACAX6w5meffQYdO3WExx9/XNRtC70u7AWv/vOfrvv/ggsugEceeYRlzfrgjTfeINfIzz//guqjA/a///1v6CSuY/2O8u+wG2+k9aAEefitw6FN6zbwlx49SNasth+lyZ07d9aDh40bNsKwYcMoWk1vm/z8Meu7c6dO+vNfu3Yd/P3vT8ImAcj45+9zYYvtv34atJiHUtOmTSFfPIcff0sEwL3wogtjHn/z58+DwRLg4mtPnAiV+Pj/+cTP+vqN4rOdMnXKLzr+5wuwPXjw4F98/pk3bz60adMGrr3+Ohg48NLf5ffn9zr/efW9+l59r35J6lsuyu7jDy5ZRgWaaZwZa/xj5t2qli00n+L5t3z8paMegwY5EQyqHtx2sq2zOfMx4hLldNYWquJa67CFS5RPMcCt2XurYmLLM6yYwvJwlXdTOZf+W1OaXKoA1zSXMqnjRPZmeSZueYPBrch6cNNYI7ICtzVZ4/IpDOCeytjbM5hEOZ3l4LYwcnD7WtLkoB6YB5WLcii6ED8U4yCJruMPxT7oXOovlQxuAosGcjwmn37gh2Ow5OGesFQsyxHg+iaUSv2SbH/zZs1hzFNjS3X7o9VHdmD8+PG/6f5XF2zIL1+hAp2UYn3+OFCfOnWafsro0aOhTdvWv/n+/y23n5cKcnkLX3A2MBDURlNqZpBMpootcwOno1+RdFHeIk/qBfJxJV3OY6HmRg4cn9FkPSq5ORtsySzPM/X7WNwL690UC8pm773nXhgwYID+EcIBfru2beGaa64RAHc0rav/gP4CNN0AixcthhEj7qU4Ll9+Pu2Lc7ufC3/7v7/BkneXQFZWU/jggw/C6k+bjgD3rLD6V111FTzwwAOUi9dHAOh//ONFes1FF15IjO+CBQuIRc5olEHPxfo3CtCH7C9uz7w35xGwemniS/Dm3DdFjc7w6KOPUf0nBXDLyEiHp556Eua+ORd6itf8FWvhY+I+lES/v3w5TJ82XQD3pvDxxx8RcENGG9lV/C536tQZbrn5Znjr7bfhPgHeW2e3Jvk2stA4ATBo0CB4699vwe233Q5dEMC77P+HRz0MmZmZ8Nf7/wr/+te/JGNrbf8aKQXPWb9eANMNsHTpMli2bCm8Leoh+H/+uedpXQjc//KXv9Bn89xzz9H6li1dSjUe/tvfYPCgwVry9dRTT4nPpDtNuk2fNo0+k48//pj218qVK6ne55+vofr/FKAcby9etMgehPjZ98bIxlVAl1yUjeOvaWYW1XCcw5cu0QB33ptviu/OCLjzzjuhQ4f2cK+4/sMPP1iPzZsHQxBUistbb70l9tX98jXz6DU333IztG3bBu69916qff7550PHDh3g2Wefpdq7du2G8849D3766SeaGMkW38/27dvD1VcPpTeHk3O4n87q0gX69etHcmZ1wWzze+6+G9q3a0eAdOzYsfq98OMfQfjAAQPh0OFDdN/YMWPhwZEPhp1/XnnlFdpf+FldeunA3+X35/c6/3n1vfpefa9+SepbEmX38YetesrXLsqavY1j/LNF5t0qAFsoJ/3JfEqaSpnjLxyfobGuwj8qDQPTL5AElFgpW+KmFowwzGAA93QD5CpMVpPJk2saObhVmEK3EsN/prqXt7YmG9gy0eV6Qmm7KCdFYHSTXBywUhjQTWYAt7wBciuxPtyqzIGrBpMoqxmC+qwXtwHLwW0kwW0my8FtxSTKfXaoHlzWQ8gZ3JCbTCEUXZJgihpCMTUTkWeP+Dqw1zbSip3yZZvxDZMo/4r6Jdn+n3/+udS3P1p9B8D9jfY/r4/bF8/nP2P6dAFo2zoePfFz6Dff/7/19qu/6ngJOmb+AhEkykF6riU93maHjbMs3C3aRbmQSZgls8vNFgqNGU190i/UUp4c8QPh7NP0O6Jf/H6WdSp7YhA4Llq8SIDELFix4hN6DNm/Bx4YCTcIQIsTFFyaiu8LgROCImQxcT1vvfVvksj26NkTHnv0Udf608T34iwFAFn9u++5G64WQBqf26dPHwFw/0GPXygALoJQ3B4E0cgyI+jDdSHobd68OV1HuS2ydchAbhTL8wL8IfjBdTz55FMEhNX2P/PMM+KxnvTY//3f/0H3c7rD0mXLNBPqJ2ZyNdVaLwDu/AXzoFXLVmQ4RRJqAei7dukKU6dOtQCueB6qE/wS8DVs2BA+/ujjsO3Pz/PB38V7OfvsbtTz3E4AqXHjxkk3Yruen8Xz3CxANTLPasa7lezbxe3EpX///vD3v//dkh3feQfcIUCjki4/hNsmAO6yZe+Fff75+Xn0vpGRx/Xmi4EF9f/6/Y76YWZTju+ST/fI8gMks2kmPZ8fU0uWCIDb6yK6PmPGDALbr854VQDsz2FA/wHwrPi8CMgKMDhUbC++9vWZM0nWjevF5zbLagZzBTjG7xv2EOO2fb7mc/o+4OTGt9/+F9BbAusf2H+AjiGcDMHP/7/ffktv7wmxr/r27Qvr1q0lMzOcPPjPV/+heg/930Nw0cUXwxdffA5vvf0WtBTgeBABXOfxjyD5kkt603kX91Xr1m2kisb9/DN//gIBmAf+Lr8/v9f5z6vv1ffqe/VLUh9dlCONP7RkWdzeIHNww3xGoox/uIsyglfFAFsMbjHrz7XHX7oHN8BJCpQo7zQZ3BbMRTmTuShnSICrFLQ8IqiOxGqnMBaXG0xVYS2pnMFNNbBiquHllGhgSzPVp9RMpsoZuugEBm4TWH6ReizVcMOqYCD2SsZSVf5VALcmA7i8D/c01ujcQM4qpEsavRnTjiuDKfzALgko9lZJKwMS4IYif4ddv+xRZ3ci52uFQuB+0LisXjG4ir0Ve1M/gUymlOmUMqASi2U0Nd5RfdKkl2jwWbVqVbjyykGwb98+Wg/O4pzf83y6/8JeF8LWggJdf9KkSfZrxGBp3769dP/Lk17W919F9++j11x2+WXEEuHlyJHDBBJOPfVUYlqeeeZp/V769u0Po0aNIqllvfr1iPki2/YY+/+zz1aRNLNKpSpiIDgUzjvvPA1wDx85AjfedCPUrlObBm5z576hX4uD4Y4dO0DFihVpYL127Xq9UmTV8H1Uq1YNLh14KezetYtq9e/XD+677z5o3LgxMVTI+jdocDrsP7AfthVtE9cbwMSJE6F27dq0L1CmSgO6BfOhRo0akJycRM9BQIAM0ZAhNhsyefJUkoXWrFETrhp0FezdY+3XKVOmwGBRa4B4H9XF+8F965Rg//7fP7fvf1CCVi5NDii5spa82IAX2W/V/1HMTKYUk0tuyVJyw+3uLQa3kOXBucxgKgdleR9KXjUQYZEufsOan+emZqRnkGz3+uuvh/976CFYtz5HAIos+OijjwTAHWZJlMVzJ06cAF26doGMhihpbkggCc2T1PoQjDVr3ox6ed3qWxLls8LqX3bZZQQ28T7N4IrryOBOEN8xBbYQGL7/wfv0GALyLAF68PVo2tSwYTqkZ8gFe4rFd5JY2if/DlddeaXefgSI3bqdQ7UQYF0ujlncfpT3IhiyzLXWiG1MJ5OhF154geTFvF/oyiuuhFGPjNIAl6S9EvQ1btIYlkpWNdL+/+CDjwR4vUWDzNWawc3Rn88rL79C7PXadevodevEX9x+3L5GGY0gA826xGuGDx9Oz0cZ8/33368HCrhtl11+ObHXnTt1Jgabf/5NM5sSOPQ7GFpuAiLvc+Thmgyui0RZgFd8DT+K0LTrwgt7SYA7Hfr1669fNGvWLGJELQZ3Pp0n8JWzBMDF8ydepovXXH7FFXqdyJTjRIW64KTBpytXkkEI1t9/4AA998Ybb4KpWqIcov350UcfwuHDR2hBdcL06dNptfj5r1jxqd6Wx0ePtiTKLsf/pg2boHWb1gTOX3755ajnnwXI4A4cWKrnn7I+/3n1vfpefa9+SeqvkRJlt/EH//3ZQC7KhdI8szCu8Q9OuCvVmyVRdgJfm8W1x1/buUSZgdxdtotyNgO4LRjAVeztmawH9zTZf1uPGQHXkcC2GsvCrSJBbZoBbisZbsrlGW5MMmKDOKBN/i0clM2GX7MPNyGCdDnZxQq6PEPvFdnGK6ctMypIgdu6TPd9mgS3pzOAmyE/jCymH9cmU2iFzbXnKtfTqTEIuU7HhKLM9cSUNoSiHz5u9ZcxGXI89YnB9TkZ3G+/+w4SExNI2vjll5uoN+q555+DgwcPWoPEW26mAeFVV10pwGEres13330LCeUSyKjky02boU3bNsQM4boSEhJh8TtsXc89S6/p1q0bMTt4wYEnGq4gSEDmoXLlSjBn9hw90Kpbry68//771A+XmpKs3UMj7f99+/dB/Xr14NrrrqP3inI9BPwWwA2R5A8loytWrCBgXqFCBZqhwksnMXi7447boeCbArjrrjuptxIvyDylpKTA1CmTSb6IIFgB0U6dOkLtWrVJNvjN1wXwY+BHkonv2btX7JvvaaIB5YJYb4p4fXJyMslSd+/aTYAItx3rHxHAe5x4j716XUDb8f77yyE1NZUGrSiVxIHlQGkmg/sxQaz3+eefp4Eo9hqi1LAsv39u33+KCdqymYFYZ1i4cwnCJnFsbdM9t+qEW+w0mZInZQ1wtXR5qy3Z4b0pLAvOKVHOtfta/Ha/p5aY6t4Xuye3kQBBCHDfFN9TlI0+/fTT0H9AP3rusBtuIIky9qs2adyEpJsbcjdYoEyASCvyxkffH+xx7SAlo271EeB2kRJlVf+Tjz8Rr2tOQAPr9eljS5SRwZ1IANcCinisLhfHjE8CXATheB2Phfvvu9/YVr8EuE/CFQRw/RLgzqDjlG8/snbPiPfcqFEGvPHGHAk4GxLgnC0AWPt27aVk11pHjx496X19+ukKYl43bdqoZVY4IWQBXHv7sUfoKfE+KAtQ7n/88cVjBKX8aySDq9ykP/roY+o5xl5fbhDVonlzeHvhQg2m+WP33HMP3HbbbWHbj/3ECAYbN2oEb8x5g16DM9u4L/F49RkO034uUTZvmwwuSpSNAwTZVNw2fsFz5UUoUQ5ZAHf4Lbfox/AcdLGUL89HibI8/8yc+Tp99wgUT59B51NV6e6774FJL0/SdXv0+AtNNhDAFfUP7N9P96OMfeqUqfQsnIREUN+0aSbtd1zwuX/7299g7949dP3777/X65w9exZJviP9/uH5Fs9/lidB5PMPfoYDBg4s1fNPWZ//vPpefa++V78k9T/XMUHh4w/++6Mkytw4M9b4R0uUi5VE2Zrwz1MxQeIxc/yljEIV/rHHawHF4LaWS3MWE5TJ+nAVyG0gMdipDOCewpJvqjMcV5UZTFVmIFdhwRR5O9kgSZMMl2QzG7fUAS5faYIBdhOZc3KSCwp3A7gpzGSqMgsErsaALV/qSJCrWNwzmEQ5g1HpWVKe3ILNSPTbuWOnlFTafbhohBN7Vib2lzoUimBHHornQAuv5GRwE2wmVzO6CY7XLB1xFgFcZHBVBdw2BKvzF/wLjh0/ruvj4Lxy5cpw4KcDcOjQYdoXWOPbb7+lLz+uG6WQFDuh17VJgDC8f77jfgK453QjFhIvp9apDe8uXaIfGznyQbjs8iss8EimL0/qx87pfg48NWZs1P2PAKJ69RqipmXydPz4Mahbty6Mn2AB+VNq1qRBP4L2Q4cOwV/OOxeeec5iOTLFwO7mm26GHTt3Ofb/HXfcCddee609ESC2e+HCRRLgdoKnGUvy448/0r7BweB3AuTjvke5oLoMHTKEpKb4+SNYaScGf+r9jx8/AS44/wK6jTLK66+7Xm8dAvuU1BQxUDxGfYUo+VMX3La2htT59/7+ud2r+mqDMmorGLQjggIa7AYgoAzcBNAvYu7IjhlFJVGWJ2/lolys8nELt4Zb5Evb/EImT1azmrg/fUz64wAmyh1X98Va96NE+eNPVoAv3wddu54tBv/NrR5C8ThJlB97jIASxvu89tprBO6xBxf7T1etWkXrwv5d7KfEx1E6vFIADrM+SpRxsgV/7FauXEHGUig1RQMnzIDF5/Tu3YcAriVRvggmTJygfxTRDAsnSPB9Yb9oM2Jw/aTOaNO6NSwRxxv+2CEbfNONw6g+Atwrr7xKbz+aQHU72wK4Ix98kIAh7v/Vaz4nVcEcAQJVDy5Khr9Y+wVtD/a04kTFSy9NhCZNmsBHApgpBhfPCYrVVADX3P8XXNALrrv+ejLRQKfol19+ibYH12FJlDMIUOP77yWA/cMPP6z7lNT2X3XVIJqEW7d+PYHiyy+/nBhErPH46Mdh4KUD9XY+OHIk3H3vPTR5gmZ0qNKY88Yb9NiHAthmZDS0vivi+Z+t/gzuvXeEvO1nBlnO+vx+vG/N6jVhRwf2CC9atMhx/I0bN57Y2JCUKFsAN6QBru7PJYA7RALcmaQesADudLhFg+IQRUvhZ64uPXv2oO8bSZQzM0llgs8bdtONNPlGrzoRgrbt2tOxaB7/+E7btm2nY4HwgpNsg4weXPXs73/4nnp1Ud6ORmfRzj/4OzHw0ktL9fxT1uc/r75X36vv1S9J/dVSouw2/uCmjLmSwVXjnHjGP1avrZ11qx7XMUHFxWHjL8Q/X+qYoO0aC+3YoSXKbSXAVf5FzRiDm8EycBuwHFwlTz7V8E1SALdKBIDL8SA3IeauyskRsnATDBa3VEymEl16cZMMiTKXKicZbzqZ9eBWNBqOVRMyZ2+ryR1Vg+3AOqz/9jSWg6skyspkqiUDuPih9UUXZdMcZ7NmcOO7hJjcAb/KoVCJjoMILQHhT14aISYoUn2SKFNU0ETHE1ESXDUtDapUqULStB07dxIYRaCG4DdRgFZkD/E2MqvqNWlV2WvEl9+6/xGoIu+/+uprxbp2UH3F4O7evZvWSfJaWR9luq3EAFqBx38KIGCDw6HwiKgVbf+jw2279u0d23/OOeeIweME2LV7lwb8iYmJtGB9kviFLHdnHHgnifvxPSJzGqKBYU94euzTrvsfXVZfY+/RArjlYB9jcHE71f5/4om/w0UXWX12BHDbtNOrRJYZZZ6gaj79tK6EgBzX+5U42SGD27t3b11z4cKFJDksy++f24VYWZ2Da50YA9puPuhgc5Vbn5s8uUjm3VoSZauvRM1EblMuyoVKqsNNpQq1+cJWw3CBXJRZoDo3dfD7nfIgtTQS4GrFJ5/QD80Tjz9OEnc8H5D0VXyHsAcXH0MJLObXNm7cBG4ZfouUKK+G8RPG00REruwhRUn7IAHGzPoIVBAU44ISWwRDd991l47KwRrYg/siSZR99H1CplRFC1C/q2QdSaLcrJlmE/G9tWzRQqy3ER0X777zLj2GzCmCQrX9JFEWAJfckd99l46HrMymAsQ2g1tvu5VYc2RwM9Ib6skCBDFdu3YlGXSnjh1h8itTqCaCU7zPkihbQBDBrwVwnft/2bJl8JfzziNQm9nEYhFf+McLVtwO1UNAvR7enDuXthONvxrRvm5MQFa5O/fr248k2emNMsQ56VrYuNFyPkb35exW2dQSgOtEsy+UYqNxFtZCdhfBLr4nbLHAXmn1+WNNfN6y95ZpJtpv9O36TIm733LANo8/PGfiMf7N19+QC/L6devICIqkwIqNvWW4PtIQ4F7USwLc+WgyZQHcWTNflz24EhRLBhckg/sSMbjW8Y891iuQwQ1aDO5+ZHDF/fidwO+Xcn6/VawD17Nv7z5ie2+66SZS9eBzbxbXcTIHz2//+c9/6HMe7ApwgVy5nxrzFHyyYoX43reHbT9ui3j+QYnyQMXgltL5p6zPf159r75X36tfkvro1xBp/OFnRpQWwOXjnNjjHwSySg2HYycVIYTjJzd5cjGXKLOoVMtkKqhigtqzmKAsyeIqXNXIIBXrsRxcJVGuycjJaoy9rcIwX0V2vQLzZ0phQFdFBqUYONPNbKrUGNxo8uRkg05ONEBtiuGknGrEBFU0mpGrSZq7hgS5tRkdXo/14Z4pF0WhZ0qA24yZTLUPA7jiAw0wkymnWMF2XivZPE8IzMmiUEx3tpBr/WUjulB9Ym8TrB5c3o+bwFyVCeDejwB3PPXhmvVRLvueGGjiLD8yAjgQPePMM6PWPypeg06lxIIOt6V1uC4cEDbNbKLZBQK406aKt3uC+nOR3VL10SxHgbdOBnjEaIxRox6Ouv8xagNlzfyCzNsEAR5x1gzB+9q1a6Puf5zpwh7XU045hV6DEr7b77hDbzn2pWEjPl46duosQPg/dX0CuGJ/Yx+yYnCRyVO1brv1VjLFsQDuNM284qMY73L+BedbskEBmJCBUZevv/kGEgTwPnBgPzG4yOCp1y1c+LbYv5ll+v1z+/7z/o0gM5YK6tnAgCMyCMFikUOaLGcS5YyiigPCE3kROxHjiZuY2QImy2HZcIUqAJ0ZMVAPrsOlkFnyO+SrPtZjyTNiGRBW/Zb59m3c7o0bN4SZRPBM1t+yPs9mNevni+83gsSS1l+/PofAX6z6uO5o9ePZfgSpKEFGIPpLtx8l0QiqHSYdYn04KYUeALw+stA4gcK3f+jQq2H046Md9fHcUNL9b5lMOY+Nw0cOk3wX5eotWraA7Nat4YnHn4CfT5ygx3GCYfjwW/XzP/zoI2KscT3zFyyAIUOHWgzurFkw7EZLovwqAdxbGcC9m9ow1JGKhmKreA+ulCgjM45xShde0IuOZZyQu+bqq4mpx8kRZO8PHT5MWxAI/AiXXnYZZRNjn/kDI0dST7l5/ONEQrduZ4vz1QG6ffttt8Gtt94a8fyD24Q9uKV5/inr859X36vv1ffql6Q+novj+f3Pyd1gg1elTIsx/tkipci0bCvSLC8BX20w5Rx/bZfJMQr/WJGOQXLilz24rRk5qPyMmrIcXB4TVJ+Z/tZlMuUaDLtVZX24isHlGbg8Iqi8SxyQW1qPW/9t0m8Bcs0c3HKG+1USY3JT2IZVZH8rMVRflUmUq7OdVYstvAf3NKYHVxR6Uzbz0IKZTPUNBoOO/lvtomzO5Lj0lIdC8aoc4p0NCkWeSQohI9u1RPUtF+WJFBWkLign7N79XJppPyHAJ86oIxjD/ZBWpTINllBu/NGHHxF7hZJTZBZQgrZt2zYanOFrMCrkgw8+JFll8bYf4cTPJ0gWqIAd78Ht168vsZbIrn7z9ddQv/5p8PwLz7sA3BDJ8pD5iLb/A4EA9fE+//wLlEk78aVJVg/uBGs7kfnCBXtgkU3Enjdkno4dPwZdu3SBt99aSM+bPWc2AVx0RMYcTtx+BMY4KLzqyquo71H1Cb/2z9d0fQS4yHDv2aMAbgL079cf9grAi5LJNAGwUVaIl7ffXigAfhq9Z7wgCL/gfMtkZtbsWWRohT2d2Bc3eNBVVAsvzz37nJwEsDZ8kVgPTkaU5ffP7ftvyY63WDN/we2ufbfcxG0zlyhvK7Iz2SRTmyclyoVSoqyigqgHl89eyt4SR9h54VYHi4szoH6f09yI/6jwPFyf2Wep/+Y7AaHfmVnHHXX9YTm7Pq9+GdVHlQdKxaPVf2/5e2T0tXHjxl9d3+7BDb/gxCB+X8l5vZSPv5Ie/8exLYVdsIUDJ/Pc6uN5MHTi55Pm98+r79X36nv1/+j1LYly7N+/XMrBLWCmUrHHPzoHt8ha1GQ/jp/CJMpy/GWN4b509t+K8RySfxLgtmUgtyUDuI2kPJn34CosVps5KNdiLG41Bm5NmXIqkyqnMoCbbDgqJxoeT0kunk+lmoNbznBOTnZpBE409NPJjLVNNpA7N5mqxEymqrNm5VOMMOF6zEG5oVwy5CxDFpMoqwU/tH4UE8QG5sQybdkcx2ERCv/Cx5RBhKI/NxQhJFoBVpccXM7gUi5uyAS4Vg6uZibFgAadWpNTUqBqlTTo2LEjFIiDiJ6/ZCn1FiYlJxHrOvElq7cLQSS+BvtDEbyhZK2g4BsaHOFMP5ozoXy5A97/jbUulEYqgItubOeedx65CadVqUIgmFgM6dj52uuv6Q0eevXV8Mijj8Tc/yjhq1evHm0zSiY7n9XZMpkSL8LP9NxzzyUDJ5QoX3nFFfDTwYO0xpcFgMdtSBPbV6dWbTITUhd0cK5WvTokJiUSuPd/9RXd37nzWa4S5b1795LRFl6/8647xeuSoGKFinD77bfr56LsGKNV8DnI9k2cMIEijdTnj8ZR1WtUhyTxWuxHxvXhBfveevfpo9eD/cAY+1GW3z+3NSpWdnsUcIsSZTUzaDG4xVoeo/JvLaa2SPbaWidsNeOIJ2W8v9BhtsBO7FvtmUyeG2cBXB6g7nQu9OfbciBnJp0NXPjr/ZqdtBlJh6zIzE31+b36ZVifYpui1N+0YSOsX7++VOpjH3BZHH9lffx79b36Xn2vvlc//vqryWQq9u9fDgFcBmbjGP+QFFnn3BZrw03LXVlm3xrjLyIepEQ5wNMwrBzc3sxkSrG4mUZMEJcon8aclOswmbKSKFdnwDaNsbeVGP7jJlNJBnZMZi2vSUYvbpKBQ0sF4Ca5IOZEw2CKM7ymg3KyweJWZKC2MtsRKgu3OpsR4C7Kp7Kde4YEuIpCz2Qsbkv5YTGJctAxIN/McnBtcYKbk1roF8wYuTcERBNNqPoIWEtSHxnf9yTANR/D6J4DB35yrb9n9244IWV0vP6Rw0fIhMqsf+TIUS1Ti/a+EOyhIVQ8248MDEbz3HffCBhx3/30974R4vb992lDF5Q/o9FTpO0/ePAQgfDwx0KU8eVWH7d7P8Ymxfn5f/+9xeBiHdw+iw0J3/5wlsR+Dvbn7XfsPzgpv39u9YPSlM0JagMOswJ+HWcKdQ8IO9GqWcW8PNstWefgavMpZargDDs33ZOVVDk3d0O4/b5k8WxjB7/uEXUaCfF+S79DbqtjYzhr6PM5ezb5/V79//n6q1evKZPjr6yPf6++V9+r79X36sdfX/fgxvj9oR5cNr6JZ/yTx3pti1QPbqECvlKebIy/tlPU42ZGSGyXJlM7EOD2k2RgNjOYai7BLXdRPp0xuPUkLqsrsdopjL2tbrC4FeXfCgz/VTDaVpPZksh6cbmLskm2JpSWRNkEtaa5lCrCnbAS2e3yzDm5oguLW5mh/uosA1fJlBWDW5/13zZg1HkTloXbkjmBtbUkytsdGZ5olPNlFJOpyDM8oYj50fzgCIWiHWihmCZTJalvm0yNL5X6v+f2T5s+FR588MGwBd1eVX/t773/3bZf9eAeOnSwTOqX9fbbBgXKKTmoJS4O6X/A6nHHmCktPWYAVs065lEPbqE0mSrWz82Tvbl85nKrIUneyuzznSZTTFaab8f1OGRCfiOXjrF1fm1UxWZZOYhyrIMxhWFxRF79/9X6msH9kx3/Xn2vvlffq+/Vj78+EjTx/P7kKgZ3K48Fij7+wXES77FVY6EtUqJcxMZUavyliAduFLo9GFASZXRR7sCUr6oPV5GHGYy9VX24dVn/bR0j/aYGY3CrMJBbmeG/FIOxLc8wo1q4a3I5oye31OTJ5QyK2GR1eQhvguGenGRkHVVgbspqwysz9jaNBQUrFvdU5qJcn/XhnskAbiMWTNyCMbjUg8szn9R1jOkIQQSL8TgmaqKbqIUivDTS4WJfQ5MpzMJdKhe8TddHdLVu88fkX8rB5SZTv6J+WW//yVgfGV88afxZt98CuJsdM390PbjdIU8OKvn/5k2OfFttiCBPyFtUDq44WRcV2TONOAPplCIblvmF9qym6lEhibIAI/naLIgZA/n9jPHzO3ssGXPH2UCnFNaIjJEOuw4wJf969f/366OcDPNiZ82yFnV99qzZZBKFGcL0d/ZM6r2fKe5Xz7WWmey6eM1s6y/e5tdn8Roz7efj/V59r75X36vv1T+562O+bTy/P6hAK2RANp7xTx6ZSTlzcAvU/dpF2Tn+crgoSxUekhU7d+5UDG47FrHagiliM5mL8pksIqg+A7jKDLi6kYXLe3CVRFnhv0oMD6YyvJhkxM4mG8k9HJOWGsAtZ7C25QxnK9PlKtnFTTmZUdMVmMFUZQZ0q7E8pZqsebk2s6U+3YgJymARQVyi3FJ+aH2Qhg/grEUwqEEu5npCWIZVKILOPmR0mzulsPH0t9svD4Xf6dX36p/E9bUDn4O1DepJo6Bkbu2YoM02a1tkuySr3DYdE4QmU0xKk6dclKVEx3IRLHSe6AvkY7JfBX9IsM/FyeT5HeYOtjGR32GK5OOsnd/uv1QSVR/LtNVAJ9+WG+neGq++V9+r79X36nv1vfpe/RLU3yBjgizwujWu8Y/F4NreJoUFtouyzsc1xl+If6wc3IBNTohx205LotyX9eBmS4CbxcBtutGHy2OClLq2liFRrsLScVQPbmWG/yoYbayJRlyQG4Ga6JKNW2omUwkGZWzGBiUbFHOKERGUzGTJ5Q2AW5UFA6ss3BoM4NZhlPhpRsOz2vnKZIozuO0cLsos5gQZXG4HHgob/oeiNJSHXFQOpjtbCVCHV9+rfxLXx+OG5C1BW46sGFsb8AYs0Bu0wDDPYlPmUsVSVoNSGhVOXsQArt2DiyfzAjmjKeU5hbbDYCF7LDcn1/HD4YiwYXlzmtnjOblcimrIYH3krOs0G/Kb/Zu8r8ar79X36nv1vfpefa++Vz/O+sjgaoZ269a4xj/ollxUZLslKzmzBr5FxWHjL0XqBRmDi8TEjh07lYtyO4mbmsuFs7cZUi2rDH5PZ55IKiJImUxxBrcqY3ErGC2qHOCWZyDXjApyw5ulzt4muNDCCQbK5tRyCmsSTnbpueVZuConqSozmaphSJR5D+5prNlZgdtGLAM3S1LtajaCGNyg6hOUOVCbjRzcyMnNTsAQitK6Hoqzmd1V4x8K6R7cZSRNPsuWIZNM+Sx9/d1RPSgWCGXJ/G9p1C/r7ffqn3z1rZ71TVqCHGCRQEE2IxiQAPhLArgs/9bhpqzybq2TtgVwt1k9uNJduZBZ4zsMFlgfirUUwgYlBTIs+f2sz0VLT1leqpalMtmr37jtyEjl0lU/f63Pq+/V9+p79b36Xn2vvle/RPVzNuRa450COb6JY/xj5eDaEmT1PGJwi+WYyxh/BXUSht1ORuO3HUHlotyOmUw1NyTKvAe3AZMn12PsrTKaqsaY3EqGQrcyw3+pjPxMMvpxkwxMmWioiEtdpmwi50QjLiiBvTEzA9dsKK7I6GplIV2J9eDWZFruWqyJuS4DuGpnn8kaobNYDm4rueCH1h/ddANKVhlQDO7mCCxWCYzTXG8bMCMUY91GfYoJilAf+3CL1r0LSx7uAUtG9SRzqfcwA5dMpiaUSv2y3n6v/slXX/dvCKAb5G7kASMDN2C1AFguyu4SZbyNQFZJbixbe+s5xOwyh2S3PhQVdq5mLS0XZdbr4vc5ImzUD1O+zw5Z9/v9DkMhvykp8vmcOa8OuZHLzK9X36vv1ffqe/W9+l59r34J6ufm5Ngs7datcY1/8piZlOWiXGi7K3MHZS5RJpOpzQ5jUOai3Jv14GYzorCpxFeKwT2TMbj1JflYl+G0mkx9m8ZIy0oM2FY02NxUAzOmurS9upkbJ7r05f4qk6lEFzflBJf7E4yw3lTG4HL9NQe61VxMplQPbk25I+uxhUuUFYPb2CUmqLWVg7tTDsADuk8QAa7JeIWiqDpDjmdHcV8LRUEloVAUYyDrYZWD61bfesyqv+ThnrAUmVwCuRPsHNxfWb8k2//+8uV0wJXm9pf1/o9V//DhQzB37htw9OixMqlfFtsfZlAgnZS54ZTqwcUTqAPg6r/F2toeT9AKpCqnPw1wHQC20BEJxJ0E7R7cXKfhkDYjcva/WD8q+c6w9bCZWlO65HdG0vh9RiSNL9zwyKvv1ffqe/W9+l59r75XP0Z9ignaWsjUabHHP5aZlC0/Voab5KKsiARj/GWN4TYZRqFBBXAHuJhMqR7cdNaHewZT0NZj6tpaDNjymKDKzEm5MjOWqsDaVlOZXDnJMJZy83tyUxKX2iUxQmRQImsS5rlFpslUqoHeKxsMblW2c2oybXdtF4myysFV4DZTsrc8KkibTDligsRgHPsE4/MGD8Uk0iKhjRBERSyut5febzG4GEuD2av4V12WSfCLTz3wwzHJ5PaQMUETSqV+Sba/efPmMGbsmFLd/njqHz5yBEaPHg27d+/+Rft/w4aNMHXK1LjqY5bw6MftWvi9KV+hAp3MfovPv6y/f271VV9tkDkmO8Gu8zr2elgn2iINbLXJVLHF4CqgaufgKomyGQdUoK9zK301q5m7IVfPlNozpD6HSYTd92KHrGvbfr89e+uYufX7HFEzZpSMY51e/T9FfZxtx/gHzMOlv2tWi2UNZR5ihBD+pcfwunycHlu9mu7H5+rn432fW9dpPatX6+tr+Drweeq+1au9+l59r75X36t/kte3vEFi//7kKpMpNaaJY/xDebdyfKUYXAv45tnA1xh/OWKCglJ9F9Q9uKZEOYsB3MYM3HKJssrB5X24tSR+q84Y3Mou5lI8KjbVRaKc4NKHa0qUEwzT41JzUE5wafjlyNtcktiGcCet8iwfKY2xt9WYwVRtw2CKS5QbMAa3CevBVSZT2TIHt7cCuEEpq8QB+qZNm2LTZq6tkKEo+SyhMHOgsM5J0/TWWB/22Eaqb8mXw5+vWNzSqF+S7f/5559Lffvjqb9/3z4Q3zwoKCj4RfWnTZ8Obdu2jav+vr37aJKhoGCrrn8Ct/s3+vzL+vsXLSYooFyTDWBrm00FKFsN2V7Vd6sc/YqVFLmomHpIVPabig7CJU/GBKmTvOUqGJ4FxyU9+EPikBsxd0S/wwDCBiz5bBbVnsk1Zmu5KYTf3UzCIXHy6v/P18fBTFkcf2V9/Hv1vfpefa++Vz/++giA4/n92ZCTY7smGyA30viHgOw21fJVpF9r5eAyaTIbf+kkjMB2B0Gx244JaisVry0YUZglsVVjF4lyXcMfqRZT3FZnitxKzGepEsN/PD421YiWTTLwY7KLRLlcaTK4bpRwstGDW87lzaUwaXKqkYVbiQFe5bZVjZlM1WR9uKeyHXqaXBoaPbiNWQ+uArjUg6v7A9mgnEuUuQQ0XgPakh0V7ut0q696cInBTSjnYHB59q2dl9vVkYOr1jZp0iRIb9gQqqZVhauuukoAtf30CIKP888/H6pWrQq9LrzQAomyPr7mzHT2mv376DG8vyGuS7xmEN6/z7r/sssug7lz50qm8wjcOGwYnHpqXcjMbALPPPOM3sh+/frBqIcfgZatWkK9evXgb3/7mzhPhGLu/01fboKOHTtCxYoVoV27drBu3To4dPAgnHbaabRfcF3PPfccPffVV1+FRhkZULlyFbF9PeHHH3+k+6dMngJXXHEFXHB+LyhfoTzMnz8fatSoAcnJyXB6gwaQn58fsf7BQ+G1dgSD0EC8bv/+/QTKGjQ4A5544gmoWbMmtGjenGbwsD6+53O7dxfP32Htn8NH4cabboTatWtDdnY27beT8fsXitaDa+bgbndKldVtnCnUGWxKglzEmFqKCbIkOEUsjFwzuAV27psyk+LZt4VsRtPKweUsH3M99JkAiffV+G3mz4XN04DKz/tt5Iysn4Mhv1f/T1IfZ+jL4vgr6+Pfq+/V9+p79b368dfHydB4fn80g1tYIE2mYo9/cJzEAax6XZ5kdh0ZuHJ8RUahm7909N8G7B5cdFFuIwGuMplS4LaRXBoyc996EoPxHtwarAe3OlPkcqCbyvAfB7em0VQSA7MJhjw5wei9LbUe3HKGJNnNRTnJkCgns8bhFEOeXNFw2VI67RpsJ50il1OZRLkBY3BNF+UsQ6JMDO5OzeBuZ1mdm2LLNh3TQCFD/um8LxRD6RmX0DSEMuSuJaqPYNcymxqvn/rdd99BQkICLHpnMQGTNm3awLMCnCE4TE9Ph+G33AKff76GwCqCLZCvSSyXAIsXv0OvaSte89xzz1rrKpcI7yxeZK/r2WepzjndzoapUy2p7y3Db4FmzZrBhx99BG+++SZUqlQJZr8xhx7r1KkTnFq3Lnzw/vvw+uuvQ2pKCuTkrI+5/zt37gS33347FHyzFe6+607o0qULnDhxAj77bDWBziXvvgM/BgIENCtVrgxTpkyBVZ99RqD4FrGNuDoEpbgvHn3sMVi1ahVJjUeL682aZcGWzZsJmEeqf+LEzyRRQak41gqIWgicsfbevfvg++++peuDBg2CtevWQs+ePSFJAOfnn39e7N/PoWFGOjz55JO0rjvuvBOyW7WCFStWwMuTJkEFAba3bN580n3/3O7Ak+Nm2b9hZ+EGtYPyduasHJBgmE6+25TJAe8LsQCuOlnbeW0S+GrHQHZSpx6VAtZ/a5sx5OTmMjMHvzZ9cEpb/Y4+Gz9/PpOn+s3HGEPo7KkxzSi8+n+G+jh5VRbHX1kf/159r75X36vv1Y+/PjK48fz+KIBbqJfY4x9kankOrkOizFIr+PhLtY7ZOMhS4u2wc3DbSPa2FXNRzmT4ihv7nh7BRbmmIVFOYxivIjObqshUvamMuU0wrie6gNlIvbilmoNrRgUlGzm4qex+nnXkFhGkenCrMQa3OnPmqu2Sg6to8jPlzs80cnCzGcDtu2OHJU1WADcQDNIgPKLSIPbkT4Svd+Q1hEJRDy193xLZZ2v14PIlQV/nr0FAvDx/PGNwQ7RtCOoWLJgPx44d0/VXrVwJlQUQ3P/TT3Do0CHaF+IDhO++/Q42fbkZEhITYP6CBfI11vpxXeVoXf/S96vL2d26wbSp0+iZderUgSVLlugtefDBB+Hyyy+TALejAHpP6dd1694dxowZE3P/N8nMhJtvvhl27NzpeC4yyLgfvpHsM3/VocMH4eGHHybGF+9HgNuhQ3vH/p82Y4YtUY7x+ataKFHGiw1w98K3BP4T4DsBdPEye/ZsOOPMBvr1dwlQPmzYMFpTjRo1YfGiRbTfDx4+DOeddx5NFJxs3z+3Vwe2q5Dw7WGL5apsA92gkijz3tsilYNrOf9ZEmXrpKxPxuSuvMUwkypk8pxCp8ugfP2G3A1hslLN9Pl8DpbOz6Sudi9neHC73SMTHtKugZXj9V79P0N97Lkqi+OvrI9/r75X36vv1ffqx19/jWRwY/3+bMjNlWOhQpvBjTH+IZOp4mI9ptLAN2+LZm3N8Rfin81yDBcI2skXO20X5faMwW3GWNzGLjm4ir3lS21DeZtmGEypJZW1qFZgWDGVMbiJxnUzBzfBhXz91QA3mV1PjNIAzN9cefbG1d+KhpOW0mgrOrsao7pV43IduVPrsh5cZTKlWFyV2dSM2V13QH35rl279KyFYqBwNsN9pibKVzqG+U/YI6EImaWhCIdSyIoCKkl9ZHCXSxdl/sgjo0ZBWpU0qFylClx77TWwS4DEKVMmEzhDUJaQUI5AMMqg3/9gOb3ukYfFa9KqQpUqleHaa8RrdlnAcpRYV9W0NKhSuQpcI+7fuXMXVekmAO7UaVOJFcX1FBb+V9d/7Z//hJYtWtJ1ZGJff+01/Z6HDhkq1vlwzP3//vvLoXHjxpCYmAjniForP10pAe5+CTotgIss7G233wa1atW2tom2sRytBpnrPn16O9Y7fRr24LaJ6/NXAHer7Pe1AG4CAVxkt3G7EbTiZf68BdCqVSv96pEPjITrb7gB9uzZTe8H14PbkpiQSNct8Htyff/c1o3HzBY6ObKIINWHG3Q6K4fn4Fon223aTKpYMrjWjGSRnIUs0u7KhWzWks1sOvLg7B8Dy2TK55St8hw6JmsNiwJwZNoZDB5zSfT7uO2/lMgaYMir/79fXzO4v/PxV9bHv1ffq+/V9+p79eOvTwxuHL8/OH5RoNZpohl5/JOXl6cdlIskwC3UEmU7tYKPv4I8CSNgY6Gdu3YqBleZTCkXZYWnGjEWtwEjF5XJlJvBVA1mGJzG+m/LM/zHzaVSDHlysktST4pLXG2pOSib/beJLqG8iYxeTjQahiswzbWipzmqT2PXaxpRQYrBVVFB9Rm4zWAaceWirJbWknbv7cjqJJbJarh2UyQ4vvZhsz6RtP2hX+R161Z/qZQom6wtgTbek6tcl8lkarw0mXKu7KgAfsuWLYPMzKYk2V26dCmcccYZUesfPnoUlr2Hr0H2dLh+6PCRo/Dee0vpflwXPrfbOeeQG3EodALSqlYlCbC6PDXmSejTu7dkcDvDawRwQQLcIQKAPxL3/scD+qpBgwWArUm9uw4GVzxn1qyZUPOUU2Dt2rVkfDV+/Hjo0vUsei0yuL1793GUmTF9OrRr2y6u+gpMf/3NN/QEzeDuEQD3++/p+qFDB+l12N+LAFet7MGRI+EGAXBxPWlpafT+Yn3+Zf39c6uPxwwy/MjOOmTKwWBYrprq11Un1iKegctzcGUPrmJvyV1ZxgRpq3w5s2nHAhUwBtea4bRMpnx2j4vDBZdLWG3bf5w0aZTRCDIyMkjOfu2118Fbb7/Ngtg5+8fC2v358O4778KLL/6Dufb64qp/jjhWMmTNRo0ydP2LL76YsZGx6ztkuiWoz7ff77dnkx1A0Ksftf4azuAax9+BAz/BTofS5Jcdf8fF+evDjz6Eo+I87Hb8f7bqM/jhhx/iPv7XrV8nzufv0fWNGzdary3F888qej/f/6bnn5Ke/3Dicc3nq8usfllvv1ffq+/VL9v6qPaJ5/dHxQTZBlOxxz8qJqhIglj13C1SoqwIAz7+UsSDwj8qDUNKlPsxg6ls5qLchDG4Z0rcdbpcTmNZuLUYyK3BiEplLqWY3FSG/0yZsgKtqUwZnOACdMu55OSWCsB10zsnGg3BiQyVJzI0ztnbioy9rcgigtJYRBB3UVYMbj0GcLnRVCP5QSgn5eaSwW0pAa4dE6QY3KBlmR3LQS1qzEokAUMIIs4Gha3PpT7PwY2n/tIRZxF7qxlccTcOkLqf0x22bdtGfaQDBw4kqS9ud5UqVcg06tjxY/DRhx9C6+xsCO4Iwofievfu6jUn6DU33XwTfPRR+P24Lix0TjcBcKdOobJoJNXrggtg985dAgx+DfXr1adeVHwe9uAiwFXbM0QA3FGPjIq6/1EOfVaXs2DhwrfpNsp/TxEgFgEsPla5ciWYOXMWrf+VV14hM6jdu3ZBYNuPkN06G7qc1UUC3GcFwO3t2J9vCzBTtVpVbUQV7fM/fvwYybpnzpxJ99kS5T1iIKUArsXgLhAAN7tlK/05jCSAez3d7NOnD/Tu0xt27d5NvbwXXXyRAOazTrrvn1v9oJmDq3s4DHkyLgErUFxn3/KoIDnjqKXISqLMTab4iV47CRbakh3uMijuz83N0WZADlOHfD/r3VQgyAJIGQJgzpkzm/qr8TMbPnw4RV4tWrTICXSUky6TrI4bN45MxBzuvXHUX/vFF2LQvQYWihrYB//Bhx+QjGn9uvV2hp4jrsa9vo/LcUtQn2+/z2+4DXv146rvNJmyjo39+/fRJBZ6GWCP/RWXX06+APz4w0k5VMRkNcuCnJycqMcfmtc1bdqUBh5ux3/vSy6ByVMm03p9+T6YN29exOP/n6/+k9Qzf//73+kuPBe9Ls9jJTn++WXx4sU0KFOFLhHvB70PfsvzT0nPfxNfegn69x8Qsz5OAL808SVtmniynn+9+l59r/4fq/5q6aIc6/eHJMqFzujDWOOfLQRwVctXkZYy51EObpEGvnz8xXNwtfJO/E7tslyU+8t2zjYuLsqKwU1nALceaxtV/kg1mX9SDcNXqTKLha3IXJRTmBExb21NMiKDEiOA2YTSBLhmk6+bPDk5Qu9tKtsY1YfLM3Ars5ggFRZ8irxem1lRn8ZMplTTc0MGcJsxF+XWUlfeH51s7X5ByTIxg59QKBTXvE8oxh0hlztDEV7onCmyb1kSZdCSVsXaahaX9eCSKdX9ymRqoq6PgAsdjlNSUqBKWhp07NBRy3mRxc3ISIfEpCRyRX5p0ktU/zC+5vLLIDklldjGTh2t1xw+7FyXuh8vCHCnTbNMplC2fO5550FychKBaATBCIhJomwwuFdfPVQA3Edi7n8E4lWrptH7RPfhuW++qR+7/6/3Q1JiEjwigDIOBs8SgBa3qUaN6vR+0ZCKAK4A2X0MBvfgwYPQomVL2p8bN22M+fnff7+oJdb98KhRNsDdt5d6lxOYRHkeMbgt9esQ4KJEGVeK7/G8c8+D1NRUSExMgMuvuILex8n2DGYTTgAAgABJREFU/XOrr1jZAHdRDlosbjBoh4YHNIO7mc0g2kYHOiYImVo5O6mMp1R8kJqxVLOSjpN/4VbN/KpsuA1SouxwxGWsm88RtG4tyJwuEgN1xQaik/bQwUNgqPhe4n34nbhVgF4ELe3bt6PPH9c7ZepUYugbNWoMHTt1hOXvLae4seH03NbQtl1b+Kt8bqT6Kz5ZIQBuQ1gngC3ev/idxeK7exZs3LBRuy0OvHQgTHppEhmm4eTQM888S+ZuncXzxo4dq9eLKpRbb70VWovH/vKXHvDK5Ff09v/jxX/Ae++957r9Ph+X+/ocAfX6B9hnv45nxPodz/EzM6b49/8ftf5q00U5ZJ0brhfAMSiObzyeb7zxRrjvvvsdx9Ps2XPgoosugrZt2kLO+pyox9/+/QcsgCvd181jGc+p6rJg/gKacIx0/N95553w4rhx+q5hNwyDma/PLPHxzx+7SWzfFJUhHnK+n9/q/PNLzn/qfUWrj6x706ymxPiezOdfr75X36v/x6qPDG48vz8kUdbRh4VxjX8wB5ePq1RvrooJUoQBH3/pmCDDFFRKlPuwmKBWzKy3MWNwz2DJNQrknsoIyJoMs1VnnkpVGbDlPkzqdoqRupNitMQmMhCbbBgblytNibKZPZRkoO1EwzmZo/BUw1wq1TCZqsKigmqwZuVabIagHnPuUhm46UyerIymWjA3MPzQ+pCLclDFBAX0hx3+LY3pJe44PEJuugaXgyb6YeIswBnceOovve8sArjLfRPC6h85ekT8iB9wrbdn9x45CHAWwH7Wn8QPv1kT71frirb9Bw8egp+PH49r+ydPniwGgyPEch/cd/99+vr9YoD4uRxMhk6EAHuo3bb/6LFjxFCr+/fs2QPHj/8c9/4/fPgwuUm71b9f/MX3p56OrPGJn0/86s//4MGfNCA+Gb9/bk93xgQp1lb14QZsoCv7cDejRJnl29q29ZKpZW7JitXVEuWtdgQQnbgLtoaxtluZyRQxuIp9c8hLfU7nXRawjnFS7yx+x+7DFM9B1geBJt5+/rnn4eyuZ5PkHPu1mzRpAm+88QbFVI16eBSc0/0cWLp0Ge0TVAh07dqVDN2mz8DnZsJc8dxI9T9Z8QkxuMTcYiZefh60a9+eXouvQZdtlC+v/mw1fPrpp5DeMB3O73k+zJs/D1544R/QWIDrOW/MofXedffdAtieRyze888/B5kCGC1dtgzy8/IIJD3wwAOu2+/IfPWZJk4+4zGfO2vqM1jPEuz/P2p9ZPz58YfnRHRj/0a2L6C65Oix4+L8eUAff3iMoOHdWvHd6dS5M6xfvz7q8YcMblbTLHh1xqvkcdC9+7nw8qSX9VPvvutuePfdd6n1BOX1zVs0h3PPPRcKCr5xrPXee++lCZoOHTrA5ZdfbjG4wwTAnfm6dT47eozi2rqe3RUuvPAimDZ9mn4v33z9DQwZPBhat24NF198CXzyySd0/wMPjITsltni+9oOhl59NT37rrvvoveDF/RBGDduPPTt0we6nd2NpPxqP+wX+wS/rzhhNHDAQJgwfiLcJQB4tPPPbbfdRgzrgP4DaDtGjBgBh8Tvi9r/b745D/r07UOTQCNG3CtqWFF4b731b5p4oElHcWzgeR1d7HEd94rnoUz78KHDpExq2jRT7OdzxPE346Q9/3r1vfpe/T9WffytiOf3B8cvPOM2nvGPBWQVS1ukmV5LorxN9uE6x1+Ifyyj0ICD7EMDXtaD25K5KGexPlzF4DZg6tn6DOSeyjJwVYtpmtF6Wom5KKcaJlMpTPWbEgVbJkTwgSo1F+XECJFB5QxmlzO9qYyCVhvFN9aMCjJzcOswOrw+04CfwQym0uWH0VTOPCiAizMS/VCCi7LQoDbICVgSZZdpnpDrdzrGQaBfGPkAcR5wRhVWHwFuSeovo5igiWQ0VRr1f8/tx0HVgw8+BA+NfBAefGgkOS+PlAvG7vwe+3/t2nWu9R986EGKQPq9P/+y/v651cc4IAvgWtFAwe22uZQ+pgKW5EXZ0dusbZHN0iqTKeaWrJldCXxtB0HbPbDQMaNp5cUVyudt4DFBPCuVs3sOmZCfFAyL31lkz66KZcG/5lOPbJ4AnOo1+eLv+pwcMVjvCk9L5nTcuBcpR9pp+e8nuWiOAC9nn302PP302Ij1P1EM7vp1uv6999wLN910I71m7NNPQ7/+/ekxBLgNBRj+97//pd87ykzvveceqt9KABg0c9u06UvYtHET9O/Xj+SoWPeTjz+m+92238/NlXhfEM/qY+7BfgejmS9lu8b2l2D//1HrWwyufXR8/fXX1MuPE2HIzqIz+8iRDzjc5kc+8ABNnuHx11kAsfWSwY10/CFIw4mKiy+5iKLG3l74NrRq2QKWL7f6aIcMGUwTL/v27YUJEyaQZPk///kajh456jj+iwSIu1qA0LFjxkDhfwup/rAbbpCtFiF49LFH4ZLeF4saX8DSd5dCmzatadIH13DtddfBXx/4Kw2knhKv79ypI70eJ6EGDboSnnrySfj22/9SHfRTmDdvPr19lGfj9x8HdwvffhtatGhJk1YKcPfr25d8CBYtWkhtJIPFtkQ7/2B+ectWrWCxeD7Kw3v1uhAefXSUHEB+Bi1btoR/LVhA+/RK8dw7br+dHsNtRLYaL+i3kJXVFGYIAPuF2J8oXcZJqROhE7BhY64AuFmkqti5c8dJe/716nv1vfp/rPpoMhXP709u7gYoLCi0pchxjH+UyZRiZ7X51BZbumyOvxD/bJZjOAK5SEyIMRwqLyWD20YCXDMHtwlzUVZ9uPVZy2gdRkZy9jaNKXOrMHJT4b8KLuZSiS45uCauTIzRNvurJcombczlyYnM8jmV6atTGWtbgYHeSqwHV9HZNYzFlCjXZxLlBsxkKov14GbLD6wdSZR37JSmOAFJzwfhy01fxuhDd29bDxk3Iun0Q6YQwjiIwo4kBlgR5NLfEV1h6f1dyHgK44CWyseWqsdGdKHHl7sxuL+wfllvv1f/5KtvsbKbSdYS0CZTEtQ6XJS5yVQxm0XcZgeQaymydUJXMhqLwc1z9KEUMimyPvlz6/yCrZCzIYf1YYbLVf3c4EH+0KQLILt40WKHZGjc+PHEmOGPz/vvv08GaciWNmrciBjXsWPG0g/Si7oH13rd8g+WU483GVeJBZ+LEViR6iPAbZieQWywqo894QhW8/PyYcCAAfDCP16g9/HppyspS3kdsb0W+Hrk0UeJkcP70hs2FI83JMl1RiPrfaJcOtb2mzPJzsxYngvrN7L87H5XnjVb0v3/R62PsjN+/CGzmZmVKUBTf5KqIwhDM7ExY8fqiDUEvj/+aPXkdurciRhcdfzhpAQCPly+EMvBnw7CAdmD++mKFfr4Gz16NDz04EP0usEIKOfPo9ejRHnAwAERj16Ur08XAE/dMWzYDSRRxse6dOkqanyqn4+tHPfcfY9jDT8f/xkKthYQCMQZf3zdMJQoo++CPH8MHjpY9wEjICXVi6yPXgvYn4vjPpwIwB509djYsU/D4EEWwP3+++9p+9W+sIywQgIwXwFPPvWkfkcffPABRdPh5fHHn5D7xPpEcNDXokULmlzAPmMy9xP/ENhSP67c6ejbMABl3SEpUW7qlCh753+vvlffq/9r6+NkaDy/PyoH1x7nxB7/bFEmU0UqB1fdn+cYT/Hxl0qOUfjHMgcNkjGi7MFtz+JVm0ss5SZRPp1JlGszmXJtQ31bnSXj8FbUCi7JOgojJhtGxW4SZTP/tlQY3EQDOZczaGQ3ybKZi5tsyJMrG+ytMpqqweju2mwnKjr8NJaBm84AblMJclUPbrZkcPsElclU0JZVWjm4IefXNhRBxBCK/KDTkjxCBlfEg8er79U/+esHWEg4d012MLkBzFcLSEOqzRaY3YbxQNK2XpseWAyuOpEXqdw2sWzZkqdP1lqis9UOPNcne+UqKJac3FwnGPEzcyG/3zFrqh5Dk6nF1INr/eCgTPjSSy8lN2W8PWjwYBh01SBiUPH5CGDHjHmK1j/uRQlw5boGDR5kPVeABXwczcQQ4EaqjxJkqwd3nUMCiz3j06fPoAH3WgK/FoOLoBX75hUIu+P2O+C2224ldrlF8+bw9sKFJd5+mxH1MSDI5bgc6PkdMl3nuv3OeJ3/8frE4LKDAydJM7OawhdffKGPPwSd2G+Lt4YMHQITJ0zQL+ncuTOx/GolOJmBJlDW0hm+8n9FEuXMrCwanKgLMpJXXXWVxeAOHgLz51uAEtlLDXBdjv9bb7sNZkyfpu8dRhLlmcT+4vcM3Y/V8f/Wv/9N312S+L79NqkU8Dm4oAu/MuS7cdiN2lgQJOCeP88C3JdfcTlN1qj6yAKPH/ci7N27j9aDQFY9NnvWLBgsjh28PnHiRNp+9GrAvxMnTbIAswC4b775pj7/Yf9ZU/FesM3j+uuvpwx2tW0oOc5smknnBATxCObx8uqM6XDLLXYaAJopXnTxhXQdW25QoqwBrnf+9+p79b36pVAfHffj+f2xJMpb2Xgn9vhH5eAWyxggBYatHFxbEcfHX5aybrPdVibHcNJFWUmUWzGD3iyWg9tIYq3TJalY35Ao15JLTcbiVjWSciqwHNyKDAtyiXKScT3ZUASbST6JpQlwy8WQJCewN5VgGE1VYH/VdS5RrsKYXGUzXZM5KZ/KGNzTGcBVUUGKSlc9uM0kwMUPrZ+jsTqwXbvCQlQpQtwTO+F3h8A1MysU8/Vefa/+yVefXMfF8RLcHh4JpIBugN3evOlL3R9SzGzrlZkUSZTliby4iDG4CHwLzZ4T5ii4tZCd6K0llwCun7FuPkfAus6fYzIh7MGdPWcOMW5vzn2TjMBQCokyUPzxQeBxy/DhJNF844050CSziQVaxTqREWrRorklQfJZzx0uBtD444J9utivO/apMRHrWyZT6RbA1SDNDw8+9BAB3yuvvEKDOQvgNiQpZ07OegLlzZs1J2M4rI2gBxfs50Xwhczuyy+/TI+NevgR6kV0234+k6zNlZQU1+8Ll1U5GE+fw7HYmR0b3/7/o9ZfYwBcdKBE4IYZ0erud955B3qc9xf46cBBeqx5ixbQKrsV9cOi9BgnJTDWLNLxd+CAxeByt2V0o0eJrwK48yTAXbAATaYGRDz+bQbXehRZTTKZEiO29u3b0+SQurwyeTIMH34LHEIDPvGeX3/9dQKS2FuP70cDXDKZmqxfN4QA7nyqgAwuAVxZH/tgx40fRzfO7nY2TcaoN4r9tIMHD456/kHAPJ5eb11QYtyxQwe6jv3DTz5ps7v//fa/0KxZMzL6mvU6MrhSoiyO11vEdqm1f/iBALgXSoD7k8XgfisArnf+9+p79b36pVV/9eo1cf3+EINbyKTIcYx/cJxUzFySHfcr4GuMv7SPSoAZhW4Pkr+NBLjZjMFVZGEmA7iKwT2DAdy6TF1bky1pjL1NM7JwORZMcTGWSomAL8sxwFuutLNw3cJ1k4xeXI62yxsGU+p6BbZUZOxtZRYMrP7WNiTKSvOtmpyVHjyDgdumLAM3W5pM9cMPMaizO20Zpcs0TaSe8ahf6BBbR1yqB7caXn2v/kla3zaZCrpKkgOst90Cw9JF2WBulWTGYmqtE7p2++MMLputtP+yTDj5g4DX8QciXLJqhqr7Hf2ZKOnF3lYEmmg8c92114qB+Vt6HXMEUMUeQZQo9+zZk+KvLNmx5bB83l/Oo9ejuQ6C2lbquT16wAW95HMj1EcGt2FDKTv226wksrT4ftA92Qa4K6kOGguhBBmlonfedacGZMgc9u3XFxoLUI39w9dccw29v7y8fDICGv3YY67b75BM+cONmzhbav9lmbBKDux39rLGu///qPVx0GIeSjffcjMMF0ASGcQ9u3cTo/rQQ/9HPV++/HzqzaZFrBNluvMFKN0tnhfp+CMGV4Cu66+/jphPzN9GVnP2nNkSUA4mt3Z8Nkp20aBsh+wfNY9/ArjTpuvbN6ge3JBl4IQs5569e+Hbb7+Fc7p1gxmvvgq7d+2GpllZ8MknH8OxY8dJbo2s6Y8BC+Ciez0aSx09dlQyuIM14MYe3LfEcaQuBHDHWQD15UmT6Fh74YUXYOTIB6Ftu3YOgOt2/kHAfFaXLlDwTQG9T1RY3HrbrfTYosWLoL1YX74vnwys7htxH9UPScb7Bsngzpg+gyag1LqRwb1QAFx83vHjx8mdfNHCRd7536vv1ffql1p9y0U59u8PTjKaxpmxxj9m3q1q2cIJeZ5/y8dfOuoxaJATwaDqwW0n2zqbMx8jLlFOZ22hKq61Dlu4RPkUA9yavbcqJrY8w4opLA9XeTeVc+m/NaXJpQpwTXMpkzpOZG+WZ+KWNxjciqwHN401IitwW5M1Lp/CAO6pjL09g0mU01kObgsjB7evJU0O6oF5ULkoh6IL8UMxDpLoOv5Q7IMuSv2Ecs54oN+7fllvv1f/5Kkf5PIWvuBsYCCojabUzCCZTBVb5gZOR78i6aK8RZ7UC+TjSrqcx0LNjRw4PqPJelRyczbYclSeZ+r3sbgXnwMQ2axfvv0j5Gevk0AHjc5M8yG1PpSn2j2c+VZP7a+oP2fOHGjVsqWMPbK2ZYWUKH+5aRNs3LgRNn25KaxHFJdNAtSioRSvT2ZZv2L7+brctp/X/y32/8lYn1yUjePvh++L4NJLB1J8FDKf1157LbGwbscf9uBiLES040/l4CI7SeyvWC/2mqrYG8vUyZIEI6ju26cvPR+3zzx6LYDLJMo3DtN53ug4f801V1P+M5pjPfLIIxCSNTCWCrcF6yNTiuvH45qkd2If9OjRA3r16kXvYeiQoVoyfeWVV8Lbb72l66OL93gBcJX/Ck4iPfR/D8Gkl16ijN4hBHAjn3+Qwf3rX/9K7uZoFIW3dXySeNlzzz5HEXjNmzcjMIxZ7XhBhhy3FS+vzphBfenqghnxisHFN/X0009Ds2bNNRD3zv9efa++V//X1rckyrF/f5SLsmZv4xj/bJF5twrAFspJfzKfkqZS5vgLx2dorKvwj0rDQIUOkoASK2VL3NSCEYYZDOCeboBchclqMnlyTSMHtwpT6FZi+M9U9/LW1mQDWya6XE8obRflpAiMbpKLA1YKA7rJDOCWN0BuJdaHW5U5cNVgEmU1Q1Cf9eI2YDm4jSS4zWQ5uK2YRLnPDtWDy3oIOYMbcpMphKJLEkxRg/m0Tz5eAf6vvgp/NBTZqFytMaFcAuz5fJxYxsOeNdZfBLn4N976bhKNeOuXZPsPHT4Mb86dC0eOHtWPoLRywfx/0fXly5dD4X+3llp9jPaZK+odO3bkpNj+P0N9dbwEHTN/gQgS5SA915Ieb7PDxlkW7hbtolzIJMyS2eVmC4XGjKY+6RdqKc//s/clcHJU5faZniUzmS0z2XeSwGSf7AhhcwFBsiACoggoKCirD2RRQdmEIIuAECGgPiH8Hzz0qaxhUyEJmyRDMluzzLQEZ4ZOZweyJ3X/9d26361zb1fPdMJABnPn96v0Vl2nqzpddc/9znfOUv8CYfZpxo3ol3gcsk7jloNuHHNUG9PNhoxthbOvepvxqD7R3ccnuSaRiJtuvtnAly7K++0nDYw+Sfy9vf+fFfyXjRxc8/dH/4epMtuZv78tW7eKrVu2dvj7JwflPf390/l0x84dafjbfOxNFGeW4fyzY8f23Tr/3H333VLtQM9v2rxJTgRwFnqm/aes8D/7hJnIPUcA2QD0GsmqP875b/uO7Tqz3Z3/Hb7Dd/gfF59amLK5/tSoHNw0n5F2xj/ookzklSvAQQW3Ffpzw/GX7sFNYpGCJMpr7AruOHBRrgIX5RGK4LKCFiOC+iqu1guquGgwVQItqVjBLbC4YoHl5RSzuKWd6tNpJlPdLF10DpDbHMgv4tcKLDesQoux97CWMnXLBLcSCC724Q6CRuchalZhuCqjjwHtOBtM0Rd2bJKrtyytTCqC62X+Pxz5n73d2R3zmb/+9S/iqquvEm+8URO6h2e4xXcSkb3mtIlinSS4/vLq7WKDT2yvOX2ifkxEdx2RX/++f7QzfCIvTX2RDf7u7j/NChUWFsrZKPq79dZbRK9evcQFF5wvH1OF4Prr5+4xPmX5XnPVNWKtGjzS90Z4ZLLT/jfy6ez/voCfUqQVpclJlitryUtIeMmOnvs/WsFkiiu50i1ZSW7Q7j6o4CYgDy5iBpMdlNVzVBHTRAQiXeKWNb+ZmxoPyRLKiOJ2HmpEBTEO24ML1cfB/9vf/y6WLF6Shk/mV4uXLP7E8ff2/n9W8KMkyu78kx0+5TpTXi2ZqU0YP0Eat61c+V67+CdJ06o/7/PnX4fv8B3+Zwv/ZSVR7uj6UyNdlBPKPDOR1fiHCgGsegskyibxDau44fhrFUqUgeSuDV2Uq4HgjgOCy9XbYdCDO0j13w4AI+C+itiWQxZuiSK1pRa57WG5KXcH3phrxQYhoc37JByU7YZfuw83J4N0OS/CCro7sPci2Hl22rKjgpjc9gfd9yBFbgcDwR2hvozRoB/XJlNkhY3ac871NDUGXuR0jNfOXE9H0gYy3Lhaktw3OtA5hPhcqb3WJ7TX+kT3an+55rRJitiqqq4iv5L4+q8bEmZhWZm38/PtrP3fuXOnvn/88ceLK668Uq+2U1UH9hR/w8aN/v7lyLgKfiF6m3tv///T8WVMUF0tkFgzLNxcUmK5/9tq0z23fMJtNU2m1ElZE1wtXW4OJTvYmwJZcKZEeVnY1xIPY1+0xLQRiZEZvI6GRPF4Y/pju4KI5kVobuTw9wl8kufui7//zsLftm2bePvtd+RvPRv8dWvXis1URd7Hz78O3+E7/M8W/is6Jqj96w9LlNE4s6Pxj5Yot7JEOZjwr+eYIP81e/zFRqHMf8LxWpIruBPVMhZigqqgD5dJ7hDFwfoBwe0FyTc9gceVgcFUMZBc5oL56nGeVSTNtVyS7WzcTie4uNEci+zGwDk5N4KFRxHcfDCZKoZA4HIgtrj0VSSXq7hDQaI8Akrpo5U8eRzMSMxZs3qNklSGfbhkhNPxrEzH/6k9L4Mdufq9kHMkmb0sX14jOvrzBBNci8y+ekf42L+/QVV1ifQSAaY+LerZRXwyqxm233BRVl4uTv76yeKDDzbIPKwhQ4aIO++6U/Tp01fKH2+77Tb9HpLEkUtmnz59pOvnQw89rPd769Yt4nvf+57o26+f2G/YMHHD9dfL5+l40jY//GCj+Po3TpbV1fLynrI3it55wokniof+56GgGrt1m9pGXzFs2H7ierUN+vv9738vho8YIYqLi2VEBbl2btq0WQwaNFAekwED+sueq1RqtRgq8QLJGk0ekBFQWWmJOPTww8SiRYvk8/TDHzxkqJh7442iX99+4sADp4lHH30sPD7zfiP3v6ysXDrRbvSJ9Cfx/bd/ov3k//91Bj731aZU1FYqFUYEJTXZTYokG7jV1gY5bVouAzOKLFFWJ292UW7lfNxEc7pFvrLNT4A8mWc1yW22EaQ/BjFhd1zdl2kZEVmzq9wnE0bGxI3YmDjIY7Gq6PD3DfyXX3p5n/z9O3yH7/AdvsPPHv8lJVHu6PqzTFVweZyTzfgn6LUNs275dR0T1NqaNv4i/rNCxwSt0lxo9WotUZ6sCC77F42BCu4IyMAdAjm4LE/uZ/kmMcEtyUBwkQ+iCTG6KudlyMLNsaq4nWIyFYvoxc21JMooVc61PnQe9OAWWQ3H3ISM1dtydaAq4AD2hf7bQZCDyxJlNpkaDwSXvrTZ5KJsm+PU6gpudn8eyB3ov7LnZfc7oB4nCrWfO3dupikm448kx0xkgyqtkiSzNPnlgNiuU69dc/okRXBz9CYp1y8nJ0c8/thjctaGnCKpv4+eJ7I4edJk8eILL8i4h7y8POnGSX/nn3++jLQgp9d58+ZJskoRGPT3g3PPEVVVVf66fxMLHlzgv1YkHvXJOxFR2ubG9Rvlj++Iww6XPYVvqv7jQw89VNx7771yT8m5k7ZBUQ3/78EHRVFRofirvw36QZb4xHb+/PliyUtLxPTp08XZZ58t+6KoakIV3MeffEIkk0nR1pYMJgHWb5A5hkOHDpWZiK++8prE7V3ZS34meQz89ebMmSN7587yt0eGPfS38t2VIuYfn8cee1Qfn1v84/NJfP+R37QnspoJ7Cr4siqrc3CDE2NS282njGouu/VFyZNbVN5tIFEO+kp4JrKNXZQTLNVBU6mENl9otgwXpIsyBKqjqYMhZ7UyT+OW4UPc6ttstCWutjRWbyfu8PcR/KVLl4kFDzwoFixYIB54YIHMcl2wIHj8oL5dIG/t+7g8qN/7gLWuv40HHzTXVc8/8KB6zuE7fIfv8B1+l8Ynd+Rsrj8BwcVxTsfjHyKyrIajsRNHCNH4KUqe3IoSZYhKDUymUhwTNBVigkarKi7zqpFWUXEA5OCyRLkSipPlUL0tAc5XBPcLwZ8pH4guRwblWzwzymyq0yq47cmT86xycswitfmWk3KBFRNUZDUjl6syd4UiuX2gHD4A+nCHqYVL6FWK4I4Bk6mpaQTX/0KTYDJlihU8S7Sf7TyPJ+zJItoWOVXec889kuByjqCt77fxiZSxsZTsv3319lCeTNVbkCevlwSXKrjjtUyZtkMOr0SUKSdxx/btGlcSXJ/YLV6yRCOfcsq3xIU/vFA+qqisEI89+qg07di8eYuMQ7np5pvken379hWPP/G4ft/iRYtETU2NjI8g7A0bNsjnZ82eLW765U36WB6mCG64jSf0nlO1dVnNG8b+b9m8WVzx05+KKVOmyOc/+GCj3H5TU5N8nHy/LSDUPh6Z8PSs6Cm2q8gKmk2jOBXKZXx35btyvXfeflu+xkS8yT8ZkIQ2Rx0fisL4pL7/9rfgfeL//zoLH/s3UmAsldKzgUkjMogmj1oMabKaSVQzihwHRCfyFjgR04lbVmabQJYD2XAJDkAHIwbZg2u4FMZNp12Ilwl7LDEjFYgQVwYb0NioMdIkAjNZHb7Dd/gO3+E7fIfv8HcHf+mympC8sjKtg/FPnZIiy6WtRVd5JfHVBlPm+GuVSo5h/hNEOpLJ1DruwZ0IxUH2MxoFObgYEzQQTH/7g0y5ArhbGfThcgUXM3AxIqh7RBxQVFpPVP9t7idBcu0c3G6W+1UuVHLzYceK4LYHsPoykCj3hIPVGxbswR0EenAuoY+CmYdxYDI1O5VKGf232kXZrmRF9JR7XrYqB/PF99uSYu6Nc+WC5DZa4B+CEAlbpwjthldVxfaV2wNTqZdDkymWMV8jJcrVaX24V175M1FaVipKSktkPib1IXNVU7t9+njXXXed+MoxR8vnOJIoJxYTMX8hEnjGGWeq13LkD8/+C4hjjk9w18vHlMt580036dcPPSwguLz9RHNT2v5v27pFVnd79+otK88YjURZkJKoEra/PhJqmjyYOnWqsbFjZx4rrr3mGmlgQiR/85awhys/P9//kS9Xx+dKUVriH5+SEnH6aafK49OZ339W632C//86Ez+QHdcFM3+pVZF9t2jiVosS5baWMJNNVWrrlUQ5oSTKHBUke3Bx9lL1lhhh54lmo4pLM6DxRtNcKB63TIisPs7QWIhvG8wLUtzMrENH3Xhazmqjw3f4Dt/hO3yH7/Ad/m7hL5M5uE1gKtXx+Efn4LYEC0/20/gpTaKsxl/BGG6F2X/rj+eo+KcI7mQgueOB4I5U8mTswWUu1gcclHtDFbccyK0tUy4AqXIBENw8y1E5Znk85UZ4PnVqDm43yzk5L6IROGbpp/OgaptnMXc0meoBJlM9oVm5lxUmPAAclPdTywg1yzAaJMq80Jc2R8YEwcBcVpnqarNuV08LdfY6Xvvuu4PKbbLt/bSVvHZoCUuUN0D/LcuRo4ymyICKKrhhD65n9NQ+/dRCKQsmyS9LlFfUhv3H3//BD8RZZ50liUxpaZl47bXX0j7dLv/F8vJy8Y8X/qH3n34YRDK1RHlD0MM6e/YsTXDp8WGHsERZbePv/9CfkLax3t8GSTp6+eSWsHfu2inuuOMOMePgg+UWNm5UBPedd+R72t4PCe7ChQvFgAEDjE87cWK1uP8Pf9D7umWTSXBpYoPxt/rH56mFfHy+36nff8Z1O/j+uyI+V2VXtUNuSaLMM4NBBbdVy2M4/zao1LaoXtvghM0zjnRSpucThtkCnNibw5lMzI0LCC4GqJvOhfEGU45q92aGPTJxMBuKhz2eYFZkG0VoR0SH7/AdvsN3+A7f4Tv83cBfKgkukNksxj9Siqxzblu14Wbgrqyyb63xlyw8KIlyEtMwghzcmWAyxVXcKismCCXKg8BJuS/IlFmi3BOIbSlUb3sA/0OTqVyLO+ZBy2uu1Yuba/HQTiG4uRGMOWYZTGGF13ZQzrOquEVAaovhQHAWbk+YEUAX5X5wcIcqgssl9Cqo4o5XXxZIlFPGgLwWcnBDcWaUk5q3BxUzTyST74v169dl+Y4QX/aXyv7bkORusKq2oflUSHDDuCAhnn/+OXHEEYeLtrZWsWuXJ52NzzrrbPGuJH054rjjjpMS39dff90ntaXigQful/izZs0Us2bOktXW5PtJ8ZVjviIefHCB3Obxx3/N3+YRstL5zjtNon//fuJ3v/+dT3CDntgNGwOJ8mySKN/0S71fhx56mJYof9X/HEcc8Xm5jSafsPbv31/87ne/E3ff/RsxcNBgmcn1ftv7otonqQcfNEO+h+THJSXFsufBrhivXrNaknKKJtqydYtcp6CgwP98b4v33gsILkmt+S9PEtxa//g8L444nI5Pm+zzPf74r4rv+cenM7//9jz42vv+uyJ+SpmymaQ2aZgV4H2aKdQ9IHCi5VnF+vrQLVnn4GrzKTZVMMPObfdkliovW1aTbr+vZlFDY4e47tE0jYSw39I2LQozUuPxdPfeuGUW4fAdvsN3+A7f4Tt8h58tvuzBhfFNNuOfeui1beEe3AQTXyVPtsZfq2TUYy0UJFYpk6nVNE6eo4qB1WAwNVaRW3RRHgwV3AGKl/VXXK0XVG97WlXcInVbCPyv0GpbzYMlBr246KJsF1tzOkuibJNa21yKQdAJKwaPu4NzclFEFbcYWH9PyMBlmTJXcAdC/+0QKJ0fAFm448EJbHIgUV5lZHiSUc6KdkymMle4vIz50UgOPK89ouF1SF5kFu7pk6T8mGOCrpG31eLq0+HxqYGDMrso8+Y2bdoiTjjhRJGfnydlytOnT5M9rFzVPP+880Vubq4o6lEozj33XI1L/9mJxOb7JJEkyieddJL4aNMm+RqR3i9+8QvytYKC7uL0008XW7dshQrueok/e84c8ctf3qz377DDieDOV9tYK7dRkF8giejp3z5NVlEJ96CDDpKfqaKiQnzthBNkXiIfoR/96EfytSuvvEJVcHOCnl//xaefflqMHDlS7v+QwUPEQw8/LI+/lCh3Y4lycGAIk+Szmzdt9o/PCf6+5EmCT6ZW3OPbFb7/roYfGhSwU3JKS1wM6X8y6HFfsbw2lB4DgeVZx3rZg5tQJlOtet161ZuLM5fNliS5GezzTZMpkBU1hHExhkwobuXSwWwtxs40NkY4HhrbgJnatDgah+/wHb7Dd/gO3+E7/Pbxl3EFtxljgdof/9A4CXtseSxUpyTKLTCm4vEXFx7QKHRVKskSZXJRngbKV+7D5eLhCKjech9uf+i/7Wul31RABbcESG4x8L98q2LbHTgjL+ia3M3qye00eXI3q0RsV3UxhDfHck/OtbKOCsFNmXe8GKq3pRAUzFXcfuCiPBD6cIcBwR0JwcTjoIIre3Ax84nv19bWAV1ox5LHy+5prx0Dn47pSjo+kTMirtXjJygSWx08VoR2/ITxmtwS4Rvv39rIW7Zuk07DocnUu6qqudknwZugP9XE37R5U5g/aH0wMqCiPMOPs/+b9DbM/Sdn5B07dkRumMygqNqa6fivVyZXu3P8qer74YcfdcnvvyvhBwS31pj5k/dTqwx5corl/7XLjXxbbYigTsh1nIPrn6xbWsKZRpqBNKXIlmV+IpzV5B4VKVH2LyoN2iwCjCEwXgalRHEwhbBmY00pkhUZoxx2jYuZunX4Dt/hO3yH7/AdvsPPFp8UaAkgstmMf+qlmZSZg9vEz2sXZXP8ZbgoKxUeFStITakquFMgYnUcKGKrwEV5GEQEDQSCy2bAPa0sXOzBZYky878ewAcLgC/mWrGzeVZyD3LSTiO43ayqbTfL2cp2ucqLcFPOg9J0IRhMFQPRLYc8pUpoXu4DttSDrZigERARhBLl8epLm0UVwiTNWqRSmuRSrqdIy7DyMvQuepbbjzC8Z7Px9wnf7qU/mQGfZMdsuJS+hGZME8ZPyApfVzU1ee3a++/wuwa+duAzqrYpPWmUUpXbMCaoNqzatoQuyZzbpmOCyGQKpDT17KKsJDqBi2DCPNE3qddUvwoFpVOfizmTGjfMHUJjiLhhCtGIs7ZGvmrYgxO3ZmjD2dl42Fvj8B2+w3f4Dt/hO3yHvxv4NSomKCCvzVmNf4IKbuhtkmgKXZR1Pq41/iL+E+TgJsPihD9uWxNIlGdDD261IrijgdwOt/pwMSaI1bW9LYlyCaTjcA9uMfC/QquNNWbFBUUVUGMR2bidZjKVY5WM7digPKvEnG9FBOWBLLm7RXDLIBiYs3ArgOD2hZL4IKvhmQ8+m0xhBXeK4aIMMSdUwcU4FC9t+O+1Y+jjRag8veycgKJYx6eEv8vbJQnG3sLf2/vv8PcMn343Ut6SCuXIXLENCW8yIL2pgAxjFhubS7UqWQ1JaTicvAUIbtiDSyfzJjWjqeQ5idBhMAGvLVu6zLhwGBb+kDenZ1YxJxWlSJYMqVE6K5pmE3G7fwf7ahy+w3f4Dt/hO3yH7/CzxKcKrq7QNjdnNf4ht+SWltAtmeXMmvi2tKaNv7iol4IKLhUmVq9ewy7KUxRvGqsWrN6OUGpZNvgdDJ5IHBHEJlNYwS2DKm6h1aKKBLc7kFw7KiiKb3Z69TYnoiycY7FsLC3nQ5NwXkTPLWbhck5SGZhMVVgSZezBHQTNzkxuR0IG7mhVaufZCFnBTXGfoMqBqrVycNOzR6MJg9eOdY+XpZlPZI+lw3f4XRQ/6FlfriXISYgESsGMYFIR4BWS4EL+reGmzHm3wUk7ILhtQQ+ucldOgDW+YbAAfSjBkhA1y5aGWXY4awp9Llp6BHl1WpYEsqO49djIyEPpUhzf2+jwHX6Xwm9Qg6iG+gZR39AgGmipp9t6Ud8YPNdYHzwv7zfQuvXwuEE/5vfWN6jX9TbrI27DbYTvc/j7Or7MFfVv3e/f4Tt8E39pzbJgvNOkxjdZjH+CHNxQgszryQpuqxpzWeOvlE7CCNvJ5PhtdYpdlKeAydRYS6KMPbhDQJ48AKq3bDRVDpXcHpZCtxj4XwEUP3Otftxci1PGLBVxp8uUbeYcs+KCcuCD2Rm4dkNxEZSr2UK6B/TgVoKWuzc0MfcHgssHexg0Qo+GHNwJaqEv7Thy502yrDLJFdzaDFWs3TCujXzstZ8t6nVURXP4Dr9r4ev+DZ/optCNPGll4CaDFoDARTlaokyPiciy5CawtQ/WkZVdcEiO6kPhsHOetQxclKHXxQhnDy9MDY1hyHo8HjcMJeK2pAgD3NPiASJmfh2+w+8C+A31jXKWvqamRrz++j9l7Nqrr7zq374qXnn1VfHqq8HjV1/F5RV9/xV+Lm0dfv418Rptw9jWa8brcnuvwXYd/j6NT+vT/8Vl/v9JkkgS6XW/f4fv8IPXli1dGlZpm5uzGv/Ug5lU4KKcCN2V0UEZJcrSZKrWMAYFF+WZ0INbDYXCUYpfcQV3GFRwB6riY3/gaZWgvi2FomUPILZFVjW3wOKMBRFtr1HmxrGIvtyPZTIVi3BTzol4PscK6y2ACi7qr5HolkeYTHEPbqU6kANgQYkyV3D3j4gJmhjk4K5RA/Ck7hMkgmtXvDxPdBikElVDa18Z6kWSiaiKm8N3+F0RP82gQDkpo+EU9+DSCdQguPq2VVvb0wmaSSo7/WmCaxDYhBEJhE6CYQ/uMtPwQZtBmP0vwUWlwQxbT5uptaVLcTMSIN5oRQI0phtOOHyHvxfwqWJGJKKutk6k/EHL9u3b3fnP4XcJfPq/SAPpOn/MRZMvVB12v3+H7/BVTFBzAtRpHY9/AjOpUH7MhpvSRZkLCdb4KxjDLbeMQlNMcL8aYTLFPbjDoQ93KChoB4C6tjcQW4wJKgYn5WIwliqEttUCkCvnWsZSUX5PUUriTvuLZYgMikGTMOYW2SZTBRZ7L7YquGVwcCpB290nQqLMObhMbqtU9RajgrTJlBET5A/GqU8wu2yWjrxoM1fbPNHuFaNdfMNICjJuPy38vb3/Dr/r4HNfbQock02ya96nKlJwom3RxFabTLUGFVwmqmEOLkuU7TigJn0frfR5VnNZzTI9U9pohbXrPLpGlBbFjQiAMAev0Zy5jTcaVv92lICxTYfv8PciPpFbIg70e3DnP4fflfHp/2hNzRuykut+/w5/X8dfxiZTPKbJYvwj827V+IoruAHxrQ+JrzX+MmKCUkp9l9I9uLZEeTQQ3P2B3KJEmXNwsQ+3t+JvPaGCWxxhLoVRsQUREuWciD5cW6KcY5ked5qDck5Ewy8yb3vJhR1BJ63ukI9UCtXbcjCY6mMZTKFEeQhUcA+AHlw2mapWObgzmeCmlKySBujLly/veNoyshXSE5mnQT091/mPv78g/3OndU7aprcR+ERo179yu9jgL3S7/tU7JNld599mi29KUzPjP/v00yKR+Fen7f/K994VixYvzhr/kzr+Dr/z8LmvNsmuyRaxDc2mkjJbjaq93HfLjn6tLEVuaZU9JJz9xtFBtNSrmCA+yQeugulZcCjpIZMpQ24E7ohxwwAivDg1wCxqHIPdLXmSnqGNR5tJGBInh+/w9xI+qZFWLF/hzn8O/zOBT1JJ6gd0v3+Hv6/j1yxdGromWyQ30/hHEtk2bvlq0e8NcnBBmgzjL52EkVxlFCjWhTFBk5XidRwUCkcrbrV/hES5v+WP1BsUtz1BkdsDfJZ6AP/D+NgCK1o21+KPeRES5W6dWcGNKgnnWT243SI+XD5IkwusLNweQHjZbascTKYqoQ+3HxzQQWrZz+rB3R96cJngyh5c3R8Ig3KUKKMEx8t6irL9q8Jf/vpXcfXVV4k3at7IOO0ZhU/k9ppTJ/rE9g5JbCXB9e9fc9rEgPTK54KFCDAR346vSuHtgw8+KJYsWaKfGjt2rLjhhhs6bf/vvfc+ceihh4psnI8+yePv8DsPHyXKRg7uKlOqzI9pplBnsLEEuQUqtTImKJDgtEAYua7gNoW5b2wmhdm3CZjRDHJwG43Q9kYjgw4vUNhXEw9nXiNmc/UFLY79NmpGNo4Xw7jDd/h7Db9BRU3QoMWd/xz+ZwGfrhX0f7ahodH9/h3+Po2vK7iJJmUy1fH4h8ZJSGD5ffWqsmtk4KrxlTQKrV1h9N8mwx5cclGepAgum0wxuR2plv3A3HeA4mDYg1sBPbg9QZGLRLcA+B+SW9toKhfIbI4lT86xem87rQe3myVJjnJRzrUkynnQOJxvyZOLLJct1mlXwEHqpZZ+IFEeAhVc20V5tCVRlhXcNbqCuwqyOpd3LNsxpiE9S35jPhd1HfnLoz7Jvepq8cYbb2Qn9JHS5BxJZK89faIktcEySVVzifTerkkvPUevk4zZ60BpxPePPvpocePcufqZnTt3dur+z7/3Xk1wvd0VOnXy8Xf4nYNPJ8da1b8RZuGmtIPyKnBWTioyLE++bWxygH0hAcHlk3WY16aIr3YMhJO67FFpgv7b0Ixh6bJlYOYQ16YPprQobvTZxHF9kCfF7ddghtbsqUkPiHf4Dn9v4Dc0Nkgzny1btuzx73/9+vXiySefFPPumuffPiU2bNig16l5o0b88MIffqrnH1IA/e3558Xzzz8nXnrpJfHhBx92ufPv2nXrxKnfOkX2PUfh08T2hT/84W7h79i+QyxdukzMnz9fPL1woVi3bu3HPv9fe+214je/+U2Xuv5s27pVmlA1YrXM/f4d/j6IzwQ3oZeOxz9UqcUcXEOiDKkVOP7i1rGQBwVKvNVhDu4kVb2dAC7KVcCv0Nh3cAYX5UpLolwKHK8IzKaKQNVbAJXbHOt+LILMZurF7dQcXDsqKM/KwS2A5zHrKCoiiHtwy6GC2xOcufpE5OBymXyYOvhVVg5uNRDc2atXB9JkJrjJVEoOwjMqbTqe/Mxwek+//5e//FVcddVVsu+kvR4Wfo7IalChvV2sU4R2nVXNxdtrffI7YcJ4+T7c0rx588R++w0TZWVl4uSTv+4PWjaK0087TXTv3l2UlpeLww8/XK534gkniIcfeli+a85xc8TlP/6xqKqqEhUVFeK3v/ud+PnPfy7K/PWHDx8hnn76afmeW2+9VVxwwQX6M//f//1JzJkzJ6jg3jdfE9w334yLESNGBPPDXnBhGzJkiPi3/4Pjzzh8v/1EeVm5+PrJJ4uNH2zs9OOv56c9kdXxd/jpzyVXcUj4qrQlcFUOiW6KJcrYe9vCObiB818gUQ5OyvpkLN2V6ywzqQTIcxKmy6B6f82ymjRZkZ5pbWw0ZmnjIDUKe2nSg9vDHpn0kHZ9YTPe7/Ad/t7BpwiWJUsW7/Hv/5233xFHHH6EOOSQQ8V3zviOmHHIDHlteOedd+R6zz77rJgydeqnev6ZMWOGOPDAA8UXv/QlMXnKZDFq9Gjx9a9/XaxZsyZr/DPPPFO8/PLLn9j5lyYFvvWtb8nJa1rj3nvvFb+67Vd67ef84zZ16pSs8ZP++OSwww8TY8aOESeddJKYPn26qBo1yh8//GW3zv9nnnmGsd/XXXedvM52tesPqcjqZXSQ+/07/H0Xn5QMwVgoEVZwOxj/SJOp1lY9ptLEt75OV23t8Rfxn1o1hkumwuSLNaGL8lSo4I6BKu7+ETm4XL3FpY+lvC21DKZ4KYAW1ULgigVQwY1Z9+0c3JyI4uvHJrh5cD/WTgMwfrju8MH5tshy0mKNNpezy6HUzY3LfdVB7Q89uGwyxVVczmwaA3bX00hfvnbtWj1rwRUoms2Inqls55TuZWe1gJvZvGWLuPHGG+WScUYUnucK7gaq0Opq7e1hBVcvt0vye83pE32CO8Ewonp35UoRy8kRjz36mFjuk43JkyeJW26+Wbz77r/EwQcfLC684ELx9ttvyXUPPexQSUrp73PTDxRDhw4Tixa9KK7/xS9EXl6umDVrlnj9n69LcjyxeqJc78orfyZOOeUUjXf//feL6QdOVxLle8UhiuCS5j8vP1+vt3nzJknE3/3Xu2LlyndFDn3Gxx/zydNyMcn/jDffdHOnH38vmxnpT/D7/0/Ap99MnTw5QkQQ9+GmTGfl9Bzc4GTbps2kWlUFN5iRbFGzkC3aXTkBs5Yws2nkwYUXg8BkqtGUDWEOHciK0qIAjEw7awYXXBLjjWj7ryRK1sXQ4Tv8vYFPBlOLFy/e49//z6/6uZg9Z7Y8N9NTmz7aJGbPni2u+OkV8vVnJVGb+qmef2b416g//fFP8v6unTvFGzU14kif7B5//NfEps2bs8KfMnmKeOKJJz618+9Pf/pTcf55F+hXJMGdMjUr/O3bt4njv3a8OPYrx4oN6zfI13bt3CVuvfUWMWH8eLFC+4V0fPwnT54s97urX38WL14UZOS637/D34fxafzCpNY00cw8/qmvr9cOyi2K4Ca0RDlMrcDxVwqTMJIhF1qzdg1XcNlkil2UmU+NhCruECgusslUlMFUBRgGl0L/bXfgf2gulW/Jk/MiknryI+JqO81B2e6/jUWE8sagvByzGoYLQXPN5Wlk9aVwv9KKCuIKLkcFDQRyOwI04uyizMtEVXafaWR1yipT0HAdpcjxMvgveO32lniREqAN69eLu++5W8yde6N4//33s1IEdfNJH1dr1wGZXScJ721QwQ3Mp0iibFZwhfyPnBPr5g8S/ghxEaFEea6UKAf4VG0lUsoE9+Zbbw1kRNu2SbJNWYr09+KiRaKsrEQR3CvFKd88RX/m++//g5g2PSS4QQXXk6Qo3yC4m+XnXPnuu/L4077+ET9jJx//3egUcvjt4NNvZjm5KKeSpkw5lUrLVeN+XT6xtmAGLubgqh5crt5Kd2UVE6St8tXMZhgL1AQV3GCGMzCZagx7XAwXRJQQge1/PJxNNS5EjaHLYRwy7nRYe7zBlCnF0VHR4Tv8Tx+/vj4guHv6+6dK5wUXXGj8/mmCVHpUeERwn5ME98677hIzZhwkK713+fc1V9nliVtuuUUcedRRYtq0aeKcc88RbW1tcmNUdSXpM/1t2bJVHHnkkeLP//dn3Rpz9NHHyJxU+/xz8IwZ8tqF578m//c+dtwY8cwzz8rHdM74/ve/L4ksEeKf/fzn8jqyY8cO8fkvfF6MGl0lDvrc58S3v/3tYP1Wtf6UKbJCTMqkbdu2px2Qeb/5jbj0ssv0+e+KK64UF1x4gT7/UkX0l7+80cfZKT7/+c/7x6nOX+cKMXHSJFFdXS2O8J+jc9uzzz0jpvnHjY4VTSrr4xbxDVGWN6mm3oy/aXxrO32S+9WvHi8nIej5hU8/Jb5x8jfEvHl3ien+tfqwww8XN910s/85PbFd7vcXxKhRo8TnaL+/c7q+VjMurUff1VHyu5ouzjnnXPF+WzAuoYn4zx9xhLjHv35/2R8jHHTQQf6+Xi8++mhTMB7wj+3cudeLQ2YcIo/hueeeK+Oo9vT6s2TxEvl/1/3+Hf6+jM8xQaHBVMfjH44JalEkltetUxJlLhjg+IsLD8x/OA1DSZTngMFUNbgoHwAV3GGKdw1WyyDIwu0NJLcCCpVsLsWV3ALgf7ZMmUlrASiDcyKIbreInNxOIbhReueY1RAcA1YeAzaO1dsiqN4WQURQKUQEoYsyV3AHAMFFo6mR6otgJ+WxqoI7XhHcMCaIK7ipwDK7IwfBdm32Mwl41EO6cNw4lyq3czW5TdtelINyt25agrxOmkoF1VxDnvzq7brKe81p1UEFlwmu2uSV/oW5tLRUlJQUi9NOO03Kuwj/y0BwCf8wIrj33ScfkizsD3/4g/FZSGZMf6+//rooLi4OCe4p39Q7LCu4iuDeN/9eWRWmzxEQ3Dy9vU1cwfUJbvAZrxBl9BlLS/Rn7KzjHzUbnc3xd/jp+Ck7B1f3cFjyZFqSQaC4zr7FqCA146ilyCxRRpMpPNFrJ8FEKNlBl0H/+WXLlmozCMPUoSEOvTN8EWrQFyLD7TAetxwQcWYW+nHA3t9wT3T4Dn8v4VOmqFHB3c3f/x8feUSSogt/eKF46qmn5OAH13/uuWfFqKpR4pJLLpG9X3fccYeoGlUlli8PWm5u+9Wv5Ln/mWefkX2n3/EJ5TFf+YpPNneIyy+/XPz4x5fL9V544QWfxB0gvv+DH8jHy1cslwaHmxWBwr8ZMw4Wf/rTn9I+75w5x4mbbw5UPhddfJH49unflkqs559/XkycOFE8/NBDQviEm8hw9YRq8dv7fitW+mSd/i6+6CJx+umnyXOTXH9StXiI1rf+6HMSgdu5a5fYsXOHTwSnivHjxvtE7yP5+hE+CXz00Ud9QrldHpdlS5f61/ekOO+888QZ3/mOaH6nSb727DPP+a9XiUsvDY7br+m4+fuv0xvg/LtgwQJJ6qPOv7+86ZfixBNOVK1A/ye/qzPOOEPGQj322GM+qZ4g7vOv30Rem5uaxER/v+/7rb/f774n33PuueeIX/ziF/I+Sain8Xf1Ro1P/k8XXznmGDkp8NGmTfLzzpo1U55TqQpMqqpHH/2rfC99HzQ+oM//1ltviZO+fpK4/vrr9/j6s3jJYnm+d79/h78v40uJcsKMPuxo/FMnCS63fLVoKXO9zMFt0cQXx1+Yg6uVd8mkWBu4KB+n2jknRbgocwV3OBDcAdA2yv5IleCfVGH5KhVDLGwRuCjngxExtrbmWpFBsQxkNqczCa7d5BslT87L0HtbADvDfbiYgVsMMUEcFtxL3e8DVtSDwGSKm573A4I7BlyUJypd+XGrUxATxFWm2lq4znhZzTt6HTyBVTau3LYRufXapy6ILyXKmtTeoVyTA0K77lVwUX41ILzXskTZf59d5du6dZt4auFCUXVAlTj77LN1BfeGG67XaxEZJVMLSXA/d6D47z/cn5Hg9lAEl3qKv3LsVzQUzRDTjLInK7hhDy7N0FKV9oONH8jHq1MpRXD/pfG3btsqB1Z0caXP2FnHX4gMaqwOjr/DT//jqmwSXZRTQRU3lQpDw5O6glsLM4ih0YGOCaJKrZqdZOMpjg/iGUuelTRO/olmXfnlbLgaJVE2HBFh1rXRCFqPg6V/3JiNDS39w4tVI1780GlRrxMHMwqH7/A/fXwymWKCu6e//4X+NeLM75whxk8Y75PX0eLb3/m2eF9WYYVPcJ8To/3nPvzwQ/meXT7xmz5tunj4fx+W2/3yl4/yrxl/0Fjkd1HlE7+6ulppWEXVVHqBzI4uv+xyMWniJCnJnT//Xp9wnh55/jl4RihRxr/zzj9fXHjhhcbOkbkWnXeOPOrLsirLOxtIdZ9M2/8tm7fISbkjjzxKXsds/M3+63Q9JYURXfO+4pP1E088UR6Hle+95x+LUWLt2nWS/NI1i84/9PeTn/xUfj4GI2n36NGjxQf+caOn5HHzyeX/Pvy/acefiOJJJ54Yef6l1AMilpJk+gSX8Nf5+LzybT5pPeGEE/R7eb/5jyrqv7guILhUuf3Df/9B49PE/6hRVbJaTxMNNHHxzLPP6feef8EF4qKLLpL3aeJ76rRp4u9//7scV3zc68+ixUukvN79/h3+vowvJco6+jCR1fiHcnBxXMW9uRwTxAUDHH/pmCDLFFRJlGdBTNAEMOvdHyq4QyG5hkluPyhAVgJn6wmeSmVAbNGHiR/nW6k7+VZLbAxIbJ5lbNytMyXKdvZQrsW2Y5ZzMrLwAstcqsAymSqBqKAKaFbuDTMEA8C5izNwh4M8mY2mxoEbGH1ps6SLcopjgpL6y87GazG6VcUzT+gRbyOZFhlRdExTTAAigLL/lqu1L9+eVrXFCq/swR0/QeRADy45UB522GHyM9CF9fjjjxffO+ss+RpVXumCuE1dpA499BAtUT7wwOlpFVz6QeoKbo+A4N7/wP2iZ8+e8oRATo804DlQ9+DeJ6vCNGigim0sFhPzfAJMs8RX/uxKXcH92/N/k2YmbW2twWf82vHirLO+12nH3/O8LGniJ/P9/yfhmzFBXLXlPtxkSHRVHy5lHLZAvm1oW68qteCWzFVdLVFuDiOA5Im7qTmtatsMJlOygsuzr4a8qNF0PoybOXZx7MMxTCQardcao2dtG61ZX4fv8PcCfkNDvVi8aHGn/P63bdsqHn/8cWk6deq3vhUQtWfSe3CPPfZY8cADC6RslaTAL764yNgiSZX/8uc/i40bN4gxY8b454J/iy8deaT0g5g5a6Z4+aWXxHfP/K74rVQOpZ9/DiGJ8p/+lPZxaXJ23p3z5Pnv8cefEF/84hf97Y+WBJCI2o9//GOzF/XJJ/Tjx594QppWjfbX/xyt75PwYP10/O+eeaas/pKZ4twb5oq77rxL/OxnPxN/9Ek3VVMJn65nJCum8w+97ac//Yk4XxJcER63aaY518yZM/3j9kDa8X/kkUckeYw6/15z9TXSzIqrqDMOnmGc/6nCSjJtvd9TpognqfdYff8kQ/7FL65T39Uo8eKiF63vaqo0sqIKLu1PLUz8E/H+/veDivuWrVvkBAL1FY8bN0784Ac/EP/617/2+PqzRBFc9/t3+PsyPp0/MOM2m/FPQGS5StuiK72BRLlN9eGa4y/iP4FRaNIo9tGEJPTgjgcX5dHQh8sV3CGgnh0IJLcfZOByi2mp1XraA1yUCyyTqXxQ/ea3wy1zMvhAdZqLcixDZFA3q7KLld4CKEHzTuHO2lFBdg5uXyiHDwQN+FAwmBquvoxRauaBCS7NSMxJ+V9iMpnUOZ30Ja9AkynPnH/02p2VbMeAwctMUMwTvhfRTxkiEQm85vQgHohurz29WscF0f1rT5uoY4SqJ0wQEyZUBxJlhbF5y2bpjpyfXyBKS0qlARS5YgYz8s9Ld+UDqvaXjw877HApKybkz33uIDkbz5+MTKDelGZUHkiUPfHhBx+Iw484XL5OEmOSjhHJJXySS5EbJP9RTFL3wu5y3VO++U1ZaSaDqc1bNokTTgw+Y5n8jNNEk/9j7grH3+GbL1EcUEBwg2ig1KrQXEr/ppKB5IXt6MOqbUtYpWWTKXBL1pVdRXxDB8HQPTBhzGgGeXEJtV4NxgRhVh3OrhoyIXA9jDeafTGYVQfuiXFjRrdByZbwgufwHf7ewW+UJlNL9vj3/+tf3yka6huM3/9dd80TBx9ycEDUnntWTJs6xXgvEbUFCwKi9oXPf0H8z//8j8aniCEim5z//o2TTxa/+tWtsi/U27VL3HzzLZJwkfyVjQ7t88/BPon745/+aJz/lixe7G93tDQnIjd+6ne9/fbbxRZlOnXGd84QV8kKbvA3xSe4RIJpA6QSoqosrb9p02a5vTPO+I646qqfR+L/93//QZx19lniuOOOEy+/8oo8X5E0+ZJLLvWP1+1yPSa4dP6RJlM/+Yk479xzNX7gojzNOP4BwV2QdpaNNzTK6ukLL7xovLLRv85+wSfxN869Iazg+iSV5dL0R5PHxx33VX38gwru42EF95xz5PEm/C98/vPBd6VQaAKCqu30XW3a9JG8T4Ngxr/+hhuUpDz8rLt27ZR9gzRpfuqpp+7x9Yckyg2N9e737/D3aXzqv080JUIpchbjHzaZ4uqsNp+qC6XL9viL+E+tGsNJkkuFCX8Mt2bNWq7gTlIE187BPQBclLkPdyC0jPaFYiRWb0tBmVsCxU3mf4UR5lKxiBxcm1fGOmib/dgSZbtsjPLkGFg+F4C+ugCqtoVAentADy6XsyusxZYoDwSJ8hAwmRoNPbjV6gubIiXKq9coU5ykKs+nxIrlKzrwQYi2TfCsB5n6JD1biGRdRNOYhPXnH1BJXCdUBwSWeorG+xfp8ZLQ+sv4alm5HV8dGEzRejY+zbp+RBKpCPztZLDxMfd/44aNYtuO7R3uP0maAply+v5v9QcrH/Jn7ELH3+F7IFFOScVDEqQtlI0b/KbQRRlNplphFrEtDCDXUuTghM4ymqCCW2/0oSRAiqxP/mid39QsltYshT6YdLlQHA0e0swhQDIUN2dutaNiRIA7Zt814jYdvsP/lPHrG+vTXJR35/dPBIic8qknlkwAaRB0zDHHiP9SGa5EcCVRg1PGzJlBBVdKj39xrTjkkENkXM6///1vKSEmZQ6d1wmfMlirqyeKSy+9TE+UVk+cJKvEmc4/1IN71513irfefNsnmC+LO++8S0yaNElG2O3yiRJd1+j6R/2/6zeuF48/9pjs5/25khzTH3lNXHb5ZVLOSwZXdK0kOe/69RvEY48/LsaMGesT4qsi8UkGONG/ntJ+b9+5Q/I22sfqiRPFG6qHdvv2nbL1h84/shf5ttukyROdu8gc6hkZrzTFOP7BcXsg8vxLn5Uq30/4n42kw0tfp+SC0+VEMkUIBQT3T+IAn4T+10X/JVr+3SpeefllMXX6VFlpDqvcX5a9z5QcQcf/XFnBvV5iXXfdtdLsqoa+q/f+LdMU+LsKenBHQQXXEzdcTwT3+/LR7bfdLr55yimy5YrIMFV2v/vd7+7x9YdUBw3aZMr9/h3+vonPObjhOKfj8U8dm0y1cA4uP19vjKdw/MXJMcx/AnPQlPS9UT24UyFedaziUlES5cEgUe4DMuU+lvq2JyTjYCtqYUSyDnPEPMuoOEqibOffdkoFN2Yx525WGTlKsmzn4uZZ8uRiq3rLRlMVUO7uAweRy+GDIAN3OBDcUYrkcg9utargzkqxyVQqlFUGObieeaXxMoi4vMwvmpb4GTLg2jUIyoxPxLXDJaebJLyfBP7e3n+H3zXwkxASjq7JRiU3SflqSWVIVRuQ2TaKB1K29dr0IKjg8om8hXPb/KWurl6frLVEpzkMPNcne3YV9Jely5aZF6M4mDvE48asKb5mS5CMxygzwt6aeKO17bgZL+DwHf6njF/PMUF7+PsnMkWSXDIvooqkNDHyH69ft0734E5D+awHBJeI3o4d4tJLLhGTJ00WVaOrxNe+9jWZrcvrN/i/ddouuymTe/L06dOkUWGm888Mn0xSfyu9j3p2qVf3nnvuFjt37dTr3O8TRariUuXzBB/z2/46V11ztd7Dhx76HzFx8iRpokRveeD++yVBpe2ecMLX5DavvvrqjOe/L33xi+KC8y/Q59+f/OQnUgnl7fR0BZcI4VJVwaXJti986YvyubfefEs89/yzYpps2wmP/8xZs8QCKVFOP//u2L5DRg1RpfsA/zsY5xN2iuIjQyc+/xPBpYmBK674qay2knrroosvlu/l75+Ms+iYHfOVo4O+5XPP9Ylt0IO7wz/2l15yqYwNpO+ZqrBvv/223PZmJVEm+SN/uhtuuEGc84NzJD6dp7/xzW/I/aPe4uP9Y06mVnt6/aH/sySvd79/h78v4wcS5WYY73Q8/uEc3FYVA8RkOMjBDRVxOP4KlHW1YVuZGsMpF2WWKE8Ag97RkIM7UnGtwaqoONCSKPdWSyVUccuspJxCyMEtAi6IEuVc636epQi2k3xinUlwu3UgSc6BD5VjGU0Vwi3fR4lyCVRy2Wa6EpyU+0EFdzAQXI4K4lI69+COUQSXvrQ5RmN1cpV2he3IICHLwlr6056IzIzzOny/w3f4XQ9fuo77v5fUqvRIICa6SXhcu3yF7g9pBdt6NpOSEmV1Im9tgQouEd+E3XMCjoLNCTjRB8sySXDjMOvaaAaxx037/jj0zYQXKZAixRvTZUXx9FD3uIHn8B3+3sGnPsYldkzQHvz+qYL31ltvSqKzJ+cfMl3axO/9lM5/RJbXKSIetf87/c8URNCFpFQbNH0C51+S4VI19OPtvycryJt13m/4Ry7KJDOmP8or3rplS+TH2rljp9i2bUdG/J34Xe3m/tP/D5RI7+n1JyC4De737/D3aXxZwU2AFDmL8Q+Nk1rBJdl4nomvNf7SPipJMApdlZJKD0Vwq6GCy8XCKiC4XMEdCgS3P6hrK2EpheptqZWFi1wwP8JYKj8Dv+wGhLdbZ2fhRoXr5lq9uMi2u1sGU3y/EJYiqN4WQzAw3/axJMqs+eYmZ9aDjwByOwoycKuVydQc+hJTOrszlFFGlMkyeSZk4/0jt5GV6jMKw+E7/C6KH5pMpSIlyUnobQ/IsHJRtiq3LJkJKrXBCV27/WEFF2Yrw1vIhFMXBLpPF4h0yZAdqh43+mMMyVA83TgCZ2vDW8jEYzlS3OzlcfgO/9PGJxflJS8tCZxt3fnvPxr/T3/6v8CV+jO+/9u2bfP/z74UxKa437/D34fxSQFiG2d2NP6x8265ZYvUF5h/i+MvHfWYsooTQbLJLFUMnAQRQeMsifJwaAvluNa+sKBEuZdFbu3eW46J7Q5cMR/ycNm7qVtE/60tTe5UgmubS9ml4xh8WMzE7W5VcIugB7cUGpGZ3FZC43IvILj9oHo7FCTKwyEHd5yVgzs7kCan9MA8xS7KXvuNiF4HF4n2+xi9jk/6Dt/hfwbwUyhvwYVmA5MpbTTFM4PSZKo1MDcwHf1alItynTqpN6nXWbpcD6HmVg4czmhCj8qypTWhHAjz7OIQtg5ZdYakqDF08dSzt3ErHsAyn7B7dAw5ksN3+J8yfmNDo3jttVfFv//d4s5//+H4ZBTz+j//+Znff+rVfu211wwXZff7d/j7Ij67KOvqbRbjnzqVd8sENqEm/aX5lDKVssdfND4jY13mP5yGQekXVARUXKla8aZxUDAcAQR3sEVymZNVgjy50srBLQGFbg/gf7a6F1tb8yxuGYu4n9PZLsq5GSq6uREOWPlAdPOA4Ha3SG4P6MMtAweuCpAo8wzBQOjFHQI5uCMVua2CHNwJIFGetZp7cKGHECu4XpRMx2tfkmSLerwONTuZZ09xG09dfJBYePEMsfCig/37/sK3/rJQ3T555RfFM413iKcbfy2ebrhDPNNAt7/uFPy9vf8Ov+vh8+8lZcz8JTNIlFNy3UB63BaGjUMWbp12UU6AhFlVdtFsIWHNaOqTfkJLeZb6FwizTyZuWP/H45B1F7ccFOOYY9eYbjZhbCucfdXbjEf16Th8h//p4i9/4w2xaPEid/5z+J8J/EWLF4tlNTXu9+/w93n8GpWDm+Yz0s74B12UibxyBTio4LZCf244/tI9uEksUpBEeY1dwR0HLspV4KI8QhFcVtBiRFBfxdV6QRUXDaZKoCUVK7gFFlcssLycYha3tFN9Os1kqpuli84BcpsD+UX8WoHlhlVoMfYe1lKmbpngVgLBxT7cQdDoPETNKgxXZfQxoB1ngyn6wo5NcvWWpZVJRXC9zOfwyJN9u7ObmfMFPU9EXzQiNk8ENhM+vdbyz8fFk1d8STzhL5LgquUZn+h2Bv7e3n+H3/XwU4q0ojQ5yXJlLXkJCS85sXL/RyuYTHElV7olK8kN2t0HFdwE5MFFzGCyg7J6joLS9UUFLP3jljW/mVsXDy9WKCOK23l4ETO4cdgeXKgcvsPfW/j0u1myZLGsirnzn8Pvyvj0f5QycOn/rPv9O/x9Hb9GuignlHlmIqvxDxUCWPUWSJRN4htWccPx1yqUKAPJXRu6KFcDwR0HBJert8OgB3eQ6r8dAEbAfRWxLYcs3BJFakstctvDclPuDrwx14oNQkKb90k4KNsNv3Yfbk4G6XJehBV0d2DvRbDz7LRlRwUxue0Puu9BitwOBoI7Qn0Zo0E/rk2myAobteec62lqbLzI6UivnbnODqU9XvuXjyh8qtJmwn/qooP0s09e8UVJdKl6+0yjWcGNwn/m2WfkD6gj/L29/10Z/8UXXxCP/PGP/zH739T0jnj++ec6xJcxQXW1QGLNsHBzSYnl/m+rTffc8gm31TSZUidlTXC1dLk5lOxgbwpkwZkS5WVhX0s8tP3XciEjdN0MXkdDiHi8Mf2xPYOL5hFoLuHwHf5exqfZ+X/84x/iqacWirffeVv2Of6nnX8d/mcTn4y33nnnHf//5lPiH3//h6irXeF+/w7f4WsX5YRhnNnR+EdLlFtZohxM+NdzTJD/mj3+YqNQ5j/heC3JFdyJahkLMUFV0IfLJHeI4mD9gOD2guSbnsDjysBgqhhILnPBfPU4zyqS5louyXY2bqcTXNxojkV2Y+CcnBvBwqMIbj6YTBVDIHA5EFtc+iqSy1XcoSBRHgGl9NFKnjwOZiTmrFm9Rkkqwz5cMsLJquekg5O652Www/eyudCkI3EFN4gBypG3/CrJlvnvw5btUqr8xBVflASXKriI8OCCB8XiJS/pDY8bN0bccMPc7C5MH2P/6WJ29dXXiLVr1+3R/u/t45/p2VtvvUX06tVLXHDhhXsFvzP2v6bmDXHvvffqVSgmY9KkyR3ic19tSkVtpVJhRFBSk92kSLKBmz/YbgF3ZGNGkSXK6uTNLsqtnI+baE63yFe2+QmQJ/Os5tKlS0M7ftVHE4cZVX0hM2Za0VAiDgYRcbNvBp7XPTfxqFB4h+/w9z7+irpa8eqrr4hnnnlWPPLIH8WCB/+feGDBApm9umDBA/r+A3T/AXVf36r7/msL8LFe/375/AJ/GwvkOgvU88FCr92/QK2rcRY4/H0c/8H/9//EHx95RDz77NPilVdflRMx7vfv8B1+XMUELTPGOdmMf4Je2zDrll/XMUGtrWnjL+I/K3RM0CrNhVav1hLlyYrgsn/RGKjgjoAM3CGQg8vy5H6WbxIT3JIMBBf5IJoQo6tyXoYs3ByritspJlOxiF7cXEuijFLlXOtD50EPbpHVcMxNyFi9LVcHqgIOYF/ovx0EObgsUWaTqfFAcOlLm00uyrY5Tq2u4Gb354HchqiE5+0WD8nQkpK+MhPcTOTXxqfnqHr7TMOdxiaPPvpocePckNBSBl42+B93/z/YsFGS8qampj3a/719/DP9ffVrx8vsxr2F3xn7/9vf/lZMnjzZwN+1a1eH2LIqq3NwgxNjUtvNp4xqLrv1RcmTW1TebSBRDvpKeCayjV2UEyzVQVOphDZfaLYMF6SLMgSqo6mDISeyMu/iluFD3OqbabQlRrY0SW8n7vAdfpfBb/AHP0Qi6Df4xhs1/vKGeKPmDTm5JR/XBI+pb7eGX3+DHof3eaH3LJfrL9fr0nN0v6ZGbYvX99cJtl0T4jp8h+/frli+Qv6f5Nxb9/t3+A4/wA8ILo5zOh7/EJFlNRyNnThCiMZPUfLkVpQoQ1RqYDKV4pigqRATNFpVcZlXjbSKigMgB5clypVQnCyH6m0JcL4iuF8I/kz5QHQ5Mijf4plRZlOdVsFtT56cZ5WTYxapzbeclAusmKAiqxm5XJW5KxTJ7QPl8AHQhztMLVxCr1IEdwyYTE1NI7j+F5oEkylTrONZTSPZ1tk8YRfrvA7dCb1IfFml9fFl9Tanm1z4j+TLbD61kE2nfnSwNJqiPlzezmmnniYKuheIsrIyccThh8vnTzjhBBkC39rWJgYPGSKuvfYaUVnZS4wZO0689PJL4ktHHiUKiwrl+qv9Y8R/9/32PjF27FjRv39/cf4F5wtvV0CUVyxfLqZPny6KehSJKVMmi3/+858yW2/goEGS4PYf0F/cfPPNct3f//73YsSIEaK4uFgcedRRou399+UnnT//XvH1k78hjp15rOhRXCJO+vpJ4vnnnhMj/XV79qwQl1x2qXw//ViH+J/5zrvuFH379Bb77befuO222/RnXL78jeCzFBWJyVOmiH++9s8Oj//8+feIk046yf88XxbdC7vL51asWC6OOvJIUVZaKicImpoDkn7yySeLwsJCUd6zXBw4fZp8jiZIjqR1y8v8db+sCf098+eLk06k7R4punfvLp+jqvZ3v/td0adPH1E9caL4H/97oE9B+zV4yFAx98a5om+//mKav+3HHn1U//8j1zvaTklJiX+Mp4jFSxbr/zG/vU99L/5xPv/88zRRzfT/75FHHhEVFRUiLz9ffv802Pjv//6DOOWUb3X4/x/7N1JgLJXSs4FJIzKIjk2LIU1WM4lqRpHjgOhE3gInYjpxy8psE8hyIBsuwQHoYMQge3ANl8K46XQI9v5hjw1m1MGFKB4604bvaYw0icBMPIfv8B2+w3f4Dt/hO/zdwV+6rCYkr6xM62D8U6ekyHJpa9FVXkl8tcGUOf5apZJjmP8EkY4pqbRUPbgToTjIfkajIAcXY4IGgulvf5ApVwB3K4M+XK7gYgYuRgR1j4gDikrrieq/zf0kSK6dg9vNcr/KhUpuPuxYEdz2AFZfBhLlnnCwesOCPbiDQA/OJfRRMPMwDkymZqdSKaP/Vrso25W0CE8Fz8tWZertZi1OROI/dfGM3cInl2Wq3pKrMv+tfPddcfDBM8SFP7xQ9mPR36GHHirlqe+tfFcS0G984xuSCH7pS18S+Xm54pZbbhGvvPyKGD58uLjuF7+Q73nhhX/4xK5I3H///eL5v/1d7DdsP/+16+Rrn/vc58S5554rid0FF1zo4x0kSRaRZf8/iHjiiSelfLW1rVUS2/k+8VuyZIkkiGeffbbcxs033SxisZiYf8/d4tlnnxU9y3uKQT5Bfu755yVmjk/u33rrbbFy5UpJ+CdPmixeePEFua28vDzx3HPPqc9ykP9Zzgk+y4UXiBkHH9zh8SfyneNv86qrrhKLFy+S5Hz4fsPlZ3v5lVckqZ04sVoefzqxHHb4EeKi/7pIvPnWm8G6w4N1X5HrfkNUV08ItnuLv085tN2rFSEVPgE9X0yYMME/ni+I38y7S5JlmtFe+e5K+V0cN2e2ePnll8XZZ50tt0uf8KOPPvKJ/DBx6qmnydfO949xeVm52LR5k+y1IzJPx+hvf/ubv57/nV13bbv/69atWyf3deyY0RKbSPcdt98hjjrqqA7//wey47pg5i+1KrLvFk3calGi3NYSZrKpSm29kignlESZo4JkDy7OXqreEiPsPNFsVHFpBjTeaJo7xOOWCYTVRxMaS/Btg3lBipuZdUbYe1rOXaPDd/gO3+E7fIfv8B3+buEvkzm4TWAq1fH4R+fgtgQLT/bT+ClNoqzGX8EYboXZf+uP56j4pwjuZCC544HgjlTyZOzBZS7WBxyUe0MVtxzIrS1TLgCpcgEQ3DzLUTlmeTzlRng+dWoObjfLOTkvohE4Zumn86Bqm2cxdzSZ6gEmUz2hWbmXFSY8AByU91PLCDXLMBokyrzQlzZHxgTBwFxWmepqs7ZLSAs197Jb28tgLui1Q4tZopwje3CtJSe4xTcFBPcO2YeL+FSBvIEkymrdQw8LCC6TqpXvrZTPL3jwQTF06FC9PSKIZ555prx/3nnn+gTrVJ9UbRZb/GXeb+bJCilts6qqSpx11lmCDLxwXzZu3CA/O5FNe/83+9u44qdXBNtQJHP6gQfq1+fMOU5ccukl+nH//v3EwoULxbsrg8+85KUlepunnHKKuFD1w9JnOdv/LKvlZ8nu+BMRnTZtmn550aLFPhEvER9+9KH8nPT/hDDfffdduc7s2bPFTTffJO8venGRJO0ffsjrJuU+/8tf92Z/nenTphn4lZWV4tHHHpXrbt60WRzx+c/Lba1U+/X228EkxPvvvy+/Yzp2L/pkuLy8TJrF0HZ27twpHnr4IbmP5517nvxe5Pboe5k3T0z1j2lH//+kRHnSZP3UHXfcISvqHf3/56rsqnbILUmUeWYwqOC2ankM598GldoW1WsbnLB5xpFOyvR8wjBbgBN7cziTiblxAcHFAHXTuTDeYMqR7N6YsEcmDmYT8bDHBswibKMI7Yjo8B2+w3f4Dt/hO3yHvxv4SyXBBTKbxfhHSpF1zm2rNtwM3JVV9q01/pKFByVRTmIaRpCDOxNMpriKW2XFBKFEeRA4KfcFmTJLlHsCsS2F6m0P4H9oMpVrccc8aHnNtXpxcy0e2ikENzeCMccsgyms8NoOynlWFbcISG0xHAjOwu0JMwLootwPDu5QRXC5hF4FVdzx6ssCiXLKGJDXQg5uKM6MchL09qBi67XrQRi1PuM/xUZSWeJTxfdpRXDxtWOOOVrMnXuDfubQQw8T84ngrnxPkioirCxdnTBhvMa/7NLLxBlnniE3X9i9UK4bi+WInJyYJtrSlfmZp8X+B+zvv5Yrq8OLFgU5jBs3Bj24zVLeSw6K28Q555wjevfu45O3HMM4iwjuzJkz9f6feOIJ4tprrtW7RxXVp556Urz3XlDBXbd2nd4fqlge85Vj1Gd5xvosL3Z4/G/R2MEr98y/V34uqurm0P52i8n7zz77jFxj9uw54qZf3iTvz+d11XHJyQnk5M8++5zap2P1dtetW68Nw3JiMfkZ6fF3zzxTE9zNmzfp45+fly9n2u65e75PWqdGfv/d1feSg98LSNkz/f+7T/bgTtGPieByBbe9//8pZcpmktqkYVaA9+nz6x4QONHyrGJ9feiWrHNwtfkUmyqYYee2ezJLlZctq0m331ezqKGxQ1z3yJhGEthvY5tGWMHtlnti3DKLcPgO3+E7fIfv8B2+w88WX/bgwvgmm/FPPfTatnAPboKJr5InW+OvVTLqsRYKEquUydRqGj/OUcXAajCYGqvILbooD4YK7gDFy/orrtYLqrc9rSpukbotBP5XaLWt5sESg15cdFG2i605nSVRtkmtbS7FIOiEFYPH3cE5uSiiilsMrL8nZOCyTJkruAOh/3YIlM4PgCzc8eAENjmQKK8yMjzJKGdFOyZTmSusXsb8ciQHntce0fA6NJnytIty9IKbCk2m7jDwjz76GHHDDdfrFVmivFJJlDdLguspgjtBb/Kyyy4T3znjDHn/DP/24h/9qN39J0c3kjv37lUpvF2e2PjBB5LMvfNOQHDJdbGX/xrl31EVkkjVwQcdrOS8NwHJJIJ7orj22ms1BvXaPvnUU5oIkpMvv/aDH/xAnPW9s4wPQz/8b3zzm6Kysrf6DjIff0lEZx0bHncfhyvZUd+/rODe9MvA7OuphcG6Ed//zbfcIo49dhYcL0+UlpZARmX4/a98D7+L4C8/P1/+3yTS3rdvXwOCiP62Hdv97+VM8SP6Xnbz/19gMjVJP33Hr29XBLf9//+hQQE7Jae0xMWQ/ieDHvcVy2tD6TEQWJ51rJc9uAllMtWq161Xvbk4c9lsSZKbwT7fNJkCWVFDaNdvyITiVi4dzNbGMwW2p138IB6g0ZzhdfgO3+E7fIfv8B2+w88GfxlXcJsxFqj98Q+Nk7DHlsdCdUqi3AJjKh5/ceEBjUJXpZIsUSYX5WmgfOU+XC4ejoDqLffh9of+275W+k0FVHBLgOQWA//Ltyq23YEz8oKuyd2sntxOkyd3s0rEdlUXQ3hzLPfkXCvrqBDclHnHi6F6WwpBwVzF7QcuygOhD3cYENyREEw8Diq4sgcXM5/4fm1tHdC1diyJvOye9toxkOqYLof3MAooG3wiuDIHF0ym6F+S8H7thBNlr2XYgzsfqoabJSrFSYQV3IDgygqu/0dW/3169RbLly8XO3bslC7Cp592mti+fbs46OCDxF/+8pcgkujBB0Vlr15il09gt/mvlRT3kO+lbd59992yr5Z+SO+/3yaqq6vFwapHlmS6M2fOggruSZLgCiC4C598SvfgHnfccWLjhg3i9ddfFyWlpRKDPgv1G/9ZfRaSXBPZJjLd3vEngnssVFqJmJWUlEq573afRFJvK33W1atSYQX3pqCCSz3dxSUlwbrbad2/iwm0rk/+bvK3O0ttl9FmzZoll3Xr1opkMimOOeYYeczelZMNOVDBVQTXPxHR8SovLxdzr79BbNmyRdx//wPStIoMwGi/e/Xm72WH/738TJzqfy8d/f/7y1/+LI3H6DPQ3+13EMH9slyTToT/9V8XyQr86jWrxQ9/+EMpP9+4foMiuLXGzJ+8n1plyJNTLP+vXW7k22pDBHVCruMcXP9k3dISzjTSDKQpRbYs8xPhrCb3qEiJsn9RadBmEWAMgfb+KCWKgymENRtrSpGsyADlsGhczNStw3f4Dt/hO3yH7/Adfrb4pEBLAJHNZvxTL82kzBzcJn5euyib4y/DRVmp8KhYQWM8VcGdAhGr40ARWwUuysMgImggEFw2A+5pZeFiDy5LlJn/9QA+WAB8MdeKnc2zknuQk3Yawe1mVW27Wc5WtstVXoSbch6UpgvBYKoYiG455ClVQvNyH7ClHmzFBI2AiCCUKI9XX9osKsMnadYildIkl6qBIi1D1MvQu+lZbk/CDErPwl8qfLuX/iTgL7xohqrg5uie24wVXJI0X3yQeKY+JLj8RwZMRBAPOOAALVG+9977FKnq5pOmzRL/j7ZEmSu46qNdcsklsheUjJNGjx4tavwfJP3Nm/cbWZksKysVfXr3EQ//7/9qbKou5ubmSkJMx/6ggw6S0tyKikqfdH9NE1ySCc+eFRLck0iizBVc/58RI0fIHlyuOpNZU66/HTJYOvfc8/Txn3fXPFHqEzcib+RU/PDDD3d4/G+59RafiM40jv/Cp56Ubs8x/7OXlZaLu+bdpV+nzykJLsczPbXQX3e4v58xf90yf915wXZvuUWSWfyjY3D44UeIgoLuUlZM7s1EaskMLMeSKBcUFMi8MnriueefE/vvv78026ro2VOaSoXfy6XB9+Jvb/So0TK2oaP/f5s2bRbjxo+Tx5LiG359x6/F0V8OCO7zPqHvUVQoGuobZF5hYfci8c/X/ykNo7QDn1G1TelJo5Sq3IYxQbVh1bYldEnm3DYdE0QmUyClqWcXZSXRCVwEE+aJvkm9pvpVKCid+lzMmdS4Ye4QGkPEDVOIRpy1NfLtwh4cO7A9nJ2Nh701Dt/hO3yH7/AdvsN3+LuBX6NiggLy2pzV+Ceo4IbeJomm0EVZ5+Na4y/iP0EObjIsTvjjtjWBRHk29OBWK4I7GsjtcKsPF2OCWF3b25Iol0A6DvfgFgP/K7TaWGNWXFBUATUWkY3baSZTOVbJ2I4NyrNKzPlWRFAeyJK7WwS3DIKBOQu3AghuXyiJD7Ianvngs8kUVnCnGC7KEHNCFVyMQ/HS6KfXjqGUF6Ey9rJzoopiXYAf5uBmh089u0RuqQ83Cp8qjLuDH7X/O3ftFOs3rE/H99+/ds3ayP3fsX2bkbG6fv16sWPnjj3CJ0MsIvubfVJODsZbt2xON6D2dsmqJz/36isvi4svvthcfnSx+JF/e88997SLT47Du3btzOr4U19wsJ8df//kgLwF5MjZ7v8649iHr1HFfP269WEf8T3z5T5efBHt50XGvpMTM/9RRTgKf6f//fAz+j65KKeUvCUVypG5YhsS3mRAelMBGcYsNjaXalWyGpLScDh5CxDcsAe3WfZwBzOaSp6TCB0GE/DasqXLjAuHYeEPeXN6ZrXRDmePGy6HoSypIeyd4dlYu38H+2ocvsN3+A7f4Tt8h+/ws8SnCq6u0DY3ZzX+IbfklpbQLZnlzJr4trSmjb+4qJeCCi4VJlavXsMuylMUbxqrFqzejlBqWTb4HQyeSBwRxCZTWMEtgypuodWiigS3O5BcOyooim92evU2J6IsnGOxbCwt50OTcF5Ezy1m4XJOUhmYTFVYEmXswR0Ezc5MbkdCBu5oVWrn2QhZwU1xn6DKgaq1cnDTs2+jCYvXjnWRl6WZVGSPr+cZLsrZ4i+8+CBJcCkmqDPw9/b+R22XnZ+3bN6cNT71vF5++Y/F5T++PLi9/HLx48svE5f5j8ls67O0/9ni3/vb++R+Xnb55fIW9z/oAd4z/KBnfbmWICchEigFM4JJRYBXSIIL+beGmzLn3QYn7YDgtgU9uMpdOQHW+IbBAvShBEtC1CxbGmbZ4aypEcoeD+358SIFF64wfB1kSEZYO0iX4vjeRofv8B2+w3f4Dt/hO/zdwl9asywY7zSp8U0W458gBzeUIPN6soLbqsZc1vgrpZMwwnYyOX5bnWIX5SlgMjXWkihjD+4QkCcPgOotG02VQyW3h6XQLQb+VwDFz1yrHzfX4pQxS0Xc6TJlmznHrLigHPhgdgau3VBcBOVqtpDuAT24laDl7g1NzP2B4PLBHgaN0KMhB3eCWuhLO27t2jVqUJ7SFSeSXkZX0XbDODnysdd+tq7XfhWVCC714dKtXC6C24tm+IR2hnouvH1Gmkz9ulPw9/b+R61DFVKqAO4t/L29/3sbX/dv+EQ3hW7kSSsDNxm0AAQuytESZXpMRJYlN4GtfbCOrOyCQ3JUHwqHnfOsZeCiDL0uRjh7eGFqaAxD1uPxuGEoEbclRRjgnhYPEDHz6/AdvsN3+A7f4Tt8h78b+MuWLg2rtM3NWY1/6sFMKnBRToTuyuigjBJlaTJVaxiDgovyTOjBrYZC4SjFr7iCOwwquANV8bE/8LRKUN+WQtGyBxDbIquaW2BxxoKIttcoc+NYRF/uxzKZikW4KedEPJ9jhfUWQAUX9ddIdMsjTKa4B7dSHcgBsKBEmSu4+0fEBE0McnDXqAF4UvcJEsG1K26eJzoM8omqobWvTPUiyURUxc/hO/yuiJ9mUKCclNFwintw6QRqEFx926qt7ekEzSSVnf40wTUIbMKIBEInQe7Bpc9FhDluyITiYNuPF6YGM2w9babWli7FzUiAeKMVCdCYbjjh8B2+w3f4Dt/hO3yH3w5+fUODHL/wWCaMCGp//BOYSYXyYzbclC7KXEiwxl/BGG65ZRSaYoL71QiTKe7BHQ59uENBQTsA1LW9gdhiTFAxOCkXg7FUIbStFoBcOdcylorye4pSEnfaXyxDZFAMmoQxt8g2mSqw2HuxVcEtg4NTCdruPhESZc7BZXJbpaq3GBWkTaaMmCB/ME59gtllA3kdFtIysQ1PeB1H5Tp8h9/F8bmvNgWOySbZNe9Tr0dwom3RxFabTLUGFVwmqmEOLkuU7TigJn0frfR5VvPNt96UF4lGvCjFGw2TiLDvJQxZ17b98XD21pi5jTcaVv92lICxTTCqcPgO3+E7fIfv8B2+w28PnwoBb775llmpzWL8I/Nu1fiKK7gB8a0Pia81/jJiglJKfZfSPbi2RHk0ENz9gdyiRJlzcLEPt7fibz2hglscYS6FUbEFERLlnIg+XFuinGOZHneag3JORMMvMm97yYUdQSet7pCPVArV23IwmOpjGUyhRHkIVHAPgB5cNpmqVjm4M5ngppSskgboFLHSYdksshXSaycfyEszh0rrnLRNlx2+w+/i+NxXm2TXZIvYhmZTSZmtRoST+27Z0a+VpcgtrbKHhLPfODqIlnoVE8Qn+cBVMD0LzpT0NIsGmgldvsK/rTfcEeOGAUR4cWqAWdQ4Brtb8iQ9QxuPNpMwJE6WnMnhO3yH7/AdvsN3+A4f8blyS+MV3WuLE/cdjH8kkW3jlq8W/d4gBxekyTD+0kkYyVVGgWJdGBM0WSlex0GhcLTiVvtHSJT7W/5IvUFx2xMUuT3AZ6kH8D+Mjy2womVzLf6YFyFR7taZFdyoknCe1YPbLeLD5YM0ucDKwu0BhJfdtsrBZKoS+nD7wQEdpJb9rB7c/aEHlwmu7MHV/YEwKEeJclpQUFYlst1hJdHbdPgO/7OAjxJlIwd3lSlV5sc0U6gz2FiC3AKVWhkTFEhwWiCMXFdwm8LcNzaTwuzbBMxossECVXLJRIF6cik6iPJxyYKfXJaXyVv/+Rp1Xy1L8X7NUnUL74lYajI8v1S/5vAdvsN3+A7f4Tt8h5+OT2Opt95803JOblImUx2Pf2ichASW31evKrtGBq4aX0mj0NoVRv9tMuzBJRflSYrgsskUk9uRatkPzH0HKA6GPbgV0IPbExS5SHQLgP8hubWNpnKBzOZY8uQcq/e203pwu1mS5CgX5VxLopwHjcP5ljy5yHLZYp12BRykXmrpBxLlIVDBtV2UR1sSZVnBXaMruKsgq3N5x7JNowzmWfLPjh10vd0Vmjp8h98F8enkWKv6N8Is3JR2UF4FzspJRYblybeNTQ6wLyQguHyyDvPaFPHVjoFwUpc9Kk3Qf2ubMSTCyq5lt5/QVvthn0sCLhbN8JwhgXb4Dt/hO3yH7/AdvsPvQvhUqcUcXEOiDKkVOP7i1rGQBwVKvNVhDu4kVb2dAC7KVcCv0Nh3cAYX5UpLolwKHK8IzKaKQNVbAJXbHOt+LILMZurF7dQcXDsqKM/KwS2A5zHrKCoiiHtwy6GC2xOcufpE5OBymXyYOvhVVg5uNRDc2atXB9JkJrjJVEoOwtsz3Mkm9sXrkEqEW/C89nsoHb7D76r4yVUcEr4qbQlclUOim2KJMvbetnAObuD8F0iUg5OyPhlLd+U6y0wqAfKchHlhMS4qpqxHV3qtfDl90TAkQQl9YdIzqA7f4Tt8h+/wHb7Dd/hdDF+aTLW26jGVJr71dbpqa4+/iP/UqjFcMhUmX6wJXZSnQgV3DFRx94/IweXqLS59LOVtqWUwxUsBtKgWAlcsgApuzLpv5+DmRBRfPzbBzYP7sXYagPHDdYcPzrdFlpMWa7S5nF0OpW5uXO6rDmp/6MFlkymu4nJm0xiwu55G+vK1a9fqWQuuQNFsRnSlrB1K4WVn9eOlVdo6qMg5fIffhfHpN1MnT44QEcR9uCnTWTk9Bzc42bZpM6lWVcENZiRb1Cxki3ZXhhN/AmY2jTw4vBgkoF8lvKjgbGqwvmnFz/jNcFExTawcvsN3+A7f4Tt8h+/wuw5+fX29dlBuUQQ3oSXKYWoFjr9SmISRDLnQmrVruILLJlPsosx8aiRUcYdAcZFNpqIMpirAMLgU+m+7A/9Dc6l8S56cF5HUkx8RV9tpDsp2/20sIpQ3BuXlmNUwXAiaay5PI6svhfuVVlQQV3A5KmggkNsRoBFnF2VeJqqy+0wjq1NWmYKGay/SeTbaf8drt7fR2yOvW4fv8D8L+PSbWU4uyqmkKVNOpdJy1bhfl0+sLZiBizm4qgeXq7fSXVnFBDUbs58JiAVqghlUuLjgTKnhQogSIrDdTzRrfONCpGdNHb7Dd/gO3+E7fIfv8LsWPscEtSgSy+vWKYkyFwxw/MWFB+Y/nIahJMpzwGCqGlyUD4AK7jDFuwarZRBk4fYGklsBhUo2l+JKbgHwP1umzKS1AJTBORFEt1tETm6nENwovXPMagiOASuPARvH6m0RVG+LICKoFCKC0EWZK7gDgOCi0dRI9UWwk/JYVcEdrwhuGBPEFdxUYJndkYNsuzErmQSknshYDUvbnsN3+J8B/JSdg6t7OCx5Mi3JIFBcZ99iVJCacdRSZJYoo8kUnuhhZhQvKIabMkh5EvjepoThthxcBJrCC0YC+1vM6CGH7/AdvsN3+A7f4Tv8roZfJwkut3y1aPx6mYPbookvjr8wB1cr75JJsTZwUT5OtXNOinBR5grucCC4A6BtlP2RKsE/qcLyVSqGWNgicFHOByNibG3NtSKDYhnIbE5nEly7yTdKnpyXofe2AHaG+3AxA7cYYoI4LLiXut8HrKgHgckUNz3vBwR3DLgoT1S68uNWpyAmiKtMtbUwzveyqnt5HTzhRTzpZXijWSlz+A6/6+JzVTaJLsqpoIqbSoWh4Uldwa2FGcTQ6EDHBFGlVs1OsvEUxwfxBUBLfRKmfT7PfGoDB+hN0eYOzQnLWh+dl3F9NH5I6HB1h+/wHb7Dd/gO3+E7/K6GTzm4OK7i7XFMEBcMcPylY4IsU1AlUZ4FMUETwKx3f6jgDoXkGia5/aAAWQmcrSd4KpUBsUUfJn6cb6Xu5FstsTEgsXmWsXG3zpQo29lDuRbbjlnOycjCCyxzqQLLZKoEooIqoFm5N8wQDADnLs7AHQ7yZDaaGgduYPSlzZIuyimOCUrqLzsbr9noVkXPJBRRb/O8LGmKw3f4XRvfjAniqi334SZDoqv6cCmupwXybUPbelWpBbdkrupqiXIz9rKwZX7ErGmUuYMh7wn7VvC1Zsut0LwgNTt8h+/wHb7Dd/gO3+F3SfyAyHKVtkVjBRLlNtWHa46/iP8ERqFJo9hHBrzQgzseXJRHQx8uV3CHgHp2IJDcfpCByy2mpVbraQ9wUS6wTKbyQfWb3w63zMngA9VpLsqxDJFB3azKLlZ6C6AEzTuFO2tHBdk5uH2hHD4QNOBDwWBquPoyRqmZBya4NCMxJ+V/iclkUud00pe8Ak2mPLP+5bVbFWvHAMjLTFBMwuFF9FM6fIffNfEpDigguEE0UGpVaC6lf1PJQPLCdvRh1bYlrNKyyRS4JevKriK+2MPSDPb64Yxmk+phaU6z5scLS1RfTMK29+cLUFM4w+rwHb7Dd/gO3+E7fIffFfHZZIqrs9p8qi6ULtvjL+I/tWoMJ0kuFSb8MdyaNWu5gjtJEVw7B/cAcFHmPtyB0DLaF4qRWL0tBWVuCRQ3mf8VRphLxSJycG1eGeugbfZjS5TtsjHKk2Ng+VwA+uoCqNoWAuntAT24XM6usBZbojwQJMpDwGRqNPTgVqsvbIqUKK9eo0xxkqo8nxIrlq/owIcn2rbHsx5k6pP0bCGoRSLSmITDd/hdFD+oytZKWUtSm0wpUmu4KKPJVCvMIraFAeRaihyc0FlGE1Rw640+lARKgZoh9JxP/k22pT7OitoXoFAO1JRm0Y+ZdQ7f4Tt8h+/wHb7Dd/hdD7+OTaZaOAeXn683xlM4/uLkGOY/gTloyie4ugd3KsSrjlVcKkqiPBgkyn1AptzHUt/2hGQcbEUtjEjWYY6YZxkVR0mU7fzbTqngxizm3M0qI0dJlu1c3DxLnlxsVW/ZaKoCyt194CByOXwQZOAOB4I7SpFc7sGtVhXcWSk2mUqFssogB9czR/peBhGnl/lFM5IlQwZpuwZBDt/hd238JISEo2uyUclNUr5aUhlS1QZkto3igZRtvTY9CCq4fCJv4dw2f6mrq4ecuOb03pZEaJ/fDCd9vhg0JZqtTLmEMWvK2XThjCheiOCxw3f4Dt/hO3yH7/AdfhfD5xzcVhUDxPhBDm6oiMPxV6Csqw3bytQYTrkos0R5Ahj0joYc3JGKaw1WRcWBlkS5t1oqoYpbZiXlFEIObhFwQZQo51r38yxFsJ3kE+tMgtutA0lyDnyoHMtoqhBu+T5KlEugkss205XgpNwPKriDgeByVBCX0rkHd4wiuPSlzTEaq5OrtCtsRwY9WRbW0p/2RGRmqdfh+x2+w+96+NJ13P+9pFalRwIx0U3C49rlK3R/SCvY1rOZlJQoqxN5awtUcIn4JprTZT2J9FB1nQ9nZMeppQnlQbZrYTNgJEJzB5YCOXyH7/AdvsN3+A7f4XdBfBontYJLsvE8E19r/KV9VJJgFLoqJdauXcsEtxoquFwsrAKCyxXcoUBw+4O6thKWUqjellpZuMgF8yOMpfIz8MtuQHi7dXYWblS4bq7Vi4tsu7tlMMX3C2EpguptMQQD820fS6LMmm9ucmY9+Aggt6MgA7damUzNoS8xpbM7QxllZGSKlxXNiO6K9LzsVJ9RGA7f4XdR/NBkKhUpSU5Cb3tAhpWLslW5ZclMUKkNTuja7Q8ruDBbGd5CJhzLgRJmL4vZuxLOgCbSsusiHAwzzJY6fIfv8B2+w3f4Dt/hdwV8O++WMcl8CvNvcfylox5TVnEileIe3CmqrXMs+BihRHk4tIVyXGtfWFCi3Msit3bvLcfEdgeumA95uOzd1C2i/9aWJncqwbXNpezScQw+LGbidrcquEXQg1sKjchMbiuhcbkXENx+UL0dChLl4ZCDO87KwZ0dSJNTemCeYhdlr/1GRK8DktB+H6PXMelw+A7/M4CfQnkLLjQbmExpoymeGZQmU62BuYHp6NeiXJTr1Em9Sb3O0uV6sNW3cuASzWlZcQkITg9mQBPgaAhh69ZMajjr2hRehBLwPofv8B2+w3f4Dt/hO/wuhl+n8m6ZwDK+NJ9SplL2+IvGZ2Ssy/yH0zAo/YKKgIorVSveNA4KhiOA4A62SC5zskqQJ1daObgloNDtAfzPVvdia2uexS1jEfdzOttFOTdDRTc3wgErH4huHhDc7hbJ7QF9uGXgwFUBEmWeIRgIvbhDIAd3pCK3VZCDOwEkyrNWcw8u9BBiBdeLkml67UtCbVGn16FmNHP1zOE7/K6Mz7+XlDHzl8wgUU7JdQPpcVsYNg5ZuHXaRTkBEmZV2UWzhYQ1o6lP+uHsZ3OU5X4zOhDCTGnCcjBMYI5dc7rZg8N3+A7f4Tt8h+/wHX4XwUcXZSKvjB9UcFuhPzccf+ke3CQWKUiivMau4I4DF+UqcFEeoQguK2gxIqiv4mq9oIqLBlMl0JKKFdwCiysWWF5OMYtb2qk+nWYy1c3SRecAuc2B/CJ+rcBywyq0GHsPaylTt0xwK4HgYh/uIGh0HqJmFYarMvoY0I6zwRR9YccmuXrL0sqkIrheZg7htee6k9HjNvqx54lo0uLwHf5nAD+lSCtKk5MsV9aSl5Dwkh0993+0gskUV3KlW7KS3KDdfVDBTRjGC2kzmIkwJB0vFGjOYPS5QKi6mU2XCC379UxswuE7fIfv8B2+w3f4Dr8GZ34xAACAAElEQVRL4lMhgFVvgUTZJL5hFTccf61CiTKQ3LWhi3I1ENxxQHC5ejsMenAHqf7bAWAE3FcR23LIwi1RpLbUIrc9LDfl7sAbc63YICS0eZ+Eg7Ld8Gv34eZkkC7nRVhBdwf2XgQ7z05bdlQQk9v+oPsepMjtYCC4I9SXMRr049pkiqywUXvOuZ6mxtOLLId57dS6OpSWeu3TB4fv8D8L+DImqK4WSKwZFm4uKbHc/2216Z5bPuG2miZT6qSsCa6WLoNkx5gNNbPlDHMH3Z+SsJ5rtkLXreB1yKZD4wiH7/AdvsN3+A7f4Tv8roavJcqtLFEO8Os5Jsh/zR5/sVEo859wvJbkCu5EtYyFmKAq6MNlkjtEcbB+QHB7QfJNT+BxZWAwVQwkl7lgvnqcZxVJcy2XZDsbt9MJLm40xyK7MXBOzo1g4VEENx9MpoohELgciC0ufRXJ5SruUJAoj4BS+mglTx4HMxJz1qxeoySVYR8uGeFkZ1DbPqnwvAxxLF42RMPhO/yuj899tSkVtZVKhRFBSU12kyLJBm61tUFOm5bLwIwiS5TVyZtdlFs5H9ew0I+aGY0KRQ+lPwlrRrUZrPnD5y0pkeV46PAdvsN3+A7f4Tt8h9/V8INe2zDrll/XMUGtrWnjL+I/K3RM0CrNhVav1hLlyYrgsn/RGKjgjoAM3CGQg8vy5H6WbxIT3JIMBBf5IJoQo6tyXoYs3ByritspJlOxiF7cXEuijFLlXOtD50EPbpHVcMxNyFi9LVcHqgIOYF/ovx0EObgsUWaTqfFAcOlLm00uyrY5Tq2u4Gb354Hck6iE5+0WD8nQEunwHX7Xx5dVWZ2DG5wYk9puPmVUc9mtL0qe3KLybgOJcnCi55nINnZR5r4Uw9QBwtUtw4WEYaFvuhUaciIrcy5hmUAkrL4Zh+/wHb7Dd/gO3+E7/K6ET0SW1XA0dmJ8Gj9FyZNbUaIMUamByVSKY4KmQkzQaFXFZV410ioqDoAcXJYoV0JxshyqtyXA+YrgfiH4M+UD0eXIoHyLZ0aZTXVaBbc9eXKeVU6OWaQ233JSLrBigoqsZuRyVeauUCS3D5TDB0Af7jC1cAm9ShHcMWAyNTWN4PpfaBJMpkyxpmc1LWZb5/KEXSzzOnSn9Ry+w/9M4GP/RgqMpVJ6NjBpRAbR5FGLIU1WM4lqRpHjgOhE3gInYjpxywtFE8hyIBsOTRpCIwbbpTCR5nTYHGW3b0iLms11HL7Dd/gO3+E7fIfv8LsYfp2SIsulrUXjS+KrDabM8dcqlRzD/CeIdCSTqXXcgzsRioPsZzQKcnAxJmggmP72B5lyBXC3MujD5QouZuBiRFD3iDigqLSeqP7b3E+C5No5uN0s96tcqOTmw44VwW0PYPVlIFHuCQerNyzYgzsI9OBcQh8FMw/jwGRqdiqVMvpvtYuyXcmKMN3xvGxVnt5u1uKEw3f4nwn8QHZcF8z8pVZF9t2iiVstSpTbWsJMNlWprVcS5YSSKHNUkOzBxdlLNGJImCYNzZaLoe5fSVgXDuh9Setz0bdN5oysw3f4Dt/hO3yH7/AdfhfD1zm4LcHC+DR+SpMoq/FXMIZbYfbf+uM5Kv4pgjsZSO54ILgjlTwZe3CZi/UBB+XeUMUtB3Jry5QLQKpcAAQ3z3JUjlkeT7kRnk+dmoPbzXJOzotoBI5Z+uk8qNrmWcwdTaZ6gMlUT2hW7mWFCQ8AB+X91DJCzTKMBokyL/SlzZExQTAwl1Wmutqs7Xo8+xkvu7W9DOa2Xta0xOE7/L2Pz1XZVe2QW5Io88xgUMFt1fIYzr8NKrUtqtc2OGHzjCOdlOn5hHERMM0YbKMG7UiIM6Uo66HXmtLlQM3oVqh7ZBJg7uDwHb7Dd/gO3+E7fIfftfClFFnn3LZq/MBdWWXfWuMvWXhQEuUkpmEEObgzwWSKq7hVVkwQSpQHgZNyX5Aps0S5JxDbUqje9gD+hyZTuRZ3zIOW11yrFzfX4qGdQnBzIxhzzDKYwgqv7aCcZ1Vxi4DUFsOB4CzcnjAjgC7K/eDgDlUEl0voVVDFHa++LJAop4wBeS3k4IbizCgnWW8PKmZeux64Ues7fIffVfFTypTNJLVJw6wA79NMoe4BgRMtzyrW14duyToHV5tPNWs3woQ9a2oErJtmDYb9fgKz6hLahp97XNCRMGH0tzQ7fIfv8B2+w3f4Dt/hd0n8eui1beEe3AQTXyVPtsZfq2TUYy0UJFYpk6nVRHDnqGJgNRhMjVXkFl2UB0MFd4DiZf0VV+sF1dueVhW3SN0WAv8rtNpW82CJQS8uuijbxdaczpIo26TWNpdiEHTCisHj7uCcXBRRxS0G1t8TMnBZpswV3IHQfzsESucHQBbueHACmxxIlFcZGZ5klLOiHZOpzBUuL4NZj0kOPK89ouF1SF4cvsPvSvihQQE7Jae0xMWQ/ieDHvcVy2tD6TEQWJ51rJc9uAllMtWq161Xvbk4c9lsS4LAPj/RbNvtq5nQptCu35AJJczZVeylSdiB7Q7f4Tt8h+/wHb7Dd/hdCJ/GSdhjy/h1SqLcAmMqHn/9f/a++7uqov2eFEKTqljAQhIIIQmhSrdjo6kUewVBxQYqvvq+YMUCFuyigL0CVkAsoPQiSQhJyEVN7k8J63LBz59w5zvPnHnm7DP3JLlo1Li+w1pn5dazT+Oe2bOfZ28WHtAo9FA8xiXK5KI8FCpfuQ+XxcMcUG+5D/dE6L893kq/6QoK7jFAcjsA/2ttKbZtgDPygq7Jraye3GYrT25lScS2qoshvGmWe3KGlXXUFtyUecc7gHrbEYKCWcU9AVyUe0Af7mlAcHMhmLgQFFzVg4uZT/y4oqIS6EIjljyJ1F5ONGLg0zRdcfgOv+XiewS3IjDzpx7HDwXKk+Nc/l9RHsi3NYYI+ge5knNw5Y91XZ0/00gzkHZIesAyP+rPagay5uTfGjNjCsYM0SjMuEaDPS5Rf+YUZ2MdvsN3+A7f4Tt8h+/wWyJ+lTKTCubg1vDrxkU5OP4KuCjrKjwSK44cOcIK7mCIWC2Eitg8cFE+DSKCegDBZTPgLlYWLvbgcoky87/2wAezgC9mWLGzmVZyD3LSZiO4rSzVtpXlbGW7XGWGuClngjTdFgymOgDR7Qx5St2gebk72FKfbMUE5UBEEJYoF+mTNoFk+BjNWsTjhuRSrqdIyhBNNNC7mLDcfkTAezYVfx//64nkFx2+w2/B+MaBL6Daxs2kUVwrt35MUIWv2tb5Lsmc22ZigshkCkppqthFOeqX7tiB6f7saNTvbakJD04PBKvXRuEzcDPCrDlwOnT4Dt/hO3yH7/AdvsNvSfiegut7mzC+ysE1+bfB8RfxHy8HN+aLE3LcdsQrUZ4IPbjFmuDmA7nNtvpwMSaIq2uPs0qUj4F0HO7B7QD8r63VxppuxQWFCajpIdm4zWYylWZJxnZsUKYlMbe2IoIyoSy5jUVwO0EwMGfhdgWCezxI4j2thmc++GwyhQru4ICLMsSckIKLcSiJpOF/ohFDn0RIlWciNSegMNbh8B1+C8an/zeqvCXulyOzYusT3phHeuMeGcYsNjaXqtdlNVRKw7ObdUBw/R5c+jGvCfaoRP3Zzyi+Z904Ahb+kDdnZlZr7XD2aDBU3eE7fIfv8B2+w3f4Dr8F4pNbcl2d75bM6zDEt64+afzFol4cFFwSJg4fPsIuyoM1byrQC6q3Obpalg1+TwZPJI4IYpMpVHA7gYrb1mpRRYLbBkiuHRUUxjebXb1NC5GF0yyWjdJya2gSzgzpucUsXM5J6gQmU12tEmXswe0Jzc5MbnMhAzdfS+08G6EU3Dj3CeocqAorBzc5ezScMCQase5JpGjmE9pj6fAdfgvF93rWy00JcgwigeIwIxjTBHifIriQfxtwU+a8W+9H2yO4B70eXO2uHAVr/GiSkQP0tfD70WjgRhK8+aDTYW3Q5CEKhg41WFbk8B2+w3f4Dt/hO3yH37LwvRxcvwSZP6cU3Ho95rLGX3GThOG3k6nx2+E4uygPBpOpAqtEGXtwT4Hy5JNAvWWjqc6g5La3KnQ7AP/LAvEzw+rHzbA4ZbpVRdzsZco2c0634oLSYMPsDFy7obgdyNVsId0eenC7QS33cdDEfCIQXD7Yp0EjdD7k4PbXC520S37//YgelMeN4kRZneEq1lEY14Y+t2hGool1O3yH38LxTf+GJLpxdCOPWRm4Ma8FwHNRDi9RpudEZHkm07O19z6jlF07QN3qQ4mGznyCVX8gHN2/MdRAyHoUwthx5rW2ttbhO3yH7/AdvsN3+A6/ReJXgZmU56Ic9d2V0UEZS5SVyVRFwBgUXJTHQw9uMQiFfTW/YgX3NFBwe2jx8UTgad2g+rYjiJbtgdi2s9TcLIszZoW0vYaZG6eH9OX+KZOp9BA35bSQ19OssN4sUHCx/hqJbucQkynuwe2mD+RJsGCJMiu4vUNiggZ4ObhH9AA8ZvoEieDailciIZoMUgnT0BqvDE2Ekokwxc3hO/yWiJ9kUKCdlNFwintw6Qc0QHDN33pjbU8/0HyTYKc/Q3ADN5Bo0JIfZ1ChjyVg+GDMGJL7X1RpEebMRSFsPRpWOuTwHb7Dd/gO3+E7fIffMvA9Mym//JjxlYsyCwnW+Msbw5VbRqFxJriXhphMcQ9uNvThngoVtCdBde1xQGwxJqgDOCl3AGOpttC2mgXlyhmWsVSY31NYJXGz/UtvIDIoHZqEMbfINpnKsth7B0vB7QQHpxvUdncPKVHmHFwmt3lavcWoIGMyFYgJkoNx6hNMLZsl0aSQ1hDbSIhGGYvDd/j/Cnzuq42DY3KQ7AYfU6+H90NbZ4itMZmq9xRcvlH4Obhcomzb8ddYdvqW6QJmyAXC0iGPDj8bCFkPWvMHZk4dvsN3+A7f4Tt8h+/wWxC+yrvV4ytWcD3iW+UTX2v8FYgJiuvqu7jpwbVLlPOB4PYGcoslypyDi324x2n+1gUU3A4h5lIYFZsVUqKcFtKHa5cop1mmx83moJwW0vCLzNteMmBH0EmrDeQjdQT1tjMYTHW3DKawRPkUUHD7QA8um0wV6xzc8Uxw47qskgbo5eXlTctmoa2QiUbyWRJJ5kBJnZO26a3Dd/gtHJ/7amPsmmwRW99sKqay1Ujt5b5bdvSr51LkunrVQ8KzmBwdREuVjglKMmWwsuCCJT3WTQI+Ew0YQPifq4FZ1CgEq9s3GYfv8B2+w3f4Dt/hO/yWgq+I7EFu+aoz3/VycKE0GcZfJgkjdiggUPyfHxM0SFe8FoJQmK+5Ve+QEuUTLX+k46DitgtU5LYHn6X2wP8wPjbLipbNsPhjZkiJcqvmVHDDJOFMqwe3VcjGtYbS5CwrC7c9EF522+oMJlPdoA/3BDigPfXSy+rB7Q09uExwVQ+u6Q+EQTmWKCcFpaQkkR0NKwlfp8N3+P8GfCxRDuTgHgqWKvNzmik0GWxcglwHSq2KCfJmO+sgjNwouDVgqx/Ffhc9I4pOhWDwEA24DlrGDvZNiV8zs6s1vlGEw3f4Dt/hO3yH7/AdfgvDp3ESElj+XpVWdgMZuHp8pYxCK/YF+m9jfg8uuSgP1ASXTaaY3ObqpReY+56kORj24HaFHtwuUJGLRDcL+B+SW9toKgPIbJpVnpxm9d42Ww9uK6skOcxFOcMqUc6ExuHWVnlyO8tli+u0u8JBOlYvJ0CJ8img4NouyvlWibJScI8YBfcQZHWWN122GZDBElb5Z9MOtomjLTR1+A6/BeLTj2OF7t/ws3DjxkH5EDgrxzQZVj++B9nkAPtCPILLP9Z+XpsmvrXRQE+LnxtXAyVByQHp/kxq0G4fZ1L92VW4ycBrgRIkh+/wHb7Dd/gO3+E7/BaET0ot5uAGSpQhtQLHX9w65vMgrxLvsJ+DO1Crt/3BRTkP+BUa+57cgItyN6tEuSNwvHZgNtUOqnqzQLlNsx6nh5DZhnpxmzUH144KyrRycLPgdcw6CosI4h7czqDgdgFnru4hObgsk5+mD36elYNbDAR34uHDXmkyE9xYPK4G4Y0Z7qQSu5Jokkr4a0gkGu+hdPgOv6Xixw5xSPihpMVzVfaJbpxLlLH3to5zcD3nP69E2ftRNj/Gyl250jJziEJ5TjR4YwncVIJlPWam1cqXMzeNQElQcnA7LStXrhTbtm9vdvxff/1NvPfBB+KXA7+o19auWSt2bN+RhP9P77/DD+J/+eWX4u133m4R+3/gQESsWLFCHPjlF3f+Hb7Dd/gO//8jfGUyVV9vxlSG+FZVGtXWHn8R/6nQY7hY3E++OOK7KA8BBbcfqLi9Q3JwWb3FpbtVedvRMpjiJQtaVNsCV8wCBTfdemzn4KaFiK9/muBmwuP0RhqAcePawIbz33aWkxbXaLOc3Rmkbm5cPl4f1BOhB5dNpljF5cymfmB3PZTqy3///Xcza8EKFM1mhCtljVCKRGpWP4kkpa0JRc7hO/wWjE//ZyrVjyNEBHEfbjzorJycg+v92B40ZlL1WsH1ZiTr9CxknXFXhh9+7FUJ2O7jzSAK/SrgSgizqd7nvZtFTnaOuO+++ww+31BKSvao9+b/b756/ZxzzhFPPLGw2fHpJkM4JXv2qPemTpsqnn3uWXBX/Gv33482qLEy/Y4ef8bNM0Ru794iNzdXFBUViilTpooXlixJCZ9I2iOPPKLKpv4oflP7/+aby8RXX38div/ee++K0aNHp7T/ixctEgMHDhD3zL3nHzn+vxw4IB6lY1W+T723YcMGkZ/fV/y06ad/9Pz/09efw3f4Dt/h//+GX1VVZRyU6zTBjZoSZT+1AsdfcUzCiPlc6MjvR1jBZZMpdlFmPpULKu4pIC6yyVSYwVRXMAzuCP23bYD/oblUa6s8OTMkqad1SFxtszko2/236SGhvOkgL6dbDcNtoeaa5Wlk9R3hcTcrKogVXI4K6gHkNgdqxNlFmZcBWnYfH8jqVCqT13CdCHWeDfffSTTa25j4Q163Dt/h/xvw6f9MObkox2PBMuV4PClXjft1+Ye1DjNwMQdX96CweqvclXVMUG1g9hPC0lWZDroSoqNhcCa11jZo0EtOTo5aPAXVvxE9+uijknhmi8cXLlTr/e233/4SfEVwJX5JSYl6Pm3qNPHss8+BwcRfu//mxgkxBLUBg4vU8adPny7mzp0rSktLxcYffxTPP/+8yO+XLx577LEm8elmnC2J/rat2/4wflP7f8Xll4snnngiFP/dd98VI0eNSmn/r7v+OjF/wYJ/7PhTSVq2vGa2bd9q8H/79bd//Pw7fIfv8B2+w/978TkmqE6TWP5spS5RZsEAx18sPDD/4TQMXaI8CQymisFFuQ8ouKdp3nWyXnpCFu5xQHK7glDJ5lKs5GYB/7PLlJm0ZkFlcFoI0W0VkpPbLAQ3rN453WoITgdWng5sHNXbdqDetoOIoI4QEYQuyqzgngQEF42mcvWJYCflAq3gFmmC68cEsYIb9yyzm3KQbTRmpaEC0oRoUA1LWp/Dd/j/Avy4nYNrejis8mRaYl6guMm+xaggPeNoSpG5RBlNpgLGDP7MKN5QAm6GUMoTMHWoiQbcDr2bQI3Iyc1VBHPFiuUwUxoVo0aPVq8vXPi4+uwNN9wglsvPEP4PP/wgLrr4ItGvXz9x4QUXiG/Wf2O++8rLL4tzzz1X9O/fX9xw/fViX3m5wj8Q+UXcfvvtYtCgQWLs2LFixfLlCr+iotIjuKWegksE97nnPIL7oySJEydOVDhnn322+PCjDxX+yy+9rMjkdXL9hEOPt2/fZvb/lVdeUYrz0CFDxD2ScP5Gs8N6/1966SW1fQMGDBA3Tb9J4dMxWbx4kfos7z8RvmuvuUatb//+/WLmrFmif3F/MWLEcPHM4mdCj//06TPEgw88EDj+y958U/TJy1P7T+t+7bVXlVJa0K9ATJk6ReyRxL66uloMG366JLjZ4vShQ8WiRYvU+l577TX52VGioKCfmDp1qvzsHrX/1furxcyZM0VxcbEYPny4WPzMYnP86dzQZ4uKihSh3bZ1q9qeWTNvEX379lWvX3LJJeb884DhnXffEaNGjVTr2LNnj1rvk5IMDxkyWFwsz/UHH3zg7eOM6SI/P18e9yL5+sXqNcKcMmWyKCqU6540SXz11VdqnXt+/lmMGDZcLHhogfx8sXhPHlM6/nS+rrryStFP7tdNN90kVq1eJcaMGa3OJR0/Ll0L2/9IRB6rYcMkwc0WQ4eeLp5++mlRVlYmt3eY/P+yH7ZnityeQrU9X9L26PN/jTynCyQ5P09eA0OHDhH/mz/fRE/g9R/oDfsL//+ZAZvDd/gO3+E7/KPGr1QEl1u+6gx+lcrBrTPEF8dfmINrKu9iMfG756J8iW7nHBjioswKbjYQ3JOgbZT9kbqBf1JXy1epA8TCtgMX5dZgRIytrRlWZFB6A2Q2rTkJrt3kG1aenNlA720W7Az34WIGbgeICeKw4GP14+5gRd0TTKa46bkXENx+4KI8QNeVX3I4DjFBrDJVVMA4P5GS7pVo4oVEyIuJBr4YVMocvsNvufisysbQRTnuqbjxuB8aHjMKbgXMIPpGByYmiJRaPTvJxlMcH8Q3AD8APWifHwVHQrw5BMwdaqOWtb7vfJib21vce+89YvKUqeb1zz//XAwdPETcKMnH448vVN+ZNHGSeFmSV1r/uHHjxJw5c8TWbdvEvffcIyaMn6A+s3rVapEnyRyRmM8++1yR4JtvnqHWS8rmeeedJz7/4guxZMkLiiRt3LBRE9xsSaq0gkslys8+q7ZtsiQpN988U6z/dr2Yd999ipQSDpGa3Jxc8bQkgp999pk4//wLxAMPPqi+84VcP6mmS5e+IVZ/tlqMHDlSPPbY4+p7q1atUtv36iuvKhI2ftx4cd2116ljRIrkzTffbHqG3li6VBO4qCKc50iC/dNPPyniS4R7u9x3+/hPv2m6eFBuBx5/Isd0jNevXy9+/nmPJHUF8vi8LL7++mu1/jvvuFPU1Pwmn69RRP/jjz9SajaRwwL87EUXizvuvFPhLXp6kSL8VJLrbU+B2CYJPhFfIoT0ubVr1ykCP3bseWpbduzcISZMmCDuufdesWXzFigh87bznXfelQR3tHp9586dIjc7R1xz7TVizZq14k65vtGjRnnr2bFdEeR58+aJTZs3q/0bMXyEuPW2W8U6iXn//fPEgIEDFXGn9dC5nTbtcnmeVou9e8vFU3Tu5PGga2TVylWiWBLfYcNOlyR3tTpnObk5YrNcb0P7T4MiPlYfyWNFanmJvHboe5XyWqLtGT5Cbs+tt4m169bJ7bxPDJTbQyX3tP0Xj7tYkvah6lp4Q+Ll9ckT38rrqxb7vkz0RfQv///nR2o4fIfv8B2+wz9afMrBxXEVr49jglgwwPGXiQmyTEF1ifIEiAnqD2a9vUHBPRWSa5jkngACZDfgbF3AU6kTEFv0YeLnra3UndZWS2w6kNhMy9i4VXOWKNvZQxkW2063nJORhWdZ5lJZlsnUMRAV1BWalY+DGYKTwLmLM3CzoTyZjaYKwQ2MTtoE5aIc55igmDnZqXjNhrcqJoKEIuxriUSKNMXhO/yWjR+MCWLVlvtwYz7R1X24FVSiDPm2vm29VmrBLZlVXVOiXIu9LGyZHzJrGmbuECjv8ftW8D3qF/1Wkq9++ZK0kYmU/NzMmbPEg/99UP69WSu4UTFpkkdwCf/MM84Ud9xxhyQsewP4RHpvuWWWwdohCc77H3yg8AcUD1AqYCQSUSrcpZddpsiOKVHeU+r34FKJMkQIUPnpJkkuiSz9vPtnRXDHnj/W7P9zzz0rLrjgAvV87tw5St30cCJiyQsvKJWZ37v1ttvM/n/73beiT14f8csvv4iHFjykCC7vy9I3lirlkp7Te0RwN27c2Ojxv2k6EdwHko7/kCFDxFsr3goc/2q5bUSqL7jwAnV86Bqg47B129akCIUD8rML5s/39lHikwJJBJcmCBCfjJ9I7STln/a9tKxUZOfkip07dqjPXH6FV6KM28aDhXcVwR2lnu/cuUuVAG/Zslk9L5VkVW0bqcES/+qrr5bn7in13pdffKmU119//VVvR41SqN95+22xc9cuhb9DX1dRPTkxTpJMxidF9YH//Mfsx5ChQ8Qnn3za6P7TwIW3hxVnek7X0pdffqG35xe1/hr5/hi5PW+95R3/i8eNU9c040+6ZJJ4YuHCQHZjbUiu41/1/8/OhHT4Dt/hO3yHnzq+R2RZpa0zWF6J8kHdhxscfxH/8YxCYwGxjwx4oQe3CFyU86EPlxXcU6B6tgeQ3BMgA5dbTDtaraftwUU5yzKZag1Vv60b4ZZpDfhANZuLcnoDkUGtLGUXld4skKB5p3Bn7aggOwf3eJDDe0AN+KlgMJWtT0ZfPfPABJdmJCbF5UmMxWImp5NO8j40mUoE9a9Eo6pYIwZAiYYJSpBwJEL6KR2+w2+Z+BQH5BFcLxoofsg3lzL/p2JeyQvb0fuqbZ2v0rLJFLglG2VXE1/sYakFe31/RrNG97DUJlnz440lrC+GXiM1jcp7ySBp/oL56ke/QJLdrVu3iJk3zxSPExmQn5t0yUSP4Er8VSs/FWeMGaO+e8mkiUoNJQwqC33qySeT8Om3xev1zVWEOrd3rirHvX32bF/BJYVNrmPatGniOUlwaX1LX18qRo0epchWTm62Wseu3bvFIkmurrrqKrP/H7z/vjjrrDMVVn7fvupzpPDm6v5iWmhbyPSJym55/yPV1eq9TZt+Egsemi9unnmzt84aTXAvulh9trSsTJFDKuceMXKkWLTo6dDjT6W3/1UKrr//dPOlfd7wwwZx4MAv4u677xaDBg0028XbVllVofZx27ZtCv/ALwfkZ+eIgeqzuWb/6fjvlcT18su97Rk5coRSmAmfVNFcs+/+/pNaSRiXX36F14MbKBPzBg+qRHm0p9Lu2rFLnZNIdcR8liYCftiwQZPSq8XTTz2lPvvSSy+Kiy66MHD9XXnlVeLRRx4Vu5SCm6PINmMRwb3q6qsM/g033Cgee+xRc/2PHDlKfPLxJ43uf1VlhZ4M2KaOf4km4ERwX5bH4MILLwpcf1dddaXaHsIfd/HFYunS1837NBmyQF73fpmcd/5NKdxf/P8vEK/h8B2+w3f4Dv+o8NlkitVZYz5V6Zcu2+Mv4j8VegynSC4JE3IMd+TI76zgDtQE187B7QMuytyH2wNaRo8HMRLV245QmXsMiJvM/9qGmEulh+Tg2rwyvYm22T9domzLxlienA6Wz1lQX50Fqm1bIL3toQeX5eyu1mKXKPeAEuVTwGQqH3pwi/UJG6xKlA8f0aY4MS3Px5UjZeM+POG2PQnrSUN9kgm7ENQiEUlMwuE7/BaK76myFaqsJWZMpjSpDbgoo8lUPcwiHvQDyE0psveDzmU0noJbFehDiWIpEASomx//GttSH2dF7RuQd6Mg8rVt+w5VUjzs9GGq//WyyZPVemjwv5BMpuT3iMi+/PIrAXwyUpo+Y4Zy1K2R2LMlYb177t0GP3LggPj5559FjXxeWFgo1q1bl4RPpIRUPiozjaoS5WmqRLlq/35VPqoey+NTsudnkZOdK3b9vFuSpEXi6quuNvv//gfvizPOOEvt/22zbxP3339/6P7Te/fee6/B37J1s9p/wnr44YfFlVdcaWaSn1+yRJUQ4/EvK90rX39e9O7dR5Vj28d/+k03iQe0gsv4RDqp95UUzjfeeEOVy37zzXpl2kXKM5V3k8pI5bXkJq1UUiqRfvNN/dlvvM8+S5+dGDj+RLyfe562p7cksavFxx9/LIaPHN7g+Z92+TTxxMInksrBCP+dd97xSpTlYyotzg4Q01p5Lvookk7411x9jSS4T6vHn3z6qRh6+tDA9Uc91kuXLlUKLhFPUqt5GxYRwaVzp/GJ4D5KJlz62hxFBPeTTxrdfypJIwK/RfcXU/lxtrw26P/kJ59+IoYOHRrY/7Fjzxevy+2hx1Rev1Q99vBJtZ+/4CErogIzG//a/398/B2+w3f4Dt/hHz1+JZtM1XEOLr9eFRhP4fiLk2OY/3jmoHFJcE0P7hCIVy3QXCqsRPlkKFHuDmXK3a3q2y6QjIOtqG1DknWYI2ZaRsVhJcp2/m2zKLjpFnNuZcnIYSXLdi5uplWe3MFSb9loqivI3d3hILIc3hMycLOB4PbVJJd7cIu1gjshziZTcb+s0svBTQRH+okGijgTDb8ZjGRpIIO0UYMgh+/wWzZ+DELC0TU5oOTGKF8tpg2pKjwye5DigbRtvTE98BRc/iGv49w2uVRWVkFOXG1yb0vUt8+vhR99vhnURGutTLloYNaU+ld6S4K3fcc2tb4xY8aIgsICsXz5MvU+9b96PbhRZdZDCu6vv/wqxo8fL9577z2FtezNZYqIEAlZvny5+v43675R5Jx6QMnoiPCuvvoqcdU1V0sSUi4JSam44oorxLJly1TcEqmFnosylyg/K/ZK8kbK3YcffigOSHJ4/7x5iizt3r1blTaTKsf7TwT3zDPPVI/fePMNMUhuzw/f/yB+/e1X8dCCBWLWrFvU/r+57E1R3L9IfPfd90pZnSHJ+biLx6n3iHySiRSpuWQ+QertRRd5JkoP/u9/ihgTSS2V204lsJQLbB9/UnDnzJ3juShv2CCeWvSU6JvX1yjSL770ohg+7HSxt7xc7S8RL+qLpfdo3VRe/MYbHhF78cUXxemnDxPl8rN71GfHer3OEufB//5X9U3T/tH2FMvtXrlqlTJbouNP0UR0nqgHmb5HrysyN/NmceMN14sDBw6Y88+RC+8qgsslyjsV2a6ujpjrjwgumTd55/Jq8RQpuPJ7e8v2qkikRYsXiwORA+r4983LE1u3bBU7dmkFV66Hrz9S36+86iqDf+ONnoLL1zf1TH/yyceN7j+VlHvH6g11/FWkVY7Xg1u2d68yl1q8aLGIHIiIN+Vn8uQ5oIoEwicF9/XXlxp8Irik3keTBmLw/C/8/xcNKBwO3+E7fIfv8I8Gn3Nw63UMEON7Obh+RRyOv7zKugq/rUyP4bSLMpco9weD3nzIwc3VXOtkLSr2sEqUj9NLN1BxO1lJOW0hB7cdcEEsUc6wHmdaFcF2kk96cxLcVk2UJKfBRqVZRlNt4S8/xhLlY0DJZZvpbuCkfAIouCcDweWoIJbSuQe3nya4dNImBRqrY4eMK2xTBj0pCmvJLydEaGZposnvO3yH3/Lwleu4/P8SP5QcCcRENwbPK8r3mf6QerCtZzMpVaKsf8jr60DBJeIbrU0u64kmh6qbfLhAdpxearA8KOhaSASXeySffPJJMXDAAHHgF69/0evB1QruJZd4JlPyMfW1kiLbv6i/ckVe8dYKgzV//v+UcRApo0RKyDCI8MskEbr00ktFXt88VaJ84003iv3V1Zrg6pggiXn5tMvFs+SiLB8/8MB/RB9JrIhckTpMnyPzISrJJZLF+6kI7hlnmm144D8PiCJJQmkbzj7rLPH9d9+Z/ScTKCKypHqSQktltCqmp6pKXCK3j5TBwqIicfU116oeXHrv+++/F2fK9ZAxVqEkkHfceYcipPbxJ8LslUfnKDOpyZMnixfkseLjv3dvmRg/YbzEzhXFA4rF9TfcoEgbn+P7/yP3V27XgoceUkRtwvhxcv97K7dkcrGeIL9LON//QNtzpjK7Kizwtuc3tT21Sv2knlPK4yXH5CWS7DI+lSpTFNAZZ4wJXE8cE0S9s0rB3bVLK7jVZtvInIvyZunz1DdLBJf3/9OVn0rMMWrfyX15xfIVPlGWrx1QRNn7LJ07VaKs13sjlyjra5O2gVThxvafPkcqvTpW8xeYzGa6luj9lZ+uFKPHjLa2x8MfJ9f5OpUoa/xZM2eJhx56CAZSUb93DErv/qr/f36mo8N3+A7f4Tv8o8WncVI9uCQHXmfia42/jI9KDIxCD8XF77//zgS3GBRcFgvzgOCygnsqENwTobq2GywdQb3taGXhIhdsHWIs1boBftkKCG+r5s7CDQvXzbB6cZFtt7EMpvhxW1jagXrbAYKB+W93q0SZa765yZnrwXOA3PaFDNxibTI1iU5i3GR3+mWUoZEpiZRoRnhXZCKRWtVnGIbDd/gtFN83mYqHliTHoLfdI8PaRdlSbrlkxlNqvR904/aHCi7MVvp/IROOy4GiwV6WYO+KPwMaTcquC3EwbGC2lP9S2XG5JO5h+L/V/AbqcxCfInGw9LUp/EjkgHLGPdr9/+23GnWOwva/pqbGW2cIPim7vmFScP9JISTV9M8efyql/Y0xLPxfyByppsZ8lq4dtT0h+1+htycMn75XQ9nFIfi/ajL8Z85/2P7TjHhzX3+N7f8vv/yqzmVDx99sz19w/f/T//8cvsN3+A7f4dcm5d0yJt3LMf8Wx18m6jFuiRPxOPfgDtZtnQXgY4QlytnQFspxrcfDgiXKx1rk1u695ZjYNsAVW0MeLns3tQrpv7VLk5uV4NrmUrZ0nA4bi5m4bSwFtx304HaERmQmt92gcflYILgngHp7KpQoZ0MObqGVgzvRK02Om4F5nF2UE403IiaaIAmN9zEmmiYdDt/h/wvw41jeggvNBsbixmiKZwaVyVS9Z24QdPSr0y7KlfpHvUa/z6XLVWCrb+XARWuTsuKiEJzuzYBGwdEQwtatmVR/1tUnVmb21OE7fIfv8B2+w3f4Dr8F4lfqvFsmsIyvzKe0qZQ9/qLxGZlfMv/hNAxKvyARUHOlYs2bCkEwzAGCe7JFcpmTdYPy5G5WDu4xUKHbHvifXd2Lra2ZFrdMD3mc1twuyhkNKLoZIQ5YrYHoZgLBbWOR3PbQh9sJHLi6QokyzxD0gF7cUyAHN1eT2zzIwe0PJcoTDnMPLvQQooKbCCvTTDReEmoXdSaarBltWD1z+A6/JePz/5d4YOYv1kCJclx91is9PuiHjUMWbqVxUY5CCbNWdtFsIWrNaJoffX/2szbMcr8WHQhhpjRqORhGMceuNtnsweE7fIfv8B2+w3f4Dr+F4KOLMpFXxvcU3Hroz/XHX6YHN4YiBZUoH7EV3EJwUc4DF+UcTXC5ghYjgo7XXO1YUHHRYOoYaElFBTfL4opZlpdTusUt7VSfZjOZamXVRacBuU2D/CJ+L8tyw2prMfb21tJJ/2WC2w0ILvbh9oRG51P0rEK2ltH7Qe04G0zRCRsXY/WWSytjmuAmGuYQicZcdxr0uA1/nkiIcNLi8B3+vwA/rkkrlibHuFzZlLz4hJfs6Ln/ox5MpljJVW7JuuQG7e49BTcaMF5ImsGM+iHpeKM4cCCiyjtLS0uUEZH6W+r9LSkp8x7T62X6r35eJv+WlJapv6Xqsb+odcjPl8nvl+j3S+Fzal1lpQbH/HX4Dt/hO3yH7/AdvsO38CnBhYwPTWSQUYKjKY1/SAjgqjevRDlIfH0V1x9/HcISZSC5v/suysVAcAuB4LJ6exr04PbU/bcngRHw8ZrYdoYs3GM0qe1okdv2lptyG+CNGVZsEBLazL/CQdlu+LX7cNMaKF3ODLGCbgPsvR3sPDtt2VFBTG5PhLrvnprcngwEN0efjHyoHzcmU2SFjbXnnOsZrPFMhMphiUa0riZLSxON0weH7/D/DfgqJqiyAkhsMCw8uMRFufy/ddD03PIPbn3QZEr/KBuCa0qXoWQnMBsazJZDcwfqL6X/z/SXHGy571X9lYv33PtLTrnqsXw/Yt73Xkt6rtdBz6sj/nu83iBWRK/T4Tt8h+/wHb7Dd/gOPxl///4qNRlPf6PW+CaV8Y8pUa7nEmVvwr+KY4Lke/b4i41Cmf/447UYK7gD9FIAMUF50IfLJPcUzcFOAIJ7LCTfdAEe1wkMpjoAyWUu2Fo/z7RE0gzLJdnOxm12gosrTbPIbjo4J2eEsPAwgtsaTKY6QCBwZyC2uByvSS6ruKdCiXIOSOn5ujy5EGYkJh05fESXVPp9uHSBpWZQ2zipSCQaiGNJpEI0HL7Db/n43Fcb11Fb8bgfERQzZDcmYmzgVlHh5bSZchmYUeQSZf3jzS7K9ZyPG7DQjwZs86NQHsSzmhGl3O7zbiSR6uCNKRLxb2T6platP8c3Ku8vft6/KeHrfEOjdXs3w4hej/++w3f4Dt/hO3yH7/AdflP4NG45cOCXJMflpsY/Xq+tn3XL75uYoPr6pPEX8Z99JibokOFChw+bEuVBmuCyf1E/UHBzIAP3FMjB5fLkEyzfJCa4xzRAcJEPogkxuipnNpCFm2apuM1iMpUe0oubYZUoY6lyhrXRmdCD285qOOYmZFRvO+sD1RUO4PHQf9sTcnC5RJlNpoqA4NJJm0guyrY5ToVRcFP7l4ByT6ISicRR8ZAGWiIdvsNv+fhKlTU5uN4PY8zYzccDai679YWVJ9fpvFuvRNkrL+aZyIPsosx9KQFTBwhXtwwXaKKqyii3Ef/mpG9EgRsYz6aaG44/E2tmZyPeTYxvXhGYwTXriuB6eKbW4Tt8h+/wHb7Dd/gOv2n8KqXk7rPGOU2Pf4jIcjUcjZ24D5jGT2HlyfVYogxRqZ7JVJxjgoZATFC+VnGZV+VaouJJkIPLJcrdQJzsDOrtMcD52sHjtuDP1BqILkcGtbZ4ZpjZVLMpuI2VJ2dacnK6RWpbW07KWVZMUDurGbmzlrm7apLbHeTwk6AP9zS9sISepwluPzCZGpJEcOUJjYHJVLBYM2E1LaaqcyWELZYlmnSnTTh8h/+vwMf+jTgYS8XNbGAsEBlEk0d1gdJkPZOoZxQ5Uod+yOvgh5h+uJUyWwNlOZANhyYNbMRAPS04+2pmUwM3ID0zq2dUq7GMCG9EPDO7H55H/PKiCM7UVvuPHb7Dd/gO3+E7fIfv8I8Gn/p/MVoolfFPpS5FVsvBOqPyKuJrDKaC469DOjmG+Y8X6UgmU//HPbgDQBxkP6O+kIOLMUE9wPT3RChT7grcrRP04bKCixm4GBHUJiQOKCytJ6z/NuOvILl2Dm4ry/0qA5Tc1rBj7eBve2D1naBEuQscrONgwR7cnlAPzhJ6X5h5KASTqYnxeDzQf2tclG0lK8R0J5FItcozcZRanHD4Dv9fge+VHVd6M3/xQ6F9t2jiVoElygfr/Ew2rdRW6RLlqC5R5qgg1YOLs5e6tyQQdm7lzJF5QwR7aCLBm0o19L5U23025u/+4A0JbmjmczxTG6kOzOJWm9Ilh+/wHb7Dd/gO3+E7/NTwafwS1XFBXILc1PjH5ODWeQtP9tP4KalEWY+/vDHcvmD/rRzPkfinCe4gILlFQHBzdXky9uAyF+sODsrHgYrbGcitXaacBaXKWUBwMy1H5XTL4ykjxPOpWXNwW1nOyZkhjcDpVv10Jqi2mRZzR5Op9mAy1QWalY+1woRPAgflXnrJ0bMM+VCizAudtEkqJggG5kplqqxI2a4nYb+SSO3TiQbMbRMp0xKH7/D/eXxWZQ81Qm6pRJlnBj0Ft96Ux3D+rafU1uleW+8Hm2cc6UeZXo8GzBbgh722Fqz5/dw4j+DCTQXLiugmsj9YjmT3xvg9MhEwm4j4PTZgFmEbVfDNzuE7fIfv8B2+w3f4Dv9o8EsUwQUym8L4R5Uim5zbemO46bkr6+xba/ylhAddohzDNAwvB3c8mEyxiptnxQRhiXJPcFI+HsqUuUS5CxDbjqDetgf+hyZTGRZ3zISW1wyrFzfD4qHNQnAzQhhzumUwhQqv7aCcaam47YDUdoADwVm4XWBGAF2UT4CDe6omuCyh54GKW6RPFpQoxwMD8grIwfWLM8OcZBN/QDFLNOqBG/Z5h+/wWyp+XJuyBUltLGBWgI9pptD0gMAPLc8qVlX5bskmB9eYT7GpghWcbrknc6lyaWlZYGaVZ2DZDKLazML6TofoiOjP2NqmERG/vyaS7J4YscwqHL7Dd/gO3+E7fIfv8FPFpwn62ijm5zY9/qmCXts67sGNMvHV5cnW+OuQinqsAEHikDaZOkwEd5IWA4vBYKpAk1t0UT4ZFNyTNC87UXO1Y0G97WKpuO3037bA/9pabauZsKRDLy66KNtia1pzlSjbpNY2l2IQdMJKh+dtwDm5XYiK2wFYfxfIwOUyZVZwe0D/7SkgnfeBLNwicAIb5JUoHwpkeJJRzr5GTKYaVrgSDZj1BMlBItEY0Ug0SV4cvsNvSfi+QQE7JcdNiUug9D/m9bjvK6/wS4+BwPKsY5XqwY1qk6l689kq3ZuLM5e1VklyLdjnR3WJctCOn3pofLv+QJlQJDi7Wg2ztWj7X10d4rgYWAfM1CbFATh8h+/wHb7Dd/gO3+E3jl/KCm4txgI1Pv6hcRL22PJYqFKXKNfBmIrHXyw8oFHooXiMS5TJRXkoVL5yHy6Lhzmg3nIf7onQf3u8lX7TFRTcY4DkdgD+19pSbNsAZ+QFXZNbWT25zVae3MqSiG1VF0N40yz35Awr66gtuCnzjncA9bYjBAWzinsCuCj3gD7c04Dg5kIwcSEouKoHFzOf+HFFRSXQhUYseRKpvZxoxMCnabri8B1+y8X3CG5FYOZPPY4fCpQnx7n8v6I8kG9rDBH0D3Il5+DKH+u6On+mkWYgg6XIlmV+1J/V5B4VVaIsbyr7jVkEGEOgvT+WEkXAFMKajQ2WIlmRAdphMXAz038dvsN3+A7f4Tt8h+/wU8WnCrQoENlUxj9VykwqmINbw68bF+Xg+Cvgoqyr8EisOHLkCCu4gyFitRAqYvPARfk0iAjqAQSXzYC7WFm42IPLJcrM/9oDH8wCvphhxc5mWsk9yEmbjeC2slTbVpazle1ylRnippwJ0nRbMJjqAES3M+QpdYPm5e5gS32yFROUAxFBWKJcpE/aBJLhYzRrEY8bkku5niIpQzTRQO9iwnL7EQHv2VT8ffyvJ5JfdPgOvwXjGwe+gGobN5NGca3c+jFBFb5qW+e7JHNum4kJIpMpKKWpYhdlXaLjuQhGgz/0Nfo93a9SWlqi+lyCM6mRgLmDbwwRCZhCVOOsbSDfzu/BsQPb/dnZiN9b4/AdvsN3+A7f4Tt8h38U+GVUosxlyVyd1sT4x1NwfW+TaI3vomzyca3xF/EfLwc35osTctx2xCtRngg9uMWa4OYDuc22+nAxJoira4+zSpSPgXQc7sHtAPyvrdXGmm7FBYUJqOkh2bjNZjKVZknGdmxQpiUxt7YigjKhLLmNRXA7QTAwZ+F2BYJ7PEjiPa2GZz74bDKFCu7ggIsyxJyQgotxKImk4X+iEUOfREiVZyI1J6Aw1uHwHX4Lxqf/N6q8Je6XI7Ni6xPemEd64x4Zxiw2Npeq12U1VErD4eR1QHD9Hlz6Ma/RM5q6PCfqOwxG4b3SktLAjSNg4Q95d2ZmtdoOZ48EXA79sqT9fu8Mz8ba/TvYV+PwHb7Dd/gO3+E7fIefIj4puEahra1NafxDbsl1db5bMpczG+JbV580/mJRLw4KLgkThw8fYRflwZo3FegF1dscXS3LBr8ngycSRwSxyRQquJ1AxW1rtagiwW0DJNeOCgrjm82u3qaFyMJpFstGabk1NAlnhvTcYhYu5yR1ApOprlaJMvbg9oRmZya3uZCBm6+ldp6NUApunPsEdQ5UhZWDm5w9Gk4YEo1Y9yRSNPMJ7bF0+A6/heJ7PevlpgQ5BpFAcZgRjGkCvE8RXMi/Dbgpc96t96PtEdyDXg+udleOgjV+wGAB+lC8JSrKSkv8LDucNQ2Eskd8e368ScGNyw9/hzKkQFg7lC5F8LvVDt/hO3yH7/AdvsN3+EeFX1JW6o13avT4JoXxj5eD65cg8+eUgluvx1zW+CtukjD8djI1fjscZxflwWAyVWCVKGMP7ilQnnwSqLdsNNUZlNz2VoVuB+B/WSB+Zlj9uBkWp0y3qoibvUzZZs7pVlxQGmyYnYFrNxS3A7maLaTbQw9uN6jlPg6amE8EgssH+zRohM6HHNz+eqGTdsnvvx/Rg/K4UZwoqzNcxToK49rQ5xbNSDSxbofv8Fs4vunfkEQ3jm7kMSsDN+a1AHguyuElyvSciCyX3Hi29t5nlLILDslhfSgcds6zlp6LMvS6BMLZ/RvT/mo/ZD0SiQQMJSJ2SREGuCfFA4TM/Dp8h+/wHb7Dd/gO3+EfBX5pSYmv0tbWpjT+qQIzKc9FOeq7K6ODMpYoK5OpioAxKLgoj4ce3GIQCvtqfsUK7mmg4PbQ4uOJwNO6QfVtRxAt2wOxbWepuVkWZ8wKaXsNMzdOD+nL/VMmU+khbsppIa+nWWG9WaDgYv01Et3OISZT3IPbTR/Ik2DBEmVWcHuHxAQN8HJwj+gBeMz0CRLBtRWvREI0GaQSpqE1XhmaCCUTYYqbw3f4LRE/yaBAOymj4RT34NIPaIDgmr/1xtqefqCZpLLTnyG4AQIbDUQCoZOg34NbGjR8MGYQwf4X76ayPxC2vnXbNrFnzx74vF26FAlGAkSqrUiA6mTDiaPAr06aKXb4/NreveVimzw/7vgn4+/cuUNUVlSG4lN7wKbNm5P6v9z15/AdvsN3+C0LX8UE1UahOq3p8Y9nJuWXH7PhpnJRZiHBGn95Y7hyyyg0zgT30hCTKe7BzYY+3FOhgvYkqK49DogtxgR1ACflDmAs1RbaVrOgXDnDMpYK83sKqyRutn/pDUQGpUOTMOYW2SZTWRZ772ApuJ3g4HSD2u7uISXKnIPL5DZPq7cYFWRMpgIxQXIwTgOB1LJZEk0KaQ2xjYRolLE4fIf/r8Dnvto4OCYHyW7wMfV6eD+0dYbYGpOpek/BZaLq5+ByibIdB1RjHqOVPs9qlpaVmpnSaius3eTRVWNpUUS88+67YuCAAaJPXh+Rk5MrLrroIrFp0yb1uTVr14jnn38+EAeAfTWBiAArCy9VfIwg8HP4qoMzxw3gr1ixQowcOeofw+fXXnv1VbF69epmx/9p009iwoQJok+fPqJ3bm8xaNBAtc+MT699vWZNYJ3vvfeeOP30oQY/NzdHrFu7LoD/xhtviDPOPMPg987NFfMXzA/gf/jBB2Lo6aerxytWLBejRo0yGHt+3iPOOussMf2m6d77y1fIaydH5Egs9Vc9zhVlZWXqu/Q8Ozdb9O7dW5x33nnirrvuEnt0OX2Tx39/tXjq6adE37754tFHHzXHf+fOneLiiy6W122e6F9UJB555BE4/vvFPffcI/Lke4WFBWLQwEFi1epVScd/0eLFajvn3D0nFH/2HbeLMWPOUIMhPtcr3lohCvoViJ9++inp+t8vl6eeom3tKx577LG/7fr7p69/h+/wHb7D/7P4pWwyxWOaFMY/Ku9Wj69YwfWIb5VPfK3xVyAmKK6r7+KmB9cuUc4HgtsbyC2WKHMOLvbhHqf5WxdQcDuEmEthVGxWSIlyWkgfrl2inGaZHjebg3JaSMMvMm97yYAdQSetNpCP1BHU285gMNXdMpjCEuVTQMHtAz24bDJVrHNwxzPBjeuyShqgl5eXNy2bhbZCJhrJZ0kkmQMldU7aprcO3+G3cHzuq42xa7JFbH2zqZjKViO1l/tu2dGvnkuR6+pVDwlnv3F0EC1VOiaIf+Q9V8HkLDgs6SGTqUC5EbgjRgIGEN5rtG39CgrEwscXiv1yO0p+/llMmTpVXHPNNer9F5a8IMaOPS9oChEJN5MIlDiliK9IAcziRjBY3iqPCsNftmyZGDli5D+Gz+ueMnWKeITIVzPi07k888wzxZVXXqGIbmVFhXju2ecUifz66zXqc0Qc13z9dWD/33v3XTFkyBCD3ys7W6xdsy6A/8bSpeIMSdwYP1t+hpZ1a9ca/Pfff99bj3xfHeeRI9T2VVRWiIkTJ4lJl1yiKn/os8uWL5Ok+nSxc8cORTx37NiplFUimvTdYacPk+/tFJu2bFbPzz33XDHu4nFib9neJo//tGnTxEhJrseMGSMefeRRc/yvufYaMWXKFFFWWiZWfbZaTQKsWrlKfe+D9z8QxcXF4scfN4r9kiDffdcccfbZZweO/6afNimiet7YsfL9u0LxS/aUSJI/VNw3b546lnvk/w86Jk8++VTo9T9t6jQ1ETBmtNzWRx/5266/f/r6d/gO3+E7/D+LX1ZS4rsmWyS3ofGPIrIHueWrznzXy8GF0mQYf5kkjNihgEDxf35M0CBd8VoIQmG+5la9Q0qUT7T8kY6DitsuUJHbHnyW2gP/w/jYLCtaNsPij5khJcqtmlPBDZOEM60e3FYhG9caSpOzrCzc9kB42W2rM5hMdYM+3BPggPbUSy+rB7c39OAywVU9uKY/EAblWKKcFJSSkkR2NKwkfJ0O3+H/G/CxRDmQg3soWKrMz2mm0GSwcQlyHSi1KibIK8GpgzByo+DW+LlvbCaF2bdRmNH0cnCrA6Ht1YEMOrxBVSsViojNDz/8YGZed0iS8tnnq8Wbb74pior7K5XwdElQvv3uO/Wd555/XpEFIhA33HCDKJE3JcKhEtrThw0T/33wQVHUv0isWL5cfLv+OzF+/DiRn58vzpJk7d133jX4n678VKnF/fL7iRkzZojr5bpefPFFtQ10zG695RYxYOBAcc4554qlry/1s/oi/g10uSRWI0aMgJuxb3Dx3bffivHjJkjsvoooEvGjz1xw4YVi+bIV5qb8lCQrtB+0TevWrROXXnqZ6FfYTymnK1evUp/54fsfJMkaafBp+4jQbdu2VW27UgoLCiXxm6jwd0sitPDxx0V5+b5Gj391oKTL3y96j5Ta4gHFimSZAYV8fd7994sXXnhBfS5Xkd2vA/v/LhHcoUPM+c/O7iXWSuKK+EuXviFGnzHa4OVKojxx0iQxVpI9mhEnHEVwh3pEedmy5WLEyJFKobzpppsUQS2hSCptQuJNNIwIHH/GJ/I7QpNjxt++fYfat6WSaNP6f5bH6zF1vMqT1IRbZt0ir7FSMVmSWU/Bjaj7FZF7te96QHbTjTeKu+6+W+F/8skn4iV5LfH1/+GHH4q+ffMCx58mJe66605x++23i7vl9+zjz/h0zebm5oqv13wtZs6cKcaNHy9J835z3iIRfzB4yy23qqgu2tbH5LY29f+vsfOPagqef7z+bfyw4+/wHb7Dd/j/Bnyj4EZrtMlU0+MfGichgeXvVWllN5CBq8dXyii0Yl+g/zbm9+CSi/JATXDZZIrJba5eeoG570mag2EPblfowe0CFblIdLOA/yG5tY2mMoDMplnlyWlW722z9eC2skqSw1yUM6wS5UxoHG5tlSe3s1y2uE67KxykY/VyApQonwIKru2inG+VKCsF94hRcA9BVmd502WbARksYZV/Nu1gmzjaQlOH7/BbID79OFbo/g0/CzduHJQPgbNyTJNh9eN7kE0OsC/EI7j8Y+3ntWniaxwD4Udd9ajUQP+tb8ZQUloKZg4RY/oQLC2KBPpszj7rLKU6LV68WGzYuNHMsJJa9b//zVfkcP369Wo/iCj06Z0nXnxhiVi1apUiqFdddZXC2LJ5k1ILSVX76OOPxe7du8Ull16qyli//uorMeeeuaK4f7FHaPbsUWXRt912m/jyiy/EHXfcroj2008/rd4n4nHO2eeIlZ+uFM8884wq+fzmm2+s3DxNnohYWQH19LlLJfZN06eLr776WsydM1f0H1Cs132XuEGSId7/iyThXbRokSqnHSYJOpGYL+Q23TN3rhhQPECSse1q/6m8lvH3le9V+7pJ7jOVc1988Tgx5+67xIYNGxT+W2+9pfbnK1JXmzj+ETxfUB42Xx77yyTZxp4me/9JzX3uuefEpytXipUrP1WTBkQCBw8ZYs4/bcdapcz6+EvfeEMpory+nJxs8flnn4lRo0eJBQsWKPwP3ntfDB0yNDCRcL8k19m9suUx2QY9VR4BHjJksFgprwm6LlauWinWrFnjnaNl+hxZ+3/FFVeIefPuU8/ffts7Xl+r44WKgn9spkyerPaNXtvww/fq+BtCLF975JGHxdRp00KvfyLJRGgZ/+133lHXH/Wc3yEJLl0T9vFH/BkzpquJAtrGDT9stHrKqpOuv8l6W1P5/9fQ+bf3P+z8N4R/NP//Hb7Dd/gOvyXgM8GNmqXp8Q8ptZiDGyhRhtQKHH9x65jPg7xKvMN+Du5Ard72BxflPOBXaOx7cgMuyt2sEuWOwPHagdlUO6jqzQLlNs16nB5CZhvqxW3WHFw7KijTysHNgtcx6ygsIoh7cDuDgtsFnLm6h+Tgskx+mj74eVYObjEQ3ImHD3ulyUxwY/G4Grw2ZriTSuxKokkq4a8hkWi8h9LhO/yWih87xCHhh5IWz1XZJ7pxLlHG3ts6zsH1nP+8EmXvR9n8GCt35UrLTCoK5TnRoMug/j6Va9plRWamtbo6MEvLN6At27aJ2bfNFgWFhSJHDuCpr3H9+m/Vd5e88IJS9bin5s477xQ3z5xlZnGJkPSRxI9ytMnMJyc7R/z0049J+HSz+e677xQ5ItWT+kQHDhyoXud1DRs+XCySBJdueP0lEX77nbfEXklgiMRMmjRJKXx2SLxSFjXB9UuhgvtPJl7fE7YkKKQyf/bZalFQUKBujPQ8V24/qdYrJUmkXs4quS/cx0mqLSnZRK579+lj8MvlOaX1bdq0WeFNmTJV9YBi5h/tbyrHvxpcJCMQqTBz1kxxs1z8HqVI0v5TzzTtS//iYnnM+itVvV+/fkZ5pc/TMV+nFVzGJ+XUI7gRU+pMJHiVPAZ5ffLk/q4X73/wvhhM69EKbS9dxtyL1rduXWBgQ+eB3qMJAdoGUmfHjRun8LG8GfefVNNrr73O7Is6XrD/EYifoPeNgkvq/6efKmXeDKzkXyL6Y88bm3T+X3vtNVVBsH79N+pzlZUVYvTo0eJ5+Xl6PvuOO8Tdd92ddPwR/6svv1T77/UhRwKuobhffP5pkucRLFH+A+ff3v+w898Q/tH8/3f4Dt/hO/yWgF9WWqrHQlFfwW1i/KNMpurrzZjKEN+qSqPa2uMv4j8VegwXi/vJF0d8F+UhoOD2AxW3d0gOLqu3uHS3Km87WgZTvGRBi2pb4IpZoOCmW4/tHNy0EPH1TxPcTHic3kgDMG5cG9hw/tvOctLiGm2WszuD1M2Ny8frg3oi9OCyyRSruJzZ1A/srodSffnvv/9uZi1YgaLZjHClrBFKkUjN6ieRpLQ1ocg5fIffgvHp/0yl+nGEiCDuw40HnZWTc3C9H9uDxkyqXiu43oxknZ6FrDPuylGYtYSZzUAenH8z8EymqoNlQ5hDB2VFdhQAkc0PPvxAnHveueKss85Ur72w5EVJGs4zs7aXXXqpV3qpb3R79+6VxCZHfP/9d2KzJLjZWlVj/JdfflmVtqoeT0mMiCRs3bpVLHn+eXGe7u1lfCrvfXrR0+LnPT97apn8PBE4Uhfp+7fcMsufWWYFV6mDI5NuxoT/8ksvS2I10hAzWt+WLVvU+yMkmaZ9JVJ02WWXqXXS4/MvON84RNJrl0+7XCyYP1+sl4SvT5/eBn+f3EdanzLjklhTp/rls3/0+AczBavFIw8/LCZNnBSYNbf33yvTXRPYfypRHmpKlL0e3DWgJBP266+/JkaPGW3w6RizykvqOR2Hd95+21dwFYHtJT5bvVpce801qtycnJ3ZEXP5Mq8MOWz/ifyqPmlr/0lRfeCBBxrc/6CJifz8lMleX6t8vnHjBnX8S8vK9PqqxYIF88UVV14ZwP/888+V+r/szWUGn6oEzjn7bG9yRT6/ffbtqge3MfzJl01W+0+TOd9/9z3EXkQMPl5/RHAffeSxP3X+7f0PO/8N4TfH9efwHb7Dd/h/Jz6NX5jUBk00Gx7/VFVVGQflOk1wo6ZE2U+twPFXHJMwYj4XOvL7EVZw2WSKXZSZT+WCinsKiItsMhVmMNUVDIM7Qv9tG+B/aC7V2ipPzgxJ6mkdElfbbA7Kdv9tekgobzrIy+lWw3BbqLlmeRpZfUd43M2KCmIFl6OCegC5zYEacXZR5mWAlt3HB7I6lcrkNVwnQp1nw/13Eo32Nib+kNetw3f4/wZ8+j9TTi7K8ViwTDkeT8pV435d/mGtwwxczMHVPbis3ip3ZR0TZKzy9cymHwtUAwquN8PpmUxV+z0uARdELCHyFlLNlix53symeuWi7yhVk0ynlrz4ojLh4XXOmnWL6VeMRParklzq3yTleLMkj6TulZfvVe9TyS+V9ZKrbNneUlXqS0Rzy5atqoyVlEYqY6abI/XxFhX1V+SDTIFIlfzqiy+Ny2IEMvZMWHxkvyZPI8C10dvPUsSWj7dt3+Zhb92ivjtv3jxxxx13SGI6VfWz0v6/Rz2ng4fAbHS1JPvniZckSf/22/WKyKuc4QhF0+wS2TlawY2wYvcoOEqmdvz9rMBI0kCACGZBYZHYsXNnYP+feOIJSVCXqv3nPlTc/3fffUcMIWKq8YdLMv/qa68E8B988EG1zYyfm5Or+3TpvO1VqvjYc88TQ4Z6bsxEYIcN85Ty3bt3icFy/bNn32pm+tVEw8iRofvvvTc8gE+934WFhWL58uUN7r/p19Lnf/LkKcZFmfqEe/fJFatXrTYqwdWSeM+dO9fg//jjj6qHm44XHv+pU6epntq8vL6ir1zY9XnSpEtC8en8kxPz1q3bVFn1+Anj1TVKxz9QpgfXn98v/MfPv73/Ydd/Q/jNcf05fIfv8B3+34nPMUG+wVTT4x+OCarTJJY/W6lLlFkwwPEXCw/MfzgNQ5coTwKDqWJwUe4DCu5pmnedrJeekIV7HJDcriBUsrkUK7lZwP/sMmUmrVlQGZwWQnRbheTkNgvBDat3TrcagtOBlacDG0f1th2ot+0gIqgjRAShizIruCcBwUWjqVx9IthJuUAruEWa4PoxQazgxj3L7KYcZBuNWWmogDQhGlTDktbn8B3+vwA/bufgmh4OqzyZlpgXKG6ybzEqSM84mlJkLlFGkyn8oTdOglG/ZAddBuXrpSZ6JeiKWL0/Ar0zfBPaL36URIPUu1defUXh7ZUEjgyXLrzwQvVZMjqiwT2V8Ko4nNdelUS0SPVX0s1IffaiC9V7mzdvEdm9enl9kWS0tGuXKnmmGKKKfZWqp5VUMCKZFfL4kUpI5JmUtwsuPF+RWlWiLLf3ckkkiEyQ+RBhExF9VWL7N8NqUzpLfbM7tHsvufju2rVbLh72u++8IyoqK8XcOfcogrt1q6fgUsluL7ktpG5SHyatd9eunaJQbsOTTz6pSpBffeUV5cxLaiGVSmfL4/TMM4vVcSKCSOsjBZf29cabblTltnRd0HZR6fPcOXNUcH1jx7/axCdgf5O3f6QSk4M19Z5ulGSNSq5eleeJyJkXSRQJEFyebfdNpjycW2+7VYwYPlKS9G8lMasypdiPP/6Ywade3rVr1xj8jz/+WO0fOQazCjt8xHBz/KkXm7Dp+mASO3zYcOOizIv6riSxw04frp5v3rxJlUdTX/cll3ouzPT9bdu3ijnqeJX6GYqBuIqIJI2TxaOPPWrenz59uuqzJrOrTz79RPTp3UeVEtNnqQpg8KBB4uorr1ITBJ6r8061/xt/3CjWyuuXruE18jogN+Ybb7xBGa3h8Y9EvIzd4v79xbPPPqte27Jls3Jepr5wjLcIuIdGPLXZM5mq/sPn395/42QaiQQGlGH4qf7/d/gO3+E7/JaCr0qUo8How6bGP5WK4HLLV50pZa5SObh1hvji+AtzcE3lXSwmfvdclC/R7ZwDQ1yUWcHNBoJ7ErSNsj9SN/BP6mr5KnWAWNh24KLcGoyIsbU1w4oMSm+AzKY1J8G1m3zDypMzG+i9zYKd4T5czMDtADFBHBZ8rH7cHayoe4LJFDc99wKC2w9clAfouvJLDschJohVpooKGOcnUtK9Ek28kAh5MdHAF4NKmcN3+C0Xn1XZGLooxz0VNx73Q8NjRsGtgBlE3+jAxASRUqtnJ9l4iuODeMaSZyUDP/7RWqP8cjZcmS5RDjgiwqxrdSBo3VteeGGJJJeFisyRK+8ZZ5whvl2/Xt2YqAz17HPOVuWgSuGTr827b54iSJQfepEkt1SaTOvdvGmzIoGe8Y+Hf9+99yoltXef3uKWW25RhHLb1m1qPTu27xCPPPKoMvAhRfKyyyarEmX6HpFj6rvt3SdPka/rr7tObUsEZ4mJgEvyRNtGuPyXiDLh36uwc+XSR8y6ZZZHcLdtM/tPz6lPtBpmp6lsmcqac+S6yBH6deXerE2f5s9XTrxE7Mixl/ZlC+27/N5HH38kSeQIMUaX/X788Scir29fpfw2dfwRP2C8IT+zafMWMW78OOVkTftBfbYU3cT7zy7KxsCDXZR1aTEtdD4o9om+T+eCSnapl5ryZRk/Wym46wL4s2+fbZRgOs50XPD4zyXTsOJiSea3i+Ur/PNAx8VbeqlJEFJp2aCJjt2555yrTJ0w0oqOF+XcEgnH/TcDIN3n/Pjjjxv8Xbt3qeuP9qugsEBFCPH2LVz4hMJnXP77vXYLx+M/e/ZsuT13hx7/6dNnqONPx4q/RxFBpD5v37Ed+siCg0PaVi8Ht/pPnX/c/2ocfKLTaQj+0fz/d/gO3+E7/JaAr0qUTfRhNKXxD1Xz4LiKe3M5JogFAxx/mZggyxRUlyhPgJig/mDW2xsU3FMhuYZJ7gkgQHYDztYFPJU6AbFFHyZ+3tpK3WlttcSmA4nNtIyNWzVnibKdPZRhse10yzkZWXiWZS6VZZlMHQNRQV2hWfk4mCE4CZy7OAM3G8qT2WiqENzA6KRNUC7KcY4JipmTnYrXbHirYiJIKMK+lkikSFMcvsNv2fjBmCBWbbkPN+YTXd2HS2plHeTb+rb1WqkFt2RWdU2Jcq0fAaR+uGtqk1TbWjCZUgouz74Gyouqg86HESvHTg7gf9y4UexQ2aWRYBSAzsv1b1jVKialVBlaBW9oETSX0Bg0U1qmeiV9fHJ7nj5jhliz5mvtirtBRe189NGHAXzq8d1Xvs/vEQoEylc3iU+l5Hsltr3/ZXK9pE6SO3Ag80+va09JSdA1Uj+m40CkrSF8MjAy5lb7q1I+/ja+ffyJDFLJ7f79R7f/iE/bTiolVQwcLf4fPf5Hs//cE3u0+NRXzdfnn8H/p/ff4Tt8h+/w/3/Gp/ELZtymMv7xiCyrtHVG6fVKlA/qPtzg+Iv4j2cUGguIfWTACz24ReCinA99uKzgngLVsz2A5J4AGbjcYtrRaj1tDy7KWZbJVGuo+m3dCLdMa8AHqtlclNMbiAxqZSm7qPRmgQTNO4U7a0cF2Tm4x4Mc3gNqwE8Fg6lsfTL66pkHJrg0IzEpLk9iLBYzOZ10kvehyVQiqH8lGlXFGjEASjRMUIKEIxHST+nwHX7LxKc4II/getFA8UO+uZT5PxXzSl7Yjt5Xbet8lZZNpsAt2Si7mvj6DoK+e2A0MKPp5cVF9efKMCYIs+pwdjVQJgSuh5HqYF8MZtWBe2IkMKO7X5ct4Q0vNXzqg6Vy26GSaJLyN+uWmeqm9Ffjf0DuwIMHq/JW1Uv5D+3/P338Hb7Dd/gO3+E7/JaCT5Pm0ZqoX4qcwviHTaZYnTXmU5V+6bI9/iL+U6HHcIrkkjAhx3BHjvzOCu5ATXDtHNw+4KLMfbg9oGX0eBAjUb3tCJW5x4C4yfyvbYi5VHpIDq7NK9ObaJv90yXKtmyM5cnpYPmcBfXVWaDatgXS2x56cFnO7motdolyDyhRPgVMpvKhB7dYn7DBqkT58BFtihPT8nxcqSSN+/CE2/YkrCcN9Ukm7EJQi0QkMQmH7/BbKL6nylaospaYMZnSpDbgoowmU/Uwi3jQDyA3pcjeDzqX0XgKblWgDyUKpcjmxx+t82tqRUlZCfTBJJcLRdDgIckcAkqGIsGZW+OoGBLgjtl31bjOFPCpx3bN2nWiZE/J34ZPmOSES/2Y//T+O3yH7/AdvsN3+A7fz8H1xzlNj38q2WSqjnNw+fWqwHgKx1+cHMP8xzMHjUuCa3pwh0C8aoHmUmElyidDiXJ3KFPublXfdoFkHGxFbRuSrMMcMdMyKg4rUbbzb5tFwU23mHMrS0YOK1m2c3EzrfLkDpZ6y0ZTXUHu7g4HkeXwnpCBmw0Et68mudyDW6wV3AlxNpmK+2WVXg5uIjjSTzRQxJlo+M1gJEsDGaSNGgQ5fIffsvFjEBKOrskBJTdG+WoxbUhV4ZHZgxQPpG3rjemBp+DyD3kd57bJpbKyyvxYmxKdWj/w3PzYs6ugXKj0N3AzioC5QyQSmDXF9+wSpMBzLDPC3ppItbXuSDBewOE7fIfv8B2+w3f4Dj8FfK9EuRbGO02PfzgHt17HADEZ9nJw/Yo4HH95lXUVfluZHsNpF2UuUe4PBr35kIObq7nWyVpU7GGVKB+nl26g4nayknLaQg5uO+CCWKKcYT3OtCqC7SSf9OYkuK2aKElOg41Ks4ym2sJffowlyseAkss2093ASfkEUHBPBoLLUUEspXMPbj9NcOmkTQo0VscOGVfYpgx6UhTWkl9OiNDM0kST33f4Dr/l4SvXcfn/JX4oORKIiW4MnleU7zP9IfVgW89mUqpEWf+Q19eBgkvEN2r3nICjYG0Ufui9pbS0FLLrwMkQMujQvj8CfTP+TQpKkSLVyWVFkeRQ90gAz+E7fIfv8B2+w3f4Dj91fKXgRqEUOYXxD42T6sElOfA6E19r/GV8VGJgFHooLn7//XcmuMWg4LJYmAcElxXcU4HgngjVtd1g6QjqbUcrCxe5YOsQY6nWDfDLVkB4WzV3Fm5YuG6G1YuLbLuNZTDFj9vC0g7U2w4QDMx/u1slylzzzU3OXA+eA+S2L2TgFmuTqUl0EuMmu9MvowyNTEmkRDPCuyITidSqPsMwHL7Db6H4vslUPLQkOQa97R4Z1i7KlnLLJTOeUuv9oBu3P1RwYbbS/wuZcPqGQI99A6SI1TuDoeqRQH9MoGQokmwcgbO1/l/IxONypEiwl8fhO3yH7/AdvsN3+A4/FXyqQLONM5sa/9h5t9yyReZTmH+L4y8T9Ri3xIl4nHtwB+u2zgLwMcIS5WxoC+W41uNhwRLlYy1ya/feckxsG+CKrSEPl72bWoX039qlyc1KcG1zKVs6ToeNxUzcNpaC2w56cDtCIzKT227QuHwsENwTQL09FUqUsyEHt9DKwZ3olSbHzcA8zi7KicYbERNNkITG+xgTTZMOh+/w/wX4cSxvwYVmA2NxYzTFM4PKZKreMzcIOvrVaRflSv2jXqPf59LlKgg1t3LgcEYTelRKS8r8ciDMs4tA2Dpk1QVKilQune90GEHjCczBgxuW3aMTKEdy+A7f4Tt8h+/wHb7DTwGfXZSNepvC+KdS590ygY3qSX9lPqVNpezxF43PyFiX+Q+nYVD6BYmAmisVa95UCIJhDhDcky2Sy5ysG5Qnd7NycI+BCt32wP/s6l5sbc20uGV6yOO05nZRzmhA0c0IccBqDUQ3EwhuG4vktoc+3E7gwNUVSpR5hqAH9OKeAjm4uZrc5kEObn8oUZ5wmHtwoYcQFdxEWJlmovGSULuoM9FkzWjD6pnDd/gtGZ//v8QDM3+xBkqU4+qzXunxQT9sHLJwK42LchRKmLWyi2YLUWtG0/zoR00pT0lpidUnEwlY/0cikHUXsRwUI5hjV51sNhFYlz/7atYZCevTcfgO3+E7fIfv8B2+w28cv0zn4Cb5jDQy/kEXZSKvrAB7Cm499Of64y/TgxtDkYJKlI/YCm4huCjngYtyjia4XEGLEUHHa652LKi4aDB1DLSkooKbZXHFLMvLKd3ilnaqT7OZTLWy6qLTgNymQX4Rv5dluWG1tRh7e2vppP8ywe0GBBf7cHtCo/MpelYhW8vo/aB2nA2m6ISNi7F6y6WVMU1wEw1ziERjrjsNetyGP08kRDhpcfgO/1+AH9ekFUuTY1yubEpefMJLdvTc/1EPJlOs5Cq3ZF1yg3b3noIbhTy4kBlMdlDWr1FQurmpgKV/xLLmD+bWRfybFZYRRew8vJAZ3AisD25UDt/hO3yH7/AdvsN3+KnilykX5ag2z4ymNP4hIYCr3rwS5SDx9VVcf/x1CEuUgeT+7rsoFwPBLQSCy+rtadCD21P3354ERsDHa2LbGbJwj9GktqNFbttbbsptgDdmWLFBSGgz/woHZbvh1+7DTWugdDkzxAq6DbD3drDz7LRlRwUxuT0R6r57anJ7MhDcHH0y8qF+3JhMkRU21p5zrmewxjMRKoclGtG6miwtTTROHxy+w/834KuYoMoKILHBsPDgEhfl8v/WQdNzyz+49UGTKf2jbAiuKV2u9Ut2sDcFsuCCJcqlfl9LxLf9N+VCgdD1YPA6GkJEItXJz+0ZXDSPQHMJh+/wHb7Dd/gO3+E7/KPA5xJlNM5savxjSpTruUTZm/Cv4pgg+Z49/mKjUOY//ngtxgruAL0UQExQHvThMsk9RXOwE4DgHgvJN12Ax3UCg6kOQHKZC7bWzzMtkTTDckm2s3GbneDiStMsspsOzskZISw8jOC2BpOpDhAI3BmILS7Ha5LLKu6pUKKcA1J6vi5PLoQZiUlHDh/RJZV+Hy4Z4aRmUNs4qUgkGohjSaRCNBy+w2/5+NxXG9dRW/G4HxEUM2Q3JmJs4FZR4eW0mXIZmFHkEmX9480uyvWcjxutTbbI17b5UShP5lnNkpIS345f99FEYEbV3MgCM61oKBEBg4hIsG8GXjc9N5GwUHiH7/AdvsN3+A7f4Tv81PFLtYLL45xUxj9er62fdcvvm5ig+vqk8Rfxn30mJuiQ4UKHD5sS5UGa4LJ/UT9QcHMgA/cUyMHl8uQTLN8kJrjHNEBwkQ+iCTG6Kmc2kIWbZqm4zWIylR7Si5thlShjqXKGtdGZ0IPbzmo45iZkVG876wPVFQ7g8dB/2xNycLlEmU2mioDg0kmbSC7KtjlOhVFwU/uXgHJPohKJxFHxkAZaIh2+w2/5+EqVNTm43g9jzNjNxwNqLrv1hZUn1+m8W69E2esr4ZnIg+yiHOVSHTSVihrzhVrLcEG5KEOgOpo6BMqJrMw7em9/9X6xedMmdcOIWH0z1XaJkV2aZNYT+cP4aDjRnPj79pWLpUuXikp5Q/sn8P/s/peXl4uff/45Jfxdu3f/IXz6u3nzJnUf+DP7v1vi75bb+k+cf/p/9NZbb6v/b82NT/8n3nrrLXV8muP8U/9XScmef8X15/AdvsN3+H8HvkdwcZzT9PiHiCxXw9HYiSOEaPwUVp5cjyXKEJXqmUzFOSZoCMQE5WsVl3lVriUqngQ5uFyi3A3Eyc6g3h4DnK8dPG4L/kytgehyZFBri2eGmU01m4LbWHlypiUnp1uktrXlpJxlxQS1s5qRO2uZu6smud1BDj8J+nBP0wtL6Hma4PYDk6khSQRXntAYmEwFizUTVtNiqjpXQthiWaJJd9qEw3f4/wp87N+Ig7FU3MwGxgKRQTQorguUJuuZRD2jyHFA9ENeBz/E9MOtlNkaKMuBbLgoB6CDEYPqwQ24FEaCTodg7889NlVV+8Xs2bNFcXGxyO3dW/TN7yvuvOsuVYatblT70ViiOtQkAjPxjhY/mJEHN0KeGf6T+N+s+0bk9c0T33333T+C/2f2/+677hb98vNFXl5fMWXqFEl294biP/Xkk6KoqFDtZ2FhoXhSPrfxq/fvF5dcconIyckVq1avNngffPChOvdFRUWid+8+4n/z/9fg/n/55Rdi9OhR4sILLgjgv/POO+Kcc84RvfvkiuzsbDFy1CixatUqgz9lyhSJmyNy5UL4Obne3yUvvKDWfeONN4r//Oc/6vMr3lohesl15ORky8/niuHDh4v//vd/TR5/ql7Ilt/Zvn17s19/tG7aph07tv+p8089Ztdcc63o06ePWs486yzxzTfrW+z15/AdvsN3+H8XfklpmU9euTKtifFPpS5FVsvBOqPyKuJrDKaC469DOjmG+Y8X6UgmU//HPbgDQBxkP6O+kIOLMUE9wPT3RChT7grcrRP04bKCixm4GBHUJiQOKCytJ6z/NuOvILl2Dm4ry/0qA5Tc1rBj7eBve2D1naBEuQscrONgwR7cnlAPzhJ6X5h5KASTqYnxeDzQf2tclG0lK8R0J5FItcozcZRanHD4Dv9fge+VHVd6M3/xQ6F9t2jiVoElygfr/Ew2rdRW6RLlqC5R5qgg1YOLs5e6tyQQdh6tDai4NAMaqQ6aO0QilgmE1Uczd+4cMXDgILFaEhJSqr766ktFKv5z/3+Cs7doRBEJmkT4660+anzf2IL/7g/eEJsBn0j8P4n/R/Z/6euvK7L67fffiV27donzx44Vd0nCa+N/9/33ktjliI8++li99vHHHyty+L18HfEXP7NYjDljjMiXhNkjn9Vivzzf/fv3F0uWvKDWu3LlSklQe3nflc+fffZZcdvts9V6lyxZIr/bT1wqSfLY8883+F988YXo27evePjhh5XSvGP7DnHPPXMlKc9TZFMNXPaUiB07d4odu3aKnTt3iPnz54tBgwaJn3fvUjjXX3+DmHfffWo7ly97U25Tsdi9a7fYtm2beOnll9Q2v/jii40e/z0So1evbIXf3NcfrTsnm8jzjj91/mny4IILLlDnk3rHZtx0k5g8eXKLvP4cvsN3+A7/78QvVTm4NWAq1fT4x+Tg1nkLT/bT+CmpRFmPv7wx3L5g/60cz5H4pwnuICC5RUBwc3V5MvbgMhfrDg7Kx4GK2xnIrV2mnAWlyllAcDMtR+V0y+MpI8TzqVlzcFtZzsmZIY3A6Vb9dCaotpkWc0eTqfZgMtUFmpWPtcKETwIH5V56ydGzDPlQoswLnbRJKiYIBuZKZaqsSNmuJ2G/kkjt04kGzG0TKdMSh+/w/3l8VmUPNUJuqUSZZwY9BbfelMdw/q2n1NbpXlvvB5tnHOlHmV6PBswW4Ie91p/JxNw4j+BigHrQuTCyP1iORIQ2T5KTFStWmBsX/X333ffEzTfPVJ+lmc5bZt2iCMmYMWPEY489Zm5ezz3/vLhBkpPLL58m+vXLl0Tlekm0PhSjRo1SiuB9992rPktE5fTTh4nFixZJMj1QDB8xXDz11JNmtvfKq66SpOgeMWr0aHHjDTeo7SfSddZZZ4rBgweLu+68S+zfX6WjBMrEjOnTRVH/IrnO08WTTzyptmXv3jIxffoMhTts2DDxxJPe+ndJYkXY6tjI/V+3bp249LJLRUFBgZgwYYJYqZVGtY3ye48++qjCvODCC8Xbb79l8v2eX/K8+Pbbb4NlVXZuYHXkqI6/3Zvk9yhFxNVXXyPmzbvP4NM5ou2y8VetWi33pZ+oouNT7ZXTFvQrEKtXrzafpX3rJ/d35acrRXFxf0VwVUmzJFrz7r9fkS3GL5KkmhRZWtfChY/L83Gjwn/o4YfE2rVrFdElss34N8+4WVx33bWB/adZ/9mzb5fX0TtJ+//jjxtFvrxW3pL7w/t/gzzntK/0eNmyZWLkyJFgdhJR1xipvPT+8/Ka+3b9t0nHXym4vbINqV637htxmTzPhYUFYsqUqWLjxh8V/oIF88Vtt91mjj9NEND1uGfPHrHuG/rOZWpigb5D26rUYSLPkvhv37HjT51/Ov5UScDnf5H8/3CePJZh5x/33/S4gVnLX339OXyH7/Ad/t+JX6IILpDZFMY/qhTZ5NzWG8NNz11ZZ99a4y8lPOgS5RimYXg5uOPBZIpV3DwrJghLlHuCk/LxUKbMJcpdgNh2BPW2PfA/NJnKsLhjJrS8Zli9uBkWD20WgpsRwpjTLYMpVHhtB+VMS8VtB6S2AxwIzsLtAjMC6KJ8AhzcUzXBZQk9D1TcIn2yoEQ5HhiQV0AOrl+cGeYkm/gDilmiUQ/csM87fIffUvHj2pQtSGpjAbMCfEwzhaYHBH5oeVaxqsp3SzY5uMZ8ik0VgmHntnsylyqXlpYl2+/rWVTf2CFiemSU+pedLfaoHk/st/FnZWffPlucecaZ4pOPP1G9rERkX3vtdfUeESAqNX3uuWfFh5LYFhX1F0OHDlUq4ssvv6LWvWHDD2LLls1KGaTB/KdykP/8c8+L3NzenuoocS6SZHKAJL5vvPGmJCIbxCfyM3375sl1vCw+/uRjMWL4CPHwQw8rzCeeWCjOPPMsRRSWSzJEquJPP/4oFsLry5Z7r/8oX9++Y7tS36jnsaxsryK/M2fOEl988aWYK0l18YBisUOSos2bN6vtvfLKK8Vnn38hbpt9mxgxYoQp46b13UeEM5LsHhmxzDJSPf5BI4/g8T/nnLPV/rMb5cYNG0WvXr0UkUf8/XLQMHHiBHHFlVfI8/C8uPzyy8X5F5yvXuf13XzzDEkQb1DrInWUSHEY/gcffKCO+/YdOy3TED8j8YUXloixY883+Oecc646/6nsP20TTSrMmD4jgH/j9dfLYztPfY4I7vARIwP448eNF3fddZci73T9zZt3f9Lxp/PbK9sjuFTKPUKug0rvP//ic0Wgzz33HLUuUqkLCgpFRUWlek4TGqRK791brr5D5/1zef5vuOF6VXZdrRXo7Gwmz8HzX1RYJPL79lWl/X375is1mx5/vWZN6PmnY0D/l6g8myYsXn311dDz39Dx/7uuP4fv8B2+w/878VUPLoxvUhn/VEGvbR334EaZ+OryZGv8dUhFPVaAIHFIm0wdJoI7SYuBxWAwVaDJLboonwwK7kmal52oudqxoN52sVTcdvpvW+B/ba221UxY0qEXF12UbbE1rblKlG1Sa5tLMQg6YaXD8zbgnNwuRMXtAKy/C2TgcpkyK7g9oP/2FJDO+0AWbhE4gQ3ySpQPBTI8yShnXyMmUw0rXIkGzHqC5CCRaIxoJJokLw7f4bckfN+ggJ2S46bEJVD6H/N63PeVV/ilx0BgedaxSvXgRrXJVL35bJXuzcWZy1qrJLkW7PODJlNQVrTft+sPlAnJv++/954cnOcHem74hsXfIcX1Pfk5/s79998vrrvuOvX8cUlwL7zwIoNJSuy9995r8AcPGSK/+4Ekj1tEtiRnqz9bbfBvuulGcffddyvsCyXBfezxxwz+nXfeqVRaMlii5ZlnnpGkaqz67oMPPiiJ7Jli/fpv/D4hub4HH+DX1wf2f/u2HUrZK9mzR6xctVIU9S8UlYrc0Gf2i1EjR4k3JbHevGWTJuQb1Pep1JWeb9y4Ud2IiSzvldsSvPlDPEJ1cIY7leMfyAW0jv+AAQPguEdU+W8vvX24DlJL77vvPrWPNLlAn5l3372G4H7xxVeinyTnW7ZsUZ/vrxVcG3/L1i1i8KBB4rFHHwuc/+rqoOMlKetjzx9rnhPxf18SY97/y6+4Qlw+bZqYJhdS+HH/n3jyCTFk6GDPNAvwSaFltZomLei4v/nmm+KVV14R10ny27tPH0VM6Tt0HsrVeQgefyShq+V5prJmmtDYu3ef2CkJO11/mzZvUmZqpw89XZL59xX+xRePUxMuq1avVMeJKgT2yfXv3LlTrW/Tpk2eOqwU3O1J5/+rL78SX375pfjyiy/V3y/k8tWXX4i9ZXtDzz+R79GjRysSTGT/u2+/a/T/n338/67rz+E7fIfv8P9O/FJWcGsxFqjx8Q+Nk7DHlsdClbpEuQ7GVDz+YuEBjUIPxWNcokwuykOh8pX7cFk8zAH1lvtwT4T+2+Ot9JuuoOAeAyS3A/C/1pZi2wY4Iy/omtzK6slttvLkVpZEbKu6GMKbZrknZ1hZR23BTZl3vAOotx0hKJhV3BPARbkH9OGeBgQ3F4KJC0HBVT24mPnEj2k2OyEaiFhJQfZq3MQ20cBXG6IrDt/ht1x8j+BWBGb+1OP4oUB5cpzL/yvKA/m2xhBB/yBXcg6u/LGuq/NnGmkGMliKbFnmR/1ZTe5RUWW4pBIZswgwhkB7f30TI7JEg3gazFdjial+TMouvf/TTz+ZG9dLL70kzj7bU7YWLlyoFEO+mV137XXioYceMvjUy/v+++8pdbRXdo5aH+M/9NACMXXqVIV14UUXqV5Lxu+TlycJSY5SXqmflMgFbQe9t3PnLlU+SqZFtP4n5DZU63JbNjMaRq8/4b1OxkBKpZYk5bnnnhPnn3++fxOX2znt8mliwYIFivwQSSQCwvvfp3dv8c0365Ich9FhMnAz139TPf4RMOOwj/95540VL734olkP9cXS8Sg3JNvDf/HFF1T5uCK++pwOlM9feulF9XzChPGecZTGJ0MpLlFmfCJ15553rrhBlSNXB7YlEgk6XlKJslJwNT6RtZdfedns/6KnF6nycFJM58yZY/b/u+9/UEonkXZ7/0lhJXW8WhNcIul0jZ199tniyiuvEO+8/XaTx98joWQEtUMR6xx9zdD1Qwtdfx99+KHCpkmYO26/XX2WeoWJcD/3/HPq/NMxzlbXXI667qjkntRhtW4ysGqm879XHnNSpVklDvv/F3b8/67rz+E7fIfv8P9OfKpAiwKRTWX8U6XMpII5uDX8unFRDo6/Ai7KugqPxIojR46wgjsYIlYLoSI2D1yUT4OIoB5AcNkMuIuVhYs9uFyizPyvPfDBLOCLGVbsbKaV3IOctNkIbitLtW1lOVvZLleZIW7KmSBNtwWDqQ5AdDtDnlI3aF7uDrbUJ1sxQTkQEYQlykX6pE0gGT5GsxbxuCG5lOspkjJEEw30LiYstx8R8J5Nxd/H/3oi+UWH7/BbML5x4AuotnEzaRTXyq0fE1Thq7Z1vksy57aZmCAymYJSmip2UdYlOp6LYDT4Q1+j39P9KhSUTn0uwZnUSMDcwTeGiKi+TVJwX331FbD3r1Z9qffee49SBwsKC8XqVatNqRGZCV0xzSO1jz++UFyhCK633muvu1Y8NH+BwafS4vfee18T3GzlGMv4s2+/Q9x6260K86KLLhQvS+LM+DNnzVL4EQyMjwQD44mUL1q0WOTm5qpSZ+7t2SUJ8KLF/PpHyhiIsMko6P333xdDhgz2y6Pkes499zxVCkwRSYZA6ht0n959VF9mGL7nTOyXW5neoqM4/mjKgcef3rv++uvEnLvnmP1/fenrqufYxp9922xx3XXXB/Cvve46VZ5Lrr2077m9cxWR65vXV5E2Kg9/Wh47Fa9TtV/1UE+aNEld1xjrMO++eWLc+PGB/eceXManSY0rr7wqaf+J+D6zeLHXFyyvM1LpZ8nzGrb/qgf3Pk/BpRLlESNHQr5iJKXj75HQXmLHtu3iPXmeqRS9oeP/1Vdfi6GnD1P9vDRBQ68R8R4uv4P7z/iqBzfHI882/rx588Q9kjDT9XqPXO69515FoEkxt/EXL35Glezz/lNPN5Fq6pG2z//R7n9zX38O3+E7fIf/d+KX6Zggj7zWpjT+8RRc39skWuO7KJt8XGv8RfzHy8GN+eKEHLcd8UqUJ0IPbrEmuPlAbrOtPlyMCeLq2uOsEuVjIB2He3A7AP9ra7WxpltxQWECanpINm6zmUylWZKxHRuUaUnMra2IoEwoS25jEdxOEAzMWbhdgeAeD5J4T6vhmQ8+m0yhgjs44KIMMSek4GIcSiJp+J9oxNAnEVLlmUjNCSiMdTh8h9+C8en/jSpvifvlyKzY+oQ35pHeuEeGMYuNzaXqdVkNldJwOHkdEFy/B5d+zGv0jKYuz4n6DoNReK+0pDRw4whY+EPenJlZrfZKfov6F4tPP/lEEpH9SrEkcuKVD1PZ8ZVi8uTLxO7dP4uNP2wQQ4YOEQufeELd7EjBvUKSI+6ruVaSnQUPPWTwh48YId6XBHfLZq/896qrrlJE5KuvvpLEuUC8+opHrEnBJYLL206lqQMGFIu169apvssHH3xATJ8xQ63z/v/cL+6ec7d6nQhH/6JC8eGHHyrXZ9pmMucipY36gakvmEqNSc0jhW/3rl3KXOoJuf2Uj0v4FNeyYeOPiuD2Ciik1aK3UnC/UaXM/5s/X3z+2ed+/IHdv4R9RUdx/KvB3MOUgsnvkMEVlf8SCSLn4bPPOUfu4/3q+++9/554+umn1eMXX3hB9dV+/vnnqiyZ/vYvKpKvvyj2y3WtXbNWLmu8Ze0atf9EUndrB2MqBycX5g0//KCcjmniYO9eT8Um5+K5c+eq/ed9XEI9uOePNfv/tTyXpJovfPxxUb63XOXgzpHngYyutmzdqr7zyMMPK+WY1GXalx07NE6Z1zN+/Q03ei7KugfX632GMmzAn79gvtpH+/irKB8ymZLXBK2b8Km0ncrRP/n4Y3GuPH67dvm9xaPHjBajJJGmHljCJTMyMuKi71TI/5PUR079xVQZwP29dC3Z5//WW2aJmTNnillymWmWm+X1tDnp/FNZ/pQpk5WhFf2/p30eMmRI6PlvaP//ruvP4Tt8h+/w/058UnCNQltbm9L4h9yS6+p8t2QuZzbEt64+afzFol4cFFwSJg4fPsIuyoM1byrQC6q3Obpalg1+TwZPJI4IYpMpVHA7gYrb1mpRRYLbBkiuHRUUxjebXb1NC5GF0yyWjdJya2gSzgzpucUsXM5J6gQmU12tEmXswe0Jzc5MbnMhAzdfS+08G6EU3Dj3CeocqAorBzc5ezScMCQase5JpGjmE9pj6fAdfgvF93rWy00JcgwigeIwIxjTBHifIriQfxtwU+a8W+9H2yO4B70eXO2uHAVr/IDBAvSheEtUlJWW+Fl2OGsaCGWP+Pb8VE4k/94x+3blSky9jpSFerMkkx7JiYifd+8Wl0yapMgeufXeeuttytGYMFSJ8hVXmJseuekSwWV8csMlNU0puJJ8UL4uKatUqnrnHXeY0qSLieCyoZK+2VFfaaEkapSdesYZZ4g1a9eq9a5ds065Oefn9xX9/h97XxpfV1Wun5wMTZqO6TzRJmnTtGnTCQUUBwaZOjAKehWKoiAiDpeCMqgo8BdUBmUSlRlEBRwYlMEiYxmahDTTqdf0eD+c9J7spn69w4ez/vtde71rP2udneQUgk3pm99v/87JmZ6zh7P3etb7vM+zLPw+F16oq91PPf1U+LojdW+j/p4Xfklvw9dMLy1VcAmLjJSo75aqfeSafOfPfqbxXno5qjLv6NhhpVlLGpfonl6K06F+4quv/q5npAF9RWCksS/b3w4SYODA+F/7+tf1tlqyZLGWX5Osqlf3KH9FHXvMMfrzidCTAzWRs8WLG/TtJeH/dFwl4beEZPhxysHV6/xylDm7KJKB6/t19eq7dh+CDM2s1y3UgwsuyvSZv/jFz7VJGOETWT78sA+qhx5+yOJ/8AMf1PufPp8qlnUG76pvfzsymdIuypdGObi/vFsdERLcJHw67tauXadVBP723277ZKOYIJoEoOOvPjzeyBWZqqe4/a+84orwWGlSbSHJ5u1PxyodG3TM6feEZDeKOTISZe7BfYf7/9XXXlOnnHKqWtywWMvfPxwer9SzO9T+T9r+/8rjT/AFX/AF/1+F39reFo13+sz4pojxT5SDG0uQ+XW6gttvxlze+CuwSRhxO5kev+0J2EV5LZhMLfckytiDuwDkyXOgestGU5OhkjveU+jWAP+rhOJnmdePW+ZxypSnIh51mbLPnFNeXFApfDE/A9dvKK6GcjVbSI+HHtxa0HJPhybm2UBweWMvhEboJsjBXWkW2mkn7907aAblga04UVZnchVrH4xrE//3aEZ+hM8WfMEf4/i2fyMkugG6kee8DNxc1AIQuSgnS5TpfyJhLLmJbO2j1+jKLjgkJ/WhcNg5z1pGLsrQ6+KEs8cXpp7eOGQ9bSRI9D2ef/75iEQ5F6fo4kOEV8fJOHKjhJnfBPyXTAWXKnzU49rRsaMAH3P0GJ/ks9vf2u7m7Jn7JBul7+zjUyWvW8feDL/+9LqR8HH9Cau3IB6huPUvZvsPhU9V+W2vbytY/57uXpf8hduKCFRPT8+o4sc9Wb0jrv+LL76kXnn5lfcUv8fsh2K3/1vb34oipvYBn95DaoZ9Xf9i93+7qXSP9vZ/L44/wRd8wRf8fwV+W3hNzqCJVBHjn24wk4pclDOxuzI6KKNEWZtMdTrGoOCivB56cFugULjU8Cuu4C6ECu5cU3ycDTytFtS3E6FoOR6IbbVXza30OGNlQttrkrlxKqEv912ZTKUS3JRLEx4v9cJ6K6GCi/prJLqTE0ymuAe31mzIObCgRJkruIsTYoJWRTm4g2YAnrN9gkRw/YpXPq9GDFJJqqENrwzNJ5KJpIqb4Av+WMQvMCgwTspoOMU9uHQCdQiuve231vZ0gmaSyk5/luA6BDbjRAKhk2Dcg9vmGj5YMwi3/yW6qPS4YesFM7W+dCntRgKke71IgN5CwwmDzxVcbeC0H/D39/oLvuALvuALvuAL/tD4OiZoVwbUaSOPfyIzqVh+zIab2kWZCwne+Csaw3V4RqEBE9xTEkymuAe3DvpwDwEF7RxQ104HYosxQTXgpFwDxlJV0LZaCXLlMs9YKsnvKUlJPGp/qSEig1LQJIy5Rb7JVKXH3mu8Cu4k2Di1oO2ekSBR5hxcJreNpnqLUUHWZMqJCQoH49QnWFw2S37EQtpQbCOvhmUsgi/4BwQ+99UG4Jjskl33PvV6RCfarCW21mSqP6rgMlGNc3BZouzHAfXZ+2ilz7Oabe1tdqa01wtrt3l0vSgtSjsRAHEOXq87c5vudaz+/SgB5zMT8Km6+Mqrr+w3/P29/oIv+IIv+IIv+II/NH4bm0zxmKaI8Y/OuzXjK67gRsS3Oya+3vjLiQkKjPousD24vkS5CQjuYiC3KFHmHFzsw51u+NsUqODWJJhLYVRsZYJEuTShD9eXKJd6psej5qBcmtDwi8zbX8pgRdBJaxzkI02E6u1kMJia4RlMoUR5AVRwl0APLptMtZgc3PVMcAMjq6QBOhmrjFg2S2yFzA+Tz5IvMAcq6Jz0TW8FX/DHOD731ebYNdkjtrHZVE5nq1G1l/tu2dGvn6XI2X7dQ8LZbxwdREu3iQnik3zkKliYBYeSHpKzOnIjcEdMOwYQ8cWpB2ZR0xjs7smT7AxtOtlMwpE4Cb7gC77gC77gC77gF4nf3toauyZ7JHeo8Y8msru55Str3xvl4II0GcZfNgkjN+AUKP4ZxwStMYrXZigUNhlutThBojzb80eaDorbKaDIHQ8+S+OB/2F8bKUXLVvm8cfyBIlyyWhWcJNKwuVeD25JwperAGlypZeFOx4IL7ttTQaTqVrow50FG3SeWRZ5PbiLoQeXCa7uwbX9gTAoR4lyQVBKUSWyfWElyZ8p+IJ/IOCjRNnJwR1wpcr8P80U2gw2liBnoVKrY4IiCU4WwshtBbcvzn1jMynMvs3AjGaUg9vrhLb3Ohl0eIHCvpp0PPOaMJtrL2hp7LcxM7JpvBimBV/wBV/wBV/wBV/w9wnfVnAzfcZkauTxD42TkMDy+7pNZdfJwDXjK20U2rnD6b/NxT245KK82hBcNplicttglkVg7jvHcDDswZ0KPbhTQJGLRLcS+B+SW99oqgzIbKknTy71em9HrQe3xJMkJ7kol3kS5XJoHK7w5MnVnssW67SnwkaaZpZZIFFeABVc30W5yZMo6wruoK3gDkBWZ8fIsk2nDJb35J8jO9jm91VoKviCPwbx6eTYafo34izcwDooD4Czcs6QYX3y3c0mB9gXEhFcPlnHeW2G+FrHQDip6x6VPui/jc0YWtvawMwhbU0fXGlR2umzSePrQZ6U9p+DGVq3p6YwIF7wBV/wBV/wBV/wBb9YfCa4GbuMPP6hSi3m4DoSZUitwPEXt47FPChS4u2Jc3BXm+rtSqv5qykAAIAASURBVHBRbgR+hca+84dwUa71JMoTgeNVg9lUNah6K6FyW+rdTyWQ2aF6cUc1B9ePCir3cnAr4XHMOkqKCOIe3MlQwZ0CzlwzEnJwuUy+0Gz8Ri8HtwUI7sY9eyJpMhPcXBDoQfhwhjvFxK7kR6QS8Sfk88P3UAq+4I9V/NwAh4QPFCyRq3JMdAOWKGPvbZZzcCPnv0iiHJ2U7clYuyt3eWZSGZDnZFyXQfP+9rb2AlmRnWnt7XVmadMgNYp7aQqD2+MemcKQdnthc94v+IIv+IIv+IIv+IJfPH57W5sZC2XiCu4I4x9tMtXfb8dUlvh2d9mqrT/+Iv7TacZwuSBOvhiMXZTXQQV3GVRxFyfk4HL1FpcZnvJ2omcwxUsltKhWAVeshApuyrvv5+CWJhRf3zXBLYf7qWEagPHLjYMvzrfVnpMWa7S5nD0ZSt3cuDzTbNTZ0IPLJlNcxeXMpmVgd30o6cv37t1rZy24AkWzGcmVsmEoRb44q598QaVthIqc4Av+GMan30yXPjlCRBD34Qaus3JhDm50st1tzaT6TQU3mpHMmlnIrHVXzsCsJcxsOnlw8cUgMpnqdWVDmEMHsqKCKAAn086bwQWXxHQv2v4biZJ3MRR8wRd8wRd8wRd8wS8Wn8YvTGpdE82hxz/d3d3WQTlrCG7GSpTj1AocfwWYhJGLudDg3kGu4LLJFLsoM59qgCruAigusslUksHUVDAMngj9t+OA/6G5VIUnTy5PSOqpSIirHTUHZb//NpUQypuC8nLKaxiuAs01l6eR1U+E+7VeVBBXcDkqaC6Q23rQiLOLMi+rTNl9vZPVqatMUcN1PtF5Ntl/Jz9sb2P+HXndCr7gHwj49JvpIBflIOfKlIOgIFeN+3X5xJrFDFzMwTU9uFy91e7KJibIWuWbmc04FqgPKrjRDGdkMtUb97g4LogoIQLb/3Q8m+pciHpjl8M0ZNzZsPZ0jytTSqOjouALvuALvuALvuALfnH4HBMUG0yNPP7hmKCsIbH82i4jUeaCAY6/uPDA/IfTMIxEeRMYTLWAi/ISqOAuNLxrvlnmQRbudCC5U6FQyeZSXMmtBP7ny5SZtFaCMrg0geiWJOTkjgrBTdI7p7yG4BSw8hSwcazeVkP1thoigiZCRBC6KHMFdw4QXDSaajA7gp2Ul5sK7gpDcOOYIK7gBpFl9kgOssPGrAwlIM2rIathBZ8n+IJ/AOAHfg6u7eHw5Mm05KJAcZt9i1FBZsbRSpFZoowmU3iit06CmViygy6D4eNtba3WDMIxdehJQ+8MX4R67IXIcTtMpz0HRJyZhX4csPd33BMFX/AFX/AFX/AFX/D3AV9LlDNu9OFI458uTXC55StrpczdOgc3a4kvjr8wB9cq73I5tTdyUT7ZtHOuTnBR5gpuHRDcOdA2yv5IteCfNNXzVaqBWNhqcFGuACNibG0t8yKDUkOQ2dLRJLh+k2+SPLl8iN7bSlgZ7sPFDNwaiAnisOBp5v4MsKKeByZT3PS8CAjuMnBRXmV05SfvCSAmiKtMnZ0wzs8XVffKj/BAPuHB/BBvdCtlgi/4Yxefq7I5dFEOoipuEMSh4Tlbwe2EGcTY6MDGBFGl1sxOsvEUxwfxjCXPSjon/8wuW/nlbLh2I1F2HBFh1rXXCVpPg6V/2pmNjS3944tVL1780GnRviYNZhSCL/iCL/iCL/iCL/jF4WuJso0+zBQ1/qEcXBxXcW8uxwRxwQDHXzYmyDMFNRLlDRATtBLMehdDBfcQSK5hkjsLCpC1wNmmgKfSJCC26MPE/1d4qTsVXktsCkhsuWdsXDKaEmU/e6jMY9spzzkZWXilZy5V6ZlMTYCooKnQrDwdZgjmgHMXZ+DWgTyZjaaawQ2MdtoG7aIccExQzu7sYrxmk1sV8y6hSHpbPl8kTRF8wR/b+G5MEFdtuQ83FxNd04fbSRJlyLeNbetNpRbckrmqayXKu+IIIH3i7ttVULXdBSZTuoLLs6+OvKjXdT5Muzl2aezDMRe3F7ZuVY888oh+7uWXXlKPPvaoEyPgZt55s76jgO+4Nvb2wgXz4MOn7U/HnGx/wRd8wRd8wX8/4tP4BTNuixn/RESWq7RZW+mNJMq7TR+uO/4i/hMZheacYh8Z8EIP7gpwUW6CPlyu4C4A9excILmzIAOXW0wneq2n48FFudIzmaoA1W/FMNyydAgfqFFzUU4NERlU4lV2sdJbCSVoXilcWT8qyM/BnQnl8LmgAT8EDKbqzM5YamYemODSjMSmINyJuVzO5nTSTt6BJlN5t/6VH7YqNowBUH5oguISjnxCP6XgC/7YxKc4oIjgRtFAwUBsLmV/U7lI8sJ29HHVNhtXadlkCtySbWXXEN/YQTB2D8w4M5pRXlzGvK4dY4Iwqw5nVx2ZUFotXtyg6hvqo6W+QTXU1auHH35IXXXlVeroY47Rr73p5pvU+g0b9EWJ1ueqq65Sb721XffhpD2J077ip9HcAvtyMCsP3BvTzozy+xv/ki2XqDVr16qGxfWqoaFBnXnWWerNN17X79ne2ho+Vqca6uk52nfhbbj/aB+2bm/Vr6H3PPnkk/qzTjzxRFW3aJHezw2LF6vjjjsu3M+/ku0v+IIv+IIv+Psdv62tXWX6MrEUuYjxD5tMcXXWmk91xdJlf/xF/KfTjOE0yaXCRDiGGxzcyxXc1Ybg+jm4S8BFmftw50LL6EwoRmL1diIocydAcZP5X1WCuVQqIQfX55WpEdpm37VE2S8bozw5BZbPlaCvroSqbRWQ3vHQg8vl7Kne4kuU54JEeQGYTDVBD26L2WFrtUR5z6AxxcmZ8nygdnTsGMGHJ9m2J+/9M1SfZN4XgnokooBJCL7gj1H8qCrbqWUtOWsyZUit46KMJlP9MIu4Ow4gt1Lk6ITOMpqogtvt9KFkQIpsT/5ond+3S7W2t0IfTKFcKI0GD+ZCUxcSooceeli9vu119frr0dIZXhz0a3t69GtuvukmtWH9hugi1Nqm6kMSvPWFrXH4e6978doXfH8m183Mw1y+dGKA/PsV/4rLr1ArV65Ujzzyq/BY6FR/evpP6hPHH6dOPeVUi79N769tatu2N9Rrr74WPv8JtXnzZotPpPeJJ57Qn3n22WerL13wJfXmm2+pZ595Vl381a+oxsal6vU33pDtL/iCL/iCL/j7FZ9zcONxzsjjny42mcpyDi4/3u2Mp3D8xckxzH8ic9AgJLi2B3cdxKsuN1wqSaI8HyTKM0CmPMNT306BZBxsRa1KSNZhjljuGRUnSZT9/NtRqeCmPOZc4pWRkyTLfi5uuSdPrvGqt2w0NRXK3TNgI3I5fB5k4NYBwV1qSC734LaYCu6GgE2mglhWGeXg5t2Rfn4IEWd+6CfdSJYhMkiHNQgSfMEf2/g5CAlH12SnkpujfLWcMaTqjMjsbooHMrb11vQgquDyiTzLuW3h0tXVbU/WVqKzKw48tyd7dhUMl9a2NvdilAZzh3TamTXl56iixySoFy5oP/3JT9XnPvc5/T4iuOvXr1dvv92h1q07VNXVLVJr161V115zrf7sp//0J3Xaqaep5c3N6ozTT1cvbH1B498Uvu/ss89Rp512qmpc2qg/96mnnlbHH3ecampqUp849lj1xz/+QT/+5ltvqmuuuUZ1dLztSqJQ5oS9Peleb93SbrxBkesfz0i7678/8Xt6esLts0zdddddDv7zzz2vPvOZz4Yk9c0C/BtuuEFXe+k5xqeKLu1bes3mczeryy7dYvE7wnP+shDj7rt/OebWX/AFX/AFX/APLvxIorwLxjsjj384B7ffxAAxGY5ycGNFHI6/ImVdZ9xWZsZwxkWZJcorwaC3CXJwGwzXmm+KinM9ifJ0s9RCFXeSl5RTBTm41cAFUaJc5t0v9xTBfpJPajQJbskIkuRS+FKlntFUFdzyfZQoT4BKLttM14KT8iyo4M4HgstRQVxK5x7cZYbg0k7b5DRW5wasK+xIBj1FFtYKH86rxMzS/IjvF3zBH3v42nU8/L0EA4WRQEx0c/B/Z8cO2x/SD7b1bCalJcrmRN6fhQouEd+M33MCjoK7MnCij5Y2TXDTMOva6waxp137fi1jra9Xjz/+uF4nUnJQb0pEmK5Xp59+WixRDgkuEa/Hf/d4SHDr1AP336+2vbZNvR0S0sMPP1x9+cIL1e9//zt17jmb1VFHH6U/+9prr1X14WuvvPIq9dhjj2r8E044Xn3l4q+orVu3qq999avqpJNO1Bj33nuv/twnnnwylkKlewtlTenCUPm0s777tv44k2zNNfYz/nPPP68nEd56662i8Lf+ZaueMKBtiPhUwX3yj09o/M2bz1GXXnqpxX/55Zf1848++uiYW3/BF3zBF3zBP7jwdQU3A1LkIsY/NE7qB5dk53Emvt74y/qo5MAodCBQe/fuZYLbAhVcLhY2AsHlCu4hQHBng7q2FpaJUL2d6GXhIhesSDCWqhiCX5YA4S0Z7SzcpHDdMq8XF9n2OM9giu9XwVIN1dsaCAbm2xmeRJk139zkzHrweiC3SyEDt8WYTG2inRjY7M5YRpkYmZIvimYkd0Xm88WpPpMwBF/wxyh+bDIVJEqSc9DbHpFh46LsVW5ZMhNVaqMTunX7wwouzFbGt5AJZy4IdJ8uEIWSIT9UPe30xxDJWRQSSyKXdNu8fLl+7vrrr9dVWSa4KFGm1201VVoiSE3Llqn29nbV0dGhtr2+TdUtqlcvvviiuua6a3SvJ+IfeeSR6sKQDL/5xhuxC6P5vs8++2yBcQXOFse3kMnHcqi020tU7Po7kql075jAv//++1Vzc7PFpwmIMz95pvrkmWfq2z8/84z9XJp0OGn9Seq8L3yhAN9W58PHzjrrLHX44Ueon//85+r6H/xAHXP0MepDH/pQuM92jLn1F3zBF3zBF/yDC58UaL5x5kjjHz/vllu2yHwK829x/GWjHgOvOBEE3IO71rR1LgcfI5Qo10FbKMe1zoQFJcrTPHLr995yTOw44IoVkIfL3k0lCf23vjR5VAmuby7ll45T8GUxE3ecV8Gthh7cidCIzOS2FhqXpwHBnQXV20NAolwHObjNXg7uxkiaHNiBecAuyvnhGxHzI5CE4fsY8yOTDsEX/AMAP0B5Cy40G5gLrNEUzwxqk6n+yNzAdfTLGhflLnNS7zPPs3S5G0LNvRw4nNGEHpW21vZYDoR5dmkIW4esukjGWq/uvede9dcXXtDLiy++pN9zAxHc007TF5+bb75Zrd+wXj9OBJeqi1SBpc+76cabQkJbp/ty9VIfkeVfPfwrLWE+KyRliP/QQw+pD334w5p80WcSQY4uqj3xRTAN39vL5kPzC79HyJFDFbn+8azz2MF/+umndeX77bff1v+/EO6X6/7f/1PXXXet3sZEeBmXZN2HrjtUbSfTLw9fV3ANwd0QbmsymfrYxz+uzcO+9KUvqb/+9a9jcv0FX/AFX/AF/+DCZxdlW70tYvzTZfJumcBmzKS/Np8yplL++IvGZ2Ssy/yH0zAo/YKKgIYrtRje1AwFw3oguPM9ksucrBbkybVeDu4EUOiOB/7nq3uxtbXc45aphPulo+2iXDZERbcswQGrAohuORDccR7JHQ99uJPAgWsqSJR5hmAu9OIugBzcBkNuGyEHdyVIlDfs4R5c6CHECm4+SaaZH14S6os68yNqRoeungm+4I9lfP69BM7MX24IiXKgXxtJj3fHYeOQhdtlXZQzIGE2lV00W8h4M5r2pJ+xUp7WtlavTybtWP+n05B1Z3pi4iof5thFFdxTT4sMjXQPLrkoh4+3aoJbpwkufe6DDz6oDjvsAwnRASRRvk6dSQQ3Af+ZZ55R527erFpWtaie3t4C/AKzC2dd4tlf+5nppD6hkdc/nnkeO/jtIbFd2tSkbr/jdgf/qSef0tuejMDotc88+6xaunRpuA8eSMSvNz24hH/O5nO1RPlAWH/BF3zBF3zBP7jw200OboHPyDDjH3RRJvLKFeCogtsP/bnx+Mv24OawSEES5UG/gtsMLsqN4KJcbwguK2gxImim4WrToIqLBlMToCUVK7iVHles9LycUh639FN9Rs1kqsTTRZcCuS2F/CJ+rtJzw6ryGPt4b5lkbpng1gLBxT7cedDovMDMKtSZMvoy0I6zwRTtsJNyXL1laWXOENz80BwiP5zrzpAet8n/5/MqmbQIvuAfAPiBIa0oTc6xXNlKXmLCS3b03P/RDyZTXMnVbslGcoN291EFNwN5cAkzmOygbB6joHR7UQFL/7RnzY+5dVQRZBKUBhlRRHCNRJlclE0Ft7uzWzUta1K33Xab/v/1199Qy5YtVz/68Y/0ReWRX/9aHX3UUdqd99prr9EVXMbv6upUJ554krr77nv0/3fccYdqaQkJbk+3evWVV9U3vvF1TaBtrICTx5cwg5yG9YEL5b6sfzxD7a7//sYnMrpmzRr129/+VnX39Kjnn39OHX3M0er0U0/XODQBQvJvMp3aZh2wt+lbOnYIv8G4KGuTqc2bw8/ccsCsv+ALvuALvuAfPPjt2kU5Y8wzM0WNf+g6yKq3SKLsEt+4ihuPvwZQogwkd2/sotwCBLcZCC5XbxdCD+480387B4yAZxpiOxmycCcYUjvRI7fjPTflccAby7zYICS05e+Fg7Lf8Ov34ZYOIV0uT7CCHgfsvRpWnp22/KggJrezQfc9z5Db+UBw683OaAL9uDWZIits1J5zrqer8cwnlsPyw9S6RpSW5oenD4Iv+AcCvo4J6uoEEuuGhbtLoN1qd9ueWz7h9rsmU+akbAmulS7viiU72JsCWXCuRLkt7mtJx7b/Vi7khK5HF5eG+rqIBGE2nZYo36BONxLlW265WW00FVz6/5JLLtHZq5dffrl+7MEHHlBHHHGEdu2l3tEf/vBHGv+6665TZ551poP/ox/9SC1vXq6X1atWq7vu+pl+7SOPPKKrkdRfmk73FnyftD+DjOYVaG6xj+uPDo8O3n7Gp4kRMuFasrhRLVmyWFduTz/jDD0RQPi//93vdC/0okWLtEScnudb6mWOYoIa1B81wU0DwT0w1l/wBV/wBV/wDx58liijceZI4x8rUe5niXI04d/NMUHhc/74i41Cmf/E47UcV3BXmWU5xAQ1Qh8uk9wFhoPNAoI7DZJvpgCPmwQGUzVAcpkLVpj/y70iaZnnkuxn4446wcUPLfXIbgqck8sSWHgSwa0Ak6kaCASeDMQWl5mG5HIV9xCQKNdDKb3JyJObYUZi0+CeQSOpjPtwyQinOIPa4UlFPj9EHEu+GKIh+II/9vG5rzYwUVtBEEcE5SzZzakcG7h1dkY5bVYuAzOKLFE2J292Ue7nfNzMrkKLfGObnwF5Ms9qtra2xnb8po8mDTOq9kLmzLSioUQaDCLSbt8MPE6PdXV2aWkxzuqS6y+ZHo2I39vjOQRHr+nu7ikaPw3yKDeU/l+z/v8q/K7w+HnuuefU9tbWg3L9BV/wBV/wBf/9j99mKrg8zilm/BP12sZZt/y8jQnq7y8YfxH/2WFjggYsF9qzx0qU1xiCy/5Fy6CCWw8ZuAsgB5flybM83yQmuBOGILjIB9GEGF2Vy4fIwi31qrijYjKVSujFLfMkyihVLvO+dDn04FZ7DcfchIzV28lmQ02FDTgT+m/nQQ4uS5TZZGoFEFzaaRvJRdk3x+m0Fdzi/vIg9yQqkc/vEw8ZoiVS8AV/7OPrqqzNwY1OjDlrNx841Vx260uSJ2dN3m0kUY76Sngmcje7KGdYqoOmUhlrvrDLM1zQLsoQqI6mDo6cyMu8S3uGD2mvb6bXlxj50iT7OWnBF3zBF3zBF3zBF/x9wo8ILo5zRh7/EJFlNRyNnThCiMZPSfLkfpQoQ1RqZDIVcEzQOogJajJVXOZVDV5RcQ7k4LJEuRaKk5OhejsBOF813K8Cf6YKILocGVTh8cwks6lRq+AOJ08u98rJKY/UVnhOypVeTFC114w82ZS5pxqSOwPK4XOgD3ehWbiE3mgI7jIwmVpXQHDDHZoDkylXrJn3mhaLrXPllV8sy4/oTpsXfME/IPCxfyMAY6nAzgbmnMggmjzKOtJkM5NoZhQ5DohO5Fk4EdOJW1dm+0CWA9lwGQ5AByMG3YPruBSmXadDsPePe2wwow4uRDwz24PGEr2JJhGYiSf4gi/4gi/4gi/4gr8v+K1t7TF5ZWXaCOOfLiNF1svurK3yauJrDabc8deASY5h/hNFOpLJ1D+5B3cVFAfZz2gp5OBiTNBcMP2dDTLlqcDdJkEfLldwMQMXI4LGJcQBJaX1JPXflr0XJNfPwS3x3K/KoJJbAStWDbfjgdVPAonyFNhY02HBHtx5oAfnEvpSmHloBpOpjUEQOP231kXZr2QlmO7k88WqPPP7WItTgi/4BwR+JDvuimb+goHEvls0cetEifLubJzJZiq13UainDESZY4K0j24OHtpekucsPPMLqeKSzOg6V7X3CGd9kwgvD6a2FiCb3vcC1Lazaxzwt4Lcu56BV/wBV/wBV/wBV/w9wm/Tefg9oGp1MjjH5uDm40Wnuyn8VOBRNmMv6Ix3A63/zYcz1HxzxDcNUByVwDBbTDyZOzBZS42AxyUp0MVdzKQW1+mXAlS5UoguOWeo3LK83gqS/B8GtUc3BLPObk8oRE45emny6FqW+4xdzSZGg8mU1OgWXmaFyY8BxyUF5ml3swyNIFEmRfaaZt0TBAMzHWVqauzaLuevP9IvrhX54cwt80XTUsEX/D3Pz5XZQeGIbckUeaZwaiC22/lMZx/G1Vqs6bXNjph84wjnZTp8YxjtgAn9l3xTCbmxkUEFwPUXefCdI8rR/J7Y+IemTSYTaTjHhswi/CNIqwjouALvuALvuALvuAL/j7gt2qCC2S2iPGPliLbnNt+a7gZuSub7Ftv/KULD0ainMM0jCgHdz2YTHEVt9GLCUKJ8jxwUp4JMmWWKE8BYjsRqrfjgf+hyVSZxx3LoeW1zOvFLfN46KgQ3LIExpzyDKawwus7KJd7VdxqILU1sCE4C3cKzAigi/Is2LiHGILLJfRGqOKuMDsLJMqBMyDvhBzcWJyZ5CSbfwcVs/ywHrhJrxd8wR+r+IExZXNJbc4xK8D7NFNoe0DgRMuzit3dsVuyzcG15lNsquCGnfvuySxVbmtrL7TfN7OosbFD2vbIuEYS2G/jm0Z4we2ee2LaM4sQfMEXfMEXfMEXfMEvFl/34ML4ppjxTzf02ma5BzfDxNfIk73x14COeuyEgsSAMZnaQwR3kykGtoDB1HJDbtFFeT5UcOcYXjbbcLVpUL2d4lVxq81tFfC/Kq9ttRyWFPTioouyX2wtHS2Jsk9qfXMpBkEnrBT8Pw6ck6sTqrg1wPqnQAYuy5S5gjsX+m8XQOl8CWThrgAnsDWRRHnAyfAko5wdw5hMDV3hyg9h1uOSg3x+OKKRH5G8CL7gjyX82KCAnZIDK3FxpP+5qMd9R0dnLD0GAsuzjt26BzdjTKb67Wu7TW8uzlzu8iTJu8A+3zWZAllRT2zX78iE0l4uHczWpocKbC+4+EE8QK87wyv4gi/4gi/4gi/4gl8MfhtXcHdhLNDw4x8aJ2GPLY+FuoxEOQtjKh5/ceEBjUIHghxLlMlF+VBQvnIfLhcP66F6y324s6H/dqaXfjMVKrgTgOTWAP+r8Cq244Az8oKuySVeT+6oyZNLvBKxX9XFEN5Szz25zMs6qgI3ZV7xGqjeToSgYK7izgIX5bnQh7sQCG4DBBM3QwVX9+Bi5hPf7+zsArowjCVPvriH88MY+IxMVwRf8McufkRwO52ZP30/GHDkyQHL/zs7nHxba4hgTshdnIMbnqyz2XimkWYgXSmyZ5mfiWc1uUdFS5TDi0qPNYsAYwi090cpURpMIbzZWFeK5EUGGIdF52JmbgVf8AVf8AVf8AVf8IvFJwVaBohsMeOfbm0m5ebg9vHj1kXZHX85LspGhUfFisHBQa7groWI1WZQxDaCi/JCiAiaCwSXzYCneFm42IPLEmXmf+OBD1YCXyzzYmfLveQe5KSjRnBLvKptieds5btclSe4KZdDaboKDKZqgOhOhjylWmhengG21PO9mKB6iAhCifIKs9M2UBk+R7MWQWBJLuV6qoIM0fwQvYt5z+1HOd6zxfj7xG/PFz4o+II/hvGtA59TtQ3spFFgKrdxTFBnXLXNxi7JnNtmY4LIZAqkNN3somwkOpGLYMY90feZ50y/CgWlU5+LO5OadswdYmOItGMK0Yuztk6+XdyD4we2x7Oz6bi3RvAFX/AFX/AFX/AFfx/w201MUERedxU1/okquLG3SaYvdlG2+bje+Iv4T5SDm4uLE+G4bTCSKG+EHtwWQ3CbgNzWeX24GBPE6trpnkR5AqTjcA9uDfC/Kq+NNeXFBSUVUFMJ2bijZjJV6pWM/digcq/EXOFFBJWDLHmcR3AnQTAwZ+FOBYI7E0ri87yGZ974bDKFFdy1josyxJxQBRfjUPIFw//8MIY++QSVZ744J6Ak1iH4gj+G8el3o+UtQSxH5optTHhzEekNIjKMWWxsLtVvZDUkpeFw8iwQ3LgHl07mfWZG08hzMrHDYAaea2ttcy4cjoU/5M3ZmdVeP5w97bgcxrKknrh3hmdj/f4d7KsRfMEXfMEXfMEXfMEvEp8quLZCu2tXUeMfckvOZmO3ZJYzW+Kb7S8Yf3FRL4AKLhUm9uwZZBfltYY3LTcLVm/rjVqWDX7ngycSRwSxyRRWcCdBFbfKa1FFgjsOSK4fFZTEN0e9eluaUBYu9Vg2lpYroEm4PKHnFrNwOSdpEphMTfUkytiDOw+anZncNkAGbpMptfNshK7gBtwnaHKgOr0c3MLs0WTCkB/GuidfpJlPYo+l4Av+GMWPetY7rAQ5B5FAAcwI5gwB3qEJLuTfOm7KnHcbnbQjgrs76sE17soZsMZ3DBagDyVaMqq9rTXOssNZUyeUPR3b8+NFCi5ccfg6yJCcsHaQLqXxvb2CL/iCL/iCL/iCL/j7hN/a3haNd/rM+KaI8U+UgxtLkPl1uoLbb8Zc3vgrsEkYcTuZHr/tCdhFeS2YTC33JMrYg7sA5MlzoHrLRlOToZI73lPo1gD/q4TiZ5nXj1vmccqUpyIedZmyz5xTXlxQKXwxPwPXbyiuhnI1W0iPhx7cWtByT4cm5tlAcHljL4RG6CbIwV1pFtppJ+/dO2gG5YGtOFFWZ3IVax+MaxP/92hGfoTPFnzBH+P4tn8jJLoBupHnvAzcXNQCELkoJ0uU6X8isiy5iWzto9foyi44JCf1oXDYOc9aRi7K0OvihLPHF6ae3jhkPW0kSK+88ora/tZb8Sxtr9+T0+te2Hrdi18vxgG8A3zM0RP8GKOttVW99PJL+3X9t2/frrZt2/ae4//u8cfVX//6V43/4ksvqccee+xftv0feuhh9dcXXnjP9n9HeM742Z0/C3/XnXL8v8f4L9Gx8+jjcv4RfME/gPDpWpdBE6kixj/dYCYVuShnYndldFBGibI2mep0jEHBRXk99OC2QKFwqeFXXMFdCBXcuab4OBt4Wi2obydC0XI8ENtqr5pb6XHGyoS21yRz41RCX+67MplKJbgplyY8XuqF9VZCBRf110h0JyeYTHEPbq3ZkHNgQYkyV3AXJ8QErYpycAfNADxn+wSJ4PoVr3xejRikklRDG14Zmk8kE0kVN8EX/LGIX2BQYJyU0XCKe3DpBOoQXHvbb63t6QTNJJWd/izBdQhsxokEQifBuAe3zTV8sGYQbv9LdFHp0Reu+++7T61evUotWbJE1dfXq+NPOCEcIL6o3//UU0+pm2+6ybH3T3tuiPjZBYYTReDjbK47U+xLpw4u/JdfflmdeNJJqiHcJ0saG1VLyyp1z933WPzFixvUE08+oV97+uln6NfV89LQoG9vuekW/XmfO/dc81xduETPffKMM0Zc/7feektt2rhRLQ6PDTo+TjzhRE0eeP3PP/+LIVa9/kzGf+SRR97x+p922mnq+uuv16+7KTzuNmzY8J5t/ztuv90SaFo++rGPqe9dffWo7X86R1x15VXqre3b9XNPP/0ntXRpo3r22ef+JcffueE+/+Zl33zf/P5ICfPtq67Sk3Aj4d98881q/fr1cv4RfME/gPB1TNCuDKjTRh7/RGZSsfyYDTe1izIXErzxVzSG6/CMQgMmuKckmExxD24d9OEeAgraOaCunQ7EFmOCasBJuQaMpaqgbbUS5MplnrFUkt9TkpJ41P5SQ0QGpaBJGHOLfJOpSo+913gV3EmwcWpB2z0jQaLMObhMbhtN9RajgqzJlBMTFA7GqU+wuGyW/IiFtKHYRl4Ny1gEX/APCHzuqw3AMdklu+596vWITrRZS2ytyVR/VMFlohrn4LJE2Y8D6rP30UqfZzXb2tvsTGmvF9Zu8+h6Y2kRzWIuX96srr3mGtXd060rdaeffrr6t3/7N/26W35yizr2mGOdOADsq3EiArwsvGLwOeTdfm46nj12Zo4PQvzPf/7z6pRTT9Ekk/bN9T+4PiRIS1UrGYmFn0eE8sknntSvbd3eql5/fZva9vrr4e3r6qqQCKxZu0a9+eabGn/zOZvVxV+9OHzuDfX6tm36NduNnH249T/n3M2aZL61/S319tsd6tOf/rT65Cc/adef7t94403qDYP7+huv64HFO11/JriapNwUkpQN69+z7X/6Gaer733vexaftvFo7v/WcLBWV1enXnhhq8Xv6e75lx1/mzdvVpdeeun75vdH/gKLFi1SW832HA7/ZjM5IucfwRf8Awe/jU2meExTxPhH592a8RVXcCPi2x0TX2/85cQEBUZ9F9geXF+i3AQEdzGQW5Qocw4u9uFON/xtClRwaxLMpTAqtjJBolya0IfrS5RLPdPjUXNQLk1o+EXm7S9lsCLopDUO8pEmQvV2MhhMzfAMplCivAAquEugB5dNplpMDu56JriBkVXSAL2jo2PkslliK2R+mHyWfIE5UEHnpG96K/iCP8bxua82x67JHrGNzaZyOluNKjncd8uOfv0sRc726x4Szn7j6CBauk1MEJ/kI1fBwiw4lPTQINCRG4E7YtoxgIge++sLf1WLwkH48889b2dSt217XT3++OPq53f9XK1csVJXCj/wgQ+oZ599Rj9PlbWPf/wotbKlRZ1zzjmqtbVVX5xeefVV/borLr9CrWheoX75y1+oZ579szpp/XrVtLRJffSjH1X3PXC/xf/tb36rTjj+BNW0bJk6LyRzNCD/6U9+or8jTSBccMEFavWq1eroo49Sd/7sZ3aG+Pe//726886fFZhZOBKrItef7vfALHIag+09eZadoU4nm2mMNv4nPvEJ9Z1vf9v57Ed+9YidxKBq6RNP/LEA/4UXXlBNTcvU3XffbfGJ4BLZScL/Xbg9f0bbN2H9afvffc/ddl0ee/QxTdrefONN/bpjjz1W/eY3v1E92oSk+PWn/fyhIz6kloXf85TTTlXbXntNY5926qkhwb1Bv5YI7obw2OF1i467j6sWfdxtVtvpuDOfffMtN+vnmpub1Wc/89mQ2L+lnxvq+DvvvM+rxsalqnl5s9qwcaPG/uxnP6t+dued8fF3/gVqzZo16sgjj1Tf//737f7/1Kc/pS4Pj/GPhXhr165V3/rWt/Q+xPV/++231aGHrlN19YvC16xT115zrXrj9TfUBz74QT2Ie/XVV9QHP/BB9e1w/9Lv6GMf/Zj+zdH6Lw2/68bwO73xxhv2+KOK5Mc+9jG1ds1a9dWvflX1GKfS3/3ud3rfJR1/mzefoy69bEu07W68SZ27+Vz1b5/5jN5G9NyLf33RHn/0+bT9VoTPfSbcfm+ZVoVPf+pT6t8v+Xf1oQ99SJ177mb9fWhbfzT8vmvCdb/44otVb09Uibkx3D+bw/PBmWeeqY+/c845W/3qkYfVEeF7VzSvVFu2bLH7/09PPx2u62l6+592xunqha1bo3PLjTeqzeeeG36Hz+hzCB23JFmn7fmBQw9VdYvq1Lp169Q14fakz/rzn/+sK7VLlzWpj4T79/777tff+6abTAX3AP79C77gH2z47eE53bomeyR3qPGPJrK7ueUra98b5eCCNBnGXzYJIzfgFCj+GccErTGK12YoFDYZbrU4QaI82/NHmg6K2ymgyB0PPkvjgf9hfGylFy1b5vHH8gSJcsloVnCTSsLlXg9uScKXqwBpcqWXhTseCC+7bU0Gk6la6MOdBRt0nlkWeT24i6EHlwmu7sG1/YEwKEeJckFQSlElsn1hJcmfKfiCfyDgo0TZycEdcKXK/D/NFNoMNpYgZ6FSq2OCIglOFsLIbQW3L859YzMpzL7NwIxmlIPb64S29zoZdHiBii4+NLj+8Ic/rG644YdqazjQ5NnU7dvfUldedaX66Ec+qv70pz/rau/DDz+spaq33HKLevTRR9WJJ5xgqr1R3xuRn9NPP0098utH1FtvvqlOOflU9fnPf0798Ykn1De+8XXVsnJlSIZI+rpdrVq9Sl144YWasNJAmYg2V+8u/trF6qijjlK/+e1v1I9++CNNRv4UDmbpe30+JCeHH364lUKl03gxTu/z+seSKgy2d2ez7QU9jf1G7y3+lVdeqaXJWy7Zoh577HE9EYL49bqC+4SDT8Rn44YNehsh/jmbN2uC8uhvH9X77dHfPmb7eqlSfMQRRySu/5EfOVLd+OMb7fr/8pe/1PvpjyGxptfRhMbZZ5+tlob759CQeFx99fdGXP9XX31VE6Abb7pRS4SPP+549eUvf1lj2AquIV1cwX344V+F2yI+7k7g4y78XJJEL1m8WJO4X//6N+r4449T537ucxp7qOPvxRdfUieedKL62te+pv7yl7/oz1m/foMm0YT35Ysu1ISYjmMikE1NTeoOTX57w88/PiSta/Rv4bbbblOLlyxWT9B+gP3fE5I+6ieuW1Sv7r//Pt2/TCS+btEiTcxfevHlcDsu0pM6f/zDH9WpIbFtCNfh2muvDUnr79XhRxymvvvd72q834TrRKT31hCL1vWw8Nj/zne+Y4k6/RaSjr9zqYK75TJ9/5rwc0mefl14S+t0zNHHqi2G/NKkCf2miaD++te/VseF++Pcz5+rt8nxJxyvJznuuusufW6gyQxSEdx62636uxxuvgvhE4knqTqR1Id/9bBauWJFSPI/EL7uV/q714Xr+5fn/6Le7nhbHX7YEerCL1+oCTqR2KOPPlrvf1r/urp6de1114bf5RF19LHHhOtwqd6ej//ucX1+uf/++20/+CmnnKKP3yf+GO7fr39DTxaQ4+tNN9+kNqzfcED//gVf8A82fFvBzfQZk6mRxz80TkICy+/rNpVdJwPXjK+0UWjnDqf/Nhf34JKL8mpDcNlkisltg1kWgbnvHMPBsAd3KvTgTgFFLhLdSuB/SG59o6kyILOlnjy51Ou9HbUe3BJPkpzkolzmSZTLoXG4wpMnV3suW6zTngobaZpZZoFEeQFUcH0X5SZPoqwruIO2gjsAWZ0dI8s2nTJY3pN/juxgm99XoangC/4YxKeTY6fp34izcAProDwAzso5Q4b1yXc3mxxgX0hEcPlkHee1GeJrHQPhpK57VPqg/zY2Y2htawMzh7Q1fXClRWmnz+bVV17RRJMqO0ReiDw880xUrb3llp/oKh331BAR/eIXzre9NDSwXxwOzClm7OWXXtTv1yZBHj6ty7PPPasHqERwHnjwQd33Sxcn+j5UATzssMMswSUicu8996qOjh1aXXLyySera665xspxX3nlZejpKQyo35f1T+PrQZ6V9p+DGWq3p+i9we/t7tEDfpp8qA+3G5EqIniMH1Vwn3Dwr7vuOrVu3aHqTarAAT4RqaVNS9WqllV6aVm1SsvS2diD+n2T1v/yb12uScPtIUEhknPY4YfpKtqvQ9JCn0vEiAgGVdJuu/WnmpD//Be/KHr9ad8SBh9jtgdXS5QjkkLv/Sodd1/8ot1eT4YEWx93XZ3q4q9crM77whfs9ifyevc99wx7/BH+GUaizNufJK1EjOj+mtWr1QP3P2Cfu+yyy3SFl+4ff9wJIZH/rt3/9D5N7L39T4M1+j1s3fqC/v+1ba9pfFI8vGgmg2hSiDBuv/12ddgHP2i3/9e++lVdQabn6DdHRJa2FS0/+uGP1Sc+cYzGaTX7Lun4o32+har2hjgefczRdvtff8MN4TY/xm7bL3zhPLv/X3zxRXXPPVGv9/HHn2Cq1/Hv/7zPn6d2hCRVf5cf/UjvuwjjGk3+Gf/Tn/q0uuSSLfb/NevWqgfD3z1NUJBqo729XX8GyduJ/NK2ICJOZJePvxv094yODVpXLfkOt6f/+6PJn+eefUZvb9q/N4FE+UD9/Qu+4B9s+ExwM3YZefxDlVrMwXUkypBageMvbh2LeVCkxNsT5+CuNtXbleCi3Aj8Co195w/holzrSZQnAserBrOpalD1VkLlttS7n0ogs0P14o5qDq4fFVTu5eBWwuOYdZQUEcQ9uJOhgjsFnLlmJOTgcpl8odn4jV4ObgsQ3I179kTSZCa4uSDQg/DhDHeKiV3Jj0gl4k/I54fvoRR8wR+r+LkBDgkfKFgiV+WY6AYsUcbe2yzn4EbOf5FEOTop25Oxdlfu8sykMiDPybgug+b97W3tBbIiO9Pa2+vM0qZBakQXGbogPPzQw+qYo4/REkR6780hoYoGsNHMK1Wavve979tZXOrLpMHpc889pwft9XX14aD1bYt/a0h6qDpIA1N6HZEjGpDfeOONdmDM+CTLvCEkN9vf2q4WLTKvb4iMi+h9519wQUFIvL2w9kJo/Dtcf0dKBcH1cY9Qer/hU5XxCyHBo23x5z8/ox9jgsuf++yzz2op7gP331+AH/Vjbtln/O6eHi1tpkrdRz76EfXTn/40JJZLNAmi1z/55JP6OOXPvejLF4XE7Pxh159+DxdddJFaHZJsqtYtov0bHh+6LzYkuDdcf0PURxmSTV3BDd9LlbrvGZkwPfd2R3zcnXpq9FzS9h/q+KPnzjjjDNuDS++jCi7Jovn4++uLL9jtT+tNkuR0OiJ9t956q93/VCG+/PLLC/CpV5owtfw2/Iyoglun+9yJhNN3IoJHn//zn/9cffyoo+z237Ll0vB4/6L+nMbGRv2+OmPiFf026kY8/s4hifKlUZWWCC5Jh/l1ZFb2kY98RP9/Svib/r6z/eL9f9wJx8frGn5P+i6LzHepq6+z25VeTxNQZ515lsX/7NmfVd/5zrft/j/8sMPUAw88qH/71Eurf9d19dFnhZ9DFXGqAtNnMP7d996jjtTfM+5p1hMG5vdH3433r/7M8PNeCffvTebYeb/8/gVf8A8G/Pa2NjMWysQV3BHGP9pkqr/fjqks8e3uslVbf/xF/KfTjOFyQZx8MRi7KK+DCu4yqOIuTsjB5eotLjM85e1Ez2CKl0poUa0CrlgJFdyUd9/PwS1NKL6+a4JbDvdTwzQA45cbB1+cb6s9Jy3WaHM5ezKUurlxeabZqLOhB5dNpriKy5lNy8Du+lDSl+/du9fOWnAFimYzkitlw1CKfHFWP/mCStsIFTnBF/wxjE+/mS59coSIIO7DDVxn5cIc3Ohku9uaSfWbCm40I5k1s5BZ666cgVlLmNl08uDii0HUn9nryoYwhw5kRXQRIlJE/Wpo/HDvffeqhsUNqickvLfc/BN17DHH2FnbCy44X8s6+UJHxItkie0hLkuUadBOn09km6SjP/jB9bpa8+qrr+kKC8URPaarOE1RLydVosJB/4qVK9X1N1yvq7nLm5erP/z+D+6FF3uF0hBJkC68GBe7/u5FGjP9vBnsfzE+GR7RYH/ba69bfLqQL1+2TP0iJENRBbchIrjhZ/WEryc56fnh/knC1wR3y6X7vP4kKSWpOq8/9Yk2LmnUfZckM7/66qt19Z7X/5ItW7Q0drj1v+OO29WqVS3qD3/4vZ5UIfOsk048UT932umnhcfADyKJMlRwqZr59a9/zW7/rea4awuPqwsu+JK6+Ctfsdt/R8cO9eprrw57/NH3I5Mp+v68/tSzScSIjr/m8PijfmPe/1d/92p11lln6tedcPzxmjjz/v/c5z6vriCC6+1/6oen38NftkamSK+9tk3jE/F9OfytkERZE9xeIrh3aYLL254mFc7/4vn6feRUfcm/X7LPx5/tuzYV3IjgRs/de8896sNHfjjathdcoL7ylYti47kdHXr7cQX31pDc8/6Pvsu/J+JTBffMs2KMsz97dtRDbvb/YYcdHhLcB3QVF6vVePxRBffMsz5pj7977rlXHRl+z7SWL0YTBlv/slXj0f5dbPdvW7h9X9XbmwguydsjifKB+fsXfME/GPFp/MKk1jXRHHr8093dbR2Us4bgZqxEOU6twPFXgEkYuZgLDe4d5Aoum0yxizLzqQao4i6A4iKbTCUZTE0Fw+CJ0H87DvgfmktVePLk8oSknoqEuNpRc1D2+29TCaG8KSgvp7yG4SrQXHN5Gln9RLhf60UFcQWXo4LmArmtB404uyjzssqU3dc7WZ26yhQ1XOcTnWeT/Xfyw/Y25t+R163gC/6BgE+/mQ5yUQ5yrkw5CApy1bhfl0+sWczAxRxc04PL1VvtrmxigqxVvpnZjGOB+qCCG81wRiZTvXGPi+OCiBKiaCE5MVVSbr/tdv09iAiQgQ9JDen1JPVc3txset4iKWXzimb15JNPhQPO7SFxOlcP+AnjpXBgSdUdXcENX0smOVSdue+++1Rn1w7179/4hh6AvvzyK5r0H3fccbqKS0Y71PdHbs6RRDkdDnLP0pUciljZ9vo2XW27/fY79HMPPPiAHtSm0z2uTCqNjo7FrX+c1ZcuvBD3xi6Pacj4s2H17zH+cZ/4hDbcocoV9SDedtsdevvqmB5yUW6oMwS3V303JGAkOyZCSi7J7Kbc/vbbUQX33HO0FJUeIxMx7aJM8TXh5zwYbk8imUnrT/hEOjvCz6H9dtJJ60My9BXzmm5twkRyXer93frCC9oA6MYf/1g/f++99+rH/PX/8Y9vVIceeqg2Mnrttde0JPWkk05ye3CNRHk9yUzT5rhrXqGPOyKI+rg74Tj9njvvvFMtC4n/H/74By1xI6Mjik0a7viLiOnntOyYJne5B5cquPTcpz71KXX6qadpqfdftv5F9xdfe+11+jn6bfz0p7fa/f+5z52rZdb+/ifpHElxqU+X1ltXcOuogtvqTQb1hgT3F9q4jbf/Fk1wv6g/87bbb1Orwn371FN/0o6hVC3+/HnnaXyqiF7/gx8kHn/soqwruNdRdfVMe/yRBJnMswiPjLWWL1uufv/7P+gKCv3+yUldE9wTTlA/vfVW+5m33XZ79F2eflpPuNB3OS/8Llgl5td+9uyz1bdNBZdwSQVAv11y8qb9RfJm2ka61zY8Bujx6DPOsutxz733mu/ZG+6nbt27rbdn+Plvvvm6rthSzBmNX74REm/6fegKrnbg3nBA//4FX/APNnyOCYoNpkYe/3BMUNaQWH5tl5Eoc8EAx19ceGD+w2kYRqK8CQymWsBFeQlUcBca3jXfLPMgC3c6kNypUKhkcymu5FYC//NlykxaK0EZXJpAdEsScnJHheAm6Z1TXkNwClh5Ctg4Vm+roXpbDRFBEyEiCF2UuYI7BwguGk01mB3BTsrLTQV3hSG4cUwQV3CDyDJ7JAfZYWNWhhKQ5tWQ1bCCzxN8wT8A8AM/B9f2cHjyZFpyUaC4zb7FqCAz42ilyCxRRpMpPNFbJ8FMLNlBl8Hw8TYb/eK6Ivb2pKF3hi9C0QWK+jqbVyzX1S6Knvnoxz6qeyrpteReSu6qNBh/8qkn9WM0aF6xYkVIsBr0YD8iXCHBfellbaITyS4jjC2XXKLNcxY3LNGVIqrAUI8cPUcVF6qg0QD5Z3fdqaWmN+gM1Igcb9q0yWbznh2Slva32zU+9QEec8wxTryA4964j+vfm/bcHtNpzwESZ6ahH+k9xqd9QNue+m9JGkoVb+otTFuTqaiCS+8nsyct9wy3fyQbrdPvo9zQXl3NOzeW6dJt+DqqYPaaHs9jTD+mv/60bz9x7LEan8yUiPi8bUhzrzF4Wr16tSYsSxYvUV/60gWajNMxTcfTb3/zm4L1p31L5mSUn0tVeyKZJ510opEon677Lun+LVSFgxzcLZduUStXFh53tP7f/OY3teM3GSkRWXr++edGPP7ICIkMm6iSGfXSrtcEVx9/b76pTg6PP3IQp6o59ahHMUK9ukedJMu8/8nQ6/IrLk/c/1TtJDXE5d+6IiS42zQ+9ZK+hBVcIri/uEvva97+muCef749VqgyTj3yDeE2I9M3koYTjv0tJBx/VEm/zDgXX3vNdeqss86yx9k9toIbvfZb3/pm+Jtu1r9/2n7PPf+8XVddrYZjYov5LnT8kcyZzgv03HXXXqer3IxvK7hm/5OUmKq39NoHH3hAHX7E4TqvmT7rhz/6YfwZnzrLYkVE/MMW/xLan+F7tCQ8fIz+p31ElVyq8tNxTfuXKrgbMUP5APz9C77gH2z4WqKccaMPRxr/dGmCyy1fWStl7tY5uFlLfHH8hTm4VnmXy6m9kYvyyaadc3WCizJXcOuA4M6BtlH2R6oF/6Spnq9SDcTCVoOLcgUYEWNra5kXGZQagsyWjibB9Zt8k+TJ5UP03lbCynAfLmbg1kBMEIcFTzP3Z4AV9TwwmeKm50VAcJeBi/Iqoys/eU8AMUFcZershHF+vqi6V36EB/IJD+aHeKNbKRN8wR+7+FyVzaGLchBVcYMgDg3P2QpuJ8wgxkYHNiaIKrVmdpKNpzg+iGcseVbSOflndtnKL2fDtRuJsuOICLOuvU7Qetz7Qr2WFC9DlT0MaOf3kOyzFy5+RGJIHpqG1+DFEfFpWxE5RXyaqSWzGs5xff75v2gS9SttXhTjU4+vrnL19hbg96Z7i8IvZv17se8oYf19/DQ6Tb7H+JQtS8Y6tM7vFT7t/+HW/+WXXgbjKhc/qt5uVW+3v22fp/1JpLerq3NIfJKlk4xsX7Y/DYqwzxzXnzJm29raijr+cP3td0zAp+OPBlDvZv/T77unp+ddH3804dXauj1h+/eMyvGnf9NtrUUd/9S+QOqKd3v8Ub+z3TZF/v66Oruc3z+dm0ip8H79/Qu+4B8s+FqibKMPM0WNf0jVguMq7s3lmCAuGOD4y8YEeaagRqK8AWKCVoJZ72Ko4B4CyTVMcmdBAbIWONsU8FSaBMQWfZj4/wovdafCa4lNAYkt94yNS0ZTouxnD5V5bDvlOScjC6/0zKUqPZOpCRAVNBWalafDDMEccO7iDNw6kCez0VQzuIHRTtugXZQDjgnK2Z1djNdscqti3iUUSW/L54ukKYIv+GMb340J4qot9+HmYqJr+nA7SaIM+baxbb2p1IJbMld1rUR5VxwBpE/cfbsKqra7wGTKDlDTvryo13U+TLs5dmnsw3FMJHq953qTZ217vVnfIvCpEkwVKZJ/ktSQKlZdbFj0L8Df3+v/fsanaJyzPvUp2f6CL/iCL/iCXxQ+jV8w47aY8U9EZLlKm7WV3kiivNv04brjL+I/kVFozin2kQEv9OCuABflJujD5QruAlDPzgWSOwsycLnFdKLXejoeXJQrPZOpClD9VgzDLUuH8IEaNRfl1BCRQSVeZRcrvZVQguaVwpX1o4L8HNyZUA6fCxrwQ8Bgqs7sjKVm5oEJLs1IbArCnZjL5WxOJ+3kHWgylXfrX/lhq2LDGADlhyYoLuHIJ/RTCr7gj018igOKCG4UDRQMxOZS9jeViyQvbEcfV22zcZWWTabALdlWdg3xjR0EY/fAjDOjGeXFZczr2jEmCLPqcHbVkQmB62G61+2Lwaw6cE9MOzO6PUa2hBe84vHJqOipp55U26kytR/w9/f6v1/xqZL65htvyPYXfMEXfMEX/KLw29raVaYvE0uRixj/sMkUV2et+VRXLF32x1/EfzrNGE6TXCpMhGO4wcG9XMFdbQiun4O7BFyUuQ93LrSMzoRiJFZvJ4IydwIUN5n/VSWYS6UScnB9XpkaoW32XUuU/bIxypNTYPlcCfrqSqjaVgHpHQ89uFzOnuotvkR5LkiUF4DJVBP04LaYHbZWS5T3DBpTnJwpzwda5jO8D0+ybU/e+2eoPsm8LwT1SEQBkxB8wR+j+FFVtlPLWnLWZMqQWsdFGU2m+mEWcXccQG6lyNEJnWU0UQW32+lDyYAU2Z780Tq/b5dqbW+FPphCuVAaDR4KzCFAMpR2Z26to2JCgDtm3/XiZwq+4Au+4Au+4Au+4BeBzzm48Thn5PFPF5tMZTkHlx/vdsZTOP7i5BjmP5E5aBASXNuDuw7iVZcbLpUkUZ4PEuUZIFOe4alvp0AyDraiViUk6zBHLPeMipMkyn7+7ahUcFMecy7xyshJkmU/F7fckyfXeNVbNpqaCuXuGbARuRw+DzJw64DgLjUkl3twW0wFd0PAJlNBLKuMcnDz7kg/P4SIMz/0k24kyxAZpMMaBAm+4I9t/ByEhKNrslPJzVG+Ws4YUnVGZHY3xQMZ23prehBVcPlEnuXctnDp6uq2J2sr0dkVB57bkz27CoYLOe46F6M0mDuk086sKT7nS5Cc/1FmhL016V7vs9NuvIDgC77gC77gC77gC34R+JFEeReMd0Ye/3AObr+JAWIyHOXgxoo4HH9FyrrOuK3MjOGMizJLlFeCQW8T5OA2GK413xQV53oS5elmqYUq7iQvKacKcnCrgQuiRLnMu1/uKYL9JJ/UaBLckhEkyaXwpUo9o6kquOX7KFGeAJVctpmuBSflWVDBnQ8El6OCuJTOPbjLDMGlnbbJaazODVhX2JEMeoosrBU+nFeJmaX5Ed8v+II/9vC163j4ewkGCiOBmOjm4P/Ojh22P6QfbOvZTEpLlM2JvD8LFVwivhm/5wQcBXdl4EQfLW2a4KZh1rXXDWJPu/b9aeibiS9SIEVK9xbKitKFoe5pB0/wBV/wBV/wBV/wBb94fF3BzYAUuYjxD42T+sEl2Xmcia83/rI+KjkwCh0I1N69e5ngtkAFl4uFjUBwuYJ7CBDc2aCurYVlIlRvJ3pZuMgFKxKMpSqG4JclQHhLRjsLNylct8zrxUW2Pc4zmOL7VbBUQ/W2BoKB+XaGJ1FmzTc3ObMevB7I7VLIwG0xJlObaCcGNrszllEmRqbki6IZyV2R+Xxxqs8kDMEX/DGKH5tMBYmS5Bz0tkdk2Lgoe5VblsxEldrohG7d/rCCC7OV8S1kwpkLAt3XLrIFkiE/VD3t9Mc4kqF0oXEEztbGt5CJx3KktNvLI/iCL/iCL/iCL/iCXww+KdB848yRxj9+3i23bJH5FObf4vjLRj0GXnEiCLgHd61p61wOPkYoUa6DtlCOa50JC0qUp3nk1u+95ZjYccAVKyAPl72bShL6b31p8qgSXN9cyi8dp+DLYibuOK+CWw09uBOhEZnJbS00Lk8DgjsLqreHgES5DnJwm70c3I2RNDmwA/OAXZTzwzci5kcgCcP3MeZHJh2CL/gHAH6A8hZcaDYwF1ijKZ4Z1CZT/ZG5gevolzUuyl3mpN5nnmfpcjeEmns5cDijCT0qba3tsRwI8+zSELYOWXWOpEjn0sVOh2k0nsAcPLhg+T06jhxJ8AVf8AVf8AVf8AW/CHx2UbbV2yLGP10m75YJbMZM+mvzKWMq5Y+/aHxGxrrMfzgNg9IvqAhouFKL4U3NUDCsB4I73yO5zMlqQZ5c6+XgTgCF7njgf766F1tbyz1umUq4XzraLsplQ1R0yxIcsCqA6JYDwR3nkdzx0Ic7CRy4poJEmWcI5kIv7gLIwW0w5LYRcnBXgkR5wx7uwYUeQqzg5pNkmvnhJaG+qDM/omZ06OqZ4Av+WMbn30vgzPzlhpAoB/q1kfR4dxw2Dlm4XdZFOQMSZlPZRbOFjDejaU/6GSvlaQ0vEG6fTNqx/k+nIesu7TkopjHHrrfQbML5rHj21X5mOqlPR/AFX/AFX/AFX/AFf3j8dpODW+AzMsz4B12UibxyBTiq4PZDf248/rI9uDksUpBEedCv4DaDi3IjuCjXG4LLClqMCJppuNo0qOKiwdQEaEnFCm6lxxUrPS+nlMct/VSfUTOZKvF00aVAbkshv4ifq/TcsKo8xj7eWyaZWya4tUBwsQ93HjQ6LzCzCnWmjL4MtONsMEU77KQcV29ZWpkzBDc/NIfID+e6M6THbfL/+bxKJi2CL/gHAH5gSCtKk3MsV7aSl5jwkh0993/0g8kUV3K1W7KR3KDdfVTBzUAeXMIMJjsom8doVrKnpyfuqUm7fS5pCFV3s+nSsWV/L9j6O3l4CTO4afg8uFAJvuALvuALvuALvuAXg9/T3aPHSmyYmeHWqxHGP1QIYNVbJFF2iW9cxY3HXwMoUQaSuzd2UW4BgtsMBJertwuhB3ee6b+dA0bAMw2xnQxZuBMMqZ3okdvxnpvyOOCNZV5sEBLa8vfCQdlv+PX7cEuHkC6XJ1hBjwP2Xg0rz05bflQQk9vZoPueZ8jtfCC49WZnNIF+3JpMkRU2as8519PVeOYTy2H5YWpdI0pL88PTB8EX/AMBX8cEdXUCiXXDwt0lUB3hb2u37bnlE26/azJlTsqW4Frp8q5YsoO9KZAFhxLlnTt36t+ynTFFcwcnm67XkSOxw2EaJUr+//4MLppHoLmE7c8RfMEXfMEXfMEXfMEfHp/GLTt3pp3xTTHjHytR7meJcjTh380xQeFz/viLjUKZ/8TjtRxXcFeZZTnEBDVCHy6T3AWGg80CgjsNkm+mAI+bBAZTNUBymQtWmP/LvSJpmeeS7GfjjjrBxQ8t9chuCpyTyxJYeBLBrQCTqRoIBJ4MxBaXmYbkchX3EJAo10MpvcnIk5thRmLT4J5BI6mM+3DJCKc4g9rhSUU+P0QcS74YoiH4gj/28bmvNjBRW0EQRwTlLNnNqRwbuHV2RjltVi4DM4osUTYnb3ZR7ud83MyuQot8Y5ufAXkyzmpSBbdzR4fq7ul2L0zsjmj7YjwjCG92desLL6hfPfIr/fqXXnpJPfrYY/pxuv/Yo4/aWd7CUHhwVxwBn2Zf77n3Hn3+8fG5TyeOLEg7nz8a+MOt/4GGT5Mur7zyykG7/oIv+IIv+IJ/YOHTOIWu/1TBtWo0liQXMf6Jem3jrFt+3sYE9fcXjL+I/+ywMUEDlgvt2WMlymsMwWX/omVQwa2HDNwFkIPL8uRZnm8SE9wJQxBc5INoQoyuyuVDZOGWelXcUTGZSiX04pZ5EmWUKpd5X7ocenCrvYZjbkLG6u1ks6GmwgacCf238yAHlyXKbDK1Aggu7bSN5KLsm+N02gpucX95kHsSlcjn94mHDNESKfiCP/bxdVXW5uBGJ8actZsPnGouu/UlyZOzJu82kihHfSU8E7mbXZQzLNVBU6mMNV/Y5RkucF/K33b+TeO2t7Zp4wZyV25va9O3tLTy/fD5+rr6cKlTi+oWqbpFdaouvH/P3Xeryy77pvr4UR/Xr7v+huvVCSecoD/j+utvUMefeEL0Oa3tuu+3tTX+7Ohz2yPMEfBfeflltWjhQrV161b7Oue17fCeVn48xGxvGxV8Wv82+7ljC/+OO+5Qi5csUY8//riDf955X1AXfeUii//8X55XGzduVI3ha+vr69XatWvVZZdeZvHvuON2tXjxYvU78zmMf95556mLLrpI498ZYuH+r1+0SN++9tprB+32F3zBF3zBF/z3Fp9iFP+2c6dDZuNxzsjjHyKyrIajsRNHCNH4KUme3I8SZYhKjUymAo4JWgcxQU2misu8qsErKs6BHFyWKNdCcXIyVG8nAOerhvtV4M9UAUSXI4MqPJ6ZZDY1ahXc4eTJ5V45OeWR2grPSbnSiwmq9pqRJ5sy91RDcmdAOXwO9OEuNAuX0BsNwV0GJlPrCghuuENzYDLlijXzXtNisXWuvPKLZfkR3Wnzgi/4BwQ+9m8EYCwV2NnAnBMZRJNHWUeabGYSzYwixwHRiTwLJ2I6cevKbB/IciAbLsMB6I4Rwy5n9hND0jM2O861229oWKx++9vfhBezdn3xoova3/7jP/Rr+vr6NP6tt96mNm7apLFvvfVWtUnff/f4nV1dqi4kZa2t2620yF68+Du+x+tvZd5jDP++++/ThPWDH/iA+o//+LvFv/DCC9WWSy7Rr+/p7lbHfuJYdcbpp6sXX3xR7fzbTvXQQw+qZcuWqeuvv15//v333Rdu4zr9OX//+98txoUXflldsmVLhBW+5rDDD7f7X9+SoyWt+0G6/QVf8AVf8AV/bON3GSmyXnZnLb4mvtZgyh1/DZjkGOY/UaQjmUz9k3twV0FxkP2MlkIOLsYEzQXT39kgU54K3G0S9OFyBRczcDEiaFxCHFBSWk9S/23Ze0Fy/RzcEs/9qgwquRWwYtVwOx5Y/SSQKE+BjTUdFuzBnQd6cC6hL4WZh2YwmdoYBIHTf2tdlP1KVoLpTj5frMozv4+1OCX4gn9A4Eey465o5i8YSOy7RRO3TpQo787GmWymUtttJMoZI1HmqCDdg4uzl+YC4ISdZ3Y5VVyevbT9KxnvwpHZVdDHYvtc7G2fWxEWfMEXfMEXfMEXfMEfY/g2BzcbLYxP46cCibIZf0VjuB1u/204nqPinyG4a4DkrgCC22DkydiDy1xsBjgoT4cq7mQgt75MuRKkypVAcMs9R+WU5/FUluD5NKo5uCWec3J5QiNwytNPl0PVttxj7mgyNR5MpqZAs/I0L0x4DjgoLzJLvZllaAKJMi+00zbpmCAYmOsqU1dn0XY9ef+RfHGvzg9hbpsvmpYIvuDvf3yuyg4MQ25Joswzg1EFt9/KYzj/NqrUZk2vbXTC5hlHOinT4xnnIgAn9l3xTKaTG2cvLhnHgIrlPJm+OF/O+cyM19/CFxPBF3zBF3zBF3zBF/wxiK+lyDbntt/iR+7KJvvWG3/pwoORKOcwDSPKwV0PJlNcxW30YoJQojwPnJRngkyZJcpTgNhOhOrteOB/aDJV5nHHcmh5LfN6ccs8HjoqBLcsgTGnPIMprPD6DsrlXhW3GkhtDWwIzsKdAjMC6KI8CzbuIYbgcgm9Eaq4K8zOAoly4AzIOyEHNxZnJjnJ5t9BxSw/rAdu0usFX/DHKn5gTNlcUptzzArwPs0U2h4QONHyrGJ3d+yWbHNwrfkUn/TdsHPfPTmWB7mv4RnQOCs3Y234uccFHQkzTn/LLsEXfMEXfMEXfMEX/DGJ3w29tlnuwc0w8TXyZG/8NaCjHjuhIDFgTKb2EMHdZIqBLWAwtdyQW3RRng8V3DmGl802XG0aVG+neFXcanNbBfyvymtbLYclBb246KLsF1tLR0ui7JNa31yKQdAJKwX/jwPn5OqEKm4NsP4pkIHLMmWu4M6F/tsFUDpfAlm4K8AJbE0kUR5wMjzJKGfHMCZTQ1e48kOY9bjkIJ8fjmjkRyQvgi/4Ywk/Nihgp+TASlwc6X8u6nHf0dEZS4+BwPKsY7fuwc0Yk6l++9pu05uLM5e7fEkQ2OdnvBlOK+vpy8QW+ygTyrizq9hLE8/CCr7gC77gC77gC77gjz18Gidhjy3jdxmJchbGVDz+4sIDGoUOBDmWKJOL8qGgfOU+XC4e1kP1lvtwZ0P/7Uwv/WYqVHAnAMmtAf5X4VVsxwFn5AVdk0u8ntxRkyeXeCViv6qLIbylnntymZd1VAVuyrziNVC9nQhBwVzFnQUuynOhD3chENwGCCZuhgqu7sHFzCe+39nZBXRhGEuefHEP54cx8BmZrgi+4I9d/Ijgdjozf/p+MODIkwOW/3d2OPm21hDBnJC7OAc3PFlns/FMI81AulIgzzI/E89q7gIHZbrtszOmYMyQycCMa8btccnEM6c4Gyv4gi/4gi/4gi/4gj8W8bu1mZSbg9vHj1sXZXf85bgoGxUeFSsGBwe5grsWIlabQRHbCC7KCyEiaC4QXDYDnuJl4WIPLkuUmf+NBz5YCXyxzIudLfeSe5CTjhrBLfGqtiWes5XvclWe4KZcDqXpKjCYqgGiOxnylGqheXkG2FLP92KC6iEiCCXKK8xO20Bl+BzNWgSBJbmU66kKMkTzQ/Qu5j23H+V4zxbj7xO/PV/4oOAL/hjGtw58TtU2sJNGgancxjFBnXHVNhu7JHNum40JIpMpkNJ0s4tyJpbuZLwZ0nh2NBP3tvT5M6kZx9whNobIxKYQmdggwsqCwOlQ8AVf8AVf8AVf8AV/LOFHFdzY24TxdQ6uzb91x1/Ef6Ic3FxcnAjHbYORRHkj9OC2GILbBOS2zuvDxZggVtdO9yTKEyAdh3twa4D/VXltrCkvLiipgJpKyMYdNZOpUq9k7McGlXsl5govIqgcZMnjPII7CYKBOQt3KhDcmVASn+c1PPPGZ5MprOCudVyUIeaEKrgYh5IvGP7nhzH0ySeoPPPFOQElsQ7BF/wxjE+/Gy1vCWI5MldsY8Kbi0hvEJFhzGJjc6l+I6shKQ3PbmaB4MY9uHQy73N7VDLx7GcGn/MuHI6F/67Y4MHOrDp9LiAFcuRBgi/4gi/4gi/4gi/4Ywuf3JKz2dgtmT/DEt9sf8H4i4t6AVRwqTCxZ88guyivNbxpuVmweltv1LJs8DsfPJE4IohNprCCOwmquFVeiyoS3HFAcv2ooCS+OerV29KEsnCpx7KxtFwBTcLlCT23mIXLOUmTwGRqqidRxh7cedDszOS2ATJwm0ypnWcjdAU34D5BkwPV6eXgFmaPJhOG/DDWPfkizXwSeywFX/DHKH7Us95hJcg5iAQKYEYwZwjwDk1wIf/WcVPmvNvopB0R3N1RD65xV86ANX6mwMgB+lr4+UzGuZC4Fx90OtzlmjxkwNChD2VFgi/4gi/4gi/4gi/4Yws/ysGNJcj8Ol3B7TdjLm/8FdgkjLidTI/f9gTsorwWTKaWexJl7MFdAPLkOVC9ZaOpyVDJHe8pdGuA/1VC8bPM68ct8zhlylMRj7pM2WfOKS8uqBS+mJ+B6zcUV0O5mi2kx0MPbi1ouadDE/NsILi8sRdCI3QT5OCuNAvttJP37h00g/LAVpwoqzO5irUPxrWJ/3s0Iz/CZwu+4I9xfNu/ERLdAN3Ic14Gbi5qAYhclJMlyvQ/EVmeyYxs7aPXpHfuVH19fW6AuteHkkmc+QSrficcPb4w9EHIegbC2HHmddcuvydG8AVf8AVf8AVf8AV//+P3hUR4ZzhOQgK7y+bgdjuVW0eirE2mOh1jUHBRXg89uC1QKFxq+BVXcBdCBXeuKT7OBp5WC+rbiVC0HA/Ettqr5lZ6nLEyoe01ydw4ldCX+65MplIJbsqlCY+XemG9lVDBRf01Et3JCSZT3INbazbkHFhQoswV3MUJMUGrohzcQTMAz9k+QSK4fsUrn1cjBqkk1dCGV4bmE8lEUsVN8AV/LOIXGBQYJ2U0nOIeXDqBOgTX3vZba3uS2PBFgp3+6PF//OMfauffdsIFJONa8uMMKvSxOIYP1oyhsP9FS4swZy4DYeuZJOmQ4Au+4Au+4Au+4Av+/sffuTOt/vGf/3BSKRhfuyhzIcEbf0VjuA7PKDRggntKgskU9+DWQR/uIaCgnQPq2ulAbDEmqAaclGvAWKoK2lYrQa5c5hlLJfk9JSmJR+0vNURkUAqahDG3yDeZqvTYe41XwZ0EG6cWtN0zEiTKnIPL5LbRVG8xKsiaTDkxQeFgnPoEi8tmyY9YSBuKbeTVsIxF8AX/gMDnvtoAHJNdsuvep16P6ESbtcTWmkz1RxVcvlDEObjRa//e16dnKPv6/m5kPn2enb5nuoAZck5YOuTR4WudkHXXmt+ZObXPCb7gC77gC77gC77g7x/8vr+bcVF4y5XZrM3BNT244bjKEl9v/OXEBAVGfRfYHlxfotwEBHcxkFuUKHMOLvbhTjf8bQpUcGsSzKUwKrYyQaJcmtCH60uUSz3T41FzUC5NaPhF5u0vZbAi6KQ1DvKRJkL1djIYTM3wDKZQorwAKrhLoAeXTaZaTA7ueia4gZFV0gC9o6Nj5LJZYitkfph8lnyBOVBB56Rveiv4gj/G8bmvNseuyR6xjc2mcjpbjaq9/fYk3B9ntpnHuoxEmRaODurvj7PbaIaSTubkqkzVXjpxU4RQl741/4fPOf938f0ufb/bLF3mtfpz9GPR+7rNa/nz9dIVP67fI/iCL/iCL/iCL/iCvx/xd+78m6ncRuOl7G5u+cpakhzl4II0GcZfNgkjN+AUKP4ZxwStMYrXZigUNhlutThBojzb80eaDorbKaDIHQ8+S+OB/2F8bKUXLVvm8cfyBIlyyWhWcJNKwuVeD25JwperAGlypZeFOx4IL7ttTQaTqVrow50FG3SeWRZ5PbiLoQeXCa7uwbX9gTAoR4lyQVBKUSWyfWElyZ8p+IJ/IOCjRNnJwR1wpcr8P80U2gw2liBn40ptFBMUzXZmIYy835Lhfqj4JuTp9rsn8NhwwTWzGgq/nx0ITWyR4Au+4Au+4Au+4Av+gYbPBle6B3cIfG0U2rnD6b/NxT245KK82hBcNplicttglkVg7jvHcDDswZ0KPbhTQJGLRLcS+B+SW99oqgzIbKknTy71em9HrQe3xJMkJ7kol3kS5XJoHK7w5MnVnssW67SnwkaaZpZZIFFeABVc30W5yZMo6wruoK3gDkBWZ8fIsk2nDJb35J8jO9jm91VoKviCPwbx6eTYafo34izcwDooD4Czcs6QYX3y3d3vnMz7geBy30ic1+ae+HeztLk/6xhV9cNFgXPg+DGLwyf/IfB3Z90LiuALvuALvuALvuAL/oGG70iUh8Dn1rGYB0VKvD1xDu5qU71dCS7KjcCv0Nh3/hAuyrWeRHkicLxqMJuqBlVvJVRuS737qQQyO1Qv7qjm4PpRQeVeDm4lPI5ZR0kRQdyDOxkquFPAmWtGQg4ul8kXmo3f6OXgtgDB3bhnTyRNZoKbC4JoEI4zIrLIIkvikhvgkPCBgiVyVY6JbsASZey9zcY9tlkrUY5OyvZknI1P2Fl7co/t7nF2k2co+TP7szD7yXLoYfDt5+LFQPAFX/AFX/AFX/AF/wDC557dKCYoGZ/4T6cZw+WCOPliMHZRXgcV3GVQxV2ckIPL1VtcZnjK24mewRQvldCiWgVcsRIquCnvvp+DW5pQfH3XBLcc7qeGaQDGLzcOvjjfVntOWqzR5nL2ZCh1c+PyTLNRZ0MPLptMcRWXM5uWgd31oaQv37t3r5214AoUzWb893//tyyyyDLCQr+ZLn1yhIgg7sMNXGflwhzc6GS7G2Yrd6bJLOHvUUyQPaljn24Wwsqz1n05m41P9vxcHGwe9/BiCHoSfvy6rOALvuALvuALvuAL/gGJT+OoPm3O+bch8QNMwsjFXGhw7yBXcNlkil2UmU81QBV3ARQX2WQqyWBqKhgGT4T+23HA/9BcqsKTJ5cnJPVUJMTVjpqDst9/m0oI5U1BeTnlNQxXgeaay9PI6ifC/VovKogruBwVNBfIbT1oxNlFmZdVpuy+3snq1FWmqOH6f/7nf2SRRZYRFvrNdJCLcpBzZcpBUJCrxv26vgQHe1T+8z//of4WnoydQHQnCD22z3fC0jN9cU4cZNHFr/VdCNHlEGz3M7tsD7DgC77gC77gC77gC/6Bip/euVPHLMZ9u+74iwsPzH84DcNIlDeBwVQLuCgvgQruQsO75ptlHmThTgeSOxUKlWwuxZXcSuB/vkyZSWslKINLE4huSUJO7qgQ3CS9c8prCE4BK08BG8fqbTVUb6shImgiRAShizJXcOcAwUWjqQazI9hJebmp4K4wBDeOCeIKbhBZZv/v//6vLLLIMsIS+Dm4tofDkyfTkosCxd2ZxrifhHtHKAZoZzqtdv29DzLkdrknehOSnnFs+OOLiPO4ly+3qy/jBKhHn90XXzDgMwRf8AVf8AVf8AVf8A8k/L///e82VtFWhvv7C8ZfmINrlXe5nNobuSifbNo5Vye4KHMFtw4I7hxoG2V/pFrwT5rq+SrVQCxsNbgoV4ARMba2lnmRQakhyGzpaBJcv8k3SZ5cPkTvbSWsDPfhYgZuDcQEcVjwNHN/BlhRzwOTKW56XgQEdxm4KK8yuvKT9wQQE8RVpnAQ7g/k/+u//ks9+OCD6nvf+5664oorZJHlgF/oWKZjmo7td0pwuSqbQxflIKriBkEcGp6zFdxOmEH0JTzck9Kv/vGP/1Q7/7Yzste3Nvxsqx/dZwt+fh4t+7v5eWPNzzb9/Ji97UIb/uh1/NmRrb/gC77gC77gC77gC/6Bg/+3v0Hl1kiZ+x1X5mjMZWOCPFNQI1HeADFBK8GsdzFUcA+B5BomubOgAFkLnG0KeCpNAmKLPkz8f4WXulPhtcSmgMSWe8bGJaMpUfazh8o8tp3ynJORhVd65lKVnsnUBIgKmgrNytNhhmAOOHdxBm4dyJPZaKoZ3MBop23QLsoBxwTl7M72ye33v/99IUWyvC8XOrbfKcl1Y4K4ast9uLmY6Jo+3E6SKGO+LdjoW6MEME7I9seZbnHFNybCtvqbzdrHY/OFrHdi3237WwRf8AVf8AVf8AVf8A9mfOI/kVFozin2kQEv9OCuABflJujD5QruAlDPzgWSOwsycLnFdKLXejoeXJQrPZOpClD9VgzDLUuH8IEaNRfl1BCRQSVeZRcrvZVQguaVwpX1o4L8HNyZUA6fCxrwQ8Bgqs7sjKVm5oEJLs1IbArCnZjL5WxOJ+1kX6JMVS4hQrK8nxc6xt+ZRDlnCG4UDRQMxOZS9jeViyQvbEcfV22zzkkY3QLxZN7P+W+2ZxdO/GjoYPPgso4LIRo39DtVY8EXfMEXfMEXfMEX/IMTn/hPpxnDaZJLhYlwDDc4uJcruKsNwfVzcJeAizL34c6FltGZUIzE6u1EUOZOgOIm87+qBHOpVEIOrs8rUyO0zb5ribJfNkZ5cgosnytBX10JVdsqIL3joQeXy9lTvcWXKM8FifICMJlqgh7cFrPD1mqJ8p5BY4qTM+X5QO3ocAmuyJJleb8vV1999Tus4AZa8ZADaQtl40a/KXRRRpMpsNHXs4pxlpvtEcHAc4glimYq4xN+f7872xk7CoL7YL9rvy/4gi/4gi/4gi/4gn+w43NyDPOfyBw0CAmu7cFdB/Gqyw2XSpIozweJ8gyQKc/w1LdTIBkHW1GrEpJ1mCOWe0bFSRJlP/92VCq4KY85l3hl5CTJsp+LW+7Jk2u86i0bTU2FcvcM2IhcDp8HGbh1QHCXGpLLPbgtpoK7IWCTqSCWVdIgXAiuLEJwR15yEBKOrslOJTdH+Wo5Y0jVGZ2Ed5M9ftY5maPxgWuPH9vgO/lvcCLPwmwn30fJT9abERV8wRd8wRd8wRd8wT+Y8SNlXWfcVmbGcMZFmSXKK8GgtwlycBsM15pviopzPYnydLPUQhV3kpeUUwU5uNXABVGiXObdL/cUwX6ST2o0CW7JCJLkUvhSpZ7RVBXc8n2UKE+ASi7bTNeCk/IsqODOB4LLUUFcSuce3GWG4NJO2+Q0VucGrCvs//3f/9nloYceUldeeaUssrxvFzrG8ZgvdtGu4+HvJRgojARiopuD/zs7dlgpDp7Is1k3qNz2mjhSHHgMe1L6s47kph9dAr2ZTgxOF3zBF3zBF3zBF3zBP1jxrY9KDoxCBwK1d+9eJrgtUMHlYmEjEFyu4B4CBHc2qGtrYZkI1duJXhYucsGKBGOpiiH4ZQkQ3pLRzsJNCtct83pxkW2P8wym+H4VLNVQva2BYGC+neFJlFnzzU3OrAevB3K7FDJwW4zJ1CbaiYHN7oxllDiI3717t7rmmmuECMnyvlzo2KZj/B0RXGsyFSRKknPQ2x6R4c4CM4X+ftccwTFd6OdZyn4rvYlP3HjCd3tT3JB0kOwkmDkIvuALvuALvuALvuAfbPg26jHwihNBwD24a01b53LwMUKJch20hXJc60xYUKI8zSO3fu8tx8SOA65YAXm47N1UktB/60uTR5Xg+uZSfuk4BV8WM3HHeRXcaujBnQiNyExua6FxeRoQ3FlQvT0EJMp1kIPb7OXgboykyYEdmAcJLsoYE0RSTpG0yvJ+kSW/25igAOUtuNBsYC6wRlM8M6hNpthEwXH0c3tGUJrjyHeyePKH90JvSnyLxgvoRCj4gi/4gi/4gi/4gn9w49P4jIx1mf9wGgalX1AR0HClFsObmqFgWA8Ed75HcpmT1YI8udbLwZ0ACt3xwP98dS+2tpZ73DKVcL90tF2Uy4ao6JYlOGBVANEtB4I7ziO546EPdxI4cE0FiTLPEMyFXtwFkIPbYMhtI+TgrgSJ8oY93IMLPYR+D64sssgyfExQ4Mz85YaQKAf6tdGs4+6Ck3Z/gUMg/O/1kbBMB2c8fev9eFY0G8+C2vcLvuALvuALvuALvuAfvPi2BzeHRQqSKA/6FdxmcFFuBBflekNwWUGLEUEzDVebBlVcNJiaAC2pWMGt9LhipefllPK4pZ/qM2omUyWeLroUyG0p5Bfxc5WeG1aVx9jHe8skc8sEtxYILvbhzoNG5wVmVqHOlNGXgXacDaZoh52U4+otSytzQnBlkaX4Cm5gc3BZ4pJjubKVvMSEl+zo3ZnHfie/zc5QeoYJ/Z70Jp6F3G3t863EJ+tKfPphlrQ/mxV8wRd8wRd8wRd8wT/o8QdQogwkd2/sotwCBLcZCC5XbxdCD+480387B4yAZxpiOxmycCcYUjvRI7fjPTflccAby7zYICS05e+Fg7Lf8Ov34ZYOIV0uT7CCHgfsvRpWnp22/KggJrezQfc9z5Db+UBw683OaAL9uDWZIits1J7HuZ4wMKeKVC6OQAkCrExx72HOeU+AxBlmRXK6wsUHUq6gZzEAMiD4gn8g4Hd2dRaN30HVXtn+gi/4gi/4gi/4gi/4+x2fOY+Pbyq4q8yyHGKCGqEPl0nuAsPBZgHBnQbJN1OAx00Cg6kaILnMBSvM/+VekbTMc0n2s3FHneDih5Z6ZDcFzsllCSw8ieBWgMlUDQQCTwZii8tMQ3K5insISJTroZTeZOTJzTAjsWlwz6CRVAawszv1Du/s7NK3ejH3O/l//ZoO87odqqsz/D9c6L5+TXjbwc+Htx2d8L6OHfq1eumg93dYzB2dOwRf8A84/MBEbfHJl35HOXuyzakcG7h1Mo75vub98eMxfnQL9zt9/B3xZ+jP4fXvNJ+zI35Pp48j+IIv+IIv+IIv+IIv+DHZDkxMkJUorzEEl/2LlkEFtx4ycBdADi7Lk2d5vklMcCcMQXCRD6IJMboqlw+RhVvqVXFHxWQqldCLW+ZJlFGqXOZ96XLowa32Go65CRmrt5PNhpoKG3Am9N/OgxxcliizydQKILi00zaSi3KBOY4nq7T9hUFQEIGiZ0WCeHZFSzRz8XM4mxIUvHcg2ZxH8AVf8AVf8AVf8AVf8AVf8AX/X4xvXJQpJmgdxAQ1mSou86oGr6g4B3JwWaJcC8XJyVC9nQCcrxruV4E/UwUQXY4MqvB4ZpLZ1KhVcIeTJ5d75eSUR2orPCflSi8mqNprRp5sytxTDcmdAeXwOdCHu9AsXEJvNAR3GZhMrSsguEFgyv45Zd2VA9NTSDtcHwA5+3iAttpBfNAEA3EVCw9ULQ/IxaY7BQeZ4Au+4Au+4Au+4Au+4Au+4Av+fsLfu/ef3IO7CoqD7Ge0FHJwMSZoLpj+zgaZ8lTgbpOgD5cruJiBixFB4xLigJLSepL6b8veC5Lr5+CWeO5XZVDJrYAVq4bb8cDqJ4FEeQpsrOmwYA/uPNCDcwl9Kcw8NIPJ1MYAZkUc/XnOfYwlmDxbQgdXLgemOjnvgAPde+FB6JvwCL7gC77gC77gC77gC77gC77g7198Kv4ZgrsGSO4KILgNRp6MPbjMxWaAg/J0qOJOBnLry5QrQapcCQS33HNUTnkeT2UJnk+jmoNb4jknlyc0Aqc8/XQ5VG3LPeaOJlPjwWRqCjQrT/PChOeAg/Iis9SbWYYmkCjzQjttk44Jgh2dQzfYIA48trMm3sxHzpktST64goF4ZiTwZlP8JnPBF3zBF3zBF3zBF3zBF3zBF/z9hR9ypPVgMsVV3EYvJgglyvPASXkmyJRZojwFiO1EqN6OB/6HJlNlHncsh5bXMq8Xt8zjoaNCcMsSGHPKM5jCCq/voFzuVXGrgdTWwIbgLNwpMCOALsqzYOMeYggul9AboYq7wuwskCgH3gEx4DiJsUQAm7Ctrj3wZkn4tfi485qceU+uAFfwBV/wBV/wBV/wBV/wBV/wBX9/4VPxj4qAphjYAgZTyw25RRfl+VDBnWN42WzD1aZB9XaKV8WtNrdVwP+qvLbVclhS0IuLLsp+sbV0tCTKPqn1zaUYBJ2wUvD/OHBOrk6o4tYA658CGbgsU+YK7lzov10ApfMlkIW7ApzA1kQSZW8WIxfE1ty+pt1bcv6BiAdSziv954zEIBd4B15O8AVf8AVf8AVf8AVf8AVf8AV/v+MbiTK5KB8Kylfuw+XiYT1Ub7kPdzb038700m+mQgV3ApDcGuB/FV7FdhxwRl7QNbnE68kdNXlyiVci9qu6GMJb6rknl3lZR1XgpswrXgPV24kQFMxV3FngojwX+nAXAsFtgGDiZqjg6h7cgswpx5ksKDxwMLMqNzDkzAsTZ5YHcG6VjiSyjeMDSvAFX/AFX/AFX/AFX/AFX/AFfyzgDw4OcgV3LUSsNoMithFclBdCRNBcILhsBjzFy8LFHlyWKDP/Gw98sBL4YpkXO1vuJfcgJx01glviVW1LPGcr3+WqPMFNuRxK01VgMFUDRHcy5CnVQvPyDLClnu/FBNVDRND/Z+87w+Sorm2r83RPzlkjTdKMMkpIAgWQBCjj+EywTTDYxjhch2tM9r2Gdwm+XAfAJtrCYJNtsE3GNtgkGxElMEGxet6oJd2/N/w5r0712V2rdp8egaaRAG99X33d06FWVZ1T0OustddGi/I0M2hrtQzvr2rkAhsANULOwYDnfP96zhR058Ke91H2iHaA0SDeO8eLvmklRvAFX/AFX/AFX/AFX/AFX/AF/xDj781blNdBDe4MQ3CHgNxOYnW42CaI3LVNzKJcBd1xqAa3EvhfBStjjbJ2QTYBNWrpjVu2kKkIk4x526A4k5gTrEVQHGzJKUZwa6AxMPXCrQeC2wKSeCcreKaLTyFTqODODqUo59iEGuWTYbToeX7y7A4lkuV8z7zZ1yh+3kzKUZZWZon5FnzBF3zBF3zBF3zBF3zBF3zBP9j4e/bspRTl2YY3TTEbqre9xi1LAb9dkIlELYIoZAoV3BpQcStYiSoS3BSQXN4qyMY3y67eRiyycISxbJSWE1AkHLfU3GIvXOqTVAMhU/XMoow1uJ1Q7Ezktg964A4ZqZ1WI3wFN0c+9VwunEqWy0v8udEgprvQhJn6UI0GPapG0Refg4jvXJCINorF4IWCccEXfMEXfMEXfMEXfMEXfMEX/EOLn9uToxTl2RAyNYVZlLEGtxvsye2g3lLQVC0ouRnm0K0E/pcE8TPG6nFjjFNGmYu47DZlzpyjrF1QBA6M98DlBcVpkKspQjoDNbgN4OVugiLmNiC4dLF7oBB6CPrgTjebHrQN+/btNYOeK6x4ZLPZ/Oa6hedu4bVs8D5u7ogayY6Yv0fM50ZC72eztD832NcI7XukeN+CL/iCL/iCL/iCL/iCL/iCL/jvET4GU0GK8hqowZ0BQuFkw69Iwe0BBbfDiI9twNMawH1bDaJlBohtmqm5ScYZk5ayV1u4cdRSlzuukKmoJU05Ynk9wpr1JkHBRf81Et1aS8gU1eA2mAvZDhtalEnB7be0CZqZ74O71/jXg4bKhQnlTTDXTAiXJpt5zYXJ5foTxg0eXZpEwff899ws7A8faTK7hUfBF3zBF3zBF3zBF3zBF3zBF/yx8PXz//qv/1L//d//rf7nf/7Huv3v//6vv+FrukxT7zccVJUjgnu8JWSKanAnQR3uBHDQtoO7tgmILbYJqoQk5UoIlqqAstUk2JVjLFjKlvdkcxKX7V+0RMugKBQJY98iHjKVZOy9kim4NXBxGsDb3WyxKFMfXCK3g0a9xVZBhZCpUDqZP9jepBkJJg+fpFmarK4bvObSa1kzkV2YhOb7+N0sTFgz8V1YuRF8wRd8wRd8wRd8wRd8wRd8wR8TfzwE18Mj/rPb1PaaGlxuUR4CgtsP5BYtytQHF+twmwx/qwMFt9ISLoWtYpMWi3LEUofLLcoRFnpctgTliKXgF5k332JwIpiklYL+SNWg3tZCwFQzC5hCi3I3KLgDUINLIVMzTB/cNURwc8aiPFqwKLshi0AwiYKVGnq/MEHp75FAAS5MbBdWdwo3AU1aN/RaMImL8W+77Tb19NNPHzL8g3n+W99+W910081q165d75vrL/iCL/iCL/iCL/iCL/iC/37BHw/BHTEWZeyt+59Bm6DDjON1KgiFQ4Zb9Vssym0sH6kJHLd14MjNQM5SBvgfto9NstayMcYf4xaLslNOBdcmCcdZDa5jObgEWJOTrBduBggvpW3VQshUA9ThtsIF7TTbRFaD2w81uERw/RrcQnNjiN0mi7JbsAMEKyjxWFw9+MCDhdf148aNG1V7e7v/mRtuuEFFo1F/i8XjatrUqeqUU09Vr7/2WmHibjj+eO/9iIpGIiqiPxuJqhNOPEE99NCDKuF958UXXwzhX3f9T1V9fb3avnOHGhwYVOecc04IP/DW480AdoiQfx++QxYIF1aK3OyY50+rVGPhP/TgQ+qKyy8fN/4dd9zhX9d3i3+oz1/wBV/wBV/wBV/wBV/wBf9g4Y9bwd0dhFEZi7JOUZ5lCC6FTBG57TPbRAj3bTccDGtw66EGtw4cuUh0k8D/kNzyoKkYkNkIsydHWO1t2WpwHWZJtqUox5hFOQ6FwwlmT06zlC3yadfDRWo0WytYlLtBweUpykPMouwruHsLCu5uqMF1wR4Q2A/0a5q4PvjAA2Zy5l/fuPFnqrW11X//up9ep9o7OtTLL72onn3uOXXdddep4SlT1Nw5c9Qbb77pf379+vXqlNNOVS+99JK/vehtb7zxhj+h+3p71aWXXhrC158/6cST/Nd27typRgpe/MCyQF5+F337BWuEC3XF2cCC7WIBe/4GG3HDNxQ/f/3Z/eFfceWVavq0qePG/8Y3v6k2bNjwrvEP9fkLvuALvuALvuALvuALvuAfLPwDJrjZLPCgfCrznqAP7iyj3k6HFOVB4FcY7NtVIkW5gVmUq4HjpSFsKg2u3iQotxH2PGohs6VqccvaB5e3CoqzPrhJeB17HdlaBFENbi0ouHWQzNVs6YNLMnmPufiDrA/uDCC46/bsyYWaKo+awQ5NRjeYsJrgPvD7BwqrMvq9n2/cmCe43nNNaLsnTAgVhb/wwgu+Anvttdf4f2vCetZZX8yvxLjgsff+/qdvfEMdseiIAv7bb72t0umMuvOuO3381WvWqGuuvtp/f+u2repTnzpBNTY2quHhYXXNNdf4x/mb++5TUzxSTfgnnniiOuXUU/LnsMtVAwMD6pGHHw7hb9q0SXV4xPwrX/uaqq2pVddff73atnWbOuH/nKCamppVb1+vOve8cwvWi63btnnYnzLv9anzvPf08f70Jz9RtbV1KhaLqc6OTvWHPzweLoR3We0BO38XUuOWLF6sLr3k0tD1DxffZ4P/sODNDv9RyWL9wrvEt42/4Au+4Au+4Au+4Au+4Av++wZ/XBblbIH/kIq7N0hRngMK7jCouP2WPrik3uLWzJy31SxgirYklKhWAFdMgoIbZc95H9yIRXwdN8GNw/PoGAXAeHApOHB6TLMkLfJok5xdC1I3FS63mIvaBjW4FDJFKi71bBqGuOu52l++b9++oLeUaY5MqyluNthoFUXbii+/7HJ19913qbvuym/nfuc7HsFt8yfbddddryZM6IYktPwEXLFipUdqz/I/s8EjuGeeeaZ6++231datW/1t166d/ntPPvGkinkkOm9Tdn17clt7m9q1M1+HOn/ePN/+q/d/qkdah4eG1N3eMVziEcFUKqUeffQx9dZbb6pEMqGeevopn9A2NDaotrZ2/xwee/xxVVlZqbZv3164KfTkfva5Z/VkVkuXLFV33H67euWVV9TJnz7ZI8P96vY7blfXXnOtSldk1I9//CP/OD5t3tOf1cQ9XZH23vux2rJli/r61//Jf+/Rxx7zzw1rCIKVrmwI3w3VJ7hqx44d/nE+8ugjrE4BCu0xdc4NbvZCgh3Gqr9L/FLjL/iCL/iCL/iCL/iCL/iC/37BH5+C6xb4D3Ghvfv2koJLIVOUokx8qg9U3G4QFylkyhYwVQ+BwdVQf5sC/ofhUglmT45bOvUkLO1qy5agzOtvo5amvFGQl6OsYLgCPNckTyOrr4bnDaxVECm41CqoA8htL3jEKUWZtplGdl/j978F77luiqx7QrmweoKrJrpmtsojXnW19aquTm91KpOp9Ahki//567WC2z3BrOIE3zvttNPUmjVr/Mm6fsM65V0Un1B6l8d/vPXWWws3w4wZM9Sll/7fgp3585//fGE/8+bPV5fr+lbvPY1/0803eYT2bY8sv6UWLlykzj//PP+9BQsXqh/84Afq4YceUlOmTlU9PT3qT088oS677DJ19PKjQxYIvd/nnnvOP458gFX+2JuamtTGjbcUzv+sL31JrVm71n/e2Nhs3svv40tnn+Wd31r/RrnyiivVtOnTQudfqCvw3r/v/vvUXXfeqe70t7vU3//+96IeX7/7/e99JXmnR+xdHo/uBv9BcTGWHewa/PojPreA2HqMlRp/wRd8wRd8wRd8wRd8wRf89w1+OSzKuXzgLtTgroeAqRmQojwACm6P4V1dZuuEXrhNQHLrQaikcClScpPA/7hNmUhrEpzBEQvRdSx9cstCcG1+5ygrCI4CK48CG0f1Ng3qbRpaBFVDiyBMUSYFtx0ILgZN9ZmBoCTlKUbBnWYIbtAmiBRcTXBHsqzgO2iQrMOhHnjgAXgtq37+85+r1rY2f+L5FuXu7pD3Xn9v2bJl6qtf+Yo/QdevW68+ffKnPTL5lE8o9aYJKnnxL7zoQrVo0SLfnpypzKgHHnywsJ/58+f5BHfz5s0qEsmT5Egk4lun9XNtG9a4//ytf/ZV1gsvushXjj/7mc+o/3vppeoTn/ykuvCCi1jDZ7eg4Orj0K/7+9eE95mnC+d61VX/roYmT1abt2zxP/vMM08XzvOqq65Sk7339HevuPIKNW3a9ND5Z+FmnzZ1mm/Zrq+v8x8ffeQRVjDvqosvvlgtP3p50fUPWz6y4evsst5jJfDDK21BPQPilxp/wRd8wRd8wRd8wRd8wRf89w3+OAiuSwpuzmyjo2pfPkV5gynnnGVJUSYFdxIQ3HYoG6V8pAbIT6pnuUqV0BY2DSnKCQgixtLWGGsZFC1BZiPlJLi8yNdmT46XqL1NwslQHS72wK2ENkHULLjRPG+GKOpOCJmioueJQHCHIUV5pvGVb9iTgzZBRsXF/lWhieZqBTcS1OCO5F/b6BNcEzJ13fV+DS4WhT/99DOqqqrKr2vVr+VrcM+yWBjyk/X55zepRCKhLr30EtXb2xvCnzc/b1He5X2uqrpK/fa3v4VC9WDF577f5Otwly9frn75y1+p62+4wVeQJ03qVQ8/8nDY5w8K7ltvvVWoDaj29n/PPfcUPvPtb5+jlq9Y7tueq6qr1a9/fW/hZv32Od9WK/R73t9Xfv9Kn8QGq1Z0E4ZvzlLnr/exevVq9Z1zv1N0/UMNs0ewBiF8/qE0ugPALzX+gi/4gi/4gi/4gi/4gi/47yf8cbUJggRlsCivhTZB0yGstx8U3AnQuYZIbisIkA3A2eogU6kGiC3mMNHfCdZ1J8FKYqNAYuMs2Ngpp0WZ9x6KMbYdZcnJyMKTLFwqyUKmqqBVUD0UKzfBCkE7JHdRD9xJYE+moKmpkAamB22tn6KcozZBeRVXD3YWYrhdWJ3R9bG+gusGCWc/3/hzCJn6qers7PSTkZ977ll19dVX+6FORxx5hNq2fbv/+XXr1vkEN8tixIOmzVlfwdXqrU4SRvzD58/Pt+Dx/l6xfIVHKleozVs2qxdfeFEdtewo9aMf/dDf1/YdO1RNbY1qqG9QW7e+rV57/TXv71o/kMrvK4skHgiuVo3phjrmmJVq8ZIl6tXNm9Wfn3zSP8cLL7jQf1+/t2Sx996rr6on/xy8p4/rxhtv9An9iy++wG7skUJ9Acfn56/t0ffee0/R9af/GOD1x4h1LNgPVtPePX6p8Rd8wRd8wRd8wRd8wRd8wX/f4I+7TdBoSOzTAbxQgzsNUpSHoA6XFNxucM92AMlthR64VGJazUpPM5CinGQhUwlw/SbG4JaREjlQZUtRjpZoGeQwZReV3iRI0HRSeLK8VRDvg9sCcngHeMAnQMDUJDMYk83KAxFcvSKxPucN4ujoqAmayvmDnB0JT6ZsYdXEVZGIR3Af/H0w8bzXfAW3tc1/fsP11yvHWIc1GR4enqJOPSXfB5cmoW59c9ZZX2KWBjeUlqZtvro+989P/jmEP3/+4eryK/I9Zl9+5RW1cOEClUwkfIvyunVrfAWWbpqVHvldunRpwR4xd+5ctX7Delg1CvDzBDdirNL511/xyOuiBYtUPB5TlZlKdeJJJ6qdu3b57+v3Fi4y73lE/MSTTvKDsvR+dciVtitri/NDDz0cKpwP1RqUOP8nnnxCJVNJj5hvK7r+hboDuP7Zon5gbiiF7t3iFxLvLOMv+IIv+IIv+IIv+IIv+IL//sEfb4pynv/szo36gt/evftIwZ1lCC7vgzsAKcpUh9sBJaMtIEaielsNztwqEDeJ/1VYwqWilj64nFdG91M2O26LMpeN0Z4chcjnJPirk6DaVgDpzUANLsnZ9WzjFuUOsCh3Q8jUENTgzjADNtu3KO/Zm08Oy40aeT7HVlGgCJysBm4w4bLZ8GpLkKgG6WfZLCs0R9/8SIni9HeOr0ntW1DDW278N73979yx04r/lnnPdv5vb337gPB1SNX8efM/MNdf8AVf8AVf8AVf8AVf8AX/UOCPT8F1C/wnn6Sc8whuoQZ3DrRXnWK4lM2i3AUW5WawKTcz920ddMbBUtQKS2cd4ohxFlRssyjz/rdlUXCjjDk7TEa2WZZ5X9w4sydXMvWWgqbqQe5uhotIcngn9MCdBAR3siG5VIM7wyi4a3MUMkX9nzyim8W0MzOhXJf1qULPfCgSPHiOlgOXrcjoRs8jGA2eDRee/yPjf/KTn1Rnn322XH/BF3zBF3zBF3zBF3zBF/yx8MdFcEcK/IdqcU2KMlmUp0NA7xD0we0zXKvLiIodzKLcZLYGUHFrWKecCuiDmwYuiBblGHseZ45g3sknWk6C6+zHkhyBg4qwoKkKeKTnaFGuAiWXYqYbIEm5FRTcLiC41CqIpHSqwR02BFcP2vpQYfXobt+qnIXY7rAVAF5DT3w2nKKWxZQ0ttKCjZtxIrtuuFH0PzK+tj+/+eabcv0FX/AFX/AFX/AFX/AFX/DHwh9XinK2wH92m3LNffv2EcGdAQouiYWDQHBJwZ0ABLcN3LUNsFWDelvNeuEiLUIC+gAAXSVJREFUF0xYgqUSJfilA4TXKXcvXFtz3RirxUW2nWIBU/S8ArY0qLeV0BiYHpuZRZk831TkTH7wXiC3k6EH7gwTMrVeD2KOGhznwinKhbQziPd2YRK7oQkX9saHmzSDZcFSTI5R36FVGsEXfMEXfMEXfMEXfMEXfMEX/BL441Jwve8T/6FNv25qcGebss4pkGOEFuVJUBZK7VpbYEOLciMjt7z2ltrEpoArJqAfLmU3OZb6W25NLivB5eFSXDqOwsFiT9wUU3DTUINbDYXIRG4boHC5EQhuK6i3E8CiPAn64E5lfXDX5a3JuUIPqIKCC9aAkH0g1JvKZXHeQb+rYALj6o1bKAqnnlbYKws9+4Iv+IIv+IIv+IIv+IIv+IIv+GPij4fgZrMF/pMzYVM5728tAhquNMPwpqkgGPYCwe1iJJc4WQPYkxtYH9wqcOhmgP9xdy+WtsYZt4xankfKnaIcK6HoxiwJWAkgunEguClGcjNQh1sDCVz1YFGmFYIOqMXthj64fYbcDkIf3OlgUV67h2pwScU1BNdlxeMu89GTTQBXXHj0NzZ8LqzCFL4/UjRps0UJaR9e/Ndef13dfPPN6ne/+616++231TXXXKO2b93mtze68aab1NZt294TfI110403qZ273PfN9de9hv/2t78d8vF/6i9Pqft/e38I/9nnnlX33HvvO8LfuXOnuskbu+3btxXev+fuu9VPf/rTMfFx/Mt5/lu2bFE7tm8vy/zX+9q+Y5vc/4Iv+IIv+IIv+IL/vsMfF8EdRQVXW5T3cgV3KqQoD0KKcq8huOSgxRZBLYarNYKKiwFTVVCSigpuknHFJMtyijJuybv6lC1kymG+6AiQ2wj0L6L3kiwNq4Ix9gzbaswjEdwGILhYh9sJhc7dZlVhkpHRh8E7TgFTesBWj5J6axRcPcihPlasaXMkGlEPP/wwrMKMqGt/cq3q7+8PLAZu2GKQdZFM8ZWXbKh/FjZ1tuG7oRjxPL6bDVZ6Dib+Qw89pK64/IoDwp89e7aaOnWq+uGPfqweeeRRlUql1B//+Ef12pbX/BZLL7zwwnty/nfecYdqb2/z/476Y/lI6Px/cu1P/LE8WNd/l0e0Gxoa1G9+85tDPv6f+cyn1RlnnBHCv9wb38NDidal8XWvZT12z296wf/8hRddqOrr69Vpp546Jj6OfznO/4EHHlR9/X0qkUx4W1KtW7e20D4L8V13l7r4u/+iKioq1LnfOdd6/R948EHV39enkt5+8vtap9586833xf0n+IIv+IIv+IIv+IJfFosykNx9QYryDCC4U4HgknrbAzW4nab+th2CgFsMsa2FXrhVhtRWM3KbYWnKKeCNMdY2CAlt/L1IUOYFv7wON1LCuhy3REGngL2n4eQpaYu3CiJy2wa+705DbruA4PaawRgC/3ghZEpHYaP3PLd7FAq3ob+VsQzoH/APP/xQyEt/7bXXqt7eScGqjXWyBl77ESwSh1WecJG5HT/k2w+t/rgHHf+KK69U06ZOe9f4O3ZsVxEnoh595JHCZ3fs3Ol//vXXthiCu+k9Of9vfOMb6vgNG/zP+WP50EOh87/22mu8sew9aNdfk7qKdIXavn37IR//yZOH1I03XB/Cv/zyy9X8+fPeEf5rr5nFiU2b/NdWHbdKffWrX31H+DT+5Tj/wYFB9eWzv+zNsx3q+eefV+3t7f55cPyjjzpK9fT0qN6+XvWd73zHij8w6O3ry2erHdu9ff3tb96+OtQVV1z+vrj/BF/wBV/wBV/wBV/wx63gGv4TqLijpODONNsUaBM0CHW4RHK7DQdrBYLbCJ1v6oDH1UDAVCWQXOKCCfN3nImkMZaSzHvjlp3g4k4jjOxGITk5ZmHhNoKbgJCpSmgIXAvEFrcWQ3JJxZ0AFuVekNKHjD15KqxIrN+7Z683wKOhOlyXelPxQu5snhRp5dIF+8C11xApyvrpvx/76MdUTW2N/8P6oosvMpPfVY8++ohavHiJqqqqVkuXLVN/eeop/73LL79MrV23Ti1ZskQlU8kgOtzgXnbZZer4j3xEbTh+g8qk02ru3Lnq/vt/6+/zeY9MdHS0q695RKK2tlbd4BGUbVu3qU+d8CnV1NTkH9f5551bWE26zPuhf/zxel/Hq0xlRs2dM1f99rf3F27IR7xjPNI7xmrvGJct9Y7xL3/xX7/sssu9Y1yrlixe7KtZP/nJT328WDzu4//hD39Qs2bN8vHpuC+++GK1fv36wvlrjM2bN6v2tnbvOkZUS2ur+qZHOF955SVvH53q73//u1EBI3kF16h7iz3Mqmp9zZaqp7zjCWwctMIVvvHDSXPZEL6+/pdccqn/vhOJGIIb/AfpGp/g9vnP9Vh+9GPeWNZ4Y9nRERrL/HEd6R/XsqX549L72LTpedXpffarX/uq/73rr79ePfLww2rmrJm+Ujh9+nT1u9/+rmBDudy7rgsXLvC/u3XrVnXCiSeoxqZm1dXVpb797XMK8++RR/PXobqqSi3zrsNf/vJU4fwvveQS1d3Vraq9Y9ngXe+/v/66//pjjz2m5s2b582ZCtU/0K82bvx5fg5cZubb0iX+WOrz37xli4pGo+qll18KzT9NDLXarudfZVWlmj5tmvrTE38qXP/zzz9fdXZ2qGbvmPV4R3yC+4Jav2G9N5dTqtq7BrNmzlS/f/D3auLEHt+OTLaahYsWqat//OPQ+AfXdrE/B/37xB/zrJoyZYq67ze/8T9z0003q0mTJvokVo/J588807te31Y7vb+vufoa9YY3djT+Cw4/XJ1z7nfgfzJ5/BNOOEG9/trr3vVc4hHcc0PN1PXzHTt2qqu9e1vPAzpfvS9Nhg90/iG+G6rLyRbh2/77k4W2AIIv+IIv+IIv+IIv+OMjuCOBi9VwoT17ChblwwzBpfyiYVBwe6EHbjf0wSV7civLTSKCW1WC4CIfxBBiTFWOl+iFG2EqbllCpqKWWtwYsyijVTnGDjoONbhpVnBMRcio3taaC1UPF7AF6m87oQ8uWZQpZGoaEFw9aOt0ijImh2mJfgQmk540I1DsnVdwHw5WXAqkqNf/zAUXXuBbXLUyd91116u0R0iffupp9dabb6kJ3d3qpJNOUvfdf79PRIaHh/3vn3/+BSrika2v/9PX1b333AurM3n8Cz0Sod//zGc/6xHb+33SpcnzW2+8qZ579q8eUXPUUo9k3X77HeqVV19RJ518shroH1B33HGHd2zXqnRFRv3YIxJ6fxdckN/XZz/zGXXffd6+Pprf15tvvuEd45veMU5QJ3vfv+/++3xyqo9Rn3/hGL/uHeO99/pE9Rve8Q70D6pHH3vUI2fb1CmnnOoTG7o2hx12mPrX730vKLh3tSV3p/r973/vE6FbbrnFI4Sb1CaPzOrrqhXA17e8lidJL2zKH88E73j0NfOOVe97eMpweMXLLa5TcF03ZOugz+3cvkNVeqRek8VsYbHi4UKNhD6+a6+5VvXpsfTev/CCC9TAwID60x//kB/LTFo99dRTvt212xtLOq51+riGp/j4umZV73fpkqX+9dctj2YfNlt91hs7TYJPO+1UNWfOnII95WMf+7hRObVF+DOqd1KvN463qx/96Ic+IdY1rW9716HbGxc9rnr818PcofG/+Wc/84nhlKlTfNKp3zviiCPVRz76UfXA7x5Qn/vcGaq+rs635V7gnVfU+44/lvfc62P/zPt+nyH2OP80wdXj8c1vflPddffd/rEfvfxo/zu3bNyo4rG4uvzKK9Sdd96phoaGjUV5k3r66afVggULfMvzE396Qu3auUu1trSom2++ydT7/kXFojH14osvhsbfv08m6HM9Sd1/X34OTvHOVV+rVatW5ZVW7/t6jurj+vWv84RXj5MmvTT+L3r7vO2229RZZ52l2lrbvOv0XEl70BJvrPIKrlt0/vp5aF9treq55547oPk3lj3JZSu0pf77E274LviCL/iCL/iCL/j/6PjlUHCDVkGjlKKs2wTNgTZBQ0bFJV7Vx0TFduiDSxblBhAna0G9rQLOl4bnFZDPlACiSy2DEoxn2sKmyqbgjmVPjjM5OcpIbYIlKSdZm6A0K0auNTJ3vSG5zSCHt0Mdbo/ZSEIfNAR3GEKm5hQRXG9AR/2QKReCptygwNvNBqQIJqomRdrmqCekJiv6x/bjjz2msiNBcbkOEkpnMuqNv3tE8u231EsvveT/QH/mmWfV+R7hmDlzJsR9h/H1+7pudNeuXT6GJpOZykqfaD5nCNXTzzxdsElo5VaTD8I/60tfUmvWrvHfu8Ajqm0eofX35auG2zzSl9/XPfeaY3zj777S9uJLL/vk6Zln9TGer2bOmFm4CfX5X6ktytOmFW7E3/z6176iqYN9Nj3/vIrHNYF5ga025YOe9DE/+8wz/vm+QATn9dfVFrK5esTnbo98aZX572/kr9nL3jXzv+cdD15/ja+J310eydJE66477/LVQLz++vO/84h1bU2N2rlrp/+dSGEsg/8YXXNN3m6u//7qV7+mBgYH1GOPPQ7R7m4wlm++4Z1Lfiz1ddLno8mP3q8meISvFz9OPOlEn+wGDcDzx6+J66233uo/b2pu8ojmzwvjr8//oYce9B7v8cn1G/o6vAV43nXQrgC98HDdddflA5Uw7MDga5VTL7joa6fHJT+WM0KriZq8afWYz7/Lr7hcdXlkns7/P676D9XZ2en/rWt2P/GJTxTORdc3O0bB1X+vPGalOu+88wrX/3Me2f3U//mU//ziiy7y3QB8/PW5ZuhcvXmiFWWt6D/z7DPq0ksuVStXrPCPY5I3Rmu9Of3P//wt9ap3XROJREG51tg33HCDR5S7/f1+6eyzjTqcLbr+Gl8vDhVUWcv9d/0NN6pub196XM/27iVSmsN1NYFNiV9/Pv85Ps4/G74bahrPe+sJvuALvuALvuAL/j80/rhTlPP8R5Nb/Xzfvv+kGtyZIA5SntFk6IOLbYI6IPS3DWzK9cDdaqAOlxRc7IGLLYJSlnZAtm49tvrb2HtBcnkfXIelX8VAyU3AiaXhMQOsvgYsynVwsZpgwxrcTvCDk4Q+GVYepkLI1LpcLheqv90dahOUDaK6zSTSP5gfeOCBwFvvvf6jH//YV970ZHvp5ZfVsmXL/BrTzs4udeEFF/qva1uoJiVRj4xEnKgfVqX//uUvb/NJ5/IVy/2VmmBlJ8DX6uniI48MUtW816ZMGfYI5vfVc88+6xOAt02IzubN+RpWTbBo1eeqq/5dDU2e7D/X6t2RRy7O30wjebuDVgO///3vq8v1MXrfjfjHGDGP3jHe9kvvGM/3jnFFsEKla3CvyBNcsklodbCzq8tXu/7t3/7Nt5nyRtRZP8l4qyHOz/j7I4Kj62+DOs4X/GsWYcej37vNOx68/hpf1wLXN9T7oUZ6e+SRR2DVK49/8XcvVsuXLy/8R0UfwwMP/L6waqavv1a6fbu59/xlj+Br+7HG7fJI3YUXXugfr39c3nf1ddfH5Zjj0mP5rEdw9XNNRAn/tttuVRMnTvQtwPPnz1f33H2vj/+35/+mYrGoT5g2b9nsn6tWiPn4XwbjorH86xEx18FfVPmaqqqs8tVprQhrIq33cdVVV6meCT3+d/3N+45Oaz7fzDcaf42jldn/8D7P598Vl1+m5uuQKTPmd9x1p2pubvafa4VYhzPR+OvFDJ9Eb9rk72PlypV5Ndlcf53QrBdftJq7YOECdeW/X1k0/oVzdYLz9K+tN6eeeOIJb4wbPGL/nK/yaoV80aJF6ucbN/oqeRb70JmwB73gMDg4oL71zW8WW4TM+S81Cm6p+4/+1vvSCx7fNPvC+UersxzfNv85Ps6/sfDx/hd8wRd8wRd8wRd8wS9HDW6o/nZ0t9LinyG4hwHJnQYEt8/Yk7EGl7hYMyQoN4GKWwvkltuUk2BVTgLBjbNE5SjLeIpZMp/K2gfXYcnJcUshcJT5p+Og2sYZc8eQqQyETNVBsXIjaybcDgnKE83Wa1YZhsCiTJsetPV+myAImfIV3BGcONnQaoqujfzRD38YasKs61+1CoQrMC97RPd7//o9n8D86le/UhtvuUV1dHRAMXrQJ0srait84gUrQICvSWlfX18QKe5t9fV1HqH6pf9jXxMArST6+93lqurqKl+Rpf2f8+1zDLHLW5T1vhBfE0O9L20Z7vCIHMfXf2sVWZMitEVoBXeqDpmC4vizvniWb7U96qhl/vtBYm1QgO8TXK3EPv2M//4LLwYWVZ/gRvIpyht/cYuvFuL1R3xbcX6wAuaG+pCRxfVcUOq6O7vUDwtjmf8Px1fNWOL5+2P5PTOWv/yV+oV/nTqs+DgeHP/xxx9TGzZs8Am4Tk++9uprzAJBHlvX0N7pEUi6/q9u3uxfj1/84hfmOpQ+/23btvqf0wstJ598kq+AJuIJ9d3vfle96T3XpFMf19/++tf8WC5fXjg+rY4nE4lCrSuOfz5kan7h+muVXNfbavxPfPIT6tRTTyvsRycOE8HVf+cJ7nmh8e/xiKm2U1ek0+q1zVuKxl/Pwc6uzpL3X3tbmx8gpS3M27ZvV3V1derMM87wxu0r/mc2bXpBnXvuuaFx0cFic+fOK8w/fv8VFFx2/z3/wqbCvghf72ue3tcBzL9S+Dj/bPd/uea/4Au+4Au+4Au+4H/48MthUR4t1OF6z/N9cNdAyBSpuIOsTRBalDshSbkFbMpkUa4DYlsN6m0G+B+GTMUYd4xDyWuM1eLGGA8tC8GNWRhzlAVMocLLE5TjTMVNA6mthAtBvXDrYEUAU5Rb4eJOMASXJPRBUHGnmcECi3Ku0PtJP45kw1HfhchubzKdcOKJfp3qHx7/g291veeeu1VNTa06/7y8UnX2l85Wp592mtqxc4d68cWX/KAhTR61squtwJdceomfInzHHbf7dZuaPPkK7vIVocQ0xNeKmyYAWt16e+tWXzXT+9LfzVuUI9AGJauOOWalWrJ0sXr11c3qySef9OsGL/CV5KxPprUqdtll/+YTTf0DvjKj9/WKvz+tAurQIp3qqxUyre6+5L2nibFPkuGmu/HGG3wyrc+TrtHDDz1cUAu3bHktuHkh+U2HKTnGnp23qL5YsKhqkhMxbYLoeHQolO6tqmtTdQ2uVlazmESH/2Fx+Wpa8Fyrh9qGTatqeiy1RfhxPZY7d6m7777HDwe7QNtqvf2cffbZ6rTTT1Pbd+z01Uk9lrd5Y6nx/bG8JD+WuvZZX6eXX34pXxOrFVyz4KCv45y5c7xrla891bW1DQ31Ht4Ov2b5tNNON3UYWXWcR8C1svnqK6+oP//5L6qlpUV9//tX+tc/fx28cdFzx8Ob4s0dbd+9/fZf+bWumlRq27lOLj7JO69XXnnZG+eoX1u7zbt2Z5xxpn9cf/3bX/0Fk+W6jtZct7vuutvDai3Mv29+/evqN7+5zz9eHYA2b/7hhfmvCbgmuPo9vYBRU1Pth67pPrF6kcYPCCsQ3GMMwQ3G/8tf+bKvvuo5Svg4/nrMqzI05jvU7d59MuTfJy/5+B//xMd9tfq6n16XT0I++ihVVVXlz1WNs9Wb01VVlb5av8MbUz1uOtjrtNNP9/FvuWWj+pd/+W7of0hawT3HLHw8v+l5dcbnPqde945F70sHa1108Xf98XrRO85p3r5OP+30A5p/odYB2fD/6AKrUvH9j//9Cf1PT/AFX/AFX/AFX/AFfxwE1/UJbs44WXebkKk9+nfZeiMGzoCAqSmG3GKKchcouO2Gl7UZrtYI6m0dU3HT5rEC+F8FK1uNwxaFWlxMUeZia6RcFmVOanm4FIFgElYU/k5BcnLaouJWAuuvgx64ZFMmBbcD6m+7QTofgF640yAJ7LC8RRl64PrSfC7cgNkNp5RpIrlq9WqVSMRV3Nt0ENApp5zihye5huBphbQiVeGToBM9srHTIyV6At7yi194P+57/HAdnYb7PY+w6O9owrFi5YowJjzX9uA5s2f7KcWaCLS2tfk1hnpSPmcssdqiTHYHHTS1aOFCP+FY1+pqwqNtoT7B9cjynNlz8vuK6H21+PuiVadbNppjjEX9FFtNqvT5a6v1CrIom+vzpoc5NHmo0DoJ65R1OnMWkuKwT5hOecZa2hcgZOi1La+bGtxN/r60oqdVv1gs5iuc37vke0FtQlFzbbe4xsHga3ur7rW69e1thTHVJHTV6lUqHoex/Owp5loFY5muyI+lDgjbvmOHv099XBN6guO6xD8u1w8zovEgfL1gUOmRMv25hoZGv2Zb42uy/pOf/qTwH7Atmzf7tt9EIun3cP3Exz/hXaut/me1atzj4UW9cakq4OXrmVev8eajdw6ajM2aOcskX7vqC1/4gj8H9Hu69lXbm5//2yYYy/y1+ta3vuUnZOtj2OmReU12v33OP+ctyldcoQ4/fH5h/usa57xF2VW7PIyTTzrZv256vujFAE1wfQXXzSu45/mBV8H4P/ro4/5nrrn66gI+jj+NuX9t/fskf66E/4Mf/MC3euuQs3xS93d9fL1oQuN/6223qc6OTr9vra7NPe7YY/z+yvr7n/3sqcZ1EPwPSKeFn3feuf456cUKvb/H//CYvy9tt9fqeTKR39cxxx6bP853Of9w/mfBWoT/Ayt1//PwiQOZ/4Iv+IIv+IIv+IL/4cQvT8gUdZMZJYuyTlGeC85XqsMl8bAX1Fuqw22D+tsW1v2mHhTcKiC5lcD/EkyxTQFnpA1Tkx1Wk1s2e7LDJGKu6mIT3ghLT46xXkcVkKZMJ14J6m01NAomFbcVUpQ7oA63BwhuHzQmngoKrl+Diz2f6HnRygn43WmFRROLJ/70Jz/AJ5xwlp/or7/+Wr6FSahYPP/9LVs2q5273LA3H28Ihp+3lK7wP7N586tB7LjrFvXZCiwPWT+RdochZISvldgVK/JK7GYdelQCX9eE7trpljx/xNdKMO1Ht1RpaW0ptAuynX+419f+z98/1s1bTDCW+47PH/G/f+UVat78+VZ8rdQVxtKCv8Ufy53W89dEa+dOd7/4WXeXH4ZE+Frd1kTNb4fEzl8vomjV1Tb/9NzR14Gf/9Zt23xbMsfXxE+3zBnr+uua8X/9138p4O/cuXO/8x/xdVDZW4bQlzz/Axj//Jjv3C9+qfF/5eVXDPEN4+evX2l82/lrh8NW754/0Pk33vl/IOcv+IIv+IIv+IIv+P8Y+AdOcF3oJpPz7cl79+4lBXc2tFidCo7YQUhR7oEWQR1AcCkMuI71wsUaXLIoE//LAB9MAl+Msbazcda5Bzlp2Qiuw1RbhyVb8ZSruCVNOQ7SdAUETFUC0a2FfkoNULzcDLHUXaxNUC+0CEKL8jQzaGu1DD+qVy38sCkTMjUCPaxCzZbDfamyvDcV9q4KregU97MKefCxb1bIfhAQ3BWmZnK8+FR/+W7w3+n53/qLW1VrS6uvQurAqXKdfzmu/yc/+Un15bPPPmT4/Pw3btyoenomHjJ8+qwmc1pdflD3dj4E+If6/AVf8AVf8AVf8AVf8D/I+OPvg5vnP76rddQjuHmL8jqowZ1hCO4QkNtJrA4X2wSRu7aJWZSroDsO1eBWAv+rYGWsUdYuyCagRi29ccsWMhVhkjFvGxRnEnOCtQiKgy05xQhuDTQGpl649UBwWyCSupMVPNPFp5ApVHBnh1KUc4H3XA921g1bAYIVFdbDKhvuT8XtCDhxA/88RHzjyg40cUZ83Sbn1c2vlgVftykKlNt3hv9Oz18f4xN/+qPa4ZGmcp5/Oa7/q6+8mlcyDxE+P3+teOo62UOFj+evk5WDAISDj3+oz1/wBV/wBV/wBV/wBf+DjH/ABHckW+A/WsHNjeoa3L2Uojzb8KYpZkP1tte4ZSngtwvILbUIopApVHBrQMWtYCWqSHBTQHJ5qyAb3yy7ehuxyMIRxrJRWk5AkXDcUnOLvXCpT1IN9E6qZxblFlBxO6HYmchtH/TAHTJSO61G+ApujloEmT5QIyGv+0iRfQD7ThXS0bLhQvHiR2ZBcFkTaIgYD010wRd8wRd8wRd8wRd8wRd8wRd8C/54CO6Itigb/pMjwW9PjlKUZ0PI1BRmUcYa3G6wJ7eBektBUxgylWEO3Urgf0kQP2OsHjfGOGWUuYjLblPmzDnK2gVF4MB4D1xeUJwGuZoipDNQg1sPXu4mKGJuBYJLF7sHCqGHoA/udLPpQduwb99e09w458vyhRpcl0WA0+qJG5DfYOWFJulIcQPnQpH4SGjlxmZRyCKG4Au+4Au+4Au+4Au+4Au+4Av+mPjjtCiP7oY63EKK8hqowZ0BQuFkw69Iwe0BBbfD8LI24GkNwN+qQbTMALFNMzU3yThj0lL2ags3jlrqcscVMhW1pClHLK9HWLPeJCi46L9GolsLBLeG1eA2gD2Z4qnRokwKbr+lTdDMfB/cvSY9bLQQkR1KPwsViGP6GTRuLhSOQ6PmkP0gG/LjZ0PR37gq44YixgVf8AVf8AVf8AVf8AVf8AVf8Evhj4vgum6oRZAW/AzBPd4SMkU1uJOgDncCOGht9bf10B6oFkTMKhAyK6BFUBzcvDxkKlYi78nmJC7bv2iJlkFRKBLGvkU8ZCrJ2HslU3Br4OI0gLe72WJRpj64RG4HjXqLrYIKIVOhNkH+YHuTZgTTzMKTtFDw7QaF5BTl7brhVRaM8MZeWUHSGhSZo49f8AVf8AVf8AVf8AVf8AVf8AV/LPxx1uAS//GV3FyhBpdblIeA4PYDuUWLcjt0uGmGvCSyJ1dbQqZQ3ExBH1xuUY5Y6nC5RTnCQo/LlqAcsRT8IvPmWwxOBJO0UtAfqRpaBNVAwFQzyOBoUaZi5x6WojwMIVPTTR/cNURwc8aiPGoU3CzEcGfRagArNdyOUJiII4ECnGUR30EfrCBRrdDniheiC77gC77gC77gC77gC77gC77gl8AfXw3uSIH/kEX5P4M2QYdBgvIUsCkPGJI7EdoEdUH9Laq45LhFR24GcpYywP+wfWyStZaNMf4Yt1iUnXIquDZJOM5qcB3LwSXAmpyEwKkkyNWVkLZVCyFTDVCH2wpbpyG6E1kNbh8juaTgri80N87tZn1waYUkCwXiwSqNy5ouh5PUeFw4vB6K/XYh9tsNx4QLvuALvuALvuALvuALvuALvuDvB3/cCq7ZRoMaXE1wZwHBHYb6W+JWE4HcthsehjW49VCDWwuOXCS6yRLklgdNxYDMRpg9OcJqb8tWg+swS7ItRTnGLMpxKBxOMIk6zVK2yKddB4XKWINLFuVu2CZBkvIg1OCSiqtXJVbvLSi4u6EG1w03W2YTbwSSzngRuOtaktMwBY0m/4i9EfSIG57Qgi/4gi/4gi/4gi/4gi/4gi/4pfAPPGQqCzxo1H80BHetIbhTDG8aNsrtAOuB22Meu1iKMtmTG5hFuRo4XhrCpipA6EyCchthz6MWMluqFresfXB5q6A464ObhNexF24aiG6aFR/Xso2SuWx9cGklgVuUByFFmQju2j178tZkIrijZrBDk9ENJmy4+DsbTGycbNioGf3zaDsg773rWmLGBV/wBV/wBV/wBV/wBV/wBV/w94M/LotytsB/SMXdmye4q71tDii4Q6DiUsDUREhQ7oT2rdjGtR62ahYwRRsPHU4wETQOeU5xlqIcYfzTKVcNLlmRHRbTbCsAxoNLsYCpBCs2ToFHGyXteqjDbTQXsAMIbjeETE00AzBoiO4Q1ODO9bZ1+/btK6xa5O3KuwurKW422AKfPBR6Y+qZmw01Yg6S0MIe+nAT6PykHAmt1tDnBF/wBV/wBV/wBV/wBV/wBV/wS+OPT8F1C/yHuNDefXtJwaUU5SnQB3fACIiUpNzNbMqt0B4IA6bqDI+rAxU3AyJnqoQIymtuo4xTRiwZUE65CK7DEpR5U94osPEoKxhOgeeaiC2y+mp4Xs9aBTVBIXM72JQnmovfCyruMGwzTJug1X7/W/Ce53K7/Z5QLqyeuDye2w0mtIux4KyvFdkLcFXGDTV9dkOrNoWbIZsVfMEXfMEXfMEXfMEXfMEXfMEfG78cFuVcPnAXanDXGb5E7tfJrAZ3EtiTO0HFJZLbyFKUKTCYwqVIyU2yfrgpwxOTQFqT4AyOWIiuY+mTWxaCa/M7R1lBcNSSeoUMPc3U2zS0CKpmzL8B4qdbWB9cSlMmktsPrYKoTdBUQ3ALKcoFBVcT3JEsK/h2i5KVXSjsDtsR0HKA34NJ6objw7OuywrGXcEXfMEXfMEXfMEXfMEXfMEX/LHxx0FwXVJwc2YbHVX7ghTlWYYvYYLyZFBwsQ9uG9iTUcWtg62K2ZPT8Jhiqi32xI2ystcIy36y8dKyKbix/fQnwgSsBPitE7BRojL2wK0E1l8HCm49KLgUMtUFIVMTIEm5H7zjU82KhJ+ivCcHbYKMiov9q0ITzWUNm0fQA58N++gxDa0wCcOTs9jCQJ58wRd8wRd8wRd8wRd8wRd8wRf8/eOPq00QJCiDRXkNpChPg7DePlBwu5mK2wbtgZrBcVvHspRqGLFNg9iZYi1lE6w7jwNc0wF+GS2nPdkp0XsoxtoClVJv40B2K4DgYshUFbQKqoMkZbQnt0HQVBfU4PZC4tdk8JBPM4O2xk9RzlGboLyKqwc7CzHcLqzO0GQsTERafXGD5svY1Dk8sUcK/voskugsK1SHlRvBF3zBF3zBF3zBF3zBF3zBF3wr/rjbBI2GxD4dwGtqcA+D7jPDENo7ACFTXUZk7DBO2jYImWqE7KQaCJlCITMDBBdDpmiLMpHU1gPXlgNVthTlaImWQQ5TdvG9JOuBm2Iny1sF1QLBrTerAySHU/+lTqjDpYs/CK2CUMVdm/MGcXR01ARN5fxBzo6EJ1O2sGriBr53iPfOFvWjckMpaFg4HvLahywNbigtTfAFX/AFX/AFX/AFX/AFX/AFf2z88aYo5/nP7tyoL/jt3buPUpRngjg4BDW4/awGtxvILam4FAhcB6ptNThzq6BzDj4mWJZTlJHbCOOVvB9uWdsEYT+iKCO2Eca0E5CanGIKbgqsyxkoQq6F+lvcSAJvhpWDbkj06oU+uJiiPMOsSqzbs2dvPjksN2rk+RxbRYEicLIauMGEy2bDqy1Bohqkn2WzrNAcffMjJYrTBV/wBV/wBV/wBV/wBV/wBV/wS+OPT8F1C/wnn6Sc8wjuXgqZmg3ZRUOQoMwtyp1gUW5ifXDRfVvHiC4JmUhsk5ZuO3FLr1u0KPP+t2VRcKOMOTslmLZNWkZvdQX4r/GkK1nQFNbfNrF+SySRTwAFd5IZkEEzOFOgVdDqHIVMUf8nj+hmMe3MTCjXZX2q0DMfigQPnqPlwGUrMrrR8whGg2fDheeCL/iCL/iCL/iCL/iCL/iCL/hj4o+L4I4U+A/V4poUZarBnWq2KeCI7QWXbCekJ7cze3IjEFusvUViW2kpVUWLMoYSc3tyzNLJJ1pOguuUYNZRRnLjwLYxaAob/CbNiVfABUiz3kn1rE1QE6Qnd0DA1EQohh4E/zjV4K4NFVaP7vatylmI7Q5bAeA19MRnwylqWUxJYyst2LgZJ7LrhhtFC77gC77gC77gC77gC77gC77gj4k/rhTlbIH/7Dblmvv27SOCOw0UXBIL+4HgkoLbBQS3Bdy19bBVgWBZZR7TwPnQ2RuHHrdx4I/cmmzjn5FyWpT5DnktLrJtlJuxoW8KiovphLFXEvZQamIWZQqYIpvyBKjD7WMW5enQJmitHsQcNTjOhVOUC2lnEO/twiR2QxMu7I0PN2kGy4KlmByjvkOrNIIv+IIv+IIv+IIv+IIv+IIv+CXwx6Xget8n/kObft3U4M5iPXCHofxzotm6wZ7czspH0aLcAKHBVZCYnLGUqSaZGBqB7CYupNr4Z1kJrsMU2yiTjqNAbHk9Liq4GBmN/ZKI3NZD4XI9EFyyKFP9bQ8UQPdBqyDq5eRblPPW5FyhB1RBwQVrQMg+EOpN5bI476DfVTCBcfXGLRSFU08r7JWFnn3BF3zBF3zBF3zBF3zBF3zBF/wx8cdDcLPZAv/JmbCpnPe3UXBJFByGGtwBILc90Ka1kyUoY90tPVayDRXcChA/U0wEjbEkZadEuHGk3CnKMUuqMu+NG7e0B4oDwU0yBTcNJ07kthouVAOsErQB0e2EfriToBh6EFoFkYK7ag/V4JKKawiuy4rHXeajJ5sArrjw6G9s+FxYhSl8f6Ro0maLEtIEX/AFX/AFX/AFX/AFX/AFX/BL44+L4I6igqstyn7I1CrDlaZDgvKA4VSo4Haw+ttmKB/lfXCpTRCWoKaZgpu0tJKNMk4ZY0KqU0LJLcu/mCXBiph0Av7GouEo+K0TIFUjwSWPto3g1gPJbYc63E6Irp4EnvEBKJbWA3bsKKm3RsHVgxzqY1XUtBljvPXkHCnEdxcsBm7YYpCFVZqsy1desqH+WdjUWfAFX/AFX/AFX/AFX/AFX/AFfyz8cVuUgeTuy6cor4UeuEOg4Pax9kAToAaXgn+bjNO2GUpM6yBkqgLaBKG4mWIpyhEguHFLS6BS/LOsxNbmg45Y+uJGGSuPW4KmKuDkqV1QDfTCrTUEtxEuZhuQ3A64+GRRJlmdVFzdKmi1jsIe3R0kh+V2j0LhNvS3QgsCb8ycpYbLEONdNFkDr/0IFonDKk+4yFzwBV/wBV/wBV/wBV/wBV/wBb80/rgVXMN/qP5W25Q9jnQciIKDIBb2QZeaHkNyScUlN20jiJHYGqgaQqa4PTnJ2sjGQSTlvW+j8H70vVJvcacRC7GNw4FxFm4juAlIUM5A2FQNq8GtZwouqbhkUe4CcjvJDMowEFy9InHs66+97ub8aOygDtel3lS8kJt88wUbAa2whCdeOOksW/hOFiK+A7uCyxLSIDpc8AVf8AVf8AVf8AVf8AVf8AV/DPwDJ7gjgYvVcKHXPG7kcaSVJWpwew2/QnJLWyu0cW1mAVPV0P+2aj/1twlLSWsCSC0XUiOWv8vWJihiKfzlCcsJFjaVAKKbZPW3KUsfXLIpN0CacrNZKWg1F7fNkNsJrB9uH3jIhw3BPfrpp59+HpPDtEQ/ApNJT5qRomJvWHFxi33yruuGbAWFgvMx7AkuW6ERfMEXfMEXfMEXfMEXfMEXfMEfC78cCi61CtqzJ6f+/Oc/b9Icybhdh83Wb1TciZYeuF3GUdvGeuDWgTiJ6m0lcL4KZlGOg5KL9bgR1i4oNgb/dMpFcEvJw3EL2Y2zg49b6nArgNVjMXI1KLlUuEzFzC1gUyYFlwqge1mS8rAZtCNvvfXWO4Ia3JzfDzfoURVMxCDam/e2CiefhQrOR7C4nDeAtsWC8x5Zgi/4gi/4gi/4gi/4gi/4gi/4JfDHnaKc5z/amqyfa27kcaQlxvE6mdmT+yBBeYIhuKTekoLbAllJtcDdqqEOlxTcFPA/UnBj5jFqaQnEg6Zs/DP2XpBc3gfXsRxMHNoEpUC9TTAVtwJaBdWYrR4ILvm7sQ63FYqdScXthTrcQTNgept35plnXqAHeHR0tOA/z6LsT6snLjRmhslZmNRu4Jl3WbF5KCocLQojbshHP0L7cAVf8AVf8AVf8AVf8AVf8AVf8PeHPz6CS/wnz4Vy6qyzzjrP40iHG3vyFOiD2wsW5S5ozYoBUw3Q/5YnKBO5zTCbcoIlKcdAzUXOGIF2QGPxz7L1wXUYKO9FFAeLcgxkZkxUTjGrMiZspUHargWiSxJ4A/TBRQUXQ6Z6YRWCSO7MeDx+7ObNm98mFddXcEeyoVZB2UKkt5mIWb7KkvW/g4lnGBk+AqsxI7BaM8I/S6s5gi/4gi/4gi/4gi/4gi/4gi/47zE+8Z/cnpx65ZVXtsZisdWQnjwM3GkS2JMnQO0t9cBtgwRlCgOuhSTlDJSfknqbZi1j40y9xTazPLi4FP8sC8G1RTZHgMTyOtwYWJTjQG4TLCo6wy5EFZBcVHEpYKoJ5PEOCJmaAAPSawZoGKKvtYp7sU5TpuLqHNbk7h5Vo6bxMRZh5+izUJRN/aPyEv/uUHBV8JlR8x0ItmKPgi/4gi/4gi/4gi/4gi/4gi/4BxPf40QXGfV2OoRLDUKp50QWMtUGCcotkI9UDwnKVSxFmcpQK4H7pUDsTEAZa4w5gCMsRdnGP2PlVHCjrOkuB4nBwUZYLW7c0uQ3Bcy+xlyMalgJQBW3AWpwO1iaMpHcPliBGACSO8vbll1/w/W/yg/uHjMpsHUQn3SB2huaiDiRoJ+UL/2Pmhrf0RybeDhx9fdygi/4gi/4gi/4gi/4gi/4gi/4BwV/T26v+tnPfnab5kSG3FLHmQHY+qD/bTfwrXazUYIyqbc1kKRcA8JlBhTcDJSpxhmpTTFxNMZSk0vxz7LV3/K+Q6jYYupVnB1knBUTE+Glk0+DelsJfXCrgdw2AcFtAasyJXt1g1+8H3rhYj/cWfF4/Libbrrptj179niTYLf6f4UVDjZxcjBxRneXXHnJ5Why5Qu3cyT/5/KrK6OhVReYaGZ/AabgC77gC77gC77gC77gC77gC3758DXn0Tj79u1TN954422xWGyVx4nmGmJL7YEGDH/qN1yKLMpEcClgChXcBtYeqJapt0R0k4brkWKbZsHDCWZVdixk18Y/y/YvVoJNR4GFR5g/OgGqbgya/KJUnQaiSxeoFlTcOkN0m6G5cAtYlEnBnQix1v3QE3cImhjrAV3+uc997uKXX375bT3oe3I5vwB7z569avfo/zOTJZdfZcmFWwuFV03ADmBWToIJmCteifH3F9gQ/Mk5ihM256/CCL7gC77gC77gC77gC77gC77gv1v8vIg36j/m+c0+9eJLL231uI+2JS/3tjmM3A6yFOVJYE3uBJLbDunJTdDppp5ZlDFcKg0EFxOUec9b/nfUwimjJUKnxm1R3l+jXbQlxyyNe22JykRwKUU5AzZltCo3QNgUtgpqByV3EoRNUR+nIbApk5KrB3ZRLBZbd8bpp59/88033/3HP/7xxddee2103769/gqHnhR79+7xH/d5xHePfu5te73386RYF2l77+3dq/buyX9HT7S9e/f5xdv6tb17iDjnJ5i/3715Qq1rgXM5s+89e/33858RfMEXfMEXfMEXfMEXfMEXfME/MHzNaZ588skXbrrppnvOOOOM8z3Os1a3TfW2w3T4LvCiqYYr9bH0ZEpQpvZAbaxFELYHqof05Crog0ucDoVN3vc2CcnJnMza+GbZ1dtICVmY9yyKAdmNs7pcrLnFv8mfzYOmqoDcNoCK2wIx1Z2sDncS2JSpWHoyeMxnwuYTXW9b6W3Hedvx3vYxb/uEt33SPJ7gbSd524lm+zRsn/G2U812ired7m2fM3+fYbbTzGufN9sXve0L5r0z4bUz4b0vmk2/d5a3fYn9TZ/7Avz9RfO5L5nX6fnn2Xtnw2fwe/Sds2D7AnznLPYZ/O5Zgi/4gi/4gi/4gi/4gi/4HzL8/f3+/gJ8Dz93BhzDFy2//+n9083fnzPb6YZHnA7PP2N4hn482fCSTxte8inDVfTjxw1/0Y+a0K4yHGext833ttmGC02DVqqk3lLt7SToTtMDycntYFHG0tFaaBFUCfyNCG2GtYWNw5ZgCcox1no2zlTbyHthU45YGHXUotxGWepVHHod0WspkKsrQbammtwa6ImLFuUGZlNuNxedVhgmAsntA3I7DEruMCO4esDnGevyQrPpiXCEaX68zGxHGVl/pdmONaRYP64xz9ea53pbZ/5eb4jzOvN8rflbbx81hFo/32D+Ph5eo/c/CtvH4FFvHzETmR7xcx8xj/T+RwwO7ePjhsQjLu5P8AVf8AVf8AVf8AVf8AVf8PeP/xHYJ312g9nWwXHQ59YDV6DH1cAlVpntOLMdYzbNQ442vGSp4ShLjEJ7JPCZBYbfzKYsIvM4A9KTKbPIlp7cBaG+7SAwNkNGUi1YlKstbYIqwJKcYmpunHHGBHDMGBNRbfyzLCnKtia7kRL2ZWTeESggTrINrcpVQHArzYWqgYJltCg3gkzexvozTTBbrxkoJLmk4k6HgSaie7jZ5psJcYTZiOwSyT3KPF8B2zGwHWu21TA515ptnWWjCf0RuAHWA+ndwEgxfQb/psf18Hi82ed6uLlpOx629ezm3gA33/FAzvG76+G54Au+4Au+4Au+4Au+4Av+hx1/XQnMdfCIv903wLae/fYnnrAe1NZV5nUitCsMpzjGPCex7ShDbJcCT1lk+AsS23lAcA8DYjvVcCPsf9sL7VaxNVAnBExR/W2jIbX1LFiqEkpObeptEizJGEIcZ5lO0THqbW0lsmVLU45YVN0o610Uc4ob92Jv3BQw+AqLRTkDfm4Mm2qCDQku2ZQnQk/cXoi8ngxEd5oZ5JlmwGng55uNyO4iM2mONBNoMZBcmmQrYDXlGDM5j4HJuRom7CqY0OuA9K6FSU6Tn/5eD6+vtdxMeINxtXgdI81r4Tm/Odey/awVfMEXfMEXfMEXfMEXfMEX/HeEv5aJV2sZmV0Lv/nXApldDUrtSiC1Kw3POBoeyVlKBPdI4CoLDH+ZB5bk2Uy1nQ725MlO0HmG7Mndhkd1gT2Zso+aQWhsAItyDViUq0CsrGB1t0lWphq3WJQdxiv3xz/H/c9W9Iv25dgYW5SpuBXA4CldC+OkMxaLcoMTbhnUwqzKnTAoE83Wy6zKQ04QNEUe9MNAxZ0LluUFhuCS3L8ELMtLQclF6zJXcY8FSwES3HUwqdeByruGPa5jN9NaCzFey2629Ywor2PkGf8jsMbcnGtK4PD/ENiOR/AFX/AFX/AFX/AFX/AF/8OOvxa+v6aEO3Mtsxjz8sXVUN54bAkLMnGJlWBHRnK7xIhuRwLBXWTUWyy9nGUILuYPkXo7xQnnFfWBNZncsJ1AcluhTLQe+FkdkNxqp7j/bRoEzTSotynghzGLioukdyz+WXblFr3PMSYdY9ugOLB29FjHgcWTjF3hFAdNkYpbDxeUVg9oVQH94UhyqRZ3EBTcATOow0B0ZxmCe5iZFPPAqkxyP04imlhHg12ZVF2yLa+ECUq2A5qsSHrp/TXwuBq2VewGwc+uY4R5FdxEtpuPaoXxplvFbrq17Pur2euCL/iCL/iCL/iCL/iCL/iCHxDU1Wxbw4juMfD7fxUotfh8JfCIo5kleQkotkuhjHKhUW0XMHI7x2yk4GJi8jA4XAfB9Uou2G7GrcieTOS2CcRHnpyMBBeJbswJMpmSTjinCVVc3iJof/yzLATXYf5oxynuU8SLgLlVmdffok2ZrMpVzKZMaco1htjSCkKTIbm0dTnhvrg94CmngRyCQaZ63JmG5NI2DyYIyf1HOEEAFfrel8G2HFZZloOFmYdTYUAV/3sl3CzHAjFe7YRre222BnyOj8eWuAFXsY2rzYIv+IIv+IIv+IIv+IIv+IK/f3z8PX8cCF1YunisEw6LWgkkljjDMsMlyDFK3IPUW7IkY93tfMNbeM3tTOA7U8GaPGixJk8EctsJLlnsYNNkqb+tAXJb6YT731aw0tQkCJ1xJ5yUzPnlO+Gf4/4XceyhUg4QWKy/jVuU3ARj8bzxbyVj/jXM24125SYgu1SHS2lfE5ziWlzsizvoBL1x0ZNOXnVa9TgcCC5NoIUwsY4AknsUKLpHO+Fi8OVM1UXCSzfACkZ6j2HKL75+LJDhY9iNeyxYoY+FG+o4eJ3XCPMb+zi4mVezfRzLrBSIJ/iCL/iCL/iCL/iCL/iC/2HB39/v71VMoV3Fyhbxe8tBpcXgqJUgkqFgRoLaEuYkXcB4CYZKzQPBbiZwnOlQrknOVkpN7gPe1GVIbhdYk4nYYmugWmZRxoApDJZCITPOSlYxjDgOpDXO/h6Lf5blXxwAohagGLwfAyIbZ6Q3CScYB4aPaVu0EkA25VpDaJHgNrFVBZLTKU25xwk3LqZU5SGQ6IeA6KKKSySXJsvhMKkWOeGi7iPBMrAYVlxwNeYosBusAOJ7FCPAfOKvhMfjGFHGFSBKWlvhhAvUV7GbERPZVjrh+mF8Hb+zklkqjrGsQAm+4Au+4Au+4Au+4Au+4H9Y8Vcy5ZV+zx/jFAfPHgs4GBS1nKmzRwM/wJY/VA5JvIKCb48AUkuq7XxQbWc74drbqcyaTMR2klOcnNzF7MnYsQY72dSDs5YykzBLKQ1El9TbuBNOUI4wERRfw1rbyDvgn2X5F2FSMZeLUWpG9k2KbQRIbwVI1mn4DF2USiC51DaIVgsaDalthdUF7Inb4YQLpSklbBJI81Ng4Kc7QfAUTQpSdOcyons4kNzFTM3FlZZlMEnJtrzEQnSPBrK7zNwQR7H3Vlgsz8ewGxWJ8dFsRYg+iytFxzACTcd1DLsRVwi+4Au+4Au+4Au+4Au+4Au+tR8ttxpjTs8Kww+WM4V2GViQiTcgt8AAqUUW1XY+CHJzwZY8ywnSkonfUJkm1d32A8HtBnGw3QnaAnH1tt4J0pPJokyqLQVMkWCZAqduBRM9uU05woRUJLGREvzzPf/Hm+5GGDgPnUqyE0uwC0DMH5OUeaIybk2M6JJdmQarG6zKk2BgB2Gwh2ECzITJMRu87LRCQhsR3oWwooJthZYA6V0MRJdILJHfpTDpj4LJfxTcNMvg+dGwj6VOuN73KMv3jiqhHh/N3l/BrNVIrI8SfMEXfMEXfMEXfMEXfMEX/BA+phsfBersMifc0gdV2sWg0CIfONIJtyZdyMgt8Q90mBKxRRcqiXfUDgjV2wHgRBPNI7leSSCkrRm2Ridcd1sLZaSVTMHFelsMFCb+h/W0mKIcYZyRq7bv+b+YBYwXCNMBJuDgY2BRjrELgDZlSlSuAqJLYVO4etAAcjmtMrQ74bZBPU7QNoi2AWZVpoEnJRfTlWc4xb1yScVFwkuTz0Z0lzCldyms1OBExxtiMXtcBuovv8mWMGsDv9GOtnx2CbvZlrIbHj8n+IIv+IIv+IIv+IIv+IIv+PZ9Lhtjf8QBMAV5iWMvdyQucTgotdS+FG3J3IpMnAVrbqcZfkOK7aATlGz2Go7UDVuXUxws1eyEs4+Q5FbCI3G2NHC5NBBcnqKcsIigPB0ZFVzuGj4oym2UeaZjTnGyMgZNJYD8YroW74mLKm41rBRQ2yDqidsMBLcVvOMd4Ccnoku9cXtBwZ0MBHcYJgYquYc5QSuhw0DRPZxNwgXMRrCIqby0MrMICDCqvEdYfPe4urMYvreU3UxL2OdwhWgJ+86RcEMucYrjx5cw0i34gi/4gi/4gi/4gi/4gi/4xfiLnXAY1GLL42IQwRYzUWwBcAa0IhPXIFI7xwm3AJoN7tMZFuV2MnOukmpLBJfaApE4iLW3rcyezIOlapiCSxblFPA7vmF7IKzB5SIp55YOsyq/J/+iJWRj7plGT3XcCactY1R0gp18mq0AVDvhsCm6sPVOuOAZSW4bqLkTYJWCBpZqcvvBtky9crGNENmWZwLZnQOElywCs80EnAeqLqm8RHQPh+e0aoP2AxsZPoJ58RfDTYE3zkJ4/Qhmm17MSDUSa9vnj2Tef8EXfMEXfMEXfMEXfMEXfMEvjc9/z6MquwgwFoEohq5QfD7PCfe1ncuEtsOcIEQKiS0F6A4Cwe0D1bYPbMmk2nYAb6LkZLQmY91tDWzVTjhQClu+JuA5T09OOMU9bx1GbjmvjB4M9daxHBBKykhkY8yinHTCLYQScBHS8IgR0yR9VzpBahf2YGoCm3IzqLidTlA0jbW4E0HR7TcbpipPYRvalmlCzYbntKoyH0gvthha6ITDqRYACV4IdoQjmOq7kN0UC+BmOgJI8xFwIy1kNyn2yrLdWPwmXAT7FnzBF3zBF3zBF3zBF3zBF/z949tyeRbCsS0oIYLh6/NAnSWldhY8IrGlHrczgLNMBfV2CMS8fkZsqSUQPbaDetsMLlnqYNMIImMNCJAZJ9waCEtNse42BfyP1+DGneI+tzaLsvNek9z9MW3b6yQzJ+CEUky9rQAFF2OmsV1QJbMpN8DqQjN4xlthFYKsyp1O0MR4EliV+8CbPggkd9hMHF6bSzYAWkGZ7YRbC81xws2W58KEpV5Vh7OVm8Nhgi9gNxe3KiBxXggEGm3S6NXHfS9iqjJ+diFTmQVf8AVf8AVf8AVf8AVf8AV///j4m34eHMM8y+//eWybw57PdcIZQDOcoK8tWZKnA6kdBsWWVFvaJprHCU6QR9TthDvPEG9qAy7VCByrxglqb2uAl2FroDRTcBNOOHcp6hRnM8WccIuguFPcLih6MOzJzhjSsQMHhcQ27oRTsrAfLiYpk3U5bV7DlQAqXsbULvKB18MANMGgoJLbDSsVE4DkTgSySynLk5mqi/W5M4DozoTtMHjENkNzGMGdB8QXbQfz4LX5oAjPZ/77BXCzHM7ex5tpDrM7LGArRnPhO/NAfebfF3zBF3zBF3zBF3zBF3zBF/yx8eez3/LzGRb+/p8NPAE5AwlomIo83QlCpKaZv3mtLW19hstw5RZ73U5wwonJ2I0GA6UagNxS71siuCRAVjLnLQmYWIubYA5eruQ67HnEwi8PWsCU45RuDYRKLQ+dirMtyeRrvDhV5jEDNmVMVEa7ciNI6s0gs7cxb3k3SPM9TrjRMam4pOgOO+FWQlPZSsl0UHcxwWwmWAkw4WymEy4Kn11C6T0MCO5sRoLnWm6QuewmnQs35Fx2I+E+5lpWjubDTXa44Au+4Au+4Au+4Au+4Au+4I+JPxvIMe9Ne5jl9/9scIDS734S0WaDYxQdpFOBj6AINwykdshwGFJve0C5xbpbUm8xs4gCpVrBJYsloTXgqk0Dwa0ESzL2wE2BQptg5atxptbGgWfGGdGNMrvye6rg2qRjpwQLj7ITilmsykhyUyBzZ+CRVNwapuLWM8sySeuk5rbBKkUX85xjj9yJYFkeYGouJS1TCNU0eJwOduZpQHLx+SyYxLPAboCTfhazPR8GRHk28+HzNLWZ7Caaw26aOZZH7PU7Bz5b6nOCL/iCL/iCL/iCL/iCL/iCH8af5YRTjWex3/P89z9yghlsI1I7BbjFkBP0tx0GS3KfEw7N7XeK26Ni2C4lJpPDtd1wpRYnHCyFNbd1TtDVphYEx0qm1qZBseWhUgkLqU1Y+GTU4gyOWvjle6ri8lpcZNkxRoS51zpu2TBWOsVWA7CIOcM84HWwwtAIKi62D2qHVQoa1C6m6KJVmdfm0qrIAFN1ccJh8vJ0ILjTLJN3BpDfGWyyz7DYn2ex1R0izDMtNw5/nGmxO8xi++D26lkWUi74gi/4gi/4gi/4gi/4gi/44TTjWZbHGfC3jQNMYxxhKii1U5lzlNr9UI0tdYAZBCsypiRPYsQW620xLRntyVhzi8ptHRDaKrAlV4IQmbbwuDhYk1G9jTnFLWXjTnEnnigrfT2o/5Bho7yMB+9Y/NZJxuZJxqYLU+GE46bp4lWDRbkKlNxaJ0j6qgOCSz7yNhjEdlByaZtoHsmj3ssmSh8oudhTigq6h2CFZQgmKHnjpzMSjM2YqZ53ClN+pzMb9HR2o5A1ejqowbS/WfDeVPDwT7fccLRf/NxMdgMKvuALvuALvuALvuALvuALfoBB79Hv/pnwXfxdz3//0/vTgTcMM4V2EDjHMDzHkso+8xz5C6m2neY1rLfFXrfNhh81GI5Ewb283pbatFaDm7YSuBkpuQkguuTUTTFxMwFEF7ljHHilzY580EluqThnlJJtKi7alpPwPOWE++QSya1kG9qVq0HJ5cFTaFemFYo2ZlumSdAJKx09TrhGt98p7p3bB6soA0B+J4OlGQvAsSAc35vuhFObeb3vVAshxhtjKrMxIJHmJHkau9F4/6zpsK+pbHVJ8AVf8AVf8AVf8AVf8AVf8MM1sfgbfyr77DTL73+spaXyRxLOUKkdAmI7AEptvxMOyyVSi1s3OFY7gNhiW9UmJwiWqmOqLbYFqgb1tgIekyBGJp1wd5ykE66/5e7euFPcBijCOGXMOYg9cB3LQcQsSi4eHCe4jlNcdJyw2JPpgmH8dAYU3Cogt1STS0oupX+Rn7wFVNxmsCtjfS5NCvKt9zj2Ol0s4CZltx9WWQZgQg7B69iXCif5ECO92LB5MiPK/P0pQIzppuM30DC7+SaX2M9UdvMjpuALvuALvuALvuALvuALvuAHfw+BSIUljPi9Yfj9P8QU2cnMEToZhDRMQibe0QvEttcJB+d2MWLbzmzJTcCHmsCWjKnJRHKrnOJ2QNyWnAHuRkHBCbAnY61tjLl8be1l8XXHOUjhUjb11tabiAdMRdnnUaLGJr8kaZOKi+2C0nDxquCxmg1AjRmcWifcJ7feDGQz2JTJtowEtxMmBsVp98DKyEQnSCXDidbHrAL9oOwOOOE+u5MZ0R1ywiFWSIAHmVVhCFZ5htl3JsONixhD8N0hdvMPWm5Een2K4Au+4Au+4Au+4Au+4Au+4I+JP8hsxVgrO9miyPaz96mmthceidxOBK7R44RrbDEZuQccqcRtyLmKDtYWJ9wOqMkJam4ptJdCpWqA3FY74WwkrLklJTfBSC4PGk4wohsFJTfKeORB7YFrsyfHStiTI4yJ84LimBPuics92gknnMzFN5tNudoJ92vCYmlcqaDVCyS5NPgYRNXNLMs9zL5MhBfbDU0ChXeAPSdCPACTmazOfWy1ZsApLiofBJvCZCDI+LpNPcbPc7JNPX/7LcRc8AVf8AVf8AVf8AVf8AVf8MfG7x8Ds9/yG5/I7CQLj0Bu0euEE5EnGm4y0SkOkMJHJLbNIPQ1gSW5yQnnGdU44dpbtCZjZ5sUPKac4tTkFCO2thBixwnnNzkWHhlliu5B/RdhB8P75EYZi3eccJoWEdw4uzC8hVCaWZXRB55hg1ID/vF68Jg3O+FWQi1gV24DGb8LHrFWl7YupvBOsHjhe5mlGT3z/TDZJzErQh8QXxsp7oe/J7G/+y2EGm+uwRL7QtW5X/AFX/AFX/AFX/AFX/AFX/DfFT7+5sf9DDABjDhCPyO3qNL2AJHtccItTjtKcJU2EO3IsUrlmQ1AcBtYWSfW3JJ6WwmCYiUQ2vQYXA2tydymHGH2ZIeptxGm3jqW5wed4EaY5Mzrc7naiz7rBDB0XAGIg687BYpuhim45AmvccLJytVAcMlPjh5zbGhMqxptoOK2wipIJ0wiJLjdTrhudwJMyglOcfE3rsJMYpbnHmZ5RktCKbLcC1s/u+Fs3++zrBRhzyzBF3zBF3zBF3zBF3zBF3zBP3B8xOmx/P6n1yYw7jARuAVxiU54rcsJh+MS0W0DgtvshMOkGkHga2DkFluu1gCxxV63FU44ORmVW7QmY3oy2pEjTMWNOsXdd0r1vz0kyq3DvNIRRnqjTnGrIG5PjjvFfXF5TW7SCTcProDXMhaiWwnktg4ILw5oI5PqW5iqi7bldphIqPJ2somGNoEJTrh2twv88hPZhnZnfK0X9tHLvo/7wc9xYt3LyDV69ydZVGfBF3zBF3zBF3zBF3zBF3zBHz9+9xi//5HE2hyixDG6gXdgF5hOEOZagccgueUtgLB0s44R22pQbzNOuO8t73GLSi52w0Ghkjhgwgl31kEVF92/tkTlQ9ID17HIx1Gm2PIk5TgjvcjYU8D6ebx0EshtmsnkaSaj0yBVMrtyDVu5qAN1t9EJmhzzlQ9eq4t9pGyWZpqMtKrSzSbtBCccaNUFN0AXTPieEt/nKzzdTnEgVo8TToXmNgfEFXzBF3zBF3zBF3zBF3zBF/z3Bp///sfHHsYZMP2YVNnOEqQWFVviKo3gUqUAKbQlI6GtNu8TmSWSmy7BtzA1mVTbGHuNB0tFLfwPRdE4E0d5C9pD/i82hswcs7DyBMjXqOZipDSuFMSdcFoX1uNWOOF0ZZ6wTJ5yGthaJwidaoDJ0AAktxHILTZEbjWvtztB7W4bWARshd7dzCffySY7hVl1wuc7mAW6k90cmJyG6WkT2E3Zzd6jG2qixWbdxVRpbpEQfMEXfMEXfMEXfMEXfMEX/ODvDkaa0eXZAb/r8fudgNvOVFq0IJObtNXCS5qZQNcEfIbKMWvN8xp4jkS3BhywVSAc8rrbFHPSouMWW7+iYptwitvJJpxw7W10DMH0kP8rxbh5vHPcCfc6QstyxAkHTaFdGcltBVNzMbIaA6jSjORWgWWZanOxRrfOMkFwawXpv4mtoLSzyYc1vG1AhLtKqMAd8D7eNBj1zftadbEVoC5mn+aEup2RaHwPPy/4gi/4gi/4gi/4gi/4gi/4B4aPv/FL/f5vd4ozf9oZv0BO0cr4SDM8NoIjFcOj6p1wC1XasPtMBrhSJeNTmJaMtbZYRpp0ilv+xJ3itOQoe40HE8ec4sDi98U/m4LL1VxObOk9zuZ5bW6C+brpoifhkacqVzE1F2tzsaC6DlY6auHvelB4m0DxxQnVDIS4na2sILHF92zkF1dpWtn32phazJs2Ex6vEe5gz1vZzSb4gi/4gi/4gi/4gi/4gi/47z1+O1Nj8TuoxrY44TLJFifIDWpxwm1P683ryFmwHLMeuA/xnxrgRzWg2pJgWAGKLedb9Bydt0mm4HJXro3/ocPXJpC+rwgul5ajFoU3ZmHuvD4XPdtYfxuDlQJOdFPMK17phNO+qpxwC6FKWLmoAw861ubWMusy76XbyAhuC3jfW53iml68YXDStjH7M9b/4g3ZAt/nNyLePGifbmbvdTjhyPBW9p02J9wIWvAFX/AFX/AFX/AFX/AFX/BL4+Nv+TbgBW1j/P7niizyCCS5vL1PA+MmjSDS1QOpxZapdcyKjHbkKuaE5ZzKZktGbpZwwrW3cUZqY8DxSnHGyPtRuXUY847sx0MddcIR0lEmX/OIaVRxk0wep1rcJFt1yICayweO7Mo44FifW29RcuudoD6X1+xiSlkTrLI0sgncBJ9rZjdXkxOu88UktBZ2M/CENLypWthNgQXozUC+W5iyLPiCL/iCL/iCL/iCL/iCL/jlx+e//9sYV8BH5BGNTnFOUCk7ch1zp9YB58k44ZTkGuZ8xUdUcFOMcyWdcCvXmBNOUY4zosvTkSPwOV7OGnu/qrec6HI1N2qxLztAamOM/OIFQIuyTclF4pt0wn2a+GoF9nfKgIpby1RdlPPrnXA/XU586y3WZkwua2JqLxJevLlQGW5iN0ULuxnwM02AiTbqRnaDcmt1I7s5m5xw2yTBF3zBF3zBF3zBF3zBF3zBL42PJY2oujaP8fu/EbBRmUVFFgkt8o9axl1q4LGWEdlqILfc4VoNomDKCdfexhm5jTHlNu4Ut/uJgYJrawsUYTbkuMXx+74nuMjKbT1yeUuhmGMPn8ILmHDCAVRJ5g1PM+syqrqo7laBTZmILp8ENTBRsBEyyv1c5W20EN165oVvsNyQ9RZPfaNT3LOKJz83su81M6UZb6J6pj4LvuALvuALvuALvuALvuAL/sHBb2Sfq3PCtbO1TFRDdymKb0hwa0CtRQ7DXayYjFwJKm0GlFvukuWW5Dhz1mL/21I5S9zhG2FE9n0ZLLU/q7Kt3y3K0vExyG2cXVjb33EnXPhcAeS2AjzjGSC3uFpR7RS3FqpkqyBoZeZ/1zjhfro4IW2KbwMou7QyU8smfKkbscHyOX7TNDr2InP8XjO83mR5X/AFX/AFX/AFX/AFX/AFX/DHh7+/3//4fi0Tw/jrtYzIoghX44TDo7C+thK4DbpbU0wATJbgUlgimmACJN+izKXL3bkRZkfmYcQfGIKLBcaREuounnTMou5G2YWjR5TPkexywpsBQpuEFQubRM8tzJWwEkLkF2t2MY25mq2sYB1vg+Xz9U44xbnWCQdd4UoNWhLw5kDvfa2FaHO/fg0j3bUllOd6diyCL/iCL/iCL/iCL/iCL/iCH+Cjgopb/bv4/V9jUWe5ixQTj2vZY4bxlkon3CKV97LNOOFyTt6dplSYFPGvJFNtOX+LMgIcGcPdG/ugWJNLWZXxpKJOcWJWlLF6ZP4RJ9xXyZbcFXeK63JJZke5PQ1KbtrydwomSBqk/Gp45JMNQ6tqLF74Kqb21lkmci0jzrVOcUujarhp6tgNwZ/jY3WJG1DwBV/wBV/wBV/wBV/wBV/wDz5+DRPQOL+oZI+1TrgTDApx3HrMSzQxbBcVWqyvTTHHLNXc8q42mJWEwmPEosjuj/99oEhtKauyrblvjNmVsQiZ91GKsVWBKJDcFCi5SSapo5qLNboVjNiiNz3NLMv4HgZUcTtzFXtezawEdRbCy28sXu9bw24CtCFUO+F64GpmYahhq0CCL/iCL/iCL/iCL/iCL/iCf+jway3EFgWzWmY3rmTW4wzjGWkn3PonbRHyUKlNO+EWQEhuU/AecrG4E66txdJRno4cfRf8L/ZBJbk2Fh+xEGGewhVhfyctsjcPoIoxlTfNVNwEW7VIMCszb3TMa3fTbLUECXE1I71VbALzMKsqp7hHbzWzIVTDY53ls7iCU2OxVNexfVUKvuALvuALvuALvuALvuAL/iHBr2R8AvfFOQN9L+PYW59Sex/OZaqAA1VYbMg8rNeWcRQDwsuDf7nb1mFW5fi74H8f6H8x5rN2mFqLxcVxdiG4pxtXDRIliG2MDRIptijBp9lKBn03w95PM+KbdorrdDNsYmLj5DTcDBUlbrRq+GyNM3Y9cCVbHapmNxa2P6oEUl0DxyL4gi/4gi/4gi/4gi/4gi/4Bxef24vTTjj4FnkFCm1piyW5Al7nWUNJ4DBxcLci0U0Bt0oxEhtnjlnOv7iaG2U8793yvw+sVZmfFCfAvNiYS92815LjhNOWY5a/eZQ1X71IssmQZnZmLMzOMFKchElVyWwAlYwcI9GtYh75DJv0Gbaqk7FMaFy1qXKKY8Cr2P6xlljwBV/wBV/wBV/wBV/wBV/wDw1+molhVYxD8P3Y+AeWWSJ3STjh0F2behsHdTbByC1939auNcJ4WoxZkaPvkv99qP5xBm+zKTuM7EZB3Y079nrdKPOL26R3VHYr4D20KuMKR8IpbjdkK9LmlmacpJVMBSbyW1WCGFeyz2EKdNUYNxqu+mQsN5/gC77gC77gC77gC77gC77gH3r8CiaoZYBTYDBuaj9EFwksEt0KJ5yInHKKw6TiTJUlLhVhBBZVWy40RixEl3fQGYv/fWjILY+HjjCZGguTeaBUxKLw2hTcmEVmxxUNDKfCVY44k+0rLB72JFNnMa2sgq2mpC1KLk5cfoOlSpBkxEixfWUs+6lgE1zwBV/wBV/wBV/wBV/wBV/w33/4SSae8SRkVGIrSnAOFO2SlkfOf5BDRffjhrUpt8jRIhbR0imh7n5oVdzIftg7j47mvXFjlhUCxyluMByFAcNY63iJ1QveSzfJSHCKqbkpmPBxtnqSsvjfS6U3889XMHWYT/i0ZYUmMcb+BF/wBV/wBV/wBV/wBV/wBf+DgW9TZTn/wPY+Cac4m4iTWiS8MbZFnOKuNeiWtfEvW83tWKImlqJ+6P8hieXJyZzwOk44ipr3yk044dZBCSdcf4vpYFFmacaVj6Rj76+LBdrYN6qC2QRSzJLMFV1ui05aPl9hmfBJtvrDb6yME05Ry1hWlDIWJTrFbBiCL/iCL/iCL/iCL/iCL/iCf2D47/b3f4ph8xaoNv6B3WES7DtJi6KL/CfO+JKt1paXiEZKEFueohxjztuIxa78of4Xc4r75DrsosQsFzlqWWmIWFYZ+EpDxCluWMz953E2KVJM2o9bJl7KYnfGz+N+0jAZ+QpOBbM/89ArPrnHWunhhF3wBV/wBV/wBV/wBV/wBV/w3z/4nHPgPpOM5CYtfCPOhLkEE/yI1HL+Y7MZx5njlTtlo4xv2UpLIxaR0vlHUm5LKbkRi5wdcezR0ty6zJsJ22p040zxjTF/OX02wiYKrpbYJlSSTeYYm5g4UbGRMt8//z6vD64oQahTgi/4gi/4gi/4gi/4gi/4gv+hwH+n/ANDoiLs7yTjQRGm5MaYkxatxfh+3EJ0OWfjz6P/aMqtzaPtsAtJF9NhFzFiWQ2wtRRKMBk+AoPKVy1Qpk/C/lJOuG7X5m2PsYkdt9gFcKUkaSHOSbZyE4fVnrhTHPWNN0ic+fETzC6NxxW3qMwpy8qR4Au+4Au+4Au+4Au+4Au+4I8Pf3+//+Pss0SmbXWzyD9QcU3Cd3l41P74D3IoBwQ+h5FU3iLIsQiLEca5PnRJyeMhuZzM2lYGSim2EWZd5hcfPefoJU9YpPiERapPWOzNuGISYzcET29OWFZkEmxVJjHG5/gqD94gSMQFX/AFX/AFX/AFX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+.S5 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
+.S6 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></h1><div  class = 'S1'><span>This tutorial is easier to follow if you have already looked at the </span><a href = "dimensionMapNoDataPipes.html"><span style=' font-weight: bold;'>dimensionMapNoDataPipes</span></a><span> tutorial or if you compare these side by side.</span></div><div  class = 'S1'><span>The key difference when using </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> instead of </span><span style=' font-weight: bold; font-family: monospace;'>VectorData</span><span> is that 1D data can be represented in HDF5 as 2D, thus allowing you to write either row or column vectors. This is made possible because of the </span><span style=' font-family: monospace;'>maxSize</span><span> property of the </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> class, which lets you specify a max size for each dimension. By setting the </span><span style=' font-family: monospace;'>maxSize</span><span> to [1, N] or [N, 1], vectors in HDF5 are represented as 2D arrays, just like in MATLAB. The flipping of the dimension order still applies, so a row vector in MATLAB becomes a column vector in HDF5 and vice versa.</span></div><div  class = 'S1'><span style=' font-weight: bold;'>Please note</span><span>: The following tutorial mixes row and column vectors and does not produce a valid dynamic table. The tutorial is only meant to showcase how data maps onto HDF5 datasets when using </span><span style=' font-weight: bold; font-family: monospace;'>DataPipe</span><span> objects.</span></div><h2  class = 'S2'><span>Create Table</span></h2><div  class = 'S1'><span>First, create an expandable </span><span style=' font-family: monospace;'>TimeIntervals</span><span> table of height 10.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% 1D column </span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >start_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'start times column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, 1:10, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, )</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, Inf </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column </span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >stop_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'stop times column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, 1:10, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, 1)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, [1 Inf], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'axis'</span><span >, 2 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >cond_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'condition column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, randi(2,10,1), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (1, 10)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, [Inf, 1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'axis'</span><span >, 1 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 4D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >randomval_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'randomvalues column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'data'</span><span >, rand(5,2,3,10), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> # maps onto HDF5 dataset of size (10, 3, 2, 5)</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'maxSize'</span><span >, [5, 2, 3, Inf], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                    </span><span style="color: #a709f5;">'axis'</span><span >, 4 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% 1D column</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >ids_col = types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, types.untyped.DataPipe( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(0:9), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # maps onto HDF5 dataset of size (10, )</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'maxSize'</span><span >, Inf </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Create table</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >trials_table = types.core.TimeIntervals(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                 </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'test dynamic table column'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'randomvalues'</span><span >, </span><span style="color: #a709f5;">'conditions'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'start_time'</span><span >, start_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'stop_time'</span><span >, stop_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'conditions'</span><span >, cond_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'randomvalues'</span><span >, randomval_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >                </span><span style="color: #a709f5;">'id'</span><span >, ids_col </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">     </span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S2'><span>Export Table</span></h2><div  class = 'S1'><span>Create NWB file with expandable </span><span style=' font-family: monospace;'>TimeIntervals</span><span> table and export.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span style="color: #008013;">% Create NwbFile object with required arguments</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >file = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(</span><span style="color: #a709f5;">'2022-01-01 00:00:00'</span><span >, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'ident1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'test file' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Assign to intervals_trials</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >file.intervals_trials = trials_table;</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Export</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwbExport(file, </span><span style="color: #a709f5;">'testFileWithDataPipes.nwb'</span><span >);</span></span></div></div></div><div  class = 'S6'><span>You can examine the dimensions of the datasets on file using </span><a href = "https://www.hdfgroup.org/downloads/hdfview/"><span>HDFView</span></a><span>. Screenshots for this file are below.</span></div><div  class = 'S1'><img class = "imageNode" src = 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" 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 </span></a><span>    </span><a href = "#H_1248"><span>Multidimensional ragged array columns
 </span></a><span>    </span><a href = "#H_9bac"><span>Adding rows to multidimensional array columns
 </span></a><a href = "#H_42c6"><span>Learn more!
-</span></a><span>    </span><a href = "#H_8e0c"><span>Python Tutorial</span></a></div></div><h2  class = 'S5' id = 'H_1987' ><span style=' font-weight: bold;'>MatNWB setup</span></h2><div  class = 'S1'><span>Start by setting up your MATLAB workspace. The code below adds the directory containing the MatNWB package to the MATLAB search path. MatNWB works by automatically creating API classes based on a defined schema.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%{</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">path_to_matnwb = '~/Repositories/matnwb'; % change to your own path location</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">addpath(genpath(pwd));</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">%}</span></span></div></div></div><h2  class = 'S9' id = 'H_869a' ><span>Constructing a table with initialized columns</span></h2><div  class = 'S1'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> class represents a column-based table to which you can add custom columns. It consists of a description, a list of columns , and a list of row IDs. You can create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> by first defining the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> objects that will make up the columns of the table. Each</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> object must contain the same number of rows. A list of rows IDs may be passed to the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> using the id argument. Row IDs are a useful way to access row information independent of row location index. The list of row IDs must be cast as an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html"><span style=' font-weight: bold;'>ElementIdentifiers</span></a><span> object before being passed to the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object. If no value is passed to id, an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html"><span style=' font-weight: bold;'>ElementIdentifiers</span></a><span> object with 0-indexed row IDs will be created for you automatically.</span></div><div  class = 'S1'><span style=' font-weight: bold;'>MATLAB Syntax Note</span><span>: Using column vectors is crucial to properly build vectors and tables. When defining individual values, make sure to use semi-colon (;) instead of instead of comma (,) when defining the data fields of these.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col1 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1;2] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col2 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #2'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a'</span><span >;</span><span style="color: #a709f5;">'b'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >row_ids = types.hdmf_common.ElementIdentifiers(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0;1]); </span><span style="color: #008013;">% 0-indexed, for compatibility with Python</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >my_table = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >, </span><span style="color: #a709f5;">'col2'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1'</span><span >, col1, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col2'</span><span >, col2, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, row_ids </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >my_table</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="5BF1DF1A" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="108" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">my_table = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DynamicTable with properties:
+</span></a><span>    </span><a href = "#H_8e0c"><span>Python Tutorial</span></a></div></div><h2  class = 'S5' id = 'H_1987' ><span style=' font-weight: bold;'>MatNWB setup</span></h2><div  class = 'S1'><span>Start by setting up your MATLAB workspace. The code below adds the directory containing the MatNWB package to the MATLAB search path. MatNWB works by automatically creating API classes based on a defined schema.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%{</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">path_to_matnwb = '~/Repositories/matnwb'; % change to your own path location</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">addpath(genpath(pwd));</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">%}</span></span></div></div></div><h2  class = 'S9' id = 'H_869a' ><span>Constructing a table with initialized columns</span></h2><div  class = 'S1'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> class represents a column-based table to which you can add custom columns. It consists of a description, a list of columns , and a list of row IDs. You can create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> by first defining the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> objects that will make up the columns of the table. Each</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> object must contain the same number of rows. A list of rows IDs may be passed to the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> using the id argument. Row IDs are a useful way to access row information independent of row location index. The list of row IDs must be cast as an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html"><span style=' font-weight: bold;'>ElementIdentifiers</span></a><span> object before being passed to the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object. If no value is passed to id, an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html"><span style=' font-weight: bold;'>ElementIdentifiers</span></a><span> object with 0-indexed row IDs will be created for you automatically.</span></div><div  class = 'S1'><span style=' font-weight: bold;'>MATLAB Syntax Note</span><span>: Using column vectors is crucial to properly build vectors and tables. When defining individual values, make sure to use semi-colon (;) instead of instead of comma (,) when defining the data fields of these.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col1 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1;2] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col2 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #2'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a'</span><span >;</span><span style="color: #a709f5;">'b'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >row_ids = types.hdmf_common.ElementIdentifiers(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0;1]); </span><span style="color: #008013;">% 0-indexed, for compatibility with Python</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >my_table = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >, </span><span style="color: #a709f5;">'col2'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1'</span><span >, col1, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col2'</span><span >, col2, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, row_ids </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >my_table</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="E88684E0" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="108" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">my_table = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DynamicTable with properties:
 
        colnames: {'col1'  'col2'}
     description: 'an example table'
              id: [1×1 types.hdmf_common.ElementIdentifiers]
      vectordata: [2×1 types.untyped.Set]
-</div></div></div></div></div></div><h2  class = 'S9' id = 'H_106f' ><span>Adding rows</span></h2><div  class = 'S1'><span>You can add rows to an existing</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> using the object's</span><span> </span><span style=' font-family: monospace;'>addRow</span><span> method. One way of using this method is to pass in the names of columns as parameter names followed by the elements to append. The class of the elements of the column must match the elements to append.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >my_table.addRow(</span><span style="color: #a709f5;">'col1'</span><span >, 3, </span><span style="color: #a709f5;">'col2'</span><span >, {</span><span style="color: #a709f5;">'c'</span><span >}, </span><span style="color: #a709f5;">'id'</span><span >, 2);</span></span></div></div></div><h2  class = 'S9' id = 'H_3afc' ><span>Adding columns</span></h2><div  class = 'S1'><span>You can add new columns to an existing</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object using the</span><span> </span><span style=' font-family: monospace;'>addColumn</span><span> method. One way of using this method is to pass in the names of each new column followed by the corresponding values for each new column. The height of the new columns must match the height of the table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col3 = types.hdmf_common.VectorData(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [100; 200; 300]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col4 = types.hdmf_common.VectorData(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #4'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a1'</span><span >; </span><span style="color: #a709f5;">'b2'</span><span >; </span><span style="color: #a709f5;">'c3'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >my_table.addColumn(</span><span style="color: #a709f5;">'col3'</span><span >, col3,</span><span style="color: #a709f5;">'col4'</span><span >, col4);</span></span></div></div></div><h2  class = 'S9' id = 'H_8036' ><span>Create MATLAB table and convert to dynamic table</span></h2><div  class = 'S1'><span>As an alternative to building a dynamic table using the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> data types, it is also possible to create a MATLAB table and convert it to a dynamic table. Lets create the same table as before, but using MATLAB's table class:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create a table with two variables (columns):</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T = table([1;2], {</span><span style="color: #a709f5;">'a'</span><span >;</span><span style="color: #a709f5;">'b'</span><span >}, </span><span style="color: #a709f5;">'VariableNames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >, </span><span style="color: #a709f5;">'col2'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >T.Properties.VariableDescriptions = {</span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #a709f5;">'column #2'</span><span >};</span></span></div></div></div><h3  class = 'S13' id = 'H_3663' ><span>Adding rows</span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >T(end+1, :) = {3, </span><span style="color: #a709f5;">'c'</span><span >};</span></span></div></div></div><h3  class = 'S13' id = 'H_34c1' ><span>Adding variables (columns)</span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >T = addvars(T, [100;200;300], </span><span style="color: #a709f5;">'NewVariableNames'</span><span >,{</span><span style="color: #a709f5;">'col3'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.Properties.VariableDescriptions{3} = </span><span style="color: #a709f5;">'column #3'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Alternatively, a new variable can be added directly using dot syntax.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.col4 = {</span><span style="color: #a709f5;">'a1'</span><span >; </span><span style="color: #a709f5;">'b2'</span><span >; </span><span style="color: #a709f5;">'c3'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.Properties.VariableDescriptions{4} = </span><span style="color: #a709f5;">'column #4'</span><span >;</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >T</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="FBF46347" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_7" widgetid="variableeditor_client_Document_7" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_7"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>T = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_7" widgetid="variableeditor_TableViewModel_7" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>300</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with T = 
+</div></div></div></div></div></div><h2  class = 'S9' id = 'H_106f' ><span>Adding rows</span></h2><div  class = 'S1'><span>You can add rows to an existing</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> using the object's</span><span> </span><span style=' font-family: monospace;'>addRow</span><span> method. One way of using this method is to pass in the names of columns as parameter names followed by the elements to append. The class of the elements of the column must match the elements to append.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >my_table.addRow(</span><span style="color: #a709f5;">'col1'</span><span >, 3, </span><span style="color: #a709f5;">'col2'</span><span >, {</span><span style="color: #a709f5;">'c'</span><span >}, </span><span style="color: #a709f5;">'id'</span><span >, 2);</span></span></div></div></div><h2  class = 'S9' id = 'H_3afc' ><span>Adding columns</span></h2><div  class = 'S1'><span>You can add new columns to an existing</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object using the</span><span> </span><span style=' font-family: monospace;'>addColumn</span><span> method. One way of using this method is to pass in the names of each new column followed by the corresponding values for each new column. The height of the new columns must match the height of the table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col3 = types.hdmf_common.VectorData(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [100; 200; 300]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col4 = types.hdmf_common.VectorData(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #4'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a1'</span><span >; </span><span style="color: #a709f5;">'b2'</span><span >; </span><span style="color: #a709f5;">'c3'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >my_table.addColumn(</span><span style="color: #a709f5;">'col3'</span><span >, col3,</span><span style="color: #a709f5;">'col4'</span><span >, col4);</span></span></div></div></div><h2  class = 'S9' id = 'H_8036' ><span>Create MATLAB table and convert to dynamic table</span></h2><div  class = 'S1'><span>As an alternative to building a dynamic table using the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> data types, it is also possible to create a MATLAB table and convert it to a dynamic table. Lets create the same table as before, but using MATLAB's table class:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create a table with two variables (columns):</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T = table([1;2], {</span><span style="color: #a709f5;">'a'</span><span >;</span><span style="color: #a709f5;">'b'</span><span >}, </span><span style="color: #a709f5;">'VariableNames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >, </span><span style="color: #a709f5;">'col2'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >T.Properties.VariableDescriptions = {</span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #a709f5;">'column #2'</span><span >};</span></span></div></div></div><h3  class = 'S13' id = 'H_3663' ><span>Adding rows</span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >T(end+1, :) = {3, </span><span style="color: #a709f5;">'c'</span><span >};</span></span></div></div></div><h3  class = 'S13' id = 'H_34c1' ><span>Adding variables (columns)</span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >T = addvars(T, [100;200;300], </span><span style="color: #a709f5;">'NewVariableNames'</span><span >,{</span><span style="color: #a709f5;">'col3'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.Properties.VariableDescriptions{3} = </span><span style="color: #a709f5;">'column #3'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Alternatively, a new variable can be added directly using dot syntax.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.col4 = {</span><span style="color: #a709f5;">'a1'</span><span >; </span><span style="color: #a709f5;">'b2'</span><span >; </span><span style="color: #a709f5;">'c3'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >T.Properties.VariableDescriptions{4} = </span><span style="color: #a709f5;">'column #4'</span><span >;</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >T</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="954BD291" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_0" widgetid="variableeditor_client_Document_0" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_0"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>T = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_0" widgetid="variableeditor_TableViewModel_0" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>300</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with T = 
     col1    col2     col3     col4 
     ____    _____    ____    ______
 
-     1      {'a'}    100"></div></div></div></div></div><h3  class = 'S13' id = 'H_6f53' ><span>Convert to dynamic table</span></h3><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >dynamic_table = util.table2nwb(T, </span><span style="color: #a709f5;">'A MATLAB table that was converted to a dynamic table'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="010825FC" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="108" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">dynamic_table = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DynamicTable with properties:
+     1      {'a'}    100"></div></div></div></div></div><h3  class = 'S13' id = 'H_6f53' ><span>Convert to dynamic table</span></h3><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >dynamic_table = util.table2nwb(T, </span><span style="color: #a709f5;">'A MATLAB table that was converted to a dynamic table'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="9DAB9288" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="108" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">dynamic_table = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DynamicTable with properties:
 
        colnames: {'col1'  'col2'  'col3'  'col4'}
     description: "A MATLAB table that was converted to a dynamic table"
              id: [1×1 types.hdmf_common.ElementIdentifiers]
      vectordata: [4×1 types.untyped.Set]
-</div></div></div></div></div></div><h2  class = 'S9' id = 'H_5ba4' ><span>Enumerated (categorical) data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html"><span style=' font-weight: bold; font-family: monospace;'>EnumData</span></a><span> is a special type of column for storing an enumerated data type. This way each unique value is stored once, and the data references those values by index. Using this method is more efficient than storing a single value many times, and has the advantage of communicating to downstream tools that the data is categorical in nature.</span></div><h3  class = 'S13' id = 'H_7077' ><span>Warning regarding EnumData</span><span> </span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html"><span style=' font-weight: bold; font-family: monospace;'>EnumData</span></a><span> is currently an experimental feature and as such should not be used in a production environment.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >cell_type_elements = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'fixed set of elements referenced by cell_type'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'aa'</span><span >, </span><span style="color: #a709f5;">'bb'</span><span >, </span><span style="color: #a709f5;">'cc'</span><span >} </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % the enumerated elements</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type = types.hdmf_experimental.EnumData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'this column holds categorical variables'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 2; 1; 0], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % zero-indexed offset to elements.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'elements'</span><span >, types.untyped.ObjectView(cell_type_elements) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type_table = types.hdmf_common.DynamicTable(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table with enum cell types'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">%Please note: the *_elements format is required for compatibility with pynwb:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type_table.vectordata.set(</span><span style="color: #a709f5;">'cell_type_elements'</span><span >, cell_type_elements);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >cell_type_table.addColumn(</span><span style="color: #a709f5;">'cell_type'</span><span >, cell_type);</span></span></div></div></div><h2  class = 'S9' id = 'H_2d5c' ><span>Ragged array columns</span></h2><div  class = 'S1'><span>A table column with a different number of elements for each row is called a "ragged array column." To define a table with a ragged array column, pass both the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> and the corresponding</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> as columns of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object. The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> columns will contain the data values. The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> column serves to indicate how to arrange the data across rows. By convention the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> object corresponding to a particular column must have have the same name with the addition of the '_index' suffix.</span></div><div  class = 'S1'><span>Below, the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> values indicate to place the 1st to 3rd (inclusive) elements of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> into the first row and 4th element into the second row. The resulting table will have the cell {'1a'; '1b'; '1c'} in the first row and the cell {'2a'} in the second row.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col1 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'1a'</span><span >; </span><span style="color: #a709f5;">'1b'</span><span >; </span><span style="color: #a709f5;">'1c'</span><span >; </span><span style="color: #a709f5;">'2a'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col1_index = types.hdmf_common.VectorIndex( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1 index'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'target'</span><span >,types.untyped.ObjectView(col1), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % object view of target column</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [3; 4] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >row_ids = types.hdmf_common.ElementIdentifiers(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1]);  </span><span style="color: #008013;">% 0-indexed, for compatibility with Python</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >table_ragged_col = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1'</span><span >, col1, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1_index'</span><span >, col1_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, row_ids </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_4351' ><span>Adding ragged array rows</span></h3><div  class = 'S1'><span>You can add a new row to the ragged array column. Under the hood, the </span><span style=' font-family: monospace;'>addRow</span><span> method will add the appropriate value to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> column to maintain proper formatting.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >table_ragged_col.addRow(</span><span style="color: #a709f5;">'col1'</span><span >, {</span><span style="color: #a709f5;">'3a'</span><span >; </span><span style="color: #a709f5;">'3b'</span><span >; </span><span style="color: #a709f5;">'3c'</span><span >}, </span><span style="color: #a709f5;">'id'</span><span >, 2);</span></span></div></div></div><h2  class = 'S9' id = 'H_5229' ><span>Accessing row elements</span></h2><div  class = 'S1'><span>You can access data from entire rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object by calling the</span><span> </span><span style=' font-family: monospace;'>getRow</span><span> method for the corresponding object. You can supply either an individual row number or a list of row numbers.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="887687C5" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_8" widgetid="variableeditor_client_Document_8" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_8"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_8" widgetid="variableeditor_TableViewModel_8" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+</div></div></div></div></div></div><h2  class = 'S9' id = 'H_5ba4' ><span>Enumerated (categorical) data</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html"><span style=' font-weight: bold; font-family: monospace;'>EnumData</span></a><span> is a special type of column for storing an enumerated data type. This way each unique value is stored once, and the data references those values by index. Using this method is more efficient than storing a single value many times, and has the advantage of communicating to downstream tools that the data is categorical in nature.</span></div><h3  class = 'S13' id = 'H_7077' ><span>Warning regarding EnumData</span><span> </span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html"><span style=' font-weight: bold; font-family: monospace;'>EnumData</span></a><span> is currently an experimental feature and as such should not be used in a production environment.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >cell_type_elements = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'fixed set of elements referenced by cell_type'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'aa'</span><span >, </span><span style="color: #a709f5;">'bb'</span><span >, </span><span style="color: #a709f5;">'cc'</span><span >} </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % the enumerated elements</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type = types.hdmf_experimental.EnumData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'this column holds categorical variables'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 2; 1; 0], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % zero-indexed offset to elements.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'elements'</span><span >, types.untyped.ObjectView(cell_type_elements) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type_table = types.hdmf_common.DynamicTable(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table with enum cell types'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">%Please note: the *_elements format is required for compatibility with pynwb:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >cell_type_table.vectordata.set(</span><span style="color: #a709f5;">'cell_type_elements'</span><span >, cell_type_elements);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >cell_type_table.addColumn(</span><span style="color: #a709f5;">'cell_type'</span><span >, cell_type);</span></span></div></div></div><h2  class = 'S9' id = 'H_2d5c' ><span>Ragged array columns</span></h2><div  class = 'S1'><span>A table column with a different number of elements for each row is called a "ragged array column." To define a table with a ragged array column, pass both the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> and the corresponding</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> as columns of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object. The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> columns will contain the data values. The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> column serves to indicate how to arrange the data across rows. By convention the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> object corresponding to a particular column must have have the same name with the addition of the '_index' suffix.</span></div><div  class = 'S1'><span>Below, the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> values indicate to place the 1st to 3rd (inclusive) elements of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold;'>VectorData</span></a><span> into the first row and 4th element into the second row. The resulting table will have the cell {'1a'; '1b'; '1c'} in the first row and the cell {'2a'} in the second row.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >col1 = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'1a'</span><span >; </span><span style="color: #a709f5;">'1b'</span><span >; </span><span style="color: #a709f5;">'1c'</span><span >; </span><span style="color: #a709f5;">'2a'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >col1_index = types.hdmf_common.VectorIndex( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'column #1 index'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'target'</span><span >,types.untyped.ObjectView(col1), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % object view of target column</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [3; 4] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >row_ids = types.hdmf_common.ElementIdentifiers(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1]);  </span><span style="color: #008013;">% 0-indexed, for compatibility with Python</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >table_ragged_col = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1'</span><span >, col1, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'col1_index'</span><span >, col1_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, row_ids </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_4351' ><span>Adding ragged array rows</span></h3><div  class = 'S1'><span>You can add a new row to the ragged array column. Under the hood, the </span><span style=' font-family: monospace;'>addRow</span><span> method will add the appropriate value to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold;'>VectorIndex</span></a><span> column to maintain proper formatting.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >table_ragged_col.addRow(</span><span style="color: #a709f5;">'col1'</span><span >, {</span><span style="color: #a709f5;">'3a'</span><span >; </span><span style="color: #a709f5;">'3b'</span><span >; </span><span style="color: #a709f5;">'3c'</span><span >}, </span><span style="color: #a709f5;">'id'</span><span >, 2);</span></span></div></div></div><h2  class = 'S9' id = 'H_5229' ><span>Accessing row elements</span></h2><div  class = 'S1'><span>You can access data from entire rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object by calling the</span><span> </span><span style=' font-family: monospace;'>getRow</span><span> method for the corresponding object. You can supply either an individual row number or a list of row numbers.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="FE1BF884" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_1" widgetid="variableeditor_client_Document_1" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_1"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_1" widgetid="variableeditor_TableViewModel_1" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     col1    col2     col3     col4 
     ____    _____    ____    ______
 
-     1      {'a'}   "></div></div></div></div></div><div  class = 'S15'><span>If you want to access values for just a subset of columns you can pass in the 'columns' argument along with a cell array with the desired column names</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1:3, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >})</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="3A7D2E95" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_9" widgetid="variableeditor_client_Document_9" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_9"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×1 table </span></div><div id="variableeditor_TableViewModel_9" widgetid="variableeditor_TableViewModel_9" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 
+     1      {'a'}   "></div></div></div></div></div><div  class = 'S15'><span>If you want to access values for just a subset of columns you can pass in the 'columns' argument along with a cell array with the desired column names</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1:3, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'col1'</span><span >})</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="F7D495B2" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_2" widgetid="variableeditor_client_Document_2" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_2"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×1 table </span></div><div id="variableeditor_TableViewModel_2" widgetid="variableeditor_TableViewModel_2" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 
     col1
     ____
 
      1  
      2  
      3  
-"></div></div></div></div></div><div  class = 'S15'><span>You can also access specific rows by their corresponding row ID's, if they have been defined, by supplying a 'true' Boolean to the 'useId' parameter</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1, </span><span style="color: #a709f5;">'useId'</span><span >, true)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="C24762E2" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_10" widgetid="variableeditor_client_Document_10" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_10"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_10" widgetid="variableeditor_TableViewModel_10" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+"></div></div></div></div></div><div  class = 'S15'><span>You can also access specific rows by their corresponding row ID's, if they have been defined, by supplying a 'true' Boolean to the 'useId' parameter</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.getRow(1, </span><span style="color: #a709f5;">'useId'</span><span >, true)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="3C1A78C7" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_3" widgetid="variableeditor_client_Document_3" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_3"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_3" widgetid="variableeditor_TableViewModel_3" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     col1    col2     col3     col4 
     ____    _____    ____    ______
 
-     2      {'b'}   "></div></div></div></div></div><div  class = 'S15'><span>For a ragged array columns, the</span><span> </span><span style=' font-family: monospace;'>getRow</span><span> method will return a cell with different number of elements for each row</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >table_ragged_col.getRow(1:2)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="2AA27305" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_11" widgetid="variableeditor_client_Document_11" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_11"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">2×1 table </span></div><div id="variableeditor_TableViewModel_11" widgetid="variableeditor_TableViewModel_11" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>3×1&nbsp;cell</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;cell</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 
+     2      {'b'}   "></div></div></div></div></div><div  class = 'S15'><span>For a ragged array columns, the</span><span> </span><span style=' font-family: monospace;'>getRow</span><span> method will return a cell with different number of elements for each row</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >table_ragged_col.getRow(1:2)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="2266836D" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_4" widgetid="variableeditor_client_Document_4" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_4"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">2×1 table </span></div><div id="variableeditor_TableViewModel_4" widgetid="variableeditor_TableViewModel_4" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>3×1&nbsp;cell</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;cell</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 
        col1   
     __________
 
     {3×1 cell}
     {1×1 cell}
-"></div></div></div></div></div><h2  class = 'S9' id = 'H_0c2a' ><span>Accessing column elements</span></h2><div  class = 'S1'><span>To access all rows from a particular column use the .get method on the vectordata field of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.vectordata.get(</span><span style="color: #a709f5;">'col2'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextMatrixElement embeddedOutputsVariableMatrixElement" uid="3EB1B81A" prevent-scroll="true" data-testid="output_7" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary headerElement" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">3×1 cell</span></div></div><div class="valueContainer" data-layout="{&quot;totalRows&quot;:3,&quot;totalColumns&quot;:1}" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 82px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">'a'         <br>'b'         <br>'c'         <br></div><div class="horizontalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 3×1 cell
+"></div></div></div></div></div><h2  class = 'S9' id = 'H_0c2a' ><span>Accessing column elements</span></h2><div  class = 'S1'><span>To access all rows from a particular column use the .get method on the vectordata field of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.vectordata.get(</span><span style="color: #a709f5;">'col2'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextMatrixElement embeddedOutputsVariableMatrixElement" uid="B2B76D08" prevent-scroll="true" data-testid="output_7" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary headerElement" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">3×1 cell</span></div></div><div class="valueContainer" data-layout="{&quot;totalRows&quot;:3,&quot;totalColumns&quot;:1}" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 82px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">'a'         <br>'b'         <br>'c'         <br></div><div class="horizontalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output ans = 3×1 cell
 'a'         
 'b'         
 'c'         
-" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2  class = 'S9' id = 'H_520f' ><span>Referencing rows of other tables</span></h2><div  class = 'S1'><span>You can create a column that references rows of other tables by adding a</span><span> </span><a href = "file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold;'>DynamicTableRegion</span></a><span> object as a column of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span>. This is analogous to a foreign key in a relational database. The</span><span> </span><a href = "file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold;'>DynamicTableRegion</span></a><span> class takes in an</span><span> </span><span style=' font-family: monospace;'>ObjectView</span><span> object as argument.</span><span> </span><span style=' font-family: monospace;'>ObjectView</span><span> objects create links from one object type referencing another.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >dtr_col = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'references multiple rows of earlier table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 1; 0], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # 0-indexed</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >,types.untyped.ObjectView(my_table) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % object view of target table</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >data_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'data column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a'</span><span >; </span><span style="color: #a709f5;">'b'</span><span >; </span><span style="color: #a709f5;">'c'</span><span >; </span><span style="color: #a709f5;">'d'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dtr_table = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'test table with DynamicTableRegion'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'data_col'</span><span >, </span><span style="color: #a709f5;">'dtr_col'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'dtr_col'</span><span >, dtr_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_col'</span><span >,data_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >,types.hdmf_common.ElementIdentifiers(</span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 2; 3]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S9' id = 'H_395d' ><span>Converting a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> </span><span style=' font-weight: bold;'>to a MATLAB table</span></h2><div  class = 'S1'><span>You can convert a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object to a MATLAB table by making use of the object's</span><span> </span><span style=' font-family: monospace;'>toTable</span><span> method. This is a useful way to view the whole table in a human-readable format.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.toTable()</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="C8C028DC" prevent-scroll="true" data-testid="output_8" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_12" widgetid="variableeditor_client_Document_12" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_12"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×5 table </span></div><div id="variableeditor_TableViewModel_12" widgetid="variableeditor_TableViewModel_12" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>300</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2  class = 'S9' id = 'H_520f' ><span>Referencing rows of other tables</span></h2><div  class = 'S1'><span>You can create a column that references rows of other tables by adding a</span><span> </span><a href = "file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold;'>DynamicTableRegion</span></a><span> object as a column of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span>. This is analogous to a foreign key in a relational database. The</span><span> </span><a href = "file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold;'>DynamicTableRegion</span></a><span> class takes in an</span><span> </span><span style=' font-family: monospace;'>ObjectView</span><span> object as argument.</span><span> </span><span style=' font-family: monospace;'>ObjectView</span><span> objects create links from one object type referencing another.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >dtr_col = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'references multiple rows of earlier table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 1; 0], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  # 0-indexed</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >,types.untyped.ObjectView(my_table) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % object view of target table</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >data_col = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'data column'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'a'</span><span >; </span><span style="color: #a709f5;">'b'</span><span >; </span><span style="color: #a709f5;">'c'</span><span >; </span><span style="color: #a709f5;">'d'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dtr_table = types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'test table with DynamicTableRegion'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'data_col'</span><span >, </span><span style="color: #a709f5;">'dtr_col'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'dtr_col'</span><span >, dtr_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_col'</span><span >,data_col, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >,types.hdmf_common.ElementIdentifiers(</span><span style="color: #a709f5;">'data'</span><span >, [0; 1; 2; 3]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S9' id = 'H_395d' ><span>Converting a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> </span><span style=' font-weight: bold;'>to a MATLAB table</span></h2><div  class = 'S1'><span>You can convert a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object to a MATLAB table by making use of the object's</span><span> </span><span style=' font-family: monospace;'>toTable</span><span> method. This is a useful way to view the whole table in a human-readable format.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >my_table.toTable()</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="411A42BC" prevent-scroll="true" data-testid="output_8" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_5" widgetid="variableeditor_client_Document_5" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_5"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×5 table </span></div><div id="variableeditor_TableViewModel_5" widgetid="variableeditor_TableViewModel_5" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>col4</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>100</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>200</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>300</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     id    col1    col2     col3     col4 
     __    ____    _____    ____    ______
 
-    0   "></div></div></div></div></div><div  class = 'S1'><span>When the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object contains a column that references other tables, you can pass in a Boolean to indicate whether to include just the row indices of the referenced table. Passing in</span><span> </span><span style=' font-family: monospace;'>false</span><span> will result in inclusion of the referenced rows as nested tables.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >dtr_table.toTable(false)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="9CB38459" prevent-scroll="true" data-testid="output_9" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_13" widgetid="variableeditor_client_Document_13" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_13"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">4×3 table </span></div><div id="variableeditor_TableViewModel_13" widgetid="variableeditor_TableViewModel_13" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 102px; min-width: 102px; max-width: 102px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>data_col</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 94px; min-width: 94px; max-width: 94px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>dtr_col</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'d'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+    0   "></div></div></div></div></div><div  class = 'S1'><span>When the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold;'>DynamicTable</span></a><span> object contains a column that references other tables, you can pass in a Boolean to indicate whether to include just the row indices of the referenced table. Passing in</span><span> </span><span style=' font-family: monospace;'>false</span><span> will result in inclusion of the referenced rows as nested tables.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >dtr_table.toTable(false)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="19DC6BCC" prevent-scroll="true" data-testid="output_9" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_6" widgetid="variableeditor_client_Document_6" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_6"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">4×3 table </span></div><div id="variableeditor_TableViewModel_6" widgetid="variableeditor_TableViewModel_6" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 102px; min-width: 102px; max-width: 102px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>data_col</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 94px; min-width: 94px; max-width: 94px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>dtr_col</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'a'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'b'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'c'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 102px; min-width: 102px; max-width: 102px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'d'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×4&nbsp;table</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     id    data_col      dtr_col  
     __    ________    ___________
 
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-.S10 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Using Dynamically Loaded Filters in MatMWB</span></h1><h2  class = 'S1'><span>Installing Dynamically Loaded Filters</span></h2><div  class = 'S2'><span>HDF5 can use various filters to compress data when writing datasets. GZIP is the default filter, and it can be read with any HDF5 installation without any setup, but many users find that other filters, such as Zstd, offer better performance. If you want to read an HDF5 Dataset that was compressed using another filter in MATLAB, such as Zstd, you will need to configure MATLAB to read using dynamically loaded filters. </span></div><div  class = 'S2'><span>The easiest way we have found to set up dynamically loaded filters is to use the Python package </span><a href = "https://github.com/silx-kit/hdf5plugin"><span>hdf5plugin</span></a><span>. This library has a sophisticated installation process that compiles several of the most popular dynamically loaded filters and works across popular operating systems. Installing this Python package is a trick that allows us to offload the tricky parts of installing dynamically loaded filters in MATLAB.</span></div><h3  class = 'S3'><span>Linux or Mac</span></h3><div  class = 'S2'><span>1. In your Terminal window, install hdf5plugin:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>pip install hdf5plugin</span></div><div  class = 'S2'><span>2. In that same Terminal window, set the environment variable </span><span style=' font-family: monospace;'>HDF5_PLUGIN_PATH</span><span>:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>export HDF5_PLUGIN_PATH=$(python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)");</span></div><div  class = 'S2'><span>3. From that same Terminal window, launch MATLAB:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>/Applications/MATLAB_R2021b.app/bin/matlab</span></div><div  class = 'S2'><span>The path above is an example of a common location for OSX. The exact path of MATLAB may vary on your computer.</span></div><h3  class = 'S3'><span>Windows</span></h3><div  class = 'S2'><span>1. Install </span><span style=' font-family: monospace;'>hdf5plugin</span><span> in the Command Prompt:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>pip install hdf5plugin</span></div><div  class = 'S2'><span>2. Determine the path of the plugin installation. In the Command Prompt, run:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)</span></div><div  class = 'S2'><span>2. Set the environment variable </span><span style=' font-family: monospace;'>HDF5_PLUGIN_PATH </span><span>to point to the local installation of the plugins (from </span><span style=' font-family: monospace;'>hdf5plugin.PLUGINS_PATH</span><span>) through </span><span style=' font-style: italic;'>System Properties &gt; Advanced &gt; Environment Variables:</span></div><div  class = 'S2'><img class = "imageNode" src = 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" width = "1084" height = "608" alt = "path-screenshot.png" style = "vertical-align: baseline; width: 1084px; height: 608px;"></img></div><div  class = 'S2'><span>3. Restart MATLAB.</span></div><div  class = 'S2'><span>That's it! Now you can read datasets that use the following filters:</span></div><ul  class = 'S4'><li  class = 'S5'><span>Bitshuffle</span></li><li  class = 'S5'><span>Blosc</span></li><li  class = 'S5'><span>FciDecomp</span></li><li  class = 'S5'><span>LZ4</span></li><li  class = 'S5'><span>Zfp</span></li><li  class = 'S5'><span>Zstd</span></li></ul><div  class = 'S2'><span>The beauty of HDF5 is that it handles the rest under the hood. When you read a dataset that uses any of these filters, HDF5 will identify the correct decompression algorithm and decompress the data on-the-fly as you access it from the dataset.</span></div><div  class = 'S2'><span>For more information about installing filter plugins, see the </span><a href = "https://www.mathworks.com/help/matlab/import_export/read-and-write-hdf5-datasets-using-dynamically-loaded-filters.html"><span>MATLAB documentation</span></a><span>.</span></div><h2  class = 'S1'><span>Writing with Dynamically Loaded Filters</span></h2><div  class = 'S2'><span>To write with dynamically loaded filters, first follow the installation steps above. </span><span style=' font-weight: bold;'>This feature requires MATLAB version ≥ 2022a.</span></div><div  class = 'S2'><span style=' font-family: monospace;'>DataPipe</span><span> objects can be used to write using Dynamically loaded filters. This tutorial will be using the </span><a href = "https://github.com/aparamon/HDF5Plugin-Zstandard"><span>Zstd dynamic filter</span></a><span> as an example.</span></div><div  class = 'S2'><span>The </span><span style=' font-family: monospace;'>DynamicFilter</span><span> property accepts an enumerated type </span><span style=' font-family: monospace;'>Filter</span><span> where the enumeration members represent a hard-coded subset of the </span><a href = "https://github.com/HDFGroup/hdf5_plugins/blob/master/docs/RegisteredFilterPlugins.md"><span>registered filter plugins</span></a><span> in HDF5.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.properties.DynamicFilter</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.dynamic.Filter</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.properties.Shuffle</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >zstdProperty = DynamicFilter(Filter.ZStandard);</span></span></div></div></div><h3  class = 'S9'><span>Parameters</span></h3><div  class = 'S2'><span>Some filter plugins allow for setting special configuration parameters to modify the filter's behavior. The </span><span style=' font-family: monospace;'>DynamicFilter</span><span> property type contains a modifiable </span><span style=' font-family: monospace;'>parameters </span><span>field which can be used to set your parameters. This is equivalent to setting the </span><span style=' font-family: monospace;'>cd_values</span><span> argument in HDF5. In the case of the </span><a href = "https://github.com/aparamon/HDF5Plugin-Zstandard"><span>Zstandard HDF5 plugin</span></a><span>, the first (and only) array argument value indicates the compression level.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >zstdProperty.parameters = 4; </span><span style="color: #008013;">% compression level.</span></span></div></div></div><h3  class = 'S9'><span>Multiple Filters</span></h3><div  class = 'S2'><span>You can use multiple dynamic filters by concatenating multiple DynamicFilter properties together. They will be applied in order of the inserted array.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ShuffleProperty = Shuffle();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >dynamicProperties = [ShuffleProperty zstdProperty];</span></span></div></div></div><h3  class = 'S3'><span>Writing</span></h3><div  class = 'S2'><span>The </span><span style=' font-family: monospace;'>DataPipe</span><span> class takes in a keyword argument called f</span><span style=' font-family: monospace;'>ilters</span><span> which is an array of </span><span style=' font-family: monospace;'>DynamicFilter</span><span> objects. Supplying a </span><span style=' font-family: monospace;'>'filters'</span><span> argument will deactivate the default GZIP compression.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% We're already compressing using zstd so we should disable</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% compressionLevel (gzip).</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dataPipe = types.untyped.DataPipe(</span><span style="color: #a709f5;">'data'</span><span >, rand(1, 10000), </span><span style="color: #a709f5;">'filters'</span><span >, dynamicProperties);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >timeseries = types.core.TimeSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, dataPipe, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'data-unit'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbFile = NwbFile(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'dynamically_loaded_filters_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'test_datapipe_filters'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(</span><span style="color: #a709f5;">"now"</span><span >, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbFile.acquisition.set(</span><span style="color: #a709f5;">'ts'</span><span >, timeseries);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbExport(nwbFile, </span><span style="color: #a709f5;">'test.nwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'>&nbsp;</div></div></div><div  class = 'S2'><span>The data is now compressed using Zstandard compression using a compression level of 4 and Shuffled</span></div><div  class = 'S2'><img class = "imageNode" src = 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+.S12 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Using Dynamically Loaded Filters in MatMWB</span></h1><h2  class = 'S1'><span>Installing Dynamically Loaded Filters</span></h2><div  class = 'S2'><span>HDF5 can use various filters to compress data when writing datasets. GZIP is the default filter, and it can be read with any HDF5 installation without any setup, but many users find that other filters, such as Zstd, offer better performance. If you want to read an HDF5 Dataset that was compressed using another filter in MATLAB, such as Zstd, you will need to configure MATLAB to read using dynamically loaded filters. </span></div><div  class = 'S2'><span>The easiest way we have found to set up dynamically loaded filters is to use the Python package </span><a href = "https://github.com/silx-kit/hdf5plugin"><span>hdf5plugin</span></a><span>. This library has a sophisticated installation process that compiles several of the most popular dynamically loaded filters and works across popular operating systems. Installing this Python package is a trick that allows us to offload the tricky parts of installing dynamically loaded filters in MATLAB.</span></div><h3  class = 'S3'><span>Linux or Mac</span></h3><div  class = 'S2'><span>1. In your Terminal window, install hdf5plugin:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>pip install hdf5plugin</span></div><div  class = 'S2'><span>2. In that same Terminal window, set the environment variable </span><span style=' font-family: monospace;'>HDF5_PLUGIN_PATH</span><span>:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>export HDF5_PLUGIN_PATH=$(python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)");</span></div><div  class = 'S2'><span>3. From that same Terminal window, launch MATLAB:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>/Applications/MATLAB_R2021b.app/bin/matlab</span></div><div  class = 'S2'><span>The path above is an example of a common location for OSX. The exact path of MATLAB may vary on your computer.</span></div><h3  class = 'S3'><span>Windows</span></h3><div  class = 'S2'><span>1. Install </span><span style=' font-family: monospace;'>hdf5plugin</span><span> in the Command Prompt:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>pip install hdf5plugin</span></div><div  class = 'S2'><span>2. Determine the path of the plugin installation. In the Command Prompt, run:</span></div><div  class = 'S2'><span style=' font-family: monospace;'>python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)"</span></div><div  class = 'S2'><span>3. Set the environment variable </span><span style=' font-family: monospace;'>HDF5_PLUGIN_PATH </span><span>to point to the local installation of the plugins (from </span><span style=' font-family: monospace;'>hdf5plugin.PLUGINS_PATH</span><span>) through </span><span style=' font-style: italic;'>System Properties &gt; Advanced &gt; Environment Variables:</span></div><div  class = 'S2'><img class = "imageNode" src = 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" width = "1084" height = "608" alt = "path-screenshot.png" style = "vertical-align: baseline; width: 1084px; height: 608px;"></img></div><div  class = 'S2'><span>4. Restart MATLAB.</span></div><div  class = 'S2'><span>That's it! Now you can read datasets that use the following filters:</span></div><ul  class = 'S4'><li  class = 'S5'><span>Bitshuffle</span></li><li  class = 'S5'><span>Blosc</span></li><li  class = 'S5'><span>FciDecomp</span></li><li  class = 'S5'><span>LZ4</span></li><li  class = 'S5'><span>Zfp</span></li><li  class = 'S5'><span>Zstd</span></li></ul><div  class = 'S2'><span>The beauty of HDF5 is that it handles the rest under the hood. When you read a dataset that uses any of these filters, HDF5 will identify the correct decompression algorithm and decompress the data on-the-fly as you access it from the dataset.</span></div><div  class = 'S2'><span>For more information about installing filter plugins, see the </span><a href = "https://www.mathworks.com/help/matlab/import_export/read-and-write-hdf5-datasets-using-dynamically-loaded-filters.html"><span>MATLAB documentation</span></a><span>.</span></div><h2  class = 'S1'><span>Writing with Dynamically Loaded Filters</span></h2><div  class = 'S2'><span>To write with dynamically loaded filters, first follow the installation steps above. </span><span style=' font-weight: bold;'>This feature requires MATLAB version ≥ 2022a.</span></div><div  class = 'S2'><span style=' font-family: monospace;'>DataPipe</span><span> objects can be used to write using Dynamically loaded filters. This tutorial will be using the </span><a href = "https://github.com/aparamon/HDF5Plugin-Zstandard"><span>Zstd dynamic filter</span></a><span> as an example.</span></div><div  class = 'S2'><span>The </span><span style=' font-family: monospace;'>DynamicFilter</span><span> property accepts an enumerated type </span><span style=' font-family: monospace;'>Filter</span><span> where the enumeration members represent a hard-coded subset of the </span><a href = "https://github.com/HDFGroup/hdf5_plugins/blob/master/docs/RegisteredFilterPlugins.md"><span>registered filter plugins</span></a><span> in HDF5.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.properties.DynamicFilter</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.dynamic.Filter</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >import </span><span style="color: #a709f5;">types.untyped.datapipe.properties.Shuffle</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >zstdProperty = DynamicFilter(Filter.ZStandard);</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsErrorElement" uid="A5D36F64" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="diagnosticMessage-wrapper diagnosticMessage-errorType eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="93" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="error output " style="max-height: 261px; white-space: normal; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"><div class="diagnosticMessage-messagePart" style="white-space: pre-wrap; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">Error using assert<br style="white-space: pre-wrap; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">Filter `ZStandard` does not appear to be installed on this system. Please doublecheck `https://www.mathworks.com/help/matlab/import_export/read-and-write-hdf5-datasets-using-dynamically-loaded-filters.html` for more information about writing with third-party filters.</div><div class="diagnosticMessage-stackPart" style="white-space: pre; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"><br style="white-space: pre; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">Error in types.untyped.datapipe.properties.DynamicFilter (line 22)<br style="white-space: pre; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">            assert(H5Z.filter_avail(uint32(filter)), ...</div></div></div></div></div></div><h3  class = 'S10'><span>Parameters</span></h3><div  class = 'S2'><span>Some filter plugins allow for setting special configuration parameters to modify the filter's behavior. The </span><span style=' font-family: monospace;'>DynamicFilter</span><span> property type contains a modifiable </span><span style=' font-family: monospace;'>parameters </span><span>field which can be used to set your parameters. This is equivalent to setting the </span><span style=' font-family: monospace;'>cd_values</span><span> argument in HDF5. In the case of the </span><a href = "https://github.com/aparamon/HDF5Plugin-Zstandard"><span>Zstandard HDF5 plugin</span></a><span>, the first (and only) array argument value indicates the compression level.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >zstdProperty.parameters = 4; </span><span style="color: #008013;">% compression level.</span></span></div></div></div><h3  class = 'S10'><span>Multiple Filters</span></h3><div  class = 'S2'><span>You can use multiple dynamic filters by concatenating multiple DynamicFilter properties together. They will be applied in order of the inserted array.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ShuffleProperty = Shuffle();</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >dynamicProperties = [ShuffleProperty zstdProperty];</span></span></div></div></div><h3  class = 'S3'><span>Writing</span></h3><div  class = 'S2'><span>The </span><span style=' font-family: monospace;'>DataPipe</span><span> class takes in a keyword argument called </span><span style=' font-family: monospace;'>filters</span><span> which is an array of </span><span style=' font-family: monospace;'>DynamicFilter</span><span> objects. Supplying a </span><span style=' font-family: monospace;'>'filters'</span><span> argument will deactivate the default GZIP compression.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% We're already compressing using zstd so we should disable</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% compressionLevel (gzip).</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dataPipe = types.untyped.DataPipe(</span><span style="color: #a709f5;">'data'</span><span >, rand(1, 10000), </span><span style="color: #a709f5;">'filters'</span><span >, dynamicProperties);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >timeseries = types.core.TimeSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, dataPipe, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'data-unit'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbFile = NwbFile(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'dynamically_loaded_filters_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'test_datapipe_filters'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(</span><span style="color: #a709f5;">"now"</span><span >, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbFile.acquisition.set(</span><span style="color: #a709f5;">'ts'</span><span >, timeseries);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbExport(nwbFile, </span><span style="color: #a709f5;">'test.nwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'>&nbsp;</div></div></div><div  class = 'S2'><span>The data is now compressed using Zstandard compression using a compression level of 4 and Shuffled</span></div><div  class = 'S2'><img class = "imageNode" src = 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width = "951" height = "534" alt = "" style = "vertical-align: baseline; width: 951px; height: 534px;"></img></div>
 <br>
 <!-- 
 ##### SOURCE BEGIN #####
@@ -52,15 +86,15 @@
 % 
 % 2. Determine the path of the plugin installation. In the Command Prompt, run:
 % 
-% |python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)|
+% |python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)"|
 % 
-% 2. Set the environment variable |HDF5_PLUGIN_PATH| to point to the local installation 
+% 3. Set the environment variable |HDF5_PLUGIN_PATH| to point to the local installation 
 % of the plugins (from |hdf5plugin.PLUGINS_PATH|) through _System Properties > 
 % Advanced > Environment Variables:_
 % 
 % 
 % 
-% 3. Restart MATLAB.
+% 4. Restart MATLAB.
 % 
 % That's it! Now you can read datasets that use the following filters:
 %% 
@@ -110,7 +144,7 @@
 ShuffleProperty = Shuffle();
 dynamicProperties = [ShuffleProperty zstdProperty];
 % Writing
-% The |DataPipe| class takes in a keyword argument called f|ilters| which is 
+% The |DataPipe| class takes in a keyword argument called |filters| which is 
 % an array of |DynamicFilter| objects. Supplying a |'filters'| argument will deactivate 
 % the default GZIP compression.
 
diff --git a/docs/source/_static/html/tutorials/ecephys.html b/docs/source/_static/html/tutorials/ecephys.html
index f624348c5..c98742fdc 100644
--- a/docs/source/_static/html/tutorials/ecephys.html
+++ b/docs/source/_static/html/tutorials/ecephys.html
@@ -85,35 +85,35 @@
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 .matrixElement .verticalEllipsis,.textElement .verticalEllipsis,.rtcDataTipElement .matrixElement .verticalEllipsis,.rtcDataTipElement .textElement .verticalEllipsis {    margin-left: 35px;    /* base64 encoded version of images-liveeditor/VEllipsis.png */    width: 12px;    height: 30px;    background-repeat: no-repeat;    background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAZCAYAAAAIcL+IAAAALklEQVR42mP4//8/AzGYgWyFMECMwv8QddRS+P//KyimlmcGUOFoOI6GI/UVAgDnd8Dd4+NCwgAAAABJRU5ErkJggg==");}
-.S22 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_112142A7' ><span>Neurodata Without Borders Extracellular Electrophysiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_112142A7"><span>Neurodata Without Borders Extracellular Electrophysiology Tutorial
-</span></a><span>    </span><a href = "#H_BE4F9B65"><span>About This Tutorial
-</span></a><span>    </span><a href = "#H_E1D22E67"><span>Before You Begin
-</span></a><span>    </span><a href = "#H_4D72F8D6"><span>Setting up the NWB File
-</span></a><span>        </span><a href = "#H_6e62"><span>Subject Information
-</span></a><span>    </span><a href = "#H_F4DE600B"><span>Electrode Information
-</span></a><span>        </span><a href = "#H_4D892966"><span>Electrodes Table
-</span></a><span>        </span><a href = "#H_2298AEF1"><span>Links
-</span></a><span>    </span><a href = "#H_1458A792"><span>Recorded Extracellular Signals
-</span></a><span>        </span><a href = "#H_DBCB9C61"><span>Referencing Electrodes
-</span></a><span>        </span><a href = "#H_0DD4A84E"><span>Raw Voltage Data
-</span></a><span>    </span><a href = "#H_A29C5644"><span>Processed Extracellular Electrical Signals
-</span></a><span>        </span><a href = "#H_165B055A"><span>LFP
-</span></a><span>        </span><a href = "#H_D949DB79"><span>Other Types of Filtered Electrical Signals
-</span></a><span>        </span><a href = "#H_3E971657"><span>Decomposition of LFP Data into Frequency Bands
-</span></a><span>    </span><a href = "#H_A3926752"><span>Spike Times and Extracellular Events
-</span></a><span>        </span><a href = "#H_EAE714B1"><span>Sorted Spike Times
-</span></a><span>            </span><a href = "#H_E2B446F0"><span>Ragged Arrays
-</span></a><span>        </span><a href = "#H_F4622D6F"><span>Unsorted Spike Times
-</span></a><span>        </span><a href = "#H_A5CE3265"><span>Detected Events
-</span></a><span>        </span><a href = "#H_0A2AE4A3"><span>Storing Spike Features (e.g Principal Components)
-</span></a><span>    </span><a href = "#H_AD8EF681"><span>Choosing NWB-Types for Electrophysiology Data (A Summary)
-</span></a><span>    </span><a href = "#H_A1ECC260"><span>Writing the NWB File
-</span></a><span>    </span><a href = "#H_10964ACC"><span>Reading NWB Data
-</span></a><span>    </span><a href = "#H_2FF05AA8"><span>Accessing Data Regions
-</span></a><a href = "#T_EB654BFC"><span>Learn more!
-</span></a><span>        </span><a href = "#H_97B86954"><span>See the API documentation to learn what data types are available.
-</span></a><span>    </span><a href = "#H_C51B621D"><span>MATLAB tutorials
-</span></a><span>    </span><a href = "#H_CD143713"><span>Python tutorials</span></a></div></div><h2  class = 'S4' id = 'H_BE4F9B65' ><span>About This Tutorial</span></h2><div  class = 'S5'><span>This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Raw voltage recording</span></li><li  class = 'S7'><span>Local field potential (LFP) and filtered electrical signals</span></li><li  class = 'S7'><span>Spike times</span></li></ul><h2  class = 'S4' id = 'H_E1D22E67' ><span>Before You Begin</span></h2><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Important</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S8' id = 'H_4D72F8D6' ><span>Setting up the NWB File</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a </span><span style=' font-family: monospace;'>session_description</span><span>, </span><span style=' font-family: monospace;'>identifier</span><span>, and </span><span style=' font-family: monospace;'>session_start_time</span><span>. Create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last Name, First Name'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="FBF3ED74" prevent-scroll="true" data-testid="output_0" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S22 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_3d99' ><span>Neurodata Without Borders Extracellular Electrophysiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_3d99"><span>Neurodata Without Borders Extracellular Electrophysiology Tutorial
+</span></a><span>    </span><a href = "#H_98dc"><span>About This Tutorial
+</span></a><span>    </span><a href = "#H_7bd1"><span>Before You Begin
+</span></a><span>    </span><a href = "#H_6ffc"><span>Setting up the NWB File
+</span></a><span>        </span><a href = "#H_9e1e"><span>Subject Information
+</span></a><span>    </span><a href = "#H_4139"><span>Electrode Information
+</span></a><span>        </span><a href = "#H_7313"><span>Electrodes Table
+</span></a><span>        </span><a href = "#H_3ff3"><span>Links
+</span></a><span>    </span><a href = "#H_9bf7"><span>Recorded Extracellular Signals
+</span></a><span>        </span><a href = "#H_46c1"><span>Referencing Electrodes
+</span></a><span>        </span><a href = "#H_7c91"><span>Raw Voltage Data
+</span></a><span>    </span><a href = "#H_861c"><span>Processed Extracellular Electrical Signals
+</span></a><span>        </span><a href = "#H_378a"><span>LFP
+</span></a><span>        </span><a href = "#H_9aad"><span>Other Types of Filtered Electrical Signals
+</span></a><span>        </span><a href = "#H_1073"><span>Decomposition of LFP Data into Frequency Bands
+</span></a><span>    </span><a href = "#H_7ffa"><span>Spike Times and Extracellular Events
+</span></a><span>        </span><a href = "#H_70ee"><span>Sorted Spike Times
+</span></a><span>            </span><a href = "#H_7e54"><span>Ragged Arrays
+</span></a><span>        </span><a href = "#H_44c4"><span>Unsorted Spike Times
+</span></a><span>        </span><a href = "#H_3265"><span>Detected Events
+</span></a><span>        </span><a href = "#H_901e"><span>Storing Spike Features (e.g Principal Components)
+</span></a><span>    </span><a href = "#H_5aa2"><span>Choosing NWB-Types for Electrophysiology Data (A Summary)
+</span></a><span>    </span><a href = "#H_71fb"><span>Writing the NWB File
+</span></a><span>    </span><a href = "#H_5f5d"><span>Reading NWB Data
+</span></a><span>    </span><a href = "#H_88a3"><span>Accessing Data Regions
+</span></a><a href = "#T_6757"><span>Learn more!
+</span></a><span>        </span><a href = "#H_2485"><span>See the API documentation to learn what data types are available.
+</span></a><span>    </span><a href = "#H_1b55"><span>MATLAB tutorials
+</span></a><span>    </span><a href = "#H_6223"><span>Python tutorials</span></a></div></div><h2  class = 'S4' id = 'H_98dc' ><span>About This Tutorial</span></h2><div  class = 'S5'><span>This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Raw voltage recording</span></li><li  class = 'S7'><span>Local field potential (LFP) and filtered electrical signals</span></li><li  class = 'S7'><span>Spike times</span></li></ul><h2  class = 'S4' id = 'H_7bd1' ><span>Before You Begin</span></h2><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Important</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S8' id = 'H_6ffc' ><span>Setting up the NWB File</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a </span><span style=' font-family: monospace;'>session_description</span><span>, </span><span style=' font-family: monospace;'>identifier</span><span>, and </span><span style=' font-family: monospace;'>session_start_time</span><span>. Create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last Name, First Name'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="6B96E776" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -166,920 +166,42 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S13' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S8' id = 'H_F4DE600B' ><span>Electrode Information</span></h2><div  class = 'S5'><span>In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an </span><span style=' font-family: monospace;'>electrodes</span><span> table, which is also a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. </span><span style=' font-family: monospace;'>electrodes</span><span> has several required fields: </span><span style=' font-family: monospace;'>x</span><span>, </span><span style=' font-family: monospace;'>y</span><span>, </span><span style=' font-family: monospace;'>z</span><span>, </span><span style=' font-family: monospace;'>impedance</span><span>, </span><span style=' font-family: monospace;'>location</span><span>, </span><span style=' font-family: monospace;'>filtering</span><span>, and </span><span style=' font-family: monospace;'>electrode_group</span><span>.</span></div><div  class = 'S5'><span>The electrodes table references a required </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span>, which is used to represent a group of electrodes. Before creating an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span>, you must define a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>serial_number</span><span> and </span><span style=' font-family: monospace;'>model</span><span> are optional, but recommended. The </span><span style=' font-family: monospace;'>model</span><span> property can contain a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DeviceModel.html"><span style=' font-weight: bold; font-family: monospace;'>DeviceModel</span></a><span> object which stores information about the device model. This property can be useful when searching a set of NWB files or a data archive for all files that use a specific device model (e.g., Neuropixels probe).</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >device_model = types.core.DeviceModel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Array Technologies'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'PRB_1_4_0480_123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Neurovoxels 0.99 - A 12-channel array with 4 shanks and 3 channels per shank' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add device model to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_devices_models.set(</span><span style="color: #a709f5;">'Neurovoxels 0.99'</span><span >, device_model);</span></span></div></div></div><h3  class = 'S13' id = 'H_C700893A' ><span></span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >device = types.core.Device(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A 12-channel array with 4 shanks and 3 channels per shank'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model'</span><span >, device_model </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'array'</span><span >, device);</span></span></div></div></div><h3  class = 'S13' id = 'H_4D892966' ><span>Electrodes Table</span></h3><div  class = 'S15'><img class = "imageNode" src = "data:image/png;base64,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" width = "576" height = "409" alt = "" style = "vertical-align: baseline; width: 576px; height: 409px;"></img></div><div  class = 'S5'><span>Since this is a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, we can add additional metadata fields. We will be adding a "label" column to the table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >numShanks = 4;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >numChannelsPerShank = 3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >numChannels = numShanks * numChannelsPerShank;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >electrodesDynamicTable = types.core.ElectrodesTable(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'group'</span><span >, </span><span style="color: #a709f5;">'group_name'</span><span >, </span><span style="color: #a709f5;">'label'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all electrodes'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >iShank = 1:numShanks</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    shankGroupName = sprintf(</span><span style="color: #a709f5;">'shank%d'</span><span >, iShank);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electrodeGroup = types.core.ElectrodeGroup( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, sprintf(</span><span style="color: #a709f5;">'electrode group for %s'</span><span >, shankGroupName), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'brain area'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >iElectrode = 1:numChannelsPerShank</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        electrodesDynamicTable.addRow( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'group'</span><span >, types.untyped.ObjectView(electrodeGroup), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'group_name'</span><span >, shankGroupName, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'label'</span><span >, sprintf(</span><span style="color: #a709f5;">'%s-electrode%d'</span><span >, shankGroupName, iElectrode));</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >electrodesDynamicTable.toTable() </span><span style="color: #008013;">% Display the table</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="21A65ABB" prevent-scroll="true" data-testid="output_1" style="width: calc(100% - 5px);" tabindex="-1"><div class="ClientDocument veSpecifier table constrictHeight" id="variableeditor_client_Document_12" widgetid="variableeditor_client_Document_12" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_12"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">12×5 table </span></div><div id="variableeditor_TableViewModel_12" widgetid="variableeditor_TableViewModel_12" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 147px; min-width: 147px; max-width: 147px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>location</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 98px; min-width: 98px; max-width: 98px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>group</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>group_name</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 133px; min-width: 133px; max-width: 133px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>label</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>5</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>6</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>7</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>9</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>11</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>10</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>12</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>11</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
-    id           location                       group                group_name            label"></div></div></div></div><div class="inlineWrapper"><div  class = 'S16'><span style="white-space: pre"><span >nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;</span></span></div></div></div><h3  class = 'S17' id = 'H_2298AEF1' ><span>Links</span></h3><div  class = 'S5'><span>In the above loop, we create </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span> objects. The </span><span style=' font-family: monospace;'>electrodes</span><span> table then uses an </span><span style=' font-family: monospace;'>ObjectView</span><span> in each row to link to the corresponding </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span> object. An </span><span style=' font-family: monospace;'>ObjectView</span><span> is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.</span></div><h2  class = 'S8' id = 'H_1458A792' ><span>Recorded Extracellular Signals</span></h2><div  class = 'S5'><span>Voltage data are stored using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> class, a subclass of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> class specialized for voltage data. </span></div><h3  class = 'S13' id = 'H_DBCB9C61' ><span>Referencing Electrodes</span></h3><div  class = 'S5'><span>In order to create our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object, we first need to reference a set of rows in the </span><span style=' font-family: monospace;'>electrodes</span><span> table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as the </span><span style=' font-family: monospace;'>electrodes</span><span> table, using row indices.</span></div><div  class = 'S5'><span>Create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references all rows of the</span><span> </span><span style=' font-family: monospace;'>electrodes</span><span> </span><span>table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >electrode_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(electrodesDynamicTable), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all electrodes'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:length(electrodesDynamicTable.id.data)-1)');</span></span></div></div></div><h3  class = 'S13' id = 'H_0DD4A84E' ><span>Raw Voltage Data</span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object to hold acquisition data collected during the experiment.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "840" height = "402" alt = "" style = "vertical-align: baseline; width: 840px; height: 402px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >raw_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30000., </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(numChannels, 3000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % nChannels x nTime</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'volts'</span><span >);</span></span></div></div></div><div  class = 'S18'><span>This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S19'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >, raw_electrical_series);</span></span></div></div></div><h2  class = 'S4' id = 'H_A29C5644' ><span>Processed Extracellular Electrical Signals</span></h2><h3  class = 'S13' id = 'H_165B055A' ><span>LFP</span></h3><div  class = 'S5'><span>LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span>. To help data analysis and visualization tools know that this </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object represents LFP data, we store it inside an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object and then place the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> named </span><span style=' font-family: monospace;'>'ecephys'</span><span>. This is analogous to how we stored the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object inside of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object and stored the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> named </span><span style=' font-family: monospace;'>'behavior'</span><span> in the </span><a href = "./behavior.html"><span>behavior</span></a><span> tutorial</span></div><div  class = 'S5'><img class = "imageNode" src = 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" width = "1311" height = "392" alt = "" style = "vertical-align: baseline; width: 1311px; height: 392px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >lfp_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1000., </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(numChannels, 100), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> nChannels x nTime</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtering'</span><span >, </span><span style="color: #a709f5;">'Low-pass filter at 300 Hz'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'volts'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >lfp = types.core.LFP(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >, lfp_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ecephys_module = types.core.ProcessingModule(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'extracellular electrophysiology'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'LFP'</span><span >, lfp);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ecephys'</span><span >, ecephys_module);</span></span></div></div></div><h3  class = 'S13' id = 'H_D949DB79' ><span>Other Types of Filtered Electrical Signals</span></h3><div  class = 'S5'><span>If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> and encapsulate it within a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> object instead of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Generate filtered data</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_data = randn(50, 12); </span><span style="color: #008013;">% 50 time points, 12 channels</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_data = permute(filtered_data, [2, 1]); </span><span style="color: #008013;">% permute timeseries for matnwb</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an ElectricalSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Data filtered in the theta range'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, filtered_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtering'</span><span >, </span><span style="color: #a709f5;">'Band-pass filtered between 4 and 8 Hz'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 200.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create a FilteredEphys object and add the filtered electrical series</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_ephys = types.core.FilteredEphys();</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_ephys.electricalseries.set(</span><span style="color: #a709f5;">'FilteredElectricalSeries'</span><span >, filtered_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the FilteredEphys object to the ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'FilteredEphys'</span><span >, filtered_ephys);</span></span></div></div></div><h3  class = 'S13' id = 'H_3E971657' ><span>Decomposition of LFP Data into Frequency Bands</span></h3><div  class = 'S5'><span>In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> object. This object allows you to include metadata about the frequency bands and metric used (e.g., </span><span style=' font-family: monospace;'>power</span><span>, </span><span style=' font-family: monospace;'>phase</span><span>, </span><span style=' font-family: monospace;'>amplitude</span><span>), as well as link the decomposed data to the original </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> signal the data was derived from.</span></div><div  class = 'S5'><span>In this tutorial, the examples for </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> may appear similar. However, the key difference is that </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> is specialized for storing the results of spectral analyses of timeseries data in general, whereas </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> is defined specifically as a container for filtered electrical signals.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Note</span><span>: When adding data to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span>, the </span><span style=' font-family: monospace;'>data</span><span> argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the </span><a href = "./dimensionMapNoDataPipes.html"><span>dimensionMapNoDataPipes</span></a><span> tutorial)</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Define the frequency bands of interest (in Hz):</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_names = {</span><span style="color: #a709f5;">'theta'</span><span >; </span><span style="color: #a709f5;">'beta'</span><span >; </span><span style="color: #a709f5;">'gamma'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_mean = [8; 21; 55];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_stdev = [2; 4.5; 12.5];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% The bands should be added to the DecompositionSeries as a dynamic table</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >bands = table(band_names, band_mean, band_stdev, band_limits, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'VariableNames'</span><span >, {</span><span style="color: #a709f5;">'band_name'</span><span >, </span><span style="color: #a709f5;">'band_mean'</span><span >, </span><span style="color: #a709f5;">'band_stdev'</span><span >, </span><span style="color: #a709f5;">'band_limits'</span><span >})</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="ADBB4E61" prevent-scroll="true" data-testid="output_2" style="width: calc(100% - 5px);" tabindex="-1"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_13" widgetid="variableeditor_client_Document_13" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_13"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>bands = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_13" widgetid="variableeditor_TableViewModel_13" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_name</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_mean</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_stdev</span></th><th class="groupedHeader4" colspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_limits</span></th></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'theta'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'beta'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>21</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>30</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'gamma'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>55</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>30</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>80</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with bands = 
+</div></div></div></div></div></div><h3  class = 'S13' id = 'H_9e1e' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S8' id = 'H_4139' ><span>Electrode Information</span></h2><div  class = 'S5'><span>In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an </span><span style=' font-family: monospace;'>electrodes</span><span> table, which is also a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. </span><span style=' font-family: monospace;'>electrodes</span><span> has several required fields: </span><span style=' font-family: monospace;'>x</span><span>, </span><span style=' font-family: monospace;'>y</span><span>, </span><span style=' font-family: monospace;'>z</span><span>, </span><span style=' font-family: monospace;'>impedance</span><span>, </span><span style=' font-family: monospace;'>location</span><span>, </span><span style=' font-family: monospace;'>filtering</span><span>, and </span><span style=' font-family: monospace;'>electrode_group</span><span>.</span></div><div  class = 'S5'><span>The electrodes table references a required </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span>, which is used to represent a group of electrodes. Before creating an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span>, you must define a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>serial_number</span><span> and </span><span style=' font-family: monospace;'>model</span><span> are optional, but recommended. The </span><span style=' font-family: monospace;'>model</span><span> property can contain a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DeviceModel.html"><span style=' font-weight: bold; font-family: monospace;'>DeviceModel</span></a><span> object which stores information about the device model. This property can be useful when searching a set of NWB files or a data archive for all files that use a specific device model (e.g., Neuropixels probe).</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >device_model = types.core.DeviceModel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Array Technologies'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'PRB_1_4_0480_123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >     </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Neurovoxels 0.99 - A 12-channel array with 4 shanks and 3 channels per shank' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add device model to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_devices_models.set(</span><span style="color: #a709f5;">'Neurovoxels 0.99'</span><span >, device_model);</span></span></div></div></div><h3  class = 'S13' id = 'H_75db' ><span></span></h3><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >device = types.core.Device(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A 12-channel array with 4 shanks and 3 channels per shank'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model'</span><span >, device_model </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'array'</span><span >, device);</span></span></div></div></div><h3  class = 'S13' id = 'H_7313' ><span>Electrodes Table</span></h3><div  class = 'S15'><img class = "imageNode" src = 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" width = "576" height = "409" alt = "" style = "vertical-align: baseline; width: 576px; height: 409px;"></img></div><div  class = 'S5'><span>Since this is a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, we can add additional metadata fields. We will be adding a "label" column to the table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >numShanks = 4;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >numChannelsPerShank = 3;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >numChannels = numShanks * numChannelsPerShank;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >electrodesDynamicTable = types.core.ElectrodesTable(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'group'</span><span >, </span><span style="color: #a709f5;">'group_name'</span><span >, </span><span style="color: #a709f5;">'label'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all electrodes'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >iShank = 1:numShanks</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    shankGroupName = sprintf(</span><span style="color: #a709f5;">'shank%d'</span><span >, iShank);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electrodeGroup = types.core.ElectrodeGroup( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, sprintf(</span><span style="color: #a709f5;">'electrode group for %s'</span><span >, shankGroupName), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'brain area'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >iElectrode = 1:numChannelsPerShank</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >        electrodesDynamicTable.addRow( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'group'</span><span >, types.untyped.ObjectView(electrodeGroup), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'group_name'</span><span >, shankGroupName, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'label'</span><span >, sprintf(</span><span style="color: #a709f5;">'%s-electrode%d'</span><span >, shankGroupName, iElectrode));</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >electrodesDynamicTable.toTable() </span><span style="color: #008013;">% Display the table</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="A7D0D6D2" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table constrictHeight" id="variableeditor_client_Document_7" widgetid="variableeditor_client_Document_7" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_7"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">12×5 table </span></div><div id="variableeditor_TableViewModel_7" widgetid="variableeditor_TableViewModel_7" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 147px; min-width: 147px; max-width: 147px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>location</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 98px; min-width: 98px; max-width: 98px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>group</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>group_name</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 133px; min-width: 133px; max-width: 133px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>label</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank1-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>5</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank2-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>6</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>7</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank3-electrode3'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>9</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode1'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>11</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>10</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode2'</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>12</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>11</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 147px; min-width: 147px; max-width: 147px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'Primary&nbsp;visual&nbsp;area'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 98px; min-width: 98px; max-width: 98px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×1&nbsp;ObjectView</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 133px; min-width: 133px; max-width: 133px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'shank4-electrode3'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+    id           location                       group                group_name            label"></div></div></div></div><div class="inlineWrapper"><div  class = 'S16'><span style="white-space: pre"><span >nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;</span></span></div></div></div><h3  class = 'S17' id = 'H_3ff3' ><span>Links</span></h3><div  class = 'S5'><span>In the above loop, we create </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span> objects. The </span><span style=' font-family: monospace;'>electrodes</span><span> table then uses an </span><span style=' font-family: monospace;'>ObjectView</span><span> in each row to link to the corresponding </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html"><span style=' font-weight: bold; font-family: monospace;'>ElectrodeGroup</span></a><span> object. An </span><span style=' font-family: monospace;'>ObjectView</span><span> is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.</span></div><h2  class = 'S8' id = 'H_9bf7' ><span>Recorded Extracellular Signals</span></h2><div  class = 'S5'><span>Voltage data are stored using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> class, a subclass of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> class specialized for voltage data. </span></div><h3  class = 'S13' id = 'H_46c1' ><span>Referencing Electrodes</span></h3><div  class = 'S5'><span>In order to create our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object, we first need to reference a set of rows in the </span><span style=' font-family: monospace;'>electrodes</span><span> table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as the </span><span style=' font-family: monospace;'>electrodes</span><span> table, using row indices.</span></div><div  class = 'S5'><span>Create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references all rows of the</span><span> </span><span style=' font-family: monospace;'>electrodes</span><span> </span><span>table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >electrode_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(electrodesDynamicTable), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all electrodes'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:length(electrodesDynamicTable.id.data)-1)');</span></span></div></div></div><h3  class = 'S13' id = 'H_7c91' ><span>Raw Voltage Data</span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object to hold acquisition data collected during the experiment.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "840" height = "402" alt = "" style = "vertical-align: baseline; width: 840px; height: 402px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >raw_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30000., </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(numChannels, 3000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % nChannels x nTime</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'volts'</span><span >);</span></span></div></div></div><div  class = 'S18'><span>This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S19'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >, raw_electrical_series);</span></span></div></div></div><h2  class = 'S4' id = 'H_861c' ><span>Processed Extracellular Electrical Signals</span></h2><h3  class = 'S13' id = 'H_378a' ><span>LFP</span></h3><div  class = 'S5'><span>LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span>. To help data analysis and visualization tools know that this </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> object represents LFP data, we store it inside an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object and then place the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> named </span><span style=' font-family: monospace;'>'ecephys'</span><span>. This is analogous to how we stored the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object inside of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object and stored the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> named </span><span style=' font-family: monospace;'>'behavior'</span><span> in the </span><a href = "./behavior.html"><span>behavior</span></a><span> tutorial</span></div><div  class = 'S5'><img class = "imageNode" src = 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" width = "1311" height = "392" alt = "" style = "vertical-align: baseline; width: 1311px; height: 392px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >lfp_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1000., </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(numChannels, 100), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> nChannels x nTime</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtering'</span><span >, </span><span style="color: #a709f5;">'Low-pass filter at 300 Hz'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'volts'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >lfp = types.core.LFP(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >, lfp_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ecephys_module = types.core.ProcessingModule(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'extracellular electrophysiology'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'LFP'</span><span >, lfp);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ecephys'</span><span >, ecephys_module);</span></span></div></div></div><h3  class = 'S13' id = 'H_9aad' ><span>Other Types of Filtered Electrical Signals</span></h3><div  class = 'S5'><span>If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> and encapsulate it within a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> object instead of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html"><span style=' font-weight: bold; font-family: monospace;'>LFP</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Generate filtered data</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_data = randn(50, 12); </span><span style="color: #008013;">% 50 time points, 12 channels</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_data = permute(filtered_data, [2, 1]); </span><span style="color: #008013;">% permute timeseries for matnwb</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an ElectricalSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_electrical_series = types.core.ElectricalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Data filtered in the theta range'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, filtered_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtering'</span><span >, </span><span style="color: #a709f5;">'Band-pass filtered between 4 and 8 Hz'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 200.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create a FilteredEphys object and add the filtered electrical series</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_ephys = types.core.FilteredEphys();</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >filtered_ephys.electricalseries.set(</span><span style="color: #a709f5;">'FilteredElectricalSeries'</span><span >, filtered_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the FilteredEphys object to the ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'FilteredEphys'</span><span >, filtered_ephys);</span></span></div></div></div><h3  class = 'S13' id = 'H_1073' ><span>Decomposition of LFP Data into Frequency Bands</span></h3><div  class = 'S5'><span>In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> object. This object allows you to include metadata about the frequency bands and metric used (e.g., </span><span style=' font-family: monospace;'>power</span><span>, </span><span style=' font-family: monospace;'>phase</span><span>, </span><span style=' font-family: monospace;'>amplitude</span><span>), as well as link the decomposed data to the original </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> signal the data was derived from.</span></div><div  class = 'S5'><span>In this tutorial, the examples for </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> may appear similar. However, the key difference is that </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span> is specialized for storing the results of spectral analyses of timeseries data in general, whereas </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html"><span style=' font-weight: bold; font-family: monospace;'>FilteredEphys</span></a><span> is defined specifically as a container for filtered electrical signals.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Note</span><span>: When adding data to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html"><span style=' font-weight: bold; font-family: monospace;'>DecompositionSeries</span></a><span>, the </span><span style=' font-family: monospace;'>data</span><span> argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the </span><a href = "./dimensionMapNoDataPipes.html"><span>dimensionMapNoDataPipes</span></a><span> tutorial)</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Define the frequency bands of interest (in Hz):</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_names = {</span><span style="color: #a709f5;">'theta'</span><span >; </span><span style="color: #a709f5;">'beta'</span><span >; </span><span style="color: #a709f5;">'gamma'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_mean = [8; 21; 55];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_stdev = [2; 4.5; 12.5];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% The bands should be added to the DecompositionSeries as a dynamic table</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >bands = table(band_names, band_mean, band_stdev, band_limits, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'VariableNames'</span><span >, {</span><span style="color: #a709f5;">'band_name'</span><span >, </span><span style="color: #a709f5;">'band_mean'</span><span >, </span><span style="color: #a709f5;">'band_stdev'</span><span >, </span><span style="color: #a709f5;">'band_limits'</span><span >})</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="2577A40A" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_8" widgetid="variableeditor_client_Document_8" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_8"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>bands = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_8" widgetid="variableeditor_TableViewModel_8" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_name</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_mean</span></th><th rowspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_stdev</span></th><th class="groupedHeader4" colspan="2" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>band_limits</span></th></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 164px; min-width: 164px; max-width: 164px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'theta'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'beta'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>21</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>30</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'gamma'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>55</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>30</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 164px; min-width: 164px; max-width: 164px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>80</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with bands = 
     band_name    band_mean    band_stdev    band_limits
-    _________    _________    __________"></div></div></div></div><div class="inlineWrapper"><div  class = 'S20'><span style="white-space: pre"><span >bands = util.table2nwb( bands, </span><span style="color: #a709f5;">'Frequency bands for lfp'</span><span >, </span><span style="color: #a709f5;">'types.core.FrequencyBandsTable'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Generate random phase data for the demonstration.</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >phase_data = randn(50, 12, numel(band_names)); </span><span style="color: #008013;">% 50 samples, 12 channels, 3 frequency bands</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >phase_data = permute(phase_data, [3,2,1]); </span><span style="color: #008013;">% See dimensionMapNoDataPipes tutorial</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >decomp_series = types.core.DecompositionSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, phase_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bands'</span><span >, bands, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'metric'</span><span >, </span><span style="color: #a709f5;">'phase'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1000.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_channels'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_timeseries'</span><span >, lfp_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add decomposition series to ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'theta'</span><span >, decomp_series);</span></span></div></div></div><h2  class = 'S8' id = 'H_A3926752' ><span>Spike Times and Extracellular Events</span></h2><h3  class = 'S13' id = 'H_EAE714B1' ><span>Sorted Spike Times</span></h3><div  class = 'S5'><span>Spike times are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table, a specialization of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> class. The default </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table is located at </span><span style=' font-family: monospace;'>/units</span><span> in the HDF5 file. You can add columns to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table just like you did for </span><span style=' font-family: monospace;'>electrodes</span><span> and </span><span style=' font-family: monospace;'>trials</span><span> (see </span><a href = "./convertTrials.m"><span>convertTrials</span></a><span>). Here, we generate some random spike time data and populate the table. Note: Spike times of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table should be sorted in ascending order.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >num_cells = 10;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_times = cell(1, num_cells);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >iShank = 1:num_cells</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    spike_times{iShank} = sort( rand(1, randi([16, 28])), </span><span style="color: #a709f5;">'ascend'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >spike_times</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="F7DA0C80" prevent-scroll="true" data-testid="output_3" style="width: calc(100% - 5px);" tabindex="-1"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_14" widgetid="variableeditor_client_Document_14" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_14"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>spike_times = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×10 cell </span></div><div id="variableeditor_TableViewModel_14" widgetid="variableeditor_TableViewModel_14" class="cell lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×19&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×18&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×19&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×19&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×18&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×18&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×28&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×28&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×17&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×28&nbsp;double</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with spike_times = 
-    {[0.1989 0.2132 0.2492 0.2631 0.2736 0.3616 0.3658 0.3735 0.3767 0.3834 0.3884 0.4547"></div></div></div></div></div><h4  class = 'S21' id = 'H_E2B446F0' ><span>Ragged Arrays</span></h4><div  class = 'S5'><span>Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table. These indexed columns have two components, the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold; font-family: monospace;'>VectorData</span></a><span> object that holds the data and the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold; font-family: monospace;'>VectorIndex</span></a><span> object that holds the indices in the vector that indicate the row breaks. You can use the convenience function </span><span style=' font-family: monospace;'>util.create_indexed_column</span><span> to create these objects. For more information about ragged arrays, we refer you to the </span><span style=' font-weight: bold;'>"Ragged Array Columns"</span><span> section of the </span><a href = "./dynamic_tables.html"><span>dynamic table</span></a><span> tutorial.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "703" height = "353" alt = "" style = "vertical-align: baseline; width: 703px; height: 353px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >[spike_times_vector, spike_times_index] = util.create_indexed_column(spike_times);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_times_vector.resolution = 1/20000; </span><span style="color: #008013;">% If original sampling rate was 20 kHz </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb.units = types.core.Units( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'units table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'spike_times'</span><span >, spike_times_vector, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'spike_times_index'</span><span >, spike_times_index </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.toTable()</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="0BB655B3" prevent-scroll="true" data-testid="output_4" style="width: calc(100% - 5px);" tabindex="-1"><div class="ClientDocument veSpecifier table constrictHeight" id="variableeditor_client_Document_15" widgetid="variableeditor_client_Document_15" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_15"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">10×2 table </span></div><div id="variableeditor_TableViewModel_15" widgetid="variableeditor_TableViewModel_15" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 126px; min-width: 126px; max-width: 126px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>spike_times</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>19×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>18×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>19×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>19×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>18×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>5</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>18×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>6</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>28×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>7</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>28×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>17×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>9</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>28×1&nbsp;double</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+    _________    _________    __________"></div></div></div></div><div class="inlineWrapper"><div  class = 'S20'><span style="white-space: pre"><span >bands = util.table2nwb( bands, </span><span style="color: #a709f5;">'Frequency bands for lfp'</span><span >, </span><span style="color: #a709f5;">'types.core.FrequencyBandsTable'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Generate random phase data for the demonstration.</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >phase_data = randn(50, 12, numel(band_names)); </span><span style="color: #008013;">% 50 samples, 12 channels, 3 frequency bands</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >phase_data = permute(phase_data, [3,2,1]); </span><span style="color: #008013;">% See dimensionMapNoDataPipes tutorial</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >decomp_series = types.core.DecompositionSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, phase_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bands'</span><span >, bands, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'metric'</span><span >, </span><span style="color: #a709f5;">'phase'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % seconds</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1000.0, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_channels'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_timeseries'</span><span >, lfp_electrical_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add decomposition series to ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'theta'</span><span >, decomp_series);</span></span></div></div></div><h2  class = 'S8' id = 'H_7ffa' ><span>Spike Times and Extracellular Events</span></h2><h3  class = 'S13' id = 'H_70ee' ><span>Sorted Spike Times</span></h3><div  class = 'S5'><span>Spike times are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table, a specialization of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> class. The default </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table is located at </span><span style=' font-family: monospace;'>/units</span><span> in the HDF5 file. You can add columns to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table just like you did for </span><span style=' font-family: monospace;'>electrodes</span><span> and </span><span style=' font-family: monospace;'>trials</span><span> (see </span><a href = "./convertTrials.m"><span>convertTrials</span></a><span>). Here, we generate some random spike time data and populate the table. Note: Spike times of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table should be sorted in ascending order.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >num_cells = 10;</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_times = cell(1, num_cells);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >iShank = 1:num_cells</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    spike_times{iShank} = sort( rand(1, randi([16, 28])), </span><span style="color: #a709f5;">'ascend'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >spike_times</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="61DA8758" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_9" widgetid="variableeditor_client_Document_9" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_9"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>spike_times = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×10 cell </span></div><div id="variableeditor_TableViewModel_9" widgetid="variableeditor_TableViewModel_9" class="cell lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×28&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×18&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×28&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×24&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×16&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×23&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×25&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×24&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×19&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>1×21&nbsp;double</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with spike_times = 
+    {[0.0264 0.0591 0.0854 0.1181 0.1203 0.1328 0.1918 0.2230 0.2389 0.2487 0.2700 0.3048"></div></div></div></div></div><h4  class = 'S21' id = 'H_7e54' ><span>Ragged Arrays</span></h4><div  class = 'S5'><span>Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table. These indexed columns have two components, the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html"><span style=' font-weight: bold; font-family: monospace;'>VectorData</span></a><span> object that holds the data and the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html"><span style=' font-weight: bold; font-family: monospace;'>VectorIndex</span></a><span> object that holds the indices in the vector that indicate the row breaks. You can use the convenience function </span><span style=' font-family: monospace;'>util.create_indexed_column</span><span> to create these objects. For more information about ragged arrays, we refer you to the </span><span style=' font-weight: bold;'>"Ragged Array Columns"</span><span> section of the </span><a href = "./dynamic_tables.html"><span>dynamic table</span></a><span> tutorial.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "703" height = "353" alt = "" style = "vertical-align: baseline; width: 703px; height: 353px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >[spike_times_vector, spike_times_index] = util.create_indexed_column(spike_times);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_times_vector.resolution = 1/20000; </span><span style="color: #008013;">% If original sampling rate was 20 kHz </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb.units = types.core.Units( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'units table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'spike_times'</span><span >, spike_times_vector, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'spike_times_index'</span><span >, spike_times_index </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.toTable()</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="DF3D3A63" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table constrictHeight" id="variableeditor_client_Document_10" widgetid="variableeditor_client_Document_10" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_10"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">10×2 table </span></div><div id="variableeditor_TableViewModel_10" widgetid="variableeditor_TableViewModel_10" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 126px; min-width: 126px; max-width: 126px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>spike_times</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>28×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>18×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>28×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>4</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>24×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>5</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>4</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>16×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>6</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>5</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>23×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>7</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>6</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>25×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>8</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>7</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>24×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>9</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>8</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>19×1&nbsp;double</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>10</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>9</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>21×1&nbsp;double</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     id     spike_times 
     __    _____________
 
-    0     {19×1 double}
+    0     {28×1 double}
     1     {18×1 double}
-"></div></div></div></div></div><h3  class = 'S17' id = 'H_F4622D6F' ><span>Unsorted Spike Times</span></h3><div  class = 'S5'><span>While the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpikeEventSeries</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% In the SpikeEventSeries the dimensions should be ordered as </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% [num_events, num_channels, num_samples].</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Define spike snippets: 20 events, 3 channels, 40 samples per event. </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_snippets = rand(20, 3, 40);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Permute spike snippets (See dimensionMapNoDataPipes tutorial)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >spike_snippets = permute(spike_snippets, [3,2,1]) </span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="D3EDBA0E" prevent-scroll="true" data-testid="output_5" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1100" data-previous-scroll-height="12984" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">spike_snippets = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">spike_snippets(:,:,1) =
-
-    0.1444    0.6720    0.2475
-    0.1849    0.2393    0.0308
-    0.7052    0.5415    0.7414
-    0.8952    0.6502    0.7821
-    0.7101    0.6679    0.5011
-    0.9481    0.6052    0.7851
-    0.5066    0.5619    0.6638
-    0.8744    0.4757    0.7377
-    0.3853    0.8357    0.8214
-    0.7259    0.8471    0.4227
-    0.6729    0.2766    0.3834
-    0.0864    0.0460    0.1778
-    0.8265    0.4889    0.3311
-    0.3498    0.3574    0.8580
-    0.7683    0.6725    0.3381
-    0.3959    0.8323    0.5376
-    0.5139    0.7670    0.0412
-    0.8239    0.3787    0.8131
-    0.1004    0.8530    0.5705
-    0.6871    0.7358    0.6569
-    0.4549    0.2117    0.9436
-    0.6752    0.2392    0.1867
-    0.3067    0.5240    0.0026
-    0.2403    0.0176    0.2402
-    0.0696    0.4419    0.0771
-    0.9021    0.4091    0.6066
-    0.3616    0.9256    0.3115
-    0.7193    0.4563    0.7156
-    0.7444    0.4037    0.9396
-    0.2174    0.5205    0.4597
-    0.2205    0.5238    0.6843
-    0.4655    0.9538    0.5020
-    0.8005    0.5715    0.5209
-    0.5550    0.1773    0.0436
-    0.1097    0.1016    0.6368
-    0.2030    0.5894    0.0582
-    0.1835    0.7810    0.3665
-    0.8316    0.1290    0.6950
-    0.8797    0.6132    0.8109
-    0.1319    0.7194    0.6273
-
-
-spike_snippets(:,:,2) =
-
-    0.6207    0.9823    0.4662
-    0.3505    0.5628    0.2342
-    0.4879    0.2671    0.8975
-    0.3916    0.6789    0.1155
-    0.4187    0.3486    0.5758
-    0.8694    0.3828    0.4271
-    0.7706    0.9734    0.9467
-    0.8791    0.2759    0.2814
-    0.1459    0.4292    0.0322
-    0.1235    0.6890    0.7291
-    0.3389    0.3991    0.6999
-    0.5855    0.5336    0.3405
-    0.6347    0.3416    0.3314
-    0.8686    0.2265    0.4078
-    0.9049    0.9808    0.9409
-    0.9946    0.3876    0.7428
-    0.3467    0.2604    0.1844
-    0.5169    0.1597    0.2657
-    0.6397    0.7284    0.1210
-    0.9291    0.3106    0.8070
-    0.1816    0.1272    0.2413
-    0.3905    0.4866    0.0261
-    0.4512    0.9274    0.1798
-    0.9241    0.6439    0.6399
-    0.4462    0.0678    0.7973
-    0.8952    0.4806    0.9674
-    0.8707    0.8339    0.0026
-    0.4562    0.9166    0.7232
-    0.5775    0.4069    0.4474
-    0.4897    0.8843    0.0116
-    0.0167    0.1441    0.1878
-    0.5687    0.3546    0.5943
-    0.3923    0.0362    0.0519
-    0.5393    0.4944    0.2547
-    0.5781    0.5714    0.6762
-    0.0079    0.5022    0.2313
-    0.9192    0.5534    0.2976
-    0.1895    0.0134    0.5093
-    0.6487    0.3452    0.9756
-    0.4354    0.3333    0.8069
-
-
-spike_snippets(:,:,3) =
-
-    0.4559    0.8201    0.4460
-    0.3314    0.8361    0.9606
-    0.8710    0.9406    0.5503
-    0.8815    0.6988    0.7684
-    0.5595    0.0226    0.5811
-    0.3760    0.9151    0.2490
-    0.5588    0.4860    0.0150
-    0.9318    0.9229    0.2608
-    0.5268    0.4037    0.4252
-    0.8998    0.3580    0.9387
-    0.6692    0.4776    0.0274
-    0.4313    0.6500    0.7542
-    0.4556    0.0279    0.4473
-    0.3926    0.8143    0.5114
-    0.3233    0.8897    0.6051
-    0.7230    0.0967    0.3433
-    0.9262    0.6666    0.7487
-    0.7678    0.1476    0.9699
-    0.4611    0.4507    0.8861
-    0.6016    0.0173    0.3626
-    0.2365    0.9621    0.5626
-    0.0381    0.3556    0.4008
-    0.0538    0.4950    0.9390
-    0.0478    0.5691    0.1786
-    0.1418    0.0435    0.6128
-    0.3837    0.0140    0.6549
-    0.5034    0.4381    0.7408
-    0.0166    0.3426    0.5471
-    0.1817    0.6953    0.8642
-    0.7921    0.8821    0.9183
-    0.7528    0.0678    0.3885
-    0.6722    0.6750    0.7363
-    0.1182    0.3140    0.0422
-    0.6676    0.1097    0.2520
-    0.5253    0.6689    0.5693
-    0.2048    0.2951    0.2889
-    0.1301    0.8063    0.2633
-    0.9100    0.9183    0.2731
-    0.3216    0.4108    0.2904
-    0.4424    0.3467    0.1573
-
-
-spike_snippets(:,:,4) =
-
-    0.1000    0.4857    0.1660
-    0.6981    0.8385    0.0896
-    0.6196    0.2742    0.0615
-    0.7073    0.5601    0.8085
-    0.2991    0.3767    0.1965
-    0.2964    0.4940    0.4756
-    0.5336    0.2417    0.7136
-    0.5967    0.0178    0.8313
-    0.2368    0.5141    0.1564
-    0.4968    0.7588    0.7818
-    0.3185    0.4744    0.1821
-    0.0372    0.8259    0.1584
-    0.9778    0.9798    0.8828
-    0.6545    0.1753    0.1312
-    0.3194    0.4269    0.6531
-    0.7898    0.3288    0.8819
-    0.9153    0.8429    0.6626
-    0.7408    0.4176    0.8818
-    0.1079    0.9244    0.6857
-    0.5685    0.3165    0.3276
-    0.9271    0.3946    0.8256
-    0.3646    0.8996    0.1311
-    0.4441    0.0409    0.0530
-    0.1846    0.8619    0.6928
-    0.1825    0.7891    0.5228
-    0.2762    0.3956    0.0371
-    0.7136    0.5357    0.3265
-    0.9898    0.2228    0.7798
-    0.7511    0.0670    0.8510
-    0.7900    0.5288    0.0462
-    0.9953    0.3280    0.2596
-    0.2779    0.3968    0.0999
-    0.3748    0.6922    0.7255
-    0.2002    0.0846    0.0068
-    0.6940    0.5829    0.5447
-    0.8487    0.2676    0.1647
-    0.4821    0.5921    0.3710
-    0.4690    0.6883    0.7351
-    0.6127    0.9537    0.5101
-    0.0795    0.6617    0.0899
-
-
-spike_snippets(:,:,5) =
-
-    0.1143    0.6740    0.7906
-    0.3094    0.0199    0.6678
-    0.7661    0.1665    0.1114
-    0.2558    0.2584    0.4177
-    0.0226    0.5149    0.1390
-    0.3079    0.9430    0.6948
-    0.4110    0.4593    0.0343
-    0.3036    0.4943    0.8871
-    0.3066    0.8911    0.1715
-    0.6799    0.8186    0.8869
-    0.4993    0.1754    0.1508
-    0.3565    0.1452    0.7939
-    0.4765    0.9670    0.4275
-    0.1080    0.3599    0.6392
-    0.4142    0.6348    0.3321
-    0.4838    0.3288    0.2213
-    0.8691    0.1060    0.0577
-    0.4015    0.5367    0.2766
-    0.6686    0.9639    0.1900
-    0.9682    0.3180    0.4473
-    0.0299    0.1971    0.1797
-    0.0399    0.6099    0.2084
-    0.9227    0.6446    0.0508
-    0.6098    0.6829    0.7880
-    0.4192    0.9505    0.2175
-    0.6120    0.9909    0.5249
-    0.8026    0.1433    0.9706
-    0.2898    0.6938    0.2578
-    0.5690    0.9092    0.3853
-    0.3696    0.7889    0.2938
-    0.8342    0.6058    0.4917
-    0.2154    0.7411    0.4758
-    0.2431    0.2182    0.3686
-    0.6200    0.2043    0.2100
-    0.9719    0.5321    0.3275
-    0.9669    0.5149    0.7707
-    0.2282    0.5357    0.5672
-    0.6834    0.6008    0.6562
-    0.2671    0.4696    0.8015
-    0.3040    0.4995    0.1790
-
-
-spike_snippets(:,:,6) =
-
-    0.7760    0.2220    0.7375
-    0.8513    0.9198    0.0247
-    0.2316    0.5590    0.3026
-    0.5701    0.4849    0.2896
-    0.9677    0.8002    0.8155
-    0.8879    0.0328    0.8741
-    0.9263    0.5921    0.3884
-    0.7929    0.9160    0.1595
-    0.6770    0.7666    0.8601
-    0.1809    0.3975    0.7103
-    0.5710    0.8944    0.0854
-    0.8374    0.1255    0.7419
-    0.3360    0.2603    0.6935
-    0.9912    0.2975    0.9370
-    0.5550    0.8287    0.7555
-    0.1897    0.3054    0.8456
-    0.5351    0.0328    0.6691
-    0.8061    0.8486    0.1438
-    0.7910    0.3581    0.1443
-    0.6832    0.5392    0.9797
-    0.0424    0.9615    0.7329
-    0.7204    0.9879    0.8833
-    0.6100    0.2854    0.3667
-    0.0038    0.1541    0.5321
-    0.3244    0.2729    0.8052
-    0.9508    0.5510    0.0695
-    0.2261    0.5353    0.7892
-    0.9706    0.8015    0.1663
-    0.9908    0.2416    0.6316
-    0.2211    0.1726    0.8809
-    0.7526    0.6859    0.9505
-    0.5379    0.5248    0.0660
-    0.0308    0.5970    0.0760
-    0.2652    0.5298    0.0642
-    0.2950    0.5689    0.1415
-    0.8117    0.0536    0.6138
-    0.7547    0.2247    0.2948
-    0.0007    0.3092    0.2946
-    0.5511    0.8039    0.2004
-    0.0257    0.3647    0.9779
-
-
-spike_snippets(:,:,7) =
-
-    0.5127    0.1808    0.4487
-    0.9982    0.8953    0.4263
-    0.3986    0.0366    0.3426
-    0.7445    0.2421    0.6813
-    0.8840    0.6673    0.8041
-    0.6977    0.7457    0.4213
-    0.0206    0.2800    0.3515
-    0.8619    0.1781    0.1270
-    0.5480    0.5048    0.5141
-    0.7576    0.4262    0.0070
-    0.4074    0.4216    0.4039
-    0.3400    0.9559    0.3761
-    0.3305    0.4264    0.9037
-    0.7139    0.7812    0.2964
-    0.1509    0.7170    0.4713
-    0.8903    0.5599    0.7648
-    0.6988    0.0679    0.4796
-    0.1433    0.0075    0.6818
-    0.5811    0.8414    0.1945
-    0.9157    0.7819    0.7466
-    0.5841    0.1106    0.0042
-    0.6576    0.5937    0.3953
-    0.8523    0.5177    0.4931
-    0.7822    0.1409    0.5373
-    0.0760    0.8614    0.8893
-    0.5927    0.4946    0.5983
-    0.3825    0.7466    0.0743
-    0.4631    0.3244    0.4177
-    0.5449    0.4068    0.7555
-    0.8111    0.3180    0.2120
-    0.9828    0.0199    0.4146
-    0.8518    0.6316    0.1907
-    0.5381    0.5196    0.1969
-    0.9656    0.8338    0.7569
-    0.3813    0.3380    0.5609
-    0.3380    0.9239    0.0827
-    0.5640    0.4920    0.5222
-    0.9829    0.2291    0.9910
-    0.2716    0.4589    0.6157
-    0.1736    0.7301    0.8249
-
-
-spike_snippets(:,:,8) =
-
-    0.9187    0.4061    0.4603
-    0.1821    0.9010    0.7259
-    0.1156    0.4338    0.2795
-    0.3540    0.3278    0.8168
-    0.6222    0.1615    0.5221
-    0.6221    0.2574    0.1261
-    0.3752    0.1322    0.0182
-    0.0059    0.6580    0.2777
-    0.8278    0.0703    0.6698
-    0.6696    0.5267    0.3051
-    0.7768    0.3987    0.6862
-    0.0879    0.9915    0.2191
-    0.0666    0.3399    0.5076
-    0.1785    0.9295    0.6607
-    0.9753    0.1954    0.6426
-    0.8853    0.5702    0.0386
-    0.8219    0.8861    0.8390
-    0.4778    0.2969    0.0213
-    0.2998    0.7831    0.0428
-    0.4679    0.7593    0.4430
-    0.0143    0.2879    0.2902
-    0.3883    0.6544    0.8635
-    0.8638    0.2517    0.1663
-    0.3882    0.2513    0.1719
-    0.6151    0.4068    0.7069
-    0.5719    0.8450    0.4389
-    0.0257    0.8055    0.2148
-    0.2106    0.5214    0.2259
-    0.7553    0.3273    0.6750
-    0.5987    0.1600    0.4018
-    0.2645    0.7885    0.5241
-    0.2666    0.4268    0.7332
-    0.4824    0.5024    0.1221
-    0.9764    0.8993    0.9908
-    0.7466    0.0465    0.4768
-    0.7639    0.4119    0.7019
-    0.8695    0.1536    0.9299
-    0.0039    0.2429    0.1077
-    0.4112    0.8943    0.2676
-    0.5745    0.7267    0.1840
-
-
-spike_snippets(:,:,9) =
-
-    0.4590    0.7380    0.8020
-    0.9905    0.0107    0.3512
-    0.4576    0.4426    0.2103
-    0.4719    0.3005    0.9191
-    0.5718    0.4305    0.1412
-    0.0086    0.5678    0.1937
-    0.0638    0.7857    0.0136
-    0.3611    0.9015    0.4975
-    0.6089    0.1974    0.8859
-    0.0793    0.1680    0.5854
-    0.9004    0.9584    0.5683
-    0.1792    0.7263    0.6732
-    0.4584    0.4530    0.0620
-    0.0501    0.0009    0.5086
-    0.0103    0.5257    0.4951
-    0.7013    0.6523    0.5873
-    0.0185    0.5632    0.9207
-    0.5332    0.1493    0.6060
-    0.9438    0.8477    0.3893
-    0.4841    0.6015    0.5007
-    0.8290    0.5104    0.7599
-    0.8354    0.9263    0.8945
-    0.9367    0.4170    0.8538
-    0.1049    0.4278    0.5463
-    0.8264    0.8576    0.4393
-    0.6504    0.8835    0.7993
-    0.2896    0.4569    0.2450
-    0.3079    0.1668    0.9766
-    0.9390    0.3559    0.9475
-    0.3326    0.8633    0.2472
-    0.4788    0.0970    0.5368
-    0.3126    0.3399    0.7317
-    0.0410    0.0182    0.0935
-    0.8088    0.9436    0.9157
-    0.6344    0.1570    0.2482
-    0.9060    0.1102    0.8557
-    0.2243    0.1310    0.8118
-    0.0715    0.8666    0.3831
-    0.1440    0.7733    0.8407
-    0.4025    0.8630    0.7172
-
-
-spike_snippets(:,:,10) =
-
-    0.9079    0.5801    0.6026
-    0.5751    0.3712    0.3070
-    0.3840    0.3639    0.4461
-    0.7169    0.4690    0.0635
-    0.4797    0.6829    0.7907
-    0.8883    0.7265    0.6067
-    0.0807    0.5207    0.1002
-    0.2075    0.0573    0.3564
-    0.1780    0.1499    0.5300
-    0.6013    0.7028    0.9039
-    0.1886    0.1242    0.7278
-    0.8682    0.6260    0.6969
-    0.3046    0.3178    0.4782
-    0.4801    0.1747    0.1846
-    0.9607    0.7523    0.7087
-    0.0266    0.4213    0.7753
-    0.9966    0.2866    0.5808
-    0.3799    0.5665    0.6226
-    0.0737    0.3664    0.3863
-    0.7813    0.7411    0.7615
-    0.9241    0.5865    0.7207
-    0.9143    0.7896    0.8260
-    0.6729    0.1919    0.4756
-    0.4643    0.1706    0.1843
-    0.2130    0.2492    0.6826
-    0.6605    0.3528    0.0475
-    0.0904    0.6021    0.8893
-    0.4828    0.4176    0.6388
-    0.7740    0.6710    0.0460
-    0.0181    0.5978    0.9145
-    0.2960    0.4343    0.0582
-    0.0278    0.9404    0.1012
-    0.8633    0.0758    0.2710
-    0.6265    0.1810    0.0041
-    0.6831    0.5061    0.2361
-    0.1624    0.3893    0.6306
-    0.6864    0.8863    0.0681
-    0.4833    0.4627    0.3871
-    0.7410    0.1173    0.8211
-    0.5852    0.2244    0.1087
-
-
-spike_snippets(:,:,11) =
-
-    0.0858    0.3339    0.7763
-    0.9176    0.5247    0.4392
-    0.3882    0.6010    0.6265
-    0.7431    0.0718    0.6106
-    0.8373    0.7766    0.9522
-    0.4108    0.3533    0.8969
-    0.2926    0.8612    0.7766
-    0.8428    0.6018    0.8342
-    0.0667    0.3935    0.5677
-    0.3172    0.8122    0.2027
-    0.0279    0.1626    0.4355
-    0.8320    0.9378    0.2799
-    0.6949    0.1419    0.4194
-    0.6980    0.4757    0.5331
-    0.0669    0.9035    0.6399
-    0.4705    0.8158    0.5026
-    0.6498    0.8962    0.8755
-    0.3154    0.7131    0.2621
-    0.4768    0.0521    0.2384
-    0.4651    0.3407    0.1474
-    0.7920    0.8513    0.0729
-    0.7333    0.1114    0.8024
-    0.8721    0.0737    0.3166
-    0.8607    0.0370    0.6942
-    0.7300    0.2168    0.8527
-    0.3261    0.3711    0.2093
-    0.4149    0.8162    0.2200
-    0.7118    0.1048    0.3534
-    0.9708    0.2513    0.9609
-    0.6460    0.8416    0.9345
-    0.6732    0.1687    0.2605
-    0.5520    0.9772    0.0485
-    0.1190    0.6116    0.5142
-    0.9117    0.8188    0.2635
-    0.8140    0.6821    0.0159
-    0.4527    0.7038    0.9307
-    0.1669    0.9979    0.0323
-    0.3275    0.3766    0.6078
-    0.8882    0.2658    0.9014
-    0.9146    0.1282    0.0285
-
-
-spike_snippets(:,:,12) =
-
-    0.2634    0.4566    0.7714
-    0.5692    0.1187    0.3631
-    0.0773    0.8546    0.3865
-    0.8734    0.7069    0.8278
-    0.1488    0.0173    0.0368
-    0.8588    0.0511    0.0955
-    0.1484    0.2192    0.9645
-    0.5338    0.0674    0.4277
-    0.9927    0.8487    0.6301
-    0.8648    0.1668    0.3151
-    0.1845    0.3485    0.9451
-    0.8727    0.5627    0.3136
-    0.4593    0.9236    0.1372
-    0.7620    0.0992    0.6461
-    0.6067    0.3025    0.2755
-    0.4283    0.0167    0.9789
-    0.2829    0.1334    0.1222
-    0.9958    0.9276    0.3061
-    0.3134    0.6553    0.1928
-    0.3875    0.6622    0.7177
-    0.4628    0.8680    0.4166
-    0.9992    0.8967    0.9360
-    0.2182    0.6017    0.8457
-    0.0065    0.0571    0.7846
-    0.8620    0.6739    0.0545
-    0.3361    0.3528    0.7622
-    0.8491    0.2702    0.3085
-    0.9694    0.9277    0.2989
-    0.5939    0.9648    0.0932
-    0.1438    0.4126    0.9271
-    0.0983    0.4620    0.3231
-    0.2156    0.1848    0.8992
-    0.8563    0.6676    0.3935
-    0.4561    0.6257    0.2109
-    0.7766    0.7318    0.2086
-    0.5883    0.7923    0.3688
-    0.3054    0.1999    0.6636
-    0.4307    0.7866    0.8561
-    0.0354    0.8115    0.7470
-    0.9437    0.3591    0.4957
-
-
-spike_snippets(:,:,13) =
-
-    0.8501    0.4964    0.9375
-    0.8436    0.6289    0.7319
-    0.3545    0.0594    0.4461
-    0.1154    0.5416    0.6205
-    0.1229    0.1616    0.2254
-    0.3808    0.5426    0.7822
-    0.3468    0.1717    0.7804
-    0.8862    0.6362    0.3807
-    0.6352    0.4613    0.4991
-    0.9984    0.3063    0.1210
-    0.2998    0.1710    0.5236
-    0.7392    0.4469    0.8351
-    0.8990    0.5165    0.3736
-    0.7349    0.5642    0.8362
-    0.3364    0.5395    0.7091
-    0.3780    0.5945    0.3575
-    0.5200    0.1461    0.6382
-    0.4294    0.5054    0.1580
-    0.2475    0.7186    0.8667
-    0.9383    0.4824    0.6139
-    0.0956    0.8394    0.9969
-    0.6328    0.2559    0.5207
-    0.8985    0.2128    0.2544
-    0.5676    0.4267    0.7001
-    0.0376    0.5098    0.9956
-    0.0646    0.7630    0.8410
-    0.7858    0.8770    0.2419
-    0.3581    0.3867    0.6712
-    0.0194    0.5224    0.8229
-    0.8545    0.1723    0.8992
-    0.8275    0.6590    0.0599
-    0.6373    0.8432    0.3216
-    0.8318    0.8014    0.6817
-    0.6521    0.7568    0.3507
-    0.4821    0.3613    0.4108
-    0.3786    0.1593    0.9497
-    0.7027    0.7708    0.1001
-    0.9218    0.7925    0.6901
-    0.7481    0.1917    0.0508
-    0.8869    0.8674    0.3866
-
-
-spike_snippets(:,:,14) =
-
-    0.7292    0.6525    0.3702
-    0.4831    0.1477    0.6170
-    0.3597    0.2578    0.5136
-    0.3916    0.8598    0.6224
-    0.0740    0.9517    0.4077
-    0.1656    0.7013    0.7553
-    0.7546    0.3066    0.4270
-    0.7486    0.2773    0.1604
-    0.0937    0.2660    0.7392
-    0.6153    0.0143    0.7880
-    0.8908    0.4546    0.4966
-    0.0703    0.7065    0.3871
-    0.6261    0.7982    0.1899
-    0.8207    0.8478    0.5721
-    0.2061    0.4610    0.4292
-    0.3661    0.7669    0.8827
-    0.5036    0.9643    0.0585
-    0.2408    0.9854    0.7621
-    0.2046    0.5717    0.1450
-    0.3434    0.9789    0.0720
-    0.7412    0.4451    0.5734
-    0.1701    0.5329    0.7581
-    0.3262    0.4676    0.2009
-    0.2627    0.7772    0.3401
-    0.2926    0.8818    0.1469
-    0.1363    0.0894    0.9833
-    0.5478    0.0078    0.5096
-    0.4297    0.6443    0.8709
-    0.9510    0.2881    0.5373
-    0.8463    0.4053    0.3140
-    0.6248    0.1064    0.1968
-    0.2691    0.2635    0.7111
-    0.9418    0.0609    0.7547
-    0.0419    0.1103    0.2623
-    0.0134    0.2175    0.0057
-    0.3027    0.3231    0.7154
-    0.8939    0.0057    0.4186
-    0.3151    0.8697    0.0247
-    0.9266    0.1603    0.6330
-    0.2889    0.3737    0.3208
-
-
-spike_snippets(:,:,15) =
-
-    0.6973    0.0030    0.9586
-    0.5837    0.9488    0.7831
-    0.7971    0.6623    0.8649
-    0.5868    0.1713    0.3172
-    0.4563    0.1007    0.0449
-    0.3452    0.9086    0.0328
-    0.6884    0.5337    0.6168
-    0.6722    0.8908    0.0798
-    0.3818    0.3363    0.7475
-    0.8579    0.8658    0.6389
-    0.1476    0.3602    0.1319
-    0.9416    0.7453    0.4435
-    0.6235    0.2004    0.3935
-    0.8467    0.1239    0.9736
-    0.8196    0.8824    0.1602
-    0.5734    0.7657    0.6363
-    0.3838    0.7422    0.9007
-    0.2635    0.9709    0.5844
-    0.9346    0.3439    0.3451
-    0.2721    0.1831    0.3469
-    0.1487    0.3357    0.5842
-    0.8917    0.0278    0.9478
-    0.8699    0.4801    0.1622
-    0.3296    0.6131    0.8321
-    0.1754    0.4413    0.0177
-    0.6423    0.4745    0.2837
-    0.8297    0.0010    0.9348
-    0.8555    0.6102    0.3513
-    0.2107    0.2172    0.1406
-    0.4766    0.2155    0.7149
-    0.8502    0.7120    0.6969
-    0.8418    0.0723    0.9048
-    0.8034    0.3691    0.5539
-    0.1285    0.8643    0.7513
-    0.8931    0.3355    0.0153
-    0.0721    0.0773    0.6590
-    0.2170    0.6720    0.3878
-    0.8708    0.5540    0.8795
-    0.5853    0.8732    0.0990
-    0.5359    0.7407    0.4088
-
-
-spike_snippets(:,:,16) =
-
-    0.3518    0.4097    0.1048
-    0.8290    0.9871    0.0220
-    0.7275    0.2216    0.3023
-    0.1259    0.4698    0.2434
-    0.1052    0.9010    0.8649
-    0.0111    0.8507    0.9125
-    0.9146    0.2248    0.1150
-    0.3095    0.3470    0.8283
-    0.1193    0.2883    0.8664
-    0.5383    0.9721    0.0399
-    0.2625    0.3832    0.5548
-    0.1842    0.8888    0.8144
-    0.8812    0.7126    0.8494
-    0.6014    0.5558    0.2773
-    0.8133    0.8459    0.6402
-    0.3513    0.6593    0.9021
-    0.7287    0.0045    0.8133
-    0.9141    0.9774    0.8192
-    0.5419    0.0286    0.9431
-    0.3515    0.0700    0.3082
-    0.9596    0.3312    0.9679
-    0.0722    0.7890    0.7038
-    0.8242    0.9796    0.4506
-    0.1925    0.7699    0.5530
-    0.6787    0.5059    0.7852
-    0.8803    0.9455    0.0964
-    0.9124    0.8199    0.5611
-    0.4066    0.5170    0.7741
-    0.0996    0.2639    0.8226
-    0.4327    0.2632    0.9906
-    0.3978    0.8399    0.8839
-    0.2182    0.2240    0.0007
-    0.4114    0.2703    0.3359
-    0.1537    0.1217    0.6842
-    0.6312    0.0353    0.8559
-    0.8920    0.2158    0.1595
-    0.7191    0.2924    0.2480
-    0.7628    0.8633    0.3716
-    0.2151    0.1804    0.2757
-    0.4395    0.7745    0.8558
-
-
-spike_snippets(:,:,17) =
-
-    0.0230    0.4015    0.3081
-    0.7345    0.5350    0.0791
-    0.2083    0.0848    0.7328
-    0.1055    0.8958    0.4566
-    0.2214    0.0180    0.2791
-    0.7746    0.5493    0.4104
-    0.2759    0.4131    0.4175
-    0.3681    0.5131    0.4415
-    0.4841    0.4907    0.9293
-    0.3083    0.3306    0.8012
-    0.5555    0.7937    0.1228
-    0.5209    0.1799    0.2600
-    0.1366    0.9773    0.1893
-    0.5960    0.9972    0.4558
-    0.6467    0.3424    0.8658
-    0.2816    0.2854    0.7079
-    0.1255    0.3305    0.0889
-    0.1021    0.6291    0.9227
-    0.3740    0.8361    0.2614
-    0.7807    0.9948    0.3148
-    0.5747    0.2271    0.7047
-    0.1851    0.3368    0.9956
-    0.4217    0.9016    0.5228
-    0.7354    0.0508    0.0500
-    0.4502    0.5064    0.9420
-    0.5022    0.5800    0.0363
-    0.8932    0.4445    0.0251
-    0.8246    0.7668    0.2660
-    0.0673    0.5567    0.2756
-    0.5963    0.5091    0.2508
-    0.2227    0.0515    0.6894
-    0.9875    0.5687    0.8913
-    0.9830    0.4408    0.7548
-    0.7308    0.5133    0.0864
-    0.0013    0.3349    0.0918
-    0.5772    0.1693    0.9915
-    0.6322    0.4938    0.8395
-    0.3257    0.1735    0.4780
-    0.7336    0.2557    0.3322
-    0.5646    0.4580    0.0716
-
-
-spike_snippets(:,:,18) =
-
-    0.0235    0.3962    0.8045
-    0.7601    0.8067    0.3132
-    0.0511    0.8155    0.3593
-    0.1843    0.0009    0.4251
-    0.4100    0.4085    0.8216
-    0.1639    0.3265    0.4865
-    0.5773    0.3798    0.2658
-    0.1619    0.0565    0.4248
-    0.0568    0.3914    0.6460
-    0.7754    0.9532    0.5893
-    0.0190    0.9003    0.2380
-    0.0245    0.6274    0.4640
-    0.6822    0.4279    0.3317
-    0.8669    0.9255    0.5963
-    0.6299    0.4869    0.4394
-    0.3101    0.4842    0.4849
-    0.2538    0.1618    0.1418
-    0.3406    0.4211    0.2686
-    0.9974    0.5401    0.0512
-    0.1689    0.8642    0.5223
-    0.7113    0.1835    0.1351
-    0.8219    0.5472    0.6757
-    0.8545    0.4525    0.8986
-    0.1392    0.5607    0.7318
-    0.8632    0.2200    0.1817
-    0.9086    0.6746    0.2013
-    0.2071    0.0019    0.2669
-    0.1129    0.1836    0.6161
-    0.4468    0.7164    0.6047
-    0.4092    0.3067    0.0939
-    0.7266    0.5096    0.0829
-    0.5197    0.1469    0.7602
-    0.0345    0.3560    0.8081
-    0.7977    0.5856    0.4265
-    0.8726    0.9852    0.3513
-    0.0440    0.3342    0.6338
-    0.0832    0.1378    0.4953
-    0.0186    0.2290    0.4266
-    0.1258    0.7205    0.9312
-    0.1084    0.3712    0.8159
-
-
-spike_snippets(:,:,19) =
-
-    0.7272    0.8207    0.8728
-    0.8097    0.3074    0.3602
-    0.4408    0.6653    0.9988
-    0.5683    0.0140    0.8489
-    0.3554    0.5821    0.2542
-    0.4205    0.1003    0.5989
-    0.7185    0.6836    0.4389
-    0.7691    0.5205    0.5715
-    0.4676    0.0891    0.4598
-    0.0387    0.2529    0.4936
-    0.0896    0.8036    0.8835
-    0.4965    0.7476    0.5928
-    0.4608    0.3198    0.4375
-    0.9891    0.6096    0.0853
-    0.5621    0.8188    0.0641
-    0.8410    0.8958    0.1027
-    0.6260    0.5967    0.8818
-    0.8905    0.7996    0.7541
-    0.8658    0.1191    0.4643
-    0.3346    0.0772    0.0405
-    0.4759    0.3197    0.4184
-    0.0660    0.4437    0.2540
-    0.8395    0.7294    0.6266
-    0.8993    0.3004    0.5067
-    0.7683    0.3697    0.8348
-    0.8459    0.1805    0.9731
-    0.9646    0.3663    0.9246
-    0.2910    0.6366    0.2908
-    0.6411    0.9995    0.6944
-    0.7586    0.2558    0.1905
-    0.4629    0.9069    0.2878
-    0.1701    0.9255    0.4694
-    0.0099    0.4622    0.5018
-    0.8086    0.9612    0.5165
-    0.3151    0.4848    0.5426
-    0.2882    0.4868    0.8601
-    0.1409    0.4406    0.6553
-    0.8214    0.5260    0.4015
-    0.3433    0.6249    0.5100
-    0.5360    0.6373    0.6101
-
-
-spike_snippets(:,:,20) =
-
-    0.2642    0.9732    0.3425
-    0.6626    0.2205    0.7332
-    0.2102    0.8743    0.7001
-    0.2827    0.9381    0.1692
-    0.3535    0.4508    0.9368
-    0.7414    0.9849    0.1459
-    0.4653    0.1087    0.6828
-    0.0959    0.4439    0.4424
-    0.4801    0.9840    0.1643
-    0.2225    0.5270    0.5432
-    0.4578    0.0928    0.8908
-    0.3590    0.1634    0.4088
-    0.9078    0.8325    0.9722
-    0.7105    0.2815    0.1357
-    0.7251    0.0271    0.4484
-    0.5002    0.4829    0.2474
-    0.2522    0.7237    0.5496
-    0.2340    0.4868    0.9903
-    0.4968    0.0209    0.8920
-    0.6967    0.4347    0.4675
-    0.2610    0.1245    0.8698
-    0.5375    0.7211    0.7748
-    0.1045    0.3661    0.6950
-    0.8736    0.5528    0.5809
-    0.4476    0.7467    0.4583
-    0.2774    0.0395    0.7790
-    0.4237    0.4871    0.9760
-    0.9504    0.3227    0.6013
-    0.2724    0.6458    0.0561
-    0.4560    0.8631    0.3045
-    0.7027    0.2820    0.2736
-    0.1239    0.5765    0.2224
-    0.0831    0.6822    0.4256
-    0.3701    0.6723    0.4143
-    0.5134    0.0696    0.8724
-    0.3234    0.7433    0.8534
-    0.4037    0.5477    0.9507
-    0.1918    0.6386    0.5263
-    0.2721    0.0165    0.2566
-    0.0916    0.6920    0.0690
-</div></div></div></div></div><div class="inlineWrapper"><div  class = 'S20'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create electrode table region referencing electrodes 0, 1, and 2</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >shank0_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(electrodesDynamicTable), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'shank0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:2)');</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Define spike event series for unsorted spike times</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_events = types.core.SpikeEventSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, spike_snippets, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, (0:19)', </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % Timestamps for each event</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'events detected with 100uV threshold'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, shank0_table_region </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add spike event series to NWB file acquisition</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'SpikeEvents_Shank0'</span><span >, spike_events);</span></span></div></div></div><h3  class = 'S17' id = 'H_A5CE3265' ><span>Detected Events</span></h3><div  class = 'S5' id = 'H_8F2CA75F' ><span>If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects in the acquisition group, and use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html"><span style=' font-weight: bold; font-family: monospace;'>EventDetection</span></a><span> class to identify the spike events in your raw traces.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9' id = 'H_8F2CA75F' ><span style="white-space: pre"><span style="color: #008013;">% Create the EventDetection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >event_detection = types.core.EventDetection( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'detection_method'</span><span >, </span><span style="color: #a709f5;">'thresholding, 1.5 * std'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_electricalseries'</span><span >, types.untyped.SoftLink(raw_electrical_series), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_idx'</span><span >, [1000; 2000; 3000], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'times'</span><span >, [.033, .066, .099] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the EventDetection object to the ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ThresholdEvents'</span><span >, event_detection);</span></span></div></div></div><h3  class = 'S13' id = 'H_0A2AE4A3' ><span>Storing Spike Features (e.g Principal Components)</span></h3><div  class = 'S5'><span>NWB also provides a way to store features of spikes, such as principal components, using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FeatureExtraction.html"><span style=' font-weight: bold; font-family: monospace;'>FeatureExtraction</span></a><span> class. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Generate random feature data (time x channel x feature)</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >features = rand(3, 12, 4); </span><span style="color: #008013;">% 3 time points, 12 channels, 4 features</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >features = permute(features, [3,2,1]); </span><span style="color: #008013;">% reverse dimension order for matnwb</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create the FeatureExtraction object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >feature_extraction = types.core.FeatureExtraction( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, {</span><span style="color: #a709f5;">'PC1'</span><span >, </span><span style="color: #a709f5;">'PC2'</span><span >, </span><span style="color: #a709f5;">'PC3'</span><span >, </span><span style="color: #a709f5;">'PC4'</span><span >}, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Feature descriptions</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'times'</span><span >, [.033; .066; .099], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Column vector for times</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'features'</span><span >, features </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the FeatureExtraction object to the ecephys module (if required)</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'PCA_features'</span><span >, feature_extraction);</span></span></div></div></div><h2  class = 'S8' id = 'H_AD8EF681' ><span>Choosing NWB-Types for Electrophysiology Data (A Summary)</span></h2><div  class = 'S5'><span>As mentioned above,</span><span style=' font-weight: bold;'> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span style=' font-weight: bold;'>API documentation</span></a><span> and the section on </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format.html#extracellular-electrophysiology"><span>Extracellular Physiology</span></a><span> in the "NWB Format Specification" for more details on using these objects.</span></div><div  class = 'S5'><span>For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects as acquisition data, and use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html"><span style=' font-weight: bold; font-family: monospace;'>EventDetection</span></a><span> class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpikeEventSeries</span></a><span> objects.</span></div><div  class = 'S5'><span>The results of spike sorting (or clustering) should be stored in the top-level </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns </span><span style=' font-family: monospace;'>waveform_mean</span><span>, </span><span style=' font-family: monospace;'>waveform_sd</span><span>, and </span><span style=' font-family: monospace;'>waveforms</span><span> in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table to store individual and mean waveform data.</span></div><h2  class = 'S8' id = 'H_A1ECC260' ><span>Writing the NWB File</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S19'><span style="white-space: pre"><span >nwbExport(nwb, </span><span style="color: #a709f5;">'ecephys_tutorial.nwb'</span><span >)</span></span></div></div></div><h2  class = 'S8' id = 'H_10964ACC' ><span>Reading NWB Data</span></h2><div  class = 'S5'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. </span><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb2 = nwbRead(</span><span style="color: #a709f5;">'ecephys_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb2.processing.get(</span><span style="color: #a709f5;">'ecephys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'LFP'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electricalseries.get(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    data.load;</span></span></div></div></div><h2  class = 'S8' id = 'H_2FF05AA8' ><span>Accessing Data Regions</span></h2><div  class = 'S5'><span>If all you need is a data region, you can index a </span><span style=' font-family: monospace;'>DataStub</span><span> object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% read section of LFP</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb2.processing.get(</span><span style="color: #a709f5;">'ecephys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'LFP'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electricalseries.get(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >    data(1:5, 1:10)</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="ABC1DA6A" prevent-scroll="true" data-testid="output_6" data-width="1107" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">5×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:5,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 732px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">    1.1010    0.5408    0.5514    0.5499   -1.4147   -0.0581   -0.2408    0.2435    0.8209    0.3412
-   -0.5912   -1.9507   -0.0309   -0.7284    1.7664    0.0516    0.2310    1.2940    2.5950   -0.2114
-    1.2611   -0.8532    0.9956    0.1800    0.3458    0.9419   -0.1981    0.2983   -0.5605    0.5547
-   -0.5501    0.1697   -0.6102    1.4202    3.0197    0.8003   -0.5024    0.5970    1.3023   -0.2280
-   -0.1100    0.6404    0.6956    1.5180    1.0416   -0.0587   -0.1316   -1.5283   -0.8390   -0.5555
+"></div></div></div></div></div><h3  class = 'S17' id = 'H_44c4' ><span>Unsorted Spike Times</span></h3><div  class = 'S5'><span>While the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpikeEventSeries</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% In the SpikeEventSeries the dimensions should be ordered as </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% [num_events, num_channels, num_samples].</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Define spike snippets: 20 events, 3 channels, 40 samples per event. </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_snippets = rand(20, 3, 40);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Permute spike snippets (See dimensionMapNoDataPipes tutorial)</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_snippets = permute(spike_snippets, [3,2,1]); </span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create electrode table region referencing electrodes 0, 1, and 2</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >shank0_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(electrodesDynamicTable), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'shank0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:2)');</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Define spike event series for unsorted spike times</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >spike_events = types.core.SpikeEventSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, spike_snippets, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, (0:19)', </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % Timestamps for each event</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'events detected with 100uV threshold'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, shank0_table_region </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add spike event series to NWB file acquisition</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'SpikeEvents_Shank0'</span><span >, spike_events);</span></span></div></div></div><h3  class = 'S17' id = 'H_3265' ><span>Detected Events</span></h3><div  class = 'S5' id = 'H_8F2CA75F' ><span>If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects in the acquisition group, and use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html"><span style=' font-weight: bold; font-family: monospace;'>EventDetection</span></a><span> class to identify the spike events in your raw traces.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9' id = 'H_8F2CA75F' ><span style="white-space: pre"><span style="color: #008013;">% Create the EventDetection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >event_detection = types.core.EventDetection( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'detection_method'</span><span >, </span><span style="color: #a709f5;">'thresholding, 1.5 * std'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_electricalseries'</span><span >, types.untyped.SoftLink(raw_electrical_series), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'source_idx'</span><span >, [1000; 2000; 3000], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'times'</span><span >, [.033, .066, .099] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the EventDetection object to the ecephys module</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ThresholdEvents'</span><span >, event_detection);</span></span></div></div></div><h3  class = 'S13' id = 'H_901e' ><span>Storing Spike Features (e.g Principal Components)</span></h3><div  class = 'S5'><span>NWB also provides a way to store features of spikes, such as principal components, using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FeatureExtraction.html"><span style=' font-weight: bold; font-family: monospace;'>FeatureExtraction</span></a><span> class. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Generate random feature data (time x channel x feature)</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >features = rand(3, 12, 4); </span><span style="color: #008013;">% 3 time points, 12 channels, 4 features</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >features = permute(features, [3,2,1]); </span><span style="color: #008013;">% reverse dimension order for matnwb</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create the FeatureExtraction object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >feature_extraction = types.core.FeatureExtraction( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, {</span><span style="color: #a709f5;">'PC1'</span><span >, </span><span style="color: #a709f5;">'PC2'</span><span >, </span><span style="color: #a709f5;">'PC3'</span><span >, </span><span style="color: #a709f5;">'PC4'</span><span >}, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Feature descriptions</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrodes'</span><span >, electrode_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'times'</span><span >, [.033; .066; .099], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Column vector for times</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'features'</span><span >, features </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the FeatureExtraction object to the ecephys module (if required)</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >ecephys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'PCA_features'</span><span >, feature_extraction);</span></span></div></div></div><h2  class = 'S8' id = 'H_5aa2' ><span>Choosing NWB-Types for Electrophysiology Data (A Summary)</span></h2><div  class = 'S5'><span>As mentioned above,</span><span style=' font-weight: bold;'> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span style=' font-weight: bold;'>API documentation</span></a><span> and the section on </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format.html#extracellular-electrophysiology"><span>Extracellular Physiology</span></a><span> in the "NWB Format Specification" for more details on using these objects.</span></div><div  class = 'S5'><span>For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ElectricalSeries</span></a><span> objects as acquisition data, and use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html"><span style=' font-weight: bold; font-family: monospace;'>EventDetection</span></a><span> class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpikeEventSeries</span></a><span> objects.</span></div><div  class = 'S5'><span>The results of spike sorting (or clustering) should be stored in the top-level </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns </span><span style=' font-family: monospace;'>waveform_mean</span><span>, </span><span style=' font-family: monospace;'>waveform_sd</span><span>, and </span><span style=' font-family: monospace;'>waveforms</span><span> in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html"><span style=' font-weight: bold; font-family: monospace;'>Units</span></a><span> table to store individual and mean waveform data.</span></div><h2  class = 'S8' id = 'H_71fb' ><span>Writing the NWB File</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S19'><span style="white-space: pre"><span >nwbExport(nwb, </span><span style="color: #a709f5;">'ecephys_tutorial.nwb'</span><span >)</span></span></div></div></div><h2  class = 'S8' id = 'H_5f5d' ><span>Reading NWB Data</span></h2><div  class = 'S5'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. </span><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb2 = nwbRead(</span><span style="color: #a709f5;">'ecephys_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb2.processing.get(</span><span style="color: #a709f5;">'ecephys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'LFP'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electricalseries.get(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >    data.load;</span></span></div></div></div><h2  class = 'S8' id = 'H_88a3' ><span>Accessing Data Regions</span></h2><div  class = 'S5'><span>If all you need is a data region, you can index a </span><span style=' font-family: monospace;'>DataStub</span><span> object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% read section of LFP</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb2.processing.get(</span><span style="color: #a709f5;">'ecephys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'LFP'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    electricalseries.get(</span><span style="color: #a709f5;">'ElectricalSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >    data(1:5, 1:10)</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="803629CB" prevent-scroll="true" data-testid="output_5" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">5×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:5,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 732px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   -0.7839    0.0147   -0.4823    0.7583    0.8135   -2.0957    0.9011   -2.3509    0.1892   -2.0754
+    0.2895   -0.2133    0.5483   -1.4372   -0.0451   -1.1182   -1.9083    1.1331   -0.5946   -0.1609
+   -0.9435    0.2274   -0.7730    0.2097   -0.3885    1.7777   -0.5676   -0.0386    0.4206    0.3531
+   -0.4632    0.7073   -0.4644    0.3453    0.4681    0.4659    1.6279    2.0310   -0.5402   -0.8882
+    0.3153    0.0440   -2.5588   -0.8699    0.6555    1.3090   -0.0109    0.4978    0.2622   -0.3127
 </div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 5×10
-    1.1010    0.5408    0.5514    0.5499   -1.4147   -0.0581   -0.2408    0.2435    0.8209" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div><div class="inlineWrapper"><div  class = 'S20'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% You can use the getRow method of the table to load spike times of a specific unit.</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% To get the values, unpack from the returned table.</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.getRow(1).spike_times{1}</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="D324A527" prevent-scroll="true" data-testid="output_7" data-width="1107" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">19×1</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:1,&quot;totalRows&quot;:19,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 75px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">    0.1989
-    0.2132
-    0.2492
-    0.2631
-    0.2736
-    0.3616
-    0.3658
-    0.3735
-    0.3767
-    0.3834
-</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 19×1
-    0.1989
-    0.2132
-    0.2492
-    0.2631
-    0.2736
-    0.3616
-    0.3658
-    0.3735
- " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h1  class = 'S22' id = 'T_EB654BFC' ><span>Learn more!</span></h1><h3  class = 'S13' id = 'H_97B86954' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_C51B621D' ><span>MATLAB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ophys.html"><span style=' text-decoration: underline;'>Optical physiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_CD143713' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul>
+   -0.7839    0.0147   -0.4823    0.7583    0.8135   -2.0957    0.9011   -2.3509    0.1892" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div><div class="inlineWrapper"><div  class = 'S20'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% You can use the getRow method of the table to load spike times of a specific unit.</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% To get the values, unpack from the returned table.</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.getRow(1).spike_times{1}</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="2AAAF7D6" prevent-scroll="true" data-testid="output_6" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">28×1</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:1,&quot;totalRows&quot;:28,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 75px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">    0.0264
+    0.0591
+    0.0854
+    0.1181
+    0.1203
+    0.1328
+    0.1918
+    0.2230
+    0.2389
+    0.2487
+</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 28×1
+    0.0264
+    0.0591
+    0.0854
+    0.1181
+    0.1203
+    0.1328
+    0.1918
+    0.2230
+ " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h1  class = 'S22' id = 'T_6757' ><span>Learn more!</span></h1><h3  class = 'S13' id = 'H_2485' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_1b55' ><span>MATLAB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ophys.html"><span style=' text-decoration: underline;'>Optical physiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_6223' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul>
 <br>
 <!-- 
 ##### SOURCE BEGIN #####
@@ -1415,7 +537,7 @@
 % Define spike snippets: 20 events, 3 channels, 40 samples per event. 
 spike_snippets = rand(20, 3, 40);
 % Permute spike snippets (See dimensionMapNoDataPipes tutorial)
-spike_snippets = permute(spike_snippets, [3,2,1]) 
+spike_snippets = permute(spike_snippets, [3,2,1]); 
 
 % Create electrode table region referencing electrodes 0, 1, and 2
 shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
diff --git a/docs/source/_static/html/tutorials/icephys.html b/docs/source/_static/html/tutorials/icephys.html
index ae9c91794..a625db48e 100644
--- a/docs/source/_static/html/tutorials/icephys.html
+++ b/docs/source/_static/html/tutorials/icephys.html
@@ -67,9 +67,9 @@
 </span></a><span>    </span><a href = "#H_1d70"><span>Add repetitions table
 </span></a><span>    </span><a href = "#H_6075"><span>Add experimental condition table
 </span></a><a href = "#H_6d41"><span>Write the NWB file
-</span></a><a href = "#H_6b40"><span>Read the NWB file</span></a></div></div><div  class = 'S4'><span>The following tutorial describes storage of intracellular electrophysiology data in NWB. NWB supports storage of the time series describing the stimulus and response, information about the electrode and device used, as well as metadata about the organization of the experiment.</span></div><div  class = 'S4'><span style=' font-weight: bold;'>Tip</span><span>: This tutorial introduces many subtypes of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> type. For a detailed tutorial demonstrating how to work with dynamic tables, see the </span><a href = "./dynamic_tables.html"><span style=' font-weight: bold;'>DynamicTables</span></a><span> tutorial.</span></div><div  class = 'S4'><img class = "imageNode" src = 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" width = "1035" height = "464" alt = "" style = "vertical-align: baseline; width: 1035px; height: 464px;"></img></div><div  class = 'S4'><span style=' font-style: italic;'>Illustration of the hierarchy of metadata tables used to describe the organization of intracellular electrophysiology experiments.</span></div><h2  class = 'S5' id = 'H_3a73' ><span>Creating an NWBFile</span></h2><div  class = 'S4'><span>When creating an NWB file, the first step is to create the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span>, which you can create using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span> command.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >session_start_time = datetime(2018, 3, 1, 12, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'my first synthetic recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'EXAMPLE_ID'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, session_start_time, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Dr. Bilbo Baggins'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_lab'</span><span >, </span><span style="color: #a709f5;">'Bag End Laboratory'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of Middle Earth at the Shire'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experiment_description'</span><span >, [</span><span style="color: #a709f5;">'I went on an adventure with thirteen dwarves '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                                       </span><span style="color: #a709f5;">'to reclaim vast treasures.'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'LONELYMTN' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S9' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S4'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h3  class = 'S10' id = 'H_86b2' ><span>Device metadata</span></h3><div  class = 'S4'><span>Device metadata is represented by</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.general_devices.set(</span><span style="color: #a709f5;">'Heka ITC-1600'</span><span >, device);</span></span></div></div></div><h3  class = 'S9' id = 'H_9715' ><span style=' font-weight: bold;'>Electrode metadata</span></h3><div  class = 'S4'><span>Intracellular electrode metadata is represented by </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrode.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularElectrode</span></a><span> </span><span>objects. Create an electrode object, which requires a link to the device of the previous step. Then add it to the NWB file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >electrode = types.core.IntracellularElectrode( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a mock intracellular electrode'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'cell_id'</span><span >, </span><span style="color: #a709f5;">'a very interesting cell' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys.set(</span><span style="color: #a709f5;">'IntracellularElectrode'</span><span >, electrode);</span></span></div></div></div><h2  class = 'S5' id = 'H_083d' ><span style=' font-weight: bold;'>Stimulus and response data</span></h2><div  class = 'S4'><span>Intracellular stimulus and response data are represented with subclasses of</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PatchClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>PatchClampSeries</span></a><span>. A stimulus is described by a time series representing voltage or current stimulation with a particular set of parameters. There are two classes for representing stimulus data:</span></div><ul  class = 'S11'><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampStimulusSeries.html"><span style=' font-weight: bold; font-family: monospace;'>VoltageClampStimulusSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampStimulusSeries.html"><span style=' font-weight: bold; font-family: monospace;'>CurrentClampStimulusSeries</span></a></li></ul><div  class = 'S4'><span>The response is then described by a time series representing voltage or current recorded from a single cell using a single intracellular electrode via one of the following classes:</span></div><ul  class = 'S11'><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>VoltageClampSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>CurrentClampSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IZeroClampSeries</span></a></li></ul><div  class = 'S4'><span>Below we create a simple example stimulus/response recording data pair for a voltage clamp recording.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >vcss = types.core.VoltageClampStimulusSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, 2, 3, 4, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(15), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.stimulus_presentation.set(</span><span style="color: #a709f5;">'VoltageClampStimulusSeries'</span><span >,  vcss);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >vcs = types.core.VoltageClampSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'capacitance_slow'</span><span >, 100e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resistance_comp_correction'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(15) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'VoltageClampSeries'</span><span >,  vcs);</span></span></div></div></div><div  class = 'S13'><span>You can add stimulus/response recording data pair from a current clamp recording in the same way:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create a CurrentClampStimulusSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ccss = types.core.CurrentClampStimulusSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, 2, 3, 4, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(16), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.stimulus_presentation.set(</span><span style="color: #a709f5;">'CurrentClampStimulusSeries'</span><span >,  ccss);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Create a CurrentClampSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ccs = types.core.CurrentClampSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bias_current'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bridge_balance'</span><span >, 70e6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'capacitance_compensation'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(16) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'CurrentClampSeries'</span><span >,  ccs);</span></span></div></div></div><div  class = 'S13'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IZeroClampSeries</span></a><span> is used when the current is clamped to 0.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create an IZeroClampSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >izcs = types.core.IZeroClampSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 345.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(17) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'IZeroClampSeries'</span><span >,  izcs);</span></span></div></div></div><h3  class = 'S10' id = 'H_478c' ><span>Adding an intracellular recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> relates electrode, stimulus and response pairs and describes metadata specific to individual recordings.</span></div><div  class = 'S4'><img class = "imageNode" src = 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FgP8B7OmDGjVx32fPIzAA68ZzHlsQyhBLNOcBwep/YAbzrZJn2fGDD70UcfeYF0vcyE4vrYuoFZgF2nsfQHzMIbD97E3333nWOf0T4AxU8++cQiEwA6AF1C0E3vH2wBXtz2AIDfqFEjv8AqgLgeFi9e7BMk6vXLOAAmgLd9TVI3ICrz2fc6mAUcd7o27XmcxglpdDD7xBNPOI7FsGHDVNfhJW8vG+AYtmA/bv8MIAnoqQdMdQywd/311yeY32nM4aGsg1l4q8JL1l6302dc86H0mMW0xfDWdqrLfsxpPOBdmxCYdcpnL1t+xhTyevAXzGK8ne5ZslynPWYzcPPU1dvAOBWgAlSAClABKkAFqAAVSIwCBLPGf+FxGghmE3NFMA8VoAJUgApQASpABaiA3wokBZg1/lc3PfPQqFCBWZTptEl4h+lWnbzZnPK4HbOD2dGjR/sNlXUwCyAJrzJ7PQA/gNaoBzDHfj59+vTmlLFyMGMZzGId2UDBil0PObbQww3M2vPIzwmBWXgpA2DBS9rNyxJlYbpurKMsA7xe4UXq5I0I70dM64rxdRp//Ghg0aJFsihzD29cJ29m2Q99r4NZwOTE2jvgot1zNrFgFoA2kB8g6P2R8VCAWVlWQnv0fciQIZYxwJTe/kBdt7LtYBZe4k72AXgNu5DXBTxt4c3uFBLjMYv1lTFVsVs7/TnuD5j1pxyZBjar/9jEHzDr9gMbwHf8yAfXlxN8xnl4PePHDgxUgApQASpABagAFaACVCDUChDMGv/lx2kgmA311cLyqAAVoAJUgApQASpABSwKhALMYupIAC1sblAGgACehQCz9umHjf/lHeEBAI2bx6yeB9BDghTp9Tl48GBHGIJ8eGEPKAaA4uTxJsvWwSymxfTl9YYpm+F9h6k/AVThrYUA7zes9SnLlHvohClYAfmQButxOnmeAQRKb8ZYBrNYW1X23b7H+EE/X9PGIk+gYBbgCx6hyJsQmLW3yekzbGz48OGW6+fAgQPmuNvTA7hj/dFz584JTIs7dOhQRy9YrG0sAzwTYUP2suRnwCdpY2gL1l5GgP2gHJnOvoetYZpaaOxkY0gPr3Ss6SyDP2AWdo5rCfkBZHF9Yxple/3yM9IDHifkMR9KMIu6cF/C2rBu17o+BrjGACNlm+172Cim7c2cObNrGjuYdZpqGTYPW4IH6TfffGNOg431rt1CYsAs1lvGvdneB3yGHWBtW7TVl2dwIGAWNonx9fXDBtz7582bp7rpD5jFlPX2PuC6xnTxuGbg4QwdkUbao0yPsbJ76arKGaECVIAKUAEqQAWoABWgAkEoQDBr/Ncdp4FgNogLg1mpABWgAlSAClABKkAFElYgWDALoDN58mRx6dIlc00/AFgJw4z/0dULdbzgx5Se8F56//33RZUqVdQ5PV25cuXEmDFjzA3TyvoCswALLVu2NIESoBLW5ezevbtZB9Yp1cuVcYABrAW6efNmsW3bNvH888+7gmIdzH744YeOYE16wqG8P/74w5wWdcqUKaJVq1am+G7rMmIq5rFjxwpMCSw3eO7ZoTXSYYwQYhXMwnsZcEyOgb4HrOvfv78JTzD9K6bBRZ/1NDLuL5iFpyrWJ4ZuWFP2gQceEBgTBLc1ZmUdbnvYGuCqPcycOVP9KEDPi/RyXOUenrV2OKiv4Ql7cLp20B+AtN27d5vw88iRI2ZbMM0vAmzPyWMQ7QGomjBhgjh48KBp7wBhbvrCa1YGX2AWNo+pzbF+85w5c0xvW8AveO2i37oOehzwdteuXWa6SpUqOeqG9KECs9AEawUDXAPg6W3R47jnyDBy5EivMUJa+cMP2CjK+uyzzxynWEdaO5jFj0X0+hCHhvCqhzctpnnHGsVOa/3KdiUGzGJaZHu9+AwPXXiubty4UezYsUMMGjRIee3a0/sLZvEjCPwYBuO7Zs0ac/1jNyiMH2nIkBCYPXv2rOOPFeBpDO3ktYU9xs5+/4T9jxo1SlbHPRWgAlSAClABKkAFqAAVCJkCBLPGt4c4DQSzIbtMWBAVoAJUgApQASpABaiAkwLBgtl69epZit23b5/jtLx4wQ/vQhkwZavxP7zXBnilBzcwC1Ai4aeeHuB3z549pueqvXzAkIcfflhPbsYbNGjguCamDmbd1loF1IFHpD0A0iLMmjXLp0eavY1On/v27WuWFatgFjbm5GEIuDJixAizb/IPxg+enU46+ANm4SEJaGgPcjrTQMEsfnhw3333iRUrVtiLND/Dnpza6u8x6CLBe5MmTRzLgmfslStXvOqXNgbw5OQJC0gKcKoHeBdiemWn9sEDESAMwQ3MwvP266+/1otUcfywwKlceFEC4uoB0+y6TdkcKjD7zDPP6FWKBQsWmN7s9jbCE14GN89j/JDk9OnTMpm5B/R1mqLaDmad1sDV24BxKlWqlHj55Zdd4WygYBZrqzpNYwxQqQN42SGsge00Hv6AWWgAKKoH2KvbdYxZAKTtJgRmcd35gv26jm5xp/7qbWWcClABKkAFqAAVoAJUgAokRgGCWeM/8DgNBLOJuSKYhwpQASpABagAFaACVMBvBYIFs3KKWFmhm4cTPAN/+uknmUwEC2ZRHuCDU4DHFqaSNb4jWDZMawlvN3vAdMIoz55eB7P169f3Oo/0r776qr04y+d33nnHMZ+9Ll+fsU4iQqyC2WnTpjlqABglgakuGtaUdJoO1R8wC1COdV/dghuYBeiH1599HHCsR48ebsWJpk2beuWxl5HQ5xkzZpjlu3l5wxvQVwCAdKoDXqlOAVPnOnnN6teoG5iFt7lb6Nmzp2M7MJWvU5g+fbrX1LPoRyjALEA1vNz1ALtygnywQxnKlCnj2Ad4+TsFeOzbtbeD2YkTJ7pO8a7nxZTCAKH6GqyyzkDBLLyrAdH18hFH27Zu3SqLVXt46+J+Z0/vD5iFZk7XHNbLdYK98J6HFz1CQmAWnsnwGLe3K5DPmEqegQpQASpABagAFaACVIAKhFoBglnjv/I4DQSzob5aWB4VoAJUgApQASpABaiARYFgwSymDtbDxYsXHV/w69AH6YMFs6VLl9artcQxNajTeqVoA6ZHtgd4vAJAGN8pLJsOZqtWrWo5J9M6TW+rlz9w4EDHfDK/P/vq1aubRcYqmAXUcuqnXIdX1wtxgCMnsO4PmHXyotbLdwOzmPbWbX1UeM0CIjoFeFs79S2QY9KG3NYwhte1r9CnTx/HNki7sed18/TEOqzwNkdwA7OY9tYtYI1Up37D49gpYPpxp/VNQwFmMSU0xloPmIYYa0vb26iDWXgN288D8rqNQfv27b3S28EsplIGnHab2levD179r7/+ut5sMx4omMUUxU5gFt7LcoztlTh52PoDZt1+AICprZ08igFmMR03QkJgFuvRQhNdo0DjGHMGKkAFqAAVoAJUgApQASoQagUIZo3/zOM0EMyG+mpheVSAClABKkAFqAAVoAIWBUINZjFFadGiRb1epIcazN5xxx2WfugfsHasE5SA95YTWIJHJ9ZdNL5TWDYdzGLtU/t5fHaDX9IT9IMPPnDMBwAEYOnPhvVBEcINZvW1Ns0KjT+YBtSp37oXJ9artKeBR6acvtZtCll4Ly5btkxWpfbPPfec4zqf/oDZBx98UJXjFHEDs5jed8mSJa7TrwJQOXkaYv1ae98B8jCtrz9jizSXL182m4opi+1l4TO8cp0CpiVGcPPKBtxet26dV9YuXbo41gN7lz9ccAOzWNfTLWCtYKf2Y5ylh6TMCw9LAFun9OECs2vXrnUE/jqYxX3FqU1O3t1YM9vJy9QOZtFn9BfXAe4jyANY6DT9NOp2urcFCmYPHz7suK4zIKf9xzRoH65Vpx+z+ANm0V8n2Is1kJ20xL3vwoULqDZBMItps53gLur09/rC84CBClABKkAFqAAVoAJUgAqEWgGCWeO//TgNBLOhvlpYHhWgAlSAClABKkAFqIBFgVgFs/BudAuXLl0SWMfQ+I7gtWHK2vnz5wt4sQGeYrpNeEs6pdXBLLwasT6jPR1gL76Q6WHv3r0mmMMxeO86eX+iTglvZV5MJ4q2wUtM3/78808zSajArNu6vRkzZrSspYppn+HdZu8zgJKcfhcNSwjMAto4gXKUC1AET0YEAEoAX6fpZpHWHzArp302C3T44wvMbt++XWCNYycvTtQPr2m5BqssGlDUyS7gCWgPgJP6uCIu4TXSunm+wsbs66Vu2rRJjBs3zqwCnolONoY2p0+fXmzZssVMBzsCMIYHMM7ZN11fNzCL/rqFpUuXiixZsniVC33gqSl/YPDrr78KQFyntGhTJMGskwcs2oQxaNOmjThz5ox53WIt7WrVqjnCVTuYxb2md+/eaip39B/AHNcNrjn7OODeZQ+BglmAYDfIDM9dTK8s1y3GPQo/nLG3A5/9AbNIB9gKO0TA/deXnTVu3Fh1LyGPWdiMU9swLT101APup19++aXXNWZfG1jPwzgVoAJUgApQASpABagAFUisAgSzxjeBOA0Es4m9KpiPClABKkAFqAAVoAJUwC8F4hHMouNuXoHG9wZz/VKABEzd6bSWKdJg08EsQBimepXn9H3mzJnNaXABde68804B70tMS4sAeFC2bFmvfAA98EYFrMOakoClACapUqUywSDgIDaAS3j0IoQKzALUAWzofZBxAOOHHnrI1A8QG1PCynNyD+/iRYsWmW3Cn4TALDw7sc6jzG/f58mTxwQ78LSGLvbz8rMODidNmuSYLlgwC/DqttYrxgPa6EB948aNjloCuA0bNswEeZjeG3GML/SUY4s9wClAPsLy5cvNNUBlf/V9tmzZROXKlQX6h/WOoZlcOxP24wbhUAagKOwd+jqBQKTBOp66N2xiwCygJTxL9XbLOGA+4DygI2A/+i3P2feRBLMzZ850tUFcDwCVuC/gGre3W362g1n8qAPXMfJivWJ4geIahEep0/TGsD97CBTMIv9rr73meP2inYDi6AfugW5QH+n8BbNICy9gaWdO/UIa2N+ECRNU9xICs7jW6tWr56U1xgLT2QPc/vHHH2Lnzp2O1xfup88++6yqjxEqQAWoABWgAlSAClABKhAqBQhmjf/w4zQQzIbqKmE5VIAKUAEqQAWoABWgAo4KxCuY3bx5s6OnlfG9weslv9sxHcwCEDz55JOuoMNeBmCsDPBsdAKOgAvwBgMwc/NixDqsAF4IoQKz8Jx0gsX2Prh9BsjBVKkyJARmke6jjz7yCcHd6tKPJwWYRVsBnd28dgG0Ro0ahWRmgF1g+mSnaWkx5oCQ2ADv9b7IeMuWLc1pblEYyoJXplNZMr2+h+4yACj6+pGBns8pXqVKFXHu3DlZnOsas748ZpEZU4UD8jrV4e+xSIJZTMXuC3L70wcdzJ48edKElXo+TPON6XkBa/XjMg54aw+JAbMHDhzwqlvW4e8+EDDrT5m478j7GfqYEJhFmsWLFzvCY0BXAHJcX25TQ+Oc0zTLKJeBClABKkAFqAAVoAJUgAoEowDBrPENIE4DwWwwVwbzUgEqQAWoABWgAlSACiSoQLyCWXQcEMkNiBnfHxyhiH5cB7MoD1Niuq0DqudDXAezmNrz/vvv9xvqyrIAbrBGrQyhArMoDx6nTrBY1u22h8cn4KEe/AGzgI6PPvqoK4xyq08/nlRgFn175JFHHKcoRnsAgzD9qwxHjhxJFMzD+OIHBHoIpCwdzEJfAL3EQlH7+rmJ8ZhFP+C9iKlqnaZ31sfSVzySYBZ9WLFihQn6fLXR1zkdzL7++uuuU2O7lfHjjz+iGZaQGDCLAjDluNMarW5124+HEszixw7wMNeDP2AWto2poAO1bVxf0J+BClABKkAFqAAVoAJUgAqEQwGCWePbQ5wGgtlwXDEskwpQASpABagAFaACVEApEM9gFp18/vnn/fIkdJp60w5mUR6mJsY0sm5TwhrfS0zgi2mP9QA4i6lo4S0n0/ja58yZ0zK1LMoKJZjFGpSYltdtnU+ntkGj++67T2AtXD34A2aRHmttYhpefzSAN7G9DUkJZgHCMB21vQ3yM6abRRoZTp06ZYIjNy9ImU/uMb2wXJNTliH38CjEuKRLl861fpSjw3/kBcB67LHH/AZxmEq5YsWKjh6FiQWzaAc8sjGlra/pin15BUcazKIPWHsaINFXO3EfcIKFALMbNmxAMaJBgwYJ3iukTeCe8emnn5r57H8SC2ZRDqaoxv1E1hPI3h8wC0/thO4jmBUAayrbgz9gFnkwHTqm8U6oHtk3pEtoWnN7W/iZClABKkAFqAAVoAJUgAoEogDBrPHfd5wGgtlArgSmpQJUgApQASpABagAFQhYgW3btpngEi/O9Q3QAev3yTBnzhyvdJiGd/DgwTKJuZdQRi8LcUAHeJzKgHVTMX2vng5phg8fLpOY+3379pnTWOrpMD1l3bp1Lel8ffjkk0/M9TixfipglPHdwQQumP6yatWq4uOPPza9IPU6EMd6mU4BkHXs2LHmurIAbLJMlIu1QJHXrgvKATibOnWqwLSxaIt9/VZAPazDCQD52WefeVWN9U/l1Md6W7FOpAw///yzCY7184iXKVNGJlF7wI5Zs2aZ7UG9TiAQfUMfy5UrJ0aPHi1+++03lV9GWrRoYa6zqteJsd2+fbtMovaAs5MnTzahIgAWpiOFbthDk6ZNm5pTCUsdZZkY82bNmqlyli1bZkIaeR57aNOnTx+VxikCjz1AGz0f4vCCxXq/eli9erWXzct86F+vXr305ALT4GIqXwBdp3U7AfNh4/CetnuoWgoyPuAHAPD4xrjpOkEr1I0fDcDr2SksWbLE1BF62MEhPFlRHqbIho3q08rqZcHTENeH7C/2WJcY15I/Ae3HtQwtAAUl4JTTd3fq1MlcaxRQT68Dbe7Xr5+qAnFoqaeBhrhmZXj44YdN29HTwEvU7p2JqX3Rdz0d7Ao/mHAKu3fvNqcvx/WFawBlor2ov06dOuYUuwDQGBN9gx1LT2j8iGH8+PFmHbjG7OAe8Bp9vuuuu8SqVaucmmEeu3Dhgjllr952xP0dE6xri7Va0Qb5oweMCeBzhQoVxOzZsx3LB5jFfQcB1weuE3sbYEtTpkwx8+O6lVrA9qS9Y41up4AfEkAvvUxcn/axk3kXLFhg3vvRb9kPWR/uIXhuYSpq3Gdxv2WgAlSAClABKkAFqAAVoALhUoBg1vhPPE4DwWy4rhqWSwWoABWgAlSAClABKpCsFLhy5YoJw+C1BcCINTkB3jD1KgJAEiCVvv3+++8+NQKgBXCYO3euWSZA2dKlSy0A2qkA2RbAacCxZ599VgwYMEAAfgPYAV4mZUB70A/ArqFDh4oXX3xRvPDCC2LIkCEmsFm3bp24fPlySJsEbdeuXWv2eeTIkWY96DtgMQLWO9XHAnG7p25IGxTCwi5evGhOdTxhwgTxxhtvKC0XLlxo+bGDP1VCp/Xr15tr9GJt2/fee08ASuvrwbqVs3//fnNMAXj79u0rBg4cKN5//32zvIRs263MQI8DKKK9uN7Qflwr+LEFAry27WOMz9IGAq0rFOlx7WH89A1aw4MW28qVK8XevXvNaxRtBVQ13kVYNsBMQGA9ABIC1uIaB7x/7rnnzGt//vz5YteuXUkCEaEr6sI9cMyYMQL3H0zbjL4iwCbs42H/IYbTfRI/xkGATrj/wcYAowFR9R/3mIlC8AdaApoDJuMeBdvGdTZ9+nTznoL7MgMVoAJUgApQASpABagAFQi3AgSzcUpljW4RzIb76mH5VIAKUAEqQAWoABWgAlSAClABKkAFDAVat25ten7CI1lu8KoFfLWv/dqyZUtHL3N40Yb6hwwcHCpABagAFaACVIAKUAEqQAWiSwGCWYLZ6LJItoYKUAEqQAWoABWgAlSAClABKkAFqECMKQAPTH1qcgBZbJj2t2TJkuaa1f379zen/3VbK7lVq1Yx1ms2lwpQASpABagAFaACVIAKUIFAFSCYJZgN1GaYngpQASpABagAFaACVIAKUAEqQAWoABXQFDh8+LC5Lq4EsoHusa4qpjxmoAJUgApQASpABagAFaACVCC+FSCYJZiNbwtn76gAFaACVIAKUAEqQAWoABWgAlSACiSBAp9++qnImTOnZd1YfwBt5syZRa9evZKghayCClABKkAFqAAVoAJUgApQgUgrQDBLMBtpG2T9VIAKUAEqQAWoABWgAlSAClABKkAF4kKBr7/+WhQvXlykTJnSL0CbP39+8fLLL4t//vknLvrPTlABKkAFqAAVoAJUgApQASrgWwGCWYJZ3xbCs1SAClABKkAFqAAVoAJUgApQASpABaiA3wr8+OOPokePHqJEiRKmBy3WmdU9Z7NkySIKFy4sWrduLdasWSP+/fdfv8tmQipABagAFaACVIAKUAEqQAViWwGC2fgFs7/ii1+rVq3EE088wY0a+G0D7du3FyVLlhTly5f3Ow9tjNcYbYA2QBugDdAGEmcDzZo1M5+7derU4XOX/6/RBmgDtIE4s4GHH35YNG/eXNSuXVtUqlRJVKxYUdSsWVPcfffdomPHjhzvGBvvRx55xHxmlypVimMXY2PH/1MT938qdaNutAHaQCzZQKNGjczn9J133snnNJ/TUW8D+JHmfz/cHBO/iDJ59uzqfwMrB5h7A1RTE2pAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDYQYRuYb9TPEEcKnDf6IlLmuV2kLFSVGzXw3wYMm4HtpE1znahfnhs1oA3QBmgDtAHaQDhtoEiea1+C0mdOI/KWzsyNGtAGaAO0AdoAbSBKbSB3iRvVj70b5k8puFED2gBtgDZAG6ANRI8N5Mt0bemI9HlvFzlqPMSNGkS1DaTNXkD+Xzksjpgku2IocBRwLWWldiJl/d7cqIH/NlCxrXlTyHzDdaLfI9yoAW2ANkAboA3QBsJpA3dWvgZms+XPKKo8UIAbNaAN0AZoA7QB2kCU2kD5FnnN78opjHctg2pyowa0AdoAbYA2QBuIJhuocUtK8zmdrXJbUfLZJdyoQVTbQObidSWY7U2aGV8KEMwSxvoPY3WtCGYJpAnkaQO0AdoAbSDJbIBgljCaQJ42QBugDdAGYsMGCGYJIKIJQLAttEfaAG2ANmC1AYJZwuhYAvIEs/EFY/XeEMzqsJFx/yEtwWySvYwPpwcWy6aHH22ANkAbiA0bIJiNjZfxhCYcJ9oAbYA2QBsgmLW+ACcQoB60AdoAbYA2EE02QDBLMEswq+NBxiOlAMEsYaz/MFbXimCWYJaecrQB2gBtgDaQZDZAMMsX/YQ9tAHaAG2ANhAbNkAwSwARTQCCbaE90gZoA7QBqw0QzBLMEsxGCkWyXl0BglkdNjLuP6QlmE2yl/H0ZosNbzaOE8eJNkAbCKcNEMzGxst4QhOOE22ANkAboA0QzFpfgBMIUA/aAG2ANkAbiCYbIJglmCWY1fEg45FSgGCWMNZ/GKtrRTBLMEtPOdoAbYA2QBtIMhsgmOWLfsIe2gBtgDZAG4gNGyCYJYCIJgDBttAeaQO0AdqA1QYIZglmCWYjhSJZr64AwawOGxn3H9ISzCbZy/hwemCxbHr40QZoA7SB2LABgtnYeBlPaMJxog3QBmgDtAGCWesLcAIB6kEboA3QBmgD0WQDBLMEswSzOh5kPFIKEMwSxvoPY3WtCGYJZukpRxugDdAGaANJZgMEs3zRT9hDG6AN0AZoA7FhAwSzBBDRBCDYFtojbYA2QBuw2gDBLMEswWykUCTr1RUgmNVhI+P+Q1qC2SR7GU9vttjwZuM4cZxoA7SBcNoAwWxsvIwnNOE40QZoA7QB2gDBrPUFOIEA9aAN0AZoA7SBaLIBglmCWYJZHQ8yHikFCGYJY/2HsbpWBLMEs8nUU27MSwXEN/OfN7dZ7zahHSTCDma/e5fScPSL+ahhIjQMJwBk2dEJmAlm+aKfsCc6baBFz5pi/Jy3ze2lkT1ELI1Tm2caqrY/M/QxS9v7jX1GnWv6ZGXLuVjqI9sanddNvI8LwSwBRDQBCLaF9kgboA3QBqw2QDBLMEswGykUyXp1BQhmddjIuP+QlmCWMCmZwqQvZvUQMvy4/cuosoM5o5uJMyf2ivGvFYuqdtlB3087v5ISioXTO0d1W+1t5+fohJbJYVwIZgkX4h1kRHv/nh/+hDh8/KDo0PcuC6R8fczT6pm2+6ftlnPR3qd3pr2h2r5u67eWth85cUid6zP0Ucu5aO8X28f7ZaRtgGDW+gKcQIB60AZoA7QB2kA02QDBLMEswayOBxmPlAIEs4Sx/sNYXSuCWcIkglkRTWB254aP1AvU99+sEtX2STBLuJkcQGqo+0gwS9AQadCQnOtftnaxesZ26dfaAikJZnltJudrg313tn+CWQKIaAIQbAvtkTZAG6ANWG2AYJZglmA2UiiS9eoKEMzqsJFx/yEtwWxUg69QAwGW5wFp0eox+9uFX9RLY4JZz3jRdqlFvNgAwazzy29CAeqSFDZw9vxp9Yy1g1lMXyzDtr0/WKBtUrQtmDp8ecwePLJfdkv0HNwxpvoVjCbMy3tKKGyAYNb6ApxAgHrQBmgDtAHaQDTZAMEswSzBrI4HGY+UAgSzhLH+w1hdK4JZgll6zEaVxyzBLAFkvABI9sPZlglmCQtCAQtYRuLsyBeYhaYNHytjbrU7Fo8pgOkLzNZ+qITqF+0mcXZD3ZKvbgSzBBDRBCDYFtojbYA2QBuw2gDBLMEswWykUCTr1RUgmNVhI+P+Q1qCWYLZOAOzb3Z1hiF2SJQYj1l/y7bXJT+/2S3htiUGzA7ofJ0YaGyyHre9ma5Lwumc8r/x6HXC3v/ETGVsL8OpLqdjTvU7peOxxI0vdUs63Qhmk+8LfsKd8Ix9nU4lRY0ORf0CqQmB2UDHqGq7gqJup1I+667WrlCCaQKt157eF5i1p+Xn8NghdY1PXQlmrS/ACQTiU4+36qURb9VPJ/wZ37cb3+RXOq+yal0nEp235jXd326UWQyunTJx9RtlDLsza6Lzyv4ktg9DG94g3qx7faLrT2y9g0Kgu+w79/F5/cf6uBLMEswSzOp4kPFIKUAwSxjrP4zVtSKYTRBmEVgkHbBIjNaAjd/Mf970ev3t4klzur7Ll34TP+/7TqxeOlwM6eHcfn/AbGLLlv0Y9lQ6sfyTV8SpYzvF31cum227cuWSOHl0h1j1+SDxVndP275dNEDs375ETTeICNLh2PdLhio7/eHbSeaxXRvniXf75hJ7Nn8mUOa///4rTh/fLRZO76zSoh3ThtYUuzd9Is6e3C/++ecfMx3iyD93/H2WtLLdcj/oievEktm9jHZsF3///ZfZtvOnD4otq6eJif1KC3/AbDAajnulsNi4cqLZL1n/n3+cN8d2w4rxYuRzOXy2X/aDe4+dUYvIa0EwG58v7wllkm5cAWHfmvyS2LxrvTh38Yz5bLr6z1Vx+PhBsXL9l2Kwcc4+Hh8sGCvWbllpppV/DhzZZx7738JJZvpHXrnX/Ix00z+dYClj/tKZ5rkV65cIgFbUj7ou/nbBLA7TBc//coZ46MV7VD6k+XbjV+K3P34106Ct329aJto+00ilke38fOU8s/w1W1aIFj1qeJ1HuhY9awqcR/sWrZhnSeMLzM5dMk31q92zjS35ZP3cJ539UuvY0ppgliAi1qGFvf1bFk4VB9Z9Jfas/ESMbVNc7P/uc/H35WvfJc/8vEd8MayXBR4C2i6f+Jo4sP5r8fu5U+bz7K8/fhNHtq0W6z4cLd5pmsuSXq9vUsdyYvNn7wuUe/XKte+Sl349b+bd9OkUMaZ1Ma+8HzxZx2wf2vhp/4fFB11qiR1fzhbnjx8y67769xWzvOUTXk0QtAJmfj99iDiyY6249N/z+rLxTEbb1380Voy85xav+tH+HxZMMtuw99vPTBC8cFBnsXv5x+LX0yfMNqAPB39YIZaO7OOYH2UAwi4e2kP8vGWV0u2fq1fF2cP7BcpdPLS7a17kT2rd9XFjnPe9WLIBglmCWYLZSKFI1qsrQDCrw0bG/Ye0BLMEOzHsMQtwd+LwFvMLktufX88fFzOG1/ca54TAbDBlA35NH1ZH/PHbtRfGbm378/dzYvxrxcy2HflprVsys48SqP164doXQiQ+e/JHrzyAuUg72PAc3rnhI6/z9gNIM7RXSi99hj+dQfxyeKs9ufr81+U/VBwROxBGG4LR8P3BlUzgbKnE9uHyn7+Kjye182q71Ir7yENIjoH3GBDMxtZLeUKU6BovQNFvNyy1PQ28PwJeNulSQUHIHfvd/1fYf2i3me71MU+rgnb/tF3lhQ0cP3VUnVu8cr6K2yPnL54VzbtXFx9/9T/7KfX5ivFDp4deaGYp/9ffL6rzj7/WynJO2mDn1+9XaS78dt6SxheYPXLi2stsZO4z9FFLPlk299Fl5xyP6BkPglkCilgCFP609bczv6hnybljB1RcRk4d2KmAIcDqL/u3yVOOe8DK2X2aqTyyDdO61jeBr2Om/w4Ckn464BFLXkBQGU4f2iOuXLJ+55TnsAfkHN2qsCW/rH/ei23Eb2ev/Whbz6PH/zDWnf/41fZe+S+c+Fkl++HjiSruFPlxzRIxpEFGSxnw6t23aqFTcssxwOcRzXJb8qL9kdBd6sY973mxZgMEswSzBLM6HmQ8UgoQzBLG+g9jda0IZgl1YhTMvt07tbj0p+dFJr7l/H7xlOFNuUqcP+N5CYnj8BSd8FoJy1j7ArPBlj325YLKwxT1IwDSHj/0g8CvfPVw4exh06sXXqjnTv2knzL7h2M7NsxRbdfBrCXxfx+++fhFM+3W1TMsp6EBPHcBW69e/dtyDt7FOkDDtMEnft5sSQMQe+zgRlNjy4n/PtjBbDAaDn7yOkOvs5Zqjh3cIA7uXi7sQBh6Du3pDb/0/jBOfaLJBghmo+eFO+FH7I3F+DlvW54NAKHrtq0yvWUtJ4wPsxZNURDyi28/FsdOHrYk+d14IYxjX69ZZKbzF8zqhcBjFuW4BXjynjx7wvw/RE/zw861qm2wQ4LZ2LNF3j+Sx5gRzBJSxBqkSKi9OpjVn0syvmJSPxMUwpv0svajIZyHx+zhrd+LC79Yn6f/Gt8zpzxcSQHGoY0yiT+N57Meju/5QRzatNILtOK73Dt336zy6mBWzw+v233fLRIXfzmiHza8Z/d5TRE8uVMFLyj856/nxNGd65X3ql6IHSzrYFZPB+3++vN3/ZAZh2esrjs8jPUAAHxwwzcmSNaPI75uzihL3kjprrefcd73YskGCGYJZglmI4UiWa+uAMGsDhsZ9x/SEsxagFQ0vbxnW3zDpB++nay+1/xjvPgEkOz/mCfP7FFNLeD24J4VlrH2BWaDLRt1yQAgunzBywKwE2OKqZW/X2J9sYy2yPFOaI1ZO5iFh+wX/+suls59VuzdskiMeDarWDC5vaze3AOyjnrhFlUHvGEP7bFO6fjp+53U+c+mPmLJj+mS9bVhv5r7nNdLZjuYDUZDeDjLgCmaPxhSQ7UNUyMv/OBRedrco/9SP+491wC1iE4tCGaTx8t8QpvwjPP2fZvU/R/T+tZ8sJgCnPf2rCXg6SrD2QtnRPX2hdV5jImvNWYDAbOYurjjC3ebZdfuWNyc1ljWK/erNy8XTZ+sbKZp9Fg5obcd/xtU71BEtY1gNjz2wuuQugZrAwSzBBSxBCj8aasdzMJDFlPyLhv/svhx9RfKA3XLog/k40xgCl4A28F1UimIOPeF+y3g9ufN36pzAJ0y4LvczJ6N1TlM0YtpfPWgTwlsB7PIv2LS6yo/pgjGdMx6+GrUc+o8yj99cJc6jbYvG/+KOg+NPh/SzQJuzx750Zw6WOpnB7OAylMeqWyWAQ3gZYtyZTi+Z5OlfEydLMNPa5daysb0zShPBsBuXddI6S77zj3vebFmAwSzBLMEszoeZDxSChDMEsb6D2N1rQhmCXRi0GMWHqn4kiYD1mt1glDT364tk5j7mcMbqHRuYDbYsgFG9aCvDyvb2P/x68w1X2W6wz+uVu0KBMxiLV2sMyvLlXsdDGPdXXigynNyD4h96rjnSyu8jeW53ZsWyKaJMyf2Kqgsz2MPzfWgg9lgNVz60TOqaHg/6/XKOLxnES79ccFcS1ge5957rKlJdGlCMEtQECwoSM75L/11ybz348/ACX0V2JSadO3fzjwPT9XjJ4+IVr3rWtKEAsxizdj7n65nKRf16AFesrUfKmFJ0/ppz4+OkLbVU562EczyviBtmPvosgWCWUKKWIMUCbVXB7NYKxbrzNrzTHywnOW79uqZb3ulQZ7/PXWX/ugTs5+5x0z3zbiX1HF419rLx2d4zyJg7VesVSvT2MEs1nuV5/T9sd0bVR0HNy5XaQCB9fDN2BfVOT0/1q/Vw5LhvVU6HcxCo3db5FfnZBnfT3tLZcd0y/I49lcu/6nOLRr8hOUczss2Au5i7dxxbUuaaSKpu95+xnnfiyUbIJglmCWYjRSKZL26AgSzOmxk3H9ISzDrCH0IMqILZNjHY8GUDurLzpUrl8RAw4vSnkZ+xvS7Mnwz/3mVzg3MBls2AKUMV/7603H9VrTtfyMamWvAfvv5QDF3XEvVrkDA7Obv3lf5ZH8HGNAXmsjgy5v0wzHNZTJz/06fTGZ5504fUMd1T1pZB/Zv90ol0D8ZdDAbrIaz37V+yd+7ZaGY+mZVi0c01tCF97HeJsapRyzYAMFsdL10JwSJrfHYc2CHfOyY0/8On9pPeaXKsWzWtarFG1Uexz4UYHbukmkW4CrLv6S9iJ0y713HNH9ryxk88sq9Kg3BbGzZoRxz7uN/3AhmCShiCVD401YdzG5bPMMLGqKMzwY+rp61f1++JN6qn84xHdLCW1QGTOGLYx8930oeMvf7v18spndvaPEMfbtRZjH8rhxe5epg9uqVv8SolgW80qCOuS+2VnVg2mQcw7Zy8hvq+K+nj4sh9TOoczKN3Ovr58ILVx7XwezG+RPUcXkee71+VKifO7HXsyTQpV/Piy9HPG3A3XyWNKPuvVW8qXkgI38kddfbzzjve7FkAwSzBLMEszoeZDxSChDMEsb6D2N1rQhmCXYMyBYLMENv48qF/dUXLkQwba7bhukCZdj03Xuqr25gNtiyVy1+U1ZnrMm6QdWnt99XPBAw+838F7zKnzKwgqofEX0KY3u9A7tcZ0k7bWgt8WY36zFf+Q/t/Vbl18FssBoCuv7991+qbBmBd+zODXMFploe9lQ6r77b+8fPsXdtJ4cxI5iN/xf5hDXhG+NpC8bJR4Jlv//QbjH90wniyf4PiGrtCingaR+LUIDZQROfdywfXrIy9BvbxzENvG1lIJgNn53Yx52fqXVibYBgloAilgCFP23Vwaw+RbCeV/cGxTML0+u6bVhfVoatn0834SOgK6CqPcA7ds+KBeZUwnZQKevXweyxXRssMFOmwR7r0uph9H2FzLQH1n2lDu9YOsc1P8pYM3OYSnt463cqrQ5ml458Rh3X68fUxnoYXDulSrd6xlD9lIoDBKPOGT0MSK2ll+VGUnfZBu55z4s1GyCYJZglmI0UimS9ugIEszpsZNx/SEswS7gTg2B2x4Y56gtOIBGARAl+3MBssGXDi1WGXZvmq/pkvQntAwGzc8ff51W+3dsUHrS+6sRUxzIsmva4eH9wJfnR3APeuuXftnamSquD2WA1RH3/G9FY/HX5d1W+PYKprDFl84TXSri2z63dPO4+ptQm/NoQzBIQJBYQMF8BUadTSbFq49f2R4Ll83nDcwYeq06ANhRgtu87TzpCVx3M9hzc0THNReOltAwEs7wX8JqOfhsgmCWkiDVIkVB7dTD78WsdFEzU8+1eNl8+qgLa63Dzw+fuFX/96fu7HNalnfJwJUsbdDC7e/nHlnN6GxGHN68MH/W9z0yre6u6TcEsy/liWC+ZXfx29qSqSwez815so47LfNiPb3ebyouIDlqHNrxB7P/uc8t5+4c/zp8Wq94faMkXSd31vjHO+14s2QDBLMEswayOBxmPlAIEs4Sx/sNYXSuCWUKdGASz+7Yttn+38evz8UM/qPF2A7PBlr11zXTVlu3rZqn6/AVegYDZaUNrepX/3qCKqn5E3njUN2i6eO6YSg+4OvOdhuozIlgP163tG1aMV2l1MBushrK+8a8WE9DQF6DFdMqYklnm4d59vKhNdGhDMBv9L+IJS6J7jABc350xSBw4sk89g5wi67d9L+o9fJsFkIYCzD71VidLmdJedDDbY9CDjmn8AbOdX2vlmPfJN9qqbl747bwlzTvTPFM3rtv6reXckROHVL4+Qx+1nJNt5z66bZ7jE7nxIZgloIglQOFPW3UwO7PXnY7Q8ac1X6rnRiARQFG9DZM7VRC7vp7rE9BiPdb5Lz+g8ulgFu3Qy7PH9bVcP361vZn24IZvVJM3zBvvM//nQ7qptL+fO6XS6mB2zjPN1XG9fl9gFukAar8e/bw4dWCnqsMpcnDDMgEPY+SJpO563xjnfS+WbIBglmCWYDZSKJL16goQzOqwkXH/IS3BLIFODILZ9cvHqu81R35cI97undqvTV+T1A3MBlv2yoWel6OH9qz0aV9vGdMGD7CtjxsImP1gSA2v8se9WkRpg4hcN9YJygG66lM9fzS2hZj0RmlL/pHP5fCqQ5a1e9MnKq0OZoPVUJYv9/DanTm8gfh+ydvi5FHP+oKy8tMn9ri2UZbBfXRASY7DdYJgNnIv2Ak34k/7Vr3riqHvvSpWrv9S/HHJ2zPn1VG9LCAyFsAsAKyTrT7ztmfNP4LZ+LNlpzHnsciPM8EsAUUsAQp/2moBsz0bO0LHHxZMkl+zxNEd68TIe27xa3NaMxZtwjqvs5+5R6ybPdIRVJ75eZ9qhw5mD6z/Wh239w116WF6twZmWkyVLIO+bqw9Pz6vmjpIJjXbJdOEAszKsrAf17akWDK8t9j77Wfirz9+U3XKyIJ+Hc22R1J3vb2M874XSzZAMEswSzCr40HGI6UAwSxhrP8wVteKYJZAJwbB7JI5veX3GAGQ6csrdHL/suLzGV3MqXHf7XuzGm83MBts2Z9MedDStv6POQMxeINiHVVMyXvx3FHxdq9UZtuCBbOAmChThjmj7lZ9tkOxyQPKyWTmfuqbVcWw3mksxz4cc49r/jMn9qq0OpgNVsMxLxUQH427V2AN3fGvFfOqH+veAnrrwRdAtvebn51tkrokjS4Es5F/0U7YEZtjAO/Xrv3biSHvvSJGTh/gBS9rdCgqBk7oa3kGfr5yniVdtIJZgFYZnnrrYUubpb2OmDFQJhEEs7Fpw3IsuY+d8SOYJaCIJUDhT1v9AbNfje6rnjeY4lefptdex9TONUzoiKmLx7YpYQLGCR3KCHiwYg3byZ0qesHVcQ+UEj9vWaXqQGRM62JmOh3MXjx5VAyq5WyDs55qask/ulVhMz+mL5bhxL4tXnXr7dc9VA/+sEKlDQbMwvt1pgG8MU3yV6OeU2XKet+se71YNPgJy/8q276YaaaLpO6yfdw72xt1iV5dCGYJZglmI4UiWa+uAMGsDhsZ9x/SEsx6QR/CiaSBE8Ho/L8RjeT3LXP/xf+6O47jm4ZH6qU/POu5bV83W6VzA7PBlm2fShig1qmvyz95VfXhwpmfVZpfL5xQx2cMr6+OyzL0804es0h3/oxn2sJjBzd6lSHL2rtlkarrr8t/iEFPXBv7Ez9vVsePHdzgmH/2KOuXYR3MBqvh4f3fq/oP7F7mWP+UgRVUGkTeG3SHYzrZV+6j/7pOLmNEMBs7L+QJT6JrrLBuqx7cPEsxhbEM9ml9z5w/JU+JXoMfsgDQ18c8rc7t/mm75dzxU0fVuXBMZXzq7C+qfKyPa7e9Wh2Lid0Htqs0BLPRZZv28eLn+BkfgtnofRlPUJK4sfEHzM55toV63iCydGQfL8AI/YfdmVVc0tZO3/XNPDPdke1rVP5Dm1Y65v3gidoqDSIfPFnHTKeDWRz/5I1OjvkBUmU4fXCXSrNwcBd52NzPfeF+dU63GQBlPXz73gCVLhgwO+vpu/VixYweDVW5ev2YwlgG6RkcSd31tjGeuGuLukVGN4JZglmCWR0PMh4pBQhmCWP9h7G6VgSzhDkx6DGLKXhPHfOs13L5z19Nj1gd7MBz1L7W6fRhddR4u4HZYMuG9y5gqAzwhp3Yr5SqF22EF+/fVy7LJGLt1yPV+XOnflLHl859Vh2XffMHzC6Z1VOVgcjar9/1Wiv2m49ftKTR18O151+1+E2LV/KYF/MLvZ0oSAezwWq4Zuk7lrY5AWodbF/686IY4GMtXKkd94Sz0WADBLPx88Ke8CVpx7Jx5/Li76t/q+fDlt0bRPX2hS0Q8+6uVYQOOcfMesty/tjJwyq//Vwkwey2vT+odl3+67K4/+l6qt1YU3f5uiXqPCIEs0lre7zWk6/eBLORedFOwBE+3f0Bs2/WSSUAO2W4/MevAh6x+rhgemLd4xRp4cWKNOs/GiOzmvvZfZpZ8iINQKgMl3+/KOBJiuN2MHvx1DHD67aCJf/yia/JrOYenrmybUMbZRLIIwO8bt9/vLo6j3Tj25W29A+zTcHLV5YRDJh95+6bxT9/e/5XObz1OzHY0FOWjf27LfKLX08fl00Uy8a/bJ6PpO56+xgP3/VHbUOvLcFsYGB2wPxdYtJXP5lbl0kbhRPUrPjyV2L80mtpkPbOwd86pmv1zmpV1pglP4oyfb90TOdUR3I9lrl4XWGAQ2y9IwUQWW94FCCY1WEj4/5DWoJZL/AVDS/u2YaEAdK0obXUlxkZ2bP5M4E1XndumCv+umxda+7wj6stY+0GZqF9sGW//2YV2SRzf+XKJbH5+6nm1Lybv3tfXNVeLP/5x3nxbt9cqm3HD3lezuJL4rnTB8TeLQvVeX/ALKZPPnnU41mDRvxyZJuxRutQserzQeLIT2st7TtxeIvQ19+FBjvWf2hJg7V8v100QGxYMV4AhNuDDmaD1XBiv9LG2rdXVRWIb1s7Uyz96BlDw+fFwd3L1TlEdKjMayfha4caRVYjgtnk+4KfcCf4scdasno4fOKgmPrxaIFpfj9cPFX8Zrw81kObPg0U4IT+ew7sUKfxjD1+8oj47odvzDSRBLNvv299yfyX8eMtePuivxc1byTZeILZ4G2J1yM19McGCGZD/xKdYCKymvoDZjFG/+vdRD5y1H7/d5+L7z54U2Ad17/+tH7XPrJjrYKP7z1WVfxzVfsuZ8R3Lv1QfDPuJQGoCi9aPez6eq7KawezSHfl0h8C68V+P+0trymQzx07YK5hq9sV1mzVw9Urf5n1AwZv/Xy6ZZ1X/C/w+ZBuqn6UEwyYRX6sJauHs0d+NHXDVMXrPxpr8TJGugntb1f1R0p3XT/GI3uNUv/A9CeYDQzMnrxwSd2eFmw46ghSm7/9nUqDyLMztzqmG/aZZ2kzpKv22jeO6ZIrhHXqN8FseKBoNJRKMEsY6z+M1bUimFXAi7AisrAiMfp/Nfc5cfXvK/gfwGc4dXyXGNrT2j9fYBZtCaZs5AdA1AGsUwMBHP83orHFBld/OcwpqRjc9Vr7/QGzqH/CayWEvgasY6HGQQDcob1SWtqA/PA4PnpgvVs2r+N2MBushktm9/Kqw+kA2jjimZu82o/6uVGDaLQBglnCAH9gANM42wm8Zo/+8rPT48ByDJ61wz/oZ4Gy0HTWoimWdPID1q+NJJhF/YePH5TNcdzv2OdZZoBg1tk+eN1Ql1DbAMFsYC/ICRSiXy9/wSzGctn4V/z6rn360B4BT1F9/LG+qj/h2O6NYvR9hVReJzDrVg68Tp3WsEU71s4eIf795x+3rOr4kuG9Vd2y/cGCWWgBYJxQgGet0zTRkdBd9p376L+GOUbWMSKYJZh1AqDReoxgNhoQanjaQDCrw0bG/Ye0BLOENzEOsMa/Vkz8vG+V45dGTHG74rPXxVvdvQHVp+93Ut+Vdm2a72gHiS1bwiBMWQwP2H8cvhT+tPNrMe6Vwl71YkreHRvmWDxG0VCZ9uzJ/ardKF/W5bTHmrFrvhphWWdXZsaasptWTRFv907tWsaw3mnEltXTvAAzdIX37MqF/WVxYsHk9o7lBKPh3HEtLVNWq8qMyIWzh80poAd09h5bJy14jDpFiw0QzBIchBocJLfyMF3xgq9miSt//6U/Fsw4pgEGwOz8+v1eUBY61ehQVHy9ZpG4qs3KgIztnm0snh/+hCpv0651lvwHjuxT57r0a205J/U/dMyzFMHjr7VyTHPyjGfqQtQp82IPOLt45XyBPugBXsBfrFog6nQqKc5fPGueOnH6mCXvoInPqywr1i+xnNt/aLc617V/O8s5vX7GeW+iDXjbAMGs9QU4gUDs63HuqOdZhXVWExpTgM/DW793/K6NKYhXTR0kht+V3bGcj19tb5kyWD2MjAimGN4wd5x4q14aS14dzB7btcGc8lhfxxZlwAN22+IZYlTLApa89r5M79ZA/LJ/qyOg/WX/NgHPWnsefD51wLNkEspwSjPqvoKqXHj0DqpltQ1MV7zp0ylmW/V+I/735Uvi6I51YlrX+o5lo76k1t2pjzxmHVPqEZ16EMwGBmYPnvLMdjDr+58dPVwbD15pvI/8V926ur+/yTFd/3meKe+v/P2PqPTy147pohWSRqJdBLPhgaLRUCrBLGGs/zBW14pg1hEmRcsLfLbDf5iEdU0BLz8cc4+YObyBGP1CnpCtOxps2QCkgKgfjbtXvDeoote0wU7j/Fa3a16vY18uqLxlndL5ewxepf8b0UjMGd1MTHqjtBj8pP/awtsY0zOj/eNfLWZZb9bf+oPREPAY00sD1M4YVtfRw9ffdjCd/+NOrcKjFcGs94tvwgBqkhgbAGRt/9ydJlB9YUQ3M25fc9at3AaP3i4efL6paPtMIxOIuqWLxHH0AW17cWR30aHvXaJqu4KEqQa4jsRYsE7qTjAbnS/iCUiSflyw/umkjuXEvJfaitnP3GOs03qbWhc2ofEYec8t5tTIALWznr5bjGiW2xVI6mD26M7119IZ0BOgEvkBM91AsFs7hjTIKN5/rJqZH1MFj21T3LV+tzISexxr5056qLyY92IbMf+Vdmbcvuasr7KTSndfbeC5pL/eqLn/mhPMBgZmIwEjWadnjAhmowGhhqcNBLM6bGTcf0hLMEswG+Mes4RH4YFH1JW60gbCYwMEs3zRT9hDG6AN0AZoA7FhAwSz/r8cJ0igVqGwAUcwa3iQhqJslkEdaQPxZwMEsx7oRwAa/VoQzIYHikZDqQSzhLH+w1hdK4JZglmCWdoAbYA2QBtIMhsgmI2Nl/GEJhwn2gBtgDZAGyCYjb+X+AQz0T2mBLPRPT68fjg+0WYDBLPRDyMJjD1jRDAbDQg1PG0gmNVhI+P+Q1qC2SR7GU/vs/B4n1FX6koboA3Ekg0QzPJFP2EPbYA2QBugDcSGDRDMEkJEG4SI9/YQzPKai3cbZ/9Ca+MEsx7oRwAa/VoQzIYHikZDqQSzhLH+w1hdK4JZgll6ytEGaAO0AdpAktkAwWxsvIwnNOE40QZoA7QB2gDBbGhfoBNIUM+EbIBgljaSkI3wPG1EtwGC2eiHkQTGnjEimI0GhBqeNhDM6rCRcf8hLcFskr2MjyWPLraVHoi0AdoAbSA8NkAwyxf9hD20AdoAbYA2EBs2QDBLAKADAMbDbw8zezYWx3ZvNLeN8ydwbVmur0sboA34tAGCWQ/0IwCNfi0IZsMDRaOhVIJZwlj/YayuFcEswSw95WgDtAHaAG0gyWyAYDY2XsYTmnCcaAO0AdoAbYBgNvwgjrCTGtMGaAO0AdpAYm2AYDb6YSSBsWeMCGajAaGGpw2/GsWKFDmKiJR5bufmosF1N90iUmTIKlJkL0iN/tMoRY7CAraTJvV14o5i3KgBbYA2QBugDQRmAyVvvU5kz3ydyJczsHzJVWfohOdu2htSi5yFM3GjBrQB2gBtgDYQMRtIf+P1Ip2x5SiQMWJtiOZnYfZbM5rPbDy3K9+ckhs1oA3QBmgDtIGot4F8N6QQOdNfJ27LFv/PrVwZUlz7bp2zsLipTFNuYdYgU5EaIk3WfCJ97lLUOhFap7kpt/y/cozxvyVDHClw1eiLHFzuqQVtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtIDpsYH4cMUl2xVDgPMDsLWXuEwWrdebmokHGbIXMG1C2AtWp0X8a3XL7vaYmN2VKIwY+VZYbNaAN0AZoA7SBgGygbdNbzedIgTwZA8qXXJ85d9W+xdQre/6Momq7gtyoAW2ANkAboA1EzAZSprrmaVK+ed6ItSGan4UVWuYzn9kpjHctL9XNxo0a0AZoA7QB2kDU20C+G1Obz677brsh6tsa7LO1ct60Zl9zFq0oSjfvxS3MGuSr2MTUO2P2fNQ6EVpnzlPM1M9geMNIM+NLAXON2Urtp4n6T63h5qJBzqINzAugSN0+1Og/je544D1TE7xQF/se5EYNaAO0AdoAbSAgG5g3qpb5HKlTKVdA+ZLrM+fdl+8w9SpWK6fo9XFdbtSANkAboA3QBiJmA6nTpDSfSQ9PqBqxNkTzs/Cx96qb+qRMcZ04/2ohbtSANkAboA3QBqLeBqrlS28+uz64/+aob2uwz9ZuVW4y+1qqSRfxwKTd3MKsQbXO75h65yxRhVonQuv8le829TOQZO/4wpLsDcGsC4zVQTXBrDe0J5gljE6ucIT9pu3TBkJjAwSzgelIMEsYHc0Qgm2jfdIGkpcNEMz6Hm+CWcLoYKEB89OGaAO0gaS2AYJZAtpwQWqC2eBsi2A2fgEuwSzBbKK8gAlmA3uhTpBDvWgDtAHagNUGCGateiRkHwSzvl+CEwpRH9oAbYA2kHQ2QDDrW2uCWQKVpAYqrI82RxugDQRrAwSzwcGzcEHNeCiXYDY42yKYJZhNFLzTvU5jOU6PWXrMJvTCnOcDAwzUi3rRBmgDBLOB2QDBrO+X4AQy1Ic2QBugDSSdDRDM+taaYJaAJFhAwvy0IdoAbSCpbYBgNjh4Fg8ANVx9IJgNzrYIZglmCWavu05wjVkPoKXHbGAv1AmhqBdtgDZAG7DaAMGsVY+E7INg1vdLcAIZ6kMboA3QBpLOBghmfWtNMEugktRAhfXR5mgDtIFgbYBgNjh4Fi6oGQ/lEswGZ1sEswSzBLMEsxYbIJgN7IV6Qi/ceZ560gZoA8nNBghmA7N5glnfL8EJZKgPbYA2QBtIOhsgmPWtNcEsAUmwgIT5aUO0AdpAUtsAwWxw8CweAGq4+kAwG5xtEcwSzFqgXCxPS5yYtnMqY4+nrNSPYDawF+rJDbiwv7QP2gBtICEbIJgNzEYIZn2/BCeQoT60AdoAbSDpbIBg1rfWBLMEKkkNVFgfbY42QBsI1gYIZoODZ+GCmvFQLsFscLZFMEswSzBLj1mLDRDMBvZCPSFAwfPUkzZAG0huNkAwG5jNE8z6fglOIEN9aAO0AdpA0tkAwaxvrQlmCUiCBSTMTxuiDdAGktoGCGaDg2fxAFDD1QeC2eBsi2CWYNYC5aTXZHLZ02OWHrPJDZiwv4EBE+pFvWgDgdsAwWxgmhHM+n4JTiBDfWgDtAHaQNLZAMGsb60JZglUkhqosD7aHG2ANhCsDRDMBgfPwgU146FcgtngbItglmA2JGC2TvcV4o62k82t2iPzAi6zbu/vjbxTzPxVHpoTcP7EgmSCWYJZQpfAAEJS6LXni+Zi3dwmYv28JuLfPR1EUtQZT3VAM2gHDfcuuYf67Ys+G48ne3PqC8FsYDZHMOv7JTiBTOzq03FMZdH6rQrm1v2j2iKWxvLBUZXMdrcdUl70nFcnptoeSzqzrdF3fRPM+h4TglkCkmABCfPThmgDtIGktgGC2eDgWTwA1HD1gWA2ONsimCWYDQkErdB6gjCkNLccReoFXGaNxz9T+TPlui3g/ASz3oA1sZpwKuPAXqg7QQkeC07DCqWyqfvBie9aRRVY/PK9+mJi/8pR1Sa7vR1fdZ/Sr0qZ7FHdVnvb+Tm4ayda9COYDWwcCWZ9vwQnuIlufbrMqCHKN88rukyv6QUv85W5ST2POoys5HU+msc2R8GMqu0AMdHcVrYtuq+RWBsfglnf9kQwS6CS1ECF9dHmaAO0gWBtgGA2OHgWLqgZD+USzAZnWwSzBLMhgaAEs6EDo4kFqqHKRzAb2Av1aAEh8dSOaASzZ9a2Fvc1yme+pH2sdeGohp0Es7yGI30/IJgNzAYJZn2/BI81qJGc2tukTymR/sY05rPxkcnVvOAlwSxtOzldD/HSV4JZ39ctwSwBSbCAhPlpQ7QB2kBS2wDBbHDwLB4Aarj6QDAbnG0RzBLMhgbMtpmoflWeo2j9gMukx2z0gF2C2cBeqEcagMRj/dEIZpe+30Dd4whmeY3E43UXyj4RzAZ2jRDM+n4JHi+wIx77kbvYjerZ6ARm85fJqs53eJces/FoA+xT/N2/CGZ9jynBLIFKUgMV1kebow3QBoK1AYLZ4OBZuKBmPJRLMBucbRHMEswGDFGdPDPr9l4taj25xNyw3qxTGl/HCGYJZkMJBVhWYFAg2vQimA1u/OgxG5x+0XY9xGJ7CGYDs0GCWd8vwQl+olefhMBs19m1zCmOnaY5jvZx5VTG0Wt30W47sd4+glnftk8wS0ASLCBhftoQbYA2kNQ2QDAbHDyLB4Aarj4QzAZnWwSzBLMBQ1RfgDWx5whmCWZjET6wzYHBB3/1IpgNTleC2eD089dOmc5dZ4JZd22c7IZg1vdL8FiHHPHc/oTAbCz3nWCW12Us228wbSeY9W37BLMEKkkNVFgfbY42QBsI1gYIZoODZ+GCmvFQLsFscLZFMEswGxIwW6PzIlG4ZjdzK9NimGOZdXutErc16SdylWgiMucuI7IXqGGmr/zgTEEwSzDr9LKaxwJ7uR9tel3Z0U7MGVFDvNCllGhSK7eodUdO0f3BYmLKwKri7Lr7Xddp9QfM7ljUTIx8qaLodG8hUbVsdtGqcX4x8KmyYuXMhq7l6vr8ufUBMfXNauLBFgXNdqFtD7csJEa/WlFc2vaAKuPiD23E0OfKm/UYjwtzSsbyJbOax3D83Ppr/UA/8fm9QVXFv3s6mPtmdfOIupVziWcfKyUOLbtXlSnbsWFeE9GvZxnRtumtovLt2UTrJreafVg2rYFXWplH7q/uai9mDa8hOrYoJGpUyCFQF+qHLv6C2cSOj2wD97F9fYZz/AhmA7MNglnfL8GDAQzM65+2PefVEc1fvl2UvTuPuLVCNpGryI2iWK2comq7guKel0qLnvPrWNaPRdoaHQuJjFmvrS+L52OFlvnMY81eLK3SNn2ulHkMabvMqOF1vH7XYuaxhydWFYgXq5VL5C6RWZRvkc9sTzfD41aO4cMTqop6TxYTRWvkFADCxevmEg26FRdou0wj952MtKgTW8t+Zb3Oy3QtXy+r0qF8eRx7NzB751Mlr+V5qJDoNsfTPj0v2l3DOI/6kV4/J+OPGmvy4jz6gT5jPV7ojz5CD5mOe/9smDqFTieCWd9aEswSkAQLSBKbf0rLXKJfg2zi3WY5xNlXCpr7O4tmFDVuzSB6VM0itvbMJ/SyT75UQCBP7+o3iQaFMwiAmccrZhbv3p1D/PTMrZa0ej7EUf6HD9wsnqh0k2hcJKOocEs60eq2TOKlutnEnLa5xTnjvD3P7qduNduHNq7qnEccf6GgmHRvTrPOSnnSiYaFM4puVW4S67pa22kvB5+Rd6TRTrS3doEMonr+DKKzEcex/X3ye9Uty5ja6ppGI4x0OLajd34xqllO0bF8ZoE2oA/9G2YTP3R3LyMxfZf1Yx+M7no5jPNeE0obIJgNDp7FA0ANVx8IZoOzLYJZgllHiBqo52uF1hPUGlI5itTzKrNqp49Ehsz5VBoJOLBPkTK1KFyjqzqXKddtXvkDbY+/6XMWvbZuZJG6fZKsTn/bFql0XGM2sBfq4YQbsVw2AGHZ4jep61q/5hHPnSO9wLqtTn30BWb/2d1e9O9VRqROlcK1bADW3za1cSwb9X0+qZ7ImtnzMtlX2/Z80dy1HuTb8mlTs55q5bKb6Qrmu8GEz/Yy766TR7Xn5Or7TNCbwshvTyc/t7nrVld4/eNXzUWxAp51/WQe7NNcn1IMeba8KrdKmeyqXl3rYMZHL4dx3i+cbIBgNjC7IJj1/RKcwCW8+nSdVdMEnfqzxB6/tVxWARghxwJw1J5Gfi5UKZtKB9goj3cY6VljNm/pzObxG7KlFYCjqdOmUulkeuwLVcxmQuFWA8qJ69M5p8lV9EYvQHqPAYdlObc1vkW1R7Zf7m9rdItKBwAtj2PvBmbdjut5H51STZWbs1AmS7lI16RPKZHSx/8x6GvDHiW88ul1MB7e6yI560sw69u2CGYJS0IJSwIpC2ARz7b8N11vAkv5nJN7AFRZ3uon8orSudKqZ5FMI/e5bkgl5rfPrdLLfNgffb6ACTFlWqd9/UIZBECsnm/RQ7lVfS/WySKq5E2vPtvL6G6AZCe4i/IAV/PcmNo1b7YMqcW0+2+21C3bAUiNutC/pY/kETe5/O+Q0fjOPNGAxjKf3Ce27zJ/MLrLMrjnPSYcNkAwGxw8CxfUjIdyCWaDsy2CWeOpHafhqNEvUan9tCQBjr7AbI3OC8X16T0vZtCujFkLiky5SokUqa73+oeLYDay3rMEs4G9UHeCEsn92Lf/ayTSGl92cK1jQ7yS4REKr9RMGT3XPMDktCHVvcChLzDbtLbnJSrKzpsrg2hYLbcoemsmoYPOkoUyi78Mj137WMx9t5YlHUDmHbdlE0gv24v9DcYXvq2f3S1+Xn6vAGzNnsXzxTa98QIZx7Dt+/Iesw4JZvUy9Pj/htUw08FLFfXp53JlS2d61ua7+doXSXkuT8704vyG1pY+/PJ9K5Ezq6ct6HOpwplNj1tdc1mGE5gNdnzsmvIz7xl2GyCYDcwmCGZ9vwRPzoAkKfoOkCqfGSlSGj+cKp5Z3Foum0hrPAflceyz5c+oQGG5e/KKG3OmM35c6fmBUUbjOYljtzfxgNCEwCzKTZXa80OrGwwP3HSZPP8n4HzhKtlFqjTX/qcAzLwpd3ovqFm1fUHVNmgWzWC29VsVREpbn6F3jgI3WPRG35v2tcLipLAH1sH7EcGsbxsgmCU0CQc08adMCWbxfHDaJGj8/KE8Io324x/Ey+dOZ3rM3vDf81TmH9vcG042LeZ5HqVMcZ2obABWeNzeaPsRVamcaSxgUwezsnzsb785rempWlibZQPH4T1r7/cHBnDV86Y2/s9AfnjsptX6hDTwzLXnl2AW5zNo7yPyZb5eZEnv/QOvNU/mtZSR2L6jHcHqbu8LP/NeE0obIJgNDp7FA0ANVx8IZoOzLYJZ44kdpyFqwOzNJZuqf67SZMgmKrSZpGBxzScWi8x5Pd5dxlgYwJYes5HylkW9BLOBvVC3A4nk/hkerWU0T9n6VXOZcFPqAjD5RNui6p6Qw3iRi+mC5Xns3cCshD24T2RIl1pMGlBFiL0dVN41H94pbjFgJs5je/OZcuocygXk1KFm7Yo5zWl/Zd3bDBCre/neZUBgeQ7evbLcx1oXVsfleTuYrV4+hwmdx7xWUdzXKJ/A1MlI+/QjJVU56MP0oVYwvXBCXZHlRs9LaWgl68AeUzfLdtycPZ1An+X5M2tbizqVcqnzSGcHs6EYH1kf97xXuNmAvFZhj25peNxjPwSzvl+CExSFT5/Hp1ZXzwzAzwdHV1aAs8fca9MbX2e8lJXPnbZD71DnMS4JrTHrD5hF2fCglXVjauLb77L+CAtpStTLJZ74bzpkePkC2Mp2ZbklvaVd0QxmAa5lu0s1zC2gs7RxAJ8Cd3j6hWml5Tnuw3cdUFurtgSzVj3s9kEwS1gSSlgSSFl2MAtgCrA6pEkOcU+JG8QxY/rfMy8XFLcZwFQ+ZzAN8Laenml7Mc3uwxU8P0iG9+nPzxVQcHKfMU2wzJvO+BHRem3aYZSN6Y31H0Mve8wDNu1g9vqUKcz2yT7CQ7ZvrayqfJSDKY/lebRTh791jLbvfdrjlYt2Njf6KdsHULulh3VaZB3MIh08ezf/lwb1L+yYW8BbVpaBqZJl/cH0PVjdZRu45/0lXDZAMBscPAsX1IyHcglmg7MtglnjiRynISrAbN0eK0WKFJ5fpsGz1g4d6/b81vh1vAcmEMzSY5Yv7D0v7GNNiwlvVFZfdArny6SApL0fWBvVuPeaW9/HS1ngjROYhffrrbdkVHkm9q9sySPL37vkHjXNMbxeT63xrGU7vp+nbfC0dfKoXTGjoaoD0yWfXtvKrCcQMAtP1wsbrZ6uaB/AsP5FFvBKtlvffz+7sWoD0svpktFefQpnpNPzIQ7wnT+3Ryc7mA3F+Njr5OfYvV7DNXYEs4HZBMGs75fg9pfi/Bw6vbAerHwWF6mewxEClqx/7X/0dDdcL+p2LmpJEwowmyl7WtH5A8/6sxjfJ/9XS0g4hPblLJzJa51bpJGep/C61deajWYwq0+F/OAoz/TO0q4fGlvZ9AiGd3DWPBlE9w9rWzSX6bgP3XVALa1aymvPvu4ydbqmE8EswUm4wElC5epgNnem1OLQcx5oKfMOb5pDPdcLZLnehLXynL5ve3smla5nNY/n6qw2numI7y15g4KWet72Za8tqQMP1KEGFJbn7GD2DQePVqRtUdJTd9fKnroRl/+TAC5jrVdZtr6vV9Azy1Sz4tY26mD2FmM6ZMBqPS/iWKNX1gNvXHk+mL4Hq7tsA/e8v4TLBghmg4Nn8QBQw9UHgtngbItg1ngix2mICjBbsf0H6p+ezHnKekFZCWmL1XtGpSOYJZgNFyxguYHBgsTohbVUjXuqudm9QfXysMapTFe+ZFYLYHQCsxvn36XSF8qX0ZJeLxfxVo09v/T9bHwdlbZJLc8XzXGvV1LH7fmffbSU6Nq+mAAsgRcqzgcCZvv1LONY9qKJdVUfMJ2xvV79c/P6eVXakS9VNNNu/sQz+wCmhdbT6/Gxr1VSee1gNhTjo9fFePivqVjUmGA2MLsgmLVCAb78Tzo92o+oqJ4XWNe0ce+SFg9OjEW32bWUp6p9bEIBZmt2KuQIHrPm87x4bdTTeb1VTGss/5fQ4W40g1nd0xcesY9MrubV/8en1vAaB7v2/Jx010ly05pg1rdtEcwSnIQLnCRUrg5mn6+dVQFFPR/WmZXPxXEO0xTLtFgLVabT4eT3XTzHMe3xhBa5TC9cmQ/74wbs1L1s5TkdzGY3fiDtBEWR9rsu+VTd1fNnUP0om9uzXM+sts7r3yL/isc97xsypU2p8uOcDmYHNPSe6hhp4KUr+w54jWPYgul7sLrLNnDP+0u4bIBgNjh4Fi6oGQ/lEswGZ1sEs8YTOU5DVIDZko1fUf/03FqpkyuYrfbIPJWOYJZgNhZBBNt8DUQUK3DtF7TGfVUM6F1WzBpew3Gb+XYNdc3Ds1XXzwnMzn7Hk754wRsdy5R1YQpi1I9t+At3qLL1NVx3Lb62Nqxer694IGAW7XAq6/nOt6l2vdqttGMamQ8ewbIPcjrjD96qpo690tU9/8FvWqh0djAbivGRbeQ+MPiWnPQimA3MNghmfb8ET25QJKn7m9G25ltawzO2aI2comGP4uLRKd7QUG9fKMDsfW+U9QKTqCN3Cc9Ui/cPKu+YRvc+BcyUbYtmMFu/azH1jJbP+ZyFMolK998q7h9Y3gCy9JCV48h9ZO6NBLO+dSeYJTgJFzhJqFwdzE6613ttWOTX13F9sU4WgXROG4CrfAZhal+9bnjjynPY32T8cKtlqRvE6Htyil1PeXvpyrw6mL3XSC+PO+3lWreYuhjnD/ctILCeLerDjFGnXvJtZ7kyembl26NNd6yDWXjAOtUNqCz7l8fwqtXTJLbvodBdbwfjvsef+gSuD8FscPAsHgBquPpAMBucbRHMGk/kOA1RAWbzlm+r/ukpXr+vK5jFdMbGOJgbwSzBbHICGPHU16u72ovrU3vWbJHXtD/7X76/NmUw9HACs/17lVH3CH/Kk2kk1ES5abX1ZH7b1MYnGLWPSyBgdsO8Jo5lY61Z2a73BlV1TCPr1eurW/naOp1PPVxC5Xebyhn5MZ2xnDJZB7OhGh/ZRu4Dg2/JSS+C2cBsg2DW90twwpnw6oN1YzNk9qxHJ59Tcp/LmEa41qOFRfePvIFhKMBsu2EVFVDVxzp3KQ+YxfS++jkZj0UwC/B6W2PPD8ikznKfNmNqcz3dB4Zb1/OVfeY+vNcD9a2rphHnVMbOtkYwGzgQIEQJjWY6mP3msVssQBEanzbWgE2d8to7NflM8Xevw02UnUMDn/YyyudOJwY1zu4FT3Uw20ubHtlp/IsbyxjIcrFOrO6teosBhp3y6Md0LVCvPKeD2W+19Wvleeyhk6zbDmYT0/dQ6a63kfHQXDPU0aMjwWxw8CxcUDMeyiWYDc62CGaNJ3KchqgAszmLNlD/9BRv+KIrmMWUxqnSXJuyjGCWYDY5AYx46ivWYzXup4nati9spkClE5jt1sHbw8SfugBDoTGgpISV+DXuv3s6qPr8GQMdlD7WurBX3mrlsqt+H1p2r9d51NGumefXuTOGVndMI9uirzMr4WrbpreqOhICuzdmvN5MK/Oi3FCNj2wj94HBt+SkF8FsYLZBMOv88puAJOl0AYAp1TC3ABR0e7bCg/Wx96tbAGkowOyDo52ha1KAWfRZ9veel0pb+qZDX4AYaY9ux+V57DE9sSwX3rD6ORmHRzKgt0xn32Pd3AbdizvmlWVwn3TXSHLSmh6zvu2KYNbzop/QI2m10GHk1p75FIyU47C/j2c5H/szJaHPAKOyHOy39cwvOpbLLDIb3rJueavkSy/2at6qOph1m2pZ1lHGWNtVlrv88bxiQzfPD5hvvckzvbBMb9/X1daZndfOGcyu7+qtEcqBN66s2w5mcT7QvodSd3s/+Tlpr7F41ptgNjh4Fg8ANVx9IJgNzrYIZo0ncpyGqACzecver/7pKVyjqyuYrdNjhUpHMEswm5wARlz1dW8HkSGd56XuAWNK3ZOr7/NrAziVWjiB2aHPlVf3iC5ti/hVJuq++IPHMzZvLs96dSe+83joynp97UMBZrF2rfG8Mbd3tCmWneqdM8IzdXOzunlMbXp2LK7yD3m2vNLLnv/y9gdUOh3MihCNj70+fg4MwiUHvQhmA7MJglnfL8GTExCJdF/hzdlqQDlzWt2cDtCwWK1cFlAYC2AW8NVNV0zZLJ/LiQGzTuvDoq4HR3nWencDs7JNjxoQF5AWbUlnTCMt2yP3Hcc4g2uZn3veP0JtAwSzvm2KYJagJFLwJiEwe+6VgiK98aMe+fzYbHiiApz6s8Hr06lfOA7g+kzNLKKctgasrOP+0plUPh3MPln5JnXcqVysQSvL2NE7vzjyvOcHzJjm2CmPfqxINs9MHwC78pzuMZtYMCvL8rfv4dBdtoF73m9CZQMEs8HBs3BBzXgol2A2ONsimDX+G4jTEBVgtnDNbuofrty33eMKZqt0nKXSEcwSzCYHcBGvfSxb/CZ1La/58E5XeIj+7/vyHnFmbWuvNE5g9pOxdVS5jWvc4pVH1/PCxtbix6+am1P66scxJbBxvzc3X22b8EZlUa5EVgFvWwBZlBEKMKuvGwu4rLfNHu/X0zN186Otrnno6nDayWtXlrH7i2aqnxYwa/QjFOMj6+E+MPiWnPQimA3MNghmfb8EDzV0YHlWvZ+YWUtgOuOus2t5AUysMVu4Wg71TEmX6XrRc14dlS5awWzzl29XbbbDZH38da9cf8GsDqzdpljGurny/w07mIV+ncZXER0MeKu3BXGcQztSGS+lZf46jxfxSmfPx89Wm6YewelBMOtbP4JZgpJQgZJAy0kIzKK80rk8nqhLH8mjgKVTXfBS/fEZ7zVjfzbWe132WF5x7AVvWIs1ZluU9Mz2kDV9KnHWAMIoXwezTYpmcK1bX+M1TaoUakpkfd3Y3T7Wsj3xYgGR1sgnn5M7euVXdQULZhPb91Do7jRGPMb7TahsgGA2OHgWDwA1XH0gmA3Otghmjad5nIaoALOV2k9T/zClTJ1O1OzyuSOczVehnUpHMEswm5wARrz1tc1dnul2OzQv6AofV81qpK75LDdeL/7c+oBK6wRmddiIdWx3Lb5HpbdrWL/qNQCLKYsHPV1WpevcpoiqU8JOe158rnmHx4Nm+fSGZv6vpnqmZX+oRSFVpszvz1TGK2Y0VPVnTJ9anFpzv1c5KA9a3Jw9nUo7/b9pj7d+drc6hvyYmljWr+91z1w7mA3F+Oh1MR4YgEsuehHMBmYXBLO+X4ITsoRPn+J1PT9Yqtq+oCMAfHxqDZEipecFqD6dce7innVgARvtY5WvjOfHWh1GekBk3tKefOGYyrhlPw8YBTy2twuf0S9MF2x8DzQ3f8EspnSWee59rYxj2eWb51VpdDDb4d1KItV/692nNbxjAcWd2la2WR6Vv/Sd7h6/Tnl5LHzXS3LRlmDWtw0RzBKUhAqUBFqOP2D23lI3qOdHa82b1V7X4k6etc4xXbGEsG1v90DXl+tmU8BTz4+pe1MZ37Pls1BOZ6yD2euN/xu2a8BUz49yZd7GRTKqOqrn98xu1dPHGrUDG3mWELJPexwMmA2m78HqruvDOO8x4bABgtng4Fm4oGY8lEswG5xtEcwa/xHEaYgKMIu1Y2/I4VkbMleJO0XdXqsscLZSh+kiZWrPVCTxDGZrdF4k4DmcIUs+UbDq46Ju7/+z9x1gchRH2yjnrNNxOkl3yijnnLNQRAkJCSQQQhICkXM2BgE2OdhEI5LAJINJJkpgEHIgGIN/w2eMCTbYHw6fbRyx+693dmu3dnZmdmZnd2dur/p5arunY3V1dfdsv9Pdey1ZjN9wv+nSb7Zp1amX6TfjlJR8Rq2+xXTsMc5AJoMXXZLyh1yLRaPW3Ga9KNdWt3IEfeoLsKD1DAYssLz23jc3dZcrja0GAA2HsY3jheXOTTuA6wTMIi12yiJP0MQRFRnHFHPed1w2MRUHd8piVy6HvfHIAgOwFukB7u67P3tHLwNKiNOpfVPzn/+XOGJZAsmzJlSm8uS8/QCzuNd21KD0H1HcGfuvtw/JyAtxjj00fWRxi2aNMuo5YkDHVP0OXdrTujuXeYD9FoG3SMNysgOzhWgfWZ668+sn5S437kfTxmT3lWLU/S+vH2xeuGOWReirbmV8uPugVLx3nljoGu/17yxIxQt67Llb2V7+Csx6L4LXFzAkinpOPTL9wRLul91y16QsoPCQy0en5pSO3VtmhHcbnAZeV148IiMM9YkKmMXxvzwPwl54eub9sdgdXNmnbUYcv8AsduBy3rhv9tgH0juIUecDTx6UCkc8Ccwibgv6GI3Tj1tTmyUz5NFrTPpd4cBTBjnGiUJftMz6MVaVCphde+Vog93lILdjwbc/NM06Xp3jOe3sh15uvnNyKq9VO7LHokLqrgKzCpoUAzTxk6cfYPbpw9P/lzHX3L6iMgV8chnYsSp3eEoA95J5adATgC3icjq2X9yU/niof+dmqXAJzKJs7Ky1H5G8b2t3017cW3vzQV1S6e8+OD2/NqcPp5x2/L66tZvpJI5BPnFSh1R68BcGmA1T97ByZ9mqreNLsXSgkMDsR7Sr/tFDqyzCRx5uPOO+Zo736pb0keP2+HtoTOF4Xrvl7encnreNS/w/GTh/s4kD8Dn/vEfMjBNvt2jJpbsdeVp908/MzJPvTMVbed3rjvGWXbUvFWf2absc45S6zlEBs+UiVwVm6Y2hTE1sgNkJRzxomrRIL9y07z7SAhkBOvaefAyBsumdYdQWFghZLNDRnm+XvoldcH2mn1Q0oFOW2X3k2tRiDOo6dOkVVrkVfaZn+I9bf6/l36oivWDWqEkLM/Xo54rOpwKzCrR4AQh+wrCjFPrNtIl2qj51ywzz0j1zzBlbBpte3Vulwpo3bWSwE1Tm6wbMvvu9xRagyvn2qGplbr1ovPnhg/PNzksnmGWzu5tGYlfPIYtqM/JFGeCF0zelXSsXbB9iAOQApD2TeIMfh1991uhU+rcfTx8PjPCV83qYYw7tZ3CPLvL1A8wiHo5QBmDMZYwe3Mncc/kkAyDogWumGIC+HAZeIDcpmw+ovIoO6SOqZozb33znhqnmBw/MN18/baTBTlpOD9sOzCKvsO0j+VG3jhdOOlBqYPb5nbNTeo/+5cQT/E4/Kg2YzJtc5RpvcN/0jrhc90G7lRXEX4HZ+gF2FBIYKFReABgai495ADTOP2mgWXvVaLP66yPN1I19CEhMfzw5ekWPDJBQHnPctrK5GTK/qwHYy/xFBcyi/EpxRy52pw5bWG0Aco5ZVWMAMMu5Em6/wKw8Jhnpqge1N9OP6mvdEwvQVu7CRbgEZsHX8MXp3bSIO2JpdwNQG0cbYwdur3HpRfGGFL71HuddtSxjtXX8KLQOlAqYlbvP3T5S2EYfUci+uurSkanxRdZ7wakDU/GaEOgjwwrtVmBWgRM3MKDY/n6AWfCwZmjmh0eHjWhnHjhkf/PE+mpzwsSOBrtMuV/hSODvH5U+8higSEvxf3jI/s3MbcsrzSubuxnc5bpjbmcj74eVwKgdmEUZAIPuXFVpnt1YbbDTFffHctnjKAz3s0q5zeqdnp8bN9zPnETA63cPqzJPEfhz7oxOGbzN6NnS4FhjmT4MMBum7mHlLuugbh1jiqEDhQRmHzk08wMQN36Po53v3N9n9nI/3nxARfq/BsYJt/z8+scNmK3oMyolhyGLj3UEU1de/0YqDmQ25/T7HONN2nJNKl7jZi0d49QXYLZc5KrALGl8mZrYALMAJkeuutE0aJR+ASSZpwYTuFu07556Lucds51qJ6TqiXr3mrjZAlpl/eE/cMFXzYzjXibQOg3AwH/0Id9SYJYW14MseGvc0ssL98bOnlCVoevQXzs1pj+C3/3mtKz2dANm0ZY41rdNK/exhMuYOrqL+cdP08cjsx6AN+zi43hu9trFmccwf/mztaayU+ZHJEiLHbrI2y8wi7gAfJuJP7xOPGBH72M3Ts+SDdK/cu9cz/R9a9JHUDkBs2Hbh2Wpdun7Vl2RuQKzwXRDgVkFVgoNHATJD7tJneYhu1/N8E7WrjSZN+4/tcdrW9EsBYpECcwuPnMwvUenjyq28wnwZtCc9MKSX2AW98BKQMmeL56x4xVANdx2YHbbfbRbt2/morlTHuAPYJOUt7p1rCiFDigw661nCswqaOIXHCh0PL/ALO6NnU6gpdPcIv1wHPGug6uyQBAAqTKemxsg6genpO+odQJm3dJip63TUcfYYTeiKvs/tz2fKTUtzW8c7sANA8yivfKtO9KGlXuh9UXz07FK6oACs+GOmw0DdpYLgOgmg6h2zJaLXBWYpRm+TE1JgVnsfiU5JhYg6EheuUuU3cMOujrjWGMrfoOGpl31MDPpqMdMk+aJHSrtqoY6pud8CmmXesfswPkXpOTUsFFTM3Hjw1Zde03ckvKHHKYds9vy33+guFOSds8Wsu5ueemO2WAL6nUFKCk5n++uMzd+ZazpVun8p3DJzG7WLlEnvsYMSR/h53QPK44jXTC1q8FuWx532MbdrNjh9ve3skFZLgvHBV9++oiMnacyvQW2Ev8cn23s7O3dPQ16Is3ZWwdZ8aaIe2k/fnFZVlrOg23swMVdtgCnuWy2J42sMM98a5ZnHgiXxxojLXYLg4/PXllhOid31SIvLjPDDtE+GfkQUKzPKgO7DpQamJX3N+OjBjs//Ixd8dzPFk6rdo03/ID0keHXnpPeOc/5FNpWYNZ7EbwUQER9LwN3srqBhe0IYBx/SE8DQNIuJxwzOnRBdeYuUVro3fbtqVZcgLnc5+RdshKwXX/D2Kx8UY68h3bDjeMd48jjiI+6I/sY5tVfG2U616RP6QAvDWgHDsDS1ZeNMAtOS48JS88dklGG3HG76faJGWHH3D/V2ukK8JTrBxvHFGNnLo4s5nIhV7vcNtOR0aOW96D/PpnpkQf8cEQ0dtDa0+mzjhWl0IFSAbPYbc79Z8K6Xo76jg8Z9hN3WaJPO8lg0Rnpvoxj2Z3iFMpPgVkFOyTYUUo3AyvoN2+73N/K/GAn6hULKkzXNpmnKXGfO7BfS4PjQzm+3caOuNFdnQHS2g5NzJnTOpjf23a7SmB2+aDW5qqFFRnHDqNs7MbdOra94xHJzAOOP8Y9tB1bZM+ROF4Z6T85PXOnLKeVgPRrx/RwrB/y5xOsetDuYU7Ldj5157Rh5c75qK3jTKF1gMePnSv3z9L5oGU9RrvYeSzBzna39NhRz/HkfdL2+PJodRwpbg8P+hy3HbNd+o1JyWHI0hMcd7muuuFNet9Jrw3OPesBx3iTj74hlVfTlm0d47gBqMXyjwqYLRe5KjBLo0SZmpICs24gn5P/+A3fto7wHb78WjN12/MlARud+IBfqYFZlDnusF2m/+wzzeQtT2XUHYDogLnnZsgEd9AOW3a1GbTwYjN9+0sZ8d3qFNZfgVkFWQoNOvzxR6vMy7vmWkfuvvbwgeZPP17lCogEKRv3v+IO2Ue/Mc08t3OmdawwdrYGyQMgJkBOHBmMY4KNAyBrz+9XLxxk3nx0gQWA+olvTy+fcccsAN+Hr59qXrx7tvnkpdygrkz/P88sMU8S7zjK9a9vrA5Ud86nWO3D+atd/8aUUgOzdV3HFJhVsKVQgEHYfDYTuLnyohHWkb/LLxzueOesUxm493HtlaMMwFe3OyCd0pXKD0AowOdl5w8r6NHAAKvXXTPGusNWAs9+6wUAF+kW0e5e7FyG2wkA95ufxtOxpBA6UCpgthC8RpGHArMKmAQFCaKOj12tOAoYu0FxHPGvTk3vcs3F2y8pLe6QxF21AGOc7pzlPOzALPv/8Oju5g4Cg3Cc8ccugCrHtds/P7HGPLyuytrZiyOXP7MdXWyPX8jnIHV3KjeM3J3yUz8de8LoQCGB2TB8lCJt3IDZYgGicck3KmA2LvUPy4cCs2WKylK1YgvMhgUOC5k+CmC2kPwXIy8FZusfiFLXQQ3lX3VWdSBeOqDAbLD2UGBWwZQowAUtU/VOdUB1wEkHFJj11gsFZhUcKQWwUBfLcANm62JdlGft5+WmAwrMRneUcVjgLu7pFZgNp1sKzCowW5JdmMUAEAuRpwKzr2a1vwKzwRbUFRBSeakOqA6oDmTqgAKzmfLIpR8KzHovgjsBB+qnMlMdUB1QHSiODigw6y1XBWYVsCk3wKZQ9VFgVvtGoXRJ8ym8LikwGw48izs4GiV/CsyG0y0FZhWYzQLmCgF41pU8FJhVYDbXgrmGBwMYVF4qL9UB1QEFZoPpgAKz3ovgCr6ofFQHVAdUB0qnAwrMestagdnCAwYKwpSHTBWYLY921P5Ynu2owGw48CxK4DPuZSswG063FJhVYFaB2f32M32mn1Sv5SCBdN0xG2xBXUEolZfqgOqA6kCmDigwmymPXPqhwKz3IrgCMiof1QHVAdWB0umAArPeslZgtjxBCwWjwrerArPhZah6qDIslg4oMBsOPIs7OBolfwrMhtMtBWYVmK3XgKTumNUds7kWzDU8GMCg8lJ5qQ6oDigwG0wHFJj1XgRXQEblozqgOqA6UDodUGDWW9YKzCpwUizgpK7n+/Lm7mZ4VTOLTpvS0dT1+ij/2tfLSQcUmA0HnkUJfMa9bAVmw+mWArMKzCowqztmM3RAd8wGW1BXEErlpTqgOqA6kKkDCsxmyiOXfigw670IroCMykd1QHVAdaB0OqDArLesFZhVsKacwBqti+qz6kD90AEFZsOBZ3EHR6PkT4HZcLqlwKwCsxmgnDzStj64dces7pjNtWCu4cEABpWXykt1QHVAgdlgOqDArPciuAIyKh/VAdUB1YHS6YACs96yVmC2foAYClZpO6sOqA6Ukw4oMBsOPIsS+Ix72QrMhtMtBWYVmFVgVnfMZuiA7pgNtqCuIJTKS3VAdUB1IFMHFJjNlEcu/VBg1nsRXAEZlY/qgOqA6kDpdECBWW9ZKzCrYE05gTVaF9Vn1YH6oQMKzIYDz+IOjkbJnwKz4XRLgVkFZjNAufqwS1bWUXfM6o7ZXAvmGh4MYFB5qbxUB1QHFJgNpgMKzHovgisgo/JRHVAdUB0onQ4oMOstawVm6weIoWCVtrPqgOpAOemAArPhwLMogc+4l63AbDjdUmBWgVkFZnXHbIYO6I7ZYAvqCkKpvFQHVAdUBzJ1QIHZTHnk0g8FZr0XwRWQUfmoDqgOqA6UTgcUmPWWtQKzCtaUE1ijdVF9Vh2oHzqgwGw48Czu4GiU/CkwG063FJhVYDYDlJO7SeuDW3fM6o7ZXAvmGh4MYFB5qbxUB1QHFJgNpgNxBma33jPFHPvAVKOgiPdCfZTy0TaKb9sUQi+0fUvTvirntJwVmE3LwqkP1zVg9vOze5oPT601Cr7EF3zRNopv2xSi32j7lqZ9Vc7eclZgNhx4FiXwGfeyFZgNp1sKzCowq8Cs7pjN0AHdMRtsQV1BKAd5vbvO/PihA81zO2dm0KcvrzA55RUm7XsOvPj1i6pcv/wVOl59q2+h5Rcyv7/9ZI3Zc9fsVP9493uLc/eNkGXm7HsFzF+B2WBjUVyB2VHLe5j9GuxnuvRuU6eA2S13TcqL3+0PTjNb7p6SV1qnxftS+GkbeQMopWiDYpZRV9u3mDIpRt4q58x+pMBspjzsOleXgNl3ju9hOrdsbGjJy9y3uqrOgLOfnllrPiPKBxT7+PRa878ERueTNoo02kbeYFIUbVLIMutq+xZSBqXIS+Wcux8pMBsOPIs7OBolfwrMhtMtBWYVmM0A5erDLllZR90xqztmg4IV//35OvPvd9aWFYgSVAa54j+/c7a1AIBFAElrFtTmlFuYtLn48gqPqlwvnooZVpfrWw598IQNB2T0jUF92ufsG8XUh0LnrcBsPIHZDTeODwQ6dhvcztLTBg0bGICW9sXpQj+jjGPuD74799gHpplZx/Q3/aZUmtYdm1o8N6OF6N4TKkyuOmM38OQNvUxVv7amcbNGVtqW7ZuagbOrzFF35AfwFlouXvlpG8W/jbzaL1dYqds3Fz9+wjfThxFrrxptjvzWxNSYsfnOySm3nzxKHaeuyXnDN8YVVZ4KzMYXmP3otFrz/sk1vkHHx9dXpd43z5zWwXe6MEAKQNV80r+6tZs5cVIHM65bC9OE3jsa0odhfTo1NdcsrDB/OMcbaH11Szdz0MDWpkf7JlZ9mzZqYIbu38zctaoyL17y4T/fNNpG8W+jfNsW6aJo3zD8Ii12nr5+TA/zo23dDffn35zR04DC5l2s9HVNzhjHS32SgQKz4cCzKIHPuJetwGw43VJgVoFZBWZ1x2yGDsR9x+xVZ440k0ZW5KRrzxldFKBjQK92prJTcwNwqNBgRrnk99u9y83CadVmwvDOZvywzqa6SwvrT/Kq+TU5ZRYmbRj5RVVuPjx/uPsgM39KVc4+wP0EcX/9/eUZsq9L9bXLqBz64LO3zzKLplebvjVtrL5xAI0r9nrW5WcFZuMFzALw7Dupi6VrQXaE9h7X2UrTsl3TooIADMJ0rG5pUFZQELjH0I4Wn/JDIHY3JYDWDcRYT+BG55pWGWkBQnNaALQbb51QkrqzDILa2kbxaqMJ6wjkP6Cdbxp/SE9P/Sp1+wbVP46/7b4pZtJhvQz6DPcf2G0qmpnKvm0tv2mb+njWlfOKwq4rcoZsMCZBtn0mVgQeK/3KVoHZeAKzT6yvNp1oTts2rr1vcOSVzd1SffKKBRW+0+ULntyyrIsFqF42P1hZjxza1TQjMFWOH9J91Oh2rryfM6OTadww/TFwIwJ0ZdrTpnR0TZtvPQuZTttoPxOnNgIgObNXS+sDAXwkkIum1bY0bxzb3VXHSt2+YXTzxU3VVt3xYQT3IfStWb1bWv0Tu+8B2oYpo1hp65KcIYP1I9qZNk0bmu8d3rVk8lRgNhx4FndwNEr+FJgNp1sKzNKMU6bmE6qXGbP2jgzQTe4WVferRnfM1r0ds9PGVKZeFKHjbjRj3P4FBzq+/NnaVHkf7VlW8PzrMhDjxftlp4yw5OYHmLXnEyatPa8gz1GV64dHHBHtpvdu/g9cM8VTX+NcXymTcuuDT9w8w2pLBWaDAZlSJ8rBXeyjjAH+YGzo0ivYkcSHXjvG2om6+usjiw6oYOcqj1+H3+R/Z+/Ruyabho0bGACwo1f0MKu/NsraqQeQiEHWmpGdHPkfs7ImVebIg7obHE+J3bfzTxpo5Ql+3NL6BTiKHU/bKF5txDu2WZdz2QAuvXSklO3rxYdXGO5mregpPnAgUKR9VQvTiPqlrP/geVWedfUqo9hhdUHOUgYdu7e0ZDtudW1RZKrAbPyA2fdO6mEqWydOdQhyJPHvaafp7SsqzbeWV5oPabdtsUARzvfkyR0s3Vw3rG2gsk6inbIYLybVtDQ3Lq00v6DdZABrx3VPfNyLsBeO7JaV52/Pqk2NM7UdmpjHDqsyvzurl/l/J9SYGT0T/aQBpX1uY3VWWuY5alvbaD8Tpza6ZlFFSqfkHOblvn5xF1f9KnX75qvPNx2U+ICT69mhRSNT1SZxDDr7wX73RP879vPlJZ90dUXOXDeMyZAnPrbBeMf+xbQVmA0HnkUJfMa9bAVmw+mWArM0GpapUWD2hGzQ0Q5GKzCbLaO475jFPaUv3DHLPH3bTIuwG5D6sLWDkP1evHu2+f0PVnoCUfks5ktQ6L2ny+tOyHzk4TdNGNAvTFq//DnFi6pcJ17sftit/dZ3F5p7Lp+U+uN4+yUTUn2C+wFs7FpG//j2VeUHzJZDH1Rgtn4Dsty3iwnMYldog+ROkrnHDSjKIr4EDPJ1S2D2sOvHBuJz4y0TDMAhe9m9Rneyxj8ca2wPw/OUjb2t8PFrs3ct9hqTSAtwd9u3gx+v7FReXffTNvIGa9C+zdskjtGcvrmvWXruUItqhid0CTb7IRxzc4u2pdmNXkzdG7qg2qpLI9r5MXNrP7Pp9sQRxtAX/igEdcWu/WLyUZ/ynnl0P0vmOH79qJ2FP85bgVnvvh7FHbOHDE3sPO9J4CMAiGIu3ofJm4HZFYPaBOIRd8K+RjsV7WVjBx/GD9DFcztnhSN+W+oH+xNoDfBapn/pqHTaUwgwlmH12a1t5H0X59dotzf0bSztln1obZVFNwvQEgAm+yMO4l5+oLNu1hU9w4cMvEt2bp9W1i5OHmf2be1u+tKR4twPf3C0++7gulLfOPCJMa+6bQL4Pn6i/1MQwvCuwGw48Czu4GiU/CkwG063FJilGaZMjQKzCszmtVs67sAsL2SzvfWQxOIWbPZzsh+7cbrBvY7Hrz/A4Dhk3BMLG8eJ4mjUJTO7mfuvnuyZRz7A7D9+usZcsH2oVS7KBv30sUVWOb+h42VvuWicWb2gxuCOyfVLe5kfP3RgFg//u2+FufErY82JRwyw+B3Yu50FuB2xvJd5+/FEXk51Zr/dd862ysUuYuzMmzupqzlj80Dz86eWmItOGGbly3Ht9r7751lymzOxyvTq3so6Pnfz6j4Guzbtcd2ew4CcftLaZfzKvXNTvP3px6vMeccMScn/lI0DDeTpxiv7+ymX48LOp43sfAfVDRxHTPOXRb964SDHOq2c18MKLwYwG0Y38tXJqPog2vile+aY0zYNtPrg0P7tLfv0owaZ7++a4yh7qR//88wSc+fXJppTjhxojQcYj6CbfoDZMHKWPJTSrUcZBwOciwnMMmgCcPIYHwDj8q8MM8MWVZthC9M078SBnoDK4jMHp9JMOaK3dU8s7NpRnQ2OJwZAeuDJgzzzCAP6uQEyuDsWY2Tz1k3M9oey78jF7th114xx5GvOcel7oNdePdoxjlu5xfbXNkqAJnFsoybNEzva5McF2CkKPZQ7RhEOP8S360uQ9sWx3xMP7ZXqrweeMsgqBzsqqwa2s3Z/467kQXO7GvhV9mlrJq3v5dgfwMeqS0dafRnHg7etbG4dyYy0y84flsUn881gNI5xZj9pHzAjsSvEa/d50HJxdDJ2i8pxCvfaolwc9QvgEkAw6nzA9P2tnfSSJ7iDyNmeFs9BeeY8AKRCVpAHjlIH9aJj48ceXGvdbd1jWEez7d7sD004PWzUn+U+alkPR7nL+EHdCszGC5iVoMlXZnXKCTD+mu6BPH1qR7NlTPsUHTO+QxZwKRf4f3lKjTmVjvxFmqPHtje4s3UPgaJHjGxn3dU6prq5wXHCdvBT5gF3vqCfPR9+BjjUmj76wHh5yTxn8Oud43u43rvLd84uOaB1TrlxmaWwtY3S4Gjc2uiiOYmPmuXHBdglCh0EyR2jKwcnrqRBGqk3QdsXu8FxRDn6H0A67HKfQrvHe3dsatbS7nPcRXob7bDEEcv4OGNh/9bW/a+yTHbjHljwA96G0D3LAFUR/6zpnVzvNGUwGh85ON0ji48mcOcz6v/8kc5H7+ZTLna4b6XxhscqHt9wpzR2zJ8wsaMZ3bW5mdijhTmb+LffNR1UziwjtvPhmdOCv9VD2li7+nEX9sQeLc1hdDwx+IQbAD7HdbMvnJ34cK9Vk4Yl2TWrwGw48CxK4DPuZSswG063FJil2aVMjQKzCswqMEtgCoMRAFKor6doxADnO+nOP3ZIKg3SAgjCfam9u7dJ3ZWKfLp0bGa6798ygyaP6mIBvlwm7B8+OD9VJpcPQBjgYUe6S4/92O5X2zaj/D+/frDpmryjleNIuzHdx/P4TdMz0nD5SHv4ssTCtEzj5AbIx+lg//WN1eaog/tYRws5xYcfAGWUIdM5uYOCnDIPP2ntMj56bb8UT9/95rQsGQM0kmU4uf2Uy+nybSM735BpEN1wA2ahw1eflbhjecvqxIcLAAKZXyc7SH3D6EY+Ohl1HwTovnZx4ihYt75w6NKe5vN9q7Jk/M+3DzFnbBlsGom7ejgP3L18JoXh2eko4zBydmrjUvopMJuee/zIvVjA7Oa7JhnsqLJ0jMARP4vz3YdmzpVIW9WvrWda+z2tGceaUnrWeQAPkgeAsQAn2hEA1IoWnzgedhG27tQsg3Bv5zH3+9+5irh8t2W/KcF36o1bU2vxg93GQe7llfUrllvbKAGaxLGNGibvSDyCdnFz+zsBswiHviM+x2M7SPvi2G/uN042jvlu26V5Vhzs2OXyYGPH+aA5XbPiyTwBdNr7AoBh3pGPDyFknuzGjvb+UyvN4rMGZ4XnW+7qyxJXZEj+8DHIqotHmGb0IYb0hxtHKzM/bAeRM6eBnS/PSIudli3aZvNn59fP8fGsVwD3C71rVoHZeAGzfMwv9ORNj7ssefEfdxbadQrP2OXHcez2nasyrwoC0CDvbeX8cMfkz21HmeJITgBAXWn3V0sCGBC3KY1teJbUvV0Tg52H9rK9nnFsM5f9o23Bdup9cnptCtTdPqE0O9K86iLDtI0SwGwc2wh3FkPnDhuevtfYDZhFHMRFmjDti6O/Wc+dbAC0dn8cM4xdl7Jc9HH0M3tcfq5s1cjsOjh7HMCHHIjTkY4v/uxM5yPPr6Q7qgE8OoXnW+7s3uIaBCofd67iKPKDCfBknqWNO6xlffPpR5w+X56RHjvwJV9O7nl9W2XwyuVKGx/dcFqA0DKsGG4FZsOBZ3EHR6PkT4HZcLqlwCyNhGVqFJhVYFaBWQHMAgQ7aeOA1MsP9XszczzdubNjvOXfuUMzK6wp/aGU98f+i4CVtq3cX3CRD1Nr+rMKMEUuxOPYWRw5C8ANu2IRdwoBuM2bJhfLaQcrdnHOm1xlgTcIk+l/9+pK06RxQ9OGeDj4wBrzzQvGmmdvn2XteK2qSByfU1vdKiMNp+fdkiizZ/fW5pyjB5ubLhxrsOO1slPmQh129HE62IfR7l2uF3YTo8z3n1tq7Q4EHxy2ZkFtRjqZB7uDgH6chm1fad9dZ+12njgicfQQ6sfp0R7XnzfaXH76CAtEB9930c5FDnezfZWb1K982yisbrgBs9AV7AJH3bCjGnX/Sw4APUh9w+hGPjoZZR9EG8l7rQ+c2tU8dN0U8/p3Flg2H6UOvZo9ocoY0kWpU8tmd0/1Few4x4cSG1f0Nn1rMv9wOgGzYeQseYjCrcBsPIBZ7IzjsXrJ2UOygAkJNrB73dVjzOTDexuALH0mJsbUXMAsQJIRS7qlykKZ3Qa3t+6nhT/v7AJIJO+PxQ7eprQIxDx62QAfnI4rZr7ZxtHNAGgYjAIwvfKiEb7qznkAPGG+cKQx+8fF1jZKAFxxayPswGYdlrrKAJrcMYtwxG1E75z23dxB2xe7ZPm4ZOQJABW7aLnfwQ/uMatqrB3seMZHC1Kf+09PgzLY4X7Q+UMN+hL6Do8DnLdMBzd2xSMMH1OsumRkVn3s8eVzvuUCEJ53wgCrr/N9q9ghDHmClw7E04il3UkuHS3gGGGyXLiDypnT58sz0tcmj1dv07mZwUkD0AMA1wDKcaoBeAdhNy6X52ZjTOf4uFPbLV4+/grMxgeY/ZRAEtz1iLYeXtXM18L95wTW4M5L7MYCtWmW6BdewCzKuWZhRcaRpdipehrtor2K/HH3K+vb4bSLVoIG5810/tiZ40sbAJBM6+a+YyXtdCdwhnfpHdivpa90Mr/jCIzlsrH7V4ZF7dY2SgCzcWwj7ByH3mAHK+uJGzCLneiIiz7AcWEHbd836IOLy5JHKCM/AKjY9Qq9Zx2GDbAPO9dxJy+eHzwkDbJil3uz5Mdh+IAC6b9Px3njKGL0Yb6jGh9NwF/yy/edIk98xIAd9DLcyx2m3L3E8xUE+ELWXE++G7o5/WfATnfUtyuB0BgL7GNYUDlzPcLwjB3DLH/s9n11azeDnbvY9SyB5lm9/Y1ZI6oSa3LYNYv6MI/FsBWYDQeeRQl8xr1sBWbD6ZYCszQDlKlRYFaBWQVmBTALcOLNRxekXvqwG1QCFjhemMYCix64JnNH5f+9drDBUbEALzkOjvKFn6S//WRNRp4yf7hXzU8DmgBbd10xKSM+dub95/+tzfBDus9eWWFw7K09v1svSu+UwLHIMvy+q9L3j2K339/fykwP8LlHVforRQnMYgcu13PprG4Z+XIZEuTGcazs72QHAf3s6YOkxVHA4FsCszK/SSMTIEOhgVmUkU8bSd7y0Q0JzOLDAuys5t3VbmC9LFO6/co5jG6E0cmo+iA+qOC+cMH2zN30LD/4c5zrzk3sVEYYjo9m/23r+hnsnuU0OEodH2RwuB2YDSNnLiNKW4HZeACzOB4TOgZw0s8xxvYFfD4qNhcwi3RrrhiV0mfsvJN54YhR1nWASDJsC+3q3XDjeMNHuyIejk2Fn6SjcxztiTxRx5a2kygAFMnycrmx07Z6UGKBqBEtCh16rfNRx7nyKVW4tlG82gjAG4BN2f5OwCzCARoivoxrd/ttXxyNi76DneUM9AKI5X634qvDrXIOOm+o5SfvtsVOVo7n9iGC/PDC/pEH35fLebStaGYdH4wd8gtOHWg201HK9nrhOWy5nKcEjrEDGYAth8HedPskA9Bc+tndfuUclmfezQvg3M4DxjscHw05AhS3h9ufcZwxA6jYkWwPD/PM+R5OPIXJp1zTlvKOWQAo3Lfsu/L8LtzzUbF2UMMp/YJ+ra3yABLY75JcTCAJeOlPHxbItDhe9KfH9TA/2d7dOvoYcXBPJZ4l/Yx2h8l0bu4ddJcs1xn2tNqWWUeYuqVl/wcO2T8Fnqyh+3nZP662tlF82ghg54CKphkgoBswe/+a/U2/zk0NbC/d8tu+fM8r91UAfgBS0Q/wcQSXgaPF4Sfvth1GH27ADwDm7k3dUnE5Dfqi20ceH51Wa2rpiGSkB2G3PABSHDGMI8TxYQPfOcv5sR2mXM4D4CaXDXtc9xbmAwEOA7D8H9sd0pxW2n7lHIbnS+cl1pMwRmJ3rywf7jOmJYB97Pq1hzk9c3zU+3U6MtopTqH8FJgNB57FHRyNkj8FZsPplgKzNAKWqVFgVoFZBWYJHJHghARmsctRhsFd0zUBVH79tJFZYQjP535LWQaDb61aNDZP3+b/jlbswnvylhl0N+UQa7fd8jn0x5ful8WdszR+WbT3vvS9qihzxdzE3aIA6L54M3MXL/P07vcWWztx8dWfPMoYoDXyBZ8fv7jMURYAoXnXL+7N5DydbL+gX9i0UQKz+bSRrG8+uiGBWQCy2BUNQtsVC5gNoxthdJJlVeo+OHV0F0ueOPocu2eZD2njYwo+Gn362MpUHN5pi2PUwbdMY7mpX+P4c7SXHZgNI+escmzjYCnCFZjNnHtyybxYRxnzEcOdaLd2PgvkfsEK5C2B2U23T8wqrw2BNdB17OJ14qVQd8wC8ADgxUfKApSeubWfY5l2PgBo8X2Y4BX3VNrjxO1Z2yj+beQGzPrRJb/ty8CsPLZ7Ee3IhB5jF+m25P3S+NAAfvvRwi2Xz0cYo68ccbMzEIcPI3g3Knaiclq2Z1Afw5HkVt7IXxKVNWR+16wd74UoF+UzMAv+l547NIs35tHL9ivnsDx36Z04LQMA7MLTB1sffgDk4w9nsIMWd9968SrD+Nh43B0s/cO6FZiNz45ZecTwTtpFms8CvV+wAnkzMLtxVOauWIRdR7tw0bdb0EdLbnwU4o7ZVzZ3s3bL4i5NHktwZ6NfYPclArNxJCrS4n5NgE5u/MbFX9so3m3kBsz61R+/7cvArDy2G8eEQ5flbvNlAxP/98+c1sHSbfQN7isAU9344t3AWPux9wvcI7uK7qVtRHM25yVtALePHpreoYsyClEu8pHA7Py+LR3vuXWrk/T3I+ewPH+bQHiWC3b6Prux2voA5Venpj88eeaI6qwj3yWf0o2jpTk/fFAiwwrtVmA2HHgWJfAZ97IVmA2nWwrM0ihYpkaBWQVmFZi1ARIMzOKoV6eFcgCeNB6YS04e7hheKFDo0pNHOObvxBN28uLeWfDlRS/dMycjTxybivi4Y9QpX/b7xbNLrGNZ+Rn2MHEfL766dCPmB8cwy/R2d7kDs/m2kZQTA7NBdEMCs9i5zfl1oPvL+Chj9stl+22jMLoRRieZ/1L2QQCuLWihF3qOu2CZByf77K2DrHjt2zRNxeM7pC86YVjKz54Wx2wjfzswG0bO9jKieFZgNh7ALI7/hX4BqMxnod4vWIG8GZhFmU5l9U7u3gWA5BReKGCW88YOXBynjPoDZNiWY8ctQFkGXZBm2KJqRz45/7jY2kbeAEoc2ikqYJZ3d+KoX5YDdkBCv0Hs17kmscBr+WNB1o2S6dzGExwvjN20Y1bWmN4TKgw+COGyYOOjBy4TdqHKZWDWaReqLM/L7bcfheV51rb+GTKR8mlOd+MCWA8CzOKObuSBY5C96hc0TIFZ73GllDtmcRQx6wkW+/NZqPcDVnC+DMziGFT2YxuAKXgBeMR+drsQwCzniV1yOIKVdwxKYIrj2G3sMOZdgTjO9YdHB7uX1p5fqZ61jbJ3HpZK9n7KiRKY5d2dt6+oTPW7tck7ablP3LUqfR0B+iiAVyfisQS223iC3e84VnkDHVmO3ep8FDrS4KhkuZO+UOUyMIs7qu335vppH47jpx+F5fljursau6SlLNmNsXFgl6bmglmdfB8HjaOROf3FdFoA16UYtgKz4cCzuIOjUfKnwGw43VJglkbBMjUKzCowq8CsCzCLO2OdwAq+/7LYwCyOR3Uq3+73px+vMt0qE3eLAEwGf7gvdPeds83zO2cbgGm4E5fGMGMHZgEQwf+MHICSvUw8Y7cl0gKQ5eNx3eze3dsYeXyrU35+Qb+waaPYMRumjWR9GZj1qxtI6wbMnnfMEHPlGaMsHQPwfsN5Y0yuY7b9tlEY3QijkyyrQgGzfuSMu20b0R8s9AX0O+bByUY49xmkw93GeAZ94/wxrmlxnDni2IHZMHJ24q/UfgrMRg/M4vhO1sEBMzMBEb+L9H7BCuTHwCzu/XTKH0AN+CkVMAse1l6dPkJ50RmDHflCPABKA2env1gfNLdr6jhYp7rEyU/byBtAiUNbRQ3Myh3zTsAsjh62xgp652tF1yJ4EXbFTtvUJ6MvYaxBH3KS9SGXj7aOV+ax6ChxrHHYcrk8BmZxtzT7BbX99qNC8Izj3HGENe9AZtlIe8Fp7uOVrNuAmelx66idzkdGy/h+3QrMeo8rpQRmj6Sdq6wbb23P75hLP2AFgwBewCzuZQQvpQJmmSfcM4lyB1dmHqHM4Wy/SMetMijbnj4SA5DMYXG3tY0UmIWOOu2YZWBW7pi3A7M3LEnsZkc/6Ujv4fu3difc1zqya3Mjd3gCDHW7V/YTAiLl/be485X7U9hyOR8GZrHTnf3ysf30o0LwjF23uB+5V0dngBbtABl/Rnd356oHgHDEB20Sss2VLp9wBWbDgWdRAp9xL1uB2XC6pcAsjYBlahSYVWBWgdkCA7PYQUfjhUWvf2eBK9jiBoIEBd8e/cY0qyzcR/vWdxdmlYejVVs2b2zFsQOzU5JHpC6Y2jUrnRt/7D9/SmKh58A80nIe0vYL+sk07A6SloHZQ5f2dKzzwN6JxY1C3jEbpo24jrCD6gbSuAGzMt+th/S19OMpOgpb+tvdfuUcRjfC6CTzW+o+yDqzfmkvT/kdRuEYG/ChAvPapWNisRtHjrOf3T5jc+KeWTswG0bO9jKieFZgNnpg9uhdky2dhF72m1IXgNnEfAd+Aeb4BRByxWveJnEU4qhlPRzzxN2TkA/P7YPzBGVxL+78kwZahHs13YCqXPwGDfcLKCHf8OC5tlHQ9kH8uAOzNcMTO/JqRnjfd+tUd+g9+g7us3YKhx/ut+X+tfLiEal4YcqVZZUSmA3LM/og7pIF/xij1109xqz+2iiz/MLhBnf1MiCKe65lHd3c2GHLssUxyG7xgvozH3rHrDNAW0pgdjst/nMb46jRfBbj/YAVnG9YYPaUyR0sfnEcKecZ1pag0G/ozk2n/HCcKO/s60Q7ZXGcsVM8L7//R2DLt5ZXmtuSBLDIK34hw7SN/AGzUbVRHHbMegGz0H8eJ54jd1DdxJ2uOMLY6W5azgt33KIM7KJlv7Dlcj6lBGbD8gyg+uF16SOd8cEMPgrBDuRdq6vMHLpfm9viu4el43Fd7TaOreb4x4xPHE1tj1OoZwVmw4FncQdHo+RPgdlwuqXALI2CZWoUmFVgVoHZAgOzADj4XtXHbpzuCLb85fWDne+TzAN8w7G2ND6ZCtrB4HRH5c5LJ6Re5OzA7CkbE4APjrGxhzFQg92eAIZmT6jK2FHJYBGOcX3v6cWO9UQeAMkAULvdYcvl+AX9OL60g6S98LihljxGDuyUxTPu0+Xdj4UEZsO0kaxnsYBZ3gX+7aumZMlElu9XzmF0I4xOSl5L2QcByKIPYmf6248vcpQhPprAxxOIt+GgNICLfgW/1rRAJI+a5rp8tGeZFYY4dmA2jJw5/yhtBWajB2axEN86+SV39yH+Fvnti/elBP1QNu8gW0x3Y9p5wTOABxx57BTm5Lf+G+OsPog+hiNE7XFwfDF2EyMcNGJJt4w4m++aZFZdOjLDz54HnjeIcpAPjhXlOyOd4hfST9so/m0Ud2AWHy1AbxvRThUcAe6mn/iIAR9NAFDkOPJoXtxfy/7SXsf32lIZACE5LEy5nAfsUgKzYXluQVdN4GORjbdOSMlB1oXHo5btmjqGy7hwA8BF27Vo6y++Pb3bswKzzoAsy6uUwOw1ixKnTaCdn9rQNQWIBFmoLyXod/7MxIceQ+leTCce/3BOz4ydehwH/uy226uHJO5mrqKdfvYwPAMUaZu8egQ7BfdtzTy++IUju5lfnJy+/9EpD/itGZp5dRCOI3WLW2h/baN4t1HcgVkcr4uTzjBOHDY8vaPVSU9/fmJNVh9B30La5YNau+r8Qcl7becS8Mj5hi2X8yklMBuW568fmBiTT5rkDKK+fXx6B+wVCypSsuK62m2At5A96PID9Shju3zyfcb9v5DpwPmbTZSAZX0pW4FZBWapv6lxkIACswrMli8w++46g92iINyhSvpvNq/uk/IzFO4ESgBERFzrKGOHOHzH7MUnut8J2adH4s/hzPGVBmAfysFxsbddPN7MmVhlvRRjt5ssn3llkOwKOmaW/WC78Xv/1ekdT+duG2w+37fKyhdg6dFr+1l3h6A+oD13zc4o8/c/WGmqKlpYYbjrEmDkv99Za8XBUavgt/v+iS8f8SL/6+8vT6X/DblxTynyBWD08q65qTCkfeXeueaC7UNT6Q9flgajuN6yfjgaGnmh/tLfrd4yTpC091yeProTx/kC+AI/++6fZ/r3TP/ZvuOyian6ML+w8yk3TBvJMoPqBtKinbj9339uaQb/XBfWaSdgluPA9ivnMLoRRidlO5WyDwJQxXgBOfeoamWevX1Whu48fdvMVD8AAPs/zyxJhePIcW6f6i4tDMDKP9OHG6CHr59q4Mfh0E/ZH8LIWcoqKrcCs/EAZvmOVXnHJC8su9nY6ck0+9jEfYiVfdum/DjMnh6ADfQZRxkD8LSH8x2z49f2zArjuO32T/QJ8H3odQmAaP0NYy1QtcfQjtbdl9ixxvFh9xjW0QI6Rq/oYYE+vFMVIFFFbeL4wwZ0JLnTzq9Ry9OLJ92HtrdAWACxTFaZVKfVl6V3+cmy2S3BKchgzCo64ox4KxZxG8DWNiK9iGEboe25nbALGzxaR2Qn+5eXbnC6IO2LI8JRRt9JXVJ6x3fM5jrKGCBhs1aJRdkO1S2N3NWKDwxW0S7XcatrTetOiVMg5NHo2CWOckFt6EjkDd8clyofddx4ywS66zlxWgnARoC7XPcw5Ur58jHpkzf0SsnckqHDOMRly/RB5ByW50aNE9cj4EjoVZdkfvSx+c7JpmX7xLGIGAMlr25u5APZV/Vv5yu+Wz52fwVmvcfvUgKzTxIYy31M3jHptVgNkPP3grq3S7zHPnDI/hn+TmAodrqivK/S3bb2MuQds05pEf+mg9K7uL+5tNJ8Skd5/pp2uT5yKF0RQLt/Kwk4xUfDTx+eBpkR1oL6xlTaiYfdqgyi4nhVecfu+hHZgBNAEBzdyjJC+di5xoQdsAib3TsNJtnrxc8MTiF+a/pQ5YNTcoO5nDaorW0U/zZCm3I7Yacu6xiOsUX/cusDMh33Q799kI8ylncj81HGsv/zUcanTemY6qfHT0zvrj9hYkcDAJL1Ev0EfQM74hnA/cn29AcMfTulj+Q9VeSJ9OiHtyzrkkr3Nbp/lvOFHaZcpIeMeGxBv2OZsS3Lsru5fTiuXzmH4fm8mYn3TujD0WPbW2Oc5EuGP7E+9+7lG2mcZN2SO3FlnoVy647ZcOBZfQFZ86mnArPhdEt3zNIoWKZGgVkFZssSmF2zoDZ1ryr13dSLjHRjZ6T9ONsVc3ukXigRtxntgLvm7MQ9nNjxxkAk5wNQk4FQCW7c+JWxGWXyvZmcDvZRB/dJgTN8xKkMt7sbN2pgAPDIcuD+v9cOToE+SIMX6VYtEotneAZgxEcZ47ld66YZwBBAJNxNy+Vh1x/AWJTHftjpd+05o7PKBpDHuwARF3fdjh7cyWAXLaeFjTztoB9kLeO4ubHz0Q4oh0mLnbu4C5fLwx9/yISfpT2oT3sLyGSZ51tumDbKVzde/fY805l2Ucv65HIXso3C6EYYneS2KmUfRJkA3zFesIwBqI4d0ikDWEX4fVdNyupH2w9LAFuc1m4DzGU/jFu4c5brGUbOnEdUtgKz8QBmeafefjR35Lp/cNu9UyxghfUxlz1pfa8UEGABI8kv9ZEO4MPUjYl7KNdeOToF+nCeAB+c+JmxpV+qPyAudp5yGrYHzemaKhdgAu8Y43CkaQuwQvAz+fDsuyc33T7RNEyCJJzWzV55kTcwK49CbkxzpLxH0w54hHnWNnJ+50O7xaWNuH0B9LvpE/yrBmYDafm075a7p5i2XRLgHJeHPrD66yPNkrOHpHgA0IaPFY64eXzKryG9C/LdywBY8cx5YLd9l95trF207AcbfQZxuZ5Lz02XgXDseq/q19a6s7lzTeLDCE4v03H6fMvtPz29kMn52218kLH03KEpXrnMfOTMaWHnyzPSMjDLvAJY7TWmkwWssvwh43XXOO8+lnzgbt8GDRN9AvKQYWHdCszGB5iVO/W2jGmfAYg4LdbvmNvZOpKUdczLxpG/AE2Rz/cIKOX7WTkNdnHy0cEMBHEYjj09fWoaFGJePjyt1vQRIA8A18ZJPeW0zclPHsv8mNgxxnF6dmiSOpoYft3bNXa8A3MrASOcxssG6Ms8Otk/FseJIh8AN07xCuGnbeQ8l8epjdDOAMmgv256hT7w0Nrso2rzaV/sNLeXtXJwG0sHR1Sl53j0Q/B26PDERgHwhrtM4Yf7TGVc5DeoS1PDu7FlPRDvd2elj48eTXnI8Oq2jc3SAW0M7nfmI8IRPryqWWrM4L6Qb7k4blzmLcuXboCJXJa085Ezp8+XZ6SXwCv4BJg8tlsLs6h/a+tuX+Z9VbL9uEw3G+M6p3nj2DRY7hY/jL8Cs+HAs3wAy/qSRoHZcLqlwCyNgmVqFJhVYLYsgVkcFUp91pMA+Nl3cQKolekAaF59VgKQ/AkBsxLwRLza6lbms1dWpEASCXDcdOFYa/eczA8ALYC2J3GXp9iNu4lAWhnPyQ1e3O4ABW+jBiWOheK0AEdx9C12uZ5yZHq3AsDlnz+V3rEHnj9+cZlZMrNbBhiLfAAkrV3c0zqKWNZNut95YqGZPrYyA9BmHrCTdsdJw1O7UmW6gw9Mf1XK8Z1syBy7CguVFvn86MH5ZnDfxO4MLhP3fj5+03TDu0fhD2AWRzFz2WF4zreN8tWNNx5ZkDoCl+voZUPObz6aeSdymPpCZvnqBtKG0Ulur1L2QZSJXfG4r1l+1ACZ4xn+CGfe7Pbtl0ww2LUu26iyU3Nz/Xmjzbd2pBfJ8SGEHdwNI2c7H6V8VmA2HsDsvBMGpPQOR956LdZvuy8b4JE6K90APCYdlgZm+0/NBEmwqD9lYwIMXXvlKAOwUqZvSzvrsOPIiZ8ZW/uZNp0zPzwB0ATgAUCTfTcuQNABM6tSO81kOa3oAxbIwKkc7ATsSLsDZXwnN8reTGU45cF+vMsN6e3HIXOcQtjaRs7vfnFqI25n3m3tpFPwA3DLcdnOp30BMkr9Q95NmtM7Iu38xhHbkA37AZjF7lDWewCA8thw3Hdq7W4VHzUw/9hJi125h9+UuB+VeQaAyHGwM5/d0saOXeze5TR2O59yB85OH/0ny5JujENLzhmSVW4+ci4Ez8jD+miE2qh9Fd3n57Dgj7ZhsNxepv155tHpD1kWnOYuX3s6P88KzMYHmMUifK/ktQQAWHItyl86r8Lxf5vsG+zGsb+8qw7HAdtBEpSHHa8oc8WgNhknNQFcdQJmEfe9k3qY6T1bGsThsmAD1Dl3RieD3Yf2euD+TIBFdhAX6VD2O7Tjz54GzzjqmXcZyrLsbvsuQHteVy1MHxkNvlEHe5xCPWsbOc/lcWojtDV2cjcTHyzZdQphiGPXi3za96I5nTP6CsoCMIq8jxqdXtc4LLlr/BtLuqT6ypjqBDCLuNjd+hU6gpuP9pY8g18cR3wf3YOKeJLvgwYkPqTCmNBSfIzM6dHHwMf7LkeC51Muji+2jzlcnrRxt63kld35yJnTws6HZ6TDSQDgrz29a3VNHgEt+YWsNoxs5/ghiSwfbuz45Z36AwlEt4cX+lmB2XDgWX0BWfOppwKz4XRLgVkaRcvUKDCrwGxZArOlBBdylfWHH640rz18oAWQ4jjYXPHzDiegF8APdtX+9LFFjvfN5sobRxBjZzAAYOSR615Ymd/f31pjAXtICxASu0RleBzdn768wjrCGHZJ+CtAG5WETwIZC1lOGN0Io5Nch5L1waTcwDPA0u/dOtP87MnFBs/Mi6ct9OPD3Qf5SyPaKoycPfkSZRQyngKzwfoZTm+gd1HTb0r6GFI/i+e54uDYUD4eGLtac8WPUzjAUOz6w1GoAJP88IbdgAtOHWjtaAMgvI3AVz/pwsTBkctoOxCAFjfAOUwZcU2rbeQN3sS13XLxBbB3zRWjLFAT/WgL3bXslWb65r5m1jGJO5wB/gIkhN/C0webtVeN9t1/g5brxVOpwoLyfASNZzhFAPwdc/9Ug/t3l1843Do+GmH2D0+86oFj3DHuAOz1O0Z65SfDFJj17tulPMoYC/M3EAjD88yrW7oVffG+UGDA5wT+4L5XADCfiGNVvfIHEIxjiK9b3MUCvXBsrFf8QoXhbk2W8eYx2UcmF6qcuOWjbZTeNRq3tgnLDz5mAHCM43Tf2t4jC4yV+aOf4lhk7NhEX0U69EHQ4+urUseLyzRu7iDluuVRav8gPOPYZBzF/hGdDgA+carB80d2Nd+hHdZ7aXz2O9YhLcY6HneuJ1kXu94KzIYDz/IBLOtLGgVmw+mWArM0EpapUWBWgVkFZosEOhQSwNC8ggEYKi+Vl+pA/HVAgdlgbVQsYBYL7XO2H2D96cfuuCO/5bxLVS7Iq9t7Qd4uH3n88pD5mccs2+PqczDZFkpe2kbRyL1Q7af5ZLdf4hjjxG7EaZsSx8YXUk4KzGbLXMq31MAsdnbhaF9aszJ+jjMu9uJ+OeZf0Spxsgd2u+E+znKsY12vk7ZR+YLIdV038+Wfj2LH3du/PSt9J3C++eVKp8BsOPCsvoCs+dRTgdlwuqXAbJmislQtBWYVmFVgVoHZwDvzFPQKBqiovFReqgPZOqDAbLZMvPSkmMDssQ9MNbjHEAu6Yw+u9dz5Jhee1e29MM/y4ftlcbzzhhszj3nlOGr7k2Wx5KRtFK38i9Wu9TnfMasSV4bgGOujd00u+LiuwKx3nyk1MIvFduymwjyO+ww/PLX4C/i5FvjLKRz33UK2ID4qtpzqVw510TZSULYc9FjW4Ven1ljjOcadr87uVJKPQRSYDQee5QNY1pc0CsyG0y0FZmkkLFOjwKwCswrMKjCrwKzqgOqA6kDJdUCB2fgAswAvcM/ifnRvZPM2TazjM+szoFHouh948iCDuzWHL+pWcHCk0LzW1/y0jbxBpvqqF3W13jgCuUVb2j1JY/ryrwwryrijwKx3n4kCmMWu2Yk9Wljg4cVzO5dkEV+CCOXs/pCOJF1Gd29Wt21sHeVaznWtq3XTNlJgtq7qrhvfO2gcJxzCTK1taXA8slu8QvorMBsOPKsvIGs+9VRgNpxuKTBbpqgsVUuBWQVmFZhVQKbkgIzXrjANCwbWqLxUXnVVBxSYDaa7xdwxy+DDnOMOsADEzTnui+T4ansvzKt8VD6qA6oDUejAlrunmE7dW5l5JwwoCiiLOikw663bUQCzWJx//+QaM69vK3PW9NLsriokIKB5KbCmOqA6oDqQ1oGzaRxfOqCNee+k0h2drsBsOPAsH8CyvqRRYDacbikwq8BsXuDdTB+gZ12I06XvLOtLpT7TT6rXcpBtNWrNbZZMaqtbKainwK7qgOqA6oDqQGAdUGA2fsBsFACClum9uK/yUfmoDqgOxFEHFJj11suogFkFNdKghspCZaE6oDqgOhBMBxSYDQee1ReQNZ96KjAbTrcUmFVgtl4DkgrMvprV/grMBltQr6s72pRvbWfVAdWBYumAArPBdKsUO2bjuPivPHkv/qt8VD6qA6oDUeiAArPeeqfAbDAwQMETlZfqgOqA6kD0OqDAbDjwLB/Asr6kUWA2nG4pMKvAbBYwJ3dPlrtbgVkFZosFTGi+wYAJlZfKS3WgfHRAgdlgbanArPcieBTAhJapbaI6oDpQX3VAgVlv3VdgNnqAQUEebQPVAdUB1YFgOqDAbDjwrL6ArPnUU4HZcLqlwGz5ArN/pqqZzn1mmK5DDlJykUGLdl2tY3vbVQ1VGSVlVNF7miWT1i0bmy2r+yqpDFQHVAdUB1QHAunAnIlV1jxSVdEiULr6OudMHFGReBfZv7kZPLerkspAdUB1QHVAdSAyHWjQcD9rTuo3pTIyHuI8F/afXmnJB2sth49sp6QyUB1QHVAdUB2IvQ5UtmpkzV0zeraMPa9h59ZBXZpadW3ffYDpPWW1UpFl0GXABEveLdpXqqzzkHXrih78Xnl9+UKU9bNmX+LPgpLKQHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHUgVjrwELWHmjKSwB+pLgBmgbifo+Qqg7eScnpMZZSS0TeTMvmDyiQlE+1DOo6oDqgOqA7414F7k/PIL3Ue8TWPPJGU109UXr7kpX3Rf19UWamsVAdUB4LqwD+Tc9LXdU5ynJMuTcrnPyofR/kE1TeNr2OU6oDqgOpA8XXgg+TctasezF0vJ+u6px7UNQ59576kvN9Xeef1XoQ1IOB3lxOpKSMJfEJ1QcMOL6M6FaMqPIBsL0bmdTTPMcQ3dAcTtxqVgEpAJaASUAkElcBKSoB5ZE/QhPU0/vFUb8gLf5TVqARUAioBlYBKIEoJfEGFY06qiZKJGJddlZTPlzHmUVlTCagEVAIqAZWAlMBL9IC5fYX0LFP3lcm67ijT+sWtWquT8n4+bozVEX6wBoS+iTUhNWUkAQVm/TWmArPZclJgNlsm6qMSUAmoBFQC/iWgwKx/WSGmArPB5KWxVQIqAZWASqB4ElBg1lu2Csx6y0dDVQIqAZWASiB+ElBgNn5tUi4cKTAbriUVmA0nv9imVmDWX9MoMJstJwVms2WiPioBlYBKQCXgXwIKzPqXFWIqMBtMXhpbJaASUAmoBIonAQVmvWWrwKy3fDRUJaASUAmoBOInAQVm49cm5cKRArPhWlKB2XDyi21qBWb9NY0Cs9lyUmA2WybqoxJQCagEVAL+JaDArH9ZIaYCs8HkpbFVAioBlYBKoHgSUGDWW7YKzHrLR0NVAioBlYBKIH4SUGA2fm1SLhwpMBuuJRWYDSe/2KZWYNZf0ygwmy0nBWazZaI+KgGVgEpAJeBfAgrM+pcVYiowG0xeGlsloBJQCagEiicBBWa9ZavArLd8NFQloBJQCagE4icBBWbj1yblwpECs+FaUoHZcPKLbWoFZv01jQKz2XJSYDZbJuqjElAJqARUAv4loMCsf1khpgKzweSlsVUCKgGVgEqgeBJQYNZbtgrMestHQ1UCKgGVgEogfhJQYDZ+bVIuHCkwG64lFZgNJ7/YplZg1l/TKDCbLScFZrNloj4qAZWASkAl4F8CCsz6lxViKjAbTF4aWyWgElAJqASKJwEFZr1lq8Cst3w0VCWgElAJqATiJwEFZuPXJuXCkQKz4VpSgdlw8ottagVm/TWNArPZclJgNlsm6qMSUAmoBFQC/iWgwKx/WSGmArPB5KWxVQIqAZWASqB4ElBg1lu2Csx6y0dDVQIqAZWASiB+ElBgNn5tUi4cKTAbriUVmA0nv9imVmDWX9MoMJstJwVms2WiPioBlYBKQCXgXwIKzPqXFWIqMBtMXhpbJaASUAmoBIonAQVmvWWrwKy3fDRUJaASUAmoBOInAQVm49cm5cKRArPhWlKB2XDyi23quAGzLUhSQ4jmE3WPkdQUmM1uDAVms2WiPioBlYBKQCXgXwIKzPqXFWLGCZhdQvxcS3QcGEuavmSPS1IT9gxg472P07cOkE6jqgTqswTC9rv6LLtcdcf/Uh6TeuaKXMLwM6ksjL+zSlimU1EKzDpJJe0Xd2DWaR4Pq/MNqfpjidBv+qdFoS6VgErAQwJh+51H1hpEEojje9Iw4gvz+KVEjYniZBSYjVNrlBcvq6k6huj58qpWyWqjwGzJRF3aguICzHahat9A9C8idFSmj8h9K1EroiiNArPZ0ldgNlsm6qMSUAmoBFQC/iWgwKx/WSFmXIDZauLlT0R4V9tGxOZZcvD72yD2DGDfLNLjA71yMfOoIltiUJm48BEDURSFhWLL1y3/sP2uKMIok0wnUT14THsgRnU6N8nXp2R3iJAvBWa9hR9nYNZtHg+r812Tuol+s89bPHUqtD1xewVRlP0NAosLH+ClHI3bPFuourq1X9h+Vyj+yjWfZ6liPJfn8/+kGHKBLnye5OuMYhQQIk8FZkMIT5N6SkCBWU/x5AxUYDaniOpmhDgAsxUkul8Q8WTpZO+l8ChfhBWYzdZvBWazZaI+KgGVgEpAJeBfAgrM+pcVYsYFmH2UeMG72jtEjYjYhF34KDdgtjMJ5iEiyAofGUZl4sJHVPUvdrnFlm+u/MP2u2LLpy7nH9fF8pYk1I+JMLZ8K0IBKzDrLfw4A7Nu83hYnS9HYHYNNfNvidDfunk3eVFD48JHUSsZUea55tlCsOXVfmH7XSH4K+c84vqetJ2EjnHl70T9YtQACszGqDHKjBUFZsM1qAKz4eQX29RxAGZ54MekhJdeHIs3m+g0ov8lgj/oZaKojAKz2ZJXYDZbJuqjElAJqARUAv4lsJKiYn7f4z9JvY4ZB2AWC0v8XrbY1hrPiLCBtjA/j+UGzM4V8ogSmI0LH350oC7GKbZ8c+Uftt/VRZmXiueJVBCPd/eXqlCf5RwheJvjM02hoykw6y3RuAKzXvN4WJ3vKvSyXHbM7hV1ihKYjQsf3lpfN0NzzbOFqJVX+4Xtd4Xgr5zzeIYqx3N5Pv9PiiWbxpTxL5K8fZ/sBsUqKGC+vD6/ImC6uhj9SmIaurGjLjJfB3lWYDZco+2i5NBXrAmpKSMJRA3MAoDlSRLHMfWxyXYAPf9GxBlvCy/VowKz2ZJWYDZbJuqjElAJqARUAv4loMCsf1khZtTALBYQPiTCe9uPiOwGu7hwukm+J5zcTGn5nXC+PfM6+DxX1EeB2TrYgD5ZLnY758o/bL/zWc16GQ2LpDym4Q7AOBmMx7yg+1ZEjCkw6y34OAKzuebxsDqvwKy3ToQJ3UuJ+R0pSoA4TB3imjbXPFsIvr3aL2y/KwR/5ZxHnN+TDifBc79eHpNGUGA2Jg1RhmwoMBuuUXdRcowXCsyGk2PsUn+SbNjhEXH23WT5UK5jXHg4VMS50yVOsb0VmM2WsAKz2TJRH5WASkAloBLwLwEFZv3LCjGjBmbXEg+8eHBEMNZ9xVZg1peYAkcqxYJjYKbKKEGx5Vvs/MuoKepdVU6lGvOYDD0ptVFg1lvicQRmiz2PKzDrrRNhQvdSYu7vCsyGkWR22lLMs9p+2XJXn/32a0JC+CMR+jZ0JA5Ggdk4tEJ58qDAbLh23UXJMVYoMBtOjrFLHSUwiy/D/kAExfovUQWRk8EXyv8kQrx/EOGLp1IbBWazJa7AbLZM1EcloBJQCagE/EtAgVn/skLMqIHZ14gHvIv9i8hpVyyOvMI1FKD2RE6mF3leSvQ40atENxDhWMVmRG7A7EkUxvnaj0+mIMvgnfJYIo43K+FdsN/qZN74QPBlItxXdQ3RFqIeRNK0owfwsZMI8gK9TsS8dSS33fQlj01ElxNBNvuI8O75VaJVRA2J7KY7eXCeB5AbdAUR5PoA0eFEpxPtJPLLB0UNZfCODtD+LiIs7IBuJ9pOlGvHId4rzydCvX9AdD/ROUQzidxMLQWwDLzafLaIVyMyA3DC6QeRG3qEuODjKaIXiCDTg4kQJk0+7SzT53L7zd+t38m6DaTCGhOhbhcTvUgEHdlG1I+ITSNyzCW6hAht9yQR6g/9zGUGU4QTiHYSQQcfJEL7TSPyMg0pcCER+hN0H22PhQekXURklzt5ZZl8ysa4g7Y/kgg8wH6MaDfR14lqiSqJWD/Ao5vJp3zOK8jYwmnY7kkOk6Rn2LOE9hfJsmtKWGZdKiqOwOxryTZzm8f96DzGkrVEdxK9TIR+g36CftCViHVyH7nZxGEeDzrWLCDmUa9PiLhOlyX9lpBtNxgnNxFdTuR3HkceucaiwylOED6QZ1gzjDLAOIi2/SHRo0SYC0YTeRnM80cRXU/0PNEeouuI4FdB5GRqyZPH2XzmceS5MpnHZjyQAXC9kegWIsxHmFNOIepDJI3feVamCer2o0de/Y7rBhnC4J0T7nuI0P/QTigDsmeDMXkLEd6n9hKhr0I30Qe8TFMKhD5eSoR3oJeI0JZI24nIy4SZy5BvPvNorvfg1ZQv3mtWELGOuf0/ibLuO4k/HmMmkjtqg3YHP5BbuRs9yri0LYw+Cd3C/KAmuAQwl0F+xwdPqiniLIFPiDk0bBQ7Zvsny0b5PyPyMnihQjzQKK+IRQrDSw3K3l6k/OtitlhAg0w+qIvMK88qAZWASkAlELkEsNiAeWRP5JzUDQbwEg554aW81GYqFYiyQU+6FA6wkuMMcohzAfl9KeJwXNjfJ3pYhM0nN5ubycFx/0HuAzhA2Fj05Tj/IvdoERbWiUUq5Mn52+0/U9jhRGzAnz2OfJbv3K0p7lVE/86RBu/BfYmkmUsPnO9F5P6DeGZ/L1vyIfPN1w3g6vcePPyGwsCz3WBBcifRf4nc+P02hTktCi4WaW4it5uROrRIRJpGbi5zG7nvEM/sz/bTFCYXloO0MyUNbPzm79bv7HXDwjrXRdrQ7fFETYgedInzd/LHYoqTwaLneUReOnw7hbdxSNyK/PYSSX7s7u9ROAAuJxOmbC73l5QxFp7t5T5OfpOE/wPktpsw5SOvoGOLvXw8/5iIeXcad53SFMpPgVlvScYNmPUzj+fS+T5U5XeJWOek/U/yP12E7SM3GzkG/4M8Mb7ZTTHn8XzGmvuIQVk/6X5EMB9mHkc2ucaiX/vkQ7CUtxPjtNs8wPWHXJo6lIB3+g+JOJ7d/h2FLXdIF3YeR5a7iVDeb4gwn/0++Wzn4S/kv46IDfTQHkc+F+I9yY8eefW73UkePyJ7NtFfXXjGHN+AaBoR6inrwW68SzYncjKDyfNNIo5rtyHbuU4JyS/MXBZmHgU/zKfbe/BoivOsiOc0T0ZVd2LLMovol+vh9K6RjFYyS4HZkom63hWE/xLQdQVm82v6XUn5KTCbn/xim+qTZMMW4qUjaCUXJstGx3wsR+J7Rdy1OeIWI5hfqLYXI/M6mqcCs3W04ZRtlYBKQCUQEwlgEQfvAHtiwk/c2YgSmMUuEV40OMJFUF4LHxeK9MjnT0T44/+xzZ/LmE/+bLCg+h4Rh+0ld0MOJBvvsFgQ5nAs7hbKjKOMZN7g9ymiN4jsQCIvONZQGMCe/yVinv6W9IN/PyI2d5GD48B+n+h7RLuJPiOSYc/RszRz6UGG290oyy8fMt983Kso0X+JmAfI7MdE+PCS/WBjsXAYEZum5EA8GQf13k30kc0f/1k6EEkTdkF3GmUmy2Y3+ITO/ccWDoCYTQ05iilfv/m79btpxB/XR9oARVC3f4twLPy/IJ7RBr8Rz0gPfW5PZDdPkIfMH30EPKEMqRPQhWZE0jxCD5wWH228QoT+9Ufhj/C3iJxMmLL3UoZctpO9jsIniTgPODAQpvx8xhYHFjKAsNOcIhTR7wvKG7KrKWIZdTnrqqR8oNtxMH7mcS+d358q8Vsi7i/o3+8Q/YAI4wr7s72P/NhEOY+Dh3zGGuykwhgvPyjDWAm/G4jYhJnHkUeusehJiuOHD+YnX7sJJXyNiNsPNuaBF4m4r3PYHeQnzQp64DDY/yJ6nQi68XciGWYfp8LO45R9CphFOQxcQj8/IPqcSJYP90AimBoiyNbP+xri52P86JFXv9tNhTL/sp9hrpV8I85DRH9LxkcbYB6Gzelhn01kN1PJQ+YN9w+J8J7+ZyJOD5luIJIm7Fz2BGXG+cMO8g4x15ZW5gM36g/zLBGHDbJ80j9R1p25aEqO/yMCj5B3Y6IoDdodvKBfl7tB/0Rdd5R7RWNSv9VJeSswm1+D7ErKD2tCaspIAlECs2tJjjxB3phDpleIuPiivdTmPioQvG4vdcExLm9MUiYfxJhHZU0loBJQCagE4isBBWaDtQ1ewvEugpfyUhsJnvV2Kdxt4aMfxZcLQzfTc1ORx2Hkti/czRfhcI4l+jcRvzeeAE8yLYh+RsT+WODBjoFCmRsoI877FnI3EhnvT+7HRPjjIgzOuSLsVlsYHquJuE5Y+D0YnsJgkfR0Ii4f9gEiXOaPMCyk7SDCQsq1RJuIYGQ8Jz4SsfL/BVgqF+uxiNtVZDeU3G8ScT2eFGG8IIKwL4jWizA4sWD7ByJOa/+/gHAOu4ncbgY6x/EWiUjThD/CsSC2hYiBf9TtbiJOi/YC2CJNseWbK/9niRnmTy442usGHdtA1DLJ/GCyf0fEaWF/SiT73nR6ln1zDT1Lw2M40qL9NhPJ/jeBngFkcBlnkptNF3Kw/9/I3Z8DyEY/W0gkgfHRIhzOMGUj/V4iLh/2K0QbiI4leogIcppExHEeILc0YcsPM7ZIPsbRA/P4PRlQAjfaHGXXlKCsulgExgrIB30vDsbPPO6l8zupEqxrGCvQv9l0JsceIg6HvY9ImrH0gDGU45RqHg871sixopusELmrRZ3QzgfbwnPN44gu84dsnMYiezw7HwgPa86iDLhtMO4fJTJsTe6TRDjioT1haojkxzTP0XMlApKmHdkPEsm8e3Ig2WHncWS1m4jzh/00US8iGMxJ04n+QsRxrie3NHPpgcNulQEFdMt2trefV7/bLXgDj3jm+RLvKqgL8872TvKD3GFaEWFO47Cfw1MYzLc/IeJwACY1IrwpufHuxeF4b+C8ES3MXBZ2HpXtBv7+QbSDyP4e/Cz5Mf/yPSnKuhNLGeYpemIex2eElP7hpSQvkGO5G/4fAr1RU3wJrKYioOcKzOYn611J+R2fX3JNFVcJRAnMbk0qFTomBkQvcxEF8kR1qlfEIoXdlyx/e5Hyr4vZjknK5IO6yLzyrBJQCagEVAKRS4D/kO+JnJO6wQBewvEuhJfyUpr2VBgDJPhavqlL4W4LHxdSfH6HQxwns4U8OQ5sCQ5x/HNFHAACNURyQerX9NyZqJDmdcqM+TrAIeM+5PevJP2MbCmbufTMaZ0W+uS77XUU183sowDOZ5GIJPNH+CkiTDplPCc+ZNx83EdTIubvY3I3c8hkuojzb3JXEAH0hD5xWowHTmYSeXIcxB8uIoVd0J0m8kYZTu/5aNP3Rbwl5Jam2PLNlb9bv7PX7TTJdNL9VbJZtrAhT7u5mDw4ztkiEO38KxGGPuxk+pMn2hx5YGG8CxEM5Mj53m/5ZP/cnozzOdnHiOCwZSOrvURcPv4PY5yzG6l7EpgtRPlhxhbJJ4N/qAvGxcYysMhulIdya4pcTl3NntvmyxhUwO887qXz3I/R5ohnNxgrPyRCOAhzl91EMY+HGWvAvxwr7IBa2Hncnr/bWGSPZ+cD4WEMwMtPibjttrlktkfEuSYZ5yrhB4APYKGTeYY8Of+HRYSw8ziy2i3y/ojc+LDGbo4kDy4f4680ueZZGTdft5ceufU7lCXrhv5lf89tQ348FqN+PyJCe0oDYP2fRAgHeCnbaGvSH2G/IHKSHXlb99QiDuhr8EiafOeyQsyjst3Al9t78LMUxrxLYDaqurPspH2T4BEfSURpXqLCIS8FZqNshfIsW4HZcO26i5KjbyowG06OsUuNlz80rFzkKBWTxybLRvl4ofMy51Eg4oHwQl9qcx8ViLKdFmxKzUtcyhuTlMkHcWFI+VAJqARUAiqBOiUBADGYW/fUKa6jYzYqYBbviGgn0Gce1Xdb+HhUpJ/pkr4p+fM7KcqZ7xAPX7bvJWJefkluBvb+Q+4ZRIU2D1GGXN4T5HZaDK0gf/BmN3PJg9Peag+k5wOIDiE6h6gvkZv5FgVwPhK8lPkDHG7skoGM58SHSzLf3vIrf7fFXGR2OdE3iKDHWFhcRMT1+jG5vYzUoRNExLALutMoL+bhj+R2akcU9x0R71B4CFNs+ebK363fybr9ifiVC7HMPo/BkMEH7GmzoaMso+tF2GjhD+DayzxIgZwHQBKYIUTsh53KkKtd/tgR347IbsKWjfz2EnH559sLSD67LZYXovwwY4tkF+2K/s916S4Di+xmMKCmyOXU1eyriHG0y5cxqIDfedxN50ck64L6YNHezeADCtbFfQ6RopjHw4w1qIIcK7rZ6hR2Hrfnf74tf/noxYeMl497HCXidvuY3E1dMhlF/vcRXUg0LxkH8zenxZzsZkZSAMfDnMQGadgf4JSbuZkCOB7eH6TZTQ8cdrIMEO5hIs4vhD+cc0XYrbawQj16tZ9bv0PZfur2NsXj+m9wYfhdEaeziPO48F8v/O3OwSKeBLbzncsKMY/KdvN6D3Z7T4qq7nbZ4vkcId/bnCKU0A9jPPRJgdkSCr2eFKXAbLiG3kXJ0TePD5eNpo6bBHgRDC/rpTbyxf3yHIV/lcL5ZcPtZStHFqGC8QKK8reHyqW8EiswW17tqbVRCagEVAKllsBKKhBz655SF1xHy8NLOOSFl/JSmjlUGL+D4Ut8N+O28PErSoD0AFGbELmZOyiAy5nvEqkX+QPE4Xhs4z2xGOYoypTLgI06YBHyIqIpRG5gKAXltdAH+fQnwkLl6URYNPorEfOAP7Rs5ILUe+zpYMt4xVhw/IjKZP4GOpTv5nWZSJer/baIuDhSj03YBd1plBHzLhcaOX+2rxLxjmDPpF1s+ebK363fybphkc3JzCJPrv9zThHID0Aqx7lBxJGA7c/Jf60HPSnykP/j+H8o5/97iof/XJBxVyI3U4iy91LmXC54dzJui+WFKD/M2GLn9ZfkwXUB+FEqo8Cst6SrKBjt8qV3tJKEzknyAn685nE3nT9cpMf852Z6UgDr4j6XSKWex8FGvmMN0sqxohs8cpgg8ziykvm7jUX2eH74yMFmRvBGeuJ2uz8jxPuhDQVDv5H2P0Sou5f5lAK5nP2TEcPO48hmt8iXP/5JZp+y2ok4H6Z8E45c86wtel6Psp3t7efW71CQrNtMl5K/T/4s18kucfCOw3EqRJx3hf+55Habyw8V8f4i0uc7lxViHpXt9p7gye50e0+Kqu52/vAMUJzb57tOEUroh3dG8KLAbAmFXk+KUmA2XEPvouTomwrMhpNj7FLzS2oUwOyGpFJBsfAFvZe5ggIRD7TJK2KRwhSYzRasArPZMlEflUBQCTSgBKWioLxpfJVAsSWgwGwwCUcFzGKRht/BHvJg2Wnho71IiwU5L3MxBXI5bsAs0ssFRMTHYlMjBBTBNKY8byJivuz2HyhsJ5HTe7RcMLqV4rgZpMXX8W8T/YvIXoZ8dgNmn6B0bsYvH27pc/njWDzmEYu0fg10idMdmSORrMNuEXexyAPt5GZupgAuS+60mSb8H3FLTP4XiXhH2OJJ3rza2ZbM92Ou/J36HTKXdcOOYyeDRV6Wyz1OEchPyvgGEec8kZbz8GNLYB3/JT7zyOeHFHYCURMiaQpR9l7KkPkFH07GbbG8EOWHGVvsvL5IHlyXefbAIj5/kSy3pohl1OWsq5Ly+TIGlfA7j7vpvPw4BR/KuJmmFPBfIuijGzCLtKWcx1FevmMN0sqxohs8HEy+8ziykvm7jUX2eG58OLDmy+sMisVjSK4NEzLDISLdxzLAxS3rOi0ZR84x+czjyGY3EfOPtnAyGHM5zoe2CHNFWDHmcRQn625vP7d+h3Sybti57mQYSEP9+jhFIL/XiLj+Fck4kEmu905OY7cZWM93LivEPCrb7YlknZwsp/ekKOvuxOMM8mQZv+oUoYR+rE8KzJZQ6PWkKAVmwzX0LkqOcUKB2XByjF3qT5IN6/YCU0yG8TUbTz4P5ygIiwUc9+AccYsRrMBstlTxxwFt8kF2kPqoBFQCHhJoQGGghjYCsMBkDyvFM/NVTJuqqEYlkJKAArMpUfhyRAXMAjTjdzCvBSunhY8DRNpci3YnirjzPSRyrIgHvn5HVOkRvxBBR1AmAIlYDnYb4ORGW0FywchNbld75PlPCnuB6Bcijhswu5PiuBk/fLilzeWPhS1ehMduGcxVfg3/uYQsD8uRSC5aysV+vwu6kD+3mRswe68HDxeL9NAFaYopX5STK3+nfod004i4zm51myni3I1EDkbK+AYRDjfnH8R+SOQBZw0R2gcfObjl830Kk328EGXvFeXVktvJSL17QEQoRPmcXT5jC6dl+xFysOwOYs8S2F8ky0UbqsmWQJyAWb/zuJvO81oI9Ax5eZn/o0DEk2O1PX4U8zj0NOhYA77lWNHNXhF6DjOP2/OvdcifvXLxwfHysS+kRGgz0AVEfk0/isjp3veRSM5X/J4n5xgvYNZtHkexuwUf/V34wAc+zOuHtjhzRRjKKYbxaj+3fgc+/NTtJYrHdevjwvxrIk5FMg5sThfUHmIrJ+hcVoh5VLbbThs/8lHq3aBkQJR1l7yxezg5uA3eZM+IbNYnBWYjaoAyLlaB2XCNu4uSY5xQYDacHGOX+pNkw2IiKLXBSxNPPq/nKPwVEXdUjrjFCOY/I9uLkXkdzXMM8Y32+6CO8q9sqwSikEADKrQhUSMi/CHBLh7sHoH7OqJriPBVOr5W/hrRpUQ7iL5KhD/NFxDhC9Ozic4kOp3oVKKTiE4gwhh1DNHRRFuIjiLCAsrhRFj4xoct+ChmHtF0ovFE+Pp2AFEvomqiTkStifAHtrGNwLdfQj1zEeQRhii5mjosAQVmgzVeVMAsgCx+X3vIg2WnhY+OIu3fPNIiCOMdl8MLdvYkGKuQD8dj+3F7xCI9Y4zE4tO9RJ8Tcfls9yU/NnLByGmhD2M2p4P9LtG1RBizJxC1JILZScTx4gbMgr+PBX9V8PBpMM9xvTCHeZk1FMhxHxMRFwt/JxlzVH6PRx7QZzbTyMH5ok3dzMUUwPHQ/tLkamcZNx93rvyd+h3K8VO3mRSP63W3C3NSxnhXYXMaOTjtTeQGcOqH2nEGNhvvG+D5IiIctcqAP5dxD/mxKUTZeykzzruWM7bZk0QcCcwWonxbUdb7l9+xxZ52j+ATY0epzBdUEGRYU6oC61g5GA8hny9jwLffedxN5zE3cX/Bfw8305wCON4+l0hRz+NBxhpUQY4V3Wx1CjuP2/OvteUvH734kPHyceO9g9sN47lf05oicro/+0j0cxGf1/TkHJPPPI5id4t8+8PDwTQhP+b1Q1t4rnnWFj2vR6/2c+t3KMhP3V6ieFy3Pi7cvSbiVCTjNCCbx3Gk70XkZx5HHPQjJ1NNnn7mskLMo7Lddjoxk/R7lmyWDwOzUdbdidXpgsennSKU0I/1aUUJy4yqqCupYOjGjqgYqGflrk7K+/l6Vu9CVXdXUn4KzBZKojHJ55Nkw0YBzGJx/+/J8v9NNl7snEwz8uR4sPFSVWrDCzrbS11wjMsbQ7xhEvsgxjwqayqBOEkAfwAaEmHswzjG4xr/UYijjeON/kL0v0SYL35J9DOiN4iw4LKHCH8evkuERcu7ifCn+gaiK4i+QnQK0VaidURLiWYSjSXCwkx3ovZEkAf+4IEgHzeC/LwIMg5KlERNRBJQYDaY4PESjnECL+WlNKOpMB6fXvQo2GnhA9Hloo99UVNm92164HLmy4CkG+MEPuTjOC+RG+MTPx9N7kIbjDc9iTBe2Q3CsNj9NyLmQb4nzhX+TouNb4lwfKSDcc/JfI88OX+Mo2xk/jvZ08GW8Zz4cEgSyGs3xWb+JnikxDyAueMhIvC0hYjT5VoIPl/EvY3cbBaSg/PA/ONmoCscD23GZho52F+BWZZKpr1YyAhzO5uDyMGyy7WIiHm+D1FTTpy025KN8YU/QpDBXekB7xVcBt5D0OdgClH2XsqH865Fpg5mEvlxHPDCphDlhxlbmA+23yYH81nLniWweWyvKUFZdbGIKmIa7fJlDJj3O4+76bwEUbzmEcyVrIv4n2A3Uczj4CHfsQZp5VjRDR7ChJ3HkZXMv1bkbXfKeHY+7HGDPk+jBNxuXuN5U4r3GtELRDcSwXxKxGmh826mOQXI/774DwgTdh5HHruJmIf+8HAw0D2O86EtfK4I89JvW7JAj17t59bvUICfusl3HMy1TgbtxvWvEBHeFP4ThL+Tsx95drYF5DuXFWIele2208aXfHyWHrjug0RAVHUXLKScKwWPO1O+0ThYnxSYjUb+5Vzqaqoc+qICs/m18q6k/LAmpKaMJPAJ1QUdY3hEdcJiPk+S6114OFjE2eMSp9je9yV52F7sgupQ/mOSMvmgDvGsrKoEopQAAEMGZVuQ+1yii4guJfo60RVEVxFdS3QdERZAv0mEBftbiLAYfTvRnUR3E91DhIXk+4keJHqI6DtEGFcfJ3qSCAv6zxA9R7SHaC8R/pi9TfQeEf6Yfkb0RyKAC/8h4jG51PZfqWzw8gsi/FH6PhH4x2Io6g257CA6iwhj8RFEq4jmE00mGkZUQ4TFl8ZJcgJ48QfSjdBGQYiiqwkhAf4TuidEHvUp6fFUWfRLvJSX0lRTYTwevOtRsNvCB8YlTn+ZS/pK8v9CxEO/tptLyIPzwVjRiegY4Yf0bgtyFBTYDKQUvIj4e3JjbHEyV5Mn84Uxm80ccrD/HeyZtNuTLXcEdrOF82MXcmBs5Hzku/Jc4b+T3G7Giw+3NEH8MUcxf5in3AzGdI43g9zTxTPqiLo6GYB2nxJxWimDWcIf85uTqSDPfxBx+kIDs8WWb6783frdNFFnvCs4mZnkyXLBe4WTWUyeHOcGEUECMP8if/QXN/M8BSAPvGOcnYx0Z9IP/uyXDEpZaLt/E3H5GCdgwpaNPKAvnG8tPBzMJPLjOHgXYRO2/LBjC/PB9ufkYD6bs2cJbIy5KLemBGXVxSIAUkE+X8aA+eokL+DHax5303m8YyMtCOM1+qaTuZw8Od4+hwilnsfBQpixBulfIeI69YRH0hRiHkdWfsYixHPjA2FhTVfKgOuIMbeXS4Zyzn0xGWcP2Zz2ay7p4C2vq3hfxJN55jOPI6vdRMyD23tgExHnQyQSJtc8K6Lm7fRqP7d+h8L81O0lisf17+PCIf7/cxzZf78t/N3eA5DlFBHvD+TGu1mYuSzsPErFZ1z1sBMeLuZZ8ue6DxJxoqq7YCHlPFbwiHEySsP6pMBslK1QnmWvpmqhL+J/gZrgEthFSSA/rAmpKSMJfEJ1QcMOj6hOa5Llgwfw0tfGB15+f0WEcNAqoijMfVQoygcYoCYhgTFkQSYfqEBUAiqBnBJoQDEaEjUmakbUmqgjERYZq4m6E/XIkzgtbD8EAIAJZdsJefQhwh8XzA3jiKYQ4U/rAqJlRBi7sTi+iQjACP5sn0F0PtHFRFiYAbh8OxEWMwES4yUffwqxIITx/k9E/ybCOFJowiLx74jeI/oB0dNE+PN1E9GlROB1KxFeDucSjSXC/IM/qk2I0E6NXAjtaCe0rx+iaGqEBBSYFcLw4YwKmEVf+CMR+ul/iToQOZlnyZP7slz4WCr8vyD3SFti5H+HiIM85tviTKXn/4g4GIdg0O/2EHG5PyI3+nAhDPjCOMJ5n+eS6SMijnxPxbjJaZ+zpW1Bz1is5/DDbOF4xBzxEyKOA3szERuMXRy2kz0dbC8+OHovcsxM0gD29GmPoHjcNhh7xzuk474Ofj8ngmwxjv6YiOuAd+2mRNIgDuYSjvM3crcTETBucxjsg0QYnC2JMAfIOIsQkDTTyOawe9nTwb5YxDvCFp5LvqjDdKKZSWpOdhCTK3+3fuenbuCJ63+3C1NuwCyiY27l9K+QW7YNwmE2EHEcjB/94EnmOCL2xyKvU1roFsd5h9zShCkb+ewl4rxr4eFgJpEfx8G7jDRhyg87tkg+etED8/hLGVACN8ZzlF1TgrLqYhFVSflgrI/aQOf+SIT2Qj/sQORkvHT+dUrAunYXufGuLM1QesAYzXH2yUByTyXiuQJxliXDizmPo4iwY83zlAfXaXKSZ1iFmMeRj5+xCPHc+EAYDMYCnmeCzuNI/zAR1/MhctvfpTA/Sx04gZ5hlhJxOrT/BHjazEB6lu9TO0R42HkcWe0mYh76i7ylE/XhOB/KAHLnmmcbUpzpRCzfoPM4JfVsP69+56duL1H+XLc+KMzB4D84x6kQ4RPJjTGBw/C+ZjeYn98k4jj8vhB2Lgszj4LHuYKnnfBwMW7vSVHW3c7qneTB8l1vDyzxM+uTArMlFnw9KE6B2XCNvIuSY5w4Plw2mjpuEviEGELDDo+IsYZUrlyU+ZSetxCNIdpE9DERT1A/JTfiR2GwWAQ+tkdReEzLRBtBJh/ElD9lSyUQJwlg0QHjV2Mi/JlrS9SZqCtRDVFPol5EvUtIKM+JwIudasnPicC7nXqQn6Tu9MzUjdxM1eRG/VEWQByAozOIsGiOl7aNRBhzzyT6KtFVRDcT3UP0CNFzRK8SYW74JRH+8P+TyISk/1L6PxH9igh/QncTobzbicDD+UTg6zCi+USjiFA/LNCgffEnVVJDerZTA/LzQxStrA2DNXvKupaFqxxewqHfeCkvteE/Aih/oUvhbgsfiP51IqQFYeEOH0cAwFtCJNNxHPQtNu3I8SsiDruXA5I2xk0GCBDnIlt4mMcrKTGX+w9yX06EhVksemJB6DtEHI7xpw0Rm8Hk4DDYAHauJ8K4C/MGEYd/RO5TiTAW4v3qFCKMbRzO9lnkxwbls/9O9nSwc/GBJNcRcV4PO+SRy+sWkR5yuIBoChHaeAeRHJtPoGc2E8iBMZfL/hG51xEBvF9FhHGew5DHgUR280Py4Di/J/c1REh7IdHbRBzGNuYYNtPIwf52veI4sC8W8Y6QAeTOJd9OIi3KGmpLn+sxV/6y/0B/2Pip20yKzPW/mxPa7MUizg22sP70/C8R/iG5NxFhPj+cCLr0JRGXgXGETRU5/krEYa+TG3P/ECLMqxjvfkvE4dAjacKUjXz2EnHetfBwMJPIj+Og/0oTtvwrKTPOO+jYIvlYL/K5VgaUwP1FsuyaEpRVF4uAjqON0QfiYND/WOcWujDkpfM9KY0E116gZ4Cr44hOJZL9GeXsI2IT5Twedqx5kCrBcnuf3BgHj0tWLOw8jmz8jEWI58UHwsPO43g3wVjEdX2V3BiThxMtJ8IYzWG/JLcE958SYZgTMGfOIML70plEUjeeoWf8F5YmzDyOfHYTMW8Ym52MFzCba54NO4+DH6/28+p3fur2EuXP9e/jVHnye03EqbDFuUOEIZ9biPC+NZXoEiLoPef/d3IPI2ITZi4LO4/OJSaYr53MkIP9rIgn35MQNaq629lEn0Jd/kNkbx973GI/sz6tKHZBMcif9XdHDHipDyxgToGeP18fKluEOvJ7HP4jqSkjCXxCdUHHwAtXVKYvFfxzIp5UnexPKbyWKCpzHxUMvrYXmYFmlP9OIvz5ryRyMlhwepwIcnMyJ5InBropToEF9BtDeUEmHxQwT81KJVCuEmhAFWtI1JgIf0bbEOFPHhYLuhPVEPW0Ef4gF4IAWsSJZJ3sda4lXpkgE0k96JkJMgN1E1RN7q5EqOsIoulES4gOIzqW6GyirxHdRHQvERYRXiF6hwhz4V+ITEj6E6X/HyIssjxKdCvRJUQnEYGP+UQAHMA79AD60MhGDenZTtAfL6LgOmlWEteQ+Z4Sc7+AyttNdIRLuRPI/zmiU13CocOPEV1HhHYplcFLOOSFl/JSm0OoQO4fO1wK91r4gE6jT3AeTvavRTj6Cpu7yMHxPyM3xk67AdjHcb4k9yR7hDyf0U/3EXHebjbGD+izNOjf4NeeZkMy0hSywas9XD7/H4XLxTzoJZu55OC4O9nTwc7FB5JAlzmvhx3yyOXVmSLsIeI83Ox7HDJC28nFYKe0WOhd7JAWXvD/J5FTOvihbW4W4YvIzWYaOTgd5gU3czEFcDz7uJFLvtBXTgt7qFshLv658nfrd9NEuW51myni3O1SPuTL/N/gEGc9+f1ZxOG4dvtFioP+JM0yevgvkT2u/RnzdQeZMOkOU/ZeUW6tQ97wwjjCvNiBWYSHKT/M2IKy2Ujdns2eJbK/oHIgn5oSlVfX5u6qpHwwzsfB+JnHc+k8wr3G6/conPsM5k42Uc7j4CHMWIP/EFwntj9IVizsPI5s/IxFiOfFB8LDzuPIYwMR92uuq93+K8UZgcjC1JBbgqv2NPz8AsVrIdKxM8w8jjx2E3EZ/TlTm91ExPnQFpZrng07j6M4r/bz6nd+6vaSqFsfW9348TURp4I9kzbe4Z4V4SxLu/1vioP/1tKEncvCzKN+34Nl3ezAbJR1Zzl2JQfL+mX2jNBmfVpRQB6aUV47ieK2zn0l8QTZ7yBSU3wJKDAbTsa7KDn0VYHZcHKMXepPkg0bJTALobQluo/o70Q8KcH+b9K/J9lRGvAGfrYXmQm5QHKyQ1l40WT5XOUQjgV+/rP0tEN4Ib3GUGbg5YNCZqp5qQTKWAINqG7oo02JWhK1I8KfgUoiLN7gpVxSNT37pW4UNxcBDHSiHuTvRDXkb6da8nMijNF26kV+knrTczFIliF5kHxyPWQ9IQuWGcv5/7N3HvByVHX7fwMKKCWQELrc3RuQYkMpKryICDZeFBRFQZTiH0SQJiCIVIUAKh0VFRQQIhhBRBBRkXClSi8iNSEmkNBDCOlh/s932F+YbLt77+69d8tzPp9nZ3bKmTPfmTln5jxzzjDt3dLm0qckbh73kQ6XfiBR6XGxdJU0XnpImibNl5J+6GWt87h0i/RH6XxpjISRu5v0SQkjl3TyQEPFAedQaAmNZzVM/6tJs5sqfEGpgdv4QU5VT2G7mAnlwm81kXQtlMqZAUcX5rPMh6XBCtyEs82xg7XBzHZW1Hic5//JTM+OXqc/pA9xv1IcltMEriFazMZyDJ+X9i8opm+r/4RPSDGN4eeZWCZwHVCZEcs+VmaZ/k5i30+WXpUi/hgy7R/SBlK58D5NfEKK5Rli8kX4uEYelLLzGadilHtPygQqMWdLTOec5J6Z8DEp1js/nVL5p7d0nJmJ68rK0VSdwzE4THpOinTFcKqm7S6RP5UL5Lv/lOIci/UYclw5D6qFTTTzful1KdZdoPG7JFpvct7EdPL2CFtoJKZfEhPLDE/ILPe1MvOr8SUPyabrvWXW721StfgrXXe17NtW2nDs/4UVEkGrmVjm7ArLdGn6tVKcp7E8Q479t6VylfGa/D/bSLdL2XUYhxnXzjES51al0KUZ/dl2j9aLbVL2lwsf1MRY5rflFtC0/m6f6OrJW1j/LRJ8SeMr0lulwQzkU2wbBoMR4pjNqLAxjhHpaZaym3t70kNe1AyhlnK8lnOe/PgeiX0LsY8cn9UkynSmk3cTmqEcJx39zWsot8j7oo6FfeMcW1oi1FOOs36c18RbKS9iud7S0YhynO28U7pRKlce/07T15bKBfKj70lx/Nmf0EsaJ33LSpVCf8tx4vubFNtap8IGSB/HjeUmlFmmWjnbiHK82vGrdt3Vsm83FvaLfcuV2Tcm3SEFo5FlliF9+0qTM8vF8gz/KH1AKhfqLcu6FGl/yvGPab1I4/nlElaYdl1mufXKLDeU+05yeAaK/TiiTPoGe1LkSTs1cMPZY3VomXg5LsHgjDLzl9S0yIMbWc99emG7Y8ps05MaT+BLBd43ND7qjohxbIHfQR2xtx20k1MKB3ajJtlnbpi4KdpZ+l9plNQMgcoxCoqBNma5SZokTZeoSCoOS2nCAxIPwtsXzyz8v1JDHo4OrjC/UZM3VUQwmdioCB2PCbQ5AW76l5DI53iYx5xdXhou8UDDQ185jdD0voq8pJJW1rysyGdDq2g8q1X1PysqXLKi0imrSsbyWloOUeGAeKjPqkv/QzmNZ5XX/1C3xkOjNV6vIq4Yxnay2490MYw0x36wT2sWtIGG/yt9VtpTOkw6WfqFdIV0k/RvaZo0X0r6qIVafqp0j3SNRLwnSN+QKA82ljg2nF88vGS1hP5nNUz/q0mzByV8QVuBw/hB2dqbGzlSowskbm7LhT00cY7EMSsXttREymkqdlYot8AATeMmHF6V0j1Am10U7UWF7ZOGLRZN7fsIzD4kfV5ar++rD9kaXFOkl2v8c4VxrqtaQk4LbSRxjXLtZQNx5KRtpB2kdaVa49WifQo5LV0pHewXx/Zsqd7Afn5CwtTLS8X7rEllw1KaynMA58ZWEnlsXwJlKRy3lZbry4oNWjaneCrxvUfz4EvZ3N+Q04qV4u9vnI1cj2vknRLnMcegW6JMqiVwn0Pe+kXpo9JwqS+hnm33ZTuVlq1n+6zbn7wFzpxT6MfSYIfXtEG23TVIGz5S22mlsrvZjFkO00VSnDP1lOPERVm1nbSNNBT5rTbbr9DfvIbnNsonrlXGs2Ewy/Fq6WhkOU55THlDebypxDNrrYF7AAxr0gOzpaVaQ7uX49WOX62MBno56iN4puVel+dLjkktob9lWcRdTzkacdQ7HIp9v1eJJl+eJVEPM9ShRwkgPTs1MCHc+06SpkublYmX/OYBifsK6jXKhSs1kXuAg8vN7Oe007Ue+zqmn+t7tb4RsDHbN17FS4/VBM7Xg4pn+H9rE5ii5HNgN2rt3Rjw1A+WMcuOcENS7eaVyq1Kb52zPqH4YeGNqY395Qadc2diY6N1bCbQ1gS4fnl4p6LyrRLX+jIS13Q1cU3XK95SzopKlGLx0J3VCvqf1XD9z4oHtax4mMmKyg/EzTgKQ3iUxkNhBIcBzMM8CsO3nNlLZX0IkzQM0xh2aRrKFZTXMNSt8axG639/lI2D8Yg/p3EUaSBNpBGRZoxc9mlD6X+lHaS9JIzcU6Qwcns0/m/pWakvRu48Lf+UdJvEA8y50lHSHtInpHdLHCPOQcqbrJbQ/6yG6X8laVbdYaiMWRLeWznZ23yuXdgNZuAmPJG4KR+KsL42ulAiDb8eigR4mwNK4BzFzrHddUC30pmRU6aRjz/WmbvvvR4gAlcrXq7Z2RLn2GCH17RBtt81iBvurWzubf5glt3NaMy6HB/Ek3UINuVyfOCguxwfOLadHPMHtPOUo+jMJgFBHQTp2anB6eG5mTK4UqDOYbDruW3MVjoaAzPdxmx9XG3M1sevade2MVvboblMi1E4HVDb4h2x1KYFJhM7Ym+9kybQOALcdIb5xQ0qBlkjheFbq5bSsuXETXM5LaPplVTOWKaCLFTOFC5nApczfsPsLTZ6w+wNk5chRm85k3cNTUcYoyhr7GbN3S7NC+U0jvIFdWtYrNGaVqti3YiPYa6g2GYlI/ddWm5b6SvSt6UfShdJ10v3SRi4YZolGu9NM7XM49JN0m+l0yTMYUyZraScxLnBORqK8zaGwzSvkjSr1zCUxmyviWvCBYbamAXJ5RLnFhXyXG8O7UHgu9oNjut8ifzQoXEEKHcmSPA9r3HRdmRMxeVNLWVR8TrtAi6vHVkgcV5hxgxFoBxg+11DsfEW2GYzGrNgczneAidPP5Locrwf0GpcxeV4jaC8WJ8JXKA1KEfnSNRPNEPoUSJIU6ON2WbYt+I02JgtJjKw/23M1sfXxmx9/Jp2bRuztR0aG7OlnDbVJArsiaWzPMUETKAXAsUVhdX+R8XjYAzDgKt1WKuhXGwUF5vBWRO42PgtNnxrMXqjlW+YvNGqF4O32NxdWdMQJlO51rtUrKFKpm5vrXS7tW5otMb7oliPCliUK6hLw9guJkq0xGV8Y+nT0p7SkRJv32K83ij9R5oukXfXIkyap6Qe6RJpjLSvRPwYxRjrnAPZ82UJ/Q8N03gladb/2JiFQu2hGYzZ9ZTc2RLnz1CZAbUQ+7AW+n91attaNtQmyxyi/Xha2qpN9qeZdoPyaLL0S4nyzKHvBKIc4d6BSh26udta4llkA4myj/Kd+ZRJxeUSZVSUSzGMOLNDLdYyYaxSSj78ssQ9ylAEG7PVqXNcOEYY6M0UXI4309FoXFpcjjeOZXFMLseLifh/IwhsqEjiBatTGxFhg+LoUTyUXTZmGwTU0SwiYGN2EYp+jcS9P3VCDm1EYIr2hUx3ozbap4HYFRuzpVRtzJYy8RQTaAUC2UrIgR6PCtBqw6ypVzweFazFw2pG79I6CKGsyUuFOApjl2G04o0unaMFLw/g5UzdMHZHaj4qZ+jS1RWVcajYzKXyOAzVLo2jXEF5DVF3RqM1XotinYgjp/UQ8cf2sgYuy39I2kH6hnSs9BPp99It0gRprsT9QW96TsvcLf1BOks6VMJw/aC0qlRcQZ49F76o+cQ/XhpWJP11KCLATTi8uCkfysAxJh3zpHWGMiFVtn2+5vV27vY2/7oq8bfbLK5L8j+HgSFAeeLQPwJRNnB/QGuS3q7b2Vrmeekp6SHpJulC6Tjpq9LmEuV0tbIpthlDLd5UYWOl5nUJFrsPYcpszFaH36zGLKl2OV792LXiXJfjA3vUXI4PLN++xN6sZXNf9oFlr5Yoxx+WqLNoltCjhJCunZolQQOYjtML+zpmALfhqN8k8KUC7xvenOSxPhCwMdsHWK206BQllkzXxmz1o2ZjtpSPjdlSJp5iAiZQnUA8SPV3mDX1Ko0Xm7v8pwK2WFlzdynND4WpyzCM3TB1s8Zu1tQtNnRX1LoojFxa6Y6UwsgdpfHocrk/Rm5e66Pugmoxb1kmlo/1c5rWVRAGbta8pQKcewNaJu0jnSBdIP1F+rf0isT9Q2+i4vYR6XoJswwTeHdpa2l/ifVvkuK4ZY/rME0vJ03uyNAsxizH6GaJY3dJkx6JI5WuO+sUD+sOJmACQ0uAMoDygTI7J90k8TLQo9Jk6WWJl0R6K4uK58/QOvdLV0mnSd+StpPWkbhfYJuVyiPNGtJAOcz+/GlIU/FGl/ako2uI09Gsm29mY9bleLOeNU6XCZhAOQLxPEjZPFrqlmJaueWbfdqHlEDKT1rMbtZkie0ppM3GbJMdmDZIjo3Z+g6ijdn6+DXt2lOUMgoEKl8dKhOwMVvKxsZsKRNPMQETGHwC8VBW6zBb0Vo8zsNesaigLVY5UzcM3WIzN1roNtrIjda4ayl9GKoYq4gK0lxBeQ0RD6+IB9neFMvGujmtg4i32LxdT9M+Ju0mYYSdK/1BwhB7Rnpd4h6jN9HC6e/SL6TvSjtLm0gY21FBznFZokjD9L+cNLktQ7MYs8DlZQTOpdX442ACJmACA0CA/J18n3KAspXeLSgXeLmJvIdyMEQZtaG0sbSl9Cnp8xIvFp0kXSLdLE2WFkrVyiV6grhGOkbaVhouUQZly6HiskezBy3w4hT5L/cdQxl48QqOsHcoJdDMxiypdTleesw8xQRMoPkIRHnL/QDPvJQ786Qok2O+JrVM4GVvynHKiWYLPUoQjG3MNtuRaf302Jit7xjamK2PX9OuzdvCZLrjpPOsigweL3Digd6c3mBwRYEJ55CZmIHPAZ8DnXIO/Fx5Xi3CZKymX2p+VufrfzldoOnoV0X6tf6jCzO6SOPo4oJ+o2FWVEyHLtV4Vtzo9VXZ9RmPuGObTLtSul6i/LxXekx6RqLF7UKJe5BqwrR9VqIcvlsaL10tsS34VjoW7Xg+whBWsGjH/fM++bj6HPA5UHwOkMeT11M+Ug5eKFHGUAYUl0GV/rMsypZNvETEC0G3Sw9Jk6SXpPlScZlEWfWCRHeD46XLpeLypzjdnfB/njjACradsL993UfuxeDD+dPXdb28mfkc8Dngc+DNcyDuBX5byFcpqymHoyw2qzdZ1cuC53TKLp7f642r2den5xT29Z4O2NdmOBZ/K/CeYt79uraoA+J8pTGEQxsRWKB94cBaZuBzwOeAzwGfAz4HfA74HPA54HPA54DPAZ8DPgd8Dvgc8Dngc8DngM8BnwM+B3wO+BzwOeBzoHnOARrJObQRgZe1L1xgOO5HWxUZPFDgxLd8zOkNBj8rMOHtcjMxA58DPgd8DvR+Dhyj/LI/OlbrVdJxmpfV8fofOkHjoe9rPPQDjaMTCzpJw9AYjaOTpVMKOlVD9MOCfqQh+nFGp2k8dLrGqymWo+Ur9yD/lYgr4o3tEActpLj5vEG6W3pSotxZKLFuJb2uedOliRLr8YYmrZx+IsEhy7O3Y9Is5/a1Sjf7y9u9zZImp8PHwueAz4GBOgcibya/Pl4i76asonyKMinKjeIyKcqnKG9iWEvZFOsSJ+URz8lXSLdJtKyNlqLZ8ody6VaJMus4iTRH+rPDgWI1FPHO1T7CAE5Dsf1m3yb3UPBZaD4+P3wO+BzwOdCvc4DyM3sPcKH+k69S5vIcy3Nutsxt9nKhFdI3UUxhPFZqhfTWk8abC/s6vgP2tR5OjVr3sgLvJ827X9dWtPDmOcWhjQhM0b6Q6fobs9UPamQgB1RfrKPmbqq95dyh4HYwARMwARNoPIFhirKaltD8rJbU/6zeov/orRktpXG0tLRMQW/T8O0FLavhctLy0goS39ZbUVpJGiGNlFaWRkmrSKtKqxe0poZrSe8oaG0Nu6SclC+oW0M0Wtpfohy5o/CfaSiWYZ1cQcRDfMQd2/igxr8oHSZRcc7LU7xINUMi3kpaqHkTpD9LVNJ/Q9pKYl/gFQyzbIdperE0aVDDQdoa+8SDsoMJmIAJdAIB8l3yZMoxyizKJ8olyqMoi6I8ijKJvHy1giif1pCy5VOlsinKoOyQ8igv5aQuiTKI9T8uHSFRjsySsuXN8/p/ofR5ifSS/ihPohzRpJYPr2kP2G+4OJQS4NyDz4LSWZ5iAiZgAibQCwHKS8pOns14VqXs31UiX71P4h6A51WeaSlnWd6hfgI9igLG/sZs/Swdw+IEvqS/nFs3LD7Z/2okQB0Q/KgTcmgjAlO0LxxYG7PVD6qN2VI+mxbOnYmlszzFBEzABExgEAlERW+lYVQIx5CH1xAPu6EwcKuZtxi3xeYtxi2ioryccUsFebnK8X01nXuQ26V8Qd0aotEVFPMZsk5O6pKypi0V8O+W/k9iG6dIv5UwgHnDmm1W0guad7N0gfQd6bPSuhJsgtkSGg9VYq5FGh5szDYcqSM0ARNocgKRx5L/kg9TARsvE2XLIipn44WieKmISlzKpuyLRWHihnlL2ZQ1bSlPclJeopwpLouYhnISy2LUssye0mXSi1K2fMG8vEpiPsYx+1FcfmhSSwYbs9UPm43Z6nw81wRMwASqERimmZSXlP28QMxz5rclyti/S5TjlPPLSDzLsrxD/QR6FAWMbczWz9IxLE7AxuziPPr6z8ZsX4m1yPJTlE4y3Y1aJL1DlUwetOF0wFAloAm3a2O2CQ+Kk2QCJmACVQjwwFpJUVEcQyqPQ2HcMuThGGHeUkGOeCCOinIenLOV5VFBXlw5zlvOe0mUrbdKWeM2WiR1aTrKSXmpuyAqwSsplmH5nBRxECctbamAf4+0o3SYdJ7Ew/0kaaFEesppjqY/KP1eOlH6qkQ5yP7BJVgFP4bDiqS/dQUbs3Xh88omYAItSiDyUvJV8trisij7MlG2XIqyKcqnSmVTvFQ0SnFjnvIyEa1sKTMoO4rLoeLyh3InJ/GCEPqc9HOJciVbnizQ/+ulL0ukiX0pLis0qWWCjdnqh8rGbHU+nmsCJmAC1QhQ9lNGUsbT+wTPjqdJlKuXSuSxPF9SnnJfwPIO9RPoURQwtjFbP0vHsDgBG7OL8+jrv7FagWuTOiGHNiIwRfvCgbUxW/2g2pgt5WNjtpSJp5iACZhAqxPgobaSeDjOKsxIHoZDWeOWB+kwbqOCPGvcYm5yD3KzlDVuefCOCnLehubBu56uKKMSvVvxoLyUk7qkbEtb5m0jfUP6ofQHCTO2uJtK0hx6XeOTpL9K50j7S8RBumESjLLcivlqsZqCjdmaMHkhEzCBNiUQeWc2P43xyGuLh72VTeVM2+HiRyvbkRJlUZRDvNiDUUu5kZPyEuXG6Iz4z3TKF4zdbSXKkwekKDcYPiedKfGiUJQV7Evso0abPtiYrX6IbMxW5+O5JmACJlCNAOUhZTrPk8tLlMfjJMrQH0k8G1JW25gVhAaGHsUFYxuzDYTqqFICNmbrOxFszNbHr2nXnqKUkenamK1+iGzMlvLZtHDuTCyd5SkmYAImYAJtTCAqjouHVCpnla0gL1c5Tqsh7kF4ACxn3PIQTovUrGlLRTnGLa2aqCynZVMYt9G6iUrzLikn5Qvq1hBlK9BjPOaxbK6grGFLvJtJu0rHSBdJt0jPS6S/kqZq3t+ls6R9pM0l9iUq4eGzREHDNCyWJi0WbMwuhsN/TMAEOphAcX7Z2//IaxlWKpuyLxNR0busRCudKIdoWRvlT7ZVbZQ3lCVRrjDkf15iPobuh6TTpWekbLlxm/7vLWEIlysXNLkpg43Z6ofFxmx1Pp5rAiZgAtUIUK5TJvKSL+Uwz313SZSf+0vksTxX2ZgVhAaGHsUFYxuzDYTqqFICNmbrOxFszNbHr2nXtjFb26GxMVvKycZsKRNPMQETMAETKDUYsxXmVIqHdtY4D343SeWMWx7EUTXTlorsMG7DtOWNaozbrGmbbenUpXk5KS9RcY6ylenZ8ZjPsjmJdcO0Jc4Npc9IB0vnSn+RJkgLJfatWK9r2lPStdKp0tekjaW3S1Q+oODDcFhGbIP4uCl3MAETMAET6BuBbH6aHc/muZEPUybRA0TWrCWfxqzlpaFoVRtGLRXElAmUDzkpL1F+RHnCONOiDNlN49dI86QoJ2Zo/FfSFlK8wJMtBzS5qYKN2eqHw8ZsdT6eawImYALVCFBOUybzHEiZy3PddIky81NSGLPMZzmWd6ifQI+igLGN2fpZOobFCdiYXZxHX//ZmO0rsRZZ3sZsbQfqMi1G4XRAbYt3xFI2ZjviMHsnTcAETKDhBHhwRl+UKFvHS/ynAjqrbAV51rilojxbWc6b0lSYR6U5LZyi4jxM22jpNErzsqYtFemVWtp2a15UqpcbMj8v5aQuiQp5WkURH/OoNDhI+qn0D+kZif0tpwWa/oh0hfR9CTYbSuxncAhj9reaBq+s9NfBBEzABEygnwSy+SnjS2REHpwtg7IvDGVb1PJyUJQvlC2UB5QNeSlbhkTZQZnxPukE6XEpWzY8pP+UH5RlbJ/0RBo12hThNaWCNLOPDqUEbMyWMvEUEzABE6iVAGUeZS/GK89zvMhKmcMLTetKGLUYtpTJlJMs71A/gR5FAWcbs/WzdAyLE7AxuziPvv4bqxW4Nnk+cGgjAjZmazuYNmZLOW2qSWQKE0tneYoJmIAJmIAJ9ErgC1qCcmR80ZJR+Vw8LK4o5yEcZSvMi1s48TAfxm22W0oe5HnIx7ClMj0MW7rJojJ1DSkq1qk875JyUl6iUh2NLqOYx3I5ifXCsCW+DaQdpe9IF0i3SC9JcCin2Zp+r3SJ9KfCMn/UkH2O/Q8uwzStWJrkYAImYAIm0A8Cxflp5LWR95IP8/IMlcKUM9GaNsoWyhUqjqMsyWk8L2XLDsoMpmPi7iDxzBmGJ2XCNOlIiTKL7ZKGSJdGhzREOinnHEoJ2JgtZeIpJmACJlArAco6ylnKV74lu4dEufiwRLnDi1B0cWxjVhAaGHoUF5xtzDYQqqNKCdiYre9EsDFbH7+mXXuKUkamu1HTprA5EmZjtvQ4bKpJnDsTS2d5igmYgAmYgAn0SqCSMVttxaiQLh5GhXkMsxXnPNRj2BabtsWGLS2TeMCnUp0KgGwrWwxbKtjDsKVVbF8MWyriu6W8lJO6pKxhy33YztIx0qXS3dJMiXK2nF7R9Fuln0h7S5tImNCx38GBYTErTXIwARMwARPoB4Fsfhr5LPlulDPRmpYeHGhNi6FKWTJKCpOWvD8nUSZQNmTLB+atLx0h8YwV+f8LGj9OIi62lc3b9XdIgo3Z6thtzFbn47kmYAImUI0A5RzPbpSnvER7jkSZSN0sZSXlKuUsL0hFmahRhzoJ9Gh9ONuYrROkVy8hYGO2BEmfJtiY7ROu1lnYxmxtx8rGbCmnTTWJAnti6SxPMQETMAETMIFeCfTHmO0t0myleXacB/ZQmJdUbkdlOg/1VKhHpXqYtjzwh2FLBXuxYcvb2lS2UwFLq6hst8g5/c9L3QVFBXx2GPNYLid1SVQ2EA/xfVD6mnSSdJ9EubuwMGQ8q7n6f4/0K+kA6X8l0h77G/vPMMuGcQcTMAETMIG+E8jmpeSt5LfZcoUXZsqZtJQZ5PPk+XmJsiDKBsZzBZGXPy5FXv+yxn8gUSHNdrL5uf4OarAxWx23jdnqfDzXBEzABKoRoHzDmOVZbGXpDomy8DsS5SdmLT1VsAzLOjSGQI+igbON2cbwdCxvErAx+yaL/ozZmO0PtRZYZ4rSSKbrFrPVD5aN2VI+NmZLmXiKCZiACZhA7QQGwpittvVsBXqM8yAfCgOTyu6oWOdhP0xbKtjDsI1ukTE+MWxpZVvcLXI5w7ZLy+WkvNRdUFTGZ4cxj+VyEusdJ3HPdpX0Eekb0k+kHqlSd8iYuP+RuJGnImNbicoN9o/9jX1nOKxI+utgAiZgAiZQI4FsHkqeSh4bZQnlCGVItrtj8uJVJXpioCvjnNQtRVnAONPI/78pkZdTBqAZ0qkS5h/byObh+jsowcZsdcw2Zqvz8VwTMAETqESA8pQylLKTZ62cNFei/OMZiJdXee7ixacoAzXq0AACPYoDzjZmGwDTUSxGwMbsYjj6/If6HK7Ng/q8pldoagJTlDoOrI3Z6ofJxmwpHxuzpUw8xQRMwARMoHYCg23MVkoZD//FopI7RMVAKCrZG2nYrq34u6SclJe6C4rK+Rj+QNO5Z7taYjnWYV0q9Kmg2EzaUzpNul6aKrF8OU3U9D9Ix0qfkViffYv9jH0fpmlZ6a+DCZiACZhALwSy+Sb5KXlrlB/0zMBLPrQC4sUeKpdHSbzMk21FG3k/w5xEfv916UEp8vWZGj9DYj3iZ1uxbY0OaLAxWx2vjdnqfDzXBEzABCoRoByj3OSFJsrJr0iUe/+VchL5Ky/FUpZS9rG8Q2MI9CgaWNuYbQxPx/ImARuzb7Loz5iN2f5Qa4F1piiNZLo2ZqsfLBuzpXxszJYy8RQTMAETMIHaCTSLMVstxVHBnR1S8R2i0gBRKYB6M2z5hi0VDFQmUBlPiykq5Gk1RaV8fMMWs7XYsP2+pnHPhjE7ukjd+p+XclKXxPpU1L9X2lU6SWK9iRJxlBNG7nXSGOmL0roS+xT7GPucZcG4gwmYgAmYQGUC2TyTfDTKDFoCUelMix9aBFEujJQoD3hZhjIgL0V+H/k803eX7pYiL5+h8aMk4iJ+thPb1eiABBuz1bHamK3Ox3NNwARMoBIByjCeQTBe+YzMRRLl3W+kLolykmcqXnSizKO8c2gMgR5FA2sbs43h6VjeJPAljXJu3fDmJI/1gYCN2T7AaqVFpyixXBg2ZqsfNRuzpXxszJYy8RQTMAETMIHaCbSCMVtpb6LCOzukEiFEJQGiUgHVatgWf8OWigcqdzFsj5W4Z7tKykn5gro1HF1GTGeZnNQlhVm7nsY/LxHf76RHpAUScReLLpL/Kp0ofVbCPGZ/2LfYV4bDMtKogwmYgAmYQBkCkVdG/hnlQ7SipatjKptHSHzDnLwfIzYnkadHXp/XONN3le6SIu9+QuOfkyhzIp9mmwMRbMxWp2pjtjofzzUBEzCBSgQoIynHKBN5iXWaRDm3h8TzzCiJXid4wSmeQzTq0AACPYoD1jZmGwDTUSxGwMbsYjj6/MfGbJ+RtcYKU5RMMl0bs9WPl43ZUj42ZkuZeIoJmIAJmEDtBFrZmK20l1Hpnh1SYRCiohxFZXw1w5YWVFTQ08IWw/YoiXu2KyUMUip9aVVFy1gq6LuknJSXuqXRZcR05ucklmc91mfZ/5P4Du1vpPukORLbK9ZETbtcOlTaUqLShH1CsZ/DNJ6V/jqYgAmYgAlkCEQeSb5J/km5EK1oiw1a8nsqo3NSNn/P6z/5+IESvR5Efv03jb9XIk7ijm1ptGHBxmx1lDZmq/PxXBMwARMoR4DyinKLF5Z4FtpRomybLr1T4tmHl5foIYLnKMpQh8YR6FFU8LYx2zimjukNAjZm6zsTbMzWx69p17YxW9uhsTFbysnGbCkTTzEBEzABE6idQDsas5X2PirFs0MqEkJUQETFPJUM5QzbwzWdB+VxUrY75JH6P0rKtq5thFnbpTg/LrHdSyVa1i6USENW8/T/Huk8aS/p3VKYAexT7GN23xl3MAETMAETeNM0jbyS/JMygEppKp5XkHg5h3y+mkFLhfXZUrxUM1/j50isR5zEH/mwRusOrykGygLKCodSAjZmS5l4igmYgAn0RoByijJrGWlF6bcSZQ3DnETeynR/X1YQBiD0KE5425gdALgdHqWN2fpOABuz9fFr2rWnKGVkuhs1bQqbI2E2ZkuPw6aaxLkzsXSWp5iACZiACZhArwQ6yZitBCMqybPDqJzPmrVUUHxbotzlnoTKCiokqLSnZRXdeVF5T0UFFfhZs3Y1/ac7TMxaWlx1STkpL3UXNFrDrGI6y7B8tKyl4v+L0knSn6WpEmkq1nRNu0EaI9GtJttnH7JGLfuZ3W/9dTABEzCBjiYQeSL5I/ll1qCl5VA1g5Z8OydtLl0rRb78vMb3l5aSIg9mO/UGG7PVCdqYrc7Hc03ABEygmABlE+UfZR/PNzyDRFnD8wf/eRGVZx5eXqJMa0R5pmgcCgR6NOT+wcasT4lGE7AxWx9RG7P18WvatW3M1nZobMyWctpUkyiwJ5bO8hQTMAETMAET6JWAjdnKiKhkyIpKioMlyl3eGKciAqMTUXlBhTsVFBi2xWZtdIUcZi3fLQyzlu7AMGsxXnNSXqJyf3QZMZ35OalLYj3M3k2k/yf9RLpFmimRzmI9pWnjJLpL3krCZGA/EPuHhhVJfx1MwARMoOMIRF5IvkgeWcmg5aUX8mLy5si3yatzEhVgD0uRF9+v8W0kyg3ijG1otF8hKsu7+rV2+69kY7b9j7H30ARMoLEEKJcon3im4TnhaIky7FGJso3njpUlf19WEAYo9ChemNuYHSDAHRytjdn6Dr6N2fr4Ne3aU5QyMl23mK1+iGzMlvKxMVvKxFNMwARMwARqJ2BjtnZWLHmQxD0bN+VRoU6lfYiKDCrcUbFZS+ta3jyPlrVZs5YKjkaYtZi7GASIyn9a+F4iYQwslEh7VnSzeZ9EF8hflUZLpJ39iH2K/YyhZjmYgAmYQMcQiLyPPJG8sZxBS/6NQUsenJfISxHj6CjpJYn893XpHInWRpHXxjY0qU/Bxmx1XDZmq/PxXBMwARMoJkBZx7MAzy28UDpZouz6nkR5Rr5KzxHRjTHLOzSWQI+ig7mN2cZydWxvvDDIuXWDYfSLgI3ZfmFr/pWmKIlcGDZmqx8rG7OlfGzMljLxFBMwARMwgdoJ2JitnRVLZo3Z7JpRqR5DKilCVLxTwYHKmbW0rsWs5a30MGtHaLy/Zm231kVUnnRJGAW83b6u9HnpB9I10jMS95/F4r70dxL7yn3GUhL7gGKfYj8ZOpiACZhAJxCIfI98kPwwa9BispJv070jvSCQ95IPjy6I/Pjd0kUSxiz57iPShyXiIb6IX6M1Bxuz1VHZmK3Ox3NNwARMIEuAcojyiHt/nk3iued5jW8g8UzBi0juxlgQBjD0KG7uE2zMDiDkDo3aLWbrO/A2Zuvj17Rr25it7dDYmC3lZGO2lImnmIAJmIAJ1E7AxmztrFgyKii4Ke8tRCV7DMPUjEp9Kj7qNWsxXGkdiwmQkzACUJgBMYzpLMOyYdZ+QON7ST+V7pLmSVQEZPWq/v9d+r70CQnzmLSjJQqKfYyhJjuYgAmYQFsSiHyO/I98EGOVLuzpEYH8kRZGdFVP/pyTsvkw/3eVnpHIZ+m1gLw12/KI+GsNNmark7IxW52P55qACZhAlgDlD88mlGm8aBR11SdqvFvixSPKOMq7pSTKwb6UWVrcoQYCPVqGewQbszXA8iJ9ImBjtk+4ShamDohrkzohhzYiEIWdW8xWP6g2Zkv52JgtZeIpJmACJmACtROwMVs7K5bsizFbLmYqL4pFpQYKs5MKESr6qfCIb9ZSaV+uZS2VI6MkKk+ogKbCBDMA4zUn5aXugsIciCHTmZ+TWB6Dl3mfk8ZImLHTpaxJy/gC6W7pXGkXiXVJM+mPfRmm8az018EETMAE2opA5HHke5FvU5lNK6MVJfJm8mXyyLwUeS/j75H+IEX+eqfG3yURD/FF3BqtGmzMVsWT8o9yq/qSnmsCJmACnU2Acofyh+cPjNcfSeSfUyRay3ZJvHTEC0jZl4n016HBBHoUH+xtzDYYrKP7Hxuz9Z0ENmbr49e0a1PQkenamK1+iGzMlvKxMVvKxFNMwARMwARqJ2BjtnZWLFmvMVtpa1EJH0MqRhBmJ4pK/2KzlooTTAC6FMMIKO4COczabKvavJbrlsIkiCHTmJeTqHxhHYzeraXDpXHSJIl71mI9pWmXSvtL3M+S3kh77MswTQtp1MEETMAE2oJA5GvkdeR95NNUWtM9Pd/ho9tH8lLy1ch7Geakb0ovS+SpmKwHS9G1MfERd7VgY7YanTeMcdjyQpGDCZiACZhAeQKUNVGGUX5tLs2VyD/3kyizePFzZYnnDso57vN7K6O0iEM/CPRoHdjbmO0HPK9SlYCN2ap4ep1pY7ZXRK25gI3Z2o6bjdlSTjZmS5l4igmYgAmYQO0EbMzWzoolB8qYLZcKKjuyosIEheFZzqylVS1mbXyvtpxZu4bmU7lCK64uKV8QlS5h0saQacxnOZZnvfdLe0u/kO6TqPCm8iArjIbrpGOkj0lU4kS62QeU3Tf9dTABEzCBliZAnkbeRl6HuUqPB+TJvDhD7wbZ7o279Z98lvx1E+kfUuShfytMJ4/P5pX6WxJszJYgWWwCLyfBlXLKwQRMwARMoDwByi/KLsoteuF5VCLvvF6irMpJ5Ke8bJRtLct6Do0n0KMo4W9jtvFsOz1GG7P1nQE2Zuvj17Rr25it7dDYmC3lZGO2lImnmIAJmIAJ1E7AxmztrFhyMI3ZcimjAiREhX2IypQwajEEeJOdLjWpPKm1C2RayGK+5qS81C1RGZMV05iXk1iWddaVdpZ+KI2XXpXCYIjhHE27Sfq+9FGJdJFmtIQ0rEj662ACJmACLUcg8jLyNfI38mPyYl6Y4UWZURIvx5B/5qXIX3MaP0oKo/UljVM+sz7xRLwaXSzE8l2LTfWfIGBjNkh4aAImYAKlBKJsoZzh2YEXOzFjuX+fJlHf2C3xYubKEvMxb6Nc0qjDABDoUZwcAxuzAwC3w6O0MVvfCWBjtj5+Tbu2jdnaDo2N2VJO3ChRYE8sneUpJmACJmACJtArARuzvSJabIGhNmYXS4z+RIVKDDEDwhCg0iTMWipbst+rxSSo1gVyuVa1VMyMLhLT8lJO6pIwatEnJEyGP0jPSNyrZDVL/2+Qjpa2kKKSJ9If+xNDLeJgAiZgAi1DIPIu8jTyYfJgXkihUpsu52mRREV3Top8Na/xraS7JfLL16XjJdYlPycu4s0GG7NZGqXjNmZLmXiKCZiACWTLKMoXyhl6drhGovyZLX1OonzqkujxgZeL3FpWEAYh9GgbHAcbs4MAu8M2YWO2vgM+VqtzbVIn5NBGBGzM1nYwbcyWcrIxW8rEU0zABEzABGonYGO2dlYs2WzGbLnUR2VLDMPspOIFFZu1tbaqxUTAdKWCJid1S1TYFIvpeYnlWJ71NpcOla6UeAOfB5qsZuj/ddIR0mZStBKLtMe+xFCLOJiACZhA0xMgzyIfI+8lX+MllOLWs+SVkZ8yROdLkUeO0/hwibybuIgzgo3ZIFF+aGO2PBdPNQET6DwCcQ+dLZcoVzBlt5celSh3MGX3kLi/z0t8Ix3T1t+WFYRBCj3aDsfCxuwgAe+gzdiYre9g25itj1/Trm1jtrZDY2O2lJON2VImnmICJmACJlA7ARuztbNiyVYwZsvtUXFlDJX7CLOg2KhdRtMwazEPaN2FIcA3paiUGSWtKlHZTUUNxmut3R9jPrAsRu1HpCOlq6UXpTAgYkg3nsw7RHq/RBpJa6Q7uz+a7GACJmACTUsg8ivyr6gEJ4/NfnuWvJQKcCrCozL8MI3PlcgX7y1Mj7ww4rQxKzBVgo3ZKnA8ywRMoCMIRHkR9/3cz+8vUb/6D2myFPffUzXOsyHlULdE2bSKRHnF8wFlEPEQp8PAEehR1BwTG7MDx7hTY7YxW9+RtzFbH7+mXXuKUkamu1HTprA5EmZjtvQ42JgtZeIpJmACJmACtROwMVs7K5ZsVWO2eC+jkiaGVLKgJSUqXRCtu3iLPmvU1tr9cU7r5aVuicqdrJjGvC4pjNptNH609BfpFSkqiGL4nKb9XvqW9C6J9JHWSHfsB0MHEzABE2hGAuRPkc+Sv5K3kqfSPSQV37zsQr4Y+SbDz0vPS+SF06StpOhRgPhszApClWBjtgoczzIBE2h7AnF/zD0z986HSuXus2dqOj01vE+Ke3bKI7owXkl6u0TZQxnme21BGODQo/gp923MDjDoDozexmx9B93GbH38mnbtKUoZme5gGrPbaXs3SntK5cKHNfHv0uHlZmoaD4p/ks6RBqtgHixjli6mLpTGSbQKKRfO0sRrpHXLzdS0Q6QbpC0rzG/UZBuzjSLpeFqVQCtdr63K2OlubwJDZcy24n0IZ0K7GLPlzuqovGFIxQuiIicqc6iQwagl3+2t+2Mqcvr6nVoqgHgzH31S+r7EvSiVRdwnZ/WM/v9W2kfiXsxGrSA0UXDZ3EQHw0lpKgKRz5K/km+Rp1LhHa1nMRJ5YaVbonKcIc/lD0rkgXOkvSXWI28ebGO21cpuG7M6SRxMoB8EXI73A1qTrRL385QVlDHUX8a99L81fpK0n8Sz4Dslypt1JMqeLon8c4TEC0RR5hCnw8AT6NEmOFaNNGab9Zo+vbCvYwYeq7cgAjZm6zsNxmp1rk3qhBzaiMBQGLOR0c+owJHKLk62hRJvSBUHWjUwH/GwOBhhsIzZj2lnYt8OLbNj62Xmn1FmPjc+PDQTx/Vl5jdyko3ZRtJ0XK1IoJWu11bk6zS3P4GhMmZb8T6Es6Gdjdnisz0MhBguoQUQ9zkYClmjlpZfmAvLSpW6P+6vUdulOD8j8cDOeTNbivu0GD6uaT+VPifR+ow0ItIb6XdlkmAMUviYthPHptnvpQcJiTdjAosRID+K/JS8lDyUym+eu3kxeC0pL40uaH0N/yTFdXWWxnlBJozZnMYHI5AHk4ZWqUOwMTsYZ4W30Y4EXI639lGNMob7de7L6bKYvJt76MMlemjgJaCc1C1hyCLGuyTyzpES5RKGHvEsIRGvw8AT6NEmOF47NXBTHyvESbzNdG9uY7aBB7mGqGzM1gCpyiI2ZqvAaeVZU5R4MsfBbDF7pLa3QOKkKhf20ETMxZvKzdQ0WoJOlyZIvH01GGGwjFluQCZJ7N9mZXZsKU17QOJBePsy85l0pQTfg/kzgMHG7ADCddQtQaCVrteWAOpEdhyBoTJmW/E+hJOjk4zZ4ouBypisqKBBYdRSaRNmLSZDfKeWSp3hEoYDefYoCeOhFqO2W8uhnERFEa1p8xIG7I+lW6V5EvfRIf7fJB0jfVAiXWHSkt7sPuivwwAQcNk8AFAdZdsRiLyIfIl8ispvXm5ZUSKfpOI8J4U5m9f4adLrEvndXyWeRxlnHvENdGi1stvG7ECfEY6/XQm4HG/dI0tZEOUK9+NhJLyqce6fcxL31pQt6xSGjOck7rNXk6KlLOtTPsX9s0YdBoFAj7ZB2b5TA7fVrNe0jdkGHuQaorIxWwOkKotEfkqdkEMbEZiifSHT3WiQ94mWDdVCb/N5eKSia7DCYBmz7A/7xf5VCtzsUOFYLfTGr9q6tc6zMVsrKS/XzgRa5Xpt52PgfWtdAkNlzEKst3Kyt/mDfR9CmjvZmGX/iwP3QyEqbRB5MqIip5xRW65FLZVAWaM2p/95qVsaXSSm5aQuiQqkdaWvSRdIT0rcU2fF9xm5h9xLonUA6SJ9pBVF+hk6NI4AjFvhXrpxe+yYTKB/BMh7yIu4ZngBmGdMXnxeWSJfJK/rlsgLGe4jhSG7UOPkd8yL/EyjAxp6K5t7mz+YZbeN2QE9FRx5mxNwOd48Bzh7r1rtfjXKE+51yWu/JVFGzJd2kShD1imIciMvUcZwP01+yUtBvBzES0KsTzyDVbZoUw4FAj0actwaacwSdTNe0zZmOTKDF2zM1sfaxmx9/Jp27SlKGZnuYBuzTQukQsIG05itkISmm2xjtukOiRNkAiZgAi1FYCiN2ZYCVUisjdnqRy1bcURFDqISAFG502ijtltxopyE6Ur3n7SS/Y50rUR3m9xjZ/Wg/p8hfUqi4inSR1qz6ddfBxMwARMYFAKR95AfkU8uI/ESC62WeHGF/I28bnRhuJ2GL0iRt5HvsV7kYxp1EAEbsz4NTMAEWplAlA3k7Zimw6XI55lXHJhGOYKpuoE0U6KcOEmi/MCUZZiTMGN5+WdViReB6NmGcoeXa3hJyKasIAxR6NF2OW6NNmaHaHeqbtbGbFU8DZ9pY7Y+pDZm6+PXtGvbmK3t0NiYLeVkY7aUiaeYgAmYgAnUTsDGbO2sWNLGbN94RYVSDKlMosII1WrUrqJla21R261l81JOosKpS9pBovvPu6VoXRZmxixNozvQw6T3SaSJtJFOFOlm6GACJmACA02AvIa8h7wIc5au4Kksp+KcPC0vUamOtpFel8jPnpCYhzlLHuY8SxAUbMy+wcG/JmACrUcgygPy9FMk8vu50h4S0+I+VaNpiOUxVXnx8M8S5cN90iHSVtJoqUuK1rGUL/TQQFlDbw0YulGOFMevWQ6DRKBH2+HY2ZgdJOAdtBkbs/UdbBuz9fFr2rVtzNZ2aGzMlnKyMVvKxFNMwARMwARqJ2BjtnZWLGljtm+8ipem0ihEhQ+icgnVYtTyRn/WqA3zNafp3QVR6RRiWk6iEopl3yXtLV0iPS1R6ZEV9+QXSrtKbIc0kTbSGelm6GACJmACA0WAPIY8h/yHSnIq2OlakjyJXgFyUuRxszUeeRjm7LrSWyXyLedVNmZ1GjiYgAm0IIFsOUC+H/k8w5ckjFTKiOz9aZQbGKyfkViWLoznFcZnavgJCVOW3hiidSwvAWHmlrvn1WSHISDQo21y/GzMDgH8Nt+kjdn6DrCN2fr4Ne3aU5QyMl13ZVz9ENmYLeVjY7aUiaeYgAmYgAnUTsDGbO2sWNLGbN949bY0FU8hKpQQhgKiggiDAWOCSiO6VsOgoCJpuESlUhi1VDKtKYVRm9d4txTmRQyZnpOi2+OPaPxo6e/SaxL346GFGr9V+p5UrTWtZjuYgAmYQEMJkC9GfkiFOfkf+d4oia4ncxL52iyJPCvqEyZpfEPJ5qwgKFA2wGcBfxxMwARMoAUIRP7PfTD3wNzfck8a96cMvyhxb8wy2ftnlqcF7F0Syz1TGMa6Z+g/5QjLsH6UFREH20YOQ0ugR5vnmNmYHdrj0I5btzFb31G1MVsfv6ZdOx6kbMxWP0Q2Zkv5bKpJFNgTS2d5igmYgAmYgAn0SsDGbK+IFlvgIP2j3OWm3KHxBKJCKCqlwpioZNTSYoDKJVqTYdRS2bSqhHFBCwMM2JyUl7qlMGgZ8p/pXRLLMb6zdK70kMRxzmpiYd6nNKQyizSRPjQsI406mIAJmEDdBCJfIa+h8pxWUOR3vJBCHkfeFcbsDhp/XCLPoiL+fVJUuBNPpwYbs5165L3fJtC6BMizubdcSuKFxJHS7VL2nvS3+s+LihixmLNRTrD85ySWnVMYZtc7UNMoQ1iO+FkvyppOLiuEoalCj1LDcbMx21SHpS0SY2O2vsNIHRDXJnVCDm1EYIb2hQM7TjrPqsggHjZvNqNFjK4QC84dziGfO2bgc8DngM8BnwN9PQeuL5QjT7scqakc5R6Ecpd7kr6y9vL1Mfu5mId+ofHQLzWOzpcukH4l/Vq6ULpIulj6jXSJdGlBPFSVU8xn2d9Lt0mTpfkSxz00V+NPSv+Q2AZpibT5ONd3nM3P/HwOlJ4D5C/kM+R15HMXSuRrkTddpXHyrJck8ikMW54TI2/qVKbk//CgtVmnMvB++9j7HGi9c4A8n/yee1rysWgBm70PvVDTuffN3g+z/BSJ5aKeOdaZrWkYuhdKsR7b8fnRfAye0XHhuF3fAcfn/sK+3tMB+9oM19rfCrzJJ5ohPa2WhscL/HiR26GNCCzQvkRh6aFZ+BzwOeBzwOeAzwGfAz4HfA74HPA54HPA54DPAZ8DPgd8Dvgc8Dngc8DngM8BnwM+B3wO+BxojnOAlx8d2ogAb7fy1sLJ0tFWRQa0YoDTz8xoEaNTCkz+ZCaLmPgacj7ic8DngM+B2s+BMwvlCL12mFvvDLgH4V6EexLzGnoGx+g4FOtYTUPHSccXdIKG35d+IJ0ojZG47+Y+6lTph9KPpB9Lp0mnF4lpzGMZlqWV7M3SNKn4AflFTbtV+rV0vERaitPIf58/ZuBzwOdAX86ByEfIU46XyM/+IfVIZ0uRd52j8SkSedPLEvkW+WHkRX3ZZqsvS75Pmf1XqdX3xen3MfQ50BnnAHk9+TX3ridJ5OHk8VOl7D3nA/rP/SxlAXndiRL3nyzzSmEYy8/X/wsk7m+5/2V5yoUoV3xuNde5FR4Bz+ntfmx+rX2knOb8bPd9bYb9O6vA+3fm3a/zLXypr4mfgwmYgAmYgAmYgAmYgAmYgAmYgAgMy2gJjaMlpbdIb5WWkpaW+E7jstJy0nBpJYnvd60irSatKb1D6pLyUrc0OiP+M535LLeJdKT0d2mOlGSEKXKZtJvEN71IDyJt2fTqr4MJmIAJ9EqAfIP8g3yN/Ix8bIS0urS2FPnVRhqfIJEf/UtaUSIfjLxHow4mYAImYAJNSIB8nnvFZSTuUbnXfJeEqVp8j8k9K8twP8t9bLyUE98fj+Wv0ryNJe5nR0ncB0eZoFEHEzABEzABEzABEzABEzABEzABEzABEzABE6ifQNb4xIxAVHRljVoqvcKoXV7jmBeYHFRarSqtIa0lYXjkpDA9qNhC/M9LOYll1pH2ksZKz0lRIcaQ1go3SYdJrEc6SM8S0rCMNOpgAiZgAhUJkF+Qb5CHZM1ZXizJmrNb6/9LEvnPFRJ5XVTEE4eDCZiACZhA8xEgf+b+kPx9BYl70fWl7aXXpey95Vf0H0OW+9bPFOYVvyS4UNN3ld4ndUm8jEh5QBniskAQHEzABEzABEzABEzABEzABEzABEzABEzABAaGAJVPIUwNKr0QFVOYFVSAYdS+XaIVGpVh2da0tEijZUKYtHmNY7COzoj/OYllaOFAJdqZ0sNStiKNcVqx0dL2nZJNWkFwMAETqJkAeVkt5uzOWm6uRJ5Dd5jkceQ3rEscDiZgAiZgAs1FIPJ37k25H8V4XUf6gPSQlL2fvEz/uTdFPy/Mm1UYxnI36/+HpQ0llqN1Lfe83AO7HBAEBxMwARMwARMwARMwARMwARMwARMwARMwgYEnQEVUCIMCZU1auj3GwKjUmpaWaVRuYb52SXmpWyo2aZnOfJbbTDpKooJsgRQVZgzvkY6WNpAwTUhLGCeRTk1yMAETMIFFBMgbyCfIM4pbzpLvRH50iMYjv/lGYVnWiTxGow4mYAImYAJNRID8nXyalwVp4Uqe/l7pbCnyc4YvSetI3IM+IWXnxfgBmh7dGGPyYva69wRBcDABEzABEzABEzABEzABEzABEzABEzABExg6AmF+htGBYRFGbfG3aenymNYGdHnMd2P70uVxl5bHpKVy7XBpvDRfisozhg9Ix0vvkaiUs0krCA4mYAJlCUSeRV6RNWdp4U9+Q2U9Bu1ZEvkLrWc/JWVbSxGHgwnUTCBJknWlzS0z8DkwMOfA/Pnzt5gxY8b/Tp48eavbb7/9E5dddtkOp5xyyq5f+MIXvqMLNXvPmGyzzTZj9tlnn28Vpi/W1fHb3va2SQceeOC+J5100m6/+c1vduzp6fnkE088sfVLL720Jdvw8RuY42eu5upzwOdAG54D1I84mIAJmIAJmIAJmIAJmIAJmMCAEahm0tLCIFrTFnd5jElLSwQMEVrTZrs8jpZrMcxrfq6wzLs0pEXb36V5UrbC7WH9P1F6v2STVhAcTMAESgiQZ1FZkjVnaWHFdwkxZyPfuUrj5C+0sOJbg+RlS0qs72ACNRNQZeNvJQcTMIEBIvD6668nCxcuTObOnZu8+uqrybRp05LHHnssueuuu5L1118/e5+Y7LTTTskJJ5yw2DRdzOn/Y445JvnXv/6VrkscMnvTOImbbTiYgAmYgAmYQI0E6EnMwQRMwARMwARMwARMwARMwAQGjQCmRSjbkjZr0kaXxyto2RUlTJFRUq1dHue0LEYuXRkfKF0vzZGyFW2P6f8p0qZS1qQlTZE+jTqYgAl0IAHygDBn6Yadlv28LII5m5MwZ9eX7pTIV56UyJ/Ix2zOCoJD7QRUgWdjtsZaTC9mAv0lgHGqVq3Ja6+9lrzwwgvJxIkTk/vvvz/Zb7/9sveGyUorrZT83//932LTdDUnq6yySnLjjTem60yaNCl58cUXk1mzZqVx2pTt71HxeiZgAibQsQRszNZ+q+wlTcAETMAETMAETMAETMAEGkwgDNAwQbJGbbUujzFpo8vj3r5Lm9OymLSYKPtL10qzpWyl2wT9/7H0IckmrSA4mIAJpC9oZM1ZXhTBnKUFf17CnN1EekoiP/mdRCWLvzUoCA61E1CVpI3Zjq2X9Y4PFgHM0wULFiRz5sxJpk+fnkyZMiX597//nVxxxRXZ+8F0fMSIESXTMHDvuOOO5JFHHkmmTp26qLUscdqYHayj6O2YgAmYQNsQsDFb+62ylzQBEzABEzABEzABEzABExhAAtVM2mxr2ujymO/SriRhlPTVpF1X6+wjXS29JmUr4Cbp/+kShkvWpM2mT7McTMAEOoAA1z3mLHkQFSjkO3SxzgsheQlzdgcpvm29l8ZpYUvewXqs72ACVQmoitHGbNvUs3pHmpUA5mm57ozvvPPOZN11183eB5aML7vsssl1112X3HfffWlLW1rc0vKWFrjE6WACJmACJmACfSRgY7bq3bFnmoAJmIAJmIAJmIAJmIAJDBWBrBGKwUH3oJgd5UxaWrIVm7RraVqXlJO6JQyUEP9zEi1p15G+Ll0pvSplK+T4Ju0xEsuwbdIQZosNF8FwMIEOIMC1zrUf5izdq/MyCOZs5C20uCfvmC7ROn9piXVY13mFIDhUJqCKPBuzfazN9OIm0B8CmLPF3Rljtu6zzz7Ze7+S8Z133jm5/fbbk//85z/J008/nbzyyitpy1u3lu3PUfA6JmACJmACImBjtvKtseeYgAmYgAmYgAmYgAmYgAk0CYEwNxhijGJ4hFGS7fJ4OU0vNmlX17QwafMaDyNltMYR/3MSJi3/95T+KGW7O35d/2+W6AoZQ4ZtI9JCmpCDCZhA+xLgGueaJ7+h1T4vgvBN2cg3aIV/l0SF/j8l8iKWjTxCow4mUJ6AKudszLqO1gQGgUB0Zzx79uy0O+PJkycnDz30UHL55ZeXmLG6WtNpSy65ZDJu3LjknnvuSSZMmJA8//zzycyZM5N58+a5tewgHDNvwgRMwATalICN2fK3xZ5qAiZgAiZgAiZgAiZgAibQpATCCGWI6YEwTGjRmjVpl9X/MGlHapzuR8OkxUzJSd3S6Iz4n5OYv550kDReWihFpd08jV8j7SJhvrDtSEekTZMcTMAE2ohAXNtc7+Qz5C8jJPKUnEQ+spUUre6P1TgVLuRL5A+s72ACZQmowtHGbJvWunq3motAue6MH3300fTbsaNHj477vMWG22yzTXLrrbem36Plu7R8nxZj161lm+vYOjUmYAIm0GIEbMyWvSv2RBMwARMwARMwARMwARMwgVYhEIZJmKMYJ402ad+vOI+T7peyFXYz9P9i6dMSZo1NWkFwMIE2JUBeQz5D/kJXxctLfON6TSkvYc4eJpFHzJU2l1iO5SOf0qiDCSxOQBWJNmZbrDbVyW1dAsXdGdMKltawe+21V/b+btH4L37xi+Suu+5KnnjiieS5555brLUscTmYgAmYgAmYQD8I2Jhd/HbY/0zABEzABEzABEzABEzABFqYQJgftZi0fCeyry1p+abkltIZ0lPSooo7jT8jnSV9UMKICZM20sTQwQRMoLUJcB2HObuMxmmVP0rKfm/2z/pP3vAfiVa18dKG8wDBcCgloMo8G7P9qNH0KibQHwLZ7oxffvnlhO6MH3zwweTSSy/N3tOl4xtttFFy8803p/P/+9//Jizv1rL9oe51TMAETMAEigjYmC29JfYUEzABEzABEzABEzABEzCBNiAQhmhvJi2t3iqZtHnN65ZoCRdiWpeEEfNZ6VfSC1K2Qu8R/T9OeqdUzqTVZAcTMIEWJUDeQr7yVonvzZJ/ZL83u7H+T5PIE86TWIZlWYd1HUxgMQKqqLMxW1Rb6b8mMFAEst0Zz5gxI5k6dWoS3RnncrnsvVwyZsyY5M4770wee+yxZNq0acmrr76azJ07N/22rFvLDtQRcrwmYAIm0BEEbMwudjfsPyZgAiZgAiZgAiZgAiZgAu1IADMkzBTMEVqzYphiltDVKA9G8U3arEm7hqZjwHZJeSlr0jLONOblpK9KV0qvSdmKvdv0f3+J1rnRijYMGtLkYAIm0HoEuHa5nmkNG9+bJb/ISbzE8TXp9YK21pDWte7SWBAcSgmo+tHGbEfUwXonm4XAwoULk3nz5iWvvfZa8vzzzyfRnfEee+yx6P6tq6sr+ec//5k88MADyaRJk5IXX3wxmTVrVjJ//vzEpmyzHEmnwwRMwARaloCN2dJbYk8xARMwARMwARMwARMwARNoYwIYKghzFFUzaVfSfL4huapUbNJivoTCpF1b02gle4B0o7RAikq+WRq/RNpGKteKljQ5mIAJtAaByEe4lnm5I743u5bG8xJ5wziJ6/+f0nKSuzQWBIdSAqpStDHbsvWqTngrEijXnfFDDz2UjB07Nu7ZksMPPzxtLUtrWlrV0rqW1rILFiywMduKB91pNgETMIHmImBjtvSW2FNMwARMwARMwARMwARMwAQ6hECYK8UmLQZKtKTFUOE7kpi0fEuSLkvXlDBhcxKmbBi0DPmfk5i/kXSs9LC0qLJP409Kx0hdUjmTVpMdTMAEmpwA+Qd5By3vqVwZLvESB9c+ecEW0myJa39niWW43lmHdR1MICWgekIbs81VWerUtDmB4u6M6aaY7orvvvvuhO6MR4wYkdx0003pt2Wfeuqp5IUXXkhb19Jalta2DiZgAiZgAiZQJwEbs34OMAETMAETMAETMAETMAETMAERwCgJowXjZEkJEwWTlm5I+U4kJi3mywipFpM2r+VyEt0hbyddJM2QwqSlRe11Upg2bDNMG9KCHEzABJqXANco1y35RHRpvLrGcxLm7M8krndezuAFD174YHlf24Lg8AYBVezZmK2zdtOrm0BfCWS7M8Z4pbtiWs3uvffeyb777pvcd999yeOPP562ln3llVeSOXPmuLVsXyF7eRMwARMwgUoEbMz6QcAETMAETMAETMAETMAETMAEighgmiBM0jBpaRWXNWnpujT7PVrMGLoxpbVcXuqWMGYQ40yjhew6El0d3yK9LoVJ+5zGz5TeJ7kVrSA4mEALEIi8ArO1XJfGtJp/WeI630/iBQ/yEvIV1nUwgf9RjZ2N2UrVlp5uAgNEILozxnDFeKW74ieeeCK55pprkltuuSVtQTt58uT027J8i5Zv0rq17AAdDEdrAiZgAp1HwMasnwFMwARMwARMwARMwARMwARMoAqBMF7CoMU0xVjBhKElLa3kik1avkeLSYsRiyEbBm2YtDlNw8Clq9OzpKlSGLQM75D2leg+GcOHbYeRYzNHMBxMoIkIcE1yfZI3UMlS3KXxiZrGdf20REt78g2W9bUsCA46OWzMdl51rPd4yAlEd8YYrjNnzkwN2KeffjqZOHFiMmHChART9rnnnku/LevWskN+uJwAEzABE2g3AjZm/RBgAiZgAiZgAiZgAiZgAiZgAjUQwEQJlTNpebjCpI3v0a6scb43iUlLV8Y5KS9lTVr+Y96ir0rXSPMkTBz0mnSxtLWEkRMm7TCNIwcTMIHmIMD1yPWZ7dKYaz8vbSBNkbimj5PIJ9xqVhAc3iCgmka3mG236lbvT0sQwJzlu7EYrzNmzEi/Jcv3ZtHzzz+ftqSdPXt2ugytZVnewQRMwARMwAQaQMDGrB8ETMAETMAETMAETMAETMAETKCPBMIYZZg1aTFlaEnLg1a179Fi1nRLYdIyzjRa0b5HOl56RAqDluHj0vekNSVMWraLIi0adTABExhCAlyLXJu0iOUFjVUkrmmu70MlruPpUpfkVrOC4PAGAVXu2ZhtQA2nozCBvhLItprFgH311VeT6dOnp2J81qxZaRfGCxYssCnbV7he3gRMwARMoBoBG7N+EDABEzABEzABEzABEzABEzCBOgiEMRpGKa3mMGd6+x4trWgxaDBkw6BliImTk5i/vXSJNEPC1EFzpUulzSW2w/Zs0AqCgwkMMQHyAq5FWsPyLVm6IqfVbE5aR3pY4ho+V6LVLHlEXLsadehUAqq1szFbrerS80xgAAlgzmK80qUxLWcxaBHjTLMpO4DwHbUJmIAJdC4BG7OdeuPv/TYBEzABEzABEzABEzABE2g4AYyZMGcwXDBNMWnCpC3u6ng1zaMFLK3qclK3lDVp8/qPebuudJD0LykMWob8313CBLJBKwgOJjDEBLj+uRZpOc+3p/mmLC9ZcG3vKXHdzpS47qPVLHmFQwcTUJ2kjdnOrZj1ng8xAYzZaDmLCUvXxojx6L6Y+Q4mYAImYAIm0EACNmY7+N7fu24CJmACJmACJmACJmACJjBwBDBoUBi0tG7FpC3X1TFdnq4urSV1SXkpa9Bi6uQkDJ5PSZdJcyRMHjRNOlHC4GU7bBNFGjTqYAImMAgE4prPtprl2s5JXNOPS1yzB0tuNSsIDjohbMw2sJ7TUZlA3wmEORsGbdaQtSnbd55ewwRMwARMoFcCNmb9EGACJmACJmACJmACJmACJmACA0ggzNEwbGhNh3mabUVLy7oVpZHSqhLdn2LC5iRM2axJm9d/zFu+RTtGeloKg3aexi+XPiKxDbZlg1YQHExgEAlwrXPtlWs1e5Kmc73eLQ2XotUs6zh0KAFV3ZVtMYs5RJeqfOfSMgOfAwN/Drz22mtJVs3OnPyBlr0OJmACJmACLUfAxmyH3vd7t03ABEzABEzABEzABEzABAafAOYLwiwNgzbbija6Oh6h+XSBmu3qOK//WZOW8VxBe2t4qxQGLcN7pP8nLSfZoBUEBxMYJAJxjXNtU+nCSxfRanYTjfOdaK7RLSWueZaLFyg06tBpBFSVWGLM0krvySefTL761a8mO+20k2UGPgd8DpScA/vtt18yfvz4lnMjnGATMAETMIHExmyn3fB7f03ABEzABEzABEzABEzABIacAMZNKGvSRitavhmbbUVLV8eVWtFi0OYlujHeVrpEmiWFSfu8xk+VWMataAXBwQQGgQDXd6VWs9dqHtfnL6UVJFrWsizrOHQgAVVOlhiztJa99957k5VXXjlZeumlLTPwOeBzoOQcWG+99ZJLLrnE/oYJmIAJmEDrEbAx24H3/N5lEzABEzABEzABEzABEzCB5iGAGYMwaMOkpQUdJi0PbNGKdiWNRyvaSt+izWuZLund0gnSJCkM2gUav1LaRrJBKwgOJjDABLieudZ40YLrN1rN7q5xrsuXJVrFM9+tZgWhU4PqEssas/fcc0+ywgorJMsss0zyyU9+MvnmN79pmYHPgQ4/B/bdd99kxx13TJZbbrkkl8slF198cevZEU6xCZiACZiAjdlOvfH3fpuACZiACZiACZiACZiACTQdAQxaFAYtpg6GDS3qilvRxrdoaSmbk7ql0QUxnpOYt4fUI4VBy/ABaS+JB0Ja6i0hxbY16mACJtAAAlxTXF/xrVlavnNNcp1OlrgW95HobpwXMViWdRw6jIDqJisas8OHD09GjBiRnH/++cnkyZMtM/A54HMgufrqq5PVVlstyefzNmZt7piACZhAaxKwMdth9/veXRMwARMwARMwARMwARMwgeYnECYpQ0xTDJviVrTDNY1v0WL20BKPVrQ5qVvC+EGM5yXMoI9KF0ozpTBpJ2v8MIlvYLINthXb1qiDCZhAHQTi+uXa5cUKrtc1Ja7J0yWuw1skruVlJF7E4Bp06DACqk+saszSnfHvf//71qx2dKpNwAQaSoDvT998883JmmuuaWO2oWQdmQmYgAkMKgEbsx12v+/dNQETMAETMAETMAETMAETaC0CYZRi2GCeYt4Uf4uWblJXlugWtbdujjfQMj+Qpklh0L6k8ZMlvmMb5lBsV5McTMAE+kmA64hrCuMVA5aW7l3SFhLdi78ubSLRZTkGbrwcoVGHTiGgasCqxuzIkSOTcePGDWptoTdmAibQnAQwZnt6elJjlq6ML7roomT+/PmLtGDBgoRlHEzABEzABJqagI3ZTrnR936agAmYgAmYgAmYgAmYgAm0NIEwSjFuEGYPRg7dpPJgR3eotHwdKdXSzXG3lvu29JgUBu1sjf9SwrwlfreiFQQHE6iDANct1xEvU3CN8gIFL09w/d0kce0dJy0vcS2zLOs4dBABVRvamG3qulMnzgSah0DWmF199dWTo48+OrnllluSm266KRk/fnxy5513Jq+88krzJNgpMQETMAETKEfAxmwH3et7V03ABEzABEzABEzABEzABNqDQNakxcjBRMX44QGPlncrSNlujt+h/zkpL40uCGMoJ9HN8Z7Sv6QwaBdq/Arpw5INWkFwMIE6CMSLFFyftG6n6/GcdJLENXe9xEsVzOd6Y3mHDiKg2jobs+WqLD3NBEyghEDWmB01alSy//77p12djx07NkHXXntt8uKLL5as5wkmYAImYAJNRcDGbAfd63tXTcAETMAETMAETMAETMAE2ovAMO0OwsjJGrR0m8o3LWmFl+3mmO9bdkl5KQxahvzHoN1RwiSie9UwacdrfHvJBq0gOJhAPwhwjXJ90iKWlyb4LjTX2w4S19nLEi3d3Z2xIHRiUDWhjdmmqit1YkygeQmUM2bp6vzSSy9NLrnkkuSaa65JXnjhhebdAafMBEzABEwAAjZmO/Gm3/tsAiZgAiZgAiZgAiZgAibQVgQwfhAGbbTOq9bNMQYtxhCGbLcUJi3/MW4/Kl0mzZPCoH1A47tLmL6YTGwntqtRBxMwgQoE4trkmsR8xYTlGlxHmilxjW0lYdq6O2NB6LSgyjkbs66kNQETqImAjdmaMHkhEzABE2h2AjZmO+2G3/trAiZgAiZgAiZgAiZgAibQ1gTCLMU4xUClpWu2m+Ph+h/dHK+h8ejmOGvQMt4lbSL9VHpVwjxCk6RDJOKxQSsIDiZQAwGuS65FKmHotji6M+7RONfVdwvTefGB5VjeoUMIqObQxmyzV586fSbQJARszDbJgXAyTMAETKA+AjZmO+Q+37tpAiZgAiZgAiZgAiZgAibQWQQqGbQYP9nv0I7Sf0yiagbthpo/RnpWCoP2OY1/R6K7ZBu0guBgAlUIcD1ynWS7M+7S/9MkrqmrJV6YoAtyWtbyYoVDhxBQvZ6N2foqN722CXQMARuzHXOovaMmYALtTcDGbIfc53s3TcAETMAETMAETMAETMAEOpNA1qDF7KE1XnRznP0ObRi0a2k+hlFeGl0QLWhzhfHDNXxSCoN2qsZpQYvZa4NWEBxMoAwBrkOuP1qvLyetLPEyxM4S19I0iWnMYxmWZR2HDiCgekcbs+1d+eq9M4GGEbAx2zCUjsgETMAEhpKAjdkOuMf3LpqACZiACZiACZiACZiACZgABDB6wiDCRC02aOliFXNoNamaQYtxe7A0UQqDdorG95d4yLRBKwgOJlBEIF6M4IWIlSS+M7ueNEfiOvqg5O/MCkKnBdUK2pgdyqpRb9sEWoiAjdkWOlhOqgmYgAlUJmBjttNu+L2/JmACJmACJmACJmACJmACHU+gkkHLAyIt9jBoR0qrSphHa0t5qbgFLS1paUGLKRsG7VMa31uiy1YbtILgYAIFAlx3tFinO3G+0cwLEDnpXxLXDy3Pufb8nVlB6KSgOjsbs5UrLgd0DibX7Nmzk1mzZlUU8+fOnZssWLAgXYb/jA9mIJ2vvPJKMmXKlOTpp59OXnvttYZvft68een+zZkzJ1m4cGHD43eEjSFgY7YxHB2LCZiACQwxARuznXSz7301ARMwARMwARMwARMwARMwgQyBYoMW04huVMOgxTzqzaANw/YoLUt3rGHQPq7x3SXis0ErCA4dT4DrjWuBlxb4NvMqUpf0M4nr5kLJ35kVhE4Lqhi0MTsEtaMYXJic++67b/LlL3+5rL70pS8lu+22W3L22Wcn9913X7LLLrsk++yzT3LvvfcOSooxgB977LHk+OOPTz772c8mW221VbL11lun6TjzzDNTo7YRCcH0HTduXMrge9/7XvLss882IlrHMQAEbMwOAFRHaQImYAKDT8DGbKfd8Ht/TcAETMAETMAETMAETMAETKCIQCWDlpZ7fDsWgxbDCCNpDalSC9p1NO846TkJowk9LO0i0W2yDVpBcOhoAvGdWa6r+M7sERrnWrmhMI15XC/+zqwgdEJQXaCN2cGvEE0wuB555JFkrbXWSpZZZplUb3vb25KsmL7iiiumRuhf/vKXdB7LX3vttQOeYtI3YcKEZMcdd0xGjRqVLL300slb3vKWZMkll0zTsfbaayd77rln8uKLL9adlueeey41f9nfLbbYInnyySfrjtMRDAwBG7MDw9WxmoAJmMAgE7Ax2wk3+d5HEzABEzABEzABEzABEzABE6iBQDmDFoMoDFq+f1nOoKVL49ESw5z0Tukk6SUpDNr7Nb6TZINWEBw6lgBmK9cA35nlWqKr8N0krpNHJV5+oDVtdAXONenQ5gRUEWhjdpBrQ2Nzzz//fHLaaaclJ5xwQkJL0Y9//ONpmbXccssle++9dzr9xBNPTH7/+9+nLVePOuqohP//+c9/IooBG9K98kUXXZS8/e1vT1ZYYYVkr732Si644ILkjDPOSLbccss0nauvvnpyxRVX1J2GmTNnJn/961+T7373u8lPfvKThpi9dSfKEZQlYGO2LBZPNAETMIFWI2Bjts3v7717JmACJmACJmACJmACJmACJtAfAlmTli6OazFoMWezBu0G+v9D6RUpDNq7NP4ZiTizLWj118EE2p4A1xXnPpUxfE92dWlrievjVYnvOvMCBC9DcH3YmBWEdg+qSbQxO0TVqXQVTItTWow+9dRTyXHHHZeWVausskpyww03JBi36OWXX06N2aOPPjo56aST0nG+83r55Zcnp59+empq/vnPf06+853vJIcffnjym9/8Jo23p6cnNXy//e1vJz/96U+TqVOnLtrT+fPnJ/fcc08yZsyY5MADD0xNUUxWuhUmMDz22GPT9KyxxhrJ1VdfnWCgvvTSSwmtdzfZZJNk8803Ty6++OJ0eb4Lyzdof/WrXyWHHnpoqvPOOy9NKwtg6E2aNClNB2n505/+lJrMbINx9hdz+mc/+9kiY5Z9vOmmm1KD+qCDDkpb1bJtTOMI06dPT9MGm29961vp/mPu/vvf/45FPGwgARuzDYTpqEzABExg6AjYmG33G3zvnwmYgAmYgAmYgAmYgAmYgAnUQSBr0GIUhUFLi79KLWgxZ1G0oH23xs+UMJ7CoB2v8Y2lMGhjO5rkYAJtS4DznOuIFrF0Eb6aRAvz1yWuDV5mwLDFmOXasDErCO0eVCdoY3boKkYXbfnVV19NTj311LSMWm211ZLbb7990bx58+alxuWaa66ZvPe9703+9re/pYbtTjvtlDBto402StZbb71kpZVWSrXuuuum3R+z7MiRI9Np+Xw+Ofjgg1NTc+7cucl1112XfPjDH04wgekumeU22GCD5IgjjkhNWUxRTFKd/2k3xh/96EdTo/aqq65KHn744dTUfeCBB5Jp06Yt6paZVrVdXV1pfKSF7o6/8IUvJHfccUeCCY3J+q53vSudvuGGGyarrrpqQloxZDGY6aZ5u+22SyZOnJhguNJi9wMf+EDalTJppEtlDOEf//jH6X5gap9zzjnJO9/5zmTllVdOhg8fnu7rO97xjrQL5jvvvHMRQ480hoCN2cZwdCwmYAImMMQEbMy2+w2+988ETMAETMAETMAETMAETMAEGkAgjFO6Yg2DFnOJ72HWatC+V8v+RJolUdm8UPq19A4JE4q4YzsadTCBtiOQNWa5bui6uEuKVuVbaHwlicoaG7OC0AlBFYM2Zoe4dpTNY8yefPLJqRGKMXvbbbctShVGKi1ihw0blpqTfGP22WefTbbZZpt0+eWXXz7Zbbfdku9///vJ6NGj0+UwMj/ykY+krVIxYFkXE/Txxx9PJk+enHziE59IDdf3vOc9ySmnnJLst99+CV0o53K5tCUuLWBpdbrVVlul2+D7shijmKB0ZXzIIYck//znP9M0vvDCC2kcGKO0riUuzFZMWKbtu+++qdH6j3/8I2HfdF2lRvCnPvWp5Otf/3ryu9/9Lk0n0zfddNPkiSeeSP71r38lm222WbLsssum+wGbXXbZJe1amWXo+vihhx5K55Huz33uc8nYsWPTFsUYzJjV559/fmoaLwLpkboJ2JitG6EjMAETMIFmIGBjthNu8r2PJmACJmACJmACJmACJmACJtAgAmGcZg3act+g5duZa0t5qbgF7WaadqUUrQRpSXustJyE6WuDVhAc2pIA1w/n91IS5/soietkqoRZ8nGJb8/SIj1eVtCoQzsTUO2gjdkmqCKtZszSYracMcs3aZdccsnkfe97X/rdWVqa7r777qnxiQFK69ann346bWGqczg1RWm9euONNy76dizft6UbZYxgDNy3ve1tyR577JG2cMUQxiClK2Ra32KAEs8SSyyRtkylK+Nbb7013TYm69JLL518+tOfTu69997k/vvvT4488sgEQxcj9b777kvoWpnv0rINWvs+8sgjaevYCRMmpN0UE/cHP/jBND66RCY+Wt2ee+656XLsz7vf/e60dewxxxyTttrl/1JLLZW8//3vT1v0XnjhhQliH+la2aGxBGzMNpanYzMBEzCBISJgY7adb+69byZgAiZgAiZgAiZgAiZgAiYwQARqMWj5XmYlg7ZL87aX7pTSimYN/yt9VaK7ZBu0guDQlgQwZjnHaW2+skSL8YkS18EO0kiJeSzDsg5tTkAVgjZmh6hWNLvZ/hqzmJLbb799aqTS/S/fmaV17DrrrJMarrNnz04uu+yytJzDrMUcvfTSS9P/b33rW9NWrdtuu23a8nTEiBGp0YvhyvdkMWb51iyG71133ZXGc9RRR6UtZtkuButhhx2Wmrq0jsUkxnilJS9dH6+//vppWuhmme/I3nLLLel8Wt7yPd0IfGM3vmeLMYupy7dvdemlLWbpzpg0Mm+FFVZIt0sLYQxlvo9LWjBx2T+2RYtevjmL8evQWAI2ZhvL07GZgAmYwBARsDHb5vf33j0TMAETMAETMAETMAETMAETGEgCWYOWFn6YSdGClm9oYjKFQdul8WwLWsaZ9k0JUzatBNbwDmlLqdig1SQHE2h5AlljlutjLelhifN/VwmzNmvMco05tDEBVQjamB2iWtHsZvtrzGJI8h1XAiYqrVRp0co3XJ955plkzpw5aVfBOoVT45Luh8OYZd0tttgi2WGHHdJvshLPl7/85eS73/1uMmnSpOTwww9P46ZVLWHWrFnpN2WJA5MUI5Z1x48fn7aoxejFEI74aBX7xS9+MTnggANS8zZazNLd8RlnnJHGyU/WmP3Qhz6UGrMnnXRSWiZjxG699dZpnHRXTBp33XXXdH2+g4v5Slyf/exn026Y2Sda6fLtXb6pyzIOjSNgY7ZxLB2TCZiACQwhARuzbXxv710zARMwARMwARMwARMwARMwgcEiUGzQ0lVrNYO2W/Oji2MM2nWlkyW6NQ6D9nKNs0x06Rrb0CQHE2hZAmHM0l0x3RZjzN4tcd5/XcKYpZtjriGWtTErCO0cVCloY3YIa0Zj0/UYs5ifhKwxSwvWcsYs5ugNN9yQtjLF9MR0ffTRR9NugY8//vj0O7W0bp06dWraLfEyyyyTfpuWrpRpQTt//vy0+2KMX4xZtk3XxbSSxRTdbrvtUmOVrosxgDF36VqYboXpXpgWtZim55xzTuz6YsYshi/ftv3FL36Rxk9XxixLGkk7LWtPPfXUNC5a8v70pz9N03z55Zen6eI/5i7m9Fb6Pi7f03VoHAEbs41j6ZhMwARMYAgJ2Jht55t775sJmIAJmIAJmIAJmIAJmIAJDDKBME8xlDBUyxm0q2k6ZlROKjZoP6Bpl0oLJYyq2RKG7UqSDVpBcGh5AnFthDFLd983S5zvB0h8d9bGrCB0SlCloI3ZIawZjU0PhjG76qqrJjfffHPaBTDmJS1LN9544+S0005Lu/7N5/PpN1zPOuustKUphiitYFkOo/drX/tastdee6XGJyYs35z94Q9/mDz77LOpYYoZipF6yCGHJKecckrysY99LG3F+pWvfCU1esOYXWuttSoas5tttlnyxBNPpOnE/H3729+exkMaaXlLGulqeezYsQnfy+X7tWyTFrN8l5b08D1c0kcL25dffjkQe9gAAjZmGwDRUZiACZjA0BOwMdspN/reTxMwARMwARMwARMwARMwARMYRAKVDFoMpxUlWgWuLvF9zZwUrWcxavn/cemfUrSenabxb0gYvUtKmFtsw8EEWo0A5y0vGVAhwwsHGLN/lTjXvyNhzC4vucWsIHRCUN2gjdmhryBNMGaj+16+lXrbbbctStW8efMSWqzqfEz4PistWjFDMT4xID//+c+ny9Ji9ogjjki/68r3XaPFLK1JWRdjllan8d1ZvseK6UorVuLlm7HE9fDDDycYcHRnjBk6cuTINE5MUsxYWsoOHz484TuvtFpdsGBBcvfdd6fdIS+77LLJSiutlMbJsnwflpazdINMS91VVlklbTF79tlnL9q/MHZJ4yabbJIasy+++GLCMl1dXQmtdkkj38AlLd/85jdTc5lWvSeffHLyjne8IzVwWRaTlrRh3v7xj39M07ZoQx6pm4CN2boROgITMAETaAYCNmY74Sbf+2gCJmACJmACJmACJmACJmACQ0QAEwoTFTM1WtDyIIrxhCmFCbWGtLaUl7IGbZf+7yE9IaWV2hrS5eumEnERJ/EjBxNoFQKcr1ljlvP/jxLn+HHSKlIYs3GOa5JDuxJQ7aCN2SaoIsV8pQUoLUPPO++81FSNZC1cuDCZMGFCOu9nP/tZOs63U8eNG5eceeaZydVXX50uSlfDGK+nn3568utf/zo1ezFN6QY44qVLYcw1TFy+DTtmzJjUfD3ssMOSX/7yl+myrENgu5izf/jDH5LjjjsuNUT322+/tHXsZZddljz55JPpMizLt2wffPDB5Oc//3lCXAceeGDyox/9KE3P9OnTFxm97BvL0P1xBEzbW265JU3jJZdckrA82+bbs9dee23aVTEG8VFHHZUwnzSRRpaZNm1aalqfeOKJyUEHHZQceuihybnnnpvcfvvt6f7HNjxsDAEbs43h6FhMwARMYIgJ2Jht1xt775cJmIAJmIAJmIAJmIAJmIAJNBEBzKisQbu0/tOVKwYU39nEjKLlIGYsrWbDoM0X/h+r4XQJ82q+dLo0XMLgitazNmgFw6HpCZQzZi9Tqjm3T5XCmOUasTErCO0eVDFoY3aIa0dj87RkpetdTFO+5ZoNGLcxj3EMMlrZYmLOnDlz0aKYnEwjDpYhYNjGutl4GadlKt9hpfUp65QLpAuT9L///W+6LC1cMYaLA0bpjBkzUlMZA/ill15KSGsEzNRIG0ZuBNIZ+84+hTHMfNLOtokPEza7r7E+y7zwwgvJ008/nW6bbWTjiOU8rJ+Ajdn6GToGEzABE2gCAjZm2/0G3/tnAiZgAiZgAiZgAiZgAiZgAk1CAEMKhUH7Vo1jPi0rrSCNlFaV+P5sOYP2/Zr+BwkDC02UtpeIBwMrDFqNOphA0xLIGrN0602X3j+XOKd/KXENcD3YmBWETgiqHLQx2wQ1pE6CCbQCARuzrXCUnEYTMAET6JWAjdlOuMn3PpqACZiACZiACZiACZiACZhAExEoZ9Auo/Rh0NIKlu/PriZV+v7sbpr3XykMWlob0trWrWcFwaHpCYQxyzkfxuwxGud8/rNkY1YQOimo6s7GbK/1l17ABEwAAjZmfR6YgAmYQFsQsDHbSTf73lcTMAETMAETMAETMAETMAETaCICWYMWU3UpiYfU5SQMK74/S2vC4u/P5jVtPek8aYGEofWitI/k1rOC4NDUBLLGLC8i8BLCXhLn8QOF/0zHuHVXxoLQ7kHVizZm26KO1TthAgNPwMbswDP2FkzABExgEAjYmG33G3zvnwmYgAmYgAmYgAmYgAmYgAk0OYEwaDGhMGjpwjW+P7uSxit9fzaneZ+W7pOi9exNGn+XRDxhahG/gwk0CwHOR85NjNcwZj+p8XjBAKPWxqwgdEpQ5Z+N2UGoAfUmTKAdCNiYbYej6H0wARMwgcTGbKfc6Hs/TcAETMAETMAETMAETMAETKDJCWBYFX9/FoM2vj+LYcX3Z3NStzS6MMxreLw0U8LcmiOdINE1MgYYcRI3cjCBoSbAech5yQsInNt0XczLBPFywboap8U4FTa8YODzVhDaOahy0sasa2hNwARqImBjtiZMXsgETMAEmp2Ajdl2vrn3vpmACZiACZiACZiACZiACZhAixEIAzVr0NKyEJM1ujdeQ+Plujf+kKb/XQqD62GNf1Ry98aC4NA0BMKYpevu5SVahHM+T5I4d3eRaCluY1YQOiGo5tDGbLNXnzp9JtAkBGzMNsmBcDJMwARMoD4CNmY74Sbf+2gCJmACJmACJmACJmACJmACLUYga9D+f/bOA9qKIvv6y4RxRAVMqDzQMeccRwWzY05gHPMoxjFjwJwTijoKhkEQFTBiGMwRw+ioo2IEMybMYra+/auP8t+29+W+77173z5r7dupurp6d90OteucwmswjT+LkDWHgJdhV6FGyHrPdtPyfsLHAiLXr8JlAvuRT/Ke1azNDLQKA9Rt6iF1mvGUGUt5fmGkQJ0dIHQS8BanUwFpbVXMgNr1LMw2r3HTe5uBdsOAhdl2c6l9ombADFQ3AxZmq/jd3qdmBsyAGTADZsAMmAEzYAbMgBmodAaSiEXoV0Qqwr/iPcsYnJ2FeQREre4CoY0B80sKQwWEWcSul4TlhOQ9S77AZgZamgHqHWIrdZG6jAhLiO4jBerqEwJ1m22ItxZmRUI1m9odLcxWd+Orz84MFMaAhdnCqHRGZsAMmIHWZMDCbDW/3PvczIAZMANmwAyYATNgBsyAGTADVcBAElERqBBok/cs3oaEfCUULN6z3YQeAuIs0xqht/ChgOD1nXCIwP7kQ34WZ0WCrcUZoO7hwY1XLB7g1N91BeopYyQj1OLlneqq66nIqFZTq2Cdwmznzp3DyJEjW7Px0Mc2A2agjTCAMPvYY4+Frl27hi5duoS+ffuGESNGhGHDhoWhQ4eG0aNHh08//bSNlNbFMANmwAyYgVoYsDBbrS/2Pi8zYAbMgBkwA2bADJgBM2AGzECVMYA4BRBVEbXwnkXYmlXA63BuAe/ZGgFxFnQXlhHGCIheYLSAp202tDH52sxASzFAfaP+0SjD2MnUR+rqRwJ1dEuBes34ytR310+RUK2mBrs6hdk55pgjXHHFFeH99983zIHrQDuvA++991649dZbw9xzz21htha1w6vNgBkwAxXAgIXZan2x93mZATNgBsyAGTADZsAMmAEzYAaqlAFEKjwOEawIB4t4hfcsAhfjdc4rLCDgNZsE2hrNHy/gjYjw9YGwoZBCG9t7VmTYWowB6jD1l84FCLCMmdxNuF2gfp4pUJ9ptEHAtTArEqrV1HhYpzA77bTThsUWWyystdZahjlwHWjndWDNNdcMyyyzTOjQoYOF2QpQXlxEM2AGzEAtDFiYrdYXe5+XGTADZsAMmAEzYAbMgBkwA2agihlAqEoCLcJVCm+MyEVoWIQuwsHWCEmc7a75DYRXBcSvX4RzBLxus96zWrSZgbIykOou9ZZOBXQooDNBf4G6+aCAFzjjzNJ5gI4DtiplQA12dQqzOu0w1VRThamnntowB64DrgOxDnBfcCjjWuQOrzYDZsAMtH0GLMxW6Xu9T8sMmAEzYAbMgBkwA2bADJgBM9AeGEgCV9Z7FjEr6z3bTcvJe5bpwsIQAQEMPC0sJmTFWfK1mYFyMoDYiuhKfUWEpSPBxgJ18huB0NyItoi31G/XSZFQjaa2wzqF2Zlmmin06dMnnHbaaYY5cB1o53Xg1FNPDXvvvXeYddZZLcy2feHFJTQDZsAM1MaAhdlqfKn3OZkBM2AGzIAZMANmwAyYATNgBtoRAwhWSaAt5T2bxp7trnRZ79m9tPyZgBD2lbCH4NDGIsHWIgxQZ6mvNMzMLhCCmzr6uUCd3FTAA5xwxxZmRUK1mlrs6hRmGWN2yJAh4dNPPzXMgetAO68Dn3zySfj3v/8d5plnHguztckdXm8GzIAZaPsMWJit1hd7n5cZMANmwAyYATNgBsyAGTADZqCdMZDE2dq8Z7uKj24CXrMItExXFh4TEMLAcIFQyFnvWS3azEDhDFBfqauMkdxRoANBjXCTQF28VkCwpeEm1UfN2qqNAbUd1inMdu7cOYwcObLtNzG6hGbADJSdgV9//TU8+uijoWvXrlGYPeCAA+L94brrrgvDhg0Lo0ePjuJ92QviA5gBM2AGzEBzGLAwW20v9D4fM2AGzIAZMANmwAyYATNgBsxAO2YAsSsJtIhZhIFlDFk8DwkXO4/AWJ5JnEWg7S6cJfwkIIi9JCwiJDEs5alVNjNQGAPUK4RZ6uifhDTObB/NUw/x4ia8MaGO0ziz7GOrMgbUqlenMNupU6cwYsSI5jT+eV8zYAaqhAGE2YcffjgKs3Ta2HfffcPQoUPDVVddFXHzzTeHjz/+uErO1qdhBsyAGahaBizMVtn7vE/HDJgBM2AGzIAZMANmwAyYATNgBv5PnEX4QtTCK5HxOvFAnEtA8KoREGaTOLu55j8QEMU+FdYTUmhji7Miw1Y4A1MrR+pYdpxZOg1MEKiHBwqItg5nLBKq1dTkaGG2attdfWJmoFgGssLsbLPNFrbccstwwgknhGOPPTb069cvXHjhheG9994r9qDOzQyYATNgBopmwMJstb7Y+7zMgBkwA2bADJgBM2AGzIAZMAPtnIEkpiJ+4f2KuIUARthYvBMZ07ObkLxnma4o/FdAFPtR2F/AoxGBl3zssSgSbIUxQH2ibtI4M5uAR3d34WyBOvikwHq2Jw9uzdqqiQG19FmYLbq50/mZgSplICvMMv70brvtFi677LIwYMCAKMriPTtx4sQqPXuflhkwA2agahiwMFtNL/M+FzNgBsyAGTADZsAMmAEzYAbMgBn4AwOIX4iqiKuIrHwI44XIWLKM6zm/gBiWvGcX1vzNAsIYuFjICmPkZzMDRTBAXaJe0mmAcNtzCoTaXlVIobVX0bzDGYuEajU1MVqYrZp2Vp+IGSgvA1lhtkuXLmH//fcPN954YwxnfO2114bbbrvNY8yW9xI4dzNgBsxAEQxYmK3WF3uflxkwA2bADJgBM2AGzIAZMANmwAz8xgACWBLBUmhjxC5CGyOGlQptjNfirwLi7BgBL9vktWhxVmTYms0A9YhOA6XCGd+j9dS9SwREW4czFgnVaGrdszBbRBOn8zAD7YCBvDDbt2/fOAb1sGHDAhg9erSF2XZQD9IpTpo0KYwZMyZcfvnlhjlosTpw3333ha+++ipVQ0+bxoCF2Wp8qfc5mQEzYAbMgBkwA2bADJgBM2AGzMAfGEjiLEIYAiveszMJhDbuLJQKbby31n8rIJC9KiwuIKLh5Zjy06zNDDSZgVQfaaBJ4YxrNE/do959ItApgLpK3SO9rYoYUHuehdmmNWp6LzPQ7hioS5gljLGF2fZVJcaNGxf22WefsMACCxjmoMXqAB1C3n777fb1Zyv+bC3MVtG7vE/FDJgBM2AGzIAZMANmwAyYATNgBupnIHkpIq4idPFhnA1tTCjZHgKhjZluJLwnIJJNmrJscVZE2AphgPqYwhlTD1M44z9r/iOBerebMIuQxjtmH1uVMKC2PguzxTd4OkczUJUMWJitysva5JN64YUXwjbbbBOmmmqq0LFjx9CtW7dQU1NjmIOy1IFZZpkl1rUdd9wxvPnmm02ut94xMmBhtkre430aZsAMmAEzYAbMgBkwA2bADJgBM9BwBpK3K4IY3rMzCAhfhDaeS8iPO7ui1j0lIJIx9udBQhLJ8GC0UCYSbE1igLpDHULsxyuWsY+7Ct2FSwXq3CMCnt3UU+qr65tIqBZT85yFWbfSmgEz0CAGLMw2iKZ2k+h///tfFGanm266sOuuuwZCzI4dO9YwB4XXgUcffTRsvPHGYYYZZgg77bRTGD9+fLv5n5XpRC3MFvAiv5jyWLsZWKOAMjgLM2AGzIAZMANmwAyYATNgBsyAGWg8A0kUQ+xiDE/GnSWcbKlxZxfW+hECQhm4WGAf9rU4KxJsTWaAepg6CCDA0jmgm7Cm8L1AfdtGoH4i4Lq+iYRqMTX4WZgtU6unszUD1caAhdlqu6LNO58kzHbo0CEcc8wxHvezeXR67zoY+Pnnn8Muu+wSZpppJguzdfDUiE0WZgt4kb9XeaQP86ZO+aiqJFtBhaURwmYGzIAZMANmwAyYATNgBsyAGah0BpI4i/dsdtxZxvXEcxGBbMEp6K7pqcIvAt9/VwnJi9FimciwNYkB6mCqf3huM+bxfEIPYbBAXfufMKtAfSMt+9iqgAE14lmYbURLppOagfbMgIXZ9nz1/3juFmb/yInXlIcBhFm8shFmEWjtMdtsni3MFvAO356EWXqN81FII8S7BXDnLMyAGTADZsAMmAEzYAbMgBkwA22BAUSuJI7hkcjHMiJYJ2EeAXEWkQyBlimhjH8WEMyuE0hvz1mRYGsyAwj71CHqEiG1qXc1wvLClwJ1bR+BcMf2mhUJ1WJq2rMw2+z2TWdgBtoHAxZm28d1buhZWphtKFNO11wGsh6zm2++eXjggQfCa6+9Fl599dXwyiuvhEmTJgXS2BrMgIXZAl7k24MwS2/cfwjpY5APQguzBVQeZ2EGzIAZMANmwAyYATNgBsxAm2EgL87imfgngTE/5xay484izv5d+FHg++gmAU9Hi7MiwdYkBlLHAMJjU+/w2KbO0RngLIF69raAN629ZkVCtZia8CzMNrgd0wnNQPtmwMJs+77++bO3MJtnxMvlYiArzK611lph0KBBYdSoUWHkyJFhxIgRYdy4ceGHH34o1+GrMV8LswW8yLcHYRZPWT4Cs7AwW0DlcRZmwAyYATNgBsyAGTADZsAMtDkGEMiS9yIiGYIrHoyM+5kXZ/fQujQG6GjN42VrcVYk2BrNQKp3eMPiFUudm1foLiwufCDwTX6s4LFmRUK1mFobLcxWY5Orz8kMlIEBC7NlILWCs7QwW8EXr8KKnhVm11hjjTBw4MBw3XXXhaFDh4YhQ4YE6uL3339fYWfVqsW1MFvAi3wpYXag8l2qgVi0gDKUOwsLs+Vm2PmbATNgBsyAGTADZsAMmAEz0JYYSCIZIiviLELYbALfRvMJNQKejGBnYbKAaHaPQDqLsyLB1mgGqHfTCNQ5RH7q2wIC9ewogTo2SaCDAF6z1DP2sVUwA2oWtDDbqm2jPrgZqBwGLMxWzrVqiZJamG0Jln0MGMgLs5dcckkYPnx4GDZsWLj22mstzDa+mliYLeDdvZQwe3QB+eazoNcsKMLIh4aFhlpbF2Ybcy5F8cgHMD3nbWbADJgBM2AGzIAZMANmwAxUJwNJnEUo6yDgxdhRIMRsV6FGQDAD2wvfCAhnDwuMTcu3B563Fs5Egq1BDKQ6R92hvhFGG6/ZGmEh4VWBOnaBwHcw9dJ1TCRUsqktz8Js4xs0vYcZaJcMWJhtl5e91pO2MFsrNd5QMAMWZgsmNAQLswW8vJdTmN1S5btK+K/A2EXgeWGIsJ+AONgQ66FE5wrPCR8Lvwp8zP0gTBQeFA4X6JGbNRobRgh3CqTP4ictj5yC4Zqmxoa1M+vT9p20rpQtrJUpTZoeUSLhdbl0nDtj7lwsjBco12cCeSwn5K0IHhGnzxIeFd4RfhY4LtOPhGHCZgIfxjYzYAbMgBkwA2bADJgBM2AGqoMBvnMA4ixi2YwC4mxnIS/ObqV1Xwp8J4wVEHAtzooEW6MYSPUNr1m+e9NYs3zX7yVQvyYLRN9KXrNTa95WoQyoqc/CbOHtnc7QDFQnAxZmq/O6NvWsLMw2lTnv11gGLMw2lrF60/NNaWsmA+UQZmdRma4R+OCqC49re41QlyF0/iLUlU/a9p7SrZTJbNkG7sf+NFRg+wgpvzSlN28pW1srU5o0RQTOW9qWpjcoAQ0daTk7zXorF8XjYTpW6v2ePVap+feVlvF/bGbADJgBM2AGzIAZMANmwAxUBwOlxFk6tZYSZzfVejqN8q3wjDCPYHFWJNgazAD1DaGVeoPX7OwC9ahGwDv7SYH6xbcz37x0DuZ7nP1sFciAmu4szNbbfukEZsAMwICFWdeDLAMWZrNseL6cDFiYLZxdC7MFvLMXLczygf+aUEr0K7XuC6VdvZbzSL1pS+1X27oPlBcff1hbFWZrKzuewDUUXFYUj3j71na82tZ/pH0Y88dmBsyAGTADZsAMmAEzYAbMQPUwgPCVPGfxVEzi7LyarxEQzcBGwicC3wtPCwhrFmdFgq3BDKS6lvWanU974zW7sfC9QP06WKBhx/VLJFSqqanPwmzh7Z3O0AxUJwMWZqvzujb1rCzMNpU579dYBizMNpaxetNbmC3gxb2UMPuk8r2wAVitxPHP0bpSgh+hg1PP6/z2J0rkwwfcR7Xk9ZXWTxRSSON8fvTyxipNmCXMcLKieHxHGeb5Yd0twr+FFKosn4brbzMDZsAMmAEzYAbMgBkwA2aguhhAMEvejHlxtpu2JXG2l+YnCXwn4NlI51eLZyLB1iAGsvWMhpvZhLmFVMdO0Tx1i8hOfLdTF6cV2A/YKogBNd21O2EWcenjjz8OgwYNCu+++270Avz222/DN998U29LZkMSfPfdd+HLL78MNCS//vrrYfDgwYXl3ZDjO40ZKBcDFmbLxWxl5mthtjKvWyWW2sJs4VfNwmwB7+qlhFk+kBqCg3LHX1jLjCOb3/cMreNDC1tFeEPIp0liKmmw3YR8mg+1bmVhGgGjt+3bQj7d8WyUERZpZ4Fy5tMg7rIN9BaStUQo41QWhOerhDHCz8J+AlYUj0srr3SsNL1d67IfunB5Yol0iOiJZ83azIAZMANmwAyYATNgBsyAGagSBvgeyIqzjANKWGM8Z7sJC07BlppOFviWuFJI4hn7Zr8ptGgzA39ggDrCNyWdrvk2T6Gzu2ueOkbHZOoWIbNnFToIpHfdEgmVZGrqa3fC7Ndffx3OP//8sO2224ZPP/00iqd9+/YNjz/+eLNaPhGt3n///XD44YeHW265JQq+CLObbbZZFGeblbl3NgNtgAELs23gIrShIlSrMDt+/PgwatSocOWVV4arrroq4pprrgk33HBDGDt2bPjss8/KchV++umn8Nxzz4UhQ4aEO+64I0ycOLEsx6nETC3MFn7VLMwW8LJ+r/JIol1jpwieWbtMC/k8rssmmDJPj9j8uLFP5tItouUDhEEC2z4WthHyhuibP+a5uURzlkjzbi5NWmwpYfZ9HZAP02TMpwpdFI89lWeem5e1bq100ClTPn7h+TxhFwFBl97wNjNgBsyAGTADZsAMmAEzYAaqkwHEr7w420nr8uLs3lpHJ1K+K04SLM6KBFuDGaCO4QnLt25HYS5hAQFhdnXhc4G6dZZAGntli4RKMzX1tSthlsbdZ555Jqy44oqx0f2HH34IDz74YKipqQn33ntvs1o+f/nll/Diiy+GBRdcMDbkkxmes+ecc05YddVVo3dusw7gnc1AKzNgYbaVL0AbO3y1CrN0rFl33XVDt27dfgfu7csuu2zYd999Y0ce7vlF2uTJk8N5550XFlpoobDVVluFhx9+uMjsKzovC7OFX76kY1Xaa2ubKm+RwuxDOrO8ELhqLWebT8vHPh9tDTUu/obCTUL+mIiMWWuLwuzx2QLm5vPccH5N4RGxN89NWn5H2/4l/E2YT7CZATNgBsyAGTADZsAMmAEz0L4YmEqnm4QzBFc8Z0uJs8dqffqO2F3zeEAitrEvedjMQG0MUD/oCIw37MzCHALif42AONtXoG7RHrCekIR/9nPdEgmVYGrqK0SY/f7776OwiQh51113BbyLDjrooHDKKaeExx57LLYoIup89NFHMYTwoYceGvbff/9w6qmnxu1s+/zzz8Nll10WBgwYEPM6/vjjwyGHHBKuvfbagAfTpZdeGvBsPeOMM8K4ceN+yxMvVbYdcMAB4cgjjww33XRTIJxwKfvqq6/CiSeeGJZZZpnojfTWW2+FPfbYI8w000xhr732ig3hNAC/9tpr4eyzz47rjj766HDffffF0MR42F588cWxTIi6pH3ppZei+HrbbbeFY445Jsw888xhm222Cbfffnug4f7ZZ58NiyyySBg4cGCpInmdGagYBizMVsylapGCVqswO3z48HjPnnbaacP8888fO/LwzJh33nnDdNNNFzp27Bh23XXX8N///rdQnnlu8SxbeeWVY/5PPPFEoflXcmYWZgu/ehZmC3hJLyXMMu7oGw1An9zxP9By+mBP04Fad3IJjC+Rlg+02oxetTsLlwqEOvpJSMfITwdrW9bKJcyuo4Pkj31n9sBT5vNpWO5ZIl1aVSSPLyvTUsfPr3te6U4XagSbGTADZsAMmAEzYAbMgBkwA+2DAcSv2sTZGm1DPAN8h/ENMVlYTUBoQ3CzgCYSbHUyQB1ByEfQR/zvIswn9BCoWyMF6tYEgXFos3VLi7a2zoCa+goRZvEM7devX2y4XnzxxcPCCy8c5pprroiePXuGN954I4Z/PPjgg8MCCywQunTpEhu8SfOXv/wlepq+8847YYUVVghdu3YNSyyxREw322yzRQ9U8sCrlfQ0ju+0007hiy++CB988EHYfffdY17kOffcc4cll1wyiqqEhczb22+/HdZYY42w9957B7ZTrq233jrMMMMMMbTxnXfeGV555ZUYfnj55ZePwiyhiGkoR/BFPOY8l1566TB69Ogo7vbu3TusvvrqUUzeb7/9osi73nrrBRr3McazJWzyBhtsEBCwbWagUhmwMFupV6485a5WYfb6668Piy66aBRgCU1P5xpEWDrb9OnTJ8wyyyzxWURHJMYnT8a9npD499xzT0zPMwojzYQJE8ILL7wQ3nvvvfjsSftMmjQpdjSisxEdf+CUZ8e///3v8OGHH6ZkMQ/S0EnogQceiBEY8s84xkknFDLHf/TRR2NHKATNajALs4VfRQuzBbyg36s88iLd0U3IlzFj8vk0dnmlEsfdQuseb2TeV+TymbPE/u/m0qTFfUqkHZA25qYblEh7Zy4Ni6V4qCmRjlVF87iy8vxaKFWGUut+UNoLBMJM2cyAGTADZsAMmAEzYAbMgBmofgZKibNE38l6NiKi3SPwDfGOQMdZC2giwVYvA6l+EaaYRpzZhBTSmHq1jECdom4NE2YSHNJYJFSKqamvEGEWwRLRdeqppw49evSIY6r+85//jI3XnTt3DiNHjoyNxHvuuWfYdNNNo8fpddddFwXcTp06BeZpsO7evXsUSbfYYos4nh9i5lRTTRXFWjxpCfOIiIpI+/zzzwca0Gefffaw1FJLxTEBBw0aFIVbhF3CCmeNhl3GB0T4JR3GeLPDhg0LqQwIt6eddloMX0mZ33zzzdjQjniLOEwjOo30iMmIsUcccURMe8UVV0ShmAZxxGMa7GmkT8cgT3jBQ9dmBiqVAQuzlXrlylPuahdmebZwL//xxx8jgXSsQZzl3s+zbpdddoljlBM9gVD4dBii085iiy0WOxkdeOCBMXQ+UR369+8f6OxDJAmiLGAIqUSJIL+dd945htW/+uqrw1prrRV4Vj711FMxHWOVn3TSSWHNNdcMdHwCG264YRRwk/hLGp5Hq6yySjw+z8Att9wylpfjVLpZmC38ClqYLeBFvShhlg/zUkJfY9ZtmTufQ7T8az355seq5XiM0Zq15gqzl2Yzy8xvpvn8+TVUmCWMUykrB4/r6kAThHxZ61q+tVThvM4MmAEzYAbMgBkwA2bADJiBqmQgiWd4NhJOdlYBcbar0F3AsxEB7U2B74iHBL5pLKCJBFu9DFC/8LBGzC8V0ng7rf9ZoG7tJaSQxlNrnn1tbZgBNfUVIszSOEzYYUTTHXbYIQqeiJw0JNO4PXjw4Ni4jTcPIX1PPvnkmG6OOeaI2xFxEWYZww+RFLEUEfTYY4+NoSMRRRF/8TiaZ555IvAawpuJcJN42l5wwQUx3/nmmy+Q79ChQ3/XkknjOY3qc845Z/RGYiMN7mPGjInrEFURUxGO8b4lLDJhjwlPvPbaa0dBl+PTOD9q1KjopYtAzHlTNsIW41WFR++VV17527EJTzlkyJDozZsa2n/b6BkzUEEMWJitoIvVAkVtL8Isz45kCKCIqHqsxygIeMgCOu/gSdtN49Kuttpq8flAxAfC78MTofF5tuGJy/ON/xKdh3he8gwjT5ZPP/30mA/iLGIvXrM8h3juzTrrrDGaBMIvIfOZ8tz65JNPouBLxAietwi9PEtJz/jmlC/vXZvOp1KmFmYLv1IWZgt4Ny9KmOUDPn1IZQW/m7V+VAOxeuZ8ttV8Np80/4HWXynsItC7tpTX6iVan7XmCrODs5ll5rfXfCpXmjZUmKUBo5QVzWM6BsfbWLhamCSk8tY1Jb3NDJgBM2AGzIAZMANmwAyYgfbBAAIYQhjfDnxsI86msLPdNY84u56QIvIwbA0CmsVZkWCrkwHqFkCcTSGNEf7nE1LdIlIV36ffC+sLpCN92leztrbIgJr6ChVmaSxmrFcMkROvHhqK8ShlfFnGlcWTh3DD66+/fvSQZfvll1/+mzCLcIroyniweJpOP/30sfGahmzGfqXhmwZoGq0JSYznEl6wiMA0iC+33HKxYTovzCKoEo6Y/B988MFYRoTZu+++O65DoKURnAZxGsHxhgI0mO+4447xWHg6IcDS0I4oS8P3mWeeGfNi/TPPPBOFWYToZAizePbiSfvII4+k1Z6agYpjwMJsxV2ysha4PQqzeL/uu+++MZIDzwrGVD///PMDkSEYS5zIDnTgYSx0nlU872688cYYgphICzPOOGP0bEVMveGGG6K4yrOBjkV08GHcdQRe8iZsMWA/noOEUb7//vvDHXfcEZ93POtGjBgRl5dddtnQoUOH2KHoySefjOvosMTxeEYRJrmSzcJs4VfPwmwBL+VFCbMUZYKQF/r4eG+K/Us75fO6WOv46M8aXral0mXTlBJm388myMzTOzef3w2Z7dlZQj7n0zZEmMULuC6boI35fJvKY/Y4fNBiTOntfpBwk/CFkD8ey8cLNjNgBsyAGTADZsAMmAEzYAbaDwN8K2TF2Y5a5ntqfoGOsXyX7COkyEbba97ejSLBVi8DqW7RGZnGnFS3iBqV6tYdmudblM7ESwt42CZxVrO2tsiAmvoKF2bxIMUQZjfaaKPovYrwitiKBxEhffFcffjhh+N4r0m4TR6zNFIjnCZhFi9cGqQRhV599dXY2I0wS5rjjjsuNkbjUUuYYo5x9NFHx0bxfChjRFjGkMUbluNjWWGWBm8ay7fZZpvYmI53L2P6IT4Q1hiPJ4RbvIMPO+ywgGcux6Xxm/DGWWH2qquuivnzM3ny5MAyYi/pbGagUhmwMFupV6485W6Pwuy7774b9tlnnyjMEkkBYZSQxXQQwkv173//ewx/vKdCEc8///zxmcezg/0OPfTQKJTyXGQMWSJCzDTTTPE5wvOQsWhPOeWUKMwixiLKXnPNNbEDE8+Piy++OD6z6OxDdAY6AvHMuvDCC2PnJMRbOhLRoYmoFHjUTjPNNLGDEWPcVrJZmC386lmYLeClvEhhtlRex5YoI72u/yOQnl6xfNivIWRFV4TTvFg4j9bl7UytyKfLe8zSEzef5qN8RlOWaVjIp322RFo+Jh8rkbYhwiw9gOuyonjcSgeBH7yWXxYYP3ZXIW/0RCZ0cf68B+cTetkMmAEzYAbMgBkwA2bADJiBqmcAAQ0xjO8zxvucTciOCYo4e7nA98MnAqKaBTSRYKuXgSTOpro1u/bgO7+bQL1aTOD7m7r1hjCfQFqLsyKhrZqa+goRZvH0QZDFY5axZjGEWcaIJawwoYppZBYP0XMUbyIEVIRagLcQwixeqFlhFu+hFB45CbMIogizjz76aPRWwruIdYQcJk8ax/GefeWVV2I50k8SThdYYIF4PNYjzOJ527Fjx+ipxD549yLe0rj98ssvx+14926++ebxnPDERVxGFEbMxSNq++23jx5JCBXsi9cw3r3Yl19+GdMuvPDC4YMPPojr/GMGKpEBC7OVeNXKV+b2KMwSNYH7Pc8yngl08kGoZZnnH88ing88ywhrzzLPO54DjKVOKGO2I+DyfMSjlZD8CKylhFmenTzvumv8dUTaZDzPkhH+mOcmIiwCLml5DnJ8PHl79+4d3njjjZS8IqcWZgu/bBZmC3gxLyUC4gnaFNtZO8UbSWb6rebXzGV2RWZ7Sk84LD74k32jmbQtTfumjVOmq2j6U4l0g3Pp6Imb8khT9ptP4MNwKSFZL82kNNnpjimBpvQgR1DObk/zDRFmJ2fyKjVbFI+UJZUrTSdq3Z9KHPS2EmntMVuCKK8yA2bADJgBM2AGzIAZMAPtgIEkziK4MiYoAtrcQjcBAW1R4X8C3xn3Cgi4CGh8K7GvzQzUxkCqW3QQnkXoJMwr1AjUrZWEtwXq1mMC3/OuWyKhrZqa+goRZml0RhilEbpfv36xBZGG5u222y42QtOg/M4770RvVIRY0i2//PK/jd36j3/8I7z11ltxHeEgH3vssThO7XnnnRcbmglZjCjE+H5LL710bNxmvFbE3+OPPz560dIATeM0QilCL43ceSMMJSIrHrg0bAPE2DXWWCPud+655wbGxkVYXXzxxaPAyxh9yTMKD1oa0zfbbLPoAfX1119HLybKPGjQoDBx4sS47c9//nMMV0n+hHBm3Fo8cRGCbWagUhmwMFupV6485a52YZZORYQpRhTEGGsWcZVQ/IwLS2h+whYnj1meaTyziMxAuosuuihGS3j++efjGK+IuoikiLGIpuTPc2b48OHxWVRKmOXZicjL843nGmPF4jGL9yxjz9I5iOcW3rl43/I8vOWWW8Ktt94awypfdtllMQ0RKCrZLMwWfvUszBbwYs5HdBLt0rSpwiy9WN8skd/PWne9cLaQer+mY6XpqdqWtce1kLalKeItPbO3E84SSom3pMX7M2s0DlCGlE+asj89vFnmYxCjwaFU2h+1/n5hmPCxkPLIT4sQZovicb1aykn5OQ88lRG7Swm4nNcSgs0MmAEzYAbMgBkwA2bADJiB9scA4ll9AhrfG3Q65duBb0g+0IksZHFWJNhqZYB6RR2hrswgzCp0EeYTuguIs9StNOTODZqnblmcFQlt0dTUV4gwS2MxYYbxPkW8xGjEJtQinrKEcSQNIuioUaPCkCFDYihiGvbvueeeGOL3m2++iWOwPqBwxJ999llsDB8/fnzcTsM2RsP1Qw89FEiDly7CJ2IoYY3/9a9/BbxZH3/88VrH0+MYNG7jtfTmm2/GPBl79j//+U8cf5bwkDQAI84ydiCN4oQxZj0iLOXifAiTjEgFEKBZx7lwjmzjHAmtTF6UB++l/Ji38eD+MQMVxICF2Qq6WC1Q1GoXZhE6t91223DppZdGIZQQ9iuuuGKM4sAz5Nprrw08UxBjEU6JxkCkBZ5XPGcIR8z+hC3mv0NY+3POOSeOaT7VVFNFD1c6CaVnZlaYTWPMIrySD0IwnYqGDRsWsdRSS8XoDAi0CMEs4zFLCOUnnngi3HbbbeGvf/1rWGWVVWKnIZ6XlWwWZgu/eryb25rJwL3aPy8uNlWYpShbCN+XyDN/jOzyeKVHEM3aEVrIpmnMPPnl7RmtqCuPZTI73FxP2pRPKW/dIoRZilIUjzc28FzSOaXpNRTCZgbMgBkwA2bADJgBM2AGzEC7ZSAvoBF5h2FisgJaPy3zDcE34PICXpB0NGVfYDMDpRhIdQuxlYadjsKcQnYsY6JW0UGa+sUQPYi4Fv5FQlszNfUVIszSZIhIijDJNFlaR6N0Mrx9aICmoRVjnzTPlOWUnml2O+nzadI68qThO3t8tmWNbQineMGeeeaZv23iOAi0HCsZ8zS6U95kqTypfGl9KlNaJi+8YxmP9sgjj4xjCOLdazMDlcwA9Z6xMLt27RpFob59+8YxNhGL6HgwevToWjtFVPJ5u+ylGahWYZb6TOh5xo0lzD3eqNR5PFynm266GPGBMVwJTc9/4sknn4xhjQm7T9rlllsuTvGMJWoEHXsw0iK0EoFB7wJRoMXbNT1jeIb1798/etQSxYFOS59++mkgVDHhjAnbTxhkQDkQifk/Iroyfi3jtXfq1Cksu+yycXxZjk80B/6XPJMq2XjG7rLLLtErGG4uueSS6GnMtUIgpy5W+jm28PWxMFvAC3nRwixFIsTw+0IS+eqavq50fNjnjY/5R4S69mXbY8KEEulW1rqsHaKFuvLaNJN4Zs2PrSf9V9q+gfBpLl1RwqyyLYRHPlxvyJWxLh7YdodAyDKbGTADZsAMmAEzYAbMgBkwA+2bgYYIaOmbkm8zvqX4lrDXbPuuNw05e+oW3/3Ul5kEhjaaW1hA6CEsKBwmpO/XvTWP8G9xViS0JVNDYGHCbAs3Kjb5cDR+4wm78cYbx/DKTc6onh1pSKZBHi8nwkrmxdx6dvdmM9DmGLAw2+YuSasWqFqFWSIgIAKus846v6Fnz57RA5WwxTfeeGMgLH7qBESHIKI2sG2llVaKYujKK68cx1tHOE3CKxeL0PaER+7Vq1fYb7/9YmSFdBERFon8wLPpoIMOihEnOAZh/gcOHBg22mijKLgSSnm33XaLUR2I5IAxhiwCLuUklD5pEIUp66RJk9IhKnZqYbbwS2dhtoCX8buUR/rQSVM+fpprfFANFyYIKd/s9COtP07Ie8pq1W/Gx9mpQikPXMLxHinwwX+UkM2b+ZOErJGOMVN/FvJp39K6jYSszaGFkcJnQjb9l1pGfF1WwCYK2e23xrW//0m9fFM6RN2GWhE8cqwNhTFCKkOp6ZPavr5gMwNmwAyYATNgBsyAGTADZsAMJAaSgDadVvCNxjcc3yndBMSzNYQ0zMwBmudD3eKZSLDVyQD1KtUtxFlEfb7D5xFS3aJ+XSTw/fqDsLVgcVYktCVTU1+7E2YRl2iofvTRR6NHa+HNnVMy5Dh4yXKc1HhermM5XzPQEgxYmG0JlivnGNUqzDJmOkInY8JmQbj+9957r+T45YivbHvuueeiBy1TlvNenAiMiLMvv/xyFFwRdZMhwhIan9DGEyZM+O04rOeZxVAAjK1O6H22ZwVf0vC8obx48DKMAGOy0xGJ/22lm4XZwq+ghdm29DJeR1no+bqO8DcBAXRRoTEemYTMWkHoI2wpEN6oqTaLdlxN2E1YR6hLGNbmKPwuremuAmWYRmgtay6PlJvxezifzYU9hL8KSwrwYjMDZsAMmAEzYAbMgBkwA2bADJRiIAloiGJ8O3QS5hVqBMSzUwTEs8+E7kISz9jPZgZqY4D6QSdqvrNrq1s9tC0NN4Q4u92UtOzDvq5jIqE1TU197U6YTc2bhCqmMbucRv7Z8MjlPJbzNgPlZsDCbLkZrqz8q1WYbc5V4D9STiG0IXk3JE1zzrE19rUwWzjrFmab8fJ9lfbF89MwB/XVgZOaUc+8qxkwA2bADJgBM2AGzIAZMAOVz0AS0PCEZaxPOnx2ERiWBuHsz8ILAuLsDYJDGosEW4MYyNetNJZxV+1dIyD8U7+ITkX9IiLVjgKdvS3OioTWNjX1tVthtvBmzgIzpGEdT6tyi7o//PBDHAu3wKI7qypmgHrpMWar+AI38tQszDaSMCdvMgMWZptMXW07Wphtxgv4eO3LR41hDuqrAwi3NjNgBsyAGTADZsAMmAEzYAbaNwNJQCOkMR/jRPSZS2BMUMQzovL8Ivwq/EVAwHVIY5Fgq5eBVLfywj/ibHeB+rWQMFLg+/UnYTfB4qxIaG1Ti13ZhFlCKDJe3rHHHhseeuih2hoHy7qextwXXnghHHnkkeHSSy8NCJENMQQoQkoyRixGKOLLLrssHHfcceHpp59uSBZNToMYy3GPPvroGJryxx9/jGEvP/300ybnmXYkb0J0EmITLrguJ5xwQvjmm29SEk/NQK0MWJitlZp2ucHCbLu87K1y0hZmC6fdwmwzXsAZm4UPGcMc1FcHejWjnnlXM2AGzIAZMANmwAyYATNgBqqHAQQ0vBSzY4IS0jiJZzdpPgj3CYQ8TsIZ+9nMQF0MUEcITZyE/46aT17ZqX4h0A4XqGM/C3sK1DF3ABAJrWVq6iubMIuQuOOOO4YFFlggDBgwoPBWxYZkiPh46623hs6dO4eePXv+bky+2vZHCH3sscdC7969wyGHHBKTMSbgRhttFGpqasKQIUNq27WQ9Rxrzz33DAceeGAUTB955JGwySabhOeff75Z+RNWGSFl8803D2PGjIlhnFmGl0GDBjUrb+/cPhiwMNs+rnNDz9LCbEOZcrrmMmBhtrkM/mF/C7Ot9eLt45oBM2AGzIAZMANmwAyYATNgBsxAu2MA8QzkPRvn1zpCGq8tMA4owtmmAh/tCG0eC1Qk2OplICvOzqTUiLNzCtSvJM5Sz4YI1DE8tP8upDGNXc9ERkubmuoKFWYRbr744ouAKIvAuO2224aOHTuGc84557dWQQTCzz//PKYp5cHKukmTJsXthPPNWzoGHq15T8/vvvsuhgDmGJ999ln48ssvw0svvRROOumkKD4iuuI1SjoaeknHsShzMuZPP/30MNtss4Uddtgh5ofH7FVXXRXOOOOM8Oyzz6akcUpeH3/88e/yYAPlTOVhnrJyLMpQm3G+N998c1h00UWjODx58uTodTzPPPOExx9//LfQxuSHRzLH/eqrr37LjnNjPeeWjLTkwzkgyM4111xhxIgRsRzwc8opp4TVVlstTJw4Me3iqRkoyQB1yaGMS1LTLldamG2Xl71VTtrCbOG0W5ht6RduH88MmAEzYAbMgBkwA2bADJgBM2AG2jUDefGMkMZzC90EPBr/JSCaPSkwXiiiGV627GczA/UxQD2hvuAJizhL/Solzl6l9dQzQmcfJFicFQmtYWrqK0yYRdC85JJLwmabbRZ69eoV9t9//7D66qtHYfbcc8+NIug777wTTjzxxLDBBhuEddddN3qFjh07NrY4Isjee++90Vt07bXXjp6ce+21V7jjjjvivohC77//fhRN//rXv4a//OUvYZdddgnDhg2LYXkRgg8++OCw9957x5DDm266aTj00EOjGLnVVluFo446KqZ78MEHw8477xzL0a9fv3gctl9++eVR5LzrrrvC4osvHqaeeurQrVu3mMebb74Z8+zTp0+47777YnkROjk2XsHrrLNO9EQ97bTTAudIWQkXDAd9+/YNAwcODFtssUU8b8pEeOVShtjMMfDORcj9z3/+EzmcYYYZAmW85557osjKcbfZZpvIIeI3XryIuu+++2708kWIRoxFGB45cmTYY489Ylm33HLL0KFDh7DeeuuFG264IQq48I8nMMKzzQzUxYCF2brYaX/bLMy2v2veWmdsYbZw5i3MtsZLt49pBsyAGTADZsAMmAEzYAbMgBkwA+2agax4RsjizsJ8QndhZeFbAdFsSwFxzV6zIsHWYAay9asucfZy5Ug9A4cJFmdFQkubmvoKEWYRAIcOHRrmm2++MMsss4QVVlghLLLIIuFPf/pTFGbPO++86CV7wAEHhLnnnjsKgaSZY445okj46quvxrFPV1111TDnnHNGcRfxlrzw5sTrFeH38MMPD3iPkma55ZaLXq1LL710uPHGG6Mo+ec//znuw/Z55503iqYIk7POOmsUYPEmRYCcaaaZoufoQgstFJZaaqkw88wzh8UWWywKnORF6OOpppoq5o/Ai5CKWMr5IYKSz+DBg0OPHj3i+XGunBfHRARFYOWcCOOMxzDlWmWVVUKXLl0iEGzxbs1aCjVM2jPPPDOK0a+//nrYddddI4/HHHNMeOaZZ8JNN90UllxyyRhq+fzzzw+I13CA0IonMqJs9+7dw7XXXhvTwzPiLePWwh+c/v3vf48euBwfsXv99deP3sFZT9ts2TxvBmDAwqzrQZYBC7NZNjxfTgYszBbOroXZln7h9vHMgBkwA2bADJgBM2AGzIAZMANmoN0zgHBG2NgU0piQs3MJCwh4zSZvxjGat9esSLA1igHqVylxljqWD2s8UOuSOHux5mko8pizIqGlTE19hQizCJGMXYrg+be//S2G+8WrE4GUkMB4zD799NNxGXETEZGQqHi+zj777AGBkVC9eHMiXiIukub444+Pnqxvv/123L9r165RNL300ktjerxPGSP1iiuuCOPHj4+C74wzzhi233776GnLWLGEBp5mmmnCGmus8ZugiuiKwHvllVcGxnClzBybc3jjjTei1y3LiLEImnii4sWLkHz11VdHr9iVV145Crp4xD7xxBNxPeeGOHvdddcFvGwJG4wQiiBKGjx0KQsibTZ8Mk2ueAzffvvtcZ8777wztsLilUsZyYdy4oWLp+zyyy8fxWJCRuNVi7DKOLQIxuPGjQuIyUsssUQUuJdZZpnIFR60eB/D7/XXXx89ajkIYi6COWIv4Z9tZqA2BizM1sZM+1xvYbZ9XvfWOGsLs4WzbmG2pV60fRwzYAbMgBkwA2bADJgBM2AGzIAZMAMZBrLC2cxa30mYV+gurCsw/idhZlcQ7DUrEmyNYoD6la1jWc/Z5J1NJwDGnD1boK4h0N4vzCPYS1sktISpqa/ZwixiDaIoHqedOnWK45fShIiIiJiZhFk8UXVOcZmxWwknjHhImF7C9+IdihcsgiieqCuuuGIg9C4hhhEMb7nlljDttNNGAZFwwXiY4u354osvhg8++CAKoYTkpQzDhw+PY6gibI4aNSrulxVmyWettdaK4ighgPFCnX766WMI4wkTJoRTTz01lmunnXaKXoKESeZckjCLyIzgilD81FNPxRZTzhfPVLxvDznkkCjwIqgiThP+GOGVcyBE8rLLLhvHh407TvlBOMULF89b8sfwREbkRURGIOa8k3du7969o9fs1ltvHT1k8f6FD8awJeQx+4DLLrssHhsP3fvvvz+KvAjAyRijlvMlbPNbb72VVntqBv7AgIXZP1DSrldYmG3Xl79FT97CbOF0W5htiZdsH8MMmAEzYAbMgBkwA2bADJgBM2AGzEAJBhDOktfsrJpnLNAFBASzewRElEGCvWZFgq3RDDRGnN1LuX8jUOfGC3QIQJxlvNqUj2ZtRTOgpr7ChFlC8OKNmbw9P/744zimKsLsOeecE8VSPFURNxEVGZs1CbRsRyBFOCTMLqIs6RA/8fjE+zaJmoTtxUMXkQjBljDDCKd4uiLMIoY+9NBDsRWTPGsTZhnjFrEUIRPhFEF40UUXjSIzY8UiGDOGLZYVZq+55pro/YqQO//884fXXnstppk0aVLYbbfdotcwoYopD2VBaCUEMceBGzxmOSf4yRrC7KBBg2J6vGAxhFnOHYH12WefDXgOI8zCz+mnnx5OOeWUiJNPPjl6HeP9ynHuvvvuGEKZEM6IrnCFMMt5IhTfdtttvx06CbOEXUZgt5mB2hiwMFsbM+1zfVaYPe6446LHfvtkwmddbga496TnK52sGM+eDlg8HwnbT13keW9rMAMWZot+oXZ+ZsAMmAEzYAbMgBkwA2bADJgBM2AGGsgAghchjRHA8JqdQ+gqdBd2EhDJEMsQa7MhZrVoMwMNYiCJqgisHQQ8ZwmdTSeAvOfselqHKJvqXR/Nsw/7Uk/Jy1YwA2rCa7YwSzPghx9+GD1KEVIPO+ywQIhdREBC+yaPWcRSxE7W4aGKB+ixxx4b9txzzyie4iVKaGK8TQlrjIct4YLJ84gjjghjx46N3qgInWzDQ/Wss86KYYEJEYzHbXeNrcp20mK1CbMIxIi4Y8aMid62J5xwQphuuumiFy2iL2ImwiveqIi/eOSuPcVjFmEW0TWNH3v22WdHofiBBx6Iwi7nO3DgwN9CGSdRtT5hNpUV4RSBGssKs/CVeF599dWjSItgTMjkM844IzAG7TfffBPFVUIwI97SkI1XMmVLwizieVaYJaQy12zhhReO5xEP7B8zUIIBC7MlSGnHq5IwS2eTNddcM96DuJca5qDoOsCwBnTK4jltYbaQm46F2YLfp52dGTADZsAMmAEzYAbMgBkwA2bADJiBxjCA2JW8ZtNYs920Dq/Z1wREsv2EWYQkklkgExm2BjNQnzhbo5yob2Bp4UGBevercLpAvaOOWpwVCUWbmvcKEWYRBAcMGBC9ThEiN9xww+gVSsjgjh07BsRLhFQEQwRPhEW8ZrspfC5eqnfddVf0Ql1wwQWjFyqetLvuumv0OMVzdsiQIWHixIkxVDB54jW63nrrxfFcGaOWcL0TFIIY0TcvzI4cOTJ6qSLyMgYr4YLFY2zgRbSkTIwLSzkZC5dzueiiiwLiLSIm5SRcMcIsx2L/L7/8Mhx11FHxfBFoGeN1qaWWisIx83ievvLKK9Hrl/Lg7Yp3Lh6z5Lv44otHL9xs8yqhGjkO+TGGLsY+jJGLOH3QQQeFRx99NG7DE5dxYRmHF29ZOES8RlRG3IaHESNGxPFnEWjhCn4Y55bzJEQzYi2G5+5mm20WNt5443i8uNI/ZqAEAxZmS5DSjlclYZb7KeOLE0a+c+fOhjkoSx2gYxd1zcJsITcdC7NFv1A7PzNgBsyAGTADZsAMmAEzYAbMgBkwA41gANEMwQvxK401i9dsD+F8gUaQMQKi7QwCApmFWZFgaxQDWXEWD+3kOdtF89S3GiGJs9S9y4TYAKfpHUJnweKsSCja1LxXiDCLYMP4p3ht4tWC4Imgue+++0ZRkHCDeIwiLDK2LMIkIJww3qWE4AVXXnllFAgROQn3y/bzzjsveuDi8UlI4L333jvmT+hkxolF9EVcZHzVLbbYImyyySZx3FmaLjkm3qcIweyHMEu4YMRdxsQlnDKiJmO+4r2LZywCKcdBqEQoRtTEm/fAAw+MoZkZn5XxbfFUJZQwnmKM78p5E4YZb13KijiL6LvpppuGV199Na7DE5iywAHnmzfE6w022CCWi3JwHDyBt99++9CzZ89w9dVXx/NEjF1nnXWiWEz5Dj/88DBu3Ljw3HPPRS9fvIsYX5fwyHCPUI6XMR63XBN4w9uY/F966aUodJ955pn54njZDPyOAQuzv6Oj3S8kYZbOJoy3TeeXVVdd1TAHZakDiP6M0W5htpBbj4XZol+onZ8ZMANmwAyYATNgBsyAGTADZsAMmIFGMoBolvWanVvL3YT1BMSxH4QaAeEWUc2eiyLB1mgG8uIsjUKMbYzoOq9AnUOURaDtLhwoTBaog+OEJQSPOysSijQ17xUizNJMiMiHVyveoYiThPtFrEX4S+OpIpQiWCJ0PvbYY3EboYIxRB/ERARGPDsRMdmXsMjJEDwZZxXhlO0vvvjib+F3yRsv1Zdffvm3sQ7JE+9WBATKg1B5xRVXRE9XRFPWcSzyo+zJCCtMPhyDfVOIYMqTysv54qHKds6Fc+J8KSNGHpwL+RCSOJ0fY+IitqZ06ZhMEY4RqhGL09i1nBflRHRFuE3HJbTxI488EkNCp/WMFwsnCMwYx8yuQ+x966234j4T5EHL8f75z3/G41FOmxmoiwHq08MPPxxFOLzJ+/btGz2zEf+HDh0aRo8e/bv/a115eVvlM5CEWcLL7rPPPtHjn/ubYQ7KUQfoeIXXrIXZQu4dFmaLfJl2XmbADJgBM2AGzIAZMANmwAyYATNgBprAAILZNAJes4QsRiibX0AkQxBDGEMk+5MwvUBa9rGZgcYyUEqcpV51EuYRGM84ibNMNxXeE6iDXwp7CdRT6uDUguuhSGiOqXmvMGE221SIgFOfITDWZfXlUd/+pfJGiLz88stjGGM8U8mjruPUtS2bf0PTZfcpNU8+CLK9evUKjJuLkFqXNYWDlB/HQvDF4xZPZ8Im28xAXQxQZyzM1sVQ+9qWhNkOHTrE+xUda2xmoBwM8CxkeANCZluYLYRhC7PNeXn2vmbADJgBM2AGzIAZMANmwAyYATNgBgpiAJELb0Q+1GcXEMlqhPMERDHCyc4msB3vWtLbzEBTGUBQpQ5RlwiRTYeAOQS8tekU0F3AcxYsLzwuUA/BzQL1M9VD8gK2JjCg5r2yCLOFNBuWIRPEx3vuuSf06dMnnHHGGXWKsmU4fIOyxLv2lltuCXvuuedvnsYN2rGRieDi3nvvDbvvvnuYIO9ZmxmojwELs/Ux1L62Z4VZOnfgnW8zA+VgAGF2l112icIswwcwrjxjrA8fPjx661MXiVJhazADFmab8M7sXcyAGTADZsAMmAEzYAbMgBkwA2bADBTNAMIWXoh4xBJedk4B78WtBcSwTwW8Gh3OWCTYCmGAOpfEWeoddYtOAdS9+YQaIYmz3TV/mkBYberjRGEzIYU2Jh/yszWSATXhtSthFmEJ8YDxYVO43wY3Y7ZgQjzP8JwtZ0MzXBAmGq/Z5njetiAtPlQrM2BhtpUvQBs7vIXZNnZBqrg4WWGWMeGPPvro2Lnq9NNPj2O9M349QxXYGsyAhdlGvi87uRkwA2bADJgBM2AGzIAZMANmwAyYgXIwkEQyhC4EMkRYxLGFhTTO5+qadzhjkWArjIFU7+gUQIjimYSOQhehq9BN6CEg0CLObiS8IiDOgssF0tt7ViQ0xdSE166E2QY3WTqhGTADf2DAwuwfKGnXKyzMtuvL36InnxVml1lmmSjMnnbaaeHUU08NJ510Uhxv3cJsoy6JhdmmvDR7HzNgBsyAGTADZsAMmAEzYAbMgBkwA2VgIHkv8rFO2OIUzjiFkT1M6xDBCD2LEGYPRZFgazYDeXGW+ofXdm3jztJZ4ArhVwFx9lWBTgPJe5b8XDdFQkNMzXgWZhvVlunEZqD9MmBhtv1e+1JnbmG2FCteVw4GssLsaqutFgYMGBD+9a9/hauvvjoMHjw4PPfcc2WNMFGOc2rlPC3MNuQl2WnMgBkwA2bADJgBM2AGzIAZMANmwAy0AAOIWdlwxnNpGY/FAQIC2I0C44Di1YgIhpBrMwNFMJDEVOofdSs77iz1MD/ubHet6y28L1A3fxJOElKngRTamHxtdTCghkELs63cOurDm4FKYcDCbKVcqZYpp4XZluHZRwkhK8yuscYa4dJLLw3XX399uO66634bY5Zx020NZsDCbB3vxt5kBsyAGTADZsAMmAEzYAbMgBkwA2agJRlAxELQIqTsLEJnAUFsLwHx63mBdYQ6Jk0SvzRrMwOFMJDqIB7ZadxZvLcZdzYf2riH1i0l3CxQP8ETwjIC+yPyuo6KhLpMTXgWZhvcjumEZqB9M2Bhtn1f//zZW5jNM+LlcjGQF2YvueSSMHz48DBs2LBw7bXXBupiOcdkL9d5tWK+Fmbrejn2NjNgBsyAGTADZsAMmAEzYAbMgBkwAy3MAEIWHot4xeIdixi2voDo9aWAQOZxZkWCrWwMZMVZOgBQFwltTKcAwmsvIHQXGHcW1Ah9hS8E6inesxcJpEegTeIs+dpyDKhR0MJsK7aM+tBmoJIYsDBbSVer/GW1MFt+jn2E/8+AhdnCa4KF2dz7sBfNgBkwA2bADJgBM2AGzIAZMANmwAy0JgOIV4hZfLCncWYX0fwvAqLXYkIaZxaPRItdIsFWOAPUKwRV6lg2tPHsWia08XxCjZDE2R6aX1m4U6Cego+F/QTEXfJJAq1mbYkBNfVZmC28vdMZmoHqZMDCbHVe16aelYXZpjLn/RrLgIXZxjJWb3oLs+lF2FMzYAbMgBkwA2bADJgBM2AGzIAZMANtgAEEMUQswsjipZjGmU1jeW6qdQi2fNAj4FqYFQm2sjBA3QIIqtQ16iTes3QMwBt2XgHvWUTZJNDWaH4HYZyQBNpnNb+OgMBrgVYkZE1NdxZm622/dAIzYAZgwMKs60GWAQuzWTY8X04GLMwWzq6F2ezLsOfNgBkwA2bADJgBM2AGzIAZMANmwAy0MgNJCMPLkJDFXQTErycFhK59BUIcI5ClMLGatZmBsjGQ6iSiKuIqjUnUTephbd6ziLXHCZ8LSaC9XvM1Qqq35Avatampz8Js4e2dztAMVCcDFmar87o29awszDaVOe/XWAYszDaWsXrTW5ht12//PnkzYAbMgBkwA2bADJgBM2AGzIAZaIsM4KGIADazgGfi/MIYAYHrCKGTwDbSkNZmBsrNQBJRqW/Je5Y6iPc2nQdKec921/qlhSFCCsX9jeZPENjXAq1IUNOdhdl62y+dwAyYARiwMOt6kGXAwmyWDc+XkwELs4Wza2FW78A2M2AGzIAZMANmwAyYATNgBsyAGTADbYmBJMziFYsIy3ieNwkIsycLiLWzCHjVkrbdex2KA1vLMEBdo84hqlL/avOe7aFthDdmWiNsIIwVkvfseM3vKhAeORveuN3VZTX1WZgtvL3TGZqB6mTAwmx1XtemnpWF2aYy5/0ay4CF2cYyVm96C7P6ALCZATNgBsyAGTADZsAMmAEzYAbMgBloSwwgTiF8IcwSLrarMERA1LpAwEORULIIY4ha7U7M0jnbWo8B6hvibAptjLiavGfpNDCPgJd3dwFxFiDQdhP2E94TkkD7huYJz00DFfmRL/m3mzqtpjsLs/W2XzqBGTADMGBh1vUgy4CF2Swbni8nAxZmC2fXwqxe9m1mwAyYATNgBsyAGTADZsAMmAEzYAbaEgOIUgizfLTPLhAm9hIBMWuwMKeAMJu8DduNiKVztrUNBpJ4ipCa956lzlJH6VDA+MjdhSTQMr+wcIbwiZAE2nc1f6hAvW5XAq2a+izMFt7e6QzNQHUyYGG2Oq9rU8/KwmxTmfN+jWXAwmxjGas3vYVZvfDbzIAZMANmwAyYATNgBsyAGTADZsAMtCUGEL0Qu2YQGMMTD8QzBUSsG4S5hFkFC7MiwdaqDCSBFjGVMY+pk3h6Uz8Jw01dJRR3NwGv2axA+2ctM97sB0ISaD/SfD8BcZf/QNV70KrpzsJsve2XTmAGzAAMWJh1PcgyYGE2y4bny8mAhdnC2bUwq5d8mxkwA2bADJgBM2AGzIAZMANmwAyYgbbEAGIXQhfCbEdhbuFYAfHqTgGxi/UWZkWCrU0wQJ1FRE0CLXWX8MbU02x44xotJ4GWaVo+QvMThCTQfq750wTqOgIt+ValSKumPguzhbd3OkMzUJ0MWJitzuva1LOyMNtU5rxfYxmwMNtYxupNb2FWL/Y2M2AGzIAZMANmwAyYATNgBsyAGTADbYmBUsLswSogotXDAkItghfiF4IV6W1moLUZoB4CBNRseONZtIwHLGMjE5Y7H944CbQ12naA8IqQBNpvNH+BgHdtVQq0arqzMFtv+6UTmAEzAAMWZl0PsgxYmM2y4flyMmBhtnB2Lczqxd5mBsyAGTADZsAMmAEzYAbMgBkwA2agLTGQhFk8YgkJi9fg7gJi1bMCoY0tzIoEW5tkIAm0dBpATE3hjRk/dg6B8WdLCbQLan13AeF2D+E5IQm0v2h+jLCDQGNW3oOWY1akqanPwmzh7Z3O0AxUJwMWZqvzujb1rCzMNpU579dYBizMNpaxetNbmK3It/a2V2jGjdlROFm4XBgqDBAItbWp4IomEmyNYmA1pV57CmhwshXPQJZjxi2zVQcDNNKm/84y1XFKTTqLRbXXccIVwhDhTOFAgfHObC3LAI2urpMty3lLH43Gdt4DzxN4B7xU4B1wLcHWOgz4WdA6vBd9VEQmhKesMLullhGp3hO4zrzD4TGL8FWxopTKbqteBqiXUwvU5fz4sw0VaHnGPCAgzFL/wUcCz53FBOp/XqTVqrIY55NFIQdR052F2XrbL53ADJgBGLAw63qQZaDahdkff/wxvPfee2HixImx7mfPvcj5zz//PLz11lvhyy+/LDLbqsrLwmzhl9N6WSFv0fVkssEGu8y8+ga7LLnaBr1XX6XXjuuuud6OPdY58UQ+HirdFtYJ3CpkP5DSh1J2OllpBgmELbK1DgOIFP1b59BNOuqP2ivVoa2blIN3qo+BLMfb15fY20syQAPQwcKKJbe2zsp+Omz677zQAkVoixzsnOEgccGUZ5VffFqgUuQO0V/L6Tq0RJ1Mh2+LdTOVrZqmdHbIejOla830jGo60XrOpa29Z7X0swB62hoH9VyyitiM+MO9DGEWL0M8DJcV+H/xTKsRCA3Ls83CrEiwtVkGkpCZFWjpUMAzBG/wUgJtD61fcAq6a9pNWFU4X/hAyD5vHtLyLgLhkvnPcByQjqvZZlnKh2nKO02z25p8EDX1WZgtvL3TGZqB6mTAwmx1XtemnlW1C7OPP/54OPbYY8Ntt90WRdMHH3wwPPbYY+GHH35oKmVxv++++y7m89BDD4Wvv/46vPrqq+Gkk04K11xzTWCb7Y8MWJj9IyfNXOP2ySa/Odez43bbndhBImzvFXv1uX/Fnr1/FcLv0Kv3tyv27HPVKuvtxMdFJdpWKnRW2Ml+GNU2/7n2WaMST7aCyzyzyn6mwLUaV0Hnka1bFmbLc+GyHFuYbTzH3MueF7jfEU6trVg/FSTdg8stgrVFDpbX+dMZKHGQnb7eVi5SOytH/8z1KHedTNS2xbqZylZt0xsy1zf7f2N+p2o72RLn01bfs1ryWdBWOShxuSpuFYIPIlMHIQmzhHf9SuA/9hfBwqxIsFUMA0nERNSkMwEetLUJtPNrW43QQ0gCLfOs6ybsJtwt/CzwfwCThIsFOjBkvWg5Xjq2ZhtlaT/ymEMYIzDmLdPNhOxxUlqtbpypYa9swuxPP/0U3njjjfCf//wnfPjhh41qQ2xqwzeN3P/973/DU089FV5++eVAY25DDM8oRCc8lp577rnCPZfggvI88cQT4fnnnw/ffPNNQ4rlNGagTTFgYbZNXY5WL0w1C7MfffRR6NevX9h3333DSy+9FJ555pmw++67h3POOafZwuybb74ZDjjggNC/f/+At+xXX30Vzj333LDLLruERx99tNWva1ssgIXZwq+KhdnGvS43LDXesSv17PPi74TYvDCbWV65545D1tluf3p3VorhHfa9kD6AmP4g3C8MES4SbhQY+yibhvnPhEUEW8swcIQOk66BhdmW4bxSjvJjpm5YmG38Vfs2w197FWbbIgdXZq4L977XhAuEk4T9BVvLM9Bfh0zPoZYSZtti3Wx55st/RBrN07Vl+p0wWjheOE+oEard2up7Vj8Rn65Nuf93bZWDaqh7iDyIQQizfCt2Efjf/U/g+u4qIBThdYjARVqbGagEBpKASZ0tJdDS4YD6Trhu6nyN0EPICrTdtUxHhRUEOiK/LaT7HlOiOZwssD0rnnLMdHzN1mkpHR0k+I+dImSPwfyrQl+BzhMcpzH5K/n/NzX1lU2YpcH54IMPDquvvnr0BKqvWfGXX34J48ePD+eff3645ZZb6kv+h+003CLKrrzyyvGYHPv777//Q7rsii+++CJceeWV4aabbgqIsw8//HBYZ511Cm8c/+yzz8KBBx4Yy9azZ88ozmbL4XkzUAkMWJithKvUcmVsijD7ySefhBdffDG88847MUwwHqiIkR988MFv4YJ5FhA+GI/Vu+++O3qXpnDCdNp5++23Y0cX1tHh5b777otT7vfvv/9+wBOVfT/++OPfyCDPtO3+++8PiKPc82uzO+64I2yxxRbh4osvjuLpjTfeGJ8r++yzT3jttdeiOEuHG0IQ33vvvYE833333cBxJk+eHDslke7bb7+Nh2D6+uuvh3HjxoXRo0eH9dZbL/Tu3Ts8++yzMf0jjzwStttuu3DqqadGL9raytVe11uYLfzKW5hNL8JFTVfp1XvblXv2ntxQUTalW6lXn9dW32THbkWVo8z53KH8sx8kV2q5ay3HXE3rn8ylH1FLWq8unoFKbSzLioZbF0+LcxQDNNrSWAHcWUIkNNK+Vfp0H7Qw23a8hh/IXJdnGnlNnbw8DPTPXJNyC0TpDNrq/zOVr1qma2euLffDzavlxBpxHm31PcvCbCMuYhtOiiiEyIMghGdyZwGR6naB/9ypQieBbRZmRYKt4hhIwif1PImfhO6mswFCZ1agnU/L3YTuQhJombJcI/Df6C3cJmS/JfmvvCVcJPQU6OjAsTgmSGVgmjfWkYb/F41nAwTyK4VPtf4MAbE4KwSn/LW6dlNTX9mEWRrgt95669CxY8foZVRfsyIi6RFHHBEWXnjhcNFFF9WX/A/baSSngb9z587h6KOPjp66NJLXZTSkr7baauGss86K3rU0pDOPp2+RhpiAUHDccceFueaaK4wdO7bI7J2XGWgRBizMtgjNFXOQxgqz1B8EzvXXXz/89a9/jVhyySXDUkstFb1RiSiAAIfIudNOOwW2LbroonGKNyniJWO+nnDCCWHNNdcMm2++efjLX/4SFl988Sh00vll++23D0sssURYZpllwuGHHx5eeeWVKKLeddddUfhkG+k32GCDcPXVVweeO3nDgxVv1s022yyKvAiq5M2zZcEFF4zerQiyN9xwQzwHOgMBnneIrgjCAwYMCFtuuWUYNWpU7CA0YsSIePwTTzwxHHrooWHOOecM888/f3wmTJgwIe7D+m233TaKtfkytfdlC7OF1wALs7W/Gjd+y0ob9llixRiiOBe2OOMdm4TYktNefZ5Urw9e+tuy8fGf/RC5SculPmKy58A+bwlpv180z4eVrfwMtNUGw/rOPPsxbWG2Pra8vTUYaKvCT0s2xrdFDp5SZUjPGhrPbK3PQH8VIV0TC7Otfz2KLMEmmWvLNaYBvb1ZW33PaslnQVvloFrqYhKFEKr4puIbCkGW/xxhXLsIeNMiNiWRSbM2M1BRDCTxkjpcSqCdTevpmDCXQIdwxM8aoYeQRFrmuwvdhCWFAwWiOHwjpPcQpgioQ4RtBcRfjpeE2lSONKU8iKyIxaRdRhgvZPPLz/MdO0xYSWiwQKumvsKEWRpOacDGA/XSSy8NyeMIYZYwjRieRHg3sZ2G6+uvvz7QKI3n0gMPPBBWXHHFMNNMM8VGekRWGvInTZoUx/hDrMV76dZbbw2IvnlLwuzcc88drr322sAyDeSUA68q1p133nmx4ZyGd8pCwzxCKZ5LNPoj1F533XXRe4uwyP/+97/jviNHjoz7khf7EiqZ8v/rX/+Knl+pLAiwiK6U9cILL4weuCksM8dDlKB8FmYTY55WEgMWZivpapW/rE0RZi+77LJ4D+S5sOGGG0YBFrFzjjnmCGeffXb0QD3yyCPDvPPOG7cfcsghYaWVVgqzzTZbOO2006LQSkjhaaedNiy00EJhxx13jOLsLLPMEjp16hQFWjxPu3XrFhZZZJF4PycMMfd47r2bbrppDBnMtjXWWCM+D6jXWUPMJd+99tor3t+55w8ZMiSWY9ddd43h7u+8886wySabhD59+oSbb7453tsRVdnvySefjOIy2xGKBw8eHEVbhFvGqWXMWgTlrbbaKj53eNbw/Bw4cGBYd911o+CbLY/nQ+QHcZ73A67bJZdcEoYPHx6GDRsWn+3UxfoiZJjH3zFgYVYvy4UYgqrGjB1XUnBtqDBLul47nFVIgcqXyarKOvvxsWkDD/W33H6t5VXBRxUfSEUYvdOba3zsFfFHTB+M+fI0tbGMsX6K4ilfpvxyKR5bW5il8au5lsLO8WFfhDW0TFw76lW5jMa/os6pvjK21DlzPg09Virzt5pJ98KmeMzSuEMdaY6Vup81pzG+sWVqCxzk+XtaK9J1OSm/sZ7lct+P6zl8nZubUkdry7CoexPPiIY8v/orXbompYTZctyzmls3a+OuoetL/Tcbum82XZHXPZtvbfPcBzlmQ413wHRtmTZm34Yeg3QNvT8XUZeo0415hjb1PSudf2Pvu2m/7LRUfWvOs6Cx94i2wEGWj2qb53+V7rd0fphX2ELgP8cQMXMKqR4hLpXrf6isbWag7AxQfwH3Yeoznda5x3Fv5hukozCH0EUgzHHWi7aHlhecAuZrBARc1u0qIJZ+ImSfW5O1fJuwt7CowH+N44JsGXgO8f9DFF5cOE4ghHE2r1LzDynN1gL31ZRnOket+j9TM10hwiyNyoRkxAuJBvH55psvLLbYYnGahFkan2l4x1sJT6GuXbtG7yMasxnDD+8mGl2nnnrqKJbSwE14y7///e/Ri5b0gP379u37h3FgkzA7zzzzhKFDh0ZhFrGVchCeeO21144NuksvvXT0iv3000/D3nvvHWaeeeboXYWnLOIwHrSE18QjatVVVw09FXqY/ddaa63o2UVjPWEoWUdZaKgnbDOhKi+//PKw3HLLxcZ1vMIQmhFo4YdGYwReC7O/axz2QgUxYGG2gi5WCxS1qcIs9+hVVlklipkvvPBCFECnn3768I9//CN6xDIuOfdw7pd0fuFeOt100wVEWsboRpjt0KFDvH+zPx1uEHK5Hw8aNCiKn3jTdunSJd5/6SyE+Mv9GJGUDjc8d2afffZw8skn/2HMb0Lab7TRRnGMWcYDp94jqG688cbRkxYvW54XePriAfv000/H8cOJ+ICIfNVVV8U8mRLKn2PzHKEj0nfffRfPYZtttoleuNlORpwvzxU6/dQX7aEFLm+bOgTPUAuzhV6ShrSn/d/LoudqZ2Dlnjts12xRNgqzfb5ZZeOdZq39SK2+ZR2VIPvR0VCBlY8Y9vtV+FzYS8jaTloYOQXHZzeUmN8sk/acEtuvm7L9Bk35kFtCuFB4SvhJ+Fmgp+tgYRGhNttSG1KZKB8fZ/sLo4SJAufDuYwVdhf44GqI9VSi4cLzAuOxwcmbws3CMQIffrUZY4GmMlG+GmGoQDnwRH5NOErYTyDdOCFdL46V9j1Z81njQ/MwgY/HDwT2+UF4UbhRoIGPD+AijMajfwj/FVIP5tc1f7WwsYD9KKRy8zGLsd8IIZ3DDqysx9bR9pQezrmGWKoj12uej+TOwtHCrcLHAseeIHCtubYNsdWU6FzhdoHzoZ6RD9flBeFyobtQm2Xr2zZTErHun8IrQirTVZrfWZhKSLasZs4UnhQ47vcCx2RdbfXpWm1L3Kyk+dpsLm04SnhG+FKgHNSll4UhwmJCU62lzzlbzlW1MFB4VPhKSPzyPzxSmEHIG/fmVAdJnwA3icsl8ztpeWbhYOEagfsF/9e07yTN3yVwT8xeUy2WtFW0lv/kuwL3jm+F+4QThdkE/qspb+pAbdbUMrUFDvLntJZWJP6/1nw6f/6Haf2A/E5TllvifgzXTbGm1NFSx2nuvSnlSf1cT7hBeFtI97iJmud/tJ9AA2re+mtFuiapTvbSuqsFnjHcI78Q7hZOEJYSmmINqZsrK+P0H6Zu7NCAA62jNKkelXqOwMf0whLChcJTwk8C/IwXBguLCA21oq57Q44HZ7wP3Cu8L3Cd+A89LvxT4L9Vyi7WSjh5UkjXlmniiekaQkOtNZ8F3OsPFygz94x0Xx2r+csE/j+lbE+tZJ9xQuLguynrWA+veWvqfTefD8urCEU8C8irqfeI1uaAsrcH4947rUBd5Tk/j7CgwPOfuvcXoaPAfWgaoSHvEkpmMwNtmgHqMZhaoF7zH+Bbm/8B3zZ/Evg/8A03lzCvML9QI/QQ+I8ksFwjLCCQhmfOZcIEId2/0/QTrbtDOF7YQOAbmWNyPI7VTVhcWEHg3nmg8JjAsyPlUWrK84VvAf6rnAvnlc5Rs9q5IGGWhmrCMCKsLrvssuGCCy4IjMVHYzvCLOPGMr4f4StpPMfzCa/S7t27RxGWBmtCPRLGmH3warrmmmuiZyqN6TR242l10kknhVlnnTX06NEjIApkLS/M4oXL2H8zzDBD9GyisR1vXRrXl19++TiGISEzEZIJU0kYY7ygCDF5zz33RO9fBARCVLIMmEdU5vzw6MWzi/1pmEcgIPQmQi0eWo8rdPFhhx0W17FsYTZ7tTxfiQxYmK3Eq1a+MjdHmMV7FJGVDjv9+vULM844Y7wPT9S4sdyX6TSDqMk9lXsyHrKMG8444nvssUd8DvAcQejEmxXxkw4zdMYhFP3OO+8cQw+fccYZAeBNi1cuHXO4j9PJhzzp5PPRRx/9jiTGte3Vq1cUbVPEA8at5bmEkEskBkRY8iQ/vFwRVOmUQ6hkvDl5HjGO7d/+9rcoInMcOhph8IYwzPkQESIZERl6SsDlvNJx07b2PrUwW3gNmDG+BPqn+Qys1LPPfYUIsxJnNU5t3+aXqGw58DGS/dh4Vss0NDXE5lAiPqxK2QVamfJ9qFSCzLqs8IA4lLeUD9NNhM+F7LrsPB9RhwqlrL9WprQDNY+Yl5ZLTV/Udj4KazM+BEcJpfbNruPDrbYGzTMy+8PZhMxyNg8airPL+XkaU5N100xqkM2nyy7TWLt52qmJ02W03wdCNt/sPNfjaOHHTJqtNZ/sNc2k9P9NK+uYZvnmozlZyoPpQsI4IbsuP08ja0ehlM2plUOF/D6llmlE4wO+lPXXyrTPKZo/J7Oc1menJ0zJZH1NU+Ncdnuaf1PbO01Jm51kOd4+uyEzv53mue4pr1JTxIezM/s0ZrZ/Ju+WOGfKRmMIx6Lcpc4nreP/TH3NWlctpO21TTfO7qB5OHy3AfuR38NCN6GU0YBzsVDbcVnP/XBQJk0SwbTqd9acMrUmB787iczCHpqvixe2TcikZ7Yl78cL545d32Jz6mg276LuTeRJw+RdQn08c79ZTMhafy2k/fhfnSTU1YD5g7bvKTTWGlo3y/Ecac67RjrPoq57yq++6e5K8KGQrk2pKdfpAmFGIWvvaKFU+uy63bI71DPfP5NfSz0LKNKyAnUyW+78PM8KysT1yVr2Xpvfh+Xsexb7Nee+mz1ukc+C5t4jWouDLB/tYR7xZhoB4ZXOFIhQ3YRHBerakQICFf9T6il1xGYGqoUB6j+gXgPq+HQC/wfq/CwC32i0M3QR6Lgwn7CAUCP0EBbMgOUagf8Q6XoKZwpjhckC/6kseAb8T7hK4BuO9AizKwprCGsL6wo7C7cI3wnZ/fPzn2n7uUJ3gXPhvz21MJW8cgrxmGXcvxRuEm8fPEife+656FGUPGZpaEbAxIuIhvQUrhKvpX/+85/h7bffjqIpwisN04i95IOnEl5QVyvsMGIv4i/hhxFas1abMEsITBrK8UBCBCBvPHonKITy+PHjY7hL1iHkJmEWQZewzAsssED04iJvGtARXfHAJeQx50NDOqIBDfmEQP7Tn/4UvboYexCPr2OPPTaG4CQEs4XZ7NXyfCUyYGG2Eq9a+crcHGGW0MLjxo2LnqWMvc19HY9YOrzgEVtTUxPDAhOFgPFm2Z4VZrmv47XKfZgw9YQ1xsuVcWoRZvGuRDglSgMetdynEVGPOuqo2LGmv8aQ5f7MPZznQtbohIPIizdsCo+LMEto4lNOOSV89tlnUUxmHFuWyYOxZelgRIcixGOExDFjxkTRlqgMRJMgIgNGJ55SwiwhjhF5KTP72/6PAQuz/8dFQXP5dha9EtoazcB2253YYcVefX4pSphVOOPbGl2Ilt3haR0u+5HxkZYPF/gAaqrR8JfyfKieTPpl0r5cIm3KJz/lQ+g+YUJm/5SmlDjbv0Q60tNQ+bxAg0j+44u8acDOGx+PfNSl46UpZaIhmzzTOqa/CH2EvJ2hFdl0peafUppLhHyepGUdeETA/iTAYTafz7X8X2Fibj1paDDno7cpxkfsF0L2WJRlvPBNbn02TVaYzV570iwh1GZ8oFPelNc+mYRpHVPqb3a51Ec52+G0lFEPsvtzTm8JzwjvCVzL/PZS5e6fS5fdh0aBrJDKNvIdIHwvpLQ/aR6k5TQdqHV5y+a3fX6jlvcW0v5pSjnGCW+X2HaY1jXW+muHlHd+Wo5zpuGD+0v+WJ9oHf+DPHfUn3WFZDSGwjvXOJ8H68B6QjJE83y6L7XuxSkoVe9vTDvnpoO0XCqvCSXWp3Qv5PJgsbllak0OSpxOXEVjWOI/nXuapvWvZnZu6ftx5tD1zja3jmYPUNS9aWZlCn+J0zSdpHXUsW9z297UcvY50T+3Pe2fpl+V2M79aSmhMdbQulmO50g6F6aNfdfgHIu87g3hbCclypaZee5trwulrsdzWt9BSPaGZvhv5fNI/zemO6bEDZj2L5FXyrsczwKKtImQfUfgeDwDeMbl3wvYNkbIGu8EdXGQ3rPYp7n33exxi3oWkGdz7xGtxUGWj/YwP5VOEvGG/yDv7dxfFxDOFqib9wtzCDMJ0wncT2xmoBoZ4L8AqOP8J6YV+F/kvWg7ad2cwjzCfAL/lxqhh7BgBix3F7oJpGG6ocB7wijhbYH/WB58Kz8u0HGXb6zjhb2FvwpbClcIfFvk98su87y5XiBKBucxjRo7r0dsAckQMAlLjKBKwzaerHUZ+9IQjscSoSMRKTG8ivBOJR8axhFaTz/99BjCEnETr1UayxFmGXOWkI6bbbZZTI9HKvkSTviggw6KnkjsgzcuYwk2Rpjt3LlzHJeWMhGWEpEUYRZvpiTMnnrqqSWFWTx6afzHaIzfd999wworrBDHp00euZwDDfOELMbbFy8vBALGTyQEJ+Ph4R1sYTbS6J8KZoD/JB0i8Dbkv44XIPeHYRrnkY4IiFOECLe1DwaaK8wyliv35CTMEnVh1KhRMWw8nWLOOeecGPUA79rkMUuHHzxmeW7khVmeN3lhlmcJIYIRZYnIwLOIccrp5MN9mnFK896pjBFLdAfKw30fQ5gl/6OPPjo+y+hMxLPgzDPPjJ126ESEiIynLh18Xn311ej1yz7HHHNMFGYJy89zJ/HG84RIEsn4D62tkPvkbfs9AxZmf89HAUsWZvUS3GxbZf0d/1yYKCuPWXnf0mjflm1dFY6PiezHRZqnQQuxaAuBRoKG2gVKmPJ4qJ6d+FhKaRFT8pa2ZacnKVG2oYKPpq+FlOZbzc8vZK2/FtL2NH1d6xbNJJpV83fk0l2c2Z5mz8qleVbLS6eNms4mkOZXIR2LhsG8yHtGZntK967WXShcKrwl7C8kO0IzKR3XJm/bakXaTqPsfgKNOthUwrJCvhHwEDY2we7RPulYTIcLfDhjfFxTZ/jQzaZhfmshGR/XlDOlgY/ajHNJ6SZrvmMmYVqfptRn6iD5Y9QFuMteD+oL1ztrq2kh5cH0buHP2QSa50P/OiGbbnAuDYv9c2lI/19hK4Hjcl04J8qazYv5dwQaA6YXaJjbRuCcUzr2SddVs9F+1G/avv2UdWkCD9nG+Z+1fLxA3snW08wHQsqD6zJv2tjAaf/M/imfcp7z3rnjPaflFTNlnVHzBwtZbl7SMg0meftWK1KZd8hvnLL870waBPTdhOx14D+2k/ChkPKC625C1tbUQtrOFEFsEyHd0+bU/Aghm4b5F4S8FVUm8m1JDvLnUdvy09qQeOC+X8rO0sqUhmlL3Y9LlSW/rqg6WuS96fwcX//TMs+GZB01c4WQ5fSfaaOm/XPbSDdB2FWYW8BoKM3WTdJcwIYmWl11k/tbOZ4jlLkp7xqcYlHXnbzqs65K8JlAeQHPhyOFWQSM+8rqwutCSsP0WCFvm2pFNg33tKZYf+2UzYf5cj4LeFa+mTkm9/yTBRr3k/Gul72fUKY+aWNmeoTmU9lLvWeRNFu328qzoMh7REtykKG+Xc3yv+T9YWaBd2fuY+sI1D3+w38W+A8jUvFO3dT/ona1mYE2zwD1G/C/ALyn8//g3s67PP8TOjHMJvB/4T15HqGrwHdZjdBjCnj/SGBdd6GbQDr+Z8sIuwsIsE8Ik4V0zy81/VzbXxbuEx4RJgql0mXXPaY026lR/AYaPBFjEV1AY4VZGgjxIKXhm8ZyxvPD8KKl4RrPpnPPPTeO20poYEIBX3HFFeGuu+6KDfDJYzYJs6SnMT2N2YqwioiLAHTzzTdHUQhhlpCVWavNYxYB6fbbb49JaxNmEYxLecwizNKQjyVhFs/gyZMnx/R4ViHM0hDPWIiEysS7lzESAd6+eOsiFliYjTT6p4IZsDBbwRevDEVPAiPjvSI+fvXVV3UehfozcODAKOrvsMMO0WMWb1X2RXilE87jCgGPhyxepnjB4pXK/X6aaaaJoidhjhFmiU7AfRtRFS9V7tV0hhk7dmyMdoBAmqI10AGH8WsZ35t0hEfmuYAXK/doypU1nl0It4xzjniMIQhvtdVWsUwIxni/7rnnnvG516dPn1hmOg7h7UvnBUIeE9qYDjvkh4jMuSDkEsKZc8ArmHMnjD7nwTY68/BstP2eAQuzv+ejgCXeW23NZWCl9Xv3KlKYXblnHwSRtm47qoDZhs/sx0Wa/1VpaFg7Vcg24mrxD3aB1qT9HvrD1t+v6JdJy4dP3lI+aXpkPsGU5XU0TWmYXjJlfZr0z21/V8t83OWND0I+vlJeNJDw8ZdsVc1kG4Hv1TIfjqWMhuqUD9PLconOyG3nY2+OXJrsYn2NZRdn8rstu2NmnrK+JaRyIWI01tbSDml/plfXksEiWv9dLu3WubR3Z7a/rfmpctvT4thMOoTRrGXLwnxWzM6m66+FbNotshs1PzyzfbzmaQgoZdQReEt5PV8iUf5YHynN/CXS5UXer5Vm3hLp9ta6dDymC+XS0BCdtm+f23ZRZhtpahMeV9G2bN0+LJdPfYv9lSCVgWk5z5nGmY8zx+MaTC+UsvW0kvtXKtuBJRJl73+l+Fksl8d+JfJIqxDf07GYspy1+7SQtv+g+YWzG6fM8z+4UkjpmL4wZVuaFFkm8mxJDtI51DfNCiknlUjcmvfjEsX53aoi62hR96YuKuHPQqpXz2h+xt+V+v8vUP9GCykdnQcQCLD+QlrP9BOhVB2m48f7mbQcq6lWX90sx3Okqe8aRV73hvB1hxJlrwf3u1I2q1Y+J6S0kzVfI2RtUy2k7UypB02x/topm89HWi7n8y/7Hslx+9RSaJ4R9wipbLwHUk+zdoQW0vZx2Q1T5ou87xb1LKBoRd0jyKslOeB47dF4j5xW4P47u8B7X3fhfwL171CB/yx1dhqhqf9F7WozAxXFAHUd8B+h7vM/qU+knVtpEGl5znQT+C/1EBbMgXWA7SnNIprnPf1Y4QqB94kXBQTZ9Cxo0lSi4tcIj3j7JHGWBtDGeMzSQIhoSUP2dNNNFz1E8RwiLCWN60mYRbycaqqpojBLyMfBgwfHBne201iPMEvDN2PCbrPNNtEL74QTTogN9owJiCcujd1sRwylYTxrjRFm8fjDc2mCwhnjRYXn38SJE/8Qyrh7CWGWMW/zwiwN6U899VQ8N7yiGEuQ88E7Fw9aym5hNnu1PF+JDFiYrcSrVr4yN0WYRXjlPo53KGO7Ikhyf8QT9ZZbbonep4QzJswwoeMZf5aICqcqqgHPDI6JB+zxxx8fx6LleUUnGERNthMSH69tPGF5fiCScgw8WMlnr732imGO2cYzJB/GGLZIj6CKR+3/Y+8swK0q1jd+FBMDpQWVsIsQUWzCwm4EBbEQEMSuqx6wsPXqNbBJuwXsxECw+NtJqJRg9713/u9vOHPunMVa++y9z9r7HA7zPc+316yZb+pduedd3wzTCyPYEWbdcD6+oR7IWj464tmH9ythnp0QwXdq6n0+QOIZw3XD1Pisk860y+RlvXOm86ce9yyibRC75A9SEYFAzFbEI4W9uLE1vVIGyQmBbXbr1SFNYnarbofNyKkB1WfMn5f7pf6gbaY/Ivxx2SChuYUiZmepPjc4HFd1dMDNtynVjt+fJPKOPNtHbI8lskwu1daVA9nT1iUkbF/17CHP+IPpZLgCriy2h7uEhG1lg2W3eeV9pXCDhHJ2VfwwKcRxpwSbTNGXK9G1mz61yGB8vWdLnigxCwnmymK7szQqnGe+zW4RAz8N8oo/83HC4JdP0PWLGD2i/U+kkGVnR9Kiu1cqwtX7RTRR+6VeOnbRfrssR0TsOC5x0kSRrj62UQw4Di49SsxO99LeVDiTuGsIDEZmMoxJK1WcawPbQvZ5SKSu9jHt8aO4t7m2fewnlIV/9dLjiNn9lf66dJ6UwZpMD1y8XFxdbHtLndRT4G+pSx/hEmK2zRT3h2fLue1LWm1yZRYLA1dfNtspMnJYxV0b1Xk/rqz9aZ6jad2bjvLwBNc9M3TCfw7OlZ27xkojZeyXoYwrPFue3/lKZedm2s+RqrxrpHncK8Orrgz8Z9qDlWTg2e+uJ7YcS1/20o6fvoyfmEOYcv1yCvksoFnfe/U9SkQG2UxpftsOi9hW9p6V1n23nupN61lAF9K6R1BWsTCgrqVVuLbqSFeSci40lbaUumfaiwrzzsq7Bv8bkt5rlRQkIFArEeAaQTn3Ua6XJJKWa6i+tJGU/0u8P68tXVfaQtpK2lq6Xpmury3K/8sNpRtLeTa0kW4p3VrKO1B36fHSi6S3SydIP5T+JPWfvf4zZbEwHql4BkHO5kPM4m2KByseQnhPtZQnUOvWra13kZvKGK+gTp062fSNN97Yru3K9MeQt4MGDTI//vijXdMPzym8aPF+wkOWcpi+GC8npjOGVCWdQW9fciVmmX6Z6ZYZfGdq44EDB1piANLXrTFLP6Ies1Fi1k3fDDnNuoW0t2vXrnY9wk033dR6RkHUBmLWP1ohvCQiEIjZJfGoFa7NuRKztISZECBk+RiIZw3nFJ62c+bMsc8AnkHcK9mHoHTxkK2s881HMTwriGcGBIQ4PoThHsyziHKZOt+3cXGQopCljhS2BcT8MJ3xoYceasld2kc7qQ/vV54b1MMzh3bRTpT2EUf7iacN1IsQT/uwIS9t5rlAO7DnWbfvvvvaaYxJD1IRAXBk3WDWGmZ5AGaigHwfq2nUIbs5F8ExSNYIZBon1utjkKwQ6N598Ipbdz3s7/TI2Z6VDRJl1a4iGjVWXcdJH5DiJbPYHwwvDtIgbmD3Gs/mJYUzyTlKdHXwZycqLo3tadHEyD6Df779Jl56qZe2UOEVvLS44DTP/l7PgOPp6hjvxScF9/bsyccfQCdRYpY/jJmkssGynsrs2sZ2tvQs6RbSNOUdFebqYTAwk/BH2Nmy5Rj5wqAUx8PZ3OonloUv9NK/Vpg/6L64vGyv8xNiwn5dg2LSiaL8aB2+KQNo/EF39c70E8vCpV76fxReLcaGqC5SVw7b3YmMkeUU59vtF7FJImY53/x8PkkYKcLurqNfBisYAMlVSpXB1VXoPt/k1fVVFg2l365tYAWevvyqHZceR8z6tsv7O5Ew5XaSQmy78o72bDhuLp5tOy8tLjjKs+eelCRVaZMrs1gYuPqy2U6RkcNrWEyG6rwfxzSnQlTa5yiFc19Ck6Sye9M4ZXR4fp5UiBe/lcKQA76UaseVwXnOPTxJBirB2f6SZJRFfGXnZtrPkaq8axTiuCdB1N7DF5w7Jhl68VO9PJwPvhSCmC30s6CJOuDOMbZH+h1KCH/h5Tk/YnO6l/ZRJC26W5X7bqGeBVW9R9DHYmEQxXNp2l9GneU9awUp74eNpJBIO0khfP4t5fpeRYoNx5U8QQICSyMCnPtOuRa4dnjf5h68opR3AD5UWlVaT8p7Cx8nc13xXuSI2nUUbiFtKW0tXU+6gXQjKWMGUXJ2B8V1kR4jHSOdLvWfN5nC07V23yd47jDw/Pvvv9vBYwaQ33rrrazXmHXDgAw0v/rqq3ZdwIsvvtg89NBD5umnnzZ3ynOIqRsZNGWaSYjOCy+80HpMMS0lXk7YMfDKgDnes8OGDTPjxo2zg/d4Uw0fPtxOW8nagHje3nHHHWbKlCmuarul3XhaMV0yA7YM8LN+H1MMMxiPYPP+++/buhncZwCcNjF1Ml68DK6PGjXKDr6TzqAvbUdoP+Qz/aIcv3ymcnb7rJHIFJtgQH7KZFAfLywG35lOkzqDBASWNAQ4j8Mas0vaUStce/MhZgvXmnRLhoRlbXCmKWbKes79QgnPD6ZaHjBggJ3WuFD1LMnlBmI29aMXiFm9LKciW3Xr+V5axOzWu/QamkqjqqcQ/vxAHJwsfUL6uzT6J+Q3xfGFqS/XaMfZveQnxIRzIWb3iMnvR7XVjquX7W5eYqmX9oYXnxR82LN/zTP61Iu/wotPCrby7GnTvp7hcC8Nsog/mpmkssEy/oT6xKOPxTdKu116kHR1aVXkO2V2ZV+ZRUH+eRMlZsl+o1fe9wrzJ9vJMgp8JXX1gVlUXBrbU6OJkf2Z2nf2QyJp0d16iuCL6b7SS6QPSj+WuvxuO0txUSlVhEufE0309rf17LDf0EuLBhngdmXuF0nk/HFph3ppDCi4eLaQhoWSUhXs6ip0n5/z6mIAkw8EMuk7nj1tXE/qy6/acW3v4SckhLk3MpjD9cw5N0L6ovQnqSvHbY9RnJNBCrh4tgwiZZKzlOjsMxGzlJFvm1z9xcLA1ZfNdoqMXP+HxWSozvtxTHMqRKV9jvqF53tvekmFODyf9AvMIVzqlRF37/OLOsyz/cNPyDGczbmZ5nOkKu8ahTzuUdh6efhyXCFxKpORMnDnwNsR4728NGyWiaRnu+ufI4V+FuwYaTP3+kzPAtI4Fx0GoxX2pbL3LN+WcL733UI+C2hXvvcI8hYLA+pamoVzB2KJ65b39+bSVlJ3n75JYd7XeSeuI833elTWIAGBWoMA14FTriGuDUfSrqDwSlIGxbiu+OiBe6FP1DbR/lpSrrd1pC2kraTrSzeSbirdQrqflGuQj9jc86Ky7WzZjpP2kW4hAvZxyEs8eRiIhjyErMyHmGXYkMFTysJ7CSKT8ly5Lh1PKTyOIHIhOJkiEi8qhMFvCFHS3XqFlIGXER5IhFHyRL1jHDGLB+vll19uvhLJSxz1s3VC/4iDSEVIw7OJep09/aAttIv6nEBeu7YSh120fGzoP30gzdnhkXz11VcHYtYiEn6WRAQCMbskHrXCtbk2E7Ogxkc1fFzDBznueVEINKmHD5H46CF4y8YjHIjZeFyqEBuIWb0EpyLbdDvszrSI2U67HsbAfW0R/uQwmDRX6v85uT/SwWu89JciadHdczzbD6OJXhr1bRKT7kfxx8tvV28vsdRLu9eLTwpe59l/UWbEHz5IIFfHCWXxmTb8WfTz+J44kIyuLAiGyiSbwbKNVchXUldu3PZ3pd8nbS3NVegPX/O7ck/KooDPPPs4Yrajl065vs3OkTT+NEfFtYWtT0pG7difIXX2Q2IM6N/h0rekfj9dnrhtHDlRqvzO9hOFkyRKzDJgkCT+ecSAgS9/acfV52PgEyOkN/MzpRwuZp9nev11/c5l2z3S91+98npE0vzd9bRzg/Qnabb1HeMVcIGX7wcvPinIuejqmZZgVNU2uWKLhYGrL5vtFBm5/g+LZKju+3GkOYvtpn2OpnFv+tjD85bFWpxdRKlXxvuVZPHvP5Bh+Uo252ZHFe7OFbZVeY5U5V0j7eOeCTP/fjI/k6GXNlRhh9MvXjzBvbw0bJYhMg8pVR5XR6Gff0d7dbk6c9m+Eenf6V55H0XS/N2q3nf9Y5fWsyCNewR9LBYGPp5LY5jri2MGkbSGtKm0pbSvlHOY9wyIIzwBIXCXlQYJCAQE/ocA15BTro86Zcp1xTUTJWr5GJKPHbje6ksbSfnfxX8jrrWdpFdIP5Bm+xzh2fuolP/3e0q7SreTtn/jjTeemDlzpiU9ITshIatCzFZhkLDKWRm4VX/sVMdt27a1a9xGydsqV5JnAZDVgwcPNm3atLHTO7MebZCAwJKGQCBml7QjVtj21nZilmcKH+3wbCyk8DEPHyyxDRKPQCBm43GpQuzKegcMkgYC2+7Wc+OOXXv+XmVyttthb3YeOpQ/BzVR+APDn5AOUkgKSM1spYUM50ndn5a/FebPkJNciNl/KJMrpzJilnozCX+0XFls+WLVSakCLm2si8ywvdaz/7LMjsETnxyDpK5M+MP4l9TVfYaXwSdmJ3vxScFsB8saqwDK/krq6o3bLlT67tJchBuNX1ZlHqqU/aaXxx8sJ80JA/yu3IdcpLa3efGve/F+0OVjm1S+s5+hgLOPErMc31e8dGfnbzmWL0khbl38LIWjUqoIlz4tmujtF5qY5Rpw7WDLQEShpJh9hlDw+5VrGMLIl1+148pIImb3l41//Tt7f/uNbG7xyiLtGKmTyxRw9j+7yAxbBnmcfdx5lEabXPXFwsDVl812itf/YZEM1X0/jjRnsd00z9G07k08y9z5dN1iLc4uotQrI+6c9EspJjFLvWk9R1r4nYgJZ3rXSPO4x1RdIepi7bnj+V2FlOSdc708v0XMCkHMZjpH0nj+neb1x2GRy/bjCAbZvGelcd9N+1mQ1j0COIqFQQT6pW6X/wf8F1tRimdfIyn/y9aTfirlPOY/EmQSBFMdKXmCBAQCAosjwLXhlOsK5ZpBfaKW643/sqtIIWlbSIdK35Nm++zgefuE9BzpQVI+luX5uat0J+nW0i2mTp36OGvm4ZGKd+eSTMxCGtEPpjtmuuGXXnqpfH2/KgxeppIVgpj2PPDAA3YqZtoZJCCwpCEQiNkl7YgVtr21nZgtLHqh9FwQCMRsLmhlZcs7ZpC0ENi6W49TqkLMQux22qXHJmm1pwDltFSZ/h+Qk3Os44ZI/lZefp+YneTFxwUv9cr5MMbAb2PHmHQ/iqmHfPvdvMRSL228F58UfNCzf80zmu/FD/Xik4LNPHva1tczHO6lTfbik4Kne/aZPDn8/G20w8Dl09I/pD4+hH+X5nrz+Mkr5xKFK5OZMnD1HphgDMHrbP5UuJ6Ugagfvfj+CseJy8c2qXyXb4YCzj5KzPrHHBs+OHhBeqUUgrOtlDYh50tdOV/bmIo/pV56WgPT//HK3K9idRXI/0O9tB29PLSXa6RQUsw+/5864fC/X+HmOWr0nP/VKy+OmGWQhWvF1cl2lnS0lOtyN2lTKcLAkH+sjrWxi35O0caV8V+FGSDKJEcr0dlHz6O02uTqLxYGrr5stlO8/g+LyVCd9+OY5lSISvMcTeve9JyHJ2XmI6XKlHRORssrNjGb1nOkY7QjkX3uow4Dtlz/TtI87q7MpO0JSnDt4KOR5ZIMvfhbvDzTvXiCe0ldeWy5l+Ujpcrkyonet/zyciFm/Xuq//w7yKuLOreU5vI8aOw3SGHu567tce9Zad1303wW0IW07hGUVSwMqGtpFq4vyKPlpXWl9aX8Z2glPUfKecj7c0Mp7yxc39gHCQgEBCpHgOvLV64dn6TlYxaurS+l7p6faQsZ+5j0LOkBUj7Q2UfaXQohu7OUZ1oH6WbS9T/66KNHmXKXaXzx1mEqXzTfqYyzGg4ssBEkKOpPQVzgKrMqHk9k1zYIriABgSUNgUDMLmlHrLDtTYOYZYpg1gBnffJPPvmk2u7bzGrw9ttv22dfrh/OcG93z84vvvjCrnX++eef27hCHgE+qmJ99rlz59pqeOZBYKYhrk+UxQwPkydPLrjncKZ2B2I2Ezp5pUXHmfVKGCRvBIYOHbpsx249X86XnO3Q7XAGCGuyQDD53l+v5djYgbL3/8Bs4OW/2kub4sXHBW/3bD+MMfDrYAAuk+ytRN9+c8+41Ev7Py8+KTjVs3/AMwInV8dILz4puJ1nTz4GPp0Ug5h1dbFl4Odg6TSp6wNb/lTmIp/I2OUfV0lGBpL88+zABPsmiocIdeX2VHh3bx9SDC+lOHF52CaV7/LNUMDZ+8Qsg2Euni193FCaJNcpwdnPjjEq9dLBO0n4E+/KYQsOSZI0MI39X1JXzqFeAWt58aTjgZlJtlLiPdIrpFzj2Qz0y8xKqX5dGwrdZ38Q+o2y+quyqYyUvMXrG4TqUCkDPnFSX5EOB7bHe0ac135apnOMbMM8+yimabWJepBiYbCotux+eX44vMAiKtV5P462Jbqf1jma5r1phBrp8Hwz2uCY/csVd5v0H9KOZeml2royoudkmUn5ptjEbFrPkaq8a6R13MtBzBDYTWnuWLBtlcHWJT3t5Ym+n/F+4peXdI9zZSVtS71yMp0jaTz/tvDqou17JDUqy/jKSMm07rtpPgvSvEcAU7EwyPKQ1GozrjHesyCJ6km5h7WQbiKdK+WcPk26qnQFKcRSvtelsgYJCCy1CHDdoBC07prj/y/XWJJ+p7SHpXy8jq1T/p/uIu0s3V7KBzvtpJtK15euK206a9ash1jLlfVeGYhl8BNlgLpevXqmQYMG5v77789r1C9kCggEBGoXAoGYrV3Hs6q9SYOY5WOVG2+80XTo0MGcf/75Zs6cOVVtVl75mdGgR48e5sADDzTPPfdcVmVA5jILwg033GBJZaY9Hj58uNl8883NxRdfbKcnzqqgPIwgYYcNG2bOOOMMM336dDNt2jRz2WWXGdYyr+qHP6zPTlmaUcOSy+PHjze9evWya+1CQFeHBGI2ddRX1jtgkDQR2Hb3Y+pv1a3n3bmQs3jKbtP18JN0eJeEP84QlO7PCGTDxjng95iXF0KNL76dXKqAK3e6i0zYfuTZVkbMjk0ow0Xf65U1V2H/y/JSLw2CK1NfN1O6T4KdoH0nIxVwffte4TVdQsL2Vs8ejNfx7IZ7aZO9+KRgpsGy1ZRpnPQd6a9S/jAmSX0l/Cl1/eB45SIM1Lu8PyicRJhS5j6eLXkOJDJBHlW8K5djeYO3f09CHqJdnsrKx3aGZz+EiDKJDpIy4J1J3laiq3dejGGpl17ogWmq/8urzydmSfvFS2NQOZNcqUTXr28VzuU+Vsw++9cO53JLaSbprcSvpRARfGjBwIkvXDOu3z38hLLwJ176azHpftS+ni1lDvQSuf5dPWyHeWlxwameffQ8SqtNrt5iYeDqy2bL8XJ4xWE10ksv9v24svandY6meW863cOLe0aLDJ3gvu5/VHN8mW2ptu6YRM/JaHHFJmapP43nSFXeNdI67lEs4/ZbKdIdC7ZD44y8OOz9ZwXvJ77spR2/vGX8xBzCpV45mc6RbT076oWUShL/nWw/z2hlhXm3cu0e4aXFBZdTJPeVz6TPSM+Q+uJfI7yfRiWt+26az4I07xH0t1gYRLFdGve5xiBbV5CuIm0gXVvaWnq+lPN6trSxdCUp52++16WyBgkILNUIcO0wNsCYRV3pJtLfpe75wXaB9CEp/xG7SruVKeEu0p2k20k7SiFjN5NuIG0pbS7lWq0vXV3r2t3PwLgjZRnQrU3ELN5YX375pbnlllvMY489Vu7BRB+//fbbjN5Z4PLss8+a0aNHG8ohD6QB26oKZeCpzOD6r7/+asnvJ554oqrFhvwBgYIiEIjZgsK7xBWeFjELibnKKquYY4891uhjoWrBYcqUKWbQoEGmf//+5vXXX8+qDRCWO++8syV0mWWCtWhHjBhh9txzT0s2MxNFoeSFF14wXbp0MWPHjrXLENx6661mp512sp6tVa2TZ9E222xjnnrqKfvM5FnVs2dPc+qpp1bb8eGZ2bt3b1O3bl2z/fbbWzL87rvvtv3nGc25yDM7SNYIMDYRpBAIbN21xyFbde05v1KCtlvPydvvccRGhWhDgcqEMPD/jHyl/UyEpWtGfwX8wb2nXULZloEuv1z+yMTJiYr07T6MMfLTGZhLwndTpfkDd7dEyirVvl9WpoFXSEBny4Aff7Sc9FLApbFlEDZJ+KP2t9TZvx4xJK9LmxxJi9vlT6Kznx4x4E8mfyZd+h2RdH+XASD/j+i5fmIW4e6ycfWwvTAhD3+AfQIT2wMTbIneX+rK/Vnhed7+HgonictTWfnknyF19uDpxB+IJH1DlxCzPVNxrgy238fYlHo2hR6Ypnr/ejw00h6fqPhTaQwIxwlEzDdS17cb44wyxBWzz9xTXDvZPpahXQy+fO7Zz1d4pYg9x9CV1zeSxu6vUpc+LibdRTVTwK+LPP55hh3XuivrR4UZwImT/RTp7NhGz6M020T9xcSA+rIRCBSHwbCYDNV5P45pToWotM7RNO9NHdRCnmkO00zX+BmeHXmaSpFSqcsfPSetgfeTFjFb2bnpVZnKc6Qq7xppHXe/T0lhBpghD93x+EnhhknGir9L6mzZ7i71ZS/t+OnL+Ik5hEu9cjKdI9t6dtTbJEMd/vsd90ZfXtKOazd2Hf3ESPh4z5Y8p0bSh3jp0yNp7KZ5303rWZDmPYI+FhMD6lvahesYwnVlKe9h3GtbSjeSzpJynp4nXUXK+zv2+V6byhokILDUIsB1w4cQK0r5oLmxlPeUidJ7pYOlnSO6k/a3l3aSMqtQWyljDvzHbylljIBnFx9V1JOuKuVaXlHTF9/LgCfEI6QLSri2eMxCfELItmjRwuy22252umamfbz88svN0UcfbdekjRvGBANN82y6d+9u7rzzTkueMvg7ZMiQKg/+MnjMgD4kBFNm4q08btw4061bN8P0l0ECAjUVgUDM1tQjUz3typeYxSMTQvGKK64wkGsnnniiWW211ew9kel58cr84IMPzG233WYuvfRSa0seJ/qgyLwgYvLaa6+16XfddZf58MMPyz+84f796aefmlGjRtl0yFKIU+69kKeTJk2yH8NMnDjR3t/HjBljnn/+efsBD967TKnM9MqsVw45iTctZVxzzTXWa5T12PFSxVu1UaNGpl27dubqq6+2bXjmmWfMhRdeaCibezvCM4d7/lVXXWXLYPphN10y5C3lsy46hPDDDz9s7cCFOuIEfCCRDzjgAEtIfvbZZ/Z5tu6665qzzjrLes1iw/PkjjvuMJpl1fYNO+LBmJkwqI8Pg5D333/f3HvvvRaHU045xay11lpm8ODB9l2AZQ44Ftttt52dOpkPuYotgZhNHXHeAYMUCoHO+/RsuE23Xodt1a3HZfKifVr6rcjaz7fu1vP+rbr1OqdT1x7dDznkPl72lySBnJghdQNabCF4GKw9VLq+1P35509HV+kjUt+ecBepLztox7eBqPAHC8Gpj9QfaMP+Q2lU/HIIfyrlD5Ev7bQzXepsIZ82kfpSqh2XzpaB5gulDHA4oV1XSX27US6xbAsekzwb+kDZfjnatdMaRbGlz74M146ra7KfkBA+zrOnXv4k+nKbdlx5bI/3E8vCDP7cJPXtdomxyxRFGe96Zfxb4ZMiGbghjfZsXH0HRuz83eW145OxLs83iufYJImzY5upfPL7x4TBRycQv345XANxMkiRvh1hjgXnhS+l2nF2xRiY5rp19XHt+rKRdvx02sOAgi8MJjwmdWWw7SzNRUpl7PIXo894vrr62N4qZUDEl9W1w4CLb3eRb1AW5hxzNnwpz+CnL29ox6VDpkbxw7aRlHuYs3Pb80n05GCFXRrbt6TreukEu0ohkH27KKZptok6i4kB9WUjlRGz1Xk/zqb9aZyjad+beKa584p79znS6P2LOnmOOrsXFXZSqoCLj56TzsZtGfB0tn+4yDy2lZ2bfpHLa6eqzxHanO+7Bm1J47hTTjayq4wcxmw/kEbff7ifXRex+0T7YOXLkkrM8g7Iuexw+Frhbn7HysJ7a8t56Ox+UXiNsjS3Oc5Lj3vPSvO+m9azIO17RDExcLgvzVvuv7zjcp3yDsP/pXWkraWnSTlfv5NCAK0k5R08es9WVJCAQECgEgTctcZ1xP+etaQbSNtKt5FCwO4o3UG6nZQ4PmhrI+W5im1LaSIZq7TlpVyjdTTYeY8jZN2w35JOzNIfJwxCMyCPF9Qll1xiB+ZffPFF07x5c7PlllsapqKME6Z3vuCCC8yOO+5ovvnmGzuI3q9fP+uhBD5RiYuL2rh9yr7ooovMZpttZuunvQyY77rrrubss892ZmEbEKhxCARitsYdkmptUD7ELIQrpGKrVq0s+bfeeuvZ+/EKK6xgjjvuOOuRCXG5//772w9qmjZtalq3bm2OOuoog1cr9+zrrrvOTn1MGS300U3Lli0tSQkpCoEHaXvIIYfYePKvs8469sMXyEjWgD3ttNPMBhtsYDbZZBObf99997VTEB966KFm7733tjMlTJgwwey+++5miy22sMQrZTRr1szssMMO9kMaiNRtt93WLLfccmbllVc2W2+9tSVBec5QNmQobaW/J598stl0001tfymDMs8991w7mwPkKQQobeGZtNFGG9l6wOX000+PJWd5JlE3BPD8+fMtscvzY4011rDPEYjn1157zWKAdyn969ixo52m+Z133rHPNPAkD5hC1OKNyjTOkNQQvquvvrrtK0QxZDbEN160PLuos9gSiNnUEYcHCRIQyBmBzZRjodQNVEW3PyltQYb0S5QWJwzY+mUxaPaK9BHp95E0Z5cNMYvtz9L7pBdKIVF+k7oy2F4gjUqpInwbF35X8ddIGbR8P2LDQAhkS1TaK4IBO1cGW/JCeF4snSD1B7VJv18alVyJ2e4qwK+TMCTR/5UVzB/HX6W+DaTPldIzpVdLP5f66ZO1n490VCZ/IJQyGbC8QgoOX0j9elz4QMVnEtrobN32skwZIvaVlT/Dsx/ilbumwgu9NOp+STpICsE9Ujpd6toU3TZTmi+l2nE2XAtJsq0SnB3bJkmGivfPuf0idn9p35VzaCSN3Wu9dOx+kj4gHSq9QTpb6vKz/Zc0VylVBldGMfoMmRk932cp7i7pP6Q3SqP3rg8VV18aFa4D13a24PmLtIcU8ftGOtcd52VfKfebF6X+gL9f1i1Ki8qjivBtOPfGSi+SPi6NXlvYRjEtVZxfRlXbVGwM1PxKZYosXB+HJVi3V7x/bWBfjPtxQnMqRKdxjq6pEtO8NzGgyLntcGX7jvQ6KefyRKmf9oP215c6KVXApUfPSWfjtmkRs5Wdm64+t63qc8T1L593DdqQxnF3fclm+5CMXJvZcl/kHsM70q3Sz6R+Ou8nW0qjspcifLtlogZZ7pd65WQ6R9J6/tEsnll+2/+r/Wek3Kc5r5+X+umEB0ij0l0RUTvure49qzSSThp19JVW17Mg7XtEsTEQdEu1cJ0tK4XMWVm6hnQtaUvpBlJ3/fIusYoUAreONN/rU1mDBASWSgS4Zrh2VpSuLm0qXU+6ubSDlP+2KM/HNtJNpFyDLaXNpfxHayCtJ11VyvVKWctLuX4pm2uZepYRoXh3dLivphCzEJjXX3+9XasP7ydIIQakGYy+8sorLVnKgDL7eE7hkYTn1YABA8xNN91kB48hZvH+hZjFOwtPqCOOOMIss8wypkmTJnZwnDX5fKH/DELjBcUafng94UnEwPnaa69tB6a/++47G8/6gpTNoDbtoCy8iRgUZ2CeAXIED6uRI0da76377rvPdOrUydSvX98SsUy1zMA3a/pB1gavWf9ohHBNQiAQszXpaFR/W3IlZvG8/Ne//mWJWIjKf/zjH/YeuPHGG9t7MvfuN954w5xwwgmmcePG5rDDDrNT1kIUQrDysczLL79sDjroILt//PHHW09QCN399tvPPPjgg+YredYys8Gaa65pZ0ngvk85lMe9mvzHHHOM/Q+1/vrr27VT8Ya9/fbb7RS5rHX7+OOP27IgUFdaaSU7NTH3Z9oB+QnpC8kLocp9nGcD6+O++uqrlvSFqMXrFK9b8vGs4d6Ohy3PqA033NDg3cratGDI+q3LLrusJXchPplNAU9cPFTx2I0KGFEebcALGA9iiF7WtmX2Babcp6/kx/P2vffeM4888ogt/7zzzrPkN/tt27a1duBO3ptvvtmmcYzAhva56ft5DtJ/sORDomJLIGZTR5x3wyABgbwQ4A8IBGV0MCrTPgN7Q6VJsosSvpdmKmOO0nt5NpAmUfHzR0kYP82Fr1QB/CmKSqkinA3byto2Uzb8UUuSvZXwrdQvMyk8WnYMpkRluCJcnsnRxJh9/gjSLpfHbRmArFtmf7C27Lu0TNvHZccAUL6yjzJWdkzelo1P0h5YSWVbKD3aZv4YZxLfvrLyZ6ggZz8kUih5XVqm7c+yGyT927M/QWFfSrXjypjmJ0TCaQ1MQyS6+uKIWR4QDFxnc24wmM0gQ65S7D7Tvu2ln0pd3zNtp8tubWmcnKLIuLyXlhmDx9QEm2i+p2Q30rOF9GawxpdVtRMlwKLlQDY+KHXx0fMo7TYVGwMfj6TwFK//w5KMFF8d9+MMzamQlMY5eqBKdOdBpm029yYat6P0qyzK5P6+l9SXUu24NkTPSd+OcFrEbGXnZrTeqj5HKnuu0f8rpXHvGq4taRx3V1ZlW57jo6TuuGTazpUd72dxwrH28zK4nI+UKpMrJ9M5ktbzjzbyjLtaGv1Iw7Ujuj2PTDFS2XtW2vfdNJ4FdCPNe0SxMYg5DEtdFNca7wmQPKtJG0nXkbaWHi7l/OXc7izlXIcE4v6T7zWqrEECAksdAu464+MG7r0NpVxn60s3lm4i3ahsv6W2zaVJZCxlcB1WIGO1X35NaqivxhKzs2fPth46eBAxOO0IY7xd27RpYwlMvGHZRxmoxssITx88lpg20k1ljJcS3kFMGYnnkzAwderUsV5MeFn5AnmA1xBlMtDO/j333GM9mYiDrGV9WgbzGbg/+OCDLZHAIDgeR0w/OW3aNDvIv9dee1lbBvohIs455xyDJxbeSwzw4xEFMQuBDIlL25g6OUhAoCYiEIjZmnhUqq9NuRKzkHx4y0JcQr5yr8SDFYIVz1MIQj6CYd1WiFhsufdCVEKA9ujRw075i2friiuuaMnEww8/3BKe2HFf5h7PtPAQn3zYg9cqH+eQzgczTNmLtygeutRLH7Dhvsw9fKuttjKssYpHLM8ZSFfuydjQNrxaue9THmuX4627xx572HqZsph7OuvlsoVApc2QxBCnfFjE/f6kk04yq666qvUQxruXD3vYZ8YEMGIKZTxwIUshX6NC+yBVmX6Z5yLCVMq0nTqpg49/6A/T9jMNMVM00x+eg3jx8jHQ8OHD7XMTEpi6Z86caT+Aom/UzXPLTVvMtc/xAVuI3mJLIGZTR5z/aUECAnkjwB+L46UvSv+WRgex3P4Cpd0h5c9LZdJSBs9JfdKIcn6RPi1dT7qh1JUdN4Dn0tgeJP2H9Dsvj0tnAP8AaZKUKsHZUs8G0tekDHS4eLa07Z9S/ohVJvVlMFL6g9Qvw4VfVvwh0iS5QAnOdlKSUSSeP4y03+VzWwainXRU4ElpHAmHJ9770rOk5X8eFc5X+KL4IWm0rt8VB/HLAN8rUtfOfRSuTPz+Ta7MWOn++VVZ+Z/L3rXlhJiyuyjuHc/G2bJleky8MB25xzFz6RMU9uUc7bi0t/yESJhj5ezAsEEk3d/9w7ONkiW/eWmZroPdZfeBNHre04ZvpX2k+Z4Xxe6zmmqFjxKukXJ8HJb+lvsF5zuDMJnkEiX+JPXzPuhl4Fy+TOofB2fLOfimFFIc/HaRujS2W0ujwoDOKdKvpb4t4RnSntJuXlrceZR2m4qNgbqXUV5XqsPm3IyWizyhR8qmmPfjSppUnpzGOdpFpaVxb3KNWl2B26RJeN2jNAYso5LtdU6+g6Xu+EEaV0UqOzejZef6HHHtZFuVdw2/HWkcd7+8ysL7yeAjadz9fYHir5U2kyYJzweHA++B+Uq250hazz+/nTtoZ6qUe7Lri799UvEQwpmksvestO+7aTwL6E+a94hiY5DpeCwtaRCtnAv8occLei1pSyn/lXgX4Tzmvsb5t4K0jnQZaZCAQEAgOwS4Xtx1tpLCvAc1lHKtQcKihBtL+T/GtcZ/B57lK0q57rhGufYoh/KcKlhRNNRXY4lZBrEhMxk0xhuWgdmpU6faAWymr8SL9s0337T7DGoziM+ANYP2DLozfSNePgywQ8Iy9SMDzwyQ4zHbStNgsm4sg/m+/PDDD+bUU0+1A9ZM2ciANOQBXlJM5UgZeN4yCA4Ry1q0eGnhrQUxjPcug94MhkPkMgVy165d7YA4nkbUx8A8tnjm4vGE4F2FtxZkQZCAQE1EIBCzNfGoVF+bciVmuU/27dvXEpfMMMBHL6xxircpZC3E36233mrvg3Xr1rX3SO6zPAeYraBnz552jVfISu6pkLWsTcsHLZ07d7beoniCQkr6pCbkIs8CtpCWtIEPeC6++OLy+y8f//CMiBKzTF3sPt7hQx3WKmdqZWZAQAnvs88+9tkEeeuIWbxjmZKZdcppH/1CeI7xjGjQoIF9VkF+9unTxz7niCed6fa7dOlin0HMsBAVZmrgWUF7uCYRn5iFOGXGBzxzaS/TM9NGMKLvPDuR5557zmLL83Os1vwFI8QRs6zP7ohZ4vECZmpkiO5iSyBmU0ec/3FBAgKpIMAfkQ7SfaXHSo+R7i3dUsofklyFPz/bSCF9GIjLpQx/QO1A5UX4E7SJ9Agp7YJkrUxKZeDKYmDDCX/KGIykLAbrVpPmIy2UibbQx67SuAFtRacma6skMN1MmtRm/kxiQ9/6loULdaPgj2w36ZFScOTPaz7Cn91vpO5YDcinkCrm4fxqKXX92UXhJtLaJAwyuGuyu8ItpfR7SRcGU7j++kp3k64vxcMpW+G8bSeFSOUaY+AlKlxv7aWQTj2lbaT5nu/KattHeYdIKZOBoVwlzTZVBwa59rcy+2Lfjytrj59elXO0UPcm8OJ5f7h0KynPjpoo2ZybtDuf54h75rCtyrtGEm5VOe5JZSbFc3/nXau3lA91eHfjPWxpEu77m0t7lCl4MNCei1T2npXmfZd20eaqPgvSvkcUG4Ncjk9ts+XYce+CAOLcgjAC/9ZS7ssLpdyfzpNyjXO+8I5SG97d1I0gAYGiIOCuM64fnou87zAWsIaU8Q/CxPF/OWcyVnnKRUN9RSdm8fCZNWuW9eTBY4epHkeNGlU+QO6GHyEw8YKFmMWbyRGzDMTjpeSIWfbxiH1N3lAQokzHiPcVXldMIcwaeewzoMw+A/dMG4nHEV65zuPI1QsZi5cT68viyYosWLDAHHnkkbZMBqoZ0IY4oEwG4BmMh1BlsJ28lEn7mVITz1iIBQbbGUT/8ccfDdNJMn0n5TrB2wrPK7xso21yNmEbEKhOBAIxW53o17y6cyVm8QbFUxZvV+6N3PO4Tw4cONAsv/zylphl6l3uvXzUcuaZZ5onn3zSeqaWlpYaSEo+ZsFTlPs8U+0Sj2co92Puwcx2gFcnZCizGnAv5QMeZjrAa/SVV16xUxnjxcqU+O4en0TMcp+mTQgfCOHJynOJtmQiZnkmQJwy7TJ1XaQpinn+cM+nXxDReO7SFzxmme6YKYR5zuH1m4mYhczl+fWCvG3ds4IZIpiJwXnMEobIpl+sK4veddddti8841gqgA+E8KKF9GaKYjetvyNmee46fOg/xDmEePCYBY0lXgrFt5S/X4ZAQKA6EIgbLM2nHaXK5Mryidl8ygp5CocAJKE7Tr8qzJ/kIAGBgEBAICAQEMgWgXyeI+65w9YRs9nWF+wCAgGBgEAaCEC08vEqf+ohippKW0jXk54m5f7Eu7H72AJbiKZAzgqEIAGBLBBw14u71vjgCwIWktYRsZC2XFt8KOE+fnD5FJWdaGix6MQsA8943DjvJLxQGaCGVPUljpidMmWK9bbyiVm8fdjHG9VNQ+yIWcr0iVk8ghjUhpht3759BWLU1c2gNWvpMTDuBr1ZUxZidqeddrLTIzNAD7nAIDUesUy1yTqHx2jtQjyXyEfdEM945zK1MtNHIhCzeO0y4E9dTubNm2c9m/BwcvW6tLANCNQEBAIxWxOOQs1pQ67ELLMD/POf/zRrrbWWvf9BXkIOcn/kPsm6qJCZzquWaeJZL5x9Pm6B3OTeyzrhPDfOOussSzZyv2aWBO7DfKADQQtRi4coa30ziwLE59FHH22JUO7TkKWQmY54hAB2zyR/KmPWmN19993NJZdcYsvB0xbvU6bRZ8YF1mLlXs409RCqp512mq2bWRfwzuUjHD7OgUjlIx76iz0Y4CHLtPesMUv7mHqZ56PzmKXPcR6z9JE0t8YsZwS4EgdZzLOT5xXev0y/zAdIlInnLKQwa7MzowP2PKNoBwQt5DVkOeXSRuLdx0s8O5khgpkjnMdtMc9EcIHAdh9EQcpDmI+Vpy/HgXPRzT5RzHYtwXUFYja71+RgtYQhkNZgaan67coKxGzNPAn4czzJO0531cxmhlYFBAICAYGAQA1FIN/niHs/YBuI2Rp6cEOzAgK1HAHIH8gg7mN47eHlzSwaraSQsy9JuUe9LeXDReywJ1+QgEBAIDsEuF5QSFeUa8ipi3M2eV9bGlQsOjHLQCbeOq+//rodRGeQmUFVBl59YXCZtfyYrvJOrfHHoCsDxnhWtdI0xL7HLPuff/55pcQs02Yy6M7UxgxEMwgdFUhYppbEMwqiF/GJWdqJVxLesQzCQwij7777rh28x9uIwX76BfmL9xf9gHRgcNsnZgk7wZsMMhiSIUhAoCYiEIjZmnhUqq9NuRKznD98oMJHLA0bNrTKGuJMuQt5yT3yK013zBqrfATTuHFju1YspCUzIPBRDaQisyyQh5kSyI93LV6zPB/4IAaSFTKVZwdlsOV+DpHHNPTHiJil/muuuab8uYNnKdMWYwf569aYJS/TFeOBSxvxlr3jjjus9ytk5y677GK9XyEyKQ/yFfKWZwMf3kDg0l/ayjMDQpj1b7HjGUZ7WCeXdMhGni8vv/yyLZfnBn2KCjNOMJXxpZdeWv5x0cSJE+3zhnbwDGL6ZYhYpjTGgxh7nkU8//Aa5lnD7A4cQ55veDKzfi7Y82wGC0hnCF+8fJnOnymiIbr5iKjYEojZ1BEPxKxeqoPUPgTSGiwtFTSurEDM1pzzhKkzGQQ/Wvqi1B0jtm2kQQICAYGAQEAgIJAJgTSeI/6zJxCzmdAOaQGBgEAhEYAYwlsPDz6mVWUa9HWlELMsFbJQyv3qcmmY0lggBAkI5ImAT75Gw3kW+b9sGuqrFmKWIUYG6X2NDjsy+MvUvhCxeDLhXYUHkFpvWmiNWQa0maKSKSHxmHXELN4zeMwysMwgvb/GLFNJPvvss5aYrVevnvWsYhDaF0jjCy64wA7GMwCOMDCNVy8EMNM7MkjNoDfrGLLeH568DJLjFYUnEuQB3kWQvwyC45kFecD0m658CAO8mRgUBwfaz6A6XmBBAgI1EQHOU0gjzmWmGIfMgTjCa40pZCF9OJ+DLB0I5ErMgsqff/5p792cM9wXOX+YGp77KPdi9/HK5MmT7b30iiuuMLfffru913PvZDYBPmKBeGRqYsqAKOVjGdYHR7jvMysDU/eSn/Vd8cQlnTQ+muHey7TInNMIa5pD6ELKcv92xCxkJLMgMC0yJCXPDzcFPe2BnL3uuutsOtMFQ8SOGzfOrjnLx0QozwtmbqAcyFsIZp4tfMDDxzmTJk2yOPBMoz2uf6zxytrjUQFDyGU8it30w/SNZxMexkw1TNv4WAhswOCWW26xGNF/PHnpH+nu4yPKIe7999+3OHFMyANWHBOWG2B6ZPCh/mJLIGZTRzwQs/97FQ6hWoRAWoOlpfzZKNNAzNacE2SGd1zc8WE7ouY0MbQkIBAQCAgEBGowAmk8R/znTyBma/DBDk0LCNRyBCCIIGeZThXidU3pWtKWUsjZAVLuV/+WdpNC4ELkkoe8QQICAYEagICG+qqNmK1smJHBYAa4ISvxVMIzCrITLyK8f76SZxUD4XgzMa0l+wyCM8DPmnn777+/wUOWwWqIJKafZBCaQWm8nCBmmUIT7yJfGHQmDx5MEE0IbWGAm7rIy4A2A/J4ZeGRxFqzDFozTSVeUBACkLQQsaxJy8A802lCNEMG4N1EvyCPWWMQG+Kok4HvIAGBmohAIGZr4lGpvjblQ8zSWshVPpL5/vvv7b2V+x/3Zu69jiiFtIREXLhwod1CzPlCHkjNpHTs/fxuymLKpx7u6S6OcrHn+UE7CD/44IP2wxqmQ4aMheiE/IySkuzTD9pCmHZRNlsn1EnZ2KF+GWDBvt8e4vy2uHL8Lc8JPliC2HbkKlva6cqnHDCASCaefcTV5/aJI+wfA2zIQzvoN9MtsxYwz7LqEI5JmMo4VeQDMVsD3sFDE9JHIK3B0lI1zZUViNn0j1O+Jb7iHRd3fJ5XHANSQQICAYGAQEAgIFAZAmk8R9zzh20gZitDPKQHBAIChUQAgrWOlDUvV5M2lK4tbS1dT3q/lHvVV1KmOsYO+0DMCoQgAYGagICG+WosMctgNgPCeOywzh5TTeJRhCcV3lFuEBpPKDxn2WdwGU9bvPogT9lnOkn2IXHZZ8AcQgHvIIhXPKWigpcS0z+yViFCWygHEvXpp5+27WLAmnLwaqJ9pDkvKKY2pl2QBggEAHH0hTgG8UmHjGUNPwbrWZOQ6Tj9dWdt5vATEKghCHAdBI/ZGnIwakAz8iVma0DTK20CzwZIWT7w4V5d0wTClWcGZCmzLRRSeFbuu+++1qOY5151SCBmU0c9ELM14SU8tCF1BG5RiXeWaVWmtt3TK+f81FsZCswXgbOV8Uvp39LXpBwbvAOCBAQCAgGBgEBAIBsE0niOpPWukU17g01AICAQEMiEAASrm9KYP/isJ9tEuq4UcratdIYUcvZp6SpSPmgMXrMCIUhAoCYgoKG+GkvMumFIyFQGg50HEuQQcU4I+/vEs4+dk+g+8ZSJ+nbOHu8lps/EO3fatGkuupzY9fPgWQQpzMCxk7g2kea3gzLoE6Qt026yHi3TZfplu/LCNiBQExDg3AzEbE04EjWjDbWZmGX9cQhZPgRyH9jUDNT/1wo+9sGbt9DThzM1MlPy8wFRdUkgZlNHPhCzNeElPLQhIBAQyAuB8JV/XrCFTAGBgEBAICBQhkB4joRTISAQEKgtCDhydgV1COK1vtSf0pgPTn+TQs5eKWUgIExpLBCCBARqAgIa6qvxxGzqw5FZFAgBNXPmTHPccceZiy66aDHiN4sisjbB84m1B4844ojgLZs1asGwOhAIxGx1oF5z66zNxCznOh/NQAgSronChz5upohCts99GFWdOARiNvUjHIjZmvASHtoQEAgIBAQCAgGBgEBAICAQEAgIBAQCAgGBKiAAORud0pipi1tJmdJ4sBRi9r/SXtKw3qxACBIQqAkIaKgvELMJ450MBLOeHuRDIQWv2Y8++shO01zIekLZAYGqIhCI2aoiWLvy12ZitnYdqfjeFJpoTbP8QMzGH8MqxAZitia8hIc2BAQCAgGBgEBAICAQEAgIBAQCAgGBgEBAoAoIOK9ZPGH5o8+Uxo2l60jderMjFIac/VnaUYqHrVtvlvxBAgIBgWpAQIN6gZitZGSTAeFCSzHqKHQfQvm1H4FAzNb+Y5xLD2sbMcuHOLfeeqs588wz7SwGaXyU8+uvv5o5c+aYn376KRdoC2rLlMQ333yzeeuttwxT8S9YsMBqGs8h1k9nnXQ+OALPG2+80Xz66adV9joOxGzqp0QgZqvhnTtUGRAICAQEAgIBgYBAQCAgEBAICAQEAgIBgbQRcOQsa8jWla4pbSptIcVrdgPpK1LI2a+lG0p9cla7QQICAYFiI6ChvkDMpj7eGQoMCNROBAIxWzuPa769qk3ELOQk09a3bNnSNGnSxHTt2tWMHz8+X2hsvi+//NJccskl5uyzzzas01pT5LbbbjM9evQwb775pvnmm2/MSSedZO67774qk8eQsuecc45dKx1yFkyPOuooc+GFF5rZs2dXqfuBmK0SfHGZAzFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKhADkLF6wEK6rShtIm0lbSiFn20k/lkLOfiJdWxrIWYEQJCBQXQhotC4Qs3FDliEuIBAQWAyBQMwuBslSHVFVYpbzCcLN99SM7vsAk4aHJ2urJgllsjZsJpu4vJ999pld53v11Vc3xx57rHn++efLyUTKwgOUcuPE1en3AzvIzrZt25p9993XkqBxeSuLo0y/L4RpB3UmSTSPbwdZ3L17dzN8+HAzb948O1X/1ltvba6//nrzww8/+Ka2z36fMtVJGuTrTjvtZM4991zrJUzesWPHms6dO5unn37allehghx2KKt3796mbt26Zvvttzc33HCDufvuu235o0ePtv1gLdwgWSMQiNnqevEO9QYEAgIBgYBAQCAgEBAICAQEAgIBgYBAQCBlBJzXLFMas47satKGUn+92U7ax2MWcnaqFPIWL9tlpeQPEhAICBQRAQ3hBWI263HMYBgQWLoRgHx5+eWXTfPmzU2jRo3MCSecYO6//35LjowZM8Y88cQT5rvvvlu6QVqKep8rMQu5CUF3/vnnW2Lt8ssvN3379jWnnnqqefTRR+25NHDgQBvHlMJ4cyKzZs0yd9xxhznuuOOsp2f//v0NZBxE4M8//2weeughc9555xk8QS+77DJz5JFHmlNOOcU8+eSThqmEZ8yYYW655RZz8cUX2+l7KfP99983V155pW0H5zRt2WCDDczKK69sycU777zT5nvmmWdsWXiY9unTx9rRb64FyMJ33nnHesVCGlLvtddea6fufffdd+3+aqutZr1wTzzxRDNp0iTbT6ZKxhsXInHu3Lm2L6Wlpea1116zfaXveJnSl8GDB9vtJ598YijzggsuML169TKUB5b0n7aAAd6q//rXv2x7jz/+ePPGG28sRoYytfA222xjnn32WVv3WWedZRo2bGh22GEHi/HChQvNhx9+aPtEn+nXiBEjLNH6+++/m9tvv92mff7557Ze2gXJO2rUKDN06FB7b+jQoYPFlr59/fXX1vv4jDPOMN9++y3Q5yWBmM0LtkyZAjFbxHftUFVAICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKjYAjZyFb+dMft95sV8UvkELOPiOFwA3krEAIEhAoNgIatQvEbKahy5AWEAgIlCMQiNlyKEJACORKzP7222/m0ksvNWuvvbZZZ511zEYbbWSJvPr165uNN97YbLHFFpbEhPTfZJNNLAmIVyfTATPFMPnWW28907RpU9OmTRtz1113WXLx9NNPt+QiNptuuqlp1qyZWXPNNc1+++1nXnnlFTN16lRz8MEHm3bt2lkvSw4eHxHsuOOOZq+99rLrrUIQQ8rWqVPHNGjQwAwaNMhcd911llSkPOql/rXWWstQH4QjxCckKe0hHuWjhSFDhliCFLKT8lZaaSVLho4cOdJOa0z/IKdZdxZi8+ijj7akMKTn22+/bcnnxo0b23Lr1atn64D4hCR1dTDdcvv27c3jjz9uwBUiGkxp4xprrGHxgrj1vXwhqQ899FCrrPsKCTts2DA7dfMuu+xisYFoPvzww822225rMWAqYuqBxGatXD7AgHhl2uevvvrKToPcqVMnc++999p1edddd13DPgQw0xlDqIJHt27dbN/yvXACMZsvcon5AjFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKjADkLFMaQ7ay3uwa0ibSdaWtpUxrvJ/0Fynk7OPSQM4KhCABgWIjoCG7QMwmjluGhIBAQMBHIBCzPhohnCsxCzGIxydepJCweJfiFeo8VfE4ZXraQw45xJKZeNJ+/PHH5qabbjLHHHOMJVAfeeQRS7hCov7jH/8w06dPt96jEKDbbbed9fqkXMpv1aqVgQydPHmy2XPPPS25iqctQjkQjpCnDz/8sN0nDEkMKcv6qw8++KDp16+f9V7F+/a0006zBDBTE7/66quWZIbE3XnnnW09eKuyNu2BBx5ovX/xgoVg3WqrrazXKXixnuuKK65o8FRlTdaPPvrIErEQrfRzypQpZu+997aELmTpVVddZR577DFL6FIW/aBdJ598siVUIU7BAO91pmGmz3iuQuRGPVSZxhjSFNwgWSE7Ia4333xzS37jmUx97ONhDPH6xRdfGLyYweb111+3+aiLPh1xxBGWQIekhaimLx07drRELMfNkcJ43G622WZmwoQJ5XG5Xj2BmM0VsUrtAzFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBASKgIAjZ1lDdhXpmtKmUshZiFn0cOnvUsjZidLVpUyDHKY1FghBAgLFQEBDd4GYrXT8MhgEBAICIBCI2XAe+AjkQ8wyRS/EbM+ePS2Rx1qurEvaokULSwYuWLDAEocQr0xZPHPmTEvO4k0KMXrAAQcYvDKXX355u8+UukzrC9kJoQoZidcn3rJ4ukIwQrLiGbv++uvbaYPpgyNmWa/0qaeeslMb41WLRyrenpCKTMXLFMsQrHjUsl4s9bBGK/kHDBhgVlllFcPUxLT7+++/t+WAC/t3ajpkiFLIVEhNpvnGe5S+nX322eXE7GGHHWa9Y2+++Wbr3QsxC+ELaU2ZeLfiVbvccstZz1sIa3DAMxYvYPpL3yFm8Xb94IMP7BTH/tq09Bkc8DS+5pprbLnEMT0yZUAqs8Ys00WDG+Q4BCxTKXN88EBm2nKmX6b8XXfd1ZLYePEyLTR1MfU05DjEL9g5AUM8oPG2xbs3HwnEbD6oZcwTiNlivGSHOgICAYGAQEAgIBAQCAgEBAICAYGAQEAgIFBkBCBmHTm7osKQs/Wla0lbSB0520vh36SQs09Lmfo4kLMCIUhAoBgIaNguELMZxy5DYkAgIOAQCMSsQyJsQaAqxOyxxx5r11NlfVe8TCHu7rnnHvPLL79YInTVVVe1xCekI2QuHrAQiBCuTKXL9MAQtXh0Qh7WrVvXrrHK9MAQhxCLEIx4azpilumI8SRFWJMVohVilil/ycM0vxCzEKSsp0p7aBvenrvttpsNQ5hCtD7wwAOWLKWdeAFDOEIeQqRCykLsMtUyxCykMJ6wxDti1nnMsp4ra7kyHbJPzEI+s64uAvkMAYpXMN7FeNJCjHbp0sUSsZThiFmmWY56ytpC9MMatuDIFM2QsAj4guv1119v41hHt3Xr1tYbljLBli3q1qyFgN1jjz0sOU678KxF8JplCmTWusUj1wnTRjNVNXjgNZ2PBGI2H9Qy5gnEbDFeskMdAYGAQECgGAiYqSXLm4kl3cwTJbssps/YAbZiNCPUUQUEzJMlay127DieE0s663HOwGqQgEDBEeg3qsO6fcds2WkxHddh44JXHioICAQEAgIBgbQR4P0B71eIVsjZVaUNpM2kPjnbQ/u/SiFnn5fiXRvWnBUIQQIChUZA7/mBmM04dhkSAwIBAYdAIGYdEmELAlUlZiHyHDHL2rCsU+oTs3hs3nfffXaqYLxdr7jiCksOQupCzEJCOmIWz9Vzzz3XeopGiVnWmN1nn30s6XrDDTdY0hXCljVZfWIWMhdiljQ8dfGIxQsVz13KoH7y4DE7ceJEO50wHrRMwcxase+9957BmxWvVcheR8zuvvvudupjPGZZC3aFFVawZUNwvvDCC7Z/UWIWcpRpmBG8TyFGqQvy+JlnnrGkLWXRH0hQ0mkrnrg+KWoLKPvBs5Xpm4cPH25JYqIdMYuXMFMrQxxDROPlyhTJkK6Q0KyJy/H+888/rScvZC794rjhlQzR64hZjoPvMQvBDTFLOXjc5iOBmM0HtYx5AjFb6BfsUH5AICAQECgWAmZCyfZmfImJ1SdK+hWrHaGe/BHQMbw09vhxXJ+w68HlX3jImTUCR45uv2/v0e2u7jOy/fVHjG13+FAzlAHtpUb6jG43vveoNiaqip8eB8LSjlccJiEuIBAQCAjUMASi5CxryTpytqXCznP2IIV/lkLOvizFuzaQswIhSECgkAho2C4QsxnHLkNiQCAg4BAIxKxDImxBIB9idtiwYXb6X8hViLyXXnrJen5C3DmP2fPOO89O9wsxy/S5eIbiqQopCUm64YYbmmWWWcaufYq3KF6dELV4auIxCwHJtMSO7HQep0x/jEcn0wKzRir7TL3rT2VMHtZ6hTSlLqYPZipfvHN31lqykKNsIUchTiGMWR8WshZPVtrZuXNnS7iOGzfOthWyl6mbWWP14osvNvXq1bOer6zRiucrhCplQI66NWbxtIXYRf766y9LxFIXhC1erbSB6Z/Bavbs2ZboZYpoPHGTiNl58+ZZLMEV4hkBvy233NJ67TKtNHgz3TGesHi6sr4tfUOZ9vjxxx+39mCOBy7HhHVrn3vuOYsZWOFdjEcyxwKBCMbLmfVso9MrW4MsfgIxmwVIuZkEYraQL9eh7IBAQCAgUEwEAjFbTLQLU1cgZguDay6l9hnTZlSUkBRJ+0K/qf0YmF4qJBdiNuC1VJwSoZMBgYBA7UDAJ2dXUpeSyNn9lfajFHL2NWlDaSBnBUKQgEChENA4XiBmcxvMDNYBgaUWgUDMLrWHPrbjuRKzTA/MNLqbb765JRAhFCdPnmw9TCE1IQKZ6vbaa6+1nph4XkIcXnXVVTYP0/tCpEISQiYOHDjQTlMM2ctUyNiRnzVZ8SDdeuutzdixY23c3Xffbb1jIV7dlMiQiAcddJAlDP08rIWKZyjryNKutdde204BzBTGO+64o+nWrZslLCFvr776akvyQqw2b97cTnc8evRo60GKNyretBCzTJtMPGvN4lGLrVt/ljpoK/VOmzbNQFrTtgcffLAc91mzZlmPXQhO8kLKQva++uqr1gsVgpZpivHqnT9/fnk+PwC5CaEKcQrJijD1Ml6ytO/kk0+2UzozrTFTOG+zzTZWIY/B8csvv7TrzjKdNPUyXfOzzz5ry2NdWTxshw4dao8NpPNnn31m29arVy9L4H788cd+c3IKB2I2J7iyMQ7EbKFerEO5AYGAQECg2AgEYrbYiKdfXyBm08c0lxL7jNxy78VI2XLP0fYn5lLWkmybLTEb8FqSj3Joe0AgILCUIhBHzkK8Mq1xS6nznN1H4e+lkLOTpY2lgZwVCEECAoVAQKN3gZjNZggz2AQEAgImELPhJPARyJWYxVsSL1m8QiH58ATFqxLylWmAWYPVt2EaXaa+xQMU78zx48fb7UcffWTefvttA9HHFLozZsywBC3kJflZ7xWiFRs3pS4E5FtvvWW9VvHcJC/tZ9pjpvCNy0M8BCbTFuNNii3EKeXifUpdTE9Mf2gbnreUSV0IZVIP8eRn7VfimPaYqY4hNfHuRWkbbWUqZ/JQr0+wUhd1QmRDYFMensBuamCwoh1gAK5JQl8gd5k2mrZQLvjhuUydxHEcaA99wp74n3/+2RLctBX8sUPY0mcXB1k9adIkezzIQzkQvG4N26R2VRYfiNnKEMo5PRCzhXipDmUGBAICiyHAgM9Y6d3Sg73UtRTeuUzbefEhmAcCgZjNA7QaliUQs9V7QHqPaXNJEjF7xOh2D1Vv64pXe7bEbMArr2PiP/fa5lVCyBQQqB0IrK5u8F6IsrZpkOIhkC05u6eatEAKOTtV2kQKOVtHShlBAgIBgZQQ0FBeIGZzHs8MGQICSycCgZhdOo97Uq9zJWaTyskmHmIOwhEiMV8hb65lkAfPUOp3wnXgi7PBLprGPvGok7g4l1bZ1tXlt6eyPH46RHbfvn3NcccdZ4ldl0Z5ftsJg5Xfbmx9G5c3Gk9ZDmu8nvfff39L0BKXr1Am0yvXrVvXej6zti5e0Hjy4onMuehI6nzrWMryBWI2pffoUEz1IsDgwBDpVtXbjFB7BgQgZRnU+bd0M8/unLJ40j704kMwDwQCMZsHaDUsSyBmq/eA9B7T/sIkYlbrqD5Wva0rXu3ZE7MBrzyOiv/cm5ZH/pAlIFCbEHheneEd8CfpOrWpY0tAXxw5C9HqpjV2nrMtFOc8Z3dX+Dspx+kdKWmBnBUIQQICaSKggchAzC5lo7GhuwGBfBGAlHn55ZftVKqNGjWy05qyJiXkCNOw4smHB2GQpQOBYhKzSweixeklHsNMNcy6sEzZXChhKmPW82XtYLxnqyKBmK0KerF5AzGb5st0KKtaENhetb4nZbAgfG1fLYeg0kr3Ljs+HKObItb+AHUgZiPg5LpbG4hZ80RJP/Xjg8V0fMm7isO7plaL+nipGV9iYvWJkta1uvMF6lzvUe179x7VbnJURT5O6vf4PnX9akXKHpZEzMqe+9VSIVkTswGvfM4H/7kXiNl8EAx5ahMCzJbyHynviONrU8eWkL7EkbMN1PZmUghYR87uovBcKcdplrST1Cdng/esAAkSEKgKAhquC8Rs7JhliAwIBASiCARiNorI0r0fiNkl8/iz1i/TMjN1clW8WCvrPd651MM2ydO2sjJceiBmHRKpbQMxW5WX55C3RiDwq1rBIAEaiNkacUgqNAIijQEcjg/eEI2kvvgD1IGY9ZHJIyxSb/tYQg+iT4RnHkUWPUtGYvLJEqYArdWSsf+BmM3r2PcZ2/6CJLL1hJHdGIAul6Fm6LK9x7R7Jsb+/SiJW56pFgayJWYDXnkdfP+5F4jZvCAMmWoZAneqP+5d/oha1rcloTuOnF1OjXWes3Hk7E5K/1TKseL/1+HSFaR1pJQRyFmBECQgkC8CGuILxGxq45yhoIBA7UYgELO1+/jm2rtAzOaKWM2xrypRmk1P0qwjELPZIJ6TTSBm831xDvlqDAKBmK0xhyK2If9UrBtsuyjGYlvFXVCm/WPSQ1QOCARiNgewaqhpIGbTPzC5ELPUPvSFzsv1HrXlCUeMbnN/nzFtHukzpu1ZSxMpCwbZErMBLxDIWQIxmzNkIUMtR6CV+ve3lPdFpsyt9bNjqI81TSojZ5mxA+/ZzaXPSTlW/5Xybu/I2WUVDuSsQAgSEMgHAQ3jBWI2p7HMYBwQWHoRCMTs0nvs43oeiNk4VEJcIRAIxGzqqAZiNp+X5pCnRiEQiNkadTgqNIZ1qn6TMnjDtr40SAERCMRsAcEtUtGBmE0f6FyJ2fRbsOSVmAsxu+T1rtpbHIjZaj8EoQE1EIG71Cb3Id9pNbB9S0OTkshZZitpIXXkLFuWJnHH62GF15TicRvIWYEQJCCQDwIa6gvEbOrjnaHAgEDtRCAQs7XzuObbq0DM5otcyJcrAoGYzRWxSu0DMZvPS3PIk4jAqkqp6pfSq6kMvrzOVqpCzNLWCusLZltpxI41lnJpcyR7hV0GNNK4MBkcWbFCycXfOV9VukGbB4pfvT0X0zi+RW+6ebKkvplYsoOmJT5IUxB3MFOzO0/TIGaNOaSO6t5IZe2j7e7mqZJ10gKAftj+PFmypXm2pIkeUYvdLzISk1Wcyth8ULKC6l+TbTZ9UltW1LFoqzwH6ljspDZXmPY2mzKSbFReU+G7LTj4Nhn7n+VUxmkfQ46Tea6kudq2s/RgaVedFxubF0q45xdd7rvvkDp9727Xss/YDl3QvuM6bNzvvl3qJTUkELNJyCTHL8HELNd2Gu8iDpyqPkN4DvM89qXoxKwZ0YH3lKxk/qCGzWYPbNhl7qDG2/8wYF3IlirJwjNb15t/QrMt5/ZvvPvsgQ06LhjcfG0ztHMUk5zqMEOHLjv/hIYbUlZOGTMYc5+bO2Dd1gtOaLzrvP7N2puTt03jXSy2RqN7WGxCDpFp4apjsdLcwc3bLhjYeDcwrWrbyD/7pKYtOYfQ7wY12Ji2ZtG1brJx74yzFM76nM2i7GCSPQKZyNl1VYwjZ1spfJL0DynH7T3pBlKOG+c35Sz2jqe4IAGBgEACAnoOLfHELGTRL7/8YubMmWN+/PFHdan65M8//zTz5s0zCxYsKOjafYXsIWsOLly40Pz000+FrCavsv/++2/zzTff2HULf/vtN4vx999/b/c/++wz89133xX9HIAw4Nz78ssv8+rTkpQpELNL0tEqfFsDMVt4jEMNixAIxGzqZ0LBxhwSXjdDdC1DAELhTOlb0h+l/DH/XcpaoaOkm0gzySpKHCK9S/q69HupG5RZoPBE6bHS6B97pji7XwrZ5+zZ0g7i0M2lcdJJkf+STpKy5in5vpI+LD1DytpK2cjWMrpXOkP6H+mf0snSa6XrS+nbfVLacrc0k3RVIjYMaoAfU4N9IaVNZ0szDQwfqnTX5/0VbikdIwVL2sVaUOdJH5I6ux4KVyadZeDsaRuEcS4CjnOl7vgk1bmPbFw9V+RSQYJtWsc3ofjCRYuo20ME4LMivWYrbCrohJL/av8LEXn3iijk/IoV5c1rjVkRbGso77XSadI/KtS9qC0/Kf4N1T3IaA3O2MoTIlUW5PL9yv+Jwv+pUPaEknlKu96StIsIv8dlt3j/HR4TSl5SOjbowVSp/Gd6cS7tcdVzg02fULKebG6UzTdScDTYJzSX8jpYnCeUfCzbf1do76J2zFH687I7Jmcsnig5QnU/LJ1VoVz6PLHkEcVtqLRLK6S5vrPNQMwW4hiqTRD0d0t/T2jTv9WmV2R3OvUnYZpGvF5/lukzsm2P3qPbvd1nVJu/YtaANb1Htftc6Vf3HdOuI3UeMbrDXkeObnt379Ftvoy3b2OOHN3+MWxQ7PuN7rBWn5Ftxrk4f+vKdf3pM7L9YD/dhRV/KTaUdeSotif3GdVubJ/RbT7uPXqLb9WWqb1Htx0RLQv7PmO26H7kmHb3ldn+KHJ0+hGjt3iq95i2pw6975DYjwkqawPl+tJnzJYHuHb62z5j2t3FFM7ONltiNhe8XNkpb5kO/0op1/Rn0n9Lee7w/GP91hHSVtIkGacEnkH3SCETmOnhLOmj0nlS947woMJHSbORbWTE8x9yiec5H5A9Jx0q5TqpMjE7b0CTwfMG1L87qnMHNLLnHuTqnIFNhmv/4zkDGvw2p38DozBtWkwgIOcOaDpszoBGr8r2e2x9ndu/0beKf2Zu/wa9F8ucELFgYLN1RMTePKd/4/l+WV749zn964+fO7Dx8XNOa8L7kpVM/eIeMHdA/VOkL6svv7iyaN/cAY0fnTew0ZBcCd/5AxpupH7fNqd/oylq66+uTLZzBzT4j/SzsrLt88a1M7qdf1LDtdSfcdHjwT5k9E+nrtZw3oBGF9Jn1TNjUfmNvmF/Xv9GF80b2LJptMy4/XxxjZYl8nnbOQMaPqy2fKrj+m+/3zrWfwjf94TLrZxH0bxx+xybeQPr95g9oMHbOh5/VSiv7HxSPZ/P7d/kavCIK0NxXH/zpe69Maw1mwBUEaKXUR286/FMWFG6qpRZbzhPfXK2tfb3k7r3fe6ZXaSBnBUIQQICuSKge2nRiNlff/3VPP/88+bBBx9UtfkLA7SffvqpueOOOywJCkE3btw4s8suu5gLL7ww/4JzzAlJNXv2bDNmzBjz/vvvG0jD1157zXTr1s0ce+yxRScIc2x+ojnE8sCBA83111+faJNrAscM4vS+++4zn3/+ea7Zy+0/+eQTc8wxx5h+/fqZ+fPnG4ihvn37mq233trsu+++ZsCAAebSSy811FcVgZyeOXOmeeihh8yMGTMyFgUZ/9xzz5njjz/etiej8RKeGIjZJfwAptz8QMymDGgoLhGBQMwmQpNvwsq5vi8G+4CAQ+AQBX6WugGUuC0DpJe7DJEt+Rm0jMsXjXtZdi28/M2zyNfdsyfI4MKFUjdoG63D7b8vm7bSJGGg4nqps4/bQir3jNhodzFZRzEM9saV4ccx2Lz9YrkXRQz38l+j8Ffevl+GH//OoqwZf/12vZrRMj6RAWxX/28Klw+8Rsz9AeoPI2m57KZ1fHOpMxVbkZ1ridi6N4H4qkjQLiLpflOeM4xHoriGiEDLmZhV3T1V95ys659Q8prq2dTVmbQ1z5SsKzsI0rg+VIx7omQX2SYTknFlyJ66Vf4zsXVMKPlWXp2bq9x5i6XHELMiFhvKdoRsK5LHcXX/L26qiMntkjBw8bJZU2Xft1g7/lfOIjwmlPwim4WJdgnEbCGOodpwoTSOmK547Bb14T/q33quv2lvIf9Eur6XRK5G4/uMbHcrbcjkKRvNwz72EKZxaYvi2vX1+5aJvOw9pl0vEbILEssa3ea/vcdu0Y/yBo/pvrrI2gcSbWmbiN1+93RYDONMbfDb6sIina9Ique0UbuV36ezLTcXvFwbUto2VjljpO45k2kLMTo4oV4/3/qy+aiSMiFx6yWUlc37Ac85zk9XL+RxzrKI4KtIoC4i+xpOx+tT5NrMKDkmkpHnegWZd0LTPUXCfRm1TdqfPaDRk3hDVigksgN5CrmXVIYfDym4cEgziB4ryf1qtFAk4iN+3riwyps8b+DaHMeMMv+MhqvNHdDw8iQSMa5sEavPzxnUZIu4giEb4/Isiqt/i0jO75LTRQL3bzDnu/4NusaV7eKqgqsrAw/oOQPqj1B9/83UHpcmux/mDmjST/8yIepiBVLaErkRQt+VsdhWhG9sQYsib9HGXRtTMtiFpMIjEEfOrqlqm0j5/9JKyjOptZSPUbiXcez+lB4v5WOiOlLui4nnj9KCBAQCAmUI6F5bFGLWkaft27c3Q4YMyXcg0XpIQvDtuuuuZptttjHffvutgfC98cYbTatWrcygQfqet0iCVylk3JZbbmkJ57/++ss888wzpmXLlqZ79+7Wa7ZITUm1GjDt2LGjOfHEE1Mrl2PWq1cvs9tuu5kpU6bkVS7e0CNGjLDH+ZxzzrHEN4T8BhtsYM466yxz0003Wdx79+5dZWKW8xUSeY899jDvvfdexvZCVkLMH3300eaUU04xv//+e0b7JTkxELNL8tFLv+2BmE0f01BiPAKBmI3HpQqxgZgN/wTyQuA45XIDJ24L4cmA5oyYtFMV58uu2nH53BaPW0hR9JeYdN/Tg0GB/0jxRHH53ZY4dBepEwYFXpI6G7fly3wGSf+OpDGo0EUaJ/6gkSsHgvoLaVx7nE20LL5A/z+pS3fbhYqLK4v+QvZGxSdmXRnR7ZvK5BOgpG8WLcjb56t4MHDlWPLAS88meL+Xn4HsJPHblS8xm+bxTWpnQeLLpoddkEjERYk7f39CyWPRRokcy4mYlf1ledU9vuRP5cU7OVZEFHYTGflz1mUXgpgFqzhSdlH8437DZddebU0mRH3c48Nn++X5YaaCVvkzs8YivvxFZGgMMauyUz+GamuvnNo7oWSs3+c0w/2m9lteJOGkJDJxsfjR7f4+ZvSWlpipLmJ2sTaJWI2NEzmLx+sRo9tNiE2P5JPH8LNRbLMlUF2+WkTMTlKf3DOKLc/f6dK3pF9LeWZG0+Oee76N8/pycb9FynDxNyg+TnzC1dnybvOV1O1Ht6kSs5YEiyFliY8Ss/JOPXsx0iwLcg1vVXmRtokDYH7/JntnS/rZNvVvcKdfThIxm0s71b6fmTbZL9cPM12v9QLOoq/RetW3f88d2GCgXx7hzMTs4gR6tFyLhTx0Zw9q2jlaNvtVxZUy8NoVNvLSza49vp1I7FGUERWmyBZhPsm3zRRW/X9XQpzvpjrcNcI13ThaZ9gvKgIQqrxjLyflf8sqUrz+OS5rS1tJIWfRjaW8l7rjd53CDDSQlzIoK0hAICCQAQEN6qVGzDJwipchXrFPPfWUmTp1qvn5558tmco0rxBXK620kjnwwAPNxx9/bCAyyQNp9+yzz9o8b731lp2WmMFGPBE/+ugjS3p9/fXX5sUXX7QeqaNGjbLlrLPOOubpp5+2XrPUddVVV5nx48fb+iAX3333XTvFLG2CMH3jjTfMDz/8UD6OiVckU9C+8MIL5qWXXjJz586109FOmzatUm9XvGNffvlls/7665s111zT3HzzzWbWrFk2/9VXX229aCHomNIYYo824F1LP1999VWLyx9//GFef/1127aoFylkM+2dOHGiAZNMZB9kGf2aNGmSeeyxx2xf6BcCvq5dYAJeeHfSFicuPxiQDnEKMRtHclMeHqT0iWNCG9988017zGgzmHPsqYNjDE7E33vvvaZ58+YWr7Fjx1qsqZ+0t99+2+ahXv/4uPa5LefCnnvuaTbddFOLGefNGWecYdZbbz3DOcF5d8ABB1gCmHYilP/OO++YJ5980kyePLnCcaXfTIPMMXj88cft+UWfiOd4Qf5C+tJ2pkjmfP3qq68sRpxPX3zxhT3XqIfz/K677jKdOnWyx4G42ihgw3nPsWzUqJE54YQTzP333284pniOP/HEExar2tj30KfFEQjE7OKYhJjCIMA9nY9u6tata7bffntzww03mLvvvtvee0aPHm1nK+CZGiRrBAIxm+HdOCTFI8Af8Z+k7o83hOx50rpSJ7so8K3U2TBA2swlavuUl/aHwkdKl5c64c/74dI5UlcG9bSQRgVPGGfTI5pYth8lkt9V/FaeLRfCEOlfUlfWBwozmODLztpx6WwXSPeRMuCA8CX5LVLfxoVJ9+Uy7bg0tm9L/YFOBj6w8cneudqnDl/iiNlZMrhWeqN0upRBRI6bP1BNviQZoATXNgamk7yDkvJz/OZ7ZWQids/x7D5MKrCS+LSObyXVpJ8sUi07j9Iksk5rn/qtUnlZE7Pyut1a5Fu2HpGLe0lOKHmHtUz9+glb79DxJV/nROwViphNws3zmDWTSlZTWz/Nqb2Ll/sf9btLFAuLx4SSh6pY9v+wjxCzhTiG8hxeSecRnrv/qzdTeELJ3+Zpu65cXPerHCcy8pRsSEtn47xlqbjGE7MR4tX1IdOW6Y59UJdSYnZbYeCeUWyflG7g46LwutJxUt/utogNu346YT7UukbKMxNZR3q61H8W8zHW6lJfdtCOXxbvB3tK3fsBRIb/wZKznab4nCUfAtMnZuf0b7K5CLU/M5FomdMaT9JfjQpEC9MIi3iTZ2t2xJ/1Vh3UpJXf+Xz6lVDf13jF+mUTZj1V1fFuQp4KUzgn2dDu6HS8aRCz1KeyX4tpc5VxpUz1e1xSn7KJZ+3daNusF2+Wx9vWkdlbluJ57/bfxZPe66NNCfuFQ4DrnPsY73srSPm/x/+CRtLm0pZSiFmU6/kKqbtfvqzw+tLlpeSnLDRIQCAgEIOAnqupELMMiD7yyCN2Gl+Is4033th6kjK1LAQa0w5D5CyzzDJ2y7SzEIUMqO6www7WnjxbbbWVOfnkky25A7mIHZ6xe+21lyXktthiC7Phhhva95kVV1zRbLvttrZePCc7dOhgzjzzTEsSMmgLuXjYYYfZKY432WQT065dOztFL1P1QhhCBO6///7l5VIHnq5dunQxr7zySsbRTYi6gw8+2NCG5ZZbzmy22WbmkksusaQrXsGHHnqoJWUhSmnXPvvsY0lF2kE6U/HikUp/wIt6IZ4hvqZPn24JL9IcJqeddloFMtU1jn5APPbs2dOSgjvuuKPF/ZBDDrHrruJlesEFF5jdd9/dtoEpf2kruHBcIKeZHpgBb9rFsejatav1+j3ppJNcNeVbiNNzzz3XHif6RBshQyFjhw4dasugzfSTcvBihazFw3n55Ze3hDrk5W233WYJY8qiXvKA0+DBgy0ZX15hWQBSFNKvWbNmFkcIUohQ8q222mq2b8OHDzf0G89cBvEhu08//XQ7zTHnCQrZDGFMOu3iYwHOL/rNudejRw9LrEIs07dVV13V7LfffrbuCRMmWByxhRjAmxZCkrI4bhC/O++8syV02a+NEojZ2nhU8+9TIGbzxy7kzA2BQMzmhlcW1ivHvBKGqIBARgT4AtoNKLJNGjTZRmk+EXhqWambaOv+sJMfEjBJDlCCXxf7UamMmIXgnCd15bynMF99xwmEst+2wRGjV7XvyoGwZLAhToYq0tm5rW+Hp6GPzbPaT2pTn0hZN/kFKQzB6upgO1taXxonDF472xkKJw2OvO7ZMbCdq7RVBlcP290zFFBVYjbN45uhmeknifhK9kqcULJAXqe3SvvK7gYRZYtPxwtZhifmU/+bJjpbYtZ8ULKCiMT3E8m3iSWvKv00pQ9RmS9msDsxioxsxyXaJxF81UnM5tPeuH4wdfIEO1BZDon2u+eMRVzZLs4jZgt1DNXebTK0mXWOr1O/bpJ+bu2eKLm9vMMFCGgd1IdjiUrrbdru3j5j2lwufURrt/6J9hvVYV3XjFpJzI5se57rH9ullJi9W113z5gvFcaLK04gE/joydny/I+KS3NbPmSKk1JFOhu2+0WMnvPSmXFiw0g6uzxzuV78copOzJr7Dqkjz8/JSWScpit+BcJt0XqvjZ5P8oDV1L5H0Skncwc3b5tUJmu1ah3Xa+V5eSPrmmKncPR9BvJQa7BmR+xmYXe9a5vbUmcW+SolaEVAf7nwzNaQU1bSImZp27wTG1X4yCANXPG4Tew30ywvmt64t2yu1/GeE2/b+FMzuPuKrs9sF61Tu/jxWnTONLpXeF++aApqPgJo/Kc/bbVfTiTMNe2ukRGRtLBbPQhw70IdOcsAAh+nNJQ2k7aQtpZCzrLtJ+UDFo7jQmkvKaQu+bkvU1aQgEBAIIKABu9SIWa/+eYbS77Vq1fPrq/KOp+QgBB0zpMOArJOnTrWCxECFWKsc+fOpmHDhnbdUKaBbdy4sSUF8SylTDwWl112WUvmQvRBvEGUUQ6EHIQinpLXXHONJf0g5SAjzzvvPEsCUzak5VFHHWVJtrXXXtt6leLJ6uLatGljyeDtttvOEq0QyJBwmQSCErJ09dVXt3kgVkeOHGk9XGkvJCMEMHGC3IDL3nvvbfuJxw/7G220kenfv78ljCEAWdf1p59+skQq7YYYBBOIQwhJiMco4Uc7sAFnvIcglK+77jqz7rrr2rVWmW75uOOOs169EMEQxcOGDbMej3j2kh/yEq9TpoPGcxRcaN+pp566GATg1qdPH4st+ENQsp7uLbfcYr1hIUppD8TvKqusYs8JPCxpA/uUC/lJW/Eybtq0qSVX8bxkbV72yc9Uwr7g2YpH9BprrGFxwpsaUgjMaDskLd6+Bx10kCVmWX8WvMAQwpjjefHFF5u2bdsaCGc8Y9nn2NN2cLv22mttOtMiQ/xi17p1a3PnnXdaT21woY2cm6w9CzHPuQVJj5DnGK1/y7kA7rVRAjFbG49q/n0KxGz+2IWcuSEQiNnc8MrCOhCzkffhsFs5AtNl4gZM3qzE/JkyWwYpR5bZ7q8txB9k6ffSTCfhqkp3dbHtLY1KZcTsEGXwy2gfLSCy73u0fOylNVX4v1JX1uVeWjS4kiK+9mzJ4wtrY7pyKLOtnxgT9glhPAmW82yGK+zKYouncZJAovu2O8cYMiDo2+wWY1NZ1NGRMjbNkKGqxGxaxzdDEwuTJGLrrUQCbEJJV79W2W0o/TPW/gk7AGbNVWZWHrOy2ye2rEUE4Mt+3YRle2esvchI31ZkbodYO0cssp1YMlFaqjZcJv3A2kPMLiKCM08nPKHkd9ljsxD7srbFrzHr1zmh5L+q6xvpH7a+Mo9Zhbey+75tNAyBPaFkpPRp2WZef7Zs3VuHifJMyVg+/ZlY8rzsnpL+kNGWdvnEbKGO4ZMlgxLbMaGkT3nf5C2n9mynduM1UzDR2rJfxxGzWnP1Hb/SfuM6NNS0wHhSlkvvMVue1Ht0u4Vx+cvjRrf5xdpgJ/tc1kxNIkUpW+vL/nHkqHZn9xm55d548ZbXl+Al22dUm5nyDj6v95j2F6o9vyXZq9yx5R1UIKkNip/u27lwLZnK+BH15xMp7xZnu74lbK9UvHumfRFj49LYQpJCGsTJmor03wEgHpzUUwBPW1fWCJcQs4XE+MOzLRgxW0aofi2S7XeINucxi+djPPGGx2ZDPhSrIJB2cfYibyvgOX9A/WPj7Ij7bkAD3v2s6M/JMmrDtj+d0LyBi3PbyohZ+qR+vCFS+DGFZyXVR7zs/uOvhzt7cNNNM9nbPP0bTVO+u1hLV+X/O5P93P71S127KyNm8SSe17/RRfMHNDxJ682+mqnc6FqzqeCaoc6FA9fE27tc7BrFZedMtJ3CpMJ7uNI5vxYnsgfUr3B/Zhpljnl5JZkDLynZXUtTM5uG1CIiAJmKQq4uL+W/Dl7pfAzKf6R1pa2ljpzlvOL4uWN5p8J8TMl/GO6zrjwFgwQEAgIgoOdjKsQsU79CguFBinfmRRddZK644gq7TickGN6xeM+uvPLK5ogjjrDekpBtTNULMcZUsxCKEKf169e3ZC7ELN6xkJaQih9++KH59NNPzcMPP2zraaU1ZfF6ZQriyy+/fDFiFk9WvBvxDMWrFE9NiD3qIY66IEDx2mW6X8g2piXOhphlkJgyIY4bNGhgiUamB8arE9LYJ2YhaukHU/4yLTHkJSQlxB/tgDhcYYUVrFcwUz7juQnhC7mMFyYkN7hCHEOk+sIUxxCFDzzwgCWywQiSEmIWvCEI8c4FK6YCxvOULQQl6+MyLTEeq47QhvBkal/WyY1bC9gRs3i/QpRDzDClL8qUthDRkLt4PUNA46XMNNMQo+BN38EIHJjSGrxpJ1Mi//Of/7THg+NEG33h/IGwhbjHAxjh/IFMpQ6OBX13Uxl/8MEHliDFIxqCnH7jQQuRi+cs0ye/9tprFjcIVerDixcs+vbtawlyiGqIW9qPVzEfBNB+1rnluHA8Of/c9JmQwZDeHD/O09oogZitjUc1/z4FYjZ/7ELO3BAIxGxueGVhnYkTCy/IAYHFENiQ/wyeVhigWcx60RSAeJXyJz5O+GOfJPxx7yRl4NXVeXSMcWXELB4ZLv9XMfmjUfTJ2fskKISEi2e7TjRjZP8fEXs/+VEvbbyfkBDe27Onbo6Dkygxy4BIkjCIwpfrrh+3xhhe6KV/rXDSIHVM1vKos7wyqGux6QTLLSuufZvPVMZpHV+vSYUPMgWwCC1IxvjpYieWjBJhV1GTbMeX/NO1WGVmR8w+WXJOYt1M6xutGzI1qf4n/je9tuofkGjHlLcTSsoH52mzMUOXVV17Mp2w14dLE8t4smQtZ+e2sq2MmL1T0/MyGFii+lcsIxPtxwKaCjiZhKS/T5acQRvL64KIHF8yJ7F9IlnLbV/ovJzs4sl0yp5Q8rbK5/5oxTxb0kRxTyWWTR6fmC3UMcxU7iKC+zW1k2O0k4+N60faW3nB/pJEUirty96j245Q+mGDx3THeydWMnnOnjCyWwVyKC1iFlLWb4yI0leT+7HFj0ff3R7CzooI5qOTbP+fvfMAs6Mq3/gCSpNmOqEkIYgoEFIhAUVQqooFCNlNSAViNhCadPiTCAiCIAoKSJUktCCilJDsbghRQMBQJQrSEpBUIIRenf/7m+xZzk5m5t67d+7d9p3n+facmfOd9p7ZuTPnne87qucBp0fcTolZBwH/mw3/n+6kF0MWXCtxv3mveHku6fKIL3UnE2L/9/NoT+eHSvv19PXy4pJTPf3SELNyGfvGpG3D/4lgSsW6kGJLjuq2A51Jcz8r4u0RrFp9EQl6Sxz5Jt3/LT2xa4O18rKJXU6M0+Nc6P5XZOfSiZ3PWD5xi0R80onZLiter+66twM03N+0uuN5SW1yfvnEzoc4/TSCM6xjYofj9eIEWRSGpUdtuasI1cQ9WZU30+mmEbPSe9cniLXqu7bI7ieS+r1iYqcRrl7iLHAV2bwqqT1/rl06UXdCpwsa9U1jS9SVVTGk/ooJ3SvdteiXTUlPU577f4r7n00palllQID/Ee67X5DwXrGRhA9Xukq2lPSS8C7i5NdKfyZhTv8jgaDn/W8diZGzAsGCIeAQ0G9QJsQsVqoQiF27dm2wTMVlLtaRWCNCKGIlC1mHlSjh3XffDV0c40K4f//+oQUpVpgQs5CLjpiFPIX0I+C6l/rYqxayE0KV/T0vuOCCNYhZdCCBCZBmuEXGYvOiiy4KyTbITghICD0CBB1WrPkQs+hDRmMFDFmItSakJqRklJiFIMbFMFagkLdYErsxsgcqRB/ELIQ2hCH7d1IGEhASkTYgQtnPlj76ATywPMbidN999w322GOPkMBlHhwxi0UrhCN4E+gDlq4QkLj1hTCFSHcB3CG04/aYdcQsxDJkOQGyDkIZghk30BCl4MqYHGkKkdmjR4+QvIf4ZY9aXE2jgy7j3HHHHcM5ZLy4dfYDBDLWqFgOY+lL4Jq67LLLwjbYF5ixOGIW0hSC13fH7PRph2vomWeeCa2CwR3csEyG0HbELG6YIbAhZrFk5hr8llwVc91BvI8YMSJ0o828EyCK6RtlIJ7bYjBiti3OatPHZMRs07GzkoUhYMRsYXjloW3ErHsQtjgvBPaSllssIYY4LTbwcv8VyQ8kP5X8XnK/5G2J3xbpwyXRkIuYnaMCrp5Plf5zDuErf6dPzMIC4f8k7vwHSrOYkBYOVqbTJ/YDCxMu75d+RkKaRQ6nTwxWLvjELEQyCx1p4XJlurpWKr2ep8yYXvbyqbsp4WIV8ttIq+N0T7cpxGxW85vWx8zzRGphARtPyhZ6XpacroMizPIjZrNy37uaLNytof17Kq5JHNesip85vbS4nvSLx6ZQYtbbSzauTRGd0xP7K3I6tsw9FT9MLDOzYpV+eMN7g0jXHRL17ql4W3k9ovUHtRWbavyvJpbzidlSzeHdFUMT249emzMrlkl3QnQcWR5jGZtEUjY6LzfGcnt889gZ/daw4G0OYnb8tAGNPiKQ5e/JjfrrWc6Onr4zH5g0hONnDNlAVrOfxOpP7/NQg6IS7ZyYdVBsqsTukjGS8yS3S56VuN8hF7+qc9Hg8oh5BkkLkERO/1hPEZLWnSeGqEgL/sdLmROzIuEYf2KQxeZ1SWRaoedXHNW9v2to6VFdf5hveZGVL0HYubIuTiNml03ownPVGoHxJrUrUvkcV0DjvjpJT3nXOD0/Xl795QOSyqhdPL+EIZ2Y7cRzV6OwrLrr+KR6l0/sNsZXLhZX3AcntVXoeSyVG/UthWBuXHeXj5ZXd7h5yaRt17g/+/XVp/kfdv9PPG9baHkI8JzjyFneI/hAYzNJF8kWkp6S3vXSSzH/u9x/mVfeVXj2X1eyjoR6wucmxRYMgXaNgN4hMiFmsRrEShGrx9NPPz0kQbfaaqvQ8hPXt5BWPjELofjAAw+EbmghBtlrFDIWwgvScurUqQ3ELMQne34SKDdv3ryQwINExZIyiZjFOhcXugSIWSw0sZjFJS7EIJa4m2++ebgvLHutQnDiSjdfYpa9YB0xi9VqEjELqYoLXDCCFIUwxMr2lltuCclaLIYdMQtZCIFJ3yCwsQjFJe/JJ58cWvpGXeSyDy8EIZadkKu4CIZAhDzEUta5MoYMdu6B6QPEK655sdilPebMBTBFP42YhUDHopdAGxCkzDflcCuNBTM4QsxCmmIRSzv0E9IUQoc+MB+4BMaKGXKda4T9gSF6/QAJzniYHz4AIEC0Uo42mE+fmMWSFbIXctoF+gkpD3GKS2dcNfNBwBlnnBHihstiCHTcY0PEOmIWspy5AyuuPeaN+aD/e++9dzg22qB+xg0JzpjbYjBiti3OatPHZMRs07GzkoUhYMRsYXjloW3EbLt++i988CyiucUS4u6FV9FQghf230niCFi/DT99eEPpzxO5iFl/IdWvK9/0AfVN0VdXhsWFXGGgFJw+sQssREAQu7yjXEZKzFfpfpnQhWu9vk/M/ielDpc1SAnXNvFBLkPxtyJ5X/XyCklO9ep5OkdBFmdcf5pCzGY1vzm6mW22yKyD8ya/omRY9Fj7zLreFUDM/jPD9sd57T+eWO+9FXldT5kSs7JwdX2Li9VWsjtpuVeOK8M5lXs91zilMyxRZ2bFn1LqviCxXGNitjRzuNpyd1ViH6LX3+rjWFIjaYyFnE8jVeOIy9HT+i4dd+Ogbfw20uooicWsSGI9gDVaaB51086HxfWXc3IvPNHvL+mR03ZaHKtvxKyDit9FLAsfk/guht3vSVwc99vt6x3qKk+IF+m80/eJ2bO9828llPVP029XT67fSL9cQzqNwFx8dCeePxKD9nr9R2PSLMYVbZx72phzK6q7DncN4a5WhOubhdQtC81GH6cljUsk6DLXTjReelTH7yS1uay6Cx5KwqA6Hk7Si7rzdWWIZe27OKnc4qM274FOGjHLXr3o+CGN8I0Ss8XiuqK62/eS+l/4+S6NnjNlzXt2IXXIynoprpJ9LGLSfOzj/j+Ic33oEFOFnSoDAvzGrS1ZR8K7zYYSrPQ7SbpL+N9grnvXxzsp9j0GzdVxTwn3cuqhPsSCIdBuEdCzY9HELKQmpBiuXr8t97aQqhCpo0ePDi09Ia8g0di7EyvV7373u8HcuXNDa1nc/EKwYsGIi1ssFiFmIcmcxSzErLOghJhlz1KsYbGehCTDzSyEG+ecS15Iwzhi1lnMYmmLy1l0IAZpj/1CIVHzJWax4oSYpU5IRYjde+65Zw2LWef2N0rM3nrrrQ3ELDr7779/SDRjwYll8bhx40K3whCuuDGGSMR1sQuQZLjMxeL00EMPDV06Q4Dffvvt4RjyIWYhdvfZZ5/QghZMIJf//Oc/hwQo7oijwVnMQsxiIUvAVfWkSZPCPtMPSFHIU8YACYqbYchY+ongIhiCE1fKWN6CP9cDJCtkJ3VBHvsBd9UQqBDaXEeENGIWt8VYa4MbJKkj3tnnl32NsXCmrQMPPDAAA8aFK2tcUztiFktmrL65NsEZPNmXFutw6mdeIMD/8pe/hP2hzxC2EO9cG20xGDHbFme16WMyYrbp2FnJwhAwYrYwvPLQNmK23T75N23gUXe+HZpWTejO1Cca/QUYl35NdV8lccfEh8e0l4uYfTdSh19fPmln0fFbr56FMf2InooSoC4ft1/+2LG0yRVYqPhY4vp7slfAJ2Yf8c6nJZ/x6vIJIsgV18bf0yrIkXezV8+DOXRP93SbQsxmNb85uplttkivsQUSX/EWpJBici/reqd0vhazizJr/+6Ksxrav6ciuV4Rfk4vLdYYsnNlPDu04khsTm0lE8kzww8VYssKu/+m4DeQQqp7VKLOvaHlfnzdsyr+L7FcY2I2Get48jT5GvLmMOz7vRWVsiZ+J7Ef8fXvEzugIk+Ombvn+rKanRNLUnpWp37+qOl9ZvnNlp2Y1b64fvukR07d6bt+H/30YdP6rGEJOGpav6d9nYa0EbPAye/o3yTu9you5jdzngTi1uW/qnQ0uDxi/0OlqB7HiyRO3ydmL/DOv4NijvBd5bt6MiVmcS8cTDpgvbT2RVA+XwiZlqa7fELHU/22cB2s+hPd5kbror8i93ZzdSQRs9rz9TmnE42XTOy0V7TehuPqzvc7/TRiFmLV6UVj9fGFhvoi5LSI3z7opxKzEQtY9HGTnFRnlJhFvxhccY2c1FbB56s7fkh/XAim7Lm+MJhTSD3s3+vKJ8T+hwv8n+Am10LLRID3E2QdyRclLCxsLOH9sJtkK0kvSe966an4BIl7dn9d6aESylKHI2iVtGAItD8EtHhXNDHLAiBWlhCJkHG44MVyEgtKLByxgMRlL4TcWmutFRKZWCZi3QlRRxksZXExiwUlpB8WnBCF5PsWsxBsWOZifQmZibUkBBpWmpC+Q4cODYmz0047LSRdIYcJWMxCwkGiQhpiPQoZi7Uu1qnbbLNNaL1L+xCz7H2bK7B3KWQqrospj4UpxCxjZCwQflgBu71uHTGLlSYE40033RTiQv/RgdQGJ8hm3BnTD3TBERIaPYgxP0AGQnSDB0QqfYCMBDNcRDNu5gWC1FnMOotY3DxD9N54443hHrS4QsbCmDbBBII0Gl5//fWQ/GaOIEoJkMFYzHIOF8oQ3r179w7xh+iE6IUcxYIVy2D2ImafWIh4iE+IePoHhswH5Kfrq2sfN8zXXnttOH/0GywhZv09ZtknFuIVchiLV3CkLdwUQ6pCQHO9YKEM0cq1wfWFG2hww00xFtQHHHBASHJDnDMHXDdcKyeddFI4LizAqQ8ClvawpCXwgQBtV1ZWrtF/N47WHhsx29pnMNv+GzGbLZ5WWzICRswmY9PEHCNm298jf1Ej/qZKu8VEYr5+LjTsogK4JvPrYbF0muQkyb4SXuYJvOx/JnG6R3AyEt7TscsfFsnj8J9e/m1K42KrEHH/JMd59bDgy+JBWmCh3fWL2A8rdODypvgZCenunj7lxnh6TSFmf+rV95HSuIHka/dV3vli3JP+2qvn30qnhdOV6bBoCjGb1fym9THzPJF2g1NIr/kixU7JW+6paLC6Vr35EbMzK2Yltl9I2+jeU9HXAaT03Sn15iI9wmo0hmyI2ZkVb7h+JcVq65bE/t5TcU5cOe2J2yexDPvozg1JowrN34BEvXsqXlTba5An+iFfS+cfSCznE7MlmkM35nDP23sqrlRfcLucTOq6vHsrfuvKliIedUPfH8q97+MNBGUCKevyx/3lqywOh6HcxKzcCy+sb7ohGj19wL6ub9GYsTUo1icSXTjnS8xO7fPxmDv23Cxa72HT+s6Mtu+OT5y675ecfr4ukgvZk9fVnUF8u+pwvxvEn0jmSi6S8AHZzhJ+0wh8OOJ01yDMvTx0ct2jFnn6PjEL0eDa4EOZNf63dc4P43Tg9J/2M/JNJxGYslhdkqsO3NEmEWnKO6UQWTZpC7BuFHBXKyvT3+VL0C6b2IF5C0PiuCZ0/NRZpzpdFy+b0On/ksYji9wrGvSqO01N0mPMTs+Pl03aqjfkcWw5kZTsc4t+ocRsofphG03EFXfTsf0PSeYuDxQ03xM7rmHdT99wt7ykuuPjye00tspecXKnhvsz5SNhko7d/wfPqBZaNgK8qyG8F2H9yv1vIwmEehfJlpKekt710kvxHpInJW6e+TB0UwnlHTlLnRYMgXaFgN4DMiFmWTSFmMKVMeQe1qfs7wk5BoEJofriiy+G7m+xasVlLa6AsVKE5MJKEZe9EGCQspCWEH6QqJB4WCi6gOtiCE+INVzVYp2LFS1k7PTp00PSDmKVetzetBB7lMGSFutbCElcKWNpi5XjL3/5y5CghEj0iWDXZlwMOVhbWxu6HKYv1113XQA5yNggimkT61EsPK+88srg448/DglDMMLaFAtbzuHmF52rr746AEdc4oILxCg4QhpCYoNjNLjytHm49mBlLBCtkJiQlmAFBrSJLgHS8re//W04NxBttDdjxoyQoGQusP4FDwjVaIA4pi/gOHv27DCbuX1Ze9/SBvuz4oIZ61cEq9IHH3wwJFH/+Mc/htcG+dQNsTpr1qyQ8OSagCBljrCUjgbawHIV62osXWnPzSFYQzaDD+UZP/qMiz1/IawhpyFWaRfrWOaOuZk8eXLYJ3ADsxtuuCEkmSGgaQPXxJRjrFiFs4cs/ecah0Bm7rAyZt64DvkgAWzbajBitq3ObNPGZcRs03CzUoUjYMRs4ZjlKOE4p3b1zGuDbToC7JnnXqKJsfpIC1iO3SL5pYTFHF64r5K4OljAnCJJevnu4OlS5ieSaMhFzPoLuA9HCxdw7Fu40JdtcpQ9VflunMR+eEgHLu8GPyMhvZunT7nveXrne3mPeOfTkl2VyUK260OV0vt5xx8ovcaCvs7lG1jgdHWvyFGoWGI2q/nN0c1ss4P5FRuK7PoslvCaWXFJU1sTqZcvMZtEfn4mQrHJ1ikiLSfHjmk1efeS+oebu0YheCC0rmg4J52kvgXat5V7UKOg9mpj25xZsbiRYsyB+ntSbNnV/f1I7fExSkMQWdlR/Uuzsn3cKUPQqu5PE+u/t+LSIJjCAmRDUHunJ+rTp8bEbBJORc0hnQlmV/QKHgotXyqCuXt+QWMerL6drPjpxP7dXVHXMJAME1PU/pgZg9zHOhVH3LRjV+0le6jIzKtF1L7vSMVoDGHoutGWiVntXXtXdOyfH/cb6TAgHj9j701Hys3y5/l9Aj89/q4DN3T6LZiY7a4+ut8X4uck27l+x8SX6pzTXxKT7/KID4rJ908t0oHT94lZfkPdeeK0/lDfzzz9jInZTgtpIC0kuZ8Vmfrx0hO7NpDzaXUk5S2f2LObrCh51qsIZgxdB8tQtXfi0tS9SDvc4+pLImYh/JZN6DAvSujVE5wfJBGCsuZs+MhMVqfHJuktndDlveUTt2j4yIj+SH8jWXf+LakMFriu34USrYXqF4MrFtQi7D+JHUd1x5+7MTQlZq7pmyu77NheXZcf1fnQcD/f6o7vx7apuWT8rkxM7P9/8D9noXUgsJa66cjZdZXm94Rnvk4Snt22lvDuBEFLvK2ED7rcB7jPKv0tiVnPCgQL7RMBLdxlQsyyAAhpA9mGG1dIWAg2yEkXIMtwScv+oeSx0IplJPuHQoJBhkEkYmkKqYY+ZBsCkeYC7aBHGdoiDWFIOchcl88xbmcJnKNO2qdPxLjQhUg74ogjQsIOQhKrzx6yPn3sscdCK0/c8MYJhAR1Qw66/lM//aRdLFVpk/FxDBlIYEyMk/YpS3B9dzqcoxwugsERy2EfR/L9gHtniEksN+kLupCv4EZ79NP1h3KuD8yVC7Tn5o3+0ReHndMhZkxgTP/B3QXq5PzLmhP6S90c0wf03JzQBuLaBi/67sZJv5MCY4MUhTy/4447wjrBjv6CgSO1adcFH0fw5NgFyjA3kP7UzXjpK1i5+sCBcuS5+rl+6S8YoEegzHnnnRfstddeDRa0rp22FDOPfAThLLoh0yH+IcP5KAIraObDQvtAwIjZ9jHPLWGURsxmPgtGzLbPx/6iRv2uSrsFSEjWtIAVhNOFJOGlnUVUd+4hpdPCD5TpdIkhd6MhFzHrE5cfqXDPaAWRYxay/yv5h+SPEhYOCD0kEMmuP7/mZEJgQWKhxOkS++EGHbi8lUrnIsKu9vTpw1YSF/zx5UvMUvYvEteHW5X+nXcMmV5MGKXCru5PlW5EPkUqLpaY9cdfzPxGulX6Q5Fcz8aSXDMr3g1qwwWshk5AyEn/75IFMXKBU1RefsQsrmqdpWM0FmHo6nNxSLjGt73A76v0vptYL+3MrFgoncqQ+LunYifpTpG8p/PfamirnMTs3RV7pfYXYvXuiqsk4yVnSXdpDv0r3TiIpfvvVP3Vc3qcSM+jhUFNqm6UmC3VHM6t2Eh9WS5ZrP5M0EcEoUVYOB4I2+j14o5nVtzpjz2rNPuvioD9dNTUna4de/Mg/95XEZKzCZazh08duKPrQ1smZkdP7XOJT65G0s+MntavCuvhcVN3/oYI7Zsj+Z8TsyJsHV7ELZiYrVL33O8L8b70NyXwsYTTXx6j5/KIm0rMcl369fwsph3/1HxPvzmI2YOSCLPlEzqf63eUtM4dI6vRR+JkxdGdIMrDAAG4tLrjKyIsn18xsWuVXll45gvDiuM6bZ7UpkhDnrXCkEbMri7fZRFEL/Uvm9D1VyGZHHEv7LfzxlFdhri62UfWz1sz3eUjrHdXTOheuXRih+NV98tr6niWn9UdG34rCyVaC9HPAleN5em4sbAnMBbODiPicK6qO8+Om+9l1R3O8nWXyYJWep8undj52jcmdm90fw7J2cS56dpwf/brq0/7z6MN5HeMnp1qeQjwP88z/zoSnh3Wl2Ad3UHCR6FcIz0lvvXsoTrmHZF76GeSKyQdJV+QUBd1NtxLlLZgCLRZBPS7mRkxm/myYQkrhACcLGvJTTbZJOjUqVPoehg3urgYxsLyqaeeCl0C42I3TiDg3L63JeymVe0hAImLxStWs1jA+iSrp1b2JIQBZCUukHHb7cjasnekDA0aMVsGkFtRE0bMtqLJauVdNWI28wk0YrbNPtmXbmA+ocdCbqOFGK9ZLC5fk7jFysvr83wi9SZPP5pkse8FiStP7FuoOH2ITaczxp304r28fPTu9PKiSb7u9tvE2pNFBRdmKOHawqp0e5cRiU/19Jy+rzI8kg+5mBS+ogzfuvXvEUWfmCyEmP2R6nF9e0dpFqzd8f6RNgo9/I5XF3V+PaWCYonZLOc3pZvZZ4no+3kiyXVPxUoRmGdKqiSTRZK9lqJ7lOud9PIjZmdXdFF9bybWObNipvLGqr4jJX9M1GOv1QUNLkMrsH6V/isp+vEuce+u2NsbQ5IlaPYWs3InrL7+s+D+OjLSj3FjLMtSNw5i1XtsJnW7dnyL2VLNYdRqd2bFMl2r92gc10j+kzgelfPHnkU63F/2hr6vOTJx1NS+H4owvH/UDf0u0z6yU0dN3fkDl9conrbTqimeNXJbJmZFTo9uNPYEojq3Tt+n/DlrwcTsSfxrebKd3+9I2vfeQBmeF6LBr+ugaGbkeJGOnX70eYTfX5e3SmmIiLjwQ510esRPxynlOpdEYMp178JcZd8+aouOshBdEUfUcW55dYeb2dN0+cQOwyDckvRoy1nH0uby6q6TfF2Rfi/JTe4tEKjq75N+np+GaHV9ThqXr59/usOTfv9wO5xuueuRromE4modCOHFR3ca6PpdCNFKmUL0s8BV1saTk3DTPC3BmnjFhK7fX01IJ++pK0L8SDdmYav9ZTu/1lCvXDuLjL1fx5fp2sBtdKwlMy6u5Tcy7YO9+9WG+x9pIO1duxa3eAQgURHmGHJ1PQmW+LwX8hEA73c9JNtInPXsTkrzTujmnffHQyRflEDyOoJWSQuGQNtFQEt97ZKYxcITy04s/SBocWV75plnhi6UsZ7E0pN9Uy+++OLQfS0ubH254oorQqvJzJdKrcJUBLBwxeUz7oKd1W1qgTJk4sp4/vz5obtq+teWgxGzbXl2Cx+bEbOFY2YlmoaAEbNNwy2llBGzbffRvmQj+6pq/ljiXp5ZVIQ89MOmOrhT4nSI95QQHpa48yxeRsuiw4s7+406PRc3+lIfRQVe3l3+n5Rel5ORwMKO0yHGAnWjiA7utu6V+HpRyxGIWCxAnQ79Hy6B0CVsLsFizuX7MfkusGDxgMTl84X4ZAkLD37YRQf+QjD6WKP6oanELIsdPhnr+gKeLIIUE8AD4trVOT6lMsgcp8ecRwMLOLM9GRdV0HFW80vVJ0tcezOVXp+TkQAR4HSID43k53UIoSki76lEosuRcWkxpFm9y1kaVX15EbOhbprFZVqbjfNOiA4W978aU7IL38blVxO1zUTMhjjMrthRuH1Q1DysHtNpa2ARDF1H9c7PoG6H0zZ+G6H1cRyehZ1rmENZP2+qviYT9mn13ttwj3ddPFsJ939ylztZSDx66s7H5yYUG7vjRX/0tL41fjttmZgdO33XrzcFo2iZUTfuDInZEEpEzGZx7+TDIfebQXx5Q6cbJ46O6KHLby2/v37w6zrIz4hJ+7/HUWIWEsGv6zEdbx2p49s65oMvX+/piE5eh0kEZj7ELA0sm9Dl4AZCLQcJmaS3fGI3MA5DMKX7hhB8Sbpp50XM7ubqSRpXWvm4PJF/n+FG2dXr4teP7rg9bovjyhRyTvj91NVJXAjRWoh+VrhCSqvPjxUyxqguJGww5fNnbEjcqE6ex43uzz6OSvNs+r7E/Y80fHhWr5fFPaS+KotKjAD32rUlkLPrSlh04F2ro6SbhPtjLwnkLNJTMlTyosTN/5+V7iGhDuqiTsSCIdAmEdCiXbskZlmshGTC6hKXtEuWLAlj5zLYuebFLW6c4NrW7dmasvBpWRkjwLxAyEKctxTLVK4jrhvcHpNuy8GI2bY8u4WPzYjZwjGzEk1DwIjZpuGWUsqI2Tb5VF/6Qf1aTbiXZuK3JZBjUyS/kyyR+Pm/1bELk5Xw8yA3L5CMkbCQf7/kQ4mv49JX6Xw0PKITLp/4Y8m7kmESF3j5f0/i672q4z9IzpCwsPuGxM//l447SKLhHJ3w9UhD1kYXW6M60Xr66QSLxL7eMzq+QvJzyUzJRxI//zYdR0NTiVnq+ZXEr580c5FFuEeVuLqnplR4uqcH5tGAta2rh5g+R0OW8ztDlfvtsYgUDbvrhK9zVlQh32MRa31EhH3UROLuMxGKI/y2CiFmKSf9m5vYNm6JH5dL4i/57bu0yNmfFVRvMxKzIQ64Ek4jHXPlzayYrR/a2MVCYdFfWH1SVP2ufc9itgHrDOdQlrFnN6mfMytmxIy/Vn10/yf/c/3NNz5x6r5fkgvjZVECMdfxqKl93htzY9++fjttmZhlnLIg/kUuXNLyZXX7Gnv3NsJsWt974sqIsF3o67GXb5ze6nN9x/i6Smdx7/yy6nlT4q4t4nkSSMKfSG6QLJT4+X66u/L84OcVQ8xS518kfn3080bJuRI+TvA/7HJ6zULMqi8VIi9vyJNIC6J6soR9NDh+SMNLhPZyPSmqk9dxdRfwaQhZEbNyqXxpQ6WRxLLqruPz6lsSYV3d4e7o/a5UxGxWuALBkkndvq5xx1qx5sIDopu9Yx2U7EWsc8tylVszf829fF2d9TFeJ9z/BjHPd37I4h7i12fp0iLAcxGyjuSLEj523EjCfbyLZEtJT8k2kt71MeT7byS8z3ENvCXh/k556nEErZIWDIG2hYB+W9otMauxWzAEDIECEDBitgCw2oGqT8yefvrpjfadbgfDtyGWEQE+yhk9enSw4YYbBrvvvnvoNv7mm28OvV5MmzYt4Fr096IvY9daa1MNaypt66nWRlNqBLhwIFtZbPcXUOLSLM7zMu0C6fmSON3oOaytWGR15yF8eSn3wwk6cPl+/AtfSWkWc/6ToOuXI71QwmJBUhinjChp6tfxkvJZFHTn3kmo6Ps6v9jTc/px8TTpxZFg53vlH1G6kIDrsGhbXyukghTdCV7d4JEUsiBmqTur+S0rMUvHRYaxz2n6XqSOmPs8hpQdRXk/6FzeFrOUC1bvJ/prtV+YhevMikfVbxbWYkOApejMiuNU79t5EX3NTMwyCJHMo9XXXHvIRl0xQ6pfiAvnWCDqTwqLwZIX8sLi8zmOthUIcxYvG4Us51D139aEPs7XPrRYyUcD9353f+G3oqAwdlqf/nJd/HYy6bempaxI2c8Om9bn4GhDbZ2YnTFj6DqHTdtpdiFYNehO6/vmmOkD+C1oFEpkMct92l0TxGc1ajT/g4Mi9fh1+ml+e1nQ/8TTP0ppP/j61JsWFinT6UctZim3keReT8fp+jEfZN3u6TytdMEhicDM12KWBkNyTXuqyhLykzUJtGS3vnLj+9Abk7Zt9NHSUhGshdSBroi9h3GH6w8+aVxh3bjKTSJL68+HY6nufGag/wm/3mh6xcROI7AAzVVfo3zan9j1fLn9ZZ4bhVIRs1nh6jq7+KjN9xDuzzQaVy5MtYcse/q6OohXHNW9v66DtwuqR+Qultp+PTHpE3XO/b88GZOf1T0kpmo7VSIEIGYRCFUsX9eT8MyAd6VOks0lW0t6SXrXC+nvSOZL3PXwkNJ9JNTB/7erV0kLhkDbQEArikbMttZlVeu3IVBmBIyYLTPgLbw5n5g97rjjgkWLFgWvv/66iWGQ+TWAl4Rhw4YFG2ywgRGz2dwXjJhtG4/xzTaK/dTyAknU8pOXaAhHiCNenKOBl/ELJHGWsXwh/aiEL/Mpu7fEvZQT7yKJhvN04m2Jr8fCZzSwEHCJZLnE13Xp13X+VMkai246Fw39deLnEshjyvFFd43kbAmElU9M/lfHSaGDMm6QUN71w4//qvNDJUmB9pz+A0lKKedZFHblCyV2U6qt2EKZkDGu7kEJyid7Ok/F6HzFy6curpukkMX83qjKXZ+5ruOuBa5Bp0PcVHJBRVcHEVtflGXlySLG3slBjEGg3i5L2yGurB8rr29K+eG+rp9WmZ0kf00pu5oknFmxUG2fBBnol09KB3MqtpD+rSIlky1GZ1Y8rXF/zdUhHP4vth8zKz4O6kIXeE41jFX/n2P1tR9qI8U8DtTPTVTfxYo/Tqjzc7KUdmdVbJtHtav7yf67q/dpfT+27pkV76rdEYnjhzyvCfdmi21SdWYyh+rD7pIZkuQ5W00eL5JOtYRF1rgwUyfd/wn3goLDpOkHbDJyev/jRk7r82IDkRizhyqErEjcG4+Y2r/hOvIbGzV951Pjyqvcx+Nn7M3vUUM4fOrAHeN0OTd62s6HNigqIRL4tljdaX3/5euRPmxa/2/G6qrew6btuE9UX8Tog3H6o6b1mRPV5RhyduTUvmOE1Utx5aLnNPYlo6f2Pa36xp1iP7DId2yF4KVuZnnv3Ev1PSFx15gf8xt/ucR9YMVvo8ufqbQfeOZweQf6GTHpFzzdKMHr1L+gxAmS/3q6rv5FOgfBBeHgzj2mdMFBpNhtcaTY8uqOa1x7uSpfclS3HZZP6Dw3rj7/nIjMl3DhC6EbV+fS6q6Dwz1lcxC96vvLWK0GnltcV18SMQvhvHRC1x2XVHd83O+Tn1b/nopzX+zqjsYQrKr3Qrk2/sivJy4tMvP2pUd37RWtwx3Tt7hynPMtTZuinwWurl1i9t1lHnMRq5DckhlJmELOr6judNyy6g4vJo2d88LuMwjmxRN7xN6f/b4p/Q+6WC8830ZDlveQaN12XFoE1lL1a0sgVb8o2UCysYT3IDw2xFnP9tT5MyR8ZMN18ZHkXAnPoNRDfdSLWDAEWj0CWt8zYjabRU6rxRBo8wgYMdvmp7igATpidu211w569uwZ7LXXXsHee+9tYhiU5BrYfPPNA641s5gt6N80SZl3IguGQNEIQIjtKoGIPUDSU5LPSzIv5P0kh0hYrORL6HUlTQmU6yth0YaXe17W00IXZX5bMkayr2RbCQsFWYUpqsgtLkFe5xN6SOn7EnCkb1tJShlY1HhN4vpZnXFjd3h1X5lx3bmqK/X80v7fJGA3loMsQj1Bu4OIyqEiCCeLbLte8hsdn6j4UO0BunUW7STVIavRLmp3T7U3UXKZ5FrJ2SLfjpR8G0vYpLJp5yHv1H9I45EiHs+VnK56D1NbX00r11x5YX9xM80+vLhlnlnxB8kvQqvaWRW75LKQTet3aE1M3TMrxqn+8xQfGWIzd08InaJDVnMoEryr+rWP5mmi4l9JuBbPkYwVJt/kWs2js9+RDv8ji/LQTVSZEkxZ+/Abdu4HMTpyet8zFF8zevrOv2UP2tHT+v3g8Bv7ce+0IAT0tLfWkTfssp1wGj5qar/zhdMVImtvGDV150tF3J7MeeG1x6SZByQR6uXAMat7J88ZPSVcZ6Mle0tY4G8Jgf8Pnm/4sIpnHD5WatFhyaRtO6+o7vYtkXbVuAJeOrHztdr/9ewV1R2OWDKx017BlCm5nqvC8S2d0LvL69Vd96aeZdXdLl42oeP1ImzP0Z60Y96c+OVvQAwmAZFGzLoyEKSvH7X5j0UInrV8QsdTaWvlcT03c/mFxhDEqnOn5RM7DFs2sdsU+otlrNKjsISNs5AttI0s9IvBNa599p3FvbHGdwhYhuOe0PnXkLaQyW9M2sJ92BBXvOEc18XyCd37UUbXzBlLqztfIxfMv2UPWhG3P5CVbr73Z94B+L1AIODSnoGzuoeoGQtlRIB7NsK9hPsAv0N86MEHUmnWs7zbzZK46+NZpXlP4j67joT6qNeCIdCqEdAznBGzScuWdt4QMAQaIWDEbCM42v3BU089FRx00EHhc9Jaa60VrLPOOiaGQcmuAT1shdfakCFDzJVx8XcfI2Zb9dO7db6cCPDC/5zkYclUyc6SpMBCwZMSt4Dw5yTFZj4Pie76+J7SLIxkGQaoMlf/KqUh8NtKYI7BjPF9ra0MysZhCGSMAJYu/I/cmnG9Vl3rRcDuna137kre83yI2ZJ3whpoLgQuVcPumfGqlE7YPSQFnFaS5ZOzfFjLggQf6+LBgQ8r+ZClh2QbSe/6uKfiIyVLJVwneOK4RtJNAsnryFkjaAWGhdaJgNb2mo2Y/fTTT4Nnn302uPzyy4PJkycH119/ffDKK6+Ey40ffPBBUFtbG9TU1ATXXXddcOmllwYvvPBC8UuRVoMhYAg0GQEjZpsMXZssuHTp0uCmm24KzjzzzEZy4oknBiNHjgx+9KMfBT/+8Y9NIhgccMABQa9evYLu3buHlqUHH3ywYRTBKO26OeaYY4JrrrkmvPZuvPHGwPaYbdLthfcgC4aAIZAnAnyh7RaN6lLKnOnpoT8xRbe5slgIeUDixvOHEnXE329vbInaKHe1uFBz43pLaVsEKvcMWHutAYEfqJPu44XjW0OHrY8lR8DunSWHuHU3YMRs656/InrPC+kbEp5JP5VAyMUFu4fEodI6z/HsjGDxGmc9C+GK1XQvCeQsQnoHCR/IQsxyvayUnCLh40/qMoJWIFhonQhoOa9ZiNmPPvoomDNnTuj6cvDgwcEPf/jDYMCAAQEL1AsWLAhWrFgR7ie3ww47BF//+tdD14V1dXVNWn20QoaAIZANAkbMZoNjW6mFj2veeuutYNmyZY3kxRdfDO6+++7gqquuCq6++moTDwM+NOJDpJ133jno3bt3AInNOcMp/+sEvKZPnx5Ayhox2+S7iRGzrfOx3XrdTAjMULuOyCQ+S7KJ1xf+oY6S4ILN6bEv0paSlhBGqBMHScZJ7pe4PhLjQq4UYYgqde28oDSWDq094JaSMTHPYGnBEDAE1kTgGZ3i/wQvAx3XzLYz7RABu3e2w0kvZMhGzBaCVpvSPVWjcc+Kf0gZmd1DUsBphVmOnIVMhVTlHWF9CQS8s57trnQPyTYSyFninpIfS56SuOvmRaUPlVCHT9Dq0IIh0DoQ0JJesxCzWFphTdWvX79g9uzZwZNPPhlazLJQjTUM+exXyJ5yWMbMnTs3XPhv8hKkFTQEDIGiETBitmgI20UFkLXz5s0LyTMsak1WY3DzzTcHt9xyS3DssccGW2+9ddChQ4dg7NixIU7kGU75XysQsk7MYrZJtxUjZlvHY7r1soUgsL36sUriFgGI+bJ/Ub18EskjvyURd/TT77tL/17nSxmuUOWurUmlbKhMdbOQ9DvJwDK1Z80YAq0RgW+p0+dJ2sLHGK0R/5bYZ7t3tsRZaUF9MmK2BU1G+brChzt4H+E5cbmksyQp2D0kCZnWfZ55RSBonfUsFrB8/Mr14axneyrtE7Q9dHycZInEvWfgDYiPQqnHCFqBYKH1IKDlvLITs5999lnwxBNPBFtuuWVw6qmnBljPEhYvXhxUVlaGlkRYXO27776hpeyqVasCLLMghSwYAoZA8yFgxGzzYd+aWl65cmVw//33hy5mHXlm8Y0h8cqHRnyUtP7664d7sfIB0mWXXRZAzBpGn5OthWBhxGyT7g5GzLaeR3XraQtB4AD1g4UjtwCQFL8tncoW0mfXjb/F9Ps+nSs1ccKX7y/Vt71CsW9lrEMLhoAhYAgYAoaAIdDeETBitl1eAZdo1O5Z+uB2iYAN2iHgyFkIVWc9+yWlN5NA2GM9u7Wkl6R3vZDeTnKRxG2f8D+lb5SQB0EL4UvdiAVDoMUioOW8shOzkKwPPvhgaC2Eq0sXIGBPOeWUoGfPnsG//vWvkJhlAduCIWAItAwEjJhtGfPQ0nvhiFnczZoV6OdWoLfeemtwwQUXBLjv32CDDYINN9wwwF0/e/RiSWtYfY5VIVhwnf3zn/8MPvzww5b+r9GS+mfEbIt9MreOtWQE+MfB8rNG8h/Jh5KPJYsk8yTHStK++Fd2s4TT1CoEKZa9D0nOkuAqrBxhTzXCQgmLbydILBgChoAhYAgYAoaAIdCAgBGzDVC0l8SmGijbQvBsOL29DNrGmYqAI1Cd9ey60ua9a2NJB0lXCVvE9JQ461lIWo7xZMO2M+59A6L2XAnXmRG0AsFCy0ZAq4RlJ2axmJ0/f37QrVu3cK89t1L5xhtvBIcddliw4447Bi+99FJIzA4dOtRlW2wIGALNjIARs808Aa2kebwgLFmyJMDzgcnnGPC7dvHFF4du+nHjv8suuwS77bZbcMYZZwTPPvusYdXE6+WFF14I3nzzzYBnCwt5I2DEbMt+PLfetRIE3CJCK+lus30xzhfwCHhZMAQMAUPAEDAEDAFDoAEBI2YboGhPCfds2J7GbGPNjQDvCpCzXB9Yz64nwXoWkrWTZHPJVpKekt71AlHL8f4SPkB1lti4Oj5SAslLfdRL/YgFQ6DFIKAlvLITsywbvvbaa8E+++wT9O3bN7SeZREfa6IePXoERxxxROjW+Dvf+U5wyCGH5L3KaIqGgCFQWgSMmC0tvm2pdkgyvCOYfI4BHx+ddtppQVVVVTBy5MhQRowYEUycODGAXDSsPseqUCy4N1koCAEjZlvMk7h1xBAwBAwBQ8AQMAQMAUPAEGinCCyf2GHYsurOv4jK8uqubWF/+nY6qzZsQ6DJCDjy1BG0znqWLVLw+JPk3hiCtofkcAmeghxB+6TS+0ogen2CVocWDIHmR0DLeM1CzOJy8K677gothiBn995775Ck/d73vhc8/vjjwdKlS4P99tsvGDZsWEErjaZsCBgCpUPAiNnSYWs1t30EHnvssWDUqFGh6+KTTjopOOaYY4Kf//zn4d7q9957b9sHwEbYkhAwYrb5H8GtB4aAIWAIGAKGgCFgCBgChoAhYAgYAoaAIRBBwBG0kKm4JMZ6dkPJJhLcG3eRbCGBjIWU7V0vverjKYrfkjiC9i6l+0ioywhagWChZSCgVcJmIWZZnXznnXeCv//978Hll18eXHjhhcG0adOCf//738HHH38cfPDBB0FdXV0wb968lrSQaX0xBNo1AkbMtuvpt8EXgQD/O1OnTg0OPfTQYMaMGcGUKVOCE044IbjzzjtDF/64OMYFtAVDoEwIGDHbMh7DrReGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCEQgwAErbOe9d0bQ9B2lHSTxO0/C0G7k+QayScSCNrPJDdKtpcYQSsQLDQ/AloAbDZilsVHFqtXrVoVvP7668G7777baD0Sq1rEgiFgCLQMBIyYbRnzYL1ofQisXLkyOP3000NX/c8880xIzB577LHBggULgpNPPjmYMGFCsHDhwtY3MOtxa0XAiNnmfwS3HhgChoAhYAgYAoaAIWAIGAKGgCFgCBgChkAKAs56FoIWQhWCdn0J7o03k+Taf/Zb0rlb8j8JBC1E7XUSrGwdQeva0CkLhkD5ENCKYrMSs611RdP6bQi0RwSMmG2Ps25jzgKB+fPnh5ax559/fvDmm28GZ511VjBp0qTg1VdfDa6++upg6NChgbkzzgJpqyNPBIyYLd+jtrVkCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgCRSDgyFNH0Ba6/+z+artW4twbf6T05ZKtJRC01OvaUNKCIVB6BLSAZ8RsnquYpmYItHcEjJht71eAjb8pCPB/gxvjQw45JLjjjjtC7xBnnnlmSMz+97//DR588MFwn9mLLrrI3Bk3BWAr0xQEjJgt/SO2tWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAIZIuDI0+j+sxurDX//WQjXXhK3/+w2SveQHCj5q8QRtO8rfYmku8QIWoFgoXwIaDXPiNmmLGlaGUOgHSJgxGw7nHQbctEIODfGhx9+ePDcc8+tQcwuX748OPHEE0N3xi+//HLR7VkFhkAeCBgxW75HbWvJEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEMkQAghYrVwhat//shkq7/We7Kr2FBDIWUtYRtJC1nDtY8rDEEbTvKP0LSWeJEbQCwULpEdDinRGzeaxgmkrTEYDMK2cod3vlHFtzt2XEbHPPgLXfGhF47LHHQjfGP//5z4P33ntvDWL2008/DX7/+9+bO+PWOLmtt89GzJb+EdtaMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgRIh4KxnfYKW/We/JNlU0lHSTbKlpKckStBiVTtc8rjEEbRvKX225MsSSF/qdu0oacEQyA4BrSlmSsx++OGHARY/EDjvv/9+cPvttweXXXZZsGDBgmZbvmTRm3383nrrrWbrQ6ENv/POO8H06dODCy+8MLj88suD119/PWcVYP/KK6+E2LOH4bXXXhs8+uijOcsVokAbtbW1Afsk/u53vwueffbZxOKLFy8Orr/++tBN50cffRQ8+eSTwdlnnx2ccsopwZVXXhnceOON4bwkVpBnBtcaeC1ZsiQc+wsvvBDW/69//SvPGkwtXwSMmM0XKdMzBFYjwP+M78aYs++++27guzLmnHNnfPHFF5s7YwCxUGoEjJjN7lHaajIEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIFmQsARp5CoWLuy/ywE7UaSzSSdJLkI2jHSeUbiCNrXlT5dggWuEbQCwUL2CGjlLxNiFvLzmWeeCY499tiQeGNFccWKFcGPfvSjYPvttw8Xpku9yhhX/6pVq0JLpEMPPTQkBuN0WuK51157LfjmN78Z9OvXLzjmmGMC9iFMC2+//Xbw61//OjjppJNCchJynPKQulkGrL1uuOGG4IADDgjnFeI9LtCfP/zhD8GPf/zjkJRfuHBhMH78+GC77bYLRowYEYwbNy7Ya6+9gr/97W9xxQs6B+l+zjnnBL/61a/Ccoz9pz/9aXDCCSeE1mkFVWbKqQgYMZsKj2UaAmsggBvj0047LXBujFGII2bNnfEa0NmJ0iJgxGz2j9RWoyFgCBgChkAuBL4xqnbrXYfNHRyVIWNmb++XzVfPL5NleuiMYJ3BlfftGu0nx/uOnI0VhgVDoFkQ2GXYrJ3jrstvHjZr82bpkDVqCBgChoAhYAi0HAQcQQuR6ghaFj98gpbfy60kPSVxFrTjdf45iSNolyp9kgQLXHNxLBAsZIeA1v0yIWZZaMaCskuXLkFVVVW4nIgV429/+9vg1FNPDf7+97/HLjFiSekHjj/77DP/VEP6448/DpCkQFk/HxLpP//5T7DDDjsEW265ZfDII4+sUTRaZg0FnUAH4tkF+se5tJCW/8knn+Qsj+Vrnz59glGjRgXPP/98gKWqH6jfx4lF/YMOOigkwtFjPu69995Yi9ZoWcbm4+a3Ex0rx1jC3nXXXUG3bt2CadOm+ephGtyfeuqpkJQ944wzgg8++CAkxb/1rW8Fw4cPD9NYUX/9618P7rvvvkbl0/pCvWBHX0m7QFv77rtvSMRyDkvtGTNmBHvssUeIgdOzuHgEwP2vf/1rsMUWWwSdO3cOjjrqqOC2224LrZ+x8L777rvzsu4uvidWgyHQOhCYP39+IzfG9DqOmOXed9VVVwWHHHKI3bdax9S29l4aMZvdo7TVZAgYAlkiMLiy5gcDh9f+amBl7WX9h9eNmDIl4Mt3C20EgYFVNfcMqKwJojKwsmahP8R89fwyWaZ3G3b/oGgf3XH/4TVjsmzL6jIE8kWA+6G7DqPxoKq6q6P12P00iogdGwKGgCFgCLQTBCBoeYdIImjZRzaNoO2h/EmSlyWOoH1D6XMlWN4aQSsQLBSPgFYWiyZmIcruv//+YMiQIcE666wTfPWrXw0tGLFkvOiii4Lq6urQMhJXwuyxd9ZZZwU333xzaEGJ9eQll1wSsAffz372s5DUPfHEExuIXIjAl2UBSTkI39GjR4dkL0QkAdIPkvDoo48OF7RHjhwZ/PKXvwxd+tIedW2wwQbBhhtuGIwdOzaoqakJy9xxxx3BxIkTwz39qJMyixYtCglQXPVieYor4AsuuCBs9/jjjw/mzJkT/OUvfwmtPSlzxRVXBFiGYkWKq0pc9N50002hi8phw4aFxyzKEyAUceULUVlZWRnWiSvLJMIaYrZv374NZCOujOkLVqgnn3xySMJiSQvutE9fttpqq2CbbbYJsYLMBbO6urqwj7gOZtGfc0OHDg2OO+64cE7++Mc/BmAGAcwYHAHMuCDaGOfBBx8cWnw98cQT4ViYk3/+858hOYdONFD2mmuuCQYNGhS2gbXshAkTgs033zzo379/cN5554VzDmE+d+7ckGBmjrH4Peyww0LyluvmxRdfDKvm+qK9yZMnB+BK/yH7H3rooWDp0qXheeoeMGBAaB3NNYH1Nta6WOm6MUX7aceFI2DEbOGYWYn2iwD3SjwMcC/605/+1ABEHDFL5gMPPBDe43Bhb/etBrgsURoEjJgt/hHaajAEDIGsERAZOzVKNvSrqp07fvz8L2bdltXXPAjkS7jmq1eqURgxWypkrd5iEEgjZvVxwzV+3XY/9dGwtCFgCBgChkA7RSBK0K4nHFgM2VjCHrK5CNpe0jlB8oLEEbTvK32FZDsJBK25ORYIFpqGgNb7iiZmIcKwnNxkk03Ca/RLX/pSsPvuu4fkGKRe7969Q4s6LC132mmnoFOnTqFLW1wcUwbrO0g8SEXIxQ4dOgQ/+MEPQss7XPpCHEK8odejR4+gZ8+eoctkLHIhUamHclhkfuUrXwnTP/nJTwL2GsWd79prrx0K/YD8mzVrVrDtttsGHTt2DL72ta8F3bt3D+uFWKVOCNAvf/nLQa9evUKSmbo322yz0PIWK0/c8dJv2mWf1DfeeCMkMOk3bdA/rEmpf//99w8JRkjq7373u2Fbu+22W0gikr/nnnsG//73v9dYdnXELIQw4aWXXgotaCG9saiCcATLffbZJ8xj8Z+2yWeMjz/+eKiPe19IbFwPM2YIAkhSSFFI0r333jskzrE4hQiG6IU0cBatEOeQ1GBLHWAK2YCVKvMRR8xCsh5xxBHB97///XBfX8aOa2Fw5Lr4zW9+E/aRPtAeY2O+aJ/4yCOPDPuG609cOLNXLNfR4MGDQ1J60qRJ4fWCe+qnn346nK+tt946zOc6hNBgzFP04sLHAmn74K4BvJ1IRcCI2VR4LNMQaIQA/y/s843Ldz78cSGJmOUDHD4a4oOaNK8Lrh6LDYEiEDBitmmPzVbKEDAESoVA/xF134+Ssg3Hw2cfU6p2rd7yIpAv4ZqvXql6b8RsqZC1eotBIF9i1u6nxaBsZQ0BQ8AQMATaGAKQsz5BywefuQja3tJBcHUMOdtDcoTkMYkjaD9V+hbJQIkRtALBQuEIaFGvaGIWou65554LCdQvfvGLIYmHJSTkIoQfhOV1110XkmyQr1iwQrZCkLIH7VprrRUSo5CLV199dQCxC9GGhSYL2pCiO+64Y+jekUVriFFISNzgQjx+4QtfCAYOHBhaJmFRiiUse6uyxy1EHe1Tx6WXXhqSoFjYYpl57rnnBvQT61HqgHzEypbz9AliFsterD8hYuk35WbOnBkSrvQTkpDFdEhCrIUhS3HtiuUspC9tX3zxxaEbZfS7du0aYA2FpSrtnn322SHZGV1cjRKzWI/SH/q4YMGCEG/qoT4sebGQhaSFCMaKFLIXrM8///zwmP1cIZL/8Y9/hHkQpZDP9A1iFLfHEKeQphCrkLZYpuIKGnIUF7UQu7/4xS9yErNYBtNP9huGmMDiFQIdYhW8sKDleoCY5fyf//znsG9YUtMXiFTIcfqL1TL1QfQyt46oxWIWrNmjdt68ecE3vvGNkHBmLmgTd8aQ5swB820hGwTA1lwZZ4Ol1dI+EOBexAc/eE1wIYmY5bcUXfL5X7NgCJQQASNmC39kthKGgCFQSgQGVNad10DEVkZc3VbV/KmUbVvd5UMgX8I1X71S9dyI2VIha/UWg0C+xKzdT4tB2cpmiMDWqutb9bJDpN60vIiqHRoChoAhkAkCSQTthqo9zoIWQjZuD9qhOj9H4gha4hrJPhIjaAWChfwR0KJf0cQsC4erVq0KCc31118/dNMLGbds2bIGYvb6668PsH6FUMViFmKSMrinhcyFwGVBGtIRS0wsZCHdIOsgPLGEhYzDgrSHrGYhWnF3C1ELYQpx2q9fv+B73/te4Fz8shCOZSd1YcH68MMPh3vFLlmyJLj11ltD97wQrRCEtLHrrrsGK1euDN39rrvuuqGFK26C6RN71Lp+o4N1Le2OGTMmJGZxTwzxyp6bWGxCCkNMOhIaopQxUC9Wt1iqUpZ+0EY0RIlZLFUhpBkbwZGd7PN55513hhaiBx54YEh0s6APUQ7WEKkQtd/+9rdDC1b2MaQ9iGswgahFn/ohXs8555zgwQcfDF0/Y8WMi2jGhKtjLHyxXKUOLFXjLGapC3ecELsQ3C5gwYt1MHNI+7hNhmyH5IZch2TFNTEBcoI07YMhrpFx+QxBjrtrLGmp3+1RC3H7ne98p8HtM3Uw95DN6ED2W8gGAebXiNlssLRa2i8CScRs+0XERt4MCBgxm/+jsmkaAoZAORAYVDnnnCRitn9VzZ3l6IO1UXoE8iVc89UrVY+NmC0VslZvMQjkS8za/bQYlK1shghcoroccTEvUm9aXkTVDg0BQ8AQyBSBfAjaLmqxu2QrSU+Js6AlhrDl4xKI2Nsln0jcve4fSg+TYJW7joR9bl17SlowBBojoMXATIhZSFX2L4WYHT58eLjGmETMYuWJC1vKYNG53nrrhfvqQfpgoQnxCpmKJST7qeKKGBJwv/32CwUSDre6EHW0gdUnFpqQlLRPWfKxIsX6ElfFjoTEegnL3D59+oREKXoQmljMQsy++eabjcbh+oR7YvoNKQWhDGEM6Tpae7BipQkxu/HGGwenn356OHbIRPBgbJCZkJEQqKSpi7IQnbQJuRwNccQsboohsgkQj1gMM2b2vQUHXAdjgUyf44hZ+kigL+w5Cy64CSY44hdilrmhf1jgQoxi5UoMQYuL41zELPOGW2JIYRcgZvfYY48GYpY5gJjFmhWcINUhwF3AWho31Lg2Zn9ZXCpjFX3QQQeFlrG4ZIZQx1oYYhaL4BNOOMEVD/vIfsK0AQltIRsEjJjNBkerpX0jYMRs+57/FjJ6I2YbPw7bkSFgCDQ3AgOH11UmEbMDh9ecXo7+6Qa9lkjg+9WPR6IyqKpuYjn60NbbyJdwzVevVHgZMVsqZK3eYhDIl5htCffTYsZpZdsMAmnka1pemwHABmIIGAItGgFHmEKeQqI6F8dY0G4i6SBhD1qfoI1a0PZQ/mDJtZL3JI6gfV5p3h2+JDGCViBYiEdA75+ZErMQkbjAZX88LDWdK2PfYhaCE/LOEbOQqZCGjlB0xCwWs+yXCmmK9SQWr1hj4oYXAhSrSqwiIUkvueSSACLuvPPOCwnPLl26hO53IRwhIGmTsljLQmButNFGIdn42GOPhe57sZjF1a5PzGJNGyVm6VMSMYs1LHVD1GIdzF6wWNFWV1eHpCPuec8888zQfTJWnAMGDAj7wXiiIYmYZd9UQi5iFlI6ajFbVVUVlo0Ss4zRJ2afeeaZkPDFOvdluTVGIEchhSFUsWhNspilgUceeSScL8bqQhIxy/z9/ve/D62BIa4JjI25hHiFmAcrXBJjWUvfIGLpC9awScQsFsUQ1pDZcfvgun5ZXBgCRswWhpdpGwJxCBgxG4eKnSszAkbMxj8W21lDwBBoLgQgHPpX1dZGydmBlTXPHDh+PgskJQ/ppEftZSXvQDtoIF/CNV+9UkFmxGypkLV6i0Eg/R5Vc42ruyXcT11fLG7XCKSRr2l57Ro0G7whYAiUHYE0gjbOxXFP9TCOoN1J5y+WvCFxBO0Spc+QdJTg5ti3oKVdC+0cAS0EZkLMstDM/qRYt+L2FytLCDRHzLJXKPuDsk+qbzHrrErZoxXSBwtXymPhiktdSD6OsS7F9S8kJ8QtRC2kKvvGQrxioQlBO378+NDaEx3y2c8UC1XIXyxjsZ7EahUSFStLyuAWl35jXbl48eJGLpkdMcv+q45QhpiFdKRO9sp1FrPsS8u+rT/84Q9Dopk01rv33HNPSArTD9wZQ3hCOIIFLpmxQo2GOGIWopX+EiAvISVxr4zFLa6TaReyEoITi1PaA1/IaCyKhw0bFpaFmL3iiitC3LBSZYwQuexJC/GL9S3kOmQohDokOkQo+DCPELO4iI5zZUwDWOFCUOP+GIKUwFywD+xJJ50UWuyyX6wjViHMsZjFepm+/+lPfwotYzmHq2OIW9piLLhevuqqq8L5xrUze99CEmONiyU11r58FIDFMmN0+9CGnbA/RSNgxGzREFoFhkC4hyy/IfxO8rtowRBoBgSMmG3nz/82fEOgRSKw55S5Xxg4ouYokXK3Days/fOAYbWnlouUBZB00sOI2SwumnwJ13z1suhTXB1GzMahYueaG4H0e9TnxCz9bO77aXNjZe23CATSyNe0vBbReeuEIWAItDsECiFotxQ6PSU+QUuac9tJzpS8KnEE7SqlfyPZXmL70AoEC6sR0GJgJsQsRCH7gGIFCom6yy67hBat48aNC0m32267LbSghUBz1q+QuZBn22+/fbhADenz0ksvBbvvvnuog5UlJChEHGUg5yBcsWyFYIV8g8idMGFCSHhCRLJP6m677RbWCwGJBSxueCFVIUIhGLGkpJ+Qv5CNELXUTznadH067rjjQtISi1FIRXQgBrH0xUIX4hJrV0fMYi1M31zdWHziqpl+QpxiMQsJTD/pC6QhBG3cwnyUmF20aFG4TyztEhzeuPfFutRhCVnJPrtz584N9RkLbeNe2u1PizvnG2+8MewrlrIECGyIash1yFRIcXCBHAUX5oR9dV999dWcxCyW0qeddlqIGf0mYOkK+Y574w8++CC4/fbbQ9Kea4Y9e7HEhVgFYzBkDOxDC3aQzFhU0xd0cGMMSY9ra8pBpkNyMB9HHnlkuN8u/YT04MOA5cuXh32wP8UjYMRs8RhaDYaAWczaNdACEDBi1l4EDAFDwBCIIpBOehgxG8WrKcf5Eq756jWlD/mUMWI2H5RMp9wIpN+jGhOz5e6btWcIxCCQRr6m5cVUZacMAUPAECgbArkI2s3Uk06SbhIIWtwZxxG0PXX+aMkCiSNo/6f0vZIDJUbQCoT2HrQ4mAkxyyIjRCjEJlaMWFlCqrLXKdaSkGMQfljROsIVy0tItUcffTR0pUsdH374YUjEoQMBChH01ltvheewnqRu6oDMI7DfKSQcbo0hXGfNmhW63fXzIR1ra2tDt8cvvvhiWK/rJ1an7GuLBSjnKJfWJ4hC2oRMhaSlPkfMssfsGWecEZ7H8pM+OVKQcaAHFrjvZW9VrIFxeUxeNEDMQkQ6N8fggmUoY3WBvlAf5DN10BaulpE33ngj1Kd+cP/3v/8dWsVSFtzRZbyQpATqBwNHEmN1CukNbm4s1EWgLepLspilLPPE/rkQwASuDSxpGRftgwXj4RqhPn+OwQcil3ME+sZ1BAGN9TG4vyyyHB36C0kNLuz/Cx6MCbfX35KVMMQ482UhGwSYK3Bm7tnfmH2H+eiCecZlNPPO3FowBAyBZASMmE3GxnLKhoARs+39BSDj8a+r+jaS8BJbTODltJiLk/LrFdAB3OMW2+f1VQftFhOyqCPf9rOaK9cefWffpBYbhs4I8u5fOulRPDFLX/Ltz+7jHth4yIh5O+05cS7/W5mHXUbVdexfVddn0Ji53Rh3oQ3sPq6m+4DhdXsNHHHf7t+o/tuX8y2fL+Gar16+7RaqlxUxW+p5jI5r/Pj57I9WllBIW029XnINpNjrWI9da31jXN02A6vq9hlSOa/fkOMfKuY3KFd3K/LFjH7tNnpO711Gzu5F2lWcfo8qPTFLX5pyv3D9dzF1DBk1Zwuset25FhpvrH7xu1lMKOdvfDH9zKcs12IhWwukka9pefn0JZdOFs87bWnucuFl+YaAIbAmAtzzEJ6TeZ9we9DyrMDzuSNouyq9hQSCtpekd71A1nK8taRKUiv5TOJI2heUPlECycvvIe0grl0lLbR1BPRslRkxq7rCADFXCjIMghGJCxBG5EHSxQX6Qz56LqTpO518YixS2U92k002Cc4555ywCBhAQMYF+ki+35eoHgQmrn733HPPkPByRHNUL3pMnVliT13+WEhD/EJAYxmNVWtcwPIZi1UsXSGJ8wn0HWyS5tjl+bj5afCmvxCDF154YWhdC4FsITsEwNuI2ezwtJraJwJGzLbPeW9hoy7pumNbf3a28VVUDBEIF0nukjwv+VTCCyYvmk9Lfi/hJTRX4KVzb8mtkkUSVw978TwgqZbEkQyH6vwf6+VHintKpktWSujDfySnSL4k8cMmOjhbUid5TUKf35E8JLlS8k1JrgAB9VPJPMliCXV8JHlGMkNyuqSzJC1kUUda/X5esXMFvg5rcCccIPmd5HEJc/aeBAwvk/SQNCl887BZmw+sqr2pf1XNzVGBJHOV5tLbc/zcToMq55wDsdd/eO2i1XvW1r4WHo+oPRci0tXlYt2g19L+ttfJffIt0T1uG46rav77eb9qr6NMrr5Q/y6Vc348aFjtLNW9dEBl7f+ob+fR9+3g2vbjgcPv+64wuEHyL7X1WX3fVab2Pyo/ddcRc7dU+nuf96MRVtPSSNxdRzy8yaBhNWeFhKfG0jAu9UfpjwZV1t41cMQc5jY2QFj1G1H3M7X9oLBaGSkf6Nxi9ggeOLxmZGwF9SfzJVzz1ctnDvz+QAQNqqr5QxyGzJXTLYaYzXIeB1TVTorr66DKul/QVwjPQSNmny/sn9V+zO8zLxof96KCQ5ZtZXW9RAdR7HVMfbsfdu9XhdU1usb+0X94zXv+tSysP9P/2vPC8y8DK+sOibYfPc4Us8Nndeive5T+h+rUfsP/mPq5SsTxvIHD6w7Pl5gt5P8i1xgGVc05UNfb9Q4vYfSh7q2PhRjmgZHDjA9BNI4L+1XVztU97m1wV/yB5OH+w2f/MpzbqrrD4q73/pW1PFeUOvDMcKzkD5K/S3im4DceYb9ALJyOkPDskhZK9RtfpUbd7/E1KR34mqeH/m4pur/ydL+boDdY538r4bnsbQl4vCy5Q3KyBPIyKVyiDIchz01+SMvz9fJNF/u8Qzulmrt8x2B6hoAh0DIR4L6PQJpC0H5BwscfG0g2kmwq6ShxBC1EbC/JNhJH0nLcQ8K96grJSom7P/IudbWkn4S6acMIWoHQHoLeKTMnZlvY4mPJu4NlJ3u/4jZ32rRpmbTHPq8jRowIiVnc8/qWspk00MRKIBSuueaaYP/99w/22Wef0Io1rioI3Llyp4zLYaySyxUgDrGkZS/i6667LpHkLVd/2lo7Rsy2tRm18TQHAkbMNgfq1mYEAd4hLBgCBSPQRSWmS9xLZFrMC+aklBZY/GKRM60O8l6UsMjoh/N14MqxsPeyd+zOE28ncWGsEkslfn40jWsp6kv6B+mhPEfoRsv6x5C9P5DEhSzqiKs3ei6ruZqsit3YwJ1FWHccF7+pfFxzFRzyJcLS9EQuXqVF/td9oiWahiDtXzXn234H08iOaHl3TJm0vojwGQPJ4vT9GEtYv31ILPX9cl8nLj2osuZNCLi4PM5B3Pr1uvSg4bUHidB9LamcOy/s7nNl/DgkGitrX3J6uWKI6MFj7u3p1+HS+RKu+erlmgPXrov3HTn7S0n9hxxyeoXWS7lSzGMaDlh6al5fiY5H1/jtbhyFxFm1leX14ve/2OsY62WIQf0PfRzFLOmY/4no/6vfp6www/p8wJofTPDRRCMRaXln9Jw7hih1fSvk+k0aA/1Re9Nc/UkxHzqk7QWuB9C1hONxELpJdXC+f9XsFwZU1T0Zp8OcubGVKB6qel+VxP2uRc/9VXo9EvrB+WKfExKqrhiuDL8v2yYonhLRuzhBj+cEPmZzdTb6XdR5yIFzJHyA5XTiYj5M21kSF3imcmXmRRTS8iKqqYdZPe/0UCulmrvUAVimIWAItBoE1lJPEUhTyFPukxC0fKDCxz2bSjpIuC91lziCtrfSTrZRmvsN9/ATJdxD3X2SmN+YSgl10gZtuXaVtNDWENBzkhGzkdXKQg+x0oQ4xVWvc11caB1RfaxDn3/++dC98IIFC0J3vlGd5jjGKhWXxriTpl/O3XBcX3BfjDtkXEOXM+Aemb7la6lbzr619raMmG3tM2j9bwkIGDHbEmah3fchiXdqa4+5Np6MEXhA9fkvjhCZCyWPSf4r8Rf40CM/zjKQF9fnJH5dpN+QYHELqevnQc52lrjgE7O+np9+1CkrHiHx80jTV6x9nfWHn/+kzvOS7YeNdfAvia+3UsdPSJZEzqPzkcTvsw4rsqiDevIJWc3VZDXmj9lPs1jLOP1zpD+RsBhRUMiXSEjTi1vQjzsnguCzwZW1e7oOloSYlQVpXNuc84meIbKSE6HyTJJuIefjiFlZw03Pt444YlbWmKflW97XE5HyLhZyDmMXJ5FAwmCh0yHOVy/teoAc9+skXSpitlTzmITDaqzXJGU5nzUxW0hbWV8vbv6KvY7l8ng7XZOJ/5P+tRtN69r8dFBV3UTXFz/OYn7qSct6C/nGRGy0L2nHmROzEVI4rW2Rs3N8XFwa183pGOU33hITs/uov9HfsVU6x2I58m5MfpxVeql/4/mgjd9X19fYa1L5dZ4OujynxIXROunq4vnrC54SRABEqst38Qqd41nI7wd5PAvsJYmGS3TClaU+P6Tl+Xq50lk875R67nKNwfINAUOgdSHgiFLulXEE7SY67xO0W+m4p2QbiU/Qcg7yFg9Ff5H491berSdLeKfi/kxbiGtbSQttAQEtTRox2+7XZ0sLAGReuUNztFnuMTZHe0bMNgfq1mZbQ8CI2bY2o61yPEbMtoWH+DKPYYjac4trxLMkX4n0gRfLmyS+XoMFj6f7q4jOP3Xc18vfVOmrIjpXevlxxOyryv+15HLJQolbsNxCaaw4XZ944T1ZgtspAi+4u0melzgd4jMkfjhEBy4fUrda8sV6BV6Q6f/VEqdDfJzED1nU4deXlM5yriarEX9MpFmU3V/Cl9wQ2HtI5kt8vZ/puKCQL8GWppdGHETzRF495DpYCmI22p5/7BOzWPn6ecWko8SsCJGfFFJflJjdbeTsHVX+o0LqiOg+oJ9I/j8aQhJJ09qJ2VLNYxJeEZwbWVWWhpiNJ9D8tkpxvXDhFHsd7zlm7vpJlphpOPp5EIPcexou5PpEsfODW2W1U8z/WMPcNycxC1a7DpuzbxSfphL1PvakS0zMzla/3e/Xh0qPlrjfd4bEPWyEZKnE6fFhUg+JH8rxG3+fGnR9YBE/GjbUCcbgdIj/J8HFZjTcphNOb2ok80gvD50nJQM9HV4kjpV8LHF1LFDaJ3d1GHohcfnzOOGFLIjZrJ53yjF33tAtaQgYAm0IAX4jEN4puQfy+8E7EvdjPvrgoxo+1t1csqWkh2QbiSNoiXvVnx+g+GLJMom7d36kNO/Xu0uofx0Jbbl2lbTQmhHQu5IRs61yTdU6bQiUHwEjZsuPubXY9hAwYrbtzWkrHJERs6354b2Z+n6z2nUviC8pjdVrXOBF8XGJ030qosSLKQuaLv8xpeMuSF427/b03lAaEpAQJWaX6BxfJceFe3TStUW8d5ySzvFlMwuPTvd9pXtKXLhMCZd3pzsZidfT8UKJ0wMHP2RRh19fUvpmZbg+FDNX1D/Zq4s6F0ri5ovFhtckrt0nlC4opBGuvuVjml50MT/X8a7Da75CJ5uLmN112NzBckUb7jubq6/55PvE7G6j5/QWmfFuPuWcjk/MDp0RrKPzj7i8aCxC6m9ydXoC+1NSLmkcg6pqx/oXQhKR1ZqJ2VLOYxJe0fnwj32y1Mc+V7qYtkp1vRR7HTNm4XGFj09T06rnpb3H1/LhUEMoBjMqYT/ZpvYnWq65iVl9nHBjAzBK7DJydi/1Kdz3ONrXQo9LSMx+TV2FuHS/XdX+GCLpH3t66HPsh3L8xkOGur7i8QMCwA/76cDl+zHEox8ot0ridA7yMjdTermXx3Pcel6+n+SZysdvkp+pdBr5mpYXqSbxMKvnnXLMXeIgLMMQMATaBAKOKOVdGPKU+yz3TkfQcm/tJOkm2UKytaSXxCdpSfesPzdR8aMSd58m5kPYwyUbS6IELe1baIUIaDHRiNlWuKJqXTYEmgMBI2abA3Vrs60hYMRsW5vRVjmeOF6lFT7FWpfLicCf1dhzEr7aPS1Hwxcp371EvhjRHevlofPdSL5/yJfBrp5lSverzzzfO08+lixxgRdhf8Hw9jgl79w+Srv2iCd7eVj+uryXle7o5flJ6viZZJRksJ+hdBZ1RKqMPcxqrqgcDNy4iYdyMiH8Wued7uIEncTTaYRrIcRsuA+r9natdw/6YBoB4Paa1W18LfZg1eL/yjR96kbQpUxan/161Je3B1bNXuLOOYtZ3H+6c3ExxCrEDXu25uob5X1iVoTprXF1unOqb/GgyrrfqP7T2UeS/R99YlZ7ce7jdKMxfYpOZP/K2t9H9eqPG90Dkois1kzMlnIek/BqjLXIfe0Hqvn7gPOlJWbj2yrV9VLsdbzr0LqvN8YqxvJ3WM3T7JPK/5muw0/T9PtXzuGe2BCKmZ9wX1nNV1p7utf8R/N6Rz4Wv6UiZtX+vXyEMXBYzQVpRKs+wvhrAzBK6Dq8JW1swm6V6v0TepKlabq6X2GZWYrwI1X6d8lyyUpJ2gMynjbcbxzxSIkfyvEbz6K934c9/A4ofbGX7z//4E3ED9/RgavnA6X9j+188hcd9+zll/fTvuXts36G0mnka1pepJrEw6yed8oxd4mDsAxDwBBoUwhAkCJrS3yClt8Xfkc2lfAe2UXSXbKVpKeE+3vveiHdU7K1ZD8JFrPcq919e6XSV0p2kZgVrUBozUHvlEbMtso1Veu0IVB+BIyYLT/m1mLbQ8CI2bY3p61wRGnrTq35sdb6XgYEeMlEkgJfAV8rcS+Or0QUebF0eS9E8uIOB+rklyMZUWKWl9i4wGKia4t4UJxS5BxfIrsy9NWFKiXceeIlklMlO0nyDVnUkW9b6BU7V9QxWeKPO8kyGd1jPN3XOVFISCM58yVmITIHj7m3p2sXS9iBw2qfSFrw7z+8rhGpn2Y5K+IAi5pGIa3PYZsiywYNr2uw0h4yas4WA0fMOWDolAXr6odjLREebyf2rarmwW+MqmVBJgzfHPvXzrlIIJ+Y7T+8dlFS3dQj966bubqJKTugau433LnQGjaZNHpE1nGX+5JMwtT+j31dXb1JY2itxGyp5zEJLze32vv06l1HPIzFfwXXVb+quiE7j75vB4d3IXExbZXqesngOj7CYRUXDxxeezxz6HAaXHnfrnx4EafLuf6VNTOdLnExmA0cUXdyYjvaBxtrdL9vA4bXDNW98L2kMqUgZnVP/Z0/3kHDaw9Kbr/2eV9X18R/k3T1IciD/v0KS2RheVuyfsmIWb/LWDclBRa+B0v4OM39Jo6LKJfrN/6fXh9+HunD017ez7z0sxE9nxS9M5J3hVfu5Uhe3CEEtcMEAh2sXPDbmedO1sdpeRHVnIfFPu+Ua+5yDsQUDAFDoM0gwLMFwv0JgpZ747oS3BzzXMyzG++4nSWbS7aU9JD0kjiClphjzu8oOVfCfdndc4mfkhwvoR7acPdD175OWWjJCOhZz4hZgWDBEDAEciNgxGxujEzDEMiFgBGzuRCy/DIgYMRsS344byV921T9xKJ1jOQ8CdaoLPz5L4qkX5X4YZ4OnM4sP6OAtE/Msgi4TkLZ4Trv2iJuIIcS9Dl9g8SV8V0R82Xzm16e0yF+TQIZfbAkJEgUx4Us6oirN9e5ps4V9U6WuLG+naMhFqmd7socumtkp5Gc+RKzEIXRikWMjk9c7B9eM8bXz5aYrf3fLsNm7ezX76dx85nUL5GcbwwaM7ebr09693EPbAyBmVTOER17jp/bKUlHVm3Lhhw+K41gD5sVIXNdUh2Fnh88bE5/N5YkIqu1ErOlnEcwS8KLOdB1kssLgIM9r7iYtkpxvWRxHUNcJ12vPpHpA8THE0llRChiWdkQisOsZlpSO4OG1V3U0IiXgKxNKuOPJ9/7KVWnjUH7Bnfxmg+TScS1/z+8y6i6jon9rKp9Pe7+NuT4hzbQdfRsXDmd57e+HIEF7a9IfiD5qeT3kvsl/P653zcXH65zfijXbzwL864P//A6wMK+O79Yaayw3DFxd4kLkOguj99uP8zRgcv7VGmsUtPkCU+fcr0lLlyihKuL5z8/pOX5eoWkN5VyU55NyzV3hYzFdA0BQ6BtIOAIUn5feGeFPOVDoPUkeHfaWMLHkrg57irZQsKHmT0l20i4pyKke0q4tx8q+ZPEt6L9UMe3SPaT0AZt0aZrn9hCC0RAi39GzJZhBdSaMATaAgJGzLaFWbQxNDcCRsw29wxY+0LAiNkW+EzeGrrES94IyWMS30WeW3SLi1+NDMwnb6+K5OV76BOz/0kpdLbyXJ9WpOj5WVO8Mu/6GUpvL3nZy3d1+zEvyDMkvDzHhSzqiKs3ei6LuaLOyRI3vn9FG4kcj/V0V0bych7mSySk6UFaRBtKJVlKSMzKGvbuaF/8Y1nq/TCOgOAcroV9XT89qHLOOUnlHDG767A5+ybpqF83+PUlpUXW/COpjoLPj6gd7tpJIoF8UgfdfPXSrgef0HftY72b1P/+w2f/0unlW28p5zENB8bQr3IuHgUyC0mY59NWKa6XbK7j2oeT5tu3EI+CKCJwcVK53Q6f08PpF4NZmnvib4yri/0NkaX7+iLkP4nrW9bELB+IuHH6sdqO3Xva/x/GTXxcHzkX3YvWrxtX0XHlykDMsuiNdXAcAet+A6Px4X7f69Pl+I3fRW25vnymNIv5hJESd979hvCM5M4dhpLCdhJ3jvJYWfnhFR24/KbEB3iVpZGvaXleFTmTWT3vlGPucg7GFAwBQ6BNI+BI0rU1ynUkjqBlcWYjCR+X8PEkH0V1l0DC9pDwTMDvFEK6lwTydgfJaZKnJf79eqGOfyZBL46k1WkLLQUBLc4ZMWtrtIaAIZAXAkbM5gWTKRkCqQgYMZsKj2WWBwEjZlvKg3gr6sf66uvfJP5LXzT9sfLnSSBuXd6rSvvhJR24vEv9jALSPjH7SEq5n3tt5eta90yvzPsxdfOiTPsve3puPH6MdS1fLMeFLOqIq9edy2quqG+yxI2Ll/60MFaZTrdZiNk4Im61a9L4fRyj+llazA6srOP6SwwDh9dVxhEQnBN5elNSQRGzJyaVc8QsLpqTdAaMmJ3X/51ImecT61AfC8obVnuqG08SkeWTOujmq5cvgeraz56YLd08puEgq8X/HTDp+fXcuLKIkzDPp61SXC+ZXMdVycQs104Sbv2rZr+QdI33r6rr48oVg1mSdSjtplm1q9y7cX3LnpitWejG6ce6P92b0H6Dvj5YGBWnw7m0+1uSe+cSE7M/0viwDHW/X3Hxa8q/KqJzuI7jQql/49dSo1jEun5W1ndimnduTP25y71z19WfO8E799f6c37ER2mu7qbErj/UmUa+puX5/UlLZ/m8Qzulnru0sVieIWAItB8EuI8jjqCFPF1Xwj3tS5JNJM7NcTelsaLtIekliZK0PXUOAndfybWStyTu3v2Z0jWSKgnWuetIaNO1T2yhmRHQ+p8Rs+VZBLVWDIFWj4ARs61+Cm0ALQABI2ZbwCRYF4yYbebn79bY/O3qtHvJI/5EMleCu8VRkp0lvFASzpI43f+GZz7/M8fLo86mhHyJ2aNUuesHi6689OYK/sLrwhzKLM6fKOGF90OJa8vFWM/m+mfLog410yhkNVdUOlnixtMqidlCiLtMidmqmp80mpXIAeROEnGhvRkXDJ0RsHiyRhg4vGZ2UrkGYjal7kGVNX9fo9KYE7LavTOpHe11eUoh4rt0TiKySkPMfm4B64Y4ZPicXZLG1RSL2VLOI31OxKtq9hI3pqziYtoqxfWShm2+17EI46lJ8801HIfdbqPn9IaMjiuncX44fvx8LFzCUAxm6tvtcW1wjg83XBt+zB7CiWUqa65xuoXc9xLHILfprj4/zkcfa+SkfkJI7zllbuzvcb+q2rlx5UpIzO6isfmuIPm9e1UCyXmShIVuFsUJLGCzyO1+E4/gZI5Qit94msTFsuvH9fV94J7gzm1Vf+4g79zC+nP3eecgaaPhnzrh6rlNaQiBQsR/7kkjX9Pyon1KOs7yeSfaRqnmLtqOHRsChkD7RcARpJClCL+NPGOsJ4FIxYoWN8e4W+8q6S7h/t5TEiVoe+lcD0lvyUTJXyX/k7j7+QqlL5X0ldAO7xm06fqgpIXmQEBrk0bM2gKtIWAI5IWAEbN5wWRKhkAqAkbMpsJjmeVBwF8zaY7HT2uzlSHAS6B7qSN+TrJdyhh46XP6UfLAX0x8NKUOl3WhEiw2nyFx1k3nK+3qf0TppMCCqtMj5oU1V4BkdWX+kUvZy+fl+RDJ0xJXnvgASb4hizqynCv6PVnixsPY0sJYZTrdlWmKcXn5Egn56rk2CtHPkpiNWuO6/rgYcgeyIY6E4Bwui52uiwcMr61O0ue8I2YhddX+e0m6IndHujr9eOiUBe7jigoRX2fHlafPWJ365QpJ50PqUF++ev2H3zcgrp/1516M9m1QZd0vkvQHVtVxvwlDvtdNKeeRjuSLw+peF/e3mLZKcb1kch0Przk2ab75H+k7Zi4LlA1hz4lzNxIOf0sqI2vPhxuUlSgGsyS3vbSt/7PFe4yd7ci1sMlwz119tJHYtxZEzMrl8mZJ/eQ8/2t63mUxuCHIyvb4pDIlJGavUgfc7xYL2FMkjfqlYxc6KOF0iX/iMvKMs/iNd019TwnXF6x5v+4d84zmAhZXn0mc7gCl+bDOHbOwHw0+2dnoeo8q5nGcRr6m5eVRdUhQuHEQF/Nsmqu9LOcuV1uWbwgYAu0TAX57EMhSSFMIWp7LWbjhuXsTCb9DuJ/fXLKlpIekl4R7OYQsQrqnZGvJYMmvJPxO+PfLR3VcLaE+SFraRFwflLRQLgT0PGTEbHkWQa0VQ6DVI2DEbKufQhtAC0DAiNkWMAnWBZ7vLRgCeSNQJU3/ZQ7CMy08rkynvzyiiAWKy/tY6R6RfP+QL4SxdHX6bhE0X2KWF1VXlniKJC2gT59cmavrlTdWfJPkCcl7kr0lSYEX3I8kro5f1CtmUUdSm/75LOeKeidL3Fie9huKSY/1dI2YjexfG4NXxYBhNU8nERGc71dVUyPLucO1F+VoERN/TNMlzxGztKXjB5L0ZZ36KcTILqPqttt1xMObYN2GJe6gYbWzXD8HDa89KKl8/xG15zo9Fw8YPvsY6bP35Bqy+7gaPhgIQ75EVr56uQigQcPqLhoyYt5OIak1ombCQFmaJo1r0IjZp7l+5kvMol/KecwXB9fvYuJi2irV9aK5Kuo6TrPcrL8OPuIawUJ1NTFY93LS9RGej7gCLwazXSrn/DitLdW9auDw2VP0v38o1r2ysF2aqt+CiNnw/6Ky9pXU/lbVzRs4ouYojfMnIrzvTtMtITH7nPrqfuMeyvH/8wNPlzITPf1y/ca7JnF36bsc9l0Wk/YDC/BujHO8dNJvuv+MxfNMT0laGKnM/0r4mO2Pkm0lLqSRr2l5rnxanNXzTrnnLm1MlmcIGAKGwFqCAIEohaD9ggSClvv+hhLuWUlWtL2U5whaYo57SCBpR0jukvjvuu/r+FbJjyTUT3tG0gqEcgatTRoxawu0hoAhkBcCRszmBZMpGQKpCBgxmwqPZZYHASNmy/mw3Qba8slUFve2SxkTriHdAiBxlKDDWsN3qxRdQPSrPtmrizLOnaC/aPiIXyCS5sXy3xLXn7eV7hTR8Q//oAOnS7xffSZfLL/h5V1Xfz4u4sXZd4t4Zr1SFnXEtRc9l+VcUfdkicMkaRHX9WGspxudd6eTGOdLhOWr5xoqRL+cFrP0b1CKNV8aSZGU14iYzWFdG1eHyJH7HG4ibTtKZ0WcHufk0vVm7aN7yKARdcNEmlybpIeLYt9tab5EVr569HdAZQ4yTf1N6p9/HqLMjb+Q66aU81gIDq7vTY2LaatU10suK3F//lzav46xaB44rPYJl1dMDDnYr3LuQB/fYjA7cPz8DbO6dhlX+faYrbknDseoO3JZp/8mTq8p50pIzPKxlfuNu8mf20iaj0te8HQpc6ynU67feK/Jijsi/XHjONhXUvq8BL2zI3rucK+I/p0uIyaGJPBxWaFjFvdduEQJ16957mR9nJYXUY09zOp5pznmLnZAdtIQMAQMAQ+BtZRGHFEKacr9aj0JiznOija6F+3Wyusp2UbiSFrSvSTk7SSZLPHfkblPc/++QrKHBDLYkbSuH8QWSoCA1v+MmC3PIqi1Ygi0egSMmG31U2gDaAEIGDHbAibBumDEbAmeqdtylftrcG5hjTiJTD06oocuLvSiL3JTPT0sYk+P0aFNLDVcu/cr7UK+xCz6+0hcHcQLJF+T+AEy9VKJr4cVDS+/LlyjhJ//E5fhxbzE8kLr6+3t5WdRh1ddbDLruZrsjceI2QjkhZB3kaLhIa5aRez8oylERVwZn5ilgUGVtXfF6SWd8wktyuv44CTdfM/LCpH7QkPIl8jKV4+KB1TV3ppvf5L0RPysPGDS8yx2haGQuS3lPBaCg+t7U+Ni2yrF9cJYir2Oh4yZvX2aa++ka2KN8yNqfhrFtmjMRsw5YI128vyQIFqupRGzu497YGPh82q0n005TiBmz9Z8zK4XrICaEh5WIfebvUrpr8RU0lnn/uXpOf2zIrrl+I33mxwT0yeeuTr4Skp/O0aPMfSP6PmHWL66cRLjQWQjX0Fp3GreK/H1zo3opJGvaXmRamIPs3zeKffcxQ7IThoChoAhkICAI0fXVj6EKe+cvL+uL+EDGe7Pzoq2i9KbS7aS9JD0kjiClngbSU8J+ftJrpQskfj38pd1fL4EEpe2jKQVCKUKWps0YtYWaA0BQyAvBIyYzQsmUzIEUhEwYjYVHsssDwJGzJbqwbqN1suXuG9K/Be2eTqGcPmJ5AbJQomf76cb3JhKh7CFxHfBh+4TEshRFlqjC31v6dy2Ehd4UXT1P+JOpsR/8vQph4XMXyTnSFhsfF7i6iOGEI4uWPbROcr5eo/p+CLJKZJfSV6Q+PnRvmVRh5pIDVnP1WS15sb0dGrLFRVjPd2VOXTXyM6XCMtXzzVQiH65LWbp4+Bhc/qLUPm0KWRFtEyUmP3m2L92FqGxOKqXdBwlZumfzt2QpJ/rvMiwR4cc/1CjH5x8iax89egje3HKgnd5rv6k5fcbPvtM6nKhkOuGMqWax0JwcH1vapxFW1lfL4wli+t40PC68WnznytP47pbz2drRbHNBLPK2ltytZ9PfksjZsFqUNWcA/Ppey6dBGK2Vk2436f/Recmz2P/N466IGcvkIyR8Dxyv+RDiWvHj6/SeT+U4zfebw/CGCLW79N8X6E+vZ7i9yN6i2L0/p+98wCTotja8I9ixMg1gldArgm9ZEQBr2RFMioSFRVUMICYAAOLEQyIWZISFUSSIjlIUEDJOagYQHKWKHr+87XUWhTdk3fZmf3qec52d3V1hbd6envqm1NlR8Gryn3n+VXj+qg9pYYf6NkziaAOEK9zq9khlPga6pydR9B+It93MrvvgtrEeBIgARIIRQDvITAItDAIpvghsRFpbS9azBKF2abyquGZnl/tErWChw37BdTyqUGkvV1tkNouNfv/yjw9flwNafxEWo1miIeAvl9SmM2cQVCWQgJJT4DCbNJ3IRuQBQhQmM0CncAqHDFOHs97JK/NPgTqaVPtL2lB+7s1HQTbP6z0D+i+G67XiDVqQfmYeAwMVncujlaYxS+I+0VQFsrcqFZZzS/cqpEY/DV1C7WF9w7KdUMi8nDzdI8T2VcdNXPTzkVuQc7xXVZaCrMRrDFr+JVsPLmsrh/5YzhxItx5V5hF/qUbjb9URY0J4a7FeT9htmrTcbm8NTgbTPgjkjxMGm3PN1i/1rTRbCMVsiJNZ/It3nBSRYg3pvxotragZfKLVpjFdRnRj9FyMPWPZZuIshJ9v5h2xHsfI5/ijSY2LtFgwrpo7g0V/Pfr2sMvl2815TRTF3ubCGa3pS09sXijca9q3f4KVze9V38KSmPfx9Hcv9G2Ier06hWs14T1nFXWfwa1TePxgyk3JEKYxYA2xEzzfy7UFt65fa2063Ufg+J2yIz/8XZ5M/TArjNEZb8wXiPtdG/5JXLiyurxKuc6Ow97/ydNd5GaG0KJr6HOufkEHSfyfSez+y6oTYwnARIggUgI5NBEMCPQQjTF/zT8GAeDPXhvOVMtt5rxosVzOp9aATU/kRYC7n/U7lUbq3ZQzTzr8UOgSWr3qCFP/P9D2TBTF91liJaAjk1SmOUALQmQQEQEKMxGhImJSCAkAQqzIfHwZOYQoDAb7Qsz03sEKujf+WrmC5q93aTx8KAwA3P2YOFojfcLEG16qcEj1s7L7ONXu/h1rhue0wiTBuVEGmprwuVq+GJprjdbeH50U8ujFiqU0pP4ouon0B7S+CVq7dTwBTUoJCKPoLxNfKL6qoNmaBjBQzhUaKInTVqsUxRVKNN03NVBg/IquNU3mUWaLpb0+vzNoeLGXr96lLx9/FED3tHWxdTJb4tpP7WdvVUc3edbfsMJu7Cea4nbJ7TzPa9et/As9MsbcSUbTWxQvNGEn/2uRZyWu6tUg0nPB11f5M7JVxVrOGFK0PUm3hOYdcpXCHR+ealAM8Sktbda/jI7faTp7GvK3DmpoJbfT/swAg/kCX9pmaOuazj5BjsPsx9r3ya6H2PhYNoQ7TaRZSXqfnHbEO99DIG1ZMOJr+i9d8C+//z29V4aek3TcQXcOtjHiWSGHxeoOLvcry6I0/t1TPl7p5wT9Iwq0XhcutgWzf0bbRuiTQ9ele+dcKY+397VdvxwtACNz+L46fhhQ9C05KUajN9mcz+8j3cL8z8H/5NjDRi4xvN9v5rJz2wxKP2tGv4H4f96ZTVzDttr1NyQGf/jTZmP645dn6rmhLN9wklX0TkfdIgpMt9QwzueXY7Z36LxeOfx/eGCxr9iXTdR9+0Q6pydLtx+ot53UE5m9l24dvE8CZAACURCAP+bjEEkhWCaU8140eI5froafjB8jtr5avi+i+/Y+dUuOWwFdQvDcX41iLRXqeH/x0w1+7vvPj3+TA0/jsHAEspE2TBTF91liISAfv+kMJs5g6AshQSSngCF2aTvQjYgCxCgMJsFOoFVoDAbyUsy0/gSwJet/GqV1O5UwyAlvuDFG/JpBrXUGquVVAsa5NNTcQd8QcXgW1O1umrF1bBGTzQB9SutBjGy2eH9aD9YichDiw4MGdVXgQXyRPwEyqdNyXnN7WOLlGo44a6SDSa+WKLh+OY4Rnz8ueuN32zKBSpMVSmh4mnxxhNeKNVwYisVbG8s32xKRJ8BiL8QM0s0mtASQhDEZBVNnkM9SzSaWAHTQSeinvHkAXH02gYTyqvQ/FipBhPfRB090/Zqu+8v3nhijXJ3T8TAU4aFjO7HDKt4gjPOqPsl3vsYXqrXNZ76X/zYoWSjcWl6n3wEz1hdE/kOeJsGecgmGI9vdtfcMfFf+Exqfdpjiu2S6nEa6kcXvplk4Uh40hdvMPF6FZpvgRh9fZOxWAvPCyUaTlzgJ0zjBx8mjbPFuwhEwp+d+FgOMXBdTA2ekw3VCqthYDvWkNH/42OtV6zXnacXVlRrpgYB+D9q8M7KCiHR7zup1ndZoY9YBxIggYwngGchDO/ix6vhu4PtRYsfTeKH0ZgK/ly1C9TyqtkibUE9NoZ35fyHz+N770tqK9TMj3OwxY+be6hVUEN5KBflw0x9dJchiICOTYYUZs855xwZPnw4hzBJgARIwCMwc+ZMyZs3r5x77rnywAMPyJAhQ2TgwIEyYMAAGTVqlGzZsoWkSIAEQhCgMBsCDk9lFoFo9aOg10jGkwAJkAAJkAAJkAAJkMA/BPBDj5K3T3oKwrKflb5tYqF/Uv+9V6zRpP/5ibKet7BOje6mP3z8lG4xMDw44DyjSYAESIAESCC7ETCCKLa2SIsfG+GHmPiRMn6AghkjMC0xRFr8SOoiNXjK5leDKGsEWmwLqOVTg4hbRe09td/UbJH2Fz3GDFTl1CjSKoRIgo4AhhRmTz31VGnSpIm8/PLLNDLgPcB7QO6//34588wzKcxmlnzCclKOAIXZlOvSZGwQhdlIXpKZhgRIgARIgARIgARIIDoC5VpOPztwymVMy6wesGWbjLnc5KrTG1dXr/u1gcJsw/Gvm7TWFrNs7FHDoPAjVjx3SYAESIAESIAE/iZgRFoItEakhRetEWnhRWumOv6X7mN2BCPSQoiFIGuLtNi3RVrMMjFQbaeaLdKu1eN31MqroTx60ioEv6Cjib7C7Jw5cyRXrlySI0cOT4Q5//zzhUYGvAd4D+TOnVuOP/54T5ht1aoVPWaTUZJhnY8pAQqzxxQ/C/+bAIVZv5dixpEACZAACZAACZAACcRPQKcP/zBIaDXxKtBuVdttjoO2mJrcp0ZLNA6DwLPUMJjMQAIkQAIkQAIkEEzAFmkhlAZNdey3Hm04kRZetc3UPlVzRdr1Gve+GjxtIQhTpFUIJuj4nK8wu3z5cilTpowULVo03YoUKSJXX3215MuXTy6++GJvi31a6jO48MILPZEeolz+/PnZ/7zvvefCE088IZ9++imnMqbUQwJREKAwGwUsJs0oAhRmzYswtyRAAiRAAiRAAiRAAoklULTZlKIqzh4MElsjjdc8Pguo2Q0a/5IaPHEYSIAESIAESIAEIiNgBFpsjRdtkEiL9WjPUTtfLY8apjI2Ii3EWGPwpM2vhqmQC6g1UftEbbsafkRlbJPu91SrpnaSWrYXaXXE7yhhFqOAe/fulYULF8r8+fOPsBkzZshrr70mnTt3pmUTBpjGunr16p6X5JVXXikvvfSSvPLKK+z/bNL/QZ91PAd69uwpH3/8MYXZjJJOmG9KEqAwm5LdmmyNojCrXwIYSIAESIAESIAESIAEMojANQ0m1VWP2D8iFWHddMUbjvv+ujsm5c2g6jFbEiABEiABEsjuBIJEWni2Qjj1W4/2Ao3H/+ag9WjNdMfmfENN219ti5oRaLHdqvaRWk01DFBlS5FWRxJ9hVm/Eca//vpLtm3b5nnIDRgwwBNkIMrQUpPBJ5984vXt66+/LhBkb7nlFilUqJC0adPGuwfY76nZ79H068CBAz1RFls8E0aNGiVbtmzxe3wwjgRI4DCBzBZmd+zcLQuXrJS5i5bJjl27Bf/LGbI9AQqz+uLPQAIkQAIkQAIkQAIkkIEESjWaUE/XkN3hiq7hjkvdPmEs1qrNwKoxaxIgARIgARIggX8IGJEWXrS2J61ZjxYiLdajPVMtt9q5aliPNhKRNp+mg1B7mxrE2I1qEGeNwbMW4m1dNZTjJ9KifikXdGgyKmF269atMnjwYE+EsUUZ7v8jUKUKCwh0H330kdSpU0fKli0r8+bNk7Zt23oibffu3QXCbaq0le2I//6lMJvthR4CiJBAZgqzW7dul6de7CbXVW0o1W67T2bPXSSHDv0ZYU2ZLIUJUJhNuTd6NogESIAESIAESIAEsiCB0o1nnVHi9gntSjWcsDG0IDvhr5KNxo+Dp21ammBQmIEESIAESIAESCDzCfiJtFg6wBVpsR4t1nk/Tw0i7UVqxlMWnrMFLcNxfjWcx5TIEGF7qWENWiPQYrtLDdMgQ8SFEAyR1gi1pl4pI9LqoGNUwiy84SDI9e3bV/r160dLYQb9+/eXZ599VgoWLOgJtH/88Yd89913grWGGzVq5N0HvAf4GbDvgZEjR8rmzZtTWMtg00ggfgKZKczOnrdESleqL/c/0kmGfTFBNm3ZRo/Z+LswFXKgMKsv9wwkQAIkQAIkQAIkQAKZRKDaQ6tPuq7B1GIlG0y8tWTjiU/o+rHvFm807tWSjSY8WKrhpJpl7pmUL5OqwmJIgARIgARIgAQiI2DEUNuLFiItpjrGwFIuNQiorkgbqSctxNxaat3V1qrZIu1ePR6ldr8axFyshZtSIq2OLkYszGIkcteuXfL111/LtGnTZPr06bQUZoBpacuXLy/16tWT7du3ewPR6P+uXbtKgQIFPLGW9wA/A/Y9MGfOHO8Z4d0s/EMCJOBLIFZhdqdOQ7zqh59l8bJVMnrCVJmzcJmuB79Pbb/MnrdYPhs5VsZNniEbNm2RP//8SzZu3irv9vpYCpa4STq88KamXy779u+X3/fslVlzFsqQEZr+q29k0+Zt8ufh6Y3X/LxWy/hJvlVB98vxU+X7H3+Wgwf/kC3qeTt5+mz5dMQYmT5rruzc/bvXtkOHDsmK1Wvk519/kyUrvpeRYyZp3abJut82yqE///HMXa91mvjVTBn6+Xj5bsFS2aP1NgFTLU+bOUeGDB8jU2Z8K9t27BQz2fIfmv+S5T/IiNGTZNioCbJUy9i//4C5lNvYCVCY1Rd6BhIgARIgARIgARIgARIgARIgARIgARIggdAEjECLbbQibThP2gKaJ36cBfG1utq7aj+p2SLtX3o8Ry1NrZSaLdKiPnb99DA5go7pRSXM/qkDrRhU3r17Ny2FGcDrsU+fPnLZZZfJpEmT0od+sTbhqlWrpEqVKtK8eXPeAyl8D8TyGd+7d69Ok3oo/X7hDgmQwNEEYhVm56oQ+8BjL0jZ6k3lqtI15aEnX5T5i1fIc6+8LzfUvFMq1b5brruxkTS9r50sWLJcpkz/Vmo2elD+VfBaKVbhNun6Xl8VNlfLsy++LeVrIP1dUlqnOL7rgQ6yaOkKb4pjpKnZ8AEprfkUuraGvNv7Y5k5Z4Hc37aTXH/zHVJRyyhdtYG079RNxdj1cuDAQXk87XWp0/RhufGWe+V6rUfhsnWk6f3tVKhd7QnEmD4Zx9fXaCqV6twtZW5qLG+818fz3v1hza/Spv3L6edKV24oDz75kqxWQRjPkkHDxkiNhq007xZa9l1yQ/U7pf+gz721co8my5goCFCYTY7XdNaSBEiABEiABEiABEiABEiABEiABEiABLIMAVsEjUakvUBbEKknLUTaimovqn2n9qeaLdT+qscfqNVUg9eu8aRNKpFWB/GiEmajGPRj0iQlAPH1hx9+8MTXhx9+WL2x9h7Rkn379nnrDBcqVEi++eabI87xgARIgARIIDSBWIXZGbPnS9U690ixcnXkrQ/6e8JrVxU4S1dpIC+81uNvr9OR46S8CrdPpHWVhUtWyXsffiKXXVNdnn+tuyxb+b289k5fKVX5dunSrZfn+Tpo2GgpW62xPK1i7fqNm6X9c2/IhZffIC1aPyMf9BksM79b6InBN1S/Q3r2+0w9bRfIG+/30zzqS+8BQ2Xr9h3SuMXjkq9wJen0yrvqsfu1PPvSW3JpqWrS55MR6r27Ve5t3VFF3abSq/9nXpmtHn9BymiZE9SD9umX3lYxubG80/Nj+ebb+dJLy7hG6/emlrHm17VS78420rD545737nT1qr397rYqSL8kP/2yLjRkng1HgMKsvrgzkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxEYgFpH2XC3KiLQQYPOrXaJW0DIcF1AznrSFdb+12pdqv6vZIi2OR6q1UIPwa3vT2vXTU1kr6Mgdhdlww5fZ7Dw8JV999VUpXry4LF269KjWQ7hdt26d3HXXXeles0clYgQJkAAJkIAvgdiF2XlS7dZ7pcXDz8pGFTwx5W/D5o/Kder1+uHA4TLtm7kyecZsuaVpaxU+m8qCxctl3KQZUvSGW+SzL8bLZp2O+NZmbaSMiqR9B41QIXeuTJw2y/NIrVjrLvWaXSntOnWVwuXqylidEnnX73u8aZPhVVtPrxs2aqIKq/N1uuLJUlLXrb3noWdkjQqkDe55TG7Ses2Zv1SwFvmCJSukZMXbpeu7fWTG7HkqtDaQZ15+S37bsMmbMnnF6h9lsE5bjCmRK2i5N+q1n3jH82TM5OlyvYrAtzV7ROYuWia1mzwoRcvfIi90/Vt4njFrnjed8d59+33ZMjJiAhRms9brOGtDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAklLwBZB4bkKL1aIpGZN2pN1312T1hZpL9bz+dVckRaCLURanEMa7DdS+0htrZot0mLK41lqz6gVU7NFWtQph2W6e2yDDuFRmI14HDP1E2LqyO+++06KFSsmb7zxhjfI7tdqDL5PnTpVypYtK59++qlfEsaRAAmQAAn4EIhVmIWQWavRA55H6gFd93XTlm1yk04fnL9oVSmvAifE0Wq33SclKt7mTQ2MqY/HT/nGE2aHfTlRftGphyvXuUsuKXajVNBpgf9Of7+UKH+rdzxfhdwnOr7uTVeMfYSv1Uu3cLnaOq1xTalcr7l3Da67umwtubNVB1n5/Rqpf8+j0kynQ8a0xAhYp7ZMtSbymnrzjhg9WYr+r6589PEIb21bnMf/md/37JNFy1ZqXeurd211qVT3Hi/vG+vf7wnDmBp5yfLv1VP2KxWTH9Hyamt8Hamt7Yeoi/V2GeIiQGH22L5+s3QSIAESIAESIAESIAESIAESIAESIAESSEkCtgAaTqQ9UwnkVrNFWnjSwlsWIqwr1OI4vxrOY/3aKmqd1eapQZi1hdqf9fhdtZvVMBCWpaY81mE9CrNxjW2m1sXbtm2TBx98UCpXriwbNmwI2bjt27dLmzZtpHbt2rJ27dqQaXmSBEiABEjgbwLxCLN1mjwkz736rv5o5pBs37FLbm7Q0vM4HTR8tHw14zv5auZ38qlOZ/zx0C9l4+at6R6zQ0eNl/WbtmjaFt4asp+OGOOln6rpBw0fq2u5jvWE3seffU0F0hY6DfJKr7JzFy71hNL72qbJ5+O+8q6ZouX0GzxSvpwwXcvYIrfp9MLw4v1RBVkECLNlIcy+85EnDBf73y3ywYeDZdfu373zW7fukJGjJ8koza9Epdukyb1PCoRjr/7ffCcDPxslw7+c7HnYLl3xvXw7b7F8MXaydHmzt9yga+PCo3bWdwu8NXG9DPknFgIUZvWFnIEESIAESIAESIAESIAESIAESIAESIAESCDjCIQSaU/UYuFJe6ra6WquSJtH48KJtBBvIdIiHbxk26qNVdujZou0u/R4mNq9arjmmHvT6mgehdlYhjRT8JoDBw7Il19+KVdeeaUMGzZMMGVxqPDnn3/KpEmTpFKlStK1a9ew6UPlxXMkQAIkkF0IxCvMPv/qB54wC3H20WdeUY/X22S4TjO8e89eb03XNu1eluatn5XVP/ycLsx+9vl47/yDT7wgpSrdrkLnFM9rdd36TdLq8eel1WPPeR6vEGZvVC/chTqtMQLWiK2uXqwQgL+du0j2HzgoC/Tc7Xc9Ki+93lNw/e3qMesnzHZ560NZulLXK7+luTS+93Fd4/YHOaizLQz4dJQnrg5QAfbWOx+RirWayVdffyf79h+Q1T/+Infe317XvO0ms1WQvUO9cl9VgXftug3eerbtn+sml5eu4U21/Id63jLETIDCrL6EM5AACZAACZAACZAACZAACZAACZAACZAACWQOgUhF2tO0OhBpz1Y7R+18tQvV4CEb6ZTHmAK5qVpftfVqtkiL/eVqb6vVUIMoDG9a41Fr11OjMybokB6F2ZjHNVPrQni9wvv1zjvvlF27dkXUuM2bN0vnzp2lQoUKsmDBgoiuYSISIAESyM4EYhVmp30zRwXSVpLW+W+PWTCcrWLpTTp9cekqDeSOlu11quMH5apra3jeqlt0TdkxE6fLVWVqedP/7lfhc7qu+Vq1bnNdl7aRTkXcXr1nW8lV19WWt7r3l63bdsgjT3XxpjLGOrEIED8//uxLb53Yijr98d0PPy3lazRTcbe+Jwbv2LlL6um0w80eeEp+/OnvqYzhOVu6SkN5/rXuKuxukZ79huias7d5Ai+mPy7+v3peXTFVMQTi629uIjdUv1PuevApqaLTJRe7vp6Kt5/rtZvlMRWKseYtpktu3voZnRa5noq8TwrWqQ3z2yGv/vwTSIDCbMa8VjNXEiABEiABEiABEiABEiABEiABEiABEiCBMARs8dOd7tj2pIVIe4YaRNp/qZ2nBpE2rxq8ZPOrFVALmvIYQi4E3RvVuqjNVjukZgu1+/V4slo7tRJqmeJNq0N2FGYDxy2zz4k9e/ZI9+7dpUSJErJo0aKIGw6v2Xnz5kmDBg3k0Ucflf3790d8LROSAAmQQHYkEKsw+8uvv6nAOlog0P75598zGuzXmQ5mzVkor77dR71nX5X2L7wpg4eN9jxMD+nz+Xv1QO3ed7AsXfGDJ7Lu3bdfZn47X7q81VvaavoOL7wlQ0aM02mDN2uef+rUw1+rKPqFbFRB1YQdOmXy6InTpNMr78ojT3fxtkiH+IPqtfvpiLHyuU41jKmVEXbs2CkfDhwq02fOlb1793mC7widqviZl96RR1Vofav7AG992QPqfbv79z0ycdpMefH1DzTvV6Tjy+94Yu1mFZUxawOmRf5wwFB5slNXafv0q9L1/b6yYNEK9dw9YKrHbWwEKMzqizYDCZAACZAACZAACZAACZAACZAACZAACZDAsSXgJ9LCe/UENYi0J6lhICuXGrxbz1Kz16WNdsrjK/T6u9X6qv2sZou02IeH7QC1O9SQd4YItTqeR2E2tkHNlLnqkHpEwdu1cOHCcs0113gCba9evcS23r17S58+feTDDz88Ih5punXrJrfeeqt3/ahRo1KGCxtCAiRAAhlBIFZhFtMA79r1u+xRsdMOmNIY3rG/rF0vv+nUwnv27k+fWv7AwYOeYIqp6k2AmIr0P6/9zVvHFWKtmbr+d50Oeeeu3bp+65HTBGOaYXi/ooyNOr0x8jVh1+7dKrDu9YRdxB069Kfs2Llb9u7bl54vBNrfNm6WX9etl20q3CKNCRBoN27ZpvVZLxs2bvGmNDbn4BW7U/Nf+9tG+eVXXLvjiGtNOm6jJkBhVl+sGUiABEiABEiABEiABEiABEiABEiABEiABLIOAVekdb1pIdL6rUsb6ZTH8KyF5Vcz3rTldL+92ji13Wq2UPuXHs9Xe0WtshoG1BIy7bEO5VGYjXo8M7Uu2Lt3rwwaNEjKlSvnTUmM6Yxr1ap1hFWrVk0KFiwo119//RHxJt1NN90kVapUkddee0326WA8AwmQAAmQgD+BWIVZ/9wYSwIxEaAwqy/SDCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAlmXgBFqIdC6Im3QlMcQae0pjyHA5lMroAZRtqBlOEZ8fjWTrp7uv6m2QA3CrC3U/q7Ho9XaqF2lFrM3rQ7nUZiNaUwzdS7C9JXr16/3pjDGNMbwnrVt/vz5Mm7cOLniiivkzTffPOKcnW7p0qXy888/H+VplTqk2BISIAESiJ8Ahdn4GTKHuAlQmNWXZwYSIAESIAESIAESIAESIAESIAESIAESIIHkIGBEWmyNSBvtlMdYbxYibX41V6SFYIs4nEMarGF7tVpLtcFqmOLYFmmx/7NaL7WGaheoRSzU6tAehdm4xzdTPwMIrsWKFZMRI0akfmPZQhIgARLIQAIUZjMQLrOOlACFWX1ZZsg4Ahdq1jcctiIZV8wROWONmKfVeqj1U+us9pAavlSZuhTV/YwKdptjLec6rZyp61kZVdEUyTeov09NkfaxGRlHwP6sZtbzKeNaw5yzEwH+j8hOvc22kgAJkAAJkAAJhCMAcdbYcboPg0gLYdRvXdozNf5sNdebFuJrPrUCahg/COVNC1G3klqa2ldq+9XEMnjXLlJ7Sw1et/9SCxRqdQSPwmykw5jZOJ0RZocPH56NKbDpJEACJBA/AQqz8TNkDnEToDCrL8cMGUegg2ZtvpzgS0lGhyZagCnP3v6p8c9a55ZlYEXsNocqB4Jix4B6HLTqWj8gDaP/7/9C9Xd2f7iFur947/xNwP6sZsbzCaWyX0LffeQTmo85y/8RhgS3JEACJEACJEACJHA0gSCR9gRNaqY8xvfFXGpnqOHH0LnVzlU7Xy2PGoRXTGecXy2UUJtPz0PQhYjbUO19taVq7rTHGJOYo/a6WnU1iMMQj2HHHTp0aNBff/0lxuIe6mMGKUmAwmxKdisbRQIkcAwIUJg9BtBZpEsgu2sX+grMkJEEMlP4KK4N2atmC7Jmf7XG23UJJZjGyyNcOfjy11ntoNrygMJwztSdwqw/pHD97X9V6sdGcn+lPoXIWmh/VjNamGW/hO4T8gnNxz3L/xEuER6TAAmQAAmQAAmQgD8BI9JiazxpIYa63rSYcek0NeNNCw/X89QwJXEeNeNNm1/3XW9aiLKIwzkj1GLa4xZqH6lhPMJ8vzfbPzTuG7UX1aps3br1UxVnvXVBsdYozIi02DKQAAhQmOV9QAIkQAKJIUBhNjEcmUtcBCjM6kswQ8YRyEzho7c2w3zJwXaV2htqndRaqdl1OZbC7ONaF1NPCrMKI8YQrr9jzDbpL4vk/kr6RiaoAfYzIaOFWfZL6E4jn9B83LMHNcL8H+GPd1w6PCYBEiABEiABEiABfwKuSGuEWoi0ft60p2s8hNrcapj2GN60WA4lrxq8afOpFVBzhVocIz6/GtLB+xbLHGFcYoDaT2rmXc7b5syZ89D1118vzz77rEyZMkX27t3ribQQa12RlkJtXIOgSX0xhdmk7j5WngRIIAsRoDCbhToj+1aFwqy+EDNkHIHMFD6maDPMl5u5Pk3CmnzPHbb7fc4nKipcOZEIEOBm6np5oiqWYvmE6+8Ua27EzYnk/oo4sxRPmJnPJ/ZL6JuJfELzcc/yf4RLhMckQAIkQAIkQAIkED0BV6g1UwsbofYkzfJkNeNNa6Y9hjftuWrGmzbSaY/z6zVGqC2l+63VBqutUzNjGd72lFNOkUqVKskLL7wgX3/9tezfv/8IodYVa7PvmGb2ajmF2ezV32wtCZBAxhGgMJtxbJlzxAQozOoLMEPGEchM4eNbbYb5MvNmxjUp7pwpQMSN0MsgWfo7Ma2NPBfeX5GzysznE/sldL+QT2g+PEsCJEACJEACJEACJJCxBFyR9jgtzkx5bLxpIdRiEA3LcBhv2rN1H960ZtpjeNO60x7DgxbTHRvD8REetcOHD5/wyiuvSO3ateXcc8814xrp29NPP12qVasmnTt3lhkzZhzlUUuhNuJB0KROSGE2qbuPlScBEshCBCjMZqHOyL5VoTCrL8QMiSGALyn4ZakdYhU+8KUIv0qNJnynic0Xl07RXBhh2mjrE5RtRgoQqCPYJSokqs3h6oMvvdE+jDK6v3EvR1uncO2M5Tz6E1/8Iw3x3F+x9EOoekV6/+BX6Cg7I0Min0+o54lqWIMq0s9bPP2C8jDwgzIzIyTi3s9sPvisJuIeQp8mKiTqeRzp5yiSeieCEz6v7v/6SMpmGhIgARIgARIgARJIJgJ4zzeG90yYK9TivQjvV0aoPUv3/aY9doVaI9Ca7SULFiz4/KeffvLWEP31119l8uTJnrfszTffLGeddZYZ50jfnnzyyYKpj9u1ayeff/65bN68Wf7444/ANWo5/XFqjPpSmE2NfmQrSIAEjj0BCrPHvg9YA8kKuoO+ujIkK4HSWvFP1X5V+0ttj9oktTQ1fCmJRpi9VtO/ozZDbZcavnSsURuu9oQavvS44XqN+Oyw7dat+aKy2oo33rM1rbhXdd8NH2sE8hqkhi9c+NVrO7WRapvUTH2G6v5dakEhqJx79ALkj3VlTT33HY5D/HNqJvTXHdOuUibSZ4uplHDdRDUz/RE4fKP2gRr4hAqJanOoMoLOVdQTn6gtVAMH3D8/qKG/26v5iRHR9LdmEVXAl+7KaoPVflY7pIZ+Wq+Ge7KlGn4p7YbGGmH66hn3pHMcdG84yf7vfxrRT22J2l411GOj2hS1d9VuVnNDNPeXfW0s/WCur6M7pu23HI5EHO69FWqo9xq1D9WaqIGxCUV1p7PabDWw3q+GdV4R59f3Gh11SOTzCVOUv6b2hRqeL+b++FP3Ue/uagXU3BBLv2BgB9Oa9VGbqbZdDSxhW9XGqDVXs3nqYcwB+cRy79sFZiYflIv/B4+p4f5Df5j/P+D1vhrqE0k4TRM9ojZPzfzfwbMUz3kwRmimZu7zuogICPE8j4/XPPG/B+XguYxwv9ocNfT7DjX8L7pWDaG/mqlTqP8RieB0tpb1qNpUtd/UUJ8Dang+faqG//PnqjGQAAmQAAmQAAmQQCoTwDuzseN0H+9vsJxq+J54khrevfBd5jQ1M+1xbt3H2ML5aheq5VX7t1o+tQJql6gVnDt37hfff/+9wH744Qf58ccfxQi1EOPGjRsnaWlpcuONN0ru3LnxPnaUXXXVVdKiRQvp06ePrFq1ikJtCo74UphNwU5lk0iABI4JAQqzxwQ7Cz2SwCn6PsdAAlETwBeRt9WO+jJgxS3T/Z7WMcQLv4AvMs+rGaEjKE8MAhdxMrhbj4PSm/g1h6+xRWLUzQ0mPbb/UVuuZse5+xgUP1PNDUHl2CzcvHAMgcqEg7pj0tQ3kc72Lj3eYKUz6e0txIo31II+6HbaeNqsRUQc8CUUooddtt8+xJayTq7R9LdzachDCA8Qu/zqYcf9oGmudHICX5NmqnPOPQy6N+x0L1j5mXz9tqiv7Ukbzf2F8uLpB1yP0FHN1A2fYfzgwRz7bZ/V8whV1Pao+aVBHDj/Sy3WkMjn03laiQFqQXW149Gmh5xKR9svt+n1v0ZY3jRNl88pL9rDeO59lJXZfFAmRH38P7DZu/v4f4J7MqdaUMD/EyM0uteb4080zUdq5jhN9/1CvM9jDOSZMvDcbmMdm3hsjWgbyf+IRHDKp2VCqLbr4Le/W9PUUmMgARIgARIgARIggexAIIc20hi+e8COV8O7J0TaE9Xwfofv4X5CLX7UdoRQu3jx4s8hxkKUNQKt2bpCLTxqp06dKq+//ro0aNBALrnkEr/3M8mTJ4/ccsst0rVrV5k1axbXqT1yMDQpjyjMJmW3sdIkQAJZkACF2SzYKdmvSkF6jb4mMpBAMAE/sWGnJl+j5vulQOP9hFl8gYGY5V6zWeMgnv7hnIOXTgU1E5roDgaxYW4eJn7l4cThRDH7+o1OfnudY5P23cN525ugcpA2VD2nW5mEG3RvrGlNHcz2T41brWa8vkw8tgvU8OXQDXaaeNrs5ht0jC+ni9XscrG/TQ1inMsHbWqoZkI0/W2uCbeFuIl7xK0TPBNxz+5xzqGe+CJtQiKF2eaaqV0P8ECfov/czwLSdVUzIZr7K95+MGV21B27vvY+hDH7PsY59OebavCONWnRLr+2vaPxsYaeeqHJ32xjeT6h/BlOXuiTn9Tmqq1VQ5tMGdji/FVqJkTTLxCs7bywj3pDhIT9ruaeh8dirCHeex/lZiYflHezGv4P2Bxw/+CHNO4zDGnGq/mFQhq5Q83OB/tr1MDcjTfHaXrODY01wpw3W9wXq9UifR7jM2muxXafc2zOof0I9mer/t9RR/xNBKfTNUf8HzZlY7tdbb7aeice59Av9rNRDxlIgARIgARIgARIIFsQMCItttEKtfhB6rkqvg7/7bffBKIrxDd4y4YTatesWZM+9fGiRYukd+/e0rJlSylRooTkzJnTfofz9nPlyiUVKlSQp59+WsaMGSPbt2/3pj4+dOiQYI1ad51aTn+c9UaIKcxmvT5hjUiABJKTAIXZ5Oy3FKs1hdls8TUhsY0sp9nZL/kQsDAIjC8gCOepDVGz02DfT5ht4aSDAFVSzQTcoK3V7EHopXqc0ySwtt/pvimzkxVvdjtY5zHY7AZzrdlisB+i20WHE/5bt4+r2eIhvIQwRZEdwpWDPEwZEBP8gt1ed9A9r14AIdPkgXo+oYbpkhDQD2XUIAqYNNg+peYG+zz2kVcsbXbzDTruoifsMufpcWEr8Vm6jzQ24416fLaVxuyG62+TLty2qyaw6wThuKh10Zm638NJ84F1HrzM9VOteL/dcPcGxDeT10zdv9TKBPW4Vc0WRDbpMX6RbYdI7q9E9UNHLdjU12zna1xdtTPUULeWarivzHmz/UXjaqhBkDpV7Ra1vWrmPK5x26ZRYUMin0/XaWmmPtiOVbP7BJW5WA1ejHa6XjjhhEj6ZZxeY/KBeH2nms0AAz2N1WxP+UN6nE8tlhDvvZ/ZfHCv/KBmGOE5+Zwapowz4QrdsZ8NSNvQnLS2k3Xf5IPtYLVzDp/HM7SS2m9qdhrsp6nZIa8ebFMz6XDfxvI8RttMHmaLvh2jhv9nE9R+UoMnBgLabtK5/yMSxQnPG1MGhGZ8ls39iHsRz8meaiYNtm3UGEiABEiABEiABEgguxPAu5IxvFvCgjxqc+m50zZt2vTZtm3bZMuWLaL7smHDBlm3bl3UQi0E3V9++UVWr14tw4YNk/bt20ulSpXkjDPOsN/ZvP3jjjtOihYtKq1atZKBAwcKhF53nVqKtVlrCJjCbNbqD9aGBEggeQlQmE3evkuhmp+i74AMJBAVgUma2rzUH9D9y3yuxpeQ3lY6pHeFWYhwEJZMXgt1HwPKfqGyRtpi3UM+iezB+E4+58OJYqYeZtvKJw9EdVQzabCtjUgrhCsnEoEm1KD7l1qWXT7Y+AUIYwvUTFqIXvnV7GDOmW2sbbbzDNq/Vk9gcN+UNVH3g/r7Disd0r+v5oZw/e2m9zs+VyMhfpg6wQvS76GI+3mUlQ4/RjAeyIkSZiEK2fd4cT32C/drpKkvtrWcROHur0T2Q0enLhv1+N9OfXDoCpf4QUMen3QtNM5u23980oSLStTzCeV8ombq86PuY9DEL2CgZZ6aSYtnmRvC9cuVeoHd/y3dDKxjCN+mLGxxHG1IxL2fmXzQPvvZinb7Ca5Ih+cKhEzD6Ffdh/hvQhXdMeew7W5OONsL9NgWwZE2zUnzpR7becX6PEad7Xywj/YGhVD/IxLF6W0t3NTp84CKoN4/WenwOWAgARIgARIgARIgARI4kgC+TxrzFWp1gHjw3r17BQPFu3btkh07dkisQq1ZpxZCqxFq4Yk7efJk6dKli9x6661y8cUXm/e8I7Z58+aVunXryksvvSQTJ06kV20WG/2lMJvFOoTVIQESSFoCFGaTtutSqeJ+GsSRb5A8IgGLALz24BFkXt6DBrRxSR41eH2ZtK4w29o6hzTF1EKFIXrS5LXCJ2E4oc4erF7mc73JG1vUFV+Y/MLZGmkLKPc6icKVE06gQXYH1Ux9bG8oiAt22UOROERwBYiOTlpTRrxtdrL1PeyssaY8tKGIb6p/Ir+20oNHzn9OeXvh+ttJ7nt4l8aaOmF7s2+qvyPLWmkhQJr7NVHCLARNuy5t/y72qL/g8JIaPj/wOD1PzQ7h7q9E9kNHLdiucz27ItZ+EyddJ+ucvXu+k66qfTKC/TM1TaKeTyhuhNpKtQNq7dVChdf0pGHxg0/CcP1SR6+ZqbZJbbtaqH/O8I43ZWHbVC3akIh7PzP5oH3gYto9MkyDr7LS4poGVnr80MPk87vuu58hK6m3ZrBJi22adTKRz2NXmF2u5RjvWKvI9N2g/xFIkChOvTQv0/Y1uv8vZO4T8H8Gn+k71K71Oc8oEiABEiABEiABEiCBfwjk0F3bPKH24MGDg+Ctqls5cOCA7Nu3T8IJtZj6eO3atZ6HLARYCLF+a9QaoRZTIyMdxD0ItXPnzpUePXpI8+bNpUiRIgIPWq3bEZYjRw4pVKiQ3HnnnfLOO+/I7Nmzvbph+mNOgZz5Y8MUZjOfOUskARJITQIUZlOzX5OsVaHGfv95c+QeCRwmUFu39ot60TBk+lnpXWHWHhzHoG+4APHBlB2LUNfBuj6cMPtWmMpss/J60EkbrpxwAg2yCxp0hxhoGGBbConDhDl63lwDz0U7mHhs42mznWfQPoQUU96XQYms+BpWelx3mXUOu4kQZsHD1Ol7J3+/w5IaebZz4g09NnlMdc65h+HuDYiAJi9sIbzXVTtDLdIQ7v5KZD901EqZ+v6p+6cHVLKClQ7pbwxIB9HZ5IctnjfRhEQ+n+xyza/a7Th7/wI96K1m6v6LffLwfrh+sS85wT5w9sEI4hfEYlPe3U6aSA4Tce+bcjKDjyva32kKD7H9Qc8ZRs9a6X604tFvocKJehLircknzUpczIrH+Xiex64w298qx2836H9EIjk1dNq3Xo/bqf3Xr0KMIwESIAESIAESIAESiImAJ9LqlMGfYE1XTB1sRM9QQu3OnTs9b1Yz9fHGjRvFFmoh4EGojWSdWuNVu2rVKhk6dKg888wzUrNmTYH3rLboKDv55JPl2muvlQcffFD69esny5cvT58CmWJtxg4JU5jNWL7MnQRIIPsQoDCbffo6C7eUwmxMr87Z9yKIkPaL+WlhUGAQ16R3hdlJ1jlMJwvvq1A230qPPAuq2SGcUBdOFDP1xPZRO2OffQgvJn1r53y4ciIRaIIG3RtZ5aL8XE7Zfod9rWvcaSZNG+Jts1+5btwqqx6vuid9jgtY6VG/Wk6acP3tJPc9nKqxhsFY3xThI9+w8kB+oUK4e+M5vdjUx97CCxR54/NUWC1UCHd/JbIfOmpFTD0x5WtQuE5PmHTYuiK7fd2fVloIrdGERD6f/Mo9UyPhOd1M7SU1COcr1Oy2Yf9XNTeE6xc3PcTOS9Vw3+N51F3tK7Vdam5592hctCER975bZkbyuV4Ls9s9X49D/b/Auf3WNf11HyGHmu1Vjc9UuLBYE5iy06zEiXweu8KsLSRbRabvHtQ9Uyd7VoVEcUJB8JDdZpVjysN2nVpvtVvUzlBjIAESIAESIAESIAESiIOADhp+goFDiLPGINIGCbX79+/3PGr37Nkju3fvFiPUbt26VTZv3iwQatevXx/XOrXwql2wYIH07dtXHnnkESlfvryceeaZ9jth+n7u3LmlatWq8vTTT8vIkSM9kZjr1SZ+KJjCbOKZMkcSIIHsSYDCbPbs9yzWagqzcbw7Z8dLbeFoRwQAGmsa87LuCrO2uGnSRLOt5pQfTqgLJ4rZZdsD3U4x3uHPVrtaOwnClROJQBM06G7z3+yUG3SYpidM2+D5ZQcTj208bbbz9NuH1xnEd1PeA36JnDh4BtrXPOacD9ffTnLfQ1tU6+GbInzkG5rEtAtiV6gQ7t6AaATBz+QXtEW9IUL6hVD3V6L7oaNWwNRxpV9lDse5wiy8+oKC3ee1gxIFxNufj3ifT6YI3Id4js1V+0vNtDfUNh5htqCW8a6anwAbVGYswmwi7n2tpjfFeGbwuVvLCmp/JPGzUFkN56rZ6SNhN8q6Jk33TbDvt3ifx64w28gUErAN+h+RKE6m2Ct0Z42azczd36fnP1W7RI2BBEiABEiABEiABEggBgI6UOgJs+6AoRFpjTdtKKEW0x8boTZonVp41UJw/eWXX7xpjY1Hrd8UyIjDeXjeGq9aXDt9+nRvWmNMgVyiRAk58cQT3fdD7zhfvnzeerZY13bKlCmeeGy8gY3ojK3dRuwzBBOgMBvMhmdIgARIIBoCFGajocW0GUSAwmwM78zZ+ZIu2njz0r07AhBYs9Okd4VZe3pIkyaabQOn/HBCXThRzC67npO3e3ishNkXLZ5b3EoFHD9tXbPXSZOoNjvZHnV4ssbYgluQqGhfCJHSFh+esE/qfrj+dpL7HtpTmr7lmyJ8ZDTC7FOanWG+LETWNfXcaLUDVnpznb2FGJJLzQ6hhNlE90NHLdjUx/1823XKLGE2kc8n1B+8pquZNvptcY9CkIdwa87/qvtuCNUvJm0d3bE/JyY/ewtPxR5qdlwk4qIpw2wTce9nJh/8MMNuc7T7EKIRzlGzr22GyDDhSz1vrkmz0ibyeewKs+4Pj6xivV372VjfOpkoTlaW3hq8L2vEGjXDwW8L79ob7Qu5TwIkQAIkQAIkQAIkEBkBHfDzFWbdgUBXxDQCJwRPe+pjeNRGu05tuOmP7bVqIdYiPQRe7I8ZM0Zefvlluf322+XSSy8VrE2rLT/CsIbtf//7X2nWrJl069ZNpk2bJjt27EifBtm0hWKt2+v/HFOY/YcF90iABEggHgIUZuOhx2sTRIDCbGSvyUx1mEBb6+Ua3mMYTA4VbO8dV7ixp4ccopnkjdLcmzecUNdB8zdfDPxEMXMO23pqocKxEmYf0EqZekLAgTdfuGCLOD85iU1e8bbZydb3cLPGmvLSfFMcGZnHSo/rmh15OiHC7CSrDExLG0uwhdkZYTLorOcNA7970L38TI3Avfiemi2kmTyw7aRmh3ACYCL7oaMWbOrifr7tOkUjzP5p5Rmtx2xb69p4n0+oP+4J0z5s/1Cbovaa2h1qRdTghYzwrJpJu9aLOfJPuH65RpPD89DkgS0E3v5quLaq2gVqCPjRgs2puRcb3Z9E3PuZyQdT5tpsiutxNP8zzrPwwJva5PWMFR+0a/+vSrMSJfJ5nChhNpGcrKam7xbWPYi/49X2qxmOZot72P3frFEMJEACJEACJEACJEACoQjoAF9Ewqw7EBhOqLXFWluojWT6Y+NVC+HVeNZCnLUNXrXGsxZetRAO4VW7YsUKGTJkiHTo0EFuvvlmueCCC8z74hFbCLiXX365J+h27txZxo0bJ5s2baJY63b04WMKswFgGE0CJEACURKgMBslMCbPCAIcPwv1csxzRxFoqDH2i/RlR6U4MgKikUnvCjf2oP6sIy+L6eg7qyxXrEKGqSDMQpwxPLEtgIaFCRhAN9eAkR1MPLb17BM++z9rnEnf2ud8uKhvrOv7hkus58tY6VFudeeacP3tJPc97K6xpk3f+qY4MvIVPeylBs/XUodPddWtycPlezhJ+qa3lTYSYTb9wsM7l+sWoqAtys12EoUTABPZDx21bNN29/NtVyuzhNlEPp/yWG1DG1eqhXrevWWlX6/7bgjXLz30AsMSonKaWg41v5BbI01abO/zSxQmLt57P7P5/Ndp801h2hfqND7rhl+/UAn13Alqu630abpvQiKfx4kSZhPJybQzaHuqnrhVDZ99wxPbamoMJEACJEACJEACJEACURDQ0b6YhFm/UUJbrDVeqGYKYVuotdepxQB10PTHodaqjWYK5Llz58pHH30kbdu29dajDRJrFZtcfPHFUqtWLenYsaO3Zi1EYrNmLdpi2mW3FfupHijMpnoPs30kQAKZRYDCbGaRZjkhCFCYjeJdmUn/7//+jZdkyzqFgTLHSusKNy9b5w7ofn61UKGpnoQnGsSvz9T+o2aHcEJdB01s6u4niplz2NazM/bZ/9nKyxUpw5UTTqBBcQet/Osj4nAooFu7nmnmRMAW6e28ejrp7LziabOTre9hX4015W3X/bN9U/0Tibqa9BCqcO/ZIVx/22mD9u2+AKd8QQk1/iw1e5pZI4bZXrA/hbgep5armTa592BjPTdeDV6S09VCBYizJh+ItLmsxHabUJ4bEtkPHTVzUw/3822Xe52VDunPt086+2iPybO2cy7cIe4Rcy22ncJcEOr51NDJCyJcqDBPT5qyN/kkDNcvK63rIZ6HCrX0pCkL21ahEgecs+sTy72f2XzwsoLngGl394B2meicuoNnxGq1CWpPqJnQW3dMPvDwvNCc8Nnic27SYptmpcHzNeiclSx9F+mDnseJEmYTxel0revHavPV9qhVVgsKufXEATXDAs9EBhIgARIgARIgARIggSgI6KBdwoRZdwDQFS+NqBlu+mPbq3bbtm2yZcsWz5t1w4YNgrVq165d601lDLEwlFdt0BTI8KxduHChDBw40POsrVmzpuTPn9+8Ux61Pe+88zxBt127djJo0CBZuXJlRGJtKgm2FGbdu5vHJEACJBAbAQqzsXHjVQklQGE2indlJv2bwGzdmJfknbqPQVm/AFHFpMPWFW4qOOc/98vkcBw8c7630m/W/ZMPnzObcEJdB+t6VxRDHnZd65lMA7Y/W+lbO2nClYP0pqyfnGvNoT14bwuzx2kCW9zbpcdYMzEo9NETpixsb3QS2ufiabOTre9hI421y3vZN9XfkZfq5g8r/UyftOH62+eSo6JKaIwt9rx3VIp/IiDsmPrjGjOtrB2P87iv/cLDGmmux3aZk6itdR7iJEScoPCMnjB5QRA53koY7v5KZD90tOqxyKqDu3udlQ71zihhFuUm6vlkC5eo82XIPCA8qfGmP7Dd7pMuXL9A/DJ5QBALCnn0hP0sxDXIO9oQ772f2XzQvqlqhhE+I6UQGRDu03iTFttHrXRX6z6uN+c/1P0c1nmze67u4IcSJh22aWomJPJ5fJJmapdTzRQSsA36H4HkieAET+GtaqZOYBQUTtQTELhN2qeDEjKeBEiABEiABEiABEjAn4AO82WYMOsOIYYSaoPE2j179ojxqt25c6ds375dtm7dKps3b5aNGzcKvGrjEWsxDTK8YiHWLl++XIYOHSqdOnWS2267zZvqGOvTKrmj7Mwzz5QbbrhBHn74YenTp48sWLBA4AlsPISNCI2t226XSzIcU5hNhl5iHUmABJKBAIXZZOillK8jhVn/12LGhiCAqQvtF+K5enyxk76iHkM8tdP5CTfwfLXT9NTj09TscIYejFGz071gJzi8H06o62Dl4YpiyMLOPx6RMlw5LayyIA5ci8KdEGrQvYqmteu6VI+vdK7HQPlbTrqVeozBdjvY+cTTZjvPoH0IHzPUTJloe0c1iBt2uEYPflYz6bC9w05weD9cf/tc4hvVT2NNWfCIRf+5Ig2mTbU9wr7SYxPK6Y65HluIZrZYDtEU9beFIKRz70F8hv5SM3nBA/NCNTdAVNqmZtKBqR3C3V+J7IeOWrCph9/n29QrM4XZRD2f0Oembdi+ZxrjbB900iEt+tq9h8L1yywrH/zg5VI1N0AoxH1j1wv7z7oJIzyO597PbD5oUlE1fEZN+9fqfiU1N9TQCHv909/1GB7vdhioByYfbD9Xu+RwAjw/b1RD/nYa7Kep2aGKHthpYn0eJ1KYTRSnXk7bIHa7IadGvK9mM6jsJuIxCZAACZAACZAACZBAaAI69JhpwqzfMKctWtpiphFqI50C2RVr161b54mtkaxX63rW2mLt6tWrZdSoUdKlSxdp0qSJFClSRE488UT7HTR9/5RTTpGSJUtKs2bN5LXXXpOxY8cK6uE3FXKyCbYUZv3uXsaRAAmQQPQEKMxGz4xXJJwAhdnQr8c8G0BgpManv/jqPoQiDHS/oPaFmj14btL5CTcQo/aomTTYwkOpj9pTahBDbK8dnIcwkVvNDeGEunCCqV2Hem7mzrEtHLreauHKgSeUXRb2IcQstsoIJcwi2TA1Ow8wRJ88rwZxe7WafR6iYnE1N9hp4mmzm2/QcTE94QqUSzQOA/svqo1WswVQ1G+Iml8I199+1/jF5dVICDc2i/l6/Jbac2rujwJ2aNx/1OywSA/s63H/T1cbobbdOWfS4T52Q3+NMOex3avWV+1ptY5qYOF+tm7WODtEcn8lqh9QJ1NfMAgKmSnMog74LJh6YRvL8+nsw9fZ+UzVOAixEKjQLz+p2eft/Tx6zg7h+qWjJrav36nHXdSaqeE+/ErNFhvttD30XCwhnns/s/mY9r2jO3bb8WOGCWpgBU6T1ezz2G+p5oYCGvGLmpsW90oQZ6TtqOaGRDyPEynMon6J4FRY83H/P+OHWK+pPanWVe17NZvhbD1mIAESIAESIAESIAESiJKADvUdU2HWHWq0hVrsG7HWeKIaofbgwYOeh+q+fftk7969As9aTIEctF4tvGrjFWshTsKzFsLtxIkTpVu3btK8eXMpXbq0nHbaafa76RH755xzjpQvX14efPBB+eCDD2TGjBme56+fYOu2H8dZIVCYzQq9wDqQAAmkAgEKs6nQi0nfhlOifF1kchLwCJymf13B6oiXXj0PAW6omokPEm7KappVVjqT3m/7k6a7SM0vhBPqOuhFJk8/Ucycw7aeXwFW3M9WXtEKs2fqtX6CAAQGTNmMcFDN1Ke+F3PkH3h/9bPSmLR+242aLsiDyU4fT5uPrF3oI3iz/aZmlx20D6EyV0B24fo74DLf6Os1do1aUD1MPESK6j45gG+QAGuu3aBpGqmZY7978GQ9D2HDpAm13aXp/PoskvtLL/2/RPRDR6uui5BpQMhsYTZRzyfwDdUH5txuTfeg2h9W+gd03w7h+uUETTxHzeQZajtO00EYNmnW6/7xarGEeO79zORj2oaXFgiC7g88DAt3+4y50Gd7vsbNUHOvsY9f1fP288qeEtlkmYjncaKF2URxulUbif9NNpOgffwoCywYSIAESIAESIAESIAEoiSgQ4tZSpj1G+p0xcogsfbAgQNHiLUY/A61Xi2mQbbFWoiP9pq1P/zwg8Cb1jbEwZAOAi2uMVMhT5s2TXr06CGPPfaYVK9eXS655BLJkSNH0Dus5MuXT2rUqCFYu3bAgAHedMgQmo0Ibdrp512b2YIthVm/O5NxJEACJBA9AQqz0TPjFQkngLE7BhKIiUBOvaqt2lo19yUXwmVDtUrWOXjaBAUIkm+obVJz88LxFrV2ahBcgsJMPWGuhYehG57QCHN+oXtSj20xtKbPeTvK9hJyBZhw5SCfy9UgZJn6mO1/cVLDXjUTV9eL8f9TW6OXq/mJFFs1vptaHrWgkKg2B+UfFJ9bT0BY2qFm2mlvp2n8bWqhQrj+DnWt37kzNLKXWlCdBum5f/tdeDguv24nqdlM0SZ4445XK6h2mZppJ/rfL+CzgHtos5pJa2/xWRithnsoKIS7v8x18fZDB83I1C3U57uUlQ4iz79MBXy2trdidZ/zkUYl6vlUQQucr2baaW/xvHpPzfxYxBb50EduCNcvEG+7qNkMTHm4r75Vq6+WQ62ymjmH7TVqsYZ47v3M5GO3r5weQMh2P2+GyVg9hx8EhAsnaoL71T5SW6yG/JYdPjb3H35UYfJtrvtBIZ7nMepxSM2Ug/4NFfbqSZM21P+IRHDC5xc8/QRa1HmJWjs13JcMJEACJEACJEACJEACMRDQob4sL8z6DUcmQqzdsWOHbNu2TbZs2XLEmrUQa9euXesJrtGItT/++GO6YGvEWoi6mNIY3rUtW7b0PGfPP/988z591PaEE06Qq6++Who0aCDPP/+8jBgxwhOC/bxrM1OwpTDrdxcyjgRIgASiJ0BhNnpmvCLhBCjMxvDOzEuOJABPr2JqENLgXYPpMeMJ5+nFFdWaqVVV+48aykjFcJE2qrTaVWqnx9FAiHkYPG+qhkH64mrwvkyGkE8rWUPtDjX0eyjxU09nSkCdaqk1ViupFuoHAXr6iADu6FO0B32SUy2WAJGjgBrY3K1WR+1itWhCNPdXVuyHaNoalDYRzyf0RX41/NDkTjWIZvC2jDWE6xc8C/BMxfMUP3AprAbhLjNCLPd+ZvOxOaB/r1a7/bDhMxdK+NfTUQV8fiE+ymHD/7lwISs+jxPBCc9BPNuaqDU7vM+XSAXBQAIkQAIkQAIkQALxEtChvqQUZv2GKG2x1vY2DVqv1kyDbDxr7WmQ3TVrMRWyLdbCWxZes8aD1vaqNfs4Z4u1tnftkiVLZOjQofLiiy/KHXfcIaVKlZLTTz/dvPsftcW5a6+91ps6+Y033pBx48Z5HrvRCLbxeNlSmPW74xhHAiRAAtEToDAbPTNekXACHFOL9wWa15MACZAACZAACSQNgfu0pqvVvlR7KkytIcTaAzJFw6TnaRIgARIgARIgARIgARKImoAO9aWMMOsOW9pCLfajEWvdNWu3b98utli7YcMGMWIt1p2FcGnEWoixEGWNQOtugwRbCL+zZ8+W/v37y1NPPSX16tWTQoUKCbxotWN9LVeuXFK8eHHPw7Zjx47y8ccfy9y5c70pnKMRbV127jGFWZcIj0mABEggNgIUZmPjxqsSSoDCbNRvzLyABEiABEiABEggWQk00IrbAyrlAhoCz9uNVlrswzuYgQRIgARIgARIgARIgAQSSkCH+VJWmPUbwgwn1hrv2oMHD4q7Zi3EWgyow7N2586dYou1mzZtEiPWuuvWQrCFWButYGumQ8b1U6ZMkQ8++EDatGkjN910kxQsWFBy5sxpf7c4Yh9r22IN26pVq8pDDz0kb7/9tkyYMEEgIkcj2IIXAoVZv7uJcSRAAiQQPQEKs9Ez4xUJJ0BhNqFv08yMBEiABEiABEggKxPA1PH2gMlCPS7jVBiesZOddN2cNDwkARIgARIgARIgARIggYQQ0KG+bCXM+g1tumItjo13LYRaW6yFYLt//37P9u7dK35irbturRFs4REbi3et7WELgdQWbKdOnSofffSR52HbsGFDKVmypJx11ln2d46j9jEtMtI1atRIOnXqJIMHD5b58+d7onOQaAtxuFixYjJs2DCxefnxZBwJkAAJkEAwAQqzwWx4JtMIUJhNyFs0MyEBEiABEiABEkgWAj20ou7gyA6NW6W23efcco3D2rEMJEACJEACJEACJEACJJBwAjoEmO2F2aBhUFuADCfW+nnX7t69+yjv2i1btojxrl2/fr3Au9Zv7dpIvGttwRbCKURbCL/Ib9GiRZ6I+vrrr0vLli09z9kCBQrIcccd534XST/GOaSBR27r1q3lvffe87xsUZfVq1dL0aJFvXVxIVQb4Rpbl5M5DuLKeBIgARLIzgQozGbn3s8ybacwm/A3amZIAiRAAiRAAiSQlQmcqJUbqpY+ABJif4yeO0eNgQRIgARIgARIgARIgAQyhIAOEVKYjWKc1IiOZmsEStuzFl6nZipkI9ja3rUQbDEV8o4dOwTetWbt2owUbI2XLUTWr776Snr16iUdOnSQ+vXre2vUnnHGGSG/n2Cd25NOOsnzmm3VqpW8+uqrnvC7YMECT3wO8rQNEm6jQM6kJEACJJBSBCjMplR3JmtjKMxmyFs1MyUBEiABEiABEsjqBIppBT9U+1ZtkxoGQrapLVFDfHk1riurEBhIgARIgARIgARIgAQyjoCOKFKYjXNY1Yi0ZmvEWmyDBFtMh7xv3z6xBVuzdq0r2G7cuDHk+rVr1qxJX78WXrTff/+9rxkPW4izuMZ42UK0hZctRNahQ4d6out9990nlStX9tayhSird2BIu/DCC6Vs2bLStGlT6dixo/Tt21dmzJgh8Aq2RVt628Z5s/FyEiCBpCdAYTbpuzAVGkBhNuNerZkzCZAACZAACZBAEhE4LonqyqqSAAmQAAmQAAmQAAmkCAEdXaQwmwFDrEaktbdBgi28a42HrSvYutMhB61fG25K5HCCrRFtXcEWUyNDuP38888lf/780qxZM3nooYekVq1aUqRIEQnnaasfE8GatoULF5Y6derIo48+Ku+++66MGTNGVq1a5a3Va8RrV7QN8rY1TDOg25glCZAACWQ4AQqzGY6YBYQnQGE2Rd7j2QwSIAESIAESIAESIAESIAESIAESIAESIIEkI6BjdxRmww9gJiyFERXtrRFsbYHSbzpk42GLQX0/wdZvSuTffvvN84Y1AivWoYX4Cq9ZWCjBFp63OA+D9+tVV10lPXr08NayNVMjw9N2yZIlntD6wQcfSPv27aVhw4ZSpkwZyZMnj+TIkSOkp+3xxx8vl1xyieede88990inTp2kT58+MnnyZK9cCNW2x61hZbY2R3c/YZ3GjEiABEgggQQozCYQJrOKlQCF2SR7X2d1SYAESIAESIAESIAESIAESIAESIAESIAEUoSAjuhRmI11WDOB17miIo6N+GgEWwiUoQTbPXv2iBFtzRq227dv99aw3bJli2zevFncaZEhrEJkhWCLqY2NaGsEWTMt8rRp06RQoULy/vvvp0+TbNLYUyPb0yNDDMa5qVOnSv/+/eWFF16QFi1aSNWqVeXSSy/11qzVj1FI4fa4446Tiy++2JsmGYJvu3bt5L333pNRo0bJ4sWLvbV6beHW9br142riEth9zIoESIAEIiZAYTZiVEyYcQQozKbIezybQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkGQEdMyPwmzGDXzGnbMREc3WiLXY+gm2QVMiG8HWrGMLwRbTIkfqZfvNN994HrPwijWethBmjXDrbm3R1hZuIQAbb1sIt3PmzJFhw4ZJt27d5LHHHvO8bcuVK+dNm3ziiSeGFG31o+ad/9e//iXFihXzplfGNMuvvvqqDB48WGbNmhV2jVtwNGyDtnF3IjMgARIgAYsAhVkLBnePFQEKs0n2vs7qkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpAgBHRGkMHushkVjLDdIQAwn2h44cEBg7jq2RrQN5WU7d+5cufrqq+XDDz8UrGdrT40ML9lQ3rZBoi3EW1u0db1tUca8efPkiy++kO7du8uzzz4rmO4YHrfw3sXatfoxDGunnHKKXHbZZVKlShXv+rS0NOnZs6d8+eWXXv4bNmw4YrpkI3jbPCngxnizpsBl5vNm3w8mDlsGEoiWAIXZaIkxfQYQoDCbIu/xbAYJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkECSEdDBPgqzGTDieayytAUjs28LSkZ0tKdFjsTLdunSpVK4cGEZMGCA52lrpkbetGmT7/TIrnBrpkg23rauWGsfG29bW7jF9a5wi2mYV6xYIZMmTZJ+/frJSy+9JA888IDUqVNHSpQoIRdccEFY0VY/rl6aE044wZsyuXTp0t71LVu2lOeee0569erlCbjz58/32ulOm+xOnRyJgEsx71h9OqIr1/78YE3ntm3bSsWKFb11kOH5bfe9SRtdCUydXQlQmM2uPZ+l2k1hNsne11ldEiABEiABEiABEiABEiABEiABEiABEiCBFCGgw4QUZrPUWGnGVMYIR+42nGhrvGxXr14tRYoU8aYI3rt3r9jr2WJ65B07dog9PTKEW1u0Xb9+vedpCzHVT7Q1wq0RZW2h1m/fpIPQa3vd2lMloyzki2mYP/vss/Tpkhs1auQJbPC6xTTI+lGO2DC9Mta8vfbaa6Vu3brSqlUref7556V3794yevRoWbBggdfuSATcSEVc9BlD5hIwnxOIr/jhAtY2tu+T448/3ps6e+TIkZ4HuhFpzXXss8ztr2QrjcJssvVYStaXwmyKvMezGSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAklGQIcbKcym5Jhr5I2yxSSz7wq2ED+LFi3qCZwQHSFW2Z62QdMjmzVtjXCLNW1tb1tMIwzR9rfffss04dYVb+GJ++2333qesRBY4Xnbpk0bb73bChUqeNMm586d+whhTj/mIY9POukkyZcvn1x33XVSu3Ztad68uXTo0EG6du0q/fv3l3HjxnnTKEM83rdvX0RTKVPIjfyejjclPgfgjXsc/VOzZs3A/v73v/8tzzzzjPcjACPI230Vb114feoRoDCben2ahC2iMJtk7+usLgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQIoQ0MFECrNJOKKaWVU2Qi3Ey2LFismwYcM8wcoVbuExCFHKFW1DrWnrirbbtm0TW7jduHGjRCLcom5r1DPWTJMMb1o/L1s7znjcYmt73SIfM2WyK+Di3OzZs2XUqFHeFMcvvviitG7dWho0aCDly5eXK6+8Us4+++xAAU8fGYHncB3WwS1btqwn5LZo0cITct944w1v+mgIuZhOGUIuRHAjAIK7MbtP7H3Th5FuM+veysrlgBUYgzXuU0zhDS/ZUH143HHHSY0aNWTEiBH0os3KnZsF6oYZB7Bm9sMPP+z9ICULVIlVyH4EKMymyHs8m0ECJEACJEACJEACJEACJEACJEACJEACJJBkBHQsksJs9huQjbrFECkhzA4fPjz92iChzxYFjWjoCrdmimTjaQuvRL8pknfu3Ok7TfLmzZuPmioZXrf2VMm//PKLoN62cBuNeAshNxYBF2VAwP3iiy+kZ8+e0rlzZ3nsscfk7rvv9kTXMmXKyOWXXy7nnHOOQMzTR0bUBg9eCLnlypXz1sSFkPvUU08JhNy+ffsKptidNm2aLFq0yJs6Gh56Rji3+8Ts233m7gf1c6j49JskSXfAAN6yENDwg4F169Z5gvwtt9wip5xyStj+uuiiizwvWtwLRkRHnoZZrFjQX/hM4AcLtORlgB95PProo54nPaY/Z18mb1/G0ndYsxrPg2McKMwm2fs6q0sCJEACJEACJEACJEACJEACJEACJEACJJAiBHRgkMLsMR4dTYbi/YTZUPU2ApS7tUU/Iwpia0RDe4pk29vWFW4hNGJwO8jrNki4hcBmi7doly3eQjAxHrRGlLU9bYP2TdogD1zjhQuxGIZ1dk09Fi9eLF999ZUMHTrUE3JffvnldCG3Vq1anhctRFish5sjR46woqA+mnzTYHrlCy64QK644gpvjdybbrrJ8/Zt2bKl553bpUsX6dGjhwwZMkQmTJggc+bM8TyP4cWMfjECo91vZt/u16B9916I5TjUPZeoc6gX2oA24z7D1Nu4R5YtWyZz586VyZMnyxNPPOGJ60GsTTyE9+rVq3s/aMA9DF7I2257NPWGFzn6CR7atORlcP3113tTnefNm9eb7px9mbx9GUvfYcp8/ODjGAcKsynyHs9mkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJJBkBHRikMHuMR0eTofhohdlwbbKFKXvfFvWM6IetEW6xtcVbeNwar1t43CbC6xbibUYJuEbYNUKuEYEhCMMg4BoRF8z9hFx4wRohF0KqEXLvuusugZD7v//9T4oUKSL58+eXs846K2avXH2UpQu8EBgx3XKBAgWkePHiUrFiRalXr57nBYzpnOGti7V53377bfnwww9l8ODBMnr0aJk6daonZq5cudJjCm9Puy/tPnb37Xsh3L59DyViH3XxE2YhVn/99ddeu9AH8IgG81NPPTWdlc3N3ocIB07oezBAGbZIG+4zg/O4H+655x6vT9EntORlgB9ZwNiHyduHsfYdngPwtD3GgcJskr2vs7okQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIpQkAHBinMHuPR0WQoPtHCbKg2hxLWXIHOFvOM4GcLt7F43W7fvt3zZrLXu920aZP4rXkbi4ALYc4ItJFsjYiLbTRCru2Vu2LFCvn22289T1h45sJjC9Mep6WlSdu2bT2xD9P0VqlSRUqVKiWXXnqpnH/++QIvW33UJdQgRp122mme927BggU9ERlr61atWtUTe++44w5p1aqV55WK+r322mvywQcfeOu8Yv3WsWPHypQpU2TmzJkyb948Wbp0qSd2wgMZ3q3wpMY9YO4Hs7XvlXD7uAb3jj2V8erVq2XhwoXeNNXTp0/3xHF4z06aNMkToTFdNbyRw/GCmFOtWjVvvWY/L9pQnw18DjElNsooWbKkPP7449KpUycaGfAeyOL3QLt27QTPO3z+KcymyAs0m0ECJEACJEACJEACJEACJEACJEACJEACJEACsRBQEYDCbCglhOc8ApkpzEaCPEi8jUS4hegWTry1PW+Dpk2ORMBdv369YO1bW8CFYArPRzCFGS9Z4zVrxFdbkI1EwHXT2NebPE0Z4bxzjagLsRPXYh1MeL9i3dz+/fvLu+++63nIQmx46KGHPLHw9ttvlxo1anjetKVLl5arr77a87A977zzJFeuXGEFS31+JTTNCSec4AnAmAIa3qrw9r3yyiulaNGigvrdcMMNnhiMOt96663SuHFjT6CGKNymTRt58skn5emnn/bWioWQ0r59ey8OYijE7EceecRLh7QwHEOcbdasmbce84knnhi2PXny5En3orU9aIM+A7YwC4F2+fLlgmm7aWTAeyBr3wN45laoUEGOP/54CrOxvKzyGhIgARIgARIgARIgARIgARIgARIgARIgARJIFQIqAFCYDVJBGJ9OIKsJs+kVC9gJEm4RH0q8hThmPCwTIeDu2LFDbAHXzwvX9cQ9VkKuEXZtQdfPS9cWde1pl3GPmKmXsYW4awu8EHnhdQqR95tvvvG8d0eOHCkff/yx9OrVS958801P7IUYCuHzvvvukyZNmkjdunU9ARVetcWKFZNChQp5nmcXXXSRnHvuuXLGGWfIySefHNf6u/o8DyuiZmQaeBFjzd9p06alT2/sd2vbwuz999+fFaZD9asm40iABBwC+D+AWQFy5sxJYTZVXqDZDhIgARIgARIgARIgARIgARIgARIgARIgARKIhYCOHVKYdQZQeXg0gWQTZo9uwdExocTbjBJwMTUuPHBdL1ysvWpE3EQJuX5euRBSIazCjBcttq4Ya0TaeLZunnZ52EcdjBmvYbPF/WabEXyN2OsKvhB94ZWM61etWiWLFy/21raFAIy1YMeNG+d5+2IaZwjBffr0ke7du8tbb73lTZWM9XE7duwo8ACGKAyv2ebNm0vTpk2lUaNGAm9geNXWqVNHatas6U1FDJEFa+3C8xaiMbxwMb0w1uDFlKVYkxfTlupzOaTBsxd5DhkyxJs6GT8IMGvPunetLcxCuIZHNgMJkEDWJ4Af5WCqeHjMwhsfzyqzLjq2+NxncuAas7G8NPMaEiABEiABEiABEiABEiABEiABEiABEiABEoiXgA4EUpjN5NHQZCwuFYXZSPshIwRcTKWMNUzNGrj79+8XrDcKM4P1EHHjEXIxtalZG9d45W7YsMET8+CVazxzzTTLEDeN8GmEUCOOGsHUCKnY2kKrLcLGI+ZGcq1dlr1v18fet+ts9k17/LamzWaLNLgOZcHjd+XKlbJs2TJZtGiRJ/7OmjXL83SF4Ash94ILLggpxOoz2zt/2WWXedMjz5kzx+uLbdu2ef2NewKe27jv3IA6mTVmKcy6dHhMAlmXgC3M4sce+JHIxIkTZfz48d7sAXgO43OfiYHCbLwv0LyeBEiABEiABEiABEiABEiABEiABEiABEiABGIhoIOAFGYzcSQ0WYuCIIRpZIcPH56sTcjwekcr4MIrEgPxttnTKNtTKccq5O7evVt27drlGbxy/TxzIQhCNDDTLG/ZssVbsxSirivswkPTrJsbibBre+3aQqcRRI1Qara2oGqLrtiPRLTNqDQQZOGJC1EW67ouXbrUE2Znzpwpzz33nJQqVSqiqZRPP/10qVWrluetO2PGDJk3b56XH9hASEdfQaRH35v7yb5xKczaNLhPAslDwBZmMT17z5495ZNPPpGBAwd6Xvx4xlGYjeUtlteQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQJIR0GFNCrPJM7Z7zGpKYTax6I3oFmrrroUbq5Bre+XanrmhvHPtqZYhFtqirjvlsp+wa8Rd47XrCrzw3DXeu7bQG0rshTev8eQ1WyP2ulsj/NpbI/66W1sMtveNMGzEXuMtC2F20KBBUr9+fTnttNPCesdiOmNMb/zYY4/Jp59+Kl9++aXnKQdhdu7cuenCLBhRmE3s54y5kUBWIWALs5gSncJskr0ss7okQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkCgCOmhJYTarjNxm4XpQmD22nRNKwDXn/IRcPzHXeOi63rlBHrq2sGumW7ZF3aCpl424C69dP89d470LoddP7I1U8HU9ezFtsz11sysCu16/7rTO7tTOxuv3u+++89ag/c9//hNWjNXns+TNm1caN24sPXr08KYtHTlypIwZM0YmTZokEGUxhfGSJUu8KZJRJtoLZpzK+Nh+1lg6CWQEAQqziXprZT4kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkOQEdACSwmxGjMKmWJ7HWpjFVL4Q4CDgZUSAh+eIESO8tf4yIv/MztOItZFug0TdIGHXT9R1hV30mT0Fsy3wwnPX9d4NJfbaa+26gq8r/LpTN7vevq4QvH37doFBGDUGEQUGj1+IvP3795dTTjklrCB76qmnyo033ihdunTxpv3GPTVq1CgZO3asJ8hOnz5dvv32W1m4cKGsWLFC4L0LYRhloR1gAI7gjr5zAz6HXGPWpcJjEsj6BCjMJvnLMqtPAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAokioMOZFGaz/pjuMa/hsRRmIe5B0Lr//vs9USsjYGB62gYNGkjr1q0zIvukyjNSMddOF0rYNeeMp27QNkjsjUTwhehrzIi+7tb29nX3jdev2RohGKIuxFpMRazPXF/LkSOHd/7JJ5/0xH1MVTx69GgZN26cN2Xx1KlTBWvRYtrixYsXe+vUQpCFlyyEXwjFEJtR31CiLG4iCrNJ9VFiZUkgnQCF2US9tTIfEiABEiABEiABEiABEiABEiABEiABEiABEkhyAjpqSGE2feiUO0EEEinMQtAz07Wa8ozIZ47tLTwJ4bFYrFgxmTZtmn3K8yp08zoigR4YYc+NxzHKhfCLKWVvvvlmueeee9KT4Vyoa9MTcseXgOnTeLZG1I1kGyT4BsWjb4MM9wSEUgimEGYrVqx4lCibJ08eueuuu+Tjjz/2PK0nTJjgCbGTJ0/27tNvvvlGMP0xvGMh/GPNWqyTi6mV4aEN0Rf5QyTGPYy6oJ2Glx9UCrN+VBhHAlmfAIXZJH9ZZvVJgARIgARIgARIgARIgARIgARIgARIgARIIFEEdDiTwmzWH9M95jVMhDCLKWYxret9990nt9xyi7Rt29ab3hWiFITRjh07yqBBgwRCGkSr4cOHS1pamifK1qxZU84++2xp1qyZfPXVVwLPRmwffvhhqVevnrRs2dLzVoSYBg/Ezz//XDp37ixvvfWWNGzYUO644w7p3bu3N2UtYCLN4MGDvWlhmzRp4tXlhhtukHvvvdcTx3788Ud5+eWXPS9aXI+paVevXn3M+4EV+FtMN+JlRmwhjuIexH0J4RQiKu61woULyznnnOMJ+LivIMAaw3l4dRsxdsGCBbJs2TLvnsFnB9MVwzsWUybjhwbwyMW9CgE4EkHW9DuFWUOCWxJILgIUZhP11sp8SIAESIAESIAESIAESIAESIAESIAESIAESCDJCejQJoXZ5BrfPSa1jVeYhcD12WefyTXXXCM1atSQxx57TCC2VqpUyfMy/P777z1vVYijELkw/WvZsmU9j8SJEyfKrbfe6olirVq18gSwSZMmSfny5b3rkVf9+vWlXLly8umnn3oiWIcOHeTf//635+mIKZCx7iemox04cKAnjH3yySdSokQJqVWrljz00ENSpkwZgQfkAw88IOvXr5dnnnlGrr32WmnXrp0XB29diLYQ1RhSmwDEXoizEGbR3xBUf/rpJ+/HA/CA/frrr737E/eoEWOxbuz8+fO9NKtWrZI1h6cqxvq08LjFDw0gxtresRB/w3nIuqSzojALYRk/dIAnMNqJdqVKgHAOMR1twzMM9wZ+FIJ7AgZxPdKAa3EPYK1siHS4v+INKB/1wH3mZ6g36o8+QVtQLtKhHqhPsga0x/QLPqP4HGX1QGE2yV+WWX0SIAESIAESIAESIAESIAESIAESIAESIAESSBQBHcykMJvVR3SzQP3iFWYhvMLz9AYVXiFurV27VsaMGSNVqlSRFi1aeIPsY8eO9QTSChUqeF6wEFohgsFj8e2335YrrrjC87hdsWKFtxYshFWItsgLIhnE27p16wrOwxv3kksukT59+sgaFclGjhwppUqVkgcffFBQl0aNGnl1mTNnjne+V69eUqBAAYHwi2lnMa0x6gGxDUJbv3795M033/QEqCzQHaxCBhIwwiyELIhxRpiFByzWiZ09e7bMmjXLW+943rx53rqxuOfcqYrN2rEQ8iDCIT9XjI1WHMuKwiw+T/B2r1y5srzyyiueiBlN90CshqfxlClTstznC32OH2dUr15dPvroI0+UxfrBWI8a057juRNpwD2AH4TUrl3be37hXoo3oHzMBgD2Qda+fXv55ZdfvOcYflyCdPiRTEb8yATPyy+++ML7zMTbtlDXQ+R84oknvB/cvPPOOxleXqi6RHqOwmyi3lqZz7EkcIoWfoNj5WKsUGEnn2tizCeSyy60yioayQXZKE0Zi82Z2ajdsTQ1h150vcUrVyyZJPCa66y6nJXAfLNyVnmsNheJs6JZiV9Zq11nxNkuXk4CsRBIxf+TWe2ZHUu/pNo19nOX7xyp1rtsDwmQAAmQQEwEdFCRwmykI6vZOF08wizEJ4ix//3vf+Wyyy7zph2GZyqmEL700ks9z1hM9QoPqHfffVfOPfdcueqqqzwhBB5h8O6CwHr11Vd7HopLly71PGHz5s0rzZs39zxaMcVx0aJFpWTJkp5Y8vjjj0vp0qUFUxIj4Bp46CIdvBuvu+46Txwx3n0QFKpWrep5xa5bt05w/YUXXuiJs23atJEBAwbIypUrU8obMBvfzmGbDg88CKm49+DxinsCAiSEOky7DYOAjzh8NuypijFlN66zpyp2BdmwFQhIkBWFWXx28IOInDlzelOD44cQkQb86OL999/3Po9PPvmk560e6bWZkW7mzJlSrVo1yZUrlzz99NOCvu3Zs6dcfPHF3vNoyJAhEVcDAjSmV8+dO7fn3T9+/PiIrw1KiPLxTD3++OM9/ugDex/H+JEJfjiAZzA8/xHXtWtX73kblG+08bi/ITpjBgQ81zN62nfMaoCyTjrpJO/HNvhxTlYPFGZjekVN/ouqVm2aq0zVpldfV7VBmdKVGlUoV7nRJeXT0nImacuu1XofteC6xl0VZXtO1PTbnLx2RJlHNMk7WGUti+bCFEh7vLahtVrJgLbY/VkvIA2j/yaAQWSbV4VMAHOFltExoJyDVn3qB6RJtWiwMH2wKM7GZRV++IyaNmFbM852JfryUPdgostifhlLIFRfpuL/yWPxzM7YHkz+3O3nLt85kr8/2QISIAESIIEEENDBTAqzWX1ENwvUL15hFh6GhQoV8qYPhtCJtWFhmEYYXnbwSoSYBQH0tNNO87xXsY+AeHirQaydNm2aLFq0yBM2Lr/88vR8TH4vvfSSJ0BgeuP//e9/3rShyAMiGjzV7rzzTs/bEd628IQzAeex7i28dyHIwUsWXrrw8oWnLURheNvC84wh9QngxwQQmyCuQoyDgAjxFf2PzwK2EGvtqYrhXRvvVMXhyGZVYRafLf135H2+/IRZ8MTnCtMe2wFCd+PGjeWEE04QTDkOwc0v4DrzIwpz3vQP8g0VcB7eqqG8k00ad0pcePJiGvSTTz5ZMD067oUePXoIfhSC5xnWqY404N7A8+mMM87wZgYYN27cEZeibCPmh6orzoEHtpi6vWDBgp4Yi1kAunXrJu+991664YcuWNcbP3qBMIt1ktFPr7/+uveDA7sCYBSOk2HucsJ1d999t/fshncxfsTiF4LyD8rXzQN8UDamg4YwCxEaz+Vff/31iKSRtOWICzLhgMJsAl5YkyWL225LO1FF2AYlKzWcXLJig7/U5Air1GBPyYoNPyxduTGEzmQKQcJspygbUUPT20IE9inMRgkxguTwwluo9v/snQe8FcX1xy0RlahYUbFgLzHGjqjRCNgrNorGnkjEglGDisoFe0ExGhvWqCD+jVFJNMYWiZrELvaGYO9iQxFN9n++6zvPw7i7d++9e997971zPp/DzM6cOTPzm9ndy/zezIJv/xR7Ow6+SJoCUlNySy7ysxv3DFEWsZ9vqj8M7AK3E7MhOuWv2wp+bZWYzTMHy6PsFm0BgTxjOUwaqu+D9vIHTC35zG4L49wIbbDPXf/N0Qgj5m10BBwBR8ARqDsCsh7pxGwLLMo2ehW1ELP0/cknn4w23XTTmBxlF+sU2VXHTjuOoTz//PPjb1Niw6L+WmutFR+3qTu9lJiFHOVoY0hTdujh7+mnn459sYORXbVnn312vJvxqKOOin4hxyazgI9YYnbSpEnxTlgIIRb6EY405juzkEPsvoJ8ueWWW+IdkXzzFgIZYhiSw6X9IwDpBQEEAQahxrGvHE0MuYLqd2MhYzmqWAk1CCbKUV61SLQahZjlXj7zzDOjUaNGRX/+85/jHZr7yh9F8IcPV1xxRXyPcZ/xRxnsmpcXXXzf821nCETuOXaojh49Oj5CnPuSo2v5Aw/Kca9z7DhkJEf5luQo5QceeKD5u62MAaQvBCSEIbs4eSZMmDBhlp2iPIfYscvOe2zYtcv3q3nmIErMzjvvvInELMQoZC19xP8f//jH5qOcISd5th1//PHRP+SYZubJ6aef/gNilu/Ajh8/Pn7G0J+999474vjhe++9N24H5Xju8Ick+KNPtBfS9ZJLLolWWmmlmKCkH/iiPap885fy4JFGzIIT48D48ExkDDhVQJ+N4IA/jj/mj2poI8c4s+uWZzl2/OEMz2eIUohi8jmqmaOWR44cGc8D2sqYDRo0KGInMuQpx39DJJOGX0hWdt5yj1mhPYwx2OADX/zBDLt/LTHLePIHNYw5/pgzzCUdT+uzpeNOzNb952zbqIDdsRv0HvjMLERsSMya6x6997xm8z0Gz9c2Wl+2FWnEbKWLuNdJTboArKETs2Xhr9hgusHZidmK4ftBgZZc5P+dGTsnZr8fipLBxXfMfo9LPWJ55mA96nWfxSOQZyydmC0ed/f4QwScmP0hJp7iCDgCjoAj0MERkMVJJ2ZbeoW2AeurlZiFMGDHGYv2EDYslkOicvQw6Syos+gPKQtJAeHBQj87YdmVOG7cuKhbt27x4jzf+ISg4JuwkBWQHizUQ6zyDUWOlx0yZEh8RLISs5APO+ywQ0y+4A/yAUKDNtx2223x8cpdu3aNQwgVvlfL928hcvgWLqQF7bn//vsbcPS8ydUgYMlZSCQIWkgulLiSsbqb0xKy1dSXp0xbJ2Y5Kpx7GRKPe3uZZZaJVl999XiH6fzzzx/vqISI5Q8yuI/79esX75aV13B8LC07Otkpz/ecKcvOVJRdphxNDqkL0ce936VLl0h9cvw5O94hZxk3iD9IRp4ZHEMMsbrQQgvFR/leeumlMdHI8b78wQX14L9z587RIossEj83aANjnIeY5XnCN63xD0nMswbh+9c8Q/BPfyDxOcrY7piFrOMZxO5/+sNpAfSJdmyzzTbRXXfdFf9BAM8rnk9gQZ+wg6CEhARPCFGIYQhc6qWcKrtJ+YOBJGKWP47p379/M05gsOiii8Z/uAIxzLznjxIglBmbBRdcMMaT+mnL/vvvH59ggA9wZhzZ/UxfIFh5TrOzmGPhFefFF1884nviELcQrd27d4/7zBiBDd8G552gxxPzxzj8kQ5jrDaUwXb22WdvJmYheSFkOWaa9mHLsdHMw5tvvrnVyVknZjvAj+0N+wzYvUfvAV/mJWXVboM+A1/aeLs9uzcARGnELDf/T3O2n+/Ufi4aPzBM6MRsTgArMHNitgKwcpi2NWIWIuWkJl01R/vbg0lJOqHPjlqJ2baCX1vdMZuHzGsPc6oj9CHPWPLtT32e/KadgNKSz+x2Alndu+HEbN0h9gocAUfAEXAEGg0BWT92YjZeRvd/shColZiFvOIIYna+sVDes2fP+DhiiFQIGshPjgwdMWJETARwbCwEDN99JQ9SAaJik002iXe48Q1Ddrexa4rvxf785z+PiRiIU8qefPLJMeGgO68ga6kL8oJjS2nLwQcfHB+tjM/evXvHu3SHDx8ekzYcUbrFFlvE7SSfdkCusHPMpeMgoOQsxBbKPFYitt67Y5NQbhRiFlIOshHijG8/c1+zAxaSbo455ogJTMhTdkvyBw/YQbyyGxSykD/egNzDlj/W4A8j2H2LX0hAiDl2ZfIHG/wRxhJLLBETchC3HIsOsUd5SDp2VFL2F7KDHtJwp512infes+NUiU5suGbHPsQof5jx+OOP5yJm+aMTniX0gXLs4kcgH3k20Q52pCYRszyH6NvSSy8d/9EIhDB/bLLccsvFRCTPHI7R5pvXnTp1itsPTvSdHf3XXHNN845jMABziHCrY8eOjZ+pITHLH63gFxIYnBgjxoNnKiQrz0vmG0cur7vuunHdfG+XnbLscIUopW98c5edwpQDX+rGL6cYsOMZjOV3Udw2iHuOmefUAv4Qh7rxQX8gqHkHQKrim13R7Eznj3MghFHi/BEORK0SwbpjFoypC2KZnbKcesBzHTtOWEg7XjnpPqtHmhOzjfbruML2brD1wDXWj48oDo4tNrtjlYhNDPsMfEhuurkqrLalzbOI2bzHGe8ujVZixYZOzBY/mk7MFotpSy7y5yFSiu1dY3grSTP1uVErMdtWeuzEbFsZifbbjo76PGnJZ3b7nT3F9syJ2WLxdG+OgCPgCDgC7QABWYB0YrYeq7DtzGetxCxwQIiym47jLf/yl7/E34udOnVqvCsLcoMdVJCqCKQX35uEHCFklyIL/ZRlZxTkGLbspsIXu2YhX3UXI37ZuYYdwu43jkDm6E2EtmBDOYhf6n722Wfj9kHGsUuM41ghJdiFxlHHED4uHQ8B5kOWtiQijULMsut9tdVWi0lOiEbuYYgxdlhCtkKOcjwt9xXfp4XUZLcj9zDH70LMQsSxu/LUU0+N71WIRO5B7svbb789Pur3zjvvjIlXdmRC8rGDFAKS3fHsImXXLEQpBB9l2LHKbl6OROZYZdrCt6jZoY8df9ABuUfbIYHz7JgNiVmeUwjPDohZ+gHpmETM8oce2NM2drqikIoQsxCKp5xySnxiAH+EMvfcc8cnDtBHnl3gpN+YlZ8iMZnJblNOAlBlN20SMcu3aHk+br755jEGffv2jcASDCA62WkKYQ6W/LEKRCkYQwaDJX8YA2kM8cp4MC7sfmaXKn9Aw9iy2xYb8OTIYcaROnluM+49evSI62bXLM9f/PKHNZoOicsf7WgbIVexAzNOOWCM6DfEPHOLfDDij3R4J9CXkhx/DGFN29lBrO+DlrxntS4nZtvBD+a0LkCoyjdjn08kXPMSs9j16X9mWh1tJD0kZh+UdilJknbcatj0P5kyU028UmJ2finbSTSPsDNO21npsct5/LMLeI48hhk280jejzLyq8mqlZidXSqlb7VKEfjkbQMYzp3XWOwqaVsti/zMVY4sB9M8Uk8ipbM0IG878rQVf9UK7eD7l3mlJIZ6LycRs9xHtd6LedpSSZvL+StHzDKfsalVKpnr1FXPOVhrXyhfaX/S6mQO1jKHQ7/MwSLGK/Rrryu9b+o9lnnxa6n7U7Gq5ZmtPmoJ6/Fez2pPJb+LsvyQV8Q8TnpO1ouYrfTdX8R9X+l9mIU5z/kifgM28vs9C5+invdZdXieI+AIOAKthoAsGjoxqyunHqYiUAQxq8458hViFPJVBeIrTWweZew1cQgA6yvNT1K6tiUpjzQW8mmrrTPN1tMdgXoj0GjELIQY3z7l/mHnJ0f+sqsS4hRyDvKMI4ghZtlND8nJH1EoMbvmmmtGN910UzOs/IEGBCI7VCFUIQ8h3thNCjHL0bkQcOxyn2eeeeKdsxCYCN8ZhRyDIOUPPnbdddd4bU9JSHZ8ctQ65dh1yXHL7OplFyeEI7twIQXZiQnZyO5fiNE0YpYds5wMkEXM8uz65z//Ge8e7dWrV9wffEMwQsxCFONfiVm+q803cFWUmIWE1u/j0mYIZZQ45Xk+2h2zkLvUy25m+fERE+DsTAYDds8yRhzNDLnM92zZRbzOOuvEJCx14w8s+e4yfUD5pis47bjjjs27U5WYZVcspC52CH8MA7EKMQ5JPG3atDidsWFHNH44sYBv97JblzZC8NMXhD+wYYyZNxCzzCWw4Zrx5Nhl+gJRDZYQw+yuZm61ljgx22o/c+tfcY/e/feomZSNidmBX2y47V4L1L/FVdcQErOHiyclSQjLHWfMouFXTWW+lvDcpjhls4hZFveGiF4t+m/RaaJa70cS/5vor0RZJEuSPMQsi1mXiEIcq+6Q5EzSWLA8usnuZQn/JwoJStsuFuVIyHKykBgcJTpR9G1R+gMmz4j+nyhtXky0UmH+3ChKHxQjwsea0kj/qaiKtdlVEucTHSF6p+jHovTtWdHLRPcVzbOruwh8pKpM6Se59AXtK7qc6HWizI3/ir4keoxouDBcS9sqWeRnDowS/Ysoc+RbUbCmbRCKl4ouLxrKgZJAn54X1bH5qimNdI4aVblWIqShG2hiQsicoNzdom+J4pfjxP8lypzfVDRLxkkmdYwXhXBaVPRY0VtF3xfF3xTRm0T3Fy0nm4nBNaLM9S9FKf+e6D9ELxTdTjRNSpKBParEbB+JXyWKP/DlWXKH6HDRNUXTJAs/5pRiu5fEIXsHi9LHd0Spn7nGPU+fId6rFTDVPhHuKLqcKFg8LsrcYbzuEz1TdEXRPFLtXD9QnNP3rDk4sMkGu8tF02R1yVAcCTdOM5T0c41t0hyotj9JVfaUxD+IPiD6mSi4TxG9WXSoKHWliZ0bPIeQbUXteE2Xa+6vC0S7ixYh1dw3ecZS28a807E6WxObQtvn3Uwaz48XRBW/KyX+S9HZRVXWlsgZog+JMpdniHLvktZZNEm457QtJyYZmLSsdpd7ZhdVjzanXu919W/DWn8X2TEtah7PLw08UvQJ0S9EmRe8/64S5R5BZoqSju4qWqnQVp0b9GE50TzvfjGbrZb7nvJINffhdyVnm03fpTdIwtyia4ieJ/qw6Dei3B+vivJMXVU0j7Sn97vtb5HPe+vX446AI+AItEkEZJHSidnWWqltoHqLJGYbqNveVEegTSHQaMQs3xXleGEEYopjbCFRlZjlKF9LzLKz1hKzHNvLblIEcpfd6xByfF+UnZF8+5QjeCFoIVMhHCFmyYNcpD4l/fjWKkQluzb1+7byUo4gfyGFITYh/44//vh4Zy022LMDNC8xC9lLnxB2bXJsexYxy459jkmH+MSWvtAndo1ypC/ELN+wVWIWkpg2qVhi9vLLL493+mteGFpiFgKbXcNKeoIXxwRbDE477bSYYOXYYtoHEc2OVYQ/WAEfdsZClkJ6KzHL2OqxwUrMQp7bHavEOfIYIpXjq3WM8MN8gEwFFzBcb7314v+/czyynqjAqQmQ3krMgrnurIVA5vu7jKWOJ7hyRDZ/ZNNa4sRsm/wJXEyjNug98J5CiFkhZ+U7tYcU06q6eGFRSxfUCDcUnWrSLGkkyT+QvSVFy98i8eHm+pMfWH+XsIcEbxg7LZ8U/lPsun9XbJZ/h5nyz82S893FvBLcbWzw/TfRpMX5tSUdAiipfk1jce1k0R+JJgltVIJMyySFkDE7JTnISFtK8pJ82TRdIMWNTT9Bri0ZY/M0/pDYLELBFCkCnxTXsySfLlfaptESn2KuNZ1wFVGVWttWbpGferqKskhs25AWh7w5TNTKZXKRZk86+KvYBW5dWNc8DSEN3xXN8vk/yQdD7oMksWVXEoNyc4RFc7BKklMk0fpLi3P/QTyEUpIELcN9OFKU9mtaGPLHDpBTSZKFn60HAo8F9dC3vaYt3ZIqyZE2Z+D7bLl+P0izdfGs7CuaJbXM9TxzcE+p3LaJeZEkx0iitTsnyUjSuG8g1dW2d2BXS3+sqx/JBc9mntFaV1LIeK4lmiQlSdQyPIeGmmtNt+HHkg95WItUe9/kGUtt1zCJaLuf08SmsGTywI85qrZJIe92ZEtRnnNJNqRNFk16n/A80jITJZ4lWe3mOaR+CHsFjoqqB7fdRev1Xg+aPVsRv4tK4lSxKWIec7+8bXyqbw15Th8rOtPYVEPMVvPuL+K+l2bPVu19SFlEsSDkj0+mBWk2H7x+K5ol7e39rn0t6nmv/jx0BBwBR6DNIyCLlE7MttZKbQPV68RsAw2WN7XdItBoxCy7L7OIWd0xKy/K+Duj7IRk16TumIV8Y3clAqnG0bnsjOVbqldeeWVMRPJ9U47s1aOMIekgSCHtttxyy3hnKMQfxxizI7ZPnz7x90s5spedpuy8hfxlZy07NNmNCzlK29h1Wm7HLEcsH3bYYc3HIrOjlZ297ARlx3AWMcv3Wfm+LAqpzI5cdghDLkLMhkcZ841XCFEVS8zSZo5j55hjq2+++Wa8U9USs7QNrCE/wYBdruwiZgcsO4XBgG/JvvXWWzEWHCnduXPnuI0cOQwh+wv5Zi+EK0cUQ85yJDEENphzvDG7apWYZRfwvffe23yyAcfKc8wzY8R3YPHHMfTgz05a0sGU/kCMs+MVkh5Sml237FpmbjFvsJsiR+QPGDAg3u2MX0hn+sI3eCFpIbghczkhobXEidk2/3O4ugbusceITuv3GfjfoohZOc54QnUtaZFSITHbQ2o9S1QXkyBssuQ2yVTb/hJn8VavP0koyKKu5mv4qaSxaI5+kZD/f5IWStbCLeTr30XVPyE7AecWDYWFNIgea/uNXNPv94J0bO4UDYUdJSx6Wx8szj0h+k6Qjg31LSaaVxYXQwgOFvVsHcRJQ7cQVQlt7PWXYkT/bBrx67VwEBaBT+Ay9dIuzobt0+uHTeki2tZF/KlvwnCRn+oeCGzAe6roY6JvilryCR/kryGqcqFESLP1qB3p96uhhHaBu59J1+heEgn9UP/Lop8l5D0paZ1EQ7E+wnnOHLH5GqcfofxKEjSfkP7QFupNmmfsoAylJAnWRxhP6hc4rRk6kuss/NLqoc2TRBnnr0Rt/VPkeiHRSiUkZq1PCMQXRJNwHpRSUa1zPc8cpJ92zAantOVuSbf94TmXJPtKotp9JPEfGaNa+6Ou2PUMyaf1aPiBpD0navtDHs/epHu8JOlaNgwZr/AdgQ2+u4lWI7XcN3nGUts0TCLaH/CwUpILzQtD+mzvJfJ5zvxedIao2oNBiDF5fxANZbQkaDnGLEuy2l3umV1UPfV+r9v+F/W7qCROFeMwrHQe/0R88RvO+uFZ+apo0u80tauVmFU/Yfiw1KtS1H1fy32obQnbqdf88cY9olNENU3D32rhINwrwbbR3+90sajnfQCXXzoCjoAj0LYRkEVKJ2Zba6W2gep1YraBBsub2m4RaDRiNmnHrD3KGIKOnZbyloy6desW8a1TyDlIVAhNiFmOBEYg1fQYYUjL7bffPibl2FEJcceuTshJiEOIXXbQciwxx9pC/tEWCL/dd9893tUKace3WCET2YW7zz77NBOifPcWAlSJWT0WWY8ypq3sIL3hhhvi3aIQf9TP8cMQluz6hATmqF5LzNI+dvtCHnJMMN9/XWWVVeJ2cnQvx/KSB0bsGj3uuOMidvqy85OdxhCzkI4q1E894Ne1a9e4P/hThbCm75DVHPcLFtiyS5WjpWkP5Cqk63bbbRftu+++8fG/4Esc8pZdshwLTF/oN+1klzGYcGzwjTfeGBPRBx54YEwmcwQyJOqECRPib+tCOlPOErOQ7Oxipc30k2OUIWAhWxlLjiCGkIesZS7ocdXs8GVHLnYQyvQFgh2MIOq7d+8e++NYaL5nTP8ZF3CD5G1NcWJWRqs9yoZb7rlyYaSs7JiV3bcQjm1VkojZ9aSx8c3YFCYRIPRnYVFdvGWhrrPocFEtm0TM/t3ks8C7r+hcoiqzS4TFKbsjkAXF7mrQFKYt3M4t+X8T1TYQ3ihq65DLWLCdLKq29OUkUburdjW5fsTYYDtQ1MrucqE+WEQ7WFTroz9ri14mqjaER4hWI9OlkPrpn+JA8214sdhuIgo5AlHHgiS7d9UGjBlPK0XhY31mxU+XTG2Phm9I2nmiF4lOFR0sihTVtnKL/BtJXdoWwjtEVxa1sqxchLsvL7cGTfHfSai+nk/IJ0nvJ+z6BTZLyTWLverjG4kPFZ1PFGGxemPRl0XVhvB40VBsPnF8QWgs3WS4jIS0l4V4tWW+LCBqhWeb5v9b4hYbsOXeeMfYvC9xvTckGktJ/lUfGk6RtH1El4gtvjvq1z47sKO9oWThVxJj9a8hWHGPq9C/20Q1n/ACzawgTCNmR4mPHzf5AYdDRcFe6/tQ4hBCVoqa6/gsNwfvFRtty622EU1xnvE8t9WGkDmyiGgoPHfV7hqTWWR/fm3qoK4nRdc3dbFjfIionRfPyjXPQSsludC2agiRuY0o7wOemZuJPiqq+YQjRauRIu6bcmNJu4aJanvpj5WSXGiehk9I2i6i3AfMT95ldn6q3euSvoMoY8mc2E30S1HNp0x4n3O/an4jELM8u7S99X6v22dbLb+LSqbN2vZq5/Fdga/r5Vrvc55vO4tOC2yosyhiNu3dL1XMVtR9X8R9qDjbkOcC72OVvhL5XFRtpkucd6yVpeSiPb7fi3zeW7w87gg4Ao5Am0dAFiqdmG3N1doGqduJ2QYZKG9mu0agrRKz7FCFUDvggAPi3ZoQhmussUa0/PLLR1dddVU8JhCmQ4cOjYlQiE8Iv88//zwmURdbbLGY+GNnJTs1IeNIgzCFwFThGGK+eQq5CaEHCcjuUkhSiMKDDjooAiN2UPJNWAhBCDyITshGCDq+UctRvOzyPOuss2IyE1+Uh1jlW6XsWuVIZXZoUgZy78QTT4x3tLLLFWIQkhNSkiOWIXDpEz6oi52y+AEDdvKOGjWqeScwZCZ5HOcL8fnb3/42JjghXvkWKyQlxCeE8V577RXjdMIJJ8TkKaQku0tVqB8SFuxR+hoqxC27ZcEbYhM7dh6zi5ijgY866qi4vbSdOsEAkvTBBx+Md6dCokKWMxbY0E5IWjA4++yzI46fRhhnJaO1Dgjy5ZZbLiZMObbZfgsc/EeOHNmMP37BDqL9+uuvj3e84hf/RxxxRDwf8MtYcWwxx1dDKLNjll3B9AeCnDlHP1Dau/HGG8ckL7uYW1OcmG3zP4era+AGWw7oUyQx26P3wM+qa0mLlOoptehiEWGPplpfMeknN6WFgV0cG9uUOdyU+yQosLpcs4iv9bHomyYsDqsdIddWkhacWTz/q6gtR7tYREwS64MyIeGqZVhYsouULBiyGK1ygUS0zgmaGIT4mCqqdo8H+XkvWdBTH3mJ2fNTnB9hfOFzx8CuKHwCt6mXp0uO9o3wHdGFU6yLaluXoM5eQX3Xm/xXJa6kWmAWL8Iyptr+SaGBXOchUmYaH/0CH7eZPOrZIsjXS0gVCCptC4TJcqJWNE/DwTbTxEsSVxtCFuNVFpWIvZ/X1Ywg/I1cWx87BflhHR9I/iqBDZfcc2+Jqq/HSAwkC7+wHu5jJRqsGxbU7xHVeiCZlrQGOeI8c7S8hseklNstsD0hsCtqruO23BwcYtrymcTnopCRrSWu/bEhJJYVyn0qqja7msyi+rOg+Hzf1ME9N7epx0a5V+xcPcxmSrwkqm0lnCoKqRsK88DOwSdCgxzXRd035caSplisIeishH1+TzJDsgj78I9OIJi6kRHIr+XaYrhSkD/a5DcCMdtS7/UifxeFYzpVMK9mHm9qxooxvUo0SVaVxK9E7bjbez2pTFLa6YGPd+R64SRDSSvqvi/qPrR9Jz40pd2bS7q1vTCwa6/vd/sMov/V/s4O4PJLR8ARcATaPgKyUOnEbGuu1jZI3U7MNshAeTPbNQJtkZiFXJ00aVLEEb4QpxCakG6Qmg888EB8HC6DAsH38ssvRxB02LP7FFKTo4Apy45HCEDISo7inThxYkwoQmipQKi+/vrr8S5a7G+99dbohRdeiJ555pnYBztDOQoZAhAimN2l7IzFFoIXwpajcBF24NIGvj0KEcg3WtnlyS5e+oBwHC7H8tLmV155JfbL8b70i/7x/VcEn/Tt5ptvjutidyj9oCz9YNxoE6HtF2kQj3fffXd0lRCbEMJgiOKDttEPjuGlDfjjyGUVsIMUBr80hWClnyi7gLEDQ7AEA/pIX6699toI0pmdqrST8VIBD/oP3uxaHj9+fNwWyjKGCPPg/vvvj/tx3XXXxW1mHKmfdPphhXL0hbkAkY9f8ANH6lO/hJQFD8YIjBTb++67Lx4vdtYi+GNn8FjZiUxfbrnllthW8239LR13Yrbt/x6uqoUbbrXnekUSs+v3GfBaVQ1pmUJpxOypUr0uIr2Q0hRLXrB7BhkuquU+iVO+/6evRP8tymL6NNGkBUNJjmU++Vf9EO79XXLzv3axhwVniIBbRW2Zq+Ta7lqQy1mENqg9ZbNkDclUW8IBxpjdkZo3ReJJZA/mW4qOFN1HFNyrkelSSOvKQ8yywysNgxDjA4MGFYVP4Db1Mlyc3SvVctadOrWMXRepQ/Ek7BXUeYtcvyj6tehxQV54OUoS1NfkMFOu8xApM42PfsYHpKQllm4yeUlR5pq2hbAUGNm8pyQvbY4sJHm23oOMn2Ukbv0cafJslN2Jp4kOEd1BtKuoFdpm/exsM4P42cYWYjWUNPywC+sZHBY215tI3LbpVyYvTzQkZqdIIZ5RafKwZGh94fuiyPuw3BxcwbSD9mwWNPgck2/nxUWBXR9jB3Fj/6ChqP4wnxQzwnVEs+RGyVT78J1WMnnY7JHh6Dxj+3aGXVpWUfdNubGk/vA9adsU9jmNUPulFFLcCHmHJcnikmjttgqMRpv8iUFeeJnV7i7GD/WFz+yi6mmp93qRv4tKATbVzuOzjB+eqd1F08QS2IxH2jxKK096Je/+ou77ou5D+qzKO6kTHUqR8A/81Kw9v9+nSScVn1p+qylWHjoCjoAj0DAIyOKkE7MtvULbgPWxUM/ONBbOXRwBR6B1EOA+ZFeqvGCiQYMGNe9WbJ3WfF8rBCNEHiFEGgrxhyrBhjVEoLUjjXzS2NEIgYZNkh22CPYQodgTcp1mT/2QfNb2Oy/f/0ubsZk+fXrsD18q+NZ+aLrWldQ32k9d9Iey+MYuLKs4UQ925fqvddpylCUd/5RPU22n9gU7bQ8+EGxot237dznf/0sZ8AYn8Ap9YKn90Hxtn7bhe2/fx+gTuOGXkHYmifpWG8qRRmiFazueaf5smZaIOzHbMD+LK2vottseNneP3gO+KY6cHVhuMaKyBhZrDUGoiyaEPZrc/yxI59rKEnLxX1HKfCyqpMPwpjTSPxFNE7VPyofIoV2QYdq2AwJDu3D7suT92dhS5jLR2UXTJFxE3jfN0KRPlri2h36qDJSIphOy2+NY0TVFi5bp4lDrykPMnlSmAe8bf0cb2yLxMW4zo+Hi7Iop1kW2rdwivzYB4hJNE+6HK0R1bF5PMMxDpLAIrj4sMQvppOmEGyT4D5Mg5bXMuCBT0wnPD/LCS+5vtT80yHzR5GEDYbyL6AKieaUkhuqfe56jY9NksGSo7RcJRmn4YWrroU9ZC+jYPyWqdd1AQgUSErNHlCm7r+RrXRCeLNQjRc51/OWZg0+LnbblVAoZsZhA0KndC8aGqCXHJpi8IvtzsfjV+qeYOtKiext75gnvGZWSRNQX4cKakRAebmw/TMjPk1TEfZNnLO178rmgYbbPvMvnD/L1EuLTYrO1ZgQheFq7nYN8OycmBnnhZVa7yz2zi6qnJd/r2v9afxfZMWUsqp3HT0hZHctbtHEp4QrGljK7pthlJed99+OjyPu+iPtQcSK0v6GS+gs21p7d0kh7fb8X+bz/Din/1xFwBByBBkJAFiSdmG2JVdkGr8OJ2QYfQG9+u0CgrRKz7QJc74QjUEcEnJhtoB/GlTZ1/T4DJxVFzPbYYs8RldbfgvYQoHahSIlZmvC8yTuZBCOHSVzLQYKq5CVmsYfoWll0J9GjRC8VvU/0M1H1reGBkmbFLtyqjQ2PscYJ8U0lzdqzEMkCZJbOMGWulbgKO2Qheqw/jb8l6RB2u4kuIFqrTBcH6rt/ijPNJwxxC4tAqKj9sSazSHyM28yoXZyFOJkzxbrItpVb5A+bgP0movuJniYKEWkxVCzfkPRQ8hAp9Ft9WGJ2T5NOvt2BGNaj1380ZR7XxKZQ6yDk3ssSSGa1HxIYnmTy1IbwG1HIF+bUz0SzpCSZWjYJN1t2gLGdYTOa4mn4kW3r+U9C2TDpZknQdv0rzCxzzdzVsoRblbHfOLBfq8m+yLmOyzxz8BTTlkea2kGwpEl/W+LLmGv62E1U5WWJaP8P0EQJi+zPPaaObyWe9ewm7wljT9tWFFUpSUTby/snS+iP2k7LMszIK+K+yTOWw0xbs4jZdzPaupHxQb9XybD9r7HdObAbbfIagZhtyfe6hWoOuaj2d1FR85g/ONA5Pso2LiX+lbGvlZjlGc7zM02KvO+LuA8VJ8Jt0hrdlL6WhNZe3wt7Bunt5f1e5PO+DLSe7Qg4Ao5A20NA1iGdmK3jYmx7ce3EbHsZSe9HIyPgxGwjj563vSMj4MRs2/v9W1iLNuwz4KqiiNmeWw7YqbCGFe+op7i0C0WWmB1h8thZYOVBudByvU1GHmKWBfELRZMIWPUZhgeaOojaBefQlmtIG92NgH0oB0hCUrm8aSGxs5r4m1LGJ4uX/ye6gmi1Ml0KahvzELO7l6noBePvWGNbND7GdWrUErMvpVrNNluRbesi9SiehL0S6mUn2F6ij4mym9Hap8XfELtQ8hApLEqrT0vMnmTSPwgdp1yPMGXC3aVaB6GtJ8nVa8bPkMBgdrk+zeRbvzbOPAt326qrkin/jCamhAOM7YwEmzT8MLX15NkBe76pa3JCXVlJEAu2/zwfsmRpybT2/CEHUuRcx1+eOcg7QNsC0bYoBUX2FtX0a+OU2WbjPtU0jrxFIO40jfKLkdgkRfbndfGp9VQTbquNkrBkfD1n0pOi+xvbaUkGOdKKuG/yjOUw09awX7bP4fvddiEkZhe3mUEcglzHYucgb7TJmxjkhZdZ7e5i/FBXr6BwUfXgtqXe69S1ouiForX8LrJjGo43dVhJm8e87+x77ghbKCVu/xCjVmKWZ0qWFHnfF3Ef6nwnzPrNSZ8WErX2PFORk0Q1vT2934t83sdA+T+OgCPgCDQSArJQ6sRsR14tztl3J2ZzAuVmjkAdEXBito7gumtHoI4IODHbSL+MK2zrRlsNXG2D3gO/qpmc7TPg4c1HjGChq61KT2mYLggRsiiv8hOJ2Ly1mjKWlVAX7t6W+BxN6QTDRbXMJyZdo30lYhdv1daG7DIdI2rTDlQHTaFduFW7u4IyD8k1BEmSHC2JWq6aEKIplK6SALk4RTTL58eSv7VoNTJdCqnvPMRsuUVS+qH+jjUNqgc+xn1i1BKzjF2aFNm2cov880gj7hdVjJLCmZIP0QBxq/lvSDyUPEQKvtRHP+PgVJOe9/jUE0yZL40voloHYbk58pqxH0LhBNlR0m4X/VrU+g7j/GFCuBuoZMo8JfEsGSCZ6nNGgmEafpjaesYmlA2TzpMErevVMLPMNc8dLUu4XBl7nh3WXv+Yp8i5ThPyzEHICp7r2h4wR64V1bT9SBC5SFTTroxTZpvtSJP2z6Y0DYrszxemHm1DJaH2i7aVjK9yc3B/YzuNwjVILfdNnrG078nngnbm7XNrELPHS1t1LMN2l3tmjzZlJwZ9Di+z6lFb7s16v9eL+l2Ud0zpW9o8nlfyFHvCcicq4OthU6bc+wT7UPK++ylX5H2v7ajlPrRYdVeHKeGCkm7t92mya6/v9yKf9ymQerIj4Ag4Am0XAVmHdGK2joux7cW1E7PtZSS9H42MgBOzjTx63vaOjIATs233d3AhLevRp/+RtRCzELs9t+hf7i/oC2lrDU56Slm7UNQj8MUiteZzxCViF4QhL6xkEbP4Zteo+iOEwGLBH59biS4hikAOsNtKbX9FohG74IwNOw4QyAEtQ3gsiQnCjjRrt65cL1WBslibJRzfyqLUnaIzRG1dxMGBBdBKZboUUF/1JGbrjU9Sv/MuzhbZtnKL/DcZvMH9G9F/iHK8I4uqa4l2EkXs3H/zu6RZ/rX3zfOz5Hx/kUYsHiImOu78YUOeP/YYY8pMlbgV9UVYbiH9NbFR+yHWSUIcPPF3keirolrOhiMl3UpJLjSf502WDJBMteW+CiUNP+xsPbeFBROu7dj/KyE/KykkZtfOMpa89UW1X4RrNNkXOddxmWcOYnepqLbnKhJE3hHVtGXilO/GWtOmNqXda+yObErToMj+PG3quVHilTy/sbXP35LxVW4OphFa4qJqqea+yTOWw6RFOj7PBa3L2+dKiFn7zt45qG+0acsDQV54eYaxDdsNVtonwl5B4aLqCdzGl/V4r/cQz0X9Lso7pnQmax5/JvmK8WkYl5HXJV/tdy1jm5Sd991P2SLv+7AtzC3aX8n7S/tNuEHoMLheU66tPb95kfb6fi/yef8dUv6vI+AIOAINhIAslDox25FXi3P23YnZnEC5mSNQRwScmK0juO7aEagjAk7MNtAP42qaOmLEiDk26DPwn9WSs+v12euoaupt4TI9pT67UMQioZXj5ULzX2jKeNSkbWiNJW7JqU+CPEsU/U/yRojOHtjo5cIS0XoJB2lGU2gXnF80eRwVZwkEyBt2/oYSLpBtExoUeN1ZfO0u+pSo7dO2VdQx3fioJzHbkvgoDHkXZ4tsW9YifzeDNePGPFtFG5sQni9pOr7MwVDyECkzpZD66GccsICr6YTLm7y06J2mzCOBkfXFQnSWvCaZaj8kyzAhb1VJg8S2hM1DgV1JrtU/90iWDJBMteXeDiUNP+xsPSzulxP7nPtTOeMgPyRmdwzyw0ueD9ovno3s1EaKnOv4yzMHsdteVNvDCQY8Q/U6fN7asV1P7PjjBbVdQeJWiuzPTeJY6/mPraSKeMn4KjcHswitKqpOLJLnvskzlsNMv54Lasrb56KI2XNNW8LnUdC0+LvsOrZhu7Oe2fgpqp6wTeF1Ue/1MeJY+1rr76K8Y0pfsuYx97i2aRzGGcIfCdlTUMq9T5Jc5X33U7bI+z6pLTYtz32oOBFCRGbJDpJp7X/aZNxe3+9FPu+zcPU8R8ARcATaJAKyDunEbB0XY9uLaydm28tIej8aGQEnZht59LztHRkBJ2bb5E/gYhu10dYHLrx+n4HXV0LOslN2w957HSE3x+zFtqYu3nqKV7tQFBKzKwf5O5vrVxNaNNzkh8SsXez7V0JZm7ST8UP7BttMiWctOLM4Zvv0sFxDlFhhtxSLoGp3qc1MiLP4yGLyy6J3iQ4VReYXZeHyCVFI0y1E02RhyfhaVOtkV1ClQh1avp7EbFH4VNK/vIuzRbYta5F/oMEazFk8zZLHJVPH5v0EwzxEShqxuLzxTR0jEvzbJOytr8tspsS1nYTlFtJfM/ZDjJ+9JH6n6Bui95v0pOgoSdQ6IfJ+bIxKJq8cKVYUMUsbVjNtCKNrSIIlHA8JDcpch8TsFWXsJ0i+4gOeKkXOdXzmmYPYQQx/IaptuiiIy2Wz8HxVu3tMPGksi+yPfV7wXF1ONEv2lsw3RXmOQ7SvJKpSyRzcXwppf6epgwrCou6bPGOZ9Z7M2+eNTH/p9+IZfbX3zM6BHe87xW1qkBdePm9sKyVmi6inJd/rL5q+1vq7KO+YgnfWPL7ctInfcQtSIEV2lHQdV8Jy75MkN/ZefijJwKRZ21ru+6LuQ9v3ckfk3yD9UPv3JD5HU7+WN+nkj2hKTwuwb4T3e5HP+zQsPN0RcAQcgTaLgKwFOTHbkVeLc/bdidmcQLmZI1BHBJyYrSO47toRqCMCTsy22Z/BxTesR+/+e6zfe+AHZQnaPgMf2mSbX65afAvq5rEcMUvFj4nqYtLnJs53sULJImYtqQiZmSbdJOMVUa2T0BJClMtacCb/JlFbHvtQJkqC2rCgvEFoYK4HGVvKHNWUN5eEH5m8K5vSk4JOkviVsT0hyahMGkSAtnm/FFvNJyy3SPqC8Xds4K8IfAKXmZd2wbXc4mxRbeti+g9evUwLLfFB3iomL4weIwkWd8YpFOaw2kwNM5uu7WJrP2PDAq4lKz6T60VNfhi9WhK0LsKtAwObV26OvGZ80QeVIyWifrh/lteMhPBEY8tiOsSlSkki6ucpTUwJiyJmqS9rEX28aRN/wLFUSnvSkkNi9lMxTHsvrC952n/CcwKnRc113OaZg1r9zRKx7dJ4uCvstBS7k9RREBbVn15BvROCeuxlZ7mw75QP5BryWaUkEe1fuTm4v7Gdpg4qCIu6b/KMZdZ7Mm+fiyJm+WMmxZiQ8UuSwyXR2lVKzBZRT0u+14v8XZR3TME9ax5vG4zByRRIkNklzf5BEuO2a4JduaRK3v3MGzs/qr3vi7oPbVt4D6Y9538ieeSr/RiJq7Tn9/tE6aT2mf5X8ztbcfLQEXAEHIGGQkDWIZ2YreNibHtx7cRsexlJ70cjI+DEbCOPnre9IyPgxGxD/TSuvbGb7zhw0Q377Dlg/T79z5RdtHeKvi1k7Ss9+gy8cf0+ew7r2bv/tnvs8X+WcKi90vp7yEPMhqSTLrLoMWy2lVnE7H/EUMtCVKxsCzbFF5OQhVi10xC/VrIWnLFbUpRFcy0PGRS2d21J+9bYsJuqj2goO0jCDFH1xU4yu4PkcpOHzSDRUH4kCReLqg/CLUKjHNdvGR9/ljhkbyi2jnKLpC9IYbU/NnBUFD6B29TLShZni2pbF9N/cGDRV2UbiSg2hBdpRhAeGthhywIki9ZWfi0X6o987r1Q0ohZ7LYU1fKEz4quLmqF+XC+qLV7Ua7nskZBfrk58pqxhwxSWVYikJZaFwv03HehcN99LKp2DwQGJZP3VJAXXg4wtjPCTLnOws/WQ1toO4SD7lqSaEwYQ4xqWwmvIaNC4T1gfRDn+bJC4GdDuX5bVG0/lLh9tmBe1FzHV545iB2yn6i2S0Pm7cKiVnrLhebbcF1rZOJF9udPQd2XyfV8pi6iC4j+TdS27RQyjJQkrvnl5uD+xpZ3TKVS1H2TZyyz3pN5+7yRdFCxIVw8o8PMD7XdObD7ucnDBqLc/nEJ98w+otYHdpUSs0XV01Lv9SJ/F5UEL8W/lnn8I/HzpPH1rcSPELXCbshrRbU+DXe1Rjnjlbz7cVnEfV/Ufaj91vAlaR8krBWeeVNF1Ybfo+G7u72+34t83gtsLo6AI+AINA4CslDqxGxHXi3O2XcnZnMC5WaOQB0RsMTs4MGDo/fee6+OtblrR8ARKAqBTz/9NNp6662jOeecM+rbt2902WWXRddff300duzYaNy4cdHkyZOjb7/9tqjq8vhhncTFEagIAcghXSwi7JFQevnABrtnEuxIGi6q/j4JbEomDxvI2TNF9xM9SfQ+UcgWLW/DMZJuJWvBWe0OlIj18ahcs+Bo5Q9yYW3+J9d3idIu2nSvqM0nfrColZ/Jhd31gs1joqNEjxE9V5RFaOvnIbmuRihn/cyUa4ji/saZzS+3SPqC8Xes8aHRIvBRX+XCShdni2hbFjHL94otoQiuE0UPFR0k+kfRqaIWbxvvJnlWtpULm0+ce+BpY8R4qk0/k67RP5t87Jh3t4qeLAop9bKolidkAXhd0VCsTbk58poUVntLzOIzXJj/UtLA5QTRkuiNoizqa3nC7UStYKf5T9mMhPgAY8uzIpQs/Gw9Wh/hk6KjRc8XfUbU5n0o1/yxSKUypxSwfjTOvXq76GmijBt90DzCw0STpIi5jt88c1Drp98hScYzNJS5JYFxt/1gzmRJUf2BXAmfvW9I2tWix4teJPqRqG3bc3K9sKiVklyoTbk5uL+xnWadVBAv4r7JM5bDTFvpt5W8fS6KmKVusFWcCXk23C96iyhY2jyNh+3uEtj1kutQiqinpd7rJWm89pWQd8KZovuJVvq7yPqqdR5vIPWHz25I5LNFLxadLGrbrfFdJb1SqfTdX9R9X8R9qP22ISfL/J/oyaK8s78UtfmMa5K0x/c7/SzqeZ+Emac5Ao6AI9BmEZDVOydm8yxhdnAbJ2Y7+ATw7rcJBCwxu9tuu0V33HFH9Mgjj7g6Bj4H2vgcuOeee6INN9zQidk2+2vYG5YHgZ5iZBeMeqQUeiiwY9E7SYZLovoLidm5JI+Ffc3PCv8udhA8avOOxOcUVclacFYbwrtF1Qdh2O55Je1c0ZCAsGVs/ESxTZLdJfF/otY2Lf4XsVswyUmOtCNT6jjDlLX1llskfcH4O9b40GhR+Ki/rLDSxdki2lZukR/8LJ5pcRZiIWy/MfaHSNwKdb0uGvpg3nRuMswiFjFh3lwjGvpIun5P7LYQTRJrX26OQLKp/ZDA2Txy/ZDJV7uk8DOxS6qrZMo/JfEsGSCZ6ntGgmEWfrYefEwzvtSnDRmrnybUkSeJZ5X1dUNwbfOIM2/4I47ZRZOkiLmO3zxz0Nb/gFzYtp5pM038zsDufJOXFC2qP/jeRPQlUdvOtPhUsVtaNJSSJGiZcnNwf2PLHKpGirhv8ozlMGmc9iskOPP2uUhiludRufvuXbHZM6Pd9Fv7RNhLNJQi6sFnS7zXi/xdlHdM6Vueebyj2IV/+GCxJ/64qCVpk57xYpIplb77cVbEfV/EfWjxKIcVtqNE56ADCdIe3+90s8jnfQJsnuQIOAKOQNtEQNgGJ2bbBOXSthvhxGzbHh9vXcdAwBKz8847b9S1a9doiSWWcHUMfA608Tmw+OKLR506dYr/T+47Ztvm72FvVXkE1hMTu7C0TkqRIwO7FVLsjjN2fMcvFBZVWdyHVLH1Ep8p+rBoP1EIChZXrY0ljYeavEkSTxPaaRfLvpbrxROMfy5pkMa0wdap8TsknQXqLGGHCXZJBC07T54RPVaUvtUi7Lb7TFTbRniTcWj7wMJqltAm9XNUhmER+GS4j7NOMm2BEMortbRtfqnEkvL4CoWF/ydEFScbvi/pF4kuLYrQbs2/PU6Z9Z9V5RLiR200XLPJ7EuTt0tTWlKwsyQ+L2rbrr4+kvTzRLuJpkklc+QVcaK+Q7IZ/5DK3I/c72pnww8lHSzoe5IMk0S1fyzJwKRBlKgtZHgoWfiVxFjLMgYri/5LNMTwC0n7vWjSc0KScwkL70rS8zxYVJR+Ml+0DYTk0ZakeSfJPxDsan1OlZuDttLfyYVt71Y208QZf2vX2+RlRYvoD/6Zg6NFQ3y1TcxBnr3ziSZJJXPwl+JA/Sa945L8J6XVet/gs9xY2nEJ35N5+8x7TfvLfF2EilNkhqSr7fYpNstJ+j2iM0XVlpD77k7RFUVXEdU87g8reZ7Z2C8nWks9+EBa4r1e1O+ivGNKv/LO45+JLTs5w981X0naX0Rp+/2iOl47SrxSqfbdX+t9TztrvQ+134S7iR4vyvPGphN/RDTrnS7ZzdKe3u/NnZJIUc9769PjjoAj4Ai0WQSEznBitmNwOjX10onZmuDzwo5AIQhYYlZeKuHveL92THwONMAccGK2zf4k9oa1UQRYXF1HFKJloCiLf51EW1vYvfJT0f5NyqJs1kK0ZP9AWPzfUJSFz/2a4uwYKFLAam3RHqJLi0IEtYQUgU+92lnPtkGmLyfaR3RfUf5ooBbijjFjjqwhyr1QrbCozBzdW5RF33VF2QXUGgJGy4vuIHqAaF/RZUXbipSkIfqDypI9C0j61qLcr/zxRS3jIcXLCmQadTGXqLsaKWKuFzUHq2l/WKaI/qjPrhKBGN5PFCJ5JVH8t1Up4r5pS2OZF2eeUzwD9xHlGfYj0XpIUfW0xHu9rf4uYlyWFNX3H8/JtvB7jXap1HrfV3sf6juFcNemxuBrdVGe87wPVxatRtrr+73I5301uHoZR8ARcARaBAFhC5yYLYQyad9OnJht3+PrvWsMBCwxu+2220bXXXdddNttt7k6Bj4H2vgcGD9+fLTOOutEc8wxh39jtkV+3XoljoAj4Ag4Ao5AdQikEbPVefNSjoAj4Ag4Ah0dgSRitqNj4v13BBwBR8AREASEjnBitjE4mVZtpROzrQq/V+4IxAhYYnbQoEHRG2+8EX399deujoHPgTY+Bz788MNoyy239G/M+i9vR8ARcAQcAUegjSPgxGwbHyBvniPgCDgCDYaAE7MNNmDeXEfAEXAEWgoBWe13YtZJn7IIODFbFiI3cATqjoAlZn/zm99E77zzTt3r9AocAUegdgQ++uijaKuttoqJ2V122SW64oorohtuuCG6/vrrY508eXL07bff1l5Rfg9Fn5baUj9bvR5HwBFwBBwBR6CuCDgxW1d43bkj4Ag4Ah0OASdmO9yQe4cdAUfAEciHgKzhOTGbfyGzw1o6Mdthh9473oYQsMQsO2admG1Dg+NNcQQyEICY1R2zHEM+evTo6NJLL40uueSSaMyYMdGLL74YffPNNxkeCs9yYjbfz2S3cgQcAUfAEehgCDgx28EG3LvrCDgCjkCdEXBits4Au3tHwBFwBBoVAVnqc2K28PXO9ufQidn2N6beo8ZDwInZxhszb7EjAAKWmN1oo42iww8/PBo6dGh09NFHx+EjjzwSzZw5syXBcmK2UX+4e7sdAUfAEXAE6oqAE7N1hdedOwKOgCPQ4RBwYrbDDbl32BFwBByBfAjIKqATsy25FNqgdTkx26AD581uVwg4MduuhtM704EQsMTs9ttvH11wwQXxccaXX355HL700ku+Yzbfz1a3cgQcAUfAEXAE6orAduL9qiYdXtea3Lkj4Ag4Ao5AR0BgjHRS3ys/6wgd9j46Ao6AI+AI5ENA1kWdmO1Ai8PVdtWJ2WqR83KOQHEIODFbHJbuyRFoSQQsMdu3b9/osssui78tO3bs2GjcuHER35j1o4zz/W51K0fAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHoKERkIVJJ2ZbcnW2QetyYrZBB86b3a4QcGK2XQ2nd6YDIZCHmP32229bEhE/yrihf7174x0BR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQaFgFZBXRitiWXQhu0LidmG3TgvNntCgEnZtvVcHpnOhACTsw27M9kb7gj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI1AsArIu6sRsB1ocrrarTsxWi5yXcwSKQ8CJ2eKwdE+OQEsi4MRssb9d3Zsj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI9CwCMjCpBOzLbk626B1OTHboAPnzW5XCDgx266G0zvTgRBwYrZhfyZ7wx0BR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgWIRkHVRJ2Y70OJwtV11YrZa5LycI1AcAk7MFoele3IEWhIBJ2aL/e3q3hwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgYZFQBYmnZhtydXZBq3LidkGHThvdrtCwInZdjWc3pkOhIATsw37M9kb7gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIFIuArIs6MduBFoer7aoTs9Ui5+UcgeIQcGK2OCzdkyPQkgg4MVvsb1f35gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AINCwCsjDpxGxLrs42aF1OzDbowHmz2xUCTsy2q+H0znQgBJyYbdifyd5wR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUegWARkXdSJ2Q60OFxtV52YrRY5L+cIFIeAE7PFYemeHIGWRMCJ2WJ/u7o3R8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUegYRGQhUknZltydbZB63JitkEHzpvdrhBwYrZdDad3pgMh4MRsw/5M9oY7Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOALFIiDrok7MdqDF4Wq76sRstch5OUegOAScmC0OS/fkCLQkAk7MFvvb1b05Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAINi4AsTDox25Krsw1alxOzDTpw3ux2hYATs+1qOL0zHQgBJ2Yb9meyN9wRcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAESgWAVkXdWK2Ay0OV9tVJ2arRc7LOQLFIeDEbHFYuidHoCURcGK22N+u7s0RcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEWhYBGRh0onZllydbdC6nJht0IHzZrcrBJyYjaL//e9/sTbSwDZimxsJ30ZoqxOzDfsz2RvexhFYUtr3iyZdu423tT03b1kzDmskdHQjk79gQn6tSfX2r+1rqXq0vlrDbuJA74+1anXWjssvZXD6WQ39nF3Kbmp8/bgGX/Uquqg4Hit6vejuphJ/lhowOlC0LY67bVNbfm4tIPOE+wjt34HmjHfVEXAEHAFHwBFwBApEQBY0nZhthFXdVm6jE7OtPABevSMgCBRBzEISPvTQQ9Fpp50WjRs3Lnr//fdrwnbGjBnR3//+92jkyJHRX//61+iLL76oyV9aYfyOHz8+uvbaa6OpU6dGf/rTn6LjjjsuGjp0aPS73/0uGjZsWHThhRdGjz32WESbapX//ve/0YMPPhhdccUV0XPPPVeTu2effTa66KKLovvuu68mP164cRFwYrbAH67uyhEwCAyTeNSkz5l0j7YsAqObxoCxmJhQ9UyT3y8hv9akPP7nlEqGiK5fQ2V56qnBfVVFV5NSpZSSpOv98VSKTVtPzupfUW0/2eD0WA1Ouxg/4N6rBl/1KgopS9u+FbV/ROHP0noh3rb9tsVxt21q68+te2V4uZ8+E12mbQ+1t84RcAQcAUfAEXAE2iICsszpxGzjrvW2WMudmG0xqL0iRyAVgaKIWUjC7t27R/vss0/0/PPPp9aXJ+OTTz6Jjj322Gj++eePjjjiiOi9997LU6xim7/97W/RnnvuGV199dXRSy+9FP3617+OllxyyWjNNdeM1l9//WiNNdaIll566ehnP/tZdMEFF0S0qxaBmL300kuj3XbbLaLuWuSDDz6IieODDz44evnll2tx5WUbFAEnZtviL2BvU3tAwC7gOjHbeiPa1onZTQSaSaIsoNeys2lmkw/81INgFre5hd2YZ4jSpudTSpUknbaibZ3gCLuQp39hmWqvOwoxu4MApPPh4gAsf5YGgHSQy7Y47rZNbf25xUkd/xXlvrqtg8wZ76Yj4Ag4Ao6AI+AIFIiArHE6MdugC70t2WwnZlsSba/LEUhGoBZi9ttvv42+/vrr6JtvvokuvvjiaPHFF4/69+8/y25QbNhtik1ewee9994bnXnmmfHO2enTpzcX1ToJk4R6KJ+Wr2XefffdmPQ98MADY1L2lVdeifbee+9o5513jiZOnBi9+eab0ZQpU+Idu9tvv3209tprRzfeeOMsRx5r38rVpXWys/idd96Jietp06Zpcuxz5syZqW2mT+RTXgWSd8KECVHfvn2jSy65JLWs2nvY/hBwYrbAH67uyhEwCNgFXCdmDTAtHG3rxOx0wUMJqfZCzP7O9Kk9ErN5+lfUNO8IxCzHrr7RNGfY3bdYAJ4/SwNAOshlWxx326a2TswyTa4S1ffLLzvIvPFuOgKOgCPgCDgCjkBBCMjypxOz7W8NuPAeOTFbOKTu0BGoGIFqiFmIyPvvvz9it+bmm28e75LdY489osUWW6yZmOWY4JtvvjnekbrpppvG6RCb7DqFdKXsySef3Ly79vXXX4/OPffceNfqTTfdFF133XUxUXrllVfGZb766quYpP3Vr34VbbbZZtG2224bnXHGGRGEKgJJNXbs2Hg3Kvn77rtvTKp++eWXiZjcfvvt0TbbbBOdffbZMempxOzAgQOjF198MS4DEQrJe8MNN8Q7aNm9y05VfFIeIrdnz54xmctxyB9//HGMywknnBDnQ54i+D7rrLPiI4w5Lpl2P/rooxH5TzzxRHxsMm3eaqutonPOOScCCy136qmnRr17947AkOOVn3zyyTiPfxg7iGX6Onny5OZ0j3QMBJyYLehHq7txBAIE+ObnSU36myDPL1sOgXLELAvtOk6r1qFZ5fwXRcyWq6cOXUt1mYe4LElpJQwageCwnc3TP2tfS7wjELO/N3PhlASw/FmaAEoHSGqL495oxOzyMk++EeVZ+6EofwTh4gg4Ao6AI+AIOAKOQC4EZEnUidmOsS5cUy/ZkcZxobfddltNfrywI+AIVI9ANcTsf/7zn5iM7Ny5c7TEEktEyy67bDTffPNFc8wxR0zAPvPMMzGxyhHACy+8cJzPbtrVVlstJifvvPPOqE+fPtEqq6wSHyMMQXnLLbfEz4ONNtooJkJPPPHEqEuXLvGuVna3/vnPf4423HDDaIEFFoiPG4YEJv/QQw+Nj/Jlx+5KK60Uk8PLLLNMHK633nqxr3BHK2Qr5OiWW27Z/PyxxOwLL7wwC6B8D5adwP369Yv4tivkKu2ESB0xYkScx7Pssssuix544IF4F+vhhx8e0W7k+uuvj37xi19Eo0ePjslnyt16663xt2vxu8EGG0RHH310BOnM8cmlUimaNGlShI+11lorGjx4cEzK0n+OigZfhH5ALNMPvsXr0rEQcGI2109SN3IEHIEGRaAcMdva3SqKmG3tftj68xCXTsxaxNLj7Z2YXVS6/qUoxBHhwqIujkBbRaDRiFlwvFpU/wjmaBJcHAFHwBFwBBwBR8ARyIOALI3+gJjVIxxZmGZB3LVjY3D66afH35BcaqmlYqLB50PHng86/pBrTz/9dMdiV1q5t5USsxypy3O8a9eu0SabbBKTlP/4xz+iXXbZJZpzzjmjvfbaK4J4ZScnpCy7PPnmLMcS871W8v/1r39FRx11VEzmDhkyJJo6dWo0cuTImHSFnIQIZdfpj3/845iYhYg85JBDIohgdqk++OCD0eWXXx7vJB00aFBMAu++++4xYXvKKadEEKvHH398TM6SrztQFWq+WYu/XXfdNSZASc8iZilPf3baaad4J+xhhx0W77a9++67o08//TQmayFM2W370EMPRccdd1xM2oILRxaDAbtzOSKZb9VuscUW8W7iP/zhD1GPHj2iCy+8MLbj6GR2yILFqFGj4l2yJ510UrxLFz9jxoyJ0/ier5LN48aNi0lf0uxRx9pXD9svAk7M5vlF6jaNjgDfhCxC5hcnnYpwlOGjc0aezZpHLn5kE+oYn118521XHZvR7HpuieXte72I2fmkDeBSq7QFYpa5NGetHTHliyBmadMcxmc10XrN2zz9y2pvJc+RcsRs3vuyizRIyRnCXlkNDPLmletaxyJw2Xw5XGLarj81p7ZcpF5zpN494PnHczCvME+KeF7lra8t2OW9N9LamvSeqZaYbc151kc6qPfYGxKfK63Dnu4IOAKOgCPgCDgCjoBFQJZBf0DMsiPq8ccfjxfK2fHk6hjMP//88Q67eeed1+eD3xPxHPjpT38ak2ztl0ppez2rlJjluF52d84zzzwxucmuUL4hC/G64IILRr/85S+ja665Jtp6661jYpUjeCFb2SFL/sYbbxzdcccd8bHDq666akxS8o1UiFV2uhKnjmHDhsXljzzyyOi+++6LiV923Z5//vkxKcl3Z6kbcoojk9l1utBCC8X+qO/nP/95TORut912MVlqkYcI3n///eMjgF999dU4K4uYhTDluODddtstbjckdPfu3eM+Qu7uuOOO8W5diGpIaY4+ZocsbYWohdDVY5AhZtkxi82xxx4b73blWGiE9+Rnn30W94mjjyGy2YlLvdQDduwUhvjlqGiEEwc46pg/dmEHrUvHQcCJWfvL0+ONikBfaTiL+uheopAIg0VvEn1HlEXJaaL/Ft1fNA+5Cpk7RPRqUcpRXhc3P5L430R/JZq22L2j5Gmbzpa4FUiw8aLkj2vK+I2Ej4pSxyeit4r2FFVZSCJHiU4UfVsUu69FnxH9P1EWjBcTLUqo+w+iD4jy3UfqmyJ6s+hQUYizJLlYErXfhOsmGTWl2XHDdj9jCy6k3SDKAvkaoueJPizK0Yzfir4qernoqqJpMloyaDsKdqFcKwna3g3CTHO9uMSPEX1M9FNR/H0lyveDrxFdXTRJkvxznOSNotSrbSPEt7blpxKvRJLq0fIW535NidtKeKHo46JgOV30X6IXiHYXrUYOlEK0/3lR7RcYaZ9OkrhKSSJqo0cZ95G0q0SZ0/8V5T64QxTybk3RPFLtvM3ju5L+qb9anyMniyPFifmB0I4Jou+Lkvey6FjR/UTTnkddJE/9EPYSTRPubXbWMW74/p8o84Pn4MWiHDFbhFDPe6Larv4pTneUdJ1D4bM0pUhmcj3nSGbFVWRyv2rfuY+XE71OlPcR98hLoseIMs+s8Izhfrtb9C1RMP5clHv8EtFNRUPpJAk8b7W+rUIDc10ydjyX0wSs1d9VaUYp6eXGfVyT7/ESzim6qOixorw79d6YIvGbRPcXzSMbihHv0zdEdd7fI/ERoguKVkLMVjvPuMcUM8Jq36FSNBaw+UCUOYD+UtTFEXAEHAFHwBFwBByBsgjIsmgiMfvYY4/F5Au7qpZccsn4GEuOsnR1DHwOdOw5ACE311xzRcstt1xM6nUcaqX1e1opMQsZ9Nvf/jaae+6545BrCMXzzjsvWmSRRWJi9uqrr44JUv7oguOM2S3KN2G33377uAxkJUcCczQw7wIIR8YeG45JZhcq5CM7ZiFm2ZkK+YktxC0CGfz+++83E7PrrrtufLQxxxdrfTvssEO885a6rCjRut9++0XEkSxilh26HBfM7luIUIhW6uOoYchVFCKZXat8n5Ydu3vuuWd0wAEHROx4ZYfsH//4x5hQ/v3vfx8Ts+PHj4/L0ed///vfcRv4h+/Xgulpp50WrbzyyjEpi/9jjjkmxmT48OExsc03dxHaA+nNTlsnZmNIOsw/TsyW/TnqBg2AQEnaqIuOkIks2Op1Ugjx0000TfaQDBZmk8qGaf8Uu+4JjuwCLuSdFYhG9cPi7xHmWtMJ6QeCf11ct/lhnIX3nShQg/xIyp4sClkX+rfXYLiWaCiHSoK1myTXc4VGct1d9BNRtYXsXFFURdMJtxOFiLBpNg6GvxVNktGSqLYTEwxmmnwlLUMz5gPYqp+kELzOCgvKdZL/pcr4wj/EaSWSVI+WL0lE23y6xIeaa0234ceSDylSqVwmBayfMP6QcVgytsylkaKMY1hGr/kjhANF06TWeZvm16ZX0j/KFfEc4V5UDJ6V+NXmWtNteIvkdxENhTRr1ys0aLpeW0LGw9qGceY67QLzWgSyTH1/KfGQXFTfWc9StckTtsQcydOOSmy4XxUjnmVTzLWmE64iqgKu74ra/DDOvYa/eUWtvCQXanudzTBxnuefGTsI4gVNvo3yhx7qj3dlJVJu3NUv4Uqiz4vatDAOyZl0b0hy/Mdctq1hWa55j9tngP5BCeWt1DrPinqH2jaNkQvt0yM2w+OOgCPgCDgCjoAj4AikISCroonELDtm+SYgu5pYXOd4SFfHwOdAx54DEIN8s5NvlS6//PJOzLYwrVQpMQshCmnIqQfsRoVIZdcpu1Qh19kxO2HChPi7q+yQhUikDr6pypG+V1xxRcT3pfHDcb3sgqUc/ji++JNPPpmFmIUEfvTRR+Mdq+zS5Rhhnhl///vfY4IUcpfdpezQ5Wj0c845J3rrrbeiG2+8MT4ume+7fvDBB7Og+vbbb0cHHXRQ8zdjyVRilva//PLLzfaQv/SB3b0c4QzxynHK7Jql77SXo5GvvfbaOB8/kKZ8+5Vjivlm7IABA6KnnnqqmZiF5OWbuewyhpS+6aab4vrYBcwuW76byw7bnj17xj6pA+X7tRCwkLHffPNNXIZ+6u5cP8q4edg6RMSJ2bRfoZ7eSAiUpLG66GhDFp8hBh8Q/SqwmSLXC4mGsqUkWB/EIQ0hK9AvRMN8dtiEkrWobIlZfIVtU/8QkvOLsiCsaYTTRJ8QfSdIJw8CazHRamQOKTRR1NZFnN02tOGbII+6QoJndkn7W2A3XK6tzCkX94vaegZagyDP2kEasoNpSoJNEjkL+aDl6VsoMyVB8/uFmXL9a5OvdhBTkACvJeQdJWlWkvwvLgaQGUlEJGnoFqKVSFI9Wr4kEW17GNIXxjFMZ6y7iVYiF4pxVp8Yc5WsNtEWS/xo2+jjmurAhEXMW+MuNVpJ/4p6jkCAav/D8H3JS5qDL0t6SHJCSNnyveQ6FJ434VxgHjDX3xO15YnfKVqL3CiF1SekWZoMkwy14zlUjbTUHKmmbVllLDGrGIThw8bBXhIP83nWMCeS7qknJb2TqMooiWh5xnx2zTDhz42N2u5s8m10irE90mbkiJcbd62bMJyfX5p6rR33cJJcJonWjjjv/SkJ6WqXRMwWMc/AvIh3qLhplq0kpu3mGd21OccjjoAj4Ag4Ao6AI+AIpCAgK6KZxCy7qlhMdnEEHAFHADLpn//8Z0yqOTHb8vOhUmKWFkKKQiiyI3attdaKCUj+6EZeCTEJyR/hQDpy7O6KK64Y7bzzzrENRC2kI8Qscu+998akIuXWXnvt+Ehi5gMkJDtEO3XqFB+XjD3fY+VoX75bC+H5k5/8JN61279///ibs+xYhdwlHdIUf3wH98QTT4yPRo4rbPoHArRUKsVkLm1AIGMhZddcc82Y/B0xYkRM7LLbFfIYMpZv30K6susVwpXjkDlGmWOEqe/ggw+O3njjjdjfPffcE22++ebxLmLIVOrku7DnnntunE45dgJzDDE7iSF2IZU5uhjymSOROd6ZvkLWcvTxHnvsEV/z/gQn/HE0cq9evfydGqPesf5xYjblR6gnNxQCJV4AgbIQvZrpBUc73hbYsEMmlL9LgvqaIfF9RdkhpMKiKYvfdkcS5FZ3NWgKsxaVQ2KW+vDBYuxI0btEp4pCYO4uqu1hgf1gUW0PbWGHW7iofISkVSMhCcmi/frG0bwSHyI6U1Tb9KzE2SFkZQm5gMxVG8ienxqDE0weNleYPI1qWRuCDQvfKn0l8rmo2kyX+DKa2RTWQswuLT4smcEYnSjauck3AQTq26LaBsaom6iKxSqJ+KXNWra/FqoizKqnZOrQuiC4thGdRxRiZjPRR0U1nxC8q5HfSSH1A6mXJCVJVBsNp0jaPqLMH2RFUXs/Ysd4hlLUvA39pl3n6Z9tdy3PkSRidqo0bEPTuBUkDjmnOBIeZ/KJdhG1+b2CfJ5Jk40N8+kkUeaHymoSeUTU+hmomRWGPLvsM+KgjPLDJE/rZN5WIy09R6ppY1KZJGL2DTE8T/Qi0amig0WRpUT5wxXF6huJDxWdTxTh2bmx6MuiakN4vKgKzwGbt45mmJB5YW2I/97ka3SNwI55WomUG/ewDfSX5wPPbYR3AfcqRKTa8r7gt4CVn8uF5hN+JLqdKHghXUVvFLU2xJOI2aLmWRHvUGlis/CO5p7WPtTyrml26hFHwBFwBBwBR8ARaN8IyNJoJjG76KKL+iJyx1o/9t46AqkIhMTsddddl2rrGcUjUA0xy/dNx44dG/H9WHY6821VdoVuttlm8VHFkydPjnfRjhw5MiZuseFY3sGDB8e7XyEUEXaynnzyyTEZym5avv2KfP755zGBuc4668T5EFDvvPNOfFwy35KFKIXEh0idOHFiNHPmzPhoZL59u/rqq8f5ELRDhw6NyVSOWrbCnGMnLUcAjxkzJiY52YULGUw5vnUMQcuxyJCmkMyQsuxSpSxEMbt02dHKMeyQ0/TtkUceiclS6qK9kMJ8HxYCFqEdfH8XQhfiFhwhXNnty45c6jzkkEPi77Gzo5hy4EqfqIf2XnnlldG0adNif+DCzloI3EmTJsVp/k/HQcCJ2fb9W7qj9K4kHdUFR0IWrhdJ6DwLrfeIqi0LuUsau9UlbhdxIUHTZBfJUD+EXFvJWlSGBLFliWOfJJDHajshyUDS8DdVVO0el3ilsqAUeF9UfUySOH6TBDLS4nRYghGkqfoifEgU/HuIgrvmPSfxzqKhaL6GEAxJsrkkqg3hhYFRLcTs+YHvtMVsSDIIWW3HUaYNdjG8n0nX6HRTLs2/2maFWfWUTB20caooJHso3AtviWo/nggNcl7nIS7DNkHSrZLgn7lh2/RYYFP0vA3cJ16W61+Rz5GQmH1dWrRYQqsgUB8R1bGDXLIEVDli1j6v8JFGuPJMuMvUw7M26f6V5ExZS3K1rYRbZ1jbtvG8qFRaY45U2sY0+9Mlw+L0jlwvnGJ8W2DLczpJmBdPiqrfLyW+nCgyp+iHopp3DImB/FuuNV9DTpMIhbKan0RihvbhdblxV98aDg4dNF2XJFQbwp0DO/ub4GvJS3oO8YcEV4haP2Gfip5nfYP6Kn2HSvFZ5FW50vZfOkuOXzgCjoAj4Ag4Ao6AI5CAgCyLZhKzvmO24ywce08dgXIIWGKWo2jZfcg3QZ9++ulYX3rppXi3YTk/nl8dAtUQs9TEt1AhUjmid4p8p/XDDz+MyUjIIghMSEi+FQtJ++STT0aM48cffxyTqNpSxh4bjh4mTwlb0j/77LM4nd2z+CINInOK1AUJyXdcIXYhZRHqxJajhJ944ok45Fp9ap0aciTxPvvsE+/gfffdd+PytEGPVmfnKwQsRxlDFFs/tIf2cYQzdeFL+63+sYFApY2QrCr4Ik2/EUtIXeBIn6wfytG2Z555Ju4zbWPnrQo7zfv27RuT1zZd8z1s3wg4MZvwA9STGg6BkrRYFxwJ0xZo6dgmge2vSGwSFkJZdH5fdJpoEnklybGwC8nWuXdTugZZi8pzB2Wfl2sWxJPkcknUeqZIPIlwptyWoiNF9xHtKVqpDJECWg/hOmUc2B1EL6TYXhb4hFx90aR9JfGfiSaJbQvkD7s60yQkiqzdaLlQXxNtRlN8pskPidOpJu/hJvu0QNvAwv4fjVGWf8ymi2r7+ptylUaz6imZOqhrjwzn5xnbtzPssrJ+Z3wwt5MkbFNIltgyZ8uFYsRcsFKPeWv9J8XL9a/I58jJ0gDtO6F9XoVt2z6w3d8YdAnyepk8otNM/q1BXni5hrGlTQNCgxzXBwQ+fpJRJutZmlGsOas15khz5TVGTpfydvz3SvHXWdLtH8vclGKnybwvrN+SZkjI80vz7jXpRBcU5eQA8m19XHcVtXK/XKifk2xGzni5cVffhJCk/OFPkiwkibatBxkj7otvRNVXFmHZTexmGNuQmK3HPLvM1EcbK3mHivksMlGutJ+cjuDiCDgCjoAj4Ag4Ao5AJgKyFOrEbPteD/beOQKFIWCJWY6+5ahbvjk7bty4WPmeJgSES30QqJaYpTWMnZKmaa3LY5NWNim9nL9y+eoTIvfyyy+PiU2+90q5SiVvXeX8lvMDxqgVSGqOSNajnG2exzsGAk7MZv4U9cwGQaAk7dQFx48lnkXi0SUWVNX+BhISZK6ENE36kUQgPyHh1A9Eg5WsReWQmL3WFgziA+Va6yB8R/RY0TVFi5SLxZnWMyWHY4hotZ8pcTAJBfL6ZVG1C8ODwwLm2toebdKTortKorVf3RiNNnksTIdC27WsJWZXMenkh8R76GcZSVhJdM4gI82/mrUGMbuwVp4QHi5pigc756qRcsQlPkuiWg/3kT0yl3wrg+VCbb+wGRKvx7wNqvjBZZ7+aaFanyOWmGU8ku4zrYvwVVHF6kyTAQGl6YSWmF08yNvXlEuLTjZlhqcZZaTzDLPtmT/DNutZmlGsOas15khz5TVGQmJ2xRR//CGNxXODFDubDDmnZcaZjN1MOvfmj02efdb+n6Q/Z2wHGDueMUrgUse6Ji9vtNy4a9sJzy/jlN8Fan+osd3ZpJO/tslLil5j7J8KDOoxz+aTOqp9hwbNm43fGYrB62GmXzsCjoAj4Ag4Ao6AIxAiIEuiTsx2jHVh76UjUDMCkFL6jVmIWY5y5RuaHJXLscZ/+ctf4t2YNVfkDhIRqIWYTXTYQInsiOUo5VGjRv3gO7RtvRvssOW7ulddddUsO3Lberu9fcUh4MRs+OvTrxsRgZI0Whcc/5OjAzcb+39l2LMDZ2XRnUQ5nvZS0ftEPxPV+jQ8UNKsZC0qh8RsFrHBDlm7qKz1Eb4leoUoC+kLiNYi90hh9c2C+i1l9AljT7k0wmBDybM7krSOcju61I5wG9EsWUsyrf1Wxni0yauEmIW4sj4h4quRtkbMMnezhD8w0H5PyzLMyMtDXJZMPW9k+CILwkfbNCOwrde8DaqZ5TJP/2yBWp4jJ4sj7XvWs0rru9PY36yJEnYx6fizxOymQd4Tcl3u/mcctF3XSrxSOUcKaPly8yzrWZqn3taYI3nalcfGErM8S+ZMKbSnpCuehJZMTSkyy87Yx40RZKAdX3Ziq1wiEa1nkMTtiQ5j1EjCvYzdaya9kmi5cdd2EPJ+zhKISLUfYgwhaTWdkL5nybGSqfYhMVuveVbtOzTsx2mm7V+FmX7tCDgCjoAj4Ag4Ao5AiIAsOzoxW9zaq3tyBNo1AuWI2b/+9a9OzNZxBnRkYlaPG+YIY3bQNpJwlDRHIvsRxo00asW21YnZ8NenXzciAiVptC6Wpu2Atf0639hPthlN8RUlvFA0iYDVesLwwMBP1qJySMyyqJ4lq0nmFNGwTnvNQis7mFYQrUbswrX1mze+bUalZ0ue9QPxy/GSWWLt7Q7YpDL4svZ7G6PRJq8SYnaAKYfvbsZnJdGZxo/dkas+ppv8/ppYRZhVT8nUwQ63LNlfMhXLcoRZmp88xKVtU9L3Ka1vOxYzbIbE6zlvg6qaL/P0D+MiniMnix8dD+7vcmKJsqeNcReJqx9CS8weEORZuzzxPH8MY5oSR68xdYYEV2g7zNiWm79hWa5bY44ktaOaNEvMvpTh4CTJ07H6IMPOZo0wZcKd6LebPN6XKrwvtR7m957m+hU1knCcSbfljUnZaLlx13YQJj1bbQWQw2pviVmL2ye2QErcEs7hvK3nPKvmHRp24TcGA7AoR0KH5f3aEXAEHAFHwBFwBDoYArL06MRsseuv7s0RaLcIODHbukPbkYnZ1kXea3cEakPAidkO9uO6nXa3JP3SRdexOfp4nrHn6E8rfeUC4lD9JYXsVB0T2NRCzGaRmlJNLF3lXxbpp4gmtUnT2F27tWilwsK8+qgmhDxLktkl0e4kUt8cT5slakfYPctQ8hYUtfb7GPtqiVl8WJ8LG5+VRGcaP0nkQUsTsyGZEPalNYjZcm3KImbrNW9DXOx1HmK2qOeIJWavtY1IiV8s6Tpv7S7ILGL2aFNGy1YSvpDSlqzk602dD2YZSl45gq5M8dlaY46Ua1PefEvMPpRR6FTJ0zH7MMPOZp1gynxpMyRuSTwdX4hYrWNqk/3iEv7PpC8rcY7b5j2ktr0lXo2UG3f1T7hrmQrSiNkzpZz6+byMD7K3M/bhc6te86zad2jYHUsq0+dyfxwVlvdrR8ARcAQcAUfAEehgCMhSoxOzta23emlHoMMg4MRs6w51UcQs4/jtt9+W/eZsPXtLG9gFSzuIVyvqI/yua7X+ssrRzs8++yz69NNPa2pzVh3ksbN22rRp0cyZM8uZen6DIODEbAf7cd1Ou1uSfuni6m05+sgxumpvjwftIensPNU8wjdEIUQgY7YSXUIUYbH0v6Jq+ysSjWQtKs8tdlqOMA8xa1zP9jO5gEy5U3SGqPVFnD7MK1qJsLtO/dwo8aUq1LT6OGJS/doQQnJV0TSxthukGTWlrymhtWecVKolZjcVB9YndVQjM6WQ+nFi9nsESwaXkOD43uq72ABjy3y3Uq95a+sI4zwLdEyfDzPlusjnyMmmrlsS6gqT/mrsuY9VsojZ3cRI+0O4rmgl939XraSC8Dyx1TqTMLSusp6l1i4t3hpzJK0tlabnJWYPEceKJ39YVO5bxLRjjCkzlQQjjL8lXLvLtSVrrzC23L9a934S38xcfyTxPG0Rsx9IuXHXOgl3/UHpWRPSiNkjxUz90F/ezVlygGSqPf22Uq95dpRUonXasNw71LaN+GHGD98OdnEEHAFHwBFwBBwBRyATAVnTdGK2QRZ2vZmOQGsjADGV9Y1ZP8q4viNUBDHLGPIt4H79+kWnnHJK9Prrr9e30SneX3jhhei4446L9t133+jee+9Nsfph8htvvBHPwcmTJ0dff/11dMMNN0S77LJLdPbZZ0fvvffeDwsUmPLEE09Exx9/fDR+/PiI44m5RmfMmFFTLRzN/Oyzz0aPPPJI9Pnnn0ePP/54dMIJJ8R9g7h2aXwEnJjN/CnqmQ2CQEnaqQuWLI6Wk0fFQO3/ZIztQjWLtCNEIWCTZGFJVB+EgwKjrEVlFn9t2UqJWVtVZ7nYXZRF4lp83mTKV3M0qRT/gUAgW+KYncYzRbWdjMNcokmiNoS7JRmYtB0kbu1/avKqJWaXDHyyUypL1pfM8aIcOcluYCUjbH+dmBVgmqQkoY5ZSHCojYZZxGw95q3WmxaWI2aLfI5YYvaxtAaZ9GckrrhC6qlkEbNripGWIdxGC9UxPMbU+UGZerKepWWKxtmtMUfytCuPDWOoY/NQRgH+GEXtCJfPsNWsO02ZRzTRhKSpz4MkfoO5HmjszjXp/BETz0Atd42xqzRabty1DsJdyzhPI2bph/WzShk/I419+Nyqxzyr5R0adsW2HTxcHAFHwBFwBBwBR8ARyERAljudmG38NV/vgSPQIgg4MdsiMKdWUhQxe9FFF0VLLrlktOeee0bPPfdcan31zHj44YejnXbaKVpjjTViojNPXZCg++yzT7TDDjtE99xzT/TVV1/FhOziiy8eHXTQQRH41EvYJXvSSSdF+++/fzMhS1tOPfXUuB211DtlypTo4IMPjoYOHRp/o5m6IM3322+/mKStxbeXbRsIODGb+VPUMxsEgZK0UxdX2cW6Wka715A8u9P1EGP7osTVz79MelJ0J2NLGcg4K1mLynmJ2fnF4TjRJ0TZHbOFaJosLBlfi2r7z0gzTEm3BAB+lkux0+S9JfKmKIv3kNsriVqhj3YHET4hTEui2kZCjuBMEmszNsnApFnC4D1Jn8PkVUvM4uILUW3HGOMzKTrK2L4t8dmbjGaadCdmm0CRoGRwCQmO762+i2URs0XP27DupOtyxGyRzxFLzPLHIssmNagpbV0Jdb4SHmhss4jZecUO31r2UlMuKcofHXDfvyx6l+hQ0UplHymg9bHD096zoa+sZ2lom3TdGnMkqR3VpNm2P5ThYHnJUzwJR2TYkoW9fTZdlmB/oqSpT0jHd8314saeP1pRO/74hh3Qer2rsas0Wm7ctQ7CcvW8Zto0xDRkGZOOn5EmLyn6qLEPn1t2rNrCOzRs/4Wm7f8JM/3aEXAEHAFHwBFwBByBEAFZsnRitm2s23orHIE2j4ATs607RNUSsxzz+/7770fPP/989Pbbb0cXXHBBtMQSS0T9+/dvJmY5Nvett96Kd26++eab8W5U29svvvgievXVV2N7dtmyY9QKOzvffffduDx22CPUDdFI/Z988klcB0QkfbnjjjuiG2+8MWL3K7tOP/zww+jjjz+OfbMz9sUXX4yP9MUP+WPHjo2J3B49esTl2F369NNPx+kPPPBAc53YU+dLL70UsTOXY4FVaCftoD0QuxBm1EPfs44OhgjWnbn0jbb36dMnOuKII+KyurOVusAZnxx7jOCXvqHsjkWwp69g9uCDD0Y77rhjTMTSZnYC33XXXVHfvn2jUaNG1bwjN67Q/2lVBJyYDX99+nUjIlCSRttF2iwib7yxhYzgyEYVyE/1AyGaJt0k4xVRtSW0i72Uy1pUnjsouy0FEmQuSftIVOu5MsFGkzpJ5Ctjy/cDK5FeYqz1EE7IKNxZ8mz/P5DreQJ7u4sKf8c35dOnSaJaFyT5z5vybKD5hNisajNN/CdN+Wo/xuQRrYWYvVXKq18W2lnETxK+cQshobYXGaOZJj2JmJ1m8vcz5SqNZtVTMnWEZEJYz/7GlrZVI9wLisXUFAeVtCmLmC163qY0d5bkcv0r8jliiVkwzSJN/yL5ijt/VLCkaXUXk4cNuFmZKBdalvttA5sZxAcZW8ocFeTnuewT+OA+TpOsZ2laGZveGnPE1l9L3JJ9WcQsxLYlRD+T60UzKr5a8nS8CbdOsF0rsFF7/uDGynxyYZ8/asf76MfWsMJ4uXHXegh3LeP7NclXe+5fK+CqeZ9KfGGbaeI7Gzvsw2dp0fOs1neoaXocvU/+1X7+Kcz0a0fAEXAEHAFHwBFwBEIEZKXSiVkBAcKJYzAvvPDC6JxzzmnW0aNHx2k333xzBFlRjbAA/8477/yA6MjyBelx0003xe2YMGFCru8ZQlJgyxGYv/nNb6KjjjoquvrqqyMIDhUIgv/85z/xYv+VV17ZTBxoflsIIVRuu+22aPjw4dEhhxwSEx/s8Js0aVJhzQPff//73zEO48aNi4mawpy3Y0dOzLbu4FZDzEIO8hzYZJNNoqWWWipab731IojNLl26NBOzPCNOP/30aO2114530q655ppRqVSKCVOIVY6v3muvvaIVVlgh6tatW7TaaqtFhx9+ePM9yfPt3HPPjdZff/24/EorrRTtvffeMeEI+XjGGWdEG220UbTVVlvF9f/iF7+I6xs8eHC0/fbbx/c7z6V95VjjbbbZJtptt90i2rDsssvG5OTdd9/dTF526tQpmnfeeaO11loruuKKK6IxY8bEvnnuQTrzfVaenfhdfvnlo2WWWSbq1atXdNVVV8UELYQsO1/BY8CAAXEefcLfWWedlXgcMs8LdsZuu+220Z133hmTroMGDYrmn3/+uL/UDdl8yy23xDbgBAYQ3xMnTowJWI5aZocw7xLuI4jdPfbYI36+sdt34YUXjhZbbLHo0EMPjYldiGKe4wMHDmwmz1t39nnttSDgxGz469OvGxGBkjRaFxwJIVwhNOwurDnl+hxRa3eNXFthF4nms0C7ss1sii8m4XOiaqfh8MA2a1F57qB8GjGLy8sDW0iRUNjBdrGotoVwi9AoxzWLtdYHu6hYdLeygFz8TdTanWINJE7djIHaPCpx2qiyrkS+EdX8KRLHrxXN0/AlyQzJm7Ulbaqo2kCeri5qZbRcaD7kUyiWUAiJ01XF2OazEB/OiS6SNkFU6yDcXFTFlg/9Y2MJ3T/LNQR7NZJVT0kcavtCMiGsa39jOy3MzHn9a+MDkq9nQrlK2pRFzOK6qHmb0MzEpHL9K/I5EhKzjONpQavmkutLRHWMCU8MbJinNh8SyQr30reiavOmxCFPQ9lBEmaIqh0EMH+YUKnwxx0Qd+rnoAwHw4wdz95QuknC340eEBrIdZFzZKip63aJh3+UQvWrGBvalnTvY1dOThcDxSiLmMXPlsaWMs+Khs9Dni/nB3YvyjVzKEmmSqLWryHP1FD+KQmaryHPxVqk3LhrPYS7lqnoNclX+yGB7e4mD5vHRJcNbHrLNX+ApD4Ik56lRc2zIt6htguM75em/YfYTI87Ao6AI+AIOAKOgCOQhIAsNDoxKyBAPvBdPxbGISxUF1xwwQjt2rVrtOmmm0b/+Mc/cq/N4hMiBYJj9913r4j8gzzguE7aw7cg8ZUm5HEcKYv82EMWdO7cOZpvvvmiRRZZJNpss83i3VeUh7yFgKFPP/nJT+KdXml+WyMdcgMy1vbjxz/+cdxeiJNLLrkkc1db3jYrOcM4b7755vFOwLxlO7KdE7OtO/rVELO33357TEIusMAC0cYbbxzv8uR5Nsccc8TEJMcDQ6pCTq688sr/z96dwO021f0f/ytzSaRkCiUyZx4rsygqMhwhY6QkykznSGZliGTKkJAIFSpziswlUYZ4Iik9KZrrefZ/vVdnnWfZXfd13cN1zrnvc37r9VpnD2vtNXz33uu6z/rs329loLnMMsvkd/Doo4/OFqdgobHEmLTvvvtmuDrPPPNkWPncc89lC9yFF144g19WpMstt1zjveWK984772z222+/pgBV9QCip512WoauSyyxRLZ4BV+NVfIpC0wFgEHY7bffvrnkkkvyVrn6It1ar4ApqLnLLrtkOHr11Vc3q622Ws4DNIPF2q4eH6Ow9uU2eMYZZ8zj+nrrrZd1MW4C1tbfbQcWwPoi2jeOXnTRRc0qq6yS3UHfc889GS5vuOGGuU/WvQWCN9hggwyoH3zwwebm9Nux8cYbZ9B62WWX5b6A0N/5zncaazPrO32ttwums6x1X+gJ+EYY2woEmO30F2icG2sKjE8NridMy/6P0nmTyCaiH2zl+V06Blnr0C4HnD0uxZ1S/HSKt6RYQ4lSj+1ZKdah26TyLCljfe0m9YWt/eXS8Z9b+U0cn5jigSl+LsXaelW5vSbwU5aOwWR0u66n0rnzU2Tx+oUUawtedT2U4twplmAf1Cn9A0uXKYnV9qgqj7wXVGl2y/X19sV0/rIUwSoQs55ols89agf3v5RxazsxHf+jSt+6Q/rJVbpyXkjR5PuEFE9P8dcplvJtT0uxDr3Kd6/q6+UHu7apCxnEfrd6xqfrSx0P9Chr5yrv8z3yDpTseS71la13qba0G0qbeoHZfjy3A/Wl0/le/av7pv8jGUc860XDevuzdN7z53mzX6d5Z4HPOsyZDuo869aJE/eVVefxccX1KR6XonfrphTrdPsfTnG44Zp0YSnvwi6FHFLlM960gw82Sjm2xsR26OczYgyq63tVu7J0vFYrz6c65BnMqaGAWeUZF+u2Gc+vTtFzdHaKj6ZYpxufV0xxoHBqSqjz2393h8z61863S4d8QznV677X9W3Ro+D/qtq3T4e8NKrL+306/kqKn0nxmynWHy2UfJ3G0n48Z3On+vrxG5qKmRRWT3ul3bbaGSEUCAVCgVAgFAgFQoGuCqSpzgCzSQRWrSbXTc6//OUvz5ZagB0rr0UWWST/jTXLLLPkSXLuKAcTTK6DsnPNNVcGGSy5BhtY77IuUyd3lgMFoIy1G0usWWedNcMGazZyuQlIzDDDDBl06Ad3odpk7ULnWXXV1rQD1TGlzmvbmWeemYGOvrDs23XXXXPfgBf3RZ9Y7400sOJjaQessNIbrjX0SNsx1q4PMDt179hQwSxXvT7E8AEC6Hfbbbdla0wWmgAoK1ggklUm0Gq8evLJJ5tTTz01W5tuscUW2foUGAVIWYzyKnDhhRfm8z6k+OlPf5rLAUsPPPDA/DEKGMzFL7BoXAVzvb9ApDSWrSxJN91002bxxRefBGZ9/AKiHnHEEfmdZM2+wgorZKh6wQUXNI6Nb8Dr17/+9WyJWvq322675bVfQWBtZa0KCmsfEOtDFWCVpbyPP7RHv370ox9lDdRtrGdd3A6sebka5oWAq+LyHmj/4YcfntsBEK+44or54xHjiXt15JFHZijOBbN7cc455+QxDHjWB3Vx0ayNPsBhQWzsV76gv6yLzzjjjEnn2m2L47GhQIDZrn+KRuIYUWB8amc96Qgo1cft/V+m9E6wkFXJPT2uLWWxgrqgygvQvTzFErpNKg8FzCrv/Sn+b4ql7m5bk8jDsWBLl+UAKDySYrc6StqTKd+CKdahDS0OqxOrfRo8mGIpy3arKr0+Dy7Ux532T0x5XlZdX3ZPqq69tZystv+o0juB2dlSOmA1GP2vT/k8Q3XoVf5+KXOn/hxbFzKI/W71jK/q6AQT6uJ3rvJ6j4YT5kwXecfa/aJhAYZDaVMvMKuNI31ulTHY0Kt//RxHajD7/dTAZ1Js61of35/S39yhI9pc51u3Qx7POqjJyrnOO9B+2yq3Q5FdT+1Z1fOLLjkPqfINF8wqvl/PSHuMG01g1th/YaXXQPfO+d+kuEGK3YL0uox/puM5OlywZiufZ+i1HfIN5VSv+163a4seBf9XSi/59+mQ95XpXNsTRMlftvp0RVXOQGPpSJ+z9vM13N/QupufrNrtg7UIoUAoEAqEAqFAKBAK9FQgTWkGmE0isDq99957s5UViywgwQQ5gHD33XdnS6ckZrYqu/322/NMMJgLYrB4sk7iddddN8nlpDSud1nKskxjrQUocvkpmHjnste5yy+/PJfBEqsEFp0FzCrDpK41DrnBZOFWJu652Dz77LMzwGUlu/POO+d1FcFjaxwCMNoNDnMHDH4CCMAsV5sFzOq/tqlDe1hwPfDAAxlYa5P+aN9dd92VLc7ACS495Xv00Udze0obuVNmIVasfIEH6ybSl3tOuhYrMW0sAczYZ599ctsAWDAEQAWUwWQgacEFF2y4ly7BWozWkGQlV+p1rgQaAy/6oq/05kJUmdqk/damrKG5vt1yyy0Z/LByswZkHWhBKxZ+7juwpXz9nNZDAVJc4rJqBrhoADyxIKTnYD9cmNa1mhz9GyqYdS8AUh86eLcce34BUwAUmDwvWXayyiyWquAiF8CuWX311TOAld89d851PuoAc1l3Wh8VsOV2GHj0jHgHreNqvPH+cHusfG0wtgmgqevaYJa1rmdK8F5xa8zCl4Uty1FWsGuvvXZ+/8BObpKBZ2Od93W77bbL4x23xNqhv8ZI7TOmGic8t8bLQw89NLeRt4T3vOc9k/qQK6/+8a6zfuVe3ZgrgNzcJU+YMCGPa34ztONNb3pTbiML/4UTgFWvNX2Nh8Y47ol9cKMN5feggFnnWCCXYFxj0asv3da/LfljO3oVCDDb88/RyDAGFBjvD8qJ0WQpMHF7im3A8Kd07pQU501xoABgHJfi31IsZZYt+HVXigDeDCm2J61XTedKOCDtlOt+XE5O3HInWVvg9Jocd9kqKX47xU6AUFkg50EpatdIA3h2Uoq/TbH0od7+Lp1Xl8nsOmycDup8LHtnrDO09ulV66DcAjbrcrZM5w9NUXp93v7dKb4vxYHC8SmhXHNDh0x/qdK7laNvP02x/UwpGyzbMcVO2g+m/KPTtS+kWNppCwAMJXSrpwYc7km3sH1KLO3gunO4YYl0oXexlFW2y04scChten9VzosTr++0Ge5z26msXud69a9f48inUkOKdqem/TemeEeK9Xsj/Q8pnpZiJ7e66XSGafWzu7aTAwRp96RovCt111vj0BopjjQskAqoxzNjXKdwQDpZ6m+PpfIb70u6rfF7oNCPZ+QrqfBSH03b46C6V63yyOs+Did8Ol1U6vr+EAp4T8r7cIr1PS/l/Hc6f3KK86fYKxiPPVvlWr+rnYJx/o8plnzf65RpiOd63ff6+dysR9mPVW0byI2vPuyX4tNV3tIfYHdciutXad3G0uE+Z35nSp226tCugYLnrB4L6t/Q+hq/k6Vcz1SEUCAUCAVCgVAgFAgFeiqQpjEDzCYRajDLataEfQngHguuJGaGUeATkMfaCUQADcAKW643WXCZhC2T/67jNhMIPeWUU/IEOxgIEsw777z5WlvAgTUakFDArOuWXHLJDAWUz6rNxL86uLo0iQ9cAK3ckJrgL9BWOQ8//HBec5Yb0LbFbAGzgCJYAYaWvoBu4Mv++++fwQVAqj/Wa+SuU9vlFcEbcIJlr35oI1em+gImPPbYY9mdp3azmOOC1HXqYA3HGgxAAcFZpNFLOYASncAkcAf4vv766zMIdm9oZD1g+qhTBMBZ7QEe+g+e0JU26p1vvvnyPoji2te//vUZDgHs2sqajmWc9imP21Vtds8FUKa4hi155FO+thfQkjNPg/8EmJ26N3WoYNZHBp/85CczCOSO2Dtj3DjxxBOze3BgFlDnYhdU9A6zmj3kkEOagw46KL+bPnzwXIOlxgDukD3zrFJZtHPb653xbnE1LhiXvLveJx9uALPyKxesFQYCs94loF/wcQvr/1K2D1O8z9z++oikBrO77757tsLVJ2O4MbJ8pMES37sMBHuXacGC1lqz3mkfb+h7gcu58uofwNeY53fhT3/6U04pYJZePjahMxjrwxdr3Iq0oZtx2LsDYtOatS7QW6z/BwKzrIJ5bjBWGc8ijF0FAsz2/HM0MowBBcb7A21iBINKYMlkonP7FIGEOVIcbJB3hRRBIROyy6UIqE7tAAKslqI+7TRxn6Xb5AqvSwWvl+JOKW6U4mIpFniadidbKPfTdouJtQCfS6ao7+9OEZCZ0sGEO/2B2E1SXCTFTkA2nR5S8Gy9NUUT7Qum+LIUp4WgL/RaOsWhvH8j7fuUem579W9yjSPK3TDF7VIEifvxDKZiXhK858ukuM3ECJy+JsV+hitTYeVd/2I/Cx5EWVPiGbltYv92HkR7JkcW45X7tkOKPjxZMcWB4H1KipAU8Nz77d8qRb//PiAYSZgSz1m39vnbpbxjf0/7C3XLHGmhQCgQCoQCoUAoEAoUBdI0Z4DZJEINZlnMAhBPJDedJtRNrAONSbMMCLjmNJEPFrACW3bZZfOaiaBsAaSsQ7m4BGxdxzqL68pzzz03W9mCG64FQ4BO672yrAVDgYMCZl3LugrUBD/AhWLt+pOf/CTDTPXPNNNMuZwCIsr0tX4BywCGiX1QpLgyBmZZcAG2AHOphyUcYKoeoIK1GDjK7an2sJrTTtZi2g8y2IK2YAPLOudYtYE6IpefynOt9LXWWmtSPjCD1ak2ghmALS3cB3mBXyBFHn3RD2s8cm0qr3xgDas/QMY1tAecWfSBr+p2D7hEZhEIBgMo+gNI00A7i0to1wAo7ov7BASxVGbh6z6AOtxDgzEs7WjHDerJJ588CYyXezAtbQPMTt27OVQw62MDz6T3xPjjAw2W7t5d75h32jEXv94jFq0gIctX74J35JprrskfHXjOfYziPQFYjW3eBVadoKj3xNqxrM1Zt3on1cPK1YcQxULVuysMBGYBXOvFgrosTY1Txl9jQwGzyrZeLAv84soYJNZ2QFkZPsQANvUPdPWOslb1HnMZbKwYLJg1nus/wAywCcCzce2www7LsJYlv3aBwGCvj3eMUR9MrtJ9zMFi3++KsWTvvffOwHWPPfbIvwfabY1wLpdB3hJcz8Wy+xFhbCsQYDb92kYY8wqMTz3IfzilbQ1mx3zHpuMOlPtpW8DsdCxHdD0UmCYVWCn1qrzrLC6BxGklAHx/TlH/fFASIRSYGgqcmiot79hZU6MBUWcoEAqEAqFAKBAKjE0F0lRngNkkQg1mQTwAj4tNrihZh7FcNbFvHUAT7qxArR/IohQgBThBBNeypOSqF0QxKQ+AgKegHksyk+1A6qtf/erslpc1KEsq4ARMkF7ArPIWTW5FAUYT+9wRg57aBFI4BwhqG8vZXqENZl3PGu6EE07IfQEbHF966aUZotIBAKnBLHfCrFfBzAJrQVkwhnUcd5/aDWqCNEAOQJPekGzRyorsl7/8ZfPlL385Q1NrUwIWIBJ9rP0KxNDcNbagDkgMrGgz/bl4BoNYsgHo6jnggAOytsAvvVmkAej0fkdaq9Ex96jSihX0qquumo+1C3DVF5bNAAmowwoXqOVOFKBxDGIBNSx4ubrWZutuehaKxXKvezEW0wPMTt27NlQwq7U+JLG+rHfA2ORZ9j4Zl4xn3gfvOADqXfQRiA9A5PMRB2jIet145R30YYOPHxwffPDB2Zod8LX2KyDK2p3rdB9rsCJ3PRBp3PJ+FotT4NXHDz7y8O74AASElE87vLfgr7YClsYMVrLApvdeW7gxZsVa1o/1MY2xkvtfH4EA0vps3wceILPxg9tjbTVWGc+LK2P9Lla/9Z0GgEFU69Y+9NBDOQngZc3rd4JFqzHRGOwDHeXLTxOwmlv2Yt0PdvMOACgbI/WB63du6I3loDc36z4sob8Pd/yeRBjbCgSYHZt/I0erX6rAeH+UTYwBZl+qzVg9KvfTNsDsWL2L0e5QoLcC16Us5X2fWpalvVs5tBw8G5R+cQU8OSyah9aiyD09KsCbxn+n6P36V4pvTDFCKBAKhAKhQCgQCoQCg1IgTXUGmE0i1GA2CZf/3wJcgAQm8MG63XbbLQM808OsNoE7MA9s4O4WsHQtaMo1MDeiLLOUAWaw9rTuovwFuAKZAvei3Okqk0VZAbNgCktQ1qTy3HrrrRnCAhbcfQLCQApQAb50goL1uU5gVt8BUZZvrL9YwSlTG8vatAXM6guQqW/ayW2wPrOwZUUMupyX1qyUD6AANguYBY/BTBoIgA1gq4/a7ry2AiV00T/AdpVVVsngRXsAIWADbGKd5loWtwA5eFQsc7k3BjOA5gJ4WNLRsNw7Lki1HRDnMvW4447Lx7RlNew+uecAEBDLIg/QZUmoLYCR9mgfi0GWgdo+LYcAs1P37g4HzPrgwYcfrD1ZtBuTfIjBitXHFKCj99na0ixnfRziowcfG4CY3hkfQsjrPOgJ6PpIBAg1fhhXQE9AUvoWW2zRnHrqqXldau+1jzBYwV522WWT1mJWpnawOrdudwGzPnzw3u2www4Z3PqAwlihHnDL+GIcES+++OJsSQuCaq9xCtA0PoCuxgN93m+//fJYIE1fgWh1+LiFlwG6sixmMWyMbYdieewjEOOkYKyjGdfJRx99dIap6gWruUw2phljQF8fsfjYwxhjDPEeGeMcW7/XvvHKGON6H3jQFqwV/TZEGNsKBJhNv7YRxrwC41MP8h9KaRtgdszfztyBcj9tA8xOG/c0ehEKdFKAm/nyvj+W9lmajvVQ1kXmOnaXsd6ZaP+YVcBa8OXdOn/M9iIaHgqEAqFAKBAKhAJTRYE01RlgNolQg1kglmXUt7/97RxN1JsoN7Eqn2hyHVRkkcVyzLqIgCXrTuc6gVmAosBaYI+lFbDaKRQwC7hyw6lOgARg0T7w0NqQrG1Zh4LI4C8wUYJrwA/ggFWWPrTBrPqBGZavLMv0hQUbN72szIBZbn8LmNU/4AbgACZYq+kLuAkuAJOs3+QDZsGGAma1G/B0rQBWOJaXVRirMcCEJRn4AUbTC5RwHiwGd0EfVnGszdTNWg58ZeEn2tceLqELmHWOxVoJ9AVN0kuXLelAZRbQjrWHvnV5CyfLadAJjGb9xgqOZWDJD9ICSkCL+zSthgCzU/fODgfMajGwaPzx4Yd3ynNs6x32oYLgfTPGyeNDDe54jSGCrXfbeenGA9eWdHkAzvr6kl4+tpCmzBK8J6Udri1g1kcePnhRh/e0uC93XSnLeW3RJmVaA7vUJ5+yXSefqO80EEpfXFPaU9yj06SMTzlz9c9dd92V3Q0DvtqtLTTTTnWps9aZTuV8aY/rit62xmNRunpppA3K4YYZ3OUauVxTNSd2x5gC7q3fOR8t+U0/++yzs2txHyD5wMDv4RS+zz7wjxAKDEmB8Sl3/sMnbQPMDkm6UZu53E/bALOj9jZFw0KBvihwRiqlvPN796XEqVsIC9nTU1x56jYjap+OFbAeNGtt79VvU3xtihFCgVAgFAgFQoFQIBQYtAJpbjPAbBLBZD03tVxoAn2slkyOl1gDCADgxBNPzJAQvLNuLCjJ+grUA2ZZj5lgLxazK620UoYAJuFN7KcblN2FcucpyAtyWksVlAATWGcBkdxlqt/kfQGzXAyzQgMcWIsqD0xkxVaCuqyhWNZ1ZZ3mXL3GLPeg6pMHlJUHdAajuSIFZk0gFzDLQrUTmOUmFATuBmZNSLtWnQIQ+47kXri4YS7unPUN9C7QGgBhdUZDoJrFKt2s0wv20ke6CKYDw1wWAyM333xzBrUsW7maLoG+4yuLWdCahR8dgWnWd86BsCbOWcNqL9Biqx7g19qPrNxY1br3rJu5KZ1Wg3tBW3p6zlk0W4OZRj4UoIlnJcLkUWC4YHbytKa/pbKu95EJN+ag5GgLIC7rVlbD2jo5g7GP9a6PQfy2RBj7CgSYHfSfpZFxFCsw3h9JE2OA2VF8o4bQtHI/bQPMDkG4yBoKjEEFuP79RYre9+dSfFWKEUKBUGD4CpyULi2/o1sOv5i4MhQIBUKBUCAUCAWmVwXSdGeA2SRCDWbnmGOOvM7fQFPB4Fxx4VvWW2UVylVneo6yK2MWn2CrtRVZdQJ34BV4eO2112YYCYaynAFxAVFgFOwCbgcLZsFa8FD5rGa1x7qOYCr3nmCt+ln0ctdbg1nngB4WsaXdoCYQoH/Ka7syHgmYVYf+gc/AJqAMgpc+a4s2ywcK04a1GBeq3JYCuDTislhe605q4zLLLJMtjm677bbmg2lNXxpwtcoCCWBmLdsNzLJ01mdt0hZ1cE3sXrHUZfnLXbG1J5VnvWDacY9qLUjWu1xZF/Bsjd5pNQSYnbp3dloGs7/4xS/yRy7GI+PpaAzaaBwwhk9O60YfhdDB2DI56xmNGk+rbQowm37ZI4x5BTZNPThvYvzUmO9NdIACZ6VY7ulyIUkoEApM8wqsk3r4vyn6D/d+KUYIBUKB4SkwZ7qMG23v0kXDKyKuCgVCgVAgFAgFQoHpXYE0CRpgNokAzFofECwFC617OFDgcpKVoLzAIKvZRRddNK9Dmp6nDBWPP/747FqTZa1zoB1gqFzQ1fqGzoONXAgDkSwuueC1niJrT9alLERZjxaLWfBRnaxKrbUosOBlrWgdXBDWeqiAqq1jUBKoZf0LzE6YMCHXLT/rVWUqT7nAJJfB4HQpCwhlBQl2aiOrNhqo17qt+rHyyitnuMliVruc49r3uuuum+TKWHn6q136rA6g15qUrFC175ZbbskQtOQFSUVa04Im7hPXp9wkq0ObiuthLoWVzVKYhdv111+fQTkNrCFZAn2L5XJpu/U0QWPlaaP7ShdtZtl7//335zUzrcNJK+1fZJFFcmS5O//882d3ydMySAkwW56gqbOdlsGsj0yMH8YV+6MxeLe1r7hAnlxtNL5xe2wbYdpQIMBs+qsgQigQCoQCoUAoEApMdQVenlogcgUcIRQIBYavQHmXhl9CXBkKhAKhQCgQCoQC07UCacozwGwSAfhkNbnVVlvlyHK0W7A+IUvOddZZJ1tsgqenn356dj9pDVQuL5XJLTCQ99a3vjW73nUNuMVVpXVfAT9rpb7tbW9r9t1332ylafKfi+LDDz88u+k96qij8jXKA2257rU2683JTW8JJvGtp8q1LLirzLXXXjsfywd4CIAC18WbbbZZhsPWaXQtt8hcJ7M+3WCDDXL7uVZmtQpgArogM8vVQw45JAMD6yvSyTqIrHS58AVsb7zxxpxvzz33zO6hyxqzoCkIe9hhh+U2AqJcPd99992TQIwygVcAW/v1o7QJUGb1C+AKAIm6dt555wyGWbJqH/fCJsHpJf+OO+7YfDBZ0nKzWwJ9reun7XRn5Up3FnGgunaql7tkfdNG9crDZbM89OJ21dq+7scll1yS71upY1rcBpidund1WgazU1fZqD0UmLwKBJidrv/Ujs6HAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAL/p0CaigwwO3E+FnRjGQpWgn7dAkAFdgJ6YAkrWNCT+2LXc3csKNOELGvMX/3qVxlwOu96QNT1Tz75ZPPss89mqFcsxaSDh8otZblOuvJFELQO0tQPkGqTMh0XkCmvcrVTuQVeOu8cK1LX2eq/euXTDteBs+XYNYJ85Zy+CuCq9oHXLL4KmGWFCpIqVxtZ62pf6XO+OP3juOR54okncj5tcq7UUfIqXz9oqzxtKRBaHn13T+XRxxL0p7RdG0q5YK77QjvlaWe7jSWPNrmvIHu7/FLPtLalW6wxO/Xu6nDBrHfioYceyhbp3OR6Zn2E4Bn2vnlXHXt/2u+jez7c4B3zHon1+zfc8npdZ7zw8Uo9BvS6ZqjpxiEa6lOEUGCwCviN2HDDDbP3DB888WLhYx5LHPhIiOv98js02DJHmG+2//tLMPZCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQYIopkCb2AsyOcHYzLu+uADC7ySabNCxmd9hhh3DP2V2uUZ0aYHbq3p7hgFnQ1RrW1kqed955s6X3Bz7wgWb55ZfP6yQr03qmSy+9dHPAAQfkjxF8hHHVVVdlS3oQd7iBK3HvPhAF6E/OYJyx7jPLfn1W96WXXpo/2hhJvUD1D37wg6wFrwo+9OAqnRt3H2ZECAUGo0CA2Sn2Z21UFAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKjG4F0oRigNnBzKpGnmErwPXvdtttl9di3XvvvV9iwTvsQuPCqaJAgNmpIvukSocDZq2NzL26tZo333zz5qyzzmqOOOKI7CqcW3GWn9yqv+pVr8ruzcFGFnxcg++xxx4NK9ThBm7Ut9xyy/z+33777cMtpud1rPS5j99mm22am266KXsisDb2SNuvYla/dFL2fffdl12kf+Mb32je//73Z6vHKWzl2FOLyDA6FQgwO7r/Fo7WhQKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCU0yBNIUZYHZ0zuNOM63ibhj8efjhh0dsvTbNiDJGOxJgdureuKGCWTDoS1/6UrPCCis0CyywQHPggQdmN7zWcbY+9C233JKtS48++ugMZnfdddfmhz/8YV53eY455sjrTV955ZXZNbl7z+26Y9da05qbc4Hr4AceeCBbloKX1nP+7ne/m9eY/vrXv56vYXnLdTLrU2s/P/LII80111yTrVpdW7tml8/1F110UW6PsePWW2/NbtFr1+zlbqgTOGXFyh0z69x3vvOdzbrrrtuonw4AqjqtsX3eeec1d955Z4auLGKtBa78Yh1szFKnPt5www257NVXXz1ryc05F+fW87Z2tTIjhAK9FAgwO8X+rI2KQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKB0a1AmkwMMNtrRjXSR6wAqGNtVtsIY1eBALNT994NFcyCm+94xzuamWeeOa9tOeecczYsSVnMLr744s0+++zTPJEsYo877rgMZnfZZZfmqKOOahZbbLFmhhlmaGaZZZZ8PdB67bXXZlA7zzzzNHPPPXe2gP/kJz/ZsIgXWacuvPDCzRvf+Mbmda97XfOud72rGT9+fLPxxhs3m266acN69uqrr27e9ra3Ncsss0y2yH3ta1/bvOY1r2lWXnnl7CoYdLUWrrLmn3/+nKYt3CwvuOCCzSGHHJIha30XPJPnnHNOs/766+cyrAl9/PHHN/PNN1+jrTvttFOGu5dffnm2EtbvpZZaKpepr0DrFVdc0bz97W9v9Me639wgA7u77bZbc/jhh2etWBxzyQxmg7xf+MIXmvXWW6+57LLL6ubEfijQUYEAs6P7b+FoXSgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoMMUUSDOIAWY7TqPGyVAgFGgrEGC2rciUPR4qmLUm6qGHHtosuuiiGVJutdVWee1YYBYQBWIff/zxSWCWxSyLWG56Z5tttgwvWdMCqtalBWRZph555JHNGmuska1wTzvttOanP/1pTgdzF1pooQxjTzrppOw2edVVV23WWmutbAH7ta99LZc566yzZlj7qU99KgNR4HjPPfdsfvGLXzSusxauNXH333//DGlZ+84444wZJD/33HMvEf2Pf/xjtgTebLPNGu6SfQDy4IMPNltvvXUGqXfccUe2oOXGWb9YxrKQBVxBa26bwVnWtiuuuGJz8MEH576wtgWj6fOhD32o2XDDDfMx62Dhuuuuy6D6M5/5zEusfV/SuDgIBSYqEGB2iv1ZGxWFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKjW4E0ZxhgNqaOQ4FQYFAKBJgdlEyTLdNQwSzLTmuusuwEOi+44ILmxRdfbE488cRmrrnm6ghmwVHpwC0Y+7Of/Sy7/11uueWaJZdcMq/lysXvXnvtla1sP/zhDze33XZbs8MOO2QLW+tIg5nqAXRXWmmll4DZYi0LiL7wwgt5fVvWqNtvv30GqADtK17xigxlWa9yKbzjjjtmq1/Wvr/73e9eoi/3w7vvvnszbty4DFwlWicXZNYmbTn//PMb7WeJC7Z+5zvfyfW+9a1vzVAX3P3Rj36U18IFn62ve+aZZzZ/+ctfGha4LGlZy3IBXYJ9sPdjH/vYf1jxljyxDQWKAgFmR/ffwtG6UCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVBgiimQJg0DzJaZ09iGAqFAVwUCzHaVZ7InDhXMahCIusEGG2SoeumllzbWVP3sZz/bEcxy3auOU045JYNZQBSoBXRZ3XJtzE0xt8LALataebhMtgU1lV3Wgf3GN77REcxyGwwYs25lcctCFpj99re/nbfKVo62AqPWxlUX18ttMMuNsmut98otswDM6ksBs6effnq25H3DG97QLL/88jkCxEsssUR2t6xMbT755JMb7pVZ3xYIOxCYZZXLAhfsBZAjhALdFAgwO8X+rI2KQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKB0a1AmkgMMNttNjXSQoFQYJICAWYnSTFVdoYDZlmztsHsQBazbTBrfdYnn3yyAXRZ3LKY5drYmrNf+tKXMsC1dux9992XrWtBW6AVcBUGArPcBFurVT7QtIBZsJalK1fHBxxwQPP73/8+r18L+s4000wdwewvf/nLbPnLupfrZqFYzBawfNZZZ2VAzO0wC1zx3nvvba655prc9r///e/5eMstt8wun61De8IJJ2Sr3wJm3/e+9+W8uYL0jz6/5z3vaVgIt90rlzyxDQWKAgFmR/ffwtG6UCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVBgiimQJg0DzJaZ09iGAqFAVwUCzHaVZ7InTgkwC2qeeuqpGVBaR/acc85prrjiirwmLEtW7oQvv/zybKEK1rJs5QaY1SowC7R6ToRuYNZarzWY5Yr4xz/+cV7vlpvlN7/5zXndWedZsVpjtpMrY/B23333zVaud999d66Xe2OQeYsttsgA9frrr2/WWWedbNX7yCOPNH/961+ze2OAWF9/9atfZRC8+uqrZzfOrGDtc8XMzTFIbI3ZG2+8MVvxquSGG27I56yTy+VxhFCgmwIBZqfYn7VRUSgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoMLoVSBOJXcHsPPPM01x55ZXd5hsjLRQIBaYjBe68885mgQUWyLDsIx/5SPO1r32t+cpXvtJcdNFF2ZKy7Wp2OpJmsnd1OGAWALXGLCtQ67pyD3zsscfm9WF33nnnbJHKCnb22WfPlqfq+OpXv5qtY2eeeebs9tcardZcXWyxxZo55pgjQ9tXvvKVGXaCnixQAVTgFugsYPaqq65qrOMKcnJTXMoFSVnaArPcJvud2XrrrZuf/vSnObKate6stWYXWWSRZuGFF86ujAHY9vNlHV11rr/++hkguwlg6mGHHZafUevAsozlppjrYmvNcqX8pje9Kbsi/sEPftCcccYZzdJLL90cdNBBzbPPPpsBLCtjrorBXtdy37zWWms13/zmN/N9Pu+88xpgl5vn0t/J/gBEBWNWgQCzo/tv4WhdKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgwxRRIs5xdwSyXkiyFWBBFDA3iGYhngPtWwIwVY4DZKcuJhgNmrX8KJnJHzNUvGMrClSticPSFF15o7r///ubcc8/N7oVffPHFBkQCVcePH59dEwOvYOf3vve95vjjj28OPfTQDDOVw/r0D3/4Q4aZAK61V0vgBpl1rbJY4j7++OMZzrKkfeaZZzLQBGPBTRaov/nNb/L1QD/L2wsvvDCf33PPPRsgeP/9989rzpbyy5ZbZOvCHnHEEdl6VR9Zxuqjfv385z/P14HIRx11VIa22iqPtuuXDwy0D2RlAWttXiCZZtwlg9ra9MADD2QXy9rC9TGXxhFCgV4KBJidYn/WRkWhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocDoViBNJnYFs6n1zSyzzJKtqVhURQwN4hmYvp8BH2vMMMMMAWZ7kZjJkD4cMAtS/u1vf8sAlXWpwGoWfLS2KhDp+M9//vOkY+dcA9r+6U9/muS+Vz7gFqSVv5RX8itTnhKkA7eidrSP67aoT13HHHNMs9BCCzVrr712Y01YxyuttFK2qv3c5z7X/OMf/yjFT9paB/aQQw5ptt1220mgVF3ao53apI2u1ScwVpo2OU+H0sZSqLzOuVY++0ULVsjcJGubciKEAr0UCDA7uv8WjtaFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAJTTIE0mdgVzM4000zNsssum11hcocZMTSIZ2D6fgZWXHHF/LFGWMz2QjH9Tx8OmO1/KyZfiSDpTTfd1Gy88cbZKnvOOedsxLnnnjuvYTuQdarrbrvttma77bbLa9SCsZMrWNP28MMPz2vsshiOEAoMRoEAs1Psz9qoKBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUGN0KpAnFrmDWhDiXj1xMRgwN4hmIZ+C6665r5ptvvrCYHQyN6XOeaR3MkovlLBfDV199dXP22Wc355xzTuOZ4064tsZtS+u6O+64I7slZtk6uQIwy3WytWe7tWdy1R/ljk0FAsyO7r+Fo3WhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocAUUyBNcXYFs/PMM09ee29sToVGq0OBUKCfCrBMtB7nAgssEGC2n8IOsqzpAcySorhfLq6IuRX27PUK8hX3zL3yDje9tC2g7HAVnD6vCzA7xf6sjYpCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoHRrUCaIu0KZl/zmtcEmJ0+55Gj16HAfyjQBrMf/ehHmyuuuKK55JJLcrzmmmsaACLC5FFgegGzk0e9KDUUmHoKBJgd3X8LR+tCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoEppkCapgwwO/XmaqPmUGBMKVCD2Ve/+tXNlltu2Rx55JHN+PHjczzttNOap59+ekz1aSw1djhglgvgb33rW3nt1t/97nc9u/uvf/2refDBB5vLL7+8ueeeexpWqwILUWu3skj961//mtPk+elPf9q4pltw3Z133pnLfPjhh7NFbLf8kTZ0BdyX73//+81ll13WPProo4N2s+xecv3MEng0B8/NV77yleZXv/pVbqY2s5DuRyjPtfHt29/+dnad/fzzz/ej6EllBJidYn/WRkWhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocDoViDNGgaYnTR1GjuhQCjQTYEazM4111zNNtts05xwwgnN0Ucf3Rx11FHNmWee2TzzzDPdioi0ESgwHDD71a9+tVlxxRWb973vfXkN1l7VA14TJkzIrqo//vGP5/up3mOOOSZD+Mcee6z59a9/3Uibd955830H97oFsP5jH/tY84Y3vKE56aST+gbUutU5vaX98Y9/bN773vc2r3vd65qzzjqreeGFF7pK4F2+9957mw996EMZeP7hD3/omn9qJnomtfOAAw7Iaw3feuutzd57793cf//9g3Jx3a3tDzzwQPORj3ykue222/Jzee211zbvfOc7M6DtF/hVf4DZ0f23cLQuFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBSYYgqk+cIAs91mbSMtFAgFJilQg9nXvva1zYc//OEG+Pvyl7+c4ze/+c1mMFaZkwqMnSEpMBwwe8cddzT7779/c/zxx2dLyl4V/u1vf2u+/vWvN7vttltz/vnnNywHzz333GbRRRdtdtxxx1wGiHfeeeflPFdddVVP0Mpqd9ddd23mnHPO5rjjjuuZv1cbI/0/FWDZDHrvsssuzQ033JCtmv8z1/+dYeW8++67N3PPPXfD0h3YHa3ha1/7WrP22mtnS1awVD/XWGON5q677hpxk1kYv/Wtb22++93vZstvEPiDH/xgBsG/+MUvRlx+KSDA7BT7szYqCgVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFRrcCadIwwGyZOY1tKBAKdFWgDWZZmnFne/HFF2erOy5zA8x2lXBEicMBsz/+8Y+bE088McNV1zs+9dRT8/1y7w488MBm3333zTDWveMSF9jjnhp05R53u+22a2afffZmmWWWydbRLBW/8Y1v5Dw33XRTdpv729/+NrvRZUkLDh5yyCG5HKCrgFnurwcLZlnleq5Y2oK6hx56aHPzzTdPcq2sXHWrB2A84ogjskWw9gsl/eCDD+6Y/vvf/z73T3t9YMDK9Mknn8zXcuvLRTMAuMcee+RIs0ceeWRS+kMPPZTTWXK6/vTTTx8U+FYAV8PnnHNOtjD/4he/mME5CP6b3/ymYcF57LHHZuh9+OGHZ/3BcgEk944B7TT59Kc/3dx3332NdPHCCy/MOv3whz/Mx9z+qmevvfbK5bFq/9GPfpTv8dVXX50tqWecccZsIXrBBRc0NAEjgdo999xz0jU/+clPMkx3T1jF04rLX5obA/RdXcYHgdU8mE8X5RRL+pLO6vrkk0/O9+UTn/hEhqIvvvhivrb9j/u47bbbNjvssEPWn3vtLbbYoplvvvmanXfeOVvNgrV3331386lPfarZfvvt8zNBF88CrU855ZTG+tcFPrO4/dznPtcAvj42YGW89dZbN86z/r7oooualVZaKWtdnqd2u4Z6HGB2dP8tHK0LBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAWmmAJpcjHA7FBnWCN/KDCdKtAJzIIb1n4EMwLMTt4HYzhglkXgCiuskN3cAnasm5dccsnshhjcmmOOOZpXvOIVzeKLL56BG2tY6waziAYtQV1pM8wwQwPisVQE8T75yU/mMoBWUA5EfP3rX5+tYl/5ylc24mqrrdZcccUVzVNPPZVB4mDBLLAHGHOVzMpWG5W31lprNayyrQkKaC699NI5bbbZZmte9apX5frAZm58O6Wvvvrq+UOC5557LgNiVsCuU/5rXvOaDJQBWevhWj+ZBgsttFDul7YD1KA0S833v//9L0nn2nvcuHE5vddTcN111zVvf/vbG2W6jv4gpvfoXe96V26Tc/oOEALUoCJg+uY3vzlDyfnnnz9fu9xyy2VgDdpq8zzzzJNhLBjO6tk94ULavVbXyiuv3Hzve9/LsFb96Y+NZtZZZ81tB765vGZFq26a00ebrPEK6q655ppZK+XSTB7lAvnAsnpBVOn6AOhL32mnnTL0pe973vOeSXXQXvsA5AJOa/0AV88vkOvZ1PYNN9wwX//ud7873ysfCXgu3d8PfOAD2QJWO31U4Fly35Zffvl8rfrXW2+9/D5ceumlWTM6rLvuunn88mw9/vjj+VnyTOtTP0KA2Sn2Z21UFAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKjG4F0oRjgNl+zLpGGaHAdKBAgNmpe5OHA2YvueSS5i1veUuz8cYbZ4tSlpmAJCtBlpfA+uabb97MNNNMed1OdbCEBNVYO4K5AC1AB4wCuwCdNHmsLwzasWgF3FhTAmWbbrpphnvA7RNPPJEhIQA2GItZ5VlPlGUky8srr7wywzggkBUr98ygHAj50Y9+NKdzPws+HnbYYdnl7WabbdYxXTsBube97W0ZcnLxrPxNNtmkATvBTxaeCy+8cLPKKqvk/nDXrR7Wt/rOjXOdrrw6vddTwnpT2TPPPHOuF/wGlFmfAqJAIotUbV1ggQWyVScrZqDVfWAh7Hi//fbLVqMAJGtn91GZIOctt9ySgab7zDpU/mJ9TL8f/OAHzTrrrJPzg6byA7PApgges8BdbLHFMiAHRFmhcvsL0rM09SEG62j3df31129YEbMsBlrB/7PPPrv50pe+1Cy77LLNEkss0XznO9/JGmoTvemg7/JzVUzbdqA1AK+uf/7zn9mileWvdZO5H9ZvQHqDDTbI4BV0Z1UL1NIHKNZXgBtMB5nBbBbDQLD2LbXUUtnam9WuMY7VrDK32mqr5uc//3m7ScM6DjA7uv8WjtaFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAJTTIE0wxhgdljTrHFRKDD9KRBgdure8+GAWdCwBrOsXYHZd7zjHRnWgVETJkzIkJV7XO56WcwWMPv0009n2MZ6lEtZ0JSFLDDLWlJeZQBgQBs3vNtss02z4IILZmtJ7nZZIIKKgwWzXNNyK8w6FvgF1EBTUPKYY47J1tnAHwtJdXLjC/4CctrG8lQ6ONdOZ737+c9/PpcnD8tfcBBYBKf32WefDINZabIkfdOb3pQhMOvJ22+/PdfFChjcm2WWWSalsxgFsQfj+labVl111XwtiEo/14LJNAWkAU4as9jVT1az3P4CryxQQVUuigFL1rIgIzAuHRznslr/WTm7FyxCwWPAl+Upy1D3ibUxcAtw0vFnP/tZA+ZzCwzm05wFLbBbwKz7yM21MmjteVKXOt1nfaCjZ8Lat6yMuWh2XOCxvoL04LP26dO11177H+sPFwgLrhp/BDCdNTbLZTAYdKWnOoF57q0BZc/4gw8+mO8J2A4OazvXxkCpoP3upXviuSsBJKdZJ1hc8gxlG2B2iv1ZGxWFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKjW4E0sRhgdiizq5E3FJiOFQgwO3Vvfj/BLMtBYAuMAztZarJ+bINZrmCtIVrALHBnrVFrrwJwwCnwCrCBmKwfQTJgDNwFbq1bOhQwKz+LWcBPeaxL7YOEwC9LzDe+8Y3ZJa11QYV//etf2aLSMwo+Swdbb7vttpekywd6sr5VnnwArQhg6wcNWAYDe3QBO22BOvDQWqzq6JQO8BWAmCvu8E8Bs6w+WRcLoO9GG22U4TDLX20p7QIxWbACnKxeF1lkkQyF6b9wsuxl2frss89m97za+oUvfCG3EbTlmlhfnec2WH9BXjCX62Vglptgx8AqCM5KVz5Wxa4tYFb9LGZBcuv70pKlr3sNzEpnbatMYNe6wwJYbd1e1qz6CBZzdcxFdukjOMqitg22lcP9MivfEgqY5XJandwUW//YM/3e974395nFK4vwsm4w61p10cDHCoCxAMyy6KVVDWZBXv0Ho/sRAsyO7r+Fo3UjV2CJVMQ7qvjqIRT5huq6pVvXzZCO31alv6KVHoejT4G3pCYdluJZKV6Y4rEp7p3i7ClG6J8Ca6Siyjs3lPetfy0YOyXFMzl27tXUaum0+FuzVhKzjBGvmlrCRr1jSoH4XRlTtysaGwqEAqFAKDAtKJAmHAPM9mPWNcoIBaYDBQLMTt2b3E8wyxUwC9OhgFkgj2vXNpjlDhlkBNfOS65nWUfuvPPOGcx+5jOfGTKYtS4u97XKBEjvvffeDPzAUVCOa2F1AW32QT+AkOtZ1p/WlwUQ63RWrltvvXVzwgknNKwwgV6WqK637qh6PvvZzzZAL9inH4AnEAsGKw/UAwrBuk7pQC8QXaDfQE9LAbOsPgFXgWWn9isDlAbAwUT90caf/OQnzY033pjdLOuL9rKaBUFB70cffTQDSQD2jDPOyOW5L7RgJau9XFHLz3KW9SqXyQXMsqKlCygMnnJdzIpXv4FZdRcwC9xyfUx3bQFmaem+s+LVB9bVgL1yAVKglPtgoFid8rmvLGH1EWzXpjbUdr/ca/elpNVglqU0cP+hD30oW7daE1Zk9av/LIEBYvce4LVGr/6xhFVeAbM0ApoF52kqH937EQLMTgt/MQ+jDxtttMMr1txoh2XW2GjbNVdbf7t1195guzeuM2HCjMMoajRfYkL7sRTzotUTt58ZQoNPqq69tXXdnFWa8tdtpcfh6FJg+9Sc+jko+/+Tzs82upo65lvzj0rrrcd8byZfB/r5TAK84ydfU6PkqajAtPZb8/KkZRl/bTebitpG1WNHgfhdGTv3KloaCoQCoUAoMI0okCYcA8z2Y9Y1yggFpgMFAAvABphhQcndJ/gBblx00UXZ6gwIiTB5FBgpmGWVWdaYBWbvvvvuDGZZvYJpxWKW++HZZ589wy4Ws+AeC0dWlCwJwcoPf/jDGbyytvUMsFrkkpZFJ5e78nL1ywUwaAh0AasgZ22Z2EkpUI3bWfB03333zXWynNUm4BNUA/64HgblrC/LnS0LUlBTP+t0a6iW9AMPPDBbZloDV59ZiHJnDCyq0xqzXB3bB4a5xbXmrv7RALxtp3MFXdKlWQu1WwBmtRuYpaXAFbFyuNrVVu0EM63HCjrKR0PvHbjJiplFJ421kWVocWXMPTIL0DXXXDODVmutapc+ArfqASt32GGHDEm1gztn93beeefNIJSbYdavXAwD0toMpBaLWevRFiAOzLKSBmZZo7Kcdg1tAVG6eTauv/76vEaw8cO91Ud1sPrlNrkTBPVsAey2PiIQWPg6x/KZq23PIgtjlsKsnT0/NPQcemc8z55HVs4gNwtZ6ya71kcALHfleSJBXnD2hRdeyGvgWkeX9XY/QoDZaeSP5sF0Y6utJsycIOy2K68/7qaV19v2f1NsXhLX3/bPK6837kurbfCB1QdT3hjIs05qYz0BbP/XKc6U4mBCgNnBqDT686yYmviXFNvPguNHR3/zx1wLYwK99y3r1zPJUp/lN80f7l1t5BiDCgSYHYM3LZrcdwXid6XvkkaBoUAoEAqEAqFAdwXShGOA2X7MukYZocB0oECA2al7k4cDZgEtMA204/oVRF9qqaWydSbLT65jQTnwDOACUcFWQHDvvffO1rHc1nILDAKCWuAliCePa10D/oGG4CjQpU7Wl1weqxewdXzSSSf1BLOPPfZYtuqcZ555cnmgWymPpSWAxpoUXAV7rQUrL9DIPTPwCwJ2SgejrY0KIHK/63ptBgv1AdhT/0EHHZSBIXgrj7Vegb5u6QAvLXoFLnvfkdwki/oheLdYzboH7oU2gbTcJ3N3/Kc//SnD0Q033DBbsAKmIkgLmHJFDIKybmV9yqoZwHSv5XOeW2J5gG0A0n0sAP2d73xnhpbcAYOq+mydXdfTlhUuC1pWt6xOuYgGZlnBsmjVF+23Xi6LVs8AV9bujTrcd66MuVymE1BLW7Dd9e6Ha9uBniA2IO96gfUsmAwig60sv1n/gr3atnACvVwaexZoDRqzEgav//rXv+Z1aNUJyrp2gw02yJqD0eoAmPWdJS9d+xECzHb/W3SaSWUdu8p64x58CYhtg9nqeNX1trtwna32euUYF4C72k4wbqtB9ivA7CCFGuXZzm09B4+kY/f2iBT3SjFCfxWICfTeevbrmdw/VVXGuACzvXUfizkCzI7FuxZt7rcC8bvSb0WjvFAgFAgFQoFQoIcCacIxwGw/Zl2jjFBgOlAgwOzUvcnDAbOAkLVDH3744Qy+gCdgjqtcsM89BfEAKq5nWSWykgUwAVCQ0znXf/Ob38wubFkaSpPnV7/6Vc4DuIF1ICJQyNKQ61sgkzWoY3Woq1dgcSofAKc8AFnbbJXHVbA2AW1c7F511VXZJa52F2vcXunKUBZXwq7XF9begKUIyGm/Ppc+DZQujz7T2rW9grJBTJGr3xL0mxte66leeeWVGS6XeyIPqKjP3Ahr8/e///2sv+vUy800CK4Mx0AnN836qDxug+laLHpZhrIulqb/7pP7Cmbqk/JoJO2pp57KlqSgpfvgWsF6u54n9RQXztI8c6Aty11pyvasgbn6DJqqlyVuuafS28GHAyzzN99881yOdPeWde0NN9yQ3R/L4/nzvHBp7UMC7dVPz4R7UzRxPWth99ZzTFP99ByBwMoCtFkb03kw91OZvUKA2R5/jE4Lyautv+37V11v278MFsqWfKusP+6RNTfdbuExqoF16/6cYoEWT1T7Nw2yTwFmBynUKM92c3Xv7x3lbZ0WmndI6sSnJ0ZrPEf4TwX69UwGmP1Pbae1MwFmp7U7Gv0ZjgLxuzIc1eKaUCAUCAVCgVBgBAqkycQAs71mVCM9FAgFsgIBZqfugzAcMFtgGCBmv32sR84BUPJ0OnZOGmgFfJay6mvkAcJAM+fbZbrGecCM2+vx48f/R7R+K2tdgFdZ6lLnQPWpo+QpsFE7SuiVrtxu12uv+ksbSrll2ykdZDzzzDP/o2/6q39cIQOFrlV/OxTd6KhtjuvguOjc7rMyxTr06mMpq1yn/NJn+66XVtphv2638+1z6ndOOfpQ5y9tkz5QH0uesvU8cMNsXV3WzoLrtb0uW13KrHUp7S9lla3r6z6VskBbltaHHnrooD4iKOX12gaYHcEfqmPh0lU2Hrf0ytlFccttcWUdW0Bsx+364+5MPssH6/p3NEmyR2pMgbLPpv0dq2PnrcnYKwSY7aXQ2Ei/KzWzPAunjI0mRyuncQX69UwGmJ3GH5TUvQCz0/49jh6GAqFAKBAKhAKhQCgw6hRIk4kBZnvNqEZ6KBAKZAWAjFhjduo9DMMBs1OvtZ1rZnG5++67Z1e33N22o7VBgU1QbywGVpbWVuVSt903x+9ILn+5l44weAU8C9aAtcYu69bJGc4999xmm222yZbHNfQdaZ0BZkfdn7/9axCgmtaMfbgjcB0smJVv/W2O61+rplhJd6aaCoy7IO3PkWK9zujJg2jJ5AKzs6e6ZxhE/VMzy6yp8hmnUAPoMdhAt6HkV+7dKZZngfvioYTh1NepfFrO0ilhgHOzpfMvGyBtOKeHqlmnOrg2n9zPrefu5Z0qH8K5Ke2C3X1yv4YSRvJM1vWMBMz269ku7fGMz1wOemz78TxaX3ekYTj3rlOdQ32/6TTY92lygtkpOc4X3bzfZTy23awkTNwaJ0c6BrSK7Hk41OfR3xODfdZ7VT7axrx+/PZMjeeqm8790Lhb+ZEWCoQCoUAoEApMkwqkycYAsyOdcY3rQ4HpRIEAs1P3Rk8LYJbrWO6PAbZO0fqu3OOO1cA9tPvUqW/OcU3MrW+EoSnAjTSXyJ3WoR1aSd1zc7ktsrztZwgwO03+Cf3vTq263jZbjRjKZjA77k+rbfIBroHHSlg6NbSe+B03seGXVOd/n/Z7wZR+gVnace16Q4q/SlHbXkzx9hS/mOLbUmwHk75fTfHyiXGjdobqeHyVrxtwXr3Kd151vd25UvxEirem+EyK2vj3FB9M8bIUuRF8bYrDDSbaL01Rfy6eWMieaXtPiur6Q4pXp6iN7eDcaSl+P8UXUpT/iRSvTPGAFE26tgNNi3a0do34aHV+IOvZ4dSXis1h6/Rvqfe9aX+RFC9K8fkU/yfFR1I8MMU2VNKHT6boWm383xT/nOIdKZ6R4hopdgs0dS2NaT1PigelSNPfplg0uyLt75ziYMK8KZO23pviH1NUxl9TfCjFC1NcMsVO4cvpZNFglVYGmpQ0WgmbpHh6ivel+K8U9fv2FD+f4sIp9gqvTBn2TdH15fnwnunrbikKO6VY6n2fEyMM66XrjSc/TpEm7tfjKXomD06xE+gZyTOZinxJ2DUd6c/DKbov5d6UPhpvOoWRPNvKK8+ZvgO7C6boGfl2it4zWtyc4qdSXCLFEnhd+FCKns9nU9TeZ1K8PsW1U+wU6mflAynDy1LcK0X39dcpKsN7dUeKnumZUxxMGM69K+UO9/32/p6Y4jdT9H57zrXfmPBAimemuGiKncKc6aS8Ja7bKdMgz82V8n0ixVtTpL8y/57iYMZ596A8X4en/W5hs5RY8p7Qymh8Kn2xlXeRFOsxwLN0S4rHpfimFPsR1Ov5066LJxa4Z9rek6J2DPQbZKzeJ8XzU/SseeZK+/877V+XonFmhhQ7hfo5Ho1j3qyp0SP57dHnkTxXnTTrdO7L6WR5pqbE70qnNsS5UCAUCAVCgVBgulIgTTwGmO3n7GuUFQpMwwoEmJ26N3daALMU9Bx1i1NX5ZHX3q1v0iIMTwHuhyd3qF0c97OuALPT8J/Wq6w37sa+gNkEZ9M6tR8ZQ1J9NrW1TJz+Le2/emLbN63OS9954vmBNidV+U1k12Gwk+XqKCCitKm9BXXUNVtdQdoH8UpecK9TADwKjJLXRH/pbzv/59OJUt73qsSF0z6QVdIG2pos37y6bii7s1Tl6+/Hq+O6vjJhruwZUzwyxQIx6nz1PqiwfIp12CUd1Hk67T9RX5D2R1JfKeqYql73VB2d6l68XJC2b01RHzrlK+doQAtt7BRKPtvFUqyBXZ1W9k1we4YHClulBPe75O+01abjOxTwj+q6AiJKtvFVGq0OqI471fH7lA7cDBTc92dS7HRtOXdJSj+vyjMh7Q83LJQuvCLFUvZAW/BtrVYlw3kmW0VMOjw77Q1Ut/N3Tsr5751+PNtKqutcOh0/1TpXp7svr0/Ru//NLvmMB/ul2A7j04lS3mlp39hQjjttvUPzpzhQGMm9K2UO9f1+XbrwohQ7tbd9zgcJe5eKqq33tM67bpU2lN2FU+aRjPPGs9KO9u9hux2HVHkfaiW+vEpTHnD729a5Uo/tH1J8b4ojDZ7DUu5gf4OMg92e8VKerd/UhVNsh/HpRMnn+RlNY14/fnv0eSTPVVuvgY6n5O/KQG2I86FAKBAKhAKhwHSlQJp4DDDbz9nXKCsUmIYVAJXClfHUu8HTCpidEgoCbP10Rdtus3dhctfRrjOOx64CAWan0T+tt9pqwswrrz/uf/oFZpM742+MEamAynqS96tVu00I1xDnriqt0263iejBTJazMCoTsmULnII2NUwtaT9K52dOsYQT005J+03an6EkVNu1qzwl73uq9Hr3iSpvASFzpHMmzsu1ts+neH+Kv26dl8a66rUpDjXUk+LKYVlX11n2N51Y8MvS1uR/OV+2z6Vz2vvPVpp21cBi+3Rs8l0s15ZtOf/zlFbCSOsr5Zh4L/UMtK2fO/3V9jqvvgGr7nl93v53U+wU6nzt6/6SLqjTy/7pnQpK53bvkB+E1ab/6pD2iXSuDoOdQC/tKFt1tLWQRo/56wom7i+VtqBNub5sn0jn/tjhfEmfkNKGEzzDP0mxlFO24PHjKbafNe/6uBRLGOozWa7rtHXv2vVpT3m2b6su6tezrcjSZ9v/bh13eqfvTHluHEQ+z+gbUqzD+HRQ11f29ZGl8vdTbNf5RDrHeq8dRnrvSnlDfb+1sbTbVtufTPHeFJ9O0TPSTge86zCY35o6f6f9fozzJ6WCS1uNzd3CISmx5DVe16ENZks+W2PAz1LsNGbtURcyjH3PQF1X+9kpaeU3aMNWfunGFR8AiH9KsVxTtpelc+0wPp0o6e3t1Bzz+vHb04/nqq3XQMdT6ndloPrjfCgQCoQCoUAoMN0pkKY5pxqYNWn+3HPPNc8880zDveXkCn/5y19yHeqaWtZK+so9JxeeXF1OrfDnP/85t4FbyOFqUe6bvkzO+1Y04vbzwQcfnKy6cVv55JNPZheWpd7Y/qcCAWb/U5MpeWY4YPa+++5rjjzyyOa0005rnnjiiSnW3N/+9rfZbe8LL7zQPP/8881XvvKV5oADDmhuuOGGDDQnZ0Nuvvnm5nOf+1yj795tuv3mN78ZcZX//Oc/m6effjr/ntj/5je/2ZxyyikN98sRQoFuCgSYnUb/vF5tw+3e3Dcomyxmk/WticixELZIjawnP8ska2n78a30lUpCh223iehek+ULpPIAm9IWcOmAFF+ZogCWrJnioymWPLaHpljC29NOnbZCSai2n27lkf+UKr3smuyvy3rjxIT3V+dBgg+nCG4LM6TIoubsFOtrP56Ohxrak+LKMyl9XYpHpHh9ik+maNJeaMNB0HrlnPLvf2ZLm31SrCdrf5qOZ/x38kv+vTsdlfarq1PoV33HpMJLXWX7VDp3copfSPHJFPdKUaDJ4ymWfPrifnItWcJb0k7dfnlr2FfylTLK9p8pwfO74MQMC6Xt/ikCQyXPi2n/VSnWQf76owH36PAUZ68ybZD26w8cPDfzV+n1Pdm6Om93fIql/rJ9KJ17Z4r67cMEz/09KZZ020737aZWnq+m43lSFLxf66dYt7OUNyGdH044Ll1UyrC9L8XlqoJenfblqTX+TTruBArre9qpb1WxXXfd09KmhwfI2a9nW/GlrnrrmV12Yt2Lpu0PB8h3VjpfnsfF0/7XW/naOoxvpavz0RS9EyV4fq9JsW7P50tite3XvRvK+71Gq13cPb+5apPdN6R4cYp1+8+RUIVevzVV1gF3+zHOG09KO28dsKZ/JxxS5fV+12EgMOtDpFdMzDhT2n40ReNYqfN3aR8IHG4w3payytb4NtBv0Heq/DxvfDBF7SphhrTzgRSfTbEub+GSYeJ2fJVe8k3tMa9fvz39eK5acg14OKV+VwZsQCSEAqFAKBAKhALTmwJpInGKgVlA0kT2eeedl+cvgdItttiiWXnllZvrrruu25zmsNP+/ve/N9/5zneapZdeuhk3blzf140bbMNA2T322KNZaaWVmi9/+cuDvayv+WjxrW99q1liiSWa7bbbbtBauG+uO/fcc3N73Lett9469wVkmZwB3DnppJOaD33oQxnuWKPx/PPPb375y1+OqFqAEaRyL6xbCB5deOGF+Rl56qmnRlT2tHxxgNnh3V3v/1e/+tXmi1/8Yn7ehvtBw3DA7KWXXtost9xyzbvf/e7mjjvuGF4HhnCVZ+T222/PY8y+++6bP3bwvu65557Na17zmubYY49t/vGPfwyhxKFltT7oRz/60Wb//fdvnn322ebqq69udtlll7wdWkn/mRvw3W233ZqLLrooJ/7gBz9odthhh+azn/1s4wOgCKHAQAoEmJ1G/7peZcNt1+8nmF11vXFgzVgI30qNLJOfoEwBfaXtS1Xp8p1dEjpsu01E95osb8MCMKtTABZAx9Lmv6T9RVIUtN1kdEk70MlWuCMdl/SyfbCVx6FrS/oDVTqIUc5/ozpf75rEfTLFku++OnGQ+8oo15etyftOAeD6bYol34/Tvus7BbrWIGzvDpl6QbB+1ndMqr+02/bXKc7doU1O1fBC3k7AVT59vz7FUu5Tab8GpelwUlrJs5eTHcL4dK7ksX1PK8+prfRtWunlcLW0A8iWsj5REtJ2KBPoT6b8s1XXlt350s6vUizl318SJm43rNLkObOVXg650q3BibwTSuIQtqunvHV/b0jHAz2TO6a00m7bM1Jsh17PZDv/QMe9wGw/n21tqPtl/+gODQNO63dSvs92yOcZfiTFUuZFrTzjqzR5PPevaeVxCMLfmGIpB8zz/JTQz3s3lPf7ktSA0qZfpP0CHUu7ylb7jaklr/GuDr1+a+q8A+33Y5w/KRVe2thvMOv3qVPYMp0sddoe1inTIM95X+uy7A/0G7RkSquf4Q93qeN9rXId12F8OqjrfTIdT+0xr1+/Pf14rmqtuu1Pid+VbvVHWigQCoQCoUAoMN0pkCYRpwiYZakJSiy00ELNXnvtlecuQbfNN9+8WX755Ztrr712oPnMEZ1nLXXFFVc0M800U7PmmmtOtclzMHOzzTZr5pprrmxhNaJODfNiWnzjG99o3vSmNzXbbLNNA9T2CmDDl770pXzfAAmhAHWw5/rrr+9VxLDTwRvlr7vuug24BG594QtfaNZZZ53m0UcfHXa5LgTGLrvssmaNNdZovv/972frYdZ173vf+5oJEyZMVheoI2r4VL44wOzwbgAL9VNPPbVZffXVm2WWWSZ/kHL66acPGdIOB8xecsklzVve8pZm4403zsC09MC99B68+OKLA44F3kEfLcjXyS2wMYVFu3GipLsGqJxnnnma7bffvnnkkUdyGT6Q+fznP9/ceeedk/Jqi/zaoA5tKsG+Mku56vKRSLd1RqX5gOQ973lPc/nllzesWs8444xmvfXWy2NIXb7xT9+UW4LrXVPnk+a8dl588cW5LGXKoz1HHXVU/lAFjI4QCgykQIDZafTP69U22m6lfoLZldff9r/GgFTzpzaygCmToMcP0OY7qzx/SvsmvjuFbhPR3SbLAYd6UveKToVX59qQyURuCRekndKfm8rJiVvQpfS3rk/+17Xy3paOSzks3Eo4J+2U80+k/U7gQ15tPCLFHVMEOoYa2pPiD6cCgOdOYZ90srTJdoVOmapzX6vy/6w6X3Z7QbB+1tcGNyyqBgrPp4TSz6sHyjTx/NJVXtds28pfyrEF3sGeTmGudLJ+Vj7UyvRkOi5l3dVKax8WWPz3lOA5LWEoE+hblYs6bE9O50pbfGRRB7CzpHmH2897nResL3ltJ9SJg9w/NuUrZdBv+R7X/aDKT48ZW/l7PZOt7AMe9gKz/Xy2NaJoYPujFAd6zh6r8v4x7c+ZYqdQ38fbWxnGp+O6vr1a6fXhWq28u1WJ/bx3Q3m/r0pt+HmK3o+Dq/Z02mUtWvr6eCsD7Uqa7bqt9MEc9mOcP6lqx609Kj2kyss6tA7G/bo/T6TjmeoMrX3jUMn/X620oRwO5TfovangO1L8bYrPp9gJpKbTObwy/VvaZ7vDv09P+nd8K300jHn6VNo8kt+efjxXk4TqsTMlfld6NCGSQ4FQIBQIBUKB6UuBNInYNzBr8ppLx2uuuSbDUMDLpL2JdZPzH/jAB5pZZpmlee9739s88MADzR/+8IdsiclqkRtZE+X3339/c++99zYmMW+88cbmyiuvzNeaLP/JT34yqVyT6SbGTaz/8Ic/zNc5py7A1zmWkM4Bsy9/+csngVnpLMceeuihXIZ2P5GsJ52zFZzTZpaiX/va15pbb7019yUnDuIfbdMHlsAstu65554MoV/96ldPArPaypIMLNVP7a0DEAAWSrvqqqvyvnMluJ5FmDr0ERgANwSgxLUiCzL9AEQAzfOSxbJrXP/rX/86ayWPe6AeMNS9ke5+7rTTTvm+vetd72p+/OMfZx2AdPcNKCqBu1Lrj379619vbrnllnwfpNECVKUBzbVZHZ6Tbi5AuUr+5Cc/2bzzne/MZbkfH/7wh5vFF1881wEQl7JZ7rJI1McCibgudVysa4ted911V37GPvWpTzWLLLJIc9ZZZ2XXpNrI/SlY+/DDD5duxbZSgN6xxmwlyCB3PXvGKhbfxhMWnautttokSOuDA+9GDQk7FT0cMOujhjaYNdYcfvjhzTve8Y7ssWDbbbfN7xRAKviQxrjFywAr/7e//e05vzYK2nHiiSc2G2ywQf6w5m1ve1vjfZJubFlrrbWaGWecsZl//vmzpwDvJ1fKLO2NVcZXYwwtnOM1QVt8FPGzn/0s12GsOfDAA5uPfexjzfjx4zNs9W4aA1iqehbbQbt23333PGYZW2666aZmww03bOaee+5GG42lfpN8lAHerrDCChlYg7nGa79LBx10UHP00Ufn8dD9MK585CMfye6g3//+9+ePa1zng5XiTWCjjTZqTjjhhJ73r93eOJ5+FAgwO43+bb3JJnvPsup62/6zf3B2XK/Ju9GgpMnvMtlou9QAjWL9UufrZGXp0m4T0XO2yqgny1dopa2isB7hnpRe2nRxlbe2GDLB/4oqbYvqmsvSvgnwUkYN7lhs/qtKWzHtlzAu7ZRrbH+d4kEpLptiP0N7UvzLXQqvYc0TXfKVJBPhpQ/DgWD9rO+Yqi3a9KbSyNZ23la+D7bSOx0CNqWfn2plKOdtT22ltQ9/n06U/B+tEhevzktvA4Yqa95dKP27WIptwD6UCXTP5kDhYymhtPN3rUy/qNLObaW1D2dOJ8DbUtaEdoZBHBv/yvXXDCL/u6v8rqNtHaYUmO3ns639RQPb4+sOtfaBu5IX4BoofCYllHz3tzKNr9I8s+5jt/BASixlfbXK2M97N9j3u6o+w+uBALZ8r0/RM1za/ksnq9Dtt6bK1nW3H+N8t9/DduVDAbMfb1/cOv5gOi7a+Chi9lb6YA+H8htUl9kNGvvgYvUU/TaXNu5SX5z2x1dp8kztMa+fvz39eK5acg14OCV+VwasPBJCgVAgFAgFQoHpUYE0LdoXMAuGce+45JJLNq997Wuzy8oFFlggW4aZrDcp/4pXvCL/LWVrgt26oZtsskm+BjgECcGKN77xjdlKct55581lmQRnZbvwwgvncsG0gw8+OENIMNUxa1guaIFJaxkuuOCCecLfMVAIEoAFgIN0lrugA+Brov6www7L9drqC3gJZtR92XLLLbuCxDLFDMLoM6D4ute9LpehT294wxuaAmZN+HPrDHqog3UZF8MszbQJJAQltFOaPIsuumgGlQAPiP3d734366gOLkKVz2UnIPlEAsxvfetbc5/o9/rXv74BD9wj9wUcL/2kFUu+ZZddNtclfauttsp9BS3LfZttttny/QFMQA36fPvb386WZEA462dt0V5lOL45uf7UH5Dds8FaEORxb+W1T4d2AF3cW/cVrAFAACZt1Q7agrtAPitXfVe+Nu2zzz5Zg7vvvjuDI9DJs/X44483O+64Y7P++utn60UWv7POOmuz9tpr57LBIgDJPTn55JPbTYrjpECA2ZE9BvQzJnm/a0hrPCyWtMZK4LR8ZFHX2A8w+6Mf/Si7GZ5jjjnyuAKevupVr2qWWmqp5pxzzsnjAhi54oorNnPOOWced+SRnztiH8iwEjXmeM95PLAFP4844ojmzDPPzGXNMMMMeezwQYd3F4x2DaBrzAWjfWShbtcb49TxwQ9+MP82eBdZxxt/vPfebePY7LPPnkFp+eCi1seYwDL4M5/5TIakIC8X8m9+85sz0DUmXHDBBRkEc6OvLcYEYJhHBx+NfPrTn85jIeteH6CAz9wVa4/fHWX5yMiHMMZhH7tI33XXXV/yoUrdrtgPBQLMTsN/Ya+8/rgf9wvMrrrBdhPGgFSPpDaWyVHuS5cfIK5T5ZP/pyl2Ct0mortNlm+XCivtsK1haqd6nLsgxXINt5YlvDLt/C3FkvaukpC2X6zO75H2a+uVs6p8rDbL9W2LIxayNagr+Wx/lSJgAA6/KsWRhPakeBss1mXfmA5KOwBllmfd4v1Vfte1YWgvCNbP+mpwYyK5DS3TqRzelv4tfbTVh259lFY/B19Ox3Woy/pEndBhH/gp+fep0n1cUM7bAg7DCfpdytm6VcD4Ku2FVlr7EOAo5TxfJc6Q9v9ZpR1UpQ20+5Mq/4SBMnU5/0h1/Qld8pWkRav8+rB5SZi47fVMtrIPeLh/SikaPdwhVz+fbcWXumz37FBfOfWdKu/F5WSH7eFVPu9AHcang1LfD+uEAfavrPLfXuXp570b7PtdVT9pd860x7J3pxSPTpEnhZ+lWPpYtk+lc3VwXUmz9Z4ONfRjnD8pVVracWuPBhxS5e1lMbtRj7LWrMpS//IT8/st9DvSLW43Ma/NUH6Dqsvy7svSv29O0XtsfD0zxVtSNIYVTcp213SuDuPTQUkbDWNeP397+vFc1Vp12/9HpePk+F3pVnekhQKhQCgQCoQC06UCabq0L2DWJDm4ZyKflSNrItANbDz77LMzRAMIAVLrvZrYByaAWCCP28mnn346g0R5QDMWXWDFy172sgzxTNizhpK+2GKLZYtWllPgmsly0AJsZP3EdbEJ9U5gtqSDe6AhS8+Pf/zjGQDYPvPMM9mSCkRlPaUv4J2+mNTvFViCWVuxtAtYBA9Z7SrThD9wSg8gwqS+voIfAC4AwNKTlsAsl6dAoT7SmBWsvoI5oAXgesghh2QXxeDGMccck61kwQzazTfffLl+5+lMP/cGpAZH0oPfvPKVr8yw071TJ8ii7yzOgEptBz5BC5arIDfIwqoOmGBJpgyQBkSXrv8gifsMmHDjrL1cEx9wwAEZHLuGi2SAow7uI4DuWQHtAS0Wvfvtt1+GPqA0+M2SDYBmhcfKFdSiq3Vp3Vf947pZfWCNewg2s8alhzTuSYEyQZmAMhes7TbV7Zte9wPM9u/O15DWO+UZZh3ayd1xsaQ1zgKA3lnA0QcHvUKxmDXe8WLApbKPIlZZZZVs9Q5WskI13niPuRz2XgGgO++8c7Yu9y56L8BVYy4rfxayLFB9HKEtPpiwFjRwazwDXF1z22235THAe278Y5XOat3HHY71mZcEH8N4d41X3lvtYOVq3DAWeb+BX2MT2KuMdjBWvyNZ3toKxndjAlfGPkoBan28UtrFQtgHQt536257/0Vjst8gY4sPOVgBuwfKcKzMMj4YZz7xiU/kNvlAJUIo0EmBALPT8J/Yq62/7Xn9ArOrb7htGyyMNuXe7gdoBNH17dBtIrrbZPmnU0GlLc+1Cx3geEJ1Deu+OlybDkp5p1YJj1fn35T2TUKXfI9V+S6uztfXlyxvSTtPVHlKGfX2rymdVe4bUxxOaE+K1xPm7fJ+mU7UdQ91f5NWgb0gWD/rq8HNI6121Ie7jLCPbVBVa9SetK7rtf9fVd37VInbVueVN3+VNpTdf1TltNsyvkp7qEehO1d5n6/yvrY6r51tEFJlnbT7reqaCZPODm5n5pSttjj/yCAuY0lXX/PJ1jW9nslW9gEPe4HZfj7bGlE/Z6zlBgo1mGW1O1A4LCWUMu9vZaqfldoCtpVt0qGxrZRlbBT6fe8G+37/u/Z/u7D2Ycy9Kf5viqV93baTA8xqz0jH+ZOq9vcTzGpXt7BgSqz12nJi5gtb5+s8Zf+WiXlthvIbVC7zu3p6ioBqKbPXtj0eja+uHQ1jXr9/e0b6XCV5BhUm9+/KoBoRmUKBUCAUCAVCgelJgTSB2DcwayJ75plnzpPpLE9NrnNpy8KT21mw0uS9CXoWUwAoOFCDWaDMRDzrJfCM1SiQCPRxQQwCgI8sp0yCi8pkedUGs1xYdgOzrHU7gVn1sh7TF5P8IIM1BUtfOk261ucAZoABdDCBr69ghTbqG8gKNoLHYCsQqx8seIFKEJE1lnQgk2XXsccem63ZlENPllzaB1DTxBqWLE9Z3AInYCkwC7Y4R38RpKGnfhUwC94Cqq4BeliRycNalQUZ6EtjlmX6Io80YBaYAVDss2jTBpCTpSwNWTmzwgN4ARVgVB4aAaP6ANAotw7K4BoUGNE/wcT2cccdl0Ex2AsoSbd2L/e6LGytpwk6K5NOXDSDSe4FrQEU51ngAkHAr2tBMgH8BZfAlwJrc0L8kxVog1naAop0F7mndp8iDE2BGtJ6Dwuk9Z4ZS1h9e498kMBaM/1GDRnMbrrppvldNQ5774plOUtxrn1ZzPtQ5vjjj294BzDGGr+kG0eNv94JgBIg9v74WMYY7t1mIQt6GoeNccY6gJOlegHK3kPjkf4Zc1iqcp+uDmMCi1zu7o2BPvowThlXPFs8EXCf7jeivV4utZVxyimn5Gt8gCLQ1W+Jj3S4TTZGANTGRmXbB22NU6xzjfF+E4xRfnOc9yFNsV42fhobfNxRgjQfrKjDWBghFOikQIDZafgv6zU2GveWVdYb99cRw9n1t71rnQkTgIbRHM5Pjes1Ydot/ZIOnes2ET1nq751q+uPqtLaLlirbC/ZrQHFX16S8m/LtNJ2FlaCCeNy7kknUuCisJ78f0M6dt9+n2LJu17a7xRel06CDk+kWPJ22ipr4xSHGtqT4m14WpdXu53t1IZe5wDGOvSCYP2sj4alfXfWjWjtA3Ul33C25TkoxdZlbFFODrAdCMzu2GrT3ANc3+v0YCfQH+hR0EBgdp5WO3fqUY7ka1IsGk1wYghh1pS3hqwfHcS1M6Q8tQ4HtK7p9Uy2sg942AvM9vPZ1oiioW2356zfYPYrAyrwfwknV+37xcTT/b53g32/Va/u21KsNWvve0ZuTRG4LWlPpf06dPutqfMNZn8k43y338N23YemE6U/D7USX16lybNIK719qM2lLNvykdhIwWy33yBteG+K9Xtft6Hs8ypxVorl2HbXFOswPh2U9NEw5vX7t0dfR/Jc1Vp126/H061bGfuhcavIOAwFQoFQIBQIBUKBNIHYFzALdAGxXFKa9GctxV2tSW+T1axIQVZWWKyrBICvE5hlzcX1LYDGstZEPUsuk+wsy7itBQ0AyE5gloUuqDkUMMtqCwQFK/TFBD/LrbovJu5ZfnYL2siySxv1A5AUuBdm2QpWKJtFV3r6ct9AAlaqoAUACtCCqHSiI8tTabRl1QVKAgSsWFddddVsGaoOwETbbeVRrjaApAKA0AnM6iMLNODDGrbul3MgKrA5Pq3vqF2s5wSwtoBZAIQ1HZDLug3UFtwnVsLqdz34rO8gEzet7i1goh5g1fNRB3qxyGOtqyzBxDZo5Jz+Aar6CNSzrBZZ9XL5DCwBSSALd8ueRaAYeBFqMGv94BK0H7hijcw1doSXKuD5LmvMuneec4DNRxki97csoj3rEYevgbHLBwaeZx9XeP/AT8+7scDYMVSLWWDWu8ArgHu3//77Txo7wFHvpneLZbl31xhrTHfPWbB7H7wfTyRrfxasxkfvlDWole2jkuLOl8W6993HHMaQNpgFSYFg1sHaJHhf99577zx2s4zngh489Xz5AERg3e4DF8+Wj1fq0AvMgs/ALKhr/PLx0JFHHpn7a01Z3hD0zTiofcYTWvN4UManALO14rE/FAUCzE7jf2+vuv42+40EzAK7q2+wzZKjXKY5Uvv+7AdoYvx12t4+iPjH6pq/p32Ti3XoNhHdbbKcRV1pi4ndwUDtenL3yboRaX+BFGvgunA63jPFUse5ab8EE7/l/E5p/+3V8X+n/cG0ZbmUz+Ttd1P8W4qlvLJlPTtbikMJQwGzP0kFl7q+lvb1fyix3bZeEKyf9Q0W3LD6Kn20XXGIfWw/q3VZ3YBZqmZAi9m2i8tlZR5GmBIT6H9I7Sp9PnwQbazv8YRB5G9neS6dKPUN5vr5q/yu2ynFOvR6Juu83fZ7gdm63/14l4oGtt2es8GCWfeulHl/q6PjqzRgvVe4ImUoZRn/S+jnvRvs+63uuj3a9c8Ub07xxBR9BLF8iix6hU+lWNr+dD7zf/90+635v1xD3xvqOH9SqqK08fs9qju2yvtQK28bzL61ld4+XLkqS/1LT8xwZNoqu1s8Z2Jem6H8Bq2a8vudK/21fSrFL6fondsoxdenKPgI439SLHl3c7IK49N+SfP73C3snBJL3udbGfs15vX7t6fVzP831Oeqff1Ax1Pid2WguuN8KBAKhAKhQCgwXSqQJhb7AmbBQJZRIJ1JeQCRW15AgfUid48FzHKbKXQDs8U6FZgFJlmMAQSA3MLJiqkNZtXF2hFw46oTmDV5D0a215gtroxZPpmEN2HK0guYBS305bHHHsvWWay71G0dW1CZ9WpxYZk70eEf7WDlVSyBS19ZhxUwCxKwMAMRQVq6aTdoC/5ysWktSJZZ4AHwAcyAkCACV6KuB0bAEkCVBSnLWlZ14DCIQyfnhYHALMALRIKl+g5KOAfUsGoGSN1H0EWowSxtQRPwXP9AV4F7UFCFe2aWrwC1vheoqi5rUQ4EZlkA66c2sBIW3KdiMcuqEGRh8ere0Yo7VO5NWRPfnGC0vtCGlSyX0Qun5wZ4Zylcg1muVksAkLhi1nagKMJLFajBrPeFhaV3fZtttsmRBSXg5d5FHLoG3l8W6yKrUVaY3nvvo48LgEwWo979oYJZ1qGedZbq3gcA3VgDuB566KG5nne/+915vPROeTdZj/swwngDaFqbWhuV5WMS4xUL2TK2GyO8Q85r99Zbb91YkxrwlAZ0cmUM7ht/jWfAp/eUu2Au411n3PWBiI89WMUPBsx6Un2U45oLL7wwP7ie12Ixa8z1++HDF+O4cdE4xFLXWERzEJmFvg+B6MOrAs0BWb8VncAsaOteGaPbsPilb08cTc8KBJidxv/ETgPZy1ZZf9z3hgtnV1r/A58YAxLtntpYJjNttxtkm03s1tcd3Lqunohm1VSHbpPlJm3rchetLxxg/7vVNaBNOzhXyvxQ2v9qdTyuyvy56rxJ5BOqYxZGQw2zpwven6IJ5VK/7SYpDiUMZVK8Bho/HEolA+SttTuiQ55+1jdYcLNsaket5zs7tGsop+qyugEzZf5XiiX/PlUl81XnpW9apXXaBUwuTdEztleKBfpPiQn0u1J9pQ+9nuuZUt4Xq/wT0v5Qw+3pglLfBYO4eM0qv+ve1bqm1zPZyj7gYS8w289nWyOKBrbdnrN+g1mAuVe4J2Uo7bu8ytzPezfY93v+qi3a9PMUF6/a1N49NZ0obf91K7Hbb00r67APBzPO178tnt9u4dyUWPrTC8xu1q2glOb3p5TlA6VZe+QfKHkov0FnteqckI5nGKDguau82rlHK9/4Kv2BVlr7cOcq7/OtxH6Nef3+7Wk18yWHg3muXnJBl4Mp8bvSpfpICgVCgVAgFAgFpj8F0kTpiMGsCWvwjPUr6yaWRya9AdhiuWrympWVyX6wgQUluNZeYxZ0ACDaYNZkeicwq14WZWXC3+S+ifRuYNbkOrABtrL4dAwyAE2sJcHF0heuM/XF2ralTBC0WwAVt9pqq9xXcJhbTO6QwUvt5BrT5D/3v+Atd6FAhYl9FoegAhDBMgwckAY0gqeACgtUbWSx5pi1L5gKIgCx3MuC5AXMApbCQGA2PfUZmh588MHZfag2FxjOxbD69J3FMOgLWgMoZY3Ze+65J1u/6RvrOZqyfAOyWZ1Z17JYzA4WzALToHRt5Wpim8Usy0z3BHxm6cuy0NqR7gsLaudowu2qZ5GO4CzAzfUzF6zg7Pnnn5/TuJVmbSeAUCAjAEWvCC9VoAazoJp33PvjmRVZXoL5gFfEoWsAUAKSnlfvO6t17zUY6j3yrnEP7J0dLJitrUx5GTC28mRgPPI+sFwFQ40XgKj3zLtnfDS2+vjBuCy/urkYLuOb8dv75lpjqo9YvJdgqLFIud4lH54YQ41X+sIjACBr/DOO+N3QFuMOOOz9NtZwJ+x95/pYMB7Lx6KYHu3AlTbgq44yTvvwRR/9hrCO52bZuMIltPJAZX2kud8T62wbh40Txl4W/aKxxbjMS4E+G9OMET7EMe4Vt83tNsVxKECBALPTwd/Xa2y869wrrz/ukqHAWZayq633gY+nZ2SgycjRpNwdfnwmRgDGZOBgwhtTJhO95don0v7LqgtPqtJurc7b7TZZvmh1nbInpNgtyF9POp7dIXNtWQa2PJtiaTcXxiVsmnbKeW4WH66O2yBljpR2cYr3p/jnFDdIcaBg8vnvKZayjx0o4wDnhzIpfkxVjzoXGaDMcnqHtPN0imABILNYinXoBcH6WV9d1p11I1r7rHrrZ+/MVnr7cMZ0Qj8eTfH6FA9IsQ7lvti273Odz/5/pVjy79NK/FOVBk50Cyz/SjnPpP0ZJmaun+WtWwWMr64ZCaSowQ/Ltvla9dSHe1R1au+EOnGQ+xekfKWvz6f9uXpc5x0u+d3nhVr5ez2TrewDHvYCs/Xz2I93qfTJtttz9p2UXvKeMWDr/9//q8c141AdxqeDUsb/pP231Imt/aXTsTwl/0eq9H7eu1rPbu/3uKot2rRR1Z5Ou/dV+X/byjBnlaasdVvpvQ77Nc4b84u+T/aotP7d6QVmz+1R1jeqelmtDjfMki4s7bft9nHRz6u8t/eocPMqr3L3auUfX6WPhjGvX789/XquWnINeDglflcGrDwSQoFQIBQIBUKB6VGBNBc0YjBr0tFEOxhnYt1EO7e7JvVtARuuMFkzJY3zBL+JctZbQJ38rJlYbpngL+53gZ0jjjgi5+eaFhQC4Ez4g7cm5llvmsgHBVhkgpXKKOuomjgHClmcga3awZqTRatz6lYWy04gkbUksAo4gcjAgTR9Ue5FF12ku10DIMASTf5SBmipLOWwGCugmiWqdusTV6CsvVh1gdZAqOullfbpw3333ZehF8s31wDK+qH9LEzvvPPODE+BM9eCHAItABL9Bq/LGrO0U462aaM2AUIgBr3Azfq+OQ8agS4sZllDszjTX23QFgCGhSorYPUAJtIAD/fQvbVur/vEEk8ZdQBKWdnqz+mnn56TWLmCqdw7s2j1zLi3QA0AvPnmm2fA45kCjwAo13s+PFugC+ttMMmzx7KWPvKwohZY3BY3p563CC9VoA1mfQTgHoB/Yqwx+1K9BnPk2Qf5AELvv2cZCGQdbowDun2owC03a1TWrN7HwYJZoJeF6Ec+8pFsvcri3zsMlAKU3lOg1jtsPHWPWYuDs8Yg6cCl9V+NLdrivffesvj3LgKT3jNWsfIYo+zzZMC61rjjwwgfURhPjJHeSeOD69XBbbNxlyW8dOXoK6DqwxTBhzfcLOvLAw88kM/V//iwYvvtt8+WvixhBfmUy6LfWMIqlkWvtuiX8cBHKQ899FC+D4AtTw3cqdPKhwd+f/x++SBGXmMGl89+K4xTPqrRv/iYo74bsV8rEGB2OvoLe9X1ttlq5fXGPdcT0K4/7s613rn9EmNEmqX88FTx/7N3HmBSFE0f/8w553zmAGYxYAAFM0hGFCQoUUQQlSwgoogBEAUTwokKKkExACIGFFRACZIVBAUURAHFrO/bX/3aa952nJnb3du9sFf1PHUz07H6PzO99/S/q4ZF+GTkLSns14fYdNJfTlzeFJeYd4xbLIfc9Remf5TrvQP1/ctcuXD9cLzUz8w7PzlQxpUPepLtLOX8BUxXDvJqp7y23AFPwu9FXZmhLiPkuK2k0YYr2y2kTFxSMoviEA+uH44szEfJjpKxVNSVXyfn2wcKz/Ty7wzkcZnO/vp4fcURN/TLM+XshlQqR2KEtJB0V5bjrYFyfl7NQF7w8ktJcOXbBjLHeXkQeYcE8t3l7nIC8e/aGewy5Og/f5kiZstKP2Dm+ufZ3UI0KPtIAmSOK8exp2iycq1U8NvgPkfJ0ZLxp6gr/2FIwfyeyZAqoUncP9fPipAS6Xy2ad71xTHuOUs3MUt/z2FAhDwv6c42iPCDvHLpvHeJvt8+YY5dx3j2BE87SoKzneOGQIHdAvnc02QkXfN8B+nUtzPKjpsD5fIjZvmkwLERA8Ir3+/zwYhyiSRvF2grjpj92Ss7IqbxAyXP//3B1uCc2sNr69OYtshq4pUNPgfpnPOmeP2k+tuTrueKcScihfG7kogdWkYRUAQUAUVAESg1CMjCYVqIWRbzWch+4YUX7LcLWcRn4ZoFdYg58ln0ZyEe8hOPJhbmITpJg6xjwR9PKzxKISBYFGeRHi8uQmkieEHy3UO8TFl4h8BjQZ1FcRbraQtCjjrYQj5EJ56WeEex4E+IWwgPPHjxrIQogLyAMIVQxVbCLLMgz3cYIUBYqKddxpKIUI7yhLgkPDJtMQa8tfB6ow/IbNL5niKYQBaAAwQMimcq5CXhdyHAyIdUZgzUBws8FPHwoj5jJmwo+eRBQoATY0HAAm82QgtDlGAjZSB/IWZGjhxp++nSpYv9fi/40w8kDe3QB+Fp8RCDhMMeCGTK4FkL2dmrVy9bDm84CAvuKR7VePAydkh6Fqhpm/DDhA+F2OOeBAUs8MCDKCGffvAohDDCDp4NCF6eMcLnQr64dIhvcOZZgZjGBtogjTFD+oA/OED+450HHtiH9x7fOFb5NwLcA7wI2XAB8c87h7c2OPIu430MWaUSjwDvJ3MN8wPe55CxeGEyL4Ivc5sjY8HcCWGDkyVmIQt5h+mTuQFxcwQkJfMj/fH8u744ck067yHl/Pq0ybtIXeZq3mn64Mh7RvtcQ5RiM5sqmCP4DeAc4Z3knD5ohz4gqZ2NzBG0x3vq5gc2inBNuitnG8v7Qx1C6UNss/ECoRzlsZP2mQfpF7uYD5dLmGXmC9Jd+8wrDgvSGAtpzMu0QVs859jFHEZ//IaoKAJRCPAM4unNxii8yCH6+c1l7uT3lN9InsFCFDbzq2QKgYpVr9n7rErX1juj0tV9xYt2kujXQtYuPbPSNaPOqHRtl7MvuvryOnVe3CpT/WegXd9rj0XRykn2UV/K+wu/r3r1+3t5LGT6kt9i+cVS2G93gVwf7zcg55CdA0X9cnjqsOAZJisk0S/LOTYG5T1JCJZ7JVgo75pv8PllW4SUw1vz0UC5ZHFOZlEcE0YH+sMDcWcyPNlVzieI+vb39vLdaSIkWLr66+PZkx8xy7cV//LKr5LzSqJBqSIJ/rd+8WqFGPXFxyCOMKPOl6KufFu/ETk/VtRfAIdIODpQhmef58m1wbGiqBO/fqaIWfp6TtS3AZuOIEOEd+tSUTD1y3DeUzRZ2UIqTBV1bUFm9BBlE4YvZ8qFjy/lG/oF8s4TeSZDqv0rqZmk+Dad/a8S6X2XXF8c456zTBCzEK53ifqY81sFWefbNVyufUnnvUv0/b5MDPBtGuwb5J3fFChHHZ4tbHaS32+NKxd3TMc8f5504I9pqVz7G464Fzzr/oYJyudHzFKG99S9u3Jq5Sz5+7Wo6/M7OQ/Oe7Zggn+289qizThi9iOvLMRxcA6UpP9j0wdjc/a5Y3cyPekh5y6P+TROmkimK7shpGC65rx0/fak47kKGWZoUmH9roR2romKgCKgCCgCikBpREAWAdNCzLrFRBaqWeRm8ZqFbH/xnIVuFsRdnlsIJ40Fb0cGcO0WKKkPQeE8kcLKsMBPX7Tr2qIOC+oIbfnXpNGfs5O26Ye6jgCgDOmujBsL9VjM51uCEINBhfCDYED8PmgXpQ/IAyekYbeznbE4YayMwc93uLgytOXyGaPL93Hy28Qmh4VPzLJQTF+MFwIlv/sGNv59wh7adveBoz9ObHD9Onsc5u4+uTG5I+3jIUvoUPftRupSHhtd+7TDNcQR/WIHQj5jdNekce7uARgxDupy5L41bdrUetxhq8q/EQAzJWb/jUt+KTy3EHmQsXjGQsbirerIWDY1QHS65xecwyQVYjasHZdGP05dWvAYlx+XRzvk5yeujUTK5tcW+Wz4IKwwG28gw6LE9RuVn0g6YeLZOMLGEOZhFUUgCgElZkvjf9jZM+ZtZChrRd1CJou7/qJ9IiPdQQqxAOraYFH50LyK/b30KXlp7pDIYvlYrz7t44WDRyLkAkTj56KuX454KJ4mGiUDJcMvzzmkXVBYGA6Wuz5YKO/6JDn63kHU+0T0AVE8ufqJsgDvt5cf4SjF/yXJLIpTmXsQtGulpOWKdhWF6PC9fbGPRfI9RYOSCAmWrv4SJW6cjY/IiY/tf+X6TdG+or1E3xb18zlvJRoUv0zNYGbg+ku5duXbBvK4HODlU+5H0dGiPUUHiX4j6upzZAy+FNYC+uHS6Veivi2crxf1iexgfg/JT0VOlUpB0mm+pLFp4W7R8aK8w35/o+Q6TBJ5JsPqBdMulwS/P84hknxP+nQ92/Tt9xX3nGWCmHV9zxE7mJsHioK/S+cIebePaFDSde8Sfb/3EAN4Dn3b+A25SbSF6NOiK0T9fP/8QMlzkshvjSsbdUzXPP+pdODb+Zdcvy/6suiGQJ4rx7zsCwSuy/OPP0k679A9ouNEg+9wG0kriGwnlf3+eHeipIdk+GV5p5iTG4syL78rGrTPlX9C8nzx2wK/OGkima4d8AxKOuc85m3XF8dUfnvS9VwFxxl2XVi/K2F9a5oioAgoAoqAIlAqEZBFxLQSs1GLktmSDrmCFyfhj8OU0J542ZYECRKzxc1miBM86QiJ2rx5881EeybsBAu8lwlviqevSjgC3BMlZsOxiUslHHHXrl2tZyxhb/GMhYyN8oyNaivdxGxUPyU5nY0afJMWstSFcc/EeNgEg7c43w+HoE0XsZwJW7XNokdAidlS+S921gy6uozEX1i8L8WRDQ600zuvHRb/XfssqvuSyGL57lJhuKhrI+64VspV9jsIOSffb+NPud4lpFz5QDnIpDCywlWtLScsyvptR53jUcy4kpVkFsVd2+fKyWeiUbb46Suk3MGiYZIoCZaO/vqIAc6u6WHGBNJ2kOt+okHCz7URPN4RqO8u/XJxhBnlvxR15du6BrwjNrFon8gzAYnMBglfCnMBfT/peKqoG0/Y8X7J9z3vgmGgfdvzO68SaCusP5f2jJTdKaLBRJ/JiOqbk5mHwshp7h1hvp2k49mmLTc2jnHPWSaI2Q2B/n1bOAeHsqJRko57l8z7DT5BG8OuN0k5CNs/vfKt5dxJIr81rmzcMR3zPL9B+d2HNVLmWlE31vyI2Re8sq6OfwQXNgltIVoQSeY3iDntY1HfjqhznvWnvbLfyDnks5MecuLqFpSYpc10zXnp+u1Jx3PlsIo7FubvSpwdmqcIKAKKgCKgCJQaBGS5UonZJNZs8XB9R0LnuvCD7tua7kj4ZL5TWhIEYoFQo3yrFZKoOAokC+GZCfmIN2+mBC9aMHjppZc2e+Jmqq+S3K4Ss6ndPYh/yLtp06ZZMjY/z9ioXpSYjULmn+l41eNBjyd8poR3gXkDT1nmKRVFIA4BJWZLzb/VWTnQIOl5YoqjPF3quYVTjniIIhC9Ln2yTfnfHwhRn0w7739Z/zqrJimLAuVdu99L+gDRA0XzExaLN4q6uh9EVNha0vHwceXeiyjnJ5eTi4miYWTcX5I+X7STaKqL49tKXdpxNrHAn4hALvUX/VbU1fWP30k6du0sGiUfSoar0y2qUF56Qfvr5fUFYZio8PxABPiLz85mjtybc0SjxK9XNapQXvpSObq2fQIoWO1SSVgg6j/nrh5EZ0PRsOfhF0l35WrIuS9d5MLlfeJnhJw38MquC8l3STxbLUWHic4TBQvIIK6vFEUgi1y/TW1K6n/2lKoQMf676NrmyPtWRzROknkm49oh71hRCB/fBs5PFPWloM82bSX6nL0uZZ09A30jAue8u67czEBeDy+P8R0typwXfB5/krSHRCGs8pOC3rtk3+8LxaDZom6M/pE5jU1BB4sizBcuf7xN+fvPLnLwx8xckaqkY57Pkc7fEvWfBezmPkwSPVL0GFE3Fu6dL1vKhSOh+b3ZW7SLaHCOJ4+6BRmvVN8szBN/iTq78vsN2k3K9hX9zavj6jL2GaJ1RZkDacvlcTxT1Aljc3nFcc4D34L89jDOdDxXtBMnv0imw7EwflfibNE8RUARUAQUAUWgVCAgC4lKzMatpobkESKXBf8whbgtSYv0kLPY7MIChwy3yJMIPwzR4kIgZ8IgSBZ3XzPRfra0qcRs6neS5xgFw1RFidlUkdN6ikDRIqDEbKn4l1oHWUwQgBhhAfM6URYWTxPdXrQ4CQTnWaINRBvnneNZUxxkXzHiItHGopeIHiUa9NaUpLRJYfeH4YynrOjVecrzspdoUQrPLc8EROzlojmikBElRbYWQ31Cpk4aDT9M2qoiCjY8m4eIFpUcLB1zn8qIQubFSVE823H2hOX1kERHxPjk3q6SzqYB5ig2K+Q3VikSKoV173hXckQriTYShcTbT7QoJR3zPL9dbl5gnuI9K6iwyYD7Clbc5+IgPF+nitYWvUaU8L2QvMVZUpnz0vHbk47nqjjjqrYpAoqAIqAIKAKlCgFZqlRitmjXa7V3RaDEIKDEbNHequJCzPIcsAGFTR0FJZuLFtHw3vFw/vbbb+13scNLFDyVjSaQdWgmN50U3FJtIR0IKDFbqv611sEqAoqAIpAVCLSQUXwu+rpo13xGBBHrCD6Op+RTXrOLBwI9xAx333xitnhYp1YoAoWLgM55hYu39qYIKAKKgCKgCJR6BGTBsdgQs3gsfvjhh2bixImGbzIWRAgVSnjhpUuX2kXvFStWmKFDh5rBgwfbUMR8JxMvzHQIJAXes3ifLl682PZbkrxm04GBtlE6EFBitmjvc3EhZiGZ7rvvPlOpUiWTm5trCdr8kGG+nTp1qlWIz+Iq/A7xW9GpUyczf/58s3r1avubQdj3gsq6devMlClTbLv8XhD6nm+ST58+vaBNa/1ijoASs6X+320FQBFQBBSBEodAPbHYkXYcz4sYAd7Ga72ynJckb9+IYZWKZCVmS8Vt1kEmiIDOeQkCpcUUAUVAEVAEFAFFID0IyFpmsSFm165daxf6jznmGLtgneo6KwQAC+sVKlSwi+qQvNWqVTP77LOPKVeunKldu7Y57bTTzFdffZVqF5vrffPNN6Zr167m+eeftwTwvHnzzPnnn18g+zc3rieKQDFDQInZor0hxYWYXbNmjbn55pvNAQccYAna/L7lyrdYBw0aZMqWLWt69OiRUU/Ugt4hyOP69eubBx980G7eefLJJ02tWrXMhAkTCtq0/Z53jRo17OYdGluwYIG54YYb7G8IxJ1K9iKgxGx6/mfVVhQBRUARUAQKDwHC9frE7Fy5Lh/oHs/YtwPlBgTK6GXxRUCJ2eJ7b9SywkdA57zCx1x7VAQUAUVAEVAESjUCsgxabIjZDRs2mM6dO9tFcTxaUxGII7xWzzzzTNOvXz8bZpPrQw891HTs2NHg9dSrVy9z2GGHGbxoCyL0tWjRInPkkUeaRx991Da1ceNG07t3b3POOefYUJgFaV/rKgLFDYHCJmbxKlz02TJz532Pmps79zGvvfFucYOkUO1JhZjdtGmToR4bX1auXGnYPMKGEsLnEllg4cKFZubMmTYv6OmPh+fs2bNt/hdffGEjAzBgiFiiGzz33HPm008/tdECeDbow5VnfiXUMf0QuaBJkyZmhx12MK1atbLXeKZyfyF5Z82aZWbMmGH8Pthgg7cqbTKGZcuW2bad7T7w3333nc375JNPNo/N5RNqmU04jBEilDFHCQQyvw916tQx06ZNM/wmde/e3ZQvX95u9vnhhx9sVcaFPXi6LlmyxDgPYPIZN/Wc0B/9gz2/SWeffbYlfSkDBgMHDjRVq1ZNC/Hr+tRj8UNAidlS/a+2Dl4RUAQUgRKLwBNiuU/Ocr5R9DPRDSF5iySN7+WqlAwElJgtGfdJrSw8BHTOKzystSdFQBFQBBQBRaDUIyDLl2kjZlnUZ2G8Z8+elgQdO3asmTRpkvWUYuEdgbjEuxSStF27dnaBeu7cuTaPRfGnn37a9O3b14Z6ZLF75MiR5rHHHrML9w8//LC57bbbDB5MLOaHCW2w0H3UUUfZRX4WxOlnt912s56yzzzzjLn33ntNTk7OZmIWm+iHcrQ/fvx4+/1E2odUgNSgTQgFvMSGDx9u1q9fb8eC95dr+/XXX7dEw8cff2yOOOII89RTT4WZqGmKQIlFoLCJ2ZWr15g2ne41Bx1f0Vzd5BYzatykEotdOgxPhZgdM2aMqVixojn33HPNGWecYQ455BBLPrL5pUWLFnau2n///c2pp55q52PmVuZeQsETdeCggw4y5B9//PGmS5cullTl+6t33HGHOfnkk+2mFOZD5tbzzjvPHHjggbb8CSecYH8L2Bhz//33m7322stsscUWZpdddrFeosyTzPcQlXje7rfffrYPfj+Ytz/44ANTr149c9FFF9mIB/SPLRdccIH9DYHUxM4XX3zRlnF2nnXWWeaJJ56w5Ch2Mneffvrp1ibm5UaNGkWGDsYmPFrbt29vv/360ksv2egKO+20k6FdsOQeYONJJ51kN/hg16233mo3/UD+NmjQwOIKCQyW/EbgcUtkhcqVK5udd97ZjnPIkCGWmH3nnXfMFVdcYcvx+6WSnQgoMVvq/91WABQBRUARKJEIbCtWjxENkrNh1xOk3N4lcpSl12glZkvvvdeRhyOgc144LpqqCCgCioAioAgoAhlAQJZA00LM4pX0/vvv2wXnXXfd1S4+EzoYEgBv1YceesgulEN+skDP4jSL3XvssYcNL8wiNp5RF198sTn88MPtYjueUSzaU/7oo482u+++u60HSdCsWbPQ7xqyEH7JJZdYjycW7vlGIG1uv/32lpTo1q2bueeeeywxywI7fbBQz6L+ZZddZi688EKbh9cUY8KbDCKA8Mp169bdTADw/UH6uvbaa60XGIQHxDHCOPCAqlKlymaC12boH0WghCOQaWL2P//57z8Qwlu2+nU3m/rNbjPfrltvfg58F/o/4qWZjGB/nJAfVyY+z0TWza/dOJv8vFSIWcK6HyYRArbeeuvNxGafPn3sRhPmaqILXH311XbuxvufDSV4w1511VV2/r7++uvthpUTTzzRHHfccXZjCp6sbFSBZGWjC99Nvfzyy+3cydwMsVmmTBlDHb5BS5vHHnus2WqrrWyEgbvuuss8++yz5tJLL7WkbM2aNe38yu8FBDGbeiZPnmzn3m222caGQMaL9ZRTTjHbbrutadiwoSVC33jjDVtmzz33tCQyczi/D5RjA9CwYcOszfym0AdzOb8jENJ45waFOvwGEAGBe0YYfMbC/N6/f39LSvNtXULhQ7RCbnNNhATGRPkBAwbYceNpSzhkykJiE63h7rvvtuODrMWDlj64p4QzJnzy559/HjRJr7MEASVmM/APrDapCCgCioAiUGgInCo9DRWdIfqtKMTsetH5oqRXFNXvygoIJUyuEHuH5Wn3Ema7mqsIZBIBnfMyia62rQgoAoqAIqAIKAIWAVnzTAsxy6Jj48aNLQHKwvarr75qvbIIXYlHKYvaq1atst5IeFXhoTpu3DhLBkC8skgP0Uke14TIZJGbRXoW5vlu68svv2xuv/12u7gPUbt8+fJ/LNni3UpYTUgIyAKEkJPvvvuuJSRYMMcGvLfwmGVhHq8wFvIfeOAB2z9hKSEc8KzCYwsb8baaOHGiIawnRC0ELQv/hKKkzMEHH2wX512YS44svENy0J+KIpAtCEAkQUaxkYGNF61btzajRo2y7yvv8GuvvWYIKxsmeL/2e/RZc3f/IaZx6y7mxtvvMguWLDMrvvra9H7gcXPl1a1M9QZtTP/BT5tv1q4zX636xtzcpa855KRKpmz5aqZn38GStsZ8sWKlPb+ibgtTvWFbM/CJ54S0/d56q3+68DOb12fAU6Z243aml4RAXvfdBvPJ3AXm5k59zGW1m5lrm3cwI8a+Lt6Kf5j1G34wI8e8bvo/9rS56/7HzFX1bzL1mt5mnhv1mvle8pC/y4w317XsZKrUa2U69upvPpmz0HrT//rrb+aNd6aZFrf0NJfXaW6atethJr41Vb5N+putu2TpCnNP/8fNVdfeJNpaxv6kWbx0uc1L5U9BiFlIVaINQKqOHj3abnphHoWEJNQwm03YKHPTTTfZb2TjZcs1hCYba/Bufeutt+w8yIYWyE021zDXEi0AcpK5GwIVL1XKv/322/Z5wAO2efPmdhPLLbfcYjfEMN+Tz28FYYP5Bi0euMzfEKoQs8z7eNNCcPJc8TvCNR6mfAsWUpTfF74hThuElsdWPFppt2XLlmbHHXe0thLCmd8dSFaUZ9UXwioTaphxQwwjpNE3m3teeeUVu9GH+Z9NN5DC/AbRL8Q2OBHGmXvEewEZzFjY+MPGI36fIKjxAAYbrhFCHRNBAmKbtlSyEwElZvWfbkVAEVAEFIFsQmDLbBqMjkURUAQUgXwQ0DkvH4A0WxFQBBQBRUARUASSR0CWQNNCzLIYzaI6nkssOuOtyqI1RKsjZlmIZoEeUoCFe7yeWMhn8X/w4MGWGGUhnjQIUxb/IWbxcGLRnm8O4smFlxbhMiFhfcHDlYV+CCPqI/RJeEo8sQhvybcKHTGLBxMevHhyOQKVNt6R0JKE1YQEoDyhliF3CdXJAjqeYChEAR65eARjnxPshACB8HUhnF2eHhWBkoxAQYjZTxcsMeUvr28OOPp8c64cmwuZOX7yVNOsbXdzwtlVTNsufUzrDr3NqRfUNO2ERF36xVemlxC2R5e7wpxZuZ4ZPOx5M3XGbNOgRSchaq8yt3S7z7S6tZc56fzq5vYeD5gfNv1kSdEDT6hgjjjlYnNpreamQ88HzZvvfGAuvKqJOeeSa0znu/qbBs07mlOkzhtvTzOQxa3a9zL7ik2VazY1rW7rZSpc2dCcVelqM+bVSWbTTz+bhx57xpxSoZap2aidufWO+8y5l9Y3dRrfYqZ/Ms+8+PIEU/6y+kL4tjAd7+xvLq/XUuo3Mi+99qZZLeRyx579TIUqDe3x5o73mFPOrW5uvr2XJZ1TeQ4KQszincpGEoSNL0QBYOMM8xdhh5nzttxySxvyHQIWj9C9997blmFeJzx806ZN7bdg2TQDMUvkAyIQsMmFMPCUo02OkL4Qo4S3J6RwmzZtLElKOGQ2r7CxBaIUUpWy9LXddttZeyAwHTHLXAv5j0D+UxYSE2K0Q4cO1oMWMpk+IFIJB0z7zNsQpnjp7rvvvnaMbMiBqOU4TMhfX/jN4vvgbCxiIw7iyFqIWYhcokJUqlTJjo/fjbJly9rNRWBHOr8T1KEsoY8Jc4zNpCH0STl+S5zwvV4iNJBO6H+V7ERAidnk/z/VGoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQFYiIEugBSZmIWsIwYiXKaSoW9TGw5SQvo6Y5ZqFerztWCiHdOUcYhavLTyowohZFuwhcyFZ58yZYxfwIWbdt2ndMi7fuOX7sJTn24BIHDHLtw+bNGliQ01CprryeFbhBcsiPWUI6YnnE16yhEnGg9YRs5SF2HjkkUdsff6w0M7CO4v1jgjZnKknikAJRqAgxOzc+YuFHK1vLhECFE/Slau/McOff8WUEZJ10JCRZsPGH8zqb9aaR54cIURobTPhzffMzDnzTZVrWpsW7XuaNWu/N7kjXjZlzqlqnhw+2paHWH1wcK45/aK65t2pM8wkIVsPFQ/bNh3uNmvWfWcWf77c3NFnoDntglrm7fenm40/bjKLlnxhbu7cx3QS0nSeeNg2b9fdnHPpNWbchLfl29EbhWydaIncvg89ZT6a+akNpdzoxs5m3qLPpM8fzYhRr1oC97HcUeb6Nt0kv62ZNn222fjDj0LWfmrqXt/etOl4t3nz3Q8sgXuNeODOFVL6u+83mrFC9uaOeMms+vrblJ6CghCzzGNuowjfZYWMhaDEk5WIAYTivfPOO+1cC/G6bNkyG7YY0rN8+fJ2wwykJgQsHqA33nijJWaZJ/nWKxtdCFtMOiHoIWfZRNO5c2f7+8D3ualPOHnIU8hWfgMgONkgg7dqBfmmLXMtBKYjZpl33XzOhhsIZUIWkw/JS2hjxoAnMBESPvroI+vdCsl5zTXXWLIXD1s8eyGRGSNh58M29lAGYhYPYMQnZkmbPn26JYXBEgz5rUOx780337S/YW7zD5uB+G0DL37bkDBilt8ewh5D/rKxSCU7EVBiNiv/hdZBKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCySMgS6AFJmZZRmVRHMKSbxay8O1IVML54vFKOEgW0vGwIo1FbEhNFsFZvH788cdjiVkWvl2bEK9hxCzeroRZhfTFaxeJI2Yhk/l+ICEn8eRFIHfxFoNgxquXhX/IYzy78Aoj1DHfA8RrC49ZxgCRgMevE0gKiGZCbpKvoghkCwIFIWbnzFtsKlZrbNp2use+l+u+32DDB++ec7YN83tD2x6W6LykZjNz0AkVzYBHh5sFi5dKeOObTOvbe5u1Eq64290DzZ5SvnqDmw3lm9zUzVSucYM5pOxF5vHcF8Q7dpoQvdXM0Of+DkWL1y1E6YHSXoOWHU3Tm7ubhkKylr/4GnNtk/ZmyrSZ1mO2QYuO5jMhi5GPZs61ZCxhkEeNe0OI5GbmwUG5Mjf8afN/kfDFELiTxBP3Yun7hLOrmvrihdtUPH/rSzvHi4dv1botbX633gPNQWUuNKdWrG29e59/abz5UkI0Ow9K22ASfwpCzOKZSqhdhDDAlStXtl79EKt4tRL+lzC7bDIZM2aMDUlMWGLOZ86caUPV49EKCcoGGcL1Mp/z3VRCvteoUcPUq1fPhpyHwGzQoIH9ri1ELSGGCWFM/euuu84SnHiJQt5CmuJpyhwLUctGF99jNoyYxfvXhT9mvidaw2OPPWbJUtpjfoaEpU/IYMIM8x1cPIXBgXD1jNkXfiuYx/kOLbYgpBE5gd8pxujwx7v1HYmswO8FhDQh9umL3wdCHkNkM068igmbTAQFPHIhZiGfIbCdQNhBTBMeGZxVshMBJWaT//9UaygCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAlmJgCyBpoWYJXQkoS+32GIL6ynKd/hYZN96660tMYs3FJ5F5ENYQlxC4OJRBXHLdwH9b8z6oYydB6wjZgl1TBtBj1lIIwgAPHd7yDdeEUfM4gEL+eu8mSBTIVohfCGGWVSHnIW4wMsXshZyARIWzycW8SFi8fKCFMbLa+3atfabsxC3eIOtXLnS9rlx40YbYtOVsYn6RxHIAgQKSsxWrtHUkqtAwXdhe9w7yOx//AVCsN5h2na917Tr2tfcInpHn0fMm1M+tMRstTxido2EBu4k4YIPkPKQsra8fIO2fdf7TI8+g8xb7003EyZPMSeLd+yYV/4OCfvZshWmdqO25sjTLjNtOt27uY/23e43Q8VzFY/clhLKGFJ1xcqv7R3C67WWfLv2TiFmR44dby6V79IOfmqkYezIX0LW/Sbfp50sHrGVqt9gzrq4nmktYYr/tv8+sf8+M0jKfy6kMB7AfK+24Y1dJORyDXNo2UrS351moXxbNxVxxKD8GFnSj3Dv+Qkh3JmjmNccGckcBXHJPMr8Rz6RDYgEAHnJPIi3KfMz8x9zGfM08yLtYQekI0QrBOuMGTNMrVq1LFHL3IpXK+UJfwwZSX8QvxC5fJeWsnjSQrpCnNIu/dAeffbt29dMmDDBnHvuuTZcMBETkOHDh9uNPZDKkMv8BkAG0wbj4LeBUMpXXnmljVZAuGPaIJ/fAMLLU46wym4zjo8fIYghXfl2LRt9kCFDhlii133n/PnnnzflypWzoYqJAAFJDD54HbMxqHbt2jaNDT7gCLlNXWxhHPwuMm7GgrcsURnA2nn9+vboefYgoMRsVv4LrYNSBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUASSR0CWPdNCzEJasNDdqFEj622K5ywL4ngysUCP1xHEJd8GhKzFU4qFchaoKcsiP0Qn3kSEAGbxG9IBbyMW0/FWgmQl/CQeUnhVBUNRsoRLG3hu4TWFVxp1CN9JyE68sCBm8d6FvMVewitDEEC2QibgacV3AylLuE2IW+qSR3hOvIIhBSAf+HYtZDIL8xAChEWmT9IIecz3DelfRRHIFgQKQszOnrdIvFubmu73DLRw/CBhhQlbfOxZV8o3Wd8y6zf8YL7fsNHMmrvQ9JEwwhCkeMw6YpYwwv0GP22Ol+/Rjp/0ni3/3foNZubs+abvwKfsccLk9ywxO1a+8Yp8Jd6prW7raUMoL/rsC1vn6zXrzMS3p5oXxk6QUMafW6LUErNfrbZ1HDHb6/7HzMR3pprLxPu1q3jq/rjpJ5s/dfos+aZtP0sq483bpHVXayf2r/p6rRkx5jXzzAuvmI8+nmuGyxGC+XvxsCWs8u3d75ewydeaFyRcciqSCjFLOHU8//EodZtHuI/MU3z/tHHjxtZbEw9YiElIVDxBCVfMhhpIQ7xMITNffvll8RZeb7/hCoGLpyfh4yEYmY8hNJn3KM9mlVdffdW2R3+QqGzeYX4mbDLzJ+GA+c2AzIQoZoMOm2QgMPHKpX88cjlHIIAheIlmQKhl7OQ73xC5kL14xrLhh7bJI6w834Xl2+CEH2YseAQvXbo01GuZdOxp1qzZZqzYwMNvBmn8DvFtXDYZMT7Gwnd33UYi+oKoZtz07b5ZzkYhcOI3B2KbDT9gz28MbfJ7MWDAgM1kcCrPhtYp3ggoMZv8/6daQxFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARyEoEZCkzLcQs4XvxJmUhnXCQhGRkwZzQmHgo4XUEScnC9NixY+03WPkWIOGE35GQkCy8s5CORxdeR5AGhH5kIZ7rb7/9+5uMEKtc4y3FeVAgCAgbCXEKsYBQjrCXhFuGOMUGyAoWziEMIBrIJ/wxRAF2Mx6EIzZBQhDeEjtXrVpl+4fUpT0W81lcp03GwOI83l+0paIIZBMCBSJmP11kv93atfcAC8lff/3HTJsxy5wjHqd8d/Z1IVtfe2OK9XA9+6I6lticL8Qs35htdVsv+65N+WCmOaNiHXPl1S3NhLemmlcmvmOq1W9jzr3kGvPx7AX2u7QnnVfdjM7zmP35l1/Nc6NfF4/ZS03zW3qYd6d9bJ5+YZyQo/VN7wceN5C1zQihLCGRl+cRsxCqNRq0Md3FC/eLL1eatvI92nKV65mHn3zOhidu0rqLuahaE/kW7Rum94OPGfrrctdD0vZM8+Aj8r3bC+tYz973PvzENLjhVlOldnMzSuzhG7cNW3YyFao0MhMnv5/SY5EKMcs8SkQDSEDmKyfcS+ZACCPmVwhHypKOUJY6RApw+XzDlXyUuRES182jfnnmRvIo7wSSEjvI4+iIU/qnjx9//NH2Rz3mcfKZu0nnHME+rkl3Y+HINe26dpy3K3WoS5sunzFFbZih7KBBg6x3MSGJETcu7MQuBDv43QAX+nQYMF7G5o/b4U9d+gU32sIOxsL3ZfGq1TDGFtqs/cM95zvCW221lalevbr9P4X/KfjfAmKfjQbumS4kEHbIyn9qdVCKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikBxR0AWANNCzLKojwcRnrB4nXbo0MF6YhFWEg9YSEsnLFqzkO0Wzzm6cxYmOffJAf+aNlx5V8a1y5E0PJwIHYmXmBPquMV4ynDtC9csnGObK+fysQl7UbdwygK+K0d71CMNHG699VZTsWJFSwa4NvSoCGQDAjzrbIwg9C3fYcbLkg0ZkAt4AOJxCVEVJni/1mt+u3nw4aGbszf9JBs65DuuFao2sp6zZc+rZknakaPHS7jg3w2hiG+4+Q7jyNyNP2yyHqnnX3FdXvnq5rJazSV08RtCeP1qJot36uV1mktI4/8Rn9+s/VYI02HmtAq1rLctIYWvFsKUcMJ4uHbq2c906PGgnK+xds0Rz97m7XqYBx55Wgi/X8zHEu64Wbuepqx8uxZv3YuqX29yR7xsNorHL/Z17jXAts23Zk+5oKZp3aG3mS3f04UUfvn1yaaKkMhlzqlqv31bsWpj8+Qzo8z6jT9sxiCZk1SI2WTa17LGficW71i8gb/++u/w1pnAhXdp0qRJ1tMXb1l+X1SyFwElZov7f8NqnyKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiUEgIyDJoWohZiE2+yYqHLKGJ+ZYgSghgwkz6HkSZXnplgRuyiNDDfHO2sAQM8AI+88wz7fcRC6tf7UcRKCwECkLM/v77H+Zr+eYqYX2d4Jv566+/2ZDDM2Z9ambOmm9Wrv7G/CJpyB9//Gm+kdDD33633l4Ll2XLf7nqazN91jzxkp1vVq1es7n8T0LOrpLwxT/9/LdnI5X+I5UIQ8w3Xz+cOcfMnr/Ytvcnm0BEv10nno/yvds///zbmxRCeM3a7yTs8Ua7+YL3mvy585eYGWLfF1+ukk0cf3vb4/W78YcfzcLPlkvbcw0evt99v3Hzxo9ff/vdfCXjwZv3o0/mmi9WrBTb/q5rB5TkHyVmkwQsheLc7ylTppihQ4dakjaFJhKqQj+TJ0+2kRq4ryrZjYASs4X0T612owgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIFHcEZCk0LcQsS6osNONhBDnJgvO7775rFi9ebMM1FvaSK6EtscOFQC6M/vGgJQTzhx9+WKhEdGGMTftQBECgIMRsfgj+KSQnHumQr4mIK59IWcrQLO1DpgYlkS4heP+279+lwYU850UfbJ8+o+oGy8ZdKzEbh0768gg/TKhhftMyKWwiIrRx1HOTyb617cJFQInZ4v7fsNqnCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgUEgKyNJk2YtYtc0JAENaXRe2iXHAuiv4Zb6YX8x3OelQEChuBTBKzhT2WktifErMl8a6pzYqAsd8V1m/MFtI/ttqNIqAIKAKKgCKgCCgCioAiEIHAtpK+S4huH1FekxUBRUARUAQUAUVAEcgIArJgmnZiVhdhSyYCBSXRIe1UshsBJWaL9v4WR2J2/fr1ZsGCBfbb3nHfScX2mTNn2qgCoMizlK45w7XDppj58+ebTz/9VKMWFO2jqr0HEFCP2Yz8C6uNlmAEcsT2xqLNRE8QVcleBLaWoV0i2la0quiOoiqKgCJQtAjkSPeNRXUOFhBUSh0CQ2XEJkQ/KnVI6IAVAUVAEVAEFAFFoEgRkLVDJWYDC6hFeQmxMX78eNO9e3fz+uuvZ9wUyNjPPvvMdOzY0Sxfvty899575oEHHkgo/DR1v/vuO0u0YPfbb79t+vTpYzZu/N83RDM+AO2gUBFQYrZQ4f5XZ8WRmH3++edNuXLlzPXXX28J2n8ZLQnMEz179jS33XabWbFihf0e+RNPPGFmzZoVVjypNIhY2po2bZoNFz1ixAjTtGlTM2nSpKTa0cKKQCYRUGK2SP/V1c6LGQItxJ7fRd2i6H/lvHcxs7EozdlCOj9c9DTRvYvSkDT0vY+0MVPU3WuOn4seI6qiCCgCRYOAzsFFg7v2WnwQyBVT/N8ld87vlYoioAgoAoqAIqAIKAKFhoAsRCoxm8nV2CTbhtS85ZZbzL777mvat2+fZO3ki+PtdtNNN5n69evbUIv9+vUzp512Wr7f5oWgg2Bp0KCBgZiBpJ0+fbq56qqrzKBBg5I3RGuUCASUmC3a21QcidmhQ4eaQw891NSoUcPMmTPnXwDxzIwePdrUqlXLDB8+3Ia5f/DBB03VqlXtZo5/VUgy4amnnjKXX3657YOqy5YtMy1btjTt2rWz3zxPsjktrghkBAElZgvt31rtKAMInCFt3p0GPUDaOF70D1G3COof8aosrXKgDPxR0Y9Ffxb1cflBrueIDhOtIApxW1LkGTHUH4s7n1ZSBqB2KgLFAIEOYkNB5+AGeePQOfhvIC6OwbRMHlZ6yF4EcmVo7vfIPyoxm733XEemCCgCioAioAgUSwRkBTLtxCzhJH/77be0hamkrbgwu4n0lV8biazE0kZB5ffff7fEhN+O3+6GDRtM27ZtzU477WQJWsrF2Q4ufn2/XXfON3/RoPAd4DfffNMcffTRZvLkyRbj1atXm9mzZxvsDIrfD/3OmzfPnHTSSZZsoSxecX379jUXXXSR9b4N1tfrko+AErNFew9TIWa5Z1988YV58cUXzUMPPWSGDBliPv74Y/PLL79sHsy6deusp/7AgQPNM888YxYvXvyPOWPlypVmzJgxtv6jjz5q3n//ffPDDz/Y+hCzhx12WCQx+/XXX9vNH9ddd51ZsmSJbbtx48Z27rjnnns2zxX0MWrUKOuxj61cI9g+YcIEO98wjzEevPzfeOMNS+xCwJ5wwgnm9ttvt23z+4ONkLUvvfSSbUP/KAJFjYASs8XyX2A1KkEE2kg5f+Ey1fPy0g7hbKPqP5SgPdlUbKs8TH6MwSWI12dStpMo38cr7rJeDAza7673LO7GF9A+vJ2fEx0pWruAbQWrnyMJFfJ092BmIV+z4cLZckqgbzYRnO/l7xTI18vEEPheirn3JtXjpLyudA7+G4j+MZg6EjsPMj1kIQK5Mqawd6moiNnrxR5+K7BL50kBQUURUAQUAUVAESgtCMhiZVqIWYg6FtAJiVupUiVTvnx5c80115hnn33W/PTTT9arslGjRoZFfMp+/vnnNvRlq1at7CL7zz//bAk+yjz99NOG0LivvPKK9bI655xzzNVXX21eeOEFw6I7AqnANV5a9HXttdeacePG2UV78p577jlDW/3797eep+eff74tk2h4YEgHwvI2adLE4JFFmM4LLrjA3HDDDeadd96x/eAx2qJFC9O6dWsDYYJtfEMRL1JCEW/atMmGBm7YsKHp1auXueuuu0zFihXtmMAFe/EaI61Tp07Wu8sRs9tvv72pUqWKadOmjR1fvXr1LB528PLnxx9/NIMHD7bkw7nnnmtDdxKGGMHrtkePHnb8EB9XXHGFgQhxRIctJH9YKG7evLmhPvcIzF9++WXraYYdELTcn4cffti2cfbZZ1uCBdIGEhYv21122cVUqFDB3jtIE8IZn3766baO60eP2YOAErNFey+TJWa5X2y+gKTca6+9zG677Wb23HNPS2RC0jLP8T7jNX/QQQeZ3Xff3eYz10FqMqd98MEHdp7de++9bf099tjDHHHEEXYTBh73+RGz9H/ZZZeZ3r1723mbdpkj6Kty5cqWdP3www9N7dq1zTHHHGPOOOMMe6xbt671wv/kk0/s/I/H7UcffWS/HwvJS3k2gtAG4zrrrLPsbwLzEMTxlVdeabp06ZJQWPaivavae2lAQInZ0vJfdXaOM53EbHuBKGwxlLSnshO+2FE9HoNHFE6k/yq6f2zLxSMTb9+ocRxSPEzMmBWQsoz9L9Eyae7lj7y2ab9umttOtrkuni0LA5V38/Kw9cJAfkm4PE6M7FHEhqaTmNU5+O+bqcRsET/URdx9rvTPnBTUoiJmL/Bs4dlUUQQUAUVAEVAEFIFSgoAsiKaFmIWog/xj0Z5QuJB1O+64oznyyCMtSQrht/POO9tFdEhFwt/usMMO5oADDjDDhg0za9euNZdccok58MAD7UI/HlIQBdQ56qij7MI7ZEBubq4lEPHq2meffWw+Hp8QhBzHjh1rickOHTrY9ln8h1CgLcjO4447zhIN+S0EO3uwkX6wgfDCeLKed9551l6+bUhaTk6OmTt3rvVcZZyMG5KAhVhCd9IvdjA2ym633XaW/GDs4IPttAOZ64jZLbbYwraD3Ycffvhm2999911L+EIagzV1ITPo85RTTrHExZo1ayzZSr+77rqrxQFym3vkxHmdHX/88Za8IJ37ApELjt988439xi3jZewQN5C4+++/v73PtAX5i90Q0W+99ZZtGo+4OnXqGEgV3yPP9avHko2AErNFe/+SJWYhXrt162bnCcKMjxw50l6zmYXNIJCyhBXmPWaOuP/++20YYOYqNtYQmhgP27Jly5qaNWvaTSqECYbkhShl3osjZtmE89hjj9nNJ2yWQZjjOnfubH8j8JBlQw9er5DB/C7gtY/HLBt82OjDXMS8D5nLRplmzZpZ8pYNM7TFN7HZXPL444/bDSb08dVXX9nNNGzYwbtWRREoagSUmC0l/1Rn6TDTScxeJBgFF0LddbMsxS9qWBfHYOEwiToOiGq0mKW/FzHGVcXMznSbU8UbNyGq0y1KzKYb0X+3h9favaJgvejf2YWakk5iVufgv29dcSFm+4k5Q0P08kJ9wkpfZ7ky5LDf16IiZrkDY/Ns+o8czyZBRRFQBBQBRUARUASyHwFZrEwLMcviOqQnRCGkKAQiYTHvvfde60XK4jgEI98ixKv0tttus/8LQfzdeOON1jsTAhRScMaMGdYbirZYsKdtvDYhHy+88ELrbcuiPfmQmUuXLrWEIoQn5ANeXNiw5ZZbWtJy0qRJloylLm3QX34CMYsnFm3iuQoJC6kBUQxpgY2kQVBw7YhZvMG22morU65cOUuE4v0rT5ElhgkFOnXqVIsBpKkjNghdvO2225pq1apZooExY3uZMmWsxy14YQu2Q4pApkDG0vcTTzxhFi1aZD1bwfLmm282ELN4t26zzTaW/IDkxpsWksQJYYwJX7zffvvZ8KGkQ8zi1cZ9ggx57bXXLLHbs2dPa9fy5cstMQKZ++2331rS5sQTT7TEjAtzTD3IFEgeSCSV7EJAidmivZ/JErN4wjNHMncwT+GhDyFLtAE8/olcwIYa8vFCJaIAUQZOPvlkG2qYOQsPfL4fTQQD5hvmKeZevGDxYI0jZpkXiBTAfE3oYYS5h40ll156qZ2D8JalLUKgQ+JC1nKkTvXq1e1vA/MN4zj44IPNIYccYs8Jv4y4spC5TrCZ3xi8ZrFRRREoagSUmM3+/6ezeYTpJGbBiXB9wQXRaZK2NZmlRLaVcX4pGsQhkWu+QbtfCcHpdLHzp8A4/5Trq0qI/amYuatUWinKvSRE9T6i6RYlZtON6L/bu12S3PuYTcQsI9U5+P/+r7gQs1Gke/d/P5KakkYEcqUt9377x6IkZo8Sm9zcvkDO+T9BRRFQBBQBRUARUASyHAFZrEwLMYsHJZ6SeHriHYqXJSF0IWdZQGdRktC3eHkS1pfwvc6TFBIRry2u8bSEiIWghRSFTMVTE0JBboUlF0aPHm2/aUh5FvSbNm1q+yKffiEcIAfxdiWMMN9LJazwgAEDLFkJyUnY3jhxxCwhMu+77z5LakIuQwxAZhIqc+HChdYbN4yYPfPMMzcTsxC1EJV4r/G9RUJ1ggMkB8QJIYkhUQk3igcYxCy2Q5hAYmA7ZAllIC/Gjx9vsYEkhtwlzDKhmrfeemvrdQZ5gwcZtkOMR33XFgLEjQUswohZyHKIXZcPyYKXL+TvggULLHmD97IT7jN44b1LKGSV7EJAidmivZ/JErPcLzZ2NJKw7kQOYE5lXmDzRdeuXW2YY8KksxGEeZv5DU9+5ic2XfBus6mDMofJd2TJx4sfIpe5F1I1jpglRD39XHzxxTYEPOgxp+GZ74hZNogwfzEXnXrqqXZ+ZI6EHGZuI5Qx4yAaAvMK3rvMXaQhYcQscyZhjInCwCYhFUWgqBFQYjbL/5nO8uGlm5jlu5NNRCEHxoi2Ey1tC5CnyZj9xeCw8y+kzERRvEv9/PvluiTJEWIsXmHjRQeJMvZsFr6V7O5X7wwNtIu02ytPj81QH4k2y/dunS0tA5V2k2uHBceSFMo4m4lZnYOVmA28qqXuMldG7M9N7rwoiVluwhDPrq4kqCgCioAioAgoAopAdiMgi5VpIWbdN2bxVCWUMaQhxCmk5e23325YoGcBH8/QE044wYbEhTzEownvq5NOOsmSAZCUq1atssQBRCRtsYAPCQDRS9hcvFIhCGgLUpfFd/LxbIWkhbxwxCzfhEUIq0v/kJcVJMxyfmF2HTHrCFTaWLZsmf22IgQCZIUjZiEynMcsHmYQsT4x6whVvMfwKMVmvF0J24kdELSUYQyOmIX4IGQnAnauDOE+X331VVsefLl2+EBet2/f3o6fUKV8SxKP3TDBFrzmGAtevEiQmKUf8qdMmfKPfIhZxoHHMPeNcM1O8FaGmIVUgVBRyS4ElJgt2vuZLDHLpgw2uuD9Dsnar18/O09C0hIa+JFHHrHkJxtBCBNMdAHm10GDBtkIARCveOETEp3Qxu/I97XZbMMcwEYSPGnjiNkoj1mIWeoTAp1v2DL3sYlm1qxZ1l42wUDYks+mH0ITE06diAvM/czrREpAIGbZbANZ64RNMHjM0gdjUFEEihoBJWaz+3/pbB8dxE/zgMZ9p3BDoKyru2e2A5XE+BpLWbcQHDxulLyzAm1BaOFBs0l0n0CeXhYfBPYWU34R5Z5yLO3PvBKz8hCkQa6VNtw86o5TJC04d7jr4SHlCZ2u8j8E1GP2f1iUxrNcGbR7X/xjUROzbGQilDE2fSu6g6iKIqAIKAKKgCKgCGQxArJYWWBiFlIWL1W8KW+99VYbOhLiEm8niFC8YyEg582bZ72xSMNri28f4gkq8FqiES8uvDDdN1IhbO+44w5L1L755pumdevWljAglC+EIG3wfUEW7SERWbjnu4OQgxCzEKQQuyzg47WLPZCZeH85b6uoxVqfmH3yySdt+SAxC3nAtxnxKp04caIlNgn7S798YxZCAWI0ipiFGI0iZmmDcMj0SRhPPN6wnW8mQl5ArNCvC0nKNxjxtIWcgDTFQxfy133XMThOCBs8b2kD0gZJlZj1+wA3Qo7i8YbtKtmFgBKzRXs/kyVm8dBnTsbLlTmEeZIwwkQWYG5k/rj77rvtXMq3syE58XAlXDnzJN/MZt7E0xbilmvaYeMIG0Ly85jltwHyl80whIJH/vzzT2sDG3PYnMI8QYh2NqzgFUsUAbxcCUvfpk0b+41YQtnjTcvcwu8MnrN9+/a10QT4tizzHWQym1gQNvewSYcxEOpdRREoagSUmM3if6RL6dCChIu/sLm0FGGyjYz1QNEtkxxzHCnwSkRb9HVqRF4iyanamkjbmS6TCdshuFG8B9MlhB9178LoJBrFhp2SKF+QojsmWHl7KZfscx1sOjhPsMEgUdlFCm6baOGIcoRH3y4iL7/k26WAu5ephDIG53Q+W0F74+aQBsHCWXzNZgjChycrxQW/dIcyThWPZPEr6vK8WwV5v3Klvnu//WMyxCxzy8Gi6SZP3/dsC0YhkCwVRUARUAQUAUVAEcgmBGSxssDELGQN3yrEAwsvTbyX8LJiIZ0F/FatWtlQwBCMNWrUsP/7QGi6b5+y6I/3KwvxEIYszBPamLDI+++/vyUjIfogESEJIF4hQAm/iQcVIZIJu4l3J+QkXqd47sp9soQCedjEN1ghK/1vEUYt1kIwQijjMQtZwRghEPi+If1AHKxevdqOEeIVD1G+h0j7kKpgATE7bNiwzV66zmOWsVAOosL3huWbitgOGRFmO2QsxCskM95lENwQKHgRuzCjEN0QsxDD9PHss8+GDhHCBC/fnJwcS3BQCGKWMNOMkTb4pqQbq58P5hA+3HPIdMhivN4QvPMgbvBuxmtNJbsQUGK2aO9nssQsYYMhUyEu2ejCvMucRiSDzp07WwITz3YITEIZM39Tjvca71rmAYhVvFSJgsAcDMlLWeZVIgQMGTLEzhnue7BBhJjniWpAWHXs4RnCy57ICYQl5hxPXcrQLwTtsccea/D6J2w99kPiMuctWbLEzsN4zDKP0v+4ceNsHeYyvHmZU/muLN/C5XdA56HgHdHrokBAidls+s9ZxwICQcLFX9iMI2YhnwjP1y1ED5M0Xy6Xi7ByDfMKQbpcJNpB9AXRSaLDRFuIYl9QDpKEzqKjRKeJQoDeL1pBNFFh0RvyDdJtoSjfS2XseEd+IjpclIVUbIuTpyWTemGKx2yZuMoJ5qXD1kTu1xFizz2ib4qCCeo8RfO7h1I0VNJhe7DhoyVhiOhs0R9FHfZ/yPlXojwX14k62+U0KYHIXCvq2r06n9oXSD7Py3xRnh/qUf8d0UGiV4hGyTOSwTOIlgsUqu7l1crLI+0x0cWi9LNcdKhoA1Gf3DhFru8VnS76l+hvop+KkhZF6FaVPGcL75MvwXniQj/TO+c5ayuaK/qh6AZR7EQhriaINhX1bZXLzVJXzpwNjDVH9FlR2sH77DPRjqL0k5/cIAVoCzLW2fBrXhrpvUTDBHKQvMmiq0Wpi4f7B6Jgf75oOqW/NObsCx65r1ECBonMwYmUO1Taoq8Borz/Y0TvFz1TNChbSgLzwROib4i+JzpMtL3oLqKJShUp+KjoFNF1om7sX8n5RNEHRcuI5ifJ4Ifd3UK0YV4nOXJsJ/qUKO/OJ6K8XzeLHisaJuTRpvsNceNwR/BxfVI2StKBR37j20Y6ry36gugMUd6nXqLOvrBj3D09JqZuK8kLE979GqIPiHLvebeYxyExB4q6TUsO16BN50kZX3LlwmHtH2f6hQLn/K5fL/qW6JeizC2uLnMN9723KM8/9qYqjMG1C9ZbptqQ1lMEFAFFQBFQBBSB4o+ALFAWmJhlkRPSEa9WwgmziA+hyuI6Xq4u5CQL5oS9hEwkxCSL/pCbeF6xyE64XoSFe/Lw3DrmmGMsmcCCPR5QeD+RD0Fxyy232NDCkAV4gNHX8uXLzcaNG234ZMheCAm+y0oZiADIAYjf/IRFVAhGQhI7Ipc+IU8hCWbMmGG9XUeMGGHJBdqHiIaQhpStWbOmtQMPMMgH2gIjSNXatWtbIgHCgm/d4rVGGcgGyFE82CB6+casbzseYtj+119/2TDKEBWQqPQNpj169LDetfTBt3ohOJw3bNh4IZ+5XxAqtEnbjz76qO0TUvntt9+2Y+UblYjLBwNIdghywi2XKVPGkjy0AS58L5J7x31SyS4EuKd4w0PO8dzxzrFZAK9pNgHwvPHsqGQGgWSJWaxgjiHsOKGMmf8gLwkTzDvMBg3IUjZUMM+5fMKXs0mE+839ZK4iOgHeqWxKgUhlbmNuZ8MK4Y9dneDIiWjA3M18Rj+IaxNSd86cOXYuxcZhspEFG4hSQGQAfjOIogD5Sj5zDMrvAITtp59+au18R0Is8/uBBy/zLJETIHqZn3UeCt4RvS4KBJSYLf7/D6uFySGwmxR3C4fB49KYpnJi6l0ZqJcbUZaF0+NFWSAO9u2u10ueT1ix+P9HTPmRkreTaJSwyNpE9DtR10fccZaUO000SnxvvLB2IIIGi54tmuwCbzptzZH+w+wjjfuFQt4Fy+wnaUiuaDCPa+5hmKTTdtc+pMKTon+JhtkSTLvbVUzyyPPh2vpFzuOeJxbvXdm444SIdvxnua6U8aWHXLg275Lz+71rl+4fu+dVvliOP8eUXSZ5e+WV9Q9dvDoL/Qw5D84TFwbyuawjulLUtynqHLLqMNGg9JEEVwfCbbl37dI5QgjlJzwrfp3g+fSQBrj3a/Kp91/Jx7YdRNMhtBW0zV03iOkgJ6Ye77OTHDlx7QWPV0leR9HfY8rkSp4jlA6Xc975YDvu+mvJ8/uWy3/JIZLysqirE3fErp6i24pGSTL45UojYf0xphaivO9h+aQxlzcTDcr3khBVJ5hO2aCkE49caTzYJ9eMj+cV0j2Y/7ikMScE0931rZIXJc9LhisXPA4JqcTvCXNhsKx/zT3vILohotw9ku5Lrlz49d05Yw4T5q7PRV25uCPPw25hjSSYdrCUY75wfeT3biTYrBZTBBQBRUARUAQUgeKIgCxQpoWYZaGTBX7CEPPtVbyw8KhkcZ/Ff4QFchbaV65caSAFSUc5h6DdtGmTLefKQrCy6P/xxx8bwhezsMmivMuHGIQYgDjEk8r15YhZPGQhjvDuZJEfeyA+ExHsguDELkfk0jeEMe2x+I8Qini5kMG0zxGbqMOY/PFCgiD+eMGCMrRP6E3KcM04uKatKNuxBXIDggJ8wMF5htEHbWEHfUQJtg8TIgRPM0gP+gYfxkgbEDr+WIP5XIMRIZ2pw/2DEIGY5tuTKtmHAPdcidmiu6+pELNY695n3lHmG+e56kZCPnOayyfcsC8ukgHzCXnUpzznzEWck0Y7QSGfUOtERHCbXHiOaNO1Rx3KMScxB3HkGqEP2nfXpHHu+qct+qYObfL7cuONN5rmzZvbEPuUV1EEihoBJWaL43/AalNBEGDR0S0aBo9LYxrOiakXXHzMjSnLYnew3+D1RikDsXlnAmWpiydOmGwliXhiBdvP7/ovqdMqrEFJq5xEe6ulbF/Rg0Tzk3TbmiMdRo3zMcmLImZYSEdyRcPqhy1+p9t2+kceFg2zISztJym7L5VSkFFSx7U5OqZ+U68c5VmAZ8F/juifoq4Nd+wnaUFJlJh1bbgjz6Rfl/T/iD4k6hPs2BFmyyOSHpSCELOQwc42d/xB0vAiRrkfLt0dX5S0oPjErCsXPLKRIxEZJIV8UsS1Qxr6fqCR+nLtyrgjmH4uikefS3NH7nMcYSjZCUkyxKLfYI5cOFuCR38OjivH+IJ1w66fk3KHin6TQHnu+4GiYXKZJIY9C2F9+mkLpN7uYQ1KWjL45Up5v91UznlGfSkIMZtuPHLFsLAx8Q5Ojsh7VNIbRuTR1irRbUSDcpQkMA+F9cfvCc+LL+fKRX6bHsLaCqYVhJg9UmzYIBpsM+r6Pn8AKZ5/4PXHnKSiCCgCioAioAgoAlmKgCxWpo2YdQufLJSzcM4xHUJbYQv+tB3WF8Qs3rQujDLlfHsgLdu2bWu9WmvVqmWCSmhgPLSSEb/9ZOolUpaxR2EZNv5E2qQMdSF6Lr74YtO+fftIjPNrj3ZQPNvw1AV7CBKV7EOA+6zEbNHd11SJ2aKz+O+eIUuZF/gO+HLZwJIpYR7GM5h5iDDHXKsoAsUBASVms/Sf6FI8rKImZqMWRIPpiRII1GOx+NiQe3qbpAXbTfQaz5mckDb3kDSIiETboRyE2nDRMK9FSbaSbltzpNVkbHRlUyFm0207gFyRpP3BxXvaSES2kELrRN34m8dUguxw5T6U86O9srxXtUV9IutbuQ4SHD65Wterz2kPUde+O86WtBqiu4rSFhsGwojXryS9iuh2ojuK1hLlGXbtUCdoS0GI2Te8tiGGGwXaB9f6oj4xw3t6mKgvfeTC2eiOKyVtgOhg0RWiN4omI7dLYdfWooiKB0n6eq8c+HQQ3VkUwWO0vOjnoq4tjl1FCyr9pQG/Tf+8QUzjOTH1EiVm/b7yO09mDn4mxG7eidUxNufXP/c/TJLBL1cayK+f/PJ5Z3NEnaRKzGYCj1wxKj/7g/kQs1uJ8m4E89x1Y8kLyhOS4PKDR0IS+8IctEI0WC6V6+DcnhvR7kxJD8oMSUi0TzZjxP1GB9uOuu7u9Rk1/0TV1XRFQBFQBBQBRUARKEEIyIJl2onZol4ExSOMUJhVq1Y1gwcP/pc5eLMSWhNCknCXQeU7uIRnLQ0CgTpx4kQbznjFihUpDxnvWsLYEq4U72aV7ERAidmiva8llZhlcwnzwtSpU21UhUyhiHct4Y0JaewiCGSqL21XEUgGASVmS9A/xmpqQgiwOBy1ULk0poWcmHo+KUATuTFlo/ouaHoTOvbkaDn3iSm//a8lj4VkyCu8bSGC/Hx3Pk7SwwQCzpVJ5rhM6h0f0mAmbM1J0cb98uzLjagfXPzOhO2Y8FBE/5AkkEB3i74oyj3eKLqHaCpyslTy7+GlEY3sLen/9cqeFlGupVeGdgkd6wv2u/7q+hly3sPLo8xa0UNEgzJCElwbHDeJHhgsJNfNRP1yeLz50kUuXP5CP0POg/PEhV4+z7CPBWRxlEAquz44cu1LkJj9RjL39AukcJ4IMfu6tOvbVTmiHwhxPGVdWZ63HNGCSH+p7NoLHosTMRu0Le76yxBAnowYJ8/Om6IQ7jeIThCFtA+2DzF8hmhQksEvVyoH203l2icHUyVmM4FHKuN7NA9Q5p8oLJgPtsgrx+EAUd8z36/3s+S53w3KIsH32i+f7LmPPW3nioa1EfxtYh4JK0fafNG+og+LzhMl7U7RdEhDacTvF+xUFAFFQBFQBBQBRSALEZCFxawjZiGPIGfxjA0LXQxJwCIpYZfDFOI2LgRwMouxJaEsIUEJewy5mqqAKUQI2KmXWqooFv96SswW7T0qqcQsqDEvsBGEuSJTwvPpQi1nqg9tVxFIBQElZrPwH+hSPqQg4eIvIBYGMQuJ1FYUAqyfqN9/2PkSKdNJFE+1H2LKs8jqy7NyEdYeBMAJfkE530fU957z65UPlHWX3eTkT1G/bCLn30odyB5fMmFrjnSQiD0bpBykiivrFthzvTSXxzG4+J0J26Wb//sgov/nyfSE5/lU7zrZ0+ulgj++4LPh2oMg9cu1dxmB49ZyDZHAM44H676iviRDzNb0K3rnDeTctyWKUOBe+uUu8drgNFVitrrU/VCUZ5nnZwfRKMED1bfhukDBIIFTP5CfymV+xOyO0qhPLI/Jp5OLJd8fQ498yueX3T/Qnt829zZKciTDL+uf+5tj4spRB29nykPMLRX12wmeg9MQ0ZtEx8eUpRy4OjlaToJtueuRrpB37B1RfpJXxp0mg19uRLvOFu49Y+slygYPlx48+vPOYikX9XtBvb/y8ilDWSRTeORK20Fbg9fY84XoT3llHTEL8To3Ly1Yh+urRJ3cLydhZUi71xXKOzJX+vNcWL1PpcwLojNEw/L9tFSJWeY7vx3/nPvhy+lysYufUIDzC6Wu3xe/AyqKgCKgCCgCioAikIUIyAJj1hGzqSyaah1FQBHIHwElZvPHKJMlSjIxm0lctG1FoLgjoMRsFv4DXcqHtJuM31809M9ZpI+SHMnwy/rnPilA/dyYsk0p4MnLcu635Z+vkrw9vLJVY8qO9spxuiCi7DpJbx2isyPKt5H0KCkjGRAHvs2JnENC+JIJW3OkgzhbPpf8sz0jIBAvF906Ly1XjmH1Z+blu0MmbKdtiISw/iHDIVNaiR4rWlCB9Pf7iVucZ5OAXxY7aogGiXZJihSfsIAY86WHXLj28RaMsiW48H+p34h3zr107XGs5uVx2kXU5S8M5AXnCfoMk23CEvPS6J9n7HdR1w9EuC9BYvZIPzPF8/yIWYh8Zw/Hcgn087FXZ0QC5eOK9Pfa8u3gvEFMxZyYeld69eLKrZByW3llT5HzoA3+dQevLEQe77+f75+X9crWiSnXT/JaB9S/Z36b33ttutNk8MuVSn57/nlP12De8byYsu8HynKJbX577rx7SNlM4ZEbYQO2QJY/IOoIc95HPJD9+8Sc4OwOHqdJHsJv8I+iwXyuIbP3FPWFeS2sLGm/iNbzC8v5ZaJRWFInVWK2odSNsmOZ5EEo897EbSyR7KSFOczvNzjnJd2gVlAEFAFFQBFQBBSB4omALGYWGjELqYPXFMeSIOmwlzY2bdpk1q9fb4fMdSY9xtKNq28r54neu2TKhtlMfZ4Vjr66NOoUdywTtY8x4dnNt5GLuzAm/cZs0d2lTBCzn332mRkxYoR54403zHfffWeWLl1qRo4caSZMmGDWrVuXscHOnDnTvPDCC7Y/3nGiHKQrUoFri2gAkyZNMi+99JLZsGFDxsaiDSsC+SGgxGzx/B9YrUodgSDh4i8gZpqY/UvM3j5gemO59m3wz/Fi8mVLufhO1C/jzsd5BbeV81S8WV1b/nGw127U6eGS0VV0kahfN+rcJxsyZWtOjC0QZYeKxkmuZIbZDzHjJFO2036ihPdCKXuLKM9GKvKgVHLj3JBPAzyPrqx/5FmbIgrJe5JonCRKzK6JaeQcyfP7PyamLASvKwsJ40s6iFnaA3s80PCwu1X0cdF3RcPInBsk3RefmAUbnzT0yyVz7pN8vJNBuVYSHCYcdwoWCLl+2qszKyQ/maT+Xlu+HZxnmpjtHWLoigh7fpb0nQPl20aUxXYIbydR70pwvIlc7+sazTsmg1+u1Anr4zdJD/4W0fyciPL+vEc5JIpMDCNmM4VHrtgRNj7SHhNNRBhbVBvnSl63mPw7QjrgGYtqj7k6TCBro+rcE6iQG1E2eI/KR5QL9sNzPlr0ONF0yHbSCKS466djOhrVNhQBRUARUAQUAUWg+CEgi4mFQsxCPD344IP2u6/Dhw/Pbw0zofw//vjDzJ0717z55psJlU+mEGF577nnHnPVVVdZ8iCZun5ZQgQ3adLEjB492hJvd9xxh7n99tsNtucnlOF7idOmTcuvaNrz6fv9998311xzjVm0aJFZvny5qV+/viVr4jqDuFuwYIG56aab7Jjjykbl0Vfbtm3NRRddZCpUqPAP5bvA3BeI7qeeesrccsstNix1VFuJpEPeYPMHH3yQSPGEyhDSecCAAYb7/csvv8TWgYyCOGrZsqX55ptvYssWdaYSs0V7BzJBzDIfn3DCCebaa681c+bMMc8884wpU6aMqVOnjoE8zYTwPWve3Y4dO5qvv/7avP7666Zz586W9C9of2wc6NSpk/3mNYTv2LFj7dz14osvlqhNMQXFQesXLwSUmC1+//+qRQVDYDep7hYMg8dME7OrQ0y/JMae2iHl50aUH+eVhRwLji3V67e9dhM5PVMKscibX3975TWWKVtzYmwYltd33CE3or6/+J0p27GrTUT/UbhCjDrPMOonKsOloGuT8JpxsoVkQhK48lHHxVLmpoiGIB9dvbqBMj28vCWBPP8ySMzu52cGztkM4fqrFsjr4uVBcPsSnCcu9DPzzvEMGyQaRsC6PoPHOGL2s5A+UknKj5j1SbJ1CXbQU8q5sRAStiCSDLHo95MjF86G4PFKr2BcuWZeOXf6gZwE2+N6vivgHWtElKX8qV65sTHlwvqKS7vAa5fTZPDLlfJhbfOOhslrkhhW3p/3XL1kiNlM4ZEbYS9jOMwZms/xspg23pI83pEwTEjfRTQor0pCWHk2iQSJfr9uFJ6pErP8HqyMsCXMvl+kbB3foAKcs7HG9fFAAdrRqoqAIqAIKAKKgCJQjBGQpctCIWZZlLz++uvNnnvuacmqgi6ZQnZBKJx88snm1ltvLWhz/6qP5xjExF577WXuvffef+UnkoCn7AMPPGDOO+88S7hB1l1++eWmYsWKBjIwP4GwqFSpkrnzzjvzK5r2fOyDyDjkkEPMhx9+aObNm2cOPfRQ88QTT8T2BXEHoXvGGWdYIj62cEQmZHv58uXNMcccY5o1a/YPbd26tRk2bJglczp06LAZ24imEkpeuHChufrqq03Xrl0TKp9IIYin5s2bmyuuuMJ6TMfVAbPZs2ebWrVqmd69eyfslRzXZqbylJjNFLKJtZsJYnbo0KH23a5WrZqZNWuWmT59uunWrZsZNGiQob90C2QpfVavXt1u3uCbsGzaufLKK613a0H7o+1LLrnEev3SFvNou3bt7DyCN7CKIlAUCCgxW4z/EVbTUkIgSLi4xUOOmSZmZ4dYfIak+Tb458EFeaqzUOyXcec+MXtxRBlXNpnjCmkrFblNKsX1c1Zeo5myNSem/3Z5fccdciPq+wRFpmzHLjwnx0TYEIXrfVRMUkZKedfetATrVpVy40V/9+q6Nvzji5K/k6gviRKzcSRxcSBmq8ugfNLXH7c7Xy1lnhB11xzjiNnpkp8OyY+YvVs6cTZ9l2CH3bw6EDgFkWSIRb+fHLlwdgePV3oF48pV88q50ygyMmxTyvlSKdi3u/aJWd4ll17QY2NnaN4xGfxypU5Y//485jfvb9Tw64WVjyISu/sN5p1nCo9cad+3050TYjgZmSqFXd1Ej3jHh8l7khjVBptbooTNIWH1UiVm6aey6LqIdsP6ArcDRAsqn0sDrv2HCtqY1lcEFAFFQBFQBBSB4omALFCmlZglLCukFB6ILIg7YVESz9FddtnlHwQY5fGQpHyYtyD5K1euNPPnz7ckAdeQQ7QN0bvTTjvZBXeIVBb4Cb3JOV6IhOikHEQA8uuvv5rPP//ceoCGheik7S+++MIsXrzYjgFv0V133dX06dPHDcP8/vvvtl28SBlTlGAjZU499VRz//33W5sdMXvBBRdYYtbZy5GxM0aHGTZDkhx55JHWswwPUbxYGRthOpcsWfKPstTHu5Y+gyFxaYu+HYa+zdhJiE/wBxtHGDtidv/997fELG0fdNBBlpilPfDDC9oJ2JJGvalTp5rTTjvNktJ+PqQIBO9XX3212WOYsWMvyn3m2cFL7/zzzzcNGjSwGIOzU3CA8AYnPI/POeeczc8NdlGOsSxbtmzzWJwNLh8iln64l/Q/ZcoUSwTfeOON1hOXsdAPNtHOmjVr7DPE8wH+fn3XNkfuz6pVq+w9gFxu0aKFJeIdTvRFW9gH4UV7TugPD9tzzz3XPn8uvbgdlZgt2juSCjHLfEE4XzxU69ata2644QYzTDY38KwiEJmHHXaYccQs7x+bQR5//HH7rlKGd3fgwIHmuuuuM40aNTIPP/ywTSOPZ4Ln/a677jL16tUzbJ545ZVXDFEHwoR3DxvQ5TL3s5GDDStsAqFtNoIwj7z77rvWc75GjRrmtttusx7tvEP0BZE7ZswYOwfx3k2ePNnOs9jMvM1cBfFLNAXec0fWPvvss8V640MYXpqWHQjw20TEh6222so+m08++aTdPPDcc8/ZUOL81vi/CYUw6nR/Dqt4/pOrVmUMgd2kZbdgGDxmmpgNW9xmMT9oh7suH4LCpIjyPjF7cEQZ2oUsI7RgotpcygYFD8WXRY8JZnjXR8m5G0fY8bS8spmyNSem/5p5fccdciPq+/cwU7b7dlWSiw9E8bYKw9FP2+BXTPB8gNfuogTruGK8S2A5WPQLUd8Wd36npPtS2MSsj1s13xA57yLq7IQU8SU4T/ges2dKwV9FXV2OeKY9Iwopeono/qIIRIxvQ1Ob+r8/fijjwiJmW0v3zva/5Hzr/5kTefaEV2dFZKnEMvp7bTk73LFBTBM5MfWu9OolWs5VYe50/ftH5tqglJcEv4x/zlzuZIic+HnufL2kJzr3unInuEbzjsnglyt1XN/+0Z/H/OaTKZ8MMZspPKLsjdvY4Y/XnfN++/jkd86mi7BQ0LT3WExb/jNCWSf7yAnvYli/97hCecfciHJR95S5jE07Ufcr2Gc7KVtQ8ftinlVRBBQBRUARUAQUgSxEQBb/0kLMsjgPOUgI2qOOOsoceOCBlpzr27evJURZlHTELN5YlIeIgwyDfGQh/fTTTzf9+/e3i/LkQ2KxIH/ccceZAw44wBx77LGmVatWlkDEmwvSdIsttjB77723qV27tiW88LrC25JFeTw8IRsgHt9+++3Ni//0BaFHG46IpK82bdpY2yEIIAZPOeWUzcQsC6YQAhAbBx98sNUKEmb3+eefD10/hRh+5JFHTE5OjrWLQo6YpR5kAkQpHrFNmzY1J510km0TT9Onn37aEoCMabvttrOERfv27c3HH39sqFuzZk2bBpnBYm7Pnj1t6FFwpD8WfyE1EEhMCD88iw8//HBTtmxZg6cpmEBAjho1yuJFvaOPPtoSKxCHYcQs42YxGazA9aGHHrJ9UHb8+PEWc8IBf/TRR/Ze4i0MKUJoUe4H9++II46wthJq9Ntvv7UkNwTNpZdeao4//njrTfzoo4+aypUrW+LGdhDyJ0jMQqISgpX7ylhoi3YhURHGm5uba84++2yLw4knnmifVUhkR/Bz3/GsYyyXXXaZ9eDjGcKbFrKe56VcuXL2eSX0K88iBDkCJni7ks7zf+aZZ1olFDbELGPt16+fvQ/YR/8Qy44cgzTmnhXE09gakuE/vJf6jdkMgxzTfLLELPMWxA8bRJj38EJnbiYSAHMr75FPzOK5TXlCGTP/MOcQ3pgw5rvvvrudD5l3OWc+Z67gHapatarN22233cwee+xh+4G8hRQOCmGL8Wi97777Ns8PzGXML2zG4Pni27O8q8zDvMdsWCC0Od+9Zc5jziR/3Lhxtjye6dTFdkIyM1bePb6bC5mLFzBzFu8cc4GKIlDYCCgxm4X/QJfyIQUJF39RcmkMNjmS55f1z31SgCZyI8qGLZyyUOu35Z9DAgQFssAv484hF3zBE87l+ccgMeTXSfT84by2f5Fje9EtQyrenVfG79ud801SvkPnJBO25kjjrr/gMXi/nB3+MTeifvAeZsJ23w53vqec1BAF+x9Fg2Ny1wdKXjIC+ePqrkumYkjZYyXtAVGfiAySjX9IvuuvbqCNHl5eHLGSjMesb0u1QH9dvP6SIWaf8Or9V857ikLAhgn3zY2XY4tAoaIgZiGOfZsOD9gUdjnJqxN8B8LKx6UlQyz67eTIhW+3f+6/04mWc20zd/ptuXPGHJTykuDyg0fmcic3yUkwn+tVrkABjsnglyv9hNkRdQ+TKe+Tb34fYR6zmcIjGXsFilh5S3L9ccSdt4pp6caYdt6XvG0Cdfn9HBVTp6DErOuOfnhG24p+IBo1Pua3gshWUpl50bXfpCCNaV1FQBFQBBQBRUARKL4IyOJkWohZvKMIBcvCPAQgnlMQXSjhb4PELEQaxACL/BCCkF8spkOEEaKY9vg+Jx62eG3iNQXhxaI/hADfF4Vg2HLLLW0feCdCKkD8bb311taTFnIB4hfPU8jdnXfe2RJihBLeYYcdbD3INsgDPLDwvqV9iE8IuG222cbaRyhjSFUW+mkDb1DKQE5AdIR9AxbvUcYEmQA5ifjELGkzZsywbTAu7ICQPeussyyJAgED6QB+EA54sb3zzjvWRghYvrNKnSFDhlhCA+whNCBZICUhkCFM8SqD1IbcgNSg3n777We9WWkT+yFNJ06caHGHHIeQ4f7wXVzfY9YRsxDqkKx8ExKB4B05cqQlJfHMc8QsmOK1zL0DM8gW+uGeYCOkMJ64ECzcd9LxeHvttdcsIcM9gNx1iucxzxJkkCNmIeGxh3YhnXnuIH4gdyFpGTfeqIwd0pTw1Hjz0RbPBH1iO0QpmEHg8L1XQm7z/PTq1cv2Sb/cJ569V1991Ya3pj2uua88s9wX2uOblnxjl3eB5wRSlnywxpsQW9mwAPHPJgX3fEDyYgM25vddWgt8EfxRYrYIQPe6TJaYhYRkI8aOO+5oiUnmFOYInk/mDjbTBIlZ9yxDtvIus7Fjn332se8pZamH1z8KEermfeYNPFd575nTIUvxfvWFDQjMWxVkgwnvHcKzzqYGNpTw7uFV37BhQzsv4U2LlzrtVqlSxW6e4X3HE5ZNLcyv1IPAffnll+1cTvvM8djp3iM2QEDm4k3LJgsVRaCwEVBitvj+H6yWpYbAblLNLRgGj9lEzE6OGOccSQ8SqSwUs/g7NETrS5ovR8jFH6I+dp/J9ZOi14tWF8ULMy7M7TzJ9yUTtuZIB76N/rlP4vh2+Oe5EfWDhEYmbMeOC0TxwoTIC3o0dpA0fzz++b6Sl4w0lMKu/l9yHnw2XFs8B5NEsQlyIU4gZ12bEKM7eYX9Z6ekErNLvPFBcMTJVZLpsOAIaeNLURCzELG+TT19g0LOKe/fN971gkgyxKLfT45c+Hb75/47nWg513YmiNnzYmyt5Tr2jpQPm39J29krx2ky+OVKeR8ndx6cx2gXyRV1ZfxjWPlkiNlM4ZGMvTK0WCkvuf6Yo86/kHJBctVv+Px82pkt+cwLR4teKjpFNKov0gtCzE6W+i+KHivqC/b/IBrW72C/YArneP/67fINXxVFQBFQBBQBRUARyEIEZHGywMQsZA1hcCG6IDYh11g8h/zadttt7QI95BWeiRCtEK54PUGiQoDhyUh4Wxbn8RCFzIKkghiD/MQzEwIBwg5vKEJqstAO6Qbh0LJlS7t4jw14I0KeErZzuYTJhBjs0aOHbZfFe9qlbzynCCkIeUcai/zU69ixoyXSICSwD3KNUMaQr/RF+4S+JbQnpJyrE1zkZfyQkRDGToLELCQgnmt4w+Jhy8ItY4VwxjuXkJ8QmJB3kJ8Qs+AH0YIXJuQpxAekIeQF3rF4uEFUMC4IQTCHdMQeCEA8O/FKJqQnpCMkLR6i4EsYZ0hoyGDahehIlJgdMWKEvf9BYpYxQ8BARjJGCFXshdiF8GGceMPh6UxZykCwQ1CDPeOHAOWI4kkLeeOIWb7fi1cs5BDPCwQsYyGkM7hxD7CJsUKEgjk48PwQzhUSCswgeNgoQP+MG6KY+uBM+4RYxU6eJwhv8CSfNiGweQZ5vnjmyIf05pmCTIIIgmjCi5b+sY8xQhTRJnWQ1atX22cBspnz4ihKzBbtXUmWmOVZZB4mqgAbT9gcAOGKtyobHPCoDRKzeJ6zyQAPUwhRoiBsv/32dk5lTmGeYa5FP/nkEzsnM2/zvBMCme9CszmH3wNHvjrUsId5nveYdxUhegD24DWPhyvRDSB98fLnO830z1zP+41NvDsQrswjzAnM08ybzjsXQhZb/L7ZnME8y9h5B1UUgcJGQInZLPwHupQPqbQQs3FeO2/LM3CB6EGiF4lGfWORxVWIVl+elQt/0TWV8yF+g3KeCVtzYuz0SZyAKZsvcyPqBwmKTNiOEe95/S+V84dEbxC9RRRyNAx3CINkpZJU8Ns6IaKB9l45yFbIuii5QzJcmxD0W3kFfYKvpBKzP3vjG+GNLXh6oCRw7xwWHNsGCmWCmKUP1+eKQH9cQr4vEnVl8MDeWzRKciXDleUImVQQ6S+V/fb88wYxDefE1PPf6UTLua4yQczyzYUVov7Y3PkmSec9PkYUwqyl6E+iLt8/zpb0oCSDX65U9ttz58F5zPWRTPlkiNlM4ZGMvW6MccfXJdNhFHVsGNeA5EF6zk+gnaj2g+mpErP8tru2/pLzsaKdRXnH4n7H8xufVI+VipLr+uVYNra0ZioCioAioAgoAopAiUVAFicLTMyyuA8Rhfcr3qos/kOIsbhPqGFIOMgnFtchFrt3727JMLxWKc8CPuUhPfGAZQEejyhIW4hBSFKERX0WNyHL8ATD+xCygfCzEEaQpbSBdxdejQhEAp5cjkDFY4vQlpCckAl4geJhSZ/Ug/xFIAnx+HLELCGL5SZbT1sIA0KD4okK8YzHKf37gi2M33mVujYJ2Ym3GOQgBAFtQGQjkK+QIowZwgPyEOIBItvZjI0Q2U7Agfp4peJhCpaQvZCEkDgQIIzD2ccRAoO2IRvxCsbTFIKRcMrYvO+++5oXX3zRYogtkLR4tsZ5zIYRs3i6QrrgsQwpcuGFF1qy5TD5niXEJ8QsYVLBwyewIWshXGvVqrWZAHJEEGQ13tQ+MUv7fPsS4hwS142FPsAD71mIU8hX921hngPIHUcEkwdZj70QszyzDmcILEIb421HPuK8hCkHqQT+5HOfEDYa8HxCzHIvGSPvB88O9oE5BDzXjijiWYXY4l2AWC6OwvOjoYyL7s4kS8xiKc8iYbQJk838y7zHBho88SE544hZiFJCdlOHzQ/MvQhzMcq7gYc3m1yIeMCzzdyI5z2bLfDQ9YV3jtDrzEtsmEB4p/Agd8QsmzjYsOA84AmjjK1sboBkZm5FmKOYt5nzCbHO7xDiiFnmJCdsYuH95j0m9LKKIlDYCCgxW2L/TVbDIxDYjX+KIxQSJUpyJCOqnk8KUD83omzYYvipEWXpCw+eoEyShDA7IBd82UIuJouGlU00bYHUpx0nJ8kJpFyi9cPKrZP6B7gG846ZsDVH2g7rn7Tg/coz4x+HXLkKqx+8h5mw/ZKIvsPs8dOe+8cIErvYUYr96vXXPKLaoZL+X6/cLDkP3keqsgi/XtTZNZVET7KBmP1IxuPG94OcH+2Nz53iNbZQ1JVzx+6uQN4xE8RsM69f3tezA31yebGos4kj7/rxor5sKxcDRf1yS+R6G79QCufJEIt+8zly4dvin/vvdKLlXNvMnX5b7py5NijMyS4/eGQu96WCXBR0vgxuXqD9ZPDLlfJBO7kOzmOSZCVX/iZaPhlilsYzgUcy9mJDfnK6FAgbv0vjnWZjQ35yrhTw50tXP5VjqsTsB2JDKv2FzWf5jdfP7+b1u1bOE8HLr6/nioAioAgoAoqAIlBCEJDFyQITs5BckE8s9kOU4qWJNyGkJN8SxZsJD0DnMQsxCyEHSQBhileUK08dPGLxUIVgxKMTD1UEEpIwtBBxEKeE46Q/SD8IIzxhWaSnjgsvjCcp4W0dgUs7EAF4gkGqUp5zCDK8yghDi9A+5IEjZvHShGSGqMRjFXvxruVIOE9HfNrK8gdbIAUhA5zQZpCYhZwbJp6bSBQxC16OmKV/QgAjeK5BuECYEjaZb9oSLhivMjxOIV8hZSEjnNAOXp+QExDlkCl4jdImCtmBpxllCIMcRcxCvjvSGbKFNhhv0GOWsKSMGa9dykM847EL6ULIU54DSFG84pyQhscczwu4BpVyPjELkYv3KSQ2WDIOvFgh07mfEMuQpJCjPA8IzwAkF4QzxCg2cF9Jh5iF8AFLBPKJMRBmmHyEe8V9h5hlPJDIEFQuHzKMbxZDzDIe7gFezODrsKZ9+uK5QDhCwkOgQUQXR+FeKDFbdHcmWWKWTQxvvfWWnSd43tiQwrzFvMcmBtJ4Z9gs4bxRnccsodu518zhELMQpHh/MzfQBs88YYMbyQYPPGohcPluNn3wbkP4Mg/6wvwDuUqoYUfa8s7gqc875uYP3hfmJ6IZsJmCcfPeYC8bGOjnWomewLyNMs/xfWsEkpa52/eYpQ3eRzbR8D6qKAKFjYASsyXkn2I1M2EESgsxCyAHi24QTWVx9nepB3njy1NykUpbfp2gB65rP9225sTY6pM4rv/gMTeifhihkW7b/7+9u4+x5iroOG5bJdZCFQXFkthKUyURMWJLRSFKn1JTYhDUQJ9QExBoISAGSAT5wxJeEjRRQiBYSjStBRooCX9ARGPCS0IgGjSxifEPLaD/iFIhRkCDT7ie78rU4fbu9tm7u73bvZ+TnL0vM3Nm5jMvezO/e+b+5S7znjsuPy8kuXTUdcq8l9if7NHA7WPYfL5fG69vG7UL8TeNeueoZ0adj/P08XpeTkIw27rO17Fw9ndHfd6orx/146P+96jzcabnt4z35+Uogtlrxwym+U2PLePyLcTrQTcN7/GroxZSvmHUblf8D6POh3dOeMKoBy37CRbn87pkvJgvz/z5/Jg+2/GmtlvneVvT84MGs7U/v6331O7ZPv7FmH5VqLUfv1tHG6vmt+o8Nkb9tv2Mv99gtvYP22M/y9v8z6YsHxdzv189mwa+Oc7N43E+7brP1wlmO++uM79+huCg5c9GA9O8+8ygECBAgAABAidUYFycPHAw2wXOejLWU6pegfX663U9Iq+55prFm970pp2erlMwW/hUcFUwMAWfnxs9authWa+pLtw3fb2uClTr7diF+N4vdC1E61ayhXn1uu0CfbfhrZdht9CcwsSWq/Cy3lj16Cps6/aZ3bq2EK3eol3Ab1kKJQoXCnFrp9CtoLnlKzQoDG7d6uFaQFBw1oX+bo3ZbYGXS8Fh4WihxVTWCWYLO1/72tfuBLMf//jHd9at+VcKKAow6mFZ8NC61psst+bde/UmzqSAo9JtdAsx6oFWoDj1Lq73W7f7rJdoPd1a38LoyXLeY7bAN8tp3WqzULNgtJ7O9XAuXGx7dpvkQp/2iXqttcw333zzznv3F8w+73nPu08oOwXg82C2oKjfzi0krfdz69IyNZ/CpELo5tXyFVJV2l8KftoPGl5v3gL+QqICo3kw2/7RlwdyzqXS/tfvybbPF+w3be71QKw0Xr3+2j/aJvUInqZv+dq/u71yx0RtVXosQC6Uzuk4FsHsZrfKfoPZzjndoru7D/TFhL7k0XHVcV2v9I7Xfq+73uWdA9tv+5JFtwfuSwX9DnY9Tzu2Oh92vuxW6Z03ujV3X95429vetvMFj36DuS9I1E7n6b4sMh0Pc7Xab1kKUNufCms7PjuWus1x07TMfXmj28i3TM2j46v3O5f1P6Tjqd7wnav7neqC4Y7VgubG7Zjs2KsnbdP0f6JjvnEUAg+0gGD2hH6I3uLV2qZgts1cD6C/GnW6UHo2j/WgXPW7cBeM91896hf32V7z7HapvznqXuUwl/WSMaPd1nUe4uy2PLfuMv1ugcZhLXs9U3cLPHZbn8LOp466bulWqlPbn92jke8cw842NG57//KKtk5CMFuP0c+MOpnt9fjnY7zC62mcfxnPzxt1KkcRzHaO++dRp3lOj/Xgq4f0VL5nPCmIn4bv9fivY7yrpwkP+LifYHE+q0vGi92WcX5Mn+14U9tHGcw+ZMzkplHnvdJ3W4f5+x8Z03S8rSr78bt1NDBvd3q+23lsP+Pvdp76nVUL/c33DttjP8u7x2J9y6AfH69W9XT+6/F+d0g42/LtY8S+5LD8ZZVpG8wf+9LE/PX8+TrB7DoBeIHq/Nw0Xu67NH1fApmWf7cvYu27YRMQIECAAAECx09gXJw8lGC22+PWM7Zws56uXVTvQn63xX3HO96xE8wWkk23FC5ELGit12o9VetR2e/JFhjU06pbzDZdt6ctnK1nZyFp4WgX67ulZoFvvVgbXghWz9p6jxYK1EuyUm/egtbCrsLZlqdwtXa7bW/BYUFZPSxru6C3Ngoe6iXWewVo9bTstxO71XLL2Po1rDCiwHS5dBG2EKDwonWp9PuuhcqFCN3KuGUsMOnWm5V6Yd5222077deLs/Cx3mCFJK1rvcmyrddYpR7EhdM5v+pVr9pxKTSpJ/Jll12200u50LHecfXWrHdvPcZavwKVAs1uf5xDgUdBcwFN4xQM1ruzda33cb3Mmve0LbutaK8LQ9qujVeIXPBdz7WCzQLtQtqeFxT3+uUvf/nOsrXN64k3BZiFnFNpXoU0bat60c1roXLr2jatN3KW9cbOsnk0r4YXELVehcdtu5Yjg261WohcaFSv3wKfevUWOOVUMFRwlF+BT6X9o57arV/bs96CBboXj8C5XsDty91+tXk3jyz7jcv2sULuAu8C48Ktlrf1zqzAvLba7pV65hbkFpy1fxzHIpjd7FbZbzDb+a9wtd/Dns6hnXPajzvP9mWJ9vPOM31Zoy9gdH4p2OxLKoWkHT/dxaBzdPt055dC0W6h3jmoZSoo7TzV+b3zUV90qLf6tG/P1TpW6407HTsN65bJHTMdk7XbeaHzbefXjuOOx46Lzot9YaRxO447R3Z8dX7sf0BfLOmc1++U91vZfWGj/xWdOzuP9L/juB5bcyPPT56AYPb4ff61RAcTeOiYfNVF1y4k/v0eTdcrcrrYuPx4zdJ09TZbHqfXn1oar5ePG3XVuL13RSMslXnvxvl0H1gab/6yXlc3jHrPqPNplp/XI64QqQvTe5UMzzag7aL0O0f9/r0anA07rGXdz/aazf7ep/vZhtNEh7XshWc3jvp3oy5vo/nrQs5bRv3hUQ9SHj0mLrSb2l61303tt2y/NeoXZ+NP0/XYPvano/7oqKtKvWyn8Z+1NMJrZ8MKQXYrLd/URsv9fbuNON6f91ydh3dN0npM7fxtb8zKw8bz+XniybNhPS38rJfsvP2prbZLX4Z49qgFOQWa07AenzjqVF4/nkzDlm/7PI2zzmP+d83anuax6tj+pTFe5775+k7j//t4v150F416WOX3RkNT+8uPz9ljJmd7TJ/teNOsOncuL0evO9cul/m+tzzN45ZHnr1+zHj+oVGXp1l+3XZ4yagPGXW3sh+//Z7H9jP+F8YCLi9/r397twWfvX9YHvtZ3tns7/fpHWOM5XW79n6nWj3Cz4y39wpe3zeGXzDqbp6dI+blbNf5J8ZEfzzqqnPUfN3a55476kFD2dHEzs8ETG3/13jdeikECBAgQIDACRUYlz8PJZgtsOkCfr2v+r3NemMVXnVbzC6MF9x2wb2wsh6fjV+Pzno1FgQ2fmFZIUFBV8O74N70/UZsw7uVbCFmYUK9oArnaq/bYtYjsVDhFa94xU5PxvmtYOuRVejVbxvWI7feWvVMLFAtdKt0y896vhaMFW4WNNTjt3CgUK6Ao55WXeif2ij8K5Cb2thp6Jt/plvdFob0+7uV5tHvkb7hDW/Y6SXWbT67HfN0q+Z6axYqtGz16OwWwd2Os5ChEKLbRTf+9JukLVPhaj3DWqYcCygKdxuv3mK10TIWuhRmFAjm3/I1v5atULRgsh5yrXfTNawws2XJsnWvzY9+9KM79lk3XW3W67PetzfddNPONmj6QuDCyNa5ELh9od699Tytp1zL2W1QW89M285TaV717GtZl2vrkV+9SwuQ2qbtD61nt10tKG5dsiiEyTinhufc/Nu+9Z7LqWnbPzNp/yoorRdh4WnrP5XGKcQvHGr69tX2xalna/tsw/ut29az/bFewt1qOusc+v3iAu2mb16tfz17K+2jhUcF0lNQP837OD0KZje7NfYbzLa0Hcvtp4Wd7WMdBx0XfWFkOs92Lv3cuGtBx0lha7csnl43Tufvxql3+MfGrYo7J0zTd3x1HHVO6NjpOOy4XhXKtjyd16cvN3ROqzRubXbM9UWLwtN6+xYqF9r22Bdber//Gy1L69S8W75Cr5a5oLa2mn9t1TO9deq82/mtthQCmxAQzJ7QD9FWaysFuvD6I6PWi6UQ7I9G/YNRXzHqr4z6A6Pup9TepaM+fdRXjloA220gf3/UgsWnjrrfNsckO+Wwl3Vq94F4PMxlL0hr27xm1FtGfeuoLx+1kPEww7IPjvamC+o3j+f3VwocC4R/cdRfH7V96odG3bbysLHCPzlqtzg9PerjR90rVBuDH9BSSHnlqD82asu6V/muMbDg8ddGfdaoTxh1t16bY5CyhsDDxzQ/O+qLRn3LqIVsN43aMVT433G1TWWbPL53bNi+pNEXQjqf91MBrf9Rl+bb/+K+nFWo3//9143acf6kUc8d9bBK//+n/yN/eFiNaocAAQIECBA4ngLjAuWhBLNd6OwCeRfBC2i7iN4F9F5Pw7q430X5+Xs9b7zGb7rli/nz9ho+9T6tzcLZQoSm7aJn4Vavqz2fly7gF651Mb/xG6fAYl6mZSkQKEhbtbwFcFMbrctuva+yKPwoUC5UrfRe7VYrLX9tzIPdns+Xf1qmaV4Nm89zCkVap5a7dWya+fo1/nybzI3nhtN612al6aZlmcablnVa9mm7ZdW8M82+dWw5WuemmW/jtmHjZlk7PW/6qfRe07fMq+oUzNd+403L23xzmixqfxpW2y1b+0nDl/fNTHpv2h7NY+7c9K3H5Ng+MAVTDatMzjk2n2kdM6gsT986T8OappB5uk33zgTH8E/L6zdmN7dh1glmp6XtWOgYqU773XzY/L3Gnb9uvN6bju/5cTVvYxq+PO00To8Nq2d9X+CpJ27TVGqzY3+atsdeN7zHqfT+8vx7b6rLbdUDvy9M1EO341shsAkBwezx/AxsqQgQIHCYAj81GpsuqP/HeF5IpxAgQIAAgbMR6EtYXx+1/yP/M+rFoyoECBAgQIDACRYYFygPLZjdxMXO4zzPgsN6EPdbut0uVyGwSqCg7GOjF2K3f60H+TyEWjX+Jt8r/BLMbm4LHCSY3dxS33fOfaGi35XtN7C7m8FRlb4sUQ/0etJ3C+X2X4XAJgQEsyf4g7RVI0CAwEzgI+P5FM4+f/a+pwQIECBAYC+BV4+B0/+PbqGsECBAgAABAidcYFygFMwe4VXaekJ2S90PfehDRzgXTT+YBep52y1W+83l496jTzC72T3tpASz7UcFVa1PPduPqvQlh25v3K3Pp565RzUv7RLYS0Awe8I/TFs9AgQIfFOg21pOF9b/cTz/DjIECBAgQOB+BC4cw/9t1P5/1Gv2slEVAgQIECBA4IQLjAuJgtm9rqYecFgBRLe0nd+C+YBNmvyECbSPdCvl9pPjXgSzm91CJyWYTbF9aapHqfpAzOMol1/bJ0NAMHvCP0xbPQIECMwE+l3AKZz9jdn7nhIgQIAAgVUCbxxvTv83blo1gvcIECBAgACBkycwLnkKZk/GdV9rQeDIBQSzR0685wxOUjC754oaSOCECQhmT97nZ2tEgACB3QQeOgZ8dtQusn9x1HpCKQQIECBAYJXARePNr47a/4y/GdWdFgaCQoAAAQIEtkFgXPsUzJ6wC8BWh8BRCQhmj0r27NoVzJ6dk7EIHDcBwew2fKK2jgQIEPh/gZ8fT78xahfaXzmqQoAAAQIEVgm8ebzZ/4rC2cevGsF7BAgQIECAwMkUGBcvBbPH7Qqu5SFwTAUEs5vdMILZzfqbO4F1BQSzJ/MztLUiQIDAXgLnjYHVc/YayTACBAgQ2GqB/kf0v+LcrVaw8gQIECBAYAsFxkVGwey6V1pNR2DLBASzm93ggtnN+ps7gXUFBLNb+AHbKhMgQIAAAQIECBAgQIAAAQIECBBYJTAuMgpm173SajoCWyYgmN3sBhfMbtbf3AmsKyCYXfUJ1HsECBAgQIAAAQIECBAgQIAAAQIEtlBgXGQUzK57pdV0BLZMQDC72Q0umN2sv7kTWFdAMLuFH7CtMgECBAgQIECAAAECBAgQIECAAIFVAuMio2B23SutpiOwZQKC2c1ucMHsZv3NncC6AoLZVZ9AvUeAAAECBAgQIECAAAECBAgQIEBgCwXGRUbB7LpXWk1HYMsEBLOb3eCC2c36mzuBdQUEs1v4AdsqEyBAgAABAgQIECBAgAABAgQIEFglMC4yCmbXvdJqOgJbJiCY3ewGF8xu1t/cCawrIJhd9QnUewQIECBAgAABAgQIECBAgAABAgS2UGBcZBTMrnul1XQEtkxAMLvZDS6Y3ay/uRNYV0Awu4UfsK0yAQIECBAgQIAAAQIECBAgQIAAgVUC4yKjYHbdK62mI7BlAoLZzW5wwexm/c2dwLoCgtlVn0C9R4AAAQIECBAgQIAAAQIECBAgQGALBcZFRsHsuldaTUdgywQEs5vd4ILZzfqbO4F1BQSzW/gB2yoTIECAAAECBAgQIECAAAECBAgQWCUwLjIKZte90mo6AlsmIJjd7AYXzG7W39wJrCsgmF31CdR7BAgQIECAAAECBAgQIECAAAECBLZQYFxkFMyue6XVdAS2TEAwu9kNLpjdrL+5E1hXQDC7hR+wrTIBAgQIECBAgAABAgQIECBAgACBVQLjIuOewewjHvGIxQc+8IF1r0WajgCBEybwqU99avHoRz968chHPnLx0pe+dHHnnXcu3vOe9yze/e53Lz784Q8v7rnnnhO2xsdndebB7Itf/OLFF77wheOzcJaEAIFdBb785S8vrrnmmsV55523eOYzn7l417vetbjjjjt2zp3vfe97F3fffffizJkzu05/BAPOX/WZ0HsECBAgQIAAAQIECBAgQIAAAQIECByxwLjYt2cw+/CHP3xx8803LwoEVAb2ge3eBz7/+c/vfFHjUY96lGD2CJKS+2tyHsxef/31i8985jPOy/432QceBPvAXXfdtXjKU54imD3iz7SaJ0CAAAECBAgQIECAAAECBAgQIHDsBe4vmD333HMXhTCPecxjVAb2AfvA4qKLLtoJF/SYvb8Y9fCHz4PZCy+8cHHxxRc7Jh2T9oEHwT5wySWXLM4///zF+FCox+yx/2RsAQkQIECAAAECBAgQIECAAAECBAgcocBuwWw9saaLiGP2i3POOUdlYB+wD+zsA50Tus35S17yErcyPvz8ddcW67H8/Oc/fyfcaRs4N/u/5H/zg2MfmI7XHp/xjGe4lfGAUAgQIECAAAECBAgQIECAAAECBAhspcCqYPYb3/jGzu9E3nrrrTsXD/sttOott9yyeOtb37q48cYbFy984QvVLTF40YtetHjBC16wOHXq1KLbp95www22/ZZs+72O85e97GWLt7zlLff+TqLfmN01Tz20AV/5ylcWn/jEJ77lvNy5+e1vf/viNa95jePScWkfeBDsA2984xsXt91226Lflu33uf3G7FZ+/LbSBAgQIECAAAECBAgQIECAAAEC2yqwKpgtRSic/drXvnafes899+wEMbfffvuiIEY9+QZdOH7zm9+8uPTSS3d6SL7vfe+z3e379+4D7R9Vweyh5a+7NtR5+etf//p9zstf+tKXFp/85CcXzssn/3zsf+6DfxtP58zpUTC7rZ/ArTcBAgQIECBAgAABAgQIECBAgMBWCowE4I5dU4ClAYUCBQDvf//77+3l0QVF9eQa3HHHHYt6Tl977bWLCy64YPHYxz528c53vnMnnLfdT+5238+2ncIFwezSCfMBfFlP2k9/+tPOy/4f+X/8INgHpnPm9Nj59u67716cOXPmATxrLM7fyg+9VpoAAQIECBAgQIAAAQIECBAgQIDApgXGVcBzRj1XZbDbPvC6173u3GrD5893G9/79iX7gH3APmAfsA8c331g0589zZ8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsLvAYrE458pTp59W3X0sQwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgbYHLT52+8fKrrltUC2nXbsiEBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIHBfgSc97fTPXX7quq8KZu9r4x0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgcWOCKq08/+4lXXfefUyirx+yBSTVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/xN4yi9c/4OXn3rOB+eB7PTcrYztJQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEDigwOgh+6zLr3rOl6cg9opT152ZnvcomD0gsMkJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBw+anTt98bxJ667p+uvPr0k5941elPTO8JZu0jBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQOKBAwewVV53+0ghib7r66hu+u+YEswdENTkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTmAj991fVPuPLa5144f08wO9fwnAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkcgIJg9AlRNEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYC4gmJ1reE6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEjEBDMHgGqJgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDAXEMzONTwnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAEQgIZo8AVZMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYCwhm5xqeEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4AgEBLNHgKpJAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIzAUEs3MNzwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIHAEAoLZI0DVJAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOYCgtm5hucECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBA4AgHB7BGgapIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJzgctPnf7o5Vddt7ji1HVnFovFOfNhnhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcKgC/wvxnQCBzDnv5gAAAABJRU5ErkJggg==" width = "888" height = "416" alt = "" style = "vertical-align: baseline; width: 888px; height: 416px;"></img></div><div  class = 'S4'><span style=' font-style: italic;'>Illustration of the structure of the IntracellularRecordingsTable</span></div><div  class = 'S4'><span>We can add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> </span><span>and add the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrodesTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularElectrodesTable</span></a><span>, </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularStimuliTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularStimuliTable</span></a><span>, and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularResponsesTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularResponsesTable</span></a><span> to it, then add them all to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ic_rec_table = types.core.IntracellularRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'categories'</span><span >, {</span><span style="color: #a709f5;">'electrodes'</span><span >, </span><span style="color: #a709f5;">'stimuli'</span><span >, </span><span style="color: #a709f5;">'responses'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'recordings_tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table to group together a stimulus and response from a single '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'electrode and a single simultaneous recording and for storing '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'metadata about the intracellular recording.'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers(</span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2])), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings_tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, repmat({</span><span style="color: #a709f5;">'Tag'</span><span >}, 3, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column for storing a custom recordings tag' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.electrodes = types.core.IntracellularElectrodesTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular electrode related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'electrode'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, repmat(types.untyped.ObjectView(electrode), 3, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column for storing the reference to the intracellular electrode' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Note: For IZeroClampSeries (izcs), idx_start=-1 and count=-1 indicate no stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% was recorded (the amplifier is disconnected, so no stimulus can reach the cell).</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% The timeseries still points to the response series as described in the NWB spec</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% for the IntracellularRecordingsTable.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.stimuli = types.core.IntracellularStimuliTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular stimulus related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'stimulus'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus'</span><span >, types.core.TimeSeriesReferenceVectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [</span><span style="color: #a709f5;">'Column storing the reference to the recorded stimulus '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                        </span><span style="color: #a709f5;">'for the recording (rows)'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, struct( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'idx_start'</span><span >, [0, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'count'</span><span >, [5, 3, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'timeseries'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(vcss), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Voltage clamp stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(ccss), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(izcs) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % Current clamp response when current is off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            ] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.responses = types.core.IntracellularResponsesTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular response related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'response'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, types.core.TimeSeriesReferenceVectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [</span><span style="color: #a709f5;">'Column storing the reference to the recorded response '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                        </span><span style="color: #a709f5;">'for the recording (rows)'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, struct( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'idx_start'</span><span >, [0, 2, 0], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'count'</span><span >, [5, 3, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'timeseries'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(vcs), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Voltage clamp response</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(ccs), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp response</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(izcs) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp response when current is off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            ] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> </span><span>table is not just a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> </span><span>but an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span> type is itself a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> </span><span>that may contain an arbitrary number of additional</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, each of which defines a "category." This is similar to a table with “sub-headings”. In the case of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span>, we have three predefined categories, i.e., electrodes, stimuli, and responses. We can also dynamically add new categories to the table. As each category corresponds to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, this means we have to create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> and add it to our table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% add category</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.categories = [ic_rec_table.categories, {</span><span style="color: #a709f5;">'recording_lab_data'</span><span >}];</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.dynamictable.set( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recording_lab_data'</span><span >, types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'category table for lab-specific recording metadata'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'location'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'location'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'Mordor'</span><span >, </span><span style="color: #a709f5;">'Gondor'</span><span >, </span><span style="color: #a709f5;">'Rohan'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Recording location in Middle Earth' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>In an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span> all category tables must align with the main table, i.e., all tables must have the same number of rows and rows are expected to correspond to each other by index.</span></div><div  class = 'S4'><span>We can also add custom columns to any of the subcategory tables, i.e., the electrodes, stimuli, and responses tables, and any custom subcategory tables. All we need to do is indicate the name of the category we want to add the column to.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Add voltage threshold as column of electrodes table</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >voltage_threshold_data = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1; 0.12; 0.13], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Just an example column on the electrodes category table' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ic_rec_table.electrodes.addColumn(</span><span style="color: #a709f5;">'voltage_threshold'</span><span >, voltage_threshold_data)</span></span></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_intracellular_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_intracellular_recordings = ic_rec_table;</span></span></div></div></div><h2  class = 'S15' id = 'H_1536' ><span>Hierarchical organization of recordings</span></h2><div  class = 'S4' id = 'H_F58007A2' ><span>To describe the organization of intracellular experiments, the metadata is organized hierarchically in a sequence of tables. All of the tables are so-called </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTables</span></a><span> enabling users to add columns for custom metadata. Storing data in hierarchical tables has the advantage that it allows us to avoid duplication of metadata. E.g., for a single experiment we only need to describe the metadata that is constant across an experimental condition as a single row in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> without having to replicate the same information across all repetitions and sequential-, simultaneous-, and individual intracellular recordings. For analysis, this means that we can easily focus on individual aspects of an experiment while still being able to easily access information about information from related tables. All of these tables are optional, but to use one you must use all of the lower level tables, even if you only need a single row.</span></div><h3  class = 'S9' id = 'H_310f' ><span>Add a simultaneous recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> groups intracellular recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> together that were recorded simultaneously from different electrodes and/or cells and describes metadata that is constant across the simultaneous recordings. In practice a simultaneous recording is often also referred to as a sweep. This example adds a custom column, "simultaneous_recording_tag".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create recordings_vector_data and recordings_vector_index using utility</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% function:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >[recordings_vector_data, recordings_vector_index] = util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    {[0, 1, 2],}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Column with references to one or more rows in the IntracellularRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ic_rec_table);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Create simultaneous recordings table with custom column 'simultaneous_recording_tag'</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_sim_recs_table = types.core.SimultaneousRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recordings from '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'the IntracellularRecordingsTable table together that were recorded '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneously from different electrodes.'</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'recordings'</span><span >, </span><span style="color: #a709f5;">'simultaneous_recording_tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(12) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings'</span><span >, recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings_index'</span><span >, recordings_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recording_tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A custom tag for simultaneous_recordings'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'LabTag1'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>Depending on the lab workflow, it may be useful to add complete columns to a table after we have already populated the table with rows. That would be done using the </span><span style=' font-family: monospace;'>addColumn</span><span> method:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >simultaneous_recording_type_data = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Description of the type of simultaneous_recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'SimultaneousRecordingType1'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ic_sim_recs_table.addColumn(</span><span style="color: #a709f5;">'simultaneous_recording_type'</span><span >, simultaneous_recording_type_data)</span></span></div></div></div><div  class = 'S4'><span>Display the simultaneous recordings table using the </span><span style=' font-family: monospace;'>toTable</span><span> method:</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >ic_sim_recs_table.toTable()</span></span></div><div  class = 'S17'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="780A2077" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_19" widgetid="variableeditor_client_Document_19" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_19"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_19" widgetid="variableeditor_TableViewModel_19" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>recordings</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 246px; min-width: 246px; max-width: 246px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>simultaneous_recording_tag</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 254px; min-width: 254px; max-width: 254px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>simultaneous_recording_type</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>[0;1;2]</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 246px; min-width: 246px; max-width: 246px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'LabTag1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 254px; min-width: 254px; max-width: 254px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'SimultaneousRecordingType1'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+</span></a><a href = "#H_6b40"><span>Read the NWB file</span></a></div></div><div  class = 'S4'><span>The following tutorial describes storage of intracellular electrophysiology data in NWB. NWB supports storage of the time series describing the stimulus and response, information about the electrode and device used, as well as metadata about the organization of the experiment.</span></div><div  class = 'S4'><span style=' font-weight: bold;'>Tip</span><span>: This tutorial introduces many subtypes of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> type. For a detailed tutorial demonstrating how to work with dynamic tables, see the </span><a href = "./dynamic_tables.html"><span style=' font-weight: bold;'>DynamicTables</span></a><span> tutorial.</span></div><div  class = 'S4'><img class = "imageNode" src = 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" width = "1035" height = "464" alt = "" style = "vertical-align: baseline; width: 1035px; height: 464px;"></img></div><div  class = 'S4'><span style=' font-style: italic;'>Illustration of the hierarchy of metadata tables used to describe the organization of intracellular electrophysiology experiments.</span></div><h2  class = 'S5' id = 'H_3a73' ><span>Creating an NWBFile</span></h2><div  class = 'S4'><span>When creating an NWB file, the first step is to create the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span>, which you can create using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span> command.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >session_start_time = datetime(2018, 3, 1, 12, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'my first synthetic recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'EXAMPLE_ID'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, session_start_time, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Dr. Bilbo Baggins'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_lab'</span><span >, </span><span style="color: #a709f5;">'Bag End Laboratory'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of Middle Earth at the Shire'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experiment_description'</span><span >, [</span><span style="color: #a709f5;">'I went on an adventure with thirteen dwarves '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                                       </span><span style="color: #a709f5;">'to reclaim vast treasures.'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'LONELYMTN' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S9' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S4'><span>It is also recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h3  class = 'S10' id = 'H_86b2' ><span>Device metadata</span></h3><div  class = 'S4'><span>Device metadata is represented by</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.general_devices.set(</span><span style="color: #a709f5;">'Heka ITC-1600'</span><span >, device);</span></span></div></div></div><h3  class = 'S9' id = 'H_9715' ><span style=' font-weight: bold;'>Electrode metadata</span></h3><div  class = 'S4'><span>Intracellular electrode metadata is represented by </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrode.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularElectrode</span></a><span> </span><span>objects. Create an electrode object, which requires a link to the device of the previous step. Then add it to the NWB file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >electrode = types.core.IntracellularElectrode( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a mock intracellular electrode'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'cell_id'</span><span >, </span><span style="color: #a709f5;">'a very interesting cell' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys.set(</span><span style="color: #a709f5;">'IntracellularElectrode'</span><span >, electrode);</span></span></div></div></div><h2  class = 'S5' id = 'H_083d' ><span style=' font-weight: bold;'>Stimulus and response data</span></h2><div  class = 'S4'><span>Intracellular stimulus and response data are represented with subclasses of</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PatchClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>PatchClampSeries</span></a><span>. A stimulus is described by a time series representing voltage or current stimulation with a particular set of parameters. There are two classes for representing stimulus data:</span></div><ul  class = 'S11'><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampStimulusSeries.html"><span style=' font-weight: bold; font-family: monospace;'>VoltageClampStimulusSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampStimulusSeries.html"><span style=' font-weight: bold; font-family: monospace;'>CurrentClampStimulusSeries</span></a></li></ul><div  class = 'S4'><span>The response is then described by a time series representing voltage or current recorded from a single cell using a single intracellular electrode via one of the following classes:</span></div><ul  class = 'S11'><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>VoltageClampSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>CurrentClampSeries</span></a></li><li  class = 'S12'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IZeroClampSeries</span></a></li></ul><div  class = 'S4'><span>Below we create a simple example stimulus/response recording data pair for a voltage clamp recording.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >vcss = types.core.VoltageClampStimulusSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, 2, 3, 4, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(15), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.stimulus_presentation.set(</span><span style="color: #a709f5;">'VoltageClampStimulusSeries'</span><span >,  vcss);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >vcs = types.core.VoltageClampSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'capacitance_slow'</span><span >, 100e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resistance_comp_correction'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(15) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'VoltageClampSeries'</span><span >,  vcs);</span></span></div></div></div><div  class = 'S13'><span>You can add stimulus/response recording data pair from a current clamp recording in the same way:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create a CurrentClampStimulusSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ccss = types.core.CurrentClampStimulusSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [1, 2, 3, 4, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 10e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(16), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.stimulus_presentation.set(</span><span style="color: #a709f5;">'CurrentClampStimulusSeries'</span><span >,  ccss);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Create a CurrentClampSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ccs = types.core.CurrentClampSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 123.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bias_current'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'bridge_balance'</span><span >, 70e6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'capacitance_compensation'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_description'</span><span >, </span><span style="color: #a709f5;">'N/A'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(16) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'CurrentClampSeries'</span><span >,  ccs);</span></span></div></div></div><div  class = 'S13'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IZeroClampSeries</span></a><span> is used when the current is clamped to 0.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create an IZeroClampSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >izcs = types.core.IZeroClampSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1, 0.2, 0.3, 0.4, 0.5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.untyped.SoftLink(electrode), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gain'</span><span >, 0.02, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_conversion'</span><span >, 1e-12, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_resolution'</span><span >, NaN, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 345.6, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 20e3, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sweep_number'</span><span >, uint64(17) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwbfile.acquisition.set(</span><span style="color: #a709f5;">'IZeroClampSeries'</span><span >,  izcs);</span></span></div></div></div><h3  class = 'S10' id = 'H_478c' ><span>Adding an intracellular recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> relates electrode, stimulus and response pairs and describes metadata specific to individual recordings.</span></div><div  class = 'S4'><img class = "imageNode" src = 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FgP8B7OmDGjVx32fPIzAA68ZzHlsQyhBLNOcBwep/YAbzrZJn2fGDD70UcfeYF0vcyE4vrYuoFZgF2nsfQHzMIbD97E3333nWOf0T4AxU8++cQiEwA6AF1C0E3vH2wBXtz2AIDfqFEjv8AqgLgeFi9e7BMk6vXLOAAmgLd9TVI3ICrz2fc6mAUcd7o27XmcxglpdDD7xBNPOI7FsGHDVNfhJW8vG+AYtmA/bv8MIAnoqQdMdQywd/311yeY32nM4aGsg1l4q8JL1l6302dc86H0mMW0xfDWdqrLfsxpPOBdmxCYdcpnL1t+xhTyevAXzGK8ne5ZslynPWYzcPPU1dvAOBWgAlSAClABKkAFqAAVSIwCBLPGf+FxGghmE3NFMA8VoAJUgApQASpABaiA3wokBZg1/lc3PfPQqFCBWZTptEl4h+lWnbzZnPK4HbOD2dGjR/sNlXUwCyAJrzJ7PQA/gNaoBzDHfj59+vTmlLFyMGMZzGId2UDBil0PObbQww3M2vPIzwmBWXgpA2DBS9rNyxJlYbpurKMsA7xe4UXq5I0I70dM64rxdRp//Ghg0aJFsihzD29cJ29m2Q99r4NZwOTE2jvgot1zNrFgFoA2kB8g6P2R8VCAWVlWQnv0fciQIZYxwJTe/kBdt7LtYBZe4k72AXgNu5DXBTxt4c3uFBLjMYv1lTFVsVs7/TnuD5j1pxyZBjar/9jEHzDr9gMbwHf8yAfXlxN8xnl4PePHDgxUgApQASpABagAFaACVCDUChDMGv/lx2kgmA311cLyqAAVoAJUgApQASpABSwKhALMYupIAC1sblAGgACehQCz9umHjf/lHeEBAI2bx6yeB9BDghTp9Tl48GBHGIJ8eGEPKAaA4uTxJsvWwSymxfTl9YYpm+F9h6k/AVThrYUA7zes9SnLlHvohClYAfmQButxOnmeAQRKb8ZYBrNYW1X23b7H+EE/X9PGIk+gYBbgCx6hyJsQmLW3yekzbGz48OGW6+fAgQPmuNvTA7hj/dFz584JTIs7dOhQRy9YrG0sAzwTYUP2suRnwCdpY2gL1l5GgP2gHJnOvoetYZpaaOxkY0gPr3Ss6SyDP2AWdo5rCfkBZHF9Yxple/3yM9IDHifkMR9KMIu6cF/C2rBu17o+BrjGACNlm+172Cim7c2cObNrGjuYdZpqGTYPW4IH6TfffGNOg431rt1CYsAs1lvGvdneB3yGHWBtW7TVl2dwIGAWNonx9fXDBtz7582bp7rpD5jFlPX2PuC6xnTxuGbg4QwdkUbao0yPsbJ76arKGaECVIAKUAEqQAWoABWgAkEoQDBr/Ncdp4FgNogLg1mpABWgAlSAClABKkAFElYgWDALoDN58mRx6dIlc00/AFgJw4z/0dULdbzgx5Se8F56//33RZUqVdQ5PV25cuXEmDFjzA3TyvoCswALLVu2NIESoBLW5ezevbtZB9Yp1cuVcYABrAW6efNmsW3bNvH888+7gmIdzH744YeOYE16wqG8P/74w5wWdcqUKaJVq1am+G7rMmIq5rFjxwpMCSw3eO7ZoTXSYYwQYhXMwnsZcEyOgb4HrOvfv78JTzD9K6bBRZ/1NDLuL5iFpyrWJ4ZuWFP2gQceEBgTBLc1ZmUdbnvYGuCqPcycOVP9KEDPi/RyXOUenrV2OKiv4Ql7cLp20B+AtN27d5vw88iRI2ZbMM0vAmzPyWMQ7QGomjBhgjh48KBp7wBhbvrCa1YGX2AWNo+pzbF+85w5c0xvW8AveO2i37oOehzwdteuXWa6SpUqOeqG9KECs9AEawUDXAPg6W3R47jnyDBy5EivMUJa+cMP2CjK+uyzzxynWEdaO5jFj0X0+hCHhvCqhzctpnnHGsVOa/3KdiUGzGJaZHu9+AwPXXiubty4UezYsUMMGjRIee3a0/sLZvEjCPwYBuO7Zs0ac/1jNyiMH2nIkBCYPXv2rOOPFeBpDO3ktYU9xs5+/4T9jxo1SlbHPRWgAlSAClABKkAFqAAVCJkCBLPGt4c4DQSzIbtMWBAVoAJUgApQASpABaiAkwLBgtl69epZit23b5/jtLx4wQ/vQhkwZavxP7zXBnilBzcwC1Ai4aeeHuB3z549pueqvXzAkIcfflhPbsYbNGjguCamDmbd1loF1IFHpD0A0iLMmjXLp0eavY1On/v27WuWFatgFjbm5GEIuDJixAizb/IPxg+enU46+ANm4SEJaGgPcjrTQMEsfnhw3333iRUrVtiLND/Dnpza6u8x6CLBe5MmTRzLgmfslStXvOqXNgbw5OQJC0gKcKoHeBdiemWn9sEDESAMwQ3MwvP266+/1otUcfywwKlceFEC4uoB0+y6TdkcKjD7zDPP6FWKBQsWmN7s9jbCE14GN89j/JDk9OnTMpm5B/R1mqLaDmad1sDV24BxKlWqlHj55Zdd4WygYBZrqzpNYwxQqQN42SGsge00Hv6AWWgAKKoH2KvbdYxZAKTtJgRmcd35gv26jm5xp/7qbWWcClABKkAFqAAVoAJUgAokRgGCWeM/8DgNBLOJuSKYhwpQASpABagAFaACVMBvBYIFs3KKWFmhm4cTPAN/+uknmUwEC2ZRHuCDU4DHFqaSNb4jWDZMawlvN3vAdMIoz55eB7P169f3Oo/0r776qr04y+d33nnHMZ+9Ll+fsU4iQqyC2WnTpjlqABglgakuGtaUdJoO1R8wC1COdV/dghuYBeiH1599HHCsR48ebsWJpk2beuWxl5HQ5xkzZpjlu3l5wxvQVwCAdKoDXqlOAVPnOnnN6teoG5iFt7lb6Nmzp2M7MJWvU5g+fbrX1LPoRyjALEA1vNz1ALtygnywQxnKlCnj2Ad4+TsFeOzbtbeD2YkTJ7pO8a7nxZTCAKH6GqyyzkDBLLyrAdH18hFH27Zu3SqLVXt46+J+Z0/vD5iFZk7XHNbLdYK98J6HFz1CQmAWnsnwGLe3K5DPmEqegQpQASpABagAFaACVIAKhFoBglnjv/I4DQSzob5aWB4VoAJUgApQASpABaiARYFgwSymDtbDxYsXHV/w69AH6YMFs6VLl9artcQxNajTeqVoA6ZHtgd4vAJAGN8pLJsOZqtWrWo5J9M6TW+rlz9w4EDHfDK/P/vq1aubRcYqmAXUcuqnXIdX1wtxgCMnsO4PmHXyotbLdwOzmPbWbX1UeM0CIjoFeFs79S2QY9KG3NYwhte1r9CnTx/HNki7sed18/TEOqzwNkdwA7OY9tYtYI1Up37D49gpYPpxp/VNQwFmMSU0xloPmIYYa0vb26iDWXgN288D8rqNQfv27b3S28EsplIGnHab2levD179r7/+ut5sMx4omMUUxU5gFt7LcoztlTh52PoDZt1+AICprZ08igFmMR03QkJgFuvRQhNdo0DjGHMGKkAFqAAVoAJUgApQASoQagUIZo3/zOM0EMyG+mpheVSAClABKkAFqAAVoAIWBUINZjFFadGiRb1epIcazN5xxx2WfugfsHasE5SA95YTWIJHJ9ZdNL5TWDYdzGLtU/t5fHaDX9IT9IMPPnDMBwAEYOnPhvVBEcINZvW1Ns0KjT+YBtSp37oXJ9artKeBR6acvtZtCll4Ly5btkxWpfbPPfec4zqf/oDZBx98UJXjFHEDs5jed8mSJa7TrwJQOXkaYv1ae98B8jCtrz9jizSXL182m4opi+1l4TO8cp0CpiVGcPPKBtxet26dV9YuXbo41gN7lz9ccAOzWNfTLWCtYKf2Y5ylh6TMCw9LAFun9OECs2vXrnUE/jqYxX3FqU1O3t1YM9vJy9QOZtFn9BfXAe4jyANY6DT9NOp2urcFCmYPHz7suK4zIKf9xzRoH65Vpx+z+ANm0V8n2Is1kJ20xL3vwoULqDZBMItps53gLur09/rC84CBClABKkAFqAAVoAJUgAqEWgGCWeO//TgNBLOhvlpYHhWgAlSAClABKkAFqIBFgVgFs/BudAuXLl0SWMfQ+I7gtWHK2vnz5wt4sQGeYrpNeEs6pdXBLLwasT6jPR1gL76Q6WHv3r0mmMMxeO86eX+iTglvZV5MJ4q2wUtM3/78808zSajArNu6vRkzZrSspYppn+HdZu8zgJKcfhcNSwjMAto4gXKUC1AET0YEAEoAX6fpZpHWHzArp302C3T44wvMbt++XWCNYycvTtQPr2m5BqssGlDUyS7gCWgPgJP6uCIu4TXSunm+wsbs66Vu2rRJjBs3zqwCnolONoY2p0+fXmzZssVMBzsCMIYHMM7ZN11fNzCL/rqFpUuXiixZsniVC33gqSl/YPDrr78KQFyntGhTJMGskwcs2oQxaNOmjThz5ox53WIt7WrVqjnCVTuYxb2md+/eaip39B/AHNcNrjn7OODeZQ+BglmAYDfIDM9dTK8s1y3GPQo/nLG3A5/9AbNIB9gKO0TA/deXnTVu3Fh1LyGPWdiMU9swLT101APup19++aXXNWZfG1jPwzgVoAJUgApQASpABagAFUisAgSzxjeBOA0Es4m9KpiPClABKkAFqAAVoAJUwC8F4hHMouNuXoHG9wZz/VKABEzd6bSWKdJg08EsQBimepXn9H3mzJnNaXABde68804B70tMS4sAeFC2bFmvfAA98EYFrMOakoClACapUqUywSDgIDaAS3j0IoQKzALUAWzofZBxAOOHHnrI1A8QG1PCynNyD+/iRYsWmW3Cn4TALDw7sc6jzG/f58mTxwQ78LSGLvbz8rMODidNmuSYLlgwC/DqttYrxgPa6EB948aNjloCuA0bNswEeZjeG3GML/SUY4s9wClAPsLy5cvNNUBlf/V9tmzZROXKlQX6h/WOoZlcOxP24wbhUAagKOwd+jqBQKTBOp66N2xiwCygJTxL9XbLOGA+4DygI2A/+i3P2feRBLMzZ850tUFcDwCVuC/gGre3W362g1n8qAPXMfJivWJ4geIahEep0/TGsD97CBTMIv9rr73meP2inYDi6AfugW5QH+n8BbNICy9gaWdO/UIa2N+ECRNU9xICs7jW6tWr56U1xgLT2QPc/vHHH2Lnzp2O1xfup88++6yqjxEqQAWoABWgAlSAClABKhAqBQhmjf/w4zQQzIbqKmE5VIAKUAEqQAWoABWgAo4KxCuY3bx5s6OnlfG9weslv9sxHcwCEDz55JOuoMNeBmCsDPBsdAKOgAvwBgMwc/NixDqsAF4IoQKz8Jx0gsX2Prh9BsjBVKkyJARmke6jjz7yCcHd6tKPJwWYRVsBnd28dgG0Ro0ahWRmgF1g+mSnaWkx5oCQ2ADv9b7IeMuWLc1pblEYyoJXplNZMr2+h+4yACj6+pGBns8pXqVKFXHu3DlZnOsas748ZpEZU4UD8jrV4e+xSIJZTMXuC3L70wcdzJ48edKElXo+TPON6XkBa/XjMg54aw+JAbMHDhzwqlvW4e8+EDDrT5m478j7GfqYEJhFmsWLFzvCY0BXAHJcX25TQ+Oc0zTLKJeBClABKkAFqAAVoAJUgAoEowDBrPENIE4DwWwwVwbzUgEqQAWoABWgAlSACiSoQLyCWXQcEMkNiBnfHxyhiH5cB7MoD1Niuq0DqudDXAezmNrz/vvv9xvqyrIAbrBGrQyhArMoDx6nTrBY1u22h8cn4KEe/AGzgI6PPvqoK4xyq08/nlRgFn175JFHHKcoRnsAgzD9qwxHjhxJFMzD+OIHBHoIpCwdzEJfAL3EQlH7+rmJ8ZhFP+C9iKlqnaZ31sfSVzySYBZ9WLFihQn6fLXR1zkdzL7++uuuU2O7lfHjjz+iGZaQGDCLAjDluNMarW5124+HEszixw7wMNeDP2AWto2poAO1bVxf0J+BClABKkAFqAAVoAJUgAqEQwGCWePbQ5wGgtlwXDEskwpQASpABagAFaACVEApEM9gFp18/vnn/fIkdJp60w5mUR6mJsY0sm5TwhrfS0zgi2mP9QA4i6lo4S0n0/ja58yZ0zK1LMoKJZjFGpSYltdtnU+ntkGj++67T2AtXD34A2aRHmttYhpefzSAN7G9DUkJZgHCMB21vQ3yM6abRRoZTp06ZYIjNy9ImU/uMb2wXJNTliH38CjEuKRLl861fpSjw3/kBcB67LHH/AZxmEq5YsWKjh6FiQWzaAc8sjGlra/pin15BUcazKIPWHsaINFXO3EfcIKFALMbNmxAMaJBgwYJ3iukTeCe8emnn5r57H8SC2ZRDqaoxv1E1hPI3h8wC0/thO4jmBUAayrbgz9gFnkwHTqm8U6oHtk3pEtoWnN7W/iZClABKkAFqAAVoAJUgAoEogDBrPHfd5wGgtlArgSmpQJUgApQASpABagAFQhYgW3btpngEi/O9Q3QAev3yTBnzhyvdJiGd/DgwTKJuZdQRi8LcUAHeJzKgHVTMX2vng5phg8fLpOY+3379pnTWOrpMD1l3bp1Lel8ffjkk0/M9TixfipglPHdwQQumP6yatWq4uOPPza9IPU6EMd6mU4BkHXs2LHmurIAbLJMlIu1QJHXrgvKATibOnWqwLSxaIt9/VZAPazDCQD52WefeVWN9U/l1Md6W7FOpAw///yzCY7184iXKVNGJlF7wI5Zs2aZ7UG9TiAQfUMfy5UrJ0aPHi1+++03lV9GWrRoYa6zqteJsd2+fbtMovaAs5MnTzahIgAWpiOFbthDk6ZNm5pTCUsdZZkY82bNmqlyli1bZkIaeR57aNOnTx+VxikCjz1AGz0f4vCCxXq/eli9erWXzct86F+vXr305ALT4GIqXwBdp3U7AfNh4/CetnuoWgoyPuAHAPD4xrjpOkEr1I0fDcDr2SksWbLE1BF62MEhPFlRHqbIho3q08rqZcHTENeH7C/2WJcY15I/Ae3HtQwtAAUl4JTTd3fq1MlcaxRQT68Dbe7Xr5+qAnFoqaeBhrhmZXj44YdN29HTwEvU7p2JqX3Rdz0d7Ao/mHAKu3fvNqcvx/WFawBlor2ov06dOuYUuwDQGBN9gx1LT2j8iGH8+PFmHbjG7OAe8Bp9vuuuu8SqVaucmmEeu3Dhgjllr952xP0dE6xri7Va0Qb5oweMCeBzhQoVxOzZsx3LB5jFfQcB1weuE3sbYEtTpkwx8+O6lVrA9qS9Y41up4AfEkAvvUxcn/axk3kXLFhg3vvRb9kPWR/uIXhuYSpq3Gdxv2WgAlSAClABKkAFqAAVoALhUoBg1vhPPE4DwWy4rhqWSwWoABWgAlSAClABKpCsFLhy5YoJw+C1BcCINTkB3jD1KgJAEiCVvv3+++8+NQKgBXCYO3euWSZA2dKlSy0A2qkA2RbAacCxZ599VgwYMEAAfgPYAV4mZUB70A/ArqFDh4oXX3xRvPDCC2LIkCEmsFm3bp24fPlySJsEbdeuXWv2eeTIkWY96DtgMQLWO9XHAnG7p25IGxTCwi5evGhOdTxhwgTxxhtvKC0XLlxo+bGDP1VCp/Xr15tr9GJt2/fee08ASuvrwbqVs3//fnNMAXj79u0rBg4cKN5//32zvIRs263MQI8DKKK9uN7Qflwr+LEFAry27WOMz9IGAq0rFOlx7WH89A1aw4MW28qVK8XevXvNaxRtBVQ13kVYNsBMQGA9ABIC1uIaB7x/7rnnzGt//vz5YteuXUkCEaEr6sI9cMyYMQL3H0zbjL4iwCbs42H/IYbTfRI/xkGATrj/wcYAowFR9R/3mIlC8AdaApoDJuMeBdvGdTZ9+nTznoL7MgMVoAJUgApQASpABagAFQi3AgSzcUpljW4RzIb76mH5VIAKUAEqQAWoABWgAlSAClABKkAFDAVat25ten7CI1lu8KoFfLWv/dqyZUtHL3N40Yb6hwwcHCpABagAFaACVIAKUAEqQAWiSwGCWYLZ6LJItoYKUAEqQAWoABWgAlSAClABKkAFqECMKQAPTH1qcgBZbJj2t2TJkuaa1f379zen/3VbK7lVq1Yx1ms2lwpQASpABagAFaACVIAKUIFAFSCYJZgN1GaYngpQASpABagAFaACVIAKUAEqQAWoABXQFDh8+LC5Lq4EsoHusa4qpjxmoAJUgApQASpABagAFaACVCC+FSCYJZiNbwtn76gAFaACVIAKUAEqQAWoABWgAlSACiSBAp9++qnImTOnZd1YfwBt5syZRa9evZKghayCClABKkAFqAAVoAJUgApQgUgrQDBLMBtpG2T9VIAKUAEqQAWoABWgAlSAClABKkAF4kKBr7/+WhQvXlykTJnSL0CbP39+8fLLL4t//vknLvrPTlABKkAFqAAVoAJUgApQASrgWwGCWYJZ3xbCs1SAClABKkAFqAAVoAJUgApQASpABaiA3wr8+OOPokePHqJEiRKmBy3WmdU9Z7NkySIKFy4sWrduLdasWSP+/fdfv8tmQipABagAFaACVIAKUAEqQAViWwGC2fgFs7/ii1+rVq3EE088wY0a+G0D7du3FyVLlhTly5f3Ow9tjNcYbYA2QBugDdAGEmcDzZo1M5+7derU4XOX/6/RBmgDtIE4s4GHH35YNG/eXNSuXVtUqlRJVKxYUdSsWVPcfffdomPHjhzvGBvvRx55xHxmlypVimMXY2PH/1MT938qdaNutAHaQCzZQKNGjczn9J133snnNJ/TUW8D+JHmfz/cHBO/iDJ59uzqfwMrB5h7A1RTE2pAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDYQYRuYb9TPEEcKnDf6IlLmuV2kLFSVGzXw3wYMm4HtpE1znahfnhs1oA3QBmgDtAHaQDhtoEiea1+C0mdOI/KWzsyNGtAGaAO0AdoAbSBKbSB3iRvVj70b5k8puFED2gBtgDZAG6ANRI8N5Mt0bemI9HlvFzlqPMSNGkS1DaTNXkD+Xzksjpgku2IocBRwLWWldiJl/d7cqIH/NlCxrXlTyHzDdaLfI9yoAW2ANkAboA3QBsJpA3dWvgZms+XPKKo8UIAbNaAN0AZoA7QB2kCU2kD5FnnN78opjHctg2pyowa0AdoAbYA2QBuIJhuocUtK8zmdrXJbUfLZJdyoQVTbQObidSWY7U2aGV8KEMwSxvoPY3WtCGYJpAnkaQO0AdoAbSDJbIBgljCaQJ42QBugDdAGYsMGCGYJIKIJQLAttEfaAG2ANmC1AYJZwuhYAvIEs/EFY/XeEMzqsJFx/yEtwWySvYwPpwcWy6aHH22ANkAbiA0bIJiNjZfxhCYcJ9oAbYA2QBsgmLW+ACcQoB60AdoAbYA2EE02QDBLMEswq+NBxiOlAMEsYaz/MFbXimCWYJaecrQB2gBtgDaQZDZAMMsX/YQ9tAHaAG2ANhAbNkAwSwARTQCCbaE90gZoA7QBqw0QzBLMEsxGCkWyXl0BglkdNjLuP6QlmE2yl/H0ZosNbzaOE8eJNkAbCKcNEMzGxst4QhOOE22ANkAboA0QzFpfgBMIUA/aAG2ANkAbiCYbIJglmCWY1fEg45FSgGCWMNZ/GKtrRTBLMEtPOdoAbYA2QBtIMhsgmOWLfsIe2gBtgDZAG4gNGyCYJYCIJgDBttAeaQO0AdqA1QYIZglmCWYjhSJZr64AwawOGxn3H9ISzCbZy/hwemCxbHr40QZoA7SB2LABgtnYeBlPaMJxog3QBmgDtAGCWesLcAIB6kEboA3QBmgD0WQDBLMEswSzOh5kPFIKEMwSxvoPY3WtCGYJZukpRxugDdAGaANJZgMEs3zRT9hDG6AN0AZoA7FhAwSzBBDRBCDYFtojbYA2QBuw2gDBLMEswWykUCTr1RUgmNVhI+P+Q1qC2SR7GU9vttjwZuM4cZxoA7SBcNoAwWxsvIwnNOE40QZoA7QB2gDBrPUFOIEA9aAN0AZoA7SBaLIBglmCWYJZHQ8yHikFCGYJY/2HsbpWBLMEs8nUU27MSwXEN/OfN7dZ7zahHSTCDma/e5fScPSL+ahhIjQMJwBk2dEJmAlm+aKfsCc6baBFz5pi/Jy3ze2lkT1ELI1Tm2caqrY/M/QxS9v7jX1GnWv6ZGXLuVjqI9sanddNvI8LwSwBRDQBCLaF9kgboA3QBqw2QDBLMEswGykUyXp1BQhmddjIuP+QlmCWMCmZwqQvZvUQMvy4/cuosoM5o5uJMyf2ivGvFYuqdtlB3087v5ISioXTO0d1W+1t5+fohJbJYVwIZgkX4h1kRHv/nh/+hDh8/KDo0PcuC6R8fczT6pm2+6ftlnPR3qd3pr2h2r5u67eWth85cUid6zP0Ucu5aO8X28f7ZaRtgGDW+gKcQIB60AZoA7QB2kA02QDBLMEswayOBxmPlAIEs4Sx/sNYXSuCWcIkglkRTWB254aP1AvU99+sEtX2STBLuJkcQGqo+0gwS9AQadCQnOtftnaxesZ26dfaAikJZnltJudrg313tn+CWQKIaAIQbAvtkTZAG6ANWG2AYJZglmA2UiiS9eoKEMzqsJFx/yEtwWxUg69QAwGW5wFp0eox+9uFX9RLY4JZz3jRdqlFvNgAwazzy29CAeqSFDZw9vxp9Yy1g1lMXyzDtr0/WKBtUrQtmDp8ecwePLJfdkv0HNwxpvoVjCbMy3tKKGyAYNb6ApxAgHrQBmgDtAHaQDTZAMEswSzBrI4HGY+UAgSzhLH+w1hdK4JZgll6zEaVxyzBLAFkvABI9sPZlglmCQtCAQtYRuLsyBeYhaYNHytjbrU7Fo8pgOkLzNZ+qITqF+0mcXZD3ZKvbgSzBBDRBCDYFtojbYA2QBuw2gDBLMEswWykUCTr1RUgmNVhI+P+Q1qCWYLZOAOzb3Z1hiF2SJQYj1l/y7bXJT+/2S3htiUGzA7ofJ0YaGyyHre9ma5Lwumc8r/x6HXC3v/ETGVsL8OpLqdjTvU7peOxxI0vdUs63Qhmk+8LfsKd8Ix9nU4lRY0ORf0CqQmB2UDHqGq7gqJup1I+667WrlCCaQKt157eF5i1p+Xn8NghdY1PXQlmrS/ACQTiU4+36qURb9VPJ/wZ37cb3+RXOq+yal0nEp235jXd326UWQyunTJx9RtlDLsza6Lzyv4ktg9DG94g3qx7faLrT2y9g0Kgu+w79/F5/cf6uBLMEswSzOp4kPFIKUAwSxjrP4zVtSKYTRBmEVgkHbBIjNaAjd/Mf970ev3t4klzur7Ll34TP+/7TqxeOlwM6eHcfn/AbGLLlv0Y9lQ6sfyTV8SpYzvF31cum227cuWSOHl0h1j1+SDxVndP275dNEDs375ETTeICNLh2PdLhio7/eHbSeaxXRvniXf75hJ7Nn8mUOa///4rTh/fLRZO76zSoh3ThtYUuzd9Is6e3C/++ecfMx3iyD93/H2WtLLdcj/oievEktm9jHZsF3///ZfZtvOnD4otq6eJif1KC3/AbDAajnulsNi4cqLZL1n/n3+cN8d2w4rxYuRzOXy2X/aDe4+dUYvIa0EwG58v7wllkm5cAWHfmvyS2LxrvTh38Yz5bLr6z1Vx+PhBsXL9l2Kwcc4+Hh8sGCvWbllpppV/DhzZZx7738JJZvpHXrnX/Ix00z+dYClj/tKZ5rkV65cIgFbUj7ou/nbBLA7TBc//coZ46MV7VD6k+XbjV+K3P34106Ct329aJto+00ilke38fOU8s/w1W1aIFj1qeJ1HuhY9awqcR/sWrZhnSeMLzM5dMk31q92zjS35ZP3cJ539UuvY0ppgliAi1qGFvf1bFk4VB9Z9Jfas/ESMbVNc7P/uc/H35WvfJc/8vEd8MayXBR4C2i6f+Jo4sP5r8fu5U+bz7K8/fhNHtq0W6z4cLd5pmsuSXq9vUsdyYvNn7wuUe/XKte+Sl349b+bd9OkUMaZ1Ma+8HzxZx2wf2vhp/4fFB11qiR1fzhbnjx8y67769xWzvOUTXk0QtAJmfj99iDiyY6249N/z+rLxTEbb1380Voy85xav+tH+HxZMMtuw99vPTBC8cFBnsXv5x+LX0yfMNqAPB39YIZaO7OOYH2UAwi4e2kP8vGWV0u2fq1fF2cP7BcpdPLS7a17kT2rd9XFjnPe9WLIBglmCWYLZSKFI1qsrQDCrw0bG/Ye0BLMEOzHsMQtwd+LwFvMLktufX88fFzOG1/ca54TAbDBlA35NH1ZH/PHbtRfGbm378/dzYvxrxcy2HflprVsys48SqP164doXQiQ+e/JHrzyAuUg72PAc3rnhI6/z9gNIM7RXSi99hj+dQfxyeKs9ufr81+U/VBwROxBGG4LR8P3BlUzgbKnE9uHyn7+Kjye182q71Ir7yENIjoH3GBDMxtZLeUKU6BovQNFvNyy1PQ28PwJeNulSQUHIHfvd/1fYf2i3me71MU+rgnb/tF3lhQ0cP3VUnVu8cr6K2yPnL54VzbtXFx9/9T/7KfX5ivFDp4deaGYp/9ffL6rzj7/WynJO2mDn1+9XaS78dt6SxheYPXLi2stsZO4z9FFLPlk299Fl5xyP6BkPglkCilgCFP609bczv6hnybljB1RcRk4d2KmAIcDqL/u3yVOOe8DK2X2aqTyyDdO61jeBr2Om/w4Ckn464BFLXkBQGU4f2iOuXLJ+55TnsAfkHN2qsCW/rH/ei23Eb2ev/Whbz6PH/zDWnf/41fZe+S+c+Fkl++HjiSruFPlxzRIxpEFGSxnw6t23aqFTcssxwOcRzXJb8qL9kdBd6sY973mxZgMEswSzBLM6HmQ8UgoQzBLG+g9jda0IZgl1YhTMvt07tbj0p+dFJr7l/H7xlOFNuUqcP+N5CYnj8BSd8FoJy1j7ArPBlj325YLKwxT1IwDSHj/0g8CvfPVw4exh06sXXqjnTv2knzL7h2M7NsxRbdfBrCXxfx+++fhFM+3W1TMsp6EBPHcBW69e/dtyDt7FOkDDtMEnft5sSQMQe+zgRlNjy4n/PtjBbDAaDn7yOkOvs5Zqjh3cIA7uXi7sQBh6Du3pDb/0/jBOfaLJBghmo+eFO+FH7I3F+DlvW54NAKHrtq0yvWUtJ4wPsxZNURDyi28/FsdOHrYk+d14IYxjX69ZZKbzF8zqhcBjFuW4BXjynjx7wvw/RE/zw861qm2wQ4LZ2LNF3j+Sx5gRzBJSxBqkSKi9OpjVn0syvmJSPxMUwpv0svajIZyHx+zhrd+LC79Yn6f/Gt8zpzxcSQHGoY0yiT+N57Meju/5QRzatNILtOK73Dt336zy6mBWzw+v233fLRIXfzmiHza8Z/d5TRE8uVMFLyj856/nxNGd65X3ql6IHSzrYFZPB+3++vN3/ZAZh2esrjs8jPUAAHxwwzcmSNaPI75uzihL3kjprrefcd73YskGCGYJZglmI4UiWa+uAMGsDhsZ9x/SEsxagFQ0vbxnW3zDpB++nay+1/xjvPgEkOz/mCfP7FFNLeD24J4VlrH2BWaDLRt1yQAgunzBywKwE2OKqZW/X2J9sYy2yPFOaI1ZO5iFh+wX/+suls59VuzdskiMeDarWDC5vaze3AOyjnrhFlUHvGEP7bFO6fjp+53U+c+mPmLJj+mS9bVhv5r7nNdLZjuYDUZDeDjLgCmaPxhSQ7UNUyMv/OBRedrco/9SP+491wC1iE4tCGaTx8t8QpvwjPP2fZvU/R/T+tZ8sJgCnPf2rCXg6SrD2QtnRPX2hdV5jImvNWYDAbOYurjjC3ebZdfuWNyc1ljWK/erNy8XTZ+sbKZp9Fg5obcd/xtU71BEtY1gNjz2wuuQugZrAwSzBBSxBCj8aasdzMJDFlPyLhv/svhx9RfKA3XLog/k40xgCl4A28F1UimIOPeF+y3g9ufN36pzAJ0y4LvczJ6N1TlM0YtpfPWgTwlsB7PIv2LS6yo/pgjGdMx6+GrUc+o8yj99cJc6jbYvG/+KOg+NPh/SzQJuzx750Zw6WOpnB7OAylMeqWyWAQ3gZYtyZTi+Z5OlfEydLMNPa5daysb0zShPBsBuXddI6S77zj3vebFmAwSzBLMEszoeZDxSChDMEsb6D2N1rQhmCXRi0GMWHqn4kiYD1mt1glDT364tk5j7mcMbqHRuYDbYsgFG9aCvDyvb2P/x68w1X2W6wz+uVu0KBMxiLV2sMyvLlXsdDGPdXXigynNyD4h96rjnSyu8jeW53ZsWyKaJMyf2Kqgsz2MPzfWgg9lgNVz60TOqaHg/6/XKOLxnES79ccFcS1ge5957rKlJdGlCMEtQECwoSM75L/11ybz348/ACX0V2JSadO3fzjwPT9XjJ4+IVr3rWtKEAsxizdj7n65nKRf16AFesrUfKmFJ0/ppz4+OkLbVU562EczyviBtmPvosgWCWUKKWIMUCbVXB7NYKxbrzNrzTHywnOW79uqZb3ulQZ7/PXWX/ugTs5+5x0z3zbiX1HF419rLx2d4zyJg7VesVSvT2MEs1nuV5/T9sd0bVR0HNy5XaQCB9fDN2BfVOT0/1q/Vw5LhvVU6HcxCo3db5FfnZBnfT3tLZcd0y/I49lcu/6nOLRr8hOUczss2Au5i7dxxbUuaaSKpu95+xnnfiyUbIJglmCWYjRSKZL26AgSzOmxk3H9ISzDrCH0IMqILZNjHY8GUDurLzpUrl8RAw4vSnkZ+xvS7Mnwz/3mVzg3MBls2AKUMV/7603H9VrTtfyMamWvAfvv5QDF3XEvVrkDA7Obv3lf5ZH8HGNAXmsjgy5v0wzHNZTJz/06fTGZ5504fUMd1T1pZB/Zv90ol0D8ZdDAbrIaz37V+yd+7ZaGY+mZVi0c01tCF97HeJsapRyzYAMFsdL10JwSJrfHYc2CHfOyY0/8On9pPeaXKsWzWtarFG1Uexz4UYHbukmkW4CrLv6S9iJ0y713HNH9ryxk88sq9Kg3BbGzZoRxz7uN/3AhmCShiCVD401YdzG5bPMMLGqKMzwY+rp61f1++JN6qn84xHdLCW1QGTOGLYx8930oeMvf7v18spndvaPEMfbtRZjH8rhxe5epg9uqVv8SolgW80qCOuS+2VnVg2mQcw7Zy8hvq+K+nj4sh9TOoczKN3Ovr58ILVx7XwezG+RPUcXkee71+VKifO7HXsyTQpV/Piy9HPG3A3XyWNKPuvVW8qXkgI38kddfbzzjve7FkAwSzBLMEszoeZDxSChDMEsb6D2N1rQhmCXYMyBYLMENv48qF/dUXLkQwba7bhukCZdj03Xuqr25gNtiyVy1+U1ZnrMm6QdWnt99XPBAw+838F7zKnzKwgqofEX0KY3u9A7tcZ0k7bWgt8WY36zFf+Q/t/Vbl18FssBoCuv7991+qbBmBd+zODXMFploe9lQ6r77b+8fPsXdtJ4cxI5iN/xf5hDXhG+NpC8bJR4Jlv//QbjH90wniyf4PiGrtCingaR+LUIDZQROfdywfXrIy9BvbxzENvG1lIJgNn53Yx52fqXVibYBgloAilgCFP23Vwaw+RbCeV/cGxTML0+u6bVhfVoatn0834SOgK6CqPcA7ds+KBeZUwnZQKevXweyxXRssMFOmwR7r0uph9H2FzLQH1n2lDu9YOsc1P8pYM3OYSnt463cqrQ5ml458Rh3X68fUxnoYXDulSrd6xlD9lIoDBKPOGT0MSK2ll+VGUnfZBu55z4s1GyCYJZglmI0UimS9ugIEszpsZNx/SEswS7gTg2B2x4Y56gtOIBGARAl+3MBssGXDi1WGXZvmq/pkvQntAwGzc8ff51W+3dsUHrS+6sRUxzIsmva4eH9wJfnR3APeuuXftnamSquD2WA1RH3/G9FY/HX5d1W+PYKprDFl84TXSri2z63dPO4+ptQm/NoQzBIQJBYQMF8BUadTSbFq49f2R4Ll83nDcwYeq06ANhRgtu87TzpCVx3M9hzc0THNReOltAwEs7wX8JqOfhsgmCWkiDVIkVB7dTD78WsdFEzU8+1eNl8+qgLa63Dzw+fuFX/96fu7HNalnfJwJUsbdDC7e/nHlnN6GxGHN68MH/W9z0yre6u6TcEsy/liWC+ZXfx29qSqSwez815so47LfNiPb3ebyouIDlqHNrxB7P/uc8t5+4c/zp8Wq94faMkXSd31vjHO+14s2QDBLMEswayOBxmPlAIEs4Sx/sNYXSuCWUKdGASz+7Yttn+38evz8UM/qPF2A7PBlr11zXTVlu3rZqn6/AVegYDZaUNrepX/3qCKqn5E3njUN2i6eO6YSg+4OvOdhuozIlgP163tG1aMV2l1MBushrK+8a8WE9DQF6DFdMqYklnm4d59vKhNdGhDMBv9L+IJS6J7jABc350xSBw4sk89g5wi67d9L+o9fJsFkIYCzD71VidLmdJedDDbY9CDjmn8AbOdX2vlmPfJN9qqbl747bwlzTvTPFM3rtv6reXckROHVL4+Qx+1nJNt5z66bZ7jE7nxIZgloIglQOFPW3UwO7PXnY7Q8ac1X6rnRiARQFG9DZM7VRC7vp7rE9BiPdb5Lz+g8ulgFu3Qy7PH9bVcP361vZn24IZvVJM3zBvvM//nQ7qptL+fO6XS6mB2zjPN1XG9fl9gFukAar8e/bw4dWCnqsMpcnDDMgEPY+SJpO563xjnfS+WbIBglmCWYDZSKJL16goQzOqwkXH/IS3BLIFODILZ9cvHqu81R35cI97undqvTV+T1A3MBlv2yoWel6OH9qz0aV9vGdMGD7CtjxsImP1gSA2v8se9WkRpg4hcN9YJygG66lM9fzS2hZj0RmlL/pHP5fCqQ5a1e9MnKq0OZoPVUJYv9/DanTm8gfh+ydvi5FHP+oKy8tMn9ri2UZbBfXRASY7DdYJgNnIv2Ak34k/7Vr3riqHvvSpWrv9S/HHJ2zPn1VG9LCAyFsAsAKyTrT7ztmfNP4LZ+LNlpzHnsciPM8EsAUUsAQp/2moBsz0bO0LHHxZMkl+zxNEd68TIe27xa3NaMxZtwjqvs5+5R6ybPdIRVJ75eZ9qhw5mD6z/Wh239w116WF6twZmWkyVLIO+bqw9Pz6vmjpIJjXbJdOEAszKsrAf17akWDK8t9j77Wfirz9+U3XKyIJ+Hc22R1J3vb2M874XSzZAMEswSzCr40HGI6UAwSxhrP8wVteKYJZAJwbB7JI5veX3GAGQ6csrdHL/suLzGV3MqXHf7XuzGm83MBts2Z9MedDStv6POQMxeINiHVVMyXvx3FHxdq9UZtuCBbOAmChThjmj7lZ9tkOxyQPKyWTmfuqbVcWw3mksxz4cc49r/jMn9qq0OpgNVsMxLxUQH427V2AN3fGvFfOqH+veAnrrwRdAtvebn51tkrokjS4Es5F/0U7YEZtjAO/Xrv3biSHvvSJGTh/gBS9rdCgqBk7oa3kGfr5yniVdtIJZgFYZnnrrYUubpb2OmDFQJhEEs7Fpw3IsuY+d8SOYJaCIJUDhT1v9AbNfje6rnjeY4lefptdex9TONUzoiKmLx7YpYQLGCR3KCHiwYg3byZ0qesHVcQ+UEj9vWaXqQGRM62JmOh3MXjx5VAyq5WyDs55qask/ulVhMz+mL5bhxL4tXnXr7dc9VA/+sEKlDQbMwvt1pgG8MU3yV6OeU2XKet+se71YNPgJy/8q276YaaaLpO6yfdw72xt1iV5dCGYJZglmI4UiWa+uAMGsDhsZ9x/SEsx6QR/CiaSBE8Ho/L8RjeT3LXP/xf+6O47jm4ZH6qU/POu5bV83W6VzA7PBlm2fShig1qmvyz95VfXhwpmfVZpfL5xQx2cMr6+OyzL0804es0h3/oxn2sJjBzd6lSHL2rtlkarrr8t/iEFPXBv7Ez9vVsePHdzgmH/2KOuXYR3MBqvh4f3fq/oP7F7mWP+UgRVUGkTeG3SHYzrZV+6j/7pOLmNEMBs7L+QJT6JrrLBuqx7cPEsxhbEM9ml9z5w/JU+JXoMfsgDQ18c8rc7t/mm75dzxU0fVuXBMZXzq7C+qfKyPa7e9Wh2Lid0Htqs0BLPRZZv28eLn+BkfgtnofRlPUJK4sfEHzM55toV63iCydGQfL8AI/YfdmVVc0tZO3/XNPDPdke1rVP5Dm1Y65v3gidoqDSIfPFnHTKeDWRz/5I1OjvkBUmU4fXCXSrNwcBd52NzPfeF+dU63GQBlPXz73gCVLhgwO+vpu/VixYweDVW5ev2YwlgG6RkcSd31tjGeuGuLukVGN4JZglmCWR0PMh4pBQhmCWP9h7G6VgSzhDkx6DGLKXhPHfOs13L5z19Nj1gd7MBz1L7W6fRhddR4u4HZYMuG9y5gqAzwhp3Yr5SqF22EF+/fVy7LJGLt1yPV+XOnflLHl859Vh2XffMHzC6Z1VOVgcjar9/1Wiv2m49ftKTR18O151+1+E2LV/KYF/MLvZ0oSAezwWq4Zuk7lrY5AWodbF/686IY4GMtXKkd94Sz0WADBLPx88Ke8CVpx7Jx5/Li76t/q+fDlt0bRPX2hS0Q8+6uVYQOOcfMesty/tjJwyq//Vwkwey2vT+odl3+67K4/+l6qt1YU3f5uiXqPCIEs0lre7zWk6/eBLORedFOwBE+3f0Bs2/WSSUAO2W4/MevAh6x+rhgemLd4xRp4cWKNOs/GiOzmvvZfZpZ8iINQKgMl3+/KOBJiuN2MHvx1DHD67aCJf/yia/JrOYenrmybUMbZRLIIwO8bt9/vLo6j3Tj25W29A+zTcHLV5YRDJh95+6bxT9/e/5XObz1OzHY0FOWjf27LfKLX08fl00Uy8a/bJ6PpO56+xgP3/VHbUOvLcFsYGB2wPxdYtJXP5lbl0kbhRPUrPjyV2L80mtpkPbOwd86pmv1zmpV1pglP4oyfb90TOdUR3I9lrl4XWGAQ2y9IwUQWW94FCCY1WEj4/5DWoJZL/AVDS/u2YaEAdK0obXUlxkZ2bP5M4E1XndumCv+umxda+7wj6stY+0GZqF9sGW//2YV2SRzf+XKJbH5+6nm1Lybv3tfXNVeLP/5x3nxbt9cqm3HD3lezuJL4rnTB8TeLQvVeX/ALKZPPnnU41mDRvxyZJuxRutQserzQeLIT2st7TtxeIvQ19+FBjvWf2hJg7V8v100QGxYMV4AhNuDDmaD1XBiv9LG2rdXVRWIb1s7Uyz96BlDw+fFwd3L1TlEdKjMayfha4caRVYjgtnk+4KfcCf4scdasno4fOKgmPrxaIFpfj9cPFX8Zrw81kObPg0U4IT+ew7sUKfxjD1+8oj47odvzDSRBLNvv299yfyX8eMtePuivxc1byTZeILZ4G2J1yM19McGCGZD/xKdYCKymvoDZjFG/+vdRD5y1H7/d5+L7z54U2Ad17/+tH7XPrJjrYKP7z1WVfxzVfsuZ8R3Lv1QfDPuJQGoCi9aPez6eq7KawezSHfl0h8C68V+P+0trymQzx07YK5hq9sV1mzVw9Urf5n1AwZv/Xy6ZZ1X/C/w+ZBuqn6UEwyYRX6sJauHs0d+NHXDVMXrPxpr8TJGugntb1f1R0p3XT/GI3uNUv/A9CeYDQzMnrxwSd2eFmw46ghSm7/9nUqDyLMztzqmG/aZZ2kzpKv22jeO6ZIrhHXqN8FseKBoNJRKMEsY6z+M1bUimFXAi7AisrAiMfp/Nfc5cfXvK/gfwGc4dXyXGNrT2j9fYBZtCaZs5AdA1AGsUwMBHP83orHFBld/OcwpqRjc9Vr7/QGzqH/CayWEvgasY6HGQQDcob1SWtqA/PA4PnpgvVs2r+N2MBushktm9/Kqw+kA2jjimZu82o/6uVGDaLQBglnCAH9gANM42wm8Zo/+8rPT48ByDJ61wz/oZ4Gy0HTWoimWdPID1q+NJJhF/YePH5TNcdzv2OdZZoBg1tk+eN1Ql1DbAMFsYC/ICRSiXy9/wSzGctn4V/z6rn360B4BT1F9/LG+qj/h2O6NYvR9hVReJzDrVg68Tp3WsEU71s4eIf795x+3rOr4kuG9Vd2y/cGCWWgBYJxQgGet0zTRkdBd9p376L+GOUbWMSKYJZh1AqDReoxgNhoQanjaQDCrw0bG/Ye0BLOENzEOsMa/Vkz8vG+V45dGTHG74rPXxVvdvQHVp+93Ut+Vdm2a72gHiS1bwiBMWQwP2H8cvhT+tPNrMe6Vwl71YkreHRvmWDxG0VCZ9uzJ/ardKF/W5bTHmrFrvhphWWdXZsaasptWTRFv907tWsaw3mnEltXTvAAzdIX37MqF/WVxYsHk9o7lBKPh3HEtLVNWq8qMyIWzh80poAd09h5bJy14jDpFiw0QzBIchBocJLfyMF3xgq9miSt//6U/Fsw4pgEGwOz8+v1eUBY61ehQVHy9ZpG4qs3KgIztnm0snh/+hCpv0651lvwHjuxT57r0a205J/U/dMyzFMHjr7VyTHPyjGfqQtQp82IPOLt45XyBPugBXsBfrFog6nQqKc5fPGueOnH6mCXvoInPqywr1i+xnNt/aLc617V/O8s5vX7GeW+iDXjbAMGs9QU4gUDs63HuqOdZhXVWExpTgM/DW793/K6NKYhXTR0kht+V3bGcj19tb5kyWD2MjAimGN4wd5x4q14aS14dzB7btcGc8lhfxxZlwAN22+IZYlTLApa89r5M79ZA/LJ/qyOg/WX/NgHPWnsefD51wLNkEspwSjPqvoKqXHj0DqpltQ1MV7zp0ylmW/V+I/735Uvi6I51YlrX+o5lo76k1t2pjzxmHVPqEZ16EMwGBmYPnvLMdjDr+58dPVwbD15pvI/8V926ur+/yTFd/3meKe+v/P2PqPTy147pohWSRqJdBLPhgaLRUCrBLGGs/zBW14pg1hEmRcsLfLbDf5iEdU0BLz8cc4+YObyBGP1CnpCtOxps2QCkgKgfjbtXvDeoote0wU7j/Fa3a16vY18uqLxlndL5ewxepf8b0UjMGd1MTHqjtBj8pP/awtsY0zOj/eNfLWZZb9bf+oPREPAY00sD1M4YVtfRw9ffdjCd/+NOrcKjFcGs94tvwgBqkhgbAGRt/9ydJlB9YUQ3M25fc9at3AaP3i4efL6paPtMIxOIuqWLxHH0AW17cWR30aHvXaJqu4KEqQa4jsRYsE7qTjAbnS/iCUiSflyw/umkjuXEvJfaitnP3GOs03qbWhc2ofEYec8t5tTIALWznr5bjGiW2xVI6mD26M7119IZ0BOgEvkBM91AsFs7hjTIKN5/rJqZH1MFj21T3LV+tzISexxr5056qLyY92IbMf+Vdmbcvuasr7KTSndfbeC5pL/eqLn/mhPMBgZmIwEjWadnjAhmowGhhqcNBLM6bGTcf0hLMEswG+Mes4RH4YFH1JW60gbCYwMEs3zRT9hDG6AN0AZoA7FhAwSz/r8cJ0igVqGwAUcwa3iQhqJslkEdaQPxZwMEsx7oRwAa/VoQzIYHikZDqQSzhLH+w1hdK4JZglmCWdoAbYA2QBtIMhsgmI2Nl/GEJhwn2gBtgDZAGyCYjb+X+AQz0T2mBLPRPT68fjg+0WYDBLPRDyMJjD1jRDAbDQg1PG0gmNVhI+P+Q1qC2SR7GU/vs/B4n1FX6koboA3Ekg0QzPJFP2EPbYA2QBugDcSGDRDMEkJEG4SI9/YQzPKai3cbZ/9Ca+MEsx7oRwAa/VoQzIYHikZDqQSzhLH+w1hdK4JZgll6ytEGaAO0AdpAktkAwWxsvIwnNOE40QZoA7QB2gDBbGhfoBNIUM+EbIBgljaSkI3wPG1EtwGC2eiHkQTGnjEimI0GhBqeNhDM6rCRcf8hLcFskr2MjyWPLraVHoi0AdoAbSA8NkAwyxf9hD20AdoAbYA2EBs2QDBLAKADAMbDbw8zezYWx3ZvNLeN8ydwbVmur0sboA34tAGCWQ/0IwCNfi0IZsMDRaOhVIJZwlj/YayuFcEswSw95WgDtAHaAG0gyWyAYDY2XsYTmnCcaAO0AdoAbYBgNvwgjrCTGtMGaAO0AdpAYm2AYDb6YSSBsWeMCGajAaGGpw2/GsWKFDmKiJR5bufmosF1N90iUmTIKlJkL0iN/tMoRY7CAraTJvV14o5i3KgBbYA2QBugDQRmAyVvvU5kz3ydyJczsHzJVWfohOdu2htSi5yFM3GjBrQB2gBtgDYQMRtIf+P1Ip2x5SiQMWJtiOZnYfZbM5rPbDy3K9+ckhs1oA3QBmgDtIGot4F8N6QQOdNfJ27LFv/PrVwZUlz7bp2zsLipTFNuYdYgU5EaIk3WfCJ97lLUOhFap7kpt/y/cozxvyVDHClw1eiLHFzuqQVtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtIDpsYH4cMUl2xVDgPMDsLWXuEwWrdebmokHGbIXMG1C2AtWp0X8a3XL7vaYmN2VKIwY+VZYbNaAN0AZoA7SBgGygbdNbzedIgTwZA8qXXJ85d9W+xdQre/6Momq7gtyoAW2ANkAboA1EzAZSprrmaVK+ed6ItSGan4UVWuYzn9kpjHctL9XNxo0a0AZoA7QB2kDU20C+G1Obz677brsh6tsa7LO1ct60Zl9zFq0oSjfvxS3MGuSr2MTUO2P2fNQ6EVpnzlPM1M9geMNIM+NLAXON2Urtp4n6T63h5qJBzqINzAugSN0+1Og/je544D1TE7xQF/se5EYNaAO0AdoAbSAgG5g3qpb5HKlTKVdA+ZLrM+fdl+8w9SpWK6fo9XFdbtSANkAboA3QBiJmA6nTpDSfSQ9PqBqxNkTzs/Cx96qb+qRMcZ04/2ohbtSANkAboA3QBqLeBqrlS28+uz64/+aob2uwz9ZuVW4y+1qqSRfxwKTd3MKsQbXO75h65yxRhVonQuv8le829TOQZO/4wpLsDcGsC4zVQTXBrDe0J5gljE6ucIT9pu3TBkJjAwSzgelIMEsYHc0Qgm2jfdIGkpcNEMz6Hm+CWcLoYKEB89OGaAO0gaS2AYJZAtpwQWqC2eBsi2A2fgEuwSzBbKK8gAlmA3uhTpBDvWgDtAHagNUGCGateiRkHwSzvl+CEwpRH9oAbYA2kHQ2QDDrW2uCWQKVpAYqrI82RxugDQRrAwSzwcGzcEHNeCiXYDY42yKYJZhNFLzTvU5jOU6PWXrMJvTCnOcDAwzUi3rRBmgDBLOB2QDBrO+X4AQy1Ic2QBugDSSdDRDM+taaYJaAJFhAwvy0IdoAbSCpbYBgNjh4Fg8ANVx9IJgNzrYIZglmCWavu05wjVkPoKXHbGAv1AmhqBdtgDZAG7DaAMGsVY+E7INg1vdLcAIZ6kMboA3QBpLOBghmfWtNMEugktRAhfXR5mgDtIFgbYBgNjh4Fi6oGQ/lEswGZ1sEswSzBLMEsxYbIJgN7IV6Qi/ceZ560gZoA8nNBghmA7N5glnfL8EJZKgPbYA2QBtIOhsgmPWtNcEsAUmwgIT5aUO0AdpAUtsAwWxw8CweAGq4+kAwG5xtEcwSzFqgXCxPS5yYtnMqY4+nrNSPYDawF+rJDbiwv7QP2gBtICEbIJgNzEYIZn2/BCeQoT60AdoAbSDpbIBg1rfWBLMEKkkNVFgfbY42QBsI1gYIZoODZ+GCmvFQLsFscLZFMEswSzBLj1mLDRDMBvZCPSFAwfPUkzZAG0huNkAwG5jNE8z6fglOIEN9aAO0AdpA0tkAwaxvrQlmCUiCBSTMTxuiDdAGktoGCGaDg2fxAFDD1QeC2eBsi2CWYNYC5aTXZHLZ02OWHrPJDZiwv4EBE+pFvWgDgdsAwWxgmhHM+n4JTiBDfWgDtAHaQNLZAMGsb60JZglUkhqosD7aHG2ANhCsDRDMBgfPwgU146FcgtngbItglmA2JGC2TvcV4o62k82t2iPzAi6zbu/vjbxTzPxVHpoTcP7EgmSCWYJZQpfAAEJS6LXni+Zi3dwmYv28JuLfPR1EUtQZT3VAM2gHDfcuuYf67Ys+G48ne3PqC8FsYDZHMOv7JTiBTOzq03FMZdH6rQrm1v2j2iKWxvLBUZXMdrcdUl70nFcnptoeSzqzrdF3fRPM+h4TglkCkmABCfPThmgDtIGktgGC2eDgWTwA1HD1gWA2ONsimCWYDQkErdB6gjCkNLccReoFXGaNxz9T+TPlui3g/ASz3oA1sZpwKuPAXqg7QQkeC07DCqWyqfvBie9aRRVY/PK9+mJi/8pR1Sa7vR1fdZ/Sr0qZ7FHdVnvb+Tm4ayda9COYDWwcCWZ9vwQnuIlufbrMqCHKN88rukyv6QUv85W5ST2POoys5HU+msc2R8GMqu0AMdHcVrYtuq+RWBsfglnf9kQwS6CS1ECF9dHmaAO0gWBtgGA2OHgWLqgZD+USzAZnWwSzBLMhgaAEs6EDo4kFqqHKRzAb2Av1aAEh8dSOaASzZ9a2Fvc1yme+pH2sdeGohp0Es7yGI30/IJgNzAYJZn2/BI81qJGc2tukTymR/sY05rPxkcnVvOAlwSxtOzldD/HSV4JZ39ctwSwBSbCAhPlpQ7QB2kBS2wDBbHDwLB4Aarj6QDAbnG0RzBLMhgbMtpmoflWeo2j9gMukx2z0gF2C2cBeqEcagMRj/dEIZpe+30Dd4whmeY3E43UXyj4RzAZ2jRDM+n4JHi+wIx77kbvYjerZ6ARm85fJqs53eJces/FoA+xT/N2/CGZ9jynBLIFKUgMV1kebow3QBoK1AYLZ4OBZuKBmPJRLMBucbRHMEswGDFGdPDPr9l4taj25xNyw3qxTGl/HCGYJZkMJBVhWYFAg2vQimA1u/OgxG5x+0XY9xGJ7CGYDs0GCWd8vwQl+olefhMBs19m1zCmOnaY5jvZx5VTG0Wt30W47sd4+glnftk8wS0ASLCBhftoQbYA2kNQ2QDAbHDyLB4Aarj4QzAZnWwSzBLMBQ1RfgDWx5whmCWZjET6wzYHBB3/1IpgNTleC2eD089dOmc5dZ4JZd22c7IZg1vdL8FiHHPHc/oTAbCz3nWCW12Us228wbSeY9W37BLMEKkkNVFgfbY42QBsI1gYIZoODZ+GCmvFQLsFscLZFMEswGxIwW6PzIlG4ZjdzK9NimGOZdXutErc16SdylWgiMucuI7IXqGGmr/zgTEEwSzDr9LKaxwJ7uR9tel3Z0U7MGVFDvNCllGhSK7eodUdO0f3BYmLKwKri7Lr7Xddp9QfM7ljUTIx8qaLodG8hUbVsdtGqcX4x8KmyYuXMhq7l6vr8ufUBMfXNauLBFgXNdqFtD7csJEa/WlFc2vaAKuPiD23E0OfKm/UYjwtzSsbyJbOax3D83Ppr/UA/8fm9QVXFv3s6mPtmdfOIupVziWcfKyUOLbtXlSnbsWFeE9GvZxnRtumtovLt2UTrJreafVg2rYFXWplH7q/uai9mDa8hOrYoJGpUyCFQF+qHLv6C2cSOj2wD97F9fYZz/AhmA7MNglnfL8GDAQzM65+2PefVEc1fvl2UvTuPuLVCNpGryI2iWK2comq7guKel0qLnvPrWNaPRdoaHQuJjFmvrS+L52OFlvnMY81eLK3SNn2ulHkMabvMqOF1vH7XYuaxhydWFYgXq5VL5C6RWZRvkc9sTzfD41aO4cMTqop6TxYTRWvkFADCxevmEg26FRdou0wj952MtKgTW8t+Zb3Oy3QtXy+r0qF8eRx7NzB751Mlr+V5qJDoNsfTPj0v2l3DOI/6kV4/J+OPGmvy4jz6gT5jPV7ojz5CD5mOe/9smDqFTieCWd9aEswSkAQLSBKbf0rLXKJfg2zi3WY5xNlXCpr7O4tmFDVuzSB6VM0itvbMJ/SyT75UQCBP7+o3iQaFMwiAmccrZhbv3p1D/PTMrZa0ej7EUf6HD9wsnqh0k2hcJKOocEs60eq2TOKlutnEnLa5xTnjvD3P7qduNduHNq7qnEccf6GgmHRvTrPOSnnSiYaFM4puVW4S67pa22kvB5+Rd6TRTrS3doEMonr+DKKzEcex/X3ye9Uty5ja6ppGI4x0OLajd34xqllO0bF8ZoE2oA/9G2YTP3R3LyMxfZf1Yx+M7no5jPNeE0obIJgNDp7FA0ANVx8IZoOzLYJZgllHiBqo52uF1hPUGlI5itTzKrNqp49Ehsz5VBoJOLBPkTK1KFyjqzqXKddtXvkDbY+/6XMWvbZuZJG6fZKsTn/bFql0XGM2sBfq4YQbsVw2AGHZ4jep61q/5hHPnSO9wLqtTn30BWb/2d1e9O9VRqROlcK1bADW3za1cSwb9X0+qZ7ImtnzMtlX2/Z80dy1HuTb8mlTs55q5bKb6Qrmu8GEz/Yy766TR7Xn5Or7TNCbwshvTyc/t7nrVld4/eNXzUWxAp51/WQe7NNcn1IMeba8KrdKmeyqXl3rYMZHL4dx3i+cbIBgNjC7IJj1/RKcwCW8+nSdVdMEnfqzxB6/tVxWARghxwJw1J5Gfi5UKZtKB9goj3cY6VljNm/pzObxG7KlFYCjqdOmUulkeuwLVcxmQuFWA8qJ69M5p8lV9EYvQHqPAYdlObc1vkW1R7Zf7m9rdItKBwAtj2PvBmbdjut5H51STZWbs1AmS7lI16RPKZHSx/8x6GvDHiW88ul1MB7e6yI560sw69u2CGYJS0IJSwIpC2ARz7b8N11vAkv5nJN7AFRZ3uon8orSudKqZ5FMI/e5bkgl5rfPrdLLfNgffb6ACTFlWqd9/UIZBECsnm/RQ7lVfS/WySKq5E2vPtvL6G6AZCe4i/IAV/PcmNo1b7YMqcW0+2+21C3bAUiNutC/pY/kETe5/O+Q0fjOPNGAxjKf3Ce27zJ/MLrLMrjnPSYcNkAwGxw8CxfUjIdyCWaDsy2CWeOpHafhqNEvUan9tCQBjr7AbI3OC8X16T0vZtCujFkLiky5SokUqa73+oeLYDay3rMEs4G9UHeCEsn92Lf/ayTSGl92cK1jQ7yS4REKr9RMGT3XPMDktCHVvcChLzDbtLbnJSrKzpsrg2hYLbcoemsmoYPOkoUyi78Mj137WMx9t5YlHUDmHbdlE0gv24v9DcYXvq2f3S1+Xn6vAGzNnsXzxTa98QIZx7Dt+/Iesw4JZvUy9Pj/htUw08FLFfXp53JlS2d61ua7+doXSXkuT8704vyG1pY+/PJ9K5Ezq6ct6HOpwplNj1tdc1mGE5gNdnzsmvIz7xl2GyCYDcwmCGZ9vwRPzoAkKfoOkCqfGSlSGj+cKp5Z3Foum0hrPAflceyz5c+oQGG5e/KKG3OmM35c6fmBUUbjOYljtzfxgNCEwCzKTZXa80OrGwwP3HSZPP8n4HzhKtlFqjTX/qcAzLwpd3ovqFm1fUHVNmgWzWC29VsVREpbn6F3jgI3WPRG35v2tcLipLAH1sH7EcGsbxsgmCU0CQc08adMCWbxfHDaJGj8/KE8Io324x/Ey+dOZ3rM3vDf81TmH9vcG042LeZ5HqVMcZ2obABWeNzeaPsRVamcaSxgUwezsnzsb785rempWlibZQPH4T1r7/cHBnDV86Y2/s9AfnjsptX6hDTwzLXnl2AW5zNo7yPyZb5eZEnv/QOvNU/mtZSR2L6jHcHqbu8LP/NeE0obIJgNDp7FA0ANVx8IZoOzLYJZ44kdpyFqwOzNJZuqf67SZMgmKrSZpGBxzScWi8x5Pd5dxlgYwJYes5HylkW9BLOBvVC3A4nk/hkerWU0T9n6VXOZcFPqAjD5RNui6p6Qw3iRi+mC5Xns3cCshD24T2RIl1pMGlBFiL0dVN41H94pbjFgJs5je/OZcuocygXk1KFm7Yo5zWl/Zd3bDBCre/neZUBgeQ7evbLcx1oXVsfleTuYrV4+hwmdx7xWUdzXKJ/A1MlI+/QjJVU56MP0oVYwvXBCXZHlRs9LaWgl68AeUzfLdtycPZ1An+X5M2tbizqVcqnzSGcHs6EYH1kf97xXuNmAvFZhj25peNxjPwSzvl+CExSFT5/Hp1ZXzwzAzwdHV1aAs8fca9MbX2e8lJXPnbZD71DnMS4JrTHrD5hF2fCglXVjauLb77L+CAtpStTLJZ74bzpkePkC2Mp2ZbklvaVd0QxmAa5lu0s1zC2gs7RxAJ8Cd3j6hWml5Tnuw3cdUFurtgSzVj3s9kEwS1gSSlgSSFl2MAtgCrA6pEkOcU+JG8QxY/rfMy8XFLcZwFQ+ZzAN8Laenml7Mc3uwxU8P0iG9+nPzxVQcHKfMU2wzJvO+BHRem3aYZSN6Y31H0Mve8wDNu1g9vqUKcz2yT7CQ7ZvrayqfJSDKY/lebRTh791jLbvfdrjlYt2Njf6KdsHULulh3VaZB3MIh08ezf/lwb1L+yYW8BbVpaBqZJl/cH0PVjdZRu45/0lXDZAMBscPAsX1IyHcglmg7MtglnjiRynISrAbN0eK0WKFJ5fpsGz1g4d6/b81vh1vAcmEMzSY5Yv7D0v7GNNiwlvVFZfdArny6SApL0fWBvVuPeaW9/HS1ngjROYhffrrbdkVHkm9q9sySPL37vkHjXNMbxeT63xrGU7vp+nbfC0dfKoXTGjoaoD0yWfXtvKrCcQMAtP1wsbrZ6uaB/AsP5FFvBKtlvffz+7sWoD0svpktFefQpnpNPzIQ7wnT+3Ryc7mA3F+Njr5OfYvV7DNXYEs4HZBMGs75fg9pfi/Bw6vbAerHwWF6mewxEClqx/7X/0dDdcL+p2LmpJEwowmyl7WtH5A8/6sxjfJ/9XS0g4hPblLJzJa51bpJGep/C61deajWYwq0+F/OAoz/TO0q4fGlvZ9AiGd3DWPBlE9w9rWzSX6bgP3XVALa1aymvPvu4ydbqmE8EswUm4wElC5epgNnem1OLQcx5oKfMOb5pDPdcLZLnehLXynL5ve3smla5nNY/n6qw2numI7y15g4KWet72Za8tqQMP1KEGFJbn7GD2DQePVqRtUdJTd9fKnroRl/+TAC5jrVdZtr6vV9Azy1Sz4tY26mD2FmM6ZMBqPS/iWKNX1gNvXHk+mL4Hq7tsA/e8v4TLBghmg4Nn8QBQw9UHgtngbItg1ngix2mICjBbsf0H6p+ezHnKekFZCWmL1XtGpSOYJZgNFyxguYHBgsTohbVUjXuqudm9QfXysMapTFe+ZFYLYHQCsxvn36XSF8qX0ZJeLxfxVo09v/T9bHwdlbZJLc8XzXGvV1LH7fmffbSU6Nq+mAAsgRcqzgcCZvv1LONY9qKJdVUfMJ2xvV79c/P6eVXakS9VNNNu/sQz+wCmhdbT6/Gxr1VSee1gNhTjo9fFePivqVjUmGA2MLsgmLVCAb78Tzo92o+oqJ4XWNe0ce+SFg9OjEW32bWUp6p9bEIBZmt2KuQIHrPm87x4bdTTeb1VTGss/5fQ4W40g1nd0xcesY9MrubV/8en1vAaB7v2/Jx010ly05pg1rdtEcwSnIQLnCRUrg5mn6+dVQFFPR/WmZXPxXEO0xTLtFgLVabT4eT3XTzHMe3xhBa5TC9cmQ/74wbs1L1s5TkdzGY3fiDtBEWR9rsu+VTd1fNnUP0om9uzXM+sts7r3yL/isc97xsypU2p8uOcDmYHNPSe6hhp4KUr+w54jWPYgul7sLrLNnDP+0u4bIBgNjh4Fi6oGQ/lEswGZ1sEs8YTOU5DVIDZko1fUf/03FqpkyuYrfbIPJWOYJZgNhZBBNt8DUQUK3DtF7TGfVUM6F1WzBpew3Gb+XYNdc3Ds1XXzwnMzn7Hk754wRsdy5R1YQpi1I9t+At3qLL1NVx3Lb62Nqxer694IGAW7XAq6/nOt6l2vdqttGMamQ8ewbIPcjrjD96qpo690tU9/8FvWqh0djAbivGRbeQ+MPiWnPQimA3MNghmfb8ET25QJKn7m9G25ltawzO2aI2comGP4uLRKd7QUG9fKMDsfW+U9QKTqCN3Cc9Ui/cPKu+YRvc+BcyUbYtmMFu/azH1jJbP+ZyFMolK998q7h9Y3gCy9JCV48h9ZO6NBLO+dSeYJTgJFzhJqFwdzE6613ttWOTX13F9sU4WgXROG4CrfAZhal+9bnjjynPY32T8cKtlqRvE6Htyil1PeXvpyrw6mL3XSC+PO+3lWreYuhjnD/ctILCeLerDjFGnXvJtZ7kyembl26NNd6yDWXjAOtUNqCz7l8fwqtXTJLbvodBdbwfjvsef+gSuD8FscPAsHgBquPpAMBucbRHMGk/kOA1RAWbzlm+r/ukpXr+vK5jFdMbGOJgbwSzBbHICGPHU16u72ovrU3vWbJHXtD/7X76/NmUw9HACs/17lVH3CH/Kk2kk1ES5abX1ZH7b1MYnGLWPSyBgdsO8Jo5lY61Z2a73BlV1TCPr1eurW/naOp1PPVxC5Xebyhn5MZ2xnDJZB7OhGh/ZRu4Dg2/JSS+C2cBsg2DW90twwpnw6oN1YzNk9qxHJ59Tcp/LmEa41qOFRfePvIFhKMBsu2EVFVDVxzp3KQ+YxfS++jkZj0UwC/B6W2PPD8ikznKfNmNqcz3dB4Zb1/OVfeY+vNcD9a2rphHnVMbOtkYwGzgQIEQJjWY6mP3msVssQBEanzbWgE2d8to7NflM8Xevw02UnUMDn/YyyudOJwY1zu4FT3Uw20ubHtlp/IsbyxjIcrFOrO6teosBhp3y6Md0LVCvPKeD2W+19Wvleeyhk6zbDmYT0/dQ6a63kfHQXDPU0aMjwWxw8CxcUDMeyiWYDc62CGaNJ3KchqgAszmLNlD/9BRv+KIrmMWUxqnSXJuyjGCWYDY5AYx46ivWYzXup4nati9spkClE5jt1sHbw8SfugBDoTGgpISV+DXuv3s6qPr8GQMdlD7WurBX3mrlsqt+H1p2r9d51NGumefXuTOGVndMI9uirzMr4WrbpreqOhICuzdmvN5MK/Oi3FCNj2wj94HBt+SkF8FsYLZBMOv88puAJOl0AYAp1TC3ABR0e7bCg/Wx96tbAGkowOyDo52ha1KAWfRZ9veel0pb+qZDX4AYaY9ux+V57DE9sSwX3rD6ORmHRzKgt0xn32Pd3AbdizvmlWVwn3TXSHLSmh6zvu2KYNbzop/QI2m10GHk1p75FIyU47C/j2c5H/szJaHPAKOyHOy39cwvOpbLLDIb3rJueavkSy/2at6qOph1m2pZ1lHGWNtVlrv88bxiQzfPD5hvvckzvbBMb9/X1daZndfOGcyu7+qtEcqBN66s2w5mcT7QvodSd3s/+Tlpr7F41ptgNjh4Fg8ANVx9IJgNzrYIZo0ncpyGqACzecver/7pKVyjqyuYrdNjhUpHMEswm5wARlz1dW8HkSGd56XuAWNK3ZOr7/NrAziVWjiB2aHPlVf3iC5ti/hVJuq++IPHMzZvLs96dSe+83joynp97UMBZrF2rfG8Mbd3tCmWneqdM8IzdXOzunlMbXp2LK7yD3m2vNLLnv/y9gdUOh3MihCNj70+fg4MwiUHvQhmA7MJglnfL8GTExCJdF/hzdlqQDlzWt2cDtCwWK1cFlAYC2AW8NVNV0zZLJ/LiQGzTuvDoq4HR3nWencDs7JNjxoQF5AWbUlnTCMt2yP3Hcc4g2uZn3veP0JtAwSzvm2KYJagJFLwJiEwe+6VgiK98aMe+fzYbHiiApz6s8Hr06lfOA7g+kzNLKKctgasrOP+0plUPh3MPln5JnXcqVysQSvL2NE7vzjyvOcHzJjm2CmPfqxINs9MHwC78pzuMZtYMCvL8rfv4dBdtoF73m9CZQMEs8HBs3BBzXgol2A2ONsimDX+G4jTEBVgtnDNbuofrty33eMKZqt0nKXSEcwSzCYHcBGvfSxb/CZ1La/58E5XeIj+7/vyHnFmbWuvNE5g9pOxdVS5jWvc4pVH1/PCxtbix6+am1P66scxJbBxvzc3X22b8EZlUa5EVgFvWwBZlBEKMKuvGwu4rLfNHu/X0zN186Otrnno6nDayWtXlrH7i2aqnxYwa/QjFOMj6+E+MPiWnPQimA3MNghmfb8EDzV0YHlWvZ+YWUtgOuOus2t5AUysMVu4Wg71TEmX6XrRc14dlS5awWzzl29XbbbDZH38da9cf8GsDqzdpljGurny/w07mIV+ncZXER0MeKu3BXGcQztSGS+lZf46jxfxSmfPx89Wm6YewelBMOtbP4JZgpJQgZJAy0kIzKK80rk8nqhLH8mjgKVTXfBS/fEZ7zVjfzbWe132WF5x7AVvWIs1ZluU9Mz2kDV9KnHWAMIoXwezTYpmcK1bX+M1TaoUakpkfd3Y3T7Wsj3xYgGR1sgnn5M7euVXdQULZhPb91Do7jRGPMb7TahsgGA2OHgWDwA1XH0gmA3Otghmjad5nIaoALOV2k9T/zClTJ1O1OzyuSOczVehnUpHMEswm5wARrz1tc1dnul2OzQv6AofV81qpK75LDdeL/7c+oBK6wRmddiIdWx3Lb5HpbdrWL/qNQCLKYsHPV1WpevcpoiqU8JOe158rnmHx4Nm+fSGZv6vpnqmZX+oRSFVpszvz1TGK2Y0VPVnTJ9anFpzv1c5KA9a3Jw9nUo7/b9pj7d+drc6hvyYmljWr+91z1w7mA3F+Oh1MR4YgEsuehHMBmYXBLO+X4ITsoRPn+J1PT9Yqtq+oCMAfHxqDZEipecFqD6dce7innVgARvtY5WvjOfHWh1GekBk3tKefOGYyrhlPw8YBTy2twuf0S9MF2x8DzQ3f8EspnSWee59rYxj2eWb51VpdDDb4d1KItV/692nNbxjAcWd2la2WR6Vv/Sd7h6/Tnl5LHzXS3LRlmDWtw0RzBKUhAqUBFqOP2D23lI3qOdHa82b1V7X4k6etc4xXbGEsG1v90DXl+tmU8BTz4+pe1MZ37Pls1BOZ6yD2euN/xu2a8BUz49yZd7GRTKqOqrn98xu1dPHGrUDG3mWELJPexwMmA2m78HqruvDOO8x4bABgtng4Fm4oGY8lEswG5xtEcwa/xHEaYgKMIu1Y2/I4VkbMleJO0XdXqsscLZSh+kiZWrPVCTxDGZrdF4k4DmcIUs+UbDq46Ju7/+z9x1gchRH2yjnrNNxOkl3yijnnLNQRAkJCSQQQhICkXM2BgE2OdhEI5LAJINJJkpgEHIgGIN/w2eMCTbYHw6fbRyx+693dmu3dnZmdmZnd2dur/p5arunY3V1dfdsv9Pdey1ZjN9wv+nSb7Zp1amX6TfjlJR8Rq2+xXTsMc5AJoMXXZLyh1yLRaPW3Ga9KNdWt3IEfeoLsKD1DAYssLz23jc3dZcrja0GAA2HsY3jheXOTTuA6wTMIi12yiJP0MQRFRnHFHPed1w2MRUHd8piVy6HvfHIAgOwFukB7u67P3tHLwNKiNOpfVPzn/+XOGJZAsmzJlSm8uS8/QCzuNd21KD0H1HcGfuvtw/JyAtxjj00fWRxi2aNMuo5YkDHVP0OXdrTujuXeYD9FoG3SMNysgOzhWgfWZ668+sn5S437kfTxmT3lWLU/S+vH2xeuGOWReirbmV8uPugVLx3nljoGu/17yxIxQt67Llb2V7+Csx6L4LXFzAkinpOPTL9wRLul91y16QsoPCQy0en5pSO3VtmhHcbnAZeV148IiMM9YkKmMXxvzwPwl54eub9sdgdXNmnbUYcv8AsduBy3rhv9tgH0juIUecDTx6UCkc8Ccwibgv6GI3Tj1tTmyUz5NFrTPpd4cBTBjnGiUJftMz6MVaVCphde+Vog93lILdjwbc/NM06Xp3jOe3sh15uvnNyKq9VO7LHokLqrgKzCpoUAzTxk6cfYPbpw9P/lzHX3L6iMgV8chnYsSp3eEoA95J5adATgC3icjq2X9yU/niof+dmqXAJzKJs7Ky1H5G8b2t3017cW3vzQV1S6e8+OD2/NqcPp5x2/L66tZvpJI5BPnFSh1R68BcGmA1T97ByZ9mqreNLsXSgkMDsR7Sr/tFDqyzCRx5uPOO+Zo736pb0keP2+HtoTOF4Xrvl7encnreNS/w/GTh/s4kD8Dn/vEfMjBNvt2jJpbsdeVp908/MzJPvTMVbed3rjvGWXbUvFWf2absc45S6zlEBs+UiVwVm6Y2hTE1sgNkJRzxomrRIL9y07z7SAhkBOvaefAyBsumdYdQWFghZLNDRnm+XvoldcH2mn1Q0oFOW2X3k2tRiDOo6dOkVVrkVfaZn+I9bf6/l36oivWDWqEkLM/Xo54rOpwKzCrR4AQh+wrCjFPrNtIl2qj51ywzz0j1zzBlbBpte3Vulwpo3bWSwE1Tm6wbMvvu9xRagyvn2qGplbr1ovPnhg/PNzksnmGWzu5tGYlfPIYtqM/JFGeCF0zelXSsXbB9iAOQApD2TeIMfh1991uhU+rcfTx8PjPCV83qYYw7tZ3CPLvL1A8wiHo5QBmDMZYwe3Mncc/kkAyDogWumGIC+HAZeIDcpmw+ovIoO6SOqZozb33znhqnmBw/MN18/baTBTlpOD9sOzCKvsO0j+VG3jhdOOlBqYPb5nbNTeo/+5cQT/E4/Kg2YzJtc5RpvcN/0jrhc90G7lRXEX4HZ+gF2FBIYKFReABgai495ADTOP2mgWXvVaLP66yPN1I19CEhMfzw5ekWPDJBQHnPctrK5GTK/qwHYy/xFBcyi/EpxRy52pw5bWG0Aco5ZVWMAMMu5Em6/wKw8Jhnpqge1N9OP6mvdEwvQVu7CRbgEZsHX8MXp3bSIO2JpdwNQG0cbYwdur3HpRfGGFL71HuddtSxjtXX8KLQOlAqYlbvP3T5S2EYfUci+uurSkanxRdZ7wakDU/GaEOgjwwrtVmBWgRM3MKDY/n6AWfCwZmjmh0eHjWhnHjhkf/PE+mpzwsSOBrtMuV/hSODvH5U+8higSEvxf3jI/s3MbcsrzSubuxnc5bpjbmcj74eVwKgdmEUZAIPuXFVpnt1YbbDTFffHctnjKAz3s0q5zeqdnp8bN9zPnETA63cPqzJPEfhz7oxOGbzN6NnS4FhjmT4MMBum7mHlLuugbh1jiqEDhQRmHzk08wMQN36Po53v3N9n9nI/3nxARfq/BsYJt/z8+scNmK3oMyolhyGLj3UEU1de/0YqDmQ25/T7HONN2nJNKl7jZi0d49QXYLZc5KrALGl8mZrYALMAJkeuutE0aJR+ASSZpwYTuFu07556Lucds51qJ6TqiXr3mrjZAlpl/eE/cMFXzYzjXibQOg3AwH/0Id9SYJYW14MseGvc0ssL98bOnlCVoevQXzs1pj+C3/3mtKz2dANm0ZY41rdNK/exhMuYOrqL+cdP08cjsx6AN+zi43hu9trFmccwf/mztaayU+ZHJEiLHbrI2y8wi7gAfJuJP7xOPGBH72M3Ts+SDdK/cu9cz/R9a9JHUDkBs2Hbh2Wpdun7Vl2RuQKzwXRDgVkFVgoNHATJD7tJneYhu1/N8E7WrjSZN+4/tcdrW9EsBYpECcwuPnMwvUenjyq28wnwZtCc9MKSX2AW98BKQMmeL56x4xVANdx2YHbbfbRbt2/morlTHuAPYJOUt7p1rCiFDigw661nCswqaOIXHCh0PL/ALO6NnU6gpdPcIv1wHPGug6uyQBAAqTKemxsg6genpO+odQJm3dJip63TUcfYYTeiKvs/tz2fKTUtzW8c7sANA8yivfKtO9KGlXuh9UXz07FK6oACs+GOmw0DdpYLgOgmg6h2zJaLXBWYpRm+TE1JgVnsfiU5JhYg6EheuUuU3cMOujrjWGMrfoOGpl31MDPpqMdMk+aJHSrtqoY6pud8CmmXesfswPkXpOTUsFFTM3Hjw1Zde03ckvKHHKYds9vy33+guFOSds8Wsu5ueemO2WAL6nUFKCk5n++uMzd+ZazpVun8p3DJzG7WLlEnvsYMSR/h53QPK44jXTC1q8FuWx532MbdrNjh9ve3skFZLgvHBV9++oiMnacyvQW2Ev8cn23s7O3dPQ16Is3ZWwdZ8aaIe2k/fnFZVlrOg23swMVdtgCnuWy2J42sMM98a5ZnHgiXxxojLXYLg4/PXllhOid31SIvLjPDDtE+GfkQUKzPKgO7DpQamJX3N+OjBjs//Ixd8dzPFk6rdo03/ID0keHXnpPeOc/5FNpWYNZ7EbwUQER9LwN3srqBhe0IYBx/SE8DQNIuJxwzOnRBdeYuUVro3fbtqVZcgLnc5+RdshKwXX/D2Kx8UY68h3bDjeMd48jjiI+6I/sY5tVfG2U616RP6QAvDWgHDsDS1ZeNMAtOS48JS88dklGG3HG76faJGWHH3D/V2ukK8JTrBxvHFGNnLo4s5nIhV7vcNtOR0aOW96D/PpnpkQf8cEQ0dtDa0+mzjhWl0IFSAbPYbc79Z8K6Xo76jg8Z9hN3WaJPO8lg0Rnpvoxj2Z3iFMpPgVkFOyTYUUo3AyvoN2+73N/K/GAn6hULKkzXNpmnKXGfO7BfS4PjQzm+3caOuNFdnQHS2g5NzJnTOpjf23a7SmB2+aDW5qqFFRnHDqNs7MbdOra94xHJzAOOP8Y9tB1bZM+ROF4Z6T85PXOnLKeVgPRrx/RwrB/y5xOsetDuYU7Ldj5157Rh5c75qK3jTKF1gMePnSv3z9L5oGU9RrvYeSzBzna39NhRz/HkfdL2+PJodRwpbg8P+hy3HbNd+o1JyWHI0hMcd7muuuFNet9Jrw3OPesBx3iTj74hlVfTlm0d47gBqMXyjwqYLRe5KjBLo0SZmpICs24gn5P/+A3fto7wHb78WjN12/MlARud+IBfqYFZlDnusF2m/+wzzeQtT2XUHYDogLnnZsgEd9AOW3a1GbTwYjN9+0sZ8d3qFNZfgVkFWQoNOvzxR6vMy7vmWkfuvvbwgeZPP17lCogEKRv3v+IO2Ue/Mc08t3OmdawwdrYGyQMgJkBOHBmMY4KNAyBrz+9XLxxk3nx0gQWA+olvTy+fcccsAN+Hr59qXrx7tvnkpdygrkz/P88sMU8S7zjK9a9vrA5Ud86nWO3D+atd/8aUUgOzdV3HFJhVsKVQgEHYfDYTuLnyohHWkb/LLxzueOesUxm493HtlaMMwFe3OyCd0pXKD0AowOdl5w8r6NHAAKvXXTPGusNWAs9+6wUAF+kW0e5e7FyG2wkA95ufxtOxpBA6UCpgthC8RpGHArMKmAQFCaKOj12tOAoYu0FxHPGvTk3vcs3F2y8pLe6QxF21AGOc7pzlPOzALPv/8Oju5g4Cg3Cc8ccugCrHtds/P7HGPLyuytrZiyOXP7MdXWyPX8jnIHV3KjeM3J3yUz8de8LoQCGB2TB8lCJt3IDZYgGicck3KmA2LvUPy4cCs2WKylK1YgvMhgUOC5k+CmC2kPwXIy8FZusfiFLXQQ3lX3VWdSBeOqDAbLD2UGBWwZQowAUtU/VOdUB1wEkHFJj11gsFZhUcKQWwUBfLcANm62JdlGft5+WmAwrMRneUcVjgLu7pFZgNp1sKzCowW5JdmMUAEAuRpwKzr2a1vwKzwRbUFRBSeakOqA6oDmTqgAKzmfLIpR8KzHovgjsBB+qnMlMdUB1QHSiODigw6y1XBWYVsCk3wKZQ9VFgVvtGoXRJ8ym8LikwGw48izs4GiV/CsyG0y0FZhWYzQLmCgF41pU8FJhVYDbXgrmGBwMYVF4qL9UB1QEFZoPpgAKz3ovgCr6ofFQHVAdUB0qnAwrMestagdnCAwYKwpSHTBWYLY921P5Ynu2owGw48CxK4DPuZSswG063FJhVYFaB2f32M32mn1Sv5SCBdN0xG2xBXUEolZfqgOqA6kCmDigwmymPXPqhwKz3IrgCMiof1QHVAdWB0umAArPeslZgtjxBCwWjwrerArPhZah6qDIslg4oMBsOPIs7OBolfwrMhtMtBWYVmK3XgKTumNUds7kWzDU8GMCg8lJ5qQ6oDigwG0wHFJj1XgRXQEblozqgOqA6UDodUGDWW9YKzCpwUizgpK7n+/Lm7mZ4VTOLTpvS0dT1+ij/2tfLSQcUmA0HnkUJfMa9bAVmw+mWArMKzCowqztmM3RAd8wGW1BXEErlpTqgOqA6kKkDCsxmyiOXfigw670IroCMykd1QHVAdaB0OqDArLesFZhVsKacwBqti+qz6kD90AEFZsOBZ3EHR6PkT4HZcLqlwKwCsxmgnDzStj64dces7pjNtWCu4cEABpWXykt1QHVAgdlgOqDArPciuAIyKh/VAdUB1YHS6YACs96yVmC2foAYClZpO6sOqA6Ukw4oMBsOPIsS+Ix72QrMhtMtBWYVmFVgVnfMZuiA7pgNtqCuIJTKS3VAdUB1IFMHFJjNlEcu/VBg1nsRXAEZlY/qgOqA6kDpdECBWW9ZKzCrYE05gTVaF9Vn1YH6oQMKzIYDz+IOjkbJnwKz4XRLgVkFZjNAufqwS1bWUXfM6o7ZXAvmGh4MYFB5qbxUB1QHFJgNpgMKzHovgisgo/JRHVAdUB0onQ4oMOstawVm6weIoWCVtrPqgOpAOemAArPhwLMogc+4l63AbDjdUmBWgVkFZnXHbIYO6I7ZYAvqCkKpvFQHVAdUBzJ1QIHZTHnk0g8FZr0XwRWQUfmoDqgOqA6UTgcUmPWWtQKzCtaUE1ijdVF9Vh2oHzqgwGw48Czu4GiU/CkwG063FJhVYDYDlJO7SeuDW3fM6o7ZXAvmGh4MYFB5qbxUB1QHFJgNpgNxBma33jPFHPvAVKOgiPdCfZTy0TaKb9sUQi+0fUvTvirntJwVmE3LwqkP1zVg9vOze5oPT601Cr7EF3zRNopv2xSi32j7lqZ9Vc7eclZgNhx4FiXwGfeyFZgNp1sKzCowq8Cs7pjN0AHdMRtsQV1BKAd5vbvO/PihA81zO2dm0KcvrzA55RUm7XsOvPj1i6pcv/wVOl59q2+h5Rcyv7/9ZI3Zc9fsVP9493uLc/eNkGXm7HsFzF+B2WBjUVyB2VHLe5j9GuxnuvRuU6eA2S13TcqL3+0PTjNb7p6SV1qnxftS+GkbeQMopWiDYpZRV9u3mDIpRt4q58x+pMBspjzsOleXgNl3ju9hOrdsbGjJy9y3uqrOgLOfnllrPiPKBxT7+PRa878ERueTNoo02kbeYFIUbVLIMutq+xZSBqXIS+Wcux8pMBsOPIs7OBolfwrMhtMtBWYVmM0A5erDLllZR90xqztmg4IV//35OvPvd9aWFYgSVAa54j+/c7a1AIBFAElrFtTmlFuYtLn48gqPqlwvnooZVpfrWw598IQNB2T0jUF92ufsG8XUh0LnrcBsPIHZDTeODwQ6dhvcztLTBg0bGICW9sXpQj+jjGPuD74799gHpplZx/Q3/aZUmtYdm1o8N6OF6N4TKkyuOmM38OQNvUxVv7amcbNGVtqW7ZuagbOrzFF35AfwFlouXvlpG8W/jbzaL1dYqds3Fz9+wjfThxFrrxptjvzWxNSYsfnOySm3nzxKHaeuyXnDN8YVVZ4KzMYXmP3otFrz/sk1vkHHx9dXpd43z5zWwXe6MEAKQNV80r+6tZs5cVIHM65bC9OE3jsa0odhfTo1NdcsrDB/OMcbaH11Szdz0MDWpkf7JlZ9mzZqYIbu38zctaoyL17y4T/fNNpG8W+jfNsW6aJo3zD8Ii12nr5+TA/zo23dDffn35zR04DC5l2s9HVNzhjHS32SgQKz4cCzKIHPuJetwGw43VJgVoFZBWZ1x2yGDsR9x+xVZ440k0ZW5KRrzxldFKBjQK92prJTcwNwqNBgRrnk99u9y83CadVmwvDOZvywzqa6SwvrT/Kq+TU5ZRYmbRj5RVVuPjx/uPsgM39KVc4+wP0EcX/9/eUZsq9L9bXLqBz64LO3zzKLplebvjVtrL5xAI0r9nrW5WcFZuMFzALw7Dupi6VrQXaE9h7X2UrTsl3TooIADMJ0rG5pUFZQELjH0I4Wn/JDIHY3JYDWDcRYT+BG55pWGWkBQnNaALQbb51QkrqzDILa2kbxaqMJ6wjkP6Cdbxp/SE9P/Sp1+wbVP46/7b4pZtJhvQz6DPcf2G0qmpnKvm0tv2mb+njWlfOKwq4rcoZsMCZBtn0mVgQeK/3KVoHZeAKzT6yvNp1oTts2rr1vcOSVzd1SffKKBRW+0+ULntyyrIsFqF42P1hZjxza1TQjMFWOH9J91Oh2rryfM6OTadww/TFwIwJ0ZdrTpnR0TZtvPQuZTttoPxOnNgIgObNXS+sDAXwkkIum1bY0bxzb3VXHSt2+YXTzxU3VVt3xYQT3IfStWb1bWv0Tu+8B2oYpo1hp65KcIYP1I9qZNk0bmu8d3rVk8lRgNhx4FndwNEr+FJgNp1sKzNKMU6bmE6qXGbP2jgzQTe4WVferRnfM1r0ds9PGVKZeFKHjbjRj3P4FBzq+/NnaVHkf7VlW8PzrMhDjxftlp4yw5OYHmLXnEyatPa8gz1GV64dHHBHtpvdu/g9cM8VTX+NcXymTcuuDT9w8w2pLBWaDAZlSJ8rBXeyjjAH+YGzo0ivYkcSHXjvG2om6+usjiw6oYOcqj1+H3+R/Z+/Ruyabho0bGACwo1f0MKu/NsraqQeQiEHWmpGdHPkfs7ImVebIg7obHE+J3bfzTxpo5Ql+3NL6BTiKHU/bKF5txDu2WZdz2QAuvXSklO3rxYdXGO5mregpPnAgUKR9VQvTiPqlrP/geVWedfUqo9hhdUHOUgYdu7e0ZDtudW1RZKrAbPyA2fdO6mEqWydOdQhyJPHvaafp7SsqzbeWV5oPabdtsUARzvfkyR0s3Vw3rG2gsk6inbIYLybVtDQ3Lq00v6DdZABrx3VPfNyLsBeO7JaV52/Pqk2NM7UdmpjHDqsyvzurl/l/J9SYGT0T/aQBpX1uY3VWWuY5alvbaD8Tpza6ZlFFSqfkHOblvn5xF1f9KnX75qvPNx2U+ICT69mhRSNT1SZxDDr7wX73RP879vPlJZ90dUXOXDeMyZAnPrbBeMf+xbQVmA0HnkUJfMa9bAVmw+mWArM0GpapUWD2hGzQ0Q5GKzCbLaO475jFPaUv3DHLPH3bTIuwG5D6sLWDkP1evHu2+f0PVnoCUfks5ktQ6L2ny+tOyHzk4TdNGNAvTFq//DnFi6pcJ17sftit/dZ3F5p7Lp+U+uN4+yUTUn2C+wFs7FpG//j2VeUHzJZDH1Rgtn4Dsty3iwnMYldog+ROkrnHDSjKIr4EDPJ1S2D2sOvHBuJz4y0TDMAhe9m9Rneyxj8ca2wPw/OUjb2t8PFrs3ct9hqTSAtwd9u3gx+v7FReXffTNvIGa9C+zdskjtGcvrmvWXruUItqhid0CTb7IRxzc4u2pdmNXkzdG7qg2qpLI9r5MXNrP7Pp9sQRxtAX/igEdcWu/WLyUZ/ynnl0P0vmOH79qJ2FP85bgVnvvh7FHbOHDE3sPO9J4CMAiGIu3ofJm4HZFYPaBOIRd8K+RjsV7WVjBx/GD9DFcztnhSN+W+oH+xNoDfBapn/pqHTaUwgwlmH12a1t5H0X59dotzf0bSztln1obZVFNwvQEgAm+yMO4l5+oLNu1hU9w4cMvEt2bp9W1i5OHmf2be1u+tKR4twPf3C0++7gulLfOPCJMa+6bQL4Pn6i/1MQwvCuwGw48Czu4GiU/CkwG063FJilGaZMjQKzCszmtVs67sAsL2SzvfWQxOIWbPZzsh+7cbrBvY7Hrz/A4Dhk3BMLG8eJ4mjUJTO7mfuvnuyZRz7A7D9+usZcsH2oVS7KBv30sUVWOb+h42VvuWicWb2gxuCOyfVLe5kfP3RgFg//u2+FufErY82JRwyw+B3Yu50FuB2xvJd5+/FEXk51Zr/dd862ysUuYuzMmzupqzlj80Dz86eWmItOGGbly3Ht9r7751lymzOxyvTq3so6Pnfz6j4Guzbtcd2ew4CcftLaZfzKvXNTvP3px6vMeccMScn/lI0DDeTpxiv7+ymX48LOp43sfAfVDRxHTPOXRb964SDHOq2c18MKLwYwG0Y38tXJqPog2vile+aY0zYNtPrg0P7tLfv0owaZ7++a4yh7qR//88wSc+fXJppTjhxojQcYj6CbfoDZMHKWPJTSrUcZBwOciwnMMmgCcPIYHwDj8q8MM8MWVZthC9M078SBnoDK4jMHp9JMOaK3dU8s7NpRnQ2OJwZAeuDJgzzzCAP6uQEyuDsWY2Tz1k3M9oey78jF7th114xx5GvOcel7oNdePdoxjlu5xfbXNkqAJnFsoybNEzva5McF2CkKPZQ7RhEOP8S360uQ9sWx3xMP7ZXqrweeMsgqBzsqqwa2s3Z/467kQXO7GvhV9mlrJq3v5dgfwMeqS0dafRnHg7etbG4dyYy0y84flsUn881gNI5xZj9pHzAjsSvEa/d50HJxdDJ2i8pxCvfaolwc9QvgEkAw6nzA9P2tnfSSJ7iDyNmeFs9BeeY8AKRCVpAHjlIH9aJj48ceXGvdbd1jWEez7d7sD004PWzUn+U+alkPR7nL+EHdCszGC5iVoMlXZnXKCTD+mu6BPH1qR7NlTPsUHTO+QxZwKRf4f3lKjTmVjvxFmqPHtje4s3UPgaJHjGxn3dU6prq5wXHCdvBT5gF3vqCfPR9+BjjUmj76wHh5yTxn8Oud43u43rvLd84uOaB1TrlxmaWwtY3S4Gjc2uiiOYmPmuXHBdglCh0EyR2jKwcnrqRBGqk3QdsXu8FxRDn6H0A67HKfQrvHe3dsatbS7nPcRXob7bDEEcv4OGNh/9bW/a+yTHbjHljwA96G0D3LAFUR/6zpnVzvNGUwGh85ON0ji48mcOcz6v/8kc5H7+ZTLna4b6XxhscqHt9wpzR2zJ8wsaMZ3bW5mdijhTmb+LffNR1UziwjtvPhmdOCv9VD2li7+nEX9sQeLc1hdDwx+IQbAD7HdbMvnJ34cK9Vk4Yl2TWrwGw48CxK4DPuZSswG063FJil2aVMjQKzCswqMEtgCoMRAFKor6doxADnO+nOP3ZIKg3SAgjCfam9u7dJ3ZWKfLp0bGa6798ygyaP6mIBvlwm7B8+OD9VJpcPQBjgYUe6S4/92O5X2zaj/D+/frDpmryjleNIuzHdx/P4TdMz0nD5SHv4ssTCtEzj5AbIx+lg//WN1eaog/tYRws5xYcfAGWUIdM5uYOCnDIPP2ntMj56bb8UT9/95rQsGQM0kmU4uf2Uy+nybSM735BpEN1wA2ahw1eflbhjecvqxIcLAAKZXyc7SH3D6EY+Ohl1HwTovnZx4ihYt75w6NKe5vN9q7Jk/M+3DzFnbBlsGom7ejgP3L18JoXh2eko4zBydmrjUvopMJuee/zIvVjA7Oa7JhnsqLJ0jMARP4vz3YdmzpVIW9WvrWda+z2tGceaUnrWeQAPkgeAsQAn2hEA1IoWnzgedhG27tQsg3Bv5zH3+9+5irh8t2W/KcF36o1bU2vxg93GQe7llfUrllvbKAGaxLGNGibvSDyCdnFz+zsBswiHviM+x2M7SPvi2G/uN042jvlu26V5Vhzs2OXyYGPH+aA5XbPiyTwBdNr7AoBh3pGPDyFknuzGjvb+UyvN4rMGZ4XnW+7qyxJXZEj+8DHIqotHmGb0IYb0hxtHKzM/bAeRM6eBnS/PSIudli3aZvNn59fP8fGsVwD3C71rVoHZeAGzfMwv9ORNj7ssefEfdxbadQrP2OXHcez2nasyrwoC0CDvbeX8cMfkz21HmeJITgBAXWn3V0sCGBC3KY1teJbUvV0Tg52H9rK9nnFsM5f9o23Bdup9cnptCtTdPqE0O9K86iLDtI0SwGwc2wh3FkPnDhuevtfYDZhFHMRFmjDti6O/Wc+dbAC0dn8cM4xdl7Jc9HH0M3tcfq5s1cjsOjh7HMCHHIjTkY4v/uxM5yPPr6Q7qgE8OoXnW+7s3uIaBCofd67iKPKDCfBknqWNO6xlffPpR5w+X56RHjvwJV9O7nl9W2XwyuVKGx/dcFqA0DKsGG4FZsOBZ3EHR6PkT4HZcLqlwCyNhGVqFJhVYFaBWQHMAgQ7aeOA1MsP9XszczzdubNjvOXfuUMzK6wp/aGU98f+i4CVtq3cX3CRD1Nr+rMKMEUuxOPYWRw5C8ANu2IRdwoBuM2bJhfLaQcrdnHOm1xlgTcIk+l/9+pK06RxQ9OGeDj4wBrzzQvGmmdvn2XteK2qSByfU1vdKiMNp+fdkiizZ/fW5pyjB5ubLhxrsOO1slPmQh129HE62IfR7l2uF3YTo8z3n1tq7Q4EHxy2ZkFtRjqZB7uDgH6chm1fad9dZ+12njgicfQQ6sfp0R7XnzfaXH76CAtEB9930c5FDnezfZWb1K982yisbrgBs9AV7AJH3bCjGnX/Sw4APUh9w+hGPjoZZR9EG8l7rQ+c2tU8dN0U8/p3Flg2H6UOvZo9ocoY0kWpU8tmd0/1Few4x4cSG1f0Nn1rMv9wOgGzYeQseYjCrcBsPIBZ7IzjsXrJ2UOygAkJNrB73dVjzOTDexuALH0mJsbUXMAsQJIRS7qlykKZ3Qa3t+6nhT/v7AJIJO+PxQ7eprQIxDx62QAfnI4rZr7ZxtHNAGgYjAIwvfKiEb7qznkAPGG+cKQx+8fF1jZKAFxxayPswGYdlrrKAJrcMYtwxG1E75z23dxB2xe7ZPm4ZOQJABW7aLnfwQ/uMatqrB3seMZHC1Kf+09PgzLY4X7Q+UMN+hL6Do8DnLdMBzd2xSMMH1OsumRkVn3s8eVzvuUCEJ53wgCrr/N9q9ghDHmClw7E04il3UkuHS3gGGGyXLiDypnT58sz0tcmj1dv07mZwUkD0AMA1wDKcaoBeAdhNy6X52ZjTOf4uFPbLV4+/grMxgeY/ZRAEtz1iLYeXtXM18L95wTW4M5L7MYCtWmW6BdewCzKuWZhRcaRpdipehrtor2K/HH3K+vb4bSLVoIG5810/tiZ40sbAJBM6+a+YyXtdCdwhnfpHdivpa90Mr/jCIzlsrH7V4ZF7dY2SgCzcWwj7ByH3mAHK+uJGzCLneiIiz7AcWEHbd836IOLy5JHKCM/AKjY9Qq9Zx2GDbAPO9dxJy+eHzwkDbJil3uz5Mdh+IAC6b9Px3njKGL0Yb6jGh9NwF/yy/edIk98xIAd9DLcyx2m3L3E8xUE+ELWXE++G7o5/WfATnfUtyuB0BgL7GNYUDlzPcLwjB3DLH/s9n11azeDnbvY9SyB5lm9/Y1ZI6oSa3LYNYv6MI/FsBWYDQeeRQl8xr1sBWbD6ZYCszQDlKlRYFaBWQVmBTALcOLNRxekXvqwG1QCFjhemMYCix64JnNH5f+9drDBUbEALzkOjvKFn6S//WRNRp4yf7hXzU8DmgBbd10xKSM+dub95/+tzfBDus9eWWFw7K09v1svSu+UwLHIMvy+q9L3j2K339/fykwP8LlHVforRQnMYgcu13PprG4Z+XIZEuTGcazs72QHAf3s6YOkxVHA4FsCszK/SSMTIEOhgVmUkU8bSd7y0Q0JzOLDAuys5t3VbmC9LFO6/co5jG6E0cmo+iA+qOC+cMH2zN30LD/4c5zrzk3sVEYYjo9m/23r+hnsnuU0OEodH2RwuB2YDSNnLiNKW4HZeACzOB4TOgZw0s8xxvYFfD4qNhcwi3RrrhiV0mfsvJN54YhR1nWASDJsC+3q3XDjeMNHuyIejk2Fn6SjcxztiTxRx5a2kygAFMnycrmx07Z6UGKBqBEtCh16rfNRx7nyKVW4tlG82gjAG4BN2f5OwCzCARoivoxrd/ttXxyNi76DneUM9AKI5X634qvDrXIOOm+o5SfvtsVOVo7n9iGC/PDC/pEH35fLebStaGYdH4wd8gtOHWg201HK9nrhOWy5nKcEjrEDGYAth8HedPskA9Bc+tndfuUclmfezQvg3M4DxjscHw05AhS3h9ufcZwxA6jYkWwPD/PM+R5OPIXJp1zTlvKOWQAo3Lfsu/L8LtzzUbF2UMMp/YJ+ra3yABLY75JcTCAJeOlPHxbItDhe9KfH9TA/2d7dOvoYcXBPJZ4l/Yx2h8l0bu4ddJcs1xn2tNqWWUeYuqVl/wcO2T8Fnqyh+3nZP662tlF82ghg54CKphkgoBswe/+a/U2/zk0NbC/d8tu+fM8r91UAfgBS0Q/wcQSXgaPF4Sfvth1GH27ADwDm7k3dUnE5Dfqi20ceH51Wa2rpiGSkB2G3PABSHDGMI8TxYQPfOcv5sR2mXM4D4CaXDXtc9xbmAwEOA7D8H9sd0pxW2n7lHIbnS+cl1pMwRmJ3rywf7jOmJYB97Pq1hzk9c3zU+3U6MtopTqH8FJgNB57FHRyNkj8FZsPplgKzNAKWqVFgVoFZBWYJHJHghARmsctRhsFd0zUBVH79tJFZYQjP535LWQaDb61aNDZP3+b/jlbswnvylhl0N+UQa7fd8jn0x5ful8WdszR+WbT3vvS9qihzxdzE3aIA6L54M3MXL/P07vcWWztx8dWfPMoYoDXyBZ8fv7jMURYAoXnXL+7N5DydbL+gX9i0UQKz+bSRrG8+uiGBWQCy2BUNQtsVC5gNoxthdJJlVeo+OHV0F0ueOPocu2eZD2njYwo+Gn362MpUHN5pi2PUwbdMY7mpX+P4c7SXHZgNI+escmzjYCnCFZjNnHtyybxYRxnzEcOdaLd2PgvkfsEK5C2B2U23T8wqrw2BNdB17OJ14qVQd8wC8ADgxUfKApSeubWfY5l2PgBo8X2Y4BX3VNrjxO1Z2yj+beQGzPrRJb/ty8CsPLZ7Ee3IhB5jF+m25P3S+NAAfvvRwi2Xz0cYo68ccbMzEIcPI3g3Knaiclq2Z1Afw5HkVt7IXxKVNWR+16wd74UoF+UzMAv+l547NIs35tHL9ivnsDx36Z04LQMA7MLTB1sffgDk4w9nsIMWd9968SrD+Nh43B0s/cO6FZiNz45ZecTwTtpFms8CvV+wAnkzMLtxVOauWIRdR7tw0bdb0EdLbnwU4o7ZVzZ3s3bL4i5NHktwZ6NfYPclArNxJCrS4n5NgE5u/MbFX9so3m3kBsz61R+/7cvArDy2G8eEQ5flbvNlAxP/98+c1sHSbfQN7isAU9344t3AWPux9wvcI7uK7qVtRHM25yVtALePHpreoYsyClEu8pHA7Py+LR3vuXWrk/T3I+ewPH+bQHiWC3b6Prux2voA5Venpj88eeaI6qwj3yWf0o2jpTk/fFAiwwrtVmA2HHgWJfAZ97IVmA2nWwrM0ihYpkaBWQVmFZi1ARIMzOKoV6eFcgCeNB6YS04e7hheKFDo0pNHOObvxBN28uLeWfDlRS/dMycjTxybivi4Y9QpX/b7xbNLrGNZ+Rn2MHEfL766dCPmB8cwy/R2d7kDs/m2kZQTA7NBdEMCs9i5zfl1oPvL+Chj9stl+22jMLoRRieZ/1L2QQCuLWihF3qOu2CZByf77K2DrHjt2zRNxeM7pC86YVjKz54Wx2wjfzswG0bO9jKieFZgNh7ALI7/hX4BqMxnod4vWIG8GZhFmU5l9U7u3gWA5BReKGCW88YOXBynjPoDZNiWY8ctQFkGXZBm2KJqRz45/7jY2kbeAEoc2ikqYJZ3d+KoX5YDdkBCv0Hs17kmscBr+WNB1o2S6dzGExwvjN20Y1bWmN4TKgw+COGyYOOjBy4TdqHKZWDWaReqLM/L7bcfheV51rb+GTKR8mlOd+MCWA8CzOKObuSBY5C96hc0TIFZ73GllDtmcRQx6wkW+/NZqPcDVnC+DMziGFT2YxuAKXgBeMR+drsQwCzniV1yOIKVdwxKYIrj2G3sMOZdgTjO9YdHB7uX1p5fqZ61jbJ3HpZK9n7KiRKY5d2dt6+oTPW7tck7ablP3LUqfR0B+iiAVyfisQS223iC3e84VnkDHVmO3ep8FDrS4KhkuZO+UOUyMIs7qu335vppH47jpx+F5fljursau6SlLNmNsXFgl6bmglmdfB8HjaOROf3FdFoA16UYtgKz4cCzuIOjUfKnwGw43VJglkbBMjUKzCowq8CsCzCLO2OdwAq+/7LYwCyOR3Uq3+73px+vMt0qE3eLAEwGf7gvdPeds83zO2cbgGm4E5fGMGMHZgEQwf+MHICSvUw8Y7cl0gKQ5eNx3eze3dsYeXyrU35+Qb+waaPYMRumjWR9GZj1qxtI6wbMnnfMEHPlGaMsHQPwfsN5Y0yuY7b9tlEY3QijkyyrQgGzfuSMu20b0R8s9AX0O+bByUY49xmkw93GeAZ94/wxrmlxnDni2IHZMHJ24q/UfgrMRg/M4vhO1sEBMzMBEb+L9H7BCuTHwCzu/XTKH0AN+CkVMAse1l6dPkJ50RmDHflCPABKA2env1gfNLdr6jhYp7rEyU/byBtAiUNbRQ3Myh3zTsAsjh62xgp652tF1yJ4EXbFTtvUJ6MvYaxBH3KS9SGXj7aOV+ax6ChxrHHYcrk8BmZxtzT7BbX99qNC8Izj3HGENe9AZtlIe8Fp7uOVrNuAmelx66idzkdGy/h+3QrMeo8rpQRmj6Sdq6wbb23P75hLP2AFgwBewCzuZQQvpQJmmSfcM4lyB1dmHqHM4Wy/SMetMijbnj4SA5DMYXG3tY0UmIWOOu2YZWBW7pi3A7M3LEnsZkc/6Ujv4fu3difc1zqya3Mjd3gCDHW7V/YTAiLl/be485X7U9hyOR8GZrHTnf3ysf30o0LwjF23uB+5V0dngBbtABl/Rnd356oHgHDEB20Sss2VLp9wBWbDgWdRAp9xL1uB2XC6pcAsjYBlahSYVWBWgdkCA7PYQUfjhUWvf2eBK9jiBoIEBd8e/cY0qyzcR/vWdxdmlYejVVs2b2zFsQOzU5JHpC6Y2jUrnRt/7D9/SmKh58A80nIe0vYL+sk07A6SloHZQ5f2dKzzwN6JxY1C3jEbpo24jrCD6gbSuAGzMt+th/S19OMpOgpb+tvdfuUcRjfC6CTzW+o+yDqzfmkvT/kdRuEYG/ChAvPapWNisRtHjrOf3T5jc+KeWTswG0bO9jKieFZgNnpg9uhdky2dhF72m1IXgNnEfAd+Aeb4BRByxWveJnEU4qhlPRzzxN2TkA/P7YPzBGVxL+78kwZahHs13YCqXPwGDfcLKCHf8OC5tlHQ9kH8uAOzNcMTO/JqRnjfd+tUd+g9+g7us3YKhx/ut+X+tfLiEal4YcqVZZUSmA3LM/og7pIF/xij1109xqz+2iiz/MLhBnf1MiCKe65lHd3c2GHLssUxyG7xgvozH3rHrDNAW0pgdjst/nMb46jRfBbj/YAVnG9YYPaUyR0sfnEcKecZ1pag0G/ozk2n/HCcKO/s60Q7ZXGcsVM8L7//R2DLt5ZXmtuSBLDIK34hw7SN/AGzUbVRHHbMegGz0H8eJ54jd1DdxJ2uOMLY6W5azgt33KIM7KJlv7Dlcj6lBGbD8gyg+uF16SOd8cEMPgrBDuRdq6vMHLpfm9viu4el43Fd7TaOreb4x4xPHE1tj1OoZwVmw4FncQdHo+RPgdlwuqXALI2CZWoUmFVgVoHZAgOzADj4XtXHbpzuCLb85fWDne+TzAN8w7G2ND6ZCtrB4HRH5c5LJ6Re5OzA7CkbE4APjrGxhzFQg92eAIZmT6jK2FHJYBGOcX3v6cWO9UQeAMkAULvdYcvl+AX9OL60g6S98LihljxGDuyUxTPu0+Xdj4UEZsO0kaxnsYBZ3gX+7aumZMlElu9XzmF0I4xOSl5L2QcByKIPYmf6248vcpQhPprAxxOIt+GgNICLfgW/1rRAJI+a5rp8tGeZFYY4dmA2jJw5/yhtBWajB2axEN86+SV39yH+Fvnti/elBP1QNu8gW0x3Y9p5wTOABxx57BTm5Lf+G+OsPog+hiNE7XFwfDF2EyMcNGJJt4w4m++aZFZdOjLDz54HnjeIcpAPjhXlOyOd4hfST9so/m0Ud2AWHy1AbxvRThUcAe6mn/iIAR9NAFDkOPJoXtxfy/7SXsf32lIZACE5LEy5nAfsUgKzYXluQVdN4GORjbdOSMlB1oXHo5btmjqGy7hwA8BF27Vo6y++Pb3bswKzzoAsy6uUwOw1ixKnTaCdn9rQNQWIBFmoLyXod/7MxIceQ+leTCce/3BOz4ydehwH/uy226uHJO5mrqKdfvYwPAMUaZu8egQ7BfdtzTy++IUju5lfnJy+/9EpD/itGZp5dRCOI3WLW2h/baN4t1HcgVkcr4uTzjBOHDY8vaPVSU9/fmJNVh9B30La5YNau+r8Qcl7becS8Mj5hi2X8yklMBuW568fmBiTT5rkDKK+fXx6B+wVCypSsuK62m2At5A96PID9Shju3zyfcb9v5DpwPmbTZSAZX0pW4FZBWapv6lxkIACswrMli8w++46g92iINyhSvpvNq/uk/IzFO4ESgBERFzrKGOHOHzH7MUnut8J2adH4s/hzPGVBmAfysFxsbddPN7MmVhlvRRjt5ssn3llkOwKOmaW/WC78Xv/1ekdT+duG2w+37fKyhdg6dFr+1l3h6A+oD13zc4o8/c/WGmqKlpYYbjrEmDkv99Za8XBUavgt/v+iS8f8SL/6+8vT6X/DblxTynyBWD08q65qTCkfeXeueaC7UNT6Q9flgajuN6yfjgaGnmh/tLfrd4yTpC091yeProTx/kC+AI/++6fZ/r3TP/ZvuOyian6ML+w8yk3TBvJMoPqBtKinbj9339uaQb/XBfWaSdgluPA9ivnMLoRRidlO5WyDwJQxXgBOfeoamWevX1Whu48fdvMVD8AAPs/zyxJhePIcW6f6i4tDMDKP9OHG6CHr59q4Mfh0E/ZH8LIWcoqKrcCs/EAZvmOVXnHJC8su9nY6ck0+9jEfYiVfdum/DjMnh6ADfQZRxkD8LSH8x2z49f2zArjuO32T/QJ8H3odQmAaP0NYy1QtcfQjtbdl9ixxvFh9xjW0QI6Rq/oYYE+vFMVIFFFbeL4wwZ0JLnTzq9Ry9OLJ92HtrdAWACxTFaZVKfVl6V3+cmy2S3BKchgzCo64ox4KxZxG8DWNiK9iGEboe25nbALGzxaR2Qn+5eXbnC6IO2LI8JRRt9JXVJ6x3fM5jrKGCBhs1aJRdkO1S2N3NWKDwxW0S7XcatrTetOiVMg5NHo2CWOckFt6EjkDd8clyofddx4ywS66zlxWgnARoC7XPcw5Ur58jHpkzf0SsnckqHDOMRly/RB5ByW50aNE9cj4EjoVZdkfvSx+c7JpmX7xLGIGAMlr25u5APZV/Vv5yu+Wz52fwVmvcfvUgKzTxIYy31M3jHptVgNkPP3grq3S7zHPnDI/hn+TmAodrqivK/S3bb2MuQds05pEf+mg9K7uL+5tNJ8Skd5/pp2uT5yKF0RQLt/Kwk4xUfDTx+eBpkR1oL6xlTaiYfdqgyi4nhVecfu+hHZgBNAEBzdyjJC+di5xoQdsAib3TsNJtnrxc8MTiF+a/pQ5YNTcoO5nDaorW0U/zZCm3I7Yacu6xiOsUX/cusDMh33Q799kI8ylncj81HGsv/zUcanTemY6qfHT0zvrj9hYkcDAJL1Ev0EfQM74hnA/cn29AcMfTulj+Q9VeSJ9OiHtyzrkkr3Nbp/lvOFHaZcpIeMeGxBv2OZsS3Lsru5fTiuXzmH4fm8mYn3TujD0WPbW2Oc5EuGP7E+9+7lG2mcZN2SO3FlnoVy647ZcOBZfQFZ86mnArPhdEt3zNIoWKZGgVkFZssSmF2zoDZ1ryr13dSLjHRjZ6T9ONsVc3ukXigRtxntgLvm7MQ9nNjxxkAk5wNQk4FQCW7c+JWxGWXyvZmcDvZRB/dJgTN8xKkMt7sbN2pgAPDIcuD+v9cOToE+SIMX6VYtEotneAZgxEcZ47ld66YZwBBAJNxNy+Vh1x/AWJTHftjpd+05o7PKBpDHuwARF3fdjh7cyWAXLaeFjTztoB9kLeO4ubHz0Q4oh0mLnbu4C5fLwx9/yISfpT2oT3sLyGSZ51tumDbKVzde/fY805l2Ucv65HIXso3C6EYYneS2KmUfRJkA3zFesIwBqI4d0ikDWEX4fVdNyupH2w9LAFuc1m4DzGU/jFu4c5brGUbOnEdUtgKz8QBmeafefjR35Lp/cNu9UyxghfUxlz1pfa8UEGABI8kv9ZEO4MPUjYl7KNdeOToF+nCeAB+c+JmxpV+qPyAudp5yGrYHzemaKhdgAu8Y43CkaQuwQvAz+fDsuyc33T7RNEyCJJzWzV55kTcwK49CbkxzpLxH0w54hHnWNnJ+50O7xaWNuH0B9LvpE/yrBmYDafm075a7p5i2XRLgHJeHPrD66yPNkrOHpHgA0IaPFY64eXzKryG9C/LdywBY8cx5YLd9l95trF207AcbfQZxuZ5Lz02XgXDseq/q19a6s7lzTeLDCE4v03H6fMvtPz29kMn52218kLH03KEpXrnMfOTMaWHnyzPSMjDLvAJY7TWmkwWssvwh43XXOO8+lnzgbt8GDRN9AvKQYWHdCszGB5iVO/W2jGmfAYg4LdbvmNvZOpKUdczLxpG/AE2Rz/cIKOX7WTkNdnHy0cEMBHEYjj09fWoaFGJePjyt1vQRIA8A18ZJPeW0zclPHsv8mNgxxnF6dmiSOpoYft3bNXa8A3MrASOcxssG6Ms8Otk/FseJIh8AN07xCuGnbeQ8l8epjdDOAMmgv256hT7w0Nrso2rzaV/sNLeXtXJwG0sHR1Sl53j0Q/B26PDERgHwhrtM4Yf7TGVc5DeoS1PDu7FlPRDvd2elj48eTXnI8Oq2jc3SAW0M7nfmI8IRPryqWWrM4L6Qb7k4blzmLcuXboCJXJa085Ezp8+XZ6SXwCv4BJg8tlsLs6h/a+tuX+Z9VbL9uEw3G+M6p3nj2DRY7hY/jL8Cs+HAs3wAy/qSRoHZcLqlwCyNgmVqFJhVYLYsgVkcFUp91pMA+Nl3cQKolekAaF59VgKQ/AkBsxLwRLza6lbms1dWpEASCXDcdOFYa/eczA8ALYC2J3GXp9iNu4lAWhnPyQ1e3O4ABW+jBiWOheK0AEdx9C12uZ5yZHq3AsDlnz+V3rEHnj9+cZlZMrNbBhiLfAAkrV3c0zqKWNZNut95YqGZPrYyA9BmHrCTdsdJw1O7UmW6gw9Mf1XK8Z1syBy7CguVFvn86MH5ZnDfxO4MLhP3fj5+03TDu0fhD2AWRzFz2WF4zreN8tWNNx5ZkDoCl+voZUPObz6aeSdymPpCZvnqBtKG0Ulur1L2QZSJXfG4r1l+1ACZ4xn+CGfe7Pbtl0ww2LUu26iyU3Nz/Xmjzbd2pBfJ8SGEHdwNI2c7H6V8VmA2HsDsvBMGpPQOR956LdZvuy8b4JE6K90APCYdlgZm+0/NBEmwqD9lYwIMXXvlKAOwUqZvSzvrsOPIiZ8ZW/uZNp0zPzwB0ATgAUCTfTcuQNABM6tSO81kOa3oAxbIwKkc7ATsSLsDZXwnN8reTGU45cF+vMsN6e3HIXOcQtjaRs7vfnFqI25n3m3tpFPwA3DLcdnOp30BMkr9Q95NmtM7Iu38xhHbkA37AZjF7lDWewCA8thw3Hdq7W4VHzUw/9hJi125h9+UuB+VeQaAyHGwM5/d0saOXeze5TR2O59yB85OH/0ny5JujENLzhmSVW4+ci4Ez8jD+miE2qh9Fd3n57Dgj7ZhsNxepv155tHpD1kWnOYuX3s6P88KzMYHmMUifK/ktQQAWHItyl86r8Lxf5vsG+zGsb+8qw7HAdtBEpSHHa8oc8WgNhknNQFcdQJmEfe9k3qY6T1bGsThsmAD1Dl3RieD3Yf2euD+TIBFdhAX6VD2O7Tjz54GzzjqmXcZyrLsbvsuQHteVy1MHxkNvlEHe5xCPWsbOc/lcWojtDV2cjcTHyzZdQphiGPXi3za96I5nTP6CsoCMIq8jxqdXtc4LLlr/BtLuqT6ypjqBDCLuNjd+hU6gpuP9pY8g18cR3wf3YOKeJLvgwYkPqTCmNBSfIzM6dHHwMf7LkeC51Muji+2jzlcnrRxt63kld35yJnTws6HZ6TDSQDgrz29a3VNHgEt+YWsNoxs5/ghiSwfbuz45Z36AwlEt4cX+lmB2XDgWX0BWfOppwKz4XRLgVkaRcvUKDCrwGxZArOlBBdylfWHH640rz18oAWQ4jjYXPHzDiegF8APdtX+9LFFjvfN5sobRxBjZzAAYOSR615Ymd/f31pjAXtICxASu0RleBzdn768wjrCGHZJ+CtAG5WETwIZC1lOGN0Io5Nch5L1waTcwDPA0u/dOtP87MnFBs/Mi6ct9OPD3Qf5SyPaKoycPfkSZRQyngKzwfoZTm+gd1HTb0r6GFI/i+e54uDYUD4eGLtac8WPUzjAUOz6w1GoAJP88IbdgAtOHWjtaAMgvI3AVz/pwsTBkctoOxCAFjfAOUwZcU2rbeQN3sS13XLxBbB3zRWjLFAT/WgL3bXslWb65r5m1jGJO5wB/gIkhN/C0webtVeN9t1/g5brxVOpwoLyfASNZzhFAPwdc/9Ug/t3l1843Do+GmH2D0+86oFj3DHuAOz1O0Z65SfDFJj17tulPMoYC/M3EAjD88yrW7oVffG+UGDA5wT+4L5XADCfiGNVvfIHEIxjiK9b3MUCvXBsrFf8QoXhbk2W8eYx2UcmF6qcuOWjbZTeNRq3tgnLDz5mAHCM43Tf2t4jC4yV+aOf4lhk7NhEX0U69EHQ4+urUseLyzRu7iDluuVRav8gPOPYZBzF/hGdDgA+carB80d2Nd+hHdZ7aXz2O9YhLcY6HneuJ1kXu94KzIYDz/IBLOtLGgVmw+mWArM0EpapUWBWgVkFZosEOhQSwNC8ggEYKi+Vl+pA/HVAgdlgbVQsYBYL7XO2H2D96cfuuCO/5bxLVS7Iq9t7Qd4uH3n88pD5mccs2+PqczDZFkpe2kbRyL1Q7af5ZLdf4hjjxG7EaZsSx8YXUk4KzGbLXMq31MAsdnbhaF9aszJ+jjMu9uJ+OeZf0Spxsgd2u+E+znKsY12vk7ZR+YLIdV038+Wfj2LH3du/PSt9J3C++eVKp8BsOPCsvoCs+dRTgdlwuqXAbJmislQtBWYVmFVgVoHZwDvzFPQKBqiovFReqgPZOqDAbLZMvPSkmMDssQ9MNbjHEAu6Yw+u9dz5Jhee1e29MM/y4ftlcbzzhhszj3nlOGr7k2Wx5KRtFK38i9Wu9TnfMasSV4bgGOujd00u+LiuwKx3nyk1MIvFduymwjyO+ww/PLX4C/i5FvjLKRz33UK2ID4qtpzqVw510TZSULYc9FjW4Ven1ljjOcadr87uVJKPQRSYDQee5QNY1pc0CsyG0y0FZmkkLFOjwKwCswrMKjCrwKzqgOqA6kDJdUCB2fgAswAvcM/ifnRvZPM2TazjM+szoFHouh948iCDuzWHL+pWcHCk0LzW1/y0jbxBpvqqF3W13jgCuUVb2j1JY/ryrwwryrijwKx3n4kCmMWu2Yk9Wljg4cVzO5dkEV+CCOXs/pCOJF1Gd29Wt21sHeVaznWtq3XTNlJgtq7qrhvfO2gcJxzCTK1taXA8slu8QvorMBsOPKsvIGs+9VRgNpxuKTBbpqgsVUuBWQVmFZhVQKbkgIzXrjANCwbWqLxUXnVVBxSYDaa7xdwxy+DDnOMOsADEzTnui+T4ansvzKt8VD6qA6oDUejAlrunmE7dW5l5JwwoCiiLOikw663bUQCzWJx//+QaM69vK3PW9NLsriokIKB5KbCmOqA6oDqQ1oGzaRxfOqCNee+k0h2drsBsOPAsH8CyvqRRYDacbikwq8BsXuDdTB+gZ12I06XvLOtLpT7TT6rXcpBtNWrNbZZMaqtbKainwK7qgOqA6oDqQGAdUGA2fsBsFACClum9uK/yUfmoDqgOxFEHFJj11suogFkFNdKghspCZaE6oDqgOhBMBxSYDQee1ReQNZ96KjAbTrcUmFVgtl4DkgrMvprV/grMBltQr6s72pRvbWfVAdWBYumAArPBdKsUO2bjuPivPHkv/qt8VD6qA6oDUeiAArPeeqfAbDAwQMETlZfqgOqA6kD0OqDAbDjwLB/Asr6kUWA2nG4pMKvAbBYwJ3dPlrtbgVkFZosFTGi+wYAJlZfKS3WgfHRAgdlgbanArPcieBTAhJapbaI6oDpQX3VAgVlv3VdgNnqAQUEebQPVAdUB1YFgOqDAbDjwrL6ArPnUU4HZcLqlwGz5ArN/pqqZzn1mmK5DDlJykUGLdl2tY3vbVQ1VGSVlVNF7miWT1i0bmy2r+yqpDFQHVAdUB1QHAunAnIlV1jxSVdEiULr6OudMHFGReBfZv7kZPLerkspAdUB1QHVAdSAyHWjQcD9rTuo3pTIyHuI8F/afXmnJB2sth49sp6QyUB1QHVAdUB2IvQ5UtmpkzV0zeraMPa9h59ZBXZpadW3ffYDpPWW1UpFl0GXABEveLdpXqqzzkHXrih78Xnl9+UKU9bNmX+LPgpLKQHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHVAdUB1QHUgVjrwELWHmjKSwB+pLgBmgbifo+Qqg7eScnpMZZSS0TeTMvmDyiQlE+1DOo6oDqgOqA7414F7k/PIL3Ue8TWPPJGU109UXr7kpX3Rf19UWamsVAdUB4LqwD+Tc9LXdU5ynJMuTcrnPyofR/kE1TeNr2OU6oDqgOpA8XXgg+TctasezF0vJ+u6px7UNQ59576kvN9Xeef1XoQ1IOB3lxOpKSMJfEJ1QcMOL6M6FaMqPIBsL0bmdTTPMcQ3dAcTtxqVgEpAJaASUAkElcBKSoB5ZE/QhPU0/vFUb8gLf5TVqARUAioBlYBKIEoJfEGFY06qiZKJGJddlZTPlzHmUVlTCagEVAIqAZWAlMBL9IC5fYX0LFP3lcm67ijT+sWtWquT8n4+bozVEX6wBoS+iTUhNWUkAQVm/TWmArPZclJgNlsm6qMSUAmoBFQC/iWgwKx/WSGmArPB5KWxVQIqAZWASqB4ElBg1lu2Csx6y0dDVQIqAZWASiB+ElBgNn5tUi4cKTAbriUVmA0nv9imVmDWX9MoMJstJwVms2WiPioBlYBKQCXgXwIKzPqXFWIqMBtMXhpbJaASUAmoBIonAQVmvWWrwKy3fDRUJaASUAmoBOInAQVm49cm5cKRArPhWlKB2XDyi21qBWb9NY0Cs9lyUmA2WybqoxJQCagEVAL+JaDArH9ZIaYCs8HkpbFVAioBlYBKoHgSUGDWW7YKzHrLR0NVAioBlYBKIH4SUGA2fm1SLhwpMBuuJRWYDSe/2KZWYNZf0ygwmy0nBWazZaI+KgGVgEpAJeBfAgrM+pcVYiowG0xeGlsloBJQCagEiicBBWa9ZavArLd8NFQloBJQCagE4icBBWbj1yblwpECs+FaUoHZcPKLbWoFZv01jQKz2XJSYDZbJuqjElAJqARUAv4loMCsf1khpgKzweSlsVUCKgGVgEqgeBJQYNZbtgrMestHQ1UCKgGVgEogfhJQYDZ+bVIuHCkwG64lFZgNJ7/YplZg1l/TKDCbLScFZrNloj4qAZWASkAl4F8CCsz6lxViKjAbTF4aWyWgElAJqASKJwEFZr1lq8Cst3w0VCWgElAJqATiJwEFZuPXJuXCkQKz4VpSgdlw8ottagVm/TWNArPZclJgNlsm6qMSUAmoBFQC/iWgwKx/WSGmArPB5KWxVQIqAZWASqB4ElBg1lu2Csx6y0dDVQIqAZWASiB+ElBgNn5tUi4cKTAbriUVmA0nv9imVmDWX9MoMJstJwVms2WiPioBlYBKQCXgXwIKzPqXFWIqMBtMXhpbJaASUAmoBIonAQVmvWWrwKy3fDRUJaASUAmoBOInAQVm49cm5cKRArPhWlKB2XDyi23quAGzLUhSQ4jmE3WPkdQUmM1uDAVms2WiPioBlYBKQCXgXwIKzPqXFWLGCZhdQvxcS3QcGEuavmSPS1IT9gxg472P07cOkE6jqgTqswTC9rv6LLtcdcf/Uh6TeuaKXMLwM6ksjL+zSlimU1EKzDpJJe0Xd2DWaR4Pq/MNqfpjidBv+qdFoS6VgErAQwJh+51H1hpEEojje9Iw4gvz+KVEjYniZBSYjVNrlBcvq6k6huj58qpWyWqjwGzJRF3aguICzHahat9A9C8idFSmj8h9K1EroiiNArPZ0ldgNlsm6qMSUAmoBFQC/iWgwKx/WSFmXIDZauLlT0R4V9tGxOZZcvD72yD2DGDfLNLjA71yMfOoIltiUJm48BEDURSFhWLL1y3/sP2uKMIok0wnUT14THsgRnU6N8nXp2R3iJAvBWa9hR9nYNZtHg+r812Tuol+s89bPHUqtD1xewVRlP0NAosLH+ClHI3bPFuourq1X9h+Vyj+yjWfZ6liPJfn8/+kGHKBLnye5OuMYhQQIk8FZkMIT5N6SkCBWU/x5AxUYDaniOpmhDgAsxUkul8Q8WTpZO+l8ChfhBWYzdZvBWazZaI+KgGVgEpAJeBfAgrM+pcVYsYFmH2UeMG72jtEjYjYhF34KDdgtjMJ5iEiyAofGUZl4sJHVPUvdrnFlm+u/MP2u2LLpy7nH9fF8pYk1I+JMLZ8K0IBKzDrLfw4A7Nu83hYnS9HYHYNNfNvidDfunk3eVFD48JHUSsZUea55tlCsOXVfmH7XSH4K+c84vqetJ2EjnHl70T9YtQACszGqDHKjBUFZsM1qAKz4eQX29RxAGZ54MekhJdeHIs3m+g0ov8lgj/oZaKojAKz2ZJXYDZbJuqjElAJqARUAv4lsJKiYn7f4z9JvY4ZB2AWC0v8XrbY1hrPiLCBtjA/j+UGzM4V8ogSmI0LH350oC7GKbZ8c+Uftt/VRZmXiueJVBCPd/eXqlCf5RwheJvjM02hoykw6y3RuAKzXvN4WJ3vKvSyXHbM7hV1ihKYjQsf3lpfN0NzzbOFqJVX+4Xtd4Xgr5zzeIYqx3N5Pv9PiiWbxpTxL5K8fZ/sBsUqKGC+vD6/ImC6uhj9SmIaurGjLjJfB3lWYDZco+2i5NBXrAmpKSMJRA3MAoDlSRLHMfWxyXYAPf9GxBlvCy/VowKz2ZJWYDZbJuqjElAJqARUAv4loMCsf1khZtTALBYQPiTCe9uPiOwGu7hwukm+J5zcTGn5nXC+PfM6+DxX1EeB2TrYgD5ZLnY758o/bL/zWc16GQ2LpDym4Q7AOBmMx7yg+1ZEjCkw6y34OAKzuebxsDqvwKy3ToQJ3UuJ+R0pSoA4TB3imjbXPFsIvr3aL2y/KwR/5ZxHnN+TDifBc79eHpNGUGA2Jg1RhmwoMBuuUXdRcowXCsyGk2PsUn+SbNjhEXH23WT5UK5jXHg4VMS50yVOsb0VmM2WsAKz2TJRH5WASkAloBLwLwEFZv3LCjGjBmbXEg+8eHBEMNZ9xVZg1peYAkcqxYJjYKbKKEGx5Vvs/MuoKepdVU6lGvOYDD0ptVFg1lvicQRmiz2PKzDrrRNhQvdSYu7vCsyGkWR22lLMs9p+2XJXn/32a0JC+CMR+jZ0JA5Ggdk4tEJ58qDAbLh23UXJMVYoMBtOjrFLHSUwiy/D/kAExfovUQWRk8EXyv8kQrx/EOGLp1IbBWazJa7AbLZM1EcloBJQCagE/EtAgVn/skLMqIHZ14gHvIv9i8hpVyyOvMI1FKD2RE6mF3leSvQ40atENxDhWMVmRG7A7EkUxvnaj0+mIMvgnfJYIo43K+FdsN/qZN74QPBlItxXdQ3RFqIeRNK0owfwsZMI8gK9TsS8dSS33fQlj01ElxNBNvuI8O75VaJVRA2J7KY7eXCeB5AbdAUR5PoA0eFEpxPtJPLLB0UNZfCODtD+LiIs7IBuJ9pOlGvHId4rzydCvX9AdD/ROUQzidxMLQWwDLzafLaIVyMyA3DC6QeRG3qEuODjKaIXiCDTg4kQJk0+7SzT53L7zd+t38m6DaTCGhOhbhcTvUgEHdlG1I+ITSNyzCW6hAht9yQR6g/9zGUGU4QTiHYSQQcfJEL7TSPyMg0pcCER+hN0H22PhQekXURklzt5ZZl8ysa4g7Y/kgg8wH6MaDfR14lqiSqJWD/Ao5vJp3zOK8jYwmnY7kkOk6Rn2LOE9hfJsmtKWGZdKiqOwOxryTZzm8f96DzGkrVEdxK9TIR+g36CftCViHVyH7nZxGEeDzrWLCDmUa9PiLhOlyX9lpBtNxgnNxFdTuR3HkceucaiwylOED6QZ1gzjDLAOIi2/SHRo0SYC0YTeRnM80cRXU/0PNEeouuI4FdB5GRqyZPH2XzmceS5MpnHZjyQAXC9kegWIsxHmFNOIepDJI3feVamCer2o0de/Y7rBhnC4J0T7nuI0P/QTigDsmeDMXkLEd6n9hKhr0I30Qe8TFMKhD5eSoR3oJeI0JZI24nIy4SZy5BvPvNorvfg1ZQv3mtWELGOuf0/ibLuO4k/HmMmkjtqg3YHP5BbuRs9yri0LYw+Cd3C/KAmuAQwl0F+xwdPqiniLIFPiDk0bBQ7Zvsny0b5PyPyMnihQjzQKK+IRQrDSw3K3l6k/OtitlhAg0w+qIvMK88qAZWASkAlELkEsNiAeWRP5JzUDQbwEg554aW81GYqFYiyQU+6FA6wkuMMcohzAfl9KeJwXNjfJ3pYhM0nN5ubycFx/0HuAzhA2Fj05Tj/IvdoERbWiUUq5Mn52+0/U9jhRGzAnz2OfJbv3K0p7lVE/86RBu/BfYmkmUsPnO9F5P6DeGZ/L1vyIfPN1w3g6vcePPyGwsCz3WBBcifRf4nc+P02hTktCi4WaW4it5uROrRIRJpGbi5zG7nvEM/sz/bTFCYXloO0MyUNbPzm79bv7HXDwjrXRdrQ7fFETYgedInzd/LHYoqTwaLneUReOnw7hbdxSNyK/PYSSX7s7u9ROAAuJxOmbC73l5QxFp7t5T5OfpOE/wPktpsw5SOvoGOLvXw8/5iIeXcad53SFMpPgVlvScYNmPUzj+fS+T5U5XeJWOek/U/yP12E7SM3GzkG/4M8Mb7ZTTHn8XzGmvuIQVk/6X5EMB9mHkc2ucaiX/vkQ7CUtxPjtNs8wPWHXJo6lIB3+g+JOJ7d/h2FLXdIF3YeR5a7iVDeb4gwn/0++Wzn4S/kv46IDfTQHkc+F+I9yY8eefW73UkePyJ7NtFfXXjGHN+AaBoR6inrwW68SzYncjKDyfNNIo5rtyHbuU4JyS/MXBZmHgU/zKfbe/BoivOsiOc0T0ZVd2LLMovol+vh9K6RjFYyS4HZkom63hWE/xLQdQVm82v6XUn5KTCbn/xim+qTZMMW4qUjaCUXJstGx3wsR+J7Rdy1OeIWI5hfqLYXI/M6mqcCs3W04ZRtlYBKQCUQEwlgEQfvAHtiwk/c2YgSmMUuEV40OMJFUF4LHxeK9MjnT0T44/+xzZ/LmE/+bLCg+h4Rh+0ld0MOJBvvsFgQ5nAs7hbKjKOMZN7g9ymiN4jsQCIvONZQGMCe/yVinv6W9IN/PyI2d5GD48B+n+h7RLuJPiOSYc/RszRz6UGG290oyy8fMt983Kso0X+JmAfI7MdE+PCS/WBjsXAYEZum5EA8GQf13k30kc0f/1k6EEkTdkF3GmUmy2Y3+ITO/ccWDoCYTQ05iilfv/m79btpxB/XR9oARVC3f4twLPy/IJ7RBr8Rz0gPfW5PZDdPkIfMH30EPKEMqRPQhWZE0jxCD5wWH228QoT+9Ufhj/C3iJxMmLL3UoZctpO9jsIniTgPODAQpvx8xhYHFjKAsNOcIhTR7wvKG7KrKWIZdTnrqqR8oNtxMH7mcS+d358q8Vsi7i/o3+8Q/YAI4wr7s72P/NhEOY+Dh3zGGuykwhgvPyjDWAm/G4jYhJnHkUeusehJiuOHD+YnX7sJJXyNiNsPNuaBF4m4r3PYHeQnzQp64DDY/yJ6nQi68XciGWYfp8LO45R9CphFOQxcQj8/IPqcSJYP90AimBoiyNbP+xri52P86JFXv9tNhTL/sp9hrpV8I85DRH9LxkcbYB6Gzelhn01kN1PJQ+YN9w+J8J7+ZyJOD5luIJIm7Fz2BGXG+cMO8g4x15ZW5gM36g/zLBGHDbJ80j9R1p25aEqO/yMCj5B3Y6IoDdodvKBfl7tB/0Rdd5R7RWNSv9VJeSswm1+D7ErKD2tCaspIAlECs2tJjjxB3phDpleIuPiivdTmPioQvG4vdcExLm9MUiYfxJhHZU0loBJQCagE4isBBWaDtQ1ewvEugpfyUhsJnvV2Kdxt4aMfxZcLQzfTc1ORx2Hkti/czRfhcI4l+jcRvzeeAE8yLYh+RsT+WODBjoFCmRsoI877FnI3EhnvT+7HRPjjIgzOuSLsVlsYHquJuE5Y+D0YnsJgkfR0Ii4f9gEiXOaPMCyk7SDCQsq1RJuIYGQ8Jz4SsfL/BVgqF+uxiNtVZDeU3G8ScT2eFGG8IIKwL4jWizA4sWD7ByJOa/+/gHAOu4ncbgY6x/EWiUjThD/CsSC2hYiBf9TtbiJOi/YC2CJNseWbK/9niRnmTy442usGHdtA1DLJ/GCyf0fEaWF/SiT73nR6ln1zDT1Lw2M40qL9NhPJ/jeBngFkcBlnkptNF3Kw/9/I3Z8DyEY/W0gkgfHRIhzOMGUj/V4iLh/2K0QbiI4leogIcppExHEeILc0YcsPM7ZIPsbRA/P4PRlQAjfaHGXXlKCsulgExgrIB30vDsbPPO6l8zupEqxrGCvQv9l0JsceIg6HvY9ImrH0gDGU45RqHg871sixopusELmrRZ3QzgfbwnPN44gu84dsnMYiezw7HwgPa86iDLhtMO4fJTJsTe6TRDjioT1haojkxzTP0XMlApKmHdkPEsm8e3Ig2WHncWS1m4jzh/00US8iGMxJ04n+QsRxrie3NHPpgcNulQEFdMt2trefV7/bLXgDj3jm+RLvKqgL8872TvKD3GFaEWFO47Cfw1MYzLc/IeJwACY1IrwpufHuxeF4b+C8ES3MXBZ2HpXtBv7+QbSDyP4e/Cz5Mf/yPSnKuhNLGeYpemIex2eElP7hpSQvkGO5G/4fAr1RU3wJrKYioOcKzOYn611J+R2fX3JNFVcJRAnMbk0qFTomBkQvcxEF8kR1qlfEIoXdlyx/e5Hyr4vZjknK5IO6yLzyrBJQCagEVAKRS4D/kO+JnJO6wQBewvEuhJfyUpr2VBgDJPhavqlL4W4LHxdSfH6HQxwns4U8OQ5sCQ5x/HNFHAACNURyQerX9NyZqJDmdcqM+TrAIeM+5PevJP2MbCmbufTMaZ0W+uS77XUU183sowDOZ5GIJPNH+CkiTDplPCc+ZNx83EdTIubvY3I3c8hkuojzb3JXEAH0hD5xWowHTmYSeXIcxB8uIoVd0J0m8kYZTu/5aNP3Rbwl5Jam2PLNlb9bv7PX7TTJdNL9VbJZtrAhT7u5mDw4ztkiEO38KxGGPuxk+pMn2hx5YGG8CxEM5Mj53m/5ZP/cnozzOdnHiOCwZSOrvURcPv4PY5yzG6l7EpgtRPlhxhbJJ4N/qAvGxcYysMhulIdya4pcTl3NntvmyxhUwO887qXz3I/R5ohnNxgrPyRCOAhzl91EMY+HGWvAvxwr7IBa2Hncnr/bWGSPZ+cD4WEMwMtPibjttrlktkfEuSYZ5yrhB4APYKGTeYY8Of+HRYSw8ziy2i3y/ojc+LDGbo4kDy4f4680ueZZGTdft5ceufU7lCXrhv5lf89tQ348FqN+PyJCe0oDYP2fRAgHeCnbaGvSH2G/IHKSHXlb99QiDuhr8EiafOeyQsyjst3Al9t78LMUxrxLYDaqurPspH2T4BEfSURpXqLCIS8FZqNshfIsW4HZcO26i5KjbyowG06OsUuNlz80rFzkKBWTxybLRvl4ofMy51Eg4oHwQl9qcx8ViLKdFmxKzUtcyhuTlMkHcWFI+VAJqARUAiqBOiUBADGYW/fUKa6jYzYqYBbviGgn0Gce1Xdb+HhUpJ/pkr4p+fM7KcqZ7xAPX7bvJWJefkluBvb+Q+4ZRIU2D1GGXN4T5HZaDK0gf/BmN3PJg9Peag+k5wOIDiE6h6gvkZv5FgVwPhK8lPkDHG7skoGM58SHSzLf3vIrf7fFXGR2OdE3iKDHWFhcRMT1+jG5vYzUoRNExLALutMoL+bhj+R2akcU9x0R71B4CFNs+ebK363fybr9ifiVC7HMPo/BkMEH7GmzoaMso+tF2GjhD+DayzxIgZwHQBKYIUTsh53KkKtd/tgR347IbsKWjfz2EnH559sLSD67LZYXovwwY4tkF+2K/s916S4Di+xmMKCmyOXU1eyriHG0y5cxqIDfedxN50ck64L6YNHezeADCtbFfQ6RopjHw4w1qIIcK7rZ6hR2Hrfnf74tf/noxYeMl497HCXidvuY3E1dMhlF/vcRXUg0LxkH8zenxZzsZkZSAMfDnMQGadgf4JSbuZkCOB7eH6TZTQ8cdrIMEO5hIs4vhD+cc0XYrbawQj16tZ9bv0PZfur2NsXj+m9wYfhdEaeziPO48F8v/O3OwSKeBLbzncsKMY/KdvN6D3Z7T4qq7nbZ4vkcId/bnCKU0A9jPPRJgdkSCr2eFKXAbLiG3kXJ0TePD5eNpo6bBHgRDC/rpTbyxf3yHIV/lcL5ZcPtZStHFqGC8QKK8reHyqW8EiswW17tqbVRCagEVAKllsBKKhBz655SF1xHy8NLOOSFl/JSmjlUGL+D4Ut8N+O28PErSoD0AFGbELmZOyiAy5nvEqkX+QPE4Xhs4z2xGOYoypTLgI06YBHyIqIpRG5gKAXltdAH+fQnwkLl6URYNPorEfOAP7Rs5ILUe+zpYMt4xVhw/IjKZP4GOpTv5nWZSJer/baIuDhSj03YBd1plBHzLhcaOX+2rxLxjmDPpF1s+ebK363fybphkc3JzCJPrv9zThHID0Aqx7lBxJGA7c/Jf60HPSnykP/j+H8o5/97iof/XJBxVyI3U4iy91LmXC54dzJui+WFKD/M2GLn9ZfkwXUB+FEqo8Cst6SrKBjt8qV3tJKEzknyAn685nE3nT9cpMf852Z6UgDr4j6XSKWex8FGvmMN0sqxohs8cpgg8ziykvm7jUX2eH74yMFmRvBGeuJ2uz8jxPuhDQVDv5H2P0Sou5f5lAK5nP2TEcPO48hmt8iXP/5JZp+y2ok4H6Z8E45c86wtel6Psp3t7efW71CQrNtMl5K/T/4s18kucfCOw3EqRJx3hf+55Habyw8V8f4i0uc7lxViHpXt9p7gye50e0+Kqu52/vAMUJzb57tOEUroh3dG8KLAbAmFXk+KUmA2XEPvouTomwrMhpNj7FLzS2oUwOyGpFJBsfAFvZe5ggIRD7TJK2KRwhSYzRasArPZMlEflUBQCTSgBKWioLxpfJVAsSWgwGwwCUcFzGKRht/BHvJg2Wnho71IiwU5L3MxBXI5bsAs0ssFRMTHYlMjBBTBNKY8byJivuz2HyhsJ5HTe7RcMLqV4rgZpMXX8W8T/YvIXoZ8dgNmn6B0bsYvH27pc/njWDzmEYu0fg10idMdmSORrMNuEXexyAPt5GZupgAuS+60mSb8H3FLTP4XiXhH2OJJ3rza2ZbM92Ou/J36HTKXdcOOYyeDRV6Wyz1OEchPyvgGEec8kZbz8GNLYB3/JT7zyOeHFHYCURMiaQpR9l7KkPkFH07GbbG8EOWHGVvsvL5IHlyXefbAIj5/kSy3pohl1OWsq5Ly+TIGlfA7j7vpvPw4BR/KuJmmFPBfIuijGzCLtKWcx1FevmMN0sqxohs8HEy+8ziykvm7jUX2eG58OLDmy+sMisVjSK4NEzLDISLdxzLAxS3rOi0ZR84x+czjyGY3EfOPtnAyGHM5zoe2CHNFWDHmcRQn625vP7d+h3Sybti57mQYSEP9+jhFIL/XiLj+Fck4kEmu905OY7cZWM93LivEPCrb7YlknZwsp/ekKOvuxOMM8mQZv+oUoYR+rE8KzJZQ6PWkKAVmwzX0LkqOcUKB2XByjF3qT5IN6/YCU0yG8TUbTz4P5ygIiwUc9+AccYsRrMBstlTxxwFt8kF2kPqoBFQCHhJoQGGghjYCsMBkDyvFM/NVTJuqqEYlkJKAArMpUfhyRAXMAjTjdzCvBSunhY8DRNpci3YnirjzPSRyrIgHvn5HVOkRvxBBR1AmAIlYDnYb4ORGW0FywchNbld75PlPCnuB6Bcijhswu5PiuBk/fLilzeWPhS1ehMduGcxVfg3/uYQsD8uRSC5aysV+vwu6kD+3mRswe68HDxeL9NAFaYopX5STK3+nfod004i4zm51myni3I1EDkbK+AYRDjfnH8R+SOQBZw0R2gcfObjl830Kk328EGXvFeXVktvJSL17QEQoRPmcXT5jC6dl+xFysOwOYs8S2F8ky0UbqsmWQJyAWb/zuJvO81oI9Ax5eZn/o0DEk2O1PX4U8zj0NOhYA77lWNHNXhF6DjOP2/OvdcifvXLxwfHysS+kRGgz0AVEfk0/isjp3veRSM5X/J4n5xgvYNZtHkexuwUf/V34wAc+zOuHtjhzRRjKKYbxaj+3fgc+/NTtJYrHdevjwvxrIk5FMg5sThfUHmIrJ+hcVoh5VLbbThs/8lHq3aBkQJR1l7yxezg5uA3eZM+IbNYnBWYjaoAyLlaB2XCNu4uSY5xQYDacHGOX+pNkw2IiKLXBSxNPPq/nKPwVEXdUjrjFCOY/I9uLkXkdzXMM8Y32+6CO8q9sqwSikEADKrQhUSMi/CHBLh7sHoH7OqJriPBVOr5W/hrRpUQ7iL5KhD/NFxDhC9Ozic4kOp3oVKKTiE4gwhh1DNHRRFuIjiLCAsrhRFj4xoct+ChmHtF0ovFE+Pp2AFEvomqiTkStifAHtrGNwLdfQj1zEeQRhii5mjosAQVmgzVeVMAsgCx+X3vIg2WnhY+OIu3fPNIiCOMdl8MLdvYkGKuQD8dj+3F7xCI9Y4zE4tO9RJ8Tcfls9yU/NnLByGmhD2M2p4P9LtG1RBizJxC1JILZScTx4gbMgr+PBX9V8PBpMM9xvTCHeZk1FMhxHxMRFwt/JxlzVH6PRx7QZzbTyMH5ok3dzMUUwPHQ/tLkamcZNx93rvyd+h3K8VO3mRSP63W3C3NSxnhXYXMaOTjtTeQGcOqH2nEGNhvvG+D5IiIctcqAP5dxD/mxKUTZeykzzruWM7bZk0QcCcwWonxbUdb7l9+xxZ52j+ATY0epzBdUEGRYU6oC61g5GA8hny9jwLffedxN5zE3cX/Bfw8305wCON4+l0hRz+NBxhpUQY4V3Wx1CjuP2/OvteUvH734kPHyceO9g9sN47lf05oicro/+0j0cxGf1/TkHJPPPI5id4t8+8PDwTQhP+b1Q1t4rnnWFj2vR6/2c+t3KMhP3V6ieFy3Pi7cvSbiVCTjNCCbx3Gk70XkZx5HHPQjJ1NNnn7mskLMo7Lddjoxk/R7lmyWDwOzUdbdidXpgsennSKU0I/1aUUJy4yqqCupYOjGjqgYqGflrk7K+/l6Vu9CVXdXUn4KzBZKojHJ55Nkw0YBzGJx/+/J8v9NNl7snEwz8uR4sPFSVWrDCzrbS11wjMsbQ7xhEvsgxjwqayqBOEkAfwAaEmHswzjG4xr/UYijjeON/kL0v0SYL35J9DOiN4iw4LKHCH8evkuERcu7ifCn+gaiK4i+QnQK0VaidURLiWYSjSXCwkx3ovZEkAf+4IEgHzeC/LwIMg5KlERNRBJQYDaY4PESjnECL+WlNKOpMB6fXvQo2GnhA9Hloo99UVNm92164HLmy4CkG+MEPuTjOC+RG+MTPx9N7kIbjDc9iTBe2Q3CsNj9NyLmQb4nzhX+TouNb4lwfKSDcc/JfI88OX+Mo2xk/jvZ08GW8Zz4cEgSyGs3xWb+JnikxDyAueMhIvC0hYjT5VoIPl/EvY3cbBaSg/PA/ONmoCscD23GZho52F+BWZZKpr1YyAhzO5uDyMGyy7WIiHm+D1FTTpy025KN8YU/QpDBXekB7xVcBt5D0OdgClH2XsqH865Fpg5mEvlxHPDCphDlhxlbmA+23yYH81nLniWweWyvKUFZdbGIKmIa7fJlDJj3O4+76bwEUbzmEcyVrIv4n2A3Uczj4CHfsQZp5VjRDR7ChJ3HkZXMv1bkbXfKeHY+7HGDPk+jBNxuXuN5U4r3GtELRDcSwXxKxGmh826mOQXI/774DwgTdh5HHruJmIf+8HAw0D2O86EtfK4I89JvW7JAj17t59bvUICfusl3HMy1TgbtxvWvEBHeFP4ThL+Tsx95drYF5DuXFWIele2208aXfHyWHrjug0RAVHUXLKScKwWPO1O+0ThYnxSYjUb+5Vzqaqoc+qICs/m18q6k/LAmpKaMJPAJ1QUdY3hEdcJiPk+S6114OFjE2eMSp9je9yV52F7sgupQ/mOSMvmgDvGsrKoEopQAAEMGZVuQ+1yii4guJfo60RVEVxFdS3QdERZAv0mEBftbiLAYfTvRnUR3E91DhIXk+4keJHqI6DtEGFcfJ3qSCAv6zxA9R7SHaC8R/pi9TfQeEf6Yfkb0RyKAC/8h4jG51PZfqWzw8gsi/FH6PhH4x2Io6g257CA6iwhj8RFEq4jmE00mGkZUQ4TFl8ZJcgJ48QfSjdBGQYiiqwkhAf4TuidEHvUp6fFUWfRLvJSX0lRTYTwevOtRsNvCB8YlTn+ZS/pK8v9CxEO/tptLyIPzwVjRiegY4Yf0bgtyFBTYDKQUvIj4e3JjbHEyV5Mn84Uxm80ccrD/HeyZtNuTLXcEdrOF82MXcmBs5Hzku/Jc4b+T3G7Giw+3NEH8MUcxf5in3AzGdI43g9zTxTPqiLo6GYB2nxJxWimDWcIf85uTqSDPfxBx+kIDs8WWb6783frdNFFnvCs4mZnkyXLBe4WTWUyeHOcGEUECMP8if/QXN/M8BSAPvGOcnYx0Z9IP/uyXDEpZaLt/E3H5GCdgwpaNPKAvnG8tPBzMJPLjOHgXYRO2/LBjC/PB9ufkYD6bs2cJbIy5KLemBGXVxSIAUkE+X8aA+eokL+DHax5303m8YyMtCOM1+qaTuZw8Od4+hwilnsfBQpixBulfIeI69YRH0hRiHkdWfsYixHPjA2FhTVfKgOuIMbeXS4Zyzn0xGWcP2Zz2ay7p4C2vq3hfxJN55jOPI6vdRMyD23tgExHnQyQSJtc8K6Lm7fRqP7d+h8L81O0lisf17+PCIf7/cxzZf78t/N3eA5DlFBHvD+TGu1mYuSzsPErFZ1z1sBMeLuZZ8ue6DxJxoqq7YCHlPFbwiHEySsP6pMBslK1QnmWvpmqhL+J/gZrgEthFSSA/rAmpKSMJfEJ1QcMOj6hOa5Llgwfw0tfGB15+f0WEcNAqoijMfVQoygcYoCYhgTFkQSYfqEBUAiqBnBJoQDEaEjUmakbUmqgjERYZq4m6E/XIkzgtbD8EAIAJZdsJefQhwh8XzA3jiKYQ4U/rAqJlRBi7sTi+iQjACP5sn0F0PtHFRFiYAbh8OxEWMwES4yUffwqxIITx/k9E/ybCOFJowiLx74jeI/oB0dNE+PN1E9GlROB1KxFeDucSjSXC/IM/qk2I0E6NXAjtaCe0rx+iaGqEBBSYFcLw4YwKmEVf+CMR+ul/iToQOZlnyZP7slz4WCr8vyD3SFti5H+HiIM85tviTKXn/4g4GIdg0O/2EHG5PyI3+nAhDPjCOMJ5n+eS6SMijnxPxbjJaZ+zpW1Bz1is5/DDbOF4xBzxEyKOA3szERuMXRy2kz0dbC8+OHovcsxM0gD29GmPoHjcNhh7xzuk474Ofj8ngmwxjv6YiOuAd+2mRNIgDuYSjvM3crcTETBucxjsg0QYnC2JMAfIOIsQkDTTyOawe9nTwb5YxDvCFp5LvqjDdKKZSWpOdhCTK3+3fuenbuCJ63+3C1NuwCyiY27l9K+QW7YNwmE2EHEcjB/94EnmOCL2xyKvU1roFsd5h9zShCkb+ewl4rxr4eFgJpEfx8G7jDRhyg87tkg+etED8/hLGVACN8ZzlF1TgrLqYhFVSflgrI/aQOf+SIT2Qj/sQORkvHT+dUrAunYXufGuLM1QesAYzXH2yUByTyXiuQJxliXDizmPo4iwY83zlAfXaXKSZ1iFmMeRj5+xCPHc+EAYDMYCnmeCzuNI/zAR1/MhctvfpTA/Sx04gZ5hlhJxOrT/BHjazEB6lu9TO0R42HkcWe0mYh76i7ylE/XhOB/KAHLnmmcbUpzpRCzfoPM4JfVsP69+56duL1H+XLc+KMzB4D84x6kQ4RPJjTGBw/C+ZjeYn98k4jj8vhB2Lgszj4LHuYKnnfBwMW7vSVHW3c7qneTB8l1vDyzxM+uTArMlFnw9KE6B2XCNvIuSY5w4Plw2mjpuEviEGELDDo+IsYZUrlyU+ZSetxCNIdpE9DERT1A/JTfiR2GwWAQ+tkdReEzLRBtBJh/ElD9lSyUQJwlg0QHjV2Mi/JlrS9SZqCtRDVFPol5EvUtIKM+JwIudasnPicC7nXqQn6Tu9MzUjdxM1eRG/VEWQByAozOIsGiOl7aNRBhzzyT6KtFVRDcT3UP0CNFzRK8SYW74JRH+8P+TyISk/1L6PxH9igh/QncTobzbicDD+UTg6zCi+USjiFA/LNCgffEnVVJDerZTA/LzQxStrA2DNXvKupaFqxxewqHfeCkvteE/Aih/oUvhbgsfiP51IqQFYeEOH0cAwFtCJNNxHPQtNu3I8SsiDruXA5I2xk0GCBDnIlt4mMcrKTGX+w9yX06EhVksemJB6DtEHI7xpw0Rm8Hk4DDYAHauJ8K4C/MGEYd/RO5TiTAW4v3qFCKMbRzO9lnkxwbls/9O9nSwc/GBJNcRcV4PO+SRy+sWkR5yuIBoChHaeAeRHJtPoGc2E8iBMZfL/hG51xEBvF9FhHGew5DHgUR280Py4Di/J/c1REh7IdHbRBzGNuYYNtPIwf52veI4sC8W8Y6QAeTOJd9OIi3KGmpLn+sxV/6y/0B/2Pip20yKzPW/mxPa7MUizg22sP70/C8R/iG5NxFhPj+cCLr0JRGXgXGETRU5/krEYa+TG3P/ECLMqxjvfkvE4dAjacKUjXz2EnHetfBwMJPIj+Og/0oTtvwrKTPOO+jYIvlYL/K5VgaUwP1FsuyaEpRVF4uAjqON0QfiYND/WOcWujDkpfM9KY0E116gZ4Cr44hOJZL9GeXsI2IT5Twedqx5kCrBcnuf3BgHj0tWLOw8jmz8jEWI58UHwsPO43g3wVjEdX2V3BiThxMtJ8IYzWG/JLcE958SYZgTMGfOIML70plEUjeeoWf8F5YmzDyOfHYTMW8Ym52MFzCba54NO4+DH6/28+p3fur2EuXP9e/jVHnye03EqbDFuUOEIZ9biPC+NZXoEiLoPef/d3IPI2ITZi4LO4/OJSaYr53MkIP9rIgn35MQNaq629lEn0Jd/kNkbx973GI/sz6tKHZBMcif9XdHDHipDyxgToGeP18fKluEOvJ7HP4jqSkjCXxCdUHHwAtXVKYvFfxzIp5UnexPKbyWKCpzHxUMvrYXmYFmlP9OIvz5ryRyMlhwepwIcnMyJ5InBropToEF9BtDeUEmHxQwT81KJVCuEmhAFWtI1JgIf0bbEOFPHhYLuhPVEPW0Ef4gF4IAWsSJZJ3sda4lXpkgE0k96JkJMgN1E1RN7q5EqOsIoulES4gOIzqW6GyirxHdRHQvERYRXiF6hwhz4V+ITEj6E6X/HyIssjxKdCvRJUQnEYGP+UQAHMA79AD60MhGDenZTtAfL6LgOmlWEteQ+Z4Sc7+AyttNdIRLuRPI/zmiU13CocOPEV1HhHYplcFLOOSFl/JSm0OoQO4fO1wK91r4gE6jT3AeTvavRTj6Cpu7yMHxPyM3xk67AdjHcb4k9yR7hDyf0U/3EXHebjbGD+izNOjf4NeeZkMy0hSywas9XD7/H4XLxTzoJZu55OC4O9nTwc7FB5JAlzmvhx3yyOXVmSLsIeI83Ox7HDJC28nFYKe0WOhd7JAWXvD/J5FTOvihbW4W4YvIzWYaOTgd5gU3czEFcDz7uJFLvtBXTgt7qFshLv658nfrd9NEuW51myni3O1SPuTL/N/gEGc9+f1ZxOG4dvtFioP+JM0yevgvkT2u/RnzdQeZMOkOU/ZeUW6tQ97wwjjCvNiBWYSHKT/M2IKy2Ujdns2eJbK/oHIgn5oSlVfX5u6qpHwwzsfB+JnHc+k8wr3G6/conPsM5k42Uc7j4CHMWIP/EFwntj9IVizsPI5s/IxFiOfFB8LDzuPIYwMR92uuq93+K8UZgcjC1JBbgqv2NPz8AsVrIdKxM8w8jjx2E3EZ/TlTm91ExPnQFpZrng07j6M4r/bz6nd+6vaSqFsfW9348TURp4I9kzbe4Z4V4SxLu/1vioP/1tKEncvCzKN+34Nl3ezAbJR1Zzl2JQfL+mX2jNBmfVpRQB6aUV47ieK2zn0l8QTZ7yBSU3wJKDAbTsa7KDn0VYHZcHKMXepPkg0bJTALobQluo/o70Q8KcH+b9K/J9lRGvAGfrYXmQm5QHKyQ1l40WT5XOUQjgV+/rP0tEN4Ib3GUGbg5YNCZqp5qQTKWAINqG7oo02JWhK1I8KfgUoiLN7gpVxSNT37pW4UNxcBDHSiHuTvRDXkb6da8nMijNF26kV+knrTczFIliF5kHxyPWQ9IQuWGcv5/7N3HvByVHX7fwMKKCWQELrc3RuQYkMpKryICDZeFBRFQZTiH0SQJiCIVIUAKh0VFRQQIhhBRBBRkXClSi8iNSEmkNBDCOlh/s932F+YbLt77+69d8tzPp9nZ3bKmTPfmTln5jxzzjDt3dLm0qckbh73kQ6XfiBR6XGxdJU0XnpImibNl5J+6GWt87h0i/RH6XxpjISRu5v0SQkjl3TyQEPFAedQaAmNZzVM/6tJs5sqfEGpgdv4QU5VT2G7mAnlwm81kXQtlMqZAUcX5rPMh6XBCtyEs82xg7XBzHZW1Hic5//JTM+OXqc/pA9xv1IcltMEriFazMZyDJ+X9i8opm+r/4RPSDGN4eeZWCZwHVCZEcs+VmaZ/k5i30+WXpUi/hgy7R/SBlK58D5NfEKK5Rli8kX4uEYelLLzGadilHtPygQqMWdLTOec5J6Z8DEp1js/nVL5p7d0nJmJ68rK0VSdwzE4THpOinTFcKqm7S6RP5UL5Lv/lOIci/UYclw5D6qFTTTzful1KdZdoPG7JFpvct7EdPL2CFtoJKZfEhPLDE/ILPe1MvOr8SUPyabrvWXW721StfgrXXe17NtW2nDs/4UVEkGrmVjm7ArLdGn6tVKcp7E8Q479t6VylfGa/D/bSLdL2XUYhxnXzjES51al0KUZ/dl2j9aLbVL2lwsf1MRY5rflFtC0/m6f6OrJW1j/LRJ8SeMr0lulwQzkU2wbBoMR4pjNqLAxjhHpaZaym3t70kNe1AyhlnK8lnOe/PgeiX0LsY8cn9UkynSmk3cTmqEcJx39zWsot8j7oo6FfeMcW1oi1FOOs36c18RbKS9iud7S0YhynO28U7pRKlce/07T15bKBfKj70lx/Nmf0EsaJ33LSpVCf8tx4vubFNtap8IGSB/HjeUmlFmmWjnbiHK82vGrdt3Vsm83FvaLfcuV2Tcm3SEFo5FlliF9+0qTM8vF8gz/KH1AKhfqLcu6FGl/yvGPab1I4/nlElaYdl1mufXKLDeU+05yeAaK/TiiTPoGe1LkSTs1cMPZY3VomXg5LsHgjDLzl9S0yIMbWc99emG7Y8ps05MaT+BLBd43ND7qjohxbIHfQR2xtx20k1MKB3ajJtlnbpi4KdpZ+l9plNQMgcoxCoqBNma5SZokTZeoSCoOS2nCAxIPwtsXzyz8v1JDHo4OrjC/UZM3VUQwmdioCB2PCbQ5AW76l5DI53iYx5xdXhou8UDDQ185jdD0voq8pJJW1rysyGdDq2g8q1X1PysqXLKi0imrSsbyWloOUeGAeKjPqkv/QzmNZ5XX/1C3xkOjNV6vIq4Yxnay2490MYw0x36wT2sWtIGG/yt9VtpTOkw6WfqFdIV0k/RvaZo0X0r6qIVafqp0j3SNRLwnSN+QKA82ljg2nF88vGS1hP5nNUz/q0mzByV8QVuBw/hB2dqbGzlSowskbm7LhT00cY7EMSsXttREymkqdlYot8AATeMmHF6V0j1Am10U7UWF7ZOGLRZN7fsIzD4kfV5ar++rD9kaXFOkl2v8c4VxrqtaQk4LbSRxjXLtZQNx5KRtpB2kdaVa49WifQo5LV0pHewXx/Zsqd7Afn5CwtTLS8X7rEllw1KaynMA58ZWEnlsXwJlKRy3lZbry4oNWjaneCrxvUfz4EvZ3N+Q04qV4u9vnI1cj2vknRLnMcegW6JMqiVwn0Pe+kXpo9JwqS+hnm33ZTuVlq1n+6zbn7wFzpxT6MfSYIfXtEG23TVIGz5S22mlsrvZjFkO00VSnDP1lOPERVm1nbSNNBT5rTbbr9DfvIbnNsonrlXGs2Ewy/Fq6WhkOU55THlDebypxDNrrYF7AAxr0gOzpaVaQ7uX49WOX62MBno56iN4puVel+dLjkktob9lWcRdTzkacdQ7HIp9v1eJJl+eJVEPM9ShRwkgPTs1MCHc+06SpkublYmX/OYBifsK6jXKhSs1kXuAg8vN7Oe007Ue+zqmn+t7tb4RsDHbN17FS4/VBM7Xg4pn+H9rE5ii5HNgN2rt3Rjw1A+WMcuOcENS7eaVyq1Kb52zPqH4YeGNqY395Qadc2diY6N1bCbQ1gS4fnl4p6LyrRLX+jIS13Q1cU3XK95SzopKlGLx0J3VCvqf1XD9z4oHtax4mMmKyg/EzTgKQ3iUxkNhBIcBzMM8CsO3nNlLZX0IkzQM0xh2aRrKFZTXMNSt8axG639/lI2D8Yg/p3EUaSBNpBGRZoxc9mlD6X+lHaS9JIzcU6Qwcns0/m/pWakvRu48Lf+UdJvEA8y50lHSHtInpHdLHCPOQcqbrJbQ/6yG6X8laVbdYaiMWRLeWznZ23yuXdgNZuAmPJG4KR+KsL42ulAiDb8eigR4mwNK4BzFzrHddUC30pmRU6aRjz/WmbvvvR4gAlcrXq7Z2RLn2GCH17RBtt81iBvurWzubf5glt3NaMy6HB/Ek3UINuVyfOCguxwfOLadHPMHtPOUo+jMJgFBHQTp2anB6eG5mTK4UqDOYbDruW3MVjoaAzPdxmx9XG3M1sevade2MVvboblMi1E4HVDb4h2x1KYFJhM7Ym+9kybQOALcdIb5xQ0qBlkjheFbq5bSsuXETXM5LaPplVTOWKaCLFTOFC5nApczfsPsLTZ6w+wNk5chRm85k3cNTUcYoyhr7GbN3S7NC+U0jvIFdWtYrNGaVqti3YiPYa6g2GYlI/ddWm5b6SvSt6UfShdJ10v3SRi4YZolGu9NM7XM49JN0m+l0yTMYUyZraScxLnBORqK8zaGwzSvkjSr1zCUxmyviWvCBYbamAXJ5RLnFhXyXG8O7UHgu9oNjut8ifzQoXEEKHcmSPA9r3HRdmRMxeVNLWVR8TrtAi6vHVkgcV5hxgxFoBxg+11DsfEW2GYzGrNgczneAidPP5Locrwf0GpcxeV4jaC8WJ8JXKA1KEfnSNRPNEPoUSJIU6ON2WbYt+I02JgtJjKw/23M1sfXxmx9/Jp2bRuztR0aG7OlnDbVJArsiaWzPMUETKAXAsUVhdX+R8XjYAzDgKt1WKuhXGwUF5vBWRO42PgtNnxrMXqjlW+YvNGqF4O32NxdWdMQJlO51rtUrKFKpm5vrXS7tW5otMb7oliPCliUK6hLw9guJkq0xGV8Y+nT0p7SkRJv32K83ij9R5oukXfXIkyap6Qe6RJpjLSvRPwYxRjrnAPZ82UJ/Q8N03gladb/2JiFQu2hGYzZ9ZTc2RLnz1CZAbUQ+7AW+n91attaNtQmyxyi/Xha2qpN9qeZdoPyaLL0S4nyzKHvBKIc4d6BSh26udta4llkA4myj/Kd+ZRJxeUSZVSUSzGMOLNDLdYyYaxSSj78ssQ9ylAEG7PVqXNcOEYY6M0UXI4309FoXFpcjjeOZXFMLseLifh/IwhsqEjiBatTGxFhg+LoUTyUXTZmGwTU0SwiYGN2EYp+jcS9P3VCDm1EYIr2hUx3ozbap4HYFRuzpVRtzJYy8RQTaAUC2UrIgR6PCtBqw6ypVzweFazFw2pG79I6CKGsyUuFOApjl2G04o0unaMFLw/g5UzdMHZHaj4qZ+jS1RWVcajYzKXyOAzVLo2jXEF5DVF3RqM1XotinYgjp/UQ8cf2sgYuy39I2kH6hnSs9BPp99It0gRprsT9QW96TsvcLf1BOks6VMJw/aC0qlRcQZ49F76o+cQ/XhpWJP11KCLATTi8uCkfysAxJh3zpHWGMiFVtn2+5vV27vY2/7oq8bfbLK5L8j+HgSFAeeLQPwJRNnB/QGuS3q7b2Vrmeekp6SHpJulC6Tjpq9LmEuV0tbIpthlDLd5UYWOl5nUJFrsPYcpszFaH36zGLKl2OV792LXiXJfjA3vUXI4PLN++xN6sZXNf9oFlr5Yoxx+WqLNoltCjhJCunZolQQOYjtML+zpmALfhqN8k8KUC7xvenOSxPhCwMdsHWK206BQllkzXxmz1o2ZjtpSPjdlSJp5iAiZQnUA8SPV3mDX1Ko0Xm7v8pwK2WFlzdynND4WpyzCM3TB1s8Zu1tQtNnRX1LoojFxa6Y6UwsgdpfHocrk/Rm5e66Pugmoxb1kmlo/1c5rWVRAGbta8pQKcewNaJu0jnSBdIP1F+rf0isT9Q2+i4vYR6XoJswwTeHdpa2l/ifVvkuK4ZY/rME0vJ03uyNAsxizH6GaJY3dJkx6JI5WuO+sUD+sOJmACQ0uAMoDygTI7J90k8TLQo9Jk6WWJl0R6K4uK58/QOvdLV0mnSd+StpPWkbhfYJuVyiPNGtJAOcz+/GlIU/FGl/ako2uI09Gsm29mY9bleLOeNU6XCZhAOQLxPEjZPFrqlmJaueWbfdqHlEDKT1rMbtZkie0ppM3GbJMdmDZIjo3Z+g6ijdn6+DXt2lOUMgoEKl8dKhOwMVvKxsZsKRNPMQETGHwC8VBW6zBb0Vo8zsNesaigLVY5UzcM3WIzN1roNtrIjda4ayl9GKoYq4gK0lxBeQ0RD6+IB9neFMvGujmtg4i32LxdT9M+Ju0mYYSdK/1BwhB7Rnpd4h6jN9HC6e/SL6TvSjtLm0gY21FBznFZokjD9L+cNLktQ7MYs8DlZQTOpdX442ACJmACA0CA/J18n3KAspXeLSgXeLmJvIdyMEQZtaG0sbSl9Cnp8xIvFp0kXSLdLE2WFkrVyiV6grhGOkbaVhouUQZly6HiskezBy3w4hT5L/cdQxl48QqOsHcoJdDMxiypdTleesw8xQRMoPkIRHnL/QDPvJQ786Qok2O+JrVM4GVvynHKiWYLPUoQjG3MNtuRaf302Jit7xjamK2PX9OuzdvCZLrjpPOsigweL3Digd6c3mBwRYEJ55CZmIHPAZ8DnXIO/Fx5Xi3CZKymX2p+VufrfzldoOnoV0X6tf6jCzO6SOPo4oJ+o2FWVEyHLtV4Vtzo9VXZ9RmPuGObTLtSul6i/LxXekx6RqLF7UKJe5BqwrR9VqIcvlsaL10tsS34VjoW7Xg+whBWsGjH/fM++bj6HPA5UHwOkMeT11M+Ug5eKFHGUAYUl0GV/rMsypZNvETEC0G3Sw9Jk6SXpPlScZlEWfWCRHeD46XLpeLypzjdnfB/njjACradsL993UfuxeDD+dPXdb28mfkc8Dngc+DNcyDuBX5byFcpqymHoyw2qzdZ1cuC53TKLp7f642r2den5xT29Z4O2NdmOBZ/K/CeYt79uraoA+J8pTGEQxsRWKB94cBaZuBzwOeAzwGfAz4HfA74HPA54HPA54DPAZ8DPgd8Dvgc8Dngc8DngM8BnwM+B3wO+BzwOeBzoHnOARrJObQRgZe1L1xgOO5HWxUZPFDgxLd8zOkNBj8rMOHtcjMxA58DPgd8DvR+Dhyj/LI/OlbrVdJxmpfV8fofOkHjoe9rPPQDjaMTCzpJw9AYjaOTpVMKOlVD9MOCfqQh+nFGp2k8dLrGqymWo+Ur9yD/lYgr4o3tEActpLj5vEG6W3pSotxZKLFuJb2uedOliRLr8YYmrZx+IsEhy7O3Y9Is5/a1Sjf7y9u9zZImp8PHwueAz4GBOgcibya/Pl4i76asonyKMinKjeIyKcqnKG9iWEvZFOsSJ+URz8lXSLdJtKyNlqLZ8ody6VaJMus4iTRH+rPDgWI1FPHO1T7CAE5Dsf1m3yb3UPBZaD4+P3wO+BzwOdCvc4DyM3sPcKH+k69S5vIcy3Nutsxt9nKhFdI3UUxhPFZqhfTWk8abC/s6vgP2tR5OjVr3sgLvJ827X9dWtPDmOcWhjQhM0b6Q6fobs9UPamQgB1RfrKPmbqq95dyh4HYwARMwARNoPIFhirKaltD8rJbU/6zeov/orRktpXG0tLRMQW/T8O0FLavhctLy0goS39ZbUVpJGiGNlFaWRkmrSKtKqxe0poZrSe8oaG0Nu6SclC+oW0M0Wtpfohy5o/CfaSiWYZ1cQcRDfMQd2/igxr8oHSZRcc7LU7xINUMi3kpaqHkTpD9LVNJ/Q9pKYl/gFQyzbIdperE0aVDDQdoa+8SDsoMJmIAJdAIB8l3yZMoxyizKJ8olyqMoi6I8ijKJvHy1giif1pCy5VOlsinKoOyQ8igv5aQuiTKI9T8uHSFRjsySsuXN8/p/ofR5ifSS/ihPohzRpJYPr2kP2G+4OJQS4NyDz4LSWZ5iAiZgAibQCwHKS8pOns14VqXs31UiX71P4h6A51WeaSlnWd6hfgI9igLG/sZs/Swdw+IEvqS/nFs3LD7Z/2okQB0Q/KgTcmgjAlO0LxxYG7PVD6qN2VI+mxbOnYmlszzFBEzABExgEAlERW+lYVQIx5CH1xAPu6EwcKuZtxi3xeYtxi2ioryccUsFebnK8X01nXuQ26V8Qd0aotEVFPMZsk5O6pKypi0V8O+W/k9iG6dIv5UwgHnDmm1W0guad7N0gfQd6bPSuhJsgtkSGg9VYq5FGh5szDYcqSM0ARNocgKRx5L/kg9TARsvE2XLIipn44WieKmISlzKpuyLRWHihnlL2ZQ1bSlPclJeopwpLouYhnISy2LUssye0mXSi1K2fMG8vEpiPsYx+1FcfmhSSwYbs9UPm43Z6nw81wRMwASqERimmZSXlP28QMxz5rclyti/S5TjlPPLSDzLsrxD/QR6FAWMbczWz9IxLE7AxuziPPr6z8ZsX4m1yPJTlE4y3Y1aJL1DlUwetOF0wFAloAm3a2O2CQ+Kk2QCJmACVQjwwFpJUVEcQyqPQ2HcMuThGGHeUkGOeCCOinIenLOV5VFBXlw5zlvOe0mUrbdKWeM2WiR1aTrKSXmpuyAqwSsplmH5nBRxECctbamAf4+0o3SYdJ7Ew/0kaaFEesppjqY/KP1eOlH6qkQ5yP7BJVgFP4bDiqS/dQUbs3Xh88omYAItSiDyUvJV8trisij7MlG2XIqyKcqnSmVTvFQ0SnFjnvIyEa1sKTMoO4rLoeLyh3InJ/GCEPqc9HOJciVbnizQ/+ulL0ukiX0pLis0qWWCjdnqh8rGbHU+nmsCJmAC1QhQ9lNGUsbT+wTPjqdJlKuXSuSxPF9SnnJfwPIO9RPoURQwtjFbP0vHsDgBG7OL8+jrv7FagWuTOiGHNiIwRfvCgbUxW/2g2pgt5WNjtpSJp5iACZhAqxPgobaSeDjOKsxIHoZDWeOWB+kwbqOCPGvcYm5yD3KzlDVuefCOCnLehubBu56uKKMSvVvxoLyUk7qkbEtb5m0jfUP6ofQHCTO2uJtK0hx6XeOTpL9K50j7S8RBumESjLLcivlqsZqCjdmaMHkhEzCBNiUQeWc2P43xyGuLh72VTeVM2+HiRyvbkRJlUZRDvNiDUUu5kZPyEuXG6Iz4z3TKF4zdbSXKkwekKDcYPiedKfGiUJQV7Evso0abPtiYrX6IbMxW5+O5JmACJlCNAOUhZTrPk8tLlMfjJMrQH0k8G1JW25gVhAaGHsUFYxuzDYTqqFICNmbrOxFszNbHr2nXnqKUkenamK1+iGzMlvLZtHDuTCyd5SkmYAImYAJtTCAqjouHVCpnla0gL1c5Tqsh7kF4ACxn3PIQTovUrGlLRTnGLa2aqCynZVMYt9G6iUrzLikn5Qvq1hBlK9BjPOaxbK6grGFLvJtJu0rHSBdJt0jPS6S/kqZq3t+ls6R9pM0l9iUq4eGzREHDNCyWJi0WbMwuhsN/TMAEOphAcX7Z2//IaxlWKpuyLxNR0busRCudKIdoWRvlT7ZVbZQ3lCVRrjDkf15iPobuh6TTpWekbLlxm/7vLWEIlysXNLkpg43Z6ofFxmx1Pp5rAiZgAtUIUK5TJvKSL+Uwz313SZSf+0vksTxX2ZgVhAaGHsUFYxuzDYTqqFICNmbrOxFszNbHr2nXtjFb26GxMVvKycZsKRNPMQETMAETKDUYsxXmVIqHdtY4D343SeWMWx7EUTXTlorsMG7DtOWNaozbrGmbbenUpXk5KS9RcY6ylenZ8ZjPsjmJdcO0Jc4Npc9IB0vnSn+RJkgLJfatWK9r2lPStdKp0tekjaW3S1Q+oODDcFhGbIP4uCl3MAETMAET6BuBbH6aHc/muZEPUybRA0TWrCWfxqzlpaFoVRtGLRXElAmUDzkpL1F+RHnCONOiDNlN49dI86QoJ2Zo/FfSFlK8wJMtBzS5qYKN2eqHw8ZsdT6eawImYALVCFBOUybzHEiZy3PddIky81NSGLPMZzmWd6ifQI+igLGN2fpZOobFCdiYXZxHX//ZmO0rsRZZ3sZsbQfqMi1G4XRAbYt3xFI2ZjviMHsnTcAETKDhBHhwRl+UKFvHS/ynAjqrbAV51rilojxbWc6b0lSYR6U5LZyi4jxM22jpNErzsqYtFemVWtp2a15UqpcbMj8v5aQuiQp5WkURH/OoNDhI+qn0D+kZif0tpwWa/oh0hfR9CTYbSuxncAhj9reaBq+s9NfBBEzABEygnwSy+SnjS2REHpwtg7IvDGVb1PJyUJQvlC2UB5QNeSlbhkTZQZnxPukE6XEpWzY8pP+UH5RlbJ/0RBo12hThNaWCNLOPDqUEbMyWMvEUEzABE6iVAGUeZS/GK89zvMhKmcMLTetKGLUYtpTJlJMs71A/gR5FAWcbs/WzdAyLE7AxuziPvv4bqxW4Nnk+cGgjAjZmazuYNmZLOW2qSWQKE0tneYoJmIAJmIAJ9ErgC1qCcmR80ZJR+Vw8LK4o5yEcZSvMi1s48TAfxm22W0oe5HnIx7ClMj0MW7rJojJ1DSkq1qk875JyUl6iUh2NLqOYx3I5ifXCsCW+DaQdpe9IF0i3SC9JcCin2Zp+r3SJ9KfCMn/UkH2O/Q8uwzStWJrkYAImYAIm0A8Cxflp5LWR95IP8/IMlcKUM9GaNsoWyhUqjqMsyWk8L2XLDsoMpmPi7iDxzBmGJ2XCNOlIiTKL7ZKGSJdGhzREOinnHEoJ2JgtZeIpJmACJlArAco6ylnKV74lu4dEufiwRLnDi1B0cWxjVhAaGHoUF5xtzDYQqqNKCdiYre9EsDFbH7+mXXuKUkamu1HTprA5EmZjtvQ4bKpJnDsTS2d5igmYgAmYgAn0SqCSMVttxaiQLh5GhXkMsxXnPNRj2BabtsWGLS2TeMCnUp0KgGwrWwxbKtjDsKVVbF8MWyriu6W8lJO6pKxhy33YztIx0qXS3dJMiXK2nF7R9Fuln0h7S5tImNCx38GBYTErTXIwARMwARPoB4Fsfhr5LPlulDPRmpYeHGhNi6FKWTJKCpOWvD8nUSZQNmTLB+atLx0h8YwV+f8LGj9OIi62lc3b9XdIgo3Z6thtzFbn47kmYAImUI0A5RzPbpSnvER7jkSZSN0sZSXlKuUsL0hFmahRhzoJ9Gh9ONuYrROkVy8hYGO2BEmfJtiY7ROu1lnYxmxtx8rGbCmnTTWJAnti6SxPMQETMAETMIFeCfTHmO0t0myleXacB/ZQmJdUbkdlOg/1VKhHpXqYtjzwh2FLBXuxYcvb2lS2UwFLq6hst8g5/c9L3QVFBXx2GPNYLid1SVQ2EA/xfVD6mnSSdJ9EubuwMGQ8q7n6f4/0K+kA6X8l0h77G/vPMMuGcQcTMAETMIG+E8jmpeSt5LfZcoUXZsqZtJQZ5PPk+XmJsiDKBsZzBZGXPy5FXv+yxn8gUSHNdrL5uf4OarAxWx23jdnqfDzXBEzABKoRoHzDmOVZbGXpDomy8DsS5SdmLT1VsAzLOjSGQI+igbON2cbwdCxvErAx+yaL/ozZmO0PtRZYZ4rSSKbrFrPVD5aN2VI+NmZLmXiKCZiACZhA7QQGwpittvVsBXqM8yAfCgOTyu6oWOdhP0xbKtjDsI1ukTE+MWxpZVvcLXI5w7ZLy+WkvNRdUFTGZ4cxj+VyEusdJ3HPdpX0Eekb0k+kHqlSd8iYuP+RuJGnImNbicoN9o/9jX1nOKxI+utgAiZgAiZQI4FsHkqeSh4bZQnlCGVItrtj8uJVJXpioCvjnNQtRVnAONPI/78pkZdTBqAZ0qkS5h/byObh+jsowcZsdcw2Zqvz8VwTMAETqESA8pQylLKTZ62cNFei/OMZiJdXee7ixacoAzXq0AACPYoDzjZmGwDTUSxGwMbsYjj6/If6HK7Ng/q8pldoagJTlDoOrI3Z6ofJxmwpHxuzpUw8xQRMwARMoHYCg23MVkoZD//FopI7RMVAKCrZG2nYrq34u6SclJe6C4rK+Rj+QNO5Z7taYjnWYV0q9Kmg2EzaUzpNul6aKrF8OU3U9D9Ix0qfkViffYv9jH0fpmlZ6a+DCZiACZhALwSy+Sb5KXlrlB/0zMBLPrQC4sUeKpdHSbzMk21FG3k/w5xEfv916UEp8vWZGj9DYj3iZ1uxbY0OaLAxWx2vjdnqfDzXBEzABCoRoByj3OSFJsrJr0iUe/+VchL5Ky/FUpZS9rG8Q2MI9CgaWNuYbQxPx/ImARuzb7Loz5iN2f5Qa4F1piiNZLo2ZqsfLBuzpXxszJYy8RQTMAETMIHaCTSLMVstxVHBnR1S8R2i0gBRKYB6M2z5hi0VDFQmUBlPiykq5Gk1RaV8fMMWs7XYsP2+pnHPhjE7ukjd+p+XclKXxPpU1L9X2lU6SWK9iRJxlBNG7nXSGOmL0roS+xT7GPucZcG4gwmYgAmYQGUC2TyTfDTKDFoCUelMix9aBFEujJQoD3hZhjIgL0V+H/k803eX7pYiL5+h8aMk4iJ+thPb1eiABBuz1bHamK3Ox3NNwARMoBIByjCeQTBe+YzMRRLl3W+kLolykmcqXnSizKO8c2gMgR5FA2sbs43h6VjeJPAljXJu3fDmJI/1gYCN2T7AaqVFpyixXBg2ZqsfNRuzpXxszJYy8RQTMAETMIHaCbSCMVtpb6LCOzukEiFEJQGiUgHVatgWf8OWigcqdzFsj5W4Z7tKykn5gro1HF1GTGeZnNQlhVm7nsY/LxHf76RHpAUScReLLpL/Kp0ofVbCPGZ/2LfYV4bDMtKogwmYgAmYQBkCkVdG/hnlQ7SipatjKptHSHzDnLwfIzYnkadHXp/XONN3le6SIu9+QuOfkyhzIp9mmwMRbMxWp2pjtjofzzUBEzCBSgQoIynHKBN5iXWaRDm3h8TzzCiJXid4wSmeQzTq0AACPYoD1jZmGwDTUSxGwMbsYjj6/MfGbJ+RtcYKU5RMMl0bs9WPl43ZUj42ZkuZeIoJmIAJmEDtBFrZmK20l1Hpnh1SYRCiohxFZXw1w5YWVFTQ08IWw/YoiXu2KyUMUip9aVVFy1gq6LuknJSXuqXRZcR05ucklmc91mfZ/5P4Du1vpPukORLbK9ZETbtcOlTaUqLShH1CsZ/DNJ6V/jqYgAmYgAlkCEQeSb5J/km5EK1oiw1a8nsqo3NSNn/P6z/5+IESvR5Efv03jb9XIk7ijm1ptGHBxmx1lDZmq/PxXBMwARMoR4DyinKLF5Z4FtpRomybLr1T4tmHl5foIYLnKMpQh8YR6FFU8LYx2zimjukNAjZm6zsTbMzWx69p17YxW9uhsTFbysnGbCkTTzEBEzABE6idQDsas5X2PirFs0MqEkJUQETFPJUM5QzbwzWdB+VxUrY75JH6P0rKtq5thFnbpTg/LrHdSyVa1i6USENW8/T/Huk8aS/p3VKYAexT7GN23xl3MAETMAETeNM0jbyS/JMygEppKp5XkHg5h3y+mkFLhfXZUrxUM1/j50isR5zEH/mwRusOrykGygLKCodSAjZmS5l4igmYgAn0RoByijJrGWlF6bcSZQ3DnETeynR/X1YQBiD0KE5425gdALgdHqWN2fpOABuz9fFr2rWnKGVkuhs1bQqbI2E2ZkuPw6aaxLkzsXSWp5iACZiACZhArwQ6yZitBCMqybPDqJzPmrVUUHxbotzlnoTKCiokqLSnZRXdeVF5T0UFFfhZs3Y1/ac7TMxaWlx1STkpL3UXNFrDrGI6y7B8tKyl4v+L0knSn6WpEmkq1nRNu0EaI9GtJttnH7JGLfuZ3W/9dTABEzCBjiYQeSL5I/ll1qCl5VA1g5Z8OydtLl0rRb78vMb3l5aSIg9mO/UGG7PVCdqYrc7Hc03ABEygmABlE+UfZR/PNzyDRFnD8wf/eRGVZx5eXqJMa0R5pmgcCgR6NOT+wcasT4lGE7AxWx9RG7P18WvatW3M1nZobMyWctpUkyiwJ5bO8hQTMAETMAET6JWAjdnKiKhkyIpKioMlyl3eGKciAqMTUXlBhTsVFBi2xWZtdIUcZi3fLQyzlu7AMGsxXnNSXqJyf3QZMZ35OalLYj3M3k2k/yf9RLpFmimRzmI9pWnjJLpL3krCZGA/EPuHhhVJfx1MwARMoOMIRF5IvkgeWcmg5aUX8mLy5si3yatzEhVgD0uRF9+v8W0kyg3ijG1otF8hKsu7+rV2+69kY7b9j7H30ARMoLEEKJcon3im4TnhaIky7FGJso3njpUlf19WEAYo9ChemNuYHSDAHRytjdn6Dr6N2fr4Ne3aU5QyMl23mK1+iGzMlvKxMVvKxFNMwARMwARqJ2BjtnZWLHmQxD0bN+VRoU6lfYiKDCrcUbFZS+ta3jyPlrVZs5YKjkaYtZi7GASIyn9a+F4iYQwslEh7VnSzeZ9EF8hflUZLpJ39iH2K/YyhZjmYgAmYQMcQiLyPPJG8sZxBS/6NQUsenJfISxHj6CjpJYn893XpHInWRpHXxjY0qU/Bxmx1XDZmq/PxXBMwARMoJkBZx7MAzy28UDpZouz6nkR5Rr5KzxHRjTHLOzSWQI+ig7mN2cZydWxvvDDIuXWDYfSLgI3ZfmFr/pWmKIlcGDZmqx8rG7OlfGzMljLxFBMwARMwgdoJ2JitnRVLZo3Z7JpRqR5DKilCVLxTwYHKmbW0rsWs5a30MGtHaLy/Zm231kVUnnRJGAW83b6u9HnpB9I10jMS95/F4r70dxL7yn3GUhL7gGKfYj8ZOpiACZhAJxCIfI98kPwwa9BispJv070jvSCQ95IPjy6I/Pjd0kUSxiz57iPShyXiIb6IX6M1Bxuz1VHZmK3Ox3NNwARMIEuAcojyiHt/nk3iued5jW8g8UzBi0juxlgQBjD0KG7uE2zMDiDkDo3aLWbrO/A2Zuvj17Rr25it7dDYmC3lZGO2lImnmIAJmIAJ1E7AxmztrFgyKii4Ke8tRCV7DMPUjEp9Kj7qNWsxXGkdiwmQkzACUJgBMYzpLMOyYdZ+QON7ST+V7pLmSVQEZPWq/v9d+r70CQnzmLSjJQqKfYyhJjuYgAmYQFsSiHyO/I98EGOVLuzpEYH8kRZGdFVP/pyTsvkw/3eVnpHIZ+m1gLw12/KI+GsNNmark7IxW52P55qACZhAlgDlD88mlGm8aBR11SdqvFvixSPKOMq7pSTKwb6UWVrcoQYCPVqGewQbszXA8iJ9ImBjtk+4ShamDohrkzohhzYiEIWdW8xWP6g2Zkv52JgtZeIpJmACJmACtROwMVs7K5bsizFbLmYqL4pFpQYKs5MKESr6qfCIb9ZSaV+uZS2VI6MkKk+ogKbCBDMA4zUn5aXugsIciCHTmZ+TWB6Dl3mfk8ZImLHTpaxJy/gC6W7pXGkXiXVJM+mPfRmm8az018EETMAE2opA5HHke5FvU5lNK6MVJfJm8mXyyLwUeS/j75H+IEX+eqfG3yURD/FF3BqtGmzMVsWT8o9yq/qSnmsCJmACnU2Acofyh+cPjNcfSeSfUyRay3ZJvHTEC0jZl4n016HBBHoUH+xtzDYYrKP7Hxuz9Z0ENmbr49e0a1PQkenamK1+iGzMlvKxMVvKxFNMwARMwARqJ2BjtnZWLFmvMVtpa1EJH0MqRhBmJ4pK/2KzlooTTAC6FMMIKO4COczabKvavJbrlsIkiCHTmJeTqHxhHYzeraXDpXHSJIl71mI9pWmXSvtL3M+S3kh77MswTQtp1MEETMAE2oJA5GvkdeR95NNUWtM9Pd/ho9tH8lLy1ch7Geakb0ovS+SpmKwHS9G1MfERd7VgY7YanTeMcdjyQpGDCZiACZhAeQKUNVGGUX5tLs2VyD/3kyizePFzZYnnDso57vN7K6O0iEM/CPRoHdjbmO0HPK9SlYCN2ap4ep1pY7ZXRK25gI3Z2o6bjdlSTjZmS5l4igmYgAmYQO0EbMzWzoolB8qYLZcKKjuyosIEheFZzqylVS1mbXyvtpxZu4bmU7lCK64uKV8QlS5h0saQacxnOZZnvfdLe0u/kO6TqPCm8iArjIbrpGOkj0lU4kS62QeU3Tf9dTABEzCBliZAnkbeRl6HuUqPB+TJvDhD7wbZ7o279Z98lvx1E+kfUuShfytMJ4/P5pX6WxJszJYgWWwCLyfBlXLKwQRMwARMoDwByi/KLsoteuF5VCLvvF6irMpJ5Ke8bJRtLct6Do0n0KMo4W9jtvFsOz1GG7P1nQE2Zuvj17Rr25it7dDYmC3lZGO2lImnmIAJmIAJ1E7AxmztrFhyMI3ZcimjAiREhX2IypQwajEEeJOdLjWpPKm1C2RayGK+5qS81C1RGZMV05iXk1iWddaVdpZ+KI2XXpXCYIjhHE27Sfq+9FGJdJFmtIQ0rEj662ACJmACLUcg8jLyNfI38mPyYl6Y4UWZURIvx5B/5qXIX3MaP0oKo/UljVM+sz7xRLwaXSzE8l2LTfWfIGBjNkh4aAImYAKlBKJsoZzh2YEXOzFjuX+fJlHf2C3xYubKEvMxb6Nc0qjDABDoUZwcAxuzAwC3w6O0MVvfCWBjtj5+Tbu2jdnaDo2N2VJO3ChRYE8sneUpJmACJmACJtArARuzvSJabIGhNmYXS4z+RIVKDDEDwhCg0iTMWipbst+rxSSo1gVyuVa1VMyMLhLT8lJO6pIwatEnJEyGP0jPSNyrZDVL/2+Qjpa2kKKSJ9If+xNDLeJgAiZgAi1DIPIu8jTyYfJgXkihUpsu52mRREV3Top8Na/xraS7JfLL16XjJdYlPycu4s0GG7NZGqXjNmZLmXiKCZiACWTLKMoXyhl6drhGovyZLX1OonzqkujxgZeL3FpWEAYh9GgbHAcbs4MAu8M2YWO2vgM+VqtzbVIn5NBGBGzM1nYwbcyWcrIxW8rEU0zABEzABGonYGO2dlYs2WzGbLnUR2VLDMPspOIFFZu1tbaqxUTAdKWCJid1S1TYFIvpeYnlWJ71NpcOla6UeAOfB5qsZuj/ddIR0mZStBKLtMe+xFCLOJiACZhA0xMgzyIfI+8lX+MllOLWs+SVkZ8yROdLkUeO0/hwibybuIgzgo3ZIFF+aGO2PBdPNQET6DwCcQ+dLZcoVzBlt5celSh3MGX3kLi/z0t8Ix3T1t+WFYRBCj3aDsfCxuwgAe+gzdiYre9g25itj1/Trm1jtrZDY2O2lJON2VImnmICJmACJlA7ARuztbNiyVYwZsvtUXFlDJX7CLOg2KhdRtMwazEPaN2FIcA3paiUGSWtKlHZTUUNxmut3R9jPrAsRu1HpCOlq6UXpTAgYkg3nsw7RHq/RBpJa6Q7uz+a7GACJmACTUsg8ivyr6gEJ4/NfnuWvJQKcCrCozL8MI3PlcgX7y1Mj7ww4rQxKzBVgo3ZKnA8ywRMoCMIRHkR9/3cz+8vUb/6D2myFPffUzXOsyHlULdE2bSKRHnF8wFlEPEQp8PAEehR1BwTG7MDx7hTY7YxW9+RtzFbH7+mXXuKUkamu1HTprA5EmZjtvQ42JgtZeIpJmACJmACtROwMVs7K5ZsVWO2eC+jkiaGVLKgJSUqXRCtu3iLPmvU1tr9cU7r5aVuicqdrJjGvC4pjNptNH609BfpFSkqiGL4nKb9XvqW9C6J9JHWSHfsB0MHEzABE2hGAuRPkc+Sv5K3kqfSPSQV37zsQr4Y+SbDz0vPS+SF06StpOhRgPhszApClWBjtgoczzIBE2h7AnF/zD0z986HSuXus2dqOj01vE+Ke3bKI7owXkl6u0TZQxnme21BGODQo/gp923MDjDoDozexmx9B93GbH38mnbtKUoZme5gGrPbaXs3SntK5cKHNfHv0uHlZmoaD4p/ks6RBqtgHixjli6mLpTGSbQKKRfO0sRrpHXLzdS0Q6QbpC0rzG/UZBuzjSLpeFqVQCtdr63K2OlubwJDZcy24n0IZ0K7GLPlzuqovGFIxQuiIicqc6iQwagl3+2t+2Mqcvr6nVoqgHgzH31S+r7EvSiVRdwnZ/WM/v9W2kfiXsxGrSA0UXDZ3EQHw0lpKgKRz5K/km+Rp1LhHa1nMRJ5YaVbonKcIc/lD0rkgXOkvSXWI28ebGO21cpuG7M6SRxMoB8EXI73A1qTrRL385QVlDHUX8a99L81fpK0n8Sz4Dslypt1JMqeLon8c4TEC0RR5hCnw8AT6NEmOFaNNGab9Zo+vbCvYwYeq7cgAjZm6zsNxmp1rk3qhBzaiMBQGLOR0c+owJHKLk62hRJvSBUHWjUwH/GwOBhhsIzZj2lnYt8OLbNj62Xmn1FmPjc+PDQTx/Vl5jdyko3ZRtJ0XK1IoJWu11bk6zS3P4GhMmZb8T6Es6Gdjdnisz0MhBguoQUQ9zkYClmjlpZfmAvLSpW6P+6vUdulOD8j8cDOeTNbivu0GD6uaT+VPifR+ow0ItIb6XdlkmAMUviYthPHptnvpQcJiTdjAosRID+K/JS8lDyUym+eu3kxeC0pL40uaH0N/yTFdXWWxnlBJozZnMYHI5AHk4ZWqUOwMTsYZ4W30Y4EXI639lGNMob7de7L6bKYvJt76MMlemjgJaCc1C1hyCLGuyTyzpES5RKGHvEsIRGvw8AT6NEmOF47NXBTHyvESbzNdG9uY7aBB7mGqGzM1gCpyiI2ZqvAaeVZU5R4MsfBbDF7pLa3QOKkKhf20ETMxZvKzdQ0WoJOlyZIvH01GGGwjFluQCZJ7N9mZXZsKU17QOJBePsy85l0pQTfg/kzgMHG7ADCddQtQaCVrteWAOpEdhyBoTJmW/E+hJOjk4zZ4ouBypisqKBBYdRSaRNmLSZDfKeWSp3hEoYDefYoCeOhFqO2W8uhnERFEa1p8xIG7I+lW6V5EvfRIf7fJB0jfVAiXWHSkt7sPuivwwAQcNk8AFAdZdsRiLyIfIl8ispvXm5ZUSKfpOI8J4U5m9f4adLrEvndXyWeRxlnHvENdGi1stvG7ECfEY6/XQm4HG/dI0tZEOUK9+NhJLyqce6fcxL31pQt6xSGjOck7rNXk6KlLOtTPsX9s0YdBoFAj7ZB2b5TA7fVrNe0jdkGHuQaorIxWwOkKotEfkqdkEMbEZiifSHT3WiQ94mWDdVCb/N5eKSia7DCYBmz7A/7xf5VCtzsUOFYLfTGr9q6tc6zMVsrKS/XzgRa5Xpt52PgfWtdAkNlzEKst3Kyt/mDfR9CmjvZmGX/iwP3QyEqbRB5MqIip5xRW65FLZVAWaM2p/95qVsaXSSm5aQuiQqkdaWvSRdIT0rcU2fF9xm5h9xLonUA6SJ9pBVF+hk6NI4AjFvhXrpxe+yYTKB/BMh7yIu4ZngBmGdMXnxeWSJfJK/rlsgLGe4jhSG7UOPkd8yL/EyjAxp6K5t7mz+YZbeN2QE9FRx5mxNwOd48Bzh7r1rtfjXKE+51yWu/JVFGzJd2kShD1imIciMvUcZwP01+yUtBvBzES0KsTzyDVbZoUw4FAj0actwaacwSdTNe0zZmOTKDF2zM1sfaxmx9/Jp27SlKGZnuYBuzTQukQsIG05itkISmm2xjtukOiRNkAiZgAi1FYCiN2ZYCVUisjdnqRy1bcURFDqISAFG502ijtltxopyE6Ur3n7SS/Y50rUR3m9xjZ/Wg/p8hfUqi4inSR1qz6ddfBxMwARMYFAKR95AfkU8uI/ESC62WeHGF/I28bnRhuJ2GL0iRt5HvsV7kYxp1EAEbsz4NTMAEWplAlA3k7Zimw6XI55lXHJhGOYKpuoE0U6KcOEmi/MCUZZiTMGN5+WdViReB6NmGcoeXa3hJyKasIAxR6NF2OW6NNmaHaHeqbtbGbFU8DZ9pY7Y+pDZm6+PXtGvbmK3t0NiYLeVkY7aUiaeYgAmYgAnUTsDGbO2sWNLGbN94RYVSDKlMosII1WrUrqJla21R261l81JOosKpS9pBovvPu6VoXRZmxixNozvQw6T3SaSJtJFOFOlm6GACJmACA02AvIa8h7wIc5au4Kksp+KcPC0vUamOtpFel8jPnpCYhzlLHuY8SxAUbMy+wcG/JmACrUcgygPy9FMk8vu50h4S0+I+VaNpiOUxVXnx8M8S5cN90iHSVtJoqUuK1rGUL/TQQFlDbw0YulGOFMevWQ6DRKBH2+HY2ZgdJOAdtBkbs/UdbBuz9fFr2rVtzNZ2aGzMlnKyMVvKxFNMwARMwARqJ2BjtnZWLGljtm+8ipem0ihEhQ+icgnVYtTyRn/WqA3zNafp3QVR6RRiWk6iEopl3yXtLV0iPS1R6ZEV9+QXSrtKbIc0kTbSGelm6GACJmACA0WAPIY8h/yHSnIq2OlakjyJXgFyUuRxszUeeRjm7LrSWyXyLedVNmZ1GjiYgAm0IIFsOUC+H/k8w5ckjFTKiOz9aZQbGKyfkViWLoznFcZnavgJCVOW3hiidSwvAWHmlrvn1WSHISDQo21y/GzMDgH8Nt+kjdn6DrCN2fr4Ne3aU5QyMl13ZVz9ENmYLeVjY7aUiaeYgAmYgAnUTsDGbO2sWNLGbN949bY0FU8hKpQQhgKiggiDAWOCSiO6VsOgoCJpuESlUhi1VDKtKYVRm9d4txTmRQyZnpOi2+OPaPxo6e/SaxL346GFGr9V+p5UrTWtZjuYgAmYQEMJkC9GfkiFOfkf+d4oia4ncxL52iyJPCvqEyZpfEPJ5qwgKFA2wGcBfxxMwARMoAUIRP7PfTD3wNzfck8a96cMvyhxb8wy2ftnlqcF7F0Syz1TGMa6Z+g/5QjLsH6UFREH20YOQ0ugR5vnmNmYHdrj0I5btzFb31G1MVsfv6ZdOx6kbMxWP0Q2Zkv5bKpJFNgTS2d5igmYgAmYgAn0SsDGbK+IFlvgIP2j3OWm3KHxBKJCKCqlwpioZNTSYoDKJVqTYdRS2bSqhHFBCwMM2JyUl7qlMGgZ8p/pXRLLMb6zdK70kMRxzmpiYd6nNKQyizSRPjQsI406mIAJmEDdBCJfIa+h8pxWUOR3vJBCHkfeFcbsDhp/XCLPoiL+fVJUuBNPpwYbs5165L3fJtC6BMizubdcSuKFxJHS7VL2nvS3+s+LihixmLNRTrD85ySWnVMYZtc7UNMoQ1iO+FkvyppOLiuEoalCj1LDcbMx21SHpS0SY2O2vsNIHRDXJnVCDm1EYIb2hQM7TjrPqsggHjZvNqNFjK4QC84dziGfO2bgc8DngM8BnwN9PQeuL5QjT7scqakc5R6Ecpd7kr6y9vL1Mfu5mId+ofHQLzWOzpcukH4l/Vq6ULpIulj6jXSJdGlBPFSVU8xn2d9Lt0mTpfkSxz00V+NPSv+Q2AZpibT5ONd3nM3P/HwOlJ4D5C/kM+R15HMXSuRrkTddpXHyrJck8ikMW54TI2/qVKbk//CgtVmnMvB++9j7HGi9c4A8n/yee1rysWgBm70PvVDTuffN3g+z/BSJ5aKeOdaZrWkYuhdKsR7b8fnRfAye0XHhuF3fAcfn/sK+3tMB+9oM19rfCrzJJ5ohPa2WhscL/HiR26GNCCzQvkRh6aFZ+BzwOeBzwOeAzwGfAz4HfA74HPA54HPA54DPAZ8DPgd8Dvgc8Dngc8DngM8BnwM+B3wO+BxojnOAlx8d2ogAb7fy1sLJ0tFWRQa0YoDTz8xoEaNTCkz+ZCaLmPgacj7ic8DngM+B2s+BMwvlCL12mFvvDLgH4V6EexLzGnoGx+g4FOtYTUPHSccXdIKG35d+IJ0ojZG47+Y+6lTph9KPpB9Lp0mnF4lpzGMZlqWV7M3SNKn4AflFTbtV+rV0vERaitPIf58/ZuBzwOdAX86ByEfIU46XyM/+IfVIZ0uRd52j8SkSedPLEvkW+WHkRX3ZZqsvS75Pmf1XqdX3xen3MfQ50BnnAHk9+TX3ridJ5OHk8VOl7D3nA/rP/SxlAXndiRL3nyzzSmEYy8/X/wsk7m+5/2V5yoUoV3xuNde5FR4Bz+ntfmx+rX2knOb8bPd9bYb9O6vA+3fm3a/zLXypr4mfgwmYgAmYgAmYgAmYgAmYgAmYgAgMy2gJjaMlpbdIb5WWkpaW+E7jstJy0nBpJYnvd60irSatKb1D6pLyUrc0OiP+M535LLeJdKT0d2mOlGSEKXKZtJvEN71IDyJt2fTqr4MJmIAJ9EqAfIP8g3yN/Ix8bIS0urS2FPnVRhqfIJEf/UtaUSIfjLxHow4mYAImYAJNSIB8nnvFZSTuUbnXfJeEqVp8j8k9K8twP8t9bLyUE98fj+Wv0ryNJe5nR0ncB0eZoFEHEzABEzABEzABEzABEzABEzABEzABEzABE6ifQNb4xIxAVHRljVoqvcKoXV7jmBeYHFRarSqtIa0lYXjkpDA9qNhC/M9LOYll1pH2ksZKz0lRIcaQ1go3SYdJrEc6SM8S0rCMNOpgAiZgAhUJkF+Qb5CHZM1ZXizJmrNb6/9LEvnPFRJ5XVTEE4eDCZiACZhA8xEgf+b+kPx9BYl70fWl7aXXpey95Vf0H0OW+9bPFOYVvyS4UNN3ld4ndUm8jEh5QBniskAQHEzABEzABEzABEzABEzABEzABEzABEzABAaGAJVPIUwNKr0QFVOYFVSAYdS+XaIVGpVh2da0tEijZUKYtHmNY7COzoj/OYllaOFAJdqZ0sNStiKNcVqx0dL2nZJNWkFwMAETqJkAeVkt5uzOWm6uRJ5Dd5jkceQ3rEscDiZgAiZgAs1FIPJ37k25H8V4XUf6gPSQlL2fvEz/uTdFPy/Mm1UYxnI36/+HpQ0llqN1Lfe83AO7HBAEBxMwARMwARMwARMwARMwARMwARMwARMwgYEnQEVUCIMCZU1auj3GwKjUmpaWaVRuYb52SXmpWyo2aZnOfJbbTDpKooJsgRQVZgzvkY6WNpAwTUhLGCeRTk1yMAETMIFFBMgbyCfIM4pbzpLvRH50iMYjv/lGYVnWiTxGow4mYAImYAJNRID8nXyalwVp4Uqe/l7pbCnyc4YvSetI3IM+IWXnxfgBmh7dGGPyYva69wRBcDABEzABEzABEzABEzABEzABEzABEzABExg6AmF+htGBYRFGbfG3aenymNYGdHnMd2P70uVxl5bHpKVy7XBpvDRfisozhg9Ix0vvkaiUs0krCA4mYAJlCUSeRV6RNWdp4U9+Q2U9Bu1ZEvkLrWc/JWVbSxGHgwnUTCBJknWlzS0z8DkwMOfA/Pnzt5gxY8b/Tp48eavbb7/9E5dddtkOp5xyyq5f+MIXvqMLNXvPmGyzzTZj9tlnn28Vpi/W1fHb3va2SQceeOC+J5100m6/+c1vduzp6fnkE088sfVLL720Jdvw8RuY42eu5upzwOdAG54D1I84mIAJmIAJmIAJmIAJmIAJmMCAEahm0tLCIFrTFnd5jElLSwQMEVrTZrs8jpZrMcxrfq6wzLs0pEXb36V5UrbC7WH9P1F6v2STVhAcTMAESgiQZ1FZkjVnaWHFdwkxZyPfuUrj5C+0sOJbg+RlS0qs72ACNRNQZeNvJQcTMIEBIvD6668nCxcuTObOnZu8+uqrybRp05LHHnssueuuu5L1118/e5+Y7LTTTskJJ5yw2DRdzOn/Y445JvnXv/6VrkscMnvTOImbbTiYgAmYgAmYQI0E6EnMwQRMwARMwARMwARMwARMwAQGjQCmRSjbkjZr0kaXxyto2RUlTJFRUq1dHue0LEYuXRkfKF0vzZGyFW2P6f8p0qZS1qQlTZE+jTqYgAl0IAHygDBn6Yadlv28LII5m5MwZ9eX7pTIV56UyJ/Ix2zOCoJD7QRUgWdjtsZaTC9mAv0lgHGqVq3Ja6+9lrzwwgvJxIkTk/vvvz/Zb7/9sveGyUorrZT83//932LTdDUnq6yySnLjjTem60yaNCl58cUXk1mzZqVx2pTt71HxeiZgAibQsQRszNZ+q+wlTcAETMAETMAETMAETMAEGkwgDNAwQbJGbbUujzFpo8vj3r5Lm9OymLSYKPtL10qzpWyl2wT9/7H0IckmrSA4mIAJpC9oZM1ZXhTBnKUFf17CnN1EekoiP/mdRCWLvzUoCA61E1CVpI3Zjq2X9Y4PFgHM0wULFiRz5sxJpk+fnkyZMiX597//nVxxxRXZ+8F0fMSIESXTMHDvuOOO5JFHHkmmTp26qLUscdqYHayj6O2YgAmYQNsQsDFb+62ylzQBEzABEzABEzABEzABExhAAtVM2mxr2ujymO/SriRhlPTVpF1X6+wjXS29JmUr4Cbp/+kShkvWpM2mT7McTMAEOoAA1z3mLHkQFSjkO3SxzgsheQlzdgcpvm29l8ZpYUvewXqs72ACVQmoitHGbNvUs3pHmpUA5mm57ozvvPPOZN11183eB5aML7vsssl1112X3HfffWlLW1rc0vKWFrjE6WACJmACJmACfSRgY7bq3bFnmoAJmIAJmIAJmIAJmIAJDBWBrBGKwUH3oJgd5UxaWrIVm7RraVqXlJO6JQyUEP9zEi1p15G+Ll0pvSplK+T4Ju0xEsuwbdIQZosNF8FwMIEOIMC1zrUf5izdq/MyCOZs5C20uCfvmC7ROn9piXVY13mFIDhUJqCKPBuzfazN9OIm0B8CmLPF3Rljtu6zzz7Ze7+S8Z133jm5/fbbk//85z/J008/nbzyyitpy1u3lu3PUfA6JmACJmACImBjtvKtseeYgAmYgAmYgAmYgAmYgAk0CYEwNxhijGJ4hFGS7fJ4OU0vNmlX17QwafMaDyNltMYR/3MSJi3/95T+KGW7O35d/2+W6AoZQ4ZtI9JCmpCDCZhA+xLgGueaJ7+h1T4vgvBN2cg3aIV/l0SF/j8l8iKWjTxCow4mUJ6AKudszLqO1gQGgUB0Zzx79uy0O+PJkycnDz30UHL55ZeXmLG6WtNpSy65ZDJu3LjknnvuSSZMmJA8//zzycyZM5N58+a5tewgHDNvwgRMwATalICN2fK3xZ5qAiZgAiZgAiZgAiZgAibQpATCCGWI6YEwTGjRmjVpl9X/MGlHapzuR8OkxUzJSd3S6Iz4n5OYv550kDReWihFpd08jV8j7SJhvrDtSEekTZMcTMAE2ohAXNtc7+Qz5C8jJPKUnEQ+spUUre6P1TgVLuRL5A+s72ACZQmowtHGbJvWunq3motAue6MH3300fTbsaNHj477vMWG22yzTXLrrbem36Plu7R8nxZj161lm+vYOjUmYAIm0GIEbMyWvSv2RBMwARMwARMwARMwARMwgVYhEIZJmKMYJ402ad+vOI+T7peyFXYz9P9i6dMSZo1NWkFwMIE2JUBeQz5D/kJXxctLfON6TSkvYc4eJpFHzJU2l1iO5SOf0qiDCSxOQBWJNmZbrDbVyW1dAsXdGdMKltawe+21V/b+btH4L37xi+Suu+5KnnjiieS5555brLUscTmYgAmYgAmYQD8I2Jhd/HbY/0zABEzABEzABEzABEzABFqYQJgftZi0fCeyry1p+abkltIZ0lPSooo7jT8jnSV9UMKICZM20sTQwQRMoLUJcB2HObuMxmmVP0rKfm/2z/pP3vAfiVa18dKG8wDBcCgloMo8G7P9qNH0KibQHwLZ7oxffvnlhO6MH3zwweTSSy/N3tOl4xtttFFy8803p/P/+9//Jizv1rL9oe51TMAETMAEigjYmC29JfYUEzABEzABEzABEzABEzCBNiAQhmhvJi2t3iqZtHnN65ZoCRdiWpeEEfNZ6VfSC1K2Qu8R/T9OeqdUzqTVZAcTMIEWJUDeQr7yVonvzZJ/ZL83u7H+T5PIE86TWIZlWYd1HUxgMQKqqLMxW1Rb6b8mMFAEst0Zz5gxI5k6dWoS3RnncrnsvVwyZsyY5M4770wee+yxZNq0acmrr76azJ07N/22rFvLDtQRcrwmYAIm0BEEbMwudjfsPyZgAiZgAiZgAiZgAiZgAu1IADMkzBTMEVqzYphiltDVKA9G8U3arEm7hqZjwHZJeSlr0jLONOblpK9KV0qvSdmKvdv0f3+J1rnRijYMGtLkYAIm0HoEuHa5nmkNG9+bJb/ISbzE8TXp9YK21pDWte7SWBAcSgmo+tHGbEfUwXonm4XAwoULk3nz5iWvvfZa8vzzzyfRnfEee+yx6P6tq6sr+ec//5k88MADyaRJk5IXX3wxmTVrVjJ//vzEpmyzHEmnwwRMwARaloCN2dJbYk8xARMwARMwARMwARMwARNoYwIYKghzFFUzaVfSfL4huapUbNJivoTCpF1b02gle4B0o7RAikq+WRq/RNpGKteKljQ5mIAJtAaByEe4lnm5I743u5bG8xJ5wziJ6/+f0nKSuzQWBIdSAqpStDHbsvWqTngrEijXnfFDDz2UjB07Nu7ZksMPPzxtLUtrWlrV0rqW1rILFiywMduKB91pNgETMIHmImBjtvSW2FNMwARMwARMwARMwARMwAQ6hECYK8UmLQZKtKTFUOE7kpi0fEuSLkvXlDBhcxKmbBi0DPmfk5i/kXSs9LC0qLJP409Kx0hdUjmTVpMdTMAEmpwA+Qd5By3vqVwZLvESB9c+ecEW0myJa39niWW43lmHdR1MICWgekIbs81VWerUtDmB4u6M6aaY7orvvvvuhO6MR4wYkdx0003pt2Wfeuqp5IUXXkhb19Jalta2DiZgAiZgAiZQJwEbs34OMAETMAETMAETMAETMAETMAERwCgJowXjZEkJEwWTlm5I+U4kJi3mywipFpM2r+VyEt0hbyddJM2QwqSlRe11Upg2bDNMG9KCHEzABJqXANco1y35RHRpvLrGcxLm7M8krndezuAFD174YHlf24Lg8AYBVezZmK2zdtOrm0BfCWS7M8Z4pbtiWs3uvffeyb777pvcd999yeOPP562ln3llVeSOXPmuLVsXyF7eRMwARMwgUoEbMz6QcAETMAETMAETMAETMAETMAEighgmiBM0jBpaRWXNWnpujT7PVrMGLoxpbVcXuqWMGYQ40yjhew6El0d3yK9LoVJ+5zGz5TeJ7kVrSA4mEALEIi8ArO1XJfGtJp/WeI630/iBQ/yEvIV1nUwgf9RjZ2N2UrVlp5uAgNEILozxnDFeKW74ieeeCK55pprkltuuSVtQTt58uT027J8i5Zv0rq17AAdDEdrAiZgAp1HwMasnwFMwARMwARMwARMwARMwARMoAqBMF7CoMU0xVjBhKElLa3kik1avkeLSYsRiyEbBm2YtDlNw8Clq9OzpKlSGLQM75D2leg+GcOHbYeRYzNHMBxMoIkIcE1yfZI3UMlS3KXxiZrGdf20REt78g2W9bUsCA46OWzMdl51rPd4yAlEd8YYrjNnzkwN2KeffjqZOHFiMmHChART9rnnnku/LevWskN+uJwAEzABE2g3AjZm/RBgAiZgAiZgAiZgAiZgAiZgAjUQwEQJlTNpebjCpI3v0a6scb43iUlLV8Y5KS9lTVr+Y96ir0rXSPMkTBz0mnSxtLWEkRMm7TCNIwcTMIHmIMD1yPWZ7dKYaz8vbSBNkbimj5PIJ9xqVhAc3iCgmka3mG236lbvT0sQwJzlu7EYrzNmzEi/Jcv3ZtHzzz+ftqSdPXt2ugytZVnewQRMwARMwAQaQMDGrB8ETMAETMAETMAETMAETMAETKCPBMIYZZg1aTFlaEnLg1a179Fi1nRLYdIyzjRa0b5HOl56RAqDluHj0vekNSVMWraLIi0adTABExhCAlyLXJu0iOUFjVUkrmmu70MlruPpUpfkVrOC4PAGAVXu2ZhtQA2nozCBvhLItprFgH311VeT6dOnp2J81qxZaRfGCxYssCnbV7he3gRMwARMoBoBG7N+EDABEzABEzABEzABEzABEzCBOgiEMRpGKa3mMGd6+x4trWgxaDBkw6BliImTk5i/vXSJNEPC1EFzpUulzSW2w/Zs0AqCgwkMMQHyAq5FWsPyLVm6IqfVbE5aR3pY4ho+V6LVLHlEXLsadehUAqq1szFbrerS80xgAAlgzmK80qUxLWcxaBHjTLMpO4DwHbUJmIAJdC4BG7OdeuPv/TYBEzABEzABEzABEzABE2g4AYyZMGcwXDBNMWnCpC3u6ng1zaMFLK3qclK3lDVp8/qPebuudJD0LykMWob8313CBLJBKwgOJjDEBLj+uRZpOc+3p/mmLC9ZcG3vKXHdzpS47qPVLHmFQwcTUJ2kjdnOrZj1ng8xAYzZaDmLCUvXxojx6L6Y+Q4mYAImYAIm0EACNmY7+N7fu24CJmACJmACJmACJmACJjBwBDBoUBi0tG7FpC3X1TFdnq4urSV1SXkpa9Bi6uQkDJ5PSZdJcyRMHjRNOlHC4GU7bBNFGjTqYAImMAgE4prPtprl2s5JXNOPS1yzB0tuNSsIDjohbMw2sJ7TUZlA3wmEORsGbdaQtSnbd55ewwRMwARMoFcCNmb9EGACJmACJmACJmACJmACJmACA0ggzNEwbGhNh3mabUVLy7oVpZHSqhLdn2LC5iRM2axJm9d/zFu+RTtGeloKg3aexi+XPiKxDbZlg1YQHExgEAlwrXPtlWs1e5Kmc73eLQ2XotUs6zh0KAFV3ZVtMYs5RJeqfOfSMgOfAwN/Drz22mtJVs3OnPyBlr0OJmACJmACLUfAxmyH3vd7t03ABEzABEzABEzABEzABAafAOYLwiwNgzbbija6Oh6h+XSBmu3qOK//WZOW8VxBe2t4qxQGLcN7pP8nLSfZoBUEBxMYJAJxjXNtU+nCSxfRanYTjfOdaK7RLSWueZaLFyg06tBpBFSVWGLM0krvySefTL761a8mO+20k2UGPgd8DpScA/vtt18yfvz4lnMjnGATMAETMIHExmyn3fB7f03ABEzABEzABEzABEzABIacAMZNKGvSRitavhmbbUVLV8eVWtFi0OYlujHeVrpEmiWFSfu8xk+VWMataAXBwQQGgQDXd6VWs9dqHtfnL6UVJFrWsizrOHQgAVVOlhiztJa99957k5VXXjlZeumlLTPwOeBzoOQcWG+99ZJLLrnE/oYJmIAJmEDrEbAx24H3/N5lEzABEzABEzABEzABEzCB5iGAGYMwaMOkpQUdJi0PbNGKdiWNRyvaSt+izWuZLund0gnSJCkM2gUav1LaRrJBKwgOJjDABLieudZ40YLrN1rN7q5xrsuXJVrFM9+tZgWhU4PqEssas/fcc0+ywgorJMsss0zyyU9+MvnmN79pmYHPgQ4/B/bdd99kxx13TJZbbrkkl8slF198cevZEU6xCZiACZiAjdlOvfH3fpuACZiACZiACZiACZiACTQdAQxaFAYtpg6GDS3qilvRxrdoaSmbk7ql0QUxnpOYt4fUI4VBy/ABaS+JB0Ja6i0hxbY16mACJtAAAlxTXF/xrVlavnNNcp1OlrgW95HobpwXMViWdRw6jIDqJisas8OHD09GjBiRnH/++cnkyZMtM/A54HMgufrqq5PVVlstyefzNmZt7piACZhAaxKwMdth9/veXRMwARMwARMwARMwARMwgeYnECYpQ0xTDJviVrTDNY1v0WL20BKPVrQ5qVvC+EGM5yXMoI9KF0ozpTBpJ2v8MIlvYLINthXb1qiDCZhAHQTi+uXa5cUKrtc1Ja7J0yWuw1skruVlJF7E4Bp06DACqk+saszSnfHvf//71qx2dKpNwAQaSoDvT998883JmmuuaWO2oWQdmQmYgAkMKgEbsx12v+/dNQETMAETMAETMAETMAETaC0CYZRi2GCeYt4Uf4uWblJXlugWtbdujjfQMj+Qpklh0L6k8ZMlvmMb5lBsV5McTMAE+kmA64hrCuMVA5aW7l3SFhLdi78ubSLRZTkGbrwcoVGHTiGgasCqxuzIkSOTcePGDWptoTdmAibQnAQwZnt6elJjlq6ML7roomT+/PmLtGDBgoRlHEzABEzABJqagI3ZTrnR936agAmYgAmYgAmYgAmYgAm0NIEwSjFuEGYPRg7dpPJgR3eotHwdKdXSzXG3lvu29JgUBu1sjf9SwrwlfreiFQQHE6iDANct1xEvU3CN8gIFL09w/d0kce0dJy0vcS2zLOs4dBABVRvamG3qulMnzgSah0DWmF199dWTo48+OrnllluSm266KRk/fnxy5513Jq+88krzJNgpMQETMAETKEfAxmwH3et7V03ABEzABEzABEzABEzABNqDQNakxcjBRMX44QGPlncrSNlujt+h/zkpL40uCGMoJ9HN8Z7Sv6QwaBdq/Arpw5INWkFwMIE6CMSLFFyftG6n6/GcdJLENXe9xEsVzOd6Y3mHDiKg2jobs+WqLD3NBEyghEDWmB01alSy//77p12djx07NkHXXntt8uKLL5as5wkmYAImYAJNRcDGbAfd63tXTcAETMAETMAETMAETMAE2ovAMO0OwsjJGrR0m8o3LWmFl+3mmO9bdkl5KQxahvzHoN1RwiSie9UwacdrfHvJBq0gOJhAPwhwjXJ90iKWlyb4LjTX2w4S19nLEi3d3Z2xIHRiUDWhjdmmqit1YkygeQmUM2bp6vzSSy9NLrnkkuSaa65JXnjhhebdAafMBEzABEwAAjZmO/Gm3/tsAiZgAiZgAiZgAiZgAibQVgQwfhAGbbTOq9bNMQYtxhCGbLcUJi3/MW4/Kl0mzZPCoH1A47tLmL6YTGwntqtRBxMwgQoE4trkmsR8xYTlGlxHmilxjW0lYdq6O2NB6LSgyjkbs66kNQETqImAjdmaMHkhEzABE2h2AjZmO+2G3/trAiZgAiZgAiZgAiZgAibQ1gTCLMU4xUClpWu2m+Ph+h/dHK+h8ejmOGvQMt4lbSL9VHpVwjxCk6RDJOKxQSsIDiZQAwGuS65FKmHotji6M+7RONfVdwvTefGB5VjeoUMIqObQxmyzV586fSbQJARszDbJgXAyTMAETKA+AjZmO+Q+37tpAiZgAiZgAiZgAiZgAibQWQQqGbQYP9nv0I7Sf0yiagbthpo/RnpWCoP2OY1/R6K7ZBu0guBgAlUIcD1ynWS7M+7S/9MkrqmrJV6YoAtyWtbyYoVDhxBQvZ6N2foqN722CXQMARuzHXOovaMmYALtTcDGbIfc53s3TcAETMAETMAETMAETMAEOpNA1qDF7KE1XnRznP0ObRi0a2k+hlFeGl0QLWhzhfHDNXxSCoN2qsZpQYvZa4NWEBxMoAwBrkOuP1qvLyetLPEyxM4S19I0iWnMYxmWZR2HDiCgekcbs+1d+eq9M4GGEbAx2zCUjsgETMAEhpKAjdkOuMf3LpqACZiACZiACZiACZiACZgABDB6wiDCRC02aOliFXNoNamaQYtxe7A0UQqDdorG95d4yLRBKwgOJlBEIF6M4IWIlSS+M7ueNEfiOvqg5O/MCkKnBdUK2pgdyqpRb9sEWoiAjdkWOlhOqgmYgAlUJmBjttNu+L2/JmACJmACJmACJmACJmACHU+gkkHLAyIt9jBoR0qrSphHa0t5qbgFLS1paUGLKRsG7VMa31uiy1YbtILgYAIFAlx3tFinO3G+0cwLEDnpXxLXDy3Pufb8nVlB6KSgOjsbs5UrLgd0DibX7Nmzk1mzZlUU8+fOnZssWLAgXYb/jA9mIJ2vvPJKMmXKlOTpp59OXnvttYZvft68een+zZkzJ1m4cGHD43eEjSFgY7YxHB2LCZiACQwxARuznXSz7301ARMwARMwARMwARMwARMwgQyBYoMW04huVMOgxTzqzaANw/YoLUt3rGHQPq7x3SXis0ErCA4dT4DrjWuBlxb4NvMqUpf0M4nr5kLJ35kVhE4Lqhi0MTsEtaMYXJic++67b/LlL3+5rL70pS8lu+22W3L22Wcn9913X7LLLrsk++yzT3LvvfcOSooxgB977LHk+OOPTz772c8mW221VbL11lun6TjzzDNTo7YRCcH0HTduXMrge9/7XvLss882IlrHMQAEbMwOAFRHaQImYAKDT8DGbKfd8Ht/TcAETMAETMAETMAETMAETKCIQCWDlpZ7fDsWgxbDCCNpDalSC9p1NO846TkJowk9LO0i0W2yDVpBcOhoAvGdWa6r+M7sERrnWrmhMI15XC/+zqwgdEJQXaCN2cGvEE0wuB555JFkrbXWSpZZZplUb3vb25KsmL7iiiumRuhf/vKXdB7LX3vttQOeYtI3YcKEZMcdd0xGjRqVLL300slb3vKWZMkll0zTsfbaayd77rln8uKLL9adlueeey41f9nfLbbYInnyySfrjtMRDAwBG7MDw9WxmoAJmMAgE7Ax2wk3+d5HEzABEzABEzABEzABEzABE6iBQDmDFoMoDFq+f1nOoKVL49ESw5z0Tukk6SUpDNr7Nb6TZINWEBw6lgBmK9cA35nlWqKr8N0krpNHJV5+oDVtdAXONenQ5gRUEWhjdpBrQ2Nzzz//fHLaaaclJ5xwQkJL0Y9//ONpmbXccssle++9dzr9xBNPTH7/+9+nLVePOuqohP//+c9/IooBG9K98kUXXZS8/e1vT1ZYYYVkr732Si644ILkjDPOSLbccss0nauvvnpyxRVX1J2GmTNnJn/961+T7373u8lPfvKThpi9dSfKEZQlYGO2LBZPNAETMIFWI2Bjts3v7717JmACJmACJmACJmACJmACJtAfAlmTli6OazFoMWezBu0G+v9D6RUpDNq7NP4ZiTizLWj118EE2p4A1xXnPpUxfE92dWlrievjVYnvOvMCBC9DcH3YmBWEdg+qSbQxO0TVqXQVTItTWow+9dRTyXHHHZeWVausskpyww03JBi36OWXX06N2aOPPjo56aST0nG+83r55Zcnp59+empq/vnPf06+853vJIcffnjym9/8Jo23p6cnNXy//e1vJz/96U+TqVOnLtrT+fPnJ/fcc08yZsyY5MADD0xNUUxWuhUmMDz22GPT9KyxxhrJ1VdfnWCgvvTSSwmtdzfZZJNk8803Ty6++OJ0eb4Lyzdof/WrXyWHHnpoqvPOOy9NKwtg6E2aNClNB2n505/+lJrMbINx9hdz+mc/+9kiY5Z9vOmmm1KD+qCDDkpb1bJtTOMI06dPT9MGm29961vp/mPu/vvf/45FPGwgARuzDYTpqEzABExg6AjYmG33G3zvnwmYgAmYgAmYgAmYgAmYgAnUQSBr0GIUhUFLi79KLWgxZ1G0oH23xs+UMJ7CoB2v8Y2lMGhjO5rkYAJtS4DznOuIFrF0Eb6aRAvz1yWuDV5mwLDFmOXasDErCO0eVCdoY3boKkYXbfnVV19NTj311LSMWm211ZLbb7990bx58+alxuWaa66ZvPe9703+9re/pYbtTjvtlDBto402StZbb71kpZVWSrXuuuum3R+z7MiRI9Np+Xw+Ofjgg1NTc+7cucl1112XfPjDH04wgekumeU22GCD5IgjjkhNWUxRTFKd/2k3xh/96EdTo/aqq65KHn744dTUfeCBB5Jp06Yt6paZVrVdXV1pfKSF7o6/8IUvJHfccUeCCY3J+q53vSudvuGGGyarrrpqQloxZDGY6aZ5u+22SyZOnJhguNJi9wMf+EDalTJppEtlDOEf//jH6X5gap9zzjnJO9/5zmTllVdOhg8fnu7rO97xjrQL5jvvvHMRQ480hoCN2cZwdCwmYAImMMQEbMy2+w2+988ETMAETMAETMAETMAETMAEGkAgjFO6Yg2DFnOJ72HWatC+V8v+RJolUdm8UPq19A4JE4q4YzsadTCBtiOQNWa5bui6uEuKVuVbaHwlicoaG7OC0AlBFYM2Zoe4dpTNY8yefPLJqRGKMXvbbbctShVGKi1ihw0blpqTfGP22WefTbbZZpt0+eWXXz7Zbbfdku9///vJ6NGj0+UwMj/ykY+krVIxYFkXE/Txxx9PJk+enHziE59IDdf3vOc9ySmnnJLst99+CV0o53K5tCUuLWBpdbrVVlul2+D7shijmKB0ZXzIIYck//znP9M0vvDCC2kcGKO0riUuzFZMWKbtu+++qdH6j3/8I2HfdF2lRvCnPvWp5Otf/3ryu9/9Lk0n0zfddNPkiSeeSP71r38lm222WbLsssum+wGbXXbZJe1amWXo+vihhx5K55Huz33uc8nYsWPTFsUYzJjV559/fmoaLwLpkboJ2JitG6EjMAETMIFmIGBjthNu8r2PJmACJmACJmACJmACJmACJtAgAmGcZg3act+g5duZa0t5qbgF7WaadqUUrQRpSXustJyE6WuDVhAc2pIA1w/n91IS5/soietkqoRZ8nGJb8/SIj1eVtCoQzsTUO2gjdkmqCKtZszSYracMcs3aZdccsnkfe97X/rdWVqa7r777qnxiQFK69ann346bWGqczg1RWm9euONNy76dizft6UbZYxgDNy3ve1tyR577JG2cMUQxiClK2Ra32KAEs8SSyyRtkylK+Nbb7013TYm69JLL518+tOfTu69997k/vvvT4488sgEQxcj9b777kvoWpnv0rINWvs+8sgjaevYCRMmpN0UE/cHP/jBND66RCY+Wt2ee+656XLsz7vf/e60dewxxxyTttrl/1JLLZW8//3vT1v0XnjhhQliH+la2aGxBGzMNpanYzMBEzCBISJgY7adb+69byZgAiZgAiZgAiZgAiZgAiYwQARqMWj5XmYlg7ZL87aX7pTSimYN/yt9VaK7ZBu0guDQlgQwZjnHaW2+skSL8YkS18EO0kiJeSzDsg5tTkAVgjZmh6hWNLvZ/hqzmJLbb799aqTS/S/fmaV17DrrrJMarrNnz04uu+yytJzDrMUcvfTSS9P/b33rW9NWrdtuu23a8nTEiBGp0YvhyvdkMWb51iyG71133ZXGc9RRR6UtZtkuButhhx2Wmrq0jsUkxnilJS9dH6+//vppWuhmme/I3nLLLel8Wt7yPd0IfGM3vmeLMYupy7dvdemlLWbpzpg0Mm+FFVZIt0sLYQxlvo9LWjBx2T+2RYtevjmL8evQWAI2ZhvL07GZgAmYwBARsDHb5vf33j0TMAETMAETMAETMAETMAETGEgCWYOWFn6YSdGClm9oYjKFQdul8WwLWsaZ9k0JUzatBNbwDmlLqdig1SQHE2h5AlljlutjLelhifN/VwmzNmvMco05tDEBVQjamB2iWtHsZvtrzGJI8h1XAiYqrVRp0co3XJ955plkzpw5aVfBOoVT45Luh8OYZd0tttgi2WGHHdJvshLPl7/85eS73/1uMmnSpOTwww9P46ZVLWHWrFnpN2WJA5MUI5Z1x48fn7aoxejFEI74aBX7xS9+MTnggANS8zZazNLd8RlnnJHGyU/WmP3Qhz6UGrMnnXRSWiZjxG699dZpnHRXTBp33XXXdH2+g4v5Slyf/exn026Y2Sda6fLtXb6pyzIOjSNgY7ZxLB2TCZiACQwhARuzbXxv710zARMwARMwARMwARMwARMwgcEiUGzQ0lVrNYO2W/Oji2MM2nWlkyW6NQ6D9nKNs0x06Rrb0CQHE2hZAmHM0l0x3RZjzN4tcd5/XcKYpZtjriGWtTErCO0cVCloY3YIa0Zj0/UYs5ifhKwxSwvWcsYs5ugNN9yQtjLF9MR0ffTRR9NugY8//vj0O7W0bp06dWraLfEyyyyTfpuWrpRpQTt//vy0+2KMX4xZtk3XxbSSxRTdbrvtUmOVrosxgDF36VqYboXpXpgWtZim55xzTuz6YsYshi/ftv3FL36Rxk9XxixLGkk7LWtPPfXUNC5a8v70pz9N03z55Zen6eI/5i7m9Fb6Pi7f03VoHAEbs41j6ZhMwARMYAgJ2Jht55t775sJmIAJmIAJmIAJmIAJmIAJDDKBME8xlDBUyxm0q2k6ZlROKjZoP6Bpl0oLJYyq2RKG7UqSDVpBcGh5AnFthDFLd983S5zvB0h8d9bGrCB0SlCloI3ZIawZjU0PhjG76qqrJjfffHPaBTDmJS1LN9544+S0005Lu/7N5/PpN1zPOuustKUphiitYFkOo/drX/tastdee6XGJyYs35z94Q9/mDz77LOpYYoZipF6yCGHJKecckrysY99LG3F+pWvfCU1esOYXWuttSoas5tttlnyxBNPpOnE/H3729+exkMaaXlLGulqeezYsQnfy+X7tWyTFrN8l5b08D1c0kcL25dffjkQe9gAAjZmGwDRUZiACZjA0BOwMdspN/reTxMwARMwARMwARMwARMwARMYRAKVDFoMpxUlWgWuLvF9zZwUrWcxavn/cemfUrSenabxb0gYvUtKmFtsw8EEWo0A5y0vGVAhwwsHGLN/lTjXvyNhzC4vucWsIHRCUN2gjdmhryBNMGaj+16+lXrbbbctStW8efMSWqzqfEz4PistWjFDMT4xID//+c+ny9Ji9ogjjki/68r3XaPFLK1JWRdjllan8d1ZvseK6UorVuLlm7HE9fDDDycYcHRnjBk6cuTINE5MUsxYWsoOHz484TuvtFpdsGBBcvfdd6fdIS+77LLJSiutlMbJsnwflpazdINMS91VVlklbTF79tlnL9q/MHZJ4yabbJIasy+++GLCMl1dXQmtdkkj38AlLd/85jdTc5lWvSeffHLyjne8IzVwWRaTlrRh3v7xj39M07ZoQx6pm4CN2boROgITMAETaAYCNmY74Sbf+2gCJmACJmACJmACJmACJmACQ0QAEwoTFTM1WtDyIIrxhCmFCbWGtLaUl7IGbZf+7yE9IaWV2hrS5eumEnERJ/EjBxNoFQKcr1ljlvP/jxLn+HHSKlIYs3GOa5JDuxJQ7aCN2SaoIsV8pQUoLUPPO++81FSNZC1cuDCZMGFCOu9nP/tZOs63U8eNG5eceeaZydVXX50uSlfDGK+nn3568utf/zo1ezFN6QY44qVLYcw1TFy+DTtmzJjUfD3ssMOSX/7yl+myrENgu5izf/jDH5LjjjsuNUT322+/tHXsZZddljz55JPpMizLt2wffPDB5Oc//3lCXAceeGDyox/9KE3P9OnTFxm97BvL0P1xBEzbW265JU3jJZdckrA82+bbs9dee23aVTEG8VFHHZUwnzSRRpaZNm1aalqfeOKJyUEHHZQceuihybnnnpvcfvvt6f7HNjxsDAEbs43h6FhMwARMYIgJ2Jht1xt775cJmIAJmIAJmIAJmIAJmIAJNBEBzKisQbu0/tOVKwYU39nEjKLlIGYsrWbDoM0X/h+r4XQJ82q+dLo0XMLgitazNmgFw6HpCZQzZi9Tqjm3T5XCmOUasTErCO0eVDFoY3aIa0dj87RkpetdTFO+5ZoNGLcxj3EMMlrZYmLOnDlz0aKYnEwjDpYhYNjGutl4GadlKt9hpfUp65QLpAuT9L///W+6LC1cMYaLA0bpjBkzUlMZA/ill15KSGsEzNRIG0ZuBNIZ+84+hTHMfNLOtokPEza7r7E+y7zwwgvJ008/nW6bbWTjiOU8rJ+Ajdn6GToGEzABE2gCAjZm2/0G3/tnAiZgAiZgAiZgAiZgAiZgAk1CAEMKhUH7Vo1jPi0rrSCNlFaV+P5sOYP2/Zr+BwkDC02UtpeIBwMrDFqNOphA0xLIGrN0602X3j+XOKd/KXENcD3YmBWETgiqHLQx2wQ1pE6CCbQCARuzrXCUnEYTMAET6JWAjdlOuMn3PpqACZiACZiACZiACZiACZhAExEoZ9Auo/Rh0NIKlu/PriZV+v7sbpr3XykMWlob0trWrWcFwaHpCYQxyzkfxuwxGud8/rNkY1YQOimo6s7GbK/1l17ABEwAAjZmfR6YgAmYQFsQsDHbSTf73lcTMAETMAETMAETMAETMAETaCICWYMWU3UpiYfU5SQMK74/S2vC4u/P5jVtPek8aYGEofWitI/k1rOC4NDUBLLGLC8i8BLCXhLn8QOF/0zHuHVXxoLQ7kHVizZm26KO1TthAgNPwMbswDP2FkzABExgEAjYmG33G3zvnwmYgAmYgAmYgAmYgAmYgAk0OYEwaDGhMGjpwjW+P7uSxit9fzaneZ+W7pOi9exNGn+XRDxhahG/gwk0CwHOR85NjNcwZj+p8XjBAKPWxqwgdEpQ5Z+N2UGoAfUmTKAdCNiYbYej6H0wARMwgcTGbKfc6Hs/TcAETMAETMAETMAETMAETKDJCWBYFX9/FoM2vj+LYcX3Z3NStzS6MMxreLw0U8LcmiOdINE1MgYYcRI3cjCBoSbAech5yQsInNt0XczLBPFywboap8U4FTa8YODzVhDaOahy0sasa2hNwARqImBjtiZMXsgETMAEmp2Ajdl2vrn3vpmACZiACZiACZiACZiACZhAixEIAzVr0NKyEJM1ujdeQ+Plujf+kKb/XQqD62GNf1Ry98aC4NA0BMKYpevu5SVahHM+T5I4d3eRaCluY1YQOiGo5tDGbLNXnzp9JtAkBGzMNsmBcDJMwARMoD4CNmY74Sbf+2gCJmACJmACJmACJmACJmACLUYga9D+f/bOA9qKIvv6y4RxRAVMqDzQMeccRwWzY05gHPMoxjFjwJwTijoKhkEQFTBiGMwRw+ioo2IEMybMYra+/auP8t+29+W+77173z5r7dupurp6d90OteucwmswjT+LkDWHgJdhV6FGyHrPdtPyfsLHAiLXr8JlAvuRT/Ke1azNDLQKA9Rt6iF1mvGUGUt5fmGkQJ0dIHQS8BanUwFpbVXMgNr1LMw2r3HTe5uBdsOAhdl2c6l9ombADFQ3AxZmq/jd3qdmBsyAGTADZsAMmAEzYAbMgBmodAaSiEXoV0Qqwr/iPcsYnJ2FeQREre4CoY0B80sKQwWEWcSul4TlhOQ9S77AZgZamgHqHWIrdZG6jAhLiO4jBerqEwJ1m22ItxZmRUI1m9odLcxWd+Orz84MFMaAhdnCqHRGZsAMmIHWZMDCbDW/3PvczIAZMANmwAyYATNgBsyAGTADVcBAElERqBBok/cs3oaEfCUULN6z3YQeAuIs0xqht/ChgOD1nXCIwP7kQ34WZ0WCrcUZoO7hwY1XLB7g1N91BeopYyQj1OLlneqq66nIqFZTq2Cdwmznzp3DyJEjW7Px0Mc2A2agjTCAMPvYY4+Frl27hi5duoS+ffuGESNGhGHDhoWhQ4eG0aNHh08//bSNlNbFMANmwAyYgVoYsDBbrS/2Pi8zYAbMgBkwA2bADJgBM2AGzECVMYA4BRBVEbXwnkXYmlXA63BuAe/ZGgFxFnQXlhHGCIheYLSAp202tDH52sxASzFAfaP+0SjD2MnUR+rqRwJ1dEuBes34ytR310+RUK2mBrs6hdk55pgjXHHFFeH99983zIHrQDuvA++991649dZbw9xzz21htha1w6vNgBkwAxXAgIXZan2x93mZATNgBsyAGTADZsAMmAEzYAaqlAFEKjwOEawIB4t4hfcsAhfjdc4rLCDgNZsE2hrNHy/gjYjw9YGwoZBCG9t7VmTYWowB6jD1l84FCLCMmdxNuF2gfp4pUJ9ptEHAtTArEqrV1HhYpzA77bTThsUWWyystdZahjlwHWjndWDNNdcMyyyzTOjQoYOF2QpQXlxEM2AGzEAtDFiYrdYXe5+XGTADZsAMmAEzYAbMgBkwA2agihlAqEoCLcJVCm+MyEVoWIQuwsHWCEmc7a75DYRXBcSvX4RzBLxus96zWrSZgbIykOou9ZZOBXQooDNBf4G6+aCAFzjjzNJ5gI4DtiplQA12dQqzOu0w1VRThamnntowB64DrgOxDnBfcCjjWuQOrzYDZsAMtH0GLMxW6Xu9T8sMmAEzYAbMgBkwA2bADJgBM9AeGEgCV9Z7FjEr6z3bTcvJe5bpwsIQAQEMPC0sJmTFWfK1mYFyMoDYiuhKfUWEpSPBxgJ18huB0NyItoi31G/XSZFQjaa2wzqF2Zlmmin06dMnnHbaaYY5cB1o53Xg1FNPDXvvvXeYddZZLcy2feHFJTQDZsAM1MaAhdlqfKn3OZkBM2AGzIAZMANmwAyYATNgBtoRAwhWSaAt5T2bxp7trnRZ79m9tPyZgBD2lbCH4NDGIsHWIgxQZ6mvNMzMLhCCmzr6uUCd3FTAA5xwxxZmRUK1mlrs6hRmGWN2yJAh4dNPPzXMgetAO68Dn3zySfj3v/8d5plnHguztckdXm8GzIAZaPsMWJit1hd7n5cZMANmwAyYATNgBsyAGTADZqCdMZDE2dq8Z7uKj24CXrMItExXFh4TEMLAcIFQyFnvWS3azEDhDFBfqauMkdxRoANBjXCTQF28VkCwpeEm1UfN2qqNAbUd1inMdu7cOYwcObLtNzG6hGbADJSdgV9//TU8+uijoWvXrlGYPeCAA+L94brrrgvDhg0Lo0ePjuJ92QviA5gBM2AGzEBzGLAwW20v9D4fM2AGzIAZMANmwAyYATNgBsxAO2YAsSsJtIhZhIFlDFk8DwkXO4/AWJ5JnEWg7S6cJfwkIIi9JCwiJDEs5alVNjNQGAPUK4RZ6uifhDTObB/NUw/x4ia8MaGO0ziz7GOrMgbUqlenMNupU6cwYsSI5jT+eV8zYAaqhAGE2YcffjgKs3Ta2HfffcPQoUPDVVddFXHzzTeHjz/+uErO1qdhBsyAGahaBizMVtn7vE/HDJgBM2AGzIAZMANmwAyYATNgBv5PnEX4QtTCK5HxOvFAnEtA8KoREGaTOLu55j8QEMU+FdYTUmhji7Miw1Y4A1MrR+pYdpxZOg1MEKiHBwqItg5nLBKq1dTkaGG2attdfWJmoFgGssLsbLPNFrbccstwwgknhGOPPTb069cvXHjhheG9994r9qDOzQyYATNgBopmwMJstb7Y+7zMgBkwA2bADJgBM2AGzIAZMAPtnIEkpiJ+4f2KuIUARthYvBMZ07ObkLxnma4o/FdAFPtR2F/AoxGBl3zssSgSbIUxQH2ibtI4M5uAR3d34WyBOvikwHq2Jw9uzdqqiQG19FmYLbq50/mZgSplICvMMv70brvtFi677LIwYMCAKMriPTtx4sQqPXuflhkwA2agahiwMFtNL/M+FzNgBsyAGTADZsAMmAEzYAbMgBn4AwOIX4iqiKuIrHwI44XIWLKM6zm/gBiWvGcX1vzNAsIYuFjICmPkZzMDRTBAXaJe0mmAcNtzCoTaXlVIobVX0bzDGYuEajU1MVqYrZp2Vp+IGSgvA1lhtkuXLmH//fcPN954YwxnfO2114bbbrvNY8yW9xI4dzNgBsxAEQxYmK3WF3uflxkwA2bADJgBM2AGzIAZMANmwAz8xgACWBLBUmhjxC5CGyOGlQptjNfirwLi7BgBL9vktWhxVmTYms0A9YhOA6XCGd+j9dS9SwREW4czFgnVaGrdszBbRBOn8zAD7YCBvDDbt2/fOAb1sGHDAhg9erSF2XZQD9IpTpo0KYwZMyZcfvnlhjlosTpw3333ha+++ipVQ0+bxoCF2Wp8qfc5mQEzYAbMgBkwA2bADJgBM2AGzMAfGEjiLEIYAiveszMJhDbuLJQKbby31n8rIJC9KiwuIKLh5Zjy06zNDDSZgVQfaaBJ4YxrNE/do959ItApgLpK3SO9rYoYUHuehdmmNWp6LzPQ7hioS5gljLGF2fZVJcaNGxf22WefsMACCxjmoMXqAB1C3n777fb1Zyv+bC3MVtG7vE/FDJgBM2AGzIAZMANmwAyYATNgBupnIHkpIq4idPFhnA1tTCjZHgKhjZluJLwnIJJNmrJscVZE2AphgPqYwhlTD1M44z9r/iOBerebMIuQxjtmH1uVMKC2PguzxTd4OkczUJUMWJitysva5JN64YUXwjbbbBOmmmqq0LFjx9CtW7dQU1NjmIOy1IFZZpkl1rUdd9wxvPnmm02ut94xMmBhtkre430aZsAMmAEzYAbMgBkwA2bADJgBM9BwBpK3K4IY3rMzCAhfhDaeS8iPO7ui1j0lIJIx9udBQhLJ8GC0UCYSbE1igLpDHULsxyuWsY+7Ct2FSwXq3CMCnt3UU+qr65tIqBZT85yFWbfSmgEz0CAGLMw2iKZ2k+h///tfFGanm266sOuuuwZCzI4dO9YwB4XXgUcffTRsvPHGYYYZZgg77bRTGD9+fLv5n5XpRC3MFvAiv5jyWLsZWKOAMjgLM2AGzIAZMANmwAyYATNgBsyAGWg8A0kUQ+xiDE/GnSWcbKlxZxfW+hECQhm4WGAf9rU4KxJsTWaAepg6CCDA0jmgm7Cm8L1AfdtGoH4i4Lq+iYRqMTX4WZgtU6unszUD1caAhdlqu6LNO58kzHbo0CEcc8wxHvezeXR67zoY+Pnnn8Muu+wSZpppJguzdfDUiE0WZgt4kb9XeaQP86ZO+aiqJFtBhaURwmYGzIAZMANmwAyYATNgBsyAGah0BpI4i/dsdtxZxvXEcxGBbMEp6K7pqcIvAt9/VwnJi9FimciwNYkB6mCqf3huM+bxfEIPYbBAXfufMKtAfSMt+9iqgAE14lmYbURLppOagfbMgIXZ9nz1/3juFmb/yInXlIcBhFm8shFmEWjtMdtsni3MFvAO356EWXqN81FII8S7BXDnLMyAGTADZsAMmAEzYAbMgBkwA22BAUSuJI7hkcjHMiJYJ2EeAXEWkQyBlimhjH8WEMyuE0hvz1mRYGsyAwj71CHqEiG1qXc1wvLClwJ1bR+BcMf2mhUJ1WJq2rMw2+z2TWdgBtoHAxZm28d1buhZWphtKFNO11wGsh6zm2++eXjggQfCa6+9Fl599dXwyiuvhEmTJgXS2BrMgIXZAl7k24MwS2/cfwjpY5APQguzBVQeZ2EGzIAZMANmwAyYATNgBsxAm2EgL87imfgngTE/5xay484izv5d+FHg++gmAU9Hi7MiwdYkBlLHAMJjU+/w2KbO0RngLIF69raAN629ZkVCtZia8CzMNrgd0wnNQPtmwMJs+77++bO3MJtnxMvlYiArzK611lph0KBBYdSoUWHkyJFhxIgRYdy4ceGHH34o1+GrMV8LswW8yLcHYRZPWT4Cs7AwW0DlcRZmwAyYATNgBsyAGTADZsAMtDkGEMiS9yIiGYIrHoyM+5kXZ/fQujQG6GjN42VrcVYk2BrNQKp3eMPiFUudm1foLiwufCDwTX6s4LFmRUK1mFobLcxWY5Orz8kMlIEBC7NlILWCs7QwW8EXr8KKnhVm11hjjTBw4MBw3XXXhaFDh4YhQ4YE6uL3339fYWfVqsW1MFvAi3wpYXag8l2qgVi0gDKUOwsLs+Vm2PmbATNgBsyAGTADZsAMmAEz0JYYSCIZIiviLELYbALfRvMJNQKejGBnYbKAaHaPQDqLsyLB1mgGqHfTCNQ5RH7q2wIC9ewogTo2SaCDAF6z1DP2sVUwA2oWtDDbqm2jPrgZqBwGLMxWzrVqiZJamG0Jln0MGMgLs5dcckkYPnx4GDZsWLj22mstzDa+mliYLeDdvZQwe3QB+eazoNcsKMLIh4aFhlpbF2Ybcy5F8cgHMD3nbWbADJgBM2AGzIAZMANmwAxUJwNJnEUo6yDgxdhRIMRsV6FGQDAD2wvfCAhnDwuMTcu3B563Fs5Egq1BDKQ6R92hvhFGG6/ZGmEh4VWBOnaBwHcw9dJ1TCRUsqktz8Js4xs0vYcZaJcMWJhtl5e91pO2MFsrNd5QMAMWZgsmNAQLswW8vJdTmN1S5btK+K/A2EXgeWGIsJ+AONgQ66FE5wrPCR8Lvwp8zP0gTBQeFA4X6JGbNRobRgh3CqTP4ictj5yC4Zqmxoa1M+vT9p20rpQtrJUpTZoeUSLhdbl0nDtj7lwsjBco12cCeSwn5K0IHhGnzxIeFd4RfhY4LtOPhGHCZgIfxjYzYAbMgBkwA2bADJgBM2AGqoMBvnMA4ixi2YwC4mxnIS/ObqV1Xwp8J4wVEHAtzooEW6MYSPUNr1m+e9NYs3zX7yVQvyYLRN9KXrNTa95WoQyoqc/CbOHtnc7QDFQnAxZmq/O6NvWsLMw2lTnv11gGLMw2lrF60/NNaWsmA+UQZmdRma4R+OCqC49re41QlyF0/iLUlU/a9p7SrZTJbNkG7sf+NFRg+wgpvzSlN28pW1srU5o0RQTOW9qWpjcoAQ0daTk7zXorF8XjYTpW6v2ePVap+feVlvF/bGbADJgBM2AGzIAZMANmwAxUBwOlxFk6tZYSZzfVejqN8q3wjDCPYHFWJNgazAD1DaGVeoPX7OwC9ahGwDv7SYH6xbcz37x0DuZ7nP1sFciAmu4szNbbfukEZsAMwICFWdeDLAMWZrNseL6cDFiYLZxdC7MFvLMXLczygf+aUEr0K7XuC6VdvZbzSL1pS+1X27oPlBcff1hbFWZrKzuewDUUXFYUj3j71na82tZ/pH0Y88dmBsyAGTADZsAMmAEzYAbMQPUwgPCVPGfxVEzi7LyarxEQzcBGwicC3wtPCwhrFmdFgq3BDKS6lvWanU974zW7sfC9QP06WKBhx/VLJFSqqanPwmzh7Z3O0AxUJwMWZqvzujb1rCzMNpU579dYBizMNpaxetNbmC3gxb2UMPuk8r2wAVitxPHP0bpSgh+hg1PP6/z2J0rkwwfcR7Xk9ZXWTxRSSON8fvTyxipNmCXMcLKieHxHGeb5Yd0twr+FFKosn4brbzMDZsAMmAEzYAbMgBkwA2aguhhAMEvejHlxtpu2JXG2l+YnCXwn4NlI51eLZyLB1iAGsvWMhpvZhLmFVMdO0Tx1i8hOfLdTF6cV2A/YKogBNd21O2EWcenjjz8OgwYNCu+++270Avz222/DN998U29LZkMSfPfdd+HLL78MNCS//vrrYfDgwYXl3ZDjO40ZKBcDFmbLxWxl5mthtjKvWyWW2sJs4VfNwmwB7+qlhFk+kBqCg3LHX1jLjCOb3/cMreNDC1tFeEPIp0liKmmw3YR8mg+1bmVhGgGjt+3bQj7d8WyUERZpZ4Fy5tMg7rIN9BaStUQo41QWhOerhDHCz8J+AlYUj0srr3SsNL1d67IfunB5Yol0iOiJZ83azIAZMANmwAyYATNgBsyAGagSBvgeyIqzjANKWGM8Z7sJC07BlppOFviWuFJI4hn7Zr8ptGgzA39ggDrCNyWdrvk2T6Gzu2ueOkbHZOoWIbNnFToIpHfdEgmVZGrqa3fC7Ndffx3OP//8sO2224ZPP/00iqd9+/YNjz/+eLNaPhGt3n///XD44YeHW265JQq+CLObbbZZFGeblbl3NgNtgAELs23gIrShIlSrMDt+/PgwatSocOWVV4arrroq4pprrgk33HBDGDt2bPjss8/KchV++umn8Nxzz4UhQ4aEO+64I0ycOLEsx6nETC3MFn7VLMwW8LJ+r/JIol1jpwieWbtMC/k8rssmmDJPj9j8uLFP5tItouUDhEEC2z4WthHyhuibP+a5uURzlkjzbi5NWmwpYfZ9HZAP02TMpwpdFI89lWeem5e1bq100ClTPn7h+TxhFwFBl97wNjNgBsyAGTADZsAMmAEzYAaqkwHEr7w420nr8uLs3lpHJ1K+K04SLM6KBFuDGaCO4QnLt25HYS5hAQFhdnXhc4G6dZZAGntli4RKMzX1tSthlsbdZ555Jqy44oqx0f2HH34IDz74YKipqQn33ntvs1o+f/nll/Diiy+GBRdcMDbkkxmes+ecc05YddVVo3dusw7gnc1AKzNgYbaVL0AbO3y1CrN0rFl33XVDt27dfgfu7csuu2zYd999Y0ce7vlF2uTJk8N5550XFlpoobDVVluFhx9+uMjsKzovC7OFX76kY1Xaa2ubKm+RwuxDOrO8ELhqLWebT8vHPh9tDTUu/obCTUL+mIiMWWuLwuzx2QLm5vPccH5N4RGxN89NWn5H2/4l/E2YT7CZATNgBsyAGTADZsAMmAEz0L4YmEqnm4QzBFc8Z0uJs8dqffqO2F3zeEAitrEvedjMQG0MUD/oCIw37MzCHALif42AONtXoG7RHrCekIR/9nPdEgmVYGrqK0SY/f7776OwiQh51113BbyLDjrooHDKKaeExx57LLYoIup89NFHMYTwoYceGvbff/9w6qmnxu1s+/zzz8Nll10WBgwYEPM6/vjjwyGHHBKuvfbagAfTpZdeGvBsPeOMM8K4ceN+yxMvVbYdcMAB4cgjjww33XRTIJxwKfvqq6/CiSeeGJZZZpnojfTWW2+FPfbYI8w000xhr732ig3hNAC/9tpr4eyzz47rjj766HDffffF0MR42F588cWxTIi6pH3ppZei+HrbbbeFY445Jsw888xhm222Cbfffnug4f7ZZ58NiyyySBg4cGCpInmdGagYBizMVsylapGCVqswO3z48HjPnnbaacP8888fO/LwzJh33nnDdNNNFzp27Bh23XXX8N///rdQnnlu8SxbeeWVY/5PPPFEoflXcmYWZgu/ehZmC3hJLyXMMu7oGw1An9zxP9By+mBP04Fad3IJjC+Rlg+02oxetTsLlwqEOvpJSMfITwdrW9bKJcyuo4Pkj31n9sBT5vNpWO5ZIl1aVSSPLyvTUsfPr3te6U4XagSbGTADZsAMmAEzYAbMgBkwA+2DAcSv2sTZGm1DPAN8h/ENMVlYTUBoQ3CzgCYSbHUyQB1ByEfQR/zvIswn9BCoWyMF6tYEgXFos3VLi7a2zoCa+goRZvEM7devX2y4XnzxxcPCCy8c5pprroiePXuGN954I4Z/PPjgg8MCCywQunTpEhu8SfOXv/wlepq+8847YYUVVghdu3YNSyyxREw322yzRQ9U8sCrlfQ0ju+0007hiy++CB988EHYfffdY17kOffcc4cll1wyiqqEhczb22+/HdZYY42w9957B7ZTrq233jrMMMMMMbTxnXfeGV555ZUYfnj55ZePwiyhiGkoR/BFPOY8l1566TB69Ogo7vbu3TusvvrqUUzeb7/9osi73nrrBRr3McazJWzyBhtsEBCwbWagUhmwMFupV6485a5WYfb6668Piy66aBRgCU1P5xpEWDrb9OnTJ8wyyyzxWURHJMYnT8a9npD499xzT0zPMwojzYQJE8ILL7wQ3nvvvfjsSftMmjQpdjSisxEdf+CUZ8e///3v8OGHH6ZkMQ/S0EnogQceiBEY8s84xkknFDLHf/TRR2NHKATNajALs4VfRQuzBbyg36s88iLd0U3IlzFj8vk0dnmlEsfdQuseb2TeV+TymbPE/u/m0qTFfUqkHZA25qYblEh7Zy4Ni6V4qCmRjlVF87iy8vxaKFWGUut+UNoLBMJM2cyAGTADZsAMmAEzYAbMgBmofgZKibNE38l6NiKi3SPwDfGOQMdZC2giwVYvA6l+EaaYRpzZhBTSmHq1jECdom4NE2YSHNJYJFSKqamvEGEWwRLRdeqppw49evSIY6r+85//jI3XnTt3DiNHjoyNxHvuuWfYdNNNo8fpddddFwXcTp06BeZpsO7evXsUSbfYYos4nh9i5lRTTRXFWjxpCfOIiIpI+/zzzwca0Gefffaw1FJLxTEBBw0aFIVbhF3CCmeNhl3GB0T4JR3GeLPDhg0LqQwIt6eddloMX0mZ33zzzdjQjniLOEwjOo30iMmIsUcccURMe8UVV0ShmAZxxGMa7GmkT8cgT3jBQ9dmBiqVAQuzlXrlylPuahdmebZwL//xxx8jgXSsQZzl3s+zbpdddoljlBM9gVD4dBii085iiy0WOxkdeOCBMXQ+UR369+8f6OxDJAmiLGAIqUSJIL+dd945htW/+uqrw1prrRV4Vj711FMxHWOVn3TSSWHNNdcMdHwCG264YRRwk/hLGp5Hq6yySjw+z8Att9wylpfjVLpZmC38ClqYLeBFvShhlg/zUkJfY9ZtmTufQ7T8az355seq5XiM0Zq15gqzl2Yzy8xvpvn8+TVUmCWMUykrB4/r6kAThHxZ61q+tVThvM4MmAEzYAbMgBkwA2bADJiBqmQgiWd4NhJOdlYBcbar0F3AsxEB7U2B74iHBL5pLKCJBFu9DFC/8LBGzC8V0ng7rf9ZoG7tJaSQxlNrnn1tbZgBNfUVIszSOEzYYUTTHXbYIQqeiJw0JNO4PXjw4Ni4jTcPIX1PPvnkmG6OOeaI2xFxEWYZww+RFLEUEfTYY4+NoSMRRRF/8TiaZ555IvAawpuJcJN42l5wwQUx3/nmmy+Q79ChQ3/XkknjOY3qc845Z/RGYiMN7mPGjInrEFURUxGO8b4lLDJhjwlPvPbaa0dBl+PTOD9q1KjopYtAzHlTNsIW41WFR++VV17527EJTzlkyJDozZsa2n/b6BkzUEEMWJitoIvVAkVtL8Isz45kCKCIqHqsxygIeMgCOu/gSdtN49Kuttpq8flAxAfC78MTofF5tuGJy/ON/xKdh3he8gwjT5ZPP/30mA/iLGIvXrM8h3juzTrrrDGaBMIvIfOZ8tz65JNPouBLxAietwi9PEtJz/jmlC/vXZvOp1KmFmYLv1IWZgt4Ny9KmOUDPn1IZQW/m7V+VAOxeuZ8ttV8Np80/4HWXynsItC7tpTX6iVan7XmCrODs5ll5rfXfCpXmjZUmKUBo5QVzWM6BsfbWLhamCSk8tY1Jb3NDJgBM2AGzIAZMANmwAyYgfbBAAIYQhjfDnxsI86msLPdNY84u56QIvIwbA0CmsVZkWCrkwHqFkCcTSGNEf7nE1LdIlIV36ffC+sLpCN92leztrbIgJr6ChVmaSxmrFcMkROvHhqK8ShlfFnGlcWTh3DD66+/fvSQZfvll1/+mzCLcIroyniweJpOP/30sfGahmzGfqXhmwZoGq0JSYznEl6wiMA0iC+33HKxYTovzCKoEo6Y/B988MFYRoTZu+++O65DoKURnAZxGsHxhgI0mO+4447xWHg6IcDS0I4oS8P3mWeeGfNi/TPPPBOFWYToZAizePbiSfvII4+k1Z6agYpjwMJsxV2ysha4PQqzeL/uu+++MZIDzwrGVD///PMDkSEYS5zIDnTgYSx0nlU872688cYYgphICzPOOGP0bEVMveGGG6K4yrOBjkV08GHcdQRe8iZsMWA/noOEUb7//vvDHXfcEZ93POtGjBgRl5dddtnQoUOH2KHoySefjOvosMTxeEYRJrmSzcJs4VfPwmwBL+VFCbMUZYKQF/r4eG+K/Us75fO6WOv46M8aXral0mXTlBJm388myMzTOzef3w2Z7dlZQj7n0zZEmMULuC6boI35fJvKY/Y4fNBiTOntfpBwk/CFkD8ey8cLNjNgBsyAGTADZsAMmAEzYAbaDwN8K2TF2Y5a5ntqfoGOsXyX7COkyEbba97ejSLBVi8DqW7RGZnGnFS3iBqV6tYdmudblM7ESwt42CZxVrO2tsiAmvoKF2bxIMUQZjfaaKPovYrwitiKBxEhffFcffjhh+N4r0m4TR6zNFIjnCZhFi9cGqQRhV599dXY2I0wS5rjjjsuNkbjUUuYYo5x9NFHx0bxfChjRFjGkMUbluNjWWGWBm8ay7fZZpvYmI53L2P6IT4Q1hiPJ4RbvIMPO+ywgGcux6Xxm/DGWWH2qquuivnzM3ny5MAyYi/pbGagUhmwMFupV6485W6Pwuy7774b9tlnnyjMEkkBYZSQxXQQwkv173//ewx/vKdCEc8///zxmcezg/0OPfTQKJTyXGQMWSJCzDTTTPE5wvOQsWhPOeWUKMwixiLKXnPNNbEDE8+Piy++OD6z6OxDdAY6AvHMuvDCC2PnJMRbOhLRoYmoFHjUTjPNNLGDEWPcVrJZmC386lmYLeClvEhhtlRex5YoI72u/yOQnl6xfNivIWRFV4TTvFg4j9bl7UytyKfLe8zSEzef5qN8RlOWaVjIp322RFo+Jh8rkbYhwiw9gOuyonjcSgeBH7yWXxYYP3ZXIW/0RCZ0cf68B+cTetkMmAEzYAbMgBkwA2bADJiBqmcAAQ0xjO8zxvucTciOCYo4e7nA98MnAqKaBTSRYKuXgSTOpro1u/bgO7+bQL1aTOD7m7r1hjCfQFqLsyKhrZqa+goRZvH0QZDFY5axZjGEWcaIJawwoYppZBYP0XMUbyIEVIRagLcQwixeqFlhFu+hFB45CbMIogizjz76aPRWwruIdYQcJk8ax/GefeWVV2I50k8SThdYYIF4PNYjzOJ527Fjx+ipxD549yLe0rj98ssvx+14926++ebxnPDERVxGFEbMxSNq++23jx5JCBXsi9cw3r3Yl19+GdMuvPDC4YMPPojr/GMGKpEBC7OVeNXKV+b2KMwSNYH7Pc8yngl08kGoZZnnH88ing88ywhrzzLPO54DjKVOKGO2I+DyfMSjlZD8CKylhFmenTzvumv8dUTaZDzPkhH+mOcmIiwCLml5DnJ8PHl79+4d3njjjZS8IqcWZgu/bBZmC3gxLyUC4gnaFNtZO8UbSWb6rebXzGV2RWZ7Sk84LD74k32jmbQtTfumjVOmq2j6U4l0g3Pp6Imb8khT9ptP4MNwKSFZL82kNNnpjimBpvQgR1DObk/zDRFmJ2fyKjVbFI+UJZUrTSdq3Z9KHPS2EmntMVuCKK8yA2bADJgBM2AGzIAZMAPtgIEkziK4MiYoAtrcQjcBAW1R4X8C3xn3Cgi4CGh8K7GvzQzUxkCqW3QQnkXoJMwr1AjUrZWEtwXq1mMC3/OuWyKhrZqa+goRZml0RhilEbpfv36xBZGG5u222y42QtOg/M4770RvVIRY0i2//PK/jd36j3/8I7z11ltxHeEgH3vssThO7XnnnRcbmglZjCjE+H5LL710bNxmvFbE3+OPPz560dIATeM0QilCL43ceSMMJSIrHrg0bAPE2DXWWCPud+655wbGxkVYXXzxxaPAyxh9yTMKD1oa0zfbbLPoAfX1119HLybKPGjQoDBx4sS47c9//nMMV0n+hHBm3Fo8cRGCbWagUhmwMFupV6485a52YZZORYQpRhTEGGsWcZVQ/IwLS2h+whYnj1meaTyziMxAuosuuihGS3j++efjGK+IuoikiLGIpuTPc2b48OHxWVRKmOXZicjL843nGmPF4jGL9yxjz9I5iOcW3rl43/I8vOWWW8Ktt94awypfdtllMQ0RKCrZLMwWfvUszBbwYs5HdBLt0rSpwiy9WN8skd/PWne9cLaQer+mY6XpqdqWtce1kLalKeItPbO3E84SSom3pMX7M2s0DlCGlE+asj89vFnmYxCjwaFU2h+1/n5hmPCxkPLIT4sQZovicb1aykn5OQ88lRG7Swm4nNcSgs0MmAEzYAbMgBkwA2bADJiB9scA4ll9AhrfG3Q65duBb0g+0IksZHFWJNhqZYB6RR2hrswgzCp0EeYTuguIs9StNOTODZqnblmcFQlt0dTUV4gwS2MxYYbxPkW8xGjEJtQinrKEcSQNIuioUaPCkCFDYihiGvbvueeeGOL3m2++iWOwPqBwxJ999llsDB8/fnzcTsM2RsP1Qw89FEiDly7CJ2IoYY3/9a9/BbxZH3/88VrH0+MYNG7jtfTmm2/GPBl79j//+U8cf5bwkDQAI84ydiCN4oQxZj0iLOXifAiTjEgFEKBZx7lwjmzjHAmtTF6UB++l/Ji38eD+MQMVxICF2Qq6WC1Q1GoXZhE6t91223DppZdGIZQQ9iuuuGKM4sAz5Nprrw08UxBjEU6JxkCkBZ5XPGcIR8z+hC3mv0NY+3POOSeOaT7VVFNFD1c6CaVnZlaYTWPMIrySD0IwnYqGDRsWsdRSS8XoDAi0CMEs4zFLCOUnnngi3HbbbeGvf/1rWGWVVWKnIZ6XlWwWZgu/eryb25rJwL3aPy8uNlWYpShbCN+XyDN/jOzyeKVHEM3aEVrIpmnMPPnl7RmtqCuPZTI73FxP2pRPKW/dIoRZilIUjzc28FzSOaXpNRTCZgbMgBkwA2bADJgBM2AGzEC7ZSAvoBF5h2FisgJaPy3zDcE34PICXpB0NGVfYDMDpRhIdQuxlYadjsKcQnYsY6JW0UGa+sUQPYi4Fv5FQlszNfUVIszSZIhIijDJNFlaR6N0Mrx9aICmoRVjnzTPlOWUnml2O+nzadI68qThO3t8tmWNbQineMGeeeaZv23iOAi0HCsZ8zS6U95kqTypfGl9KlNaJi+8YxmP9sgjj4xjCOLdazMDlcwA9Z6xMLt27RpFob59+8YxNhGL6HgwevToWjtFVPJ5u+ylGahWYZb6TOh5xo0lzD3eqNR5PFynm266GPGBMVwJTc9/4sknn4xhjQm7T9rlllsuTvGMJWoEHXsw0iK0EoFB7wJRoMXbNT1jeIb1798/etQSxYFOS59++mkgVDHhjAnbTxhkQDkQifk/Iroyfi3jtXfq1Cksu+yycXxZjk80B/6XPJMq2XjG7rLLLtErGG4uueSS6GnMtUIgpy5W+jm28PWxMFvAC3nRwixFIsTw+0IS+eqavq50fNjnjY/5R4S69mXbY8KEEulW1rqsHaKFuvLaNJN4Zs2PrSf9V9q+gfBpLl1RwqyyLYRHPlxvyJWxLh7YdodAyDKbGTADZsAMmAEzYAbMgBkwA+2bgYYIaOmbkm8zvqX4lrDXbPuuNw05e+oW3/3Ul5kEhjaaW1hA6CEsKBwmpO/XvTWP8G9xViS0JVNDYGHCbAs3Kjb5cDR+4wm78cYbx/DKTc6onh1pSKZBHi8nwkrmxdx6dvdmM9DmGLAw2+YuSasWqFqFWSIgIAKus846v6Fnz57RA5WwxTfeeGMgLH7qBESHIKI2sG2llVaKYujKK68cx1tHOE3CKxeL0PaER+7Vq1fYb7/9YmSFdBERFon8wLPpoIMOihEnOAZh/gcOHBg22mijKLgSSnm33XaLUR2I5IAxhiwCLuUklD5pEIUp66RJk9IhKnZqYbbwS2dhtoCX8buUR/rQSVM+fpprfFANFyYIKd/s9COtP07Ie8pq1W/Gx9mpQikPXMLxHinwwX+UkM2b+ZOErJGOMVN/FvJp39K6jYSszaGFkcJnQjb9l1pGfF1WwCYK2e23xrW//0m9fFM6RN2GWhE8cqwNhTFCKkOp6ZPavr5gMwNmwAyYATNgBsyAGTADZsAMJAaSgDadVvCNxjcc3yndBMSzNYQ0zMwBmudD3eKZSLDVyQD1KtUtxFlEfb7D5xFS3aJ+XSTw/fqDsLVgcVYktCVTU1+7E2YRl2iofvTRR6NHa+HNnVMy5Dh4yXKc1HhermM5XzPQEgxYmG0JlivnGNUqzDJmOkInY8JmQbj+9957r+T45YivbHvuueeiBy1TlvNenAiMiLMvv/xyFFwRdZMhwhIan9DGEyZM+O04rOeZxVAAjK1O6H22ZwVf0vC8obx48DKMAGOy0xGJ/22lm4XZwq+ghdm29DJeR1no+bqO8DcBAXRRoTEemYTMWkHoI2wpEN6oqTaLdlxN2E1YR6hLGNbmKPwuremuAmWYRmgtay6PlJvxezifzYU9hL8KSwrwYjMDZsAMmAEzYAbMgBkwA2bADJRiIAloiGJ8O3QS5hVqBMSzUwTEs8+E7kISz9jPZgZqY4D6QSdqvrNrq1s9tC0NN4Q4u92UtOzDvq5jIqE1TU197U6YTc2bhCqmMbucRv7Z8MjlPJbzNgPlZsDCbLkZrqz8q1WYbc5V4D9STiG0IXk3JE1zzrE19rUwWzjrFmab8fJ9lfbF89MwB/XVgZOaUc+8qxkwA2bADJgBM2AGzIAZMAOVz0AS0PCEZaxPOnx2ERiWBuHsz8ILAuLsDYJDGosEW4MYyNetNJZxV+1dIyD8U7+ITkX9IiLVjgKdvS3OioTWNjX1tVthtvBmzgIzpGEdT6tyi7o//PBDHAu3wKI7qypmgHrpMWar+AI38tQszDaSMCdvMgMWZptMXW07Wphtxgv4eO3LR41hDuqrAwi3NjNgBsyAGTADZsAMmAEzYAbaNwNJQCOkMR/jRPSZS2BMUMQzovL8Ivwq/EVAwHVIY5Fgq5eBVLfywj/ibHeB+rWQMFLg+/UnYTfB4qxIaG1Ti13ZhFlCKDJe3rHHHhseeuih2hoHy7qextwXXnghHHnkkeHSSy8NCJENMQQoQkoyRixGKOLLLrssHHfcceHpp59uSBZNToMYy3GPPvroGJryxx9/jGEvP/300ybnmXYkb0J0EmITLrguJ5xwQvjmm29SEk/NQK0MWJitlZp2ucHCbLu87K1y0hZmC6fdwmwzXsAZm4UPGcMc1FcHejWjnnlXM2AGzIAZMANmwAyYATNgBqqHAQQ0vBSzY4IS0jiJZzdpPgj3CYQ8TsIZ+9nMQF0MUEcITZyE/46aT17ZqX4h0A4XqGM/C3sK1DF3ABAJrWVq6iubMIuQuOOOO4YFFlggDBgwoPBWxYZkiPh46623hs6dO4eePXv+bky+2vZHCH3sscdC7969wyGHHBKTMSbgRhttFGpqasKQIUNq27WQ9Rxrzz33DAceeGAUTB955JGwySabhOeff75Z+RNWGSFl8803D2PGjIlhnFmGl0GDBjUrb+/cPhiwMNs+rnNDz9LCbEOZcrrmMmBhtrkM/mF/C7Ot9eLt45oBM2AGzIAZMANmwAyYATNgBsxAu2MA8QzkPRvn1zpCGq8tMA4owtmmAh/tCG0eC1Qk2OplICvOzqTUiLNzCtSvJM5Sz4YI1DE8tP8upDGNXc9ERkubmuoKFWYRbr744ouAKIvAuO2224aOHTuGc84557dWQQTCzz//PKYp5cHKukmTJsXthPPNWzoGHq15T8/vvvsuhgDmGJ999ln48ssvw0svvRROOumkKD4iuuI1SjoaeknHsShzMuZPP/30MNtss4Uddtgh5ofH7FVXXRXOOOOM8Oyzz6akcUpeH3/88e/yYAPlTOVhnrJyLMpQm3G+N998c1h00UWjODx58uTodTzPPPOExx9//LfQxuSHRzLH/eqrr37LjnNjPeeWjLTkwzkgyM4111xhxIgRsRzwc8opp4TVVlstTJw4Me3iqRkoyQB1yaGMS1LTLldamG2Xl71VTtrCbOG0W5ht6RduH88MmAEzYAbMgBkwA2bADJgBM2AG2jUDefGMkMZzC90EPBr/JSCaPSkwXiiiGV627GczA/UxQD2hvuAJizhL/Solzl6l9dQzQmcfJFicFQmtYWrqK0yYRdC85JJLwmabbRZ69eoV9t9//7D66qtHYfbcc8+NIug777wTTjzxxLDBBhuEddddN3qFjh07NrY4Isjee++90Vt07bXXjp6ce+21V7jjjjvivohC77//fhRN//rXv4a//OUvYZdddgnDhg2LYXkRgg8++OCw9957x5DDm266aTj00EOjGLnVVluFo446KqZ78MEHw8477xzL0a9fv3gctl9++eVR5LzrrrvC4osvHqaeeurQrVu3mMebb74Z8+zTp0+47777YnkROjk2XsHrrLNO9EQ97bTTAudIWQkXDAd9+/YNAwcODFtssUU8b8pEeOVShtjMMfDORcj9z3/+EzmcYYYZAmW85557osjKcbfZZpvIIeI3XryIuu+++2708kWIRoxFGB45cmTYY489Ylm33HLL0KFDh7DeeuuFG264IQq48I8nMMKzzQzUxYCF2brYaX/bLMy2v2veWmdsYbZw5i3MtsZLt49pBsyAGTADZsAMmAEzYAbMgBkwA+2agax4RsjizsJ8QndhZeFbAdFsSwFxzV6zIsHWYAay9asucfZy5Ug9A4cJFmdFQkubmvoKEWYRAIcOHRrmm2++MMsss4QVVlghLLLIIuFPf/pTFGbPO++86CV7wAEHhLnnnjsKgaSZY445okj46quvxrFPV1111TDnnHNGcRfxlrzw5sTrFeH38MMPD3iPkma55ZaLXq1LL710uPHGG6Mo+ec//znuw/Z55503iqYIk7POOmsUYPEmRYCcaaaZoufoQgstFJZaaqkw88wzh8UWWywKnORF6OOpppoq5o/Ai5CKWMr5IYKSz+DBg0OPHj3i+XGunBfHRARFYOWcCOOMxzDlWmWVVUKXLl0iEGzxbs1aCjVM2jPPPDOK0a+//nrYddddI4/HHHNMeOaZZ8JNN90UllxyyRhq+fzzzw+I13CA0IonMqJs9+7dw7XXXhvTwzPiLePWwh+c/v3vf48euBwfsXv99deP3sFZT9ts2TxvBmDAwqzrQZYBC7NZNjxfTgYszBbOroXZln7h9vHMgBkwA2bADJgBM2AGzIAZMANmoN0zgHBG2NgU0piQs3MJCwh4zSZvxjGat9esSLA1igHqVylxljqWD2s8UOuSOHux5mko8pizIqGlTE19hQizCJGMXYrg+be//S2G+8WrE4GUkMB4zD799NNxGXETEZGQqHi+zj777AGBkVC9eHMiXiIukub444+Pnqxvv/123L9r165RNL300ktjerxPGSP1iiuuCOPHj4+C74wzzhi233776GnLWLGEBp5mmmnCGmus8ZugiuiKwHvllVcGxnClzBybc3jjjTei1y3LiLEImnii4sWLkHz11VdHr9iVV145Crp4xD7xxBNxPeeGOHvdddcFvGwJG4wQiiBKGjx0KQsibTZ8Mk2ueAzffvvtcZ8777wztsLilUsZyYdy4oWLp+zyyy8fxWJCRuNVi7DKOLQIxuPGjQuIyUsssUQUuJdZZpnIFR60eB/D7/XXXx89ajkIYi6COWIv4Z9tZqA2BizM1sZM+1xvYbZ9XvfWOGsLs4WzbmG2pV60fRwzYAbMgBkwA2bADJgBM2AGzIAZMAMZBrLC2cxa30mYV+gurCsw/idhZlcQ7DUrEmyNYoD6la1jWc/Z5J1NJwDGnD1boK4h0N4vzCPYS1sktISpqa/ZwixiDaIoHqedOnWK45fShIiIiJiZhFk8UXVOcZmxWwknjHhImF7C9+IdihcsgiieqCuuuGIg9C4hhhEMb7nlljDttNNGAZFwwXiY4u354osvhg8++CAKoYTkpQzDhw+PY6gibI4aNSrulxVmyWettdaK4ighgPFCnX766WMI4wkTJoRTTz01lmunnXaKXoKESeZckjCLyIzgilD81FNPxRZTzhfPVLxvDznkkCjwIqgiThP+GOGVcyBE8rLLLhvHh407TvlBOMULF89b8sfwREbkRURGIOa8k3du7969o9fs1ltvHT1k8f6FD8awJeQx+4DLLrssHhsP3fvvvz+KvAjAyRijlvMlbPNbb72VVntqBv7AgIXZP1DSrldYmG3Xl79FT97CbOF0W5htiZdsH8MMmAEzYAbMgBkwA2bADJgBM2AGzEAJBhDOktfsrJpnLNAFBASzewRElEGCvWZFgq3RDDRGnN1LuX8jUOfGC3QIQJxlvNqUj2ZtRTOgpr7ChFlC8OKNmbw9P/744zimKsLsOeecE8VSPFURNxEVGZs1CbRsRyBFOCTMLqIs6RA/8fjE+zaJmoTtxUMXkQjBljDDCKd4uiLMIoY+9NBDsRWTPGsTZhnjFrEUIRPhFEF40UUXjSIzY8UiGDOGLZYVZq+55pro/YqQO//884fXXnstppk0aVLYbbfdotcwoYopD2VBaCUEMceBGzxmOSf4yRrC7KBBg2J6vGAxhFnOHYH12WefDXgOI8zCz+mnnx5OOeWUiJNPPjl6HeP9ynHuvvvuGEKZEM6IrnCFMMt5IhTfdtttvx06CbOEXUZgt5mB2hiwMFsbM+1zfVaYPe6446LHfvtkwmddbga496TnK52sGM+eDlg8HwnbT13keW9rMAMWZot+oXZ+ZsAMmAEzYAbMgBkwA2bADJgBM2AGGsgAghchjRHA8JqdQ+gqdBd2EhDJEMsQa7MhZrVoMwMNYiCJqgisHQQ8ZwmdTSeAvOfselqHKJvqXR/Nsw/7Uk/Jy1YwA2rCa7YwSzPghx9+GD1KEVIPO+ywQIhdREBC+yaPWcRSxE7W4aGKB+ixxx4b9txzzyie4iVKaGK8TQlrjIct4YLJ84gjjghjx46N3qgInWzDQ/Wss86KYYEJEYzHbXeNrcp20mK1CbMIxIi4Y8aMid62J5xwQphuuumiFy2iL2ImwiveqIi/eOSuPcVjFmEW0TWNH3v22WdHofiBBx6Iwi7nO3DgwN9CGSdRtT5hNpUV4RSBGssKs/CVeF599dWjSItgTMjkM844IzAG7TfffBPFVUIwI97SkI1XMmVLwizieVaYJaQy12zhhReO5xEP7B8zUIIBC7MlSGnHq5IwS2eTNddcM96DuJca5qDoOsCwBnTK4jltYbaQm46F2YLfp52dGTADZsAMmAEzYAbMgBkwA2bADJiBxjCA2JW8ZtNYs920Dq/Z1wREsv2EWYQkklkgExm2BjNQnzhbo5yob2Bp4UGBevercLpAvaOOWpwVCUWbmvcKEWYRBAcMGBC9ThEiN9xww+gVSsjgjh07BsRLhFQEQwRPhEW8ZrspfC5eqnfddVf0Ql1wwQWjFyqetLvuumv0OMVzdsiQIWHixIkxVDB54jW63nrrxfFcGaOWcL0TFIIY0TcvzI4cOTJ6qSLyMgYr4YLFY2zgRbSkTIwLSzkZC5dzueiiiwLiLSIm5SRcMcIsx2L/L7/8Mhx11FHxfBFoGeN1qaWWisIx83ievvLKK9Hrl/Lg7Yp3Lh6z5Lv44otHL9xs8yqhGjkO+TGGLsY+jJGLOH3QQQeFRx99NG7DE5dxYRmHF29ZOES8RlRG3IaHESNGxPFnEWjhCn4Y55bzJEQzYi2G5+5mm20WNt5443i8uNI/ZqAEAxZmS5DSjlclYZb7KeOLE0a+c+fOhjkoSx2gYxd1zcJsITcdC7NFv1A7PzNgBsyAGTADZsAMmAEzYAbMgBkwA41gANEMwQvxK401i9dsD+F8gUaQMQKi7QwCApmFWZFgaxQDWXEWD+3kOdtF89S3GiGJs9S9y4TYAKfpHUJnweKsSCja1LxXiDCLYMP4p3ht4tWC4Imgue+++0ZRkHCDeIwiLDK2LMIkIJww3qWE4AVXXnllFAgROQn3y/bzzjsveuDi8UlI4L333jvmT+hkxolF9EVcZHzVLbbYImyyySZx3FmaLjkm3qcIweyHMEu4YMRdxsQlnDKiJmO+4r2LZywCKcdBqEQoRtTEm/fAAw+MoZkZn5XxbfFUJZQwnmKM78p5E4YZb13KijiL6LvpppuGV199Na7DE5iywAHnmzfE6w022CCWi3JwHDyBt99++9CzZ89w9dVXx/NEjF1nnXWiWEz5Dj/88DBu3Ljw3HPPRS9fvIsYX5fwyHCPUI6XMR63XBN4w9uY/F966aUodJ955pn54njZDPyOAQuzv6Oj3S8kYZbOJoy3TeeXVVdd1TAHZakDiP6M0W5htpBbj4XZol+onZ8ZMANmwAyYATNgBsyAGTADZsAMmIFGMoBolvWanVvL3YT1BMSxH4QaAeEWUc2eiyLB1mgG8uIsjUKMbYzoOq9AnUOURaDtLhwoTBaog+OEJQSPOysSijQ17xUizNJMiMiHVyveoYiThPtFrEX4S+OpIpQiWCJ0PvbYY3EboYIxRB/ERARGPDsRMdmXsMjJEDwZZxXhlO0vvvjib+F3yRsv1Zdffvm3sQ7JE+9WBATKg1B5xRVXRE9XRFPWcSzyo+zJCCtMPhyDfVOIYMqTysv54qHKds6Fc+J8KSNGHpwL+RCSOJ0fY+IitqZ06ZhMEY4RqhGL09i1nBflRHRFuE3HJbTxI488EkNCp/WMFwsnCMwYx8yuQ+x966234j4T5EHL8f75z3/G41FOmxmoiwHq08MPPxxFOLzJ+/btGz2zEf+HDh0aRo8e/bv/a115eVvlM5CEWcLL7rPPPtHjn/ubYQ7KUQfoeIXXrIXZQu4dFmaLfJl2XmbADJgBM2AGzIAZMANmwAyYATNgBprAAILZNAJes4QsRiibX0AkQxBDGEMk+5MwvUBa9rGZgcYyUEqcpV51EuYRGM84ibNMNxXeE6iDXwp7CdRT6uDUguuhSGiOqXmvMGE221SIgFOfITDWZfXlUd/+pfJGiLz88stjGGM8U8mjruPUtS2bf0PTZfcpNU8+CLK9evUKjJuLkFqXNYWDlB/HQvDF4xZPZ8Im28xAXQxQZyzM1sVQ+9qWhNkOHTrE+xUda2xmoBwM8CxkeANCZluYLYRhC7PNeXn2vmbADJgBM2AGzIAZMANmwAyYATNgBgpiAJELb0Q+1GcXEMlqhPMERDHCyc4msB3vWtLbzEBTGUBQpQ5RlwiRTYeAOQS8tekU0F3AcxYsLzwuUA/BzQL1M9VD8gK2JjCg5r2yCLOFNBuWIRPEx3vuuSf06dMnnHHGGXWKsmU4fIOyxLv2lltuCXvuuedvnsYN2rGRieDi3nvvDbvvvnuYIO9ZmxmojwELs/Ux1L62Z4VZOnfgnW8zA+VgAGF2l112icIswwcwrjxjrA8fPjx661MXiVJhazADFmab8M7sXcyAGTADZsAMmAEzYAbMgBkwA2bADBTNAMIWXoh4xBJedk4B78WtBcSwTwW8Gh3OWCTYCmGAOpfEWeoddYtOAdS9+YQaIYmz3TV/mkBYberjRGEzIYU2Jh/yszWSATXhtSthFmEJ8YDxYVO43wY3Y7ZgQjzP8JwtZ0MzXBAmGq/Z5njetiAtPlQrM2BhtpUvQBs7vIXZNnZBqrg4WWGWMeGPPvro2Lnq9NNPj2O9M349QxXYGsyAhdlGvi87uRkwA2bADJgBM2AGzIAZMANmwAyYgXIwkEQyhC4EMkRYxLGFhTTO5+qadzhjkWArjIFU7+gUQIjimYSOQhehq9BN6CEg0CLObiS8IiDOgssF0tt7ViQ0xdSE166E2QY3WTqhGTADf2DAwuwfKGnXKyzMtuvL36InnxVml1lmmSjMnnbaaeHUU08NJ510Uhxv3cJsoy6JhdmmvDR7HzNgBsyAGTADZsAMmAEzYAbMgBkwA2VgIHkv8rFO2OIUzjiFkT1M6xDBCD2LEGYPRZFgazYDeXGW+ofXdm3jztJZ4ArhVwFx9lWBTgPJe5b8XDdFQkNMzXgWZhvVlunEZqD9MmBhtv1e+1JnbmG2FCteVw4GssLsaqutFgYMGBD+9a9/hauvvjoMHjw4PPfcc2WNMFGOc2rlPC3MNuQl2WnMgBkwA2bADJgBM2AGzIAZMANmwAy0AAOIWdlwxnNpGY/FAQIC2I0C44Di1YgIhpBrMwNFMJDEVOofdSs77iz1MD/ubHet6y28L1A3fxJOElKngRTamHxtdTCghkELs63cOurDm4FKYcDCbKVcqZYpp4XZluHZRwkhK8yuscYa4dJLLw3XX399uO66634bY5Zx020NZsDCbB3vxt5kBsyAGTADZsAMmAEzYAbMgBkwA2agJRlAxELQIqTsLEJnAUFsLwHx63mBdYQ6Jk0SvzRrMwOFMJDqIB7ZadxZvLcZdzYf2riH1i0l3CxQP8ETwjIC+yPyuo6KhLpMTXgWZhvcjumEZqB9M2Bhtn1f//zZW5jNM+LlcjGQF2YvueSSMHz48DBs2LBw7bXXBupiOcdkL9d5tWK+Fmbrejn2NjNgBsyAGTADZsAMmAEzYAbMgBkwAy3MAEIWHot4xeIdixi2voDo9aWAQOZxZkWCrWwMZMVZOgBQFwltTKcAwmsvIHQXGHcW1Ah9hS8E6inesxcJpEegTeIs+dpyDKhR0MJsK7aM+tBmoJIYsDBbSVer/GW1MFt+jn2E/8+AhdnCa4KF2dz7sBfNgBkwA2bADJgBM2AGzIAZMANmwAy0JgOIV4hZfLCncWYX0fwvAqLXYkIaZxaPRItdIsFWOAPUKwRV6lg2tPHsWia08XxCjZDE2R6aX1m4U6Cego+F/QTEXfJJAq1mbYkBNfVZmC28vdMZmoHqZMDCbHVe16aelYXZpjLn/RrLgIXZxjJWb3oLs+lF2FMzYAbMgBkwA2bADJgBM2AGzIAZMANtgAEEMUQswsjipZjGmU1jeW6qdQi2fNAj4FqYFQm2sjBA3QIIqtQ16iTes3QMwBt2XgHvWUTZJNDWaH4HYZyQBNpnNb+OgMBrgVYkZE1NdxZm622/dAIzYAZgwMKs60GWAQuzWTY8X04GLMwWzq6F2ezLsOfNgBkwA2bADJgBM2AGzIAZMANmwAy0MgNJCMPLkJDFXQTErycFhK59BUIcI5ClMLGatZmBsjGQ6iSiKuIqjUnUTephbd6ziLXHCZ8LSaC9XvM1Qqq35Avatampz8Js4e2dztAMVCcDFmar87o29awszDaVOe/XWAYszDaWsXrTW5ht12//PnkzYAbMgBkwA2bADJgBM2AGzIAZaIsM4KGIADazgGfi/MIYAYHrCKGTwDbSkNZmBsrNQBJRqW/Je5Y6iPc2nQdKec921/qlhSFCCsX9jeZPENjXAq1IUNOdhdl62y+dwAyYARiwMOt6kGXAwmyWDc+XkwELs4Wza2FW78A2M2AGzIAZMANmwAyYATNgBsyAGTADbYmBJMziFYsIy3ieNwkIsycLiLWzCHjVkrbdex2KA1vLMEBdo84hqlL/avOe7aFthDdmWiNsIIwVkvfseM3vKhAeORveuN3VZTX1WZgtvL3TGZqB6mTAwmx1XtemnpWF2aYy5/0ay4CF2cYyVm96C7P6ALCZATNgBsyAGTADZsAMmAEzYAbMgBloSwwgTiF8IcwSLrarMERA1LpAwEORULIIY4ha7U7M0jnbWo8B6hvibAptjLiavGfpNDCPgJd3dwFxFiDQdhP2E94TkkD7huYJz00DFfmRL/m3mzqtpjsLs/W2XzqBGTADMGBh1vUgy4CF2Swbni8nAxZmC2fXwqxe9m1mwAyYATNgBsyAGTADZsAMmAEzYAbaEgOIUgizfLTPLhAm9hIBMWuwMKeAMJu8DduNiKVztrUNBpJ4ipCa956lzlJH6VDA+MjdhSTQMr+wcIbwiZAE2nc1f6hAvW5XAq2a+izMFt7e6QzNQHUyYGG2Oq9rU8/KwmxTmfN+jWXAwmxjGas3vYVZvfDbzIAZMANmwAyYATNgBsyAGTADZsAMtCUGEL0Qu2YQGMMTD8QzBUSsG4S5hFkFC7MiwdaqDCSBFjGVMY+pk3h6Uz8Jw01dJRR3NwGv2axA+2ctM97sB0ISaD/SfD8BcZf/QNV70KrpzsJsve2XTmAGzAAMWJh1PcgyYGE2y4bny8mAhdnC2bUwq5d8mxkwA2bADJgBM2AGzIAZMANmwAyYgbbEAGIXQhfCbEdhbuFYAfHqTgGxi/UWZkWCrU0wQJ1FRE0CLXWX8MbU02x44xotJ4GWaVo+QvMThCTQfq750wTqOgIt+ValSKumPguzhbd3OkMzUJ0MWJitzuva1LOyMNtU5rxfYxmwMNtYxupNb2FWL/Y2M2AGzIAZMANmwAyYATNgBsyAGTADbYmBUsLswSogotXDAkItghfiF4IV6W1moLUZoB4CBNRseONZtIwHLGMjE5Y7H944CbQ12naA8IqQBNpvNH+BgHdtVQq0arqzMFtv+6UTmAEzAAMWZl0PsgxYmM2y4flyMmBhtnB2Lczqxd5mBsyAGTADZsAMmAEzYAbMgBkwA2agLTGQhFk8YgkJi9fg7gJi1bMCoY0tzIoEW5tkIAm0dBpATE3hjRk/dg6B8WdLCbQLan13AeF2D+E5IQm0v2h+jLCDQGNW3oOWY1akqanPwmzh7Z3O0AxUJwMWZqvzujb1rCzMNpU579dYBizMNpaxetNbmK3It/a2V2jGjdlROFm4XBgqDBAItbWp4IomEmyNYmA1pV57CmhwshXPQJZjxi2zVQcDNNKm/84y1XFKTTqLRbXXccIVwhDhTOFAgfHObC3LAI2urpMty3lLH43Gdt4DzxN4B7xU4B1wLcHWOgz4WdA6vBd9VEQmhKesMLullhGp3hO4zrzD4TGL8FWxopTKbqteBqiXUwvU5fz4sw0VaHnGPCAgzFL/wUcCz53FBOp/XqTVqrIY55NFIQdR052F2XrbL53ADJgBGLAw63qQZaDahdkff/wxvPfee2HixImx7mfPvcj5zz//PLz11lvhyy+/LDLbqsrLwmzhl9N6WSFv0fVkssEGu8y8+ga7LLnaBr1XX6XXjuuuud6OPdY58UQ+HirdFtYJ3CpkP5DSh1J2OllpBgmELbK1DgOIFP1b59BNOuqP2ivVoa2blIN3qo+BLMfb15fY20syQAPQwcKKJbe2zsp+Omz677zQAkVoixzsnOEgccGUZ5VffFqgUuQO0V/L6Tq0RJ1Mh2+LdTOVrZqmdHbIejOla830jGo60XrOpa29Z7X0swB62hoH9VyyitiM+MO9DGEWL0M8DJcV+H/xTKsRCA3Ls83CrEiwtVkGkpCZFWjpUMAzBG/wUgJtD61fcAq6a9pNWFU4X/hAyD5vHtLyLgLhkvnPcByQjqvZZlnKh2nKO02z25p8EDX1WZgtvL3TGZqB6mTAwmx1XtemnlW1C7OPP/54OPbYY8Ntt90WRdMHH3wwPPbYY+GHH35oKmVxv++++y7m89BDD4Wvv/46vPrqq+Gkk04K11xzTWCb7Y8MWJj9IyfNXOP2ySa/Odez43bbndhBImzvFXv1uX/Fnr1/FcLv0Kv3tyv27HPVKuvtxMdFJdpWKnRW2Ml+GNU2/7n2WaMST7aCyzyzyn6mwLUaV0Hnka1bFmbLc+GyHFuYbTzH3MueF7jfEU6trVg/FSTdg8stgrVFDpbX+dMZKHGQnb7eVi5SOytH/8z1KHedTNS2xbqZylZt0xsy1zf7f2N+p2o72RLn01bfs1ryWdBWOShxuSpuFYIPIlMHIQmzhHf9SuA/9hfBwqxIsFUMA0nERNSkMwEetLUJtPNrW43QQ0gCLfOs6ybsJtwt/CzwfwCThIsFOjBkvWg5Xjq2ZhtlaT/ymEMYIzDmLdPNhOxxUlqtbpypYa9swuxPP/0U3njjjfCf//wnfPjhh41qQ2xqwzeN3P/973/DU089FV5++eVAY25DDM8oRCc8lp577rnCPZfggvI88cQT4fnnnw/ffPNNQ4rlNGagTTFgYbZNXY5WL0w1C7MfffRR6NevX9h3333DSy+9FJ555pmw++67h3POOafZwuybb74ZDjjggNC/f/+At+xXX30Vzj333LDLLruERx99tNWva1ssgIXZwq+KhdnGvS43LDXesSv17PPi74TYvDCbWV65545D1tluf3p3VorhHfa9kD6AmP4g3C8MES4SbhQY+yibhvnPhEUEW8swcIQOk66BhdmW4bxSjvJjpm5YmG38Vfs2w197FWbbIgdXZq4L977XhAuEk4T9BVvLM9Bfh0zPoZYSZtti3Wx55st/RBrN07Vl+p0wWjheOE+oEard2up7Vj8Rn65Nuf93bZWDaqh7iDyIQQizfCt2Efjf/U/g+u4qIBThdYjARVqbGagEBpKASZ0tJdDS4YD6Trhu6nyN0EPICrTdtUxHhRUEOiK/LaT7HlOiOZwssD0rnnLMdHzN1mkpHR0k+I+dImSPwfyrQl+BzhMcpzH5K/n/NzX1lU2YpcH54IMPDquvvnr0BKqvWfGXX34J48ePD+eff3645ZZb6kv+h+003CLKrrzyyvGYHPv777//Q7rsii+++CJceeWV4aabbgqIsw8//HBYZ511Cm8c/+yzz8KBBx4Yy9azZ88ozmbL4XkzUAkMWJithKvUcmVsijD7ySefhBdffDG88847MUwwHqiIkR988MFv4YJ5FhA+GI/Vu+++O3qXpnDCdNp5++23Y0cX1tHh5b777otT7vfvv/9+wBOVfT/++OPfyCDPtO3+++8PiKPc82uzO+64I2yxxRbh4osvjuLpjTfeGJ8r++yzT3jttdeiOEuHG0IQ33vvvYE833333cBxJk+eHDslke7bb7+Nh2D6+uuvh3HjxoXRo0eH9dZbL/Tu3Ts8++yzMf0jjzwStttuu3DqqadGL9raytVe11uYLfzKW5hNL8JFTVfp1XvblXv2ntxQUTalW6lXn9dW32THbkWVo8z53KH8sx8kV2q5ay3HXE3rn8ylH1FLWq8unoFKbSzLioZbF0+LcxQDNNrSWAHcWUIkNNK+Vfp0H7Qw23a8hh/IXJdnGnlNnbw8DPTPXJNyC0TpDNrq/zOVr1qma2euLffDzavlxBpxHm31PcvCbCMuYhtOiiiEyIMghGdyZwGR6naB/9ypQieBbRZmRYKt4hhIwif1PImfhO6mswFCZ1agnU/L3YTuQhJombJcI/Df6C3cJmS/JfmvvCVcJPQU6OjAsTgmSGVgmjfWkYb/F41nAwTyK4VPtf4MAbE4KwSn/LW6dlNTX9mEWRrgt95669CxY8foZVRfsyIi6RFHHBEWXnjhcNFFF9WX/A/baSSngb9z587h6KOPjp66NJLXZTSkr7baauGss86K3rU0pDOPp2+RhpiAUHDccceFueaaK4wdO7bI7J2XGWgRBizMtgjNFXOQxgqz1B8EzvXXXz/89a9/jVhyySXDUkstFb1RiSiAAIfIudNOOwW2LbroonGKNyniJWO+nnDCCWHNNdcMm2++efjLX/4SFl988Sh00vll++23D0sssURYZpllwuGHHx5eeeWVKKLeddddUfhkG+k32GCDcPXVVweeO3nDgxVv1s022yyKvAiq5M2zZcEFF4zerQiyN9xwQzwHOgMBnneIrgjCAwYMCFtuuWUYNWpU7CA0YsSIePwTTzwxHHrooWHOOecM888/f3wmTJgwIe7D+m233TaKtfkytfdlC7OF1wALs7W/Gjd+y0ob9llixRiiOBe2OOMdm4TYktNefZ5Urw9e+tuy8fGf/RC5SculPmKy58A+bwlpv180z4eVrfwMtNUGw/rOPPsxbWG2Pra8vTUYaKvCT0s2xrdFDp5SZUjPGhrPbK3PQH8VIV0TC7Otfz2KLMEmmWvLNaYBvb1ZW33PaslnQVvloFrqYhKFEKr4puIbCkGW/xxhXLsIeNMiNiWRSbM2M1BRDCTxkjpcSqCdTevpmDCXQIdwxM8aoYeQRFrmuwvdhCWFAwWiOHwjpPcQpgioQ4RtBcRfjpeE2lSONKU8iKyIxaRdRhgvZPPLz/MdO0xYSWiwQKumvsKEWRpOacDGA/XSSy8NyeMIYZYwjRieRHg3sZ2G6+uvvz7QKI3n0gMPPBBWXHHFMNNMM8VGekRWGvInTZoUx/hDrMV76dZbbw2IvnlLwuzcc88drr322sAyDeSUA68q1p133nmx4ZyGd8pCwzxCKZ5LNPoj1F533XXRe4uwyP/+97/jviNHjoz7khf7EiqZ8v/rX/+Knl+pLAiwiK6U9cILL4weuCksM8dDlKB8FmYTY55WEgMWZivpapW/rE0RZi+77LJ4D+S5sOGGG0YBFrFzjjnmCGeffXb0QD3yyCPDvPPOG7cfcsghYaWVVgqzzTZbOO2006LQSkjhaaedNiy00EJhxx13jOLsLLPMEjp16hQFWjxPu3XrFhZZZJF4PycMMfd47r2bbrppDBnMtjXWWCM+D6jXWUPMJd+99tor3t+55w8ZMiSWY9ddd43h7u+8886wySabhD59+oSbb7453tsRVdnvySefjOIy2xGKBw8eHEVbhFvGqWXMWgTlrbbaKj53eNbw/Bw4cGBYd911o+CbLY/nQ+QHcZ73A67bJZdcEoYPHx6GDRsWn+3UxfoiZJjH3zFgYVYvy4UYgqrGjB1XUnBtqDBLul47nFVIgcqXyarKOvvxsWkDD/W33H6t5VXBRxUfSEUYvdOba3zsFfFHTB+M+fI0tbGMsX6K4ilfpvxyKR5bW5il8au5lsLO8WFfhDW0TFw76lW5jMa/os6pvjK21DlzPg09Virzt5pJ98KmeMzSuEMdaY6Vup81pzG+sWVqCxzk+XtaK9J1OSm/sZ7lct+P6zl8nZubUkdry7CoexPPiIY8v/orXbompYTZctyzmls3a+OuoetL/Tcbum82XZHXPZtvbfPcBzlmQ413wHRtmTZm34Yeg3QNvT8XUZeo0415hjb1PSudf2Pvu2m/7LRUfWvOs6Cx94i2wEGWj2qb53+V7rd0fphX2ELgP8cQMXMKqR4hLpXrf6isbWag7AxQfwH3Yeoznda5x3Fv5hukozCH0EUgzHHWi7aHlhecAuZrBARc1u0qIJZ+ImSfW5O1fJuwt7CowH+N44JsGXgO8f9DFF5cOE4ghHE2r1LzDynN1gL31ZRnOket+j9TM10hwiyNyoRkxAuJBvH55psvLLbYYnGahFkan2l4x1sJT6GuXbtG7yMasxnDD+8mGl2nnnrqKJbSwE14y7///e/Ri5b0gP379u37h3FgkzA7zzzzhKFDh0ZhFrGVchCeeO21144NuksvvXT0iv3000/D3nvvHWaeeeboXYWnLOIwHrSE18QjatVVVw09FXqY/ddaa63o2UVjPWEoWUdZaKgnbDOhKi+//PKw3HLLxcZ1vMIQmhFo4YdGYwReC7O/axz2QgUxYGG2gi5WCxS1qcIs9+hVVlklipkvvPBCFECnn3768I9//CN6xDIuOfdw7pd0fuFeOt100wVEWsboRpjt0KFDvH+zPx1uEHK5Hw8aNCiKn3jTdunSJd5/6SyE+Mv9GJGUDjc8d2afffZw8skn/2HMb0Lab7TRRnGMWcYDp94jqG688cbRkxYvW54XePriAfv000/H8cOJ+ICIfNVVV8U8mRLKn2PzHKEj0nfffRfPYZtttoleuNlORpwvzxU6/dQX7aEFLm+bOgTPUAuzhV6ShrSn/d/LoudqZ2Dlnjts12xRNgqzfb5ZZeOdZq39SK2+ZR2VIPvR0VCBlY8Y9vtV+FzYS8jaTloYOQXHZzeUmN8sk/acEtuvm7L9Bk35kFtCuFB4SvhJ+Fmgp+tgYRGhNttSG1KZKB8fZ/sLo4SJAufDuYwVdhf44GqI9VSi4cLzAuOxwcmbws3CMQIffrUZY4GmMlG+GmGoQDnwRH5NOErYTyDdOCFdL46V9j1Z81njQ/MwgY/HDwT2+UF4UbhRoIGPD+AijMajfwj/FVIP5tc1f7WwsYD9KKRy8zGLsd8IIZ3DDqysx9bR9pQezrmGWKoj12uej+TOwtHCrcLHAseeIHCtubYNsdWU6FzhdoHzoZ6RD9flBeFyobtQm2Xr2zZTErHun8IrQirTVZrfWZhKSLasZs4UnhQ47vcCx2RdbfXpWm1L3Kyk+dpsLm04SnhG+FKgHNSll4UhwmJCU62lzzlbzlW1MFB4VPhKSPzyPzxSmEHIG/fmVAdJnwA3icsl8ztpeWbhYOEagfsF/9e07yTN3yVwT8xeUy2WtFW0lv/kuwL3jm+F+4QThdkE/qspb+pAbdbUMrUFDvLntJZWJP6/1nw6f/6Haf2A/E5TllvifgzXTbGm1NFSx2nuvSnlSf1cT7hBeFtI97iJmud/tJ9AA2re+mtFuiapTvbSuqsFnjHcI78Q7hZOEJYSmmINqZsrK+P0H6Zu7NCAA62jNKkelXqOwMf0whLChcJTwk8C/IwXBguLCA21oq57Q44HZ7wP3Cu8L3Cd+A89LvxT4L9Vyi7WSjh5UkjXlmniiekaQkOtNZ8F3OsPFygz94x0Xx2r+csE/j+lbE+tZJ9xQuLguynrWA+veWvqfTefD8urCEU8C8irqfeI1uaAsrcH4947rUBd5Tk/j7CgwPOfuvcXoaPAfWgaoSHvEkpmMwNtmgHqMZhaoF7zH+Bbm/8B3zZ/Evg/8A03lzCvML9QI/QQ+I8ksFwjLCCQhmfOZcIEId2/0/QTrbtDOF7YQOAbmWNyPI7VTVhcWEHg3nmg8JjAsyPlUWrK84VvAf6rnAvnlc5Rs9q5IGGWhmrCMCKsLrvssuGCCy4IjMVHYzvCLOPGMr4f4StpPMfzCa/S7t27RxGWBmtCPRLGmH3warrmmmuiZyqN6TR242l10kknhVlnnTX06NEjIApkLS/M4oXL2H8zzDBD9GyisR1vXRrXl19++TiGISEzEZIJU0kYY7ygCDF5zz33RO9fBARCVLIMmEdU5vzw6MWzi/1pmEcgIPQmQi0eWo8rdPFhhx0W17FsYTZ7tTxfiQxYmK3Eq1a+MjdHmMV7FJGVDjv9+vULM844Y7wPT9S4sdyX6TSDqMk9lXsyHrKMG8444nvssUd8DvAcQejEmxXxkw4zdMYhFP3OO+8cQw+fccYZAeBNi1cuHXO4j9PJhzzp5PPRRx/9jiTGte3Vq1cUbVPEA8at5bmEkEskBkRY8iQ/vFwRVOmUQ6hkvDl5HjGO7d/+9rcoInMcOhph8IYwzPkQESIZERl6SsDlvNJx07b2PrUwW3gNmDG+BPqn+Qys1LPPfYUIsxJnNU5t3+aXqGw58DGS/dh4Vss0NDXE5lAiPqxK2QVamfJ9qFSCzLqs8IA4lLeUD9NNhM+F7LrsPB9RhwqlrL9WprQDNY+Yl5ZLTV/Udj4KazM+BEcJpfbNruPDrbYGzTMy+8PZhMxyNg8airPL+XkaU5N100xqkM2nyy7TWLt52qmJ02W03wdCNt/sPNfjaOHHTJqtNZ/sNc2k9P9NK+uYZvnmozlZyoPpQsI4IbsuP08ja0ehlM2plUOF/D6llmlE4wO+lPXXyrTPKZo/J7Oc1menJ0zJZH1NU+Ncdnuaf1PbO01Jm51kOd4+uyEzv53mue4pr1JTxIezM/s0ZrZ/Ju+WOGfKRmMIx6Lcpc4nreP/TH3NWlctpO21TTfO7qB5OHy3AfuR38NCN6GU0YBzsVDbcVnP/XBQJk0SwbTqd9acMrUmB787iczCHpqvixe2TcikZ7Yl78cL545d32Jz6mg276LuTeRJw+RdQn08c79ZTMhafy2k/fhfnSTU1YD5g7bvKTTWGlo3y/Ecac67RjrPoq57yq++6e5K8KGQrk2pKdfpAmFGIWvvaKFU+uy63bI71DPfP5NfSz0LKNKyAnUyW+78PM8KysT1yVr2Xpvfh+Xsexb7Nee+mz1ukc+C5t4jWouDLB/tYR7xZhoB4ZXOFIhQ3YRHBerakQICFf9T6il1xGYGqoUB6j+gXgPq+HQC/wfq/CwC32i0M3QR6Lgwn7CAUCP0EBbMgOUagf8Q6XoKZwpjhckC/6kseAb8T7hK4BuO9AizKwprCGsL6wo7C7cI3wnZ/fPzn2n7uUJ3gXPhvz21MJW8cgrxmGXcvxRuEm8fPEife+656FGUPGZpaEbAxIuIhvQUrhKvpX/+85/h7bffjqIpwisN04i95IOnEl5QVyvsMGIv4i/hhxFas1abMEsITBrK8UBCBCBvPHonKITy+PHjY7hL1iHkJmEWQZewzAsssED04iJvGtARXfHAJeQx50NDOqIBDfmEQP7Tn/4UvboYexCPr2OPPTaG4CQEs4XZ7NXyfCUyYGG2Eq9a+crcHGGW0MLjxo2LnqWMvc19HY9YOrzgEVtTUxPDAhOFgPFm2Z4VZrmv47XKfZgw9YQ1xsuVcWoRZvGuRDglSgMetdynEVGPOuqo2LGmv8aQ5f7MPZznQtbohIPIizdsCo+LMEto4lNOOSV89tlnUUxmHFuWyYOxZelgRIcixGOExDFjxkTRlqgMRJMgIgNGJ55SwiwhjhF5KTP72/6PAQuz/8dFQXP5dha9EtoazcB2253YYcVefX4pSphVOOPbGl2Ilt3haR0u+5HxkZYPF/gAaqrR8JfyfKieTPpl0r5cIm3KJz/lQ+g+YUJm/5SmlDjbv0Q60tNQ+bxAg0j+44u8acDOGx+PfNSl46UpZaIhmzzTOqa/CH2EvJ2hFdl0peafUppLhHyepGUdeETA/iTAYTafz7X8X2Fibj1paDDno7cpxkfsF0L2WJRlvPBNbn02TVaYzV570iwh1GZ8oFPelNc+mYRpHVPqb3a51Ec52+G0lFEPsvtzTm8JzwjvCVzL/PZS5e6fS5fdh0aBrJDKNvIdIHwvpLQ/aR6k5TQdqHV5y+a3fX6jlvcW0v5pSjnGCW+X2HaY1jXW+muHlHd+Wo5zpuGD+0v+WJ9oHf+DPHfUn3WFZDSGwjvXOJ8H68B6QjJE83y6L7XuxSkoVe9vTDvnpoO0XCqvCSXWp3Qv5PJgsbllak0OSpxOXEVjWOI/nXuapvWvZnZu6ftx5tD1zja3jmYPUNS9aWZlCn+J0zSdpHXUsW9z297UcvY50T+3Pe2fpl+V2M79aSmhMdbQulmO50g6F6aNfdfgHIu87g3hbCclypaZee5trwulrsdzWt9BSPaGZvhv5fNI/zemO6bEDZj2L5FXyrsczwKKtImQfUfgeDwDeMbl3wvYNkbIGu8EdXGQ3rPYp7n33exxi3oWkGdz7xGtxUGWj/YwP5VOEvGG/yDv7dxfFxDOFqib9wtzCDMJ0wncT2xmoBoZ4L8AqOP8J6YV+F/kvWg7ad2cwjzCfAL/lxqhh7BgBix3F7oJpGG6ocB7wijhbYH/WB58Kz8u0HGXb6zjhb2FvwpbClcIfFvk98su87y5XiBKBucxjRo7r0dsAckQMAlLjKBKwzaerHUZ+9IQjscSoSMRKTG8ivBOJR8axhFaTz/99BjCEnETr1UayxFmGXOWkI6bbbZZTI9HKvkSTviggw6KnkjsgzcuYwk2Rpjt3LlzHJeWMhGWEpEUYRZvpiTMnnrqqSWFWTx6afzHaIzfd999wworrBDHp00euZwDDfOELMbbFy8vBALGTyQEJ+Ph4R1sYTbS6J8KZoD/JB0i8Dbkv44XIPeHYRrnkY4IiFOECLe1DwaaK8wyliv35CTMEnVh1KhRMWw8nWLOOeecGPUA79rkMUuHHzxmeW7khVmeN3lhlmcJIYIRZYnIwLOIccrp5MN9mnFK896pjBFLdAfKw30fQ5gl/6OPPjo+y+hMxLPgzDPPjJ126ESEiIynLh18Xn311ej1yz7HHHNMFGYJy89zJ/HG84RIEsn4D62tkPvkbfs9AxZmf89HAUsWZvUS3GxbZf0d/1yYKCuPWXnf0mjflm1dFY6PiezHRZqnQQuxaAuBRoKG2gVKmPJ4qJ6d+FhKaRFT8pa2ZacnKVG2oYKPpq+FlOZbzc8vZK2/FtL2NH1d6xbNJJpV83fk0l2c2Z5mz8qleVbLS6eNms4mkOZXIR2LhsG8yHtGZntK967WXShcKrwl7C8kO0IzKR3XJm/bakXaTqPsfgKNOthUwrJCvhHwEDY2we7RPulYTIcLfDhjfFxTZ/jQzaZhfmshGR/XlDOlgY/ajHNJ6SZrvmMmYVqfptRn6iD5Y9QFuMteD+oL1ztrq2kh5cH0buHP2QSa50P/OiGbbnAuDYv9c2lI/19hK4Hjcl04J8qazYv5dwQaA6YXaJjbRuCcUzr2SddVs9F+1G/avv2UdWkCD9nG+Z+1fLxA3snW08wHQsqD6zJv2tjAaf/M/imfcp7z3rnjPaflFTNlnVHzBwtZbl7SMg0meftWK1KZd8hvnLL870waBPTdhOx14D+2k/ChkPKC625C1tbUQtrOFEFsEyHd0+bU/Aghm4b5F4S8FVUm8m1JDvLnUdvy09qQeOC+X8rO0sqUhmlL3Y9LlSW/rqg6WuS96fwcX//TMs+GZB01c4WQ5fSfaaOm/XPbSDdB2FWYW8BoKM3WTdJcwIYmWl11k/tbOZ4jlLkp7xqcYlHXnbzqs65K8JlAeQHPhyOFWQSM+8rqwutCSsP0WCFvm2pFNg33tKZYf+2UzYf5cj4LeFa+mTkm9/yTBRr3k/Gul72fUKY+aWNmeoTmU9lLvWeRNFu328qzoMh7REtykKG+Xc3yv+T9YWaBd2fuY+sI1D3+w38W+A8jUvFO3dT/ona1mYE2zwD1G/C/ALyn8//g3s67PP8TOjHMJvB/4T15HqGrwHdZjdBjCnj/SGBdd6GbQDr+Z8sIuwsIsE8Ik4V0zy81/VzbXxbuEx4RJgql0mXXPaY026lR/AYaPBFjEV1AY4VZGgjxIKXhm8ZyxvPD8KKl4RrPpnPPPTeO20poYEIBX3HFFeGuu+6KDfDJYzYJs6SnMT2N2YqwioiLAHTzzTdHUQhhlpCVWavNYxYB6fbbb49JaxNmEYxLecwizNKQjyVhFs/gyZMnx/R4ViHM0hDPWIiEysS7lzESAd6+eOsiFliYjTT6p4IZsDBbwRevDEVPAiPjvSI+fvXVV3UehfozcODAKOrvsMMO0WMWb1X2RXilE87jCgGPhyxepnjB4pXK/X6aaaaJoidhjhFmiU7AfRtRFS9V7tV0hhk7dmyMdoBAmqI10AGH8WsZ35t0hEfmuYAXK/doypU1nl0It4xzjniMIQhvtdVWsUwIxni/7rnnnvG516dPn1hmOg7h7UvnBUIeE9qYDjvkh4jMuSDkEsKZc8ArmHMnjD7nwTY68/BstP2eAQuzv+ejgCXeW23NZWCl9Xv3KlKYXblnHwSRtm47qoDZhs/sx0Wa/1VpaFg7Vcg24mrxD3aB1qT9HvrD1t+v6JdJy4dP3lI+aXpkPsGU5XU0TWmYXjJlfZr0z21/V8t83OWND0I+vlJeNJDw8ZdsVc1kG4Hv1TIfjqWMhuqUD9PLconOyG3nY2+OXJrsYn2NZRdn8rstu2NmnrK+JaRyIWI01tbSDml/plfXksEiWv9dLu3WubR3Z7a/rfmpctvT4thMOoTRrGXLwnxWzM6m66+FbNotshs1PzyzfbzmaQgoZdQReEt5PV8iUf5YHynN/CXS5UXer5Vm3hLp9ta6dDymC+XS0BCdtm+f23ZRZhtpahMeV9G2bN0+LJdPfYv9lSCVgWk5z5nGmY8zx+MaTC+UsvW0kvtXKtuBJRJl73+l+Fksl8d+JfJIqxDf07GYspy1+7SQtv+g+YWzG6fM8z+4UkjpmL4wZVuaFFkm8mxJDtI51DfNCiknlUjcmvfjEsX53aoi62hR96YuKuHPQqpXz2h+xt+V+v8vUP9GCykdnQcQCLD+QlrP9BOhVB2m48f7mbQcq6lWX90sx3Okqe8aRV73hvB1hxJlrwf3u1I2q1Y+J6S0kzVfI2RtUy2k7UypB02x/topm89HWi7n8y/7Hslx+9RSaJ4R9wipbLwHUk+zdoQW0vZx2Q1T5ou87xb1LKBoRd0jyKslOeB47dF4j5xW4P47u8B7X3fhfwL171CB/yx1dhqhqf9F7WozAxXFAHUd8B+h7vM/qU+knVtpEGl5znQT+C/1EBbMgXWA7SnNIprnPf1Y4QqB94kXBQTZ9Cxo0lSi4tcIj3j7JHGWBtDGeMzSQIhoSUP2dNNNFz1E8RwiLCWN60mYRbycaqqpojBLyMfBgwfHBne201iPMEvDN2PCbrPNNtEL74QTTogN9owJiCcujd1sRwylYTxrjRFm8fjDc2mCwhnjRYXn38SJE/8Qyrh7CWGWMW/zwiwN6U899VQ8N7yiGEuQ88E7Fw9aym5hNnu1PF+JDFiYrcSrVr4yN0WYRXjlPo53KGO7Ikhyf8QT9ZZbbonep4QzJswwoeMZf5aICqcqqgHPDI6JB+zxxx8fx6LleUUnGERNthMSH69tPGF5fiCScgw8WMlnr732imGO2cYzJB/GGLZIj6CKR+3/Y+8swK0q1jd+FBMDpQWVsIsQUWzCwm4EBbEQEMSuqx6wsPXqNbBJuwXsxECw+NtJqJRg9713/u9vOHPunMVa++y9z9r7HA7zPc+316yZb+pduedd3wzTCyPYEWbdcD6+oR7IWj464tmH9ythnp0QwXdq6n0+QOIZw3XD1Pisk860y+RlvXOm86ce9yyibRC75A9SEYFAzFbEI4W9uLE1vVIGyQmBbXbr1SFNYnarbofNyKkB1WfMn5f7pf6gbaY/Ivxx2SChuYUiZmepPjc4HFd1dMDNtynVjt+fJPKOPNtHbI8lskwu1daVA9nT1iUkbF/17CHP+IPpZLgCriy2h7uEhG1lg2W3eeV9pXCDhHJ2VfwwKcRxpwSbTNGXK9G1mz61yGB8vWdLnigxCwnmymK7szQqnGe+zW4RAz8N8oo/83HC4JdP0PWLGD2i/U+kkGVnR9Kiu1cqwtX7RTRR+6VeOnbRfrssR0TsOC5x0kSRrj62UQw4Di49SsxO99LeVDiTuGsIDEZmMoxJK1WcawPbQvZ5SKSu9jHt8aO4t7m2fewnlIV/9dLjiNn9lf66dJ6UwZpMD1y8XFxdbHtLndRT4G+pSx/hEmK2zRT3h2fLue1LWm1yZRYLA1dfNtspMnJYxV0b1Xk/rqz9aZ6jad2bjvLwBNc9M3TCfw7OlZ27xkojZeyXoYwrPFue3/lKZedm2s+RqrxrpHncK8Orrgz8Z9qDlWTg2e+uJ7YcS1/20o6fvoyfmEOYcv1yCvksoFnfe/U9SkQG2UxpftsOi9hW9p6V1n23nupN61lAF9K6R1BWsTCgrqVVuLbqSFeSci40lbaUumfaiwrzzsq7Bv8bkt5rlRQkIFArEeAaQTn3Ua6XJJKWa6i+tJGU/0u8P68tXVfaQtpK2lq6Xpmury3K/8sNpRtLeTa0kW4p3VrKO1B36fHSi6S3SydIP5T+JPWfvf4zZbEwHql4BkHO5kPM4m2KByseQnhPtZQnUOvWra13kZvKGK+gTp062fSNN97Yru3K9MeQt4MGDTI//vijXdMPzym8aPF+wkOWcpi+GC8npjOGVCWdQW9fciVmmX6Z6ZYZfGdq44EDB1piANLXrTFLP6Ies1Fi1k3fDDnNuoW0t2vXrnY9wk033dR6RkHUBmLWP1ohvCQiEIjZJfGoFa7NuRKztISZECBk+RiIZw3nFJ62c+bMsc8AnkHcK9mHoHTxkK2s881HMTwriGcGBIQ4PoThHsyziHKZOt+3cXGQopCljhS2BcT8MJ3xoYceasld2kc7qQ/vV54b1MMzh3bRTpT2EUf7iacN1IsQT/uwIS9t5rlAO7DnWbfvvvvaaYxJD1IRAXBk3WDWGmZ5AGaigHwfq2nUIbs5F8ExSNYIZBon1utjkKwQ6N598Ipbdz3s7/TI2Z6VDRJl1a4iGjVWXcdJH5DiJbPYHwwvDtIgbmD3Gs/mJYUzyTlKdHXwZycqLo3tadHEyD6Df779Jl56qZe2UOEVvLS44DTP/l7PgOPp6hjvxScF9/bsyccfQCdRYpY/jJmkssGynsrs2sZ2tvQs6RbSNOUdFebqYTAwk/BH2Nmy5Rj5wqAUx8PZ3OonloUv9NK/Vpg/6L64vGyv8xNiwn5dg2LSiaL8aB2+KQNo/EF39c70E8vCpV76fxReLcaGqC5SVw7b3YmMkeUU59vtF7FJImY53/x8PkkYKcLurqNfBisYAMlVSpXB1VXoPt/k1fVVFg2l365tYAWevvyqHZceR8z6tsv7O5Ew5XaSQmy78o72bDhuLp5tOy8tLjjKs+eelCRVaZMrs1gYuPqy2U6RkcNrWEyG6rwfxzSnQlTa5yiFc19Ck6Sye9M4ZXR4fp5UiBe/lcKQA76UaseVwXnOPTxJBirB2f6SZJRFfGXnZtrPkaq8axTiuCdB1N7DF5w7Jhl68VO9PJwPvhSCmC30s6CJOuDOMbZH+h1KCH/h5Tk/YnO6l/ZRJC26W5X7bqGeBVW9R9DHYmEQxXNp2l9GneU9awUp74eNpJBIO0khfP4t5fpeRYoNx5U8QQICSyMCnPtOuRa4dnjf5h68opR3AD5UWlVaT8p7Cx8nc13xXuSI2nUUbiFtKW0tXU+6gXQjKWMGUXJ2B8V1kR4jHSOdLvWfN5nC07V23yd47jDw/Pvvv9vBYwaQ33rrrazXmHXDgAw0v/rqq3ZdwIsvvtg89NBD5umnnzZ3ynOIqRsZNGWaSYjOCy+80HpMMS0lXk7YMfDKgDnes8OGDTPjxo2zg/d4Uw0fPtxOW8nagHje3nHHHWbKlCmuarul3XhaMV0yA7YM8LN+H1MMMxiPYPP+++/buhncZwCcNjF1Ml68DK6PGjXKDr6TzqAvbUdoP+Qz/aIcv3ymcnb7rJHIFJtgQH7KZFAfLywG35lOkzqDBASWNAQ4j8Mas0vaUStce/MhZgvXmnRLhoRlbXCmKWbKes79QgnPD6ZaHjBggJ3WuFD1LMnlBmI29aMXiFm9LKciW3Xr+V5axOzWu/QamkqjqqcQ/vxAHJwsfUL6uzT6J+Q3xfGFqS/XaMfZveQnxIRzIWb3iMnvR7XVjquX7W5eYqmX9oYXnxR82LN/zTP61Iu/wotPCrby7GnTvp7hcC8Nsog/mpmkssEy/oT6xKOPxTdKu116kHR1aVXkO2V2ZV+ZRUH+eRMlZsl+o1fe9wrzJ9vJMgp8JXX1gVlUXBrbU6OJkf2Z2nf2QyJp0d16iuCL6b7SS6QPSj+WuvxuO0txUSlVhEufE0309rf17LDf0EuLBhngdmXuF0nk/HFph3ppDCi4eLaQhoWSUhXs6ip0n5/z6mIAkw8EMuk7nj1tXE/qy6/acW3v4SckhLk3MpjD9cw5N0L6ovQnqSvHbY9RnJNBCrh4tgwiZZKzlOjsMxGzlJFvm1z9xcLA1ZfNdoqMXP+HxWSozvtxTHMqRKV9jvqF53tvekmFODyf9AvMIVzqlRF37/OLOsyz/cNPyDGczbmZ5nOkKu8ahTzuUdh6efhyXCFxKpORMnDnwNsR4728NGyWiaRnu+ufI4V+FuwYaTP3+kzPAtI4Fx0GoxX2pbL3LN+WcL733UI+C2hXvvcI8hYLA+pamoVzB2KJ65b39+bSVlJ3n75JYd7XeSeuI833elTWIAGBWoMA14FTriGuDUfSrqDwSlIGxbiu+OiBe6FP1DbR/lpSrrd1pC2kraTrSzeSbirdQrqflGuQj9jc86Ky7WzZjpP2kW4hAvZxyEs8eRiIhjyErMyHmGXYkMFTysJ7CSKT8ly5Lh1PKTyOIHIhOJkiEi8qhMFvCFHS3XqFlIGXER5IhFHyRL1jHDGLB+vll19uvhLJSxz1s3VC/4iDSEVIw7OJep09/aAttIv6nEBeu7YSh120fGzoP30gzdnhkXz11VcHYtYiEn6WRAQCMbskHrXCtbk2E7Ogxkc1fFzDBznueVEINKmHD5H46CF4y8YjHIjZeFyqEBuIWb0EpyLbdDvszrSI2U67HsbAfW0R/uQwmDRX6v85uT/SwWu89JciadHdczzbD6OJXhr1bRKT7kfxx8tvV28vsdRLu9eLTwpe59l/UWbEHz5IIFfHCWXxmTb8WfTz+J44kIyuLAiGyiSbwbKNVchXUldu3PZ3pd8nbS3NVegPX/O7ck/KooDPPPs4Yrajl065vs3OkTT+NEfFtYWtT0pG7difIXX2Q2IM6N/h0rekfj9dnrhtHDlRqvzO9hOFkyRKzDJgkCT+ecSAgS9/acfV52PgEyOkN/MzpRwuZp9nev11/c5l2z3S91+98npE0vzd9bRzg/Qnabb1HeMVcIGX7wcvPinIuejqmZZgVNU2uWKLhYGrL5vtFBm5/g+LZKju+3GkOYvtpn2OpnFv+tjD85bFWpxdRKlXxvuVZPHvP5Bh+Uo252ZHFe7OFbZVeY5U5V0j7eOeCTP/fjI/k6GXNlRhh9MvXjzBvbw0bJYhMg8pVR5XR6Gff0d7dbk6c9m+Eenf6V55H0XS/N2q3nf9Y5fWsyCNewR9LBYGPp5LY5jri2MGkbSGtKm0pbSvlHOY9wyIIzwBIXCXlQYJCAQE/ocA15BTro86Zcp1xTUTJWr5GJKPHbje6ksbSfnfxX8jrrWdpFdIP5Bm+xzh2fuolP/3e0q7SreTtn/jjTeemDlzpiU9ITshIatCzFZhkLDKWRm4VX/sVMdt27a1a9xGydsqV5JnAZDVgwcPNm3atLHTO7MebZCAwJKGQCBml7QjVtj21nZilmcKH+3wbCyk8DEPHyyxDRKPQCBm43GpQuzKegcMkgYC2+7Wc+OOXXv+XmVyttthb3YeOpQ/BzVR+APDn5AOUkgKSM1spYUM50ndn5a/FebPkJNciNl/KJMrpzJilnozCX+0XFls+WLVSakCLm2si8ywvdaz/7LMjsETnxyDpK5M+MP4l9TVfYaXwSdmJ3vxScFsB8saqwDK/krq6o3bLlT67tJchBuNX1ZlHqqU/aaXxx8sJ80JA/yu3IdcpLa3efGve/F+0OVjm1S+s5+hgLOPErMc31e8dGfnbzmWL0khbl38LIWjUqoIlz4tmujtF5qY5Rpw7WDLQEShpJh9hlDw+5VrGMLIl1+148pIImb3l41//Tt7f/uNbG7xyiLtGKmTyxRw9j+7yAxbBnmcfdx5lEabXPXFwsDVl812itf/YZEM1X0/jjRnsd00z9G07k08y9z5dN1iLc4uotQrI+6c9EspJjFLvWk9R1r4nYgJZ3rXSPO4x1RdIepi7bnj+V2FlOSdc708v0XMCkHMZjpH0nj+neb1x2GRy/bjCAbZvGelcd9N+1mQ1j0COIqFQQT6pW6X/wf8F1tRimdfIyn/y9aTfirlPOY/EmQSBFMdKXmCBAQCAosjwLXhlOsK5ZpBfaKW643/sqtIIWlbSIdK35Nm++zgefuE9BzpQVI+luX5uat0J+nW0i2mTp36OGvm4ZGKd+eSTMxCGtEPpjtmuuGXXnqpfH2/KgxeppIVgpj2PPDAA3YqZtoZJCCwpCEQiNkl7YgVtr21nZgtLHqh9FwQCMRsLmhlZcs7ZpC0ENi6W49TqkLMQux22qXHJmm1pwDltFSZ/h+Qk3Os44ZI/lZefp+YneTFxwUv9cr5MMbAb2PHmHQ/iqmHfPvdvMRSL228F58UfNCzf80zmu/FD/Xik4LNPHva1tczHO6lTfbik4Kne/aZPDn8/G20w8Dl09I/pD4+hH+X5nrz+Mkr5xKFK5OZMnD1HphgDMHrbP5UuJ6Ugagfvfj+CseJy8c2qXyXb4YCzj5KzPrHHBs+OHhBeqUUgrOtlDYh50tdOV/bmIo/pV56WgPT//HK3K9idRXI/0O9tB29PLSXa6RQUsw+/5864fC/X+HmOWr0nP/VKy+OmGWQhWvF1cl2lnS0lOtyN2lTKcLAkH+sjrWxi35O0caV8V+FGSDKJEcr0dlHz6O02uTqLxYGrr5stlO8/g+LyVCd9+OY5lSISvMcTeve9JyHJ2XmI6XKlHRORssrNjGb1nOkY7QjkX3uow4Dtlz/TtI87q7MpO0JSnDt4KOR5ZIMvfhbvDzTvXiCe0ldeWy5l+Ujpcrkyonet/zyciFm/Xuq//w7yKuLOreU5vI8aOw3SGHu567tce9Zad1303wW0IW07hGUVSwMqGtpFq4vyKPlpXWl9aX8Z2glPUfKecj7c0Mp7yxc39gHCQgEBCpHgOvLV64dn6TlYxaurS+l7p6faQsZ+5j0LOkBUj7Q2UfaXQohu7OUZ1oH6WbS9T/66KNHmXKXaXzx1mEqXzTfqYyzGg4ssBEkKOpPQVzgKrMqHk9k1zYIriABgSUNgUDMLmlHrLDtTYOYZYpg1gBnffJPPvmk2u7bzGrw9ttv22dfrh/OcG93z84vvvjCrnX++eef27hCHgE+qmJ99rlz59pqeOZBYKYhrk+UxQwPkydPLrjncKZ2B2I2Ezp5pUXHmfVKGCRvBIYOHbpsx249X86XnO3Q7XAGCGuyQDD53l+v5djYgbL3/8Bs4OW/2kub4sXHBW/3bD+MMfDrYAAuk+ytRN9+c8+41Ev7Py8+KTjVs3/AMwInV8dILz4puJ1nTz4GPp0Ug5h1dbFl4Odg6TSp6wNb/lTmIp/I2OUfV0lGBpL88+zABPsmiocIdeX2VHh3bx9SDC+lOHF52CaV7/LNUMDZ+8Qsg2Euni193FCaJNcpwdnPjjEq9dLBO0n4E+/KYQsOSZI0MI39X1JXzqFeAWt58aTjgZlJtlLiPdIrpFzj2Qz0y8xKqX5dGwrdZ38Q+o2y+quyqYyUvMXrG4TqUCkDPnFSX5EOB7bHe0ac135apnOMbMM8+yimabWJepBiYbCotux+eX44vMAiKtV5P462Jbqf1jma5r1phBrp8Hwz2uCY/csVd5v0H9KOZeml2royoudkmUn5ptjEbFrPkaq8a6R13MtBzBDYTWnuWLBtlcHWJT3t5Ym+n/F+4peXdI9zZSVtS71yMp0jaTz/tvDqou17JDUqy/jKSMm07rtpPgvSvEcAU7EwyPKQ1GozrjHesyCJ6km5h7WQbiKdK+WcPk26qnQFKcRSvtelsgYJCCy1CHDdoBC07prj/y/XWJJ+p7SHpXy8jq1T/p/uIu0s3V7KBzvtpJtK15euK206a9ash1jLlfVeGYhl8BNlgLpevXqmQYMG5v77789r1C9kCggEBGoXAoGYrV3Hs6q9SYOY5WOVG2+80XTo0MGcf/75Zs6cOVVtVl75mdGgR48e5sADDzTPPfdcVmVA5jILwg033GBJZaY9Hj58uNl8883NxRdfbKcnzqqgPIwgYYcNG2bOOOMMM336dDNt2jRz2WWXGdYyr+qHP6zPTlmaUcOSy+PHjze9evWya+1CQFeHBGI2ddRX1jtgkDQR2Hb3Y+pv1a3n3bmQs3jKbtP18JN0eJeEP84QlO7PCGTDxjng95iXF0KNL76dXKqAK3e6i0zYfuTZVkbMjk0ow0Xf65U1V2H/y/JSLw2CK1NfN1O6T4KdoH0nIxVwffte4TVdQsL2Vs8ejNfx7IZ7aZO9+KRgpsGy1ZRpnPQd6a9S/jAmSX0l/Cl1/eB45SIM1Lu8PyicRJhS5j6eLXkOJDJBHlW8K5djeYO3f09CHqJdnsrKx3aGZz+EiDKJDpIy4J1J3laiq3dejGGpl17ogWmq/8urzydmSfvFS2NQOZNcqUTXr28VzuU+Vsw++9cO53JLaSbprcSvpRARfGjBwIkvXDOu3z38hLLwJ176azHpftS+ni1lDvQSuf5dPWyHeWlxwameffQ8SqtNrt5iYeDqy2bL8XJ4xWE10ksv9v24svandY6meW863cOLe0aLDJ3gvu5/VHN8mW2ptu6YRM/JaHHFJmapP43nSFXeNdI67lEs4/ZbKdIdC7ZD44y8OOz9ZwXvJ77spR2/vGX8xBzCpV45mc6RbT076oWUShL/nWw/z2hlhXm3cu0e4aXFBZdTJPeVz6TPSM+Q+uJfI7yfRiWt+26az4I07xH0t1gYRLFdGve5xiBbV5CuIm0gXVvaWnq+lPN6trSxdCUp52++16WyBgkILNUIcO0wNsCYRV3pJtLfpe75wXaB9CEp/xG7SruVKeEu0p2k20k7SiFjN5NuIG0pbS7lWq0vXV3r2t3PwLgjZRnQrU3ELN5YX375pbnlllvMY489Vu7BRB+//fbbjN5Z4PLss8+a0aNHG8ohD6QB26oKZeCpzOD6r7/+asnvJ554oqrFhvwBgYIiEIjZgsK7xBWeFjELibnKKquYY4891uhjoWrBYcqUKWbQoEGmf//+5vXXX8+qDRCWO++8syV0mWWCtWhHjBhh9txzT0s2MxNFoeSFF14wXbp0MWPHjrXLENx6661mp512sp6tVa2TZ9E222xjnnrqKfvM5FnVs2dPc+qpp1bb8eGZ2bt3b1O3bl2z/fbbWzL87rvvtv3nGc25yDM7SNYIMDYRpBAIbN21xyFbde05v1KCtlvPydvvccRGhWhDgcqEMPD/jHyl/UyEpWtGfwX8wb2nXULZloEuv1z+yMTJiYr07T6MMfLTGZhLwndTpfkDd7dEyirVvl9WpoFXSEBny4Aff7Sc9FLApbFlEDZJ+KP2t9TZvx4xJK9LmxxJi9vlT6Kznx4x4E8mfyZd+h2RdH+XASD/j+i5fmIW4e6ycfWwvTAhD3+AfQIT2wMTbIneX+rK/Vnhed7+HgonictTWfnknyF19uDpxB+IJH1DlxCzPVNxrgy238fYlHo2hR6Ypnr/ejw00h6fqPhTaQwIxwlEzDdS17cb44wyxBWzz9xTXDvZPpahXQy+fO7Zz1d4pYg9x9CV1zeSxu6vUpc+LibdRTVTwK+LPP55hh3XuivrR4UZwImT/RTp7NhGz6M020T9xcSA+rIRCBSHwbCYDNV5P45pToWotM7RNO9NHdRCnmkO00zX+BmeHXmaSpFSqcsfPSetgfeTFjFb2bnpVZnKc6Qq7xppHXe/T0lhBpghD93x+EnhhknGir9L6mzZ7i71ZS/t+OnL+Ik5hEu9cjKdI9t6dtTbJEMd/vsd90ZfXtKOazd2Hf3ESPh4z5Y8p0bSh3jp0yNp7KZ5303rWZDmPYI+FhMD6lvahesYwnVlKe9h3GtbSjeSzpJynp4nXUXK+zv2+V6byhokILDUIsB1w4cQK0r5oLmxlPeUidJ7pYOlnSO6k/a3l3aSMqtQWyljDvzHbylljIBnFx9V1JOuKuVaXlHTF9/LgCfEI6QLSri2eMxCfELItmjRwuy22252umamfbz88svN0UcfbdekjRvGBANN82y6d+9u7rzzTkueMvg7ZMiQKg/+MnjMgD4kBFNm4q08btw4061bN8P0l0ECAjUVgUDM1tQjUz3typeYxSMTQvGKK64wkGsnnniiWW211ew9kel58cr84IMPzG233WYuvfRSa0seJ/qgyLwgYvLaa6+16XfddZf58MMPyz+84f796aefmlGjRtl0yFKIU+69kKeTJk2yH8NMnDjR3t/HjBljnn/+efsBD967TKnM9MqsVw45iTctZVxzzTXWa5T12PFSxVu1UaNGpl27dubqq6+2bXjmmWfMhRdeaCibezvCM4d7/lVXXWXLYPphN10y5C3lsy46hPDDDz9s7cCFOuIEfCCRDzjgAEtIfvbZZ/Z5tu6665qzzjrLes1iw/PkjjvuMJpl1fYNO+LBmJkwqI8Pg5D333/f3HvvvRaHU045xay11lpm8ODB9l2AZQ44Ftttt52dOpkPuYotgZhNHXHeAYMUCoHO+/RsuE23Xodt1a3HZfKifVr6rcjaz7fu1vP+rbr1OqdT1x7dDznkPl72lySBnJghdQNabCF4GKw9VLq+1P35509HV+kjUt+ecBepLztox7eBqPAHC8Gpj9QfaMP+Q2lU/HIIfyrlD5Ev7bQzXepsIZ82kfpSqh2XzpaB5gulDHA4oV1XSX27US6xbAsekzwb+kDZfjnatdMaRbGlz74M146ra7KfkBA+zrOnXv4k+nKbdlx5bI/3E8vCDP7cJPXtdomxyxRFGe96Zfxb4ZMiGbghjfZsXH0HRuz83eW145OxLs83iufYJImzY5upfPL7x4TBRycQv345XANxMkiRvh1hjgXnhS+l2nF2xRiY5rp19XHt+rKRdvx02sOAgi8MJjwmdWWw7SzNRUpl7PIXo894vrr62N4qZUDEl9W1w4CLb3eRb1AW5hxzNnwpz+CnL29ox6VDpkbxw7aRlHuYs3Pb80n05GCFXRrbt6TreukEu0ohkH27KKZptok6i4kB9WUjlRGz1Xk/zqb9aZyjad+beKa584p79znS6P2LOnmOOrsXFXZSqoCLj56TzsZtGfB0tn+4yDy2lZ2bfpHLa6eqzxHanO+7Bm1J47hTTjayq4wcxmw/kEbff7ifXRex+0T7YOXLkkrM8g7Iuexw+Frhbn7HysJ7a8t56Ox+UXiNsjS3Oc5Lj3vPSvO+m9azIO17RDExcLgvzVvuv7zjcp3yDsP/pXWkraWnSTlfv5NCAK0k5R08es9WVJCAQECgEgTctcZ1xP+etaQbSNtKt5FCwO4o3UG6nZQ4PmhrI+W5im1LaSIZq7TlpVyjdTTYeY8jZN2w35JOzNIfJwxCMyCPF9Qll1xiB+ZffPFF07x5c7PlllsapqKME6Z3vuCCC8yOO+5ovvnmGzuI3q9fP+uhBD5RiYuL2rh9yr7ooovMZpttZuunvQyY77rrrubss892ZmEbEKhxCARitsYdkmptUD7ELIQrpGKrVq0s+bfeeuvZ+/EKK6xgjjvuOOuRCXG5//772w9qmjZtalq3bm2OOuoog1cr9+zrrrvOTn1MGS300U3Lli0tSQkpCoEHaXvIIYfYePKvs8469sMXyEjWgD3ttNPMBhtsYDbZZBObf99997VTEB966KFm7733tjMlTJgwwey+++5miy22sMQrZTRr1szssMMO9kMaiNRtt93WLLfccmbllVc2W2+9tSVBec5QNmQobaW/J598stl0001tfymDMs8991w7mwPkKQQobeGZtNFGG9l6wOX000+PJWd5JlE3BPD8+fMtscvzY4011rDPEYjn1157zWKAdyn969ixo52m+Z133rHPNPAkD5hC1OKNyjTOkNQQvquvvrrtK0QxZDbEN160PLuos9gSiNnUEYcHCRIQyBmBzZRjodQNVEW3PyltQYb0S5QWJwzY+mUxaPaK9BHp95E0Z5cNMYvtz9L7pBdKIVF+k7oy2F4gjUqpInwbF35X8ddIGbR8P2LDQAhkS1TaK4IBO1cGW/JCeF4snSD1B7VJv18alVyJ2e4qwK+TMCTR/5UVzB/HX6W+DaTPldIzpVdLP5f66ZO1n490VCZ/IJQyGbC8QgoOX0j9elz4QMVnEtrobN32skwZIvaVlT/Dsx/ilbumwgu9NOp+STpICsE9Ujpd6toU3TZTmi+l2nE2XAtJsq0SnB3bJkmGivfPuf0idn9p35VzaCSN3Wu9dOx+kj4gHSq9QTpb6vKz/Zc0VylVBldGMfoMmRk932cp7i7pP6Q3SqP3rg8VV18aFa4D13a24PmLtIcU8ftGOtcd52VfKfebF6X+gL9f1i1Ki8qjivBtOPfGSi+SPi6NXlvYRjEtVZxfRlXbVGwM1PxKZYosXB+HJVi3V7x/bWBfjPtxQnMqRKdxjq6pEtO8NzGgyLntcGX7jvQ6KefyRKmf9oP215c6KVXApUfPSWfjtmkRs5Wdm64+t63qc8T1L593DdqQxnF3fclm+5CMXJvZcl/kHsM70q3Sz6R+Ou8nW0qjspcifLtlogZZ7pd65WQ6R9J6/tEsnll+2/+r/Wek3Kc5r5+X+umEB0ij0l0RUTvure49qzSSThp19JVW17Mg7XtEsTEQdEu1cJ0tK4XMWVm6hnQtaUvpBlJ3/fIusYoUAreONN/rU1mDBASWSgS4Zrh2VpSuLm0qXU+6ubSDlP+2KM/HNtJNpFyDLaXNpfxHayCtJ11VyvVKWctLuX4pm2uZepYRoXh3dLivphCzEJjXX3+9XasP7ydIIQakGYy+8sorLVnKgDL7eE7hkYTn1YABA8xNN91kB48hZvH+hZjFOwtPqCOOOMIss8wypkmTJnZwnDX5fKH/DELjBcUafng94UnEwPnaa69tB6a/++47G8/6gpTNoDbtoCy8iRgUZ2CeAXIED6uRI0da76377rvPdOrUydSvX98SsUy1zMA3a/pB1gavWf9ohHBNQiAQszXpaFR/W3IlZvG8/Ne//mWJWIjKf/zjH/YeuPHGG9t7MvfuN954w5xwwgmmcePG5rDDDrNT1kIUQrDysczLL79sDjroILt//PHHW09QCN399tvPPPjgg+YredYys8Gaa65pZ0ngvk85lMe9mvzHHHOM/Q+1/vrr27VT8Ya9/fbb7RS5rHX7+OOP27IgUFdaaSU7NTH3Z9oB+QnpC8kLocp9nGcD6+O++uqrlvSFqMXrFK9b8vGs4d6Ohy3PqA033NDg3cratGDI+q3LLrusJXchPplNAU9cPFTx2I0KGFEebcALGA9iiF7WtmX2Babcp6/kx/P2vffeM4888ogt/7zzzrPkN/tt27a1duBO3ptvvtmmcYzAhva56ft5DtJ/sORDomJLIGZTR5x3wyABgbwQ4A8IBGV0MCrTPgN7Q6VJsosSvpdmKmOO0nt5NpAmUfHzR0kYP82Fr1QB/CmKSqkinA3byto2Uzb8UUuSvZXwrdQvMyk8WnYMpkRluCJcnsnRxJh9/gjSLpfHbRmArFtmf7C27Lu0TNvHZccAUL6yjzJWdkzelo1P0h5YSWVbKD3aZv4YZxLfvrLyZ6ggZz8kUih5XVqm7c+yGyT927M/QWFfSrXjypjmJ0TCaQ1MQyS6+uKIWR4QDFxnc24wmM0gQ65S7D7Tvu2ln0pd3zNtp8tubWmcnKLIuLyXlhmDx9QEm2i+p2Q30rOF9GawxpdVtRMlwKLlQDY+KHXx0fMo7TYVGwMfj6TwFK//w5KMFF8d9+MMzamQlMY5eqBKdOdBpm029yYat6P0qyzK5P6+l9SXUu24NkTPSd+OcFrEbGXnZrTeqj5HKnuu0f8rpXHvGq4taRx3V1ZlW57jo6TuuGTazpUd72dxwrH28zK4nI+UKpMrJ9M5ktbzjzbyjLtaGv1Iw7Ujuj2PTDFS2XtW2vfdNJ4FdCPNe0SxMYg5DEtdFNca7wmQPKtJG0nXkbaWHi7l/OXc7izlXIcE4v6T7zWqrEECAksdAu464+MG7r0NpVxn60s3lm4i3ahsv6W2zaVJZCxlcB1WIGO1X35NaqivxhKzs2fPth46eBAxOO0IY7xd27RpYwlMvGHZRxmoxssITx88lpg20k1ljJcS3kFMGYnnkzAwderUsV5MeFn5AnmA1xBlMtDO/j333GM9mYiDrGV9WgbzGbg/+OCDLZHAIDgeR0w/OW3aNDvIv9dee1lbBvohIs455xyDJxbeSwzw4xEFMQuBDIlL25g6OUhAoCYiEIjZmnhUqq9NuRKzkHx4y0JcQr5yr8SDFYIVz1MIQj6CYd1WiFhsufdCVEKA9ujRw075i2friiuuaMnEww8/3BKe2HFf5h7PtPAQn3zYg9cqH+eQzgczTNmLtygeutRLH7Dhvsw9fKuttjKssYpHLM8ZSFfuydjQNrxaue9THmuX4627xx572HqZsph7OuvlsoVApc2QxBCnfFjE/f6kk04yq666qvUQxruXD3vYZ8YEMGIKZTxwIUshX6NC+yBVmX6Z5yLCVMq0nTqpg49/6A/T9jMNMVM00x+eg3jx8jHQ8OHD7XMTEpi6Z86caT+Aom/UzXPLTVvMtc/xAVuI3mJLIGZTR5z/aUECAnkjwB+L46UvSv+WRgex3P4Cpd0h5c9LZdJSBs9JfdKIcn6RPi1dT7qh1JUdN4Dn0tgeJP2H9Dsvj0tnAP8AaZKUKsHZUs8G0tekDHS4eLa07Z9S/ohVJvVlMFL6g9Qvw4VfVvwh0iS5QAnOdlKSUSSeP4y03+VzWwainXRU4ElpHAmHJ9770rOk5X8eFc5X+KL4IWm0rt8VB/HLAN8rUtfOfRSuTPz+Ta7MWOn++VVZ+Z/L3rXlhJiyuyjuHc/G2bJleky8MB25xzFz6RMU9uUc7bi0t/yESJhj5ezAsEEk3d/9w7ONkiW/eWmZroPdZfeBNHre04ZvpX2k+Z4Xxe6zmmqFjxKukXJ8HJb+lvsF5zuDMJnkEiX+JPXzPuhl4Fy+TOofB2fLOfimFFIc/HaRujS2W0ujwoDOKdKvpb4t4RnSntJuXlrceZR2m4qNgbqXUV5XqsPm3IyWizyhR8qmmPfjSppUnpzGOdpFpaVxb3KNWl2B26RJeN2jNAYso5LtdU6+g6Xu+EEaV0UqOzejZef6HHHtZFuVdw2/HWkcd7+8ysL7yeAjadz9fYHir5U2kyYJzweHA++B+Uq250hazz+/nTtoZ6qUe7Lri799UvEQwpmksvestO+7aTwL6E+a94hiY5DpeCwtaRCtnAv8occLei1pSyn/lXgX4Tzmvsb5t4K0jnQZaZCAQEAgOwS4Xtx1tpLCvAc1lHKtQcKihBtL+T/GtcZ/B57lK0q57rhGufYoh/KcKlhRNNRXY4lZBrEhMxk0xhuWgdmpU6faAWymr8SL9s0337T7DGoziM+ANYP2DLozfSNePgywQ8Iy9SMDzwyQ4zHbStNgsm4sg/m+/PDDD+bUU0+1A9ZM2ciANOQBXlJM5UgZeN4yCA4Ry1q0eGnhrQUxjPcug94MhkPkMgVy165d7YA4nkbUx8A8tnjm4vGE4F2FtxZkQZCAQE1EIBCzNfGoVF+bciVmuU/27dvXEpfMMMBHL6xxircpZC3E36233mrvg3Xr1rX3SO6zPAeYraBnz552jVfISu6pkLWsTcsHLZ07d7beoniCQkr6pCbkIs8CtpCWtIEPeC6++OLy+y8f//CMiBKzTF3sPt7hQx3WKmdqZWZAQAnvs88+9tkEeeuIWbxjmZKZdcppH/1CeI7xjGjQoIF9VkF+9unTxz7niCed6fa7dOlin0HMsBAVZmrgWUF7uCYRn5iFOGXGBzxzaS/TM9NGMKLvPDuR5557zmLL83Os1vwFI8QRs6zP7ohZ4vECZmpkiO5iSyBmU0ec/3FBAgKpIMAfkQ7SfaXHSo+R7i3dUsofklyFPz/bSCF9GIjLpQx/QO1A5UX4E7SJ9Agp7YJkrUxKZeDKYmDDCX/KGIykLAbrVpPmIy2UibbQx67SuAFtRacma6skMN1MmtRm/kxiQ9/6loULdaPgj2w36ZFScOTPaz7Cn91vpO5YDcinkCrm4fxqKXX92UXhJtLaJAwyuGuyu8ItpfR7SRcGU7j++kp3k64vxcMpW+G8bSeFSOUaY+AlKlxv7aWQTj2lbaT5nu/KattHeYdIKZOBoVwlzTZVBwa59rcy+2Lfjytrj59elXO0UPcm8OJ5f7h0KynPjpoo2ZybtDuf54h75rCtyrtGEm5VOe5JZSbFc3/nXau3lA91eHfjPWxpEu77m0t7lCl4MNCei1T2npXmfZd20eaqPgvSvkcUG4Ncjk9ts+XYce+CAOLcgjAC/9ZS7ssLpdyfzpNyjXO+8I5SG97d1I0gAYGiIOCuM64fnou87zAWsIaU8Q/CxPF/OWcyVnnKRUN9RSdm8fCZNWuW9eTBY4epHkeNGlU+QO6GHyEw8YKFmMWbyRGzDMTjpeSIWfbxiH1N3lAQokzHiPcVXldMIcwaeewzoMw+A/dMG4nHEV65zuPI1QsZi5cT68viyYosWLDAHHnkkbZMBqoZ0IY4oEwG4BmMh1BlsJ28lEn7mVITz1iIBQbbGUT/8ccfDdNJMn0n5TrB2wrPK7xso21yNmEbEKhOBAIxW53o17y6cyVm8QbFUxZvV+6N3PO4Tw4cONAsv/zylphl6l3uvXzUcuaZZ5onn3zSeqaWlpYaSEo+ZsFTlPs8U+0Sj2co92Puwcx2gFcnZCizGnAv5QMeZjrAa/SVV16xUxnjxcqU+O4en0TMcp+mTQgfCOHJynOJtmQiZnkmQJwy7TJ1XaQpinn+cM+nXxDReO7SFzxmme6YKYR5zuH1m4mYhczl+fWCvG3ds4IZIpiJwXnMEobIpl+sK4veddddti8841gqgA+E8KKF9GaKYjetvyNmee46fOg/xDmEePCYBY0lXgrFt5S/X4ZAQKA6EIgbLM2nHaXK5Mryidl8ygp5CocAJKE7Tr8qzJ/kIAGBgEBAICAQEMgWgXyeI+65w9YRs9nWF+wCAgGBgEAaCEC08vEqf+ohippKW0jXk54m5f7Eu7H72AJbiKZAzgqEIAGBLBBw14u71vjgCwIWktYRsZC2XFt8KOE+fnD5FJWdaGix6MQsA8943DjvJLxQGaCGVPUljpidMmWK9bbyiVm8fdjHG9VNQ+yIWcr0iVk8ghjUhpht3759BWLU1c2gNWvpMTDuBr1ZUxZidqeddrLTIzNAD7nAIDUesUy1yTqHx2jtQjyXyEfdEM945zK1MtNHIhCzeO0y4E9dTubNm2c9m/BwcvW6tLANCNQEBAIxWxOOQs1pQ67ELLMD/POf/zRrrbWWvf9BXkIOcn/kPsm6qJCZzquWaeJZL5x9Pm6B3OTeyzrhPDfOOussSzZyv2aWBO7DfKADQQtRi4coa30ziwLE59FHH22JUO7TkKWQmY54hAB2zyR/KmPWmN19993NJZdcYsvB0xbvU6bRZ8YF1mLlXs409RCqp512mq2bWRfwzuUjHD7OgUjlIx76iz0Y4CHLtPesMUv7mHqZ56PzmKXPcR6z9JE0t8YsZwS4EgdZzLOT5xXev0y/zAdIlInnLKQwa7MzowP2PKNoBwQt5DVkOeXSRuLdx0s8O5khgpkjnMdtMc9EcIHAdh9EQcpDmI+Vpy/HgXPRzT5RzHYtwXUFYja71+RgtYQhkNZgaan67coKxGzNPAn4czzJO0531cxmhlYFBAICAYGAQA1FIN/niHs/YBuI2Rp6cEOzAgK1HAHIH8gg7mN47eHlzSwaraSQsy9JuUe9LeXDReywJ1+QgEBAIDsEuF5QSFeUa8ipi3M2eV9bGlQsOjHLQCbeOq+//rodRGeQmUFVBl59YXCZtfyYrvJOrfHHoCsDxnhWtdI0xL7HLPuff/55pcQs02Yy6M7UxgxEMwgdFUhYppbEMwqiF/GJWdqJVxLesQzCQwij7777rh28x9uIwX76BfmL9xf9gHRgcNsnZgk7wZsMMhiSIUhAoCYiEIjZmnhUqq9NuRKznD98oMJHLA0bNrTKGuJMuQt5yT3yK013zBqrfATTuHFju1YspCUzIPBRDaQisyyQh5kSyI93LV6zPB/4IAaSFTKVZwdlsOV+DpHHNPTHiJil/muuuab8uYNnKdMWYwf569aYJS/TFeOBSxvxlr3jjjus9ytk5y677GK9XyEyKQ/yFfKWZwMf3kDg0l/ayjMDQpj1b7HjGUZ7WCeXdMhGni8vv/yyLZfnBn2KCjNOMJXxpZdeWv5x0cSJE+3zhnbwDGL6ZYhYpjTGgxh7nkU8//Aa5lnD7A4cQ55veDKzfi7Y82wGC0hnCF+8fJnOnymiIbr5iKjYEojZ1BEPxKxeqoPUPgTSGiwtFTSurEDM1pzzhKkzGQQ/Wvqi1B0jtm2kQQICAYGAQEAgIJAJgTSeI/6zJxCzmdAOaQGBgEAhEYAYwlsPDz6mVWUa9HWlELMsFbJQyv3qcmmY0lggBAkI5ImAT75Gw3kW+b9sGuqrFmKWIUYG6X2NDjsy+MvUvhCxeDLhXYUHkFpvWmiNWQa0maKSKSHxmHXELN4zeMwysMwgvb/GLFNJPvvss5aYrVevnvWsYhDaF0jjCy64wA7GMwCOMDCNVy8EMNM7MkjNoDfrGLLeH568DJLjFYUnEuQB3kWQvwyC45kFecD0m658CAO8mRgUBwfaz6A6XmBBAgI1EQHOU0gjzmWmGIfMgTjCa40pZCF9OJ+DLB0I5ErMgsqff/5p792cM9wXOX+YGp77KPdi9/HK5MmT7b30iiuuMLfffru913PvZDYBPmKBeGRqYsqAKOVjGdYHR7jvMysDU/eSn/Vd8cQlnTQ+muHey7TInNMIa5pD6ELKcv92xCxkJLMgMC0yJCXPDzcFPe2BnL3uuutsOtMFQ8SOGzfOrjnLx0QozwtmbqAcyFsIZp4tfMDDxzmTJk2yOPBMoz2uf6zxytrjUQFDyGU8it30w/SNZxMexkw1TNv4WAhswOCWW26xGNF/PHnpH+nu4yPKIe7999+3OHFMyANWHBOWG2B6ZPCh/mJLIGZTRzwQs/97FQ6hWoRAWoOlpfzZKNNAzNacE2SGd1zc8WE7ouY0MbQkIBAQCAgEBGowAmk8R/znTyBma/DBDk0LCNRyBCCIIGeZThXidU3pWtKWUsjZAVLuV/+WdpNC4ELkkoe8QQICAYEagICG+qqNmK1smJHBYAa4ISvxVMIzCrITLyK8f76SZxUD4XgzMa0l+wyCM8DPmnn777+/wUOWwWqIJKafZBCaQWm8nCBmmUIT7yJfGHQmDx5MEE0IbWGAm7rIy4A2A/J4ZeGRxFqzDFozTSVeUBACkLQQsaxJy8A802lCNEMG4N1EvyCPWWMQG+Kok4HvIAGBmohAIGZr4lGpvjblQ8zSWshVPpL5/vvv7b2V+x/3Zu69jiiFtIREXLhwod1CzPlCHkjNpHTs/fxuymLKpx7u6S6OcrHn+UE7CD/44IP2wxqmQ4aMheiE/IySkuzTD9pCmHZRNlsn1EnZ2KF+GWDBvt8e4vy2uHL8Lc8JPliC2HbkKlva6cqnHDCASCaefcTV5/aJI+wfA2zIQzvoN9MtsxYwz7LqEI5JmMo4VeQDMVsD3sFDE9JHIK3B0lI1zZUViNn0j1O+Jb7iHRd3fJ5XHANSQQICAYGAQEAgIFAZAmk8R9zzh20gZitDPKQHBAIChUQAgrWOlDUvV5M2lK4tbS1dT3q/lHvVV1KmOsYO+0DMCoQgAYGagICG+WosMctgNgPCeOywzh5TTeJRhCcV3lFuEBpPKDxn2WdwGU9bvPogT9lnOkn2IXHZZ8AcQgHvIIhXPKWigpcS0z+yViFCWygHEvXpp5+27WLAmnLwaqJ9pDkvKKY2pl2QBggEAHH0hTgG8UmHjGUNPwbrWZOQ6Tj9dWdt5vATEKghCHAdBI/ZGnIwakAz8iVma0DTK20CzwZIWT7w4V5d0wTClWcGZCmzLRRSeFbuu+++1qOY5151SCBmU0c9ELM14SU8tCF1BG5RiXeWaVWmtt3TK+f81FsZCswXgbOV8Uvp39LXpBwbvAOCBAQCAgGBgEBAIBsE0niOpPWukU17g01AICAQEMiEAASrm9KYP/isJ9tEuq4UcratdIYUcvZp6SpSPmgMXrMCIUhAoCYgoKG+GkvMumFIyFQGg50HEuQQcU4I+/vEs4+dk+g+8ZSJ+nbOHu8lps/EO3fatGkuupzY9fPgWQQpzMCxk7g2kea3gzLoE6Qt026yHi3TZfplu/LCNiBQExDg3AzEbE04EjWjDbWZmGX9cQhZPgRyH9jUDNT/1wo+9sGbt9DThzM1MlPy8wFRdUkgZlNHPhCzNeElPLQhIBAQyAuB8JV/XrCFTAGBgEBAICBQhkB4joRTISAQEKgtCDhydgV1COK1vtSf0pgPTn+TQs5eKWUgIExpLBCCBARqAgIa6qvxxGzqw5FZFAgBNXPmTHPccceZiy66aDHiN4sisjbB84m1B4844ojgLZs1asGwOhAIxGx1oF5z66zNxCznOh/NQAgSronChz5upohCts99GFWdOARiNvUjHIjZmvASHtoQEAgIBAQCAgGBgEBAICAQEAgIBAQCAgGBKiAAORud0pipi1tJmdJ4sBRi9r/SXtKw3qxACBIQqAkIaKgvELMJ450MBLOeHuRDIQWv2Y8++shO01zIekLZAYGqIhCI2aoiWLvy12ZitnYdqfjeFJpoTbP8QMzGH8MqxAZitia8hIc2BAQCAgGBgEBAICAQEAgIBAQCAgGBgEBAoAoIOK9ZPGH5o8+Uxo2l60jderMjFIac/VnaUYqHrVtvlvxBAgIBgWpAQIN6gZitZGSTAeFCSzHqKHQfQvm1H4FAzNb+Y5xLD2sbMcuHOLfeeqs588wz7SwGaXyU8+uvv5o5c+aYn376KRdoC2rLlMQ333yzeeuttwxT8S9YsMBqGs8h1k9nnXQ+OALPG2+80Xz66adV9joOxGzqp0QgZqvhnTtUGRAICAQEAgIBgYBAQCAgEBAICAQEAgIBgbQRcOQsa8jWla4pbSptIcVrdgPpK1LI2a+lG0p9cla7QQICAYFiI6ChvkDMpj7eGQoMCNROBAIxWzuPa769qk3ELOQk09a3bNnSNGnSxHTt2tWMHz8+X2hsvi+//NJccskl5uyzzzas01pT5LbbbjM9evQwb775pvnmm2/MSSedZO67774qk8eQsuecc45dKx1yFkyPOuooc+GFF5rZs2dXqfuBmK0SfHGZAzFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKhADkLF6wEK6rShtIm0lbSiFn20k/lkLOfiJdWxrIWYEQJCBQXQhotC4Qs3FDliEuIBAQWAyBQMwuBslSHVFVYpbzCcLN99SM7vsAk4aHJ2urJgllsjZsJpu4vJ999pld53v11Vc3xx57rHn++efLyUTKwgOUcuPE1en3AzvIzrZt25p9993XkqBxeSuLo0y/L4RpB3UmSTSPbwdZ3L17dzN8+HAzb948O1X/1ltvba6//nrzww8/+Ka2z36fMtVJGuTrTjvtZM4991zrJUzesWPHms6dO5unn37allehghx2KKt3796mbt26Zvvttzc33HCDufvuu235o0ePtv1gLdwgWSMQiNnqevEO9QYEAgIBgYBAQCAgEBAICAQEAgIBgYBAQCBlBJzXLFMas47satKGUn+92U7ax2MWcnaqFPIWL9tlpeQPEhAICBQRAQ3hBWI263HMYBgQWLoRgHx5+eWXTfPmzU2jRo3MCSecYO6//35LjowZM8Y88cQT5rvvvlu6QVqKep8rMQu5CUF3/vnnW2Lt8ssvN3379jWnnnqqefTRR+25NHDgQBvHlMJ4cyKzZs0yd9xxhznuuOOsp2f//v0NZBxE4M8//2weeughc9555xk8QS+77DJz5JFHmlNOOcU8+eSThqmEZ8yYYW655RZz8cUX2+l7KfP99983V155pW0H5zRt2WCDDczKK69sycU777zT5nvmmWdsWXiY9unTx9rRb64FyMJ33nnHesVCGlLvtddea6fufffdd+3+aqutZr1wTzzxRDNp0iTbT6ZKxhsXInHu3Lm2L6Wlpea1116zfaXveJnSl8GDB9vtJ598YijzggsuML169TKUB5b0n7aAAd6q//rXv2x7jz/+ePPGG28sRoYytfA222xjnn32WVv3WWedZRo2bGh22GEHi/HChQvNhx9+aPtEn+nXiBEjLNH6+++/m9tvv92mff7557Ze2gXJO2rUKDN06FB7b+jQoYPFlr59/fXX1vv4jDPOMN9++y3Q5yWBmM0LtkyZAjFbxHftUFVAICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKjYAjZyFb+dMft95sV8UvkELOPiOFwA3krEAIEhAoNgIatQvEbKahy5AWEAgIlCMQiNlyKEJACORKzP7222/m0ksvNWuvvbZZZ511zEYbbWSJvPr165uNN97YbLHFFpbEhPTfZJNNLAmIVyfTATPFMPnWW28907RpU9OmTRtz1113WXLx9NNPt+QiNptuuqlp1qyZWXPNNc1+++1nXnnlFTN16lRz8MEHm3bt2lkvSw4eHxHsuOOOZq+99rLrrUIQQ8rWqVPHNGjQwAwaNMhcd911llSkPOql/rXWWstQH4QjxCckKe0hHuWjhSFDhliCFLKT8lZaaSVLho4cOdJOa0z/IKdZdxZi8+ijj7akMKTn22+/bcnnxo0b23Lr1atn64D4hCR1dTDdcvv27c3jjz9uwBUiGkxp4xprrGHxgrj1vXwhqQ899FCrrPsKCTts2DA7dfMuu+xisYFoPvzww822225rMWAqYuqBxGatXD7AgHhl2uevvvrKToPcqVMnc++999p1edddd13DPgQw0xlDqIJHt27dbN/yvXACMZsvcon5AjFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBAQKjADkLFMaQ7ay3uwa0ibSdaWtpUxrvJ/0Fynk7OPSQM4KhCABgWIjoCG7QMwmjluGhIBAQMBHIBCzPhohnCsxCzGIxydepJCweJfiFeo8VfE4ZXraQw45xJKZeNJ+/PHH5qabbjLHHHOMJVAfeeQRS7hCov7jH/8w06dPt96jEKDbbbed9fqkXMpv1aqVgQydPHmy2XPPPS25iqctQjkQjpCnDz/8sN0nDEkMKcv6qw8++KDp16+f9V7F+/a0006zBDBTE7/66quWZIbE3XnnnW09eKuyNu2BBx5ovX/xgoVg3WqrrazXKXixnuuKK65o8FRlTdaPPvrIErEQrfRzypQpZu+997aELmTpVVddZR577DFL6FIW/aBdJ598siVUIU7BAO91pmGmz3iuQuRGPVSZxhjSFNwgWSE7Ia4333xzS37jmUx97ONhDPH6xRdfGLyYweb111+3+aiLPh1xxBGWQIekhaimLx07drRELMfNkcJ43G622WZmwoQJ5XG5Xj2BmM0VsUrtAzFb7BfuUF9AICAQEAgIBAQCAgGBgEBAICAQEAgIBASKgIAjZ1lDdhXpmtKmUshZiFn0cOnvUsjZidLVpUyDHKY1FghBAgLFQEBDd4GYrXT8MhgEBAICIBCI2XAe+AjkQ8wyRS/EbM+ePS2Rx1qurEvaokULSwYuWLDAEocQr0xZPHPmTEvO4k0KMXrAAQcYvDKXX355u8+UukzrC9kJoQoZidcn3rJ4ukIwQrLiGbv++uvbaYPpgyNmWa/0qaeeslMb41WLRyrenpCKTMXLFMsQrHjUsl4s9bBGK/kHDBhgVlllFcPUxLT7+++/t+WAC/t3ajpkiFLIVEhNpvnGe5S+nX322eXE7GGHHWa9Y2+++Wbr3QsxC+ELaU2ZeLfiVbvccstZz1sIa3DAMxYvYPpL3yFm8Xb94IMP7BTH/tq09Bkc8DS+5pprbLnEMT0yZUAqs8Ys00WDG+Q4BCxTKXN88EBm2nKmX6b8XXfd1ZLYePEyLTR1MfU05DjEL9g5AUM8oPG2xbs3HwnEbD6oZcwTiNlivGSHOgICAYGAQEAgIBAQCAgEBAICAYGAQEAgIFBkBCBmHTm7osKQs/Wla0lbSB0520vh36SQs09Lmfo4kLMCIUhAoBgIaNguELMZxy5DYkAgIOAQCMSsQyJsQaAqxOyxxx5r11NlfVe8TCHu7rnnHvPLL79YInTVVVe1xCekI2QuHrAQiBCuTKXL9MAQtXh0Qh7WrVvXrrHK9MAQhxCLEIx4azpilumI8SRFWJMVohVilil/ycM0vxCzEKSsp0p7aBvenrvttpsNQ5hCtD7wwAOWLKWdeAFDOEIeQqRCykLsMtUyxCykMJ6wxDti1nnMsp4ra7kyHbJPzEI+s64uAvkMAYpXMN7FeNJCjHbp0sUSsZThiFmmWY56ytpC9MMatuDIFM2QsAj4guv1119v41hHt3Xr1tYbljLBli3q1qyFgN1jjz0sOU678KxF8JplCmTWusUj1wnTRjNVNXjgNZ2PBGI2H9Qy5gnEbDFeskMdAYGAQECgGAiYqSXLm4kl3cwTJbssps/YAbZiNCPUUQUEzJMlay127DieE0s663HOwGqQgEDBEeg3qsO6fcds2WkxHddh44JXHioICAQEAgIBgbQR4P0B71eIVsjZVaUNpM2kPjnbQ/u/SiFnn5fiXRvWnBUIQQIChUZA7/mBmM04dhkSAwIBAYdAIGYdEmELAlUlZiHyHDHL2rCsU+oTs3hs3nfffXaqYLxdr7jiCksOQupCzEJCOmIWz9Vzzz3XeopGiVnWmN1nn30s6XrDDTdY0hXCljVZfWIWMhdiljQ8dfGIxQsVz13KoH7y4DE7ceJEO50wHrRMwcxase+9957BmxWvVcheR8zuvvvudupjPGZZC3aFFVawZUNwvvDCC7Z/UWIWcpRpmBG8TyFGqQvy+JlnnrGkLWXRH0hQ0mkrnrg+KWoLKPvBs5Xpm4cPH25JYqIdMYuXMFMrQxxDROPlyhTJkK6Q0KyJy/H+888/rScvZC794rjhlQzR64hZjoPvMQvBDTFLOXjc5iOBmM0HtYx5AjFb6BfsUH5AICAQECgWAmZCyfZmfImJ1SdK+hWrHaGe/BHQMbw09vhxXJ+w68HlX3jImTUCR45uv2/v0e2u7jOy/fVHjG13+FAzlAHtpUb6jG43vveoNiaqip8eB8LSjlccJiEuIBAQCAjUMASi5CxryTpytqXCznP2IIV/lkLOvizFuzaQswIhSECgkAho2C4QsxnHLkNiQCAg4BAIxKxDImxBIB9idtiwYXb6X8hViLyXXnrJen5C3DmP2fPOO89O9wsxy/S5eIbiqQopCUm64YYbmmWWWcaufYq3KF6dELV4auIxCwHJtMSO7HQep0x/jEcn0wKzRir7TL3rT2VMHtZ6hTSlLqYPZipfvHN31lqykKNsIUchTiGMWR8WshZPVtrZuXNnS7iOGzfOthWyl6mbWWP14osvNvXq1bOer6zRiucrhCplQI66NWbxtIXYRf766y9LxFIXhC1erbSB6Z/Bavbs2ZboZYpoPHGTiNl58+ZZLMEV4hkBvy233NJ67TKtNHgz3TGesHi6sr4tfUOZ9vjxxx+39mCOBy7HhHVrn3vuOYsZWOFdjEcyxwKBCMbLmfVso9MrW4MsfgIxmwVIuZkEYraQL9eh7IBAQCAgUEwEAjFbTLQLU1cgZguDay6l9hnTZlSUkBRJ+0K/qf0YmF4qJBdiNuC1VJwSoZMBgYBA7UDAJ2dXUpeSyNn9lfajFHL2NWlDaSBnBUKQgEChENA4XiBmcxvMDNYBgaUWgUDMLrWHPrbjuRKzTA/MNLqbb765JRAhFCdPnmw9TCE1IQKZ6vbaa6+1nph4XkIcXnXVVTYP0/tCpEISQiYOHDjQTlMM2ctUyNiRnzVZ8SDdeuutzdixY23c3Xffbb1jIV7dlMiQiAcddJAlDP08rIWKZyjryNKutdde204BzBTGO+64o+nWrZslLCFvr776akvyQqw2b97cTnc8evRo60GKNyretBCzTJtMPGvN4lGLrVt/ljpoK/VOmzbNQFrTtgcffLAc91mzZlmPXQhO8kLKQva++uqr1gsVgpZpivHqnT9/fnk+PwC5CaEKcQrJijD1Ml6ytO/kk0+2UzozrTFTOG+zzTZWIY/B8csvv7TrzjKdNPUyXfOzzz5ry2NdWTxshw4dao8NpPNnn31m29arVy9L4H788cd+c3IKB2I2J7iyMQ7EbKFerEO5AYGAQECg2AgEYrbYiKdfXyBm08c0lxL7jNxy78VI2XLP0fYn5lLWkmybLTEb8FqSj3Joe0AgILCUIhBHzkK8Mq1xS6nznN1H4e+lkLOTpY2lgZwVCEECAoVAQKN3gZjNZggz2AQEAgImELPhJPARyJWYxVsSL1m8QiH58ATFqxLylWmAWYPVt2EaXaa+xQMU78zx48fb7UcffWTefvttA9HHFLozZsywBC3kJflZ7xWiFRs3pS4E5FtvvWW9VvHcJC/tZ9pjpvCNy0M8BCbTFuNNii3EKeXifUpdTE9Mf2gbnreUSV0IZVIP8eRn7VfimPaYqY4hNfHuRWkbbWUqZ/JQr0+wUhd1QmRDYFMensBuamCwoh1gAK5JQl8gd5k2mrZQLvjhuUydxHEcaA99wp74n3/+2RLctBX8sUPY0mcXB1k9adIkezzIQzkQvG4N26R2VRYfiNnKEMo5PRCzhXipDmUGBAICiyHAgM9Y6d3Sg73UtRTeuUzbefEhmAcCgZjNA7QaliUQs9V7QHqPaXNJEjF7xOh2D1Vv64pXe7bEbMArr2PiP/fa5lVCyBQQqB0IrK5u8F6IsrZpkOIhkC05u6eatEAKOTtV2kQKOVtHShlBAgIBgZQQ0FBeIGZzHs8MGQICSycCgZhdOo97Uq9zJWaTyskmHmIOwhEiMV8hb65lkAfPUOp3wnXgi7PBLprGPvGok7g4l1bZ1tXlt6eyPH46RHbfvn3NcccdZ4ldl0Z5ftsJg5Xfbmx9G5c3Gk9ZDmu8nvfff39L0BKXr1Am0yvXrVvXej6zti5e0Hjy4onMuehI6nzrWMryBWI2pffoUEz1IsDgwBDpVtXbjFB7BgQgZRnU+bd0M8/unLJ40j704kMwDwQCMZsHaDUsSyBmq/eA9B7T/sIkYlbrqD5Wva0rXu3ZE7MBrzyOiv/cm5ZH/pAlIFCbEHheneEd8CfpOrWpY0tAXxw5C9HqpjV2nrMtFOc8Z3dX+Dspx+kdKWmBnBUIQQICaSKggchAzC5lo7GhuwGBfBGAlHn55ZftVKqNGjWy05qyJiXkCNOw4smHB2GQpQOBYhKzSweixeklHsNMNcy6sEzZXChhKmPW82XtYLxnqyKBmK0KerF5AzGb5st0KKtaENhetb4nZbAgfG1fLYeg0kr3Ljs+HKObItb+AHUgZiPg5LpbG4hZ80RJP/Xjg8V0fMm7isO7plaL+nipGV9iYvWJkta1uvMF6lzvUe179x7VbnJURT5O6vf4PnX9akXKHpZEzMqe+9VSIVkTswGvfM4H/7kXiNl8EAx5ahMCzJbyHynviONrU8eWkL7EkbMN1PZmUghYR87uovBcKcdplrST1Cdng/esAAkSEKgKAhquC8Rs7JhliAwIBASiCARiNorI0r0fiNkl8/iz1i/TMjN1clW8WCvrPd651MM2ydO2sjJceiBmHRKpbQMxW5WX55C3RiDwq1rBIAEaiNkacUgqNAIijQEcjg/eEI2kvvgD1IGY9ZHJIyxSb/tYQg+iT4RnHkUWPUtGYvLJEqYArdWSsf+BmM3r2PcZ2/6CJLL1hJHdGIAul6Fm6LK9x7R7Jsb+/SiJW56pFgayJWYDXnkdfP+5F4jZvCAMmWoZAneqP+5d/oha1rcloTuOnF1OjXWes3Hk7E5K/1TKseL/1+HSFaR1pJQRyFmBECQgkC8CGuILxGxq45yhoIBA7UYgELO1+/jm2rtAzOaKWM2xrypRmk1P0qwjELPZIJ6TTSBm831xDvlqDAKBmK0xhyK2If9UrBtsuyjGYlvFXVCm/WPSQ1QOCARiNgewaqhpIGbTPzC5ELPUPvSFzsv1HrXlCUeMbnN/nzFtHukzpu1ZSxMpCwbZErMBLxDIWQIxmzNkIUMtR6CV+ve3lPdFpsyt9bNjqI81TSojZ5mxA+/ZzaXPSTlW/5Xybu/I2WUVDuSsQAgSEMgHAQ3jBWI2p7HMYBwQWHoRCMTs0nvs43oeiNk4VEJcIRAIxGzqqAZiNp+X5pCnRiEQiNkadTgqNIZ1qn6TMnjDtr40SAERCMRsAcEtUtGBmE0f6FyJ2fRbsOSVmAsxu+T1rtpbHIjZaj8EoQE1EIG71Cb3Id9pNbB9S0OTkshZZitpIXXkLFuWJnHH62GF15TicRvIWYEQJCCQDwIa6gvEbOrjnaHAgEDtRCAQs7XzuObbq0DM5otcyJcrAoGYzRWxSu0DMZvPS3PIk4jAqkqp6pfSq6kMvrzOVqpCzNLWCusLZltpxI41lnJpcyR7hV0GNNK4MBkcWbFCycXfOV9VukGbB4pfvT0X0zi+RW+6ebKkvplYsoOmJT5IUxB3MFOzO0/TIGaNOaSO6t5IZe2j7e7mqZJ10gKAftj+PFmypXm2pIkeUYvdLzISk1Wcyth8ULKC6l+TbTZ9UltW1LFoqzwH6ljspDZXmPY2mzKSbFReU+G7LTj4Nhn7n+VUxmkfQ46Tea6kudq2s/RgaVedFxubF0q45xdd7rvvkDp9727Xss/YDl3QvuM6bNzvvl3qJTUkELNJyCTHL8HELNd2Gu8iDpyqPkN4DvM89qXoxKwZ0YH3lKxk/qCGzWYPbNhl7qDG2/8wYF3IlirJwjNb15t/QrMt5/ZvvPvsgQ06LhjcfG0ztHMUk5zqMEOHLjv/hIYbUlZOGTMYc5+bO2Dd1gtOaLzrvP7N2puTt03jXSy2RqN7WGxCDpFp4apjsdLcwc3bLhjYeDcwrWrbyD/7pKYtOYfQ7wY12Ji2ZtG1brJx74yzFM76nM2i7GCSPQKZyNl1VYwjZ1spfJL0DynH7T3pBlKOG+c35Sz2jqe4IAGBgEACAnoOLfHELGTRL7/8YubMmWN+/PFHdan65M8//zTz5s0zCxYsKOjafYXsIWsOLly40Pz000+FrCavsv/++2/zzTff2HULf/vtN4vx999/b/c/++wz89133xX9HIAw4Nz78ssv8+rTkpQpELNL0tEqfFsDMVt4jEMNixAIxGzqZ0LBxhwSXjdDdC1DAELhTOlb0h+l/DH/XcpaoaOkm0gzySpKHCK9S/q69HupG5RZoPBE6bHS6B97pji7XwrZ5+zZ0g7i0M2lcdJJkf+STpKy5in5vpI+LD1DytpK2cjWMrpXOkP6H+mf0snSa6XrS+nbfVLacrc0k3RVIjYMaoAfU4N9IaVNZ0szDQwfqnTX5/0VbikdIwVL2sVaUOdJH5I6ux4KVyadZeDsaRuEcS4CjnOl7vgk1bmPbFw9V+RSQYJtWsc3ofjCRYuo20ME4LMivWYrbCrohJL/av8LEXn3iijk/IoV5c1rjVkRbGso77XSadI/KtS9qC0/Kf4N1T3IaA3O2MoTIlUW5PL9yv+Jwv+pUPaEknlKu96StIsIv8dlt3j/HR4TSl5SOjbowVSp/Gd6cS7tcdVzg02fULKebG6UzTdScDTYJzSX8jpYnCeUfCzbf1do76J2zFH687I7Jmcsnig5QnU/LJ1VoVz6PLHkEcVtqLRLK6S5vrPNQMwW4hiqTRD0d0t/T2jTv9WmV2R3OvUnYZpGvF5/lukzsm2P3qPbvd1nVJu/YtaANb1Htftc6Vf3HdOuI3UeMbrDXkeObnt379Ftvoy3b2OOHN3+MWxQ7PuN7rBWn5Ftxrk4f+vKdf3pM7L9YD/dhRV/KTaUdeSotif3GdVubJ/RbT7uPXqLb9WWqb1Htx0RLQv7PmO26H7kmHb3ldn+KHJ0+hGjt3iq95i2pw6975DYjwkqawPl+tJnzJYHuHb62z5j2t3FFM7ONltiNhe8XNkpb5kO/0op1/Rn0n9Lee7w/GP91hHSVtIkGacEnkH3SCETmOnhLOmj0nlS947woMJHSbORbWTE8x9yiec5H5A9Jx0q5TqpMjE7b0CTwfMG1L87qnMHNLLnHuTqnIFNhmv/4zkDGvw2p38DozBtWkwgIOcOaDpszoBGr8r2e2x9ndu/0beKf2Zu/wa9F8ucELFgYLN1RMTePKd/4/l+WV749zn964+fO7Dx8XNOa8L7kpVM/eIeMHdA/VOkL6svv7iyaN/cAY0fnTew0ZBcCd/5AxpupH7fNqd/oylq66+uTLZzBzT4j/SzsrLt88a1M7qdf1LDtdSfcdHjwT5k9E+nrtZw3oBGF9Jn1TNjUfmNvmF/Xv9GF80b2LJptMy4/XxxjZYl8nnbOQMaPqy2fKrj+m+/3zrWfwjf94TLrZxH0bxx+xybeQPr95g9oMHbOh5/VSiv7HxSPZ/P7d/kavCIK0NxXH/zpe69Maw1mwBUEaKXUR286/FMWFG6qpRZbzhPfXK2tfb3k7r3fe6ZXaSBnBUIQQICuSKge2nRiNlff/3VPP/88+bBBx9UtfkLA7SffvqpueOOOywJCkE3btw4s8suu5gLL7ww/4JzzAlJNXv2bDNmzBjz/vvvG0jD1157zXTr1s0ce+yxRScIc2x+ojnE8sCBA83111+faJNrAscM4vS+++4zn3/+ea7Zy+0/+eQTc8wxx5h+/fqZ+fPnG4ihvn37mq233trsu+++ZsCAAebSSy811FcVgZyeOXOmeeihh8yMGTMyFgUZ/9xzz5njjz/etiej8RKeGIjZJfwAptz8QMymDGgoLhGBQMwmQpNvwsq5vi8G+4CAQ+AQBX6WugGUuC0DpJe7DJEt+Rm0jMsXjXtZdi28/M2zyNfdsyfI4MKFUjdoG63D7b8vm7bSJGGg4nqps4/bQir3jNhodzFZRzEM9saV4ccx2Lz9YrkXRQz38l+j8Ffevl+GH//OoqwZf/12vZrRMj6RAWxX/28Klw+8Rsz9AeoPI2m57KZ1fHOpMxVbkZ1ridi6N4H4qkjQLiLpflOeM4xHoriGiEDLmZhV3T1V95ys659Q8prq2dTVmbQ1z5SsKzsI0rg+VIx7omQX2SYTknFlyJ66Vf4zsXVMKPlWXp2bq9x5i6XHELMiFhvKdoRsK5LHcXX/L26qiMntkjBw8bJZU2Xft1g7/lfOIjwmlPwim4WJdgnEbCGOodpwoTSOmK547Bb14T/q33quv2lvIf9Eur6XRK5G4/uMbHcrbcjkKRvNwz72EKZxaYvi2vX1+5aJvOw9pl0vEbILEssa3ea/vcdu0Y/yBo/pvrrI2gcSbWmbiN1+93RYDONMbfDb6sIina9Ique0UbuV36ezLTcXvFwbUto2VjljpO45k2kLMTo4oV4/3/qy+aiSMiFx6yWUlc37Ac85zk9XL+RxzrKI4KtIoC4i+xpOx+tT5NrMKDkmkpHnegWZd0LTPUXCfRm1TdqfPaDRk3hDVigksgN5CrmXVIYfDym4cEgziB4ryf1qtFAk4iN+3riwyps8b+DaHMeMMv+MhqvNHdDw8iQSMa5sEavPzxnUZIu4giEb4/Isiqt/i0jO75LTRQL3bzDnu/4NusaV7eKqgqsrAw/oOQPqj1B9/83UHpcmux/mDmjST/8yIepiBVLaErkRQt+VsdhWhG9sQYsib9HGXRtTMtiFpMIjEEfOrqlqm0j5/9JKyjOptZSPUbiXcez+lB4v5WOiOlLui4nnj9KCBAQCAmUI6F5bFGLWkaft27c3Q4YMyXcg0XpIQvDtuuuuZptttjHffvutgfC98cYbTatWrcygQfqet0iCVylk3JZbbmkJ57/++ss888wzpmXLlqZ79+7Wa7ZITUm1GjDt2LGjOfHEE1Mrl2PWq1cvs9tuu5kpU6bkVS7e0CNGjLDH+ZxzzrHEN4T8BhtsYM466yxz0003Wdx79+5dZWKW8xUSeY899jDvvfdexvZCVkLMH3300eaUU04xv//+e0b7JTkxELNL8tFLv+2BmE0f01BiPAKBmI3HpQqxgZgN/wTyQuA45XIDJ24L4cmA5oyYtFMV58uu2nH53BaPW0hR9JeYdN/Tg0GB/0jxRHH53ZY4dBepEwYFXpI6G7fly3wGSf+OpDGo0EUaJ/6gkSsHgvoLaVx7nE20LL5A/z+pS3fbhYqLK4v+QvZGxSdmXRnR7ZvK5BOgpG8WLcjb56t4MHDlWPLAS88meL+Xn4HsJPHblS8xm+bxTWpnQeLLpoddkEjERYk7f39CyWPRRokcy4mYlf1ledU9vuRP5cU7OVZEFHYTGflz1mUXgpgFqzhSdlH8437DZddebU0mRH3c48Nn++X5YaaCVvkzs8YivvxFZGgMMauyUz+GamuvnNo7oWSs3+c0w/2m9lteJOGkJDJxsfjR7f4+ZvSWlpipLmJ2sTaJWI2NEzmLx+sRo9tNiE2P5JPH8LNRbLMlUF2+WkTMTlKf3DOKLc/f6dK3pF9LeWZG0+Oee76N8/pycb9FynDxNyg+TnzC1dnybvOV1O1Ht6kSs5YEiyFliY8Ss/JOPXsx0iwLcg1vVXmRtokDYH7/JntnS/rZNvVvcKdfThIxm0s71b6fmTbZL9cPM12v9QLOoq/RetW3f88d2GCgXx7hzMTs4gR6tFyLhTx0Zw9q2jlaNvtVxZUy8NoVNvLSza49vp1I7FGUERWmyBZhPsm3zRRW/X9XQpzvpjrcNcI13ThaZ9gvKgIQqrxjLyflf8sqUrz+OS5rS1tJIWfRjaW8l7rjd53CDDSQlzIoK0hAICCQAQEN6qVGzDJwipchXrFPPfWUmTp1qvn5558tmco0rxBXK620kjnwwAPNxx9/bCAyyQNp9+yzz9o8b731lp2WmMFGPBE/+ugjS3p9/fXX5sUXX7QeqaNGjbLlrLPOOubpp5+2XrPUddVVV5nx48fb+iAX3333XTvFLG2CMH3jjTfMDz/8UD6OiVckU9C+8MIL5qWXXjJz586109FOmzatUm9XvGNffvlls/7665s111zT3HzzzWbWrFk2/9VXX229aCHomNIYYo824F1LP1999VWLyx9//GFef/1127aoFylkM+2dOHGiAZNMZB9kGf2aNGmSeeyxx2xf6BcCvq5dYAJeeHfSFicuPxiQDnEKMRtHclMeHqT0iWNCG9988017zGgzmHPsqYNjDE7E33vvvaZ58+YWr7Fjx1qsqZ+0t99+2+ahXv/4uPa5LefCnnvuaTbddFOLGefNGWecYdZbbz3DOcF5d8ABB1gCmHYilP/OO++YJ5980kyePLnCcaXfTIPMMXj88cft+UWfiOd4Qf5C+tJ2pkjmfP3qq68sRpxPX3zxhT3XqIfz/K677jKdOnWyx4G42ihgw3nPsWzUqJE54YQTzP333284pniOP/HEExar2tj30KfFEQjE7OKYhJjCIMA9nY9u6tata7bffntzww03mLvvvtvee0aPHm1nK+CZGiRrBAIxm+HdOCTFI8Af8Z+k7o83hOx50rpSJ7so8K3U2TBA2swlavuUl/aHwkdKl5c64c/74dI5UlcG9bSQRgVPGGfTI5pYth8lkt9V/FaeLRfCEOlfUlfWBwozmODLztpx6WwXSPeRMuCA8CX5LVLfxoVJ9+Uy7bg0tm9L/YFOBj6w8cneudqnDl/iiNlZMrhWeqN0upRBRI6bP1BNviQZoATXNgamk7yDkvJz/OZ7ZWQids/x7D5MKrCS+LSObyXVpJ8sUi07j9Iksk5rn/qtUnlZE7Pyut1a5Fu2HpGLe0lOKHmHtUz9+glb79DxJV/nROwViphNws3zmDWTSlZTWz/Nqb2Ll/sf9btLFAuLx4SSh6pY9v+wjxCzhTiG8hxeSecRnrv/qzdTeELJ3+Zpu65cXPerHCcy8pRsSEtn47xlqbjGE7MR4tX1IdOW6Y59UJdSYnZbYeCeUWyflG7g46LwutJxUt/utogNu346YT7UukbKMxNZR3q61H8W8zHW6lJfdtCOXxbvB3tK3fsBRIb/wZKznab4nCUfAtMnZuf0b7K5CLU/M5FomdMaT9JfjQpEC9MIi3iTZ2t2xJ/1Vh3UpJXf+Xz6lVDf13jF+mUTZj1V1fFuQp4KUzgn2dDu6HS8aRCz1KeyX4tpc5VxpUz1e1xSn7KJZ+3daNusF2+Wx9vWkdlbluJ57/bfxZPe66NNCfuFQ4DrnPsY73srSPm/x/+CRtLm0pZSiFmU6/kKqbtfvqzw+tLlpeSnLDRIQCAgEIOAnqupELMMiD7yyCN2Gl+Is4033th6kjK1LAQa0w5D5CyzzDJ2y7SzEIUMqO6www7WnjxbbbWVOfnkky25A7mIHZ6xe+21lyXktthiC7Phhhva95kVV1zRbLvttrZePCc7dOhgzjzzTEsSMmgLuXjYYYfZKY432WQT065dOztFL1P1QhhCBO6///7l5VIHnq5dunQxr7zySsbRTYi6gw8+2NCG5ZZbzmy22WbmkksusaQrXsGHHnqoJWUhSmnXPvvsY0lF2kE6U/HikUp/wIt6IZ4hvqZPn24JL9IcJqeddloFMtU1jn5APPbs2dOSgjvuuKPF/ZBDDrHrruJlesEFF5jdd9/dtoEpf2kruHBcIKeZHpgBb9rFsejatav1+j3ppJNcNeVbiNNzzz3XHif6RBshQyFjhw4dasugzfSTcvBihazFw3n55Ze3hDrk5W233WYJY8qiXvKA0+DBgy0ZX15hWQBSFNKvWbNmFkcIUohQ8q222mq2b8OHDzf0G89cBvEhu08//XQ7zTHnCQrZDGFMOu3iYwHOL/rNudejRw9LrEIs07dVV13V7LfffrbuCRMmWByxhRjAmxZCkrI4bhC/O++8syV02a+NEojZ2nhU8+9TIGbzxy7kzA2BQMzmhlcW1ivHvBKGqIBARgT4AtoNKLJNGjTZRmk+EXhqWambaOv+sJMfEjBJDlCCXxf7UamMmIXgnCd15bynMF99xwmEst+2wRGjV7XvyoGwZLAhToYq0tm5rW+Hp6GPzbPaT2pTn0hZN/kFKQzB6upgO1taXxonDF472xkKJw2OvO7ZMbCdq7RVBlcP290zFFBVYjbN45uhmeknifhK9kqcULJAXqe3SvvK7gYRZYtPxwtZhifmU/+bJjpbYtZ8ULKCiMT3E8m3iSWvKv00pQ9RmS9msDsxioxsxyXaJxF81UnM5tPeuH4wdfIEO1BZDon2u+eMRVzZLs4jZgt1DNXebTK0mXWOr1O/bpJ+bu2eKLm9vMMFCGgd1IdjiUrrbdru3j5j2lwufURrt/6J9hvVYV3XjFpJzI5se57rH9ullJi9W113z5gvFcaLK04gE/joydny/I+KS3NbPmSKk1JFOhu2+0WMnvPSmXFiw0g6uzxzuV78copOzJr7Dqkjz8/JSWScpit+BcJt0XqvjZ5P8oDV1L5H0Skncwc3b5tUJmu1ah3Xa+V5eSPrmmKncPR9BvJQa7BmR+xmYXe9a5vbUmcW+SolaEVAf7nwzNaQU1bSImZp27wTG1X4yCANXPG4Tew30ywvmt64t2yu1/GeE2/b+FMzuPuKrs9sF61Tu/jxWnTONLpXeF++aApqPgJo/Kc/bbVfTiTMNe2ukRGRtLBbPQhw70IdOcsAAh+nNJQ2k7aQtpZCzrLtJ+UDFo7jQmkvKaQu+bkvU1aQgEBAIIKABu9SIWa/+eYbS77Vq1fPrq/KOp+QgBB0zpMOArJOnTrWCxECFWKsc+fOpmHDhnbdUKaBbdy4sSUF8SylTDwWl112WUvmQvRBvEGUUQ6EHIQinpLXXHONJf0g5SAjzzvvPEsCUzak5VFHHWVJtrXXXtt6leLJ6uLatGljyeDtttvOEq0QyJBwmQSCErJ09dVXt3kgVkeOHGk9XGkvJCMEMHGC3IDL3nvvbfuJxw/7G220kenfv78ljCEAWdf1p59+skQq7YYYBBOIQwhJiMco4Uc7sAFnvIcglK+77jqz7rrr2rVWmW75uOOOs169EMEQxcOGDbMej3j2kh/yEq9TpoPGcxRcaN+pp566GATg1qdPH4st+ENQsp7uLbfcYr1hIUppD8TvKqusYs8JPCxpA/uUC/lJW/Eybtq0qSVX8bxkbV72yc9Uwr7g2YpH9BprrGFxwpsaUgjMaDskLd6+Bx10kCVmWX8WvMAQwpjjefHFF5u2bdsaCGc8Y9nn2NN2cLv22mttOtMiQ/xi17p1a3PnnXdaT21woY2cm6w9CzHPuQVJj5DnGK1/y7kA7rVRAjFbG49q/n0KxGz+2IWcuSEQiNnc8MrCOhCzkffhsFs5AtNl4gZM3qzE/JkyWwYpR5bZ7q8txB9k6ffSTCfhqkp3dbHtLY1KZcTsEGXwy2gfLSCy73u0fOylNVX4v1JX1uVeWjS4kiK+9mzJ4wtrY7pyKLOtnxgT9glhPAmW82yGK+zKYouncZJAovu2O8cYMiDo2+wWY1NZ1NGRMjbNkKGqxGxaxzdDEwuTJGLrrUQCbEJJV79W2W0o/TPW/gk7AGbNVWZWHrOy2ye2rEUE4Mt+3YRle2esvchI31ZkbodYO0cssp1YMlFaqjZcJv3A2kPMLiKCM08nPKHkd9ljsxD7srbFrzHr1zmh5L+q6xvpH7a+Mo9Zhbey+75tNAyBPaFkpPRp2WZef7Zs3VuHifJMyVg+/ZlY8rzsnpL+kNGWdvnEbKGO4ZMlgxLbMaGkT3nf5C2n9mynduM1UzDR2rJfxxGzWnP1Hb/SfuM6NNS0wHhSlkvvMVue1Ht0u4Vx+cvjRrf5xdpgJ/tc1kxNIkUpW+vL/nHkqHZn9xm55d548ZbXl+Al22dUm5nyDj6v95j2F6o9vyXZq9yx5R1UIKkNip/u27lwLZnK+BH15xMp7xZnu74lbK9UvHumfRFj49LYQpJCGsTJmor03wEgHpzUUwBPW1fWCJcQs4XE+MOzLRgxW0aofi2S7XeINucxi+djPPGGx2ZDPhSrIJB2cfYibyvgOX9A/WPj7Ij7bkAD3v2s6M/JMmrDtj+d0LyBi3PbyohZ+qR+vCFS+DGFZyXVR7zs/uOvhzt7cNNNM9nbPP0bTVO+u1hLV+X/O5P93P71S127KyNm8SSe17/RRfMHNDxJ682+mqnc6FqzqeCaoc6FA9fE27tc7BrFZedMtJ3CpMJ7uNI5vxYnsgfUr3B/Zhpljnl5JZkDLynZXUtTM5uG1CIiAJmKQq4uL+W/Dl7pfAzKf6R1pa2ljpzlvOL4uWN5p8J8TMl/GO6zrjwFgwQEAgIgoOdjKsQsU79CguFBinfmRRddZK644gq7TickGN6xeM+uvPLK5ogjjrDekpBtTNULMcZUsxCKEKf169e3ZC7ELN6xkJaQih9++KH59NNPzcMPP2zraaU1ZfF6ZQriyy+/fDFiFk9WvBvxDMWrFE9NiD3qIY66IEDx2mW6X8g2piXOhphlkJgyIY4bNGhgiUamB8arE9LYJ2YhaukHU/4yLTHkJSQlxB/tgDhcYYUVrFcwUz7juQnhC7mMFyYkN7hCHEOk+sIUxxCFDzzwgCWywQiSEmIWvCEI8c4FK6YCxvOULQQl6+MyLTEeq47QhvBkal/WyY1bC9gRs3i/QpRDzDClL8qUthDRkLt4PUNA46XMNNMQo+BN38EIHJjSGrxpJ1Mi//Of/7THg+NEG33h/IGwhbjHAxjh/IFMpQ6OBX13Uxl/8MEHliDFIxqCnH7jQQuRi+cs0ye/9tprFjcIVerDixcs+vbtawlyiGqIW9qPVzEfBNB+1rnluHA8Of/c9JmQwZDeHD/O09oogZitjUc1/z4FYjZ/7ELO3BAIxGxueGVhnYkTCy/IAYHFENiQ/wyeVhigWcx60RSAeJXyJz5O+GOfJPxx7yRl4NXVeXSMcWXELB4ZLv9XMfmjUfTJ2fskKISEi2e7TjRjZP8fEXs/+VEvbbyfkBDe27Onbo6Dkygxy4BIkjCIwpfrrh+3xhhe6KV/rXDSIHVM1vKos7wyqGux6QTLLSuufZvPVMZpHV+vSYUPMgWwCC1IxvjpYieWjBJhV1GTbMeX/NO1WGVmR8w+WXJOYt1M6xutGzI1qf4n/je9tuofkGjHlLcTSsoH52mzMUOXVV17Mp2w14dLE8t4smQtZ+e2sq2MmL1T0/MyGFii+lcsIxPtxwKaCjiZhKS/T5acQRvL64KIHF8yJ7F9IlnLbV/ovJzs4sl0yp5Q8rbK5/5oxTxb0kRxTyWWTR6fmC3UMcxU7iKC+zW1k2O0k4+N60faW3nB/pJEUirty96j245Q+mGDx3THeydWMnnOnjCyWwVyKC1iFlLWb4yI0leT+7HFj0ff3R7CzooI5qOTbP+fvfMAs6Mq3/gCSpNmOqEkIYgoEFIhAUVQqooFCNlNSAViNhCadPiTCAiCIAoKSJUktCCilJDsbghRQMBQJQrSEpBUIIRenf/7m+xZzk5m5t67d+7d9p3n+facmfOd9p7ZuTPnne87qucBp0fcTolZBwH/mw3/n+6kF0MWXCtxv3mveHku6fKIL3UnE2L/9/NoT+eHSvv19PXy4pJTPf3SELNyGfvGpG3D/4lgSsW6kGJLjuq2A51Jcz8r4u0RrFp9EQl6Sxz5Jt3/LT2xa4O18rKJXU6M0+Nc6P5XZOfSiZ3PWD5xi0R80onZLiter+66twM03N+0uuN5SW1yfvnEzoc4/TSCM6xjYofj9eIEWRSGpUdtuasI1cQ9WZU30+mmEbPSe9cniLXqu7bI7ieS+r1iYqcRrl7iLHAV2bwqqT1/rl06UXdCpwsa9U1jS9SVVTGk/ooJ3SvdteiXTUlPU577f4r7n00palllQID/Ee67X5DwXrGRhA9Xukq2lPSS8C7i5NdKfyZhTv8jgaDn/W8diZGzAsGCIeAQ0G9QJsQsVqoQiF27dm2wTMVlLtaRWCNCKGIlC1mHlSjh3XffDV0c40K4f//+oQUpVpgQs5CLjpiFPIX0I+C6l/rYqxayE0KV/T0vuOCCNYhZdCCBCZBmuEXGYvOiiy4KyTbITghICD0CBB1WrPkQs+hDRmMFDFmItSakJqRklJiFIMbFMFagkLdYErsxsgcqRB/ELIQ2hCH7d1IGEhASkTYgQtnPlj76ATywPMbidN999w322GOPkMBlHhwxi0UrhCN4E+gDlq4QkLj1hTCFSHcB3CG04/aYdcQsxDJkOQGyDkIZghk30BCl4MqYHGkKkdmjR4+QvIf4ZY9aXE2jgy7j3HHHHcM5ZLy4dfYDBDLWqFgOY+lL4Jq67LLLwjbYF5ixOGIW0hSC13fH7PRph2vomWeeCa2CwR3csEyG0HbELG6YIbAhZrFk5hr8llwVc91BvI8YMSJ0o828EyCK6RtlIJ7bYjBiti3OatPHZMRs07GzkoUhYMRsYXjloW3ErHsQtjgvBPaSllssIYY4LTbwcv8VyQ8kP5X8XnK/5G2J3xbpwyXRkIuYnaMCrp5Plf5zDuErf6dPzMIC4f8k7vwHSrOYkBYOVqbTJ/YDCxMu75d+RkKaRQ6nTwxWLvjELEQyCx1p4XJlurpWKr2ep8yYXvbyqbsp4WIV8ttIq+N0T7cpxGxW85vWx8zzRGphARtPyhZ6XpacroMizPIjZrNy37uaLNytof17Kq5JHNesip85vbS4nvSLx6ZQYtbbSzauTRGd0xP7K3I6tsw9FT9MLDOzYpV+eMN7g0jXHRL17ql4W3k9ovUHtRWbavyvJpbzidlSzeHdFUMT249emzMrlkl3QnQcWR5jGZtEUjY6LzfGcnt889gZ/daw4G0OYnb8tAGNPiKQ5e/JjfrrWc6Onr4zH5g0hONnDNlAVrOfxOpP7/NQg6IS7ZyYdVBsqsTukjGS8yS3S56VuN8hF7+qc9Hg8oh5BkkLkERO/1hPEZLWnSeGqEgL/sdLmROzIuEYf2KQxeZ1SWRaoedXHNW9v2to6VFdf5hveZGVL0HYubIuTiNml03ownPVGoHxJrUrUvkcV0DjvjpJT3nXOD0/Xl795QOSyqhdPL+EIZ2Y7cRzV6OwrLrr+KR6l0/sNsZXLhZX3AcntVXoeSyVG/UthWBuXHeXj5ZXd7h5yaRt17g/+/XVp/kfdv9PPG9baHkI8JzjyFneI/hAYzNJF8kWkp6S3vXSSzH/u9x/mVfeVXj2X1eyjoR6wucmxRYMgXaNgN4hMiFmsRrEShGrx9NPPz0kQbfaaqvQ8hPXt5BWPjELofjAAw+EbmghBtlrFDIWwgvScurUqQ3ELMQne34SKDdv3ryQwINExZIyiZjFOhcXugSIWSw0sZjFJS7EIJa4m2++ebgvLHutQnDiSjdfYpa9YB0xi9VqEjELqYoLXDCCFIUwxMr2lltuCclaLIYdMQtZCIFJ3yCwsQjFJe/JJ58cWvpGXeSyDy8EIZadkKu4CIZAhDzEUta5MoYMdu6B6QPEK655sdilPebMBTBFP42YhUDHopdAGxCkzDflcCuNBTM4QsxCmmIRSzv0E9IUQoc+MB+4BMaKGXKda4T9gSF6/QAJzniYHz4AIEC0Uo42mE+fmMWSFbIXctoF+gkpD3GKS2dcNfNBwBlnnBHihstiCHTcY0PEOmIWspy5AyuuPeaN+aD/e++9dzg22qB+xg0JzpjbYjBiti3OatPHZMRs07GzkoUhYMRsYXjloW3EbLt++i988CyiucUS4u6FV9FQghf230niCFi/DT99eEPpzxO5iFl/IdWvK9/0AfVN0VdXhsWFXGGgFJw+sQssREAQu7yjXEZKzFfpfpnQhWu9vk/M/ielDpc1SAnXNvFBLkPxtyJ5X/XyCklO9ep5OkdBFmdcf5pCzGY1vzm6mW22yKyD8ya/omRY9Fj7zLreFUDM/jPD9sd57T+eWO+9FXldT5kSs7JwdX2Li9VWsjtpuVeOK8M5lXs91zilMyxRZ2bFn1LqviCxXGNitjRzuNpyd1ViH6LX3+rjWFIjaYyFnE8jVeOIy9HT+i4dd+Ogbfw20uooicWsSGI9gDVaaB51086HxfWXc3IvPNHvL+mR03ZaHKtvxKyDit9FLAsfk/guht3vSVwc99vt6x3qKk+IF+m80/eJ2bO9828llPVP029XT67fSL9cQzqNwFx8dCeePxKD9nr9R2PSLMYVbZx72phzK6q7DncN4a5WhOubhdQtC81GH6cljUsk6DLXTjReelTH7yS1uay6Cx5KwqA6Hk7Si7rzdWWIZe27OKnc4qM274FOGjHLXr3o+CGN8I0Ss8XiuqK62/eS+l/4+S6NnjNlzXt2IXXIynoprpJ9LGLSfOzj/j+Ic33oEFOFnSoDAvzGrS1ZR8K7zYYSrPQ7SbpL+N9grnvXxzsp9j0GzdVxTwn3cuqhPsSCIdBuEdCzY9HELKQmpBiuXr8t97aQqhCpo0ePDi09Ia8g0di7EyvV7373u8HcuXNDa1nc/EKwYsGIi1ssFiFmIcmcxSzErLOghJhlz1KsYbGehCTDzSyEG+ecS15Iwzhi1lnMYmmLy1l0IAZpj/1CIVHzJWax4oSYpU5IRYjde+65Zw2LWef2N0rM3nrrrQ3ELDr7779/SDRjwYll8bhx40K3whCuuDGGSMR1sQuQZLjMxeL00EMPDV06Q4Dffvvt4RjyIWYhdvfZZ5/QghZMIJf//Oc/hwQo7oijwVnMQsxiIUvAVfWkSZPCPtMPSFHIU8YACYqbYchY+ongIhiCE1fKWN6CP9cDJCtkJ3VBHvsBd9UQqBDaXEeENGIWt8VYa4MbJKkj3tnnl32NsXCmrQMPPDAAA8aFK2tcUztiFktmrL65NsEZPNmXFutw6mdeIMD/8pe/hP2hzxC2EO9cG20xGDHbFme16WMyYrbp2FnJwhAwYrYwvPLQNmK23T75N23gUXe+HZpWTejO1Cca/QUYl35NdV8lccfEh8e0l4uYfTdSh19fPmln0fFbr56FMf2InooSoC4ft1/+2LG0yRVYqPhY4vp7slfAJ2Yf8c6nJZ/x6vIJIsgV18bf0yrIkXezV8+DOXRP93SbQsxmNb85uplttkivsQUSX/EWpJBici/reqd0vhazizJr/+6Ksxrav6ciuV4Rfk4vLdYYsnNlPDu04khsTm0lE8kzww8VYssKu/+m4DeQQqp7VKLOvaHlfnzdsyr+L7FcY2I2Get48jT5GvLmMOz7vRWVsiZ+J7Ef8fXvEzugIk+Ombvn+rKanRNLUnpWp37+qOl9ZvnNlp2Y1b64fvukR07d6bt+H/30YdP6rGEJOGpav6d9nYa0EbPAye/o3yTu9you5jdzngTi1uW/qnQ0uDxi/0OlqB7HiyRO3ydmL/DOv4NijvBd5bt6MiVmcS8cTDpgvbT2RVA+XwiZlqa7fELHU/22cB2s+hPd5kbror8i93ZzdSQRs9rz9TmnE42XTOy0V7TehuPqzvc7/TRiFmLV6UVj9fGFhvoi5LSI3z7opxKzEQtY9HGTnFRnlJhFvxhccY2c1FbB56s7fkh/XAim7Lm+MJhTSD3s3+vKJ8T+hwv8n+Am10LLRID3E2QdyRclLCxsLOH9sJtkK0kvSe966an4BIl7dn9d6aESylKHI2iVtGAItD8EtHhXNDHLAiBWlhCJkHG44MVyEgtKLByxgMRlL4TcWmutFRKZWCZi3QlRRxksZXExiwUlpB8WnBCF5PsWsxBsWOZifQmZibUkBBpWmpC+Q4cODYmz0047LSRdIYcJWMxCwkGiQhpiPQoZi7Uu1qnbbLNNaL1L+xCz7H2bK7B3KWQqrospj4UpxCxjZCwQflgBu71uHTGLlSYE40033RTiQv/RgdQGJ8hm3BnTD3TBERIaPYgxP0AGQnSDB0QqfYCMBDNcRDNu5gWC1FnMOotY3DxD9N54443hHrS4QsbCmDbBBII0Gl5//fWQ/GaOIEoJkMFYzHIOF8oQ3r179w7xh+iE6IUcxYIVy2D2ImafWIh4iE+IePoHhswH5Kfrq2sfN8zXXnttOH/0GywhZv09ZtknFuIVchiLV3CkLdwUQ6pCQHO9YKEM0cq1wfWFG2hww00xFtQHHHBASHJDnDMHXDdcKyeddFI4LizAqQ8ClvawpCXwgQBtV1ZWrtF/N47WHhsx29pnMNv+GzGbLZ5WWzICRswmY9PEHCNm298jf1Ej/qZKu8VEYr5+LjTsogK4JvPrYbF0muQkyb4SXuYJvOx/JnG6R3AyEt7TscsfFsnj8J9e/m1K42KrEHH/JMd59bDgy+JBWmCh3fWL2A8rdODypvgZCenunj7lxnh6TSFmf+rV95HSuIHka/dV3vli3JP+2qvn30qnhdOV6bBoCjGb1fym9THzPJF2g1NIr/kixU7JW+6paLC6Vr35EbMzK2Yltl9I2+jeU9HXAaT03Sn15iI9wmo0hmyI2ZkVb7h+JcVq65bE/t5TcU5cOe2J2yexDPvozg1JowrN34BEvXsqXlTba5An+iFfS+cfSCznE7MlmkM35nDP23sqrlRfcLucTOq6vHsrfuvKliIedUPfH8q97+MNBGUCKevyx/3lqywOh6HcxKzcCy+sb7ohGj19wL6ub9GYsTUo1icSXTjnS8xO7fPxmDv23Cxa72HT+s6Mtu+OT5y675ecfr4ukgvZk9fVnUF8u+pwvxvEn0jmSi6S8AHZzhJ+0wh8OOJ01yDMvTx0ct2jFnn6PjEL0eDa4EOZNf63dc4P43Tg9J/2M/JNJxGYslhdkqsO3NEmEWnKO6UQWTZpC7BuFHBXKyvT3+VL0C6b2IF5C0PiuCZ0/NRZpzpdFy+b0On/ksYji9wrGvSqO01N0mPMTs+Pl03aqjfkcWw5kZTsc4t+ocRsofphG03EFXfTsf0PSeYuDxQ03xM7rmHdT99wt7ykuuPjye00tspecXKnhvsz5SNhko7d/wfPqBZaNgK8qyG8F2H9yv1vIwmEehfJlpKekt710kvxHpInJW6e+TB0UwnlHTlLnRYMgXaFgN4DMiFmWTSFmMKVMeQe1qfs7wk5BoEJofriiy+G7m+xasVlLa6AsVKE5MJKEZe9EGCQspCWEH6QqJB4WCi6gOtiCE+INVzVYp2LFS1k7PTp00PSDmKVetzetBB7lMGSFutbCElcKWNpi5XjL3/5y5CghEj0iWDXZlwMOVhbWxu6HKYv1113XQA5yNggimkT61EsPK+88srg448/DglDMMLaFAtbzuHmF52rr746AEdc4oILxCg4QhpCYoNjNLjytHm49mBlLBCtkJiQlmAFBrSJLgHS8re//W04NxBttDdjxoyQoGQusP4FDwjVaIA4pi/gOHv27DCbuX1Ze9/SBvuz4oIZ61cEq9IHH3wwJFH/+Mc/htcG+dQNsTpr1qyQ8OSagCBljrCUjgbawHIV62osXWnPzSFYQzaDD+UZP/qMiz1/IawhpyFWaRfrWOaOuZk8eXLYJ3ADsxtuuCEkmSGgaQPXxJRjrFiFs4cs/ecah0Bm7rAyZt64DvkgAWzbajBitq3ObNPGZcRs03CzUoUjYMRs4ZjlKOE4p3b1zGuDbToC7JnnXqKJsfpIC1iO3SL5pYTFHF64r5K4OljAnCJJevnu4OlS5ieSaMhFzPoLuA9HCxdw7Fu40JdtcpQ9VflunMR+eEgHLu8GPyMhvZunT7nveXrne3mPeOfTkl2VyUK260OV0vt5xx8ovcaCvs7lG1jgdHWvyFGoWGI2q/nN0c1ss4P5FRuK7PoslvCaWXFJU1sTqZcvMZtEfn4mQrHJ1ikiLSfHjmk1efeS+oebu0YheCC0rmg4J52kvgXat5V7UKOg9mpj25xZsbiRYsyB+ntSbNnV/f1I7fExSkMQWdlR/Uuzsn3cKUPQqu5PE+u/t+LSIJjCAmRDUHunJ+rTp8bEbBJORc0hnQlmV/QKHgotXyqCuXt+QWMerL6drPjpxP7dXVHXMJAME1PU/pgZg9zHOhVH3LRjV+0le6jIzKtF1L7vSMVoDGHoutGWiVntXXtXdOyfH/cb6TAgHj9j701Hys3y5/l9Aj89/q4DN3T6LZiY7a4+ut8X4uck27l+x8SX6pzTXxKT7/KID4rJ908t0oHT94lZfkPdeeK0/lDfzzz9jInZTgtpIC0kuZ8Vmfrx0hO7NpDzaXUk5S2f2LObrCh51qsIZgxdB8tQtXfi0tS9SDvc4+pLImYh/JZN6DAvSujVE5wfJBGCsuZs+MhMVqfHJuktndDlveUTt2j4yIj+SH8jWXf+LakMFriu34USrYXqF4MrFtQi7D+JHUd1x5+7MTQlZq7pmyu77NheXZcf1fnQcD/f6o7vx7apuWT8rkxM7P9/8D9noXUgsJa66cjZdZXm94Rnvk4Snt22lvDuBEFLvK2ED7rcB7jPKv0tiVnPCgQL7RMBLdxlQsyyAAhpA9mGG1dIWAg2yEkXIMtwScv+oeSx0IplJPuHQoJBhkEkYmkKqYY+ZBsCkeYC7aBHGdoiDWFIOchcl88xbmcJnKNO2qdPxLjQhUg74ogjQsIOQhKrzx6yPn3sscdCK0/c8MYJhAR1Qw66/lM//aRdLFVpk/FxDBlIYEyMk/YpS3B9dzqcoxwugsERy2EfR/L9gHtniEksN+kLupCv4EZ79NP1h3KuD8yVC7Tn5o3+0ReHndMhZkxgTP/B3QXq5PzLmhP6S90c0wf03JzQBuLaBi/67sZJv5MCY4MUhTy/4447wjrBjv6CgSO1adcFH0fw5NgFyjA3kP7UzXjpK1i5+sCBcuS5+rl+6S8YoEegzHnnnRfstddeDRa0rp22FDOPfAThLLoh0yH+IcP5KAIraObDQvtAwIjZ9jHPLWGURsxmPgtGzLbPx/6iRv2uSrsFSEjWtIAVhNOFJOGlnUVUd+4hpdPCD5TpdIkhd6MhFzHrE5cfqXDPaAWRYxay/yv5h+SPEhYOCD0kEMmuP7/mZEJgQWKhxOkS++EGHbi8lUrnIsKu9vTpw1YSF/zx5UvMUvYvEteHW5X+nXcMmV5MGKXCru5PlW5EPkUqLpaY9cdfzPxGulX6Q5Fcz8aSXDMr3g1qwwWshk5AyEn/75IFMXKBU1RefsQsrmqdpWM0FmHo6nNxSLjGt73A76v0vptYL+3MrFgoncqQ+LunYifpTpG8p/PfamirnMTs3RV7pfYXYvXuiqsk4yVnSXdpDv0r3TiIpfvvVP3Vc3qcSM+jhUFNqm6UmC3VHM6t2Eh9WS5ZrP5M0EcEoUVYOB4I2+j14o5nVtzpjz2rNPuvioD9dNTUna4de/Mg/95XEZKzCZazh08duKPrQ1smZkdP7XOJT65G0s+MntavCuvhcVN3/oYI7Zsj+Z8TsyJsHV7ELZiYrVL33O8L8b70NyXwsYTTXx6j5/KIm0rMcl369fwsph3/1HxPvzmI2YOSCLPlEzqf63eUtM4dI6vRR+JkxdGdIMrDAAG4tLrjKyIsn18xsWuVXll45gvDiuM6bZ7UpkhDnrXCkEbMri7fZRFEL/Uvm9D1VyGZHHEv7LfzxlFdhri62UfWz1sz3eUjrHdXTOheuXRih+NV98tr6niWn9UdG34rCyVaC9HPAleN5em4sbAnMBbODiPicK6qO8+Om+9l1R3O8nWXyYJWep8undj52jcmdm90fw7J2cS56dpwf/brq0/7z6MN5HeMnp1qeQjwP88z/zoSnh3Wl2Ad3UHCR6FcIz0lvvXsoTrmHZF76GeSKyQdJV+QUBd1NtxLlLZgCLRZBPS7mRkxm/myYQkrhACcLGvJTTbZJOjUqVPoehg3urgYxsLyqaeeCl0C42I3TiDg3L63JeymVe0hAImLxStWs1jA+iSrp1b2JIQBZCUukHHb7cjasnekDA0aMVsGkFtRE0bMtqLJauVdNWI28wk0YrbNPtmXbmA+ocdCbqOFGK9ZLC5fk7jFysvr83wi9SZPP5pkse8FiStP7FuoOH2ITaczxp304r28fPTu9PKiSb7u9tvE2pNFBRdmKOHawqp0e5cRiU/19Jy+rzI8kg+5mBS+ogzfuvXvEUWfmCyEmP2R6nF9e0dpFqzd8f6RNgo9/I5XF3V+PaWCYonZLOc3pZvZZ4no+3kiyXVPxUoRmGdKqiSTRZK9lqJ7lOud9PIjZmdXdFF9bybWObNipvLGqr4jJX9M1GOv1QUNLkMrsH6V/isp+vEuce+u2NsbQ5IlaPYWs3InrL7+s+D+OjLSj3FjLMtSNw5i1XtsJnW7dnyL2VLNYdRqd2bFMl2r92gc10j+kzgelfPHnkU63F/2hr6vOTJx1NS+H4owvH/UDf0u0z6yU0dN3fkDl9conrbTqimeNXJbJmZFTo9uNPYEojq3Tt+n/DlrwcTsSfxrebKd3+9I2vfeQBmeF6LBr+ugaGbkeJGOnX70eYTfX5e3SmmIiLjwQ510esRPxynlOpdEYMp178JcZd8+aouOshBdEUfUcW55dYeb2dN0+cQOwyDckvRoy1nH0uby6q6TfF2Rfi/JTe4tEKjq75N+np+GaHV9ThqXr59/usOTfv9wO5xuueuRromE4modCOHFR3ca6PpdCNFKmUL0s8BV1saTk3DTPC3BmnjFhK7fX01IJ++pK0L8SDdmYav9ZTu/1lCvXDuLjL1fx5fp2sBtdKwlMy6u5Tcy7YO9+9WG+x9pIO1duxa3eAQgURHmGHJ1PQmW+LwX8hEA73c9JNtInPXsTkrzTujmnffHQyRflEDyOoJWSQuGQNtFQEt97ZKYxcITy04s/SBocWV75plnhi6UsZ7E0pN9Uy+++OLQfS0ubH254oorQqvJzJdKrcJUBLBwxeUz7oKd1W1qgTJk4sp4/vz5obtq+teWgxGzbXl2Cx+bEbOFY2YlmoaAEbNNwy2llBGzbffRvmQj+6pq/ljiXp5ZVIQ89MOmOrhT4nSI95QQHpa48yxeRsuiw4s7+406PRc3+lIfRQVe3l3+n5Rel5ORwMKO0yHGAnWjiA7utu6V+HpRyxGIWCxAnQ79Hy6B0CVsLsFizuX7MfkusGDxgMTl84X4ZAkLD37YRQf+QjD6WKP6oanELIsdPhnr+gKeLIIUE8AD4trVOT6lMsgcp8ecRwMLOLM9GRdV0HFW80vVJ0tcezOVXp+TkQAR4HSID43k53UIoSki76lEosuRcWkxpFm9y1kaVX15EbOhbprFZVqbjfNOiA4W978aU7IL38blVxO1zUTMhjjMrthRuH1Q1DysHtNpa2ARDF1H9c7PoG6H0zZ+G6H1cRyehZ1rmENZP2+qviYT9mn13ttwj3ddPFsJ939ylztZSDx66s7H5yYUG7vjRX/0tL41fjttmZgdO33XrzcFo2iZUTfuDInZEEpEzGZx7+TDIfebQXx5Q6cbJ46O6KHLby2/v37w6zrIz4hJ+7/HUWIWEsGv6zEdbx2p49s65oMvX+/piE5eh0kEZj7ELA0sm9Dl4AZCLQcJmaS3fGI3MA5DMKX7hhB8Sbpp50XM7ubqSRpXWvm4PJF/n+FG2dXr4teP7rg9bovjyhRyTvj91NVJXAjRWoh+VrhCSqvPjxUyxqguJGww5fNnbEjcqE6ex43uzz6OSvNs+r7E/Y80fHhWr5fFPaS+KotKjAD32rUlkLPrSlh04F2ro6SbhPtjLwnkLNJTMlTyosTN/5+V7iGhDuqiTsSCIdAmEdCiXbskZlmshGTC6hKXtEuWLAlj5zLYuebFLW6c4NrW7dmasvBpWRkjwLxAyEKctxTLVK4jrhvcHpNuy8GI2bY8u4WPzYjZwjGzEk1DwIjZpuGWUsqI2Tb5VF/6Qf1aTbiXZuK3JZBjUyS/kyyR+Pm/1bELk5Xw8yA3L5CMkbCQf7/kQ4mv49JX6Xw0PKITLp/4Y8m7kmESF3j5f0/i672q4z9IzpCwsPuGxM//l447SKLhHJ3w9UhD1kYXW6M60Xr66QSLxL7eMzq+QvJzyUzJRxI//zYdR0NTiVnq+ZXEr580c5FFuEeVuLqnplR4uqcH5tGAta2rh5g+R0OW8ztDlfvtsYgUDbvrhK9zVlQh32MRa31EhH3UROLuMxGKI/y2CiFmKSf9m5vYNm6JH5dL4i/57bu0yNmfFVRvMxKzIQ64Ek4jHXPlzayYrR/a2MVCYdFfWH1SVP2ufc9itgHrDOdQlrFnN6mfMytmxIy/Vn10/yf/c/3NNz5x6r5fkgvjZVECMdfxqKl93htzY9++fjttmZhlnLIg/kUuXNLyZXX7Gnv3NsJsWt974sqIsF3o67GXb5ze6nN9x/i6Smdx7/yy6nlT4q4t4nkSSMKfSG6QLJT4+X66u/L84OcVQ8xS518kfn3080bJuRI+TvA/7HJ6zULMqi8VIi9vyJNIC6J6soR9NDh+SMNLhPZyPSmqk9dxdRfwaQhZEbNyqXxpQ6WRxLLqruPz6lsSYV3d4e7o/a5UxGxWuALBkkndvq5xx1qx5sIDopu9Yx2U7EWsc8tylVszf829fF2d9TFeJ9z/BjHPd37I4h7i12fp0iLAcxGyjuSLEj523EjCfbyLZEtJT8k2kt71MeT7byS8z3ENvCXh/k556nEErZIWDIG2hYB+W9otMauxWzAEDIECEDBitgCw2oGqT8yefvrpjfadbgfDtyGWEQE+yhk9enSw4YYbBrvvvnvoNv7mm28OvV5MmzYt4Fr096IvY9daa1MNaypt66nWRlNqBLhwIFtZbPcXUOLSLM7zMu0C6fmSON3oOaytWGR15yF8eSn3wwk6cPl+/AtfSWkWc/6ToOuXI71QwmJBUhinjChp6tfxkvJZFHTn3kmo6Ps6v9jTc/px8TTpxZFg53vlH1G6kIDrsGhbXyukghTdCV7d4JEUsiBmqTur+S0rMUvHRYaxz2n6XqSOmPs8hpQdRXk/6FzeFrOUC1bvJ/prtV+YhevMikfVbxbWYkOApejMiuNU79t5EX3NTMwyCJHMo9XXXHvIRl0xQ6pfiAvnWCDqTwqLwZIX8sLi8zmOthUIcxYvG4Us51D139aEPs7XPrRYyUcD9353f+G3oqAwdlqf/nJd/HYy6bempaxI2c8Om9bn4GhDbZ2YnTFj6DqHTdtpdiFYNehO6/vmmOkD+C1oFEpkMct92l0TxGc1ajT/g4Mi9fh1+ml+e1nQ/8TTP0ppP/j61JsWFinT6UctZim3keReT8fp+jEfZN3u6TytdMEhicDM12KWBkNyTXuqyhLykzUJtGS3vnLj+9Abk7Zt9NHSUhGshdSBroi9h3GH6w8+aVxh3bjKTSJL68+HY6nufGag/wm/3mh6xcROI7AAzVVfo3zan9j1fLn9ZZ4bhVIRs1nh6jq7+KjN9xDuzzQaVy5MtYcse/q6OohXHNW9v66DtwuqR+Qultp+PTHpE3XO/b88GZOf1T0kpmo7VSIEIGYRCFUsX9eT8MyAd6VOks0lW0t6SXrXC+nvSOZL3PXwkNJ9JNTB/7erV0kLhkDbQEArikbMttZlVeu3IVBmBIyYLTPgLbw5n5g97rjjgkWLFgWvv/66iWGQ+TWAl4Rhw4YFG2ywgRGz2dwXjJhtG4/xzTaK/dTyAknU8pOXaAhHiCNenKOBl/ELJHGWsXwh/aiEL/Mpu7fEvZQT7yKJhvN04m2Jr8fCZzSwEHCJZLnE13Xp13X+VMkai246Fw39deLnEshjyvFFd43kbAmElU9M/lfHSaGDMm6QUN71w4//qvNDJUmB9pz+A0lKKedZFHblCyV2U6qt2EKZkDGu7kEJyid7Ok/F6HzFy6curpukkMX83qjKXZ+5ruOuBa5Bp0PcVHJBRVcHEVtflGXlySLG3slBjEGg3i5L2yGurB8rr29K+eG+rp9WmZ0kf00pu5oknFmxUG2fBBnol09KB3MqtpD+rSIlky1GZ1Y8rXF/zdUhHP4vth8zKz4O6kIXeE41jFX/n2P1tR9qI8U8DtTPTVTfxYo/Tqjzc7KUdmdVbJtHtav7yf67q/dpfT+27pkV76rdEYnjhzyvCfdmi21SdWYyh+rD7pIZkuQ5W00eL5JOtYRF1rgwUyfd/wn3goLDpOkHbDJyev/jRk7r82IDkRizhyqErEjcG4+Y2r/hOvIbGzV951Pjyqvcx+Nn7M3vUUM4fOrAHeN0OTd62s6HNigqIRL4tljdaX3/5euRPmxa/2/G6qrew6btuE9UX8Tog3H6o6b1mRPV5RhyduTUvmOE1Utx5aLnNPYlo6f2Pa36xp1iP7DId2yF4KVuZnnv3Ev1PSFx15gf8xt/ucR9YMVvo8ufqbQfeOZweQf6GTHpFzzdKMHr1L+gxAmS/3q6rv5FOgfBBeHgzj2mdMFBpNhtcaTY8uqOa1x7uSpfclS3HZZP6Dw3rj7/nIjMl3DhC6EbV+fS6q6Dwz1lcxC96vvLWK0GnltcV18SMQvhvHRC1x2XVHd83O+Tn1b/nopzX+zqjsYQrKr3Qrk2/sivJy4tMvP2pUd37RWtwx3Tt7hynPMtTZuinwWurl1i9t1lHnMRq5DckhlJmELOr6judNyy6g4vJo2d88LuMwjmxRN7xN6f/b4p/Q+6WC8830ZDlveQaN12XFoE1lL1a0sgVb8o2UCysYT3IDw2xFnP9tT5MyR8ZMN18ZHkXAnPoNRDfdSLWDAEWj0CWt8zYjabRU6rxRBo8wgYMdvmp7igATpidu211w569uwZ7LXXXsHee+9tYhiU5BrYfPPNA641s5gt6N80SZl3IguGQNEIQIjtKoGIPUDSU5LPSzIv5P0kh0hYrORL6HUlTQmU6yth0YaXe17W00IXZX5bMkayr2RbCQsFWYUpqsgtLkFe5xN6SOn7EnCkb1tJShlY1HhN4vpZnXFjd3h1X5lx3bmqK/X80v7fJGA3loMsQj1Bu4OIyqEiCCeLbLte8hsdn6j4UO0BunUW7STVIavRLmp3T7U3UXKZ5FrJ2SLfjpR8G0vYpLJp5yHv1H9I45EiHs+VnK56D1NbX00r11x5YX9xM80+vLhlnlnxB8kvQqvaWRW75LKQTet3aE1M3TMrxqn+8xQfGWIzd08InaJDVnMoEryr+rWP5mmi4l9JuBbPkYwVJt/kWs2js9+RDv8ji/LQTVSZEkxZ+/Abdu4HMTpyet8zFF8zevrOv2UP2tHT+v3g8Bv7ce+0IAT0tLfWkTfssp1wGj5qar/zhdMVImtvGDV150tF3J7MeeG1x6SZByQR6uXAMat7J88ZPSVcZ6Mle0tY4G8Jgf8Pnm/4sIpnHD5WatFhyaRtO6+o7vYtkXbVuAJeOrHztdr/9ewV1R2OWDKx017BlCm5nqvC8S2d0LvL69Vd96aeZdXdLl42oeP1ImzP0Z60Y96c+OVvQAwmAZFGzLoyEKSvH7X5j0UInrV8QsdTaWvlcT03c/mFxhDEqnOn5RM7DFs2sdsU+otlrNKjsISNs5AttI0s9IvBNa599p3FvbHGdwhYhuOe0PnXkLaQyW9M2sJ92BBXvOEc18XyCd37UUbXzBlLqztfIxfMv2UPWhG3P5CVbr73Z94B+L1AIODSnoGzuoeoGQtlRIB7NsK9hPsAv0N86MEHUmnWs7zbzZK46+NZpXlP4j67joT6qNeCIdCqEdAznBGzScuWdt4QMAQaIWDEbCM42v3BU089FRx00EHhc9Jaa60VrLPOOiaGQcmuAT1shdfakCFDzJVx8XcfI2Zb9dO7db6cCPDC/5zkYclUyc6SpMBCwZMSt4Dw5yTFZj4Pie76+J7SLIxkGQaoMlf/KqUh8NtKYI7BjPF9ra0MysZhCGSMAJYu/I/cmnG9Vl3rRcDuna137kre83yI2ZJ3whpoLgQuVcPumfGqlE7YPSQFnFaS5ZOzfFjLggQf6+LBgQ8r+ZClh2QbSe/6uKfiIyVLJVwneOK4RtJNAsnryFkjaAWGhdaJgNb2mo2Y/fTTT4Nnn302uPzyy4PJkycH119/ffDKK6+Ey40ffPBBUFtbG9TU1ATXXXddcOmllwYvvPBC8UuRVoMhYAg0GQEjZpsMXZssuHTp0uCmm24KzjzzzEZy4oknBiNHjgx+9KMfBT/+8Y9NIhgccMABQa9evYLu3buHlqUHH3ywYRTBKO26OeaYY4JrrrkmvPZuvPHGwPaYbdLthfcgC4aAIZAnAnyh7RaN6lLKnOnpoT8xRbe5slgIeUDixvOHEnXE329vbInaKHe1uFBz43pLaVsEKvcMWHutAYEfqJPu44XjW0OHrY8lR8DunSWHuHU3YMRs656/InrPC+kbEp5JP5VAyMUFu4fEodI6z/HsjGDxGmc9C+GK1XQvCeQsQnoHCR/IQsxyvayUnCLh40/qMoJWIFhonQhoOa9ZiNmPPvoomDNnTuj6cvDgwcEPf/jDYMCAAQEL1AsWLAhWrFgR7ie3ww47BF//+tdD14V1dXVNWn20QoaAIZANAkbMZoNjW6mFj2veeuutYNmyZY3kxRdfDO6+++7gqquuCq6++moTDwM+NOJDpJ133jno3bt3AInNOcMp/+sEvKZPnx5Ayhox2+S7iRGzrfOx3XrdTAjMULuOyCQ+S7KJ1xf+oY6S4ILN6bEv0paSlhBGqBMHScZJ7pe4PhLjQq4UYYgqde28oDSWDq094JaSMTHPYGnBEDAE1kTgGZ3i/wQvAx3XzLYz7RABu3e2w0kvZMhGzBaCVpvSPVWjcc+Kf0gZmd1DUsBphVmOnIVMhVTlHWF9CQS8s57trnQPyTYSyFninpIfS56SuOvmRaUPlVCHT9Dq0IIh0DoQ0JJesxCzWFphTdWvX79g9uzZwZNPPhlazLJQjTUM+exXyJ5yWMbMnTs3XPhv8hKkFTQEDIGiETBitmgI20UFkLXz5s0LyTMsak1WY3DzzTcHt9xyS3DssccGW2+9ddChQ4dg7NixIU7kGU75XysQsk7MYrZJtxUjZlvHY7r1soUgsL36sUriFgGI+bJ/Ub18EskjvyURd/TT77tL/17nSxmuUOWurUmlbKhMdbOQ9DvJwDK1Z80YAq0RgW+p0+dJ2sLHGK0R/5bYZ7t3tsRZaUF9MmK2BU1G+brChzt4H+E5cbmksyQp2D0kCZnWfZ55RSBonfUsFrB8/Mr14axneyrtE7Q9dHycZInEvWfgDYiPQqnHCFqBYKH1IKDlvLITs5999lnwxBNPBFtuuWVw6qmnBljPEhYvXhxUVlaGlkRYXO27776hpeyqVasCLLMghSwYAoZA8yFgxGzzYd+aWl65cmVw//33hy5mHXlm8Y0h8cqHRnyUtP7664d7sfIB0mWXXRZAzBpGn5OthWBhxGyT7g5GzLaeR3XraQtB4AD1g4UjtwCQFL8tncoW0mfXjb/F9Ps+nSs1ccKX7y/Vt71CsW9lrEMLhoAhYAgYAoaAIdDeETBitl1eAZdo1O5Z+uB2iYAN2iHgyFkIVWc9+yWlN5NA2GM9u7Wkl6R3vZDeTnKRxG2f8D+lb5SQB0EL4UvdiAVDoMUioOW8shOzkKwPPvhgaC2Eq0sXIGBPOeWUoGfPnsG//vWvkJhlAduCIWAItAwEjJhtGfPQ0nvhiFnczZoV6OdWoLfeemtwwQUXBLjv32CDDYINN9wwwF0/e/RiSWtYfY5VIVhwnf3zn/8MPvzww5b+r9GS+mfEbIt9MreOtWQE+MfB8rNG8h/Jh5KPJYsk8yTHStK++Fd2s4TT1CoEKZa9D0nOkuAqrBxhTzXCQgmLbydILBgChoAhYAgYAoaAIdCAgBGzDVC0l8SmGijbQvBsOL29DNrGmYqAI1Cd9ey60ua9a2NJB0lXCVvE9JQ461lIWo7xZMO2M+59A6L2XAnXmRG0AsFCy0ZAq4RlJ2axmJ0/f37QrVu3cK89t1L5xhtvBIcddliw4447Bi+99FJIzA4dOtRlW2wIGALNjIARs808Aa2kebwgLFmyJMDzgcnnGPC7dvHFF4du+nHjv8suuwS77bZbcMYZZwTPPvusYdXE6+WFF14I3nzzzYBnCwt5I2DEbMt+PLfetRIE3CJCK+lus30xzhfwCHhZMAQMAUPAEDAEDAFDoAEBI2YboGhPCfds2J7GbGPNjQDvCpCzXB9Yz64nwXoWkrWTZHPJVpKekt71AlHL8f4SPkB1lti4Oj5SAslLfdRL/YgFQ6DFIKAlvLITsywbvvbaa8E+++wT9O3bN7SeZREfa6IePXoERxxxROjW+Dvf+U5wyCGH5L3KaIqGgCFQWgSMmC0tvm2pdkgyvCOYfI4BHx+ddtppQVVVVTBy5MhQRowYEUycODGAXDSsPseqUCy4N1koCAEjZlvMk7h1xBAwBAwBQ8AQMAQMAUPAEGinCCyf2GHYsurOv4jK8uqubWF/+nY6qzZsQ6DJCDjy1BG0znqWLVLw+JPk3hiCtofkcAmeghxB+6TS+0ogen2CVocWDIHmR0DLeM1CzOJy8K677gothiBn995775Ck/d73vhc8/vjjwdKlS4P99tsvGDZsWEErjaZsCBgCpUPAiNnSYWs1t30EHnvssWDUqFGh6+KTTjopOOaYY4Kf//zn4d7q9957b9sHwEbYkhAwYrb5H8GtB4aAIWAIGAKGgCFgCBgChoAhYAgYAoaAIRBBwBG0kKm4JMZ6dkPJJhLcG3eRbCGBjIWU7V0vverjKYrfkjiC9i6l+0ioywhagWChZSCgVcJmIWZZnXznnXeCv//978Hll18eXHjhhcG0adOCf//738HHH38cfPDBB0FdXV0wb968lrSQaX0xBNo1AkbMtuvpt8EXgQD/O1OnTg0OPfTQYMaMGcGUKVOCE044IbjzzjtDF/64OMYFtAVDoEwIGDHbMh7DrReGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCEQgwAErbOe9d0bQ9B2lHSTxO0/C0G7k+QayScSCNrPJDdKtpcYQSsQLDQ/AloAbDZilsVHFqtXrVoVvP7668G7777baD0Sq1rEgiFgCLQMBIyYbRnzYL1ofQisXLkyOP3000NX/c8880xIzB577LHBggULgpNPPjmYMGFCsHDhwtY3MOtxa0XAiNnmfwS3HhgChoAhYAgYAoaAIWAIGAKGgCFgCBgChkAKAs56FoIWQhWCdn0J7o03k+Taf/Zb0rlb8j8JBC1E7XUSrGwdQeva0CkLhkD5ENCKYrMSs611RdP6bQi0RwSMmG2Ps25jzgKB+fPnh5ax559/fvDmm28GZ511VjBp0qTg1VdfDa6++upg6NChgbkzzgJpqyNPBIyYLd+jtrVkCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgCRSDgyFNH0Ba6/+z+artW4twbf6T05ZKtJRC01OvaUNKCIVB6BLSAZ8RsnquYpmYItHcEjJht71eAjb8pCPB/gxvjQw45JLjjjjtC7xBnnnlmSMz+97//DR588MFwn9mLLrrI3Bk3BWAr0xQEjJgt/SO2tWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAIZIuDI0+j+sxurDX//WQjXXhK3/+w2SveQHCj5q8QRtO8rfYmku8QIWoFgoXwIaDXPiNmmLGlaGUOgHSJgxGw7nHQbctEIODfGhx9+ePDcc8+tQcwuX748OPHEE0N3xi+//HLR7VkFhkAeCBgxW75HbWvJEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEMkQAghYrVwhat//shkq7/We7Kr2FBDIWUtYRtJC1nDtY8rDEEbTvKP0LSWeJEbQCwULpEdDinRGzeaxgmkrTEYDMK2cod3vlHFtzt2XEbHPPgLXfGhF47LHHQjfGP//5z4P33ntvDWL2008/DX7/+9+bO+PWOLmtt89GzJb+EdtaMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgRIh4KxnfYKW/We/JNlU0lHSTbKlpKckStBiVTtc8rjEEbRvKX225MsSSF/qdu0oacEQyA4BrSlmSsx++OGHARY/EDjvv/9+cPvttweXXXZZsGDBgmZbvmTRm3383nrrrWbrQ6ENv/POO8H06dODCy+8MLj88suD119/PWcVYP/KK6+E2LOH4bXXXhs8+uijOcsVokAbtbW1Afsk/u53vwueffbZxOKLFy8Orr/++tBN50cffRQ8+eSTwdlnnx2ccsopwZVXXhnceOON4bwkVpBnBtcaeC1ZsiQc+wsvvBDW/69//SvPGkwtXwSMmM0XKdMzBFYjwP+M78aYs++++27guzLmnHNnfPHFF5s7YwCxUGoEjJjN7lHaajIEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIFmQsARp5CoWLuy/ywE7UaSzSSdJLkI2jHSeUbiCNrXlT5dggWuEbQCwUL2CGjlLxNiFvLzmWeeCY499tiQeGNFccWKFcGPfvSjYPvttw8Xpku9yhhX/6pVq0JLpEMPPTQkBuN0WuK51157LfjmN78Z9OvXLzjmmGMC9iFMC2+//Xbw61//OjjppJNCchJynPKQulkGrL1uuOGG4IADDgjnFeI9LtCfP/zhD8GPf/zjkJRfuHBhMH78+GC77bYLRowYEYwbNy7Ya6+9gr/97W9xxQs6B+l+zjnnBL/61a/Ccoz9pz/9aXDCCSeE1mkFVWbKqQgYMZsKj2UaAmsggBvj0047LXBujFGII2bNnfEa0NmJ0iJgxGz2j9RWoyFgCBgChkAuBL4xqnbrXYfNHRyVIWNmb++XzVfPL5NleuiMYJ3BlfftGu0nx/uOnI0VhgVDoFkQ2GXYrJ3jrstvHjZr82bpkDVqCBgChoAhYAi0HAQcQQuR6ghaFj98gpbfy60kPSVxFrTjdf45iSNolyp9kgQLXHNxLBAsZIeA1v0yIWZZaMaCskuXLkFVVVW4nIgV429/+9vg1FNPDf7+97/HLjFiSekHjj/77DP/VEP6448/DpCkQFk/HxLpP//5T7DDDjsEW265ZfDII4+sUTRaZg0FnUAH4tkF+se5tJCW/8knn+Qsj+Vrnz59glGjRgXPP/98gKWqH6jfx4lF/YMOOigkwtFjPu69995Yi9ZoWcbm4+a3Ex0rx1jC3nXXXUG3bt2CadOm+ephGtyfeuqpkJQ944wzgg8++CAkxb/1rW8Fw4cPD9NYUX/9618P7rvvvkbl0/pCvWBHX0m7QFv77rtvSMRyDkvtGTNmBHvssUeIgdOzuHgEwP2vf/1rsMUWWwSdO3cOjjrqqOC2224LrZ+x8L777rvzsu4uvidWgyHQOhCYP39+IzfG9DqOmOXed9VVVwWHHHKI3bdax9S29l4aMZvdo7TVZAgYAlkiMLiy5gcDh9f+amBl7WX9h9eNmDIl4Mt3C20EgYFVNfcMqKwJojKwsmahP8R89fwyWaZ3G3b/oGgf3XH/4TVjsmzL6jIE8kWA+6G7DqPxoKq6q6P12P00iogdGwKGgCFgCLQTBCBoeYdIImjZRzaNoO2h/EmSlyWOoH1D6XMlWN4aQSsQLBSPgFYWiyZmIcruv//+YMiQIcE666wTfPWrXw0tGLFkvOiii4Lq6urQMhJXwuyxd9ZZZwU333xzaEGJ9eQll1wSsAffz372s5DUPfHEExuIXIjAl2UBSTkI39GjR4dkL0QkAdIPkvDoo48OF7RHjhwZ/PKXvwxd+tIedW2wwQbBhhtuGIwdOzaoqakJy9xxxx3BxIkTwz39qJMyixYtCglQXPVieYor4AsuuCBs9/jjjw/mzJkT/OUvfwmtPSlzxRVXBFiGYkWKq0pc9N50002hi8phw4aFxyzKEyAUceULUVlZWRnWiSvLJMIaYrZv374NZCOujOkLVqgnn3xySMJiSQvutE9fttpqq2CbbbYJsYLMBbO6urqwj7gOZtGfc0OHDg2OO+64cE7++Mc/BmAGAcwYHAHMuCDaGOfBBx8cWnw98cQT4ViYk3/+858hOYdONFD2mmuuCQYNGhS2gbXshAkTgs033zzo379/cN5554VzDmE+d+7ckGBmjrH4Peyww0LyluvmxRdfDKvm+qK9yZMnB+BK/yH7H3rooWDp0qXheeoeMGBAaB3NNYH1Nta6WOm6MUX7aceFI2DEbOGYWYn2iwD3SjwMcC/605/+1ABEHDFL5gMPPBDe43Bhb/etBrgsURoEjJgt/hHaajAEDIGsERAZOzVKNvSrqp07fvz8L2bdltXXPAjkS7jmq1eqURgxWypkrd5iEEgjZvVxwzV+3XY/9dGwtCFgCBgChkA7RSBK0K4nHFgM2VjCHrK5CNpe0jlB8oLEEbTvK32FZDsJBK25ORYIFpqGgNb7iiZmIcKwnNxkk03Ca/RLX/pSsPvuu4fkGKRe7969Q4s6LC132mmnoFOnTqFLW1wcUwbrO0g8SEXIxQ4dOgQ/+MEPQss7XPpCHEK8odejR4+gZ8+eoctkLHIhUamHclhkfuUrXwnTP/nJTwL2GsWd79prrx0K/YD8mzVrVrDtttsGHTt2DL72ta8F3bt3D+uFWKVOCNAvf/nLQa9evUKSmbo322yz0PIWK0/c8dJv2mWf1DfeeCMkMOk3bdA/rEmpf//99w8JRkjq7373u2Fbu+22W0gikr/nnnsG//73v9dYdnXELIQw4aWXXgotaCG9saiCcATLffbZJ8xj8Z+2yWeMjz/+eKiPe19IbFwPM2YIAkhSSFFI0r333jskzrE4hQiG6IU0cBatEOeQ1GBLHWAK2YCVKvMRR8xCsh5xxBHB97///XBfX8aOa2Fw5Lr4zW9+E/aRPtAeY2O+aJ/4yCOPDPuG609cOLNXLNfR4MGDQ1J60qRJ4fWCe+qnn346nK+tt946zOc6hNBgzFP04sLHAmn74K4BvJ1IRcCI2VR4LNMQaIQA/y/s843Ldz78cSGJmOUDHD4a4oOaNK8Lrh6LDYEiEDBitmmPzVbKEDAESoVA/xF134+Ssg3Hw2cfU6p2rd7yIpAv4ZqvXql6b8RsqZC1eotBIF9i1u6nxaBsZQ0BQ8AQMATaGAKQsz5BywefuQja3tJBcHUMOdtDcoTkMYkjaD9V+hbJQIkRtALBQuEIaFGvaGIWou65554LCdQvfvGLIYmHJSTkIoQfhOV1110XkmyQr1iwQrZCkLIH7VprrRUSo5CLV199dQCxC9GGhSYL2pCiO+64Y+jekUVriFFISNzgQjx+4QtfCAYOHBhaJmFRiiUse6uyxy1EHe1Tx6WXXhqSoFjYYpl57rnnBvQT61HqgHzEypbz9AliFsterD8hYuk35WbOnBkSrvQTkpDFdEhCrIUhS3HtiuUspC9tX3zxxaEbZfS7du0aYA2FpSrtnn322SHZGV1cjRKzWI/SH/q4YMGCEG/qoT4sebGQhaSFCMaKFLIXrM8///zwmP1cIZL/8Y9/hHkQpZDP9A1iFLfHEKeQphCrkLZYpuIKGnIUF7UQu7/4xS9yErNYBtNP9huGmMDiFQIdYhW8sKDleoCY5fyf//znsG9YUtMXiFTIcfqL1TL1QfQyt46oxWIWrNmjdt68ecE3vvGNkHBmLmgTd8aQ5swB820hGwTA1lwZZ4Ol1dI+EOBexAc/eE1wIYmY5bcUXfL5X7NgCJQQASNmC39kthKGgCFQSgQGVNad10DEVkZc3VbV/KmUbVvd5UMgX8I1X71S9dyI2VIha/UWg0C+xKzdT4tB2cpmiMDWqutb9bJDpN60vIiqHRoChoAhkAkCSQTthqo9zoIWQjZuD9qhOj9H4gha4hrJPhIjaAWChfwR0KJf0cQsC4erVq0KCc31118/dNMLGbds2bIGYvb6668PsH6FUMViFmKSMrinhcyFwGVBGtIRS0wsZCHdIOsgPLGEhYzDgrSHrGYhWnF3C1ELYQpx2q9fv+B73/te4Fz8shCOZSd1YcH68MMPh3vFLlmyJLj11ltD97wQrRCEtLHrrrsGK1euDN39rrvuuqGFK26C6RN71Lp+o4N1Le2OGTMmJGZxTwzxyp6bWGxCCkNMOhIaopQxUC9Wt1iqUpZ+0EY0RIlZLFUhpBkbwZGd7PN55513hhaiBx54YEh0s6APUQ7WEKkQtd/+9rdDC1b2MaQ9iGswgahFn/ohXs8555zgwQcfDF0/Y8WMi2jGhKtjLHyxXKUOLFXjLGapC3ecELsQ3C5gwYt1MHNI+7hNhmyH5IZch2TFNTEBcoI07YMhrpFx+QxBjrtrLGmp3+1RC3H7ne98p8HtM3Uw95DN6ED2W8gGAebXiNlssLRa2i8CScRs+0XERt4MCBgxm/+jsmkaAoZAORAYVDnnnCRitn9VzZ3l6IO1UXoE8iVc89UrVY+NmC0VslZvMQjkS8za/bQYlK1shghcoroccTEvUm9aXkTVDg0BQ8AQyBSBfAjaLmqxu2QrSU+Js6AlhrDl4xKI2Nsln0jcve4fSg+TYJW7joR9bl17SlowBBojoMXATIhZSFX2L4WYHT58eLjGmETMYuWJC1vKYNG53nrrhfvqQfpgoQnxCpmKJST7qeKKGBJwv/32CwUSDre6EHW0gdUnFpqQlLRPWfKxIsX6ElfFjoTEegnL3D59+oREKXoQmljMQsy++eabjcbh+oR7YvoNKQWhDGEM6Tpae7BipQkxu/HGGwenn356OHbIRPBgbJCZkJEQqKSpi7IQnbQJuRwNccQsboohsgkQj1gMM2b2vQUHXAdjgUyf44hZ+kigL+w5Cy64CSY44hdilrmhf1jgQoxi5UoMQYuL41zELPOGW2JIYRcgZvfYY48GYpY5gJjFmhWcINUhwF3AWho31Lg2Zn9ZXCpjFX3QQQeFlrG4ZIZQx1oYYhaL4BNOOMEVD/vIfsK0AQltIRsEjJjNBkerpX0jYMRs+57/FjJ6I2YbPw7bkSFgCDQ3AgOH11UmEbMDh9ecXo7+6Qa9lkjg+9WPR6IyqKpuYjn60NbbyJdwzVevVHgZMVsqZK3eYhDIl5htCffTYsZpZdsMAmnka1pemwHABmIIGAItGgFHmEKeQqI6F8dY0G4i6SBhD1qfoI1a0PZQ/mDJtZL3JI6gfV5p3h2+JDGCViBYiEdA75+ZErMQkbjAZX88LDWdK2PfYhaCE/LOEbOQqZCGjlB0xCwWs+yXCmmK9SQWr1hj4oYXAhSrSqwiIUkvueSSACLuvPPOCwnPLl26hO53IRwhIGmTsljLQmButNFGIdn42GOPhe57sZjF1a5PzGJNGyVm6VMSMYs1LHVD1GIdzF6wWNFWV1eHpCPuec8888zQfTJWnAMGDAj7wXiiIYmYZd9UQi5iFlI6ajFbVVUVlo0Ss4zRJ2afeeaZkPDFOvdluTVGIEchhSFUsWhNspilgUceeSScL8bqQhIxy/z9/ve/D62BIa4JjI25hHiFmAcrXBJjWUvfIGLpC9awScQsFsUQ1pDZcfvgun5ZXBgCRswWhpdpGwJxCBgxG4eKnSszAkbMxj8W21lDwBBoLgQgHPpX1dZGydmBlTXPHDh+PgskJQ/ppEftZSXvQDtoIF/CNV+9UkFmxGypkLV6i0Eg/R5Vc42ruyXcT11fLG7XCKSRr2l57Ro0G7whYAiUHYE0gjbOxXFP9TCOoN1J5y+WvCFxBO0Spc+QdJTg5ti3oKVdC+0cAS0EZkLMstDM/qRYt+L2FytLCDRHzLJXKPuDsk+qbzHrrErZoxXSBwtXymPhiktdSD6OsS7F9S8kJ8QtRC2kKvvGQrxioQlBO378+NDaEx3y2c8UC1XIXyxjsZ7EahUSFStLyuAWl35jXbl48eJGLpkdMcv+q45QhpiFdKRO9sp1FrPsS8u+rT/84Q9Dopk01rv33HNPSArTD9wZQ3hCOIIFLpmxQo2GOGIWopX+EiAvISVxr4zFLa6TaReyEoITi1PaA1/IaCyKhw0bFpaFmL3iiitC3LBSZYwQuexJC/GL9S3kOmQohDokOkQo+DCPELO4iI5zZUwDWOFCUOP+GIKUwFywD+xJJ50UWuyyX6wjViHMsZjFepm+/+lPfwotYzmHq2OIW9piLLhevuqqq8L5xrUze99CEmONiyU11r58FIDFMmN0+9CGnbA/RSNgxGzREFoFhkC4hyy/IfxO8rtowRBoBgSMmG3nz/82fEOgRSKw55S5Xxg4ouYokXK3Days/fOAYbWnlouUBZB00sOI2SwumnwJ13z1suhTXB1GzMahYueaG4H0e9TnxCz9bO77aXNjZe23CATSyNe0vBbReeuEIWAItDsECiFotxQ6PSU+QUuac9tJzpS8KnEE7SqlfyPZXmL70AoEC6sR0GJgJsQsRCH7gGIFCom6yy67hBat48aNC0m32267LbSghUBz1q+QuZBn22+/fbhADenz0ksvBbvvvnuog5UlJChEHGUg5yBcsWyFYIV8g8idMGFCSHhCRLJP6m677RbWCwGJBSxueCFVIUIhGLGkpJ+Qv5CNELXUTznadH067rjjQtISi1FIRXQgBrH0xUIX4hJrV0fMYi1M31zdWHziqpl+QpxiMQsJTD/pC6QhBG3cwnyUmF20aFG4TyztEhzeuPfFutRhCVnJPrtz584N9RkLbeNe2u1PizvnG2+8MewrlrIECGyIash1yFRIcXCBHAUX5oR9dV999dWcxCyW0qeddlqIGf0mYOkK+Y574w8++CC4/fbbQ9Kea4Y9e7HEhVgFYzBkDOxDC3aQzFhU0xd0cGMMSY9ra8pBpkNyMB9HHnlkuN8u/YT04MOA5cuXh32wP8UjYMRs8RhaDYaAWczaNdACEDBi1l4EDAFDwBCIIpBOehgxG8WrKcf5Eq756jWlD/mUMWI2H5RMp9wIpN+jGhOz5e6btWcIxCCQRr6m5cVUZacMAUPAECgbArkI2s3Uk06SbhIIWtwZxxG0PXX+aMkCiSNo/6f0vZIDJUbQCoT2HrQ4mAkxyyIjRCjEJlaMWFlCqrLXKdaSkGMQfljROsIVy0tItUcffTR0pUsdH374YUjEoQMBChH01ltvheewnqRu6oDMI7DfKSQcbo0hXGfNmhW63fXzIR1ra2tDt8cvvvhiWK/rJ1an7GuLBSjnKJfWJ4hC2oRMhaSlPkfMssfsGWecEZ7H8pM+OVKQcaAHFrjvZW9VrIFxeUxeNEDMQkQ6N8fggmUoY3WBvlAf5DN10BaulpE33ngj1Kd+cP/3v/8dWsVSFtzRZbyQpATqBwNHEmN1CukNbm4s1EWgLepLspilLPPE/rkQwASuDSxpGRftgwXj4RqhPn+OwQcil3ME+sZ1BAGN9TG4vyyyHB36C0kNLuz/Cx6MCbfX35KVMMQ482UhGwSYK3Bm7tnfmH2H+eiCecZlNPPO3FowBAyBZASMmE3GxnLKhoARs+39BSDj8a+r+jaS8BJbTODltJiLk/LrFdAB3OMW2+f1VQftFhOyqCPf9rOaK9cefWffpBYbhs4I8u5fOulRPDFLX/Ltz+7jHth4yIh5O+05cS7/W5mHXUbVdexfVddn0Ji53Rh3oQ3sPq6m+4DhdXsNHHHf7t+o/tuX8y2fL+Gar16+7RaqlxUxW+p5jI5r/Pj57I9WllBIW029XnINpNjrWI9da31jXN02A6vq9hlSOa/fkOMfKuY3KFd3K/LFjH7tNnpO711Gzu5F2lWcfo8qPTFLX5pyv3D9dzF1DBk1Zwuset25FhpvrH7xu1lMKOdvfDH9zKcs12IhWwukka9pefn0JZdOFs87bWnucuFl+YaAIbAmAtzzEJ6TeZ9we9DyrMDzuSNouyq9hQSCtpekd71A1nK8taRKUiv5TOJI2heUPlECycvvIe0grl0lLbR1BPRslRkxq7rCADFXCjIMghGJCxBG5EHSxQX6Qz56LqTpO518YixS2U92k002Cc4555ywCBhAQMYF+ki+35eoHgQmrn733HPPkPByRHNUL3pMnVliT13+WEhD/EJAYxmNVWtcwPIZi1UsXSGJ8wn0HWyS5tjl+bj5afCmvxCDF154YWhdC4FsITsEwNuI2ezwtJraJwJGzLbPeW9hoy7pumNbf3a28VVUDBEIF0nukjwv+VTCCyYvmk9Lfi/hJTRX4KVzb8mtkkUSVw978TwgqZbEkQyH6vwf6+VHintKpktWSujDfySnSL4k8cMmOjhbUid5TUKf35E8JLlS8k1JrgAB9VPJPMliCXV8JHlGMkNyuqSzJC1kUUda/X5esXMFvg5rcCccIPmd5HEJc/aeBAwvk/SQNCl887BZmw+sqr2pf1XNzVGBJHOV5tLbc/zcToMq55wDsdd/eO2i1XvW1r4WHo+oPRci0tXlYt2g19L+ttfJffIt0T1uG46rav77eb9qr6NMrr5Q/y6Vc348aFjtLNW9dEBl7f+ob+fR9+3g2vbjgcPv+64wuEHyL7X1WX3fVab2Pyo/ddcRc7dU+nuf96MRVtPSSNxdRzy8yaBhNWeFhKfG0jAu9UfpjwZV1t41cMQc5jY2QFj1G1H3M7X9oLBaGSkf6Nxi9ggeOLxmZGwF9SfzJVzz1ctnDvz+QAQNqqr5QxyGzJXTLYaYzXIeB1TVTorr66DKul/QVwjPQSNmny/sn9V+zO8zLxof96KCQ5ZtZXW9RAdR7HVMfbsfdu9XhdU1usb+0X94zXv+tSysP9P/2vPC8y8DK+sOibYfPc4Us8Nndeive5T+h+rUfsP/mPq5SsTxvIHD6w7Pl5gt5P8i1xgGVc05UNfb9Q4vYfSh7q2PhRjmgZHDjA9BNI4L+1XVztU97m1wV/yB5OH+w2f/MpzbqrrD4q73/pW1PFeUOvDMcKzkD5K/S3im4DceYb9ALJyOkPDskhZK9RtfpUbd7/E1KR34mqeH/m4pur/ydL+boDdY538r4bnsbQl4vCy5Q3KyBPIyKVyiDIchz01+SMvz9fJNF/u8Qzulmrt8x2B6hoAh0DIR4L6PQJpC0H5BwscfG0g2kmwq6ShxBC1EbC/JNhJH0nLcQ8K96grJSom7P/IudbWkn4S6acMIWoHQHoLeKTMnZlvY4mPJu4NlJ3u/4jZ32rRpmbTHPq8jRowIiVnc8/qWspk00MRKIBSuueaaYP/99w/22Wef0Io1rioI3Llyp4zLYaySyxUgDrGkZS/i6667LpHkLVd/2lo7Rsy2tRm18TQHAkbMNgfq1mYEAd4hLBgCBSPQRSWmS9xLZFrMC+aklBZY/GKRM60O8l6UsMjoh/N14MqxsPeyd+zOE28ncWGsEkslfn40jWsp6kv6B+mhPEfoRsv6x5C9P5DEhSzqiKs3ei6ruZqsit3YwJ1FWHccF7+pfFxzFRzyJcLS9EQuXqVF/td9oiWahiDtXzXn234H08iOaHl3TJm0vojwGQPJ4vT9GEtYv31ILPX9cl8nLj2osuZNCLi4PM5B3Pr1uvSg4bUHidB9LamcOy/s7nNl/DgkGitrX3J6uWKI6MFj7u3p1+HS+RKu+erlmgPXrov3HTn7S0n9hxxyeoXWS7lSzGMaDlh6al5fiY5H1/jtbhyFxFm1leX14ve/2OsY62WIQf0PfRzFLOmY/4no/6vfp6www/p8wJofTPDRRCMRaXln9Jw7hih1fSvk+k0aA/1Re9Nc/UkxHzqk7QWuB9C1hONxELpJdXC+f9XsFwZU1T0Zp8OcubGVKB6qel+VxP2uRc/9VXo9EvrB+WKfExKqrhiuDL8v2yYonhLRuzhBj+cEPmZzdTb6XdR5yIFzJHyA5XTiYj5M21kSF3imcmXmRRTS8iKqqYdZPe/0UCulmrvUAVimIWAItBoE1lJPEUhTyFPukxC0fKDCxz2bSjpIuC91lziCtrfSTrZRmvsN9/ATJdxD3X2SmN+YSgl10gZtuXaVtNDWENBzkhGzkdXKQg+x0oQ4xVWvc11caB1RfaxDn3/++dC98IIFC0J3vlGd5jjGKhWXxriTpl/O3XBcX3BfjDtkXEOXM+Aemb7la6lbzr619raMmG3tM2j9bwkIGDHbEmah3fchiXdqa4+5Np6MEXhA9fkvjhCZCyWPSf4r8Rf40CM/zjKQF9fnJH5dpN+QYHELqevnQc52lrjgE7O+np9+1CkrHiHx80jTV6x9nfWHn/+kzvOS7YeNdfAvia+3UsdPSJZEzqPzkcTvsw4rsqiDevIJWc3VZDXmj9lPs1jLOP1zpD+RsBhRUMiXSEjTi1vQjzsnguCzwZW1e7oOloSYlQVpXNuc84meIbKSE6HyTJJuIefjiFlZw03Pt444YlbWmKflW97XE5HyLhZyDmMXJ5FAwmCh0yHOVy/teoAc9+skXSpitlTzmITDaqzXJGU5nzUxW0hbWV8vbv6KvY7l8ng7XZOJ/5P+tRtN69r8dFBV3UTXFz/OYn7qSct6C/nGRGy0L2nHmROzEVI4rW2Rs3N8XFwa183pGOU33hITs/uov9HfsVU6x2I58m5MfpxVeql/4/mgjd9X19fYa1L5dZ4OujynxIXROunq4vnrC54SRABEqst38Qqd41nI7wd5PAvsJYmGS3TClaU+P6Tl+Xq50lk875R67nKNwfINAUOgdSHgiFLulXEE7SY67xO0W+m4p2QbiU/Qcg7yFg9Ff5H491berSdLeKfi/kxbiGtbSQttAQEtTRox2+7XZ0sLAGReuUNztFnuMTZHe0bMNgfq1mZbQ8CI2bY2o61yPEbMtoWH+DKPYYjac4trxLMkX4n0gRfLmyS+XoMFj6f7q4jOP3Xc18vfVOmrIjpXevlxxOyryv+15HLJQolbsNxCaaw4XZ944T1ZgtspAi+4u0melzgd4jMkfjhEBy4fUrda8sV6BV6Q6f/VEqdDfJzED1nU4deXlM5yriarEX9MpFmU3V/Cl9wQ2HtI5kt8vZ/puKCQL8GWppdGHETzRF495DpYCmI22p5/7BOzWPn6ecWko8SsCJGfFFJflJjdbeTsHVX+o0LqiOg+oJ9I/j8aQhJJ09qJ2VLNYxJeEZwbWVWWhpiNJ9D8tkpxvXDhFHsd7zlm7vpJlphpOPp5EIPcexou5PpEsfODW2W1U8z/WMPcNycxC1a7DpuzbxSfphL1PvakS0zMzla/3e/Xh0qPlrjfd4bEPWyEZKnE6fFhUg+JH8rxG3+fGnR9YBE/GjbUCcbgdIj/J8HFZjTcphNOb2ok80gvD50nJQM9HV4kjpV8LHF1LFDaJ3d1GHohcfnzOOGFLIjZrJ53yjF33tAtaQgYAm0IAX4jEN4puQfy+8E7EvdjPvrgoxo+1t1csqWkh2QbiSNoiXvVnx+g+GLJMom7d36kNO/Xu0uofx0Jbbl2lbTQmhHQu5IRs61yTdU6bQiUHwEjZsuPubXY9hAwYrbtzWkrHJERs6354b2Z+n6z2nUviC8pjdVrXOBF8XGJ030qosSLKQuaLv8xpeMuSF427/b03lAaEpAQJWaX6BxfJceFe3TStUW8d5ySzvFlMwuPTvd9pXtKXLhMCZd3pzsZidfT8UKJ0wMHP2RRh19fUvpmZbg+FDNX1D/Zq4s6F0ri5ovFhtckrt0nlC4opBGuvuVjml50MT/X8a7Da75CJ5uLmN112NzBckUb7jubq6/55PvE7G6j5/QWmfFuPuWcjk/MDp0RrKPzj7i8aCxC6m9ydXoC+1NSLmkcg6pqx/oXQhKR1ZqJ2VLOYxJe0fnwj32y1Mc+V7qYtkp1vRR7HTNm4XGFj09T06rnpb3H1/LhUEMoBjMqYT/ZpvYnWq65iVl9nHBjAzBK7DJydi/1Kdz3ONrXQo9LSMx+TV2FuHS/XdX+GCLpH3t66HPsh3L8xkOGur7i8QMCwA/76cDl+zHEox8ot0ridA7yMjdTermXx3Pcel6+n+SZysdvkp+pdBr5mpYXqSbxMKvnnXLMXeIgLMMQMATaBAKOKOVdGPKU+yz3TkfQcm/tJOkm2UKytaSXxCdpSfesPzdR8aMSd58m5kPYwyUbS6IELe1baIUIaDHRiNlWuKJqXTYEmgMBI2abA3Vrs60hYMRsW5vRVjmeOF6lFT7FWpfLicCf1dhzEr7aPS1Hwxcp371EvhjRHevlofPdSL5/yJfBrp5lSverzzzfO08+lixxgRdhf8Hw9jgl79w+Srv2iCd7eVj+uryXle7o5flJ6viZZJRksJ+hdBZ1RKqMPcxqrqgcDNy4iYdyMiH8Wued7uIEncTTaYRrIcRsuA+r9natdw/6YBoB4Paa1W18LfZg1eL/yjR96kbQpUxan/161Je3B1bNXuLOOYtZ3H+6c3ExxCrEDXu25uob5X1iVoTprXF1unOqb/GgyrrfqP7T2UeS/R99YlZ7ce7jdKMxfYpOZP/K2t9H9eqPG90Dkois1kzMlnIek/BqjLXIfe0Hqvn7gPOlJWbj2yrV9VLsdbzr0LqvN8YqxvJ3WM3T7JPK/5muw0/T9PtXzuGe2BCKmZ9wX1nNV1p7utf8R/N6Rz4Wv6UiZtX+vXyEMXBYzQVpRKs+wvhrAzBK6Dq8JW1swm6V6v0TepKlabq6X2GZWYrwI1X6d8lyyUpJ2gMynjbcbxzxSIkfyvEbz6K934c9/A4ofbGX7z//4E3ED9/RgavnA6X9j+188hcd9+zll/fTvuXts36G0mnka1pepJrEw6yed8oxd4mDsAxDwBBoUwhAkCJrS3yClt8Xfkc2lfAe2UXSXbKVpKeE+3vveiHdU7K1ZD8JFrPcq919e6XSV0p2kZgVrUBozUHvlEbMtso1Veu0IVB+BIyYLT/m1mLbQ8CI2bY3p61wRGnrTq35sdb6XgYEeMlEkgJfAV8rcS+Or0QUebF0eS9E8uIOB+rklyMZUWKWl9i4wGKia4t4UJxS5BxfIrsy9NWFKiXceeIlklMlO0nyDVnUkW9b6BU7V9QxWeKPO8kyGd1jPN3XOVFISCM58yVmITIHj7m3p2sXS9iBw2qfSFrw7z+8rhGpn2Y5K+IAi5pGIa3PYZsiywYNr2uw0h4yas4WA0fMOWDolAXr6odjLREebyf2rarmwW+MqmVBJgzfHPvXzrlIIJ+Y7T+8dlFS3dQj966bubqJKTugau433LnQGjaZNHpE1nGX+5JMwtT+j31dXb1JY2itxGyp5zEJLze32vv06l1HPIzFfwXXVb+quiE7j75vB4d3IXExbZXqesngOj7CYRUXDxxeezxz6HAaXHnfrnx4EafLuf6VNTOdLnExmA0cUXdyYjvaBxtrdL9vA4bXDNW98L2kMqUgZnVP/Z0/3kHDaw9Kbr/2eV9X18R/k3T1IciD/v0KS2RheVuyfsmIWb/LWDclBRa+B0v4OM39Jo6LKJfrN/6fXh9+HunD017ez7z0sxE9nxS9M5J3hVfu5Uhe3CEEtcMEAh2sXPDbmedO1sdpeRHVnIfFPu+Ua+5yDsQUDAFDoM0gwLMFwv0JgpZ747oS3BzzXMyzG++4nSWbS7aU9JD0kjiClphjzu8oOVfCfdndc4mfkhwvoR7acPdD175OWWjJCOhZz4hZgWDBEDAEciNgxGxujEzDEMiFgBGzuRCy/DIgYMRsS344byV921T9xKJ1jOQ8CdaoLPz5L4qkX5X4YZ4OnM4sP6OAtE/Msgi4TkLZ4Trv2iJuIIcS9Dl9g8SV8V0R82Xzm16e0yF+TQIZfbAkJEgUx4Us6oirN9e5ps4V9U6WuLG+naMhFqmd7socumtkp5Gc+RKzEIXRikWMjk9c7B9eM8bXz5aYrf3fLsNm7ezX76dx85nUL5GcbwwaM7ebr09693EPbAyBmVTOER17jp/bKUlHVm3Lhhw+K41gD5sVIXNdUh2Fnh88bE5/N5YkIqu1ErOlnEcwS8KLOdB1kssLgIM9r7iYtkpxvWRxHUNcJ12vPpHpA8THE0llRChiWdkQisOsZlpSO4OG1V3U0IiXgKxNKuOPJ9/7KVWnjUH7Bnfxmg+TScS1/z+8y6i6jon9rKp9Pe7+NuT4hzbQdfRsXDmd57e+HIEF7a9IfiD5qeT3kvsl/P653zcXH65zfijXbzwL864P//A6wMK+O79Yaayw3DFxd4kLkOguj99uP8zRgcv7VGmsUtPkCU+fcr0lLlyihKuL5z8/pOX5eoWkN5VyU55NyzV3hYzFdA0BQ6BtIOAIUn5feGeFPOVDoPUkeHfaWMLHkrg57irZQsKHmT0l20i4pyKke0q4tx8q+ZPEt6L9UMe3SPaT0AZt0aZrn9hCC0RAi39GzJZhBdSaMATaAgJGzLaFWbQxNDcCRsw29wxY+0LAiNkW+EzeGrrES94IyWMS30WeW3SLi1+NDMwnb6+K5OV76BOz/0kpdLbyXJ9WpOj5WVO8Mu/6GUpvL3nZy3d1+zEvyDMkvDzHhSzqiKs3ei6LuaLOyRI3vn9FG4kcj/V0V0bych7mSySk6UFaRBtKJVlKSMzKGvbuaF/8Y1nq/TCOgOAcroV9XT89qHLOOUnlHDG767A5+ybpqF83+PUlpUXW/COpjoLPj6gd7tpJIoF8UgfdfPXSrgef0HftY72b1P/+w2f/0unlW28p5zENB8bQr3IuHgUyC0mY59NWKa6XbK7j2oeT5tu3EI+CKCJwcVK53Q6f08PpF4NZmnvib4yri/0NkaX7+iLkP4nrW9bELB+IuHH6sdqO3Xva/x/GTXxcHzkX3YvWrxtX0XHlykDMsuiNdXAcAet+A6Px4X7f69Pl+I3fRW25vnymNIv5hJESd979hvCM5M4dhpLCdhJ3jvJYWfnhFR24/KbEB3iVpZGvaXleFTmTWT3vlGPucg7GFAwBQ6BNI+BI0rU1ynUkjqBlcWYjCR+X8PEkH0V1l0DC9pDwTMDvFEK6lwTydgfJaZKnJf79eqGOfyZBL46k1WkLLQUBLc4ZMWtrtIaAIZAXAkbM5gWTKRkCqQgYMZsKj2WWBwEjZlvKg3gr6sf66uvfJP5LXzT9sfLnSSBuXd6rSvvhJR24vEv9jALSPjH7SEq5n3tt5eta90yvzPsxdfOiTPsve3puPH6MdS1fLMeFLOqIq9edy2quqG+yxI2Ll/60MFaZTrdZiNk4Im61a9L4fRyj+llazA6srOP6SwwDh9dVxhEQnBN5elNSQRGzJyaVc8QsLpqTdAaMmJ3X/51ImecT61AfC8obVnuqG08SkeWTOujmq5cvgeraz56YLd08puEgq8X/HTDp+fXcuLKIkzDPp61SXC+ZXMdVycQs104Sbv2rZr+QdI33r6rr48oVg1mSdSjtplm1q9y7cX3LnpitWejG6ce6P92b0H6Dvj5YGBWnw7m0+1uSe+cSE7M/0viwDHW/X3Hxa8q/KqJzuI7jQql/49dSo1jEun5W1ndimnduTP25y71z19WfO8E799f6c37ER2mu7qbErj/UmUa+puX5/UlLZ/m8Qzulnru0sVieIWAItB8EuI8jjqCFPF1Xwj3tS5JNJM7NcTelsaLtIekliZK0PXUOAndfybWStyTu3v2Z0jWSKgnWuetIaNO1T2yhmRHQ+p8Rs+VZBLVWDIFWj4ARs61+Cm0ALQABI2ZbwCRYF4yYbebn79bY/O3qtHvJI/5EMleCu8VRkp0lvFASzpI43f+GZz7/M8fLo86mhHyJ2aNUuesHi6689OYK/sLrwhzKLM6fKOGF90OJa8vFWM/m+mfLog410yhkNVdUOlnixtMqidlCiLtMidmqmp80mpXIAeROEnGhvRkXDJ0RsHiyRhg4vGZ2UrkGYjal7kGVNX9fo9KYE7LavTOpHe11eUoh4rt0TiKySkPMfm4B64Y4ZPicXZLG1RSL2VLOI31OxKtq9hI3pqziYtoqxfWShm2+17EI46lJ8801HIfdbqPn9IaMjiuncX44fvx8LFzCUAxm6tvtcW1wjg83XBt+zB7CiWUqa65xuoXc9xLHILfprj4/zkcfa+SkfkJI7zllbuzvcb+q2rlx5UpIzO6isfmuIPm9e1UCyXmShIVuFsUJLGCzyO1+E4/gZI5Qit94msTFsuvH9fV94J7gzm1Vf+4g79zC+nP3eecgaaPhnzrh6rlNaQiBQsR/7kkjX9Pyon1KOs7yeSfaRqnmLtqOHRsChkD7RcARpJClCL+NPGOsJ4FIxYoWN8e4W+8q6S7h/t5TEiVoe+lcD0lvyUTJXyX/k7j7+QqlL5X0ldAO7xm06fqgpIXmQEBrk0bM2gKtIWAI5IWAEbN5wWRKhkAqAkbMpsJjmeVBwF8zaY7HT2uzlSHAS6B7qSN+TrJdyhh46XP6UfLAX0x8NKUOl3WhEiw2nyFx1k3nK+3qf0TppMCCqtMj5oU1V4BkdWX+kUvZy+fl+RDJ0xJXnvgASb4hizqynCv6PVnixsPY0sJYZTrdlWmKcXn5Egn56rk2CtHPkpiNWuO6/rgYcgeyIY6E4Bwui52uiwcMr61O0ue8I2YhddX+e0m6IndHujr9eOiUBe7jigoRX2fHlafPWJ365QpJ50PqUF++ev2H3zcgrp/1516M9m1QZd0vkvQHVtVxvwlDvtdNKeeRjuSLw+peF/e3mLZKcb1kch0Przk2ab75H+k7Zi4LlA1hz4lzNxIOf0sqI2vPhxuUlSgGsyS3vbSt/7PFe4yd7ci1sMlwz119tJHYtxZEzMrl8mZJ/eQ8/2t63mUxuCHIyvb4pDIlJGavUgfc7xYL2FMkjfqlYxc6KOF0iX/iMvKMs/iNd019TwnXF6x5v+4d84zmAhZXn0mc7gCl+bDOHbOwHw0+2dnoeo8q5nGcRr6m5eVRdUhQuHEQF/Nsmqu9LOcuV1uWbwgYAu0TAX57EMhSSFMIWp7LWbjhuXsTCb9DuJ/fXLKlpIekl4R7OYQsQrqnZGvJYMmvJPxO+PfLR3VcLaE+SFraRFwflLRQLgT0PGTEbHkWQa0VQ6DVI2DEbKufQhtAC0DAiNkWMAnWBZ7vLRgCeSNQJU3/ZQ7CMy08rkynvzyiiAWKy/tY6R6RfP+QL4SxdHX6bhE0X2KWF1VXlniKJC2gT59cmavrlTdWfJPkCcl7kr0lSYEX3I8kro5f1CtmUUdSm/75LOeKeidL3Fie9huKSY/1dI2YjexfG4NXxYBhNU8nERGc71dVUyPLucO1F+VoERN/TNMlzxGztKXjB5L0ZZ36KcTILqPqttt1xMObYN2GJe6gYbWzXD8HDa89KKl8/xG15zo9Fw8YPvsY6bP35Bqy+7gaPhgIQ75EVr56uQigQcPqLhoyYt5OIak1ombCQFmaJo1r0IjZp7l+5kvMol/KecwXB9fvYuJi2irV9aK5Kuo6TrPcrL8OPuIawUJ1NTFY93LS9RGej7gCLwazXSrn/DitLdW9auDw2VP0v38o1r2ysF2aqt+CiNnw/6Ky9pXU/lbVzRs4ouYojfMnIrzvTtMtITH7nPrqfuMeyvH/8wNPlzITPf1y/ca7JnF36bsc9l0Wk/YDC/BujHO8dNJvuv+MxfNMT0laGKnM/0r4mO2Pkm0lLqSRr2l5rnxanNXzTrnnLm1MlmcIGAKGwFqCAIEohaD9ggSClvv+hhLuWUlWtL2U5whaYo57SCBpR0jukvjvuu/r+FbJjyTUT3tG0gqEcgatTRoxawu0hoAhkBcCRszmBZMpGQKpCBgxmwqPZZYHASNmy/mw3Qba8slUFve2SxkTriHdAiBxlKDDWsN3qxRdQPSrPtmrizLOnaC/aPiIXyCS5sXy3xLXn7eV7hTR8Q//oAOnS7xffSZfLL/h5V1Xfz4u4sXZd4t4Zr1SFnXEtRc9l+VcUfdkicMkaRHX9WGspxudd6eTGOdLhOWr5xoqRL+cFrP0b1CKNV8aSZGU14iYzWFdG1eHyJH7HG4ibTtKZ0WcHufk0vVm7aN7yKARdcNEmlybpIeLYt9tab5EVr569HdAZQ4yTf1N6p9/HqLMjb+Q66aU81gIDq7vTY2LaatU10suK3F//lzav46xaB44rPYJl1dMDDnYr3LuQB/fYjA7cPz8DbO6dhlX+faYrbknDseoO3JZp/8mTq8p50pIzPKxlfuNu8mf20iaj0te8HQpc6ynU67feK/Jijsi/XHjONhXUvq8BL2zI3rucK+I/p0uIyaGJPBxWaFjFvdduEQJ16957mR9nJYXUY09zOp5pznmLnZAdtIQMAQMAQ+BtZRGHFEKacr9aj0JiznOija6F+3Wyusp2UbiSFrSvSTk7SSZLPHfkblPc/++QrKHBDLYkbSuH8QWSoCA1v+MmC3PIqi1Ygi0egSMmG31U2gDaAEIGDHbAibBumDEbAmeqdtylftrcG5hjTiJTD06oocuLvSiL3JTPT0sYk+P0aFNLDVcu/cr7UK+xCz6+0hcHcQLJF+T+AEy9VKJr4cVDS+/LlyjhJ//E5fhxbzE8kLr6+3t5WdRh1ddbDLruZrsjceI2QjkhZB3kaLhIa5aRez8oylERVwZn5ilgUGVtXfF6SWd8wktyuv44CTdfM/LCpH7QkPIl8jKV4+KB1TV3ppvf5L0RPysPGDS8yx2haGQuS3lPBaCg+t7U+Ni2yrF9cJYir2Oh4yZvX2aa++ka2KN8yNqfhrFtmjMRsw5YI128vyQIFqupRGzu497YGPh82q0n005TiBmz9Z8zK4XrICaEh5WIfebvUrpr8RU0lnn/uXpOf2zIrrl+I33mxwT0yeeuTr4Skp/O0aPMfSP6PmHWL66cRLjQWQjX0Fp3GreK/H1zo3opJGvaXmRamIPs3zeKffcxQ7IThoChoAhkICAI0fXVj6EKe+cvL+uL+EDGe7Pzoq2i9KbS7aS9JD0kjiClngbSU8J+ftJrpQskfj38pd1fL4EEpe2jKQVCKUKWps0YtYWaA0BQyAvBIyYzQsmUzIEUhEwYjYVHsssDwJGzJbqwbqN1suXuG9K/Be2eTqGcPmJ5AbJQomf76cb3JhKh7CFxHfBh+4TEshRFlqjC31v6dy2Ehd4UXT1P+JOpsR/8vQph4XMXyTnSFhsfF7i6iOGEI4uWPbROcr5eo/p+CLJKZJfSV6Q+PnRvmVRh5pIDVnP1WS15sb0dGrLFRVjPd2VOXTXyM6XCMtXzzVQiH65LWbp4+Bhc/qLUPm0KWRFtEyUmP3m2L92FqGxOKqXdBwlZumfzt2QpJ/rvMiwR4cc/1CjH5x8iax89egje3HKgnd5rv6k5fcbPvtM6nKhkOuGMqWax0JwcH1vapxFW1lfL4wli+t40PC68WnznytP47pbz2drRbHNBLPK2ltytZ9PfksjZsFqUNWcA/Ppey6dBGK2Vk2436f/Recmz2P/N466IGcvkIyR8Dxyv+RDiWvHj6/SeT+U4zfebw/CGCLW79N8X6E+vZ7i9yN6i2L0/p+98wCTotja8I9ixMg1gldArgm9ZEQBr2RFMioSFRVUMICYAAOLEQyIWZISFUSSIjlIUEDJOagYQHKWKHr+87XUWhTdk3fZmf3qec52d3V1hbd6envqm1NlR8Gryn3n+VXj+qg9pYYf6NkziaAOEK9zq9khlPga6pydR9B+It93MrvvgtrEeBIgARIIRQDvITAItDAIpvghsRFpbS9azBKF2abyquGZnl/tErWChw37BdTyqUGkvV1tkNouNfv/yjw9flwNafxEWo1miIeAvl9SmM2cQVCWQgJJT4DCbNJ3IRuQBQhQmM0CncAqHDFOHs97JK/NPgTqaVPtL2lB+7s1HQTbP6z0D+i+G67XiDVqQfmYeAwMVncujlaYxS+I+0VQFsrcqFZZzS/cqpEY/DV1C7WF9w7KdUMi8nDzdI8T2VcdNXPTzkVuQc7xXVZaCrMRrDFr+JVsPLmsrh/5YzhxItx5V5hF/qUbjb9URY0J4a7FeT9htmrTcbm8NTgbTPgjkjxMGm3PN1i/1rTRbCMVsiJNZ/It3nBSRYg3pvxotragZfKLVpjFdRnRj9FyMPWPZZuIshJ9v5h2xHsfI5/ijSY2LtFgwrpo7g0V/Pfr2sMvl2815TRTF3ubCGa3pS09sXijca9q3f4KVze9V38KSmPfx9Hcv9G2Ier06hWs14T1nFXWfwa1TePxgyk3JEKYxYA2xEzzfy7UFt65fa2063Ufg+J2yIz/8XZ5M/TArjNEZb8wXiPtdG/5JXLiyurxKuc6Ow97/ydNd5GaG0KJr6HOufkEHSfyfSez+y6oTYwnARIggUgI5NBEMCPQQjTF/zT8GAeDPXhvOVMtt5rxosVzOp9aATU/kRYC7n/U7lUbq3ZQzTzr8UOgSWr3qCFP/P9D2TBTF91liJaAjk1SmOUALQmQQEQEKMxGhImJSCAkAQqzIfHwZOYQoDAb7Qsz03sEKujf+WrmC5q93aTx8KAwA3P2YOFojfcLEG16qcEj1s7L7ONXu/h1rhue0wiTBuVEGmprwuVq+GJprjdbeH50U8ujFiqU0pP4ouon0B7S+CVq7dTwBTUoJCKPoLxNfKL6qoNmaBjBQzhUaKInTVqsUxRVKNN03NVBg/IquNU3mUWaLpb0+vzNoeLGXr96lLx9/FED3tHWxdTJb4tpP7WdvVUc3edbfsMJu7Cea4nbJ7TzPa9et/As9MsbcSUbTWxQvNGEn/2uRZyWu6tUg0nPB11f5M7JVxVrOGFK0PUm3hOYdcpXCHR+ealAM8Sktbda/jI7faTp7GvK3DmpoJbfT/swAg/kCX9pmaOuazj5BjsPsx9r3ya6H2PhYNoQ7TaRZSXqfnHbEO99DIG1ZMOJr+i9d8C+//z29V4aek3TcQXcOtjHiWSGHxeoOLvcry6I0/t1TPl7p5wT9Iwq0XhcutgWzf0bbRuiTQ9ele+dcKY+397VdvxwtACNz+L46fhhQ9C05KUajN9mcz+8j3cL8z8H/5NjDRi4xvN9v5rJz2wxKP2tGv4H4f96ZTVzDttr1NyQGf/jTZmP645dn6rmhLN9wklX0TkfdIgpMt9QwzueXY7Z36LxeOfx/eGCxr9iXTdR9+0Q6pydLtx+ot53UE5m9l24dvE8CZAACURCAP+bjEEkhWCaU8140eI5froafjB8jtr5avi+i+/Y+dUuOWwFdQvDcX41iLRXqeH/x0w1+7vvPj3+TA0/jsHAEspE2TBTF91liISAfv+kMJs5g6AshQSSngCF2aTvQjYgCxCgMJsFOoFVoDAbyUsy0/gSwJet/GqV1O5UwyAlvuDFG/JpBrXUGquVVAsa5NNTcQd8QcXgW1O1umrF1bBGTzQB9SutBjGy2eH9aD9YichDiw4MGdVXgQXyRPwEyqdNyXnN7WOLlGo44a6SDSa+WKLh+OY4Rnz8ueuN32zKBSpMVSmh4mnxxhNeKNVwYisVbG8s32xKRJ8BiL8QM0s0mtASQhDEZBVNnkM9SzSaWAHTQSeinvHkAXH02gYTyqvQ/FipBhPfRB090/Zqu+8v3nhijXJ3T8TAU4aFjO7HDKt4gjPOqPsl3vsYXqrXNZ76X/zYoWSjcWl6n3wEz1hdE/kOeJsGecgmGI9vdtfcMfFf+Exqfdpjiu2S6nEa6kcXvplk4Uh40hdvMPF6FZpvgRh9fZOxWAvPCyUaTlzgJ0zjBx8mjbPFuwhEwp+d+FgOMXBdTA2ekw3VCqthYDvWkNH/42OtV6zXnacXVlRrpgYB+D9q8M7KCiHR7zup1ndZoY9YBxIggYwngGchDO/ix6vhu4PtRYsfTeKH0ZgK/ly1C9TyqtkibUE9NoZ35fyHz+N770tqK9TMj3OwxY+be6hVUEN5KBflw0x9dJchiICOTYYUZs855xwZPnw4hzBJgARIwCMwc+ZMyZs3r5x77rnywAMPyJAhQ2TgwIEyYMAAGTVqlGzZsoWkSIAEQhCgMBsCDk9lFoFo9aOg10jGkwAJkAAJkAAJkAAJkMA/BPBDj5K3T3oKwrKflb5tYqF/Uv+9V6zRpP/5ibKet7BOje6mP3z8lG4xMDw44DyjSYAESIAESCC7ETCCKLa2SIsfG+GHmPiRMn6AghkjMC0xRFr8SOoiNXjK5leDKGsEWmwLqOVTg4hbRe09td/UbJH2Fz3GDFTl1CjSKoRIgo4AhhRmTz31VGnSpIm8/PLLNDLgPcB7QO6//34588wzKcxmlnzCclKOAIXZlOvSZGwQhdlIXpKZhgRIgARIgARIgARIIDoC5VpOPztwymVMy6wesGWbjLnc5KrTG1dXr/u1gcJsw/Gvm7TWFrNs7FHDoPAjVjx3SYAESIAESIAE/iZgRFoItEakhRetEWnhRWumOv6X7mN2BCPSQoiFIGuLtNi3RVrMMjFQbaeaLdKu1eN31MqroTx60ioEv6Cjib7C7Jw5cyRXrlySI0cOT4Q5//zzhUYGvAd4D+TOnVuOP/54T5ht1aoVPWaTUZJhnY8pAQqzxxQ/C/+bAIVZv5dixpEACZAACZAACZAACcRPQKcP/zBIaDXxKtBuVdttjoO2mJrcp0ZLNA6DwLPUMJjMQAIkQAIkQAIkEEzAFmkhlAZNdey3Hm04kRZetc3UPlVzRdr1Gve+GjxtIQhTpFUIJuj4nK8wu3z5cilTpowULVo03YoUKSJXX3215MuXTy6++GJvi31a6jO48MILPZEeolz+/PnZ/7zvvefCE088IZ9++imnMqbUQwJREKAwGwUsJs0oAhRmzYswtyRAAiRAAiRAAiRAAoklULTZlKIqzh4MElsjjdc8Pguo2Q0a/5IaPHEYSIAESIAESIAEIiNgBFpsjRdtkEiL9WjPUTtfLY8apjI2Ii3EWGPwpM2vhqmQC6g1UftEbbsafkRlbJPu91SrpnaSWrYXaXXE7yhhFqOAe/fulYULF8r8+fOPsBkzZshrr70mnTt3pmUTBpjGunr16p6X5JVXXikvvfSSvPLKK+z/bNL/QZ91PAd69uwpH3/8MYXZjJJOmG9KEqAwm5LdmmyNojCrXwIYSIAESIAESIAESIAEMojANQ0m1VWP2D8iFWHddMUbjvv+ujsm5c2g6jFbEiABEiABEsjuBIJEWni2Qjj1W4/2Ao3H/+ag9WjNdMfmfENN219ti5oRaLHdqvaRWk01DFBlS5FWRxJ9hVm/Eca//vpLtm3b5nnIDRgwwBNkIMrQUpPBJ5984vXt66+/LhBkb7nlFilUqJC0adPGuwfY76nZ79H068CBAz1RFls8E0aNGiVbtmzxe3wwjgRI4DCBzBZmd+zcLQuXrJS5i5bJjl27Bf/LGbI9AQqz+uLPQAIkQAIkQAIkQAIkkIEESjWaUE/XkN3hiq7hjkvdPmEs1qrNwKoxaxIgARIgARIggX8IGJEWXrS2J61ZjxYiLdajPVMtt9q5aliPNhKRNp+mg1B7mxrE2I1qEGeNwbMW4m1dNZTjJ9KifikXdGgyKmF269atMnjwYE+EsUUZ7v8jUKUKCwh0H330kdSpU0fKli0r8+bNk7Zt23oibffu3QXCbaq0le2I//6lMJvthR4CiJBAZgqzW7dul6de7CbXVW0o1W67T2bPXSSHDv0ZYU2ZLIUJUJhNuTd6NogESIAESIAESIAEsiCB0o1nnVHi9gntSjWcsDG0IDvhr5KNxo+Dp21ammBQmIEESIAESIAESCDzCfiJtFg6wBVpsR4t1nk/Tw0i7UVqxlMWnrMFLcNxfjWcx5TIEGF7qWENWiPQYrtLDdMgQ8SFEAyR1gi1pl4pI9LqoGNUwiy84SDI9e3bV/r160dLYQb9+/eXZ599VgoWLOgJtH/88Yd89913grWGGzVq5N0HvAf4GbDvgZEjR8rmzZtTWMtg00ggfgKZKczOnrdESleqL/c/0kmGfTFBNm3ZRo/Z+LswFXKgMKsv9wwkQAIkQAIkQAIkQAKZRKDaQ6tPuq7B1GIlG0y8tWTjiU/o+rHvFm807tWSjSY8WKrhpJpl7pmUL5OqwmJIgARIgARIgAQiI2DEUNuLFiItpjrGwFIuNQiorkgbqSctxNxaat3V1qrZIu1ePR6ldr8axFyshZtSIq2OLkYszGIkcteuXfL111/LtGnTZPr06bQUZoBpacuXLy/16tWT7du3ewPR6P+uXbtKgQIFPLGW9wA/A/Y9MGfOHO8Z4d0s/EMCJOBLIFZhdqdOQ7zqh59l8bJVMnrCVJmzcJmuB79Pbb/MnrdYPhs5VsZNniEbNm2RP//8SzZu3irv9vpYCpa4STq88KamXy779u+X3/fslVlzFsqQEZr+q29k0+Zt8ufh6Y3X/LxWy/hJvlVB98vxU+X7H3+Wgwf/kC3qeTt5+mz5dMQYmT5rruzc/bvXtkOHDsmK1Wvk519/kyUrvpeRYyZp3abJut82yqE///HMXa91mvjVTBn6+Xj5bsFS2aP1NgFTLU+bOUeGDB8jU2Z8K9t27BQz2fIfmv+S5T/IiNGTZNioCbJUy9i//4C5lNvYCVCY1Rd6BhIgARIgARIgARIgARIgARIgARIgARIggdAEjECLbbQibThP2gKaJ36cBfG1utq7aj+p2SLtX3o8Ry1NrZSaLdKiPnb99DA5go7pRSXM/qkDrRhU3r17Ny2FGcDrsU+fPnLZZZfJpEmT0od+sTbhqlWrpEqVKtK8eXPeAyl8D8TyGd+7d69Ok3oo/X7hDgmQwNEEYhVm56oQ+8BjL0jZ6k3lqtI15aEnX5T5i1fIc6+8LzfUvFMq1b5brruxkTS9r50sWLJcpkz/Vmo2elD+VfBaKVbhNun6Xl8VNlfLsy++LeVrIP1dUlqnOL7rgQ6yaOkKb4pjpKnZ8AEprfkUuraGvNv7Y5k5Z4Hc37aTXH/zHVJRyyhdtYG079RNxdj1cuDAQXk87XWp0/RhufGWe+V6rUfhsnWk6f3tVKhd7QnEmD4Zx9fXaCqV6twtZW5qLG+818fz3v1hza/Spv3L6edKV24oDz75kqxWQRjPkkHDxkiNhq007xZa9l1yQ/U7pf+gz721co8my5goCFCYTY7XdNaSBEiABEiABEiABEiABEiABEiABEiABLIMAVsEjUakvUBbEKknLUTaimovqn2n9qeaLdT+qscfqNVUg9eu8aRNKpFWB/GiEmajGPRj0iQlAPH1hx9+8MTXhx9+WL2x9h7Rkn379nnrDBcqVEi++eabI87xgARIgARIIDSBWIXZGbPnS9U690ixcnXkrQ/6e8JrVxU4S1dpIC+81uNvr9OR46S8CrdPpHWVhUtWyXsffiKXXVNdnn+tuyxb+b289k5fKVX5dunSrZfn+Tpo2GgpW62xPK1i7fqNm6X9c2/IhZffIC1aPyMf9BksM79b6InBN1S/Q3r2+0w9bRfIG+/30zzqS+8BQ2Xr9h3SuMXjkq9wJen0yrvqsfu1PPvSW3JpqWrS55MR6r27Ve5t3VFF3abSq/9nXpmtHn9BymiZE9SD9umX3lYxubG80/Nj+ebb+dJLy7hG6/emlrHm17VS78420rD545737nT1qr397rYqSL8kP/2yLjRkng1HgMKsvrgzkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxEYgFpH2XC3KiLQQYPOrXaJW0DIcF1AznrSFdb+12pdqv6vZIi2OR6q1UIPwa3vT2vXTU1kr6Mgdhdlww5fZ7Dw8JV999VUpXry4LF269KjWQ7hdt26d3HXXXeles0clYgQJkAAJkIAvgdiF2XlS7dZ7pcXDz8pGFTwx5W/D5o/Kder1+uHA4TLtm7kyecZsuaVpaxU+m8qCxctl3KQZUvSGW+SzL8bLZp2O+NZmbaSMiqR9B41QIXeuTJw2y/NIrVjrLvWaXSntOnWVwuXqylidEnnX73u8aZPhVVtPrxs2aqIKq/N1uuLJUlLXrb3noWdkjQqkDe55TG7Ses2Zv1SwFvmCJSukZMXbpeu7fWTG7HkqtDaQZ15+S37bsMmbMnnF6h9lsE5bjCmRK2i5N+q1n3jH82TM5OlyvYrAtzV7ROYuWia1mzwoRcvfIi90/Vt4njFrnjed8d59+33ZMjJiAhRms9brOGtDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAklLwBZB4bkKL1aIpGZN2pN1312T1hZpL9bz+dVckRaCLURanEMa7DdS+0htrZot0mLK41lqz6gVU7NFWtQph2W6e2yDDuFRmI14HDP1E2LqyO+++06KFSsmb7zxhjfI7tdqDL5PnTpVypYtK59++qlfEsaRAAmQAAn4EIhVmIWQWavRA55H6gFd93XTlm1yk04fnL9oVSmvAifE0Wq33SclKt7mTQ2MqY/HT/nGE2aHfTlRftGphyvXuUsuKXajVNBpgf9Of7+UKH+rdzxfhdwnOr7uTVeMfYSv1Uu3cLnaOq1xTalcr7l3Da67umwtubNVB1n5/Rqpf8+j0kynQ8a0xAhYp7ZMtSbymnrzjhg9WYr+r6589PEIb21bnMf/md/37JNFy1ZqXeurd211qVT3Hi/vG+vf7wnDmBp5yfLv1VP2KxWTH9Hyamt8Hamt7Yeoi/V2GeIiQGH22L5+s3QSIAESIAESIAESIAESIAESIAESIAESSEkCtgAaTqQ9UwnkVrNFWnjSwlsWIqwr1OI4vxrOY/3aKmqd1eapQZi1hdqf9fhdtZvVMBCWpaY81mE9CrNxjW2m1sXbtm2TBx98UCpXriwbNmwI2bjt27dLmzZtpHbt2rJ27dqQaXmSBEiABEjgbwLxCLN1mjwkz736rv5o5pBs37FLbm7Q0vM4HTR8tHw14zv5auZ38qlOZ/zx0C9l4+at6R6zQ0eNl/WbtmjaFt4asp+OGOOln6rpBw0fq2u5jvWE3seffU0F0hY6DfJKr7JzFy71hNL72qbJ5+O+8q6ZouX0GzxSvpwwXcvYIrfp9MLw4v1RBVkECLNlIcy+85EnDBf73y3ywYeDZdfu373zW7fukJGjJ8koza9Epdukyb1PCoRjr/7ffCcDPxslw7+c7HnYLl3xvXw7b7F8MXaydHmzt9yga+PCo3bWdwu8NXG9DPknFgIUZvWFnIEESIAESIAESIAESIAESIAESIAESIAESCDjCIQSaU/UYuFJe6ra6WquSJtH48KJtBBvIdIiHbxk26qNVdujZou0u/R4mNq9arjmmHvT6mgehdlYhjRT8JoDBw7Il19+KVdeeaUMGzZMMGVxqPDnn3/KpEmTpFKlStK1a9ew6UPlxXMkQAIkkF0IxCvMPv/qB54wC3H20WdeUY/X22S4TjO8e89eb03XNu1eluatn5XVP/ycLsx+9vl47/yDT7wgpSrdrkLnFM9rdd36TdLq8eel1WPPeR6vEGZvVC/chTqtMQLWiK2uXqwQgL+du0j2HzgoC/Tc7Xc9Ki+93lNw/e3qMesnzHZ560NZulLXK7+luTS+93Fd4/YHOaizLQz4dJQnrg5QAfbWOx+RirWayVdffyf79h+Q1T/+Infe317XvO0ms1WQvUO9cl9VgXftug3eerbtn+sml5eu4U21/Id63jLETIDCrL6EM5AACZAACZAACZAACZAACZAACZAACZAACWQOgUhF2tO0OhBpz1Y7R+18tQvV4CEb6ZTHmAK5qVpftfVqtkiL/eVqb6vVUIMoDG9a41Fr11OjMybokB6F2ZjHNVPrQni9wvv1zjvvlF27dkXUuM2bN0vnzp2lQoUKsmDBgoiuYSISIAESyM4EYhVmp30zRwXSVpLW+W+PWTCcrWLpTTp9cekqDeSOlu11quMH5apra3jeqlt0TdkxE6fLVWVqedP/7lfhc7qu+Vq1bnNdl7aRTkXcXr1nW8lV19WWt7r3l63bdsgjT3XxpjLGOrEIED8//uxLb53Yijr98d0PPy3lazRTcbe+Jwbv2LlL6um0w80eeEp+/OnvqYzhOVu6SkN5/rXuKuxukZ79huias7d5Ai+mPy7+v3peXTFVMQTi629uIjdUv1PuevApqaLTJRe7vp6Kt5/rtZvlMRWKseYtpktu3voZnRa5noq8TwrWqQ3z2yGv/vwTSIDCbMa8VjNXEiABEiABEiABEiABEiABEiABEiABEiCBMARs8dOd7tj2pIVIe4YaRNp/qZ2nBpE2rxq8ZPOrFVALmvIYQi4E3RvVuqjNVjukZgu1+/V4slo7tRJqmeJNq0N2FGYDxy2zz4k9e/ZI9+7dpUSJErJo0aKIGw6v2Xnz5kmDBg3k0Ucflf3790d8LROSAAmQQHYkEKsw+8uvv6nAOlog0P75598zGuzXmQ5mzVkor77dR71nX5X2L7wpg4eN9jxMD+nz+Xv1QO3ed7AsXfGDJ7Lu3bdfZn47X7q81VvaavoOL7wlQ0aM02mDN2uef+rUw1+rKPqFbFRB1YQdOmXy6InTpNMr78ojT3fxtkiH+IPqtfvpiLHyuU41jKmVEXbs2CkfDhwq02fOlb1793mC7widqviZl96RR1Vofav7AG992QPqfbv79z0ycdpMefH1DzTvV6Tjy+94Yu1mFZUxawOmRf5wwFB5slNXafv0q9L1/b6yYNEK9dw9YKrHbWwEKMzqizYDCZAACZAACZAACZAACZAACZAACZAACZDAsSXgJ9LCe/UENYi0J6lhICuXGrxbz1Kz16WNdsrjK/T6u9X6qv2sZou02IeH7QC1O9SQd4YItTqeR2E2tkHNlLnqkHpEwdu1cOHCcs0113gCba9evcS23r17S58+feTDDz88Ih5punXrJrfeeqt3/ahRo1KGCxtCAiRAAhlBIFZhFtMA79r1u+xRsdMOmNIY3rG/rF0vv+nUwnv27k+fWv7AwYOeYIqp6k2AmIr0P6/9zVvHFWKtmbr+d50Oeeeu3bp+65HTBGOaYXi/ooyNOr0x8jVh1+7dKrDu9YRdxB069Kfs2Llb9u7bl54vBNrfNm6WX9etl20q3CKNCRBoN27ZpvVZLxs2bvGmNDbn4BW7U/Nf+9tG+eVXXLvjiGtNOm6jJkBhVl+sGUiABEiABEiABEiABEiABEiABEiABEiABLIOAVekdb1pIdL6rUsb6ZTH8KyF5Vcz3rTldL+92ji13Wq2UPuXHs9Xe0WtshoG1BIy7bEO5VGYjXo8M7Uu2Lt3rwwaNEjKlSvnTUmM6Yxr1ap1hFWrVk0KFiwo119//RHxJt1NN90kVapUkddee0326WA8AwmQAAmQgD+BWIVZ/9wYSwIxEaAwqy/SDCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAlmXgBFqIdC6Im3QlMcQae0pjyHA5lMroAZRtqBlOEZ8fjWTrp7uv6m2QA3CrC3U/q7Ho9XaqF2lFrM3rQ7nUZiNaUwzdS7C9JXr16/3pjDGNMbwnrVt/vz5Mm7cOLniiivkzTffPOKcnW7p0qXy888/H+VplTqk2BISIAESiJ8Ahdn4GTKHuAlQmNWXZwYSIAESIAESIAESIAESIAESIAESIAESIIHkIGBEWmyNSBvtlMdYbxYibX41V6SFYIs4nEMarGF7tVpLtcFqmOLYFmmx/7NaL7WGaheoRSzU6tAehdm4xzdTPwMIrsWKFZMRI0akfmPZQhIgARLIQAIUZjMQLrOOlACFWX1ZZsg4Ahdq1jcctiIZV8wROWONmKfVeqj1U+us9pAavlSZuhTV/YwKdptjLec6rZyp61kZVdEUyTeov09NkfaxGRlHwP6sZtbzKeNaw5yzEwH+j8hOvc22kgAJkAAJkAAJhCMAcdbYcboPg0gLYdRvXdozNf5sNdebFuJrPrUCahg/COVNC1G3klqa2ldq+9XEMnjXLlJ7Sw1et/9SCxRqdQSPwmykw5jZOJ0RZocPH56NKbDpJEACJBA/AQqz8TNkDnEToDCrL8cMGUegg2ZtvpzgS0lGhyZagCnP3v6p8c9a55ZlYEXsNocqB4Jix4B6HLTqWj8gDaP/7/9C9Xd2f7iFur947/xNwP6sZsbzCaWyX0LffeQTmo85y/8RhgS3JEACJEACJEACJHA0gSCR9gRNaqY8xvfFXGpnqOHH0LnVzlU7Xy2PGoRXTGecXy2UUJtPz0PQhYjbUO19taVq7rTHGJOYo/a6WnU1iMMQj2HHHTp0aNBff/0lxuIe6mMGKUmAwmxKdisbRQIkcAwIUJg9BtBZpEsgu2sX+grMkJEEMlP4KK4N2atmC7Jmf7XG23UJJZjGyyNcOfjy11ntoNrygMJwztSdwqw/pHD97X9V6sdGcn+lPoXIWmh/VjNamGW/hO4T8gnNxz3L/xEuER6TAAmQAAmQAAmQgD8BI9JiazxpIYa63rSYcek0NeNNCw/X89QwJXEeNeNNm1/3XW9aiLKIwzkj1GLa4xZqH6lhPMJ8vzfbPzTuG7UX1aps3br1UxVnvXVBsdYozIi02DKQAAhQmOV9QAIkQAKJIUBhNjEcmUtcBCjM6kswQ8YRyEzho7c2w3zJwXaV2htqndRaqdl1OZbC7ONaF1NPCrMKI8YQrr9jzDbpL4vk/kr6RiaoAfYzIaOFWfZL6E4jn9B83LMHNcL8H+GPd1w6PCYBEiABEiABEiABfwKuSGuEWoi0ft60p2s8hNrcapj2GN60WA4lrxq8afOpFVBzhVocIz6/GtLB+xbLHGFcYoDaT2rmXc7b5syZ89D1118vzz77rEyZMkX27t3ribQQa12RlkJtXIOgSX0xhdmk7j5WngRIIAsRoDCbhToj+1aFwqy+EDNkHIHMFD6maDPMl5u5Pk3CmnzPHbb7fc4nKipcOZEIEOBm6np5oiqWYvmE6+8Ua27EzYnk/oo4sxRPmJnPJ/ZL6JuJfELzcc/yf4RLhMckQAIkQAIkQAIkED0BV6g1UwsbofYkzfJkNeNNa6Y9hjftuWrGmzbSaY/z6zVGqC2l+63VBqutUzNjGd72lFNOkUqVKskLL7wgX3/9tezfv/8IodYVa7PvmGb2ajmF2ezV32wtCZBAxhGgMJtxbJlzxAQozOoLMEPGEchM4eNbbYb5MvNmxjUp7pwpQMSN0MsgWfo7Ma2NPBfeX5GzysznE/sldL+QT2g+PEsCJEACJEACJEACJJCxBFyR9jgtzkx5bLxpIdRiEA3LcBhv2rN1H960ZtpjeNO60x7DgxbTHRvD8REetcOHD5/wyiuvSO3ateXcc8814xrp29NPP12qVasmnTt3lhkzZhzlUUuhNuJB0KROSGE2qbuPlScBEshCBCjMZqHOyL5VoTCrL8QMiSGALyn4ZakdYhU+8KUIv0qNJnynic0Xl07RXBhh2mjrE5RtRgoQqCPYJSokqs3h6oMvvdE+jDK6v3EvR1uncO2M5Tz6E1/8Iw3x3F+x9EOoekV6/+BX6Cg7I0Min0+o54lqWIMq0s9bPP2C8jDwgzIzIyTi3s9sPvisJuIeQp8mKiTqeRzp5yiSeieCEz6v7v/6SMpmGhIgARIgARIgARJIJgJ4zzeG90yYK9TivQjvV0aoPUv3/aY9doVaI9Ca7SULFiz4/KeffvLWEP31119l8uTJnrfszTffLGeddZYZ50jfnnzyyYKpj9u1ayeff/65bN68Wf7444/ANWo5/XFqjPpSmE2NfmQrSIAEjj0BCrPHvg9YA8kKuoO+ujIkK4HSWvFP1X5V+0ttj9oktTQ1fCmJRpi9VtO/ozZDbZcavnSsURuu9oQavvS44XqN+Oyw7dat+aKy2oo33rM1rbhXdd8NH2sE8hqkhi9c+NVrO7WRapvUTH2G6v5dakEhqJx79ALkj3VlTT33HY5D/HNqJvTXHdOuUibSZ4uplHDdRDUz/RE4fKP2gRr4hAqJanOoMoLOVdQTn6gtVAMH3D8/qKG/26v5iRHR9LdmEVXAl+7KaoPVflY7pIZ+Wq+Ge7KlGn4p7YbGGmH66hn3pHMcdG84yf7vfxrRT22J2l411GOj2hS1d9VuVnNDNPeXfW0s/WCur6M7pu23HI5EHO69FWqo9xq1D9WaqIGxCUV1p7PabDWw3q+GdV4R59f3Gh11SOTzCVOUv6b2hRqeL+b++FP3Ue/uagXU3BBLv2BgB9Oa9VGbqbZdDSxhW9XGqDVXs3nqYcwB+cRy79sFZiYflIv/B4+p4f5Df5j/P+D1vhrqE0k4TRM9ojZPzfzfwbMUz3kwRmimZu7zuogICPE8j4/XPPG/B+XguYxwv9ocNfT7DjX8L7pWDaG/mqlTqP8RieB0tpb1qNpUtd/UUJ8Dang+faqG//PnqjGQAAmQAAmQAAmQQCoTwDuzseN0H+9vsJxq+J54khrevfBd5jQ1M+1xbt3H2ML5aheq5VX7t1o+tQJql6gVnDt37hfff/+9wH744Qf58ccfxQi1EOPGjRsnaWlpcuONN0ru3LnxPnaUXXXVVdKiRQvp06ePrFq1ikJtCo74UphNwU5lk0iABI4JAQqzxwQ7Cz2SwCn6PsdAAlETwBeRt9WO+jJgxS3T/Z7WMcQLv4AvMs+rGaEjKE8MAhdxMrhbj4PSm/g1h6+xRWLUzQ0mPbb/UVuuZse5+xgUP1PNDUHl2CzcvHAMgcqEg7pj0tQ3kc72Lj3eYKUz6e0txIo31II+6HbaeNqsRUQc8CUUooddtt8+xJayTq7R9LdzachDCA8Qu/zqYcf9oGmudHICX5NmqnPOPQy6N+x0L1j5mXz9tqiv7Ukbzf2F8uLpB1yP0FHN1A2fYfzgwRz7bZ/V8whV1Pao+aVBHDj/Sy3WkMjn03laiQFqQXW149Gmh5xKR9svt+n1v0ZY3jRNl88pL9rDeO59lJXZfFAmRH38P7DZu/v4f4J7MqdaUMD/EyM0uteb4080zUdq5jhN9/1CvM9jDOSZMvDcbmMdm3hsjWgbyf+IRHDKp2VCqLbr4Le/W9PUUmMgARIgARIgARIggexAIIc20hi+e8COV8O7J0TaE9Xwfofv4X5CLX7UdoRQu3jx4s8hxkKUNQKt2bpCLTxqp06dKq+//ro0aNBALrnkEr/3M8mTJ4/ccsst0rVrV5k1axbXqT1yMDQpjyjMJmW3sdIkQAJZkACF2SzYKdmvSkF6jb4mMpBAMAE/sWGnJl+j5vulQOP9hFl8gYGY5V6zWeMgnv7hnIOXTgU1E5roDgaxYW4eJn7l4cThRDH7+o1OfnudY5P23cN525ugcpA2VD2nW5mEG3RvrGlNHcz2T41brWa8vkw8tgvU8OXQDXaaeNrs5ht0jC+ni9XscrG/TQ1inMsHbWqoZkI0/W2uCbeFuIl7xK0TPBNxz+5xzqGe+CJtQiKF2eaaqV0P8ECfov/czwLSdVUzIZr7K95+MGV21B27vvY+hDH7PsY59OebavCONWnRLr+2vaPxsYaeeqHJ32xjeT6h/BlOXuiTn9Tmqq1VQ5tMGdji/FVqJkTTLxCs7bywj3pDhIT9ruaeh8dirCHeex/lZiYflHezGv4P2Bxw/+CHNO4zDGnGq/mFQhq5Q83OB/tr1MDcjTfHaXrODY01wpw3W9wXq9UifR7jM2muxXafc2zOof0I9mer/t9RR/xNBKfTNUf8HzZlY7tdbb7aeice59Av9rNRDxlIgARIgARIgARIIFsQMCItttEKtfhB6rkqvg7/7bffBKIrxDd4y4YTatesWZM+9fGiRYukd+/e0rJlSylRooTkzJnTfofz9nPlyiUVKlSQp59+WsaMGSPbt2/3pj4+dOiQYI1ad51aTn+c9UaIKcxmvT5hjUiABJKTAIXZ5Oy3FKs1hdls8TUhsY0sp9nZL/kQsDAIjC8gCOepDVGz02DfT5ht4aSDAFVSzQTcoK3V7EHopXqc0ySwtt/pvimzkxVvdjtY5zHY7AZzrdlisB+i20WHE/5bt4+r2eIhvIQwRZEdwpWDPEwZEBP8gt1ed9A9r14AIdPkgXo+oYbpkhDQD2XUIAqYNNg+peYG+zz2kVcsbXbzDTruoifsMufpcWEr8Vm6jzQ24416fLaVxuyG62+TLty2qyaw6wThuKh10Zm638NJ84F1HrzM9VOteL/dcPcGxDeT10zdv9TKBPW4Vc0WRDbpMX6RbYdI7q9E9UNHLdjU12zna1xdtTPUULeWarivzHmz/UXjaqhBkDpV7Ra1vWrmPK5x26ZRYUMin0/XaWmmPtiOVbP7BJW5WA1ejHa6XjjhhEj6ZZxeY/KBeH2nms0AAz2N1WxP+UN6nE8tlhDvvZ/ZfHCv/KBmGOE5+Zwapowz4QrdsZ8NSNvQnLS2k3Xf5IPtYLVzDp/HM7SS2m9qdhrsp6nZIa8ebFMz6XDfxvI8RttMHmaLvh2jhv9nE9R+UoMnBgLabtK5/yMSxQnPG1MGhGZ8ls39iHsRz8meaiYNtm3UGEiABEiABEiABEgguxPAu5IxvFvCgjxqc+m50zZt2vTZtm3bZMuWLaL7smHDBlm3bl3UQi0E3V9++UVWr14tw4YNk/bt20ulSpXkjDPOsN/ZvP3jjjtOihYtKq1atZKBAwcKhF53nVqKtVlrCJjCbNbqD9aGBEggeQlQmE3evkuhmp+i74AMJBAVgUma2rzUH9D9y3yuxpeQ3lY6pHeFWYhwEJZMXgt1HwPKfqGyRtpi3UM+iezB+E4+58OJYqYeZtvKJw9EdVQzabCtjUgrhCsnEoEm1KD7l1qWXT7Y+AUIYwvUTFqIXvnV7GDOmW2sbbbzDNq/Vk9gcN+UNVH3g/r7Disd0r+v5oZw/e2m9zs+VyMhfpg6wQvS76GI+3mUlQ4/RjAeyIkSZiEK2fd4cT32C/drpKkvtrWcROHur0T2Q0enLhv1+N9OfXDoCpf4QUMen3QtNM5u23980oSLStTzCeV8ombq86PuY9DEL2CgZZ6aSYtnmRvC9cuVeoHd/y3dDKxjCN+mLGxxHG1IxL2fmXzQPvvZinb7Ca5Ih+cKhEzD6Ffdh/hvQhXdMeew7W5OONsL9NgWwZE2zUnzpR7becX6PEad7Xywj/YGhVD/IxLF6W0t3NTp84CKoN4/WenwOWAgARIgARIgARIgARI4kgC+TxrzFWp1gHjw3r17BQPFu3btkh07dkisQq1ZpxZCqxFq4Yk7efJk6dKli9x6661y8cUXm/e8I7Z58+aVunXryksvvSQTJ06kV20WG/2lMJvFOoTVIQESSFoCFGaTtutSqeJ+GsSRb5A8IgGLALz24BFkXt6DBrRxSR41eH2ZtK4w29o6hzTF1EKFIXrS5LXCJ2E4oc4erF7mc73JG1vUFV+Y/MLZGmkLKPc6icKVE06gQXYH1Ux9bG8oiAt22UOROERwBYiOTlpTRrxtdrL1PeyssaY8tKGIb6p/Ir+20oNHzn9OeXvh+ttJ7nt4l8aaOmF7s2+qvyPLWmkhQJr7NVHCLARNuy5t/y72qL/g8JIaPj/wOD1PzQ7h7q9E9kNHLdiucz27ItZ+EyddJ+ucvXu+k66qfTKC/TM1TaKeTyhuhNpKtQNq7dVChdf0pGHxg0/CcP1SR6+ZqbZJbbtaqH/O8I43ZWHbVC3akIh7PzP5oH3gYto9MkyDr7LS4poGVnr80MPk87vuu58hK6m3ZrBJi22adTKRz2NXmF2u5RjvWKvI9N2g/xFIkChOvTQv0/Y1uv8vZO4T8H8Gn+k71K71Oc8oEiABEiABEiABEiCBfwjk0F3bPKH24MGDg+Ctqls5cOCA7Nu3T8IJtZj6eO3atZ6HLARYCLF+a9QaoRZTIyMdxD0ItXPnzpUePXpI8+bNpUiRIgIPWq3bEZYjRw4pVKiQ3HnnnfLOO+/I7Nmzvbph+mNOgZz5Y8MUZjOfOUskARJITQIUZlOzX5OsVaHGfv95c+QeCRwmUFu39ot60TBk+lnpXWHWHhzHoG+4APHBlB2LUNfBuj6cMPtWmMpss/J60EkbrpxwAg2yCxp0hxhoGGBbConDhDl63lwDz0U7mHhs42mznWfQPoQUU96XQYms+BpWelx3mXUOu4kQZsHD1Ol7J3+/w5IaebZz4g09NnlMdc65h+HuDYiAJi9sIbzXVTtDLdIQ7v5KZD901EqZ+v6p+6cHVLKClQ7pbwxIB9HZ5IctnjfRhEQ+n+xyza/a7Th7/wI96K1m6v6LffLwfrh+sS85wT5w9sEI4hfEYlPe3U6aSA4Tce+bcjKDjyva32kKD7H9Qc8ZRs9a6X604tFvocKJehLircknzUpczIrH+Xiex64w298qx2836H9EIjk1dNq3Xo/bqf3Xr0KMIwESIAESIAESIAESiImAJ9LqlMGfYE1XTB1sRM9QQu3OnTs9b1Yz9fHGjRvFFmoh4EGojWSdWuNVu2rVKhk6dKg888wzUrNmTYH3rLboKDv55JPl2muvlQcffFD69esny5cvT58CmWJtxg4JU5jNWL7MnQRIIPsQoDCbffo6C7eUwmxMr87Z9yKIkPaL+WlhUGAQ16R3hdlJ1jlMJwvvq1A230qPPAuq2SGcUBdOFDP1xPZRO2OffQgvJn1r53y4ciIRaIIG3RtZ5aL8XE7Zfod9rWvcaSZNG+Jts1+5btwqqx6vuid9jgtY6VG/Wk6acP3tJPc9nKqxhsFY3xThI9+w8kB+oUK4e+M5vdjUx97CCxR54/NUWC1UCHd/JbIfOmpFTD0x5WtQuE5PmHTYuiK7fd2fVloIrdGERD6f/Mo9UyPhOd1M7SU1COcr1Oy2Yf9XNTeE6xc3PcTOS9Vw3+N51F3tK7Vdam5592hctCER975bZkbyuV4Ls9s9X49D/b/Auf3WNf11HyGHmu1Vjc9UuLBYE5iy06zEiXweu8KsLSRbRabvHtQ9Uyd7VoVEcUJB8JDdZpVjysN2nVpvtVvUzlBjIAESIAESIAESIAESiIOADhp+goFDiLPGINIGCbX79+/3PGr37Nkju3fvFiPUbt26VTZv3iwQatevXx/XOrXwql2wYIH07dtXHnnkESlfvryceeaZ9jth+n7u3LmlatWq8vTTT8vIkSM9kZjr1SZ+KJjCbOKZMkcSIIHsSYDCbPbs9yzWagqzcbw7Z8dLbeFoRwQAGmsa87LuCrO2uGnSRLOt5pQfTqgLJ4rZZdsD3U4x3uHPVrtaOwnClROJQBM06G7z3+yUG3SYpidM2+D5ZQcTj208bbbz9NuH1xnEd1PeA36JnDh4BtrXPOacD9ffTnLfQ1tU6+GbInzkG5rEtAtiV6gQ7t6AaATBz+QXtEW9IUL6hVD3V6L7oaNWwNRxpV9lDse5wiy8+oKC3ee1gxIFxNufj3ifT6YI3Id4js1V+0vNtDfUNh5htqCW8a6anwAbVGYswmwi7n2tpjfFeGbwuVvLCmp/JPGzUFkN56rZ6SNhN8q6Jk33TbDvt3ifx64w28gUErAN+h+RKE6m2Ct0Z42azczd36fnP1W7RI2BBEiABEiABEiABEggBgI6UOgJs+6AoRFpjTdtKKEW0x8boTZonVp41UJw/eWXX7xpjY1Hrd8UyIjDeXjeGq9aXDt9+nRvWmNMgVyiRAk58cQT3fdD7zhfvnzeerZY13bKlCmeeGy8gY3ojK3dRuwzBBOgMBvMhmdIgARIIBoCFGajocW0GUSAwmwM78zZ+ZIu2njz0r07AhBYs9Okd4VZe3pIkyaabQOn/HBCXThRzC67npO3e3ishNkXLZ5b3EoFHD9tXbPXSZOoNjvZHnV4ssbYgluQqGhfCJHSFh+esE/qfrj+dpL7HtpTmr7lmyJ8ZDTC7FOanWG+LETWNfXcaLUDVnpznb2FGJJLzQ6hhNlE90NHLdjUx/1823XKLGE2kc8n1B+8pquZNvptcY9CkIdwa87/qvtuCNUvJm0d3bE/JyY/ewtPxR5qdlwk4qIpw2wTce9nJh/8MMNuc7T7EKIRzlGzr22GyDDhSz1vrkmz0ibyeewKs+4Pj6xivV372VjfOpkoTlaW3hq8L2vEGjXDwW8L79ob7Qu5TwIkQAIkQAIkQAIkEBkBHfDzFWbdgUBXxDQCJwRPe+pjeNRGu05tuOmP7bVqIdYiPQRe7I8ZM0Zefvlluf322+XSSy8VrE2rLT/CsIbtf//7X2nWrJl069ZNpk2bJjt27EifBtm0hWKt2+v/HFOY/YcF90iABEggHgIUZuOhx2sTRIDCbGSvyUx1mEBb6+Ua3mMYTA4VbO8dV7ixp4ccopnkjdLcmzecUNdB8zdfDPxEMXMO23pqocKxEmYf0EqZekLAgTdfuGCLOD85iU1e8bbZydb3cLPGmvLSfFMcGZnHSo/rmh15OiHC7CSrDExLG0uwhdkZYTLorOcNA7970L38TI3Avfiemi2kmTyw7aRmh3ACYCL7oaMWbOrifr7tOkUjzP5p5Rmtx2xb69p4n0+oP+4J0z5s/1Cbovaa2h1qRdTghYzwrJpJu9aLOfJPuH65RpPD89DkgS0E3v5quLaq2gVqCPjRgs2puRcb3Z9E3PuZyQdT5tpsiutxNP8zzrPwwJva5PWMFR+0a/+vSrMSJfJ5nChhNpGcrKam7xbWPYi/49X2qxmOZot72P3frFEMJEACJEACJEACJEACoQjoAF9Ewqw7EBhOqLXFWluojWT6Y+NVC+HVeNZCnLUNXrXGsxZetRAO4VW7YsUKGTJkiHTo0EFuvvlmueCCC8z74hFbCLiXX365J+h27txZxo0bJ5s2baJY63b04WMKswFgGE0CJEACURKgMBslMCbPCAIcPwv1csxzRxFoqDH2i/RlR6U4MgKikUnvCjf2oP6sIy+L6eg7qyxXrEKGqSDMQpwxPLEtgIaFCRhAN9eAkR1MPLb17BM++z9rnEnf2ud8uKhvrOv7hkus58tY6VFudeeacP3tJPc97K6xpk3f+qY4MvIVPeylBs/XUodPddWtycPlezhJ+qa3lTYSYTb9wsM7l+sWoqAtys12EoUTABPZDx21bNN29/NtVyuzhNlEPp/yWG1DG1eqhXrevWWlX6/7bgjXLz30AsMSonKaWg41v5BbI01abO/zSxQmLt57P7P5/Ndp801h2hfqND7rhl+/UAn13Alqu630abpvQiKfx4kSZhPJybQzaHuqnrhVDZ99wxPbamoMJEACJEACJEACJEACURDQ0b6YhFm/UUJbrDVeqGYKYVuotdepxQB10PTHodaqjWYK5Llz58pHH30kbdu29dajDRJrFZtcfPHFUqtWLenYsaO3Zi1EYrNmLdpi2mW3FfupHijMpnoPs30kQAKZRYDCbGaRZjkhCFCYjeJdmUn/7//+jZdkyzqFgTLHSusKNy9b5w7ofn61UKGpnoQnGsSvz9T+o2aHcEJdB01s6u4niplz2NazM/bZ/9nKyxUpw5UTTqBBcQet/Osj4nAooFu7nmnmRMAW6e28ejrp7LziabOTre9hX4015W3X/bN9U/0Tibqa9BCqcO/ZIVx/22mD9u2+AKd8QQk1/iw1e5pZI4bZXrA/hbgep5armTa592BjPTdeDV6S09VCBYizJh+ItLmsxHabUJ4bEtkPHTVzUw/3822Xe52VDunPt086+2iPybO2cy7cIe4Rcy22ncJcEOr51NDJCyJcqDBPT5qyN/kkDNcvK63rIZ6HCrX0pCkL21ahEgecs+sTy72f2XzwsoLngGl394B2meicuoNnxGq1CWpPqJnQW3dMPvDwvNCc8Nnic27SYptmpcHzNeiclSx9F+mDnseJEmYTxel0revHavPV9qhVVgsKufXEATXDAs9EBhIgARIgARIgARIggSgI6KBdwoRZdwDQFS+NqBlu+mPbq3bbtm2yZcsWz5t1w4YNgrVq165d601lDLEwlFdt0BTI8KxduHChDBw40POsrVmzpuTPn9+8Ux61Pe+88zxBt127djJo0CBZuXJlRGJtKgm2FGbdu5vHJEACJBAbAQqzsXHjVQklQGE2indlJv2bwGzdmJfknbqPQVm/AFHFpMPWFW4qOOc/98vkcBw8c7630m/W/ZMPnzObcEJdB+t6VxRDHnZd65lMA7Y/W+lbO2nClYP0pqyfnGvNoT14bwuzx2kCW9zbpcdYMzEo9NETpixsb3QS2ufiabOTre9hI421y3vZN9XfkZfq5g8r/UyftOH62+eSo6JKaIwt9rx3VIp/IiDsmPrjGjOtrB2P87iv/cLDGmmux3aZk6itdR7iJEScoPCMnjB5QRA53koY7v5KZD90tOqxyKqDu3udlQ71zihhFuUm6vlkC5eo82XIPCA8qfGmP7Dd7pMuXL9A/DJ5QBALCnn0hP0sxDXIO9oQ772f2XzQvqlqhhE+I6UQGRDu03iTFttHrXRX6z6uN+c/1P0c1nmze67u4IcSJh22aWomJPJ5fJJmapdTzRQSsA36H4HkieAET+GtaqZOYBQUTtQTELhN2qeDEjKeBEiABEiABEiABEjAn4AO82WYMOsOIYYSaoPE2j179ojxqt25c6ds375dtm7dKps3b5aNGzcKvGrjEWsxDTK8YiHWLl++XIYOHSqdOnWS2267zZvqGOvTKrmj7Mwzz5QbbrhBHn74YenTp48sWLBA4AlsPISNCI2t226XSzIcU5hNhl5iHUmABJKBAIXZZOillK8jhVn/12LGhiCAqQvtF+K5enyxk76iHkM8tdP5CTfwfLXT9NTj09TscIYejFGz071gJzi8H06o62Dl4YpiyMLOPx6RMlw5LayyIA5ci8KdEGrQvYqmteu6VI+vdK7HQPlbTrqVeozBdjvY+cTTZjvPoH0IHzPUTJloe0c1iBt2uEYPflYz6bC9w05weD9cf/tc4hvVT2NNWfCIRf+5Ig2mTbU9wr7SYxPK6Y65HluIZrZYDtEU9beFIKRz70F8hv5SM3nBA/NCNTdAVNqmZtKBqR3C3V+J7IeOWrCph9/n29QrM4XZRD2f0Oembdi+ZxrjbB900iEt+tq9h8L1yywrH/zg5VI1N0AoxH1j1wv7z7oJIzyO597PbD5oUlE1fEZN+9fqfiU1N9TQCHv909/1GB7vdhioByYfbD9Xu+RwAjw/b1RD/nYa7Kep2aGKHthpYn0eJ1KYTRSnXk7bIHa7IadGvK9mM6jsJuIxCZAACZAACZAACZBAaAI69JhpwqzfMKctWtpiphFqI50C2RVr161b54mtkaxX63rW2mLt6tWrZdSoUdKlSxdp0qSJFClSRE488UT7HTR9/5RTTpGSJUtKs2bN5LXXXpOxY8cK6uE3FXKyCbYUZv3uXsaRAAmQQPQEKMxGz4xXJJwAhdnQr8c8G0BgpManv/jqPoQiDHS/oPaFmj14btL5CTcQo/aomTTYwkOpj9pTahBDbK8dnIcwkVvNDeGEunCCqV2Hem7mzrEtHLreauHKgSeUXRb2IcQstsoIJcwi2TA1Ow8wRJ88rwZxe7WafR6iYnE1N9hp4mmzm2/QcTE94QqUSzQOA/svqo1WswVQ1G+Iml8I199+1/jF5dVICDc2i/l6/Jbac2rujwJ2aNx/1OywSA/s63H/T1cbobbdOWfS4T52Q3+NMOex3avWV+1ptY5qYOF+tm7WODtEcn8lqh9QJ1NfMAgKmSnMog74LJh6YRvL8+nsw9fZ+UzVOAixEKjQLz+p2eft/Tx6zg7h+qWjJrav36nHXdSaqeE+/ErNFhvttD30XCwhnns/s/mY9r2jO3bb8WOGCWpgBU6T1ezz2G+p5oYCGvGLmpsW90oQZ6TtqOaGRDyPEynMon6J4FRY83H/P+OHWK+pPanWVe17NZvhbD1mIAESIAESIAESIAESiJKADvUdU2HWHWq0hVrsG7HWeKIaofbgwYOeh+q+fftk7969As9aTIEctF4tvGrjFWshTsKzFsLtxIkTpVu3btK8eXMpXbq0nHbaafa76RH755xzjpQvX14efPBB+eCDD2TGjBme56+fYOu2H8dZIVCYzQq9wDqQAAmkAgEKs6nQi0nfhlOifF1kchLwCJymf13B6oiXXj0PAW6omokPEm7KappVVjqT3m/7k6a7SM0vhBPqOuhFJk8/Ucycw7aeXwFW3M9WXtEKs2fqtX6CAAQGTNmMcFDN1Ke+F3PkH3h/9bPSmLR+242aLsiDyU4fT5uPrF3oI3iz/aZmlx20D6EyV0B24fo74DLf6Os1do1aUD1MPESK6j45gG+QAGuu3aBpGqmZY7978GQ9D2HDpAm13aXp/PoskvtLL/2/RPRDR6uui5BpQMhsYTZRzyfwDdUH5txuTfeg2h9W+gd03w7h+uUETTxHzeQZajtO00EYNmnW6/7xarGEeO79zORj2oaXFgiC7g88DAt3+4y50Gd7vsbNUHOvsY9f1fP288qeEtlkmYjncaKF2URxulUbif9NNpOgffwoCywYSIAESIAESIAESIAEoiSgQ4tZSpj1G+p0xcogsfbAgQNHiLUY/A61Xi2mQbbFWoiP9pq1P/zwg8Cb1jbEwZAOAi2uMVMhT5s2TXr06CGPPfaYVK9eXS655BLJkSNH0Dus5MuXT2rUqCFYu3bAgAHedMgQmo0Ibdrp512b2YIthVm/O5NxJEACJBA9AQqz0TPjFQkngLE7BhKIiUBOvaqt2lo19yUXwmVDtUrWOXjaBAUIkm+obVJz88LxFrV2ahBcgsJMPWGuhYehG57QCHN+oXtSj20xtKbPeTvK9hJyBZhw5SCfy9UgZJn6mO1/cVLDXjUTV9eL8f9TW6OXq/mJFFs1vptaHrWgkKg2B+UfFJ9bT0BY2qFm2mlvp2n8bWqhQrj+DnWt37kzNLKXWlCdBum5f/tdeDguv24nqdlM0SZ4445XK6h2mZppJ/rfL+CzgHtos5pJa2/xWRithnsoKIS7v8x18fZDB83I1C3U57uUlQ4iz79MBXy2trdidZ/zkUYl6vlUQQucr2baaW/xvHpPzfxYxBb50EduCNcvEG+7qNkMTHm4r75Vq6+WQ62ymjmH7TVqsYZ47v3M5GO3r5weQMh2P2+GyVg9hx8EhAsnaoL71T5SW6yG/JYdPjb3H35UYfJtrvtBIZ7nMepxSM2Ug/4NFfbqSZM21P+IRHDC5xc8/QRa1HmJWjs13JcMJEACJEACJEACJEACMRDQob4sL8z6DUcmQqzdsWOHbNu2TbZs2XLEmrUQa9euXesJrtGItT/++GO6YGvEWoi6mNIY3rUtW7b0PGfPP/988z591PaEE06Qq6++Who0aCDPP/+8jBgxwhOC/bxrM1OwpTDrdxcyjgRIgASiJ0BhNnpmvCLhBCjMxvDOzEuOJABPr2JqENLgXYPpMeMJ5+nFFdWaqVVV+48aykjFcJE2qrTaVWqnx9FAiHkYPG+qhkH64mrwvkyGkE8rWUPtDjX0eyjxU09nSkCdaqk1ViupFuoHAXr6iADu6FO0B32SUy2WAJGjgBrY3K1WR+1itWhCNPdXVuyHaNoalDYRzyf0RX41/NDkTjWIZvC2jDWE6xc8C/BMxfMUP3AprAbhLjNCLPd+ZvOxOaB/r1a7/bDhMxdK+NfTUQV8fiE+ymHD/7lwISs+jxPBCc9BPNuaqDU7vM+XSAXBQAIkQAIkQAIkQALxEtChvqQUZv2GKG2x1vY2DVqv1kyDbDxr7WmQ3TVrMRWyLdbCWxZes8aD1vaqNfs4Z4u1tnftkiVLZOjQofLiiy/KHXfcIaVKlZLTTz/dvPsftcW5a6+91ps6+Y033pBx48Z5HrvRCLbxeNlSmPW74xhHAiRAAtEToDAbPTNekXACHFOL9wWa15MACZAACZAACSQNgfu0pqvVvlR7KkytIcTaAzJFw6TnaRIgARIgARIgARIgARKImoAO9aWMMOsOW9pCLfajEWvdNWu3b98utli7YcMGMWIt1p2FcGnEWoixEGWNQOtugwRbCL+zZ8+W/v37y1NPPSX16tWTQoUKCbxotWN9LVeuXFK8eHHPw7Zjx47y8ccfy9y5c70pnKMRbV127jGFWZcIj0mABEggNgIUZmPjxqsSSoDCbNRvzLyABEiABEiABEggWQk00IrbAyrlAhoCz9uNVlrswzuYgQRIgARIgARIgARIgAQSSkCH+VJWmPUbwgwn1hrv2oMHD4q7Zi3EWgyow7N2586dYou1mzZtEiPWuuvWQrCFWButYGumQ8b1U6ZMkQ8++EDatGkjN910kxQsWFBy5sxpf7c4Yh9r22IN26pVq8pDDz0kb7/9tkyYMEEgIkcj2IIXAoVZv7uJcSRAAiQQPQEKs9Ez4xUJJ0BhNqFv08yMBEiABEiABEggKxPA1PH2gMlCPS7jVBiesZOddN2cNDwkARIgARIgARIgARIggYQQ0KG+bCXM+g1tumItjo13LYRaW6yFYLt//37P9u7dK35irbturRFs4REbi3et7WELgdQWbKdOnSofffSR52HbsGFDKVmypJx11ln2d46j9jEtMtI1atRIOnXqJIMHD5b58+d7onOQaAtxuFixYjJs2DCxefnxZBwJkAAJkEAwAQqzwWx4JtMIUJhNyFs0MyEBEiABEiABEkgWAj20ou7gyA6NW6W23efcco3D2rEMJEACJEACJEACJEACJJBwAjoEmO2F2aBhUFuADCfW+nnX7t69+yjv2i1btojxrl2/fr3Au9Zv7dpIvGttwRbCKURbCL/Ib9GiRZ6I+vrrr0vLli09z9kCBQrIcccd534XST/GOaSBR27r1q3lvffe87xsUZfVq1dL0aJFvXVxIVQb4Rpbl5M5DuLKeBIgARLIzgQozGbn3s8ybacwm/A3amZIAiRAAiRAAiSQlQmcqJUbqpY+ABJif4yeO0eNgQRIgARIgARIgARIgAQyhIAOEVKYjWKc1IiOZmsEStuzFl6nZipkI9ja3rUQbDEV8o4dOwTetWbt2owUbI2XLUTWr776Snr16iUdOnSQ+vXre2vUnnHGGSG/n2Cd25NOOsnzmm3VqpW8+uqrnvC7YMECT3wO8rQNEm6jQM6kJEACJJBSBCjMplR3JmtjKMxmyFs1MyUBEiABEiABEsjqBIppBT9U+1ZtkxoGQrapLVFDfHk1riurEBhIgARIgARIgARIgAQyjoCOKFKYjXNY1Yi0ZmvEWmyDBFtMh7xv3z6xBVuzdq0r2G7cuDHk+rVr1qxJX78WXrTff/+9rxkPW4izuMZ42UK0hZctRNahQ4d6out9990nlStX9tayhSird2BIu/DCC6Vs2bLStGlT6dixo/Tt21dmzJgh8Aq2RVt628Z5s/FyEiCBpCdAYTbpuzAVGkBhNuNerZkzCZAACZAACZBAEhE4LonqyqqSAAmQAAmQAAmQAAmkCAEdXaQwmwFDrEaktbdBgi28a42HrSvYutMhB61fG25K5HCCrRFtXcEWUyNDuP38888lf/780qxZM3nooYekVq1aUqRIEQnnaasfE8GatoULF5Y6derIo48+Ku+++66MGTNGVq1a5a3Va8RrV7QN8rY1TDOg25glCZAACWQ4AQqzGY6YBYQnQGE2Rd7j2QwSIAESIAESIAESIAESIAESIAESIAESIIEkI6BjdxRmww9gJiyFERXtrRFsbYHSbzpk42GLQX0/wdZvSuTffvvN84Y1AivWoYX4Cq9ZWCjBFp63OA+D9+tVV10lPXr08NayNVMjw9N2yZIlntD6wQcfSPv27aVhw4ZSpkwZyZMnj+TIkSOkp+3xxx8vl1xyieede88990inTp2kT58+MnnyZK9cCNW2x61hZbY2R3c/YZ3GjEiABEgggQQozCYQJrOKlQCF2SR7X2d1SYAESIAESIAESIAESIAESIAESIAESIAEUoSAjuhRmI11WDOB17miIo6N+GgEWwiUoQTbPXv2iBFtzRq227dv99aw3bJli2zevFncaZEhrEJkhWCLqY2NaGsEWTMt8rRp06RQoULy/vvvp0+TbNLYUyPb0yNDDMa5qVOnSv/+/eWFF16QFi1aSNWqVeXSSy/11qzVj1FI4fa4446Tiy++2JsmGYJvu3bt5L333pNRo0bJ4sWLvbV6beHW9br142riEth9zIoESIAEIiZAYTZiVEyYcQQozKbIezybQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkGQEdMyPwmzGDXzGnbMREc3WiLXY+gm2QVMiG8HWrGMLwRbTIkfqZfvNN994HrPwijWethBmjXDrbm3R1hZuIQAbb1sIt3PmzJFhw4ZJt27d5LHHHvO8bcuVK+dNm3ziiSeGFG31o+ad/9e//iXFihXzplfGNMuvvvqqDB48WGbNmhV2jVtwNGyDtnF3IjMgARIgAYsAhVkLBnePFQEKs0n2vs7qkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpAgBHRGkMHushkVjLDdIQAwn2h44cEBg7jq2RrQN5WU7d+5cufrqq+XDDz8UrGdrT40ML9lQ3rZBoi3EW1u0db1tUca8efPkiy++kO7du8uzzz4rmO4YHrfw3sXatfoxDGunnHKKXHbZZVKlShXv+rS0NOnZs6d8+eWXXv4bNmw4YrpkI3jbPCngxnizpsBl5vNm3w8mDlsGEoiWAIXZaIkxfQYQoDCbIu/xbAYJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkECSEdDBPgqzGTDieayytAUjs28LSkZ0tKdFjsTLdunSpVK4cGEZMGCA52lrpkbetGmT7/TIrnBrpkg23rauWGsfG29bW7jF9a5wi2mYV6xYIZMmTZJ+/frJSy+9JA888IDUqVNHSpQoIRdccEFY0VY/rl6aE044wZsyuXTp0t71LVu2lOeee0569erlCbjz58/32ulOm+xOnRyJgEsx71h9OqIr1/78YE3ntm3bSsWKFb11kOH5bfe9SRtdCUydXQlQmM2uPZ+l2k1hNsne11ldEiABEiABEiABEiABEiABEiABEiABEiCBFCGgw4QUZrPUWGnGVMYIR+42nGhrvGxXr14tRYoU8aYI3rt3r9jr2WJ65B07dog9PTKEW1u0Xb9+vedpCzHVT7Q1wq0RZW2h1m/fpIPQa3vd2lMloyzki2mYP/vss/Tpkhs1auQJbPC6xTTI+lGO2DC9Mta8vfbaa6Vu3brSqlUref7556V3794yevRoWbBggdfuSATcSEVc9BlD5hIwnxOIr/jhAtY2tu+T448/3ps6e+TIkZ4HuhFpzXXss8ztr2QrjcJssvVYStaXwmyKvMezGSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAklGQIcbKcym5Jhr5I2yxSSz7wq2ED+LFi3qCZwQHSFW2Z62QdMjmzVtjXCLNW1tb1tMIwzR9rfffss04dYVb+GJ++2333qesRBY4Xnbpk0bb73bChUqeNMm586d+whhTj/mIY9POukkyZcvn1x33XVSu3Ztad68uXTo0EG6du0q/fv3l3HjxnnTKEM83rdvX0RTKVPIjfyejjclPgfgjXsc/VOzZs3A/v73v/8tzzzzjPcjACPI230Vb114feoRoDCben2ahC2iMJtk7+usLgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQIoQ0MFECrNJOKKaWVU2Qi3Ey2LFismwYcM8wcoVbuExCFHKFW1DrWnrirbbtm0TW7jduHGjRCLcom5r1DPWTJMMb1o/L1s7znjcYmt73SIfM2WyK+Di3OzZs2XUqFHeFMcvvviitG7dWho0aCDly5eXK6+8Us4+++xAAU8fGYHncB3WwS1btqwn5LZo0cITct944w1v+mgIuZhOGUIuRHAjAIK7MbtP7H3Th5FuM+veysrlgBUYgzXuU0zhDS/ZUH143HHHSY0aNWTEiBH0os3KnZsF6oYZB7Bm9sMPP+z9ICULVIlVyH4EKMymyHs8m0ECJEACJEACJEACJEACJEACJEACJEACJJBkBHQsksJs9huQjbrFECkhzA4fPjz92iChzxYFjWjoCrdmimTjaQuvRL8pknfu3Ok7TfLmzZuPmioZXrf2VMm//PKLoN62cBuNeAshNxYBF2VAwP3iiy+kZ8+e0rlzZ3nsscfk7rvv9kTXMmXKyOWXXy7nnHOOQMzTR0bUBg9eCLnlypXz1sSFkPvUU08JhNy+ffsKptidNm2aLFq0yJs6Gh56Rji3+8Ts233m7gf1c6j49JskSXfAAN6yENDwg4F169Z5gvwtt9wip5xyStj+uuiiizwvWtwLRkRHnoZZrFjQX/hM4AcLtORlgB95PProo54nPaY/Z18mb1/G0ndYsxrPg2McKMwm2fs6q0sCJEACJEACJEACJEACJEACJEACJEACJJAiBHRgkMLsMR4dTYbi/YTZUPU2ApS7tUU/Iwpia0RDe4pk29vWFW4hNGJwO8jrNki4hcBmi7doly3eQjAxHrRGlLU9bYP2TdogD1zjhQuxGIZ1dk09Fi9eLF999ZUMHTrUE3JffvnldCG3Vq1anhctRFish5sjR46woqA+mnzTYHrlCy64QK644gpvjdybbrrJ8/Zt2bKl553bpUsX6dGjhwwZMkQmTJggc+bM8TyP4cWMfjECo91vZt/u16B9916I5TjUPZeoc6gX2oA24z7D1Nu4R5YtWyZz586VyZMnyxNPPOGJ60GsTTyE9+rVq3s/aMA9DF7I2257NPWGFzn6CR7atORlcP3113tTnefNm9eb7px9mbx9GUvfYcp8/ODjGAcKsynyHs9mkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJJBkBHRikMHuMR0eTofhohdlwbbKFKXvfFvWM6IetEW6xtcVbeNwar1t43CbC6xbibUYJuEbYNUKuEYEhCMMg4BoRF8z9hFx4wRohF0KqEXLvuusugZD7v//9T4oUKSL58+eXs846K2avXH2UpQu8EBgx3XKBAgWkePHiUrFiRalXr57nBYzpnOGti7V53377bfnwww9l8ODBMnr0aJk6daonZq5cudJjCm9Puy/tPnb37Xsh3L59DyViH3XxE2YhVn/99ddeu9AH8IgG81NPPTWdlc3N3ocIB07oezBAGbZIG+4zg/O4H+655x6vT9EntORlgB9ZwNiHyduHsfYdngPwtD3GgcJskr2vs7okQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIpQkAHBinMHuPR0WQoPtHCbKg2hxLWXIHOFvOM4GcLt7F43W7fvt3zZrLXu920aZP4rXkbi4ALYc4ItJFsjYiLbTRCru2Vu2LFCvn22289T1h45sJjC9Mep6WlSdu2bT2xD9P0VqlSRUqVKiWXXnqpnH/++QIvW33UJdQgRp122mme927BggU9ERlr61atWtUTe++44w5p1aqV55WK+r322mvywQcfeOu8Yv3WsWPHypQpU2TmzJkyb948Wbp0qSd2wgMZ3q3wpMY9YO4Hs7XvlXD7uAb3jj2V8erVq2XhwoXeNNXTp0/3xHF4z06aNMkToTFdNbyRw/GCmFOtWjVvvWY/L9pQnw18DjElNsooWbKkPP7449KpUycaGfAeyOL3QLt27QTPO3z+KcymyAs0m0ECJEACJEACJEACJEACJEACJEACJEACJEACsRBQEYDCbCglhOc8ApkpzEaCPEi8jUS4hegWTry1PW+Dpk2ORMBdv369YO1bW8CFYArPRzCFGS9Z4zVrxFdbkI1EwHXT2NebPE0Z4bxzjagLsRPXYh1MeL9i3dz+/fvLu+++63nIQmx46KGHPLHw9ttvlxo1anjetKVLl5arr77a87A977zzJFeuXGEFS31+JTTNCSec4AnAmAIa3qrw9r3yyiulaNGigvrdcMMNnhiMOt96663SuHFjT6CGKNymTRt58skn5emnn/bWioWQ0r59ey8OYijE7EceecRLh7QwHEOcbdasmbce84knnhi2PXny5En3orU9aIM+A7YwC4F2+fLlgmm7aWTAeyBr3wN45laoUEGOP/54CrOxvKzyGhIgARIgARIgARIgARIgARIgARIgARIgARJIFQIqAFCYDVJBGJ9OIKsJs+kVC9gJEm4RH0q8hThmPCwTIeDu2LFDbAHXzwvX9cQ9VkKuEXZtQdfPS9cWde1pl3GPmKmXsYW4awu8EHnhdQqR95tvvvG8d0eOHCkff/yx9OrVS958801P7IUYCuHzvvvukyZNmkjdunU9ARVetcWKFZNChQp5nmcXXXSRnHvuuXLGGWfIySefHNf6u/o8DyuiZmQaeBFjzd9p06alT2/sd2vbwuz999+fFaZD9asm40iABBwC+D+AWQFy5sxJYTZVXqDZDhIgARIgARIgARIgARIgARIgARIgARIgARKIhYCOHVKYdQZQeXg0gWQTZo9uwdExocTbjBJwMTUuPHBdL1ysvWpE3EQJuX5euRBSIazCjBcttq4Ya0TaeLZunnZ52EcdjBmvYbPF/WabEXyN2OsKvhB94ZWM61etWiWLFy/21raFAIy1YMeNG+d5+2IaZwjBffr0ke7du8tbb73lTZWM9XE7duwo8ACGKAyv2ebNm0vTpk2lUaNGAm9geNXWqVNHatas6U1FDJEFa+3C8xaiMbxwMb0w1uDFlKVYkxfTlupzOaTBsxd5DhkyxJs6GT8IMGvPunetLcxCuIZHNgMJkEDWJ4Af5WCqeHjMwhsfzyqzLjq2+NxncuAas7G8NPMaEiABEiABEiABEiABEiABEiABEiABEiABEoiXgA4EUpjN5NHQZCwuFYXZSPshIwRcTKWMNUzNGrj79+8XrDcKM4P1EHHjEXIxtalZG9d45W7YsMET8+CVazxzzTTLEDeN8GmEUCOOGsHUCKnY2kKrLcLGI+ZGcq1dlr1v18fet+ts9k17/LamzWaLNLgOZcHjd+XKlbJs2TJZtGiRJ/7OmjXL83SF4Ash94ILLggpxOoz2zt/2WWXedMjz5kzx+uLbdu2ef2NewKe27jv3IA6mTVmKcy6dHhMAlmXgC3M4sce+JHIxIkTZfz48d7sAXgO43OfiYHCbLwv0LyeBEiABEiABEiABEiABEiABEiABEiABEiABGIhoIOAFGYzcSQ0WYuCIIRpZIcPH56sTcjwekcr4MIrEgPxttnTKNtTKccq5O7evVt27drlGbxy/TxzIQhCNDDTLG/ZssVbsxSirivswkPTrJsbibBre+3aQqcRRI1Qara2oGqLrtiPRLTNqDQQZOGJC1EW67ouXbrUE2Znzpwpzz33nJQqVSqiqZRPP/10qVWrluetO2PGDJk3b56XH9hASEdfQaRH35v7yb5xKczaNLhPAslDwBZmMT17z5495ZNPPpGBAwd6Xvx4xlGYjeUtlteQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQJIR0GFNCrPJM7Z7zGpKYTax6I3oFmrrroUbq5Bre+XanrmhvHPtqZYhFtqirjvlsp+wa8Rd47XrCrzw3DXeu7bQG0rshTev8eQ1WyP2ulsj/NpbI/66W1sMtveNMGzEXuMtC2F20KBBUr9+fTnttNPCesdiOmNMb/zYY4/Jp59+Kl9++aXnKQdhdu7cuenCLBhRmE3s54y5kUBWIWALs5gSncJskr0ss7okQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkCgCOmhJYTarjNxm4XpQmD22nRNKwDXn/IRcPzHXeOi63rlBHrq2sGumW7ZF3aCpl424C69dP89d470LoddP7I1U8HU9ezFtsz11sysCu16/7rTO7tTOxuv3u+++89ag/c9//hNWjNXns+TNm1caN24sPXr08KYtHTlypIwZM0YmTZokEGUxhfGSJUu8KZJRJtoLZpzK+Nh+1lg6CWQEAQqziXprZT4kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkOQEdACSwmxGjMKmWJ7HWpjFVL4Q4CDgZUSAh+eIESO8tf4yIv/MztOItZFug0TdIGHXT9R1hV30mT0Fsy3wwnPX9d4NJfbaa+26gq8r/LpTN7vevq4QvH37doFBGDUGEQUGj1+IvP3795dTTjklrCB76qmnyo033ihdunTxpv3GPTVq1CgZO3asJ8hOnz5dvv32W1m4cKGsWLFC4L0LYRhloR1gAI7gjr5zAz6HXGPWpcJjEsj6BCjMJvnLMqtPAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAokioMOZFGaz/pjuMa/hsRRmIe5B0Lr//vs9USsjYGB62gYNGkjr1q0zIvukyjNSMddOF0rYNeeMp27QNkjsjUTwhehrzIi+7tb29nX3jdev2RohGKIuxFpMRazPXF/LkSOHd/7JJ5/0xH1MVTx69GgZN26cN2Xx1KlTBWvRYtrixYsXe+vUQpCFlyyEXwjFEJtR31CiLG4iCrNJ9VFiZUkgnQCF2US9tTIfEiABEiABEiABEiABEiABEiABEiABEiABEkhyAjpqSGE2feiUO0EEEinMQtAz07Wa8ozIZ47tLTwJ4bFYrFgxmTZtmn3K8yp08zoigR4YYc+NxzHKhfCLKWVvvvlmueeee9KT4Vyoa9MTcseXgOnTeLZG1I1kGyT4BsWjb4MM9wSEUgimEGYrVqx4lCibJ08eueuuu+Tjjz/2PK0nTJjgCbGTJ0/27tNvvvlGMP0xvGMh/GPNWqyTi6mV4aEN0Rf5QyTGPYy6oJ2Glx9UCrN+VBhHAlmfAIXZJH9ZZvVJgARIgARIgARIgARIgARIgARIgARIgARIIFEEdDiTwmzWH9M95jVMhDCLKWYxret9990nt9xyi7Rt29ab3hWiFITRjh07yqBBgwRCGkSr4cOHS1pamifK1qxZU84++2xp1qyZfPXVVwLPRmwffvhhqVevnrRs2dLzVoSYBg/Ezz//XDp37ixvvfWWNGzYUO644w7p3bu3N2UtYCLN4MGDvWlhmzRp4tXlhhtukHvvvdcTx3788Ud5+eWXPS9aXI+paVevXn3M+4EV+FtMN+JlRmwhjuIexH0J4RQiKu61woULyznnnOMJ+LivIMAaw3l4dRsxdsGCBbJs2TLvnsFnB9MVwzsWUybjhwbwyMW9CgE4EkHW9DuFWUOCWxJILgIUZhP11sp8SIAESIAESIAESIAESIAESIAESIAESIAESCDJCejQJoXZ5BrfPSa1jVeYhcD12WefyTXXXCM1atSQxx57TCC2VqpUyfMy/P777z1vVYijELkw/WvZsmU9j8SJEyfKrbfe6olirVq18gSwSZMmSfny5b3rkVf9+vWlXLly8umnn3oiWIcOHeTf//635+mIKZCx7iemox04cKAnjH3yySdSokQJqVWrljz00ENSpkwZgQfkAw88IOvXr5dnnnlGrr32WmnXrp0XB29diLYQ1RhSmwDEXoizEGbR3xBUf/rpJ+/HA/CA/frrr737E/eoEWOxbuz8+fO9NKtWrZI1h6cqxvq08LjFDw0gxtresRB/w3nIuqSzojALYRk/dIAnMNqJdqVKgHAOMR1twzMM9wZ+FIJ7AgZxPdKAa3EPYK1siHS4v+INKB/1wH3mZ6g36o8+QVtQLtKhHqhPsga0x/QLPqP4HGX1QGE2yV+WWX0SIAESIAESIAESIAESIAESIAESIAESIAESSBQBHcykMJvVR3SzQP3iFWYhvMLz9AYVXiFurV27VsaMGSNVqlSRFi1aeIPsY8eO9QTSChUqeF6wEFohgsFj8e2335YrrrjC87hdsWKFtxYshFWItsgLIhnE27p16wrOwxv3kksukT59+sgaFclGjhwppUqVkgcffFBQl0aNGnl1mTNnjne+V69eUqBAAYHwi2lnMa0x6gGxDUJbv3795M033/QEqCzQHaxCBhIwwiyELIhxRpiFByzWiZ09e7bMmjXLW+943rx53rqxuOfcqYrN2rEQ8iDCIT9XjI1WHMuKwiw+T/B2r1y5srzyyiueiBlN90CshqfxlClTstznC32OH2dUr15dPvroI0+UxfrBWI8a057juRNpwD2AH4TUrl3be37hXoo3oHzMBgD2Qda+fXv55ZdfvOcYflyCdPiRTEb8yATPyy+++ML7zMTbtlDXQ+R84oknvB/cvPPOOxleXqi6RHqOwmyi3lqZz7EkcIoWfoNj5WKsUGEnn2tizCeSyy60yioayQXZKE0Zi82Z2ajdsTQ1h150vcUrVyyZJPCa66y6nJXAfLNyVnmsNheJs6JZiV9Zq11nxNkuXk4CsRBIxf+TWe2ZHUu/pNo19nOX7xyp1rtsDwmQAAmQQEwEdFCRwmykI6vZOF08wizEJ4ix//3vf+Wyyy7zph2GZyqmEL700ks9z1hM9QoPqHfffVfOPfdcueqqqzwhBB5h8O6CwHr11Vd7HopLly71PGHz5s0rzZs39zxaMcVx0aJFpWTJkp5Y8vjjj0vp0qUFUxIj4Bp46CIdvBuvu+46Txwx3n0QFKpWrep5xa5bt05w/YUXXuiJs23atJEBAwbIypUrU8obMBvfzmGbDg88CKm49+DxinsCAiSEOky7DYOAjzh8NuypijFlN66zpyp2BdmwFQhIkBWFWXx28IOInDlzelOD44cQkQb86OL999/3Po9PPvmk560e6bWZkW7mzJlSrVo1yZUrlzz99NOCvu3Zs6dcfPHF3vNoyJAhEVcDAjSmV8+dO7fn3T9+/PiIrw1KiPLxTD3++OM9/ugDex/H+JEJfjiAZzA8/xHXtWtX73kblG+08bi/ITpjBgQ81zN62nfMaoCyTjrpJO/HNvhxTlYPFGZjekVN/ouqVm2aq0zVpldfV7VBmdKVGlUoV7nRJeXT0nImacuu1XofteC6xl0VZXtO1PTbnLx2RJlHNMk7WGUti+bCFEh7vLahtVrJgLbY/VkvIA2j/yaAQWSbV4VMAHOFltExoJyDVn3qB6RJtWiwMH2wKM7GZRV++IyaNmFbM852JfryUPdgostifhlLIFRfpuL/yWPxzM7YHkz+3O3nLt85kr8/2QISIAESIIEEENDBTAqzWX1ENwvUL15hFh6GhQoV8qYPhtCJtWFhmEYYXnbwSoSYBQH0tNNO87xXsY+AeHirQaydNm2aLFq0yBM2Lr/88vR8TH4vvfSSJ0BgeuP//e9/3rShyAMiGjzV7rzzTs/bEd628IQzAeex7i28dyHIwUsWXrrw8oWnLURheNvC84wh9QngxwQQmyCuQoyDgAjxFf2PzwK2EGvtqYrhXRvvVMXhyGZVYRafLf135H2+/IRZ8MTnCtMe2wFCd+PGjeWEE04QTDkOwc0v4DrzIwpz3vQP8g0VcB7eqqG8k00ad0pcePJiGvSTTz5ZMD067oUePXoIfhSC5xnWqY404N7A8+mMM87wZgYYN27cEZeibCPmh6orzoEHtpi6vWDBgp4Yi1kAunXrJu+991664YcuWNcbP3qBMIt1ktFPr7/+uveDA7sCYBSOk2HucsJ1d999t/fshncxfsTiF4LyD8rXzQN8UDamg4YwCxEaz+Vff/31iKSRtOWICzLhgMJsAl5YkyWL225LO1FF2AYlKzWcXLJig7/U5Air1GBPyYoNPyxduTGEzmQKQcJspygbUUPT20IE9inMRgkxguTwwluo9v/snQe8FcX1xy0RlahYUbFgLzHGjqjRCNgrNorGnkjEglGDisoFe0ExGhvWqCD+jVFJNMYWiZrELvaGYO9iQxFN9n++6zvPw7i7d++9e997971zPp/DzM6cOTPzm9ndy/zezIJv/xR7Ow6+SJoCUlNySy7ysxv3DFEWsZ9vqj8M7AK3E7MhOuWv2wp+bZWYzTMHy6PsFm0BgTxjOUwaqu+D9vIHTC35zG4L49wIbbDPXf/N0Qgj5m10BBwBR8ARqDsCsh7pxGwLLMo2ehW1ELP0/cknn4w23XTTmBxlF+sU2VXHTjuOoTz//PPjb1Niw6L+WmutFR+3qTu9lJiFHOVoY0hTdujh7+mnn459sYORXbVnn312vJvxqKOOin4hxyazgI9YYnbSpEnxTlgIIRb6EY405juzkEPsvoJ8ueWWW+IdkXzzFgIZYhiSw6X9IwDpBQEEAQahxrGvHE0MuYLqd2MhYzmqWAk1CCbKUV61SLQahZjlXj7zzDOjUaNGRX/+85/jHZr7yh9F8IcPV1xxRXyPcZ/xRxnsmpcXXXzf821nCETuOXaojh49Oj5CnPuSo2v5Aw/Kca9z7DhkJEf5luQo5QceeKD5u62MAaQvBCSEIbs4eSZMmDBhlp2iPIfYscvOe2zYtcv3q3nmIErMzjvvvInELMQoZC19xP8f//jH5qOcISd5th1//PHRP+SYZubJ6aef/gNilu/Ajh8/Pn7G0J+999474vjhe++9N24H5Xju8Ick+KNPtBfS9ZJLLolWWmmlmKCkH/iiPap885fy4JFGzIIT48D48ExkDDhVQJ+N4IA/jj/mj2poI8c4s+uWZzl2/OEMz2eIUohi8jmqmaOWR44cGc8D2sqYDRo0KGInMuQpx39DJJOGX0hWdt5yj1mhPYwx2OADX/zBDLt/LTHLePIHNYw5/pgzzCUdT+uzpeNOzNb952zbqIDdsRv0HvjMLERsSMya6x6997xm8z0Gz9c2Wl+2FWnEbKWLuNdJTboArKETs2Xhr9hgusHZidmK4ftBgZZc5P+dGTsnZr8fipLBxXfMfo9LPWJ55mA96nWfxSOQZyydmC0ed/f4QwScmP0hJp7iCDgCjoAj0MERkMVJJ2ZbeoW2AeurlZiFMGDHGYv2EDYslkOicvQw6Syos+gPKQtJAeHBQj87YdmVOG7cuKhbt27x4jzf+ISg4JuwkBWQHizUQ6zyDUWOlx0yZEh8RLISs5APO+ywQ0y+4A/yAUKDNtx2223x8cpdu3aNQwgVvlfL928hcvgWLqQF7bn//vsbcPS8ydUgYMlZSCQIWkgulLiSsbqb0xKy1dSXp0xbJ2Y5Kpx7GRKPe3uZZZaJVl999XiH6fzzzx/vqISI5Q8yuI/79esX75aV13B8LC07Otkpz/ecKcvOVJRdphxNDqkL0ce936VLl0h9cvw5O94hZxk3iD9IRp4ZHEMMsbrQQgvFR/leeumlMdHI8b78wQX14L9z587RIossEj83aANjnIeY5XnCN63xD0nMswbh+9c8Q/BPfyDxOcrY7piFrOMZxO5/+sNpAfSJdmyzzTbRXXfdFf9BAM8rnk9gQZ+wg6CEhARPCFGIYQhc6qWcKrtJ+YOBJGKWP47p379/M05gsOiii8Z/uAIxzLznjxIglBmbBRdcMMaT+mnL/vvvH59ggA9wZhzZ/UxfIFh5TrOzmGPhFefFF1884nviELcQrd27d4/7zBiBDd8G552gxxPzxzj8kQ5jrDaUwXb22WdvJmYheSFkOWaa9mHLsdHMw5tvvrnVyVknZjvAj+0N+wzYvUfvAV/mJWXVboM+A1/aeLs9uzcARGnELDf/T3O2n+/Ufi4aPzBM6MRsTgArMHNitgKwcpi2NWIWIuWkJl01R/vbg0lJOqHPjlqJ2baCX1vdMZuHzGsPc6oj9CHPWPLtT32e/KadgNKSz+x2Alndu+HEbN0h9gocAUfAEXAEGg0BWT92YjZeRvd/shColZiFvOIIYna+sVDes2fP+DhiiFQIGshPjgwdMWJETARwbCwEDN99JQ9SAaJik002iXe48Q1Ddrexa4rvxf785z+PiRiIU8qefPLJMeGgO68ga6kL8oJjS2nLwQcfHB+tjM/evXvHu3SHDx8ekzYcUbrFFlvE7SSfdkCusHPMpeMgoOQsxBbKPFYitt67Y5NQbhRiFlIOshHijG8/c1+zAxaSbo455ogJTMhTdkvyBw/YQbyyGxSykD/egNzDlj/W4A8j2H2LX0hAiDl2ZfIHG/wRxhJLLBETchC3HIsOsUd5SDp2VFL2F7KDHtJwp512infes+NUiU5suGbHPsQof5jx+OOP5yJm+aMTniX0gXLs4kcgH3k20Q52pCYRszyH6NvSSy8d/9EIhDB/bLLccsvFRCTPHI7R5pvXnTp1itsPTvSdHf3XXHNN845jMABziHCrY8eOjZ+pITHLH63gFxIYnBgjxoNnKiQrz0vmG0cur7vuunHdfG+XnbLscIUopW98c5edwpQDX+rGL6cYsOMZjOV3Udw2iHuOmefUAv4Qh7rxQX8gqHkHQKrim13R7Eznj3MghFHi/BEORK0SwbpjFoypC2KZnbKcesBzHTtOWEg7XjnpPqtHmhOzjfbruML2brD1wDXWj48oDo4tNrtjlYhNDPsMfEhuurkqrLalzbOI2bzHGe8ujVZixYZOzBY/mk7MFotpSy7y5yFSiu1dY3grSTP1uVErMdtWeuzEbFsZifbbjo76PGnJZ3b7nT3F9syJ2WLxdG+OgCPgCDgC7QABWYB0YrYeq7DtzGetxCxwQIiym47jLf/yl7/E34udOnVqvCsLcoMdVJCqCKQX35uEHCFklyIL/ZRlZxTkGLbspsIXu2YhX3UXI37ZuYYdwu43jkDm6E2EtmBDOYhf6n722Wfj9kHGsUuM41ghJdiFxlHHED4uHQ8B5kOWtiQijULMsut9tdVWi0lOiEbuYYgxdlhCtkKOcjwt9xXfp4XUZLcj9zDH70LMQsSxu/LUU0+N71WIRO5B7svbb789Pur3zjvvjIlXdmRC8rGDFAKS3fHsImXXLEQpBB9l2LHKbl6OROZYZdrCt6jZoY8df9ABuUfbIYHz7JgNiVmeUwjPDohZ+gHpmETM8oce2NM2drqikIoQsxCKp5xySnxiAH+EMvfcc8cnDtBHnl3gpN+YlZ8iMZnJblNOAlBlN20SMcu3aHk+br755jEGffv2jcASDCA62WkKYQ6W/LEKRCkYQwaDJX8YA2kM8cp4MC7sfmaXKn9Aw9iy2xYb8OTIYcaROnluM+49evSI62bXLM9f/PKHNZoOicsf7WgbIVexAzNOOWCM6DfEPHOLfDDij3R4J9CXkhx/DGFN29lBrO+DlrxntS4nZtvBD+a0LkCoyjdjn08kXPMSs9j16X9mWh1tJD0kZh+UdilJknbcatj0P5kyU028UmJ2finbSTSPsDNO21npsct5/LMLeI48hhk280jejzLyq8mqlZidXSqlb7VKEfjkbQMYzp3XWOwqaVsti/zMVY4sB9M8Uk8ipbM0IG878rQVf9UK7eD7l3mlJIZ6LycRs9xHtd6LedpSSZvL+StHzDKfsalVKpnr1FXPOVhrXyhfaX/S6mQO1jKHQ7/MwSLGK/Rrryu9b+o9lnnxa6n7U7Gq5ZmtPmoJ6/Fez2pPJb+LsvyQV8Q8TnpO1ouYrfTdX8R9X+l9mIU5z/kifgM28vs9C5+invdZdXieI+AIOAKthoAsGjoxqyunHqYiUAQxq8458hViFPJVBeIrTWweZew1cQgA6yvNT1K6tiUpjzQW8mmrrTPN1tMdgXoj0GjELIQY3z7l/mHnJ0f+sqsS4hRyDvKMI4ghZtlND8nJH1EoMbvmmmtGN910UzOs/IEGBCI7VCFUIQ8h3thNCjHL0bkQcOxyn2eeeeKdsxCYCN8ZhRyDIOUPPnbdddd4bU9JSHZ8ctQ65dh1yXHL7OplFyeEI7twIQXZiQnZyO5fiNE0YpYds5wMkEXM8uz65z//Ge8e7dWrV9wffEMwQsxCFONfiVm+q803cFWUmIWE1u/j0mYIZZQ45Xk+2h2zkLvUy25m+fERE+DsTAYDds8yRhzNDLnM92zZRbzOOuvEJCx14w8s+e4yfUD5pis47bjjjs27U5WYZVcspC52CH8MA7EKMQ5JPG3atDidsWFHNH44sYBv97JblzZC8NMXhD+wYYyZNxCzzCWw4Zrx5Nhl+gJRDZYQw+yuZm61ljgx22o/c+tfcY/e/feomZSNidmBX2y47V4L1L/FVdcQErOHiyclSQjLHWfMouFXTWW+lvDcpjhls4hZFveGiF4t+m/RaaJa70cS/5vor0RZJEuSPMQsi1mXiEIcq+6Q5EzSWLA8usnuZQn/JwoJStsuFuVIyHKykBgcJTpR9G1R+gMmz4j+nyhtXky0UmH+3ChKHxQjwsea0kj/qaiKtdlVEucTHSF6p+jHovTtWdHLRPcVzbOruwh8pKpM6Se59AXtK7qc6HWizI3/ir4keoxouDBcS9sqWeRnDowS/Ysoc+RbUbCmbRCKl4ouLxrKgZJAn54X1bH5qimNdI4aVblWIqShG2hiQsicoNzdom+J4pfjxP8lypzfVDRLxkkmdYwXhXBaVPRY0VtF3xfF3xTRm0T3Fy0nm4nBNaLM9S9FKf+e6D9ELxTdTjRNSpKBParEbB+JXyWKP/DlWXKH6HDRNUXTJAs/5pRiu5fEIXsHi9LHd0Spn7nGPU+fId6rFTDVPhHuKLqcKFg8LsrcYbzuEz1TdEXRPFLtXD9QnNP3rDk4sMkGu8tF02R1yVAcCTdOM5T0c41t0hyotj9JVfaUxD+IPiD6mSi4TxG9WXSoKHWliZ0bPIeQbUXteE2Xa+6vC0S7ixYh1dw3ecZS28a807E6WxObQtvn3Uwaz48XRBW/KyX+S9HZRVXWlsgZog+JMpdniHLvktZZNEm457QtJyYZmLSsdpd7ZhdVjzanXu919W/DWn8X2TEtah7PLw08UvQJ0S9EmRe8/64S5R5BZoqSju4qWqnQVp0b9GE50TzvfjGbrZb7nvJINffhdyVnm03fpTdIwtyia4ieJ/qw6Dei3B+vivJMXVU0j7Sn97vtb5HPe+vX446AI+AItEkEZJHSidnWWqltoHqLJGYbqNveVEegTSHQaMQs3xXleGEEYopjbCFRlZjlKF9LzLKz1hKzHNvLblIEcpfd6xByfF+UnZF8+5QjeCFoIVMhHCFmyYNcpD4l/fjWKkQluzb1+7byUo4gfyGFITYh/44//vh4Zy022LMDNC8xC9lLnxB2bXJsexYxy459jkmH+MSWvtAndo1ypC/ELN+wVWIWkpg2qVhi9vLLL493+mteGFpiFgKbXcNKeoIXxwRbDE477bSYYOXYYtoHEc2OVYQ/WAEfdsZClkJ6KzHL2OqxwUrMQp7bHavEOfIYIpXjq3WM8MN8gEwFFzBcb7314v+/czyynqjAqQmQ3krMgrnurIVA5vu7jKWOJ7hyRDZ/ZNNa4sRsm/wJXEyjNug98J5CiFkhZ+U7tYcU06q6eGFRSxfUCDcUnWrSLGkkyT+QvSVFy98i8eHm+pMfWH+XsIcEbxg7LZ8U/lPsun9XbJZ/h5nyz82S893FvBLcbWzw/TfRpMX5tSUdAiipfk1jce1k0R+JJgltVIJMyySFkDE7JTnISFtK8pJ82TRdIMWNTT9Bri0ZY/M0/pDYLELBFCkCnxTXsySfLlfaptESn2KuNZ1wFVGVWttWbpGferqKskhs25AWh7w5TNTKZXKRZk86+KvYBW5dWNc8DSEN3xXN8vk/yQdD7oMksWVXEoNyc4RFc7BKklMk0fpLi3P/QTyEUpIELcN9OFKU9mtaGPLHDpBTSZKFn60HAo8F9dC3vaYt3ZIqyZE2Z+D7bLl+P0izdfGs7CuaJbXM9TxzcE+p3LaJeZEkx0iitTsnyUjSuG8g1dW2d2BXS3+sqx/JBc9mntFaV1LIeK4lmiQlSdQyPIeGmmtNt+HHkg95WItUe9/kGUtt1zCJaLuf08SmsGTywI85qrZJIe92ZEtRnnNJNqRNFk16n/A80jITJZ4lWe3mOaR+CHsFjoqqB7fdRev1Xg+aPVsRv4tK4lSxKWIec7+8bXyqbw15Th8rOtPYVEPMVvPuL+K+l2bPVu19SFlEsSDkj0+mBWk2H7x+K5ol7e39rn0t6nmv/jx0BBwBR6DNIyCLlE7MttZKbQPV68RsAw2WN7XdItBoxCy7L7OIWd0xKy/K+Duj7IRk16TumIV8Y3clAqnG0bnsjOVbqldeeWVMRPJ9U47s1aOMIekgSCHtttxyy3hnKMQfxxizI7ZPnz7x90s5spedpuy8hfxlZy07NNmNCzlK29h1Wm7HLEcsH3bYYc3HIrOjlZ297ARlx3AWMcv3Wfm+LAqpzI5cdghDLkLMhkcZ841XCFEVS8zSZo5j55hjq2+++Wa8U9USs7QNrCE/wYBdruwiZgcsO4XBgG/JvvXWWzEWHCnduXPnuI0cOQwh+wv5Zi+EK0cUQ85yJDEENphzvDG7apWYZRfwvffe23yyAcfKc8wzY8R3YPHHMfTgz05a0sGU/kCMs+MVkh5Sml237FpmbjFvsJsiR+QPGDAg3u2MX0hn+sI3eCFpIbghczkhobXEidk2/3O4ugbusceITuv3GfjfoohZOc54QnUtaZFSITHbQ2o9S1QXkyBssuQ2yVTb/hJn8VavP0koyKKu5mv4qaSxaI5+kZD/f5IWStbCLeTr30XVPyE7AecWDYWFNIgea/uNXNPv94J0bO4UDYUdJSx6Wx8szj0h+k6Qjg31LSaaVxYXQwgOFvVsHcRJQ7cQVQlt7PWXYkT/bBrx67VwEBaBT+Ay9dIuzobt0+uHTeki2tZF/KlvwnCRn+oeCGzAe6roY6JvilryCR/kryGqcqFESLP1qB3p96uhhHaBu59J1+heEgn9UP/Lop8l5D0paZ1EQ7E+wnnOHLH5GqcfofxKEjSfkP7QFupNmmfsoAylJAnWRxhP6hc4rRk6kuss/NLqoc2TRBnnr0Rt/VPkeiHRSiUkZq1PCMQXRJNwHpRSUa1zPc8cpJ92zAantOVuSbf94TmXJPtKotp9JPEfGaNa+6Ou2PUMyaf1aPiBpD0navtDHs/epHu8JOlaNgwZr/AdgQ2+u4lWI7XcN3nGUts0TCLaH/CwUpILzQtD+mzvJfJ5zvxedIao2oNBiDF5fxANZbQkaDnGLEuy2l3umV1UPfV+r9v+F/W7qCROFeMwrHQe/0R88RvO+uFZ+apo0u80tauVmFU/Yfiw1KtS1H1fy32obQnbqdf88cY9olNENU3D32rhINwrwbbR3+90sajnfQCXXzoCjoAj0LYRkEVKJ2Zba6W2gep1YraBBsub2m4RaDRiNmnHrD3KGIKOnZbyloy6desW8a1TyDlIVAhNiFmOBEYg1fQYYUjL7bffPibl2FEJcceuTshJiEOIXXbQciwxx9pC/tEWCL/dd9893tUKace3WCET2YW7zz77NBOifPcWAlSJWT0WWY8ypq3sIL3hhhvi3aIQf9TP8cMQluz6hATmqF5LzNI+dvtCHnJMMN9/XWWVVeJ2cnQvx/KSB0bsGj3uuOMidvqy85OdxhCzkI4q1E894Ne1a9e4P/hThbCm75DVHPcLFtiyS5WjpWkP5Cqk63bbbRftu+++8fG/4Esc8pZdshwLTF/oN+1klzGYcGzwjTfeGBPRBx54YEwmcwQyJOqECRPib+tCOlPOErOQ7Oxipc30k2OUIWAhWxlLjiCGkIesZS7ocdXs8GVHLnYQyvQFgh2MIOq7d+8e++NYaL5nTP8ZF3CD5G1NcWJWRqs9yoZb7rlyYaSs7JiV3bcQjm1VkojZ9aSx8c3YFCYRIPRnYVFdvGWhrrPocFEtm0TM/t3ks8C7r+hcoiqzS4TFKbsjkAXF7mrQFKYt3M4t+X8T1TYQ3ihq65DLWLCdLKq29OUkUburdjW5fsTYYDtQ1MrucqE+WEQ7WFTroz9ri14mqjaER4hWI9OlkPrpn+JA8214sdhuIgo5AlHHgiS7d9UGjBlPK0XhY31mxU+XTG2Phm9I2nmiF4lOFR0sihTVtnKL/BtJXdoWwjtEVxa1sqxchLsvL7cGTfHfSai+nk/IJ0nvJ+z6BTZLyTWLverjG4kPFZ1PFGGxemPRl0XVhvB40VBsPnF8QWgs3WS4jIS0l4V4tWW+LCBqhWeb5v9b4hYbsOXeeMfYvC9xvTckGktJ/lUfGk6RtH1El4gtvjvq1z47sKO9oWThVxJj9a8hWHGPq9C/20Q1n/ACzawgTCNmR4mPHzf5AYdDRcFe6/tQ4hBCVoqa6/gsNwfvFRtty622EU1xnvE8t9WGkDmyiGgoPHfV7hqTWWR/fm3qoK4nRdc3dbFjfIionRfPyjXPQSsludC2agiRuY0o7wOemZuJPiqq+YQjRauRIu6bcmNJu4aJanvpj5WSXGiehk9I2i6i3AfMT95ldn6q3euSvoMoY8mc2E30S1HNp0x4n3O/an4jELM8u7S99X6v22dbLb+LSqbN2vZq5/Fdga/r5Vrvc55vO4tOC2yosyhiNu3dL1XMVtR9X8R9qDjbkOcC72OVvhL5XFRtpkucd6yVpeSiPb7fi3zeW7w87gg4Ao5Am0dAFiqdmG3N1doGqduJ2QYZKG9mu0agrRKz7FCFUDvggAPi3ZoQhmussUa0/PLLR1dddVU8JhCmQ4cOjYlQiE8Iv88//zwmURdbbLGY+GNnJTs1IeNIgzCFwFThGGK+eQq5CaEHCcjuUkhSiMKDDjooAiN2UPJNWAhBCDyITshGCDq+UctRvOzyPOuss2IyE1+Uh1jlW6XsWuVIZXZoUgZy78QTT4x3tLLLFWIQkhNSkiOWIXDpEz6oi52y+AEDdvKOGjWqeScwZCZ5HOcL8fnb3/42JjghXvkWKyQlxCeE8V577RXjdMIJJ8TkKaQku0tVqB8SFuxR+hoqxC27ZcEbYhM7dh6zi5ijgY866qi4vbSdOsEAkvTBBx+Md6dCokKWMxbY0E5IWjA4++yzI46fRhhnJaO1Dgjy5ZZbLiZMObbZfgsc/EeOHNmMP37BDqL9+uuvj3e84hf/RxxxRDwf8MtYcWwxx1dDKLNjll3B9AeCnDlHP1Dau/HGG8ckL7uYW1OcmG3zP4era+AGWw7oUyQx26P3wM+qa0mLlOoptehiEWGPplpfMeknN6WFgV0cG9uUOdyU+yQosLpcs4iv9bHomyYsDqsdIddWkhacWTz/q6gtR7tYREwS64MyIeGqZVhYsouULBiyGK1ygUS0zgmaGIT4mCqqdo8H+XkvWdBTH3mJ2fNTnB9hfOFzx8CuKHwCt6mXp0uO9o3wHdGFU6yLaluXoM5eQX3Xm/xXJa6kWmAWL8Iyptr+SaGBXOchUmYaH/0CH7eZPOrZIsjXS0gVCCptC4TJcqJWNE/DwTbTxEsSVxtCFuNVFpWIvZ/X1Ywg/I1cWx87BflhHR9I/iqBDZfcc2+Jqq/HSAwkC7+wHu5jJRqsGxbU7xHVeiCZlrQGOeI8c7S8hseklNstsD0hsCtqruO23BwcYtrymcTnopCRrSWu/bEhJJYVyn0qqja7msyi+rOg+Hzf1ME9N7epx0a5V+xcPcxmSrwkqm0lnCoKqRsK88DOwSdCgxzXRd035caSplisIeishH1+TzJDsgj78I9OIJi6kRHIr+XaYrhSkD/a5DcCMdtS7/UifxeFYzpVMK9mHm9qxooxvUo0SVaVxK9E7bjbez2pTFLa6YGPd+R64SRDSSvqvi/qPrR9Jz40pd2bS7q1vTCwa6/vd/sMov/V/s4O4PJLR8ARcATaPgKyUOnEbGuu1jZI3U7MNshAeTPbNQJtkZiFXJ00aVLEEb4QpxCakG6Qmg888EB8HC6DAsH38ssvRxB02LP7FFKTo4Apy45HCEDISo7inThxYkwoQmipQKi+/vrr8S5a7G+99dbohRdeiJ555pnYBztDOQoZAhAimN2l7IzFFoIXwpajcBF24NIGvj0KEcg3WtnlyS5e+oBwHC7H8tLmV155JfbL8b70i/7x/VcEn/Tt5ptvjutidyj9oCz9YNxoE6HtF2kQj3fffXd0lRCbEMJgiOKDttEPjuGlDfjjyGUVsIMUBr80hWClnyi7gLEDQ7AEA/pIX6699toI0pmdqrST8VIBD/oP3uxaHj9+fNwWyjKGCPPg/vvvj/tx3XXXxW1mHKmfdPphhXL0hbkAkY9f8ANH6lO/hJQFD8YIjBTb++67Lx4vdtYi+GNn8FjZiUxfbrnllthW8239LR13Yrbt/x6uqoUbbrXnekUSs+v3GfBaVQ1pmUJpxOypUr0uIr2Q0hRLXrB7BhkuquU+iVO+/6evRP8tymL6NNGkBUNJjmU++Vf9EO79XXLzv3axhwVniIBbRW2Zq+Ta7lqQy1mENqg9ZbNkDclUW8IBxpjdkZo3ReJJZA/mW4qOFN1HFNyrkelSSOvKQ8yywysNgxDjA4MGFYVP4Db1Mlyc3SvVctadOrWMXRepQ/Ek7BXUeYtcvyj6tehxQV54OUoS1NfkMFOu8xApM42PfsYHpKQllm4yeUlR5pq2hbAUGNm8pyQvbY4sJHm23oOMn2Ukbv0cafJslN2Jp4kOEd1BtKuoFdpm/exsM4P42cYWYjWUNPywC+sZHBY215tI3LbpVyYvTzQkZqdIIZ5RafKwZGh94fuiyPuw3BxcwbSD9mwWNPgck2/nxUWBXR9jB3Fj/6ChqP4wnxQzwnVEs+RGyVT78J1WMnnY7JHh6Dxj+3aGXVpWUfdNubGk/vA9adsU9jmNUPulFFLcCHmHJcnikmjttgqMRpv8iUFeeJnV7i7GD/WFz+yi6mmp93qRv4tKATbVzuOzjB+eqd1F08QS2IxH2jxKK096Je/+ou77ou5D+qzKO6kTHUqR8A/81Kw9v9+nSScVn1p+qylWHjoCjoAj0DAIyOKkE7MtvULbgPWxUM/ONBbOXRwBR6B1EOA+ZFeqvGCiQYMGNe9WbJ3WfF8rBCNEHiFEGgrxhyrBhjVEoLUjjXzS2NEIgYZNkh22CPYQodgTcp1mT/2QfNb2Oy/f/0ubsZk+fXrsD18q+NZ+aLrWldQ32k9d9Iey+MYuLKs4UQ925fqvddpylCUd/5RPU22n9gU7bQ8+EGxot237dznf/0sZ8AYn8Ap9YKn90Hxtn7bhe2/fx+gTuOGXkHYmifpWG8qRRmiFazueaf5smZaIOzHbMD+LK2vottseNneP3gO+KY6cHVhuMaKyBhZrDUGoiyaEPZrc/yxI59rKEnLxX1HKfCyqpMPwpjTSPxFNE7VPyofIoV2QYdq2AwJDu3D7suT92dhS5jLR2UXTJFxE3jfN0KRPlri2h36qDJSIphOy2+NY0TVFi5bp4lDrykPMnlSmAe8bf0cb2yLxMW4zo+Hi7Iop1kW2rdwivzYB4hJNE+6HK0R1bF5PMMxDpLAIrj4sMQvppOmEGyT4D5Mg5bXMuCBT0wnPD/LCS+5vtT80yHzR5GEDYbyL6AKieaUkhuqfe56jY9NksGSo7RcJRmn4YWrroU9ZC+jYPyWqdd1AQgUSErNHlCm7r+RrXRCeLNQjRc51/OWZg0+LnbblVAoZsZhA0KndC8aGqCXHJpi8IvtzsfjV+qeYOtKiext75gnvGZWSRNQX4cKakRAebmw/TMjPk1TEfZNnLO178rmgYbbPvMvnD/L1EuLTYrO1ZgQheFq7nYN8OycmBnnhZVa7yz2zi6qnJd/r2v9afxfZMWUsqp3HT0hZHctbtHEp4QrGljK7pthlJed99+OjyPu+iPtQcSK0v6GS+gs21p7d0kh7fb8X+bz/Din/1xFwBByBBkJAFiSdmG2JVdkGr8OJ2QYfQG9+u0CgrRKz7QJc74QjUEcEnJhtoB/GlTZ1/T4DJxVFzPbYYs8RldbfgvYQoHahSIlZmvC8yTuZBCOHSVzLQYKq5CVmsYfoWll0J9GjRC8VvU/0M1H1reGBkmbFLtyqjQ2PscYJ8U0lzdqzEMkCZJbOMGWulbgKO2Qheqw/jb8l6RB2u4kuIFqrTBcH6rt/ijPNJwxxC4tAqKj9sSazSHyM28yoXZyFOJkzxbrItpVb5A+bgP0movuJniYKEWkxVCzfkPRQ8hAp9Ft9WGJ2T5NOvt2BGNaj1380ZR7XxKZQ6yDk3ssSSGa1HxIYnmTy1IbwG1HIF+bUz0SzpCSZWjYJN1t2gLGdYTOa4mn4kW3r+U9C2TDpZknQdv0rzCxzzdzVsoRblbHfOLBfq8m+yLmOyzxz8BTTlkea2kGwpEl/W+LLmGv62E1U5WWJaP8P0EQJi+zPPaaObyWe9ewm7wljT9tWFFUpSUTby/snS+iP2k7LMszIK+K+yTOWw0xbs4jZdzPaupHxQb9XybD9r7HdObAbbfIagZhtyfe6hWoOuaj2d1FR85g/ONA5Pso2LiX+lbGvlZjlGc7zM02KvO+LuA8VJ8Jt0hrdlL6WhNZe3wt7Bunt5f1e5PO+DLSe7Qg4Ao5A20NA1iGdmK3jYmx7ce3EbHsZSe9HIyPgxGwjj563vSMj4MRs2/v9W1iLNuwz4KqiiNmeWw7YqbCGFe+op7i0C0WWmB1h8thZYOVBudByvU1GHmKWBfELRZMIWPUZhgeaOojaBefQlmtIG92NgH0oB0hCUrm8aSGxs5r4m1LGJ4uX/ye6gmi1Ml0KahvzELO7l6noBePvWGNbND7GdWrUErMvpVrNNluRbesi9SiehL0S6mUn2F6ij4mym9Hap8XfELtQ8hApLEqrT0vMnmTSPwgdp1yPMGXC3aVaB6GtJ8nVa8bPkMBgdrk+zeRbvzbOPAt326qrkin/jCamhAOM7YwEmzT8MLX15NkBe76pa3JCXVlJEAu2/zwfsmRpybT2/CEHUuRcx1+eOcg7QNsC0bYoBUX2FtX0a+OU2WbjPtU0jrxFIO40jfKLkdgkRfbndfGp9VQTbquNkrBkfD1n0pOi+xvbaUkGOdKKuG/yjOUw09awX7bP4fvddiEkZhe3mUEcglzHYucgb7TJmxjkhZdZ7e5i/FBXr6BwUfXgtqXe69S1ouiForX8LrJjGo43dVhJm8e87+x77ghbKCVu/xCjVmKWZ0qWFHnfF3Ef6nwnzPrNSZ8WErX2PFORk0Q1vT2934t83sdA+T+OgCPgCDQSArJQ6sRsR14tztl3J2ZzAuVmjkAdEXBito7gumtHoI4IODHbSL+MK2zrRlsNXG2D3gO/qpmc7TPg4c1HjGChq61KT2mYLggRsiiv8hOJ2Ly1mjKWlVAX7t6W+BxN6QTDRbXMJyZdo30lYhdv1daG7DIdI2rTDlQHTaFduFW7u4IyD8k1BEmSHC2JWq6aEKIplK6SALk4RTTL58eSv7VoNTJdCqnvPMRsuUVS+qH+jjUNqgc+xn1i1BKzjF2aFNm2cov880gj7hdVjJLCmZIP0QBxq/lvSDyUPEQKvtRHP+PgVJOe9/jUE0yZL40voloHYbk58pqxH0LhBNlR0m4X/VrU+g7j/GFCuBuoZMo8JfEsGSCZ6nNGgmEafpjaesYmlA2TzpMErevVMLPMNc8dLUu4XBl7nh3WXv+Yp8i5ThPyzEHICp7r2h4wR64V1bT9SBC5SFTTroxTZpvtSJP2z6Y0DYrszxemHm1DJaH2i7aVjK9yc3B/YzuNwjVILfdNnrG078nngnbm7XNrELPHS1t1LMN2l3tmjzZlJwZ9Di+z6lFb7s16v9eL+l2Ud0zpW9o8nlfyFHvCcicq4OthU6bc+wT7UPK++ylX5H2v7ajlPrRYdVeHKeGCkm7t92mya6/v9yKf9ymQerIj4Ag4Am0XAVmHdGK2joux7cW1E7PtZSS9H42MgBOzjTx63vaOjIATs233d3AhLevRp/+RtRCzELs9t+hf7i/oC2lrDU56Slm7UNQj8MUiteZzxCViF4QhL6xkEbP4Zteo+iOEwGLBH59biS4hikAOsNtKbX9FohG74IwNOw4QyAEtQ3gsiQnCjjRrt65cL1WBslibJRzfyqLUnaIzRG1dxMGBBdBKZboUUF/1JGbrjU9Sv/MuzhbZtnKL/DcZvMH9G9F/iHK8I4uqa4l2EkXs3H/zu6RZ/rX3zfOz5Hx/kUYsHiImOu78YUOeP/YYY8pMlbgV9UVYbiH9NbFR+yHWSUIcPPF3keirolrOhiMl3UpJLjSf502WDJBMteW+CiUNP+xsPbeFBROu7dj/KyE/KykkZtfOMpa89UW1X4RrNNkXOddxmWcOYnepqLbnKhJE3hHVtGXilO/GWtOmNqXda+yObErToMj+PG3quVHilTy/sbXP35LxVW4OphFa4qJqqea+yTOWw6RFOj7PBa3L2+dKiFn7zt45qG+0acsDQV54eYaxDdsNVtonwl5B4aLqCdzGl/V4r/cQz0X9Lso7pnQmax5/JvmK8WkYl5HXJV/tdy1jm5Sd991P2SLv+7AtzC3aX8n7S/tNuEHoMLheU66tPb95kfb6fi/yef8dUv6vI+AIOAINhIAslDox25FXi3P23YnZnEC5mSNQRwScmK0juO7aEagjAk7MNtAP42qaOmLEiDk26DPwn9WSs+v12euoaupt4TI9pT67UMQioZXj5ULzX2jKeNSkbWiNJW7JqU+CPEsU/U/yRojOHtjo5cIS0XoJB2lGU2gXnF80eRwVZwkEyBt2/oYSLpBtExoUeN1ZfO0u+pSo7dO2VdQx3fioJzHbkvgoDHkXZ4tsW9YifzeDNePGPFtFG5sQni9pOr7MwVDyECkzpZD66GccsICr6YTLm7y06J2mzCOBkfXFQnSWvCaZaj8kyzAhb1VJg8S2hM1DgV1JrtU/90iWDJBMteXeDiUNP+xsPSzulxP7nPtTOeMgPyRmdwzyw0ueD9ovno3s1EaKnOv4yzMHsdteVNvDCQY8Q/U6fN7asV1P7PjjBbVdQeJWiuzPTeJY6/mPraSKeMn4KjcHswitKqpOLJLnvskzlsNMv54Lasrb56KI2XNNW8LnUdC0+LvsOrZhu7Oe2fgpqp6wTeF1Ue/1MeJY+1rr76K8Y0pfsuYx97i2aRzGGcIfCdlTUMq9T5Jc5X33U7bI+z6pLTYtz32oOBFCRGbJDpJp7X/aZNxe3+9FPu+zcPU8R8ARcATaJAKyDunEbB0XY9uLaydm28tIej8aGQEnZht59LztHRkBJ2bb5E/gYhu10dYHLrx+n4HXV0LOslN2w957HSE3x+zFtqYu3nqKV7tQFBKzKwf5O5vrVxNaNNzkh8SsXez7V0JZm7ST8UP7BttMiWctOLM4Zvv0sFxDlFhhtxSLoGp3qc1MiLP4yGLyy6J3iQ4VReYXZeHyCVFI0y1E02RhyfhaVOtkV1ClQh1avp7EbFH4VNK/vIuzRbYta5F/oMEazFk8zZLHJVPH5v0EwzxEShqxuLzxTR0jEvzbJOytr8tspsS1nYTlFtJfM/ZDjJ+9JH6n6Bui95v0pOgoSdQ6IfJ+bIxKJq8cKVYUMUsbVjNtCKNrSIIlHA8JDcpch8TsFWXsJ0i+4gOeKkXOdXzmmYPYQQx/IaptuiiIy2Wz8HxVu3tMPGksi+yPfV7wXF1ONEv2lsw3RXmOQ7SvJKpSyRzcXwppf6epgwrCou6bPGOZ9Z7M2+eNTH/p9+IZfbX3zM6BHe87xW1qkBdePm9sKyVmi6inJd/rL5q+1vq7KO+YgnfWPL7ctInfcQtSIEV2lHQdV8Jy75MkN/ZefijJwKRZ21ru+6LuQ9v3ckfk3yD9UPv3JD5HU7+WN+nkj2hKTwuwb4T3e5HP+zQsPN0RcAQcgTaLgKwFOTHbkVeLc/bdidmcQLmZI1BHBJyYrSO47toRqCMCTsy22Z/BxTesR+/+e6zfe+AHZQnaPgMf2mSbX65afAvq5rEcMUvFj4nqYtLnJs53sULJImYtqQiZmSbdJOMVUa2T0BJClMtacCb/JlFbHvtQJkqC2rCgvEFoYK4HGVvKHNWUN5eEH5m8K5vSk4JOkviVsT0hyahMGkSAtnm/FFvNJyy3SPqC8Xds4K8IfAKXmZd2wbXc4mxRbeti+g9evUwLLfFB3iomL4weIwkWd8YpFOaw2kwNM5uu7WJrP2PDAq4lKz6T60VNfhi9WhK0LsKtAwObV26OvGZ80QeVIyWifrh/lteMhPBEY8tiOsSlSkki6ucpTUwJiyJmqS9rEX28aRN/wLFUSnvSkkNi9lMxTHsvrC952n/CcwKnRc113OaZg1r9zRKx7dJ4uCvstBS7k9RREBbVn15BvROCeuxlZ7mw75QP5BryWaUkEe1fuTm4v7Gdpg4qCIu6b/KMZdZ7Mm+fiyJm+WMmxZiQ8UuSwyXR2lVKzBZRT0u+14v8XZR3TME9ax5vG4zByRRIkNklzf5BEuO2a4JduaRK3v3MGzs/qr3vi7oPbVt4D6Y9538ieeSr/RiJq7Tn9/tE6aT2mf5X8ztbcfLQEXAEHIGGQkDWIZ2YreNibHtx7cRsexlJ70cjI+DEbCOPnre9IyPgxGxD/TSuvbGb7zhw0Q377Dlg/T79z5RdtHeKvi1k7Ss9+gy8cf0+ew7r2bv/tnvs8X+WcKi90vp7yEPMhqSTLrLoMWy2lVnE7H/EUMtCVKxsCzbFF5OQhVi10xC/VrIWnLFbUpRFcy0PGRS2d21J+9bYsJuqj2goO0jCDFH1xU4yu4PkcpOHzSDRUH4kCReLqg/CLUKjHNdvGR9/ljhkbyi2jnKLpC9IYbU/NnBUFD6B29TLShZni2pbF9N/cGDRV2UbiSg2hBdpRhAeGthhywIki9ZWfi0X6o987r1Q0ohZ7LYU1fKEz4quLmqF+XC+qLV7Ua7nskZBfrk58pqxhwxSWVYikJZaFwv03HehcN99LKp2DwQGJZP3VJAXXg4wtjPCTLnOws/WQ1toO4SD7lqSaEwYQ4xqWwmvIaNC4T1gfRDn+bJC4GdDuX5bVG0/lLh9tmBe1FzHV545iB2yn6i2S0Pm7cKiVnrLhebbcF1rZOJF9udPQd2XyfV8pi6iC4j+TdS27RQyjJQkrvnl5uD+xpZ3TKVS1H2TZyyz3pN5+7yRdFCxIVw8o8PMD7XdObD7ucnDBqLc/nEJ98w+otYHdpUSs0XV01Lv9SJ/F5UEL8W/lnn8I/HzpPH1rcSPELXCbshrRbU+DXe1Rjnjlbz7cVnEfV/Ufaj91vAlaR8krBWeeVNF1Ybfo+G7u72+34t83gtsLo6AI+AINA4CslDqxGxHXi3O2XcnZnMC5WaOQB0RsMTs4MGDo/fee6+OtblrR8ARKAqBTz/9NNp6662jOeecM+rbt2902WWXRddff300duzYaNy4cdHkyZOjb7/9tqjq8vhhncTFEagIAcghXSwi7JFQevnABrtnEuxIGi6q/j4JbEomDxvI2TNF9xM9SfQ+UcgWLW/DMZJuJWvBWe0OlIj18ahcs+Bo5Q9yYW3+J9d3idIu2nSvqM0nfrColZ/Jhd31gs1joqNEjxE9V5RFaOvnIbmuRihn/cyUa4ji/saZzS+3SPqC8Xes8aHRIvBRX+XCShdni2hbFjHL94otoQiuE0UPFR0k+kfRqaIWbxvvJnlWtpULm0+ce+BpY8R4qk0/k67RP5t87Jh3t4qeLAop9bKolidkAXhd0VCsTbk58poUVntLzOIzXJj/UtLA5QTRkuiNoizqa3nC7UStYKf5T9mMhPgAY8uzIpQs/Gw9Wh/hk6KjRc8XfUbU5n0o1/yxSKUypxSwfjTOvXq76GmijBt90DzCw0STpIi5jt88c1Drp98hScYzNJS5JYFxt/1gzmRJUf2BXAmfvW9I2tWix4teJPqRqG3bc3K9sKiVklyoTbk5uL+xnWadVBAv4r7JM5bDTFvpt5W8fS6KmKVusFWcCXk23C96iyhY2jyNh+3uEtj1kutQiqinpd7rJWm89pWQd8KZovuJVvq7yPqqdR5vIPWHz25I5LNFLxadLGrbrfFdJb1SqfTdX9R9X8R9qP22ISfL/J/oyaK8s78UtfmMa5K0x/c7/SzqeZ+Emac5Ao6AI9BmEZDVOydm8yxhdnAbJ2Y7+ATw7rcJBCwxu9tuu0V33HFH9Mgjj7g6Bj4H2vgcuOeee6INN9zQidk2+2vYG5YHgZ5iZBeMeqQUeiiwY9E7SYZLovoLidm5JI+Ffc3PCv8udhA8avOOxOcUVclacFYbwrtF1Qdh2O55Je1c0ZCAsGVs/ESxTZLdJfF/otY2Lf4XsVswyUmOtCNT6jjDlLX1llskfcH4O9b40GhR+Ki/rLDSxdki2lZukR/8LJ5pcRZiIWy/MfaHSNwKdb0uGvpg3nRuMswiFjFh3lwjGvpIun5P7LYQTRJrX26OQLKp/ZDA2Txy/ZDJV7uk8DOxS6qrZMo/JfEsGSCZ6ntGgmEWfrYefEwzvtSnDRmrnybUkSeJZ5X1dUNwbfOIM2/4I47ZRZOkiLmO3zxz0Nb/gFzYtp5pM038zsDufJOXFC2qP/jeRPQlUdvOtPhUsVtaNJSSJGiZcnNwf2PLHKpGirhv8ozlMGmc9iskOPP2uUhiludRufvuXbHZM6Pd9Fv7RNhLNJQi6sFnS7zXi/xdlHdM6Vueebyj2IV/+GCxJ/64qCVpk57xYpIplb77cVbEfV/EfWjxKIcVtqNE56ADCdIe3+90s8jnfQJsnuQIOAKOQNtEQNgGJ2bbBOXSthvhxGzbHh9vXcdAwBKz8847b9S1a9doiSWWcHUMfA608Tmw+OKLR506dYr/T+47Ztvm72FvVXkE1hMTu7C0TkqRIwO7FVLsjjN2fMcvFBZVWdyHVLH1Ep8p+rBoP1EIChZXrY0ljYeavEkSTxPaaRfLvpbrxROMfy5pkMa0wdap8TsknQXqLGGHCXZJBC07T54RPVaUvtUi7Lb7TFTbRniTcWj7wMJqltAm9XNUhmER+GS4j7NOMm2BEMortbRtfqnEkvL4CoWF/ydEFScbvi/pF4kuLYrQbs2/PU6Z9Z9V5RLiR200XLPJ7EuTt0tTWlKwsyQ+L2rbrr4+kvTzRLuJpkklc+QVcaK+Q7IZ/5DK3I/c72pnww8lHSzoe5IMk0S1fyzJwKRBlKgtZHgoWfiVxFjLMgYri/5LNMTwC0n7vWjSc0KScwkL70rS8zxYVJR+Ml+0DYTk0ZakeSfJPxDsan1OlZuDttLfyYVt71Y208QZf2vX2+RlRYvoD/6Zg6NFQ3y1TcxBnr3ziSZJJXPwl+JA/Sa945L8J6XVet/gs9xY2nEJ35N5+8x7TfvLfF2EilNkhqSr7fYpNstJ+j2iM0XVlpD77k7RFUVXEdU87g8reZ7Z2C8nWks9+EBa4r1e1O+ivGNKv/LO45+JLTs5w981X0naX0Rp+/2iOl47SrxSqfbdX+t9TztrvQ+134S7iR4vyvPGphN/RDTrnS7ZzdKe3u/NnZJIUc9769PjjoAj4Ai0WQSEznBitmNwOjX10onZmuDzwo5AIQhYYlZeKuHveL92THwONMAccGK2zf4k9oa1UQRYXF1HFKJloCiLf51EW1vYvfJT0f5NyqJs1kK0ZP9AWPzfUJSFz/2a4uwYKFLAam3RHqJLi0IEtYQUgU+92lnPtkGmLyfaR3RfUf5ooBbijjFjjqwhyr1QrbCozBzdW5RF33VF2QXUGgJGy4vuIHqAaF/RZUXbipSkIfqDypI9C0j61qLcr/zxRS3jIcXLCmQadTGXqLsaKWKuFzUHq2l/WKaI/qjPrhKBGN5PFCJ5JVH8t1Up4r5pS2OZF2eeUzwD9xHlGfYj0XpIUfW0xHu9rf4uYlyWFNX3H8/JtvB7jXap1HrfV3sf6juFcNemxuBrdVGe87wPVxatRtrr+73I5301uHoZR8ARcARaBAFhC5yYLYQyad9OnJht3+PrvWsMBCwxu+2220bXXXdddNttt7k6Bj4H2vgcGD9+fLTOOutEc8wxh39jtkV+3XoljoAj4Ag4Ao5AdQikEbPVefNSjoAj4Ag4Ah0dgSRitqNj4v13BBwBR8AREASEjnBitjE4mVZtpROzrQq/V+4IxAhYYnbQoEHRG2+8EX399deujoHPgTY+Bz788MNoyy239G/M+i9vR8ARcAQcAUegjSPgxGwbHyBvniPgCDgCDYaAE7MNNmDeXEfAEXAEWgoBWe13YtZJn7IIODFbFiI3cATqjoAlZn/zm99E77zzTt3r9AocAUegdgQ++uijaKuttoqJ2V122SW64oorohtuuCG6/vrrY508eXL07bff1l5Rfg9Fn5baUj9bvR5HwBFwBBwBR6CuCDgxW1d43bkj4Ag4Ah0OASdmO9yQe4cdAUfAEciHgKzhOTGbfyGzw1o6Mdthh9473oYQsMQsO2admG1Dg+NNcQQyEICY1R2zHEM+evTo6NJLL40uueSSaMyYMdGLL74YffPNNxkeCs9yYjbfz2S3cgQcAUfAEehgCDgx28EG3LvrCDgCjkCdEXBits4Au3tHwBFwBBoVAVnqc2K28PXO9ufQidn2N6beo8ZDwInZxhszb7EjAAKWmN1oo42iww8/PBo6dGh09NFHx+EjjzwSzZw5syXBcmK2UX+4e7sdAUfAEXAE6oqAE7N1hdedOwKOgCPQ4RBwYrbDDbl32BFwBByBfAjIKqATsy25FNqgdTkx26AD581uVwg4MduuhtM704EQsMTs9ttvH11wwQXxccaXX355HL700ku+Yzbfz1a3cgQcAUfAEXAE6orAduL9qiYdXtea3Lkj4Ag4Ao5AR0BgjHRS3ys/6wgd9j46Ao6AI+AI5ENA1kWdmO1Ai8PVdtWJ2WqR83KOQHEIODFbHJbuyRFoSQQsMdu3b9/osssui78tO3bs2GjcuHER35j1o4zz/W51K0fAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHoKERkIVJJ2ZbcnW2QetyYrZBB86b3a4QcGK2XQ2nd6YDIZCHmP32229bEhE/yrihf7174x0BR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQaFgFZBXRitiWXQhu0LidmG3TgvNntCgEnZtvVcHpnOhACTsw27M9kb7gj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI1AsArIu6sRsB1ocrrarTsxWi5yXcwSKQ8CJ2eKwdE+OQEsi4MRssb9d3Zsj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI9CwCMjCpBOzLbk626B1OTHboAPnzW5XCDgx266G0zvTgRBwYrZhfyZ7wx0BR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgWIRkHVRJ2Y70OJwtV11YrZa5LycI1AcAk7MFoele3IEWhIBJ2aL/e3q3hwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgYZFQBYmnZhtydXZBq3LidkGHThvdrtCwInZdjWc3pkOhIATsw37M9kb7gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIFIuArIs6MduBFoer7aoTs9Ui5+UcgeIQcGK2OCzdkyPQkgg4MVvsb1f35gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AINCwCsjDpxGxLrs42aF1OzDbowHmz2xUCTsy2q+H0znQgBJyYbdifyd5wR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUegWARkXdSJ2Q60OFxtV52YrRY5L+cIFIeAE7PFYemeHIGWRMCJ2WJ/u7o3R8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUegYRGQhUknZltydbZB63JitkEHzpvdrhBwYrZdDad3pgMh4MRsw/5M9oY7Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOALFIiDrok7MdqDF4Wq76sRstch5OUegOAScmC0OS/fkCLQkAk7MFvvb1b05Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAINi4AsTDox25Krsw1alxOzDTpw3ux2hYATs+1qOL0zHQgBJ2Yb9meyN9wRcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAESgWAVkXdWK2Ay0OV9tVJ2arRc7LOQLFIeDEbHFYuidHoCURcGK22N+u7s0RcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEWhYBGRh0onZllydbdC6nJht0IHzZrcrBJyYjaL//e9/sTbSwDZimxsJ30ZoqxOzDfsz2RvexhFYUtr3iyZdu423tT03b1kzDmskdHQjk79gQn6tSfX2r+1rqXq0vlrDbuJA74+1anXWjssvZXD6WQ39nF3Kbmp8/bgGX/Uquqg4Hit6vejuphJ/lhowOlC0LY67bVNbfm4tIPOE+wjt34HmjHfVEXAEHAFHwBFwBApEQBY0nZhthFXdVm6jE7OtPABevSMgCBRBzEISPvTQQ9Fpp50WjRs3Lnr//fdrwnbGjBnR3//+92jkyJHRX//61+iLL76oyV9aYfyOHz8+uvbaa6OpU6dGf/rTn6LjjjsuGjp0aPS73/0uGjZsWHThhRdGjz32WESbapX//ve/0YMPPhhdccUV0XPPPVeTu2effTa66KKLovvuu68mP164cRFwYrbAH67uyhEwCAyTeNSkz5l0j7YsAqObxoCxmJhQ9UyT3y8hv9akPP7nlEqGiK5fQ2V56qnBfVVFV5NSpZSSpOv98VSKTVtPzupfUW0/2eD0WA1Ouxg/4N6rBl/1KgopS9u+FbV/ROHP0noh3rb9tsVxt21q68+te2V4uZ8+E12mbQ+1t84RcAQcAUfAEXAE2iICsszpxGzjrvW2WMudmG0xqL0iRyAVgaKIWUjC7t27R/vss0/0/PPPp9aXJ+OTTz6Jjj322Gj++eePjjjiiOi9997LU6xim7/97W/RnnvuGV199dXRSy+9FP3617+OllxyyWjNNdeM1l9//WiNNdaIll566ehnP/tZdMEFF0S0qxaBmL300kuj3XbbLaLuWuSDDz6IieODDz44evnll2tx5WUbFAEnZtviL2BvU3tAwC7gOjHbeiPa1onZTQSaSaIsoNeys2lmkw/81INgFre5hd2YZ4jSpudTSpUknbaibZ3gCLuQp39hmWqvOwoxu4MApPPh4gAsf5YGgHSQy7Y47rZNbf25xUkd/xXlvrqtg8wZ76Yj4Ag4Ao6AI+AIFIiArHE6MdugC70t2WwnZlsSba/LEUhGoBZi9ttvv42+/vrr6JtvvokuvvjiaPHFF4/69+8/y25QbNhtik1ewee9994bnXnmmfHO2enTpzcX1ToJk4R6KJ+Wr2XefffdmPQ98MADY1L2lVdeifbee+9o5513jiZOnBi9+eab0ZQpU+Idu9tvv3209tprRzfeeOMsRx5r38rVpXWys/idd96Jietp06Zpcuxz5syZqW2mT+RTXgWSd8KECVHfvn2jSy65JLWs2nvY/hBwYrbAH67uyhEwCNgFXCdmDTAtHG3rxOx0wUMJqfZCzP7O9Kk9ErN5+lfUNO8IxCzHrr7RNGfY3bdYAJ4/SwNAOshlWxx326a2TswyTa4S1ffLLzvIvPFuOgKOgCPgCDgCjkBBCMjypxOz7W8NuPAeOTFbOKTu0BGoGIFqiFmIyPvvvz9it+bmm28e75LdY489osUWW6yZmOWY4JtvvjnekbrpppvG6RCb7DqFdKXsySef3Ly79vXXX4/OPffceNfqTTfdFF133XUxUXrllVfGZb766quYpP3Vr34VbbbZZtG2224bnXHGGRGEKgJJNXbs2Hg3Kvn77rtvTKp++eWXiZjcfvvt0TbbbBOdffbZMempxOzAgQOjF198MS4DEQrJe8MNN8Q7aNm9y05VfFIeIrdnz54xmctxyB9//HGMywknnBDnQ54i+D7rrLPiI4w5Lpl2P/rooxH5TzzxRHxsMm3eaqutonPOOScCCy136qmnRr17947AkOOVn3zyyTiPfxg7iGX6Onny5OZ0j3QMBJyYLehHq7txBAIE+ObnSU36myDPL1sOgXLELAvtOk6r1qFZ5fwXRcyWq6cOXUt1mYe4LElpJQwageCwnc3TP2tfS7wjELO/N3PhlASw/FmaAEoHSGqL495oxOzyMk++EeVZ+6EofwTh4gg4Ao6AI+AIOAKOQC4EZEnUidmOsS5cUy/ZkcZxobfddltNfrywI+AIVI9ANcTsf/7zn5iM7Ny5c7TEEktEyy67bDTffPNFc8wxR0zAPvPMMzGxyhHACy+8cJzPbtrVVlstJifvvPPOqE+fPtEqq6wSHyMMQXnLLbfEz4ONNtooJkJPPPHEqEuXLvGuVna3/vnPf4423HDDaIEFFoiPG4YEJv/QQw+Nj/Jlx+5KK60Uk8PLLLNMHK633nqxr3BHK2Qr5OiWW27Z/PyxxOwLL7wwC6B8D5adwP369Yv4tivkKu2ESB0xYkScx7Pssssuix544IF4F+vhhx8e0W7k+uuvj37xi19Eo0ePjslnyt16663xt2vxu8EGG0RHH310BOnM8cmlUimaNGlShI+11lorGjx4cEzK0n+OigZfhH5ALNMPvsXr0rEQcGI2109SN3IEHIEGRaAcMdva3SqKmG3tftj68xCXTsxaxNLj7Z2YXVS6/qUoxBHhwqIujkBbRaDRiFlwvFpU/wjmaBJcHAFHwBFwBBwBR8ARyIOALI3+gJjVIxxZmGZB3LVjY3D66afH35BcaqmlYqLB50PHng86/pBrTz/9dMdiV1q5t5USsxypy3O8a9eu0SabbBKTlP/4xz+iXXbZJZpzzjmjvfbaK4J4ZScnpCy7PPnmLMcS871W8v/1r39FRx11VEzmDhkyJJo6dWo0cuTImHSFnIQIZdfpj3/845iYhYg85JBDIohgdqk++OCD0eWXXx7vJB00aFBMAu++++4xYXvKKadEEKvHH398TM6SrztQFWq+WYu/XXfdNSZASc8iZilPf3baaad4J+xhhx0W77a9++67o08//TQmayFM2W370EMPRccdd1xM2oILRxaDAbtzOSKZb9VuscUW8W7iP/zhD1GPHj2iCy+8MLbj6GR2yILFqFGj4l2yJ510UrxLFz9jxoyJ0/ier5LN48aNi0lf0uxRx9pXD9svAk7M5vlF6jaNjgDfhCxC5hcnnYpwlOGjc0aezZpHLn5kE+oYn118521XHZvR7HpuieXte72I2fmkDeBSq7QFYpa5NGetHTHliyBmadMcxmc10XrN2zz9y2pvJc+RcsRs3vuyizRIyRnCXlkNDPLmletaxyJw2Xw5XGLarj81p7ZcpF5zpN494PnHczCvME+KeF7lra8t2OW9N9LamvSeqZaYbc151kc6qPfYGxKfK63Dnu4IOAKOgCPgCDgCjoBFQJZBf0DMsiPq8ccfjxfK2fHk6hjMP//88Q67eeed1+eD3xPxHPjpT38ak2ztl0ppez2rlJjluF52d84zzzwxucmuUL4hC/G64IILRr/85S+ja665Jtp6661jYpUjeCFb2SFL/sYbbxzdcccd8bHDq666akxS8o1UiFV2uhKnjmHDhsXljzzyyOi+++6LiV923Z5//vkxKcl3Z6kbcoojk9l1utBCC8X+qO/nP/95TORut912MVlqkYcI3n///eMjgF999dU4K4uYhTDluODddtstbjckdPfu3eM+Qu7uuOOO8W5diGpIaY4+ZocsbYWohdDVY5AhZtkxi82xxx4b73blWGiE9+Rnn30W94mjjyGy2YlLvdQDduwUhvjlqGiEEwc46pg/dmEHrUvHQcCJWfvL0+ONikBfaTiL+uheopAIg0VvEn1HlEXJaaL/Ft1fNA+5Cpk7RPRqUcpRXhc3P5L430R/JZq22L2j5Gmbzpa4FUiw8aLkj2vK+I2Ej4pSxyeit4r2FFVZSCJHiU4UfVsUu69FnxH9P1EWjBcTLUqo+w+iD4jy3UfqmyJ6s+hQUYizJLlYErXfhOsmGTWl2XHDdj9jCy6k3SDKAvkaoueJPizK0Yzfir4qernoqqJpMloyaDsKdqFcKwna3g3CTHO9uMSPEX1M9FNR/H0lyveDrxFdXTRJkvxznOSNotSrbSPEt7blpxKvRJLq0fIW535NidtKeKHo46JgOV30X6IXiHYXrUYOlEK0/3lR7RcYaZ9OkrhKSSJqo0cZ95G0q0SZ0/8V5T64QxTybk3RPFLtvM3ju5L+qb9anyMniyPFifmB0I4Jou+Lkvey6FjR/UTTnkddJE/9EPYSTRPubXbWMW74/p8o84Pn4MWiHDFbhFDPe6Larv4pTneUdJ1D4bM0pUhmcj3nSGbFVWRyv2rfuY+XE71OlPcR98hLoseIMs+s8Izhfrtb9C1RMP5clHv8EtFNRUPpJAk8b7W+rUIDc10ydjyX0wSs1d9VaUYp6eXGfVyT7/ESzim6qOixorw79d6YIvGbRPcXzSMbihHv0zdEdd7fI/ERoguKVkLMVjvPuMcUM8Jq36FSNBaw+UCUOYD+UtTFEXAEHAFHwBFwBByBsgjIsmgiMfvYY4/F5Au7qpZccsn4GEuOsnR1DHwOdOw5ACE311xzRcstt1xM6nUcaqX1e1opMQsZ9Nvf/jaae+6545BrCMXzzjsvWmSRRWJi9uqrr44JUv7oguOM2S3KN2G33377uAxkJUcCczQw7wIIR8YeG45JZhcq5CM7ZiFm2ZkK+YktxC0CGfz+++83E7PrrrtufLQxxxdrfTvssEO885a6rCjRut9++0XEkSxilh26HBfM7luIUIhW6uOoYchVFCKZXat8n5Ydu3vuuWd0wAEHROx4ZYfsH//4x5hQ/v3vfx8Ts+PHj4/L0ed///vfcRv4h+/Xgulpp50WrbzyyjEpi/9jjjkmxmT48OExsc03dxHaA+nNTlsnZmNIOsw/TsyW/TnqBg2AQEnaqIuOkIks2Op1Ugjx0000TfaQDBZmk8qGaf8Uu+4JjuwCLuSdFYhG9cPi7xHmWtMJ6QeCf11ct/lhnIX3nShQg/xIyp4sClkX+rfXYLiWaCiHSoK1myTXc4VGct1d9BNRtYXsXFFURdMJtxOFiLBpNg6GvxVNktGSqLYTEwxmmnwlLUMz5gPYqp+kELzOCgvKdZL/pcr4wj/EaSWSVI+WL0lE23y6xIeaa0234ceSDylSqVwmBayfMP6QcVgytsylkaKMY1hGr/kjhANF06TWeZvm16ZX0j/KFfEc4V5UDJ6V+NXmWtNteIvkdxENhTRr1ys0aLpeW0LGw9qGceY67QLzWgSyTH1/KfGQXFTfWc9StckTtsQcydOOSmy4XxUjnmVTzLWmE64iqgKu74ra/DDOvYa/eUWtvCQXanudzTBxnuefGTsI4gVNvo3yhx7qj3dlJVJu3NUv4Uqiz4vatDAOyZl0b0hy/Mdctq1hWa55j9tngP5BCeWt1DrPinqH2jaNkQvt0yM2w+OOgCPgCDgCjoAj4AikISCroonELDtm+SYgu5pYXOd4SFfHwOdAx54DEIN8s5NvlS6//PJOzLYwrVQpMQshCmnIqQfsRoVIZdcpu1Qh19kxO2HChPi7q+yQhUikDr6pypG+V1xxRcT3pfHDcb3sgqUc/ji++JNPPpmFmIUEfvTRR+Mdq+zS5Rhhnhl///vfY4IUcpfdpezQ5Wj0c845J3rrrbeiG2+8MT4ume+7fvDBB7Og+vbbb0cHHXRQ8zdjyVRilva//PLLzfaQv/SB3b0c4QzxynHK7Jql77SXo5GvvfbaOB8/kKZ8+5Vjivlm7IABA6KnnnqqmZiF5OWbuewyhpS+6aab4vrYBcwuW76byw7bnj17xj6pA+X7tRCwkLHffPNNXIZ+6u5cP8q4edg6RMSJ2bRfoZ7eSAiUpLG66GhDFp8hBh8Q/SqwmSLXC4mGsqUkWB/EIQ0hK9AvRMN8dtiEkrWobIlZfIVtU/8QkvOLsiCsaYTTRJ8QfSdIJw8CazHRamQOKTRR1NZFnN02tOGbII+6QoJndkn7W2A3XK6tzCkX94vaegZagyDP2kEasoNpSoJNEjkL+aDl6VsoMyVB8/uFmXL9a5OvdhBTkACvJeQdJWlWkvwvLgaQGUlEJGnoFqKVSFI9Wr4kEW17GNIXxjFMZ6y7iVYiF4pxVp8Yc5WsNtEWS/xo2+jjmurAhEXMW+MuNVpJ/4p6jkCAav/D8H3JS5qDL0t6SHJCSNnyveQ6FJ434VxgHjDX3xO15YnfKVqL3CiF1SekWZoMkwy14zlUjbTUHKmmbVllLDGrGIThw8bBXhIP83nWMCeS7qknJb2TqMooiWh5xnx2zTDhz42N2u5s8m10irE90mbkiJcbd62bMJyfX5p6rR33cJJcJonWjjjv/SkJ6WqXRMwWMc/AvIh3qLhplq0kpu3mGd21OccjjoAj4Ag4Ao6AI+AIpCAgK6KZxCy7qlhMdnEEHAFHADLpn//8Z0yqOTHb8vOhUmKWFkKKQiiyI3attdaKCUj+6EZeCTEJyR/hQDpy7O6KK64Y7bzzzrENRC2kI8Qscu+998akIuXWXnvt+Ehi5gMkJDtEO3XqFB+XjD3fY+VoX75bC+H5k5/8JN61279///ibs+xYhdwlHdIUf3wH98QTT4yPRo4rbPoHArRUKsVkLm1AIGMhZddcc82Y/B0xYkRM7LLbFfIYMpZv30K6susVwpXjkDlGmWOEqe/ggw+O3njjjdjfPffcE22++ebxLmLIVOrku7DnnntunE45dgJzDDE7iSF2IZU5uhjymSOROd6ZvkLWcvTxHnvsEV/z/gQn/HE0cq9evfydGqPesf5xYjblR6gnNxQCJV4AgbIQvZrpBUc73hbYsEMmlL9LgvqaIfF9RdkhpMKiKYvfdkcS5FZ3NWgKsxaVQ2KW+vDBYuxI0btEp4pCYO4uqu1hgf1gUW0PbWGHW7iofISkVSMhCcmi/frG0bwSHyI6U1Tb9KzE2SFkZQm5gMxVG8ienxqDE0weNleYPI1qWRuCDQvfKn0l8rmo2kyX+DKa2RTWQswuLT4smcEYnSjauck3AQTq26LaBsaom6iKxSqJ+KXNWra/FqoizKqnZOrQuiC4thGdRxRiZjPRR0U1nxC8q5HfSSH1A6mXJCVJVBsNp0jaPqLMH2RFUXs/Ysd4hlLUvA39pl3n6Z9tdy3PkSRidqo0bEPTuBUkDjmnOBIeZ/KJdhG1+b2CfJ5Jk40N8+kkUeaHymoSeUTU+hmomRWGPLvsM+KgjPLDJE/rZN5WIy09R6ppY1KZJGL2DTE8T/Qi0amig0WRpUT5wxXF6huJDxWdTxTh2bmx6MuiakN4vKgKzwGbt45mmJB5YW2I/97ka3SNwI55WomUG/ewDfSX5wPPbYR3AfcqRKTa8r7gt4CVn8uF5hN+JLqdKHghXUVvFLU2xJOI2aLmWRHvUGlis/CO5p7WPtTyrml26hFHwBFwBBwBR8ARaN8IyNJoJjG76KKL+iJyx1o/9t46AqkIhMTsddddl2rrGcUjUA0xy/dNx44dG/H9WHY6821VdoVuttlm8VHFkydPjnfRjhw5MiZuseFY3sGDB8e7XyEUEXaynnzyyTEZym5avv2KfP755zGBuc4668T5EFDvvPNOfFwy35KFKIXEh0idOHFiNHPmzPhoZL59u/rqq8f5ELRDhw6NyVSOWrbCnGMnLUcAjxkzJiY52YULGUw5vnUMQcuxyJCmkMyQsuxSpSxEMbt02dHKMeyQ0/TtkUceiclS6qK9kMJ8HxYCFqEdfH8XQhfiFhwhXNnty45c6jzkkEPi77Gzo5hy4EqfqIf2XnnlldG0adNif+DCzloI3EmTJsVp/k/HQcCJ2fb9W7qj9K4kHdUFR0IWrhdJ6DwLrfeIqi0LuUsau9UlbhdxIUHTZBfJUD+EXFvJWlSGBLFliWOfJJDHajshyUDS8DdVVO0el3ilsqAUeF9UfUySOH6TBDLS4nRYghGkqfoifEgU/HuIgrvmPSfxzqKhaL6GEAxJsrkkqg3hhYFRLcTs+YHvtMVsSDIIWW3HUaYNdjG8n0nX6HRTLs2/2maFWfWUTB20caooJHso3AtviWo/nggNcl7nIS7DNkHSrZLgn7lh2/RYYFP0vA3cJ16W61+Rz5GQmH1dWrRYQqsgUB8R1bGDXLIEVDli1j6v8JFGuPJMuMvUw7M26f6V5ExZS3K1rYRbZ1jbtvG8qFRaY45U2sY0+9Mlw+L0jlwvnGJ8W2DLczpJmBdPiqrfLyW+nCgyp+iHopp3DImB/FuuNV9DTpMIhbKan0RihvbhdblxV98aDg4dNF2XJFQbwp0DO/ub4GvJS3oO8YcEV4haP2Gfip5nfYP6Kn2HSvFZ5FW50vZfOkuOXzgCjoAj4Ag4Ao6AI5CAgCyLZhKzvmO24ywce08dgXIIWGKWo2jZfcg3QZ9++ulYX3rppXi3YTk/nl8dAtUQs9TEt1AhUjmid4p8p/XDDz+MyUjIIghMSEi+FQtJ++STT0aM48cffxyTqNpSxh4bjh4mTwlb0j/77LM4nd2z+CINInOK1AUJyXdcIXYhZRHqxJajhJ944ok45Fp9ap0aciTxPvvsE+/gfffdd+PytEGPVmfnKwQsRxlDFFs/tIf2cYQzdeFL+63+sYFApY2QrCr4Ik2/EUtIXeBIn6wfytG2Z555Ju4zbWPnrQo7zfv27RuT1zZd8z1s3wg4MZvwA9STGg6BkrRYFxwJ0xZo6dgmge2vSGwSFkJZdH5fdJpoEnklybGwC8nWuXdTugZZi8pzB2Wfl2sWxJPkcknUeqZIPIlwptyWoiNF9xHtKVqpDJECWg/hOmUc2B1EL6TYXhb4hFx90aR9JfGfiSaJbQvkD7s60yQkiqzdaLlQXxNtRlN8pskPidOpJu/hJvu0QNvAwv4fjVGWf8ymi2r7+ptylUaz6imZOqhrjwzn5xnbtzPssrJ+Z3wwt5MkbFNIltgyZ8uFYsRcsFKPeWv9J8XL9a/I58jJ0gDtO6F9XoVt2z6w3d8YdAnyepk8otNM/q1BXni5hrGlTQNCgxzXBwQ+fpJRJutZmlGsOas15khz5TVGTpfydvz3SvHXWdLtH8vclGKnybwvrN+SZkjI80vz7jXpRBcU5eQA8m19XHcVtXK/XKifk2xGzni5cVffhJCk/OFPkiwkibatBxkj7otvRNVXFmHZTexmGNuQmK3HPLvM1EcbK3mHivksMlGutJ+cjuDiCDgCjoAj4Ag4Ao5AJgKyFOrEbPteD/beOQKFIWCJWY6+5ahbvjk7bty4WPmeJgSES30QqJaYpTWMnZKmaa3LY5NWNim9nL9y+eoTIvfyyy+PiU2+90q5SiVvXeX8lvMDxqgVSGqOSNajnG2exzsGAk7MZv4U9cwGQaAk7dQFx48lnkXi0SUWVNX+BhISZK6ENE36kUQgPyHh1A9Eg5WsReWQmL3WFgziA+Va6yB8R/RY0TVFi5SLxZnWMyWHY4hotZ8pcTAJBfL6ZVG1C8ODwwLm2toebdKTortKorVf3RiNNnksTIdC27WsJWZXMenkh8R76GcZSVhJdM4gI82/mrUGMbuwVp4QHi5pigc756qRcsQlPkuiWg/3kT0yl3wrg+VCbb+wGRKvx7wNqvjBZZ7+aaFanyOWmGU8ku4zrYvwVVHF6kyTAQGl6YSWmF08yNvXlEuLTjZlhqcZZaTzDLPtmT/DNutZmlGsOas15khz5TVGQmJ2xRR//CGNxXODFDubDDmnZcaZjN1MOvfmj02efdb+n6Q/Z2wHGDueMUrgUse6Ji9vtNy4a9sJzy/jlN8Fan+osd3ZpJO/tslLil5j7J8KDOoxz+aTOqp9hwbNm43fGYrB62GmXzsCjoAj4Ag4Ao6AIxAiIEuiTsx2jHVh76UjUDMCkFL6jVmIWY5y5RuaHJXLscZ/+ctf4t2YNVfkDhIRqIWYTXTYQInsiOUo5VGjRv3gO7RtvRvssOW7ulddddUsO3Lberu9fcUh4MRs+OvTrxsRgZI0Whcc/5OjAzcb+39l2LMDZ2XRnUQ5nvZS0ftEPxPV+jQ8UNKsZC0qh8RsFrHBDlm7qKz1Eb4leoUoC+kLiNYi90hh9c2C+i1l9AljT7k0wmBDybM7krSOcju61I5wG9EsWUsyrf1Wxni0yauEmIW4sj4h4quRtkbMMnezhD8w0H5PyzLMyMtDXJZMPW9k+CILwkfbNCOwrde8DaqZ5TJP/2yBWp4jJ4sj7XvWs0rru9PY36yJEnYx6fizxOymQd4Tcl3u/mcctF3XSrxSOUcKaPly8yzrWZqn3taYI3nalcfGErM8S+ZMKbSnpCuehJZMTSkyy87Yx40RZKAdX3Ziq1wiEa1nkMTtiQ5j1EjCvYzdaya9kmi5cdd2EPJ+zhKISLUfYgwhaTWdkL5nybGSqfYhMVuveVbtOzTsx2mm7V+FmX7tCDgCjoAj4Ag4Ao5AiIAsOzoxW9zaq3tyBNo1AuWI2b/+9a9OzNZxBnRkYlaPG+YIY3bQNpJwlDRHIvsRxo00asW21YnZ8NenXzciAiVptC6Wpu2Atf0639hPthlN8RUlvFA0iYDVesLwwMBP1qJySMyyqJ4lq0nmFNGwTnvNQis7mFYQrUbswrX1mze+bUalZ0ue9QPxy/GSWWLt7Q7YpDL4svZ7G6PRJq8SYnaAKYfvbsZnJdGZxo/dkas+ppv8/ppYRZhVT8nUwQ63LNlfMhXLcoRZmp88xKVtU9L3Ka1vOxYzbIbE6zlvg6qaL/P0D+MiniMnix8dD+7vcmKJsqeNcReJqx9CS8weEORZuzzxPH8MY5oSR68xdYYEV2g7zNiWm79hWa5bY44ktaOaNEvMvpTh4CTJ07H6IMPOZo0wZcKd6LebPN6XKrwvtR7m957m+hU1knCcSbfljUnZaLlx13YQJj1bbQWQw2pviVmL2ye2QErcEs7hvK3nPKvmHRp24TcGA7AoR0KH5f3aEXAEHAFHwBFwBDoYArL06MRsseuv7s0RaLcIODHbukPbkYnZ1kXea3cEakPAidkO9uO6nXa3JP3SRdexOfp4nrHn6E8rfeUC4lD9JYXsVB0T2NRCzGaRmlJNLF3lXxbpp4gmtUnT2F27tWilwsK8+qgmhDxLktkl0e4kUt8cT5slakfYPctQ8hYUtfb7GPtqiVl8WJ8LG5+VRGcaP0nkQUsTsyGZEPalNYjZcm3KImbrNW9DXOx1HmK2qOeIJWavtY1IiV8s6Tpv7S7ILGL2aFNGy1YSvpDSlqzk602dD2YZSl45gq5M8dlaY46Ua1PefEvMPpRR6FTJ0zH7MMPOZp1gynxpMyRuSTwdX4hYrWNqk/3iEv7PpC8rcY7b5j2ktr0lXo2UG3f1T7hrmQrSiNkzpZz6+byMD7K3M/bhc6te86zad2jYHUsq0+dyfxwVlvdrR8ARcAQcAUfAEehgCMhSoxOzta23emlHoMMg4MRs6w51UcQs4/jtt9+W/eZsPXtLG9gFSzuIVyvqI/yua7X+ssrRzs8++yz69NNPa2pzVh3ksbN22rRp0cyZM8uZen6DIODEbAf7cd1Ou1uSfuni6m05+sgxumpvjwftIensPNU8wjdEIUQgY7YSXUIUYbH0v6Jq+ysSjWQtKs8tdlqOMA8xa1zP9jO5gEy5U3SGqPVFnD7MK1qJsLtO/dwo8aUq1LT6OGJS/doQQnJV0TSxthukGTWlrymhtWecVKolZjcVB9YndVQjM6WQ+nFi9nsESwaXkOD43uq72ABjy3y3Uq95a+sI4zwLdEyfDzPlusjnyMmmrlsS6gqT/mrsuY9VsojZ3cRI+0O4rmgl939XraSC8Dyx1TqTMLSusp6l1i4t3hpzJK0tlabnJWYPEceKJ39YVO5bxLRjjCkzlQQjjL8lXLvLtSVrrzC23L9a934S38xcfyTxPG0Rsx9IuXHXOgl3/UHpWRPSiNkjxUz90F/ezVlygGSqPf22Uq95dpRUonXasNw71LaN+GHGD98OdnEEHAFHwBFwBBwBRyATAVnTdGK2QRZ2vZmOQGsjADGV9Y1ZP8q4viNUBDHLGPIt4H79+kWnnHJK9Prrr9e30SneX3jhhei4446L9t133+jee+9Nsfph8htvvBHPwcmTJ0dff/11dMMNN0S77LJLdPbZZ0fvvffeDwsUmPLEE09Exx9/fDR+/PiI44m5RmfMmFFTLRzN/Oyzz0aPPPJI9Pnnn0ePP/54dMIJJ8R9g7h2aXwEnJjN/CnqmQ2CQEnaqQuWLI6Wk0fFQO3/ZIztQjWLtCNEIWCTZGFJVB+EgwKjrEVlFn9t2UqJWVtVZ7nYXZRF4lp83mTKV3M0qRT/gUAgW+KYncYzRbWdjMNcokmiNoS7JRmYtB0kbu1/avKqJWaXDHyyUypL1pfM8aIcOcluYCUjbH+dmBVgmqQkoY5ZSHCojYZZxGw95q3WmxaWI2aLfI5YYvaxtAaZ9GckrrhC6qlkEbNripGWIdxGC9UxPMbU+UGZerKepWWKxtmtMUfytCuPDWOoY/NQRgH+GEXtCJfPsNWsO02ZRzTRhKSpz4MkfoO5HmjszjXp/BETz0Atd42xqzRabty1DsJdyzhPI2bph/WzShk/I419+Nyqxzyr5R0adsW2HTxcHAFHwBFwBBwBR8ARyERAljudmG38NV/vgSPQIgg4MdsiMKdWUhQxe9FFF0VLLrlktOeee0bPPfdcan31zHj44YejnXbaKVpjjTViojNPXZCg++yzT7TDDjtE99xzT/TVV1/FhOziiy8eHXTQQRH41EvYJXvSSSdF+++/fzMhS1tOPfXUuB211DtlypTo4IMPjoYOHRp/o5m6IM3322+/mKStxbeXbRsIODGb+VPUMxsEgZK0UxdX2cW6Wka715A8u9P1EGP7osTVz79MelJ0J2NLGcg4K1mLynmJ2fnF4TjRJ0TZHbOFaJosLBlfi2r7z0gzTEm3BAB+lkux0+S9JfKmKIv3kNsriVqhj3YHET4hTEui2kZCjuBMEmszNsnApFnC4D1Jn8PkVUvM4uILUW3HGOMzKTrK2L4t8dmbjGaadCdmm0CRoGRwCQmO762+i2URs0XP27DupOtyxGyRzxFLzPLHIssmNagpbV0Jdb4SHmhss4jZecUO31r2UlMuKcofHXDfvyx6l+hQ0UplHymg9bHD096zoa+sZ2lom3TdGnMkqR3VpNm2P5ThYHnJUzwJR2TYkoW9fTZdlmB/oqSpT0jHd8314saeP1pRO/74hh3Qer2rsas0Wm7ctQ7CcvW8Zto0xDRkGZOOn5EmLyn6qLEPn1t2rNrCOzRs/4Wm7f8JM/3aEXAEHAFHwBFwBByBEAFZsnRitm2s23orHIE2j4ATs607RNUSsxzz+/7770fPP/989Pbbb0cXXHBBtMQSS0T9+/dvJmY5Nvett96Kd26++eab8W5U29svvvgievXVV2N7dtmyY9QKOzvffffduDx22CPUDdFI/Z988klcB0QkfbnjjjuiG2+8MWL3K7tOP/zww+jjjz+OfbMz9sUXX4yP9MUP+WPHjo2J3B49esTl2F369NNPx+kPPPBAc53YU+dLL70UsTOXY4FVaCftoD0QuxBm1EPfs44OhgjWnbn0jbb36dMnOuKII+KyurOVusAZnxx7jOCXvqHsjkWwp69g9uCDD0Y77rhjTMTSZnYC33XXXVHfvn2jUaNG1bwjN67Q/2lVBJyYDX99+nUjIlCSRttF2iwib7yxhYzgyEYVyE/1AyGaJt0k4xVRtSW0i72Uy1pUnjsouy0FEmQuSftIVOu5MsFGkzpJ5Ctjy/cDK5FeYqz1EE7IKNxZ8mz/P5DreQJ7u4sKf8c35dOnSaJaFyT5z5vybKD5hNisajNN/CdN+Wo/xuQRrYWYvVXKq18W2lnETxK+cQshobYXGaOZJj2JmJ1m8vcz5SqNZtVTMnWEZEJYz/7GlrZVI9wLisXUFAeVtCmLmC163qY0d5bkcv0r8jliiVkwzSJN/yL5ijt/VLCkaXUXk4cNuFmZKBdalvttA5sZxAcZW8ocFeTnuewT+OA+TpOsZ2laGZveGnPE1l9L3JJ9WcQsxLYlRD+T60UzKr5a8nS8CbdOsF0rsFF7/uDGynxyYZ8/asf76MfWsMJ4uXHXegh3LeP7NclXe+5fK+CqeZ9KfGGbaeI7Gzvsw2dp0fOs1neoaXocvU/+1X7+Kcz0a0fAEXAEHAFHwBFwBEIEZKXSiVkBAcKJYzAvvPDC6JxzzmnW0aNHx2k333xzBFlRjbAA/8477/yA6MjyBelx0003xe2YMGFCru8ZQlJgyxGYv/nNb6KjjjoquvrqqyMIDhUIgv/85z/xYv+VV17ZTBxoflsIIVRuu+22aPjw4dEhhxwSEx/s8Js0aVJhzQPff//73zEO48aNi4mawpy3Y0dOzLbu4FZDzEIO8hzYZJNNoqWWWipab731IojNLl26NBOzPCNOP/30aO2114530q655ppRqVSKCVOIVY6v3muvvaIVVlgh6tatW7TaaqtFhx9+ePM9yfPt3HPPjdZff/24/EorrRTtvffeMeEI+XjGGWdEG220UbTVVlvF9f/iF7+I6xs8eHC0/fbbx/c7z6V95VjjbbbZJtptt90i2rDsssvG5OTdd9/dTF526tQpmnfeeaO11loruuKKK6IxY8bEvnnuQTrzfVaenfhdfvnlo2WWWSbq1atXdNVVV8UELYQsO1/BY8CAAXEefcLfWWedlXgcMs8LdsZuu+220Z133hmTroMGDYrmn3/+uL/UDdl8yy23xDbgBAYQ3xMnTowJWI5aZocw7xLuI4jdPfbYI36+sdt34YUXjhZbbLHo0EMPjYldiGKe4wMHDmwmz1t39nnttSDgxGz469OvGxGBkjRaFxwJIVwhNOwurDnl+hxRa3eNXFthF4nms0C7ss1sii8m4XOiaqfh8MA2a1F57qB8GjGLy8sDW0iRUNjBdrGotoVwi9AoxzWLtdYHu6hYdLeygFz8TdTanWINJE7djIHaPCpx2qiyrkS+EdX8KRLHrxXN0/AlyQzJm7Ulbaqo2kCeri5qZbRcaD7kUyiWUAiJ01XF2OazEB/OiS6SNkFU6yDcXFTFlg/9Y2MJ3T/LNQR7NZJVT0kcavtCMiGsa39jOy3MzHn9a+MDkq9nQrlK2pRFzOK6qHmb0MzEpHL9K/I5EhKzjONpQavmkutLRHWMCU8MbJinNh8SyQr30reiavOmxCFPQ9lBEmaIqh0EMH+YUKnwxx0Qd+rnoAwHw4wdz95QuknC340eEBrIdZFzZKip63aJh3+UQvWrGBvalnTvY1dOThcDxSiLmMXPlsaWMs+Khs9Dni/nB3YvyjVzKEmmSqLWryHP1FD+KQmaryHPxVqk3LhrPYS7lqnoNclX+yGB7e4mD5vHRJcNbHrLNX+ApD4Ik56lRc2zIt6htguM75em/YfYTI87Ao6AI+AIOAKOgCOQhIAsNDoxKyBAPvBdPxbGISxUF1xwwQjt2rVrtOmmm0b/+Mc/cq/N4hMiBYJj9913r4j8gzzguE7aw7cg8ZUm5HEcKYv82EMWdO7cOZpvvvmiRRZZJNpss83i3VeUh7yFgKFPP/nJT+KdXml+WyMdcgMy1vbjxz/+cdxeiJNLLrkkc1db3jYrOcM4b7755vFOwLxlO7KdE7OtO/rVELO33357TEIusMAC0cYbbxzv8uR5Nsccc8TEJMcDQ6pCTq688sr/z96dwO021f0f/ytzSaRkCiUyZx4rsygqMhwhY6QkykznSGZliGTKkJAIFSpziswlUYZ4Iik9KZrrefZ/vVdnnWfZXfd13cN1zrnvc37r9VpnD2vtNXz33uu6z/rs329loLnMMsvkd/Doo4/OFqdgobHEmLTvvvtmuDrPPPNkWPncc89lC9yFF144g19WpMstt1zjveWK984772z222+/pgBV9QCip512WoauSyyxRLZ4BV+NVfIpC0wFgEHY7bffvrnkkkvyVrn6It1ar4ApqLnLLrtkOHr11Vc3q622Ws4DNIPF2q4eH6Ow9uU2eMYZZ8zj+nrrrZd1MW4C1tbfbQcWwPoi2jeOXnTRRc0qq6yS3UHfc889GS5vuOGGuU/WvQWCN9hggwyoH3zwwebm9Nux8cYbZ9B62WWX5b6A0N/5zncaazPrO32ttwums6x1X+gJ+EYY2woEmO30F2icG2sKjE8NridMy/6P0nmTyCaiH2zl+V06Blnr0C4HnD0uxZ1S/HSKt6RYQ4lSj+1ZKdah26TyLCljfe0m9YWt/eXS8Z9b+U0cn5jigSl+LsXaelW5vSbwU5aOwWR0u66n0rnzU2Tx+oUUawtedT2U4twplmAf1Cn9A0uXKYnV9qgqj7wXVGl2y/X19sV0/rIUwSoQs55ols89agf3v5RxazsxHf+jSt+6Q/rJVbpyXkjR5PuEFE9P8dcplvJtT0uxDr3Kd6/q6+UHu7apCxnEfrd6xqfrSx0P9Chr5yrv8z3yDpTseS71la13qba0G0qbeoHZfjy3A/Wl0/le/av7pv8jGUc860XDevuzdN7z53mzX6d5Z4HPOsyZDuo869aJE/eVVefxccX1KR6XonfrphTrdPsfTnG44Zp0YSnvwi6FHFLlM960gw82Sjm2xsR26OczYgyq63tVu7J0vFYrz6c65BnMqaGAWeUZF+u2Gc+vTtFzdHaKj6ZYpxufV0xxoHBqSqjz2393h8z61863S4d8QznV677X9W3Ro+D/qtq3T4e8NKrL+306/kqKn0nxmynWHy2UfJ3G0n48Z3On+vrxG5qKmRRWT3ul3bbaGSEUCAVCgVAgFAgFQoGuCqSpzgCzSQRWrSbXTc6//OUvz5ZagB0rr0UWWST/jTXLLLPkSXLuKAcTTK6DsnPNNVcGGSy5BhtY77IuUyd3lgMFoIy1G0usWWedNcMGazZyuQlIzDDDDBl06Ad3odpk7ULnWXXV1rQD1TGlzmvbmWeemYGOvrDs23XXXXPfgBf3RZ9Y7400sOJjaQessNIbrjX0SNsx1q4PMDt179hQwSxXvT7E8AEC6Hfbbbdla0wWmgAoK1ggklUm0Gq8evLJJ5tTTz01W5tuscUW2foUGAVIWYzyKnDhhRfm8z6k+OlPf5rLAUsPPPDA/DEKGMzFL7BoXAVzvb9ApDSWrSxJN91002bxxRefBGZ9/AKiHnHEEfmdZM2+wgorZKh6wQUXNI6Nb8Dr17/+9WyJWvq322675bVfQWBtZa0KCmsfEOtDFWCVpbyPP7RHv370ox9lDdRtrGdd3A6sebka5oWAq+LyHmj/4YcfntsBEK+44or54xHjiXt15JFHZijOBbN7cc455+QxDHjWB3Vx0ayNPsBhQWzsV76gv6yLzzjjjEnn2m2L47GhQIDZrn+KRuIYUWB8amc96Qgo1cft/V+m9E6wkFXJPT2uLWWxgrqgygvQvTzFErpNKg8FzCrv/Sn+b4ql7m5bk8jDsWBLl+UAKDySYrc6StqTKd+CKdahDS0OqxOrfRo8mGIpy3arKr0+Dy7Ux532T0x5XlZdX3ZPqq69tZystv+o0juB2dlSOmA1GP2vT/k8Q3XoVf5+KXOn/hxbFzKI/W71jK/q6AQT6uJ3rvJ6j4YT5kwXecfa/aJhAYZDaVMvMKuNI31ulTHY0Kt//RxHajD7/dTAZ1Js61of35/S39yhI9pc51u3Qx7POqjJyrnOO9B+2yq3Q5FdT+1Z1fOLLjkPqfINF8wqvl/PSHuMG01g1th/YaXXQPfO+d+kuEGK3YL0uox/puM5OlywZiufZ+i1HfIN5VSv+163a4seBf9XSi/59+mQ95XpXNsTRMlftvp0RVXOQGPpSJ+z9vM13N/QupufrNrtg7UIoUAoEAqEAqFAKBAK9FQgTWkGmE0isDq99957s5UViywgwQQ5gHD33XdnS6ckZrYqu/322/NMMJgLYrB4sk7iddddN8nlpDSud1nKskxjrQUocvkpmHjnste5yy+/PJfBEqsEFp0FzCrDpK41DrnBZOFWJu652Dz77LMzwGUlu/POO+d1FcFjaxwCMNoNDnMHDH4CCMAsV5sFzOq/tqlDe1hwPfDAAxlYa5P+aN9dd92VLc7ACS495Xv00Udze0obuVNmIVasfIEH6ybSl3tOuhYrMW0sAczYZ599ctsAWDAEQAWUwWQgacEFF2y4ly7BWozWkGQlV+p1rgQaAy/6oq/05kJUmdqk/damrKG5vt1yyy0Z/LByswZkHWhBKxZ+7juwpXz9nNZDAVJc4rJqBrhoADyxIKTnYD9cmNa1mhz9GyqYdS8AUh86eLcce34BUwAUmDwvWXayyiyWquAiF8CuWX311TOAld89d851PuoAc1l3Wh8VsOV2GHj0jHgHreNqvPH+cHusfG0wtgmgqevaYJa1rmdK8F5xa8zCl4Uty1FWsGuvvXZ+/8BObpKBZ2Od93W77bbL4x23xNqhv8ZI7TOmGic8t8bLQw89NLeRt4T3vOc9k/qQK6/+8a6zfuVe3ZgrgNzcJU+YMCGPa34ztONNb3pTbiML/4UTgFWvNX2Nh8Y47ol9cKMN5feggFnnWCCXYFxj0asv3da/LfljO3oVCDDb88/RyDAGFBjvD8qJ0WQpMHF7im3A8Kd07pQU501xoABgHJfi31IsZZYt+HVXigDeDCm2J61XTedKOCDtlOt+XE5O3HInWVvg9Jocd9kqKX47xU6AUFkg50EpatdIA3h2Uoq/TbH0od7+Lp1Xl8nsOmycDup8LHtnrDO09ulV66DcAjbrcrZM5w9NUXp93v7dKb4vxYHC8SmhXHNDh0x/qdK7laNvP02x/UwpGyzbMcVO2g+m/KPTtS+kWNppCwAMJXSrpwYc7km3sH1KLO3gunO4YYl0oXexlFW2y04scChten9VzosTr++0Ge5z26msXud69a9f48inUkOKdqem/TemeEeK9Xsj/Q8pnpZiJ7e66XSGafWzu7aTAwRp96RovCt111vj0BopjjQskAqoxzNjXKdwQDpZ6m+PpfIb70u6rfF7oNCPZ+QrqfBSH03b46C6V63yyOs+Did8Ol1U6vr+EAp4T8r7cIr1PS/l/Hc6f3KK86fYKxiPPVvlWr+rnYJx/o8plnzf65RpiOd63ff6+dysR9mPVW0byI2vPuyX4tNV3tIfYHdciutXad3G0uE+Z35nSp226tCugYLnrB4L6t/Q+hq/k6Vcz1SEUCAUCAVCgVAgFAgFeiqQpjEDzCYRajDLataEfQngHguuJGaGUeATkMfaCUQADcAKW643WXCZhC2T/67jNhMIPeWUU/IEOxgIEsw777z5WlvAgTUakFDArOuWXHLJDAWUz6rNxL86uLo0iQ9cAK3ckJrgL9BWOQ8//HBec5Yb0LbFbAGzgCJYAYaWvoBu4Mv++++fwQVAqj/Wa+SuU9vlFcEbcIJlr35oI1em+gImPPbYY9mdp3azmOOC1HXqYA3HGgxAAcFZpNFLOYASncAkcAf4vv766zMIdm9oZD1g+qhTBMBZ7QEe+g+e0JU26p1vvvnyPoji2te//vUZDgHs2sqajmWc9imP21Vtds8FUKa4hi155FO+thfQkjNPg/8EmJ26N3WoYNZHBp/85CczCOSO2Dtj3DjxxBOze3BgFlDnYhdU9A6zmj3kkEOagw46KL+bPnzwXIOlxgDukD3zrFJZtHPb653xbnE1LhiXvLveJx9uALPyKxesFQYCs94loF/wcQvr/1K2D1O8z9z++oikBrO77757tsLVJ2O4MbJ8pMES37sMBHuXacGC1lqz3mkfb+h7gcu58uofwNeY53fhT3/6U04pYJZePjahMxjrwxdr3Iq0oZtx2LsDYtOatS7QW6z/BwKzrIJ5bjBWGc8ijF0FAsz2/HM0MowBBcb7A21iBINKYMlkonP7FIGEOVIcbJB3hRRBIROyy6UIqE7tAAKslqI+7TRxn6Xb5AqvSwWvl+JOKW6U4mIpFniadidbKPfTdouJtQCfS6ao7+9OEZCZ0sGEO/2B2E1SXCTFTkA2nR5S8Gy9NUUT7Qum+LIUp4WgL/RaOsWhvH8j7fuUem579W9yjSPK3TDF7VIEifvxDKZiXhK858ukuM3ECJy+JsV+hitTYeVd/2I/Cx5EWVPiGbltYv92HkR7JkcW45X7tkOKPjxZMcWB4H1KipAU8Nz77d8qRb//PiAYSZgSz1m39vnbpbxjf0/7C3XLHGmhQCgQCoQCoUAoEAoUBdI0Z4DZJEINZlnMAhBPJDedJtRNrAONSbMMCLjmNJEPFrACW3bZZfOaiaBsAaSsQ7m4BGxdxzqL68pzzz03W9mCG64FQ4BO672yrAVDgYMCZl3LugrUBD/AhWLt+pOf/CTDTPXPNNNMuZwCIsr0tX4BywCGiX1QpLgyBmZZcAG2AHOphyUcYKoeoIK1GDjK7an2sJrTTtZi2g8y2IK2YAPLOudYtYE6IpefynOt9LXWWmtSPjCD1ak2ghmALS3cB3mBXyBFHn3RD2s8cm0qr3xgDas/QMY1tAecWfSBr+p2D7hEZhEIBgMo+gNI00A7i0to1wAo7ov7BASxVGbh6z6AOtxDgzEs7WjHDerJJ588CYyXezAtbQPMTt27OVQw62MDz6T3xPjjAw2W7t5d75h32jEXv94jFq0gIctX74J35JprrskfHXjOfYziPQFYjW3eBVadoKj3xNqxrM1Zt3on1cPK1YcQxULVuysMBGYBXOvFgrosTY1Txl9jQwGzyrZeLAv84soYJNZ2QFkZPsQANvUPdPWOslb1HnMZbKwYLJg1nus/wAywCcCzce2www7LsJYlv3aBwGCvj3eMUR9MrtJ9zMFi3++KsWTvvffOwHWPPfbIvwfabY1wLpdB3hJcz8Wy+xFhbCsQYDb92kYY8wqMTz3IfzilbQ1mx3zHpuMOlPtpW8DsdCxHdD0UmCYVWCn1qrzrLC6BxGklAHx/TlH/fFASIRSYGgqcmiot79hZU6MBUWcoEAqEAqFAKBAKjE0F0lRngNkkQg1mQTwAj4tNrihZh7FcNbFvHUAT7qxArR/IohQgBThBBNeypOSqF0QxKQ+AgKegHksyk+1A6qtf/erslpc1KEsq4ARMkF7ArPIWTW5FAUYT+9wRg57aBFI4BwhqG8vZXqENZl3PGu6EE07IfQEbHF966aUZotIBAKnBLHfCrFfBzAJrQVkwhnUcd5/aDWqCNEAOQJPekGzRyorsl7/8ZfPlL385Q1NrUwIWIBJ9rP0KxNDcNbagDkgMrGgz/bl4BoNYsgHo6jnggAOytsAvvVmkAej0fkdaq9Ex96jSihX0qquumo+1C3DVF5bNAAmowwoXqOVOFKBxDGIBNSx4ubrWZutuehaKxXKvezEW0wPMTt27NlQwq7U+JLG+rHfA2ORZ9j4Zl4xn3gfvOADqXfQRiA9A5PMRB2jIet145R30YYOPHxwffPDB2Zod8LX2KyDK2p3rdB9rsCJ3PRBp3PJ+FotT4NXHDz7y8O74AASElE87vLfgr7YClsYMVrLApvdeW7gxZsVa1o/1MY2xkvtfH4EA0vps3wceILPxg9tjbTVWGc+LK2P9Lla/9Z0GgEFU69Y+9NBDOQngZc3rd4JFqzHRGOwDHeXLTxOwmlv2Yt0PdvMOACgbI/WB63du6I3loDc36z4sob8Pd/yeRBjbCgSYHZt/I0erX6rAeH+UTYwBZl+qzVg9KvfTNsDsWL2L0e5QoLcC16Us5X2fWpalvVs5tBw8G5R+cQU8OSyah9aiyD09KsCbxn+n6P36V4pvTDFCKBAKhAKhQCgQCoQCg1IgTXUGmE0i1GA2CZf/3wJcgAQm8MG63XbbLQM808OsNoE7MA9s4O4WsHQtaMo1MDeiLLOUAWaw9rTuovwFuAKZAvei3Okqk0VZAbNgCktQ1qTy3HrrrRnCAhbcfQLCQApQAb50goL1uU5gVt8BUZZvrL9YwSlTG8vatAXM6guQqW/ayW2wPrOwZUUMupyX1qyUD6AANguYBY/BTBoIgA1gq4/a7ry2AiV00T/AdpVVVsngRXsAIWADbGKd5loWtwA5eFQsc7k3BjOA5gJ4WNLRsNw7Lki1HRDnMvW4447Lx7RlNew+uecAEBDLIg/QZUmoLYCR9mgfi0GWgdo+LYcAs1P37g4HzPrgwYcfrD1ZtBuTfIjBitXHFKCj99na0ixnfRziowcfG4CY3hkfQsjrPOgJ6PpIBAg1fhhXQE9AUvoWW2zRnHrqqXldau+1jzBYwV522WWT1mJWpnawOrdudwGzPnzw3u2www4Z3PqAwlihHnDL+GIcES+++OJsSQuCaq9xCtA0PoCuxgN93m+//fJYIE1fgWh1+LiFlwG6sixmMWyMbYdieewjEOOkYKyjGdfJRx99dIap6gWruUw2phljQF8fsfjYwxhjDPEeGeMcW7/XvvHKGON6H3jQFqwV/TZEGNsKBJhNv7YRxrwC41MP8h9KaRtgdszfztyBcj9tA8xOG/c0ehEKdFKAm/nyvj+W9lmajvVQ1kXmOnaXsd6ZaP+YVcBa8OXdOn/M9iIaHgqEAqFAKBAKhAJTRYE01RlgNolQg1kglmXUt7/97RxN1JsoN7Eqn2hyHVRkkcVyzLqIgCXrTuc6gVmAosBaYI+lFbDaKRQwC7hyw6lOgARg0T7w0NqQrG1Zh4LI4C8wUYJrwA/ggFWWPrTBrPqBGZavLMv0hQUbN72szIBZbn8LmNU/4AbgACZYq+kLuAkuAJOs3+QDZsGGAma1G/B0rQBWOJaXVRirMcCEJRn4AUbTC5RwHiwGd0EfVnGszdTNWg58ZeEn2tceLqELmHWOxVoJ9AVN0kuXLelAZRbQjrWHvnV5CyfLadAJjGb9xgqOZWDJD9ICSkCL+zSthgCzU/fODgfMajGwaPzx4Yd3ynNs6x32oYLgfTPGyeNDDe54jSGCrXfbeenGA9eWdHkAzvr6kl4+tpCmzBK8J6Udri1g1kcePnhRh/e0uC93XSnLeW3RJmVaA7vUJ5+yXSefqO80EEpfXFPaU9yj06SMTzlz9c9dd92V3Q0DvtqtLTTTTnWps9aZTuV8aY/rit62xmNRunpppA3K4YYZ3OUauVxTNSd2x5gC7q3fOR8t+U0/++yzs2txHyD5wMDv4RS+zz7wjxAKDEmB8Sl3/sMnbQPMDkm6UZu53E/bALOj9jZFw0KBvihwRiqlvPN796XEqVsIC9nTU1x56jYjap+OFbAeNGtt79VvU3xtihFCgVAgFAgFQoFQIBQYtAJpbjPAbBLBZD03tVxoAn2slkyOl1gDCADgxBNPzJAQvLNuLCjJ+grUA2ZZj5lgLxazK620UoYAJuFN7KcblN2FcucpyAtyWksVlAATWGcBkdxlqt/kfQGzXAyzQgMcWIsqD0xkxVaCuqyhWNZ1ZZ3mXL3GLPeg6pMHlJUHdAajuSIFZk0gFzDLQrUTmOUmFATuBmZNSLtWnQIQ+47kXri4YS7unPUN9C7QGgBhdUZDoJrFKt2s0wv20ke6CKYDw1wWAyM333xzBrUsW7maLoG+4yuLWdCahR8dgWnWd86BsCbOWcNqL9Biqx7g19qPrNxY1br3rJu5KZ1Wg3tBW3p6zlk0W4OZRj4UoIlnJcLkUWC4YHbytKa/pbKu95EJN+ag5GgLIC7rVlbD2jo5g7GP9a6PQfy2RBj7CgSYHfSfpZFxFCsw3h9JE2OA2VF8o4bQtHI/bQPMDkG4yBoKjEEFuP79RYre9+dSfFWKEUKBUGD4CpyULi2/o1sOv5i4MhQIBUKBUCAUCAWmVwXSdGeA2SRCDWbnmGOOvM7fQFPB4Fxx4VvWW2UVylVneo6yK2MWn2CrtRVZdQJ34BV4eO2112YYCYaynAFxAVFgFOwCbgcLZsFa8FD5rGa1x7qOYCr3nmCt+ln0ctdbg1nngB4WsaXdoCYQoH/Ka7syHgmYVYf+gc/AJqAMgpc+a4s2ywcK04a1GBeq3JYCuDTislhe605q4zLLLJMtjm677bbmg2lNXxpwtcoCCWBmLdsNzLJ01mdt0hZ1cE3sXrHUZfnLXbG1J5VnvWDacY9qLUjWu1xZF/Bsjd5pNQSYnbp3dloGs7/4xS/yRy7GI+PpaAzaaBwwhk9O60YfhdDB2DI56xmNGk+rbQowm37ZI4x5BTZNPThvYvzUmO9NdIACZ6VY7ulyIUkoEApM8wqsk3r4vyn6D/d+KUYIBUKB4SkwZ7qMG23v0kXDKyKuCgVCgVAgFAgFQoHpXYE0CRpgNokAzFofECwFC617OFDgcpKVoLzAIKvZRRddNK9Dmp6nDBWPP/747FqTZa1zoB1gqFzQ1fqGzoONXAgDkSwuueC1niJrT9alLERZjxaLWfBRnaxKrbUosOBlrWgdXBDWeqiAqq1jUBKoZf0LzE6YMCHXLT/rVWUqT7nAJJfB4HQpCwhlBQl2aiOrNhqo17qt+rHyyitnuMliVruc49r3uuuum+TKWHn6q136rA6g15qUrFC175ZbbskQtOQFSUVa04Im7hPXp9wkq0ObiuthLoWVzVKYhdv111+fQTkNrCFZAn2L5XJpu/U0QWPlaaP7ShdtZtl7//335zUzrcNJK+1fZJFFcmS5O//882d3ydMySAkwW56gqbOdlsGsj0yMH8YV+6MxeLe1r7hAnlxtNL5xe2wbYdpQIMBs+qsgQigQCoQCoUAoEApMdQVenlogcgUcIRQIBYavQHmXhl9CXBkKhAKhQCgQCoQC07UCacozwGwSAfhkNbnVVlvlyHK0W7A+IUvOddZZJ1tsgqenn356dj9pDVQuL5XJLTCQ99a3vjW73nUNuMVVpXVfAT9rpb7tbW9r9t1332ylafKfi+LDDz88u+k96qij8jXKA2257rU2683JTW8JJvGtp8q1LLirzLXXXjsfywd4CIAC18WbbbZZhsPWaXQtt8hcJ7M+3WCDDXL7uVZmtQpgArogM8vVQw45JAMD6yvSyTqIrHS58AVsb7zxxpxvzz33zO6hyxqzoCkIe9hhh+U2AqJcPd99992TQIwygVcAW/v1o7QJUGb1C+AKAIm6dt555wyGWbJqH/fCJsHpJf+OO+7YfDBZ0nKzWwJ9reun7XRn5Up3FnGgunaql7tkfdNG9crDZbM89OJ21dq+7scll1yS71upY1rcBpidund1WgazU1fZqD0UmLwKBJidrv/Ujs6HAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAL/p0CaigwwO3E+FnRjGQpWgn7dAkAFdgJ6YAkrWNCT+2LXc3csKNOELGvMX/3qVxlwOu96QNT1Tz75ZPPss89mqFcsxaSDh8otZblOuvJFELQO0tQPkGqTMh0XkCmvcrVTuQVeOu8cK1LX2eq/euXTDteBs+XYNYJ85Zy+CuCq9oHXLL4KmGWFCpIqVxtZ62pf6XO+OP3juOR54okncj5tcq7UUfIqXz9oqzxtKRBaHn13T+XRxxL0p7RdG0q5YK77QjvlaWe7jSWPNrmvIHu7/FLPtLalW6wxO/Xu6nDBrHfioYceyhbp3OR6Zn2E4Bn2vnlXHXt/2u+jez7c4B3zHon1+zfc8npdZ7zw8Uo9BvS6ZqjpxiEa6lOEUGCwCviN2HDDDbP3DB888WLhYx5LHPhIiOv98js02DJHmG+2//tLMPZCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQYIopkCb2AsyOcHYzLu+uADC7ySabNCxmd9hhh3DP2V2uUZ0aYHbq3p7hgFnQ1RrW1kqed955s6X3Bz7wgWb55ZfP6yQr03qmSy+9dHPAAQfkjxF8hHHVVVdlS3oQd7iBK3HvPhAF6E/OYJyx7jPLfn1W96WXXpo/2hhJvUD1D37wg6wFrwo+9OAqnRt3H2ZECAUGo0CA2Sn2Z21UFAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKjG4F0oRigNnBzKpGnmErwPXvdtttl9di3XvvvV9iwTvsQuPCqaJAgNmpIvukSocDZq2NzL26tZo333zz5qyzzmqOOOKI7CqcW3GWn9yqv+pVr8ruzcFGFnxcg++xxx4NK9ThBm7Ut9xyy/z+33777cMtpud1rPS5j99mm22am266KXsisDb2SNuvYla/dFL2fffdl12kf+Mb32je//73Z6vHKWzl2FOLyDA6FQgwO7r/Fo7WhQKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCU0yBNIUZYHZ0zuNOM63ibhj8efjhh0dsvTbNiDJGOxJgdureuKGCWTDoS1/6UrPCCis0CyywQHPggQdmN7zWcbY+9C233JKtS48++ugMZnfdddfmhz/8YV53eY455sjrTV955ZXZNbl7z+26Y9da05qbc4Hr4AceeCBbloKX1nP+7ne/m9eY/vrXv56vYXnLdTLrU2s/P/LII80111yTrVpdW7tml8/1F110UW6PsePWW2/NbtFr1+zlbqgTOGXFyh0z69x3vvOdzbrrrtuonw4AqjqtsX3eeec1d955Z4auLGKtBa78Yh1szFKnPt5www257NVXXz1ryc05F+fW87Z2tTIjhAK9FAgwO8X+rI2KQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKB0a1AmkwMMNtrRjXSR6wAqGNtVtsIY1eBALNT994NFcyCm+94xzuamWeeOa9tOeecczYsSVnMLr744s0+++zTPJEsYo877rgMZnfZZZfmqKOOahZbbLFmhhlmaGaZZZZ8PdB67bXXZlA7zzzzNHPPPXe2gP/kJz/ZsIgXWacuvPDCzRvf+Mbmda97XfOud72rGT9+fLPxxhs3m266acN69uqrr27e9ra3Ncsss0y2yH3ta1/bvOY1r2lWXnnl7CoYdLUWrrLmn3/+nKYt3CwvuOCCzSGHHJIha30XPJPnnHNOs/766+cyrAl9/PHHN/PNN1+jrTvttFOGu5dffnm2EtbvpZZaKpepr0DrFVdc0bz97W9v9Me639wgA7u77bZbc/jhh2etWBxzyQxmg7xf+MIXmvXWW6+57LLL6ubEfijQUYEAs6P7b+FoXSgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoMMUUSDOIAWY7TqPGyVAgFGgrEGC2rciUPR4qmLUm6qGHHtosuuiiGVJutdVWee1YYBYQBWIff/zxSWCWxSyLWG56Z5tttgwvWdMCqtalBWRZph555JHNGmuska1wTzvttOanP/1pTgdzF1pooQxjTzrppOw2edVVV23WWmutbAH7ta99LZc566yzZlj7qU99KgNR4HjPPfdsfvGLXzSusxauNXH333//DGlZ+84444wZJD/33HMvEf2Pf/xjtgTebLPNGu6SfQDy4IMPNltvvXUGqXfccUe2oOXGWb9YxrKQBVxBa26bwVnWtiuuuGJz8MEH576wtgWj6fOhD32o2XDDDfMx62Dhuuuuy6D6M5/5zEusfV/SuDgIBSYqEGB2iv1ZGxWFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKjW4E0ZxhgNqaOQ4FQYFAKBJgdlEyTLdNQwSzLTmuusuwEOi+44ILmxRdfbE488cRmrrnm6ghmwVHpwC0Y+7Of/Sy7/11uueWaJZdcMq/lysXvXnvtla1sP/zhDze33XZbs8MOO2QLW+tIg5nqAXRXWmmll4DZYi0LiL7wwgt5fVvWqNtvv30GqADtK17xigxlWa9yKbzjjjtmq1/Wvr/73e9eoi/3w7vvvnszbty4DFwlWicXZNYmbTn//PMb7WeJC7Z+5zvfyfW+9a1vzVAX3P3Rj36U18IFn62ve+aZZzZ/+ctfGha4LGlZy3IBXYJ9sPdjH/vYf1jxljyxDQWKAgFmR/ffwtG6UCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVBgiimQJg0DzJaZ09iGAqFAVwUCzHaVZ7InDhXMahCIusEGG2SoeumllzbWVP3sZz/bEcxy3auOU045JYNZQBSoBXRZ3XJtzE0xt8LALataebhMtgU1lV3Wgf3GN77REcxyGwwYs25lcctCFpj99re/nbfKVo62AqPWxlUX18ttMMuNsmut98otswDM6ksBs6effnq25H3DG97QLL/88jkCxEsssUR2t6xMbT755JMb7pVZ3xYIOxCYZZXLAhfsBZAjhALdFAgwO8X+rI2KQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKB0a1AmkgMMNttNjXSQoFQYJICAWYnSTFVdoYDZlmztsHsQBazbTBrfdYnn3yyAXRZ3LKY5drYmrNf+tKXMsC1dux9992XrWtBW6AVcBUGArPcBFurVT7QtIBZsJalK1fHBxxwQPP73/8+r18L+s4000wdwewvf/nLbPnLupfrZqFYzBawfNZZZ2VAzO0wC1zx3nvvba655prc9r///e/5eMstt8wun61De8IJJ2Sr3wJm3/e+9+W8uYL0jz6/5z3vaVgIt90rlzyxDQWKAgFmR/ffwtG6UCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVBgiimQJg0DzJaZ09iGAqFAVwUCzHaVZ7InTgkwC2qeeuqpGVBaR/acc85prrjiirwmLEtW7oQvv/zybKEK1rJs5QaY1SowC7R6ToRuYNZarzWY5Yr4xz/+cV7vlpvlN7/5zXndWedZsVpjtpMrY/B23333zVaud999d66Xe2OQeYsttsgA9frrr2/WWWedbNX7yCOPNH/961+ze2OAWF9/9atfZRC8+uqrZzfOrGDtc8XMzTFIbI3ZG2+8MVvxquSGG27I56yTy+VxhFCgmwIBZqfYn7VRUSgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCoUAoMLoVSBOJXcHsPPPM01x55ZXd5hsjLRQIBaYjBe68885mgQUWyLDsIx/5SPO1r32t+cpXvtJcdNFF2ZKy7Wp2OpJmsnd1OGAWALXGLCtQ67pyD3zsscfm9WF33nnnbJHKCnb22WfPlqfq+OpXv5qtY2eeeebs9tcardZcXWyxxZo55pgjQ9tXvvKVGXaCnixQAVTgFugsYPaqq65qrOMKcnJTXMoFSVnaArPcJvud2XrrrZuf/vSnObKate6stWYXWWSRZuGFF86ujAHY9vNlHV11rr/++hkguwlg6mGHHZafUevAsozlppjrYmvNcqX8pje9Kbsi/sEPftCcccYZzdJLL90cdNBBzbPPPpsBLCtjrorBXtdy37zWWms13/zmN/N9Pu+88xpgl5vn0t/J/gBEBWNWgQCzo/tv4WhdKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgwxRRIs5xdwSyXkiyFWBBFDA3iGYhngPtWwIwVY4DZKcuJhgNmrX8KJnJHzNUvGMrClSticPSFF15o7r///ubcc8/N7oVffPHFBkQCVcePH59dEwOvYOf3vve95vjjj28OPfTQDDOVw/r0D3/4Q4aZAK61V0vgBpl1rbJY4j7++OMZzrKkfeaZZzLQBGPBTRaov/nNb/L1QD/L2wsvvDCf33PPPRsgeP/9989rzpbyy5ZbZOvCHnHEEdl6VR9Zxuqjfv385z/P14HIRx11VIa22iqPtuuXDwy0D2RlAWttXiCZZtwlg9ra9MADD2QXy9rC9TGXxhFCgV4KBJidYn/WRkWhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocDoViBNJnYFs6n1zSyzzJKtqVhURQwN4hmYvp8BH2vMMMMMAWZ7kZjJkD4cMAtS/u1vf8sAlXWpwGoWfLS2KhDp+M9//vOkY+dcA9r+6U9/muS+Vz7gFqSVv5RX8itTnhKkA7eidrSP67aoT13HHHNMs9BCCzVrr712Y01YxyuttFK2qv3c5z7X/OMf/yjFT9paB/aQQw5ptt1220mgVF3ao53apI2u1ScwVpo2OU+H0sZSqLzOuVY++0ULVsjcJGubciKEAr0UCDA7uv8WjtaFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAJTTIE0mdgVzM4000zNsssum11hcocZMTSIZ2D6fgZWXHHF/LFGWMz2QjH9Tx8OmO1/KyZfiSDpTTfd1Gy88cbZKnvOOedsxLnnnjuvYTuQdarrbrvttma77bbLa9SCsZMrWNP28MMPz2vsshiOEAoMRoEAs1Psz9qoKBQIBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUGN0KpAnFrmDWhDiXj1xMRgwN4hmIZ+C6665r5ptvvrCYHQyN6XOeaR3MkovlLBfDV199dXP22Wc355xzTuOZ4064tsZtS+u6O+64I7slZtk6uQIwy3WytWe7tWdy1R/ljk0FAsyO7r+Fo3WhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocAUUyBNcXYFs/PMM09ee29sToVGq0OBUKCfCrBMtB7nAgssEGC2n8IOsqzpAcySorhfLq6IuRX27PUK8hX3zL3yDje9tC2g7HAVnD6vCzA7xf6sjYpCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoHRrUCaIu0KZl/zmtcEmJ0+55Gj16HAfyjQBrMf/ehHmyuuuKK55JJLcrzmmmsaACLC5FFgegGzk0e9KDUUmHoKBJgd3X8LR+tCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoEppkCapgwwO/XmaqPmUGBMKVCD2Ve/+tXNlltu2Rx55JHN+PHjczzttNOap59+ekz1aSw1djhglgvgb33rW3nt1t/97nc9u/uvf/2refDBB5vLL7+8ueeeexpWqwILUWu3skj961//mtPk+elPf9q4pltw3Z133pnLfPjhh7NFbLf8kTZ0BdyX73//+81ll13WPProo4N2s+xecv3MEng0B8/NV77yleZXv/pVbqY2s5DuRyjPtfHt29/+dnad/fzzz/ej6EllBJidYn/WRkWhQCgQCoQCoUAoEAqEAqFAKBAKhAKhQCgQCoQCocDoViDNGgaYnTR1GjuhQCjQTYEazM4111zNNtts05xwwgnN0Ucf3Rx11FHNmWee2TzzzDPdioi0ESgwHDD71a9+tVlxxRWb973vfXkN1l7VA14TJkzIrqo//vGP5/up3mOOOSZD+Mcee6z59a9/3Uibd955830H97oFsP5jH/tY84Y3vKE56aST+gbUutU5vaX98Y9/bN773vc2r3vd65qzzjqreeGFF7pK4F2+9957mw996EMZeP7hD3/omn9qJnomtfOAAw7Iaw3feuutzd57793cf//9g3Jx3a3tDzzwQPORj3ykue222/Jzee211zbvfOc7M6DtF/hVf4DZ0f23cLQuFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFQoFQIBSYYgqk+cIAs91mbSMtFAgFJilQg9nXvva1zYc//OEG+Pvyl7+c4ze/+c1mMFaZkwqMnSEpMBwwe8cddzT7779/c/zxx2dLyl4V/u1vf2u+/vWvN7vttltz/vnnNywHzz333GbRRRdtdtxxx1wGiHfeeeflPFdddVVP0Mpqd9ddd23mnHPO5rjjjuuZv1cbI/0/FWDZDHrvsssuzQ033JCtmv8z1/+dYeW8++67N3PPPXfD0h3YHa3ha1/7WrP22mtnS1awVD/XWGON5q677hpxk1kYv/Wtb22++93vZstvEPiDH/xgBsG/+MUvRlx+KSDA7BT7szYqCgVCgVAgFAgFQoFQIBQIBUKBUCAUCAVCgVAgFAgFRrcCadIwwGyZOY1tKBAKdFWgDWZZmnFne/HFF2erOy5zA8x2lXBEicMBsz/+8Y+bE088McNV1zs+9dRT8/1y7w488MBm3333zTDWveMSF9jjnhp05R53u+22a2afffZmmWWWydbRLBW/8Y1v5Dw33XRTdpv729/+NrvRZUkLDh5yyCG5HKCrgFnurwcLZlnleq5Y2oK6hx56aHPzzTdPcq2sXHWrB2A84ogjskWw9gsl/eCDD+6Y/vvf/z73T3t9YMDK9Mknn8zXcuvLRTMAuMcee+RIs0ceeWRS+kMPPZTTWXK6/vTTTx8U+FYAV8PnnHNOtjD/4he/mME5CP6b3/ymYcF57LHHZuh9+OGHZ/3BcgEk944B7TT59Kc/3dx3332NdPHCCy/MOv3whz/Mx9z+qmevvfbK5bFq/9GPfpTv8dVXX50tqWecccZsIXrBBRc0NAEjgdo999xz0jU/+clPMkx3T1jF04rLX5obA/RdXcYHgdU8mE8X5RRL+pLO6vrkk0/O9+UTn/hEhqIvvvhivrb9j/u47bbbNjvssEPWn3vtLbbYoplvvvmanXfeOVvNgrV3331386lPfarZfvvt8zNBF88CrU855ZTG+tcFPrO4/dznPtcAvj42YGW89dZbN86z/r7oooualVZaKWtdnqd2u4Z6HGB2dP8tHK0LBUKBUCAUCAVCgVAgFAgFQoFQIBQIBUKBUCAUCAWmmAJpcjHA7FBnWCN/KDCdKtAJzIIb1n4EMwLMTt4HYzhglkXgCiuskN3cAnasm5dccsnshhjcmmOOOZpXvOIVzeKLL56BG2tY6waziAYtQV1pM8wwQwPisVQE8T75yU/mMoBWUA5EfP3rX5+tYl/5ylc24mqrrdZcccUVzVNPPZVB4mDBLLAHGHOVzMpWG5W31lprNayyrQkKaC699NI5bbbZZmte9apX5frAZm58O6Wvvvrq+UOC5557LgNiVsCuU/5rXvOaDJQBWevhWj+ZBgsttFDul7YD1KA0S833v//9L0nn2nvcuHE5vddTcN111zVvf/vbG2W6jv4gpvfoXe96V26Tc/oOEALUoCJg+uY3vzlDyfnnnz9fu9xyy2VgDdpq8zzzzJNhLBjO6tk94ULavVbXyiuv3Hzve9/LsFb96Y+NZtZZZ81tB765vGZFq26a00ebrPEK6q655ppZK+XSTB7lAvnAsnpBVOn6AOhL32mnnTL0pe973vOeSXXQXvsA5AJOa/0AV88vkOvZ1PYNN9wwX//ud7873ysfCXgu3d8PfOAD2QJWO31U4Fly35Zffvl8rfrXW2+9/D5ceumlWTM6rLvuunn88mw9/vjj+VnyTOtTP0KA2Sn2Z21UFAqEAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKjG4F0oRjgNl+zLpGGaHAdKBAgNmpe5OHA2YvueSS5i1veUuz8cYbZ4tSlpmAJCtBlpfA+uabb97MNNNMed1OdbCEBNVYO4K5AC1AB4wCuwCdNHmsLwzasWgF3FhTAmWbbrpphnvA7RNPPJEhIQA2GItZ5VlPlGUky8srr7wywzggkBUr98ygHAj50Y9+NKdzPws+HnbYYdnl7WabbdYxXTsBube97W0ZcnLxrPxNNtmkATvBTxaeCy+8cLPKKqvk/nDXrR7Wt/rOjXOdrrw6vddTwnpT2TPPPHOuF/wGlFmfAqJAIotUbV1ggQWyVScrZqDVfWAh7Hi//fbLVqMAJGtn91GZIOctt9ySgab7zDpU/mJ9TL8f/OAHzTrrrJPzg6byA7PApgges8BdbLHFMiAHRFmhcvsL0rM09SEG62j3df31129YEbMsBlrB/7PPPrv50pe+1Cy77LLNEkss0XznO9/JGmoTvemg7/JzVUzbdqA1AK+uf/7zn9mileWvdZO5H9ZvQHqDDTbI4BV0Z1UL1NIHKNZXgBtMB5nBbBbDQLD2LbXUUtnam9WuMY7VrDK32mqr5uc//3m7ScM6DjA7uv8WjtaFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAJTTIE0wxhgdljTrHFRKDD9KRBgdure8+GAWdCwBrOsXYHZd7zjHRnWgVETJkzIkJV7XO56WcwWMPv0009n2MZ6lEtZ0JSFLDDLWlJeZQBgQBs3vNtss02z4IILZmtJ7nZZIIKKgwWzXNNyK8w6FvgF1EBTUPKYY47J1tnAHwtJdXLjC/4CctrG8lQ6ONdOZ737+c9/PpcnD8tfcBBYBKf32WefDINZabIkfdOb3pQhMOvJ22+/PdfFChjcm2WWWSalsxgFsQfj+labVl111XwtiEo/14LJNAWkAU4as9jVT1az3P4CryxQQVUuigFL1rIgIzAuHRznslr/WTm7FyxCwWPAl+Upy1D3ibUxcAtw0vFnP/tZA+ZzCwzm05wFLbBbwKz7yM21MmjteVKXOt1nfaCjZ8Lat6yMuWh2XOCxvoL04LP26dO11177H+sPFwgLrhp/BDCdNTbLZTAYdKWnOoF57q0BZc/4gw8+mO8J2A4OazvXxkCpoP3upXviuSsBJKdZJ1hc8gxlG2B2iv1ZGxWFAqFAKBAKhAKhQCgQCoQCoUAoEAqEAqFAKBAKhAKjW4E0sRhgdiizq5E3FJiOFQgwO3Vvfj/BLMtBYAuMAztZarJ+bINZrmCtIVrALHBnrVFrrwJwwCnwCrCBmKwfQTJgDNwFbq1bOhQwKz+LWcBPeaxL7YOEwC9LzDe+8Y3ZJa11QYV//etf2aLSMwo+Swdbb7vttpekywd6sr5VnnwArQhg6wcNWAYDe3QBO22BOvDQWqzq6JQO8BWAmCvu8E8Bs6w+WRcLoO9GG22U4TDLX20p7QIxWbACnKxeF1lkkQyF6b9wsuxl2frss89m97za+oUvfCG3EbTlmlhfnec2WH9BXjCX62Vglptgx8AqCM5KVz5Wxa4tYFb9LGZBcuv70pKlr3sNzEpnbatMYNe6wwJYbd1e1qz6CBZzdcxFdukjOMqitg22lcP9MivfEgqY5XJandwUW//YM/3e974395nFK4vwsm4w61p10cDHCoCxAMyy6KVVDWZBXv0Ho/sRAsyO7r+Fo3UjV2CJVMQ7qvjqIRT5huq6pVvXzZCO31alv6KVHoejT4G3pCYdluJZKV6Y4rEp7p3i7ClG6J8Ca6Siyjs3lPetfy0YOyXFMzl27tXUaum0+FuzVhKzjBGvmlrCRr1jSoH4XRlTtysaGwqEAqFAKDAtKJAmHAPM9mPWNcoIBaYDBQLMTt2b3E8wyxUwC9OhgFkgj2vXNpjlDhlkBNfOS65nWUfuvPPOGcx+5jOfGTKYtS4u97XKBEjvvffeDPzAUVCOa2F1AW32QT+AkOtZ1p/WlwUQ63RWrltvvXVzwgknNKwwgV6WqK637qh6PvvZzzZAL9inH4AnEAsGKw/UAwrBuk7pQC8QXaDfQE9LAbOsPgFXgWWn9isDlAbAwUT90caf/OQnzY033pjdLOuL9rKaBUFB70cffTQDSQD2jDPOyOW5L7RgJau9XFHLz3KW9SqXyQXMsqKlCygMnnJdzIpXv4FZdRcwC9xyfUx3bQFmaem+s+LVB9bVgL1yAVKglPtgoFid8rmvLGH1EWzXpjbUdr/ca/elpNVglqU0cP+hD30oW7daE1Zk9av/LIEBYvce4LVGr/6xhFVeAbM0ApoF52kqH937EQLMTgt/MQ+jDxtttMMr1txoh2XW2GjbNVdbf7t1195guzeuM2HCjMMoajRfYkL7sRTzotUTt58ZQoNPqq69tXXdnFWa8tdtpcfh6FJg+9Sc+jko+/+Tzs82upo65lvzj0rrrcd8byZfB/r5TAK84ydfU6PkqajAtPZb8/KkZRl/bTebitpG1WNHgfhdGTv3KloaCoQCoUAoMI0okCYcA8z2Y9Y1yggFpgMFAAvABphhQcndJ/gBblx00UXZ6gwIiTB5FBgpmGWVWdaYBWbvvvvuDGZZvYJpxWKW++HZZ589wy4Ws+AeC0dWlCwJwcoPf/jDGbyytvUMsFrkkpZFJ5e78nL1ywUwaAh0AasgZ22Z2EkpUI3bWfB03333zXWynNUm4BNUA/64HgblrC/LnS0LUlBTP+t0a6iW9AMPPDBbZloDV59ZiHJnDCyq0xqzXB3bB4a5xbXmrv7RALxtp3MFXdKlWQu1WwBmtRuYpaXAFbFyuNrVVu0EM63HCjrKR0PvHbjJiplFJ421kWVocWXMPTIL0DXXXDODVmutapc+ArfqASt32GGHDEm1gztn93beeefNIJSbYdavXAwD0toMpBaLWevRFiAOzLKSBmZZo7Kcdg1tAVG6eTauv/76vEaw8cO91Ud1sPrlNrkTBPVsAey2PiIQWPg6x/KZq23PIgtjlsKsnT0/NPQcemc8z55HVs4gNwtZ6ya71kcALHfleSJBXnD2hRdeyGvgWkeX9XY/QoDZaeSP5sF0Y6utJsycIOy2K68/7qaV19v2f1NsXhLX3/bPK6837kurbfCB1QdT3hjIs05qYz0BbP/XKc6U4mBCgNnBqDT686yYmviXFNvPguNHR3/zx1wLYwK99y3r1zPJUp/lN80f7l1t5BiDCgSYHYM3LZrcdwXid6XvkkaBoUAoEAqEAqFAdwXShGOA2X7MukYZocB0oECA2al7k4cDZgEtMA204/oVRF9qqaWydSbLT65jQTnwDOACUcFWQHDvvffO1rHc1nILDAKCWuAliCePa10D/oGG4CjQpU7Wl1weqxewdXzSSSf1BLOPPfZYtuqcZ555cnmgWymPpSWAxpoUXAV7rQUrL9DIPTPwCwJ2SgejrY0KIHK/63ptBgv1AdhT/0EHHZSBIXgrj7Vegb5u6QAvLXoFLnvfkdwki/oheLdYzboH7oU2gbTcJ3N3/Kc//SnD0Q033DBbsAKmIkgLmHJFDIKybmV9yqoZwHSv5XOeW2J5gG0A0n0sAP2d73xnhpbcAYOq+mydXdfTlhUuC1pWt6xOuYgGZlnBsmjVF+23Xi6LVs8AV9bujTrcd66MuVymE1BLW7Dd9e6Ha9uBniA2IO96gfUsmAwig60sv1n/gr3atnACvVwaexZoDRqzEgav//rXv+Z1aNUJyrp2gw02yJqD0eoAmPWdJS9d+xECzHb/W3SaSWUdu8p64x58CYhtg9nqeNX1trtwna32euUYF4C72k4wbqtB9ivA7CCFGuXZzm09B4+kY/f2iBT3SjFCfxWICfTeevbrmdw/VVXGuACzvXUfizkCzI7FuxZt7rcC8bvSb0WjvFAgFAgFQoFQoIcCacIxwGw/Zl2jjFBgOlAgwOzUvcnDAbOAkLVDH3744Qy+gCdgjqtcsM89BfEAKq5nWSWykgUwAVCQ0znXf/Ob38wubFkaSpPnV7/6Vc4DuIF1ICJQyNKQ61sgkzWoY3Woq1dgcSofAKc8AFnbbJXHVbA2AW1c7F511VXZJa52F2vcXunKUBZXwq7XF9begKUIyGm/Ppc+DZQujz7T2rW9grJBTJGr3xL0mxte66leeeWVGS6XeyIPqKjP3Ahr8/e///2sv+vUy800CK4Mx0AnN836qDxug+laLHpZhrIulqb/7pP7Cmbqk/JoJO2pp57KlqSgpfvgWsF6u54n9RQXztI8c6Aty11pyvasgbn6DJqqlyVuuafS28GHAyzzN99881yOdPeWde0NN9yQ3R/L4/nzvHBp7UMC7dVPz4R7UzRxPWth99ZzTFP99ByBwMoCtFkb03kw91OZvUKA2R5/jE4Lyautv+37V11v278MFsqWfKusP+6RNTfdbuExqoF16/6cYoEWT1T7Nw2yTwFmBynUKM92c3Xv7x3lbZ0WmndI6sSnJ0ZrPEf4TwX69UwGmP1Pbae1MwFmp7U7Gv0ZjgLxuzIc1eKaUCAUCAVCgVBgBAqkycQAs71mVCM9FAgFsgIBZqfugzAcMFtgGCBmv32sR84BUPJ0OnZOGmgFfJay6mvkAcJAM+fbZbrGecCM2+vx48f/R7R+K2tdgFdZ6lLnQPWpo+QpsFE7SuiVrtxu12uv+ksbSrll2ykdZDzzzDP/o2/6q39cIQOFrlV/OxTd6KhtjuvguOjc7rMyxTr06mMpq1yn/NJn+66XVtphv2638+1z6ndOOfpQ5y9tkz5QH0uesvU8cMNsXV3WzoLrtb0uW13KrHUp7S9lla3r6z6VskBbltaHHnrooD4iKOX12gaYHcEfqmPh0lU2Hrf0ytlFccttcWUdW0Bsx+364+5MPssH6/p3NEmyR2pMgbLPpv0dq2PnrcnYKwSY7aXQ2Ei/KzWzPAunjI0mRyuncQX69UwGmJ3GH5TUvQCz0/49jh6GAqFAKBAKhAKhQCgw6hRIk4kBZnvNqEZ6KBAKZAWAjFhjduo9DMMBs1OvtZ1rZnG5++67Z1e33N22o7VBgU1QbywGVpbWVuVSt903x+9ILn+5l44weAU8C9aAtcYu69bJGc4999xmm222yZbHNfQdaZ0BZkfdn7/9axCgmtaMfbgjcB0smJVv/W2O61+rplhJd6aaCoy7IO3PkWK9zujJg2jJ5AKzs6e6ZxhE/VMzy6yp8hmnUAPoMdhAt6HkV+7dKZZngfvioYTh1NepfFrO0ilhgHOzpfMvGyBtOKeHqlmnOrg2n9zPrefu5Z0qH8K5Ke2C3X1yv4YSRvJM1vWMBMz269ku7fGMz1wOemz78TxaX3ekYTj3rlOdQ32/6TTY92lygtkpOc4X3bzfZTy23awkTNwaJ0c6BrSK7Hk41OfR3xODfdZ7VT7axrx+/PZMjeeqm8790Lhb+ZEWCoQCoUAoEApMkwqkycYAsyOdcY3rQ4HpRIEAs1P3Rk8LYJbrWO6PAbZO0fqu3OOO1cA9tPvUqW/OcU3MrW+EoSnAjTSXyJ3WoR1aSd1zc7ktsrztZwgwO03+Cf3vTq263jZbjRjKZjA77k+rbfIBroHHSlg6NbSe+B03seGXVOd/n/Z7wZR+gVnace16Q4q/SlHbXkzx9hS/mOLbUmwHk75fTfHyiXGjdobqeHyVrxtwXr3Kd151vd25UvxEirem+EyK2vj3FB9M8bIUuRF8bYrDDSbaL01Rfy6eWMieaXtPiur6Q4pXp6iN7eDcaSl+P8UXUpT/iRSvTPGAFE26tgNNi3a0do34aHV+IOvZ4dSXis1h6/Rvqfe9aX+RFC9K8fkU/yfFR1I8MMU2VNKHT6boWm383xT/nOIdKZ6R4hopdgs0dS2NaT1PigelSNPfplg0uyLt75ziYMK8KZO23pviH1NUxl9TfCjFC1NcMsVO4cvpZNFglVYGmpQ0WgmbpHh6ivel+K8U9fv2FD+f4sIp9gqvTBn2TdH15fnwnunrbikKO6VY6n2fEyMM66XrjSc/TpEm7tfjKXomD06xE+gZyTOZinxJ2DUd6c/DKbov5d6UPhpvOoWRPNvKK8+ZvgO7C6boGfl2it4zWtyc4qdSXCLFEnhd+FCKns9nU9TeZ1K8PsW1U+wU6mflAynDy1LcK0X39dcpKsN7dUeKnumZUxxMGM69K+UO9/32/p6Y4jdT9H57zrXfmPBAimemuGiKncKc6aS8Ja7bKdMgz82V8n0ixVtTpL8y/57iYMZ596A8X4en/W5hs5RY8p7Qymh8Kn2xlXeRFOsxwLN0S4rHpfimFPsR1Ov5066LJxa4Z9rek6J2DPQbZKzeJ8XzU/SseeZK+/877V+XonFmhhQ7hfo5Ho1j3qyp0SP57dHnkTxXnTTrdO7L6WR5pqbE70qnNsS5UCAUCAVCgVBgulIgTTwGmO3n7GuUFQpMwwoEmJ26N3daALMU9Bx1i1NX5ZHX3q1v0iIMTwHuhyd3qF0c97OuALPT8J/Wq6w37sa+gNkEZ9M6tR8ZQ1J9NrW1TJz+Le2/emLbN63OS9954vmBNidV+U1k12Gwk+XqKCCitKm9BXXUNVtdQdoH8UpecK9TADwKjJLXRH/pbzv/59OJUt73qsSF0z6QVdIG2pos37y6bii7s1Tl6+/Hq+O6vjJhruwZUzwyxQIx6nz1PqiwfIp12CUd1Hk67T9RX5D2R1JfKeqYql73VB2d6l68XJC2b01RHzrlK+doQAtt7BRKPtvFUqyBXZ1W9k1we4YHClulBPe75O+01abjOxTwj+q6AiJKtvFVGq0OqI471fH7lA7cDBTc92dS7HRtOXdJSj+vyjMh7Q83LJQuvCLFUvZAW/BtrVYlw3kmW0VMOjw77Q1Ut/N3Tsr5751+PNtKqutcOh0/1TpXp7svr0/Ru//NLvmMB/ul2A7j04lS3mlp39hQjjttvUPzpzhQGMm9K2UO9f1+XbrwohQ7tbd9zgcJe5eKqq33tM67bpU2lN2FU+aRjPPGs9KO9u9hux2HVHkfaiW+vEpTHnD729a5Uo/tH1J8b4ojDZ7DUu5gf4OMg92e8VKerd/UhVNsh/HpRMnn+RlNY14/fnv0eSTPVVuvgY6n5O/KQG2I86FAKBAKhAKhwHSlQJp4DDDbz9nXKCsUmIYVAJXClfHUu8HTCpidEgoCbP10Rdtus3dhctfRrjOOx64CAWan0T+tt9pqwswrrz/uf/oFZpM742+MEamAynqS96tVu00I1xDnriqt0263iejBTJazMCoTsmULnII2NUwtaT9K52dOsYQT005J+03an6EkVNu1qzwl73uq9Hr3iSpvASFzpHMmzsu1ts+neH+Kv26dl8a66rUpDjXUk+LKYVlX11n2N51Y8MvS1uR/OV+2z6Vz2vvPVpp21cBi+3Rs8l0s15ZtOf/zlFbCSOsr5Zh4L/UMtK2fO/3V9jqvvgGr7nl93v53U+wU6nzt6/6SLqjTy/7pnQpK53bvkB+E1ab/6pD2iXSuDoOdQC/tKFt1tLWQRo/56wom7i+VtqBNub5sn0jn/tjhfEmfkNKGEzzDP0mxlFO24PHjKbafNe/6uBRLGOozWa7rtHXv2vVpT3m2b6su6tezrcjSZ9v/bh13eqfvTHluHEQ+z+gbUqzD+HRQ11f29ZGl8vdTbNf5RDrHeq8dRnrvSnlDfb+1sbTbVtufTPHeFJ9O0TPSTge86zCY35o6f6f9fozzJ6WCS1uNzd3CISmx5DVe16ENZks+W2PAz1LsNGbtURcyjH3PQF1X+9kpaeU3aMNWfunGFR8AiH9KsVxTtpelc+0wPp0o6e3t1Bzz+vHb04/nqq3XQMdT6ndloPrjfCgQCoQCoUAoMN0pkKY5pxqYNWn+3HPPNc8880zDveXkCn/5y19yHeqaWtZK+so9JxeeXF1OrfDnP/85t4FbyOFqUe6bvkzO+1Y04vbzwQcfnKy6cVv55JNPZheWpd7Y/qcCAWb/U5MpeWY4YPa+++5rjjzyyOa0005rnnjiiSnW3N/+9rfZbe8LL7zQPP/8881XvvKV5oADDmhuuOGGDDQnZ0Nuvvnm5nOf+1yj795tuv3mN78ZcZX//Oc/m6effjr/ntj/5je/2ZxyyikN98sRQoFuCgSYnUb/vF5tw+3e3Dcomyxmk/WticixELZIjawnP8ska2n78a30lUpCh223iehek+ULpPIAm9IWcOmAFF+ZogCWrJnioymWPLaHpljC29NOnbZCSai2n27lkf+UKr3smuyvy3rjxIT3V+dBgg+nCG4LM6TIoubsFOtrP56Ohxrak+LKMyl9XYpHpHh9ik+maNJeaMNB0HrlnPLvf2ZLm31SrCdrf5qOZ/x38kv+vTsdlfarq1PoV33HpMJLXWX7VDp3copfSPHJFPdKUaDJ4ymWfPrifnItWcJb0k7dfnlr2FfylTLK9p8pwfO74MQMC6Xt/ikCQyXPi2n/VSnWQf76owH36PAUZ68ybZD26w8cPDfzV+n1Pdm6Om93fIql/rJ9KJ17Z4r67cMEz/09KZZ020737aZWnq+m43lSFLxf66dYt7OUNyGdH044Ll1UyrC9L8XlqoJenfblqTX+TTruBArre9qpb1WxXXfd09KmhwfI2a9nW/GlrnrrmV12Yt2Lpu0PB8h3VjpfnsfF0/7XW/naOoxvpavz0RS9EyV4fq9JsW7P50tite3XvRvK+71Gq13cPb+5apPdN6R4cYp1+8+RUIVevzVV1gF3+zHOG09KO28dsKZ/JxxS5fV+12EgMOtDpFdMzDhT2n40ReNYqfN3aR8IHG4w3payytb4NtBv0Heq/DxvfDBF7SphhrTzgRSfTbEub+GSYeJ2fJVe8k3tMa9fvz39eK5acg14OKV+VwZsQCSEAqFAKBAKhALTmwJpInGKgVlA0kT2eeedl+cvgdItttiiWXnllZvrrruu25zmsNP+/ve/N9/5zneapZdeuhk3blzf140bbMNA2T322KNZaaWVmi9/+cuDvayv+WjxrW99q1liiSWa7bbbbtBauG+uO/fcc3N73Lett9469wVkmZwB3DnppJOaD33oQxnuWKPx/PPPb375y1+OqFqAEaRyL6xbCB5deOGF+Rl56qmnRlT2tHxxgNnh3V3v/1e/+tXmi1/8Yn7ehvtBw3DA7KWXXtost9xyzbvf/e7mjjvuGF4HhnCVZ+T222/PY8y+++6bP3bwvu65557Na17zmubYY49t/vGPfwyhxKFltT7oRz/60Wb//fdvnn322ebqq69udtlll7wdWkn/mRvw3W233ZqLLrooJ/7gBz9odthhh+azn/1s4wOgCKHAQAoEmJ1G/7peZcNt1+8nmF11vXFgzVgI30qNLJOfoEwBfaXtS1Xp8p1dEjpsu01E95osb8MCMKtTABZAx9Lmv6T9RVIUtN1kdEk70MlWuCMdl/SyfbCVx6FrS/oDVTqIUc5/ozpf75rEfTLFku++OnGQ+8oo15etyftOAeD6bYol34/Tvus7BbrWIGzvDpl6QbB+1ndMqr+02/bXKc7doU1O1fBC3k7AVT59vz7FUu5Tab8GpelwUlrJs5eTHcL4dK7ksX1PK8+prfRtWunlcLW0A8iWsj5REtJ2KBPoT6b8s1XXlt350s6vUizl318SJm43rNLkObOVXg650q3BibwTSuIQtqunvHV/b0jHAz2TO6a00m7bM1Jsh17PZDv/QMe9wGw/n21tqPtl/+gODQNO63dSvs92yOcZfiTFUuZFrTzjqzR5PPevaeVxCMLfmGIpB8zz/JTQz3s3lPf7ktSA0qZfpP0CHUu7ylb7jaklr/GuDr1+a+q8A+33Y5w/KRVe2thvMOv3qVPYMp0sddoe1inTIM95X+uy7A/0G7RkSquf4Q93qeN9rXId12F8OqjrfTIdT+0xr1+/Pf14rmqtuu1Pid+VbvVHWigQCoQCoUAoMN0pkCYRpwiYZakJSiy00ELNXnvtlecuQbfNN9+8WX755Ztrr712oPnMEZ1nLXXFFVc0M800U7PmmmtOtclzMHOzzTZr5pprrmxhNaJODfNiWnzjG99o3vSmNzXbbLNNA9T2CmDDl770pXzfAAmhAHWw5/rrr+9VxLDTwRvlr7vuug24BG594QtfaNZZZ53m0UcfHXa5LgTGLrvssmaNNdZovv/972frYdZ173vf+5oJEyZMVheoI2r4VL44wOzwbgAL9VNPPbVZffXVm2WWWSZ/kHL66acPGdIOB8xecsklzVve8pZm4403zsC09MC99B68+OKLA44F3kEfLcjXyS2wMYVFu3GipLsGqJxnnnma7bffvnnkkUdyGT6Q+fznP9/ceeedk/Jqi/zaoA5tKsG+Mku56vKRSLd1RqX5gOQ973lPc/nllzesWs8444xmvfXWy2NIXb7xT9+UW4LrXVPnk+a8dl588cW5LGXKoz1HHXVU/lAFjI4QCgykQIDZafTP69U22m6lfoLZldff9r/GgFTzpzaygCmToMcP0OY7qzx/SvsmvjuFbhPR3SbLAYd6UveKToVX59qQyURuCRekndKfm8rJiVvQpfS3rk/+17Xy3paOSzks3Eo4J+2U80+k/U7gQ15tPCLFHVMEOoYa2pPiD6cCgOdOYZ90srTJdoVOmapzX6vy/6w6X3Z7QbB+1tcGNyyqBgrPp4TSz6sHyjTx/NJVXtds28pfyrEF3sGeTmGudLJ+Vj7UyvRkOi5l3dVKax8WWPz3lOA5LWEoE+hblYs6bE9O50pbfGRRB7CzpHmH2897nResL3ltJ9SJg9w/NuUrZdBv+R7X/aDKT48ZW/l7PZOt7AMe9gKz/Xy2NaJoYPujFAd6zh6r8v4x7c+ZYqdQ38fbWxnGp+O6vr1a6fXhWq28u1WJ/bx3Q3m/r0pt+HmK3o+Dq/Z02mUtWvr6eCsD7Uqa7bqt9MEc9mOcP6lqx609Kj2kyss6tA7G/bo/T6TjmeoMrX3jUMn/X620oRwO5TfovangO1L8bYrPp9gJpKbTObwy/VvaZ7vDv09P+nd8K300jHn6VNo8kt+efjxXk4TqsTMlfld6NCGSQ4FQIBQIBUKB6UuBNInYNzBr8ppLx2uuuSbDUMDLpL2JdZPzH/jAB5pZZpmlee9739s88MADzR/+8IdsiclqkRtZE+X3339/c++99zYmMW+88cbmyiuvzNeaLP/JT34yqVyT6SbGTaz/8Ic/zNc5py7A1zmWkM4Bsy9/+csngVnpLMceeuihXIZ2P5GsJ52zFZzTZpaiX/va15pbb7019yUnDuIfbdMHlsAstu65554MoV/96ldPArPaypIMLNVP7a0DEAAWSrvqqqvyvnMluJ5FmDr0ERgANwSgxLUiCzL9AEQAzfOSxbJrXP/rX/86ayWPe6AeMNS9ke5+7rTTTvm+vetd72p+/OMfZx2AdPcNKCqBu1Lrj379619vbrnllnwfpNECVKUBzbVZHZ6Tbi5AuUr+5Cc/2bzzne/MZbkfH/7wh5vFF1881wEQl7JZ7rJI1McCibgudVysa4ted911V37GPvWpTzWLLLJIc9ZZZ2XXpNrI/SlY+/DDD5duxbZSgN6xxmwlyCB3PXvGKhbfxhMWnautttokSOuDA+9GDQk7FT0cMOujhjaYNdYcfvjhzTve8Y7ssWDbbbfN7xRAKviQxrjFywAr/7e//e05vzYK2nHiiSc2G2ywQf6w5m1ve1vjfZJubFlrrbWaGWecsZl//vmzpwDvJ1fKLO2NVcZXYwwtnOM1QVt8FPGzn/0s12GsOfDAA5uPfexjzfjx4zNs9W4aA1iqehbbQbt23333PGYZW2666aZmww03bOaee+5GG42lfpN8lAHerrDCChlYg7nGa79LBx10UHP00Ufn8dD9MK585CMfye6g3//+9+ePa1zng5XiTWCjjTZqTjjhhJ73r93eOJ5+FAgwO43+bb3JJnvPsup62/6zf3B2XK/Ju9GgpMnvMtlou9QAjWL9UufrZGXp0m4T0XO2yqgny1dopa2isB7hnpRe2nRxlbe2GDLB/4oqbYvqmsvSvgnwUkYN7lhs/qtKWzHtlzAu7ZRrbH+d4kEpLptiP0N7UvzLXQqvYc0TXfKVJBPhpQ/DgWD9rO+Yqi3a9KbSyNZ23la+D7bSOx0CNqWfn2plKOdtT22ltQ9/n06U/B+tEhevzktvA4Yqa95dKP27WIptwD6UCXTP5kDhYymhtPN3rUy/qNLObaW1D2dOJ8DbUtaEdoZBHBv/yvXXDCL/u6v8rqNtHaYUmO3ns639RQPb4+sOtfaBu5IX4BoofCYllHz3tzKNr9I8s+5jt/BASixlfbXK2M97N9j3u6o+w+uBALZ8r0/RM1za/ksnq9Dtt6bK1nW3H+N8t9/DduVDAbMfb1/cOv5gOi7a+Chi9lb6YA+H8htUl9kNGvvgYvUU/TaXNu5SX5z2x1dp8kztMa+fvz39eK5acg14OCV+VwasPBJCgVAgFAgFQoHpUYE0LdoXMAuGce+45JJLNq997Wuzy8oFFlggW4aZrDcp/4pXvCL/LWVrgt26oZtsskm+BjgECcGKN77xjdlKct55581lmQRnZbvwwgvncsG0gw8+OENIMNUxa1guaIFJaxkuuOCCecLfMVAIEoAFgIN0lrugA+Brov6www7L9drqC3gJZtR92XLLLbuCxDLFDMLoM6D4ute9LpehT294wxuaAmZN+HPrDHqog3UZF8MszbQJJAQltFOaPIsuumgGlQAPiP3d734366gOLkKVz2UnIPlEAsxvfetbc5/o9/rXv74BD9wj9wUcL/2kFUu+ZZddNtclfauttsp9BS3LfZttttny/QFMQA36fPvb386WZEA462dt0V5lOL45uf7UH5Dds8FaEORxb+W1T4d2AF3cW/cVrAFAACZt1Q7agrtAPitXfVe+Nu2zzz5Zg7vvvjuDI9DJs/X44483O+64Y7P++utn60UWv7POOmuz9tpr57LBIgDJPTn55JPbTYrjpECA2ZE9BvQzJnm/a0hrPCyWtMZK4LR8ZFHX2A8w+6Mf/Si7GZ5jjjnyuAKevupVr2qWWmqp5pxzzsnjAhi54oorNnPOOWced+SRnztiH8iwEjXmeM95PLAFP4844ojmzDPPzGXNMMMMeezwQYd3F4x2DaBrzAWjfWShbtcb49TxwQ9+MP82eBdZxxt/vPfebePY7LPPnkFp+eCi1seYwDL4M5/5TIakIC8X8m9+85sz0DUmXHDBBRkEc6OvLcYEYJhHBx+NfPrTn85jIeteH6CAz9wVa4/fHWX5yMiHMMZhH7tI33XXXV/yoUrdrtgPBQLMTsN/Ya+8/rgf9wvMrrrBdhPGgFSPpDaWyVHuS5cfIK5T5ZP/pyl2Ct0mortNlm+XCivtsK1haqd6nLsgxXINt5YlvDLt/C3FkvaukpC2X6zO75H2a+uVs6p8rDbL9W2LIxayNagr+Wx/lSJgAA6/KsWRhPakeBss1mXfmA5KOwBllmfd4v1Vfte1YWgvCNbP+mpwYyK5DS3TqRzelv4tfbTVh259lFY/B19Ox3Woy/pEndBhH/gp+fep0n1cUM7bAg7DCfpdytm6VcD4Ku2FVlr7EOAo5TxfJc6Q9v9ZpR1UpQ20+5Mq/4SBMnU5/0h1/Qld8pWkRav8+rB5SZi47fVMtrIPeLh/SikaPdwhVz+fbcWXumz37FBfOfWdKu/F5WSH7eFVPu9AHcang1LfD+uEAfavrPLfXuXp570b7PtdVT9pd860x7J3pxSPTpEnhZ+lWPpYtk+lc3VwXUmz9Z4ONfRjnD8pVVracWuPBhxS5e1lMbtRj7LWrMpS//IT8/st9DvSLW43Ma/NUH6Dqsvy7svSv29O0XtsfD0zxVtSNIYVTcp213SuDuPTQUkbDWNeP397+vFc1Vp12/9HpePk+F3pVnekhQKhQCgQCoQC06UCabq0L2DWJDm4ZyKflSNrItANbDz77LMzRAMIAVLrvZrYByaAWCCP28mnn346g0R5QDMWXWDFy172sgzxTNizhpK+2GKLZYtWllPgmsly0AJsZP3EdbEJ9U5gtqSDe6AhS8+Pf/zjGQDYPvPMM9mSCkRlPaUv4J2+mNTvFViCWVuxtAtYBA9Z7SrThD9wSg8gwqS+voIfAC4AwNKTlsAsl6dAoT7SmBWsvoI5oAXgesghh2QXxeDGMccck61kwQzazTfffLl+5+lMP/cGpAZH0oPfvPKVr8yw071TJ8ii7yzOgEptBz5BC5arIDfIwqoOmGBJpgyQBkSXrv8gifsMmHDjrL1cEx9wwAEZHLuGi2SAow7uI4DuWQHtAS0Wvfvtt1+GPqA0+M2SDYBmhcfKFdSiq3Vp3Vf947pZfWCNewg2s8alhzTuSYEyQZmAMhes7TbV7Zte9wPM9u/O15DWO+UZZh3ayd1xsaQ1zgKA3lnA0QcHvUKxmDXe8WLApbKPIlZZZZVs9Q5WskI13niPuRz2XgGgO++8c7Yu9y56L8BVYy4rfxayLFB9HKEtPpiwFjRwazwDXF1z22235THAe278Y5XOat3HHY71mZcEH8N4d41X3lvtYOVq3DAWeb+BX2MT2KuMdjBWvyNZ3toKxndjAlfGPkoBan28UtrFQtgHQt536257/0Vjst8gY4sPOVgBuwfKcKzMMj4YZz7xiU/kNvlAJUIo0EmBALPT8J/Yq62/7Xn9ArOrb7htGyyMNuXe7gdoBNH17dBtIrrbZPmnU0GlLc+1Cx3geEJ1Deu+OlybDkp5p1YJj1fn35T2TUKXfI9V+S6uztfXlyxvSTtPVHlKGfX2rymdVe4bUxxOaE+K1xPm7fJ+mU7UdQ91f5NWgb0gWD/rq8HNI6121Ie7jLCPbVBVa9SetK7rtf9fVd37VInbVueVN3+VNpTdf1TltNsyvkp7qEehO1d5n6/yvrY6r51tEFJlnbT7reqaCZPODm5n5pSttjj/yCAuY0lXX/PJ1jW9nslW9gEPe4HZfj7bGlE/Z6zlBgo1mGW1O1A4LCWUMu9vZaqfldoCtpVt0qGxrZRlbBT6fe8G+37/u/Z/u7D2Ycy9Kf5viqV93baTA8xqz0jH+ZOq9vcTzGpXt7BgSqz12nJi5gtb5+s8Zf+WiXlthvIbVC7zu3p6ioBqKbPXtj0eja+uHQ1jXr9/e0b6XCV5BhUm9+/KoBoRmUKBUCAUCAVCgelJgTSB2DcwayJ75plnzpPpLE9NrnNpy8KT21mw0uS9CXoWUwAoOFCDWaDMRDzrJfCM1SiQCPRxQQwCgI8sp0yCi8pkedUGs1xYdgOzrHU7gVn1sh7TF5P8IIM1BUtfOk261ucAZoABdDCBr69ghTbqG8gKNoLHYCsQqx8seIFKEJE1lnQgk2XXsccem63ZlENPllzaB1DTxBqWLE9Z3AInYCkwC7Y4R38RpKGnfhUwC94Cqq4BeliRycNalQUZ6EtjlmX6Io80YBaYAVDss2jTBpCTpSwNWTmzwgN4ARVgVB4aAaP6ANAotw7K4BoUGNE/wcT2cccdl0Ex2AsoSbd2L/e6LGytpwk6K5NOXDSDSe4FrQEU51ngAkHAr2tBMgH8BZfAlwJrc0L8kxVog1naAop0F7mndp8iDE2BGtJ6Dwuk9Z4ZS1h9e498kMBaM/1GDRnMbrrppvldNQ5774plOUtxrn1ZzPtQ5vjjj294BzDGGr+kG0eNv94JgBIg9v74WMYY7t1mIQt6GoeNccY6gJOlegHK3kPjkf4Zc1iqcp+uDmMCi1zu7o2BPvowThlXPFs8EXCf7jeivV4utZVxyimn5Gt8gCLQ1W+Jj3S4TTZGANTGRmXbB22NU6xzjfF+E4xRfnOc9yFNsV42fhobfNxRgjQfrKjDWBghFOikQIDZafgv6zU2GveWVdYb99cRw9n1t71rnQkTgIbRHM5Pjes1Ydot/ZIOnes2ET1nq751q+uPqtLaLlirbC/ZrQHFX16S8m/LtNJ2FlaCCeNy7kknUuCisJ78f0M6dt9+n2LJu17a7xRel06CDk+kWPJ22ipr4xSHGtqT4m14WpdXu53t1IZe5wDGOvSCYP2sj4alfXfWjWjtA3Ul33C25TkoxdZlbFFODrAdCMzu2GrT3ANc3+v0YCfQH+hR0EBgdp5WO3fqUY7ka1IsGk1wYghh1pS3hqwfHcS1M6Q8tQ4HtK7p9Uy2sg942AvM9vPZ1oiioW2356zfYPYrAyrwfwknV+37xcTT/b53g32/Va/u21KsNWvve0ZuTRG4LWlPpf06dPutqfMNZn8k43y338N23YemE6U/D7USX16lybNIK719qM2lLNvykdhIwWy33yBteG+K9Xtft6Hs8ypxVorl2HbXFOswPh2U9NEw5vX7t0dfR/Jc1Vp126/H061bGfuhcavIOAwFQoFQIBQIBUKBNIHYFzALdAGxXFKa9GctxV2tSW+T1axIQVZWWKyrBICvE5hlzcX1LYDGstZEPUsuk+wsy7itBQ0AyE5gloUuqDkUMMtqCwQFK/TFBD/LrbovJu5ZfnYL2siySxv1A5AUuBdm2QpWKJtFV3r6ct9AAlaqoAUACtCCqHSiI8tTabRl1QVKAgSsWFddddVsGaoOwETbbeVRrjaApAKA0AnM6iMLNODDGrbul3MgKrA5Pq3vqF2s5wSwtoBZAIQ1HZDLug3UFtwnVsLqdz34rO8gEzet7i1goh5g1fNRB3qxyGOtqyzBxDZo5Jz+Aar6CNSzrBZZ9XL5DCwBSSALd8ueRaAYeBFqMGv94BK0H7hijcw1doSXKuD5LmvMuneec4DNRxki97csoj3rEYevgbHLBwaeZx9XeP/AT8+7scDYMVSLWWDWu8ArgHu3//77Txo7wFHvpneLZbl31xhrTHfPWbB7H7wfTyRrfxasxkfvlDWole2jkuLOl8W6993HHMaQNpgFSYFg1sHaJHhf99577zx2s4zngh489Xz5AERg3e4DF8+Wj1fq0AvMgs/ALKhr/PLx0JFHHpn7a01Z3hD0zTiofcYTWvN4UManALO14rE/FAUCzE7jf2+vuv42+40EzAK7q2+wzZKjXKY5Uvv+7AdoYvx12t4+iPjH6pq/p32Ti3XoNhHdbbKcRV1pi4ndwUDtenL3yboRaX+BFGvgunA63jPFUse5ab8EE7/l/E5p/+3V8X+n/cG0ZbmUz+Ttd1P8W4qlvLJlPTtbikMJQwGzP0kFl7q+lvb1fyix3bZeEKyf9Q0W3LD6Kn20XXGIfWw/q3VZ3YBZqmZAi9m2i8tlZR5GmBIT6H9I7Sp9PnwQbazv8YRB5G9neS6dKPUN5vr5q/yu2ynFOvR6Juu83fZ7gdm63/14l4oGtt2es8GCWfeulHl/q6PjqzRgvVe4ImUoZRn/S+jnvRvs+63uuj3a9c8Ub07xxBR9BLF8iix6hU+lWNr+dD7zf/90+635v1xD3xvqOH9SqqK08fs9qju2yvtQK28bzL61ld4+XLkqS/1LT8xwZNoqu1s8Z2Jem6H8Bq2a8vudK/21fSrFL6fondsoxdenKPgI439SLHl3c7IK49N+SfP73C3snBJL3udbGfs15vX7t6fVzP831Oeqff1Ax1Pid2WguuN8KBAKhAKhQCgwXSqQJhb7AmbBQJZRIJ1JeQCRW15AgfUid48FzHKbKXQDs8U6FZgFJlmMAQSA3MLJiqkNZtXF2hFw46oTmDV5D0a215gtroxZPpmEN2HK0guYBS305bHHHsvWWay71G0dW1CZ9WpxYZk70eEf7WDlVSyBS19ZhxUwCxKwMAMRQVq6aTdoC/5ysWktSJZZ4AHwAcyAkCACV6KuB0bAEkCVBSnLWlZ14DCIQyfnhYHALMALRIKl+g5KOAfUsGoGSN1H0EWowSxtQRPwXP9AV4F7UFCFe2aWrwC1vheoqi5rUQ4EZlkA66c2sBIW3KdiMcuqEGRh8ere0Yo7VO5NWRPfnGC0vtCGlSyX0Qun5wZ4Zylcg1muVksAkLhi1nagKMJLFajBrPeFhaV3fZtttsmRBSXg5d5FHLoG3l8W6yKrUVaY3nvvo48LgEwWo979oYJZ1qGedZbq3gcA3VgDuB566KG5nne/+915vPROeTdZj/swwngDaFqbWhuV5WMS4xUL2TK2GyO8Q85r99Zbb91YkxrwlAZ0cmUM7ht/jWfAp/eUu2Au411n3PWBiI89WMUPBsx6Un2U45oLL7wwP7ie12Ixa8z1++HDF+O4cdE4xFLXWERzEJmFvg+B6MOrAs0BWb8VncAsaOteGaPbsPilb08cTc8KBJidxv/ETgPZy1ZZf9z3hgtnV1r/A58YAxLtntpYJjNttxtkm03s1tcd3Lqunohm1VSHbpPlJm3rchetLxxg/7vVNaBNOzhXyvxQ2v9qdTyuyvy56rxJ5BOqYxZGQw2zpwven6IJ5VK/7SYpDiUMZVK8Bho/HEolA+SttTuiQ55+1jdYcLNsaket5zs7tGsop+qyugEzZf5XiiX/PlUl81XnpW9apXXaBUwuTdEztleKBfpPiQn0u1J9pQ+9nuuZUt4Xq/wT0v5Qw+3pglLfBYO4eM0qv+ve1bqm1zPZyj7gYS8w289nWyOKBrbdnrN+g1mAuVe4J2Uo7bu8ytzPezfY93v+qi3a9PMUF6/a1N49NZ0obf91K7Hbb00r67APBzPO178tnt9u4dyUWPrTC8xu1q2glOb3p5TlA6VZe+QfKHkov0FnteqckI5nGKDguau82rlHK9/4Kv2BVlr7cOcq7/OtxH6Nef3+7Wk18yWHg3muXnJBl4Mp8bvSpfpICgVCgVAgFAgFpj8F0kTpiMGsCWvwjPUr6yaWRya9AdhiuWrympWVyX6wgQUluNZeYxZ0ACDaYNZkeicwq14WZWXC3+S+ifRuYNbkOrABtrL4dAwyAE2sJcHF0heuM/XF2ralTBC0WwAVt9pqq9xXcJhbTO6QwUvt5BrT5D/3v+Atd6FAhYl9FoegAhDBMgwckAY0gqeACgtUbWSx5pi1L5gKIgCx3MuC5AXMApbCQGA2PfUZmh588MHZfag2FxjOxbD69J3FMOgLWgMoZY3Ze+65J1u/6RvrOZqyfAOyWZ1Z17JYzA4WzALToHRt5Wpim8Usy0z3BHxm6cuy0NqR7gsLaudowu2qZ5GO4CzAzfUzF6zg7Pnnn5/TuJVmbSeAUCAjAEWvCC9VoAazoJp33PvjmRVZXoL5gFfEoWsAUAKSnlfvO6t17zUY6j3yrnEP7J0dLJitrUx5GTC28mRgPPI+sFwFQ40XgKj3zLtnfDS2+vjBuCy/urkYLuOb8dv75lpjqo9YvJdgqLFIud4lH54YQ41X+sIjACBr/DOO+N3QFuMOOOz9NtZwJ+x95/pYMB7Lx6KYHu3AlTbgq44yTvvwRR/9hrCO52bZuMIltPJAZX2kud8T62wbh40Txl4W/aKxxbjMS4E+G9OMET7EMe4Vt83tNsVxKECBALPTwd/Xa2y869wrrz/ukqHAWZayq633gY+nZ2SgycjRpNwdfnwmRgDGZOBgwhtTJhO95don0v7LqgtPqtJurc7b7TZZvmh1nbInpNgtyF9POp7dIXNtWQa2PJtiaTcXxiVsmnbKeW4WH66O2yBljpR2cYr3p/jnFDdIcaBg8vnvKZayjx0o4wDnhzIpfkxVjzoXGaDMcnqHtPN0imABILNYinXoBcH6WV9d1p11I1r7rHrrZ+/MVnr7cMZ0Qj8eTfH6FA9IsQ7lvti273Odz/5/pVjy79NK/FOVBk50Cyz/SjnPpP0ZJmaun+WtWwWMr64ZCaSowQ/Ltvla9dSHe1R1au+EOnGQ+xekfKWvz6f9uXpc5x0u+d3nhVr5ez2TrewDHvYCs/Xz2I93qfTJtttz9p2UXvKeMWDr/9//q8c141AdxqeDUsb/pP231Imt/aXTsTwl/0eq9H7eu1rPbu/3uKot2rRR1Z5Ou/dV+X/byjBnlaasdVvpvQ77Nc4b84u+T/aotP7d6QVmz+1R1jeqelmtDjfMki4s7bft9nHRz6u8t/eocPMqr3L3auUfX6WPhjGvX789/XquWnINeDglflcGrDwSQoFQIBQIBUKB6VGBNBc0YjBr0tFEOxhnYt1EO7e7JvVtARuuMFkzJY3zBL+JctZbQJ38rJlYbpngL+53gZ0jjjgi5+eaFhQC4Ez4g7cm5llvmsgHBVhkgpXKKOuomjgHClmcga3awZqTRatz6lYWy04gkbUksAo4gcjAgTR9Ue5FF12ku10DIMASTf5SBmipLOWwGCugmiWqdusTV6CsvVh1gdZAqOullfbpw3333ZehF8s31wDK+qH9LEzvvPPODE+BM9eCHAItABL9Bq/LGrO0U462aaM2AUIgBr3Azfq+OQ8agS4sZllDszjTX23QFgCGhSorYPUAJtIAD/fQvbVur/vEEk8ZdQBKWdnqz+mnn56TWLmCqdw7s2j1zLi3QA0AvPnmm2fA45kCjwAo13s+PFugC+ttMMmzx7KWPvKwohZY3BY3p563CC9VoA1mfQTgHoB/Yqwx+1K9BnPk2Qf5AELvv2cZCGQdbowDun2owC03a1TWrN7HwYJZoJeF6Ec+8pFsvcri3zsMlAKU3lOg1jtsPHWPWYuDs8Yg6cCl9V+NLdrivffesvj3LgKT3jNWsfIYo+zzZMC61rjjwwgfURhPjJHeSeOD69XBbbNxlyW8dOXoK6DqwxTBhzfcLOvLAw88kM/V//iwYvvtt8+WvixhBfmUy6LfWMIqlkWvtuiX8cBHKQ899FC+D4AtTw3cqdPKhwd+f/x++SBGXmMGl89+K4xTPqrRv/iYo74bsV8rEGB2OvoLe9X1ttlq5fXGPdcT0K4/7s613rn9EmNEmqX88FTx/7N3HmBSFE0f/8w553zmAGYxYAAFM0hGFCQoUUQQlSwgoogBEAUTwokKKkExACIGFFRACZIVBAUURAHFrO/bX/3aa952nJnb3du9sFf1PHUz07H6PzO99/S/q4ZF+GTkLSns14fYdNJfTlzeFJeYd4xbLIfc9Remf5TrvQP1/ctcuXD9cLzUz8w7PzlQxpUPepLtLOX8BUxXDvJqp7y23AFPwu9FXZmhLiPkuK2k0YYr2y2kTFxSMoviEA+uH44szEfJjpKxVNSVXyfn2wcKz/Ty7wzkcZnO/vp4fcURN/TLM+XshlQqR2KEtJB0V5bjrYFyfl7NQF7w8ktJcOXbBjLHeXkQeYcE8t3l7nIC8e/aGewy5Og/f5kiZstKP2Dm+ufZ3UI0KPtIAmSOK8exp2iycq1U8NvgPkfJ0ZLxp6gr/2FIwfyeyZAqoUncP9fPipAS6Xy2ad71xTHuOUs3MUt/z2FAhDwv6c42iPCDvHLpvHeJvt8+YY5dx3j2BE87SoKzneOGQIHdAvnc02QkXfN8B+nUtzPKjpsD5fIjZvmkwLERA8Ir3+/zwYhyiSRvF2grjpj92Ss7IqbxAyXP//3B1uCc2sNr69OYtshq4pUNPgfpnPOmeP2k+tuTrueKcScihfG7kogdWkYRUAQUAUVAESg1CMjCYVqIWRbzWch+4YUX7LcLWcRn4ZoFdYg58ln0ZyEe8hOPJhbmITpJg6xjwR9PKzxKISBYFGeRHi8uQmkieEHy3UO8TFl4h8BjQZ1FcRbraQtCjjrYQj5EJ56WeEex4E+IWwgPPHjxrIQogLyAMIVQxVbCLLMgz3cYIUBYqKddxpKIUI7yhLgkPDJtMQa8tfB6ow/IbNL5niKYQBaAAwQMimcq5CXhdyHAyIdUZgzUBws8FPHwoj5jJmwo+eRBQoATY0HAAm82QgtDlGAjZSB/IWZGjhxp++nSpYv9fi/40w8kDe3QB+Fp8RCDhMMeCGTK4FkL2dmrVy9bDm84CAvuKR7VePAydkh6Fqhpm/DDhA+F2OOeBAUs8MCDKCGffvAohDDCDp4NCF6eMcLnQr64dIhvcOZZgZjGBtogjTFD+oA/OED+450HHtiH9x7fOFb5NwLcA7wI2XAB8c87h7c2OPIu430MWaUSjwDvJ3MN8wPe55CxeGEyL4Ivc5sjY8HcCWGDkyVmIQt5h+mTuQFxcwQkJfMj/fH8u744ck067yHl/Pq0ybtIXeZq3mn64Mh7RvtcQ5RiM5sqmCP4DeAc4Z3knD5ohz4gqZ2NzBG0x3vq5gc2inBNuitnG8v7Qx1C6UNss/ECoRzlsZP2mQfpF7uYD5dLmGXmC9Jd+8wrDgvSGAtpzMu0QVs859jFHEZ//IaoKAJRCPAM4unNxii8yCH6+c1l7uT3lN9InsFCFDbzq2QKgYpVr9n7rErX1juj0tV9xYt2kujXQtYuPbPSNaPOqHRtl7MvuvryOnVe3CpT/WegXd9rj0XRykn2UV/K+wu/r3r1+3t5LGT6kt9i+cVS2G93gVwf7zcg55CdA0X9cnjqsOAZJisk0S/LOTYG5T1JCJZ7JVgo75pv8PllW4SUw1vz0UC5ZHFOZlEcE0YH+sMDcWcyPNlVzieI+vb39vLdaSIkWLr66+PZkx8xy7cV//LKr5LzSqJBqSIJ/rd+8WqFGPXFxyCOMKPOl6KufFu/ETk/VtRfAIdIODpQhmef58m1wbGiqBO/fqaIWfp6TtS3AZuOIEOEd+tSUTD1y3DeUzRZ2UIqTBV1bUFm9BBlE4YvZ8qFjy/lG/oF8s4TeSZDqv0rqZmk+Dad/a8S6X2XXF8c456zTBCzEK53ifqY81sFWefbNVyufUnnvUv0/b5MDPBtGuwb5J3fFChHHZ4tbHaS32+NKxd3TMc8f5504I9pqVz7G464Fzzr/oYJyudHzFKG99S9u3Jq5Sz5+7Wo6/M7OQ/Oe7Zggn+289qizThi9iOvLMRxcA6UpP9j0wdjc/a5Y3cyPekh5y6P+TROmkimK7shpGC65rx0/fak47kKGWZoUmH9roR2romKgCKgCCgCikBpREAWAdNCzLrFRBaqWeRm8ZqFbH/xnIVuFsRdnlsIJ40Fb0cGcO0WKKkPQeE8kcLKsMBPX7Tr2qIOC+oIbfnXpNGfs5O26Ye6jgCgDOmujBsL9VjM51uCEINBhfCDYED8PmgXpQ/IAyekYbeznbE4YayMwc93uLgytOXyGaPL93Hy28Qmh4VPzLJQTF+MFwIlv/sGNv59wh7adveBoz9ObHD9Onsc5u4+uTG5I+3jIUvoUPftRupSHhtd+7TDNcQR/WIHQj5jdNekce7uARgxDupy5L41bdrUetxhq8q/EQAzJWb/jUt+KTy3EHmQsXjGQsbirerIWDY1QHS65xecwyQVYjasHZdGP05dWvAYlx+XRzvk5yeujUTK5tcW+Wz4IKwwG28gw6LE9RuVn0g6YeLZOMLGEOZhFUUgCgElZkvjf9jZM+ZtZChrRd1CJou7/qJ9IiPdQQqxAOraYFH50LyK/b30KXlp7pDIYvlYrz7t44WDRyLkAkTj56KuX454KJ4mGiUDJcMvzzmkXVBYGA6Wuz5YKO/6JDn63kHU+0T0AVE8ufqJsgDvt5cf4SjF/yXJLIpTmXsQtGulpOWKdhWF6PC9fbGPRfI9RYOSCAmWrv4SJW6cjY/IiY/tf+X6TdG+or1E3xb18zlvJRoUv0zNYGbg+ku5duXbBvK4HODlU+5H0dGiPUUHiX4j6upzZAy+FNYC+uHS6Veivi2crxf1iexgfg/JT0VOlUpB0mm+pLFp4W7R8aK8w35/o+Q6TBJ5JsPqBdMulwS/P84hknxP+nQ92/Tt9xX3nGWCmHV9zxE7mJsHioK/S+cIebePaFDSde8Sfb/3EAN4Dn3b+A25SbSF6NOiK0T9fP/8QMlzkshvjSsbdUzXPP+pdODb+Zdcvy/6suiGQJ4rx7zsCwSuy/OPP0k679A9ouNEg+9wG0kriGwnlf3+eHeipIdk+GV5p5iTG4syL78rGrTPlX9C8nzx2wK/OGkima4d8AxKOuc85m3XF8dUfnvS9VwFxxl2XVi/K2F9a5oioAgoAoqAIlAqEZBFxLQSs1GLktmSDrmCFyfhj8OU0J542ZYECRKzxc1miBM86QiJ2rx5881EeybsBAu8lwlviqevSjgC3BMlZsOxiUslHHHXrl2tZyxhb/GMhYyN8oyNaivdxGxUPyU5nY0afJMWstSFcc/EeNgEg7c43w+HoE0XsZwJW7XNokdAidlS+S921gy6uozEX1i8L8WRDQ600zuvHRb/XfssqvuSyGL57lJhuKhrI+64VspV9jsIOSffb+NPud4lpFz5QDnIpDCywlWtLScsyvptR53jUcy4kpVkFsVd2+fKyWeiUbb46Suk3MGiYZIoCZaO/vqIAc6u6WHGBNJ2kOt+okHCz7URPN4RqO8u/XJxhBnlvxR15du6BrwjNrFon8gzAYnMBglfCnMBfT/peKqoG0/Y8X7J9z3vgmGgfdvzO68SaCusP5f2jJTdKaLBRJ/JiOqbk5mHwshp7h1hvp2k49mmLTc2jnHPWSaI2Q2B/n1bOAeHsqJRko57l8z7DT5BG8OuN0k5CNs/vfKt5dxJIr81rmzcMR3zPL9B+d2HNVLmWlE31vyI2Re8sq6OfwQXNgltIVoQSeY3iDntY1HfjqhznvWnvbLfyDnks5MecuLqFpSYpc10zXnp+u1Jx3PlsIo7FubvSpwdmqcIKAKKgCKgCJQaBGS5UonZJNZs8XB9R0LnuvCD7tua7kj4ZL5TWhIEYoFQo3yrFZKoOAokC+GZCfmIN2+mBC9aMHjppZc2e+Jmqq+S3K4Ss6ndPYh/yLtp06ZZMjY/z9ioXpSYjULmn+l41eNBjyd8poR3gXkDT1nmKRVFIA4BJWZLzb/VWTnQIOl5YoqjPF3quYVTjniIIhC9Ln2yTfnfHwhRn0w7739Z/zqrJimLAuVdu99L+gDRA0XzExaLN4q6uh9EVNha0vHwceXeiyjnJ5eTi4miYWTcX5I+X7STaKqL49tKXdpxNrHAn4hALvUX/VbU1fWP30k6du0sGiUfSoar0y2qUF56Qfvr5fUFYZio8PxABPiLz85mjtybc0SjxK9XNapQXvpSObq2fQIoWO1SSVgg6j/nrh5EZ0PRsOfhF0l35WrIuS9d5MLlfeJnhJw38MquC8l3STxbLUWHic4TBQvIIK6vFEUgi1y/TW1K6n/2lKoQMf676NrmyPtWRzROknkm49oh71hRCB/fBs5PFPWloM82bSX6nL0uZZ09A30jAue8u67czEBeDy+P8R0typwXfB5/krSHRCGs8pOC3rtk3+8LxaDZom6M/pE5jU1BB4sizBcuf7xN+fvPLnLwx8xckaqkY57Pkc7fEvWfBezmPkwSPVL0GFE3Fu6dL1vKhSOh+b3ZW7SLaHCOJ4+6BRmvVN8szBN/iTq78vsN2k3K9hX9zavj6jL2GaJ1RZkDacvlcTxT1Aljc3nFcc4D34L89jDOdDxXtBMnv0imw7EwflfibNE8RUARUAQUAUWgVCAgC4lKzMatpobkESKXBf8whbgtSYv0kLPY7MIChwy3yJMIPwzR4kIgZ8IgSBZ3XzPRfra0qcRs6neS5xgFw1RFidlUkdN6ikDRIqDEbKn4l1oHWUwQgBhhAfM6URYWTxPdXrQ4CQTnWaINRBvnneNZUxxkXzHiItHGopeIHiUa9NaUpLRJYfeH4YynrOjVecrzspdoUQrPLc8EROzlojmikBElRbYWQ31Cpk4aDT9M2qoiCjY8m4eIFpUcLB1zn8qIQubFSVE823H2hOX1kERHxPjk3q6SzqYB5ig2K+Q3VikSKoV173hXckQriTYShcTbT7QoJR3zPL9dbl5gnuI9K6iwyYD7Clbc5+IgPF+nitYWvUaU8L2QvMVZUpnz0vHbk47nqjjjqrYpAoqAIqAIKAKlCgFZqlRitmjXa7V3RaDEIKDEbNHequJCzPIcsAGFTR0FJZuLFtHw3vFw/vbbb+13scNLFDyVjSaQdWgmN50U3FJtIR0IKDFbqv611sEqAoqAIpAVCLSQUXwu+rpo13xGBBHrCD6Op+RTXrOLBwI9xAx333xitnhYp1YoAoWLgM55hYu39qYIKAKKgCKgCJR6BGTBsdgQs3gsfvjhh2bixImGbzIWRAgVSnjhpUuX2kXvFStWmKFDh5rBgwfbUMR8JxMvzHQIJAXes3ifLl682PZbkrxm04GBtlE6EFBitmjvc3EhZiGZ7rvvPlOpUiWTm5trCdr8kGG+nTp1qlWIz+Iq/A7xW9GpUyczf/58s3r1avubQdj3gsq6devMlClTbLv8XhD6nm+ST58+vaBNa/1ijoASs6X+320FQBFQBBSBEodAPbHYkXYcz4sYAd7Ga72ynJckb9+IYZWKZCVmS8Vt1kEmiIDOeQkCpcUUAUVAEVAEFAFFID0IyFpmsSFm165daxf6jznmGLtgneo6KwQAC+sVKlSwi+qQvNWqVTP77LOPKVeunKldu7Y57bTTzFdffZVqF5vrffPNN6Zr167m+eeftwTwvHnzzPnnn18g+zc3rieKQDFDQInZor0hxYWYXbNmjbn55pvNAQccYAna/L7lyrdYBw0aZMqWLWt69OiRUU/Ugt4hyOP69eubBx980G7eefLJJ02tWrXMhAkTCtq0/Z53jRo17OYdGluwYIG54YYb7G8IxJ1K9iKgxGx6/mfVVhQBRUARUAQKDwHC9frE7Fy5Lh/oHs/YtwPlBgTK6GXxRUCJ2eJ7b9SywkdA57zCx1x7VAQUAUVAEVAESjUCsgxabIjZDRs2mM6dO9tFcTxaUxGII7xWzzzzTNOvXz8bZpPrQw891HTs2NHg9dSrVy9z2GGHGbxoCyL0tWjRInPkkUeaRx991Da1ceNG07t3b3POOefYUJgFaV/rKgLFDYHCJmbxKlz02TJz532Pmps79zGvvfFucYOkUO1JhZjdtGmToR4bX1auXGnYPMKGEsLnEllg4cKFZubMmTYv6OmPh+fs2bNt/hdffGEjAzBgiFiiGzz33HPm008/tdECeDbow5VnfiXUMf0QuaBJkyZmhx12MK1atbLXeKZyfyF5Z82aZWbMmGH8Pthgg7cqbTKGZcuW2bad7T7w3333nc375JNPNo/N5RNqmU04jBEilDFHCQQyvw916tQx06ZNM/wmde/e3ZQvX95u9vnhhx9sVcaFPXi6LlmyxDgPYPIZN/Wc0B/9gz2/SWeffbYlfSkDBgMHDjRVq1ZNC/Hr+tRj8UNAidlS/a+2Dl4RUAQUgRKLwBNiuU/Ocr5R9DPRDSF5iySN7+WqlAwElJgtGfdJrSw8BHTOKzystSdFQBFQBBQBRaDUIyDLl2kjZlnUZ2G8Z8+elgQdO3asmTRpkvWUYuEdgbjEuxSStF27dnaBeu7cuTaPRfGnn37a9O3b14Z6ZLF75MiR5rHHHrML9w8//LC57bbbDB5MLOaHCW2w0H3UUUfZRX4WxOlnt912s56yzzzzjLn33ntNTk7OZmIWm+iHcrQ/fvx4+/1E2odUgNSgTQgFvMSGDx9u1q9fb8eC95dr+/XXX7dEw8cff2yOOOII89RTT4WZqGmKQIlFoLCJ2ZWr15g2ne41Bx1f0Vzd5BYzatykEotdOgxPhZgdM2aMqVixojn33HPNGWecYQ455BBLPrL5pUWLFnau2n///c2pp55q52PmVuZeQsETdeCggw4y5B9//PGmS5cullTl+6t33HGHOfnkk+2mFOZD5tbzzjvPHHjggbb8CSecYH8L2Bhz//33m7322stsscUWZpdddrFeosyTzPcQlXje7rfffrYPfj+Ytz/44ANTr149c9FFF9mIB/SPLRdccIH9DYHUxM4XX3zRlnF2nnXWWeaJJ56w5Ch2Mneffvrp1ibm5UaNGkWGDsYmPFrbt29vv/360ksv2egKO+20k6FdsOQeYONJJ51kN/hg16233mo3/UD+NmjQwOIKCQyW/EbgcUtkhcqVK5udd97ZjnPIkCGWmH3nnXfMFVdcYcvx+6WSnQgoMVvq/91WABQBRUARKJEIbCtWjxENkrNh1xOk3N4lcpSl12glZkvvvdeRhyOgc144LpqqCCgCioAioAgoAhlAQJZA00LM4pX0/vvv2wXnXXfd1S4+EzoYEgBv1YceesgulEN+skDP4jSL3XvssYcNL8wiNp5RF198sTn88MPtYjueUSzaU/7oo482u+++u60HSdCsWbPQ7xqyEH7JJZdYjycW7vlGIG1uv/32lpTo1q2bueeeeywxywI7fbBQz6L+ZZddZi688EKbh9cUY8KbDCKA8Mp169bdTADw/UH6uvbaa60XGIQHxDHCOPCAqlKlymaC12boH0WghCOQaWL2P//57z8Qwlu2+nU3m/rNbjPfrltvfg58F/o/4qWZjGB/nJAfVyY+z0TWza/dOJv8vFSIWcK6HyYRArbeeuvNxGafPn3sRhPmaqILXH311XbuxvufDSV4w1511VV2/r7++uvthpUTTzzRHHfccXZjCp6sbFSBZGWjC99Nvfzyy+3cydwMsVmmTBlDHb5BS5vHHnus2WqrrWyEgbvuuss8++yz5tJLL7WkbM2aNe38yu8FBDGbeiZPnmzn3m222caGQMaL9ZRTTjHbbrutadiwoSVC33jjDVtmzz33tCQyczi/D5RjA9CwYcOszfym0AdzOb8jENJ45waFOvwGEAGBe0YYfMbC/N6/f39LSvNtXULhQ7RCbnNNhATGRPkBAwbYceNpSzhkykJiE63h7rvvtuODrMWDlj64p4QzJnzy559/HjRJr7MEASVmM/APrDapCCgCioAiUGgInCo9DRWdIfqtKMTsetH5oqRXFNXvygoIJUyuEHuH5Wn3Ema7mqsIZBIBnfMyia62rQgoAoqAIqAIKAIWAVnzTAsxy6Jj48aNLQHKwvarr75qvbIIXYlHKYvaq1atst5IeFXhoTpu3DhLBkC8skgP0Uke14TIZJGbRXoW5vlu68svv2xuv/12u7gPUbt8+fJ/LNni3UpYTUgIyAKEkJPvvvuuJSRYMMcGvLfwmGVhHq8wFvIfeOAB2z9hKSEc8KzCYwsb8baaOHGiIawnRC0ELQv/hKKkzMEHH2wX512YS44svENy0J+KIpAtCEAkQUaxkYGNF61btzajRo2y7yvv8GuvvWYIKxsmeL/2e/RZc3f/IaZx6y7mxtvvMguWLDMrvvra9H7gcXPl1a1M9QZtTP/BT5tv1q4zX636xtzcpa855KRKpmz5aqZn38GStsZ8sWKlPb+ibgtTvWFbM/CJ54S0/d56q3+68DOb12fAU6Z243aml4RAXvfdBvPJ3AXm5k59zGW1m5lrm3cwI8a+Lt6Kf5j1G34wI8e8bvo/9rS56/7HzFX1bzL1mt5mnhv1mvle8pC/y4w317XsZKrUa2U69upvPpmz0HrT//rrb+aNd6aZFrf0NJfXaW6atethJr41Vb5N+putu2TpCnNP/8fNVdfeJNpaxv6kWbx0uc1L5U9BiFlIVaINQKqOHj3abnphHoWEJNQwm03YKHPTTTfZb2TjZcs1hCYba/Bufeutt+w8yIYWyE021zDXEi0AcpK5GwIVL1XKv/322/Z5wAO2efPmdhPLLbfcYjfEMN+Tz28FYYP5Bi0euMzfEKoQs8z7eNNCcPJc8TvCNR6mfAsWUpTfF74hThuElsdWPFppt2XLlmbHHXe0thLCmd8dSFaUZ9UXwioTaphxQwwjpNE3m3teeeUVu9GH+Z9NN5DC/AbRL8Q2OBHGmXvEewEZzFjY+MPGI36fIKjxAAYbrhFCHRNBAmKbtlSyEwElZvWfbkVAEVAEFIFsQmDLbBqMjkURUAQUgXwQ0DkvH4A0WxFQBBQBRUARUASSR0CWQNNCzLIYzaI6nkssOuOtyqI1RKsjZlmIZoEeUoCFe7yeWMhn8X/w4MGWGGUhnjQIUxb/IWbxcGLRnm8O4smFlxbhMiFhfcHDlYV+CCPqI/RJeEo8sQhvybcKHTGLBxMevHhyOQKVNt6R0JKE1YQEoDyhliF3CdXJAjqeYChEAR65eARjnxPshACB8HUhnF2eHhWBkoxAQYjZTxcsMeUvr28OOPp8c64cmwuZOX7yVNOsbXdzwtlVTNsufUzrDr3NqRfUNO2ERF36xVemlxC2R5e7wpxZuZ4ZPOx5M3XGbNOgRSchaq8yt3S7z7S6tZc56fzq5vYeD5gfNv1kSdEDT6hgjjjlYnNpreamQ88HzZvvfGAuvKqJOeeSa0znu/qbBs07mlOkzhtvTzOQxa3a9zL7ik2VazY1rW7rZSpc2dCcVelqM+bVSWbTTz+bhx57xpxSoZap2aidufWO+8y5l9Y3dRrfYqZ/Ms+8+PIEU/6y+kL4tjAd7+xvLq/XUuo3Mi+99qZZLeRyx579TIUqDe3x5o73mFPOrW5uvr2XJZ1TeQ4KQszincpGEoSNL0QBYOMM8xdhh5nzttxySxvyHQIWj9C9997blmFeJzx806ZN7bdg2TQDMUvkAyIQsMmFMPCUo02OkL4Qo4S3J6RwmzZtLElKOGQ2r7CxBaIUUpWy9LXddttZeyAwHTHLXAv5j0D+UxYSE2K0Q4cO1oMWMpk+IFIJB0z7zNsQpnjp7rvvvnaMbMiBqOU4TMhfX/jN4vvgbCxiIw7iyFqIWYhcokJUqlTJjo/fjbJly9rNRWBHOr8T1KEsoY8Jc4zNpCH0STl+S5zwvV4iNJBO6H+V7ERAidnk/z/VGoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQFYiIEugBSZmIWsIwYiXKaSoW9TGw5SQvo6Y5ZqFerztWCiHdOUcYhavLTyowohZFuwhcyFZ58yZYxfwIWbdt2ndMi7fuOX7sJTn24BIHDHLtw+bNGliQ01CprryeFbhBcsiPWUI6YnnE16yhEnGg9YRs5SF2HjkkUdsff6w0M7CO4v1jgjZnKknikAJRqAgxOzc+YuFHK1vLhECFE/Slau/McOff8WUEZJ10JCRZsPGH8zqb9aaR54cIURobTPhzffMzDnzTZVrWpsW7XuaNWu/N7kjXjZlzqlqnhw+2paHWH1wcK45/aK65t2pM8wkIVsPFQ/bNh3uNmvWfWcWf77c3NFnoDntglrm7fenm40/bjKLlnxhbu7cx3QS0nSeeNg2b9fdnHPpNWbchLfl29EbhWydaIncvg89ZT6a+akNpdzoxs5m3qLPpM8fzYhRr1oC97HcUeb6Nt0kv62ZNn222fjDj0LWfmrqXt/etOl4t3nz3Q8sgXuNeODOFVL6u+83mrFC9uaOeMms+vrblJ6CghCzzGNuowjfZYWMhaDEk5WIAYTivfPOO+1cC/G6bNkyG7YY0rN8+fJ2wwykJgQsHqA33nijJWaZJ/nWKxtdCFtMOiHoIWfZRNO5c2f7+8D3ualPOHnIU8hWfgMgONkgg7dqBfmmLXMtBKYjZpl33XzOhhsIZUIWkw/JS2hjxoAnMBESPvroI+vdCsl5zTXXWLIXD1s8eyGRGSNh58M29lAGYhYPYMQnZkmbPn26JYXBEgz5rUOx780337S/YW7zD5uB+G0DL37bkDBilt8ewh5D/rKxSCU7EVBiNiv/hdZBKQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCySMgS6AFJmZZRmVRHMKSbxay8O1IVML54vFKOEgW0vGwIo1FbEhNFsFZvH788cdjiVkWvl2bEK9hxCzeroRZhfTFaxeJI2Yhk/l+ICEn8eRFIHfxFoNgxquXhX/IYzy78Aoj1DHfA8RrC49ZxgCRgMevE0gKiGZCbpKvoghkCwIFIWbnzFtsKlZrbNp2use+l+u+32DDB++ec7YN83tD2x6W6LykZjNz0AkVzYBHh5sFi5dKeOObTOvbe5u1Eq64290DzZ5SvnqDmw3lm9zUzVSucYM5pOxF5vHcF8Q7dpoQvdXM0Of+DkWL1y1E6YHSXoOWHU3Tm7ubhkKylr/4GnNtk/ZmyrSZ1mO2QYuO5jMhi5GPZs61ZCxhkEeNe0OI5GbmwUG5Mjf8afN/kfDFELiTxBP3Yun7hLOrmvrihdtUPH/rSzvHi4dv1botbX633gPNQWUuNKdWrG29e59/abz5UkI0Ow9K22ASfwpCzOKZSqhdhDDAlStXtl79EKt4tRL+lzC7bDIZM2aMDUlMWGLOZ86caUPV49EKCcoGGcL1Mp/z3VRCvteoUcPUq1fPhpyHwGzQoIH9ri1ELSGGCWFM/euuu84SnHiJQt5CmuJpyhwLUctGF99jNoyYxfvXhT9mvidaw2OPPWbJUtpjfoaEpU/IYMIM8x1cPIXBgXD1jNkXfiuYx/kOLbYgpBE5gd8pxujwx7v1HYmswO8FhDQh9umL3wdCHkNkM068igmbTAQFPHIhZiGfIbCdQNhBTBMeGZxVshMBJWaT//9UaygCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAlmJgCyBpoWYJXQkoS+32GIL6ynKd/hYZN96660tMYs3FJ5F5ENYQlxC4OJRBXHLdwH9b8z6oYydB6wjZgl1TBtBj1lIIwgAPHd7yDdeEUfM4gEL+eu8mSBTIVohfCGGWVSHnIW4wMsXshZyARIWzycW8SFi8fKCFMbLa+3atfabsxC3eIOtXLnS9rlx40YbYtOVsYn6RxHIAgQKSsxWrtHUkqtAwXdhe9w7yOx//AVCsN5h2na917Tr2tfcInpHn0fMm1M+tMRstTxido2EBu4k4YIPkPKQsra8fIO2fdf7TI8+g8xb7003EyZPMSeLd+yYV/4OCfvZshWmdqO25sjTLjNtOt27uY/23e43Q8VzFY/clhLKGFJ1xcqv7R3C67WWfLv2TiFmR44dby6V79IOfmqkYezIX0LW/Sbfp50sHrGVqt9gzrq4nmktYYr/tv8+sf8+M0jKfy6kMB7AfK+24Y1dJORyDXNo2UrS351moXxbNxVxxKD8GFnSj3Dv+Qkh3JmjmNccGckcBXHJPMr8Rz6RDYgEAHnJPIi3KfMz8x9zGfM08yLtYQekI0QrBOuMGTNMrVq1LFHL3IpXK+UJfwwZSX8QvxC5fJeWsnjSQrpCnNIu/dAeffbt29dMmDDBnHvuuTZcMBETkOHDh9uNPZDKkMv8BkAG0wbj4LeBUMpXXnmljVZAuGPaIJ/fAMLLU46wym4zjo8fIYghXfl2LRt9kCFDhlii133n/PnnnzflypWzoYqJAAFJDD54HbMxqHbt2jaNDT7gCLlNXWxhHPwuMm7GgrcsURnA2nn9+vboefYgoMRsVv4LrYNSBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUASSR0CWPdNCzEJasNDdqFEj622K5ywL4ngysUCP1xHEJd8GhKzFU4qFchaoKcsiP0Qn3kSEAGbxG9IBbyMW0/FWgmQl/CQeUnhVBUNRsoRLG3hu4TWFVxp1CN9JyE68sCBm8d6FvMVewitDEEC2QibgacV3AylLuE2IW+qSR3hOvIIhBSAf+HYtZDIL8xAChEWmT9IIecz3DelfRRHIFgQKQszOnrdIvFubmu73DLRw/CBhhQlbfOxZV8o3Wd8y6zf8YL7fsNHMmrvQ9JEwwhCkeMw6YpYwwv0GP22Ol+/Rjp/0ni3/3foNZubs+abvwKfsccLk9ywxO1a+8Yp8Jd6prW7raUMoL/rsC1vn6zXrzMS3p5oXxk6QUMafW6LUErNfrbZ1HDHb6/7HzMR3pprLxPu1q3jq/rjpJ5s/dfos+aZtP0sq483bpHVXayf2r/p6rRkx5jXzzAuvmI8+nmuGyxGC+XvxsCWs8u3d75ewydeaFyRcciqSCjFLOHU8//EodZtHuI/MU3z/tHHjxtZbEw9YiElIVDxBCVfMhhpIQ7xMITNffvll8RZeb7/hCoGLpyfh4yEYmY8hNJn3KM9mlVdffdW2R3+QqGzeYX4mbDLzJ+GA+c2AzIQoZoMOm2QgMPHKpX88cjlHIIAheIlmQKhl7OQ73xC5kL14xrLhh7bJI6w834Xl2+CEH2YseAQvXbo01GuZdOxp1qzZZqzYwMNvBmn8DvFtXDYZMT7Gwnd33UYi+oKoZtz07b5ZzkYhcOI3B2KbDT9gz28MbfJ7MWDAgM1kcCrPhtYp3ggoMZv8/6daQxFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARyEoEZCkzLcQs4XvxJmUhnXCQhGRkwZzQmHgo4XUEScnC9NixY+03WPkWIOGE35GQkCy8s5CORxdeR5AGhH5kIZ7rb7/9+5uMEKtc4y3FeVAgCAgbCXEKsYBQjrCXhFuGOMUGyAoWziEMIBrIJ/wxRAF2Mx6EIzZBQhDeEjtXrVpl+4fUpT0W81lcp03GwOI83l+0paIIZBMCBSJmP11kv93atfcAC8lff/3HTJsxy5wjHqd8d/Z1IVtfe2OK9XA9+6I6lticL8Qs35htdVsv+65N+WCmOaNiHXPl1S3NhLemmlcmvmOq1W9jzr3kGvPx7AX2u7QnnVfdjM7zmP35l1/Nc6NfF4/ZS03zW3qYd6d9bJ5+YZyQo/VN7wceN5C1zQihLCGRl+cRsxCqNRq0Md3FC/eLL1eatvI92nKV65mHn3zOhidu0rqLuahaE/kW7Rum94OPGfrrctdD0vZM8+Aj8r3bC+tYz973PvzENLjhVlOldnMzSuzhG7cNW3YyFao0MhMnv5/SY5EKMcs8SkQDSEDmKyfcS+ZACCPmVwhHypKOUJY6RApw+XzDlXyUuRES182jfnnmRvIo7wSSEjvI4+iIU/qnjx9//NH2Rz3mcfKZu0nnHME+rkl3Y+HINe26dpy3K3WoS5sunzFFbZih7KBBg6x3MSGJETcu7MQuBDv43QAX+nQYMF7G5o/b4U9d+gU32sIOxsL3ZfGq1TDGFtqs/cM95zvCW221lalevbr9P4X/KfjfAmKfjQbumS4kEHbIyn9qdVCKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikBxR0AWANNCzLKojwcRnrB4nXbo0MF6YhFWEg9YSEsnLFqzkO0Wzzm6cxYmOffJAf+aNlx5V8a1y5E0PJwIHYmXmBPquMV4ynDtC9csnGObK+fysQl7UbdwygK+K0d71CMNHG699VZTsWJFSwa4NvSoCGQDAjzrbIwg9C3fYcbLkg0ZkAt4AOJxCVEVJni/1mt+u3nw4aGbszf9JBs65DuuFao2sp6zZc+rZknakaPHS7jg3w2hiG+4+Q7jyNyNP2yyHqnnX3FdXvnq5rJazSV08RtCeP1qJot36uV1mktI4/8Rn9+s/VYI02HmtAq1rLctIYWvFsKUcMJ4uHbq2c906PGgnK+xds0Rz97m7XqYBx55Wgi/X8zHEu64Wbuepqx8uxZv3YuqX29yR7xsNorHL/Z17jXAts23Zk+5oKZp3aG3mS3f04UUfvn1yaaKkMhlzqlqv31bsWpj8+Qzo8z6jT9sxiCZk1SI2WTa17LGficW71i8gb/++u/w1pnAhXdp0qRJ1tMXb1l+X1SyFwElZov7f8NqnyKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiUEgIyDJoWohZiE2+yYqHLKGJ+ZYgSghgwkz6HkSZXnplgRuyiNDDfHO2sAQM8AI+88wz7fcRC6tf7UcRKCwECkLM/v77H+Zr+eYqYX2d4Jv566+/2ZDDM2Z9ambOmm9Wrv7G/CJpyB9//Gm+kdDD33633l4Ll2XLf7nqazN91jzxkp1vVq1es7n8T0LOrpLwxT/9/LdnI5X+I5UIQ8w3Xz+cOcfMnr/Ytvcnm0BEv10nno/yvds///zbmxRCeM3a7yTs8Ua7+YL3mvy585eYGWLfF1+ukk0cf3vb4/W78YcfzcLPlkvbcw0evt99v3Hzxo9ff/vdfCXjwZv3o0/mmi9WrBTb/q5rB5TkHyVmkwQsheLc7ylTppihQ4dakjaFJhKqQj+TJ0+2kRq4ryrZjYASs4X0T612owgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIFHcEZCk0LcQsS6osNONhBDnJgvO7775rFi9ebMM1FvaSK6EtscOFQC6M/vGgJQTzhx9+WKhEdGGMTftQBECgIMRsfgj+KSQnHumQr4mIK59IWcrQLO1DpgYlkS4heP+279+lwYU850UfbJ8+o+oGy8ZdKzEbh0768gg/TKhhftMyKWwiIrRx1HOTyb617cJFQInZ4v7fsNqnCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgUEgKyNJk2YtYtc0JAENaXRe2iXHAuiv4Zb6YX8x3OelQEChuBTBKzhT2WktifErMl8a6pzYqAsd8V1m/MFtI/ttqNIqAIKAKKgCKgCCgCioAiEIHAtpK+S4huH1FekxUBRUARUAQUAUVAEcgIArJgmnZiVhdhSyYCBSXRIe1UshsBJWaL9v4WR2J2/fr1ZsGCBfbb3nHfScX2mTNn2qgCoMizlK45w7XDppj58+ebTz/9VKMWFO2jqr0HEFCP2Yz8C6uNlmAEcsT2xqLNRE8QVcleBLaWoV0i2la0quiOoiqKgCJQtAjkSPeNRXUOFhBUSh0CQ2XEJkQ/KnVI6IAVAUVAEVAEFAFFoEgRkLVDJWYDC6hFeQmxMX78eNO9e3fz+uuvZ9wUyNjPPvvMdOzY0Sxfvty899575oEHHkgo/DR1v/vuO0u0YPfbb79t+vTpYzZu/N83RDM+AO2gUBFQYrZQ4f5XZ8WRmH3++edNuXLlzPXXX28J2n8ZLQnMEz179jS33XabWbFihf0e+RNPPGFmzZoVVjypNIhY2po2bZoNFz1ixAjTtGlTM2nSpKTa0cKKQCYRUGK2SP/V1c6LGQItxJ7fRd2i6H/lvHcxs7EozdlCOj9c9DTRvYvSkDT0vY+0MVPU3WuOn4seI6qiCCgCRYOAzsFFg7v2WnwQyBVT/N8ld87vlYoioAgoAoqAIqAIKAKFhoAsRCoxm8nV2CTbhtS85ZZbzL777mvat2+fZO3ki+PtdtNNN5n69evbUIv9+vUzp512Wr7f5oWgg2Bp0KCBgZiBpJ0+fbq56qqrzKBBg5I3RGuUCASUmC3a21QcidmhQ4eaQw891NSoUcPMmTPnXwDxzIwePdrUqlXLDB8+3Ia5f/DBB03VqlXtZo5/VUgy4amnnjKXX3657YOqy5YtMy1btjTt2rWz3zxPsjktrghkBAElZgvt31rtKAMInCFt3p0GPUDaOF70D1G3COof8aosrXKgDPxR0Y9Ffxb1cflBrueIDhOtIApxW1LkGTHUH4s7n1ZSBqB2KgLFAIEOYkNB5+AGeePQOfhvIC6OwbRMHlZ6yF4EcmVo7vfIPyoxm733XEemCCgCioAioAgUSwRkBTLtxCzhJH/77be0hamkrbgwu4n0lV8biazE0kZB5ffff7fEhN+O3+6GDRtM27ZtzU477WQJWsrF2Q4ufn2/XXfON3/RoPAd4DfffNMcffTRZvLkyRbj1atXm9mzZxvsDIrfD/3OmzfPnHTSSZZsoSxecX379jUXXXSR9b4N1tfrko+AErNFew9TIWa5Z1988YV58cUXzUMPPWSGDBliPv74Y/PLL79sHsy6deusp/7AgQPNM888YxYvXvyPOWPlypVmzJgxtv6jjz5q3n//ffPDDz/Y+hCzhx12WCQx+/XXX9vNH9ddd51ZsmSJbbtx48Z27rjnnns2zxX0MWrUKOuxj61cI9g+YcIEO98wjzEevPzfeOMNS+xCwJ5wwgnm9ttvt23z+4ONkLUvvfSSbUP/KAJFjYASs8XyX2A1KkEE2kg5f+Ey1fPy0g7hbKPqP5SgPdlUbKs8TH6MwSWI12dStpMo38cr7rJeDAza7673LO7GF9A+vJ2fEx0pWruAbQWrnyMJFfJ092BmIV+z4cLZckqgbzYRnO/l7xTI18vEEPheirn3JtXjpLyudA7+G4j+MZg6EjsPMj1kIQK5Mqawd6moiNnrxR5+K7BL50kBQUURUAQUAUVAESgtCMhiZVqIWYg6FtAJiVupUiVTvnx5c80115hnn33W/PTTT9arslGjRoZFfMp+/vnnNvRlq1at7CL7zz//bAk+yjz99NOG0LivvPKK9bI655xzzNVXX21eeOEFw6I7AqnANV5a9HXttdeacePG2UV78p577jlDW/3797eep+eff74tk2h4YEgHwvI2adLE4JFFmM4LLrjA3HDDDeadd96x/eAx2qJFC9O6dWsDYYJtfEMRL1JCEW/atMmGBm7YsKHp1auXueuuu0zFihXtmMAFe/EaI61Tp07Wu8sRs9tvv72pUqWKadOmjR1fvXr1LB528PLnxx9/NIMHD7bkw7nnnmtDdxKGGMHrtkePHnb8EB9XXHGFgQhxRIctJH9YKG7evLmhPvcIzF9++WXraYYdELTcn4cffti2cfbZZ1uCBdIGEhYv21122cVUqFDB3jtIE8IZn3766baO60eP2YOAErNFey+TJWa5X2y+gKTca6+9zG677Wb23HNPS2RC0jLP8T7jNX/QQQeZ3Xff3eYz10FqMqd98MEHdp7de++9bf099tjDHHHEEXYTBh73+RGz9H/ZZZeZ3r1723mbdpkj6Kty5cqWdP3www9N7dq1zTHHHGPOOOMMe6xbt671wv/kk0/s/I/H7UcffWS/HwvJS3k2gtAG4zrrrLPsbwLzEMTxlVdeabp06ZJQWPaivavae2lAQInZ0vJfdXaOM53EbHuBKGwxlLSnshO+2FE9HoNHFE6k/yq6f2zLxSMTb9+ocRxSPEzMmBWQsoz9L9Eyae7lj7y2ab9umttOtrkuni0LA5V38/Kw9cJAfkm4PE6M7FHEhqaTmNU5+O+bqcRsET/URdx9rvTPnBTUoiJmL/Bs4dlUUQQUAUVAEVAEFIFSgoAsiKaFmIWog/xj0Z5QuJB1O+64oznyyCMtSQrht/POO9tFdEhFwt/usMMO5oADDjDDhg0za9euNZdccok58MAD7UI/HlIQBdQ56qij7MI7ZEBubq4lEPHq2meffWw+Hp8QhBzHjh1rickOHTrY9ln8h1CgLcjO4447zhIN+S0EO3uwkX6wgfDCeLKed9551l6+bUhaTk6OmTt3rvVcZZyMG5KAhVhCd9IvdjA2ym633XaW/GDs4IPttAOZ64jZLbbYwraD3Ycffvhm2999911L+EIagzV1ITPo85RTTrHExZo1ayzZSr+77rqrxQFym3vkxHmdHX/88Za8IJ37ApELjt988439xi3jZewQN5C4+++/v73PtAX5i90Q0W+99ZZtGo+4OnXqGEgV3yPP9avHko2AErNFe/+SJWYhXrt162bnCcKMjxw50l6zmYXNIJCyhBXmPWaOuP/++20YYOYqNtYQmhgP27Jly5qaNWvaTSqECYbkhShl3osjZtmE89hjj9nNJ2yWQZjjOnfubH8j8JBlQw9er5DB/C7gtY/HLBt82OjDXMS8D5nLRplmzZpZ8pYNM7TFN7HZXPL444/bDSb08dVXX9nNNGzYwbtWRREoagSUmC0l/1Rn6TDTScxeJBgFF0LddbMsxS9qWBfHYOEwiToOiGq0mKW/FzHGVcXMznSbU8UbNyGq0y1KzKYb0X+3h9favaJgvejf2YWakk5iVufgv29dcSFm+4k5Q0P08kJ9wkpfZ7ky5LDf16IiZrkDY/Ns+o8czyZBRRFQBBQBRUARUASyHwFZrEwLMcviOqQnRCGkKAQiYTHvvfde60XK4jgEI98ixKv0tttus/8LQfzdeOON1jsTAhRScMaMGdYbirZYsKdtvDYhHy+88ELrbcuiPfmQmUuXLrWEIoQn5ANeXNiw5ZZbWtJy0qRJloylLm3QX34CMYsnFm3iuQoJC6kBUQxpgY2kQVBw7YhZvMG22morU65cOUuE4v0rT5ElhgkFOnXqVIsBpKkjNghdvO2225pq1apZooExY3uZMmWsxy14YQu2Q4pApkDG0vcTTzxhFi1aZD1bwfLmm282ELN4t26zzTaW/IDkxpsWksQJYYwJX7zffvvZ8KGkQ8zi1cZ9ggx57bXXLLHbs2dPa9fy5cstMQKZ++2331rS5sQTT7TEjAtzTD3IFEgeSCSV7EJAidmivZ/JErN4wjNHMncwT+GhDyFLtAE8/olcwIYa8vFCJaIAUQZOPvlkG2qYOQsPfL4fTQQD5hvmKeZevGDxYI0jZpkXiBTAfE3oYYS5h40ll156qZ2D8JalLUKgQ+JC1nKkTvXq1e1vA/MN4zj44IPNIYccYs8Jv4y4spC5TrCZ3xi8ZrFRRREoagSUmM3+/6ezeYTpJGbBiXB9wQXRaZK2NZmlRLaVcX4pGsQhkWu+QbtfCcHpdLHzp8A4/5Trq0qI/amYuatUWinKvSRE9T6i6RYlZtON6L/bu12S3PuYTcQsI9U5+P/+r7gQs1Gke/d/P5KakkYEcqUt9377x6IkZo8Sm9zcvkDO+T9BRRFQBBQBRUARUASyHAFZrEwLMYsHJZ6SeHriHYqXJSF0IWdZQGdRktC3eHkS1pfwvc6TFBIRry2u8bSEiIWghRSFTMVTE0JBboUlF0aPHm2/aUh5FvSbNm1q+yKffiEcIAfxdiWMMN9LJazwgAEDLFkJyUnY3jhxxCwhMu+77z5LakIuQwxAZhIqc+HChdYbN4yYPfPMMzcTsxC1EJV4r/G9RUJ1ggMkB8QJIYkhUQk3igcYxCy2Q5hAYmA7ZAllIC/Gjx9vsYEkhtwlzDKhmrfeemvrdQZ5gwcZtkOMR33XFgLEjQUswohZyHKIXZcPyYKXL+TvggULLHmD97IT7jN44b1LKGSV7EJAidmivZ/JErPcLzZ2NJKw7kQOYE5lXmDzRdeuXW2YY8KksxGEeZv5DU9+5ic2XfBus6mDMofJd2TJx4sfIpe5F1I1jpglRD39XHzxxTYEPOgxp+GZ74hZNogwfzEXnXrqqXZ+ZI6EHGZuI5Qx4yAaAvMK3rvMXaQhYcQscyZhjInCwCYhFUWgqBFQYjbL/5nO8uGlm5jlu5NNRCEHxoi2Ey1tC5CnyZj9xeCw8y+kzERRvEv9/PvluiTJEWIsXmHjRQeJMvZsFr6V7O5X7wwNtIu02ytPj81QH4k2y/dunS0tA5V2k2uHBceSFMo4m4lZnYOVmA28qqXuMldG7M9N7rwoiVluwhDPrq4kqCgCioAioAgoAopAdiMgi5VpIWbdN2bxVCWUMaQhxCmk5e23325YoGcBH8/QE044wYbEhTzEownvq5NOOsmSAZCUq1atssQBRCRtsYAPCQDRS9hcvFIhCGgLUpfFd/LxbIWkhbxwxCzfhEUIq0v/kJcVJMxyfmF2HTHrCFTaWLZsmf22IgQCZIUjZiEynMcsHmYQsT4x6whVvMfwKMVmvF0J24kdELSUYQyOmIX4IGQnAnauDOE+X331VVsefLl2+EBet2/f3o6fUKV8SxKP3TDBFrzmGAtevEiQmKUf8qdMmfKPfIhZxoHHMPeNcM1O8FaGmIVUgVBRyS4ElJgt2vuZLDHLpgw2uuD9Dsnar18/O09C0hIa+JFHHrHkJxtBCBNMdAHm10GDBtkIARCveOETEp3Qxu/I97XZbMMcwEYSPGnjiNkoj1mIWeoTAp1v2DL3sYlm1qxZ1l42wUDYks+mH0ITE06diAvM/czrREpAIGbZbANZ64RNMHjM0gdjUFEEihoBJWaz+3/pbB8dxE/zgMZ9p3BDoKyru2e2A5XE+BpLWbcQHDxulLyzAm1BaOFBs0l0n0CeXhYfBPYWU34R5Z5yLO3PvBKz8hCkQa6VNtw86o5TJC04d7jr4SHlCZ2u8j8E1GP2f1iUxrNcGbR7X/xjUROzbGQilDE2fSu6g6iKIqAIKAKKgCKgCGQxArJYWWBiFlIWL1W8KW+99VYbOhLiEm8niFC8YyEg582bZ72xSMNri28f4gkq8FqiES8uvDDdN1IhbO+44w5L1L755pumdevWljAglC+EIG3wfUEW7SERWbjnu4OQgxCzEKQQuyzg47WLPZCZeH85b6uoxVqfmH3yySdt+SAxC3nAtxnxKp04caIlNgn7S798YxZCAWI0ipiFGI0iZmmDcMj0SRhPPN6wnW8mQl5ArNCvC0nKNxjxtIWcgDTFQxfy133XMThOCBs8b2kD0gZJlZj1+wA3Qo7i8YbtKtmFgBKzRXs/kyVm8dBnTsbLlTmEeZIwwkQWYG5k/rj77rvtXMq3syE58XAlXDnzJN/MZt7E0xbilmvaYeMIG0Ly85jltwHyl80whIJH/vzzT2sDG3PYnMI8QYh2NqzgFUsUAbxcCUvfpk0b+41YQtnjTcvcwu8MnrN9+/a10QT4tizzHWQym1gQNvewSYcxEOpdRREoagSUmM3if6RL6dCChIu/sLm0FGGyjYz1QNEtkxxzHCnwSkRb9HVqRF4iyanamkjbmS6TCdshuFG8B9MlhB9178LoJBrFhp2SKF+QojsmWHl7KZfscx1sOjhPsMEgUdlFCm6baOGIcoRH3y4iL7/k26WAu5ephDIG53Q+W0F74+aQBsHCWXzNZgjChycrxQW/dIcyThWPZPEr6vK8WwV5v3Klvnu//WMyxCxzy8Gi6SZP3/dsC0YhkCwVRUARUAQUAUVAEcgmBGSxssDELGQN3yrEAwsvTbyX8LJiIZ0F/FatWtlQwBCMNWrUsP/7QGi6b5+y6I/3KwvxEIYszBPamLDI+++/vyUjIfogESEJIF4hQAm/iQcVIZIJu4l3J+QkXqd47sp9soQCedjEN1ghK/1vEUYt1kIwQijjMQtZwRghEPi+If1AHKxevdqOEeIVD1G+h0j7kKpgATE7bNiwzV66zmOWsVAOosL3huWbitgOGRFmO2QsxCskM95lENwQKHgRuzCjEN0QsxDD9PHss8+GDhHCBC/fnJwcS3BQCGKWMNOMkTb4pqQbq58P5hA+3HPIdMhivN4QvPMgbvBuxmtNJbsQUGK2aO9nssQsYYMhUyEu2ejCvMucRiSDzp07WwITz3YITEIZM39Tjvca71rmAYhVvFSJgsAcDMlLWeZVIgQMGTLEzhnue7BBhJjniWpAWHXs4RnCy57ICYQl5hxPXcrQLwTtsccea/D6J2w99kPiMuctWbLEzsN4zDKP0v+4ceNsHeYyvHmZU/muLN/C5XdA56HgHdHrokBAidls+s9ZxwICQcLFX9iMI2YhnwjP1y1ED5M0Xy6Xi7ByDfMKQbpcJNpB9AXRSaLDRFuIYl9QDpKEzqKjRKeJQoDeL1pBNFFh0RvyDdJtoSjfS2XseEd+IjpclIVUbIuTpyWTemGKx2yZuMoJ5qXD1kTu1xFizz2ib4qCCeo8RfO7h1I0VNJhe7DhoyVhiOhs0R9FHfZ/yPlXojwX14k62+U0KYHIXCvq2r06n9oXSD7Py3xRnh/qUf8d0UGiV4hGyTOSwTOIlgsUqu7l1crLI+0x0cWi9LNcdKhoA1Gf3DhFru8VnS76l+hvop+KkhZF6FaVPGcL75MvwXniQj/TO+c5ayuaK/qh6AZR7EQhriaINhX1bZXLzVJXzpwNjDVH9FlR2sH77DPRjqL0k5/cIAVoCzLW2fBrXhrpvUTDBHKQvMmiq0Wpi4f7B6Jgf75oOqW/NObsCx65r1ECBonMwYmUO1Taoq8Borz/Y0TvFz1TNChbSgLzwROib4i+JzpMtL3oLqKJShUp+KjoFNF1om7sX8n5RNEHRcuI5ifJ4Ifd3UK0YV4nOXJsJ/qUKO/OJ6K8XzeLHisaJuTRpvsNceNwR/BxfVI2StKBR37j20Y6ry36gugMUd6nXqLOvrBj3D09JqZuK8kLE979GqIPiHLvebeYxyExB4q6TUsO16BN50kZX3LlwmHtH2f6hQLn/K5fL/qW6JeizC2uLnMN9723KM8/9qYqjMG1C9ZbptqQ1lMEFAFFQBFQBBSB4o+ALFAWmJhlkRPSEa9WwgmziA+hyuI6Xq4u5CQL5oS9hEwkxCSL/pCbeF6xyE64XoSFe/Lw3DrmmGMsmcCCPR5QeD+RD0Fxyy232NDCkAV4gNHX8uXLzcaNG234ZMheCAm+y0oZiADIAYjf/IRFVAhGQhI7Ipc+IU8hCWbMmGG9XUeMGGHJBdqHiIaQhpStWbOmtQMPMMgH2gIjSNXatWtbIgHCgm/d4rVGGcgGyFE82CB6+casbzseYtj+119/2TDKEBWQqPQNpj169LDetfTBt3ohOJw3bNh4IZ+5XxAqtEnbjz76qO0TUvntt9+2Y+UblYjLBwNIdghywi2XKVPGkjy0AS58L5J7x31SyS4EuKd4w0PO8dzxzrFZAK9pNgHwvPHsqGQGgWSJWaxgjiHsOKGMmf8gLwkTzDvMBg3IUjZUMM+5fMKXs0mE+839ZK4iOgHeqWxKgUhlbmNuZ8MK4Y9dneDIiWjA3M18Rj+IaxNSd86cOXYuxcZhspEFG4hSQGQAfjOIogD5Sj5zDMrvAITtp59+au18R0Is8/uBBy/zLJETIHqZn3UeCt4RvS4KBJSYLf7/D6uFySGwmxR3C4fB49KYpnJi6l0ZqJcbUZaF0+NFWSAO9u2u10ueT1ix+P9HTPmRkreTaJSwyNpE9DtR10fccZaUO000SnxvvLB2IIIGi54tmuwCbzptzZH+w+wjjfuFQt4Fy+wnaUiuaDCPa+5hmKTTdtc+pMKTon+JhtkSTLvbVUzyyPPh2vpFzuOeJxbvXdm444SIdvxnua6U8aWHXLg275Lz+71rl+4fu+dVvliOP8eUXSZ5e+WV9Q9dvDoL/Qw5D84TFwbyuawjulLUtynqHLLqMNGg9JEEVwfCbbl37dI5QgjlJzwrfp3g+fSQBrj3a/Kp91/Jx7YdRNMhtBW0zV03iOkgJ6Ye77OTHDlx7QWPV0leR9HfY8rkSp4jlA6Xc975YDvu+mvJ8/uWy3/JIZLysqirE3fErp6i24pGSTL45UojYf0xphaivO9h+aQxlzcTDcr3khBVJ5hO2aCkE49caTzYJ9eMj+cV0j2Y/7ikMScE0931rZIXJc9LhisXPA4JqcTvCXNhsKx/zT3vILohotw9ku5Lrlz49d05Yw4T5q7PRV25uCPPw25hjSSYdrCUY75wfeT3biTYrBZTBBQBRUARUAQUgeKIgCxQpoWYZaGTBX7CEPPtVbyw8KhkcZ/Ff4QFchbaV65caSAFSUc5h6DdtGmTLefKQrCy6P/xxx8bwhezsMmivMuHGIQYgDjEk8r15YhZPGQhjvDuZJEfeyA+ExHsguDELkfk0jeEMe2x+I8Qini5kMG0zxGbqMOY/PFCgiD+eMGCMrRP6E3KcM04uKatKNuxBXIDggJ8wMF5htEHbWEHfUQJtg8TIgRPM0gP+gYfxkgbEDr+WIP5XIMRIZ2pw/2DEIGY5tuTKtmHAPdcidmiu6+pELNY695n3lHmG+e56kZCPnOayyfcsC8ukgHzCXnUpzznzEWck0Y7QSGfUOtERHCbXHiOaNO1Rx3KMScxB3HkGqEP2nfXpHHu+qct+qYObfL7cuONN5rmzZvbEPuUV1EEihoBJWaL43/AalNBEGDR0S0aBo9LYxrOiakXXHzMjSnLYnew3+D1RikDsXlnAmWpiydOmGwliXhiBdvP7/ovqdMqrEFJq5xEe6ulbF/Rg0Tzk3TbmiMdRo3zMcmLImZYSEdyRcPqhy1+p9t2+kceFg2zISztJym7L5VSkFFSx7U5OqZ+U68c5VmAZ8F/juifoq4Nd+wnaUFJlJh1bbgjz6Rfl/T/iD4k6hPs2BFmyyOSHpSCELOQwc42d/xB0vAiRrkfLt0dX5S0oPjErCsXPLKRIxEZJIV8UsS1Qxr6fqCR+nLtyrgjmH4uikefS3NH7nMcYSjZCUkyxKLfYI5cOFuCR38OjivH+IJ1w66fk3KHin6TQHnu+4GiYXKZJIY9C2F9+mkLpN7uYQ1KWjL45Up5v91UznlGfSkIMZtuPHLFsLAx8Q5Ojsh7VNIbRuTR1irRbUSDcpQkMA+F9cfvCc+LL+fKRX6bHsLaCqYVhJg9UmzYIBpsM+r6Pn8AKZ5/4PXHnKSiCCgCioAioAgoAlmKgCxWpo2YdQufLJSzcM4xHUJbYQv+tB3WF8Qs3rQujDLlfHsgLdu2bWu9WmvVqmWCSmhgPLSSEb/9ZOolUpaxR2EZNv5E2qQMdSF6Lr74YtO+fftIjPNrj3ZQPNvw1AV7CBKV7EOA+6zEbNHd11SJ2aKz+O+eIUuZF/gO+HLZwJIpYR7GM5h5iDDHXKsoAsUBASVms/Sf6FI8rKImZqMWRIPpiRII1GOx+NiQe3qbpAXbTfQaz5mckDb3kDSIiETboRyE2nDRMK9FSbaSbltzpNVkbHRlUyFm0207gFyRpP3BxXvaSES2kELrRN34m8dUguxw5T6U86O9srxXtUV9IutbuQ4SHD65Wterz2kPUde+O86WtBqiu4rSFhsGwojXryS9iuh2ojuK1hLlGXbtUCdoS0GI2Te8tiGGGwXaB9f6oj4xw3t6mKgvfeTC2eiOKyVtgOhg0RWiN4omI7dLYdfWooiKB0n6eq8c+HQQ3VkUwWO0vOjnoq4tjl1FCyr9pQG/Tf+8QUzjOTH1EiVm/b7yO09mDn4mxG7eidUxNufXP/c/TJLBL1cayK+f/PJ5Z3NEnaRKzGYCj1wxKj/7g/kQs1uJ8m4E89x1Y8kLyhOS4PKDR0IS+8IctEI0WC6V6+DcnhvR7kxJD8oMSUi0TzZjxP1GB9uOuu7u9Rk1/0TV1XRFQBFQBBQBRUARKEEIyIJl2onZol4ExSOMUJhVq1Y1gwcP/pc5eLMSWhNCknCXQeU7uIRnLQ0CgTpx4kQbznjFihUpDxnvWsLYEq4U72aV7ERAidmiva8llZhlcwnzwtSpU21UhUyhiHct4Y0JaewiCGSqL21XEUgGASVmS9A/xmpqQgiwOBy1ULk0poWcmHo+KUATuTFlo/ouaHoTOvbkaDn3iSm//a8lj4VkyCu8bSGC/Hx3Pk7SwwQCzpVJ5rhM6h0f0mAmbM1J0cb98uzLjagfXPzOhO2Y8FBE/5AkkEB3i74oyj3eKLqHaCpyslTy7+GlEY3sLen/9cqeFlGupVeGdgkd6wv2u/7q+hly3sPLo8xa0UNEgzJCElwbHDeJHhgsJNfNRP1yeLz50kUuXP5CP0POg/PEhV4+z7CPBWRxlEAquz44cu1LkJj9RjL39AukcJ4IMfu6tOvbVTmiHwhxPGVdWZ63HNGCSH+p7NoLHosTMRu0Le76yxBAnowYJ8/Om6IQ7jeIThCFtA+2DzF8hmhQksEvVyoH203l2icHUyVmM4FHKuN7NA9Q5p8oLJgPtsgrx+EAUd8z36/3s+S53w3KIsH32i+f7LmPPW3nioa1EfxtYh4JK0fafNG+og+LzhMl7U7RdEhDacTvF+xUFAFFQBFQBBQBRSALEZCFxawjZiGPIGfxjA0LXQxJwCIpYZfDFOI2LgRwMouxJaEsIUEJewy5mqqAKUQI2KmXWqooFv96SswW7T0qqcQsqDEvsBGEuSJTwvPpQi1nqg9tVxFIBQElZrPwH+hSPqQg4eIvIBYGMQuJ1FYUAqyfqN9/2PkSKdNJFE+1H2LKs8jqy7NyEdYeBMAJfkE530fU957z65UPlHWX3eTkT1G/bCLn30odyB5fMmFrjnSQiD0bpBykiivrFthzvTSXxzG4+J0J26Wb//sgov/nyfSE5/lU7zrZ0+ulgj++4LPh2oMg9cu1dxmB49ZyDZHAM44H676iviRDzNb0K3rnDeTctyWKUOBe+uUu8drgNFVitrrU/VCUZ5nnZwfRKMED1bfhukDBIIFTP5CfymV+xOyO0qhPLI/Jp5OLJd8fQ498yueX3T/Qnt829zZKciTDL+uf+5tj4spRB29nykPMLRX12wmeg9MQ0ZtEx8eUpRy4OjlaToJtueuRrpB37B1RfpJXxp0mg19uRLvOFu49Y+slygYPlx48+vPOYikX9XtBvb/y8ilDWSRTeORK20Fbg9fY84XoT3llHTEL8To3Ly1Yh+urRJ3cLydhZUi71xXKOzJX+vNcWL1PpcwLojNEw/L9tFSJWeY7vx3/nPvhy+lysYufUIDzC6Wu3xe/AyqKgCKgCCgCioAikIUIyAJj1hGzqSyaah1FQBHIHwElZvPHKJMlSjIxm0lctG1FoLgjoMRsFv4DXcqHtJuM31809M9ZpI+SHMnwy/rnPilA/dyYsk0p4MnLcu635Z+vkrw9vLJVY8qO9spxuiCi7DpJbx2isyPKt5H0KCkjGRAHvs2JnENC+JIJW3OkgzhbPpf8sz0jIBAvF906Ly1XjmH1Z+blu0MmbKdtiISw/iHDIVNaiR4rWlCB9Pf7iVucZ5OAXxY7aogGiXZJihSfsIAY86WHXLj28RaMsiW48H+p34h3zr107XGs5uVx2kXU5S8M5AXnCfoMk23CEvPS6J9n7HdR1w9EuC9BYvZIPzPF8/yIWYh8Zw/Hcgn087FXZ0QC5eOK9Pfa8u3gvEFMxZyYeld69eLKrZByW3llT5HzoA3+dQevLEQe77+f75+X9crWiSnXT/JaB9S/Z36b33ttutNk8MuVSn57/nlP12De8byYsu8HynKJbX577rx7SNlM4ZEbYQO2QJY/IOoIc95HPJD9+8Sc4OwOHqdJHsJv8I+iwXyuIbP3FPWFeS2sLGm/iNbzC8v5ZaJRWFInVWK2odSNsmOZ5EEo897EbSyR7KSFOczvNzjnJd2gVlAEFAFFQBFQBBSB4omALGYWGjELqYPXFMeSIOmwlzY2bdpk1q9fb4fMdSY9xtKNq28r54neu2TKhtlMfZ4Vjr66NOoUdywTtY8x4dnNt5GLuzAm/cZs0d2lTBCzn332mRkxYoR54403zHfffWeWLl1qRo4caSZMmGDWrVuXscHOnDnTvPDCC7Y/3nGiHKQrUoFri2gAkyZNMi+99JLZsGFDxsaiDSsC+SGgxGzx/B9YrUodgSDh4i8gZpqY/UvM3j5gemO59m3wz/Fi8mVLufhO1C/jzsd5BbeV81S8WV1b/nGw127U6eGS0VV0kahfN+rcJxsyZWtOjC0QZYeKxkmuZIbZDzHjJFO2036ihPdCKXuLKM9GKvKgVHLj3JBPAzyPrqx/5FmbIgrJe5JonCRKzK6JaeQcyfP7PyamLASvKwsJ40s6iFnaA3s80PCwu1X0cdF3RcPInBsk3RefmAUbnzT0yyVz7pN8vJNBuVYSHCYcdwoWCLl+2qszKyQ/maT+Xlu+HZxnmpjtHWLoigh7fpb0nQPl20aUxXYIbydR70pwvIlc7+sazTsmg1+u1Anr4zdJD/4W0fyciPL+vEc5JIpMDCNmM4VHrtgRNj7SHhNNRBhbVBvnSl63mPw7QjrgGYtqj7k6TCBro+rcE6iQG1E2eI/KR5QL9sNzPlr0ONF0yHbSCKS466djOhrVNhQBRUARUAQUAUWg+CEgi4mFQsxCPD344IP2u6/Dhw/Pbw0zofw//vjDzJ0717z55psJlU+mEGF577nnHnPVVVdZ8iCZun5ZQgQ3adLEjB492hJvd9xxh7n99tsNtucnlOF7idOmTcuvaNrz6fv9998311xzjVm0aJFZvny5qV+/viVr4jqDuFuwYIG56aab7Jjjykbl0Vfbtm3NRRddZCpUqPAP5bvA3BeI7qeeesrccsstNix1VFuJpEPeYPMHH3yQSPGEyhDSecCAAYb7/csvv8TWgYyCOGrZsqX55ptvYssWdaYSs0V7BzJBzDIfn3DCCebaa681c+bMMc8884wpU6aMqVOnjoE8zYTwPWve3Y4dO5qvv/7avP7666Zz586W9C9of2wc6NSpk/3mNYTv2LFj7dz14osvlqhNMQXFQesXLwSUmC1+//+qRQVDYDep7hYMg8dME7OrQ0y/JMae2iHl50aUH+eVhRwLji3V67e9dhM5PVMKscibX3975TWWKVtzYmwYltd33CE3or6/+J0p27GrTUT/UbhCjDrPMOonKsOloGuT8JpxsoVkQhK48lHHxVLmpoiGIB9dvbqBMj28vCWBPP8ySMzu52cGztkM4fqrFsjr4uVBcPsSnCcu9DPzzvEMGyQaRsC6PoPHOGL2s5A+UknKj5j1SbJ1CXbQU8q5sRAStiCSDLHo95MjF86G4PFKr2BcuWZeOXf6gZwE2+N6vivgHWtElKX8qV65sTHlwvqKS7vAa5fTZPDLlfJhbfOOhslrkhhW3p/3XL1kiNlM4ZEbYS9jOMwZms/xspg23pI83pEwTEjfRTQor0pCWHk2iQSJfr9uFJ6pErP8HqyMsCXMvl+kbB3foAKcs7HG9fFAAdrRqoqAIqAIKAKKgCJQjBGQpctCIWZZlLz++uvNnnvuacmqgi6ZQnZBKJx88snm1ltvLWhz/6qP5xjExF577WXuvffef+UnkoCn7AMPPGDOO+88S7hB1l1++eWmYsWKBjIwP4GwqFSpkrnzzjvzK5r2fOyDyDjkkEPMhx9+aObNm2cOPfRQ88QTT8T2BXEHoXvGGWdYIj62cEQmZHv58uXNMcccY5o1a/YPbd26tRk2bJglczp06LAZ24imEkpeuHChufrqq03Xrl0TKp9IIYin5s2bmyuuuMJ6TMfVAbPZs2ebWrVqmd69eyfslRzXZqbylJjNFLKJtZsJYnbo0KH23a5WrZqZNWuWmT59uunWrZsZNGiQob90C2QpfVavXt1u3uCbsGzaufLKK613a0H7o+1LLrnEev3SFvNou3bt7DyCN7CKIlAUCCgxW4z/EVbTUkIgSLi4xUOOmSZmZ4dYfIak+Tb458EFeaqzUOyXcec+MXtxRBlXNpnjCmkrFblNKsX1c1Zeo5myNSem/3Z5fccdciPq+wRFpmzHLjwnx0TYEIXrfVRMUkZKedfetATrVpVy40V/9+q6Nvzji5K/k6gviRKzcSRxcSBmq8ugfNLXH7c7Xy1lnhB11xzjiNnpkp8OyY+YvVs6cTZ9l2CH3bw6EDgFkWSIRb+fHLlwdgePV3oF48pV88q50ygyMmxTyvlSKdi3u/aJWd4ll17QY2NnaN4xGfxypU5Y//485jfvb9Tw64WVjyISu/sN5p1nCo9cad+3050TYjgZmSqFXd1Ej3jHh8l7khjVBptbooTNIWH1UiVm6aey6LqIdsP6ArcDRAsqn0sDrv2HCtqY1lcEFAFFQBFQBBSB4omALFCmlZglLCukFB6ILIg7YVESz9FddtnlHwQY5fGQpHyYtyD5K1euNPPnz7ckAdeQQ7QN0bvTTjvZBXeIVBb4Cb3JOV6IhOikHEQA8uuvv5rPP//ceoCGheik7S+++MIsXrzYjgFv0V133dX06dPHDcP8/vvvtl28SBlTlGAjZU499VRz//33W5sdMXvBBRdYYtbZy5GxM0aHGTZDkhx55JHWswwPUbxYGRthOpcsWfKPstTHu5Y+gyFxaYu+HYa+zdhJiE/wBxtHGDtidv/997fELG0fdNBBlpilPfDDC9oJ2JJGvalTp5rTTjvNktJ+PqQIBO9XX3212WOYsWMvyn3m2cFL7/zzzzcNGjSwGIOzU3CA8AYnPI/POeeczc8NdlGOsSxbtmzzWJwNLh8iln64l/Q/ZcoUSwTfeOON1hOXsdAPNtHOmjVr7DPE8wH+fn3XNkfuz6pVq+w9gFxu0aKFJeIdTvRFW9gH4UV7TugPD9tzzz3XPn8uvbgdlZgt2juSCjHLfEE4XzxU69ata2644QYzTDY38KwiEJmHHXaYccQs7x+bQR5//HH7rlKGd3fgwIHmuuuuM40aNTIPP/ywTSOPZ4Ln/a677jL16tUzbJ545ZVXDFEHwoR3DxvQ5TL3s5GDDStsAqFtNoIwj7z77rvWc75GjRrmtttusx7tvEP0BZE7ZswYOwfx3k2ePNnOs9jMvM1cBfFLNAXec0fWPvvss8V640MYXpqWHQjw20TEh6222so+m08++aTdPPDcc8/ZUOL81vi/CYUw6nR/Dqt4/pOrVmUMgd2kZbdgGDxmmpgNW9xmMT9oh7suH4LCpIjyPjF7cEQZ2oUsI7RgotpcygYFD8WXRY8JZnjXR8m5G0fY8bS8spmyNSem/5p5fccdciPq+/cwU7b7dlWSiw9E8bYKw9FP2+BXTPB8gNfuogTruGK8S2A5WPQLUd8Wd36npPtS2MSsj1s13xA57yLq7IQU8SU4T/ges2dKwV9FXV2OeKY9Iwopeono/qIIRIxvQ1Ob+r8/fijjwiJmW0v3zva/5Hzr/5kTefaEV2dFZKnEMvp7bTk73LFBTBM5MfWu9OolWs5VYe50/ftH5tqglJcEv4x/zlzuZIic+HnufL2kJzr3unInuEbzjsnglyt1XN/+0Z/H/OaTKZ8MMZspPKLsjdvY4Y/XnfN++/jkd86mi7BQ0LT3WExb/jNCWSf7yAnvYli/97hCecfciHJR95S5jE07Ufcr2Gc7KVtQ8ftinlVRBBQBRUARUAQUgSxEQBb/0kLMsjgPOUgI2qOOOsoceOCBlpzr27evJURZlHTELN5YlIeIgwyDfGQh/fTTTzf9+/e3i/LkQ2KxIH/ccceZAw44wBx77LGmVatWlkDEmwvSdIsttjB77723qV27tiW88LrC25JFeTw8IRsgHt9+++3Ni//0BaFHG46IpK82bdpY2yEIIAZPOeWUzcQsC6YQAhAbBx98sNUKEmb3+eefD10/hRh+5JFHTE5OjrWLQo6YpR5kAkQpHrFNmzY1J510km0TT9Onn37aEoCMabvttrOERfv27c3HH39sqFuzZk2bBpnBYm7Pnj1t6FFwpD8WfyE1EEhMCD88iw8//HBTtmxZg6cpmEBAjho1yuJFvaOPPtoSKxCHYcQs42YxGazA9aGHHrJ9UHb8+PEWc8IBf/TRR/Ze4i0MKUJoUe4H9++II46wthJq9Ntvv7UkNwTNpZdeao4//njrTfzoo4+aypUrW+LGdhDyJ0jMQqISgpX7ylhoi3YhURHGm5uba84++2yLw4knnmifVUhkR/Bz3/GsYyyXXXaZ9eDjGcKbFrKe56VcuXL2eSX0K88iBDkCJni7ks7zf+aZZ1olFDbELGPt16+fvQ/YR/8Qy44cgzTmnhXE09gakuE/vJf6jdkMgxzTfLLELPMWxA8bRJj38EJnbiYSAHMr75FPzOK5TXlCGTP/MOcQ3pgw5rvvvrudD5l3OWc+Z67gHapatarN22233cwee+xh+4G8hRQOCmGL8Wi97777Ns8PzGXML2zG4Pni27O8q8zDvMdsWCC0Od+9Zc5jziR/3Lhxtjye6dTFdkIyM1bePb6bC5mLFzBzFu8cc4GKIlDYCCgxm4X/QJfyIQUJF39RcmkMNjmS55f1z31SgCZyI8qGLZyyUOu35Z9DAgQFssAv484hF3zBE87l+ccgMeTXSfT84by2f5Fje9EtQyrenVfG79ud801SvkPnJBO25kjjrr/gMXi/nB3+MTeifvAeZsJ23w53vqec1BAF+x9Fg2Ny1wdKXjIC+ePqrkumYkjZYyXtAVGfiAySjX9IvuuvbqCNHl5eHLGSjMesb0u1QH9dvP6SIWaf8Or9V857ikLAhgn3zY2XY4tAoaIgZiGOfZsOD9gUdjnJqxN8B8LKx6UlQyz67eTIhW+3f+6/04mWc20zd/ptuXPGHJTykuDyg0fmcic3yUkwn+tVrkABjsnglyv9hNkRdQ+TKe+Tb34fYR6zmcIjGXsFilh5S3L9ccSdt4pp6caYdt6XvG0Cdfn9HBVTp6DErOuOfnhG24p+IBo1Pua3gshWUpl50bXfpCCNaV1FQBFQBBQBRUARKL4IyOJkWohZvKMIBcvCPAQgnlMQXSjhb4PELEQaxACL/BCCkF8spkOEEaKY9vg+Jx62eG3iNQXhxaI/hADfF4Vg2HLLLW0feCdCKkD8bb311taTFnIB4hfPU8jdnXfe2RJihBLeYYcdbD3INsgDPLDwvqV9iE8IuG222cbaRyhjSFUW+mkDb1DKQE5AdIR9AxbvUcYEmQA5ifjELGkzZsywbTAu7ICQPeussyyJAgED6QB+EA54sb3zzjvWRghYvrNKnSFDhlhCA+whNCBZICUhkCFM8SqD1IbcgNSg3n777We9WWkT+yFNJ06caHGHHIeQ4f7wXVzfY9YRsxDqkKx8ExKB4B05cqQlJfHMc8QsmOK1zL0DM8gW+uGeYCOkMJ64ECzcd9LxeHvttdcsIcM9gNx1iucxzxJkkCNmIeGxh3YhnXnuIH4gdyFpGTfeqIwd0pTw1Hjz0RbPBH1iO0QpmEHg8L1XQm7z/PTq1cv2Sb/cJ569V1991Ya3pj2uua88s9wX2uOblnxjl3eB5wRSlnywxpsQW9mwAPHPJgX3fEDyYgM25vddWgt8EfxRYrYIQPe6TJaYhYRkI8aOO+5oiUnmFOYInk/mDjbTBIlZ9yxDtvIus7Fjn332se8pZamH1z8KEermfeYNPFd575nTIUvxfvWFDQjMWxVkgwnvHcKzzqYGNpTw7uFV37BhQzsv4U2LlzrtVqlSxW6e4X3HE5ZNLcyv1IPAffnll+1cTvvM8djp3iM2QEDm4k3LJgsVRaCwEVBitvj+H6yWpYbAblLNLRgGj9lEzE6OGOccSQ8SqSwUs/g7NETrS5ovR8jFH6I+dp/J9ZOi14tWF8ULMy7M7TzJ9yUTtuZIB76N/rlP4vh2+Oe5EfWDhEYmbMeOC0TxwoTIC3o0dpA0fzz++b6Sl4w0lMKu/l9yHnw2XFs8B5NEsQlyIU4gZ12bEKM7eYX9Z6ekErNLvPFBcMTJVZLpsOAIaeNLURCzELG+TT19g0LOKe/fN971gkgyxKLfT45c+Hb75/47nWg513YmiNnzYmyt5Tr2jpQPm39J29krx2ky+OVKeR8ndx6cx2gXyRV1ZfxjWPlkiNlM4ZGMvTK0WCkvuf6Yo86/kHJBctVv+Px82pkt+cwLR4teKjpFNKov0gtCzE6W+i+KHivqC/b/IBrW72C/YArneP/67fINXxVFQBFQBBQBRUARyEIEZHGywMQsZA1hcCG6IDYh11g8h/zadttt7QI95BWeiRCtEK54PUGiQoDhyUh4Wxbn8RCFzIKkghiD/MQzEwIBwg5vKEJqstAO6Qbh0LJlS7t4jw14I0KeErZzuYTJhBjs0aOHbZfFe9qlbzynCCkIeUcai/zU69ixoyXSICSwD3KNUMaQr/RF+4S+JbQnpJyrE1zkZfyQkRDGToLELCQgnmt4w+Jhy8ItY4VwxjuXkJ8QmJB3kJ8Qs+AH0YIXJuQpxAekIeQF3rF4uEFUMC4IQTCHdMQeCEA8O/FKJqQnpCMkLR6i4EsYZ0hoyGDahehIlJgdMWKEvf9BYpYxQ8BARjJGCFXshdiF8GGceMPh6UxZykCwQ1CDPeOHAOWI4kkLeeOIWb7fi1cs5BDPCwQsYyGkM7hxD7CJsUKEgjk48PwQzhUSCswgeNgoQP+MG6KY+uBM+4RYxU6eJwhv8CSfNiGweQZ5vnjmyIf05pmCTIIIgmjCi5b+sY8xQhTRJnWQ1atX22cBspnz4ihKzBbtXUmWmOVZZB4mqgAbT9gcAOGKtyobHPCoDRKzeJ6zyQAPUwhRoiBsv/32dk5lTmGeYa5FP/nkEzsnM2/zvBMCme9CszmH3wNHvjrUsId5nveYdxUhegD24DWPhyvRDSB98fLnO830z1zP+41NvDsQrswjzAnM08ybzjsXQhZb/L7ZnME8y9h5B1UUgcJGQInZLPwHupQPqbQQs3FeO2/LM3CB6EGiF4lGfWORxVWIVl+elQt/0TWV8yF+g3KeCVtzYuz0SZyAKZsvcyPqBwmKTNiOEe95/S+V84dEbxC9RRRyNAx3CINkpZJU8Ns6IaKB9l45yFbIuii5QzJcmxD0W3kFfYKvpBKzP3vjG+GNLXh6oCRw7xwWHNsGCmWCmKUP1+eKQH9cQr4vEnVl8MDeWzRKciXDleUImVQQ6S+V/fb88wYxDefE1PPf6UTLua4yQczyzYUVov7Y3PkmSec9PkYUwqyl6E+iLt8/zpb0oCSDX65U9ttz58F5zPWRTPlkiNlM4ZGMvW6MccfXJdNhFHVsGNeA5EF6zk+gnaj2g+mpErP8tru2/pLzsaKdRXnH4n7H8xufVI+VipLr+uVYNra0ZioCioAioAgoAopAiUVAFicLTMyyuA8Rhfcr3qos/kOIsbhPqGFIOMgnFtchFrt3727JMLxWKc8CPuUhPfGAZQEejyhIW4hBSFKERX0WNyHL8ATD+xCygfCzEEaQpbSBdxdejQhEAp5cjkDFY4vQlpCckAl4geJhSZ/Ug/xFIAnx+HLELCGL5SZbT1sIA0KD4okK8YzHKf37gi2M33mVujYJ2Ym3GOQgBAFtQGQjkK+QIowZwgPyEOIBItvZjI0Q2U7Agfp4peJhCpaQvZCEkDgQIIzD2ccRAoO2IRvxCsbTFIKRcMrYvO+++5oXX3zRYogtkLR4tsZ5zIYRs3i6QrrgsQwpcuGFF1qy5TD5niXEJ8QsYVLBwyewIWshXGvVqrWZAHJEEGQ13tQ+MUv7fPsS4hwS142FPsAD71mIU8hX921hngPIHUcEkwdZj70QszyzDmcILEIb421HPuK8hCkHqQT+5HOfEDYa8HxCzHIvGSPvB88O9oE5BDzXjijiWYXY4l2AWC6OwvOjoYyL7s4kS8xiKc8iYbQJk838y7zHBho88SE544hZiFJCdlOHzQ/MvQhzMcq7gYc3m1yIeMCzzdyI5z2bLfDQ9YV3jtDrzEtsmEB4p/Agd8QsmzjYsOA84AmjjK1sboBkZm5FmKOYt5nzCbHO7xDiiFnmJCdsYuH95j0m9LKKIlDYCCgxW2L/TVbDIxDYjX+KIxQSJUpyJCOqnk8KUD83omzYYvipEWXpCw+eoEyShDA7IBd82UIuJouGlU00bYHUpx0nJ8kJpFyi9cPKrZP6B7gG846ZsDVH2g7rn7Tg/coz4x+HXLkKqx+8h5mw/ZKIvsPs8dOe+8cIErvYUYr96vXXPKLaoZL+X6/cLDkP3keqsgi/XtTZNZVET7KBmP1IxuPG94OcH+2Nz53iNbZQ1JVzx+6uQN4xE8RsM69f3tezA31yebGos4kj7/rxor5sKxcDRf1yS+R6G79QCufJEIt+8zly4dvin/vvdKLlXNvMnX5b7py5NijMyS4/eGQu96WCXBR0vgxuXqD9ZPDLlfJBO7kOzmOSZCVX/iZaPhlilsYzgUcy9mJDfnK6FAgbv0vjnWZjQ35yrhTw50tXP5VjqsTsB2JDKv2FzWf5jdfP7+b1u1bOE8HLr6/nioAioAgoAoqAIlBCEJDFyQITs5BckE8s9kOU4qWJNyGkJN8SxZsJD0DnMQsxCyEHSQBhileUK08dPGLxUIVgxKMTD1UEEpIwtBBxEKeE46Q/SD8IIzxhWaSnjgsvjCcp4W0dgUs7EAF4gkGqUp5zCDK8yghDi9A+5IEjZvHShGSGqMRjFXvxruVIOE9HfNrK8gdbIAUhA5zQZpCYhZwbJp6bSBQxC16OmKV/QgAjeK5BuECYEjaZb9oSLhivMjxOIV8hZSEjnNAOXp+QExDlkCl4jdImCtmBpxllCIMcRcxCvjvSGbKFNhhv0GOWsKSMGa9dykM847EL6ULIU54DSFG84pyQhscczwu4BpVyPjELkYv3KSQ2WDIOvFgh07mfEMuQpJCjPA8IzwAkF4QzxCg2cF9Jh5iF8AFLBPKJMRBmmHyEe8V9h5hlPJDIEFQuHzKMbxZDzDIe7gFezODrsKZ9+uK5QDhCwkOgQUQXR+FeKDFbdHcmWWKWTQxvvfWWnSd43tiQwrzFvMcmBtJ4Z9gs4bxRnccsodu518zhELMQpHh/MzfQBs88YYMbyQYPPGohcPluNn3wbkP4Mg/6wvwDuUqoYUfa8s7gqc875uYP3hfmJ6IZsJmCcfPeYC8bGOjnWomewLyNMs/xfWsEkpa52/eYpQ3eRzbR8D6qKAKFjYASsyXkn2I1M2EESgsxCyAHi24QTWVx9nepB3njy1NykUpbfp2gB65rP9225sTY6pM4rv/gMTeifhihkW7b/7+9u4+x5iroOG5bJdZCFQXFkthKUyURMWJLRSFKn1JTYhDUQJ9QExBoISAGSAT5wxJeEjRRQiBYSjStBRooCX9ARGPCS0IgGjSxifEPLaD/iFIhRkCDT7ie78rU4fbu9tm7u73bvZ+TnL0vM3Nm5jMvezO/e+b+5S7znjsuPy8kuXTUdcq8l9if7NHA7WPYfL5fG69vG7UL8TeNeueoZ0adj/P08XpeTkIw27rO17Fw9ndHfd6orx/146P+96jzcabnt4z35+Uogtlrxwym+U2PLePyLcTrQTcN7/GroxZSvmHUblf8D6POh3dOeMKoBy37CRbn87pkvJgvz/z5/Jg+2/GmtlvneVvT84MGs7U/v6331O7ZPv7FmH5VqLUfv1tHG6vmt+o8Nkb9tv2Mv99gtvYP22M/y9v8z6YsHxdzv189mwa+Oc7N43E+7brP1wlmO++uM79+huCg5c9GA9O8+8ygECBAgAABAidUYFycPHAw2wXOejLWU6pegfX663U9Iq+55prFm970pp2erlMwW/hUcFUwMAWfnxs9authWa+pLtw3fb2uClTr7diF+N4vdC1E61ayhXn1uu0CfbfhrZdht9CcwsSWq/Cy3lj16Cps6/aZ3bq2EK3eol3Ab1kKJQoXCnFrp9CtoLnlKzQoDG7d6uFaQFBw1oX+bo3ZbYGXS8Fh4WihxVTWCWYLO1/72tfuBLMf//jHd9at+VcKKAow6mFZ8NC61psst+bde/UmzqSAo9JtdAsx6oFWoDj1Lq73W7f7rJdoPd1a38LoyXLeY7bAN8tp3WqzULNgtJ7O9XAuXGx7dpvkQp/2iXqttcw333zzznv3F8w+73nPu08oOwXg82C2oKjfzi0krfdz69IyNZ/CpELo5tXyFVJV2l8KftoPGl5v3gL+QqICo3kw2/7RlwdyzqXS/tfvybbPF+w3be71QKw0Xr3+2j/aJvUInqZv+dq/u71yx0RtVXosQC6Uzuk4FsHsZrfKfoPZzjndoru7D/TFhL7k0XHVcV2v9I7Xfq+73uWdA9tv+5JFtwfuSwX9DnY9Tzu2Oh92vuxW6Z03ujV3X95429vetvMFj36DuS9I1E7n6b4sMh0Pc7Xab1kKUNufCms7PjuWus1x07TMfXmj28i3TM2j46v3O5f1P6Tjqd7wnav7neqC4Y7VgubG7Zjs2KsnbdP0f6JjvnEUAg+0gGD2hH6I3uLV2qZgts1cD6C/GnW6UHo2j/WgXPW7cBeM91896hf32V7z7HapvznqXuUwl/WSMaPd1nUe4uy2PLfuMv1ugcZhLXs9U3cLPHZbn8LOp466bulWqlPbn92jke8cw842NG57//KKtk5CMFuP0c+MOpnt9fjnY7zC62mcfxnPzxt1KkcRzHaO++dRp3lOj/Xgq4f0VL5nPCmIn4bv9fivY7yrpwkP+LifYHE+q0vGi92WcX5Mn+14U9tHGcw+ZMzkplHnvdJ3W4f5+x8Z03S8rSr78bt1NDBvd3q+23lsP+Pvdp76nVUL/c33DttjP8u7x2J9y6AfH69W9XT+6/F+d0g42/LtY8S+5LD8ZZVpG8wf+9LE/PX8+TrB7DoBeIHq/Nw0Xu67NH1fApmWf7cvYu27YRMQIECAAAECx09gXJw8lGC22+PWM7Zws56uXVTvQn63xX3HO96xE8wWkk23FC5ELGit12o9VetR2e/JFhjU06pbzDZdt6ctnK1nZyFp4WgX67ulZoFvvVgbXghWz9p6jxYK1EuyUm/egtbCrsLZlqdwtXa7bW/BYUFZPSxru6C3Ngoe6iXWewVo9bTstxO71XLL2Po1rDCiwHS5dBG2EKDwonWp9PuuhcqFCN3KuGUsMOnWm5V6Yd5222077deLs/Cx3mCFJK1rvcmyrddYpR7EhdM5v+pVr9pxKTSpJ/Jll12200u50LHecfXWrHdvPcZavwKVAs1uf5xDgUdBcwFN4xQM1ruzda33cb3Mmve0LbutaK8LQ9qujVeIXPBdz7WCzQLtQtqeFxT3+uUvf/nOsrXN64k3BZiFnFNpXoU0bat60c1roXLr2jatN3KW9cbOsnk0r4YXELVehcdtu5Yjg261WohcaFSv3wKfevUWOOVUMFRwlF+BT6X9o57arV/bs96CBboXj8C5XsDty91+tXk3jyz7jcv2sULuAu8C48Ktlrf1zqzAvLba7pV65hbkFpy1fxzHIpjd7FbZbzDb+a9wtd/Dns6hnXPajzvP9mWJ9vPOM31Zoy9gdH4p2OxLKoWkHT/dxaBzdPt055dC0W6h3jmoZSoo7TzV+b3zUV90qLf6tG/P1TpW6407HTsN65bJHTMdk7XbeaHzbefXjuOOx46Lzot9YaRxO447R3Z8dX7sf0BfLOmc1++U91vZfWGj/xWdOzuP9L/juB5bcyPPT56AYPb4ff61RAcTeOiYfNVF1y4k/v0eTdcrcrrYuPx4zdJ09TZbHqfXn1oar5ePG3XVuL13RSMslXnvxvl0H1gab/6yXlc3jHrPqPNplp/XI64QqQvTe5UMzzag7aL0O0f9/r0anA07rGXdz/aazf7ep/vZhtNEh7XshWc3jvp3oy5vo/nrQs5bRv3hUQ9SHj0mLrSb2l61303tt2y/NeoXZ+NP0/XYPvano/7oqKtKvWyn8Z+1NMJrZ8MKQXYrLd/URsv9fbuNON6f91ydh3dN0npM7fxtb8zKw8bz+XniybNhPS38rJfsvP2prbZLX4Z49qgFOQWa07AenzjqVF4/nkzDlm/7PI2zzmP+d83anuax6tj+pTFe5775+k7j//t4v150F416WOX3RkNT+8uPz9ljJmd7TJ/teNOsOncuL0evO9cul/m+tzzN45ZHnr1+zHj+oVGXp1l+3XZ4yagPGXW3sh+//Z7H9jP+F8YCLi9/r397twWfvX9YHvtZ3tns7/fpHWOM5XW79n6nWj3Cz4y39wpe3zeGXzDqbp6dI+blbNf5J8ZEfzzqqnPUfN3a55476kFD2dHEzs8ETG3/13jdeikECBAgQIDACRUYlz8PJZgtsOkCfr2v+r3NemMVXnVbzC6MF9x2wb2wsh6fjV+Pzno1FgQ2fmFZIUFBV8O74N70/UZsw7uVbCFmYUK9oArnaq/bYtYjsVDhFa94xU5PxvmtYOuRVejVbxvWI7feWvVMLFAtdKt0y896vhaMFW4WNNTjt3CgUK6Ao55WXeif2ij8K5Cb2thp6Jt/plvdFob0+7uV5tHvkb7hDW/Y6SXWbT67HfN0q+Z6axYqtGz16OwWwd2Os5ChEKLbRTf+9JukLVPhaj3DWqYcCygKdxuv3mK10TIWuhRmFAjm3/I1v5atULRgsh5yrXfTNawws2XJsnWvzY9+9KM79lk3XW3W67PetzfddNPONmj6QuDCyNa5ELh9od699Tytp1zL2W1QW89M285TaV717GtZl2vrkV+9SwuQ2qbtD61nt10tKG5dsiiEyTinhufc/Nu+9Z7LqWnbPzNp/yoorRdh4WnrP5XGKcQvHGr69tX2xalna/tsw/ut29az/bFewt1qOusc+v3iAu2mb16tfz17K+2jhUcF0lNQP837OD0KZje7NfYbzLa0Hcvtp4Wd7WMdBx0XfWFkOs92Lv3cuGtBx0lha7csnl43Tufvxql3+MfGrYo7J0zTd3x1HHVO6NjpOOy4XhXKtjyd16cvN3ROqzRubXbM9UWLwtN6+xYqF9r22Bdber//Gy1L69S8W75Cr5a5oLa2mn9t1TO9deq82/mtthQCmxAQzJ7QD9FWaysFuvD6I6PWi6UQ7I9G/YNRXzHqr4z6A6Pup9TepaM+fdRXjloA220gf3/UgsWnjrrfNsckO+Wwl3Vq94F4PMxlL0hr27xm1FtGfeuoLx+1kPEww7IPjvamC+o3j+f3VwocC4R/cdRfH7V96odG3bbysLHCPzlqtzg9PerjR90rVBuDH9BSSHnlqD82asu6V/muMbDg8ddGfdaoTxh1t16bY5CyhsDDxzQ/O+qLRn3LqIVsN43aMVT433G1TWWbPL53bNi+pNEXQjqf91MBrf9Rl+bb/+K+nFWo3//9143acf6kUc8d9bBK//+n/yN/eFiNaocAAQIECBA4ngLjAuWhBLNd6OwCeRfBC2i7iN4F9F5Pw7q430X5+Xs9b7zGb7rli/nz9ho+9T6tzcLZQoSm7aJn4Vavqz2fly7gF651Mb/xG6fAYl6mZSkQKEhbtbwFcFMbrctuva+yKPwoUC5UrfRe7VYrLX9tzIPdns+Xf1qmaV4Nm89zCkVap5a7dWya+fo1/nybzI3nhtN612al6aZlmcablnVa9mm7ZdW8M82+dWw5WuemmW/jtmHjZlk7PW/6qfRe07fMq+oUzNd+403L23xzmixqfxpW2y1b+0nDl/fNTHpv2h7NY+7c9K3H5Ng+MAVTDatMzjk2n2kdM6gsT986T8OappB5uk33zgTH8E/L6zdmN7dh1glmp6XtWOgYqU773XzY/L3Gnb9uvN6bju/5cTVvYxq+PO00To8Nq2d9X+CpJ27TVGqzY3+atsdeN7zHqfT+8vx7b6rLbdUDvy9M1EO341shsAkBwezx/AxsqQgQIHCYAj81GpsuqP/HeF5IpxAgQIAAgbMR6EtYXx+1/yP/M+rFoyoECBAgQIDACRYYFygPLZjdxMXO4zzPgsN6EPdbut0uVyGwSqCg7GOjF2K3f60H+TyEWjX+Jt8r/BLMbm4LHCSY3dxS33fOfaGi35XtN7C7m8FRlb4sUQ/0etJ3C+X2X4XAJgQEsyf4g7RVI0CAwEzgI+P5FM4+f/a+pwQIECBAYC+BV4+B0/+PbqGsECBAgAABAidcYFygFMwe4VXaekJ2S90PfehDRzgXTT+YBep52y1W+83l496jTzC72T3tpASz7UcFVa1PPduPqvQlh25v3K3Pp565RzUv7RLYS0Awe8I/TFs9AgQIfFOg21pOF9b/cTz/DjIECBAgQOB+BC4cw/9t1P5/1Gv2slEVAgQIECBA4IQLjAuJgtm9rqYecFgBRLe0nd+C+YBNmvyECbSPdCvl9pPjXgSzm91CJyWYTbF9aapHqfpAzOMol1/bJ0NAMHvCP0xbPQIECMwE+l3AKZz9jdn7nhIgQIAAgVUCbxxvTv83blo1gvcIECBAgACBkycwLnkKZk/GdV9rQeDIBQSzR0685wxOUjC754oaSOCECQhmT97nZ2tEgACB3QQeOgZ8dtQusn9x1HpCKQQIECBAYJXARePNr47a/4y/GdWdFgaCQoAAAQIEtkFgXPsUzJ6wC8BWh8BRCQhmj0r27NoVzJ6dk7EIHDcBwew2fKK2jgQIEPh/gZ8fT78xahfaXzmqQoAAAQIEVgm8ebzZ/4rC2cevGsF7BAgQIECAwMkUGBcvBbPH7Qqu5SFwTAUEs5vdMILZzfqbO4F1BQSzJ/MztLUiQIDAXgLnjYHVc/YayTACBAgQ2GqB/kf0v+LcrVaw8gQIECBAYAsFxkVGwey6V1pNR2DLBASzm93ggtnN+ps7gXUFBLNb+AHbKhMgQIAAAQIECBAgQIAAAQIECBBYJTAuMgpm173SajoCWyYgmN3sBhfMbtbf3AmsKyCYXfUJ1HsECBAgQIAAAQIECBAgQIAAAQIEtlBgXGQUzK57pdV0BLZMQDC72Q0umN2sv7kTWFdAMLuFH7CtMgECBAgQIECAAAECBAgQIECAAIFVAuMio2B23SutpiOwZQKC2c1ucMHsZv3NncC6AoLZVZ9AvUeAAAECBAgQIECAAAECBAgQIEBgCwXGRUbB7LpXWk1HYMsEBLOb3eCC2c36mzuBdQUEs1v4AdsqEyBAgAABAgQIECBAgAABAgQIEFglMC4yCmbXvdJqOgJbJiCY3ewGF8xu1t/cCawrIJhd9QnUewQIECBAgAABAgQIECBAgAABAgS2UGBcZBTMrnul1XQEtkxAMLvZDS6Y3ay/uRNYV0Awu4UfsK0yAQIECBAgQIAAAQIECBAgQIAAgVUC4yKjYHbdK62mI7BlAoLZzW5wwexm/c2dwLoCgtlVn0C9R4AAAQIECBAgQIAAAQIECBAgQGALBcZFRsHsuldaTUdgywQEs5vd4ILZzfqbO4F1BQSzW/gB2yoTIECAAAECBAgQIECAAAECBAgQWCUwLjIKZte90mo6AlsmIJjd7AYXzG7W39wJrCsgmF31CdR7BAgQIECAAAECBAgQIECAAAECBLZQYFxkFMyue6XVdAS2TEAwu9kNLpjdrL+5E1hXQDC7hR+wrTIBAgQIECBAgAABAgQIECBAgACBVQLjIuOewewjHvGIxQc+8IF1r0WajgCBEybwqU99avHoRz968chHPnLx0pe+dHHnnXcu3vOe9yze/e53Lz784Q8v7rnnnhO2xsdndebB7Itf/OLFF77wheOzcJaEAIFdBb785S8vrrnmmsV55523eOYzn7l417vetbjjjjt2zp3vfe97F3fffffizJkzu05/BAPOX/WZ0HsECBAgQIAAAQIECBAgQIAAAQIECByxwLjYt2cw+/CHP3xx8803LwoEVAb2ge3eBz7/+c/vfFHjUY96lGD2CJKS+2tyHsxef/31i8985jPOy/432QceBPvAXXfdtXjKU54imD3iz7SaJ0CAAAECBAgQIECAAAECBAgQIHDsBe4vmD333HMXhTCPecxjVAb2AfvA4qKLLtoJF/SYvb8Y9fCHz4PZCy+8cHHxxRc7Jh2T9oEHwT5wySWXLM4///zF+FCox+yx/2RsAQkQIECAAAECBAgQIECAAAECBAgcocBuwWw9saaLiGP2i3POOUdlYB+wD+zsA50Tus35S17yErcyPvz8ddcW67H8/Oc/fyfcaRs4N/u/5H/zg2MfmI7XHp/xjGe4lfGAUAgQIECAAAECBAgQIECAAAECBAhspcCqYPYb3/jGzu9E3nrrrTsXD/sttOott9yyeOtb37q48cYbFy984QvVLTF40YtetHjBC16wOHXq1KLbp95www22/ZZs+72O85e97GWLt7zlLff+TqLfmN01Tz20AV/5ylcWn/jEJ77lvNy5+e1vf/viNa95jePScWkfeBDsA2984xsXt91226Lflu33uf3G7FZ+/LbSBAgQIECAAAECBAgQIECAAAEC2yqwKpgtRSic/drXvnafes899+wEMbfffvuiIEY9+QZdOH7zm9+8uPTSS3d6SL7vfe+z3e379+4D7R9Vweyh5a+7NtR5+etf//p9zstf+tKXFp/85CcXzssn/3zsf+6DfxtP58zpUTC7rZ/ArTcBAgQIECBAgAABAgQIECBAgMBWCowE4I5dU4ClAYUCBQDvf//77+3l0QVF9eQa3HHHHYt6Tl977bWLCy64YPHYxz528c53vnMnnLfdT+5238+2ncIFwezSCfMBfFlP2k9/+tPOy/4f+X/8INgHpnPm9Nj59u67716cOXPmATxrLM7fyg+9VpoAAQIECBAgQIAAAQIECBAgQIDApgXGVcBzRj1XZbDbPvC6173u3GrD5893G9/79iX7gH3APmAfsA8c331g0589zZ8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsLvAYrE458pTp59W3X0sQwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgbYHLT52+8fKrrltUC2nXbsiEBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIHBfgSc97fTPXX7quq8KZu9r4x0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgcWOCKq08/+4lXXfefUyirx+yBSTVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/xN4yi9c/4OXn3rOB+eB7PTcrYztJQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEDigwOgh+6zLr3rOl6cg9opT152ZnvcomD0gsMkJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBw+anTt98bxJ667p+uvPr0k5941elPTO8JZu0jBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQOKBAwewVV53+0ghib7r66hu+u+YEswdENTkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTmAj991fVPuPLa5144f08wO9fwnAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkcgIJg9AlRNEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYC4gmJ1reE6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEjEBDMHgGqJgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDAXEMzONTwnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAEQgIZo8AVZMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYCwhm5xqeEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4AgEBLNHgKpJAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIzAUEs3MNzwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIHAEAoLZI0DVJAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOYCgtm5hucECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBA4AgHB7BGgapIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJzgctPnf7o5Vddt7ji1HVnFovFOfNhnhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcKgC/wvxnQCBzDnv5gAAAABJRU5ErkJggg==" width = "888" height = "416" alt = "" style = "vertical-align: baseline; width: 888px; height: 416px;"></img></div><div  class = 'S4'><span style=' font-style: italic;'>Illustration of the structure of the IntracellularRecordingsTable</span></div><div  class = 'S4'><span>We can add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> </span><span>and add the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrodesTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularElectrodesTable</span></a><span>, </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularStimuliTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularStimuliTable</span></a><span>, and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularResponsesTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularResponsesTable</span></a><span> to it, then add them all to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ic_rec_table = types.core.IntracellularRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'categories'</span><span >, {</span><span style="color: #a709f5;">'electrodes'</span><span >, </span><span style="color: #a709f5;">'stimuli'</span><span >, </span><span style="color: #a709f5;">'responses'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'recordings_tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table to group together a stimulus and response from a single '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'electrode and a single simultaneous recording and for storing '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'metadata about the intracellular recording.'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers(</span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2])), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings_tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, repmat({</span><span style="color: #a709f5;">'Tag'</span><span >}, 3, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column for storing a custom recordings tag' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.electrodes = types.core.IntracellularElectrodesTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular electrode related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'electrode'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'electrode'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, repmat(types.untyped.ObjectView(electrode), 3, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column for storing the reference to the intracellular electrode' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Note: For IZeroClampSeries (izcs), idx_start=-1 and count=-1 indicate no stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% was recorded (the amplifier is disconnected, so no stimulus can reach the cell).</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% The timeseries still points to the response series as described in the NWB spec</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% for the IntracellularRecordingsTable.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.stimuli = types.core.IntracellularStimuliTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular stimulus related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'stimulus'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus'</span><span >, types.core.TimeSeriesReferenceVectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [</span><span style="color: #a709f5;">'Column storing the reference to the recorded stimulus '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                        </span><span style="color: #a709f5;">'for the recording (rows)'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, struct( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'idx_start'</span><span >, [0, 1, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'count'</span><span >, [5, 3, -1], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'timeseries'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(vcss), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Voltage clamp stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(ccss), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp stimulus</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(izcs) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % Current clamp response when current is off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            ] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.responses = types.core.IntracellularResponsesTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Table for storing intracellular response related metadata.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'response'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'response'</span><span >, types.core.TimeSeriesReferenceVectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [</span><span style="color: #a709f5;">'Column storing the reference to the recorded response '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                        </span><span style="color: #a709f5;">'for the recording (rows)'</span><span >], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, struct( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'idx_start'</span><span >, [0, 2, 0], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'count'</span><span >, [5, 3, 5], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'timeseries'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(vcs), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Voltage clamp response</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(ccs), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp response</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >                types.untyped.ObjectView(izcs) </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Current clamp response when current is off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            ] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    )</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> </span><span>table is not just a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> </span><span>but an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span> type is itself a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> </span><span>that may contain an arbitrary number of additional</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, each of which defines a "category." This is similar to a table with “sub-headings”. In the case of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span>, we have three predefined categories, i.e., electrodes, stimuli, and responses. We can also dynamically add new categories to the table. As each category corresponds to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, this means we have to create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> and add it to our table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% add category</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.categories = [ic_rec_table.categories, {</span><span style="color: #a709f5;">'recording_lab_data'</span><span >}];</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_rec_table.dynamictable.set( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recording_lab_data'</span><span >, types.hdmf_common.DynamicTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'category table for lab-specific recording metadata'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'location'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, int64([0; 1; 2]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'location'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'Mordor'</span><span >, </span><span style="color: #a709f5;">'Gondor'</span><span >, </span><span style="color: #a709f5;">'Rohan'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Recording location in Middle Earth' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>In an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>AlignedDynamicTable</span></a><span> all category tables must align with the main table, i.e., all tables must have the same number of rows and rows are expected to correspond to each other by index.</span></div><div  class = 'S4'><span>We can also add custom columns to any of the subcategory tables, i.e., the electrodes, stimuli, and responses tables, and any custom subcategory tables. All we need to do is indicate the name of the category we want to add the column to.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Add voltage threshold as column of electrodes table</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >voltage_threshold_data = types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0.1; 0.12; 0.13], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Just an example column on the electrodes category table' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ic_rec_table.electrodes.addColumn(</span><span style="color: #a709f5;">'voltage_threshold'</span><span >, voltage_threshold_data)</span></span></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_intracellular_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_intracellular_recordings = ic_rec_table;</span></span></div></div></div><h2  class = 'S15' id = 'H_1536' ><span>Hierarchical organization of recordings</span></h2><div  class = 'S4' id = 'H_F58007A2' ><span>To describe the organization of intracellular experiments, the metadata is organized hierarchically in a sequence of tables. All of the tables are so-called </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTables</span></a><span> enabling users to add columns for custom metadata. Storing data in hierarchical tables has the advantage that it allows us to avoid duplication of metadata. E.g., for a single experiment we only need to describe the metadata that is constant across an experimental condition as a single row in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> without having to replicate the same information across all repetitions and sequential-, simultaneous-, and individual intracellular recordings. For analysis, this means that we can easily focus on individual aspects of an experiment while still being able to easily access information about information from related tables. All of these tables are optional, but to use one you must use all of the lower level tables, even if you only need a single row.</span></div><h3  class = 'S9' id = 'H_310f' ><span>Add a simultaneous recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> groups intracellular recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>IntracellularRecordingsTable</span></a><span> together that were recorded simultaneously from different electrodes and/or cells and describes metadata that is constant across the simultaneous recordings. In practice a simultaneous recording is often also referred to as a sweep. This example adds a custom column, "simultaneous_recording_tag".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% Create recordings_vector_data and recordings_vector_index using utility</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% function:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >[recordings_vector_data, recordings_vector_index] = util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    {[0, 1, 2],}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Column with references to one or more rows in the IntracellularRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ic_rec_table);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Create simultaneous recordings table with custom column 'simultaneous_recording_tag'</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ic_sim_recs_table = types.core.SimultaneousRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recordings from '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'the IntracellularRecordingsTable table together that were recorded '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneously from different electrodes.'</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'recordings'</span><span >, </span><span style="color: #a709f5;">'simultaneous_recording_tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(12) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings'</span><span >, recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'recordings_index'</span><span >, recordings_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recording_tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A custom tag for simultaneous_recordings'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'LabTag1'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S13'><span>Depending on the lab workflow, it may be useful to add complete columns to a table after we have already populated the table with rows. That would be done using the </span><span style=' font-family: monospace;'>addColumn</span><span> method:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >simultaneous_recording_type_data = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Description of the type of simultaneous_recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'SimultaneousRecordingType1'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ic_sim_recs_table.addColumn(</span><span style="color: #a709f5;">'simultaneous_recording_type'</span><span >, simultaneous_recording_type_data)</span></span></div></div></div><div  class = 'S4'><span>Display the simultaneous recordings table using the </span><span style=' font-family: monospace;'>toTable</span><span> method:</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >ic_sim_recs_table.toTable()</span></span></div><div  class = 'S17'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="D1753624" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_11" widgetid="variableeditor_client_Document_11" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_11"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×4 table </span></div><div id="variableeditor_TableViewModel_11" widgetid="variableeditor_TableViewModel_11" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>recordings</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 246px; min-width: 246px; max-width: 246px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>simultaneous_recording_tag</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 254px; min-width: 254px; max-width: 254px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>simultaneous_recording_type</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>12</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>[0;1;2]</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 246px; min-width: 246px; max-width: 246px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'LabTag1'</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 254px; min-width: 254px; max-width: 254px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>'SimultaneousRecordingType1'</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     id    recordings     simultaneous_recording_tag     simultaneous_recording_type  
-    __ "></div></div></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_simultaneous_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_simultaneous_recordings = ic_sim_recs_table;</span></span></div></div></div><h3  class = 'S10' id = 'H_4279' ><span>Add a sequential recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span> groups simultaneously recorded intracellular recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> together and describes metadata that is constant across the simultaneous recordings. In practice a sequential recording is often also referred to as a sweep sequence. A common use of sequential recordings is to group together simultaneous recordings where a sequence of stimuli of the same type with varying parameters have been presented in a sequence (e.g., a sequence of square waveforms with varying amplitude).</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[simultaneous_recordings_vector_data, simultaneous_recordings_vector_index] = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        {0,}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Column with references to one or more rows in the SimultaneousRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ic_sim_recs_table);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >sequential_recordings = types.core.SequentialRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recording '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneous_recordings from the SimultaneousRecordingsTable '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'table together. This is typically used to group together '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneous_recordings where a sequence of stimuli of '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'the same type with varying parameters have been presented in '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'a sequence.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'simultaneous_recordings'</span><span >, </span><span style="color: #a709f5;">'stimulus_type'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(15) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recordings'</span><span >, simultaneous_recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recordings_index'</span><span >, simultaneous_recordings_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_type'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column storing the type of stimulus used for the sequential recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'square'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_sequential_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_sequential_recordings = sequential_recordings;</span></span></div></div></div><h3  class = 'S10' id = 'H_1d70' ><span>Add repetitions table</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> groups sequential recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span>. In practice, a repetition is often also referred to a run. A typical use of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> is to group sets of different stimuli that are applied in sequence that may be repeated.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[sequential_recordings_vector_data, sequential_recordings_vector_index] = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        {0,}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Column with references to one or more rows in the SequentialRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        sequential_recordings);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_repetitions = types.core.RepetitionsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recording sequential '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'recordings together. With each SimultaneousRecording typically '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'representing a particular type of stimulus, the RepetitionsTable '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'table is typically used to group sets of stimuli applied in sequence.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'sequential_recordings'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(17) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sequential_recordings'</span><span >, sequential_recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sequential_recordings_index'</span><span >, sequential_recordings_vector_index </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S10' id = 'H_6075' ><span>Add experimental condition table</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ExperimentalConditionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>ExperimentalConditionsTable</span></a><span> groups repetitions of intracellular recording from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> together that belong to the same experimental conditions.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[repetitions_vector_data, repetitions_vector_index] = util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    {0, 0}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Column with references to one or more rows in the RepetitionsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    nwbfile.general_intracellular_ephys_repetitions);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_experimental_conditions = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    types.core.ExperimentalConditionsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'A table for grouping different intracellular recording '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'repetitions together that belong to the same experimental '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'conditions.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'repetitions'</span><span >, </span><span style="color: #a709f5;">'tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, int64([19; 21]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'repetitions'</span><span >, repetitions_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'repetitions_index'</span><span >, repetitions_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'integer tag for an experimental condition'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, [1; 3] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_6d41' ><span>Write the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbExport(nwbfile, </span><span style="color: #a709f5;">'icephys_tutorial.nwb'</span><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_6b40' ><span>Read the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwbfile2 = nwbRead(</span><span style="color: #a709f5;">'icephys_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S17'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="EC5F47A9" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwbfile2 = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+    __ "></div></div></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_simultaneous_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_simultaneous_recordings = ic_sim_recs_table;</span></span></div></div></div><h3  class = 'S10' id = 'H_4279' ><span>Add a sequential recording</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span> groups simultaneously recorded intracellular recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SimultaneousRecordingsTable</span></a><span> together and describes metadata that is constant across the simultaneous recordings. In practice a sequential recording is often also referred to as a sweep sequence. A common use of sequential recordings is to group together simultaneous recordings where a sequence of stimuli of the same type with varying parameters have been presented in a sequence (e.g., a sequence of square waveforms with varying amplitude).</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[simultaneous_recordings_vector_data, simultaneous_recordings_vector_index] = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        {0,}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Column with references to one or more rows in the SimultaneousRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ic_sim_recs_table);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >sequential_recordings = types.core.SequentialRecordingsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recording '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneous_recordings from the SimultaneousRecordingsTable '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'table together. This is typically used to group together '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'simultaneous_recordings where a sequence of stimuli of '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'the same type with varying parameters have been presented in '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'a sequence.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'simultaneous_recordings'</span><span >, </span><span style="color: #a709f5;">'stimulus_type'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(15) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recordings'</span><span >, simultaneous_recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'simultaneous_recordings_index'</span><span >, simultaneous_recordings_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'stimulus_type'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Column storing the type of stimulus used for the sequential recording'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'square'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span> is added to the </span><span style=' font-family: monospace;'>general_intracellular_ephys_sequential_recordings</span><span> property of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_sequential_recordings = sequential_recordings;</span></span></div></div></div><h3  class = 'S10' id = 'H_1d70' ><span>Add repetitions table</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> groups sequential recordings from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html"><span style=' font-weight: bold; font-family: monospace;'>SequentialRecordingsTable</span></a><span>. In practice, a repetition is often also referred to a run. A typical use of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> is to group sets of different stimuli that are applied in sequence that may be repeated.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[sequential_recordings_vector_data, sequential_recordings_vector_index] = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        {0,}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'Column with references to one or more rows in the SequentialRecordingsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        sequential_recordings);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_repetitions = types.core.RepetitionsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'A table for grouping different intracellular recording sequential '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'recordings together. With each SimultaneousRecording typically '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'representing a particular type of stimulus, the RepetitionsTable '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'table is typically used to group sets of stimuli applied in sequence.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'sequential_recordings'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'data'</span><span >, int64(17) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sequential_recordings'</span><span >, sequential_recordings_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sequential_recordings_index'</span><span >, sequential_recordings_vector_index </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S10' id = 'H_6075' ><span>Add experimental condition table</span></h3><div  class = 'S4'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ExperimentalConditionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>ExperimentalConditionsTable</span></a><span> groups repetitions of intracellular recording from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html"><span style=' font-weight: bold; font-family: monospace;'>RepetitionsTable</span></a><span> together that belong to the same experimental conditions.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >[repetitions_vector_data, repetitions_vector_index] = util.create_indexed_column( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    {0, 0}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'Column with references to one or more rows in the RepetitionsTable table'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    nwbfile.general_intracellular_ephys_repetitions);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile.general_intracellular_ephys_experimental_conditions = </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    types.core.ExperimentalConditionsTable( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, [ </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'A table for grouping different intracellular recording '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'repetitions together that belong to the same experimental '</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'conditions.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'repetitions'</span><span >, </span><span style="color: #a709f5;">'tag'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'id'</span><span >, types.hdmf_common.ElementIdentifiers( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, int64([19; 21]) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'repetitions'</span><span >, repetitions_vector_data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'repetitions_index'</span><span >, repetitions_vector_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'tag'</span><span >, types.hdmf_common.VectorData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'integer tag for an experimental condition'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, [1; 3] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        ) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_6d41' ><span>Write the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >nwbExport(nwbfile, </span><span style="color: #a709f5;">'icephys_tutorial.nwb'</span><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_6b40' ><span>Read the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwbfile2 = nwbRead(</span><span style="color: #a709f5;">'icephys_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S17'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="A83E4329" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwbfile2 = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: [1×1 types.untyped.DataStub]
diff --git a/docs/source/_static/html/tutorials/images.html b/docs/source/_static/html/tutorials/images.html
index 55c5304d7..527315974 100644
--- a/docs/source/_static/html/tutorials/images.html
+++ b/docs/source/_static/html/tutorials/images.html
@@ -62,7 +62,7 @@
 </span></a><span>    </span><a href = "#H_8cdc"><span>GrayscaleImage: for grayscale images
 </span></a><span>    </span><a href = "#H_0c5e"><span>Images: a container for images
 </span></a><a href = "#H_100c"><span>IndexSeries for repeated images
-</span></a><a href = "#H_44e2"><span>Writing the images to an NWB File</span></a></div></div><h2  class = 'S9' id = 'H_6a48' ><span>Create an NWB File</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011' </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="6DE5FCB7" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</span></a><a href = "#H_44e2"><span>Writing the images to an NWB File</span></a></div></div><h2  class = 'S9' id = 'H_6a48' ><span>Create an NWB File</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011' </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="D56219DF" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -115,7 +115,7 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S13' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_2581' ><span>ImageSeries: Storing series of images as acquisition</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> is a general container for time series of images acquired during an experiment. Image data can be stored directly in the NWB file or referenced from external image files (see section </span><a href = "#H_9b57"><span>External files</span></a><span> below). For color images stored in the NWB file, the channel order must be RGB. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 50, 50, 3], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_images = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Image data of an animal in the environment'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ImageSeries'</span><span >, behavior_images);</span></span></div></div></div><h2  class = 'S9' id = 'H_5693' ><span>OpticalSeries: Storing series of images as stimuli</span></h2><div  class = 'S1'><span>We will use the</span><span style=' font-weight: bold;'> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> class to store time series of images presented to the subject as stimuli. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> class extends the ImageSeries with additional fields that describe the spatial relationship between the subject and the stimuli, such as: </span></div><ul  class = 'S2'><li  class = 'S3'><span style=' font-weight: bold; font-family: monospace;'>field_of_view</span><span> </span><span>– physical extent of the image in the visual field or target area.</span></li><li  class = 'S3'><span style=' font-weight: bold; font-family: monospace;'>distance</span><span> </span><span>– distance from the image source to the subject.</span></li></ul><div  class = 'S1'><span>Here we create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span> containing synthetic RGB image data (As in all </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, the first dimension is time. The second and third dimensions represent x and y and the fourth dimension represents the RGB value (length of 3) for color images):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 50, 50, 3], </span><span style="color: #a709f5;">'uint8'</span><span >); </span><span style="color: #008013;">% time, x, y, RGB</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >optical_series = types.core.OpticalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'distance'</span><span >, 0.7, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % recommended</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'field_of_view'</span><span >, [0.2, 0.3, 0.7], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % recommended</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The images presented to the subject as stimuli' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >, optical_series);</span></span></div></div></div><h2  class = 'S9' id = 'H_1478' ><span>AbstractFeatureSeries: Storing features of visual stimuli</span></h2><div  class = 'S1'><span>While full image data is usually stored in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span>, you can also store derived features of the visual stimuli—either instead of, or in addition to, the raw images. Examples of derived features include</span><span> </span><span style=' font-weight: bold;'>mean luminance</span><span>,</span><span> </span><span style=' font-weight: bold;'>contrast</span><span>, or</span><span> </span><span style=' font-weight: bold;'>spatial frequency</span><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AbstractFeatureSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AbstractFeatureSeries</span></a><span> class is a general</span><span> </span><span style=' font-family: monospace;'>TimeSeries</span><span> </span><span>container for storing such feature data over time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Create some fake feature data</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >feature_data = rand(200, 3);  </span><span style="color: #008013;">% 200 time points, 3 features</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an AbstractFeatureSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >abstract_feature_series = types.core.AbstractFeatureSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(feature_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, linspace(0, 1, 200), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Features of the visual stimuli'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'features'</span><span >, {</span><span style="color: #a709f5;">'luminance'</span><span >, </span><span style="color: #a709f5;">'contrast'</span><span >, </span><span style="color: #a709f5;">'spatial frequency'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'feature_units'</span><span >, {</span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #a709f5;">'cycles/degree'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the AbstractFeatureSeries to the NWBFile</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'StimulusFeatures'</span><span >, abstract_feature_series);</span></span></div></div></div><h2  class = 'S9' id = 'H_9b57' ><span>External files</span></h2><div  class = 'S1'><span>External files (e.g., videos of the behaving animal) can be added to an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> </span><span>by creating an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> </span><span>with the</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> field set to the relative path(s) of the file(s) on disk. The path must be relative to the NWB file’s location.</span></div><div  class = 'S1'><span>Either</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> </span><span style=' font-weight: bold;'>or</span><span> </span><span style=' font-family: monospace;'>data</span><span> </span><span>must be specified — not both.</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> </span><span>can be a cell array of multiple video files. </span></div><div  class = 'S1'><span>The </span><span style=' font-family: monospace;'>starting_frame</span><span> attribute is a zero-based index into the full </span><span style=' font-family: monospace;'>ImageSeries</span><span> that indicates the first frame contained in each file listed in </span><span style=' font-family: monospace;'>external_file</span><span>. For example, if three files contain 5, 10, and 20 frames respectively, </span><span style=' font-family: monospace;'>starting_frame</span><span> would be </span><span style=' font-family: monospace;'>[0, 5, 15]</span><span>. If one file contains all frames, it is </span><span style=' font-family: monospace;'>[0]</span><span>. This indexing allows random access to frames without loading all preceding files.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >external_files = {</span><span style="color: #a709f5;">'video1.pmp4'</span><span >, </span><span style="color: #a709f5;">'video2.pmp4'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >timestamps = [0.0, 0.04, 0.07, 0.1, 0.14, 0.16, 0.21];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_external_file = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Behavior video of animal moving in environment'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'external_file'</span><span >, external_files, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'external'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'external_file_starting_frame'</span><span >, [0, 2], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >);</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsWarningElement" uid="BFC040E4" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="diagnosticMessage-wrapper diagnosticMessage-warningType eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="34" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="warning output " style="max-height: 261px; white-space: normal; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;"><div class="diagnosticMessage-messagePart" style="white-space: pre-wrap; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;">Warning: The property "data_unit" of type "types.core.ImageSeries" depends on the property "data", which is unset. If you do not set a value for "data, the value of "data_unit" will not be exported to file.</div><div class="diagnosticMessage-stackPart" style="white-space: pre; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;"></div></div></div></div></div><div class="inlineWrapper"><div  class = 'S15'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ExternalVideos'</span><span >, behavior_external_file);</span></span></div></div></div><h2  class = 'S9' id = 'H_81aa' ><span>Static images</span></h2><div  class = 'S1'><span>Static images can be stored in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBAImage</span></a><span>, </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBImage</span></a><span> or </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html"><span style=' font-weight: bold; font-family: monospace;'>GrayscaleImage</span></a><span> classes. All of these image types provide an optional </span><span style=' font-family: monospace;'>description</span><span> parameter to include text description about the image and the </span><span style=' font-family: monospace;'>resolution</span><span> parameter to specify the pixels/cm resolution of the image.</span></div><h3  class = 'S13' id = 'H_7dfe' ><span>RGBAImage: for color images with transparency</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBAImage</span></a><span> is for storing data of color image with transparency. </span><span style=' font-family: monospace;'>data</span><span> must be 3D where the first and second dimensions represent x and y. The third dimension has length 4 and represents the RGBA value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200, 4], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgba_image = types.core.RGBAImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'RGBA image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_84b0' ><span>RGBImage: for color images</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBImage</span></a><span> is for storing data of RGB color image. </span><span style=' font-family: monospace;'>data</span><span> must be 3D where the first and second dimensions represent x and y. The third dimension has length 3 and represents the RGB value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200, 3], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_image = types.core.RGBImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'RGB image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_8cdc' ><span style=' font-weight: bold;'>GrayscaleImage: for grayscale images</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html"><span style=' font-weight: bold; font-family: monospace;'>GrayscaleImage</span></a><span> is for storing grayscale image data. </span><span style=' font-family: monospace;'>data</span><span> must be 2D where the first and second dimensions represent x and y.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >grayscale_image = types.core.GrayscaleImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, image_data, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Grayscale image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_0c5e' ><span>Images: a container for images</span></h3><div  class = 'S1'><span>Add these images to an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> container that accepts any mix of these image types.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_collection = types.core.Images( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A collection of images presented to the subject.'</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'rgba_image'</span><span >, rgba_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'rgb_image'</span><span >, rgb_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'grayscale_image'</span><span >, grayscale_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ImageCollection'</span><span >, image_collection);</span></span></div></div></div><h2  class = 'S9' id = 'H_100c' ><span>IndexSeries for repeated images</span></h2><div  class = 'S1'><span>If the same images are presented multiple times, storing each copy in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> </span><span>would duplicate data. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span> </span><span>avoids this by referencing a set of unique images stored in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> </span><span>container.</span></div><div  class = 'S1'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> </span><span>container holds the unique images, and an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html"><span style=' font-weight: bold; font-family: monospace;'>ImageReferences</span></a><span> </span><span>object defines their order. The </span><span style=' font-family: monospace;'>data</span><span> field of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span> </span><span>is a zero-based index into that order, indicating which image is shown at each time point. </span><span style=' font-family: monospace;'>timestamps</span><span> define when each indexed image was presented.</span></div><div  class = 'S1'><span>For example, if</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span style=' font-family: monospace;'>.data</span><span> </span><span>is</span><span> </span><span style=' font-family: monospace;'>[0, 1, 0, 1]</span><span>, then the first and third presentations use the first image in</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html"><span style=' font-weight: bold; font-family: monospace;'>ImageReferences</span></a><span>, and the second and fourth presentations use the second image:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Define images that were used for repeated presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_image = imread(</span><span style="color: #a709f5;">'street2.jpg'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >gray_image = uint8(mean(rgb_image, 3));</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >gs_street = types.core.GrayscaleImage(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, gray_image, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Grayscale image of a street.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 28 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_street = types.core.RGBImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(rgb_image), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 28, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Color (rgb) image of a street.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an image collection with ordered images</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_order = types.core.ImageReferences(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [types.untyped.ObjectView(rgb_street), types.untyped.ObjectView(gs_street)] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >template_image_collection = types.core.Images( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gs_street'</span><span >, gs_street, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rgb_street'</span><span >, rgb_street, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A collection of images of a street.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'order_of_images'</span><span >, image_order </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% The image collection is added to the stimulus_templates group</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb.stimulus_templates.set(</span><span style="color: #a709f5;">'StreetImages'</span><span >, template_image_collection);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >street_image_presentation = types.core.IndexSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Alternating presentation of color and grayscale versions of an image'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0, 1, 0, 1], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NOTE: 0-indexed</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indexed_images'</span><span >, template_image_collection, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [0.1, 0.2, 0.3, 0.4] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="F6575E9C" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="315" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 326px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">street_image_presentation = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  IndexSeries with properties:
+</div></div></div></div></div></div><h3  class = 'S13' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_2581' ><span>ImageSeries: Storing series of images as acquisition</span></h2><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> is a general container for time series of images acquired during an experiment. Image data can be stored directly in the NWB file or referenced from external image files (see section </span><a href = "#H_9b57"><span>External files</span></a><span> below). For color images stored in the NWB file, the channel order must be RGB. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 50, 50, 3], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_images = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Image data of an animal in the environment'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ImageSeries'</span><span >, behavior_images);</span></span></div></div></div><h2  class = 'S9' id = 'H_5693' ><span>OpticalSeries: Storing series of images as stimuli</span></h2><div  class = 'S1'><span>We will use the</span><span style=' font-weight: bold;'> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> class to store time series of images presented to the subject as stimuli. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> class extends the ImageSeries with additional fields that describe the spatial relationship between the subject and the stimuli, such as: </span></div><ul  class = 'S2'><li  class = 'S3'><span style=' font-weight: bold; font-family: monospace;'>field_of_view</span><span> </span><span>– physical extent of the image in the visual field or target area.</span></li><li  class = 'S3'><span style=' font-weight: bold; font-family: monospace;'>distance</span><span> </span><span>– distance from the image source to the subject.</span></li></ul><div  class = 'S1'><span>Here we create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span> named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span> containing synthetic RGB image data (As in all </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, the first dimension is time. The second and third dimensions represent x and y and the fourth dimension represents the RGB value (length of 3) for color images):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 50, 50, 3], </span><span style="color: #a709f5;">'uint8'</span><span >); </span><span style="color: #008013;">% time, x, y, RGB</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >optical_series = types.core.OpticalSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'distance'</span><span >, 0.7, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % recommended</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'field_of_view'</span><span >, [0.2, 0.3, 0.7], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % recommended</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'The images presented to the subject as stimuli' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >, optical_series);</span></span></div></div></div><h2  class = 'S9' id = 'H_1478' ><span>AbstractFeatureSeries: Storing features of visual stimuli</span></h2><div  class = 'S1'><span>While full image data is usually stored in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalSeries</span></a><span>, you can also store derived features of the visual stimuli—either instead of, or in addition to, the raw images. Examples of derived features include</span><span> </span><span style=' font-weight: bold;'>mean luminance</span><span>,</span><span> </span><span style=' font-weight: bold;'>contrast</span><span>, or</span><span> </span><span style=' font-weight: bold;'>spatial frequency</span><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AbstractFeatureSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AbstractFeatureSeries</span></a><span> class is a general</span><span> </span><span style=' font-family: monospace;'>TimeSeries</span><span> </span><span>container for storing such feature data over time.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Create some fake feature data</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >feature_data = rand(200, 3);  </span><span style="color: #008013;">% 200 time points, 3 features</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an AbstractFeatureSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >abstract_feature_series = types.core.AbstractFeatureSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(feature_data), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, linspace(0, 1, 200), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Features of the visual stimuli'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'features'</span><span >, {</span><span style="color: #a709f5;">'luminance'</span><span >, </span><span style="color: #a709f5;">'contrast'</span><span >, </span><span style="color: #a709f5;">'spatial frequency'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'feature_units'</span><span >, {</span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #a709f5;">'n.a.'</span><span >, </span><span style="color: #a709f5;">'cycles/degree'</span><span >} </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Add the AbstractFeatureSeries to the NWBFile</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'StimulusFeatures'</span><span >, abstract_feature_series);</span></span></div></div></div><h2  class = 'S9' id = 'H_9b57' ><span>External files</span></h2><div  class = 'S1'><span>External files (e.g., videos of the behaving animal) can be added to an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> </span><span>by creating an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> </span><span>with the</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> field set to the relative path(s) of the file(s) on disk. The path must be relative to the NWB file’s location.</span></div><div  class = 'S1'><span>Either</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> </span><span style=' font-weight: bold;'>or</span><span> </span><span style=' font-family: monospace;'>data</span><span> </span><span>must be specified — not both.</span><span> </span><span style=' font-family: monospace;'>external_file</span><span> </span><span>can be a cell array of multiple video files. </span></div><div  class = 'S1'><span>The </span><span style=' font-family: monospace;'>starting_frame</span><span> attribute is a zero-based index into the full </span><span style=' font-family: monospace;'>ImageSeries</span><span> that indicates the first frame contained in each file listed in </span><span style=' font-family: monospace;'>external_file</span><span>. For example, if three files contain 5, 10, and 20 frames respectively, </span><span style=' font-family: monospace;'>starting_frame</span><span> would be </span><span style=' font-family: monospace;'>[0, 5, 15]</span><span>. If one file contains all frames, it is </span><span style=' font-family: monospace;'>[0]</span><span>. This indexing allows random access to frames without loading all preceding files.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >external_files = {</span><span style="color: #a709f5;">'video1.pmp4'</span><span >, </span><span style="color: #a709f5;">'video2.pmp4'</span><span >};</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >timestamps = [0.0, 0.04, 0.07, 0.1, 0.14, 0.16, 0.21];</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >behavior_external_file = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Behavior video of animal moving in environment'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'external_file'</span><span >, external_files, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'external'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'external_file_starting_frame'</span><span >, [0, 2], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, timestamps </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >);</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsWarningElement" uid="FA73BA14" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="diagnosticMessage-wrapper diagnosticMessage-warningType eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="34" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="warning output " style="max-height: 261px; white-space: normal; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;"><div class="diagnosticMessage-messagePart" style="white-space: pre-wrap; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;">Warning: The property "data_unit" of type "types.core.ImageSeries" depends on the property "data", which is unset. If you do not set a value for "data, the value of "data_unit" will not be exported to file.</div><div class="diagnosticMessage-stackPart" style="white-space: pre; font-style: normal; color: rgb(179, 98, 5); font-size: 12px;"></div></div></div></div></div><div class="inlineWrapper"><div  class = 'S15'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ExternalVideos'</span><span >, behavior_external_file);</span></span></div></div></div><h2  class = 'S9' id = 'H_81aa' ><span>Static images</span></h2><div  class = 'S1'><span>Static images can be stored in an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBAImage</span></a><span>, </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBImage</span></a><span> or </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html"><span style=' font-weight: bold; font-family: monospace;'>GrayscaleImage</span></a><span> classes. All of these image types provide an optional </span><span style=' font-family: monospace;'>description</span><span> parameter to include text description about the image and the </span><span style=' font-family: monospace;'>resolution</span><span> parameter to specify the pixels/cm resolution of the image.</span></div><h3  class = 'S13' id = 'H_7dfe' ><span>RGBAImage: for color images with transparency</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBAImage</span></a><span> is for storing data of color image with transparency. </span><span style=' font-family: monospace;'>data</span><span> must be 3D where the first and second dimensions represent x and y. The third dimension has length 4 and represents the RGBA value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200, 4], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgba_image = types.core.RGBAImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'RGBA image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_84b0' ><span>RGBImage: for color images</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html"><span style=' font-weight: bold; font-family: monospace;'>RGBImage</span></a><span> is for storing data of RGB color image. </span><span style=' font-family: monospace;'>data</span><span> must be 3D where the first and second dimensions represent x and y. The third dimension has length 3 and represents the RGB value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200, 3], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_image = types.core.RGBImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(image_data), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'RGB image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_8cdc' ><span style=' font-weight: bold;'>GrayscaleImage: for grayscale images</span></h3><div  class = 'S1'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html"><span style=' font-weight: bold; font-family: monospace;'>GrayscaleImage</span></a><span> is for storing grayscale image data. </span><span style=' font-family: monospace;'>data</span><span> must be 2D where the first and second dimensions represent x and y.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_data = randi(255, [200, 200], </span><span style="color: #a709f5;">'uint8'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >grayscale_image = types.core.GrayscaleImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, image_data, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 70.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Grayscale image' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >);</span></span></div></div></div><h3  class = 'S13' id = 'H_0c5e' ><span>Images: a container for images</span></h3><div  class = 'S1'><span>Add these images to an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> container that accepts any mix of these image types.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >image_collection = types.core.Images( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A collection of images presented to the subject.'</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'rgba_image'</span><span >, rgba_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'rgb_image'</span><span >, rgb_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_collection.baseimage.set(</span><span style="color: #a709f5;">'grayscale_image'</span><span >, grayscale_image);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'ImageCollection'</span><span >, image_collection);</span></span></div></div></div><h2  class = 'S9' id = 'H_100c' ><span>IndexSeries for repeated images</span></h2><div  class = 'S1'><span>If the same images are presented multiple times, storing each copy in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSeries</span></a><span> </span><span>would duplicate data. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span> </span><span>avoids this by referencing a set of unique images stored in an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> </span><span>container.</span></div><div  class = 'S1'><span>The</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html"><span style=' font-weight: bold; font-family: monospace;'>Images</span></a><span> </span><span>container holds the unique images, and an</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html"><span style=' font-weight: bold; font-family: monospace;'>ImageReferences</span></a><span> </span><span>object defines their order. The </span><span style=' font-family: monospace;'>data</span><span> field of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span> </span><span>is a zero-based index into that order, indicating which image is shown at each time point. </span><span style=' font-family: monospace;'>timestamps</span><span> define when each indexed image was presented.</span></div><div  class = 'S1'><span>For example, if</span><span> the </span><span style=' font-family: monospace;'>data</span><span> property of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html"><span style=' font-weight: bold; font-family: monospace;'>IndexSeries</span></a><span> </span><span>is</span><span> </span><span style=' font-family: monospace;'>[0, 1, 0, 1]</span><span>, then the first and third presentations use the first image in</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html"><span style=' font-weight: bold; font-family: monospace;'>ImageReferences</span></a><span>, and the second and fourth presentations use the second image:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Define images that were used for repeated presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_image = imread(</span><span style="color: #a709f5;">'street2.jpg'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >gray_image = uint8(mean(rgb_image, 3));</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >gs_street = types.core.GrayscaleImage(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, gray_image, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Grayscale image of a street.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 28 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >rgb_street = types.core.RGBImage( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, reverseDims(rgb_image), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'resolution'</span><span >, 28, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Color (rgb) image of a street.' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Create an image collection with ordered images</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >image_order = types.core.ImageReferences(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [types.untyped.ObjectView(rgb_street), types.untyped.ObjectView(gs_street)] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >template_image_collection = types.core.Images( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'gs_street'</span><span >, gs_street, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rgb_street'</span><span >, rgb_street, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A collection of images of a street.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'order_of_images'</span><span >, image_order </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% The image collection is added to the stimulus_templates group</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >nwb.stimulus_templates.set(</span><span style="color: #a709f5;">'StreetImages'</span><span >, template_image_collection);</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >street_image_presentation = types.core.IndexSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Alternating presentation of color and grayscale versions of an image'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [0, 1, 0, 1], </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % NOTE: 0-indexed</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indexed_images'</span><span >, template_image_collection, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [0.1, 0.2, 0.3, 0.4] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S11'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S12'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="2DC19E55" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="315" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 326px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">street_image_presentation = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  IndexSeries with properties:
 
          indexed_images: [1×1 types.untyped.SoftLink]
      indexed_timeseries: []
@@ -360,9 +360,9 @@
 % is shown at each time point. |timestamps| define when each indexed image was 
 % presented.
 % 
-% For example, if <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html 
-% |*IndexSeries*|>|.data| is |[0, 1, 0, 1]|, then the first and third presentations 
-% use the first image in <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html 
+% For example, if the |data| property of <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html 
+% |*IndexSeries*|> is |[0, 1, 0, 1]|, then the first and third presentations use 
+% the first image in <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html 
 % |*ImageReferences*|>, and the second and fourth presentations use the second 
 % image:
 
diff --git a/docs/source/_static/html/tutorials/intro.html b/docs/source/_static/html/tutorials/intro.html
index cb1507137..e5036f754 100644
--- a/docs/source/_static/html/tutorials/intro.html
+++ b/docs/source/_static/html/tutorials/intro.html
@@ -93,7 +93,7 @@
 </span></a><a href = "#H_098c"><span>Write
 </span></a><a href = "#H_9060"><span>Read
 </span></a><span>    </span><a href = "#H_2a80"><span>Reading Data
-</span></a><a href = "#H_0b60"><span>Next Steps</span></a></div></div><h2  class = 'S4' id = 'H_5755' ><span>Installing MatNWB</span></h2><div  class = 'S5'><span>Use the code below within the brackets to install MatNWB from source. MatNWB works by automatically creating API classes based on the schema.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%{</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">!git clone https://github.com/NeurodataWithoutBorders/matnwb.git</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">addpath(genpath(pwd));</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">%}</span></span></div></div></div><h2  class = 'S4' id = 'H_51bb' ><span>Set up the NWB File</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object with those and additional metadata using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span> command. For all MatNWB classes and functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value. Ellipses are used for clarity.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="880515F0" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</span></a><a href = "#H_0b60"><span>Next Steps</span></a></div></div><h2  class = 'S4' id = 'H_5755' ><span>Installing MatNWB</span></h2><div  class = 'S5'><span>Use the code below within the brackets to install MatNWB from source. MatNWB works by automatically creating API classes based on the schema.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%{</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">!git clone https://github.com/NeurodataWithoutBorders/matnwb.git</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">addpath(genpath(pwd));</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">%}</span></span></div></div></div><h2  class = 'S4' id = 'H_51bb' ><span>Set up the NWB File</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> object with those and additional metadata using the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span> command. For all MatNWB classes and functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value. Ellipses are used for clarity.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="BAD0E63C" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -146,7 +146,7 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h2  class = 'S4' id = 'H_3b84' ><span>Subject Information</span></h2><div  class = 'S5'><span>You can also provide information about your subject in the NWB file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store information such as age, species, genotype, sex, and a freeform description. Then set </span><span style=' font-weight: bold; font-family: monospace;'>nwb.general_subject</span><span> to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object.</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "159" height = "193" alt = "" style = "vertical-align: baseline; width: 159px; height: 193px;"></img></div><div  class = 'S5'><span>Each of these fields is free-form, so any values will be valid, but here are our recommendations:</span></div><ul  class = 'S12'><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>age</span><span>, we recommend using the </span><a href = "https://en.wikipedia.org/wiki/ISO_8601#Durations"><span>ISO 8601 Duration format</span></a></li><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>species</span><span>, we recommend using the formal latin binomal name (e.g. mouse -&gt; </span><span style=' font-style: italic;'>Mus musculus</span><span>, human -&gt; </span><span style=' font-style: italic;'>Homo sapiens</span><span>)</span></li><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>sex</span><span>, we recommend using F (female), M (male), U (unknown), and O (other)</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >subject</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="18A7ACE0" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="197" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">subject = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Subject with properties:
+</div></div></div></div></div></div><h2  class = 'S4' id = 'H_3b84' ><span>Subject Information</span></h2><div  class = 'S5'><span>You can also provide information about your subject in the NWB file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store information such as age, species, genotype, sex, and a freeform description. Then set </span><span style=' font-weight: bold; font-family: monospace;'>nwb.general_subject</span><span> to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object.</span></div><div  class = 'S11'><img class = "imageNode" src = "data:image/png;base64,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" width = "159" height = "193" alt = "" style = "vertical-align: baseline; width: 159px; height: 193px;"></img></div><div  class = 'S5'><span>Each of these fields is free-form, so any values will be valid, but here are our recommendations:</span></div><ul  class = 'S12'><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>age</span><span>, we recommend using the </span><a href = "https://en.wikipedia.org/wiki/ISO_8601#Durations"><span>ISO 8601 Duration format</span></a></li><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>species</span><span>, we recommend using the formal latin binomal name (e.g. mouse -&gt; </span><span style=' font-style: italic;'>Mus musculus</span><span>, human -&gt; </span><span style=' font-style: italic;'>Homo sapiens</span><span>)</span></li><li  class = 'S13'><span>For </span><span style=' font-family: monospace;'>sex</span><span>, we recommend using F (female), M (male), U (unknown), and O (other)</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >subject</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="B03D7F93" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="197" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">subject = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Subject with properties:
 
               age: 'P90D'
     age_reference: 'birth'
@@ -158,14 +158,14 @@
            strain: ''
        subject_id: '005'
            weight: ''
-</div></div></div></div></div></div><div  class = 'S14' id = 'H_FF7B59CD' ><span>Note: the DANDI archive requires all NWB files to have a subject object with subject_id specified, and strongly encourages specifying the other fields.</span></div><h2  class = 'S15' id = 'H_47a8' ><span>Time Series Data</span></h2><div  class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>is a common base class for measurements sampled over time, and provides fields for</span><span> </span><span style=' font-family: monospace;'>data</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>(regularly or irregularly sampled). You will also need to supply the</span><span> </span><span style=' font-family: monospace;'>name</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>unit</span><span> </span><span>of measurement (</span><a href = "https://en.wikipedia.org/wiki/International_System_of_Units"><span>SI unit</span></a><span>).</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "206" height = "221" alt = "" style = "vertical-align: baseline; width: 206px; height: 221px;"></img></div><div  class = 'S5'><span>For instance, we can store a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>data where recording started</span><span> </span><span style=' font-family: monospace;'>0.0</span><span> </span><span>seconds after</span><span> </span><span style=' font-family: monospace;'>start_time</span><span> </span><span>and sampled every second (1 Hz):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >time_series_with_rate = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0);</span></span></div></div></div><div  class = 'S14'><span>For irregularly sampled recordings, we need to provide the </span><span style=' font-family: monospace;'>timestamps</span><span> for the </span><span style=' font-family: monospace;'>data</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >time_series_with_timestamps = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, linspace(0, 1, 10));</span></span></div></div></div><div  class = 'S5'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> class serves as the foundation for all other time series types in the NWB format. Several specialized subclasses extend the functionality of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, each tailored to handle specific kinds of data. In the next section, we’ll explore one of these specialized types. For a full overview, please check out the </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format.html#type-hierarchy"><span>type hierarchy</span></a><span> in the NWB schema documentation.</span></div><h2  class = 'S4' id = 'H_336b' ><span>Other Types of Time Series </span></h2><div  class = 'S5'><span>As mentioned previously, there are many subtypes of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> in MatNWB that are used to store different kinds of data. One example is </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span>, a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> that stores text-based records about the experiment. Similar to our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> example above, we can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span> object with text information about a stimulus and add it to the stimulus_presentation group in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span>. Below is an example where we create an AnnotationSeries object with annotations for airpuff stimuli and add it to the NWBFile.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6' id = 'H_341153F5' ><span style="white-space: pre"><span style="color: #008013;">% Create an AnnotationSeries object with annotations for airpuff stimuli</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >annotations = types.core.AnnotationSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Airpuff events delivered to the animal'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'Left Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [1.0, 3.0, 8.0] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Add the AnnotationSeries to the NWBFile's stimulus group</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'Airpuffs'</span><span >, annotations)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="503D6AF9" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="64" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Set with entries:
+</div></div></div></div></div></div><div  class = 'S14' id = 'H_FF7B59CD' ><span>Note: the DANDI archive requires all NWB files to have a subject object with subject_id specified, and strongly encourages specifying the other fields.</span></div><h2  class = 'S15' id = 'H_47a8' ><span>Time Series Data</span></h2><div  class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>is a common base class for measurements sampled over time, and provides fields for</span><span> </span><span style=' font-family: monospace;'>data</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>(regularly or irregularly sampled). You will also need to supply the</span><span> </span><span style=' font-family: monospace;'>name</span><span> </span><span>and</span><span> </span><span style=' font-family: monospace;'>unit</span><span> </span><span>of measurement (</span><a href = "https://en.wikipedia.org/wiki/International_System_of_Units"><span>SI unit</span></a><span>).</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "206" height = "221" alt = "" style = "vertical-align: baseline; width: 206px; height: 221px;"></img></div><div  class = 'S5'><span>For instance, we can store a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> </span><span>data where recording started</span><span> </span><span style=' font-family: monospace;'>0.0</span><span> </span><span>seconds after</span><span> </span><span style=' font-family: monospace;'>start_time</span><span> </span><span>and sampled every second (1 Hz):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >time_series_with_rate = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0);</span></span></div></div></div><div  class = 'S14'><span>For irregularly sampled recordings, we need to provide the </span><span style=' font-family: monospace;'>timestamps</span><span> for the </span><span style=' font-family: monospace;'>data</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >time_series_with_timestamps = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'an example time series'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, linspace(0, 100, 10), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, linspace(0, 1, 10));</span></span></div></div></div><div  class = 'S5'><span>The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> class serves as the foundation for all other time series types in the NWB format. Several specialized subclasses extend the functionality of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, each tailored to handle specific kinds of data. In the next section, we’ll explore one of these specialized types. For a full overview, please check out the </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format.html#type-hierarchy"><span>type hierarchy</span></a><span> in the NWB schema documentation.</span></div><h2  class = 'S4' id = 'H_336b' ><span>Other Types of Time Series </span></h2><div  class = 'S5'><span>As mentioned previously, there are many subtypes of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> in MatNWB that are used to store different kinds of data. One example is </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span>, a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> that stores text-based records about the experiment. Similar to our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span> example above, we can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html"><span style=' font-weight: bold; font-family: monospace;'>AnnotationSeries</span></a><span> object with text information about a stimulus and add it to the stimulus_presentation group in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span>. Below is an example where we create an AnnotationSeries object with annotations for airpuff stimuli and add it to the NWBFile.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6' id = 'H_341153F5' ><span style="white-space: pre"><span style="color: #008013;">% Create an AnnotationSeries object with annotations for airpuff stimuli</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >annotations = types.core.AnnotationSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Airpuff events delivered to the animal'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, {</span><span style="color: #a709f5;">'Left Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >, </span><span style="color: #a709f5;">'Right Airpuff'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps'</span><span >, [1.0, 3.0, 8.0] </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Add the AnnotationSeries to the NWBFile's stimulus group</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'Airpuffs'</span><span >, annotations)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="23E5E83A" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="64" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Set with entries:
 
     Airpuffs: types.core.AnnotationSeries
-</div></div></div></div></div></div><h2  class = 'S4' id = 'H_8d44' ><span>Behavior</span></h2><h3  class = 'S16' id = 'H_298a' ><span>SpatialSeries and Position</span></h3><div  class = 'S5'><span>Many types of data have special data types in NWB. To store the spatial position of a subject, we will use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> classes. </span></div><div  class = 'S11'><img class = "imageNode" src = 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rKql4PYJZs+r/ljFnOVTLHa5YEqYvdksczD69c63R1ZJZ7Ocre6ZItSvaoUx1HjWhlatUPLs+zGLtlil3M+Np7moaNnqY7Yn4+j53nfaN/F7t9ippnhngmkGAAADl6Ke4N3VCPaSaKqwTsqOWq9/JW/pm8NOqFpsW/dqVKs9eda5jx4R85Ke3unDt4xtiuzKXW4vZcgYztiNntM+1olM7G0xPs99/kc2+LzgKGeCaQYAABOhBRHOVZGMA6lXA0HrN95SGhlSnvDPHs4ZmUVnW1s1Rt7/YqhoZPzOuKQzFE8ncnWMnsv83l7XPLRG+Y5bntr6OZWW/MQ1GNetqtquN2+vP6hYxOH3e7th7jdnqbj6IeZXH5BEEgxAACcGCk+KXFSekWII8+1HiZoF4EUAwAAIMUnvqswb0v1cuOuohohjyCQYgAAQIqR4q1I6D4MpxwlveqRgiCQYgAAQIr3JKbU7R5KGUWvDnib4fri1rDdBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwAAUkwQBIEUAwDA3koxAMAhgBQDAABSDABIMVIMAABIMQAgxUgxAAAgxQCAFCPFAACAFAMAUowUAwAAUgwASDFSDAAA+yLFH3zwwdE777xz9NFHH3GXBgCkGAAATqcUP/nkk7oxHYsxAABSDAAASPESvP7668fLAABAigEA4NRK8bVr144/DwCAFAMAwMFIsTK7L7/88tGNGzeO64h7Uqxpmleh2uPIhx9+eDz94sWLx5/3fHkerUfTX3311eP/AwAgxQAAsDMplgxbYB1Xrlw5jizF+necz/HQQw/dlOPWPOaFF14of0+pBQAgxQAAsBMplshGKdX/lb2V5Hp6lGKLsuZRdle/c5nE9evXj+dRljmKsf7tZegznu6MdJRyTQMApBgpBgCArUqxRFYyquxtS5aj0Go+SXAuhXC2OHbfljPEziLr87mcQv/XvHnZAIAUI8UAALBxKXbdcJbUKLWtF+2cAc5Z4Z4UR5xRVqiueEYJBQAgxQAAsAspruqGe7/Tv3P9cYwxUpzLM2bUFQMAUgwAALuUYgtuJcX5dzEjLGFWdndqptjlGrNFqYTqid2TBVIMAEgxAADsRIqnlE+414jY00SW5SEpdoY4y6+XjRQDAFIMAABbl+LWi3bqBSKLrgU6vwznLG9LiquX73Jm2qUayk4DAFKMFAMAwFalOMqvZLfXJZtKHTxNMh27Y3OpRZRri66mad44IIizze6OrdUFHAAgxUgxAABsXIotu9XgHXlEO0lt9YKca4tn6WW5PIiH/h8lfJYG/oiDelTlHACAFHNZBwCAjUlxlGOJqX66uzQP0pGzy5ovD8/sl+5ivbHm8VDOLqPQT/1f2WYP4OHpXgbDPgMgxUgxAADsRIoBAJBiAABAigEAkGIAAECKAQCQYgAAQIoBAJBiAABAigEAkGIAAECKAQCQYgAAQIoBAJBiAABAigEAkGIAAECKd4UH7liVOAjIvmyTBygBAKQYAACQ4i6zxRDPq+LhovVz12g0v31pCwBSDAAASPEBSLEEUhF58sknj4ednpKZlYRevHjxZnZWw05rOXH46U2htmpdMWuttqhN+5C5BkCKAQAAKT4wPvzww2OZzKLcoxJQieq2pFRCnLPdlagDAFIMAAAHLMUSVUlqlb2V/Ol3UT7jvMrUvvrqq8fTtJxeplifuX79+rFMSjTzcnulCvpcnGZR1fS8bv1b6/K6b9y4cdvv/btKaP27P/uzPzv++dBDD90U+Sjz+1TSAYAUAwAAUrwiznrGEoEspVEGPa8FN0ZexizUFFtkYwxljJ0ljmUSeRkxYywBtsTG0LQo4G5LFlpv75UrV27ulxxGbfKyAQApBgCAUyjFDomxs8WW0UpgLZEWaf3UeqvscpZOiXFur6RVv9N63ROEwm3w8rWMa9eu3ZRXZ4bjvJblKLlql3u8iOKdM9uVtAMAUgwAAKdIiiWkEctnNW9veS20fM2r5bbqfKOk9uZ322KblVWO0u1l5u2adXrQcAnFlBppAKQYKQYAgBMkxTk76nnjclaR4t68lRRX4mv8Yl4WZmeunXmuhLonxVO2BwApRooBAGCLUlzV7vZeaFtWilvz7kqKq+2eDdQ8x+7VYnnFVCmOLwICAFIMAAB7IMX6Sl/yF6NX83rSpNg9W1SRe9hQ7XB8Ma96eOhJca9kAwCQYgAA2KEUr7N8wmUFhyTFU7pIi2UTs0a/wzPKJwCQYgAAOPlS7AE1svjF6euW4jHi2itNsADrZbmctZXoZjSfssSxt4vcXotxbhtSDIAUAwDAKZDimDGVEHrwC9farlOKnZV2v8FjyjoqyXWvD1qXu3aL3ax5uqbFvou9vtz9muuLPV+Ube+HarAR77c4PwAgxQAAcKBS7CxrDAlf1eXYKlIcs7yzRslGxJKa+zOOAhvb1xq8Q+F6Yn029nOcM8qz9NKdp+Vt0e89jaGeAZBiAAA4AVJsyfNQzBJBD15RDfNclQt43vgyWzVvHGZ5aJhnd5lWrU9t9HKiNHs7JPSK1jDPVddtfkDIbfO0+BlesgNAigEA4ARK8T7ijHAsc9gH3GvFvrULACkGAACk+ITijOxQqcU2aY1+BwBIMQAAIMWnQkJdY7xPkg6AFAMAAFJ8ChiqA9525lpt6fWcAQBIMQAAIMUAAEgxAAAgxQAASDEAACDFAABIMQAAIMUAAEgxAAAgxQAASDEAACDFAABIMQAAIMUAAEgxAAAgxQAASDEAAGxBit977z2CIIi9D6QYAACQYoIgkGKkGAAAkGKCIJBipBgAAJBigiCQYqQYAACQYoIgkGKkGAAAkGKCIJBipBgAAJDi3cbbb7999MYbbxz/PNRteOutt4634d1332V/r7GN2q9xuqZ5P3uf7/N2HEIbkWIAAECK9yRefPFF3ZCPfx7qNjzxxBPH2yABYn+vHpcvXz5u42uvvXb8f0nwhQsXjqd5P3uf7/N2qP1qo7YHKQYAAKR4gyL2wx/+ECku4plnnjl67rnnNtLel1566Xjfx2k6Dmr/vmUElalUW2PGVUKptu6rwPt8eOqpp25ps6ZJjHVctZ+3KcXVfhwb2o59l3ekGAAADlaKdXM+9OzqpqRYWUUtL4vrurPCh7RvDyGDHc/rc+fO3VKKUp0j25TiVfajtkPbo88vI9VIMQAAnEgpjlk63Sz1M2d7lQVz5lE/c52qpikTavHLWT/Nr69tNd3LmHozjstQZrT6vNoZ1+1t0/y92lrPp+VrvqlS7H3jbYvZWS3b+0ZZxSojqm1RG92GlgS5PT5GN27cuNlWt9fHLmeKl903alv1uWVkTJ9zyYH2SdyevMy4LW5DPu5xemsb4rkbj/GyZRPej3k7eplirS+2Qe3tZfC9P1r7ubUfq8+31qX2zPa4jAIpBgCArUtxFEDfaGNG0zf6HLoZ9+bxjVriYrHIoRv2WOmsPq/2RsHxV9pqW7XOLA8SnTyfMmhjv16WbMS60mrborTmduS61N6+idP97z/6oz+67XM+drmmeOq+0bZZ5h36nEoFls2GVtvZyrrG8yx/Jj5oxOOWH+b0f2dE83kz5qFM+8DLzudY1aZKirWeqg3VMdb6qnk1LT4stfZjFPOh46vwuvbxpTukGAAAdibFukFKFiSElgu/lKPpziTrRp5vprrxW1L0M77d7pu0hMpv70dRHMo4xq96naWOMhvFIgqLPqP1aH0WuSg3LmmwSDr7GiV1SPy8XO0zrcdt03oU2gdRLr0PvG+8H1wbquleptpRCaWWq/k135/+6Z/ess2xd4SWFI/ZN3F9brPmj8K2jBRrOfG4uW09KfZ0zeuHFbdD+zWeezHraaGN56TrcKv926rVzg+A/jYlb4emZymODz36qfMryvMslS94+3w+xXMnPqi29qPb6/Mpno/V35qXPfbhFCkGAIBTIcWz8BVxzLTpBp2n+2YaJaYSG9dj5oxuSzhaX99rnjxffNGpkuIsAJYTb4vXX319PFb8vMwsFVpH/No6ym/e95qWM3X5K/ssqa329nqfmLJvPG9eVxTNZetmq14xelIc921v/Z7u88znaHw5Lu/foZdC3dZqvmo7shT7HKsE3A8jPq/Vbk3TMvLfirctnie99efzUfNUZRT+BmZTte5IMQAAHJQUW4ByprDVT2v86jreTCuxqd7ar4R5Sl2x2+AsdpTB3rbkkgi3reoRwtI0JH75gaJVq9qS4qr/2JgFHJLEZaR4zL7pPbA467gtKc5lDi2x9z7zdK+nqtEeWzPe69ZujBT3zv+Y1R17PlTritNimdFQrXg8J5BiAABAigdujM5ezYoaxSlSPBRjvsZu1WVWUlxtS25fr6eAsTXFVU2wBCa/jNiS4l4NaF5/JUbLSPGYfVN9E7CuHhamSvGYba2W2ztfZkVdfO8lu2WluLevqoeUWN4xa9QtDwl7rhfX/1uC3PpGACkGAACkuHHjd0ZVWbdlMsW66fpt+CrG1HVG4XT976akeKr4eZ9EQY4Z8EqKY620a3x7meJe1nLdUtx7KFi1j9ttS7F7Z6hiqHzCx3MbUhxLQ/L5ULWjdz64Vjyej9U3BFUJElIMAABIcZKl+CJazqj5hjxFilftg1fLiNmuVaXYGfCqfMIysezLZDmzV0lxLP/IclaVKGxTinulJdsun1hWitfRZ/A2yyfiw1/uznBKprgS5PiiKuUTAACAFE+U4ih3rbrOSoqjSLlueBZegMp98w51jdWSMGcsl5Xi1ot2UUJ6QuX+jKtsozPp3heVFMeXsOK+iftsV1Lc+lq91bZ9lOLq24z4QFL1t91aZlWXPEaKe30Bx95a4kNabm98eIrtyOv3+VjVnMdeUqplI8UAAIAUD8iSM6YSU3fLpWlxut9o9w3W8+auwdwlVdWl1Jhuz5zpUsRlxgzYFPGLX1drfg8MMrbbsVhPHN/sVxtyl3XxAUPrcXdbs1AW4l423DWe22Vx60lxfHDImdJlpDg+GKhtcd+smimOvUK4K7NNSHE+vl6XRVXbMfRA1vs2YYwUx78hd2sYu5ZrnSPaR7E7tlgKUvWu4W2LZTexu8RWGUhv+5BiAABAihu9K8zSgBmx3jiLWFxeb/COoRedeoMfWJBnqS/bseLnTF5+UU7r6r1o1sqcDm1bHGwiD7AxSy9GxXpjC2BPivPgJqtKsefP7XNt+CpSHGXV7duEFPcG75iNHB6510PKWCnuDd6Rv2Wo/k4syLNioI68Pa3BP4YeevdxqGekGAAAti7FHra29dKRfq9MqF+0q7poc/bKA3NUw9M626ibfB4KeUxXbPqM2lANo2wh6G1Lb3hiD1PtZU8ZyjgOP62sXWtYXbctD+Xsl6I8mEbVJVc1bHNrGGhn3lvDPE/dN+7j1m1zlnPoJbVexCG/3c1eNcxzJd6t6a39E89fn8NThnm2ZGZxrNbnaXk/xvNX0Rvm2S9tVkNax/Oh2o+x20L3gd0qURrbFSNSDAAAp0aKCaL1EFLJp8tnTsu+cFnQPnZbtkpUA8QgxQAAgBQTRJJilwJUQySvkiU+xFi1G7p9i16vGEgxAAAgxQTReClulmqu88t/Y2NMOcq+PiS0yigOLVwnHY8jUgwAAEgxQYwYTbA12IVLLMbGlDryfQvX6e5rucHUeu59f0BBigEAACkmCOLUB1IMAABIMUEQSDFSDAAASDFBEEgxUgwAAEgxQRBIMVIMAABIMUEQSDFSDAAASDFBEEgxUgwAAEgxQRBIMVIMAABIMUEQSDFSDAAA+y3FGgkrDtygf+eBGfR//07za7AADQTBDZ8gCKQYAAAOXoo1IpaHvM0RRzyTBGuahgKOwwFzwycIAikGAICDlmJlfC3EFy5cOJZgyW+UXmeMLcWaX/HUU08dZ4654RMEgRQDAMBBS7HKHyS6ly9fvu13zzzzzPHv9DNKseK5557jRk8QBFIMAAAnQ4qz+MZQFni2KJfIUpzrjQmCIJBiAAA4WCl2mUT1wpwlWGUVWYq5yRMEgRQDAABSzE2eIAikGAAATooUqzZ4tkT5BDd5giCQYgAAODFS3HvRTr1LzBov2nGTJwgCKQYAgBMjxbFLNmWEJb6tLtneeustpJggCKQYAABOnhQ7A1wN3qFpcfAOBVJMEARSDAAAJ1KKPYSzBFg1xgqVVVTdrnkYaG7yBEEgxQAAcOKkmCAIAikGAACkmCAIAikGAACkmCAIAikGAACkmCAIAikGAACkmCAIAikGAACkmCAIAikGAACkmCAIAikGAACkmCAIAikGAICTLcUaplmj12loZ27S+xM+Lo5q0JRlB2RZ5/K2uU3+XLVM/dQ57H9ve9t2uW6kGAAAYA1S/MQTTxwP0Zxlg9ht+Lg41jVioJazzuVta5s0mqI+89RTT92c9swzz9xclpatc9j/3oUUX7hwgb8lpBgAAA5Bij1cc5ymYZwlJ7vIHC4jVWrvaZJiyeCymV3tqyyIWpaO9y7EzVldC+5YKda2nzt37jjifrCESpS1rduWYq837lutX+3imxekGAAA9liKdbPeRRZtHWHh2EWGc5dSvIq8OpN66McyPiBEUda0y5cv37bcbZzjXn/eBu/z/PBJIMUAALAHUuzsoLNY+rczXDlTHDOJyna99tprN+f3PHF6T9qUGdQ8Cmc8e1+Pe16tK2ba9H9l5Sw81Xrd7l6b4na7bVpvrAON03O2r7VvetsV25XXNVWKtf/jNnjZWm7MoHo+Z1K9/ipT3FtmbKun9zL1WlY8jq1a9SlSrPVl0dU0y2c8n3tSHI/D0DHTdvjvIp+Lef35fNS8ymjrd9QXI8UAALBnUmwhjmFxyPLleZXp8s3dIQGRMFi28vRYX2mJzSGZiJKh9eblOdymXIsahcoZuxxqu6Q17ofYhjy/5s3TtYwogb1945rWuA+qds9SXewUKY7SV22DpsX5clQ1xXGZ1TGT2OXp1b51SUQOZXJz6ccUKfZ+iOur9murprh3Lmq+LLyteeMx652PLlMiW4wUAwDAHkqxM3iWlPiWfEuKLbuSEYmhPmch0nRN0+88PX617WVYnrRs/bRIRoHw+iUQamdsq2sz1VZLoH7GGtv8+Thvls8sMVqOJcht02fjMuLX83HfaH79P+4DhdvlDKe2wfs2PgBkqZwixXH98VsAhfaXe0KIDxf5+GYpztsU90tcl6fHfRuX4W8EFN4vcR9OkWJtR9yumM2vzudKiuO+8bHVtHi8c7v0O2e5dRw9r49t73zMtcWIL1IMAAB7JMW9esueFEcRidOjAFj+oiRZIqrusywdWVTzV81abvzquuo1Qb+TDFcZv6qtUdxybWglaXk/tPaNIj8ctGpLLXRDX60PSXEuY/D8cXrMELd6n4jLjKLeWpf3VzyGFuVKcv0QkEsxxkjxUDlE/l01zedifgjxuRiPpct04vnRyvwO9eJR7XsCKQYAgAOU4pzdk1RU2S8v1/NX4ltJktdneay+kp/alVjsB7fqHqsl4LNUrpFFN++bKgNocapKGHLN77I1xXGZWcq9/rh/pkrxmAeLan/1amh9fGO7xkpxVU88RYor8R1z3GN/zgrv2yoD3dqG1oMhgRQDAMCBSXHOcLY+n6e36llnjXphd7c1S/WbORPakpD4NX1vPb3sXWt6a99Uklb9Lten+qWwMYLck+L8wNLaP1OkuFrm0P6yaI453stIcetcHCvFQ2UM+aHH3zy0tmGKFNP/N1IMAAAnRIor+ZwqxfFt/xxZDP2iWxTkWO9ZtStmAvW5WM9aZerWJcXVi3ItYdZ2SrSyuA/1Yzv0ot26pbha5tj9FY9X63hX2zFWiqv5NiHF8SFG2+J6+NjTBFKMFAMAAFI8WYqXHRAk9mTgZVTtivPFr+5jnfAmpHhM+USrvKN62fDQpdjbNHZglanlE9VDyNTyiV7dr1+qqx7GoixPkWJGt0OKAQBgz6U4f02+binuvdzk5Xtd7gmgmi+/uFa1q8rgRZnKbViXFPfmdXv1M7+wNVQnuykprl6YXKcUWxqrBwL3UpK74Ztt+UW7ql/ruH+iQOd5vYxKilvdro39RgApRooBAGDLUhxrdz3QwaakOHbJpn/nbtWc+YvD90Zx8kt9mh57BpiFLs70Wc83K7pSi11u5drXdUixu6VTO2K/tc5sR1F0Fls/LftDWdV1SHHsrs7Z0E1IcRRMbW/uWi8exylSXPV0MVWK87nowUqqLtmc3XWXbGq/psV+tOMgN3HePADMrPOyKYEUAwDADqW4GsRjU1LcGzBBUhgFKYrtrPNyVswex99VA39YzGbpBanZGmuKxwze0RqUZEz/teuQ4vzSY+zTeJ1S3Bu8Q/sp90oxZfCOXPc7VYqnDN5RbYPbHwdLqQaViev0cnqlNARSDAAAO5RiZwrHDvOcs3bVML+t6bE8QnLQG+bZWV9lNDVvHFK6NWx0FC2/pFcN2RyzeHG44yyU1fS8b6JQeihgD94wNMyzxCy3e9VhnltDUFfb4GGrh4Z5HrNfWtOdHdZxdFnMqsM8e59nwaza3dsWD8ntlwFb3f/FbYjzeN/Foau9vrzfxw7QQiDFAACwIykmVosxfSWvK05y7wVTpDhm3A9hXzhLPFQzTiDFAACAFCPFSPGk/XgowybHGvlle15BipFiAABAipHiQopnjfrqQ4xVtqkasW9ft6/qdYRAigEAACk+URnOqm53E+Fa4Kpm9VBjlW2K9fD7miV27TZ/K0gxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAADsjLvm8QBSTBDEgUnx78zj8jy+oGvY4loGAACwkhTfjxQTBHFgUvyfz+Nb8/i8rmFIMQAArMqd8/gUUkwQxIFJ8X86j0fncV7XsMW1DAAAYGnOzOOTuskQxKrxu7/7u8fBviA2HfPr1qV5fGken9E1bHEtAwAAWJo75vGJ2c9r8n5lcZNR9kVfS6peTy+y/FWCGBNnzpz5bz72sY+9xr4gNhC/tbgmfWtxjfrS4pr1wOIadgeXcwAAWAW9ra2vHc8ubi7Kupyf/bxOTy+w6M3uLxLEiHjkE5/4xJ/PfyqL99fYH8Sa49cW16TPL65Rn1lcs84urmH0PAEAACtzx+KmomyLvoZUfd79ixuOujr6ZYIYirvuuuu/vXjx4tG1a9eO7rnnnv+dfUKsOR5cXJPuX1yjPrm4Zt1JlhgAANadMdaN5cziJqM3ue9exCcIYiCUxTt65513jj788MOjBx988KP5//8r9guxxvD16K7FNerM4ppFhhgAADYqyDHuIIhe3HPPPf+bMsTm5ZdfVrb4/57/7j9i/xBrinxdAgAAANgrrj744IN/oQxx5Etf+tJf3H333f+I3QMAAAAAJ52z99xzz/+lzHDmxo0beuFOcZ7dBAAAAAAnFmWClRE+avDkk08e3XvvvT9mTwEAAADASeX8bPFyXQuVVMx+ni1+nN0FAAAAACeOu+66639RJngIlVbM5/0/Z4w4BgAAAAAnjKvzOMov11V89NFHR+fOnfsPZ86c+X12GwAAAACcFJov17V4/fXXeekOAAAAAE4O995773+tl+uUAZ6CSi3uu+++P2YPAgAAAMChc34ex92tTeWDDz5wtvgSuxEAAAAADhZ1rzbm5boWL7zwwtGdd97572e8dAcAAAAAB4q6VRv1ct3QS3dzMb7G7gQAAACAQ+OMulWb8nJdi1dffdVlFOfYrQAAAABwOEZ85szvL/NyXYsrV65opLv/nj0LAAAAAIfC+dmSL9e10Ch4s59nix9m9wIAAADA3rPqy3Utrl+/fnTPPff8rzNeugMAAACAPef45Tp1p7ZuVIrx4IMP/sVdd931X7KbAQAAAGBfOX65TllilTtsIq5du+YyirPsbgAAAADYR55dCOs24ll2NwAAAACcJI7YBQAAAACAFAMAAAAAIMWQUA8b52cMWAIAAACAFJ9izi/2yyvsCgAAAACkeBdIRC8hxQAAAABwWqX47KI9V5FiAAAAADgkKVbd7cMLkX14dnsd7rlZuz73bPjducUy3OXb+dnt/SFr2qXFfPr3mYbQnl/8+8yiTY/P+n0rn1nM8/DsF/XELSn28q/OqDkGAAAAQIqDxPb6MbZgvp/EVPL55uJ3jy8ENC/napi3+v37s9tLLfy7S8X8zxdSfnWxnNz+LMXnQ3tzG5BjAAAAgFMqxVE6H19I4+NBMGMJxNOFZF5N084FGc2Z4leCgD68+N3TYf3nCin2sh9O4vtwmPf5sNyri216M33e/Ci17Xxo7484jQAAAABOpxTHLG/kbBDNWSGVlxYS63nOFKIchTrOmzOyT89uz0y3Rt3zvE+ndlbDVv8oSfGZMG8u2Xh80d4znEoAAAAAp0uKoyT2hPl8ktv3F2HpfDh9rpJiT3u+WI+F+UeFFOdlX0qie76zDTmLPZv9Ilv9oxnlEgAAAABIcRLKXpxPn3t81q7vHZLiochSfKbRZovuw7P2y3SXZnVNcVyfxP9ZBBkAAAAAKbY4tuJcQ3qnSLHLHt4fWFeW4tmAFD/ekeKWMLuXivzS31VOIwAAAIDTJ8W9etwWsTbY5ROPj5Diq7NpfQaPleLzs7r2eew6JcjPzuraaAAAAAA4BVI8m/2iN4dq9Dm/TBcFMtYRR0E+OyDFD3dE9+xiXWeWkOJznXlfSfOeXQh8VSrx5qyuYQYAAACAUyDFFtg3F8J4ZhEud4gvv3na88XnX0ky7WVW4vnK7Bd1ymeDaD+9hBTH5b45+0U3a7E0osoqnyuE/U1OIwAAAIDTKcVnZr/o57ca0OJMIZS5xMBSe7WYFqefm9UDZ1Rdr02R4nOzelCQavCOq2n74vyXOI0AAAAATqcUGwnh0wuB1M+Hk/zGYZkzHtr5amPa+SThVxfC+kpnmXl5xqPXXWoI8/Oh/bPGvJrv8cW8r8zaJRUAAAAAcMqkGAAAAAAAKQYAAAAAQIoBAAAAAJBiAAAAAACkGAAAAE4p52f1m+/LcnbNywNAiuFE8DGCIIg9CLgddb8U+1FdF1fXuDzOXWJKHLEPiH26f9wx+3n/e3ct4m6CIIg9CF+T7lxco+445bL8+Oz2Tv5XQfv04UKKz87G9X2qY/HxxXLu5B5CLBlH7ANiC/ePj4+9f+jDn5zH/fN4YB4PEgRB7EE8sLgufWpxjfrE4gJ3xyn+mtnDwa5DiuOQts4+e3StHw3I8JnFvUMC/UvzuI97CLFkHLEPiA3eP+5bXKPOLq5ZZ4bkWDecT8/jc/P4wjx+jSAIYg9C16PPLyTwM4uL3NmFGJ/GjPHTs1+M1LUOKT67EONnw/LeXGSNzw1kmO9d3HB07/js4v7xq9xDiCXiiH1AbOj+8auLa9NnF9eq+xfXrjO3XdW+//3vHxEEQRxKzC9bv7IQ40+c4mzxbI1SnMsy3h/IEDtLfDfnI0EQB3b/uDCPv7wQ47tvS6xopvv/xt8nCILY+1hc1L60yBh/snzSR4qX4Vwom3BZxtOd+VWXd5abLEEQBybF31qI8acX35J9HCkmCOKQpfjRhbh96hSXUKxbis8vSibOLv7/yoAU62Hkl7h/EARxYPePb8/jq4tSil+6LbHCRY0giAO7qH1rUWN8/+KtYqR4++hh5D7uHwRBHNj944l5fHNRY3zf4lqGFBMEcbAXtcuLlyceKGvCkOJtoIeR+7l/EARxYPeP78zjNxYv3zmxghQTBHGwF7XfWrxV/CBSvFMpfoD7B0EQB3b/+O48fjMkVpBigiAO+qL2V+fxxXn8Mj1Q7ESKP7Z4GHmQ+wdBEAd2//jb3cQKFzWCIJDiEyvFrywicmkx7RJSTBAEUowUEwSBFB8iV2e3D/Gc4+qANF8t5kOKCYJAirmoEQSBFB8Ml0L2txWXyBQTBEEgxQRBIMVATTFBEARSTGwvvv3MD46+94f/7Dj+0t+6vvb5CQIpRooJgiCQYuI2ofzn//rNW+Kf/PGf3JRM/X7bbVIbzMW/8w/XPj9BIMVIMUEQBFJM3BIS3yH+zb99/3i+XUvxpb/7wvE0/R4pJpBipJiYFvom7bv/4J/ekvTYp2um2hJZ9Z7Wum/pXuJ9oNB6f/m3n92rZA+BFBM7luJ/97M/78qxLiS7bOsfvPQvSykmCKQYKSba8fkn/+D4G8AWuqZuu/RM9x6tN34buWkp/nv/+F8073Oavsl7HFKMFLOzDkyKs2zqYhXRRbX1xK3f6YKjLETv4qr5NZ/m17y6WA/VCOsCon/7YubMtS+mQzXFY9voZej3vpHo35JxLmJIMSDFhxpRyMS/+unbx5GlsLoebyp8PY/iqmytrrWOdUpxFm6tX/slS7LuEZt6MPF2bTorTSDFxAakuCqviF/D5QttvNjkJ3TN38tS9J6oWyUe/lzrCXxqGyP5gcAyzsUMKQak+NBKJuJ1LyYE9O8ohVEILXBRlPP/hwSwSj5oupINxkkHzTtGij1Pqx0tKY73gnzt97eQ3kfVdX5Iap3Ayb93O4c+H7d9zD5WskfBy+VIMbFFKc4Xi1bmQfNJGiPxohaXowuvvzobunhpGX4ZMD/h+wLekuKpbczSrMjz7+LFQwIpRoqJdVzjqwd7f5OWZSxeC3OSQNfWSlr1bVzOvCojHa+bLdyGVvmElpGz29qenNltSXHvOq59Emus4z7S//N9wPeTOF9cr5bjzwwlb7SMqrSlyty3Ej1aF/cmpJjYsBTnDK3LJ/IFMv6B6wIV/1BzpiJflGNPF0O1V2Mzy7mN+eJStTFfNNUutVMRhb4qISGQYkCKDyFTbEkd843XGFxqpojZ3/yOSsy+LiPFeRt6yY2WFMfr+NgER25P71vO/PAx9hvN1reZVWY/boOOY/7sNstfCKT41PY+kV+064l066IWn4R1wdAFtPX12DqkeJk2tl4qXMfLHwRSDEjxrl9SzmKp6S05zCIt4ZKgxWu5kwQS3ijBWm5eb1zP2Lrf6hpvqXdvRDlZMXbZ8VvH1nsmcb3Ojufl+HP5Xqp2aprvJdV9Km6DS/q0bTEj7n0ZHwz0+/zAsO1eogik+NRKcc60xoti9UdYPbnqD736CkrLVuY2Zi7WIcW9NmpdVRurC101P+cNUgxI8aFli3u9Twy9YxGvw1EK4zdt8ffVvK2s6hgpjvW58V5RzdvrfUL/r+5DrV44IvkbzryOfC/Nkj2UvIlt1XbmssXqwYN6YqSY2HC9Wey7UU/21dcx8YKQ67myQMY/WncLVHWJE5ezbike28bexRgpRooPlDNf/vKXH//qV7/6/COPPPLm/OfRoQTn5fpD1z5lRXV9rOSwqinOQtj6nUvNct3vuqTY76G0ulSb2k+x7gtVW6vMbI9KiquHheo+1SudqLatle1WmymdQIqJLbxoN/UzY0sN9Acca3tjzdk+lE8gxUjxoUvxXCwfnsf7i3h+Hlfngnx+X+PixYv/yRe/+MUvfPazn/3K9773Pc7LLUR+P8RCOOV66LKK6gW7KJ2rSHH1gl0WyilSnB8U8vLdE8QYvF1xvVW3bqtIcUwu5dptXgRHiokdS3G+WPgPXBeSXIPl+ZWZ0IUn1urGjG18oWBIivNFYkyt1lAbkWIuaidJiucC/Owi4/q8ssWUT5zezLB7l8hlai3J6pXBVV285WXEcrs4fVkpzhlbi18sMRiSYvc5r31QiWMu7/MLhL1yiLFdwfXuU7Hmemo9sLsrrV7qI5BiYotS3OoMPmcKYj2xf6efEmRdDOL8Y3qfyC/rTX2rt9dGpJiL2kmR4kWG+OjLX/7yJWqKkeLqBbgY8Ru7VqY4Xp9jdtkvuEW5i/eRVgKilVWtpDhPs9i3ZL6S09y1aBbcLNj+fdx3MaHjB43YT/AyUhy3Ib8854GkPK/F3i/j8SI4UkzskRS3+lZsDZeZLzq9QTF6/TlmqV2m/8dWG5FipPgESPEZlUs88sgjT/OiHZGlN9agKnLioPXNnAU492Jh+atGP+317Vv1ulD17FC9A1J1Reb7l+4zrUxxVd7hOuXY1vhgkF+e0+/iPSVmxZeR4nxPc5uqB5m4j933s6L3/gyBFBNLvJU8ZRSdqjbYT6++sPX6wIzz6w+6+kqqN/JPbG98gu6NFDS2jb2RlFYdepRAireUJb6qF+oOqGQCKd5C6PrXekGt1Qd7Fsje4BVVwsLzRAFuvQw91E9xldxwN3H5G8eWnPZGOI0JmnxP6vXaMaabuaFkj5bROjYxe1w94OSHHUZcRYqJJQNWh/MIKd5DKX7lQLPESPEWSikka84QKyRdrT56c8mC5vPnqvn9lb+WKZGMwhiTEv6se6uIyQpJrtcRv7nUvB7Z1P0KWwBdyuBlxPmq+mFNc4bY+8BDTfdeSHTPGl5/TiINrVdC699XI9W5TVqH5tV2VZLr7XVPHN7XdM+GFBNIMVJMIMW3SvH7B1hLjBTv+T2C/UEQSDFBEEjxoUmxXrA7jxQTQNKGQIoJgiCQYqQYKQakmECKCYIgkGKkmFgtqtpegiCQYoIgkGKkGCkmCIJAigmCQIqRYqSYIAgCKSYIAilGipFigiAIpJggCKQYKUaKCYIgkGKCIJBipBgpJgiCQIoJgkCKkWKkmCAIAikmCAIpRoqRYoIgCKSYIAikGClGigmCIJaQYoIgiEMJpBgpJgiC2JgUAwAcAkjx/kkxQRDEASVVkGIAQIqR4s1IMQDAAd0/kGIAQIqRYqQYAJBipBgAkGKkGCkGAKQYKQYApBgpRooBAClGik8tH3744dE777xz/LPHBx98MGo+AKQYKV5Fin2t+eijj/bq3HK7HOtq375u767uRYrTtK0KnQOAFJ9odKK//PLLN//A9W+Fef31129Ou3bt2tELL7xw8/+vvvrq1i6QWt/8MN/Stoonn3xy1Hyb3m8ASPHJlmJfa/ZNjtwux7rat6/bu01u3Lhxy7496fi+69A5AEjxqTjpLXT5jz1fYHM89NBDx5/dtBxnCbWw5z9SS/ymL9x5v128ePFUXCQBKUaK1yOJ1fVr3fK6bGZXCY/WtfU0fwunpJD27ZUrV7aSBNEx0H7fFc4U63xAipHiUynF+mOvpPj69eu3fI2i0DTLsf5oto0y17sS0bzfvJ8AkGKkeJfXr3Vci7ie1fj+uI0yAq1jG996jk1KIcUnRIr9dKunZT31jMkkxvn0s3oyduZSv9OyPb++XvGTeZzeW5+X5ZKEbdbtWO7U7upiOFSO4CdIXSyqP2r93tsW903rOLX2ecwU63f6t7OzsXShtb/jsfB+zuvwcv1k7vZX82oepBiQ4tMjxfka35PieE3P1/Pe9SvOo/W0rlVThTZ/0+b/a5viNdltc7IjXg9zptjzeplub75+e7rvMS0BjPeKsQ8anr/aP17emHuv7+NxG6r54n7Jx2zKfVy/i46R74ua5ocmHc9876uyx9W3qW6Lf5/Ppeg6vXs0UnyCpNgXCMtbDF2U8okbs58xdIJWMqnpKiHIdTdabjU9nnDxwppDX9OMvUgPRe+CarnzH9JUKdY2uM1xPf6aKYfkOe5z/ds3hxwx+xwzs/4DzdFqb3UsqnXEP/zqPIgXBM/bepgAQIpPhhTrGpmvUbqOOWsYJaR1bdL1x9e93vWrd+0cKyTVtcjXTy3b7Y7h62XVNq83PwR4Xt0D8zK1vbof5Onaj/l+1NveodIPf7Yq9fD6esuI95V8j9D/fX3PtbXxmPXu47qPtI5Pb95qnnjvq86F6r2b6BO5xlz7qLovxnMVKT7BUuwDqoOrE91/rFE+feB1UrgWK5448USJfyRalpYZ59VP/UF6ui+q8WnZ4uU/Pj8texlDtURD9b7reNFiSIr94oHanC9I/kPXdsWLTvzD8j7QhVXz+Wld8/rCmv/gNU+8eMc3gXN7Na/Xq2OgtmneuO98XLWufJHKx4S3bwEpPj1S7K+wY7IjC1S8vvq+4mSEfueMn6Wnd/2K1yBn7Vr3j2WkOL4Hom2xLGn5rbb5mteSYi9T7Yz3Vk/X9VPzVvfceK/wfApvbyWVOSnj5fp+Ga/5Q/e+uF/i/cHHzMkwH8vqmOX7uKZHF4iJF98vtS47Rry/+H6nfe7lunQxtmGqFMeMs5YTz7NYGuljHO/nSPEJleL89b5Pzjhd0/RHkOtjfbLFP1BPyydPzDC2ntTzxS+LVvzD2TU9KdY2+Pcxk+4LWt6PMavsbfa8+QHADxOVFOc/+F57/Q1BtS99Q4jHtVUj7YvkLmqnASlGincjxb6e52/uoixHodV8+VtFX+t1r4hZy+r6ZSHL19v4zeSqUpzvN9UDf+/aWklxzP5G0a2y6PGeO+ZeMZQt9rHw/vW1esy3rXG/VMcsrz/P27uPe3vjvcdlEfnhxudZ69vRMXLau0fmh4uYjGqdP/GejBSfQCmuarZ63ar4iTnWEsUTonWRquaNouvp1QUisi9dvnj/6Q/bZQX6GTOtfurNba+e0nPGIz5c9GqOl5Xi3s2keqO2dYFrHVcApPjkSnEvKTD0TVx8Kbmad+gaH+9BU97+70lxlRyotnGKFOdlxu0duuf29kFVnjIkt/5M677a+lx1f6iyzbm9rX0wZvtiH8CW1Mox1iHFWcJ9LKtvHqauFw5Uiqs/rHzC6iLUqh0ac8L2pueTqqpxrmLXHaX3SjT0Oz2N5xc1ehcCP8XH8oaqXi+/fLCsFPduarFcZugihhQDUnz6pLgnZq2a4tY7EmOluKpvnVJX3HrRrvX5VaU4L7O3rrjcVm31suV/VUncWCmu7g+VO+T9ErPivXB7dE9rvbe0SSluJad6ER8UkOJTKsUWNp8Qrg+qivnXJcU6OVd5SW7VF+3GXmjGdgszVYrj/tHFIv6xxqdvpBiQYqR421Ls61HvW6+qnEDCHGtkx0pxFCZdK13jOuX6s6wUx6/Lty3FvfvXGCnOyZUxtdfrlOKx9/FYby2vsGNsOlPcOne13labKZ9Aim8+neevUsaesFOkuPUV07qyuNt60a73ldGY8omW7OdlrFo+Ub2w0SufQIoBKUaKp5RPOHmS396fIsXVy8hx2ZuU4qG2rVuK4/9XfYE5vhgXe7/YtBRPuY/HhFGud/a6xkpxVa5RJZxabZt6X0eKT6EUVy9OZJGLdUqrSnH8A6n61K36t9wFy0hx6+W5fBH0dlbz+UEky231Bx/LLHJ7LdhVjVl8u3esFDOUMyDFvGgXh/vNPd+03jNpCVb1Ile+JzjDOObl60OS4taLdk5ajMn4xhfTYwnkGIFbVYqn3MejQOd7e698Ip57rRcQ4/QxUlwl+uL+zOWLSPEpzRT7wuMu2dwVTqzv8sm8qhTHEzMOlazlx1HkDlGKc5ds7iDcF0Dvg/iVV+xU3F2yxbeaq/3qz+qi0eqSLc7nFwJjlzv5zWmkGJBipLiSX3cb2eqSLV/34nUmXqvy/cbXr9jfrbPN1T1o6Ju/dUix1xfLFzYhxd5nWp/vFe7Fw8mM3ns1mrd6Ic77dqi3oFWluHcfz13xxf3qTLbPpXiP8v0olma4S7b88BXPkSlSHGXeXdW6O9Tetx1I8SmT4qpo3idHrPWKNV6rSHGv0+99OfmWkWIxZfCOVtlH1QVe9VXSbGDwDs3XenElZ6mRYkCKkeL8kF+9DJzvLbF/3FkaICheq6oSOi8nSni+B8Vrau86tA4pzvfCTUlx716Ru7DrbWury7xqIIp1S3HsBq6637W+NYjzZBfIDyL5289qoBHvxzFSnJNPOXKGHik+QVKch6bMJ2jVVZsuYi6Cj0M2O8vof1cvAbSm94Zn9NCSksBtD/M8Jus7ZdjNfGHydvWG+YzDa7qf6N4wz/krtjiU5dAwzzquitbQqdU50Vs/AFJ8eoZ51rXDXy3HIYLz9by6xvj+Ea/v8foV7zX6v66brXtQr162kuLe/ad1j4xDFlfztZbZW1fr+prvFWOus16Ps7PL3Lt61/Vqv7TaP+U+7nbbMeL06Bhxv+RySt/PtK58PuZhnnsPUNF3svMgxSdUigEAkGKkeFUpPhQYch7WDVKMFAMAIMVIMVIMSDFSjBQDACDFSPGhSvE6uuSE002ugUaKkWIAAKQYKT4Y8qBOqwzeBKcb1ylXA3oAUgwAgBQjxQAASDEAAFKMFAMAIMUAAEgxUgwAgBQDACDFSDEAAFIMAIAUI8VbxwM7AAD3D6QYAJBipPjUSvGsM2TvNogjvPVGrTst5BHvhkaPA0CKAQCQYqR4DVTSpX5jtyWm7v/Yw0zvut9aD028K+L+8EPLxYsXubgBUgwAgBQjxdvEYrqt7GQlgbuU4l2v/9q1a3u1PwApvk2K33vvPYIgiL0PpPhkSnGvrMADIcS64DjvBx98cPTqq682B9yImWJ95vr16zdFLC932xKYt9vbqv9ru/I+6GW3437wdn300Ue3LMPbrsxstbw46ISWFdtgXP7gder/cR9qWhy4Ii/Do70hxYAUEwRB7E6Kz8xl4CxSvH9SLEFqyZElKmZ2Pa8lb9YZUjfWFOehnIcyxnmUu1b0cPurTG3c7ty2hx566Fgoq+k3btwo15HjypUrt9TutvaV5LnaN4pcauH54vK8D6rjkY8JUgxIMUEQxG6l+Mw3v/nN//Gxxx77P77yla+cQ4oPX4odEjFnSSWMmhazk1GKNd3ipp+u8x0qfRiKVbfbsusMr2TW0zxdIuyss9peLUP7wL1teBkxK63fW5Y1j/eT94mFO+/PmFGO+0T/ljR7nV6GhTe2Q8sDQIoJgiB2K8XHQjy/Of+H3/u93zv6+te//u93LcZI8XqkOIuWpbGat7e8XRGFNpYgxOlRSCWrFs+8zZV0Wmot/tV+do11fpgQEuT8IpylOL8c5/2q9lSlMVUpBgBSTBAEsT0pvinEP/vZz46/Jt4HMUaK1yPFrXrVuJxDkeKIzlNPz5nsPH9rX0RhjjXLef94mjK6FXl9sXwiy2/M3vcy8ABIMUEQxHal+BYhjsKxazE+6VJclR30XmhbVopb8x6aFFfb3eqqrCXFvfC2VutzScVQWHJzbxoRlVLEz6j9moYgA1K8oXjjjTeOY5nPvv3228ef1c9938533333uK1vvfXW3uxztekQzpFVj/Mq59i6Q8d/k+fsPp1na5biUoj3RYxPuhRLhPzymKP39fm+SvE6XrRbRYqrbWxJsfZ5q32xxrclxe6RohVjpDjWLruWuJXxBkCK1xD+A1vmsy+++OLxZ/XzEORfbX3iiSd22o7XXnvtuB2XL1++TaZ++MMfHj311FPHbVRov2rarvfdqse5dY7tYpu1jk2es9qmc+fOHa/jEMR4pBR3hXgfxJjyifFyaLnahRRv60W7dUhx7pFi7PpaJRxD+2RMV3b6G5Ns55cDAZDiJULykYVDcrCsIBySFCszuGvJbAmTRNnTFfHfFvltCZb2T35w0AOF9t2y2d5Kisds8yayudq+VbZllQefA5XiUUIcb9o/+MEPti7GSPFRsx61NX3dUrzLUd3WKcWW1Gp7lLGVLLu/4qEX7bLo6nP5JbmWFLuP5Ng3svDLevHlQACkeAkhW7fAHpIU78tDSbW/Lly4cDz9mWeeuUUEJW4SK0vjNtqoNiz7zcEUKd6nbd7msT4wKZ4kxBGJ8fyzP9uGGEuGJcX70mfyvoxo54ywa1BVuuAs4zqlOHcdtsteEdYhxTHT6xfcYvdr2ocW1SjA7npNxC7ZLLaxj+SY5W1JseuJ3UWe19d6MQ/g4KRYYvrSSy/dzM4qo5TrSp3J0nRlBz1/L2um3/lz+pnn1TTLjr+idqaslSkeWua6pNgZXEcvg+eMpUL7pcqexu3R/G57L1M8Zrljj9/U7KHLOiSA1bLUbglkzBbHzK0+o2WPyYLH7czb6P1jWc37sXVs4jJbxy5LsbdZ6xq7zdV56WOQz814rF3jq+PWyxTH/dg758ees5rH2e99rrnvSPHSQhzFeH6+/z+bFuO5FD8+l+L3Z4fLRqS4euFLomzZWpcU57KIXQ4csQ4pjtudwwOAROIAG2MG78i9UvTKJ3IdcWxHziADHJQU56+LY8Qbv2seLbExclYtZvZyKFOVlxnDwlNl8Z577rlymflr9VWl2J/PkQVR2+zsYWs7soBJgvxvv+iVt0HrcEav2tfLHL+hDKzlLAui2zllv6nteb9IJrMcq32t/ex1xnbEaB1nHZOhZQ5J8ZRt7h2DvM54rOO536op1v7xw0Bebt6Xrb+NqlSidcwPQIrvkhB/73vf+3+XFeIsxnNpfXiDpRPPzuMVpLguZ/FQzP7avzXMc5V5rIZDruZ137nbGOZ5qGykNXxzaxtb050d1r7rbZdLIjwgSC510HQtY2iY59aLc1qvPqvu4Ha9fwHWJsWWF2d9FRa3mDGLN3ELjuazDMSbb8wyOmuo+XPdqn76Bq2f8e37LCxRoJwJ1TItDVE+VpFil3PE9cR2VlKv7dK8zqB7O6O4RKFxVty9J2Qp9rq8XGeWvdwoM73jp98NZYx7WcO4fWMkMT9MSNT0uXjuxOV4uubT+rX//DBg+XdGNT9ItI5zb5nx2LUevKZuc6zH9jkc1xkfpOLfhULzuO2VFPvc1nH0cqP8+pj5QcDni89Zt0GfObTa4kKKPzkX4v9JQryuTJTEeFHze2kDQnlGWWJli5FiAIADkOIogDmrKLHy17zxpl31TpBv0rrp6oacM1GWmJjtbAlsFpZ1LHNKbxDVV+j+WjxLet532m9ZdFuZuyzFvWMS25azokPHb2hbW1/HV1l5LbtXtlJlWrPs6vOSNS0vtjFuf5xeCWw+zpq/Wmb8fGx3tcwqe93bZj+AZOGO2+xzNsp9Po+zFPscqs5Di7FltzrfvD9aJRmr9O6yAyn+j7/xjW/8eJ1CvGkxni/z+UceeeRNyTFSDABwIJninO1cpsuo3lfOzvS5xjPfvMdKcWuZlpIxyxwbMUPXKkFwtq3qSq2SjpYMZSkeElVnJfNx0fxTey4Y0x2cM89VqUveP1GKW8LcWpePp8LbGLdnjBS3+v5VVN8otNo6ZZs9T1WKkIU5SnEW7Pz31du2fO7lbPWYspkDkuJf//rXv/4nmxDiTYnxopb4kHudQIoB4HRKsW6guR5SN/EsyL5pVy9N5d85Q9Xq63FZKdY8rfrZdUqxpSOWPPir8aG646r2tffgkMXUWb+hiDW3Y47fVLHvPYzE41Dt9+pr+WpdUVZ72zhFimMvEcssc+o2V+LcOjd9rKv9k6V4ynKjgOdztiXI1UPCHkrxX3vsscf+57kQ/8WmX95ZhxirlwnVEC+W8/js8EGKAeB0SXH8yl8yFQUrliT4Jl3JVpbi+JKYBU03X3/tu4wUxxf33K/x1Ozz1NriKlvobfQ6JBfxjf8cy0rx0HKrnjx6x691zJcdOKR6Ka2XDc5SHMsUtK3a1/HFw2WkOC5T+yEuc2z2eeo2+/xwfXAVPmd6mfmWFLv+fOj86mW4K/H1g8O+SvFchI8effTRN7chxFmMv/KVr3xHWd6RcemRRx552jI8//ePdjWkNFIMALCBfopjFjTXFFeimYXD/891lrH7talSXGXpYn3luqW4JS0ua7Dkje2zdqwUR/FatZ/mXJc7pXzCpSm9z+c64SnlEz5u2n8xmxnFdqoUx2xpXGasje4tc5ltji/2rVKu0iqfqGqVxz7UWXyr9Y05P3YZ3/nOd44F9cc//vHWLqSS7ytXrvx/X/va147XPSFekRgvssxnZicHpBgATo8USxxa/Zn6hurMcMxc9V60a0lNrtXtvSiXhSWuIy/T7ayWuYwUu0/Y/LVzfgGs96Kdy0eq2tMhKW4JXLXc3vHzvu6VUbS+zo9C1ZKy2N+tM6EtKY0PRJZHy2U+n3KXdT2Jy8e5tcxYkhL3Rz7Hxmxz/sYg9vRRnUuxX+EpUuw2Vw9d7uXEy3Uf1VlwfY5WyziEmuJf//Vf/+8knT/96U+3IsSqW1aXbydMbJFiAECKx0qxu4dyV07u6iy/7BW/ktW/dYPXZyrRjSN/uTsxzRe/2s+iYGGxTOWbdnwhUPNUy/RnV5Fib7u7wYq9GmR5t4T563/Xn3r7Y/ZwrBTnLtnigCk5Sznl+A11P9cSqliu4ocebWs1uls8lm57bHc8RlFUXa/t7Y6jylVdrbmbv3ycYy2462njvszL7HX7N3ab40tu/rvwgBu5C8IpUhwfbPQ79yQSl2vBj7XcPr/Ubm97/jsYeplzn160u3jx4j/atBgjxEgxACDFgy92xZfqpgzeUS3TMhs/7xt4lCYLQ9Ul25hlutZylfKJ1sAjudcASUrrhaicbZwixb3BO3Lfw2OP31A5QDVv7Bu59aJXNTiF2lgN3hHnjTKZ+zaOclqVlXg/Vse5enHP5+fQObbMNg8NoBL361Qpbg3eUZ3brfOwGpEwd+m2712yPfLII3+4KTHWICAaHQ8hRooBACm+ZYha3SQlEFX/tlHqnDnzAAm9rrw0Txx2OHan5mlxeNqY/co3fr/8pja2lukBMYaGZh4Kb6PW5W0YKrnQvK3hmD2Mb2/o39ZQxb3ljj1+rej1s5u3b2gI4Sh9bpPb01u22p37AXZ3anGbvQ9jVr5qS1xmrleOy2wNJT5lm+O56WPg82XKse4N8+zfeV+2/uZ8zupYOqPe63liTNdt+zJ4x6OPPvrcusUYIUaKAQApXulFs319W51YPnL9+LIxpt9jYrex6gt8uxzm+Wtf+9r1dYkxQowUAwBSjBQTZYax9VU7Usxx3hcpnscdjz322O+vKsYIMVIMAEjxqZTiMYMgVINtkEFEivlGYP+kWNeyVcQYIV5eivkbIgjiEGIrUtyqid33cLvHxmn/ar3q2mts9Gpmid2Gj001HPWhSfGyYowQI8UEQSDFa5FigiCIfZHiqWKMECPFBEEgxUgxQRAnUorHijFCjBQTBIEUI8UEQZxoKR4SY4QYKSYIAilGigmCOBVS3BJjhBgpJggCKUaKCYI4VVKcxRghPkwp9uBL+zywzD5GHLzqNGznPnRvGQcgI5BigiCITV/U2he0ATFGiA9Pij26Jt06Tgt3tVgN/36SYszoq9tuC+cpUkwQBLGti9rleXxhHg+MlWKL8Tz+B4T4sKTYfcpLeujWcVwoU+kBeTQM/EkdXEsZcG2jwtlwbbvOmW2cK+rbPQuwz9dD6eYSKUaKCYI47Ivat+bx+XncP4+7xkoxHKYUn/YBlJYtJziEYdvX9cAUBdTbvo3suB448rkpOfcDyUkvWzmxUkwQBHEoMb9sPTqP8/P41DzuRIr3T4olJpIS11fqZ87cqf5SMuPBgXI2U/M/99xzx4Jx4cKF2wYA0nKVqfPn9buq5jgOwKR1aJ1RVvTvOJiTlplrQvMARN4+LatXP+oaUy2zla2ttmMVmdIyLIv66X0bj4lrtPMxiftCP6ttiwN2xfnjfovTe9sy5vi1QvPnUgVNy9se1z9mX/t3Vbt9vt64ceP4p85LC3iU8HWMBEvsSIoDf2UeX5/Hb8/jO/P4LkEQxB6ErkdPzOPbiyzxl+bxmXl8klKI/ZRiS0EUB8uLxMQZthySCIuV5SaGlyF5ijWzMfLX1rGu1v+2oEqQlNHLy9C0KGhxqHq1Mc8veW9lEXvzaR3ePzmW/fq/WpalzW3Ix6S1vxXajlZ2tjp+2qbWcYvyPfb4DWWJ4/D01Tb4WLf2tY513Nc+bllovb1axk9+8pOy7bl8RcFLd4crxQ8t3uiWGP/GPH6TIAhiD0LXo2/O46vzuDCPX1nUEw/2PAG7lWIJhyQi1gNHOZVoOItpYbGEOZOsaRKo2PuE5SdOr6Q3SqLaErOH/prb63Q7LL2a30JjKfZ0LSOvM8qP26dt0na79jQLorfZ2+Fsrpe5TG8bWobFztvlTHfrmKh9bq+z+9r3fmCIWdMonvq3Phvn9X7WdC3f0+O2DB2/KLq9mum837WcvO3+fd7XmtffRMRt1Pye1+3QNG9H/PYjnm/5mwBvI3XwhyvF+iry04uM8a8uXmQhCILYdeh69LlFycRnFkJ8ltKJ/ZfiSnAsFzkjGGsxs4xGkXQmUuKSs3CW6JjdjFnMOK+FqPqK22IcyyVa2+OMp6fH9lXb53b76/9qO7z/cpZ2SglFrqvtHRO1Rducp1sw43Ki0FYSmLfby/Dx9v6Jx7lXEtGrma7mq7a9t699HsQsfm6jz4dcp9yrd6/aQRyWFN+9+Dry/sVN50GCIIg9iAcW16VPLa5Rn1gIMVniPa4ptlS0MnzV55yhc9atkp9evWYlpK1yhF4mz3JtUYpSnKUqi6MFbKietCe+PelbVYrzMWn1CR0zzlWZRS4FaW2Pp3sZvW7LYvZ+aj3xmG2vylycJc/L8mf80JMfAoakmO7ZDl+K71jU5921iLsJgiD2IHxNunNxjbqDDPFhSHEWgiiXvRgjxUMxJC6tmtaqFnZKZtKZx6EMYauuOte7rluKq22Q6MdSgl5NcFXL28uMZiGdevx621dJbtWOVr10jEp64zlSlbL02rrqgw2xeymuLnwEQRC7DjiBUuyXv6pwfWdPiiUsvWWMlWJldFvLyOUTY6S4KjnoSbHrnIe2Y1Up7m1DlEaJpnvL6GWKcw3tkBR7uv/vHkWW2e5eaUJPiqfs61hH3KpzRopPlxQDAACsTYrj1+Njuh0bkuJV+jlulQGsWsM69mvzXuZ21ZgixbGkpdXTxDqlOPbisOz29UpUelnyKV2kuY7YDwZVDXRPisfWRxNIMQAAnEIpji/atWp5Y0au96LdrKjvdY8V8avulri4XKBqo3tO6GWsWxLWehEwPxBYmqq2eTuW7a94ihTH7H0uEeiVTywrxfH45e3T//PxW+eLdlU5SrWvPb8fvFoPMLNOt2u9Eg8CKQYAAKT4lhe+XCoRu9KKGeDWcnKXZ57XJRGxZ4uWeHqIYC/fXW35xavYV/FUCYvt0/K0nJh5zC8W5u3w9GW785paPuH1aZvdLZy7bpul0dlWleIxx2+ofCL2VNLK0Lp7Obc7dskW15kf0uJ5EeXcn4/nVvy2IXb/FjPNDPeMFAMAAFLc/Oq49VJXzq62ltMbvCOXAMw6X3G3Bu/IQjpVilsDRWhazEj2tmOVDONUKa5efvOxiPXGWsY6pLg3eMfYbuj8+WqkwLjvY4a6NVBKJex5O2KPKvkbhFnRP3bVxzOBFAMAwCmT4jwscmsIZM0jCaqGeR6zHA/Zq2W0hkceenHLpQySUL9kNjTMc2tI61yrG5fbKgnwfEPbMXUAj9ymoX3p33sfVF20xbbmNrb2Q2t6PH5a55iyiSld2vm4x2UO7Wvvg9b54s/GbfG0OOR3Lr8gkGIAADilUkwQm4448lwl3LSLQIoBAAApJk5FuHxhlZ4s1h3VCHkEUgwAAEgxsWKMGXhi1qhrPQ0xdrCUbUt61SMFgRQDAABSTKzQJ29vwInWwCenqYxilYFONnGsptRGE0gxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAAAgxQRBEEgxAAAcLHfN4wGkmCCIA5Pi787jN+fxBV3DFtcyAACAlaT4fqSYIIgDk+LvzOM35vGruoYhxQAAsCp3zuM+pJggiAOT4ifm8c15fE7XsMW1DAAAYGnOzOOXdJMhCII4lJhft749j6/O47O6hi2uZQAAAEvz8Xmcncen53FhkXn56/P4m/P4L+bxO7Of1+4RBEHsMn5ncU36m4tr1DcX16xPL65hH+dyDgAAq+AeKFST95cXN5lH5vGNeVyax+XZz19mIQiC2GVcXlyTvrG4Rl1YXLPun9HzBAAArAl97Xjv4uairIu+jlSdnl5g0Zvdv0YQBLHj+MLimvS5xTXq04tr1r0zSicAAGCN2eIzi2yLvoZUfd59ixuOujp6kCAIYsfxwOKadN/iGnV2cc06Q5YYAAA2IccfX9xk9Cb3XYu4myAIYsfh69Gdi2vUx5FhAADYhSwTBEHsMgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAs/z8Uv1eRzZOpjQAAAABJRU5ErkJggg==" width = "492" height = "309" alt = "" style = "vertical-align: baseline; width: 492px; height: 309px;"></img><span> </span></div><div  class = 'S5'><span>Note: These diagrams follow a standard convention called "UML class diagram" to express the object-oriented relationships between NWB classes. For our purposes, all you need to know is that an open triangle means "extends" (i.e., is a specialized subtype of), and an open diamond means "is contained within." Learn more about class diagrams on </span><a href = "https://en.wikipedia.org/wiki/Class_diagram"><span>the wikipedia page</span></a><span>.</span></div><div  class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, a common base class for measurements sampled over time, and provides fields for data and time (regularly or irregularly sampled). Here, we put a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object called </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span> in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object. If the data is sampled at a regular interval, it is recommended to specify the </span><span style=' font-family: monospace;'>starting_time</span><span> and the sampling rate (</span><span style=' font-family: monospace;'>starting_time_rate</span><span>), although it is still possible to specify </span><span style=' font-family: monospace;'>timestamps</span><span> as in the </span><span style=' font-weight: bold; font-family: monospace;'>time_series_with_timestamps</span><span> example above.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create SpatialSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >spatial_series_ts = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [linspace(0,10,100); linspace(0,8,100)], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is bottom left corner'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 200 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% create Position object and add SpatialSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >position = types.core.Position(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >, spatial_series_ts);</span></span></div></div></div><div  class = 'S14'><span>NWB differentiates between raw, </span><span style=' font-style: italic;'>acquired </span><span>data, which should never change, and </span><span style=' font-style: italic;'>processed </span><span>data, which are the results of preprocessing algorithms and could change. Let's assume that the animal's position was computed from a video tracking algorithm, so it would be classified as processed data. Since processed data can be very diverse, NWB allows us to create processing modules, which are like folders, to store related processed data or data that comes from a single algorithm. </span></div><div  class = 'S5'><span>Create a processing module called "behavior" for storing behavioral data in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> and add the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object to the module.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >behavior_module = types.core.ProcessingModule(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'contains behavioral data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the Position object (that holds the SpatialSeries object) to the module </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% and name the Position object "Position"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >behavior_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Position'</span><span >, position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the processing module to the NWBFile object, and name the processing module "behavior"</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'behavior'</span><span >, behavior_module);</span></span></div></div></div><h3  class = 'S16' id = 'H_2d94' ><span>Trials</span></h3><div  class = 'S5' id = 'H_FF7B86AB' ><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> objects are used to store tabular metadata throughout NWB, including for trials, electrodes, and sorted units. They offer flexibility for tabular data by allowing required columns, optional columns, and custom columns.</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "372" height = "368" alt = "" style = "vertical-align: baseline; width: 372px; height: 368px;"></img></div><div  class = 'S5'><span>The trials </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> can be thought of as a table with this structure:</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "378" height = "193" alt = "" style = "vertical-align: baseline; width: 378px; height: 193px;"></img></div><div  class = 'S5'><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which subclasses </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. Here, we are adding</span><span> </span><span style=' font-family: monospace;'>'correct'</span><span>, which will be a</span><span> logical array.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >trials = types.core.TimeIntervals( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'correct'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'trial data and properties'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.1, </span><span style="color: #a709f5;">'stop_time'</span><span >, 1.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 1.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 2.0, </span><span style="color: #a709f5;">'correct'</span><span >, true)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 2.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 3.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >trials.toTable() </span><span style="color: #008013;">% visualize the table</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="5503F8E5" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_21" widgetid="variableeditor_client_Document_21" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_21"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_21" widgetid="variableeditor_TableViewModel_21" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>start_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>stop_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 94px; min-width: 94px; max-width: 94px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>correct</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0.1000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>1</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+</div></div></div></div></div></div><h2  class = 'S4' id = 'H_8d44' ><span>Behavior</span></h2><h3  class = 'S16' id = 'H_298a' ><span>SpatialSeries and Position</span></h3><div  class = 'S5'><span>Many types of data have special data types in NWB. To store the spatial position of a subject, we will use the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> and </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> classes. </span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "492" height = "309" alt = "" style = "vertical-align: baseline; width: 492px; height: 309px;"></img><span> </span></div><div  class = 'S5'><span>Note: These diagrams follow a standard convention called "UML class diagram" to express the object-oriented relationships between NWB classes. For our purposes, all you need to know is that an open triangle means "extends" (i.e., is a specialized subtype of), and an open diamond means "is contained within." Learn more about class diagrams on </span><a href = "https://en.wikipedia.org/wiki/Class_diagram"><span>the wikipedia page</span></a><span>.</span></div><div  class = 'S5'><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span>, a common base class for measurements sampled over time, and provides fields for data and time (regularly or irregularly sampled). Here, we put a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object called </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span> in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object. If the data is sampled at a regular interval, it is recommended to specify the </span><span style=' font-family: monospace;'>starting_time</span><span> and the sampling rate (</span><span style=' font-family: monospace;'>starting_time_rate</span><span>), although it is still possible to specify </span><span style=' font-family: monospace;'>timestamps</span><span> as in the </span><span style=' font-weight: bold; font-family: monospace;'>time_series_with_timestamps</span><span> example above.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create SpatialSeries object</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >spatial_series_ts = types.core.SpatialSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, [linspace(0,10,100); linspace(0,8,100)], </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'reference_frame'</span><span >, </span><span style="color: #a709f5;">'(0,0) is bottom left corner'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 200 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% create Position object and add SpatialSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >position = types.core.Position(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >, spatial_series_ts);</span></span></div></div></div><div  class = 'S14'><span>NWB differentiates between raw, </span><span style=' font-style: italic;'>acquired </span><span>data, which should never change, and </span><span style=' font-style: italic;'>processed </span><span>data, which are the results of preprocessing algorithms and could change. Let's assume that the animal's position was computed from a video tracking algorithm, so it would be classified as processed data. Since processed data can be very diverse, NWB allows us to create processing modules, which are like folders, to store related processed data or data that comes from a single algorithm. </span></div><div  class = 'S5'><span>Create a processing module called "behavior" for storing behavioral data in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> and add the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object to the module.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% create processing module</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >behavior_module = types.core.ProcessingModule(</span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'contains behavioral data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the Position object (that holds the SpatialSeries object) to the module </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% and name the Position object "Position"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >behavior_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Position'</span><span >, position);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% add the processing module to the NWBFile object, and name the processing module "behavior"</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'behavior'</span><span >, behavior_module);</span></span></div></div></div><h3  class = 'S16' id = 'H_2d94' ><span>Trials</span></h3><div  class = 'S5' id = 'H_FF7B86AB' ><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which is a subclass of </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> objects are used to store tabular metadata throughout NWB, including for trials, electrodes, and sorted units. They offer flexibility for tabular data by allowing required columns, optional columns, and custom columns.</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "372" height = "368" alt = "" style = "vertical-align: baseline; width: 372px; height: 368px;"></img></div><div  class = 'S5'><span>The trials </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span> can be thought of as a table with this structure:</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "378" height = "193" alt = "" style = "vertical-align: baseline; width: 378px; height: 193px;"></img></div><div  class = 'S5'><span>Trials are stored in a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html"><span style=' font-weight: bold; font-family: monospace;'>TimeIntervals</span></a><span> object which subclasses </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>. Here, we are adding</span><span> </span><span style=' font-family: monospace;'>'correct'</span><span>, which will be a</span><span> logical array.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >trials = types.core.TimeIntervals( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'start_time'</span><span >, </span><span style="color: #a709f5;">'stop_time'</span><span >, </span><span style="color: #a709f5;">'correct'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'trial data and properties'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 0.1, </span><span style="color: #a709f5;">'stop_time'</span><span >, 1.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 1.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 2.0, </span><span style="color: #a709f5;">'correct'</span><span >, true)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >trials.addRow(</span><span style="color: #a709f5;">'start_time'</span><span >, 2.5, </span><span style="color: #a709f5;">'stop_time'</span><span >, 3.0, </span><span style="color: #a709f5;">'correct'</span><span >, false)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >trials.toTable() </span><span style="color: #008013;">% visualize the table</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="7C2C4FAA" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_12" widgetid="variableeditor_client_Document_12" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_12"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">3×4 table </span></div><div id="variableeditor_TableViewModel_12" widgetid="variableeditor_TableViewModel_12" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>id</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>start_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 110px; min-width: 110px; max-width: 110px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>stop_time</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 94px; min-width: 94px; max-width: 94px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>correct</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>0.1000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>2</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>1</span></td></tr><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>3</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>2.5000</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 110px; min-width: 110px; max-width: 110px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>3</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 94px; min-width: 94px; max-width: 94px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: center;"><span>0</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
     id    start_time    stop_time    correct
     __    __________    _________    _______
 
-  "></div></div></div></div><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.intervals_trials = trials;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% If you have multiple trials tables, you will need to use custom names for</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% each one:</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.intervals.set(</span><span style="color: #a709f5;">'custom_intervals_table_name'</span><span >, trials);</span></span></div></div></div><h2  class = 'S4' id = 'H_098c' ><span>Write</span></h2><div  class = 'S5' id = 'H_66678991' ><span>Now, to write the NWB file that we have built so far:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S18'><span style="white-space: pre"><span >nwbExport(nwb, </span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >)</span></span></div></div></div><div  class = 'S14' id = 'H_2D49648F' ><span>We can use the </span><a href = "https://www.hdfgroup.org/downloads/hdfview/"><span>HDFView</span></a><span> application to inspect the resulting NWB file.</span></div><div  class = 'S11'><img class = "imageNode" src = 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" width = "483" height = "413" alt = "" style = "vertical-align: baseline; width: 483px; height: 413px;"></img></div><h2  class = 'S4' id = 'H_9060' ><span>Read</span></h2><div  class = 'S5'><span>We can then read the file back in using MatNWB and inspect its contents. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_nwbfile = nwbRead(</span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="0C7C2C82" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_nwbfile = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+  "></div></div></div></div><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.intervals_trials = trials;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% If you have multiple trials tables, you will need to use custom names for</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% each one:</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.intervals.set(</span><span style="color: #a709f5;">'custom_intervals_table_name'</span><span >, trials);</span></span></div></div></div><h2  class = 'S4' id = 'H_098c' ><span>Write</span></h2><div  class = 'S5' id = 'H_66678991' ><span>Now, to write the NWB file that we have built so far:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S18'><span style="white-space: pre"><span >nwbExport(nwb, </span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >)</span></span></div></div></div><div  class = 'S14' id = 'H_2D49648F' ><span>We can use the </span><a href = "https://www.hdfgroup.org/downloads/hdfview/"><span>HDFView</span></a><span> application to inspect the resulting NWB file.</span></div><div  class = 'S11'><img class = "imageNode" src = 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dWAakA1oBpQDcxbA2pg1MCogVENqAZUA6oB1YBqQDWgGlANrKYG1MCoWFdTrPrXjHn/NUP8il/VgGpANaAaUA2oBtZQA2pgTtjA3PuZ393ddfVt3XOf+9xJm4iOXk95ylM6f5C7Se1TyJzQz2vjvq8+cwurD13VgGpANaAaUA2oBlQD518Dkzcw3dVVx4eKKC6il/+Nl3f3/s17u3uv7u3uuLqj+/xP+fzuK7/0KydpZKxJQPMQNQx9G4k+1/CUc/dZp7BxXYoX8aIaUA2oBlQDqgHVwNJrYNIGxhoXPmE0Mmw7ho55IY8xZ5850Lx8x9X/1n3n1Uu677r6R5tG5r7ub119XfelV7d3d37LnQc89slt2FM2Eaecuy9PwusDWjWgGlANqAZUA6oB1cD6amCyBsY3LyiGzA7/HBKNyynmrp0PmpfvvHrR9unLt159e/eab/3M7n3/7M91r/v7n7ttaL766qu3jcxzvuw5gxoZbiLwJGaotFy1c2L/KefmdUhf34eRrpmumWpANaAaUA2oBlQDLTUwWQOTTXaKJoIbmFPMn3HBzYs1MPYE5v/9V1fdx7rN1+42xyOPXW3H339196aRedH29zH2ROZpn/u0Xk3E9itiv7vJWTiurh7dULM7SjjLlZ1PZI/mxjyZzObvO3e0Htn0QagaUA2oBlQDqgHVgGrgvGpglgbGNxA8HlJAtpHlo5aDmxbMXYuZ228/2LffvFhjgsN+/3LvU1/Qfddf/eLuf7n6vu59b9k1Mvd821V399XmNzIbv2E/5zM+Z3QTwU2CbyTY5/W+TYThfY7D+ewnUreaJ9M9HuO+c899DZX/vD78dD11PVUDqgHVgGpANbDOGpilgbFiQOPAckiRbDfE9r0kevXd2Noahsw9VYw9eXnR/3CrcUEDY79/+Y6/9D91P/XXv777qbuftn0S86GPXnXf9d/9l9sGxv5C2VOvntp77VETgabAZGsDYdi+XEdzH86nBmaqulKedX7o6rrpuqkGVAOqAdWAamBcDTQ1MFebBoCPVtLHNi82j22Ihx5Y5ykbGP+1MWtedk9WXtK9+Mv/cve+D++eurzzoavuna99avedX/IV++bl8/6Dz+v+/F/885M2MIfNxPG/QsaNVE3v2zzhekuO+1AQf+JPNaAaUA2oBlQDqoEl10C1gbHGZbvRtCcg9puKzYubmUzHSY9pHqJ/za9tetl/6g2wb17wlTB78vIPr75n+/Tlp99x1b3qRVfd3/0Lz+vsB/32F8nsyYt9beyVr37loP9HTIk3fGWLGxnmzOt9OYzmthLaHfnXxfy8Nu47N2pOUh+6qgHVgGpANaAaUA2oBs63BtoaGOtarpuXFmlNzRRFE22Go41uZjvlBth+oP/p//6nbw/7q2J48mKNy13PeG73ngd3jctdn/KN3bdvGhb85gXNyz0vu6f7zQ/8ZjfkL5FFvPmGxY+n4rA2N89rejav2U95/aaoX+U43w9OXVtdW9WAakA1oBpQDZyuBtoamB7NC57QTHFRo81wacPrfafcANv/nPKznv5Z26coz37Ws7s/+2l/dvv/ebGnLC96+n/f/dqmgXnl3VebxuYl28Oezljz8rSrp3XWvPziL/1i965fe9f2f275Z67+TK+GMOKNGwfjyY89dxj35bA2N8+rBuZ0b/wp3p/KoeunGlANqAZUA6oB1cApaqCtgdETmF4NhF3IOz71ju3Tk9/+7d/u3vmL7+x+/Md/fPvnkO13LfZExhqZl129cvtk5gc2f3Hsm66+efu1MTQv1sC89ntf233zp31z76+R+SaCmwY0JpENPpZzNzClJqbv3Kd4A2lOfXCrBlQDqgHVgGpANaAaOG4NtDUwwROY7LcvNXufC+w34ryxbtFPuQG2JzBP/7KnW+u3f/36r/169+offfX2ycwz/91nbv9nldy8vPBFL+x++f/65e3Tlx/+nh/unv9pz+/dvBi/njduVjI947Mvh35uzmu/g+Gx6bwe7+s7d5/aEva4HzTiW3yrBlQDqgHVgGpANTBVDbQ1MLYFpybGmhS/2WwZ9/1tTGkz3DLfKTfA9psXe3ry4//rj3c/9jP/tPvjR/9438jYUxlrVr70r31p91lXn7V/8sLNy5AnLygKzxs3CZme8dmXQz93lhd2Xg9skH3nxvlL6gNSNaAaUA2oBlQDqgHVwPnWQFsDc4LmxYqu72YYG1/IU2+A7fcsf+Xqr3T2g/6X3fUD3Y/9kx/bNzGm/OEf/mF357fcuf/Ni31tzJ683PmUOwf9eB9vVM8bNwmZDs687Muhn9vn82Nej/f1nRvnL3m+H1i6trq2qgHVgGpANaAaUA20NTC2295+3ec4T15QmH03w0vdAD/tc5+2/d3L3/t3/l73khfc3f3sT/xs9weP/oGxun1ZI2O/k5mieTHuWngrNQ7MY98momVuzl9aR9+5UTeS+mBTDagGVAOqAdWAakA1cL410NbAnKB5saLruxnmjbHpS9oAf+WXfuX2qYr9gP+b/pNv6u79m/duf+tifyr5Y3/yse6HX7L7zcuQP5vs36Al3rhhYN1zh3FfDv3cPEesb3o4e8IXHH3n9jxofL4fXLq2uraqAdWAakA1oBq43BpoamCO9ZsXX4h+Mxxtcku2JW6A7StjX/C0L9h+teyuz76rs//ZpT2Rsb9a9tznPrf3XzvznNm4xFvcROT/P5a+HPade23XL+Jbtsv9ANW117VXDagGVAOqAdXA8WuguYGp/XWxyD/2gpY2w6WNL3x9N99j19s3/vM/5fO3f4nMfvMyVfNiayjxpgbm+G+yvnUhvK6RakA1oBpQDagGVAOqgbwGqg3MKcmzjfjY45Trb53bnsq0YltwYznz8S1zAuNjx46RVzJ/E4sbcaMaUA2oBlQDqgHVwCXVwKIbmEu6EDpXffCoBlQDqgHVgGpANaAaUA2oBuo1oAZm85UrFYo4UA2oBlQDqgHVgGpANaAaUA2sowbUwKiBUQOnGlANqAZUA6oB1YBqQDWgGlhNDaiBUbGuplj1ryLr+FcRXSddJ9WAakA1oBpQDagG5qwBNTBqYNTAqAZUA6oB1YBqQDWgGlANqAZWUwNqYFSsqynWOTt55da/FKkGVAOqAdWAakA1oBpYRw2ogVEDowZGNaAaUA2oBlQDqgHVgGpANbCaGlADo2JdTbHqX0XW8a8iuk66TqoB1YBqQDWgGlANzFkDamBW3MC88IUv7HSIA9WAakA1oBpQDagGVAOnq4E5N+rKHTeCaQPzwAMPdDqWwUFWvPZhpZcYEANiQAyIATEgBsTAaRiwvVi2T9M+evw+OuO22MB8+MMf7nSclgMr/uziqYE5zYeVZhUDYkAMiAExIAbEgDFQa2C0jx6+jy7tgdXALLxJK108NTD68BQDYkAMiAExIAbEwOkYUAMzvEGpNXelPbAaGDUwp3vXa2YxIAbEgBgQA2JADKyYATUwamD0dbWgmSp1n3oCs+JPPC1dDIgBMSAGxIAYWD0DamDUwKiBUQOz+g8ynYAYEANiQAyIATFwOQyogVEDowZmpgbm6uqq46P0sWK46JXZDVvyRbnmsGVrMHt0zLGGWs41rLF2DsfyZ1xF82fYFnuGieZhG8eZHh0Znu1T6rymsXmH5hoa59fbNw/wkH3yZTGWw3x8+LzAtNpruZAPOM5rtiW/lrK+oesYGuevSd88ffF+vj7jY87VZ11rx6qBUQOjBmbiBsY+rKIPrMxuHyIRvmSv+cx/jFffdWf4OdeazVmyZ74517mE3H3OO8OaPfKxjXV/3q2+DMd21v08U42nnGNorqFxYznAvJA+X2Y3XOQzW6s9w9XW0BLHGNZ97iWMl7K+oesYGjeW+2POe8y5xvKypng1MCtqYOxNEB21vyawBL+tu7aO6NzYZvGch/Va7r7+Mb+BsXVlr8zX1275s5hs7jns2Roy+ynWna0ls59ijXNcmyE5S5z4fBnW7JHP2/wY+VvtGc7ywAeJ3HPIKecYmmto3Fg+MC+kz5fZDRf5Ihtyel9tPFWcnwd5lyKXsr6h6xgaN5b/Y857zLnG8rKm+LENjF0XPvw+0XzeNveY15PpU62hdH6lPfCgv0KWTZbZpzrJKfK0rLEFw2vpi+fYml66ePamyV62ptqLMdAhfWxmN1zkMxsfPh/igPF+5PR+jCERBzzGkJnd/OxjHbERxnB8RNian2P8HN6X+bM5OD7DmD16sR06cjA+skV+5PA+GyOHx7AdOuIxhmQ7dJaGsxckfLVxhsvsPh9wJuGDzHzm9xg/5ljWgWNpOsaMremIY2kxWS7YGQ+d54INeO/DHPBDAsfx0OFDLEv2leyRz8/tc/kYxrPOcS32GibzYz3mH4NBfJQDtjEY5GBesHbYOH+GB9ZLH4t4yAhvtiwOePbDBoncwJgdNsbADwlfhI98iItyMz7TEccSObMY2dsZGNrA4Br4faC329hjjj2ecw2l3KU98KQNjBFaWsixCY/ma1lfC4Zz98VzbE0vXbyxDUz29rTz8a/IBoz3+bHhvK1lXMP4vB7v/TbmF+NZjzA1v8UYxuNqY8TxnF6v5fD+KCdjWOe52G66H/u87Eceb4vGkQ3xkH0xHs9r9b7auBTLvmytmT2aN7Jl8dHcbLNcpXzI2yJb87TgahjzlzDeh/WzPdMjLGyQHGs2PwYuk8BDRrjM5+19xzZXLaYFU8th/rEYHw+eYIeEPVo3+zLd5/FjxHm7H0fze4yNI1s2R2T38cBgfu/nMeuIK9nM5/1+jDyS7QyMaWCyPaBdF/hYh+3Ycs41lHKX9sBHa2BsgTgy4uHPTqbkR0yGYTv00joyH+yYz8ass780D3yQiPOydPHW1MDwR4Gdc/RiO+vARjb4TEb+yNaKzWKzOTM821lHnsgGn19rhmU761Gemt/PiRxRHNtYR4zP1YLxMdGY82d+nivTkYf9nC+zIw5Yw/mDMcCxzeeGj+2s13J4bJQPtpr0ufwY8d5eG0dxPsYwkQ2xkIzJdMYaJjuAM8m52J7pyJn5zZ7ljOzIl/l4ngjj56than6fL5t/SB6OYZ3n6Kv7PH6MfN5eG0dxPsYwkQ2xkIxhHX7IzMd21i2uNPa+2jzwX5r8pV/6pfSUI9+QBsauhd/v+TEwkOw3Gw62Q4ePJXwmzQ4Jnf1er2Fq80T5OAa6x5X2wEdpYPyJ+7Et2NuGjGsx0TyerCGY2rw1f2nO0sVTA3P4GWM8+5fZosPjbOzj/bgWk+HZzjryRTb4TLKf9b4Y4IfmiOLYxjrmMsl21jOMj4nGHJv5ea5MRx72c77MjjjGsi2yt+TycbUY9rPOa8nsjPG6j/Fj4L29No7ifIxhIhtiIRmT6REWNkiONZsfA5dJ4CEjXObz9rFjzM15WIefZeZnO+tZ7BAMx7DOc/TVfR4/Rj5vr42jOB9jmMiGWEjGsA4/ZOZjO+sWVxp7X20e+C9JPvLII91XfdVXdbb38i+zmc8w/JqrgcH+1K4b9Gi/WPMjlnGm8xiYTJaw3jd2zGso7YEnbWD8om0Rkc3ba5ia3+fjk/exfsxY6IbJDsbUdPhN8rysMybSSxdvqQ2MvbE9f/xm9z4eA2e26MVYj/Fji49sLfYozmzRgXVGMX6uCBPZkLMlvhWDnNl8bGe9FMc407OjlMOvPxrDxvmREz4eRzasFTLDez/GkFmct/s1+Hg/Rry3Y5zJLK5mhz+SmAs+P87sHufHUVyE8TYbR8fQfIgzGc3F/prO8axzXIt9CMZisgPzZ3nZPzQH52YduU16O8aQHmt2HOxr1X1eP0Yeb6+NozgfYxhvs3F0RPlgg/S5Mjtwmczianb4L01GTUzWvBg3x2xg7BpHe8XMzljGsM6YTM/wLXaPqY15DaU98OAGxhbgD54Uul9oZM8wERY2kxzHeobxMYxjPcuVYRjPegueMZFeunhjGxhba/byPj/muJIPOMawDr+XLRiL8bjamOfxWM7nfX6MPGxnHX7OCZvH+TFwkOxnHX6TbGedMdAzP9tZL8UxjnXEeJlhvN2PfR4be0zr2OOQG3bImt37MWaZ5TKM9yHO2zHOZBZXs8MfScwFnx9ndo/z4yguwrCNdcSbZDvr3heNzYZXLRY4lhzDumH8OLN5exTnMX6cxRgOrxqm5rc8GYbtrGPuKBY4SMZ6vQVTi8lyeHttjHkYx3qr33AcxzpyQGY+b8c4k33zAX/JkpuYUvNiHKmBuflnlK0WeU+LMWTkYxv00h54cAOD5CyzhZk9OhAbxcFnMoqFDbgsh7f7MeJZ9sUw3vTs8HMwjn2sly5eqYGxN5Xlz14lXxRbwpd8mJ8xrMPv58wwjPcxLWOOz+aI7JHNz9eC8THRuLTGljlqmJo/W1MUxzbWs3NowWTzc84I43P7MWIiO3JnvsyOnIj30sfxmHWOi+ze5seldURYni/SfYwfI8bba+MozscYhm2sI76G8TF+nOWBvQ8+wrbYPMaPs7UwjnXgTbKd9QhT8/t8UY5WDGKzOeGHbMUBb9LH+DGw3l4bR3E+xs8f+VsxHof5M7ufy4+zuJKd57xEHU1M9LUx5uPYDYxd2+jgvWPkNxswrMNWkhk+myfCRzabM7Obr7QHnrSBiRZSWhjIqmFq/mjeLPeYXMjp5+OcrDO+pmdxpYvX0sBYXv8yW2RnXOSf0uZz1ca2No+JbB7jx7VzjHJmNm/P5vL22hhrNJzH+jmjcWTzeaIx21jn9UCH9Li+46F5LG7IXBbj47CGKCd8U8VwHtYxT7YGj/VjxHm7H/M8JT2K8zYbRzaft4bxfpwL8kT+GsbH+DFy+zywGz6KiewRLsrrcX7cEtOCGZLXx0TjyAa+onXB5+MybCsOeTPZkscwHufH0To9xo99TORvxXicjfGK8nqbH1us2bzdjzGH5I4Ba2LsKL3mamDs2pT2m9m+EnHez3bWPS4aZ/jM7nMAB8n+yAZ/aQ98sgaGF8w6Fm0Sdkj2sd/rjPOxfsxY6H0xjGcd+UxmdmAyf+ni1RoYvOEsNx+wszQ/v/wYPs6TYQzbgmMM8kNmuTkmwnibHyM/pPf7MXAmzecP2Fmazi+fMxr7vB7D+UxnvPdhXMN4P8/JOueDDpnhkBs4yAhvvsjubcgJiZyQER4+lh7X4qvFYE0sOS90zsM6/CYju7f5MceZDwfn7atnc3DuDOPnQkwfPOfgeOjmRz5IxNTGwHEOtkHHXJCws/RzlXzIU4sBjnNBj2KBj3wcBxxsLOGLcsDWguGcpiMWdj+GHdjSHIwt6dEcPm+G8Xl9HPtbcnA8dMuBWEjk5THr8HNsyRbFwmYSB+eQPoyBIQ1MbV9o1yfaH7Idfp9rDIZzst6SM8P72NqY85T2wLM3MLaQlsXWMH39IKAWBxxLH8M+6Ixh3fx9x1EM5ildvNYGZthb8tYH7ND4U8UZ/0NfY2L7zHmsefqs6Rywa+B1DWtULSybgWPX0JTzTZlr2VdJq7sUBsY0MPZ+wH4P0mxsZz3aL9b8nDfSYStJPwdjvW/smHOX9sCTNzA2sV88bGaPfFgs/Bmm5C/FID9khm31G45zsM45zB75EA9/hjFc6eLN3cBcyocPztOug15i4FwZ4M+bTD/Xc9d5jWNgqs/GoXmyemX7uDNUtBgYzsDQBibaL1pNww6Z2VD/wLGEj6X5kQuSY0p6De/nQa4sztv9GPGlPfCgBgaJJW/+5YWpOSldPDUwwz9wokh7A+klBsSAGBADhwxM9dk4VZ7D1WkkBk7LwNgGZup94znlK+2B1cBsOtIlX+zSxVMDM92Hlm6s03GpTGJADJwXA1N8Pk6R47xY1dmcCwNqYObbR5f2wGpg1MCcy2eIzkMMiAExIAbEgBgQA0dlQA2MGphFPwk51VOaUvepJzBH/YzSZGJADIgBMSAGxIAYOGBADYwaGDUwwdMgNTAHnxMaiAExIAbEgBgQA2JgMQyogVEDowZGDcxiPpC0EDEgBsSAGBADYkAM1BhQA6MGRg3MgAbG3jg6xIFqQDWgGlANqAZUA6qB09TAww8/3EWHfYvmVD9BOId5S99CKv6I3wJ1nJ6D6E0hW/xhIV7Ei2pANaAaUA2oBlQDS6gB7aHH76Gz65g2MFmA7PpQUA2oBlQDqgHVgGpANaAaUA2oBk5VA2pgksd+p7ogmlcfBqoB1YBqQDWgGlANqAZUA6qBvAbUwKiBCb+3qTdN/qYRN+JGNaAaUA2oBlQDqgHVwOlqQA2MGhg1MKoB1YBqQDWgGlANqAZUA6qB1dSAGhgV62qKVf/Scbp/6RD34l41oBpQDagGVAOqgaXUgBoYNTBqYFQDqgHVgGpANaAaUA2oBlQDq6mBsIF5wwM/3ekQB6qB49ZA679q6Loc97qIb/GtGlANqAZUA6qB09VAtD9KG5ja/3lUfjEgBqZjwD4YozdoZDOsXmJADIgBMSAGxIAYOHcGsv2RGphzv/I6v1UwkL1BSw3M+9///k7HZXEQ1YNs+k66akA1oBpQDSytBmzzNXZNliPbH6mBMXb0EgMnZiB7g0ZvfsPay5oXvS6HAbveUT3Ipo2LakA1oBpQDSytBuzuPHZNliPbH6mBMXb0EgMnZiB7g0ZvfjUwJ75YJ5peT9vW+7Qteh/Lpg2nakA1cM41YLfKsednObL9kRoYY0cvMXBiBrI3aPTmVwNz4ot1oun1BGadmx1dt3Vet+izVzZdS9VAew3YrXIsX5Yj2x8dvYF5ylOe0vFhi9NLDFw6A9kbNHrzq4G5zGrRRrj9xhm9b05l03Vb53U7Vb1oXtXLudSA3anHnovlyPZHR21grHHxr8jmMWsYX11dLWKZQ9cxNM6fdN88ffF+vj7jY87VZ12Gzd6g0ZtfDUxfds8Dr43wOjc2um7rvG7RZ69supaqgfYasDvvWL4sR7Y/OnoDw09fWnRb/BpeS9kcD13H0Lix1+aY8x5zrr68ZG/Q6M3fu4F5y9d0du44bvv+h2h5b+q+4eqLu+/7TTJB/eD3dM+5ek7sA2ak/Lk7Nuv6+p8fl+VgnYXzGTdLEG1zfUPX9EetcQ2e/YPdB4JMLSZthNtvnNH75lQ2Xbd1XrdT1YvmVb2cSw3Yfa10Ls973vOKfou1V7Y/OloDs33S8rubpxTXx9XVo5sN1c0Dfsi1PKFZyuZ46DqGxm2ra8R/jjnvMefqS0n2Bo3e/L0amO3GmZuQ93cv/+Kr7lYTM9eGvyHvpvH44i/6hu4bvviLXJPkY/24xG4fLOcZEmcxLQ2M4a66v/0zPF9/XRvhdW5sdN3Wed2iz17ZdC1VA+01YHe5jC9rXnBkGLPbK9sfLaCB6Q4aGTQukC0NjG1McWzPNvgP/NkmtuRHzBgMcvilsZ3zs93HRGMfi3hIHwN7Fgc8+2GD9DnMDhtjOEfkB9ZLYDmeMfCzLdKBY4mcEf4UtuwNGr2x2xuYXbNyY+O8fWLx/OsnB0M27i0M1fN+6OVP3zZSkLey+lg/voW8qfXBcvSQOItpbWCSp1y8hIpe2gg/8u4Xdc+mz8GrL7y3e3Dz4R/Vz+y2R/6P7muvntW9+Fc+Hs7/8Tf+j/vPa3sffsFLfvUWrhC7O8dnp3kHn9d2zq/tfuLhR26tI+Fuv/Ye/Jau2+A1J+tTvhPVvK5H9b2j2ry82rRbWnTd0biwjHBms1e2PxrcwDz55JPbxPyfyAZ/9gQGjUomaw0MNqWYx4/N7m1Dxi0xJYz3+fVG/siGuEz6GD9GnLf7seG8LRpHtmyOyO7jgTFpPu/nMeuIK9nM5/1+jDzHltkbNHpTNzcwB40Kn9Gmsfmi/+r6qcdu4/6yt7xs83WxHT/p05ltPvxjARogy7trlMDv1735LdsnDhjHXxGjNdiTmP1Xq2w9mGMj79g8oeHx9utmuzXf8Q3P3lzP53c/s2nFbn0Nrsf5dDvs933wZ4M5NqeVni+4tHg0MLtcP/Dyr9/X7Y5Hs986nz23B7nbns5kG+FoY//G5/2tpg15VF+jbYUmZNcAUBPyyLu6e55FTUwhdtS6SnlbG5gt7qr7qp/8/8Kbcra+7LpleNkvb6Ona65rfo41YHdKf16+afFjj7cc2f5oUANz3333dXfddVf3+OOPW+7ty3Sz3X///TAdyJsNjG1QH91/pWxIA5NtPtnOOi8Idkj2mc521hnHdtZbMRzDOsf31X0eP0Y+b6+NozgfY5jIhlhIxrAOP2TmYzvrFlcae19tHviPIbM3qH8z27hPA3OrMeCz4Ccz1xtsNBDbjTW+crbblO9+H8P6Jtfmq2m7zfgu135jvp/G4ff2a+WgaaFmZuv2sdE4+xqcYTcNQ/V8bCLOy7r3bcb78zUfXhbDDcxmXvyeZ/vVPTR5UW5qWg44R+6bMtwIXzcAfTfVUV1NZsuahWSt1oA96+pv7xquLHbsv3CX8m59DU9gSjkK6wuvWwE/2XXQHDc2T+JWjYJq4Hg1YHcxzzcaFrZHNvgtR7Y/6t3APPHEE9tG5fbbb983MWheYDOMf91sYOq/f7GmpvQEpmVDmmGwvszPdtYRZ5LtrLdiOIZ1ju+r+zx+jHzeXhtHcT7GMJENsZCMYR1+yMzHdtYtrjT2vto88B9DZm9QvIlZ9mlgnrN5QnHzR+bcMPjNddfZD+t3Xzsjn3tiYFxuN+tbezQHxQYE+q+N2Xi/+T9oLCzY5yqNvS85n+2aGMv6xpmd7zYO/7EYbmD4a2Kcj3XkPuTsFufIfVNGG+GDzX+yYd09ocFToMNG4aX33PoDD/xVrlLM8772Czfvs12eB56HvBv5NQ/sblbJRj9dqzU2z7xt99Ww69i733j3/itx+3W5vOEajYPrRmlbo5s6/eo3vHHzlbZgneBrm/e6gbme4wYvW/utHFjT4RripzPRdeP3s/TjbajEtbhWDRyvBuwu5vm2ZsXbbJzZLUe2P+rdwFiyxx577KCJsScvaF74qYxh8WppYKInMrUGBjcpLzGv2UsvH8djxGU52M464kx6O8aQHmt2HOxr1X1eP0Yeb6+NozgfYxhvs3F0RPlgg/S5Mjtwmczianb4jyGzN2j0Rm9uYK6/2tXvNzD+6cz1pjxrVDL7jaaDWbQNfVQX2NS7Df+NXCW/9yXns10OY1nfONPz8udx+gbmmfR7jPfc87nX77frr2m5Tb99hWu7+caG/Lrp2H2167C5wW9YfAw27we1yfOwjiZhI22zz2vdx/OTGazr+px2DUJwLm6OW2t0X0nD/A6/n9v8Wx83MJvabOBlF3eraTlYK+bdSDUwx9swHVxXugay6xqoBo5fA3a3HMu75cj2R4MaGEtoLzx58V8n23kP/+sbGHu6su0tgr9Mxl8nqzUwh7PcHGFTe9Ozs9T8hsowbGed5/J2jCEZ6/UWTC0my+HttTHmYRzrrX7DcRzryAGZ+bwd40z2zQf8MWX2Bo3e/O0NzOYMDr7KZGdkG3X/9SsaH2zceVPv48DOrkHo9RUyWxO+4oU0B80Wz4s1Z083vN+t8+B8uJnZxG25ib4uh5zEi5luvGyuAQ3M9TXYN5YHa7wxyd4QbYRt07z/+hU2bLRZ322qXbNom3PCbGuMxs0xm/l2jQ/yB40G1rSR4VrNHzyBQfNka7OnPNuvyDWsMZ+j8IcFtnm5gaE/QEBzes6iufZrpfOOrlv0vpbt+BsscS7OVQPz1YDdvMbyazmy/dGoBsYSWxNjT2Rqr6iB4UZl19DsvlbG9iENDDaytibWeY2wQ7LPx02FwRxZPvghW3HAm/Qxfgyst9fGUZyP8fNH/laMx2H+zO7n8uMsrmTnOefWszdo9Obv1cDYwrcbdWwy8fUwnNGuWdj9IH6H2W+s/ZOP7Ub7Vp7Dr3zdsiN++/94sSct+F3I9ZRmv9nwdB1/jczHHo5LDU7pfBwX2z8QcKsxOpxjg03Pl7kb0sD43GiikDeW4UaYN9jYNJMt2mRva4owftwac4CrNCG7OXZPR/zvdQ7zuEbjxtOZXXNxEIPz3sjM7puPg/fVlgs1MAecEKeyz7fBE7fi9pxrwO5kY8/PcmT7o9ENjCVvec3RwNi8fqPqxy0YHxONIxuft/fDF9nH2JA3ky25DeNxfmz5va029jEejzWznXX4ITNfZPc2P8bavN2PMfexZfYGjd78vRuYwSfDTxgGJ1HgRAyEDcxmo7n72tj1179s48nNyVanv/KFjSljBsZsn77UvuqF+a7l7omNXyutz633oCHhNTvc/n1y3fDc+Jobx7o17fjq38Ds4g6/Qnbjadhmruy67dfs16Px6E2PuFVjoBo4fQ3YrW/sdbAc2f7oJA1M9D+w3Nk2K6WvlJleegJjJ2Yv24Ti2Flu/hf+bMNa8iOmBeNnRizsfgy7Sc5fwnFMpEexnBtz+diWOI7J8B7DcyPGS8TAbmPW4c/sHuvHHGc+HJz3lHr2Bo3e/EdrYMKvep2Spcueu7QRPvwqFzUEm82wNQEH/4+YylfIrObaYnZPVLbvpWd+bfe1z3rmwQ/x+WtgXMd+rQdPZK4bjd0fCti9T/d+14SEa7TN/3Vzg/c44vd/cOD69y37NW3xAxqYGzzRn4e2dVwfpesGjOQtvsSFuFANnEcN2B177LW0HNn+6CQNDJoU38jAzrKlgbETnPNlN8KpXlPmmmpNynN6BrI3aPTmn72B2X91qu2rTadn7zJWcPEbYW40qEGI3iNLsl38dVvRtVpS3Wgt57GJv+TraHfmBx98cPBh3Nkr2x8dtYGxZmTIsT2DE/5nqqZjaB6Lqx0npEdTT8BA9gaNPvxmb2AmOB+lmJ6BS98I81fWovfFUm2Xft2Wel20LjUIqoF5a8DugmfRwEx/Oz9exqGNh1/hVHl8Xo3Xz4AamPVfw7nP4FI3wre+KhZ/RWvpm5BLvW5Lvy5a37ybV/Erfu2eqAZm7p1BJf8UjccUOSrLlHvFDKiBWfHFO9LStRFe54ZA122d100bcF031cC4GrBboxqYI20QNI0YOBUDamBOxfx65tVGeNzN9FSbEV23dV63U9WL5lW9nEsN2N1VDcx69hhaqRgYxIAamEG0XVSQbYR1iAPVgGpANaAaWHINoAGzG7QamIvapuhkL5EBNTCXeNV1zmJADIgBMSAGzocBa6zUwJzP9dSZiIEqA1M0ML/1W7/V6RAHqgHVgGpANaAaUA0cuwZso6MGprrdE0AMnBcDamB0szn2zUbzqeZUA6oB1YBqYKoasF2ZGpjz2pvqbMRAlQE1MLqJTHUTUR7VkmpANaAaUA0cuwZso6MGprrdE0AMnBcDamB0szn2zUbzqeZUA6oB1YBqYKoasF2ZGpjC3tT+fyp8FKCrcvX5/8R4bG28KiIudLFqYHQTmeomojyqJdWAakA1oBo4dg3Y9k0NTLKJ3W7UH3zF5u+y3Tr2tiTmEsy+gbmEcz63c1QDo5vNsW82mk81pxpQDagGVANT1YDty9TABLtT26S/5o5nHDQv1shENrNf0qb+ks41KI2zMKmB0U1kqpuI8qiWVAOqAdWAauDYNWCbMTUwwZZ0u0m/blisacHBT2NYN3/Lxt4w/gimP8C0+Hlu1jmW7awbxsZ8RHHsh45YxsPGGPabvYZhvPTpGVADo5vNsW82mk81pxpQDagGVANT1YDtjNTABPvD7SabvjrGzUqkWwNz2223bZuAIN3WhI2793v7kDHHsM5zsT3TgS/52Wf4IeNaDNYhOQ8DamB0E5nqJqI8qiXVgGpANaAaOHYN2O5IDUywR9xusCduYIJptibezLPOeNgh2Wc621lnHNsznfHQGWu20tj7ohwtGMRJzsOAGhjdbI59s9F8qjnVgGpANaAamKoGbHekBibYI2432QtqYLDEls1/X0yGz+b0eB6zjniTbGc9w7Bd+vQMTNHAfPCDH+x0iAPVgGpANaAaUA2oBo5dA7YzUgMT7A+3m+wZGhjLGx1YQra5r/k5jnXEmWQ76/CZDUcWB2zm93mBYzvr8Ed52Sd9WgamamCm+pcU5dG/yqkGVAOqAdWAakA10FID1izZSw1MsDfcbrInbmBaNu4ZBkvM/GxnHXEm2c46Y6Czn3Wfx489tiVfhIFNch4G1MDoJtFykxBGdaIaUA2oBlQDS6sBNTCFveF2I76gBgaNAaRfOttZZxzbWWcMdPazbv7S2Pta8kUY2CTnYUANjG5IS7shaT2qSdWAakA1oBpoqQE1MJW9oW3G8eeTa9L+AtlS/gqZnZZvJGzMtkwHJSU/+7K5kKfFD6zPC7vk9AyogdFNouUmIYzqRDWgGlANqAaWVgNqYBr2hbapRnNSk8CW0hrGH4Y3G78Yw3bo3u/jDVfCeDxjIx/mhWQM6+xHTtggI7z5MjviJKdjQA2MbkhLuyFpPapJ1YBqQDWgGmipATUwjftBbMRr0hqcU720+T8V8+ucVw2MbhItNwlhVCeqAdWAakA1sLQaUAOzzr1nuGo1MCEtMiYMqIHRDWlpNyStRzWpGlANqAZUAy01oAYm2dyt0awGZo1X7XRrVgOjm0TLTUIY1YlqQDWgGlANLK0G1MCcbv84+cxqYCan9KwTqoHRDWlpNyStRzWpGlANqAZUAy01oAbmrLeoOjkxkDOgBkY3iZabhDCqE9WAakA1oBpYWg2ogcn3d/KIgbNmQA2MbkhLuyFpPapJ1YBqQDWgGmipATUwZ71F1cmJgZwBNTC6SbTcJIRRnagGVAOqAdXA0mpADUy+v5NHDJw1A2pgdENa2g1J61FNqgZUA6oB1UBLDaiBOestqk5ODOQMqIHRTaLlJiGM6kQ1oBpQDagGllYDamDy/Z08YuCsGThGA/Pwww93OsTBmmsgumm/8IUv7HSIA9WAakA1MG8NRJ+/sKmBOestqk5ODOQMqIFRY7HmxuJYa8fNkqVtWvQSA2JADIiB+Riwz1n+3PW6GpgG7u3/r8JHQ8giIFP9f2GmyrMIUrSIPQNqYNTAHKsJWPM8/qZpYzQwaz4vrV3vf9WAamCpNWAbFTUw++3aMGW7eX/wFV1Hx942LOXRotR4HI3qVU6kBkY3r6XevJa0LjUwep8sqR61FtXjJdSAbarUwIzYWloD8Jo7nnHQvFgjE9nMvrSGYWnrGXEpFDoDA6dqYKwuSx/AkT+y+RyMYd3j+o6nzGVzWz4++q5nLN6fjx+PzV+L53OfYu4pcpTWrAZGG8ZSfcin+lANTF8DtuVRAzNi42c3RjQs1rTg4KcxrJt/G1OZ0zD+4BDkYAz7obMfOnwmOQ/bocMPLHKwHT7EYJxhGSd92QycWwPDNxGrTx6P0afKhfeMX0tm97ipxlOdT9/1ZOeZ2Vvzz30+amCm35y0XlvhxL1q4DJrwHZPamBG7CHtxsgNSk23Bua2227bNw7R1NucgYPtpvPY4LUxUjIOOiQwkLBDwm6SbZkOPPthk1w+A6doYKxW7IYEGd2cIl9ki2Jh64tHXCSnylXKU/JFaxpjO+ZcvM7SvCUf54j0MbFRPm9TA3OZGyhfBxqrDlQDx6sB20GpgRmxj7QbY61pYX9LA5MtZzvXtZN1xmf2DMN41g3PY9Y5F3T2sw6/5DoZWFoDY7XVcthNxHCQrOMGwzbkhA8SGIwhYUccS2BMZnbGQEdOjCMJDEvowNsYB2ws4WMJP9ugm890YCDhz3yIAw5xmYzyeCxjoEf5YWNMlAu4yGc270c+jx/TwGAOSJ977rE/Jz8+xvw49ynmniJHdM5Z3sxuOczHh8/LvkyfIsbnGDO2dY6JL8XOmbs0r3zHazqm5Np2VWpgRuwt7Q3HDUpNVwMzgmyFHp2BYzcw/gbmx/jwi+zeZuPIxjlKfsN5P8dCz3A+1o85PsvhMRhbriift9XGnA+6yVpci7+G4fmiOb3fYyy/n8NjMPa4lrHHROuBbUgDY/mjOTI75ppaRmuYeo4oX3aemT3KEdnmOp9sXdF8JWyEx3mUfMB4OSTG5+gznnO+OXP3OUdh19HQ2IZIDcyIbaG94WpNC/tbGxjLGx1Y6nZeDEh6e5SDMaxbGowhKfWN9XifH1sOHOyTvh4G1t7A+BuR1SNsrMNmku2sZxgfE40Rm+UrxSCWZZQnsrXm9bGlsfdhXWxnHf7aWrKYLD7CRzY/7xgMr4X1oQ0M52A9WyNjptKPORevuTRvycc5In1MbJQPNssb5W61cR7oXka5PMaPh8T4HH3Gc843Z+4+5yisGpgHH3ywyw6rD3tl+6OrqIAMvLSXveG4QanpLQ3MNmdwomxnnaFsZ70VgxhIjvM6Y1j3OBvX/FGMbKdnIHuDlt6f73//+w8Wbv8jqWiDBxtyWY1AZxnZW2w1TOS3ednOemlNHufHiM3sfl7gMxnliWyteX1saex9WCPbWYe/tpYsJouP8JHNzzsGw2thHbXM0v5l0F6Mg56tAX6TwLCEDpyNccDGEj6W8LMNuvlMBwYS/syHOOAQl8koj8cyBnqUHzbGRLmAi3xmy/zAZ/lh9ziMI+ljgMns8Ecyi4HdJHSLx5hllhcY9nMu2DNbFI8Yk/AjHpIx0tfRTJziOtnnq57AGAsDX/aGqzUt7F96A2M0bM+pgQ/GsR6F1vxRjGynZ0ANzM3NHG68/IFt9e3HZosOxrHuc7DP6xE2mgs2jofNS4/JxhbHPuhsZx1+k5m95kMOjmc98sPmc1tcdiAmyg1fJLlxgT62gcE8WCvGkH6NtfHQuFreaH0+BnND1vyGY0w0h8dgzHGwmcQR+b0NWJbAQMJXGwPH0sfAl9nhj2QWY3bv82Pk8/bSuORrzWe4KI+3IZ/krfoVF7e4sB2SGpiR+0R701lj0nLYXyCb6q+QRcu2teDFOmwm2c46MENsHMN6KSd8kstl4FgNjNVM7eAPbsPy2HRv82OPifxDMD4mGputdmTr4ThgICMf27wexRnG20tj78McbGcd/mieVh9wnJf1yA+bScayzhjWWzCMR9PCcsoGhucyPVtfZud4jymNvQ952M46/KU11nzIwXlZj/yw+dxRXCuGc5Zi/Bx+7PP4XOxviWX8VLl4XtZ5Ltghs7nZH8VncSU755Fev5dcCke2c1IDM8H+0d60aE5qEtjStIbxh+HNxnI7oP/AD5PPAb+XjIfupc/FfuSDrYQFRnL5DByzgSl96Fo9sd+PzedtfuwxkX8IxsdEY7OV7C1+Xi/rtVjGso64aF0ex2PWsxwtGI6FnsW1rDHCRHmzOdjOOnKUJDcu0NXAHL5nmb8WfhnDOvJENvOxnXXEtWIYX4rxc/ixz+Nzsb8llvFT5eJ5Wfdz8XwZrsXegonmlk3NC9eA7aDUwEy0j7Q3ZcthDc5SX7Z+vcQAGDhGA5PdzPiDynTGsQ6ct/lxlMNj/NjHYOxxfgycSRwRBj5Iw0Q4b48wlsPba2OeF/qQPEPn4Tkxr8+V2SMcsJzXcB7bd8z5Ih1NC8u5Gxicl5e8Pu/D2GOyseHZB53trMNvMrPXfMjB8axHfth8bovLDsREueFj6XEYQwLrx7CzzDCZnWO9nsWU7ObzB/JmcexHLGws4YskcNkcmR1xkrfuJ+Ji90N6NTDYrV24tA8PvcQAM7DUBsY+vKObHdtYx4c926CbxAGcl/BzTISJbIj1vtIYMZAei3V4u40Rk2HYDx1xnI/jWQcGsZkPOJYRlv3QOXcWk9ktB8djjNyQHgN7hi/ZuXGBPraBwflBtqyvBePzlcbeh/xsZx1+k5m95kMOjmc98sPmc0dxjPV47+Oxz4UxJLB+DDvLDJPZOdbrWUxkj2yWj+2s+7lasLV4n4PnaIllvPTLbmhsr6IGhndsF6zbh4deYoAZOEYDo5vQZd+EzuH6o2lhWWpg7JxLmzX2sQ6uIpvP2YLxMX7ckqMFg3WzzOL8GqJxZvP2bA62s27x2RHhzJbZ++QBNsoFXyazmMge2Swv21nnOWGHhK82jnA+JsLAJpnX5KVzY3sVNTC8Y7tQ3T5Q9BIDngE1MLp5XPpNsuX8uXGB3tLARBs5s7GddV6Lt9fGiK3hxvqzeWCHtHn8XOaL7BEOWOTD2GP7jjkf6z5PNt8QO+aJ5oAvk1lMZI9sWC/n9zges44Yb6uNszl9HPJL6j4U1YDtV9TA+F2bxmJADGwZUAOjG0d045DtsC7QtLCsNTDg0DZtfMAOWdrUleIsnv3QYUd+P47mQ2zm41zQIyx8LDl3FpPZLQ/HY8z5YQMu8nlbNM7WkNn9vCUc5mvBAAuZxZTs5uMDa0VOjIHxdh5D9/Mh1tuBN+kxJSzHST/8/LlUPmyTogZGm1UxIAZCBtTA6EZxqTfHPufNjQv01gamzzzC6v2oGlANqAZ2NWCbFjUw4dZNRjEgBtTA6Gapm2W9BtC0sFQDU+dNtSWOVAOqgaE1YDs0NTDap4oBMRAyoAZGN5ehN5dLiuPGBboaGL13Luk9oHNVvR+7BmzTogYm3LrJKAbEgBoY3ZSOfVNa43xoWliqgdF7Z421rDWrbtdSA7ZDUwOjfaoYEAMhA2pgdDNby83slOvkxgW6Ghi9d05Zk5pb9XfuNWCbFjUw4datzfi+b3tWVzve++Iv69700KtuHG0zCCUGTseAGhjdBM/9JjjF+aFpYYkG5nTvXs0sBsSAGDhvBtTAjLi+1rxkL/M98u63dtbARE1MFgc7/sSgjU3Hi3XY1i6PfU5D5hsSs/brogZGDcwUG/xzz8GNC3S7seoQB6oB1YBqYN4awGduJD/4wQ9ut2Hvf//79/+TWjM8+OCDgw+7n9kr2x9dRTc8Ay/tVWtgsN6oiYEvkqXNcskX5VqD7djnNGS+ITFr4L60xuwNWnp/2gcFv+wDJPpgke23xMtviQO9D1QDqgHVgGpgnhpQA8O7MaejgbHNLQ5A4LPxYx954saTGOAiWdosl3xRrjXYjn1OQ+YbErMG7ktrVAMzz4eqblbiVTWgGlANqAZUA/PWgBqYwg6PmxQPi3xmw+9hPN7Gtkn2B+zARxtpjgFuiMzyYE74OTdswLDPdPZDBwZjlvD5WLb31X1+G/OL/dDhx5glfCYzO2PWqquBmffDVTcv8asaUA2oBlQDqoF5akANTGH3iSYl2sSaLzpKDQymsnz84jHrhqmNOU9JL+Uxn/e3zB3FtMS1YkrnA59fgz8X7y/FwQfpY/0YuLVKNTDzfKjqZiVeVQOqAdWAakA1MG8NqIEp7D7RwBQgBy7DT9nAZBvmzH6wGBpkeNghKSRsaMwfYTkuwvgYP0Z8Zoffywyf2TneY2pjxHoc7GuUamDm/XDVzUv8qgZUA6oB1YBqYJ4aUANT2HmigbFNa3Zw+NoaGKw92pRHNsNnduSKMD7GjxGb2eH3MsNndo73mNoYsR4H+xrlMRqY6A8CyKa/frbEGtAmY55NxhJ5XWL9aU03Pxej2hFPN3k6Z06iGoBNDUxh54kGpgA5cM3RwNiGOToOJq4MapvuyB/NCRtPB5uXHuPHHo8x42q6xUQvb0duLzm2NcbjOMfadDUwl3UjOOeb3BTnhpui5Pk3MlYvDzzwgI4Fc2DXKHov6tpdTt1mNYC6UANT2HWigfEbXx5z+BwNDOcfqtc23ZE/svn5M4y318Y+b+vY50Uc21mH36S318Ycey66Ghg1MHaD0LHjADdFyctpYD784Q93OpbHgTWX2ebV7ObXdVvedZvympRqAJ/RamAKu1E0MAXIgetYDYzfbB8sIhhkeNghOTSymZ/trJdiPc6PEZvZ4fcyw7OddY739toYsR4H+xqlGhht3tW83KoB3BQl1cBMuRFTrv4b7dLmVQ1Mfz7XWIOlGsBntBqYws4TDYxtWrODw6duYCy33zD7Mc9f0n0cj1nnHN5eGyO2BdeCQb6SjPKwjXXO4+1+bFhv82POt0b9mA2McRdtljN7hB1jO9Y8fddo6xq7tig+svm1MYZ1j+s7njJX37lb8LY+HIzHTVFSDcwaN3zntObS5tXes+Y/p/PVudxsyko1gM9oNTCFnScamALkwDVHA2MT4GZrcswry1PKm8VgHeyHbj6f04+B4Rjk7CuRA3NAIg/7oWN+YKIxbBzD+LXrx25gjEfeMJoe2TxmivGx5umz1qnWFOWJbKW19cUfK1dpniE+f548xk1RUg2MNpQ3N5TH5KS0ebX3vRqY016fY9RCqQbwGa0GprALnauBKUwplxg4GgNqYG59fWjIZnhsDG+ex+SK8kS20hx98cfKVZqnry87R9hxU5RUA3OMDZrmyDfhpc2rve/VwOTcnUtdlWoAn9FqYArbRWtg+h4t/x+YwpS9XHbjrR29Ei4EXDsn8+s1noFjNzB247FrxxvP2hhYxkE3CZ1x3sbzIibC+BwRBjbkQUwkgUEMMGyHDp+X8LMEhm0l3fDmh2Sdc7EfGPjh4zF0YE36AxjEw8926PBBwj6VtLxRLthxU5RUA3Mum8C1nkdp82rvYTUwamDsc1oNzPh9oDKIgVUysOYGBptOu5lBh2QbNqzmY3+EiWxRjLdhDpYe48fRXBxf8vtcfhzFGsbjeFzzRzmxXs6T4WoY78/y8JxYs5fAtEieV43L+TcuuMZWG9oEL3cTrAZmudfmWE1xqQbwPlYDs8qtpxYtBsYzcIoGxjYOvGlk3ft4A8o41oGp2SK/n28MBuvwOUv2bD6OiXQf58fRGmqYyO/ztGB8TDQ2m7dnuYGdUtpcODgvboqS59/I2HVvbWCsVvpu2lpiWjCYt4aN/JEN+SAZwzr8Q+XYXKXNa+3a2dzRccxzGXv+0VprOSN/ZPO5GcO6x/Udj81VqgF8RquBGb8PVAYxsEoG1MCUmylsbu2DONJh85Lx7PN2P2ZsSfdxfmyx3ubHHhP5h2B8TDQ2m7dn8wM7l+R5cVOUVAPDmzWrER636C0xLRjMVcNG/siGfJHsi49ywDY2V2nzap8FpeYzm9vsmQ/rjuSxYqK52VZbR+SPbJzT633xPp7HY3OVagCf0WpgVrn11KLFwHgGTtXA8ObVPuR4c+rH8LGd9cgf2aIYw7Hd9OyIcsLmJedkn7f7MWOh19ZjuCiPt/mxj4v8QzA+BmPLHx3mx+H9sEfSY3kc4Us2izU/boqSamCwEbPaMB0S9ppswbdgeO4SPvJFttK6++LnzFXavNp7dUgDw1yW1u59Q3gZEuPn5THyQbIPeuSLbMBHsi8+ygHb2FylGsBntBqY8ftAZRADq2RADczh5t8+cEsbXfONwfhYP/ZzZ35v9+NonTVM5Pd5WjA+JhqbreXI5muJjTBZPthxU5RUA+M3YVYjsEXS/DjMH+Hhhw8yysc24CC9z+y1g9cELNtYh98kz8WYzM6x0BkLW5SbcaXNq723hzYw2TmU1hWtNcOzHTqfF2wm2V7TgYdkPOcs6RaDeODYxjr8wPv5eAwdWI6FDoxJ2IBnH+ulGsBntBqYVW49tWgxMJ6BUzYwdhPCBxlvNs3GY+hsZz3yR7YoxnBsZx05WjEZvmTP5kNM5vd2P/ZrjsbeFuUYgvEx0dhsJXurH7hWWTtH3BQl1cBgo8UbKqsfHkP3dhtHNuBNRhj2Q6/lyXCYA36MS/miNZXwPndpjPlrGPhLm1d7v0/ZwNTOsa8/OtfI5vPi3L30OD8GPrJ7m40jG+co+Q3n/RwLPcP5WD/m+FIN4DNaDcz4faAyiIFVMrDEBsZuTvahxptSG7ONdeBqNp8jmiey+bx+jPm99Dg/juaq5YDf5/LjKHcNY36P8eMsr8f5cRaH84n8mY1jhuh+bTzGTVFSDUy0AbNa4Q1WhIGfsazDX4otYfrk8lg/9muI/EMwPiYam61kL21e7X0/VQPT95xb8NF5tcaBF5Y+1o+Bjeze5sd+rZF/CMbHRGOzleylGsBntBqYVW49tWgxMJ6BUzcwpU2qfZDi8Diz+81rzQY/cmLs82Au4Ly/FBdhszyYx8f4MeJZRrHRutjGOuZgG3STOIDzEn6OiTCRDbHeZ2P4ICPMFDbkN8n5cFOUVANjtYENFktv92Ng2c46/CYzOzCZP7K32GqYyO/X2YLxMdG4do6lzau9Zy+lgWnlO+PYx/uxj4v8QzA+JhqbrWQv1QA+o9XAjN8HKoMYWCUDx2xgeKMove03IOLpuDzhpiipBqZlI1fafHE869i0lWKB6RMXYb3Nj/0aIv8QjI/B2PJHh/n9Udq8Tt3ARGtiLrxew+N8+ZyyGM7NeOiZP7K32GqYyO/PpwXjYzC22OjA+bIs1QA+o9XANGw9PeENIWcPMU70WjcDamCOu0FWQ7JsvnFTlLzsBsbf76MxNlrmg86S7axnGLabHs3pbRwTzeFtfox5kCfyD8H4mGiMOTNZ2rxO3cBka4CdeWEd/kh6nB9HMd5mMbWDY6I5vM2PLZ5trGe5WzA+bzTm/JFeqgF8RquBqexB7WJ1D77i4NjbKrHn7N5ycM4neAHnpgZm2RtqNTzHvT64KUqqgYk2VLDxBo51+E2ynfUMw3Yf732RP5rD2/zY54n8QzA+JhqbrWQvbV7HNDD+HP04WhdjWAc2Og+P82PEZvYoJ2IgfawfRzlqmMjv87RgfEw0NlvJXqoBfEargSlsVO1CveaOZxw0L9bMRDazG/5SXpd0rud6TdXAHHeDrIZk2Xzjpih5uQ1MtjnDZguScaxjQxbZEJth4PexsHvJONaB8zY/NhzbTOex95fyDo1DTi9Lm9ehDUx0ftE51s6l5o9yRjafhzko+TJcFONtfuzXZX6P8WMfg7HH+TFwJnFEGPhKNYDP6ItsYJ588smmPamRi4bFmhYc/okMxubfxjRkNxwOg0dx8Gc+xAGXTQt/Sx7k4Bjo8GFeHktfHwNqYJa9oVbDc9zrg5uipBoYbKIyafdE9uEeCTtkX4zho1jOA93j/NjnavUbDgfm8hJ+5IRkXGZDLGO9Xtq8tjQwmIOln4PHJZz5GGt6CQ/s0DjkR56S9HP4sc/V6jccjmx++JETkvGZDbGM9XqpBvAZfXENzHvf+97uHe94R9Nu00hGc9IirYG57bbbthe+NME2LwFwMcl0I8eQGMsXxfl5ahjgGcc6/JLrYkANzHE3yGpIls03boqSl9vA+E2Uxrf+tfyYXJQ2r7UG5pjr1Fzz1UepBvAZfVENjD15sebl9a9/ffenf/qn1d3mdpPufv9SamRaGphs48921nmRbGd9DgznhM5zsg6/5LoYUAOz7A21Gp7jXh/cFCXVwGhjOt/GtIXb0uZVDcxpr03L9ZsCU6oBfEZfVANj20trXN6xaWLsqDUx2026Gpj9rpybFtb3ACmrYkANzHE3yGpIls03boqSamCm2IApx/CNdmnzqgZmOK9rqslSDeAz+uIaGNthWuNiT2FqTcwpGxibOzqwQ84aCLZH8bC15AGWZS0OfsnlM6AGZtkbajU8x70+uClKqoFZ00bvHNda2ryqgVEDg89oNTCbZiZ72ca99JUx75v7K2S8zu3a2HCts531ALo1RZjIZmC2s57lln3ZDKiBOe4GWQ3JsvnGTVFSDcw5NgVrOic1MJfRpJRqslQD+Iy+uAZGXyGrNyJZc8J21pe9TdfqMgbUwCx7Q62G57jXBzdFSTUwpY2VfPNvrkubVz2BmZ//JdR4qQbwGX1RDUzfH/Hbxs826vZkpeWwv0B2zL9CFm1MfWPRd4xzruX2eSO8bMtmQA3McTfIakiWzTduipJqYJawgbvkNZQ2r2pg1MDgM/qiGhjbTvb5M8rYftpmHc1JTQKL2EwaDodhTPcv+DOfx9s4wyKXj4nwyIMYSNhZ+nwar4cBNTDL3lCr4Tnu9cFNUfJyGhjbKOtYJgf2+Re9F9HA6Lot87pNeV2yGkBdXFwDY9vL1v+RJW9FsYmvSWtwhrwsr15i4JgMqIE57gZZDcmy+cZNUfIyGhi9H5f9fsw2r7puy79uU16j0ufxRTYwx9wkts6lBqaVKeGmYuAYDUzpw0e+898o6hrrGqsGVAOqAdXAHDWgBmaq3eCIPGpeRpCn0MEMqIHRTWWOm4pyqq5UA6oB1YBqYO4aUAMzePs3PNB/DW14JkWKgeEMqIHRDWbuG4zyq8ZUA6oB1YBqYI4aUAMzfP+nSDGwagbUwOimMsdNRTlVV6oB1YBqQDUwdw2ogVn1FlSLFwPDGVADoxvM3DcY5VeNqQZUA6oB1cAcNaAGZvj+T5FiYNUMHKOB+a57Xt7pEAeqAdWAakA1oBpQDfStgVLjowZm1VtQLV4MDGfgWA3M8BUqUgyIATEgBsSAGLhEBqzZUQNziVde5ywGKgyogakQtBL3Rz7ykZWsVMsUA9MyoNqflk9kE69gQvKUDKiBOSX7mlsMLJgBNTALvjg9lqbNRg+yBD0rBlT781xO8ToPr8rajwE1MP34OkC/79ue1dWO9774y7o3PfSqG8dBIg3EwAIZUAOzwIsyYEnabAwgTSFnwYBqf57LKF7n4VVZ+zGgBqYfXwdoa16yl/keefdbO2tgoiYmi2v5n1a2YLL8JbvP68cWazbYIUs55VsvA2pg1nvteOXabDAb0i+JAdX+PFdbvM7Dq7L2Y0ANTD++DtC1BgbgqImBb4icq3Go5a35h5yLYpbLgBqY5V6bPivTZqMPW8KeEwOq/Xmupnidh1dl7ceAGph+fB2g0cDYxh4HAPDZ+LGPPHHjSQxwQ+RcjUQtb80/5FwUs1wG1MAs99r0WZk2G33YEvacGFDtz3M1xWt/Xsfsn8bE9l/peiLUwIy4Vtyk+DSRz2z4PYzHYxwVqtlwGK4Fg3yMRw4fz3boWVzk93MxxvuQ1zB6LZuBJTUwWb1k9mUze9zVlTYbeK+W5HFXq9nEwHQMlGo/mmXJnyfRe9Sfw7HWX+K1dQ2tODvHPljPSTa2nJwX40j6HIzJfGznedjeR58iR5/51oBVAzPiKqFJiYrZfNHRt4HxRYu5eNkRxvtrGMPXMFP4fQ5ep/RlMbC0Biaqnci2LBZPv5rSZsOvTnx6RjReMwN9at/Oc6n1n60rs899zUq8tq6pFTfXdfHz+3HGocf5cbTeCJPlz+xT5Mhyr9WuBmbElUMD05rC8H0amKxg2c46r4PtrGcYs3tcn7HHYh62sw6/5HIZUAOz3GvTZ2WlzYbPo/eoZ0TjNTMwpPaX9h6orafmn+P61Xitranm92vui/fx0djn9OMoxmwe58etmCx/Zo/mybCXYlcDM+JKo4GxwsoOTq8GRl8b43pYur60Bsb48h/ifgyMfz8y14gBxvvgZzt0xJQwwC5F1jYbvM7ovCKbxcDO0nSMOS90+EsYYCXFwFgGhtR+Vpuwo4ZtbZENduAgcS6IwRiyrx1xLDkH5vWS8aaz3/uycY1XXkeUw/t5DdA5jvGsZxjYkSuK8TY/Rg6WGcbbecw65xqiT5lryPxLi1EDM+KKoIFpTTFXA2NFHR1YV1b03j5mHM0PW20d8Esui4E1NjC+hsEo27O69BjEQrLfbH4M3NJkbbPB643OKbJZDOwmoSOXHzO+hIFPUgxMwUBr7ft69WNbi9m8PbNFa0cspMdkdszt8dG4lCPK4/F+HM1hthZes1ze7seYk+2ZDqxJxowZWx4cnD/KCX9pbu9DzBA5Za4h8y8tRg3MiCuCBgbFHklOP1cDw3NEelb03j5m7GP7rCPCynZ6BpbYwBgrXGuslxhjHOuIqdkiv18Lci1Ntmw2sObW82Qc68hjku2sZxi2SxcDUzDQWvu+Pv3Y1tJqy9bN8axnuX0ei/FHhPE2jFvn9DjEs2zhNcuT2Tm/6YzLdI4Zi7F4zuHXEI0xfynO+xAzRE6Za8j8S4tRAzPiiqCBaU1xzAaGC511Xqu3jxn7WMzDdtbhl1wuA2pgbn3lMavdzL6kq9qy2cB6s/Pxdh6zjjwm2c56hmG7dDEwBQMttd9amxEusmXrZizrhvfjLIe3+zg/Bj6yRzbDZ3bkMtnCq+F8Lj82TPZibKZz7FgMx4/Ja7Gci3XOO0SfMteQ+ZcWowZmxBVBA2NFlR2cvm8DY7G+YDEP540wJT98feOmxmMdkstkYKkNjLGFWoRkBs0WHcBkMfBDMi7KBxvwS5Wtmw1bP5+zPx/4IOH348humOwAXlIMTM1AS+231K+tK8JFNmCjeufzQywk+/roHM86ckQ285k9OxCbyRZeMQfn6LsWxHIc6/D7ubLz4ljWOY/XGcc647ydx6xzzBB9ylxD5l9ajBqYEVcEDUxriiENjOW2osWBsZ8T/qjAI1spD3L7uNoYOQ3nsfAht+TyGVhjAxPVna+9CFOzRf7lX8HdCls3G54nf37gABJ+P47sGQZYSTEwBwO12re6rB1YV1TDrTbL4bEYQ2KevpLjWUeeyGa+zI64kqzxyrGYB5J9prfYGcM652I764xh3WP8GFi2sw6/SW/nMescM0SfMteQ+ZcWowZmxBWZo4EZsRyFioFJGVhyA2Mnah/m/gPdj0EI21mP/JEtijFcZkeOJcghm41s3dH5RjaLZzvrnDuzM0a6GBjKQK32a/XHftaxnlab4ftgkT+Ly/x+Dj/O4lrsjKnxylisAZJ9prfYGcM652I7662YlphovVEc21jntQzRp8w1ZP6lxaiBGXFFrIHpe9T+PzAjlqNQMTApA2pgbv0Gxoj1Nw8/npT8CZMN2Wxk00fnbDZv92PL521+nM0puxgYykCp9lvrDzhIXkurzWL6YHmOLDay8xys+3wYe4wfA+dliVePtXEpb+ZjO+tRPvO3YHhtHp/l5ZghmGgen7N1PGWu1jmXjFMDs+Sro7WJgRMysPQGxqiJPtDN5g/GZjGe6gyH3B6/1HGfzUZ0zjivzAc7eMEYcSxbMIyXLgbGMFCq/VKd8pzAQUY+tpnOdQ4ddmCjfPBFEnlYehznZBzrUQz83peNS7xGMbyuzI81QBoOcZAcCxx8kCWM9/EYus8LO8saxq/Fjy2Xt/Ud83ouVVcDc6lXXuctBioMLKmBqSxV7gIDfTcbWSp/gwUus8MvKQZOxcBUtT/H+tf8vlkyr63Xak7+fW4/bl0j46bIwfnOQVcDcw5XUecgBmZgQA3MDKSeIOUUm43SzbPkO8HpakoxsGdgitrfJ5tQWft7Zqm89rlEc14Dn9uP+6wT2ClyINe5SDUw53IldR5iYGIG1MBMTOiJ0k2x2SjdPEu+E52yphUDWwamqP05qFz7e2apvPa5VnYN5rgOWd4xc42J7cPJ2rBqYNZ2xbReMXAkBtTAHInomacZu9mo3Txr/plPT+nFQMrA2NpPE49wnMP7ZYm8jrgkCl0pA2pgVnrhtGwxMDcDamDmZvg4+bXZOA7PmmV5DKj257km4nUeXpW1HwNqYPrxJbQYuBgG1MCcx6XWZuM8rqPOoj8Dqv3+nLVEiNcWloSZmwE1MHMzrPxiYKUMqIFZ6YVzy/79P+q6hx9+2Fk1FAPnz4Bqf55rLF7n4VVZ+zGgBqYfX0KLgYthQA3MeVxqbTbO4zrqLPozoNrvz1lLhHhtYUmYuRlQAzMRw/bDPD4mSnt2ac7hB4xnd1GSE1IDkxCzMrM2Gyu7YFruZAyo9iej8iCReD2gQ4MTMaAGZgLit5vyB1/RdXRwMxPpE0yrFGJgVgbUwMxK79GSa7NxNKo10cIYUO3Pc0HE6zy8Kms/BtTA9OPrBjpqXriRifRtzI1MMoiBZTGgBmZZ12PoarTZGMqc4tbOgGp/nisoXufhVVn7MaAGph9fN9BTNzBobkzi8JN6DPsRAwz7oNcwffzRPBzv/TyGznis0UuPQazHaTwdA2pgpuPylJm02Tgl+5r7lAyo9udhX7zOw6uy9mNADQzx9eSTT9Jop0Y2gF5zxzO6Pgeextx2221IcUNio84Ov1mPMIaPcJynBVPL4f0+Zx+/YT3ej31+jCOc+fSajgE1MNNxmWU6Rh23bjZsLTiy9bK9Ze1DMC0xtg5ea2sMr79F93n9uCXHKTFD1jsk5pTnWJp7SO3b+c/FwVx5SxzM4Wvl1ebue87gv2/cHOepnMtmQA3M9fW57777urvuuqt7/PHH91fMdLPdf//9exsrfZoXYK2JqTUwPAd0fjOzHvlhM8lY1iNMze/zcQ7oWY7In2HZzjpymMzsjJE+jgE1MOP4W0p0y2bDv5/82J+L+WsYi2nBRLm9zY99Xj/2+KHjufIOXc8x4s7pnIfUPjieg4c5cmK9x5StvNr59jlnj/XjY56j5lo+A2pgNtfoiSee2DYqt99++76JQfMCm2H8C01JX7m0BgbnlX1YsJ11xLHs48+wbGe9zzyMlT6MATUww3hbWlRtszHkPWYxWRyffwuG8aa3xHiMH/ucQ8dz5R26nmPEndM5D6198Dw1F1PnwzqPLWu88nr6nLPH+jHnlS4G1MBc18Bjjz120MTYkxc0L/xUxpdM3+bF8EtuYOwDIzr4vL2ffaaX/ObDi3XYTLKd9QzDdunTMbC0BsZqAUd0lvBFNQNbhoHf5/X2LN7igAUGuTCGhJ1jvC3CMh5+k7VXbbPRkoPnAB6SfV4HxiSOFkwJCx+k5TPdv+CPfIhhDOO8HT5IxEMC79eAMfyIh4TfS/h9nMfZuIRBHuAgEWNjvGBjCd9a5dS1bzyU+GEfdObObPwChqX32xh+9p1Sr/HKa/PnzD7WM1xm51jpl8mAGhh33fHkxX+dzMH2w3NrYPYn1kOpfcCwP9N5ur4YjpU+HQNLamC4JuwMh4xLMd4HFtnOeraGGsbH1fCRP7JhvZGsbTaQzySOKE9t7VFMlA/zAV8bA8eyFtPXj9x94gxbw1veCONtmB9yTG7kMMnzDM3J+dam12rfc1Q7P+bTx3ofcrE904GNcnIM406pt/CK9bWuP8NlduSXvFwG1MAE196aGHsi0/rq28Qs7QkMPiAg/XlnduD6+BnLOnKZZDvrGYbt0qdjYCkNTK0Gan5jZAiGY1hnhtnOOjCRDT6T7Gd9DIZjTa9tNmxeP7cfIyfbWYffywzDdtYt3o99zgjDMaxzbGYvYXwMj1nPcrRgOBZ6S1xfTAve5s9wWNuaZK32cS52zv6ADzLjJbNHcTWsxTCGdeRbgmzl1Z9Pae3ZuWb2Ui75LoMBNTATXefWJsaal1oD49+wtTFOoQVXw/T129wcw3q0LvazDqzPl42zWM4jfRwDS29gcHZZLbCddcSZZDvrNR9ycAzrkR82lhzD+hgMx5pe22y0zGt5PI7HpvsjijGbvXzszrr7L/vYzrrH8Jj1Ugz7oPvY0tj7ohw1jPn9YXlqcUMwLTlLec23tlet9kvn4/ny41Is+ziOdcawzhjWGXNqvQ+vreeQ4TL7qTnQ/KdnQA3MRNfA3mRoTmqy9IaEzyQOv0RgvN3GiBmDqeVgfzRPyc941vlcIjvnxHlyjPTpGbi0Bobrytcg15/XwbyPYTvHwM7zeX0MhmNNr202SutGrggT2YCHzDBsZ93i/Bi5WHoMj1mvxRjWH7UY+FvmacEgH8uWuL6YFrytIcPx+tai12q/dh7MBetZnGGiA3ifI8IyhnXkWILsw2vrOWS4zL4EHrSG0zKgBmZC/u2N1nKUptSbtcTOLZ94usXFXJoamFvMttRbC8YyMi7Tb82c4zMM202vbTZ4DRzLdtOzg2O8zjnYx3bWDePHHAfdY3jMOvAm2c56hvExftySowXD80NvieuLacH7c8R61iprtV87L+aM9Sgu87M90zlfC4bxp9D78MrnU1prhsvspVzyXQYDamAWdp31Zm27IOKpjacxqKU3MKgBSH+ubGedcZG91WZ5GMs6z+F1xmU6x/TFcKzptc0G5+fYzA5MzW+4DMN21ksxmDfCcA7Ws5gWzDHn4XVG88LP62Ydfh/LGNYzvI9n3Br1obWPc2XOWIffJOyQ7GN/SecYzsM6Y06t13jl9fU5B4/1Y84rXQyogVlYDegNW78g4qjO0RSIpTQwdi7+mo8dgx+fJ5orw/pYP85yMY71CF/zZ2uD3WTLZqN1Hs7rY9gH3TAe13eMXCz75qjhkbuGYz/riDfp7dHY2zgeOTzGj4HjWI/hMeutMYxboz6k9nGeEV/exmPWkcMk2zO9Bc+YU+stvGKNfM6wZdJj/TiLk/0yGVADs7DrrjfszQtinPBxEyHLHAwsqYGx86vVQMlvvugV2SMbYofOkcVFc2VYW0OEL9nN17rZKM1refwrWwvjgCnlBiaKYxvrPsaPDVua0/uBhd0kXpw704E1yRjYkR8+SPi9hN/HeZyNSxjkAS6L93aO8741jYfUPvjMzhP+iCP2Qbc8wEIiNzAsS3jEnVq28srnEq3Z8wE8+IhiZBMDYEANDJiQFANi4ICBpTUwB4vToJmBPpuN5qQCjmIg2rhxwpqfsdJzBlT7OTdjPOJ1DHuKnYoBNTBTMak8YuDMGFADcx4XVJuN5V3HWoNS8y/vjJa5ItX+PNdFvM7Dq7L2Y0ANTD++hBYDF8OAGpjzuNTabCzrOrY0Jy2YZZ3VMlej2p/nuojXeXhV1n4MqIHpx5fQYuBiGFADcx6XWpuN015Ha0b4aFmNGpgWluoY1X6doyEI8TqENcVMzYAamKkZVT4xcCYMqIE5jwupzcZ5XEedRX8GVPv9OWuJEK8tLAkzNwNqYOZmWPnFwEoZOGYD8/DDD3c6xIFqQDWgGlANqAZUA7UasG2VGpiVbi61bDEwNwPHbGDmPpdLzq9/Lb3kq3/Z567an+f6i9d5eFXWfgyogenH12xo/o70mO8/j4nlk5sqD+f0+jHm8HNq3M6AGph2rpaM1GZjyVdHa5uTAdX+POyK13l4VdZ+DKiB6cfXLOjtRv7BV3QdHWYrHdlCpmoKpsqTrVP25TOgBmb516hlhdpstLAkzDkyoNqf56qK13l4VdZ+DKiB6cfX5Ohto0CNCzcxmV5qLkq+PoufKk+fOYVdFgNqYJZ1PYauRpuNocwpbu0MqPbnuYLidR5elbUfA2pg+vE1OXquBsby4sgWDf92DQ4EWwljIeyHjlTIgTEk7JCwm0SOzMcYjpM+PQNqYKbn9BQZtdk4BeuacwkMqPbnuQridR5elbUfA2pg+vFVRD/55JM3/JENoNfc8Yyuz4EnMrfddhtS3JBoANhRagaA85iWPD7G5+rr9/ho7G2YU3J6BtTATM/pKTJqs3EK1jXnEhhQ7c9zFcTrPLwqaz8G1MD04ytF33fffd1dd93VPf7443uM6Wa7//779zZW+jQvwFoTU2tgeA7ovPFnHX6TbGc9w7CddY5l3TA8zvTWXIyTPj0DamAO67WVYa5ri/Hj1jxT4Vo3G7ZOHC1zt5zXEExLjK2P19oa03JejPF5/ZixS9SHrHdIzBLP3dY0pPbt/OfiYK68x+a/lddsXX15wDXpG5fNL/t5MKAGZoLr+MQTT2wbldtvv33fxKB5gc0w/oWmpK9UA+OZ1HgOBtTADGs+lnaTbdls+DX7sa8v89cwFtOCiXJ7mx/7vH7s8UPHc+Udup5jxJ3TOQ+pfXA8Bw9z5MR6jylbeI3WY+ePI/JHNs+ZH0cxsl0GA2pgJrrOjz322EETY09e0LzwUxk/XWvzsnnX779uNkUDgw8RL7G+7EPC2308xshjEjGQ8PGYdfg51uuMkT4PA2pgbtVuH4azWu6TY0psbbORrTez29rMV/Jj/S0YYCFbYjzGj5FrrJwr79h1zRl/Tuc8tPbB79RcTJ0P6zy2rPFaW08fHjzWj2tzyX++DKiBmfja4smL/zpZNk1LA2PNCw7DT9HAZOuBPfuQYDvriDPp7RhDAstj1uH3uTIM46VPx8BSGhhcd5PQcZaweXvkb8EgDpJjWIffJOwm/cF+HwMs2xkPv8kxr9pmo29+4CFLawPGJA6PjzAlLHyQlg85ODf8kQ8xjGGct8MHiXhI4Hl+1uFHPCRjWIffxzEGegmDPIaFnuHZDh1zrFVOXfvgMeMHdpbMndn5xTjo3m/jyMe4Y+s1Xmvr8Txk+AyX2bM8sp8nA2pgZriu1sTYE5nWV62J2Xx62SfY9pizgeEPBdb5PNjOeoaBPcKyjXXEmGQ764yRPg8DS2pgomvvbbWxsVTDlPzeB9a9fYpxLQfmbpG1zQbmMokjywus+Vkv4T2u7zjK3TdHDY85ajj2m85jy+HHkS2Kw/yQEaY1N3L4uYfm5Hxr02u17zmqnZ+/BjxmnfOwPdNLeI5h3Cn1Fl5L62s9pwyX2Utzynd+DKiBWcg1zZqYzR3R7oq7VV7rtScw/s3tx5bM22pj0MQ41uGPcrfafL7amOeUPj0DS2pg/Nn52oCf7azDzzLzsz3TS3k4xnA8Zj3L0YLh2Jpe22zYfH5OP8YcbGcdfi8zDNtZt3g/9jkjDMewzrGZvYTxMTxmPcvRguFY6C1xfTEteJs/w2Fta5K12se52Dn7Az7IjJfMHsXVsBbDGNaRbwmylddsra3nleEyezaf7OfJgBqYhVxXe0NaY+KPzaeZfaIdHKU3L3wmcWSnCD9iGBfZzO/tnAN6huP8EQY2zsMxfm72SZ+eATUwh/We1Z+3l8beh6vGdtbhN5nZGRPptc1GltfbS2Pz+aO0Zs7FeimGz60U432Iy+zwR3P7GB6znuWoYczvj2gdyM/5WIffxzKG9Qzv4xm3Rr1W+6Vz8nz5cSmWfRzHOmNYZwzrjDm1XuLV1uwPv97W88pwmd3Po/F5M6AGZkHX98abfvNBsPkkiI8FrVtLOU8Glt7A+PcLxnw1YIP0Ph5DNyxemQ6/ScbUxh6LPGxnHf4oL/tKemmzUcrL62Adc0U2+CAzDNtZL60HOSMM52C9FmNYf9Ri4G+ZpwWDfCxb4vpiWvC2hgzH61uLXqv92nkwF6xncYaJDuB9jgjLGNaRYwlySl5L55Odf2Yv5ZLv/BhQA7Pka4rmhdcY2dgvXQxMxMDSG5ghp8k3PtY5F9szPcObnWP82PuQh+2sw+/zsL2m1zYbLfMZJjtK87fm5hxZTAnDMaxnMS0Yi/U4HrM+dh6Oj+aFn+dkHX4fyxjWM7yPZ9wa9Vrt186JOWM9isv8bM90zteCYfwp9Cl5La2fuWBcZmeM9PNnQA3M+V9jnaEYGMTAGhuY2o2N/awzQWzP9Axvdo7xY+9DHrazDr/Pw/aaXttsDJ0vi+P1ZBi2s956nqUY78N62M46/NHcHsdj1rMcLRiOhd4S1xfTgo84wJrWKIfWPs6VOWMdfpOwQ7KP/SWdYzgP64w5tV7jtba+PuflsX5cm0v+82VADcz5XludmRgYxcCSGxg7MX8jq42HxNRymr8FwxeiLx6xPg72mmzZbPjcfhzN0YrxuL7jlrn75vR4zOHtpbH39cmRxXIOj/Fjw3pbaex9PBf0KCf71qYPqX2cY8SXt/GYdeQwyfZMb8Ez5tR6C6+lNTIPJZz5PNaPa/Hyny8DamDO99rqzMTAKAaW3sDYydnNDEd0svBB1jDeb3H+hVzwQTKObawDYzYcsEFGePNldsRlsnWzgfW0ztOCA6aUGxhef2Qr+SN8aU7LxX7osGdz8TysZ3jYkR8xkPB7Cb+P8zgblzDIA1wW7+0c531rGg+pffCZnSf8EUfsg255gIVEbmBYlvCIO7Vs5TVbp+eBcZHP88N46ZfLgBqYy732OnMxUGRgKQ1McZFyVhkYu9moTiBAbwaiTRonqfkZKz1nQLWfczPGI17HsKfYqRhQAzMVk8ojBs6MATUw53FBtdlY3nWsNSg1//LOaJkrUu3Pc13E6zy8Kms/BtTA9ONLaDFwMQyogTmPS63NxrKuY0tz0oJZ1lktczWq/Xmui3idh1dl7ceAGph+fAktBi6GATUw53Gptdk47XW0ZoSPltWogWlhqY5R7dc5GoIQr0NYU8zUDKiBmZpR5RMDZ8KAGpjzuJDabJzHddRZ9GdAtd+fs5YI8drCkjBzM6AGZm6GlV8MrJSBYzYwDz/8cKdDHKgGVAOqAdWAakA1UKsB21apgVnp5tIvm7+iMPbrBz4Xxn7OsWO/ztp47HyK78fAMRuYfisTug8D+tfSPmwJe04MqPbnuZridR5elbUfA2pg+vG1SPR24//gK7qODjQdmSydiG8kgM3s8I+Vc+cfu75Li1cDcx5XXJuN87iOOov+DKj2+3PWEiFeW1gSZm4G1MDMzfDM+bebfmpcuInJ9FqjUPKXfGNPdc7cY9d2ifFqYM7jqmuzcR7XUWfRnwHVfn/OWiLEawtLwszNgBqYuRmeOf9203/CBsbmxxGdKnyQjDGbveBjCfsWQP/xGHId/N+OgWO/9H4MqIHpx9dS0dpsLPXKaF1zM6Dan4dh8ToPr8rajwE1MP34mhX95JNP3sgf2QB6zR3P6PoceCJz2223IUUobfOfvdjHuuFrY4+p4Yf4azHZecl+kwE1MDc5WaNFm401XjWteQoGVPtTsHgzh3i9yYksx2dADczxOQ9nvO+++7q77rqre/zxx/d+0812//33722s9GlegLUmZmgDw80B67wmtrPOGOjeXxp7X5SjBYM4yToDamDqHK0Boc3GGq6S1jgHA6r9OVjtOvE6D6/K2o8BNTD9+JoF/cQTT2wbldtvv33fxKB5gc0w/oWmpK9saWCsGfAHz9/SLGQY5PH+0tj7ohwtGMRJ1hk4twbG14cf1xlZJ6J1s2F8+CM64xbeWjBR7jG2U8yJ9fq5/Ri4Nci+a2/Bt2Dm4Gbq2h+7xlPxMHbdPr6VVx83xfhcOJyCi0vPoQZmIRXw2GOPHTQx9uQFzQs/lfHLbW1eNjuT/dfNWhoYP48fZx8i3m5jPjhPhM38Hgsc21mH32RmZ4z0mwycWwPjz/BS6qJls5FxEdkjm+f2FONTrsvP7cen4ONYc7acawtmjvVOXftj13gqHsau28e38OpjphqfC4dT8XHJedTALOzq48mL/zpZtsyWBsaaFxyGP2YD49fNHz6sG6409j7kZTvr8Ed52Sc9Z0ANTM7Nmjy1zUb2vsE5er8fA3dqecp1+bn9+NTczDl/y7m2YOZY49S1P3aNp+Jh7Lp9fI1Xj59yfC4cTsnJpeZSA7PAK29NjD2RaX3Vmhg0LybVwLSyKtxSGpjshsV26CZx+CsYYTKsj13zuLbZAC+1cwRXLC0G8bCzjXX4gTeff3lMCRvFRjbkjHxmg780l8cAy3boyAnJdrNN9cIafD62Y26WjAcWfvPBBhx8LOFjfOZnjI9DDNun1KeqfV4T1mzSv9gHnTE+BhiWER5+9p1Sb+U1Wndk43OB3yS/2A4987PddOTycRhD+jiNl82AGphlX5/m1WVNzOada+/eXZ5rfYonMJYQHwpYZG3sY2r4sf5sXbBLlhlYWwMzVb2UWVmft7bZsDPy3JXO0mNtHNmQo+ZnHHSTURz7vV5aA/JxTJTf5xga15o7Wg9iWTIOerRWXm/ND6zH8Zh1zIs4jA3jcWPHyD1WHqP2sUZ/zpH9/2fvXWPtyK4zMQZQkPyfXwMMggES5GFkjATySxoksOEEwQSYQQIbTiYPq2FrWm7RD8q2rJZsSbZl2RNblix7rCjyq23PtMe2xtYr1sOUZIWCSKrVJNUUKTbZZPMlvh8STdM0Xzv3q8vv3nXW2btqn9r1Oud8BdTde6+19tq7vlpVe32n6pxrbWydtiitHHXbtnZj1ptw9fPmMbD0x8ljsfpcm5hdzE9MxnFZehvKVU4TARGYaZ6XhWeFCw/ExO8bdz/cEWf2pou0SW8nB1vuVs46dSwpRwmZ36zM1mlHPykd7WwZs7V61eMILBuBiR2FPfe2DlvfjvVfBVlTssFjBB5+p86WHjffhq2V2XrKT46N7RurWx+2bm2t3NZLbNDX+/Jt+k/JqV+09P58O+bP2tg6bWMy6lhaG1unHqWVp+opeysvqXcZ+/YY7JxSctpYva1T70trY+vebsx2E66xeTfJYnoco5c3tYmLtbP1mJ4ylcuFgAjMcp2v2tniIrX7RgNXf3yv9SSlEAhBBGY1oqAp2ag7ypyFv8kmpseYVm7rdj4pubVh3draOvUordzWS2y831ib/lNjUr9o6f35dsyftbF12sZk1LG0NrZOPUorT9VT9lZeUu8y9u0xLDIn28/WUz6sja2n7MeQN+Eam3eTLKbHsXl5U5t4WDtbj+kpU7lcCIjALNf5Wmy2JC+2V0xm9aoLgccIiMCsRig0JRtNR+kX/6Y2/FkbW7djWbmtp2ysPFa3PlBP7exr7SlDaeW2nrLxfWJt9k35Yx/oYzv7x0r6ZGltYr6sna2zn5e18QFf1o+vN/nkXErLLmPfHkNqXk3H5X0sap8ad2h5E67+ODG/JlkKC98v1k71JS6+j5XbvpSrXA4ERGCW4zxplkJgcAREYAaHvJcBm5KNpkH94t/Uhj9rY+t2LCu39ZSNlcfq1oetx2whS9lYua1bP17e1GZfb0d5SUmfLOnLt2PymI2V2Tr7o7RyW8+xSdnbvl3Vu4z9pnmn9FaeqtvjzbGx9mPUm3C1x8D5Nclieva1pbfzbWvLeo4NbHPt6FfluAiIwIyLv0YXApNFQASm31Pz6NGjMMR+5a8fhps3bybHwqJdNw+vb2rDl7WxdTuOldt6ysbKY3Xrw9atrZXbeokN+npfvk3/KTn1bcuY35jMzzVmY2W2budm5baeY5Njb/2U1LuM/aZ5N+lxHNbG1u0xWrmtW5ux621wjR2Lldm6PT4vb2qzr7WzdepjZa6d7dvvKiHvdQiIwNShI50QWGMEpkJgcAqwsNgNbSuzdW/Htrfxbdp1UdoFbux6U7KB+QGL2Dxjci/zbe8vpvc2qXaqb85cfd+mNn022UGfY0N/tvT9rK6kHvMbk2EMK7d1jm9ltk59zIe3K23bsUrqQ8a+P2bO28pTddqizLGx9mPUm3C1x8D55ci8jW97fFK+fT/fzvVD/6VlF2uKfMwjIAIzj4kkQkAIbCAwJQKDE4JFiDvbKLFBHtus3NZpG5NRt0i5yAL38OHDMOR++daDcOPGjcYxgYXfU/OEHXW2HpPF9LCLySHjHrOJ9YmNaWXWH+Ux39TFxqAP6liyj/cX03sb27e0XjcedHa384j18zLbl/WYD+p8f2trj7POPtbH+/Vt65v1vmKfc+c4LCm3JXScK8u29uw3dtmEqz9Oi4Gde8oO8piOfWM69knp2NeWtk+sn7Vtqi+yBiyytsg2jYAITBobaYTAWiMwNQIztZORs2DVLXoPHjwIQ+yXvnE/XL9+fZCxuj4eJBX0aeuUjVVOaS5jYbAM4y5z7E8ZX+G6fe+uu8fnrBFTW9eWaT4iMMt0tjRXITAgAiIwabDrFia/oKUSkfv374ch9otfvxeuXbs2yFhdHw+IQtc+u/A31Xl1cWyr5GOZY3/K50G4bt+7U/d3vw7UrRnplUaaOgREYOrQkU4IrDECIjDzJz+1CPnFyi9qPhm5d+9eGGq/cPPvwtWrVwcbr6vjAknoyleXfqY6ry6PcVV8LWvsTx1/4Tp7//b3d3//9+tDah2ZX3EkqUNABKYOHemEwBojIAIze/JTi45dnOzCZRc1m5D83d/9XbD73bt3Q5/71278bbhy5UqvY3QxfxADu3fhswsfdk6od+FTPvqNeeK7LLHP+S5LKVzvztzDcT+393h777drgl0rUuvJ7KqjVh0CIjB16EgnBNYYARGY2ZPvFxy7GKHOhYqLl13QSFhsgvK3f/u3we537twJfeznrv1NuHTpUi+++5ivfPYTB+uIq2K/n1haZ1ztPRt1e0/nfd7e+7kecH3w64ZfV2ZXHbXqEBCBqUNnjXT+U8bSQ/f+0O5z8/59u2nsmD1kfm/yk6uPjZfbdyi7IQnM17/+9TDlHf9Hxe74VS/u+II8d3zXBDte2eKOpx/YL1++XO0gE9wvXrwYuF+4cCFoFwaKAcWAYmC6McD7NUrex1Hy/s77Pe//KLkucJ1AyfUDpV1bUJ/yWjiVuSEPEoEZKhuc8DhVMv38b4Zgdp+4+3bd4aSS85S8zleurtS37+/bnEdKTv0qlUMSmCnj5j8hs5+g8VM1fsrGT97wSRw/mcOndPz0+2/+5m8C9tu3b1f7X//1Xwfut27dCn3sL1+6Fc6fP9+L7z7mK5/9xME64qrY7yeW1hlX3q9R8j7O+zrv8/bJjH29jOsE1w27lvh1Zspr4lTmJgIzlTMx0jyqhNwQF0tiUvW6JL5Oh0Ns0reFodSv7W/rsfk06WN9llEmArN51vzCwkWHixAXJZAXvkIA8sJXDbCocYHjgsdF0CaV3/jGN0If+6kLXw9nz57txXcf85XPfuJgHXFV7PcTS+uMq71n8z7O+zrv87jn8/6PtYDrAtYIrhdcP7ie+HVmGXOGoecsAjM04hMbr0rGByQw9vBJBFByt3rWqbNlkw62drN9Wfd6tn1fyutK+oz1pYw28EOZ9Ul9TMc+TTbWX2ldBGYTQbuwcLHh4sPFiOTFP3XhgoYFDosdFz8mk3wc718h6LL90vkb4cyZM3OvKXQ5hnzNvmIoPKaBh2K/n/OwTrjWveLF+zjv63wqw/s+iQzXBT6N4brBdYTril1rStfvdegvArNCZxnB77eYjDYfePKbwyI7n8i88pWvpItomUrAvTGTcSv3fX2btl5e1/a6mA9v49vsEyu9bawdk1lfi+rR1/ex/rqoi8CEYBcULjIosfBwEbLkhZ+6YeGypMUSFSyIfBea70bb96W7rh87cyWcPHly6zs5XfuXv+3vOwmLaWGh2O/nfKwTrvhOi7+ued/mfRwfWOAeT0JjiQxJDF8ri5EYu7bYNaeLdXyVfYjArMjZ/b3f+73wpje9qUqqeEhIsCB75plnKJopFyEvtAWJaSIwGATJtd9nBn9s42Vo5yTm3qap3TSO7895QG537yfWj31pG7OxMltnnxwf1raPughMnMDwUzNcX568YLHCp29YwLCYYVHD4oaFjl/u5xf68cVPfBGUX9j92te+FvrYD7/0tXDs2LFefPcxX/nsJw7WEVfFfj+xtM648n7NL/Hjy/sgOLi/82kN7v14KoN1AOsB1gUSGb5SxtfJuJ6QxIjA5GczIjD5WE3WEhcAiMp3fud3bpEYkhfKYOM3kpJFyxwC48dC2yfqvs0+KTn1Ob4W9ZFjnzMu52j92Xqu3o8V80FffZUiMPMEhotNirzwqQuIC0gLFjYQFpAVLHxIIvGF+nPnzlXfS8GrXadPn672l19+OfSxHzhyKrzwwgu9+O5jvvLZTxysI66K/X5iaZ1x5f0a9258txD3c9zXQWhAZkhksAaQxGBdIJGJPYnhugISIwKTn9GIwORjNWlLsHlLYmwdyVZqyyUvG+xj63WztgQGc7CJuK3b+Xk52rG9qY/X1/nwY9q+vm5tYz4pYz9rH5PRPlbSHqXXW10fdRGYbQLDT8ew0OB6ihEYPnnBwoVFDIsZFjaQFSx2SCBPnTpVvc710ksvhRMnToTjx4+HF198sdrxlKSPfe/Br4bnnnuuF999zFc++4mDdcRVsd9PLK0Lrl/96lcDd14/vF/j3o17OO7luK+D2OBeDyKDD67wAVaMxMQIDNYTrC1cZ0hi+ljXV8mnCMwqnc2NY8GFAPKCHfWmLYfAgLxwh/2QBCaW/OOYvLyu7XXExMptnfpUaW1tPceeNrafrVOfU7btl+MbNutOYLiIoMTCwk/JYuQFrwfgUza8OoCFC+Rl165dk9if+uFdYefOnZOYy1Qw0TymEZt9nwfFfj/nWbimccVTGZIYvFLmSUzOq2R27cldr9fRTgRmBc86E6zcQ2siMSQvKJsITFNSbfW2budq5baesoHc29m2rad8WBtbt/asW72tU4/Sym2dNlZm69R7H1bOeqof9aWlCMyjrcf5JDC8tvBFTLzLjE/TsCDx6Qu+74JXxvDkBcmZNiEgBISAEFgPBHDPxw+m4EkMXhvGk3j7BX9+ub+OxGCtEYHJixcRmDycVt4qRWK2yAsQePwkpukJTCqx9nK0YzILttdT5+V1ba+L+fA2vh3rk5L5vr6Nfl62aDvmg/PpqhSB2SQwfKzP18fsF/dJXvD0Be874xM3PH3Bp3AiMF1FovwIASEgBKaPAO75eLUMrwzjO4/4MAtP5Eli+J0Yfh8m9oV+rjckMdM/6vFmKAIzHvaTGhkJNIiJ30labOmT7diBwMbv3o5+rJ23QdvqWafc2tNfSse+LK2d7UuftLMldb6ss0n5XsQHbO0YMZ/eX2lbBGabwPjXx2JPX/jdF7w+gPehRWBKI1D9hYAQEALLgwDu+fjODL4TY5/C+F8nwwde9ilM7BfJRGCaz7sITDNGa2MxlyA/fuJiyctWvQNUhkjCO5jm2roQgZklMP71Mf/0BZ+y4cubeH0MC5gIzNpeOjpwISAE1hAB3PO/8pWvVF/sx1N4PoWxv0yG70nan1dOPYURgWkOIBGYZozW14IExiIQk1n9AnURmAXAGsFUBCZNYPjdF3ySxv/5wtfH8LOaeA9aBGaEoNWQQkAICIGREMA9Hz9Zj9fI8BQeawG+C8NfJcOHXPwfMfYpjP3nlvw1MhGY5pMoAtOMkSx6QkAEpidgO3IrAhMnMHx9jF/cx3vN/PUxLFZ4dQA/rSkC01Egyo0QEAJCYAkQwD3/0KFD1U/W8zUyvFKM78LYpzD2uzD8WWX7Gpn9Iv8SHPZoUxSBGQ16DSwCM+0YEIHZ/PlkLCb8Aj9/fYyvj+HpCxYj+/0XfIETn8DlEJin/9nfC4vs73vjP5l20Gh2QkAICIE1RQD3/IMHD4ajR4/OvEYWewoTe40MrynzCQxJzJpCmXXYIjBZMMlICKwfAiIwswTG//oYf3kMT1/4/Re884xXB/DPzhYhMC8f2Rdu3bhS7X/wzteEv/p3/6qqf/IPfzH8yXt+uKof+tyfBRGY9bsOdcRCQAgsBwK45z///PPhyJEj1T+55GtkqacwfI3Mfg9GBCb/XIvA5GMlSyGwVgisM4Hh+8f26QsJjP3+C18fw/df+P9fXn755eoVglwCA/Ly22/7nvCrr391tb/9f/2H4Z3f/01V/R3/538efu6f/ydV/Zd+4L8Kb/3ef1DV3//0P12rWNTBCgEhIASmjgDu+V/60pfC4cOHqw+xsBbglWJ8sIWf1+d3YfjPLUlgsKboezCLn10RmMUxW/se+HR5HfZ1P9EiMJtPYPj6mCUw/P4Lnr5gMSKBwZc28e4zfkozl8DgyQvIyxc/9W+qJyyf+eNfDWeOfal68oKnL6hDhlfN3vPD/2348p4PhXc+8U3rHp46fiEgBAZCAK975+wDTSd7GMwZhCK1Qdflq+y45z/33HPVF/mPHTtWrQX4NTL8MmXda2QiMKkzVC8XganHR9oIAutAXnCM676JwMwTGP8FfhIY/LMyfMKGT9vwC2R4BzqXwPzWT//P4W3f9x9VROV3f/Z/Ce/d9V0B5S//i2+pdspAYH72n//HAU9ffuZ7/n5lkxejHwmv27HxdOdEnvXoVh///rDjtZ8cfRqNE3jpV8I/3vGPu8N167jbnK82fRJH2MVxzRyLTX4L8KqbV50ucZgS5yOQk+Tn2OSP2I0lCMq//x/8h1ESU6drOzru+fv37w9f/vKXqw+xsBbgO5H4YMu+RsZfI+P3YPRF/naIi8C0w22te4nArMfpF4HJIzBYjDyBwTvQuQRm38d/P/ziE/8o/M7bv3fjycp/GT76gZ8Oh/6/Pw9/8AvfX+2oY//E7/9CeNfrvr2qow8ITd6Wk9zm2OSNNmu1qN8Xw3tf9R3dkYLZyUy4ZY97UcxwWG369AVH+lhOvvebw45v//VwvHjoKR1v8cFM3kEOOcmxGeNAY0QlJutibrjn79u3r/olMn6RP/U9GHz4JQJThroITBl+a9lbBGY9TrsITJzA4BfI+AV+vD4GAoOfyORPKONTt0UIDF8h46+Rgaxg+8Tvv6PaGW0vPv+ZrS/xo89KEpiNT9K/fRmevvCkdFXOHHeb5LxNn64m7/zUHktX8+zKj5u7mlEEcshJjk3U+QBCS1hsveuhQWD27t1b/RKZ/SK//x4MXjn234PB0339lPJiZ0QEZjG8ZL2BAAkME67c0oJHH1Mu7XzXsS4CMwyB+cj/8+bw8//7fxo+9H+/Kbzrh76jesKCeCsiMNUrNZuv7nzrr7135hWyTzxpXumpyAKSQS/bGH/Orv4qmLeP+63zgk/of+BjtNhMUt/z8fdsvKq1Ob9vefepx8pN3ZMbT6R27Pih8FFIzTEjmdr2A+XGU4FXbx/jlm6mz2M/UdtY/805bL6at1n/tfe+duu7AttznZ2bPx+YXf5xz/raOvbHT2DiWMXO5ebxbOGASeC1r+rpyEdn4iWNa+QcJI5l+/VFi5k/FnvOavB+6f+NxGuuXxyotkURyCEnOTaLjtulPYlL6pWyLsYSgekCxXwfIjD5WMnyMQIkHSQuFhj7qXCqDnv6yC1xc8y1pV2bPuyLct03EZhhCQy+rI/vu/zRu34ofPaDvxE+8Jb/qdpRx/7BX/+x8C9/8L+u6vh5ZXt9zcYqkrntZLB6bSf6XQ2b9Nn6rLfFX0+yvmzd+/Vt2L5uk4xUKrQ3SAdfOarIBr9DsanbJgmb7a2EfMZ2MxnetuW46GO+G7SRwFc2W9/foN1GGZPNvLb1eK58egR7EqvKru58oO8ixx2Z8+MxtrCaGd8ch52zOyYQ0E38LC6bxxXHdVM3i2vsWLbnO/sKWY1vN7fNI4A9fdk6tLZd49dCMVId61LOPtL0osNivk1bjk2Tjz71IjB9ojuObxGYcXBf6lGZ5DOBQrnIjoOnj9wSN8dcW9q16cO+KNd9G5LA4HH6lHa8FoYdj/rx/Ra8IoafwMRPJeNVMfwsJr6YiV+YwZc08ctj+OeVeO8Z/4kZ70Hv3LmzMYRw3fAVso8/8/MbvzL234Tffuv3hj9/3xvDr+/67mpHHfszP/+/hV/4/v+iquPXyXj9zQ2ykby/ikl/pbTJ3YagSm6ZRFlCwOTwsceo3dxo24KovRt723q+ttF/PhmenVM8yd5wtXHMr94iDJuut2wjusqiIjnE4XEJAvJYPjOXmGwuabZzNce90bfpfMyMNeN3/lj4NAr3t2rfeoqWGB8ukueGxGljvt/G76bMzj2Ja2SeGGf+WCzGfMq1MacNXJK+C/FO+t2EU38XRABx1rTl2DT56EtP8oLS1rseD/f83bt3hz179lRf5j9w4ED1k8pYF/CrZCdOnKjWCnwvBmsH1hCsJfiCP39mGWsN1hysPVyHprQ2TmUuOHciMF1H8Br4Y5LPBMqXgIAy1tlmSR+5JW6Ouba0a9OHfVGu+zYkgZka1qn/A4P3lCf/HZi6hHkmadx4MrH1hXmTtOJkJO0SZypp7/wmuleveG3NhUa+r33tyelmxt/sn0NgfKLLkVFuPrniU4lNzazMzsHWYWvaG3NLExh7DjbHmOlbicxxR45zs5cZrxKY9kyf2fFwPCAcLOd8zfTd1G7hao+xUs36nvO1Kdj+W+t706wt3v68bs95e3jV8hHIISc5NvkjdmcZIywxWRcj6hWyLlDM9yECk4+VLB8jwCQfZIQ7VKznlPSRKnEz5A4b1K0tdbak3spYz9HRhuW6n3ARmGFeITv0uT8L+CeV73vjPwnv+D/+s+rXxvCF/Wd/+V9UO+rYP/PH767+Xwzq6IPrLL4hed1OvDeTwMdPWvBJPJ/ObCSQ2z8DbBJeOE3axUdM2zu/ie4gTPNf3t88jq1P9GcSXu9309a+6rSdxG4SgC0/W3Nw/rfk25XZxH5Tvi2zc7B12Nn25jic28z5aHXcO9xTDj+ea9edS2D6qteF123s1feI0LVm7rPE1h7jRrfksdgnQ9UAj//M4jLre9uuFO9Zv3bOqfr22KptIoB1tGnLsWny0bW+jqjU6drOQwSmLXLt+onAtMNtrXsxwWcCZQkLgaEu1oaOPmIlboRWHiMhVs96rB91KL2eupScc1/XUgSmfwID0oL9rd/7D7b+Dwx+Khky/LQydtrgn12+/fv+YdX+jTf8dzUEZiNikbRuXEfYZ780vpnMVzokrq9+1dZPFuNT6kpevZKUtotfD2n7Wb/x3khSmeBvW2wmmJtf1N+c27aNTT4f99hIoLdfr+KrcfQG++0PRbb8zPTZ0OPYDXZb32OJyeYSfZuou/mZ/vZ8LH7cG8cTm/PMXHDMdvz0udn6cYPqnFuszLHMjGdxtWP4HyJI+KKYZcq3wWvrHMwck/lhgtgrdCm/Mz7s/G2dk1NJBHgvaSppP5US8wVRSW3QwaarTQSmKyTz/IjA5OEkK4MAE3+SlC4JDG4m9G/LlLzOJqcP+qfszCGvZVUEpn8Cw8ACOfnyng9V/6Sy+59R5ihTLTeSx63vX9g5rmhSuZFc4+nQxzaeecSP22KwLPXUOVyW+WueQqAcARGYcgwX8SACswhasq0QIGnwxIWEBka27tvQ0YcvU2QiJbf9vY1vW1tbT9lVB7vGf0RghiMw73/6n4Z3PvFN4We+5+8H/JNKfLEfP6+MHXXseG0MT15Qv/jykblrbPVCdTUJTPVEiq/xrd5J0xEJgbVFQARm2FMvAjMs3isxGpN/khRboh7bNx5zzMjpw5cpMuHlaMd268/3gS7WJ2YH23XfRGCGIzCMNfyMcuz6ScnYbzXLVSEw5hWu6r5lfolrNU+cjkoIrCUCIjDDnnYRmGHxXonRSBKQVGGzyRUPkLqqvbFog8BU+2N7+vBlikxYua3b/l7e1GZfb0c5j2VdSxGY4QnMusaajlsICAEhsOwIiMAMewZFYIbFeyVGY4JPkiICsxKnde4gRGBEYOaCQgIhIASEgBCIIiACE4WlN6EITG/Qrq5jS2BIXnC0rNvSPnlhHXr6iJX+iQjaVmbrtr+XN7XZ19tRvrpnMO/IRGBEYPIiRVZCQAgIASEgAjNsDIjADIv3SozmE3wQEmwsZ+p8dcyUTQQG/kEquLPNcdmmniXl3s63ac8y1g+ydd9EYERg1v0a0PELASEgBHIREIHJRaobOxGYbnBcKy8kBDxoEBLuVsYnLrGSPqZc8ljWtRSBEYFZ19jXcQsBISAEFkVABGZRxMrsRWDK8FvL3iQd/uBJYlhuERdr+PhJDH1MubTTXse6CIwIzDrGvY5ZCAgBIdAGARGYNqi17yMC0x67te05ZdLR5dzW9gQ/PnARGBGYdb8GdPxCQAgIgVwERGBykerGTgSmGxzXykuXJGHKvtbqpEYOVgRGBCYSFhIJASEgBIRABAERmAgoPYpEYHoEd1VdT5l0dDm3VT1/ucclAiMCkxsrshMCQkAIrDsCIjDDRoAIzLB4r8RoXZKEKftaiZNVcBAiMCIwBeGjrkJACAiBtUJABGbY0y0CMyzeGk0ILA0CIjAiMEsTrJqoEBACQmBkBERghj0BIjDD4q3RhMDSICACIwKzNMGqiQoBISAERkZABGbYEyACMyzeGk0ILA0CIjAiMEsTrJqoEBACQmBkBERghj0BIjDD4q3RhMDSICAC0y+B+cSTOzb+VdL8/spfPZWIkY+E1+14VXjXiZi6ThezX0QG368LH12ky1C2H//+sOO1n9wYDXOcxfIHPjbUJGLjzM8nfV5j/fuSDTmvhrFe+uXw6h2vTsRz0/HDd+paaOorvRDoBwERmH5wTXkVgUkhI3kjAkef/tbQtL/wju8KHzn1/rm90bkMRkdABKZfArN9gnOTsTq7Ot32SO1q8D0mgUkd24vh177j2x8nwCmbdkec16tuTK9De4NgVWSrzrvvV2fbRuf99zmvtmPlHJf3ndNHNkKgXwREYPrF13sXgfGIqJ2NAMhLaoPuGwc/FUBgYiQm1a9Uzk+0S/0sY/+uj10ERgRm8zpAsjhBArPxCf63bxGCMRLaujEjuuqJww81PMmK9Ov0ZhTx39u82o6Vc8AR3zndZCMEekRABKZHcCOuRWAioEiUh0ATgaGXGImhrssSCfy6bn0cuwjMeARm5vUyl6S/++Pv3nj1ZvN1qe3XklxCVyWlfKWqKWluumrgmwRmcxw7B7yqdfLX/tHW63B+TtZ2W7cx5swcd4TtV742x3jydd+25ZPk3D7BwJi+z+zrdRtPaF61I2yNidfNvu294fgGhcDrR33NaxNNdz4q4eZ8OOf5c4w+PGfbT2vm7ZrOV51+yHk1jWX1m9jwPG9itKmPnyfbN4R5jGaxrhB5fP7r0JFOCJQgIAJTgt7ifUVgFsdMPR4jQALDRccm0dTB9N7XHsw9iekDRDt+H/6n7LOPYx+SwHz9618PU9pv3rwZsN+4cSNcv349XLt2LVy9ejVcuXIlXLp0KVy4cCGcP38+nD17Npw5cyacOnUqHD9+PBw9ejQcOnQo7Nu3L+zcuTMzZGaTsdlOVof6RmJbJeEbVhUB4HcIvJ35fsBG4raVxM86z2zBtyUwZg5ICjfmtOW/apMwNc3XkJa5YzE+q++3mOOpZm3nBMHjsbYIgJ3D68LHKtKSOIbI2CQZ8xg3zYuQYj5+zpGkeutYaBvrV+eTutwy5r+veTWNZfSImy2izmOB3sTa3HkiZrRHmfYJkrN1Xm0X1YVARwjgnr979+6wZ8+esH///nDgwIFw+PDhal04duxYOHHiRLVWnD59ulo7sIZgLbl48WK4fPlytcZgrcGag7WH69CU1sapzAWnTASmo8BdRzeWpPjjj+kg4/dhvD3bTMRRsm51TfImPX2x5Bil/eiHfm1J3ymbJr315eu2L+uw4VhWRjllLK1P9oNsSAJj5zCF+qNHjwL2hw8fhgcPHoT79++He/fuhbt374Y7d+6E27dvh1u3blWkC4sMFh0Qm3PnzoWTJ0+GI0eOBHwal7eZpIsdHhODzXMUIymbhttJmfFRJXqb1w/P8XxyyIFySvi2yb9NHM24lSvbtnU332qOJBlOZ5PQOZ+btmEDny3SlLJ5bApb4LCdvPY4L445dwxQbBCFre/sbDQzz3HcbmugBSvzx97fvJrGMvrHMdt0TqPxDgSSWJq4JfFfEDGZC4FcBPQEJhepbuxEYLrBcS29kKRsJUkbSQI36GJ7DoGBP795mW/DPkfmbdD2spgvbxPr521y/cCOW8wHdXWl75c7P/i0fW0/EZiRCMxMcm+TXpPwVcFgPzk3upn+dVGTq4NvkwjOPFkw41bubNvWoTTzjcwxmZzOkQGLCY/Bj0X5RtlIYLqalxlzbs4bOnvMtj5DbNxxJO3sWIvUnX90tWPYevG8GsaKYMRXEe0rZNuvBZrzZPsm57z5aiNIEfzOkqNFMJOtEMhDQAQmD6eurERgukJyDf2QwOQeOuxzCIz3ZxNsq/Pypjb7Wjtbj+kpQ2ltbb2tTY4P67uu7n35dm5f208EZiQCg4SbnxZXyZl9AmNeYZpJ3GyyiLqxqzv5WTr4a0tgzDzm5mueiszpap7ybNhuf3mfB2CPnzKUm3OPvUK2ldDOjd1yXnZYm2BX8s1zsvUUqPYcm2NP2s0MtkDD49TnvBrGmsNo8zC2ycbm3NLn6TFOdRhtnNtXfcfrwute9R0tf655AWhluvYIiMAMGwIiMMPivVKjkcAg6U3t9oBFYCwas4TIaiyJsPK6uu/j27l9bT8RmJEIzOMnFTgXO2aSr82E0H65fSsh9slglZSb63Lu+wV1EeF1GLctgXlViM93Y4yZOZKkYWyf+JovaW8cx+yX9znXzWS3wuzx/ehb3v2JmS/xV5/umy/xdz0vzmSz9POxxweLzacJ8+d49ljr7GbHy20NOa+msaA3JGRrHeGrhZv6+HkyfWuwBCp4slf2CmUutrJbdwREYIaNABGYYfFeqdFIYHIPqoTA2MTE1u3YkNvN2vk67XwfyL2tbdf1Y98mG6u3vm2dNrkl+trNt6mzY9i61bMuAjMUgSHiq1baJLOrY9vwyadTrV32Ma/Wk1HHJALdnKftVxOTA0khBDpBQASmExiznYjAZEMlQ48ACYxNhH3d9ikhMNZPqo6x7ebbVsd6zCYmoz3LlI2V2zr72bJJb22b6t6Xb6N/TObl1kYERgSmKe7q9d0koPVjtNFOdV5tjmWV+3RwnvAKWTHhXWWMdWxdIiAC0yWazb5EYJoxkkUCARKYhHpO3DWBsck2Bmtqc0LWztZjespQWltbb2uT48P6rqt7X76NvjGZl1sbERgRmLqYa9Z1kIA2D9LCYqrzanEoK92l5DzxFT3/6t5KA6aDGxkBEZhhT4AIzLB4r9RoJDBIelO7PeC2BAY+bGIda+fKcvzEfPXVL9cv5lS35fjxNvRn5bYuAiMCwxhRKQSEgBAQAvUIiMDU49O1VgSma0TXyB8JTO4hlxAYjIHkmntsTJt8Wz37xPQxGfu26RfzV+cHYzXpOZ+60o/r2+xrx2IdOtqzhEwEppzA4H/FaBcGigHFgGJg9WNABAaZw3CbCMxwWK/cSH0QmJUDaYkPSASmnMBM5b8Wax5fr/7pqHAQDooBxUBfMSACM2zCIwIzLN4rNRoIzKJ70/+BWSmACg+GT0jqysIharuLwIjA9LXQy6+SSMWAYmDVYkAEpjal6FwpAtM5pHIoBFYDAREYEZhVSzB0PEqaFQOKgb5iQARm2NxHBGZYvDWaEFgaBERgRGD6WujlV0mkYkAxsGoxIAIzbHojAjMs3hpNCCwNAiIwIjCrlmDoeJQ0KwYUA33FgAjMsOmNCMyweGs0IbA0CIjAiMD0tdDLr5JIxYBiYNViQARm2PRGBGZYvDWaEFgaBERg+iEwzzzzTNAuDBQDigHFwHLGABbxGPkSgRk2vRGBGRZvjSYElgYBEZj+CMxTTz0VtAsDxYBiQDGwXDFw7ty5ag0XgRk/lRGBGf8caAZCYJIIiMD0S2BiC6Bkeq1GMaAYUAxMNwZEYKaTrojATOdcaCZCYFIIiMCIwCiRmm4ipXOjc6MYGD4GRGCmk6aIwEznXCzdTJ7+Z38vLLIv3QGu+YRFYERglCANnyAJc2GuGJhuDIjATCcxEoGZzrlYupmQvNiJQ8YtVac+VeI/z9vNt61uyPpU5jHUMYvADEdgXvGKV0S/FKpEZrqJjM6Nzo1iYP1iQARmqAykeRwRmGaMZJFAgASFRCa3TLjbEk+VKEx1XlvAdVwRgVk+AgPur6Rq/ZIqnXOdc8XAMDEgAtNxolHgTgSmALx172oJDLBgO1anjmUddlMlClOdVx2WJToRmOUiMCQvLJXQDJPQCGfhrBhYnxgQgSnJKrrtKwLTLZ5r5Q1khDsOnPWcMgYUCILfYWeJA+u0s35ispiePqwOdfZnST3btszR0SdL9Lcb/VkZbVmmbLze+/Y+27SHJDBTSwBu3rwZsN+4cSNcv349XLt2LVy9ejVcuXIlXLp0KVy4cCGcP38+nD17Npw5cyacOnUqHD9+PBw9ejQcOnQo7Nu3L+zcuTP6NAT/+wE/nWqPufQVMpKWX/z3dgTs1rfq65Nc6VzrXCsG+o2BOgKDe/7u3bvDnj17wv79+8OBAwfC4cOHq3Xh2LFj4cSJE9Vacfr06WrtwBqCteTixYvh8uXL1RqDtQZrDtYerkM6p/PnFDmNCEybzE59KgRAVLBZwlIJHstYR0lbX7c2rPtk3LZ9Qk8dS/iw9RyfXfbxvtrM1/fxPmPtlAzyttuQBKbtHPvq9+jRo4D94cOH4cGDB+H+/fvh3r174e7du+HOnTvh9u3b4datWxVRwCKDRQfEBovbyZMnw5EjRwL+qVls4WkiMCQjsb4xmbUXgZlf6GKYSSacFAOKgTYxUEdg9I8s+1qR435FYOK4SJqBAEnJ0ATGT62JsMT08GHltu79s51jk+M35sfKbJ1j5/i1tl3URWCGJzAkIyybFlhvJwKjpKwpZqRXjCgG2seACEwX2UU3PkRgusFxLb144kJCAzBs3be9zoPnE3jbtnX2a5LF9Ohr5bZOv77MscnxG/NjZbZu52Dltm5tuqyLwAxLYDwZ8W2fdMT0IjDtExOPr9rCUjGgGPAxIALTZZZR5ksEpgy/te5NImJL1GP7BluYkdcB55Nz27Z1+miSQZ/a6QOlt7E66mMy3w9tbraeK4v1QV8vR9vu9N9VKQIzLIHhQmlJCEKJcltCbu2oi8moU6lkTDGgGFAMlMWACExXGUa5HxGYcgzX1kOMuBAM6qo2si3uG4IZHTuYMpaoU+11kDfJYnr6qyt9v6Y2fVk7W4/pY7JYH9il5DEflJWUIjDjExgkG57EsB0jKzGZEpayhEX4CT/FgGKAMSACU5JVdNtXBKZbPNfKG4kISu4EgLqqTfKCcmOb0VWS2T8+UbdtW2evJllMj74pecqvt/ftWL+YTZMspm8zX86nbSkCMw0Cg4WTpIUlZDGyEpNx4VWpJEwxoBhQDJTFgAhM24yi+34iMN1jujYeSVpISGyb9SrzekxcWKd9CiifwNu2rbN/jszbNLXhu8nG62Pzidk0yaD3Nk3t2Hw5n7alCMx0CAySDkte0I6RlZhMCUtZwiL8hJ9iQDHAGBCBaZtRdN9PBKZ7TNfOIwkJSwCwVUfW5fYtXQ1SNmFP1dnd6ptksI3Zox91LOnLlr4vbW1JX7as8+HtOIb3aX2wT5ON77NIWwRmWgSGCyjLGFmJyWivUkmYYkAxoBgoiwERmEWyiH5tRWD6xXctvIOQcOcBVyTFEZcZIkNDlXMIkMDMKQYWiMCMR2A2aPbMl/djxCRXpoSlLGERfsJPMaAYYAyIwAyciNQMJwJTA45UiyFAEsNyi7BYNyQ1Vqb6DAIiMDNwjNIY9R9ZbpAXkBNLYnLJiu/HRVelEjDFgGJAMVAeAyIwoyzJ0UFFYKKwSCgExkNABGY87DnyWAQGpMWSFZIYK0MS4u2YmFDOtsryhEUYCkPFgGKAMSACw1Vy/FIEZvxzoBkIgRkERGBm4BilMQaBySUrtPNkhW2QHS62KpV4KQYUA4qB7mJABGaUJTk6qAhMFBYJhYAQ0HdgxvsOjCchJCckL0hIYMM2S/+kRolLd4mLsBSWigHFgAjMdHIjEZjpnAvNRAhMCgERmOkQGEtWmESRrJC8QE4ZbVQq4VIMKAYUA93FgAjMdNIUEZjpnAvNRAhMCgERmGkRGJATm4jEyEpMZvuo3l0iIyyFpWJg/WJABGY6aYoIzHTOhWYiBCaFgAjMsATmFa94RehqV2K1fomVzrnOuWKg/xgQgZlOmiICM51zoZkIgUkhIAIzHIFR4tF/4iGMhbFiQDFQGgMiMNNJU0RgpnMuNBMhMCkERGBEYEoXe/VXwqgYUAysUgyIwEwnTRGBKTgXR5/+1tC0v/CO7wofOfX+ub1gWHUVAoMgIAIjArNKiYeORYm0YkAxUBoDIjCDpB9Zg4jAZMEUNwJ5SW3QfePgpwIITIzEpPpNQT6V/0NCLKYwnzZz8H18m8c31VIEpj8Cg0VQuzBQDCgGFAPLFwNYs2NEaNeuXWHv3r3h4MGD4ciRI+HEiRPh9OnT1b3+woUL4fLly+Hq1avhxo0bVf9bt26F27dvhzt37oS7d++Ge/fuhfv374cHDx6Ehw8fBv4/sqnmCGPPSwSm4Aw0ERi6jpEY6lQ2I7BsiT+PaFnnzfmLwPRDYIivSiEgBISAEFhOBERgxj9vIjAF54AEBokqd7qjDu17X3sw9ySGdiqbEVhWIrCs8+YZEYHph8DEFj7J9GqLYkAxoBhY7hjQExhmD8OUIjAFOFuS4t3EdJDx+zDe3rZJhlhaHevUpZJkq4/Z1Omb7DkHlrS3PqlrU3o/9G99eRurQ93qY/29je1Pe/qgLW28nm2rZ1+W3kfMljKW9EsfbFPfdykCIwKjhGq5EyqdP50/xcBwMSAC03dWMutfBGYWj4VaJCmxBBO62N5EYGJJqpct2sZB2T62zgO2Mlv3fVPtpj4cp6mM+YnJrJ8mfWrOKR/wV+ezSU+/dT5y59Tkg2P1UQ5JYKa2yN68eTNgx7vK169fD9euXaveXb5y5Uq4dOlSwPvM58+fD2fPng1nzpwJp06dCsePHw9Hjx4Nhw4dCvv27Qs7d+6Mvic9tWPVfIZLcIS1sFYMrG4M4J6/e/fusGfPnrB///5w4MCBcPjw4WpdOHbsWPWdGKwV+F4M1g6sIVhLLl68uPX9GKw1WHOw9nAdUszMxwxyHhGYgsyPBCbXBezbEBjr3ye01Fm5rVNvy0X0KVsrt/VFxrG2qOf4KbHheE0+Ynors3X6jM3f29m2rad85NjYvl3XhyQwXc+91B+/PIkvUuILlfhiJb5giS9a4guX+OIlvoCJhQWLDBYdEBt8IffkyZPVFzjxaZwWnvmFR5gIE8WAYmAVY0BPYEpX3sX6i8AshteMNQkMEs3UbjuIwFg05us5CXuJDUdM+ajT2z62zj4ovbyu7XX0Y+W2Tn1sHKvrsi4Co1fIVjHJ0DEpeVYMKAb6iAERmC4zkGZfIjDNGCUtSGCSBk6RQ2DQBYmr3a0bK/f1OjurQ72pL+1hF9us3NatbUpubWw9ZW/lqKd278vaeZ1t+7odjzors3XqUXp5Xdvr6MfKbZ362DhW12VdBEYEpo9FXj6VPCoGFAOrGAMiMF1mIM2+RGCaMUpakMAg0UzttnMugbF9ULeJrK17u7p2Uz+rT9Wt/0VtbN9U3fq0NlZu69amqW772XqsX0xvZbZu+3t5Xdvr6MfKbZ16lCm5temiLgIjArOKSYaOScmzYkAx0EcMiMB0kXnk+xCBycdqzpIEZk6REPRJYJqS2kX01tbW7WFZua2nbKw8Vc/xk2MT82/72bq1pZxlTAdZTB+TezvbtvXScWz/LusiMCIwfSzy8qnkUTGgGFjFGBCB6TIDafYlAtOMUdKCBAbJaGq3nXMITCyx9bJF25iD7WPrnJ+V2brvm9OO+aSsqYyNHZNZP0162DbZWL2tcxwrQ922Y/5jsqY+TfrYXCjroxSBEYFZxSRDx6TkWTGgGOgjBkRg+shE0j5FYNLYNGpIYBoNHxvkEBiYMkFmGfNPnU96aWv1MZs6fZM9x2AZs4cuJWe/VGnnlvLjbbwvq0/Nw9rY/jF7K2M91T/li/28nn6sHPWYfZ3c9y9ti8BMg8AwPlDmLLq5djm++rJZhjn2dezyq+RZMbCaMSACU5p1LNZfBGYxvGas+yIwM4OoMTkEkHytwyYCMz6B8Ym+b8cSoRybWL8hZcswxyHx0FirmdDqvK7XeRWBGTYzEoEpwBsEZtG96f/AFExnsl2RrDTtk518ZGI4lnXYRGBEYPpKwERg1iux6yuO5FdxNKUYEIEZNjMSgRkWb422AgiIwMwvmiA72F588cWZM/zSSy9V/9wR/+AxtuMGNMVtCv/IMpXkp+RcyKlHyZ06W1JnS6tH3epQ93pvE9PHbFK+Uv0ln7/mhIkwUQxMKwZEYIZdzUVghsVbo60AAki+1mHTE5hxn8CkkvyUnMkM9N6mqW37xuopWY7fmI2X0b/KaSVkOh86H4qB/BgQgRk2MxKBGRZvjSYElgYBEZjlJTCxpCOHNFgbW1/En+1n69ZHSm5tVM9PnISVsFIMjB8DIjDDpjciMMPirdGEwNIgIALTD4HBa3Wp3SYhqSSfcpR+R3/qra86ubWzfW3d2rCe0lu5rbMfSi9P4SF5OlaEjbBRDIwXA/Z+xroIzLDpjQjMsHhrNCGwNAiIwPRHYLjg1ZU+yadtSt6k9/3Qju30g9Lr63TWlnaQsW7LlNzaqD7+J8o6BzoHioH5GABxjOEiAjNseiMCMyzeGk0ILA0CIjCrS2BSBCIl52Jt9bZOvS9TNim576/2fPIkTISJYmDcGBCBmUYaIwIzjfOgWQiBySEgAjMugUGS4hN9344lMikbK7d16yMlp43V2zr1KK3c1lM2Vq76uImZ8Bf+ioHmGBCBmUa6IgIzjfOgWQiBySEgArO8BMYTh6Y2kxZrZ+sxPWTexrdTNjE7jqGyOYESRsJIMTBeDIjATCNdEYEpOA85/8TyhXd8V+A/r7RlwbDqKgQGQUAEZnwCgyQFyT73nKSF5IB92PZ9rZ51jkdbyllSbkvqUFq5rXubOlvbT/XxkjRhL+wVA/EYEIEZJAVpHEQEphGitAEITGqD7hsHPxVAYGIkJtVvCnIkF1PdupxbW1++n29PFbtF5yUCMw0CoyQinkQIF+GiGFAMjBEDIjCLZhP92IvAFODaRGDoOkZiqFO5GAJdkoW2vtr2W+xIx7cWgRGBGSM50JhKShUDioEpx4AIzPj5CWYgAlNwHkhgkNBypzvq0L73tQdzT2Jop3IxBLokD219te232JGOby0CIwIz5SRCc1OSqxhQDIwRAyIw4+cnmIEITMF5sCTFu4npIOP3YLy9bZMMsbQ61qlLJdNWH7Op0zfZcw4saW99UrdoaX3QL3x4ea6OfVnG/FhfsEttsb6wtf1Zp631FZPF9PRhdWPURWBEYMZIDjSmklLFgGJgyjEgAjNGRjI/pgjMPCbZEpIUJqY28YQutjcRGOuDE/GyRdvwY/vYemwMr89pN9lwnLrS+4CtlzW16d/aoW7bMRvKckrvy7b9WNSxhH9b53he5tu0G7IUgRGBmXISobkpyVUMKAbGiAERmCEzkfRYIjBpbBo1JDCNho8NYN+GwFj/qcTWym3d9mV9EX3K1sptnWOgTMmtja3n2OfY+LFTfVJyO6dY3fezbVtn3yZZTO+Pgb6GLIckMGMsQnVj3rx5M2C/ceNGuH79erh27Vq4evVquHLlSrh06VK4cOFCOH/+fDh79mw4c+ZMOHXqVDh+/Hg4evRoOHToUNi3b1/YuXNn9Fe5Uotf3XykU6KmGFAMKAamEQOpezju+bt37w579uwJ+/fvDwcOHAiHDx+u1oVjx46FEydOVGvF6dOnq7UDawjWkosXL4bLly9XawzWGqw5WHu4Dum8z5935EIiMAUZIQkMEtDUbt2LwFg05uupRN5a5tjA3trZehtfto/37duxsZpkMb336+cwRHtIAjPE8SwyxqNHjwL2hw8fhgcPHoT79++He/fuhbt374Y7d+6E27dvh1u3blUEBYsMFh0Qm3PnzoWTJ0+GI0eOhF27donAbPwMtBZfYaAYUAysUgykCAzu+Xv37g0HDx6s1gAQFpAVrAsgKiQpICfAA2sI1hKsKVhbsMZgrcGag7WH69Aia9c62YrAFJxtEphcFzkEBr6Q0Nrd+rdyX6+zszrUm/rSHnaxzcpt3dqm5NbG19HH7jF9TGb7sE47tGNbSh6ztTLfz7ZtnX2aZNCndvoYoxSBEYFZpaRDx6IkWjGgGOgiBkRgxshI5scUgZnHJFtCApNKPn3imktg/ASsH1v3dnXtpn5Wn6pb/4va2L6L1O046NfUpm9rZ+vUx3xZXV3d+7NtW6ePJllMz75jliIwIjBdLPbyoaRRMaAYWKUYEIEZMzPZHlsEZhuLhWskMLkd+yQwTUnwInpra+v2OK3c1lM2Vr5I3ftuatO3tbN16lGm5NYmVvf9bNvW2bdJFtOjb0pOv32XIjDjEJhnnnkmrPKOuG1KZr70pS8F7cJAMaAYaBMDOfeYpntQnV4Epu/sI8+/CEweTlErEhgkmqnddswhMLGk1csWbWMOto+tc35WZuu+b0475pOyutKPmzNWrI/vl2NTNy+v8/5s29bZL0fmbXybvoYsRWDGIzB1i+ey6xDDTceApEWbEBACQmBRBHjvaLrHlOhFYBY9K/3Yi8AU4EoCk+sih8DAF5JXu8f8L6KPJcO2v9f7tp+Tn0/Mnn28bVO7bl7s68fzfaj3JfvbkjZWllO3/VJ1+rH6JhlsY/bsN2QpAiMCU7LIp/oihlM6ypmEhHBzw1q7MFAMKAZyYiBUT25z7jG817QpRWCA8PibCEzBOeiLwBRMSV2FQGcIiMCIwLRZ3Jv6IECbbERgcpI12SipVwzMxoAITGcJwBI4EoEpOEkgMIvuTf8HpmA6k+3Kpwp15diTr5sbdWPPcejxRWBEYJqIRhs94ripnwiMEtPZxFR4CI+cGMgjME888UTjPajuHqUnMENnI/HxRGDiuEgqBNYeAREYEZi6RbytDhdWU18RmJxkTTZK6hUDszHQTGBAXrg33YdSehGYaaRHIjDTOA+ahRCYHAIiMCIwqQW8RI5Ab+ovAqPEdDYxFR7CIycG6gkMiYstm+5FMb0IzDTSFRGYaZwHzUIITA4BERgRmNjiXSpDoDf5EIHJSdZko6ReMTAbA2kC40mLbzfdk6xeBGYa6YoIzDTOg2YhBCaHgAiMCIxdtLuqI9CbfInAKDGdTUyFh/DIiYE0gWm65yyiF4GZRroiAjON86BZCIHJISACIwKzyKKea4tAb7IVgclJ1mSjpF4xMBsDIjCTSyR6nJAITI/gyrUQWGYERGBEYJqIRhs9rommfiIwSkxnE1PhITxyYkAEZplzjkXnLgKzKGKyFwJrgoAIjAhME9Foo8fl09RPBCYnWZONknrFwGwMiMCsSXpSHaYIzDqdbR2rEFgAAREYEZgmotFGjxBs6icCo8R0NjEVHsIjJwbSBAZf2o/dd1LymC1l+g7MAolEj6YiMD2CK9dCYJkREIERgeGC3WWJa6LJnwhMTrImGyX1ioHZGKgnMJ6s8JfImu5HXi8CM43MRgSmg/PA/9TOsgOXo7vAsfjNHl9M7+0XbXufvr2oP9mXISACIwLjF+4u2ojKJj8iMEpMZxNT4SE8cmKgmcCQxJC8sN10T7J6EZiy3KKr3iIwhUhWSfbzvxmC2bdkhb6n1H0IMjHEGFPCdOpzEYEZh8BMPS66mJ9NBmJ1EZicZE02SuoVA7MxkCYwuM9Y0sJ67P7TJBOB6WIVKPchAlOAIRLuDzz5zTPkBUQmJoN8mRP0IeY+xBgFp3vtuorAjENgmhZP6b/e+ARHGAkjxYBioK8YEIGZRjokAhM5D48ePYpI50VVwv2YsIC0cLdPY2wd+pwkHTZ2tyOzf0rvbWln5bZOPf1Sx7bVsw4b6mlPmbVJ6bwN27akP+uDMtrFdE02Xg9f2uIIiMCIwPSVAMivkkvFgGJgWWNABCaeMwwtFYFxiL/wwgvhs5/9rJPGm1Xya14ds2QlVgeBeeUrXxlN/jlCLKG2MtRtG/18OyZb1Mbbl7R939Sxers27UX7xLDi/Na9FIERgVnWBEPzVnKsGFAM9BUDIjDTyI5EYMx5wJMXkJdnn302PHz40Gji1SpZHoDA2NF9gk6dlds69Sit3NZjNl5f1/Y6+kvJU3pvb9u2zv4ordzWF7Wx9qpvIiACIwLTVwIgv0ouFQOKgWWNARGYaWRJIjDuPIC4fHaDxGBvIjFVwrxiBIZweDJQ1/Y6+mgqfb+6ttfRt5XbOvUordzWrY3q8wgMSWCmtpDdvHkzYL9x40a4fv16uHbtWrh69Wq4cuVKuHTpUrhw4UI4f/58OHv2bDhz5kw4depUOH78eDh69Gg4dOhQ2LdvX9i5c2f0uxqpxW9qGGg+SjAVA4oBxcB8DKTu4bjn7969O+zZsyfs378/HDhwIBw+fLhaF44dOxZOnDhRrRWnT5+u1g6sIVhLLl68GC5fvlytMVhrsOZg7eE6pHMwfw6QsYjAzOdtFXHBU5gmElMlwx0TGEwHfu1up5hKwK3c9vV1+rL2lNnS6+vaXmf9sA6b2E49Su/Htm091SfHhuPAlrv1p/o2AkMSmO1Rp1HD01js+BDjwYMH4f79++HevXvh7t274c6dO+H27dvh1q1bFUHBIoNFB8Tm3Llz4eTJk+HIkSNh165dIjAbv/yjxVcYKAYUA6sUAykCg3v+3r17w8GDB6s1AIQFZAXrAogKSQrICfDAGoK1BGsK1hasMVhrsOZg7eE6NI1VcXqzEIGJnBMEzpgExk/JJua2bu2s3Natja032Xh9Xdvr7Diop/ReXtf2Oo5h5bZOfd34tEn1o35dSxEYEZhVSjp0LEqiFQOKgS5iQARmGlmRCIw7DyAvnx3xFTI3nappE2xbt7ZWbuttbbyPurbXcUzKWVLO0svr2l4X85Fjw362TPWzNutYF4ERgelisZcPJY2KAcXAKsWACMw0MiIRGHMeFv0SP7oi+eXPJzeV+AUy/QrZNuCeOHTd5kjWr63H9JSpDEEERgRmlZIOHYuSaMWAYqCLGBCBmUaGJALjzsMiP6PMrkiKSU6aStqyb6yEjd2tDeTYUnpvSzsrt3Xq6Ze6Rdvol/LldbSjHCU3O66tW73tTznKmH1Mzv4srQ/VtxEQgRGB6WKxlw8ljYoBxcAqxYAIzHaeMGZNBCaCPp7ELLoxGW4qQXBKNvjXJgSGQEAEZhwCgy98TnF/5plnwlNPPTXJuU0RLz8n4VcW18RvlRJhHUs/xA7rY5/YisAMkYE0jyEC04zRpCxEYCZ1OlZ6MiIw4xGYKQYWE0gk5toWR0D4LY6Z7UH8+kxM5bsfQjE0roibPscUgbFX5nh1EZjxsG81sghMK9jUqQUCIjDjEhj/Cf7YbSaQJDBjz2fZxhd+egLTZ1It39vkC8tdn3iIwLRIKHroIgLTA6h9uhSB6RNd+bYIiMCIwFiSoAS8mwRcBLAdjoy/PhNT+d4mAcuMBdaxPucvAmMzhfHqIjDjYa+RhcCkERCBEYERgWmXbFvcWGcCLgLTDlPi12diKt8iMDkxIAIzjdRFBGYa50GzEAKTQ0AERgSGyTdKJpBKwMsScOFXhl9Ogimb1SAibc8jFtO2fXP6icBMI10RgZnGedAshMDkEBCBEYERgWmXbFvcWBcBLMOS+OUkmLIRgamLgSeeeKKI4IjATCNdEYGZxnnQLITA5BAQgRGBYfKNkgkk6tisTvXm5Fz4NWNUF0fEry4xlW69iQvPP+5PrPsS5IW71+W2RWCA8PibCMz450AzEAKTREAERgTGJpRMICHDZnVTquOHTrhPaV7Lgh8wA35Twg5zIX65Sabs1pfM4P4UO/8kLraM2TXJRGCA8PibCMz450AzEAKTREAERgTGJrFMICHDZnVTqfvE27fHnOey4DdF8ofzRvyakkvp15e48Nzj/sQ6S09afJt2OaUIDBAefxOBGf8caAZCYJIIiMCIwNiEnwkkZNisbip1T1h8e8x5LgN+xGdKuHFOxC8nwZTNepMY3J98DJCwWHlMZvWpuggMEB5/E4Hp4BzwEyuWHbisdYFx7ObbVtdVfYgxupqr/HSDgAiMCAyTR5RMIFHHZnVTqOMeFZtHSh6z7VM2dfzssU8FMzsn4pdKKiVfb9Jizz/uT7aNOsiKl9XJY7aUicAA4fE3EZjCc1Al9s//Zghm35IV+k51r/ynlD3Jxxizp0OR20wERGBEYGIJJGTYrG4KddyjYvNIyWO2fcqYgGOMKeJnj30qmNk5ET8mkSpFWFIxgOsrpetCLgJT3cJG/yMCU3AKcJP/wJPfPENeQGRiMshh38XWlZ9F5jLGmIvMT7bdIyACIwITSyCnmoDjHmXny3pKTv1QJRNwjIdtqHHbjDMVzOzciV8XCah8rDb5wfXV5zkWgQHC428iMJFz8OjRo4h0XlQl9Y8JC0gLd/s0xtahr/rMu5qRwMbuVFoZ69Chzo11q7c66ilDGZN5ubWx9To/sLO7tVV9+giIwIjAxBJIyLBZ3RTquNfE5pGSx2z7lDEBxxhTxM8e+1Qws3Mifn0mpvK9GsQG11ef51IEprqFjf5HBMadghdeeCF89rOfddJ4Ezd5S1Ca6iAwr3zlK5OEAaNUPt1wXlbXhs7qWWcZG8Pq7NBWnqrn2NPG+qBM5XQREIERgYklkFNNwHF/sfNlPSWnfqiSCTjGwzbUuG3GmQpmdu7Er8/EVL5FYHJiQARmGnmLCIw5D3jyAvLy7LPPhocPHxpNvFol5Oa7L30RGD96Na4R2rat06RJFtOjr5Wn6hyjzt7aqL48CIjAiMDEEkjIsFndFOq4R8XmkZLHbPuUMQHHGFPEzx77VDCzcyJ+OQmmbFaDiLQ9j7i+2vbN6ScCU93CRv8jAuNOAYjLZzdIDPYmElMl9SIwWwhWeDxu2fqWgSpLhYAIjAhMLIGccgLuE2/ftsczdJ0J+JTxIyZTwo1zIn45CaZsRGCef/750HZvih8RmGmkMiIwkfMA4oKnME0kpkrSOyYwmA782t1PsRrXCG3b1mnSJIvp0dfKU3WO4e3ZRj/u1lb16SMwJIFpWjCG1t+8eTNgv3HjRrh+/Xq4du1auHr1arhy5Uq4dOlSuHDhQjh//nw4e/ZsOHPmTDh16lQ4fvx4OHr0aDh06FDYt29f2LlzZ/RTwNTix2OcaoLLBHKq88O8cK9hwhtrW93Q9WXAj5h4HCkfsyR+vE5UrjdJqTv/WF3bkhf0q/MNXeoejnv+7t27w549e8L+/fvDgQMHwuHDh6t14dixY+HEiRPVWnH69Olq7cAagrXk4sWL4fLly9Uag7UGaw7WHq5DTfNZRz3OsQgMUHDb2ATGTWeGSECHxcVutm3rtGmSxfToa+WpOsfw9lbOuvVBmcrpIjAkgZkaCnidFDvuBQ8ePAj3798P9+7dC3fv3g137twJt2/fDrdu3aoWOiwyWHRAbJDgnTx5Mhw5ciTs2rUruhCmFj8uQvCBbcxkMTY2E8ipzo9zxn2GO2VTKJcFP2AF/KaAmZ0D8eN1olIEJhUDuH+OQWBwz9+7d284ePBgtQaAsICsII5BVEhSQE4wd6whWEuwpmBtwRqDtQZrDtYerkM4Hm3zCIjAOEwQNJ8d8RUyN52q6RP/urbXwUGTLKb3/ayNrVcTfPwnJadNk552KqeBgAiMCEwsgYQMm9Wpfq4RDybgwq8Zq1g8Eb9U0iq5CA1jAPcnERigsNqbCIw5v4t+iR9dkZTz55ObSvwC2RC/QmYOqarGiIOXxdpWZus8bjsO9NbG1mkXk1GncnoIiMCIwNhEkgmkEvCyBFz4leHHJFWlCEsqBrCaisBML6foekYiMA7RRX5GmV2RmJOcNJW0Zd9YCRu7p2wohy03W28j47joa33ZuvWbsmd/6mP96UflNBEQgRGBEYFpl2xb3FgXASzDkvilklbJRWgYA1hRRWCmmVd0OSsRmAiaeBKz6GYT9bo6CI42IbAMCIjAiMAw+UbJBBJ1bFanenNyLvyaMaqLI+LHJFWlCEsqBnB/EoEBCqu9icCs9vnV0QmB1giIwIjA2ISSCSRk2KxO9ebkXPg1Y1QXR8QvlbRKLkLDGMD9SQQGKKz2JgKz2udXRycEWiMgAiMCYxNKJpCQYbM61ZuTc+HXjFFdHBE/JqkqRVhSMYD7kwgMUFjtTQRmtc+vjk4ItEZABGYcAoNETbswUAzEYyCVtEouQsMYwKInAtN66V+ajiIwS3OqNFEhMCwCIjDjEJinnnoqaBcGioF4DDBJVSnCkooBrJQiMMPmC2OMJgIzBuoaUwgsAQIiMOMQmCl/8v5Xf/VXSxC5052i8Cs7N8AvlbRKLkLDGECUicCUXWvL0FsEZhnOkuYoBEZAQARGBMaTKSXgZRei8CvHj0mqShGWVAwgykRgyq61ZegtArMMZ0lzFAIjICACIwIjAtPthScCU4annsCItKRIi5UjykRgyq61ZegtArMMZ0lzFAIjICACIwIjAtPthScCU4anCIwIjCUqqTqiTASm7Fpbht4iMMtwljRHITACAiIwIjAiMN1eeCIwZXiKwIjApEiLlSPKRGDKrrVl6C0CU3CWjj79raFpf+Ed3xU+cur9c3vBsOoqBAZBQARGBEYEpttLTQSmDE8RGBEYS1RSdUSZCEzZtbYMvUVgCs4SyEtqg+4bBz8VQGBiJCbVD/IdO3bMqH17RjlgYyrzGPCQa4cCHquMiQjMahAYxilKT0gWbfsE3PquvViMsk0f032pq8Kv7PSJwIjApEiLlSPKRGDKrrVl6C0CU3CWmggMXcdIDHWxEgv8FLepzmsMrNYBCxGY5ScwiFNLUnzb6nLqNgH314Bvx65Lb+PbsT6rJBN+ZWdTBEYExhKVVB1RJgJTdq0tQ28RmIKzRAKDRZg73VGH9r2vPZh7EkO7WDnVRX2q84ph2LdsHbAQgRGB8aRGCXjZnUX4leOXSlolF7lhDCDKRGDKrrVl6C0CU3CWLEnxbmI6yPh9GG+PNkmQLSmnPRNn2lBOO+qtnHX2SdlYvbXx8lwd58TS9kvJKGfJsdH2G3Usvb6pzfnE+lNGG/qyctapQ0mZ70cdS69fpJ/vC5/c6vzApklPPyhFYJabwOBcewKCdkoes/UyJuDwEdtSctimdCl5zP+yy4Rf2RkEfkxSVYqwpGIAUSYCU3atLUNvEZiCs0SSggWYO91BF9vrCAz7+gXdtv041LGED1vP8dllH++rzXx9H+8z1k7JIE9tsXFg6/Hz7ZhNTOb7tR0v1s/7zh0fdtxiPqhDKQIjAiMCY6+I8roITBmGIjAiLSnSYuWIMhGYsmttGXqLwBScJRKYXBew74LA+PFiiaiV2brta+W2bm1sPccG9tbO1umrSRbT5/il/9wyNk5M5sfOaXMO1p+tx/SUef+xfova5Piw46MuAiMCIwLjr4qytghMOX42UVVdhCYWA4gyEZiya20ZeovAFJwlEhgkh6nduheB2UQjlkxbma1b/Kzc1q3NIvWYj5gMPr28qc15WDtbj+kpQ2ltbX1RG9rn+KAtSxEYERgRGF4N3ZQiMGU46gmMCEuMsHgZokwEpuxaW4beIjAFZ4kEJtfFmAQGCWxst3P3eqtDPZYE+z5ss2+qD/UsrZ2tU4/Sy9G2u7XNqXt/HMP6tHXr0/e1dr7Ofr5P7nixfuxb55s62vp5sW3tbF0ERgRGBMZeEeV1EZgyDEVgRGA8WYm1EWUiMGXX2jL0FoEpOEskMEwEY6V1PyaBsfPIreN47NbUpq21s/WYPiaL9YFdSh7zQVldGfMXk8V8eDvfzukDm7b9fN8mP0362HyHJDCxRWhM2c2bNwP2GzduhOvXr4dr166Fq1evhitXroRLly6FCxcuhPPnz4ezZ8+GM2fOhFOnToXjx4+Ho0ePhkOHDoV9+/aFnTt3Rr9w/NJLL0XlPF5PGtq2cc5jfVPymK2XKQGPXSn5MuGXj1XMUgRGBIb3yboSsTMGgcE9f/fu3WHPnj1h//794cCBA+Hw4cPVunDs2LFw4sSJaq04ffp0tXZgDcFacvHixXD58uVqjcFagzUHaw/XobpjXVcdzrEIDFBouZHA5HafGoFpSmq9vqlNHKydrcf0MVmsD+xS8pgPyurKmL+YDD68vKnNca2drcf0lKG0trbe1ibHh/WN+pAExo89dvvRo0cB+8OHD8ODBw/C/fv3w71798Ldu3fDnTt3wu3bt8OtW7cqIoJFBosOiM25c+fCyZMnw5EjR8KuXbuiRGUoAgPygfNuSYhvW11OnQk4zo+PKd+OnUNv49uxPqskE35lZ1MERgQmJ2FHlOXYtbVJ3cNxz9+7d284ePBgtQaAsICsYF0AUSFJATnB2FhDsJZgTcHagjUGaw3WHKw9XIfKrprV7S0CU3BuSWCwCKd2634sAoM5+EShqd22j+/nx/F6tLFZO9Rt2+tj7ZQM8tTmx6Cdl/s27HJk3sa3c8frqp/349ucD0sRGBEYT2qUgPPqaFcKv3a4sZcIjAhMDulAvOTYtbURgeEVOW4pAlOAPwlMrotcAgN/NrlM1Tmu1TfJYBuz55jU19lwjFgf9vOl72Pb9EOZ7Ys629SzpI4l5bllyi/602fKpk7Ovn4eqT5N46X6xeQcO6ZrGsfPVwRm+QkMCIiNCU9IFm3bBDwnnmJxaOfjY27V28Kv7AyLwIjA5JAORFmOXVsbEZiy67ir3iIwBUj2SWAKprX0XWNJz9If1BIegAjMahCYRUlKnb1PwJcwrEedsvArg18ERgQmh3QgynLs2tqIwJRdx131FoEpQBIEZtE95//AFExpJbqWEhj7CW+qvhJAdXgQHie4FoERgfFkRgl42UUn/Mrxa5t0qt/6kB9EWZ/nWwSm7DruqrcITFdIyk9nCJQSmM4msuaORGBEYERgur0JiMCU4aknMOtDQkoICKKspH9TXxGYsuu4q94iMF0hKT+dISAC0xmURY5EYERgRGCKLqG5ziIwc5AsJBCBEYFpIhfQY8uxa2sjArPQZdubsQhMb9DKsRBYbgREYERgRGC6vYZFYMrwFIERgckhHYiyHLu2NiIwZddxV71FYLpCUn6EwIohIAIjAiMC0+1FLQJThqcIjAhMDulAlOXYtbURgSm7jrvqLQLTFZLyIwRWDAERmHEIDJI07cJAMRCPgbZJp/qtD/nBUtzn+RaBmUayIwIzjfOgWQiBySEgAjMOgfFPPabURlKtrT0Cwq89dugJ/PpMTOV7NUgOYqXPcykCA4TH30Rgxj8HmoEQmCQCIjAiMJ48KQEvu1SFXzl+fSam8i0CkxMDIjBl13FXvUVgukJSfoTAiiEgAiMCIwLT7UUtAlOGp57ArAbByCEJJTaIspL+TX1FYMqu4656i8B0haT8CIEVQ0AERgRGBKbbi1oEpgxPERgRmCZyAT22HLu2NiIwZddxV71FYLpCUn6EwIohIAIjAiMC0+1FLQJThqcIjAhMDulAlOXYtbURgSm7jrvqLQLTFZLyIwRWDAERGBEYEZhuL2oRmDI8RWBEYHJIB6Isx66tjQhM2XXcVW8RmK6QlB8hsGIIiMCsDoHZsWNH8GSkTdsn4PDLPTf8aY8yteXY2L4pXzl+cmzsWCX1LvDD+Knj5dxyjmlIG86rtBSBEYHJIR2Isxy7tjYiMKVXcjf9RWC6wVFehMDKISACs/wExiapbQiL72MTcJ9E+3bsgvA2vo0+Xubb3i/0MRsv8+02Y/mxF213gV/qeDkXf5y+nXvcvp9v5/rhvLooRWBEYHJIB2Itx66tjQhMF1dzuQ8RmHIM5UEIrCQCIjDLT2BIQJB8sl5SliTgsQQYF46XN7X9xQZ736fEb8yXH7NtuwQ/O2ZqjjnyIW3snLuoi8CIwOSQDsRajl1bGxGYLq7mch8iMOUYyoMQWEkERGBEYDzZYQKekwT7iyKnT46N9Ut7ltT5dkyeY8N+XZUl+Nk5lMw9p2+JjZ1n13URGBGYHNKBuMuxa2sjAtP1ld3OnwhMO9zUSwisPAIiMCIwXRKY1AVjk2Vbt/YxuZXZOvr5Nn15eVOb/boqhyIwOC7uOXP3OKT6WDvWOQ7bqb5dyEVgRGBySAdiLceurY0ITBdXc7kPEZhyDOVBCKwkAiIwIjB9Exif9Po2L6yY3MpsHX18u8kP7FN92LeLcggC44/Dt3kcPOaUnnYsvR37U4/S21hdF3URGBGYHNKBWMuxa2sjAtPF1VzuQwSmHEN5EAIriYAIjAhMnwQmluzGZLi4vHzRNi/Qtv3Yv7QcgsDE5uiP29u00af6pOR+zDZtERgRmBzSgdjKsWtrIwLT5urtvo8ITPeYyqMQWAkEhiQwbReSvvrdvHkzYL9x40a4fv16uHbtWrh69Wq4cuVKuHTpUrhw4UI4f/58OHv2bDhz5kw4depUOH78eDh69Gg4dOhQ2LdvX9i5c2d0EU0tfjwWTxq6aCOp7MLPFBLwWILsZb7NC9LKbZ16lCm5tWlbnwJ+qbmnjrsreWrcReQiMCIwvE/WlYipOn2pLnUPxz1/9+7dYc+ePWH//v3hwIED4fDhw9W6cOzYsXDixIlqrTh9+nS1dmANwVpy8eLFcPny5WqNwVqDNQdrD9eh0vmuYn+cYxEYoKBNCAiBOQSGJDBzg48sePToUcD+8OHD8ODBg3D//v1w7969cPfu3XDnzp1w+/btcOvWrWqRxCKDRQfE5ty5c+HkyZPhyJEjYdeuXdFFNLX4cZHpgmh4H0hCvaxNu4sEPJUQ45SndFaOempn2Fh7yrz/HBvbt4t6F/j547DzyjmmHBv6TNm2mQN9lpQiMCIwvE/WlYixOn2pLnUPxz1/79694eDBg9UaAMICsoJ1AUSFJAXkBHPAGoK1BGsK1hasMVhrsOZg7eE6VHLNrHJfEZhVPrs6NiFQgIAIjAiMJzlMwBFWPrn17VjotbFp0ydnfim/KXnseBaVleLH8VJzzJHn2MTw49gsc/3QvotSBEYEJod8INZy7NraiMB0cTWX+xCBKcdQHoTASiIgAiMC0yWBSSW8/uLxdr7t7dGO2XiZb8f6xWxi47WV9U1gco/JH2dTO3W8bful/DXJRWBEYHJIB+Iox66tjQhM05U6jF4EZhicNYoQWDoERGBEYOoIDAIaCSz3WIDbBJd2sdL3tTZeF2vbcaw+x0+Tjffd1Lbj+7olMNAtOjb9+TlQzrLJL+zqbKzO1zkGS6unzJbQ262pbW19XQRGBCaHdCBucuza2ojA+CtznLYIzDi4a1QhMHkERGBWh8B4ItK27RPwyQfxxCYo/MpOiAiMCEwO6UCU5di1tRGBKbuOu+otAtMVkvIjBFYMAREYERhPdJSAl13kwq8cv7ZJp/qtD/lBlPV5vkVgyq7jrnqLwHSFpPwIgRVDQARGBEYEptuLWgSmDE89gVkfElJCQBBlJf2b+orAlF3HXfUWgekKSfkRAiuGgAiMCIwITLcXtQhMGZ4iMCIwTeQCemw5dm1tRGDKruOueovAdIWk/AiBFUNABEYERgSm24taBKYMTxEYEZgc0oEoy7FrayMCU3Ydd9VbBKYrJOVHCKwYAiIwIjAiMN1e1CIwZXiKwIjA5JAORFmOXVsbEZiy67ir3iIwXSEpP0JgxRAQgRmHwCBJ0y4MFAPxGGibdKrf+pAfLMV9nm8RmGkkOyIw0zgPmoUQmBwCIjDjEBj/1GNKbSTV2tojIPzaY4eewK/PxFS+V4PkIFb6PJciMEB4/E0EZvxzoBkIgUkiIAIjAuPJkxLwsktV+JXj12diKt8iMDkxIAJTdh131VsEpisk5UcIrBgCIjAiMCIw3V7UIjBleOoJzGoQjBySUGKDKCvp39RXBKbsOu6qtwhMV0jKjxBYMQREYERgRGC6vahFYMrwFIERgWkiF9Bjy7FrayMCU3Ydd9VbBKYrJOVHCKwYAiIwIjAiMN1e1CIwZXiKwIjA5JAORFmOXVsbEZiy67ir3iIwXSEpP0JgxRAQgRGBEYHp9qIWgSnDUwRGBCaHdCDKcuza2ojAlF3HXfUWgekKSfkRAiuGgAjMahCYHTt2BO6ekCza9gk4/aJss6X65fjNscGcUmNwvrl+aF9SdoFfznxLbWx/X/fHb/Ve13VbBEYEJod0IO5y7NraiMB0fWW38ycC0w439RICK4+ACMzyExgkl5ak+LbV5dRtAg5fdvNtq4vVYR/r42W+DV9e5tu0gTym43y8zrdp11VZip+fn2/zuO1829jE+lifrHs736ZdV6UIjAhMDulAvOXYtbURgenqii7zIwJThp96C4GVRUAEZrkJDJLJGClJyWO2XlaagNuLBfOIJbxe1tSmT2/XtZz+SsoS/HKOr28be+w5Y1n7LuoiMCIwOaQDsZZj19ZGBKaLq7nchwhMOYbyIARWEgERGBGYFIEpTV7ZnyUvIN+OyXNs2A/lova2b9d1Epg2c8rpU2JjjzXlJ8cmp6/1s0hdBEYEJod0IKZy7NraiMAsctX2ZysC0x+28iwElhoBEZjlJjCefLCNBJP1RcuSBJwXg01wbR163471idml+sVsvU/05U5dX2UX+Nm51R13yo59eMxsx+zrbKw96zFf1HVRisCIwOSQDsRajl1bGxGYLq7mch8iMOUYyoMQWEkERGBWj8AgwVyUtFj7LhJwm+TaOi4i3+aFFZNDxp12sTLWF3axvinbmN82sq7wi809NR9/TLG+bWw4Hv15H9R3WYrAiMDkkA7EXI5dWxsRmC6v6va+RGDaY6eeQmClERiSwLRdSPrqd/PmzYD9xo0b4fr16+HatWvh6tWr4cqVK+HSpUvhwoUL4fz58+Hs2bPhzJkz4dSpU+H48ePh6NGj4dChQ2Hfvn1h586d0UU0tfjxWCxh6LKOBLPUX2kC7pPcpjYvsCY7r2c/lCndonLrs229FD8/buoYaBfTx2Swt3Jbpy9vY+Wsp/pRX1qKwIjA8D5ZVyLO6vSlutQ9HPf83bt3hz179oT9+/eHAwcOhMOHD1frwrFjx8KJEyeqteL06dPV2oE1BGvJxYsXw+XLl6s1BmsN1hysPVyHSue7iv1xjkVggII2ISAE5hAYksDMDT6y4NGjRwH7w4cPw4MHD8L9+/fDvXv3wt27d8OdO3fC7du3w61bt6pFEosMFh0Qm3PnzoWTJ0+GI0eOhF27dkUX0dTix0WmlGTE+iOxjMkXlZUk4LHk1st8m2Fg5bZOPcq+5XastvUS/FJjDnncqbHs3HJsrP0idREYERjeJ+tKxFSdvlSXuofjnr93795w8ODBag0AYQFZwboAokKSAnKCOWANwVqCNQVrC9YYrDVYc7D2cB1a5BpZJ1sRmHU62zpWIbAAAiIwq0FgkFAuSlRS9iUJOOaR2hmWqeTXym2d/VD2Lbdjta2X4hcbN3bcMRn7pnRWbuvsh9LKbT1lY+Vd1EVgRGByyAdiLceurY0ITBdXc7kPEZhyDOVBCKwkAiIwy09gkGSmyEgbORNwBLxPYH0756KI9fGypjbH8XZdy+mvpCzBL/f4Unacd0pv5bbOfiit3NZTNlbeRV0ERgQmh3Qg1nLs2tqIwHRxNZf7EIEpx1AehMBKIiACs9wEBglmG5JS16ckAY9dJLEk2Mt8G368zLftWIvo6mytz7b1Uvz8/JraqXnm9OvKJjWHNnIRGBGYHNKB2Mqxa2sjAtPm6u2+jwhM95jKoxBYCQREYJafwCAJje11JKVOZxNwBLn1HQt6nwR7m5S+yW/O2BwrNYbVczzKbOn7N7VtX1/vAj/O1c8DY1mdr/u5WL3Xsd2FDXzYraltbX1dBEYEJod0IG5y7NraiMD4K3OctgjMOLhrVCEweQREYJabwNQRkbY6n4BPPognNkHhV3ZCRGBEYHJIB6Isx66tjQhM2XXcVW8RmK6QlB8hsGIIiMCIwHiiowS87CIXfuX4tU061W99yA+irM/zLQJTdh131VsEpisk5UcIrBgCIjAiMCIw3V7UIjBleOoJzPqQkBICgigr6d/UVwSm7DruqrcITFdIyo8QWDEERGBEYERgur2oRWDK8BSBEYFpIhfQY8uxa2sjAlN2HXfVWwSmKyTlRwisGAIiMCIwIjDdXtQiMGV4isCIwOSQDkRZjl1bGxGYsuu4q94iMF0hKT9CYMUQEIERgRGB6faiFoEpw1MERgQmh3QgynLs2tqIwJRdx131FoHpCkn5EQIrhoAIzDgEBkmadmGgGIjHQNukU/3Wh/xgKe7zfIvATCPZEYGZxnnQLITA5BAQgRmHwPinHlNqI6nW1h4B4dceO/QEfn0mpvK9GiQHsdLnuRSBAcLjbyIw458DzUAITBIBERgRGE+elICXXarCrxy/PhNT+RaByYkBEZiy67ir3iIwXSEpP0JgxRAQgRGBEYHp9qIWgSnDU09gVoNg5JCEEhtEWUn/pr4iMGXXcVe9RWC6QlJ+hMCKISACIwIjAtPtRS0CU4anCIwITBO5gB5bjl1bGxGYsuu4q94iMF0hKT9CYMUQEIERgRGB6faiFoEpw1MERgQmh3QgynLs2tqIwJRdx131FoHpCkn5EQIrhoAIjAiMCEy3F7UITBmeIjAiMDmkA1GWY9fWRgSm7DruqrcITFdIyo8QWDEEhiQwbRcS2+/5558PXe1f+tKXAvbnnnuu2r/4xS+G/fv3h3379oUvfOEL4fOf/3zYs2dP+NznPlf9MtJnPvOZ8KlPfSr8xV/8Rfjwhz8c/vRP/zS85jWviS6iqcWPx+JJQ2l7x44dgXupL5+A0y/K3C23T5PPHD85NnbeTWNa2zb1LvCz46bmm3PcdTZW5+t2fNSt3uu6bovAiMDwPllXIu7q9KW61D0c93zcY5999tnwwQ9+MHzoQx8KH/vYx6p14ZOf/GT4y7/8y/DpT3+6WjOwdmANwVqCNWXv3r3VGoO1BjvWHq5DXaxrpcc8tf44xyIwQEGbEBACcwiIwCw/gUFyaUmLb1tdTt0m4PBlN9+2Ota9jW/DDjLu7OdL38+36cf2i9l4fZONtW9TL8XPjom5xubrZb4NH17m23YcW/d2TW3bt4u6CIwITE4ijVjLsWtrIwIzfhziHIvAAAVtQkAIzCEgArPcBAbJZYyUpOQxWy8rScB9ssuA60O+qE/MBX1S/TjX0rIEPz92bL6p+Vu5rVufKXnKJmWfkls/besiMOMnjm2T/iH7Ib76HE8EZvw4xDkWgQEK2oSAEJhDQARGBCZFYFJJakqO4KrTzQVfjX3Kj5XbuvXdJE/prY+SOglMapyU3I9JO5bU+3ZMnmPDfrb0/Xybtik59SWlCMz4iWOfxKAr34ixrnzF/IjAjB+HOMciMEBBmxAQAnMITInAPPHEE4F7bEGBrIv3hOmD7x6vwndgLAlBcmnbi9ZLEnAmtii5zwWdEdDeiGqrOfYxGyuz9drBWipL8OOQdo62HtND5m18O9WPcpa+n2+n7CjvohSBGT9xTN1/pyRHrPU5HxGYfuKQazzKpvOHcywCAxS0CQEhMIfAVAhM7k2N5KOLcpUIDBJN7osSFm9fkoBzDjbQUkkwbOp01keubcqfldu6H6OLdgl+HN/O0dapRwk5dyunzsvq5Cld3dgx/13IRGD6SRybksVl0yPW+pyzCEx/cZi73uMci8AABW1CQAjMITAFApN7M8Ni1QVxoY9VIjCWhCDptO1F6yUJ+KIJb8reB2qOXcrGy33bj1XaLsEPY/v5+XaJTcwXjzel83K0vYw+uihFYPpLHPtM+If2jVjrc0wRmH7jMGfdxzkWgQEK2oSAEJhDYGwCk3MTs4sUyUcX5aoSGBAWJJiLEhfalyTgqcR2UbkN1FTfHJtY35jM+iqtd42fn69vc74xOWTcYRezqetvdbl+2KdtKQLTb+Jo76fLXEd89Tl/EZj+47Bp/cc5FoEBCtqEgBCYQ2BsAoMFqOkmZhepLogLfawCgUFSSeJhy5Tc2qTqXSfgCLpU4pySM1Cb9HW+qYOP2M4xui5L8YvN1eJg63buKXmOTU7fHD/Wpm1dBKb/xNHeU5e1jvjqc+4iMP3GYc66j3MsAgMUtAkBITCHwBQIDBahnJsZ7Eg+uihFYJ6Jkh8m4AgWn9j6tg+olH5ReWxsP1auje+Xmou3a9suwS82pp+vb7OPlds69Sj7ltux2tZFYPpNHPtM+of0jfjqczwRmP7iMHe9xzluS2BKYwNjp/KjHTHnMNYmBITAcAikLtC66/PFF1+cmSBu9OfOnUvuuAFhi/m0spybWhfEhT5WgcDgKQqSUvs0xbetLqdemoD7JNm3bfCkdCl5Tl9rE6vn+I71y5WV4ufHic3Xy3wbPrzMt+04i+jqbK3PtnURmP4SR3u/XfY64qvPYxCB6ScOc9Z5nlecYxEYoKBNCAiBOQSmRGB406orST66KFeFwJDEILHEnkNS6mxsAo6Aod9U4hqTN/VhIMb6+jGtL2vv5bZN/7HS+qDey5ra7Bcru8DP+vVzoQ5y7pT5kvqUD9rn6OmLfWzp+ze1bV9fF4HpJ3Gsu68uow5x0+e8RWDGj0Oc46UjMF0EZRcJjnw83+krO6uOZxdxO6QP3BxEYJb7H1nWEZG2Op+AI0605SMg/PKxilmKwIyfOA65DrUdC7HTtm9OPxGY8eMQ51gE5nkl4qtOHqZwfDk3xSnZ4OYgAiMC44mOEnBcGe034dceO/QUgRk/cZzSOpWaC2IlpetCLgIzfhziHIvAiMDoScoAMdDFTXNIH7g5iMCIwIjA4ErobhOBKcNSBGb8xHHIdajtWIiytn1z+onAjB+HOMciMAMkr1N4AqA5jPukLeemOCUb3ByWjcB0id/NmzcD9hs3boTr16+Ha9euhatXr4YrV66ES5cuhQsXLoTz58+Hs2fPhjNnzoRTp06F48ePh6NHj4ZDhw6Fffv2hZ07d0YX0dTix/l70jClthJwXBntN+HXHjv0FIEZP3HkfWrKJWKlz/ml7uG45+/evTvs2bMn7N+/Pxw4cCAcPny4WheOHTsWTpw4Ua0Vp0+frtYOrCFYSy5evBguX75crTFYa7DmYO3hOtTnsSyrb5zjlSMwOb9ioGR+3GR+HfGfwk0i59rgPHFzGJLAYLwpbY8ePQrYHz58GB48eBDu378f7t27F+7evRvu3LkTbt++HW7dulUtklhksOiA2OAX106ePBmOHDkSdu3aFV1EU4sfsZ8SYfFzUQJeFqXCrxw/XicqRWZSMYAoS+m6kKfu4bjn7927Nxw8eLBaA0BYQFawLoCokKSAnGAeWEOwlmBNwdqCNQZrDdYcrD1ch8qumtXtvVIEJjdBW8cEWsc8Lmnr4qbZhY/cawS3vKEIDG5CU9t/7hffE7j/7DvfHbC//Rd+Nbz9He8Kb/v5Xwlv/blfDj/zs/9X+Om3/8vwlrf9Unj6re8MP/XT7wg/+fTPhl1vfGv4kTc8Hf77/+F/jC6iqcWP59eThim1lYCXJQPCrxw/XicqRWBSMYAoS+m6kKfu4bjnP/n6N4TX/+gbN9aAN4ddP/kz4cff9LZqXXjjW34+vGljjcBa8eaNNeMtG2vHT2+sIT+zsZa8dWNNedvG2oI1husN1x+UU1sfpzSfun/TgPOEDf/egecdbdZZIi9i3ZYpOXyk8qPa/wNjndt6bmKGPkrmx03m1xF/G6tj13OulboLNDZ/XMzYFv0/MHU3nzF1eDWMr4fhE7SXX3652vF0BZ+q4TjxSgBeGfvKV74SXnjhhep1gS9+8Yvh85//fPj0pz8dnnzyyehNMbX4EVckudqFgWIgHgO8TlSKwKRiAGtRSteFPHUPxz3/wx/+cPjEJz4RPvOZz1SvkuGJDNYF/DQ/nsxgrcCagbXjq1/9arWWYE3B2oJXkbHWYM3Bq8lch8ZcC5d57EUIjCcrzJNi8YL46ozAcCA/gdjAkK1jAq1jHpe0pWJxLHnTNVN3gcbmLALTHYGZ0hMXPxc9QcCV0X4Tfu2xQ0/gF7v/SCYyY2MAsWLbXddFYNL/nHpKhGdRAkMOUZIf6QmMflhg5Uhm1zfQEn9NFyd8Y0t9whAbWwRGBKYKGv2pRUAEphaeRqUIjIhKbP3xMgSSl3XZFoFZLQKD2LB5EeupmEF8pfKjVgTGTyA1MOR6GjHu04h1xL8uHofU8cJEWTdu3QUa6ycCIwKDmNFWj4AITD0+TVoRGBGY2PrjZYgjL+uyLQKzegQG8dFFftSawOROYB0TaB3zuKSty5tnW1+5Fyf8Y0t9whAbXwRGBKYKGv2pRUAEphaeRqUIjAhMbP3xMgSSl3XZFoFZTQKDGGn6cBc22FL5URGByQlSJfPjJvPriH9OXE7Jpu4Cjc1TBEYEprqr608tAiIwtfA0KkVgRGBi64+XIZC8rMu2CMzqEpicOEF8icDouy5r8zpfzkUxJZu6CzQ2zzoCg5v9su34VRjs+OeU+MUx/6tj+OdkX/7yl6tflcE/K8MvzOCfV+IXyPDrM/gVmte+9rXRRTS1+BFX/8X5KbWVgOPKaL8Jv/bYoacIjAgM75N1JWKlTl+qS93Dcc//4Ac/GD760Y9WawB+jfJzn/tctS584QtfqP655XPPPVf9YiV+kQxrCNYS+6tk+HVLrDdYe7gOLdv6OaX5IhaAJ895F7EBHyIwIjAiMBuPI3lhTamsu0Bj80wRmCn9Iskic+HPV+KnLIf+GeU+CcuOHTtCiX+fgMMfd8RMzkZ7lHVbjp6+Un6or/OVY5Pyv6i8b/zssfi6n6vVex3bXdnQX2kpAjPN9SK2JowpQ5z1OX6KwOhnlKf3ZAaxIAIjsrE2ZKOPV9z6vJn24RsXfeoThth4KQIDP8u48T8g478h478i478j478k478l478m478n478oA4ubN2+Ga9euhUuXLlX/cRm/53/kyJGA/8ocwyq1+NG2hGDU9WUyWmfTpLMJOPzZzbetjnVv49uwg4w7+/nS9/Nt+rH92tpYH6X1vvGLHWNszt7Ot9HHy3w71yY2fluZCIwIDO+TdSXiq05fqkvdw3HPx/99wdMVrAF4goIPwPDh2YULF8Lly5fD1atXw40bN6r5YQ3BWoI1BWsL1hisNVhzsPZwHWp7vajfJgIrRWBKg1f9dRNd9RjAZS8C86haRERgntl6alOSgMcSYMRZH/Icnzk2mF+XW9/4pY7JHkPKxssXbXMM34/yLkoRGK29OWsvYi3Hrq2NCEwXV/NwPiZDYIY7ZI0kBNYbgTYEZlUQ4ydfq/IEBkklnq6wbHrSktIzAU8lqSk54iKl60puYy/HZ46N9dlFvQ/87LxSx5RjY/vaeqpvysbad10XgRGBySEdiLscu7Y2IjBdX9nL5y+VHyV/hQwdtAsDxcBwMZB7g1+1c/InH/xI4P7Hf/rh8G//5EPV/uy//bPwb/7o34V//eyfhj/8138Sfv8P/zg88wd/FH73mWfDb/3OH4b3f+D3wr9632+F9/zab4bv+d7viy6iqcWPWKfIQ1s5Ek32tXXKFilLEnAsUT7p9W27jKV0lKPkbvul6uyX0lOea0f7RcoS/DgvHjPbdnzK6mysPevsl2rH5Oyz6Fj01aYUgRGB4X2yrkRs1elLdal7OO75v/hL7wq/8q73hve8933hN37zA+F97/+dal34wG//fvjt3/3DjbXi31RrBtaOP9hYQ7CWYE3B2vJHf/zn1TqDNQc716BVW19X5XhicRQlMDFDyXQzUwwoBvqIAXyvBTveVb5+/Xr1HRe8u3zlypXquy54n/n8+fMBX/bHF/1PnTpV/WrM0aNHw6FDh6pfJNu5c2d0EU0tfjyORQhFji0STNrZOmWLlCUJOBPW3IQXdrGN/a2uyTaltz5Qz7Xz/XLbJfjlHHeODedK29gxx2ToZ+XsT39eb+Vd1UVgdL/nfbKuRLzV6Ut1qXs47vm7d+8Oe/bsqX5xDL9QiV8Zw7qAXxfDd2KwVuB7MVg7sIZgLbl48eLW92PwfUqsOVh7uA6Vzlf9h7tuRGAm+stUugiGuwiE9bhYc+FYdgKDJNMSFN+2upx6SQKOpMImwLE2ZNy8bVt5U79cPe1KyhL8cvDIsYnN3/fzbfaxclunHmVKbm3a1kVgxr0vLsu6hPjqc64iMIrDVHyJwIjA9HrzSQWe5LopMQZWgcAgkfSkJCbzNnXtvhNwm9imEuFF5SU+bd8u6kPiZ+ebwixlk7K3cltP+bHyLuoiMLpH8x5dVyLW6vSlOhEYxWEqhkRgRGB6vfmkAk9y3ZQYA6tCYJBkxvY6klKnGzIBXzRBtva2bhPnmDwms326rPeNX+pYrNzW7bFZua2X2Ni+XdRFYHSP5j26rkSs1elLdSIwisNUDInAiMD0evNJBZ7kuikxBlaBwMSICBLTmDxXxgQcCYJPcn0bNnZL6fuQ5/pM2dl5d1nvG7/U8Vi5rdtj8/JF2/Tl+1HeRSkCo3s079F1JWKtTl+qE4FRHKZiSARGBKbXm08q8CTXTYkxIALzTJTolCTgSCp8cuvbsOG2iC5m62VNbY7bZzkEfjnHOaRNl3iKwOgezXt0XYmYq9OX6kRgFIepGBKBEYHp9eaTCjzJdVNiDIjANBMYJAlIhLmj7TefKENP+5jO9s/R05ftZ+vUx3xZna97H4u0ra2vWwIDnR3X21Lv5U192I92vj/b1KNMbV3YeP9N7dRcIBeB0T2a9+i6ErFSpy/VicAoDlMxJAIjAtPrzScVeJLrpsQYWFUCk/uqWMrOJ+BIFLTlIyD88rGKWYrA6B7Ne3Rdidip05fqRGAUh6kYEoERgen15pMKPMl1U2IMiMDkPYGJJZmSpREQgUljk6MRgdE9mvfouhKxVKcv1YnAKA5TMSQCIwLT680nFXiS66bEGBCBEYHJSagXtRGBWRSxWXsRGN2jeY+uKxE1dfpSnQiM4jAVQ1EC85a3vCVoFwaKAcXAUDHw5je/OXB/+umnA/Y3velN4ad+6qfCG9/4xvCTP/mT4Sd+4ifCj//4j4c3vOEN4cd+7MfCj/zIj4TXv/714cknnwzf/d3fHV1EU4sfb4ip17emIFcCPptQL9oSfosiNmsvAqPEkffJuhJRU6cv1aXu4bjnv+Y1rwk/+IM/WK0BTz31VNi5c2e1Lvzoj/5otUZgrcCagbUDawjWEqwpWFuwc63h2jPUeqdxFs+tYnGUJDCztzK1hIAQEAL9IPDo0aOA/eHDh+HBgwfh/v374d69e+Hu3bvhzp074fbt2+HWrVvVIomnNdeuXQuXLl0K586dCydPngxHjhwJu3btii6iqcWPN8MpEJXUHJSAl8Wb8CvHj9eJSpGZVAwgylK6LuSpezju+Xv37g0HDx6s1oATJ06E06dPV+vChQsXwuXLl8PVq1fDjRs3qvlhDcFagjUFawvWGKw1WHOw9nAdKrtq1LsPBED4YrFUS2CQIGgXBooBxUCfMXD27NmA/cyZM9UC9PLLLwfsICdYlF588cVw7P9v78pa5ai6aP6Gv8ef4LsGBEFFP+fhzSeffBAURBRxnvDBi6hBjFNi1JjcaIyJA0YF5xFERJzO52q/Rda3Pbvq1NC53X1XQd2zz95r71N1et/aZ3VVd7/3Xjl58mQ5ceJEOX78eNne3i6HDx8uBw8eLPv371+8A1e7wGXFj9iMPKyC3gvwaaXQ8zd9/vh/4tYEJssBZFlmm0OfXcNx531ra6vs27evvPDCC+XAgQMLQoO6cOTIkQWxQa1AzUDtOHXq1KKWoKagtpw+fXpRZ0B6UHtYh5ZZ63Zb7DlyAzFMYEzGTEadAyuZAywcO0FgsMj17jlwDtRzYI4FqGNsNvmZY5HalSMmMOv7BuocuWEC44XrSi5cd9u7ET7f+oV4JwnMKtxpyY7BdxBQusZvnr/xcwdPzF/XwtK2zSYmra8vcqUVOwZnAlOvm+uwnpgjNxDDd2BMYkxinAMrmQMmMP4WMhSpuTcTmGkzagJjgtJCOJBlLbixGBMYExgTGC9eV3Lxug7vIvgYl3sBNYExgZm21K57m8DU56VVawJjAtNCOpBPLbixGBOY5dbfZa5v5sgNxFg6gdmzZ8/gBfIYn7kme+zYY/3mOu6zFWeZ58nYbKee01xxuo5jzjE0FuTa3nUsY2w6Jvz7+mPGyHxqY8VzBonZic/AZI9vrYLeC3CUrvGb52/83MHTBMYEpoV0IFdacGMxJjD/rtdZra3VdmJjHa7pMwyxWZv5zZEbiGECE+7AZBOevUDUj/Wjv9sz/4xzzeVccc7Wa6PHq7KOn+kVM0Vednw9Nh1LZWJAXqA3gfn/OzFegKN0jd88f+PnDp4mMCYwLaQDudKCG4sxgTmzZmLN7GprNRZ46Gs21akcxxhj68uNvXv39uYOYpjAmMAMvkMWE3iuPv8R2E6NO1ecqcfR6q/Hq3L077JF7ND+MmPHY+FYbKOdj5DBjq+0xFcoY/fXKL+Ea7e3kTNgAjNy4v7nZgJjAtNCOpAuLbixGBOYs0dgUJuzOj1Uj1hduQHywr0rNxBjdgKDk+FeO2natOXCRXWUW2zE9LWMqS19VEeZtpYWPsBpm8WJGI1PH2LURrkPM8ReG0f9o137lBXPY4xtxNBXcdRpSz/FqUw7faIN/S5MtBPLOLW49NGWeOpiHNoZr2anjTHoE1vFEVuLRz/a2FJPX7a0s6Ve8dQpRu3Q92GIVyx12pLA6B0Y+IDAcHy0td+Bof2cc86pFtGs+PGCOdejYjyOWjt2jNoCHPGHbHo8md9cGI2fHWfLWBpnihznb8zYrT7Z+fL4W+LMheGYU1sTGBMYXie7WuRZl32qLbuG6+/A6P9O9jswitHfgYEetYd1iLUJ+igzBvVoa7qaXePRTh1jsK92tam9S09/benLlra+fobr08Oe5QaJi7ZZniDGrASmdsKqU5kniTbq+/r0jTjqa22GjfrYr8WKOvWBrH1ga/2oy3BdY7X4xHFiP8YYYgc24mM/xmc/4rQPWfv0Qat7H6YlTozB+NSzpZ4t9WyjvqsffYCNuthnvIiNuL7+VP+++LD3YeIx1PrQsXBEAoP4+kOW6OsPWaK/Kj9kiWMZS1QyP12AIz73RVVo+AO8brEPW9TFfismjjNHHI05Ro7zpzFqx6d2yBET+8RAX7MxXrTFPuMQX+vXdLU4GmOqbAJjApMtKlWPPNP+3HIfgcH/gf6QJfrxhyyh0x+yRJ8/ZAm5hcAAh3qldaymI0Zx1Cme9pqOeLZDMRHPsbQdE7srLuNpW8uNSFpiP+YPYsxGYGonUJsUPQnK0Tf2iYttKy76aT/GiH3FZrL6qKx41atMTE0Hm+pVpp9i+uyKVX+VsxjEqF1l2tGqXuUMM8anJW4LRo9JZfVVuetYI47xVK8y7TFmrU+s+qtMe/TNMMRHe1c/2moxWjDxGDUO/HUHiYkEpvYIGXzw68rb29sF77TtFgKDizg2nH/rFrF9fcbtw0U7/djCXsNEXezTf652CoHJjm0Z+paYLZi55o1xTGBMYOJistZHvtT0c+m6CAz+L/bt2/d/BAZ1Afpjx44tagVk3L1XAsO7+3hUGfYWAsP6xRZ+lNmqTmXa0ape5QwTfWp99c3sOlYmM47aNV6mp19sa7lBwqL5UdPRjhgmMCHZ4gsRJ77WVx+VFat6lYmp6WBTvcr00zazq15l9aU8xJ5hVa8yx0Ab9dpXOfOZC6PxVdb4KsdjV5vKrbGIi77o13bi43Fk+hhXcbUYEa99lTWO6lXOMLVxFUsZ5AXxTGD6P8SPeWrZMpzqVdaYqlc5w9T00S/26ZPpaZ/SksBkY2R6jJnZ5tLrebXEbMFozDlkExgTGC4iu1rkWpd9qm1ZBGboHRjWK7b4n6TMVnUq045W9SpnmOhT66tvZtexMplx1K7xMj39YlvLDZCVWk5kesQ46wQGJ1rb9QTjZHCi+vw0Rk2u+cexYr8WJ+rUR2XFqV5lYmo62FSvMv20hT3bu3BqgxxjqF2PQeUpGI7JGK1x43GyPyQOx6avtoxDjLa0AV+TqYs+is8w0UdxKg+J1XdejBtjal9l4tGqXuUME30UpzIfIUNMfogfcssdGOCwr8JnYPAoGI8HbfZoWKueC3BcwLkhbsuW4TK9xlSMyhmGesWqDHvs13yom6vl/I0dO/rFvh5nZqMeLXf1y2T6ZXbqW3HED2lNYExgagvNqENORd2c/XUmMPyfjy3rH/SUtY362Ac2i0mbxqvpGJNtho929tlGP+3PkRuIcVYJTHZiUd/X50REHPW1NsNGfezXYqku4mOfWNWrXLNTh1axKiuGcp+duNj2+ak9kzXmVIz6D42b4TN9y1jwJY4t42lfZdrVN8rE1PxqOuLZZphMX/OL2K5+tLXEG4Ihlu1YAoPjXKVHyHA8Sk5iX20tMhfguIBzQ8yWLcNlesaM9tjPcNArVuVoY4wuvWLGypy/eCyMl+lpRwsMd9VHOYtV8+3DZvbWMSNubN8ExgSmhYggv1pwYzHrTGBY47IW/+s1W9TH/hifGIN9tjEm9WxpZ58t9bV2jtxADBOYkCgtk68vSMTHPrGqV7lmpw6tYlWuYfrs6qNy5keM2jOZWLRTMeo/Z1zG0vgq0x7PgfoaVnUq0yfGqmFadRozxlVbLV5mj9iufrQxpupVph2t6lWONvXZFAJTIyWYg5q+RccFOC7g3BCvZctwmR4xa7aaroaNuL4+zyHiqJ+j5fxlY2R6jh3tsU8c2sw2VM+YmV+rnbgprQmMCUwL6UCOteDGYpZFYPA/tq6fgdH6SRnnQxltX5+YiOuKQVuXDzFo58gNxJiNwOCg4sGjrzqV9WSivq9P34ijvtZm2KiP/Vos1UU8+jVdlw9tfX7A9WGG2mPM6N9lr2EjPuurr8rE13Sw6d6HiXb6ql5l2tHW9C26iOnrDxlLj49yX/xoj+NF+9z92nHGMeIx0Sf7DAz81+VbyDJCgnPIbH16LsAXVeB/fxCvZctwy9DXYkZd7PMcMj3tU1rOXzZGpseYmW3Zej3foWOp7xyyCYwJTAvpQK614MZiugjM1tbW4n91yreQ4bFl/K/xjTTWKehYo1Qeoot+ff0sdotfHybaa+fJ8dnWfOhHTFc7R24gxqwEhieAk+MJsuXJ0KYt/YjJ+urDuGzVN5OjP33Z0i/2qa+1Ecs+Wu7Rj5ioR58+UzB9MdReG6fLrniV9Vxqeo3J86RPxLMffYjXtgvDOIqHHPUag3KG64tFP42jPnFs4hWT6SKGfY5Vi81YGabmozqVW8ar4XkM6k9ZW/jGHcWj9iF+xa3678DUCAmOv6Zv0XEBjgs4N8Rr2TJcTV/TcYzMpnrI2T4kDrFztZw/PVaNnemByWzL0A+JmWH1vOaSTWBMYFpIB/KtBTcW00dg8C1k+L/gPvR3YEBgUHvoj7allhEXa5v2GYexa7aoo0/Ux/EYk20LPmKysYiLY/bpaWc7R24gxuwEhge4G9rai1jT7Ya5GHqOnCe2Q/03Bb/bzn/I+fKdrxqB0TswNQKzSp+BqZESzENN36LjAhwXcG6I17pFbOwjTk0X40dM7Ed8Fjf6xX4tzhSdzl8cK/bjOJl9GfrWmBkuHvtcfRMYE5gW0oF8a8GNxbQQGL0DkxGY+DXK/BYyEhjWoU1Zc+A8cM3YyfOZIzcQwwTm7xdzzhdypxNjznNZZizP07x5t8zXaqdis3CsM4EBIUGuKzGJfbW1yLoAx0UcG2K2bhHb18/ijvGLPogddbGfjT9Wr/MXx4r92hgRE/vqM8RWw0ZdX1/HXpZsAmMC00I6kH8tuLEYE5j1XUMgN44ePTp6R85gM4ExgZmVwLUsdlGEW3DGrO8Fao7XblMIDEkM8h57C0npwugCfHEV//tPXNhSj7Zm47H02RTXh9UxM7kWg8fIsWq+0a+vX4tBXZw/jhtjEl/T9/l0+dKGtiVOF0ZtUY7jDOkrNsomMCYwLaQDedOCG4sxgVnf9QFywwRmZvIxx6ILRWSOOJsUIxbWTTo3n8vyLqKbRGC6CMlQW1yAoxh4a58Bz1/7XNWQJjAmMC2kA7nTghuLMYFZXu1d9roGuWECs4IEZtkvvOOv7z+tX7thr12NwODrLWs/ZHnixIly/Pjxsr29XfCs86p8BmYoOWnBewGO8jd+8/yNnzt4msCYwLSQDuRKC24sxgRmWD1dpfUHcsMExgTGd3ucAxubAyYw91YfN/MCHOVv/Ob5Gz938DSBMYFpIR3IlRbcWIwJjAmMPwPjBfDGLoBX6R0HH8vwi60JjAkMFkFzbyYw02bUBMYEpoV0IMtacGMxJjDDa+qqrEOQG74DY/Jh8uEc2NgcMIExgUGhm3szgZk2oyYwJjAtpANZ1oIbizGBMYHxHRgvgDd2Abwq7zb4OMZdaE1gTGCmLbXr3iYw9Xlp1ZrAmMC0kA7kUwtuLMYEZlxdXYX1CHLDd2BMPkw+nAMbmwM7SWCwSPPuOXAO1HNg7KLTfruH/JjArC/BWDbJMYHxwnVjF67L/udx/PW4sO4kgWn5NrCdwmBR7W38DHj+xs8dPDF/JiK7h4iMfa2RK2N9W/x8B2Y96nhtvYXc8B0YkxiTGOfAxuaACYwfIUOhm3szgZk2oyYwJi8tBANZ1oIbizGBMYHxZ2C8AN7YBXCN+Vu3Phc9ExgTmGlL7bq3CUx9Xlq1JjAmMC2kA/nUghuLMYFZn1oe113IDd+BMfkw+XAObGwOmMCYwKDQzb2ZwEybURMYE5gW0oEsa8GNxZjAmMD4DowXwBu7AI6s3/31uuCZwJjATFtq171NYOrz0qo1gTGBaSEdyKcW3FiMCcx61XNdfyE3fAfG5MPkwzmwsTlgAmMCg0I392YCM21GTWBMYFpIB7KsBTcWYwJjAuM7MF4Ab+wCWBm/5fW72G0SgdmzZ0/hPvXby2oLcMQesvFYuvxaMDpmVyziMszQsRhvTBvnb8zYXT5qi3I8XrV32RQHOW5qj7a5+yYwJjAtpAN514IbizGBWb+aznUYcsN3YEw+TD6cAxubA5tCYLC4VNIS+2prkXUBPmbhCh/dYh+2qIt99Sd+LCb6xX4ca2o/zp/Gaxk7YmJf46kccUP7GkvlvjiKnUM2gTGBaSEdyLUW3FiMCYwJjO/AeAG8sQtgsn2363mh2wQCg8VljZRk+ho26nQBzgVpXMRSH9sMp3qV1T/TAwNblz3DZD6ZXo9nrKzzF8eJ/ThGZs/06q8YlYdiMnyLXjFjZRMYE5gW0oH8asGNxZjArGddx3psjtxADBMYExgTGOfASubAJhOYSEqG9HUBvqgEf//JFsS0s81wqleZfmj79JldfSMm9jlepqd9Ssv5y8bI9Hoecfwun5pfhle9ynE89jNMpqfflNYExgSmhXQgxyJu7969/9IBk+mjv/ZNYExgTGC8eF3JxavvmqzvxWmu126TCAwWlNyHkJUalgtwXYROXbC2+NcwqlM5O7YaJupiX2PNIXP+snEyPcbObJmexxvtsV/DEYOWO3FsiWGfbaanfUprAmMCo0Qik5Fj0QaiEslKTRf9an0TmPVdI9Ryo/Yad+kQwwTGBMYExjmwkjmwKQQGi0klIrGvthaZC3BcwLlNWbC2+GYY1avM40KrepUjBrbMrtipMucvGyvTY9zMlukznwyvesjar8WKdmCwZfp/rNP+msCYwHQtLGlDllFmS7JCEhP7xLW0JjAmMCYwXryu5OJ1rnfxHWd9L3KbQmBqpAQLzJq+RccFuC5Dxy5YW/wyTNTHPo4v6mK/FaPnOlXm/NWOpXY8cbzoh37UqU9mi/oYJ9oZM+pr/aij7xytCYwJTAvBQK7VcEpaKNdwfToTmPWt7Vlu9L3makcMExgTGBMY58BK5oAJzL1VksMFOC7g3MYsWFt8MkxNH3Wxj2ONutifcj707Ws5f1PGhi93jJfFarG1xuF51cZiDNrY0mfO1gTGBEYXkpmMnMtsJC5oM0yf3gTGBMYExovXlVy8+s7J+l6c5nrtTGCWR2BaFrhdGNiynYvlzK5xVaYf2kyvmLHyHAQmjp0db6aP/uwrXmXa0Wb6oRjFD5FNYExg+sgF7Ni6cFPIC+KawKzvGqGWG1k+ZHrEMIExgTGBcQ6sZA6YwCyHwCxrATwmbuaT6VG0pm4kMIgTx4n9OFZmX4a+JWYLJp7D1L4JjAlMFzGhDXlGeRmtCczmEZhIVtCPOuYS8ssExovXlVy8zvUuvuOs70VuEwgMPtOCRaZ+tiX21dYi6wIcF3Fs2UL2H+uZvy24FsyZiGekFr8aJupi/8wI80g6f3Gs2K+NGDGxrz5DbDVs1MU+xoq62NfjmUM2gTGB4SKyq0Wuddmn2kxg1re213KDZIWEJfZjviCGCYwJjAmMc2Alc2BTCAxJDBaW2FtIShdGF+C4iGPrWrSqjcdQa/+J9E+sml3jEKttnx3YDKPjaUzK0a+vT79aG+dv6NiI2efDceNxUs+2Jc4cmHgcfX0eX601gTGBiYvJWh+5U9PPpTOB2SwCg7xQ0kI5yxfklwmMF68ruXj1nZP1vTjN9dptEoHpIiRDbXEBjgu5t/YZ8Py1z1UNaQJjApMtKlWP3NH+3LIJzPquEbpyg8QFbVfOIIYJjAmMCYxzYCVzwASm/TMwuJh7a5sBE5i2ecpQJjAmMF0LS9qQP5SX0ZrAbCaBQa70kRdgsJnAePG6kovXud7Fd5z1vcjtJIFZRsF1TC/8nAPOAefAPDlgArO+tR3kY+r/gQmMyYvJi3NgZXPABGaeQj+1UNjfr4NzwDmwajnQSmBeeeWV8tprr5XDhw+XI0eOlGPHjpXjx4+XEydOlJMnT5ZTp06V999/v3z44Yflo48+KqdPny4ff/xx+eSTT8qnn35aWIf8Zuh8hMkExgvPlV14+h99vn/03TyXLBwoIigmKCrYUWRQbFB03nvvvUURQjFCUdre3l4UqoMHD5b9+/eXiy66qPpOT1b8Vq1I+3i8cHQOOAecA//Ogewajmv+1tZWefbZZxc1AATm0KFD5Y033jCBWZF1swnMirwQu3mB6XM3UVlmDpjA/LtoeyHjOXEOOAecA/0/ZNlFYN5+++3yzjvv+A7MDq2jTWB2aOKXuWBzbBMC58CZHDCB8ULNCzXngHPAOVDLAd+BOVMr123dsKMEBoN78wx4BjwDy5yBv/76q2D/888/yx9//FF+//338ttvv5Vff/21/PLLL+Xnn38uP/300+IRsR9//LF899135auvvlo8WonHzPBY2RVXXOFHyP7+xpbaAsA6z4tzwDmwrjmQERhc8/GZF3zW5d133y0ffPDB4tFjLPK/+OKL8vXXX5dvv/22/PDDD4vrImoIaglqCmoLagxqDWoOag/r0DJrnWOPm4HB30IGB++eA+eAc+Bs5MCNN95YuN9www0F+/XXX1+uu+66cu2115ZrrrmmXH311eWqq64qV155ZfnPf/5TLrvssnLJJZcsPv9y7rnnVhfvKH7ePQfOAeeAc2B9c6BGvnDNv+CCC8qFF164qAEXX3xxufTSSxd14fLLL1/UCNQK1AzUDtQQ1BLUFNQW7Kw1rD1no9Z5jHFrqloO7KkprfO7Nc4B58DZygHcVcGOd8q+//77xR0WvHP2zTffLN5F+/LLL8vnn3++uOOCD/p//PcH/PHhfnyzzFtvvbX40CYK19k6Xo/j/w3ngHPAObCzOYBrPr7ABV/kgm8ew50Yft4FX/wCwopvGsMXw+CuDGoIagnu3vPuDO7mo+ag9rAO+XXd2dd1yPybwPixCy/8nAM7mgMsHCYw61M4hhQZY/26OgecA3PngBKYN998c/FmFr8ymQQGb3bhTS8TmM3MPxMYL153dPE690XN8dbvQjWUwOAdNby7hu/1x7fM4N0334FZv9fd/6t+zZwDzoGxOYBr/osvvrj46mT87gtqAT4Lg7qAz8Pg85EZgcHdfdzl9x2Y9c4/ExgTGBMY58CO5kAfgcEtf3wo87PPPlv82BgIDIoTfhsGjwygeJnArHchGruIsZ9fd+fA7swBXPNfeumlxQf5jx49uvh9MDxWjLqAR4z5+Bi+5RK1AzUEtQS7Ccxm5IwJjBevO7p4dfHZjAvJ1NcRJAaPkPExMn4GBoUmEhg8EoDihHfZ8MgAftTSBMZ5NDUH7e8ccg6sTw7gmo8fr8QPV/LzL7j7oo+P4c2uSGDw+RcSGH7+hZ+B8eu/Pq8/Xqs9jz/+ePHuOXAOOAd2Mgcee+yxwv3RRx8tjzzySHn44YfLQw89VB588MHywAMPlPvvv7/cd9995d577y333HNPueuuu8odd9xRbrvttnLeeeeZiPvNGOeAc8A5sEtyANf8W265pdx6663l9ttvX9SCO++8c1EX7r777kWNQK1AzUDtQA1BLcGOuoIdNUbrDGysM6wxN910k9fIK8oT9oCd8ht9jh8/vmCyeCQDz5VjB7v17jlwDjgHlpkDr7/+esGO7/XHfujQofLqq68uvmHmwIED5eWXX148LoBnnl944YXy/PPPl3379pWnn366bG1tlfPPP9+758A54BxwDuyiHMCbXU888UR58skny1NPPVWeeeaZRV147rnnFjUC31KGmoFHzVBDcMcGO2oKd+ixA4cdtQX+iIXacvPNN3sNvEM8gDwEX9IAXoJHBfHEBb59FI+P/xfyijGGBgqGuwAAAABJRU5ErkJggg==" width = "483" height = "413" alt = "" style = "vertical-align: baseline; width: 483px; height: 413px;"></img></div><h2  class = 'S4' id = 'H_9060' ><span>Read</span></h2><div  class = 'S5'><span>We can then read the file back in using MatNWB and inspect its contents. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_nwbfile = nwbRead(</span><span style="color: #a709f5;">'intro_tutorial.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="9C8ACE0E" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_nwbfile = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: [1×1 types.untyped.DataStub]
@@ -218,7 +218,7 @@
                                    stimulus_presentation: [1×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><div  class = 'S14'><span>We can print the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> data traversing the hierarchy of objects. The processing module called </span><span style=' font-family: monospace;'>'behavior'</span><span> contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object named </span><span style=' font-family: monospace;'>'Position'</span><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object named </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >read_spatial_series = read_nwbfile.processing.get(</span><span style="color: #a709f5;">'behavior'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Position'</span><span >).spatialseries.get(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="399D9332" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="300" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 311px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_spatial_series = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  SpatialSeries with properties:
+</div></div></div></div></div></div><div  class = 'S14'><span>We can print the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> data traversing the hierarchy of objects. The processing module called </span><span style=' font-family: monospace;'>'behavior'</span><span> contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object named </span><span style=' font-family: monospace;'>'Position'</span><span>. The </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html"><span style=' font-weight: bold; font-family: monospace;'>Position</span></a><span> object contains our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span> object named </span><span style=' font-family: monospace;'>'SpatialSeries'</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >read_spatial_series = read_nwbfile.processing.get(</span><span style="color: #a709f5;">'behavior'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Position'</span><span >).spatialseries.get(</span><span style="color: #a709f5;">'SpatialSeries'</span><span >)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="C35D3459" prevent-scroll="true" data-testid="output_5" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="300" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 311px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">read_spatial_series = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  SpatialSeries with properties:
 
         reference_frame: '(0,0) is bottom left corner'
      starting_time_unit: 'seconds'
@@ -237,17 +237,17 @@
           starting_time: 0
      starting_time_rate: 200
              timestamps: []
-</div></div></div></div></div></div><h3  class = 'S16' id = 'H_2a80' ><span>Reading Data</span></h3><div  class = 'S5'><span>Counter to normal MATLAB workflow, data arrays are read passively from the file. Calling </span><span style=' font-weight: bold; font-family: monospace;'>read_spatial_series.data</span><span> does not read the data values, but presents a </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="54FBBF22" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
+</div></div></div></div></div></div><h3  class = 'S16' id = 'H_2a80' ><span>Reading Data</span></h3><div  class = 'S5'><span>Counter to normal MATLAB workflow, data arrays are read passively from the file. Calling </span><span style=' font-weight: bold; font-family: monospace;'>read_spatial_series.data</span><span> does not read the data values, but presents a </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="B2AFD880" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
 
     filename: 'intro_tutorial.nwb'
         path: '/processing/behavior/Position/SpatialSeries/data'
         dims: [2 100]
        ndims: 2
     dataType: 'double'
-</div></div></div></div></div></div><div  class = 'S14'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access all the data in the matrix using the </span><span style=' font-weight: bold; font-family: monospace;'>load</span><span> method with no arguments. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data.load</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="5DB56E82" prevent-scroll="true" data-testid="output_7" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×100</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:100,&quot;totalRows&quot;:2,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1024px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091    1.0101    1.1111    1.2121    1.3131    1.4141    1.5152    1.6162    1.7172    1.8182    1.9192    2.0202    2.1212    2.2222    2.3232    2.4242    2.5253    2.6263    2.7273    2.8283    2.9293    3.0303    3.1313    3.2323    3.3333    3.4343    3.5354    3.6364    3.7374    3.8384    3.9394    4.0404    4.1414    4.2424    4.3434    4.4444    4.5455    4.6465    4.7475    4.8485    4.9495
+</div></div></div></div></div></div><div  class = 'S14'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access all the data in the matrix using the </span><span style=' font-weight: bold; font-family: monospace;'>load</span><span> method with no arguments. </span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data.load</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="6161D6DD" prevent-scroll="true" data-testid="output_7" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×100</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:100,&quot;totalRows&quot;:2,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1024px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091    1.0101    1.1111    1.2121    1.3131    1.4141    1.5152    1.6162    1.7172    1.8182    1.9192    2.0202    2.1212    2.2222    2.3232    2.4242    2.5253    2.6263    2.7273    2.8283    2.9293    3.0303    3.1313    3.2323    3.3333    3.4343    3.5354    3.6364    3.7374    3.8384    3.9394    4.0404    4.1414    4.2424    4.3434    4.4444    4.5455    4.6465    4.7475    4.8485    4.9495
          0    0.0808    0.1616    0.2424    0.3232    0.4040    0.4848    0.5657    0.6465    0.7273    0.8081    0.8889    0.9697    1.0505    1.1313    1.2121    1.2929    1.3737    1.4545    1.5354    1.6162    1.6970    1.7778    1.8586    1.9394    2.0202    2.1010    2.1818    2.2626    2.3434    2.4242    2.5051    2.5859    2.6667    2.7475    2.8283    2.9091    2.9899    3.0707    3.1515    3.2323    3.3131    3.3939    3.4747    3.5556    3.6364    3.7172    3.7980    3.8788    3.9596
 </div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 2×100
-         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S14'><span>If you only need a section of the data, you can read only that section by indexing the </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data(:, 1:10)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="2EF5CE61" prevent-scroll="true" data-testid="output_8" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:2,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 732px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091
+         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S14'><span>If you only need a section of the data, you can read only that section by indexing the </span><span style=' font-weight: bold; font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S19'><span style="white-space: pre"><span >read_spatial_series.data(:, 1:10)</span></span></div><div  class = 'S10'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="0FD67C88" prevent-scroll="true" data-testid="output_8" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:2,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 732px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">         0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091
          0    0.0808    0.1616    0.2424    0.3232    0.4040    0.4848    0.5657    0.6465    0.7273
 </div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 2×10
          0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><h2  class = 'S4' id = 'H_0b60' ><span>Next Steps</span></h2><div  class = 'S5'><span>This concludes the introductory tutorial. Please proceed to one of the specialized tutorials, which are designed to follow this one.</span></div><ul  class = 'S12'><li  class = 'S13'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S13'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li><li  class = 'S13'><a href = "./ophys.html"><span style=' text-decoration: underline;'>Optical physiology</span></a></li></ul><div  class = 'S5'><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></div><div  class = 'S5'></div>
diff --git a/docs/source/_static/html/tutorials/ogen.html b/docs/source/_static/html/tutorials/ogen.html
index 856c74d31..fa4a1666e 100644
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+++ b/docs/source/_static/html/tutorials/ogen.html
@@ -44,11 +44,11 @@
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 .S10 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }
-.S11 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Optogenetics</span></h1><div  class = 'S1'><span>This tutorial will demonstrate how to write optogenetics data.</span></div><h2  class = 'S2'><span>Creating an NWBFile object</span></h2><div  class = 'S1'><span>When creating a NWB file, the first step is to create the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span> </span><span>object using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, char(java.util.UUID.randomUUID), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First M.'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="EC7F63C5" prevent-scroll="true" data-testid="output_0" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S11 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Optogenetics</span></h1><div  class = 'S1'><span>This tutorial will demonstrate how to write optogenetics data.</span></div><h2  class = 'S2'><span>Creating an NWBFile object</span></h2><div  class = 'S1'><span>When creating a NWB file, the first step is to create the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span> </span><span>object using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, char(java.util.UUID.randomUUID), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First M.'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="4337F5E0" prevent-scroll="true" data-testid="output_0" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
-                                              identifier: 'af38b64a-0c6d-48d7-b068-88de1a94d771'
+                                              identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
                                      session_description: 'mouse in open exploration'
                                       session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                timestamps_reference_time: []
@@ -97,11 +97,11 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S9'><span>Adding optogenetic data</span></h2><div  class = 'S1'><span>The</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>ogen </span><span>module contains two data types that you will need to write optogenetics data,</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>, which contains metadata about the stimulus site, and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>, which contains the values of the time series.</span></div><div  class = 'S1'><span>First, you need to create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>object linked to the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div></div><div  class = 'S10'><span>Now, you can create and add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_stim_site = types.core.OptogeneticStimulusSite( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'This is an example optogenetic site.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'VISrl'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_optogenetics.set(</span><span style="color: #a709f5;">'OptogeneticStimulusSite'</span><span >, ogen_stim_site);</span></span></div></div></div><div  class = 'S10'><span>With the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span> </span><span>added, you can now create and add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>. Here, we will generate some random data and specify the timing using</span><span> </span><span style=' font-family: monospace;'>rate</span><span>. If you have samples at irregular intervals, you should use</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>instead.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_series = types.core.OptogeneticSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(20, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'site'</span><span >, types.untyped.SoftLink(ogen_stim_site), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30.0);  </span><span style="color: #008013;">% Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'OptogeneticSeries'</span><span >, ogen_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="8A96283F" prevent-scroll="true" data-testid="output_1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S9'><span>Adding optogenetic data</span></h2><div  class = 'S1'><span>The</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>ogen </span><span>module contains two data types that you will need to write optogenetics data,</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>, which contains metadata about the stimulus site, and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>, which contains the values of the time series.</span></div><div  class = 'S1'><span>First, you need to create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>object linked to the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div></div><div  class = 'S10'><span>Now, you can create and add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_stim_site = types.core.OptogeneticStimulusSite( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'This is an example optogenetic site.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'VISrl'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_optogenetics.set(</span><span style="color: #a709f5;">'OptogeneticStimulusSite'</span><span >, ogen_stim_site);</span></span></div></div></div><div  class = 'S10'><span>With the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span> </span><span>added, you can now create and add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>. Here, we will generate some random data and specify the timing using</span><span> </span><span style=' font-family: monospace;'>rate</span><span>. If you have samples at irregular intervals, you should use</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>instead.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_series = types.core.OptogeneticSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(20, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'site'</span><span >, types.untyped.SoftLink(ogen_stim_site), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30.0);  </span><span style="color: #008013;">% Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'OptogeneticSeries'</span><span >, ogen_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="E1F68076" prevent-scroll="true" data-testid="output_1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
-                                              identifier: 'af38b64a-0c6d-48d7-b068-88de1a94d771'
+                                              identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
                                      session_description: 'mouse in open exploration'
                                       session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                timestamps_reference_time: []
diff --git a/docs/source/_static/html/tutorials/ophys.html b/docs/source/_static/html/tutorials/ophys.html
index cfd3c416f..53ddf2901 100644
--- a/docs/source/_static/html/tutorials/ophys.html
+++ b/docs/source/_static/html/tutorials/ophys.html
@@ -95,7 +95,7 @@
 </span></a><a href = "#T_C8BDC905"><span>Learn more!
 </span></a><span>        </span><a href = "#H_872126D0"><span>See the API documentation to learn what data types are available.
 </span></a><span>    </span><a href = "#H_6F0B7108"><span>Other MatNWB tutorials
-</span></a><span>    </span><a href = "#H_1310FB5A"><span>Python tutorials</span></a></div></div><h2  class = 'S4' id = 'H_C9814973' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_11954BED' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="2B2762A0" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</span></a><span>    </span><a href = "#H_1310FB5A"><span>Python tutorials</span></a></div></div><h2  class = 'S4' id = 'H_C9814973' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_11954BED' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="B9672902" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -148,14 +148,14 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S12' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_3CE571F5' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_A752E725' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span style=' font-weight: bold; font-family: monospace;'>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_35FE25AF' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span style=' font-family: monospace;'>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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RYhCBsAAAAA2FPYjBLhdmFTO/dTUUVICIGauyXlTR3Ns/RJVClBRiNcpoTNaNnRe0uEzdRIpFpm5nOZE0ZLJUcKoqXCpk5hW/LdZOT3MxJhS4VNSDeiQRA2AAAAAHAOwqaPfgmB04VNSoHoqMdroxiNoonX4jOjvDgpH/pToQ4hbEb5XzI3zL7CZiRQer6ZQwibuelGcyNs4nNLv5us92qQ+2YXYTMadSQEYQMAAAAAZxA2mbuk51oZ5bCpUmDfzlhKmxyVMTdKIyTOklEzu0yJGuWiWe2Qw2b0GOte5iGEzb5TopaOdql5azKPUOazMSVKEDYAAAAAcMnCpj6NqU6Lqo+I7tKiJxLOESFRRpU7Mc2mL9eTDNfcNKNkxF2C7Cps6qiR+vSpOqJnNUhQPFVmr2eUWUce1YS+hxA2IwEzmp6V+2u0z/t3MxoxNJVUeTUzZW60L4UgbAAAAADgAMKmTouJzn503DOvSX9cdXTa87V8DHeIguy4V5GTCW2zzChj9AjsKg9SIIRgmHpU9q7CJj9THz3eR5Lk5+NnCoxteWIy8XIts0qNswqblD6j3DJ93+a21O8t9mF8N6OndeXn+5SxLp5GyYxHo7O2PaJcCMIGAAAAAHYUNik46oia1Wb0xkhahByogqVKkdEjtLNTn5EyoefL6UlzU0acNelw3a6UTH20SH2iVUqW0bbH50bbHnVfOpVol2lEKWBGQqQmc65Cbap+WUb8nBItdWRQvtfl1xKhJARhAwAAAAAHEDZVXFRBEZ3y+Hs0HSZey2S228qNckIAbRuJkesfLZfrm0pqXOvZ5U5+tkuVbXVesu1TZU7tm9zGJXXZN6nv3Hczt4/rdm+r39xTrIQgbAAAAADggMLmusTcaJyrFjmSZokYu6joT8USgrABAAAAAMLmRgmbmux4NNrnoiPqcIwSSRA2AAAAAEDYEDYXGjElanXGx6lfx7oIwgYAAAAACJsrFHP5bq7y9hzDiJasxzGM9hGEDQAAAAAQNkIIQdgAAAAAAGEjhCBsCBsAAAAAhI0QQhA2AAAAAEDYCCEEYQMAAACAsBFCCMIGAAAAAAgbIYQgbAAAAACAsBFCEDYAAAAAQNgIIQRhAwAAAAB7CBsAOHYIGwAAAACEDQAQNgAAAABA2AAAYQMAAAAAhA0AwgYAAAAACBsAIGwAAAAAgLABQNgAAAAAwM0QNq+99tppfPbZZ3qRAAgbAAAAADgGYfPoo49Gx+nko48+0osEQNgAAAAAwDEIm6eeeupMwubZZ589LQMACBsAAAAAOBJhEyN0CBsAhA0AAAAA7ClsQsqEXHn88cdPXn755ZNPP/10KGw+//zzk3feeefkhRdeOF02lnnzzTdPl0/i/fzsQw89dPp7lJlETpz4TIzACakTP+MzUTYAwoawAQAAAHDjhU3Ik5QrEZm3JkTLKIdNiJdcNoRNLJd/p7QJ+RLvTQmbuo5cLtcNgLAhbAAAAADceGETT4FKWfL+++9/OdqmipQUNiF38rV8clSMiskyYtRNHbETr/UpUblsfT3KkuAYAGEDAAAAgLDZkKNrYlRMnyI1Ejbx+9SyMWJmm7CJUTghhuoUqipyqvQBQNgQNgAAAABupLDpUqaOepl6L2RLSJuQLBm57DZhk6Ny4v3IY5OfT3EkSTFA2BA2AAAAAAibjWgZJfztwiaWiQTB+footgmbeD2nP42CsAEIG8IGAAAAAGHTEgbXUTBd2NSEwzEqpo64WSpsasLhus4sm7ABCBvCBgAAAMCNFzb5lKdMONyFSxU2mYg4RtlUYmrTEmFTp1n1PDhZNmEDEDaEDQAAAIAbL2xyilP8rNOiIvnvlLCpiYFDwtQnSuXTo2oi4ix3SthUOUTYAIQNYQMAAADgxgubGFmTsiSmK8VUpzptqQqbOpImpE1KnVg+kwbHaznVqY7IySlUVe7UZMM1N06sZ5RTBwBhAwAAAAA3QtjkCJc6oma1GQGTMmUu6XAImBA0dZRMypn62mozXSpeq9ImIvLXBDVHTn8yFQDCRksOAAAA4EYJmzq9qYqSFDF9tEs+lvsso2CybAAgbAAAAAAQNgBA2AAAAAAAYQMAhA0AAAAAEDYACBsAAAAAIGwAgLABAAAAAMIGAGEDAAAAAIQNABA2AAAAAEDYACBsCBsAAAAAhA0AEDYAAAAAQNic8tlnn5289tprJ++8886Zy/r0008PVtZZiXpEfP7553riAGEDAAAAAFdL2Hz00UfRSTt56qmnzlROiJFHH330tKwo8xiETdTl2Wef1RMHCBsAAAAAuFrCJkRLCJYYHVNH3YTsCOmxlJdffvn0M/EziZE2FyVwQjh16fT444+frv/999/XGwcIGwAAAAC4OsJmRAiOXYRNjtKJETZ1CtILL7xwYcLmoYce+oawCQkV64/3TI0CCBsAAAAAOLiwidEqISRG+WFiVEu8l6Nk4mf8Ha/HaJn4GaNN8vNVXtRls6yc2hQ/a7lTjEayxOdClMTrue6+PTFdKdcRcqeKnah3jprpsiWWi9fj87lfUszUbanSqL4GgLABAAAAQNgcNCfLaNRLCosUHjniJURJlS/xM1/vo2NSqOwqbHL6VMiSUZ1GwiZES74X66h1q0IqlwvpUsnl33zzzdPlUxiNhE2OsonPACBsAAAAAODShU3Kknw95UVEiJaRsNm2rk5Ik9VEct9cVx05k3ltupzJcur0pfiZo3SyjKxbre+2xMlZxraRQgBhQ9gAAAAAIGwuRNh0SZHLhiA5hLCZW3YkbHL9c9tRRU7Nj1Nz0qRwWiJs+v4BQNgAAAAAIGyGnaOQBz3mEuPuI2z6NKVROWcVNiPJMidscjpTTHOK8mtMyZx8AlWOlOlPfdombHJq1S5PvQIIG8IGAAAAwA0UNikzasyNANlH2IwExnkJm1HdR9s12u4eowTBNYHxSH7NCZtdtgcgbAgbAAAAADdY2PTRJRF1ms91FTYpXmJK1miUUUSfxpX1mRJbhA1A2AAAAADAQYTNvjlsenLfmEbVRcZFCpt8bHbmxDlLDpsRNW9NJiyOaVV1+tjSHDajaVsACBsAAAAAhM3eTD2eOp+udB7CZjQ1aZfRK1mvmnMml4+pTT1nT8ifiDrSKB/ZnUIo89lUcVUTE+86CggAYQMAAACAsDkTmbA3JEYIjBQR/dHXZxU2NWlxLDcnOkLGbFtX1DenfNVHdWf5MfIlEwPXHDUpZ2rZ8fncD1UE9WTGoxFIc0mdAcKGsAEAAABA2OxFCIoccZJRxc2hhE0dlbLaMn1pTohUOVPrF6OF+nasNqNmsowqjXpun5RE9b18LaMvO0pWDICwAQAAAEDYHIwQHnXUS0iL+ljw+DlK3luXTdExt+xSMo/Nrkl9sy6jETz53lS9ljwKXf4agLABAAAAgAsTNsdGHUlzFvFzaDK/z1QyYgCEDQAAAADC5lpzbFOPYoROSKTRlCoAhA0AAAAAwubGdPgycfDoEd8XTU6FOoa6AIQNAAAAABA2lzo16qz5cA7FVG4cAIQNAAAAANwoYQOAsAEAAAAAwgYACBsAAAAAIGwAEDYAAAAAQNgAAGEDAAAAAIQNAMKGsAEAAABA2AAAYQMAAAAAhA0AEDYAAAAACBsAIGwAAAAA4HiEzQ9/+EMhhDjqIGwAAAAAEDZCCEHYAAAAAABhI4QQhA0AAAAAEDZCCMIGAAAAAAgbIYQgbAAAAACAsBFCEDYAAAAAQNhc53jjjTdOXnnllZMPP/zwym5D1P+qbMO77757Wtf4eax1fPvtt0/r+MEHH3yj3rmfr8Jxk3Ws20HYAAAAAABhcyXiySefjA7jUQuEbRH1vyrbEAIh6ho/j7F+ITeifg8//PDJxx9//KXAyX2c+/kqHDdRt6jjY489RtgAAAAAwE0QNtmpvcqS47yETXTyz1NIRH0jesc8IgXDscmZ+lqMSIm6HuvIlJAbUeeQNH07nnvuuS/380UKm9F+XBpR52MWZIQNAAAAAMLmwFMtCJv5UQ3n1UGOsruwOfZ9e5WmlkV9n3nmma3HyEUKm7Psx5BLd+7cOf38MU6NImwAAAAAEDYb0RKdvxg98Oqrr57efX/xxRe/NnImOq2xTIw0iPdj2TpyI96L6SKrzVSL+Lvn+ojPxXsR0fmtoxWWdDCzbrGe+Hz83Udk5LbEz1xnLj+1vigj6xY/o967dLxjmSg/R7lEGbH+fD/2Ze6b+Jn1q7lRYpnch/H7aL3xfqynfh+/+Zu/+eXoj+iAx2v53WV98nvYZ9/Efo/l4nM1T0v/fpeOwIrPpSioo4Jq3eqysS3xe+6f/N7ze8t9m6NcRuuN5XPf1jKX1DnWkfs2j/esa25HPd6njpvYv1H3Jcd/Hg+5bK/v3H6sx3O+HuuKOveRVilYj1H0ETYAAAAACJsygiA7m7UTF52/7BjGz5QDqzbiYKoDW8sflREdy11GE/R61o50Hw2R9UlZMlpfLSuXi8/l79uETciA/Hx2sPPvHE0zJ2xy5E2+V+uaYqLnpKl1/o3f+I1JYbNqOWx23TexfP3u6zpWe4wiqUKji4aew6bmWel1iIj9N9qGus/iuKjfR92/8dklwjClRj3WU9j07ZgSNjn9KNdbt6Pv81xf3/bc5m37sebViW2t218lbJ2mF3Fs0+YIGwAAAACETRMqeec/R65kZ7N2LKPDmB3fOsJl1FnNEQq1w5kdy+yMbuv4Zye0JnyNn1HXXm7flqxfdoSjjN45jnpkR793hrfVLfdD7fxXyZXrn0qqm5/v+6bXtQqYeD3qm3VLudFHSkwJmyX7Jsus+yZeqwJg32k/+fm5pMNVZMV29f2YQqNvW90HKdNiG1IexnGTy/b9O4rRMTa3Hf1/oO7HWG+sP+vQj5E6Tanu29G2bVt/F1dTAnJu+wgbAAAAADgiYdPvtEcHLzpzferRqGM4EjZT+T960tZt01Iyge62KR11W6Y62Nl5z85qlyhVMm2TEtnB7h3evr9Gwib2dZTfR9LUcutUmNXESIl9hM22fZOyo++bKlIuStj0qXWj9dfvbC5RcBdl27Yhyxgtt0TYjIRnXza3OY+HfizVkTD1uBqtP7erHyPxudEommN9OhdhAwAAAICw2SJVakcylsvonc0pYZMd1syBUqO+tzRxb3Rm5z4/ty29fiPptOsjsasEiU5yz+2zpGMcnen4XN03I6Ew6rTvI2yW7JuUWWfZN4cQNiGulnx+9Hr9Xvqxl/t3tH2jMkff6RJhM/o/2TZyJgRVPx5G+3y0/jqlKnMpzU13WipNCRsAAAAAuERhM+pU1qlLo9gmbOr0oqnYJmz6NKW5z89tS6/f3CiLpXlaMilv30eZvHhbvWpnfBTbOuj7CJuz7pu5USeHFjajY2O1mQ61VNjMxbaRJVP7fKmwmduPOfUttzGOpZBLZz0eRsdjCLhRHeb2M2EDAAAAAEcqbOpUjJ7cdzSdaE7Y5FN8RrHtiT25rlWbejQ3JWqJlMi/R1NmlubXqfsq6lM7y1UqjOpVp/d0ATFa/0UKm0Pum/MQNqsdRtjkU7FG0UcrHVrY5N+jHDF9hE0fHTP6P1xyPFQhlE8CW03k7OnSiLABAAAAgCsgbOoTZ7pUyU77NmEzNe1jnw5+z8sxNyVqFykxJ6r2kRKxr5bIkhxNUZMpT33+ooVN1m0uv8+26USXLWym8gvtU9eR2FmdcUpUz2+TYjLkXT0e6v/hLsKmHk9T+yL3e//fImwAAAAA4AoIm/qUnf4o6zoSYNQ5raNIeqc3ypmaqjGSQ3VdUVZ9RPM+wialRNRh9GjwbcIm9k+UOcr/kdOGsoM8yhWS6+8d9DotpnawtwmbqadK7SNs6uilqbrtm6h2dDych7BJIdJz9uQTxuL9bSNs5qbGLRE29QlcdT/GevsxkvWtx0N9GtpqS06jPB5H+2zqaVBTYo6wAQAAAIAjFjZVluTjnVOSZOcyHzHdBU+U1UcZ5OdiuewoxrLbOs25rtVmJEAdiVCfihPl7CIl6miRqEeUkeWtFo6wyU53JnjNaShdNlRxlfumjqSJcuqjnnMbo4ycljQlKupjm7MeZxU2dVRG3zdnnRJVty3qMvreDiFs6vcbdY+y41jNbZ1Ksj069uaSL28TPHU/1mTZXbLVkTT1eIjIcuNnTwxd92NdLo/HOpJr6vgdTX0jbAAAAADgiIVNdPqqxMjOexcxoyTD9XHFdZRA7ZRuy1+T8iA7lquSRDXzxtT17SIlslNfO9ApPfK1bVJiVLfctv7Zup6sX+zLmiA29nU+2SdfT2mxmpkCU/dDX34fYZPfW5VIsU2xnqUJmece096l2HkIm/x+uoRbDUZVzY2iWi1Mcjx3jI2OkT56aZQwOOoe21D3We6T0X6M8kYJumP9Xcrk50dP4iJsAAAAAOAIhE103LYlYI1O4xK5si0xbyZ7XdJZnqrn3GfntiXqP/X5XvaSdY22bdtjlOceWb5tpFHuu13KHG3TrvsmXquvp1DYZzuXfm+5P0fH3NR+mNs/Wf4+kmnqSU+j8pYcY9uOq9z2bcfDku8/jsepcnLkzbE90puwAQAAAEDYCLHlUep9JEpO4zrGTv55RY72GY2yucrf72rw9DfCBgAAAAAIG3HkUZPgxlSlmnA4R23kNLSlcdX3xVXehquQu4awAQAAAEDYCLGDqFiVHEZVWqxarpRtcVX3Q80pdNapgZcdmS9oSdJlwgYAAAAACBtxxLLiLLmHrkssyfV0VaZDHft3SdgAAAAAIGyEEIKwAQAAAADCRgghCBsAAAAAIGyEEIQNAAAAABA2QghB2AAAAAAAYSOEIGwAAAAAgLARQgjCBgAAAAAIGyEEYQMAAAAAhI0QQhA2AAAAAHBFhM0bb7xx8uSTT548/PDDJ4899tjJM888c/L2229/bZkPPvjgdJlYNt578cUXT//WIRVCEDYAAAAAcGBh89xzz0Vn6jTu3LlzGvl3SJlc7t133z19LYROXUaHVAhB2AAAAADAAYVNSpgQMK+88srJxx9/fBrxe0qZDz/88GvLRuRImxh1o0MqhCBsAAAAAOCAwiamPq3aSJqMkDLxXsibLmz6dCkhhCBsAAAAAOBAwialzKuvvvqN90LixHsxZaoKm8hzoxMqhCBsAAAAAOCchM1qM2ImZEx/LyTOajP9qQobiYaFEIQNAAAAAJyjsIkEwquJKU4xFYqwEUIQNgAAAABwwcKm56kZ5bfpOWwIGyEEYQMAAAAA5yhs6iiaeDpUvh5PhopcNfFePA2KsBFCEDYAAAAAcEHCJiRNPr47H+0dSYZXm9w2NcEwYSOEIGwAAAAA4AKETcQHH3zwZS6bGjElqo66IWyEEIQNAAAAAFyQsKnToELKRFRRU0fjxHsheHRChRCEDQAAAABcgLARQgjCBgAAAAAIGyEEYQMAAAAAhI0QQhA2AAAAAEDYCCEIG8IGAAAAAGEjhBCEDQAAAAAQNkIIQdgAAAAAAGEjhCBsAAAAAICwEUIIwgYAAAAADi1sXnnlldPQgTyueOONN778biI+/PDDg5Z7qPLOsk3vvvvu4s9+8MEHp5959dVXv3wttqGWFRG/x3ouetuyfpexbsIGAAAAAK6hsFl/5DR0II8rnnzyyS+/m4hd5MaScg9V3lm2aRdRmJ99++23v3zt4Ycf/lpZEfF7LHsZ31nW5zL2LWEDAAAAAFdY2ESHsndmc2TCsXfuoo43qTOcguIsZTz33HPfKCNGgsQ+/Pjjjy9t21KsLBU2Maomln/mmWe+NromXotjOiROjra5KGGT66/bkMdo1Oky9y9hAwAAAABXSNhkB/OyRh+cNbLTTtgsj8cee+woR0/tImziuL1z585p1GlcIWmijJBSvdyLOMZz/X0bXnzxxdPX4ycpQ9gAAAAAwKywibwa2XmPjm90aDPXRvxeO7jxevwdIzHi9xjVEH9Hxzhey85q/B0jCaLDOhpNEJ3r6LTGZ3NkT0iX0bIhYbIeua469SXLiPrHdmT9ap1jXfFeRNSpvp8jS+JzsVz8nnWL7cu8KFnnKCPq0OVQfqbum237IMrJ7cp1LRl9MRI2+d3Ez9yGqGuUW3On5LauNtOFcrv7NtRls8ysb5SZ30GuK7a1Hgc1Ypti2+KzeYzFZ0a5cnYRNlGPWLbmrqnHQx5bmR9nJGzq91DrNvoeMhdNfrfxuSi7Lpv7oq4/90ksF+uI90b7SRA2AAAAAAibr3X0s4PZhc2q5bCpnd7skOYy8XuOdMlOaUbt0IboqO9XeRCCoXd+63t1fSkMsqM9EjZZn3yvrrdKn5yuUpepy8b+yL9rHbooWG2m5uT7tay6bK6vdurz7zq1Zxdhk99NfH5U1yx3Ttj0HDZ1Gs9ov8Q2Te2vKmtSCI72cRcXuwib0fG1i7CJY6DWux/PVShV2RI/6zbVMueETZ2KVo8HQdgAAAAAIGxmp6H00QdTwiY71NGJjU5tT/Car6cAqB34OhomO9qxbHaAs6Me72WZtcObEiE6zfn5UbLc2sGur1exMRIoUVZ21Ov2Rv2ynNH+qhIkOuVRRtQhO+gRWW6+VqVEFRtVJu0qbHLbcl1127YllJ4SNrXMqGeVUvX1FGyjqUhVztT90o+5pcImpx3FPlsySmf0neWxGK/Vbc7X63aMti22J5etcmduG+bqLQgbAAAAAITNmYRN72xmZzY6r6Nys5M7ki2jpKw1r85qMH2k/z0lbOLv/ijlWu5IaozkUC+7ltHr0PdBFQM5qiKFR+38Z7n7TomqwqZPNcr1VxG0q7Cp9UrpMLW/6j4YSbNeh1rGUmEzt9wSYTMlsvpxWl/LBMajaVmjdY3qVkUkMUPYAAAAAMBBhU1PmtrFTO/YZ7khT7JDn49artHXl6MwouMc65waeTL3OOqQAfG50Xq6mKgd9G2PNp8SNqMpTfleduD7yJXYL6N8LrsIm7mRG339uwqbXubS/VXlxOj7Hj3q+qKETR6Lo/01EnJVAtZtGO3bbdtA2BA2AAAAAHAuwqZ3RKdez459lltlyVzUqVJ1+k1KghA5PW/JajDCpubAGUUXE6MnCC0VOaOO+zaB0HP9RBnbpkNNCZu5bTirsBk97n1uf2W5VUzNRR0FtVTYjEa27CJstj01qh8jPd9Nj32EzS6SjrAhbAAAAAAQNucmbDIJcOaDmYo+LSj+zicvZae5juYZCZuacLguW0dPLBU2qx1G2Iwe2TzKWVNHxtREtaPpYocSNlWMXISwidFNdV9PxdL8L6NpePsKmzw+tgmbrFsVhrW+u06JmkqWLAgbAAAAAITNpQmbubwhSyOnstTO9EjYZEe6T3mpnz8PYbN0hMsot0lKm22yYl9hU/fPRQib0UiVbXFMOWxWg5FCfTtSIJoSRdgAAAAAwLkJmy43Di1s6pObejLgfNx0vh5lRX3644+r2OjCppZZhU2OZOhPODoPYbNqU13i99zm2MZ8elZ/hPm2USPnJWzmppYdQthkmT2/UX4Xsc21DrsKm17uUmEz9RSx+j3ksjXJcl22vl5zF03leqqjjkbT7ARhAwAAAICw+UbnOTuv0clM8XFoYdPz2GTOlprPJddd6xSd8lgup0X1jnpPZpxPh1qVR3LHdkV5ETVBcE7LOZSwqXXujwXvU6TiMyGkMpHt6pynRFXZUPdBfm/nIWzq6JT4fuJ7iW3OR5h34bJU2KT4GD2Va+ljvfuxGPWqj6iv+6tOWavL1e89Xs/ExPlaHo99qt4oObUgbAAAAAAQNpOd3NqpPQ9h0/PL1M58H00Tn68d6Fq/nrA1BUCtR4qRKk1ylEst65DCJoRErUtKitHoktE+6Muel7DpCYHPS9jk8j1hbyaP3ndKVJUo/THvS4XNVCLsqFtP/hzfSz0W88ll/TjLfVdHXNV65Pe+5HsmbAgbAAAAAITNMHL0SZ3e05PEzr2eow16h7qXv+1pOVFOdKAjdk3UmnWYW8dcPfs+mHq956nJfbJkHx/isd5z2xCvjZI5b1tuqsx99lctf+p42FXYTE09Gh2P+dq2Y3GubnUb9pEQdTqUhMOEDQAAAADsLWzE8thFNJw1RsLmpu7HHPWyr0S5yMiRV0se3U7YEDYAAAAACBtxRYXN1GPQr2LkyJW5R6BPjYxZbaaTHfN+2PYYcUHYAAAAACBsxDURNqsdH5l9zNG3aZf9mE91Gj2V6RginxQWscvUN8KGsAEAAABA2IgDdMqX5OM55GiU6zjCZmleo1FOnWMVV9vy5wjCBgAAAABhozMohCBsAAAAAICwEUIIwgYAAAAACBshBGEDAAAAAISNEEIQNgAAAABA2AghCBsAAAAAIGyEEOJChc2fXsdj6/iZdXxvHfdrxQEAAAAQNkIIcbnC5j9fxx9bx0+t47uEDQAAAADCRgghLl/Y/Cfr+CPruLuOH1/HfVpxAAAAANdW2Ahx1viVX/kV+0Gce6ybrHvr+Ll1/Dvr+NF13NKKAwAAALju/MQ6/uA6/ujqixwRkdjzPxZiQTy5juhM/wX7Qhw4fmnTHsU0qD+ykTW/d/VF/prfto5vaboBAAAAXHe+s5E2P7n6IqHnz24EjhCz8eCDD/5vDz300Ml99933r9Z//3v2iThg/OymPYqcNXdXX4ysCVlze/XFdChPiAIAAABw7Ym71THF4LubDlE8Mvd3CLElfnkdJx999NHJnTt3/r9bt279j/aJOGB8f9MeRbv045s26rdtZI3RNQAAAABuBN9efZEPIp668sAmfpsQM/Gj99133794+eWXT4I333wzpkWdbDrZ9o84RGRbdP9G0tzaiBojawAAAADcOH6kxLeEmIr77rvvv49RNZ9//vlJ8vjjj588+OCD/4v9Iw4UP9ICAAAAAADMcGcdp6NqKjE1avXFKJtH7CIAAAAAAIAL5MEHH/x7MZpmxAsvvHDy23/7b/+/Vx65DAAAAAAAcGHcW20SDY+IKVLf//73/83999//X9lVAAAAAAAA58+tH/3RH/1/YhTNHK+99lpOjbptlwEAAAAAAJwjMWrmzp07/7YmGp7i0UcfPXnggQf+ur0GAAAAAABwfsRomW8kGp6iJCC+a9cBAAAAAACcA/fdd9/fnko0PMWzzz57cv/99/9f9h4AAAAAAMDhicd0TyYanuKzzz47TUC8/uzTdiEAAAAAAMDhuBWP6d6WaHguAfH68/9qJQExAAAAAADAYYhEwzFKZkmi4anHfP/cz/3cv3nwwQf/ir0JAAAAAABwdk4TDccombPw/vvvS0AMAAAAAABwCCLRcDye+xA89dRTJw8++OD/Ya8CAAAAAADsz16JhucSEK++GGXzhF0LAAAAAACwB5FoOB7LfUhiatX999//L9bF37KHAQAAAAAAdiATDceomEMSCYjv3Lnzb2/duvU/2csAAAAAAADLuR2P4T5rouEp3nnnnZwadceuBgAAAAAAWMADDzzw11ebJ0OdVzz00EMn3/nOd/6+vQ0AAAAAALCd29/61rf+VsiU8454AtXKKBsAAAAAAIBz48QuAAAAAAAAOC4IGwAAAAAAgCODsAEAAAAAADgyCJsx99bx9Dpu2xUAAAAAAOCiIWzGvL7ZN3ftCgAAAAAAcNEcm7B5+kjqRNgAAAAAAIBL49iEzesrwgYAAAAAANxwzipH7qy+GBXz+ibi90fK+7c2r43ywdT3fufm5yebOuXrlcgr8/xmPS9NlHl38/rdzXtZtx9s6jridik3y5wSNr0O91by3AAAAAAAgANzFmFzb/P5UVTZ8oPNa6+3z9fX706Uk7w08X4InipickrVS6uv5M9UvYLnW1n5873V14XNrfLaSfv9LYcRAAAAAAA4JPsKm1urr4TF8+X1J1ZfiY868uStzWtPbP6+u/pKjtxq9el1urf6ppy5tfpKtrxXln26lJEi6FZ5vS77SCn36c1yd1dfja6pwibr8FbbBymSnnYoAQAAAACAQ7GvsHliNR41E+TImSpyQrR8sokQOSlwHhnUp9dpatkgR7qkyKnC5tZE2SmSpmTLndU3hU1u00tt2ZQ8txxKAAAAAADgUOwrbHJ0S/y82yKlSZc5KXk+WX1T6NT6nEy8dm+wrhwNkyN36pSoToqfu5u/+2dH68xlb5fXouyQRyQNAAAAAAA4F/YVNnXa0FSMRt+kNOlToWp9ap1uL1hPHSXz9Gp6ilJPJNzz1MwtG9TpXl3eAAAAAAAAHIyzCpvRCJuM/lSmOtVo28iW0Wv3ZtaV05x2ETZ9xE3lk5n3Htlsd008fMehBAAAAAAADsW+wmYqp8sUMZqmJh6u+Wx6feaEzTZ2ETavTyxbEyrfXbhNzzuUAAAAAADAodhX2NQnN90avBcCo4466YInxcpbg/r0J0xlcuCRFHl+9dUTnmq5S4TN86uxdKqPK6/LvjTY1nzS1OsOJQAAAAAAcChOzvDZnBKUj8W+u/pKzNRHcN9djeVOjk6pciWlykvl9fx8PpY7hMojq69ETpUluwibO4Nye26eu63ct1ZfTc26t5rPgwMAAAAAALAXZxE2MQpmlHw4xMzdzTK3Vl/lg+nJeaswuTN4rdbtkdU3E/6mrKkSaBdhs1p9/UlTGTmapi/70mqc8Nh0KAAAAAAAcFBODlBGSJYQKjHipOekCZlydzU9AuXO6utJg+tro8/Ea0+sph+rfXtQXi/31kS5tf63J5bN7Xl65dHeAAAAAADgnDixCwAAAAAAAI4LwgYAAAAAAODIIGwAAAAAAACODMIGAAAAl8FcfsF9uHXg8gDgXPgRIRbGiX0gziEA5zUhhHC+nBMrz6++euLm3QOVe/fA5QHAmfnWOr69jvvWcf8mHhBiYZzYB+IcItui+zYXZd8mcrCDmPn25rhxXhNCOF9eP0KmvFdkzSEEy93VV6NrTsrfj7iRIIS4bLEejX08gvg76/juOr63ju8LsTBO7ANxDvG9TXsU7dKPbdqoBzYXT8QNpi58b5Vz2o85rwkhnC+vJa+v45N1PLH5/azC5tamjE9KebmOk9XZp0e5kSAEsX4msf7j63hoHX9gHT+9jp9Zx88KsTBO7ANxDvEzm/boJ9fx+9bxE5sL0gdX5pVj+oL7wc1x8hOb4+YnndeEEM6X146nV1/krlkdUNjESJofFEnzyebvR86wH91IEEIcRKw/tGnw/8N13FvHY+v4RSEWxol9IM4hHtu0R390HX9oHQ+v4/dsGrsHjLLB4KL4gc3x8Xs2x8sfXsfPr+MXNsfULwkhxDWMX9y0cz+/afdu2vnyEMImubMRNRl33EgQQlyaWP+1X/u1EyGEuAqxuRB9eNPA3d4MJQSSb2+Oizg+HvY/I4S44efMm3S+PJSwiU7TWxtRc3vz860zjq55wPEohGjt83KxbocJIa5Q4/bHNw3c79sMJTQtCv1C+8c2x8cf9j8jhNAhuDHny0MKmyjric3fmR9n3/13eiPB8SiEaO3zn1jHH1sk1uMD3/3lvySEEEcdm8btyc0om5/czP+8j6NA4b7NcRHHx887vwkhbvg58yadLw85JergNxKcj4QQrX1+aiNttot1DYgQ4go1br+ymaP/05vhg/dzFCjcvzku4vj4Bec3IcQNP2fepPPlsQqb0xsJzkdCiNY+P7P4RrQGRAhxhRq3X119kVgxknZ9j7DBQNh8b3N8/KLzmxDihp8zb9L58liFzemNBOcjIURrn//bxTeiNSBCiCvUuP2Z1RdPw4hM699feVIUviKfEPX9zfHxS85vQogbfs68SefLYxY233M+EkK09vnPL74RrQERQhA2IGyEEIKwucbC5gebZeojuu9sXnv9vM9LzkdCiNY+/3eL+zUaECEEYQPCRgghCJsrxN2NoJmL17cInVoGYSOEIGyEEIKwAWEjhBCEzRmpo2Om4gdG2AghCBshhCBsQNgIIQRhA8JGCEHYCCEIGxeg15Zbq/2SRhI2QghB2BA2QgjCRgghCBucE09s8gm8tI7bhI0QQhA2hI0QgrARVzr+xHO/cfLn/urfOY3f/adesE8OGLE/c9/GfrZPCBucOzHC5r11fLKOpwkboX3/IrKuEb4/QdgQNkIIwuZaxl/8G//g5O/+1nuL46ee+vULq1usa1SHvED71b/8mye/408+/43PxTLJo3/2r13bC+vY/n/0Tz48jdjmv/n3//HkPjlUxP5MYp0aGsIGF8bTG2nz3oJpUoTNJcZlnTOvavsecqif5+N8tk0eVRx3grAhbI4t4no82tWLvHlc29OLEO+1H7ntfJf741DlXadrBe0zYbP1YNmFixQg9eJxjl9/9R9+rTG8CGETYuSypEU0ZnP803/2ybltN2FD2OBSub2ZHpWPaL1N2BxfXNY586q27yFlthHntT6ShrARhA1hc4w3VEM4//N/+a+/0Y7FDdZ7f/7lg7bZ/RxT29OLGH24rc8V2xv7I9rwSnwu+lJdZt2Em+7OYYTNmUfYRGOSRGNzmSNsurAZNX5V2lzkP3vup4uWFmGn637IEUfxPfbG0HQwwgbXfprUyWbkzS3ChrC5qlE7GHPn+aB2dlzsCsKGsDm2lAzb2rC8dj/raPjoj43avzjn5OjEizj/zPW5Yn8skfF1XxA2hA1hc4Y7bHVu+8jmjnLGTH0m/jEzz0wKh/i7N15d2PSGqjaK8fvUP3tOHwrDO/fPn8uF/AgBFPXqJjzr3e/69WGHUb94PcrJskayq+bbyTrEa/GZkYWv647tmZM5sS39/dy+nD7V69RzGOTwxfxut+U4yGVzOHtsw9RJKfdRHgM6NoQNduaJMk3qEcLmeIVNv4iOdjHax9H5L16Pc8DccPZ6rhjdpZw61+R6an3ys1Pt++g8FeucOk/Vc07PMTPKMVfPa/0mSL/gr+e9uYvdvn9G59+6D+L9/E7yM1PnrnounStfEDa4OcIm2pzRiJpshzpnHf1SR9sfy0yNeh0/kjV5zoif9SZzbdcJG8KGsDmjsBn9Y3WpUiVBThsK8uIvLmr6SJAqXerF35ywGb2f/9j1n3001DrW3y9u54ZkVxM+NYUs91UsN2qYk2hgp0zyyMxHXeuFYC07TgT9gjKWHV24x36dsv71wrx///Fd1/07dXxEPeqy/YTV6zl3DJxnDh7CBteQGF3zfJsmRdgcmbCp55jRiMi8y1hHuU5NcZ06D/U7lSlM6rqijc0cByOpNGrf67JxLhmtt56ncr31nBPLxGdH650TNqPz87aL3alzUd8//Tvp+35042PpuVQQNrhZwqa2baN2O67Ne0qDvF6vOTtjmdpWRlm1bcll+2j7bDu35bCJekWZmf9y6oZyFyux/EhOTwmWub7P6Mb7qA8Xr2W+zux/jm4SRFm1DY86x9+jET+5b+K9nK5V5VqtZ/1e4r34TE53yxv2U32ufjzE5/o+IGwIm3OZw57/tHU0S7+QqjKnyoU4oPsIkFy+j5TJg3+bsOn2NhuR0cVsvziuF2H9AjSW7dIl90W/qMtpY/H6KL9M/qNPWfX6Xu6HftGYZY8Mfs6Hnbu7OtrvvU657+o+75+ZEzZ9u2Mf1n1ep6zV7y1H4tT9LTcOYYO9p0m9tRE3f2EjbgibIxQ22baOBP3o9XoO6OernhugXqCPzm2j8+MuwmbqnFrrWG/W5LIjGbJU2NTzQ73+GF0f9OuGvn/quX/0nYyE2S7nUlORCRvcTGEzJ3qnruFzudpuRZszlf8mPj+X33Muh03KhyU3S+t0q9Gy24RN//xUuxjnqC5JanlT9a3SZtQuT627z1LYlmKj7us4x019L0vzjPYb2IQNYXMuwqYLmJrLJX/Wf+S8SMoLnj6VKMuIg7f+U+Y/y7YpUVMNSC0rG7i+/voPVi/meuO2TWpMjULqDUqVFLVh7AZ2VNd6wbjt7mo0FL1hrBfPdX8s2ed5V3Ru22NbRncZe1m53rq/q6nPXDwex07Y4MzTpP75Jp4gbI5P2NR2st8cyLa4ttv1HBDv1zux/dxTz2394jTPO12o7CJslpyn6jbVc2rfB0uEzdxNodH1wWj/1GVrPafqEz/rhXmep+qFeJQ/dy4VhA1ujrCZ6i/M5cCsonuUszOnj9a2qKYqGN1QnRM2VVDkaJJa9tS0pFg2tqd+vt4YGAmbqfPXPg/DyVFAta61na3tciwTdaiv1fNAb/OjbnUET+9Pjr6XTOUw6t/0fmAs10dlVplH2BA25yJsqnTIRqP+XRuq+l7+Y/WpSlMXZbnepU+J6v+QdT31H6P/I41kQ29k6z9x1nlqH20bEbRt+lbdhrl/5MwfMDWtqDdmtZGtj0WvF9XZoM5NcZva9j4qp+Y0qnUcNeS53XO5bgRhc45Tie5e0/jpdfzPm9E2//s6/gvnt+MRNvUCtre52Q72c9No5GRepE+de/pUpiV5drYJm3runqrjqN0fnYOnZNahkg5v2z9TN3Gmtrm+Vh87boQoYXNkIy2f2EyTzRGXxxZ/L35e175TH4Eyl3tmqr8zNSpkJKF7+zclbKo06E+0qtOEan61nvdsac6Z2ib2vs0uwqaup0qxuo9rXWsfbrRsH1VZ1zuanjX3vYz6mrX82g+bEliEDWFzLsKmXmzl3MB6kZlyIw7MOhQ7TWy9iOsXj6PGbqmw6XM05xJW9X+ObZJl1OhM7aNtw7qr/Mk6z11Q1+XnrH4mf+xD9UZ3/+bY1jhNbfuSLPBzpr+LJskbCZtzFDR5IfvekV7Inlf8n85vxyNspvLA9TuRo3NTtI+j6UXbcrz0tryWsYuw6efUel6fujEytU3ZEVgibOL81s8No/XE+pfun6mL696pynPXtnJdeBM2l8jTpb3/ZJPP7Okju5Hw+zf7/2ev2/loW19iql8xEjb9PFDfq2J5F2EzVca2bcqHiPSnCG87P9Rt3HXU4dRN97ltrg96GaWiWPLglm153fr3MtrPtW+TU5Dn6uO8Qdici7CpF2c5UqP+8+cFTt556hdlc8KmCp4pYVNHbsw9VehYhc1o++fqumum9J7AK+s7dVewx66JnnMb63eXSbhG0Z8U1rPE7zt8krAhbBbwyOqrpym9VC5kb13T7f3lzZSo/3cdT5kSdVzCpsrruXPuqC3uiXyzfd22bJcd2+4m7itspqTM3DaNnrxYn1Y1JfHPun+mvpOp9/rUp7lzqSBsLog7ZSTN05vcZaZEXfITouau2UdpGObOA1PX5bsIm239kx5TN1WXnh8ONSWq78fRjewlwn/XNn/bOXHJOWKOPC8SNoTNuQmbbobrgVo77vleNblnnRJ1iH/2bY3e3JSoUXKwpVOi+tDxqWzoU416vJdzSOMz/WkXo2F3Wbfa8G67mNxH2Oz7PfXHry492QnCZsdRNS9tLmafv8aCZpR4+H9Yx++Sw+b4hM3UeWObsFmSV622wVM5B6Y6F/sIm9F6R6N35uq5a2di6f6ZSoC5TaItuRgnZgibIxpV89ZG3MhhcyTt/tQ0oN72jnI+nscIm3qNva2NHaVGyCcqLT0/9DKmpHvUL58+Ncrt2c83fVpX71tFHzU+MzXSc9c2f1dhU8+5WZdREDaEzbkLm9H0l/yH6v84c8me6mNA4x+ryompJFznJWz6uvMfaeopVFMN61T29yivJ8Xata6jBqhPH4r3e3KykenOofHxeg7VO8sIm96I5nrr3Nioa/wdkUMW+wltbnqYIGz24PZmVM0nmxE2111M1Ud7/86Vx3pfO2EzSjQZ55d+N3Qq0X6eR/ryhxY2/cZOnKfic0uGqZ9F2PT9k7nRemLnPL/sevE+lcC/nkunng4jCJtzGDWao2okHT6C6E+z6+kaeuLe+gCSs+awqTdwR21Xl9aja+986uxUe1z7MVM34+v5ofatYrv7uaPXacnMg7k2v+7P/oTEJcJmJOR3FTZzU65GN6IJG8Lm3ITNSMpMjUhZ8ui1fgE314Cdl7AZZQHvU3XqHMyoX30/tjkb0VEG8r7N9YJuF2EzNfRvybSivszU97SvsOn1ivLrdk9d/Ke4qfWTtJGwORCvb6ZAXfdRNU+U6V6P9Atjwub6CJsux+P3/Lu2ofFaPt1vLifMeQmbpXnNDi1slu6fPOftKmz6NUz83s+lRocSNhck6D/ZjB5dETbHMy2qX+/n01tHU2VqX2D0hNbMIVPLrH2qfrM5Bf5UuzZ68tNo6lJvv2OZXo+pXJy1/evSpKbT6O1m7WPtcr7p57goJ+rV2/uRUDkPYVNvrOdDWPrj1JeMkiJsCJszC5suZfoy/fnzfdrOXMLEbED2SeJ1FmGTd8h6Y1QbgL4dvW71Edd9H/R/3n3q2oeVzyU/nDLZU5+pJ41tTyaZOz6mtjvWW/dfF159H7ngJWwOJDFOrsgw8UNMf3q6iSnC5siEzWi66j7CZtTO5jlqlBshRzX2i9fRk5wOJWyyrN5J6R2B8xA2/SmEtTPQn/q4q7DZdi7td9QFYXNOvLQRNrcIm+OK/gjnJU+2HY0UGVFHyE89mCUlz7Z8aXPt1+jGfNarn0t6O9/PD0vyy/Q+wlmm4Pbz3CgP6nkKm7m+UO7D+h0SNoTN3lEfFTo15zCHN+fw6m6Yt32+Zh+fSyDcH1u6dBtq/UaiZaq8qHt9PNxcssO+nr5c5mXJsqLcvq92qesoX02+l+uoc0CX7PepfTq3j7YdH/Fabnf8HG3zaB9F3T0dirA58FSop6/x9r1Upj/dnrswJmwu58ZHb7un2s65NnWqLY62M9rMiNrG1vPv1Ll5bjj9VF32PafWz849anXpdcPSc1XdP33acN0/S/bX6L3cT9vOpYKwOcepUFdxmu+1Fzb1oRpdJIRwGd1QHYmBuDbOz8fnpmRwyoFYNqefzkmJLpxTwvQ0BHX9eSM429J4PZ+A1EePjLYtp4L1hPC53n5O2WeWRJ1qFp/P7Yn9E+vNfXoRwiZvHNSbIrm9S294EDaEzeSBAhwzLjgJmx3vPr51Tbft6cH0pxVh45wK5y1xI4TN8xtRvyJsrkYskdBTYmDupudF1Os81n9eN2dD/Gy7aX1ZU+W0wYSNi0u48CVsCJuv895mStR1m/703g4jhwgb51Q4b4nrJ2zeusKjR6+lsAGcNwgbIYQgbHZLxniyERzXbfrTSxPTnwgbMXvndpQzbJRT7ZARw89HuRhGT08RgrBZzFWdDkXYAIQNYSOEIGwIm1NRc3KNtufeZmTN3X0vjAkbkXPqa964ixg6Huuo612SZ00Iwmbr+e02YXPzHvgiBGFD2AghCJvrkuPlrRUIGyGEuF7C5onNtNgVYSOEIGyEEIKwuarC5nW+hrARQohrJmyu+vmNsBFCEDZCCMKGsCFsCBshhCBsCBshBGEjhBCEjQtawkYIIQgb5zfCRghB2AghBGFD2BA2QghB2BA2QgjCRgghCBsXtISNEEIQNs5vhI0QgrARQgjChrAhbIQQgrAhbIQQhA1hI4QgbAgbwkYIIQgb5zfCRghB2AghBGHjgpawEUIIwoawEUIQNguFjRBCXIUgbAibXYWNEELc4HMmYXNEwkYIIUr7vJuwAYBjh7AhbPYRNgBwg8+ZhM0RCRsAKO0zYQOAsCFsCBsAIGwIG8IGAGEDAITNZfADwoawAQDChrABQNgAAGFzXLy+kTaEDWEDAIQNYQPgJgibd9555+S11147+eyzz45+oz/66KPTusbPq8pV2t+VqG/U+80337yS+/3Yj51jrF/UJ+Lzzz8nbI6D2C/3+BrC5pj+Xzvvv//+lT9PA4QNYUPYADgaYfPUU09FQVfi4iouAqOu8fOqcpX296jeKWzyu4jXr0pn5qKOnVxXjdhPGSHtrsKxnXV69tlnCZvL587mWLrD1xA2U/+rL7/88qXXJeR+1OWhhx66dHkEEDaEDWFzeX2G0fXuRZ+P4lzkfIQrL2w+/fTTU3lwFQ7k+MeLul610SlXXdiEpOly5qoJm4s8dnLfPProo6cniS5v8r343zt2Gfn444+f1ivumhM2l8q9zbEDwuYb5/CRILmo9iQuyPs57bJkL0DYEDaEzfH1GXIU+UWt3/kI107YgLDZJjrSTlfZcdWEzUUyJ1/ie08JUk8exypspjqDhM2F89JKwmHCZqFUjXbmotqTGNUzOqddhuwFCBvChrA5vj5D9n0ugtG5z/kIV17YxMVW/CPl3f64W5bTNuIC7IUXXjj9OzqXeaDn6/EPED9HoxbitbCc8bkYTRDLRZlTnb4oM5aNiM/FcrnupNYtO5Pxd2xDLB//oPF31GtuXVlOlB/rreXsK2GynlmH2ObRUMApYRPLxvqj7hFRTh2B0bc39m9dfmp74zO5H6NOuX+XEp8dNX4jYZP1i7rE73lsxTrrdxav5zHRt7EfO7Ft+fmp0THxem5jHjtzx3X9LvqxE/XKMrYdO/H/MDp2tsmXlCARuZ6pz8Q6cluibvH7SPblfh5ty9T/Z5YbJ9b4OZX/Io+Bi5puQdh8g0c2x8sjXM1uwqa2A/G/m+ejeH1b25Pt5ajtyXYg/hfjfzTbnzyn5P9u/B3lTOUtG/0fzrU/nbyTWeVvnhdyJF9v95acE+K1eH90cVvfy3rHumKd/XyQshcAYUPYjPs+S0Z+R3s6OmfNLT8qd+r1Spx/sm+0T3+opk9I8jwxJ3r2XWffn6Nr6cu4+QgcVNh0gVCHjqUlrdM64sIv/66v1wu7+GeIC8FcJi8e8wJyJCHy/SyzvjbVEc5/zJQWWf7UuqKOtV65ruyQ7jtapOYp6fsl1lc72CNhU3OexHbUz9fldt3eHKre67fL0MCsS2/gR8Im61enAtVtiQZ89HoXW7lt2YGZ2r6Uev3YqfunH9e1Ec8y6/pqWfseO0tGy/Tvd/SZ3J9Zz/pd95Ph3DFYxVD+z9X3e6ern8zyRNf3P2FzIdxaxyebETbYUdjk/8PU/0UXErW96eelUfs31XakSOnrq+3orv+Hc9tXtyOF+EjYLD0nRHmji9u63bn+KWETZD0kIAYIm5subOp1XrTTvf3v56Noe2sbnW1yv1Ge14rZ1vc+SJTTXx/dMI2/a3+tLruEmr9sdB1bt7W+P0odUK9xp85H9RyYNz56OfWaOq/Xr+rDU0DYDDuOeQEX/4DxDxK/z70+GmFQ/ylj+bx4G+XuiEYi61A79kuETf/81LpqJzvqniMzRo3IPsImys9RClVE1Y5u3985fHBK4tSGcmp764V/NuJRh7q9tYxskLclA6sCZurEMxI2/Rip+zhfn9rG7OjUdcbyo45JXTb3RWxTFRvbhE3uyyy3Hjt1XbnP8kTZt2sXYZMnn20jbLIe9XvKz/bvpB6DuXzUc/Rd576sncK6j0cjafJYOuvdD8Jmr6lQn2zEDfYUNvm/Fcd7/d+tbU/KjN725Dmu/g/V82SWW//f8v8o1zcqY9v/4bbpTPGZkZCdak92PSfkVKcUOfVifDTUfSRlsoxjSIYMEDaEzTEIm2xvc2RzXuulCO+CIV6P690qeUY39+oN77psvcFbX6/ngWzfc/kulZa04XlN3svt58s8z/Q+Q7xXr6tHsmV0Y6FKqXqTpj+tMJfv+xm48sKmUu/Mjcxu/QeITl1OFxldvNV/5qmLvVE95oRNN9N5gZzLZmM0kg+1kTuLsOkWul5Q577o25tDD3v9a+M5EiJ9f/VhiPl9jRqmfG/bKJvRcPulwqZ2IKqgqMfESAjlsTN1PNS65Hfc913We6mw6eKqH6f1e+zUE+8SYVOF0lwOmzwuRncCRvJkauRNl2Kj6Vh1309NYbjI3EuEzZcja14yFeowwqa3YfUCd5+2p4806W3H1Iics/4fjrZh1MaP2qC5i9U8J9R2rI6UzelPozZmrm3Idcp1BhA2hM1rk9edvX819XSj0Yjneu09NUq79k9GZVQ5VNdXbypvm0o1urHat2+0fO87jc6B/XxUb0CMbnaPrr/nruWBKyts+gXW1GiL2iD0f4zscEaZmVejlz3VENRO/hJhs+0O49yFY3b8zypsRp3rLg6mLm6zs5D7aiSsRp2Mqe2tHYpaZjXY26a4zDV8c8Kmdwjm6j3VePZjZ/QdTQ23H4mtOWGzbbpXHW7amZsSNRex/CiJc9/XsUw/LkbbPbUt/biv/8f9uJiStSMBStice86aTzZB1hxA2PS2ee7Crbc9c//j/a7j1NMoehuy7//hXJnb2u4l54TeRlexNLWuOWEzV0cAhM1NEjajfs22c9VcnyPb3HrNW68B63mut899+Z5GYHT9t61uc+eC0XZPXbdOXd9X0TSVW3HbCPe5dQJXUtj0g33qwmskbOK1Oi1lav5ivRhcYkKnhM2SC9Zd5cM+wmYqGeycsOlTa0axzwX6tjKXdAj2FTZTx8io3qNtHM2hHXUY5rZhF2GzbdvmHgk42g/1sd6ZkDqTAsd7o7sPcx2sqRgJm20dpiUyaTT16SKfYnVDhc2d1ReP7s5RNS+ZBnU4YbPkAnJq/v4u0x6Xnj/3/T+sjB6dOlePHP2z6zmhnktGF7lLhM1F5b8CCBvC5liFzdy18GgqbH2AxijPSxc2o3Z2ql3vZdTpUPW6NZPTL7n+mztPjM639dzaYzR6pp87l44uXXq+Aq6UsJnKsL2tM17/cWpy1nrBmReMWca2u5yHFDZz03sOlXR41BHvlrjv7zp9Z2o+6T7CpoqE+NxUHJuwqR2mUa6jkbDpJ4e67w4hbEbTBZYIm13kxtzosb7e0YlsqbCpeTrmjoslOTHOuXH7C+v4K+t4bh3/zTr+63X8l5uLuusSP9hcoNaLsNeNqrkcYVPbnrr8SIycVdjs+384ulu76wibXc4JdTj81PlzibCRMwAgbAib5cKmtr35AI7oI1Rh0fttS66xp16fEzYZ2/JeLrmJOurjTa1vtM6e94awwY0XNn1O4dLOeLWmU0//WTKtpYqMbZ3aJVOE5hq0Ub32ETajBmLblKiaeLJScwadRdic5c7mRQubKlr6sTOaTjf3iPRDCpu5pySN6nUIYZNyM5Opje5K7CNs5qYRzpHbue2EfcDGLeIfruN/XcffW8ffXsff2lzQXaf4wWZkzR0e5vKETb15MHrE9aGFzb7/h0vb1Tlhs8s5oXYiRsnYlwobU6IAwoaw2S5ssn2dynczugY8pLAZ3XjetS+0ZIRNPecunZ5U89bkz15fwgY3Ttgs7XQvFTZTIwZGuTH60za2SZi5Tveogz56VPYhhE1P1lUfRTe1v3P762f7U7lypMkuF+hVfvRh9ZlJfVvne9eRJeclbOoxVcVWTQ48te8OIWx2PXYOKWz6MVWnNdR9tFTY1BNe//7zyWajpwFIOoybImx625P/L1V0nFXYLPk/3JYvYMlT/KZGbS45J0zl8upJMOcSTc6dQwAQNoTNyezN3D6KZPRQjUMIm7mnhOaTUZcK/l1z2Iz6IqPpwPWpxFPnIzlsQNjs8Hp/tGlteOpdzHwMW75WHzlXc+AcStjUR7zGhWz+fainRGVG95oktzeAfX/X7c8hj1lO3W85XH2XO6o190D8HuvK/dunq42oj3G9CGFTG+TclppFPr+nPHZqArd67NQh/P0R4vsKm/4I7yrazkPYdEkVr+d21Tv+2UlaKmz6nZsso5bfT55zjw8mbHBdpkRNtT3xen2UavwPnFXY7PN/OGIqaX9tP+qjTWs7Fts1dU6oT+moZfdpzXU7sp2qy+f6liTSBEDYEDYffeP8kO1yfcpoPUccQthkXyTa/KmbFtskR55DR+eu0U3Peu4Y3QytMqY/wruej6rUSqE1upHhKVG4lsJm21Mu5l4fJR1OaVEvGHOd0Qj0DvEoIfFZhU1v8LKTf9b8HDW7e5UFmStgbn/3ESHZeMX21zuiWdYuwqY2aDViu5dObcl9NZWE9iKSDuexOJq7u++xs6uwybpVyRfrHeWROISwye+uHk/5vdW787neXYRNntR6Ert8Yk1n7nHAhA2ui7AZJR2OtqcKmizrEMKm3y2t/4dLBcecEKnt4rbkw/WcENub+2H0dK08J+R79bW+r5de6AMgbAibr1/b9pvBeR1W5Ur2rc4qbPpN7WjT6/lwSZ8hz2c9pUa/fq8jW+s64vfRuaTm86nnl9H5qPZb+j7J/TbKfQMcvbCJi9Sa3DAfx90vsPL13nGfer2WvZS6zux013+2eL/WLdc9WkdfdlS3fgG7b36O3tBG2VOjV/r+7vtx7sJ2bl9v294lSSznREJv4HJ9tS77HCPbvr9djp3ctjzRVcM+2j/b1j31HY4ex11PFtu+i6kypz6zpKypbZnb9/V4nCv/IvPXEDY4pLCZam/P2vZM/b9Ovb7t/zDrsqvYOEtS36lzwtx5dUn72C/eTYcCCBvCZn7aec5GqO1qtLM5ujyuwXN0eV57xntZ1tx09ilBNHo91pFPaYrzSpS5NK/N3Kj8qHdNJlxfzxGtdZ19JE68P7qhmDdSax8l6pvrqftk6ePJgaMUNpdNNg69ocmLvbMkwOqNRfyz9gYxDe9ZkvNe9yRWacAP9V0cisy70I+dtOiHFAw5ha6WmcdOn0N7nbiMThdhg0MJm5tA3nC4iCe47XK+zTui28QOAMLmJggb56PLo+Z8u67X6yBszv3CLoVACJWa/+UsEmWqg5/2N9ZTn3yRxjWHuy+NMMrXXdjUfAbH1NBd5LGT83hHx86xnZgORQ5D7XksCBsQNsd1Dj02OTJ6mAAAwoawcT66jDpNPZEYIGz27HjX+ZPnMaKjJvnteQqCPs9yW9S8Jte5IRg9deSYj53zmL5Tk6/V+bHXNT9Dzc10CY0bYQPCZiEXnWfqqtQFIGwIG8LmZp8DctTPaMoYQNjsQc6NvwjxsSR3x65lXfdhdhf13ewrbq7iseP7vhLC5t467q7jDldC2Bz7/+tln4duSvsIEDaEDWEz5ljyxdSclqZCgbABgOspbF4qI6pe50oIGwAgbAgbwgYAYQOAsLn8C9Coyyebi8lHuBLCBgAIG8KGsAFA2AAgbC73AvSukTWEDQAQNoQNYQOAsAGAwwibu5uLv9ubvDNPbPLQJLc2f/9gc4E4Gj3zdJkO9dbm71rG7c1rz28i17caLHNv8/sjm79HdX1pU597m/qNlrm7KefezDrrNubn8rMj+nY8MVNmLe8yRxwRNgBA2BA2AAgbALikxu1X1/GL6/iZdXxvHffveOF3shEgPf/MnY2AGT317fkiS0bvZxn3NtOkRsvcmxih83pZJvnBRBnvrb6e4Lhuz2i9TwwkzGgbuyy6N7H+TwYy5vWJZd8ibACAsCFsCBsAhA2Am9O4/co6fmEdP72O7+4pbOromLtNPLxV5MwjRYQ8MSjn6TZy5ZMiePK1us7bTdikBPlBETr3BpLndqvf1PbkiJ0fFMFTZc0nRRTdbpLq+bLcyWA7flDqW/dPruf2oK5PEzYAQNgQNoQNAMIGwM1o3P6zdfz8On5yHd9Zx317Cps6vedOeb1PO3pkID9GwuaJmbw2z7fl786Mgnl94vXVpg4nZZTN0zP17tt5r0mYOtrnrc3Uqzq656XB+l9qdXt6Ytlbm/12h7ABAMKGsCFsABA2AG5G4/bH1/GH1vH71vFjA1GxRNi8NJFE+K3N7z36lKWRsHmpCJH++R80mXN3Qhytmsjp5fSRK0/PTD/KZe9OSKMpXi/TrPr6n58ZjfPSTD4cwgYACBvCBgD2FTZCCHEVYjO65g+u4yc2wuDbewibp2dG3szFrZlyXl/w+ZGwWU0Im7l4esv2jITNW+3vKd7aYTum9t3zlyRvhsLmhz/8oRBC3LggbAgbANdE2BQe2iTy/A/W8cc2eSIeE0KIS4xf2LRH/9E6/v2NrPl3N/lrdr343CZs3lt9/WlHPW4vEDavz3z+3g7C5vmZcu7uIWzy721PcHq9jJjZth11CtS9zWc+mRk9RNgIIQRhc2OFjeNSCFHa572EzY+v43et4/ev46c2ST1/RgghLjF+etMe/YF1/N6NWA5Z8+CO06HmBMecQFlazvMTOWJG3J2ZypTC494ZtmfXKVF3i1ypU6L24dbqm4mMCRshhCBsCBvHpRDijMLmgc1F+3c2HaLvbT4ohBCXFd/btEff2eSsub1pq27tceE5JThqLpa7g/eebyNT5pIOvzdY7702QufuajpB8UszsuPpzXpu7SFs7k1Ionut3k/MyKR7g5FGLw3E2b2ZfUHYCCEEYUPYCCEImz2Ezbc2+SDiqSv3b+IBIYS4xMi26L6NGPj2GS445wRHjkDJx2zf2UiatwafmXqs93tFVOQjtu+Vcu8sEDZ1tM/rq28mLn5rT2GzGtTvXtm+e2U7PmnL3SnrqttRkyk/stneu4MyCRshhCBsCBvHpRDijMJmqqERQojLikNf+E0JjltldMsoie6Scu6sppP23ls4JSpHqHwyKOOT1dfzwuwqbO5MlDt6atZ7E9vRRyBNbe9bl3lhTNgIIXQICBvCRghxE4QNAFwXcgTIXDLcO2XqzyOrcZ6cuXJubd7rCYJHy9zdUtdHVl9Ng7qzYz3ubN67NVPuvdV0HqAl21HLvFfKvH1J3++Zhc3HH3988sorr5zGsZ/sz6Oex7Tt77777mld3n777Uutx1U6JoQgbAgbIQRhAwDAUV8Yr84wwma1GSV07Cf786jnsWx7SJI7d+6cxocffnjp9XnuuedO9wtpIwgbwuYihc2TTz552vaEwD72GwiHbiPPo8yz3ECIujz88MOXXpcXX3zR+YiwAQCAsDn2k31cQJ7lIv6xxx477QwcssxDxTPPPPONC9K8eL+I9cdFed03KZBi/R988IGLTUHYEDaEzQHlSoj5+GwV9McibKL9j3NC/x6iXhfxvcQ5p+4D5yPCBgAAwuYKCJtDjKbpwuYYIqZARd1CKI06LhdxcT7aN1P1EoKwIWxuurA5lPA5xu3MES3xs4+4uQiZNLpZ4XxE2AAAcCOEzRtvvPHlaIq4KMrO+kgMvPrqq6fL5fJx8Ta6uxV3CGMKTSwTEaNF4rNR9tSds1y+X6xG/eL1WC7KiOXyojHLrxeVWUZczMV6o665XbXMuMiLbYy7dLktozKntj3K7rllsq7xM+obZcZ6Ytl4belFTL9zGD+j3Hx9VMdt303UNes9EkT53mjf1LrnyJ9YnwtOQdgQNocWNtFW1/PANmGzdFRknJdiuUOPyNhlymqse2r5bFt3ETa57UvrEMvtuv11KlQ9j8Y5YJuwyfqNzv37SLslI1EFYQMAwLUQNnERlUOc82Is71Z1YRMXW/X1+D2Xj059FwO1zLzQigjZ0gVHvpcyot/FyztrtZyUFb2euUyta5YbP/Oibk7YjGRVXXdsU63raMpSXETmMnUfj2TJ1MVxvWuYwqZuf+6D/t3E+up3Uy/+c7mpoeXx3W0TNvmdHePIJCEIm6srbFL0r8rTFlPCd5ER7VZ21mtE29XlQLRrtR3u54O+bG3f43N1vdk+R51SWOT5ok9fqstGu1nLje2skmU1eNLk1JSo2L4c8dK3qcuefC/OIf18v1Ry5HdSJX09H2XkuqN+/XvM76Zu8+h8VPdbbM9v/dZvTe6bPGZyWe0dYQMAwLUSNnnBlaNXsjNeLyq7iKhypl40VrkwGoFR57/nqJS82I3XUwjExVcul5/PdedFbnw+L/qm6lkviPvFY362XkzPTQdLQRHbXutZy8wOQl1/7Ie+rtWC6UyjoedzU9Wmvpt8vSaIjPr00Tu5HaOh7iMpU0dgnfWuqRCEDWFTO95VpnQpMBp1k8tGVFE9KjfauDh/xLJVUvcRI3n+iOX7+aiONuk3EKaETb1pEGXWc2xuUz93ZRkjYVPPPXmzoZY5EkGZwD6WraJryWibLLuWm6NJsw4198627ybPG7HuUR6aFDmxzzOvT71B0gVPLn/ZT1MUhA0AAAcVNlMXbPXiqF+wjS6I+gVXXpj10TQpT+bu2uUdzngv5UitTxcEU8Jm9BSLehG4i7DJi8/R3cheZq1rH6LehdW2od+j5UbCZu5idZQkMjsl8bmpoe5zwqau87rnlBCEDWFzMcKmjk7s01e73KgipJ6/4nPZfub5I9u7ProxRy1mu15HaoxG1OT5p/7dy5wSNnk+zO2qo4NqGSMx1cusAmpqxFE991axMzr/jm4MbBvxOZcQeeq7mbqZU89HfUTt0rx6nhhF2AAAcO2EzdyIjzqlqV8s5d2tGl0K1AvszIkyml+/tNOfF3BdAM0Jm5Fo6HPdlwqbOdHSH3Wd6x9d3PaOxD4JNkffWb17PPXddCnWpxL0dW0TNjclCaggbAibixE2U2K8jujro1FGsiEFQLZd9Vw31/bmeW/U5tUcObW8fk6YEzaj3Gz95sISYdMFx2gbRmX2bV86tXXuXDASNnPn66l15vkoRwpF9GuGOWEzt05B2AAAcKWFzeiir8uc+vdc9CHbdYh2ypt6IbpaOK1m7rGmU8JmtOxcfoG5MlcTI2b6XeFtwmip5Mj9tkTY1M7M0u+m36UdXeQuFTa7JFIWgrAhbLYJmyUjBXPZzJFWI28E1Lar5yCLNq+3r3MSaOnNjjlhs6ScJcJmTk7EiJapMvt5dlsbv0SGjM63KV+Wfjc9j9pqh9Glu26LIGwAALhywmaUqK9fSFYpEBdSebexx0hoxPI1D0Cdv75aOId+H2Ezurjsw6Z3FTYjgdLzzRxC2MyNPFrNjLAJebL0u+kJK/t3sFTYyBkgCBvC5hDCZk5U97ZzTthkVPES55yaTL3Km2wblz5t6FDCpkrzfYTNqJ6jJzxuE0ZLhc2SmyBLv5u58+PUNQFhQ9gAAHCjhE29WOx33kY5bObufC19lGhekOeojKlRGrFsPhZ7X2Ezd0G4q7DJz40eY93LnBtmvlTY7Dslaulol5q3ZmoKlylRgrAhbC5S2MyN2usio06h2fV4SHmT56Mu27c9yW9fYTM1wmVfYTM1deu8hM3SETZ5M2CX6Uk1cf5qYuQvYUPYAABwo4RNvaPZHxc6eqx3Xkz3i7BMdJhPZMpEjqMLp55keCrJZE+GuI+wWbUpTPXpUz0XQU9QPFVmr+co+ePcheOuwmau41K3LfdX72j076YPPe93q0eJI0cXzlPJjIUgbAibfYXN1AiXOs2n57AZtU/RxvW2f9ROZb6XHGWaf4/KjLpFO5ll7SNspnLY1HPFEmHT6z3K31O34azCZt8cNqPE//27qd9v5q2ZymW0ksOGsAEA4KYJmzotpj4adPRY73rhmY/hjovDlDv1QikvvKPMWCanRa3alKgqD+JnLJvyoS63r7BZlcej5t/1Qrauvz6VajXIE1PrGeX1x6ouubhdKmzyont08Vn37eipITlqJsoYSbbRnek6+inrVrc5lq3yqD5NxQWnIGwIm0MImxQY0a5kexPtUZ0qk+1TbfOivasiPc9JfSRnP3+MOvn1CYf9/DNKYryLsMmn8uXrua5ar3yttre9zH7eSgESnxmVeVZhk0JlJGBGo31q/fp3M/ou+pMW6/moTo3qI3Q9JYqwAQDg2gubvFtXL4jzUZyji7x6kVmXnxrZsWqJb1P09IvBnpw4LuDq1Kt9hE1cwNWRQqN19yda5UXnaNujnn3bV4NRN4cQNnNCpHZUah2X1K8+laQPz09JVN/L1/r2zAklIQgbwmYfYVMfyb0q02Pi52hEXxXxsUz9bD0ndeFek9+u2mjFej7oNy9GsmipsIn6Z1m1ne7nj/qExlzn6PzX61nLjG2rZZ5V2FRZ0kfH9GTBfWrw6Lup60vR0uswOh/VMvuxkNu/LR+eIGwAALhywqZ2+OvFTn2M6UgaxHtLLo5iuanHevf1TyUuzvWN3uv17Be3+dltT6KaK3NUl6ky47WpfZPbuKQu+yb1nftu5vZx3e65+sV7pkMJwoawObSwqe1QtH/1aU5xDonXRsnRQ3qEpIjl4/fo7Pc2LNq8OCdkuxrLhywYtWFZZpQXy8Xn+nqnpv1mPesIoRQSUUZsTybjnWo/Y5ksPz7Ty6zteW57SpHRTY2puub05SV5gPp05i5Xch21jvW7yRs79bvJ6U851WxqP9RzYE5Nq8dCnVKlvSNsAAC4tsLmusTcaJyrFDksvE4NO6b9uy0xpxCEDWGzj7C5TnFdkuEe6/lo20MBBGEDAABhQ9jcGDlS72Qe20W7EIQNYUPY3CxZn1OnPB2KsAEAgLAhbC4lMtfCvo9Tv651EYKwIWwIm5t7DqijfrZNuRaEDQAAhM0RDd2ey9NyVbfnspMpZm4eeWsEYUPYEDY3U9gcU76Yubw6grABAICwEUIIwoawIWyEEIQNAACEjRBCEDaEjRCCsAEAgLARQgjChrAhbIQQhA0AAISNEEIQNoSNEIKwcUkPACBshBCCsCFsCBshBGEDAABhI4QQhA1hI4TQPhM2AADCxgWyEIKwIWwIGyEEYQMAAGEjhBCEDWFD2AghCBsAALYIGwC4iRA2hI0QgrABAICwAQDChrDZImwAoLTPhA0AgLABAMKGsCFsABA2AAAQNgBA2BA2hA0AwgYAAMIGAAgbwgYAYQMAAGEDAIQNYUPYACBsAAC4LsLmnXfeOXnttddOPvvss3NfV6wn4piI7Y46xX64yuv/6KOPTsuJn+fB559//o3vL1578803v6z/Ze7LrF/UB4QNYUPYACBsAAC48sLmqaeeipP0uXX0K7GeiGMitjvqFPvhKq8/ZEWUc15C7Nlnn/1G+XnsZP0ve19mfUgbwoawuTrC5rzPQZfdLl1Wm31R6z/v7y/a8yg/zkG17g899NCX7f1l7sv333//dN2PP/44YQMAAGFzeD799NPTC60YoZC88MILZxYro4unWM9FiCHC5nzq2C9I84I5RtTkcXSR+7Jvb4zwiTpFXMSIMRA2hA1hQ9ic3/c31abnjYJ4PUcJX+S+7KNZRzc0CBsAAAibcyM65mcRNtF5vyoXL4TNPCHyHn300dOy43vt33Gt90Xuy6ljLO/GHlvnDIQNYQPCZr+y66jJ0XnmIvdlrr9PD84bGPU8SdgAAHCNhE2c8F9++eXTi5DoIMfP+LuOfImLlnz9/2/vbGItq9LzvO26BWqq1AKSa4Hidl+Z7rYZFAP3pEl1kQmKLeK4ke3uuPAgaqPEsYUYkVlUCQryIMoEgZQow6AiSqxERAmTEJIMkCVGEZBRhILEgLKRFUsOMwY35721X/j4+Nb+uefnnp/nkT7de/fZZ++11zl3772e/a21Mn4t37AoXVfLlDWj1/PNhPep5YrczSXuS0+4tB89TVIZ9dNjl8TtWfj4ODymiX7PDWmPPaLluuHRT5U1l1PbcDl1TFpH29fPZW4UW5JB+3G5dDzaj/YbP49Yfx7PxccYPzsvV3nz+C5x/9qn9uP9tW78XGdaT5+BP+PqhjUeh+tL68+9qdf7qs/Yn5mWteqy9d2usmDid8zHN+c7FoXjRY1LBAibfRE2+t+dmxmZMzbHlo9lWOh9Y43gqeu15O+c97pO1i1MXK45x1GdU1vL8/59XGOfUT7+MWHje4u5tOS7l8d6G6tLf4enZF76u9Sqh2r/vtfqdrhrFMIGAAAQNgPCRjcIzmLwBd9Pa/TTNzvxKU5sdDvbID/d8U2MG7b+vRqHxDesbuxWwsbbcCM9bjs2pr1ebkx3aQwbHU/cXyxjPkYfS1zfdaGoJNZ5hY3TsF2muM8oLmL9OSU6HoOWuXtZLGu8ofP+9b74mcfPIN44xs/UqeKxXnIXoVi3sWz5OFr4PfFmfI6wiXWZ6yd+t7NsOe93LN5QTz1GQNggbL7eKI//f/GcFs9H8X8+jivic5Llclyu/8u4jarBr//nfD3Q33l8qinrtUSy1onl8nkn78Pl0/Uo1onWzceyrLDRtlTHsVzeT5YNXegSlM+xWjfXjbZb3R/op69f3qaW5ePSNnXuzdfd6vPz92fKcYxde2KZ8/fR5W8Jm3i9jOWoHlrE60qss0pOxYiv+/q1bV2/ETYAAABLChs36n2DG2/cujTYnm8+ddPhdau0YTdadQPim5PYeI5ZL/EGw9vvUpco3/jkxrhvcCq5km+e8na9Xsw80TZdH7GRHm+8YnaGj/O8T7Wqm3nXe2zw66bRdVc1NnzjnOswLo/bcPnjuvGzijfh/lyjgHEGj8L11ZJxcVn1/Rl60uh1p9Rbtcxl8KDEXi8ur76zsQy+CZ7yHRsrNyBsEDbjwsbndf3vuUEc5XB1PvH/s85H8e8o/7289dAgP4DwrHPxvW7AT11vqAuNzzf6OzbWowCO53hfF+IxnneQ8+ocFh9MeDBdlysL6HiN0TpROPihTz62qg7iZ6VtxO3G+5G4PIqaSlx4O/E4vJ8pD1f82eZzuLNFoyT0DIm5LuN1UeuqHPEzjuWN32N/l2IWZyUIvf+c/bnMAySEDQAAwJYKG99A5CdP8SapuinQDUkrbbiVpqz1YzeiqcLGXZFyRkTVZWaqsKmyhfK6Lle8uZxSR8sIGy3L3XBaYszL8g2ab/ZaGTmV3MmfVZZkQ+Oz+CYyyxntJ4uZXIa53cWmCpv42eRji/LJ9Tz2HcuzhHQDKfDe9nmffgPC5lCFjc4L+QGC/59zxl08f8XzibfRNcYfidInX4Oqa4q7nMRsuqnrVeeqShzlhwi5fFoW68PHGM9LywibmEEbrz0+j2bJ3hXZklFixeXxXFxJq/jZxQcxXh4fjFR1ED+/1nHMEelDWZJDosvL4vUlX3v8QKZ6sJCviVXGTPUgZMo1E2EDAACwo8ImPyXMUXVJyV1HqllxqhumiqnCJt6Q+IlZTKWeMgBg3m5XdOPKxzd2c75sGvLQuCt+cueouh3FdPkpUsSyzdsYqu/cJ74SF2M3ke6PX32nxgZorKZTnSNsxr5LrRvqZb5j+5CajrBB2FyksKnE9JTzV9VVqiUf4jkhX4MsAaouk5VYGltv6LxUdfvJr7l8+doTr92ryrAZy3iK57PW+bMruhRX22g9BInZKS7bkKRolaPC9yVj6w7VzRRh42vnUHbolM8t18NYXcTvD8IGAABgT4RNvHkYinyD4xuS6sZ6zo3kVGGj5bl/d4y5wmbspilPkzl0A7fMLBWVeMhjAnRFn/mxfbeW5+OIY9iM3VhWN49DdV6N0dA6jrk3zXOETau7WvXdi+MFnec7torvBCBsEDZ3/289iLrD/59T/sdb57UxYRPlgrctUVRJmSnrtc5LVdkqAdGS8tWxLCts4uD11fW2EjZTswtbmUxVNkh+bSgrs3qQkScTGLufmSJK5giboWOrpKGv+x7sfso1fyy7E2EDAACwh8LGMxlU0br56opU6XhDMtYlZKqwiTcx1cCFc4XNWBnzTdGmhE0sV06Br27UViVshp4EumHh91b947Pgik/6chp7dYM91KCo9jdH2LRS4PMT31V8xxA2CBuEzXLCJo6DkoVNnAVvrAvI0PIxYWNxkRvPVTetsfVyGZ2Z0+q2krPzhs4lqxQ2efIBjwcUx3+ZI2zGzolDGZu5++1QHeTr4tBxTBU2Q/ubI2xaA8/n60y8n/L3fiyrFmEDAABwIMImCoKpU1/GmymnqOebrlZXKu1DN6z5RmVI2MTGf37CVg0eO7dLVHVTlm9QNyVshtLsuzVm2HRF97V80zz01DDfWLaOI37fxoSNs7jOO4ZNNU5NVQ5Pt7qK71j+/pxnOllA2ByysBlrkI6dB1YhbHI3E58Lu4FBflvrzRXJXSPDZt3CpjXuT2sssFUJm6EMGwuPoW5yeTyg+BnksXGmCpvc9W6usBka7y1nIMdy5fsohA3CBgAAEDZfkSs5k8FTY8blcfYE3WjEgSBjyrZTiltTqOa+6ZWw8ftajem4bkyJbz3dat2o5/UqiXHRwibehMbPY5XCJtZtnCXM9WOB4mlbq/oaEzbxKedYg2zZQYfjdzvvy+Vz9lDrOxZvrqvvWGvAz129aUbYIGwuWthUsxPGRndrWu9VCRuPu9V6UOHzwNT15ojk6oHFpoRNNVZXPO+vS9hU6+aMTf+d71FiXeZ7ivz9qQYonlo3c4XNlLHhLOz8fRnKVGYMG4QNAAAcuLDJUzvrxiam48abJC+rZt6Igw/HGwdP7embrtjVp7oZjTdc+SbMNypRtsTpTbXdfBPnBni+kcnH7b77XTHLxSbHsPHx6GecCtRP/eI05KvsEtU1ph+NjYq4XPWTp1aNM450octc3Gb8HgzNFDXUbW2qsIk36Z7iNZa5mj53me9Y1ZUMEDYIm+nCJp6T4ixzQ7NErVLY5AHnW1kXU9eryhiz9uL039VDhIvIsPExxWmk83V/lcJGx+vrTBz7zHUTHwDEzKV8zm5l2OTjGJuhcGjA+ynCJl4r47HFcdKq7sP+3POxxXLEh2FzslIRNgAAADssbHyhz4Pz6e8oa4ZSmH2TmhvQeZu68ajkTFyWB0LONzr55qbqm+7yxDJVN5LVcXcD061uQthkYaLyVeJqlcLG/fxz/VZdieKYBlGEdMWgw/k4fKMclw9huZMH3JwqbHJDJEukvM38HfONcvUdi8viPv3dq8beAYQNwmZY2MRGqf9XWyJ9HcJGksDnN52n4gC8MQti6npVGeN7fQ6NxxvPu5sSNlGQ+5hctpgV4vPaqoRNPPZ4XcjnzzxuXv5uVAIkH0fMZhk6Pw9NAT5V2OR7i3icrXHdYnn13ngPFTO04nbjPnf52oOwAQAAhM2IsIk3krpJqMbe8GvVIL2ehrq6qRx639BrQ+U7D63yxe0OjWPgsU6WPYaq3lr1PTYGSmvfreX5OPL+/ffYdOx5vaH3tY5vCq0BOqt6G6rLse92VUfnQWWYOqU9IGwQNqeDDxEsQRQSNTkzQv/LudtubOC3lmfBGjNdfM7y8m5kBqix9Vpl1PkhZilamudzdlW+1rHMPbfqvbkLqPZnEeKMQgt4lc/H0dp3a3k+jrh/fdaS49pv9aAglk+v6/ug9fW+1nE4MzIfhwRHPI4Wlij5YUG1v2pZLIe25bJo/9V12Q84/B1yHWj/rrv4MMT1HPdp2bOLY6chbAAAAGEzUdgAbBtDA05uG3nsBUDYIGzOJ2wALhJnFVVTxm8jQwMdI2wAAAAQNtCjhvrUGOtHD18+fdTTydxVYNtw14dducFH2CBsEDYAuy/gd+UaibABAABA2Fw4XRorZSh29UkYTw+/TjUgKiBsEDYIm03gbldTg4cF08/reQDkbWSXslARNgAAAAgb2FOGxhjahhv7ZcbqAYQNwgYQNtvHts+81BrnDWEDAACAsAEAQNggbBA2AICwAQAAQNgAACBsEDYAgLABAABA2AAAIGwQNggbAEDYAAAAIGwAABA2CBsAQNggbAAAAGEDAICwQdggbAAAYQMAAICwAQBA2BygsBH3LOJBrkcAkM7Pf7iInyziiUV8T+eJ/nwBAACAsAEAQNggbDYkbB7gegQA6fz8Dxbx24v44SK+q/MEwgYAABA2AAAIG4TN5ri8iPu5HgFAOj8/u4inF/H4Ih7ReaI/XwAAAByOsPnkk08IgiAOLhA2W8PRIr6pz4MgCMKxOC/8ziJ+bRHfX8S3dZ7ozxcAAAAIG4IgCIQNwmYDXFrElUU8vIhHu7tP0391ET9axG8t4sfd3XEsCILY//hx/3//o/488Hh/Xni4P09c4nYeAAAQNgRBEAgbhM1mr00an+JbfePsVxbxg0VcX8SN7u6gowRB7H/c6P/vf9CfBx7tzwsPdMwQBQAACBuCIAiEDcJm46ibw319o0xP0tX9QWNWfKe7O9jo9wiCOIj4bv9//0h/Hni4Py/c19EdCgAAEDYEQRAIG4TNhVyfjvrPQN0eNFbF/X1D7cH+8yEIYv/jwf7//v7+PHClPy8cdWTXAAAAwoYgCAJhg7C50OvUpb5xptlg7unjXoIgDiL8P3+5Pw9cQtQAAADCJsUbb7xx+uqrr55++OGHK20gvf3222fbfffdd8/+1vb1t/a3zw3DfNwXFR9//PFZORQ02AmEDcJmx65fBEHsfwAAACBsxoTN008/rYbMygWDRIG2a2Gg7etv7W9fG4WSUsfHx2exagF2nnj22We/8hkQBMIGYQMAAAAAADsibD744IMzmaKMjHUKG21f+9H+1t04uyhJYfn12muvfbHspZdeOlu2if1LFMXjVp1rmfa/iXonCIQNwgYAAAAAAFYkbNYVWdhsKpzJs+n9StJ0RQaRJc4msnuq426ViyAQNggbAAAAAADYYmGjDBA15p2BoTFm9Ld+aplev3Hjxln3mtb4M5IFL7zwwumjjz569tPjuESBoG1pu9pea9wX7UNRZYNoH5IPet37UXliZpDLqv1qHR9HzCbS+7Tcr8dsmChZFB53J9bPUHaL9uuuUD5eLbMwydJE+45lUfnjflQvWq4yV2MP+bWx43Y59n38IIJA2AAAAAAAwN4ImzyGjUWLxIglhCSAxYOWx/dLGPi1uJ4FwtgYNvH93p9+RpEiKeNt6zVv2/uMwsbrZXGhn35PVdYofqJk8e9DY/xIrHg7UQ61hI32FY9Bv8fjs7SJ68X68Fg5LtfQcccsm/zZEQTCBmEDAAAAAAA7JmzcwHfGiIVLF7r3eFkcO0Xre7DbMWHj8V0kJbz/uB+JkFim+F7tx5IiZqVUXbHiWC4SRD4mSQ1vIwqRKJC0fGxAZkunKlsn11ksY5YzsT6i+PG6LrfrN2YrDXVBc3cpbYPGO4GwQdgAAAAAAMCOC5s805HlRhYpVebGFGFTCRdvVzLCZdLr2mdez4IjdhmqxIWzayphUY3x4rJXXZHmzrRVCRvLI9dj9Vo81ljGKptnyphBrc+UIBA2CBsAAAAAANghYZOFQFzXUiD/XcmdlrBRRkklM1rhWaYkL7RNjy2TZUslLqoMnZwlFMvhv6fOrORuS1OFTZRZOVxveVuxe1VLcg0Jm3VN304QCBuEDQAAAAAAbFDYVHIjD17rv6tMkSxzsrDx35UYqqRKHHMmx5iwcZelSmZU4miOSIrrV1Ojd41uZGORy+quUV0j8wdhQxAIGwAAAAAA2GNh0xocuFrXf1djt4wNOhwFxFhjqxqQN3aJmpphU4mOqhxzhU2eIWpI2ERBJNGleqkibyuOCxTHs0HYEATCBgAAAAAAEDZfWbca/HbOGDZdY1wVrSuZoeVRqORpqS2FxoSNx4Cpsnk8vk2cbWqusJk7hk2XBlUeizhuTTXmzhRhM9RtiyAQNggbAAAAAADYI2FjeSDZEaVLnEJ7SNi0xsBxRo220xI2sWtRJWxiNo1nSeqKsV/cXSquf15hUwmYrugu5fVzxo/WkQSLM1nFKbxd9py9NNaVbazbFkEgbBA2AAAAAACwR8Imdu+RVFCmjdexZBgSNlG6SNJIAPn9MRvGgqLrs3m8TuwmpPeqPFHwaN8uq8WMRYlecxm7lOUzV9hYllSZRrGsuS583BJRKr+PM4qcSmrFY7TEiWJH5Yhyy+vHLCKCQNggbAAAAAAAYE+FjUWBBEGUHxYiY8LG2TjxvZYbMRMmDzpsOZRFTCVnvH/JnLg8CpOcdTNX2AwJkShXujT4cB5IWccVp0gf6spVzeYVp2SP9eyxfiqhRBAIG4QNAAAAAABsobCRUJA8cFcZT59dTWmd162m3fZrEjlx8Nyh7Y5tO6+zTMPN5agG9o0yZe5+nB0zdVyaKLzG6nuZcup4W1OFEwTCBmEDAAAAAABbKmyI1YQzaZQls03jxJBdQxAIGwAAAAAAQNgcdAyNZXMR4W5oyrBhsGECYYOwAQAAAAAAhM3BxrZMn01XKIJA2AAAAAAAAMKGSGPStMad2aSwGRoziCAQNggbAAAAAABA2BAEQSBsEDYAAAAAAAgbhA1BEATCBgAAAAAAEDYEQRAIG4QNAAAAAAAgbAiCIBA2AAAAAACAsCEIgkDYIGwAAAAAAABhQxAEgbBB2AAAAAAAIGwIgiAQNggbAAAAAABA2BAEQSBsEDYAAAAAAAcqbAAADhGEDQAAAAAAIGwAABA2CBsAAAAAAEDYAAAgbAAAAAAAAGEDAICwQdgAAAAAAADCBgAAYYOwAQAAAABA2AAAIGwQNgAAAAAAcDHC5p133jm9ffv26WeffbbVDS6X86OPPrrwsqiuVJY333xza+rl008/pVUOgLABAAAAAIB9ETY3b95UQ2YrRMgQLqfkxEXz/PPPn5VlG4SNPjeV5cknn6RVDoCwAQAAAAAAhM36kJRRmSLKIFEZLzoT6P333z8rm+otlm1TdVjVjQXSNsgsAIQNwgYAAAAAAPZU2LhM28bnn39++tBDD52VLXZBUqbNpuqwqptWuQAAYQMAAAAAAHsmbJRJoswNdbVR6Hcta4kMZXe88sorZ9trZXro/VrH28xjr+h3vd/yQb87k8Xb1pgtuUuQy/nYY4+drZO7Knm72obLqr/1Hq2rZVN48cUXv5bJou1ov13fLUl/x2Maq0ft28eZZUt8TcfZqhuheulS5g8AIGwAAAAAAGCPhI3FhEKCwJJAodeyDPHrcT1tN3ZfchaKxUZc1xLGYiVuwwKiGsPG3YMcFiddo8uSpUkuq36fgrcfxUqUKFnYxPJpHe8716PXy+PQuKuTfg7VjeWOX5sqoAAQNggbAAAAAADYEWHjQWwlGCQS1Ph3VorFhEWMfnpZXNeiwQIidtmJYsiiQutHLB6qclrYaN9RfrhMEkPeV8zGibLIZdB7LGFa2UNZ+mj9KdIrli+WQ/upui/l4/PnoP1FAVPVTRY8OQsJAGGDsAEAAAAAgB0XNm7050yaSiq4G04WLpIVWub1JBy0/SwSYlZIzMaZImwse6ouQNVrlTyJ2UTqLnLz9roAADIxSURBVDXEUJejSti4DFU9al/5tSi/JHIsknJXtSFh430y+DAAwgYAAAAAAPZM2PjvasrqLEIsHqYKAokIZZhofYcFRBQTU4RNJT2G5ErX6C40JH6mypBK2Fh8eUyfGPG1PMZPF7prVRJpirCp6gQAYYOwAQAAAACAHRY2Vdellgjxe8e6E0mSWLC0Yq6wqca0Me5OFLfREh3rEjZxvJlWVPuMY+xUDAmbaspxAEDYAAAAAADAHggb/12Ng5LlhjNFxjJsLHq6lP0Ru0TNFTZD+47jv1y0sNHxankVeVaoKJpa+xoSNswUBYCwAQAAAACAPRc2lSzI470MdcFRl6o8Lo6yR2KXJGeEnEfYnHcMm2WFTdVNqRI2zijK4/sMZSF53BqJl2pgYsawAUDYcHkDAAAAADhQYeMsjSxXNCiuu+s4+yZmssRBg70NZ7hUwibOJtUSNnGbQ7MoxfW03Uo6jQmbMbHi/Q0NOhz35zqQeInls8zS/qoxbyyEPA16nuq7qpssiRA2AAgbAAAAAADYM2ETp+D21N7OrOmKaa3jWC0SBgq/32PbxEwaCQivo/D79TNn5HjA3pYUiftWGePU4/o5ZTrsqVkpnqa7GlsmCiptJ2cruXxaz1Ills/1k6fw9vtjVk9VN3n8m7ExhQAQNggbAAAAAADYMWGTs2m6NEhunmUpZrREqZNlQpQ+XkfdfSxCupC9EpdZslTCJmfpRCk0tSvRnG5ErpNqQOZYB3E687HyRUGWtxun+vZrVd2MCSUAhA3CBgAAAAAAdkzYSBxIBmQRYwngAXKr17O4qQbSrdapuvOM4XJW7/V2h7bt11vHOKVMzqSZOi5NVb5KeA3V29BxV92hqjF2ABA2CBuYdN0kiEMLAAAA2GZhA/OYOpX5JvH4OrkbGAAgbKC8Pl5axNEiLi/inj7uJYgDDH//L/f/E5cQOQAAAAibnUUZL9smRxi7BgBhA5Oui0f9Z39lEd9cxP2LeGARD/bfC4I4tHiw/x+4v/+fuNL/jxwhbgAAABA2O8nUmaUOrSwACBuEzRajBuh9+i4QBPH1WPx/fHsRD/cC577+fwYAAAAQNrtHa0yci8j4mTLGEADCBmHDNbF7QN+FB/7WPyIIIkR/jvz+In55Ed/qpc0unicBAAAQNgAACBuEzQ6hcTnU1eNhhA1BNIXNry7i8V7aPNz/z1zi9AEAAICwAQBA2CBs1oW6dmh8jm8jbAiiKWx+p5c2v7KIX+j/Z+gWBQAAgLABAEDYIGzWhmbA0aCqjyBsCKIpbH5vET9axA8W8Yv9/8xlTh8AAAAIGwAAhA3CZl1o2mKNyfFdhA1BNIXN7y/iNxdxfRHf6f9n7uH0AQAAgLABAEDYIGzWKWw0ffH3EDYE0RQ2f7CI317EDcnN/n8GYQMAAICwAQBA2CBs1n89RNgQRFPY/OEifryIJyQ3d/hcCQAAgLABAEDYIGwQNgSBsAEAAACEDQAAwuYw0Uw1t7q70wwjbAgCYQMAALB/wuaTTz4hCII4uEDY7DwSNW8tQp/jc928qYYRNgSBsAEAAEDYEARB7KGwOe7uzpqCsLl4nlrEnT6un+d6iLDZnvjOzT86/eP/8d5Z/MN/8Z+oE4QNAAAAwoYgCAJhM7kRcnz58uU/7+5mdly0tEHY3EXZNc/1n4nq4wRhMy0q/tf/uXP6L//jn5z+vX/2704f++k/32h5tD8jaYM4QdgAAAAgbAiCIBA2UxohZ7Lm5s2bpy+++OLpFkgbhE36fPr60Odyq2t3k0LYDAibjOTNX/3btzZSnr/2my+eiSLFr73wrxAnCBsAAACEDUEQBMJmtBFyJmskaswWSBuETY0+j/e6u92knkLYTBM2f/Tafz0TJeqKpCybnHWj7kqIDIQNwgYAAABhQxAEsU3C5muyxty+ffsipc1zCJsmR73QOu3lzQnCZljY5O5P+vtP/+9fDnZRkuDRcq3nrlTX//CVL16X/PGYNFXGTHxd7xsbw0YZOBJLfl37r0SSMoK87Vg2pBPCBgAAAGEzM1599dWzWOc+3n333bN96OeuH8tFxXnq8IMPPjh7z2uvvbYVx/DGG2+clefDDz9c2z60be1D+9qV7+02/O+//fbbZ7/rO7NlwqYpa8w777xzUdLm9V7aQJsr3ZfdpF7u/0bYTBA2eUwZEYXIf/+fHzb/Jyxn9NNo/SxVIpIxrTFstK7kTIWEjN4bpU7OEKrKRiBsAAAAEDYTor+RXnvDUPtYt0zZxLFcZON6bh3euHHj7D1qjMflH3/88dl2nn766dPj4+Mv6k1/v/DCC2sTKtq+9rNOAaJt+1iq11UXL7300hd18+ijj54+++yzZ1IrH/emvrfb8L/velN96PuxJcJmVNZkaXP16tUnNnhe34aBj3eFk+7LblK/i7CZJmwUMcvG62R5ouwVZbREiSLJUkkZbzfKHL1/aNDhuD9tW39HKRPXVQZO7uoVl+l9CBmEDQAAAMKmCDXOc+NTDbVNZL4gbDZbh15fMiILiyhp1ECX3NBPL9Pry2blaL/5s1D2hr5r6xQCQ8JGMioeYxZW+j1Km0MSNgqJLP2tn1sgbCbLmixt7r333l/fwDn9uK+/Yy5vs1A3qT9dxAeLuIGwGRc2kiHmJ//4X39N4sQuUDHrxutKxlTZLdXylrCJ23W3ppjlIwnjclSCSOu2ykwgbAAAABA2fVeRi2p8Imw2W4cSIxYQUY7oO2BBoQyTnFGidS1auiUzYSyALqLrWCVsLGtUrtztR387+0f14jo7NGGj4/bntm6JOyJsjr/xjW/8xRxZk6XN0dHRP1nzOf2pvv42gaTQ9V527EM8u4j/0tfff/vpT3+KsJkobJTZkt/nMWcUMevFY9BUmTQx80YiZWha75ylM3Q8kjlTyubjIBA2AAAAOyls1HDSU25nPrS6qngdNTiVEaHGttZXAzM21HMXEL3HY3zo9yobwWNaaL96vWrAaR/ar9bZRNcSbdtdebTPnKnhBqjL5frT71VWh8d48Xrapo47r+t61nHFetby3PiPn4m25brRz9b4ILGs+lltd04dWrrkcVy8vNVdKK+n70wcf8bfm3xc8fOO8sP7csZGrJtq3Bm/3sru0b79XVZUY65UwkbbqwRW/i7reLSO6y3WubarffuYW0JjShldR1pPx+71Xb+xjHHd2JVN6w99r/1/6+9UVaeV4NRn6/PEBQmb46tXr/7leWTNBqWNxmG508uHdXGt+3Lsl9N+f6/vSfzxIv53f1x/grCZ1yUqS5EWMUMmbiNLHHVXGhI2cd1q4OOhMXdaIGwQNgAAADsrbNSAi91TcleV2PhzwzhmRTiDInZrcUOzEjZVo80N3Lz/LAtywzyWITYklxU22lbcfjyW2HD2666PXHexTPo91pWFVld0I/K+/TN2o+nSGDFexxkdWjeunyVKPi6XOYqDOXWo44riqmqgj2VPOBsrrhu7WOXvmo/XsiDWZRQ21Rg2+j1+DvEzqL5DFklx3by9LGxc5rG6a4lGvd/7G/p/mFtGrRO/y/G91bpRusbPYKjLW/x+xeyhoYw0v3+dg0M3hM3PLStrNiRtJB3eWtO1QrMrvdx/Nre6u5k1V/boWkiXqBnCRl2HIu6OFNE6em+OOCNTHENG68e/434rYROXxWycKRk2Vbly2QiEDQAAwE4JGzf0Y1aLfkZpUDX2nemgiI3qse4dudFmWaNGoYVB7EpjCVQ9iY/7Po9sGBs8V9t2IzI2jn2MUXzEsrueYplczxYNlg1uDMfGaqxnZy8pqm201q3GlFF5KhHndWPdTq1DbzNn0bibVDexm5KPwzIq1re/m/G44rqWDHlfWdjErluxrvwdcl3F758/l7jvWP+VsPF7586AFI8tZhLFAXqr7mZTy2iJkusjbqO1bhS7UQZ5Wf5e+38oZtq0vg/+Xq9zdrFC2Pz81atX/98qZM2apY27Qq1j7Bpl1SiTJk+DvQ+cdAw6PEvYSGq0pvWekpnTEj+SNd5uHgC4NYZNa+BiTyuu8Jg5lWAiEDYAAAB7I2y6RhZEzJxww7OSOLmRmhvcY8Km1Z3GXZ+83API5nJa+MQyLSNs4nFXXXfiNNFeLzc0W41mHUvOIvDxx23ETKaq+01suMexUMaESZYiQzM8Ta3D1noxU2OOsPF2orxodaFyJs1UYePvSvxcovjzvvUd0HtyPVV1moVNzBY677hB3UAWiv8XtZ+5Zaz+z11H/v7FdfP/pMVKrveqO1MlWFvH5uNe5+DDSdj80qplzZqkzTN9na2jK9S1ftuaJvxoj659TOs9UdhIpEiAKKopu1sZM1rXmStxnBgLFEc11XbumtQSNnEcHe1P49rk7k8WOXFdjZmj5RJG1WxXBMIGAABgp4TNWBaEG4lu5MXuN1Mb3GPCxk/op2YjWHxoux7zZpXCZmya5upYpoxrEkWAy+7xQXJZcyN6SCZ53aqxm9ftQvZGLIPCwmbs88vhmX6WFTa5G1GV9ZM/X9fvVGEzN5PD4wLFehoTNnMzi6bMtDU0RfmcMubuTNXnHOsyd3HL5YvZcfn75O9F9f1ryZ0p/3MrEDaPr0vWrFDaKJtGXaCUHfLUmqTGnV7W7AtHQXDljCGETSFsWkh0xNmdFJIgUYAMjV/jiNN+V9kyQ8JG600dk2ZsHJs4Zg6BsAEAANgpYTP0hLwSB5VcyE/f5wiboWyWqvEcxyrJsSphU2XHzJ0lqhI2eayPHJWwqcZ+aQmbKeu29l2VY2odVt2/VtklqmrE525YU4XNUF0NzV5VxdBn3aVuRquYmSuXfdkyThE2Y+tGOTSlHHP+X9YkbH565cqVz9Ypa5aQNpIo13qJove+3q1vLJnXe6mxL5k117svuz89NXY9RNi0pYvkhrJZqvdKosRsFndxar2nmsFpaNDg/LrenzN/9LcyefL+qnVVNkmj1vEQCBsAAICdETatRrUFSW7wDjUo52bYTG3cxsZp7AZSjaGyjLCpulitQth0YdDWeKzVALXrFjY6Rncvy5HH7Bmrw6pL19xBh+OAzHnQ4SHJcF5hk+VS6zuQxV3V3an6rKfuR8ddjSU0RdjEMsbsqqllXKWw8Vg3rdgGYfMbv/EbZ/vYhKzJ0ubSpUv/oWtPOf16LxpOg6hZ52xQHhPnZA+uc8fdl92fbg0IKITNikOiJWfLrCvcHWqqeNlk2RA2AAAAsFZhU41TM9Tgbo2tEuVONUXxkETIUig2PLUtl6trzJRTTR29ii5RVfcRlSdKjakNUHf5qOrZomITwsbdz6Z0CVp2DJuxMY+qbVSDHlefg7vbWKZMFTZ+X9V9zBJrqNyWg2MyZKg715DsmiNsWmWMIuc8wmbKWDNe19/r6jOaIzhbA1fvi7D52Z/92Q+69pTTyqi53q1nUOGKt3q5scscdV/NRDqZcz08RGEDX04xTiBsAAAAdkbYRGGSG4lxRiGPZeFGohqiMUukmm2mlSGRG21xXIw4ZkbsYhUzMHI2QZyieVWzROVsjyy3zits8uxMUeRUMz/NETZVNkdetxrvJ8qDODPRXGFTSZD4vdC285goWTDEzKnY3SZ/DhZPPubWuCu5HmMXwPj9zcLAIiWOv5OnevfnWMmQWEYtz8etvy2PYsbVeYTNecu4CmETs3ny4MeeaS5+L6du90C7RG1KdOx6do0yhO70cf0810OEDcKGQNgAAADsjLCJWQOenjpPFZ0bjRYaaoTHwUVjgzDKFK3jhltutMX1PHipG8xRFlk2uEyxUe3Gsfaj7S3bAMxTSksqWGxFsdLN6OLhMuqYtD3/7ePQ33msoDnCpjrWqq670DVL79E+q2yNqXU4Ng5SFDJax7MxqR7jmERZ+MQMG5dNy6KgiyIkTsXuMlf16PX0fu1T5ciCLv5P6HWvo4hZZu7yU8kQLY9ldfnzuDPn7RK1ijIuK2xi1pK37wHBK+nZ2u7ULmQHNOjwujjpP4NdHLtG4/m81Z1vZiu6RBEEwgYAAGA3hY0bf9WAuLnrTGxYWThE0TMmPlqNNjVa8/61fnxqXw067Kyc2HiNM+Us88Q+NkTd6I4ZKHOFTa4zC4NYr97WuoTN0ODNOcNpTh36s2uNQ6R9xmOMoTqpjjPuP77XoilnrcQsm6F61Pvy8VsUDg06rPfoOKL00vaHZIjWrY5b+9Nnn4972UGHz1PGZYVNa/Dh6pxQbTdmrlUZWAc+rfeq0dg47+3oNU2C5lZ3voGYETYEgbABAADYXWETG9Z5oNChRqMahmMDylYyo/Ue73+o4eZ9Tlln7iw9c8vbes3Td1fjAmnZ2BTmQ/WQ9+l1q2MdKrvraOz1KXU4NDbM3O9YSw64Tuc06ofqccrxeZ9T1pn6mQ5ta6hMrWNZtozVPlufz1id5YGrp3wf58zKtkJho/Piz69a2my5rBEv93HQ10OEDUEgbAAAAHZS2EydenmupCH2O+KYLav6bmxiXBPiYsOZQLl724aEjc6PP3f16tW/XIW02QFZI9Y9AxXChiAQNgAAAICwmR4eU2Nq0JA+fyZSN2FmJIQNkc8nU2YtW5Ow0XnyeFlpsyOyBmGDsCEIhA0AAADCZjsbhVODhvT5w12jViFZEDb7HR6Mep1TeU8UNuL4G9/4xl+cR9rskKxB2CBsCAJhAwAAsM/CZsoYM8Rhd42aMj7N1O4yqxqHiNi+2PS5ZETYnEmby5cv//kcaWNZc++99/76jlwXEDYIG4JA2AAAAOyrsCEIgtjFmCBsZkkby5qrV68+sUPXBYQNwoYgEDYAAAAIG4IgiJ0TNpOkjWXNIq7v2HUBYYOwIQiEDQAAAMKGIAhiJ4XNoLS5ffv2rsoahA3ChiAQNgAAAAgbgiCInRY2pbTR7zssaxA2CBuCQNgAAAAgbAiCIHZe2HwhbW7evLkPsgZhE4QNQRBfD4QNAAAAwoYgCGJXhM0X0mYPZA3CBmFDEAgbAAAAhA1BEMTeCJszabMHsgZhE4QNAHwVhA0AAADChiAIYheFzb6AsEHYACBsAAAAtlXYAAAccGMEYYOwQdgAIGwAAAAQNgAACBuEDcIGAGEDAAAACBsAAIQNwgZhA4CwAQAAQNgAACBsEDYIm93jo48+OottKcfnn3/Oh4KwAQAAQNgAACBsEDYIm8Pl9u3b+n84feWVV7amLM8//zwnKoQNAAAAwgYAAGFzkI2Q9xA2CJtPP/30TJA89thjX2S1aNnNmzfPfq6bd95552xfkSeffPKsTNuQ8YOwQdgAAADshLDRjZyefCnWiW7QtI+LvlH77LPPzsrx5ptvbt2N1LrrKH7WpKUDwmYvOVKDeBEnCJvDFjaWI++///5XJMqmhIlkjfaVr3FZIgHCBgAAAGEzQn+Dv9abBKdDr1sMTb2J1I1rLFcM3Uy++OKLZ+ts4knkJutIqfHdlqTIAyBsVs5Jfx47Qtj8/cFzrc+zOsdL4OvvofN9XE8SpBIOumbodT0YUHj9OHaLlsf1WnJd+3A5tX5r3aFrSex+pGW+/un6lvevfbpcPsb4uh92tIS/X/uzP/uzs58PPfTQF9ezeE3jGoSwAQAAQNhsobDRzZ5uWufcdK4a3TzrOGOatm9sJWm03E8lY+imdxNPAzdRRzoOHWtHWjogbPYRdYV6i+vhsLDxud0ZJzF0Dcjn++q6UMl1CxFLiRhaJgmSl0ueZDHU2p8fNIzhc3wUUNX2fA3QTwuWGFoWrxPx+CpBpHI7iyZHvM5524CwAQAAQNhsibC5aHSTqBtERRQirawWP031TaxuRC9SNq0S0tIBYbO3aMDhW1wPpwkbCxOdEyVDfL6P3Ygs+nW+dKaMM0gU8RxqoeEHA7qOxCxOra/l2n68vsRri2WL5Ye7ynobY1mfLSGi7VgEad8+FoXL4brQPvSgwvv0McZ1LXLi/vQ+rROlTTVLFQ8NEDYAAAAImwa6KXU2iVObW8JGN3Vaz+vrqVq8WdTfvvms0A2fb1o9AGF+QhjHVfE+Wunoupl0ebTtOePQ+OYzv2esG5L26xvUPICi61Pbdh2pjLGOfFytsvqYVIetOvJn4W3pPZVo0Y2zPxOXp7XfVn0AIGx2lqf6c/kx18NpwiZnt/h6EJf7OpjlQu5eG5flmZAsSvRzaBvOwKlkelW21jW+db3y/uKxVJmn8fqUrxO5jN5mvpYMPQhyBtJFd5FG2CBsAAAAtkbY6AbNT7V8s+UbyHxjpZuwuFy/e30/RYs3XdU0nX7C5qd8lRiJqd9+Wun9RekR09b9hLJ1w92iepI5RdjEY8k3n7Ecqp9Yv74Bd9nzjXp8MulyVWXJn4X34ae98SY6prTHslUp/r7prsoFgLDZOa4s4k53mLNDnVvY5IcNPi9W8sLnbGeMWGbE83VLXnjdVlcib2NIynjWp1bZplzTKmEzJO9b8sfXfl+bqjINCZttGdMOYYOwAQAA2Bph4xu1OKVnTAGPN1a+mYpyRg3+eJOWhUMWAvkGtbpBi2nYfr/3ISGRU7DjTWwUUK0MnyxcKjkx9caxS+novpGNdZTrLqebZ1nkJ5sWXlVZfEOsdfz+mB7venNdxDqKQqw6viiLABA2O426Qr3F9XCesMnXLV8rooDQOr6edSPj2FRCJJ6z87Uqn/OH9tMV48GsQthEud+KLGTiWGi5W9gUYZOvfYCwAQAAOHhh0zX6v8fGfxYplQjxa95ONXVotV6+iaxujH0j6O5a8cauki1Tb/q876q71VRhk5+ctp6kVmnuVVp5rLs8Y5XLMtRdTcfsWT7imDQtWVW91mpcACBsdoYrvaw55K5Q5xY2rfNlvC7FzEkPHKz1nJkyR9i0lmdh42tgK9YhbIb2mbvpeky4Lg1ePFXYDHXbAoQNAADAwQmbVpeemAIeX+vCk8McebBAS5OY2VF1t5mT+l3dfFY3k/Hm9rw3sOcVNjGjJZfL63qbVX1UA0Pmsoyl5+ebX5Wp+sxanz3CBhA2O43GrFE3qPeQNesRNu6GVF0jquzFZYVNvN6dl/N2iZravThux9fgasydji5RCBsAAACYJ2yqLJUsc1pTcnaNNHBngUTxUGWU5Bu06unk0I1ht0SKeDXWwNwbxy49SZxTR1HwuPtRJbqWlVpz62ioXgAQNluJxMz17susmucWccSlcD3CxtI8d/uJ2Y8xc3NZYRPHa8t45sKxLqxDmacuX2sWrKo+VKZqZkXXka8j+TqFsEHYAAAAwExhk6f5rIRNvBF16ncV8QbO8sU3gU6Vjje4+QbNY9W0ZoUycWDjVlnGMkSWzbCJT1l9TP7b06OO1ZGPwxLLT2er8W9yd7CpGURxGtgpdcRMUbCnjZF/uogXFvH7i/i7i/jd7u5gvLsazwVBo7jT/33CJXC9wiaOQabzqweHz4Pk+1y/rLCJcl8/3RUpjtlWzSJYXa8qARNli69R8Ri9XMviQPYut7et5T7m+P4oglxe7TMfd6srNSBsAAAADlLYDA0OXHWZ6RozaLTwU0E9YfMTyfx0r/UksZIRliCe8rtbcjajZYRNnKUpljV3exrDN7q+ua9uqFvj/FSiTfXnG+6hLm9TspfoEgV71hhR/OdF/PtF/NtF/JtecOxySNxc6+6OWQMbEjZRtsfwzHtxfBu9dxXCJg4Wn2PZWRGjXMnXmrg8RhRELleW/FUmUuxu3aVZKFv3I4CwAQAAOEhhE2/ghqaMzg35fHOo9SVitDzfCPqJo9Ojx2bDiGO4xJvYKCm0vygj8j51I6myjj1xHEoRHxI2OoZYP1VaeDVltuqgqiNvK2fbDJUlP+HMN7z5CWmuCzciqjpyeRA2sGeNkUOcJQpmCJvW4L3OoKkG2dV5WxHPl3Gab52H9T4PBp/Pw0PL8znYGS4eqy13SxpjqLur95nLE8uv48z7dN20MjK1PB+Ll8X3MEMUwgYAAABhUxCfEqoBr79jWncUNlGSOC1bN1lu4FdP+eJ0pFVGSCUj4nv0u8pUpX3HcWycxePjydNqnzdFXK9pP45YL/o938BHSaKfvsl3N6OhWa1aAqqqo5gBpXK1Poe8nm/2WzN+xawrAIQNHJKw2Xfi9WmO6Fk3cXapbSoXwoZzJQAAwIULG2eMRPlh2VE13GOf+bh+66lYlDxTp8/WTWWUNhYn+f1ar5WWPrXblo9laFrzavs63taNZSt1Xe+pUr2jJKm6grWyffKxO5OpJXxyfVbZNe6SxlNOQNgAwmb/mDrL4CaZOtkAIGwAAAAOUthE0RDFxdDAvU75HstiidtpyYo8EG8UMqvYxxCWGVlQxJR2x5RytLYxpe5b9TBUR2OfU7Xe0BNMBn0EhA0gbPYbP6DZhoHlLZCWGY8OEDYAAAAHIWwOFcZsGZZXAAgbQNjsD2PjzmwSj2cz94EIIGwAAAAQNgdCHMvmUGeniNOyMkMHIGwAYQOAsOFcCQAAgLDZquySapydQ8Ap8nSFAoQNIGwAEDacKwEAABA2W4PHyznEblGHfOyAsLlgrizi+iJOuGwhbAAQNgAAAAgbAACEzcU3Qq51X529DRA2AAgbAAAAhA0AAMLmgs/Tt3pR8/IinuKyhbABQNgAAAAgbAAAEDYX3wh5qxc2V7hkIWwAEDYAAAAIGwAAhM35GyGSK890d8ed0e/X+r8jJ/0yZc481697VLzurlDPFNu43r/39f61a0VZrvevHYVtnoTXtVyZO7f6suj142I7Pp6u389z/XtOBurhOOy/lR10NPE4Yr0+k+oXYQOAsAEAANhL7lnEg/1F9wluUgHgwBsjboQ82J8fz8NJL1le7yOPP/Nc99VxaRzvBVnyTGMdS463Gq+/nMSP938ryR8LlTuN7WTBctqve6tR7kx1jG8lwXI8cBy30nE81VjvtCGYEDYACBsAAIC9EDYPLOK7i/ghN6kAcOCNkR/258MHViBs7vThDJr42mlYdqUXLZX8qAYbvhW2fxy2+14SMl0SRs6gsTR5L4iUoyCK7hQi5DTs0xk718M2TgpZ8173ZRbMc+H9R6lscf/XwjajNLqT6uwoSJy3ktxZibD55JNPCIIIgbABAADYPJcXcf8iHlnE4wgbADhwYfN4fz68vz8/LiNsqkyVlxvLu4b8yMLmStfOLDkupM/rDRF0PSzPssPZPbeKcjzVkEfPjUgml+X1vpzHYb2jRv29l46rOuZr/foIG4JA2AAAAOwdusn95iK+vYjvI2wA4MCFzff78+E3l5AAUdjkMVbuBPFxksJy5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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_26A56A9B' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_71940743' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_CF66BA0A' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_531DA023' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = randi(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_58FDA251' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>"</span><span style=' font-family: monospace;'>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_A4473BF5' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, NaN(n_rois, 100), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_16ABF2B0' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_F9A25D9C' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="8B629195" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
+</div></div></div></div></div></div><h3  class = 'S12' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_3CE571F5' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_A752E725' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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yoQ0/CEYNHTjf8Flzk/v0HjpvLvMxBOudFURj+OgNiA0wpiNfRfHaODsIaD8g3vx638WxAbUjmY5IUQEHvqEKv2U5Nwvf2P9jc7VqLtje2+n5ww3pcI3NTWq93Vtkgnbw9HRgbGyF99mtvUuRYef5rLlvsd/97Lpd+746XVHzk0yzGT83KYjX1iW3baILT3EKuLOW6AMF3M8de9q2ok/KasAtTVGHm6sa83AHHjK78qDZXNgTXS0PaxUID4jfLKN7Y3lr6/GkM/7mhEVG4vn8IEBLfeQHnjp/JWoKo09mHCGUJALBC7DxDrI2XudJBzIENXbI/fxme14d62CxQ9Hd9XbbTyq/oB7x+Un9rvqW24nDeebvQVub9UP+OAG/2MjrECUIfVFtxqhFTHnFfJUR+fnS0I7T3E5oBu3Tz+xBhPoXFTZBirgsKzRzJDW8tzqxtQQxsVgE4dhtcx5DK0IFY3fLV/dXNHudQoKnSgeortQWwV4I8QAmJPGWKrWft5tM77iDyCl50tDoMV3Gb4q9LwPs37pFY5ev1ttSKB0qj297JlKK/6o/g+f+71fZXjB6GDgtgMXr2LOW6I2F9DPnoydBBrLWnVmgXpwz35JlU+blNutJzmGMR6I5Xz0zFmcPYGrJVXD2J7ZUQIAbGnBLEezjW1r6qkNBR+4LDqoOn9XG6f8/7yDlftuLygY2Xr9QXVcffguAJU7zfVZ7X64NZI5lQgR2ivIHbKTdWKB6oArnWjtLZnmHNQ7Vk1+UzS0M1UiPWhoZ5l+fBOdeNPgVh/okYIAbGnCLG9h/oqjCwgNNr5+K7qm9Tm9trZVrue09BnzW1wE1COhbj1+olcDoWnqT6yKbQh9yvej1VzR8YgttdfIXTQEOuNhO9fweKqEOvxt/Fdy3qTu3pD9T1PrFZVaJkrGpo8+WAZiK1CGBBCQCwQW0NstL15glaYQ1yG2CrdMYjNn3s2FWKnfFdN+Kose39bIRJALDppiPVhl2q5qXVDrN+Iyy73sWw4wRyvqDesHh87F2IJJ0AIiAVixyFW8aRynuQYU/dgbgJiV1lHdY4n1vvcqUP7Hgfb8tQCsejkITavqaqbpQqCXxZifSZoDoKPPLVe7BSIraA0TyTLjV8OU9CszmoFhgzeeZmTapkvTxeIRQiIBWKnQaz6qtakJ4+rXSfE9lbFmQqZy8bEVhPBqjw9DrbVHxETi04SYt3jqhvAVzbwZU3ihlkVYn1fbQ8I9LUDp7xzunozikDYj0XH7/GtepGDllwJE9z6CgmetoayquPQhDHfn3X5EAJigdj5EOsr3ahdzmu95qWqVoFYD3Fzh4RCynJfUKlaSjLn5w4UX0KsNYFLgKrwOd+Wl4F0D3bl3GF1AnTUEFtN7Iqbw6FVaa0DYv2Gc4ubbyr09SCxtd5eNcEr8lTDE8ereqhWdVAjqu98W67rFmQjhIBYILbdV/nIWbSjvgasg5pm568KsSHv//SinFaYXaVqndjc7yjdqu+I/L0MgmkfufT+xX/v/ZfXla9eIFCv5nYgtJcQG2DamiBVTWRS4zEWL6N9Mpy1tuuVeq3VBlSWubC3ysSp1uQxlbVVBzrG3soJ3qARSoAQEAvE/iJUrHpxgL8iXdKry32kS+21Xv+ttFrpakLY1O0+CqmlrKaMCjp4V2ucR7l99QHfrmP3FzW4N1grNFQhAlrDPL9x0ifCTXH6ILSXEHsK0hPu2MzRbcqfnMdgFyEExJ4CxJ6CFFKwT/2RO3yqV9ciBMQCjAfRkCEExAKxQOzpOTAUprDK6gsIAbEbUu+VsqdcFoSAWCAWiD3tPqD1Jk2EgNg90pQXJGxDikNmMhdCQCwQe5rqrVSwTXmcLmEECIhFCCEgFogFYhFCQCxCCAGxQCxCCIhFCCEgFogFYhFCQCxCCAGxQCwQixACYhFCCIgFYhFCQCwQixCikQRigViEEBCLEEJALBALxCKEgFiEEAJigViEEBDbhNiPP/4YwzDs5AyIBWIRQkAshmEYEAvErgyxXJcYhln7DMRiGIYBsUAshmFALIZhGBALxAKxGIYBsRiGYUAsEEt/hGEYEIthGAbEArEYhgGxbh999NHtV1555cL2vQI2Uc59OvZ33333oixvv/32TstxSNcEhgGxQCyGYSfsiV387ML2vQI2Uc59OfYAx/Pz8wv74IMPdl6e559//qJeAFkMiAVitwmxTz/99EXbEw/1++5UWXcbuYk0l7X333//oiyPPvoo/REGxK7LU7lKw3bt2rWLBnKdaa7Lnnnmmc/dpGrQtpF/NFReN4LqyD8aM25ADIgFYoHY9QFnOCvit+602CeIjf4yn4co1zbOS/Q5Xgf0R0DsUUDsOryuGWL3wSJ8IMoWjUbVmG/DC1zVTatcGAbEArGnDrHrguB9PE6VLTyg2TO7DcCuHDj0R0Dshb3xxht3vG5xoQhgKlh69dVXL/bT/i+++GL5FBRPknGxxz5h4VWM30barScs7Z9v4ChfbI/9Io3YL/JV4+agFduVRlzgkW+UVcflaeqpMp7mdCxVmq1jj7RzrKrKGn+jvJFm5BP7xrapJzY/YcbfSFfbqzKOnZsoq8pdQbO+q+rGyy4PceTHTYgBsUDsuiE22mpvu8YgNvad6o2Lvmnd4VlVv9bzKLbyV9s6B2Ijvdh/ahly3c4JI4j+wPOJPmAMYlW+Veu45cCpRiyxE4HYuLACeASs+l8A4xdMXLi+Pf7X/nFhZ1jyNHXx5ac4QZ++E6CFCSj9CczTEcDlcmofL6vSjb+60HsQWwG85x3H5GWthvvjxtI+XscVQFbnJT9dCmL9+FUH+dxEfn5uvAHRfq1hmTh3YxCrc7aPHmwMA2IPF2I1gdTbbDkmMtzFvoqLdIt9M9DF77z9VDtZgU/VL3r/pvY50guI077V0L/vG+1m7uO8nPk4Wmm6wybvn8vq6cb23E9MdUSonn3/XJ9+fuK4qvLlc1P1Rw7NYT/96U+bdaOHEl0rtHcnBrG6COXlFKD4jZbhzIHVL1QHrspTF/vqZpf3Uk9xsV2QFBek9tPvlbcgOH6vJ9lWObVv7JcbO/3WG5heKIWgLY7dy+lp6sb0/KMecl5TQgFUpw7yvTCP1rnRdg/Cj/JkL6+Ow/Nr1Y2HGvhxYxgQC8SuArH+MC14dZjMEKv+S84Jh19v87y9irYu+pVIW/t6mu5UifRjf+2n/sidDPpO/V8LYv24PE13MuS+S2lUEOt9jxww3m+7p9edOfmhYGo8qdL2dDXqqDJ4LG91btyxon7DYdXTdrhVnLA7jTL0Ku1jDzcBYhtAlC9iv2DyRVwt9ZShSBdU9roKKHtPd/LkxncCRi9PhqYWxFazJ3Vj5MZoDGJ1Q1ZP7TlNL2seMsoQPzZsUu1XQazKUO1f3dx6eIjfqQ5iP6/bHsS2AvwxDIgFYpeFWLWd0bZUE5u8vXE49P4r2jC1n+o/BKZ5FEzeWT28C3bz6FV2QPjnSNPbzRbEqj/Uvl5O7ycrWM9pyvNYeablQKrSzF7QqbP7vY+YMpGtd25UPh/ZU3+kviZ/njJPR44fQgpOCGJ7nkEPB8gXkJ6C3DIo+dOsYiyrGKCpIFQFlI9BbAVfOXZmKsT24DM3BN4Qt+B0LDa2F/9VnTMfnmqdm/ygoLqoGsMpEHsqEy0wIBaI3Q7EtpwF7knNXstqtCpDkPd1vbZX/V7V5mnFmiiLp5f7hB7EVnM9MhxOgVh3QlTzW1pp5mNvwWKLFar9Kojt9detUDSde3mUq2UlexDbyxM7coitboTek2fP8nCHD28IaP3mnDok3VtipAWx1b69eKVemi3Pao6BHYPoqeBXDXO1yuYN/NRzk5/mqxt/KsTOmayGYUAsEDsGsVNGlLSv5ly4yTnibZcPn8f31YTkHhhPdQD1IHZKOlMgNvc5rVjSsf5rrI2fAoi9UIep5yaHubX6lR7ETj0W7AghtgqGzjeXg1I0MHoqzVZBXuzvsU0eDzM1JmcZiK1uuDzkMBdiK6jM8avrgNieh7rniY0bf+q5yUH3+RxMhdhdv0kMw4DY44DYXlxjbjurSUXVJCJ/2PdJWA606o+mznJfF8RWqwDNgdiqnL00lwW/OY6hqeem4g7/XcUSQCwQ2/TEZU9oFRPbGj6Zs4iznrT0lNXy5sW+WqJqWYjtQeRciNXvqpmcOc3ezP2pELtsOMFUr6jHOLXCHwgnwIBYIHYXntieF07tjYBzzGva6ot8AnOGw7Eh6WUhtloxYRWIbYU9bApip3pi5SCZM7Sv0AZfQhKIBWJHJ3blm7harik3MPnCVLC2VgLQUlDVxZQncvmwSBVwnr2bcyA2P835qgd5ckAOWG+lmctZBdj3bqa5ENtrzP3YVF95eCmfmzwDOHs1quD81iLSzAbFgFggdhsxsT5EnmNiq/Yp2rgKGFvxmfIK9hbOj3Y0yhft6LIQ24qJ9fymQGwV91pN2l0XxC4bE1uVrzo3cX49DrZ1HRATC8Q2h8LzMh3VElt+M2pJrLhhBLx+8egpWevoKaQghxPkJVX0IoO837IQ60uV+PBRtaSLr4ZQxZ3mtWbzEidTbvipEKuGqLohvW6r2aryrurFB61z4x4M95L7On/aFvs6ULMuHwbEArHrhthqKcO8vqvaJ2/z8kt01CcpjbyCTK+d1cO5r+Hamyg2B2IFwdqucnlbrPy9vc1petvsL/HxNL2vXBVi9RDRW53AHSh+bvJLhqpRzXx+qmUg3elWOXdYneCE39ilNzV5rEoVHF4tAq39Wx7AaiHmfAHqKSzHKXnYwjIQqzdl9fKu1gVs3fRRznzslXd2HRDbg0RvILyMU8rnT/D5adjX7M2Ndz6eHmRjGBALxC67TmyegKV2sBr5ceeEXspSDUc7FEU68qpWo1r55TveplYAPRVi/QU5+aU1Vf46Hr3NMvd/uZz5RUTVSxRWGYKv1onNdesvxfG+I58bdyS1QiPco5z7I6+bDP9z30SGHclrZwVB+a0kLdjSkMqUCyb2ay2xlfNvTQ5TftV3uZzVmnpzXsk39dhbaWoJlqpu5rwecNmJU71z06vjvIxM7406hBJgQCwQu26Izeu3ykGitwhmcHEnjEYQW68391eACyrjIbxqwzzN2K9ayaAVMuevHc+gGGnoNejV69U9PEzpx29ymrme1B5rNDUfe6usCv2bElfcWtNdcKk8vIxePoGne2b10JJBPodweB+obX4tMDIIxB6V9by2h2R6ws3vqt6X+p3y+lwMA2KB2LkQe0x2LBOONDq7b/1RL5YaA2KBWIDxYBoyDANigVgg9rQcGL3JeBgQC8TugfVeKXvKZcEwIBaIBWJPtw/wCW7EwgKxR2O9+NlDPp5d36SK9SUOFgNigVgg9jQhtrdSwbYtv/IdA2IxDMOAWCAWiMUwDIjFMAwDYoFYIBbDMCAWwzAMiAViMQwDYjEMw4BYIBaIxTAMiMUwDANigVggFsMwIBbDMAyIBWIxDANiMQzDgFggFojFMAyIxTAMA2KBWCAWw7ANQixCCJ2igFggFsMwIBYhhIBYIHZliEUIIWufgViEEAJigViEEBCLEEJALBALxCKEgFiEEAJigVj6I4QQEIsQQkAsEIsQAmK3rXfeeef2a6+9dvvTTz/deF6RT9g+KY47yhT1cMj5f/jhhxfpxN9N6LPPPvvc+Yttb7755p3y77IuVb4oDwJigVggFiF0AhB7/fr1KPjG4McV+YTtk+K4o0xRD4ecfwBcpLOph4Tnnnvuc+nr2lH5d12XKg8gC8QCsYcDsZvug3bdLu2qzd5W/ps+f9GeR/rRB3nZH3744Tvt/S7r8r333rvI+8knnwRid6FPPvnk4uILT5b0wgsvrAyb1QUV+WwDloHYzZQx36RqRMLzqutom3WZjzc8wVGmsG2MLCAgFogFYoHYzZ2/Vpsu50ls12jyNusyj3pWTh4gdocKWFkFYgNoDuWEArF9xcPN448/fpF2nNd8jr3c26zL1jWmp/Z967AQEAvEIiB2ubR9dK3qZ7ZZl8o/h9bJqeP95ElBbFTCyy+/fHFiAhrib3x2D2mcSG3P0nf5JIarO7aFdzW+zxWsPGN7WB4i9rziSSjyiaeOKGP8VSykpycI1nEoRjL+z3ChWMbYHhdB/I2y5nJGGipnHFPsE+nH31VunhZ4RT4qVxxP5BP5+vnw+lN8qI7Rz522R3lzvKjnH3lGPsqvdTOozmK/OAc6x9VN7Meh+or95zZ08bvqHOucxbZWXbau7cpb6teYjm/ONeYPYbuKc0ZA7LFAbNy7c0fQ8sje2PYxT1z8bgwMpu7XeiCe81vVyaYhUuWacxxVm9ranvPXcY2do3z8YxArtpirlkNC273exupS1/CUETpdS616qPIXax1yWMFKEBuVJm+XKkFUH391ATjtO4jIK5WfAnRi1dnr/yquUTexAKCCWKUhcPG0HTC0XwaMHBMbx+P5eRnzMepYfH/VRVgF9stCrIYwVCbP02HO60/DCX4MsU2hGV5Wv8iVf/zOz7mfA7+Z/JxqmMXrJQ+ve9162fJxtKTfeAM1B2K9LnP9+LWdAXTZa8wbmanHiIBYIPbzoOL3n7dp3h75Pe9ximqT9MDt2+O+9DQqCIr7OfcH8TnHu0/Zr/VwHft4udTu5DxUvuiPvE5i33wsq0JspBV17OVSPhnAfDg9t7Gxb66bSLfig/ir/ktpxrZ8XJFmtL25363On66fKccx1vd4mfP1qPK3INb7Sy9H5cjxfsXrrAJ2N/9e/de+hU1uHGIFOrrp/WLOAc26IeNEaN/K5a6OPE6KTpgDhXtHvdKVfg4n0MWQAUUnvQLOfEHldLWfeygjTdWHg4tfjO7F03Eu+/RTNXCqd4eguJFUd1UDrMYk16Fv9zRUft/Xz5U3TDqvDqXy9IapvloPKL6tun56T6Tad0q9VdtUBk380n6+vbpmvQxqGKZcY2PlRkAsEDsOsWrX494TJPgDc9We6H6O9sg/u0NE21uOlOyU0Won/ltBzdT9esPPam/iswOMPxR7G69+wY9x2YmkVRvmzhpNWFK58kO59zGxj0OYHGH52Ko68HMVaXi6ziO+3eG1gjml48ehfKY4nHRucxuuUUV/cNLKPLkuvV+MfaMcfo69vH4d61ry0b7qoUn551HCVZxqBwuxqtT8hOIXTlVRcZJaLveWiz/29yH4qRCrYfzsOauGm6dCbOVVzvuqXH7DTamjVSA2tuUh7NbDgrbli1Y3QMtzWwFvPlf5waEX76kbKwNr5JNhNZdhbqjFVIj1c5OPzYFc9Tx2jeXZqb3hI6W9rJcEAbGnCrHRLmSniu7nPDLj7Ze3J0qjFc/oIJz7oKpP0XCtj7pM3a9qqyqYzo6VXL7Y5vWhY/R2aRWI9ZFW73vUjmbHQzWq5mDv270trkDez507p7TdnUVVHfj5ax3HHOdCbzStB//a5v1L7nvkpKqcLblPrDyrlXNoSp95tBCbnyazVcO5edi1mo1dXUSVpkKsnyQ9WfkwxJQg65xuFQKRj2+swVrVhd+L49QTnqwasvehpimgqAcQpdGr7xxjU8Hc2I2l+J7qmhoLgq+WNpkDsWPXUquRWeUaO4ZhHSAWiN0lxPaWquu1X1WYQQvIvE3IfZDAqAo3qmB7bL9eu1QNmefvVL7c93jfvS5P7Jhn3NuzVvtZheNVabQcQ+7FVNl64NYqRyVxydi+vbqZArHqO3ujiFPOW66Hsbrw6+dkINYrtGf5pOskVY3NnJtrKsTG9hwv4jYXYscupLxkRe+iXmV2ZAVjOcaoisEZy7u1PR+Hx8SO3WzVDdWr8yrmq3UccxuSORDbCvWorj2PP17mGlvHNYGAWCD25/etJqrKdH9Oucdb7doYxDpwKe2A5wpUp+zXapeqslVQ1nJUVMeyKsT6BOGqv60gduooVMvjXXkN83e90bvKuZMnbI/xzBR4nAOxvWOrHqTU72tC8ZQ+f2wU8CQhVjPoKmtdkNUwg5+kseHUqRDrJ7YKDp8LsWNlzBfKtiDWy5WHj6qLd10Q23tiVGOr31bxNhn6/YkwDwFVjU6vka3ymwOxreGj7BlYxzUGxAKxQOxqEOtxlRliffWVseHT3vYxiBXMZaCoQhzG9stllAe3NeSbR3F6bck6ITZP8FZ8sceTzoHYsTaxN7KXQ9d6dZD7xd5xTIXYXn5zILY1uTf3M85Tuu7HRl+B2AI4py5D4ReYhnfyhdgKQ4g8tDj9VIh1IMpPYtUEnbnhBNWFmm/abUFsb4hqk57YKvQjNyS9p8t8s7WOw6+3MYiVt3/ZmNgq7rUqh5Y+Wcc1lq+fQ12zD4gFYnftiZ0yzL0piM1DtGoLexOpWvvNfbhueWI3DbGtOOLW3IJ1QWzPEysI7IWY5PhiPwc51nYqxOawlbkQ25s/kkeqvVyZo4DYiRO7BJzZ46VlKny7z9qLyvdgex/ukDu+tZxJjnWpIFa/awGG7+vDSa2noFbjlferwG7XEOs3pp+PdUKs162vTqH6EVRqCZWqvsYg1p+GxzqpVSd2+bWd81L55GVuXWPe4FTXWGtSxaE2JEAsELtriK1WxXEQaS2xtS6IVRx/y3mjdmDqfnMerisnzrYgtor993Z/UxBb7ZtH9vQ5M4rXZWaKfP1Uk8Cm1s1ciJ0y10QPMbpeeiPaxMSOQGxeZkmvUdNTgF842lbN+PQJXnmNUJ8g48Pk1Q3qF2G+MHXyHEB9qZFIN1/YrXVi83ErFqiaXbnNmFhfr9WX5dDToS8Jts5wgtZSIN7Q5rVe8zInPtM1r32o3/p10FuhoBfyMRViveHScite5mopm1WusSoMAwGxQOx0iPU2yVc36a1OsE6Ibb2sJHvnpu43tvSfL8VVOVZ24YnVMeW1Xr3fXyfE+vqtPpdCdeNOEfdw5za75YnNxzG2Mk5vUvEUiPW+0o/N511UoXc67/nYvBzuIJwzennUEKuDrxZedoDtuf9142aoyGm2Fu+t1kzLE7Fy3JFOYhXr4uvNqUzVzVUdd2/pk21AbIZIrR2XQWudEKu4oVy/1TB8tah49RaR6jjUePj2ngS8eVLDVIjNjXNvUfHqGlPjUV1jvs3zPOS1+oBYIHYf1on1tii/eMQ7701ArK+nrZeZ+It2BNVT96vK6L89K16i4+3utiDWnQZn9mKYKJt7D9WurQtiWy8cyu1nnoeTr40KCvNxuNez1z73luOaCrGZLfw4W/NEvLzxW2co9+R7up7nya4Tm+M1Wq/A03fVRKje6wF7v+t91yvfMuq9vlDp9uKiWq+Nm3sMVb216nssprKVd2t7Po6cvz6PLY2W9+v9btnXMTqAtpYgy/G2vbymvt6xNUw49XxOXV4OAbFA7O2uY0VgqFdWZw+aXlVeddq97fmhM78SPe5/X1C/t/LA2H6tMurFOu6Vrd7AVZWvdSxz29b8tsHIQy9s0Kicv748yqfjaOXd2p6Pw/OPc603hfXeqqVXo+u18/G71nFoBC0fR0CfH0dL1UtuWvVWbfNy6OUPmrBV9cty+ugaUh1E/qo7dxCpnj3P6i1jJwexCO2bekH9+6Ycy4WAWCB2OYhFaJfK66Tvu3qTyYBYNFnVCyNaNhaXg37xlNp6qcY+aWxdWgTEArFALDocHYpT4lD6SCD2ADTlxRFVHCU67KfMatIJAmKBWCB2G1LIwlTDgTK9XT+E5RIPabQSiEUnq17M8j40dqvE/iIgFohFQOz+ad9n/I+9PAOIRQghIBaIBWIRQkAsQggBsUAs/RFCCIhFCCEgFohFCAGxCCEExAKxQCxCCIhFCCEgFogFYhFCQCxCCAGxB6t7F/YQ/RFCKLXPP1nYjxf2w4V9O9qJob04A2IRQjSSQOy+QOyD9EcIodQ+//2F/f7C/vrCvhXtBBCLEEJA7D7p8sIeoD9CCKX2+ZmFPb2w7y/sG9FODO3FLyD2448/xjAMOzkDYvdGlxb2lTgfGIZhskW78HcW9jsL++7Cvh7txNBeALEYhgGxQOxe6J6FXVnYIwt7dPC6/PbCfndhf3thfzDExWEYdvz2B8N9/7tDO/D9oV14ZGgn7gFiMQwDYoHYfVuhIOLdvjZ0WL+xsO8t7ImFXRsmdmAYdvx2bbjvvze0A48O7cKDd61MAMRiGAbEArF7FFLw5aGjemQYOowYuG8OEzq+jWHYSdi3hvv+G0M78MjQLnz5rlACIBbDMCAWiN0jb+ylwdNyZYh9e2DovB4a1ofEMOz47aHhvn9gaAeuDO3Cpbu8sEAshmFALBC7hzB7z9BhXR6W07l36MQwDDt+0z1/eWgH7vkcvI5B7BtvvHH7lVdeuf3BBx+stdN4++23L9J99913Lz5H+vE58jvmzjIf967so48+uihHGBCDAbFA7IHBLYZhx2/T1YLYp59+Ohr3tUNXwFOkK4iK9ONz5HesHWWA+vn5+YWt+6FgGXvmmWfuOgcYBsQCsQghdHBqQez7779/AZjhudskxEb6kU/kt+kOa1fgpgeCV1999c62F1988WLbNvIPePbjjjqPbZH/Nuodw4BYIBYhhLYGsZuyDLHbMnl8t51vgGvlaRbYbsMLXB13q1wYBsQCsQghdNAQG57CABx56iJmNT7H39gW31+7du1iaLoVzxoA9fzzz99+9NFHL/4qLtShKtKKdCO9Vhxp5BFWeQ0jjwCy+F75RHncg6yyRr6xj47Dvc7xu9iu791r6uAZpjher5+eFzTyVRiBjje2CSIzSEbeXpYov+cT9RLbo8xVLLO+GztulePY45ExDIhFCKETgtgcEyv4DFgUmAUYCcZiu/8+IErf+X6CqrGYWP+98ou/DpcBqko7vlPaytMhVvtlmIu/+k1VVodhB0/934sZDthUOg7MLYiNvPwY4n8/PoGs7+f1odhblat33O6NzecOw4BYIBYhhI4OYgU98iwKQs9saFzbPBYz9teEojGIVbxogJry93wCDr1M/tvIR+Dm3ssqjMFjQwOadUwBekrDIdGhOraPTXoTiFde3VxnXsYMrF4fDsPaV+VW/bpXuxe+oVCDSAOgwYBYIBYhhI4eYvMMewFfhsvKwzcFYisIVboBaCpTfB955v0EfT7cXsGcvLAVxFUxoyp7NYw/d4WHCmIF1KrH6js/Vi9j5fWdEoPcOqcYBsQCsQghdFQQmyHJ9xUo5c8V8LYgNjyPFeD11j2NNALotP6p8ncArWCu8uRmb7KXQ5+nzujXkP9UiHXAz6Z6y2l5aEIL/HsQu6ml1DAMiAViEUJoryC2Ar48QUifK49iBtwMsfpcwXIFmh7Dmm0MYjXcXwFeBdNz4Nr3r5YpO2uEYIxZLqvCCloeYiAWw4BYhBA6aYjtvZQg71uti5q9ky2IdSgb64CqSU8eTjDVE1vBX1WOuRCbVyboQaxDc8B/1EtlOS2PM65epgDEYhgQixBCQOxEiK0mGM2JiT1rxGnGvgF4sd0hMy8RJVAeg1jFlFZeX8XL+ioHcyF2bkzsWZq4NuVVtip/a93XMYjthTxgGBALxCKE0ElBrIAqANBB1Jez6kFsK6ZWnletVVtBrA/LVxDrXlfNzj8rYkkVauD7LwuxFZSeFaEG2j97hmOfeDDwFRR8OS2VPXu5x8JAxkIeMAyIBWIRQuikINaHxgO09OKEM1vztQexDqIBrlr8P3tNfU1Vz8OH2OO3UR6H3shbZfX1aOP/+E5lzN7guRArgKw80l7WXBc67oDzKL+O0+G2An0/RoGtw26UI7/kIXubMQyIBWIRQuhkIVbwFNDkQChIPJvwsoOALf+tgM89pnlil4A5w2kFrMo/ANe3O0Rm7+xciO1BogPnWZrglSerxXH5cmW9MIhqFQlfHq1aj7eCbAwDYoFYhBA6SIgNyAqg0jCzlrKqlpfK+1ZLYOm7gFufoNRLdyztvM8qnZnKUU2ecsCcm4+8qFPjXP0hYKy+Vymnv+2MeFgMiAViEULoaCAWW4/5m7X2Ke4ULyyGAbEIIQTEYkvHxu7CFMIRnlgmdGFALBCLEEJALLbU27u2aYQRYBgQixBCQCw2O8a1Fce6TYjtxSBjGBALxCKEEBCLYRgGxAKxCCEExGIYhgGxQCxCCAGxGIYBsUAsQggBsRiGYUAsEIsQQkAshmEYEAvEIoQQEIthGAbEIoQQEIthGAbEArEIIQTEYhiGAbFALEIITYJYhBA6RQGxCCEExCKEEBALxCKEEBCLEEJALBCLEEJALEIIAbEIIQTEIoQQEAvEIoQQEIsQQkAsEIsQQqtC7DvvvHP7tddeu/3pp5/udSekcn744Yc7L0vUVZTlzTff3Jt6+eSTTyAVhIBYhBA6HYi9fv16NO57AYc9qZwBbLvWc889d1GWfYDYOG9RlieffBJSQQiIRQghIHaXClCNMrnC0xhl3LXH+L333rsoW9Sbl21bdVjVjaB6HwAfISAWiEUIoZOFWJVp3/TZZ5/dfvjhhy/K5sP34ZHdVh1WddMqF0IIiEUIoZOD2PA4hocvhqnD4v/Y1oK78AK+/PLLF+m1PILx+9hHaeZYzvg/fi8gi//l8VTaEQOah9NVzscff/xinzzMr3QjDZU1PsdvYt/YNkUvvPDC5zyekU7kezYM6cdnP6axeoy8dZwZQP27OM5W3YSiXrKHGCEExCKE0ElBrGAtLKBJ4BQW32VA1Pe+X6TrQ//yVgr2fF+BqWDT0xCUVTGxGlqXCSbPGsP9Aslc1vh/ipS+w6aDZYZYL1/so7xzPWq/HNeqMIH426sbAa++mwrlCAGxQCxCCB0NxGqiUEBXwFUAkbyXgjXBafzVNt9X8CUo8+Fuh2XBW+zvEoxV5RTERt4OhCpTwLLycq+tA7TKEL8RmLa8zBmEY/8pDwJePi9H5FMN/efj03mI/BxKq7rJ0Ju91QgBsUAsQggdPcQKhLLHtQItDWFnCA2Ai23aLyAs0s9w5d5D99pOgVgBcDV8Xn1XAaV7nSPUoKfecH0FsSpDVY+RV/7OHwgCbgXXOcyjB7HKkwleCAGxCCF0chCrz9XyURkOBWNToSngLDyRsb9MUOawNgViKxDsAedZY6i9B8NTAbGCWD0MKEbYzb/LMcNnFupQgfUUiK3qBCEgFohFCKGjhthq2L8Fh/rt2FB8gKOgs2VzIba3bqyG4j2NFvxtCmI9frVlVZ4es1upB7HV8l8IISAWIYROyhNbxVVm4Ju6PqngN3sJPZxgLsT28vZ40l1DbBxvbK8sr0bg8N3KqwexrFCAEBCLEEInD7EVQOX40d7wdYQj5Djb8DL6cL48h8tA7LIxsatCbDXEX0GsPM85XrjnrVYcbMBoa93XM2JiEQJiEUIIiP08gMmbl4EzJh5pqFteWvd4+sQspSFPaAWxvopBC2I9zd7sfd9Pa6tmmBuD2DHYVH69iV2en+ogYDS/ZSwAP/KrYmgFyVqSLC+7VdVNBmcgFiEgFiGETg5ifTksLbPl68bmJaY89jMgKky/V6yse1wDyrRPmH6vBf0d6PzFCRUoet5RRl8GLP5OWZpqqvdSS2ZVsaoO7ZFO9mqrfLGfQNPLp/rJy2np9+79reomx9OOxSgjBMQCsQghdHQQm72uZ2kiUp7d755PB90MWA7C2ieGyn09VXk5fZvAs4LY7M11UJ46DD9nCF51Uk168zrwpcXGytdaQ1f1kL+r6mYMshECYoFYhBA6OogNmApAqt7yFGCkSUhjb4HSWrAZHqt9qqHwMamc1W+Vbi9tfd86xillaq2LOyXetVWPY/XWO+4qlGBsvVuEgFggFi2lL2DYCdp+Qyyap6nLim1T/pY1XjmLEBCLVobVexZ2aWGXF3bvYPdh2Amarv/Lwz1xz07gFohdj/T62X0CRmJhEQJi0Vrg9dLQcV9Z2FcW9sDCHlzYQwv7KoadoD003AMPDPfEleEeubRVmAVi16epKxqcWlkQAmKB2ANWdMpfjg47rhcMw+62xb3x9YU9MkDtl4d7Bog9RLVibHfhGZ4Ss4wQEAvEoq4X9r6hc/5aXC8P/o1/hGHYYEMb+t2hDf3acK/ctzVvLBCLEAJigVhU6p5hmDS8TI8CsRhWQuxvL+z7Qzv6yHDP3APEIoQQEAvE7jaU4CvDcOl3gVgMKyH27wwg+xsL+9XhnrkExCKEEBALxO5Ol4eJK99Y2PeAWAwrIfaPFva7cY8s7NeGe+YyEIsQQkAsELs73TvE+H1rYU8AsRhWQuyfLOz34h5Z2DeHe+ZeIBYhhIBYIHa3EBtLCX17YdeAWAwrIfYfLOz34x4ZHvgeAmIRQgiIBWJ3vzJBrIn5nYX9EIjFsBJif7KwP4h7ZHjg++rWVigAYhFCQCwQi4BYDANiEUIIiAVi90c3F3YViMWwI4XYjz/+GMMw7OQMiD0JvbSw28PfK0AshgGxGIZhpw6x58NsXCB2/xWe2J8t7NZQX0DsAds3r//p7X/z05/dMeoEiMUwDANip0Ps+eXLl//L4OF7Aog9KK/1rQFoHwNip1mAYtZf/Ne/vNj+9/7Jv779xE9evv0rf+vm1srz+N/9p3eVBZgEYjEMw4DYaRB7AbDXr1+//cILL9zeA5AFYufpioUYvN4JMQBiOxCb9R//0wcXHtJtlOev/t4LF/AsAyaBWAzDMCB2HGIvADbgVdoDkAViVwsxuD3U4SUgdhxi//TV/3AHHgNcs3c2vLLAHRALxGIYhu0XxH4OYKXXXnttlyD7LBC7kp5qhBgAsQXExlB+9oo6zAbI5t8H2EYaf/6fb90JQ/jxP/6Xd77/nef/xZ341n/4z/79534f++r7gOexmNgIbYj94jtBdy53TjvKFfbP/92fAeJA7Hx75ZVXLmyTebz77rsXecTfQz+WXdkydfj+++9f/ObVV1/di2N44403LsrzwQcfbCyPSDvyiLwO5brdh3v/7bffvvg/rpk9g9gmwErvvPPOrkD29QFk0fIKL+zNFGIAxE6AWE20CgCUPKwgILKl+E6/78W4BvxKAZi9mNiAUi+LK5e9FyZBmAIQO8uGxmPjnWXksWnA3Max7BI45tbhtWvXLn4TgOLbP/roo4t0nn766dvn5+d36i0+P//88xuDzEg/8tkkFEbaOpbq+6iLF1988U7dPProo7efeeaZC9DPx72t63Yf7n3VW9RHXB97ArGjAJtB9v777//hFgFsHyaXHVOIwVtDnf4fA8wCsSMQm/eRlzXDpjypFVhmUHVAzl7eFsTG7zKIupc48tAEtPD+5rKFF9gVXmZAFYj9nAWw5A45Oq9teEiB2O3WofYPQMsQ5+Aa0BLAF3+1Lb5f1Xsb+eZzEV6+uNY2CUk9iA1A92PMEB//O8ieEsSGBdzH5/i7BxA7GWAzyN53331/cwvQdT7U3zn8ufYQg78Y7CkgdhxiHU5jSD7/zj2b8X32xvrvtS1vV6hBC2IdQgNSFVogb3BAsADby6Z9WzCOAbF3DbPuqkMGYrdbhwGLgjIHxrgGBG3hicyex9hX8Lmqx1RQvIuwiwpiBbBRrjxkHp/lJY56UZ2dGsTGceu8bfrBdgRiz7/0pS/9tzkAm0H20qVL/+cWYOv2jkBP6+TeOFL7Xxf26lC/f7awHwGx8yDWvaDxv2JYfbtiWsPr6R5Tpev7KkyhBbHuzR1bKSGvrNArG3agEBudSXhD5CFrDfNqn+iEw3MWABL7R6fr8JKHT+M3ihmM/yuvlWLkIt/4vurUIo/IN/bZxrBspK1h8Mgze/TUKatcqr/4v/L+KWZU+0Wacdx5X9VzHJfXc2zPQOTnJNJS3cTfVryhlzX+VunOqUOBaI4L1fbWUHveL64Zj2fVdZOPy8+3A6HykmfP66aKY9X3LS9w5K1rOayK4awgNtKroD5fy3E8sY/qzes80o28dcwtyJtSRtVR7BfHrv1Vv15G39fDQGL/3nWt+1bXVFWn1UNfnFu1EzuC2PP777//L5cB2C2C7JWzeYv3r0OPDTGjt3Tehs/HaP9qYf/vcIx/DsRODyeQ13WKfCJYBlYH2/iut06s7ztl7dgpAmIPGGKjU/Oh3TzM6x2iYMG9Z/K0+ZCwOt8KYquOTJ1+zj8DVIYVL4N3rqtCbKTl6fuxOEzoe9VHrjsvU/zvdSXIr4bglbf++hB0jjnVPvL8xb6+fwbLfFwqs8PUnDqM43KYr6BlzMsmr73v6+EJ+VrT8QqgvC4dYquY2Pjfz4Ofg+oaElz7vjm9DLEq81jdtR6+4vfKr3c/zC1j7OPXsv+22tcfRP0c9MJF/PpyL3Nv5EK/3+QEvAbE/nerAuyWQPb1IX5z2xOfXho8sMccwkA4wQoTuxTT6lAaQ/Px28p8UpaDsH/2of0KYiNsYE48q6tVtm2tewvEbgBiBT/u/Yy/DlIVAMkjFuagMTY0mjsyAWx0lIIoH4YWGFceG897GQAbm6AUaatjdWDQMToMetlVT14m1bPgSwAmQPAO3OtZXu6wKo3WvlWMapSnejjRvl63U+tQaWZvq0IMpg7x6zgE6F7fujb9uHxfgVfOK0Oshz14XekaUl359afz4nl7/VcQq9/OnXnvx+YeZ58EVYVqTC2jwDLXh6fR2tcfdh2QtS1f17qH3CPbuh50XW9yVYsCYr+2ANj/bx0Au2GQfWqLsbBaT/XW2fhbrpjYxRJbdyZPtWJiW+YgGml6uv5GsFY4ge/vk8O0xJcv4eX7ekwsdiQQ2/KWuYdNnXEFtrnjzhAyBrGtoWiFDWi7JunkcgqCvUyrQKwfdzXs7Us2ab/c+bZAIo4le5t0/J6Ge7yroWuHGY+tHIPIDIq9lQWm1mFrP/fozYFYpeNA1wo/kMd1KsTqWvHz4g9DyjuugfhNrqeqTjPEuld52TjknrdS92LkM7eM1X2uOtL15/vme1Kwmeu9CgWoHjpbx6bj3uQErwSxv75ugN0QyN44+8UC/duAOnlfL7HEFhDrLzvwCVoVEPoKABGvGjAZ0JvT87yqNBVjOwaxHpcbMK2QhGrlA983gFaeV4dbltk6UIgd85ap41TH50PXUyFkDGLlyZnqtRIMas1J9ySvA2LHlkyqjmVKnKTDkcqueMNc1gwWPcDWvhUA5H3dy+dlCBPEjp2/bJphvirE5iH4yjucz6/qdyrEzvX4Kc7Y62kMYud6oKes8NBbLmxOGXMoQHWevS5zeEgun4+i5OtJ10V1/bWAd8o9twaI/cGVK1c+3QTArhFkzwcv4a3BE7sNsPvZALDHvhIBLztY8bWzLejLb/Vy+ZJXFfhWXtUexEZarTVic4zr2L4eg4sdGMT2PCkVTFXAlb00cyC25/WsgMJjH7OtC2IrL+rc1QkqiM2xg9kqiK1iSVsQO2XfVt5VOabWYRU6sc5wggpscgjDVIidum5sXjWhst65zkP061gRIpd91TJOgdixfR2Yp5Rjzv2yIYj9o00D7Aoge2UAq2eTl3AbujnA3bF6YBUmwWtnV4DY+C48pa34UV/iyr2krd9kuKzeANZ72UH8Ppc3YDnCCHKcbGvfKC9rxB4BxLZAQ9CYIaDXyc71xE7t8L3D9iHUKiZzFYitwhPWAbFnjbVBq0lAm4bYOEaFZmTLMcBjdViFQ8yd2OWT3vLErh54LQuxY2/X8omG/jBThQpU53pqPnHcVWzyFIj1MroXfmoZ1wmxip1t2T5A7I9+9KOLPLYBsBlk77nnnv+7s7xTtQLAtlchuH2kMbBXBu/y2EMBELtmC2jd5mSpgN2pMMpEriOC2CrutQchrVhNB95quaAeWGVQ9s440lK5WjO0q2Wc1hFOUA29Rnkc9KZ2yhourepZ8LYNiFXoxpTh9FVjYsdiqKs0qoll1XnQULUAcyrE9hbXF9j3yq0HpjFA7IVC9B4A5kBsq4wOt8tA7JTYVX91bOsczXnoa00OPBaI/eIXv/h+Z3mnZ3e8AsDrRxpGcKMROgDELrkM1SkIWD3AiV05FrKaya7YOHWc0TnntTqzR7XlScsdmcfZeQyehye4py57nXy5pHWtTpC9ghn4l4XYvCqAw2214sAciK28fnnfKn7YgcpnxM+F2AoM/bqo1tnN0OUedh+qzudBMK5jbsVx5nr08Bm/fjNEVevW5mXXdB4rQPQyxvZ83FqXOXvml4HYZcu4Doh1r2+eYKYVTvy6nJruiYYT7Eq3zo7rVba+wsJUjzYQi4DYQ4VY9y5pqai8bFPuSAV5ASY+gcM7SQfM2Ke1TqzvpwkigggHaH+Fp+/ja19qMfdVO8W8vFOAlmDfYXPO8Kivxxrp6bOvMZtjj+dAbHWsVV17WEP8Ri8+WNabPRZXndcA1ioAUY8e45wh2D2xKlts84cWh0NfFk1lrupR+8XvI88oR35o8Xsivtc+enWs8tFweQWIeT1alT/HsS4bTrCOMq4Kse7dVvqadFk9CLbSnRp+cUITu7alY3uV7Uu2wsKceGLCCTDskN/YFZ1HNekoDzt7Z5NfkFB1QBkGWx1ZdOQ5/9jfvTvVxC55b71D9xnaq3h2vHMWiOS3Rc2B2Fxngqi81usmIbY3Qa612P+UOhxbrD6/Vcstv0Ciyj+/2CK/IS57Y3v1GL/Lx6+Hp97ErvhNHIc/CGj92hYg+prBOb849/m4V53YtUwZV4XY1gSvqk2o0u29LIMltjauJ8529yrbTU1Qu7rE74BYDDtkiHXYqNZibXWkWqtybsxp6zfKv9eZKc8p+6zjDUC98ra+01JaVZxxbBtbTqxXDzlP7Vsda6/sqqOx76fUYS/WdO411gIm1ekc0OnV45TjU55T9pl6Tntp9crUOpZVy1jl2To/Y3WWJwdOuR7nrAZyoi872HTc6OtnCIjFsGOA2KnLIM0FV+y4zWNA13VtbCNOEtutyWOcQ0N47SwQC8RiGBALxA7D+Hkh9p4BF8t7rKfMyAdisbGXemwRYr8Y8aCrguyBAiwQC8RiGBB7KB3lVAMulrfW27uAWGyV9Zg3DLEXE5y+9KUv/bdlQPaAARaIBWIx7LQgdkrMKnbaYQVT4l2nDjWvK64Z2z/bdlsyArEXIHv58uX/MgdkBbD33Xff3zxQeANigVgMOx2IxTAMO0SbALGzQFYAe//99//wgOENiAViMQyIxTAMOwKInQSyAtizw39JABALxGIYEIthGHYkENsF2ddee+1YABaIBWIxDIjFMAw7MogtQTb+PyKABWKBWAwDYjEMw44QYu+A7PXr148RYIHYEYjFMOxuA2IxDMMOB2LvgOwRAiwQC8RiGBCLYRh2xBB7AbJHCLBA7AjEIoR+ISAWwzDsMCH2WAXEArEIHQ7EIoTQCTfAQCwQC8QiBMQihBAQC8QCsQgBsUAsQggBsUAsEIsQEAvEIoQQEAvEArHL6cMPP7ywzz77bKfl+OSTTy7K8emnn9KQAbEIIQTEArEXehaIBWIrvffeexfrIj/55JOUBQGxCCEExO6dXh9AFogFYu8oPK8PP/zwBTiGF1SKl3688847W/G8Rl6e93PPPXdRnnjtMwJiJ1/IccFs+qKJYYLII/7uUjFUEeV488039+7i2XQd+bne9dARQkDs1nSML3AAYldUBYwBlNuCyMgj8vL+rgXWCIjtamjktnLB7voJK578ohx60lS53B5//PGL10/GPtu8kbZRRy+//PJFHvEXISD26HU+tGvnMOx0iI22Xw/74fjQ556DwfeLv1VspxwV8V2krf1jKF2OBd/ey09pySkzp6+qhu4jT712OfrJnH+UK36nPKv+Ud9VZYkyKs34G/1s5BV5ep8X6RJWAMTuHcTGTbvroO24iXSDZnCMGyq2x42ToTaeWLfhudxGHcVxqPHYtVccISB24woP7C34dR7EytmhPiM7OTKkCf6qvqNyVMR2eRxlGlqvtnv/E/+rfNmmOidUXh+RrNIUXEa5qr7RHUJ+fBlABc1xbFH+Kh2X6oBJXkDs3kDsrhU3Q9wYYX5jtLyfcdPGDa6bKW7KY7mhAl7VGBNWgIDYo9ZLTOpaHmIFkdFmBogJ5BwW1Z5GX6FZ/gF21bC4j/xFWpGm7xt/o13Wdjkc4nMG0Ng3tue+ako8awWJkY7Sjr/uUFF9RJlje+zrx+IOEdWRANkdJzqO2N/3yw6VCrIREHvHVS+vo4ZLWhAbF1Dsp/3jxvUbMj7Hdr/BXPG0qafLyLcKGPc4TeVRSTeYyhNpz7nAFf+TfzM2hO9Pxu7B9fqMtFVHUcbcaMX2Vll1TGq0WkH1GopRHhV8RoOjc6LytPJt1QdCQOzR6LGhbX8Mfl0OYlseRd8e26r+SH1LtNd5W/QpLU9ulYb6xWjjtV/2Bqts0fZPdWJkVf2h4mQrp4c81X6M2WGkEDbfx+u4GhFUSEHV56IThdi4UPQ0pAtSN2O+eeJC9e3xv/aPC1M3jy7OPGSSn057N4fy0UWv/PwG1QWt/fwpOd8Yc548p0CsH0tuYLwcUT9evwLRXnyPN0iacJbLks+F8oi/fvNHA+bDUF62+H1ufFjOBAGxR61LQxjBTdh1eYjN/YK32ZWindW6q2rPq/C1VphBhja109quvqjVbk8ZVVUaFSBWfVAF4xXgVnCrvqsC4B7E9sqIThRidcH4khY+jOEXvj8tat+4AAWtuoH8hs4XqJ4s9QRZ3RzK2z2LykM3hc9W9JvIobzlCc43RHXjT51MlZ9+BadeR7nuouxe/gzQutHVoFVlUSMQ++j3PoyjevMg+eohoTo+B2iEgNijCyP42QCzaEmIrQCrcvpUE4TdoTDW37S2Z5irYnQr64WJZTAeK4f65DFrjfa16nEKxOJkAWKbEJYvWr8IBV0VHOY4H0FS3jfvl2+O1pOWgta1nz/RZWUIbCkPySwDsR7oX32u9pU3thXfo7rLKyWoLL1QjzhmzXLtDQ/1vus1IggBsQfrgX1paDeuwq2bh1iHtfg/+sL4neBvExAb7bnC8CrrOSZ6Q/U9T2z0V708cz/uI4i9frIHsWOhEehEILY1HO5PZf6dz0zMlme2VzEx1VB1vjl6QxStYZZcFl8OZEoaFaguC7F5eRC3PBRV1Ye82B4blcvSe2KuGqVWw9Y690AsAmKPLgb21uCBBWC3BLFy2GRnStU/rQqxvb58qpYNJ5jjFXWAV6hgdqARTgDEzobY6iLMN4V/7ln2FjqMVZ7HfHNMfTNHaymRsaGMqjFZBWLzsMicOnLo1RNyBf+rgv7cOurVC0JA7EEo1n99wryvNwkh2B7EKia02tfnnawLYj2EL+ensIax9WKnhNdVqy9UYQpaGcHz9PkwsX9rNLVXx1MdOOjEIDbPiKwg1oewNSxSmQ9XCEgVUqAnU7/g8006ddF9nzzWKsuYJ3FVT6w3VDomHyaZUkc6DoG9hlqqeNocSjHV06zlT6bWESsUoCNtgF9c2PML+5OF/W8L+18WduOILF4h+7o9oN4aPrMKwQ48sT45OfbXCkA+36QVVjcXYt35EOlrhSG9wnWK08P7+AylPtejWmIrjkvhb75Mlrb5HBAPMawmyuk41Ld7WXgpDxB7l3oTsKrhZofYKdKFHxelnqBasy91EfficgSGHjC/SoD3KhDrsT1e1tbs1TEQ1nqzVbxPK264eviI+lMjs+wQE+EE6Egb4LD/Z2H/dmH/amH/1wB5x2TPDh5Y3sS1Y4j11XPObBUdX3dVaa0DYnsvO5jquZQDo1rO0dOe8rIDh2alm/v/amkw35brWl5sXj0LxN6Rno56yzflizg/0cX+cXHG9hw4rrgX3bQZgPNN6jGhfvHmoQgHtJxnb13VDMWtCWA9iPXFrXP+DtfVygxVHflC2WcT16zVecuv//My+dNvrov4XauOVB4gFh1ZA8zqBGgliNVrX6sJUtVEptgv2vNo2/MrZDUSpv+r18m2tmvpxar91itgo6+Z+9rZXp+oFxlUoQmeZ361rq/5Xq2OoN/6sfhrbPWbnvMGnTDE+jIZeqmAr83qEOvgqOGDuOgFPdVwhT9xVhdfBWj+m/g/yqQnML/Q87qwceHreKqA8ZYXtLe4s14OIPN6if+rlzT4W1Z0c+pJtLeaQgvKe0H1Om+t85D3U+PQWmlibL1DhIBYdKoQewpqrSxEuWhDz/b1jV1xUTgQCgArmMkvR9D+reWsHHynLmUVIJjfOR155t/7GrVuem3fFLWGJ3qToiJ9X5+1guNqiCV+03qjVm/Yp+UVzscuj3cLgnN9Vk/xGgIbW54MISAWAbHHJ/X9GvXcB7VeBoGA2M9dvHk2YWtIWTGXU4YqfNiklU4FhBpmWTWPZW4OlcttbhyOpzGl7lv10KujsfNU7ddbK5CnXQTEIiD2j0/6ftknaNxHqAZi9xRiT1XEgPK0i4BYBMQCsXf3B6041m1KIXDM0QBiOQsjT3rVe5xPrQ542kVALAJi6S8RAmIP0At5quvPKSaaMAIExCIglv4SISD2gORLnnDsCAGxO9CVs5+v8XoVrgRiEQJigViEEDoEiH0srSCCgFiEgFggFiFEA7z3EHtzgNeXFvYUXAnEIgTEArEIIXQIEPvWALFXYEogFiEgFohFCKFNQmwA540hjvXKEBJwI+1zddgWHtZnh30vFd8rjOBGkcYTw29fH757rCjLE8N3lyxNj629NHh4bw5lie/Pi3R0PApxeHb4TS9O99zyb3mRL008Dq/XG6l+gViEgFiEEAJi1wCxVwfwfH2wHM/67Fn91r+fGUDeaOwj8Hur8f1LCYaV/80ExILMW410MnTeHva92Sh3VnWMbyXoPO8cx810HE819rvdgG4gFiEgFiGEjr4B/q0BUtbV8F416Ltlnlb/7rZtuzLAZwWE1YSum5b+uaX7swSpZwmi5WkVSP7M4PKSwfOtAg5vW543zIOqNK4WAPsz85Y+a7+/lMrm+T9maTpI30p1dsnA9q0EvBuF2I8//hjDsMGAWIQQ2i3EXhsa3ofWDLGVR/OlxvazBhBmiL3S8UCeFyD8egOOn7Dtl4rQAXlDczmeagD1syPgfWae6XMr6+0i/6upzOedY35s2B+IxTAgFiGETg5if7Cwby7swYXdu2aIzTGbtwwGryZ7vfCkZiB0wLtaWN6/StNB9aUijacsHCKX47wBw683AHSsjt6aeRy3ivjhrcbEAi4YtmcQi2EYdqq2aAZ/cwCnX17Y5TVD7FkjPKBnPYh9YmIaVxP8XW14hHt2a4J39WqC2AqAzxoTxaYcx6UiXMJjbDcJtEAshu07xJp+bWHfW9iPhsL8IYZh2BFatG//88L+pwGC/trC/srC7l8TEE2B2Jtppr1bL5zgsRSb2rIrIxB702Czl85ciH1iIsQ+ZR7bKcfh+T2bgPYmEIthQOzDwwzd7w0xYr+FYRh2hHZtCCH4zQFgf3WIh13XGrFXC09ma4LSmFrhBLcnAncLYm9MhM25ENs79itWjh7oT9WNYrIYEIthJwqxMZT2yMK+MRTiOxiGYUdo3x5iYK8OHtiHBsC6vKZGtxcX+lLHe6j1VC914PGSgXBeT/XSkO5jEyD2iREwfbaxOsEYxDqoX+lMXLtShD447PpxPFYcV07zfB8h9qOPPrr97rvvXti+w8gmyrlPx/7BBx9clOX999/faTkO6Zo4NIi9bxhOe3Bo1L+KYRh2hPbQ0M798tDm/dIAsOt6U9fVCdAn8Ltq0FgtF1WlcyMNpZ8PgPdWAcgtiD2z/TWkr5cTVJA8B2LzSxoeM3h/vQhpuGXQ7MeR17PVpK4rw7a53uSdeGJVF/sOI5so5z4d+7Vr1y7K8vbbb1OWI4XYLw6N572D3YdhGHaEpjbu8tDmfXHNje3YULmDYp5IdT5hGP/SWfuFCS9NDCeQx7P1soGnlgwncI9w9VKEq2m/1gSzmx3Pca6zq0Ds6vbKK69c2LK/f/HFFy9snWmu89jiHDzzzDN3tr3xxhu3n3766a3kn+smvMFRnvPz8wvPLBC7uRsYwzDs2GzTumQe1pb8dbRPdYbDe+n4a11br2A9PxtfR/XqUIYbnbxa5dCxnnfSfeqs/3pYP47HOmX1V+fe2CC8niTErmoBZNuCwjnWAsYA2m2dl8gn100F1kAsQgghhHYCsRF3GXDy6quv3om9bEFsfB/7yVvZi5EMr6H2i/8jn7F4y0g7e/lie6QRvw+L4exIr/KaKk/tq8+5nPE5vIxxjI8++uidMvY8sZGeH3uUo1VW5afPkfYc72Ueutc5irLG9qqM1bnxPFWW2Kc6r/67559//q668fpTGQ45rACIRQghhA4YYgNwBCtuATkVxAr6sgVwOSwFcAl0qrQzPAnYZOF9dGiS98/zl4cwlzO2y1MY6Xi68Z3SVZr5+xbAV/urrD7pysuaj0vwOWUyl9J2AK3yHyufn5v4qzrJAKqyCmR75db1EdcOEIsQQgihrUOsADbARt5LQWCGpPAkOogGVAUICVZ9eFnpxrYAvICn2DfyCZNHNr4TVGnfyCeDlkNVwFakL69pLqeDro7LyxkW5ZFXVWn6CgA5TQdIgXDkrzTjbwWTyj97fccAq4JEeauVtq8U4Nt1bsIEph4SEHWhcghuvR7c4+x14170CrKBWIQQQghtDWIFPtkr5yCYh5CroWjtK9Bp7aswAO2nfKqJVe79czDMIQk9j6R7h+N/lUsALPjLcZ85TcWh5nL6sXoogn6fl8USnI8tl6UHCaU5Fqus8lXnRnl6vXm9x3Y9XHi5WnWTj/tQl9wCYhFCCKEDhVhNHDor4l6rcIIKDDN05aH6AKMqbrT1u7FZ+hVQtSC22lf5CY6nQqw8mhVU5jSVf+Vx1b4VbE6tl+qctQDfAdfL7kCvY8tlGoPYqecOiEUIIYTQWiFWkFLBlg9P+/DxmAmEHJLOLAwgT27qwVfPMzsFYiuvaU5nKsS2PKuepob+p0D0GPgJLKdAbNTnlHNTTQI7S7HAy0DsoU7uAmIRQgihE4NYTfyprFoBIODOgdbz2yTEVvseCsT2hup7ntgA96nnxmOcPT52LsRW3mkgFiGEEEIbh9izmeEEyw4fa0KRp9HzOK4KsbsKJ1DdrQKxy4YTTPWKKg72bJhQd1asNEA4AUIIIYT2EmJ78OPLbmXvYAWSAW5KIzx6vuZqla7S0OfWxC6teLAMxFYz5zOMCtQ0K38dE7t68DcXYisoPStik7V/VT6dm2r/qCsP/fD8xiB26gMIEIsQQgihtUOs4MyXgpqyxFbAp14mIGjUeqQORQFQChWItPNMeQ9TqJbYyhPF5kDs2TBMHun5cTncev4BcFOW2Ipy6iUK1RJb64DY1qoNDs3xXbXElp8bXyZLEOvnK5fZ3w7Wqpsch3uor58FYhFCCKEDhtgAlTkvO+gt+O9xrT5pKFsetva8zhoTkZaB2NbLDnL87ZQXKPTKGVA5dWmqqRCr+huLVz6b8LIDXzrLz0uO7/W1fSuPvB+PHmgO+dWzQCxCCCF0wBCbYdYnALVevarXk+b9q7eBhQcv9gnYca9sL83q9az5Va4ZrFvAK2+key2rcvprZMeOPdLTsVdllXe6CqfwV+KOQVZvTVm9WjYvi6XjbZ0bnY8qTMHrQeWr6mbOUmFALEIIIYQ2CrHHZD2v7SGZJsLleF3KBcQihBBCQCwQu9emmOV9OZbwzE596xgQixBCCCEg9kQhdt+gcd+gGohFCCGEgNijsV787CEfz67fjOXxscdQr0AsQgghBMRiGBCLEEIIISAWw4BYhBBCCIgFYjEMiEUIIYSAWAwDYhFCCCEExGIYEIsQQggBsUAshgGxCCGEEBCLYUAsQgghhIBYDANiEUIIITQBYhFCvxAQixBCCAGxCAGxCCGEEAJiEQJiEUIIISAWiEUIiEUIIYSAWISAWIQQQggBsQgBsQghhBAQe6oQ+8knn9z+8MMPL/6itj777LO9qacoRxgQixBCCKGThNgXXnghoOTCrl+/Dql2FPUT9fTmm2+ebFmAWIQQQgiI3QsFCD388MMXMPvOO+9sLJ9I+5AhOWAxg/577723tWN6+eWXL0z69NNPL85bWPwPxCKEEEJA7MlAbAyLb8sD+9xzz13kdYhqAWNA5baOKfLO56kCayAWIYQQAmKbEBtexddee+0iRjKgJmAiPnuMom/vecriN7FP/r0rvpOXNMCzl67SqkDM04n/FUrw+OOP3/Wdx38qvbE4UMVohncy10P8PvKIvLx8qscpx6GyxN/YP/LJnmMvb9RRVWY/d/F9fB6LLRWAa+i+d0x+3MorLMobeeZ6qcIB9J2ON/62zpPCCjbpRQdiEUIIoSOBWIGDPHFusS2AIm8PYMygkvc5G4b2M1TpO8GM25NPPlnuW0Gme+2qvPWd9q3KVoGhx9V6PfTS8nqsIDIfh4490o1yeHkDDgWaY/WuPL3MFfQ7zGs/QWjvmNxDWp0r1V+kJQjOIKtjiTJW59w9r7rWtuWNBWIRQgihI4BYwUOAicOGhn7D+xbbBV3uiXMQDqCNNAQv8bcCOoGNvLFK18FyKsTGZ4Fc/PVZ9wFbgrsoW3znYOXSvgFkAVSKE1Ve8uiqXD6rfhmIVV4ew6vjiPqQJzi+U/04JPq5i7JH/fc8sYJEPye9Y/LtXq/KN8qewzk8TCHKr/3c06/y5tURKsgGYhFCCCEgtguxGTYFdNkrlod8AzYCngR5Fbz5EHv2bmYPqG+fCrEOhu6JjHwjvXxs8hwHcKnMgrDsDda+DmxVuZaF2BbEZS+xjtnLUcFkT1Ud9Y6p9SASkufVj1fpx/5xLAJv36c6d1PrEYhFCCGEgNjR5Y3cuzoVhHyd1rAKdFqQVg0lrwqxrVjX7HlseSl7qyCsA2IzMFegmidEeTqtczS2lFUVc1odkx5kqmNS+Emub3/4qb6fCrHhxQViEUIIISB2FGIzqLRgooLF+K3gpbIKYqeA6Tog1mG6VzZ5gnsQvG6IzXlV8ce9Ms/1Ws4pYyvWOFs1CUywnSF9CsS24BiIRQghhIDYWRCbt2cA0zD82TA8Hx5deTur4eRtQqxiMgVUXrZ9hljN3G+ZwjO2BbGalFVZTsvr1sM1pkLsFI86EIsQQggBsStDrM9e9xABX7FgVYjNICRv3RjE+iQpL1sFsR7PuQrE5qF6h/wpXuNW/awjfrQ3VH/WCSeY+iYtj4NVPG2G1anhBNtYZguIRQghhE4YYluw4kPjDk1zILaKqW1NOqvAsDVpzSdWKW3BZo5HdRiXB7SCa4F1K4Z4CsS2wF+AmNe4nQuxvaH6s2ISXmsVB3m54xx7HWh/1UEFwTrPVagBE7sQQgghtDWI9SF7LZnlHtAMQXMg1j2uCgPIS4L1wNBBWktE+Yz+DJ3+XRyXLyflx+C/zRAcxxz5qh5UB1MgNnuP/UUGFVDOBT55zSso9eOM9PLSWzq3vkyWL6floRB5xQf3hHvMbPVyBn23jdfPArEIIYTQCUOsQ91ZWgPWPYv5xQRTIDbAxyFQVr2itCqXD2/n3zugKZ3IrzqW2OZQlV8QUK3bemZLic2B2N7LDsJ7Wa3POxVifWmxalWJs2LymK9Rm60C09b14p7X/NKDHMPc8tICsQghhBAQ+7lXl2bPV2u7vzI1Dy8HsOVXpGrJLW2b+irZnLZeCBD7Vfu2yuW/j7JlGK0W3I9041jCWq+o1etyc7xo7B+/86F/1eWUsuZ9VO7ea2fneC01xF/FxSq9yC+/Vja2qU7ya2dV1lYca56QptCI/Js5k+uAWIQQQujEIRadlnwt2m28FWufywXEIoQQQkAsOiC1JqHtSgGtrUl8QCxCCCEExEJvaKfQ2JJiZLcN1UAsQgghBMSiA1PE2PbiWLcNsVqJAYhFCCGEgFggFiEgFiGEEAJiEQJiEUIIIQTEIgTEIoQQQgiIRQiIRQghhIBYhIBYhBBCCG1Z9y7soaFjvgbEIlRC7D9Y2O/HPbKwbw33zL00HwghhNBuIfbBoWN+AohFqITYP1nY78U9srBvDvcMEIsQQgjtUJcX9sDCvrGw7wGxCJUQ+0cL+924Rxb2a8M9c5nmAyGEENqdLi3sKwv7+sK+Gx02hmF32+Le+MOF/fbCfmNhvzrcM5doPhBCCKHd6Z6FXVnYIwt7dGE/GDrr8Dr97bOfT2b5MYadoP3BcA/EvfA/Luz7C/v14V65Mtw7CCGEENqRtEJBxPh9bQDZ8DbFsGnE/8VElh9i2AnateEe+N5wT/z6cI88eMbKBAghhNBeKIZFvzx0zuFlitCCiJGNyV6xasF3MOwE7dvDPfCN4Z54ZLhHvnxGKAFCCCG0F/rC0CmHdymGSSPe74Ghw46lhL6KYSdoDw33wAPDPXFluEcuneGFRQghhPYOZu8ZOumYeX3vYPdh2Amarv/Lwz1xD/CKEEIIHR7cYtipGUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIoVPU/w8W+buTamhwKAAAAABJRU5ErkJggg==" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_35FE25AF' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_26A56A9B' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_71940743' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_CF66BA0A' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_531DA023' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = randi(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_58FDA251' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_A4473BF5' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, NaN(n_rois, 100), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_16ABF2B0' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_F9A25D9C' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="BD4F90C5" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
 
     filename: 'ophys_tutorial.nwb'
         path: '/processing/ophys/Fluorescence/RoiResponseSeries/data'
         dims: [20 100]
        ndims: 2
     dataType: 'double'
-</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="31DE5435" prevent-scroll="true" data-testid="output_2" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">20×100</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:100,&quot;totalRows&quot;:20,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1058px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="28C4B862" prevent-scroll="true" data-testid="output_2" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">20×100</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:100,&quot;totalRows&quot;:20,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1058px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
@@ -166,14 +166,14 @@
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
 </div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 20×100
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    data(1:5, 1:10)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="8E7723BA" prevent-scroll="true" data-testid="output_3" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">5×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:5,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 442px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    data(1:5, 1:10)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="AA486BFE" prevent-scroll="true" data-testid="output_3" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">5×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:5,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 442px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
 </div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 5×10
    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first roi in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = plane_segmentation.image_mask.data(:,:,1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    row = plane_segmentation.getRow(1, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask(ind) = pixel_mask.weight;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span><span >    </span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >imshow(roi_mask)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="BBE1101E" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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style="width: 157px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_C8BDC905' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_872126D0' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_6F0B7108' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_1310FB5A' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h2  class = 'S4' id = 'H_8B4EF750' ></h2>
+   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first roi in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = plane_segmentation.image_mask.data(:,:,1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    row = plane_segmentation.getRow(1, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask(ind) = pixel_mask.weight;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span><span >    </span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >imshow(roi_mask)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="4AE72B27" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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style="width: 157px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_C8BDC905' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_872126D0' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_6F0B7108' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_1310FB5A' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h2  class = 'S4' id = 'H_8B4EF750' ></h2>
 <br>
 <!-- 
 ##### SOURCE BEGIN #####
@@ -248,7 +248,7 @@
 % |manufacturer|, |model_number|, |model_name|, and |serial_number| are optional, 
 % but recommended. Then create an <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html 
 % |*OpticalChannel*|> and add both of these to the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html 
-% |*ImagingPlane*|>|*.*|
+% |*ImagingPlane*|>.
 
 device = types.core.Device( ...
     'description', 'My two-photon microscope', ...
@@ -281,7 +281,7 @@
 % |*TwoPhotonSeries*|>, like <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html 
 % |*SpatialSeries*|>, inherits from <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html 
 % |*TimeSeries*|> and is similar in behavior to <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html 
-% |*OnePhotonSeries*|>|.|
+% |*OnePhotonSeries*|>.
 % 
 % 
 
@@ -451,7 +451,7 @@
 img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation);
 %% 
 % Add the |img_seg| object to the "ophys" <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html 
-% |*ProcessingModule*|> we created before, naming it "|ImageSegmentation|"|.|
+% |*ProcessingModule*|> we created before, naming it "|ImageSegmentation|".
 
 ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);
 % Storing fluorescence of ROIs over time
diff --git a/docs/source/_static/html/tutorials/read_demo.html b/docs/source/_static/html/tutorials/read_demo.html
index 2f1c4e6d0..9be65ca66 100644
--- a/docs/source/_static/html/tutorials/read_demo.html
+++ b/docs/source/_static/html/tutorials/read_demo.html
@@ -88,7 +88,7 @@
 </span></a><span>    </span><a href = "#H_505b"><span>Stimulus
 </span></a><a href = "#H_4391"><span>Quick PSTH and raster
 </span></a><a href = "#H_0768"><span>Plot results
-</span></a><a href = "#H_5128"><span>Conclusion</span></a></div></div><h2  class = 'S2' id = 'H_39af' ><span>Download the data</span></h2><div  class = 'S1'><span>First, let's download an NWB data file from the</span><span> </span><a href = "https://dandiarchive.org/"><span style=' text-decoration: underline;'>DANDI neurophysiology data archive</span></a><span>.</span></div><div  class = 'S1'><span>A NWB file represents a single session of an experiment. It contains all the data of that session and the metadata required to understand the data.</span></div><div  class = 'S1'><span>We will use data from one session of an experiment by</span><span> </span><a href = "https://www.nature.com/articles/s41597-020-0415-9"><span style=' text-decoration: underline;'>Chandravadia et al. (2020)</span></a><span>, where the authors recorded single neuron electrophysiological activity from the medial temporal lobes of human subjects while they performed a visual recognition memory task.</span></div><ol  class = 'S6'><li  class = 'S7'><span>Go to the DANDI page for this dataset:</span><span> </span><a href = "https://dandiarchive.org/dandiset/000004/draft"><span>https://dandiarchive.org/dandiset/000004/draft</span></a></li><li  class = 'S7'><span>Toward the top middle of the page, click the "Files" button.</span></li></ol><div  class = 'S8'><img class = "imageNode" src = 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44DDzaG/uI8qJ+kHdDXCJzsLmLXLrIpiRbQgNWMPoeYQ8i2fV4j0HKDuH/8QTxrlot37TJjWCH+bTsb/XrSwODKpx+VymceFUf/fpI+4UzJPvqXRrfurUIOgQkEEKhZwOFw0OV3TTTRdvl997i20qtdek1NMQ8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGh2Ak0rQ6VfKr75XMrf/rd4P/+k8bML2o0YyXyjW+6+g8V58KGSZgTtafCkPSu72V0nqXRAvvIyM8jSvXKJeFZ8K94fFotv81aRyDrJjRuVZjl1HH60ZJ18nmQOOdzYjhG9S0EAgRoFNm/eLHa7nS6/q+q8vbxEnvxyT9XZYafH9sqWC4bmSl6WcdejIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACzVigKQRUenfvkLKZ/5HKt583sgfuarSzYcvLFseAYeIcOFzS+w2R9IP6iy0zq9H2hw0nTsBfUS6uNcvEtfwb8Xy/ULxLF4m/uCxxO1BlS7aObSTj5HMl+/gzxdG6XZWlTCKAgNXlt83lciVLAuOkOCt+Q+OG94pkxXZXVPuTZUR0nzWolUzs30KcdqK5o8KjMgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0GYFkDqisNILXyl59UryfGdkoPYkPibF37SSOIUdImhFAmaGBlJ26NZnzyo7GX8BTuFEqjcBKtwZYfvOZ+DYVxn+jVbZgcxpZK484WrLPmCwZRpAvBQEE9gts27aNLr9ruxiKSr3yl493yQ9FNQdV9mmfLvmtnPLR2upR412M+RcPz5UR3TJra575CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAkxVIuoBKr1fK5s2U8v88Lr6VaxLqas/vII5DR0v6kNGSMXgkmf8Sqt/0N6aZVCsXLxDXN/PF++18o5vw7Qk9KHufgyTrzMsl+6jjRRyOhG6bjSGQbAKaoZIuv8OcFZ/xI4V3VpTIwo0Vsm6nW5wOm/Ro7ZTDC7JkQp8WojkoVxkBl08Y3YOvrCHwckh+hvxueJ50y3OG2QqLEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBpiWQLAGV/spyKXnnJan8zz/Fv2NvQhBt2WniGHq4pI04RjIPGyPODl0Ssl02khoCnu1bpOKrueL+8iPxfv25+MvcCTlwW7tWknHmZZJz0m/ElkGX9AlBZyNJJ0CGyhidEk0O/eGaMnlu0R7ZXe4LaVV7/j7p4Bw5+9CW0iLdHrKMCQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCkKNHZApa+8VEpnTDcCKZ8U/57yuBPau3WWtCOPl4xRYyXj4MFk8ou7OBswBYzMq5UrFkvlF3PE/elM8W3cGncYW26WEVg5WVqccr7Ys1rEfXtsAIFkErDubTaXy6UxgZQGCpS7/fLy4r3y1vIS8YTGVUqrDLucN7SVTOhtZLbU1JYUBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgiQpYQScFBQUJPQJfeZmUvPYvcRmDv8QVv20bObMc/fqbQZSZo8dLWpfEHmf8DoyWm7KAe8t6qZg/2wyu9C5fJlIlPimWx2bLSZf00y+RHGOwZ2XHsmnaQiBpBQoLC8Xv9wsBlTE+RVv2emSa0Q34V5sqqrV8QJs0mTwiVwZ2yjCXLd/mkrk/lslPu9xGoKVNDmybJqN7ZMnBHdKrrcsMBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgGQQSHVDp93qk9J0XpWL6w+IvLosPwc9BlOnHTZKso04QR+t28dkOrSIQAwHv7h1SPu89cX3wX4lncKUtL1syz79WWpx0ttgczhjsOU0gkLwCBFTG+dwsMgIqNbBysxFgWbUcYQRNZqfZZM7q6jd5zWA5qX+OnG9ktHRqn+EUBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgiQQSGVBZ9vE7UjbtPvEX7oyLgL1XT8kYf4ZkHX2iONp2iMs2aBSBeAp4d26X8k/elcrZr4pv9bq4bMrWqa1k/+5Wyf7FSXFpn0YRSAaBLVu2mEkRyVAZx7PhNVLrzjC6AH/F6Aq8zOgSPJpy2sCWcuGwVtGsQl0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDuAokIqHSt+0FKHr5ZvEuXx/x4bG1yJG3caZJ9/BmS1v2gmLdPgwg0loB7wxopm/mquN9/Xfy7SmK+G44B/STn2r9Ies++MW+bBhFobAG9t2khoDIBZ6K43CfPfb2nxoyUtW1ek1M+fGIHOahdWm1VmI8AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCRcIJ4Blb7SvbLvqanievtVESORVcyK0aW3a+Ag8Rx7msjA4UbEjDGDgkBzFfAbT57vF4rzw9cl/fvvYv5cqjx6vHhPu1QkO6e5CnJcjSDgcDgkPT1dMjIyJCsryxxP5G7Q5XcitX/e1qodLrn3o12yo9Qb0dbPOKSl2fV3RJWphAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJEAgXgGVZfPek7KHbhH/nvKYHYUvN0tcR50ovmMmibRuH7N2aQiBJiOwa7vYP54h6fPeFXssn1s5GVJx/nUih/2iyVCwo01LoFWrVpKXl2d2w52IPdcuv7U4E7ExtrFfoHe7dGmX7Yg4oHLdLjd0CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAsxbwFu+UvQ/fIp55n8TsOG09C8R5+u8k88gTRIysZ82p2GxGt6eUmAj4/f6YtJPUjXTsKHLwQJHJt0rFp++J57Vp4l+3vsG7bC+plOzH7hPbEZ9K9lV3iz23TYPbpIHUFbDb7aLPR4/HI5WVlVJSUiJ79+4Vl8slHTp0SEhQpe6Dz+cjoDLRl2FORuQpo32p8KKd6BPA9hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEUlrAu22zODrmp7RBMh28mZXywZvFv7ei4btlxBnahg2X9NMnS/rAwxreXpK2kBJBgElq36R3ywgszjz6JBFjcH3/lbhee1L8ixaKNDCm1P/Zp1K6ZLykX32PZIwe36SJ2PnGE9BARi1Op9Ps8rtFixZSVFQkFRUVUlxcLK1bt477zulrq3Y7TobKuFOHbqBnmzRZtCmyNwEHtkkPXZkpBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGiQQNn0h8Q5+HBJHzSSwMoGSTZsZb+7Uvb8/Q5xv/tmwxrStY3cVrbDj5LMc34vzoJeDW+PFhBo5gIacKyDZ/1qqXjhEfF/Pk9kfzxb/Y58X6W47r1BPMcb2SovmyK2tIz6tcNaKS9gZanUwMb27dvLtm3bzEyVOTk5kpaWFlcfzf7r9XrFZqTFbGCccVz3s9k1vnqHS/7wbpH46lC3G7+aePDE9qLdhFMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIDYCxeP6i619rtgHjSKwsp6kW7duNbtmLSgoqFcL7g1rZe8dF4vvp831Wj+wkhFIaT/yGMk452pxdu0ZmM0IAghEJ+DZtE4qX/i7+D79qGGBlcZmbd27SOaUx3lORncKqF2DgGar1G6/9+3bJxpQ2bZt2xpqxW6W3tu0EFAZO9OIW5r+9V55dcm+sPVPH9hSLhjWKmwdFiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAdAIaUGkVAistiegeGxJQWTZ/tpTde434K7zRbbRKbdvQoZJ58S3i7NG7yhImEUCgvgKeH1dKxdN/Ef/XX9e3if3rZTgk/aapkjFqbMPaYe2UFtCMkZqxUrNUanBlfn5+XD22bNkidrudgMq4KtfSuNdIkfvS4v1BlVUzVWpmytMPaSlnD24lDuOXFBQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJ1AcECl1SqBlZZEZI/1Dajc+8KjUvnMoyJ19OoZbi9sB/aQ9N/dJumHjAhXjWUIINAAAdeSL8U17V7xr/2p/q0YMVCO8y6T7F9fVv82WDPlBbTrbw101FLfrMiRIhYWFppVyVAZqVgc6q3d6ZYvN5TLul1us/WebdJkRPcsObBtfPt7j8Oh0CQCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJNQqCmgEprxwmstCTCP0YbUOn3uKX4z9eI56M54RsOtzQnQ9IuukEyJ5xu1DIitSgIIBBfAb9PKma/Lu6n7xcpqaz3tuxjjpHs6+8Xm5N4qHojpvCKmjHS6oo73gGV1r2NgMoUvuA4dAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFJNIFxApWVBYKUlUfOjFXQSSXCLv7Jcdt92oXi//qbmxuqaa8RO2iecLFm/vUHsObl11WY5AgjEWMBXskfKn7lffLPernd2WdvgwdLizmliy8iM8d7RXCoIaJffWiK55zTEwwrcJKCyIYqsiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJNSiCSgErrgAistCRCHyMNqPSV7ZPiG88W77IfQhuIcMrbLlcqfzdFOh01Xvz+BvQTHuH2qIYAArULuJd9LRUPXCdSuKv2SmGW2A7uIy3ueVZs2TlharEIgeoCiQyo1IyY9uq7wBwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABf9Ee8X4wS1zTH5Sy6Q9J+ftviHfbZmAiENBgyt2/P61+wZRGVsr0U8+SyvteEOk9KIKtUQUBBOItkNZ/qOQ8MUvsE08XMZ6j0Rb/ipVSesNZ4i8riXZV6iOQMAGPx0NAZcK02RACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIINEkBAiujO21+d6UU33Se+Nb8GN2KRm1bbpa0vP8Zyb3yT0ZUJd0DRw3ICgjEUUC77G5x6RTJuPcJkVbRPz/969ZL6ZSLRF8jKAgkowAZKpPxrLBPCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJCUAgRWRnBavF4pvmOyeJcui6ByaBXHoEOk9bMfSObQ0aELmEIAgaQSSD/0cGnxxHtiG9g/wYmSEQAAQABJREFU6v3yL18hpfdcKWK8VlAQSEYBuvxOxrPCPiGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA0goQWFn7qSn++x/F88XntVeoZUn6GedJm4dfFUfr9rXUYDYCCCSTgL11O8n564vi+NVvot4t/8IvpfRxIwstBYEkEtDslD6fT5xJtE/sCgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0GQErMBK33dfiGfQKHEOPlzSB40UR8f8JnMMsdzR0tmvifvtN6Jq0ua0Sda1d0mLE86Maj0qI4BAEgjY7JJ9yc1S0f1Acf/jHhGPP+Kd8r03Qyr6DpbMsb+KeB0qIhBPAb/fLw6Hg4DKeCLTNgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0fwECK0Vcq5dJ+YNTojrZtkyH5Nz3tGQa3QdTEECg6Qpkjj9d7J26SeWdl4lURN6Vt/vRu8TRs6+kHdiv6R48e95sBLxGN/QaUEmX383mlHIgCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINCYAlZgpWv6g1I2/SEpf/8N8W7b3Ji7lJBt+10Vsu/OS8Tv9kW8PVuLNGn50EsEU0YsRkUEkltAs/Nm/uU5key0yHfUeM2ouOcK8bsqI1+HmgjEScBms5ktE1AZJ2CaRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHUFEi1wMq9T90vvi07Ij7Zthbp0urhVyXj4EMjXoeKCCCQ/AJpfQZJ5tTnRYyA6YhL4U4zAD3i+lREIE4CdrtdfD4fGSrj5EuzCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJDiAqkQWFn5w2JxvWEEUEVYbGl2aWlksUvv1T/CNaiGAAJNSUC778740zQR47keafHNeFncK5dEWp16CMRFwO/3iwZVRn7lxmU3aBQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGjeAs03sNIvJQ/dLBJpT99GlEr27Y9IxoBhzfuEc3QIpLhAev+hkn7L/RJxZJrxGlL56B8NNX+Ky3H4jSmgAZVmUGVj7gTbRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEUkWguQVWls2bKb7V6yI+fRnnXS7Zo8dHXJ+KCCDQdAUyRo0Vx28uifgA/Gt/ksr570dcn4oIxFrA4XCYTZKhMtaytIcAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCIQRaBaBlT6vlE+7N8xRhi5yDDlUWp13dejMCKdsNluENamGAALJJJD9myvENmhQxLvkeuavIl5vxPWpiEAsBTwej9kcAZWxVKUtBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgQoEmHVj59TzxbS6K6EhtOemSO+VxERthKhGBUQmB5iJgPOezb3lExHgNiKhs3SGVX3wQUVUqIRBrASt43xnrhmkPAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBaAVK/20EXFAQSFEBK7DS990X4hk0SpyDD5f0QSPF0TE/aUXSPngt4n3LvPQ2ceS1jbh+1Yp+v7/qrLhN7yjzyoINlbLLeAxXNGnm+F7Z0iFnfxex4eqyDIFUFrDntpG0i24U9yP3RMTgfme6ZIweH1FdKiEQSwHt8tvn8wkBlbFUpS0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXoJuF+YVq/1WAmB5iTQZAIrt26QtJWrI6K39zlIcn7564jqNnalNTvd8sf3d0mZO7IAzk/WVcifx7chqLKxT1w9tl9RUSHFxcXSsWNHsbLS1aMZVolQIHPC6eJ57yXxr15X5xr+JUvFs+lHcXY9oM66VEAglgJeo7t5u91OQGUsUWkLAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoqECyB1baPp8d8SFmX3yzUddI59gEylNf7Ys4mFIPp6jUK7fM3pX0QZUPP/ywLFq0KOIzcMQRR8jll19erf6bb74pb7zxhjn/b3/7m7Rv375aHZ1hbS8rK0ueeuqpGuvUNXPDhg1yyy231FUtZPk111wjhx12WMi84ImtW7fKiy++KN99953s2rXLXJSZmSm9e/eWvn37ytixY6VLly7BqzAeMwGbpF9wvVTeVv26qmkTrk/eEec5V9e0iHkIxE2ADJVxo6VhBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg4QLJGljpXDw/ooOzH3SAZA07KqK6yVBp7S531LthBVUed1BWVOvmZdllRLdMaWM8xruUlJSY2Rgj3U5ZWVmNVa2sjrpQu8WtrVjbq6ysrK1KnfM9Hk9U+6wN6jq1lddff13+/e9/i2agCy56TEuWLDGHWbNmyZQpU6Rfv37BVRiPkUD6kNHi6lkg/nXr62zRt+ADEQIq63SiQmwF9HXN7/eToTK2rLSGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEVqBqYGX6hDMk45ARsd1IhK35ineKc+PmiGpnnnZxRPWSpZLHF1lX31X3V4MqX/6upOrsOqef/6ZE7hnXWnq2SauzbiwqaDbGO++8s86m8vLy6qyTyAonnniijB49us5NFhQU1Fjns88+k+eee84MlOrevbucffbZZtBkbm6umaly+fLl8vzzz4tmsLztttvkxhtvlFGjRtXYFjMbJuA85UJxP3RnnY341/0k+lpjz2tbZ10qIBArAZttfzZlZ6wapB0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB5i3g+v4rkQjiDm1pNsk6ckLzxmjg0ZW4fKLdjN83vk0DW4psde3OduDAgZFVTqJa2g13Q/b7ySefNIMpO3bsKPfff7/k5OQEjk67LR8zZozZVfiVV14p27Ztk0cffVSGDh0q6enpgXqMxEYg44hx4v77n4x0onW8iBiL3UsXScbo8bHZMK0gEIVA/PMGR7EzVEUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIFbC1zxXHcRMk/fzrJPv8PzRadkrdK++PK0J3rpYp+6HDxZ7dspalzLYE1uyMvptxa10e6xYoKiqSnTt3mhWPPvrokGDK4LWzs7PlN7/5jTlrz5498vXXXwcvZjxGArbsHLENGhJRa94ff4ioHpUQiJWAdvmthQyVsRKlHQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIIYCGkhpHzRKnIMPl/RBI8XRMT+GrdevKe+6yIKcnAOH128DKbaWu57djKcYU70Pd/fu3YF1gzNTBmYGjfTu3Vvy8/c/x/bt2xe0hNFYCtgHHCbeCAJWfQRUxpKdtiIQoMvvCJCoggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJFogGQMpLQP/5nXWaNjH9L6Dwy5nIQKJECgoKBC73S6aeW7+/PkyceJE0a7Payrdu3eXadOm1bSIeTEUcPYeJN5I2tv8YyS1qINAzAQ0oNLv9wtdfseMlIYQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH6C1Tt2jtr3KlJkZUy5IhKIsvc58wvCFmNCQQaQyAjI0PGjBljbnrlypVy1113SWFhYWPsCtv8WcDRpXtEFv7SyF5rImqMSghEIKCB1xpQSZffEWBRBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvgJp5/wuvhugdQR+FnC/kHwZ6JI5I2XVC8dfVl51Vo3T9pa5Nc5nJgLRCqxdu1bmzp0bdrXRo0fXmnnyiiuuEG1jw4YNsmjRIpk8ebIMGzbMDLQcPny4ZGZmhm2bhbEVsOVE+NoQ4WtNbPeO1lJZwMpmS0BlKl8FHDsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQJAItzvt9kuwJu9HcBYqTKKCyKQVSBq6LSk9gNNyIPSsn3GKWNYJAWVmZnHHGGWG3fMstt8ihhx4atk6iF3744YeiQ7iiAZItWrSosUpWVpY88sgj8vzzz8uMGTPE4/HIggULzCE9PV0OO+wwGTdunBlkWWMDzIypgD2r5vNUbSMRvtZUW48ZCNRTQLNTOhwOMlTW04/VEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBegk0yUBK60gzjNxd5XUHVfrKS8QeaSY6q20e4yqgwUKlpaVht+H1esMub4yF7du3l9zc8FkNNQgqXNHAyYsuukhOOOEEMzjzo48+km3btonL5ZLPPvvMHHr16iW33Xab6PYo8RPwlYe/BgNb1tcaCgIJFLDZbKKvgVx5CURnUwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQugJNOpDy59Nmy84Sf/m+Ok+ib98eAirrVEpsBc3UeN9994XdaH5+ftjljbFw0qRJMnHixJhsunPnznLOOefI2WefLcuXLze7Ep8/f77s2bNHVq9eLTfccIPce++9kowOMQFIgkb8JXsi2wvjtYaCQCIFNOhcgyoJqEykOttCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPg/9s4DXo6q7MPv3p5KGqRCEkLoIF0BqdKsIChFQHqVIkVABAEBaSpFQPEDBUERUBBpgiJI7723ECQhISSQfvt+8wyc5dy523d27957/+8vm5mdOXPmnGf2zpw553/eVwREoN8R6AtCytRFGzzEbG5uQWX7jOlWN3aF1GFa6XkCNTU1tvLKK/d8QaqgBIim1lhjjfCD58qbb77Z/vSnP9mcOXPs5JNPtquvvjoUVlVBUftcETpmvpdXnRKDgnuNTAQqSABBJfdJCSorCF2nEgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER6D8E+pSQ8rPLlhi/oiWnz8x5EVtfe86aNtgsZzolEIGeJtDY2Gh77LFHGOr3+uuvt48++simTZtmK664Yk8XrU+ev/2N5/Or1/jJ+aVTKhGIiQBiSkyCypiAKhsREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERgEBfFFK6K1u74qrW+chD7mvGZfuLjwf7jsy4XztEoBIEzjvvvNDrJCG8jznmmKyn3GyzzQxBJfbCCy9IUJmVVvE7O196Mq+Dayavmlc6JRKBuAjgoZKPBJVxEVU+IiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiAC/ZpAXxZSugtbO3k1a3Nfsiw7n33SOpcstJqBCtubBZN2lZnA4sWL7dVXX7W33nrLjjjiCKuvr894xvb29oz7tCMeAskliyz5/DN5ZVYrQWVenPp6okQiUdEqcr5P/VRW9LQ6mQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAj0HQIIKWu32cEa9jnOBu5zrA3YbherHT2+71TQq0nDWhsGLji9DRlWk21JW/rgPzPs1WYRqAyBrbbaKjxRW1ubXXHFFVlPeu+996b2r7qqvCOmYMS40vLwPWbtydw5BveY+jU3yJ1OKUQgZgKE/ZaHypihKjsREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIH+QaA/eKSMXsmaYSOtffnxVvfejOiubt+b/3qlDdr+u922a0P/IEDo3JkzZ+as7LBhw2zgwIEZ082fPz+vfIYOHWqDBw/uks8WW2xhd999t7344ot211132dy5c2233XazKVOmhN4qKeOsWbPs5ptvtjvvvDM8dq211rKpU6d2yUdf4iHQ/vc/5JVRYsVJxr1GJgKVJoCnWgkqK01d5xMBERABERABERABERABERABERABERABERABERABERABERABERABERABERABEejVBPqjkNK/YO3rbhYIKv/ib0q73vnWNFv61AM2YIPN0+7Xxr5NoLm52Q466KCclfzhD39o2267bcZ0N9xwg/HJZQcccIDtvPPOXZLhbe7000+3c845x5566il74oknwg9hfZdZZhkjJDjeK51NmDDBTj75ZKutrXWbtIyJQOszD1nynel55VbzpW3ySqdEIhA3AXmojJuo8hMBERABERABERABERABERABERABERABERABERABERABERABERABERABERABEeizBPq7kNJd2OTG25ndmltQSfolV54bCCo3C9byiBPuTtBDy7qahHV05hGOOMbycU5ZeQk0NTXZGWecYY8//rg9/PDD9sYbb9j7779vn3zySerE48aNs1122SUUdkpMmcIS40rSWq/+Rd75NWz5zbzTKqEIxE0g0draWtknQdw1UH4iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikCeBT7ZbI8+UnyeTkPJzFqx98MEHVvfzw6z+9Te77sjwbcDxZ9jgr++RYW/mzdOnf+rNbsyYMUZo5nLbSf+cZ6982Fru03TJf/XlGuzcHUZ02aYv5SewdOlS4/c1aNAgGz16tDU0NJT/pP34DM3/vNHaLj4rLwKJtde0wef9Oa+0StT3CeBNdtasWWFFJ06cWNYKz5492xTyu6yIlbkIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEBvJiAhZear17bNdwNB5c8zJ/D2NP/2bBuw6XZWO2ykt7X6Vg/ccIidcs88W9JWfvEmtR9YnzDOKas8gQEDBtiqq65a+RP3wzN2zp9nbVedn3fN67+5T95plVAE4iSAcB8PtXVxZqq8REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKC3E5CQMo8ruP7mVjP+/6xzxpyciZOLWm3+mYfbiF9cH0T+rsmZvqcSrDSy3i7dcZQ99l6LzVvSUdZiDB9QaxtPbLRRA2vLeh5lLgI9SiDZaUvOOdosuAfkZWNHWePG2+SVVIlEIG4CHR0dElTGDVX5iYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI9F4CElIWcO1qam3AwT+xxaf9MK+DOp551hb88RIbuk9+6fPKtAyJEDh+Y9WBZchZWYpA/yOw5M+XWfL55/OueMP+J1qgaMs7vRKKQJwECC+OyUNlnFSVlwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQK8jICFlcZds4OZftaVTL7HON9/JK4OWP15uS6asZgO/vH1e6ZVIBESg9xJoefRf1vHn/8u7Aokpk6xR94a8eSlh/ARqamqss7PTqtePcvx1Vo4iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikCKAkLJ2mx2sYZ/jbOA+x9qA7Xax2tHjU/u1kotAwgYfe67lrT7pNFvys6Ot5aWncmWs/SIgAr2YQOvLT1vrOT8yC/7m87JAwdZ45Fl5JVUiESgXgWQyaYgq5aGyXISVrwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQFUSkEfK+C5L46rrWMMue1vrTdfmlWmyrdMWnrSvJS68wRqmrpHXMUokAiLQewi0vf2KtZx2sFnwt56v1ey0h9Wvsna+yZVOBMpCAEElJg+VZcGrTEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABKqRgDxSxn9Vhh74I6sZNyrvjJOLW23BMbtay6vP5n2MEoqACFQ/gbbXn7fmE/Y2W9yWf2HHjAw9BOd/gFL2JwJO5FiJOtfW1oankaCyErR1DhEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgaogoNDe8V+GREOTDTn9/yxRn78MJRkIrhYe+z1rfvaR+AukHEVABCpOoPX5x6w58D5rSwoQUwb3jKZTLrNEQ2PFy6sTikCUQHt7e7gp/ydZNAd9FwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFeRqB29PheVuLeUVzCdw847syCCpts7rBFJ+xni++8oaDjlFgERKC6CDTffZO1nHKIWfA3XYjVH/lTq5+yeiGHKK0IlI1AIpEI85agsmyIlbEIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI9B8Cg7b/rtV/a5eCKpxsT9qSC061+b85yyzZWdCxSiwCItDDBIK/2SX/d661XRSIqYO/5UKs5ms7WdO2OxdyiNKKQFkJKOR3WfEqcxEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARHofwSGHXWW1W28ScEVb73xjzbvmF2t4+M5BR+rA0RABCpPoPPjj2zRiXtax81/LvjkiY2+aIMOP63g43SACJSTQEdHh+GlUh4qy0lZeYuACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAfyJQW2vDzrjCatdco+Badzz/gn283zbW/PRDBR+rA0RABCpHoPXZR2zxoV+z5IsvF3zSxOqr2aBTLjUL7hUyEagmAnioTCaTElRW00VRWURABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgtxNI1DfasPP+aDUrTS64Ksn5S23hj/a3+ZcG3utamws+XgeIgAiUj0CypdkW//ZMa/nJoWYLCv/7TKw40QadeZVxj5CJQLUR6OzslKCy2i6KyiMCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACfYFAzcAhNvziv1rtGqsWXp1koKX82/XWePJeZm88X/jxOkIERCB2Am0vP22LDt3BOm+9ySz4Gy3UEquubIMuuN4SAwcXeqjSi0BFCBDuWyG/K4JaJxEBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCB/kcgFFX+8garXX+9oipf+9F8G3jO8bbo4p9Y56L5ReWhg0RABEojwN/e4ktOseYf7Wc2a15RmSXWWccGnXudxJRF0dNBlSZQU+kT6nwiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAL9g0CicYANP/daq9t62+IqHHjC67zrH7Z4v22s+Z83BnkU4RqvuDPrKBHo5wSS4d8cf3v8DRb7p1ezxdafhvlubOrnPFX9aidAyG9Mgspqv1IqnwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAj0YgKJunobfupl1njAkWaJIiuyqMXaLj7LFh2xo7W+8HiRmegwERCBfAjwN8bfGn9zFvztFWXB33rtPofZoJMuMu4BMhEohkBNTeXkjYT7xhKtra2S7hdztXSMCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACPRDAh988IElk0mbOHFiwbVf8tDdtuTsH1qyuaPgY/0DEuuvb00H/tjqJq3sb9a6CIhACQTap71uzVeda8mnny4hl+DQxlprOPF8a9y4SM+0pZ1dR/chArW1tTZz5sywRsU8cwpBMWvWrPDZJkFlIdSUVgREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAT6OYFSBJWga3vvbVtw2oHW+e6M0kgGjstqNtvaGvc6yuomrFhaXjpaBPoxgfb337GW6y6xzgf/Y/Zp1OOiaSRWGGdNp16uv8miCepARwCPkYj3Z8+ebXV1dTZ+/Hi3qyxLJ9yUoLIseJWpCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACPRNAqUKKqGSbGux+ZecZm2331w6pEBYmdhkc2va62irmzi19PyUgwj0EwLt09+05usutuQjD5QspARZzVe/ZQMPO9US9Y39hKCqWU4CiCgXLFhgCxcutMGDB9vIkSPLeTrDQ2VnZ6dCfpeVsjIXAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgT5GIA5BpUOy5IE7bckvT7Lkgma3qfhlIhBWbrCRNXz3EGtYa8Pi89GRItDHCbS++KS13nSFJZ96IlA3x1DZIY3WcNRZ1vjl7WPITFmIgBmhvhE34p0SL5Xjxo2z+vr6sqLh2VZTUyNBZVkpK3MREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER6GME4hRUgqbjk7m24MIfW/sD98dGKrHiRKv77sHWtNnXUObElq8yEoFeS6Cjw5ofvNPab/qdJd+ZHls1EptuZgOPPNNqlhkRW57KqP8SIMw3YsqO4Pc6Z86ccDl06FAbPnx42aHIQ2XZEesEIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACItD3CMQtqHSEQm+Vv/qxJecvdZtKXw4fZLVf3d0av7a71Y4cXXp+ykEEehmBjrmzreXOv1jHXX8x+3hxfKUf2mQNgZDSeaVECCeLhwAeGfub4RmSere3t1tLS4stWrQo/N7U1GTLLbecVeL3xbMNS7S2tva/K9DffnGqrwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjERKBcgkqK17l4gS288nxr/ceNwZeYCkw2NWata33B2r/yHbO1NgoUM8EGmQj0VQLJ4I/nxSes7t6/WsOLz8f+t9Sy5fbW8Z1DzQYO7qsEVa8qIIBnymHDhlVETEl1Z86cqZDfVXDdVQQREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER6FUEyimodCBa33nNFl14knW89IrbFNuyc5kB1rrJ9ta52dfNxk2KLV9lJAI9TmDmu1bz4B3W8MjdVhOnp9fPKtY2ZbK17Xui2fJTeryqKkDfI0Co74aGBmtsbLQBAwaE65WsJSG/MXmorCR1nUsEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEejmBSggqHaIl991mS373c0vOmus2xbpsX2GctW36dUt+cWuzYaNizVuZiUBFCHzykSUe/4/VP3yH1b03syyn7Bg51Fp3PSz4O9mmLPkrUxGAQE8LKt2zTYJK/R5FQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATyJuBEJxMnTsz7mFISdra32Uc3XGnJv1xuNYtaSskq87FBBPC2FVe09i9tb8mNtjIbOjxzWu0RgZ4msOBjSzxxn9U9drfVv/NOvCG9vbp1Dm60lp32s+RWOwZqtzpvj1ZFoPwEKh3ym2cbJkFl+a+tziACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACfYZAJQWVyWTSPvzwQ2tubg5ELs3WeO/NVnP7dWaL28rHMxBX1q6+htVv9jVr+vJ2Vj+uMsLR8lVIOfcFAm0zp1vzQ/dY24N3WscrL5dNRAmrxOAGa/juQTY4+NQMGNgX8KkOvYBAR0eHtba22tKlS23RokXG/b+pqcmWW245SyQSZa8Bz7aamhoJKstOWicQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgT5EoJKCyo8//tgWLFgQhoJFVNPQ0GCdSxfb4r9fYy03XGHJ+UvLTrZm+bGBuPKr1rjxtta42jrEpS37OXUCEbBAXNby6nPW8ui/AhHlXdb5v0+955WTTGKZAda42yE2aKd9AiHloHKeSnmLQFYCCCsR0yOyxFPl8OHl9xrMs62zs1OCyqxXRjtFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAS6EKiUoLKtrc1mzpwZeiYbM2ZMKKb0C5JsCbyY3fbnQFj5G0t+tMDfVbb1xMB6q11/E6v/4tbWtNGWVrfs2LKdSxn3PwLtcz6w5ifut7bH/2MdTz9iySVl9MTq4U2MGhoIKQ+zwd/8niUaB3h7tCoCPUcAUeWsWbNCT5Xjxo2z+vr6shaGZxumkN9lxazMRUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKBvEaiUoHLu3Llh2NchQ4bYiBEjMkMMPJgteeAuW3rD5db5+luZ05VhT8345ax23S9bw3pftsZ1vmS1w0eV4SzKsq8S6Pj4I2t57jFrfeYh63j2QeucMaeiVa1ZZSUbsNvhNnDzr8rzakXJ62T5Epg3b54tXLjQBg8ebCNHjsz3sKLSSVBZFDYdJAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAL9m0ClBJUzZsyw9vZ2Gzt2bDfvlJmuQMsrz9iSG6+wjofvt2R7MlOysm2vmTDGatfb1OrX+qI1rrmB1Y2ZULZzKePeR6B91vvW8tJT1vbi49bxzMPW+f6silciUZew2k23tIG7HmKNq69X8fPrhCJQCAG8VPLMqaurs/HjxxdyaMFpZ8+eHT5z5KGyYHQ6QAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAT6L4FKCSqnT58eQl5hhRXCsN+FEMfz35K7brCWf1xrydnzCjk01rSJYQOtFmHlWhtZQyBea1hpDUs0KaRyrJCrNLNk81Jrfetlaw1Evu0vPmEdgZAy+cmSHittYvQIa/zW3jbwq7vJk2qPXQWduFACyWTS3nvvvfCwiRMnFnp4QekJL45JUFkQts8TdwTuoltaWkJVKuvRDxdTJgIi0HcIJBLBDI3a2m4fFPCNjY3h9r5TW9VEBERABERABERABERABERABERABERABERABERABERABERABERABERABDITqLSgsjQRTdKan37Ylt52rXU8EnitbOthPUeNWc2EsVa7yjpWt9q6Vr/K2lY/eVWrGTAwM3DtqXoCnUuXWNu016zt9Res/dVnreP15wLvkx+YdfZs0RP1gdZhky1twDf3tqb1Nw0Kk+jZAunsIlAEASeuL+1ZkPvEeEVGG1SXO6lSOAIIKJcuXRp+2tra3GYtRUAE+gEBRNK4kueTzurr623AgAHhB4GlTAREQAREQAREQAREQAREQAREQAREQAREQAREQAR6mkBnZ8+O3tbUBGoBmQiIgAiIgAiIgAj0OIFEICT7cvjpXLzAltx3u7XefaN1vPyKWU9oK4MmWud7H4Sftn/dZUvhE2jcEqNHWu2U1a028GBZN2U1q18x+IxdPlBf1vY4QRXAI9DZYW0f/M/a3nnV2t9+1ToCD5Qdb78SeEGd2zO/J69oqdXg91S7xurWsP2uNnCrb1jNoKGpXVoRARHITABna5g8VGZmFO6hs2HRokW2cOHC0AulSw5ARFOIqHyvdXir47sD7NJrKQIi0LsJIKjEEy2CSt8jLeJqxNa+V1ruAUOGDLHBgwebOgx793VX6UVABERABERABERABERABERABERABERABESgNxL45JNP7O2337YlS3ounCDcBg4caFOmTLFhw4b1RowqswiIgAiIgAiIQBYCvctDZfqKtM9+35b86xZru/dm63x3RvpEPbwV74KJsWOsZuLKVjtxavipW36K1Y+faDWDl+nh0vXt03cumm9tM6Zb+//eto7pb4afzulvWPKDWT3v5TQD+ppJ463+KzvbwG2/bXWjJ2RIpc0i0PsIVMpDJSG/0QpKUJnhN4I4ChHlggULQlAkQySFBzo6ABBTSjSZAZ42i0A/I8D9AlElnZN4sUVwiSGmHDp0aCiu1P2in/0oVF0REAEREAEREAEREAEREAEREAEREAEREAER6EECzz77bOgsogeLkDo1E8/XXXfd1HetiIAIiIAIiIAI9A0CfUFQ6V+JtvenWfN/77DW+261zren+7uqdj0xqN4SY8YH4cMnW824SVY7bqLVjplgdcuNs9plxwYhxAdVbdmroWCdSxdbx5wPrP3DmdYx633rmDndOme+G4TpnmbJWTMsubh3RK6tmTLRGrba0Zq2+LrVB78FmQj0RQKVElTybEPfo5DfaX5FixcvNmZvOlEU4klEUYgpZSIgAiIQJcDNtKmpKfywD1ElYmxEltxLEGczA3vQIDVYo+z0XQREQAREQAREQAREQAREID2B1tZWmzZtWvhuwXoptvzyy9sKK6xQShY6VgREQAREQAREoJcRIPJWtVg1laVamKgcIiACIiACIiAC1UcAIVr9nkeYBZ/2Wf+zpQ/cZW0P3x2EBX/JrKMn4oLnZoTgL/l2IAAMPuksMTAQXC67bPAZZzUILEcsZzUjR1vN8FHBcjmrHTbKakcE64OGBId/GuY2XT69a1vSOhcHEWjnfWQdn3xknXM/tM6PWc62znnBeiCgTM6ZGXzmWHJJ7xBMduNfmwjCea9p9ZtubwM2/6rVjQnCwstEQARiIeCi00pQ6eEkdO+8efNCERSbGxoaQhEUQimZCIiACORLAPE1n+bm5lBQyeDn3LlzwxnhI0aMsPr6+nyzUjoREAEREAEREAEREAEREIF+SIDJWc8884y1t7fHUntm79IRNHHixFjyUyYiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0JcJIFAbsuvBZsGnc8lCa37iv9b6yD3W/vj9llzQ3GuqjmAwOX2mWfD5NMZihqLXBHLKwU2WGBIIK4cMs8TQ4VazzAhLDBpqiYGDg+WQT5cDB1liwODQ82WiMdDRBJqaRH0Q3bXhs0998D3YZoma4F9tEAaWjFkPlnywIJRuMvgE/wVCVdaDkgXryWBMPdkWfFpbPv20tZixraXZ8CSZXLooEEAGyyXBMhBMfroMIs7Onxdck4/NFn5iycDRUXJRcH2CrPuaJYY2Wd0Xt7SGTbazpo22sJqBiGBlIiACcRMgejUhvyWo/IwsHuTwJMcAA3DkTS7un5zyE4H+RwAx9pgxY8x5vWVQdNasWeH9ZQiNUZkIiIAIiIAIiIAIiIAIiIAIpCHw9ttvxyamdNm/99574apElY6IliIgAiIgAiLQvwhsuummwRj2p4PYDA49/PDDXQBsttlm4aBRdHuXRPoiAiIgAiIgAiIgAv2QAMK1gVt+I/wg/Gt982VreeoBa3vqv9b50guBCLAPqPfQNwZC0U/FonPCq5xVgNkPfweVrnKivsZq1lzb6jfYwho32Nwapq4RilMrXQ6dTwT6GwGcHPDu3O8FlQgo8Ry3ZMmS8DdASN7hwwO1vVPH97dfhuorAiIQOwHuK3is/Pjjj0NxJUvElSNHjjTChctEQAREQAREQAREQAREQAREwCcwf/58/2tR6/RrjB071mbMmJE6XqLKFAqtiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDhBAJviw0rrxV+7Hs/CMSULdby4lOhwLL9uYct+dabwbbqDA9eeGV1RCUJJOoTllhpqtWts2kooGxca4PQ+2cly6BziYAIBLrlzzQ8/VpQiZjyww8/DIVNAEHcNHDgQP0+REAERCB2Agxmco9BWOlE3B0dHbbccsulbsixn1QZioAIVC0BJnLMnj3bFi1aFH4o6ODBg8PP6NGj1R6p2iungomACIiACIhA+QngMSquUN8rrrhi+L7x/vvvpwouUWUKhVZEQAREQAREQAREQAREQAREQAREQAREoCQChLtuWm/T8ENGydZma3n1OWt9/nFrf/4R63wl8GDZLF+PJUHuowcnmmqtZvW1re4Lm1jDF75ojautE4RLD8Koy0RABHqUQL8P+Y2Ycs6cOaGYEhiImurr63v0oujkIiACfZ8Aom3uNU7MzX1o2WWXlaiy71961VAEUgQQNEyfPj0Mo5XaGKx88skn4WfmzJlGKM4JEyb4u7UuAiIgAiIgAiIgAkURmDx5cnicRJVF4dNBIiACIiACIiACIiACIiACIiACIiACIpA3AQRxTV/4UvgxOzpQWAYhwt9909oCkWXbK89Yx6tPW+f0/5l1yItl3lD7QsLahNVMXN5qV1vf6ldfz+oD8WTDpKkK4d0Xrq3q0OcI4BitX4f8JuRuc3NzCAFPUHV1/dpZZ5/7gfeVCjHghXeS8ePHG8JfWd8ggKCS+86sWbPC+xD3oxEjRvSNyqkWIiACWQm8/PLLNm/evKxpuO9PmzbNCPW5xhprZE3bEztbW1utoaGhJ06tc5aZQFtbW0ETjG655RY78cQTu5Tq3//+t62wwgpdtvXFL48++qjts88+Xap2zTXX2MYbb9xlm750J5AvuzfffNP++c9/2korrWRbbbWVNTVpZm53mtWz5Xvf+5499dRTqQJtsMEG9uc//zn1XSs9TwBRJZE5/ve/oMP+M+ttnippI9GRFbWFCxfa+uuvH25eeeWV7fbbb48m6fHvdMJheq8v/6VgAjW81c9XftY6gwiIQO8jQFs8mz388MPZdve5fZnaFj//+c/t6quvDuv761//2rbffvuqqTvPOd7dC+2XyVTXqqlYLyhIS0uLNTY2Vqykaj9WDLVOJAIiIALlJ0CI8MmrhB/72m7h+ZItS631zVes7a2XrP2NF63jzRctGTjiSLZ1lr88OkPZCSTqaywROE6pnbqW1QXh4etXWtMapq5uicYBZT+3TiACIlA6gX7toZLOdkJsMpiAZ0p1spb+g1IOIiAChRHgvsP9x4X8RWQ5ZMiQwjJRahEQgV5FAM+TucSUfoVIyzHjxo3zN1d8fcaMGXbttdcaAy+sL1iwwEaNGmUIMxA67bvvvuF6xQumE5ZMANHuTTfdZIggmcSB1+QBAwaEHlK5vgiidtttt4JEliUXShmIQEDgvvvus0MOOSTFohrFebfddpvdcMMNqTKystdee9kOO+zQZVv0y29+8xuLDlT/4Q9/0N9ZFJS+l4XApEmTwnwrKark987vvlBbc8017aSTTgoPQwBw+OGH22OPPWa77757anuheVYyPQPg//rXv+yvf/2rvfPOO+FkOvqg8EJOGPZNN93Udt11VwksY7ooCLpprzIpCU/wCB5oQ8MbUT6i72jfH5OsDzzwwJJKwGQKiWRLQqiDRUAEKkyAZ2o2y7U/27GV2nfccceF/amFnu+nP/2pMfEC621ti3vvvdeY0MgzjgkxS5cutWWWWcZo22233Xa24447hv3M6Zj0trqmq0OmbUR6ufnmm+25554L+6vos1p++eXDfo5MxxSynfz+9re/hW06+kwWL15sw4YNCyeRTpkyxQ444IDUb6qQfDOl5breeuutdscdd4SToBg3oP1Im4Z60QfHxM5cEW3222+/UHSb6TyDBw+2MWPGhB/y3XrrrcO+oEzptV0EREAERKA8BBDWNa65fvhJnSHoS2h7/51AaPmydUx7Lfx0Tn/DOmd9FDRgUqm0Uk0Egnm/NWNGBZ4nV7bayauGn4apa1j9hBWZVVtNJVVZREAECiDAexST2fqdW0a8KuENDhs5cmTBM/kKYKykIiACIpCVADOJuQ999NFH4X2JGa6Fzi7OegLtFAERqBoCdIoywFuocczw4cN7pGMTIcDZZ58dehiLDqpw3+Lz5JNPhkIBOmt/8IMf9Eg5C2Wq9J8SuPLKKw1PG/w2fVuyZIm9+uqr4efOO+80hAInnHCCbbvttn4yrYtAWQkwKOYbQhkGDxHGVIt98MEH9sQTT3Qpzttvvx16KWVwM5MhrIoex4u5TAQqRYCBdwZmnXdKzuvWy/E3Nnfu3G6/+Xzq6ovfeC795z//CQ/juXT00UdXdZvjhRdeCMvIIHzU3nrrLeNzzz332PXXX2+nnXZayrtmNK2+5yaAkOLUU0+1u+++u1tihA98EPVed911dtZZZ9lGG22USse9N3o/Tu3UigiIgAj0EQIIp3AsUQ1GWeIwnrO8GxRqPofe0rZ45ZVX7IwzzrBnn322W3WZIPn888+Hn8svv9yOP/5423PPPbul6y117VbwLBt4F7vooouMPgsmUfgWx6Rk2gi0OS+44IJuwkTaHnz4HSJ+3Gmnnez0008vOaICwk08o+IMJmq01fnQpiEKABOMjjjiiLC/MJqW77w/R7mkS+e20e+4xx57hBOmEYzKREAEREAEepBAIMCrnzg1/PilSLa3Wtv/pll7EDa8ffqb1jHjHesMhJfJmf+z5IJmP6nWy0QgMbTJEuOWt5pAKFk7fkWrC65TXRCuu375ICJMnSK6lQm7shWBHiNA/znWrwSViAEY/MfwBDdw4MBwXf+JgAiIQE8R4D7E/YjOEu5PzA5NF8aup8qn84qACMRDAM9/UVFiPjlzDMdWOoQyojo8QT3yyCNhMWk44slhrbXWCmfEf/jhh/baa6+F+wk19bvf/c5ef/31cOkamfnUr6fS4BEKDwbYgw8+aKNHj+6pouR13oceesj233//MC1lRxBQrCGURbxx44035pUFA1WIZY899lg79NBD8zpGiUSgVAJ4pvPFMSNGjLCxY8eWmm3Zj0c4duGFF4YDamU/mU4gAiUQcMJJJ6QkK7fu9pWQfcZD8dC/6qqrZtzv71httdVSX/HGw+A4nrsRxOFNuVoNr5R4zsL7IYbAeu211w7bUE1NTfbuu+8az3XXlsKz7Z/+9Cdbb731qrVKsZcrznbNj3/8Y8NrVy6DO+2Zf/zjH7E9T3pDmzcXF+0XARHo+wTwisxETTzr9aQNGjSoLJEt8GSf7xiPHxmoN7QtaPfQD+AinSBIZaIjHgWpM203hJYIJrm+CC95HznqqKO6XOreUNcuBc7xBbHgkUceGdY1R9Kid59//vl21VVX5TyePjsmA9LGQ1RZrP3iF78I+9PyOZ4+OLxyI+hEXEnUq1INBzSIcmmj0S6tZFjzUsuu40VABESgvxBAsJcKGR6pdOeSRdY+Y7q1z3zXOj54zzpnzwg+74ef5IezLLmoNXKEvqYjkBjcYInlgjH60RM++4y32rErWN24SVY3fqLVDIxnclC6c2ubCIhA9RLoV4JKXgza29tDD3CaaVW9P0qVTAT6GwHuR8wadR508VopEwER6FsEfE8IhdaslGMLPZdLf95556XElAx8MCt/m222cbtTSzqyEQ3gHeC///1v2Kn7/e9/P7VfK9VH4P/+7//SiikRBUwKvJbROc/1RHjp28UXXxx60Npwww39zVoXgbIQINQ8A794cCOU2kEHHdRrvHj/5S9/sV122SUUT5UFjjIVgZgIIJzk3u97eCq3qHLjjTcO2xSFVoFBXURzlG/y5MmFHl6x9IRSxzuUE1NuscUWocg66pGLiSu//OUvw3YTz1uOwcORL/SoWKF78YkIp55OTMlzY/XVVw89qc+aNStVQzx5EUYej1POll12Wbeac4k3KtpJzrbcckuF+3YwtBQBEahaAgj711lnnaotX6kFO/fcc4uagFrtbQuEekxydWLK7bffPpxYGfWEjydFJgv85Cc/CfuVL7300nCSxpe//OUU2mqva6qgeaww6Y7Jnv7z2B2Gg4Lx48cbk/NKMbyiR8WU5L3++uuHYlY8jSNm9A1hI31mPnd/f7Z1PFMySTmd0UfD5ELawM5RjEuHd9JzzjnHCGUfl1EvPH8jKJWJgAiIgAj0HgII/QgxzSedJZuXWvucmdbx0WzrnDfHOuZ+GCyDz9xAbDl3tiU/nmPJ+Z+YLVpiyba+FUUnUZ8wGzzQEssMs8TwZS0xcrTVjAxEkyOWs9qRywXLZa121GirW3acJZqqd/JuuuuqbSIgAuUlwDsZfef9RlBJhzYz9aj0qFGjwmV5ESt3ERABEciPgLsvIWDhPoV4iZmtMhEQgb5DoBRRZCnHFkMQkSSCIIz70Q033BB6p0yXF94g6GhFRElHPutf//rXTcLwdLR6fhvemS677LIuBakNwoggLkAA5gQfhAH//e9/H6ZlMhKG4IOBCwRu1ewZrEvl9KXXEmCyCYOjhDzrbZ67edHGCyxCn95W9l77g1HBiybgPGBXUlRZbGF5XlWzmJJ6MQDNMxTjuXr22WenvQ/gVeqUU04JQ38/+uijYVjqX//613byySeHx+q//Aj8+9//7pYQQQJCR4x36+9+97sh53BD8N9jjz1mCFq5BrRnCJ2Zjy1YsMAQyPoCjn322SefQ5VGBERABESgSglUc9sC0R7hvrEJEyaEE1LS9RXTp7zjjjuG3hp5f8J++9vfdhP2VXNdw0Ln8R/evRGO+s9iDvviF79oRx99dOgRvKGh9HCb8PMNQSrbNt1009Tm3/zmN+GkmdSGYOX+++/vxt3fn24dj6Luuvn7CSNOXX0BLZNIaD9yjLPrrrvOdt5555wi0k022cQoszP6GfGAinfN66+/3m0Ol7fddlvo7VT9Pl2w6IsIiEA/J/D6nBa7/JF59uacVmvvLE5wuNzgOvvlN8fY6CGVl+YgFKxffkr4yXUpEV92LPjYOhd88uln4ceWXLzIOhcvtGTgCTP1WbzAbOliS7YEfSCtLZYMPtba/Nky8IjJZMSOTgvCtlkwcBQsg0+wTHaw/KwUgdYxUct/wafGLWvMaoMPHpiD53qioTFYBqG2w2WjJRoD0eOAQZYYNNQSgZDUfWoGDQm2DbaaIcOtZuiw8FM7dLhEkrkuuPaLgAhkJMC7Flb5u3bGIpVvBwP8bjYfLyF1df2i2uUDqpxFQARiJ8B9ifsTXi+4XxHW0t2oYz+ZMhQBEag4gVL+nks5tpiKXnnllaE4kmN33333jGJKlzed11tttZUxix9PuwxUI6rMZnSA03lL3QhXVWodEf3hzY5Qon6Hc7YyFLsP70azZ88OBzXyDS0WPRdiC+pPxzweFEqtfzT/TN8ZBMAjsm+IYL/5zW/6m0KBAWExSesPJlDvp59+Ou9BAkSYiDjxqDB8+PAu58jnC78TjkdcV4gHqUx54zmM522hoaNdPcaMGROKjDPlX8h2PGbxuyWEbSbRXdz1L6R8Lm2pZSiVXSY2rnzR5Zw5cwzRC4OO/H31lL300kvhwNSee+4ZaxHirB/3If4muAcXOlhG5Ac4I8ar1P0rVpDKrAsBdx253zrDCw6/jUL/Bt3xvWHJM46/AdoNcTxjXn755ZR3bwQPP/rRj7Ly42/nhBNOsG9/+9shrpGFI9YAAEAASURBVAceeCAvQWXcbai4rhX3JwSM/G4QbVTCCHHq29SpU1NiSrYzMQiRCd5AndE/+NprrxUcYp3JRtTPGedCnCATAREQAREQAUcgzrYF3ged0eeSTkzp9rPkvYPIIrx/PfHEE6kJ+36aSq7HycKVG4Eh7yDOaKdS52h/httfzJJJRs8991yXQ/fee+8uYkp2fu973zMmw7gJqGx7/fXXWRRkf/zjH40+Jt+I1nDmmWf6m8L1r3zlK+GEkOiEDqLF5PLKST+I/87HOu3fL3zhC2G/D8JKZ/yGaCutu+66bpOWIiACItCvCcxb0mEn3THbFrcGwsAS7MNF7XbcbbN6TFSZb9ERX9bhqXG5cfkeonQiIAIi0CcJ0HdLP2K/UBYuXLgwfLmpD9TsQ4cO7ZMXVJUSARHo/QS4PzFIwyAZM0UV8q33X1PVQAQcAQZ0XfhHty3fJcdWyrgHPfTQQ+HpmNm///7753XqzTbbLBRUkjiboBLRJWEWH3/88WBy4qedEHhFXHXVVe1b3/pWKODMdMIHH3zQTjzxxHA33gfoZMYDHB9EDHTYY3giP+CAA2y//fbrImTYddddQw9QpPE7rBlod4KRSy65xPC6GTUGJJi1/+KLL4ahltx+BJzkSzhiv3Pa7feXdLTTSY0HATqnnXHcGmusEYb7XG+99dxmI5TVGWecEX7nueCMcF5wxOj89r0MuDTplnSKu+PcfsK9fuMb33Bfuy3h6AsqSYAnrVxhrKgfngUJF4UXKAyvpbA68sgjs05uYuAI1ogk8Mrh6o54da211go9T6S7Rj4vzodA5q677rI33ngj9JxKWZi0gPGbI2Qa3h2y/X3dd999Roh0fl+Iz3iBWnHFFcMw99mO4xx4ej3qqKNYTRkDHZSJa/b222+H28kfj1fOiqk/vxPq7wwhyz//+c9udePlD7Gz40D6tddeu9s1LqYM7txuWQo78oDRtdde67ILl4888kiX73zh90E4NnjjwcWFQeNaIYBFqMTfZ67r1S3jGDZceOGF4e+Me1KxVkr9/Hse58fDLN5LLr/88vD3wd8X92Huf5MmTQo9oHAvz2Q8H/CewnVAhIbRVkVQH2eYuUzn1/byEkAEh/miyvKesbDceTfabrvtwoO4D/MsLdZoT1xxxRVhOGjuixjPKAaiEUGuvPLKRWXt34fxishkglxGWGrSMaHunXfeMTwv0bZIZ6W0ofCWSTQEjPpnEjwSrpJQodgPf/jD8LkdfvnsPwbw33zzzfA5isdqRJTnnXdeONlixowZYSr63QiJybPJ9yjqP6fds50Dim3XcO3g5RuCk6il2xY9LnpM9DsThvww4eyPihmix+i7CIiACIhAdROo9raFLxzk2ZrLmEzGezoTNzGe0e4dKFddeQegvyBfy9YWK1c7C5Ej79O+0S8Up5iSvJmoGzX/fd3to79hww03DPtH3DbH3n3PZ/mvf/2rSzKEj/SZZLKNN97YVllllS7iTbxtMyG2WNt8883Dvir/eNqlElT6RLQuAiLQnwk8Nn1JyWLKDSYMsKfeX2q9RVTZn6+36i4CIiACjoAbw+7zgko6Wd0LKN5tZCIgAiJQzQS4T9HphdgHwQeCAJkIiEDvJ8Dfsx+Wp5AacWyljI5YBo0xBsPz9diEoMaJMNJ5/2PgHMHN3/72t25VoXMfMRQfOt8R4qXzMkm5nFiKgfDTTz/dGPiPGmkY3H/mmWe6hLfGo5o73j/GeTFnmz/Az3fakQjeEEalG2CgHAgPbrrpplDYOXr0aA7rZtzT6eBGmBk1hHrUfY899gi9HBAqGEMgmq68CHOdONcXhkbzjX7n3L6Qjv3f+c53sj5nuA5TpkxJiac4Bg+V2YzwUITbpF6+8ftHJIcYCy9P6cQceGfg2kWvA/kgzESIiycIRGHRsFhRXlxvxIs//vGPU6JOVx5+c/wW4Y6QI50YFk+tv/jFL1LCX47l94AQ8vDDD7cvfelLLru0S+oQvX6If3/2s591+S35HIqtP4Mqf/rTn7qUA2EzHix8w2MGIjrfokKTYsvg51kqO/Liekf5+edgHfEOg2iIZaPGtUI8hHiQ3xu/ScSjlTTeQfEAy6cYK7V+0Xse4uKDDz7Y8ILnGx0DDJgdeOCB4X2Ve1HUKMuhhx7aZeCONExc5G/o2WefTXk2jh6r7z1LgOvLs7W3G3/T7p6Qj1AxXX15jhOCO50Yk2cUg/Q8ZwjbjSCyUENI7uyrX/2qW8265F3vmGOOSd2bea5HBZVxtKGi94NMhaJ94ThHn+McQzuC/SyZZMLzEO/VvlFenkFMWEEYz+QPLPqcdscU266BHV6encCb/NK1UdJt47hC7NZbbw3f0d0xeN1mIpBMBERABHoDAe7ZtPV8L7s9UW7EfQjxqmV8ptrbFky6dEb7nXfYXH1D9F2ks1x1pR3ke1pMl4e/LV1brNztrDvuuMMvQthPwfsJxrnxLEm5Sv19MenUTWx1J2QCTDqj/963TP1Bfhp/nT606Ps57/DRtqB/DOvHH398OOnTbUeEWYql63/JVYZSzqdjRUAERKC3EZgbeKgs1U7ddjn7+b0f2uPvVYeokr5KN07BPf+yyy7LOkYQrT/vyP5EePr8M40j+eeK5pPuO89iP8oEaZgkTiSVYgxnEvvuu2/OQ105mTxOm6qQ5yvPdJ7PUSMPuMCYPngmaZQSUck/z/e///2c/RL0zTC+UawxTpArQluhZSq2LDpOBHqCAI4o6FsvrbXdEyUv8JwM8lBRblDpBksLzE7JRUAERKCsBLhPcb9iwIn7l7zqlhW3MheBihHgpen999/vIqTK5+SIrSrZkUkZnRUy2Exawi9lMjxL3n777eFuBlLw6oM3RsJWOW94nJvZ+XgEQKCT7aUVz1IILGGDmJOXUQbkETMg6MPIi5dGJ3xD0Om8JfJSzsseRigl1+ke9UrFMTfccEOYjnNts802tvXWW4cho/HQRGcDS8QEeLW6+uqrU94uw4OC/2iHIlSinhghvgkZhScsBjaefPJJQ4BG2RDFMcjEfrwwEkIKw/MeHRMY4jlEhZgrd/glx3+UM2qIJXMZosRC7OKLL86aHA546oyKVRCwONZZMwh2cjz89tprr4xJGVRBbJfNGHSh8weRmW+33HJLThEcv62ocT2z2emBCDiaxv3OS6k/HiX4u/IHSfE0GxVUOu+zroyIUXbYYQf3NRQRlXoNysUuVcjPVhAHcv2jIuFoOr4jmkawRGdfrkHIdMcXso17ke855e9//3soXN5oo40KySb0rBp3/bg/ZTN+mxdddJHttNNOXd6b+Vui45G/l0zmC5pcmuhv3W3XUgR6igCD4wisMTxSMgiP52Oev4j9+RuhLUF7hjDoUcF5rnIX24bC43Y2i7sNle1c+e5DdIH4mvsD9zeeN7QpEOzgNZh2ESyZJOEmv8TdrqGseJumDeUM79Z4ycbzOUY577zzTrc7XOKN1RepdNmZ5gvtOOrkGx6Ac4Ve9dNrXQREQAR6kkA1iCmpP+8qlMWPytCTXOI4dznbFkxIwOP+rFmzQq+TTGJgUmEh/TT51pHICtna7gju6INxwrt0ESvKyYJnMREYfKNvhndy+nZ49ruJwRMmTLAtt9zSTjjhhKKe1XjX9j1s++f013k3ioohC/1tu6gVfr75tFF45+QTh8HWD/dNnghJ5J0yDrrKQwREQAQ+J1Bfa3badsvZGfdUh6iSiRpMvMRYMlG6kOcY/b/uePLI1o7wz0XaXMYExqgx+dQ/X3R/tu9+f3mmdEyKv/fee8M+BM5DhK5sUXyi+dD3kKl8TMBgbOf+++8PJ/7TF+X3x0fzyvad9pA7D2NguSZ60j5y6bPlm2kf7YRcVmiZcuWn/SJQTQS4tzFG3y8ElYCXKKmafn4qiwiIQDYC3K9oZElQmY2S9olA7yLAoCudstEO11y14JhKDtj6M+zj6qhH2OjElAwI4CnODXRTfwbiCUV52GGHhS/vMMJ7FAKeTMbLIC/5zJIjNLkzZhxSbjr7MUJHO0Gl/xLsD4zT2Z7OkwACxBtvvDHMBxEk3gURYDibOnVqKOYkhDIiCjrzeTlG6OcbZXRiynXWWScMr+x7dECwQR54XeQFH/EBgwPUw10DxHJOUIkQAAFBoeY8TvnHlUusy/XlujKDkQ4IV393bvj7gkq4RYV8hLFCWMqSsKJcC98jJ5wQNPjX3+XvLxGobrLJJuGgADNvo56qELH6gko6CqJhzsmP60o+cMSTa9Qjl3/OTOvpOpeY5VZq/blHIPR1YmLOn84jXXQbgyTOo2ypZeCc5WRH/r4h/IuKKbln8LeD51h+L77oFcEfYqkjjjjCzyb2dcKLM7jmBNucACFtLpF4tCDlqh+Tdvgd83vmt0y5/I5FOtnw/oL3Wmfcv6NiSu5JCKi4R9HpirdXhFUyEahWAs47MuVD+IeYftSoUaniIgzgs99++4UDAXimduLLVKIsK3hzdH9LdLSla1dkOTzjrnK0oTKerMAdDBgwscQX7yPwJ/TmtttuG3rS4t5Au26llVaKvV1Dcbnn8kxGSOmMSSe0B3mn5rnn72PyIoIP3zu0Oy7TkkEVBEDOmAix5557uq9aioAIiEDVE3DPp2ooaDWVpVQe5W5b8MzCoxDeh3jP4t2GwXcmYjB4zqSQuMx5esyUH1FEnJiSPo2oB6Zys8BbfvT9m3d7+pai79j0zdCfRBuEaCJMkimHcW7fmKxIn04hVsk+GoQihE13xsQX3vHo5/L7a+if4PqWeyKiK4eWIiACItCfCNTVJKpKVOmzp+8vX0Elz410E6v9/NKt07YhakguSzcWxkQJnl1RY1I7E+gx0jD2FDW/7ye6z33/97//3cVbNyJBfyzJpctnSbQON/GBMSyet0yiYIyEMf8LLrggnEjPhJZCjD4Y+oic0U9BNCrGTjIZ0Zp+9atfpd1NVC8cOzGJB+cn6Yxrls2KKVO2/LRPBKqNAG18xjz6tKCSTu2wkkGHZ64/+mq7QOUuD1wIg8fDxXnGKeSc/f34QlgprQgUSoD7FX+XNEa4j+n+VShBpReB6iSAcAmxSr6hvxHvObFTpWrkh7+OSwxAyF1nvKj5Ykq3HYEhAiLEUHTU0/HNy2e6GYkcg9cB8k0npmPAwQkqoyIgd758lnTEuxDBiAN8MaU7nvszwkCEFxjhL6OCymuuucYlD8M9+2JKtwPhLN4UGajgRZvBAeofp/liWZdvplAcbn8xSwYRqAcd8RgDHHi88wUNXBeeca4NSodC1PAOuuGGG4abebGH9SWXXJJKxgs/Io1M4bdIiMiQ35I7D9sQivkhovH4QYeM66yhwwQvqb4hzKCzwwkw6PzYfffdQ++kfrp81hHs4FWVzg5+63hFJfx41Aqt/9e+9rUugkrCoPBxA0i0J6JiUj8kbRzXoNzsfEbR8iKcPvnkk1NJuJcgVqbezl5++WW3WrYlgkWu3f777586B7/T3//+912Eu6mdGVbKUT9+44Ss8b3u8Vv0BcUUxxeD8vcbFRgjpuS+5u6PHHPTTTdl9VJMGpkIQIDOXud9ORsR7rFxPqN4FmB0hOGZOl2HOmJjnvcIjZ955plQuOAmZWQrK/v89hN5u+dFruNy7S9HGyrXOfPdz2C7L6Z0x9Fe4rnpBKk88xFUlsNoJzIxgkk5hGvH8H7hzu2fk7SIH1wIcn9ftvVo+FQmtaQbpMmWh/aJgAiIgAiUjwATGJdZZpmsJ2CSHZMY47Ryty0oK5MUaQvwfsGkMd5baYfz4T2P9zlElvl4NSy27gzcMzENI0IG/Tb++zXby82CukeNSV3ZDK+VRClAMEj7L07j/Z33H9/oZ+B3VoilE1TG1Q8XLQdiSiakZjPaSoguNt1002zJtE8EREAERKAEAtUqqmSiIhPX84mGxUTsYox+kkLfx915MokG/YhcjDkRLrwYcxG6aF/Rn87YDGN46caDcuXPuJVfTze+QXsGQelLL70UikBpN9B3kq8xuYZrBEf6JJhwcvfdd2cVVHI9M11T12/Ffr+8+ZaHdMWUqZD8lVYEepoAYyO8S/RpQaWb9ViJGVUMkrlQQ7xU+QIIbujp1N28fHLTw7PHloF3JF6A3WBuOX4giCDxbsQMe8rEgH19fX3Yuc1gGh5boi/Efjn6+/E+C7fOTAxCbvizIf/zn/+U9Tq6cxe6bHn3FZtx0WHdDktYwmoGL2N1I8Za44SpNvTLO1nDuCnd0rkNmfJx+2saB1j9qPFWN3K8DVjpCzZkk29Zoq7e7c65/PDq023Rc/el0o34xsE2bJvsHiBylSmVmbcyYOX1bezhv7J3T9zBOls+H2z3kuS1OvYHF9qAqevllbaQRNy3aBxxH5OgshBySisC1U0A8dfMmTNDwVYml/kI0RDYOe+ElayRXybaCKUawhwnYqJO2Twr0nbCUyWD4Myef+CBBzKKCmm3pBMmUl4EGC78cTGzJV2dOQefXOa/qPtCJI7jBZmXWwyhRjoxabgz+I+2oPNygAArbks3izOOaxwtJ+0iJ6ZkHy8ccPQFlbQp4eLY4WHDn/XJMXgA9Q3Rhi+oZB9hn7MJKpmZGm3bIrL0BZW04fibdIMfUSEbdSHcqutk4LyEwGIbodwLMQbxCEnm8+H4OOoPP9oOCEmcEeLbhYcntDxtf2cw9gWVcZShnOxcuVlyn4p6NI2Ko3inYiCIGcbO+L1UwhDNwtZ1xnFOBkLpJMvnvl6u+vE35IspKRf3JTzJEvLYmbtn8R0hdjQUHYIzX0xJOoTleAX1vZuwXSYCUQIMbvPJZXg4jEtQiUcjJyjnOUzo6UyGGBtBJUb7JV9BJc81Z3E9W8vVhnLlLHXpe7KN5jVp0qTUplLaYqlMsqzw7DvggANSgspMSbm20XtXprRuO96tfG9ObE/Xt+fSaykCIiACvYEA7T/eBTDehfAu6Btt6HTb/TTVtI6gMpfRrohTUFmJtoWrE++vtF2YxPn3v/895RGbwX4mkvKhfXPIIYeE7yDRd02XTzFLJpkyKRbjN0OUiOj7TCVYpJscSpnwRs27F30C9Dfw/uX3aTHhFaFBsWE1OUfUiOxw+umnd9lMm5W+h0ItXR9NujEA+lNyhetE+JFuwnEhZaLdS3QWmQiIgAiIQHkJVJOokon+9Pvh+ICJ8kxuzWZuzIY0OJVg7KO3G3oZF5XiuOOOCyeyECWLSSW5eBRSdyKF0Z77wQ9+EI5poNfZeOON8xZtcn0w2oV4E3V6HzyNl9oGKKQeftpqLJNfPq2LQKkEeC/m/arPCiqpoPNIwiBRuY1BKDerzH9x47w8iNy+aDnwikMH7Z133hkq00866aRQTBBNV+p3XjyZlcfLlW94xuGFkw9KcmYZpptt39+P95m5da7rKaecEg40um0s+e1VoyU72qxjwdz0RVvwkbXOfNuWvPSQffzPP9jA1Te20QecbXXDl+uWPms+QWqGktrmvB8et+Chm23ebVfYyO8cY0O++NVueUU3dC5dZAsevd2Sbc2pXfPvvzGnoDJXmVKZeSudixeE3zoWzAsElZ8PIntJ8lpNtrflla7QRNy3EFRyH+M35To7C81H6UVABKqPAB3QzPxmVhqe7twEEESADAozIz1dJ2olauLfa+J4njkBA2XnBTFX5z6DN86rUFScWEj9uYfC1bUFCzk2W1qY0Kaj/eYESO6Fm+OizOhsd5bLawTig+hgljs2jmW6STu0ZSphhAWLGrM8naCSAYiocIaOCzjz4Tr64b5dXu5vx32PLv3fs9sX9SDKdp63zhg08o2yp/MSkU4wk+58fl577LFH2r+BOOpPxwliEQbZnPmCymi47/XXX9/8kO9xlKGc7FydWHIfocPPN37LCAH5vfDb4n0s+vvwxab+seVYx1smonBXBu4XzEImPG4uq2T9+N0gXvaFkP5gXfSaUvZMwvjo30Suv4dcHLS/bxJA5I4n11yWq72Q63h/P6I493yOior9dKz7Ex/S/f6j6d13//fuzuX2FbvsqTZUseX1j/P74OJui/nnYf36668PvY5Gt0e/40mK5z3hU/NtZ0e9U+K1IV2bJnoufRcBERCBaibAM8s9Z6NjGJSbfem2V2uduKe7+mQqY9yDzJVoW/h1QTh42mmnhaK9e+65x2655ZZwHMddJ+eNkWciHgb99zw/n0LWGTMiigbCCYzJC+kmnFaCRbpxteWXXz70EulPtGUCG16rfbvqqqtiE1QyEZP8iZbhjN8W3tdzeUl16f1lut+tPwnSpeVd0vU/uW3lWCJIZULi+eefH6sAuRxlVZ4iIAIi0NsJVIuokjYGYzb0IeN5crfddss6Fs1EBdoIOB6gH7ovCCrdhHjaFowb0N6hfz0fgWmhv0Oe/bSpcNTAxIp8RZtERXH6HiYK0S/BpBr6url2TMCptFVjmSrNQOfr+wRce73PCip5saEjm4GdqGeanry8e+21V2oQiocOHogQM+LVkIFiZv3xAOIlOU7j5uxutpnyJawV6ntCJkWtvx8f5cF3XsgJWdYXbckrj9p7p3/Hxh5xUcneF9vmzrTZvzvJaoeOsIGrfTErrkVP3t1FTElihJ7N016ypslrZj22r+3kvsX9i/sE97N0Qpi+VmfVRwT6EwE6/Z2YrJrq7QvH/PCVxZYRbwXOop4M3HZ/yYurs1IElS6POJbch2+//XbD8wUe7vieyaIiCj80le+9PNPx5dxOuImoIZxKN5Emmq7U7/m2xZnAc91114XeB/P5/UV551NO9xKUKa3vnY80lbxupdYfr4i+oJLJUnhNo5OLzhXfCBGezkopQ6XZcf3/+9//2h/+8IdwQLGY30M6BnFs41561FFH2TnnnJPKjk6y+++/P/U910ql6ucLwaJlil5T9lfybyJaHn3v/QTwDnXBBRdUtCL+s/jqq682PvlYIYJKhAv8LfF3m8/zK5/z94U2VD71LCUNHhnw8ODbjjvuGHp+RtSJyIM0ToTAfXjPPfcMPermapvgpSJ6z/7+97/vn0rrIiACIiACVUDgtttuC8NfV7IolWhbpKsP/Ug85/jw3oYAAHGliwrC2A8RCoieVozAzz/nueeea3h4xPCCxJhROqsEC98TuCsD0St8MSXb8ciPyIDnvbO4on8g6tx3333tgw8+cFmHy7POOivk02Vjnl+iURY4zG//5ZlNXsnwPonY1jcmmhA6/eKLLw4nfLOP/i6uNZOMpkzJHMHMz0frIiACIiACxRFIJ6q8+41F9v31hxWXYRFHIeQn+gT9xkwc4LnAcz+TuXDfeDxHjNnbjece+hyMNoRb0r9OtA30OumcM4QJi/wPD5M8Y/EMyrnz8YLJhBraQ/RzwJ6JyuRD24/2YE8IKquxTEVeEh0mAhkJ0M/Lp08LKql9tYmQEE/4XhEIS4DxMnbssceGoagQNPLSwkyAOIywhv4gKh5oCNHAQwAxJyEJ3Msa4QAff/zxLmXs78enuwbTp0+3Sy+9NN2uXrOtdtAyNvLbR4blbf94trW897otefVxS7Z/OsuyY+E8++DSH9rEn91stcuMylgvPx8S4e2x5f03bdHjd1qy89PQZ8lkh8264gSbfP7dlmhoypjXgodvTbtvYbC9EEFltEzpMq0bMTrcPGr3ICRHR3cPXXNu+EUXceeIbx5qdWk4NIyZnC77WLZx/6JBJ0FlLDiViQiIQB4EfJFntKM4j8O7JfG9CvpizW4JP9vgd4gXImTIlF+p2xETMfBOh4IzXlwpJwMUiCeoY6Zwlr5HvHSd5S7PSixXWGGFbqfBk1+1GG1VwpRlE6xWoqxRIYz/myzn+eOoP+8VdGY5LyL8/ugII2yXH7YZUWk6L4OllqGS7PBIScj1cnp1LfV6I7y5+eabu0zAOvPMM7OGqXfnrJb6Ra8p5avU34RjoaUIlEqg2Gddut9/prLQmU07x3lVZoB62LDSBkF6exsqE6u4tjMp5Le//W2X7Giz+ZOT8cZMmG8/TDcDIrfeemvOyDBMoKXT1BnXl4kLMhEQARHo7QS4tznPhv59ztWLfem2u/1aWugRvxgOhbQtcuXP+A7vG3wYSCcMNc9GQoEjNjz11FNzZZFxPx6Zrr322nA/0VWIaMYkvXRWiXYW0VyilskLJ+/EvqCSiAGIT+FVrPF+jTep6KTf448/3nbaaadis00bCcON0fmZHnTQQaEXK7eN68Nk30IMT5rRPim+r7TSSrbaaquF7SL3d8894He/+104fljIOZRWBERABEQgM4FrnvwkGEdIv3/5YfX2zIxma+v4/P0zfcr4tyKoRJjH5GnGgpiokklQSXQbNwaCV2g/wk38JatMjvSFE0GOMR4mZmDoc3BMwdgUz9y4BZWcY1IQqYy++mnTpoXt8lwOIPAMim222WapqC8IQBFU0vdP+yGfsbcwk5j+q8YyxVQ1ZSMCXQhwf+izgkrnHj/ukA5dCMb4hYcVHnm++93vhop3vJngVjjTy2Ehp46GKfrZz35mTsiJK2c8RPgz7Xlh8kWf/f34dKx/+tOfdgnvkC5NtW+raRpky2y9e5ditrz3qn1w2bFByO7/hdsRVc7+/U9t3DGXd0nnf0mXD/sXb7i9zbzkiFRSwo23vPeaNa20Tmqbv9L24f9s6ZvP+JtS6wsDceao3X9kibqG1LZsK5nKlO6YZbb4TrrN9tHfLu4iqBzypa9Zw9gV06Yt10Z3/3L3s3KdR/mKgAiIgCPgi+4KEVQSooAwUxhicBcy0/dk5kRe7lzplnR0O/PDRbptlVzyMk3HtRNTMtGFGYPR0N28PB555KcTFGhc++a/yPrCCD9Npdb9a+vOmc81ZgKJP/BDnRA+xml4gcKbny+mHDJkiG244YZhJ87kyZND4SphlMttDLT4v1Xn1aqc542r/ni2JtwKIj5ndAxFvZPCNTqgFEcZKsmOtnhUTMlAEJ1+hGQdPHhw+G4VTeO4VGLJoCMDm3iJcYNTdDzm83dXLfVL9y7K30SpQrFK8Nc5RMAR8L0m7Lzzzl36OlyadMtCf+dMSkFQifF3nu/xDM4jwMToVHfH9eY2VFiZMv/36KOPWjScuC+cdKenz2vllVfuIjzAC8Quu+ziknRbMhjBQJJv3MtzebX002tdBERABKqVQK728cMPP1ytRa+aclWqbZFvhRH8M+lp7733Dg/JdY2z5Ysg8yc/+UmYhP4Nxo2yRbWoBAv6A6JG/0r0nZY06cqKF810aaN5pvtOW4N+IZyS+Ebo74MPPtjfVPA6Ex+j5k+EdPuYSOgbaQoVVPrHR9cRihAthmvv7JVXXnGrWoqACIiACMRA4Ibn5+eVS9eRhbwOKSkRY8887xFIogWhDYFQkgkVUSOCF0YUMiYvulDZ0XSZvvveIDOl4Xk0derUTLtj345gEuNZ6LchECsSEYmoFUcccUTszttcfw+TGJickq2dwjPZPaOdF03KvOmmm4YeK+mnZXzK1/mwv5xWjWUqZ32Vd/8mgOC5zwoq8eqBMbDYW4xBN7xU7r///mFIImbN88LmDKECs+15USXswsiRI92urEv/BQhxwhZbbNEl/UYbbRQ+HN1sAj89Cf3v/fH4LrCCL4TSoPMeQzRCYyPakR/u7IX/Na6wmo076hKbfurOQek/nQ2z+MUHrHXWu9YwZlJBNRr0hc0Dj47LWvv8z8Ux2QSVCx75R5f8B666kS157YlwW8fi+bb4uftt8AbbdUnT17+4+5e7n/X1+qp+IiACPU/A91DJ7DZeqp24O1vp7rvvPjv66KPDJNttt13Ki7Pf8Z2Px0knXiQjX4yY7dzl2kcngQsPRdvspJNOKvhUvojRr1vBGcVwgF8Wlx0dH0zmyWS0by677LIwpIRLgxglbvvLX/5ivjdP8ue8hKVyls5TQ1TA6tKWsox6c0x33lLyT3dsnPUnlHdUUBkdqEnnYSuOMlSKHYNnf/3rX7ugRCSKl3/foqFa/X2VWqeDkb8Zwu45S9euc4JL0lRT/egkjRp/E+m2R9PpuwhUCwE/XCFig29/+9tlKRptKKJ9YCwReedjDMi7wXPaHk5Q2ZvbUPnUu9Q06dqVmdqODFb4woN0x/rlISy8P8kDD6T5hOHy89C6CIiACIhA3yVQ7rYFkTKuuOKKECDPth/84Ac5YeIcgzY6/Q60K4rxykj/D/06TC7FEAxuvvnmWc9dbhac3O+ncoVhEgveFaPmT9J1+9KJQty+bEvaAocffnjo+clPh2DhmGOO8TcVtb766quH/V5+nwN9NPQ9FVvmogoSHMSERN96ekKwXxati4AIiIAIlI+Ac+azww47hAJC+iwRGe6xxx5dTspz4cEHHwy3ff3rXw91Ee7YLgmzfCES49lnn50lhYUTHyslqGTCxdNPPx2WxxcqsgGHBfQLMDZC/zJ84jRfQMkEW/979DxOuEqaddb53GEV/RR4rERMyYeJNeUYK4mWh+/VWKZ05dQ2EYiDAPfFmjgyqsY8Ojo+DTXc22aQ4znSeXWKzohncBRxw3//+19jRn0+hrrdeUkgPQr7KBOUtf5AK2p4JxDs78dHGSM6RczqjA6NdGEn3P7euGwYP9UGr7d1l6IveOjvXb7n9SVRYwNW/1wIwTEdSxakPzQIN7Pw4c8FlYTsXvb7P+2StqgydMmh931xf6vuftb7aqASi4AIOAKIZeiUpnObAd0XX3wxfKY/+eSToUgfoT7rPOfZRxrScowvtHH5lWtJZz3e3TDaA0zuyMfcCzVpnRds1icFnpacUbdcBgNn5Qin4PLOZ0kYC2ff+ta33GpBS7/j34kzM2VApwIdFnxeeOGFTMmK3k6HvAtd4TJh1mk2UQFliT6DmIgTt/Gb941BG19M6e8r93pUKEbHihtQ8s9NGzkui7P+m2yySUqMQ/kIber/fTKBK1247zjKUCl20bJSzxNOOIFFVRplcwKpfApYTfWLXlPKn0moGuffRD6clEYE8iWw4oqfRxngnlguY0KJs2uuuabb89Pt85dO9MA2PMLiSdFZnG0of5A8OoHBna+3LdMNOEyfPj1tNVxYMrczGvLSbWfJM59JBr7RDqy0sME/v9ZFQAREoBQC1dRvXU1lKYVpudsWDJDzLOLzm9/8xtJNyEpXfiaOOPPHg9y2XEsipr388sthMsJ//vCHP8x1iJWbBQXguU0EC9/8sN7+9scee8z/Gk4QzjThokvCyBf6Qah/1GMrHq6dB8/IIQV/RfTAhEjfiP6CR6xKGhFJ3OQed95KiVnc+bQUAREQgd5CYM0xTbb3+sNi/ZBnT5kTRdJviTgPu+OOO7qNR91zzz3hpEPGrJ240J+EmE/50aLQ55Htk+49P5+8i0mDCJFxNxwbRR2RoaVxY1NOPFjMOfI5JtvYH+NFrh+WcZ2oYNIJQWn35TP2lk95cqWpxjLlKrP2i0ApBLh39VlBpRvQoZK9zdwLC6H3/EFsQoDzYoyHIT8kd7b6MSvPf6j5L9b+cdHOYdfh3N+P9xmx/vOf/zx0d836KqusYgcccACrfc4GrrFJlzo1ZwjF3SVRmi9ts7sOaNSPGJsmldnS15+ytrkzUvsGrbt16BGzadIaqW1LXnrYOuZ/lPreH1bc/cvdz/pDnVVHEehLBHgZ+uijj8IOaTphn3vuubCTktnnzDxjQJvOUjrH+bDONvaRhg5NjuFYOrXJK9sLVlzsfvSjH6Wy+v3vfx+WK7UhzQpl5aXamS+oJEyDEwdQF+fh2aX1l9T7pptuCjdx/3Mv536acqxnusf6YXkzpaE82eqEGMKFi2AyjGtfpasH+RD2ms+9996bLkm4LVtZMh702Y7jjjvOENQ5Iy/CY74bhBuNGuJW2j2+cWy+bVD/uFzr/LZ98z00uO3+b8xtK8cyWj8mGTGI5RsdB7/+9a/9TeF6sX+fcdafji3XmUKhuMa+eAZBbDov93GUoVLs0nkdiQ4YIgh3s4y7XagKb+A9i7+9fK2a6sfgpe91hjrceOON3YTY//73v8PnlV/HYv8e/Dy0LgJxEKD/xIWvZ5A9OtAePQfPoHyFC/6xTAZw90E8S/3977knJV533XWpLAjX5FucbSjycpatLUIfVKWt2HYNXp2ixrM5mh+TQ955550uSdMd6xJcf/31tnjxYvc1XKYLJd4lgb6IgAiIQBUTQPBWDUJGyuCL76oYWc6ilbttQRvcTaxA6ICwIZfx3vr666+HyRAGMG5RiCEWcB7/GT+66KKLuvQdZMqr3CzceXfbbTe3Gi5pZ/lR1dj41FNP2Z133tklHe/Gfh8IO3nfoq+L0Kbp+h54jzn55JMtKtpE/HjWWWd1EzN0OWGBX6hXNCrMb3/7Wzv//PO7tWnImgmxuSbr5lsE+iDpp8JhCH0cvhX6+/GP1boIiIAI9GUCa41tjFVMiTiTPHvKnKCS83/zm98Mi8GYyDPPPNOlSC7cN6JLN2ncP7ZL4gxfiIBKH3u2T7ZIWhmyLWozz3oEldjGG2/czVMz292kWSbm0scTp/l9v45nuvxxsEZIb8zv73dp8Vjp+rvo+6iEVWOZKlFvnaN/E+izIb+5rFGldm+51K6zGzElN1U3EM9NHVFF9CUwW72ig6NOoBU9xnnCc9td+O/+frzjwRL2zlMXHM8888xu3j799L15vW5k1/B+7fNmF1SdziULbf59N1jzO5972ErU1gUeK7+YNp8FD3f1gDZ4w0+9ewzecAdrfvfTmbHJzg5b8OjtNnyHfdPm4W/sXBoIku75o7+py3rd8NE2eMPtu2yr1i/cxzQgXa1XR+USgfQEGMhlsJqXT39SQ/rUubdyD2DGOB86xglbSFsh0zM9d47ZUyC4YlYeL0eIOglnRLip6OQLcuGF7vjjj7cFCxaEmdIZPGHChNQJKCOz991A9M9+9rOw45o6+EbnLemch+xvfOMbqZdBP11c676g7LXXXguZRvPGY7gTS+IZgo7zqP3qV7+yP//5z6nNdEj7Rvvq2GOPDb3n0a4jj4svvtiamrrOPEXwdsEFF4SHct+PhmT2J8RQ3mKNkFiEOvXDJfNbpcOEWZcIDLgGXPd0sxrx0OB7NS+2HNHjCIvqe8pkIOjUU081fgewYSbmDTfcED0sp9i32wF5bEDIy+CR7+XqyiuvDEWniF0YcKE8pVyHaDHirj+/HydOjp4r6gHD7Y+jDJVil04I84tf/CK8R7IP77IMBk6bNs1VL1xGB4nYyOCR88bB3165Zh3vuuuu4d+d7/m2S+G8L3HWz8u2qFWYHHLIIV08gCJWJfQtoczx9kInK4ONURFTUSfUQSJQBgL0nzAo7rws/fSnP7WrrroqbEtFT0dH+UEHHRSGlrzkkksKboswKeU73/lOmC3PMf6G+FtJZwxgUw4Mkcmhhx7aJVmcbSjfY3amNg3hrP72t7+lylDOd9A42jW0abgH+UII7kXcn3jW4ZWTe67f5nGVw5tzOmNACO+ivtEP5yYH+du1LgIiIAK9hQCDtOutt15vKW6vKGcl2haHHXaYnXjiiSEP2hQ88zJFcaCvgbSu/4moI1GRXjawTLCkrYTR/uDdyg3OZzuOfZVgwXkIvU30NvrFMPot9txzT6MPijYBUV7om/HfSeg/I/ylb0yaQCDhJk/Q3iOigy9mIBzpLbfc4h+W6n9z16TLTu8Lf2vf+973vC3ZVxEZE2bd9Qe51PRB8G669tprG85XmIBMnwllddfZpWWZq93GBGc/fCucXn311bR9KnAjnKtMBERABESg7xPwJ5PSfmCixHvvvWcIKNdff/0QAO/Vrt/eiS7Z4R/b20gRHcw50xg6dGha5xb+8xaxYpwOtohWgtFn5PePRDk6keQyyywT9l+7Pmw/HWM1hC9/6KGHwvZNuSdSVWOZfB5aF4E4CfBuxL2uTwsqc71IxAk0zrycgJI8GbDyvxcipkxXJm7O6SzT9mjaTOkybe8rxyOQYODFGS+gKP/7qiE49K39kw/9r13WOxYFntT+8Dmb9o8/tKWvPmbJjvYu6YZutovVDVuuyza+JFuW2qKn7kltrx041AauvnH4HWHlRzf9MrUP4WU+gkpCi8/5y/mp46IrA1bZoNcIKnvrfSzKXN9FoL8QQFiIdx83cyzuevMix0stEx7oVOWFrxxGZzqiNry+0fGKOIDBaZ59vKTxwknn6x//+MdUhzYCxFNOOaVbcRiIZgYdg9y89BEi6cgjjwwHdXjB46WcQXwnNMIbQ7p8umVcwgZfWHD55ZeHHdKcl/C2rt1FWGQG1eloxiMbXkIRjCF2wnsjL6nR0KHz58/vVqodd9wxzIfj77vvvrATG7HGGmusEQ4+4EUPj0pcV2y//fYzWPrmBLSUhXxIj+iVgX9CcRVixxxzTJgH188ZQjPKkc2jH+c7+OCD3SGxLgmlGfX+gIAynYjSP7EbTPG3lbpOWxsBmRtQcvnhgY9POSzu+vM3hwDaTZByZUbg62bXum1uGUcZKsWOATMEoP5vGAHsaaed5qqTdsl9mTa9L2imQ9B5Lsv3fSZt5jk2kvcZZ5wR3v/8CATpDouzfunyL3QbnaXcc3yvdnTU4TlFJgK9hQBtGITmTJJENMCz+YgjjggHCHju0wbh2U4a7hWEfcYDQb5iAseBQe8DDzzQGASnw+2kk06yBx54wDbccMNwQJznNvcdPE0hznfGhJPJkye7r6llXG0oRN20aWhH0qahQ5CBcsQW1J3nf6U8QVO5ONo1PHOYALHXXnt1iexCu5VPJkN8Add0xgRa31MEaZhYJBMBERABERCBKIFyty122mkne+KJJ8LJDrzD8DwiGgjn5TnK+x7PLPqf8Hjt+hMYcI9GmoiW3f9O2wBBnxMYIqZgPMo5lfDT+uuIO10o7XKz4LxMlMDrvx9umzLjaTKd0U745S9/2U1MjAdLV1eOoy8Eb5f77rtvKht/gonbSF9M1Pul2+cvee8rRFDJsQg0aLtE+0Roi/LJNemPcapcAlr6KrP19/h1QDRKf5VMBERABESgO4GXZ7XY9c927//vnjL/LeRZLYZzA8ZKHnnkkbBfmfbGbbfdFhYPsSXthL5g/rMV8ajzwJmpbvSXMGZDX0oc5tptjLNFHZ65/BFduvEyxpxyte9o0zBx1xe9urziWlZjmeKqm/IRgXQE0OjwXtGnBZXpKt4btpVzMK831L8ay+gPItJZUEjYvmqsT64yJaKNgmRnxkM6W5bYggdvzrifHYO+sKUtu+eP06ZZ+HTg0SbIwxnhvvFmidWPGm9Nk9YMvFS+FH5vnfGmtbz7ijVO6h7eK0yg/0RABESgBwkg7iLkUCWE0Az2M5MOcV+2WWzF4mBQnw50nncIBxHS4KUyk+HV8pxzzrHGxvQhKvBwgHdGhAqIQTMJn3gx/93vftfFO0Cmc5ayHe9FiEERO/BiiqgCu/DCC1Mz8fEsgPATj5IYQkY+viEIxSOC68RHpBE12nWww2sVHi/Jw3nJ8tPyQk7HNS/nURsyZEgo/sBLAr8v2iV88GbgZgVGj8n0fdlllw3FFLyEE9Yyl/FSTx3xtFmuNiriVQQRfujTaLkmTZpkdB74IsF0szKjxxXzHdEvv/loqG8/LwYt+D3HMRs37vrzkodwMipIZdArnadZ6hVXGSrBjvrhSYRzOe+4/rVhnd8qwibX8eT285vJNDgUV6eYO1d0yf2awbVrr702uqvL93LVr8tJCvhCeQiHh6DadfhFD+f9iE7VSgqyomXQdxHIRYBn/Omnnx4OhuP9+Nxzz017CM929q277rpp9+faeMIJJ4TtMyaH0F5j8D3TADzigKOOOiprh3ccbSieoQgxnEdMvFTy8Q2xOSHL8VBebourXYPHDNqUtEGdl/NsZd96663txz9O3y+BUMLxcXnQLt1qq63cVy1FQAREQAREoAuBcrYteJ/BUyITP+lvQPhI3wyfTEbfEJMNeO7na0yU8ieqEakiXbSKaH702zhBJfvKycKdmzCghAvFYydtuUzGex0hs5kQG7UpU6ZEN4UeLrttrOAGynvppZeGfTyXXXZZ3n2KvNvTZ0VfShxGJBcml2oySRw0lYcIiEBfJfDCB83Gp68a/clMEGUyKKJDJmLiHRkrp1Cvkjzpp2HiK8Y4CZNRMhkc6AtlPItJuPSZlGpE+nnrrbfCbLL1N7gxH9oJjAFlM5yi0DbimpXzOlVjmbJx0T4RKJUAjjEYG+nTgspyDfqWCj/X8c7NMOkyDXrmyqPU/aWy60vHE9LxD3/4QwopAyMMfPRli3qkrB22bNHVHb3/WTb0yztlPH5ht3Df23dJO3gjwn5/Kqhkx4KH/27L5hBUJmqCm9vIsV3y8b+k85Tp76+mdf6WKiHOqqY6qywi0BsJMMOd50Ul/145F+dExFIOV/60QXiBpvOe2W2IRXnhdMYMeLxkEjJ3m222cZvTLgcMGBAOBhAymY53vCgwIOCMjlsEaoQGz/YS69KXumSAgZBKuTriqRue8BBW+GJJ6o63KTxKEb6c8MLMAuR6IKyNilzxcEVbAp6EnsSbnvut4GUU8SYek/ACmcnwckXecQgdEL7iLY+Ql4TN4tr6ITMpAwM3CBXwaJnrpT1TmQvZjidwzsd1cWEvOB7WzI5l/5lnntklHCmexqIeBws5Z6a0PHtdvZmVy7V314uOFgS4/F7pUHIhTzLlle/2uOuPl5CooJJt2SyOMlSKHV5mESjxe2GmNEIYZ3hs4ffN73bLLbd0m8Plvffe20VQ6R/ne67sclCMXxBT0/kU9YAWPUVc9YvmW+x3WHLvwiMMs9SdVxfCwSGoZ8CSwV0JKoslrOMqQYBQjogM8JqNh1W80/ptEQbov/KVr4RemhDSlWI8t2g/MPnhxRdf7OLhlXx5xtJ2wiMUbaBsFlcbikkbiAj4eyVspDP+jhGg06ZBdB5HO8PlnW0ZV7uGSTJcN1jj+RPvor5Rv7XWWit8rmcb/KCt6zwWu+MJE1pusb07l5YiIAIiIAK9j0C52xY8g5jkykQyJoTiwTD63g41Im3wzGLyFu2GnrBys3B1QthBu4XJFAg/8abpjHdRJhHiLXyzzTZzm7ssXd8LXil5x+cdmX6qnjbKziQb9x5P+dJFQKG9Sr8J77lEkinletPnR5t3woQJYb8iE2lLya+nGer8IiACIiACpRNg8iPPGPr36HelLYIzAcYS6EvpC8a7P+M4GJNuo5HC/DpSdyJ+8EymPzdbn4J/XKZ12h7OaQlOLDIxJZ3rYyU6Ge2ebEafLY4hiDg3bdq0tBFQsh2fz75qLFM+5VYaESiFAG10LBGoq5OlZFStxzrvGaV2hOdbP2aR4SkFI5wjbnqdMcuPkFIYg1CIBbIZHoBwL8xFwotRJne/2fJw+xjkZRa+M14ao54I2EfoSb/jnAcl4eb6+/EMsvKwxAsYxosmHRS+IY7wBSaHH354eM0YRJlUwIxQP0+3Dn/KwO8JBXQp1vz28/a/s/dMZVE/cpxNuuDzUNupHcHK/PtvtA//+LPUpqYVv2DLn/Kn8Hs0n0Rdow1cbaNU2qVvPWedSz8fxFhu71Ntma12S+33V9rnfmDTfrRdsMndhhI2arfjLVFXn0rWPm+2fXzXVanvtYOG2eQL7+uSJlqmbHVLZZRj5e0fbNylHhPP/oc1jF0xx1Hx7670vSz+GihHEegfBHhZYWZ/TxhivVVWWaXsp+Z5xAsZ9USwRKd9sYPMCBgQVfIyyjMujudcMQDwZoR4AHEQdSLsZ6Y6MRORgXYmVay88soltc9oN/CbobOavDKdM12dEBsi8KNNguiKDvU4DM+Pb7zxRlg/8u3JySP8xvitMShDe7TUNlCpfPidIpZloMFv45eab6bjq6H+cZWhEuzoBONvgt8wf09RQXMmzmxnMMqJbzg2V4iXbHmVa18p9StHmXgWMJOa+yZeNzN5Ji7HuftTnnBGNN6TxuByIc+nnixrMeemLUIbgOc7bSnaAeUaRHb3Qp6tpT5jS21D0QnOJF4mdyDsxCt5KX1OxbD3j4m7XYNYlLrR1uKaMvGlp9sRfn21LgIiIAKVJsAziHcrNyGm0ud352MSKM+cSkygdOes9LLcbQue4Tw3EVXynKP/gvfTcrVfSuFXbhaubLy38m7C7woW9CHkY0zMhGc1sqP8lI02Ktebv2Em4tIHRB17st2WD1ulEQER6J8EeMfk3sX7bjmNdz2s3Ofx63Dt058YH2ztsU22wYTCJy9c/dTH1umGwf3MI+t7rz/M+JTbmFRPnxN9FEQp8w3HC3hAxph8zjMTz5VM0vTtmmuuCSd8sI0oWPSrpLNs50qXvpBtTkjIMTgFyed3Qd2oI/381CGXMXmTiQ48f2+88ca0bVnaInh5xnDQsfPOO3fLluf6eeedZ3ioxA444IBwMky3hMEGvGEyCRVDV+TrfMKNkf/oE0fLQl8iEy4OO+ywSIruX/FkSXsSBx9co1xWiTLlKoP2i4AjUKlnAd5f+bvqsx4q6fingnx62yAAnfoYYrxSX5Bc5zEuSTHfE0G44bP//PCJbEIkgfX341966aWUmBIe/8/eecDLUlSJu1/gPXjkzIIsGBB1DYiKIEHMiooJBVkE1GUVUTCCaVGCigkzKIoCawITZl0w/GVRYRVzAHVFWSUHAR8vv//7Gs+8unV7ZnpmeubOvfc7v9/cnttdXV39dU/VqVOnTsEpHGf5v0qIZISwnOCgDpVV+Y9i3x2X/2jCZda9630n/J/+M3/jzYttX3Z6a9fN3zyruOHcd7T+v+lLHyw22vMpxZwF67b2xZdbf/DlNV9TLXKNseDct8fhyu3Kv99S/P2n3y02ePDMmA1TeZP/2En9hUy3OuwfxXcjgVlFoF37OgoIo7o2dRGRjaqWSOr1PokWhCPOVAvG8/vet30bl5YPpwM+TQhOkP0uJYqOFnpaE2WJPDDODzrLMvIadIsBpp0RZtC8+zmfwZlRshmH+2+qDKNgh0NfP07lGHvDmZL3gkiL4yj93t+w7oW2AOdTRQLTnQC6SKdIBE3eX5N14aA6FBN3h6VL9MOs6bLgSFHXmaKf8nqOBCQggelGgEmBnZZHHtX9oHdTln77waMq5yDXGbZuQRuOQx2fcZdhs4j777ffOorVCaKM/Wx51qyOwUeRgAQkIIHxIfAv2ywsDnpg+yWi25X0nDUOmavWOJxOB2HMhpVq0NtwpkSaWEaa8e6//OUvXRGEzaJrwj4SEMAIZ0qk24prkT1RJHGoJFrlhRdeWEYOj2NVW8bq4j6Z0EQQAFgS4TLs0Pvss0+5+lXV+eyLpbUZu6oTSZtxHSJZXnrppWUZCaI2qH9RXrZxLFNeRv+XQNMEmCjAWMiMdahkBjqVMxUcSwVOF2EJNSJaIixhMKjAAafIWI6QGYyx3nuad7rMOMb+iHY0288nvPVsk2XXXFncduk3Jtw2DpF1ZZNHPLu45RtnFyv+dn15CttbLvh4sekT/21SFrf+98SZL5MStNlx65plwmeDQyX1F2JEjTYvgrslMCYE0DfWRPyestJwbcqg8/WUPQIvLAEJTEMCn/vc5yaUumr28IQE/iMBCUhAAhKQgAQkIIFpRmAcnCkD2TiVJcrkVgISkIAEJCABCUhgIgEcKN/znveUOwmu0URgDKIhHnrooRMvVPEfkx6++tWvVhwZfNfXv/71ViZ1HSqZiMvqF1dddVXp6PiMZzyjlUfVl0984hMFnyphwsnhhx9eHHTQQVWHy304XcaKNXvvvXftlYGIIopDJQ6dP/jBDwrObUrGsUxN3Zv5SKATARwqkRnrUInnNUsL8JkuDpU4QxDuF6FSjWXCyx0D/CGCSDhU4g1PRbrXXnu1cvzZz35WXH/9nc5v7MwjvMzm8zfffPNZc1cfAABAAElEQVTiUY96VItV1ZeLLrpogiPNvvvuWzrANRXFquqaw9q3/LqrimtOf8WaoJF3RkbkOiz3vXDH+lHE5ixYWDpPXv/Jt7SKedPXzlyz7PezirmLNmrtW7JmafDl190Znp2dc+bNL+ZtsGnreP4lHDTZv/gXFxUrb72xmLfR5nmyGfU/9RfS9EySGQXJm5GABCQgAQlIQAI9EmAZ7XPPPbd1Fga9utFqWyf5RQISkIAEJCABCUhAAtOMQD4Rk/GIVOJ4vj9N43cJSEACEpCABCQwGwn84uqlBdEme5XpEp0y7gtnwzPOOKO44447iic96Umxe1pvCTZ2wQUXlPfACqMEI6srOCueeeaZZaTJK664ovbKPfj6EE176623LvbYY4+CaJfdAnkRBTN8A+o6fXIfD3vYw8pgaTiuEk2ySYfKcSxT3WdnOgkMQiACOM5Yh0qcKKnoidq0/vrrD8JqJOcSOfK4445rRac8+uijix122KGRaz/vec8rvv3tb7fyOv744wuWpcarnlDDr33ta1vH+EL6VGbz+RtttFFx+ulrl7NOucR3Gqkbbrgh/i1nbRCGedxl1bIlxd9/9r2ymCtuurpY+ufflpEpV91xW6vocxcuKrY54s2t/+t+2XjfZxY3f+NjxYqbrilPIc+bvvbRYosDXtrKgiiTqWy4+5OKrZ9/crprwverTj64WPK/Py/3rV61srj1B18pNn3cYRPSzLR/IuLddHEKn2n8vR8JSEACEpCABGYmgd/97nct49T973//sh82M+/Uu5KABCQgAQlIQAISkMBaAgzmpk6TEQEnUrCsIM6U+f447lYCEpCABCQgAQnMVgK/vGZJwWe6y4knntjxFljF9Ctf+UrHNIcddljBp5t0u1a38zsdP+CAAwo+dQTHqM9+9rN1kk5Kc/DBBxd82sk97nGP4lvf+la7wz3tf9rTnlbw6VUWLlxYfPnLX659Wi9pR1Wm2oU3oQRGRICVZOk7z1iHSioOZMmS8WrYbrrpptKJkbIRLRJP9ssvv7xgybm//e1v7C4e97jHFUcccUT5Pf3zhz/8oYxguemmmxave93rCpz96shuu+1W8CHUL0K0yv33378ME0x0llRwsnzEIx6R7irPnc3nT4Axg/5ZedtNxV/f86K2dzR3wXrFNv9+SrHO1r079s6Zv6DY7MkvKK47+4RW/rdc+PFik0f/azF/ky2L1cuXTlpWfIOHPK6VturLhrs9vuVQyfHb/vv8Ge9QGfVX1GdVXNwnAQlIQAISkIAEJNAbAaJRMuHs7LPPLvtFzBhWJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQkEgc0XzYuvQ9+O8lpDvxkvIAEJSGCaE5gzZ055BzPaoZKbJCwu3qPjsmQu0Q7bRTwkCt0xxxxT6UzJ0/rUpz5VfPe73y0f3O67796Th/opp5xSHHXUUa0ImGSSO1Pe7W53K971rncV8XKUF/rHn9l+fspiNnxfuN1OxTYvfHuxYLt79H27G+31tOLmr55ZLL/h/8o8Vq+JiHnTlz9YbPWc/yhuv+zbxYRImOttWCz6lz06XmuDBz+2uP7Tb1+TZnWZbulfflcs/dOvi4U71F+OvOMFxuwg9Rb1F79HHSrH7OFYHAlkBJihQhSHqZSIMDGVZfDaEpCABKYTAWZbH3nkkdOpyJZ1FhEoZ7/On1/aMqbitrGfqFtMBXmvKQEJSEACEpCABCQgAQlIQAISkMC4ENh9h0XF+pfcXPx92aqhFmn9BXMLrqVIQAISkMB4EIglv+eOR3GaLwVOSLHs8uLFi5u/QAM54kDJst4sGU3ESZbSqIpMGZfCiZJ72myzzYpddtkldtfa3uUudynOO++84jnPeU6x9dZbTzhnyy23LJ7+9KeXUTLvfve7TzgW/8z284PDTN3OW7RRsfCf711suMeTi7sc+9Hin0/6wkDOlHCaM29+sdn+L5yA7NbvfW6Ng+Vfiny57w12fVSZfkLi7J/5m21TrHePB0zYm+cz4eA0/yfqLX7zVU7O0/z2LL4EZhwBnA6m8jPjgHpDEpCABCQggVlOYOONN54yAlN57Sm7aS8sAQlIQAISkIAEJCABCUhAAhKQgAQSAputiVB5yhO3Lu699cJi/tw7o5Ulhwf+Sp7kzTW4liIBCUhAAuNBYOXKlaWPzpxly5bdGe5tPMrVaCnuuOOO4vrrry+jU2677baN5j1VmXFPOGLiETuI3HzzzeXS49tvv32xxRZb9JzVbD+/Z2B9nsDy7KtWrSq22267gZ95n0XwtCki8Ne//rWMSIPDcziHT1FRvKwEJCABCUhAAhKQgAQkMGICrChx2WWXjTxKJdEpd911V6Pkj/h5ezkJSEACEpDAMAhcdNFFw8i27zz33nvvvs/1RAlIQAISkIAExpPA1VdfXaxevboMIjXMEv7pT38qsydYlSIBCUhAArOTwKjagmuvvbb005qxS37z+uCEhOMhS+fiiDgTnJKauodNN9204NOvzPbz++XmeRKoQ4D6inqL+qup33yd65pGAhKQgAQkIAEJSEACEhgPAgsXLiwe9KAHFX/84x+LW2+9tVgzGXaoBWPi5kYbbVTc9a53LSdxDvViZi4BCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGEMCBL1jssCMdqiE+4Ybbljccsst5QCEjklj+CZaJAlIYBIBBkwR6i9FAhKQgAQkIAEJSEACEpidBHBy3HnnnWfnzXvXEpCABCQgAQkMTGCDDTYobr/99oHzaSIDyqJIQAISkIAEJCABCUhAAhKQgATGncCcOXPKIs4d94IOWj4ckubOnVuwXBZR3xQJSEAC40yAeor6inpLh8pxflKWTQISkIAEJCABCUhAAhKQgAQkIAEJSEAC40uAyNOLFi2a8gJSBsqiSEACEpCABCQgAQlIQAISkIAEpguBGR+hEs9Rlq0iSiUfo1ROl1fTckpgdhKgnkI23njjIjzfZycJ71oCEpCABCQgAQlIQAISkIAEJCABCUhAAhLol8Amm2xSPOhBDypYrmwqhYnjigQkIAEJSEACEpCABCQgAQlIYDoQoA+Nr86Md6jkYRDljaUtli9fXi79jYOlIgEJSGDcCLDUN/XU/PnzC5fBGbenY3kkIAEJSEACEpCABCQgAQlIQAISkIAEJDD9COjQOP2emSWWgAQkIAEJSEACEpCABCQggakhEIHPZsXUQG52s802K0n/7W9/K1asWDE11L2qBCQggTYEqJeonxDqq6ik2yR3twQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCTQEIHw1ZkVDpUwW3fddYv111+/WL16dXHDDTeU24ZYmo0EJCCBgQik9RL1FPWVIgEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQmMhgBLfuPDM2scKsG66aablkvpLlu2rLjllltGQ9qrSEACEuhCgPqIeomlvqmnFAlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhgdATmzr3TlXJWOVRy01tssUVJ+bbbbisWL148OuJeSQISkEAFAeoh6iOE+ikq54qk7pKABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEhkCA6JTz5s2bXREq4bhgwYJWBLgbb7yxjAo3BL5mKQEJSKArAaJSUg8hRKakflIkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggdESmDNnTrFy5crZ51AJ5g033LDYYIMNyjXPr7vuumLFihWjpe/VJCCBWU+Aeof6B+926iPqJUUCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGD0BfHhwqpw/+kuPxxWJBodD05IlS4prr7222GqrrYp11llnPApnKSTwDwJ3uctdZDEDCSxfvrx0ply1alWx7rrrtqLmzsBb9ZYkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQw9gRwqJw7d+7sjFDJ08GbdMsttywWLlxYhuq85pprisWLF4/9g7OAEpDA9CZAPUN9Q4hg6h/qIeojRQISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKYGgI4UyJ3/p2aMkz5VXFiIjLlokWLymV3b7jhhuLGG28siBqnSEACEmiSAPUK9Qv1DB7t1DvUPzpTNknZvCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggZlIgDFWRQISkIAEZh+BUdb/XAv/nlm75He8XjgzbbHFFsVtt91W3HLLLcXf//73chnwTTbZpFh//fUjmVsJSEACfROgXqF+ISoldQ71y4Ybbth3fp4oAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYDYQmD9/frFixYpi+fLlxYIFC2bDLXuPEpCABCSQEKD+R2gPRiH49YzmSqO4mwGvgXPTuuuuW9x0003F0qVLy0hyOFni+MR+RQISkECvBJYsWVI6Ui5btqw8lSW+N9tss2KdddbpNSvTS0ACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEph1BPDXuP3228sPY62KBCQgAQnMLgK0Acio/PdY9luHyuQdw8lp6623LqNUEk0OJ6jrrruuwAlqo402KtZbb70ktV8lIAEJVBO44447iltvvbV0zibFvHnzSudso95W83KvBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggSoC+GqEQ+UGG2xglMoqSO6TgAQkMEMJ4LsXDpW0B6MQoiLrUFlBGqenRYsWlcuAh1PU9ddfXzpF4VTJMZwsCfGpSEACEli9enXpPLl48eICZ0qW9kbwWqdCJwKu9YXviQQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIHeCBAYizFXfDcIiLXVVlvpVNkbQlNLQAISmJYEIhAiPjm0A6NaDdYIlR1eF5yfeBjMcMDTleW/cZKKmQ8cx6mSh0X0ufiwXjvfdZ7qANdDEpiGBKigqQPwRGcbn+XLl5fOlBwPoQ7AiZL6g4pWkYAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCB/ghssskm5QqjS5YsKa655ppyHJZgWAsWLCj9M/rL1bMkIAEJSGDcCOCLgyMlwczw0cMXh6W+aQdGKXPWFGKtF9AorzwNr7V06dLygfHQcKJSJCABCQQBnKtR2vngbK1IQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISmKkErr766tLRZYcddhjJLeJUc8stt5SRKkdyQS8iAQlIQAJTToBgiDhTjiqw4bXXXlsGWnPJ7x4ePU5SfHhQeMTiYJlHq4uodWm0uh4uYVIJSGBMCVA5RyTadEtUWuoF9ikSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNA8AcZrN9100zI65eLFi0t/DaKY4aOhSEACEpDAzCCA7w3Rh/HDiUjEo7wz/P0ogw6VfVIH3qJFi/o829MkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABHohwMqBG2+8cS+nmFYCEpCABCRQiwBO+vgEzq2V2kQSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGAGEoilxXWonIEP11uSgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIoB6BuXPnFqtWrTJCZT1cppKABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhgJhJYvXp1gVPl/Jl4c96TBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKoQwCHSsQlv+vQMo0EJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACM5LAvHnzyvvSoXJGPl5vSgISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIIE6BFasWFEm06GyDi3TSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDAjCcyZM6e8Lx0qZ+Tj9aYkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISqEPAJb/rUDKNBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAjOawMqVKwuiVBqhckY/Zm9OAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggU4EiFC5evVqHSo7QfKYBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAjObwKpVq0qHyvkz+za9OwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQk0J4Ay30jLvndnpFHJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpglBHSonCUP2tuUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYDIBlvxGdKiczMY9EpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCcwSAi75PUsetLcpAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQHsC4VA5v30Sj0hAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggZlNwCW/Z/bz9e4kIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISqEFg7ty5Zao7/9Y4wSQSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGCmEVi9enUxb968QofKmfZkvR8JSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEahOYM2dOsXLlSh0qaxMzoQQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIzjgARKnGqNELljHu03pAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhIYHgEcThQJSEACEpDATCKgQ+VMepreiwQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAJ9EZg7987YlPP7OtuTJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDADCBAhEo+Lvk9Ax6mtyABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBA/wTmzJmjQ2X/+DxTAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggZlAgGW/jVA5E56k9yABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBA3wRWrFihQ2Xf9DxRAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggRlBwAiVM+IxehMSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJDErAJb8HJej5EpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUxbAkSnXLVqlUt+T9snaMElIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISGJjA6tWri3nz5hXzB87JDCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJDBkAsuXLy8WL15cLF26tFi2bFmxcuXKIV/R7CUgAQlIYFQEcGZcsGBBsXDhwmK99dYrv4/q2lyHNkWHylES91oSkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAj0TIGrYLbfcUtx66609n+sJEpCABCQwPQjg0HjHHXeUH+r8jTbaqNhkk02KOXPmjOQG4jpGqBwJbi8iAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJNArAZwpr7vuumLJkiWlU80GG2zQilxGJDFFAhKQgARmBgEcKok+jFPl7bffXjrR8/9WW201EqfKuXPnFqtWrXLJ75nxOnkXEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYOYRIEoZzpQ4T+JUw3KwigQkIAEJzDwC1PMs9c0H5/lwpqcd2HTTTYd+wzjw41Q5d+hX8gISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhLokcDy5cvLCGUsw6ozZY/wTC4BCUhgGhPAeT4iU956660F7cGwBYfK0qly2BcyfwlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCfRKACcahEhlRqbslZ7pJSABCUxvAtT71P9ItAfDvCMiZCJGqBwmZfOWgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEuiLAEt9I+FQ01cmniQBCUhAAtOWQNT/0R4M80ZWrFhRZq9D5TApm7cEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQQF8EwrllnXXW6et8T5KABCQggelNIOr/aA+GeTdz5swps9ehcpiUzVsCEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYCAC4eQyUCaeLAEJSEAC047AKOt/l/yedq+HBZaABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUigaQIrV64scOA0QmXTZM1PAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQggWlDgAiVq1ev1qFy2jwxCyoBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpBA4wRWrVpVOlTObzxnM5SABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAtOEAMt9Iy75PU0emMWUgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYHgEdKgcHltzloAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSGDMCbDkN6JD5Zg/KIsnAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQwPAIuOT38NiaswQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQALThEA4VM6fJuUdejEjZGdcaO7cwYJ3Np1flMutBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAs0RCH8/HSr/wfTiiy+eQHfPPfcs+nWqBG6e39577z0hf/+RgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIYOoJ4CuI399gYRin/j4sgQQkIAEJSEACEpCABCQgAQlIQAISkEAbAitXriyWL1/e5qi7JSABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISgMDq1auLefPmFUao9H2QgAQkIAEJSEACEpCABCQgAQlIQAIzkMBf//rX4sorryzvbPvtty/4KBKQgAQkIAEJSEACEpCABCQgAQlIQALNEfjdDcuKs//n5uKX1ywtM73vNguLwx6yabHTFguau4g5SUACEpDASAjMmTOnIEiBDpUjwe1FJCABCUhAAhKQgAQkIAEJSEACEpDA6AhcddVVLWdKropjJYagHXfccXSF8EoSkIAEJCABCUhAAhKQgAQkIAEJSGAGE/jrrSuKV335mmLx8lWtu7z0qjtK58rTnrFtse1GuuS0wPhFAhKQwDQgQIRKnCpnZe29dOnS4o9//GPxt7/9rVi2bFnl47r44osr9/e786KLLipPXbBgQbHxxhsXd73rXYuFCxf2m53nSUACEpCABCQwjQmw7OYNN9xQLFmypPzccccdBfoJsu6667Y+6623XrH55psX66yzzjS+W4suAQlIQAISkMCoCfz5z38u/vSnP026LE6WW221VbFo0aJJx9whAQlIQAISkIAEJCABCUhAAhKQgAQkUJ/AspWri5MvuG6CM2WcjYMlx9791H8qFsybE7vdSkACEpDAmBPAoXLu3Lmzz6ESZ4XLLrusWLFixZQ8Ihw4r7/++uLmm28udt11V50qp+QpeFEJSEACEpDA1BC47bbbCpbexJly1aq1sxXT0tx+++0Fn5A//OEPxZZbbllsu+22xQYbbBC73UpAAhKQgAQkIIFKAu2cKSPx3//+dx0qA4ZbCUhAAhKQgAQkIAEJSEACEpCABCTQhsDl1y8tTvv+TcXvrl9WrFi1uk2qO3dvtmhecdLjti7/+Y9vXlvctHhl8fsblxVPOnPyhFcSzZ87p9hpywXFix62WbHzlgbiupOifyUgAQlMPQGcKZFZF6GSyJRT5UyZPnbKQFnuda97pbv9LgEJSEACEpDADCRw6623Fv/7v/9b4FDZq+B4ee2115afDTfcsLj73e9esFUkIAEJSEACEpBAToColDhUthOiX2+xxRbtDrtfAhKQgAQkIIFZRGDx4sWlrSGd2MlETj5bb721EzBm0bvgrUpAAhKQgAQkMJkADpGv/uq1xd+XVQfHyM948F3WKx0k2c/3/7pibeCMPC3/46D5m2uXltc481nbFThkKhKQgAQkMPUEiFDJZ9Y5VLLM97jIOJVlXJhYDglIQAISkMBMI0BESpwpUbwGFRwyf/aznxV3u9vdyoiVg+bn+RKQgAQkIAEJzBwCdZwp73e/+xVz5rjM1Mx56t6JBCQgAQlIoD8C//d//1egO+SrZ9xyyy0FH2wZO+ywQ3GXu9ylvwt4lgTGjMDKlSuLeO833njjYscddyzYKhKQgAQkIIF2BH74p8W1nSnJ49Kr7ih+f8OyMju+1xUcNrnWfvc2kEZdZqbrTGDJkiXlirnbbLONdsDOqDwqgbYEsKHPOodKltweFxlFWbjGggULxuWWZ1U5iAry6Ec/urznhz70ocV//ud/zqr7H/bNfuELXyiOO+64CZe58MILi3/+53+esM9/eiOwdOnSYuHCZsLKY6SaN2+w2VTkgQyaD3kQGXj+/FnX7HHryhQR4P393e9+V1x//fWVJSBC1FZbbVWwXXfddcvfHsoZbTcfolreeOONBctypoJjJsuAc3ynnXZq5PeR5u/3mUPgVa96VfHFL36xvKGPf/zjxW677TZzbs47kUAbAk3qDm0uMfLd46afAaCpMs3E5zXyF+QfF7zyyiuLq666quPl0TOmqzMlzh6x1ErHm/SgBKaAwMEHH1z86Ec/al35wQ9+cPHJT36y9f+4fFm+fHmxzjrrNFqcYeTZaAHNbBIB2t4mbCyTMnbHtCLwq1/9qrjppps6lpm2lxW2CArxL//yLx3TelAC40yAd/mcc84p3vOe90ywsT3jGc8o3vKWt4xz0ad92dThp/0j9AYkMOsJ3LAmQmUvcssdK4sXff6vvZzSStvrtVon9vjlbW97W3HppZeWZzE+9YEPfKAnWxHjDanPw+mnn15sueWWtUuB/eqVr3xlmf7QQw8t9t9//9rnkjDKj33rhBNOKO5zn/t0Pf+AAw4o0+C38cIXvrBt+mc+85llYJR99923ePGLX1yZLi1/ZYKKne9///sLHBtT6ScfxnvwOWknTIg666yzip/+9Kfl2CLpGH/ceeedS06Pf/zji+22267y9B/+8IfFO97xjspjdXaiay1atKhOUtNIYNoQwBatZ8m0eVz1CvqXv/ylbMR+8IMfFHzH2YLlvO5617sW97jHPYrDDz+8/F4vN1NNJYHnPve5BYbpXGgsH/CAB+S7K//HoP/ud7970jEM6B/72Mcm7XdH7wRwbPrJT35SOqzc+973Lg466KDeM1lzxiWXXFJ89atfLZfnQ+G5+uqrCxSshz/84X3lV/ck6onPfe5zxQUXXFDO0MVxa5NNNikdU1lW+PnPf35xz3ves1Z2v/3tb8u8fvOb3xR8v+OOO8p65173ulex9957F0960pO65oNh/9vf/naBw+wvfvGL0hENQ/8//dM/lWVCiX32s59daxCIvBjEuuyyywrKhHK6+eabF5SHZ/Wc5zynXL6oa6FMIIE+CPD+EUkyd4YkK97DbbfdtvytVWVNHb3++usXm266aRkNgt8SUSNyx0z+Z3ku2oQmB8Re8YpXlEt+UTY6p5RVkcBMIfAf//Ef5aBg3M9mm21WvPe9741/3U5DAk3qDvntq5+tJdKUzjjM57W2tLPvG3puN2dKqKA3/PznPy/uf//7j2zi5cUXX1xgXEd23333tgbpMkGbP9/5zneK448/vlx+lIHwuv2TNtm5WwKzhgBOUJ/5zGcKJp8SkYv+A4MpRJzDTojj54EHHlirfx3Q/ud//qfsZ9Pnx3ZB1AsGhnZcE+WLKPr015v+jbazj0WZWJqYMvDZfvvti0c+8pHlfcZxtr/85S+LU045Jd1VDiS99a1vnbAv/4cVAhhIo/0Kob+GvYY+W1Py3e9+t/jIRz4yITvu+1GPetSEff3804Stpp/res74EuC3282ZMi09aTln3G0Dn/3sZ4vzzz+/LDr2zEc84hHpbfh9TAhce+21BXYnhGXl3/nOdw69ZK9+9atb70Z6MaNTpjSa/Y4j5Yte9KIC5wzGS3gGigQkIAEJjAeB22+/vYxcSGluvvnmcox71113rV04xm85L6TXldm+8Y1vtM7/8pe/3LNDZVr+t7/97cUZZ5zRtU8b5a0ar4v7YBs6cqd0BO+J/NJzO31P+5ORrp98qvxGIr9Pf/rTpe8H+abC+CIOlny+8pWvFCeddFJx3/veN01SfifIS6/3lWZC269IYKYRKIN1zbSbmq33Q0X8pje9qTRq5hXWDTfcUPDB6IlhAYPcUUcdNcm4OFvZjet94wxJ5JdceIZ1HSoxnMcskzSfpiIQpnnOtu8s38ssHJSPGLw88sgje8aAM+OZZ55Z/P73v590bv5bnpRggB0ouGeffXaBspkrYLG0D4Ot3ONTn/rU4o1vfGMZQa/dJXnXTjzxxEnvLI6MfFCwGSBAUWMAp0rg+PKXv7x0QkuPw4GIr3z++7//uyw3s4722muvNNmE7wwUHXPMMROihJCA/Xwuuuiisj7EaLfnnntOONd/JNAEASJT5p0uBt6YCYajZC/CbwZHYJbZCmflOJ9rcC2ONyX89nHgRKraoaauYz4SmAoCv/71r0uH/bh2PjM09rudHgSa1B3SO1Y/W0ujSZ1xWM9rbWln5zciR+EoVVcwpDLpg8lTRMLecMMNy8HsYUWu5BrRJ+0lYkF6PxiFGXzng7E9BuHTNH6XgAQmEsA5733ve1850TE9gmN19NO/9rWvlf3rY489tnjMYx6TJpv0Hcd6+tj0FXJhQigfJnfzeyX6x0tf+tJyIlmetp//29nH2uVFfwvHTiaVM2EUIboek87pT6VCWR/ykIekuyZ8/9SnPlXeV7qTiKRNOlMyIMikH+q4VJ74xCem//b1vQlbTV8X9qSxJYAegO7Qq3AOv612Nr2q/KgX0AN4x9Ez+N0wwZRJ08OQD3/4w617Y4UaHSqHQXnwPLEzhW6Ig/+whUly4WjLSm5Epdpnn31Kxwv0YGU4BNA1CJqAMAaBDtFL/TGcUpmrBCQgAQlUEWCcu65DJfakGBOvyqvbPpyTCPATwlj75ZdfXo6bxb5etowbEy3zec97Xi+nNZb2KU95Sq3ASAQ/6yR182EiY5V873vfKyfoYcclDZE/cZqkP4w+zgRDAm0xSYrAXa9//esnjY0z+frUU0+tyr54zWteU44V4p9y2GGHVaaxna/E4s5pTsAIldP8AUbxMYYy2+v73/9+uQsDBbPB73e/+5XR4a677rrSYMhxHKfw1KdxYjusQZMo26i2OFlFY/msZz2rOPnkk0d16ZFf55vf/GYZnQPHnE5ClID/+q//6pTEYz0SwBGPKJJf+tKXSuWjx9MnJGemB1FWPv/5z0/YP6p/iDqHI2c3wZmRMrIcMU6VVfKud72rFXWm6njsg9sVV1xR5pcvvc3gDAMZdWa/oKy/5CUvKZ09q5Z4v+aaa4qnP/3ppSN5XLtqy0wjZqzjVPrkJz+5Kon7JNAXAd5n6otUNtpoo9LpcRCHdiKv7LLLLmWbnv5WuBb5j3u0iJTHqL7PJv1gVEyrroPuxQxHBId1okwoEhg2gSZ1hyir+tn8QNHaNqUzDuN5tQo5i7/06kwZqOgrstQngu6MjLODOdHu/9//+38FfYg99tijLK9/JCCBagJMuH7DG95QnHfeedUJsr1MpGLSNZMb2y19hhPlC17wgtaSYVkWE/7l+ueee245oZIJmkQDH7XQVzrttNPKCZmf+MQnCvpg2D9x5sgnwuKA1c6hEr0AJ5BUyCvPIz3ez3cGrXJnyn7yyc9pwlaT5+n/058A9oN+JnBzDudW2eFyKvx2cF4mSm4q7Of3ST5MNh3EPpLmy/cf//jHLWdK/icq3nSIqklZleESwB4dwhLf7ZbvjDRuuxOoYwNitTzslPwOd9ttN50pu2M1hQQkIIEpI8DKIgTaiclonQrCGPkggo7GtVhxDTsU9kL8HtAN+xUm9TFZgrZn1EIQlLpBsDqVbdB8WLYdZ0qYsnJpOmmEZd1ZxYFVY4444ojSDkgflBUrUn2c59/uHYgV8jjexP12YuExCYwbgbnjViDL0zsBlqcJZ0pmelJpErXhzW9+c+lkSDj9s846q4xeGTNAGYzAY1+ZfgRQNJhp0E2IBphHR+t2jserCWAExGEXhYzfFTM5BhEMiCw33c6ZEkWHmSDDGnhgdmjuTImHPYMIOCJy7VxYOhvHpFxw2M7zQgFjoPOxj31skc+6gSVRn1JByTv66KMnOVPikLPffvuV+eRLsPBuM4umShgQISpvKiz7RXQHFD3uNQSDMIpjHgI9jruVQK8EiHqSR3vA2ZHfVdo56TXfSI8jAdFV8iiXzORjOTpFAhKQwGwg0LTuADP1s4n6GUya0hmH8bwo32wX2v5eIlN24kXkqHGWQw45pHSoxO7xsIc9bJyLatkkMOUE6A9XOVPiUMgy3wzUxGBIWtj3vOc95co26T6+4xCFoyVRLXLBdsHAVzpYE2lwEKTPThswVYIjKJEfQ1g+O7d3YB8l4n+V4BCK41cqRL5scvLQT37yk9Jem16jie9N2GqaKId5jB+BQdr8uucSyCF3pkxJcCyPFpse7+d7bmuMCeL95OU5M4tAaqN7/OMfP7NubozvBhvot771rdJJJp+cMMbFtmgSkIAEZhUBgnPRN2R8lGW4uwljX+GfULVkdLfzOR7XwZlv//33L0+hvcAu26uUkePWjJcxqY/AOWxno9D3izFx+rxV/XO4LFq0qIxcyXd8TVjZVpGABNoToI6hX7nWq6R9Wo+MMQGWvcHzHsGZklngj370oytLTONEhJGISsn3KoNo5cnuHCsCOMx2kzppuuXh8TsJ4ECJM2E3ZSx+W924MTsEo3kqRJ4jIgQzgZhV3cvS7mk+db5/8IMfnJAMAwdOkURuOOWUU8prv+xlL5uQhn9w0s2FZcRSRRdFjXwwlLz//e8vQ7fnoeK5furAyD3/4he/mJA19RgRVmFFPixFFg7hkRCGuYGWwY588IhQ6eRFdAaWu2KbDiAxAyo3vMY13EqgVwJ/+MMfJgwaEk343ve+d6vt7TW/qvTUNSzxnYbQZ6CSaysSkIAEZgOBJnWH4KV+NlE/g0tTOuMwnlc8t9m6vfLKK8tZ/E3d/7AmcjVVPvLBgYlJKooEJNCeAHUDk6xToe/7ute9rrQzEPkDZ/nLLrusjNaYrhyBvQObBMsBp4LtMAZnYj8TvIhMgk0S2xM2DPrarJSTCpHLzznnnHRXI99xrGapufjQznB9nB1zoXzpPbEUeSr0o/JJohzHaJ7vZwCKSJ1NCXYRHD65VtPShK2m6TKZ33gQqOsUWVXaOhP3iXzNAC2C7QJH7oc+9KHlh+9hO2VAHntcE8LqXV//+tfLrJjgHVE0v/CFL0ywzzRxLfOYfgSIkBhCpERldATQQfjdKxKQgAQkMJ4EsLHEKiD077pNhqM/yUqo1O/tfFE63SmrBl566aVlksc85jEFzn84LKGfVgX06ZQXx1hZ8eCDDy6T/f73vy99ZLqdMxOPwzWknTNlHGdckUmWfAzQElTcSqCaAHUi9d2sd6jECLfnnnuO5DOMSAoYyKKBO+igg8qlvqsf+Z17MWA84hGPKP/BCYrQynUEwyqOGhg7OsnSpUsLGq18Bnenc/JjGBKZOXj11Ve37i1P0+T/TZQZQxGG62EYQavuFQN4JyMWz6nK+a0qr077UIyYqT/I84z8MagRQaUbIxzkWBaaZegGkXhn02VxB8kvPZfKk99SP8LAxUc/+tEJpz7oQQ8qBzWI+rDllltOONb0PyznFcuyRt5Ey6QeTAUlNB1c4RgzzFOhDsGJO5VnPvOZEyI+4Oj9mte8Jk1SGkyZcRRy/vnnx9dyyyDFSSedNMFZDC5PeMITJqTjn7xMOFPymw7BmY3BI55ZCPnk92vE3qDjdhAC1L3p4AQDBThTLliwoGu2DALGp2viNQn4fdL5SYUOUHr99Ng4fI/6ncGOfoX2iDaiUxvYb95TeR73g57VzXG/XRmjzWuiE9zEc2pXzqr9PFN0DXSOcZYmdSLu86qrrip13am85ybvqa6e19T9Nqk7UCb1s2KSftakztj082rqPZqu+eCA05QDQjCgDUonSdGuEF2uiXYlrjHqLQ5U3Bd90LCZ9FqGptoonhltXT4ZrG556BtzPg4B3frT7fJswu7Rjx7Wb3nz++Cd5L2HQ+ocl6er8z/vA3VcvxOMsTFwfr/vVZ0yVqXhepdcckn5aXdtHOHT/jD54BB52GGHFUziDGFiFst8/9u//VvsKrf87nGODIE5joqp3Oc+9ykjKu60004txyiOsxrEhz70oSJfXWLQJeHSa8d3+kLcQ3ywF3D9E044oVx1I9Kx5d1JI+Httdde5dJmaRqcLonqkQp2NyIBp4LtZPPNN093DfQdmy59m6alKVtN0+Uyv/EgEA6N/ZSmXd2T5pUO6O6www7lYC02ET4M3KbOVWnaNI9ev+NMGTYCJlU/9alPLbOgz0W92Y9wr7R72B0j717zaSKPqWq/03ttQg9J8xvkez86VaqLpPbhOuWgzR/kHYhrhM2m29hapIc5Y2zd7CS8Y4xJ8a72K6O2AXUqJ/eLjvXnP/+5ET2r1/clfVc6ldNjEpCABGYKAfoNT3rSk8rbwd6QBwLK7zP6dvi79DPhlcA3tImMAZMHq7ARDAyJyTH5Nbv9/6//+q8t/ZLJfLQhs03Qr2NlRlZgoP1rJ+jnBETiUzXm3u4890tgNhKgvkLmz8abT+8ZI0JUMun+YXxvWiGnMx8e+xglWJK4juy9996l8xZpcahkGdwQZo8fd9xx5b/Pfe5zC5yjcEb6wQ9+UDppMMPzwgsvjOStLedhOCU8cBhXMDIS8pklfggb3UnoJGL4ZWbCb37zm5ahgsh5GEWZJZ9HuWMmBMZSJO1cfulLX2rdH+eefvrplZduosyEkCa8NR09BCUA4/Kxxx7b+HLNsA9FAF4XXHBBy0CU3yBKScpkxx13LDvXebqq/5nh/6lPfap8DmnHnXsj2sAxxxzTUnDS84lMwLLNqdBwowTxDBjMQlh+6uEPf3iarIxqwPuDkRsDPsJvc7vttitnuZBvavyfcHL2D3mwLDdLO4XTDu/is571rOIlL3nJJCfB7PS2/3L/GN6ZdYNTMoME3d7rqsyIFpHWBdtss03xvve9r9hkk02qkje+L13mJDLPnwf7uT+WAOe3H5LO6GUf/+eDWcwqyoXfIRFl4tlyPB2YyMtEvVE1SAH73Bk1L1OaL9dBGa9iy3LkEZqedBifeC6jag+4pjLzCOTvI0ve5wOK7e46rRfapcn3Uy9utdVWEwb/KEM/dVOed1P/067TphCFNtow8qbc1MtHHHHEBOfpquvSfhBZFp0n6nXSMXD6tKc9rfj3f//3CR34QfWDqjI84xnPaDm/oT+0M8J/8pOfLKPqkgfRb7jHkFTHesMb3lDsu+++xTvf+c7iu2ui/8agPLMq0SOe//znF1X1aeQVW+po2lj4xiDP9ttvX1DHVUUajvPybT/PiXuLpWZTBxEGsKIufe9731upMzBwdtZZZ5VO+THxAQf4u9/97gVLcBH5px3jvOzD/L9fnSh9Bykf9QD6EAPmODWgo0TUFn7Hj3vc44rXv/71ZbT59H6IznzyySenu0qnBvSjVFgC95WvfGW6q9Sr8yUtSdDvPfWi52G8Qp8P4VmynAuTLFKhQ8rzjneXY7y/+WSN9Jz43qTuQJ7qZ3eSTfWonDEp+tUZ87wG0fXuLOns/othtB+9oRM1+pfUTdQb5E1dEQ6WRIm/xz3u0en0oR9jKeKoG970pje1JonmF4bN5z//+eLjH//4BAcqnK6IqEddmdsV8jz6baPSdp4+84EHHlhG/mflgV/96lctJzf0Q9p57C3RXuZl4H/uhXum/5M6hWL7oV9OHzkiSVSdH/sGtXv0qofFdXl/mDCIwzp6VLrscqTptuVdZMAGGwLlSG0c9Bfpl7/oRS8qHXTyvI488sjyPY79OA8yWPDGN76xoN0MhwbaVKJVvfa1r+2ot9NWsZoD5+KggxD5gTIcf/zxcZnKLTY2nkMI7SKrOkQEtdjPM6W9DcEexjuA3sz7wEBX1NPYeL7yla9MmLhFu4oTYCoMlsQAWbo/vvMu5tGI0S+xfSDYlnLh3eU3VSW83+iCaVRKVvxg0lddm05Vvr3s22effcp6ID0Hbg984ANbu/iN4hwZwruFbootL+SMM86Ir+WW8ucOqBMS9PgPNog0mmhuM+kxuwnJm7LVTMjUf2YMAXRy2v1+pM7vOJ3kSf2VC+961GVp2jxdL//HqjPUTfStcJinL4rQDu++++61s8MW/rGPfaysY1ObJ/oQ9RvtS7s6MC7SRB5T1X7HPTSlh0R+vWyb0KlS3TF13E0DnrDM6Ktf/epJRcM5kYn3TNqPcxmnoE1FV2acatttt510HjvSsncbW/vOd75TjrtxHnli32KMgLaX/hPtOroAeh/jdziMhBB9FbsDelbYFvhtYwtC7+gWfX4UNiB+35QHudvd7lbq51H+fIv+gmMHDtDRz6G+YSI5z4lAMu0kZT6IDo79hTEl9DvG/PpdyrZdOd0vAQlIYBwJ0G9nDBU9hyBXTDRrZzPBThR9YfqYYdPv5b5ouxD8VGjjEMZAaJdw5mQMGV2xF2GyHX4oL37xi0ubwTve8Y4CPWCQSUS9XH8c0sLykY98ZOm/g00e+wv2hXzVx3Eoq2WQwHQiEPXIrHeonE4PLS8rkaxikIMId3Uj22HwvXKN4Q7JK1Pyi6V86LgdeuihEwYi4sUpT17zh44dAxoMWOSC8QKHOjpCVN44Z1YJZaFiT2eMRzpmYdKQ0mnCGY5PCMeirLGPLUahMAylA/yRpokyM3Mdwz2DTqng6IEBnA5up45eek7d73RAmb0eguNozLiNfbFF6UkFhYRBiG6C0futb33rhIGKOId741kSufDpT396OZgQx9higM6fB0arE088sXxPIm3qIIFxBgMXhuroLEc6HHMZOMKozSAKDgh5VMFIG1vum7xSgxfHeBdxOGHw49Of/nTPThqEHX/yk5/cUvDier1uUTBTB0XOx8jBoAPCcT4YaFJO5cGG/pB3OCJHljjvVEk+uzVXZFGwc2lnUGJ/6lAZTjicT7SM1JmCwaEqyd8v0nQrU+50Evnm5aTu43eNg6sigX4IpO1nnJ+/Z7G/yS2/aX6rMZmB7xgq8wizTV6zTl6Uh3YHR0ja3Vz4vb3//e8vncNoK/LfcqRnEJmB76o8uFfqfIzcDHjgKIH0qx/ENau21M1VdVCeFv0j0uVtUfqOEPmHwWscLFLhfAziGBDQeRj8z3WvSI/zJs52tKWpYNhgeUImzTAZoJMM8pzaMYkBB66bOj5EOX7961+XDpNpmxBp0QX50F6feuqppQNBnDfq7SA6Uf4OwgqHSiI2p07B3BODDAwAoj+i26WDc3k+pK/6LaTvFmmQ/L1g3yD31Iueh4MPgxqp4BDN4GYq/A5iUlLsP+CAA+Jrx22TugPPR/3sTtypftakztjk8+r4YsySgzjf03+I9iZuG0c7hDqhH6HdwkGfflmaBzo/xlkcnqdK6CvE/aZlS8tD3592E/tBLtwb9SzLAdOfZWJDlQzSRqV1MXoO+gttdS7cB31u2vvUmSpNh7MfNoU8Gj9puA71J7+rqn555NOE3aMfPSyujx4SE4BxSuh1NQaiox5++OHlOxl5plv6+OiQX/ziF8vBE9KmwvsQ7wz7WSGBSZxEVE6FCIw4JaB7cTyNnBbpSEP58+dBGWm7OTd08Tgn3TJRBOfHVC9iUg2DPCGwYvJBKlwznJEYeAoHJNJgR0O3wLknhHc/nCpiH+1qO12SNEz6YEJLDIyxL41QyUTeVHBu6BbFggmVUR/FubzvODqOQlLOcb3gGP+z4gj6Str+YyvCzsc9Ul/kK3swib3uZLm4TqctjrjoegjPCKeefEJUp2fXKe+mbDWdruGx6UuAd5w6tB/hEXXvwwAAQABJREFU3G6S/gaZNJdLav/tNLEgP6/d/9SH/GYR7Lc4lfHBGYC6B+c06utuyx9yPnZl+mxVuga/K/rw1Pu032F/4LxUBs1jqttv7qUJPSRl0uv3JnSqVHdMr5/qBlUOvbwztAWpXYPzaed51/gw3oUtJu/fki4te7extTQtOigRodEpUqGd4P3mQxnQD9Ej84ADnMM9o9MwaZf3MG/7SDNKGxDXCt7tHDypL2gPwymaMobwfOLesS/g7FjVDqccB9HBsStyPh/sjDpUxpNwKwEJzGQC1KH0OXCQZCwHmzx2UiJH5kKfFmHMC58U+qO9CG1dBNxIA0kw7s8YBjZz+r34pfQqO++8c+mDgg2D8RbaQ+wl00lYjSOfIJmXnz51u3E/JhWQB7Z2bAPYSOj34mjJ5CJsiYoEJNAbAfqr9F91qMy4pZ16DuUd+0GPZ5cb6N90wKsXpw3SEnWym2CcRmhMmQ2GoSA3nOCYhdERIUIABl9mzdHw0fDiDIdzANdj9n2+TDJRBnC0pIPEdajUcfhktiCdd6JhMvjLoDDGCmZKRBQGvjNrD6EhjkiUHGeQBKmKTDdomXkHmGFIw4SgWHAPGI0xXmOQpeOaLiFMB3JQQSFgECsM3VyHTmk440X+dPrS5UwwjteJKILTK8pGHSHqB53KQw45pGNyBpHye08be47TQe0mOM1gWMaxspPjcDogUZUnM2goezvn3qpz2FelPLZL22k/ymDq4IBhkfcYp1MGKsK5BGcKlgkmulkvs6g7XTuOMUBUNUgUx2OL0sWM6lTymUl5ND7Stns+OcO0/mKmaR3Jo+MysJsbMPMy5b+PuE5eHvZTJh0qg5DbXglQH6f1HQb8fpY96PW6dIQYGIgoO7RRDI60c1DsNf9+02MQjTYFIy4RZum8MZGD9pM2nS31HjMI0RdynYuo17TZDChwn5zP7EXaQwzTOA3S/tP2MgORjjLtfr/6Qb/32s953C9CWTEaUJdxT7QFYShnwgHs0iiX5Ulr/jBznjY0hKhxfHAixwCPsTl31kzfzzhvkOfEueEciFMAAwvISSed1NK/8mippGEAIJzo99tvv4IP7wVtDlyYRUmnnwgNVRN2yosM+U/TOhHvMEaNTkK7i+5IxNVhSNP3RBl5B/P3Cj2PZ0pEh/QYBqF8wImIHLl0iqKVpm1Sd1A/W0s21c+a1BmbfF5rSzu7v9EW8hsLh4iIWggVnCIxivcjUa/n5/Ju0MbkbXWebqr+R/8hchz9PYSyEn2OPiuc0CmYnMj9ERmQySdpdDrOabKNwjGPZ0A7jn2DNhq7CPUeA9wIbT5Oh1X9PfqB4bxHW4oOhL7AvRBlHxsJ+dO3ZdllogzmMqjdo189LMqBDYn+LgPl2DGqViCItPmWdpP+P+9yNyH/t7zlLeU1cKRpJ+Hc2e44z58BBxwvU5sFZWF/PgEgzSdsNOm+tA3kfUP/Qc8JYfAJu9Iuu+xS6rpEv0wFXR77WkjVhMt8X9io4hy22IO6SaeBsDxPnmXuLJnnz2RUPlMh1AX8LlKhr5RGp4xjvGOpQyV2Sfov/J7yCcn0MXgPmhLKyO8/BH2ffkFTkttFyLcfW01T5TGf8SJA20S7Tv3Wi1DncG43Cbs96ar0htSxvcpu3y3//Hj6m08DD+BwjnMc7S8OAEyq6CS00a94xStaSYgWiP1hxzWTvqnn6ZvSNvL7IjDE+eefP8lJs4k8prr9BkATekgL5IBf+tWpcKLA2QOhLxwTDmIsif0sQZ8KuhdOHKFH8y4TGRKdBj0AOwvPnUkbTIAlAlanfk6dsbW4ftg+0PfQDQl+gL2MiSMxzoMTJ2027yGOhUyuwZbEWAMTU3EIpC3DEYb+OJ9cRm0Dyq+f/0+giXDQwY7KPTH+gA0QvZ4Vb6iv0JuxmeHQnOppeX79vi/kQ/TR6EugnykSkIAEZgOBaPNYQYh2hDaFiX653kTbF5PnaRvx54hz63KKfif9krSeZZwXnQv7LB9sNeTfq9CG0O9nQiTjRviZjGK8F1tT2tfPy41NB52hmzAJqGqFiPQ8fGzatYM4peInw3NkogLPksBsfGDMuUyOZIUORQISqEcA2x592vn1ks+OVBjeqFhSoRJPBWMb6RAA0slJJT+f41XGg/Scfr/jZBbSi0NlnFNnS8eTCrjKwBHR/sgHIy2DwKnzEksE8cFIzAtHlKpwvoxr4yxARw/BaJEPImNQxGCLMQHuNARhmOGe477pcIVDJenzAdu4XhNlxqkvjMpEbqGxZBuCsx6dLxrvdgNSkbaXLQYgjEFE9EIwfOFQkc/WoHzxjpKOjn23cuCQEo4vnIMwSIfiwpZGHMdHlKYQZkNi9O1kTE8HEeK8+D3g6BGGhTjGIAvPjvcI43IaXQTDBwP37aJ4RB4YOVAIUB74vUZHOI6jSPXqUBnnDrrFkJcKszNRTNPBa44TQYW0/HYw8ue/izSPYX3Pl7dCgc1n9KRG0ChHO2UujbhF2qpzI4+qLdE4codKZjGlDmu8b+GUGnlUzYbnWF4e9vVaJs5RJBAE8miEdQYa4txBt9Sd4VBJXnlZBs2/1/NpI8NZHocG6vp0EJ2Bf5wL6HxT/1Hf0w7ls7+J6hRO6EQppmMfQlqctqgnOZ+OMoZm2t5+9IPId5Rb2lgM+yEsAU6bjpEcZw8EYzDLZKYD5rTx6SAAgzk4lIbAlgEQHBLRE9rJoM8p1ZFpW0O4jyqHXupoltMMZ0qWuE51mFjGijYPfQ0dAL0617XjOsPaDksnorw4VGDMYWCf+0sjQHGc5z6MNn9Y99ROz+M3SB8C5+AQonjw7oYeyH4mIKVC3ZA74abHe/1eR3cgT/WztWT70YXq6Ixrr9D+W93n1T6H2XWE3xIDrfSRiFxD/ynaCurTQZwqq0jSF+jHoF2V1zD24WAY/T4M80SQSiPhYDhG76A/QztEX5bJHrFiR9NtFAMLDAZTrrS/jNM4dSTtO8Igce5QyWBxOP/xjIlWlPZpSE/biAMpNgHulch56fNpwu7Rrx4WzxddmHac5Z4f8pCHTKj/I027LfUyjqOp4JDIMuq0oQyqowsRcQzh+fFMOzlUko5nAT+cD9Ad6V+mthKij+EokUZLZpA/d6bE/sS1eJZMZKG96+aYhK6GE0Aa8Yoy8w7wjK+44gqK2BKWp08jjdP3Rf+FJ8IEzNzuFVGgWpms+TJIn4T3OLUBke8g+aXlGvQ7fZ80giT2Mp4T/Y6oC7gGdSUs88nhHKOuQG9N9REGwtDV0n2k5fdWlQfHehXegdSBlgFFJpg12Yeras+HZavp9f5NP/UEcFRi4kw+ibpbyTiHc7tJqu/naXFeT53QqyY75+d0+p+6l4mVCLpQ2nfEXkDfGsd7BpVzx4A0X9qCiF5Ne0r9nDpnUv9ijyANYxvcA5MKsVOHNJHHOLTfTeghwaSJbb86FeMDfBBWVwvJ287Yz7tEpOBwDsG2RLTSeJ9xtmOyAHoEtgxsVTgs0j5X2Zgj305ja5EmtkTGxJaTCjoJY2ZMyKE/jTMl9hYciVNHecqBkwTjQJQNnYPxtFSXGLUNKL2Pqu/oc+FMicML/cp4ZqRnfAd72JFHHlnqW9RZOJ52muDQ7/vC9bDP0V9gHBTbjSIBCUhgNhCIdo+6D1s/k0PwMWAFztTGgI8AOhV9ihij4f+6gt0q+lj0pdO8yYN2DGdK+jH0sfPgPnWug9MgfT/GROgfoqtVTS6ok1cvabg3Pu2kbj+PvnaVL06ab+gl6b70OwyYmIk+wDOjrYUp5cPGwgefD3TYcenbp+X3uwTGjUCMf80dt4JZnvoEUkNs1cB1/ZyqU9KRwUjdrgIPxz4aPqIRpc6UkSOGyJiZjlE8nYFNGhpcIjsyWzyPEBF50HDHIEQYj+NYr9tBy0zHNXXoo6OVOlNGebinphtqGt181mNEt4jrsk338Ww4p1uDnc7Kj7yYsYjDJPeCcRdDQio0wHWMbzjVwokOLw6xMVBOFAl4hvAOY/ymIWewgfTx7kQaFK5wrol96ZYBMgZAMGygNOHQw0BDKhjZO+WRpm36e+7sx/+5M2V6TYw5zFzpFuY7PaeJ7xgaGdRJBQNObkzoRWFO8+J7+uzzY/n/GCV5J9LBLhTDPNoXeeYDWbliHnlX7e+lTJGPWwkEATppqXQyqKbpmvieL3mTl6WJa/SSB/UaxmbaDxweU2fKyAc+qXN7VQSin//852VyOoo4PeSC40gauSfS5+nG8X8MFLRVuXBPtJlR3zJQEwbmSItRgwjfCIxTZ8pIAzP0EJxq2klTz6ld/vl+oiqEgx36YepMGWnR99A/wiko1bkizbC3w9KJiC7Gs+T+mCyBAwW/k1QwcAzj9zuse6Ls7fS8PNIk/Zb0N8oSL/EeB4P8nNjfz7au7kDe6mdrCfeqC9XVGddeofpbL8+rOofZu5e+Ov2oqDchwcApExqaFFabqNKfm7zGIHmdffbZrdOJ/p86U8YBHEGi/8CgQTp5r+k2iohHp512WsuOEWVgm7Z/uaMex9O6Emev1JmS4wgOIxHRAYe3PJ9B7R5cI8oxiB7G+0k7Qd+tF8mXu8TQz6onDLLzbjOQTuSwVK5c46QT0afS/fGdgR8m9DIhlnaYfjbvTepoQFqOh8GULQ6rqeBMyXmcj82BicW8c92EiYBcNxUmVuAIlK+0wbPNbT/8xnGewN7Bh4lE+buRTrqO66TOFrGv7rbKQXOQ/Opet046nCmxV8WH3xU6dOpMiaMWk346OVFRJ6R1GzoB/Yt4BygLfZl29so6Zc3TsGRp+q7SJ0gniubp+/l/VLaafsrmOeNBgAkFVf30dqUjbUxCaJem2350/9SZknGHQQdymYAQujyOUKk+hJ2EyYYIbVruuJ6Wl0hM4YiM7SF1pox01BW0RVEPRpSmON5EHuPQfkf7z331q4cEkya2g+hUvVyfCYCxygcT/ZgMWuW0wNhD2LJ493I9Ib1mt7G1NC02ktBT0/18P+qooybsomzxHqYHWKEMvQuhHcM5NpVR24DSa1d9R1cOwXk1daaM/ej07373u1s6D78RInC2k0HfFxx4wh7X7hrul4AEJDCTCIRDJfcU4/FMNMwnn8f4BGMa4TOSntuNCe1sjO/iPJkLfeDQC9Gp+hVs7dGX5h4iKma/+dU5D72ANq3d59hjj62TTalfEDis06fumCOTP7Gb4GeBvYFgXfHciMiNztHJN6FWgU0kgVlAIPqX82fBvc7YW0wHvHJDahM3TaNW5SRJ3nQYI6oOnZ1OS9NgiGBWHELHNI3AkHacygQVf3hZiTqJ4QWDLhEt+5kZ3kSZiWJA9BSEzlUanSkvOl7+IamBNvb1ukXZ2HHNMiM4T4SRmC2DJ+HUySBC6nTKLEgGHLo5VKJgpPdCeTEepMKsEQYgUsEYRdSMdoIxi1kguQGCqCDpABbnY+TOB/6IQpE6iGIUhn84ZebXZSZnei3uA+MZy4eG4HDHcwxmsX8U26oBDq4Lw3322ad0MowlLNLynHrqqaURK7239HiT34lihVNrKhhp6ip96XlNfcdgmTvv4kA0Fc+wqXsyn5lHIHeC6nXgeBAiMekg8sjLEvtHtaXejYGLTtdMf8O0X7ng+MCAC/oO+gPtXy7oFOGk1y7qSn7OOPxPe95ON6CuZ6lB6j6ECAR0ekPSgZvUKSOOxxbdEF0lnFXz6zX1nOJ63bY4goZ0GpSmzHyInkCbNGoZlk5EO5q/ozhZpgNWDHqwhBz336QM657a6XmUndnKRIRJJ7EwQSQcgJjxnEuTDpW96A7qZ/mTqPd/kzpjL8+rXulmdyr6zFXtar9UaLMwxo6r0P+lf4cwGF01GBtlxzEgoqqm/Yum2yja2CqnTspB3wqnPPrEqWNJlBHeITGwH/+n249+9KPlBFX2kV9IE3YP8ppKPSwfmMEpH7tHunw1/UEiMaWS68TpMQbIaXdTQbfEaYZonCHYG/gN8ZxoH3LnfwaZ8gkRDKAwmTPsNJFXviUd10rtEzgQpLY99DUcA3O9LfLqZH+p6gMMYiusirIxSH5xD0SBjN9s7Mu3aVSw/Fjd/+kn5Dam/FyilT72sY8to6HEMQYQU3nBC14wKfoY7wmTYzoJTi0Pf/jDJyTB+SvsoxzgeP4eTzjBfyQwRALUJ/Q9cLpK66H0kvRNaQ8G1QOYTJUuRc+AeW53Tq9b9zuTe0KqnCDZF0sn4pROBMIqSQfu6Yu3E+w8tCXf+ta3yolwOEfH4PSgeYxL+z2oHtKOXb/7B9GperlmOIpwDqtGtGuHOU67QFAIxinQIfMJE6RBOo2t3Zli7V/GOtpdE9sZtgT61vwmw2ly7dlrv6XPD92Jdi5k1DaguG7Vlr5K6LnUMe0ih3IuztxEqiQ6LBGqia6V2sjS/Ef1vqTX9LsEJCCB6UwgtdvSP2YyLXob7SL+BQj93HC+C6dL9qfn8n8nCT2JCS/0sfN+NufSd7vuuuvKFTuwl6R2jk5558eOOOKIcvVK8mLiAxGPe5lIlOfX7X/02tQfpFv6UR5HtyDSOh/0F+wWTBCFDdE86dO20z9GWU6vJYFxJRD6tw6V4/qEapQrreTS2dM1Th04Cc4LcU2Wz+ok6YBGNLqd0uP8x6xQjNhh0EkN6nHdTnlUHWuizGn5USbSZ1B1zSb3hVMkxqDUUI/DYUSnSp0PuXYYk1A+OgmDBXxSIdIFz4EP186XeiJtt3xxkqxyAqwa4KsyBjDowVJIadTBXp1pY+A+vbcbb7xxSpzxuG4uGPNYoi0Ep1AUPhxoQnBcxfCO0+UwBeMmy2ikAycMShGpNo+AN8xypHnjxJsOcHIMJ20YKRIYJwL5AGadpbCaKn8+eJz+hpu6xqD50HbTrtOmxGzEmKBA3lVtOwOMsdwjBm1+9zhcMcs/hDam6YgukfdUblkaMyQfcE51kTRdpB9k289zqnu9aNd4Zp0m4pAfRggcKnlXcKJo55RS99q9pBuWTlSlM2LMyCWNWJQf6/f/Yd1TOz2PcvLMcGyK3zD7iDRONC8kd6jEaMcEqiakV91B/ax36k3qjL0+r95LO7vOoK397W9/W9mu9kOCQdmmfpv9XL/OOanzfTpoXHUu98NymrmMuo0iKiJ96ejjp+UhujR1KO0fURzQgYj4TZufOuYTmaAqOkETdg/KM5V6WN6Hx4B5yCGHlBEbiM7JCic4sMQAT8qv3feqdpi0DOCnDpXso46j7Ux1LvYj7Qb8c0fDquuhAxGNkImjIWHziv+JfNUpwnikq9pW9T96GeTK86yaIDZIfpH/1772tQnLdcf+dNvO6SlN0+070UhY1v1tb3tbuYRnu/RHH310uRRa/ixITwTgqgiXvCNpZNyqvBkATB0q+b3HksKkpx7IJ7NW5eM+CQyTAI6SRHNlUPW2225r2Xl5f7G/8huoamt6KRP9mzSSMnoF7fGgQr44NiI43FfpAPwGaS9IS2RfBo7TtjTKkDq65ysdRZrYskoXn1wGzWNc2u9B9ZCcy7D/76RT9XLtNBJX1RhFmhe/G5wAsddjr8GxMtcD0vSDfifYCPeJMyHX2XDDDdtmSbqQOrbBYdqAohxV2wjUwrE99tijcvwoPQ/9D4dKpGpcKU3b6XtT70una3hMAhKQwHQmwNgLgbCwmxARGD0x/A5wtqwKeNHtfuk7hU8DfgZE7O8ktF9Myk+dNzulz4+hu2J/pk9JgC4iNNZZVSLPZ6b9T/+eIBf4W7DSRkwgJcK1IgEJVBMI254OlQkfjJtplL7kUOsrynsn6XZ+p3N7PYZRIwRj+ygFQ0sIs8v51JEqgzTnMRuWCAvMGu0Utp+0VU4X7O8mTZQ5jSCR8u927SaOhwPKfvvtVyocsYQPM0WqHCpxsCE6EFI1UFNVJhpQQkCfe+655QBOVZp0X7/Poqrj2y4aKgMKg0iVoWyQ/AY5N3UMJR9mNafOlOyjY09EighLzj4Ex5JhOlQy+EoI8DwaA9GlcGxtWqhvuwn1Six3E2lx0GZ5tTrnxzl1t8PIs+61TTd9CNBetWvL0ruoWmKXTmcalTFNn37HSNqLk2C/dXF6zWF8p52ijTrnnHNKQ3O0W1XXqrqHgw8+uJwRyWAlugGDoXwYLEEfQ+diO071fNW99bMvXU4No0MqoYtQZ+WTIdJ0db8P+pzqXoeIGwiD1b04gvJ7G6VDZdzPKHSiUbc7o7in4McWw1fqUImTF+8zkzTCeSnSE+2yCelHd1A/W0u+zjvZpM7Yz/NaW1q/5QSqnCkx/OC0wGBvOkkxP7fqf84bd2dKyp3289P2s+qe2u0bpzaK3yG2ESa60TfDGZ0Pzi20nwzy049kScMqSXnwG+NTR3L9dir1MO6RqIERVYzy4/xOdAk+OA7iTIkuSMSiQSb/Vb0ztFUMFOWTWihHVXr21xWeITYdnApz4RnHxIP8WJ3/q94JdOh0MlKdfCINNi8cN1Idvpu9Ls4d9pbok6zkkQoOUywXz4BZ/A4o+yte8Yoycm0a4TQ9jyh56CExUJge43dY5Viapqn7nYmqocdzDo6c3SJo1s27yXR1dIEmr2deU0+AQec6dop+S5raGYngg37RhOAgGfVTBBTI86UOo87FcZ52hEld+TKT9E9jghVOA/04kDaRR9Rb3ANtN5860nT7PageUqfM45YGexR9ZYQ6v479AR0Zh0r6ktTtTa8yMUxGo7IBdbqH0L1JUycCbtonqRpX6nQtj0lAAhKQQH0C9MM/8pGPlPYjJqjRT4rVsvp1cIzolOgY3dpLgnHgBMm1+70ed0u/m8mQ3/zmN4uLL7540sT++kSmR0rG8dElsQl0W22SCUc4VCI//elPCx0qp8cztpRTQ4CJTfT1dKicGv6NXDU1dqTGiUYy75JJGBq6JJt0uMrx893vfndx+umntxwlaVSZPUoI5jBc4kxWZ1bdpAsmO5ooc2rcaMKJISle16/hUIlxh4EDZmggRPhiiQQ6/2mHkiUOwiGnjkMlERAJ+RzGqK4FGiBB2mmObAYZBIk8xn3LAAmOUiEYEqsEp0EcTBkUDUlnc8e+praUiSVt0veHvJm53c4oyfGqMOm8P1WzcvNIFt1+Pyyd8pa3vIXLtATjCcp8Otu2dXDNFxq2mHke+9sNHFe9593KFHm6nd0EmIWO5EbrblTqOlOSDzP2GMBkoCGPPll1namMjllVHvYxAE30ndQZkN8uhmnqexw8mJGYDirmefGbZhlYohR96EMfKiNukSaWhcBRk98tkX3TSD95PtPxf/Qf2gyiV6W6R2rohyWMBpEmnlOd66PDdYtq3S6fKt2xXdqm9o9SJ2qqzN3ymYp7IqI0Ti9pHcUgJjpO2g6j+zfhUNmP7gA39bO1b083XahfnXHtFdZ+6/d5rc3BbymBds6U0a+gzfjFL35R26kSHSQduEyvNW7fMbaHtJukF8ertuPYRrEMK0uTMpEMxzv687Sj4Vz5pje9qZxsR5SF3MGvCbsHnKZaD2MSDX27z3zmMy1bUTw/2hUGRfgwAY8Bg4MOOigO97StivIUuleVDlLHyaJbARgYqnKoJLJzt3q4U970N3Lp930gH94BHP5SO0Fqo8ivNcr/6SPlv3f+ZzCI6HI42saELYzfZ5xxRvHWt761bRFf8pKXTHKoxBH1Wc96VttzejlAJN2PfexjrVOYEIYezm85JGx+8T9bBhLp+/AseonaOUxbTVo+v0ugDoHUDtqvg3fVddLlvvn9sGRhlURUEY6hf+YOlbSxYbOs+u1U5ZnvayKPfuvrvK1qov0eRA/J2UyH/9Gxon+KrT59Z9qVP9UHsA92cxBpl8+o94/KBtTtvtKV0OoELcl5d8vf4xKQgAQk0B8B+sf77rtvObmRPis2W/QkxipyHarOFeiTxURJVqzKx3zzPNDV8Be5/PLLy0BcMRaXp6vzP5PjmNCPrpQH7qlz/nRKg10MPxEme7zsZS+rHKOP+0kDC9TReeI8txKYjQT4vVAP6lA5jZ9+aiztxaES4zMRYhAGOdMlueviCEc90hNBsNuy35EvBvFUzjvvvDJ8NPvwnMeISaOcL+tMmGkaAqTfCr6JMqfG7XTgpizYkP+kTpFPecpTWg6VXPZLX/rSpKunjnBVhtn0BBxWmR0fxgOOoTgxi4PohCgtdHRf+9rXpqf1/b2qo8zSMgxoz2RhYDL9rabGg/y+MTKmgxVVTqj5Of38z3vFMrrp0jTkg7LJ8nKdpF0EjCpH0TySRafZp8xYIkpnDIBQBt4ZnKeq8k7LSJmITBGSX7fT/k5livPcSgAC1Im8nxgi60gdZ0qWVfr973/fiijMbx6DOpMnGCjv1PalzkqUJyYj1CnbMNJQn1OvhDPlgQceWA5y50twMUOQdh/pdH84WvGBD5E/GTy/5JJLSscCogiccMIJxS9/+ctyW8cBdRj33HSe1M3hgMhEihA4MVjMQP+gekjTzynKWLXluYRjHfoYjrJ1pR89tW7eVelGrRNVlaHpfVN1T+h1TPDBGSAkHCrjf7b0I1IdOz1W9/sguoP62VrKnXShQXTGtVe489sgzyvPy/+Lss+QL/NNexHOlDBiUgO6Rh2nSn4T08WZkntL+5ad+lekrZJxbaNw6mCwgX4Reg86EM7x6EPooSwHji2GgYF06esm7B4pp6nSw3hnibLAcss4VdJ+pH3pKCP60PHHH1/+249TZdp3jDxj0L6q74ltJbdrxXl1tjj3tRvMYelaBkFynblOvqRJbYRxThWzOBZbypM65PCbYrItQp6pQyURQ7oJ9cwXvvCFCclwTAyd7swzzywjek1IkP2DQ1C/ct/73resw/785z+3ssChsZNQ7+WCbaFdJHwiZF566aX5KRP+T/tkOHqlA1cMSkZUkAknZf/wu+fTq0PlsGw1WfH8VwJdCdBeha0Z3aTKib1rJhUJqCtjbIPDDLzXESIsYUNI+x70WfjQ/06dP+vkF2mayGPc2u9+9ZBgMp22jEPxbmIj4VNHYvIFaXn+00FGaQPqxiOdEFTndxcRRMm3XaCFbtf0uAQkIAEJ1CPABECcIOlLRp+FqIb96HE/+tGPWlGg6zhkPupRjyoDa9BvxnbIOHW/QnmPOeaY4g1veMOEMeN03LnfvMftPNjiPIreTf8ep8p2wphcCJMRFQlIoD0BbDFGqGzPZ1ocSSNUsqwNkdjqOBJgiKYRQQjf3M6Y2wlCulQOxuanPe1pnZK3PUZ0SoSG7ROf+MSk6AptT+zjQBNlTgeW6him+yhm21NSp0iW+KKzH84UzBRJlQCcL1BwQlJnzNiXbj/96U+38or9H/jABwqMxCFVDn2dHGDivKptlbEax6EmZypXXXeq93HfGMNDOg1GpIYC0segTpzbxBblishu1B+pHHrooR0Vrkib/h5iH+WuGnjK7yc1nMS5bBkkZEmudLCBe2fJ3zrLYVEmHKtCUgNX7GObl4d3eaa/f+n9+31wAsw+p94Np8F2OdZxpuRcBmYZDGdmO4N/KGkMtBGRhLqCaCupgT29Xl7H47g2lcIy3wz2I8973vN6iqbSqdww4POc5zynZMOyfG9/+9tLRxKiU+AowvKUM0HSNjcfEKWeo27DkRan8dThspd7H9ZzqioDdSyOyDjvY8RnyY0mjeC0Y7k++9znPrdcFjUtD21LOrjOsdwYNGqdKC3fsL5P5T0xKSp1qPzhD384acCJNIPIoLqD+tla+u30s0F1xrVXGFzXS/Pyez1nyl448Xuo0vF7yWPUaVMnsm56WVXZht1GVV2zl320lzin80FwbmNJ469+9avl5BsmpzDZhPtAmrB7lBllf6ZKD8O5kM8b3/jGMkoF/WnunYjuqbzrXe8q7VKpE1t6vN33vF9IutC9qpzM0dGq9rfLP9+PzoojUJWg/xOxkKVp+5H0txDn0wY/85nPjH8nbelHYPtJ+9846oakdkf20S9B73rgAx8YSSZtL7jgguLjH//4hP2siBGS616xv8ltPkm7H2frTuXB0bIXx9pRO9tU1eOD2mo68fCYBNoRoG3aa6+92h3ue38anbKXTLCx4PCdTyCnrsPxmt8Jem/VyjvdrjNoHuPafveqh3TjNK7HsVf8/Oc/Lx0uGG/J25G83Om4UJUdPE8/Dv+P0gbU7X7hHVJnBaBUx08nU0UebiUgAQlIoDkCRKpm7IvVMSOQR7/Lb+MUiay33nrFnnvu2bWQjHMQyZLJaxdeeGEZtKPdJLeuma1JgB5KxE1W+xh3YbJnnQAujBHmfWrsRWEnob0nWBMruu20006lXhvjmEQAjQBdD3jAA4qdd9553LFYPglMKQHsdPx+jFA5pY9hsIvTeaDCw5CMM9oXv/jFjobSuBqzMUP6NWqkyxikzkuRb50tDUNE4Nttt92G6kxJeZooc2qU7Pe+67CpSsOPNoRBgsc//vHlUiXsSx0v+P8JT3jChJn0qXGc47kQQSCVffbZZ4IzZXqsie+5UZ48mamSR2LAUeSQQw6ZYNxngCGNwNFEeUaVR2os4Jpw59nlhoA//vGPE5Z4JW367vH/oMI78dKXvrQcfEvzYmmsupHDcHDEOJo68xIpJX+O/Na5p1RikCrd9+Mf/7g46qijWrPXOYZyyNJY6e83PSf/nnNC8WYZv3xgDeeLVDB+9WMwTfPw++wjEAbv1LCXUqjrTBnn8HviHeZ9xJEylnwiUgJtPU6/dIJyyR2H6aBOpaQD3Pvvv39fReGe4r6oa3KnQTrSTOZgQBsHSwT9ZlgOlakxHeM6y5YPU1IdI3cm5x2hvkQYkO9Xl2viOfXCgHoch0raDMpNFOymhOeT6rfki46cs0HXyAfUc6eIUetEnRhUOZ3iSFPlMNEpn6m8p5jBHJE+mADGJ4S2lwle/UoTuoP62Vr6VfpZEzpjXKGJ5xV5ue3NmZLnSF2Q/v5yhjtOQ2dK7iHtW8aEjvze4n/6BUwwRaj/73//+5ffh9lGlRfo4Q9tJG0l9WOVcZvndOqpp5ZOH/R1sKnQvjLogaT9plSf6KEIpQ42VXoYAzb5ctjYJ4jQDQ8+hx9+ePHBD36w5BD3he2APmdEQYz93bbf/va3JyWJujDXEUjIQEyVM2Fqr5mU4T920Bby7FJBh4mJsuxHV2EApJ/JBujLRPQg0mXI97///XLCVtxT7I8tA1y5vQj7XAjOlThHpn3+c889t5IB51DHxJJukQe6bK7PxrFhbIm2SV8qlao+VHp82N97cb5soizwbtJW00SZzGN8CPB7pt6hTmJL24ijYXwoKf3t+GBPo65i4JYt71YvktaPLJc2qFDPMLkSYTIpg+3dJpXi/BaOAJ0cKmFD/dGpLaGdvuqqq8rr0z5Fny0cKvvNY1za70H1kBLMNPyDfoVDJfLTn/50ki0hvSUcb8POTRCAKn0hTT8u30dtA+p03/AOyQM9xP50S9StECJRKxKQgAQkMFwC6E1M5EQYAwt7Qy9XRddk8iey9957TxqjbZcXdmJsHTgYMqGScwcRJqFedtllfUciH+TavZxL0DE+3YTVJFgBIhV0bHwnTjrppHKFEwIa8EFvpy+Kzs+koRDGFt64ZtLqICtDRF5uJTCTCUTfd/Be7EymNA3u7VWvelWrlB/96EdbswVaO7MvOG+lxs18oDlL3vZfKtuYfRcVc9vEaw5wXQwzqaQz+XIDbpqOgeM0bXqs6ntqqEmPN1FmjJLR4aNz3anD120wJy1bP99Z9ruddDpWdU44tsax3EGT/el7E+n63WKAI5JYKkRhyJd4YFkklqlkJkx80mVZ0vOnw3cGRdLIBPwmiK6WvrMoNW9+85snDFhwbzjQNiUY91i+ncgRqey3337lsmrRQKTHqr7jtJWHaWc5tvx9OuOMMyacDoPceYKZ4MwQTyPtke4jH/lI0UvYcRT9tPw4ohHdMhXqlPx9xpFUkUA/BOhQVkX0os1JB/h7yZsBCzqpOCenAwNVTr/Um+kgLJ0nlmaaSknb7LR+y8uURuzNjzEQzAAunw996EP54db/DGjHbz4fOG0l+seXTmXJ0+b/p87aMXiSp+F/2qw6krd3+TlpvcWszFTS2Zzofu0E3Yr6rp008Zyq8m7HOb2PiFBedX7sqzMjM9LiEJf/PphxGU58ke7zn/98fG1tc8fOvA0btk7UKkjFl6o6JHWS4BQGQLsZW6bynoien7f56a1iFOvXQbkp3UH97M4nUqWfNaUzcoWmnlf6/szm7/yuuy3znfJh4HcmOlNyj/TPI9I8znGd2ml0j6OPPrr8pPXpMNuo9DnU+X7EEUeU+g99+nb6ArrPrrvu2sou1YGasHsMQw9rFbbLF9pzljl/9atf3fqcdtppk85Kn1kcbKeDcBz7TToYzz6iVjMxORX44byJsI0JVJHmvPPOKx0U43+2OPOQfyrUn7kQTTsmTMUxHEOJ/pnK2972tgn94jjG75jlsukjh8NrHIstqz2kgyIwOeywwyp1Qnhge0iFcx/60Ie2dtEfwU6QCvoUEUFzoe/N8uvYblJJddd0f9PfccalDmCSJvpRKlXOyenxYX8ncvoll1zS8YMjbS78Djgv7zfxXNl3yimnlKt85DbVJm01eZn8f3oSoE5Cd8BZjoFt6izaDvo6DFZjU+A3FE6VfGcfx0hDWs7hXPIgr6p6LqfDu8o5fPL3OE9b939smTFJDidyxieYjN3pQx0Q7Sb3wqB6Kmm9l/bF0zTxnXoOXeLlL395kTqIDprHuLTfg+ohwWm6bR/96Ee3ilyld7QOrvnCOxJOCay+kba7abpx+z5qG1Cn+8fGds973rNMQt3SqX6gHmK8AeE31+QYSZmpfyQgAQlIYBIB2sUI2tHPZD8ypJ8c7WXazk66WLbjYQ97WGvCSkS4zJL09C8OhS9+8Yt7Omc6JuZ50b8/+eSTS1s8uiXC5NN4Dvi4oMN++MMfbrsa3nS8d8ssgWETmD/sC5j/cAkwc5zIL0SFwyDAUr04HuSRnCgFS0a/8pWvbBnlDzzwwNZSRr2Wko4iDllEuEMwJmDYTR0OIk+iItAZZ1D4ve99b8sRE2MGAwEYYJhtwMy+PEIMIf+5p3RAHKNOHs44XQ6ZgaUqaaLMdNpYIvnYY48tL3HiiSeWxuzceeWKK64oWUc56hiZIm3dbUT1TDvDnAvnqogJnfLFYS1dXuHyyy8vBzFQlDCgEYWBKAS58Cz6lde85jUTIonx/FmK6qlPfWo54xkDGe9FKrvsskvPUZnS86f6OwMyRx55ZPGOd7yjVRTCa7NsFo6JvCcRlruVYM0XfuPpTGWMDCeccEIrCdHZCN9dV5ipwozsVMIIeNxxx6W7J33HAJlGgGN2D88q3nEcYDjOACC/SyJB5o6bHE+dJ3D4YVng9HfOhZnhSzSMfLmwvFAMMsSgF/UKLFOHSXhTPxLZlEiCONmkDmjMJmeQSZFAvwQYBOU3wG8ZobNS5QjVa/78hujw4RjAgEV0gtJ88uiYOJ0PsgxCmne/34nmEMZQlhqmE5cL0XnS5Qzz9gQnt9ARzj///DIKZVVkGwZmov7Jo+NyzTr6QV62qv/T59nuns4666wiXXosylWVH4P3tONVSzAymSAiB+Csm0+SoG1maUbqTupYDMtV+bz+9a9v5UMZ8vI08Zzi3lI9CD2sysn4oIMOKrg3dCQG8BmkoE0Mh9jIiwFhZkjSTnEPnNdNeOdpKzHUhPB7xAGAAQ70RgajifqUCs7LuUP9VOhEaZnS77x3tM+pgwj6GA4gGKJoc3kHqnTf9HlP9T3xzqa/jfQe+zXKNak7NKWfcV84caBzILzb6XLn5c4Of8ZNP6OoTZWpyefVAeGsOdSrMyVg0gkaOSgcEqv68Xm6cf2fNgCDMH102hD0DiIp5PeM/s9kNoTfJ6s6hAyzjYpr1N3ikEGfkLofZ7uwPaTnc59MbA1JdaAm7B5N6WE45vD7zx0Go9xVW8q/75oludI+JLoOg+609azuQETO3KEPp/BOTnM42BHJAd2AyKTRL2RwIZUXvvCFLd2E94Rz0meAQyTvC5N+KAtOOZQ1bavT/OI7ToZ5v5ayoA9iG0mXxEaHYVLiMcccE6eXfWX6udj1EGxrROdO+9XshzVR3OnzhtCXYOIhEZ2YtMUkRtqqqgnC2PhynftlL3tZ2b+OQRjyPf3008sIcUyUhQM6CVGIiQ6ZCsf4fTYtOF88+9nPbmULf96LvE9BApx0n/jEJ7bSTsUXdE4+naTK6R1bRZV9Fz2adwBhghXvT27LacJW06m8HpseBPhtUAdgO05/w/2Wnj4Gv3M+/Lbo96FDhE2x33x7OS/tV+R95U75YG8OR0ryCAdLziHKD05y2Kax07LSxh577DEpO+rWiP7P0oqprjFoHuPSfg+qh0yCNk124KTHZA1sBnyoW7FV54KtKyY08huIFVPydOP4/6htQJ0YUGewQlbY4xljg3luS0J/I10EYMB+EEFeOuXf7zH0GexHOGgrEpCABGYyAerdTkI/pGrCV3oOdXjU4+n++E6/lE+vQr8popG3O7db+fPzmITDp46kk2/bpaffXSddu/Njf1P5RH5s0WFDj8V+cOUamwxBruibd+uTpvnk37s9kzy9/0tgJhCgP41tUIfKGfA0cWzEAQ6jK0bFAw44oDQS43yGIRSjCYbFc845p2XcpAPFIPUggvEXwzbODFTIGDHw8sdpiQgROAMwYE4aKm2cpeiURKeHzgkRMjECc5wBbRo0otgw05R8OZZHZeBYHqUwjDe82MyUfd/73lca2zFIppELBi0zvDBEYzzGyMK1MJigNGCUx7hOx5oBkNRhaxDO7c7lB4yBJ4/c1c/yqpyTDlhwTQbsq5wo0/LkxvL0WLfvPBcGsdKBZpjmAyORDx3ZuktRxznjuOVd4Tfxpz/9qVU8HEdz59E4yO8pQqvHPn4vadSHfBAo0rXbpsbHSMNvJ19aLY6lW9671KGSQSucVdKZQtQHeZkjDwa6iM6QClEkqt4lIr3VifbGzPBUqIdQZtMoDTjm5E6kcQ73UzVIEcfdSqAOgVhGjmhsqfNdnXM7pcHISH44U/L7S4VZ2jhVpNL0UkMMGKROiem10u90xg455JByF3UCgxHUK0Txoa3EOM0ALnoBDmD5EpQRYSLypDOJPkN9Sf3AwDUD3Bj3aQ/Qd3CQi4iDsKlyzKqrH8R1221jcIRBKO6J58LALM8bfYcB5NSRu10+6X6iL+EIx6xLBripO2kTU0dTIvfmDrIMtOC0FVHKaRsZEN93jfMBxmfKwkQIlnjsJE08p8g/fecZ4IUTTmq8jxE1jHIzGYA6l4E4olQyoMWzxeGP95n3A47h+Pi9732vYAJQ/u7HddMteXMe+YQQTQX9t0qYtUlZU2dQ0k2FTlRVPvZRRhw2UqcIflfoyHzqylTf0+67716+D3l9xf3VNWbl99q07tCEfvb/2bsPOKmqu//jvy30LkUQpIjYFRsWLFHEXhARHqwxllgfozFRYy8xlgRj1EQTK7FHjRpEsKHJo4CKvSAgoEgRFOl9y3+/h/+Z3Lk7Mzt9Znc/5/XanTu3nHvu+9659XfPURn1gpI/R0tmuw0uV7Gdn6ls2SpTttdX0K2xdacTTCkjvaCh/XL4d9g7z8GU+n3oRcxkkvYdwUDBRNPoXoTOPXTOoSa9FWillzo1vY4/OjbqHoEC0pV0PaJ7Ij7l+hjl55PMp17g1Auzup+gmgh1D0Uvz2ldqYZCXTfqWKlzISWdQ4RfTM30vkc2zsN0PNb5n87jdO0fPL+py0EBeDpH8LU56mG6XuJV0rHDP1QP5qOABq3HREmmOo/TX6yk8xYF3AST7v9o2wnWfKraIRXsmkpSTRHBYCadQ+qFZyXV1qxmY3Xe45PWvc6FfXCjHmbpPoBPChzUAw1//u3761MBkPot6D6gTzLU70B/8ZICVnXuGU66DlGw3s033xx1ja1jXvDF3PB0OofVfYFkriXC09b1XfcJEy1LcHqVPdl9SXC6Yu4O36vT8VrbU/A3kI17NcVsQNnqFtDvRPfUgvuOuqdKfgzt03Rc1bmF7ofEC0DywZb+M/k51B5Tzzj8da72Lak0Aal70HrpQs8KdP9Tz0V0TFHS/krB83rhT8ulIGXtD/XcQsdfHYt1b8Jfl+m5hL8e96XMRh7FcPzOxnmIN6lvn7q3oudTuuZWDcCqYV/nAbpfofMAXYPr+O9bYdN9GZ0z1ZeU73tAdbko2EMvi+iZlF70kL1eBlCws54f6F7bwzUvLvsXjnUtk+nzzERlUk3iOudToKxeYtZ5EQkBBBCQQKeWia8zs6mUz3lls9zkVZwCCoxNp7n24lwaSoVA/gX88x0CKvNvn/U56ua5ajpS0z4KUtAFXjjILjhTvQGvG6GZRKL7/BT8dl1NLUK6EaGb07rYjJV0EaRh4ZoTb731VveGgh54a3otR7jJJd28UNNB/u15BRyEL1YVnKmHKAqY0kN6XfzoT7X6BQO9VLZMy6wblHoIpBsratZb3uHat/QD01uMiZrijOWUaj89IA+va/VLNemCWjfiwzUmBPPRDSQFvASD93wNPMHxUunWQwUd0GM9rA3mowcbeqAQXu/BcepLt353eoCjG3V6UJYo9e/f31W9LaNEKRs3JRPlX9cwNUmmMvrApnjja1+l36VugOQy6eGobrjoQVL4oXFwvvqdKkArWPNHcDjdCKQq4IMqU50umfH9iZsfVw9FFYwXrIVOx0K9bZbNpOr/k0l6eOIf6Ormp26C+sBqPdDVXzDpvECBcv44qWN7OOnYqv2FXmLQeUKwZt7wuPodxwrMSuX8IJxn8LuOgaox2weBqJZK/QWTaqdQwGdd+3ZNo5cw9AD6kUcecX/BfHy3ziOCNe/4/vrUOY9MRo0a5bYBPdjxD3f8eHr4rQep4Rcm/PBsrSflp6BIBS7qwYJudvtmNHTOFawRSC8J6DigoAgFWChwIBg84MumTwWaqobh8LYfHCfYrYdaGl8PzX0ARnB4sFvbhbbtYK0kfnihzon8/MOfOpbpJYHg+Vd4HD3kSfSWaKGXSefOeoCp7T2YBg0aFHmIGexfiG7Oz3J/flaI9drQ5pluMKV30Dmyrl91/ND1g4LhfbCWHyfXnwrwCgZ5JZqfHmonGwSlY4WuixXcoBccdd7hW9MIzkPLreNE+AUvjZPLY1SwDHV163xSQRu6TtFLeHrZIvgSYnB6vUihB/uxjpWZ3vfI9DxMLzn4l+bUre032etANbOta0sFKoRfwokVTKlaL3S8TJR0nqBpdd8pVurRo4fbhmK9yKJzBt3/8QG54elV04Ou3eO9XKMg3/D5zmmnnRbVYoxaXVCQkA/S0Lm+7qHpvEkp3PR4vH7qr2XVPQfdb1Ht4HUlLbPOyxXIGmtb0vQ6D9bvUc6+Vv5E+epenH7Dsc61Ek2XzWF6aUY1jKrsDS1pe9C+3CddqwSDKX3/YrtX48vFZ+4FtP9VMFjwfkGu5qqAzU8++cQ9rA0HUOu4u88++2Rt1jo2KNhNSdeZsbb7eDNTjb6qVfLll192zxl0bNXLaz7pfoJvJlHLpJfv9BdOusbWOUesl1mzkUehj9/ZOg8Ju9WH7zrO6QUdBahrH6vaSvUXKynwVAG49SkV4h5QXT66h6NzTp376lzx2muvjTmJ7m+p9m69JJarpHtn2mcq6FovBBJQmStp8kWg/gns1aultXpnia1av/EcJFdL0KppqWleJAQQQACB4hDQPTKdH5YWR3EoRaYCql1NAWcK0tLb/+EALL39rotC3QhQ0Fy2mvTSRYxqGNLFvm4o6A2uYFI59JBXQZJ6mBtOuqGuC1MFDIRvXOumtGrfVPCC3tb3yb+J6r/7T92ATuZCJ9Mya3662a43wnXzRg/lfdIPS0FjclZ5sh3Y4ufjP3WTI/h2gQJWg7VE+fGS+VRwg9Zj+IaQth3dYFKTq3rwHUx6MzNWk0rBcRJ1K6BGwb1660/bp+blkyy1fLqxr4vqhhBM6ZdNv1fdBND2rbdcw78b3fjXAxtt+7G2oWDNi8oz2MSMn0c+PzV/PezRjcdYy6MHtapdQ4GzaqotH0lBTdrvqFYUPVAKJr2BrgdeCtLSA6FCB6QGy0Z3wxXI5oMMPUDQw5FgLTfaZ8Z60FooUT0A1wP+3jUP94JJ+3k9UNGxXzep/csdCg71D939+Fom/UbVnJ2C62LdONUb7WpuSTez46Vkzw/iTe/7KwBD+7lwObQPVxCG9nHh5qP9tOFP7YNUm7Vq5Q7vw3Ucv/HGG915RKL9k44TstHxUVY+6bihcy+9ZKImOxOlbKwn5a/1rGZUYx2zwvPXMmvZVcZw7ZAaV+eTelnnoYceqnU+G84r/F3ni7oJrlrJwg/zNK62Px0XtM0kesBfiHOi8LL47zqGqVYqNfEd3h50Pq9aFJJp5qTQyxSrBtlY/fxyF+Iz0/Mzldk/4FV3+LetfvlMxXh+ls/lb2jzyjSYUh7++krHBp0r5zuYMtfrRC1h6Nih8wsdl4LHRr34ccABB7gAiFjBlL5suTxG+Xkk86lzOr0ooWthXROHA0Z0LqKXSXR88C2AhPPN9L5HpudhepFX90aUVPtQssGUfjkULKpgQAWW6j5HeJ+q8un8UC906EXd4Pr2eQQ/FeCn60PdLwuOq3sSCoDRPQdZx0qaVutDy6HxfdI5oM5rFeCjPGIlna/rnkcw6fxHyxVMOp/TCyrBpKAf1ciuJE8N1/z1p9rTfTNewWl8t86x9TKSAjIVQKT7a+GkdaL7dNqOFMAQvLcVHlffdb6rc3i9RKWaUf15vB9X51kyVF4610ul5jifRyafOo7rHFv36XSvRcEQClxtiEmtdOgempKC5eM1q865QENc+3UvkyolCL98WfdUmY2h+x2ap+adq6R5BF/k1kuGqaZgLcThFxKVl+5d6jpalTsEr2113ND9TNVqrxcrw/fOg+XINI9CH7+1LNk4Dwma1KdunSPrWKcKK8L3k7UdaNvQ+aZe3AmeT9SXZSzEPaBENrpHf9NNN7nni3rGFesZiV7Y1f02nY/lMukFHZ3L6HxCtcWSEEAAAS+wSU0Nlbccualtu2kzKy/97z14PzzTT+WpvDUPzYuEAAIIIFAcAnrWo+vQkpo3bqqLo0j5KYWakA4m3fwMP5wMDs9mt9DDzfPl6gaj5qXmk9UUkR626kI/H8upm8WqtVAPfHRjX/P2zWfUZam3v1QDg6bVTdhUb7j7/OfPn2+q6UoXPwpKCAeX+vH8ZyZlVh76IammDzWlohuZwRvsfh717VPbjbYf3cTRjf3wA5xcLY8CBbXutA3oIrquG/q5Kke+89W2r2Z4VqxY4ZprVcBuInPVvqigHp/0kCRWwLIfnu9P1arhfxMKjNFb4IVO2qZUJu2Xetc8YM3H/rDQy8z8i0dAxzYFC2q/Fn7wmGopFcSump3Ctevohneim/qpzieb4+v3pyY+9UBCDyHCL1CkMi/V6ihP/Za1r0z2HEPzSPX8IF65dNxXU2Mqh85VFCyQzDKplkFfg4GapVST30o6Z1ONR6rtR/tMnb+kmnQOou1CzWvHah4+mfyysZ5U85POA/UgTeeAKk9d+1s1X6r9s34bCp7QX7aSjquyVe2Ofpup67wwPO9CnROFy6Hv+v3rt6Tl0jlnusfXQiyTahgL1gCjsuslqfADk1jLXah+qZ6fqZwKNtH6UdL+TkEqxZKK8fysWGyKvRzaP4cDI/QAV/uBdK+Zi32Zs1E+1S41bdo0d66g32Ndx6NY88zlMSrW/OL10/FVx3mdg+j4qgf8qT7Ez/S+h8qWznmYjsHpnNuELXSvwN9r0jmv/vTQO1468cQTTTVj+qQACV9Tsg820rlpqvc7dIRuLY0AAEAASURBVN6m8xblkY1ze1++ZD99DZ2pnAP7vLUupk+f7s7Jda8sGCzkx0nl068T1Qyu80+dw6XzO0tlnowbLZDq74tzgWi/hvpNxz6d7xci6ZpL17QNIemYq6a+dezTy+PpXHtlI49CHb+D6zAb5yHB/OpTt84Fdc9e61/3oNLZDop1eQt1DyiRh7Z3eauVNL38pb9Ez0gS5ZXOMF3L6xwrmXt86eTPNAg0ZAHd19a1UroV/iRro/vxSrmeT7LlYTwEEEAAgfwL5OtY4I9tBFQ20IDK/G+6zBEBBPIloNoW/JvUuqmg5sPCb83mqyzMBwEEEgt8++23LlBcY+mhrx7op/tQWUGZejjimwL0c9bvX0EVpOIWiBdQWdylpnQIZEfgkksuiWqWfPjw4a4miuzkXhy5KNA6WFu+avBSzaAkBDIRIJgyEz2mbcwCiQIqG7MLy44AAg1fQDXb6sWgQiTd81AAOwkBBBBAAAEEGpeADzrJdaBjvoJoGtfaY2kRQACB+iWQr2OBnvfoxeHy+sVDaRFAAIHGLaCaVsaPHx9BUC23BFNGOOhAoKgEFi5cGAmmVMH0UEO1tCmgUjWlJlsrjGoeUe2K4SaxladqRY7XPKGGkxBAAIFCCixdutQ1mzdmzJioYqgJtYaW1AxZMAVr5Az2pxuBZAUIpkxWivEQQAABBBBAQAKqGapQwZSav+atMlBbrTRICCCAAAIIIIAAAggggAAC9VVArQOpdZZGH1Cpphd0oZ+PpHmREEAAgUwEVDOlb+q3bdu21HyUCSbTIpBjATXDqeaBFBAZTPquPzU93LFjR9dkkGpy0J/OFdSsr/9TU12+ab9gHupWMLWCKfPZ/E24DHxHAAEEYgmoqXXVDharuUE1A7jHHnvEmqze9lu3bp099dRTkfKfeuqptsMOO0S+04FAqgK5CKZM9b4HwRCprjXGRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEKjvAnper6DKRhdQqWCF4JuaEydOLNi6VFlICCCAQCoCX3zxRWT0W265xXr06BH5TgcCCBSXgAIdt99+e1dL5dy5c2sVTjXO6k/NgqeSdAK3xRZb2GabbZbKZIyLAAII5E1Ab+7FCqZUAa6++mp3IZq3wuRhRjNmzLANGza4Oe2000522WWX5WGuzKKhCmQzmFIvaMyaNcuWLVtmFRUVKZGVl5e7lz769u1rzZo1S2laRkYAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoD4KKKCyUTb53a5dO1dbVDGsNJWFhAACCKQioCDKESNG2Oeff26DBw9OZVLGRQCBAggo+LFPnz6uJsrZs2fb8uXLMypFmzZtTIEN+iQhgAAC9UlAQeY33HBDg6udUutAtVFOmDDBRo8ebWrOvEmTJvVp1VDWIhLIZjClak798MMPUw6k9BwKwFy8eLELxtxtt91cTdp+GJ8IIIAAAggggAACCCCAAAIIIIAAAggggAACCDREAd96U6OroVJBDWpmM9XaGbK9Eai2B5WFhAACCKQqsOuuu5r+SAggUH8E2rZta/3793eBCfPnz3fBCXq7JZmkkzY1760aKVu3bp3MJIxThAKqVfTCCy90JVMNdiQEGrKAXhwbOnSoffPNNy4Ia6uttnJNgOt30FBTy5Yt7dxzz22oi8dy5UFA9ym+/PJLC54f6MWMbbbZxjp16pRyCVQzZTbueygPvRSy9dZbp1wGJkCgUALDhg2zgQMHRmbfvXv3SDcdCCCAQEMV0L2DffbZp6CL5x86FbQQzBwBBBBAAAEE8iqg439VVVVe58nMEEAAAQQQyKWA7tHrr6Sm+evknubnsjR5zls1NeiBgJq9Cjb/nY9iqJlvPWBUMCXNZuVDnHkggAACCCBQfAIKTvjxxx/dSx46L9H5iP4UONG8efPIX4sWLVztltR2VnzrkBIhgAACCCCQTYGPP/44qibrTIIpVa533nkna/c7dG4yYMCAbC4ueSGAAAIIIIAAAggggAACCCCAQAMQWLRokXuhs1evXjldGr24rZTr+eR0IcgcAQQQQCAjgXwdCxYsWODK2ehqqNRSK5BRtTyQEEAAAQQQQACBQgiopuouXbq4v0LMn3kigAACCCCAQPEKZBpMqSXL5suj2cyreNUpGQIIIIAAAggggAACCCCAAAIIIIAAAggggAACZqqBuVEGVLLyEUAAAQQQQAABBBBAAAEEEEAAgWIRUCsWaklDqWfPntahQ4eMipbNJjezmVdGC8XECCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkWECtTRJQmWNkskcAAQQQQAABBBBAAAEEEEAAAQQSCbRt29b69++faJSUhu2zzz4pjc/ICCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggsLGGylIgEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcYuQEBlY98CWH4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEGrFAaWmpVVVVGQGVjXgjYNERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFUBaqrq1OdhPERQAABBBAoagEd28rKygioLOq1ROEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFGLkAAZyPfAFh8BBBotAL53P9XVlY6Z2qobLSbGwuOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUr0B5ebkr3IYNG4q3kJQMAQQQQCBnAn7/748HOZtRTcYlJSUuewIqc6lM3ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJpCTRv3txNt3LlyrSmZyIEEEAAgfot4Pf//niQy6UpLS21qqoqmvzOJTJ5I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKQn0LZtWzehAmrWr1+fXiZMhQACCCBQLwW03/cBlf54kMsFUfPiCqqkhspcKpM3AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQFoCTZo0MQXRKMhl0aJFBFWmpchECCCAQP0TUDCl9vva/+s4oONBrpPmpb/yXM+I/BFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEegffv2LpBy7dq19t1331nr1q2tRYsW1rRpUysrK0snS6ZBAAEEEChCgcrKSre/X7NmjauZUsGNaupbx4F8JB1T1OQ3AZX50GYeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAikLlJSUWJcuXWzp0qW2fPlyW7FihftLOSMmQAABBBCoVwKqmVLBlDoO5CNVVFS4Jr8JqMyHNvNAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSEtAwTQdOnSwVq1amWouW7dunavFTLWZkRBAAAEEGoaAaohU7cPNmjWL1ESczyXzgZsEVOZTnXkhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIpCWgQBv9kRBAAAEEEMi2gG/yuzTbGZMfAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUF8EVOuxaqkkoLK+rDHKiQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkIHDiiSfaVlttFfnT9/qeJk2aFFkev2zqF0xqFvTFF1+0u+++22bNmhUclNXu9evXZzU/MkMAAQQQQAABBBBAAAEEEMifgGqorK6uNpr8zp85c0IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMizwEknnWSfffaZm+udd95po0ePtr333jvjUnz99df26KOP2vvvv29z5861ZcuWWZs2baxHjx62884728knn2z9+vXLeD4NJYOVK1faeeedZ1VVVZFFOuyww5xTpAcdCCCAAAIIIIAAAggggECBBHStQkBlgfCZLQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQO4Fpk+fHgmm9HN79tlnMwqoXL16tV155ZX20ksvuYdtPl99rlixwqZOner+nnjiCRs0aJDdeuut1q5du+BojbJ7/PjxNnny5KhlVyCqakotLaVRvSgYviCAAAIIIIAAAggggEDeBdTctxJXJ3mnZ4YIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAL5ENh8882tbdu2UbPaYYcdor6n8mXBggU2cuRIGzt2bK1gylj5TJgwwUaMGGHffPNNrMGNqt8///nPWss7f/58e+edd2r1pwcCCCCAAAIIIIAAAgggUCgBAioLJc98EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEcirQokULu+GGG0xBlAqsHD58uB133HFpzbOystLOOecc+/LLL2tNrxoWFbxZXl5ea9js2bPtrLPOsrVr19Ya1lh6zJkzx6ZMmRJzcWMFWsYckZ4IIIAAAggggAACCCCAQA4F1OS3EgGVOUQmawQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcIKHHHEEaagvXfffdduuummWjVWJlu6+++/3zXlHRxfQZp33323ffDBB/b666/b+++/b3/729+sY8eOwdHs66+/trvuuiuqX2P68vzzz8dd3FdeecVWrVoVdzgDEEAAAQQQQAABBBBAAIF8CNDkdz6UmQcCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUACBJUuWuGamq6urM577hg0bbMaMGfb9999nnJcy+Pbbb01NZ6eaVEPkzJkz0w6+Uy2S8ZKc1PS0/mKZrVixwgVOBqdv166dPfHEE3bIIYdYy5Yt3SDViHnAAQfYk08+aV26dAmObg8++KAtWrQoql+iLyqHmgpfvHhxotESDvvhhx9s2rRptnr16oTjJTswnXWn5UgUULlmzRobN25cskWIGk/bxKxZs+y7776L6p/Kl2zkkamzyjBv3jz3O5NHOknbqNb1woULY27D6eTJNAgggAACCCCAAAIINCYBH1BZu92BxqTAsiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACDURAtfzdcsstNnHiRBe0qMVq06aNHXjggXbNNdektJQff/yxCxacOnWqffXVV6agSiUFDu644472i1/8wnbfffdaeb788st2/fXXR/or6FDBctOnT7fbbrvNPvnkE1u6dKkb3rp1azv00EPtqquuslatWkWmCXe88cYbdt9999nnn39uCjbTQ64tttjCLrnkkoTTKZ/169e7AMdgnmeccYbpz6eKigo76qijXGCe+vXu3dtefPFFa9q0qR/FJkyYYOvWrYt8V8fIkSOtX79+Uf38l169etmpp55qf/jDH3wvU9CcfE455ZRIv3PPPddk7dP5559vhx9+uF133XVuPS5fvtwN6t69u+2xxx52xRVXmEwTJa2vv//97zZmzJhI8KnMevToYTvttJP9+te/ts0226xWFrlYd5rJe++9Z3Pnzo3MT4GtgwcPNtVM6ZNqED3++OP914SfajpdtX1qO9ey+vWibWjrrbd22+bee++d8zzSdfYFU3OCY8eOddu2AoX9b0zDVcOpfrfnnXeeW29+mvDn/PnzXaCvakdVELVP2nb79u1rp59+utu2y8rK/CA+EUAAAQQQQAABBBBAII6Ab/K7pOZCMvNXE+PMhN4IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK5F1Dtduecc46roS7W3DbffHNXa10wsE0BkY8//nit0RWMd+utt0YFeNUaqabHcccd5wI4g8P+9a9/2a9+9atILwVy3X777fab3/wmbi2JCj7UdKrdMZzUzLaCEv2DrfDwvfbayyZPnhzVe/To0eYD6hRQucMOO0QNv/DCC+2CCy6I9FNQ28UXXxz5ro7f//73NmTIkEg/BT4qaM0nLZeCLLt16+Z71fpU4Oh+++0XCfjTCAMGDLDHHnssMu6JJ55oU6ZMiXzfd999XZBgvBoXFez517/+1fr06ROZJtjx9NNPu2DMYHBecLi6FWSrANvg8ql/tted8lS6/PLLXZPrG7+ZCwzVOjj55JN9L/f52muvWc+ePaP6hb988cUXLpBWAYiJkgJktQ03adKk1mjZyCMTZxVItUmedtpp9umnn9YqX7CHyq8AWI0bTs8884xde+21df5OFfSr30SnTp3CWfAdAQQQyEhANeLqZQEdx0kIIIAAAgg0BAFdh+naM37bBg1hKVkGBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCBCyiYQQFXau43XlJTzcFgSo0Xq2nrq6++2n7729/WGaSl6VWr4KOPPqrOuEllU22WiZqcVrPWjzzySK08nnvuOVerZbxgSk0QDqZUv1jLpf7xUqza+8L9VLNmMKnGyETBlBq3ffv2ttVWWwUnczV0RvUIfXnrrbcSNl/99ddfu3WtWjXDSc2MX3nllXWuOwXzKUhP80qUMll3Pl/VKKqaL4NJNXAqmDfcJLrWd6I0Z84cGzFihGv2PdF4GqYaRlWLZThlI49MneWqAN66gilVdgXG3nzzzVHBvOr/7rvvJhVMqXFnzJhhqvlUwcUkBBBAAAEEEEAAAQQQiC+ga0ldCxJQGd+IIQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA0QsoeExBicGk5o+POeYYV2vlnnvu6R4KBYfH6lbNfU899VTUIDWhrABL1ch31lln1WpuWrUAJhOopSa6VSOhanrcbbfdouahL8FaG/VdQZT33nuvOqOSapv8+c9/7mrH3HTTTaOGpfvl4IMPjqrFctttt3VNkfv8VJYff/zRf3Wfyc47HDSo5qkV0Jgoqbnm/fff31lpHaqZ9WBasGCBPf/888FeLlhW6yKY9CBQ0yt4b/jw4dasWbPgYFe7qIL76kqprrtgfmrWW03R+6Tmvg855BDTp5p7DyYtU6Jg2Fjb2sCBA021XaoZddW8GUy+mfhgv0zzUFByps4ffPCB/ec//wkWy0466SRXQ6iat1ez7sFlkUl4nnfffXdU4KyacL/zzjvtnXfesQcffNAGDRoUlf+HH35YK7A1agS+IIAAAggggAACCCCAgJWUlLjal8uxQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqJ8CCrYKBx4qmFJN/O60006RhVJApGovTJQmTZpUa7CahlYz1Ur9+/d3zXIrcMsnBQh+9dVXtt122/letT4V3KUAsPLy/z6WOv7446Nqa1TTamvXrrXmzZu76cePH2+zZ8+OykvNOKspbl97pAITR44c6WrgixoxxS/KT7VtKqBUKbwsixcvdg/VgtmmG1CpPL7//vuogLlgvjKSbzAg7uOPP7af/vSnUbV8qin0YcOGuQd+mv6hhx6KClxUP9XQOHjwYHW6pCbaTzjhBP/Vpk+f7oL4hg4dGukX7khn3QXzkGswKZi2c+fOrpdqqgzWTKpm6xUQqGbcw0n9X3311aje5513nl100UWRfgrYVZ6+RlMFiypAcfvtt3fjZCOPbDgruDGYFHSr36b/fZxWU9usAjf//ve/R0ZTzaRqQl61nmq5wjWmKtj5sMMOc+Or2XgFBSsoN9j0+0cffWRHH310JE86EEAAAQQQQAABBBBAIFpA19cKqqSGymgXviGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC9UZAwXkzZ86MKq+CpoLBlBqoGgoVEJkoqTZD1Xbp/8aOHRsJpvTTHXTQQb4z8qnAvETp0ksvjQSL+fGCAYPqpwdX8+fP94MtHNypGg0vu+yySDClRlQtfuqXraRAynAwpfJeuXJlrVm0bt26Vr9YPYI1DfrhiWqo3HXXXaOCKTWN1tuxxx7rJ3efs2bNsh9++MF1K8Bu3LhxUcP79u0bFUypgQpmVG2TwRQO7gsOU3c6687nofUZbpJdAY8+qTzhwNRwAKYfV4GRwST/s88+O9jL+vTpY3vvvXdUvy+//DLyPdM8suUcrtFVtZ+Ga5i94IIL7Iknnoj6U82lSgoYDTf5Hl6PHTt2dLXKBvMIBtNGUOhAAAEEEEAAAQQQQACBiIAPqPzvq4CRQXQggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUBwHVZBdO4aaU/fAmTZr4TvepmjeCSTUH+toDff9ly5aZao/U35o1a0zfwynYpHN4mL6H56N+aro7nFQDn0/h5dp5551rBd9p3PAyqV+s+al/uskHsgWnV82cyaRY4/laOGNNH6/sWqePP/541CQKWNT6CgZX+hGGDBliy5cv918jn6q1UOP7FA7k8/39Z6zy1LXu/LQvvPBCVBPe4Wa+lbeWK1gTo5oIv+6662o1c64aGoNJwZgtWrQI9nLdl19+eVTtjR06dIiMk2ke2XLWthxMCo5U7ZoKet5nn31sl112cTVRahljJW3zqnUzGET5r3/9y5YsWWJHHHGEq+Gze/fuMYODY+VHPwQQQAABBBBAAAEEENgooGsWJQIqN3rwHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCodwJqJjmcunXrFu6V0nfVevnoo4/aU089Zao9r66kWjxSTf5BVbzpwsuV6TLFm08y/Tt16lRrNAWYJpMWLVpUazTVHphqirX8CqhU7ZWx5nH77beb/upK3377bV2j1Bpe17rzEzz33HO+032q9s1wwK4CAIMBlatXr3a1bao582AKB0PGWicaf+utt3Z/wWl9d6Z5ZMtZTXIfcsghpuBRn9Ss/L333uv+FHCrYMr99tvPNeverl07P1rk86qrrnLNtwdru/y///s/059Sz549XXCmfPfcc8/IdHQggAACCCCAAAIIIIBAfAFd2+qPgMr4RgxBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIpaIFbA4yabbJJ2md966y3XlPKGDRvSziMbE4aXK5NlyrQ8zZo1czUGBmvQjBVcF2s+CxcujOpdVlZm6SxLrKbDfRl8099RM0ryi4Jnc5FUe2I4gFHL/pe//CVqdu5hZXl5VBPWavY7HFAZdowVZBiVcYwvmeaRTefbbrvNbVNPP/10VC2eKvbatWvt7bffdn933323a3Z95MiRUUu04447uuDLa6+91mIFxc6ZM8f0pya/99hjD/vjH/9YK5g1KkO+IIAAAggggAACCCCAgBNQTfoEVLIxIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAL1VKBLly61Sq5a/tq3b1+rf109ZsyYYRdeeKEFgykVyDdgwABT7YJ9+vRxTX5fccUVdWWV8XDVZBhsslrLVMikZQ82sawmyVW+tm3bxi2WmnKePXt21PBevXpZsjU8BicMBnP6/j4w03/6/ql8plO7aDL5KygynN555x3TX11pypQpLkhw8803j4y66aabRjVVvmLFisiwZDsyzSObzi1btrTf/va3rpZJBVW+8cYbtmDBglqLsnLlSrvmmmtc/3BQpWq6fOmll0zTv/rqqya34G/XZ/buu++6+YwZMyZmM+l+PD4RQAABBBBAAAEEEEDA3PUaAZVsCQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAPRXYbLPNapVcNfHF6l9rxFCPJ5980hTAFUx//vOfba+99or0CtfypwGqwSPbqXv37jZz5sxItrHmGxmYhw410RwMqFy3bp0paPC0006LO/eXX37ZlixZEjX84IMPjvqe7JdYNUn26NHDTd6vX79a2fzhD3+wI488slb/fPSQjQL90k0K8lRz4Qru9UmBqLNmzfJfLZ3aIjPNIxfO22+/venvuuuus2nTptmkSZNs7Nix9vHHH0eWVR2qYXLo0KGm2lKDSd9PPvlk96cgUwWsqtlvBU8Gf8uqrfLxxx+3M844Izg53QgggEBGArkKys+oUEyMAAIIIIBAhgJ6Ma40wzyYHAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoEACsQIn33zzzZilqaqqitnf9/z00099p/vcf//9o4Ipowbm+Et4ud5//32LVSthXcuUrWIefvjhtQJHH3300Zhl0jxVU+DDDz9ca/aHHnporX7J9JgwYUKt0XxAZdeuXS3cJLgCGtXEdrw/NSvth9XKOMMer7/+elyXZLN+/vnno5rC7t27d9Skn3zySVQz4X6gmhQ/7LDDIn8XX3yxH2SZ5pENZ7krEDf454NDt956axegqxonf/nLX0bKrQ4F5vraThXYG5z+rbfecuNqGxg8eLBdf/31pmDebt26ReXxwQcfRH3nCwIIIIAAAggggAACCNQWUIsCBFTWdqEPAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFAvBDp16mR9+/aNKus//vEPU5PUwfTaa6/ZRx99FOwVFbCmAT6wy48Uq1bIV155xQ/O6eeee+4Zlf+aNWvsnnvuieqnmhDvuuuuqH76kk6NWaoB8oEHHrD777/fFi1aVCtPBXgec8wxUf1V659qBwzXHrlq1So788wza9UyuM8++9gOO+wQlUf4i9bRe++9F9Vb83nhhRei+vXs2dO07n0aOHCg73SfCsCMFdApm7vvvtsFHM6bNy9qmmx9Ue2SwaQHkrfffrsp2DHe3xFHHBGcxG2/aqrap7333tt3us/FixfbU089FdVPzcJrHaomS/9XXv7fxvqykUemzk2aNLGrr77aLr/88sifTMJp9913D/cyHzysWiyD059zzjkW/q127ty5VgCpn75WxvRAAAEEEEAAAQQQQACBKIH/XkVE9eYLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAsQuoue2zzz7bLr300khRFWw2cuRIO+6442zTTTc11Uz36quvRgKyIiOGOrbddtuoQEw1Qazgr6OOOso1H6yaL8NBbMpCte5lO6mWwTvuuMO++eabSNYKdvz6669NgYkKIFN5vvzyy8jwdDtU86Wa4lZAntKdd97pmk1u165dVJZXXXWVvf3221GBp1OnTrUDDzzQBUpus802rhZB1Z6ooMpgatmypd14443BXjG7FSSq9akAw5122snmz59vzzzzTK2mwxVEF2xqXWWbOHFiVM2Qv/vd70yBtAcccIC1b9/eWU2ZMsW++OILN+/TTz/drU8Ny1ZScKmvMdHnueuuu7ptyH+P9dmxY8dazYSrFkYfWKtlUECkggl9uuGGG0y1qip/bavjx4+PWn6NN2TIED+6c8g0j0ydVSuolkW/R59UI+VWW21lP/nJT9zvVduUmvgOplatWplqsFTS9vanP/0pEji8fv169ztVc9677babM1AtoUErTadhJAQQQAABBBBAAAEEEIgvoJfB1OQ3AZXxjRiCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBS9wNFHH+1qavz2228jZVUti/fee2/kezIdqoExGOilaRRAGSuIMpjfjz/+GPyalW4Fnimw8IorrojKTwGC+stmevHFFyPBlMpXAaJjxoxxtU8G56MASwW6nXfeeVGBewpoU9BqvCaVmzZtarfccov5JrqDecbqXrlypamWUf3FSqot89hjj40apMDZ3/zmN7W8VMtjsKbH4EQK1lRA4n777RfsnVG3atKsrKyMykPNpdeVdt55Z9dE9YIFCyKjqtnqa6+91hSMqnTllVe6AEmfv2rb9E1fRyYKdCgQeN999w30yTyPbDirOW9tKwp8VlIQ7TXXXOO6W7RoYaqNNZxOOeUU10S7+ivwWTWjPvLII5HRFFysP02v7TdcS6tqMw3XAhqZmA4EEEAAAQQQQAABBBBwAjqP1rUoTX6zQSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC9VhAD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" width = "961" height = "399" alt = "demo_dandi_view_files_in_dataset.png" style = "vertical-align: baseline; width: 961px; height: 399px;"></img></div><div  class = 'S1'><span>          3. Click on the folder "sub-P11MHM" (click the folder name, not the checkbox).</span></div><div  class = 'S8'><img class = "imageNode" src = 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" width = "960" height = "228" alt = "demo_dandi_select_folder.png" style = "vertical-align: baseline; width: 960px; height: 228px;"></img></div><div  class = 'S1'><span>          4. Then click on the download symbol to the right of the filename "sub-P11HMH_ses-20061101_ecephys+image.nwb" to download the data file (69 MB) to your computer.</span></div><div  class = 'S8'><img class = "imageNode" src = 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lBpeGVsWERpbWVuc2lvbj4yNjQ4PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjY4MjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgICAgIDx0aWZmOlJlc29sdXRpb25Vbml0PjI8L3RpZmY6UmVzb2x1dGlvblVuaXQ+CiAgICAgICAgIDx0aWZmOllSZXNvbHV0aW9uPjE0NDwvdGlmZjpZUmVzb2x1dGlvbj4KICAgICAgICAgPHRpZmY6WFJlc29sdXRpb24+MTQ0PC90aWZmOlhSZXNvbHV0aW9uPgogICAgICAgICA8dGlmZjpPcmllbnRhdGlvbj4xPC90aWZmOk9yaWVudGF0aW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KrmUsAQAAQABJREFUeAHsnQd4FVX6xr+QhARI6AFC702qKIgNBHvH3rura1kbYl/77qrr2lZd17Ura/uLLvZCVYpSFOnSew8E0hP4n3fiuTl3MnPv3H4T3u95kpk5c+pvZs7MvfPe70spKCjYJzQSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAGpU6eO7Nu3D8s6kpaWRiQkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIksN8TSElJEfzVAYm9e/fu90AIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ0AcuDJQWWGgeXJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEAC+zMBramsU1FRwRDh+/OZwLGTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn4CCA8OKwOVrTa0reXKyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSwHxLQAss0jJ0Cy/3wDOCQSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEqhGApnLfvn2VHizT09OrZWACCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACexvBOrUqSPwYplWUVFBD5b729HneEmABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABBwJwHtlamqq1ElLSxOoLWkkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIicF6ZVl5ebrmyJBASIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAGxdJXKeWUdgTvL/cHWrl0r77//vtxwww3SrVs3adCggXTt2lWuu+462bJly/6AgGMkARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIQACaypSUFEkpLCzcB5El3FnWVps5c6Z8/fXX8tVXX8ncuXMdh3nCCSfIhx9+6LiPiSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAvsHgdTUVGugKSUlJfv27t0r+KtttnDhQks0OXbsWIH3ykAGkel3330ngwcPDpSN+0iABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABGoxAR0ZPKWgoMDyYFmbBJabN2+WcePGyXvvvSc//vij58PYqVMnmT59umRnZ3suw4wkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK1hwAElrBaFyJ86tSp8sorr8gHH3zg+WjBnacOkX7RRRfJSy+95LksM5IACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACdRcAvv27au5nWfPSYAESCCKBFJSUqJYW82uCgJL6ApTioqKrLtETfdguWvXLnnzzTctceTKlStDOjqAYY7/rbfekjPOOCOkOpiZBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEggeQlQSJm8x4Y9IwESSH4C+5v4UmsKU0pKSvZBXGgKDJP/cPn38JdffrGElW+88Yb/Dg9bPXr0kCVLlvjlbNKkicycOVPatGnjl84NEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBmkMgFFFlKHlrDgH2lARIgAS8EQhFQBlKXm+tJ18ujBF/aTVZWAms48ePt8SVEydODIsyRJR2gWVeXp5cddVV8tlnn4mOpR5W5SxEAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQEALBBJPB9utOe82n83NJAiRAAslKIJAw0j7XeckbKE+yMvDaLz22NIDRG14LJ0u+l19+WR599FHZunVrWF0666yzZNGiRY5lp0yZIk8//bTceuutjvuZSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkFwE7AIhs3du+0JNN+vkOgmQAAnUJAJ6vgumF8R+nVePz6mMzuO0T5erqUs4ZiwvL5eUoqKifRhgRUVFjRkLBJUQVz711FNSWFgYVr+PPPJI6dKli7z22muu5evWrSvwjDlgwADXPNxBAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQOAJa4GPvgVO6mWau67JOaV726TxckgAJkEAyEvAigLTnMbfNdT0+pzTsc0vX5WrKMjU11eqqJbDEWk0JFQ5x5RNPPCHPP/982Kw7d+4s5557rrzzzjuyZs2agPV0795dfvjhB6lfv37AfNxJAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQXwJOgkh7mrmt1/VS9zbYts7HJQmQAAnUJgJ2MaTbtpluroOFfdstrSZyg8jSChGOlZogsFyyZIk8++yz8vrrr4fNG0LJ0aNHy9dffx1UXIlGli5dKnfddZc888wzYbfJgiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAtElYBdFonYzTa/rpbnfKc3cj3VtZl6dxiUJkAAJ1FQCpiBSz286Ddt6Pdj4dD6nMk5pwepLtv16fDXGgyU8TT7yyCOW18lIYD7++OOyc+dO+ctf/hJSNe+//76cdNJJIZVhZhIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIggegS0IIgs1YzTa97WeYX75VVuypkff5e2bRnr2wp2Ct5xftkp/orKtsnJRVmK1wnARIggZpPIEUNIa2OSHbdFMnOSJFm9VMkp34daZVVR9o2TJV2jepI48w6PqGlT2iYgpJVHivt6eY+K+Pv/3Q+M60mrGunlSmFhYX7MIhk9mAJL5JPPvmkvP322xGxffjhhyU3N1duuukmKSgoCKmu5s2by48//igtW7YMqRwzkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJRIeAFk3q2sxtvY6luY68Og3LpdvLZcn2CvlN/S3bUSmo1PVxSQIkQAL7O4Fm9VKkU5M60r1pqvRsnirdmqVZYktoDLVY0lya65qdTnPb1unJvKxTp1JkanmwxIAqKpJTcp+Xlyd//vOf5dVXX42I5wMPPCD9+/eXhx56SObOnRtWXcccc4yMGzfOd6KEVQkLkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJhExAiyZ1Qb1tLt3Wt+6pkDmbyuVn9Td/S4WU7dW1cEkCJEACJOBGIFV5uuzfIlX6t0qTAa1SpWVWpdgS+aE51EJK+7quT+/X+XV6TVlaIkt4sESH9Q0mmTqPUN5PPPGEPP300xF1C6HFhw0bJnfccYdMmzYtorrQn+uuu86qY+XKlbJ+/XrZsGGDFBUVSWZmpuTk5MigQYOkUaNGEbXDwiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAtU1LabGBet6276OaK5rVPjvmevK5KcNFbJWhQGnkQAJkAAJhEegvQodflBuqgxuky4dGqeK9vCI2uwCSzdhpZkeXi/iVyotLc2KCp5SUlKi7i/7ks6DJWKY//3vf5d77703bCqNGze2yo8YMULuv/9+GT9+fNh16YJ169aVK6+8UhC2fNasWbJr1y69y7ccMGCAXH755XLVVVf50rhCAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQGgEtntSlzG2s6z/s1+sQVm4r3CtTVpXKD2vLZcMey/eYroJLEiABEiCBCAi0yU6Rw9qly+Ht0ySnQZXQUosn7WJLNKX32dcj6EZcilpjKSgoSLq7CNSt7733ntx+++2ydevWsGB06tRJ7rvvPjn44IPl7rvvjoq4MpSONG/eXO655x75wx/+4LnYPnWD37Njh5QWFFrq14wG9SVb1ZOieNBIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIYH8iYIopMW69bS61qFIvIa6cuqZMJq0qk0Xb6LFyfzpfOFYSIIH4EujdvI4M65guR7RPtwSU2qOlXWCpxZV6iV6a6/HttffW9DhSVGjrfdioqKjwXjqGOdGXuXPnytVXXy0LFy4Mq6XDDz9cbrnlFunYsaM8+uij8tFHH4VVT6SFevfsKRMnT5asrCzXqgqVB8wVM3+Sdb/+KttWrZa9ZeV+edMyMySncydp1b27dDtsqGRmZ/vt5wYJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ1DYCWkSpx6W3tZAS6XodSwgrN+6ukK+Xl8p3K8uljNpKjY5LEiABEogZgdQUkWM6p8lxXepKq2x3b5ZaUKmX6JC5HrMORlCxL0R4YWHhPqhHk0FgCWilpaWW58qXX345rOGdf/75Mnr0aCkuLpYHH3xQvv7667DqiVahJ2+5TS69/Tap16iRX5VF+fny8/jPZPn0mbK33F9U6ZfR2EhNT5cuQw+RgaedIvBuSSMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCB2khACyoxNr2uBZU6TQsrIa6cu7FMvlpeJvO2UFlZG88HjokESCC5CfRtUUeO75IuA3PTBVpE05sleg5doBZU6qVOT9aR6T6nKCGiFSI8GQSWUH2+++67cumll4bMrWnTpnL99dfLFVdcIQsWLJAnn3xSJk6cGHI90S5w48hjZUC3bnLoRRdI+4EDrOpX/jhLZrz7npQVFoXVXP0mjeWIKy6Tlt26hlWehUiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEggWQloQSX6p9e1uNLchtYF2xNXlsr4pWWyqcCSwCTrsNgvEiABEqjVBFpnpcjJ3dJleKe6lpjSFFpi4FqwqNc1DFNwqdOSYenzYFlQkBx3FwBdu3atXHvttTJhwoSQGA0aNMgKCX7hhRfK66+/Lo899pgsXrw4pDpilXn08SdJl5wWOEOk34nHS1lJiSz6NrTxOfWtjhKjDr/2amnb5wCn3UwjARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIggRpHQAso0XG9jqX9D14rkfbZ0hJLXJlfWuOGyg6TAAmQQK0jkF1X5NTu6XJS9ww/kSUGqgWWWlCpl3pfssGAnhH3mTStFC33GKY6VgOB4nP69OkhiSvR9xtuuEEuv/xy6dKlizzyyCPy0ksvybp162LVzZDqravGlJWRUVlGwZ732RchlQ+UGWHFJ730shx3y02S07lToKzcRwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJJTwBCFrtpYaVOxzbElfj735IS+WhxuZQxKrjGwyUJkAAJJJTAbiV2/2BRmVTs3Sen9sy05mp0SIsVdecgrsR8rkWW5rrOk+il7lsddAQ3nURaamqqQGA5efJkz90YOXKkjB07Vp555hmpX7++5cHyvvvuSxpxJQbSqXmOtGzYyPOYQs24t6xcprzympQWFoZalPlJgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIGkJQGxjGrbxp8WVnyvPlZ8sobjSZMR1EiABEkgGAqUVIh+p+fnTJcW+Ods+pydDP732wfJgmQzeK9HhOXPmBO33QQcdJOecc46MGjVKunbtKu+88468/PLLIYkzgzYSpQy9W7cJqSaoXiEWzawLF6kihUVFUlhcpHxeu1dTsH2HzP1kvAw5/1z3TNxDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAklMwBTf6HUszb+KigpLrDNxZal8vLRcipWIh0YCJEACJJB8BCCyHP9budRPT5ERnTME8zecMJqW7F4sIehHH9PQeXiPTJTIUocoBzyIC91s4MCBlrDytNNOk169esnq1atlzJgx8tprr8m2bdvciiUsvWFmPTmgdVtv7asD0aJZM2mhPF6mq2NhWll5mWzYtEl27NxpJvutL536g/QYPkwa57byS+cGCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACSQ7AS2oRD/1uimsxDqELlj+vKlMPl9WLgVlyT4q9o8ESIAE9m8CCBf+mZqvG2emyIGt61rzOLSCpiWzyBJ9g6VhJZEhwgFNd+aCCy6QnUpIuHjxYqtPnTt3lhEjRsgRRxwhhx56qOWxEjfLN954Q95++2359ttvTd5JtX7yIUOlQ4sWUl4W+I6empYqndt3kKz6DRz7n56WLh3atpPMzExLaOmUaZ96iJj/5Vdy+OWXOu1mGgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkkJQHoQOym07DEnxZXrs8vly+WKUdVe6qXsdfBbRIgARIggcQT2Kjm66+Wl0mrrDrSumGl40EnkWXie1q9Bz7xZ1FRkdLn7a2eI04p6enpgj/TfvvtNylTwsTu3btb3jX1vokTJ1oeK9966y2dlJTLJlnZ8sptY6Rnm7YyfeYMKS1VclwHq6Pcnnbv1EnqKW+XXmzl2jWyc9cux6wpqq4zH31Q6jdu7LifiSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQbAS0mBL90utaWKnFldC1IELrW/NK5KsVjAuebMeQ/SEBEiCBYASO65wqF/fLsMKEQ2CpnTJCxKidM+ol6jLXg9Udq/3oA+5DlgdLCBwhaEwW69atm19XvvrqKxk3bpx8/PHHsnnzZr99ybhx0dHHyJBeva2uHao8WU6DyLKkpFpX27du41lcicJtWuXKrt35sm9v9V9i7FMPEtPf/q80adNaMrOzpX6TJtKqRzfJzMqq1i4TSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESCAZCZjiSlNg+f2aUpmwiuLKZDxm7BMJkAAJBCPw7coK6dioVI7smGFltQsotZjRnh6s3ljuhwgUAv80KPwT6cGyvLzcp0w1B7x7925599135csvv5Tx48cnlQDU7Kd9/eAePeW84SN8yQ2V2PHQIUNk2o8/SmlxsZHeUJo0auTb9rJSVwlhmzVuKtt2bHfMvn7+AsGfthR1kFt26yp9jz9Wcnv11MlckgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEDCCUBAqU2vmwJL6FmwvWVPhUxaXSFliQvQqrvJJQmQAAmQQBgEKtR0P2VNhfTKqZAWWSmWXlGHCncSVWLud0oPo+mwi6APqSqqdJoy62YEoWUiDB1BCG0NDH1AGvoF8SU8V2K7JliaAtpPeZncvG6dtG7WXCCIhDXMbihDDz1Upk+fLqVFRfBhqrxRtgxrSDnNmsm2PCWw9IBExX6XTUuWWn9t+hwgh15yodRr2DCsdlmIBEiABEiABEiABEiABCIhAE/0jz/+uMyePVu2bt0aVlULFlT9mAgVDBw40PosEWpl9npCLc/8JEACJEACoRHgPSA0Xsy9/xHgNbL/HXOOmATiSYBzTDxpsy0SIAESIAESIIFICGhdiF6iLqzjD3qW79eUyeLtHoQSkXSCZUmABEiABGJKYJGax6euLpVRvepY4knM8dpzJRrW61pYqffHtFNBKsc9KA0iRt2pIPljthu/OHDyonnppZdaIsubbrqpRogsh/foJQe172iFMf91/q8yaOCBPmaN6tWXQw8eLD/NniVNGjeWxo2bSDHEliFaZkaGZDfIkt179oRUEp4txz/yVxlx3bXSvGOHkMoyMwmQAAmQAAmQAAmQAAlEQgAv9EaNGiW7du2KpBqWJQESIAESqIEEeA+ogQeNXY4rAV4jccXNxkhgvyPAOWa/O+QcMAmQAAmQAAnUOAIQzjgZ0vUftCRrdlXIjPWJcRrm1D+mkQAJkAAJhE9g+vq9MrBVuXRqmmJVonWLdnFl+C1EtyT6laZjhUe36ujVduWVV1rhwUePHp3UIsv+7drLsO5VYbi3bNtWDQLChQ8/4khrHPDQWaBEknk7doQ8rhbKO2aoAkt0pjh/t3z99LMy8vo/WqHDq3Uwjgll5RXyyMufBG0RXkEbZ9eXDrnNZEDPDtK+VbOgZULJ8OzYr2X7rupi1T5d28rZxwz2XNW2vN3y3LvfOObv3qGVXHjioY77zMRxE2bJz0vWmEm+9XuuOlV5RE3zbb/92TT5bc0m37bTSh11gTeolyGtc5pI7y5tpK8aU2pqHaesrmkP/MvZg2yvTq3lvOMPcS3HHSRAAiRAAiRAAiRgEoDnSoorTSJcJwESIIH9hwDvAfvPseZIwyPAayQ8bixFAiTgjQDnGG+cmIsESIAESIAESCAxBExxpV7Xokr0SIcGx/LH9eWyvvpr/cR0nK2SAAmQAAlERGCDms9/2lAuHRqnWhGvMc8jDDfuAVpsGVEDUSyMPkFbmYYVdDJRIcK9jOnaa6+1PFnecccdXrLHPU9r5ZHyqJ69pbkSUGprrkJ5OxlYa2uQlWWFQt+6ZUtIIksINRvUry8FhYW6Ks/L8uISmfDiS3LC6Fulcetcz+WinbFcuU995/PpIVfbvX1LS9h3znFDJLNuZQj2kCv5vcDK9Vvluf9+4xhtvYkSdZ5+1CBJT6s6XoHa2VVQ5DqeNCVqHNyns3Rr3ypQFTL9l2Xy0YTZjnnuuPxkJbCs2jXxp4UyefaSqgQPaxjTqcMHyhWnD5M2LZp4KCEyVh2jvWqOsNsxh/ShwNIOhdskQAIkQAIkQAKuBBAWnEYCJEACJLB/EuA9YP887hy1dwK8RryzYk4SIIHQCXCOCZ0ZS5AACZAACZAACSSOALQrMCz1H0Q3m3aXy+xNexPXMbZMAiRAAiQQdQJz1Lx+ZPtyadWwjiVg1PcAsyEttsQ+vW7uj8c6xJUw5SAw1TE8dzw6EUobN9xwg+XJ8t577w2lWMzzNqnfQI7r0096tPpdrKi8l7Zr01b6HtDHU9sZmZnSPCdHILIMxdrmtpalK5fLvr3VxW/B6ikrLJJJL70sJ901RtJV+zXJlq7ZLA/9+xP5z7jJ8tB1Z8rwg6q8hoY6jk8mznEUV6KevN2FMvGnRXLsUG/HMVDb5RV75a+vjJdXH7w6ULaY78OY3hj/g/z3yxlyzZkj5PrzRmICiHm7bIAESIAESIAESIAEtm7d6gdhxowZUrduXb+0cDZmzpzp+EOlr7/+Wu6+++4a8TknnHGzDAmQAAnUJAK8B9Sko8W+JoIAr5FEUGebJLD/EOAcs/8ca46UBEiABEiABGoLAS2wgbASBkdh8zZXyLrdtWWEHAcJkAAJkAAIrFXz+s+bKuSYBhWWeFJ7sUw2Olrwn1ZeXm4pQZOtg079ueWWWyxPlg888IDT7oSkHaOElIM7dfG13atHT+nWpatv28tKZr160rBRI8nftctLditPfVWmXW4bWbNhnfoJh0h6erq0aN5c6mVkSnFpiWzbsV2KlbdKN8vfvEVm/d84GXrh+W5Zkjp9w9adcvVDr8ifLjhWbjzvmJD7igvgk0lzApZDyO5oCCzRyJQ5S2XyrMUyLAJBaMDOhrCztKzCCmf+4/zl8uK9l0nDBvVCKM2sJEACJEACJEACJBA5AYgrMzIyIq7ISaQJceU999xDcWXEdFkBCZAACcSGAO8BseHKWmsPAV4jtedYciQkkIwEOMck41Fhn0iABEiABEhg/ySghZQYvRavaBJ6H8Q2+Pt1K71XajZckgAJkEBtIoD5fWSnvT5nKnr+194qsa3XEzlu9KEO/iVDZ7yCuP32260Xpl7zxyofmN1yxllyXP+Bfk3USal0DeqX6GGjYeNGVrhwD1l9WZo1aSI9OneVTu07SO/u3aVFs+aSrcKO5zRtJj27dJMctR3IfvthmmxfsyZQlqTep64jeeadr60w36F2dNbClbJ2846AxSYpQWRefkHAPKHs/Our4wXeLJPFZs5fIVfc/x8pLilLli6xHyRAAiRAAiRAAiQQEQGIK0ePHm39qllXpF33620uSYAESIAEaicB3gNq53HlqKJHgNdI9FiyJhIggeoEOMdUZ8IUEiABEiABEiCB0AlooSWWEFbCe+WyHeWyYFvodbEECZAACZBA8hOYr+b3ZTsqrPle3wPQa3M9GUaB6OCWwBIdq0mGcH9jxoxJaJdvVuLK0eecL4MG+AssV61d41PWhtJBJXOVRo0bh1LEygtPlo0bNlQH0l/YCQFo29xcaanCj7uaCi8+95NPXXcnakeH3GZy/vGHWH9njBgkA3u0l9Q6Kva6iz37369lkgrnHYp9PGF20Oxl5RXy6ZSfg+bzmmHZ2i3y7pfTvWYPK5+6oOW844ZYf2cfc7AceWAPyarn7h3q5yVr5MGXxoXVFguRQDQJ4EPS5s2bZdOmTWHNodHsC+uKD4GysjJZu3atlJS4e1uOT09i28qOHTusczu2rbD2ZCWwbds2WbduneWBPVn7WJv65fRCDx94/vKXv9SmYdbasRQXF8vq1atlVwhe/b3AKCoqsurFkkYCNY1ArK6LmsbBS395D/BCiXn2ZwK8Rvbno8+xk0DsCXCOiT1jtkACJEACJEAC+xsBLazB+8Ml2/dJWfL4MIrZochIS5E2jdIlO8Nf9xFpg1l160jL7DSlJ4m0JpYngfAJ8DwMn11tL1mu5vcl2yu9Feu53z5mrWnUS/v+eGxD25AGjy64MdU0u//++62X1f/4xz/i3vWbzzhbrjvldKvdHBWWu2nTpgIBBaxgzx5ZuXqVdO7YydoO5V/9Bg1kZ16en8edUMo75W3dspUUKfFKfn6+027ZsHCR7Ny4SRrntnLcn4jEvt3aycPXn+nX9DrlbfLPL3ykQm0v8UvHBvTB973wf/L1v8aoEOl1q+23J5SUlskXP8yzJztuj1NCzItPPsxxXziJz4z9Wk4ddqA0zIpNWO466snokRvO8utaUUmp/OejyfLPd7+RCiWqtduH3/wkpw0/UA7pF1poe3s9tWH77bffln//+9+uQ8lSHmI7dOhg/R111FEyZMgQ17zYcc0118iiRZXi36OPPlr+/Oc/u+b/5ptv5OGHH/bt/9///ieNHUTXgfqIED8tWrSQXCWu7tWrl5xzzjnSUAmwA5lZX1pamkyYMCFQdtd9119/vfz666/W/mHDhvmNxa0QfnWGcb/zzjtWWdyUYJmZmRbjE088US655BLBuKJhI0eOFN2Grg9i9EaNGlnMunbtKqNGjRIswzXcT8866yyBqAp2/vnnyx//+EfP1ZnHA3178skn5aCDDnItD1Hq2Wef7dsPEdHhhx/u2zZXoj3+WbNmya233upr4o033pBOnQLf+zZu3CgvvviiTJs2zSemxXnXXXlh7tOnj+A6OeywwHOuycjXeJCVG2+80Y/T559/Ln/729+ClHLffdttt8lpp53mmAHnwBdffCFvvvmmrFy5UgoKKj0hN2vWzBpj37595aKLLpLs7GzH8kwMTiCa15m9tXDmUbMOPNh///331vGfPXu2T0CM521cH5iXMc80UM98wSya95BYnvPBxhGv/W4v9P7+978L7kt33nlnvLpSq9pxm3PT09OlZcuW1v3zyCOPlOOPPz6skO+4X7777rvy8ccf++4LAIhrZPjw4XLxxRcL5s1QDcJmPF989tlnsn37dqs47qt4Turdu7fccMMN0rNnz4DVRvMa1A058Xz99delc+fOOkvAJX5oiDlGW/369eXLL7/Um35Lp7b8Mjhs2O+XOov5DNGvXz/55z//qXc5LnGPxbwD69atm7zyyit++WI9J4Vzn4hFn8J5VgKoaF4X0T52fgcyiTZ4D4jdwTDPoauvvtqalwO1Zl7/ePbBZwQnM+t12m9Pw2dxPa+Y+8y5LpLPs4H6g+c4fMbu2LGj9VkRz3M17Vme14h51kR33enc4fcMkX3P4vX7pVCvf/v3Jw899JD1vGk/I+L1DKjbjfQzqK4nkUvOMYmkz7ZJgARIgARIoHYQ0IIZLPGH73ZgentZXvX324ka+TsXtJW01BSr+Xfn7pJx8501GF7716R+qpzSO1uO7a6ilGalKZdclVZYuldmrCmy6l+yJXRHJajvsE71pXOzutJS1QsrVzqBzbvL5ce1RTJ2zi7ZVVxR2ViE//vmZsq9R1c5/Ro9fpOs3ekcQdPkt1iN6/6vtgRs/dqhTeWorpXvU1btKJM7PtsUML/bzp4tMuTB41r4do/5dJOszqveRwhRnz0918pXWrFPLn93vcXNV/D3lRGqT9eovsEQwPTy99ZJSbn/efqQaq+Hahc2c3WR/GOKvxtWjAvjC9f+MzNPvlm6x6/46X0ayvkDG1lp6NcF76z122/faN4gVZ4/o7Uv+R+Tt8vMNYW+7UhXonUe1iaWA9tkyp0jqq6XWz7ZKBvyy11RP6POx1bqvIR9tWSPvPpjXrW85nG37yxT5/G2ggrZWlAuy7aVymeLdsuekso51p43kduY5/X8j+/isA7DdyzJYOgP+pWmb1DJ0KlQ+wAx0oYNG6yXcqGWDTf/6LPPk5vPPNt3c0M9rVvl+gSW2F60ZInkKmFjPeVdMlSrp16S7dm9O9RiAfO3b91GFhbskb1O4anVibB8xkwZNMpZKBKw4jjubNuyqbx8/xVyzcOvCUJ3223jtl0y9vPpcuWoYfZd1ba/m7lQ8guKq6U7Jfz621pZrjxPdmlXdcN1yuc1LS+/UP753rdy95WneC0ScT6ITm88/xgVOr6h3PvPD6vVh6npybe+lA+euKHavv0tAZ6N9Et4p7FjHzwrwd566y3ri1gINtq1a+eUXXbu3Omrz03krAvCO43ZttvcHKyPELD98ssv1st2iEUhIIBI0c3M+uDlK1wzx7rbwxyWp8TkeIk/d+7cak2CxRI1j+Lvgw8+sDyPDRo0qFq+UBPA1y6wRB14ib18+XJLsACRw9ChQ602IcII1WbOnGn1W5dD//HlPG64Xsw8Hsj/4IMPykcffSQQsjgZbubmeRPIG2S0x19aWurXdnm5+8Mf+v7qq6/Ks88+W+0YoNzChQutP/C65ZZb5Morr3QarpVmZ+Sa0diBMqbZrzdzn5d1N854YYPj/dtvv1WrBvwnT55s/UEM88ILL0ibNm2q5WNCcALRvs7sLYY6j+ryOK8QmnrixIk6ybfEnI555q9//au89tprltAYwuJAZs6rkd5DYnXOB+p/PPcFeqF37LHH+oSu8exTbWkr0JwLr9P6mQOCGszfEJ14talTp1pC/cLC6l/YQJwOcST+Lr30Uuva8nov/eSTTwQ/xrPf83HPxDyNv0mTJlk/iLjvvvvE7fknmtegZuLEc9y4cQLhfjBDfyAuMseFa9vNnNpyy6vT7fdLnW4+Q2COww+BTj31VL272hLHTz+fNFc/SLRbrOekcO4TsehTqM9K4BTt6yLax85+LJNhm/eA2B4F8xxymq/trZvXP36M7GZmvW55zHS3+c6c69zmc7Met/Vg/dm6davMmzfPKo7PNficjR/T1QTjNRLbo+R27vB7hvC/ZzGfwQJ9vxTq9W///sR8pjLPErP9SD+H6XrNvuo0cxnuZ1CzjkStc45JFHm2SwIkQAIkQAI1nwCez0yzb+M7dfztKKyQlbuQNzlENhBEpv3uCrJeemR9OrhdPSVMbCFO9dRXXich4sPf/83Ll3/P3KEERyYx5/UGqtwtRzaTIztXd/KAfsND5ij1d3yPLHlh2g5LsOVck/fUdCU4bVKv6h1zaoBXoia/oR3qy9HdsuTb3/xFgmbLGI+uOy8zfEHoyh2llmdQfex65GQ4CiwHtM70tYd+9GhRVxZsqi5wPaBVVT6I1uziSpRtmJnqq6uBg1fSujZuKBOKobzdMpUXVM3LSRpkzw/xms6PfelVh9GeNaTtaJ+HtYml/XqBA7VA1sg8j9T14GTmcXfa38ISWWfIMDUvXKAEuG/M2ikf/ZrvlDVhaatUd3YWVUgzpVfROplIvuuL9kC00LOO/WYV7YZiXR88p8TDmmY3lPsuukT+NOrMarfvJk38Q3tXKLHIL/MrPbmF2reMTPdwzqHWpfOnK+9gLZpVf7Gl96/5+Re9mtTLVCVQ+suNZ0tmXWeh0TufT/P0YPHxxNmO4+zVqUqdrzPgOWXchFl6MyrLtz/9QVZt8P+FQlQqDlIJQocPdfFSOXfxapm/fF2QGva/3fBSYf7ZvUHi5TyEYHuU59pEmdk/vMDWkzv6g5dbjz/+uHz77beJ6p5ju/iCHC+jTHElPJLAIyi8P3Tp0sVXDh6o/vSnP0U9xDLaA7tWrVpV85A5ffp0S3ShX6D5OuNhBR64TIOIA/WFaytWrJD//Oc/4RZ3LRer8bs1CEEIvHGaLzBycnIEXkqHDx8u8O4IwzMJPFMH8vZqb8O8BtzW4eHLNGw75dX90HlxTTnls9eH/HjpixetprgSD34HHnigJULp37+/rlaWLVsm5513nixYsMCXxhXvBKJ9nZnHONx5FOf2ZZdd5ieuxHyM+WzAgAF+nv0gSoMXU+3h2PvIw88Zi3M+/N5Et2SwF3rRbY216eulSZMmfjDw8hk/CoBgUX/49ctg24AH6+uuu05MsQ7uTfDabBefw/vZXXfdZavBeXPs2LECL4/6foPrsGPHjgKhLepGGzDcbyDqh/A/0TZ+/HhPzCA21eMKp8/62AVaOt3fnNrC8yXEB+FarOekcO4Tse6TF1axui7MtiM9dmZdybDOe0AyHIXI+6A/mwSan/C5LV5m7w++dzRF/pj/HnnkEcE9J9mN10h8j5A+d/g9Q3S/Z4nvUYxPa+Z8F+5n0Pj0NHArnGMC8+FeEiABEiABEiCB8Alo/Qq+Z1y/e6/sLAksQAq/pcSVPFx5l3z4+JZ+4soC5bVy3sZiy8uk2bMz+zWUO46q8nhn7jPX66XXkReUN0JTXIk6f95QLBOWFQg8RmqNJvLefERzgffJRNo1Q5soIaKzaCya/YIAcrkSQmrTniX1tl72VwJL0/q78OmeU9eXbcFm9x/B+zI5rBSX7ZMte8qr/eUpkZlpEBk75SsqSz4vhOh3Is7D2srSPA9CWTfPF5w/pjgbxwfeVw/r6P/uPJT6Y5E3T11G63ZXerHU9wC0g3VzW6fFog+B6sR3g7gnpeGlkynICVQo2fZNmTIlLl9otmmeIzefcZZcMOJoRwTZWdmVP5rQdySVa8uWLbJRvUDPDfFL4NTUSveujg1FkNi8SVPZtFW5WDb6qKvbvXmLFOzIkwZN/V+U6v3JtGyhvDAed2gf+WRSda93azbtkAVKJNina1vXLm/fuccxzHhm3TS57w+nyQV3vVit7CeT5sitF5+gvlCPzsNbaXmFPPbap/LiPZdVayvWCZeocOfT5y1zbObL7+dJny7u7BwL1eJECKOchIkQzCHEMV7Kw9avXy8IK4SXlfE2pz4i7DbCfN97772W8BM3HPQNoZeTxRCude3atb7uwOMfhGnmS6sffvjB8lYFwQZeYI0ZM8Y1vJyvohBWrrhCecRV7cLADKGc4VXwpZdesm6O8EIBYSe8YCGEuBeDcBQvxu0GD5TBwl7by5jb8ER6wgknWAIRMz2S9ViM360/eNh44IEHfLsRKvSJJ56wQobqRIgT4dnvww8/tJL+7//+T4455hg54ogjdBbHpdM14JjRlohQtvizG0S/EL5pw7WOULJe7L///a/lhVPnveOOO+SMM87wiXmQDtEuQsbjnN6xY4fAEzdC49K8E4j2deZ0DoUzj0II/euvVT+uGTFihDz66KOihfmo8/3337e84+KagAAe8zTS0IdYWyzO+Vj32Uv9fKHnhVL08tivF3ijhgdizOlaXI57XocOHeSqq65ybRhzIO4LuBZgCAeOZxWEctefSxGl4KabbvLNq59++qn1LIN7g5vh2QLPGNrgYRzegs3w25hDIIpBfTCErj700EOtH3nocvFewisawn4j1Hogs4sGA+W177MfO/v+ULdx7MEaLMOxWM5J4d4nYtknL4xidV3Y24702NnrS+Q27wGJpB/dts3PJtGtObzanPoDj7TTpk2znu9wj4Lh/gfxfjCv5OH1IvJSvEYiZxhqDea5w+8ZovM9S6jHoCbkd3ouC+czaKLHyjkm0UeA7ZMACZAACZBA7SHgJpzBd4fYt14JbpLFe2W0qENQeJMSN2rpAYRyf52wTYWRLvRJORDW+8/HtpBuzSuFfPBk+f3KAvVXPRqQ7tdZSoiZ2zBNb8onKnz5i9N3iIoM7jPUh3pRPzxNIrT3xWPXCcJhJ8Lgne/qIU3lycmxd061YHOJL2S3KZA0xz2gtX+E2n5KcDlWhYI3DV4wu6jQ69qcPFzqfYGWk1cUCP7sdkCrDHnq1Mow5dh375ebrdDO9nzJup2I87C2sgznGMNz6UXqmjYN1zo8xt42rLnAuyhUTwhP/8Mq9/nELB+v9fX5e6VPi8q5H23iHqDfGTltx6tfum18nrfk4PYbVzw7Eklb8IiFF1KxtEN69ZaHLr3cVVyJttPS0lQ48OoK38W/LQ25a3Xr1pWs7Gy/E8WpknL1on7L9m2yduMG2a3CfwczhJet79BHXW776jV6NemXRxzYw7WPM39d7roPOz6d8rOUO/hDPrR/Nzn4gM6S27y6kArhx2e4iBIDNvb7TidZ5rczFqg6A/fVS92h5jlsQDf1sOTUI5GZ81eEWt1+mR/eKyAKOOmkk3zjx1zkFrLMlylOK5jYIT6AYFEbRKDBwhrpvLFeIkQuxHPaIMCAkNEUV2IfBIm33367ziazZs2yRJC+hCiugFnXrl0tZggRnpFR6UkY95dQhAMQaOrQ0Z06dfL1EILXSPjjZR68gsXKojV+t/5BdAMxmbY777zTT1yJ9MzMTMvrWdu2VSLvSIQkuq14Lk2PrPBaeckll/iJK9GXfv36WeFwdb8gyIOXUpp3ArG6zswehDqP4kc1EEJrgyfeZ555xieuRDrqhOdehFHWtnjxYoEYjRYeAb7QC49bNEvBiyXu1++9957lIVLX/dxzz1meevW2ffn888/7vB/i/o9Qq/BmbH5Qbt26tbz55pvSs2dPX3EI8bUo05dorDz99NO++zBEm/AsZoorkTVbfc7CD2MgAoXhc7D+0YyVEMd/mBe0BbvnLV261Cc2xWfPZDCENv/xxx+ToSt+fYjHfcKvwShtxOq6cOpesh47p766pfEe4EaG6bEigO/qcK+CcB/fr8HwOU0L9mPVbrj18hoJl1z0ykXrc3as7ms1/XuG6B2p5Kgp1M+gie4155hEHwG2TwIkQAIkQAK1lwC+qzP/8F3gloLECP9iSfniQY2l0e9eGyF+vP3TTTLDEFei7c3Ks+Ft4zfK8u1VXhevO7SZ+g7VuWeNVYjus/pV6Rw+X7RbnlchwE1xJUr+prw4/uXbKn0NwkP3c/HS6NxS9FOPU+HK7Z4jo9+KyPxNVZ4mIZDU4cJ1W62VOLV5g6rvbJF+QMvMavk6NU33S4Nwk1ZJoCafh7X5GEIeBXH2myo0uLaW2WmS5RC6Xu9PxHKz0ntqcb1eJqIfTm3ih5EwK0S4XdjiVCDZ0pYsWSJffPFFTLt12qGHyb0XXiLHHzwkaDtNbWHyUGC38ry2K99f0R6sIrxYbNK0qeS2aV0tbK0uu6ewQBYp8eb6jRtlm/KytmzVKikuqboh6Hz2ZZYtRKq5f5fytllTrEfHKsW+vc9LVgcexziX8OAjhxxgPZBg6WTjJjiHFXfKa0876uBekgZZuGF4FPzLK/9TE0R8Hwrr18uQti2bGj2pWl26emPVBteCEkB4X2142MeclEzWq1cvv+5oLxt+iQnY+Oqrr6wPR2gaL6kuvfRS115AxKpfXiHTzJkzXfNGa8egQYOs0M26PvQX3iy9GF5Wa7vssstEh4TGiwuE9YzEIGAIJr6IpH5dNpLx6zrsy43qXmUaRMpOBsHI4YcfLgiPhb9du0K7fzrVGc80c5xuY0R/Bg8e7BsjxmmWi2d/a2pbsbzO7Ey8zqPwdozrXNuNN95YTTSu95144ol+gi8I9GmhEwj1hR6er3HPMf9Cb5Ul3AjghwnwyAqxPKy8vNwSXTrlx4diCAW0wcN2nz599Kbfsl69enLllVf60uBFfM6cOb5tcwU/dJk0aZIv6ayzzpKm6jOVk6G/8AytLVEiQXg90885EydODHjfM58B4HEzkWYKYfEDEHP+S2S/dNvxvE/oNiNdxuq6sPcr2Y+dvb9u27wHuJFhejwIwBs/PjNpww9mks14jSTbERHrnDG/Q+L3DN6/Z0m+oxnbHnn9DBrbXgSunXNMYD7cSwIkQAIkQAIkEB0CeO+K70vwtz24BCI6jcapFggkh3Vu4GvtB+WVcunWqvcLvh1qBWGP3/u56l0ZxH99WlV+B2vmw/opvbN94cYVPnn/l6py9ryLVKjw1XllvuTB7f29Nvp2xHjFVEncdHgzSU91UY9GqR+mEBLiys7NKn9Aqas3vVfmFytFmrKMtBTl9bLKWyXSuudUOurB+lYlhMUfrZJATTwP96djt8wQbGPc8GSbTLatsGru1/3C/QCml/Z0vR3rpf5uvQ5WcHOqaTZ9+vSAnlEiGU+DzHpykwoJ/sAll8vArt08VdUmt7Vjvvz83Y7pwRIRKryFCi+erl4GmwbPlSvWrLZeXPrS1UlVUFjk23RbycxwvuEif4EK81ZTDGHC3WzzdveHhWVrNsuCZf7ucFGPughkxODKELBHuwgsv5r+qxQWh/frg67tWsr5Jwyt1uWFKzbI/333U7X0WCe48SsoKlXeUGvZU2oMYcKzkmnwEplMZoq2EOI6WcKWQUCgDeGf3cQPyAMBBMI1H3vssdaf/cap64n2El41tXco/BrBiwAKIcZ/+eUXqysoCy+ippdT82W/1/7Cg8Fpp53my44QdAjrGGsLZ/yB+gRvjvqhA/lMkYi93H333SeTJ0+2/hByuSYZhDLa8IwCIZCTIWStHiOWkYSPd6q/NqfF4joLxMvrPPrNN9/4qsF5gBfvboZr4dxzz/XthndehIWleScQ6gs91AxhJbzMmn/eW2ROLwSaNWsmZ599ti8rfginf9HnS1QrEEju2LHDlwTProHsuOOOE3jK1AZBs5PNmDHDz5v46aef7pTNl3bUUUf5ni8glDH75MsU4xUISOENDQaRotvzBjhqD2mYQxDKOpF2yimnWNcU+rBK/dDP9OCbyH6h7XjfJ6I13lhdF/b+JfOxs/fVbZv3ADcyTI8nAfO7gGT7HoDXSDzPhNDaCudzdizua7Xle4bQ6Nec3F4/gyZqRJxjEkWe7ZIACZAACZDA/kUA7wLNv7zi2Iru4k0XAkl4+dP2v4WBtSRTlQBzV3Gl5zSUOaxj9aiqSG/fpEosCE+NG/IDi/7u/WKzXPfRBuvvYxVKPBH23W97pOz30ORtG6fL+QMbxbQbeYUVstHg0sMQSqJh04vmez9Xvbfpbwsbbpabvyk8/UhMB5rAymvieZhAXHFvuoUhqNxdsldW7nAWd8e9Y783mFeS4jf/x0sb4mW8cFoJXaXlwdJLgWTLA/FCLGxQtx7y2NXXyJhzzpcWjate5gVrq5XyyOUkFNJeSYKVd9qPF2jNlHcr07aqsOAV5VU3Ub0vI8NfiKnTzaUWDJlper2sqOYI6+plVD0g6P7rZWGR+03sY+W90vwlhC7Tv3s7yWmSbW0O6dtFGjaoLkQtLC6VL6f9qouEvPzT+cdIo6zqv/546q0vpSBAn0NuyEOBegHOlXBFpB6arXVZ1qxZ4zcmHWrSLzFBG6tXr7ZCl+nmIWRMFk/FJjczHLTuq305evRoeeqpp6y/YCIMe9lwtzGXt2nTxld83rx5vnW3FVNACS+MELVCAKG5L1iwQBAqO1QbM2aMNG7c2CoGEdbjjz8eahUh5w9n/IEaadiwoc+bJ/JBOHnXXXdJsr2MDDQGL/twnWnDsbrwwgtl/PjxNfJHLHocybaM1XXmNM5Q5tFly5b5qggkrtSZunTpolet8wNt0bwRCOeFnreamSsaBPr27eurBj8IWLt2rW9br5jXC9KCXTMQAXTs2FEXlxUrVvjWzRXz+QLp5n3czKfX4TVTP19g6fQ5TueN1RJeN8844wxf9eYc50tUK1OnTvV504YYNJCXZLNcrNZxPK6++mpf9bivQwCSDGYyjObzWKzHFqvrwt7vZD529r46bfMe4ESFaYkgYN5zkul7AF4jiTgbvLcZzufsWN3XasP3DN7J15ycoXwGTcSoOMckgjrbJAESIAESIIH9i4CTkAZpe6ocLdYKIB0NISQGtCqIwAmhfdftrBJLmgI2E0hudpV+Ipi4EuUQgnyZCheOPy/5zbaitb5uZ5m8a3joPG9AI2mnhJaxtAWbqzQxpidKtKkFltuVEPPTRXuk/PdIpP1tIdS751Tpcsz6YtnvmlJ3TTwPawrbSPvZplG6XDyoUnOAun5aWyRxDrYbdAiY793uBUELxzgD+gXdRxpEfPiraTZt2rSodjkjPV0uGHG0nDt8hPTt1Dmsug85eLAsWLRQ1q5fJ3vV3Q7eVlrk5IRVly4EgWY9Fdq7qLBQKpQidqtDqFqIK7PqV7mS1mXtS3hqdLOKiqobs1ueZEkPdL7uVSe2kyH9k0nOIQXNsODpaakybFBPGT/l52rVfKzChJ8xospDWbUMARKaNGwgN5x3tDz6n/F+ubbk7ZaXPpwot14cP0804fDz6zQ3rIn95Zdf9pHAy/+uXbv6tuO1ApX8008/7Wtu9+7dAvG5KdZBGEmI2ZLBICTYs2ePryu5ubm+9WRbgVcSzdHNE6HuM47D//73P73p81wJb15Dhgyxjgl2wnPj7bff7svnZQXiSrz8uPvuu63saAdeLQ855BAvxcPOE8r4vTTy2GOPybgvl8AAAEAASURBVBVXXOETVWIcEB9CKHLyySdbXsQgSg3VwP6ee+4JWOzmm2+WnAjvxQEb+H0nrrXrrrvOJ26G54k777zTEsVCbAuPpgMGDPBSFfM4EIjVdRbpPIrypmdZL/NaK+Wh3LStW7eam1x3IcAXei5gkijZ9OiFbuH+2dEQRyJt27ZtWFiGkOKmd0qdbl+agkK368WsNzs7Wxo0CP7ZyN5OvLeLiooEIkDcozAu/BgDQjv7M6UproAgE89ToVq075cQWMLjJoSVZWVl8sADD8jrr7+e0O8VYnWfCJV1OPnN8zea14VTX5Lx2Dn1057Ge4CdSGK24UXYFBc69QLeokO1YPX269fPzwN4qPVHM//s2bMFf9p69uypVxO65DWSUPyeGw/lc3Ys72u15XsGE7yXZx08eyWDoa816bs8MOMckwxnDvtAAiRAAiRAAvsfAYhZ8FdY7q5vqIlUmtSv8l5ZUr5PeacMHml2awF0HJVhqZsZ5c3x5zasCvW7pQaFrP7v3F0yvEsDS1iJsN03H9FMRo/f5Og0yxxvuOsLlMfJo7tlWcVNT5QQdjb9ne3c9UVSVLZXlmwplQNaZUjvlhmCvkFwiZDhHZoYAssk9mCptGAyeri/Mzc7t0w1nmhaTT0PgzFIBMtgfXLbr05VuWJwlSPBBnXryIFtMgUCS22z1xXJC9O2682kWRaUwYPlXmvux2dn7cgqGTqo+5Omb07J0CmvfUAIt2h6vhrSs7dcOPJoOfOIYV674JgPHiL79+0nfXofYIV5Q9i3aFj9BpUCy21q3E6h9lrltPDUzF51MrpZalrVBeWWJ1nSK353Fe3Un7RUdadwsJnzlsvGbbsc9ojYw4IffcgBjgLLGahj607JzalSdjtW6JJ40UmHydjPp8vKDVUvlpH11Y+nyHnHDXEpFf1kCHXdLBV3B5pFAHOj3eNgoRI6Q3Q3duxY+fXXKo+m8KyIcNbxNvTRFHra2x8+fLg899xzSXPzyc/3dzEPz4bJaqYAynzp7dTfH374wRJGYB/mfYQd1QZRnfa4DEHhrbfeKhDkhmIQVH7yyScyc+ZMq9hDDz0kEFvE8pwLZfxexgJvpW+99Zbl8Wr58uVWEZy/CI+Mv0cffVSGDRsm1157rfTq1ctLlb46AoUcRyaEYouHwBJtXX/99dY5AG9oeNCC4ZkFcwb+4FENYe/POeccgYCC5p1ArK6zSOdRiCtRhzYv51pzm3dyU6Cp6+HSnwBf6PnzSNYt896BPjrdP81Q3PZrwW1c5nXldr3s2lX1nJ/MzxfmGCGUxBcECGeun+dwfzd/jAGPyJMnT7aKQTR67LHHyqRJk8xqPK1jnorm/RI/AoSo8rLLLrPmQNzLP/roIznzzDM99ScWmWJ1n4hFX+11xuq6sLeD7WQ8dk79NNN4DzBpJHZ94cKFgr9oW7B6MV+ee+650W7Wtb7t6ofN5ncBmEPxo4GffvpJ3nnnHd+zX1ZWVkLnPT0AXiOaRPIvzWclp+ckcwSxvq/Vhu8ZTF5ennXM/IlcR1/1s59TP5LtuzzOMU5HiWkkQAIkQAIkQAKxJoD3K3huwl/53tr17rpxZtU7wh3KU6IXyyuqytfIKK/LQvSXnVHFKd8IKa7ztFcCwnpKaOVkxUpMuDovMa5CIVp8eup2+fsprQRSv77KW+RxPbPky8VVDnuc+hxumulxEt5AM9NTpLhsnwxoXfXObu76yh+4z91QZAkswbdni7qCcOBdmtUVLUeBCDPZQiybXMDz2O6VYlIzPVbrNfk8DMYk3iyD9SfQfvjcgzdYN5uxulDu/3qLml/dciQuvVy9Wse8r9+xoyfYDuRALl69hRbQEllC6JFMyk8vACAqicaLs1zlYRKhwJ++7saIxZVmv8E0WuJK1JtRN8M6cRAe3G5op4nHUOZlZe5eKuvWj44Y1N6/WGzvKXT32pLdwHkc41R4cCfrmNtcurZv6bcLHizrKk+WdoMXzI9dvGDa8zptwzvmHVecXG1XcWmZPPHG59XSY5Xgxg83hoYu/GLVl2SuFxMkXnibfxdccIHlDdIUV/bu3VsQxjoZDS/gH3zwQT+vkcnYz2Tsk3mjNm/iTn01PUtBXGnO/0cffbTUrVv5Sya8qJsyZYpTFUHT7r//fl89EPm+9NJLQctEkiGU8XttBx7IwOrhhx+W7t27+xWD9yt4rjn77LPl2Wef9dtX0zbgqfPTTz+15g7zXMA48OMQePM89dRTHUPn1rSxxrO/8bjOnMYTbB4NNj841cm00AhE44VeSUmJHHDAAX5/ofWCub0QsH+mdLo+nNKC1W3ek4LlrUn7tSfKUaNG+bqN+4f5g7rPPvvM8hCJDPCGjPuKLucrlKCVgw46SMy+P/nkk9aPChLUHesZQ7cdq+cxXX+0l/G+LpLt2AXiyXtAIDrcFysCEFGa3wNgrsMPwV555RW/ORifa/ADqkQar5FE0g+9bfOZJtjcH4/PP7Xhe4bQj0Lylwj2GTSeI+AcE0/abIsESIAESIAESMBOAKKa2mjwLheqBUNhr9P87KHbuvnI5vLc6bmOf7cOC+zlUNcRq+WvG4vlK0NQefWQptK4XnWtRjTaX72jTPaUVjpIAbeuSjAJ0+HBsT5HefeD/fy70BLr/VpXalDMsOKLNpckXYhl9DVRVtPPw0Rxi3e7h3SoLzcpT7H1XQTX8e6Pvb1knfvLy8stzV4aVmqawXsHxBluIeKCjSdbeY06pEtXObhjZzl32FHSXIVxTWZLVWrYXSr0LwQodmujwkx6vQ8XFVfeDOx1YDu9BnnSWr8lz2kIVlqbFlXubnWmopJS+WpalbdBnY7l4D6dZfP2Ko83el+/7u1k1sJVetO3RJjwP549wrcd6gq8ZQ7t11Wmz1vmV/RTFZK8Sztvnkj9Coax4cavaaMsycyoOZ5Mwxh6VItgHrr88svl0ksvtTzBRLVyj5VBzG0PwYaQbQjZCC9FGzZskA8//NDyoIiQzIn2mGcXmyGkebLali1bfF1DqG83g8esiRMn+nbDY6VpCFN6xBFHyHfffWcl4yWJ6eHSzBtovUOHDnLNNddYHkmR79VXX7VCTscqBKrX8Qfqs9M+nLMIb4o/eIWBkATnK85VGB6aIB7F/e62225zqsIvDfV98803fmn2Da8e0uzlItnG8YJXzvvuu8869hgnPJBo0QyElpdccom89tpr1cLnRtJubS0by+ss0nkU4fVMg5A6mJmeypDXXkew8vvT/mi80NufeCV6rPDuZZrT/dMMCe7lekF9Zj6368V8xkjm5wuTjw5TiXvGoEGDrJCz8GY1depUgecimOl1EvdOWDgCy1jdL/EjI4gAMK9hrsaPCPAXb4vlfSIeY4nVdRGo78ly7AL1kfeAQHQSs+/iiy8O6kkSYW/xw6lQDD9Qgpd3N4ul5363NgOlQ6R88803y8CBAwNli/k+XiMxRxz1Brx+zo7Xfa22fM+AA4Uf+uB7r0CG9wj4Hi/RFuln0Hj1n3NMvEizHRIgARIgARIggUAE8M4oNWWfVOzzqoYIVFty7DNDgpvhwgP1zhQb7nLwTlmsQo1XKM2g9qzYKNPZU2WgNhK97+WZO+SQDvUsYSW8cV47tKn8bcLWqHcLsl0IIw9uVyWYRNhwLbCEJ8/tv3sWXajyIYw7PDP2V541x6qy3ZpXCjLRsQVqfzKbcg4qV32wPmAXEXL+iZNbBczjdWdtOA/dxhpvlm798JKOueDkV1f5Zc1tmC5HdVERsnpkScusNDmxZ7YMalPPOj9wjieLpRlTV7IJLSFcx18avoDAin7pnyzwgvUD3p8gVgjFurdtJ4M6dZL2WQ2lY/Mcq+g6JXJIlMASApKVq1dZLw3LlNAVbkUhlmmqPFK2yMnxC/+6Y1d1USGEO9kNvLv1LVJhldxsyeSp0nvkUZKp6kx2+3XZWtcu9u5c3XvAN9PnS0GR8w3u/W9+FPx5teXrtsgvS9dI/+7tvRaplu/uq06R029+Wj3oVE1WWFu2tkrQVa1QlBI2qTDpW/OcRW0HdKnOLkrN1shqMC/awxwiBBhCHeOvX79+0qiRu3vleA0aX8ya1knNcQhTjP7BywYMYi6EbYQHzkQa5iwIILSgwHy5kMh+ObW9dm3VPAPPi24GcWBpaam1G+eMDiFv5kcoRm0I8wkRQtOmTXWS5yVCXaM9hNjG/eMBFZozViIGr+P33HmHjN26dZNbbrnFejkJLk888YSsWrXKygkBKcQmWmDiUNyXFOj4+DIlaAWiZohu8Yfj/q9//csKFY6HQpz/CAP7wQcfJKh3NafZWF9nkcyjuL4h+EIYX5hdYOZE2Z4nESJgp34lWxpf6CXbEQneH/PegdxO87MZ7hvPAxBD4vkgkJnXjNv1YraVn58v8FqabIIc+xhNoSTEk7NnV3rcx48xcP9bunSpLxRv586dZcCAAVYV+jnKXl+wbZNRsLxe9+NZ+I477rD+UAY/KEDI0XhbrO8TsR5PrK6LQP1OlmPn1kfeA9zIJDYdYmB83gxk4USbwefsWMxRgfoZaB+8Xvfq1cuXBc+K6B++B8D4EcUi0cZrJNFHILz2zWelQOd8PO9rteV7BnwfE2x+Mn+QE94RjF6pSD6DRq8X7jVxjnFnwz0kQAIkQAIkQALxJ9AgfZ+UltQegeWOwirnZ5lKuJelvMhpj4pudJs3qHoXbIYL1/nh4XK7qreFEk7BWmZXLvV+LD+cpxzGLKuq5wQlsOpqiAXNvIlY312yV/41fYfcOaJSwzOiawP5emmMwoRvKvYJLHvkZEiHpumiQ6/PXV/lsAzhy+ervIPa1pPeLTOUsCpFerTI8OGBMDOZDefFup3VHbiZfY6muK42nIcmG3M93izNtsNZh8jSNJwHb83eKYu3lMijJ1TqHjBPHK8El58scNYOmeXjtd4gbZ/ljCnZxJUYPz7HIxpJGjqHLyFqmkFABCHLCy+8ELDrDTIy5cj+/WV4/4EyYsCBUpS/SxYsXOgrs3Vb9JXvvsoDrAD+DzOmC14AmgavJRhXipqgW+a0lO7dukqWElHmO3h6a90iNI+HEHoUFBaazfnWy9QLzvlffysHnTnKl5asKxBMOhnO4sMGdKu2yy08eLWMHhPGKS+WkQgse3VqLWcdPVje+3qmxxajl+2bGc7s0IITu+i1XPNqgvgc4bUjMdNjpJMHWrNuCAFMi1QUMHToUEsACs8HMDOsudlOvNdzc3NlxYoVVrObNm0K2jxEBlqICUHeiBEjgpaJNAPmZYhStfXs2VOvVltCuKoN99PHH39cbzou4TUa4gN4LwzVIMJ/QIkqURZtzZkzx/JQGmo9wfKHMv5gdWE/7nf6QUj/qMMsh2cQCEkgCoYoQ3v3mzlzppVu5k3WdYxPh3jDeDBOu0FUe/fdd1tivOeff97avWjRIk/iIntd+9t2PK8zk63XeRSeX7TAUs9vZj32dXga1obzpX175x9tJPIeovuXqCVf6CWKfGTtLjQ+Y+FHYxCi2A3Xi2m4Zvqrz2puhvnVFCPYy+tyeL4wDc8YbnmRD9fhF1984Sty3nnnVfvhTKyvQfPZ77jjjrM8Hxeqz2r40QHmFNN7pRmK2yznG0ACV04++WSrr9OnT7d68dBDD0mfPn3i2qNE3SeiNUj7uRqt6yJY/5Lh2Dn1kfcAJypMiyeBkSNHWtED4tlmKG3xGgmFVvLkDeVzdjzvazX1e4ZYHtlYPwMG6rvXz6CB6oh0H+eYSAmyPAmQAAmQAAmQQLQJQHCT5/8KNdpNxLW+dbuqBJZouF3jdFmkRE9uBv1Da+V9Ttt6W3mdvml3lcCybaOq/Hr/tFX+GpF+rTOTSmCJfk5YViDHds+SA5WgEfanw5vJ0q3ubKxMYfwzPU9CMDng9/DfqGqOERYc2z9vqBRYwovlgW0zpd3vbOFHa9EWd+dmKLs/Wm04D2N93Oyi1nSlCwtkdVOr9pdG6G1yrjq/IWaGl1hYZcj7JBJYKkF9shocVv6uAaguAkjWTpv9Qufvv/9+uffqa+TI7j2lS04Laa08P3Zo1lz6KU+Vxx7QV644Ypi8d+/98p9bx8hFI4+R1irMa+NG/qEc4QEklJdUO5VYadWa1bJ7T2QnWt7OvGriSnN8+9SsvGnzJpmivHTO/nmuYNu0LPXSsn69+mZS0PXcFi0lLb36LxZ0weUzZqp2bHJmvTNJlrMWrpQf51eKs+xdGtirg7Rt6e8VbsuOfJn282/2rBFtf6bCeZeVV0RUxy0XHydZ9ap+4RBRZR4LF5eWyasfT3HMnarEQCce4f5y27EQE4MSgLchbevWrdOrjktzPzwZRBr6GV+Smy9rQ5nnHDsYpcTDDjvMVxMEBIG8MEGM+Mgjj8izzz5r/Wnvhr4KYrTy8ssvC9qG4V6Dl89OZnqWctrvlma+LHHL45Z+4IEHyllnneXb/corr/jWo7Xidfxe2zvyyCMt8SQElH/7299ci0GAeMghh/j2a09evoQkXnn33Xd9Y+zbt2/A+/uJJ57oG4kWyvoSuFKNQCKuM90Jr/MoXsJrmzZtmp8YTKebS3MOwHXhFEYZ+RN5DzH7G+91vtCLN/HotAdx/Pvvv++r7Nhjj3X0IDl48GCBpzJt7733nl51XOKaMj1Yuv3QAvcP0xOQKZ50qvjLL7/0PV/gXurkRTPW16D+8QH6B49Kxx9/vNVV/Cjnk08+kfHjx1vbGFcivEJajXv8h8/l+sdBEMSGGh7YYzOO2RJ5n3DsUBiJsbouvHQlkcfOqX+8BzhRYRoJVBHgNVLFoqatef2cnYj7Wk38niGWxz/Wz4CB+u71M2igOiLZxzkmEnosSwIkQAIkQAIkECsCjev6ayRi1U686v1FCfYKSqv0GCf3DhzdB94TTQ+W021CSd3v6aurBJQ9lWjQDGWt89SE5bPfb7fCcqOvrRumyeGdQtPDeBkjvPjBO6Vu47COlW3A6x+Oj2mmR8sz+zZS740r967YXipFZbXr3DTHHe56bTkPwx2/l3JrbF5Fc9V57mbpSlzZzPBgu3pnZVRNt/zB0nHer99V5dUUwuFkssZ1q+bGZOoX+qLfP6XVtNDgJsiMVCUgUvPW+UOGmsm+9U4dO0rfbt1921hpqkIa4eWPKTYqVCJL/ULIL7OxsWfPHvl14QLZurXS4yU8TB486CBppUSL4ViF3S+rWyXKa4v5UlFna6rCUYZq6Upw1alte/ltlRIoOsz3Jbv3yA4lAmvm4kkp1PainX/T9l1y+z/edeq61dSVpw+r1uT4yXP9QnFXyxBGQt7uQpn40yI5dmj4nlmaN86Wa88eIX9/s8pzThhd8VwEntXuee5DWbt5h2OZEw/vJ7nNQz+nHCtjoo8APC5qQ1hniCidvDnh+EydOlVnlR49evjWw12B56Nly5b5ipt98SUmYAXigbfeestqGfMqvDOdf/75jj2ZOHGimOEzTfGdY4EoJML71jvvvOOr6fDDD5fWrVv7ts0V07MUQjveeuut5m6/dXjKevHFF6203377TRYsWCAIPReO3XbbbTJhwgTZvn27TwgaTj1OZUIZv1N5pzS8nNBiyZ9++sny9Ojk4RFlcU5oq18/+h+adN3RXpovYFA3vG8ec8wxjs2YY0SGmjROxwHFODFR1xmG5XUehee5p556yue2/t///rc8/PDDjmRmzZolv/zyi28fyrqZOW/H+x7i1qdYpy9evFhGjx4t5ucTfGj5+9//LhDs0ZKTAI4XPDjD+yIMP044++yzHTubnp5uzY/wUA1DCMxrr73W0ZMrno9effVVXz2N1eefIUOG+LbNFb0PgkwYxJ5XXHGF1K1b18xmraPer776ypd+8MEHO3oejvc1iDDhWoD9z3/+08cTP1RwE2L7BpHglXbt2sl1111nzYXoSjDP7dHsbiLvE9EaR6yuCy/9S+Sxs/eP9wA7EW6TgD8BXiP+PGrSViifsxN1X6tp3zPE8vjH+xnQHIvXz6BmmWitc46JFknWQwIkQAIkQAIkEA0C+H4Rf7AmmQ6ihmg0kqA6IHD6fmWhHKdC88KOUqGw35mzUzbk+3u2xD4gOLt/I6xall+81/KoqLfN5eTlBfKHIU19AsCLBzWW+7/a4qqrMMsm0zo4gMcVg5tY3UJY7mgbPAgu31bqC/fdX3nzhC1R3jKLyvwFXr+pfAjhjlDuA9tU5kPeBZv9hZhIo4nUlvMwlscyr7BCcC03zKx0hDikfT2xe5jV7Q9uV0/MK2Dl9ipxpM4TyhJtdmhS9c5k5Y7I6gulbS95m6r53pz/vZSJVx68V4LTDKVxqCP4dWRNtNVz50p5ibNKN0WNq2uXLtWGhQPStk1bv/Si4iK/bXOjtLRU5ith5cSpU3ziSuyHR8lffp3nCwlqlvGy3kx56mqhBDl46ddEiT57du8hI48aISOHHyUD+w9w9KJi1tswK/CvGcy85jo8X2arkONulr9pi9uuhKXvLiyW91U47VG3POMqEDyod0cleOxbrY8I5x0LGzdhVsTVXn7akcrjZuXDQcSVuVQAIe+Mecvlwrv/JZ9MmuOYq15Gutx6caXHHMcMTAybAMR5+sU+XjTfe++9vvDHulJMxniJji8ytUUqItm4caPgy3EtdEC98JKWDDZgwAABF22PPfaYJa7Q23q5ZMkSP4ESvuAOFKpblwt3iVDqr7/+ulxwwQU+AX7Dhg3lgQcecKwSYhLtWQoZIBw99dRTXf/++Mc/WqGhdWXmSxOd5nUJL1t33nmn1+ye8oU6fk+V/p7JFNnguOKYmz9y0HX9oDw2409bspyzuj+BlhDndOzY0ZcF4VHNa1rvwLifeeYZvWkJeuIdRtXXeA1YSeR1Fso8CuH8xRdf7CMKgdSDDz5Y7RkR5zcESNogakFYYjdL1D3ErT/xSMc8j3lYG8WVmkRyLvEMA8HAhRde6HdPvOyyywKG/b7xxht9nrrxfHTRRRfJ/Pnz/QaJH1jcfPPNMmPGDF/6LbfcEvBz6/XXX+/74hU/ULv66qtl927/qAP4bAcxqDlH4/7tZPG+BgcOHOi7l5jPcBBe1gTDcTcFCfHocyLvE9EeX6yuCy/9TMSxc+oX7wFOVJhGAlUEeI1Usagpa6F+zk7kfa2mfc8Qy3Mg3s+AeiyhfAbVZaK55BwTTZqsiwRIgARIgARIIFoEoOvIyfQXvEWr7kTW88asPJ+QDwLCp07LVaF6q0RP6Bs8y91/TAs/Ud+rP+b5PC/a+7+toEI++jXfl3xIh/pynyqfmW7Ksyp3w7tlt+bxjbTp65iHlQ/n5cvKHc4aIA/FPWUxw4TrAnPWV9cMKT2VzLN5tUT+BZuiH7pc96MmL2vTeRjL42AKKo/vkS3DuzSo1lz7xulyzdCq6L3rlOfJFRFcFy2y0uSekS2knjEnLFHeXJPJcupVCSy1yD5Z+qeFn5ayEi/HaqKt+bnKA5C9/61zc6VeZj17srXdtk0bWb5iuW8fvvBq3SrXt40VvHxbtmKFrFy9Sip+DxXrl0FtlBSXyHYVDi+neXP7rqDbeGF8yGBnDywIDWz1ceUKWQjRFWZuwyCKhZeLcC1TefDcbXgJM+vZuzey0NdmXeGuT/hxoRx11V+t4iVl5bItb7fstTEw686unymP3XSu7xcZet/ilRtl8aqNetNvOfygnvLYzef6pTltnD36OVmzqbrXx0mzFktefoE0aVh9snOqxykto26ajLnsJPnTY2877Q4rDYJKza5CXdfbd+2RktLqv3gxK7/rilOkXatmZhLXo0SgvfIGC6+GOiwyvPch3PTQoUMtT5YIpzlXCcVXrlzpa/Goo46SUaNG+bYDrWDuNkU9yLtt2zZZv369n/cveEgzQ3O71elUn1PeN954w9HTk1NepzSIFk8//XTLWyGEFWPGjBGEy4KgDnPjqlWr5Mcff7R+BYDy8PAH73BuXg+d2giW9uGHH8r3339vtQHPoto7sS6HefiJJ56QVq1a6SS/JcKb4/hpO+mkk/Sq4xLjgggToaRhn332mdx+++0+Aa5joQCJCDON8KEYQzgW6fhDaRNs0Nfp06dbxd5++21B2ClcB/AOCtEhhC7a6xgywRPZOeecE7QZr+cs+hBIyBa0oSAZcG5CQA0hLc5pnBsI5Q6vq127dpVGjRpZ3qi151FdHURJCAtLcyYQj+vM6RwKZx7905/+ZF2PK9SzIwze8xCiGPMazmeIx1avXu0bKJ7jIMQN5EE91vcQX2eSbEULyMeOHUvPlUl2bMzrpaCgwHp+wWcm0+Bt8aabbjKTqq23bNnS+qHAfffdZ+2DR2bM0V3Uj+N69+4teLkMT8+myBD3jDPPPLNaXWYCfsSB56I333zTSobHWPxopX///tKpUyfrXj9nzhy/6ACo082TbCKuQTwD4plHGzxXDhs2TG+GvTSPXaBKIrlf4kebeMaDYNYMfx6ovUj3xeM+EWkfvZaP1XXhpf1EHDu3fvEe4EamdqfrzybBRvnKK68E/Pzkda6L9PNssH7Gcj+vkVjSjbxufS7jPsjvGUL/niXyIxCdGmL9DOg0V4XzGTQ6o/WvhXOMPw9ukQAJkAAJkAAJxJcAxCv275SQ1rK+v04ivr0K3NoJvbLloHaBo7F9u3SPfL7Y/0fgEKG9MG2H3DasUmPSpF6qPDeqtazJK5OlyosihFDdczL8hFBz1hXJF7Z67L177ac8ObBtpnRqWinWRHjtfrntBCGxIVhslJkqrRulSd9WmX66iliLGe39DLYNL5/PTN1uCU+ry0ODlfa2f/6mYjmjb0O/zHPWFftt6w2ECT/09zDiOs1JoKn37e/L2nIexvI4/mvGDutaxbWupjm5e2SOjFLn4zLlMbW4fK+0bZQuByvvldqDK4IjPzZxm5RVBJ8P1Str+cep/tq3pvVTpaVqK7XSaaY1tCkrCmSWmleSyXIbVI3PFFia64nubxpEAeUuAsJEdy5Q+/uUsGjT0t9cs7Rv2851XyPllQwvEfS41ypxTTvlfSgzI1Pyd+fLuvUbZO36tUpYGVxsWFYeG7epOEm6du5iiUTn/PKz8phZJYKF2BCnVrg3lAIVEt3N0pT4MtFWWFwqhcVVwqVA/cmsmy7P332JdGhdXeQ6bqK7l8njlLfLZo3cPXnqNo8+pI+8+vEUvelblqlz49MpP8vFJx/mSwtn5cTD+8sb47+X2QtXhVO8WhmcF25hwKtlVglXnHaEXHDiUKddTIsSAbxkhoASYht8MICg+8svv3SsffDgwZbgxnGnQyLqg1AgkOGX9/Ck5sW81Id67B9wvNRt5slVAniE4YYnN4hBUd/SpUutPzMf1uHh95FHHrGEEfZ9kWxv2LBB8OdkvXr1skQ9HTt2dNptpZkeKOGFrm/f6h507YUhrtUCS5wHCIHuJuqwl3Xa/vOf/2x5zDTDqDvlc0qLdPxOdbql4TkDXlrxRf0333xjZduyZYslunQqA8EhxCXwChjMvJ6zEN3E2iD+efrppy2hZV5ennVeQ1SqhaX29iGmhriY5k4gHteZl3PIyzyK8xbiYQgtIeqCwXOe6ZVVjxTeYZ599lnBnB/MYnkPCdZ2IvdjvoAQH15UaMlDIND1Ao/dEJnDa6SXD7vwygiBMcTpEGmi7mXLlll/9hHjBwp//etfPdWLeRWf83Ro8fz8fJk6dar1Z6935MiRcvfdd9uT/bbjfQ2edtpplqdjvHiHYRs/0ojUAh07s+5I75cof+655/qed8y6Y7Eej/tELPrtVmesrgu39sz0eB87s237Ou8BdiK1fzvQZxNz9HpuNNPMda9zHfLVZOM1krxHL9C5zO8Zgn/PkkxHNpbPgF7mKi+fQWPFi3NMrMiyXhIgARIgARIgAa8E9HeLegnBTaO6e2VXqaEM8lpZjPNBsIS/QDZvo7No76sleyyxFESW6akqJLqqpEOTdOvPXt8kFf77iUnbgob7LlXiq9v+t0nuUmItiLNgCAk8WIUgxp/d8On4s4W75Z8/bLfvSvj2ws0l8qnq2ym9s2PSF9RvWlHZPkuIaqbp9bnr/Y/h1j3lgj+aM4HadB46jzDy1EIVdv6Rb7dawspW2ZVzSK8WGYI/uyHvi9N3iFdvk5hL+rSqXo9Z709ri+SpKcl13TdW83xu/b1+74H0fcDse6LW8b0k+pOG8COm2DBRHQq13fwtW6VceZB0MngFaq68fQQyhBDXBnHKhEmT9KbnJTyc5TTP8Zw/nIxtlHcvHKOf5xneOtWXwcUqrLmbh85A7ezYudPPG4w9b9O2bexJSbud0yRbnrvzYjmod6dqfYT3xvGT51ZLR0IddeIfNbi34z574tFDDnAUWCIfwo9HKrBEPfdcdaqcddtzAb10Il80LVW5G7/5wuPkj+eMjGa1rMuBACZaCOHgIQmisV9++cXvGsR8Ba9K1157bURiO900PB21UV568Yt7vOA+8MAD9a6kWsKr3wcffCD//e9/rZfwdg+SOTk5lnjwD3/4g2RlBRdDRzI4iDgh+oSQB4KeQYMGBawO3gnhMUkbvEl6Mby4hsdGLewcN25cRMccxxnhUJ988kkvzbvmCXX8rhUF2JGZmWmJD+GlEgJjeCaDkEb/0AFF4eUPXh8vvfRSadq0yuV5gGqTbtfw4cMtr4UQEONaxzjhmU0bRDLwpHbNNdcIXpjQ3Akk8joLdx6Fp1J4VoKHWogtETrZNOzHOQ7PpfBU5sXifQ/x0qd45aG4Ml6kw2sHgkp4ecZ9DXMfwmzjHA/F4C3xgAMOkLfeessS3RfZfgR20EEHySWXXCIjRozw+1AdqA1cM7fddps1x8JD2ZQpU/x+GAJRJ7xkQgztReQc72sQzz9HHHGE7znDq1fzQEzivQ+h3b/77rtq3sGj3Y9E3ieiPRazvlhcF2b9gdbjdewC9UHv4z1Ak+CSBJwJ8Bpx5pJMqaF+zk6W+1pN+p4hlsc73s+A4X4GjRUDzjGxIst6SYAESIAESIAEvBKA4w48k+GvkdIJtW1QrgSWlV4ZvdZRE/JNWFagPFaWWp7rjumeJZkqLLhpEGci7Pf0VYVBxZW63B4lxrr3y81yZOcGljixX26m3mUtd5fsldXKUya8VkJcGUnIYb+KY7CBkOiHKc+R8L4XbdtRWCEb88slt2GluA2s4TnTydbsVJHrVH7dD3qvdKLkn1abzkP/kUVvC55lr3p/vZzVr6HAG67lzdKofldxhcxYXSSvKs+0eer8i8Tyiipk8+5y2aDO+U8X5sv8JAxx37ZBhTTMqJz3cQ/QptdxPzDNvm3ui8W6bi9FvciyZopgvwSPRSciqXP9/AXy3T9fdKyiZYsWMuRgd69A8GQyaeoUx7JeEyEWOfzQQ6V+vcBun73WFyzf7J/nWp7edL7G6gVmp3bt9aan5TYlClq7UXlsc/m1foNmTeXMRx/yVFekmYpKSqXvWfeEVQ3EgaePGCR3XHayNG3kHKJ76pwlcvn9/3Gsf1CvDvLe4zc47rMnQqg59OKHZIcKB243XMJfvnC7dGnXQpav2yLH/fEJexZr+w9nDJcxlwcOH3z7P96VcRNnO5ZH4rwPHpX6mVUPjlc+8B+ZPHuJa/5AO/p2bSt/vuZ0GdizQ6Bs1r7up45xFH4eozx7vnjPpUHLM4MzAXjvg+dGfIEKL336xuCcu/an4tf7EKGBCeZWiEMZNrn2H3d4LIPnUoiMIdCBV7/aaJs3b7ZEtRDUQYy0v1/vtfEYu41pz5491ryGMMd4UQnhlH4AdyvjJb2m3kMgpDMNHpgDhUg380a6XlJSUu0HBxBA05KHAD6LYr5EePCGKtoAno/wTBCp4Yd0qBOehXGvwVwc6XVYU6/BSFmyfPwJxOq6iP9IxBJTm+3yHmDS4DoJ8BrhOUACNYFATX4G5Gex/2fvPqAsu8o70e8KXZ2qc7c6KeeMkBASkpBgEIggkm0B9hiJ8MDGBtsz76HBBMPy8jLGjKMQGDBmYQ8DGpIJZpAQBoQAIUASCOXYdFC3OudQ6Z3vSrv63Fu3UneFW1W/vdbtk8/Z53dvn9tS/evbE+ETpo8ECBAgQGDqCeTRBcrTmI/CU/Hq6OhI8f914//v3fR4St/49eH/v8JGVi7iD2nx7NYiZNWSIgS5vghD7e+sH/gbzn1ML0KbUWUzKlmuLcJVhxvUGs617UsgC/gcZon+p2G0Yu60FNM12zvSruI5MJXalUfvS1cc11T5uWH87DDyA1G4KF7xM538c53a6VgZRT/iO6oSyZ5o4cpAOlBT4aQMN9AP4+Kmf3VfdUWh8rGDzhdfbketPDKdfuppY/ZD4ejT2WecmZ7cuDF1FIGUaNuK4WXXta1PK5YuqywP9MeewmrdhvVpZ/GD/oHaqc+/bKDN47otwozHrVySoqLka664IB1bZ0jwcgejumR/7QXFOYbaWop09PPPPy196TtPDfdZPi7+SfOV//xZ+v+uGVr1uvKxtfP/79UvSd/60S/T3v0dtZtGZHnBnFnpknNPSb9RBFMveeYpxQNoRE7rJIcocEQRAo+X9pRAfBEuXry48mIydQSi+tmZZ5456W84wjxDrVY46TGm2A1G9d1TTjllxO/ad8iIkzphAwhE+DyqScdrJFv8d2FUCo/XSDV/B0dK0nkGExitvxeDXdd2AgQIECBAoK+AfwP2NbGGAAECBAgQIDASAvEzwhyyjPOVwzMxH/9/5IS5HWlac0/q6J68P+CO4olPFsNOx2skW4Q0owKjRmA8BXwOB9cPo6gsOxVbS1NPOn5uV/G8b6s888vfA2WPvL68bqzmI1NZCVhGJyL9Gb8FMJHaQHgDZfnvKcKVmzZtGtatRkI2KmstKQJAK5YtTzE0+Fi3yjDCxxyTHnzood5LbygCl3uL39xYumhxmj1rVu8/OGKH/UUQc+fuXWlrMST4rj1F9cWBUIr9o3rlyc8du+FK26a1pn9+/5t776W/mdaW5jS3fWY6ZvmiNK996NVCr3rhs9PLL3tm3dOec8rwKn/+0e+8KL344rPrnmte0bdoyxfPS5/8szfV3efoZQMPVx8HLSuO/+wH35Y2b6sfgp3e9lR56nyBGN779VcO/H7FUOizZrYVfZufViyZX/X5yOcZbPqJ972x7kdncTE8u0aAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEZKIEKVkQWJabyOnZfSSUXI8t5tB0d7HKlrOQ8BAgQIjK/AycXz/bjiOR/P/fIrepWXyz2MdWPd4rsoQpatUWJ5IlawbCsChf21KBVdrz30yMPp0cceq7cpzZo9K82fO68SpJw5Y2aaMXNGmjl9RmVougg3NkI7/tjj0oMPP1w1xHcMdx6vpqJu9LTWp/rZ2dWZuruGXjK2qfgwXPKGq9O0Ikg6Vi0qQz7vWaeO2uWe84wTR+zcK49YkOI1UJs1Y3ql0uVA+wy27eyTjhpsl97tZw1j396DDmHmeUX1To0AAQIECBAgMNICB56uyj7S5613vrG8Vr3rW0eAAAEC1QJj+Vwey2tV36UlAocuMJaf27G81qGLOJIAgZEUGMu/92N5rZE0ci4CBAgQIEBg9AUiIFNbubK8nMOVeXrK/E4By9F/W1yBAAECYy5wavF8j2d9DMOdn/njEaIc6Mbj+yn611q0ypdXBC0nUpu9cGG/3d2xc2efbeueeCLd98D9VesD4Nhjjk1HHXlkmltUqGz0FsOpzps7N20vhgevbT1F3ehD+h8WxQf14mt+Ny09aeQCibV9s0yAAAECBAgQIDC1BZYsWZI2FtXXc7vwwgvz7JhPoy8aAQIECIydgO+AsbN2pYkp4O/IxHzf9JrARBHwjJko75R+EiBAgACBqSuQw5YxjXBNtCgQlpcj03H6wo70o/UdacO+xiiMNXXfLXdOgACBkRNYPrOzeL53F+HFtt5nfn725++Dkbva4Z0pMpXNnZ1FtcPiC2qitTlLFqeWfipL7tu7N+0qhsfObdPmzemOX9xVNUz2ggUL0vMuvTSdcdppEyJcme9l0QDB0rzPUKetRcXKS9/0hnT8Bc8e6iH2I0CAAAECBAgQIDBsgfPOO2/Yx4zWAY3Ul9G6R+clQIBAIwk00nO3kfrSSO+RvoyvQCN9LhupL+P7rrg6gckj0Eh/rxupL5PnHXYnBAgQIEBgcgrUVjNbNKMnnbngwOS8WXdFgACBKSpwxoKOtGjmU0OB5wqWEbCsbXldntZuH4vlp4OfzVXll8fiwiNxjebiNxUWHXN0v6d64KGHUkdnR3r816vSbT+9vRgy+2CFzkWLFqWLLrgwzZ41u9/jh7ohhiPfvGVL2vDkhvRkUZUnhusezcDqSKV0Fx59VHrZu96Zjn3WuUO9VfsRIECAAAECBAgQOCSBa6+9Ns2bN++Qjh3Jg6IP0ReNAAECBMZOwHfA2Fm70sQU8HdkYr5vek1gogh4xkyUd0o/CRAgQIAAgRCIAEsO0OSQZYRu4nX2ws50xPQOUAQIECAwCQSWFM/zeK7nZ3w88+OVvwPK3wfjfbsxRHj0p+U973nPB6LDsWKitb3bd6T1DzxYt9s7i2HCH37kkSL4+GTVvU2fMT1d8pyLYmz0uscNtjKcNm7alB58+OH0y3t+lR586MG0es3qtHbdurRm7dpKoPPhxx5JUTUz9m2f056am54qZT3YuYey/YkNG9K2bduGsmvdfWbOn5fOv+o304W/87o0YwIMi173JqwkQIAAAQIECBCYUALt7e3pyiuvTBuKf8tu37497dmzZ0z7H8PiXVpUr7/uuuvS0qVLx/TaLkaAAIGpLuA7YKp/Atz/YAL+jgwmZDsBAocj4BlzOHqOJUCAAAECBEZTIIdo4hrl+RxkyfmVmEaBq9mt3WnXge706M620eyWcxMgQIDAGAhctGRPOrf4cV1bW1uaVoxeHa9y2DIHLMvfD+X5Mehi7yWiX9Ga9u/f3xNfSKNZdbH3qiM8s2vzlvSV930gFTcw5DOf98xz08oVK4a8f96xu/jiXr16dXro0UfSnt278+pBp9NnzEinnXxKOvqoowbdd7Ad4j26+bv/maJq5rBakaRdcsJx6dTLLk3HnPvMFNU/NQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAeAjkEGVcO+bzclcxOmlkIzo6OiqvAwcOpCLXkp7YfiB9/uHp6fE9M8aju65JgAABAiMgcNysfem1J+5Ly+dNT9OnT68KWZYrWeaQZb7keAUso0/x/dTa2dlZKbOZOzSRpu2LFqbjLzg/PfLjnwyp2/HGrFi+fEj71u50y60/qAz/Xbt+sOX9RRjyrl/+Im3dvi0948yzBtt9wO0x3PlA4cq22bMqv90xrQh1zpo/P81bviwtOe64tPy0U9PsBfMHPLeNBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHxEIjwTIRYcqgmQi25ollMl8xuTs9avD+tWT09dfY0jUcXXZMAAQIEDkOguaknPWvJgbR4VvXzvV6wshyoLM8fxuUP+dC4fmv+cjrks4zzgef95qvTuvvuT3u3bR+0J8uK4QAPFT1+S+Jw2qpVq9LMIvh48oknDfs08Y+IRx57NN13//39HjtvxfL0yj97T7/bbSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINIJAZDdy1co8H9NcLSxClbE9pq2trSkyG886Ym9as2t3um1LeyPcgj4QIECAwDAEzl+wq3iOdxVDgs+oPNfj+Z7D9HGamG/EVvkeyl9UjdjBofRpRnt7esHb/yDd/I8fSft27BzwkJkzZw64faCN5Tcxxn6PsOaihYvS7NmzK296VALdtXtX2rR5c1q/YX3q6uwbyLz/wQeKL//WdEJRVXIoraOzIz3xxPr0cBGu3LVz4Hs7+bkXD+WU9iFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjLtAf3mVHLSMaYQrY8jwCFi2tbWli5d3pA379qbH9hx6/mPcb1wHCBAgMMUEjpu5J1289EDxHD8YrswBy3jWxytanjYST0dHR2qN4GB8GU3ktvDIlellf3pt+snnbkhr7v5VSj317+Zw3oR5c+emAwcOpJNPOjkdfeSRld+SqL3KooUL0zFHHZ0OFLAPPPhgemzVY9V9Kfp1z733pNVrVqfly5aluXPmphlFVcuWlqfeg46OzrS3+IfAzp270tZtW4vXttQzhPemffGidPIlApa174dlAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPEFIs9RHiI8V6/M0yiEFdmWZe1d6aIle9KOdc1pc8f0xr8xPSRAgMAUF1g47UC6qKhAvGzOUxWJ43ke4fkIWMYrnv+RX8y5vjwNtvL8eDDGd1Clb3v37q3EESd6yDIjrrvn3nTzdR/Ni1XTFStWpGc989yqdUNd6OzqLMKOPUWZ0mlDPSStW/9E+vmdd1SOG/JBw9yxubUlvfCP35GWnnTiMI+0OwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgfAUiwJJbzMcrMiwxjcqVUT0sXlEUK1579uxJt67pSd/ZuCDt627Jh5oSIECAQIMJzGjuTC9cuj0996iWShXi6dOnV6aRv4vXQAHL8Q5XBmUe8bq5/EXVYMaH1J3lp52aWmfU/y2FDU8+mfbt23dI520thvYeTrgyLrJi2fJ05ulnHNL1hnJQU5HeveSN1whXDgXLPgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDDC0SoJlczi2kEcKLaWX5FQOeiFT3p4oXb0rSmroa/Hx0kQIDAVBRobepOFy3Yli5Y2lXJ3EXuLj/H47lefs7nMGWeNopX9DEC/83RsUbr3OEgRehwRRGyrNe6OjvTL+/5VeW3HGq3B8bdxbbv/eCWtHbd2trNh7x83DHHpvnz5x/y8f0d2DZrVnre778lHXveoVXk7O+81hMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExkOgnF8ph28ijJMrnsV05syZ6bIVHenCeZtTS+oej666JgECBAj0I9BaPJcvnr85PW9lZ5oxY0YlWJkDlvE8j1dtZrH8/C/P93OJMVkdhSsr/X3ve9/7gbjipKpkWYRGV91xZ13IXbt2pW3bt6dlS5f2lvGMEtK3//xnad26dWn//v3pifXr0779+9IRS5aMTPi0KaUNGzbU7c+wVxbnOvLsM9Pz3/bWtOS4Y4d9uAMIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAo0iUC9IU15Xno8+x3JkXOJ15Mz9qadzf1q/vy11FlFLjQABAgTGV2BGU2e6cP6m9LwVB9KsooBgW1tb1bDgUcUyByxzkD56XH7Wl+fH827ie6bSxz179vREpzuL6o6TpXV3daUvv/f9ac/Wbf3eUpSMPmLJEUUZz660YePG1NnR0WffBQsWpLPOODPNnzevz7b+Vuzesztt376j+M2J1tRVVMWMQOeq1avT7mJ6OC0qc6488/R0xgtfUAwJftLhnMqxBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGGEigXBsvzMY0RSePVVWRBOopsR+RbonhWFNOKaeQyfrSuKf1s56K0vXtmQ92TzhAgQGAqCcxr3pvOn7ulGBa8M7W3t1cFLCNYGVUsI6cYocVGD1fG+xZBz0o/9+7d2xMr4stoMrWHf/Tj9KN//eyI3NLs9tlpTvucyhsc4c2O4ss6vriffd6zUgQ1c1tfVKn86R0/Tz0jZNlalEg94sTj08ozTk/HnHNOmrVg5Icaz303JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMp0AOVkYf8ny9kGUELSNgGa+isFjavXt3+tXGnvSz7fPS2q4F43kLrk2AAIEpKbCyZVt65pyt6ZxlLWn27Nlp5syZfapXTqRwZbyJ0d/IVLbGF1GjlNUcyU/XCc+5MD16+8/S+vsfOOzT7t61u6hAubvPeQ4UX9g5YBlf2nf+4q5hhSubWprTKZc+t3JMc5HSbS1Kos6cOzfNXrggzVu+LM0ZqSHK+/TcCgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA4wpEliWHLKOXUUUsliPwEtP8ihBP7Htm2pXmND+Z7tyxL913YGnqTs2Ne3N6RoAAgUkiUDyZ02lt69O583akI+e39YYro1plvPKQ4OWKlRPl1uN7JvrdGl8ykzFgGfd06ZvfkL7513+Tdm3cNCrvS3NxjdweW/V4pRR1Xh7K9ORLLk7Pfu1VQ9nVPgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBSS0QWY8ItJRbrIuAS1QRi2kELMsttscr2nHF9iUzt6XFm/ak+/cvShu6jRRatjJPgACBkRRY1rwtnTpzSzpnwb40Z86cypDgM4oRm9uKIoPlgGWELHPAMk+jH/nZXTs/kn08nHPl751KBcvaL6fDOXEjHTujeOOu+G9/nG6+7vq0/Yn1I9615qe/tMPv16tXD+v8i48/Lp33m68e1jF2JkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMZoEI3OQcS56PaTlkGcvxyi0vxz7xOjcVldR2rU737dmRHj6wOO3omZV3NSVAgACBwxSY17Q7nTBtczp19va0fG5UrSxGbH56WPBctTJPcwXLuORECldGf6PvlSHCc+nkrq6uWD/pWgy3/bJ3vTP99ItfTg/98EcpdVf/psPh3HBr61O/FbFl69a0d+/eIZ/qmHOfmS66+ncrQ4IP+SA7EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSmgEAEJvsLWcbt520xn8OVeRoBngjFzJy5Ny3evTMdt7MIWe6dm1Z1LEzbUnscohEgQIDAIQjMb9qVjmndkk6YsSMdNbepCFXOKZ61M1NUrZw+fXqlcmU8f3O4MnKJ8UyONtHCldHnzs7OyndMa8xM9tZavIHP+a+/nU57/vPSA9+/Jf36l3envVu3Hbzt4pca5ixZko4+5xmpfdGidPeNN6U9W7Ye3F5vrvgyb21prWz59ZrBq1c2tTSn5aeekk5/wX9JK04/rd4ZrSNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgRyIHLmEZIJw/bmnfL62NbBHry0LQxf2zrnrR0xpZ06t7N6fH9c9OazvlpffeC1JWeCv3kc5gSIECAQF+B4ombljdvSUe1bk/HztiVFs7oqQQq29vbK0OC52BlDlXmipU5XJmfzzGNlqe185WNDfZH9LXyKiov9sTMZK1g2Z/7vh07094dO+JdS7MXzE9tsw6Wg+4uqnmuuftXadUdd6bHbv9Z3VNMK8aKf8kLX1RJqt74nW+nrs76FUBPvvSSdPQzz0mLjz0mtRWJXY0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBwgXKlytg7L8c0vyLvEoHLmEaRsfw6cOBA6ujoSPv376+89u3bl/bs2ZNiunpPW9rQNSdt6p6bNvfMTXvT9ME7Yw8CBAhMEYFZaV9a2LQzLWnekZa27EgrZx6oVKqMapXxilBlvCJU2VZk6CJUmV85WBnTaBOxcmV+m+OeKkOExxdOORmad5js0xlz56R41WvNxRsc1SwXH3tsvwHLtuIDEu2J9ev7DVe2tE1L577qFVXhzXrXs44AAQIECBAgQIAAAQIeae8wAABAAElEQVQECBAgQIAAAQIECBAgQIAAAQIECBAgQKBaIPIsOVQZW/JyTOMVwZcI8cR8hHjyK9bFK4d/8vC1EZaJdSe07U9Hd2wpjt+U9nQ1p81ds9K2rhlpe/Ha1TMj7e2ZXrympY7UmjrTUyGh6p5ZIkCAwMQWaCkqU04vnnLTmzrSrKb9qb1pX5rXsj8taNlbec1uKer8Fs/Vp56jc4rhwGdXKlbG8zRClfGMLYcqY778PM7P6ZhGy9Pa+crGBv0jgvvR79aA0OoLdBS/tdBfiw9PtLVPrOtvl3TMuc8UruxXxwYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwMACEW6pF7KMoyLzkocLz/vE/jlgmQNAeRoV13Jly6huGa8I0CytVL7cXczvqFTAjHNGJcyY5gqZA/fSVgIECEwcgXh25udkzOdwZF43bdrcyvbIx+VXhCpjPgfV83M1pnGO/IpncLxiObdYzq08n9c16jTuLb4HWuOLQKsv0NXZUX9DsTYA40t346ZN/e5zwoUX9LvNBgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcEFciCnHKKMo2I5QjwxjVfsF69Yl+cj3xGvyMdEwDIPIR7THLCM/Edsj1esL8/HeSNgk68xeG/tQYAAgcYUyM/SeEbmZ2N5moOWuUJlbaCyfFx5vva5m68TCv3NN6ZQda/ys79SwTJuOL4gtGqBnu6e6hWlpcjVrl67JvUUX6L1Wgw/vuzkk+ptso4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCYAhHUiaBjbnk5puX5HLaMPEwEZCJAFNMITkaAKKaxHFmZvD7WxXF5GvOxLV4xHy+NAAECE1kgnpPRYhrPx3jl52c8J2M+By5jWzwvYxrr8rS8Xz5PTKPFPrnldf0t5/WNPM330BqdjC8Dra9Ac/Hh6K91F1+ca9au7W9zZXjwptKHpt8dbSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBiSQAReymHHHICJdTEfr8jBlENAOSgZ62K/HKLM01hXDlLmHE2+Tkzz/JA6aScCBAg0qEB+ZubnZXQzgpF5uTxffo7G9tiWX3n/fHy+3Vhf2+qtq92nkZcrFSxVr6z/Fk2bMb3+hmLtnr170r59+/rdftLFz+l3mw0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKHJpDDOuXQY3ldBIDKLbbFutg/hy9jGtXZ8rqYll9xfCyXp5UFfxAgQGCCC+TnZXka8/kVz8va+fK6fFz5WZvXlWnqrStvb/T5+J6Ie2iNNH58YQhZ9n3LZrS3FzVRi/V1qjxXwpV11sdZ5i5dmhYedVTfE1pDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwIgIRfMkhyHzC8roc/snBydgn1sVyuUpl3p6nsV95PpY1AgQITDaBeF7mV9xbns/TWFcOVpb3ifncYv/aVm9d7T6NvpzvoTVm8pdGo3d6rPvXOn16mr1gYdq9ZUvfS/cTrowd5y1f1nd/awgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEZUIHIvEYYstxyKyetjOa/LFcly0DLvUzuN8+V1+dy1y3m9KQECBBpdID8Dy/2sXVd+Vpbn45gcWB/o+Lyt9rx5/USbxn3Ec781Oi5g2f/bt/LM09ODt9za/w51thx55hl11lpFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw0gLlME85BFleH9eMbbUhoVhXPqbct/7Wl/cxT4AAgYkoUPt8rL2H2mdlbB/omIG21Z57oixHpjK+ByoVLKdNm5Y6OjomSt/HtJ+nv+C/pId/fFvq7ugc0nVnLVyQjr/g/CHtaycCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGREyiHfGoDkuVtccXYHutq19f2pvY8tdstEyBAYKIIDPa8K9/HUPYdyj7lc06k+QiZRsiytaurSwXLAd65uUuPSOdf9ZvpJ5+7ofhmHWDHYlPztNZ06ZvekFqKwKpGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwfgIR/BkoHFkbDOpv39r9xu+OXJkAAQKjJzCcZ91w9h29Ho/umeM7oaWlJTXt37+/mO9JEbTU+hd4+Ec/Tj+54Qupa/+BujvNWjA/PfdN16SlJ51Ud7uVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC4yfQX4By/HrkygQIEJg4AlMhVFl+N6KCZXxvNO3evbsnbt6XSJmn/vze7dvT/d//QVp3z71p1+YtxU49af6KFenoc56RTrzowjRtxoz6B1pLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQkAIyMw35tugUAQLjIDDVQpQDEUfAMlrT3r17e2KscI0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMNUFImwaIcvmSOHHWOEaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCqC+QKls0RrlTqeKp/HNw/AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgEAKRqYyRwZs7OzuJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIPC1QGSY8/oiXRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkFKMDt4c4UpDhPs4ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSeEujo6EjNReNBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQCETRyshWNnd3dwMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoBGJk8GjNhgevOPiDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCpXhnFK5sjaZnTllwIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAlNZIApXtrS0pOZAUMVyKn8U3DsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBbq6uiqzlSHCm5srOcu8zZQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMCUF8qjglSHCY6xwjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw1QWiaGXkKisVLKc6hvsnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIRAT09PipBlpYJlLmeJhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwlQWiemUlZBl/GCJ8Kn8U3DsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBVpbWyuzrVu2bEkHDhxI27dvTx0dHZVXZ2dnilcEL3MSM4KY8YqWp5WFOst5vSkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYKwFakf2zssxza/KMODFUOARqIzXtGnTKq/29vY0ffr01JqrV7a0tPSGKePgOLAcsMz7xU2W58f6pl2PAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDAcgchE5hbzOScZ85GfLL8ibBnbi2nvTOXY2DmqV8Y0gpR5CPFy1cryfL5geTrY9vK+5gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChyMQgciBWnl7DlfmaYQrI0uZp1HJMjKUrbFDXpl3jmnsHAHLrq6uyjUjNFkOTpbnB+pU3jbc/fNxpgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBLBAZx+G08v4xn5cjOxlByljOActYF8uVXGUkLSNEGdNYmXeOQGQELOOgaPWGBReaHM5bZF8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgLAVymLJ8zchJRitnJiMrWalaWUxznvKp9GSxY05dRtgydsoByxyijGmer5y5+KN2Oa83JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMt0BtwDKW87pywDJyk+WKlrGtCF0+NW54VKiMV+yQhwXPVStj2l+Ysr/1443i+gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDUFchBylqBHKyM9bmaZQ5X5iqWlYBlLESgMoKWuXpl7JhDlTlAGdM8X75YvXXl7eYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmMtUC9gGevy+jwfOco8n4OWsVypYBkhyVgZK2I+wpU5lRk3lCtZ1rs5Act6KtYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC4ykQmcj+WjkjGfvlkGWeVgKWEZCcNm1a5RyxoRyujG3xivBlvSZcWU/FOgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKARBAYKWca2vD2mOVwZ03i1RngygpKxEC3vXK5aWRukrF1uBAR9IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUE8jZyLytvJzzk3laDA1e2e2pP4vZXKUydigHLvPJYipYWdYwT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEwkgXKwMvc71uVXXhd5yUoFy9jQ2dlZCVDGfDlIWZ7PB5oSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCayQOQlc8vzMc2FKltjKPDyighU5h3jwP4Clv2tzxczJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMt0A5E1nuS+36WM6vGBW8qQhYFlnJnhRBy3qhyXrryhcwT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYaAK1Acvofw5YRnayUsEy31R/O+fttVPhy1oRywQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECjCNTLRQ7WtxyybI2ZCErGmOG51QtO1lt3KBfO1zAlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIy1QL3sY3ldzlQ2dXV19eSQZX+drBeu7G9f6wkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECjSxQDlTW9jNvayrCk9Fqt1eqWvZZOYwV9c45jMPtSoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYVCAHIgfdsZ8d6h0fGcimzs7OnvLw4P0cbzUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYMoIFNnK5lQvfTllBNwoAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAkELnKyhDhpXWVWcN714pYJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCarQG2hyu7u7voBy4EAhC8H0rGNAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaESB2hBlf32MnGS/FSz7O8h6AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBUEGhWkXIqvM3ukQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBiqwCENET7Uk9uPAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDBRBZonasf1mwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw0gJRvTKaIcJHWtb5CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQkr0NTUVOl7U0/RJuxd6DgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYIQFIlqpguUIozodAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMLEFooplcy5lObFvRe8JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAocv0N3dXTlJUzFTGSJc0PLwUZ2BAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmBwCrYKVk+ONdBcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDA4QtEBcvKEOExk8tZHv5pnYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMHEFIlxZefUUbeLehp4TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEZeoFm+cuRRnZEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYmAI5V9mkguXEfAP1mgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBg9gebu7u4UL40AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCClqGKpgqVPAgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgRqC5ZtkiAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDKC7SGQAwR3twsaznlPw0NCBBlVjUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCanQFNT0+S8MXc1oQUqw4MXn82mIlzZ40M6od/LSdX5QwlUHsoxkwrNzRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoUIHhZtOGu3+D3rZuTRKBpiKcpkTgJHkzJ/JtDPQxHGjbRL5nfSdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwlQUGClMOtG0qm7n3sRWoBCwNET626K5WLVAvQFlvXRzV3/rqM1oiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKARBfoLTtZbX29dI96TPk0+gcipxUsFy8n33k6YO6oXliyvK8/nm6q3Lm8zJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgsQXqhSbL68rz+U7qrcvbTAmMlkBk1Zq6urp64gPoQzhazM5bT6A2KFlezvN5GseX5+st17uGdQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINIZAbT6tvJzn8zR6XJ6vt9wYd6UXk11ABcvJ/g434P31F5bM6weblm8p71teZ54AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcYQqA1KRq/yuqFO853k/fOyKYHRFIhsmgqWoyns3HUFyqHIPF+e9jefT5a35+XydKBt5f3MEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwOgL9BSHL6/N8TPubj97lbbmntct5vSmB0RCoVLDs7u5Ozc3No3F+5yRQJVAOQOb5mNbOx3I8DONzmV9VJ7JAgAABAgSmiED+jpwit+s2CRAg0BAC/sdMQ7wNOkGAAAECBAgQIECAAAECBAgQIECAAAECk1wgMmv5lbNC8f/o8ytuP/8/+zwtr5vkPG5vnAV6P5PFh7Sn/AEc5365/CQWKAdE8nxM8ytuPR6a0VpaWiovn80Khz8IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITEqByA51dXVVXnGDUYwtZ4ZiWp7PAHldXjYlMFoCzT5so0XrvGWBHKgcaF08KGO/CFe2trb2PhzLx5gnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDyCER+LbJCkRmK7FAUaMtZo5iW5yfPXbuTiSAQn73mXDFwInRYHyeHQO2DL5bjcxivHK6cHHfqLggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGIpADllGkbZyyLLesTl0WW+bdQRGSiDCvypYjpSm8wxbIActc7gyyvtOmzZt2OdxAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECE18gskORIcp5opwvyoHKPJ34d+oOJoqAgOVEeacmST9rH3L5IRgPxXg4agQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITF2BHLCMXFFkinKrlzvK20wJjIZAJdM2Gid2TgIDCeRQZZ7GBzFK+wpYDqRmGwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHJLxAZojxMeNxtzhhN/jt3h40kEJ+7+CwqGdhI78ok7Ut82Oq1WB/hyvwSsKynZB0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqSMQGaJcsC2mtdmj2uWpI+NOx1KgqampcrlmH7ixZJ/a16r9rOXl/EDMH8qpreTuCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECExdgcgQRQXLyBblV9bIeaP+lvN6UwIjIRC5tmahtpGgdI7hCuSHXU6Zx0NRI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQB4iPGeMaoOWhAiMhYAhwsdC2TX6FcgPwAhZxksjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBALtoWEjljRIXAWArkPJshwsdS3bXqlu2Nh2D+QCIiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBqC+SApUzR1P4cjOfd55HBDRE+nu+Ca/cJXCIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAuXJlzNcuEyIwmgIRsIzPnAqWo6ns3P0KlB94/e5kAwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGAeBCFmqYDkO8C7ZV0A5374m1hAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYigJ5iPDaypVT0cI9j49AzrNVKliqJjg+b8JUumq9z1i9dVPJxL0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDB0AXmjoVvZ8/AEmpubKydojTKWGoHxFPDgG0991yZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBskBUsKwMER4zuZxleQfzBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGpJhDhyni15lKWUw3A/TaGgOqVjfE+6AUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBiSAQeSOjNk+Ed2pi9zF/xpoF3Cb2G6n3BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwMgJ5Fxlc05ajtypnYkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMDEFcq6yubu7O8VLI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCmqWLY2NzezIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQeFogqlhKV/o4ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqBCoBS0OE16hYJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKakQAwPHq05ZgwTPiU/A26aAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqBGI4cGjNeeZmu0WCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJTVsAQ4VP2rXfjBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQK1AjAze3d2dWmODIcJreSwTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECU1EgjwzeHCnLSFtqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECKUXIslX1Sh8FAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBBgShcqYLlQQ9zBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFKBcuigGWzIcJ9GAgQGHWBVatWpXhpBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaGSBqF4ZrblSxrIIWWoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaku0NTUVCFozjNTHcT9EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRCoFK8sru7mwYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDTAlG8UgVLHwcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI2AgGUNiEUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgagvE6ODNU5vA3RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEDgr09PSk5uZmAcuDJOYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqS7Q1NRUIWiOpKVGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDwlEBliPCctIRCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQDBHuQ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAtE9cpohggvq5gnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEprRAHhm8Oc9MaQ03T4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoBCJX2dPTo4KlTwMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCwQIUsVLMsi5gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEpLdDd3V25/+YoYxkvjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw1QWam5srBK1RxlIjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIKSpYVoYIj5lczhIMAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAqC0S4Ml6tuZTlVMZw7wQaTeAb3/hGpUtXXnllo3VNfwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCpBfLI4K09PT2VpOWkvls3R2CCCHR1daX/83/+T7rzzjsrPd65c2d6zWtek1paWibIHegmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEJrZAzlW25qTlxL4dvScw8QUOHDiQ/vVf/zU9+OCDvTcTQcvdu3enq6++OrW1tfWuN0OAAIGpKnD9Dzenr96zs+r2P3nVynTMgmlV6ywQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgrAW2bNmSbr755hTT0047LV122WVj3QXXI0BghARyrrK1u7u7ckpDhY+QrNMQOASBXbt2pX/5l39Ja9as6XN0BC7/6Z/+Kb3pTW9K7e3tfbZbMXYCm/d0pYc3HUiPbI7X/rRpV1c6ugh1Hb+wLR2/qC2dtLgtzWprPqQOOXdfttE06Xu1g2tuemBX2nXgqe/GvPYFJ81O82aMXyXZL33pS2nHjh2V7sRz4KqrrspdM20Agb0dPen/3l8d+uyvW03FhsWzW9PKea1pxbxpaUZrrBn59sCT+9Ptq/em1583f8RPPtxz1/OZO6M5XX7S8L7THiqev3c/sa/qfs5cNj2dvGR61bpYqHfNvNN5R848rEDu41sOpDvWVvcjn/vlp89J01r6vqf1+jPaBrlPpgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAv0LPPDAA+m73/1uevjhh9OmTZsqwcjZs2enJUuWpOXLl1cCkhdccMGQRh6Nc/32b/922rZtW+8FX/SiF6WPfexjRhfuFTFDYGIJRBXLVsHKifWm6e3kE4jfWvjkJz+ZNm/e3O/NRfDy+uuvT295y1vSwoUL+93PhtET+Pxd29Onf7o1Fc/NqnZfEWLKbX4RwHv3C5akc1bOyKuGNHXuvkyjadL3agfX/PDxPel/fn/TwRVPz52zYsa4BSzXrVuXrr322uKz99SH77d+67cELPu8Q+O7YncRyP2nH285pE4smtWSLjthdvrdc+en9umHFtDOF964qzPd/NDu9O0Hd6U12ztS5PxGKmB5OOeu5xMVR4cbsPzFur3pE7dtzbdbmb752QvqBizrXTMf+PzC+0+LZ/Whtn+7Y1v6waN76h7+opPb6wYs6/VntA3qdtBKAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBFMXoosjNddddV7cQVpko8hwLFiyojDz6tre9bcDRR//mb/6mKlwZ57npppvSrbfemp773OeWT2ueAIEJIhBVLFsnSF91k8CkFFi7dm361Kc+laKC5WAtApgf+chH0pvf/Oa0cuXKwXa3fYQEourYh7+3Md36WP0wTfky2/Z1pXd9c3164/kL0mvOmZf61jAr7/1UhTXnHjuT6iv1XYoA1Edu7T/o3PeIsVnz1a9+tTdcGVd89atfPTYXdpUxEYhKrV++e0clFBlhyJefPje1DCNnGc+oWx97KlR517r6VRUP9UZG89yH2qeROC6C1PH3ffYhVBzeub87/fjxvSPRDecgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYB4EocPPWt7413XvvvUO++tatW9M//MM/pK9//euV6RlnnFH32NWrV9dd/+tf/7rueisJEJgYApWAZSSzVbKcGG+YXk4egSgv/ZnPfCbt33+wAuJgdxdBzBgu/JprrkknnnjiYLvbPgIC7/vWhvTLmiFpBzptd1Fk8FO3by3Ke6f0mmfMG2jX5Nx9eUbTpO/Vqtf880+2pgi7NVr793//994uRQn6KD+vTT6BCO599Edb0t3r96X3XX7EgDcYxUzvLKo5RqXKHxbh732dNaV1Bzx64I2jee6Brzx2Ww909aTvPbI7vey0OcO+6Hcf3pU640GvESBAgAABAgQIECBAgAABAgQIECBAgAABAgQITDiB9evXp9/5nd9Jhxp4fPTRRyt5jah+ecwxx/S5/7POOivFMOG1LdZrBAhMPIEYabRSwTJmhCsn3huoxxNb4K677ko33HBD6uoafpgrAplR9fK1r31tOueccyY2RIP3/r4N+/uEK9uKMXffcuGCdM6KmWnpnNb00Mb96TvFkLzfvH9n1d186Zc70qvPnFt3qNjY0bmruCoLo2nS92rVa35VhNr+477q97B6j/FZ+tWvfpUeeuih3ou/8pWv9J3dq9HYM3OL4b5fdEq9AF9PJcj7+JaOtGrrgVSb1Yuhp79ZfBZf2k/472v37Eifu3P7qISBR/PcjfZu3fTArkMKWN5UhFo1AgQIECBAgAABAgQIECBAgAABAgQIECBAgACBiSnwrne9a8Bw5fTp09OSJUtSjDC6d2/9Uc22bNlSGXn0xhtvTC0tLVUQ1157bbr//vtT/Jw3Wmx/xzvekc4+++yq/SwQIDAxBCJcGa01z0yMbuslgYkvcOutt1bKRke4+VBbBDM/97nPVYYWv+SSSw71NI4bROCLd2+v2mNaEa784EuXprOWz+hdH/PxmlOEqW74xcH9t+7tqlSX6y8k5dy9hL0zo2nSe5E6Mx1FNbu/u6XxhgaPrn7lK1+p6rHhwas4GnphwayW9NYijD1Qe3TzgfT3P9ic7n+yupJxVLI8Y9mMdMyCaX0Ov+3Xe4cernzq33p9ztHfitE8d3/XHK/19xXmq7d1pKPm9zXur0+PbzmQHtx4oL/N1hMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSwwB133JFuueWWuj186Utfmt7+9renU045pVKtLnaK4b4/+tGPpi984QspRgYut6hk+Y1vfCNFgZxyW7x4cYoRCmP48QhpxvmWLVtW3sU8AQITUKA5+lz7IJiA96HLBCaEwDe/+c30ta99LR1OuDLfaJwjzhXn1EZeYP3OzsrQu+UzX3LsrKpwZXnbNefPT+1tlUdq7+ovFIHLejla5+4l6p0ZTZPei/Qz87+LaoARtGq0FkHqr3/9673dOuOMM9JJJ53Uu2xm4gscv6gt/c3Ll/UJ+cUQ1jc+MPSKqmcsnZ7+8OKF6aTFbSOOMprnHvHODvOEUcVyOO2mB3cPZ3f7EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQINJDAzTffXLc3L3/5y9NHPvKRdOqpp/aGK2PHo446Kn3wgx9Mb3vb2+oe95nPfKbu+hhF+Mwzz0yXXXaZcGVdISsJTByByGZFrrI1umyI8InzxunpxBSIv2zxWw0///nPR/wGvve976WdO3emq666yt/lEdS9/dd7+gzde/nJ7f1eobW5KV1y3Kz0rVJgZ+2OzrRxd2c6or3yqO091rnH1qQXvs7Mqq0d6fN3bevd0lJkZJ+xfGa6Y239cu+9O47BzA9+8IO0adOm3iu96lWv6p0f7Zknd3Wmtds707odHWn7vu7iM9ySVsydllbOa03zZlSXua/Xl21FBde9HdVVepfNaS3+g6Te3k+t21Bcs/yLX1ExdvHswa9Ve8Yte7rSw5sOpMeKaoNLZremU45oSyvmTUsDXLr2FGO6HPf59osXpf/xH+urrhv3MFA7ecn09LwTZqXLjp+dljz9jLn1sT0DHTLkbaN57iF3YhR2XD63NT1RPJdzu/mhXemNz16Qisf3oK2r+KXE7xT7l9vK4nzxnNcIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaXyCqTtZrV199db3Vvev+5E/+JH36059Oe/ZU/ywuhgKP8FV55OBt27alHTt29B6bZ1auXFk1nPj+/fvThg0b8uZDms6ePTstWrRo0GNXrVqVHn744bRmzZrKsOcnnnhipbLmkUceWdX3QU9kBwJTUCD//W6N4Fcs5BVT0MItExhVgQMHDqT/9b/+V4ov19FqEdzcvXt3+t3f/d3U1jbyFcxGq9+NfN6Nu7v6dO/MYsjegdq5R86sCljGvhFUqw1YOvfYmvT3nkV10b+9ZVOK4FRuV509L0X1wEYIWEbp+NxaWlrSK17xirw4atN7N+xPn7p9a7r7iX39XuPSItB3zbPm96m6WD7gH2/dnGrDfl++5ujUPr26ymv5mD/88rq0owhz5nbswmnpE7+1Mi8OOo0qpH/5nY3pkWLY7do2u6gu+6oz56ari34PIUtXe/ioL5+5bHol5NddyqTWu4/oyMtPn5PeUQQyIyw40m00zz3SfT2U80UY9fN3be89dHMRxv35mr3p/KNm9q7rb+anxX5bi+BwbhHKfG7N+fI2UwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcYTiFBjvTZz5sA/K4qf1Z511lnpF7/4RZ/Do2DOkiVLetd//OMfT/GqbVFcJ0KWuUXGI/Idh9Ne8pKXpOuvv77fU9x4440pqmzedtttdfeJgGYU+fnTP/3TNGvWrLr7WEmAQKpkKovilc3ClT4NBEZJIEKP8eU5muHK3PW4RlwrrqkdvsDmmoDljNamNHPawNGs+TP7Vtt7ctfBQE7ulXOPrUl2r51+7d4d6b4iUJhbBNb+67nz8+K4TuPv8be//e3ePlx88cVV/zDv3TBCM5Hr+/D3NqU/+eoTA4Yr43K3PLo7veULa9Onf7p1hK5++Kf57sO7UwQ0+wsl7j7QnT57x7b0gRuf7FNZ8/CvfvhniCqWS2sq3e7c310V6MtXec4xs0YlXBnnH81z5/6P5zQqotYG5W8sVR0eqG831QzZ/qwiUD9QYHigc9lGgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw9gLHHHNM3YvecMMNddeXV37uc59L9957b59XOVxZ3n885/ft25f++I//uDK0eX/hyuhf/Ez6s5/9bLryyivTXXfdNZ5ddm0CDS0QlWqbo4JlzGgECIyswNatW9NHP/rRtHr16pE98QBni2vFNePa2uEJbCqG9i63RbP6hifL22N+3oy+1fme3Fl9ntjPucfWJMxr28aisui/FJUay+2Pn7soTS+CtI3QvvWtb1XKs+e+vPrVr86zozL9dGHx7Qerhz8e6EJRafFzd25P37p/6McMdL7D3fa/79yW9nUO/m+ZH6/ak975jfVp8D0Pt0fDOz76s6VUHTGObitCl0MZjn14V5rae+8vPiMvPqW9CiE+E7uKMOtALSqr3rZqb9UuV5w6J8X5NAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYkhcMEFF9Tt6L/927+l97///XWH9q57QAOvjHDl6173uvT1r399yL18/PHH01VXXZV+/OMfD/kYOxKYSgIxKnilgqWA5VR6293rWAg88cQTlVLMGzduHIvLVV0jrhlloKMP2qEL1A7jPXfG4AHLevvEEOG1zbnH1qTWP5av++HmqkqGl5/Uns5dOXDp93rnGa11X/nKV3pPHeXYX/jCF/Yuj/RMDAdeHjY5zj+/+Lz/1tlz01+9bGn6wtVHpetetTyFUW2LocBjaO5GaUcUVSBfVATo/vDihemVZ8xNR8+f1qdrD27cn3742J4+68dzxZrCsDasd+zCtsqw4ePZr8l27TC+9ITZVdWIO7p60ncfGbjy83cf2ZU6S+O3zy3C9M85Zmaf92yyebkfAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBkErjiiivS8ccfX/eWImR56aWXpg9/+MPpoYceqrtPo608//zz+3Qp+v/LX/6yz/rBVnR1daW/+Iu/SFGkTyNA4KBAzlS2VspYFsOEawQIjIzAI488kj7zmc+k+M2A8Wo7duxIH/vYx9I111yTTjjhhPHqxoS+7raaanJDuZnWOo/SHXUqozl333+UjaZJ7Xv3/WKI63I1urnTm9PvPWdB7W7jtrx+/fpULtUe/9CPkOVotZ+v6fusimDl8Yvaei8ZlRSvPWJ6imDZl+/e0bs+Qmd3rt2bjqoTZOzdaYxmrnnW/D5DvEf//u6WzX2qc0bFy0uOGz3T4d7yx2/b0ueQExb1DYf22WkCr9i8u6vy3gznFh7bcmA4u/fZNwKWM4oqtZcdPzt9qzQ0eAwT/vLT5/TZP6+4qbRvrIuwcWtz02EHLMfDIN+TKQECBAgQIECAAAECBAgQIECAAAECBAgQIEBgqgkUFejShz70ofT617++bp4j5ywia3HGGWdUqjq+8pWvTPPmzRtxquOOOy69733vG/S8H//4x9OTTz7ZZ78o0POGN7yhav2dd95ZyapUrSwWWlpa0h/8wR+kF7/4xWnRokUpMi1xj7feemvVrvfdd1/64he/mF7zmtdUrbdAYCoLRPXKaK15ZipjuHcCIyUQvwnw+c9/PnV29q3QN1LXGOp5IuD5qU99qlL++eyzzx7qYfZ7WqC7p2bo1xEcOdq5+37MRtOkfLUYCvj6H1aH2d564cKGGor5a1/7WtVvBo328OD3bqgOWC6f21oVriz7XfWMeVUBy9h2/5MH0ivOKO819vMvOGl2n3Bl9CJCcO983uIUwbyHNx0M58X8z9fsTecdOb5VSzfv6Uqf+snWdPuvq4efjr5fdOzsmEzatutAd/q/9+8c0/vLw8i/uBjeuxywjKqmq7Z2pGMW9A21Pl58dh4qfXaiw1c8Pcz4/s6+YfHh3NB4GAynf/YlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEw2gfPOO68yImgEDvfv39/v7d1zzz0pXh/84AcrwcTXvva16cILL+x3/+FuWL58eXrjG9844GFRVbNeuPKoo46qVNqsPTj2r1eB8u///u/Ty172st7djzjiiPSc5zynEtC85ZZbetfHzN/+7d8KWFaJWCCQUhSvbI2/XJHS1ggQODyBH/3oR+mrX/1q5S/W4Z1p5I6OoOdnP/vZtGvXrnTRRReN3ImdicAEFYhKgeVqmc9YMaMypHQj3U55ePClS5eO+t/dmihxiqp6Efxqb+v7b4NFs1rSda9eXjznDorNnNZ3v4Nbx2budefMH/BCMVz433x/U9U+v1q/v0/AsnRbVfuWF4aatd5aVMGN8GS9tqXYtm5HR3po44F0oBiiurZFNcULjh7f8GdtnybDcg5Enr50ejpy3rS0ZvvB4e1venBXessFfSvZxvpyO3lJWzquGL49Wu2w7uX9zBMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSmwPOf//z0H//xH+md73xniqqPA7UIYUYOJF5RNTIClwsXLhzokBHZdvfdd1eG7K492bRp09JHPvKRNHfu3KpN0c9vf/vbVetiIYKU5XBleYc/+qM/SrUBywh0xitCmBoBAk8JRPFKFSx9GgiMkEAEGOuFGN/97nePWUXL1tbW9Jd/+ZcjdEdOQ2ByCdy1bl+KoYBzm9bSlP7kuYvyYkNMo+z6Aw880NuXl7/85aP+SxCnLJmeflHY5BaBv2u/sT79P0XY7NyVfUN+sf9Ea5ceP6sIWFb3esPO6krDf1sEMMtVDav3PrgU1TL/x/OXHFzRz9yOfd3phl9s72dr/6tjaPbfe87o/0dZ/z2YvFvKgcgXn9qe/rkUgP3OQ7vSm5+9IBVFT3tbV1Gg8uaHdvcux8wVpxwcSjxXxKzawQIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDDCxx//PHpC1/4Qvr3f//39IlPfCI9+OCDg/Y5Aox33HFHJWR5+eWXD7r/oe4QQ5W//e1vTx0dB4uF5HO95z3vSWeddVZe7J3+5Cc/Sbt3V/9cKzZeccUVvfvUzpx66qkpgmNRna/cVq1aJWBZBjFPoBAQsPQxIECAAIFJLxChwb+/pbqC4e88c15aWVSxa6QW/4Avt9EeHjyu9eqz5qav3bMjlcNiMYT2u/5jQ4qKleesnFEJWp5bDKcdyxOxRZXNWcVrT8fBIZ2f3FUdsBzv+2opCoG+tBi6+vXnzU9tRfh3srf4LEWIdzgthlL/7iN9/8NwqOcof8YvP6k9ffr2rSkXEN1SDNf+09V7qyqH/rQYRr5c8Tbel+efeHDo9nJgc6h9KO83Hgbl65snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECExlgRjt9zd+4zdS/Ez2tttuq4Qtv/Wtb6WdO3f2y7J58+b0e7/3eymG465XgKvfA4exISprrl69us8RL33pS9PVV1/dZ32sWLt2bd31MRLrI488UndbrAyDrq6uqu0RsDz//POr1lkgMJUFYnTw1qkM4N4JECDQn8C0oozZ3lT6TY3SbH/H1Fs/a1rfoJRzj61JvC//9vNtxZDMBwN1R8+fll57zrx6b9m4rYsv5a997Wu91z/llFPSaaed1rs8WjMR8vrzK5am935rQ5/hqjcXobPvFBX84hXtmAXT0gtObE8vOa09zZsxscKWC4v73LO98QKWMRT7BcfMTFeftyAtnzt1/lnWPr05vaAIOQ6nxbDrhxOwLAci4/NwfjEM+22r9vZ24aYHd1YFLG96oPo/ni8+blaK9yu3POR4Xh7udDwMhttH+xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEJrtAVHGMobTj9ed//ueVoba/9KUvpVtvvbVP+DAsouLjtddemyKM2d4+vJ93DWb5qU99qu5Q38cee2z6q7/6q34Pj2G967Wbbrqp3uoB1z3++OMDbreRwFQSiL/vEUSeOj/Jn0rvrnslQOCwBZa0t6Yd+w/0nmfXgYPBrN6VNTPl8E7etHh238esc4+tyaObD6Qv1gzVHOHKJ3dV/yZOfs927e/7Xm/c3ZVmTHsqoDmjtSlFOGukW/z20IYNG3pPOxbVK/PFokrlJ65amT7yw83pZ0UVv/7aqq0d6V9+ujX92x3b0jXPmp9+6+x5VUMq93dcI6yfM6MIxpVG7I6wXrnF39VjFw5e0bTe3+nyefL8gpkt6beLKqn12qJZrWlZEaaMQGU5sFdvX+tGTqA2EPniYrjvcsAy5ncWf//nFOHPGOK9vC16EfuXW71nfnm7eQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYklMH369HTllVdWXmvWrEkf/vCH0ze+8Y0+w2ivW7cu/eM//mN697vfPWI3GMOPf+hDH+pzvujTlMhRhwAAQABJREFU9ddfP2CYMyprjlTbvr30Q9WROqnzEJigAhHAjtYaScu8MEHvRbcJTHiB1tbWym9CDHQjf/Znf5Y6Ow9W4BtoX9sOX+CIImD5SBHMy237vuowVl5fnm4tqv3VtsWz+wbxnHtsTf7uls29wwDn9+fD36seLjyv72/6vqK6Y27PKobK/suXLs2LIzb9yle+0nuu+A2IV7ziFb3LYzGzogj7/eVLlqYIUd74wK70szV70uNbOupeuqMYV/mff7K1sv3a5y+uu0+jrdxZBObKLQKQ5XZ1ERiN10i1uUWg81Vnzh2p0znPCAjsz+OBP32uZxcVLOcXlVi3Pf18j8/1fz68K73yjLlFpcxdqbP7YOniZXNaUwSRy632fOVt5gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBCa2wJFHHpn+4R/+oTLq4F//9V/3uZnbb7+9z7pDXbF169b0jne8o24m5P3vf/+gIx8uWrToUC/tOAIEBhGoDBEuXDmIks0ExkggQpZa4whECLLcoqpZkUcvAunltdXzW2oq4sXWRXUqWDp3tW04jabJqq0Hg7JxrUZse/bsSTfeeGNv1y688MK0bNmy3uWxnIlhwN964YL01rQgbSs+03et25fuWLM3/eCxPWl3TSXXmx/alS4/aXY6twidNnrbtLs6oF77mWv0/uvf4QvEM7zcWpub0uUnz05f/OWO3tU3FeHiCFjGtNxeeHJ7qn38l/KX5V3NEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQINJjAX/zFX6QbbrihT6+uu+669LznPa/P+vKK3//930+f/OQnU4Qgy+3BBx+sDCHe0lJd2KW8z1DmozDef//v/z098cQTfXZ/1atelV73utf1WV+74pRTTqldVVl+73vfm04++eS62/pbuXz58v42WU9gSgoYInxKvu1umgCBoQgsnVP9j6AI5vx6W0eK8Fl/7Ykd1QGu2G9JnQqWzl1tG06jaTJzWnPa19m3umhct1Hat7/97RQhy9zGcnjwfM160/lFlcfnnTC78nrbRd3pYz/akr5VEzz72Zp9fQKWzXWSyF216bZ6FxyldTHc877O6nRdVCTUCFxRDPtdDlg+tOlAUcVyd4ppbvFxftEp7XnRlAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYIIJLF26NO3evbtPr++7775BA5Zx0PHHH59+/vOfVx2/b9++9OSTT6bDDSR+7GMfS9///verzh0LJ554Yopg6FDaSSedVHe3uOdLLrmk7jYrCRAYXCCqV1YCloYIHxzLHgQITD2BI+f1DVJ+/9Hd6erz+h9C+AfF9nKb1tKU6oW4nLtvsG00TZ573KxUr7po+b0qzz9aDA2/riYs+8xiaODZbc2V3U5cPL28+4jMl4cHnzlzZnrxi188Iucd7CQ/fHxP+m4RJiu3t120MC2a1TcEG0HV/3bZ4nT76r1py56DgdU71+4tDl9QPkXdYPGm3V1pXjEc82i08lDO9c7/vWK459q2VMCylmRKLkdo/tQjpqf7n9zfe/9/f8um3vmYeeaKmWlpTVXjqh0sECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINLRAhBXrta9+9avpTW96U5o+vf+fAUeu6tFHH+1zeHt7e4rg5uG0n/zkJ+nv/u7v+pwifmZ8/fXXp1mzZvXZVm/Fsccem9ra2tKBAweLiMR+n//859PrX//6NH9+/zmH/fv3V8KjF110Ub1TW0dgSgvkkcFb88yU1nDzBAgQqBE4/6hZlWGrn9x1sCplBNF++5x5KYKTtW3t9o50z4aDAZ3YfvlJ7WnW06G88v7O/VRQcaxM3n7JovKlBp3/px9vSV++++CQwXHA7124MB2/qG3QYw9lh40bN6Yf/vCHvYdefvnlafbs2b3Lozmzv6Mn3VITDL7g6JkphkOu1+KTv7ioyloOWNYLNy6f2zdEe2/x9+OEfgxjqPGoMnmo7aNFZc0PvWxpiiGfa1sM4/yVmvcz9jn/qMYf1rz2XiyPjsAVRXXKcsCyttrpi0+t//dhdHrjrAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiMtcOmll6aVK1emtWvXVp06hvmOYbQ/8IEP9Psz2g9/+MN9hgePk5x11lmVynZVJxzGwqZN/z979wEmWVUmDPir6uowecgSBMGABAVEXSWsoiLrKorIIsIuqCRBWMTfAIgCS1JUTIthVRR0MSOK+q/8sooBVwUUZAZYBAQHkDQzPal7OlT9dWq8Q8fpqp7q7uqu9zxPTd2698T3Vg811NffeTxOO+20yjbjQ5udf/75MVpWyqF10+u0TXn6nvlHP/rRoMtp2/E0RtoKff78+YOupRd//etf48QTT4zFixfHf/zHf8QBBxwwrI4TBJpZIMVVpiDrggyWzfw2sHYCBEYTaCnHAP7THvPjsl8tXV8lBVH+2/97NM555ZaDArlSZr6z/+vR9fWygzc8d/gHlHRN35nQk88TafLkKI15dO211w760DyZ24M/d5uOynt5YJDk5b9dVtnye6Qslmnb5HvKGT4Hll236hj4snK89fzhGWC/8YfOSMGbWw7JBJgyw37kZ4MzBlY6Gbyj97AxBp7448Pd8fFfPBGn7rtZtBeeDLLs6i3GBT95LB4cISNpylqoEEgCL336nPhMOUi3p3/4m25uOUh+n6dV95uBNAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBBpTIAUgnnDCCXHOOecMm+B3vvOduP766+Owww6LHXfcMbbZZptKJsglS5bE1VdfHYsWLRrWJp3Ye++9Rzxfzcm07XAKfExbjA8tm2yySaStyy+88MKhlwa9Tms644wz1p9LQaI33nhjLF++fP25dPCLX/wiXv7yl8exxx4bu+yySyXQMgVU3nbbbZV1L126LibilFNOiauuuir22GOPQe29INDsAinIUgbLZn8XWD8BAqMK/MPO8+IrNy8flFnvNw90xbHffDD2LAembVveRvyecsDZLQ92R2f3k1smpw5TdrztFw4PMssG03cm8eTzRJo8OUrjHQ3cHnzzzTeP/ffff9ImmbJRvqqcne/axSvXj/lEefvv48rv8UN2nx+7btUem8xqieXl93cKYvz+opXRPyTR5AvLQZNDyx7ln4/Udwo+zkrKBnvitx+KF+8wOzYrX7uvHKh552NrB/18ZXXH83zdXasq253vUg6cTMGTD63ojdv/ujaWdz05h6zfI/caPQV+Vsdz8wjMKQdR7r/T7Lj+7tXDFv2yZ8yJthGyFg+r6AQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBDCxx11FGRtuQemuUxTToFJX7hC1+oev4777xzJfNj1Q2GVLziiivi17/+9ZCz614uW7YsvvjFL454beDJoQGW6bvmFEB6+umnD6xWOX7iiSfikksuGXZ+4Imurq44+eST44YbbohCYfiOhQPrOibQLAIpGDqfz6/LYJkWbavwZrn11kmAQLUCKRPeKeWMeB8uZ9frHZDZ7OFyNryHV6watZsUWHb8320y6vV0Qd/DeSbSZPhojXHm7rvvHvQbTwcffHAlfftkzu748vbnfyoHCt/x6JNb3K/uKcZ/3jL4N5tGmtOhz5kf+46Q3S8FpB3z/E3iozcMzkyZ+k3bgdezpNi3lvLW4Cn7YPo5va0cCJoeo5X9dpwdKQBUITBQ4KByQP1IAZYHPXvewGqOCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEpqlACpL62Mc+FimQ8Kc//em4V7HZZptVgjHnzJkz7j4eeuihcbfdUMPXve51lSDSr3/96xuqNuK1FLCZtiYXXDkij5NNKpD+3kglnwIrBVc26bvAsgkQGFMgbR176WufEluUgyarKbs9pT0uO3SbeNqmbWNW1/dwook0GT7a1J8ZmL0yzWYytwfPVt9RDiS+8FVbRQo8rLaUPzqUM1/OixPLwZmjlQOfNbey9fJo17PzaXv4Y1+4SSUrbHaulufN5xTi46/betjW4yP1cdhz58f7X7HlSJeca3KBFHS79bzBv4m302Zt8czNx/67vMnpLJ8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMG0EWltbK9khP/rRj0bK+FhreelLXxpf/epXY9ttt6216aTVv+iii+Lyyy+vbHVe7aA77LBDpKyaL3vZy6ptoh6BphBIGSxLpVIU0kEqWcRlU6zeIgkQIFCDwM5btMen37BNfP5/lsWiR9ZWth4u//25vrSWU+g9bZPWeH55W/B/2XthFMrZ9Kot+h4uNZEmw0ebujPpv7/f+9731k/gmc98Zuy+++7rX0/mwdz2fHzgwC3jd3/pih+Utwu/aUnXoKyt2VxSMOQBT58bb9prQTx1YWt2esTn9GNw1su3qGwJ/tlfL41lQ7bqTj8nTy8HsL19300rW3rfVd4ufDwlbTf+jHIQ3GWHbh2f+MUT8fsHuyNlysxKynC5Y3mcQ3abH6/ceW522jOBQQLpb+30/rjipiczt/6D98sgIy8IECBAgAABAgQIECBAgAABAgQIECBAgAABAjNFICW+OfDAAyvbhf/4xz+OX/3qV9HT0zPi8ubNmxf77bdfZfvs3XbbbcQ6jXYyBYJed9118clPfjJ+/vOfx7333jtsfSkZ35577hmvec1r4sgjj4z29vZGW4b5EJhygSxxZa4cZTkgTGjK52UCM1AgvcWyt1l2nAKL+vv7o7e3t/KXeHd3dyXCfyZmUz3rrLOir69vg3c2pVhOv0WwoVKvfjY0xkReu//++yvdp998mO6lu68U9z7RE4+u6osdyoGV2y9sK29RXJ9V6Xu440SaDB9t8s78+te/jqOOOmr9gO9+97vjpJNOWv96Kg+6ekvxwPKeWLqmP7p6SrH53JbYqpzdb4tytsga4ocHLSEFWN6/rDc6u/tjh3Jw5lPr+HMzaKDyi4dW9MU95Z/RzWa3VII40/bzCgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEYSSNuGL1myJB577LF4/PHHY9asWbHddttV4ljmz58/UpNpdS7F56SYjbvvvjtSvM6WW24Z22+/fWyxxRYNu44UX/Tggw9GR0dHtLW1Rco+mrYxXx/w9rcdm7M4o+y5YRdkYtNaoJDekN5k0/oemjwBApMskLZU3nWr9sqj3kPre7joRJoMH23yzgzcHjz9d/h1r3vd5A0+xkizWnORMonWs2wyqyXSYzLKNvMLkR4KAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExhJIAZVpx8H0mIklBSbutNNOlcdMXJ81EZgogSyuUgbLiRLW73qBLGtlOpEdN1MGy7GyV2ZQKYvlhkq9+tnQGBN5bSZlsJxIJ303j8A3v/nNWL16dWXBKa38YYcd1jyLt1ICBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCKQIovksFyFBynJ12gkALdUsnn67S/7aQvwYAEGltgrMDJamdfr36qHU89AgQmVuDwww+f2AH0ToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMG6BFOxbEFg5bj8NCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRkokMvlQtrKGXhjLYkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYOIFKgGW2TfjGdaU1AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGB6C6TtwVPJpwPbhE/vm2n2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH0E0vbgqeSzg/p0qxcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwPQXsEX49L+HVkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUSSDtDF4sFqOQ+rNFeJ1UdUOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMa4FsZ/B8irJM0ZYKAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBARAqyLMhe6a1AgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEHhSICWulMHySQ9HBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFKBstyAsu8LcK9GQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECZYGUvTKVfCWNZTnIUiFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINLtALperEOSzg2YHsX4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBKoJK8sFos0CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE/iaQklfKYOntQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYIiDAcgiIlwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBzC6TdwfPNTWD1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEnBUqlUuTzeQGWT5I4IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJpdIJfLVQgKKdIye9HsKNZPgMDECeywww4T17meCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUEeByhbhgivrKKorAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYNoL2CJ82t9CCyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqKZCyV6aST1uEKwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhHZzuD57AAKAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDZBVJcZUpeKYNls78TrJ8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0KACa+/8Qyw99ZDof+KRBp2haREgQIDATBVIQZYyWM7Uu2tdBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFpLNC/7LFY+b63Rv/ti2PZcQfF2ttvnsarMXUCBAgQmE4CxWKxMt1KBsuUylIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0AgCpb7eWH72W6O0dFVlOqXla2Ll6UfGqmv/sxGmZw4ECBAgMMMF8vl8ZYWVDJYplaVCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBEEOj/+viguvmvQVEp9pei69LxY/pH3RgrAVAgQIECAwEQJpAyWKXFlPh1k6SwnajD9EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhGYNX3vxq9P7xm1Kq9P/xuLD3t0EhbiCsECBAgQGAiBFLSyvQoZ7LMpz8mYgx9EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhaYO3tN0f3p84fs37KbrnsuINi7Z1/GLOuCgQIECBAoFaBbFfwfEpjqRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYSoH+Jx6Jle8/trz9d3WxLKWlq2L1ZedO5ZSNTYAAAQIzVCCLq8xnkZYzdJ2WRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0OACpb6eWH7Wm6O0fE3VM80tnB0Lzv1c1fVVJECAAAEC1QpkcZX5YrEY6aEQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmAqBzo+8N4r/e0/VQ+cKuZh3/hejZbOtqm6jIgECBAgQqEUgZbEs5PP5WtqoS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBuAqu+++Xo/fEPa+qv418/EO27711TG5UJECBAgEAtAimLpejKWsTUJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJtA922/ia7LLq6pv9ZXvz7mHnxUTW1UJkCAAAEC4xGoBFjaInw8dNoQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiMV6Dv0Ydi1fuPj+gvVd1FftedY8E7Lqi6vooECBAgQGA8Aml78FTy6cA24eMh1IYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGA8AqWe7ug8681RWtFddfPcpnNj4QWXR67QWnUbFQkQIECAwHgE0vbgqeSzg/F0og0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBWgWWX/KuKN7z56qb5VpzMe/Cy6Nlky2qbqMiAQIECBDYWAFbhG+soPYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVC6z85uej7/rrqq6fKs46/fxof/aeNbVRmQABAgQIjFcg7QxeLBajkDqwRfh4GbUjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoVqD7ll9F9+c+XG31Sr22Q/4p5rzq8JraqEyAAAECBDZGINsZPJ+iLFO0pUKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgogT6/rokVp37tohi9SO07L5bzD/lvOobqEmAAAECBOokkIIsC7JX1klTNwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiMKlNZ2RedZR0dp5doRr490Mrf5/Fhw/hci11LZoHWkKs4RIECAAIEJE0iJK2WwnDBeHRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCSB5Re/I4r3LakaI9eaj/kXfTlaFm5WdRsVCRAgQIBAPQVSBstyAsu8LcLrqaovAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB9QIrr7os+m746frX1RzMevdF0fbM3aupqg4BAgQIEKi7QMpemUq+ksayHGSpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKinQNfvbojuL36ipi7bDjsy5hx4aE1tVCZAgAABAvUUSNkrU8lnB/XsXF8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNLdD70P2x+ryTI4rVO7TsuUcsOOn91TdQkwABAgQITJBAJXllsVjDf8UmaCK6JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmDkCxa41seLMo6O0urfqReW2XBgLz/t8OV1YS9VtVCRAgAABAhMlkJJXymA5Ubr6JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0pUApOi88NYoPPFz16nPt+Zh/8RWRn7+w6jYqEiBAgACBiRYQYDnRwvonQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSRwIorPxl9v/pFTSue/d4PR9tOu9TURmUCBAgQIDCRAml38PxEDqBvArUIpD3rFQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGD6CnT9+vpY++XLalpA2xFvjtkHHFxTG5UJECBAgMBECqRYtnw+L8ByIpH1TYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoFoHev9wbq88/NaKG/Eotez8vFpxwRrMQWScBAgQITBOBXC5XmWkhRVpmL6bJ3E1zhgr8+c9/jpaWlhm6OssiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIzWKB7dbR/4C3R0tVX9SL7N5sfa449O1Y+8Jeq26hIgMDMF0jxbIVCYeYv1AobXqCyRbjgyoa/TyZIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhggXIw1Kc/EC2PLqt6jqX2fKx954cjZs+ruo2KBAgQIEBgMgXSFuFCfSdT3FgbFNhxxx1lU92gkIsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDxBFZ86SOx9rbbqp9YedfVue/7WGy5zwHVt1GTAIGmEliyZElTrddiG0+gkr2yHGCZTylVFQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1Cqz55Y9j7Vf+o6Zm7UcdF7P3f1VNbVQmQIAAAQKTKZDtDJ7PDiZzcGMRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMb4He+++ONRe+I6KG3F4tL/y7mP/Wd03vhZs9AQIECMx4gRRXmZJXymA542+1BRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6itQXL0iOs88Okrd/VV3nN92i1h4zqcjcvmq26hIgAABAgSmSiAFWcpgOVX6xiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITEeBUjGWn/u2KD38RNWzz80qxPyLr4z87HlVt1GRAAECBAhMlUCxWKwMXclgmVJZKgSmWsB29VN9B4xPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTGFuj8/Iei/6abxq6Y1chFzDn7k9H61KdnZzwTIECAAIGGFsjn12VbLghqa+j7ZHIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYYRWPOzH0TP175U03zajz4pZu3zipraqEyAAAECBKZSIGWwrGwRng6ydJZTOSFjEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINK5Az713xpoPvqumCRb23S/mv/kdNbVRmQABAgQITLVACq5Mj0KWynKqJ2R8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBxhQorlgeK846Jkpri1VPMP/UrWPB+z5Vrl/eI1whQIAAAQLTSCDbGTxfKpWm0bRNlQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYFIFiv2x/NwTovTIsqqHzc1pjfkXXxH5WXOqbqMiAQIECBBoFIEsrjKfRVo2ysTMgwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHEEOj97YfT//g/VTygfMecDl0Xrtk+rvo2aBAgQIECggQSyuMp8sViM9FAIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIDBRY/ZNroudbXx14aszjjmNPi1kvfOmY9VQgQIAAAQKNLJCyWBby+fKvDSgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBgj03L0ouj58xoAzYx8W/v6lMe/It49dUQ0CBAgQINDgAimLpejKBr9JpkeAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmGyB/s6lseJ9b45ST/W7ouaftm0sPPMTkz1V4xEgQIAAgQkTqARY2iJ8wnx1TIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYXgL9/dH5/uOi9Fhn1fPOzW2LBRdfGbmOWVW3UZEAAQIECDSqQNoePJV8OrBNeKPeJvMiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEyuQOdl50b/H2+vftByeq+55342Ck95avVt1CRAgAABAg0skLYHTyWfHTTwXE2NAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgEgRW//hb0fPdb9Q0UscJ/yc69t6vpjYqEyBAgACB6SBgi/DpcJfMkQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwwQJr77o1uj56dk2jFA54Rcx744k1tVGZAAECBAg0ukDaGbxYLEYhTdQW4Y1+u8yPAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDBxAv3LHo+VZ70lSr2lqgfJP32HWHjGpVXXV5EAAQIECEwXgWxn8HyKskzRlgoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDzCZT6+6Lz/W+N0tJVVS8+N78jFlz45ci1dVTdRkUCBAgQIDCdBFKQZUH2yul0y8yVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBfgc6Pnx39i+6svtOWXMw973NR2Grb6tuoSYAAAQIEpplASlwpg+U0u2mmS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCol8CqH1wVvT+4uqbuZp303ujY88U1tVGZAAECBAhMN4GUwbKcwDJvi/DpdufMlwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwkQJrF90S3Z88r6ZeWg98Vcx9w1traqMyAQIECBCYbgIpe2Uq+Uoay3KQpUKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAcAv1PPBorz35rlHrXBZBUs+r8M3eKBe++pJqq6hAgQIAAgWktkLJXppLPDqb1akyeAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgKoFSX08sf9+bo7R8TVX1U6Xcwtmx4KIvR661veo2KhIgQIAAgeksUEleWSwWp/MazJ0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAGgc5Lz4ziXX+qukWukIt5538hCps/peo2KhIgQIAAgekukJJXymA53e+i+RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhRYdc0V0ft/r62y9rpqHaeeHe27P7+mNioTIECAAIGZICDAcibcRWsgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECIwh0P3H30b3ZReNUWvw5dZ/fF3Mfe2/DD7pFQECBAgQaAKBtDt4vgnWaYkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaYW6Hvs4Vj1/uOj1Feq2iH/7GfGgtNrC8isunMVCRAgQIBAAwuUSqXI5/MCLBv4HpkaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCjBUq9a6PzrDdHqbOr6r5ym8yJhRd+KXKF1qrbqEiAAAECBGaKQC6XqywlnyItFQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZkp0Pnhd0fxT/dVvbhcay7mXXh5tGy6ZdVtVCRAgAABAjNNoLJFeBZpOdMWZz0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWYXWPntL0bv//uvmhg6Tjsv2nfZq6Y2KhMgQIAAgZkmYIvwmXZHrYcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8DeB7t/fGN2f+VBNHq2vfUPMffURNbVRmQABAgQIzDSBlL0yFVuEz7Q7az0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA0wv0PbIkVp1zYsS6+JCqPFp22yUW/Ov5VdVViQABAgQIzGSBbGfwfHYwkxdrbQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBZhEore2KzjOPidLKtVUvObfZvFhw/hcj11Kouo2KBAgQIEBgpgqkuMpSqSSD5Uy9wdZFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINKfA8g++M4r3/aXqxeda8zH/oi9HyyabV91GRQIECBAgMNMFUpClDJYz/S5bHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINA0Aiu//pno+9n1Na131rsuiLZnPaemNioTIECAAIGZLFAsFivLy6c0lumhECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITF+B7pt+Ed2f/1hNC2g79IiY88rDamqjMgECBAgQmOkC+Xy+ssRCSmOpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITF+BvocfiFXnnhSxLuFWVQtp2eO5seDkc6qqqxIBAgQIEGgmgZTBsrJFeDrI0lk2E4C1EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRmgkCxa010nnl0lFb3VL2c3BYLYsF5n49oaam6jYoECBAgQKBZBFJwZXoUslSWzbJw6yRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIzByBUnRefFoU73+o6iXl2vIx/6IromXBJlW3UZEAAQIECDSTQLYzeL5UKjXTuq2VAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDBjBFZ89d+j7xc31LSe2e+9JNqesWtNbVQmQIAAAQLNJJDFVeazSMtmWry1EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSmu0DXb/471n7pUzUto+3wo2P2y15bUxuVCRAgQIBAswlkcZX5YrEY6aEQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMD4HeJffF6n87NaKGkI+W5+0VC048c3os0CwJECBAgMAUC6QsloV8Pj/F0zA8AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAtQLFNatixZlHR2lNb7VNIrfVprHw3P+IyLdU3UZFAgQIECDQzAIpi6XoymZ+B1g7AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMM0EStF5wSlRXPJI1fPOdbTE/IuviPy8BVW3UZEAAQIECBAo/15CQrBFuLcCAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDxBVZ86dLo+/WNNU109hkfibYdd66pjcoECBAgQKCZBdL24KkU0oFtwpv5rWDt1Qr09K/7oammfq5cqbUl/akQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6CKz55XWx9iufq6mz9iPfGrNf8uqa2qhMgAABAgSaXSBtD55KITtodhDrJzCSwKOr+uLTNy6NWx/qjtU9xZGqjHrun/deGEeXHwoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIENhYgd4H/hRrLnpHRPW5gaLlBS+I+ce9Z2OH1p4AAQIECDStQCGtPG0RLotl074HLHwUgcdX98dJ33koVq6tLbAy6+6rNy+vHAqyzEQ8EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMB4BIqrV8SKM4+OUldf1c3z22weC8/5TEQuX3UbFQkQIECAAIF1Amln8PSoBFgKrvS2IDBc4Iu/XTru4MqsN0GWmYRnAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBiXQKkYy//t5Cg+9HjVzXOzCjH/4isjP2d+1W1UJECAAAECBJ4UyHYGz6fslSnSUiFAYLDA7Q+vHXxinK9SkOWVf8tmOc4uNCNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJhVY8YVLov+3v61+9bmI2Wd9PFq3f0b1bdQkQIAAAQIEhgmkIMuC7JXDXJwgUBFY2tVfN4kUZJllsxxvpwtntcSe23TEiS/eNDab3TLebrQjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWkisOaGH8baqy6vabbtR78tZu/3ypraqEyAAAECBAgMF0iJK2WwHO7iDIGGFFheDvj82T2r46RvPxRL19Qv+LMhF2tSBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaHKBnvvuijUffFdNCoUX7xPz33x6TW1UJkCAAAECBEYWSBksywks87YIH9nHWQINKbC8uz+++NtlDTk3kyJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBjRcoruyMFWceE6Xy98PVlvx2W8WCs/+9XL28R7hCgAABAgQIbJRAyl6ZSr6SxrIcZKkQIDB9BP74cPf0mayZEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgED1AsX+WH7uCVF6ZGnVbXKzW2P+xVdGfvbcqtuoSIAAAQIECIwukLJXppLPDkav6goBAo0m8IQtwhvtlpgPAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6iLQ+bmLo/+W31ffVzmn1pwPfCpat9ux+jZqEiBAgAABAmMKVJJXFovFMSuqQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMLECa67/XvR888qaBml/y6kx6+9eVlMblQkQIECAAIGxBVLyShksx3ZSgwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwoQI9f1ocay55b01jFPZ/Scz/51NqaqMyAQIECBAgUL2AAMvqrdQkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRdoL9zWaw465go9VS/C2l+h21iwZmfKM8lV/f56JAAAQIECBCISLuD50EQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMkUB/f3Sec3yUHuusegK5OW2x4OIrIz9rdtVtVCRAgAABAgSqFyiVSpHP5wVYVk+mJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgvgKdnz4v+m+9rfpOy6m05p77mShsvX31bdQkQIAAAQIEahLI5dZliM6nSEuFAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgcgVWX/ft6Ln66zUN2nH86dHx/P1raqMyAQIECBAgULtAZYvwLNKy9uZaECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjEdg7V23RddHzq6paeGlL495R5xUUxuVCRAgQIAAgfEJ2CJ8fG5aESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTGLdC/7PFY+b63RKm3WHUf+R2fGgvPuLTq+ioSIECAAAEC4xdI2StTsUX4+A21JECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUJFDq74vO9x8bpSdWVt0uN689Flx8ReTaZ1XdRkUCBAgQIEBg/ALZzuD57GD8XWlJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJjeAn1/XRK9D9wz4Yvo/OT7o3/RHdWPk4+Ye97norDVdtW3UZMAAQIECBDYKIEUV1kqlWSw3ChFjQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEZIdB908+j820HR9eNP5mw9az64dej9/vfqan/jpPeGx177VNTG5UJECBAgACBjRdIQZYyWG68ox4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBaS7Qt+imKHX1xaqzT44VV3y8vJpSXVe09o7fR/cnzqmpz9YD/yHmHXZsTW1UJkCAAAECBDZeoFgsVjrJpzSW6aEQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJpVoH/RzeuWXg6hWPvlT8ey9701il2r68LRv/TRWFnur9RbfXxG/hk7xoJ3f7gu4+uEAAECBAgQqE0gn89XGlQyWKZUlgoBAgQygSeeeCIeeeSR7OWw57GuD2vgBAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBpYoLhmVRQffHjQDPtu/FUsO+FV0fvgnwedr/VFqa83lr/vLVFaVn2wZm7BrFhw0Zcj19pe63DqEyBAgAABAnUQSBksU+LKfDrI0lnWoV9dECAwzQW+//3vx4te9KLYd99944orrhi2mrGuD2vgBAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBpcoOfOWyPW7QQ6aKbFJX+NzhNeHV2//emg87W86PzYWVG88+6qm+QKuZh7/uejsMXWVbdRkQABAgQIEKivQEpamR7lTJb59Ed9e9cbAQLTVuAnP/lJ9Pf3VwKvf/zjHw9bx1jXhzVwggABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAg0u0JNtDz7CPEtremPVmSfGyqsuG+Hqhk+t+v5XovdH39twpSFXO95+VnQ854VDznpJgAABAgQITKZAtit4PqWxVAgQIJAJvOY1r4m5c+fG7Nmz49BDD81Or38e6/r6ig4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwTQT6Ft204ZmWs1t2f/4TseycE6LU3bXhun+7uvb2m6L7UxdUVTer1Pqqg2PuIcdkLz0TIECAAAECUySQxVXmygciLKfoJjTLsOktlr3NsuO0LX3Kktjb2xs9PT3R3d0d2223XUORvPqL90dvf2P+eLS25OKHx+4wYV7pfqR7lIIsRypjXR+pjXMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoVIHHD9kjSp3VBU7mn7ZtLLj4yig85amjLqfv8b/G8uP/IUrL14xaZ+iF/M7PiE3//ZrIFdqGXvKaAAECTSewZMmS6OjoiLa2tmhtbY2WlpbKds3Zts3Zc4LJMg02HZIFT4pAPgVRpYdCgACBTCD9B2q04MpUZ6zrWT+eCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0OgCfX/9S9XBlWktxT8/WA6efFV03/zLEZdW6l0bnWe9uabgytzC2bHwwi8LrhxR1EkCBAgQIDA1AimZYL5c0h9TMwOjEmhggU1ntTTs7Bp5bg2LZmIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAYQaBn0S0jnN3wqdKqnlj5nrfGym98bljFzo+8N4p33zvs/Ggncq25mHfB5dGy2ZajVXGeAAECBAgQmAKBlB1VZOUUwBtyegjsvnV7w060kefWsGgmRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgREEehffPMLZKk6VNwvt/uxHY9m/nRylteu2F1/1ncuj97ofVdH4ySod/3pOtO/2vCdPOCJAgAABAgQaRqASYGmL8Ia5HybSQALHvnDTmNfeeDHIaU7H/d2mDSRlKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJi+An23/26jJt/305/E0pMPjjX/fW10feZDNfXV+ppDY+5rjqypjcoECBAgQIDAxAuk7cFTyZWDK0splaVCYKIE0pste8Nlxymot7+/P3p7e6Onpye6u7tju+22m6gpjLvfR1f1xadvXBq3PtQdq3vKv340hWVOWz722KYjTt5n09hybmEKZ2JoAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIzAyBUl9vPPGPu0epd10QxUatKuXvqeFr5Zbdnh2bfOLqyLX4/nej3DUmQGBGCixZsiQ6Ojqira0tWltbo6WlJVKM29BHWrzYtxn5FmiYRRW8wRrmXphIAwqkQMZzX7llZWY9/XX4QL0Ra2xrEQi9EXyaEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYJhAz9231ye4MvVcQ3BlbtO5seD8ywVXDrsjThAgQIAAgcYSqPwaRMommM833lbIjUVlNs0uIMCx2d8B1k+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwEwT6F38+0lfUq41F/Mu+lK0bLL5pI9tQAIECBAgQKA6gWyn5kqApeDK6tDUIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGaOQO/tv5v0xcz6PxdE+857TPq4BiRAgAABAgSqF8h2Bs+n7JUp2lIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSTQP8dt0zqctte/8aYc9A/TeqYBiNAgAABAgTGJ5CCLAuyV44PTysCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg+gr0dy6N0iPLJm0BLc/ZPRa8/dxJG89ABAgQIECAwMYJpMSVMlhunKHWBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwDQU6Fn8+0mddW6rbco7jPZP6pgGI0CAAAECBMYvkDJYlhNY5m0RPn5DLQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFpKNC76KZJnXXfT66Lpae+PvqfeHRSxzUYAQIECBAgULtAyl6ZSr6SxrIcZKkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJpFoG/xzZO+1OKdd8ey4w+KtYtvmfSxDUiAAAECBAhUL5CyV6aSzw6qb6omAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAaC5SKUbzz9ilZQGnZ6lj5jjfFqh9+bUrGNygBAgQIECBQnUAleWWxWKyutloECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRkg0PvAPVHq6puylZR6S9H1kXNi+aVnRKl/6uYxZQAGJkCAAAEC00AgJa+UwXIa3ChTJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOon0LOoMbbo7r326lh22huif9nj9VucnggQIECAAIG6CQiwrBuljggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHpINC7+OaGmWb/ojti2XGvjLV33dYwczIRAgQIECBAICLtDp4HQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoJoH+Rb9rqOWWlq6Klaf+U6z+8bcaal4mQ4AAAQIEmlWgVCpFPp8XYNmsbwDrJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzShQ7FoTxQcebLill3pLseaD74vOT30gor+/4eZnQgQIECBAoJkEcrlcZbn5FGmpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAheBCYwAAEAASURBVASaQaDnrlvLe3427kp7rv56LH3n4dHfubRxJ2lmBAgQIECgCQQqW4RnkZZNsF5LJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJpcoHfxLQ0v0H/bH2PZ8QdFz92LGn6uJkiAAAECBGaqgC3CZ+qdtS4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgRIG+RTeNeL7RTpYe64wVp7whVv/kmkabmvkQIECAAIEZL5CyV6Zii/AZf6stkAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMgE+hf/Pjts+OdSTzHWXPieWPX9rzT8XE2QAAECBAjMJIFsZ/BCdjCTFmctBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGhAn2PPBil5WuGnm7Y1/ldnhWzjjw1Zu/3yoado4kRIECAAIGZKJDiKkulUhTSH4IsZ+IttiYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgoEDPHdMge2UuovDifWN2ObCyfbfnDZy+YwIECBAgQGASBVJcpQyWkwhuKAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDqBHoX3Tx1g48xcq61HMRx4GtjzptOjtbtdhyjtssECBAgQIDARAoUi8XI5/PrMlimgWSxnEhufRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJTLdC3+KapnsKw8XNz2qLtdf8ccw47Llo22XzYdScIECBAgACByRdIwZWpyGA5+fZGJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCZZoNTfF6W7757kUUcfLrfFgmg//G0x9zVHRq5j1ugVXSFAgAABAgQmXSBlsKxsEZ4OUskiLid9JgYkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEywQO89d0Spd12MxAQPtcHu8zttHx1vOiXmHHBwREvLBuu6SIAAAQIECEyNQAqurARYCqycmhtgVAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDyBHoW3zJ5g40wUstee8bsI0+NjufvP8JVpwgQIECAAIFGEkjBlakUSqVSJdKykSZnLgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBegr0Lrqpnt1V11dLLgp///KYc9Qp0fb0XatroxYBAgSaXCDFsykEplogi6ssZJGWUz0h4xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEJkqgfxIDLHMdLdH66sNjzuEnRmHLbSZqSfolQIAAAQIEJkggi6ssFIvFyhC2Cp8gad0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECUypQXLE8Sg8/MeFzyC2cHW1vODbmHnJ05OcumPDxDECAAAECBAhMnEDKYlkQWDlxwHomQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEpl5g7Z2/n9BJ5LfdMtrf9PaYc9AbIldom9CxdE6AAAECBAhMjkDKYlmYnKGMQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYGoHeRbdMyMD5XXeOWW86NWbvd2C5/9yEjKFTAgQIECBAYOoEKgGWaZtwmSyn7iYYmQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJg4gb5Fv6tf5+U4ysKL943ZR54a7bs9r3796okAAQIECBBoGIG0PXglg2U6EFzZMPfFRAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIG6CpSieOcfN7rHXGt5m9ADXxtzjjgpWp+600b3pwMCBAgQIECgcQVScGUqheygcadqZs0i0NXV1SxLtU4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmCSBviX3RWl17/hHm9Ma+VcfGR2HHBP5hZtFX7mnPt9vj99TSwIECIwhkBIGKgQaRcAW4Y1yJ8yDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqLtA311/GF+fm8+PlkOPjY5/ODxyHbPH14dWBAgQIECAwLQUSIG+6VEJsLRF+LS8hyZNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIjCFQXHzLGDWGXH7adtF6+EnRvv+rIlpahlz0kgABAgQIEGgGgWxn8EKxWIz0IjvRDIu3RgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA5BIp3/r6qheb22KMcWPm2aNtrn6rqq0SAAAECBAjMbIEUU1mQvXJm32SrI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzSpQWtsd8cCS0ZffUk5Itd8B0X74iVHY8dmj13OFAAECBAgQaDqByhbhMlg23X23YAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0BQCfX9aFNFfGr7WjpbIv/LQaH/DsdGyxdbDrztDgAABAgQINL3A+gyWWZBl04sAIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGaMQN8dQ7YHXzArWg45JtpffVTk586fMeu0EAIECBAgQKC+Ail7ZSXAMh3YJry+uHojQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEpl6geOct6yaxzRZROOyEaH/FIZErtE39xMyAAAECBAgQaGiBFFyZSiE7aOjZmhwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoFaBfEu0vv+j0f7il5dbrguUqLUL9QkQIECAAIHmFEjJKwtpe3AZLJvzDWDVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgJgvMOetTM3l51kaAAAECBAhMoEBKXpmXwXIChXVNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITEsBAZbT8raZNAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDBRApXdwSeqc/0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKabQKlUinw+H/npNnHzJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMlEAul6t0nU+RlgoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMA6gcoW4VmkJRQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGwRbg3AQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgoEDKXpmKLcIHqjgmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEmlog2xk8nx00tYbFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKAimuslQqyWDp3UCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCiQgixlsBwo4pgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoaoFisVhZfz6lsUwPhQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ7AL5fL5CUEhpLBUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGIlMGyskV4OsjSWYIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSzQAquTI9ClsqymTGsnQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJhYgVKpVEkANdJzGjmdTyXbibOSNaq8PedIz5WK/iBAgAABAgQITJBA9nmkkD6gZC8maCzdEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAk0okHbU7O/vrwRWVrvDZhZomQViDmRLSaSyR0tLS+V44HXHBAgQIECAAIF6CGRxlQXBlfXg1AcBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQBFJAQgqqzB5ZwGQ9dAYGaaZ4hxRkmT3EP9RDWB8ECBAgQIBAEsg+VxTSh49UbBVeYfAHAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMA6BLFtlX19fJWPlOLqoqUkK3ExjpUeKeSgUCpVgS/EPNTGqTIAAAQIECIwikD5rFHywGEXHaQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBMgYGBjlmSpzEb1blCGrenp2d9oGUKtswyT9V5KN0RIECAAAECTSKQPksUmmStlkmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUWSDLIJm2A2+EkgVapvmkIMv0UAgQIECAAAEC4xWofJJIHzBkshwvoXYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKC5BFKcQRZcmTJYNlpJAZZpjumRgizFRDTaHTIfAgQIECDQ2ALp800lg2U68EGisW+W2REgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUYRSMGLvb290ShZK0dzSfEQaZ4pyLK1tTVaWlpGq+o8AQIECBAgQGCQQAquTKWQHQy66gUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYIhAylqZBS0OudSwL1MgaAq2TEGWtgxv2NtkYgQIECBAoCEF8mlW6bc1FAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAaAIpsLKnp2daxhikuIg097QGhQABAgQIECAwlkD65Yz0+aGQKtoifCwu1wkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQPMKpMDE9EjBBtO1ZFuGp/mnbJYKAQIECBAgQGA0gWxn8HyKspzOH4BGW6DzBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwMYLzITgykwhC7KUyTIT8UyAAAECBAiMJpCCLMvJK/ORRVuOVtF5AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPkE+vr6pn3myqF3LQuyTGtTCBAgQIAAAQKjCaTPDDJYjqbjPAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaGKB/v7+GRdcmd3OLMgyrVEhQIAAAQIECIwksD6DZfrgoBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEkkCxWKwEV6bnmVqaYY0z9d5ZFwECBAgQmGiBLKYyX0ljWd4mXCFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJIG0fXYzZHdMa7RVuPc8AQIECBAgMFQgZa9MpZAdDK3gNQECwwWeWFOMK29ZGYsf7Y1l3cXIIpWH1xz7zD/tPieO2GPu2BXVIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwCQKpIDDZgo6TGvNlxNTFQqFSVTe8FCPr+mPm5b0xF2P98TS8vfUy7rWPVKrhbPysWn5sUn58ewt2uL527XF5rNbNtyhqwQIECBAgEDNAik2rJBSXqcPCgoBAhsWeHR1f5z+g6Wxqqc+KfC/dtvqKJWHfJMgyw3Du0qAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwKQJpECCFHC4MclmJm2ydRooW3NLS0tMZZKqlWuL8cM718T//GVt3Lesb9TVrSjXe2D5uss/vbc74jcRO25SiBc9tT1e/ezZMa9dDMioeC4QIECAAIEaBNLnAhksawBTtbkFvvC7lXULrswkv14OskxFkGUm4pkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgakUSMGVk7E1+JFXr6ppmVcdOrG7A2Zbhbe2ttY0r3pU7u4rxTWL15Qfq6OrN6Xpqb2kgMz0uOaONXHIrnPKj9nRUVi3tWntvWlBgAABAgQIZAL5qfzti2wSnglMB4HbH+nZ6GkWyr8o9NpdZg/qJwVZfu3W2v7xMKgDLwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAHgbQDZjNtDT6ULK09GUxm+dX93XH81Y9XvjMeb3DlwPmmPtL3z6nP1LdCgAABAgQIjF8gfS4ojL+5lgSaR6C3vxSre8b3m0JDlY59/rzIl39RKP0GUlZksswkPBMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMFUCKYvjZAcYTtVaRxo3rT0Z5PMTv8V2+vb5qj+sim/+cd2uhyPNZ2POpW3EL/l5Zxz+nL44cs+5MZNzWT68sj/SjpR3PLYuadIuW7TFcS+YF1vPa9kYQm0JECBAoMkFSqVS5TOBAMsmfyNY/tQIvGXveZUPsN8VZDk1N8CoBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgMEkhBBM2cvTLDSAaFQiEmcjfQtCX4pb/sjN/8ZW027IQ9pwDO+5f3xTv3WzAjtwxPwZXv+METkUyzctODayPtUPnx12wmyDJD8UyAAAECNQtknwXy6UOSQoDA5Au8uRxkeehucwYNbLvwJzkOPfTQ2HXXXSuP5cuXP3nBEQECDSfQ29tblznV63/a1KufuiyqATvh04A3pYYp/fd///f6/z5eeOGFNbScuVWb+bfpZ+5dre/KfK6sr6feCBAgQIAAAQIECBAgQIAAgZkr0OzZK7M7m2WxzF7X+zlFaExWcGU29xTImcacidEhKXPlwODKbM3pXLqmzCyBen0vObNUrIYAgYkUSJ8LClmk5UQOpG8CBEYWOOZ5cyvbhX/79ifTvtsufGSriTz77ne/Ox599NFRh0i/obbNNtvEDjvsEE972tNi//33j/b29lHrj3YhBTXdcMMNcfXVV8drX/vaOOigg0arusHztfZz4403xuc+97lKn/vss0+ceOKJG+x/4MV3vOMdsWzZssqpj370o7H55psPvBxD7S6++OKK1aBKVby444474oMf/OD6mk9/+tPjAx/4wPrX2cHA8S655JLYaqutsksbfE7ul19+eaVOun/HHXfcBuu7uGGBRx55JL7whS9Eum/33ntvpCDoLbbYovLzkd5jxxxzTHR0dGy4k/LV7u7uuPLKK+O2226LO++8Mx5++OHK+2fnnXeOPffcs9JPa2vrpPUzdKD77rsvfv7zn0d6Tn9HbL/99nHGGWcMrTbm67/85S/xve99L/7whz9U3MZsMEqF8fZTL+eh0xrvfIb24zWBjRH42c9+Fueee27MmzcvLr300njmM5+5Md1pS4AAAQIECBAgQIAAAQIECBAgQKCpBVKApbJOIFmk7wgnoqRtwScjc+XQuacx09hHlbcLn0kl2xZ8pDVt6NpI9Z0bv0D6HuxjH/vYuDu47LLLYu7c4e/Nen0vWevEurq64je/+U387ne/q3xP+Pjjj8dZZ5017u8hBn5nn+bywhe+MN7+9rfXOq1K/fSdfPpuNSvvete74jnPeU72svI8dLxBF8sv5syZE1tvvXXlu9nddtstXvSiFw2t4jUBAn8TyOfzMTGfCBATIFC1wL/sNbecXj7iW+XU7FkRZJlJTM7zH//4x3jggQeqHiwFW77zne+Mf/zHf6yqTQpCu+aaa+Laa6+tBKKlRgcccEBVbQdWGm8/S5curXzwS3095SlPGdjlmMe33nprpA+tqfT09AyrP9TuW9/6Vpx22mnD6o114qqrrlo/x1R3pLHS+YHjrV1b/ZYJ6QNv+vCbynbbbVd59kftAuk3M77yla/EJz/5yUj/qBhYHnvssUiP5PzNb34zLrjggnjxi188sMqg4/S+OuWUU2LRokWDzj/44IORHilL3/XXXx+f+MQnYssttxxUZ+CLevUzsM//+Z//iRTAO/AfJul6Lf+wSD7/9V//Fd/97nfj5ptvjpQxvNqA4IFz2dh+6u2zsfMZuDbHBOohkP6+SQHQ6ZH+O5v++6wQIECAAAECBAgQIECAAAECBAgQIFC7wERnbax9RlPbIsvmmYIq6ll+dX93pC27p6qksXfcpBD77DB2ooypmqNxp6dAZ2fn+u9jx7OCoQHe9fxespb5rF69Oj796U/H1772tUqymIFtV64cf0bUgd/Zpz5vv/32SrKZkYJKB4459DglrPnP//zPSD5ZSfZDy9Dxhl4f+vq5z31upORLtXwfOrQPrwnMRIH0s1YJsExf+MtiORNvsTVNJ4F/Lv+WUL4cZPmN2578MC3Icmru4N577x2zZ88eNHj6C3PJkiWRsrWl44ceeijSb4Gk3/pIQWQjlfSB5Qc/+EElW+X//u//jlSlqnP16qeqwepQKWXnTEFzLS0tVfeWPqT+6Ec/qrq+ilMnkIIrP/ShD1UmkD47HHjggbHHHntUslemLI8pS2P6+Ugf7E8//fRKYPFIQb3pHx+HH354JSAzdfasZz0rXvKSl8ROO+0Uf/rTnyJlpLvnnnsiBfi+8Y1vrLw/Zs2aNWzh9eon6zh9Jkq/8fXVr341O1V5bmtrq8xtr732GnR+6IvU/re//W1l3dddd92wINSh9Ud7Xa9+6uVTr/mMtl7nCWyMwL777lvJNJt+k3xDQd0bM4a2BAgQIECAAAECBAgQIECAAAECBJpBIAU3pf8frKwTSBbJpJ4BlmnL6s/+ZvwBWoUhsZ59T8ZX1XTbPlOew/O2bY+OQvkL6hlQdtmiLW56cOTELOmaMjkC6TvB17/+9VUPlpLpZN8RpwyKCxYsGNS2Xt9LDup0jBd33313ZSfElFRmYEnJYNIOjEPnOLBOrccpqUlKHPGmN72ppqbf/va3BwVXVtM47UT47Gc/e1DV9B19StSSvttNf9+lHQfTTpgpsHTXXXcdVNcLAs0skMVU2iK8md8F1t5QAkfuUQ6yLM/oa4Isp/S+XHjhhZVtgEeaRPbbKmlL4/QPqhRMmLZDHprJ8pe//GWcdNJJlTpZP+kfX+kDV7bddnZ+Q8/16mdDY9T7WvqwmYLjXv7yl1fddfrgmD5AKo0tkLYC//jHP16ZZAp2/OxnPxsveMELBk362GOPrQTYpgyQK1asiPPOOy8+85nPDKqTXqSsjtk/TP7+7/++kqWyvb19fb2TTz450iOl3U8f7FPg5hFHHLH+enZQr36y/lLAdPpHQ1YOPvjgOP7442PHHXesKmj46KOPrmSrzNqn58033zxSBtVaSr36qZdPveZTi4G6BKoVOOqoo+IVr3hF5Zcj0jbhCgECBAgQIECAAAECBAgQIECAAAEC4xMYmA1tfD3MvFb1Nrlm8ZpYsXacUZFl3q8fsWW0tqwLiuztL8VhVz06LvQ0h++V5/LG584ZV/tGa3TcC+bF7Y/0RApgHVhSAGm6pkyOwM477xzpu/Zqy3e+8531AZZvectbBjWr5/eSgzrewIs05jHHHLN+R8oUVPme97ynEg+wcOHCDbQc/6VvfOMbNQVYphiF5FZrSVkpsyQ6Q9umdX/wgx+MFJvQ29tb2SksfX/f2to6tKrXBJpSIAVYpiDkvN9Cacr7b9ENKnBEOcgyBVoOLCmT5dduXTXwlOMpEpjz/9m7E3hZzrJOwHXPPTcJWQhJIBiWYVEMgbAHUEAEZmQfNhmVHTUIyKAsOsiOosAg4oYCgiABCYgDYRdh2ARkCSAYCIFBCBA2SUKAhCR3m/Ovm69Tt273Od2nq5fT/Xy/X5+qruWrqqeruutUvfV+hx1W/e7v/m71pCc9qbcGCSDbtWtX7316kjUuJzcpP/VTP1U98YlPrJs6Tia+UkqUe3nfr9tVPf3q7npY8wQrzbWOUsr0yXo5SubLUZZh2vEF/u7v/q4qzbI/9rGPPSC4MktI9tdnP/vZVTI+piSbY/viQ8470iR8SvabnMw3gyszPAGcf/zHf9zbH5JRsn2+0lU9WV5KmgMvwZUJivzbv/3bet1yDA+7X5YA6qz/Pe5xj+pv/uZvqve85z37FjDC3y7q6dKni/UZYfNNusQCyWybY+c+97nPSApXvepVK8GVI5GZmAABAgQIECBAgAABAgQIECBAgMB+Armm3L6ev98ES/omJu37E5ul+OFaUONpn7+8NcPN1tPVfG9aW5es0yKU447YXv3ZPY+pTlrLynnYQdvqV/ozLOOU+RPIcfXKV76yXrFkvrzLXe6y30p2dV9yv0o3ePOnf/qnveDK+93vftXb3/72OtHSJIIry731tISZzJHDlg984APVd7+7L7C6fX912Dra06WFwSTZKa0Sfu1rX6tbHGxP5z2BZRZIfM/KMEE+y4xk2wlMWyBPCj1orcnwZkmQ5c49+z9x0xw/6f5kmks67IsuumjSi9qw/pxspanuc889d8NpJzVBsmUlBXhKgiDzVEezJMgjGd+Snvstb3lLlax+Cf4YtXRVz6jL3cz0eYKnpAr/8Ic/XJ1zzjlDVfPv//7vdWBbJr7DHe5QpYnXRShdHDPJepjmsvOk0GbKxRdfXM+f5rq7uChy1lln9VYj/1QMKle/+tXrwOKMT2bSnIQ3y6c+9anesGSvHJRKP0GOt771retZc4ydeeaZzWqqruoplf7VX/1V6a2e85znbKqp4Zvf/Ob1k3n/8i//UgeI3u52txs6OLO38LWeLurp0qeL9WluXxf9CfbN8TFqdtCy7PyW5DjNP67j/LaNe5yW9ck6fPnLX+4F55fho3YT8B+XCy64YNRZx56+i++9s88+u/rKV75SpTvpUr4jN/qOzb7y1a9+tZd1dzPrlYy++VzGydb8/e9/v3bJ+oxTxj12Rln2LPfHYddzHs4rh11X0xEgQIAAAQIECBAgQIAAAQIEJimQ+whd3EuY5DrOou4uXd7+hYuqH+8c7/pelwZZl6zTopQEUj79TleqXvvLx9av9AuunN9P933ve1/vHvuDH/zgA+6ndXVfcliBLO+9731vPfnNbnaz6g/+4A+qJF+aVMl98VKSxXLY0px2lBYlN6o/SXRudatb9SZr+vcG6iGwpALl/Gi13KQTaLmke4LNnkuBX7rRYWvRz1X16k83MldO+Xw7gR5//ud/XiVYrhkQkKCnZJZKs7n9skW9853vrIOTApsU2ieffPJA4zyJkqCS1POOd7xj4HQZkeCqZLT73Oc+Vwc1Zthxxx1X/9D/3u/93sAgrUzXdcn35YknnlgHw6TurNtP//RP9xaToKq8xi1d1TPuegwzfwI2kqHzmc98Zv0P+Bve8IbqcY973IazNk8Ck7msnLhuOOMcTrDZY6a5KckU+KpXvapKVs8E06Qk6DRPDt31rnetj7v1sikmmCX2earrG9/4Ru+pymSUvOENb1gl82RSwI9acq6QoNlkZjz66KOrK17xiutWccwxx/TGt4OXEiBdSgmgLO/b3azrRz7ykXrwN7/5zV4QbwZ0VU/qSpBd2ff+x//4H5s+fvPPVheli3q69OlifbpwSR0JXn35y19enX766b39O/tk9u9kGE7G0fVKgtxyjOU3p/nblqfy0iT8ox71qHo/X6+OzR6nWfenPOUpddXPeMYzqgQY52nIPG2YQOQcZ4ccckh1wgknVL/2a79WjfKP8Rvf+Mb62E8m1pJpNsdh6slv8crKSm+Tsv0xTPmFX/iFKusyqLz61a+uM7FmfH77k5G5Wbr43mvWt5n+v/zLv6y/MzNvMug2L0q8//3vr57+9KfX1T7+8Y+v7n3ve1cJpk5m2QRyJtt0nvDM/pPMmQ94wAN6q3DaaadV//zP/1x9+tOf7gWs5gJDzNIkyFFHHdWbtl/PJz7xiep1r3tdfd7SDDS/ylWuUuV7Jg9f5Dt1vZJ99AUveEF9LlbqyDrkgYb8xib4OQHvCW7NNrzkJS8ZWN24x86gipv7dX5j8lue/TFNlHz+85/fcH/MAzzZT1PudKc7VckM3q80P8uYDPr9yGfzr//6r3UV2X+vfe1r96turs4r+66ggQQIECBAgAABAgQIECBAgACBKQuU4IEpL3ZLLC42692bGXYjPvr1S4addGrTZZ3SwqJCYNoCaUkuJdfJc828Wbq8L9msd73+3DvIcrM+z33uc/e7r7LefJsdl3ugSUT07W9/u/qnf/qn6slPfnJ1+OHrH4u5V5rYjZQb3/jG9f3jzS6/33zNTJ2bTXDSr17DCGx1gXKfdVVg5Vb/KK3/VhLYtZZl/d6v/s7cr3KCuxKkUJq5bq5wfkwTmJEgrpz4lKyFZZoEdpTskhtlBUtdCR649NJLy+x9u29605uqv/iLv+jdpC8TJTPfm9/85jr44a//+q87P4koy+nXbZ5gJDPVspd8hve85z2r5z//+dWFF15Y5TNLoMV6/3BmugTkplzjGteog2Vz4roVyzjHTNneBOo++tGP7qV1L8MTNJkAwLwSNJJA42QMbZdkU33IQx5ST9cel88nQUIJYrnvfe9bZ1lsT7Pe+5wrfOhDH1pvkt64XGj4t3/7t/p9Pv//8l/+S29cepon5AnYXq8kcK6U/IPRLF3VkzrTlHfZ99YLCm8uf977u/SZh23N71H+oS3NyzfX6bzzzqsDLxPQlgDG+9///s3Rvf405fDUpz61729O9q+Xvexl1dve9rYqAXvt37ZSyTjHaYKNy+/jJz/5yeqlL31pHYBW6k43WRVzrP7Wb/1W9ZjHPKb+TljvXD3HdgLSmsHqpb4sK98XyWaabSrl53/+56v//b//d/0225uHFBKE3a/ErKzzbW972/0m6eJ7b78KN/kmvyVlHdsB3U3zBPsls3T5fiqLy3lLjPLK99cDH/jA2i2B6u2S85qcdyQoNgGY/b6L812S86M8pNLvPCrBkDlnSQBiAjAHZbjOPpl9IPtcs2QdEmD88Ic/vA70THbN7DclKL85bfq7OHbadTbfN42zbQnIzna1y6D9MUHRMctx/H//7/+tnvWsZ1X99vkEu5bP+d3vfnffAMt8fvls8nuYoOlBwZXzeF7Z9vKeAAECBAgQIECAAAECBAgQIDBtgXKNvOvlvuWsS6vXfW79+4CjLPOBb2wkp2nN+Cs3PKi61/H9r3W2Jh3pbRc237tod/WV83eNtNx+E9//tfuaBu43bjPDsk5ZtysfqhntzfiZZ3MCuQ+SV0ru6bSTOnV5X3KYNUxLerk+nZIkC+17m8PUMeo0uXafwNLcv0m8RFrFzP2J9UruceQ6eEoSH+X+QJelmbVyGgZdrru6CExSIMddvpdWywFYIi4nuVB1EyAw/wK5aV+CK5NJK5mgfu7nfq7O0Jgf6Ve84hX1zf40P5oAkPyQN4MNJ7GFCRDJ0yLJbHSb29ymvml+xhln1EF83/nOd+rMXwkcy833QUEiXa9Xs1nw0lx418vYSvUlyCef0b3uda/q1FNPrbNpJbX7f/tv/23gZuREsWSQS8arfoEoA2eeoxFdHDNpDjfp74tHslXe/e53r/f1knEvJ7UJIEuWvmSga5cnPOEJveDKBKyU4yW/8wmOfM1rXlMHliWwJON/9Vd/tV1FJ+8TDJTglpR8fyQ7XLN897uX//M/ToBlV/Vk3fKEWEoyy13zmtess2Z+8IMfrL/r8jRYjvEE3CWD31b5h6JLnxpnxn/+8A//sBdEmMDbRz7ykXUm4QTXffSjH62PiQSZJRtjPqNmMwZZ9WS/y7FTSj7LZAm+1rWuVWdDTeBmjuUE7iczYDLwtZ8U7OI4Lcs/5ZRT6t5kIEzgYvav/MYmcCyBfrlg96IXvajeJ9tPbpY60s165rsz++4d73jH+vc6AYP5/k1wZEouCsSoZK9N0NmNbnSjer//0Y9+VO/vOVbbJcFyzWOjadrF9157eZN+X4Jzcx6R7IfXv/7169+q/BZ9/OMfrxefIN53vetd9b6QTL0PfehD688mxskOmqDL7HMJZvyTP/mTXqBqc90TIJiHUFLyuSQTaXyvdrWrVcnW+OIXv7huLjznLwluTTBm+/+wLC8ZLpNlMyXrkv3gJje5Sb3sfJ7Zp5/znOfU49f7M+6xs17d7XEJUs75wCj7Y/4hzXlmAlcTZBmjZlbwLCMe2d5SknH4aU97Wnnb6+YzKr8/CSQeVObxvHLQuhpOgAABAgQIECBAgAABAgQIEJiWQBdBhP3WtQQ8dhlk2W85kwquzLK6sDn9G90Fmfbb/nGGZd3u+tPrt7YzTv3mJdAWKK1sJVFL7o+OUza6LzlM3Yk7KMd57tvkOnMSW6Tu3J/Nvc7cxznppJPqe+H9kgQMs5zmNLmX84u/+It1QoZcA09Si/UCLDNN7gml5J7B3e52tzrRQ7POcfpzf+pjH/tYXUWu8acVNoUAgX0COebrAMv2DT1ABAgst0CyDuUHOuV5z3tedec737kHkuYnE/iVDG/5gU3g0Vvf+tY6a15vogn0pInkBCQ0gzsSPJYnM9JUeQLQEkyUk4pf+ZVfmcAa7F9lml5OcEkp7UCAMnyZujkJTMlnkgDLlJwIrhdgmfEpO3bsqJs4LXXUA7fQn3GPmZywJ1CkBFcmA1/zn4k0R59gy2S3TAbLBFmm2ewECZWSIJySEj7NC2ed4lpKAqsyfYLScnwnc16yn3XxD0BZRuk2m6h90IMeVAb3us3Av3YAW2+iy3qaT6wl2KtZuqonAagJqkvJd9wf/dEfVX//93/fXFTdPHoCLtPcbIKV7nKXu+w3fh7fdOUzD9uW/b5kaLzuda9bB1M2m6FPoGSCFPPblOMpmQObn2GOrdLscPb5BKWlqehS8rnnM03mvSwn3/EvfOEL92s6u4vjtCyvdHN8JKNmKQkIy7Gf9Svfo7nIkCagB2UDzvF8s5vdrHrlK1+53wMG+c447rjjek2BZ7tKgGWWl+bQS/Bkmp7oF2CZILZyQSH/qDf/Zxj3e69s87S7aQI+D4c0S3wTfJuA1Hwf5Pc9WSXzG5WLCKUkUDL7SbIAxz3ZPxNMmYcLSsn5SB48SUlAZepoZuJNcHus8zDCOeecU59L5QJN9sFmybqU4MqrX/3q9Xd29v1S8kRvMnEmCLP8dpRxze64x06zrmH6E1y5mf0x+34CLFMSPNo+r0rQcTNDZ7J7JiNpO9NsCZRNPesFWM7beWXWVyFAgAABAgQIECBAgAABAgQIzFqgJGWaxHpMOshyksGV8ejC5gv/Ob8Bllm3RQiw/NYPd1cv/8QPqzMvsz7hKgdVJ9/yiOq4I2TnnMRxvdk6v/zlL/ceqE/iiCQ+GadsdF9ymLoT81BK7oXkPkBzWMblWn5apso9lcRQHHnkkWWWTXVzXzytZMXgPe95T51E5zOf+UydaKFfhUlCUO79JYlIEmXlmvywJS0mtu8nJKHE17/+9TreoySOyDr96Z/+aV3/sHWbjsCiC5SYipVy43TRN9j2ESAwnEAJuMjJQ4IY2yVBHr/xG7/RG/zZz3621z+pngScNYMry3ISAJFsZaUku2YJDi3Duu4mIOKJT3xi7wQkWZc2ysLX9TqMU1+CNm5605sO/UqGrWFKfkvSTGiCIhJckZIgwAQq9SvZb0qa8QStJABllJPA1JkAoWG3JUEwkyrjHjMJVk5zwSk/+7M/u19wZVnnZGZNIFYJskrAcbOUdciwBJU0gyvLdAmwvPGNb1y/TbP2X/va18qozrppMreZQr8Z0FUW0vycy8lIGdfuNgO62sd2V/UkcDP/VKQkiLIE5uUptIc97GF11rjrXOc69fj8o/H4xz++/uepHjDHf7rymYdNTHPKKdlfEijZDK4s65dj5x73uEf9Ns1KJEirlPyzW77L8rvWDK4s06TuZBMs3+clYLmM7+I4LXWlm6DQ/La1S47xHOtln8s/tu94xzvak/XeJ/gumS77ZW9O9sVS2sd7rMr3SQIp201rZ74ML6XYlvflO2cz5wo5jj73uc/1fZVjMRcsB01TMhSWdRm2m33ksY99bN/JE8DeLE960pP2C64s4xIgmc8uJb97JQiyjM9vXrKD5rs2QezN4MoyTQIym83YF8syPtvevCCU/bIZXFmmy+9faeq9DGt3xz122vVt9H6z+2MCpMv+mKDQdim/K3E49NBD69FlWHPaEmCZ4yFZSgeVeTqvHLSOhhMgQIAAAQIECBAgQIAAAQIEFk0gQZYJhOy6TDq4sqv1Pf/He7qqqvN65nndht3YBFc+7m3nVqefc0l14aV761f6MyzjlPkRSMKIEqM0bmt7w9yXHGbLm8GUuaec92nBM/eTktwj8QAJaEz5wAc+UN8/bN+3HGY5zWlK4qFm8qiSnKg5XelvjistnzXvBZbpBnXf+c53Vre4xS32eyVLZZKBlKQaua6ehFblXv+gugwnsGwC5TtrdaPghmWDsb0Ell3g2mtNhyagIzf4kx2oBGQ1XfLjWlJEJwvQpEuyZg4qCYJKM59pFjKBDQmAHKcJ3wSUtIN3coKUbEl5oibNkJdAlARODNM856B1n8XwfK6jnGyNso6pN4F9ae47AU5ZVjJ5pbnddmmeBCbrZUo5kWxPO+h9+RwGjZ/W8HGPmQR/ldIv42MZl8CSBF0lQ1qOzWbJOpSSoKRBJdnwSgBTCVIZNO2owxMkVLIE5qmtpz/96aNWMZPpm/80ZZ+NSzJ85p+MUvIdkCZlS7POaRo4gXr9gtrKPLrdCCQwMtnrUo4//vj9Ppf2EpIxN1kFU/IkYQnwzfd2Kb/2a79Weg/opomHfK4J3MpTfMmYl3+gU7o4TpsLTNPkg87BE7SYiwrlAYJkrU1Aeb+SbIhHHXVUv1F1sGiC+bIt+V1vlsyTQMFcCEgz4QkoTV2lXHTRRb3f+fymJmiwWcb53svvQ/NBjWa9pT/f7+UCQRlWugko7fcASBk/qHu9611voHk+jwT45ViPfwmi7FdXpi0lzcY3syjGsOlYpmt3m+cpaRq+WZJRtwxLwOJ69TWX3d6fujh2mus1TH/WdTP7Y7IVJ2A0DxskSDKfQwm4zHJLsG8yiMYmF4JynDYDZjNPyS6eh3KamUXb6z7N88r2sr0nQIAAAQIECBAgQIAAAQIECMyrQAkemOT6dZ3JclrBlV3YdBXEeOqvHFvtWNn3Ke1ci9l8wOu+O/ZH1tW6jb0iY1SQzJUX79p7QA0ZlnFPv9O+a/0HTGDAVAWSgTGtY6YkBmGcQL4u70u27xXmnkwCLZvXmXPf6TGPeUwdM/DFL36xzmaZe+KbLeW+eJJD5J5BEmXkXtSTn/zkqt0CYOIfSmKQxEb85E/+ZL3YUsdm16E9XxL0JAYkrZopBAhcLlDuwa0mmCClmSXq8sn0ESCwbAJ5AuNf/uVf6s1OU5p5KiOZq5ItspR8XzSb7i3DZ9XNCVgCLFNyApSTkATZtLP8NdcvTcgmE1W7PP/5z28P6vs+zXkm+KUdjNl34jkamACl9QIL2quagLxkGxum5CTusMMOq+tPavQLLrigfsolARDNQIkE8yQ4IiUBgyXL1KiBn8nONiiQo72+Z5xxRi+zYnvcuO/HPWYSPJWS4yonxeuVZAhNgGUCps4///ze9qcZ8VhkWI7fZGNL9sXU1wyCbv4jUJaz2WOlzJ9uTuzzT0X2gXzWaV65ZAJsTjeP/aV58LJuaSK8GVyZ4dmmZJHLfpRgv2xvmgF4wAMeUGbTnZBAAq7Kxat+mYybi202K9zMnpt/ektJQP56Jf8w59UuXRyn7TrXe9/8Lmj+Y7/ePP3G5Ts53xd5tUsuECTAMuVd73pXleC4UvI9Ur6T+/0jPe73XlnOvHRzjCe4Otkx86BA++JFcz2bwenDXLzI/nvuuefWF10SuJrSznzZrL+57+biUvP3szndRv1dHDsbLWPU8evtj8m+nHXOvpos1+XCWi5UFZNkvE6wcM4hyvBrXOMa9WrkPDDnFyl56nac0u+8cpz6zEuAAAECBAgQIECAAAECBAgQIHC5QFdBltMKrrx8zcfr6yqIMcGVO7Zvu2xlDgwo3MxadrVum1l2V/OUZsH71bfeuH7TGzY5gVe/+tW9REYPX2sFarOl6/uSzfswSWzw7Gc/+4AkKyeccEKdeKkk8kgyiLRWtdk4q3IPJoFbSTiR5C65Pv7mN7+5zirZtElCoxLX1cx4WepoTjuoP9fc2/c1k7ggLf196Utfqq+7535aWvJMMp/f+Z3fGVSV4QSWUiD3+1Y3e8AvpZiNJrAEAvlhzY3sZGpL9q4XvOAF9StPQqSJ4TTjmG4zaGvWLM3gzxIslScs1gtgGOWEI9uXwLRkksqN/KQDv/Od7zwwE9asPdZbfgKQHvnIR643yX7jkhJ8lADLzJwscDHKPvS9732vet/73lcls1wpb3nLW3rBPiV7ZcaN+pk88IEPHDpbaYLh+jUnWtZpnO64x0xpujgnxiXj3jDrk+O0BJjmtzzBsOXJqQRN5ZX9NkFpOW4TPFUCUZr1j3us5HviEY94RP1Zp940b5ynrYYpJXBumGn7NXte5hunngQ+lfKLv/iLVTKkDSrJupfA85QEALX/ERk036yHj+Mz63XPP3al5DulZBEtwwZ1S0BWjqvyGScbZb8g40F1NId3cZw269uo/7jjjutN0vzHvjewg55kgUywYIL+8v2YrJHlOHvPe97TW0K7efCMGOd7L99DeXijX8nnm9+CBBUOahqkmf2xXx3zMCyWb3/726vXvOY19YWJUTIu58JQKc3zmzJs2O64x86wy+lqugRYJjg/Jc2ElwDL8tv9Ez/xE3W20DyYkfOMBLcms2UetkgpzYOnf9wAy6Z7Oa9MvQoBAgQIECBAgAABAgQIECBAgEA3AuMGWW614Mpu1NRCYGsL5H7z61//+nojrna1q1W/8Au/sKkNGue+5KAFlvtIuTeRJE2DWrDLfdy0+JXsmbkHnvs3/e69DlpOc3gzgcP97ne/6i/+4i/q+zRveMMb9guwTBBkmu1OyX3hptso99aznve85z2bq7Bff+4x575Mrom/4hWvqG5+85tvqjWx/Sr1hsACCSQYenWBtsemECDQgUBOHJKpLdl7/uZv/qbOEJRq0zx2XnmyJJnpkiFvXoKLShOuWc+kFk9JRr8SCFUPaP0ZlF3v1FNPPeBEKCbNZbSq8vYygWYASQInSyBUTpabAZalefASiFkAS9PV5f1W6Y5zzOTkuWQ0G3V7zzvvvP1myZNTeYIpTzglhXzJWleCLZ/73OfWzd6mGe8EqpSy2WMl82cZCdj96mVN3P76r/961Xxyqiyj2W1mfU1w53qladM+ZruqpwSUZT02CtxqNpNctnm99Z/luK58ZrkNWXb5p3bU9SjHx8UXX1w3N5z52/vQsHV2eZwOu8x8PyYYNMdYCe4cdt5hpzvkkEPqgOI3velN+zUTnu/iD37wg3U1Ccrvl+15nO+9NC/+hCc8oe9q5vciFwTyEMegafrOOEcDEyCZoL9mYF4+y6OPPro68sgj6zVNpsx2s+1lE8q+m/fHHntsGTxyd9xjZ+QFjjlDmnBPYGP2949+9KPVb/7mb9Y1lgDLZK9MiWUe9MnDGxlXAizTbElK9q+NvsvrCdf50zznK+eV60xuFAECBAgQIECAAAECBAgQIECAwCYENhtkuVWDK4+6wkr1g0v2tSy6Ca6JzpJ12+rlhKscVJ1+ziV9NyPjlNkL5Pp/aYXoIQ95yKZab9rMfclhtrwEVCahTRIurVdyXzUBlim5V7jZAMvmffESOPmOd7yjjs34t3/7t+qmN71pvYz3v//9dZbJvLnvfe/bS5SR9wm+7KrkuvrTnva0OplP6nzpS18qwLIrXPUsjEAdYJnsPjJZLsxnakMIdCJw97vfvcorzRHnRvdHPvKRKjevc+KSJzKSGjvN5T7zmc8c+BRHJysyRCXNYIRyUzwBonmNWnIC0wxMGnV+0+8TKE1/Z5/JvpNscjnB/MxnPtML2k2mwBJssghumzlmcsKeIKcEgR1xxBHVk5/85KEp+jV1nACeNHGdE+DYl2M3x3GyGKbZ36Srz1NQefIoZbPHSk7aH/e4x/X+iUj2x6SN36g0M/OlSfP1Sr5rSmkHGnVVzzWvec2yiN4/KL0BrZ58P5Sgt0llFWwtctNvu/LZ9Ap0NOMVr3jFXk35x/GWt7xl7/16PeW3IBkaS5bGCy64YL1ZBo7r+jgduKDGiPzW5pWS43pSJc2EJ8AypTQTnubQEwCY0i97ZT3isj+b+d5rzr9o/bk4lAdQSnBlmvXIAwdpUqRZ3v3ud1e//du/3RzU629+15XPoTdyhJ5xj50RFtXZpMlimYcwcq6Q/T/H7Oc///m6/hJgmTd5QjcBlp/61KfqbOv5/Uzz4injZq9MHf3OKzNcIUCAAAECBAgQIECAAAECBAgsskAyM43SGlIXFqMGWc4quDI245YEMZ79/XFrmcz8ixBgefItj6jO+M6l1cW79m82/ZDVbVXGKbMVSJKeJHFKOeyww+p7laOu0WbvSw6znNwrTNKCrGeuS693/zrZN0vp8l5hEtgkwDIl18lLgGXJ+pnvoV/6pV8qi55INxk6y29BmguPeRJuKASWXSDnRzk2VtMjuHLZdwfbT2CwQDJX5fXgBz+4ypMUb3vb2+oMeTnJSFBGMrptlLFucO3djGlmF9rsUyLdrIlamgI5yUuQX35nks788Y9/fH1CWKZpNg9ehi1Cd5RjJj/E117LtvWFL3yhfmorQacJ4Bu3pI40CZ5Xytlnn10HVb7zne+sA0cS2JMMdVn+ZsvTn/70OmAz8ydD6bOe9ayhqmoG/jWDWPrN3Gzitp19sKt6mpnOSrPS/dYlw5LJMMGwKVe5ylXq7rz+6cpn1tt33etet7cKCXC9z33u03s/bM+1rnWtKv8IJmA3/xw3s5YOU8ekjtP1lj2t37Vb3epWvayBpZnwNLtcyt3udrfSu253lO+9dSva4iNzjpSA9pSHPexh1ZOe9KSRt6j5dOy3v/3tkecvM3Rx7JS6ptVNcGQuHOU4Pf3006uvfe1r9aITLNoMrr7jHe9YX9TJxZ08vfuTP/mTVZqXSUmQ5rhlWsffuOtpfgIECBAgQIAAAQIECBAgQIDAIggMG2Q5q+DKroznOYhxntdtWP/jjthe/dk9j6le/okfVmf+56X1bMlcmeDKjFNmK5Br56W1riRsSZDlqGWz9yWHWU7uFSZrZEpaqVovwLLZOl8zYcIwy1lvmpNOOqm+1p0WRdNSYZLyZFkf/vCH69l+9md/duzWm9Zbfsbl/nISGmS5iQtJN/fmFALLLlBiKlZKz7KD2H4CBPY1r/25z32uyuv73z/wMao02Znglhe84AU9rmTEa5b86JZS0nyX95PqZn1LEWBZJGbfTdBdybz2xje+sd6nckKYkmZnb3azm81+JcdcgwRhjHvMlCCYBKI29+VRVi0ZvjLvF7/4xb6zJcAsx20JUEmAdII6N1te+MIXVqeddlo9+61vfeu67mGfYGoG/pWMY4PWI0FxpTSbNc+wrurJk2b5bktJVt40TzyoJAAzn1NKsrTOc+nKZ9bb2HTe7PFRgmjz2f3Hf/zHupuUY+mf//kXczf3AABAAElEQVSf61fJIJkZujhO111wa2RzW5tPQ7YmG/ttHrIqWSrzm51/1EuTzDe5yU36Nm3Rxffe2Cs+pxWUZkGyeve61702tZbNAMt8J222dHHsbHbZm50vF4hKUyzJwFz2xQRNNn9jcnErwcEpmebjH/943Z+LP7kINW5pHn/OK8fVND8BAgQIECBAgAABAgQIECCwVQRmmZApQZYJoBxUZh1c2YXN9ee4mep5XrdB+0S/4QmkfPqdrlS99pePrV/pF1zZT2q6w3Jv5pWvfGW90FznfehDHzryCoxzX3KYheU+ailpOWm98vWvf703unkdvjdwjJ6SoTLJXt785jfXCYzKfclpJLxKfEgJIE3iA8GVY3yYZl1IgZVsVZoIVwgQIJCsVWnOMq+XvvSlA0ESGFeCs9vBKs0b0c0TjHZlyUqUDEXDlDQxPajkaZLSfORVr3rVvsEgg+Y1fLICyRJ3v/vdr15IAvqe8IQn9Jq8LSeIk12DydfexTFzi1vcoreiabp7o5Inp9rlkY98ZH3cxntQk7I5Zks6+czfPnbbdQ56/5rXvKZ6+ctfXo++4Q1vWP3VX/1VLyBm0DzN4fn+KCfkaeK1GcTWnC7BXiWTXoIr05R5s3RVT/6ZKwFmyXBYAkebyyr9pSnlvE/GvnkuXfnMehsTHFmyhZZm79dbpzwBmafqmiVBwKWccsoppbdv95nPfGb1uMc9rm7uvvzOZcIujtPmAss/qM1hzf5XvepVvbddBIz1KuvT0wwEzHdQyZpYjov2LF1877XrXJT3zeZA1vv/6l//9V8HbnLOZZKRMSWB8GmyfVAZFFSf6bs4dgYtd1LDmwGSCZws254HNtolzYSn5BwxGZlTbnOb2wyVodZ5Zc3lDwECBAgQIECAAAECBAgQIEBgP4Hm9dD9RkzpzaAgy1kHV2bzu7A56RqDA0inRDxwMfO8bgNX2ogtI/CBD3yg1/JTrvWOmlRi3PuSw0Dd/e537z3kn2DQ9n2mUkda5sv2pBxyyCGdxyXc+973rutN/WntKQmMUnKfLC07Tbok02gpxx9/fOnVJbD0Agl0zn2/OsCyi6cull4UAIEFEEgQR/kn4S1veUvVDBRobl5uTJenJW5wgxs0R9U39Mt3SjJh9QsGO//886vf+I3f6J2clLr2q6jx5qlPfWr12c9+tjFkX+8FF1xQPe95z+sN//Vf//XeyU9voJ6ZCiSQsuxTyUaVkgCKnCAuQunimIlRCdZLk6gveclLesdX0yhNoSb4K0FPr3/965ujepm88sM+KDg685csX5n5hBNO2K+OYd4kA2k55vJU1ste9rLq0EMPHWbW3jQHH3xwVZ6ySnDln/zJn/TGNXte9KIX1U1yZ1iak29mL8uwrupJXf/zf/7PXlBOllua+M24Us4666zq1FNPrd8mU++8N3HfpU8xmEU3n/vv/d7v9Rb9+7//+9Wg4P1knUvTEmmaudnE7/3vf//q2te+dl3HW9/61qp8F/Uqvazn//yf/9PLInuHO9yh909sRndxnDaXl/X4x3/8x+agXv/rXve6qmRCTHDxf//v/703bhI9ySicV0rJbBv3u971rn0X18X3Xt+KF2Bg84JD+3u6bN6f/dmfVfmMS7nkkktKb93Nb2aC5kt5znOeUzdvX96X7pe+9KXqf/2v/1XeHvC70cWxUyrPRaPmMVWGT6JbmvjOgzj53cp32e1ud7sDFvVf/+t/rc8v8jtSAjHLvAdM3BrgvLIF4i0BAgQIECBAgAABAgQIECBAYE2g3MuZJUY7yHIegivj0YXNlQ/dXl3nqH2tac3SuL3srFPWTSEwKYG//du/7VX98Ic/vNc/TE8X9yWznMQi5N7foMQIyWCZ+0sp3/rWt6o//uM/PuCae65XZ3hJHJN7Ue17l3UFY/xJ1si73e1udQ1Z3ySGScm6db2suuLGnyQ9aN6zXZR7+Y1N1Etg0wI5D8hrNV8i5c2mazMjAQILIZAgr2TAS5BJgiAf+MAH1oGQaYYxP+gJckwGsZLFLd8d97znPffb9hI8l2nydEfSfCcb2PWud73q7LPPrj796U9X73//+6vcOB+2JPggAZl3vvOdq9ve9rb1ky0JPskTK1/96lfrao455pgqQTTKfAkko2kCI5pNySdA8LDDDpuvFd3k2nRxzOSE+FnPelb1kIc8pD5ZTwa5HCcJ6EoQZNKxJ/AyzRaXtPTxbAavPvrRj64zeF144YXVK17xijqwOcdv/iHIyXeCTzL/Zz7zmXpLExidAMlRSoIzn/SkJ+17OmOtWeGcWDc/1/XqyndIsrKVknXLP1Rpjvu1r31tnW4+WfQS5PXlL3+5TnufIO+UBNcMOra7qidN8qauZA2M14Mf/OA66DKZD7OO8fvLv/zL3j9NCci80pWuVDZnbrtd+cx6A/PPZH6XEtyf35H8Tj3mMY+pbn7zm9f7VX4Pcoy84Q1vqD+jZD/NAwLHHntsveppAv53f/d363nyu/SIRzyifuX35NprgZd5ECDBjuVJwCtf+crV7/zO7+y32V0cp/tVuPYmx30Cd5N1L8dkfs/e9a537Rd8d/LJJ/easG/P3+X7HH8veMELelXmmI1Dv9LF916/ejPsZ37mZ6o0fTHpCwWDlj/u8Lvc5S71uUn+v8o+lQzbGXb961+/+uQnP1l96EMf6mXdLsvKd3y7ZJ9PduDs79lHEpSeizU3utGN6vOzBAm//e1vr7Kvr1fGPXZSd5aTIOdcgEo334+TLD/3cz9XPfe5z+0tIs2G59yyXfLEbrIy5/eylMw7THFeOYySaQgQIECAAAECBAgQIECAAIFlEyjJU2a93QmyLKXZX4bNotuVzc9c8+DqK+fv3wLTLLanucysk0JgUgJpiTLXxlPS8tpNbnKToRfV1X3JXK+/z33uUwdYHnfccfU172SfbJff/M3frJIcIwGUr371q+v7TAlsTItTCXb8+7//+/o+VebLPc/ELkyiJMFLicVI/fn+Seuj45bcfy1NtZe6EjSagNIzzjijl/gj45JptLRSWabVJbDsAnWAZVcnBMuOafsJLIrAH/zBH9RBFcmCl2xFf/iHfzhw0x772Mf2TUf9W7/1W9V73vOeuqni/CgnKKtdEhiTYJJkRVqv5KZ6TmiS8SnBCv0yfuUk5sUvfvF+2cbWq9O46QrkRLAZiDfvmf9G1enimMnx8Od//ud1hsoEN8eradZcpwSb5Amp/IiXkoCnZERL0FmClxOklVe/kox4f/3Xf73f/P2maw9LWvidO3fWg/PPSI7JYUuOz2aAZQKiE2D527/92/V3QOpupp0v9Wa6BDam2690VU/qTgBemrVP8+dpvjlZ49ol5g960IPqYMz2uHl836XPrLcvwX851vK0YgKJn//85/ddpfxmJMtqAq+aJU0n5Pfsj/7oj+p/jpMpNq92SZBv9tf8k90u4x6nzfpOPPHEOrAz/5Dn1a8koK5ke+03vsthCXx/4Qtf2Ht6c1Dz4GWZXXzvlbqa3XwPbuWSC0T5Hs73VkoCLPNqlmT9zcWQ0gx8zoXaJQGmOQ/L+c9//Md/1BdymkGHZfpc5Mj51npl3GMnTcLnIktKljXpAMsEPad58/Igzp3udKeBm5dmwkuAZYJYm78zg2ZyXjlIxnACBAgQIECAAAECBAgQIEBg2QUSM5BXrv/PusxLYGUciksXJve4/qHVaWdeVP14594uqhu7jivs2FZlnRalfOuHu6uXf+KH1Zn/eWm9SSdc5aDq5FseUR13hAyds/qMx8le2dV9yVyDLy3XJW4hSTtuectbHkCSpB2515okK2lJLRkd82qXJBfKdfd+iQHa027m/Y1vfOM6aUNpcez2t79933tWo9bd735Fu47cm0hQ6ROf+MT2KO8JLL1AEpGs5CRpo+Z5l14KAIElEkgAUYKeEjyVQK4jjzzygK1PhqlTTjmletSjHnXAuAzIDe5kn0smoWYQWMYl4CeBmQksOProozNo3ZIscWn6O4EwCXxpZrXKiUuW8Q//8A/1ica6FRk5M4E02VmCHm54wxtWeS1S6eKYiUcCZfJkVAKb+h0b2f+f8Yxn1AGA/U7a8wRVjoXSlHHzWEn9OZZz7OXYLJn9MnxW5Ra3uEWdcTD7RzLkNkuO+wzP9rQD5ZrTpb+renKR5AlPeEIdZJbMoU2/9CeINd9DT3nKU/Yb116feXvflc+styv7RAIA809rjoVkpWyWHBM5dvJUX4Ku+pU8bZcg/WSubGbRzTGczzcZl5NRtV9wZalv3OO01JOnNHO8p772k5IJLkt2yzyckP1yGiXf0dlXUg466KCBhmVduvreK/UtUjcZhRPEngzCzRLXZCvNPpqLE8nOm/LFL36xzkrZnDb92Q9OPfXUuqn2ww8/fL/R17zmNesgzmYT4c3vrObE4x47d7/73evjJcfYtJoEKU19Z/9fL8Ayx08pucg0THFeOYySaQgQIECAAAECBAgQIECAAIFlFMg1v2ldj9xKvjGJTRfliINXqvvcYH5aeLvv2rpknRahJLjycW87tzr9nEuqCy/dW7/Sn2EZp0xf4Ctf+Ur1vve9r15wghLXu9Y7ybVLi34loDIP6q+XRTMtcubeZFrwbN+rzX3WBB/mGn+CICdZcq+3lGZ/GdZVN/cV8tkkJuQBD3hAddppp9X3p4444oiuFqEeAgsjkHOBbWvBlXsTZOmEaWE+17nbkATwliDe0p99Ltlwkg0tza+mOchBGcrmYYN27t5b3f+1353pqvzjA4+tdmzv5gR+1A35xje+UWcSSlBWmtLtF9w1qM6k0c4JVJqxzEnJUUcdNWjSoYanvjSXmYCU1DcooGCoyky09AJJ8d4vK9hGMDkO3v3udw+cbJxjplSapqrzRFWCcBLwlcyTo5QcK3m6Kd+7OTlOc6pdXQQYZT2GnTZNOidjWYKS1gtw26i+rurJ79KZZ55Z+yd4tQRDbbT8eR/flc+w2zmpYyznD8nsl+Mkv03Zx0f5bcpxkabBc6wmoLbfwwTDbOMox2kyAebJx5RkQn3qU59a9+ecKPv+t7/97fphgQSATbvEI4Gp2T9ykeNFL3rRyKvQxffeyAud8xnOPffcej9NgGTOWdqBwcOufj6fNN9xwQUX1AHG5eJGmlZ5yEMeUleT4Mdhfs82c+zk+zDrMMoxNuy2zXo655Wz/gQsnwABAgQIECBAgAABAgQIEJgngXLfdp7Wadbrkgen0/pVV+XiXXurR7zxe9UPLtlcptDVVjzkrs1VU11xLbDyZfe7cnXI6mzuPXflWep59nu/XwdXlvfN7klXP7h6+p2mf929uQ76Zy/w/e9/vxr1/ku5p5YgzZJMaPZbslxrkHsTaRE1sSHl+zjxIXWwWwLeGq/IzPO98OX65BZra7MfZt9aTY/gysX6cG0Nga4FEriS12ZKbsbf4AY32MysfedJfRtltOs7o4EEpigwzjFTVvPKV75ylddmS46VNFe7VcrVrna1Kq9xS1f15ER9K/kN69aVz7DLm9R0uaB1/PHH16/NLCMnwfNwnGbdcx5+7bVmkfOaVfnYxz5WB1dm+Rs1Dz5oHbvwHFT3Vh2eh4e6eIAo+2uyrLZLsl+WkkDwYcpmjp12ltVhlrNVpnFeuVU+KetJgAABAgQIECBAgAABAgQITEOgBK0kfkDZF6jTdaKXBDQ+6tZHVM//4AWbIt5sQGV7YY9eW4dFCa7MtpVmwdvbudG4ftMbtpgCowZXRmFR7qkt5idqqwhMT6AE7q6Wnukt2pIIECBAgMB8CKSp4Oc///kjr0wCMhQCBDYWcIxtbDQPU/zd3/1dvRrJtHiHO9xhHlZp6dfhec97Xh1kf/LJJ/e1SObFl7/85b1xHj7pUeghQIAAAQIECBAgQIAAAQIECBDYpEAeBk9A4a5duzZZw4GzPfS0H1VdBQUeWPv+Q5Ld8ZT7HL7/wDHexWISiapue61Dql+60a7qH/79wjHWbvOz/tKNDqtus7YOCgECBAgQIDCcQB4+WdU8+HBYpiJAgACBxRO47nWvW+WlECAwGQHH2GRcu6o1TWKceuqp1Qc/+MG6yjRdLoC8K93N13PaaadVp5xySl1BmgZ/1KMeVV3rWtfqVZhhz3jGM6pvfetb9bC73vWu1UknndQbr4cAAQIECBAgQIAAAQIECBAgQIDAZgW6DrDc7HrMw3xdZ69sbtMDb3p4dfb3d1Uf+/olzcET77/1NQ+usuxFKydc5aCBTYRnnEKAAAECBMYRSPJKGSzHETQvAQIECBAgQIAAgS0m8KIXvah673vfW5111llVae7n2GOPrR7+8IdvsS1ZzNX9+Z//+eo617lO9ZWvfKV685vfXL+OPvroKs2An3POOXVgZfNze9rTnraYELaKAAECBAgQIECAAAECBAgQIEBg6gIla2OSNC1zKdk8J2Wwba3iJ9zuyOqFH7pgakGWCa7MMrPsRSsn3/KI6ozvXFpdvGv/5u3TDHrGKQQIECBAYFyBFU2Ej0tofgIECBAgQIAAAQJbRyCBlV/4whd6wZVXvOIVqxe/+MXVkUceuXU2YoHX9Kijjqre8IY3VA996EN7GUXPO++86hOf+ET1zW9+s/7c8j/cL//yL1dvfetbqwRfKgQIECBAgAABAgQIECBAgAABAgS6EKgzNK2udlHVlq5jdc1g0nEUCf578h2utNZc+GETt8oysqwscxHLcUdsr/7snsdUJ1394Oqwg7bVr/RnWMYpBAgQIEBgHIE8eLJtLfvJ/mH849RoXgJ9BLKLld2s9Gfn2717d7Vz587q0kvXnia5+OLqmGOO6TP3/Ax64Ou/W1146WwOl5wIvvaXj50fDGtCgAABAgQIjCSQbITveMc76nludKMbVbe//e1Hmr/LidP8dNbl0EMPrbIuv/qrv1pd6UpX6nIR6upIIOfLCYj97Gc/W5155pn1+XIyWd7gBjeos1x2tBjVECBAgAABAgQIECBAgAABAgQIEOgJ5D7uJZdcUnWRxXLnlBNh7ljpbcame5K98uCDD67SnVb58NkXVy/52A+rH1zSLdgVD16pHnXrI6rbXuuQaW2K5RAgQKAzgcQXJQHFIYccUh100EHVjh07qmRaTgB8+5WFTjowvrMNU9GWEsh+WO9vaz2ziRjbUlxWdhyB7GJlNyv9WzHA8jnv//7UUrS3vZOy/SlrTxUpBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmKRASZIzyWXMa90liGfa65fmrU/7/EVrrwurH+8cL4TjCju2Vfe5wWFrr0MXNmvltD8fyyNAYPoCiS8SYDl9d0vsL7CaHVIUb38cQwk0BU6+5RHV576zs/rRpd0+OdRcRr/+ww9aqR6xtmyFAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECkxZIE9lpXSWvZSrJjJZtn0VJ892/cuPDqnscf4Xq7V+4qPro1y+pvnL+rpFW5TpHrVY/s5a45x7XP7Q6Yi17pUKAAAECBAiML5AkgpoIH99RDRsIlKyVmaz0b8UMlln/cy/aU53yqR9Wn//uzur8iycbaHnUISvVDY7dUT3s5kdURx/qBDj+CgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECkxfYtWtXdemll/ZaKpz8Eme7hCSlSvbKWQVY9tv67120uzr9G5dWZ33v0uq8tfvU5/943yvTHnWFld7r+lc5qDrpGgdVVz50e79qDCNAgMCWFJDBckt+bAu70gIsF/ajnZ8NK0GVWaPSv1UDLOdH1ZoQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgcgIJsExz4ctQduzYUQdYLsO22kYCBAhsFYFzzz23OuSQQ+rv53xPJ9NwAuLbr2yP1pu3yqe6tdYz8W0rKyvVSgLeFAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEUg2xwSzLHqZZdPgi25r+wgQIECAwFYWKIG7K6VnK2+MdSdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6E0jGpmQMS3dRyzJs46J+draLAAECBAhMWiBxlUleKYPlpKXVT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEtqBAsjsmyHIREzdlm0qTs1vwo7HKBAgQIECAwBQEcr4gg+UUoC2CAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsRYE0Fb5oQZYluDLbphAgQIAAAQIE+gns2bOnHlxnsEwqS4UAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0BZIgOWiBFmW4Mpsj0KAAAECBAgQGCSwsrJSj1rNyYNCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBgkUAISd+7cWW3VJE6CKwd9uoYTIECAAAECbYFksMy5w2ovleVlEZftCb0nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQslgmyLLEGW0UlWaiy/poF3yqfmPUkQIAAAQKzFUhwZR1gWVJZznZ1LJ0AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYd4EEKCbYIEGWu3fvnvfVrddv+/btdXBlugoBAgQIECBAYBiBnO+krCZ1d3kzzIymIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJZXIIGKiTPYtWtX/ZrXJsOzjgkIzUvyqeXdX205AQIECBDYjECJq1wVXLkZPvMQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHlFUjA4kEHHVQHLibQct6yWSYItARXLu+nZMsJECBAgACBzQqUuMrVPXv21HV4WmOzlOYjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLLKZAgxgQzlmyWJQZhVhqJfSiBlSUwYlbrYrkECBAgQIDA1hZIFstVgZVb+0O09gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYJYCCWTcsWNHHWiZTJYJtpx2oGUJrEywpziIWe4Nlk2AAAECBBZHIOc4q4uzObaEAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmJVAAhtLoGMCLcsr2Z8mURL0kIDK8pKxchLK6iRAgAABAsstUAdY5skRT3As945g6wkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBcCdbantabD01R34hESaJlueY2zjBLEma5sleNImpcAAQIECBBYTyAPiNTnNOkRXLkelXEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAZgRKQGTmTXxCgiz7dcv4dEsmynQzf79uplMIECBAgAABApMSKOcjq6VnUgtSLwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAg8QnJOqkQIECAAAECBLaKwEpWNE+IKAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBZRcoGbdXA6GJ8GXfHWw/AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgEIHSMvhKslcm2lIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYF2S5KnulXYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcLlAElfKYHm5hz4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQN1M+FoCyxVNhNsZCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrAslembJSp7FcC7JUCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLLLrBt27aaYKX0LDuI7SdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIRKBOXrlnzx4aBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEClwkkeaUMlnYHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBLQIBlC8RbAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYLkF0jr4ynIT2HoCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwOUCe/furVZWVgRYXk6ijwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFh2gW3bttUEK4m0VAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPYJ1E2El0hLKAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABApUmwu0EBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGmQLJXpmgivKminwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFhqgdIy+ErpWWoNG0+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWBNIXOXevXtlsLQ3ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSaAgmylMGyKaKfAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWGqBPXv21Nu/kjSWeSkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWUXWFlZqQlWk8ZSIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqKpksKybCE9PSWcJhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwzAIJrsxrtaSyXGYM2z4fAle4whXmY0WsBQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsrUBpGXxl7969S4tgwwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECTYESV7lSIi2bI/UTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJZRoMRVruzZs6fKSyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKiqZLFcXVlZYUFgZgIl0ndmK2DBBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhsGQHxRlvmo9ryK5p9TXTllv8Yt/4G+NLb+p+hLSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCiCdQBlpoIX7SPdWtsj8DKrfE5WUsCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECsxBIfFEzxqjZP4v1sczlEUjz4Ckr6dFM+PJ88LPa0o2+3DYaP6v1tlwCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKYrkFiidjxR+/1018jSlk2g7G8rpWfZAGzvbAXKfle6WZsS9TvbNbN0AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRmJdAvhqgZY5T1ar+f1bpa7uILaCJ88T/jud/CfOHl1e/Lce5X3goSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCZgBiizihVNIZA9sM9e/ZUdYClJsLHkDTrSAIlmDIzNSPJ079r166R6jIxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKLJZAYosSzlTijZozRYm2prZlngbL/rSTKUtTvPH9Ui7lu5Yuv7IjpXnTRRYu5sbaKAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGhBBJDVGKKygwl1qi81yUwDYHsd2vBvvuifaexQMsg0BQoX3xlH/zhD39YXXLJJc1J9BMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsCQCiR1KDFEJsCzdbH6zf0k4bOaMBZK4UgbLGX8Iy7T4ElDZ3OYEV6aU7n/+539WO3fubE6inwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBBRdIzFBih1JKwrYSU9Tc9GYMUrO/OY1+Al0IZP9a2wdXNBHehaY6Bgq0v8jyvrwyU/bB7du316/yRam58IGcRhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYKIHECpXEbCWOqARXNuOMFmqjbcxcCyR7ZcpqncbysiyCc73GVm4hBPKFV3a+bFC+CPPK8Hw5rq6uVvnC/O53v1sdeeSR1THHHFMddthh9biFALARBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUu3fvri688MLq3HPPrS644ILq8MMPrw4++OA6TqjEFJUgy3AlvqhZ2u+b4/QTGFeg7F+rpWfcCs1PYFSB7Ht55YswgZV79uypgy8TgJkvy+9973vVV7/61fqL9OKLL6527drVm6bfspqBm/3GG0aAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwOQF+sWklWElVuiQQw6pE68lsDKvgw46qNqxY0cdR5REbZmuxBeVeUt38ltgCQSqOpZtNUFtzUhfMAQmIZAvtxIA2fyiK/texiXIMt28rnCFK9SrUbJaJuAyAZaJXE8pddVvGn8GDW9MopcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgY4FmjFB/aou40s8UAmwTJxQXiW4Mt1M0wyybNdX6moP955AlwLZz2Sw7FJUXSMJZAdMQGS6CbTMl2PpTzdfkolMP/TQQ3vBlc0slyMtzMQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECMxMIPFAJTaoBFkmXqhkrkw3CdryyvhMm9LuzmwDLHgpBQRYLuXHPpuNzpddyTBZ+hNY2cyimi/IlIzPF2W+RDO+BFamm5J6Sl31AH8IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJhbgdLSbbqJDUo38UHplqDK0s34Mk02KP2lNPvLMF0CkxBIrNq+aLZJ1K5OAn0E8gVXAiNLf/nyzOQZllf5As1OmmbBS0Bl6WbaUk/6FQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE5lMg8UApJTaodEuAZYkVSjevEj9U5qln9ofAFAUSm5Z9UYDlFNEt6kCBfBmWnbGMLV+QzayVJbCyGVTZ7C/z6hIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMF8CiQcqJf3l1QymLDFDmS7DU5rz9XtfT+QPgQkIlH1v21oQ297yZgLLUSWBvgLt4MjyPt3SnwDLlDKsDC/dvhUbSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAXAqUOLV0yysr2gyobE5TNqIMK+91CUxDIPFr29aC1fZOY2GWQaAt0N71mu/T337fnL85rjlcPwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC8yfQDpJsvk9/+31zC5rjmsP1E5i0gADLSQurf12BfoGS7WHt980K1xvXnE4/AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLTF1gvOLI9rv0+a9tv2PS3whKXTSDZK+sm7DURvmwf/Xxu76BAyUHD+23FKNP2m98wAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6ExgmOHLQNIOGd7d2aiIwWCCxaNkHZbAcbGTMDASGCZIcZpoZrLpFEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwQGDYgMlhpxuwGIMJdCZQB1nKYNmZp4o6FBBE2SGmqggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjMuYDAyjn/gJZ09WSwXNIPfitttmDLrfRpWVcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECwwkIqhzOyVTTF1hLXFmtrKxU25LBMou3s07/Q7BEAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYD4FVgVWzucHY60IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB6Qskg2ViK1fSk5dCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFh2gQRX1q+9a2XZMWw/AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKApsJr4Ss2EN0n0EyBAgAABAgQIECBAgAABAgQIECDQlcCkn+92bbOrT0o9BAgQIECAAAECBAgQIECAAAECBAgUgRJXuW2tRwbLoqJLgAABAgQIECBAgAABAgQIECBAgEBfgUW7jCgws+/HbCABAgQIECBAgAABAgQIECBAgAABAg2B1T179tRvV1ZWGoP1EiBAgAABAgQIECBAgAABAgQIECCwjAKLFkg56DMctJ0CLweJGU6AAAECBAgQIECAAAECBAgQIEBguQRyDVEGy+X6zG0tAQIECBAgQIAAAQIECBAgQIAAgf0EBgUa7jfRGG8mXf+kAyInXf8YtGYlQIAAAQIECBAgQIAAAQIECBAgQGDCAgIsJwysegIECBAgQIAAAQIECBAgQIAAAQLzKDBO4OM4887SYpxgyXHmneU2WzYBAgQIECBAgAABAgQIECBAgAABApsXqAMs00y4JsI3j2hOAgQIECBAgAABAgQIECBAgAABAltJYJQAyVGm3UoGZV1HCZwcZdpSvy4BAgQIECBAgAABAgQIECBAgAABAltPINdFcz1w21pw5V4XBrfeB2iNCRAgQIAAAQIECBAgQIAAAQIECIwqMEyw5DDTZLnDTjfqOk5q+mGvgQ4z3TDTTGo71EuAAAECBAgQIECAAAECBAgQIECAwPQENBE+PWtLIkCAAAECBAgQIECAAAECBAgQIDAzgY0CItcbv964jTZonHk3qjvjxwl2XG/e9caNu9xhtss0BAgQIECAAAECBAgQIECAAAECBAjMXkAT4bP/DKwBAQIECBAgQIAAAQIECBAgQIAAgYkKrBfkOGjcoOHNFR1mmub0s+rfKFgy6zVomkHD15tnVttpuQQIECBAgAABAgQIECBAgAABAgQIdCOQa595yWDZjadaCBAgQIAAAQIECBAgQIAAAQIECMylwHpBkP3G9RuWDRs0fKNxs0TZTHBkv3n6DSvbtd64Mo0uAQIECBAgQIAAAQIECBAgQIAAAQJbTyDXRLft3r17by4CuhC49T5Aa0yAAAECBAgQIECAAAECBAgQIEBgPYFBQZH9hg8zrN80ZfnrjSvTTLO73vXO9rj2+6znsMMGTTvNbbUsAgQIECBAgAABAgQIECBAgAABAgQmIyCD5WRc1UqAAAECBAgQIECAAAECBAgQINChwLwF73W1af2C+Lqqe5BZe/io77N+7XnKOg8aXsZPqzvItd/w9rBR35dtas9XhnfRnRfXLralWcckzZrL0U+AAAECBAgQIECAAAECBAgQIEBgMwK5LieD5WbkzEOAAAECBAgQIECAAAECBAgQIDBRgUUNKNsIrauAs0F+7eHrvW+Oa/aXbeg3LOMGDS/zTbo7yLDf8OawQf1Z3+a4fu/LNrWnK8NH7c7acNT17Wr6rvy6Wh/1ECBAgAABAgQIECBAgAABAgQIEKgzWO7Zs6daWVmhQYAAAQIECBAgQIAAAQIECBAgQGCmAssaWNZGHzfQrJ9je1jz/aj9Wd/mPO31H2Z8v3nGGbaRWXt88/2o/VnP5jxlvfsNK+OG6W5kOkwdizDNuI6LYGAbCBAgQIAAAQIECBAgQIAAAQIEZiuQa3W5TrVtLbhyrwtWs/0wLJ0AAQIECBAgQIAAAQIECBAgQGDjgL2m0VYNRBvlOtwo025k0/Zqvi/9pZu6Sn/pNoeVZZVxpdseXt5PuzvIrf2AeXO60l+6WefSX7rNYWWbmuPWG1bGrddtO3Y17Xr1THtcP69B6zDKtIPqMJwAAQIECBAgQIAAAQIECBAgQIDAuAJ1BstxKzE/AQIECBAgQIAAAQIECBAgQIAAgc0KbBRYttH4zS53XubbKJBso/HN7Rhk1Rzer78Ma3ZLf1q/KaUMy/tmf7/xZdgsuv3MmsOa/SXwsnSzvmV8u9sc1+7P+1LKfOX9et1+js3pNxrfnHYr9m9ktdH4rbjN1pkAAQIECBAgQIAAAQIECBAgQGBrCOTa3Lbdu3fvbV483Bqrbi0JECBAgAABAgQIECBAgAABAgQWQWC94LF+49rD2u/n1aQdJNZ+n/XuN6xsz3rjyjTp9vNoDuvXn2HllTpKQGV72jJNGV66mSel/X7f0Nn9bZuV9+k2X2UNy/hyrbR0y/DSzfSD+tt1lfeDuuuZ9RvXHtZ+P2g5sx7e9Mq6tN8PGlbWu9/0ZZwuAQIECBAgQIAAAQIECBAgQIAAgUkKaCJ8krrqJkCAAAECBAgQIECAAAECBAgQGCgwKDisPby8L91+Fa43rt/00xq2XmBYGVe6ZZ3a7zcaXsb3M2gO69efYe1X6suwT3/zkurj37ik+uy3d1bn/GB3Wcy63e3bqmr7Sl7bqtW1/tW1/pQ9e5uvvdXuy97vujw55r4Jp/z36lfcXt34J3ZUt7rGwdXNrnZwL/Avn0H7lVVrfjaD+ssmNMeXYc1u8/NYb3iZrnSb05b+9caVaWbRXc+gjCvdsn7t9xsNL+N1CRAgQIAAAQIECBAgQIAAAQIECExCQBPhk1BVJwECBAgQIECAAAECBAgQIECAwIYC/YLCmsNKf+mmwmb/egsYdrr16tjsuEEBYs36mtOU/tLNdM3+QfM1h6e/3zY3h5X+Zjf9zYyVef/NH+yq3nHWRdU/feniatYBkO1tnNT7BILe9XqHVHc//tDqaldc7QVXZnnJZJnPo3wm7W6mKcPSX0q/YWVcuuVzGDSsjC/dQfM05y/9zXnKsGl1N9rurEdzmtJfuu3xzfVuTtMcrp8AAQIECBAgQIAAAQIECBAgQIDAJARy7VSA5SRk1UmAAAECBAgQIECAAAECBAgQILCuQL8AsOaw9Dfft4Pc1q38spHN+cv0CdDK8BKo1ewv00yjm+XmVYIbs8ysU1mv8r69Ls3x7XGpr1ma79v9eZ9XWX76d+/eXX3rh7urV3/6wuqj37i0WdXS9P/MNQ6qHnKzw6rjjthebd++vfd5lObCy35YQJqfR7M/49vvyzzpNj+PMrw5LP3N92W569VZ6ind5vxlWObP8FJPs79MM41ulptX2f+yzKxTWa/yvr0uzfHtcd4TIECAAAECBAgQIECAAAECBAgQ6FKgXDu7rJGeLqtWFwECBAgQIECAAAECBAgQIECAAIHBArkw1S5lWLrllWkSWLa6utrLItieb733JWCr2c30zSCtZv96dXU9Lsttblvqb253ed9ebnEadvig6ZrLSn+CK1OSuXJZgyuz/dn2GKTEpAQAFvemWz3ROn/KPO1J+g0vw0r95X1zHxl1X8307VfWpVlPs7+9npN8n+U2ty3LKttellsMyvsyTfO9fgIECBAgQIAAAQIECBAgQIAAAQKTEijXzlb6Xaia1ELVS4AAAQIECBAgQIAAAQIECBAgsNwC/a5FtYclqC3DEoCV16KXsp3Z5vIq29y2KcM36jbnK/2l2wwaTH/x/uQ3fly96/9dslHVCz8+BrFIiVnxKe8zrFiWbhmX7nqlOX2Zrj2sLK/sF2W6Re2W7YxDeZVtbduU4boECBAgQIAAAQIECBAgQIAAAQIEpiGQa3UrJdJyGgu0DAIECBAgQIAAAQIECBAgQIAAAQJNgWYAVQmuSrdkuGtOu8j9CTLLNpfguqZLe7vXG9eetv2+zJtueWWZeZ1+zs5q1572HMv3PgaxKBksm2Zda5S6U2/5PNK1/x+Y5bbYN83KMF0CBAgQIECAAAECBAgQIECAAAECkxCoHw6eRMXqJECAAAECBAgQIECAAAECBAgQIDCKQDu4LM2CL1vJNiewrmkRg3ECysq8/erMsBLQmWDCf//OrmUjH7i9sYhJMSpO5fMonuX9wIqGHFHqSzf7gP2/f5bQITlNRoAAAQIECBAgQIAAAQIECBAgQGBsgVwTTNFE+NiUKiBAgAABAgQIECBAgAABAgQIEBhXoASY5aJVAsyWtWTbSzBfTEpp9mdY832zv0zf7janSX9zGQkkzOubP5K+srjForjEq2lWphmm23Rfb/rmMuz/e2rvpl2zP47t9+vZGkeAAAECBAgQIECAAAECBAgQIEBgMwLlOp0mwjejZx4CBAgQIECAAAECBAgQIECAAIGxBUqQVLOb/jS7sqwl2x6DpsmoFmXe9nxleOmWIMt081L2Fygu8So+xS7d0t+cq9+w5vhmf5m22U2//f9y22LTdNNPgAABAgQIECBAgAABAgQIECBAYBoCCbDM9anV/CnRltNYsGUQIECAAAECBAgQIECAAAECBAgsp8CgYKkML68Esi17gFkJ5ivX7Ep31L2meJdu5i/OpVuyNKar7C8Qk9iXVzFr7qOlP+M2+pwyTb9S6k231NdvumUYlmO/q/1/GbxsIwECBAgQIECAAAECBAgQIECAwGQFcs1PBsvJGqudAAECBAgQIECAAAECBAgQIEBgSIESYDbk5As7WQkwawfkbfR+GJDUUV5l+rwvyyzDdKvapGk1aadJ179VPtOyL8ajWTZ635xWPwECBAgQIECAAAECBAgQIECAAIFxBcp1qpVcmGpfnBq3cvMTIECAAAECBAgQIECAAAECBAgQGEagXJfKxSrXqfaJxaF4ZEgxGsaz3zRt1/K+mb1SBssD5YpPPov0tz+H9vsDa9h4SKmjfN7l/cZzLu4UMSge2Uomi/tZ2zICBAgQIECAAAECBAgQIECAwDwLlJaW6gyWGzVfM88bYt0IECBAgAABAgQIECBAgAABAgS2tkAJoEq39G/tLRpv7ZsGzf7xat0/UK3U2+6Ou4xFmj825ZXtKlbpTwBgKc3hZdgo3TJ/c1mjzL9o0xaPbFezf9G20/YQIECAAAECBAgQIECAAAECBAjMt0B5CHglPc0LgvO92taOAAECBAgQIECAAAECBAgQIEBgEQTagVMluKw9fBG2ddRtGMdiPb9Sb9an9JdrgzJYHvgpxaT4FK90N7qWmmk2Ku1pmvVvNO+ij2ex6J+w7SNAgAABAgQIECBAgAABAgQIbA2BJK3May2T5Ur+bI21tpYECBAgQIAAAQIECBAgQIAAAQILLdAOPFvojR2wcW2DEnA2YPK+g0sdpducqNSXQMH0l25zGv2XB6EWr9Jt22R4Sum2x4/yvos6RlnePE7bNsj79rB5XG/rRIAAAQIECBAgQIAAAQIECBAgsFgCpVXwFRenFuuDtTUECBAgQIAAAQIECBAgQIAAga0k4NrUdD+tZrBa6S/d6a7J/C+tuKS7UdbKzW5N6lYIECBAgAABAgQIECBAgAABAgQIEJg/gXLtbqVEWs7fKlojAgQIECBAgAABAgQIECBAgAABAsspUC7ebbT1w0zXb5r2sEkFEG60/vM8vm2U9/2GbbQN7Xk2mt74brKBciRAgAABAgQIECBAgAABAgQIECAwjkCJq1zJxVMXUMehNC8BAgQIECBAgAABAgQIECBAgEBXAoLRphdcVqxzbTD9K9u6+hQXo55y3TQ2xSpbVvpLt8utnUSdXa7fNOpiMA1lyyBAgAABAgQIECBAgAABAgQIEBhGINeqVldWVoaZ1jQECBAgQIAAAQIECBAgQIAAAQIEJiogsKp73mLa7wHrjCvD079929r7vaIsy6cQk+arDE83w1PKU+z1mzH/lDrHrMbsBAgQIECAAAECBAgQIECAAAECBAh0JJDrf6IrO8JUDQECBAgQIECAAAECBAgQIECAAIF5FmgG8DX7yzpvF1tZKPp2m2bN/kzcft+3AgMJECBAgAABAgQIECBAgAABAgQIENhyAnWAZXlSfcutvRUmQIAAAQIECBAgQIAAAQIECBBYOAHBaoM/0nFt+s1fhiWDpXKgQPFpdw+cspshZTnd1LZYtbBZrM/T1hAgQIAAAQIECBAgQIAAAQIE5lmgXItaSY9mwuf5o7JuBAgQIECAAAECBAgQIECAAIHlEigXrpZrq6e3tYN85yGD5erKtuq21z60+v27HFvd/rqHTQ9lgyUVs9LdYPKxRk9jGWOtoJkJECBAgAABAgQIECBAgAABAgQILIFAmgdPWS09S7DNNpEAAQIECBAgQIAAAQIECBAgQIDASAJnnXVWtXPnzurEE08cab5ZTDxOYN4483axrT915YOqO//04dUdf+rw6shD6kZ3qo+e/eMuqt50Hf1a/RnWKdPN+3XXM844o9qxY0d1/PHHb9rIjAQIECBAgAABAgQIECBAgAABAgQWXWA1G5iLhbJYLvpHbfsIECBAgAABAgQIECBAgAABAgRGEUhwZYLQStkKQZZlXdfrJvivvJrT7Wm+mUL/la6wvbrTTx1W3eX4w6vrHH3QFJZoEUUg+3X271IEWRYJXQIECBAgQIAAAQIECBAgQIAAAQL7BMo11DrAUnCl3YIAAQIECBAgQIAAAQIECBAgQIDA5QLt4Mq83759e3XCCSdcPtGc9+UCYLO032dcGbbvYmFz6sn2n3SNK1TPvutVq+37klXWC8vq/uCS3WsZLLdPduETqD1+856xsmz2mWeeuV9wZQkiFmRZhHQJECBAgAABAgQIECBAgAABAgQIVL3rfSvJXlkupIIhQIAAAQIECBAgQIAAAQIECBAgsOwC7eDKeKQp5ate9aoLSVOuDe7Zu21q23fYwSu94Mqzz99Zvfxj51cPeu3Xq7d9/oe9dVjLs9nrn1VPv2bCZ7UuXS03+3H252ZpZ7RsjtNPgAABAgQIECBAgAABAgQIECBAYFkF8lD1quyVy/rx224CBAgQIECAAAECBAgQIECAAIG2wKDgytvd7nbV0Ucf3Z58od5Ps4nwCy/ZU73pjB9U7/7ij6r/971LF8px3jcm+3H25w996EPVzp07e6srk2WPQg8BAgQIECBAgAABAgQIECBAgACBWiAPp8tgaWcgQIAAAQIECBAgQIAAAQIECBCYK4FZNbW8zMGV2QH2TDFh5Onf+HH14o+c4ujRaQAAGbZJREFUJ7iyz5E3jf2/BFnKZNnnAzCIAAECBAgQIECAAAECBAgQIECAwGUCuVa3lsByRRPhdgkCBAgQIECAAAECBAgQIECAAIG5EZhGgFl7Y5c9uDIe02wivO3v/eUC09r/BVlebq6PAAECBAgQIECAAAECBAgQIECAQFsg2StTVuo0lmtBlgoBAgQIECBAgAABAgQIECBAgACBeRAoF66mtS6CK/dJ755iBstpfbZbcTnT3P8FWW7FPcQ6EyBAgAABAgQIECBAgAABAgQITEOgPAi9UnqmsVDLIECAAAECBAgQIECAAAECBAgQILCRwDSvVwmu3OjTMH7aAtPc/7Ntgiyn/QlbHgECBAgQIECAAAECBAgQIECAwFYRqJNX7tmzZ6usr/UkQIAAAQIECBAgQIAAAQIECBAg0JmA4MrOKFW0xQUEWW7xD9DqEyBAgAABAgQIECBAgAABAgQITEQgD0PLYDkRWpUSIECAAAECBAgQIECAAAECBAjMs4Dgynn+dKzbLAQEWc5C3TIJECBAgAABAgQIECBAgAABAgTmXUCA5bx/QtaPAAECBAgQIECAAAECBAgQIECgUwHBlZ1yqmyBBARZLtCHaVMIECBAgAABAgQIECBAgAABAgTGFkjr4Ctj16ICAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCCCOzdu7daWVkRYLkgn6fNIECAAAECBAgQIECAAAECBAgQGFLg+OOPr0488cT9pt65c2f1oQ99qDrvvPP2G+4NgWUSyP6f4yDHQ7PkeMlxoxAgQIAAAQIECBAgQIAAAQIECBBYFoFt27bVm7qSSEuFAAECBAgQIECAAAECBAgQIECAwDIJCLJcpk/btg4jILhyGCXTECBAgAABAgQIECBAgAABAgQILJNA3UR4ibRcpg23rQQIECBAgAABAgQIECBAgAABAgQEWdoHCOwTEFxpTyBAgAABAgQIECBAgAABAgQIECBwoIAmwg80MYQAAQIECBAgQIAAAQIECBAgQGCJBOYtyFJrM0u0883Jps5TcKX9f052CqtBgAABAgQIECBAgAABAgQIECBQJXtliibC7QwECBAgQIAAAQIECBAgQIAAAQJLLTBPQZZam5mPXXH7tvlYj0mvxTwFV2Zb7f+T/sTVT4AAAQIECBAgQIAAAQIECBAgMKxAuVa1UnqGndF0BAgQIECAAAECBAgQIECAAAECBBZNYF6CLGXwm489a2Xb3vlYkQmuxbwFV2ZT7f8T/MBVTYAAAQIECBAgQIAAAQIECBAgMJJA4ipzvUoGy5HYTEyAAAECBAgQIECAAAECBAgQILCoAvMQZCnAbD72rpUFz2A5j8GV+eTt//Ox/1sLAgQIECBAgAABAgQIECBAgACBfQIJspTB0t5AgAABAgQIECBAgAABAgQIECBA4DKBWQdZrqys+CzmQGCRP4V5Da7Mx27/n4Od3yoQIECAAAECBAgQIECAAAECBAjUAnv27Km7dQZLTwbbKwgQIECAAAECBAgQIECAAAECBAjsExgUZPmd73wH0ZIILHIT4dmPd+7cud8neeKJJ1bZ7xUCBAgQIECAAAECBAgQIECAAAECBPYJlIeBV5PGUiFAgAABAgQIECBAgAABAgQIECBA4HKBEmx2xhln1APz/oQTTrh8An0TEXjV6d+v8pp1WeRLptmPd+/eXZ111lk1s+DKWe9tlk+AAAECBAgQIECAAAECBAgQIDCPAslgmdjK1V4qS80PzePnZJ0IECBAgAABAgQIECBAgAABAgRmJFCCLJPtL0Fo0yjlWt00lmUZgwUWuYnwbHXZn3fs2DFXmSvt/4P3SWMIECBAgAABAgQIECBAgAABAgSmK5DgyjrAsqSynO7iLY0AAQIECBAgQIAAAQIECBAgQIDA/AuUIMv5X1NrSGA0gRJkOdpcpiZAgAABAgQIECBAgAABAgQIECCwHAKlZfCVvXv3LscW20oCBAgQIECAAAECBAgQIECAAAECcy5QLtpNczVnscxpbt9mlrX7skumbDajt/l5eG/ezpwECBAgQIAAAQIECBAgQIAAAQLdCpS4yhUXrbqFVRsBAgQIECBAgAABAgQIECBAgACBzQp0ea2uy7o2uz1bdb7de7eNtOqsR+IaODHHgTRGECBAgAABAgQIECBAgAABAgQITFmgXKta2bNnT5WXQoAAAQIECBAgQIAAAQIECBAgQIAAAQJVVTJYsiBAgAABAgQIECBAgAABAgQIECBAYHkFksVydWVlZXkFbDkBAgQIECBAgAABAgQIECBAgACBBRHIE9Wl2ZpRN6k8jT3qfIs6fTJYjmMyzryLamq7CBAgQIAAAQIECBAgQIAAAQIECGw1gVznE1251T4160uAAAECBAgQIECAAAECBAgQIEBgDAHBfxvj7dnbfxp2/V0MJUCAAAECBAgQIECAAAECBAgQILCoAnWApSbCF/XjtV0ECBAgQIAAAQIECBAgQIAAAQKLJDBugN+48y+S5Wa3heFm5cafj/34hmogQIAAAQIECBAgQIAAAQIECBAYTqC0FrSSHs2ED4dmKgIECBAgQIAAAQIECBAgQIAAAQJbVaAZnNbs36rbM+31bpo1+6e9HpZHgAABAgQIECBAgAABAgQIECBAgMDkBco1wJXSM/lFWgIBAgQIECBAgAABAgQIECBAgACBwQKuUw22GXdMP9sM6zd83GUt2vyDnLq267q+RfscbA8BAgQIECBAgAABAgQIECBAgACBWQhoInwW6pZJgAABAgQIECBAgAABAgQIECBAYE4EBgUQzsnqzWw1uMyM3oIJECBAgAABAgQIECBAgAABAgQIzFwgLYPv2bOnqgMsNRE+88/DChAgQIAAAQIECBAgQIAAAQIECKwJyOK3z2BYh2GmG2YaO9/mBIaxHWaasvRRpi3zLFo3BhwW7VO1PQQIECBAgAABAgQIECBAgACBrSdQrlOtJMoy0ZYKAQIECBAgQIAAAQIECBAgQIAAgWkLCKSatrjlzZOA/X+ePg3rQoAAAQIECBAgQIAAAQIECBAgQGB/gVy/W0teueKJ4P1dvCNA4P+3d3+9bR35GYDpREqyWbfdbYDdtkBRoFgUDdrrAgu0X6AX/eC9L9Cb3uYurRMYsR1ZdvXS+67Hs6REihRFkc8Bjub/nJnn0DeDn0kCBAgQIECAAAECBAgQIECAwNEI9H9Kb7OgBu41Hcd2vjEd2+U/CoxGzX9s/ZBLfa6mH2r93ZfAOvd9zW8eAgQIECBAgAABAgQIECBAgAABAusE8sWVvsFynY56AgQIECBAgAABAgQIECBAgACBBxOYg9HG8tu3bx/succ+8a57Hx1X7XVd+7r6VXOcS11MVrmsqhtN7mpP37nPWN71MzCu5anlz3nvT+1dWS8BAgQIECBAgAABAgQIECBA4BwEcm63/AZLPxF+Dq/bHgkQIECAAAECBAgQIECAAAECxynQ4LKkuc85yCp7nz329dbySzbrrj5zXfs51t9mclvbtladK2lun/+P3wham21N9SdAgAABAgQIECBAgAABAgQIECCwq0BjKj9bfo3lLYeruz7IeAIECBAgQIAAAQIECBAgQIAAAQLrBOYAqpR//vnnRQ+v1o07xfrsOXuPwegy5u+773GOzp+Ay+aT/uYX7+47/cmNi8Vosyo4Ne27XvMcKfv87//zv+t7Mp4AAQIECBAgQIAAAQIECBAgQOD8BHp291kz50dgxwQIECBAgAABAgQIECBAgAABAscocHV1tXj16tUxLu1B15Q9Z+/rrvkcby6vGzfXd1zSBg4m/w9/cT13PdtyLEanQIxeDwnj879at++jrXO59VICBAgQIECAAAECBAgQIECAAAEC+xJYfnnlu3f+Z/q+QM1DgAABAgQIECBAgAABAgQIECCwXuC2gKjx2xSTT7Dh69ev1092Yi3Za/Y8O+yyzXo3zVzNj2memfufv7lefP7s/S6PPImxMYhFXWrVzY3l5pu2z6r0tj7ze/f5/xj8u8pSHQECBAgQIECAAAECBAgQIECAAIFDCORMzzdYHkLaMwgQIECAAAECBAgQIECAAAECBP5EoAFnY9pAs/zP4JcvXy5+/PHHk/658Owze8xecy0P7P7w090tLxu2+FPPechYn3zLTf/x1+8Wv//Nz/OwsyvH4Nu//DTQtJ/LYtSs5abr6ts+pu07pn2Oz/+Hn1+vzegmT4AAAQIECBAgQIAAAQIECBAgQOCQAhcOqQ7J7VkECBAgQIAAAQIECBAgQIAAAQKrBHJG1TtBZp9//vny57J/+OGHxYsXLxZff/314vnz54uvvvpq1fAHrUuw23znganr2VoD45LedWXcmzdvlkGVP/3003KOL7/8cnF5efnJN1h27sw35udy2jLnplf6j3fWXPN//eurxf+9WSz+68WXm053Uv3+6VdvFv/2N29vPC7++A2W47ud30M3v219xzWd34fP/8cA4BjNvnO5jlICBAgQIECAAAECBAgQIECAAAEC+xTIr4Nf7HNCcxEgQIAAAQIECBAgQIAAAQIECBDYViDBUgkQTCBb0gSX5Ur++vp6GYz4/fffL7777rvF27dvl3Vpy/2UrgbRZX8XFxeLBFX2Tjn1uRvwmL11zH33mblyCNh0DEzr3EnTnmf/1fPrxb//7evFn19cL/7z+18srt9/+CbB+z7/qYzLz4L/yzevFgkw/e0vLz95D3XqXlpOmqtp27dNMz6f5byDpHkPuZL3+f800HJbW/0JECBAgAABAgQIECBAgAABAgQI3FegZ3YCLO8raBwBAgQIECBAgAABAgQIECBAgMDOAg0ua5Bag8waaPbFF18sn5H63A2wTNDgfGXMsVzdz7ye7CEBdA2wzLdWZo8NrGya8evmWFc/P2tduXMnrWvT1P32l88W//F3rxa/+7PXi/9+cbn4n5dfLf736nLddAepvwk9XHx2EwSZNGGNNyGJy3LDP69vat7dBIO+G9JNFvbN5dXi75+/Xnz7q7c3Pwv+bvkuapF0tBrzmTvlXa/Mkc9t58ozU/b5f7jP/67vzHgCBAgQIECAAAECBAgQIECAAIHzEOiZ3cV4gHceW7dLAgQIECBAgAABAgQIECBAgACBxxJoQNn4/B5U9ZwqQYap652gswQkJhAx3+jXALSkuZqOcx5TvvvrfpJmj7nHb65scGX2m6v977OXjK1L82PauZvmmV1Pfb/99dXid89f3pj/sAxsTVDr7D8HuvaZ91nzPsdkX+OV/Y17TTl3Alyz76R9F03bZx6beTt/07FufO6cT//ZqHOkPvmsJ2nvPN/nf5ZUJkCAAAECBAgQIECAAAECBAgQIPDwAjn/vOgB3sM/zhMIECBAgAABAgQIECBAgAABAgQIfAhOWxVklkCyHFiN51XJpz5BZw3my9jeo+c859j2GPlxH3l+yr1Tzr5W3W2bx6d+VV3rN9l/jTtmtF0eFt4Ess7zdI0Jrsx7SHv6tl/TzJn8WE7doa8arUqzl9R339lPghdzj0GWqe/dMd1H5215XbquX+pno67J5/9DcPFsXuN1pm2XEiBAgAABAgQIECBAgAABAgQIENinQM6p/ET4PkXNRYAAAQIECBAgQIAAAQIECBAgsJHAbUFmCT5rgNUYeNbgvQanNd3ogUfQqcFhSXt3n01b3767LDtz1GjMZ848L21NE0zYoMnUj+MSWJmfZk8QYvr0bp/MN+ZT7rWuvu37Std5jfXJZ7+1Tpo9NchyTBtc2TFNx/XOc49td+UzdrbpM1LfNbZu1buZx9/1zMdur1fS3t1n09a372Ov2fMJECBAgAABAgQIECBAgAABAgTOVyBncsszxBzEObA63w+CnRMgQIAAAQIECBAgQIAAAQIEjkmg51RJc4CVQLcGnCXtPa859cd4dT/j2lI332nPYV2uVWNuq18O2uBP5o1Tn53njeVatz1Blck32HAMrMy4lHtt679t/z4n69nmGvuPQXzJ5+7ekvabLFvXPpmjY5tus4Zt+na9SX3+P5Wrzae1SgQIECBAgAABAgQIECBAgAABAgQeRqDnUX4i/GF8zUqAAAECBAgQIECAAAECBAgQIHCHQA6oVgXatT7BbLkSaNbDrLn/XL7jkY/W3PV3AWO5+0zbWN++t9XPfWaPzNe6OZ9ynp32BBf2OVdXV8ufy257/PMtlknTt2nn7RpSnuva9hjp6Nq9dU9Nx2DKGKQ+acamLWnu1OfuNc495ts+9m3dnKbPKq/Wd16f//X/LmZTZQIECBAgQIAAAQIECBAgQIAAAQL7Eug53UUO8TY58NvXg81DgAABAgQIECBAgAABAgQIECBAoAI9pGq56Xhe1UCztiVdFZg2th9zftxb1jmX57Xf1T73n8vxS5BernGu1MexvgkozHV5ebkMqEzftCW4sn3H4Mr5HczlzLWqLvUPcY17y/yryqnrvpqOQZYNqmxam6Sdr+mqZ2y7r8y1ymh8Rtcwzr1qzNh+zPlxb1nnXJ7Xflf73F+ZAAECBAgQIECAAAECBAgQIECAwL4EcjblGyz3pWkeAgQIECBAgAABAgQIECBAgACBewk0gGpd0Fjb7zX5kQ3aZi/b9O02M2adY/p0zvZp4GTTtCegMmm/ObFtqc/VsQ3abHlsW3Z8pD/dYx/fctLeaUsQZcrZ3xhQOdanPeVe6XvXlTHbXO0/Oo7j2z7WPdX8NnvZpu9T9bBuAgQIECBAgAABAgQIECBAgACB4xXI+WfOA5/dZN5nmQ6sjvdlWRkBAgQIECBAgAABAgQIECBA4FwE1gWZncv+u89dz+pmx5Rb1zQHhK1PfrxTP/4seMrtnzW23HzXPabpc8hrldlYtzwM/UPwY+pzp671Y2Bl69vWvtlP5+wc4x7bNtZtkz+02TZrO2TfXR0PuVbPIkCAAAECBAgQIECAAAECBAgQOG0B32B52u/X7ggQIECAAAECBAgQIECAAAECT0qggVXnGmjW/T/ES8vccR3TPmcMJBzbE1SZqz8TnnzfTdLmU99rVV3bDpGuMkxd68e0QZWpq0GDKzumbVn7OLb5fe6pcz624T73tM1c3f82Y/QlQIAAAQIECBAgQIAAAQIECBAg8BACORvNedVFD0lzcOgiQIAAAQIECBAgQIAAAQIECBAgcAwCc6DVqQaczfvcl33mHc1aHtNVz+oZYcYmn7Q/GZ7+nTNp8+M8q+rG9kPl17l2f21P2iDLrC3l9Ek63m0b0+THK/33dc1zHYvrvvbXeeZ9tl5KgAABAgQIECBAgAABAgQIECBA4LEF/ng+eHM4d9jf6nnsnXs+AQIECBAgQIAAAQIECBAgQIAAgTMRmI/+Wk465sMx/wR42vufs9u35fK1PuUx3/bHTBu817Rr6cFoys2PaevHYMzOMafjnM1LCRAgQIAAAQIECBAgQIAAAQIECBA4HYFnN4ei73sweDrbshMCBAgQIECAAAECBAgQIECAAAECBCIwBz6O5QZMti5p746d20bVsW2sP5Z8zz2bdl0p905d21vXtP0bbNly+49j2yYlQIAAAQIECBAgQIAAAQIECBAgQODpC+Tsc3lOeJPxDZZP/33aAQECBAgQIECAAAECBAgQIECAAIG1AvMR4FhOvuUxHeszcdvGhzRAc6w7xvwcIJk1zkGSY1Dl2DaPHdvG/DHu25oIECBAgAABAgQIECBAgAABAgQIENhN4Nn19fUywHI+KNxtWqMJECBAgAABAgQIECBAgAABAgQIEDgmgTlAci7PwZJj+9yWfY3tt+1z0363zXFb26ZBjqv6jWeic/vY1uePfcZ826UECBAgQIAAAQIECBAgQIAAAQIECJyOQM42n9388Q2Wp/NO7YQAAQIECBAgQIAAAQIECBAgQIDAWoH5KHAuZ2Dq5vq53H5rH3SEDasCIue6lOe6bGWum8tHuF1LIkCAAAECBAgQIECAAAECBAgQIEBgDwICLPeAaAoCBAgQIECAAAECBAgQIECAAAECT0lgDJgc8/Me0nZbe/tv0qd9D5luEgiZPrf1G9vG/CH34VkECBAgQIAAAQIECBAgQIAAAQIECDyOwDLAMj/xs+onbx5nSZ5KgAABAgQIECBAgAABAgQIECBAgMBDC4xBkWP+ruem7zb975rvkO13BVOOa5mDKefy2FeeAAECBAgQIECAAAECBAgQIECAAIHTEsgZ6PI88Sa48r3DwdN6uXZDgAABAgQIECBAgAABAgQIECBAYFOBVcGSq+o2nW/ut8+55rlT3tfZ5rp51tWvWos6AgQIECBAgAABAgQIECBAgAABAgROS8BPhJ/W+7QbAgQIECBAgAABAgQIECBAgAABAjsJbBsQuW3/nRa3w+BNAyU37bfDUgwlQIAAAQIECBAgQIAAAQIECBAgQOCJCPiJ8CfyoiyTAAECBAgQIECAAAECBAgQIECAwGMKPJVAyk2NBFJuKqUfAQIECBAgQIAAAQIECBAgQIAAgfMTyHlobt9geX7v3o4JECBAgAABAgQIECBAgAABAgQIHEzgoQMzBUoe7FV6EAECBAgQIECAAAECBAgQIECAAIGzElgGWF5fX7/PIaSDyLN69zZLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI3CLgGyxvwdFEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQInJ9AvsHy/wGH6IpuKfzRywAAAABJRU5ErkJggg==" width = "960" height = "247" alt = "demo_dandi_download_data.png" style = "vertical-align: baseline; width: 960px; height: 247px;"></img></div><h2  class = 'S9' id = 'H_531c' ><span>Installing matnwb</span></h2><div  class = 'S1'><span>Use the code below to install MatNWB from source using </span><span style=' font-family: monospace;'>git</span><span>. Ensure </span><span style=' font-family: monospace;'>git</span><span> is on your path before running this line. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~exist(</span><span style="color: #a709f5;">'nwbRead'</span><span >, </span><span style="color: #a709f5;">'file'</span><span >) </span><span style="color: #008013;">% Skip if MatNWB is on MATLAB's search path</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #8b6606;">!git clone https://github.com/NeurodataWithoutBorders/matnwb.git</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% add the path to matnwb and generate the core classes</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    addpath(</span><span style="color: #a709f5;">'matnwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><div  class = 'S13'><span>MatNWB works by automatically creating API classes based on the schema. For most NWB files, the classes are generated automatically by calling </span><span style=' font-family: monospace;'>nwbRead</span><span> farther down. This particular NWB file was created before this feature was supported, so we must ensure that these classes for the correct schema versions are properly generated before attempting to read from the file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Reminder: YOU DO NOT NORMALLY NEED TO CALL THIS FUNCTION. Only attempt this </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% method if you encounter read errors.</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >generateCore(util.getSchemaVersion(</span><span style="color: #a709f5;">'sub-P11HMH_ses-20061101_ecephys+image.nwb'</span><span >));</span></span></div></div></div><h2  class = 'S9' id = 'H_30ba' ><span>Read the NWB file</span></h2><div  class = 'S1'><span>You can read any NWB file using </span><span style=' font-family: monospace;'>nwbRead</span><span>. You will find that the print out for this shows a summary of the data within.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% ignorecache informs the `nwbRead` call to not generate files by default. Since </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% we have already done this, we can skip this automated step when reading. If </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% you are reading the file before generating, you can omit this argument flag.</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >nwb = nwbRead(</span><span style="color: #a709f5;">'sub-P11HMH_ses-20061101_ecephys+image.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="7DBCCD75" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="699" data-hashorizontaloverflow="true" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</span></a><a href = "#H_5128"><span>Conclusion</span></a></div></div><h2  class = 'S2' id = 'H_39af' ><span>Download the data</span></h2><div  class = 'S1'><span>First, let's download an NWB data file from the</span><span> </span><a href = "https://dandiarchive.org/"><span style=' text-decoration: underline;'>DANDI neurophysiology data archive</span></a><span>.</span></div><div  class = 'S1'><span>A NWB file represents a single session of an experiment. It contains all the data of that session and the metadata required to understand the data.</span></div><div  class = 'S1'><span>We will use data from one session of an experiment by</span><span> </span><a href = "https://www.nature.com/articles/s41597-020-0415-9"><span style=' text-decoration: underline;'>Chandravadia et al. (2020)</span></a><span>, where the authors recorded single neuron electrophysiological activity from the medial temporal lobes of human subjects while they performed a visual recognition memory task.</span></div><ol  class = 'S6'><li  class = 'S7'><span>Go to the DANDI page for this dataset:</span><span> </span><a href = "https://dandiarchive.org/dandiset/000004/draft"><span>https://dandiarchive.org/dandiset/000004/draft</span></a></li><li  class = 'S7'><span>Toward the top middle of the page, click the "Files" button.</span></li></ol><div  class = 'S8'><img class = "imageNode" src = 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" width = "961" height = "399" alt = "demo_dandi_view_files_in_dataset.png" style = "vertical-align: baseline; width: 961px; height: 399px;"></img></div><div  class = 'S1'><span>          3. Click on the folder "sub-P11MHM" (click the folder name, not the checkbox).</span></div><div  class = 'S8'><img class = "imageNode" src = 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" width = "960" height = "228" alt = "demo_dandi_select_folder.png" style = "vertical-align: baseline; width: 960px; height: 228px;"></img></div><div  class = 'S1'><span>          4. Then click on the download symbol to the right of the filename "sub-P11HMH_ses-20061101_ecephys+image.nwb" to download the data file (69 MB) to your computer.</span></div><div  class = 'S8'><img class = "imageNode" src = 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" width = "960" height = "247" alt = "demo_dandi_download_data.png" style = "vertical-align: baseline; width: 960px; height: 247px;"></img></div><h2  class = 'S9' id = 'H_531c' ><span>Installing matnwb</span></h2><div  class = 'S1'><span>Use the code below to install MatNWB from source using </span><span style=' font-family: monospace;'>git</span><span>. Ensure </span><span style=' font-family: monospace;'>git</span><span> is on your path before running this line. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~exist(</span><span style="color: #a709f5;">'nwbRead'</span><span >, </span><span style="color: #a709f5;">'file'</span><span >) </span><span style="color: #008013;">% Skip if MatNWB is on MATLAB's search path</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #8b6606;">!git clone https://github.com/NeurodataWithoutBorders/matnwb.git</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% add the path to matnwb and generate the core classes</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    addpath(</span><span style="color: #a709f5;">'matnwb'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><div  class = 'S13'><span>MatNWB works by automatically creating API classes based on the schema. For most NWB files, the classes are generated automatically by calling </span><span style=' font-family: monospace;'>nwbRead</span><span> farther down. This particular NWB file was created before this feature was supported, so we must ensure that these classes for the correct schema versions are properly generated before attempting to read from the file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% Reminder: YOU DO NOT NORMALLY NEED TO CALL THIS FUNCTION. Only attempt this </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% method if you encounter read errors.</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >generateCore(util.getSchemaVersion(</span><span style="color: #a709f5;">'sub-P11HMH_ses-20061101_ecephys+image.nwb'</span><span >));</span></span></div></div></div><h2  class = 'S9' id = 'H_30ba' ><span>Read the NWB file</span></h2><div  class = 'S1'><span>You can read any NWB file using </span><span style=' font-family: monospace;'>nwbRead</span><span>. You will find that the print out for this shows a summary of the data within.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% ignorecache informs the `nwbRead` call to not generate files by default. Since </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% we have already done this, we can skip this automated step when reading. If </span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% you are reading the file before generating, you can omit this argument flag.</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >nwb = nwbRead(</span><span style="color: #a709f5;">'sub-P11HMH_ses-20061101_ecephys+image.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="4B2CD2CA" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="699" data-hashorizontaloverflow="true" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                 nwb_version: '2.1.0'
                            file_create_date: [1×1 types.untyped.DataStub]
@@ -134,10 +134,10 @@
                       stimulus_presentation: [1×1 types.untyped.Set]
                          stimulus_templates: [0×1 types.untyped.Set]
                                       units: [1×1 types.core.Units]
-</div></div></div></div></div></div><div  class = 'S13'><span>You can also use </span><span style=' font-family: monospace;'>util.nwbTree</span><span> to actively explore the NWB file.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >util.nwbTree(nwb);</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="1F040CBC" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h3  class = 'S17' id = 'H_505b' ><span>Stimulus</span></h3><div  class = 'S1'><span>Now lets take a look at the visual stimuli presented to the subject. They will be in </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.stimulus_presentation</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="ABB70F39" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="64" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Set with properties:
+</div></div></div></div></div></div><div  class = 'S13'><span>You can also use </span><span style=' font-family: monospace;'>util.nwbTree</span><span> to actively explore the NWB file.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >util.nwbTree(nwb);</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="7A3149A6" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h3  class = 'S17' id = 'H_505b' ><span>Stimulus</span></h3><div  class = 'S1'><span>Now lets take a look at the visual stimuli presented to the subject. They will be in </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.stimulus_presentation</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="ADCAA85B" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="64" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  Set with properties:
 
     StimulusPresentation: [types.core.OpticalSeries]
-</div></div></div></div></div></div><div  class = 'S13'><span>This results shows us that </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span><span> is a </span><span style=' font-family: monospace;'>Set</span><span> object that contains a single data object called </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>, which is an </span><span style=' font-family: monospace;'>OpticalSeries</span><span> neurodata type. Use the </span><span style=' font-family: monospace;'>get</span><span> method to return this </span><span style=' font-family: monospace;'>OpticalSeries</span><span>. </span><span style=' font-family: monospace;'>Set</span><span> objects store a collection of other NWB objects.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="C5631514" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="359" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 370px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  OpticalSeries with properties:
+</div></div></div></div></div></div><div  class = 'S13'><span>This results shows us that </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span><span> is a </span><span style=' font-family: monospace;'>Set</span><span> object that contains a single data object called </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>, which is an </span><span style=' font-family: monospace;'>OpticalSeries</span><span> neurodata type. Use the </span><span style=' font-family: monospace;'>get</span><span> method to return this </span><span style=' font-family: monospace;'>OpticalSeries</span><span>. </span><span style=' font-family: monospace;'>Set</span><span> objects store a collection of other NWB objects.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="A73BFA1B" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="359" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 370px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  OpticalSeries with properties:
 
                         distance: 0.7000
                    field_of_view: [1×1 types.untyped.DataStub]
@@ -160,15 +160,15 @@
                    starting_time: []
               starting_time_rate: []
                       timestamps: [1×1 types.untyped.DataStub]
-</div></div></div></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>OpticalSeries</span><span> is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the </span><span style=' font-family: monospace;'>OpticalSeries</span><span> object named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>. The images are stored in </span><span style=' font-family: monospace;'>StimulusPresentation.data</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >StimulusImageData = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).data</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="2726653C" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">StimulusImageData = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
+</div></div></div></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>OpticalSeries</span><span> is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the </span><span style=' font-family: monospace;'>OpticalSeries</span><span> object named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>. The images are stored in </span><span style=' font-family: monospace;'>StimulusPresentation.data</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >StimulusImageData = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).data</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="EDB4302A" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">StimulusImageData = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
 
     filename: 'sub-P11HMH_ses-20061101_ecephys+image.nwb'
         path: '/stimulus/presentation/StimulusPresentation/data'
         dims: [3 300 400 200]
        ndims: 4
     dataType: 'uint8'
-</div></div></div></div></div></div><div  class = 'S13'><span>When calling a data object directly, the data is not read but instead a </span><span style=' font-family: monospace;'>DataStub</span><span> is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any </span><span style=' font-family: monospace;'>DataStub</span><span> as you would any MATLAB matrix.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% get the image and display it</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% the dimension order is provided as follows:</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% [rgb, y, x, image index]</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >img = StimulusImageData(1:3, 1:300, 1:400, 32);</span></span></div></div></div><div  class = 'S13'><span>A bit of manipulation allows us to display the image using MATLAB's </span><span style=' font-family: monospace;'>imshow</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >img = permute(img,[3, 2, 1]);  </span><span style="color: #008013;">% fix orientation</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >img = flip(img, 3); </span><span style="color: #008013;">% reverse color order</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >F = figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >imshow(img, </span><span style="color: #a709f5;">'InitialMagnification'</span><span >, </span><span style="color: #a709f5;">'fit'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >daspect([3, 5, 5]);</span></span></div><div  class = 'S15'><div 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S1'><span>To read an entire dataset, use the </span><span style=' font-family: monospace;'>DataStub.load</span><span> method without any input arguments. We will use this approach to read all of the image display timestamps into memory.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S18'><span style="white-space: pre"><span >stimulus_times = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).timestamps.load();</span></span></div></div></div><h2  class = 'S9' id = 'H_4391' ><span>Quick PSTH and raster</span></h2><div  class = 'S1'><span>Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.</span></div><div  class = 'S1'><span>First, let us show the first row of the NWB Units table representing the first unit.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.units.getRow(1)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="4937B8E4" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_5" widgetid="variableeditor_client_Document_5" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_5"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×8 table </span></div><div id="variableeditor_TableViewModel_5" widgetid="variableeditor_TableViewModel_5" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 142px; min-width: 142px; max-width: 142px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>origClusterID</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 214px; min-width: 214px; max-width: 214px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_encoding</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 238px; min-width: 238px; max-width: 238px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_recognition</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 142px; min-width: 142px; max-width: 142px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>IsolationDist</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>SNR</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 254px; min-width: 254px; max-width: 254px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_sampling_rate</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 126px; min-width: 126px; max-width: 126px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>spike_times</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>electrodes</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 142px; min-width: 142px; max-width: 142px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1102</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 214px; min-width: 214px; max-width: 214px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>256×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 238px; min-width: 238px; max-width: 238px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>256×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 142px; min-width: 142px; max-width: 142px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>11.2917</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1.4407</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 254px; min-width: 254px; max-width: 254px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>98400</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>373×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>0</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
-    origClusterID    waveform_mean_encoding    waveform_mean_recognition    IsolationDist    "></div></div></div></div></div><div  class = 'S1'><span>Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">%% Align spikes by stimulus presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >unit_ind =</span></span><span>8</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >before =</span></span><span>1</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >after =</span></span><span>3</span><span style="white-space: pre"><span >;</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>getRow</span><span> provides a convenient method for reading this data out.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >unit_spikes = nwb.units.getRow(unit_ind, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}).spike_times{1}</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="EC002FF1" prevent-scroll="true" data-testid="output_7" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">unit_spikes = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2116×1</span></div></div><div class="veScalingFactor" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">10<sup>3</sup><span class="multiply" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> ×</span></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:1,&quot;totalRows&quot;:2116,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 75px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">    5.9338
+</div></div></div></div></div></div><div  class = 'S13'><span>When calling a data object directly, the data is not read but instead a </span><span style=' font-family: monospace;'>DataStub</span><span> is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any </span><span style=' font-family: monospace;'>DataStub</span><span> as you would any MATLAB matrix.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">% get the image and display it</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% the dimension order is provided as follows:</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #008013;">% [rgb, y, x, image index]</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >img = StimulusImageData(1:3, 1:300, 1:400, 32);</span></span></div></div></div><div  class = 'S13'><span>A bit of manipulation allows us to display the image using MATLAB's </span><span style=' font-family: monospace;'>imshow</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >img = permute(img,[3, 2, 1]);  </span><span style="color: #008013;">% fix orientation</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >img = flip(img, 3); </span><span style="color: #008013;">% reverse color order</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >F = figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >imshow(img, </span><span style="color: #a709f5;">'InitialMagnification'</span><span >, </span><span style="color: #a709f5;">'fit'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >daspect([3, 5, 5]);</span></span></div><div  class = 'S15'><div 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S1'><span>To read an entire dataset, use the </span><span style=' font-family: monospace;'>DataStub.load</span><span> method without any input arguments. We will use this approach to read all of the image display timestamps into memory.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S18'><span style="white-space: pre"><span >stimulus_times = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).timestamps.load();</span></span></div></div></div><h2  class = 'S9' id = 'H_4391' ><span>Quick PSTH and raster</span></h2><div  class = 'S1'><span>Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.</span></div><div  class = 'S1'><span>First, let us show the first row of the NWB Units table representing the first unit.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >nwb.units.getRow(1)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableTableElement" uid="44B35309" prevent-scroll="true" data-testid="output_6" tabindex="-1" style="width: calc(100% - 5px);"><div class="ClientDocument veSpecifier table" id="variableeditor_client_Document_13" widgetid="variableeditor_client_Document_13" tabindex="0" aria-labelledby="variableeditor_views_SummaryBar_13"><div class="summaryBar" style="font-size: 12px; font-family: Consolas, Inconsolata, Menlo, monospace;"><span>ans = </span><span style="color: rgb(179, 179, 179); font-style: normal;">1×8 table </span></div><div id="variableeditor_TableViewModel_13" widgetid="variableeditor_TableViewModel_13" class="table lightWeightView ClientViewDiv hasSummaryBar" data-viewid="__1" style="width: 100%; overflow: auto;"><table cellspacing="0" style="border-spacing: 0px; border-collapse: collapse;"><thead><tr><th rowspan="1" style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border: 1px solid rgb(191, 191, 191); text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>&nbsp;</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 142px; min-width: 142px; max-width: 142px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>origClusterID</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 214px; min-width: 214px; max-width: 214px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_encoding</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 238px; min-width: 238px; max-width: 238px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_recognition</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 142px; min-width: 142px; max-width: 142px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>IsolationDist</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 82px; min-width: 82px; max-width: 82px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>SNR</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 254px; min-width: 254px; max-width: 254px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>waveform_mean_sampling_rate</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 126px; min-width: 126px; max-width: 126px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>spike_times</span></th><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 118px; min-width: 118px; max-width: 118px; border: 1px solid rgb(191, 191, 191); text-align: center; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>electrodes</span></th></tr></thead><tbody><tr><th style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 6px 3px 3px; width: 34px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left; background-color: rgb(245, 245, 245); color: rgba(0, 0, 0, 0.75); font-weight: 700; box-sizing: border-box;"><span>1</span></th><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 142px; min-width: 142px; max-width: 142px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1102</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 214px; min-width: 214px; max-width: 214px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>256×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 238px; min-width: 238px; max-width: 238px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>256×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 142px; min-width: 142px; max-width: 142px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>11.2917</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 82px; min-width: 82px; max-width: 82px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>1.4407</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 254px; min-width: 254px; max-width: 254px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: right;"><span>98400</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 126px; min-width: 126px; max-width: 126px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>373×1&nbsp;double</span></td><td style="text-overflow: ellipsis; font-family: Arial, sans-serif; font-size: 12px; overflow: hidden; padding: 3px; width: 118px; min-width: 118px; max-width: 118px; border-width: 0px 1px 1px; border-style: solid; border-color: rgb(191, 191, 191); border-image: initial; text-align: left;"><span>0</span></td></tr></tbody></table></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 
+    origClusterID    waveform_mean_encoding    waveform_mean_recognition    IsolationDist    "></div></div></div></div></div><div  class = 'S1'><span>Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span style="color: #008013;">%% Align spikes by stimulus presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >unit_ind =</span></span><span>8</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >before =</span></span><span>1</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span >after =</span></span><span>3</span><span style="white-space: pre"><span >;</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>getRow</span><span> provides a convenient method for reading this data out.</span></div><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S16'><span style="white-space: pre"><span >unit_spikes = nwb.units.getRow(unit_ind, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}).spike_times{1}</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="3F2DE0B9" prevent-scroll="true" data-testid="output_7" data-width="1115" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1115px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">unit_spikes = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">2116×1</span></div></div><div class="veScalingFactor" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">10<sup>3</sup><span class="multiply" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"> ×</span></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:73,&quot;totalColumns&quot;:1,&quot;totalRows&quot;:2116,&quot;charsPerColumn&quot;:10}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 75px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">    5.9338
     5.9343
     5.9346
     5.9358
@@ -187,7 +187,7 @@
     5.9358
     5.9364
     5.9375
-  " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S13'><span>Spike times from this unit are aligned to each stimulus time and compiled in a cell array</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >results = cell(1, length(stimulus_times));</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >itime = 1:length(stimulus_times)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    stimulus_time = stimulus_times(itime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = unit_spikes - stimulus_time;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = spikes(spikes &gt; -before);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = spikes(spikes &lt; after);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    results{itime} = spikes;</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S9' id = 'H_0768' ><span>Plot results</span></h2><div  class = 'S1'><span>Finally, here is a (slightly sloppy) peri-stimulus time histogram</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">on</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(results)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = results{i};</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    yy = ones(length(spikes)) * i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    plot(spikes, yy, </span><span style="color: #a709f5;">'k.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">off</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'trial'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="997E8CD3" prevent-scroll="true" data-testid="output_8" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >all_spikes = cat(1, results{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >histogram(all_spikes, 30);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'count'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="71AD7704" prevent-scroll="true" data-testid="output_9" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h2  class = 'S9' id = 'H_5128' ><span>Conclusion</span></h2><div  class = 'S1'><span>This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find </span><a href = "https://github.com/rutishauserlab/recogmem-release-NWB"><span>here</span></a><span>. For more datasets, or to publish your own NWB data for free, check out the DANDI archive </span><a href = "http://dandiarchive.org/"><span>here</span></a><span>. Also, make sure to check out the DANDI breakout session later in this event.</span></div>
+  " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S13'><span>Spike times from this unit are aligned to each stimulus time and compiled in a cell array</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >results = cell(1, length(stimulus_times));</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >itime = 1:length(stimulus_times)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    stimulus_time = stimulus_times(itime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = unit_spikes - stimulus_time;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = spikes(spikes &gt; -before);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = spikes(spikes &lt; after);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    results{itime} = spikes;</span></span></div></div><div class="inlineWrapper"><div  class = 'S12'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S9' id = 'H_0768' ><span>Plot results</span></h2><div  class = 'S1'><span>Finally, here is a (slightly sloppy) peri-stimulus time histogram</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">on</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(results)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    spikes = results{i};</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    yy = ones(length(spikes)) * i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >    plot(spikes, yy, </span><span style="color: #a709f5;">'k.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">off</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'trial'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="64DF5008" prevent-scroll="true" data-testid="output_8" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >all_spikes = cat(1, results{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >histogram(all_spikes, 30);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'count'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div><div  class = 'S15'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="BD6C35BF" prevent-scroll="true" data-testid="output_9" tabindex="-1" style="width: 1145px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h2  class = 'S9' id = 'H_5128' ><span>Conclusion</span></h2><div  class = 'S1'><span>This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find </span><a href = "https://github.com/rutishauserlab/recogmem-release-NWB"><span>here</span></a><span>. For more datasets, or to publish your own NWB data for free, check out the DANDI archive </span><a href = "http://dandiarchive.org/"><span>here</span></a><span>. Also, make sure to check out the DANDI breakout session later in this event.</span></div>
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-.S14 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_B8DB5017' ><span>Reading NWB Data in MATLAB (DandiHub edition)</span></h1><div  class = 'S1' id = 'H_5D05D5B9' ><span>Authors: Ryan Ly, with modification by Lawrence Niu</span></div><div  class = 'S1'><span>DandiHub edition* authors: Thomas Kuenzel &amp; Vijay Iyer</span></div><div  class = 'S1'><span>Last Updated: 2023-09-05</span></div><div  class = 'S1'><span>(*) minimally modified to utilize </span><span style=' font-family: monospace;'>dandi</span><span> package for download and to skip the MatNWB installation </span></div><h2  class = 'S2' id = 'H_AEF597F8' ><span>Introduction</span></h2><div  class = 'S1'><span>In this tutorial, we will read single neuron spiking data that is in the NWB standard format and do a basic visualization of the data. More thorough documentation regarding reading files as well as the </span><span style=' font-family: monospace;'>NwbFile</span><span> class, can be found in the </span><a href = "https://nwb-overview.readthedocs.io/en/latest/file_read/file_read.html#reading-with-matnwb"><span>NWB Overview Documentation</span></a></div><div  class = 'S3'><div  class = 'S4'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S5'><a href = "#H_AEF597F8"><span>Introduction
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+.S14 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_B8DB5017' ><span>Reading NWB Data in MATLAB (DandiHub edition)</span></h1><div  class = 'S1' id = 'H_5D05D5B9' ><span>Authors: Ryan Ly, with modification by Lawrence Niu</span></div><div  class = 'S1'><span>DandiHub edition* authors: Thomas Kuenzel &amp; Vijay Iyer</span></div><div  class = 'S1'><span>Last Updated: 2023-09-05</span></div><div  class = 'S1'><span>(*) minimally modified to utilize </span><span style=' font-family: monospace;'>dandi</span><span> package for download and to skip the MatNWB installation </span></div><h2  class = 'S2' id = 'H_AEF597F8' ><span>Introduction</span></h2><div  class = 'S1'><span>In this tutorial, we will read single neuron spiking data that is in the NWB standard format and do a basic visualization of the data. More thorough documentation regarding reading files as well as the </span><span style=' font-family: monospace;'>NwbFile</span><span> class, can be found in the </span><a href = "https://nwb-overview.readthedocs.io/en/latest/file_read/file_read.html#reading-with-matnwb"><span>NWB Overview Documentation</span></a></div><div  class = 'S3'><div  class = 'S4'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S5'><a href = "#H_AEF597F8"><span>Introduction
 </span></a><a href = "#H_4596"><span>Download the Dataset
 </span></a><a href = "#H_CB19E5AC"><span>Read the NWB file
 </span></a><span>    </span><a href = "#H_69924E02"><span>Stimulus
 </span></a><a href = "#H_01DD834C"><span>Quick PSTH and raster
 </span></a><a href = "#H_716AA928"><span>Plot results
-</span></a><a href = "#H_E2BE29FD"><span>Conclusion</span></a></div></div><h2  class = 'S2' id = 'H_4596' ><span>Download the Dataset</span></h2><div  class = 'S1'><span>Use the </span><span style=' font-family: monospace;'>dandi</span><span> Python package to download the dataset to the user-local dandisets folder. If you are running this livescript on DandiHub, the </span><span style=' font-family: monospace;'>dandi</span><span> package is already pre-installed. On a local environment, you need to install the </span><span style=' font-family: monospace;'>dandi</span><span> package in the python environment returned by running </span><span style=' font-family: monospace;'>pyenv()</span><span> in MATLAB.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >environment = </span></span><span style="color: rgb(167, 9, 245);">"DandiHub"</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">switch </span><span >environment</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">"DandiHub"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        targetFolder = </span><span style="color: #a709f5;">"/home/jovyan/dandisets/000004"</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">"Local"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        targetFolder = fullfile(userpath(), </span><span style="color: #a709f5;">"dandisets"</span><span >, </span><span style="color: #a709f5;">"000004"</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >py.dandi.download.download(</span><span style="color: #a709f5;">"dandi://dandi/000004/sub-P11HMH/"</span><span >, targetFolder, existing=</span><span style="color: #a709f5;">'overwrite'</span><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextElement" uid="DC0FF8FB" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="32" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">PATH                                                 SIZE    DONE    DONE% CHECKSUM STATUS  MESSAGE  
-sub-P11HMH/sub-P11HMH_ses-20061101_ecephys+image.nwb 72.6 MB                        error   OSError  </div></div><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsErrorElement" uid="F812C168" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="diagnosticMessage-wrapper diagnosticMessage-errorType eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="34" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: normal; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"><div class="diagnosticMessage-messagePart" style="white-space: pre-wrap; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">Error using download<br style="white-space: pre-wrap; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">Python Error: RuntimeError: Encountered 1 error while downloading.</div><div class="diagnosticMessage-stackPart" style="white-space: pre; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"></div></div></div><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsTextElement" uid="D03A7B76" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="32" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">Summary:                                             72.6 MB 0 Bytes                1 error 1 OSError
-                                                             0.00%                                   </div></div></div></div></div><h2  class = 'S10' id = 'H_CB19E5AC' ><span>Read the NWB file</span></h2><div  class = 'S1'><span>You can read any NWB file using </span><span style=' font-family: monospace;'>nwbRead</span><span>. You will find that the print out for this shows a summary of the data within.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb = nwbRead(fullfile(targetFolder, </span><span style="color: #a709f5;">"sub-P11HMH"</span><span >, </span><span style="color: #a709f5;">"sub-P11HMH_ses-20061101_ecephys+image.nwb"</span><span >))</span></span></div></div></div><h3  class = 'S12' id = 'H_69924E02' ><span>Stimulus</span></h3><div  class = 'S1'><span>Now lets take a look at the visual stimuli presented to the subject. They will be in </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.stimulus_presentation</span></span></div></div></div><div  class = 'S13'><span>This results shows us that </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span><span> is a </span><span style=' font-family: monospace;'>Set</span><span> object that contains a single data object called </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>, which is an </span><span style=' font-family: monospace;'>OpticalSeries</span><span> neurodata type. Use the </span><span style=' font-family: monospace;'>get</span><span> method to return this </span><span style=' font-family: monospace;'>OpticalSeries</span><span>. </span><span style=' font-family: monospace;'>Set</span><span> objects store a collection of other NWB objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >)</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>OpticalSeries</span><span> is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the </span><span style=' font-family: monospace;'>OpticalSeries</span><span> object named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>. The images are stored in </span><span style=' font-family: monospace;'>StimulusPresentation.data</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >StimulusImageData = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).data</span></span></div></div></div><div  class = 'S13'><span>When calling a data object directly, the data is not read but instead a </span><span style=' font-family: monospace;'>DataStub</span><span> is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any </span><span style=' font-family: monospace;'>DataStub</span><span> as you would any MATLAB matrix.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% get the image and display it</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% the dimension order is provided as follows:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% [rgb, y, x, image index]</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >img = StimulusImageData(1:3, 1:300, 1:400, 32);</span></span></div></div></div><div  class = 'S13'><span>A bit of manipulation allows us to display the image using MATLAB's </span><span style=' font-family: monospace;'>imshow</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >img = permute(img,[3, 2, 1]);  </span><span style="color: #008013;">% fix orientation</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >img = flip(img, 3); </span><span style="color: #008013;">% reverse color order</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >F = figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >imshow(img, </span><span style="color: #a709f5;">'InitialMagnification'</span><span >, </span><span style="color: #a709f5;">'fit'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >daspect([3, 5, 5]);</span></span></div></div></div><div  class = 'S1'><span>To read an entire dataset, use the </span><span style=' font-family: monospace;'>DataStub.load</span><span> method without any input arguments. We will use this approach to read all of the image display timestamps into memory.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >stimulus_times = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).timestamps.load();</span></span></div></div></div><h2  class = 'S10' id = 'H_01DD834C' ><span>Quick PSTH and raster</span></h2><div  class = 'S1'><span>Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.</span></div><div  class = 'S1'><span>First, let us show the first row of the NWB Units table representing the first unit.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.getRow(1)</span></span></div></div></div><div  class = 'S1'><span>Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%% Align spikes by stimulus presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >unit_ind =</span></span><span>8</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >before =</span></span><span>1</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >after =</span></span><span>3</span><span style="white-space: pre"><span >;</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>getRow</span><span> provides a convenient method for reading this data out.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >unit_spikes = nwb.units.getRow(unit_ind, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}).spike_times{1}</span></span></div></div></div><div  class = 'S13'><span>Spike times from this unit are aligned to each stimulus time and compiled in a cell array</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >results = cell(1, length(stimulus_times));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >itime = 1:length(stimulus_times)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    stimulus_time = stimulus_times(itime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = unit_spikes - stimulus_time;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = spikes(spikes &gt; -before);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = spikes(spikes &lt; after);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    results{itime} = spikes;</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S10' id = 'H_716AA928' ><span>Plot results</span></h2><div  class = 'S1'><span>Finally, here is a (slightly sloppy) peri-stimulus time histogram</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">on</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(results)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = results{i};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    yy = ones(length(spikes)) * i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    plot(spikes, yy, </span><span style="color: #a709f5;">'k.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'trial'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >all_spikes = cat(1, results{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >histogram(all_spikes, 30);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'count'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div></div></div><h2  class = 'S10' id = 'H_E2BE29FD' ><span>Conclusion</span></h2><div  class = 'S1'><span>This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find </span><a href = "https://github.com/rutishauserlab/recogmem-release-NWB"><span>here</span></a><span>. For more datasets, or to publish your own NWB data for free, check out the DANDI archive </span><a href = "http://dandiarchive.org/"><span>here</span></a><span>. </span></div><div  class = 'S1'></div>
+</span></a><a href = "#H_E2BE29FD"><span>Conclusion</span></a></div></div><h2  class = 'S2' id = 'H_4596' ><span>Download the Dataset</span></h2><div  class = 'S1'><span>Use the </span><span style=' font-family: monospace;'>dandi</span><span> Python package to download the dataset to the user-local dandisets folder. If you are running this livescript on DandiHub, the </span><span style=' font-family: monospace;'>dandi</span><span> package is already pre-installed. On a local environment, you need to install the </span><span style=' font-family: monospace;'>dandi</span><span> package in the python environment returned by running </span><span style=' font-family: monospace;'>pyenv()</span><span> in MATLAB.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >environment = </span></span><span style="color: rgb(167, 9, 245);">"DandiHub"</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">switch </span><span >environment</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">"DandiHub"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        targetFolder = </span><span style="color: #a709f5;">"/home/jovyan/dandisets/000004"</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">case </span><span style="color: #a709f5;">"Local"</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >        targetFolder = fullfile(userpath(), </span><span style="color: #a709f5;">"dandisets"</span><span >, </span><span style="color: #a709f5;">"000004"</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >py.dandi.download.download(</span><span style="color: #a709f5;">"dandi://dandi/000004/sub-P11HMH/"</span><span >, targetFolder, existing=</span><span style="color: #a709f5;">'overwrite'</span><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsErrorElement" uid="9883A5C6" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="diagnosticMessage-wrapper diagnosticMessage-errorType eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="19" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="error output " style="max-height: 261px; white-space: normal; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"><div class="diagnosticMessage-messagePart" style="white-space: pre-wrap; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;">No module or function named 'dandi'.</div><div class="diagnosticMessage-stackPart" style="white-space: pre; font-style: normal; color: rgb(183, 49, 44); font-size: 12px;"></div></div></div></div></div></div><h2  class = 'S10' id = 'H_CB19E5AC' ><span>Read the NWB file</span></h2><div  class = 'S1'><span>You can read any NWB file using </span><span style=' font-family: monospace;'>nwbRead</span><span>. You will find that the print out for this shows a summary of the data within.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb = nwbRead(fullfile(targetFolder, </span><span style="color: #a709f5;">"sub-P11HMH"</span><span >, </span><span style="color: #a709f5;">"sub-P11HMH_ses-20061101_ecephys+image.nwb"</span><span >))</span></span></div></div></div><h3  class = 'S12' id = 'H_69924E02' ><span>Stimulus</span></h3><div  class = 'S1'><span>Now lets take a look at the visual stimuli presented to the subject. They will be in </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.stimulus_presentation</span></span></div></div></div><div  class = 'S13'><span>This results shows us that </span><span style=' font-family: monospace;'>nwb.stimulus_presentation</span><span> is a </span><span style=' font-family: monospace;'>Set</span><span> object that contains a single data object called </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>, which is an </span><span style=' font-family: monospace;'>OpticalSeries</span><span> neurodata type. Use the </span><span style=' font-family: monospace;'>get</span><span> method to return this </span><span style=' font-family: monospace;'>OpticalSeries</span><span>. </span><span style=' font-family: monospace;'>Set</span><span> objects store a collection of other NWB objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >)</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>OpticalSeries</span><span> is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the </span><span style=' font-family: monospace;'>OpticalSeries</span><span> object named </span><span style=' font-family: monospace;'>StimulusPresentation</span><span>. The images are stored in </span><span style=' font-family: monospace;'>StimulusPresentation.data</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >StimulusImageData = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).data</span></span></div></div></div><div  class = 'S13'><span>When calling a data object directly, the data is not read but instead a </span><span style=' font-family: monospace;'>DataStub</span><span> is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any </span><span style=' font-family: monospace;'>DataStub</span><span> as you would any MATLAB matrix.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% get the image and display it</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% the dimension order is provided as follows:</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% [rgb, y, x, image index]</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >img = StimulusImageData(1:3, 1:300, 1:400, 32);</span></span></div></div></div><div  class = 'S13'><span>A bit of manipulation allows us to display the image using MATLAB's </span><span style=' font-family: monospace;'>imshow</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >img = permute(img,[3, 2, 1]);  </span><span style="color: #008013;">% fix orientation</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >img = flip(img, 3); </span><span style="color: #008013;">% reverse color order</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >F = figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >imshow(img, </span><span style="color: #a709f5;">'InitialMagnification'</span><span >, </span><span style="color: #a709f5;">'fit'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >daspect([3, 5, 5]);</span></span></div></div></div><div  class = 'S1'><span>To read an entire dataset, use the </span><span style=' font-family: monospace;'>DataStub.load</span><span> method without any input arguments. We will use this approach to read all of the image display timestamps into memory.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >stimulus_times = nwb.stimulus_presentation.get(</span><span style="color: #a709f5;">'StimulusPresentation'</span><span >).timestamps.load();</span></span></div></div></div><h2  class = 'S10' id = 'H_01DD834C' ><span>Quick PSTH and raster</span></h2><div  class = 'S1'><span>Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.</span></div><div  class = 'S1'><span>First, let us show the first row of the NWB Units table representing the first unit.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwb.units.getRow(1)</span></span></div></div></div><div  class = 'S1'><span>Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">%% Align spikes by stimulus presentations</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >unit_ind =</span></span><span>8</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >before =</span></span><span>1</span><span style="white-space: pre"><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >after =</span></span><span>3</span><span style="white-space: pre"><span >;</span></span></div></div></div><div  class = 'S13'><span style=' font-family: monospace;'>getRow</span><span> provides a convenient method for reading this data out.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >unit_spikes = nwb.units.getRow(unit_ind, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'spike_times'</span><span >}).spike_times{1}</span></span></div></div></div><div  class = 'S13'><span>Spike times from this unit are aligned to each stimulus time and compiled in a cell array</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >results = cell(1, length(stimulus_times));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >itime = 1:length(stimulus_times)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    stimulus_time = stimulus_times(itime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = unit_spikes - stimulus_time;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = spikes(spikes &gt; -before);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = spikes(spikes &lt; after);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    results{itime} = spikes;</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h2  class = 'S10' id = 'H_716AA928' ><span>Plot results</span></h2><div  class = 'S1'><span>Finally, here is a (slightly sloppy) peri-stimulus time histogram</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">on</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:length(results)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    spikes = results{i};</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    yy = ones(length(spikes)) * i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    plot(spikes, yy, </span><span style="color: #a709f5;">'k.'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >hold </span><span style="color: #a709f5;">off</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'trial'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div></div></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >figure();</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >all_spikes = cat(1, results{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >histogram(all_spikes, 30);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ylabel(</span><span style="color: #a709f5;">'count'</span><span >)</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'time (s)'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S14'><span style="white-space: pre"><span >axis(</span><span style="color: #a709f5;">'tight'</span><span >)</span></span></div></div></div><h2  class = 'S10' id = 'H_E2BE29FD' ><span>Conclusion</span></h2><div  class = 'S1'><span>This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find </span><a href = "https://github.com/rutishauserlab/recogmem-release-NWB"><span>here</span></a><span>. For more datasets, or to publish your own NWB data for free, check out the DANDI archive </span><a href = "http://dandiarchive.org/"><span>here</span></a><span>. </span></div><div  class = 'S1'></div>
 <br>
 <!-- 
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-.S10 { margin: 20px 10px 5px 4px; padding: 0px; line-height: 25px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Remote read of NWB files</span></h1><div  class = 'S1'><span>It is possible to read an NWB file (or any HDF5 file) in MATLAB directly from several different kinds of remote locations, including AWS, Azure Blob Storage and HDFS. This tutorial will walk you through specifically loading a MATLAB file from AWS S3, which is the storage used by the DANDI archive. See </span><a href = "https://www.mathworks.com/help/matlab/ref/h5read.html"><span>MATLAB documentation</span></a><span> for more general information.</span></div><div  class = 'S1'><span>To read an NWB file file from an s3 store, first you need to figure out the s3 path of that resource. The easiest way to do this is to use the DANDI web client. </span></div><ul  class = 'S2'><li  class = 'S3'><span>(skip if on DANDI Hub) Make sure you do not have a file </span><span style=' font-family: monospace;'>~/.aws/credentials</span><span>. If you do, rename it to something else. On Windows this file would be somewhere like </span><span style=' font-family: monospace;'>C:/Users/username/.aws/credentials</span><span>.</span></li><li  class = 'S3'><span>Find and select a dandiset you want on the </span><a href = "https://dandiarchive.org/dandiset"><span>DANDI Archive</span></a><span>, then click </span><img class = "imageNode" src = "data:image/png;base64,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" width = "121" height = "29" alt = "" style = "vertical-align: baseline; width: 121px; height: 29px;"></img></li><li  class = 'S3'><span>Navigate to the NWB file of interest and click </span><img class = "imageNode" src = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACIAAAAaCAYAAADSbo4CAAABL0lEQVR4AdWWwQrCMAxAdxIvfoef4mf4SR4VFK/evXrwEwQ/wItMDyKIHkTcFn1CYQhtMrZhDRTKmiZvSZM2kUgkiYRD/hskL0TW+7uMNlcZrs7SXxw/gznfWEOnilSOyO7ylMHyJJ1pGhzooGsVMwh/ON7epDc/BAHKgOiyxxIdMwgGy06qzCfvvZqYQAhxKBLdWSoMHxx7tTSpIIRVOxNZLsLwgfAdG6EUqSBUQMgBaxYQ9LDlExWEctRArOvY8okKQm+wOtL0sOUTFYRmpTl4ZIUwND1s+aQREOsZqQViSY0VpFZqLIfVClLrsEZTvtE0NE55FC3elVsUlx4wpIhbNHT5ffeRVp4BLjKkSbsEAWrtYeRAXHSoJsqR3vCTp2IZqMm52uKbdBayFQ3IC7o1HAqj04nRAAAAAElFTkSuQmCC" width = "30" height = "23" alt = "" style = "vertical-align: baseline; width: 30px; height: 23px;"></img></li><li  class = 'S3'><span>Find the second entry of "contentURL"</span><img class = "imageNode" src = "data:image/png;base64,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" width = "688" height = "62" alt = "Screenshot 2025-07-15 at 11.58.35 AM.png" style = "vertical-align: baseline; width: 688px; height: 62px;"></img></li><li  class = 'S3'><span>In your MATLAB session, take the end of that url (the blob id) and add it to this expression: </span><span style=' font-family: monospace;'>s3 = 's3://dandiarchive/blobs/&lt;blob_id&gt;'</span><span>. In this case, you would have:</span></li><li  class = 'S3'><span>Use </span><span style=' font-family: monospace;'>setenv</span><span> to specify AWS region. The DandiArchive datasets are located on </span><span style=' font-family: monospace;'>us-east-2</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Example S3 path for a 380KiB file from dandiset 001199:</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >s3 = </span><span style="color: #a709f5;">"s3://dandiarchive/blobs/3fb/2c8/3fb2c8b9-26db-47c0-86c2-9594678a8263"</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #008013;">% Set AWS region (NB: Changes environment variable)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >setenv(</span><span style="color: #a709f5;">'AWS_DEFAULT_REGION'</span><span >, </span><span style="color: #a709f5;">'us-east-2'</span><span >) </span></span></div></div></div><ul  class = 'S2'><li  class = 'S3'><span>Read from that s3 path directly with:</span></li></ul><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile = nwbRead(s3) </span><span style="color: #008013;">% this may take a minute</span></span></div><div  class = 'S8'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="B6202E0B" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="773" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwbfile = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S10 { margin: 20px 10px 5px 4px; padding: 0px; line-height: 25px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Remote read of NWB files</span></h1><div  class = 'S1'><span>It is possible to read an NWB file (or any HDF5 file) in MATLAB directly from several different kinds of remote locations, including AWS, Azure Blob Storage and HDFS. This tutorial will walk you through specifically loading a MATLAB file from AWS S3, which is the storage used by the DANDI archive. See </span><a href = "https://www.mathworks.com/help/matlab/ref/h5read.html"><span>MATLAB documentation</span></a><span> for more general information.</span></div><div  class = 'S1'><span>To read an NWB file file from an s3 store, first you need to figure out the s3 path of that resource. The easiest way to do this is to use the DANDI web client. </span></div><ul  class = 'S2'><li  class = 'S3'><span>(skip if on DANDI Hub) Make sure you do not have a file </span><span style=' font-family: monospace;'>~/.aws/credentials</span><span>. If you do, rename it to something else. On Windows this file would be somewhere like </span><span style=' font-family: monospace;'>C:/Users/username/.aws/credentials</span><span>.</span></li><li  class = 'S3'><span>Find and select a dandiset you want on the </span><a href = "https://dandiarchive.org/dandiset"><span>DANDI Archive</span></a><span>, then click </span><img class = "imageNode" src = "data:image/png;base64,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" width = "121" height = "29" alt = "" style = "vertical-align: baseline; width: 121px; height: 29px;"></img></li><li  class = 'S3'><span>Navigate to the NWB file of interest and click </span><img class = "imageNode" src = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACIAAAAaCAYAAADSbo4CAAABL0lEQVR4AdWWwQrCMAxAdxIvfoef4mf4SR4VFK/evXrwEwQ/wItMDyKIHkTcFn1CYQhtMrZhDRTKmiZvSZM2kUgkiYRD/hskL0TW+7uMNlcZrs7SXxw/gznfWEOnilSOyO7ylMHyJJ1pGhzooGsVMwh/ON7epDc/BAHKgOiyxxIdMwgGy06qzCfvvZqYQAhxKBLdWSoMHxx7tTSpIIRVOxNZLsLwgfAdG6EUqSBUQMgBaxYQ9LDlExWEctRArOvY8okKQm+wOtL0sOUTFYRmpTl4ZIUwND1s+aQREOsZqQViSY0VpFZqLIfVClLrsEZTvtE0NE55FC3elVsUlx4wpIhbNHT5ffeRVp4BLjKkSbsEAWrtYeRAXHSoJsqR3vCTp2IZqMm52uKbdBayFQ3IC7o1HAqj04nRAAAAAElFTkSuQmCC" width = "30" height = "23" alt = "" style = "vertical-align: baseline; width: 30px; height: 23px;"></img></li><li  class = 'S3'><span>Find the second entry of "contentURL"</span><img class = "imageNode" src = "data:image/png;base64,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" width = "688" height = "62" alt = "Screenshot 2025-07-15 at 11.58.35 AM.png" style = "vertical-align: baseline; width: 688px; height: 62px;"></img></li><li  class = 'S3'><span>In your MATLAB session, take the end of that url (the blob id) and add it to this expression: </span><span style=' font-family: monospace;'>s3 = 's3://dandiarchive/blobs/&lt;blob_id&gt;'</span><span>. In this case, you would have:</span></li><li  class = 'S3'><span>Use </span><span style=' font-family: monospace;'>setenv</span><span> to specify AWS region. The DandiArchive datasets are located on </span><span style=' font-family: monospace;'>us-east-2</span></li></ul><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span style="color: #008013;">% Example S3 path for a 380KiB file from dandiset 001199:</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span >s3 = </span><span style="color: #a709f5;">"s3://dandiarchive/blobs/3fb/2c8/3fb2c8b9-26db-47c0-86c2-9594678a8263"</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S5'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S5'><span style="white-space: pre"><span style="color: #008013;">% Set AWS region (NB: Changes environment variable)</span></span></div></div><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >setenv(</span><span style="color: #a709f5;">'AWS_DEFAULT_REGION'</span><span >, </span><span style="color: #a709f5;">'us-east-2'</span><span >) </span></span></div></div></div><ul  class = 'S2'><li  class = 'S3'><span>Read from that s3 path directly with:</span></li></ul><div class="CodeBlock"><div class="inlineWrapper outputs"><div  class = 'S7'><span style="white-space: pre"><span >nwbfile = nwbRead(s3) </span><span style="color: #008013;">% this may take a minute</span></span></div><div  class = 'S8'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="5F2046CD" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="773" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwbfile = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.6.0'
                                         file_create_date: [1×1 types.untyped.DataStub]
diff --git a/docs/source/_static/html/tutorials/scratch.html b/docs/source/_static/html/tutorials/scratch.html
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@@ -47,12 +47,12 @@
 .S11 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
 .S12 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_792A91EB' ><span>Scratch Data</span></h1><div  class = 'S1'><span>This tutorial will focus on the basics of working with a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html"><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span></a><span> for storing non-standardizable data. For example, you may want to store results from one-off analyses of some temporary utility. NWB provides in-file </span><span style=' font-weight: bold;'>scratch space </span><span>as a dedicated location where miscellaneous non-standard data may be written.</span></div><div  class = 'S2'><div  class = 'S3'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S4'><a href = "#H_6D5A695F"><span>Setup
 </span></a><a href = "#H_F0AB2427"><span>Warning Regarding the Usage of Scratch Space
-</span></a><a href = "#H_AB310D43"><span>Writing Data to Scratch Space</span></a></div></div><h2  class = 'S5' id = 'H_6D5A695F' ><span>Setup</span></h2><div  class = 'S1'><span>Let us first set up an environment with some "acquired data".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ContextFile = NwbFile(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'demonstrate NWBFile scratch'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'SCRATCH-0'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2019, 4, 3, 11, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'file_create_date'</span><span >, datetime(2019, 4, 15, 12, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Niu, Lawrence'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'NWB' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Simulate some data</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >timestamps = 0:100:1024;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >data = sin(0.333 .* timestamps) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    + cos(0.1 .* timestamps) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    + randn(1, length(timestamps));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >RawTs = types.core.TimeSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 100, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'simulated acquired data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile.acquisition.set(</span><span style="color: #a709f5;">'raw_timeseries'</span><span >, RawTs);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% "Analyze" the simulated data</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% We provide a re-implementation of scipy.signal.correlate(..., mode='same')</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Ideally, you should use MATLAB-native code though using its equivalent </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% function (xcorr) requires the Signal Processing Toolbox</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >correlatedData = sameCorr(RawTs.data, ones(128, 1)) ./ 128;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% If you are unsure of how HDF5 paths map to MatNWB property structures, we </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% suggest using HDFView to verify. In most cases, MatNWB properties map </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% directly to HDF5 paths.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >FilteredTs = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, correlatedData, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 100, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'cross-correlated data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="A43978B2" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="286" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 297px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">FilteredTs = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  TimeSeries with properties:
+</span></a><a href = "#H_AB310D43"><span>Writing Data to Scratch Space</span></a></div></div><h2  class = 'S5' id = 'H_6D5A695F' ><span>Setup</span></h2><div  class = 'S1'><span>Let us first set up an environment with some "acquired data".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ContextFile = NwbFile(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'demonstrate NWBFile scratch'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'SCRATCH-0'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2019, 4, 3, 11, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % required</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'file_create_date'</span><span >, datetime(2019, 4, 15, 12, 0, 0, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Niu, Lawrence'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'NWB' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Simulate some data</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >timestamps = 0:100:1024;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >data = sin(0.333 .* timestamps) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    + cos(0.1 .* timestamps) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    + randn(1, length(timestamps));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >RawTs = types.core.TimeSeries(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 100, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'simulated acquired data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile.acquisition.set(</span><span style="color: #a709f5;">'raw_timeseries'</span><span >, RawTs);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% "Analyze" the simulated data</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% We provide a re-implementation of scipy.signal.correlate(..., mode='same')</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Ideally, you should use MATLAB-native code though using its equivalent </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% function (xcorr) requires the Signal Processing Toolbox</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >correlatedData = sameCorr(RawTs.data, ones(128, 1)) ./ 128;</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% If you are unsure of how HDF5 paths map to MatNWB property structures, we </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% suggest using HDFView to verify. In most cases, MatNWB properties map </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% directly to HDF5 paths.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >FilteredTs = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, correlatedData, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'m'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 100, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'cross-correlated data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="5D5FA57F" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="286" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 297px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">FilteredTs = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  TimeSeries with properties:
 
      starting_time_unit: 'seconds'
     timestamps_interval: 1
         timestamps_unit: 'seconds'
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-</div></div></div></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ProcModule = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a module to store filtering results'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtered_timeseries'</span><span >, FilteredTs </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile.processing.set(</span><span style="color: #a709f5;">'core'</span><span >, ProcModule);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwbExport(ContextFile, </span><span style="color: #a709f5;">'context_file.nwb'</span><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_F0AB2427' ><span>Warning Regarding the Usage of Scratch Space</span></h2><div  class = 'S1'><span style=' font-weight: bold;'>Scratch data written into the scratch space should not be intended for reuse or sharing. Standard NWB types, along with any extensions, should always be used for any data intended to be shared. Published data should not include scratch data and any reuse should not require scratch data for data processing.</span></div><h2  class = 'S5' id = 'H_AB310D43' ><span>Writing Data to Scratch Space</span></h2><div  class = 'S1'><span>Let us first copy what we need from the processed data file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ScratchFile = NwbFile(</span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'SCRATCH-1'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile = nwbRead(</span><span style="color: #a709f5;">'./context_file.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% again, copy the required metadata from the processed file.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ScratchFile.session_description = ContextFile.session_description;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ScratchFile.session_start_time = ContextFile.session_start_time;</span></span></div></div></div><div  class = 'S12'><span>We can now do an analysis lacking specification but that we still wish to store results for.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% ProcessingModule stores its timeseries inside of the "nwbdatainterface" property which is a </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Set of NWBDataInterface objects. This is not directly mapped to the NWB file but is used to </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% distinguish it and DynamicTable objects which it stores under the "dynamictable" property.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >FilteredTs = ContextFile.processing.get(</span><span style="color: #a709f5;">'core'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'filtered_timeseries'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% note: MatNWB does not currently support complex numbers. If you wish to store the data, consider</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% storing each number as a struct which will write the data to HDF5 using compound types.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dataFft = real(fft(FilteredTs.data.load()));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ScratchData = types.core.ScratchData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, dataFft, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'notes'</span><span >, </span><span style="color: #a709f5;">'discrete Fourier transform from filtered data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="0743228E" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="79" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ScratchData = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  ScratchData with properties:
+</div></div></div></div></div><div class="inlineWrapper"><div  class = 'S10'><span style="white-space: pre"><span >ProcModule = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a module to store filtering results'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'filtered_timeseries'</span><span >, FilteredTs </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile.processing.set(</span><span style="color: #a709f5;">'core'</span><span >, ProcModule);</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >nwbExport(ContextFile, </span><span style="color: #a709f5;">'context_file.nwb'</span><span >);</span></span></div></div></div><h2  class = 'S5' id = 'H_F0AB2427' ><span>Warning Regarding the Usage of Scratch Space</span></h2><div  class = 'S1'><span style=' font-weight: bold;'>Scratch data written into the scratch space should not be intended for reuse or sharing. Standard NWB types, along with any extensions, should always be used for any data intended to be shared. Published data should not include scratch data and any reuse should not require scratch data for data processing.</span></div><h2  class = 'S5' id = 'H_AB310D43' ><span>Writing Data to Scratch Space</span></h2><div  class = 'S1'><span>Let us first copy what we need from the processed data file.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span >ScratchFile = NwbFile(</span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'SCRATCH-1'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ContextFile = nwbRead(</span><span style="color: #a709f5;">'./context_file.nwb'</span><span >, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% again, copy the required metadata from the processed file.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ScratchFile.session_description = ContextFile.session_description;</span></span></div></div><div class="inlineWrapper"><div  class = 'S11'><span style="white-space: pre"><span >ScratchFile.session_start_time = ContextFile.session_start_time;</span></span></div></div></div><div  class = 'S12'><span>We can now do an analysis lacking specification but that we still wish to store results for.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S6'><span style="white-space: pre"><span style="color: #008013;">% ProcessingModule stores its timeseries inside of the "nwbdatainterface" property which is a </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% Set of NWBDataInterface objects. This is not directly mapped to the NWB file but is used to </span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% distinguish it and DynamicTable objects which it stores under the "dynamictable" property.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >FilteredTs = ContextFile.processing.get(</span><span style="color: #a709f5;">'core'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'filtered_timeseries'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% note: MatNWB does not currently support complex numbers. If you wish to store the data, consider</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span style="color: #008013;">% storing each number as a struct which will write the data to HDF5 using compound types.</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >dataFft = real(fft(FilteredTs.data.load()));</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >ScratchData = types.core.ScratchData( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, dataFft, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S7'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'notes'</span><span >, </span><span style="color: #a709f5;">'discrete Fourier transform from filtered data' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S8'><span style="white-space: pre"><span >)</span></span></div><div  class = 'S9'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="FAD0A24B" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1108" data-previous-scroll-height="79" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ScratchData = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  ScratchData with properties:
 
     notes: 'discrete Fourier transform from filtered data'
      data: [11×1 double]
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diff --git a/docs/source/_static/tutorials/media/ophys/image_0.png b/docs/source/_static/tutorials/media/ophys/image_0.png
new file mode 100644
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diff --git a/docs/source/pages/tutorials/basicUsage.rst b/docs/source/pages/tutorials/basicUsage.rst
index a95946db3..2961f9369 100644
--- a/docs/source/pages/tutorials/basicUsage.rst
+++ b/docs/source/pages/tutorials/basicUsage.rst
@@ -6,14 +6,461 @@ Basic Usage of MatNWB
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/basicUsage.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/basicUsage.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/basicUsage.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+*last updated: January 1st, 2023*
+
+In this tutorial, we demonstrate the reading and usage of the NWB file produced in the `File Conversion Tutorial <https://matnwb.readthedocs.io/en/latest/pages/tutorials/convertTrials.html>`_. The output is a near-reproduction of Figure 1e from the `Li et al <https://www.ncbi.nlm.nih.gov/pubmed/27074502>`_ publication, showing raster and peristimulus time histogram (PSTH) plots for neural recordings from anterior lateral motor cortex (ALM). This figure illustrates the main finding of the publication, showing the robustness of motor planning behavior and neural dynamics following short unilateral network silencing via optogenetic inhibition.
+
+Reading NWB Files
+-----------------
+
+NWB files can be read in using the *nwbRead()* function.  This function returns a *nwbfile* object which is the in-memory representation of the NWB file structure.
+
+.. code-block:: matlab
+
+   nwb = nwbRead('out\ANM255201_20141124.nwb', 'ignorecache');
+
+**Constrained Sets**
+--------------------
+
+Analyzed data in NWB is placed under the *analysis* property, which is a **Constrained Set**.  A constrained set consists of an arbitrary amount of key-value pairs similar to `Map containers <https://www.mathworks.com/help/matlab/map-containers.html>`_ in MATLAB or a dictionary in Python.  However, constrained sets also have the ability to validate their own properties closer to how a typed Object would.
+
+You can get/set values in constrained sets using their respective *.get()/.set()* methods and retrieve all Set properties using the  *keys()* method, like in a *containers.Map.*
+
+.. code-block:: matlab
+
+   unit_names = keys(nwb.analysis);
+
+Dictionaries with MATLAB R2022b
+-------------------------------
+
+As noted above, constrained sets uses Map container objects, a custom class that allows user to retrieve values using a corresponding key.  For users working with MATLAB version R2022b, a notable addition of the `dictionary <https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta>`_ object offers significantly faster functionality.
+
+You can initialize a dictionary and configure its contents from relevant analysed NWB data with the same key/values noted above.
+
+Number of entries in a dictionary can be done with the new `numEntries() <https://www.mathworks.com/help/matlab/ref/dictionary.numentries.html>`_ function. One can also return all the keys and values of a dictionary as arrays using `keys(dict_name) <https://www.mathworks.com/help/matlab/ref/dictionary.keys.html>`_ and `values(dict_name) <https://www.mathworks.com/help/matlab/ref/dictionary.values.html>`_ functions.
+
+Dynamic Tables
+--------------
+
+*nwb.intervals_trials* returns a unique type of table called a **Dynamic Table**.  Dynamic tables inherit from the NWB type *types.hdmf_common.DynamicTable* and allow for a table-like interface in NWB.  In the case below, we grab the special column *start_time*.  Dynamic Tables allow adding your own vectors using the *vectordata* property, which are Constrained Sets.  All columns are represented by either a  *types.hdmf_common.VectorData* or a *types.hdmf_common.VectorIndex* type.
+
+Data Stubs
+----------
+
+The *data* property of the column *id* in *nwb.units* is a *types.untyped.DataStub.*  This object is a representation of a dataset that is not loaded in memory, and is what allows MatNWB to lazily load its file data.  To load the data into memory, use the  *.load()* method which extracts all data from the NWB file. Alternatively, you can index into the DataStub directly using conventional MATLAB syntax.
+
+Jagged Arrays in Dynamic Tables
+-------------------------------
+
+With the new addition of *addRow* and *getRow* to Dynamic Tables, the concept of jagged arrays can be worked around and no longer require full understanding outside of specific data format concerns or low-level nwb tool development. The below paragraph is retained in its entirety from its original form as purely informational.
+
+All data in a Dynamic Table must be aligned by row and column, but not all data fits into this paradigm neatly.  In order to represent variable amounts of data that is localised to each row and column, NWB uses a concept called **Jagged Arrays**.  These arrays consist of two column types: the familiar *types.core.VectorData*, and the new *types.core.VectorIndex*.  A Vector Index holds no data, instead holding a reference to another Vector Data and a vector of indices that align to the Dynamic Table dimensions.  The indices represent the last index boundary in the Vector Data object for the Vector Index row.  As an example, an index of three in the first row of the Vector Index column points to the first three values in the referenced Vector Data column.  Subsequently, if the next index were a five, it would indicate the fourth and fifth elements in the referenced Vector Data column.
+
+The jagged arrays serve to represent multiple trials and spike times associated to each unit by id. A convenient way to represent these in MATLAB is to use `Map containers <https://www.mathworks.com/help/matlab/map-containers.html>`_ where each unit's data is indexed directly by its unit id. Below, we utilize *getRow* in order to build the same Map.
+
+Once again, for users with MATLAB R2022b versions or newer, we can utilize MATLAB  `dictionary <https://www.mathworks.com/help/matlab/ref/dictionary.html?s_tid=doc_ta>`_ objects instead of map containers to build the Map.
+
+.. code-block:: matlab
+
+   unit_ids = nwb.units.id.data.load(); % array of unit ids represented within this
+   
+   % Initialize trials & times Map containers indexed by unit_ids
+   unit_trials = containers.Map('KeyType',class(unit_ids),'ValueType','any');
+   unit_times = containers.Map('KeyType',class(unit_ids),'ValueType','any');
+
+MATLAB R2022b Versions
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: matlab
+
+   % unit_trials = dictionary();
+   % unit_times  = dictionary();
+
+Adding key-value pairs, one at a time:
+
+.. code-block:: matlab
+
+   last_idx = 0;
+   for i = 1:length(unit_ids)
+       unit_id = unit_ids(i);
+       
+       row = nwb.units.getRow(unit_id, 'useId', true, 'columns', {'spike_times', 'trials'});
+       unit_trials(unit_id) = row.trials{1};
+       unit_times(unit_id) = row.spike_times{1};
+   end
+
+Process Units
+-------------
+
+We now do the following for each Unit:
+
+* Filter out invalid trials
+* Separate datasets based on resulting mouse behavior (right/left licks).
+* Derive "sample", "delay", and "response" times for this analyzed neuron.
+* Compose a peristimulus time histogram from the data.
+
+.. code-block:: matlab
+
+   
+   sorted_ids = sort(unit_ids);
+   Photostim = struct(...
+       'ind', true,... % mask into xs and ys for this photostim
+       'period', 'none',...
+       'duration', 0,... % in seconds
+       'ramp_offset', 0); % in seconds
+   
+   % Initialize Map container of plotting data for each unit, stored as structure
+   Unit = containers.Map('KeyType',class(unit_ids),'ValueType','any');
+
+MATLAB R2022b Versions
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: matlab
+
+   % Unit = dictionary();
+
+Initializing unit structure:
+
+.. code-block:: matlab
+
+   unit_struct = struct(...
+       'id', [],...
+       'xs', [],...
+       'ys', [],...
+       'xlim', [-Inf Inf],...
+       'sample', 0,...
+       'delay', 0,...
+       'response', 0,...
+       'left_scatter', false,...
+       'right_scatter', false,...
+       'photostim', Photostim); % can have multiple photostim
+
+.. code-block:: matlab
+
+   for unit_id = unit_ids'
+
+We first extract trial IDs from the Unit IDs.
+
+.. code-block:: matlab
+
+       unit_trial_id = unit_trials(unit_id);
+
+Then filter out outliers from the Sample, Delay, and Response time points with which we derive a "good enough" estimate.
+
+.. code-block:: matlab
+
+       trial = nwb.intervals_trials.getRow(unit_trial_id, 'useId', true,...
+           'columns', {'PoleInTime', 'PoleOutTime', 'CueTime', 'GoodTrials'});
+       unit_sample = trial.PoleInTime;
+       unit_delay = trial.PoleOutTime;
+       unit_response = trial.CueTime;
+       unit_good_trials = trial.GoodTrials;
+       
+       % Subjective parameters
+       delay_threshold = 0.064;
+       response_threshold = 0.43;
+       expected_delay_offset = 1.3; % determined from figure 1a
+       expected_response_offset = 1.3;
+       
+       expected_delay = unit_sample + expected_delay_offset;
+       expected_response = unit_delay + expected_response_offset;
+       good_delay = (unit_delay > expected_delay - delay_threshold) &...
+           (unit_delay < expected_delay + delay_threshold);
+       good_response = (unit_response > expected_response - response_threshold) &...
+           (unit_response < expected_response + response_threshold);
+       avg_sample = mean(unit_sample(good_delay & good_response));
+       avg_delay = mean(unit_delay(good_delay & good_response));
+       avg_response = mean(unit_response(good_delay & good_response));
+
+Filter the rest of the data by "good" trials.
+
+.. code-block:: matlab
+
+       unit_good_trials = unit_good_trials & good_delay & good_response;
+       
+       unit_trial_id = unit_trial_id(unit_good_trials);
+       
+       unit_spike_time = unit_times(unit_id);
+       unit_spike_time = unit_spike_time(unit_good_trials);
+
+Retrieve good trial data and organize by stimulation type.
+
+.. code-block:: matlab
+
+       trial = nwb.intervals_trials.getRow(unit_trial_id, 'useId', true,...
+           'columns', {'start_time', 'HitR', 'HitL', 'StimTrials', 'PhotostimulationType'});
+       unit_is_photostim = logical(trial.StimTrials);
+       unit_stim_type = trial.PhotostimulationType;
+       
+       unit_no_stim = ~unit_is_photostim & 0 == unit_stim_type;
+       unit_sample_stim = unit_is_photostim & 1 == unit_stim_type;
+       unit_early_stim = unit_is_photostim & 2 == unit_stim_type;
+       unit_middle_stim = unit_is_photostim & 3 == unit_stim_type;
+
+Compose Scatter Plots and the Peristimulus Time Histogram zeroed on the Response time.
+
+.. code-block:: matlab
+
+       xs = unit_spike_time - trial.start_time - avg_response;
+       ys = unit_trial_id;
+       
+       curr_unit = unit_struct;
+       
+       curr_unit.xs = xs;
+       curr_unit.ys = ys;
+       curr_unit.left_scatter = logical(trial.HitL);
+       curr_unit.right_scatter = logical(trial.HitR);
+       curr_unit.sample = avg_sample - avg_response;
+       curr_unit.delay = avg_delay - avg_response;
+       curr_unit.response = 0;
+       
+       % Photostim periods
+       curr_unit.photostim.ind = unit_no_stim;
+       
+       % Sample
+       if any(unit_sample_stim)
+           SampleStim = Photostim;
+           SampleStim.ind = unit_sample_stim;
+           SampleStim.period = 'Sample';
+           SampleStim.duration = 0.5;
+           SampleStim.ramp_offset = 0.1;
+           curr_unit.photostim(end+1) = SampleStim;
+       end
+       
+       % Early Delay
+       if any(unit_early_stim)
+           early_stim_types = unique(unit_stim_type(unit_early_stim));
+           for i_early_types=1:length(early_stim_types)
+               early_type = early_stim_types(i_early_types);
+               EarlyStim = Photostim;
+               EarlyStim.period = 'Early Delay';
+               EarlyStim.ind = early_type == unit_stim_type & unit_early_stim;
+               if early_type == 2
+                   EarlyStim.duration = 0.5;
+                   EarlyStim.ramp_offset = 0.1;
+               else
+                   EarlyStim.duration = 0.8;
+                   EarlyStim.ramp_offset = 0.2;
+               end
+               curr_unit.photostim(end+1) = EarlyStim;
+           end
+       end
+       
+       % Middle Delay
+       if any(unit_middle_stim)
+           MiddleStim = Photostim;
+           MiddleStim.ind = unit_middle_stim;
+           MiddleStim.period = 'Middle Delay';
+           MiddleStim.duration = 0.5;
+           MiddleStim.ramp_offset = 0.1;
+           curr_unit.photostim(end+1) = MiddleStim;
+       end
+       
+       Unit(unit_id) = curr_unit;
+   end
+
+Plot Example Neurons
+--------------------
+
+.. code-block:: matlab
+
+   neuron_labels = [2, 3]; % neuron labels from Figure 1e
+   neuron_ids = [11, 2]; % neuron unit IDs corresponding to the Fig 1e labels
+   num_conditions = 4; % photostim conditions: nostim, sample, early, middle if applicable
+   num_neurons = length(neuron_ids);
+   
+   % Initialize data structures for each summary plot of categorized neural spike data at specified stimulus condition
+   RasterPlot = struct(...
+       'xs', 0,...
+       'ys', 0);
+   ConditionPlot = struct(...
+       'label', '',...
+       'xlim', 0,...
+       'sample', 0,...
+       'delay', 0,...
+       'response', 0,...
+       'right_scatter', RasterPlot,...
+       'left_scatter', RasterPlot,...
+       'psth_bin_window', 0,...
+       'stim_type', '');
+   fig = figure;
+   
+   % Plot neural spike data for each neuron and stimulus condition in a subplot array: num_neurons (rows) x num_conditions (columns)
+   for nn=1:num_neurons
+       
+       Neuron = Unit(neuron_ids(nn));
+       
+       % Initialize structure with neural + stimulus condition data
+       CurrPlot = ConditionPlot;
+       CurrPlot.xlim = [min(Neuron.xs) max(Neuron.xs)];
+       CurrPlot.sample = Neuron.sample;
+       CurrPlot.delay = Neuron.delay;
+       CurrPlot.response = Neuron.response;
+       
+       % Plot each neuron/condition
+       plot_row = (nn - 1) * num_conditions;
+       for cc=1:num_conditions
+           ax = subplot(num_neurons, num_conditions, plot_row + cc, 'Parent', fig);
+           Stim = Neuron.photostim(cc);
+           
+           CurrPlot.stim_type = Stim.period;
+           if strcmp(Stim.period, 'none')
+               CurrPlot.label = sprintf('Neuron %d', neuron_labels(nn));
+               CurrPlot.psth_bin_window = 9;
+           else
+               CurrPlot.label = Stim.period;
+               CurrPlot.psth_bin_window = 2;
+           end
+           
+           stim_left_scatter_ind = Stim.ind & Neuron.left_scatter;
+           stim_left_scatter_trials = Neuron.ys(stim_left_scatter_ind);
+           
+           CurrPlot.left_scatter.xs = Neuron.xs(stim_left_scatter_ind);
+           [~,CurrPlot.left_scatter.ys] = ismember(stim_left_scatter_trials,unique(stim_left_scatter_trials));
+           
+           stim_right_scatter_ind = Stim.ind & Neuron.right_scatter;
+           stim_right_scatter_trials = Neuron.ys(stim_right_scatter_ind);
+           
+           CurrPlot.right_scatter.xs = Neuron.xs(stim_right_scatter_ind);
+           [~,CurrPlot.right_scatter.ys] = ismember(stim_right_scatter_trials,unique(stim_right_scatter_trials));
+           
+           plot_condition(ax, CurrPlot);
+       end
+   end
+
+.. image:: ../../_static/tutorials/media/basicUsage/figure_0.png
+   :class: tutorial-media
+   :alt: figure_0.png
+
+Helper Functions
+----------------
+
+PSTH helper function
+
+.. code-block:: matlab
+
+   function [psth_xs, psth_ys] = calculate_psth(xs, bin_window, bin_width)
+   [bin_counts, edges]  = histcounts(xs, 'BinWidth', bin_width);
+   psth_xs = edges(1:end-1) + (bin_width / 2);
+   
+   moving_avg_b = (1/bin_window) * ones(1,bin_window);
+   psth_ys = filter(moving_avg_b, 1, bin_counts);
+   end
+
+Plotter function for each stimulus condition
+
+.. code-block:: matlab
+
+   function plot_condition(ax, ConditionPlot)
+   left_cdata = [1 0 0]; % red
+   right_cdata = [0 0 1]; % blue
+   hist_margin = 50;
+   scatter_margin = 10;
+   
+   % Calculate PSTH values
+   % moving average over 200 ms as per figure 1e
+   hist_bin_width = 0.2 / ConditionPlot.psth_bin_window;
+   [left_psth_xs, left_psth_ys] =...
+       calculate_psth(ConditionPlot.left_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);
+   [right_psth_xs, right_psth_ys] =...
+       calculate_psth(ConditionPlot.right_scatter.xs, ConditionPlot.psth_bin_window, hist_bin_width);
+   
+   right_scatter_offset = min(ConditionPlot.right_scatter.ys);
+   right_scatter_height = max(ConditionPlot.right_scatter.ys) - right_scatter_offset;
+   left_scatter_offset = min(ConditionPlot.left_scatter.ys);
+   left_scatter_height = max(ConditionPlot.left_scatter.ys) - left_scatter_offset;
+   psth_height = max([left_psth_ys right_psth_ys]);
+   
+   left_y_offset = hist_margin...
+       + psth_height...
+       - left_scatter_offset;
+   
+   right_y_offset = scatter_margin...
+       + left_y_offset...
+       + left_scatter_offset...
+       + left_scatter_height...
+       - right_scatter_offset;
+   
+   subplot_height = right_y_offset...
+       + right_scatter_offset...
+       + right_scatter_height;
+   
+   hold(ax, 'on');
+   
+   % PSTH
+   plot(ax, left_psth_xs, left_psth_ys, 'Color', left_cdata);
+   plot(ax, right_psth_xs, right_psth_ys, 'Color', right_cdata);
+   
+   % Scatter Plot
+   scatter(ax,...
+       ConditionPlot.left_scatter.xs,...
+       left_y_offset + ConditionPlot.left_scatter.ys,...
+       'Marker', '.',...
+       'CData', left_cdata,...
+       'SizeData', 1);
+   scatter(ax,...
+       ConditionPlot.right_scatter.xs,...
+       right_y_offset + ConditionPlot.right_scatter.ys,...
+       'Marker', '.',...
+       'CData', right_cdata,...
+       'SizeData', 1);
+   
+   % sample, delay, response lines
+   line(ax, repmat(ConditionPlot.sample, 1, 2), [0 subplot_height],...
+       'Color', 'k', 'LineStyle', '--');
+   line(ax, repmat(ConditionPlot.delay, 1, 2), [0 subplot_height],...
+       'Color', 'k', 'LineStyle', '--');
+   line(ax, repmat(ConditionPlot.response, 1, 2), [0 subplot_height],...
+       'Color', 'k', 'LineStyle', '--');
+   
+   % blue bar for photoinhibition period
+   if ~strcmp(ConditionPlot.stim_type, 'none')
+       stim_height = subplot_height;
+       stim_width = 0.5; % seconds
+       % end time relative to 'go' cue as described in the paper.
+       switch ConditionPlot.stim_type
+           case 'Sample'
+               end_offset = 1.6;
+           case 'Early Delay'
+               end_offset = 0.8;
+           case 'Middle Delay'
+               end_offset = 0.3;
+           otherwise
+               error('Invalid photostim period `%s`', ConditionPlot.stim_type);
+       end
+       stim_offset = ConditionPlot.response - stim_width - end_offset;
+       patch_vertices = [...
+           stim_offset, 0;...
+           stim_offset, stim_height;...
+           stim_offset+stim_width, stim_height;...
+           stim_offset+stim_width, 0];
+       patch(ax,...
+           'Faces', 1:4,...
+           'Vertices', patch_vertices,...
+           'FaceColor', '#B3D3EC',...  % light blue shading
+           'EdgeColor', 'none',...
+           'FaceAlpha', 0.8);
+   end
+   
+   title(ax, ConditionPlot.label);
+   xlabel(ax, 'Time (Seconds)');
+   ylabel(ax, 'Spikes s^{-1}')
+   xticks(ax, [-2 0 2]);
+   yticks(ax, [0 max(10, round(psth_height, -1))]);
+   %     legend(ax, [scatter_left_plot, scatter_right_plot], {'Left Lick', 'Right Lick'},...
+   %         'location', 'northwestoutside');
+   ax.TickDir = 'out';
+   ax.XLim = ConditionPlot.xlim;
+   ax.YLim = [0 subplot_height];
+   
+   hold(ax, 'off');
+   end
diff --git a/docs/source/pages/tutorials/behavior.rst b/docs/source/pages/tutorials/behavior.rst
index 9569e830f..cd17d329d 100644
--- a/docs/source/pages/tutorials/behavior.rst
+++ b/docs/source/pages/tutorials/behavior.rst
@@ -6,14 +6,363 @@ Behavior Data
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/behavior.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/behavior.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/behavior.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This tutorial will guide you in writing behavioral data to NWB.
+
+Creating an NWB File
+--------------------
+
+Create an NWBFile object with the required fields (``session_description``, ``identifier``, and ``session_start_time``) and additional metadata.
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', 'Mouse5_Day3', ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'general_experimenter', 'My Name', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', 'DOI:10.1016/j.neuron.2016.12.011'); % optional
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: []
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'My Name'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: 'DOI:10.1016/j.neuron.2016.12.011'
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is also recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '005', ...
+       'age', 'P90D', ...
+       'description', 'mouse 5', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+SpatialSeries: Storing continuous spatial data
+----------------------------------------------
+
+`SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ is a subclass of `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ that represents data in space, such as the spatial direction e.g., of gaze or travel or position of an animal over time.
+
+Create data that corresponds to x, y position over time.
+
+.. code-block:: matlab
+
+   position_data = [linspace(0, 10, 50); linspace(0, 8, 50)]; % 2 x nT array
+
+In `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ data, the first dimension is always time (in seconds), the second dimension represents the x, y position. However, as described in the `dimensionMapNoDataPipes <dimensionMapNoDataPipes>`_ tutorial, when a MATLAB array is exported to HDF5, the array is transposed. Therefore, in order to correctly export the data, in MATLAB the last dimension of an array should be time. `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ data should be stored as one continuous stream as it is acquired, not by trials as is often reshaped for analysis. Data can be trial-aligned on-the-fly using the trials table. See the trials tutorial for further information.
+
+For position data ``reference_frame`` indicates the zero-position, e.g. the 0,0 point might be the bottom-left corner of an enclosure, as viewed from the tracking camera.
+
+.. code-block:: matlab
+
+   timestamps = linspace(0, 50, 50)/ 200;
+   position_spatial_series = types.core.SpatialSeries( ...
+       'description', 'Postion (x, y) in an open field.', ...
+       'data', position_data, ...
+       'timestamps', timestamps, ...
+       'reference_frame', '(0,0) is the bottom left corner.' ...
+       )
+
+.. code-block:: text
+
+   position_spatial_series = 
+     SpatialSeries with properties:
+   
+           reference_frame: '(0,0) is the bottom left corner.'
+        starting_time_unit: 'seconds'
+       timestamps_interval: 1
+           timestamps_unit: 'seconds'
+                      data: [2x50 double]
+                 data_unit: 'meters'
+                  comments: 'no comments'
+                   control: []
+       control_description: ''
+           data_continuity: ''
+           data_conversion: 1
+               data_offset: 0
+           data_resolution: -1
+               description: 'Postion (x, y) in an open field.'
+             starting_time: []
+        starting_time_rate: []
+                timestamps: [0 0.0051 0.0102 0.0153 0.0204 0.0255 0.0306 0.0357 0.0408 0.0459 0.0510 0.0561 0.0612 0.0663 0.0714 0.0765 0.0816 0.0867 0.0918 0.0969 0.1020 0.1071 0.1122 0.1173 0.1224 0.1276 0.1327 0.1378 0.1429 0.1480 0.1531 … ] (1x50 double)
+
+Position: Storing position measured over time
+---------------------------------------------
+
+To help data analysis and visualization tools know that this `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object represents the position of the subject, store the `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object inside a `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object, which can hold one or more `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ objects.
+
+.. code-block:: matlab
+
+   position = types.core.Position();
+   position.spatialseries.set('SpatialSeries', position_spatial_series);
+
+Create a Behavior Processing Module
+-----------------------------------
+
+Create a processing module called "behavior" for storing behavioral data in the NWBFile, then add the `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object to the processing module.
+
+.. code-block:: matlab
+
+   behavior_processing_module = types.core.ProcessingModule('description', 'stores behavioral data.');
+   behavior_processing_module.nwbdatainterface.set("Position", position);
+   nwb.processing.set("behavior", behavior_processing_module);
+
+CompassDirection: Storing view angle measured over time
+-------------------------------------------------------
+
+Analogous to how position can be stored, we can create a `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object for representing the view angle of the subject.
+
+For direction data ``reference_frame`` indicates the zero direction, for instance in this case "straight ahead" is 0 radians.
+
+.. code-block:: matlab
+
+   view_angle_data = linspace(0, 4, 50);
+   direction_spatial_series = types.core.SpatialSeries( ...
+       'description', 'View angle of the subject measured in radians.', ...
+       'data', view_angle_data, ...
+       'timestamps', timestamps, ...
+       'reference_frame', 'straight ahead', ...
+       'data_unit', 'radians' ...
+       );
+   direction = types.core.CompassDirection();
+   direction.spatialseries.set('spatial_series', direction_spatial_series);
+
+We can add a `CompassDirection <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CompassDirection.html>`_ object to the behavior processing module the same way we have added the position data.
+
+.. code-block:: matlab
+
+   behavior_processing_module.nwbdatainterface.set('CompassDirection', direction);
+
+BehaviorTimeSeries: Storing continuous behavior data
+----------------------------------------------------
+
+`BehavioralTimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralTimeSeries.html>`_ is an interface for storing continuous behavior data, such as the speed of a subject.
+
+.. code-block:: matlab
+
+   speed_data = linspace(0, 0.4, 50);
+   
+   speed_time_series = types.core.TimeSeries( ...
+       'data', speed_data, ...
+       'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
+       'starting_time_rate', 10.0, ... % Hz
+       'description', 'he speed of the subject measured over time.', ...
+       'data_unit', 'm/s' ...
+       );
+   
+   behavioral_time_series = types.core.BehavioralTimeSeries();
+   behavioral_time_series.timeseries.set('speed', speed_time_series);
+   
+   % Add behavioral_time_series to the processing module
+   behavior_processing_module.nwbdatainterface.set('BehavioralTimeSeries', behavioral_time_series);
+
+BehavioralEvents: Storing behavioral events
+-------------------------------------------
+
+`BehavioralEvents <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEvents.html>`_ is an interface for storing behavioral events. We can use it for storing the timing and amount of rewards (e.g. water amount) or lever press times.
+
+.. code-block:: matlab
+
+   reward_amount = [1.0, 1.5, 1.0, 1.5];
+   event_timestamps = [1.0, 2.0, 5.0, 6.0];
+   
+   time_series = types.core.TimeSeries( ...
+       'data', reward_amount, ...
+       'timestamps', event_timestamps, ...
+       'description', 'The water amount the subject received as a reward.', ...
+       'data_unit', 'ml' ...
+       );
+   
+   behavioral_events = types.core.BehavioralEvents();
+   behavioral_events.timeseries.set('lever_presses', time_series);
+   
+   % Add behavioral_events to the processing module
+   behavior_processing_module.nwbdatainterface.set('BehavioralEvents', behavioral_events);
+
+Storing only the timestamps of the events is possible with the ndx-events NWB extension. You can also add labels associated with the events with this extension. You can find information about installation and example usage `here <https://github.com/nwb-extensions/ndx-events-record>`_.
+
+BehavioralEpochs: Storing intervals of behavior data
+----------------------------------------------------
+
+`BehavioralEpochs <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html>`_ is for storing intervals of behavior data. `BehavioralEpochs <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html>`_ uses `IntervalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html>`_ to represent the time intervals. Create an `IntervalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html>`_ object that represents the time intervals when the animal was running. IntervalSeries uses 1 to indicate the beginning of an interval and -1 to indicate the end.
+
+.. code-block:: matlab
+
+   run_intervals = types.core.IntervalSeries( ...
+       'description', 'Intervals when the animal was running.', ...
+       'data', [1, -1, 1, -1, 1, -1], ...
+       'timestamps', [0.5, 1.5, 3.5, 4.0, 7.0, 7.3] ...
+       );
+   
+   behavioral_epochs = types.core.BehavioralEpochs();
+   behavioral_epochs.intervalseries.set('running', run_intervals);
+
+You can add more than one `IntervalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html>`_ to a `BehavioralEpochs <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html>`_ object.
+
+.. code-block:: matlab
+
+   sleep_intervals = types.core.IntervalSeries( ...
+       'description', 'Intervals when the animal was sleeping', ...
+       'data', [1, -1, 1, -1], ...
+       'timestamps', [15.0, 30.0, 60.0, 95.0] ...
+       );
+   behavioral_epochs.intervalseries.set('sleeping', sleep_intervals);
+   
+   % Add behavioral_epochs to the processing module
+   behavior_processing_module.nwbdatainterface.set('BehavioralEpochs', behavioral_epochs);
+
+Another approach: TimeIntervals
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Using `TimeIntervals <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html>`_ to represent time intervals is often preferred over `BehavioralEpochs <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/BehavioralEpochs.html>`_ and `IntervalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntervalSeries.html>`_. `TimeIntervals <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html>`_ is a subclass of `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, which offers flexibility for tabular data by allowing the addition of optional columns which are not defined in the standard DynamicTable class.
+
+.. code-block:: matlab
+
+   sleep_intervals = types.core.TimeIntervals( ...
+       'description', 'Intervals when the animal was sleeping.', ...
+       'colnames', {'start_time', 'stop_time', 'stage'} ...
+       );
+   
+   sleep_intervals.addRow('start_time', 0.3, 'stop_time', 0.35, 'stage', 1);
+   sleep_intervals.addRow('start_time', 0.7, 'stop_time', 0.9, 'stage', 2);
+   sleep_intervals.addRow('start_time', 1.3, 'stop_time', 3.0, 'stage', 3);
+   
+   nwb.intervals.set('sleep_intervals', sleep_intervals);
+
+EyeTracking: Storing continuous eye-tracking data of gaze direction
+-------------------------------------------------------------------
+
+`EyeTracking <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html>`_ is for storing eye-tracking data which represents direction of gaze as measured by an eye tracking algorithm. An `EyeTracking <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EyeTracking.html>`_ object holds one or more `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ objects that represent the gaze direction over time extracted from a video.
+
+.. code-block:: matlab
+
+   eye_position_data = [linspace(-20, 30, 50); linspace(30, -20, 50)];
+   
+   right_eye_position = types.core.SpatialSeries( ...
+       'description', 'The position of the right eye measured in degrees.', ...
+       'data', eye_position_data, ...
+       'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
+       'starting_time_rate', 50.0, ... % Hz
+       'reference_frame', '(0,0) is middle', ...
+       'data_unit', 'degrees' ...
+       );
+   
+   left_eye_position = types.core.SpatialSeries( ...
+       'description', 'The position of the right eye measured in degrees.', ...
+       'data', eye_position_data, ...
+       'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
+       'starting_time_rate', 50.0, ... % Hz
+       'reference_frame', '(0,0) is middle', ...
+       'data_unit', 'degrees' ...
+       );
+   
+   eye_tracking = types.core.EyeTracking();
+   eye_tracking.spatialseries.set('right_eye_position', right_eye_position);
+   eye_tracking.spatialseries.set('left_eye_position', left_eye_position);
+   
+   behavior_processing_module.nwbdatainterface.set('EyeTracking', eye_tracking);
+
+PupilTracking: Storing continuous eye-tracking data of pupil size
+-----------------------------------------------------------------
+
+`PupilTracking <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html>`_ is for storing eye-tracking data which represents pupil size. `PupilTracking <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PupilTracking.html>`_ holds one or more `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ objects that can represent different features such as the dilation of the pupil measured over time by a pupil tracking algorithm.
+
+.. code-block:: matlab
+
+   pupil_diameter = types.core.TimeSeries( ...
+       'description', 'Pupil diameter extracted from the video of the right eye.', ...
+       'data', linspace(0.001, 0.002, 50), ...
+       'starting_time', 1.0, ... % NB: Important to set starting_time when using starting_time_rate
+       'starting_time_rate', 20.0, ... % Hz
+       'data_unit', 'meters' ...
+       );
+   
+   pupil_tracking = types.core.PupilTracking();
+   pupil_tracking.timeseries.set('pupil_diameter', pupil_diameter);
+   
+   behavior_processing_module.nwbdatainterface.set('PupilTracking', pupil_tracking);
+
+Writing the behavior data to an NWB file
+----------------------------------------
+
+All of the above commands build an NWBFile object in-memory. To write this file, use `nwbExport <https://matnwb.readthedocs.io/en/latest/pages/functions/nwbExport.html>`_.
+
+.. code-block:: matlab
+
+   % Save to tutorials/tutorial_nwb_files folder
+   nwbFilePath = misc.getTutorialNwbFilePath('behavior_tutorial.nwb');
+   nwbExport(nwb, nwbFilePath);
+   fprintf('Exported NWB file to "%s"\n', 'behavior_tutorial.nwb')
+
+.. code-block:: text
+
+   Exported NWB file to "behavior_tutorial.nwb"
diff --git a/docs/source/pages/tutorials/convertTrials.rst b/docs/source/pages/tutorials/convertTrials.rst
index ab3ebd61a..31c6f5407 100644
--- a/docs/source/pages/tutorials/convertTrials.rst
+++ b/docs/source/pages/tutorials/convertTrials.rst
@@ -4,16 +4,592 @@ Converting Trials to NWB Format
 ===============================
 
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
-   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/convertTrials.mlx
+   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/convertTrials.m
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/convertTrials.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/convertTrials.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+How to convert trial-based experimental data to the Neurodata Without Borders file format using MatNWB. This example uses the `CRCNS ALM-3 <https://crcns.org/data-sets/motor-cortex/alm-3>`* data set.  Information on how to download the data can be found on the `CRCNS Download Page <https://crcns.org/data-sets/motor-cortex/download>`*.  One should first familiarize themselves with the file format, which can be found on the `ALM-3 About Page <https://crcns.org/data-sets/motor-cortex/alm-3/about-alm-3>`_ under the Documentation files.
+
+**Author:** Lawrence Niu
+**Contact:** lawrence@vidriotech.com
+**Last Updated:** Sep 14, 2024
+
+Script Configuration
+--------------------
+
+The following section describes configuration parameters specific to the publishing script, and can be skipped when implementing your own conversion. The parameters can be changed to fit any of the available sessions.
+
+.. code-block:: matlab
+
+   animal = 'ANM255201';
+   session = '20141124';
+   
+   identifier = [animal '_' session];
+
+Specify the local path for the downloaded data:
+
+.. code-block:: matlab
+
+   data_root_path = 'data';
+   
+   metadata_loc = fullfile(data_root_path, 'metadata', ['meta_data_' identifier '.mat']);
+   datastructure_loc = fullfile(data_root_path, 'data_structure_files',...
+       ['data_structure_' identifier '.mat']);
+   rawdata_loc = fullfile(data_root_path, 'RawVoltageTraces', [identifier '.tar']);
+
+The animal and session specifier can be changed with the ``animal`` and ``session`` variable name respectively.  ``metadata_loc``, ``datastructure_loc``, and ``rawdata_loc`` should refer to the metadata .mat file, the data structure .mat file, and the raw .tar file.
+
+.. code-block:: matlab
+
+   output_directory = 'out';
+   
+   if ~isfolder(output_directory)
+       mkdir(output_directory);
+   end
+   
+   source_file = [mfilename() '.m'];
+   [~, source_script, ~] = fileparts(source_file);
+
+The NWB file will be saved in the output directory indicated by ``output_directory``
+
+General Information
+-------------------
+
+.. code-block:: matlab
+
+   nwb = NwbFile();
+   nwb.identifier = identifier;
+   nwb.general_source_script = source_script;
+   nwb.general_source_script_file_name = source_file;
+   nwb.general_lab = 'Svoboda';
+   nwb.general_keywords = {'Network models', 'Premotor cortex', 'Short-term memory'};
+   nwb.general_institution = ['Janelia Research Campus,'...
+       ' Howard Huges Medical Institute, Ashburn, Virginia 20147, USA'];
+   nwb.general_related_publications = ...
+       ['Li N, Daie K, Svoboda K, Druckmann S (2016).',...
+       ' Robust neuronal dynamics in premotor cortex during motor planning.',...
+       ' Nature. 7600:459-64. doi: 10.1038/nature17643'];
+   nwb.general_stimulus = 'photostim';
+   nwb.general_protocol = 'IACUC';
+   nwb.general_surgery = ['Mice were prepared for photoinhibition and ',...
+       'electrophysiology with a clear-skull cap and a headpost. ',...
+       'The scalp and periosteum over the dorsal surface of the skull were removed. ',...
+       'A layer of cyanoacrylate adhesive (Krazy glue, Elmer''s Products Inc.) ',...
+       'was directly applied to the intact skull. A custom made headpost ',...
+       'was placed on the skull with its anterior edge aligned with the suture lambda ',...
+       '(approximately over cerebellum) and cemented in place ',...
+       'with clear dental acrylic (Lang Dental Jet Repair Acrylic; 1223-clear). ',...
+       'A thin layer of clear dental acrylic was applied over the cyanoacrylate adhesive ',...
+       'covering the entire exposed skull, ',...
+       'followed by a thin layer of clear nail polish (Electron Microscopy Sciences, 72180).'];
+   nwb.session_description = sprintf('Animal `%s` on Session `%s`', animal, session);
+
+All properties with the prefix ``general`` contain context for the entire experiment such as lab, institution, and experimentors.  For session-delimited data from the same experiment, these fields will all be the same.  Note that most of this information was pulled from the publishing paper and not from any of the downloadable data.
+
+The only required property is the ``identifier``, which distinguishes one session from another within an experiment.  In our case, the ALM-3 data uses a combination of session date and animal ID.
+
+The ALM-3 File Structure
+------------------------
+
+Each ALM-3 session has three files: a metadata .mat file describing the experiment, a data structures .mat file containing analyzed data, and a raw .tar archive containing multiple raw electrophysiology data separated by trials as .mat files. All files will be merged into a single NWB file.
+
+Metadata
+--------
+
+ALM-3 Metadata contains information about the reference times, experimental context, methodology, as well as details of the electrophysiology, optophysiology, and behavioral portions of the experiment.  A vast majority of these details are placed in ``general`` prefixed properties in NWB.
+
+.. code-block:: matlab
+
+   fprintf('Processing Meta Data from `%s`\n', metadata_loc);
+   loaded = load(metadata_loc, 'meta_data');
+   meta = loaded.meta_data;
+
+Experiment-specific treatment for animals with the ReaChR gene modification
+
+.. code-block:: matlab
+
+   isreachr = any(cell2mat(strfind(meta.animalGeneModification, 'ReaChR')));
+
+Sessions are separated by date of experiment.
+
+.. code-block:: matlab
+
+   nwb.general_session_id = meta.dateOfExperiment;
+
+ALM-3 data start time is equivalent to the reference time.
+
+.. code-block:: matlab
+
+   nwb.session_start_time = datetime([meta.dateOfExperiment meta.timeOfExperiment],...
+       'InputFormat', 'yyyyMMddHHmmss', 'TimeZone', 'America/New_York'); % Eastern Daylight Time
+   nwb.timestamps_reference_time = nwb.session_start_time;
+   
+   nwb.general_experimenter = strjoin(meta.experimenters, ', ');
+
+.. code-block:: matlab
+
+   nwb.general_subject = types.core.Subject(...
+       'species', meta.species{1}, ...
+       'subject_id', meta.animalID{1}(1,:), ... %weird case with duplicate Animal ID
+       'sex', meta.sex, ...
+       'age', meta.dateOfBirth, ...
+       'description', [...
+           'Whisker Config: ' strjoin(meta.whiskerConfig, ', ') newline...
+           'Animal Source: ' strjoin(meta.animalSource, ', ')]);
+
+Ideally, if a raw data field does not correspond directly to a NWB field, one would
+
+.. code-block:: matlab
+
+   % create their own using a
+
+`custom NWB extension class <https://pynwb.readthedocs.io/en/latest/extensions.html>`_. However, since these fields are mostly experimental annotations, we instead pack the extra values into the ``description`` field as a string.
+
+The formatStruct function simply prints the field and values given the struct. An optional cell array of field names specifies whitelist of fields to print. This function is provided with this script in the tutorials directory.
+
+.. code-block:: matlab
+
+   nwb.general_subject.genotype = formatStruct(...
+       meta, ...
+       {'animalStrain'; 'animalGeneModification'; 'animalGeneCopy';...
+       'animalGeneticBackground'});
+   
+   weight = {};
+   if ~isempty(meta.weightBefore)
+       weight{end+1} = 'weightBefore';
+   end
+   if ~isempty(meta.weightAfter)
+       weight{end+1} = 'weightAfter';
+   end
+   weight = weight(~cellfun('isempty', weight));
+   if ~isempty(weight)
+       nwb.general_subject.weight = formatStruct(meta, weight);
+   end
+   
+   % general/experiment_description
+   nwb.general_experiment_description = [...
+       formatStruct(meta, {'experimentType'; 'referenceAtlas'}), ...
+       newline, ...
+       formatStruct(meta.behavior, {'task_keyword'})];
+
+Miscellaneous collection information from ALM-3 that didn't quite fit any NWB properties are stored in general/data_collection.
+
+.. code-block:: matlab
+
+   nwb.general_data_collection = formatStruct(meta.extracellular,...
+       {'extracellularDataType';'cellType';'identificationMethod';'amplifierRolloff';...
+       'spikeSorting';'ADunit'});
+
+Device objects are essentially just a list of device names.  We store the probe and laser hardware names here.
+
+.. code-block:: matlab
+
+   probetype = meta.extracellular.probeType{1};
+   probeSource = meta.extracellular.probeSource{1};
+   deviceName = [probetype ' (' probeSource ')'];
+   nwb.general_devices.set(deviceName, types.core.Device());
+   
+   if isreachr
+       laserName = 'laser-594nm (Cobolt Inc., Cobolt Mambo 100)';
+   else
+       laserName = 'laser-473nm (Laser Quantum, Gem 473)';
+   end
+   nwb.general_devices.set(laserName, types.core.Device());
+
+.. code-block:: matlab
+
+   structDesc = {'recordingCoordinates';'recordingMarker';'recordingType';'penetrationN';...
+       'groundCoordinates'};
+   if ~isempty(meta.extracellular.referenceCoordinates)
+       structDesc{end+1} = 'referenceCoordinates';
+   end
+   recordingLocation = meta.extracellular.recordingLocation{1};
+   egroup = types.core.ElectrodeGroup(...
+       'description', formatStruct(meta.extracellular, structDesc),...
+       'location', recordingLocation,...
+       'device', types.untyped.SoftLink(['/general/devices/' deviceName]));
+   nwb.general_extracellular_ephys.set(deviceName, egroup);
+
+The NWB *ElectrodeGroup* object stores experimental information regarding a group of probes.  Doing so requires a *SoftLink* to the probe specified under ``general_devices``.  SoftLink objects are direct maps to `HDF5 Soft Links <https://portal.hdfgroup.org/display/HDF5/H5L_CREATE_SOFT>`_ on export, and thus, require a true HDF5 path.
+
+You can specify column names and values as key-value arguments in the DynamicTable constructor.
+
+.. code-block:: matlab
+
+   dtColNames = {'x', 'y', 'z', 'imp', 'location', 'filtering','group', 'group_name'};
+   dynTable = types.hdmf_common.DynamicTable(...
+       'colnames', dtColNames,...
+       'description', 'Electrodes',...
+       'x', types.hdmf_common.VectorData('description', 'x coordinate of the channel location in the brain (+x is posterior).'),...
+       'y', types.hdmf_common.VectorData('description', 'y coordinate of the channel location in the brain (+y is inferior).'),...
+       'z', types.hdmf_common.VectorData('description', 'z coordinate of the channel location in the brain (+z is right).'),...
+       'imp', types.hdmf_common.VectorData('description', 'Impedance of the channel.'),...
+       'location', types.hdmf_common.VectorData('description', ['Location of the electrode (channel). '...
+       'Specify the area, layer, comments on estimation of area/layer, stereotaxic coordinates if '...
+       'in vivo, etc. Use standard atlas names for anatomical regions when possible.']),...
+       'filtering', types.hdmf_common.VectorData('description', 'Description of hardware filtering.'),...
+       'group', types.hdmf_common.VectorData('description', 'Reference to the ElectrodeGroup this electrode is a part of.'),...
+       'group_name', types.hdmf_common.VectorData('description', 'Name of the ElectrodeGroup this electrode is a part of.'));
+
+Raw HDF5 path to the above electrode group. Referenced by
+
+.. code-block:: matlab
+
+   % the general/extracellular_ephys Dynamic Table
+   egroupPath = ['/general/extracellular_ephys/' deviceName];
+   eGroupReference = types.untyped.ObjectView(egroupPath);
+   for i = 1:length(meta.extracellular.siteLocations)
+       location = meta.extracellular.siteLocations{i};
+
+% Add each row in the dynamic table. The `id` column is populated % dynamically.
+
+.. code-block:: matlab
+
+       dynTable.addRow(...
+           'x', location(1), 'y', location(2), 'z', location(3),...
+           'imp', 0,...
+           'location', recordingLocation,...
+           'filtering', '',...
+           'group', eGroupReference,...
+           'group_name', probetype);
+   end
+
+The ``group`` column in the Dynamic Table contains an *ObjectView* to the previously created ``ElectrodeGroup``.  An ``ObjectView`` can be best thought of as a direct pointer to another typed object.  It also directly maps to a `HDF5 Object Reference <https://portal.hdfgroup.org/display/HDF5/H5R_CREATE>`_, thus the HDF5 path requirement.  ``ObjectViews`` are slightly different from ``SoftLinks`` in that they can be stored in datasets (data columns, tables, and ``data`` fields in ``NWBData`` objects).
+
+.. code-block:: matlab
+
+   nwb.general_extracellular_ephys_electrodes = dynTable;
+
+The ``electrodes`` property in ``extracellular_ephys`` is a special keyword in NWB that must be paired with a *Dynamic Table*.  These are tables which can have an unbounded number of columns and rows, each as their own dataset.  With the exception of the ``id`` column, all other columns must be *VectorData* or *VectorIndex* objects.  The ``id`` column, meanwhile, must be an *ElementIdentifiers* object.  The names of all used columns are specified in the in the ``colnames`` property as a cell array of strings.
+
+.. code-block:: matlab
+
+   % general/optogenetics/photostim
+   nwb.general_optogenetics.set('photostim', ...
+       types.core.OptogeneticStimulusSite(...
+       'excitation_lambda', meta.photostim.photostimWavelength{1}, ...
+       'location', meta.photostim.photostimLocation{1}, ...
+       'device', types.untyped.SoftLink(['/general/devices/' laserName]), ...
+       'description', formatStruct(meta.photostim, {...
+       'stimulationMethod';'photostimCoordinates';'identificationMethod'})));
+
+Analysis Data Structure
+-----------------------
+
+The ALM-3 data structures .mat file contains analyzed spike data, trial-specific parameters, and behavioral analysis data.
+
+Hashes
+------
+
+ALM-3 stores its data structures in the form of *hashes* which are essentially the same as python's dictionaries or MATLAB's maps but where the keys and values are stored under separate struct fields.  Getting a hashed value from a key involves retrieving the array index that the key is in and applying it to the parallel array in the values field.
+
+You can find more information about hashes and how they're used on the `ALM-3 about page <https://crcns.org/data-sets/motor-cortex/alm-3/about-alm-3>`_.
+
+.. code-block:: matlab
+
+   fprintf('Processing Data Structure `%s`\n', datastructure_loc);
+   loaded = load(datastructure_loc, 'obj');
+   data = loaded.obj;
+
+wherein each cell is one trial. We must populate this way because trials may not be in trial order. Trial timeseries will be a compound type under intervals/trials.
+
+.. code-block:: matlab
+
+   trial_timeseries = cell(size(data.trialIds)); 
+
+NWB comes with default support for trial-based data.  These must be *TimeIntervals* that are placed in the ``intervals`` property.  Note that ``trials`` is a special keyword that is required for PyNWB compatibility.
+
+.. code-block:: matlab
+
+   ephus = data.timeSeriesArrayHash.value{1};
+   ephusUnit = data.timeUnitNames{data.timeUnitIds(ephus.timeUnit)};
+
+Lick direction and timestamps trace
+
+.. code-block:: matlab
+
+   tsIdx = strcmp(ephus.idStr, 'lick_trace');
+   bts = types.core.BehavioralTimeSeries();
+   
+   bts.timeseries.set('lick_trace_ts', ...
+       types.core.TimeSeries(...
+       'data', ephus.valueMatrix(:,tsIdx),...
+       'data_unit', ephusUnit,...
+       'description', ephus.idStrDetailed{tsIdx}, ...
+       'timestamps', ephus.time, ...
+       'timestamps_unit', ephusUnit));
+   nwb.acquisition.set('lick_trace', bts);
+   bts_ref = types.untyped.ObjectView('/acquisition/lick_trace/lick_trace_ts');
+
+Acousto-optic modulator input trace
+
+.. code-block:: matlab
+
+   tsIdx = strcmp(ephus.idStr, 'aom_input_trace');
+   ts = types.core.TimeSeries(...
+       'data', ephus.valueMatrix(:,tsIdx), ...
+       'data_unit', 'Volts', ...
+       'description', ephus.idStrDetailed{tsIdx}, ...
+       'timestamps', ephus.time, ...
+       'timestamps_unit', ephusUnit);
+   nwb.stimulus_presentation.set('aom_input_trace', ts);
+   ts_ref = types.untyped.ObjectView('/stimulus/presentation/aom_input_trace');
+
+Laser power
+
+.. code-block:: matlab
+
+   tsIdx = strcmp(ephus.idStr, 'laser_power');
+   ots = types.core.OptogeneticSeries(...
+       'data', ephus.valueMatrix(:, tsIdx), ...
+       'data_conversion', 1e-3, ... % data is stored in mW, data unit for OptogeneticSeries is watts
+       'description', ephus.idStrDetailed{tsIdx}, ...
+       'timestamps', ephus.time, ...
+       'timestamps_unit', ephusUnit, ...
+       'site', types.untyped.SoftLink('/general/optogenetics/photostim'));
+   nwb.stimulus_presentation.set('laser_power', ots);
+   ots_ref = types.untyped.ObjectView('/stimulus/presentation/laser_power');
+
+Append trials timeseries references in order
+
+.. code-block:: matlab
+
+   [ephus_trials, ~, trials_to_data] = unique(ephus.trial);
+   for i=1:length(ephus_trials)
+       i_loc = i == trials_to_data;
+       t_start = find(i_loc, 1);
+       t_count = sum(i_loc);
+       trial = ephus_trials(i);
+   
+       trial_timeseries{trial}(end+(1:3), :) = {...
+           bts_ref int64(t_start) int64(t_count);...
+           ts_ref  int64(t_start) int64(t_count);...
+           ots_ref int64(t_start) int64(t_count)};
+   end
+
+The ``timeseries`` property of the ``TimeIntervals`` object is an example of a *compound data type*.  These types are essentially tables of data in HDF5 and can be represented by a MATLAB table, an array of structs, or a struct of arrays. Beware: validation of column lengths here is not guaranteed by the type checker until export.
+
+*VectorIndex* objects index into a larger *VectorData* column.  The object that is being referenced is indicated by the ``target`` property, which uses an ObjectView. Each element in the VectorIndex marks the *last* element in the corresponding vector data object for the VectorIndex row.  Thus, the starting index for this row would be the previous index + 1.  Note that these indices must be 0-indexed for compatibility with pynwb.  You can see this in effect with the ``timeseries`` property which is indexed by the ``timeseries_index`` property.
+
+Though TimeIntervals is a subclass of the DynamicTable type, we opt for populating the Dynamic Table data by column instead of using `addRow` here because of how the data is formatted. DynamicTable is flexible enough to accommodate both styles of data conversion.
+
+.. code-block:: matlab
+
+   trials_epoch = types.core.TimeIntervals(...
+       'colnames', {'start_time'}, ...
+       'description', 'trial data and properties', ...
+       'start_time', types.hdmf_common.VectorData(...
+           'data', data.trialStartTimes', ...
+           'description', 'Start time of epoch, in seconds.'), ...
+       'id', types.hdmf_common.ElementIdentifiers(...
+           'data', data.trialIds' ) );
+
+Add columns for the trial types
+
+.. code-block:: matlab
+
+   for i=1:length(data.trialTypeStr)
+       columnName = data.trialTypeStr{i};
+       columnData = types.hdmf_common.VectorData(...
+            'data', data.trialTypeMat(i,:)', ... % transpose for column vector 
+            'description', data.trialTypeStr{i});
+       trials_epoch.addColumn( columnName, columnData )
+   end
+
+Add columns for the trial properties
+
+.. code-block:: matlab
+
+   for i=1:length(data.trialPropertiesHash.keyNames)
+       columnName = data.trialPropertiesHash.keyNames{i};
+       descr = data.trialPropertiesHash.descr{i};
+       if iscellstr(descr)
+           descr = strjoin(descr, newline);
+       end
+       columnData = types.hdmf_common.VectorData(...
+            'data', data.trialPropertiesHash.value{i},...
+            'description', data.trialTypeStr{i});
+       trials_epoch.addColumn( columnName, columnData )
+   end
+   
+   nwb.intervals_trials = trials_epoch;
+
+Ephus spike data is separated into units which directly maps to the NWB property of the same name.  Each such unit contains a group of analysed waveforms and spike times, all linked to a different subset of trials IDs.
+
+The waveforms are placed in the ``analysis`` Set and are paired with their unit name ('unitx' where 'x' is some unit ID).
+
+Trial IDs, wherever they are used, are placed in a relevant ``control`` property in the data object and will indicate what data is associated with what trial as defined in ``trials``'s ``id`` column.
+
+.. code-block:: matlab
+
+   nwb.units = types.core.Units('colnames',...
+       {'spike_times', 'trials', 'waveforms'},...
+       'description', 'Analysed Spike Events');
+   esHash = data.eventSeriesHash;
+   ids = regexp(esHash.keyNames, '^unit(\d+)$', 'once', 'tokens');
+   ids = str2double([ids{:}]);
+   nwb.units.spike_times = types.hdmf_common.VectorData(...
+       'description', 'timestamps of spikes');
+   
+   for i=1:length(ids)
+       esData = esHash.value{i};
+
+% Add trials ID reference
+
+.. code-block:: matlab
+
+       good_trials_mask = ismember(esData.eventTrials, nwb.intervals_trials.id.data);
+       eventTrials = esData.eventTrials(good_trials_mask);
+       eventTimes = esData.eventTimes(good_trials_mask);
+       waveforms = esData.waveforms(good_trials_mask,:);
+       channel = esData.channel(good_trials_mask);
+
+% Add waveform data to "unitx" and associate with "waveform" column as ObjectView.
+
+.. code-block:: matlab
+
+       ses = types.core.SpikeEventSeries(...
+           'control', ids(i),...
+           'control_description', 'Units Table ID',...
+           'data', waveforms .', ...
+           'description', esHash.descr{i}, ...
+           'timestamps', eventTimes, ...
+           'timestamps_unit', data.timeUnitNames{data.timeUnitIds(esData.timeUnit)},...
+           'electrodes', types.hdmf_common.DynamicTableRegion(...
+               'description', 'Electrodes involved with these spike events',...
+               'table', types.untyped.ObjectView('/general/extracellular_ephys/electrodes'),...
+               'data', channel - 1));
+       ses_name = esHash.keyNames{i};
+       ses_ref = types.untyped.ObjectView(['/analysis/', ses_name]);
+       if ~isempty(esData.cellType)
+           ses.comments = ['cellType: ' esData.cellType{1}];
+       end
+       nwb.analysis.set(ses_name, ses);
+       nwb.units.addRow(...
+           'id', ids(i), 'trials', eventTrials, 'spike_times', eventTimes, 'waveforms', ses_ref);
+
+% Add this timeseries into the trials table as well.
+
+.. code-block:: matlab
+
+       [s_trials, ~, trials_to_data] = unique(eventTrials);
+       for j=1:length(s_trials)
+           trial = s_trials(j);
+           j_loc = j == trials_to_data;
+           t_start = find(j_loc, 1);
+           t_count = sum(j_loc);
+   
+           trial_timeseries{trial}(end+1, :) = {ses_ref int64(t_start) int64(t_count)};
+       end
+   end
+
+To better understand how ``spike_times_index`` and ``spike_times`` map to each other, refer to `this diagram <https://neurodatawithoutborders.github.io/matnwb/tutorials/html/ecephys.html#13>`_ from the Extracellular Electrophysiology Tutorial.
+
+Raw Acquisition Data
+--------------------
+
+Each ALM-3 session is associated with a large number of raw voltage data grouped by trial ID. To map this data to NWB, each trial is created as its own *ElectricalSeries* object under the name 'trial n' where 'n' is the trial ID.  The trials are then linked to the ``trials`` dynamic table for easy referencing.
+
+.. code-block:: matlab
+
+   fprintf('Processing Raw Acquisition Data from `%s` (will take a while)\n', rawdata_loc);
+   untarLoc = strrep(rawdata_loc, '.tar', '');
+   if ~isfolder(untarLoc)
+       untar(rawdata_loc, fileparts(rawdata_loc));
+   end
+   
+   rawfiles = dir(untarLoc);
+   rawfiles = fullfile(untarLoc, {rawfiles(~[rawfiles.isdir]).name});
+   
+   nrows = length(nwb.general_extracellular_ephys_electrodes.id.data);
+   tablereg = types.hdmf_common.DynamicTableRegion(...
+       'description','Relevent Electrodes for this Electrical Series',...
+       'table',types.untyped.ObjectView('/general/extracellular_ephys/electrodes'),...
+       'data',(1:nrows) - 1);
+   objrefs = cell(size(rawfiles));
+   
+   endTimestamps = trials_epoch.start_time.data;
+   for i=1:length(rawfiles)
+       tnumstr = regexp(rawfiles{i}, '_trial_(\d+)\.mat$', 'tokens', 'once');
+       tnumstr = tnumstr{1};
+       rawdata = load(rawfiles{i}, 'ch_MUA', 'TimeStamps');
+       tnum = str2double(tnumstr);
+   
+       if tnum > length(endTimestamps)
+           continue; % sometimes there are extra trials without an associated start time.
+       end
+   
+       es = types.core.ElectricalSeries(...
+           'data', rawdata.ch_MUA,...
+           'description', ['Raw Voltage Acquisition for trial ' tnumstr],...
+           'electrodes', tablereg,...
+           'timestamps', rawdata.TimeStamps);
+       tname = ['trial ' tnumstr];
+       nwb.acquisition.set(tname, es);
+   
+       endTimestamps(tnum) = endTimestamps(tnum) + rawdata.TimeStamps(end);
+       objrefs{tnum} = types.untyped.ObjectView(['/acquisition/' tname]);
+   end
+
+Link to the raw data by adding the acquisition column with ObjectViews
+
+.. code-block:: matlab
+
+   % to the data
+   emptyrefs = cellfun('isempty', objrefs);
+   objrefs(emptyrefs) = {types.untyped.ObjectView('')};
+   
+   trials_epoch.addColumn('acquisition', types.hdmf_common.VectorData(...
+       'description', 'soft link to acquisition data for this trial',...
+       'data', [objrefs{:}]'));
+   
+   trials_epoch.stop_time = types.hdmf_common.VectorData(...
+        'data', endTimestamps',...
+        'description', 'the end time of each trial');
+   trials_epoch.colnames{end+1} = 'stop_time';
+
+Add timeseries to trials_epoch
+------------------------------
+
+First, we'll format and store ``trial_timeseries`` into ``intervals_trials``. note that ``timeseries_index`` data is 0-indexed.
+
+.. code-block:: matlab
+
+   ts_len = cellfun('size', trial_timeseries, 1);
+   nwb.intervals_trials.timeseries_index = types.hdmf_common.VectorIndex(...
+       'description', 'Index into Timeseries VectorData', ...
+       'data', cumsum(ts_len)', ...
+       'target', types.untyped.ObjectView('/intervals/trials/timeseries') );
+
+Intervals/trials/timeseries is a compound type so we use cell2table to convert this 2-d cell array into a compatible table.
+
+.. code-block:: matlab
+
+   is_len_nonzero = ts_len > 0;
+   trial_timeseries_table = cell2table(vertcat(trial_timeseries{is_len_nonzero}),...
+       'VariableNames', {'timeseries', 'idx_start', 'count'});
+   trial_timeseries_table = movevars(trial_timeseries_table, 'timeseries', 'After', 'count');
+   
+   interval_trials_timeseries = types.core.TimeSeriesReferenceVectorData(...
+       'description', 'Index into TimeSeries data', ...
+       'data', trial_timeseries_table);
+   nwb.intervals_trials.timeseries = interval_trials_timeseries;
+   nwb.intervals_trials.colnames{end+1} = 'timeseries';
+
+Export
+------
+
+.. code-block:: matlab
+
+   nwbFilePath = fullfile(output_directory, [identifier '.nwb']);
+   if isfile(nwbFilePath)
+       delete(nwbFilePath);
+   end
+   nwbExport(nwb, nwbFilePath);
diff --git a/docs/source/pages/tutorials/dataPipe.rst b/docs/source/pages/tutorials/dataPipe.rst
index 1f0f9c45f..3ece2b49b 100644
--- a/docs/source/pages/tutorials/dataPipe.rst
+++ b/docs/source/pages/tutorials/dataPipe.rst
@@ -4,18 +4,205 @@ Advanced Writing Using DataPipes 🎬
 ===================================
 
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
-   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dataPipe.mlx
+   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dataPipe.m
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/dataPipe.html
-   :alt: View full page
+   :alt: View rendered Live Script
 .. image:: https://img.shields.io/badge/View-Youtube-red
    :target: https://www.youtube.com/watch?v=PIE_F4iVv98&ab_channel=NeurodataWithoutBorders
    :alt: View tutorial on YouTube
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/dataPipe.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+How to utilize HDF5 compression using dataPipe
+
+**Authors:** Ivan Smalianchuk and Ben Dichter
+**Contact:** smalianchuk.ivan@gmail.com, ben.dichter@catalystneuro.com
+**Last Edited:** Jan 04, 2021
+
+Neurophysiology data can be quite large, often in the 10s of GB per session and sometimes much larger. Here, we demonstrate methods in MatNWB that allow you to deal with large datasets. These methods are compression and iterative write. Both of these techniques use the ``types.untyped.DataPipe`` object, which sends specific instructions to the HDF5 backend about how to store data.
+
+Compression - basic implementation
+----------------------------------
+
+To compress experimental data (in this case a 3D matrix with dimensions [250 250 70]) one must assign it as a ``DataPipe`` type:
+
+.. code-block:: matlab
+
+   DataToCompress = randi(100, 250, 250, 70);
+   DataPipe = types.untyped.DataPipe('data', DataToCompress);
+
+This is the most basic way to achieve compression, and all of the optimization decisions are automatically determined by MatNWB.
+
+Background
+----------
+
+HDF5 has built-in ability to compress and decompress individual datasets. If applied intelligently, this can dramatically reduce the amount of space used on the hard drive to represent the data. The end user does not need to worry about the compression status of the dataset- HDF5 will automatically decompress the dataset on read.
+
+The above example uses default chunk size and compression level (3). To optimize compression, ``compressionLevel`` and ``chunkSize`` must be considered. compressionLevel ranges from 0 - 9 where 9 is the highest level of compression and 0 is the lowest. ``chunkSize`` is less intuitive to adjust; to implement compression, chunk size must be less than data size.
+
+``DataPipe`` Arguments
+----------------------
+
+.. list-table::
+   :widths: 25 75
+
+   * - ``maxSize``
+     - Sets the maximum size of the HDF5 Dataset. Unless using iterative writing, this should match the size of Data. To append data later, use the maxSize for the full dataset. You can use Inf for a value of a dimension if you do not know its final size.
+   * - ``data``
+     - The data to compress. Must be numerical data.
+   * - ``axis``
+     - Set which axis to increment when appending more data.
+   * - ``dataType``
+     - Sets the type of the experimental data. This must be a numeric data type. Useful to include when using iterative write to append data as the appended data must be the same data type. If data is provided and dataType is not, the dataType is inferred from the provided data.
+   * - ``chunkSize``
+     - Sets chunk size for the compression. Must be less than maxSize.
+   * - ``compressionLevel``
+     - Level of compression ranging from 0-9 where 9 is the highest level of compression. The default is level 3.
+   * - ``offset``
+     - Axis offset of dataset to append. May be used to overwrite data.
+
+Chunking
+--------
+
+HDF5 Datasets can be either stored in continuous or chunked mode. Continuous means that all of the data is written to one continuous block on the hard drive, and chunked means that the dataset is automatically split into chunks that are distributed across the hard drive. The user does not need to know the mode used- HDF5 handles the gathering of chunks automatically. However, it is worth understanding these chunks because they can have a big impact on space used and read and write speed. When using compression, the dataset MUST be chunked. HDF5 is not able to apply compression to continuous datasets.
+
+If chunkSize is not explicitly specified, dataPipe will determine an appropriate chunk size. However, you can optimize the performance of the compression by manually specifying the chunk size using *chunkSize* argument.
+
+We can demonstrate the benefit of chunking by exploring the following scenario. The following code utilizes DataPipe's default chunk size:
+
+.. code-block:: matlab
+
+   fData = randi(250, 100, 1000); % Create fake data
+   
+   % create an nwb structure with required fields
+   nwb = NwbFile( ...
+       'session_start_time', datetime('2020-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'DataPipeTutorial');
+   
+   fData_compressed = types.untyped.DataPipe('data', fData);
+   
+   fdataNWB=types.core.TimeSeries( ...
+       'data', fData_compressed, ...
+       'data_unit', 'mV', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 30.0);
+   
+   nwb.acquisition.set('data', fdataNWB);
+   
+   nwbExport(nwb, 'DefaultChunks.nwb');
+
+This results in a file size of 47MB (too large), and the process takes 11 seconds (far too long). Setting the chunk size manually as in the example code below resolves these issues:
+
+.. code-block:: matlab
+
+   fData_compressed = types.untyped.DataPipe( ...
+       'data', fData, ...
+       'chunkSize', [1, 1000], ...
+       'axis', 1);
+
+This change results in the operation completing in 0.7 seconds and resulting file size of 1.1MB. The chunk size was chosen such that it spans each individual row of the matrix.
+
+Use the combination of arguments that fit your need. When dealing with large datasets, you may want to use iterative write to ensure that you stay within the bounds of your system memory and use chunking and compression to optimize storage, read and write of the data.
+
+Iterative Writing
+-----------------
+
+If experimental data is close to, or exceeds the available system memory, performance issues may arise. To combat this effect of large data, ``DataPipe`` can utilize iterative writing, where only a portion of the data is first compressed and saved, and then additional portions are appended.
+
+To demonstrate, we can create a nwb file with a compressed time series data:
+
+.. code-block:: matlab
+
+   dataPart1 = randi(250, 1, 1000); % "load" 1/4 of the entire dataset
+   fullDataSize = [1 40000]; % this is the size of the TOTAL dataset
+   
+   % create an nwb structure with required fields
+   nwb=NwbFile( ...
+       'session_start_time', datetime('2020-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'DataPipeTutorial');
+   
+   % compress the data
+   fData_use = types.untyped.DataPipe( ...
+       'data', dataPart1, ...
+       'maxSize', fullDataSize, ...
+       'axis', 2);
+
+Set the compressed data as a time series
+
+.. code-block:: matlab
+
+   fdataNWB = types.core.TimeSeries( ...
+       'data', fData_use, ...
+       'data_unit', 'mV', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 30.0);
+   
+   nwb.acquisition.set('time_series', fdataNWB);
+   
+   nwbExport(nwb, 'DataPipeTutorial_iterate.nwb');
+
+To append the rest of the data, simply load the NWB file and use the append method:
+
+.. code-block:: matlab
+
+   nwb = nwbRead('DataPipeTutorial_iterate.nwb', 'ignorecache'); %load the nwb file with partial data
+
+"load" each of the remaining 1/4ths of the large dataset
+
+.. code-block:: matlab
+
+   for i = 2:4 % iterating through parts of data
+       dataPart_i=randi(250, 1, 10000); % faked data chunk as if it was loaded
+       nwb.acquisition.get('time_series').data.append(dataPart_i); % append the loaded data
+   end
+
+The axis property defines the dimension in which additional data will be appended. In the above example, the resulting dataset will be 4000x1. However, if we set axis to 2 (and change fullDataSize appropriately), then the resulting dataset will be 1000x4.
+
+Timeseries example
+------------------
+
+Following is an example of how to compress and add a timeseries to an NWB file:
+
+.. code-block:: matlab
+
+   fData=randi(250, 1, 10000); % create fake data;
+   
+   %assign data without compression
+   nwb=NwbFile(...
+       'session_start_time', datetime(2020, 1, 1, 0, 0, 0, 'TimeZone', 'local'), ...
+       'identifier','ident1', ...
+       'session_description', 'DataPipeTutorial');
+   
+   ephys_module = types.core.ProcessingModule( ...
+       'description', 'holds processed ephys data');
+   
+   nwb.processing.set('ephys', ephys_module);
+   
+   % compress the data
+   fData_compressed=types.untyped.DataPipe( ...
+       'data', fData, ...
+       'compressionLevel', 3, ...
+       'chunkSize', [100 1], ...
+       'axis', 1);
+
+Assign the data to appropriate module and write the NWB file
+
+.. code-block:: matlab
+
+   fdataNWB=types.core.TimeSeries( ...
+       'data', fData_compressed, ...
+       'data_unit', 'mV', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 30.0);
+   
+   ephys_module.nwbdatainterface.set('data', fdataNWB);
+   nwb.processing.set('ephys', ephys_module);
+   
+   % write the file
+   nwbExport(nwb, 'Compressed.nwb');
diff --git a/docs/source/pages/tutorials/dimensionMapNoDataPipes.rst b/docs/source/pages/tutorials/dimensionMapNoDataPipes.rst
index 6f644442e..13f8a9f55 100644
--- a/docs/source/pages/tutorials/dimensionMapNoDataPipes.rst
+++ b/docs/source/pages/tutorials/dimensionMapNoDataPipes.rst
@@ -6,14 +6,88 @@ Mapping Dimensions without DataPipes
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dimensionMapNoDataPipes.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/dimensionMapNoDataPipes.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/dimensionMapNoDataPipes.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This tutorial demonstrates how the dimensions of a MATLAB array maps onto a dataset in HDF5. There are two main differences between the way MATLAB and HDF5 represents dimensions:
+
+1. **C-ordering vs F-ordering:** HDF5 is C-ordered, which means it stores data in a rows-first pattern, whereas MATLAB is F-ordered, storing data in the reverse pattern, with the last dimension of the array stored consecutively. The result is that the data in HDF5 is effectively the transpose of the array in MATLAB.
+2. **1D data (i.e vectors):** HDF5 can store 1-D arrays, but in MATLAB the lowest dimensionality of an array is 2-D.
+
+Due to differences in how MATLAB and HDF5 represent data, the dimensions of datasets are flipped when writing to/from file in MatNWB. Additionally, MATLAB represents 1D vectors in a 2D format, either as row vectors or column vectors, whereas HDF5 treats vectors as truly 1D. Consequently, when a 1D dataset from HDF5 is loaded into MATLAB, it is always represented as a column vector. To avoid unintentional changes in data dimensions, it is therefore recommended to avoid writing row vectors into an NWB file for 1D datasets.
+
+Contrast this tutorial with the `dimensionMapWithDataPipes <dimensionMapWithDataPipes>`_ tutorial that illustrates how vectors are represented differently when using ``DataPipe`` objects within ``VectorData`` objects.
+
+Create Table
+------------
+
+First, create a ``TimeIntervals`` table of height 10.
+
+.. code-block:: matlab
+
+   % Define VectorData objects for each column
+   % 1D column
+   start_col = types.hdmf_common.VectorData( ...
+                   'description', 'start_times column', ...
+                   'data', (1:10)' ... # maps onto HDF5 dataset of size (10,)
+   );
+   % 1D column
+   stop_col = types.hdmf_common.VectorData( ...
+                   'description', 'stop_times column', ...
+                   'data', (2:11)' ... # maps onto HDF5 dataset of size (10,)
+   );
+   % 4D column
+   randomval_col = types.hdmf_common.VectorData( ...
+                   'description', 'randomvalues column', ...
+                   'data', rand(5,2,3,10) ... # maps onto HDF5 dataset of size (10, 3, 2, 5)
+   );
+   
+   % 1D column
+   id_col = types.hdmf_common.ElementIdentifiers(...
+                   'data', int64(0:9)'); % maps onto HDF5 dataset of size (10,)
+   
+   % Create table
+   trials_table = types.core.TimeIntervals(...
+                    'description', 'test dynamic table column',...
+                   'colnames', {'start_time','stop_time','randomvalues'}, ...
+                   'start_time', start_col, ...
+                   'stop_time', stop_col, ...
+                   'randomvalues', randomval_col, ...
+                   'id', id_col ...     
+   );
+
+Export Table
+------------
+
+Create NWB file with ``TimeIntervals`` table and export.
+
+.. code-block:: matlab
+
+   % Create NwbFile object with required arguments
+   file = NwbFile( ...
+       'session_start_time', datetime('2022-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'test file' ...
+   );
+   % Assign to intervals_trials
+   file.intervals_trials = trials_table;
+   % Export
+   nwbExport(file, 'testFileNoDataPipes.nwb');
+
+You can examine the dimensions of the datasets on file using `HDFView <https://www.hdfgroup.org/downloads/hdfview/>`_. Screenshots for this file are below.
+
+.. image:: ../../_static/tutorials/media/dimensionMapNoDataPipes/image_0.png
+   :class: tutorial-media
+   :width: 950px
+   :alt: image_0.png
+
+.. image:: ../../_static/tutorials/media/dimensionMapNoDataPipes/image_1.png
+   :class: tutorial-media
+   :width: 952px
+   :alt: image_1.png
diff --git a/docs/source/pages/tutorials/dimensionMapWithDataPipes.rst b/docs/source/pages/tutorials/dimensionMapWithDataPipes.rst
index 20336ac98..af6832db1 100644
--- a/docs/source/pages/tutorials/dimensionMapWithDataPipes.rst
+++ b/docs/source/pages/tutorials/dimensionMapWithDataPipes.rst
@@ -6,14 +6,117 @@ Mapping Dimensions with DataPipes
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dimensionMapWithDataPipes.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/dimensionMapWithDataPipes.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/dimensionMapWithDataPipes.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This tutorial is easier to follow if you have already looked at the `dimensionMapNoDataPipes <dimensionMapNoDataPipes>`_ tutorial or if you compare these side by side.
+
+The key difference when using ``DataPipe`` instead of ``VectorData`` is that 1D data can be represented in HDF5 as 2D, thus allowing you to write either row or column vectors. This is made possible because of the ``maxSize`` property of the ``DataPipe`` class, which lets you specify a max size for each dimension. By setting the ``maxSize`` to [1, N] or [N, 1], vectors in HDF5 are represented as 2D arrays, just like in MATLAB. The flipping of the dimension order still applies, so a row vector in MATLAB becomes a column vector in HDF5 and vice versa.
+
+**Please note**: The following tutorial mixes row and column vectors and does not produce a valid dynamic table. The tutorial is only meant to showcase how data maps onto HDF5 datasets when using ``DataPipe`` objects.
+
+Create Table
+------------
+
+First, create an expandable ``TimeIntervals`` table of height 10.
+
+.. code-block:: matlab
+
+   % 1D column 
+   start_col = types.hdmf_common.VectorData( ...
+       'description', 'start times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', 1:10, ...  # maps onto HDF5 dataset of size (10, )
+           'maxSize', Inf ...
+       ) ...
+   );
+   % 1D column 
+   stop_col = types.hdmf_common.VectorData( ...
+       'description', 'stop times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', 1:10, ...  # maps onto HDF5 dataset of size (10, 1)
+           'maxSize', [1 Inf], ...
+           'axis', 2 ...
+       ) ...
+   );
+   % 1D column
+   cond_col = types.hdmf_common.VectorData( ...
+       'description', 'condition column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', randi(2,10,1), ...  # maps onto HDF5 dataset of size (1, 10)
+           'maxSize', [Inf, 1], ...
+           'axis', 1 ...
+       ) ...
+   );
+   % 4D column
+   randomval_col = types.hdmf_common.VectorData( ...
+                   'description', 'randomvalues column', ...
+                   'data', types.untyped.DataPipe( ...
+                       'data', rand(5,2,3,10), ... # maps onto HDF5 dataset of size (10, 3, 2, 5)
+                       'maxSize', [5, 2, 3, Inf], ...
+                       'axis', 4 ...
+                   ) ...
+   );
+   % 1D column
+   ids_col = types.hdmf_common.ElementIdentifiers( ...
+       'data', types.untyped.DataPipe( ...
+           'data', int64(0:9), ...  # maps onto HDF5 dataset of size (10, )
+           'maxSize', Inf ...
+       ) ...
+   );
+   % Create table
+   trials_table = types.core.TimeIntervals(...
+                    'description', 'test dynamic table column',...
+                   'colnames', {'start_time', 'stop_time', 'randomvalues', 'conditions'}, ...
+                   'start_time', start_col, ...
+                   'stop_time', stop_col, ...
+                   'conditions', cond_col, ...
+                   'randomvalues', randomval_col, ...
+                   'id', ids_col ...     
+   );
+
+Export Table
+------------
+
+Create NWB file with expandable ``TimeIntervals`` table and export.
+
+.. code-block:: matlab
+
+   % Create NwbFile object with required arguments
+   file = NwbFile( ...
+       'session_start_time', datetime('2022-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'test file' ...
+   );
+   % Assign to intervals_trials
+   file.intervals_trials = trials_table;
+   % Export
+   nwbExport(file, 'testFileWithDataPipes.nwb');
+
+You can examine the dimensions of the datasets on file using `HDFView <https://www.hdfgroup.org/downloads/hdfview/>`_. Screenshots for this file are below.
+
+.. image:: ../../_static/tutorials/media/dimensionMapWithDataPipes/image_0.png
+   :class: tutorial-media
+   :width: 952px
+   :alt: image_0.png
+
+.. image:: ../../_static/tutorials/media/dimensionMapWithDataPipes/image_1.png
+   :class: tutorial-media
+   :width: 952px
+   :alt: image_1.png
+
+.. image:: ../../_static/tutorials/media/dimensionMapWithDataPipes/image_2.png
+   :class: tutorial-media
+   :width: 952px
+   :alt: image_2.png
+
+.. image:: ../../_static/tutorials/media/dimensionMapWithDataPipes/image_3.png
+   :class: tutorial-media
+   :width: 952px
+   :alt: image_3.png
diff --git a/docs/source/pages/tutorials/dynamic_tables.rst b/docs/source/pages/tutorials/dynamic_tables.rst
index d43723f56..93d96898c 100644
--- a/docs/source/pages/tutorials/dynamic_tables.rst
+++ b/docs/source/pages/tutorials/dynamic_tables.rst
@@ -6,14 +6,638 @@ Using Dynamic Tables in MatNWB
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dynamic_tables.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/dynamic_tables.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/dynamic_tables.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This is a user guide to interacting with `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ objects in MatNWB.
+
+**MatNWB setup**
+----------------
+
+Start by setting up your MATLAB workspace. The code below adds the directory containing the MatNWB package to the MATLAB search path. MatNWB works by automatically creating API classes based on a defined schema.
+
+.. code-block:: matlab
+
+   %{
+   path_to_matnwb = '~/Repositories/matnwb'; % change to your own path location
+   addpath(genpath(pwd));
+   %}
+
+Constructing a table with initialized columns
+---------------------------------------------
+
+The `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ class represents a column-based table to which you can add custom columns. It consists of a description, a list of columns , and a list of row IDs. You can create a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ by first defining the `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ objects that will make up the columns of the table. Each `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ object must contain the same number of rows. A list of rows IDs may be passed to the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ using the id argument. Row IDs are a useful way to access row information independent of row location index. The list of row IDs must be cast as an `ElementIdentifiers <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html>`_ object before being passed to the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object. If no value is passed to id, an `ElementIdentifiers <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html>`_ object with 0-indexed row IDs will be created for you automatically.
+
+**MATLAB Syntax Note**: Using column vectors is crucial to properly build vectors and tables. When defining individual values, make sure to use semi-colon (;) instead of instead of comma (,) when defining the data fields of these.
+
+.. code-block:: matlab
+
+   col1 = types.hdmf_common.VectorData( ...
+       'description', 'column #1', ...
+       'data', [1;2] ...
+   );
+   
+   col2 = types.hdmf_common.VectorData( ...
+       'description', 'column #2', ...
+       'data', {'a';'b'} ...
+   );
+   
+   row_ids = types.hdmf_common.ElementIdentifiers(...
+       'data', [0;1]); % 0-indexed, for compatibility with Python
+   
+   my_table = types.hdmf_common.DynamicTable( ...
+       'description', 'an example table', ...
+       'colnames', {'col1', 'col2'}, ...
+       'col1', col1, ...
+       'col2', col2, ...
+       'id', row_ids ...
+   );
+   my_table
+
+.. code-block:: text
+
+   my_table = 
+     DynamicTable with properties:
+   
+          colnames: {'col1'  'col2'}
+       description: 'an example table'
+                id: [1x1 types.hdmf_common.ElementIdentifiers]
+        vectordata: [2x1 types.untyped.Set]
+
+Adding rows
+-----------
+
+You can add rows to an existing `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ using the object's ``addRow`` method. One way of using this method is to pass in the names of columns as parameter names followed by the elements to append. The class of the elements of the column must match the elements to append.
+
+.. code-block:: matlab
+
+   my_table.addRow('col1', 3, 'col2', {'c'}, 'id', 2);
+
+Adding columns
+--------------
+
+You can add new columns to an existing `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object using the ``addColumn`` method. One way of using this method is to pass in the names of each new column followed by the corresponding values for each new column. The height of the new columns must match the height of the table.
+
+.. code-block:: matlab
+
+   col3 = types.hdmf_common.VectorData('description', 'column #3', ...
+       'data', [100; 200; 300]);
+   col4 = types.hdmf_common.VectorData('description', 'column #4', ...
+       'data', {'a1'; 'b2'; 'c3'});
+   
+   my_table.addColumn('col3', col3,'col4', col4);
+
+Create MATLAB table and convert to dynamic table
+------------------------------------------------
+
+As an alternative to building a dynamic table using the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ and `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ data types, it is also possible to create a MATLAB table and convert it to a dynamic table. Lets create the same table as before, but using MATLAB's table class:
+
+.. code-block:: matlab
+
+   % Create a table with two variables (columns):
+   T = table([1;2], {'a';'b'}, 'VariableNames', {'col1', 'col2'});
+   T.Properties.VariableDescriptions = {'column #1', 'column #2'};
+
+Adding rows
+~~~~~~~~~~~
+
+.. code-block:: matlab
+
+   T(end+1, :) = {3, 'c'};
+
+Adding variables (columns)
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: matlab
+
+   T = addvars(T, [100;200;300], 'NewVariableNames',{'col3'});
+   T.Properties.VariableDescriptions{3} = 'column #3';
+   
+   % Alternatively, a new variable can be added directly using dot syntax.
+   T.col4 = {'a1'; 'b2'; 'c3'};
+   T.Properties.VariableDescriptions{4} = 'column #4';
+   T
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - col1
+     - col2
+     - col3
+     - col4
+   * - 1
+     - 1
+     - 'a'
+     - 100
+     - 'a1'
+   * - 2
+     - 2
+     - 'b'
+     - 200
+     - 'b2'
+   * - 3
+     - 3
+     - 'c'
+     - 300
+     - 'c3'
+
+Convert to dynamic table
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: matlab
+
+   dynamic_table = util.table2nwb(T, 'A MATLAB table that was converted to a dynamic table')
+
+.. code-block:: text
+
+   dynamic_table = 
+     DynamicTable with properties:
+   
+          colnames: {'col1'  'col2'  'col3'  'col4'}
+       description: "A MATLAB table that was converted to a dynamic table"
+                id: [1x1 types.hdmf_common.ElementIdentifiers]
+        vectordata: [4x1 types.untyped.Set]
+
+Enumerated (categorical) data
+-----------------------------
+
+`EnumData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html>`_ is a special type of column for storing an enumerated data type. This way each unique value is stored once, and the data references those values by index. Using this method is more efficient than storing a single value many times, and has the advantage of communicating to downstream tools that the data is categorical in nature.
+
+Warning regarding EnumData
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`EnumData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_experimental/EnumData.html>`_ is currently an experimental feature and as such should not be used in a production environment.
+
+.. code-block:: matlab
+
+   cell_type_elements = types.hdmf_common.VectorData(...
+       'description', 'fixed set of elements referenced by cell_type', ...
+       'data', {'aa', 'bb', 'cc'} ... % the enumerated elements
+   );
+   cell_type = types.hdmf_experimental.EnumData( ...
+       'description', 'this column holds categorical variables', ...
+       'data', [0; 1; 2; 1; 0], ... % zero-indexed offset to elements.
+       'elements', types.untyped.ObjectView(cell_type_elements) ...
+   );
+   
+   cell_type_table = types.hdmf_common.DynamicTable(...
+       'description', 'an example table with enum cell types');
+   %Please note: the *_elements format is required for compatibility with pynwb:
+   cell_type_table.vectordata.set('cell_type_elements', cell_type_elements);
+   cell_type_table.addColumn('cell_type', cell_type);
+
+Ragged array columns
+--------------------
+
+A table column with a different number of elements for each row is called a "ragged array column." To define a table with a ragged array column, pass both the `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ and the corresponding `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ as columns of the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object. The `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ columns will contain the data values. The `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ column serves to indicate how to arrange the data across rows. By convention the `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ object corresponding to a particular column must have have the same name with the addition of the '_index' suffix.
+
+Below, the `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ values indicate to place the 1st to 3rd (inclusive) elements of the `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ into the first row and 4th element into the second row. The resulting table will have the cell {'1a'; '1b'; '1c'} in the first row and the cell {'2a'} in the second row.
+
+.. code-block:: matlab
+
+   col1 = types.hdmf_common.VectorData( ...
+       'description', 'column #1', ...
+       'data', {'1a'; '1b'; '1c'; '2a'} ...
+   );
+   
+   col1_index = types.hdmf_common.VectorIndex( ...
+       'description', 'column #1 index', ...
+       'target',types.untyped.ObjectView(col1), ...  % object view of target column
+       'data', [3; 4] ...
+   );
+   
+   row_ids = types.hdmf_common.ElementIdentifiers(...
+       'data', [0; 1]);  % 0-indexed, for compatibility with Python
+   
+   table_ragged_col = types.hdmf_common.DynamicTable( ...
+       'description', 'an example table', ...
+       'colnames', {'col1'}, ...
+       'col1', col1, ...
+       'col1_index', col1_index, ...
+       'id', row_ids ...
+   );
+
+Adding ragged array rows
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can add a new row to the ragged array column. Under the hood, the ``addRow`` method will add the appropriate value to the `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ column to maintain proper formatting.
+
+.. code-block:: matlab
+
+   table_ragged_col.addRow('col1', {'3a'; '3b'; '3c'}, 'id', 2);
+
+Accessing row elements
+----------------------
+
+You can access data from entire rows of a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object by calling the ``getRow`` method for the corresponding object. You can supply either an individual row number or a list of row numbers.
+
+.. code-block:: matlab
+
+   my_table.getRow(1)
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - col1
+     - col2
+     - col3
+     - col4
+   * - 1
+     - 1
+     - 'a'
+     - 100
+     - 'a1'
+
+If you want to access values for just a subset of columns you can pass in the 'columns' argument along with a cell array with the desired column names
+
+.. code-block:: matlab
+
+   my_table.getRow(1:3, 'columns', {'col1'})
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - col1
+   * - 1
+     - 1
+   * - 2
+     - 2
+   * - 3
+     - 3
+
+You can also access specific rows by their corresponding row ID's, if they have been defined, by supplying a 'true' Boolean to the 'useId' parameter
+
+.. code-block:: matlab
+
+   my_table.getRow(1, 'useId', true)
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - col1
+     - col2
+     - col3
+     - col4
+   * - 1
+     - 2
+     - 'b'
+     - 200
+     - 'b2'
+
+For a ragged array columns, the ``getRow`` method will return a cell with different number of elements for each row
+
+.. code-block:: matlab
+
+   table_ragged_col.getRow(1:2)
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - col1
+   * - 1
+     - 3x1 cell
+   * - 2
+     - 1x1 cell
+
+Accessing column elements
+-------------------------
+
+To access all rows from a particular column use the .get method on the vectordata field of the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object
+
+.. code-block:: matlab
+
+   my_table.vectordata.get('col2').data
+
+.. code-block:: text
+
+   ans = 3x1 cell
+   'a'         
+   'b'         
+   'c'         
+
+Referencing rows of other tables
+--------------------------------
+
+You can create a column that references rows of other tables by adding a `DynamicTableRegion <file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html>`_ object as a column of a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_. This is analogous to a foreign key in a relational database. The `DynamicTableRegion <file:///Users/cesar/Repositories/matnwb/doc/+types/+hdmf_common/DynamicTableRegion.html>`_ class takes in an ``ObjectView`` object as argument. ``ObjectView`` objects create links from one object type referencing another.
+
+.. code-block:: matlab
+
+   dtr_col = types.hdmf_common.DynamicTableRegion( ...
+       'description', 'references multiple rows of earlier table', ...
+       'data', [0; 1; 1; 0], ...  # 0-indexed
+       'table',types.untyped.ObjectView(my_table) ...  % object view of target table
+   );
+   
+   data_col = types.hdmf_common.VectorData( ...
+       'description', 'data column', ...
+       'data', {'a'; 'b'; 'c'; 'd'} ...
+   );
+   
+   dtr_table = types.hdmf_common.DynamicTable( ...
+       'description', 'test table with DynamicTableRegion', ...
+       'colnames', {'data_col', 'dtr_col'}, ...
+       'dtr_col', dtr_col, ...
+       'data_col',data_col, ...
+       'id',types.hdmf_common.ElementIdentifiers('data', [0; 1; 2; 3]) ...
+   );
+
+Converting a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ **to a MATLAB table**
+------------------------------------------------------------------------------------------------------------------------------------------------
+
+You can convert a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object to a MATLAB table by making use of the object's ``toTable`` method. This is a useful way to view the whole table in a human-readable format.
+
+.. code-block:: matlab
+
+   my_table.toTable()
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - col1
+     - col2
+     - col3
+     - col4
+   * - 1
+     - 0
+     - 1
+     - 'a'
+     - 100
+     - 'a1'
+   * - 2
+     - 1
+     - 2
+     - 'b'
+     - 200
+     - 'b2'
+   * - 3
+     - 2
+     - 3
+     - 'c'
+     - 300
+     - 'c3'
+
+When the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object contains a column that references other tables, you can pass in a Boolean to indicate whether to include just the row indices of the referenced table. Passing in ``false`` will result in inclusion of the referenced rows as nested tables.
+
+.. code-block:: matlab
+
+   dtr_table.toTable(false)
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - data_col
+     - dtr_col
+   * - 1
+     - 0
+     - 'a'
+     - 1x4 table
+   * - 2
+     - 1
+     - 'b'
+     - 1x4 table
+   * - 3
+     - 2
+     - 'c'
+     - 1x4 table
+   * - 4
+     - 3
+     - 'd'
+     - 1x4 table
+
+Creating an expandable table
+----------------------------
+
+When using the default HDF5 backend, each column of these tables is an HDF5 Dataset, which by default are set to an unchangeable size. This means that once a file is written, it is not possible to add a new row. If you want to be able to save this file, load it, and add more rows to the table, you will need to set this up when you create the `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ and `ElementIdentifiers <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/ElementIdentifiers.html>`_ columns of a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_. Specifically, you must wrap the column data with a **DataPipe** object. The **DataPipe** class takes in ``maxSize`` and ``axis`` as arguments to indicate the maximum desired size for each axis and the axis to which to append to, respectively. For example, creating a **DataPipe** object with a *maxSize* value equal to ``[Inf, 1]`` indicates that the number of rows may increase indefinitely. In contrast, setting *maxSize* equal to ``[8, 1]`` would allow the column to grow to a maximum height of 8.
+
+.. code-block:: matlab
+
+   % create NwbFile object with required fields
+   file = NwbFile( ...
+       'session_start_time', datetime('2021-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'ExpandableTableTutorial' ...
+   );
+   
+   % create VectorData objects with DataPipe objects
+   start_time_exp = types.hdmf_common.VectorData( ...
+       'description', 'start times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', [1, 2], ...  # data must be numerical
+           'maxSize', Inf ...
+       ) ...
+   );
+   
+   stop_time_exp = types.hdmf_common.VectorData( ...
+       'description', 'stop times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', [2, 3], ...  #data must be numerical
+           'maxSize', Inf ...
+       ) ...
+   );
+   
+   random_exp = types.hdmf_common.VectorData( ...
+       'description', 'random data column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', rand(5, 2), ...  #data must be numerical
+           'maxSize', [5, Inf], ...
+           'axis', 2 ...
+       ) ...
+   );
+   
+   ids_exp = types.hdmf_common.ElementIdentifiers( ...
+       'data', types.untyped.DataPipe( ...
+           'data', int32([0; 1]), ...  # data must be numerical
+           'maxSize', Inf ...
+       ) ...
+   );
+   % create expandable table
+   colnames = {'start_time', 'stop_time', 'randomvalues'};
+   file.intervals_trials = types.core.TimeIntervals( ...
+       'description', 'test expdandable dynamic table', ...
+       'colnames', colnames, ...
+       'start_time', start_time_exp, ...
+       'stop_time', stop_time_exp, ...
+       'randomvalues', random_exp, ...
+       'id', ids_exp ...
+   );
+   % export file
+   nwbExport(file, 'expandableTableTestFile.nwb');
+
+Now, you can read in the file, add more rows, and save again to file
+
+.. code-block:: matlab
+
+   read_file = nwbRead('expandableTableTestFile.nwb', 'ignorecache');
+   read_file.intervals_trials.addRow( ...
+       'start_time', 3, ...
+       'stop_time', 4, ...
+       'randomvalues', rand(5,1), ...
+       'id', 2 ...
+       )
+   nwbExport(read_file, 'expandableTableTestFile.nwb');
+
+**Note: DataPipe** objects change how the dimension of the datasets for each column map onto the shape of HDF5 datasets. See the `documentation <https://matnwb.readthedocs.io/en/latest/pages/concepts/dimension_ordering.html>`_ for more details.
+
+Multidimensional columns
+------------------------
+
+The order of dimensions of multidimensional columns in MatNWB is reversed relative to the Python HDMF package (see `documentation <https://matnwb.readthedocs.io/en/latest/pages/concepts/dimension_ordering.html>`_ for detailed explanation). Therefore, the height of a multidimensional column belonging to a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object is defined by the shape of its last dimension. A valid `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ must have matched height across columns.
+
+Constructing multidimensional columns
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: matlab
+
+   % Define 1D column
+   simple_col = types.hdmf_common.VectorData( ...
+       'description', '1D column',...
+       'data', rand(10,1) ...
+   );
+   % Define ND column
+   multi_col = types.hdmf_common.VectorData( ...
+       'description', 'multidimensional column',...
+       'data', rand(3,2,10) ...
+   );
+   % construct table
+   multi_dim_table = types.hdmf_common.DynamicTable( ...
+       'description','test table', ...
+       'colnames', {'simple','multi'}, ...
+       'simple', simple_col, ...
+       'multi', multi_col, ...
+       'id', types.hdmf_common.ElementIdentifiers('data', (0:9)') ...
+   );
+
+Multidimensional ragged array columns
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ objects with multidimensional ragged array columns can be constructed by passing in the corresponding `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ column
+
+.. code-block:: matlab
+
+   % Define column with data
+   multi_ragged_col = types.hdmf_common.VectorData( ...
+       'description', 'multidimensional ragged array column',...
+       'data', rand(2,3,5) ...
+   );
+   % Define column with VectorIndex
+   multi_ragged_index = types.hdmf_common.VectorIndex( ...
+       'description', 'index to multi_ragged_col', ...
+       'target', types.untyped.ObjectView(multi_ragged_col),'data', [2; 3; 5] ...
+   );
+   
+   multi_ragged_table = types.hdmf_common.DynamicTable( ...
+       'description','test table', ...
+       'colnames', {'multi_ragged'}, ...
+       'multi_ragged', multi_ragged_col, ...
+       'multi_ragged_index', multi_ragged_index, ...
+       'id', types.hdmf_common.ElementIdentifiers('data', [0; 1; 2]) ...
+   );
+
+Adding rows to multidimensional array columns
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ objects with multidimensional array columns can also be constructed by adding a single row at a time. This method makes use of **DataPipe** objects due to the fact that MATLAB doesn't support singleton dimensions for arrays with more than 2 dimensions. The code block below demonstrates how to build a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ object with a multidimensional ragged array column in this manner.
+
+.. code-block:: matlab
+
+   % Create file
+   file = NwbFile( ...
+       'session_start_time', datetime('2021-01-01 00:00:00', 'TimeZone', 'local'), ...
+       'identifier', 'ident1', ...
+       'session_description', 'test_file' ...
+   );
+   
+   % Define Vector Data Objects with first row of table
+   start_time_exp = types.hdmf_common.VectorData( ...
+       'description', 'start times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', 1, ...
+           'maxSize', Inf ...
+       ) ...
+   );
+   stop_time_exp = types.hdmf_common.VectorData( ...
+       'description', 'stop times column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', 10, ...
+           'maxSize', Inf ...
+       ) ...
+   );
+   random_exp = types.hdmf_common.VectorData( ...
+       'description', 'random data column', ...
+       'data', types.untyped.DataPipe( ...
+           'data', rand(3,2,5), ...  #random data
+           'maxSize', [3, 2, Inf], ...
+           'axis', 3 ...
+       ) ...
+   );
+   random_exp_index = types.hdmf_common.VectorIndex( ...
+       'description', 'index to random data column', ...
+       'target', types.untyped.ObjectView(random_exp), ...
+       'data', types.untyped.DataPipe( ...
+           'data', uint64(5), ...
+           'maxSize', Inf ...
+       ) ...
+   );
+   ids_exp = types.hdmf_common.ElementIdentifiers( ...
+       'data', types.untyped.DataPipe( ...
+           'data', int64(0), ...  # data must be numerical
+           'maxSize', Inf ...
+       ) ...
+   );
+   % Create expandable table
+   colnames = {'start_time', 'stop_time', 'randomvalues'};
+   file.intervals_trials = types.core.TimeIntervals( ...
+       'description', 'test expdandable dynamic table', ...
+       'colnames', colnames, ...
+       'start_time', start_time_exp, ...
+       'stop_time', stop_time_exp, ...
+       'randomvalues', random_exp, ...
+       'randomvalues_index', random_exp_index, ...
+       'id', ids_exp ...
+   );
+   % Export file
+   nwbExport(file, 'multiRaggedExpandableTableTest.nwb');
+   % Read in file
+   read_file = nwbRead('multiRaggedExpandableTableTest.nwb', 'ignorecache');
+   % add individual rows
+   read_file.intervals_trials.addRow( ...
+       'start_time', 2, ...
+       'stop_time', 20, ...
+       'randomvalues', rand(3,2,6), ...
+       'id', 1 ...
+   );
+   read_file.intervals_trials.addRow( ...
+       'start_time', 3, ...
+       'stop_time', 30, ...
+       'randomvalues', rand(3,2,3), ...
+       'id', 2 ...
+   );
+   read_file.intervals_trials.addRow( ...
+       'start_time', 4, ...
+       'stop_time', 40, ...
+       'randomvalues', rand(3,2,8), ...
+       'id', 3 ...
+   );
+
+Learn more!
+-----------
+
+Python Tutorial
+~~~~~~~~~~~~~~~
+
+`DynamicTable Tutorial <https://hdmf.readthedocs.io/en/stable/tutorials/plot_dynamictable_tutorial.html#sphx-glr-tutorials-plot-dynamictable-tutorial-py>`_
diff --git a/docs/source/pages/tutorials/dynamically_loaded_filters.rst b/docs/source/pages/tutorials/dynamically_loaded_filters.rst
index 9c72a560f..681bd15f9 100644
--- a/docs/source/pages/tutorials/dynamically_loaded_filters.rst
+++ b/docs/source/pages/tutorials/dynamically_loaded_filters.rst
@@ -6,14 +6,143 @@ Implementing Dynamically Loaded Filters
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/dynamically_loaded_filters.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/dynamically_loaded_filters.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/dynamically_loaded_filters.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+Installing Dynamically Loaded Filters
+-------------------------------------
+
+HDF5 can use various filters to compress data when writing datasets. GZIP is the default filter, and it can be read with any HDF5 installation without any setup, but many users find that other filters, such as Zstd, offer better performance. If you want to read an HDF5 Dataset that was compressed using another filter in MATLAB, such as Zstd, you will need to configure MATLAB to read using dynamically loaded filters.
+
+The easiest way we have found to set up dynamically loaded filters is to use the Python package `hdf5plugin <https://github.com/silx-kit/hdf5plugin>`_. This library has a sophisticated installation process that compiles several of the most popular dynamically loaded filters and works across popular operating systems. Installing this Python package is a trick that allows us to offload the tricky parts of installing dynamically loaded filters in MATLAB.
+
+Linux or Mac
+~~~~~~~~~~~~
+
+1. In your Terminal window, install hdf5plugin:
+
+``pip install hdf5plugin``
+
+2. In that same Terminal window, set the environment variable ``HDF5_PLUGIN_PATH``:
+
+``export HDF5_PLUGIN_PATH=\$(python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)");``
+
+3. From that same Terminal window, launch MATLAB:
+
+``/Applications/MATLAB_R2021b.app/bin/matlab``
+
+The path above is an example of a common location for OSX. The exact path of MATLAB may vary on your computer.
+
+Windows
+~~~~~~~
+
+1. Install ``hdf5plugin`` in the Command Prompt:
+
+``pip install hdf5plugin``
+
+2. Determine the path of the plugin installation. In the Command Prompt, run:
+
+``python -c "import hdf5plugin; print(hdf5plugin.PLUGINS_PATH)"``
+
+3. Set the environment variable  ``HDF5_PLUGIN_PATH`` to point to the local installation of the plugins (from ``hdf5plugin.PLUGINS_PATH``) through *System Properties > Advanced > Environment Variables:*
+
+   .. image:: ../../_static/tutorials/media/dynamically_loaded_filters/image_0.png
+      :class: tutorial-media
+      :width: 1084px
+      :alt: image_0.png
+4. Restart MATLAB.
+
+That's it! Now you can read datasets that use the following filters:
+
+* Bitshuffle
+* Blosc
+* FciDecomp
+* LZ4
+* Zfp
+* Zstd
+
+The beauty of HDF5 is that it handles the rest under the hood. When you read a dataset that uses any of these filters, HDF5 will identify the correct decompression algorithm and decompress the data on-the-fly as you access it from the dataset.
+
+For more information about installing filter plugins, see the `MATLAB documentation <https://www.mathworks.com/help/matlab/import_export/read-and-write-hdf5-datasets-using-dynamically-loaded-filters.html>`_.
+
+Writing with Dynamically Loaded Filters
+---------------------------------------
+
+To write with dynamically loaded filters, first follow the installation steps above. **This feature requires MATLAB version ≥ 2022a.**
+
+``DataPipe`` objects can be used to write using Dynamically loaded filters. This tutorial will be using the `Zstd dynamic filter <https://github.com/aparamon/HDF5Plugin-Zstandard>`_ as an example.
+
+The ``DynamicFilter`` property accepts an enumerated type ``Filter`` where the enumeration members represent a hard-coded subset of the `registered filter plugins <https://github.com/HDFGroup/hdf5_plugins/blob/master/docs/RegisteredFilterPlugins.md>`_ in HDF5.
+
+.. code-block:: matlab
+
+   import types.untyped.datapipe.properties.DynamicFilter
+   import types.untyped.datapipe.dynamic.Filter
+   import types.untyped.datapipe.properties.Shuffle
+   
+   zstdProperty = DynamicFilter(Filter.ZStandard);
+
+.. code-block:: text
+
+   Error using assert
+   Filter `ZStandard` does not appear to be installed on this system. Please doublecheck `https://www.mathworks.com/help/matlab/import_export/read-and-write-hdf5-datasets-using-dynamically-loaded-filters.html` for more information about writing with third-party filters.
+   
+   Error in types.untyped.datapipe.properties.DynamicFilter (line 22)
+               assert(H5Z.filter_avail(uint32(filter)), ...
+
+Parameters
+~~~~~~~~~~
+
+Some filter plugins allow for setting special configuration parameters to modify the filter's behavior. The ``DynamicFilter`` property type contains a modifiable  ``parameters`` field which can be used to set your parameters. This is equivalent to setting the ``cd_values`` argument in HDF5. In the case of the `Zstandard HDF5 plugin <https://github.com/aparamon/HDF5Plugin-Zstandard>`_, the first (and only) array argument value indicates the compression level.
+
+.. code-block:: matlab
+
+   zstdProperty.parameters = 4; % compression level.
+
+Multiple Filters
+~~~~~~~~~~~~~~~~
+
+You can use multiple dynamic filters by concatenating multiple DynamicFilter properties together. They will be applied in order of the inserted array.
+
+.. code-block:: matlab
+
+   ShuffleProperty = Shuffle();
+   dynamicProperties = [ShuffleProperty zstdProperty];
+
+Writing
+~~~~~~~
+
+The ``DataPipe`` class takes in a keyword argument called ``filters`` which is an array of ``DynamicFilter`` objects. Supplying a ``'filters'`` argument will deactivate the default GZIP compression.
+
+.. code-block:: matlab
+
+   % We're already compressing using zstd so we should disable
+   % compressionLevel (gzip).
+   dataPipe = types.untyped.DataPipe('data', rand(1, 10000), 'filters', dynamicProperties);
+   
+   timeseries = types.core.TimeSeries(...
+       'data', dataPipe, ...
+       'data_unit', 'data-unit', ...
+       'starting_time_rate', 1.0, ...
+       'starting_time', 0.0);
+   
+   nwbFile = NwbFile(...
+       'identifier', 'dynamically_loaded_filters_tutorial', ...
+       'session_description', 'test_datapipe_filters', ...
+       'session_start_time', datetime("now", 'TimeZone', 'local') );
+   nwbFile.acquisition.set('ts', timeseries);
+   
+   nwbExport(nwbFile, 'test.nwb');
+
+The data is now compressed using Zstandard compression using a compression level of 4 and Shuffled
+
+.. image:: ../../_static/tutorials/media/dynamically_loaded_filters/image_1.png
+   :class: tutorial-media
+   :width: 951px
+   :alt: image_1.png
diff --git a/docs/source/pages/tutorials/ecephys.rst b/docs/source/pages/tutorials/ecephys.rst
index 1a6f26893..60dc18aa7 100644
--- a/docs/source/pages/tutorials/ecephys.rst
+++ b/docs/source/pages/tutorials/ecephys.rst
@@ -6,16 +6,731 @@ Extracellular Electrophysiology 🎬
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/ecephys.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/ecephys.html
-   :alt: View full page
+   :alt: View rendered Live Script
 .. image:: https://img.shields.io/badge/View-Youtube-red
    :target: https://www.youtube.com/watch?v=W8t4_quIl1k&ab_channel=NeurodataWithoutBorders
    :alt: View tutorial on YouTube
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/ecephys.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+About This Tutorial
+-------------------
+
+This tutorial describes storage of hypothetical data from extracellular electrophysiology experiments in NWB for the following data categories:
+
+* Raw voltage recording
+* Local field potential (LFP) and filtered electrical signals
+* Spike times
+
+Before You Begin
+----------------
+
+It is recommended to first work through the `Introduction to MatNWB tutorial <intro>`_, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
+
+**Important**: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB time should be stored in the last dimension of the data. This is explained in more detail here: `MatNWB <-> HDF5 Dimension Mapping <dimensionMapNoDataPipes>`_.
+
+Setting up the NWB File
+-----------------------
+
+An NWB file represents a single session of an experiment. Each file must have a ``session_description``, ``identifier``, and ``session_start_time``. Create a new `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html>`_ object these required fields along with any additional metadata. In MatNWB, arguments are specified using MATLAB's keyword argument pair convention, where each argument name is followed by its value.
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', 'Mouse5_Day3', ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
+       'general_experimenter', 'Last Name, First Name', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: {[2018-04-25T03:00:45.000000+02:00]}
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'Last Name, First Name'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is also recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '005', ...
+       'age', 'P90D', ...
+       'description', 'mouse 5', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+Electrode Information
+---------------------
+
+In order to store extracellular electrophysiology data, you first must create an electrodes table describing the electrodes that generated this data. Extracellular electrodes are stored in an ``electrodes`` table, which is also a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_. ``electrodes`` has several required fields: ``x``, ``y``, ``z``, ``impedance``, ``location``, ``filtering``, and ``electrode_group``.
+
+The electrodes table references a required `ElectrodeGroup <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html>`_, which is used to represent a group of electrodes. Before creating an `ElectrodeGroup <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html>`_, you must define a `Device <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html>`_ object. The fields ``description``, ``serial_number`` and ``model`` are optional, but recommended. The ``model`` property can contain a `DeviceModel <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DeviceModel.html>`_ object which stores information about the device model. This property can be useful when searching a set of NWB files or a data archive for all files that use a specific device model (e.g., Neuropixels probe).
+
+.. code-block:: matlab
+
+   device_model = types.core.DeviceModel( ...
+        'manufacturer', 'Array Technologies', ...
+        'model_number', 'PRB_1_4_0480_123', ...
+        'description', 'Neurovoxels 0.99 - A 12-channel array with 4 shanks and 3 channels per shank' ...
+   );
+   % Add device model to nwb object
+   nwb.general_devices_models.set('Neurovoxels 0.99', device_model);
+
+.. code-block:: matlab
+
+   
+   device = types.core.Device(...
+       'description', 'A 12-channel array with 4 shanks and 3 channels per shank', ...
+       'serial_number', '1234567890', ...
+       'model', device_model ...
+   );
+   
+   % Add device to nwb object
+   nwb.general_devices.set('array', device);
+
+Electrodes Table
+~~~~~~~~~~~~~~~~
+
+.. image:: ../../_static/tutorials/media/ecephys/image_0.png
+   :class: tutorial-media
+   :width: 576px
+   :alt: image_0.png
+
+Since this is a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, we can add additional metadata fields. We will be adding a "label" column to the table.
+
+.. code-block:: matlab
+
+   numShanks = 4;
+   numChannelsPerShank = 3;
+   numChannels = numShanks * numChannelsPerShank;
+   
+   electrodesDynamicTable = types.core.ElectrodesTable(...
+       'colnames', {'location', 'group', 'group_name', 'label'}, ...
+       'description', 'all electrodes');
+   
+   for iShank = 1:numShanks
+       shankGroupName = sprintf('shank%d', iShank);
+       electrodeGroup = types.core.ElectrodeGroup( ...
+           'description', sprintf('electrode group for %s', shankGroupName), ...
+           'location', 'brain area', ...
+           'device', types.untyped.SoftLink(device) ...
+       );
+       
+       nwb.general_extracellular_ephys.set(shankGroupName, electrodeGroup);
+       for iElectrode = 1:numChannelsPerShank
+           electrodesDynamicTable.addRow( ...
+               'location', 'Primary visual area', ...
+               'group', types.untyped.ObjectView(electrodeGroup), ...
+               'group_name', shankGroupName, ...
+               'label', sprintf('%s-electrode%d', shankGroupName, iElectrode));
+       end
+   end
+   electrodesDynamicTable.toTable() % Display the table
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - location
+     - group
+     - group_name
+     - label
+   * - 1
+     - 0
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank1'
+     - 'shank1-electrode1'
+   * - 2
+     - 1
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank1'
+     - 'shank1-electrode2'
+   * - 3
+     - 2
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank1'
+     - 'shank1-electrode3'
+   * - 4
+     - 3
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank2'
+     - 'shank2-electrode1'
+   * - 5
+     - 4
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank2'
+     - 'shank2-electrode2'
+   * - 6
+     - 5
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank2'
+     - 'shank2-electrode3'
+   * - 7
+     - 6
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank3'
+     - 'shank3-electrode1'
+   * - 8
+     - 7
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank3'
+     - 'shank3-electrode2'
+   * - 9
+     - 8
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank3'
+     - 'shank3-electrode3'
+   * - 10
+     - 9
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank4'
+     - 'shank4-electrode1'
+   * - 11
+     - 10
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank4'
+     - 'shank4-electrode2'
+   * - 12
+     - 11
+     - 'Primary visual area'
+     - 1x1 ObjectView
+     - 'shank4'
+     - 'shank4-electrode3'
+
+.. code-block:: matlab
+
+   nwb.general_extracellular_ephys_electrodes = electrodesDynamicTable;
+
+Links
+~~~~~
+
+In the above loop, we create `ElectrodeGroup <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html>`_ objects. The ``electrodes`` table then uses an ``ObjectView`` in each row to link to the corresponding `ElectrodeGroup <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectrodeGroup.html>`_ object. An ``ObjectView`` is a construct that enables linking one neurodata type to another, allowing a neurodata type to reference another within the NWB file.
+
+Recorded Extracellular Signals
+------------------------------
+
+Voltage data are stored using the `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ class, a subclass of the `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ class specialized for voltage data.
+
+Referencing Electrodes
+~~~~~~~~~~~~~~~~~~~~~~
+
+In order to create our `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ object, we first need to reference a set of rows in the ``electrodes`` table to indicate which electrode (channel) each entry in the electrical series were recorded from. We will do this by creating a `DynamicTableRegion <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html>`_, which is a type of link that allows you to reference specific rows of a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, such as the ``electrodes`` table, using row indices.
+
+Create a `DynamicTableRegion <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html>`_ that references all rows of the ``electrodes`` table.
+
+.. code-block:: matlab
+
+   electrode_table_region = types.hdmf_common.DynamicTableRegion( ...
+       'table', types.untyped.ObjectView(electrodesDynamicTable), ...
+       'description', 'all electrodes', ...
+       'data', (0:length(electrodesDynamicTable.id.data)-1)');
+
+Raw Voltage Data
+~~~~~~~~~~~~~~~~
+
+Now create an `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ object to hold acquisition data collected during the experiment.
+
+.. image:: ../../_static/tutorials/media/ecephys/image_1.png
+   :class: tutorial-media
+   :width: 840px
+   :alt: image_1.png
+
+.. code-block:: matlab
+
+   raw_electrical_series = types.core.ElectricalSeries( ...
+       'starting_time', 0.0, ... % seconds
+       'starting_time_rate', 30000., ... % Hz
+       'data', randn(numChannels, 3000), ... % nChannels x nTime
+       'electrodes', electrode_table_region, ...
+       'data_unit', 'volts');
+
+This is the voltage data recorded directly from our electrodes, so it goes in the acquisition group.
+
+.. code-block:: matlab
+
+   nwb.acquisition.set('ElectricalSeries', raw_electrical_series);
+
+Processed Extracellular Electrical Signals
+------------------------------------------
+
+LFP
+~~~
+
+LFP refers to data that has been low-pass filtered, typically below 300 Hz. This data may also be downsampled. Because it is filtered and potentially resampled, it is categorized as processed data. LFP data would also be stored in an `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_. To help data analysis and visualization tools know that this `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ object represents LFP data, we store it inside an `LFP <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html>`_ object and then place the `LFP <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html>`_ object in a `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ named ``'ecephys'``. This is analogous to how we stored the `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object inside of a `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object and stored the `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object in a `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ named ``'behavior'`` in the `behavior <behavior>`_ tutorial
+
+.. image:: ../../_static/tutorials/media/ecephys/image_2.png
+   :class: tutorial-media
+   :width: 1311px
+   :alt: image_2.png
+
+.. code-block:: matlab
+
+   lfp_electrical_series = types.core.ElectricalSeries( ...
+       'starting_time', 0.0, ... % seconds
+       'starting_time_rate', 1000., ... % Hz
+       'data', randn(numChannels, 100), ... nChannels x nTime
+       'filtering', 'Low-pass filter at 300 Hz', ...
+       'electrodes', electrode_table_region, ...
+       'data_unit', 'volts');
+   
+   lfp = types.core.LFP('ElectricalSeries', lfp_electrical_series);
+   
+   ecephys_module = types.core.ProcessingModule(...
+       'description', 'extracellular electrophysiology');
+   
+   ecephys_module.nwbdatainterface.set('LFP', lfp);
+   nwb.processing.set('ecephys', ecephys_module);
+
+Other Types of Filtered Electrical Signals
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If your acquired data is filtered for frequency ranges other than LFP—such as Gamma or Theta—you can store the result in an `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ and encapsulate it within a `FilteredEphys <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html>`_ object instead of the `LFP <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/LFP.html>`_ object.
+
+.. code-block:: matlab
+
+   % Generate filtered data
+   filtered_data = randn(50, 12); % 50 time points, 12 channels
+   filtered_data = permute(filtered_data, [2, 1]); % permute timeseries for matnwb
+   
+   % Create an ElectricalSeries object
+   filtered_electrical_series = types.core.ElectricalSeries( ...
+       'description', 'Data filtered in the theta range', ...
+       'data', filtered_data, ...
+       'electrodes', electrode_table_region, ...
+       'filtering', 'Band-pass filtered between 4 and 8 Hz', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 200.0 ...
+       );
+   
+   % Create a FilteredEphys object and add the filtered electrical series
+   filtered_ephys = types.core.FilteredEphys();
+   filtered_ephys.electricalseries.set('FilteredElectricalSeries', filtered_electrical_series);
+   
+   % Add the FilteredEphys object to the ecephys module
+   ecephys_module.nwbdatainterface.set('FilteredEphys', filtered_ephys);
+
+Decomposition of LFP Data into Frequency Bands
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In some cases, you may want to further process the LFP data and decompose the signal into different frequency bands for additional downstream analyses. You can then store the processed data from these spectral analyses using a `DecompositionSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html>`_ object. This object allows you to include metadata about the frequency bands and metric used (e.g., ``power``, ``phase``, ``amplitude``), as well as link the decomposed data to the original `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ signal the data was derived from.
+
+In this tutorial, the examples for `FilteredEphys <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html>`_ and `DecompositionSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html>`_ may appear similar. However, the key difference is that `DecompositionSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html>`_ is specialized for storing the results of spectral analyses of timeseries data in general, whereas `FilteredEphys <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FilteredEphys.html>`_ is defined specifically as a container for filtered electrical signals.
+
+**Note**: When adding data to a `DecompositionSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DecompositionSeries.html>`_, the ``data`` argument is assumed to be 3D where the first dimension is time, the second dimension is channels, and the third dimension is bands. As mentioned in the beginning of this tutorial, in MatNWB the data needs to be permuted because the dimensions are written to file in reverse order (See the `dimensionMapNoDataPipes <dimensionMapNoDataPipes>`_ tutorial)
+
+.. code-block:: matlab
+
+   % Define the frequency bands of interest (in Hz):
+   band_names = {'theta'; 'beta'; 'gamma'};
+   band_mean = [8; 21; 55];
+   band_stdev = [2; 4.5; 12.5];
+   band_limits = [band_mean - 2*band_stdev, band_mean + 2*band_stdev];
+   
+   % The bands should be added to the DecompositionSeries as a dynamic table
+   bands = table(band_names, band_mean, band_stdev, band_limits, ...
+       'VariableNames', {'band_name', 'band_mean', 'band_stdev', 'band_limits'})
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - band_name
+     - band_mean
+     - band_stdev
+     - band_limits
+     - 
+   * - 
+     - 
+     - 
+     - 
+     - 1
+     - 2
+   * - 1
+     - 'theta'
+     - 8
+     - 2
+     - 4
+     - 12
+   * - 2
+     - 'beta'
+     - 21
+     - 4.5000
+     - 12
+     - 30
+   * - 3
+     - 'gamma'
+     - 55
+     - 12.5000
+     - 30
+     - 80
+
+.. code-block:: matlab
+
+   bands = util.table2nwb( bands, 'Frequency bands for lfp', 'types.core.FrequencyBandsTable');
+   
+   % Generate random phase data for the demonstration.
+   phase_data = randn(50, 12, numel(band_names)); % 50 samples, 12 channels, 3 frequency bands
+   phase_data = permute(phase_data, [3,2,1]); % See dimensionMapNoDataPipes tutorial
+   
+   decomp_series = types.core.DecompositionSeries(...
+       'data', phase_data, ...
+       'bands', bands, ...
+       'metric', 'phase', ...
+       'starting_time', 0.0, ... % seconds
+       'starting_time_rate', 1000.0, ... % Hz
+       'source_channels', electrode_table_region, ...
+       'source_timeseries', lfp_electrical_series);
+   
+   % Add decomposition series to ecephys module
+   ecephys_module.nwbdatainterface.set('theta', decomp_series);
+
+Spike Times and Extracellular Events
+------------------------------------
+
+Sorted Spike Times
+~~~~~~~~~~~~~~~~~~
+
+Spike times are stored in a `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table, a specialization of the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ class. The default `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table is located at ``/units`` in the HDF5 file. You can add columns to the `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table just like you did for ``electrodes`` and ``trials`` (see `convertTrials <convertTrials>`_). Here, we generate some random spike time data and populate the table. Note: Spike times of a `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table should be sorted in ascending order.
+
+.. code-block:: matlab
+
+   num_cells = 10;
+   spike_times = cell(1, num_cells);
+   for iShank = 1:num_cells
+       spike_times{iShank} = sort( rand(1, randi([16, 28])), 'ascend');
+   end
+   spike_times
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - 1
+     - 2
+     - 3
+     - 4
+     - 5
+     - 6
+     - 7
+     - 8
+     - 9
+     - 10
+   * - 1
+     - 1x28 double
+     - 1x18 double
+     - 1x28 double
+     - 1x24 double
+     - 1x16 double
+     - 1x23 double
+     - 1x25 double
+     - 1x24 double
+     - 1x19 double
+     - 1x21 double
+
+Ragged Arrays
+^^^^^^^^^^^^^
+
+Spike times are an example of a ragged array- it's like a matrix, but each row has a different number of elements. We can represent this type of data as an indexed column of the `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table. These indexed columns have two components, the `VectorData <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorData.html>`_ object that holds the data and the `VectorIndex <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/VectorIndex.html>`_ object that holds the indices in the vector that indicate the row breaks. You can use the convenience function ``util.create_indexed_column`` to create these objects. For more information about ragged arrays, we refer you to the **"Ragged Array Columns"** section of the `dynamic table <dynamic_tables>`_ tutorial.
+
+.. image:: ../../_static/tutorials/media/ecephys/image_3.png
+   :class: tutorial-media
+   :width: 703px
+   :alt: image_3.png
+
+.. code-block:: matlab
+
+   [spike_times_vector, spike_times_index] = util.create_indexed_column(spike_times);
+   spike_times_vector.resolution = 1/20000; % If original sampling rate was 20 kHz 
+   
+   nwb.units = types.core.Units( ...
+       'colnames', {'spike_times'}, ...
+       'description', 'units table', ...
+       'spike_times', spike_times_vector, ...
+       'spike_times_index', spike_times_index ...
+   );
+   
+   nwb.units.toTable()
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - spike_times
+   * - 1
+     - 0
+     - 28x1 double
+   * - 2
+     - 1
+     - 18x1 double
+   * - 3
+     - 2
+     - 28x1 double
+   * - 4
+     - 3
+     - 24x1 double
+   * - 5
+     - 4
+     - 16x1 double
+   * - 6
+     - 5
+     - 23x1 double
+   * - 7
+     - 6
+     - 25x1 double
+   * - 8
+     - 7
+     - 24x1 double
+   * - 9
+     - 8
+     - 19x1 double
+   * - 10
+     - 9
+     - 21x1 double
+
+Unsorted Spike Times
+~~~~~~~~~~~~~~~~~~~~
+
+While the `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table is used to store spike times and waveform data for spike-sorted, single-unit activity, you may also want to store spike times and waveform snippets of unsorted spiking activity. This is useful for recording multi-unit activity detected via threshold crossings during data acquisition. Such information can be stored using `SpikeEventSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html>`_ objects.
+
+.. code-block:: matlab
+
+   % In the SpikeEventSeries the dimensions should be ordered as 
+   % [num_events, num_channels, num_samples].
+   % Define spike snippets: 20 events, 3 channels, 40 samples per event. 
+   spike_snippets = rand(20, 3, 40);
+   % Permute spike snippets (See dimensionMapNoDataPipes tutorial)
+   spike_snippets = permute(spike_snippets, [3,2,1]); 
+   
+   % Create electrode table region referencing electrodes 0, 1, and 2
+   shank0_table_region = types.hdmf_common.DynamicTableRegion( ...
+       'table', types.untyped.ObjectView(electrodesDynamicTable), ...
+       'description', 'shank0', ...
+       'data', (0:2)');
+   
+   % Define spike event series for unsorted spike times
+   spike_events = types.core.SpikeEventSeries( ...
+       'data', spike_snippets, ...
+       'timestamps', (0:19)', ...  % Timestamps for each event
+       'description', 'events detected with 100uV threshold', ...
+       'electrodes', shank0_table_region ...
+   );
+   
+   % Add spike event series to NWB file acquisition
+   nwb.acquisition.set('SpikeEvents_Shank0', spike_events);
+
+Detected Events
+~~~~~~~~~~~~~~~
+
+If you need to store the complete, continuous raw voltage traces, along with unsorted spike times, you should store the traces in `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ objects in the acquisition group, and use the `EventDetection <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html>`_ class to identify the spike events in your raw traces.
+
+.. code-block:: matlab
+
+   % Create the EventDetection object
+   event_detection = types.core.EventDetection( ...
+       'detection_method', 'thresholding, 1.5 * std', ...
+       'source_electricalseries', types.untyped.SoftLink(raw_electrical_series), ...
+       'source_idx', [1000; 2000; 3000], ...
+       'times', [.033, .066, .099] ...
+   );
+   
+   % Add the EventDetection object to the ecephys module
+   ecephys_module.nwbdatainterface.set('ThresholdEvents', event_detection);
+
+Storing Spike Features (e.g Principal Components)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+NWB also provides a way to store features of spikes, such as principal components, using the `FeatureExtraction <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/FeatureExtraction.html>`_ class.
+
+.. code-block:: matlab
+
+   % Generate random feature data (time x channel x feature)
+   features = rand(3, 12, 4); % 3 time points, 12 channels, 4 features
+   features = permute(features, [3,2,1]); % reverse dimension order for matnwb
+   
+   % Create the FeatureExtraction object
+   feature_extraction = types.core.FeatureExtraction( ...
+       'description', {'PC1', 'PC2', 'PC3', 'PC4'}, ... % Feature descriptions
+       'electrodes', electrode_table_region, ...
+       'times', [.033; .066; .099], ... % Column vector for times
+       'features', features ...
+   );
+   
+   % Add the FeatureExtraction object to the ecephys module (if required)
+   ecephys_module.nwbdatainterface.set('PCA_features', feature_extraction);
+
+Choosing NWB-Types for Electrophysiology Data (A Summary)
+---------------------------------------------------------
+
+As mentioned above, `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ objects are meant for storing electrical timeseries data like raw voltage signals or processed signals like LFP or other filtered signals. In addition to the `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ class, NWB provides some more classes for storing event-based electropysiological data. We will briefly discuss them here, and refer the reader to the `API documentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html>`_ and the section on `Extracellular Physiology <https://nwb-schema.readthedocs.io/en/latest/format.html#extracellular-electrophysiology>`_ in the "NWB Format Specification" for more details on using these objects.
+
+For storing unsorted spiking data, there are two options. Which one you choose depends on what data you have available. If you need to store complete and/or continuous raw voltage traces, you should store the traces with `ElectricalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ElectricalSeries.html>`_ objects as acquisition data, and use the `EventDetection <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/EventDetection.html>`_ class for identifying the spike events in your raw traces. If you do not want to store the entire raw voltage traces, only the waveform ‘snippets’ surrounding spike events, you should use `SpikeEventSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpikeEventSeries.html>`_ objects.
+
+The results of spike sorting (or clustering) should be stored in the top-level `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table. The `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table can hold just the spike times of sorted units or, optionally, include additional waveform information. You can use the optional predefined columns ``waveform_mean``, ``waveform_sd``, and ``waveforms`` in the `Units <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Units.html>`_ table to store individual and mean waveform data.
+
+Writing the NWB File
+--------------------
+
+.. code-block:: matlab
+
+   nwbExport(nwb, 'ecephys_tutorial.nwb')
+
+Reading NWB Data
+----------------
+
+Data arrays are read passively from the file. Calling ``TimeSeries.data`` does not read the data values, but presents an HDF5 object that can be indexed to read data. This allows you to conveniently work with datasets that are too large to fit in RAM all at once. ``load`` with no input arguments reads the entire dataset:
+
+.. code-block:: matlab
+
+   nwb2 = nwbRead('ecephys_tutorial.nwb', 'ignorecache');
+   nwb2.processing.get('ecephys'). ...
+       nwbdatainterface.get('LFP'). ...
+       electricalseries.get('ElectricalSeries'). ...
+       data.load;
+
+Accessing Data Regions
+----------------------
+
+If all you need is a data region, you can index a ``DataStub`` object like you would any normal array in MATLAB, as shown below. When indexing the dataset this way, only the selected region is read from disk into RAM. This allows you to handle very large datasets that would not fit entirely into RAM.
+
+.. code-block:: matlab
+
+   % read section of LFP
+   nwb2.processing.get('ecephys'). ...
+       nwbdatainterface.get('LFP'). ...
+       electricalseries.get('ElectricalSeries'). ...
+       data(1:5, 1:10)
+
+.. code-block:: text
+
+   ans = 5x10
+      -0.7839    0.0147   -0.4823    0.7583    0.8135   -2.0957    0.9011   -2.3509    0.1892   -2.0754
+       0.2895   -0.2133    0.5483   -1.4372   -0.0451   -1.1182   -1.9083    1.1331   -0.5946   -0.1609
+      -0.9435    0.2274   -0.7730    0.2097   -0.3885    1.7777   -0.5676   -0.0386    0.4206    0.3531
+      -0.4632    0.7073   -0.4644    0.3453    0.4681    0.4659    1.6279    2.0310   -0.5402   -0.8882
+       0.3153    0.0440   -2.5588   -0.8699    0.6555    1.3090   -0.0109    0.4978    0.2622   -0.3127
+
+.. code-block:: matlab
+
+   
+   % You can use the getRow method of the table to load spike times of a specific unit.
+   % To get the values, unpack from the returned table.
+   nwb.units.getRow(1).spike_times{1}
+
+.. code-block:: text
+
+   ans = 28x1
+       0.0264
+       0.0591
+       0.0854
+       0.1181
+       0.1203
+       0.1328
+       0.1918
+       0.2230
+       0.2389
+       0.2487
+
+Learn more!
+-----------
+
+See the `API documentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html>`_ to learn what data types are available.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+MATLAB tutorials
+----------------
+
+* `Optical physiology <ophys>`_
+* `Intracellular electrophysiology <icephys>`_
+
+Python tutorials
+----------------
+
+See our tutorials for more details about your data type:
+
+* `Calcium imaging <https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py>`_
+* `Extracellular electrophysiology <https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py>`_
+* `Intracellular electrophysiology <https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology>`_
+
+**Check out other tutorials that teach advanced NWB topics:**
+
+* `Iterative data write <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py>`_
+* `Extensions <https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py>`_
+* `Advanced HDF5 I/O <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py>`_
diff --git a/docs/source/pages/tutorials/icephys.rst b/docs/source/pages/tutorials/icephys.rst
index 087afe319..38ed685b0 100644
--- a/docs/source/pages/tutorials/icephys.rst
+++ b/docs/source/pages/tutorials/icephys.rst
@@ -6,14 +6,548 @@ Intracellular Electrophysiology
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/icephys.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/icephys.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/icephys.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+The following tutorial describes storage of intracellular electrophysiology data in NWB. NWB supports storage of the time series describing the stimulus and response, information about the electrode and device used, as well as metadata about the organization of the experiment.
+
+**Tip**: This tutorial introduces many subtypes of the `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ type. For a detailed tutorial demonstrating how to work with dynamic tables, see the `DynamicTables <dynamic_tables>`_ tutorial.
+
+.. image:: ../../_static/tutorials/media/icephys/image_0.png
+   :class: tutorial-media
+   :width: 1035px
+   :alt: image_0.png
+
+*Illustration of the hierarchy of metadata tables used to describe the organization of intracellular electrophysiology experiments.*
+
+Creating an NWBFile
+-------------------
+
+When creating an NWB file, the first step is to create the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_, which you can create using the `NwbFile <https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html>`_ command.
+
+.. code-block:: matlab
+
+   session_start_time = datetime(2018, 3, 1, 12, 0, 0, 'TimeZone', 'local');
+   
+   nwbfile = NwbFile( ...
+       'session_description', 'my first synthetic recording', ...
+       'identifier', 'EXAMPLE_ID', ...
+       'session_start_time', session_start_time, ...
+       'general_experimenter', 'Dr. Bilbo Baggins', ...
+       'general_lab', 'Bag End Laboratory', ...
+       'general_institution', 'University of Middle Earth at the Shire', ...
+       'general_experiment_description', ['I went on an adventure with thirteen dwarves ', ...
+                                          'to reclaim vast treasures.'], ...
+       'general_session_id', 'LONELYMTN' ...
+   );
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is also recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '001', ...
+       'age', 'P90D', ...
+       'description', 'mouse 1', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+Device metadata
+~~~~~~~~~~~~~~~
+
+Device metadata is represented by `Device <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html>`_ objects.
+
+.. code-block:: matlab
+
+   device = types.core.Device();
+   nwbfile.general_devices.set('Heka ITC-1600', device);
+
+**Electrode metadata**
+~~~~~~~~~~~~~~~~~~~~~~
+
+Intracellular electrode metadata is represented by `IntracellularElectrode <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrode.html>`_ objects. Create an electrode object, which requires a link to the device of the previous step. Then add it to the NWB file.
+
+.. code-block:: matlab
+
+   electrode = types.core.IntracellularElectrode( ...
+       'description', 'a mock intracellular electrode', ...
+       'device', types.untyped.SoftLink(device), ...
+       'cell_id', 'a very interesting cell' ...
+   );
+   nwbfile.general_intracellular_ephys.set('IntracellularElectrode', electrode);
+
+**Stimulus and response data**
+------------------------------
+
+Intracellular stimulus and response data are represented with subclasses of `PatchClampSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PatchClampSeries.html>`_. A stimulus is described by a time series representing voltage or current stimulation with a particular set of parameters. There are two classes for representing stimulus data:
+
+* `VoltageClampStimulusSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampStimulusSeries.html>`_
+* `CurrentClampStimulusSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampStimulusSeries.html>`_
+
+The response is then described by a time series representing voltage or current recorded from a single cell using a single intracellular electrode via one of the following classes:
+
+* `VoltageClampSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/VoltageClampSeries.html>`_
+* `CurrentClampSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CurrentClampSeries.html>`_
+* `IZeroClampSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html>`_
+
+Below we create a simple example stimulus/response recording data pair for a voltage clamp recording.
+
+.. code-block:: matlab
+
+   vcss = types.core.VoltageClampStimulusSeries( ...
+       'data', [1, 2, 3, 4, 5], ...
+       'starting_time', 123.6, ...
+       'starting_time_rate', 10e3, ...
+       'electrode', types.untyped.SoftLink(electrode), ...
+       'gain', 0.02, ...
+       'sweep_number', uint64(15), ...
+       'stimulus_description', 'N/A' ...
+   );
+   nwbfile.stimulus_presentation.set('VoltageClampStimulusSeries',  vcss);
+   
+   vcs = types.core.VoltageClampSeries( ...
+       'data', [0.1, 0.2, 0.3, 0.4, 0.5], ...
+       'data_conversion', 1e-12, ...
+       'data_resolution', NaN, ...
+       'starting_time', 123.6, ...
+       'starting_time_rate', 20e3, ...
+       'electrode', types.untyped.SoftLink(electrode), ...
+       'gain', 0.02, ...
+       'capacitance_slow', 100e-12, ...
+       'resistance_comp_correction', 70.0, ...
+       'stimulus_description', 'N/A', ...
+       'sweep_number', uint64(15) ...
+   );
+   nwbfile.acquisition.set('VoltageClampSeries',  vcs);
+
+You can add stimulus/response recording data pair from a current clamp recording in the same way:
+
+.. code-block:: matlab
+
+   % Create a CurrentClampStimulusSeries object
+   ccss = types.core.CurrentClampStimulusSeries(...
+       'data', [1, 2, 3, 4, 5], ...
+       'starting_time', 123.6, ...
+       'starting_time_rate', 10e3, ...
+       'electrode', types.untyped.SoftLink(electrode), ...
+       'gain', 0.02, ...
+       'sweep_number', uint64(16), ...
+       'stimulus_description', 'N/A' ...
+   );
+   nwbfile.stimulus_presentation.set('CurrentClampStimulusSeries',  ccss);
+   
+   % Create a CurrentClampSeries object
+   ccs = types.core.CurrentClampSeries(...
+       'data', [0.1, 0.2, 0.3, 0.4, 0.5], ...
+       'data_conversion', 1e-12, ...
+       'data_resolution', NaN, ...
+       'starting_time', 123.6, ...
+       'starting_time_rate', 20e3, ...
+       'electrode', types.untyped.SoftLink(electrode), ...
+       'gain', 0.02, ...
+       'bias_current', 1e-12, ...
+       'bridge_balance', 70e6, ...
+       'capacitance_compensation', 1e-12, ...
+       'stimulus_description', 'N/A', ...
+       'sweep_number', uint64(16) ...
+   );
+   nwbfile.acquisition.set('CurrentClampSeries',  ccs);
+
+`IZeroClampSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IZeroClampSeries.html>`_ is used when the current is clamped to 0.
+
+.. code-block:: matlab
+
+   % Create an IZeroClampSeries object
+   izcs = types.core.IZeroClampSeries(...
+       'data', [0.1, 0.2, 0.3, 0.4, 0.5], ...
+       'electrode', types.untyped.SoftLink(electrode), ...
+       'gain', 0.02, ...
+       'data_conversion', 1e-12, ...
+       'data_resolution', NaN, ...
+       'starting_time', 345.6, ...
+       'starting_time_rate', 20e3, ...
+       'sweep_number', uint64(17) ...
+   );
+   nwbfile.acquisition.set('IZeroClampSeries',  izcs);
+
+Adding an intracellular recording
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_ relates electrode, stimulus and response pairs and describes metadata specific to individual recordings.
+
+.. image:: ../../_static/tutorials/media/icephys/image_1.png
+   :class: tutorial-media
+   :width: 888px
+   :alt: image_1.png
+
+*Illustration of the structure of the IntracellularRecordingsTable*
+
+We can add an `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_ and add the `IntracellularElectrodesTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularElectrodesTable.html>`_, `IntracellularStimuliTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularStimuliTable.html>`_, and `IntracellularResponsesTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularResponsesTable.html>`_ to it, then add them all to the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object.
+
+.. code-block:: matlab
+
+   ic_rec_table = types.core.IntracellularRecordingsTable( ...
+       'categories', {'electrodes', 'stimuli', 'responses'}, ...
+       'colnames', {'recordings_tag'}, ...
+       'description', [ ...
+           'A table to group together a stimulus and response from a single ', ...
+           'electrode and a single simultaneous recording and for storing ', ...
+           'metadata about the intracellular recording.'], ...
+       'id', types.hdmf_common.ElementIdentifiers('data', int64([0; 1; 2])), ...
+       'recordings_tag', types.hdmf_common.VectorData( ...
+           'data', repmat({'Tag'}, 3, 1), ...
+           'description', 'Column for storing a custom recordings tag' ...
+           ) ...
+   );
+   
+   ic_rec_table.electrodes = types.core.IntracellularElectrodesTable( ...
+       'description', 'Table for storing intracellular electrode related metadata.', ...
+       'colnames', {'electrode'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64([0; 1; 2]) ...
+       ), ...
+       'electrode', types.hdmf_common.VectorData( ...
+           'data', repmat(types.untyped.ObjectView(electrode), 3, 1), ...
+           'description', 'Column for storing the reference to the intracellular electrode' ...
+       ) ...
+   );
+   
+   % Note: For IZeroClampSeries (izcs), idx_start=-1 and count=-1 indicate no stimulus
+   % was recorded (the amplifier is disconnected, so no stimulus can reach the cell).
+   % The timeseries still points to the response series as described in the NWB spec
+   % for the IntracellularRecordingsTable.
+   ic_rec_table.stimuli = types.core.IntracellularStimuliTable( ...
+       'description', 'Table for storing intracellular stimulus related metadata.', ...
+       'colnames', {'stimulus'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64([0; 1; 2]) ...
+       ), ...
+       'stimulus', types.core.TimeSeriesReferenceVectorData( ...
+           'description', ['Column storing the reference to the recorded stimulus ', ...
+                           'for the recording (rows)'], ...
+           'data', struct( ...
+               'idx_start', [0, 1, -1], ...
+               'count', [5, 3, -1], ...
+               'timeseries', [ ...
+                   types.untyped.ObjectView(vcss), ... % Voltage clamp stimulus
+                   types.untyped.ObjectView(ccss), ... % Current clamp stimulus
+                   types.untyped.ObjectView(izcs) ...  % Current clamp response when current is off
+               ] ...
+           )...
+       )...
+   );
+   
+   ic_rec_table.responses = types.core.IntracellularResponsesTable( ...
+       'description', 'Table for storing intracellular response related metadata.', ...
+       'colnames', {'response'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64([0; 1; 2]) ...
+       ), ...
+       'response', types.core.TimeSeriesReferenceVectorData( ...
+           'description', ['Column storing the reference to the recorded response ', ...
+                           'for the recording (rows)'], ...
+           'data', struct( ...
+               'idx_start', [0, 2, 0], ...
+               'count', [5, 3, 5], ...
+               'timeseries', [ ...
+                   types.untyped.ObjectView(vcs), ... % Voltage clamp response
+                   types.untyped.ObjectView(ccs), ... % Current clamp response
+                   types.untyped.ObjectView(izcs) ... % Current clamp response when current is off
+               ] ...
+           )...
+       )...
+   );
+
+The `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_ table is not just a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ but an `AlignedDynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html>`_. The `AlignedDynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html>`_ type is itself a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ that may contain an arbitrary number of additional `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, each of which defines a "category." This is similar to a table with “sub-headings”. In the case of the `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_, we have three predefined categories, i.e., electrodes, stimuli, and responses. We can also dynamically add new categories to the table. As each category corresponds to a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, this means we have to create a new `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ and add it to our table.
+
+.. code-block:: matlab
+
+   % add category
+   ic_rec_table.categories = [ic_rec_table.categories, {'recording_lab_data'}];
+   ic_rec_table.dynamictable.set( ...
+       'recording_lab_data', types.hdmf_common.DynamicTable( ...
+           'description', 'category table for lab-specific recording metadata', ...
+           'colnames', {'location'}, ...
+           'id', types.hdmf_common.ElementIdentifiers( ...
+               'data', int64([0; 1; 2]) ...
+           ), ...
+           'location', types.hdmf_common.VectorData( ...
+               'data', {'Mordor', 'Gondor', 'Rohan'}, ...
+               'description', 'Recording location in Middle Earth' ...
+           ) ...
+       ) ...
+   );
+
+In an `AlignedDynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/AlignedDynamicTable.html>`_ all category tables must align with the main table, i.e., all tables must have the same number of rows and rows are expected to correspond to each other by index.
+
+We can also add custom columns to any of the subcategory tables, i.e., the electrodes, stimuli, and responses tables, and any custom subcategory tables. All we need to do is indicate the name of the category we want to add the column to.
+
+.. code-block:: matlab
+
+   % Add voltage threshold as column of electrodes table
+   voltage_threshold_data = types.hdmf_common.VectorData( ...
+       'data', [0.1; 0.12; 0.13], ...
+       'description', 'Just an example column on the electrodes category table' ...
+   );
+   ic_rec_table.electrodes.addColumn('voltage_threshold', voltage_threshold_data)
+
+The `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_ is added to the ``general_intracellular_ephys_intracellular_recordings`` property of the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object:
+
+.. code-block:: matlab
+
+   nwbfile.general_intracellular_ephys_intracellular_recordings = ic_rec_table;
+
+Hierarchical organization of recordings
+---------------------------------------
+
+To describe the organization of intracellular experiments, the metadata is organized hierarchically in a sequence of tables. All of the tables are so-called `DynamicTables <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ enabling users to add columns for custom metadata. Storing data in hierarchical tables has the advantage that it allows us to avoid duplication of metadata. E.g., for a single experiment we only need to describe the metadata that is constant across an experimental condition as a single row in the `SimultaneousRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html>`_ without having to replicate the same information across all repetitions and sequential-, simultaneous-, and individual intracellular recordings. For analysis, this means that we can easily focus on individual aspects of an experiment while still being able to easily access information about information from related tables. All of these tables are optional, but to use one you must use all of the lower level tables, even if you only need a single row.
+
+Add a simultaneous recording
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `SimultaneousRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html>`_ groups intracellular recordings from the `IntracellularRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IntracellularRecordingsTable.html>`_ together that were recorded simultaneously from different electrodes and/or cells and describes metadata that is constant across the simultaneous recordings. In practice a simultaneous recording is often also referred to as a sweep. This example adds a custom column, "simultaneous_recording_tag".
+
+.. code-block:: matlab
+
+   % Create recordings_vector_data and recordings_vector_index using utility
+   % function:
+   [recordings_vector_data, recordings_vector_index] = util.create_indexed_column( ...
+       {[0, 1, 2],}, ...
+       'Column with references to one or more rows in the IntracellularRecordingsTable table', ...
+       ic_rec_table);
+   
+   % Create simultaneous recordings table with custom column 'simultaneous_recording_tag'
+   ic_sim_recs_table = types.core.SimultaneousRecordingsTable( ...
+       'description', [ ...
+           'A table for grouping different intracellular recordings from ', ...
+           'the IntracellularRecordingsTable table together that were recorded ', ...
+           'simultaneously from different electrodes.'...
+       ], ...
+       'colnames', {'recordings', 'simultaneous_recording_tag'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64(12) ...
+       ), ...
+       'recordings', recordings_vector_data, ...
+       'recordings_index', recordings_vector_index, ...
+       'simultaneous_recording_tag', types.hdmf_common.VectorData( ...
+           'description', 'A custom tag for simultaneous_recordings', ...
+           'data', {'LabTag1'} ...
+       ) ...
+   );
+
+Depending on the lab workflow, it may be useful to add complete columns to a table after we have already populated the table with rows. That would be done using the ``addColumn`` method:
+
+.. code-block:: matlab
+
+   simultaneous_recording_type_data = types.hdmf_common.VectorData(...
+           'description', 'Description of the type of simultaneous_recording', ...
+           'data', {'SimultaneousRecordingType1'} ...
+   );
+   ic_sim_recs_table.addColumn('simultaneous_recording_type', simultaneous_recording_type_data)
+
+Display the simultaneous recordings table using the ``toTable`` method:
+
+.. code-block:: matlab
+
+   ic_sim_recs_table.toTable()
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - recordings
+     - simultaneous_recording_tag
+     - simultaneous_recording_type
+   * - 1
+     - 12
+     - [0;1;2]
+     - 'LabTag1'
+     - 'SimultaneousRecordingType1'
+
+The `SimultaneousRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html>`_ is added to the ``general_intracellular_ephys_simultaneous_recordings`` property of the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object:
+
+.. code-block:: matlab
+
+   nwbfile.general_intracellular_ephys_simultaneous_recordings = ic_sim_recs_table;
+
+Add a sequential recording
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `SequentialRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html>`_ groups simultaneously recorded intracellular recordings from the `SimultaneousRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SimultaneousRecordingsTable.html>`_ together and describes metadata that is constant across the simultaneous recordings. In practice a sequential recording is often also referred to as a sweep sequence. A common use of sequential recordings is to group together simultaneous recordings where a sequence of stimuli of the same type with varying parameters have been presented in a sequence (e.g., a sequence of square waveforms with varying amplitude).
+
+.. code-block:: matlab
+
+   [simultaneous_recordings_vector_data, simultaneous_recordings_vector_index] = ...
+       util.create_indexed_column( ...
+           {0,}, ...
+           'Column with references to one or more rows in the SimultaneousRecordingsTable table', ...
+           ic_sim_recs_table);
+   
+   sequential_recordings = types.core.SequentialRecordingsTable( ...
+       'description', [ ...
+           'A table for grouping different intracellular recording ', ...
+           'simultaneous_recordings from the SimultaneousRecordingsTable ', ...
+           'table together. This is typically used to group together ', ...
+           'simultaneous_recordings where a sequence of stimuli of ', ...
+           'the same type with varying parameters have been presented in ', ...
+           'a sequence.' ...
+       ], ...
+       'colnames', {'simultaneous_recordings', 'stimulus_type'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64(15) ...
+       ), ...
+       'simultaneous_recordings', simultaneous_recordings_vector_data, ...
+       'simultaneous_recordings_index', simultaneous_recordings_vector_index, ...
+       'stimulus_type', types.hdmf_common.VectorData( ...
+           'description', 'Column storing the type of stimulus used for the sequential recording', ...
+           'data', {'square'} ...
+       ) ...
+   );
+
+The `SequentialRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html>`_ is added to the ``general_intracellular_ephys_sequential_recordings`` property of the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object:
+
+.. code-block:: matlab
+
+   nwbfile.general_intracellular_ephys_sequential_recordings = sequential_recordings;
+
+Add repetitions table
+~~~~~~~~~~~~~~~~~~~~~
+
+The `RepetitionsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html>`_ groups sequential recordings from the `SequentialRecordingsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SequentialRecordingsTable.html>`_. In practice, a repetition is often also referred to a run. A typical use of the `RepetitionsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html>`_ is to group sets of different stimuli that are applied in sequence that may be repeated.
+
+.. code-block:: matlab
+
+   [sequential_recordings_vector_data, sequential_recordings_vector_index] = ...
+       util.create_indexed_column( ...
+           {0,}, ...
+           'Column with references to one or more rows in the SequentialRecordingsTable table', ...
+           sequential_recordings);
+   
+   nwbfile.general_intracellular_ephys_repetitions = types.core.RepetitionsTable( ...
+       'description', [ ...
+           'A table for grouping different intracellular recording sequential ', ...
+           'recordings together. With each SimultaneousRecording typically ', ...
+           'representing a particular type of stimulus, the RepetitionsTable ', ...
+           'table is typically used to group sets of stimuli applied in sequence.' ...
+       ], ...
+       'colnames', {'sequential_recordings'}, ...
+       'id', types.hdmf_common.ElementIdentifiers( ...
+           'data', int64(17) ...
+       ), ...
+       'sequential_recordings', sequential_recordings_vector_data, ...
+       'sequential_recordings_index', sequential_recordings_vector_index ...
+   );
+
+Add experimental condition table
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The `ExperimentalConditionsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ExperimentalConditionsTable.html>`_ groups repetitions of intracellular recording from the `RepetitionsTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RepetitionsTable.html>`_ together that belong to the same experimental conditions.
+
+.. code-block:: matlab
+
+   [repetitions_vector_data, repetitions_vector_index] = util.create_indexed_column( ...
+       {0, 0}, ...
+       'Column with references to one or more rows in the RepetitionsTable table', ...
+       nwbfile.general_intracellular_ephys_repetitions);
+   
+   nwbfile.general_intracellular_ephys_experimental_conditions = ...
+       types.core.ExperimentalConditionsTable( ...
+           'description', [ ...
+               'A table for grouping different intracellular recording ', ...
+               'repetitions together that belong to the same experimental ', ...
+               'conditions.' ...
+           ], ...
+           'colnames', {'repetitions', 'tag'}, ...
+           'id', types.hdmf_common.ElementIdentifiers( ...
+               'data', int64([19; 21]) ...
+           ), ...
+           'repetitions', repetitions_vector_data, ...
+           'repetitions_index', repetitions_vector_index, ...
+           'tag', types.hdmf_common.VectorData( ...
+               'description', 'integer tag for an experimental condition', ...
+               'data', [1; 3] ...
+           ) ...
+   );
+
+Write the NWB file
+------------------
+
+.. code-block:: matlab
+
+   nwbExport(nwbfile, 'icephys_tutorial.nwb');
+
+Read the NWB file
+-----------------
+
+.. code-block:: matlab
+
+   nwbfile2 = nwbRead('icephys_tutorial.nwb', 'ignorecache')
+
+.. code-block:: text
+
+   nwbfile2 = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: [1x1 types.untyped.DataStub]
+                                                 identifier: 'EXAMPLE_ID'
+                                        session_description: 'my first synthetic recording'
+                                         session_start_time: [1x1 types.untyped.DataStub]
+                                  timestamps_reference_time: [1x1 types.untyped.DataStub]
+                                                acquisition: [3x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [1x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: 'I went on an adventure with thirteen dwarves to reclaim vast treasures.'
+                                       general_experimenter: [1x1 types.untyped.DataStub]
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of Middle Earth at the Shire'
+                                general_intracellular_ephys: [1x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: [1x1 types.core.ExperimentalConditionsTable]
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: [1x1 types.core.IntracellularRecordingsTable]
+                    general_intracellular_ephys_repetitions: [1x1 types.core.RepetitionsTable]
+          general_intracellular_ephys_sequential_recordings: [1x1 types.core.SequentialRecordingsTable]
+        general_intracellular_ephys_simultaneous_recordings: [1x1 types.core.SimultaneousRecordingsTable]
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: 'Bag End Laboratory'
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: ''
+                                         general_session_id: 'LONELYMTN'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: [1x1 types.untyped.DataStub]
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [2x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
diff --git a/docs/source/pages/tutorials/images.rst b/docs/source/pages/tutorials/images.rst
index 2ee6fad06..5765da6b7 100644
--- a/docs/source/pages/tutorials/images.rst
+++ b/docs/source/pages/tutorials/images.rst
@@ -6,14 +6,367 @@ Image Data
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/images.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/images.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/images.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+In NWB, image data may be stored as collections of single images or as movie segments and can describe the subject, the environment, presented stimuli, or other experiment components. In this tutorial you will:
+
+* Use `types.core.ImageSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html>`_ to store acquired image series (movie segments).
+* Use `types.core.OpticalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html>`_ and `types.core.AbstractFeatureSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AbstractFeatureSeries.html>`_ to store series of images or image features that were presented as stimuli.
+* Use `types.core.GrayscaleImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html>`_, `types.core.RGBImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html>`_ and `types.core.RGBAImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html>`_ to store static images.
+
+**Preliminary note:** As described in the `dimensionMapNoDataPipes <dimensionMapNoDataPipes>`_ tutorial, when a MATLAB array is exported to HDF5, the dimensions of the array are reversed. Therefore, in order to correctly export the data, we will need to reverse the order of dimensions in image and timeseries data before adding these to NWB objects. The function below will be used for this purpose throughout the tutorial:
+
+.. code-block:: matlab
+
+   % Anonymous function for reversing data dimensions for NWB compliance
+   reverseDims = @(data) permute(data, ndims(data):-1:1);
+
+Create an NWB File
+------------------
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', 'Mouse5_Day3', ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
+       'general_experimenter', 'LastName, FirstName', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', 'DOI:10.1016/j.neuron.2016.12.011' ... % optional
+   );
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: {[2018-04-25T03:00:45.000000+02:00]}
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'LastName, FirstName'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: 'DOI:10.1016/j.neuron.2016.12.011'
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '005', ...
+       'age', 'P90D', ...
+       'description', 'mouse 5', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+ImageSeries: Storing series of images as acquisition
+----------------------------------------------------
+
+`ImageSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html>`_ is a general container for time series of images acquired during an experiment. Image data can be stored directly in the NWB file or referenced from external image files (see section External files below). For color images stored in the NWB file, the channel order must be RGB.
+
+.. code-block:: matlab
+
+   image_data = randi(255, [200, 50, 50, 3], 'uint8');
+   behavior_images = types.core.ImageSeries( ...
+       'data', reverseDims(image_data), ...
+       'description', 'Image data of an animal in the environment', ...
+       'data_unit', 'n.a.', ...
+       'starting_time_rate', 1.0, ...
+       'starting_time', 0.0 ...
+   );
+   
+   nwb.acquisition.set('ImageSeries', behavior_images);
+
+OpticalSeries: Storing series of images as stimuli
+--------------------------------------------------
+
+We will use the `OpticalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html>`_ class to store time series of images presented to the subject as stimuli. The `OpticalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html>`_ class extends the ImageSeries with additional fields that describe the spatial relationship between the subject and the stimuli, such as:
+
+* ``field_of_view`` – physical extent of the image in the visual field or target area.
+* ``distance`` – distance from the image source to the subject.
+
+Here we create an `OpticalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html>`_ named ``StimulusPresentation`` containing synthetic RGB image data (As in all `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_, the first dimension is time. The second and third dimensions represent x and y and the fourth dimension represents the RGB value (length of 3) for color images):
+
+.. code-block:: matlab
+
+   image_data = randi(255, [200, 50, 50, 3], 'uint8'); % time, x, y, RGB
+   optical_series = types.core.OpticalSeries( ...
+       'distance', 0.7, ...  % recommended
+       'field_of_view', [0.2, 0.3, 0.7], ...  % recommended
+       'data', reverseDims(image_data), ...
+       'data_unit', 'n.a.', ...
+       'starting_time_rate', 1.0, ...
+       'starting_time', 0.0, ...
+       'description', 'The images presented to the subject as stimuli' ...
+   );
+   
+   nwb.stimulus_presentation.set('StimulusPresentation', optical_series);
+
+AbstractFeatureSeries: Storing features of visual stimuli
+---------------------------------------------------------
+
+While full image data is usually stored in an `OpticalSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalSeries.html>`_, you can also store derived features of the visual stimuli—either instead of, or in addition to, the raw images. Examples of derived features include **mean luminance**, **contrast**, or **spatial frequency**. The `AbstractFeatureSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AbstractFeatureSeries.html>`_ class is a general ``TimeSeries`` container for storing such feature data over time.
+
+.. code-block:: matlab
+
+   % Create some fake feature data
+   feature_data = rand(200, 3);  % 200 time points, 3 features
+   
+   % Create an AbstractFeatureSeries object
+   abstract_feature_series = types.core.AbstractFeatureSeries( ...
+       'data', reverseDims(feature_data), ...
+       'timestamps', linspace(0, 1, 200), ...
+       'description', 'Features of the visual stimuli', ...
+       'features', {'luminance', 'contrast', 'spatial frequency'}, ...
+       'feature_units', {'n.a.', 'n.a.', 'cycles/degree'} ...
+   );
+   
+   % Add the AbstractFeatureSeries to the NWBFile
+   nwb.stimulus_presentation.set('StimulusFeatures', abstract_feature_series);
+
+External files
+--------------
+
+External files (e.g., videos of the behaving animal) can be added to an `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ by creating an `ImageSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html>`_ with the ``external_file`` field set to the relative path(s) of the file(s) on disk. The path must be relative to the NWB file’s location.
+
+Either ``external_file`` **or** ``data`` must be specified — not both. ``external_file`` can be a cell array of multiple video files.
+
+The ``starting_frame`` attribute is a zero-based index into the full ``ImageSeries`` that indicates the first frame contained in each file listed in ``external_file``. For example, if three files contain 5, 10, and 20 frames respectively, ``starting_frame`` would be ``[0, 5, 15]``. If one file contains all frames, it is ``[0]``. This indexing allows random access to frames without loading all preceding files.
+
+.. code-block:: matlab
+
+   external_files = {'video1.pmp4', 'video2.pmp4'};
+   
+   timestamps = [0.0, 0.04, 0.07, 0.1, 0.14, 0.16, 0.21];
+   behavior_external_file = types.core.ImageSeries( ...
+       'description', 'Behavior video of animal moving in environment', ...
+       'external_file', external_files, ...
+       'data_unit', 'n/a', ...
+       'format', 'external', ...
+       'external_file_starting_frame', [0, 2], ...
+       'timestamps', timestamps ...
+   );
+
+.. code-block:: text
+
+   Warning: The property "data_unit" of type "types.core.ImageSeries" depends on the property "data", which is unset. If you do not set a value for "data, the value of "data_unit" will not be exported to file.
+
+.. code-block:: matlab
+
+   nwb.acquisition.set('ExternalVideos', behavior_external_file);
+
+Static images
+-------------
+
+Static images can be stored in an `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object using the `RGBAImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html>`_, `RGBImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html>`_ or `GrayscaleImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html>`_ classes. All of these image types provide an optional ``description`` parameter to include text description about the image and the ``resolution`` parameter to specify the pixels/cm resolution of the image.
+
+RGBAImage: for color images with transparency
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`RGBAImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBAImage.html>`_ is for storing data of color image with transparency. ``data`` must be 3D where the first and second dimensions represent x and y. The third dimension has length 4 and represents the RGBA value.
+
+.. code-block:: matlab
+
+   image_data = randi(255, [200, 200, 4], 'uint8');
+   
+   rgba_image = types.core.RGBAImage( ...
+       'data', reverseDims(image_data), ...  % required
+       'resolution', 70.0, ...
+       'description', 'RGBA image' ...
+   );
+
+RGBImage: for color images
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`RGBImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RGBImage.html>`_ is for storing data of RGB color image. ``data`` must be 3D where the first and second dimensions represent x and y. The third dimension has length 3 and represents the RGB value.
+
+.. code-block:: matlab
+
+   image_data = randi(255, [200, 200, 3], 'uint8');
+   
+   rgb_image = types.core.RGBImage( ...
+       'data', reverseDims(image_data), ...  % required
+       'resolution', 70.0, ...
+       'description', 'RGB image' ...
+   );
+
+**GrayscaleImage: for grayscale images**
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`GrayscaleImage <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/GrayscaleImage.html>`_ is for storing grayscale image data. ``data`` must be 2D where the first and second dimensions represent x and y.
+
+.. code-block:: matlab
+
+   image_data = randi(255, [200, 200], 'uint8');
+   
+   grayscale_image = types.core.GrayscaleImage( ...
+       'data', image_data, ...  % required
+       'resolution', 70.0, ...
+       'description', 'Grayscale image' ...
+   );
+
+Images: a container for images
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Add these images to an `Images <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html>`_ container that accepts any mix of these image types.
+
+.. code-block:: matlab
+
+   image_collection = types.core.Images( ...
+       'description', 'A collection of images presented to the subject.'...
+   );
+   
+   image_collection.baseimage.set('rgba_image', rgba_image);
+   image_collection.baseimage.set('rgb_image', rgb_image);
+   image_collection.baseimage.set('grayscale_image', grayscale_image);
+   
+   nwb.acquisition.set('ImageCollection', image_collection);
+
+IndexSeries for repeated images
+-------------------------------
+
+If the same images are presented multiple times, storing each copy in an `ImageSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSeries.html>`_ would duplicate data. The `IndexSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html>`_ avoids this by referencing a set of unique images stored in an `Images <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html>`_ container.
+
+The `Images <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Images.html>`_ container holds the unique images, and an `ImageReferences <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html>`_ object defines their order. The ``data`` field of `IndexSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html>`_ is a zero-based index into that order, indicating which image is shown at each time point. ``timestamps`` define when each indexed image was presented.
+
+For example, if the ``data`` property of `IndexSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/IndexSeries.html>`_ is ``[0, 1, 0, 1]``, then the first and third presentations use the first image in `ImageReferences <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageReferences.html>`_, and the second and fourth presentations use the second image:
+
+.. code-block:: matlab
+
+   % Define images that were used for repeated presentations
+   rgb_image = imread('street2.jpg');
+   gray_image = uint8(mean(rgb_image, 3));
+   
+   gs_street = types.core.GrayscaleImage(...
+       'data', gray_image, ...
+       'description', 'Grayscale image of a street.', ...
+       'resolution', 28 ...
+   );
+   rgb_street = types.core.RGBImage( ...
+       'data', reverseDims(rgb_image), ...
+       'resolution', 28, ...
+       'description', 'Color (rgb) image of a street.' ...
+   );
+   
+   % Create an image collection with ordered images
+   image_order = types.core.ImageReferences(...
+       'data', [types.untyped.ObjectView(rgb_street), types.untyped.ObjectView(gs_street)] ...
+   );
+   template_image_collection = types.core.Images( ...
+       'gs_street', gs_street, ...
+       'rgb_street', rgb_street, ...
+       'description', 'A collection of images of a street.', ...
+       'order_of_images', image_order ...
+   );
+   
+   % The image collection is added to the stimulus_templates group
+   nwb.stimulus_templates.set('StreetImages', template_image_collection);
+   
+   street_image_presentation = types.core.IndexSeries(...
+       'description', 'Alternating presentation of color and grayscale versions of an image', ...
+       'data', [0, 1, 0, 1], ... % NOTE: 0-indexed
+       'indexed_images', template_image_collection, ...
+       'timestamps', [0.1, 0.2, 0.3, 0.4] ...
+   )
+
+.. code-block:: text
+
+   street_image_presentation = 
+     IndexSeries with properties:
+   
+            indexed_images: [1x1 types.untyped.SoftLink]
+        indexed_timeseries: []
+        starting_time_unit: 'seconds'
+       timestamps_interval: 1
+           timestamps_unit: 'seconds'
+                      data: [0 1 0 1]
+                 data_unit: 'N/A'
+                  comments: 'no comments'
+                   control: []
+       control_description: ''
+           data_continuity: ''
+           data_conversion: 1
+               data_offset: 0
+           data_resolution: -1
+               description: 'Alternating presentation of color and grayscale versions of an image'
+             starting_time: []
+        starting_time_rate: []
+                timestamps: [0.1000 0.2000 0.3000 0.4000]
+
+.. code-block:: matlab
+
+   nwb.stimulus_presentation.set('StreetImagePresentation', street_image_presentation);
+
+Here *data* contains the (0-indexed) index of the displayed image as they are ordered in the ``ImageReference``.
+
+Writing the images to an NWB File
+---------------------------------
+
+Now use `nwbExport <https://matnwb.readthedocs.io/en/latest/pages/functions/nwbExport.html>`_ to write the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object and all its contents to disk:
+
+.. code-block:: matlab
+
+   nwbExport(nwb, "images_tutorial.nwb");
+
+This creates an NWB file containing the acquired image series, stimulus presentations, static images, and any feature or index series you added.
+
+You can reopen the file with ``nwbRead`` to verify its contents and check that data shapes, timestamps, and metadata are as expected.
diff --git a/docs/source/pages/tutorials/intro.rst b/docs/source/pages/tutorials/intro.rst
index 7ecc01aa8..433c63697 100644
--- a/docs/source/pages/tutorials/intro.rst
+++ b/docs/source/pages/tutorials/intro.rst
@@ -6,14 +6,477 @@ Getting Started with MatNWB
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/intro.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/intro.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/intro.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+Installing MatNWB
+-----------------
+
+Use the code below within the brackets to install MatNWB from source. MatNWB works by automatically creating API classes based on the schema.
+
+.. code-block:: matlab
+
+   %{
+   !git clone https://github.com/NeurodataWithoutBorders/matnwb.git
+   addpath(genpath(pwd));
+   %}
+
+Set up the NWB File
+-------------------
+
+An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ object with those and additional metadata using the `NwbFile <https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html>`_ command. For all MatNWB classes and functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value. Ellipses are used for clarity.
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', 'Mouse5_Day3', ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'general_experimenter', 'Last, First', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: []
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'Last, First'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+-------------------
+
+You can also provide information about your subject in the NWB file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store information such as age, species, genotype, sex, and a freeform description. Then set ``nwb.general_subject`` to the `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object.
+
+.. image:: ../../_static/tutorials/media/intro/image_0.png
+   :class: tutorial-media
+   :width: 159px
+   :alt: image_0.png
+
+Each of these fields is free-form, so any values will be valid, but here are our recommendations:
+
+* For ``age``, we recommend using the `ISO 8601 Duration format <https://en.wikipedia.org/wiki/ISO_8601#Durations>`_
+* For ``species``, we recommend using the formal latin binomal name (e.g. mouse -> *Mus musculus*, human -> *Homo sapiens*)
+* For ``sex``, we recommend using F (female), M (male), U (unknown), and O (other)
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '005', ...
+       'age', 'P90D', ...
+       'description', 'mouse 5', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+   
+   subject
+
+.. code-block:: text
+
+   subject = 
+     Subject with properties:
+   
+                 age: 'P90D'
+       age_reference: 'birth'
+       date_of_birth: []
+         description: 'mouse 5'
+            genotype: ''
+                 sex: 'M'
+             species: 'Mus musculus'
+              strain: ''
+          subject_id: '005'
+              weight: ''
+
+Note: the DANDI archive requires all NWB files to have a subject object with subject_id specified, and strongly encourages specifying the other fields.
+
+Time Series Data
+----------------
+
+`TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ is a common base class for measurements sampled over time, and provides fields for ``data`` and ``timestamps`` (regularly or irregularly sampled). You will also need to supply the ``name`` and ``unit`` of measurement (`SI unit <https://en.wikipedia.org/wiki/International_System_of_Units>`_).
+
+.. image:: ../../_static/tutorials/media/intro/image_1.png
+   :class: tutorial-media
+   :width: 206px
+   :alt: image_1.png
+
+For instance, we can store a `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ data where recording started ``0.0`` seconds after ``start_time`` and sampled every second (1 Hz):
+
+.. code-block:: matlab
+
+   time_series_with_rate = types.core.TimeSeries( ...
+       'description', 'an example time series', ...
+       'data', linspace(0, 100, 10), ...
+       'data_unit', 'm', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 1.0);
+
+For irregularly sampled recordings, we need to provide the ``timestamps`` for the ``data``:
+
+.. code-block:: matlab
+
+   time_series_with_timestamps = types.core.TimeSeries( ...
+       'description', 'an example time series', ...
+       'data', linspace(0, 100, 10), ...
+       'data_unit', 'm', ...
+       'timestamps', linspace(0, 1, 10));
+
+The `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ class serves as the foundation for all other time series types in the NWB format. Several specialized subclasses extend the functionality of `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_, each tailored to handle specific kinds of data. In the next section, we’ll explore one of these specialized types. For a full overview, please check out the `type hierarchy <https://nwb-schema.readthedocs.io/en/latest/format.html#type-hierarchy>`_ in the NWB schema documentation.
+
+Other Types of Time Series
+--------------------------
+
+As mentioned previously, there are many subtypes of `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ in MatNWB that are used to store different kinds of data. One example is `AnnotationSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html>`_, a subclass of `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ that stores text-based records about the experiment. Similar to our `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ example above, we can create an `AnnotationSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/AnnotationSeries.html>`_ object with text information about a stimulus and add it to the stimulus_presentation group in the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_. Below is an example where we create an AnnotationSeries object with annotations for airpuff stimuli and add it to the NWBFile.
+
+.. code-block:: matlab
+
+   % Create an AnnotationSeries object with annotations for airpuff stimuli
+   annotations = types.core.AnnotationSeries( ...
+       'description', 'Airpuff events delivered to the animal', ...
+       'data', {'Left Airpuff', 'Right Airpuff', 'Right Airpuff'}, ...
+       'timestamps', [1.0, 3.0, 8.0] ...
+   );
+   
+   % Add the AnnotationSeries to the NWBFile's stimulus group
+   nwb.stimulus_presentation.set('Airpuffs', annotations)
+
+.. code-block:: text
+
+   ans = 
+     Set with entries:
+   
+       Airpuffs: types.core.AnnotationSeries
+
+Behavior
+--------
+
+SpatialSeries and Position
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Many types of data have special data types in NWB. To store the spatial position of a subject, we will use the `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ and `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ classes.
+
+.. image:: ../../_static/tutorials/media/intro/image_2.png
+   :class: tutorial-media
+   :width: 492px
+   :alt: image_2.png
+
+Note: These diagrams follow a standard convention called "UML class diagram" to express the object-oriented relationships between NWB classes. For our purposes, all you need to know is that an open triangle means "extends" (i.e., is a specialized subtype of), and an open diamond means "is contained within." Learn more about class diagrams on `the wikipedia page <https://en.wikipedia.org/wiki/Class_diagram>`_.
+
+`SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ is a subclass of `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_, a common base class for measurements sampled over time, and provides fields for data and time (regularly or irregularly sampled). Here, we put a `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object called ``'SpatialSeries'`` in a `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object. If the data is sampled at a regular interval, it is recommended to specify the ``starting_time`` and the sampling rate (``starting_time_rate``), although it is still possible to specify ``timestamps`` as in the ``time_series_with_timestamps`` example above.
+
+.. code-block:: matlab
+
+   % create SpatialSeries object
+   spatial_series_ts = types.core.SpatialSeries( ...
+       'data', [linspace(0,10,100); linspace(0,8,100)], ...
+       'reference_frame', '(0,0) is bottom left corner', ...
+       'starting_time', 0, ...
+       'starting_time_rate', 200 ...
+   );
+   
+   % create Position object and add SpatialSeries
+   position = types.core.Position('SpatialSeries', spatial_series_ts);
+
+NWB differentiates between raw,  *acquired* data, which should never change, and  *processed* data, which are the results of preprocessing algorithms and could change. Let's assume that the animal's position was computed from a video tracking algorithm, so it would be classified as processed data. Since processed data can be very diverse, NWB allows us to create processing modules, which are like folders, to store related processed data or data that comes from a single algorithm.
+
+Create a processing module called "behavior" for storing behavioral data in the `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ and add the `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object to the module.
+
+.. code-block:: matlab
+
+   % create processing module
+   behavior_module = types.core.ProcessingModule('description', 'contains behavioral data');
+   
+   % add the Position object (that holds the SpatialSeries object) to the module 
+   % and name the Position object "Position"
+   behavior_module.nwbdatainterface.set('Position', position);
+   
+   % add the processing module to the NWBFile object, and name the processing module "behavior"
+   nwb.processing.set('behavior', behavior_module);
+
+Trials
+~~~~~~
+
+Trials are stored in a `TimeIntervals <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html>`_ object which is a subclass of `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_. `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ objects are used to store tabular metadata throughout NWB, including for trials, electrodes, and sorted units. They offer flexibility for tabular data by allowing required columns, optional columns, and custom columns.
+
+.. image:: ../../_static/tutorials/media/intro/image_3.png
+   :class: tutorial-media
+   :width: 372px
+   :alt: image_3.png
+
+The trials `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_ can be thought of as a table with this structure:
+
+.. image:: ../../_static/tutorials/media/intro/image_4.png
+   :class: tutorial-media
+   :width: 378px
+   :alt: image_4.png
+
+Trials are stored in a `TimeIntervals <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeIntervals.html>`_ object which subclasses `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_. Here, we are adding ``'correct'``, which will be a logical array.
+
+.. code-block:: matlab
+
+   trials = types.core.TimeIntervals( ...
+       'colnames', {'start_time', 'stop_time', 'correct'}, ...
+       'description', 'trial data and properties');
+   
+   trials.addRow('start_time', 0.1, 'stop_time', 1.0, 'correct', false)
+   trials.addRow('start_time', 1.5, 'stop_time', 2.0, 'correct', true)
+   trials.addRow('start_time', 2.5, 'stop_time', 3.0, 'correct', false)
+   
+   trials.toTable() % visualize the table
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - id
+     - start_time
+     - stop_time
+     - correct
+   * - 1
+     - 0
+     - 0.1000
+     - 1
+     - 0
+   * - 2
+     - 1
+     - 1.5000
+     - 2
+     - 1
+   * - 3
+     - 2
+     - 2.5000
+     - 3
+     - 0
+
+.. code-block:: matlab
+
+   nwb.intervals_trials = trials;
+   
+   % If you have multiple trials tables, you will need to use custom names for
+   % each one:
+   nwb.intervals.set('custom_intervals_table_name', trials);
+
+Write
+-----
+
+Now, to write the NWB file that we have built so far:
+
+.. code-block:: matlab
+
+   nwbExport(nwb, 'intro_tutorial.nwb')
+
+We can use the `HDFView <https://www.hdfgroup.org/downloads/hdfview/>`_ application to inspect the resulting NWB file.
+
+.. image:: ../../_static/tutorials/media/intro/image_5.png
+   :class: tutorial-media
+   :width: 483px
+   :alt: image_5.png
+
+Read
+----
+
+We can then read the file back in using MatNWB and inspect its contents.
+
+.. code-block:: matlab
+
+   read_nwbfile = nwbRead('intro_tutorial.nwb', 'ignorecache')
+
+.. code-block:: text
+
+   read_nwbfile = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: [1x1 types.untyped.DataStub]
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: [1x1 types.untyped.DataStub]
+                                  timestamps_reference_time: [1x1 types.untyped.DataStub]
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: [1x1 types.untyped.DataStub]
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: [1x1 types.untyped.DataStub]
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: [1x1 types.core.Subject]
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: [1x1 types.untyped.DataStub]
+                                                  intervals: [1x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: [1x1 types.core.TimeIntervals]
+                                                 processing: [1x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [1x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+We can print the `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ data traversing the hierarchy of objects. The processing module called ``'behavior'`` contains our `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object named ``'Position'``. The `Position <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Position.html>`_ object contains our `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_ object named ``'SpatialSeries'``.
+
+.. code-block:: matlab
+
+   read_spatial_series = read_nwbfile.processing.get('behavior'). ...
+       nwbdatainterface.get('Position').spatialseries.get('SpatialSeries')
+
+.. code-block:: text
+
+   read_spatial_series = 
+     SpatialSeries with properties:
+   
+           reference_frame: '(0,0) is bottom left corner'
+        starting_time_unit: 'seconds'
+       timestamps_interval: 1
+           timestamps_unit: 'seconds'
+                      data: [1x1 types.untyped.DataStub]
+                 data_unit: 'meters'
+                  comments: 'no comments'
+                   control: []
+       control_description: ''
+           data_continuity: ''
+           data_conversion: 1
+               data_offset: 0
+           data_resolution: -1
+               description: 'no description'
+             starting_time: 0
+        starting_time_rate: 200
+                timestamps: []
+
+Reading Data
+~~~~~~~~~~~~
+
+Counter to normal MATLAB workflow, data arrays are read passively from the file. Calling ``read_spatial_series.data`` does not read the data values, but presents a ``DataStub`` object that can be indexed to read data.
+
+.. code-block:: matlab
+
+   read_spatial_series.data
+
+.. code-block:: text
+
+   ans = 
+     DataStub with properties:
+   
+       filename: 'intro_tutorial.nwb'
+           path: '/processing/behavior/Position/SpatialSeries/data'
+           dims: [2 100]
+          ndims: 2
+       dataType: 'double'
+
+This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access all the data in the matrix using the ``load`` method with no arguments.
+
+.. code-block:: matlab
+
+   read_spatial_series.data.load
+
+.. code-block:: text
+
+   ans = 2x100
+            0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091    1.0101    1.1111    1.2121    1.3131    1.4141    1.5152    1.6162    1.7172    1.8182    1.9192    2.0202    2.1212    2.2222    2.3232    2.4242    2.5253    2.6263    2.7273    2.8283    2.9293    3.0303    3.1313    3.2323    3.3333    3.4343    3.5354    3.6364    3.7374    3.8384    3.9394    4.0404    4.1414    4.2424    4.3434    4.4444    4.5455    4.6465    4.7475    4.8485    4.9495
+            0    0.0808    0.1616    0.2424    0.3232    0.4040    0.4848    0.5657    0.6465    0.7273    0.8081    0.8889    0.9697    1.0505    1.1313    1.2121    1.2929    1.3737    1.4545    1.5354    1.6162    1.6970    1.7778    1.8586    1.9394    2.0202    2.1010    2.1818    2.2626    2.3434    2.4242    2.5051    2.5859    2.6667    2.7475    2.8283    2.9091    2.9899    3.0707    3.1515    3.2323    3.3131    3.3939    3.4747    3.5556    3.6364    3.7172    3.7980    3.8788    3.9596
+
+If you only need a section of the data, you can read only that section by indexing the ``DataStub`` object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
+
+.. code-block:: matlab
+
+   read_spatial_series.data(:, 1:10)
+
+.. code-block:: text
+
+   ans = 2x10
+            0    0.1010    0.2020    0.3030    0.4040    0.5051    0.6061    0.7071    0.8081    0.9091
+            0    0.0808    0.1616    0.2424    0.3232    0.4040    0.4848    0.5657    0.6465    0.7273
+
+Next Steps
+----------
+
+This concludes the introductory tutorial. Please proceed to one of the specialized tutorials, which are designed to follow this one.
+
+* `Extracellular electrophysiology <ecephys>`_
+* `Intracellular electrophysiology <icephys>`_
+* `Optical physiology <ophys>`_
+
+See the `API documentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html>`_ to learn what data types are available.
diff --git a/docs/source/pages/tutorials/ogen.rst b/docs/source/pages/tutorials/ogen.rst
index 58e29ab9c..712fa9d55 100644
--- a/docs/source/pages/tutorials/ogen.rst
+++ b/docs/source/pages/tutorials/ogen.rst
@@ -6,14 +6,203 @@ Optogenetics
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/ogen.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/ogen.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/ogen.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This tutorial will demonstrate how to write optogenetics data.
+
+Creating an NWBFile object
+--------------------------
+
+When creating a NWB file, the first step is to create the ``NWBFile`` object using `NwbFile <https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html>`_.
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', char(java.util.UUID.randomUUID), ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'general_experimenter', 'Last, First M.', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', 'DOI:10.1016/j.neuron.2016.12.011'); % optional
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: []
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'Last, First M.'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: 'DOI:10.1016/j.neuron.2016.12.011'
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '001', ...
+       'age', 'P90D', ...
+       'description', 'mouse 1', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+Adding optogenetic data
+-----------------------
+
+The  ``ogen`` module contains two data types that you will need to write optogenetics data, `OptogeneticStimulusSite <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html>`_, which contains metadata about the stimulus site, and `OptogeneticSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html>`_, which contains the values of the time series.
+
+First, you need to create a `Device <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html>`_ object linked to the ``NWBFile``:
+
+.. code-block:: matlab
+
+   device = types.core.Device();
+   nwb.general_devices.set('Device', device);
+
+Now, you can create and add an `OptogeneticStimulusSite <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html>`_.
+
+.. code-block:: matlab
+
+   ogen_stim_site = types.core.OptogeneticStimulusSite( ...
+       'device', types.untyped.SoftLink(device), ...
+       'description', 'This is an example optogenetic site.', ...
+       'excitation_lambda', 600.0, ...
+       'location', 'VISrl');
+   
+   nwb.general_optogenetics.set('OptogeneticStimulusSite', ogen_stim_site);
+
+With the `OptogeneticStimulusSite <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html>`_ added, you can now create and add a `OptogeneticSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html>`_. Here, we will generate some random data and specify the timing using ``rate``. If you have samples at irregular intervals, you should use ``timestamps`` instead.
+
+.. code-block:: matlab
+
+   ogen_series = types.core.OptogeneticSeries( ...
+       'data', randn(20, 1), ...
+       'site', types.untyped.SoftLink(ogen_stim_site), ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 30.0);  % Hz
+   nwb.stimulus_presentation.set('OptogeneticSeries', ogen_series);
+   
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: []
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [1x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'Last, First M.'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [1x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: 'DOI:10.1016/j.neuron.2016.12.011'
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: [1x1 types.core.Subject]
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [1x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Now you can write the NWB file.
+
+.. code-block:: matlab
+
+   nwbExport(nwb, 'ogen_tutorial.nwb');
diff --git a/docs/source/pages/tutorials/ophys.rst b/docs/source/pages/tutorials/ophys.rst
index 198be9bbe..46d966684 100644
--- a/docs/source/pages/tutorials/ophys.rst
+++ b/docs/source/pages/tutorials/ophys.rst
@@ -6,16 +6,521 @@ Calcium Imaging 🎬
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/ophys.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/ophys.html
-   :alt: View full page
+   :alt: View rendered Live Script
 .. image:: https://img.shields.io/badge/View-Youtube-red
    :target: https://www.youtube.com/watch?v=OBidHdocnTc&ab_channel=NeurodataWithoutBorders
    :alt: View tutorial on YouTube
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/ophys.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+Introduction
+------------
+
+In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:
+
+* Acquired two-photon images
+* Image segmentation (ROIs)
+* Fluorescence and dF/F response
+
+It is recommended to first work through the `Introduction to MatNWB tutorial <intro>`_, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.
+
+**Please note**: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: `MatNWB <-> HDF5 Dimension Mapping <dimensionMapNoDataPipes>`_.
+
+Set up the NWB file
+-------------------
+
+An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new ``NWBFile`` object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.
+
+.. code-block:: matlab
+
+   nwb = NwbFile( ...
+       'session_description', 'mouse in open exploration',...
+       'identifier', 'Mouse5_Day3', ...
+       'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
+       'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
+       'general_experimenter', 'LastName, FirstName', ... % optional
+       'general_session_id', 'session_1234', ... % optional
+       'general_institution', 'University of My Institution', ... % optional
+       'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
+   nwb
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.9.0'
+                                           file_create_date: []
+                                                 identifier: 'Mouse5_Day3'
+                                        session_description: 'mouse in open exploration'
+                                         session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
+                                  timestamps_reference_time: {[2018-04-25T03:00:45.000000+02:00]}
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: ''
+                                            general_devices: [0x1 types.untyped.Set]
+                                     general_devices_models: [0x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: 'LastName, FirstName'
+                                general_extracellular_ephys: [0x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: []
+                                        general_institution: 'University of My Institution'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: ''
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
+                                         general_session_id: 'session_1234'
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: ''
+                                            general_subject: []
+                                            general_surgery: ''
+                                              general_virus: ''
+                                   general_was_generated_by: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: []
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: []
+
+Subject Information
+~~~~~~~~~~~~~~~~~~~
+
+It is recommended to store information about the experimental subject in the file. Create a `Subject <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html>`_ object to store metadata about the subject, then assign it to ``nwb.general_subject``.
+
+.. code-block:: matlab
+
+   subject = types.core.Subject( ...
+       'subject_id', '005', ...
+       'age', 'P90D', ...
+       'description', 'mouse 5', ...
+       'species', 'Mus musculus', ...
+       'sex', 'M' ...
+   );
+   nwb.general_subject = subject;
+
+Optical Physiology
+------------------
+
+Optical physiology results are written in four steps:
+
+1. Create imaging plane
+2. Acquired two-photon images
+3. Image segmentation
+4. Fluorescence and dF/F responses
+
+Imaging Plane
+~~~~~~~~~~~~~
+
+First, you must create an `ImagingPlane <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html>`_ object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a `Device <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html>`_ object for the microscope and an `OpticalChannel <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html>`_ object. Then you can create an `ImagingPlane <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html>`_.
+
+.. image:: ../../_static/tutorials/media/ophys/image_0.png
+   :class: tutorial-media
+   :width: 527px
+   :alt: image_0.png
+
+Create a `Device <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html>`_ representing a two-photon microscope. The fields ``description``, ``manufacturer``, ``model_number``, ``model_name``, and ``serial_number`` are optional, but recommended. Then create an `OpticalChannel <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html>`_ and add both of these to the `ImagingPlane <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html>`_.
+
+.. code-block:: matlab
+
+   device = types.core.Device( ...
+       'description', 'My two-photon microscope', ...
+       'manufacturer', 'Loki Labs', ...
+       'model_number', 'ABC-123', ...
+       'model_name', 'Loki 1.0', ...
+       'serial_number', '1234567890');
+   
+   % Add device to nwb object
+   nwb.general_devices.set('Device', device);
+   
+   optical_channel = types.core.OpticalChannel( ...
+       'description', 'description', ...
+       'emission_lambda', 500.);
+   
+   imaging_plane_name = 'imaging_plane';
+   imaging_plane = types.core.ImagingPlane( ...
+       'optical_channel', optical_channel, ...
+       'description', 'a very interesting part of the brain', ...
+       'device', types.untyped.SoftLink(device), ...
+       'excitation_lambda', 600., ...
+       'imaging_rate', 5., ...
+       'indicator', 'GFP', ...
+       'location', 'Primary visual area');
+   
+   nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);
+
+Storing Two-Photon Data
+~~~~~~~~~~~~~~~~~~~~~~~
+
+You can create a `TwoPhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html>`_ class representing two photon imaging data. `TwoPhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html>`_, like `SpatialSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html>`_, inherits from `TimeSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html>`_ and is similar in behavior to `OnePhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html>`_.
+
+.. image:: ../../_static/tutorials/media/ophys/image_1.png
+   :class: tutorial-media
+   :width: 669px
+   :alt: image_1.png
+
+.. code-block:: matlab
+
+   InternalTwoPhoton = types.core.TwoPhotonSeries( ...
+       'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 3.0, ...
+       'data', ones(200, 100, 1000), ...
+       'data_unit', 'lumens');
+   
+   nwb.acquisition.set('2pInternal', InternalTwoPhoton);
+
+Storing One-Photon Data
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Now that we have our `ImagingPlane <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html>`_, we can create a `OnePhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html>`_ object to store raw one-photon imaging data.
+
+.. code-block:: matlab
+
+   % using internal data. this data will be stored inside the NWB file
+   InternalOnePhoton = types.core.OnePhotonSeries( ...
+       'data', ones(100, 100, 1000), ... 
+       'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
+       'starting_time', 0., ...
+       'starting_time_rate', 1.0, ...
+       'data_unit', 'normalized amplitude' ...
+   );
+   nwb.acquisition.set('1pInternal', InternalOnePhoton);
+
+Motion Correction (optional)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can also store the result of motion correction using a `MotionCorrection <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html>`_ object, a container type that can hold one or more `CorrectedImageStack <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html>`_ objects.
+
+.. code-block:: matlab
+
+   % Create the corrected ImageSeries
+   corrected = types.core.ImageSeries( ...
+       'description', 'A motion corrected image stack', ...
+       'data', ones(100, 100, 1000), ...  % 3D data array
+       'data_unit', 'n/a', ...
+       'format', 'raw', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 1.0 ...
+   );
+   
+   % Create the xy_translation TimeSeries
+   xy_translation = types.core.TimeSeries( ...
+       'description', 'x,y translation in pixels', ...
+       'data', ones(2, 1000), ...  % 2D data array
+       'data_unit', 'pixels', ...
+       'starting_time', 0.0, ...
+       'starting_time_rate', 1.0 ...
+   );
+   
+   % Create the CorrectedImageStack
+   corrected_image_stack = types.core.CorrectedImageStack( ...
+       'corrected', corrected, ...
+       'original', types.untyped.SoftLink(InternalOnePhoton), ... % Ensure `InternalOnePhoton` exists
+       'xy_translation', xy_translation ...
+   );
+   
+   % Create the MotionCorrection object
+   motion_correction = types.core.MotionCorrection();
+   motion_correction.correctedimagestack.set('CorrectedImageStack', corrected_image_stack);
+
+The motion corrected data is considered processed data and will be added to the ``processing`` field of the ``nwb`` object using a `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ called "``ophys``". First, create the `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ object and then add the ``motion_correction`` object to it, naming it "``MotionCorrection``".
+
+.. code-block:: matlab
+
+   ophys_module = types.core.ProcessingModule( ...
+       'description', 'Contains optical physiology data');
+   ophys_module.nwbdatainterface.set('MotionCorrection', motion_correction);
+
+Finally, add the "ophys" `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ to the ``nwb`` (Note that we can continue adding objects to the "ophys" `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ without needing to explicitly update the nwb):
+
+.. code-block:: matlab
+
+   nwb.processing.set('ophys', ophys_module);
+
+Plane Segmentation
+~~~~~~~~~~~~~~~~~~
+
+Image segmentation stores the detected regions of interest in the `TwoPhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html>`_ data. `ImageSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html>`_ allows you to have more than one segmentation by creating more `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ objects.
+
+.. image:: ../../_static/tutorials/media/ophys/image_2.png
+   :class: tutorial-media
+   :width: 958px
+   :alt: image_2.png
+
+Regions of interest (ROIs)
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ROIs can be added to a `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ either as an image_mask **or** as a pixel_mask. An image mask is an array that is the same size as a single frame of the `TwoPhotonSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html>`_, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a `ragged array <https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays>`_ and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ object accordingly.
+
+.. code-block:: matlab
+
+   selection = "Create Image Mask"; % "Create Image Mask" or "Create Pixel Mask"
+   
+   % generate fake image_mask data
+   imaging_shape = [100, 100];
+   x = imaging_shape(1);
+   y = imaging_shape(2);
+   
+   n_rois = 20;
+   image_mask = zeros(y, x, n_rois);
+   center = randi(90,2,n_rois);
+   for i = 1:n_rois
+       image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
+   end
+   
+   if selection == "Create Pixel Mask"
+       ind = find(image_mask);
+       [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);
+   
+       pixel_mask_struct = struct();
+       pixel_mask_struct.x = uint32(x_ind); % Add x coordinates to struct field x
+       pixel_mask_struct.y = uint32(y_ind); % Add y coordinates to struct field y
+       pixel_mask_struct.weight = single(ones(size(x_ind))); 
+       
+       % Create pixel mask vector data
+       pixel_mask = types.hdmf_common.VectorData(...
+               'data', struct2table(pixel_mask_struct), ...
+               'description', 'pixel masks');
+   
+       % When creating a pixel mask, it is also necessary to specify a
+       % pixel_mask_index vector. See the documentation for ragged arrays linked
+       % above to learn more.
+       num_pixels_per_roi = zeros(n_rois, 1); % Column vector
+       for i_roi = 1:n_rois
+           num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);
+       end
+   
+       pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); % Note: Use an integer 
+       % type that can accommodate the maximum value of the cumulative sum
+   
+       % Create pixel_mask_index vector
+       pixel_mask_index = types.hdmf_common.VectorIndex(...
+               'description', 'Index into pixel_mask VectorData', ...
+               'data', pixel_mask_index, ...
+               'target', types.untyped.ObjectView(pixel_mask) );
+   
+       plane_segmentation = types.core.PlaneSegmentation( ...
+           'colnames', {'pixel_mask'}, ...
+           'description', 'roi pixel position (x,y) and pixel weight', ...
+           'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
+           'pixel_mask_index', pixel_mask_index, ...
+           'pixel_mask', pixel_mask ...
+       );
+   
+   else % selection == "Create Image Mask"
+       plane_segmentation = types.core.PlaneSegmentation( ...
+           'colnames', {'image_mask'}, ...
+           'description', 'output from segmenting my favorite imaging plane', ...
+           'imaging_plane', types.untyped.SoftLink(imaging_plane), ...
+           'image_mask', types.hdmf_common.VectorData(...
+               'data', image_mask, ...
+               'description', 'image masks') ...
+       );
+   end
+
+Adding ROIs to NWB file
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Now create an `ImageSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html>`_ object and put the ``plane_segmentation`` object inside of it, naming it "``PlaneSegmentation"``.
+
+.. code-block:: matlab
+
+   img_seg = types.core.ImageSegmentation();
+   img_seg.planesegmentation.set('PlaneSegmentation', plane_segmentation);
+
+Add the ``img_seg`` object to the "ophys" `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ we created before, naming it "``ImageSegmentation``".
+
+.. code-block:: matlab
+
+   ophys_module.nwbdatainterface.set('ImageSegmentation', img_seg);
+
+Storing fluorescence of ROIs over time
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Now that ROIs are stored, you can store fluorescence data for these regions of interest. This type of data is stored using the `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ class.
+
+.. image:: ../../_static/tutorials/media/ophys/image_3.png
+   :class: tutorial-media
+   :width: 958px
+   :alt: image_3.png
+
+To create a `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ object, we will need to reference a set of rows from the `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a `DynamicTableRegion <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html>`_, which is a type of link that allows you to reference specific rows of a `DynamicTable <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html>`_, such as a `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ table by row indices.
+
+First, we create a `DynamicTableRegion <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html>`_ that references the ROIs of the `PlaneSegmentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html>`_ table.
+
+.. code-block:: matlab
+
+   roi_table_region = types.hdmf_common.DynamicTableRegion( ...
+       'table', types.untyped.ObjectView(plane_segmentation), ...
+       'description', 'all_rois', ...
+       'data', (0:n_rois-1)');
+
+Then we create a `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ object to store fluorescence data for those ROIs.
+
+.. code-block:: matlab
+
+   roi_response_series = types.core.RoiResponseSeries( ...
+       'rois', roi_table_region, ...
+       'data', NaN(n_rois, 100), ... % [nRoi, nT]
+       'data_unit', 'lumens', ...
+       'starting_time_rate', 3.0, ...
+       'starting_time', 0.0);
+
+To help data analysis and visualization tools know that this `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ object represents fluorescence data, we will store the `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ object inside of a `Fluorescence <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html>`_ object. Then we add the `Fluorescence <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html>`_ object into the same `ProcessingModule <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html>`_ named ``"ophys"`` that we created earlier.
+
+.. code-block:: matlab
+
+   fluorescence = types.core.Fluorescence();
+   fluorescence.roiresponseseries.set('RoiResponseSeries', roi_response_series);
+   
+   ophys_module.nwbdatainterface.set('Fluorescence', fluorescence);
+
+**Tip**: If you want to store dF/F data instead of fluorescence data, then store the `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html>`_ object in a `DfOverF <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html>`_ object, which works the same way as the `Fluorescence <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html>`_ class.
+
+Writing the NWB file
+--------------------
+
+.. code-block:: matlab
+
+   nwb_file_name = 'ophys_tutorial.nwb';
+   if isfile(nwb_file_name); delete(nwb_file_name); end
+   nwbExport(nwb, nwb_file_name);
+
+Reading the NWB file
+--------------------
+
+.. code-block:: matlab
+
+   read_nwb = nwbRead(nwb_file_name, 'ignorecache');
+
+Data arrays are read passively from the file. Calling ``TimeSeries.data`` does not read the data values, but presents an HDF5 object that can be indexed to read data.
+
+.. code-block:: matlab
+
+   read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence')...
+       .roiresponseseries.get('RoiResponseSeries').data
+
+.. code-block:: text
+
+   ans = 
+     DataStub with properties:
+   
+       filename: 'ophys_tutorial.nwb'
+           path: '/processing/ophys/Fluorescence/RoiResponseSeries/data'
+           dims: [20 100]
+          ndims: 2
+       dataType: 'double'
+
+This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the ``load`` method.
+
+``load`` with no input arguments reads the entire dataset:
+
+.. code-block:: matlab
+
+   read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
+       roiresponseseries.get('RoiResponseSeries').data.load
+
+.. code-block:: text
+
+   ans = 20x100
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+
+If all you need is a section of the data, you can read only that section by indexing the ``DataStub`` object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
+
+.. code-block:: matlab
+
+   read_nwb.processing.get('ophys'). ...
+       nwbdatainterface.get('Fluorescence'). ...
+       roiresponseseries.get('RoiResponseSeries'). ...
+       data(1:5, 1:10)
+
+.. code-block:: text
+
+   ans = 5x10
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+
+Finally, read back the image/pixel masks and display the first roi in a figure:
+
+.. code-block:: matlab
+
+   plane_segmentation = read_nwb.processing.get('ophys'). ...
+       nwbdatainterface.get('ImageSegmentation'). ...
+       planesegmentation.get('PlaneSegmentation');
+   
+   if ~isempty(plane_segmentation.image_mask)
+       roi_mask = plane_segmentation.image_mask.data(:,:,1);
+   elseif ~isempty(plane_segmentation.pixel_mask)
+       row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
+       pixel_mask = row.pixel_mask{1};
+       roi_mask = zeros(imaging_shape);
+       ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
+       roi_mask(ind) = pixel_mask.weight;
+   end    
+   imshow(roi_mask)
+
+.. image:: ../../_static/tutorials/media/ophys/figure_0.png
+   :class: tutorial-media
+   :alt: figure_0.png
+
+Learn more!
+-----------
+
+See the `API documentation <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html>`_ to learn what data types are available.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Other MatNWB tutorials
+----------------------
+
+* `Extracellular electrophysiology <ecephys>`_
+* `Intracellular electrophysiology <icephys>`_
+
+Python tutorials
+----------------
+
+See our tutorials for more details about your data type:
+
+* `Extracellular electrophysiology <https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py>`_
+* `Calcium imaging <https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py>`_
+* `Intracellular electrophysiology <https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology>`_
+
+**Check out other tutorials that teach advanced NWB topics:**
+
+* `Iterative data write <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py>`_
+* `Extensions <https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py>`_
+* `Advanced HDF5 I/O <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py>`_
diff --git a/docs/source/pages/tutorials/read_demo.rst b/docs/source/pages/tutorials/read_demo.rst
index c3844e715..7adda8d65 100644
--- a/docs/source/pages/tutorials/read_demo.rst
+++ b/docs/source/pages/tutorials/read_demo.rst
@@ -6,14 +6,357 @@ Reading NWB Files with MatNWB
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/read_demo.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/read_demo.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/read_demo.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+Authors: Ryan Ly, with modification by Lawrence Niu
+
+Last Updated: 2023-09-05
+
+Introduction
+------------
+
+In this tutorial, we will read single neuron spiking data that is in the NWB standard format and do a basic visualization of the data. More thorough documentation regarding reading files as well as the ``NwbFile`` class, can be found in the `NWB Overview Documentation <https://nwb-overview.readthedocs.io/en/latest/file_read/file_read.html#reading-with-matnwb>`_
+
+Download the data
+-----------------
+
+First, let's download an NWB data file from the `DANDI neurophysiology data archive <https://dandiarchive.org/>`_.
+
+A NWB file represents a single session of an experiment. It contains all the data of that session and the metadata required to understand the data.
+
+We will use data from one session of an experiment by `Chandravadia et al. (2020) <https://www.nature.com/articles/s41597-020-0415-9>`_, where the authors recorded single neuron electrophysiological activity from the medial temporal lobes of human subjects while they performed a visual recognition memory task.
+
+1. Go to the DANDI page for this dataset: `https://dandiarchive.org/dandiset/000004/draft <https://dandiarchive.org/dandiset/000004/draft>`_
+2. Toward the top middle of the page, click the "Files" button.
+
+   .. image:: ../../_static/tutorials/media/read_demo/image_0.png
+      :class: tutorial-media
+      :width: 961px
+      :alt: image_0.png
+3. Click on the folder "sub-P11MHM" (click the folder name, not the checkbox).
+
+   .. image:: ../../_static/tutorials/media/read_demo/image_1.png
+      :class: tutorial-media
+      :width: 960px
+      :alt: image_1.png
+4. Then click on the download symbol to the right of the filename "sub-P11HMH_ses-20061101_ecephys+image.nwb" to download the data file (69 MB) to your computer.
+
+   .. image:: ../../_static/tutorials/media/read_demo/image_2.png
+      :class: tutorial-media
+      :width: 960px
+      :alt: image_2.png
+
+Installing matnwb
+-----------------
+
+Use the code below to install MatNWB from source using ``git``. Ensure ``git`` is on your path before running this line.
+
+.. code-block:: matlab
+
+   if ~exist('nwbRead', 'file') % Skip if MatNWB is on MATLAB's search path
+       !git clone https://github.com/NeurodataWithoutBorders/matnwb.git
+       % add the path to matnwb and generate the core classes
+       addpath('matnwb');
+   end
+
+MatNWB works by automatically creating API classes based on the schema. For most NWB files, the classes are generated automatically by calling ``nwbRead`` farther down. This particular NWB file was created before this feature was supported, so we must ensure that these classes for the correct schema versions are properly generated before attempting to read from the file.
+
+.. code-block:: matlab
+
+   % Reminder: YOU DO NOT NORMALLY NEED TO CALL THIS FUNCTION. Only attempt this 
+   % method if you encounter read errors.
+   generateCore(util.getSchemaVersion('sub-P11HMH_ses-20061101_ecephys+image.nwb'));
+
+Read the NWB file
+-----------------
+
+You can read any NWB file using ``nwbRead``. You will find that the print out for this shows a summary of the data within.
+
+.. code-block:: matlab
+
+   % ignorecache informs the `nwbRead` call to not generate files by default. Since 
+   % we have already done this, we can skip this automated step when reading. If 
+   % you are reading the file before generating, you can omit this argument flag.
+   nwb = nwbRead('sub-P11HMH_ses-20061101_ecephys+image.nwb', 'ignorecache')
+
+.. code-block:: text
+
+   nwb = 
+     NwbFile with properties:
+   
+                                   nwb_version: '2.1.0'
+                              file_create_date: [1x1 types.untyped.DataStub]
+                               general_devices: [1x1 types.untyped.Set]
+                                    identifier: 'H11_9'
+                           session_description: 'New/Old recognition task for ID: 9. '
+                            session_start_time: 2006-11-01T00:00:00.000000-07:00
+                     timestamps_reference_time: 2006-11-01T00:00:00.000000-07:00
+                                   acquisition: [2x1 types.untyped.Set]
+                                      analysis: [0x1 types.untyped.Set]
+                                       general: [0x1 types.untyped.Set]
+                       general_data_collection: 'learning: 80, recognition: 81'
+                general_experiment_description: 'The data contained within this file describes a new/old recogntion task performed in patients with intractable epilepsy implanted with depth electrodes and Behnke-Fried microwires in the human Medical Temporal Lobe (MTL).'
+                          general_experimenter: ''
+                   general_extracellular_ephys: [9x1 types.untyped.Set]
+        general_extracellular_ephys_electrodes: [1x1 types.core.DynamicTable]
+                           general_institution: 'Hunigton Memorial Hospital'
+                   general_intracellular_ephys: [0x1 types.untyped.Set]
+         general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_sweep_table: []
+                              general_keywords: [1x1 types.untyped.DataStub]
+                                   general_lab: 'Rutishauser'
+                                 general_notes: ''
+                          general_optogenetics: [0x1 types.untyped.Set]
+                        general_optophysiology: [0x1 types.untyped.Set]
+                          general_pharmacology: ''
+                              general_protocol: ''
+                  general_related_publications: [1x1 types.untyped.DataStub]
+                            general_session_id: ''
+                                general_slices: ''
+                         general_source_script: ''
+               general_source_script_file_name: ''
+                              general_stimulus: ''
+                               general_subject: [1x1 types.core.Subject]
+                               general_surgery: ''
+                                 general_virus: ''
+                                     intervals: [0x1 types.untyped.Set]
+                              intervals_epochs: []
+                       intervals_invalid_times: []
+                              intervals_trials: [1x1 types.core.TimeIntervals]
+                                    processing: [0x1 types.untyped.Set]
+                                       scratch: [0x1 types.untyped.Set]
+                         stimulus_presentation: [1x1 types.untyped.Set]
+                            stimulus_templates: [0x1 types.untyped.Set]
+                                         units: [1x1 types.core.Units]
+
+You can also use ``util.nwbTree`` to actively explore the NWB file.
+
+.. code-block:: matlab
+
+   util.nwbTree(nwb);
+
+.. image:: ../../_static/tutorials/media/read_demo/figure_0.png
+   :class: tutorial-media
+   :alt: figure_0.png
+
+Stimulus
+~~~~~~~~
+
+Now lets take a look at the visual stimuli presented to the subject. They will be in ``nwb.stimulus_presentation``
+
+.. code-block:: matlab
+
+   nwb.stimulus_presentation
+
+.. code-block:: text
+
+   ans = 
+     Set with properties:
+   
+       StimulusPresentation: [types.core.OpticalSeries]
+
+This results shows us that ``nwb.stimulus_presentation`` is a ``Set`` object that contains a single data object called ``StimulusPresentation``, which is an ``OpticalSeries`` neurodata type. Use the ``get`` method to return this ``OpticalSeries``. ``Set`` objects store a collection of other NWB objects.
+
+.. code-block:: matlab
+
+   nwb.stimulus_presentation.get('StimulusPresentation')
+
+.. code-block:: text
+
+   ans = 
+     OpticalSeries with properties:
+   
+                           distance: 0.7000
+                      field_of_view: [1x1 types.untyped.DataStub]
+                        orientation: 'lower left'
+                          dimension: [1x1 types.untyped.DataStub]
+                      external_file: ''
+       external_file_starting_frame: []
+                             format: 'raw'
+                 starting_time_unit: 'seconds'
+                timestamps_interval: 1
+                    timestamps_unit: 'seconds'
+                               data: [1x1 types.untyped.DataStub]
+                          data_unit: 'meters'
+                           comments: 'no comments'
+                            control: []
+                control_description: ''
+                    data_conversion: 1
+                    data_resolution: -1
+                        description: 'no description'
+                      starting_time: []
+                 starting_time_rate: []
+                         timestamps: [1x1 types.untyped.DataStub]
+
+``OpticalSeries`` is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the ``OpticalSeries`` object named ``StimulusPresentation``. The images are stored in ``StimulusPresentation.data``
+
+.. code-block:: matlab
+
+   StimulusImageData = nwb.stimulus_presentation.get('StimulusPresentation').data
+
+.. code-block:: text
+
+   StimulusImageData = 
+     DataStub with properties:
+   
+       filename: 'sub-P11HMH_ses-20061101_ecephys+image.nwb'
+           path: '/stimulus/presentation/StimulusPresentation/data'
+           dims: [3 300 400 200]
+          ndims: 4
+       dataType: 'uint8'
+
+When calling a data object directly, the data is not read but instead a ``DataStub`` is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any ``DataStub`` as you would any MATLAB matrix.
+
+.. code-block:: matlab
+
+   % get the image and display it
+   % the dimension order is provided as follows:
+   % [rgb, y, x, image index]
+   img = StimulusImageData(1:3, 1:300, 1:400, 32);
+
+A bit of manipulation allows us to display the image using MATLAB's ``imshow``.
+
+.. code-block:: matlab
+
+   img = permute(img,[3, 2, 1]);  % fix orientation
+   img = flip(img, 3); % reverse color order
+   F = figure();
+   imshow(img, 'InitialMagnification', 'fit');
+   daspect([3, 5, 5]);
+
+.. image:: ../../_static/tutorials/media/read_demo/figure_1.png
+   :class: tutorial-media
+   :alt: figure_1.png
+
+To read an entire dataset, use the ``DataStub.load`` method without any input arguments. We will use this approach to read all of the image display timestamps into memory.
+
+.. code-block:: matlab
+
+   stimulus_times = nwb.stimulus_presentation.get('StimulusPresentation').timestamps.load();
+
+Quick PSTH and raster
+---------------------
+
+Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.
+
+First, let us show the first row of the NWB Units table representing the first unit.
+
+.. code-block:: matlab
+
+   nwb.units.getRow(1)
+
+.. list-table::
+   :header-rows: 1
+
+   * - 
+     - origClusterID
+     - waveform_mean_encoding
+     - waveform_mean_recognition
+     - IsolationDist
+     - SNR
+     - waveform_mean_sampling_rate
+     - spike_times
+     - electrodes
+   * - 1
+     - 1102
+     - 256x1 double
+     - 256x1 double
+     - 11.2917
+     - 1.4407
+     - 98400
+     - 373x1 double
+     - 0
+
+Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time.
+
+.. code-block:: matlab
+
+   %% Align spikes by stimulus presentations
+   
+   unit_ind =8;
+   before =1;
+   after =3;
+
+``getRow`` provides a convenient method for reading this data out.
+
+.. code-block:: matlab
+
+   unit_spikes = nwb.units.getRow(unit_ind, 'columns', {'spike_times'}).spike_times{1}
+
+.. code-block:: text
+
+   unit_spikes = 2116x1
+   1.0e+03 *
+   
+       5.9338
+       5.9343
+       5.9346
+       5.9358
+       5.9364
+       5.9375
+       6.0772
+       6.0776
+       6.0797
+       6.0798
+
+Spike times from this unit are aligned to each stimulus time and compiled in a cell array
+
+.. code-block:: matlab
+
+   results = cell(1, length(stimulus_times));
+   for itime = 1:length(stimulus_times)
+       stimulus_time = stimulus_times(itime);
+       spikes = unit_spikes - stimulus_time;
+       spikes = spikes(spikes > -before);
+       spikes = spikes(spikes < after);
+       results{itime} = spikes;
+   end
+
+Plot results
+------------
+
+Finally, here is a (slightly sloppy) peri-stimulus time histogram
+
+.. code-block:: matlab
+
+   figure();
+   hold on
+   for i = 1:length(results)
+       spikes = results{i};
+       yy = ones(length(spikes)) * i;
+   
+       plot(spikes, yy, 'k.');
+   end
+   hold off
+   ylabel('trial');
+   xlabel('time (s)');
+   axis('tight')
+
+.. image:: ../../_static/tutorials/media/read_demo/figure_2.png
+   :class: tutorial-media
+   :alt: figure_2.png
+
+.. code-block:: matlab
+
+   figure();
+   all_spikes = cat(1, results{:});
+   histogram(all_spikes, 30);
+   ylabel('count')
+   xlabel('time (s)');
+   axis('tight')
+
+.. image:: ../../_static/tutorials/media/read_demo/figure_3.png
+   :class: tutorial-media
+   :alt: figure_3.png
+
+Conclusion
+----------
+
+This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find `here <https://github.com/rutishauserlab/recogmem-release-NWB>`_. For more datasets, or to publish your own NWB data for free, check out the DANDI archive `here <http://dandiarchive.org/>`_. Also, make sure to check out the DANDI breakout session later in this event.
diff --git a/docs/source/pages/tutorials/read_demo_dandihub.rst b/docs/source/pages/tutorials/read_demo_dandihub.rst
index af0a6eb52..48d611860 100644
--- a/docs/source/pages/tutorials/read_demo_dandihub.rst
+++ b/docs/source/pages/tutorials/read_demo_dandihub.rst
@@ -6,14 +6,173 @@ Reading NWB Files with MatNWB on DandiHub
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/read_demo_dandihub.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/read_demo_dandihub.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/read_demo_dandihub.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+Authors: Ryan Ly, with modification by Lawrence Niu
+
+DandiHub edition* authors: Thomas Kuenzel & Vijay Iyer
+
+Last Updated: 2023-09-05
+
+(*) minimally modified to utilize ``dandi`` package for download and to skip the MatNWB installation
+
+Introduction
+------------
+
+In this tutorial, we will read single neuron spiking data that is in the NWB standard format and do a basic visualization of the data. More thorough documentation regarding reading files as well as the ``NwbFile`` class, can be found in the `NWB Overview Documentation <https://nwb-overview.readthedocs.io/en/latest/file_read/file_read.html#reading-with-matnwb>`_
+
+Download the Dataset
+--------------------
+
+Use the ``dandi`` Python package to download the dataset to the user-local dandisets folder. If you are running this livescript on DandiHub, the ``dandi`` package is already pre-installed. On a local environment, you need to install the ``dandi`` package in the python environment returned by running ``pyenv()`` in MATLAB.
+
+.. code-block:: matlab
+
+   environment = "DandiHub";
+   switch environment
+       case "DandiHub"
+           targetFolder = "/home/jovyan/dandisets/000004";
+       case "Local"
+           targetFolder = fullfile(userpath(), "dandisets", "000004");
+   end
+   py.dandi.download.download("dandi://dandi/000004/sub-P11HMH/", targetFolder, existing='overwrite')
+
+.. code-block:: text
+
+   No module or function named 'dandi'.
+
+Read the NWB file
+-----------------
+
+You can read any NWB file using ``nwbRead``. You will find that the print out for this shows a summary of the data within.
+
+.. code-block:: matlab
+
+   nwb = nwbRead(fullfile(targetFolder, "sub-P11HMH", "sub-P11HMH_ses-20061101_ecephys+image.nwb"))
+
+Stimulus
+~~~~~~~~
+
+Now lets take a look at the visual stimuli presented to the subject. They will be in ``nwb.stimulus_presentation``
+
+.. code-block:: matlab
+
+   nwb.stimulus_presentation
+
+This results shows us that ``nwb.stimulus_presentation`` is a ``Set`` object that contains a single data object called ``StimulusPresentation``, which is an ``OpticalSeries`` neurodata type. Use the ``get`` method to return this ``OpticalSeries``. ``Set`` objects store a collection of other NWB objects.
+
+.. code-block:: matlab
+
+   nwb.stimulus_presentation.get('StimulusPresentation')
+
+``OpticalSeries`` is a neurodata type that stores information about visual stimuli presented to subjects. This print out shows all of the attributes in the ``OpticalSeries`` object named ``StimulusPresentation``. The images are stored in ``StimulusPresentation.data``
+
+.. code-block:: matlab
+
+   StimulusImageData = nwb.stimulus_presentation.get('StimulusPresentation').data
+
+When calling a data object directly, the data is not read but instead a ``DataStub`` is returned. This is because data is read "lazily" in MatNWB. Instead of reading the entire dataset into memory, this provides a "window" into the data stored on disk that allows you to read only a section of the data. In this case, the last dimension indexes over images. You can index into any ``DataStub`` as you would any MATLAB matrix.
+
+.. code-block:: matlab
+
+   % get the image and display it
+   % the dimension order is provided as follows:
+   % [rgb, y, x, image index]
+   img = StimulusImageData(1:3, 1:300, 1:400, 32);
+
+A bit of manipulation allows us to display the image using MATLAB's ``imshow``.
+
+.. code-block:: matlab
+
+   img = permute(img,[3, 2, 1]);  % fix orientation
+   img = flip(img, 3); % reverse color order
+   F = figure();
+   imshow(img, 'InitialMagnification', 'fit');
+   daspect([3, 5, 5]);
+
+To read an entire dataset, use the ``DataStub.load`` method without any input arguments. We will use this approach to read all of the image display timestamps into memory.
+
+.. code-block:: matlab
+
+   stimulus_times = nwb.stimulus_presentation.get('StimulusPresentation').timestamps.load();
+
+Quick PSTH and raster
+---------------------
+
+Here, I will pull out spike times of a particular unit, align them to the image display times, and finally display the results.
+
+First, let us show the first row of the NWB Units table representing the first unit.
+
+.. code-block:: matlab
+
+   nwb.units.getRow(1)
+
+Let us specify some parameters for creating a cell array of spike times aligned to each stimulus time.
+
+.. code-block:: matlab
+
+   %% Align spikes by stimulus presentations
+   
+   unit_ind =8;
+   before =1;
+   after =3;
+
+``getRow`` provides a convenient method for reading this data out.
+
+.. code-block:: matlab
+
+   unit_spikes = nwb.units.getRow(unit_ind, 'columns', {'spike_times'}).spike_times{1}
+
+Spike times from this unit are aligned to each stimulus time and compiled in a cell array
+
+.. code-block:: matlab
+
+   results = cell(1, length(stimulus_times));
+   for itime = 1:length(stimulus_times)
+       stimulus_time = stimulus_times(itime);
+       spikes = unit_spikes - stimulus_time;
+       spikes = spikes(spikes > -before);
+       spikes = spikes(spikes < after);
+       results{itime} = spikes;
+   end
+
+Plot results
+------------
+
+Finally, here is a (slightly sloppy) peri-stimulus time histogram
+
+.. code-block:: matlab
+
+   figure();
+   hold on
+   for i = 1:length(results)
+       spikes = results{i};
+       yy = ones(length(spikes)) * i;
+   
+       plot(spikes, yy, 'k.');
+   end
+   hold off
+   ylabel('trial');
+   xlabel('time (s)');
+   axis('tight')
+
+.. code-block:: matlab
+
+   figure();
+   all_spikes = cat(1, results{:});
+   histogram(all_spikes, 30);
+   ylabel('count')
+   xlabel('time (s)');
+   axis('tight')
+
+Conclusion
+----------
+
+This is an example of how to get started with understanding and analyzing public NWB datasets. This particular dataset was published with an extensive open analysis conducted in both MATLAB and Python, which you can find `here <https://github.com/rutishauserlab/recogmem-release-NWB>`_. For more datasets, or to publish your own NWB data for free, check out the DANDI archive `here <http://dandiarchive.org/>`_.
diff --git a/docs/source/pages/tutorials/remote_read.rst b/docs/source/pages/tutorials/remote_read.rst
index e668dd62b..9ff603626 100644
--- a/docs/source/pages/tutorials/remote_read.rst
+++ b/docs/source/pages/tutorials/remote_read.rst
@@ -6,14 +6,113 @@ Reading NWB Files from Remote Locations
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/remote_read.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/remote_read.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/remote_read.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+It is possible to read an NWB file (or any HDF5 file) in MATLAB directly from several different kinds of remote locations, including AWS, Azure Blob Storage and HDFS. This tutorial will walk you through specifically loading a MATLAB file from AWS S3, which is the storage used by the DANDI archive. See `MATLAB documentation <https://www.mathworks.com/help/matlab/ref/h5read.html>`_ for more general information.
+
+To read an NWB file file from an s3 store, first you need to figure out the s3 path of that resource. The easiest way to do this is to use the DANDI web client.
+
+* (skip if on DANDI Hub) Make sure you do not have a file ``~/.aws/credentials``. If you do, rename it to something else. On Windows this file would be somewhere like ``C:/Users/username/.aws/credentials``.
+* Find and select a dandiset you want on the `DANDI Archive <https://dandiarchive.org/dandiset>`_, then click
+
+   .. image:: ../../_static/tutorials/media/remote_read/image_0.png
+      :class: tutorial-media
+      :width: 121px
+      :alt: image_0.png
+* Navigate to the NWB file of interest and click
+
+   .. image:: ../../_static/tutorials/media/remote_read/image_1.png
+      :class: tutorial-media
+      :width: 30px
+      :alt: image_1.png
+* Find the second entry of "contentURL"
+
+   .. image:: ../../_static/tutorials/media/remote_read/image_2.png
+      :class: tutorial-media
+      :width: 688px
+      :alt: image_2.png
+* In your MATLAB session, take the end of that url (the blob id) and add it to this expression: ``s3 = 's3://dandiarchive/blobs/<blob_id>'``. In this case, you would have:
+* Use ``setenv`` to specify AWS region. The DandiArchive datasets are located on ``us-east-2``
+
+.. code-block:: matlab
+
+   % Example S3 path for a 380KiB file from dandiset 001199:
+   s3 = "s3://dandiarchive/blobs/3fb/2c8/3fb2c8b9-26db-47c0-86c2-9594678a8263";
+   
+   % Set AWS region (NB: Changes environment variable)
+   setenv('AWS_DEFAULT_REGION', 'us-east-2') 
+
+* Read from that s3 path directly with:
+
+.. code-block:: matlab
+
+   nwbfile = nwbRead(s3) % this may take a minute
+
+.. code-block:: text
+
+   nwbfile = 
+     NwbFile with properties:
+   
+                                                nwb_version: '2.6.0'
+                                           file_create_date: [1x1 types.untyped.DataStub]
+                                                 identifier: 'BH590_30_200_67_15_NZ'
+                                        session_description: 'Neural Spikes for neural pathways to tFUS'
+                                         session_start_time: 2024-08-16T20:02:00.000000-04:00
+                                  timestamps_reference_time: 2024-09-30T18:09:15.858000-04:00
+                                                acquisition: [0x1 types.untyped.Set]
+                                                   analysis: [0x1 types.untyped.Set]
+                                                    general: [0x1 types.untyped.Set]
+                                    general_data_collection: 'tFUS parameters {PRF:30Hz PD:200us UD:67ms PRESSURE:67V}'
+                                            general_devices: [1x1 types.untyped.Set]
+                             general_experiment_description: ''
+                                       general_experimenter: ''
+                                general_extracellular_ephys: [1x1 types.untyped.Set]
+                     general_extracellular_ephys_electrodes: [1x1 types.hdmf_common.DynamicTable]
+                                        general_institution: 'Carnegie Mellon University'
+                                general_intracellular_ephys: [0x1 types.untyped.Set]
+        general_intracellular_ephys_experimental_conditions: []
+                      general_intracellular_ephys_filtering: ''
+       general_intracellular_ephys_intracellular_recordings: []
+                    general_intracellular_ephys_repetitions: []
+          general_intracellular_ephys_sequential_recordings: []
+        general_intracellular_ephys_simultaneous_recordings: []
+                    general_intracellular_ephys_sweep_table: []
+                                           general_keywords: ''
+                                                general_lab: ''
+                                              general_notes: 'Recording:S1; stim at:S1'
+                                       general_optogenetics: [0x1 types.untyped.Set]
+                                     general_optophysiology: [0x1 types.untyped.Set]
+                                       general_pharmacology: ''
+                                           general_protocol: ''
+                               general_related_publications: ''
+                                         general_session_id: ''
+                                             general_slices: ''
+                                      general_source_script: ''
+                            general_source_script_file_name: ''
+                                           general_stimulus: 'intervals contains each tFUS trial start and end timestamps'
+                                            general_subject: [1x1 types.core.Subject]
+                                            general_surgery: ''
+                                              general_virus: ''
+                                                  intervals: [0x1 types.untyped.Set]
+                                           intervals_epochs: []
+                                    intervals_invalid_times: []
+                                           intervals_trials: [1x1 types.core.TimeIntervals]
+                                                 processing: [0x1 types.untyped.Set]
+                                                    scratch: [0x1 types.untyped.Set]
+                                      stimulus_presentation: [0x1 types.untyped.Set]
+                                         stimulus_templates: [0x1 types.untyped.Set]
+                                                      units: [1x1 types.core.Units]
+
+That's it! MATLAB will automatically detect that this is an S3 path instead of a local filepath and will set up a remote read interface for that NWB file. This approach works on any computer with a fairly recent version of MATLAB and an internet connection. It works particularly well on the `DANDI Hub <http://hub.dandiarchive.org>`_, which has a very fast connection to the DANDI S3 store and which provides a MATLAB environment for free provided you have a license.
+
+Note: MATLAB vs. Python remote read
+-----------------------------------
+
+Python also allows you to remotely read a file, and has several advantages over MATLAB. Reading in Python is faster. On DANDI Hub, for MATLAB, reading the file takes about 51 seconds, while the analogous operation takes less than a second in Python. Python also allows you to create a local cache so you are not repeatedly requesting the same data, which can further speed up data access. Overall, we recommend remote reading using Python instead of MATLAB.
diff --git a/docs/source/pages/tutorials/scratch.rst b/docs/source/pages/tutorials/scratch.rst
index 5e584bc5a..e817e1c77 100644
--- a/docs/source/pages/tutorials/scratch.rst
+++ b/docs/source/pages/tutorials/scratch.rst
@@ -6,14 +6,156 @@ Working with Scratch Space in MatNWB
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
    :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/scratch.mlx
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: ../../_static/html/tutorials/scratch.html
-   :alt: View full page
+   :alt: View rendered Live Script
 
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="../../_static/html/tutorials/scratch.html"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+This tutorial will focus on the basics of working with a `NWBFile <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/NWBFile.html>`_ for storing non-standardizable data. For example, you may want to store results from one-off analyses of some temporary utility. NWB provides in-file  **scratch space** as a dedicated location where miscellaneous non-standard data may be written.
+
+Setup
+-----
+
+Let us first set up an environment with some "acquired data".
+
+.. code-block:: matlab
+
+   ContextFile = NwbFile(...
+       'session_description', 'demonstrate NWBFile scratch', ... % required
+       'identifier', 'SCRATCH-0', ...  % required
+       'session_start_time', datetime(2019, 4, 3, 11, 0, 0, 'TimeZone', 'local'), ... % required
+       'file_create_date', datetime(2019, 4, 15, 12, 0, 0, 'TimeZone', 'local'), ... % optional
+       'general_experimenter', 'Niu, Lawrence', ...
+       'general_institution', 'NWB' ...
+   );
+   
+   % Simulate some data
+   timestamps = 0:100:1024;
+   data = sin(0.333 .* timestamps) ...
+       + cos(0.1 .* timestamps) ...
+       + randn(1, length(timestamps));
+   RawTs = types.core.TimeSeries(...
+       'data', data, ...
+       'data_unit', 'm', ...
+       'starting_time', 0., ...
+       'starting_time_rate', 100, ...
+       'description', 'simulated acquired data' ...
+   );
+   ContextFile.acquisition.set('raw_timeseries', RawTs);
+   
+   % "Analyze" the simulated data
+   
+   % We provide a re-implementation of scipy.signal.correlate(..., mode='same')
+   % Ideally, you should use MATLAB-native code though using its equivalent 
+   % function (xcorr) requires the Signal Processing Toolbox
+   correlatedData = sameCorr(RawTs.data, ones(128, 1)) ./ 128;
+   
+   % If you are unsure of how HDF5 paths map to MatNWB property structures, we 
+   % suggest using HDFView to verify. In most cases, MatNWB properties map 
+   % directly to HDF5 paths.
+   FilteredTs = types.core.TimeSeries( ...
+       'data', correlatedData, ...
+       'data_unit', 'm', ...
+       'starting_time', 0, ...
+       'starting_time_rate', 100, ...
+       'description', 'cross-correlated data' ...
+   )
+
+.. code-block:: text
+
+   FilteredTs = 
+     TimeSeries with properties:
+   
+        starting_time_unit: 'seconds'
+       timestamps_interval: 1
+           timestamps_unit: 'seconds'
+                      data: [-4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04 -4.7892e-04]
+                 data_unit: 'm'
+                  comments: 'no comments'
+                   control: []
+       control_description: ''
+           data_continuity: ''
+           data_conversion: 1
+               data_offset: 0
+           data_resolution: -1
+               description: 'cross-correlated data'
+             starting_time: 0
+        starting_time_rate: 100
+                timestamps: []
+
+.. code-block:: matlab
+
+   ProcModule = types.core.ProcessingModule( ...
+       'description', 'a module to store filtering results', ...
+       'filtered_timeseries', FilteredTs ...
+   );
+   ContextFile.processing.set('core', ProcModule);
+   nwbExport(ContextFile, 'context_file.nwb');
+
+Warning Regarding the Usage of Scratch Space
+--------------------------------------------
+
+**Scratch data written into the scratch space should not be intended for reuse or sharing. Standard NWB types, along with any extensions, should always be used for any data intended to be shared. Published data should not include scratch data and any reuse should not require scratch data for data processing.**
+
+Writing Data to Scratch Space
+-----------------------------
+
+Let us first copy what we need from the processed data file.
+
+.. code-block:: matlab
+
+   ScratchFile = NwbFile('identifier', 'SCRATCH-1');
+   ContextFile = nwbRead('./context_file.nwb', 'ignorecache');
+   % again, copy the required metadata from the processed file.
+   ScratchFile.session_description = ContextFile.session_description;
+   ScratchFile.session_start_time = ContextFile.session_start_time;
+
+We can now do an analysis lacking specification but that we still wish to store results for.
+
+.. code-block:: matlab
+
+   % ProcessingModule stores its timeseries inside of the "nwbdatainterface" property which is a 
+   % Set of NWBDataInterface objects. This is not directly mapped to the NWB file but is used to 
+   % distinguish it and DynamicTable objects which it stores under the "dynamictable" property.
+   FilteredTs = ContextFile.processing.get('core').nwbdatainterface.get('filtered_timeseries');
+   % note: MatNWB does not currently support complex numbers. If you wish to store the data, consider
+   % storing each number as a struct which will write the data to HDF5 using compound types.
+   dataFft = real(fft(FilteredTs.data.load()));
+   ScratchData = types.core.ScratchData( ...
+       'data', dataFft, ...
+       'notes', 'discrete Fourier transform from filtered data' ...
+   )
+
+.. code-block:: text
+
+   ScratchData = 
+     ScratchData with properties:
+   
+       notes: 'discrete Fourier transform from filtered data'
+        data: [11x1 double]
+
+.. code-block:: matlab
+
+   ScratchFile.scratch.set('dft_filtered', ScratchData);
+   nwbExport(ScratchFile, 'scratch_analysis.nwb');
+
+The ``scratch_analysis.nwb`` file will now have scratch data stored in it:
+
+.. image:: ../../_static/tutorials/media/scratch/image_0.png
+   :class: tutorial-media
+   :width: 1029px
+   :alt: image_0.png
+
+.. code-block:: matlab
+
+   function C = sameCorr(A, B)
+       % SAMECORR scipy.signals.correlate(..., mode="same") equivalent
+       for iDim = 1:ndims(B)
+           B = flip(B, iDim);
+       end
+       C = conv(A, conj(B), 'same');
+   end
diff --git a/tools/documentation/_rst_templates/tutorial.rst.template b/tools/documentation/_rst_templates/tutorial.rst.template
index d4b552838..2bcc28553 100644
--- a/tools/documentation/_rst_templates/tutorial.rst.template
+++ b/tools/documentation/_rst_templates/tutorial.rst.template
@@ -4,16 +4,15 @@
 {{tutorial_title_underline}}
 
 .. image:: https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg
-   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file=tutorials/{{tutorial_name}}.mlx
+   :target: https://matlab.mathworks.com/open/github/v1?repo=NeurodataWithoutBorders/matnwb&file={{tutorial_source_path}}
    :alt: Open in MATLAB Online
-.. image:: https://img.shields.io/badge/View-Full_Page-blue
+.. image:: https://img.shields.io/badge/View-Rendered_Live_Script-blue
    :target: {{static_html_path}}
-   :alt: View full page
+   :alt: View rendered Live Script
 {{youtube_badge_block}}
 
-.. raw:: html
+.. contents:: On this page
+   :local:
+   :depth: 2
 
-   <iframe class="autoresize"
-           src="{{static_html_path}}"
-           style="width:100%; border:none; display:block;">
-   </iframe>
+{{tutorial_body}}
diff --git a/tools/documentation/matnwb_exportTutorials.m b/tools/documentation/matnwb_exportTutorials.m
index 96dcc7344..c021d228e 100644
--- a/tools/documentation/matnwb_exportTutorials.m
+++ b/tools/documentation/matnwb_exportTutorials.m
@@ -114,6 +114,10 @@ function matnwb_exportTutorials(options)
             end
         end
     end
+
+    if ~isempty(filePaths)
+        generateRstForTutorials()
+    end
 end
 
 function mustStartWithDot(value)
diff --git a/tools/documentation/private/convertTutorialMCodeToRst.m b/tools/documentation/private/convertTutorialMCodeToRst.m
new file mode 100644
index 000000000..6c6bf6f7d
--- /dev/null
+++ b/tools/documentation/private/convertTutorialMCodeToRst.m
@@ -0,0 +1,412 @@
+function rstText = convertTutorialMCodeToRst(mCodeFilePath)
+% convertTutorialMCodeToRst - Convert exported tutorial m-code to reStructuredText
+
+    arguments
+        mCodeFilePath (1,1) string {mustBeFile}
+    end
+
+    sourceLines = string(splitlines(fileread(mCodeFilePath)));
+
+    rstLines = strings(0, 1);
+    commentLines = strings(0, 1);
+    codeLines = strings(0, 1);
+
+    inBlockCommentCode = false;
+    isFirstHeading = true;
+    for i = 1:numel(sourceLines)
+        thisLine = sourceLines(i);
+        trimmedLine = strtrim(thisLine);
+
+        if inBlockCommentCode
+            if trimmedLine == "%}"
+                rstLines = appendBlock(rstLines, renderCodeBlock(codeLines));
+                codeLines = strings(0, 1);
+                inBlockCommentCode = false;
+            else
+                codeLines(end+1, 1) = thisLine;
+            end
+            continue
+        end
+
+        if startsWith(trimmedLine, "%%")
+            rstLines = appendBlock(rstLines, renderCommentBlock(commentLines));
+            rstLines = appendBlock(rstLines, renderCodeBlock(codeLines));
+            commentLines = strings(0, 1);
+            codeLines = strings(0, 1);
+
+            headingText = strtrim(extractAfter(trimmedLine, 2));
+            if headingText == ""
+                continue
+            end
+
+            if isFirstHeading
+                isFirstHeading = false;
+                continue
+            end
+
+            headingText = normalizeInlineMarkup(headingText);
+            rstLines = appendBlock(rstLines, [ ...
+                headingText
+                string(repmat('-', 1, strlength(headingText))) ...
+            ]);
+            continue
+        end
+
+        if trimmedLine == "%{"
+            rstLines = appendBlock(rstLines, renderCommentBlock(commentLines));
+            rstLines = appendBlock(rstLines, renderCodeBlock(codeLines));
+            commentLines = strings(0, 1);
+            codeLines = strings(0, 1);
+            inBlockCommentCode = true;
+            continue
+        end
+
+        if trimmedLine == ""
+            if ~isempty(codeLines)
+                codeLines(end+1, 1) = "";
+            elseif ~isempty(commentLines)
+                commentLines(end+1, 1) = "";
+            end
+        elseif startsWith(trimmedLine, "%")
+            if shouldKeepCommentAsCode(sourceLines, i)
+                rstLines = appendBlock(rstLines, renderCommentBlock(commentLines));
+                commentLines = strings(0, 1);
+                codeLines(end+1, 1) = thisLine;
+            else
+                rstLines = appendBlock(rstLines, renderCodeBlock(codeLines));
+                codeLines = strings(0, 1);
+                commentLines(end+1, 1) = extractCommentText(thisLine);
+            end
+        else
+            rstLines = appendBlock(rstLines, renderCommentBlock(commentLines));
+            commentLines = strings(0, 1);
+            codeLines(end+1, 1) = thisLine;
+        end
+    end
+
+    rstLines = appendBlock(rstLines, renderCommentBlock(commentLines));
+    rstLines = appendBlock(rstLines, renderCodeBlock(codeLines));
+    rstLines = trimBlankLines(rstLines);
+
+    rstText = join(rstLines, newline);
+end
+
+function rstLines = renderCommentBlock(commentLines)
+    commentLines = trimBlankLines(commentLines);
+    if isempty(commentLines)
+        rstLines = strings(0, 1);
+        return
+    end
+
+    rstLines = strings(0, 1);
+    numLines = numel(commentLines);
+    i = 1;
+
+    while i <= numLines
+        currentLine = strtrim(commentLines(i));
+
+        if currentLine == ""
+            if ~isempty(rstLines) && rstLines(end) ~= ""
+                rstLines(end+1, 1) = "";
+            end
+            i = i + 1;
+            continue
+        end
+
+        if currentLine == "<html>"
+            [htmlTableLines, nextIndex] = renderHtmlTable(commentLines, i);
+            rstLines = appendBlock(rstLines, htmlTableLines);
+            i = nextIndex;
+            continue
+        end
+
+        if isMetadataLine(currentLine)
+            rstLines(end+1, 1) = renderMetadataLine(currentLine);
+            i = i + 1;
+            continue
+        end
+
+        [isListItem, listMarker, itemText] = parseListItem(currentLine);
+        if isListItem
+            j = i + 1;
+            while j <= numLines
+                nextLine = strtrim(commentLines(j));
+                if nextLine == "" || isListItemLine(nextLine)
+                    break
+                end
+                itemText = itemText + " " + nextLine;
+                j = j + 1;
+            end
+
+            rstLines(end+1, 1) = listMarker + " " + normalizeInlineMarkup(itemText);
+            i = j;
+            continue
+        end
+
+        paragraphText = currentLine;
+        j = i + 1;
+        while j <= numLines
+            nextLine = strtrim(commentLines(j));
+            if nextLine == "" || isListItemLine(nextLine)
+                break
+            end
+            paragraphText = paragraphText + " " + nextLine;
+            j = j + 1;
+        end
+
+        rstLines(end+1, 1) = normalizeInlineMarkup(paragraphText);
+        i = j;
+    end
+
+    rstLines = trimBlankLines(rstLines);
+end
+
+function [rstLines, nextIndex] = renderHtmlTable(commentLines, startIndex)
+    tableRows = strings(0, 2);
+    nextIndex = startIndex + 1;
+
+    while nextIndex <= numel(commentLines)
+        currentLine = strtrim(commentLines(nextIndex));
+        if currentLine == "</html>"
+            nextIndex = nextIndex + 1;
+            break
+        end
+
+        rowTokens = regexp(currentLine, '<tr><td>(.*?)</td><td>(.*?)</td></tr>', 'tokens', 'once');
+        if ~isempty(rowTokens)
+            tableRows(end+1, :) = [ ...
+                cleanHtmlTableCell(string(rowTokens{1}))
+                cleanHtmlTableCell(string(rowTokens{2})) ...
+            ];
+        end
+
+        nextIndex = nextIndex + 1;
+    end
+
+    if isempty(tableRows)
+        rstLines = strings(0, 1);
+        return
+    end
+
+    rstLines = [
+        ".. list-table::"
+        "   :widths: 25 75"
+        ""
+    ];
+
+    for i = 1:size(tableRows, 1)
+        rstLines(end+1, 1) = "   * - " + normalizeInlineMarkup(tableRows(i, 1));
+        rstLines(end+1, 1) = "     - " + normalizeInlineMarkup(tableRows(i, 2));
+    end
+end
+
+function rstLines = renderCodeBlock(codeLines)
+    codeLines = trimBlankLines(codeLines);
+    if isempty(codeLines)
+        rstLines = strings(0, 1);
+        return
+    end
+
+    rstLines = [".. code-block:: matlab"; ""];
+    for i = 1:numel(codeLines)
+        thisLine = codeLines(i);
+        thisLine = replace(thisLine, sprintf('\t'), '    ');
+        rstLines(end+1, 1) = "   " + thisLine;
+    end
+end
+
+function rstLines = appendBlock(rstLines, blockLines)
+    blockLines = trimBlankLines(blockLines);
+    if isempty(blockLines)
+        return
+    end
+
+    if ~isempty(rstLines) && rstLines(end) ~= ""
+        rstLines(end+1, 1) = "";
+    end
+
+    rstLines = [rstLines; blockLines];
+end
+
+function text = extractCommentText(lineText)
+    if startsWith(lineText, "% ")
+        text = extractAfter(lineText, 2);
+    else
+        text = extractAfter(lineText, 1);
+    end
+end
+
+function text = normalizeInlineMarkup(text)
+    text = strtrim(text);
+    text = convertLinks(text);
+
+    text = replace(text, "|*.*|", ".");
+    text = replace(text, "|.|", ".");
+    text = replace(text, "|>|", ">");
+    text = replace(text, "|>|.", ">.");
+
+    text = regexprep(text, '\|\*([^|]+)\*\|', '``$1``');
+    text = regexprep(text, '\|([^|]+)\|', '``$1``');
+    text = regexprep(text, '(^|[^A-Za-z0-9])_([^_\n]+?)_([^A-Za-z0-9]|$)', '$1*$2*$3');
+end
+
+function text = convertLinks(text)
+    arrowPlaceholder = "CODExLeftRightArrow";
+    text = replace(text, "<->", arrowPlaceholder);
+
+    expression = '<([^ <>\n]+)\s+([^>]+)>';
+    [matches, tokens] = regexp(text, expression, 'match', 'tokens');
+
+    for i = 1:numel(matches)
+        url = string(tokens{i}{1});
+        label = cleanLinkLabel(string(tokens{i}{2}));
+        label = replace(label, arrowPlaceholder, "<->");
+        if label == ""
+            label = url;
+        end
+
+        if isTutorialLink(url)
+            [~, targetName] = fileparts(url);
+            replacement = "`" + label + " <" + targetName + ">`_";
+        else
+            replacement = "`" + label + " <" + url + ">`_";
+        end
+
+        text = replace(text, matches{i}, replacement);
+    end
+
+    text = replace(text, arrowPlaceholder, "<->");
+end
+
+function tf = isTutorialLink(url)
+    [~, ~, extension] = fileparts(url);
+    tf = ismember(extension, [".mlx", ".m", ".rst"]);
+end
+
+function label = cleanLinkLabel(label)
+    label = strtrim(label);
+    label = replace(label, "|*.*|", ".");
+    label = replace(label, "|.|", ".");
+    label = replace(label, "|", "");
+    label = regexprep(label, '^\*+', '');
+    label = regexprep(label, '\*+$', '');
+    label = regexprep(label, '^_+', '');
+    label = regexprep(label, '_+$', '');
+    label = strtrim(label);
+end
+
+function tf = isMetadataLine(lineText)
+    tf = ~isempty(regexp(lineText, '^(authors?|contact|last edited|last updated):', 'once'));
+end
+
+function lineText = renderMetadataLine(lineText)
+    tokens = regexp(lineText, '^([^:]+):(.*)$', 'tokens', 'once');
+    label = strtrim(string(tokens{1}));
+    value = strtrim(string(tokens{2}));
+    label = formatMetadataLabel(label);
+    lineText = "**" + label + ":** " + normalizeInlineMarkup(value);
+end
+
+function cellText = cleanHtmlTableCell(cellText)
+    cellText = replace(cellText, "<em>", "|");
+    cellText = replace(cellText, "</em>", "|");
+    cellText = regexprep(cellText, '<[^>]+>', '');
+    cellText = strtrim(cellText);
+end
+
+function label = formatMetadataLabel(label)
+    words = split(lower(label), " ");
+    for i = 1:numel(words)
+        if words(i) == ""
+            continue
+        end
+        words(i) = upper(extractBefore(words(i), 2)) + extractAfter(words(i), 1);
+    end
+    label = strjoin(words, " ");
+end
+
+function [tf, listMarker, itemText] = parseListItem(lineText)
+    itemText = "";
+    listMarker = "";
+
+    if startsWith(lineText, "* ")
+        tf = true;
+        listMarker = "*";
+        itemText = strtrim(extractAfter(lineText, 2));
+        return
+    end
+
+    if startsWith(lineText, "# ")
+        tf = true;
+        listMarker = "1.";
+        itemText = strtrim(extractAfter(lineText, 2));
+        return
+    end
+
+    match = regexp(lineText, '^(\d+\.)\s+(.*)$', 'tokens', 'once');
+    if isempty(match)
+        tf = false;
+        return
+    end
+
+    tf = true;
+    listMarker = string(match{1});
+    itemText = string(match{2});
+end
+
+function tf = isListItemLine(lineText)
+    [tf, ~, ~] = parseListItem(lineText);
+end
+
+function tf = shouldKeepCommentAsCode(sourceLines, lineIndex)
+    commentText = strtrim(extractCommentText(sourceLines(lineIndex)));
+    if commentText == ""
+        tf = false;
+        return
+    end
+
+    tf = ~isMetadataLine(commentText) && ...
+        isProbablyCodeComment(commentText) && ...
+        nextNoncommentLineLooksLikeCode(sourceLines, lineIndex + 1);
+end
+
+function tf = isProbablyCodeComment(commentText)
+    startsLowercase = ~isempty(regexp(commentText, '^[a-z]', 'once'));
+    endsLikeSentence = ~isempty(regexp(commentText, '[\.\:\!\?]$', 'once'));
+    hasSentencePunctuation = ~isempty(regexp(commentText, '[\.\,\;\:]', 'once'));
+    hasManyWords = numel(split(commentText)) > 8;
+
+    tf = startsLowercase && ~endsLikeSentence && ~hasSentencePunctuation && ~hasManyWords;
+end
+
+function tf = nextNoncommentLineLooksLikeCode(sourceLines, startIndex)
+    tf = false;
+
+    for i = startIndex:numel(sourceLines)
+        trimmedLine = strtrim(sourceLines(i));
+        if trimmedLine == ""
+            continue
+        end
+
+        if startsWith(trimmedLine, "%")
+            continue
+        else
+            tf = true;
+        end
+        return
+    end
+end
+
+function lines = trimBlankLines(lines)
+    if isempty(lines)
+        return
+    end
+
+    while ~isempty(lines) && strtrim(lines(1)) == ""
+        lines(1) = [];
+    end
+
+    while ~isempty(lines) && strtrim(lines(end)) == ""
+        lines(end) = [];
+    end
+end
diff --git a/tools/documentation/private/convertTutorialMarkdownToRst.m b/tools/documentation/private/convertTutorialMarkdownToRst.m
new file mode 100644
index 000000000..c0568de35
--- /dev/null
+++ b/tools/documentation/private/convertTutorialMarkdownToRst.m
@@ -0,0 +1,477 @@
+function rstText = convertTutorialMarkdownToRst(markdownFilePath, options)
+% convertTutorialMarkdownToRst - Convert exported tutorial markdown to reStructuredText
+
+    arguments
+        markdownFilePath (1,1) string {mustBeFile}
+        options.MediaRelativePath (1,1) string = ""
+        options.ImageNames (1,:) string = string.empty
+        options.ImageDisplayWidths (1,:) double = double.empty
+    end
+
+    markdownLines = string(splitlines(fileread(markdownFilePath)));
+
+    rstLines = strings(0, 1);
+    inTableOfContents = false;
+    isFirstHeading = true;
+
+    i = 1;
+    while i <= numel(markdownLines)
+        thisLine = markdownLines(i);
+        trimmedLine = normalizeBlockLine(thisLine);
+
+        if trimmedLine == '<a name="beginToc"></a>'
+            inTableOfContents = true;
+            i = i + 1;
+            continue
+        elseif trimmedLine == '<a name="endToc"></a>'
+            inTableOfContents = false;
+            i = i + 1;
+            continue
+        elseif inTableOfContents || startsWith(trimmedLine, "<a name=")
+            i = i + 1;
+            continue
+        end
+
+        if trimmedLine == ""
+            i = i + 1;
+            continue
+        end
+
+        if startsWith(trimmedLine, "```")
+            [blockLines, i] = readCodeBlock(markdownLines, i);
+            rstLines = appendBlock(rstLines, blockLines);
+            continue
+        end
+
+        if startsWith(trimmedLine, "|")
+            [blockLines, i] = readTableBlock(markdownLines, i);
+            rstLines = appendBlock(rstLines, blockLines);
+            continue
+        end
+
+        if isImageLine(trimmedLine)
+            [blockLines, i] = readImageBlock(markdownLines, i, options);
+            rstLines = appendBlock(rstLines, blockLines);
+            continue
+        end
+
+        headingMatch = regexp(trimmedLine, '^(#+)\s+(.*)$', 'tokens', 'once');
+        if ~isempty(headingMatch)
+            headingLevel = strlength(headingMatch{1});
+            headingText = normalizeInlineMarkdown(string(headingMatch{2}));
+            if isFirstHeading
+                isFirstHeading = false;
+            else
+                blockLines = [
+                    headingText
+                    string(repmat(getHeadingUnderline(headingLevel), 1, strlength(headingText)))
+                ];
+                rstLines = appendBlock(rstLines, blockLines);
+            end
+            i = i + 1;
+            continue
+        end
+
+        if isListLine(trimmedLine)
+            [blockLines, i] = readListBlock(markdownLines, i, options);
+            rstLines = appendBlock(rstLines, blockLines);
+            continue
+        end
+
+        [blockLines, i] = readParagraphBlock(markdownLines, i);
+        rstLines = appendBlock(rstLines, blockLines);
+    end
+
+    rstLines = trimBlankLines(rstLines);
+    rstText = join(rstLines, newline);
+end
+
+function blockLines = createImageDirective(imagePath, imageAltText, imageDisplayWidth)
+    blockLines = ".. image:: " + imagePath;
+    blockLines(end+1, 1) = "   :class: tutorial-media";
+    if ~isnan(imageDisplayWidth)
+        blockLines(end+1, 1) = "   :width: " + string(round(imageDisplayWidth)) + "px";
+    end
+    if imageAltText ~= ""
+        blockLines(end+1, 1) = "   :alt: " + normalizeInlineMarkdown(imageAltText);
+    end
+end
+
+function [blockLines, nextIndex] = readCodeBlock(markdownLines, startIndex)
+    openingLine = normalizeBlockLine(markdownLines(startIndex));
+    blockLanguage = strtrim(extractAfter(openingLine, 3));
+
+    blockLines = [
+        ".. code-block:: " + getCodeBlockLanguage(blockLanguage)
+        ""
+    ];
+
+    nextIndex = startIndex + 1;
+    while nextIndex <= numel(markdownLines)
+        thisLine = markdownLines(nextIndex);
+        if startsWith(normalizeBlockLine(thisLine), "```")
+            nextIndex = nextIndex + 1;
+            return
+        end
+
+        blockLines(end+1, 1) = "   " + thisLine;
+        nextIndex = nextIndex + 1;
+    end
+end
+
+function [blockLines, nextIndex] = readTableBlock(markdownLines, startIndex)
+    tableLines = strings(0, 1);
+    nextIndex = startIndex;
+
+    while nextIndex <= numel(markdownLines)
+        thisLine = normalizeBlockLine(markdownLines(nextIndex));
+        if thisLine == "" || ~startsWith(thisLine, "|")
+            break
+        end
+
+        tableLines(end+1, 1) = thisLine;
+        nextIndex = nextIndex + 1;
+    end
+
+    if numel(tableLines) < 2
+        blockLines = normalizeInlineMarkdown(tableLines);
+        return
+    end
+
+    tableRows = cell(0, 1);
+    for i = 1:numel(tableLines)
+        if i == 2 && isMarkdownAlignmentRow(tableLines(i))
+            continue
+        end
+        tableRows{end+1, 1} = splitTableRow(tableLines(i)); %#ok<AGROW>
+    end
+
+    blockLines = [
+        ".. list-table::"
+        "   :header-rows: 1"
+        ""
+    ];
+
+    for i = 1:numel(tableRows)
+        thisRow = tableRows{i};
+        for j = 1:numel(thisRow)
+            prefix = "     - ";
+            if j == 1
+                prefix = "   * - ";
+            end
+            blockLines(end+1, 1) = prefix + normalizeInlineMarkdown(thisRow(j));
+        end
+    end
+end
+
+function [blockLines, nextIndex] = readImageBlock(markdownLines, startIndex, options)
+    blockLines = strings(0, 1);
+    nextIndex = startIndex;
+
+    while nextIndex <= numel(markdownLines)
+        thisLine = normalizeBlockLine(markdownLines(nextIndex));
+        if ~isImageLine(thisLine)
+            break
+        end
+
+        imageTokens = regexp(thisLine, '!\[([^\]]*)\]\(([^)]+)\)', 'tokens', 'once');
+        imageAltText = string(imageTokens{1});
+        imagePath = resolveImagePath(string(imageTokens{2}), options.MediaRelativePath);
+        imageDisplayWidth = resolveImageDisplayWidth( ...
+            imagePath, options.ImageNames, options.ImageDisplayWidths);
+
+        blockLines = [blockLines; createImageDirective(imagePath, imageAltText, imageDisplayWidth)]; %#ok<AGROW>
+        blockLines(end+1, 1) = "";
+
+        nextIndex = nextIndex + 1;
+        while nextIndex <= numel(markdownLines) && normalizeBlockLine(markdownLines(nextIndex)) == ""
+            nextIndex = nextIndex + 1;
+        end
+    end
+end
+
+function [blockLines, nextIndex] = readListBlock(markdownLines, startIndex, options)
+    blockLines = strings(0, 1);
+    nextIndex = startIndex;
+
+    while nextIndex <= numel(markdownLines)
+        thisLine = normalizeBlockLine(markdownLines(nextIndex));
+        if ~isListLine(thisLine)
+            break
+        end
+
+        tokens = regexp(thisLine, '^([-*]|\d+\.)\s+(.*)$', 'tokens', 'once');
+        marker = string(tokens{1});
+        itemText = string(tokens{2});
+        childBlockLines = strings(0, 1);
+        [itemText, inlineImageLines] = extractInlineImages(itemText, options);
+        childBlockLines = [childBlockLines; inlineImageLines];
+
+        nextIndex = nextIndex + 1;
+        while nextIndex <= numel(markdownLines)
+            continuationLine = normalizeBlockLine(markdownLines(nextIndex));
+            if isListLine(continuationLine) || isSpecialBlockStart(continuationLine)
+                break
+            end
+
+            if continuationLine == ""
+                lookaheadIndex = nextIndex + 1;
+                while lookaheadIndex <= numel(markdownLines) && normalizeBlockLine(markdownLines(lookaheadIndex)) == ""
+                    lookaheadIndex = lookaheadIndex + 1;
+                end
+
+                if lookaheadIndex > numel(markdownLines) || isListLine(normalizeBlockLine(markdownLines(lookaheadIndex)))
+                    nextIndex = lookaheadIndex;
+                    break
+                end
+
+                nextBlockLine = normalizeBlockLine(markdownLines(lookaheadIndex));
+                if isImageLine(nextBlockLine)
+                    imageTokens = regexp(nextBlockLine, '!\[([^\]]*)\]\(([^)]+)\)', 'tokens', 'once');
+                    imageAltText = string(imageTokens{1});
+                    imagePath = resolveImagePath(string(imageTokens{2}), options.MediaRelativePath);
+                    imageDisplayWidth = resolveImageDisplayWidth( ...
+                        imagePath, options.ImageNames, options.ImageDisplayWidths);
+                    childBlockLines = [childBlockLines; ""; createImageDirective(imagePath, imageAltText, imageDisplayWidth)]; %#ok<AGROW>
+                    nextIndex = lookaheadIndex + 1;
+                    continue
+                end
+
+                if startsWith(nextBlockLine, "```")
+                    [nestedCodeBlock, lookaheadIndex] = readCodeBlock(markdownLines, lookaheadIndex);
+                    childBlockLines = [childBlockLines; ""; indentBlock(nestedCodeBlock, "   ")]; %#ok<AGROW>
+                    nextIndex = lookaheadIndex;
+                    continue
+                end
+
+                nextIndex = lookaheadIndex;
+                break
+            end
+
+            itemText = itemText + " " + continuationLine;
+            nextIndex = nextIndex + 1;
+        end
+
+        [itemPrefix, itemBody] = getListMarkerPrefix(marker, itemText);
+        blockLines(end+1, 1) = itemPrefix + normalizeInlineMarkdown(itemBody);
+        if ~isempty(childBlockLines)
+            blockLines(end+1, 1) = "";
+            blockLines = [blockLines; indentBlock(trimBlankLines(childBlockLines), "   ")]; %#ok<AGROW>
+        end
+    end
+end
+
+function [blockLines, nextIndex] = readParagraphBlock(markdownLines, startIndex)
+    paragraphParts = strings(0, 1);
+    nextIndex = startIndex;
+
+    while nextIndex <= numel(markdownLines)
+        thisLine = normalizeBlockLine(markdownLines(nextIndex));
+        if thisLine == "" || isSpecialBlockStart(thisLine)
+            break
+        end
+
+        paragraphParts(end+1, 1) = thisLine;
+        nextIndex = nextIndex + 1;
+    end
+
+    blockLines = normalizeInlineMarkdown(strjoin(paragraphParts, " "));
+end
+
+function tf = isSpecialBlockStart(lineText)
+    tf = startsWith(lineText, "```") || ...
+        startsWith(lineText, "|") || ...
+        isImageLine(lineText) || ...
+        isListLine(lineText) || ...
+        ~isempty(regexp(lineText, '^(#+)\s+', 'once')) || ...
+        startsWith(lineText, "<a name=");
+end
+
+function tf = isListLine(lineText)
+    tf = ~isempty(regexp(lineText, '^([-*]|\d+\.)\s+', 'once'));
+end
+
+function tf = isImageLine(lineText)
+    tf = ~isempty(regexp(lineText, '^!\[[^\]]*\]\(([^)]+)\)$', 'once'));
+end
+
+function tf = isMarkdownAlignmentRow(lineText)
+    cells = splitTableRow(lineText);
+    tf = ~isempty(cells);
+    for i = 1:numel(cells)
+        tf = tf && ~isempty(regexp(cells(i), '^:?-+:?$', 'once'));
+    end
+end
+
+function cells = splitTableRow(lineText)
+    cells = split(lineText, "|");
+    if ~isempty(cells) && cells(1) == ""
+        cells(1) = [];
+    end
+    if ~isempty(cells) && cells(end) == ""
+        cells(end) = [];
+    end
+    cells = strtrim(cells);
+end
+
+function imagePath = resolveImagePath(imagePath, mediaRelativePath)
+    imagePath = strtrim(imagePath);
+
+    if mediaRelativePath == "" || ~contains(imagePath, "_media/")
+        return
+    end
+
+    pathParts = split(imagePath, "/");
+    imagePath = mediaRelativePath + "/" + pathParts(end);
+end
+
+function [text, imageBlockLines] = extractInlineImages(text, options)
+    imageBlockLines = strings(0, 1);
+    imagePattern = '!\[([^\]]*)\]\(([^)]+)\)';
+    imageTokens = regexp(text, imagePattern, 'tokens');
+
+    for i = 1:numel(imageTokens)
+        imageAltText = string(imageTokens{i}{1});
+        imagePath = resolveImagePath(string(imageTokens{i}{2}), options.MediaRelativePath);
+        imageDisplayWidth = resolveImageDisplayWidth( ...
+            imagePath, options.ImageNames, options.ImageDisplayWidths);
+        imageBlockLines = [imageBlockLines; ""; createImageDirective(imagePath, imageAltText, imageDisplayWidth)]; %#ok<AGROW>
+    end
+
+    text = strtrim(regexprep(text, imagePattern, ''));
+end
+
+function imageDisplayWidth = resolveImageDisplayWidth(imagePath, imageNames, imageDisplayWidths)
+    imageDisplayWidth = nan;
+    if isempty(imageNames) || isempty(imageDisplayWidths)
+        return
+    end
+
+    [~, baseName, extension] = fileparts(imagePath);
+    imageName = string(baseName) + string(extension);
+    matchIndex = find(imageNames == imageName, 1);
+    if isempty(matchIndex)
+        return
+    end
+
+    if matchIndex <= numel(imageDisplayWidths)
+        imageDisplayWidth = imageDisplayWidths(matchIndex);
+    end
+end
+
+function [itemPrefix, itemBody] = getListMarkerPrefix(marker, itemText)
+    if endsWith(marker, ".")
+        itemPrefix = marker + " ";
+    else
+        itemPrefix = "* ";
+    end
+    itemBody = itemText;
+end
+
+function language = getCodeBlockLanguage(blockLanguage)
+    if blockLanguage == "matlab"
+        language = "matlab";
+    else
+        language = "text";
+    end
+end
+
+function underlineCharacter = getHeadingUnderline(headingLevel)
+    if headingLevel <= 1
+        underlineCharacter = '-';
+    elseif headingLevel == 2
+        underlineCharacter = '~';
+    elseif headingLevel == 3
+        underlineCharacter = '^';
+    else
+        underlineCharacter = '"';
+    end
+end
+
+function text = normalizeInlineMarkdown(text)
+    text = replace(text, "&nbsp;", " ");
+    text = replace(text, "&emsp;", " ");
+    text = replace(text, "\_", "_");
+    text = replace(text, "\-", "-");
+    text = replace(text, "\>", ">");
+    text = replace(text, "\*", "*");
+    text = replace(text, "\(", "(");
+    text = replace(text, "\)", ")");
+    text = replace(text, "\[", "[");
+    text = replace(text, "\]", "]");
+
+    text = regexprep(text, '`([^`]+)`', '``$1``');
+    text = convertMarkdownLinks(text);
+    text = regexprep(text, '\*\*``([^`]+)``\*\*', '``$1``');
+end
+
+function text = convertMarkdownLinks(text)
+    expression = '\[([^\]]+)\]\(([^)]+)\)';
+    [matches, tokens] = regexp(text, expression, 'match', 'tokens');
+
+    for i = 1:numel(matches)
+        label = normalizeLinkLabel(string(tokens{i}{1}));
+        url = string(tokens{i}{2});
+
+        if isTutorialLink(url)
+            [~, targetName] = fileparts(url);
+            replacement = "`" + label + " <" + targetName + ">`_";
+        else
+            replacement = "`" + label + " <" + url + ">`_";
+        end
+
+        text = replace(text, matches{i}, replacement);
+    end
+end
+
+function label = normalizeLinkLabel(label)
+    label = strtrim(label);
+    label = replace(label, "**", "");
+    label = replace(label, "*", "");
+    label = replace(label, "``", "");
+    label = regexprep(label, '`([^`]+)`', '$1');
+    label = replace(label, "\_", "_");
+end
+
+function tf = isTutorialLink(url)
+    [~, ~, extension] = fileparts(url);
+    tf = ismember(extension, [".mlx", ".m", ".rst"]);
+end
+
+function rstLines = appendBlock(rstLines, blockLines)
+    blockLines = trimBlankLines(blockLines);
+    if isempty(blockLines)
+        return
+    end
+
+    if ~isempty(rstLines) && rstLines(end) ~= ""
+        rstLines(end+1, 1) = "";
+    end
+
+    rstLines = [rstLines; blockLines];
+end
+
+function blockLines = indentBlock(blockLines, indentation)
+    blockLines = string(blockLines);
+    for i = 1:numel(blockLines)
+        if blockLines(i) == ""
+            continue
+        end
+        blockLines(i) = indentation + blockLines(i);
+    end
+    blockLines = reshape(blockLines, [], 1);
+end
+
+function lines = trimBlankLines(lines)
+    while ~isempty(lines) && strtrim(lines(1)) == ""
+        lines(1) = [];
+    end
+
+    while ~isempty(lines) && strtrim(lines(end)) == ""
+        lines(end) = [];
+    end
+end
+
+function lineText = normalizeBlockLine(lineText)
+    lineText = strtrim(replace(lineText, "&nbsp;", " "));
+end
diff --git a/tools/documentation/private/generateRstForTutorials.m b/tools/documentation/private/generateRstForTutorials.m
index bc028ce60..64330eb2f 100644
--- a/tools/documentation/private/generateRstForTutorials.m
+++ b/tools/documentation/private/generateRstForTutorials.m
@@ -1,29 +1,74 @@
-function generateRstForTutorials()
-% generateRstForTutorials - Generate rst files for all the tutorial HTML files
+function generateRstForTutorials(options)
+% generateRstForTutorials - Generate rst files for tutorial pages
+
+    arguments
+        options.FileNames (1,:) string = string.empty
+    end
 
     docsSourceRootDir = fullfile(misc.getMatnwbDir, 'docs', 'source');
-    
+    tutorialStaticRootDir = fullfile(docsSourceRootDir, '_static', 'tutorials', 'media');
     tutorialHtmlSourceDir = fullfile(docsSourceRootDir, '_static', 'html', 'tutorials');
     tutorialRstTargetDir = fullfile(docsSourceRootDir, 'pages', 'tutorials');
+    legacyTutorialStaticRootDir = fullfile(docsSourceRootDir, '_static', 'markdown');
     if ~isfolder(tutorialRstTargetDir); mkdir(tutorialRstTargetDir); end
+    if ~isfolder(tutorialStaticRootDir); mkdir(tutorialStaticRootDir); end
     
     tutorialConfigFilePath = fullfile(docsSourceRootDir, '_config', 'tutorial_config.json');
     S = jsondecode(fileread(tutorialConfigFilePath));
 
     rstTemplate = fileread( getRstTemplateFile('tutorial') );
+    tempMarkdownRootDir = fullfile(tempdir, 'matnwbTutorialMarkdown');
+    safeRemoveDir(tempMarkdownRootDir)
+    mkdir(tempMarkdownRootDir);
+    cleanupDeleteTempMarkdown = onCleanup(@() safeRemoveDir(tempMarkdownRootDir)); %#ok<NASGU>
 
-    % List all html files in source dir
-    L = dir(fullfile(tutorialHtmlSourceDir, '*.html'));
-    
-    for i = 1:numel(L)
-        thisFilePath = fullfile(L(i).folder, L(i).name);
-        relPath = strrep(thisFilePath, docsSourceRootDir, '../..');
-    
-        [~, name] = fileparts(relPath);
+    tutorialNames = string(fieldnames(S.titles));
+    tutorialNames = sort(tutorialNames);
+
+    if ~isempty(options.FileNames)
+        tutorialNames = intersect(tutorialNames, options.FileNames, 'stable');
+    end
+
+    for i = 1:numel(tutorialNames)
+        name = tutorialNames(i);
+        [sourceFilePath, sourceRepoPath] = resolveTutorialSource(name);
+        if sourceFilePath == ""
+            warning('Could not find a tutorial source file for "%s"', name)
+            continue
+        end
+
+        htmlFilePath = fullfile(tutorialHtmlSourceDir, name + ".html");
+        relPath = strrep(htmlFilePath, docsSourceRootDir, '../..');
         title = S.titles.(name);
     
         rstOutput = replace(rstTemplate, '{{static_html_path}}', relPath);
         rstOutput = replace(rstOutput, '{{tutorial_name}}', name);
+        rstOutput = replace(rstOutput, '{{tutorial_source_path}}', sourceRepoPath);
+
+        [~, ~, sourceExtension] = fileparts(sourceFilePath);
+        if sourceExtension == ".mlx"
+            markdownFilePath = fullfile(tempMarkdownRootDir, name + ".md");
+            export(char(sourceFilePath), char(markdownFilePath));
+
+            sourceMediaDir = fullfile(tempMarkdownRootDir, name + "_media");
+            targetMediaDir = fullfile(tutorialStaticRootDir, name);
+            safeRemoveDir(targetMediaDir)
+            if isfolder(sourceMediaDir)
+                copyfile(sourceMediaDir, targetMediaDir);
+            end
+
+            [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths( ...
+                markdownFilePath, htmlFilePath );
+            mediaRelativePath = "../../_static/tutorials/media/" + name;
+            tutorialBody = convertTutorialMarkdownToRst(markdownFilePath, ...
+                "MediaRelativePath", mediaRelativePath, ...
+                "ImageNames", imageNames, ...
+                "ImageDisplayWidths", imageDisplayWidths);
+        else
+            tutorialBody = convertTutorialMCodeToRst(sourceFilePath);
+        end
+
+        rstOutput = replace(rstOutput, '{{tutorial_body}}', tutorialBody);
 
         % Add the youtube badge block if the tutorial has a corresponding youtube video
         if isfield(S.youtube, name)
@@ -36,20 +81,102 @@ function generateRstForTutorials()
         rstOutput = replace(rstOutput, '{{youtube_badge_block}}', youtubeBadge);
         rstOutput = replace(rstOutput, '{{tutorial_title}}', title);
         rstOutput = replace(rstOutput, '{{tutorial_title_underline}}', repmat('=', 1, numel(title)));
-        rstOutputFile = fullfile(tutorialRstTargetDir, [name, '.rst']);
-        fid = fopen(rstOutputFile, 'wt');
-        fwrite(fid, rstOutput);
-        fclose(fid);
+        rstOutputFile = fullfile(tutorialRstTargetDir, name + ".rst");
+        filewrite(char(rstOutputFile), rstOutput)
     end
 
     % Create index
     indexTemplate = fileread( getRstTemplateFile('index_tutorials') );
-    [~, fileNames] = fileparts(string({L.name}));
-    data.file_list = strjoin("   "+fileNames, newline);
+    data.file_list = strjoin("   "+tutorialNames, newline);
     
     thisRst = fillTemplate(indexTemplate, data);
     rstFilePath = fullfile(tutorialRstTargetDir, ['index', '.rst']);
 
     % Commented out because currently this index file is edited manually.
     %filewrite(rstFilePath, thisRst);
-end
\ No newline at end of file
+
+    safeRemoveDir(legacyTutorialStaticRootDir)
+end
+
+function [sourceFilePath, sourceRepoPath] = resolveTutorialSource(tutorialName)
+    tutorialRootDir = fullfile(misc.getMatnwbDir, 'tutorials');
+    candidateFilePaths = [ ...
+        fullfile(tutorialRootDir, tutorialName + ".mlx")
+        fullfile(tutorialRootDir, tutorialName + ".m")
+        fullfile(tutorialRootDir, 'private', 'mcode', tutorialName + ".m")
+    ];
+    candidateRepoPaths = [ ...
+        fullfile('tutorials', tutorialName + ".mlx")
+        fullfile('tutorials', tutorialName + ".m")
+        fullfile('tutorials', tutorialName + ".mlx")
+    ];
+
+    sourceFilePath = "";
+    sourceRepoPath = "";
+
+    for i = 1:numel(candidateFilePaths)
+        if isfile(candidateFilePaths(i))
+            sourceFilePath = candidateFilePaths(i);
+            sourceRepoPath = strrep(candidateRepoPaths(i), filesep, '/');
+            return
+        end
+    end
+end
+
+function maybeRemovePath(folderPath)
+    pathEntries = string(split(path, pathsep));
+    if any(pathEntries == folderPath)
+        rmpath(folderPath)
+    end
+end
+
+function [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths(markdownFilePath, htmlFilePath)
+    imageNames = string.empty(1, 0);
+    imageDisplayWidths = double.empty(1, 0);
+
+    markdownText = fileread(markdownFilePath);
+    markdownMatches = regexp(markdownText, '!\[[^\]]*\]\(([^)]+)\)', 'tokens');
+    if isempty(markdownMatches)
+        return
+    end
+
+    imagePaths = strings(numel(markdownMatches), 1);
+    for i = 1:numel(markdownMatches)
+        imagePaths(i) = string(markdownMatches{i}{1});
+    end
+    imageNames = cellfun(@extractImageName, cellstr(imagePaths), 'UniformOutput', false);
+    imageNames = string(imageNames);
+
+    if ~isfile(htmlFilePath)
+        imageDisplayWidths = nan(size(imageNames));
+        return
+    end
+
+    htmlText = fileread(htmlFilePath);
+    widthTokens = regexp(htmlText, '<img class = "imageNode"[^>]*width = "(\d+)"', 'tokens');
+    if isempty(widthTokens)
+        imageDisplayWidths = nan(size(imageNames));
+        return
+    end
+
+    htmlWidths = nan(numel(widthTokens), 1);
+    for i = 1:numel(widthTokens)
+        htmlWidths(i) = str2double(widthTokens{i}{1});
+    end
+
+    imageDisplayWidths = nan(size(imageNames));
+    numMatches = min(numel(imageNames), numel(htmlWidths));
+    imageDisplayWidths(1:numMatches) = htmlWidths(1:numMatches);
+end
+
+function imageName = extractImageName(imagePath)
+    [~, baseName, extension] = fileparts(imagePath);
+    imageName = string(baseName) + string(extension);
+end
+
+function safeRemoveDir(folderPath)
+    if isfolder(folderPath)
+        maybeRemovePath(folderPath)
+        rmdir(folderPath, 's');
+    end
+end
diff --git a/tools/githooks/pre-commit-linux b/tools/githooks/pre-commit-linux
index 9f5befa74..333e1e70f 100644
--- a/tools/githooks/pre-commit-linux
+++ b/tools/githooks/pre-commit-linux
@@ -4,6 +4,8 @@
 # Relative paths
 TUTORIAL_FOLDER="tutorials"
 PRIVATE_FOLDER="$TUTORIAL_FOLDER/private"
+TUTORIAL_HTML_FOLDER="docs/source/_static/html/tutorials"
+TUTORIAL_RST_FOLDER="docs/source/pages/tutorials"
 
 # Define file mappings (script files and their corresponding documentation)
 SCRIPT_FILES=("generateCore.m" "generateExtension.m" "nwbRead.m" "nwbExport.m")
@@ -23,17 +25,19 @@ if [[ -n "$TUTORIAL_FILES" ]]; then
         # Get the base name without extension
         BASENAME=$(basename "$TUTORIAL_FILE" .mlx)
 
-        # Find corresponding .html and .m files
-        HTML_FILE=$(find "$TUTORIAL_FOLDER" -name "$BASENAME.html" -print -quit)
+        # Find corresponding generated files
+        HTML_FILE=$(find "$TUTORIAL_HTML_FOLDER" -name "$BASENAME.html" -print -quit)
         MC_FILE=$(find "$PRIVATE_FOLDER" -name "$BASENAME.m" -print -quit)
+        RST_FILE=$(find "$TUTORIAL_RST_FOLDER" -name "$BASENAME.rst" -print -quit)
 
         # Get modification dates (default to 0 if file doesn't exist)
         TUTORIAL_FILE_DATE=$(stat -c "%Y" "$TUTORIAL_FILE")
         HTML_FILE_DATE=$(stat -c "%Y" "$HTML_FILE" 2>/dev/null || echo 0)
         MC_FILE_DATE=$(stat -c "%Y" "$MC_FILE" 2>/dev/null || echo 0)
+        RST_FILE_DATE=$(stat -c "%Y" "$RST_FILE" 2>/dev/null || echo 0)
 
-        # Check if .html or .m file is outdated
-        if [[ "$TUTORIAL_FILE_DATE" -gt "$HTML_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$MC_FILE_DATE" ]]; then
+        # Check if generated files are outdated
+        if [[ "$TUTORIAL_FILE_DATE" -gt "$HTML_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$MC_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$RST_FILE_DATE" ]]; then
             echo "Error: Please re-export live script \"$BASENAME.mlx\"." >&2
             exit 1
         fi
diff --git a/tools/githooks/pre-commit-mac b/tools/githooks/pre-commit-mac
index 4e693d80c..ac4004da2 100644
--- a/tools/githooks/pre-commit-mac
+++ b/tools/githooks/pre-commit-mac
@@ -3,6 +3,8 @@
 # Relative paths
 TUTORIAL_FOLDER="tutorials"
 PRIVATE_FOLDER="$TUTORIAL_FOLDER/private"
+TUTORIAL_HTML_FOLDER="docs/source/_static/html/tutorials"
+TUTORIAL_RST_FOLDER="docs/source/pages/tutorials"
 
 # Define file mappings (script files and their corresponding documentation)
 SCRIPT_FILES=("generateCore.m" "generateExtension.m" "nwbRead.m" "nwbExport.m")
@@ -21,18 +23,20 @@ if [[ -n "$TUTORIAL_FILES" ]]; then
         # Get the base name without extension
         BASENAME=$(basename "$TUTORIAL_FILE" .mlx)
 
-        # Find corresponding .html and .m files
-        HTML_FILE=$(find "$TUTORIAL_FOLDER" -name "$BASENAME.html" -print -quit)
+        # Find corresponding generated files
+        HTML_FILE=$(find "$TUTORIAL_HTML_FOLDER" -name "$BASENAME.html" -print -quit)
         MC_FILE=$(find "$PRIVATE_FOLDER" -name "$BASENAME.m" -print -quit)
+        RST_FILE=$(find "$TUTORIAL_RST_FOLDER" -name "$BASENAME.rst" -print -quit)
 
         # Get modification dates (default to 0 if file doesn't exist)
         TUTORIAL_FILE_DATE=$(stat -f "%m" "$TUTORIAL_FILE")
         HTML_FILE_DATE=$(stat -f "%m" "$HTML_FILE" 2>/dev/null || echo 0)
         MC_FILE_DATE=$(stat -f "%m" "$MC_FILE" 2>/dev/null || echo 0)
+        RST_FILE_DATE=$(stat -f "%m" "$RST_FILE" 2>/dev/null || echo 0)
 
-        # Check if .html or .m file is outdated
-        if [[ "$TUTORIAL_FILE_DATE" -gt "$HTML_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$MC_FILE_DATE" ]]; then
-            echo "Error: Please re-export html/m-files for live script \"$BASENAME.mlx\"." >&2
+        # Check if generated files are outdated
+        if [[ "$TUTORIAL_FILE_DATE" -gt "$HTML_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$MC_FILE_DATE" || "$TUTORIAL_FILE_DATE" -gt "$RST_FILE_DATE" ]]; then
+            echo "Error: Please re-export html/m/rst files for live script \"$BASENAME.mlx\"." >&2
             exit 1
         fi
     done

From 9e22cd1740fd443860b3ede0e0673eea30d7e797 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Sun, 19 Apr 2026 21:13:40 +0200
Subject: [PATCH 3/9] Update ogen tutorial - remove random uuid generation

---
 docs/source/_static/html/tutorials/ogen.html |  12 ++++++------
 docs/source/pages/tutorials/ogen.rst         |   8 ++++----
 tutorials/ogen.mlx                           | Bin 5508 -> 5479 bytes
 tutorials/private/mcode/ogen.m               |   4 ++--
 4 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/docs/source/_static/html/tutorials/ogen.html b/docs/source/_static/html/tutorials/ogen.html
index fa4a1666e..91acc2c60 100644
--- a/docs/source/_static/html/tutorials/ogen.html
+++ b/docs/source/_static/html/tutorials/ogen.html
@@ -44,11 +44,11 @@
 .S8 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 0px none rgb(33, 33, 33); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 0px 0px 4px 4px; padding: 0px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
 .S9 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 25px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 20px; font-weight: 700; text-align: left;  }
 .S10 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }
-.S11 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Optogenetics</span></h1><div  class = 'S1'><span>This tutorial will demonstrate how to write optogenetics data.</span></div><h2  class = 'S2'><span>Creating an NWBFile object</span></h2><div  class = 'S1'><span>When creating a NWB file, the first step is to create the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span> </span><span>object using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, char(java.util.UUID.randomUUID), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First M.'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="4337F5E0" prevent-scroll="true" data-testid="output_0" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S11 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }</style></head><body><div class = rtcContent><h1  class = 'S0'><span>Optogenetics</span></h1><div  class = 'S1'><span>This tutorial will demonstrate how to write optogenetics data.</span></div><h2  class = 'S2'><span>Creating an NWBFile object</span></h2><div  class = 'S1'><span>When creating a NWB file, the first step is to create the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span> </span><span>object using </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/functions/NwbFile.html"><span style=' font-weight: bold; font-family: monospace;'>NwbFile</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'matnwb_optogenetics_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'Last, First M.'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, </span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="CA2063BA" prevent-scroll="true" data-testid="output_0" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
-                                              identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                              identifier: 'matnwb_optogenetics_tutorial'
                                      session_description: 'mouse in open exploration'
                                       session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                timestamps_reference_time: []
@@ -97,11 +97,11 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S9'><span>Adding optogenetic data</span></h2><div  class = 'S1'><span>The</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>ogen </span><span>module contains two data types that you will need to write optogenetics data,</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>, which contains metadata about the stimulus site, and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>, which contains the values of the time series.</span></div><div  class = 'S1'><span>First, you need to create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>object linked to the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div></div><div  class = 'S10'><span>Now, you can create and add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_stim_site = types.core.OptogeneticStimulusSite( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'This is an example optogenetic site.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'VISrl'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_optogenetics.set(</span><span style="color: #a709f5;">'OptogeneticStimulusSite'</span><span >, ogen_stim_site);</span></span></div></div></div><div  class = 'S10'><span>With the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span> </span><span>added, you can now create and add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>. Here, we will generate some random data and specify the timing using</span><span> </span><span style=' font-family: monospace;'>rate</span><span>. If you have samples at irregular intervals, you should use</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>instead.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_series = types.core.OptogeneticSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(20, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'site'</span><span >, types.untyped.SoftLink(ogen_stim_site), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30.0);  </span><span style="color: #008013;">% Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'OptogeneticSeries'</span><span >, ogen_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="E1F68076" prevent-scroll="true" data-testid="output_1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+</div></div></div></div></div></div><h3  class = 'S7' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S1'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'001'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 1'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S9'><span>Adding optogenetic data</span></h2><div  class = 'S1'><span>The</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>ogen </span><span>module contains two data types that you will need to write optogenetics data,</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>, which contains metadata about the stimulus site, and</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>, which contains the values of the time series.</span></div><div  class = 'S1'><span>First, you need to create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> </span><span>object linked to the</span><span> </span><span style=' font-weight: bold; font-family: monospace;'>NWBFile</span><span>:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >device = types.core.Device();</span></span></div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div></div><div  class = 'S10'><span>Now, you can create and add an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span>. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_stim_site = types.core.OptogeneticStimulusSite( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'This is an example optogenetic site.'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'VISrl'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb.general_optogenetics.set(</span><span style="color: #a709f5;">'OptogeneticStimulusSite'</span><span >, ogen_stim_site);</span></span></div></div></div><div  class = 'S10'><span>With the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticStimulusSite.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticStimulusSite</span></a><span> </span><span>added, you can now create and add a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OptogeneticSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OptogeneticSeries</span></a><span>. Here, we will generate some random data and specify the timing using</span><span> </span><span style=' font-family: monospace;'>rate</span><span>. If you have samples at irregular intervals, you should use</span><span> </span><span style=' font-family: monospace;'>timestamps</span><span> </span><span>instead.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S3'><span style="white-space: pre"><span >ogen_series = types.core.OptogeneticSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, randn(20, 1), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'site'</span><span >, types.untyped.SoftLink(ogen_stim_site), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 30.0);  </span><span style="color: #008013;">% Hz</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'><span style="white-space: pre"><span >nwb.stimulus_presentation.set(</span><span style="color: #a709f5;">'OptogeneticSeries'</span><span >, ogen_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S4'>&nbsp;</div></div><div class="inlineWrapper outputs"><div  class = 'S5'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S6'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="D76CAB1F" prevent-scroll="true" data-testid="output_1" style="width: 1145px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" tabindex="-1"><div class="textElement eoOutputContent scrollArea" data-previous-available-width="1108" data-previous-scroll-height="802" data-hashorizontaloverflow="false" style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " tabindex="-1"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
-                                              identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                              identifier: 'matnwb_optogenetics_tutorial'
                                      session_description: 'mouse in open exploration'
                                       session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                timestamps_reference_time: []
@@ -162,8 +162,8 @@
 % |*NwbFile*|>.
 
 nwb = NwbFile( ...
-    'session_description', 'mouse in open exploration',...
-    'identifier', char(java.util.UUID.randomUUID), ...
+    'identifier', 'matnwb_optogenetics_tutorial', ...
+    'session_description', 'mouse in open exploration', ...
     'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
     'general_experimenter', 'Last, First M.', ... % optional
     'general_session_id', 'session_1234', ... % optional
diff --git a/docs/source/pages/tutorials/ogen.rst b/docs/source/pages/tutorials/ogen.rst
index 712fa9d55..7674206da 100644
--- a/docs/source/pages/tutorials/ogen.rst
+++ b/docs/source/pages/tutorials/ogen.rst
@@ -25,8 +25,8 @@ When creating a NWB file, the first step is to create the ``NWBFile`` object usi
 .. code-block:: matlab
 
    nwb = NwbFile( ...
-       'session_description', 'mouse in open exploration',...
-       'identifier', char(java.util.UUID.randomUUID), ...
+       'identifier', 'matnwb_optogenetics_tutorial', ...
+       'session_description', 'mouse in open exploration', ...
        'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
        'general_experimenter', 'Last, First M.', ... % optional
        'general_session_id', 'session_1234', ... % optional
@@ -41,7 +41,7 @@ When creating a NWB file, the first step is to create the ``NWBFile`` object usi
    
                                                 nwb_version: '2.9.0'
                                            file_create_date: []
-                                                 identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                                 identifier: 'matnwb_optogenetics_tutorial'
                                         session_description: 'mouse in open exploration'
                                          session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                   timestamps_reference_time: []
@@ -151,7 +151,7 @@ With the `OptogeneticStimulusSite <https://matnwb.readthedocs.io/en/latest/pages
    
                                                 nwb_version: '2.9.0'
                                            file_create_date: []
-                                                 identifier: '141f8f81-2f93-4258-a7db-5ef3757e0f02'
+                                                 identifier: 'matnwb_optogenetics_tutorial'
                                         session_description: 'mouse in open exploration'
                                          session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                   timestamps_reference_time: []
diff --git a/tutorials/ogen.mlx b/tutorials/ogen.mlx
index f0b0ea0c3be2878d5cc807629bd2d6d842dd5ff2..28d010003b866bf14a0a1dd910c86b3ebac1a739 100644
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diff --git a/tutorials/private/mcode/ogen.m b/tutorials/private/mcode/ogen.m
index 29b7f6912..b4c3e8a11 100644
--- a/tutorials/private/mcode/ogen.m
+++ b/tutorials/private/mcode/ogen.m
@@ -6,8 +6,8 @@
 % |*NwbFile*|>.
 
 nwb = NwbFile( ...
-    'session_description', 'mouse in open exploration',...
-    'identifier', char(java.util.UUID.randomUUID), ...
+    'identifier', 'matnwb_optogenetics_tutorial', ...
+    'session_description', 'mouse in open exploration', ...
     'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
     'general_experimenter', 'Last, First M.', ... % optional
     'general_session_id', 'session_1234', ... % optional

From c4840f6aa3455bf0eeb73d767d7dcdef619bbcb9 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 12:29:12 +0200
Subject: [PATCH 4/9] Update .codespellrc

---
 .codespellrc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.codespellrc b/.codespellrc
index 5c4c3a73d..8820ec9f9 100644
--- a/.codespellrc
+++ b/.codespellrc
@@ -1,3 +1,3 @@
 [codespell]
 skip = *.html,*.svg,*fastsearch.m,*.yaml,*testResults.xml
-ignore-words-list = DNE,nd,whos
+ignore-words-list = DNE,nd,whos,ans

From fb1ceb5d4fcdc7f65da36e8dd5578f9d38157418 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 13:53:47 +0200
Subject: [PATCH 5/9] Update ophys tutorial

Updated file identifier. Should be globally unique and not confused with session id
Used reproducible random number generation to produce predictable files / output
---
 +types/+core/Units.m                          |   2 +-
 docs/source/_static/html/tutorials/ophys.html | 201 ++++++++++--------
 .../tutorials/media/ophys/figure_0.png        | Bin 2865 -> 37830 bytes
 .../tutorials/media/ophys/figure_1.png        | Bin 0 -> 17935 bytes
 .../tutorials/media/ophys/figure_2.png        | Bin 0 -> 4444 bytes
 docs/source/pages/tutorials/ophys.rst         | 148 +++++++++----
 tutorials/ophys.mlx                           | Bin 174228 -> 228591 bytes
 tutorials/private/mcode/ophys.m               | 111 ++++++++--
 8 files changed, 311 insertions(+), 151 deletions(-)
 create mode 100644 docs/source/_static/tutorials/media/ophys/figure_1.png
 create mode 100644 docs/source/_static/tutorials/media/ophys/figure_2.png

diff --git a/+types/+core/Units.m b/+types/+core/Units.m
index e6af0fb04..a09fa714e 100644
--- a/+types/+core/Units.m
+++ b/+types/+core/Units.m
@@ -131,7 +131,7 @@
         obj.waveforms_index_index = p.Results.waveforms_index_index;
         obj.waveforms_sampling_rate = p.Results.waveforms_sampling_rate;
         obj.waveforms_unit = p.Results.waveforms_unit;
-
+        
         % Only execute validation/setup code when called directly in this class's
         % constructor, not when invoked through superclass constructor chain
         if strcmp(class(obj), 'types.core.Units') %#ok<STISA>
diff --git a/docs/source/_static/html/tutorials/ophys.html b/docs/source/_static/html/tutorials/ophys.html
index 53ddf2901..500120066 100644
--- a/docs/source/_static/html/tutorials/ophys.html
+++ b/docs/source/_static/html/tutorials/ophys.html
@@ -52,54 +52,30 @@
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 .S18 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left;  }
-.variableValue { width: 100% !important; }
-.embeddedOutputsMatrixElement,.eoOutputWrapper .matrixElement {    min-height: 18px;    box-sizing: border-box;}
-.embeddedOutputsMatrixElement .matrixElement,.eoOutputWrapper  .matrixElement,.rtcDataTipElement .matrixElement {    position: relative;}
-.matrixElement .variableValue,.rtcDataTipElement .matrixElement .variableValue {    white-space: pre;    display: inline-block;    vertical-align: top;    overflow: hidden;}
-.embeddedOutputsMatrixElement.inlineElement {}
-.embeddedOutputsMatrixElement.inlineElement .topHeaderWrapper {    display: none;}
-.embeddedOutputsMatrixElement.inlineElement .veTable .body {    padding-top: 0 !important;    max-height: 100px;}
-.inlineElement .matrixElement {    max-height: 300px;}
-.embeddedOutputsMatrixElement.rightPaneElement {}
-.rightPaneElement .matrixElement,.rtcDataTipElement .matrixElement {    overflow: hidden;    padding-left: 9px;}
-.rightPaneElement .matrixElement {    margin-bottom: -1px;}
-.embeddedOutputsMatrixElement .matrixElement .valueContainer,.eoOutputWrapper .matrixElement .valueContainer,.rtcDataTipElement .matrixElement .valueContainer {    white-space: nowrap;    margin-bottom: 3px;}
-.embeddedOutputsMatrixElement .matrixElement .valueContainer .horizontalEllipsis.hide,.embeddedOutputsMatrixElement .matrixElement .verticalEllipsis.hide,.eoOutputWrapper .matrixElement .valueContainer .horizontalEllipsis.hide,.eoOutputWrapper .matrixElement .verticalEllipsis.hide,.rtcDataTipElement .matrixElement .valueContainer .horizontalEllipsis.hide,.rtcDataTipElement .matrixElement .verticalEllipsis.hide {    display: none;}
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-.embeddedOutputsVariableElement {    white-space: pre-wrap;    word-wrap: break-word;    min-height: 18px;    max-height: 250px;    overflow: auto;}
-.eoOutputWrapper .variableElement {    padding-top: 2px;}
-.embeddedOutputsVariableElement.inlineElement {}
-.inlineElement .variableElement {}
-.embeddedOutputsVariableElement.rightPaneElement {    min-height: 16px;}
-.rightPaneElement .variableElement {    padding-left: 9px;}
-.outputsOnRight .embeddedOutputsVariableElement.rightPaneElement .eoOutputContent {    /* Remove extra space allocated for navigation border */    margin-top: 0;    margin-bottom: 0;}
-.variableNameElement {    margin-bottom: 3px;    display: inline-block;}
-/* * Ellipses as base64 for HTML export. */.matrixElement .horizontalEllipsis,.rtcDataTipElement .matrixElement .horizontalEllipsis {    display: inline-block;    margin-top: 3px;    /* base64 encoded version of images-liveeditor/HEllipsis.png */    width: 30px;    height: 12px;    background-repeat: no-repeat;    background-image: url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAJCAYAAADO1CeCAAAAJUlEQVR42mP4//8/A70xw0i29BUDFPxnAEtTW37wWDqakIa4pQDvOOG89lHX2gAAAABJRU5ErkJggg==");}
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-.S19 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_B7893FC2' ><span>MatNWB Optical Physiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_B7893FC2"><span>MatNWB Optical Physiology Tutorial
-</span></a><span>    </span><a href = "#H_C9814973"><span>Introduction
-</span></a><span>    </span><a href = "#H_11954BED"><span>Set up the NWB file
-</span></a><span>        </span><a href = "#H_6e62"><span>Subject Information
-</span></a><span>    </span><a href = "#H_3CE571F5"><span>Optical Physiology
-</span></a><span>        </span><a href = "#H_A752E725"><span>Imaging Plane
-</span></a><span>        </span><a href = "#H_35FE25AF"><span>Storing Two-Photon Data
-</span></a><span>        </span><a href = "#H_26A56A9B"><span>Storing One-Photon Data
-</span></a><span>        </span><a href = "#H_71940743"><span>Motion Correction (optional)
-</span></a><span>        </span><a href = "#H_CF66BA0A"><span>Plane Segmentation
-</span></a><span>        </span><a href = "#H_531DA023"><span>Regions of interest (ROIs)
-</span></a><span>        </span><a href = "#H_58FDA251"><span>Adding ROIs to NWB file 
-</span></a><span>        </span><a href = "#H_A4473BF5"><span>Storing fluorescence of ROIs over time
-</span></a><span>    </span><a href = "#H_16ABF2B0"><span>Writing the NWB file
-</span></a><span>    </span><a href = "#H_F9A25D9C"><span>Reading the NWB file
-</span></a><a href = "#T_C8BDC905"><span>Learn more!
-</span></a><span>        </span><a href = "#H_872126D0"><span>See the API documentation to learn what data types are available.
-</span></a><span>    </span><a href = "#H_6F0B7108"><span>Other MatNWB tutorials
-</span></a><span>    </span><a href = "#H_1310FB5A"><span>Python tutorials</span></a></div></div><h2  class = 'S4' id = 'H_C9814973' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_11954BED' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'session_1234'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="B9672902" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S19 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_17e7' ><span>MatNWB Optical Physiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_17e7"><span>MatNWB Optical Physiology Tutorial
+</span></a><span>    </span><a href = "#H_8c0c"><span>Introduction
+</span></a><span>    </span><a href = "#H_6792"><span>Set up the NWB file
+</span></a><span>        </span><a href = "#H_46e0"><span>Subject Information
+</span></a><span>    </span><a href = "#H_5e37"><span>Optical Physiology
+</span></a><span>        </span><a href = "#H_2387"><span>Imaging Plane
+</span></a><span>        </span><a href = "#H_9082"><span>Storing Two-Photon Data
+</span></a><span>        </span><a href = "#H_9054"><span>Storing One-Photon Data
+</span></a><span>        </span><a href = "#H_8c47"><span>Motion Correction (optional)
+</span></a><span>        </span><a href = "#H_68e4"><span>Plane Segmentation
+</span></a><span>        </span><a href = "#H_5763"><span>Regions of interest (ROIs)
+</span></a><span>        </span><a href = "#H_6b62"><span>Adding ROIs to NWB file 
+</span></a><span>        </span><a href = "#H_47d3"><span>Storing fluorescence of ROIs over time
+</span></a><span>    </span><a href = "#H_5d01"><span>Writing the NWB file
+</span></a><span>    </span><a href = "#H_8378"><span>Reading the NWB file
+</span></a><a href = "#T_389a"><span>Learn more!
+</span></a><span>        </span><a href = "#H_7f6d"><span>See the API documentation to learn what data types are available.
+</span></a><span>    </span><a href = "#H_6a4f"><span>Other MatNWB tutorials
+</span></a><span>    </span><a href = "#H_379a"><span>Python tutorials
+</span></a><a href = "#T_7f67"><span>... Helper functions ...</span></a></div></div><h2  class = 'S4' id = 'H_8c0c' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_6792' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'matnwb_ophys_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="53C0C548" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
-                                              identifier: 'Mouse5_Day3'
+                                              identifier: 'matnwb_ophys_tutorial'
                                      session_description: 'mouse in open exploration'
                                       session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                timestamps_reference_time: {[2018-04-25T03:00:45.000000+02:00]}
@@ -130,7 +106,7 @@
                                     general_pharmacology: ''
                                         general_protocol: ''
                             general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
-                                      general_session_id: 'session_1234'
+                                      general_session_id: 'Mouse5_Day3'
                                           general_slices: ''
                                    general_source_script: ''
                          general_source_script_file_name: ''
@@ -148,32 +124,14 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S12' id = 'H_6e62' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_3CE571F5' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_A752E725' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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yoQ0/CEYNHTjf8Flzk/v0HjpvLvMxBOudFURj+OgNiA0wpiNfRfHaODsIaD8g3vx638WxAbUjmY5IUQEHvqEKv2U5Nwvf2P9jc7VqLtje2+n5ww3pcI3NTWq93Vtkgnbw9HRgbGyF99mtvUuRYef5rLlvsd/97Lpd+746XVHzk0yzGT83KYjX1iW3baILT3EKuLOW6AMF3M8de9q2ok/KasAtTVGHm6sa83AHHjK78qDZXNgTXS0PaxUID4jfLKN7Y3lr6/GkM/7mhEVG4vn8IEBLfeQHnjp/JWoKo09mHCGUJALBC7DxDrI2XudJBzIENXbI/fxme14d62CxQ9Hd9XbbTyq/oB7x+Un9rvqW24nDeebvQVub9UP+OAG/2MjrECUIfVFtxqhFTHnFfJUR+fnS0I7T3E5oBu3Tz+xBhPoXFTZBirgsKzRzJDW8tzqxtQQxsVgE4dhtcx5DK0IFY3fLV/dXNHudQoKnSgeortQWwV4I8QAmJPGWKrWft5tM77iDyCl50tDoMV3Gb4q9LwPs37pFY5ev1ttSKB0qj297JlKK/6o/g+f+71fZXjB6GDgtgMXr2LOW6I2F9DPnoydBBrLWnVmgXpwz35JlU+blNutJzmGMR6I5Xz0zFmcPYGrJVXD2J7ZUQIAbGnBLEezjW1r6qkNBR+4LDqoOn9XG6f8/7yDlftuLygY2Xr9QXVcffguAJU7zfVZ7X64NZI5lQgR2ivIHbKTdWKB6oArnWjtLZnmHNQ7Vk1+UzS0M1UiPWhoZ5l+fBOdeNPgVh/okYIAbGnCLG9h/oqjCwgNNr5+K7qm9Tm9trZVrue09BnzW1wE1COhbj1+olcDoWnqT6yKbQh9yvej1VzR8YgttdfIXTQEOuNhO9fweKqEOvxt/Fdy3qTu3pD9T1PrFZVaJkrGpo8+WAZiK1CGBBCQCwQW0NstL15glaYQ1yG2CrdMYjNn3s2FWKnfFdN+Kose39bIRJALDppiPVhl2q5qXVDrN+Iyy73sWw4wRyvqDesHh87F2IJJ0AIiAVixyFW8aRynuQYU/dgbgJiV1lHdY4n1vvcqUP7Hgfb8tQCsejkITavqaqbpQqCXxZifSZoDoKPPLVe7BSIraA0TyTLjV8OU9CszmoFhgzeeZmTapkvTxeIRQiIBWKnQaz6qtakJ4+rXSfE9lbFmQqZy8bEVhPBqjw9DrbVHxETi04SYt3jqhvAVzbwZU3ihlkVYn1fbQ8I9LUDp7xzunozikDYj0XH7/GtepGDllwJE9z6CgmetoayquPQhDHfn3X5EAJigdj5EOsr3ahdzmu95qWqVoFYD3Fzh4RCynJfUKlaSjLn5w4UX0KsNYFLgKrwOd+Wl4F0D3bl3GF1AnTUEFtN7Iqbw6FVaa0DYv2Gc4ubbyr09SCxtd5eNcEr8lTDE8ereqhWdVAjqu98W67rFmQjhIBYILbdV/nIWbSjvgasg5pm568KsSHv//SinFaYXaVqndjc7yjdqu+I/L0MgmkfufT+xX/v/ZfXla9eIFCv5nYgtJcQG2DamiBVTWRS4zEWL6N9Mpy1tuuVeq3VBlSWubC3ysSp1uQxlbVVBzrG3soJ3qARSoAQEAvE/iJUrHpxgL8iXdKry32kS+21Xv+ttFrpakLY1O0+CqmlrKaMCjp4V2ucR7l99QHfrmP3FzW4N1grNFQhAlrDPL9x0ifCTXH6ILSXEHsK0hPu2MzRbcqfnMdgFyEExJ4CxJ6CFFKwT/2RO3yqV9ciBMQCjAfRkCEExAKxQOzpOTAUprDK6gsIAbEbUu+VsqdcFoSAWCAWiD3tPqD1Jk2EgNg90pQXJGxDikNmMhdCQCwQe5rqrVSwTXmcLmEECIhFCCEgFogFYhFCQCxCCAGxQCxCCIhFCCEgFogFYhFCQCxCCAGxQCwQixACYhFCCIgFYhFCQCwQixCikQRigViEEBCLEEJALBALxCKEgFiEEAJigViEEBDbhNiPP/4YwzDs5AyIBWIRQkAshmEYEAvErgyxXJcYhln7DMRiGIYBsUAshmFALIZhGBALxAKxGIYBsRiGYUAsEEt/hGEYEIthGAbEArEYhgGxbh999NHtV1555cL2vQI2Uc59OvZ33333oixvv/32TstxSNcEhgGxQCyGYSfsiV387ML2vQI2Uc59OfYAx/Pz8wv74IMPdl6e559//qJeAFkMiAVitwmxTz/99EXbEw/1++5UWXcbuYk0l7X333//oiyPPvoo/REGxK7LU7lKw3bt2rWLBnKdaa7Lnnnmmc/dpGrQtpF/NFReN4LqyD8aM25ADIgFYoHY9QFnOCvit+602CeIjf4yn4co1zbOS/Q5Xgf0R0DsUUDsOryuGWL3wSJ8IMoWjUbVmG/DC1zVTatcGAbEArGnDrHrguB9PE6VLTyg2TO7DcCuHDj0R0Dshb3xxht3vG5xoQhgKlh69dVXL/bT/i+++GL5FBRPknGxxz5h4VWM30barScs7Z9v4ChfbI/9Io3YL/JV4+agFduVRlzgkW+UVcflaeqpMp7mdCxVmq1jj7RzrKrKGn+jvJFm5BP7xrapJzY/YcbfSFfbqzKOnZsoq8pdQbO+q+rGyy4PceTHTYgBsUDsuiE22mpvu8YgNvad6o2Lvmnd4VlVv9bzKLbyV9s6B2Ijvdh/ahly3c4JI4j+wPOJPmAMYlW+Veu45cCpRiyxE4HYuLACeASs+l8A4xdMXLi+Pf7X/nFhZ1jyNHXx5ac4QZ++E6CFCSj9CczTEcDlcmofL6vSjb+60HsQWwG85x3H5GWthvvjxtI+XscVQFbnJT9dCmL9+FUH+dxEfn5uvAHRfq1hmTh3YxCrc7aPHmwMA2IPF2I1gdTbbDkmMtzFvoqLdIt9M9DF77z9VDtZgU/VL3r/pvY50guI077V0L/vG+1m7uO8nPk4Wmm6wybvn8vq6cb23E9MdUSonn3/XJ9+fuK4qvLlc1P1Rw7NYT/96U+bdaOHEl0rtHcnBrG6COXlFKD4jZbhzIHVL1QHrspTF/vqZpf3Uk9xsV2QFBek9tPvlbcgOH6vJ9lWObVv7JcbO/3WG5heKIWgLY7dy+lp6sb0/KMecl5TQgFUpw7yvTCP1rnRdg/Cj/JkL6+Ow/Nr1Y2HGvhxYxgQC8SuArH+MC14dZjMEKv+S84Jh19v87y9irYu+pVIW/t6mu5UifRjf+2n/sidDPpO/V8LYv24PE13MuS+S2lUEOt9jxww3m+7p9edOfmhYGo8qdL2dDXqqDJ4LG91btyxon7DYdXTdrhVnLA7jTL0Ku1jDzcBYhtAlC9iv2DyRVwt9ZShSBdU9roKKHtPd/LkxncCRi9PhqYWxFazJ3Vj5MZoDGJ1Q1ZP7TlNL2seMsoQPzZsUu1XQazKUO1f3dx6eIjfqQ5iP6/bHsS2AvwxDIgFYpeFWLWd0bZUE5u8vXE49P4r2jC1n+o/BKZ5FEzeWT28C3bz6FV2QPjnSNPbzRbEqj/Uvl5O7ycrWM9pyvNYeablQKrSzF7QqbP7vY+YMpGtd25UPh/ZU3+kviZ/njJPR44fQgpOCGJ7nkEPB8gXkJ6C3DIo+dOsYiyrGKCpIFQFlI9BbAVfOXZmKsT24DM3BN4Qt+B0LDa2F/9VnTMfnmqdm/ygoLqoGsMpEHsqEy0wIBaI3Q7EtpwF7knNXstqtCpDkPd1vbZX/V7V5mnFmiiLp5f7hB7EVnM9MhxOgVh3QlTzW1pp5mNvwWKLFar9Kojt9detUDSde3mUq2UlexDbyxM7coitboTek2fP8nCHD28IaP3mnDok3VtipAWx1b69eKVemi3Pao6BHYPoqeBXDXO1yuYN/NRzk5/mqxt/KsTOmayGYUAsEDsGsVNGlLSv5ly4yTnibZcPn8f31YTkHhhPdQD1IHZKOlMgNvc5rVjSsf5rrI2fAoi9UIep5yaHubX6lR7ETj0W7AghtgqGzjeXg1I0MHoqzVZBXuzvsU0eDzM1JmcZiK1uuDzkMBdiK6jM8avrgNieh7rniY0bf+q5yUH3+RxMhdhdv0kMw4DY44DYXlxjbjurSUXVJCJ/2PdJWA606o+mznJfF8RWqwDNgdiqnL00lwW/OY6hqeem4g7/XcUSQCwQ2/TEZU9oFRPbGj6Zs4iznrT0lNXy5sW+WqJqWYjtQeRciNXvqpmcOc3ezP2pELtsOMFUr6jHOLXCHwgnwIBYIHYXntieF07tjYBzzGva6ot8AnOGw7Eh6WUhtloxYRWIbYU9bApip3pi5SCZM7Sv0AZfQhKIBWJHJ3blm7harik3MPnCVLC2VgLQUlDVxZQncvmwSBVwnr2bcyA2P835qgd5ckAOWG+lmctZBdj3bqa5ENtrzP3YVF95eCmfmzwDOHs1quD81iLSzAbFgFggdhsxsT5EnmNiq/Yp2rgKGFvxmfIK9hbOj3Y0yhft6LIQ24qJ9fymQGwV91pN2l0XxC4bE1uVrzo3cX49DrZ1HRATC8Q2h8LzMh3VElt+M2pJrLhhBLx+8egpWevoKaQghxPkJVX0IoO837IQ60uV+PBRtaSLr4ZQxZ3mtWbzEidTbvipEKuGqLohvW6r2aryrurFB61z4x4M95L7On/aFvs6ULMuHwbEArHrhthqKcO8vqvaJ2/z8kt01CcpjbyCTK+d1cO5r+Hamyg2B2IFwdqucnlbrPy9vc1petvsL/HxNL2vXBVi9RDRW53AHSh+bvJLhqpRzXx+qmUg3elWOXdYneCE39ilNzV5rEoVHF4tAq39Wx7AaiHmfAHqKSzHKXnYwjIQqzdl9fKu1gVs3fRRznzslXd2HRDbg0RvILyMU8rnT/D5adjX7M2Ndz6eHmRjGBALxC67TmyegKV2sBr5ceeEXspSDUc7FEU68qpWo1r55TveplYAPRVi/QU5+aU1Vf46Hr3NMvd/uZz5RUTVSxRWGYKv1onNdesvxfG+I58bdyS1QiPco5z7I6+bDP9z30SGHclrZwVB+a0kLdjSkMqUCyb2ay2xlfNvTQ5TftV3uZzVmnpzXsk39dhbaWoJlqpu5rwecNmJU71z06vjvIxM7406hBJgQCwQu26Izeu3ykGitwhmcHEnjEYQW68391eACyrjIbxqwzzN2K9ayaAVMuevHc+gGGnoNejV69U9PEzpx29ymrme1B5rNDUfe6usCv2bElfcWtNdcKk8vIxePoGne2b10JJBPodweB+obX4tMDIIxB6V9by2h2R6ws3vqt6X+p3y+lwMA2KB2LkQe0x2LBOONDq7b/1RL5YaA2KBWIDxYBoyDANigVgg9rQcGL3JeBgQC8TugfVeKXvKZcEwIBaIBWJPtw/wCW7EwgKxR2O9+NlDPp5d36SK9SUOFgNigVgg9jQhtrdSwbYtv/IdA2IxDMOAWCAWiMUwDIjFMAwDYoFYIBbDMCAWwzAMiAViMQwDYjEMw4BYIBaIxTAMiMUwDANigVggFsMwIBbDMAyIBWIxDANiMQzDgFggFojFMAyIxTAMA2KBWCAWw7ANQixCCJ2igFggFsMwIBYhhIBYIHZliEUIIWufgViEEAJigViEEBCLEEJALBALxCKEgFiEEAJigVj6I4QQEIsQQkAsEIsQAmK3rXfeeef2a6+9dvvTTz/deF6RT9g+KY47yhT1cMj5f/jhhxfpxN9N6LPPPvvc+Yttb7755p3y77IuVb4oDwJigVggFiF0AhB7/fr1KPjG4McV+YTtk+K4o0xRD4ecfwBcpLOph4Tnnnvuc+nr2lH5d12XKg8gC8QCsYcDsZvug3bdLu2qzd5W/ps+f9GeR/rRB3nZH3744Tvt/S7r8r333rvI+8knnwRid6FPPvnk4uILT5b0wgsvrAyb1QUV+WwDloHYzZQx36RqRMLzqutom3WZjzc8wVGmsG2MLCAgFogFYoHYzZ2/Vpsu50ls12jyNusyj3pWTh4gdocKWFkFYgNoDuWEArF9xcPN448/fpF2nNd8jr3c26zL1jWmp/Z967AQEAvEIiB2ubR9dK3qZ7ZZl8o/h9bJqeP95ElBbFTCyy+/fHFiAhrib3x2D2mcSG3P0nf5JIarO7aFdzW+zxWsPGN7WB4i9rziSSjyiaeOKGP8VSykpycI1nEoRjL+z3ChWMbYHhdB/I2y5nJGGipnHFPsE+nH31VunhZ4RT4qVxxP5BP5+vnw+lN8qI7Rz522R3lzvKjnH3lGPsqvdTOozmK/OAc6x9VN7Meh+or95zZ08bvqHOucxbZWXbau7cpb6teYjm/ONeYPYbuKc0ZA7LFAbNy7c0fQ8sje2PYxT1z8bgwMpu7XeiCe81vVyaYhUuWacxxVm9ranvPXcY2do3z8YxArtpirlkNC273exupS1/CUETpdS616qPIXax1yWMFKEBuVJm+XKkFUH391ATjtO4jIK5WfAnRi1dnr/yquUTexAKCCWKUhcPG0HTC0XwaMHBMbx+P5eRnzMepYfH/VRVgF9stCrIYwVCbP02HO60/DCX4MsU2hGV5Wv8iVf/zOz7mfA7+Z/JxqmMXrJQ+ve9162fJxtKTfeAM1B2K9LnP9+LWdAXTZa8wbmanHiIBYIPbzoOL3n7dp3h75Pe9ximqT9MDt2+O+9DQqCIr7OfcH8TnHu0/Zr/VwHft4udTu5DxUvuiPvE5i33wsq0JspBV17OVSPhnAfDg9t7Gxb66bSLfig/ir/ktpxrZ8XJFmtL25363On66fKccx1vd4mfP1qPK3INb7Sy9H5cjxfsXrrAJ2N/9e/de+hU1uHGIFOrrp/WLOAc26IeNEaN/K5a6OPE6KTpgDhXtHvdKVfg4n0MWQAUUnvQLOfEHldLWfeygjTdWHg4tfjO7F03Eu+/RTNXCqd4eguJFUd1UDrMYk16Fv9zRUft/Xz5U3TDqvDqXy9IapvloPKL6tun56T6Tad0q9VdtUBk380n6+vbpmvQxqGKZcY2PlRkAsEDsOsWrX494TJPgDc9We6H6O9sg/u0NE21uOlOyU0Won/ltBzdT9esPPam/iswOMPxR7G69+wY9x2YmkVRvmzhpNWFK58kO59zGxj0OYHGH52Ko68HMVaXi6ziO+3eG1gjml48ehfKY4nHRucxuuUUV/cNLKPLkuvV+MfaMcfo69vH4d61ry0b7qoUn551HCVZxqBwuxqtT8hOIXTlVRcZJaLveWiz/29yH4qRCrYfzsOauGm6dCbOVVzvuqXH7DTamjVSA2tuUh7NbDgrbli1Y3QMtzWwFvPlf5waEX76kbKwNr5JNhNZdhbqjFVIj1c5OPzYFc9Tx2jeXZqb3hI6W9rJcEAbGnCrHRLmSniu7nPDLj7Ze3J0qjFc/oIJz7oKpP0XCtj7pM3a9qqyqYzo6VXL7Y5vWhY/R2aRWI9ZFW73vUjmbHQzWq5mDv270trkDez507p7TdnUVVHfj5ax3HHOdCbzStB//a5v1L7nvkpKqcLblPrDyrlXNoSp95tBCbnyazVcO5edi1mo1dXUSVpkKsnyQ9WfkwxJQg65xuFQKRj2+swVrVhd+L49QTnqwasvehpimgqAcQpdGr7xxjU8Hc2I2l+J7qmhoLgq+WNpkDsWPXUquRWeUaO4ZhHSAWiN0lxPaWquu1X1WYQQvIvE3IfZDAqAo3qmB7bL9eu1QNmefvVL7c93jfvS5P7Jhn3NuzVvtZheNVabQcQ+7FVNl64NYqRyVxydi+vbqZArHqO3ujiFPOW66Hsbrw6+dkINYrtGf5pOskVY3NnJtrKsTG9hwv4jYXYscupLxkRe+iXmV2ZAVjOcaoisEZy7u1PR+Hx8SO3WzVDdWr8yrmq3UccxuSORDbCvWorj2PP17mGlvHNYGAWCD25/etJqrKdH9Oucdb7doYxDpwKe2A5wpUp+zXapeqslVQ1nJUVMeyKsT6BOGqv60gduooVMvjXXkN83e90bvKuZMnbI/xzBR4nAOxvWOrHqTU72tC8ZQ+f2wU8CQhVjPoKmtdkNUwg5+kseHUqRDrJ7YKDp8LsWNlzBfKtiDWy5WHj6qLd10Q23tiVGOr31bxNhn6/YkwDwFVjU6vka3ymwOxreGj7BlYxzUGxAKxQOxqEOtxlRliffWVseHT3vYxiBXMZaCoQhzG9stllAe3NeSbR3F6bck6ITZP8FZ8sceTzoHYsTaxN7KXQ9d6dZD7xd5xTIXYXn5zILY1uTf3M85Tuu7HRl+B2AI4py5D4ReYhnfyhdgKQ4g8tDj9VIh1IMpPYtUEnbnhBNWFmm/abUFsb4hqk57YKvQjNyS9p8t8s7WOw6+3MYiVt3/ZmNgq7rUqh5Y+Wcc1lq+fQ12zD4gFYnftiZ0yzL0piM1DtGoLexOpWvvNfbhueWI3DbGtOOLW3IJ1QWzPEysI7IWY5PhiPwc51nYqxOawlbkQ25s/kkeqvVyZo4DYiRO7BJzZ46VlKny7z9qLyvdgex/ukDu+tZxJjnWpIFa/awGG7+vDSa2noFbjlferwG7XEOs3pp+PdUKs162vTqH6EVRqCZWqvsYg1p+GxzqpVSd2+bWd81L55GVuXWPe4FTXWGtSxaE2JEAsELtriK1WxXEQaS2xtS6IVRx/y3mjdmDqfnMerisnzrYgtor993Z/UxBb7ZtH9vQ5M4rXZWaKfP1Uk8Cm1s1ciJ0y10QPMbpeeiPaxMSOQGxeZkmvUdNTgF842lbN+PQJXnmNUJ8g48Pk1Q3qF2G+MHXyHEB9qZFIN1/YrXVi83ErFqiaXbnNmFhfr9WX5dDToS8Jts5wgtZSIN7Q5rVe8zInPtM1r32o3/p10FuhoBfyMRViveHScite5mopm1WusSoMAwGxQOx0iPU2yVc36a1OsE6Ibb2sJHvnpu43tvSfL8VVOVZ24YnVMeW1Xr3fXyfE+vqtPpdCdeNOEfdw5za75YnNxzG2Mk5vUvEUiPW+0o/N511UoXc67/nYvBzuIJwzennUEKuDrxZedoDtuf9142aoyGm2Fu+t1kzLE7Fy3JFOYhXr4uvNqUzVzVUdd2/pk21AbIZIrR2XQWudEKu4oVy/1TB8tah49RaR6jjUePj2ngS8eVLDVIjNjXNvUfHqGlPjUV1jvs3zPOS1+oBYIHYf1on1tii/eMQ7701ArK+nrZeZ+It2BNVT96vK6L89K16i4+3utiDWnQZn9mKYKJt7D9WurQtiWy8cyu1nnoeTr40KCvNxuNez1z73luOaCrGZLfw4W/NEvLzxW2co9+R7up7nya4Tm+M1Wq/A03fVRKje6wF7v+t91yvfMuq9vlDp9uKiWq+Nm3sMVb216nssprKVd2t7Po6cvz6PLY2W9+v9btnXMTqAtpYgy/G2vbymvt6xNUw49XxOXV4OAbFA7O2uY0VgqFdWZw+aXlVeddq97fmhM78SPe5/X1C/t/LA2H6tMurFOu6Vrd7AVZWvdSxz29b8tsHIQy9s0Kicv748yqfjaOXd2p6Pw/OPc603hfXeqqVXo+u18/G71nFoBC0fR0CfH0dL1UtuWvVWbfNy6OUPmrBV9cty+ugaUh1E/qo7dxCpnj3P6i1jJwexCO2bekH9+6Ycy4WAWCB2OYhFaJfK66Tvu3qTyYBYNFnVCyNaNhaXg37xlNp6qcY+aWxdWgTEArFALDocHYpT4lD6SCD2ADTlxRFVHCU67KfMatIJAmKBWCB2G1LIwlTDgTK9XT+E5RIPabQSiEUnq17M8j40dqvE/iIgFohFQOz+ad9n/I+9PAOIRQghIBaIBWIRQkAsQggBsUAs/RFCCIhFCCEgFohFCAGxCCEExAKxQCxCCIhFCCEgFogFYhFCQCxCCAGxB6t7F/YQ/RFCKLXPP1nYjxf2w4V9O9qJob04A2IRQjSSQOy+QOyD9EcIodQ+//2F/f7C/vrCvhXtBBCLEEJA7D7p8sIeoD9CCKX2+ZmFPb2w7y/sG9FODO3FLyD2448/xjAMOzkDYvdGlxb2lTgfGIZhskW78HcW9jsL++7Cvh7txNBeALEYhgGxQOxe6J6FXVnYIwt7dPC6/PbCfndhf3thfzDExWEYdvz2B8N9/7tDO/D9oV14ZGgn7gFiMQwDYoHYfVuhIOLdvjZ0WL+xsO8t7ImFXRsmdmAYdvx2bbjvvze0A48O7cKDd61MAMRiGAbEArF7FFLw5aGjemQYOowYuG8OEzq+jWHYSdi3hvv+G0M78MjQLnz5rlACIBbDMCAWiN0jb+ylwdNyZYh9e2DovB4a1ofEMOz47aHhvn9gaAeuDO3Cpbu8sEAshmFALBC7hzB7z9BhXR6W07l36MQwDDt+0z1/eWgH7vkcvI5B7BtvvHH7lVdeuf3BBx+stdN4++23L9J99913Lz5H+vE58jvmzjIf967so48+uihHGBCDAbFA7IHBLYZhx2/T1YLYp59+Ohr3tUNXwFOkK4iK9ONz5HesHWWA+vn5+YWt+6FgGXvmmWfuOgcYBsQCsQghdHBqQez7779/AZjhudskxEb6kU/kt+kOa1fgpgeCV1999c62F1988WLbNvIPePbjjjqPbZH/Nuodw4BYIBYhhLYGsZuyDLHbMnl8t51vgGvlaRbYbsMLXB13q1wYBsQCsQghdNAQG57CABx56iJmNT7H39gW31+7du1iaLoVzxoA9fzzz99+9NFHL/4qLtShKtKKdCO9Vhxp5BFWeQ0jjwCy+F75RHncg6yyRr6xj47Dvc7xu9iu791r6uAZpjher5+eFzTyVRiBjje2CSIzSEbeXpYov+cT9RLbo8xVLLO+GztulePY45ExDIhFCKETgtgcEyv4DFgUmAUYCcZiu/8+IErf+X6CqrGYWP+98ou/DpcBqko7vlPaytMhVvtlmIu/+k1VVodhB0/934sZDthUOg7MLYiNvPwY4n8/PoGs7+f1odhblat33O6NzecOw4BYIBYhhI4OYgU98iwKQs9saFzbPBYz9teEojGIVbxogJry93wCDr1M/tvIR+Dm3ssqjMFjQwOadUwBekrDIdGhOraPTXoTiFde3VxnXsYMrF4fDsPaV+VW/bpXuxe+oVCDSAOgwYBYIBYhhI4eYvMMewFfhsvKwzcFYisIVboBaCpTfB955v0EfT7cXsGcvLAVxFUxoyp7NYw/d4WHCmIF1KrH6js/Vi9j5fWdEoPcOqcYBsQCsQghdFQQmyHJ9xUo5c8V8LYgNjyPFeD11j2NNALotP6p8ncArWCu8uRmb7KXQ5+nzujXkP9UiHXAz6Z6y2l5aEIL/HsQu6ml1DAMiAViEUJoryC2Ar48QUifK49iBtwMsfpcwXIFmh7Dmm0MYjXcXwFeBdNz4Nr3r5YpO2uEYIxZLqvCCloeYiAWw4BYhBA6aYjtvZQg71uti5q9ky2IdSgb64CqSU8eTjDVE1vBX1WOuRCbVyboQaxDc8B/1EtlOS2PM65epgDEYhgQixBCQOxEiK0mGM2JiT1rxGnGvgF4sd0hMy8RJVAeg1jFlFZeX8XL+ioHcyF2bkzsWZq4NuVVtip/a93XMYjthTxgGBALxCKE0ElBrIAqANBB1Jez6kFsK6ZWnletVVtBrA/LVxDrXlfNzj8rYkkVauD7LwuxFZSeFaEG2j97hmOfeDDwFRR8OS2VPXu5x8JAxkIeMAyIBWIRQuikINaHxgO09OKEM1vztQexDqIBrlr8P3tNfU1Vz8OH2OO3UR6H3shbZfX1aOP/+E5lzN7guRArgKw80l7WXBc67oDzKL+O0+G2An0/RoGtw26UI7/kIXubMQyIBWIRQuhkIVbwFNDkQChIPJvwsoOALf+tgM89pnlil4A5w2kFrMo/ANe3O0Rm7+xciO1BogPnWZrglSerxXH5cmW9MIhqFQlfHq1aj7eCbAwDYoFYhBA6SIgNyAqg0jCzlrKqlpfK+1ZLYOm7gFufoNRLdyztvM8qnZnKUU2ecsCcm4+8qFPjXP0hYKy+Vymnv+2MeFgMiAViEULoaCAWW4/5m7X2Ke4ULyyGAbEIIQTEYkvHxu7CFMIRnlgmdGFALBCLEEJALLbU27u2aYQRYBgQixBCQCw2O8a1Fce6TYjtxSBjGBALxCKEEBCLYRgGxAKxCCEExGIYhgGxQCxCCAGxGIYBsUAsQggBsRiGYUAsEIsQQkAshmEYEAvEIoQQEIthGAbEIoQQEIthGAbEArEIIQTEYhiGAbFALEIITYJYhBA6RQGxCCEExCKEEBALxCKEEBCLEEJALBCLEEJALEIIAbEIIQTEIoQQEAvEIoQQEIsQQkAsEIsQQqtC7DvvvHP7tddeu/3pp5/udSekcn744Yc7L0vUVZTlzTff3Jt6+eSTTyAVhIBYhBA6HYi9fv16NO57AYc9qZwBbLvWc889d1GWfYDYOG9RlieffBJSQQiIRQghIHaXClCNMrnC0xhl3LXH+L333rsoW9Sbl21bdVjVjaB6HwAfISAWiEUIoZOFWJVp3/TZZ5/dfvjhhy/K5sP34ZHdVh1WddMqF0IIiEUIoZOD2PA4hocvhqnD4v/Y1oK78AK+/PLLF+m1PILx+9hHaeZYzvg/fi8gi//l8VTaEQOah9NVzscff/xinzzMr3QjDZU1PsdvYt/YNkUvvPDC5zyekU7kezYM6cdnP6axeoy8dZwZQP27OM5W3YSiXrKHGCEExCKE0ElBrGAtLKBJ4BQW32VA1Pe+X6TrQ//yVgr2fF+BqWDT0xCUVTGxGlqXCSbPGsP9Aslc1vh/ipS+w6aDZYZYL1/so7xzPWq/HNeqMIH426sbAa++mwrlCAGxQCxCCB0NxGqiUEBXwFUAkbyXgjXBafzVNt9X8CUo8+Fuh2XBW+zvEoxV5RTERt4OhCpTwLLycq+tA7TKEL8RmLa8zBmEY/8pDwJePi9H5FMN/efj03mI/BxKq7rJ0Ju91QgBsUAsQggdPcQKhLLHtQItDWFnCA2Ai23aLyAs0s9w5d5D99pOgVgBcDV8Xn1XAaV7nSPUoKfecH0FsSpDVY+RV/7OHwgCbgXXOcyjB7HKkwleCAGxCCF0chCrz9XyURkOBWNToSngLDyRsb9MUOawNgViKxDsAedZY6i9B8NTAbGCWD0MKEbYzb/LMcNnFupQgfUUiK3qBCEgFohFCKGjhthq2L8Fh/rt2FB8gKOgs2VzIba3bqyG4j2NFvxtCmI9frVlVZ4es1upB7HV8l8IISAWIYROyhNbxVVm4Ju6PqngN3sJPZxgLsT28vZ40l1DbBxvbK8sr0bg8N3KqwexrFCAEBCLEEInD7EVQOX40d7wdYQj5Djb8DL6cL48h8tA7LIxsatCbDXEX0GsPM85XrjnrVYcbMBoa93XM2JiEQJiEUIIiP08gMmbl4EzJh5pqFteWvd4+sQspSFPaAWxvopBC2I9zd7sfd9Pa6tmmBuD2DHYVH69iV2en+ogYDS/ZSwAP/KrYmgFyVqSLC+7VdVNBmcgFiEgFiGETg5ifTksLbPl68bmJaY89jMgKky/V6yse1wDyrRPmH6vBf0d6PzFCRUoet5RRl8GLP5OWZpqqvdSS2ZVsaoO7ZFO9mqrfLGfQNPLp/rJy2np9+79reomx9OOxSgjBMQCsQghdHQQm72uZ2kiUp7d755PB90MWA7C2ieGyn09VXk5fZvAs4LY7M11UJ46DD9nCF51Uk168zrwpcXGytdaQ1f1kL+r6mYMshECYoFYhBA6OogNmApAqt7yFGCkSUhjb4HSWrAZHqt9qqHwMamc1W+Vbi9tfd86xillaq2LOyXetVWPY/XWO+4qlGBsvVuEgFggFi2lL2DYCdp+Qyyap6nLim1T/pY1XjmLEBCLVobVexZ2aWGXF3bvYPdh2Amarv/Lwz1xz07gFohdj/T62X0CRmJhEQJi0Vrg9dLQcV9Z2FcW9sDCHlzYQwv7KoadoD003AMPDPfEleEeubRVmAVi16epKxqcWlkQAmKB2ANWdMpfjg47rhcMw+62xb3x9YU9MkDtl4d7Bog9RLVibHfhGZ4Ss4wQEAvEoq4X9r6hc/5aXC8P/o1/hGHYYEMb+t2hDf3acK/ctzVvLBCLEAJigVhU6p5hmDS8TI8CsRhWQuxvL+z7Qzv6yHDP3APEIoQQEAvE7jaU4CvDcOl3gVgMKyH27wwg+xsL+9XhnrkExCKEEBALxO5Ol4eJK99Y2PeAWAwrIfaPFva7cY8s7NeGe+YyEIsQQkAsELs73TvE+H1rYU8AsRhWQuyfLOz34h5Z2DeHe+ZeIBYhhIBYIHa3EBtLCX17YdeAWAwrIfYfLOz34x4ZHvgeAmIRQgiIBWJ3vzJBrIn5nYX9EIjFsBJif7KwP4h7ZHjg++rWVigAYhFCQCwQi4BYDANiEUIIiAVi90c3F3YViMWwI4XYjz/+GMMw7OQMiD0JvbSw28PfK0AshgGxGIZhpw6x58NsXCB2/xWe2J8t7NZQX0DsAds3r//p7X/z05/dMeoEiMUwDANip0Ps+eXLl//L4OF7Aog9KK/1rQFoHwNip1mAYtZf/Ne/vNj+9/7Jv779xE9evv0rf+vm1srz+N/9p3eVBZgEYjEMw4DYaRB7AbDXr1+//cILL9zeA5AFYufpioUYvN4JMQBiOxCb9R//0wcXHtJtlOev/t4LF/AsAyaBWAzDMCB2HGIvADbgVdoDkAViVwsxuD3U4SUgdhxi//TV/3AHHgNcs3c2vLLAHRALxGIYhu0XxH4OYKXXXnttlyD7LBC7kp5qhBgAsQXExlB+9oo6zAbI5t8H2EYaf/6fb90JQ/jxP/6Xd77/nef/xZ341n/4z/79534f++r7gOexmNgIbYj94jtBdy53TjvKFfbP/92fAeJA7Hx75ZVXLmyTebz77rsXecTfQz+WXdkydfj+++9f/ObVV1/di2N44403LsrzwQcfbCyPSDvyiLwO5brdh3v/7bffvvg/rpk9g9gmwErvvPPOrkD29QFk0fIKL+zNFGIAxE6AWE20CgCUPKwgILKl+E6/78W4BvxKAZi9mNiAUi+LK5e9FyZBmAIQO8uGxmPjnWXksWnA3Max7BI45tbhtWvXLn4TgOLbP/roo4t0nn766dvn5+d36i0+P//88xuDzEg/8tkkFEbaOpbq+6iLF1988U7dPProo7efeeaZC9DPx72t63Yf7n3VW9RHXB97ArGjAJtB9v777//hFgFsHyaXHVOIwVtDnf4fA8wCsSMQm/eRlzXDpjypFVhmUHVAzl7eFsTG7zKIupc48tAEtPD+5rKFF9gVXmZAFYj9nAWw5A45Oq9teEiB2O3WofYPQMsQ5+Aa0BLAF3+1Lb5f1Xsb+eZzEV6+uNY2CUk9iA1A92PMEB//O8ieEsSGBdzH5/i7BxA7GWAzyN53331/cwvQdT7U3zn8ufYQg78Y7CkgdhxiHU5jSD7/zj2b8X32xvrvtS1vV6hBC2IdQgNSFVogb3BAsADby6Z9WzCOAbF3DbPuqkMGYrdbhwGLgjIHxrgGBG3hicyex9hX8Lmqx1RQvIuwiwpiBbBRrjxkHp/lJY56UZ2dGsTGceu8bfrBdgRiz7/0pS/9tzkAm0H20qVL/+cWYOv2jkBP6+TeOFL7Xxf26lC/f7awHwGx8yDWvaDxv2JYfbtiWsPr6R5Tpev7KkyhBbHuzR1bKSGvrNArG3agEBudSXhD5CFrDfNqn+iEw3MWABL7R6fr8JKHT+M3ihmM/yuvlWLkIt/4vurUIo/IN/bZxrBspK1h8Mgze/TUKatcqr/4v/L+KWZU+0Wacdx5X9VzHJfXc2zPQOTnJNJS3cTfVryhlzX+VunOqUOBaI4L1fbWUHveL64Zj2fVdZOPy8+3A6HykmfP66aKY9X3LS9w5K1rOayK4awgNtKroD5fy3E8sY/qzes80o28dcwtyJtSRtVR7BfHrv1Vv15G39fDQGL/3nWt+1bXVFWn1UNfnFu1EzuC2PP777//L5cB2C2C7JWzeYv3r0OPDTGjt3Tehs/HaP9qYf/vcIx/DsRODyeQ13WKfCJYBlYH2/iut06s7ztl7dgpAmIPGGKjU/Oh3TzM6x2iYMG9Z/K0+ZCwOt8KYquOTJ1+zj8DVIYVL4N3rqtCbKTl6fuxOEzoe9VHrjsvU/zvdSXIr4bglbf++hB0jjnVPvL8xb6+fwbLfFwqs8PUnDqM43KYr6BlzMsmr73v6+EJ+VrT8QqgvC4dYquY2Pjfz4Ofg+oaElz7vjm9DLEq81jdtR6+4vfKr3c/zC1j7OPXsv+22tcfRP0c9MJF/PpyL3Nv5EK/3+QEvAbE/nerAuyWQPb1IX5z2xOfXho8sMccwkA4wQoTuxTT6lAaQ/Px28p8UpaDsH/2of0KYiNsYE48q6tVtm2tewvEbgBiBT/u/Yy/DlIVAMkjFuagMTY0mjsyAWx0lIIoH4YWGFceG897GQAbm6AUaatjdWDQMToMetlVT14m1bPgSwAmQPAO3OtZXu6wKo3WvlWMapSnejjRvl63U+tQaWZvq0IMpg7x6zgE6F7fujb9uHxfgVfOK0Oshz14XekaUl359afz4nl7/VcQq9/OnXnvx+YeZ58EVYVqTC2jwDLXh6fR2tcfdh2QtS1f17qH3CPbuh50XW9yVYsCYr+2ANj/bx0Au2GQfWqLsbBaT/XW2fhbrpjYxRJbdyZPtWJiW+YgGml6uv5GsFY4ge/vk8O0xJcv4eX7ekwsdiQQ2/KWuYdNnXEFtrnjzhAyBrGtoWiFDWi7JunkcgqCvUyrQKwfdzXs7Us2ab/c+bZAIo4le5t0/J6Ge7yroWuHGY+tHIPIDIq9lQWm1mFrP/fozYFYpeNA1wo/kMd1KsTqWvHz4g9DyjuugfhNrqeqTjPEuld52TjknrdS92LkM7eM1X2uOtL15/vme1Kwmeu9CgWoHjpbx6bj3uQErwSxv75ugN0QyN44+8UC/duAOnlfL7HEFhDrLzvwCVoVEPoKABGvGjAZ0JvT87yqNBVjOwaxHpcbMK2QhGrlA983gFaeV4dbltk6UIgd85ap41TH50PXUyFkDGLlyZnqtRIMas1J9ySvA2LHlkyqjmVKnKTDkcqueMNc1gwWPcDWvhUA5H3dy+dlCBPEjp2/bJphvirE5iH4yjucz6/qdyrEzvX4Kc7Y62kMYud6oKes8NBbLmxOGXMoQHWevS5zeEgun4+i5OtJ10V1/bWAd8o9twaI/cGVK1c+3QTArhFkzwcv4a3BE7sNsPvZALDHvhIBLztY8bWzLejLb/Vy+ZJXFfhWXtUexEZarTVic4zr2L4eg4sdGMT2PCkVTFXAlb00cyC25/WsgMJjH7OtC2IrL+rc1QkqiM2xg9kqiK1iSVsQO2XfVt5VOabWYRU6sc5wggpscgjDVIidum5sXjWhst65zkP061gRIpd91TJOgdixfR2Yp5Rjzv2yIYj9o00D7Aoge2UAq2eTl3AbujnA3bF6YBUmwWtnV4DY+C48pa34UV/iyr2krd9kuKzeANZ72UH8Ppc3YDnCCHKcbGvfKC9rxB4BxLZAQ9CYIaDXyc71xE7t8L3D9iHUKiZzFYitwhPWAbFnjbVBq0lAm4bYOEaFZmTLMcBjdViFQ8yd2OWT3vLErh54LQuxY2/X8omG/jBThQpU53pqPnHcVWzyFIj1MroXfmoZ1wmxip1t2T5A7I9+9KOLPLYBsBlk77nnnv+7s7xTtQLAtlchuH2kMbBXBu/y2EMBELtmC2jd5mSpgN2pMMpEriOC2CrutQchrVhNB95quaAeWGVQ9s440lK5WjO0q2Wc1hFOUA29Rnkc9KZ2yhourepZ8LYNiFXoxpTh9FVjYsdiqKs0qoll1XnQULUAcyrE9hbXF9j3yq0HpjFA7IVC9B4A5kBsq4wOt8tA7JTYVX91bOsczXnoa00OPBaI/eIXv/h+Z3mnZ3e8AsDrRxpGcKMROgDELrkM1SkIWD3AiV05FrKaya7YOHWc0TnntTqzR7XlScsdmcfZeQyehye4py57nXy5pHWtTpC9ghn4l4XYvCqAw2214sAciK28fnnfKn7YgcpnxM+F2AoM/bqo1tnN0OUedh+qzudBMK5jbsVx5nr08Bm/fjNEVevW5mXXdB4rQPQyxvZ83FqXOXvml4HYZcu4Doh1r2+eYKYVTvy6nJruiYYT7Eq3zo7rVba+wsJUjzYQi4DYQ4VY9y5pqai8bFPuSAV5ASY+gcM7SQfM2Ke1TqzvpwkigggHaH+Fp+/ja19qMfdVO8W8vFOAlmDfYXPO8Kivxxrp6bOvMZtjj+dAbHWsVV17WEP8Ri8+WNabPRZXndcA1ioAUY8e45wh2D2xKlts84cWh0NfFk1lrupR+8XvI88oR35o8Xsivtc+enWs8tFweQWIeT1alT/HsS4bTrCOMq4Kse7dVvqadFk9CLbSnRp+cUITu7alY3uV7Uu2wsKceGLCCTDskN/YFZ1HNekoDzt7Z5NfkFB1QBkGWx1ZdOQ5/9jfvTvVxC55b71D9xnaq3h2vHMWiOS3Rc2B2Fxngqi81usmIbY3Qa612P+UOhxbrD6/Vcstv0Ciyj+/2CK/IS57Y3v1GL/Lx6+Hp97ErvhNHIc/CGj92hYg+prBOb849/m4V53YtUwZV4XY1gSvqk2o0u29LIMltjauJ8529yrbTU1Qu7rE74BYDDtkiHXYqNZibXWkWqtybsxp6zfKv9eZKc8p+6zjDUC98ra+01JaVZxxbBtbTqxXDzlP7Vsda6/sqqOx76fUYS/WdO411gIm1ekc0OnV45TjU55T9pl6Tntp9crUOpZVy1jl2To/Y3WWJwdOuR7nrAZyoi872HTc6OtnCIjFsGOA2KnLIM0FV+y4zWNA13VtbCNOEtutyWOcQ0N47SwQC8RiGBALxA7D+Hkh9p4BF8t7rKfMyAdisbGXemwRYr8Y8aCrguyBAiwQC8RiGBB7KB3lVAMulrfW27uAWGyV9Zg3DLEXE5y+9KUv/bdlQPaAARaIBWIx7LQgdkrMKnbaYQVT4l2nDjWvK64Z2z/bdlsyArEXIHv58uX/MgdkBbD33Xff3zxQeANigVgMOx2IxTAMO0SbALGzQFYAe//99//wgOENiAViMQyIxTAMOwKInQSyAtizw39JABALxGIYEIthGHYkENsF2ddee+1YABaIBWIxDIjFMAw7MogtQTb+PyKABWKBWAwDYjEMw44QYu+A7PXr148RYIHYEYjFMOxuA2IxDMMOB2LvgOwRAiwQC8RiGBCLYRh2xBB7AbJHCLBA7AjEIoR+ISAWwzDsMCH2WAXEArEIHQ7EIoTQCTfAQCwQC8QiBMQihBAQC8QCsQgBsUAsQggBsUAsEIsQEAvEIoQQEAvEArHL6cMPP7ywzz77bKfl+OSTTy7K8emnn9KQAbEIIQTEArEXehaIBWIrvffeexfrIj/55JOUBQGxCCEExO6dXh9AFogFYu8oPK8PP/zwBTiGF1SKl3688847W/G8Rl6e93PPPXdRnnjtMwJiJ1/IccFs+qKJYYLII/7uUjFUEeV488039+7i2XQd+bne9dARQkDs1nSML3AAYldUBYwBlNuCyMgj8vL+rgXWCIjtamjktnLB7voJK578ohx60lS53B5//PGL10/GPtu8kbZRRy+//PJFHvEXISD26HU+tGvnMOx0iI22Xw/74fjQ556DwfeLv1VspxwV8V2krf1jKF2OBd/ey09pySkzp6+qhu4jT712OfrJnH+UK36nPKv+Ud9VZYkyKs34G/1s5BV5ep8X6RJWAMTuHcTGTbvroO24iXSDZnCMGyq2x42ToTaeWLfhudxGHcVxqPHYtVccISB24woP7C34dR7EytmhPiM7OTKkCf6qvqNyVMR2eRxlGlqvtnv/E/+rfNmmOidUXh+RrNIUXEa5qr7RHUJ+fBlABc1xbFH+Kh2X6oBJXkDs3kDsrhU3Q9wYYX5jtLyfcdPGDa6bKW7KY7mhAl7VGBNWgIDYo9ZLTOpaHmIFkdFmBogJ5BwW1Z5GX6FZ/gF21bC4j/xFWpGm7xt/o13Wdjkc4nMG0Ng3tue+ako8awWJkY7Sjr/uUFF9RJlje+zrx+IOEdWRANkdJzqO2N/3yw6VCrIREHvHVS+vo4ZLWhAbF1Dsp/3jxvUbMj7Hdr/BXPG0qafLyLcKGPc4TeVRSTeYyhNpz7nAFf+TfzM2hO9Pxu7B9fqMtFVHUcbcaMX2Vll1TGq0WkH1GopRHhV8RoOjc6LytPJt1QdCQOzR6LGhbX8Mfl0OYlseRd8e26r+SH1LtNd5W/QpLU9ulYb6xWjjtV/2Bqts0fZPdWJkVf2h4mQrp4c81X6M2WGkEDbfx+u4GhFUSEHV56IThdi4UPQ0pAtSN2O+eeJC9e3xv/aPC1M3jy7OPGSSn057N4fy0UWv/PwG1QWt/fwpOd8Yc548p0CsH0tuYLwcUT9evwLRXnyPN0iacJbLks+F8oi/fvNHA+bDUF62+H1ufFjOBAGxR61LQxjBTdh1eYjN/YK32ZWindW6q2rPq/C1VphBhja109quvqjVbk8ZVVUaFSBWfVAF4xXgVnCrvqsC4B7E9sqIThRidcH4khY+jOEXvj8tat+4AAWtuoH8hs4XqJ4s9QRZ3RzK2z2LykM3hc9W9JvIobzlCc43RHXjT51MlZ9+BadeR7nuouxe/gzQutHVoFVlUSMQ++j3PoyjevMg+eohoTo+B2iEgNijCyP42QCzaEmIrQCrcvpUE4TdoTDW37S2Z5irYnQr64WJZTAeK4f65DFrjfa16nEKxOJkAWKbEJYvWr8IBV0VHOY4H0FS3jfvl2+O1pOWgta1nz/RZWUIbCkPySwDsR7oX32u9pU3thXfo7rLKyWoLL1QjzhmzXLtDQ/1vus1IggBsQfrgX1paDeuwq2bh1iHtfg/+sL4neBvExAb7bnC8CrrOSZ6Q/U9T2z0V708cz/uI4i9frIHsWOhEehEILY1HO5PZf6dz0zMlme2VzEx1VB1vjl6QxStYZZcFl8OZEoaFaguC7F5eRC3PBRV1Ye82B4blcvSe2KuGqVWw9Y690AsAmKPLgb21uCBBWC3BLFy2GRnStU/rQqxvb58qpYNJ5jjFXWAV6hgdqARTgDEzobY6iLMN4V/7ln2FjqMVZ7HfHNMfTNHaymRsaGMqjFZBWLzsMicOnLo1RNyBf+rgv7cOurVC0JA7EEo1n99wryvNwkh2B7EKia02tfnnawLYj2EL+ensIax9WKnhNdVqy9UYQpaGcHz9PkwsX9rNLVXx1MdOOjEIDbPiKwg1oewNSxSmQ9XCEgVUqAnU7/g8006ddF9nzzWKsuYJ3FVT6w3VDomHyaZUkc6DoG9hlqqeNocSjHV06zlT6bWESsUoCNtgF9c2PML+5OF/W8L+18WduOILF4h+7o9oN4aPrMKwQ48sT45OfbXCkA+36QVVjcXYt35EOlrhSG9wnWK08P7+AylPtejWmIrjkvhb75Mlrb5HBAPMawmyuk41Ld7WXgpDxB7l3oTsKrhZofYKdKFHxelnqBasy91EfficgSGHjC/SoD3KhDrsT1e1tbs1TEQ1nqzVbxPK264eviI+lMjs+wQE+EE6Egb4LD/Z2H/dmH/amH/1wB5x2TPDh5Y3sS1Y4j11XPObBUdX3dVaa0DYnsvO5jquZQDo1rO0dOe8rIDh2alm/v/amkw35brWl5sXj0LxN6Rno56yzflizg/0cX+cXHG9hw4rrgX3bQZgPNN6jGhfvHmoQgHtJxnb13VDMWtCWA9iPXFrXP+DtfVygxVHflC2WcT16zVecuv//My+dNvrov4XauOVB4gFh1ZA8zqBGgliNVrX6sJUtVEptgv2vNo2/MrZDUSpv+r18m2tmvpxar91itgo6+Z+9rZXp+oFxlUoQmeZ361rq/5Xq2OoN/6sfhrbPWbnvMGnTDE+jIZeqmAr83qEOvgqOGDuOgFPdVwhT9xVhdfBWj+m/g/yqQnML/Q87qwceHreKqA8ZYXtLe4s14OIPN6if+rlzT4W1Z0c+pJtLeaQgvKe0H1Om+t85D3U+PQWmlibL1DhIBYdKoQewpqrSxEuWhDz/b1jV1xUTgQCgArmMkvR9D+reWsHHynLmUVIJjfOR155t/7GrVuem3fFLWGJ3qToiJ9X5+1guNqiCV+03qjVm/Yp+UVzscuj3cLgnN9Vk/xGgIbW54MISAWAbHHJ/X9GvXcB7VeBoGA2M9dvHk2YWtIWTGXU4YqfNiklU4FhBpmWTWPZW4OlcttbhyOpzGl7lv10KujsfNU7ddbK5CnXQTEIiD2j0/6ftknaNxHqAZi9xRiT1XEgPK0i4BYBMQCsXf3B6041m1KIXDM0QBiOQsjT3rVe5xPrQ542kVALAJi6S8RAmIP0At5quvPKSaaMAIExCIglv4SISD2gORLnnDsCAGxO9CVs5+v8XoVrgRiEQJigViEEDoEiH0srSCCgFiEgFggFiFEA7z3EHtzgNeXFvYUXAnEIgTEArEIIXQIEPvWALFXYEogFiEgFohFCKFNQmwA540hjvXKEBJwI+1zddgWHtZnh30vFd8rjOBGkcYTw29fH757rCjLE8N3lyxNj629NHh4bw5lie/Pi3R0PApxeHb4TS9O99zyb3mRL008Dq/XG6l+gViEgFiEEAJi1wCxVwfwfH2wHM/67Fn91r+fGUDeaOwj8Hur8f1LCYaV/80ExILMW410MnTeHva92Sh3VnWMbyXoPO8cx810HE819rvdgG4gFiEgFiGEjr4B/q0BUtbV8F416Ltlnlb/7rZtuzLAZwWE1YSum5b+uaX7swSpZwmi5WkVSP7M4PKSwfOtAg5vW543zIOqNK4WAPsz85Y+a7+/lMrm+T9maTpI30p1dsnA9q0EvBuF2I8//hjDsMGAWIQQ2i3EXhsa3ofWDLGVR/OlxvazBhBmiL3S8UCeFyD8egOOn7Dtl4rQAXlDczmeagD1syPgfWae6XMr6+0i/6upzOedY35s2B+IxTAgFiGETg5if7Cwby7swYXdu2aIzTGbtwwGryZ7vfCkZiB0wLtaWN6/StNB9aUijacsHCKX47wBw683AHSsjt6aeRy3ivjhrcbEAi4YtmcQi2EYdqq2aAZ/cwCnX17Y5TVD7FkjPKBnPYh9YmIaVxP8XW14hHt2a4J39WqC2AqAzxoTxaYcx6UiXMJjbDcJtEAshu07xJp+bWHfW9iPhsL8IYZh2BFatG//88L+pwGC/trC/srC7l8TEE2B2Jtppr1bL5zgsRSb2rIrIxB702Czl85ciH1iIsQ+ZR7bKcfh+T2bgPYmEIthQOzDwwzd7w0xYr+FYRh2hHZtCCH4zQFgf3WIh13XGrFXC09ma4LSmFrhBLcnAncLYm9MhM25ENs79itWjh7oT9WNYrIYEIthJwqxMZT2yMK+MRTiOxiGYUdo3x5iYK8OHtiHBsC6vKZGtxcX+lLHe6j1VC914PGSgXBeT/XSkO5jEyD2iREwfbaxOsEYxDqoX+lMXLtShD447PpxPFYcV07zfB8h9qOPPrr97rvvXti+w8gmyrlPx/7BBx9clOX999/faTkO6Zo4NIi9bxhOe3Bo1L+KYRh2hPbQ0M798tDm/dIAsOt6U9fVCdAn8Ltq0FgtF1WlcyMNpZ8PgPdWAcgtiD2z/TWkr5cTVJA8B2LzSxoeM3h/vQhpuGXQ7MeR17PVpK4rw7a53uSdeGJVF/sOI5so5z4d+7Vr1y7K8vbbb1OWI4XYLw6N572D3YdhGHaEpjbu8tDmfXHNje3YULmDYp5IdT5hGP/SWfuFCS9NDCeQx7P1soGnlgwncI9w9VKEq2m/1gSzmx3Pca6zq0Ds6vbKK69c2LK/f/HFFy9snWmu89jiHDzzzDN3tr3xxhu3n3766a3kn+smvMFRnvPz8wvPLBC7uRsYwzDs2GzTumQe1pb8dbRPdYbDe+n4a11br2A9PxtfR/XqUIYbnbxa5dCxnnfSfeqs/3pYP47HOmX1V+fe2CC8niTErmoBZNuCwjnWAsYA2m2dl8gn100F1kAsQgghhHYCsRF3GXDy6quv3om9bEFsfB/7yVvZi5EMr6H2i/8jn7F4y0g7e/lie6QRvw+L4exIr/KaKk/tq8+5nPE5vIxxjI8++uidMvY8sZGeH3uUo1VW5afPkfYc72Ueutc5irLG9qqM1bnxPFWW2Kc6r/67559//q668fpTGQ45rACIRQghhA4YYgNwBCtuATkVxAr6sgVwOSwFcAl0qrQzPAnYZOF9dGiS98/zl4cwlzO2y1MY6Xi68Z3SVZr5+xbAV/urrD7pysuaj0vwOWUyl9J2AK3yHyufn5v4qzrJAKqyCmR75db1EdcOEIsQQgihrUOsADbARt5LQWCGpPAkOogGVAUICVZ9eFnpxrYAvICn2DfyCZNHNr4TVGnfyCeDlkNVwFakL69pLqeDro7LyxkW5ZFXVWn6CgA5TQdIgXDkrzTjbwWTyj97fccAq4JEeauVtq8U4Nt1bsIEph4SEHWhcghuvR7c4+x14170CrKBWIQQQghtDWIFPtkr5yCYh5CroWjtK9Bp7aswAO2nfKqJVe79czDMIQk9j6R7h+N/lUsALPjLcZ85TcWh5nL6sXoogn6fl8USnI8tl6UHCaU5Fqus8lXnRnl6vXm9x3Y9XHi5WnWTj/tQl9wCYhFCCKEDhVhNHDor4l6rcIIKDDN05aH6AKMqbrT1u7FZ+hVQtSC22lf5CY6nQqw8mhVU5jSVf+Vx1b4VbE6tl+qctQDfAdfL7kCvY8tlGoPYqecOiEUIIYTQWiFWkFLBlg9P+/DxmAmEHJLOLAwgT27qwVfPMzsFYiuvaU5nKsS2PKuepob+p0D0GPgJLKdAbNTnlHNTTQI7S7HAy0DsoU7uAmIRQgihE4NYTfyprFoBIODOgdbz2yTEVvseCsT2hup7ntgA96nnxmOcPT52LsRW3mkgFiGEEEIbh9izmeEEyw4fa0KRp9HzOK4KsbsKJ1DdrQKxy4YTTPWKKg72bJhQd1asNEA4AUIIIYT2EmJ78OPLbmXvYAWSAW5KIzx6vuZqla7S0OfWxC6teLAMxFYz5zOMCtQ0K38dE7t68DcXYisoPStik7V/VT6dm2r/qCsP/fD8xiB26gMIEIsQQgihtUOs4MyXgpqyxFbAp14mIGjUeqQORQFQChWItPNMeQ9TqJbYyhPF5kDs2TBMHun5cTncev4BcFOW2Ipy6iUK1RJb64DY1qoNDs3xXbXElp8bXyZLEOvnK5fZ3w7Wqpsch3uor58FYhFCCKEDhtgAlTkvO+gt+O9xrT5pKFsetva8zhoTkZaB2NbLDnL87ZQXKPTKGVA5dWmqqRCr+huLVz6b8LIDXzrLz0uO7/W1fSuPvB+PHmgO+dWzQCxCCCF0wBCbYdYnALVevarXk+b9q7eBhQcv9gnYca9sL83q9az5Va4ZrFvAK2+key2rcvprZMeOPdLTsVdllXe6CqfwV+KOQVZvTVm9WjYvi6XjbZ0bnY8qTMHrQeWr6mbOUmFALEIIIYQ2CrHHZD2v7SGZJsLleF3KBcQihBBCQCwQu9emmOV9OZbwzE596xgQixBCCCEg9kQhdt+gcd+gGohFCCGEgNijsV787CEfz67fjOXxscdQr0AsQgghBMRiGBCLEEIIISAWw4BYhBBCCIgFYjEMiEUIIYSAWAwDYhFCCCEExGIYEIsQQggBsUAshgGxCCGEEBCLYUAsQgghhIBYDANiEUIIITQBYhFCvxAQixBCCAGxCAGxCCGEEAJiEQJiEUIIISAWiEUIiEUIIYSAWISAWIQQQggBsQgBsQghhBAQe6oQ+8knn9z+8MMPL/6itj777LO9qacoRxgQixBCCKGThNgXXnghoOTCrl+/Dql2FPUT9fTmm2+ebFmAWIQQQgiI3QsFCD388MMXMPvOO+9sLJ9I+5AhOWAxg/577723tWN6+eWXL0z69NNPL85bWPwPxCKEEEJA7MlAbAyLb8sD+9xzz13kdYhqAWNA5baOKfLO56kCayAWIYQQAmKbEBtexddee+0iRjKgJmAiPnuMom/vecriN7FP/r0rvpOXNMCzl67SqkDM04n/FUrw+OOP3/Wdx38qvbE4UMVohncy10P8PvKIvLx8qscpx6GyxN/YP/LJnmMvb9RRVWY/d/F9fB6LLRWAa+i+d0x+3MorLMobeeZ6qcIB9J2ON/62zpPCCjbpRQdiEUIIoSOBWIGDPHFusS2AIm8PYMygkvc5G4b2M1TpO8GM25NPPlnuW0Gme+2qvPWd9q3KVoGhx9V6PfTS8nqsIDIfh4490o1yeHkDDgWaY/WuPL3MFfQ7zGs/QWjvmNxDWp0r1V+kJQjOIKtjiTJW59w9r7rWtuWNBWIRQgihI4BYwUOAicOGhn7D+xbbBV3uiXMQDqCNNAQv8bcCOoGNvLFK18FyKsTGZ4Fc/PVZ9wFbgrsoW3znYOXSvgFkAVSKE1Ve8uiqXD6rfhmIVV4ew6vjiPqQJzi+U/04JPq5i7JH/fc8sYJEPye9Y/LtXq/KN8qewzk8TCHKr/3c06/y5tURKsgGYhFCCCEgtguxGTYFdNkrlod8AzYCngR5Fbz5EHv2bmYPqG+fCrEOhu6JjHwjvXxs8hwHcKnMgrDsDda+DmxVuZaF2BbEZS+xjtnLUcFkT1Ud9Y6p9SASkufVj1fpx/5xLAJv36c6d1PrEYhFCCGEgNjR5Y3cuzoVhHyd1rAKdFqQVg0lrwqxrVjX7HlseSl7qyCsA2IzMFegmidEeTqtczS2lFUVc1odkx5kqmNS+Emub3/4qb6fCrHhxQViEUIIISB2FGIzqLRgooLF+K3gpbIKYqeA6Tog1mG6VzZ5gnsQvG6IzXlV8ce9Ms/1Ws4pYyvWOFs1CUywnSF9CsS24BiIRQghhIDYWRCbt2cA0zD82TA8Hx5deTur4eRtQqxiMgVUXrZ9hljN3G+ZwjO2BbGalFVZTsvr1sM1pkLsFI86EIsQQggBsStDrM9e9xABX7FgVYjNICRv3RjE+iQpL1sFsR7PuQrE5qF6h/wpXuNW/awjfrQ3VH/WCSeY+iYtj4NVPG2G1anhBNtYZguIRQghhE4YYluw4kPjDk1zILaKqW1NOqvAsDVpzSdWKW3BZo5HdRiXB7SCa4F1K4Z4CsS2wF+AmNe4nQuxvaH6s2ISXmsVB3m54xx7HWh/1UEFwTrPVagBE7sQQgghtDWI9SF7LZnlHtAMQXMg1j2uCgPIS4L1wNBBWktE+Yz+DJ3+XRyXLyflx+C/zRAcxxz5qh5UB1MgNnuP/UUGFVDOBT55zSso9eOM9PLSWzq3vkyWL6floRB5xQf3hHvMbPVyBn23jdfPArEIIYTQCUOsQ91ZWgPWPYv5xQRTIDbAxyFQVr2itCqXD2/n3zugKZ3IrzqW2OZQlV8QUK3bemZLic2B2N7LDsJ7Wa3POxVifWmxalWJs2LymK9Rm60C09b14p7X/NKDHMPc8tICsQghhBAQ+7lXl2bPV2u7vzI1Dy8HsOVXpGrJLW2b+irZnLZeCBD7Vfu2yuW/j7JlGK0W3I9041jCWq+o1etyc7xo7B+/86F/1eWUsuZ9VO7ea2fneC01xF/FxSq9yC+/Vja2qU7ya2dV1lYca56QptCI/Js5k+uAWIQQQujEIRadlnwt2m28FWufywXEIoQQQkAsOiC1JqHtSgGtrUl8QCxCCCEExEJvaKfQ2JJiZLcN1UAsQgghBMSiA1PE2PbiWLcNsVqJAYhFCCGEgFggFiEgFiGEEAJiEQJiEUIIIQTEIgTEIoQQQgiIRQiIRQghhIBYhIBYhBBCCG1Z9y7soaFjvgbEIlRC7D9Y2O/HPbKwbw33zL00HwghhNBuIfbBoWN+AohFqITYP1nY78U9srBvDvcMEIsQQgjtUJcX9sDCvrGw7wGxCJUQ+0cL+924Rxb2a8M9c5nmAyGEENqdLi3sKwv7+sK+Gx02hmF32+Le+MOF/fbCfmNhvzrcM5doPhBCCKHd6Z6FXVnYIwt7dGE/GDrr8Dr97bOfT2b5MYadoP3BcA/EvfA/Luz7C/v14V65Mtw7CCGEENqRtEJBxPh9bQDZ8DbFsGnE/8VElh9i2AnateEe+N5wT/z6cI88eMbKBAghhNBeKIZFvzx0zuFlitCCiJGNyV6xasF3MOwE7dvDPfCN4Z54ZLhHvnxGKAFCCCG0F/rC0CmHdymGSSPe74Ghw46lhL6KYSdoDw33wAPDPXFluEcuneGFRQghhPYOZu8ZOumYeX3vYPdh2Amarv/Lwz1xD/CKEEIIHR7cYtipGUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIoVPU/w8W+buTamhwKAAAAABJRU5ErkJggg==" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_35FE25AF' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_26A56A9B' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_71940743' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_CF66BA0A' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_531DA023' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = randi(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_58FDA251' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_A4473BF5' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, NaN(n_rois, 100), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_16ABF2B0' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_F9A25D9C' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="BD4F90C5" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
+</div></div></div></div></div></div><h3  class = 'S12' id = 'H_46e0' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_5e37' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_2387' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_9082' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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RYhCBsAAAAA2FPYjBLhdmFTO/dTUUVICIGauyXlTR3Ns/RJVClBRiNcpoTNaNnRe0uEzdRIpFpm5nOZE0ZLJUcKoqXCpk5hW/LdZOT3MxJhS4VNSDeiQRA2AAAAAHAOwqaPfgmB04VNSoHoqMdroxiNoonX4jOjvDgpH/pToQ4hbEb5XzI3zL7CZiRQer6ZQwibuelGcyNs4nNLv5us92qQ+2YXYTMadSQEYQMAAAAAZxA2mbuk51oZ5bCpUmDfzlhKmxyVMTdKIyTOklEzu0yJGuWiWe2Qw2b0GOte5iGEzb5TopaOdql5azKPUOazMSVKEDYAAAAAcMnCpj6NqU6Lqo+I7tKiJxLOESFRRpU7Mc2mL9eTDNfcNKNkxF2C7Cps6qiR+vSpOqJnNUhQPFVmr2eUWUce1YS+hxA2IwEzmp6V+2u0z/t3MxoxNJVUeTUzZW60L4UgbAAAAADgAMKmTouJzn503DOvSX9cdXTa87V8DHeIguy4V5GTCW2zzChj9AjsKg9SIIRgmHpU9q7CJj9THz3eR5Lk5+NnCoxteWIy8XIts0qNswqblD6j3DJ93+a21O8t9mF8N6OndeXn+5SxLp5GyYxHo7O2PaJcCMIGAAAAAHYUNik46oia1Wb0xkhahByogqVKkdEjtLNTn5EyoefL6UlzU0acNelw3a6UTH20SH2iVUqW0bbH50bbHnVfOpVol2lEKWBGQqQmc65Cbap+WUb8nBItdWRQvtfl1xKhJARhAwAAAAAHEDZVXFRBEZ3y+Hs0HSZey2S228qNckIAbRuJkesfLZfrm0pqXOvZ5U5+tkuVbXVesu1TZU7tm9zGJXXZN6nv3Hczt4/rdm+r39xTrIQgbAAAAADggMLmusTcaJyrFjmSZokYu6joT8USgrABAAAAAMLmRgmbmux4NNrnoiPqcIwSSRA2AAAAAEDYEDYXGjElanXGx6lfx7oIwgYAAAAACJsrFHP5bq7y9hzDiJasxzGM9hGEDQAAAAAQNkIIQdgAAAAAAGEjhCBsCBsAAAAAhI0QQhA2AAAAAEDYCCEEYQMAAACAsBFCCMIGAAAAAAgbIYQgbAAAAACAsBFCEDYAAAAAQNgIIQRhAwAAAAB7CBsAOHYIGwAAAACEDQAQNgAAAABA2AAAYQMAAAAAhA0AwgYAAAAACBsAIGwAAAAAgLABQNgAAAAAwM0QNq+99tppfPbZZ3qRAAgbAAAAADgGYfPoo49Gx+nko48+0osEQNgAAAAAwDEIm6eeeupMwubZZ589LQMACBsAAAAAOBJhEyN0CBsAhA0AAAAA7ClsQsqEXHn88cdPXn755ZNPP/10KGw+//zzk3feeefkhRdeOF02lnnzzTdPl0/i/fzsQw89dPp7lJlETpz4TIzACakTP+MzUTYAwoawAQAAAHDjhU3Ik5QrEZm3JkTLKIdNiJdcNoRNLJd/p7QJ+RLvTQmbuo5cLtcNgLAhbAAAAADceGETT4FKWfL+++9/OdqmipQUNiF38rV8clSMiskyYtRNHbETr/UpUblsfT3KkuAYAGEDAAAAgLDZkKNrYlRMnyI1Ejbx+9SyMWJmm7CJUTghhuoUqipyqvQBQNgQNgAAAABupLDpUqaOepl6L2RLSJuQLBm57DZhk6Ny4v3IY5OfT3EkSTFA2BA2AAAAAAibjWgZJfztwiaWiQTB+footgmbeD2nP42CsAEIG8IGAAAAAGHTEgbXUTBd2NSEwzEqpo64WSpsasLhus4sm7ABCBvCBgAAAMCNFzb5lKdMONyFSxU2mYg4RtlUYmrTEmFTp1n1PDhZNmEDEDaEDQAAAIAbL2xyilP8rNOiIvnvlLCpiYFDwtQnSuXTo2oi4ix3SthUOUTYAIQNYQMAAADgxgubGFmTsiSmK8VUpzptqQqbOpImpE1KnVg+kwbHaznVqY7IySlUVe7UZMM1N06sZ5RTBwBhAwAAAAA3QtjkCJc6oma1GQGTMmUu6XAImBA0dZRMypn62mozXSpeq9ImIvLXBDVHTn8yFQDCRksOAAAA4EYJmzq9qYqSFDF9tEs+lvsso2CybAAgbAAAAAAQNgBA2AAAAAAAYQMAhA0AAAAAEDYACBsAAAAAIGwAgLABAAAAAMIGAGEDAAAAAIQNABA2AAAAAEDYACBsCBsAAAAAhA0AEDYAAAAAQNic8tlnn5289tprJ++8886Zy/r0008PVtZZiXpEfP7553riAGEDAAAAAFdL2Hz00UfRSTt56qmnzlROiJFHH330tKwo8xiETdTl2Wef1RMHCBsAAAAAuFrCJkRLCJYYHVNH3YTsCOmxlJdffvn0M/EziZE2FyVwQjh16fT444+frv/999/XGwcIGwAAAAC4OsJmRAiOXYRNjtKJETZ1CtILL7xwYcLmoYce+oawCQkV64/3TI0CCBsAAAAAOLiwidEqISRG+WFiVEu8l6Nk4mf8Ha/HaJn4GaNN8vNVXtRls6yc2hQ/a7lTjEayxOdClMTrue6+PTFdKdcRcqeKnah3jprpsiWWi9fj87lfUszUbanSqL4GgLABAAAAQNgcNCfLaNRLCosUHjniJURJlS/xM1/vo2NSqOwqbHL6VMiSUZ1GwiZES74X66h1q0IqlwvpUsnl33zzzdPlUxiNhE2OsonPACBsAAAAAODShU3Kknw95UVEiJaRsNm2rk5Ik9VEct9cVx05k3ltupzJcur0pfiZo3SyjKxbre+2xMlZxraRQgBhQ9gAAAAAIGwuRNh0SZHLhiA5hLCZW3YkbHL9c9tRRU7Nj1Nz0qRwWiJs+v4BQNgAAAAAIGyGnaOQBz3mEuPuI2z6NKVROWcVNiPJMidscjpTTHOK8mtMyZx8AlWOlOlPfdombHJq1S5PvQIIG8IGAAAAwA0UNikzasyNANlH2IwExnkJm1HdR9s12u4eowTBNYHxSH7NCZtdtgcgbAgbAAAAADdY2PTRJRF1ms91FTYpXmJK1miUUUSfxpX1mRJbhA1A2AAAAADAQYTNvjlsenLfmEbVRcZFCpt8bHbmxDlLDpsRNW9NJiyOaVV1+tjSHDajaVsACBsAAAAAhM3eTD2eOp+udB7CZjQ1aZfRK1mvmnMml4+pTT1nT8ifiDrSKB/ZnUIo89lUcVUTE+86CggAYQMAAACAsDkTmbA3JEYIjBQR/dHXZxU2NWlxLDcnOkLGbFtX1DenfNVHdWf5MfIlEwPXHDUpZ2rZ8fncD1UE9WTGoxFIc0mdAcKGsAEAAABA2OxFCIoccZJRxc2hhE0dlbLaMn1pTohUOVPrF6OF+nasNqNmsowqjXpun5RE9b18LaMvO0pWDICwAQAAAEDYHIwQHnXUS0iL+ljw+DlK3luXTdExt+xSMo/Nrkl9sy6jETz53lS9ljwKXf4agLABAAAAgAsTNsdGHUlzFvFzaDK/z1QyYgCEDQAAAADC5lpzbFOPYoROSKTRlCoAhA0AAAAAwubGdPgycfDoEd8XTU6FOoa6AIQNAAAAABA2lzo16qz5cA7FVG4cAIQNAAAAANwoYQOAsAEAAAAAwgYACBsAAAAAIGwAEDYAAAAAQNgAAGEDAAAAAIQNAMKGsAEAAABA2AAAYQMAAAAAhA0AEDYAAAAACBsAIGwAAAAA4HiEzQ9/+EMhhDjqIGwAAAAAEDZCCEHYAAAAAABhI4QQhA0AAAAAEDZCCMIGAAAAAAgbIYQgbAAAAACAsBFCEDYAAAAAQNhc53jjjTdOXnnllZMPP/zwym5D1P+qbMO77757Wtf4eax1fPvtt0/r+MEHH3yj3rmfr8Jxk3Ws20HYAAAAAABhcyXiySefjA7jUQuEbRH1vyrbEAIh6ho/j7F+ITeifg8//PDJxx9//KXAyX2c+/kqHDdRt6jjY489RtgAAAAAwE0QNtmpvcqS47yETXTyz1NIRH0jesc8IgXDscmZ+lqMSIm6HuvIlJAbUeeQNH07nnvuuS/380UKm9F+XBpR52MWZIQNAAAAAMLmwFMtCJv5UQ3n1UGOsruwOfZ9e5WmlkV9n3nmma3HyEUKm7Psx5BLd+7cOf38MU6NImwAAAAAEDYb0RKdvxg98Oqrr57efX/xxRe/NnImOq2xTIw0iPdj2TpyI96L6SKrzVSL+Lvn+ojPxXsR0fmtoxWWdDCzbrGe+Hz83Udk5LbEz1xnLj+1vigj6xY/o967dLxjmSg/R7lEGbH+fD/2Ze6b+Jn1q7lRYpnch/H7aL3xfqynfh+/+Zu/+eXoj+iAx2v53WV98nvYZ9/Efo/l4nM1T0v/fpeOwIrPpSioo4Jq3eqysS3xe+6f/N7ze8t9m6NcRuuN5XPf1jKX1DnWkfs2j/esa25HPd6njpvYv1H3Jcd/Hg+5bK/v3H6sx3O+HuuKOveRVilYj1H0ETYAAAAACJsygiA7m7UTF52/7BjGz5QDqzbiYKoDW8sflREdy11GE/R61o50Hw2R9UlZMlpfLSuXi8/l79uETciA/Hx2sPPvHE0zJ2xy5E2+V+uaYqLnpKl1/o3f+I1JYbNqOWx23TexfP3u6zpWe4wiqUKji4aew6bmWel1iIj9N9qGus/iuKjfR92/8dklwjClRj3WU9j07ZgSNjn9KNdbt6Pv81xf3/bc5m37sebViW2t218lbJ2mF3Fs0+YIGwAAAACETRMqeec/R65kZ7N2LKPDmB3fOsJl1FnNEQq1w5kdy+yMbuv4Zye0JnyNn1HXXm7flqxfdoSjjN45jnpkR793hrfVLfdD7fxXyZXrn0qqm5/v+6bXtQqYeD3qm3VLudFHSkwJmyX7Jsus+yZeqwJg32k/+fm5pMNVZMV29f2YQqNvW90HKdNiG1IexnGTy/b9O4rRMTa3Hf1/oO7HWG+sP+vQj5E6Tanu29G2bVt/F1dTAnJu+wgbAAAAADgiYdPvtEcHLzpzferRqGM4EjZT+T960tZt01Iyge62KR11W6Y62Nl5z85qlyhVMm2TEtnB7h3evr9Gwib2dZTfR9LUcutUmNXESIl9hM22fZOyo++bKlIuStj0qXWj9dfvbC5RcBdl27Yhyxgtt0TYjIRnXza3OY+HfizVkTD1uBqtP7erHyPxudEommN9OhdhAwAAAICw2SJVakcylsvonc0pYZMd1syBUqO+tzRxb3Rm5z4/ty29fiPptOsjsasEiU5yz+2zpGMcnen4XN03I6Ew6rTvI2yW7JuUWWfZN4cQNiGulnx+9Hr9Xvqxl/t3tH2jMkff6RJhM/o/2TZyJgRVPx5G+3y0/jqlKnMpzU13WipNCRsAAAAAuERhM+pU1qlLo9gmbOr0oqnYJmz6NKW5z89tS6/f3CiLpXlaMilv30eZvHhbvWpnfBTbOuj7CJuz7pu5USeHFjajY2O1mQ61VNjMxbaRJVP7fKmwmduPOfUttzGOpZBLZz0eRsdjCLhRHeb2M2EDAAAAAEcqbOpUjJ7cdzSdaE7Y5FN8RrHtiT25rlWbejQ3JWqJlMi/R1NmlubXqfsq6lM7y1UqjOpVp/d0ATFa/0UKm0Pum/MQNqsdRtjkU7FG0UcrHVrY5N+jHDF9hE0fHTP6P1xyPFQhlE8CW03k7OnSiLABAAAAgCsgbOoTZ7pUyU77NmEzNe1jnw5+z8sxNyVqFykxJ6r2kRKxr5bIkhxNUZMpT33+ooVN1m0uv8+26USXLWym8gvtU9eR2FmdcUpUz2+TYjLkXT0e6v/hLsKmHk9T+yL3e//fImwAAAAA4AoIm/qUnf4o6zoSYNQ5raNIeqc3ypmaqjGSQ3VdUVZ9RPM+wialRNRh9GjwbcIm9k+UOcr/kdOGsoM8yhWS6+8d9DotpnawtwmbqadK7SNs6uilqbrtm6h2dDych7BJIdJz9uQTxuL9bSNs5qbGLRE29QlcdT/GevsxkvWtx0N9GtpqS06jPB5H+2zqaVBTYo6wAQAAAIAjFjZVluTjnVOSZOcyHzHdBU+U1UcZ5OdiuewoxrLbOs25rtVmJEAdiVCfihPl7CIl6miRqEeUkeWtFo6wyU53JnjNaShdNlRxlfumjqSJcuqjnnMbo4ycljQlKupjm7MeZxU2dVRG3zdnnRJVty3qMvreDiFs6vcbdY+y41jNbZ1Ksj069uaSL28TPHU/1mTZXbLVkTT1eIjIcuNnTwxd92NdLo/HOpJr6vgdTX0jbAAAAADgiIVNdPqqxMjOexcxoyTD9XHFdZRA7ZRuy1+T8iA7lquSRDXzxtT17SIlslNfO9ApPfK1bVJiVLfctv7Zup6sX+zLmiA29nU+2SdfT2mxmpkCU/dDX34fYZPfW5VIsU2xnqUJmece096l2HkIm/x+uoRbDUZVzY2iWi1Mcjx3jI2OkT56aZQwOOoe21D3We6T0X6M8kYJumP9Xcrk50dP4iJsAAAAAOAIhE103LYlYI1O4xK5si0xbyZ7XdJZnqrn3GfntiXqP/X5XvaSdY22bdtjlOceWb5tpFHuu13KHG3TrvsmXquvp1DYZzuXfm+5P0fH3NR+mNs/Wf4+kmnqSU+j8pYcY9uOq9z2bcfDku8/jsepcnLkzbE90puwAQAAAEDYCLHlUep9JEpO4zrGTv55RY72GY2yucrf72rw9DfCBgAAAAAIG3HkUZPgxlSlmnA4R23kNLSlcdX3xVXehquQu4awAQAAAEDYCLGDqFiVHEZVWqxarpRtcVX3Q80pdNapgZcdmS9oSdJlwgYAAAAACBtxxLLiLLmHrkssyfV0VaZDHft3SdgAAAAAIGyEEIKwAQAAAADCRgghCBsAAAAAIGyEEIQNAAAAABA2QghB2AAAAAAAYSOEIGwAAAAAgLARQgjCBgAAAAAIGyEEYQMAAAAAhI0QQhA2AAAAAHBFhM0bb7xx8uSTT548/PDDJ4899tjJM888c/L2229/bZkPPvjgdJlYNt578cUXT//WIRVCEDYAAAAAcGBh89xzz0Vn6jTu3LlzGvl3SJlc7t133z19LYROXUaHVAhB2AAAAADAAYVNSpgQMK+88srJxx9/fBrxe0qZDz/88GvLRuRImxh1o0MqhCBsAAAAAOCAwiamPq3aSJqMkDLxXsibLmz6dCkhhCBsAAAAAOBAwialzKuvvvqN90LixHsxZaoKm8hzoxMqhCBsAAAAAOCchM1qM2ImZEx/LyTOajP9qQobiYaFEIQNAAAAAJyjsIkEwquJKU4xFYqwEUIQNgAAAABwwcKm56kZ5bfpOWwIGyEEYQMAAAAA5yhs6iiaeDpUvh5PhopcNfFePA2KsBFCEDYAAAAAcEHCJiRNPr47H+0dSYZXm9w2NcEwYSOEIGwAAAAA4AKETcQHH3zwZS6bGjElqo66IWyEEIQNAAAAAFyQsKnToELKRFRRU0fjxHsheHRChRCEDQAAAABcgLARQgjCBgAAAAAIGyEEYQMAAAAAhI0QQhA2AAAAAEDYCCEIG8IGAAAAAGEjhBCEDQAAAAAQNkIIQdgAAAAAAGEjhCBsAAAAAICwEUIIwgYAAAAADi1sXnnlldPQgTyueOONN778biI+/PDDg5Z7qPLOsk3vvvvu4s9+8MEHp5959dVXv3wttqGWFRG/x3ouetuyfpexbsIGAAAAAK6hsFl/5DR0II8rnnzyyS+/m4hd5MaScg9V3lm2aRdRmJ99++23v3zt4Ycf/lpZEfF7LHsZ31nW5zL2LWEDAAAAAFdY2ESHsndmc2TCsXfuoo43qTOcguIsZTz33HPfKCNGgsQ+/Pjjjy9t21KsLBU2Maomln/mmWe+NromXotjOiROjra5KGGT66/bkMdo1Oky9y9hAwAAAABXSNhkB/OyRh+cNbLTTtgsj8cee+woR0/tImziuL1z585p1GlcIWmijJBSvdyLOMZz/X0bXnzxxdPX4ycpQ9gAAAAAwKywibwa2XmPjm90aDPXRvxeO7jxevwdIzHi9xjVEH9Hxzhey85q/B0jCaLDOhpNEJ3r6LTGZ3NkT0iX0bIhYbIeua469SXLiPrHdmT9ap1jXfFeRNSpvp8jS+JzsVz8nnWL7cu8KFnnKCPq0OVQfqbum237IMrJ7cp1LRl9MRI2+d3Ez9yGqGuUW3On5LauNtOFcrv7NtRls8ysb5SZ30GuK7a1Hgc1Ypti2+KzeYzFZ0a5cnYRNlGPWLbmrqnHQx5bmR9nJGzq91DrNvoeMhdNfrfxuSi7Lpv7oq4/90ksF+uI90b7SRA2AAAAAAibr3X0s4PZhc2q5bCpnd7skOYy8XuOdMlOaUbt0IboqO9XeRCCoXd+63t1fSkMsqM9EjZZn3yvrrdKn5yuUpepy8b+yL9rHbooWG2m5uT7tay6bK6vdurz7zq1Zxdhk99NfH5U1yx3Ttj0HDZ1Gs9ov8Q2Te2vKmtSCI72cRcXuwib0fG1i7CJY6DWux/PVShV2RI/6zbVMueETZ2KVo8HQdgAAAAAIGxmp6H00QdTwiY71NGJjU5tT/Car6cAqB34OhomO9qxbHaAs6Me72WZtcObEiE6zfn5UbLc2sGur1exMRIoUVZ21Ov2Rv2ynNH+qhIkOuVRRtQhO+gRWW6+VqVEFRtVJu0qbHLbcl1127YllJ4SNrXMqGeVUvX1FGyjqUhVztT90o+5pcImpx3FPlsySmf0neWxGK/Vbc7X63aMti22J5etcmduG+bqLQgbAAAAAITNmYRN72xmZzY6r6Nys5M7ki2jpKw1r85qMH2k/z0lbOLv/ijlWu5IaozkUC+7ltHr0PdBFQM5qiKFR+38Z7n7TomqwqZPNcr1VxG0q7Cp9UrpMLW/6j4YSbNeh1rGUmEzt9wSYTMlsvpxWl/LBMajaVmjdY3qVkUkMUPYAAAAAMBBhU1PmtrFTO/YZ7khT7JDn49artHXl6MwouMc65waeTL3OOqQAfG50Xq6mKgd9G2PNp8SNqMpTfleduD7yJXYL6N8LrsIm7mRG339uwqbXubS/VXlxOj7Hj3q+qKETR6Lo/01EnJVAtZtGO3bbdtA2BA2AAAAAHAuwqZ3RKdez459lltlyVzUqVJ1+k1KghA5PW/JajDCpubAGUUXE6MnCC0VOaOO+zaB0HP9RBnbpkNNCZu5bTirsBk97n1uf2W5VUzNRR0FtVTYjEa27CJstj01qh8jPd9Nj32EzS6SjrAhbAAAAAAQNucmbDIJcOaDmYo+LSj+zicvZae5juYZCZuacLguW0dPLBU2qx1G2Iwe2TzKWVNHxtREtaPpYocSNlWMXISwidFNdV9PxdL8L6NpePsKmzw+tgmbrFsVhrW+u06JmkqWLAgbAAAAAITNpQmbubwhSyOnstTO9EjYZEe6T3mpnz8PYbN0hMsot0lKm22yYl9hU/fPRQib0UiVbXFMOWxWg5FCfTtSIJoSRdgAAAAAwLkJmy43Di1s6pObejLgfNx0vh5lRX3644+r2OjCppZZhU2OZOhPODoPYbNqU13i99zm2MZ8elZ/hPm2USPnJWzmppYdQthkmT2/UX4Xsc21DrsKm17uUmEz9RSx+j3ksjXJcl22vl5zF03leqqjjkbT7ARhAwAAAICw+UbnOTuv0clM8XFoYdPz2GTOlprPJddd6xSd8lgup0X1jnpPZpxPh1qVR3LHdkV5ETVBcE7LOZSwqXXujwXvU6TiMyGkMpHt6pynRFXZUPdBfm/nIWzq6JT4fuJ7iW3OR5h34bJU2KT4GD2Va+ljvfuxGPWqj6iv+6tOWavL1e89Xs/ExPlaHo99qt4oObUgbAAAAAAQNpOd3NqpPQ9h0/PL1M58H00Tn68d6Fq/nrA1BUCtR4qRKk1ylEst65DCJoRErUtKitHoktE+6Muel7DpCYHPS9jk8j1hbyaP3ndKVJUo/THvS4XNVCLsqFtP/hzfSz0W88ll/TjLfVdHXNV65Pe+5HsmbAgbAAAAAITNMHL0SZ3e05PEzr2eow16h7qXv+1pOVFOdKAjdk3UmnWYW8dcPfs+mHq956nJfbJkHx/isd5z2xCvjZI5b1tuqsx99lctf+p42FXYTE09Gh2P+dq2Y3GubnUb9pEQdTqUhMOEDQAAAADsLWzE8thFNJw1RsLmpu7HHPWyr0S5yMiRV0se3U7YEDYAAAAACBtxRYXN1GPQr2LkyJW5R6BPjYxZbaaTHfN+2PYYcUHYAAAAACBsxDURNqsdH5l9zNG3aZf9mE91Gj2V6RginxQWscvUN8KGsAEAAABA2IgDdMqX5OM55GiU6zjCZmleo1FOnWMVV9vy5wjCBgAAAABhozMohCBsAAAAAICwEUIIwgYAAAAACBshBGEDAAAAAISNEEIQNgAAAABA2AghCBsAAAAAIGyEEOJChc2fXsdj6/iZdXxvHfdrxQEAAAAQNkIIcbnC5j9fxx9bx0+t47uEDQAAAADCRgghLl/Y/Cfr+CPruLuOH1/HfVpxAAAAANdW2Ahx1viVX/kV+0Gce6ybrHvr+Ll1/Dvr+NF13NKKAwAAALju/MQ6/uA6/ujqixwRkdjzPxZiQTy5juhM/wX7Qhw4fmnTHsU0qD+ykTW/d/VF/prfto5vaboBAAAAXHe+s5E2P7n6IqHnz24EjhCz8eCDD/5vDz300Ml99933r9Z//3v2iThg/OymPYqcNXdXX4ysCVlze/XFdChPiAIAAABw7Ym71THF4LubDlE8Mvd3CLElfnkdJx999NHJnTt3/r9bt279j/aJOGB8f9MeRbv045s26rdtZI3RNQAAAABuBN9efZEPIp668sAmfpsQM/Gj99133794+eWXT4I333wzpkWdbDrZ9o84RGRbdP9G0tzaiBojawAAAADcOH6kxLeEmIr77rvvv49RNZ9//vlJ8vjjj588+OCD/4v9Iw4UP9ICAAAAAADMcGcdp6NqKjE1avXFKJtH7CIAAAAAAIAL5MEHH/x7MZpmxAsvvHDy23/7b/+/Vx65DAAAAAAAcGHcW20SDY+IKVLf//73/83999//X9lVAAAAAAAA58+tH/3RH/1/YhTNHK+99lpOjbptlwEAAAAAAJwjMWrmzp07/7YmGp7i0UcfPXnggQf+ur0GAAAAAABwfsRomW8kGp6iJCC+a9cBAAAAAACcA/fdd9/fnko0PMWzzz57cv/99/9f9h4AAAAAAMDhicd0TyYanuKzzz47TUC8/uzTdiEAAAAAAMDhuBWP6d6WaHguAfH68/9qJQExAAAAAADAYYhEwzFKZkmi4anHfP/cz/3cv3nwwQf/ir0JAAAAAABwdk4TDccombPw/vvvS0AMAAAAAABwCCLRcDye+xA89dRTJw8++OD/Ya8CAAAAAADsz16JhucSEK++GGXzhF0LAAAAAACwB5FoOB7LfUhiatX999//L9bF37KHAQAAAAAAdiATDceomEMSCYjv3Lnzb2/duvU/2csAAAAAAADLuR2P4T5rouEp3nnnnZwadceuBgAAAAAAWMADDzzw11ebJ0OdVzz00EMn3/nOd/6+vQ0AAAAAALCd29/61rf+VsiU8454AtXKKBsAAAAAAIBz48QuAAAAAAAAOC4IGwAAAAAAgCODsAEAAAAAADgyCJsx99bx9Dpu2xUAAAAAAOCiIWzGvL7ZN3ftCgAAAAAAcNEcm7B5+kjqRNgAAAAAAIBL49iEzesrwgYAAAAAANxwzipH7qy+GBXz+ibi90fK+7c2r43ywdT3fufm5yebOuXrlcgr8/xmPS9NlHl38/rdzXtZtx9s6jridik3y5wSNr0O91by3AAAAAAAgANzFmFzb/P5UVTZ8oPNa6+3z9fX706Uk7w08X4InipickrVS6uv5M9UvYLnW1n5873V14XNrfLaSfv9LYcRAAAAAAA4JPsKm1urr4TF8+X1J1ZfiY868uStzWtPbP6+u/pKjtxq9el1urf6ppy5tfpKtrxXln26lJEi6FZ5vS77SCn36c1yd1dfja6pwibr8FbbBymSnnYoAQAAAACAQ7GvsHliNR41E+TImSpyQrR8sokQOSlwHhnUp9dpatkgR7qkyKnC5tZE2SmSpmTLndU3hU1u00tt2ZQ8txxKAAAAAADgUOwrbHJ0S/y82yKlSZc5KXk+WX1T6NT6nEy8dm+wrhwNkyN36pSoToqfu5u/+2dH68xlb5fXouyQRyQNAAAAAAA4F/YVNnXa0FSMRt+kNOlToWp9ap1uL1hPHSXz9Gp6ilJPJNzz1MwtG9TpXl3eAAAAAAAAHIyzCpvRCJuM/lSmOtVo28iW0Wv3ZtaV05x2ETZ9xE3lk5n3Htlsd008fMehBAAAAAAADsW+wmYqp8sUMZqmJh6u+Wx6feaEzTZ2ETavTyxbEyrfXbhNzzuUAAAAAADAodhX2NQnN90avBcCo4466YInxcpbg/r0J0xlcuCRFHl+9dUTnmq5S4TN86uxdKqPK6/LvjTY1nzS1OsOJQAAAAAAcChOzvDZnBKUj8W+u/pKzNRHcN9djeVOjk6pciWlykvl9fx8PpY7hMojq69ETpUluwibO4Nye26eu63ct1ZfTc26t5rPgwMAAAAAALAXZxE2MQpmlHw4xMzdzTK3Vl/lg+nJeaswuTN4rdbtkdU3E/6mrKkSaBdhs1p9/UlTGTmapi/70mqc8Nh0KAAAAAAAcFBODlBGSJYQKjHipOekCZlydzU9AuXO6utJg+tro8/Ea0+sph+rfXtQXi/31kS5tf63J5bN7Xl65dHeAAAAAADgnDixCwAAAAAAAI4LwgYAAAAAAODIIGwAAAAAAACODMIGAAAAl8FcfsF9uHXg8gDgXPgRIRbGiX0gziEA5zUhhHC+nBMrz6++euLm3QOVe/fA5QHAmfnWOr69jvvWcf8mHhBiYZzYB+IcItui+zYXZd8mcrCDmPn25rhxXhNCOF9eP0KmvFdkzSEEy93VV6NrTsrfj7iRIIS4bLEejX08gvg76/juOr63ju8LsTBO7ANxDvG9TXsU7dKPbdqoBzYXT8QNpi58b5Vz2o85rwkhnC+vJa+v45N1PLH5/azC5tamjE9KebmOk9XZp0e5kSAEsX4msf7j63hoHX9gHT+9jp9Zx88KsTBO7ANxDvEzm/boJ9fx+9bxE5sL0gdX5pVj+oL7wc1x8hOb4+YnndeEEM6X146nV1/krlkdUNjESJofFEnzyebvR86wH91IEEIcRKw/tGnw/8N13FvHY+v4RSEWxol9IM4hHtu0R390HX9oHQ+v4/dsGrsHjLLB4KL4gc3x8Xs2x8sfXsfPr+MXNsfULwkhxDWMX9y0cz+/afdu2vnyEMImubMRNRl33EgQQlyaWP+1X/u1EyGEuAqxuRB9eNPA3d4MJQSSb2+Oizg+HvY/I4S44efMm3S+PJSwiU7TWxtRc3vz860zjq55wPEohGjt83KxbocJIa5Q4/bHNw3c79sMJTQtCv1C+8c2x8cf9j8jhNAhuDHny0MKmyjric3fmR9n3/13eiPB8SiEaO3zn1jHH1sk1uMD3/3lvySEEEcdm8btyc0om5/czP+8j6NA4b7NcRHHx887vwkhbvg58yadLw85JergNxKcj4QQrX1+aiNttot1DYgQ4go1br+ymaP/05vhg/dzFCjcvzku4vj4Bec3IcQNP2fepPPlsQqb0xsJzkdCiNY+P7P4RrQGRAhxhRq3X119kVgxknZ9j7DBQNh8b3N8/KLzmxDihp8zb9L58liFzemNBOcjIURrn//bxTeiNSBCiCvUuP2Z1RdPw4hM699feVIUviKfEPX9zfHxS85vQogbfs68SefLYxY233M+EkK09vnPL74RrQERQhA2IGyEEIKwucbC5gebZeojuu9sXnv9vM9LzkdCiNY+/3eL+zUaECEEYQPCRgghCJsrxN2NoJmL17cInVoGYSOEIGyEEIKwAWEjhBCEzRmpo2Om4gdG2AghCBshhCBsQNgIIQRhA8JGCEHYCCEIGxeg15Zbq/2SRhI2QghB2BA2QgjCRgghCBucE09s8gm8tI7bhI0QQhA2hI0QgrARVzr+xHO/cfLn/urfOY3f/adesE8OGLE/c9/GfrZPCBucOzHC5r11fLKOpwkboX3/IrKuEb4/QdgQNkIIwuZaxl/8G//g5O/+1nuL46ee+vULq1usa1SHvED71b/8mye/408+/43PxTLJo3/2r13bC+vY/n/0Tz48jdjmv/n3//HkPjlUxP5MYp0aGsIGF8bTG2nz3oJpUoTNJcZlnTOvavsecqif5+N8tk0eVRx3grAhbI4t4no82tWLvHlc29OLEO+1H7ntfJf741DlXadrBe0zYbP1YNmFixQg9eJxjl9/9R9+rTG8CGETYuSypEU0ZnP803/2ybltN2FD2OBSub2ZHpWPaL1N2BxfXNY586q27yFlthHntT6ShrARhA1hc4w3VEM4//N/+a+/0Y7FDdZ7f/7lg7bZ/RxT29OLGH24rc8V2xv7I9rwSnwu+lJdZt2Em+7OYYTNmUfYRGOSRGNzmSNsurAZNX5V2lzkP3vup4uWFmGn637IEUfxPfbG0HQwwgbXfprUyWbkzS3ChrC5qlE7GHPn+aB2dlzsCsKGsDm2lAzb2rC8dj/raPjoj43avzjn5OjEizj/zPW5Yn8skfF1XxA2hA1hc4Y7bHVu+8jmjnLGTH0m/jEzz0wKh/i7N15d2PSGqjaK8fvUP3tOHwrDO/fPn8uF/AgBFPXqJjzr3e/69WGHUb94PcrJskayq+bbyTrEa/GZkYWv647tmZM5sS39/dy+nD7V69RzGOTwxfxut+U4yGVzOHtsw9RJKfdRHgM6NoQNduaJMk3qEcLmeIVNv4iOdjHax9H5L16Pc8DccPZ6rhjdpZw61+R6an3ys1Pt++g8FeucOk/Vc07PMTPKMVfPa/0mSL/gr+e9uYvdvn9G59+6D+L9/E7yM1PnrnounStfEDa4OcIm2pzRiJpshzpnHf1SR9sfy0yNeh0/kjV5zoif9SZzbdcJG8KGsDmjsBn9Y3WpUiVBThsK8uIvLmr6SJAqXerF35ywGb2f/9j1n3001DrW3y9u54ZkVxM+NYUs91UsN2qYk2hgp0zyyMxHXeuFYC07TgT9gjKWHV24x36dsv71wrx///Fd1/07dXxEPeqy/YTV6zl3DJxnDh7CBteQGF3zfJsmRdgcmbCp55jRiMi8y1hHuU5NcZ06D/U7lSlM6rqijc0cByOpNGrf67JxLhmtt56ncr31nBPLxGdH650TNqPz87aL3alzUd8//Tvp+35042PpuVQQNrhZwqa2baN2O67Ne0qDvF6vOTtjmdpWRlm1bcll+2j7bDu35bCJekWZmf9y6oZyFyux/EhOTwmWub7P6Mb7qA8Xr2W+zux/jm4SRFm1DY86x9+jET+5b+K9nK5V5VqtZ/1e4r34TE53yxv2U32ufjzE5/o+IGwIm3OZw57/tHU0S7+QqjKnyoU4oPsIkFy+j5TJg3+bsOn2NhuR0cVsvziuF2H9AjSW7dIl90W/qMtpY/H6KL9M/qNPWfX6Xu6HftGYZY8Mfs6Hnbu7OtrvvU657+o+75+ZEzZ9u2Mf1n1ep6zV7y1H4tT9LTcOYYO9p0m9tRE3f2EjbgibIxQ22baOBP3o9XoO6OernhugXqCPzm2j8+MuwmbqnFrrWG/W5LIjGbJU2NTzQ73+GF0f9OuGvn/quX/0nYyE2S7nUlORCRvcTGEzJ3qnruFzudpuRZszlf8mPj+X33Muh03KhyU3S+t0q9Gy24RN//xUuxjnqC5JanlT9a3SZtQuT627z1LYlmKj7us4x019L0vzjPYb2IQNYXMuwqYLmJrLJX/Wf+S8SMoLnj6VKMuIg7f+U+Y/y7YpUVMNSC0rG7i+/voPVi/meuO2TWpMjULqDUqVFLVh7AZ2VNd6wbjt7mo0FL1hrBfPdX8s2ed5V3Ru22NbRncZe1m53rq/q6nPXDwex07Y4MzTpP75Jp4gbI5P2NR2st8cyLa4ttv1HBDv1zux/dxTz2394jTPO12o7CJslpyn6jbVc2rfB0uEzdxNodH1wWj/1GVrPafqEz/rhXmep+qFeJQ/dy4VhA1ujrCZ6i/M5cCsonuUszOnj9a2qKYqGN1QnRM2VVDkaJJa9tS0pFg2tqd+vt4YGAmbqfPXPg/DyVFAta61na3tciwTdaiv1fNAb/OjbnUET+9Pjr6XTOUw6t/0fmAs10dlVplH2BA25yJsqnTIRqP+XRuq+l7+Y/WpSlMXZbnepU+J6v+QdT31H6P/I41kQ29k6z9x1nlqH20bEbRt+lbdhrl/5MwfMDWtqDdmtZGtj0WvF9XZoM5NcZva9j4qp+Y0qnUcNeS53XO5bgRhc45Tie5e0/jpdfzPm9E2//s6/gvnt+MRNvUCtre52Q72c9No5GRepE+de/pUpiV5drYJm3runqrjqN0fnYOnZNahkg5v2z9TN3Gmtrm+Vh87boQoYXNkIy2f2EyTzRGXxxZ/L35e175TH4Eyl3tmqr8zNSpkJKF7+zclbKo06E+0qtOEan61nvdsac6Z2ib2vs0uwqaup0qxuo9rXWsfbrRsH1VZ1zuanjX3vYz6mrX82g+bEliEDWFzLsKmXmzl3MB6kZlyIw7MOhQ7TWy9iOsXj6PGbqmw6XM05xJW9X+ObZJl1OhM7aNtw7qr/Mk6z11Q1+XnrH4mf+xD9UZ3/+bY1jhNbfuSLPBzpr+LJskbCZtzFDR5IfvekV7Inlf8n85vxyNspvLA9TuRo3NTtI+j6UXbcrz0tryWsYuw6efUel6fujEytU3ZEVgibOL81s8No/XE+pfun6mL696pynPXtnJdeBM2l8jTpb3/ZJPP7Okju5Hw+zf7/2ev2/loW19iql8xEjb9PFDfq2J5F2EzVca2bcqHiPSnCG87P9Rt3HXU4dRN97ltrg96GaWiWPLglm153fr3MtrPtW+TU5Dn6uO8Qdici7CpF2c5UqP+8+cFTt556hdlc8KmCp4pYVNHbsw9VehYhc1o++fqumum9J7AK+s7dVewx66JnnMb63eXSbhG0Z8U1rPE7zt8krAhbBbwyOqrpym9VC5kb13T7f3lzZSo/3cdT5kSdVzCpsrruXPuqC3uiXyzfd22bJcd2+4m7itspqTM3DaNnrxYn1Y1JfHPun+mvpOp9/rUp7lzqSBsLog7ZSTN05vcZaZEXfITouau2UdpGObOA1PX5bsIm239kx5TN1WXnh8ONSWq78fRjewlwn/XNn/bOXHJOWKOPC8SNoTNuQmbbobrgVo77vleNblnnRJ1iH/2bY3e3JSoUXKwpVOi+tDxqWzoU416vJdzSOMz/WkXo2F3Wbfa8G67mNxH2Oz7PfXHry492QnCZsdRNS9tLmafv8aCZpR4+H9Yx++Sw+b4hM3UeWObsFmSV622wVM5B6Y6F/sIm9F6R6N35uq5a2di6f6ZSoC5TaItuRgnZgibIxpV89ZG3MhhcyTt/tQ0oN72jnI+nscIm3qNva2NHaVGyCcqLT0/9DKmpHvUL58+Ncrt2c83fVpX71tFHzU+MzXSc9c2f1dhU8+5WZdREDaEzbkLm9H0l/yH6v84c8me6mNA4x+ryompJFznJWz6uvMfaeopVFMN61T29yivJ8Xata6jBqhPH4r3e3KykenOofHxeg7VO8sIm96I5nrr3Nioa/wdkUMW+wltbnqYIGz24PZmVM0nmxE2111M1Ud7/86Vx3pfO2EzSjQZ55d+N3Qq0X6eR/ryhxY2/cZOnKfic0uGqZ9F2PT9k7nRemLnPL/sevE+lcC/nkunng4jCJtzGDWao2okHT6C6E+z6+kaeuLe+gCSs+awqTdwR21Xl9aja+986uxUe1z7MVM34+v5ofatYrv7uaPXacnMg7k2v+7P/oTEJcJmJOR3FTZzU65GN6IJG8Lm3ITNSMpMjUhZ8ui1fgE314Cdl7AZZQHvU3XqHMyoX30/tjkb0VEG8r7N9YJuF2EzNfRvybSivszU97SvsOn1ivLrdk9d/Ke4qfWTtJGwORCvb6ZAXfdRNU+U6V6P9Atjwub6CJsux+P3/Lu2ofFaPt1vLifMeQmbpXnNDi1slu6fPOftKmz6NUz83s+lRocSNhck6D/ZjB5dETbHMy2qX+/n01tHU2VqX2D0hNbMIVPLrH2qfrM5Bf5UuzZ68tNo6lJvv2OZXo+pXJy1/evSpKbT6O1m7WPtcr7p57goJ+rV2/uRUDkPYVNvrOdDWPrj1JeMkiJsCJszC5suZfoy/fnzfdrOXMLEbED2SeJ1FmGTd8h6Y1QbgL4dvW71Edd9H/R/3n3q2oeVzyU/nDLZU5+pJ41tTyaZOz6mtjvWW/dfF159H7ngJWwOJDFOrsgw8UNMf3q6iSnC5siEzWi66j7CZtTO5jlqlBshRzX2i9fRk5wOJWyyrN5J6R2B8xA2/SmEtTPQn/q4q7DZdi7td9QFYXNOvLQRNrcIm+OK/gjnJU+2HY0UGVFHyE89mCUlz7Z8aXPt1+jGfNarn0t6O9/PD0vyy/Q+wlmm4Pbz3CgP6nkKm7m+UO7D+h0SNoTN3lEfFTo15zCHN+fw6m6Yt32+Zh+fSyDcH1u6dBtq/UaiZaq8qHt9PNxcssO+nr5c5mXJsqLcvq92qesoX02+l+uoc0CX7PepfTq3j7YdH/Fabnf8HG3zaB9F3T0dirA58FSop6/x9r1Upj/dnrswJmwu58ZHb7un2s65NnWqLY62M9rMiNrG1vPv1Ll5bjj9VF32PafWz849anXpdcPSc1XdP33acN0/S/bX6L3cT9vOpYKwOcepUFdxmu+1Fzb1oRpdJIRwGd1QHYmBuDbOz8fnpmRwyoFYNqefzkmJLpxTwvQ0BHX9eSM429J4PZ+A1EePjLYtp4L1hPC53n5O2WeWRJ1qFp/P7Yn9E+vNfXoRwiZvHNSbIrm9S294EDaEzeSBAhwzLjgJmx3vPr51Tbft6cH0pxVh45wK5y1xI4TN8xtRvyJsrkYskdBTYmDupudF1Os81n9eN2dD/Gy7aX1ZU+W0wYSNi0u48CVsCJuv895mStR1m/703g4jhwgb51Q4b4nrJ2zeusKjR6+lsAGcNwgbIYQgbHZLxniyERzXbfrTSxPTnwgbMXvndpQzbJRT7ZARw89HuRhGT08RgrBZzFWdDkXYAIQNYSOEIGwIm1NRc3KNtufeZmTN3X0vjAkbkXPqa964ixg6Huuo612SZ00Iwmbr+e02YXPzHvgiBGFD2AghCJvrkuPlrRUIGyGEuF7C5onNtNgVYSOEIGyEEIKwuarC5nW+hrARQohrJmyu+vmNsBFCEDZCCMKGsCFsCBshhCBsCBshBGEjhBCEjQtawkYIIQgb5zfCRghB2AghBGFD2BA2QghB2BA2QgjCRgghCBsXtISNEEIQNs5vhI0QgrARQgjChrAhbIQQgrAhbIQQhA1hI4QgbAgbwkYIIQgb5zfCRghB2AghBGHjgpawEUIIwoawEUIQNguFjRBCXIUgbAibXYWNEELc4HMmYXNEwkYIIUr7vJuwAYBjh7AhbPYRNgBwg8+ZhM0RCRsAKO0zYQOAsCFsCBsAIGwIG8IGAGEDAITNZfADwoawAQDChrABQNgAAGFzXLy+kTaEDWEDAIQNYQPgJgibd9555+S11147+eyzz45+oz/66KPTusbPq8pV2t+VqG/U+80337yS+/3Yj51jrF/UJ+Lzzz8nbI6D2C/3+BrC5pj+Xzvvv//+lT9PA4QNYUPYADgaYfPUU09FQVfi4iouAqOu8fOqcpX296jeKWzyu4jXr0pn5qKOnVxXjdhPGSHtrsKxnXV69tlnCZvL587mWLrD1xA2U/+rL7/88qXXJeR+1OWhhx66dHkEEDaEDWFzeX2G0fXuRZ+P4lzkfIQrL2w+/fTTU3lwFQ7k+MeLul610SlXXdiEpOly5qoJm4s8dnLfPProo6cniS5v8r343zt2Gfn444+f1ivumhM2l8q9zbEDwuYb5/CRILmo9iQuyPs57bJkL0DYEDaEzfH1GXIU+UWt3/kI107YgLDZJjrSTlfZcdWEzUUyJ1/ie08JUk8exypspjqDhM2F89JKwmHCZqFUjXbmotqTGNUzOqddhuwFCBvChrA5vj5D9n0ugtG5z/kIV17YxMVW/CPl3f64W5bTNuIC7IUXXjj9OzqXeaDn6/EPED9HoxbitbCc8bkYTRDLRZlTnb4oM5aNiM/FcrnupNYtO5Pxd2xDLB//oPF31GtuXVlOlB/rreXsK2GynlmH2ObRUMApYRPLxvqj7hFRTh2B0bc39m9dfmp74zO5H6NOuX+XEp8dNX4jYZP1i7rE73lsxTrrdxav5zHRt7EfO7Ft+fmp0THxem5jHjtzx3X9LvqxE/XKMrYdO/H/MDp2tsmXlCARuZ6pz8Q6cluibvH7SPblfh5ty9T/Z5YbJ9b4OZX/Io+Bi5puQdh8g0c2x8sjXM1uwqa2A/G/m+ejeH1b25Pt5ajtyXYg/hfjfzTbnzyn5P9u/B3lTOUtG/0fzrU/nbyTWeVvnhdyJF9v95acE+K1eH90cVvfy3rHumKd/XyQshcAYUPYjPs+S0Z+R3s6OmfNLT8qd+r1Spx/sm+0T3+opk9I8jwxJ3r2XWffn6Nr6cu4+QgcVNh0gVCHjqUlrdM64sIv/66v1wu7+GeIC8FcJi8e8wJyJCHy/SyzvjbVEc5/zJQWWf7UuqKOtV65ruyQ7jtapOYp6fsl1lc72CNhU3OexHbUz9fldt3eHKre67fL0MCsS2/gR8Im61enAtVtiQZ89HoXW7lt2YGZ2r6Uev3YqfunH9e1Ec8y6/pqWfseO0tGy/Tvd/SZ3J9Zz/pd95Ph3DFYxVD+z9X3e6ern8zyRNf3P2FzIdxaxyebETbYUdjk/8PU/0UXErW96eelUfs31XakSOnrq+3orv+Hc9tXtyOF+EjYLD0nRHmji9u63bn+KWETZD0kIAYIm5subOp1XrTTvf3v56Noe2sbnW1yv1Ge14rZ1vc+SJTTXx/dMI2/a3+tLruEmr9sdB1bt7W+P0odUK9xp85H9RyYNz56OfWaOq/Xr+rDU0DYDDuOeQEX/4DxDxK/z70+GmFQ/ylj+bx4G+XuiEYi61A79kuETf/81LpqJzvqniMzRo3IPsImys9RClVE1Y5u3985fHBK4tSGcmp764V/NuJRh7q9tYxskLclA6sCZurEMxI2/Rip+zhfn9rG7OjUdcbyo45JXTb3RWxTFRvbhE3uyyy3Hjt1XbnP8kTZt2sXYZMnn20jbLIe9XvKz/bvpB6DuXzUc/Rd576sncK6j0cjafJYOuvdD8Jmr6lQn2zEDfYUNvm/Fcd7/d+tbU/KjN725Dmu/g/V82SWW//f8v8o1zcqY9v/4bbpTPGZkZCdak92PSfkVKcUOfVifDTUfSRlsoxjSIYMEDaEzTEIm2xvc2RzXuulCO+CIV6P690qeUY39+oN77psvcFbX6/ngWzfc/kulZa04XlN3svt58s8z/Q+Q7xXr6tHsmV0Y6FKqXqTpj+tMJfv+xm48sKmUu/Mjcxu/QeITl1OFxldvNV/5qmLvVE95oRNN9N5gZzLZmM0kg+1kTuLsOkWul5Q577o25tDD3v9a+M5EiJ9f/VhiPl9jRqmfG/bKJvRcPulwqZ2IKqgqMfESAjlsTN1PNS65Hfc913We6mw6eKqH6f1e+zUE+8SYVOF0lwOmzwuRncCRvJkauRNl2Kj6Vh1309NYbjI3EuEzZcja14yFeowwqa3YfUCd5+2p4806W3H1Iics/4fjrZh1MaP2qC5i9U8J9R2rI6UzelPozZmrm3Idcp1BhA2hM1rk9edvX819XSj0Yjneu09NUq79k9GZVQ5VNdXbypvm0o1urHat2+0fO87jc6B/XxUb0CMbnaPrr/nruWBKyts+gXW1GiL2iD0f4zscEaZmVejlz3VENRO/hJhs+0O49yFY3b8zypsRp3rLg6mLm6zs5D7aiSsRp2Mqe2tHYpaZjXY26a4zDV8c8Kmdwjm6j3VePZjZ/QdTQ23H4mtOWGzbbpXHW7amZsSNRex/CiJc9/XsUw/LkbbPbUt/biv/8f9uJiStSMBStice86aTzZB1hxA2PS2ee7Crbc9c//j/a7j1NMoehuy7//hXJnb2u4l54TeRlexNLWuOWEzV0cAhM1NEjajfs22c9VcnyPb3HrNW68B63mut899+Z5GYHT9t61uc+eC0XZPXbdOXd9X0TSVW3HbCPe5dQJXUtj0g33qwmskbOK1Oi1lav5ivRhcYkKnhM2SC9Zd5cM+wmYqGeycsOlTa0axzwX6tjKXdAj2FTZTx8io3qNtHM2hHXUY5rZhF2GzbdvmHgk42g/1sd6ZkDqTAsd7o7sPcx2sqRgJm20dpiUyaTT16SKfYnVDhc2d1ReP7s5RNS+ZBnU4YbPkAnJq/v4u0x6Xnj/3/T+sjB6dOlePHP2z6zmhnktGF7lLhM1F5b8CCBvC5liFzdy18GgqbH2AxijPSxc2o3Z2ql3vZdTpUPW6NZPTL7n+mztPjM639dzaYzR6pp87l44uXXq+Aq6UsJnKsL2tM17/cWpy1nrBmReMWca2u5yHFDZz03sOlXR41BHvlrjv7zp9Z2o+6T7CpoqE+NxUHJuwqR2mUa6jkbDpJ4e67w4hbEbTBZYIm13kxtzosb7e0YlsqbCpeTrmjoslOTHOuXH7C+v4K+t4bh3/zTr+63X8l5uLuusSP9hcoNaLsNeNqrkcYVPbnrr8SIycVdjs+384ulu76wibXc4JdTj81PlzibCRMwAgbAib5cKmtr35AI7oI1Rh0fttS66xp16fEzYZ2/JeLrmJOurjTa1vtM6e94awwY0XNn1O4dLOeLWmU0//WTKtpYqMbZ3aJVOE5hq0Ub32ETajBmLblKiaeLJScwadRdic5c7mRQubKlr6sTOaTjf3iPRDCpu5pySN6nUIYZNyM5Opje5K7CNs5qYRzpHbue2EfcDGLeIfruN/XcffW8ffXsff2lzQXaf4wWZkzR0e5vKETb15MHrE9aGFzb7/h0vb1Tlhs8s5oXYiRsnYlwobU6IAwoaw2S5ssn2dynczugY8pLAZ3XjetS+0ZIRNPecunZ5U89bkz15fwgY3Ttgs7XQvFTZTIwZGuTH60za2SZi5Tveogz56VPYhhE1P1lUfRTe1v3P762f7U7lypMkuF+hVfvRh9ZlJfVvne9eRJeclbOoxVcVWTQ48te8OIWx2PXYOKWz6MVWnNdR9tFTY1BNe//7zyWajpwFIOoybImx625P/L1V0nFXYLPk/3JYvYMlT/KZGbS45J0zl8upJMOcSTc6dQwAQNoTNyezN3D6KZPRQjUMIm7mnhOaTUZcK/l1z2Iz6IqPpwPWpxFPnIzlsQNjs8Hp/tGlteOpdzHwMW75WHzlXc+AcStjUR7zGhWz+fainRGVG95oktzeAfX/X7c8hj1lO3W85XH2XO6o190D8HuvK/dunq42oj3G9CGFTG+TclppFPr+nPHZqArd67NQh/P0R4vsKm/4I7yrazkPYdEkVr+d21Tv+2UlaKmz6nZsso5bfT55zjw8mbHBdpkRNtT3xen2UavwPnFXY7PN/OGIqaX9tP+qjTWs7Fts1dU6oT+moZfdpzXU7sp2qy+f6liTSBEDYEDYffeP8kO1yfcpoPUccQthkXyTa/KmbFtskR55DR+eu0U3Peu4Y3QytMqY/wruej6rUSqE1upHhKVG4lsJm21Mu5l4fJR1OaVEvGHOd0Qj0DvEoIfFZhU1v8LKTf9b8HDW7e5UFmStgbn/3ESHZeMX21zuiWdYuwqY2aDViu5dObcl9NZWE9iKSDuexOJq7u++xs6uwybpVyRfrHeWROISwye+uHk/5vdW787neXYRNntR6Ert8Yk1n7nHAhA2ui7AZJR2OtqcKmizrEMKm3y2t/4dLBcecEKnt4rbkw/WcENub+2H0dK08J+R79bW+r5de6AMgbAibr1/b9pvBeR1W5Ur2rc4qbPpN7WjT6/lwSZ8hz2c9pUa/fq8jW+s64vfRuaTm86nnl9H5qPZb+j7J/TbKfQMcvbCJi9Sa3DAfx90vsPL13nGfer2WvZS6zux013+2eL/WLdc9WkdfdlS3fgG7b36O3tBG2VOjV/r+7vtx7sJ2bl9v294lSSznREJv4HJ9tS77HCPbvr9djp3ctjzRVcM+2j/b1j31HY4ex11PFtu+i6kypz6zpKypbZnb9/V4nCv/IvPXEDY4pLCZam/P2vZM/b9Ovb7t/zDrsqvYOEtS36lzwtx5dUn72C/eTYcCCBvCZn7aec5GqO1qtLM5ujyuwXN0eV57xntZ1tx09ilBNHo91pFPaYrzSpS5NK/N3Kj8qHdNJlxfzxGtdZ19JE68P7qhmDdSax8l6pvrqftk6ePJgaMUNpdNNg69ocmLvbMkwOqNRfyz9gYxDe9ZkvNe9yRWacAP9V0cisy70I+dtOiHFAw5ha6WmcdOn0N7nbiMThdhg0MJm5tA3nC4iCe47XK+zTui28QOAMLmJggb56PLo+Z8u67X6yBszv3CLoVACJWa/+UsEmWqg5/2N9ZTn3yRxjWHuy+NMMrXXdjUfAbH1NBd5LGT83hHx86xnZgORQ5D7XksCBsQNsd1Dj02OTJ6mAAAwoawcT66jDpNPZEYIGz27HjX+ZPnMaKjJvnteQqCPs9yW9S8Jte5IRg9deSYj53zmL5Tk6/V+bHXNT9Dzc10CY0bYQPCZiEXnWfqqtQFIGwIG8LmZp8DctTPaMoYQNjsQc6NvwjxsSR3x65lXfdhdhf13ewrbq7iseP7vhLC5t467q7jDldC2Bz7/+tln4duSvsIEDaEDWEz5ljyxdSclqZCgbABgOspbF4qI6pe50oIGwAgbAgbwgYAYQOAsLn8C9Coyyebi8lHuBLCBgAIG8KGsAFA2AAgbC73AvSukTWEDQAQNoQNYQOAsAGAwwibu5uLv9ubvDNPbPLQJLc2f/9gc4E4Gj3zdJkO9dbm71rG7c1rz28i17caLHNv8/sjm79HdX1pU597m/qNlrm7KefezDrrNubn8rMj+nY8MVNmLe8yRxwRNgBA2BA2AAgbALikxu1X1/GL6/iZdXxvHffveOF3shEgPf/MnY2AGT317fkiS0bvZxn3NtOkRsvcmxih83pZJvnBRBnvrb6e4Lhuz2i9TwwkzGgbuyy6N7H+TwYy5vWJZd8ibACAsCFsCBsAhA2Am9O4/co6fmEdP72O7+4pbOromLtNPLxV5MwjRYQ8MSjn6TZy5ZMiePK1us7bTdikBPlBETr3BpLndqvf1PbkiJ0fFMFTZc0nRRTdbpLq+bLcyWA7flDqW/dPruf2oK5PEzYAQNgQNoQNAMIGwM1o3P6zdfz8On5yHd9Zx317Cps6vedOeb1PO3pkID9GwuaJmbw2z7fl786Mgnl94vXVpg4nZZTN0zP17tt5r0mYOtrnrc3Uqzq656XB+l9qdXt6Ytlbm/12h7ABAMKGsCFsABA2AG5G4/bH1/GH1vH71vFjA1GxRNi8NJFE+K3N7z36lKWRsHmpCJH++R80mXN3Qhytmsjp5fSRK0/PTD/KZe9OSKMpXi/TrPr6n58ZjfPSTD4cwgYACBvCBgD2FTZCCHEVYjO65g+u4yc2wuDbewibp2dG3szFrZlyXl/w+ZGwWU0Im7l4esv2jITNW+3vKd7aYTum9t3zlyRvhsLmhz/8oRBC3LggbAgbANdE2BQe2iTy/A/W8cc2eSIeE0KIS4xf2LRH/9E6/v2NrPl3N/lrdr343CZs3lt9/WlHPW4vEDavz3z+3g7C5vmZcu7uIWzy721PcHq9jJjZth11CtS9zWc+mRk9RNgIIQRhc2OFjeNSCFHa572EzY+v43et4/ev46c2ST1/RgghLjF+etMe/YF1/N6NWA5Z8+CO06HmBMecQFlazvMTOWJG3J2ZypTC494ZtmfXKVF3i1ypU6L24dbqm4mMCRshhCBsCBvHpRDijMLmgc1F+3c2HaLvbT4ohBCXFd/btEff2eSsub1pq27tceE5JThqLpa7g/eebyNT5pIOvzdY7702QufuajpB8UszsuPpzXpu7SFs7k1Ionut3k/MyKR7g5FGLw3E2b2ZfUHYCCEEYUPYCCEImz2Ezbc2+SDiqSv3b+IBIYS4xMi26L6NGPj2GS445wRHjkDJx2zf2UiatwafmXqs93tFVOQjtu+Vcu8sEDZ1tM/rq28mLn5rT2GzGtTvXtm+e2U7PmnL3SnrqttRkyk/stneu4MyCRshhCBsCBvHpRDijMJmqqERQojLikNf+E0JjltldMsoie6Scu6sppP23ls4JSpHqHwyKOOT1dfzwuwqbO5MlDt6atZ7E9vRRyBNbe9bl3lhTNgIIXQICBvCRghxE4QNAFwXcgTIXDLcO2XqzyOrcZ6cuXJubd7rCYJHy9zdUtdHVl9Ng7qzYz3ubN67NVPuvdV0HqAl21HLvFfKvH1J3++Zhc3HH3988sorr5zGsZ/sz6Oex7Tt77777mld3n777Uutx1U6JoQgbAgbIQRhAwDAUV8Yr84wwma1GSV07Cf786jnsWx7SJI7d+6cxocffnjp9XnuuedO9wtpIwgbwuYihc2TTz552vaEwD72GwiHbiPPo8yz3ECIujz88MOXXpcXX3zR+YiwAQCAsDn2k31cQJ7lIv6xxx477QwcssxDxTPPPPONC9K8eL+I9cdFed03KZBi/R988IGLTUHYEDaEzQHlSoj5+GwV9McibKL9j3NC/x6iXhfxvcQ5p+4D5yPCBgAAwuYKCJtDjKbpwuYYIqZARd1CKI06LhdxcT7aN1P1EoKwIWxuurA5lPA5xu3MES3xs4+4uQiZNLpZ4XxE2AAAcCOEzRtvvPHlaIq4KMrO+kgMvPrqq6fL5fJx8Ta6uxV3CGMKTSwTEaNF4rNR9tSds1y+X6xG/eL1WC7KiOXyojHLrxeVWUZczMV6o665XbXMuMiLbYy7dLktozKntj3K7rllsq7xM+obZcZ6Ytl4belFTL9zGD+j3Hx9VMdt303UNes9EkT53mjf1LrnyJ9YnwtOQdgQNocWNtFW1/PANmGzdFRknJdiuUOPyNhlymqse2r5bFt3ETa57UvrEMvtuv11KlQ9j8Y5YJuwyfqNzv37SLslI1EFYQMAwLUQNnERlUOc82Is71Z1YRMXW/X1+D2Xj059FwO1zLzQigjZ0gVHvpcyot/FyztrtZyUFb2euUyta5YbP/Oibk7YjGRVXXdsU63raMpSXETmMnUfj2TJ1MVxvWuYwqZuf+6D/t3E+up3Uy/+c7mpoeXx3W0TNvmdHePIJCEIm6srbFL0r8rTFlPCd5ER7VZ21mtE29XlQLRrtR3u54O+bG3f43N1vdk+R51SWOT5ok9fqstGu1nLje2skmU1eNLk1JSo2L4c8dK3qcuefC/OIf18v1Ry5HdSJX09H2XkuqN+/XvM76Zu8+h8VPdbbM9v/dZvTe6bPGZyWe0dYQMAwLUSNnnBlaNXsjNeLyq7iKhypl40VrkwGoFR57/nqJS82I3XUwjExVcul5/PdedFbnw+L/qm6lkviPvFY362XkzPTQdLQRHbXutZy8wOQl1/7Ie+rtWC6UyjoedzU9Wmvpt8vSaIjPr00Tu5HaOh7iMpU0dgnfWuqRCEDWFTO95VpnQpMBp1k8tGVFE9KjfauDh/xLJVUvcRI3n+iOX7+aiONuk3EKaETb1pEGXWc2xuUz93ZRkjYVPPPXmzoZY5EkGZwD6WraJryWibLLuWm6NJsw4198627ybPG7HuUR6aFDmxzzOvT71B0gVPLn/ZT1MUhA0AAAcVNlMXbPXiqF+wjS6I+gVXXpj10TQpT+bu2uUdzngv5UitTxcEU8Jm9BSLehG4i7DJi8/R3cheZq1rH6LehdW2od+j5UbCZu5idZQkMjsl8bmpoe5zwqau87rnlBCEDWFzMcKmjk7s01e73KgipJ6/4nPZfub5I9u7ProxRy1mu15HaoxG1OT5p/7dy5wSNnk+zO2qo4NqGSMx1cusAmpqxFE991axMzr/jm4MbBvxOZcQeeq7mbqZU89HfUTt0rx6nhhF2AAAcO2EzdyIjzqlqV8s5d2tGl0K1AvszIkyml+/tNOfF3BdAM0Jm5Fo6HPdlwqbOdHSH3Wd6x9d3PaOxD4JNkffWb17PPXddCnWpxL0dW0TNjclCaggbAibixE2U2K8jujro1FGsiEFQLZd9Vw31/bmeW/U5tUcObW8fk6YEzaj3Gz95sISYdMFx2gbRmX2bV86tXXuXDASNnPn66l15vkoRwpF9GuGOWEzt05B2AAAcKWFzeiir8uc+vdc9CHbdYh2ypt6IbpaOK1m7rGmU8JmtOxcfoG5MlcTI2b6XeFtwmip5Mj9tkTY1M7M0u+m36UdXeQuFTa7JFIWgrAhbLYJmyUjBXPZzJFWI28E1Lar5yCLNq+3r3MSaOnNjjlhs6ScJcJmTk7EiJapMvt5dlsbv0SGjM63KV+Wfjc9j9pqh9Glu26LIGwAALhywmaUqK9fSFYpEBdSebexx0hoxPI1D0Cdv75aOId+H2Ezurjsw6Z3FTYjgdLzzRxC2MyNPFrNjLAJebL0u+kJK/t3sFTYyBkgCBvC5hDCZk5U97ZzTthkVPES55yaTL3Km2wblz5t6FDCpkrzfYTNqJ6jJzxuE0ZLhc2SmyBLv5u58+PUNQFhQ9gAAHCjhE29WOx33kY5bObufC19lGhekOeojKlRGrFsPhZ7X2Ezd0G4q7DJz40eY93LnBtmvlTY7Dslaulol5q3ZmoKlylRgrAhbC5S2MyN2usio06h2fV4SHmT56Mu27c9yW9fYTM1wmVfYTM1deu8hM3SETZ5M2CX6Uk1cf5qYuQvYUPYAABwo4RNvaPZHxc6eqx3Xkz3i7BMdJhPZMpEjqMLp55keCrJZE+GuI+wWbUpTPXpUz0XQU9QPFVmr+co+ePcheOuwmau41K3LfdX72j076YPPe93q0eJI0cXzlPJjIUgbAibfYXN1AiXOs2n57AZtU/RxvW2f9ROZb6XHGWaf4/KjLpFO5ll7SNspnLY1HPFEmHT6z3K31O34azCZt8cNqPE//27qd9v5q2ZymW0ksOGsAEA4KYJmzotpj4adPRY73rhmY/hjovDlDv1QikvvKPMWCanRa3alKgqD+JnLJvyoS63r7BZlcej5t/1Qrauvz6VajXIE1PrGeX1x6ouubhdKmzyont08Vn37eipITlqJsoYSbbRnek6+inrVrc5lq3yqD5NxQWnIGwIm0MImxQY0a5kexPtUZ0qk+1TbfOivasiPc9JfSRnP3+MOvn1CYf9/DNKYryLsMmn8uXrua5ar3yttre9zH7eSgESnxmVeVZhk0JlJGBGo31q/fp3M/ou+pMW6/moTo3qI3Q9JYqwAQDg2gubvFtXL4jzUZyji7x6kVmXnxrZsWqJb1P09IvBnpw4LuDq1Kt9hE1cwNWRQqN19yda5UXnaNujnn3bV4NRN4cQNnNCpHZUah2X1K8+laQPz09JVN/L1/r2zAklIQgbwmYfYVMfyb0q02Pi52hEXxXxsUz9bD0ndeFek9+u2mjFej7oNy9GsmipsIn6Z1m1ne7nj/qExlzn6PzX61nLjG2rZZ5V2FRZ0kfH9GTBfWrw6Lup60vR0uswOh/VMvuxkNu/LR+eIGwAALhywqZ2+OvFTn2M6UgaxHtLLo5iuanHevf1TyUuzvWN3uv17Be3+dltT6KaK3NUl6ky47WpfZPbuKQu+yb1nftu5vZx3e65+sV7pkMJwoawObSwqe1QtH/1aU5xDonXRsnRQ3qEpIjl4/fo7Pc2LNq8OCdkuxrLhywYtWFZZpQXy8Xn+nqnpv1mPesIoRQSUUZsTybjnWo/Y5ksPz7Ty6zteW57SpHRTY2puub05SV5gPp05i5Xch21jvW7yRs79bvJ6U851WxqP9RzYE5Nq8dCnVKlvSNsAAC4tsLmusTcaJyrFDksvE4NO6b9uy0xpxCEDWGzj7C5TnFdkuEe6/lo20MBBGEDAABhQ9jcGDlS72Qe20W7EIQNYUPY3CxZn1OnPB2KsAEAgLAhbC4lMtfCvo9Tv651EYKwIWwIm5t7DqijfrZNuRaEDQAAhM0RDd2ey9NyVbfnspMpZm4eeWsEYUPYEDY3U9gcU76Yubw6grABAICwEUIIwoawIWyEEIQNAACEjRBCEDaEjRCCsAEAgLARQgjChrAhbIQQhA0AAISNEEIQNoSNEIKwcUkPACBshBCCsCFsCBshBGEDAABhI4QQhA1hI4TQPhM2AADCxgWyEIKwIWwIGyEEYQMAAGEjhBCEDWFD2AghCBsAALYIGwC4iRA2hI0QgrABAICwAQDChrDZImwAoLTPhA0AgLABAMKGsCFsABA2AAAQNgBA2BA2hA0AwgYAAMIGAAgbwgYAYQMAAGEDAIQNYUPYACBsAAC4LsLmnXfeOXnttddOPvvss3NfV6wn4piI7Y46xX64yuv/6KOPTsuJn+fB559//o3vL1578803v6z/Ze7LrF/UB4QNYUPYACBsAAC48sLmqaeeipP0uXX0K7GeiGMitjvqFPvhKq8/ZEWUc15C7Nlnn/1G+XnsZP0ve19mfUgbwoawuTrC5rzPQZfdLl1Wm31R6z/v7y/a8yg/zkG17g899NCX7f1l7sv333//dN2PP/44YQMAAGFzeD799NPTC60YoZC88MILZxYro4unWM9FiCHC5nzq2C9I84I5RtTkcXSR+7Jvb4zwiTpFXMSIMRA2hA1hQ9ic3/c31abnjYJ4PUcJX+S+7KNZRzc0CBsAAAibcyM65mcRNtF5vyoXL4TNPCHyHn300dOy43vt33Gt90Xuy6ljLO/GHlvnDIQNYQPCZr+y66jJ0XnmIvdlrr9PD84bGPU8SdgAAHCNhE2c8F9++eXTi5DoIMfP+LuOfImLlnz9/2/vbGItq9LzvO26BWqq1AKSa4Hidl+Z7rYZFAP3pEl1kQmKLeK4ke3uuPAgaqPEsYUYkVlUCQryIMoEgZQow6AiSqxERAmTEJIMkCVGEZBRhILEgLKRFUsOMwY35721X/j4+Nb+uefnnp/nkT7de/fZZ++11zl3772e/a21Mn4t37AoXVfLlDWj1/PNhPep5YrczSXuS0+4tB89TVIZ9dNjl8TtWfj4ODymiX7PDWmPPaLluuHRT5U1l1PbcDl1TFpH29fPZW4UW5JB+3G5dDzaj/YbP49Yfx7PxccYPzsvV3nz+C5x/9qn9uP9tW78XGdaT5+BP+PqhjUeh+tL68+9qdf7qs/Yn5mWteqy9d2usmDid8zHN+c7FoXjRY1LBAibfRE2+t+dmxmZMzbHlo9lWOh9Y43gqeu15O+c97pO1i1MXK45x1GdU1vL8/59XGOfUT7+MWHje4u5tOS7l8d6G6tLf4enZF76u9Sqh2r/vtfqdrhrFMIGAAAQNgPCRjcIzmLwBd9Pa/TTNzvxKU5sdDvbID/d8U2MG7b+vRqHxDesbuxWwsbbcCM9bjs2pr1ebkx3aQwbHU/cXyxjPkYfS1zfdaGoJNZ5hY3TsF2muM8oLmL9OSU6HoOWuXtZLGu8ofP+9b74mcfPIN44xs/UqeKxXnIXoVi3sWz5OFr4PfFmfI6wiXWZ6yd+t7NsOe93LN5QTz1GQNggbL7eKI//f/GcFs9H8X8+jivic5Llclyu/8u4jarBr//nfD3Q33l8qinrtUSy1onl8nkn78Pl0/Uo1onWzceyrLDRtlTHsVzeT5YNXegSlM+xWjfXjbZb3R/op69f3qaW5ePSNnXuzdfd6vPz92fKcYxde2KZ8/fR5W8Jm3i9jOWoHlrE60qss0pOxYiv+/q1bV2/ETYAAABLChs36n2DG2/cujTYnm8+ddPhdau0YTdadQPim5PYeI5ZL/EGw9vvUpco3/jkxrhvcCq5km+e8na9Xsw80TZdH7GRHm+8YnaGj/O8T7Wqm3nXe2zw66bRdVc1NnzjnOswLo/bcPnjuvGzijfh/lyjgHEGj8L11ZJxcVn1/Rl60uh1p9Rbtcxl8KDEXi8ur76zsQy+CZ7yHRsrNyBsEDbjwsbndf3vuUEc5XB1PvH/s85H8e8o/7289dAgP4DwrHPxvW7AT11vqAuNzzf6OzbWowCO53hfF+IxnneQ8+ocFh9MeDBdlysL6HiN0TpROPihTz62qg7iZ6VtxO3G+5G4PIqaSlx4O/E4vJ8pD1f82eZzuLNFoyT0DIm5LuN1UeuqHPEzjuWN32N/l2IWZyUIvf+c/bnMAySEDQAAwJYKG99A5CdP8SapuinQDUkrbbiVpqz1YzeiqcLGXZFyRkTVZWaqsKmyhfK6Lle8uZxSR8sIGy3L3XBaYszL8g2ab/ZaGTmV3MmfVZZkQ+Oz+CYyyxntJ4uZXIa53cWmCpv42eRji/LJ9Tz2HcuzhHQDKfDe9nmffgPC5lCFjc4L+QGC/59zxl08f8XzibfRNcYfidInX4Oqa4q7nMRsuqnrVeeqShzlhwi5fFoW68PHGM9LywibmEEbrz0+j2bJ3hXZklFixeXxXFxJq/jZxQcxXh4fjFR1ED+/1nHMEelDWZJDosvL4vUlX3v8QKZ6sJCviVXGTPUgZMo1E2EDAACwo8ImPyXMUXVJyV1HqllxqhumiqnCJt6Q+IlZTKWeMgBg3m5XdOPKxzd2c75sGvLQuCt+cueouh3FdPkpUsSyzdsYqu/cJ74SF2M3ke6PX32nxgZorKZTnSNsxr5LrRvqZb5j+5CajrBB2FyksKnE9JTzV9VVqiUf4jkhX4MsAaouk5VYGltv6LxUdfvJr7l8+doTr92ryrAZy3iK57PW+bMruhRX22g9BInZKS7bkKRolaPC9yVj6w7VzRRh42vnUHbolM8t18NYXcTvD8IGAABgT4RNvHkYinyD4xuS6sZ6zo3kVGGj5bl/d4y5wmbspilPkzl0A7fMLBWVeMhjAnRFn/mxfbeW5+OIY9iM3VhWN49DdV6N0dA6jrk3zXOETau7WvXdi+MFnec7torvBCBsEDZ3/289iLrD/59T/sdb57UxYRPlgrctUVRJmSnrtc5LVdkqAdGS8tWxLCts4uD11fW2EjZTswtbmUxVNkh+bSgrs3qQkScTGLufmSJK5giboWOrpKGv+x7sfso1fyy7E2EDAACwh8LGMxlU0br56opU6XhDMtYlZKqwiTcx1cCFc4XNWBnzTdGmhE0sV06Br27UViVshp4EumHh91b947Pgik/6chp7dYM91KCo9jdH2LRS4PMT31V8xxA2CBuEzXLCJo6DkoVNnAVvrAvI0PIxYWNxkRvPVTetsfVyGZ2Z0+q2krPzhs4lqxQ2efIBjwcUx3+ZI2zGzolDGZu5++1QHeTr4tBxTBU2Q/ubI2xaA8/n60y8n/L3fiyrFmEDAABwIMImCoKpU1/GmymnqOebrlZXKu1DN6z5RmVI2MTGf37CVg0eO7dLVHVTlm9QNyVshtLsuzVm2HRF97V80zz01DDfWLaOI37fxoSNs7jOO4ZNNU5NVQ5Pt7qK71j+/pxnOllA2ByysBlrkI6dB1YhbHI3E58Lu4FBflvrzRXJXSPDZt3CpjXuT2sssFUJm6EMGwuPoW5yeTyg+BnksXGmCpvc9W6usBka7y1nIMdy5fsohA3CBgAAEDZfkSs5k8FTY8blcfYE3WjEgSBjyrZTiltTqOa+6ZWw8ftajem4bkyJbz3dat2o5/UqiXHRwibehMbPY5XCJtZtnCXM9WOB4mlbq/oaEzbxKedYg2zZQYfjdzvvy+Vz9lDrOxZvrqvvWGvAz129aUbYIGwuWthUsxPGRndrWu9VCRuPu9V6UOHzwNT15ojk6oHFpoRNNVZXPO+vS9hU6+aMTf+d71FiXeZ7ivz9qQYonlo3c4XNlLHhLOz8fRnKVGYMG4QNAAAcuLDJUzvrxiam48abJC+rZt6Igw/HGwdP7embrtjVp7oZjTdc+SbMNypRtsTpTbXdfBPnBni+kcnH7b77XTHLxSbHsPHx6GecCtRP/eI05KvsEtU1ph+NjYq4XPWTp1aNM450octc3Gb8HgzNFDXUbW2qsIk36Z7iNZa5mj53me9Y1ZUMEDYIm+nCJp6T4ixzQ7NErVLY5AHnW1kXU9eryhiz9uL039VDhIvIsPExxWmk83V/lcJGx+vrTBz7zHUTHwDEzKV8zm5l2OTjGJuhcGjA+ynCJl4r47HFcdKq7sP+3POxxXLEh2FzslIRNgAAADssbHyhz4Pz6e8oa4ZSmH2TmhvQeZu68ajkTFyWB0LONzr55qbqm+7yxDJVN5LVcXcD061uQthkYaLyVeJqlcLG/fxz/VZdieKYBlGEdMWgw/k4fKMclw9huZMH3JwqbHJDJEukvM38HfONcvUdi8viPv3dq8beAYQNwmZY2MRGqf9XWyJ9HcJGksDnN52n4gC8MQti6npVGeN7fQ6NxxvPu5sSNlGQ+5hctpgV4vPaqoRNPPZ4XcjnzzxuXv5uVAIkH0fMZhk6Pw9NAT5V2OR7i3icrXHdYnn13ngPFTO04nbjPnf52oOwAQAAhM2IsIk3krpJqMbe8GvVIL2ehrq6qRx639BrQ+U7D63yxe0OjWPgsU6WPYaq3lr1PTYGSmvfreX5OPL+/ffYdOx5vaH3tY5vCq0BOqt6G6rLse92VUfnQWWYOqU9IGwQNqeDDxEsQRQSNTkzQv/LudtubOC3lmfBGjNdfM7y8m5kBqix9Vpl1PkhZilamudzdlW+1rHMPbfqvbkLqPZnEeKMQgt4lc/H0dp3a3k+jrh/fdaS49pv9aAglk+v6/ug9fW+1nE4MzIfhwRHPI4Wlij5YUG1v2pZLIe25bJo/9V12Q84/B1yHWj/rrv4MMT1HPdp2bOLY6chbAAAAGEzUdgAbBtDA05uG3nsBUDYIGzOJ2wALhJnFVVTxm8jQwMdI2wAAAAQNtCjhvrUGOtHD18+fdTTydxVYNtw14dducFH2CBsEDYAuy/gd+UaibABAABA2Fw4XRorZSh29UkYTw+/TjUgKiBsEDYIm03gbldTg4cF08/reQDkbWSXslARNgAAAAgb2FOGxhjahhv7ZcbqAYQNwgYQNtvHts+81BrnDWEDAACAsAEAQNggbBA2AICwAQAAQNgAACBsEDYAgLABAABA2AAAIGwQNggbAEDYAAAAIGwAABA2CBsAQNggbAAAAGEDAICwQdggbAAAYQMAAICwAQBA2BygsBH3LOJBrkcAkM7Pf7iInyziiUV8T+eJ/nwBAACAsAEAQNggbDYkbB7gegQA6fz8Dxbx24v44SK+q/MEwgYAABA2AAAIG4TN5ri8iPu5HgFAOj8/u4inF/H4Ih7ReaI/XwAAAByOsPnkk08IgiAOLhA2W8PRIr6pz4MgCMKxOC/8ziJ+bRHfX8S3dZ7ozxcAAAAIG4IgCIQNwmYDXFrElUU8vIhHu7tP0391ET9axG8t4sfd3XEsCILY//hx/3//o/488Hh/Xni4P09c4nYeAAAQNgRBEAgbhM1mr00an+JbfePsVxbxg0VcX8SN7u6gowRB7H/c6P/vf9CfBx7tzwsPdMwQBQAACBuCIAiEDcJm46ibw319o0xP0tX9QWNWfKe7O9jo9wiCOIj4bv9//0h/Hni4Py/c19EdCgAAEDYEQRAIG4TNhVyfjvrPQN0eNFbF/X1D7cH+8yEIYv/jwf7//v7+PHClPy8cdWTXAAAAwoYgCAJhg7C50OvUpb5xptlg7unjXoIgDiL8P3+5Pw9cQtQAAADCJsUbb7xx+uqrr55++OGHK20gvf3222fbfffdd8/+1vb1t/a3zw3DfNwXFR9//PFZORQ02AmEDcJmx65fBEHsfwAAACBsxoTN008/rYbMygWDRIG2a2Gg7etv7W9fG4WSUsfHx2exagF2nnj22We/8hkQBMIGYQMAAAAAADsibD744IMzmaKMjHUKG21f+9H+1t04uyhJYfn12muvfbHspZdeOlu2if1LFMXjVp1rmfa/iXonCIQNwgYAAAAAAFYkbNYVWdhsKpzJs+n9StJ0RQaRJc4msnuq426ViyAQNggbAAAAAADYYmGjDBA15p2BoTFm9Ld+aplev3Hjxln3mtb4M5IFL7zwwumjjz569tPjuESBoG1pu9pea9wX7UNRZYNoH5IPet37UXliZpDLqv1qHR9HzCbS+7Tcr8dsmChZFB53J9bPUHaL9uuuUD5eLbMwydJE+45lUfnjflQvWq4yV2MP+bWx43Y59n38IIJA2AAAAAAAwN4ImzyGjUWLxIglhCSAxYOWx/dLGPi1uJ4FwtgYNvH93p9+RpEiKeNt6zVv2/uMwsbrZXGhn35PVdYofqJk8e9DY/xIrHg7UQ61hI32FY9Bv8fjs7SJ68X68Fg5LtfQcccsm/zZEQTCBmEDAAAAAAA7JmzcwHfGiIVLF7r3eFkcO0Xre7DbMWHj8V0kJbz/uB+JkFim+F7tx5IiZqVUXbHiWC4SRD4mSQ1vIwqRKJC0fGxAZkunKlsn11ksY5YzsT6i+PG6LrfrN2YrDXVBc3cpbYPGO4GwQdgAAAAAAMCOC5s805HlRhYpVebGFGFTCRdvVzLCZdLr2mdez4IjdhmqxIWzayphUY3x4rJXXZHmzrRVCRvLI9dj9Vo81ljGKptnyphBrc+UIBA2CBsAAAAAANghYZOFQFzXUiD/XcmdlrBRRkklM1rhWaYkL7RNjy2TZUslLqoMnZwlFMvhv6fOrORuS1OFTZRZOVxveVuxe1VLcg0Jm3VN304QCBuEDQAAAAAAbFDYVHIjD17rv6tMkSxzsrDx35UYqqRKHHMmx5iwcZelSmZU4miOSIrrV1Ojd41uZGORy+quUV0j8wdhQxAIGwAAAAAA2GNh0xocuFrXf1djt4wNOhwFxFhjqxqQN3aJmpphU4mOqhxzhU2eIWpI2ERBJNGleqkibyuOCxTHs0HYEATCBgAAAAAAEDZfWbca/HbOGDZdY1wVrSuZoeVRqORpqS2FxoSNx4Cpsnk8vk2cbWqusJk7hk2XBlUeizhuTTXmzhRhM9RtiyAQNggbAAAAAADYI2FjeSDZEaVLnEJ7SNi0xsBxRo220xI2sWtRJWxiNo1nSeqKsV/cXSquf15hUwmYrugu5fVzxo/WkQSLM1nFKbxd9py9NNaVbazbFkEgbBA2AAAAAACwR8Imdu+RVFCmjdexZBgSNlG6SNJIAPn9MRvGgqLrs3m8TuwmpPeqPFHwaN8uq8WMRYlecxm7lOUzV9hYllSZRrGsuS583BJRKr+PM4qcSmrFY7TEiWJH5Yhyy+vHLCKCQNggbAAAAAAAYE+FjUWBBEGUHxYiY8LG2TjxvZYbMRMmDzpsOZRFTCVnvH/JnLg8CpOcdTNX2AwJkShXujT4cB5IWccVp0gf6spVzeYVp2SP9eyxfiqhRBAIG4QNAAAAAABsobCRUJA8cFcZT59dTWmd162m3fZrEjlx8Nyh7Y5tO6+zTMPN5agG9o0yZe5+nB0zdVyaKLzG6nuZcup4W1OFEwTCBmEDAAAAAABbKmyI1YQzaZQls03jxJBdQxAIGwAAAAAAQNgcdAyNZXMR4W5oyrBhsGECYYOwAQAAAAAAhM3BxrZMn01XKIJA2AAAAAAAAMKGSGPStMad2aSwGRoziCAQNggbAAAAAABA2BAEQSBsEDYAAAAAAAgbhA1BEATCBgAAAAAAEDYEQRAIG4QNAAAAAAAgbAiCIBA2AAAAAACAsCEIgkDYIGwAAAAAAABhQxAEgbBB2AAAAAAAIGwIgiAQNggbAAAAAABA2BAEQSBsEDYAAAAAAAcqbAAADhGEDQAAAAAAIGwAABA2CBsAAAAAAEDYAAAgbAAAAAAAAGEDAICwQdgAAAAAAADCBgAAYYOwAQAAAABA2AAAIGwQNgAAAAAAcDHC5p133jm9ffv26WeffbbVDS6X86OPPrrwsqiuVJY333xza+rl008/pVUOgLABAAAAAIB9ETY3b95UQ2YrRMgQLqfkxEXz/PPPn5VlG4SNPjeV5cknn6RVDoCwAQAAAAAAhM36kJRRmSLKIFEZLzoT6P333z8rm+otlm1TdVjVjQXSNsgsAIQNwgYAAAAAAPZU2LhM28bnn39++tBDD52VLXZBUqbNpuqwqptWuQAAYQMAAAAAAHsmbJRJoswNdbVR6Hcta4kMZXe88sorZ9trZXro/VrH28xjr+h3vd/yQb87k8Xb1pgtuUuQy/nYY4+drZO7Knm72obLqr/1Hq2rZVN48cUXv5bJou1ov13fLUl/x2Maq0ft28eZZUt8TcfZqhuheulS5g8AIGwAAAAAAGCPhI3FhEKCwJJAodeyDPHrcT1tN3ZfchaKxUZc1xLGYiVuwwKiGsPG3YMcFiddo8uSpUkuq36fgrcfxUqUKFnYxPJpHe8716PXy+PQuKuTfg7VjeWOX5sqoAAQNggbAAAAAADYEWHjQWwlGCQS1Ph3VorFhEWMfnpZXNeiwQIidtmJYsiiQutHLB6qclrYaN9RfrhMEkPeV8zGibLIZdB7LGFa2UNZ+mj9KdIrli+WQ/upui/l4/PnoP1FAVPVTRY8OQsJAGGDsAEAAAAAgB0XNm7050yaSiq4G04WLpIVWub1JBy0/SwSYlZIzMaZImwse6ouQNVrlTyJ2UTqLnLz9roAADIxSURBVDXEUJejSti4DFU9al/5tSi/JHIsknJXtSFh430y+DAAwgYAAAAAAPZM2PjvasrqLEIsHqYKAokIZZhofYcFRBQTU4RNJT2G5ErX6C40JH6mypBK2Fh8eUyfGPG1PMZPF7prVRJpirCp6gQAYYOwAQAAAACAHRY2Vdellgjxe8e6E0mSWLC0Yq6wqca0Me5OFLfREh3rEjZxvJlWVPuMY+xUDAmbaspxAEDYAAAAAADAHggb/12Ng5LlhjNFxjJsLHq6lP0Ru0TNFTZD+47jv1y0sNHxankVeVaoKJpa+xoSNswUBYCwAQAAAACAPRc2lSzI470MdcFRl6o8Lo6yR2KXJGeEnEfYnHcMm2WFTdVNqRI2zijK4/sMZSF53BqJl2pgYsawAUDYcHkDAAAAADhQYeMsjSxXNCiuu+s4+yZmssRBg70NZ7hUwibOJtUSNnGbQ7MoxfW03Uo6jQmbMbHi/Q0NOhz35zqQeInls8zS/qoxbyyEPA16nuq7qpssiRA2AAgbAAAAAADYM2ETp+D21N7OrOmKaa3jWC0SBgq/32PbxEwaCQivo/D79TNn5HjA3pYUiftWGePU4/o5ZTrsqVkpnqa7GlsmCiptJ2cruXxaz1Ills/1k6fw9vtjVk9VN3n8m7ExhQAQNggbAAAAAADYMWGTs2m6NEhunmUpZrREqZNlQpQ+XkfdfSxCupC9EpdZslTCJmfpRCk0tSvRnG5ErpNqQOZYB3E687HyRUGWtxun+vZrVd2MCSUAhA3CBgAAAAAAdkzYSBxIBmQRYwngAXKr17O4qQbSrdapuvOM4XJW7/V2h7bt11vHOKVMzqSZOi5NVb5KeA3V29BxV92hqjF2ABA2CBuYdN0kiEMLAAAA2GZhA/OYOpX5JvH4OrkbGAAgbKC8Pl5axNEiLi/inj7uJYgDDH//L/f/E5cQOQAAAAibnUUZL9smRxi7BgBhA5Oui0f9Z39lEd9cxP2LeGARD/bfC4I4tHiw/x+4v/+fuNL/jxwhbgAAABA2O8nUmaUOrSwACBuEzRajBuh9+i4QBPH1WPx/fHsRD/cC577+fwYAAAAQNrtHa0yci8j4mTLGEADCBmHDNbF7QN+FB/7WPyIIIkR/jvz+In55Ed/qpc0unicBAAAQNgAACBuEzQ6hcTnU1eNhhA1BNIXNry7i8V7aPNz/z1zi9AEAAICwAQBA2CBs1oW6dmh8jm8jbAiiKWx+p5c2v7KIX+j/Z+gWBQAAgLABAEDYIGzWhmbA0aCqjyBsCKIpbH5vET9axA8W8Yv9/8xlTh8AAAAIGwAAhA3CZl1o2mKNyfFdhA1BNIXN7y/iNxdxfRHf6f9n7uH0AQAAgLABAEDYIGzWKWw0ffH3EDYE0RQ2f7CI317EDcnN/n8GYQMAAICwAQBA2CBs1n89RNgQRFPY/OEifryIJyQ3d/hcCQAAgLABAEDYIGwQNgSBsAEAAACEDQAAwuYw0Uw1t7q70wwjbAgCYQMAALB/wuaTTz4hCII4uEDY7DwSNW8tQp/jc928qYYRNgSBsAEAAEDYEARB7KGwOe7uzpqCsLl4nlrEnT6un+d6iLDZnvjOzT86/eP/8d5Z/MN/8Z+oE4QNAAAAwoYgCAJhM7kRcnz58uU/7+5mdly0tEHY3EXZNc/1n4nq4wRhMy0q/tf/uXP6L//jn5z+vX/2704f++k/32h5tD8jaYM4QdgAAAAgbAiCIBA2UxohZ7Lm5s2bpy+++OLpFkgbhE36fPr60Odyq2t3k0LYDAibjOTNX/3btzZSnr/2my+eiSLFr73wrxAnCBsAAACEDUEQBMJmtBFyJmskaswWSBuETY0+j/e6u92knkLYTBM2f/Tafz0TJeqKpCybnHWj7kqIDIQNwgYAAABhQxAEsU3C5muyxty+ffsipc1zCJsmR73QOu3lzQnCZljY5O5P+vtP/+9fDnZRkuDRcq3nrlTX//CVL16X/PGYNFXGTHxd7xsbw0YZOBJLfl37r0SSMoK87Vg2pBPCBgAAAGEzM1599dWzWOc+3n333bN96OeuH8tFxXnq8IMPPjh7z2uvvbYVx/DGG2+clefDDz9c2z60be1D+9qV7+02/O+//fbbZ7/rO7NlwqYpa8w777xzUdLm9V7aQJsr3ZfdpF7u/0bYTBA2eUwZEYXIf/+fHzb/Jyxn9NNo/SxVIpIxrTFstK7kTIWEjN4bpU7OEKrKRiBsAAAAEDYTor+RXnvDUPtYt0zZxLFcZON6bh3euHHj7D1qjMflH3/88dl2nn766dPj4+Mv6k1/v/DCC2sTKtq+9rNOAaJt+1iq11UXL7300hd18+ijj54+++yzZ1IrH/emvrfb8L/velN96PuxJcJmVNZkaXP16tUnNnhe34aBj3eFk+7LblK/i7CZJmwUMcvG62R5ouwVZbREiSLJUkkZbzfKHL1/aNDhuD9tW39HKRPXVQZO7uoVl+l9CBmEDQAAAMKmCDXOc+NTDbVNZL4gbDZbh15fMiILiyhp1ECX3NBPL9Pry2blaL/5s1D2hr5r6xQCQ8JGMioeYxZW+j1Km0MSNgqJLP2tn1sgbCbLmixt7r333l/fwDn9uK+/Yy5vs1A3qT9dxAeLuIGwGRc2kiHmJ//4X39N4sQuUDHrxutKxlTZLdXylrCJ23W3ppjlIwnjclSCSOu2ykwgbAAAABA2fVeRi2p8Imw2W4cSIxYQUY7oO2BBoQyTnFGidS1auiUzYSyALqLrWCVsLGtUrtztR387+0f14jo7NGGj4/bntm6JOyJsjr/xjW/8xRxZk6XN0dHRP1nzOf2pvv42gaTQ9V527EM8u4j/0tfff/vpT3+KsJkobJTZkt/nMWcUMevFY9BUmTQx80YiZWha75ylM3Q8kjlTyubjIBA2AAAAOyls1HDSU25nPrS6qngdNTiVEaHGttZXAzM21HMXEL3HY3zo9yobwWNaaL96vWrAaR/ar9bZRNcSbdtdebTPnKnhBqjL5frT71VWh8d48Xrapo47r+t61nHFetby3PiPn4m25brRz9b4ILGs+lltd04dWrrkcVy8vNVdKK+n70wcf8bfm3xc8fOO8sP7csZGrJtq3Bm/3sru0b79XVZUY65UwkbbqwRW/i7reLSO6y3WubarffuYW0JjShldR1pPx+71Xb+xjHHd2JVN6w99r/1/6+9UVaeV4NRn6/PEBQmb46tXr/7leWTNBqWNxmG508uHdXGt+3Lsl9N+f6/vSfzxIv53f1x/grCZ1yUqS5EWMUMmbiNLHHVXGhI2cd1q4OOhMXdaIGwQNgAAADsrbNSAi91TcleV2PhzwzhmRTiDInZrcUOzEjZVo80N3Lz/LAtywzyWITYklxU22lbcfjyW2HD2666PXHexTPo91pWFVld0I/K+/TN2o+nSGDFexxkdWjeunyVKPi6XOYqDOXWo44riqmqgj2VPOBsrrhu7WOXvmo/XsiDWZRQ21Rg2+j1+DvEzqL5DFklx3by9LGxc5rG6a4lGvd/7G/p/mFtGrRO/y/G91bpRusbPYKjLW/x+xeyhoYw0v3+dg0M3hM3PLStrNiRtJB3eWtO1QrMrvdx/Nre6u5k1V/boWkiXqBnCRl2HIu6OFNE6em+OOCNTHENG68e/434rYROXxWycKRk2Vbly2QiEDQAAwE4JGzf0Y1aLfkZpUDX2nemgiI3qse4dudFmWaNGoYVB7EpjCVQ9iY/7Po9sGBs8V9t2IzI2jn2MUXzEsrueYplczxYNlg1uDMfGaqxnZy8pqm201q3GlFF5KhHndWPdTq1DbzNn0bibVDexm5KPwzIq1re/m/G44rqWDHlfWdjErluxrvwdcl3F758/l7jvWP+VsPF7586AFI8tZhLFAXqr7mZTy2iJkusjbqO1bhS7UQZ5Wf5e+38oZtq0vg/+Xq9zdrFC2Pz81atX/98qZM2apY27Qq1j7Bpl1SiTJk+DvQ+cdAw6PEvYSGq0pvWekpnTEj+SNd5uHgC4NYZNa+BiTyuu8Jg5lWAiEDYAAAB7I2y6RhZEzJxww7OSOLmRmhvcY8Km1Z3GXZ+83API5nJa+MQyLSNs4nFXXXfiNNFeLzc0W41mHUvOIvDxx23ETKaq+01suMexUMaESZYiQzM8Ta3D1noxU2OOsPF2orxodaFyJs1UYePvSvxcovjzvvUd0HtyPVV1moVNzBY677hB3UAWiv8XtZ+5Zaz+z11H/v7FdfP/pMVKrveqO1MlWFvH5uNe5+DDSdj80qplzZqkzTN9na2jK9S1ftuaJvxoj659TOs9UdhIpEiAKKopu1sZM1rXmStxnBgLFEc11XbumtQSNnEcHe1P49rk7k8WOXFdjZmj5RJG1WxXBMIGAABgp4TNWBaEG4lu5MXuN1Mb3GPCxk/op2YjWHxoux7zZpXCZmya5upYpoxrEkWAy+7xQXJZcyN6SCZ53aqxm9ftQvZGLIPCwmbs88vhmX6WFTa5G1GV9ZM/X9fvVGEzN5PD4wLFehoTNnMzi6bMtDU0RfmcMubuTNXnHOsyd3HL5YvZcfn75O9F9f1ryZ0p/3MrEDaPr0vWrFDaKJtGXaCUHfLUmqTGnV7W7AtHQXDljCGETSFsWkh0xNmdFJIgUYAMjV/jiNN+V9kyQ8JG600dk2ZsHJs4Zg6BsAEAANgpYTP0hLwSB5VcyE/f5wiboWyWqvEcxyrJsSphU2XHzJ0lqhI2eayPHJWwqcZ+aQmbKeu29l2VY2odVt2/VtklqmrE525YU4XNUF0NzV5VxdBn3aVuRquYmSuXfdkyThE2Y+tGOTSlHHP+X9YkbH565cqVz9Ypa5aQNpIo13qJove+3q1vLJnXe6mxL5k117svuz89NXY9RNi0pYvkhrJZqvdKosRsFndxar2nmsFpaNDg/LrenzN/9LcyefL+qnVVNkmj1vEQCBsAAICdETatRrUFSW7wDjUo52bYTG3cxsZp7AZSjaGyjLCpulitQth0YdDWeKzVALXrFjY6Rncvy5HH7Bmrw6pL19xBh+OAzHnQ4SHJcF5hk+VS6zuQxV3V3an6rKfuR8ddjSU0RdjEMsbsqqllXKWw8Vg3rdgGYfMbv/EbZ/vYhKzJ0ubSpUv/oWtPOf16LxpOg6hZ52xQHhPnZA+uc8fdl92fbg0IKITNikOiJWfLrCvcHWqqeNlk2RA2AAAAsFZhU41TM9Tgbo2tEuVONUXxkETIUig2PLUtl6trzJRTTR29ii5RVfcRlSdKjakNUHf5qOrZomITwsbdz6Z0CVp2DJuxMY+qbVSDHlefg7vbWKZMFTZ+X9V9zBJrqNyWg2MyZKg715DsmiNsWmWMIuc8wmbKWDNe19/r6jOaIzhbA1fvi7D52Z/92Q+69pTTyqi53q1nUOGKt3q5scscdV/NRDqZcz08RGEDX04xTiBsAAAAdkbYRGGSG4lxRiGPZeFGohqiMUukmm2mlSGRG21xXIw4ZkbsYhUzMHI2QZyieVWzROVsjyy3zits8uxMUeRUMz/NETZVNkdetxrvJ8qDODPRXGFTSZD4vdC285goWTDEzKnY3SZ/DhZPPubWuCu5HmMXwPj9zcLAIiWOv5OnevfnWMmQWEYtz8etvy2PYsbVeYTNecu4CmETs3ny4MeeaS5+L6du90C7RG1KdOx6do0yhO70cf0810OEDcKGQNgAAADsjLCJWQOenjpPFZ0bjRYaaoTHwUVjgzDKFK3jhltutMX1PHipG8xRFlk2uEyxUe3Gsfaj7S3bAMxTSksqWGxFsdLN6OLhMuqYtD3/7ePQ33msoDnCpjrWqq670DVL79E+q2yNqXU4Ng5SFDJax7MxqR7jmERZ+MQMG5dNy6KgiyIkTsXuMlf16PX0fu1T5ciCLv5P6HWvo4hZZu7yU8kQLY9ldfnzuDPn7RK1ijIuK2xi1pK37wHBK+nZ2u7ULmQHNOjwujjpP4NdHLtG4/m81Z1vZiu6RBEEwgYAAGA3hY0bf9WAuLnrTGxYWThE0TMmPlqNNjVa8/61fnxqXw067Kyc2HiNM+Us88Q+NkTd6I4ZKHOFTa4zC4NYr97WuoTN0ODNOcNpTh36s2uNQ6R9xmOMoTqpjjPuP77XoilnrcQsm6F61Pvy8VsUDg06rPfoOKL00vaHZIjWrY5b+9Nnn4972UGHz1PGZYVNa/Dh6pxQbTdmrlUZWAc+rfeq0dg47+3oNU2C5lZ3voGYETYEgbABAADYXWETG9Z5oNChRqMahmMDylYyo/Ue73+o4eZ9Tlln7iw9c8vbes3Td1fjAmnZ2BTmQ/WQ9+l1q2MdKrvraOz1KXU4NDbM3O9YSw64Tuc06ofqccrxeZ9T1pn6mQ5ta6hMrWNZtozVPlufz1id5YGrp3wf58zKtkJho/Piz69a2my5rBEv93HQ10OEDUEgbAAAAHZS2EydenmupCH2O+KYLav6bmxiXBPiYsOZQLl724aEjc6PP3f16tW/XIW02QFZI9Y9AxXChiAQNgAAAICwmR4eU2Nq0JA+fyZSN2FmJIQNkc8nU2YtW5Ow0XnyeFlpsyOyBmGDsCEIhA0AAADCZjsbhVODhvT5w12jViFZEDb7HR6Mep1TeU8UNuL4G9/4xl+cR9rskKxB2CBsCAJhAwAAsM/CZsoYM8Rhd42aMj7N1O4yqxqHiNi+2PS5ZETYnEmby5cv//kcaWNZc++99/76jlwXEDYIG4JA2AAAAOyrsCEIgtjFmCBsZkkby5qrV68+sUPXBYQNwoYgEDYAAAAIG4IgiJ0TNpOkjWXNIq7v2HUBYYOwIQiEDQAAAMKGIAhiJ4XNoLS5ffv2rsoahA3ChiAQNgAAAAgbgiCInRY2pbTR7zssaxA2CBuCQNgAAAAgbAiCIHZe2HwhbW7evLkPsgZhE4QNQRBfD4QNAAAAwoYgCGJXhM0X0mYPZA3CBmFDEAgbAAAAhA1BEMTeCJszabMHsgZhE4QNAHwVhA0AAADChiAIYheFzb6AsEHYACBsAAAAtlXYAAAccGMEYYOwQdgAIGwAAAAQNgAACBuEDcIGAGEDAAAACBsAAIQNwgZhA4CwAQAAQNgAACBsEDYIm93jo48+OottKcfnn3/Oh4KwAQAAQNgAACBsEDYIm8Pl9u3b+n84feWVV7amLM8//zwnKoQNAAAAwgYAAGFzkI2Q9xA2CJtPP/30TJA89thjX2S1aNnNmzfPfq6bd95552xfkSeffPKsTNuQ8YOwQdgAAADshLDRjZyefCnWiW7QtI+LvlH77LPPzsrx5ptvbt2N1LrrKH7WpKUDwmYvOVKDeBEnCJvDFjaWI++///5XJMqmhIlkjfaVr3FZIgHCBgAAAGEzQn+Dv9abBKdDr1sMTb2J1I1rLFcM3Uy++OKLZ+ts4knkJutIqfHdlqTIAyBsVs5Jfx47Qtj8/cFzrc+zOsdL4OvvofN9XE8SpBIOumbodT0YUHj9OHaLlsf1WnJd+3A5tX5r3aFrSex+pGW+/un6lvevfbpcPsb4uh92tIS/X/uzP/uzs58PPfTQF9ezeE3jGoSwAQAAQNhsobDRzZ5uWufcdK4a3TzrOGOatm9sJWm03E8lY+imdxNPAzdRRzoOHWtHWjogbPYRdYV6i+vhsLDxud0ZJzF0Dcjn++q6UMl1CxFLiRhaJgmSl0ueZDHU2p8fNIzhc3wUUNX2fA3QTwuWGFoWrxPx+CpBpHI7iyZHvM5524CwAQAAQNhsibC5aHSTqBtERRQirawWP031TaxuRC9SNq0S0tIBYbO3aMDhW1wPpwkbCxOdEyVDfL6P3Ygs+nW+dKaMM0gU8RxqoeEHA7qOxCxOra/l2n68vsRri2WL5Ye7ynobY1mfLSGi7VgEad8+FoXL4brQPvSgwvv0McZ1LXLi/vQ+rROlTTVLFQ8NEDYAAAAImwa6KXU2iVObW8JGN3Vaz+vrqVq8WdTfvvms0A2fb1o9AGF+QhjHVfE+Wunoupl0ebTtOePQ+OYzv2esG5L26xvUPICi61Pbdh2pjLGOfFytsvqYVIetOvJn4W3pPZVo0Y2zPxOXp7XfVn0AIGx2lqf6c/kx18NpwiZnt/h6EJf7OpjlQu5eG5flmZAsSvRzaBvOwKlkelW21jW+db3y/uKxVJmn8fqUrxO5jN5mvpYMPQhyBtJFd5FG2CBsAAAAtkbY6AbNT7V8s+UbyHxjpZuwuFy/e30/RYs3XdU0nX7C5qd8lRiJqd9+Wun9RekR09b9hLJ1w92iepI5RdjEY8k3n7Ecqp9Yv74Bd9nzjXp8MulyVWXJn4X34ae98SY6prTHslUp/r7prsoFgLDZOa4s4k53mLNDnVvY5IcNPi9W8sLnbGeMWGbE83VLXnjdVlcib2NIynjWp1bZplzTKmEzJO9b8sfXfl+bqjINCZttGdMOYYOwAQAA2Bph4xu1OKVnTAGPN1a+mYpyRg3+eJOWhUMWAvkGtbpBi2nYfr/3ISGRU7DjTWwUUK0MnyxcKjkx9caxS+novpGNdZTrLqebZ1nkJ5sWXlVZfEOsdfz+mB7venNdxDqKQqw6viiLABA2O426Qr3F9XCesMnXLV8rooDQOr6edSPj2FRCJJ6z87Uqn/OH9tMV48GsQthEud+KLGTiWGi5W9gUYZOvfYCwAQAAOHhh0zX6v8fGfxYplQjxa95ONXVotV6+iaxujH0j6O5a8cauki1Tb/q876q71VRhk5+ctp6kVmnuVVp5rLs8Y5XLMtRdTcfsWT7imDQtWVW91mpcACBsdoYrvaw55K5Q5xY2rfNlvC7FzEkPHKz1nJkyR9i0lmdh42tgK9YhbIb2mbvpeky4Lg1ePFXYDHXbAoQNAADAwQmbVpeemAIeX+vCk8McebBAS5OY2VF1t5mT+l3dfFY3k/Hm9rw3sOcVNjGjJZfL63qbVX1UA0Pmsoyl5+ebX5Wp+sxanz3CBhA2O43GrFE3qPeQNesRNu6GVF0jquzFZYVNvN6dl/N2iZravThux9fgasydji5RCBsAAACYJ2yqLJUsc1pTcnaNNHBngUTxUGWU5Bu06unk0I1ht0SKeDXWwNwbxy49SZxTR1HwuPtRJbqWlVpz62ioXgAQNluJxMz17susmucWccSlcD3CxtI8d/uJ2Y8xc3NZYRPHa8t45sKxLqxDmacuX2sWrKo+VKZqZkXXka8j+TqFsEHYAAAAwExhk6f5rIRNvBF16ncV8QbO8sU3gU6Vjje4+QbNY9W0ZoUycWDjVlnGMkSWzbCJT1l9TP7b06OO1ZGPwxLLT2er8W9yd7CpGURxGtgpdcRMUbCnjZF/uogXFvH7i/i7i/jd7u5gvLsazwVBo7jT/33CJXC9wiaOQabzqweHz4Pk+1y/rLCJcl8/3RUpjtlWzSJYXa8qARNli69R8Ri9XMviQPYut7et5T7m+P4oglxe7TMfd6srNSBsAAAADlLYDA0OXHWZ6RozaLTwU0E9YfMTyfx0r/UksZIRliCe8rtbcjajZYRNnKUpljV3exrDN7q+ua9uqFvj/FSiTfXnG+6hLm9TspfoEgV71hhR/OdF/PtF/NtF/JtecOxySNxc6+6OWQMbEjZRtsfwzHtxfBu9dxXCJg4Wn2PZWRGjXMnXmrg8RhRELleW/FUmUuxu3aVZKFv3I4CwAQAAOEhhE2/ghqaMzg35fHOo9SVitDzfCPqJo9Ojx2bDiGO4xJvYKCm0vygj8j51I6myjj1xHEoRHxI2OoZYP1VaeDVltuqgqiNvK2fbDJUlP+HMN7z5CWmuCzciqjpyeRA2sGeNkUOcJQpmCJvW4L3OoKkG2dV5WxHPl3Gab52H9T4PBp/Pw0PL8znYGS4eqy13SxpjqLur95nLE8uv48z7dN20MjK1PB+Ll8X3MEMUwgYAAABhUxCfEqoBr79jWncUNlGSOC1bN1lu4FdP+eJ0pFVGSCUj4nv0u8pUpX3HcWycxePjydNqnzdFXK9pP45YL/o938BHSaKfvsl3N6OhWa1aAqqqo5gBpXK1Poe8nm/2WzN+xawrAIQNHJKw2Xfi9WmO6Fk3cXapbSoXwoZzJQAAwIULG2eMRPlh2VE13GOf+bh+66lYlDxTp8/WTWWUNhYn+f1ar5WWPrXblo9laFrzavs63taNZSt1Xe+pUr2jJKm6grWyffKxO5OpJXxyfVbZNe6SxlNOQNgAwmb/mDrL4CaZOtkAIGwAAAAOUthE0RDFxdDAvU75HstiidtpyYo8EG8UMqvYxxCWGVlQxJR2x5RytLYxpe5b9TBUR2OfU7Xe0BNMBn0EhA0gbPYbP6DZhoHlLZCWGY8OEDYAAAAHIWwOFcZsGZZXAAgbQNjsD2PjzmwSj2cz94EIIGwAAAAQNgdCHMvmUGeniNOyMkMHIGwAYQOAsOFcCQAAgLDZquySapydQ8Ap8nSFAoQNIGwAEDacKwEAABA2W4PHyznEblGHfOyAsLlgrizi+iJOuGwhbAAQNgAAAAgbAACEzcU3Qq51X529DRA2AAgbAAAAhA0AAMLmgs/Tt3pR8/IinuKyhbABQNgAAAAgbAAAEDYX3wh5qxc2V7hkIWwAEDYAAAAIGwAAhM35GyGSK890d8ed0e/X+r8jJ/0yZc481697VLzurlDPFNu43r/39f61a0VZrvevHYVtnoTXtVyZO7f6suj142I7Pp6u389z/XtOBurhOOy/lR10NPE4Yr0+k+oXYQOAsAEAANhL7lnEg/1F9wluUgHgwBsjboQ82J8fz8NJL1le7yOPP/Nc99VxaRzvBVnyTGMdS463Gq+/nMSP938ryR8LlTuN7WTBctqve6tR7kx1jG8lwXI8cBy30nE81VjvtCGYEDYACBsAAIC9EDYPLOK7i/ghN6kAcOCNkR/258MHViBs7vThDJr42mlYdqUXLZX8qAYbvhW2fxy2+14SMl0SRs6gsTR5L4iUoyCK7hQi5DTs0xk718M2TgpZ8173ZRbMc+H9R6lscf/XwjajNLqT6uwoSJy3ktxZibD55JNPCIIIgbABAADYPJcXcf8iHlnE4wgbADhwYfN4fz68vz8/LiNsqkyVlxvLu4b8yMLmStfOLDkupM/rDRF0PSzPssPZPbeKcjzVkEfPjUgml+X1vpzHYb2jRv29l46rOuZr/foIG4JA2AAAAOwdusn95iK+vYjvI2wA4MCFzff78+E3l5AAUdjkMVbuBPFxksJy5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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_9054' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_8c47' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_68e4' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_5763' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = generateCenterCoords(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_6b62' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_47d3' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >data = generateCalciumResponses(n_rois, 100); </span><span style="color: #008013;">% [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_5d01' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_8378' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="7450DA71" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
 
     filename: 'ophys_tutorial.nwb'
         path: '/processing/ophys/Fluorescence/RoiResponseSeries/data'
         dims: [20 100]
        ndims: 2
     dataType: 'double'
-</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="28C4B862" prevent-scroll="true" data-testid="output_2" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">20×100</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:100,&quot;totalRows&quot;:20,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 1058px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-</div><div class="horizontalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 20×100
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    data(1:5, 1:10)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableMatrixElement" uid="AA486BFE" prevent-scroll="true" data-testid="output_3" data-width="1107" tabindex="-1" style="width: 1137px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="matrixElement veSpecifier saveLoad eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="veVariableName variableNameElement double" style="width: 1107px; white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="headerElementClickToInteract" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span><span class="veVariableValueSummary veMetaSummary" style="white-space: normal; font-style: italic; color: rgb(97, 97, 97); font-size: 12px;">5×10</span></div></div><div class="valueContainer" data-layout="{&quot;columnWidth&quot;:44,&quot;totalColumns&quot;:10,&quot;totalRows&quot;:5,&quot;charsPerColumn&quot;:6}" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="variableValue" style="width: 442px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-</div><div class="horizontalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="meatballMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div><div class="verticalEllipsis hide" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><mw-icon icon-id="kebabMenuUI" icon-width="16" icon-height="16" icon-config="{}" svg-path="" style="white-space: nowrap; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></mw-icon></div></div></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="variable output starting with ans = 5×10
-   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-   NaN   NaN   NaN   NaN   NaN" style="white-space: normal; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first roi in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = plane_segmentation.image_mask.data(:,:,1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    row = plane_segmentation.getRow(1, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roi_mask(ind) = pixel_mask.weight;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span><span >    </span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >imshow(roi_mask)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="4AE72B27" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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style="width: 157px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_C8BDC905' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_872126D0' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_6F0B7108' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_1310FB5A' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h2  class = 'S4' id = 'H_8B4EF750' ></h2>
+</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signals = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for all RoIs</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signals' + (1:size(signals, 1)))</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(</span><span style="color: #a709f5;">'Fluorescence signals'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="25B0CDD2" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signal_subset = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data(1:5, 1:20);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for a subset of RoIs and timepoints</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signal_subset' + (1:size(signal_subset, 1)))</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="A16EF8BE" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first 10 RoIs in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >num_rois = plane_segmentation.id.data.dims(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Replace this with the correct image size if needed</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Example: imaging_shape = [512, 512];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >label_image = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:10</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% image_mask is assumed to be height x width x nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        image_mask_data = plane_segmentation.image_mask.data;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        roi_mask = image_mask_data(:,:,i) &gt; 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(roi_mask) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        row = plane_segmentation.getRow(i, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% pixel_mask is assumed to have fields x, y, and weight</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% Mark ROI pixels with the ROI index</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(ind) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create one distinct color per ROI</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >cmap = lines(num_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Convert label image to RGB</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >rgb_image = label2rgb(label_image, cmap, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'shuffle'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imshow(rgb_image)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(sprintf(</span><span style="color: #a709f5;">'First %d ROIs'</span><span >, 10))</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="9C6CF6E1" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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NwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBG4H+WxyEAV9nUsAAAAABJRU5ErkJggg==" style="width: 314px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_389a' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_7f6d' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_6a4f' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_379a' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h1  class = 'S0' id = 'T_7f67' ><span>... Helper functions ...</span></h1><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >X = generateCenterCoords(varargin)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    X = randi(varargin{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >data = generateCalciumResponses(nRois, nTime)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">arguments</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nRois </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nTime </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data = zeros(nRois, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    eventProbability = 0.05;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    decayTimeConstant = 3;                 </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernelLength = 16;                     </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernel = exp(-(0:kernelLength-1) / decayTimeConstant);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i = 1:nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        spikeTrain = rand(1, nTime) &lt; eventProbability;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        response = conv(double(spikeTrain), kernel, </span><span style="color: #a709f5;">"same"</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        noise = 0.05 * randn(1, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        baseline = 0.1 * randn;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        data(i, :) = baseline + response + noise;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div>
 <br>
 <!-- 
 ##### SOURCE BEGIN #####
@@ -204,12 +162,12 @@
 % as name followed by value.
 
 nwb = NwbFile( ...
+    'identifier', 'matnwb_ophys_tutorial', ...
     'session_description', 'mouse in open exploration',...
-    'identifier', 'Mouse5_Day3', ...
     'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
     'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
     'general_experimenter', 'LastName, FirstName', ... % optional
-    'general_session_id', 'session_1234', ... % optional
+    'general_session_id', 'Mouse5_Day3', ... % optional
     'general_institution', 'University of My Institution', ... % optional
     'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
 nwb
@@ -388,7 +346,7 @@
 
 n_rois = 20;
 image_mask = zeros(y, x, n_rois);
-center = randi(90,2,n_rois);
+center = generateCenterCoords(90,2,n_rois);
 for i = 1:n_rois
     image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
 end
@@ -483,9 +441,10 @@
 % Then we create a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html 
 % |*RoiResponseSeries*|> object to store fluorescence data for those ROIs.
 
+data = generateCalciumResponses(n_rois, 100); % [nRoi, nT]
 roi_response_series = types.core.RoiResponseSeries( ...
     'rois', roi_table_region, ...
-    'data', NaN(n_rois, 100), ... % [nRoi, nT]
+    'data', data, ...
     'data_unit', 'lumens', ...
     'starting_time_rate', 3.0, ...
     'starting_time', 0.0);
@@ -529,8 +488,13 @@
 % 
 % |load| with no input arguments reads the entire dataset:
 
-read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
-    roiresponseseries.get('RoiResponseSeries').data.load
+signals = read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
+    roiresponseseries.get('RoiResponseSeries').data.load();
+
+% Plot signals for all RoIs
+plot(signals' + (1:size(signals, 1)))
+xlabel('timepoints'); ylabel('rois')
+title('Fluorescence signals')
 %% 
 % If all you need is a section of the data, you can read only that section by 
 % indexing the |DataStub| object like a normal array in MATLAB. This will just 
@@ -538,27 +502,55 @@
 % useful if you are dealing with a large dataset that is too big to fit entirely 
 % into your available RAM.
 
-read_nwb.processing.get('ophys'). ...
+signal_subset = read_nwb.processing.get('ophys'). ...
     nwbdatainterface.get('Fluorescence'). ...
     roiresponseseries.get('RoiResponseSeries'). ...
-    data(1:5, 1:10)
+    data(1:5, 1:20);
+
+% Plot signals for a subset of RoIs and timepoints
+plot(signal_subset' + (1:size(signal_subset, 1)))
+xlabel('timepoints'); ylabel('rois')
 %% 
-% Finally, read back the image/pixel masks and display the first roi in a figure:
+% Finally, read back the image/pixel masks and display the first 10 RoIs in 
+% a figure:
 
 plane_segmentation = read_nwb.processing.get('ophys'). ...
     nwbdatainterface.get('ImageSegmentation'). ...
     planesegmentation.get('PlaneSegmentation');
 
-if ~isempty(plane_segmentation.image_mask)
-    roi_mask = plane_segmentation.image_mask.data(:,:,1);
-elseif ~isempty(plane_segmentation.pixel_mask)
-    row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
-    pixel_mask = row.pixel_mask{1};
-    roi_mask = zeros(imaging_shape);
-    ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
-    roi_mask(ind) = pixel_mask.weight;
-end    
-imshow(roi_mask)
+num_rois = plane_segmentation.id.data.dims(1);
+
+% Replace this with the correct image size if needed
+% Example: imaging_shape = [512, 512];
+label_image = zeros(imaging_shape);
+
+for i = 1:10
+    if ~isempty(plane_segmentation.image_mask)
+        % image_mask is assumed to be height x width x nRois
+        image_mask_data = plane_segmentation.image_mask.data;
+        roi_mask = image_mask_data(:,:,i) > 0;
+        label_image(roi_mask) = i;
+    
+    elseif ~isempty(plane_segmentation.pixel_mask)
+        row = plane_segmentation.getRow(i, 'columns', {'pixel_mask'});
+        pixel_mask = row.pixel_mask{1};
+
+        % pixel_mask is assumed to have fields x, y, and weight
+        ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
+
+        % Mark ROI pixels with the ROI index
+        label_image(ind) = i;
+    end
+end
+
+% Create one distinct color per ROI
+cmap = lines(num_rois);
+
+% Convert label image to RGB
+rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+
+imshow(rgb_image)
+title(sprintf('First %d ROIs', 10))
 %% Learn more!
 % See the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html API documentation> to learn what data types are available.
 %% Other MatNWB tutorials
@@ -583,7 +575,44 @@
 % Extensions>
 % * <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py 
 % Advanced HDF5 I/O>
-%%
+%% ... Helper functions ...
+
+function X = generateCenterCoords(varargin)
+    % Use reproducible random number generation for consistent 
+    % tutorial output and export.
+    rndGeneratorState = rng(2, 'twister');
+    rngCleanup = onCleanup(@() rng(rndGeneratorState));
+    X = randi(varargin{:});
+end
+
+function data = generateCalciumResponses(nRois, nTime)
+    arguments
+        nRois (1,1) double {mustBeInteger, mustBePositive}
+        nTime (1,1) double {mustBeInteger, mustBePositive}
+    end
+
+    % Use reproducible random number generation for consistent 
+    % tutorial output and export.
+    rndGeneratorState = rng(2, 'twister');
+    rngCleanup = onCleanup(@() rng(rndGeneratorState));
+
+    data = zeros(nRois, nTime);
+
+    eventProbability = 0.05;
+    decayTimeConstant = 3;                 % samples
+    kernelLength = 16;                     % samples
+
+    kernel = exp(-(0:kernelLength-1) / decayTimeConstant);
+
+    for i = 1:nRois
+        spikeTrain = rand(1, nTime) < eventProbability;
+        response = conv(double(spikeTrain), kernel, "same");
+        noise = 0.05 * randn(1, nTime);
+        baseline = 0.1 * randn;
+
+        data(i, :) = baseline + response + noise;
+    end
+end
 ##### SOURCE END #####
 -->
 </div>
diff --git a/docs/source/_static/tutorials/media/ophys/figure_0.png b/docs/source/_static/tutorials/media/ophys/figure_0.png
index e4d3f29b8b745eda994dee6a021dc14060fb0d15..352acece5685c37a2222b8029a0a3e19330a47a7 100644
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diff --git a/docs/source/_static/tutorials/media/ophys/figure_2.png b/docs/source/_static/tutorials/media/ophys/figure_2.png
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diff --git a/docs/source/pages/tutorials/ophys.rst b/docs/source/pages/tutorials/ophys.rst
index 46d966684..8d3ec2a87 100644
--- a/docs/source/pages/tutorials/ophys.rst
+++ b/docs/source/pages/tutorials/ophys.rst
@@ -38,12 +38,12 @@ An NWB file represents a single session of an experiment. Each file must have a
 .. code-block:: matlab
 
    nwb = NwbFile( ...
+       'identifier', 'matnwb_ophys_tutorial', ...
        'session_description', 'mouse in open exploration',...
-       'identifier', 'Mouse5_Day3', ...
        'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
        'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
        'general_experimenter', 'LastName, FirstName', ... % optional
-       'general_session_id', 'session_1234', ... % optional
+       'general_session_id', 'Mouse5_Day3', ... % optional
        'general_institution', 'University of My Institution', ... % optional
        'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
    nwb
@@ -55,7 +55,7 @@ An NWB file represents a single session of an experiment. Each file must have a
    
                                                 nwb_version: '2.9.0'
                                            file_create_date: []
-                                                 identifier: 'Mouse5_Day3'
+                                                 identifier: 'matnwb_ophys_tutorial'
                                         session_description: 'mouse in open exploration'
                                          session_start_time: {[2018-04-25T02:30:03.000000+02:00]}
                                   timestamps_reference_time: {[2018-04-25T03:00:45.000000+02:00]}
@@ -86,7 +86,7 @@ An NWB file represents a single session of an experiment. Each file must have a
                                        general_pharmacology: ''
                                            general_protocol: ''
                                general_related_publications: {'DOI:10.1016/j.neuron.2016.12.011'}
-                                         general_session_id: 'session_1234'
+                                         general_session_id: 'Mouse5_Day3'
                                              general_slices: ''
                                       general_source_script: ''
                             general_source_script_file_name: ''
@@ -286,7 +286,7 @@ ROIs can be added to a `PlaneSegmentation <https://matnwb.readthedocs.io/en/late
    
    n_rois = 20;
    image_mask = zeros(y, x, n_rois);
-   center = randi(90,2,n_rois);
+   center = generateCenterCoords(90,2,n_rois);
    for i = 1:n_rois
        image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
    end
@@ -382,9 +382,10 @@ Then we create a `RoiResponseSeries <https://matnwb.readthedocs.io/en/latest/pag
 
 .. code-block:: matlab
 
+   data = generateCalciumResponses(n_rois, 100); % [nRoi, nT]
    roi_response_series = types.core.RoiResponseSeries( ...
        'rois', roi_table_region, ...
-       'data', NaN(n_rois, 100), ... % [nRoi, nT]
+       'data', data, ...
        'data_unit', 'lumens', ...
        'starting_time_rate', 3.0, ...
        'starting_time', 0.0);
@@ -440,42 +441,36 @@ This allows you to conveniently work with datasets that are too large to fit in
 
 .. code-block:: matlab
 
-   read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
-       roiresponseseries.get('RoiResponseSeries').data.load
-
-.. code-block:: text
+   signals = read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
+       roiresponseseries.get('RoiResponseSeries').data.load();
+   
+   % Plot signals for all RoIs
+   plot(signals' + (1:size(signals, 1)))
+   xlabel('timepoints'); ylabel('rois')
+   title('Fluorescence signals')
 
-   ans = 20x100
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+.. image:: ../../_static/tutorials/media/ophys/figure_0.png
+   :class: tutorial-media
+   :alt: figure_0.png
 
 If all you need is a section of the data, you can read only that section by indexing the ``DataStub`` object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.
 
 .. code-block:: matlab
 
-   read_nwb.processing.get('ophys'). ...
+   signal_subset = read_nwb.processing.get('ophys'). ...
        nwbdatainterface.get('Fluorescence'). ...
        roiresponseseries.get('RoiResponseSeries'). ...
-       data(1:5, 1:10)
-
-.. code-block:: text
+       data(1:5, 1:20);
+   
+   % Plot signals for a subset of RoIs and timepoints
+   plot(signal_subset' + (1:size(signal_subset, 1)))
+   xlabel('timepoints'); ylabel('rois')
 
-   ans = 5x10
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
-      NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN
+.. image:: ../../_static/tutorials/media/ophys/figure_1.png
+   :class: tutorial-media
+   :alt: figure_1.png
 
-Finally, read back the image/pixel masks and display the first roi in a figure:
+Finally, read back the image/pixel masks and display the first 10 RoIs in a figure:
 
 .. code-block:: matlab
 
@@ -483,20 +478,43 @@ Finally, read back the image/pixel masks and display the first roi in a figure:
        nwbdatainterface.get('ImageSegmentation'). ...
        planesegmentation.get('PlaneSegmentation');
    
-   if ~isempty(plane_segmentation.image_mask)
-       roi_mask = plane_segmentation.image_mask.data(:,:,1);
-   elseif ~isempty(plane_segmentation.pixel_mask)
-       row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
-       pixel_mask = row.pixel_mask{1};
-       roi_mask = zeros(imaging_shape);
-       ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
-       roi_mask(ind) = pixel_mask.weight;
-   end    
-   imshow(roi_mask)
+   num_rois = plane_segmentation.id.data.dims(1);
+   
+   % Replace this with the correct image size if needed
+   % Example: imaging_shape = [512, 512];
+   label_image = zeros(imaging_shape);
+   
+   for i = 1:10
+       if ~isempty(plane_segmentation.image_mask)
+           % image_mask is assumed to be height x width x nRois
+           image_mask_data = plane_segmentation.image_mask.data;
+           roi_mask = image_mask_data(:,:,i) > 0;
+           label_image(roi_mask) = i;
+       
+       elseif ~isempty(plane_segmentation.pixel_mask)
+           row = plane_segmentation.getRow(i, 'columns', {'pixel_mask'});
+           pixel_mask = row.pixel_mask{1};
+   
+           % pixel_mask is assumed to have fields x, y, and weight
+           ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
+   
+           % Mark ROI pixels with the ROI index
+           label_image(ind) = i;
+       end
+   end
+   
+   % Create one distinct color per ROI
+   cmap = lines(num_rois);
+   
+   % Convert label image to RGB
+   rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+   
+   imshow(rgb_image)
+   title(sprintf('First %d ROIs', 10))
 
-.. image:: ../../_static/tutorials/media/ophys/figure_0.png
+.. image:: ../../_static/tutorials/media/ophys/figure_2.png
    :class: tutorial-media
-   :alt: figure_0.png
+   :alt: figure_2.png
 
 Learn more!
 -----------
@@ -524,3 +542,45 @@ See our tutorials for more details about your data type:
 * `Iterative data write <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py>`_
 * `Extensions <https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py>`_
 * `Advanced HDF5 I/O <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py>`_
+
+... Helper functions ...
+------------------------
+
+.. code-block:: matlab
+
+   function X = generateCenterCoords(varargin)
+       % Use reproducible random number generation for consistent 
+       % tutorial output and export.
+       rndGeneratorState = rng(2, 'twister');
+       rngCleanup = onCleanup(@() rng(rndGeneratorState));
+       X = randi(varargin{:});
+   end
+   
+   function data = generateCalciumResponses(nRois, nTime)
+       arguments
+           nRois (1,1) double {mustBeInteger, mustBePositive}
+           nTime (1,1) double {mustBeInteger, mustBePositive}
+       end
+   
+       % Use reproducible random number generation for consistent 
+       % tutorial output and export.
+       rndGeneratorState = rng(2, 'twister');
+       rngCleanup = onCleanup(@() rng(rndGeneratorState));
+   
+       data = zeros(nRois, nTime);
+   
+       eventProbability = 0.05;
+       decayTimeConstant = 3;                 % samples
+       kernelLength = 16;                     % samples
+   
+       kernel = exp(-(0:kernelLength-1) / decayTimeConstant);
+   
+       for i = 1:nRois
+           spikeTrain = rand(1, nTime) < eventProbability;
+           response = conv(double(spikeTrain), kernel, "same");
+           noise = 0.05 * randn(1, nTime);
+           baseline = 0.1 * randn;
+   
+           data(i, :) = baseline + response + noise;
+       end
+   end
diff --git a/tutorials/ophys.mlx b/tutorials/ophys.mlx
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diff --git a/tutorials/private/mcode/ophys.m b/tutorials/private/mcode/ophys.m
index 3d906700e..e22a53345 100644
--- a/tutorials/private/mcode/ophys.m
+++ b/tutorials/private/mcode/ophys.m
@@ -25,12 +25,12 @@
 % as name followed by value.
 
 nwb = NwbFile( ...
+    'identifier', 'matnwb_ophys_tutorial', ...
     'session_description', 'mouse in open exploration',...
-    'identifier', 'Mouse5_Day3', ...
     'session_start_time', datetime(2018, 4, 25, 2, 30, 3, 'TimeZone', 'local'), ...
     'timestamps_reference_time', datetime(2018, 4, 25, 3, 0, 45, 'TimeZone', 'local'), ...
     'general_experimenter', 'LastName, FirstName', ... % optional
-    'general_session_id', 'session_1234', ... % optional
+    'general_session_id', 'Mouse5_Day3', ... % optional
     'general_institution', 'University of My Institution', ... % optional
     'general_related_publications', {'DOI:10.1016/j.neuron.2016.12.011'}); % optional
 nwb
@@ -209,7 +209,7 @@
 
 n_rois = 20;
 image_mask = zeros(y, x, n_rois);
-center = randi(90,2,n_rois);
+center = generateCenterCoords(90,2,n_rois);
 for i = 1:n_rois
     image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;
 end
@@ -304,9 +304,10 @@
 % Then we create a <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html 
 % |*RoiResponseSeries*|> object to store fluorescence data for those ROIs.
 
+data = generateCalciumResponses(n_rois, 100); % [nRoi, nT]
 roi_response_series = types.core.RoiResponseSeries( ...
     'rois', roi_table_region, ...
-    'data', NaN(n_rois, 100), ... % [nRoi, nT]
+    'data', data, ...
     'data_unit', 'lumens', ...
     'starting_time_rate', 3.0, ...
     'starting_time', 0.0);
@@ -350,8 +351,13 @@
 % 
 % |load| with no input arguments reads the entire dataset:
 
-read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
-    roiresponseseries.get('RoiResponseSeries').data.load
+signals = read_nwb.processing.get('ophys').nwbdatainterface.get('Fluorescence'). ...
+    roiresponseseries.get('RoiResponseSeries').data.load();
+
+% Plot signals for all RoIs
+plot(signals' + (1:size(signals, 1)))
+xlabel('timepoints'); ylabel('rois')
+title('Fluorescence signals')
 %% 
 % If all you need is a section of the data, you can read only that section by 
 % indexing the |DataStub| object like a normal array in MATLAB. This will just 
@@ -359,27 +365,55 @@
 % useful if you are dealing with a large dataset that is too big to fit entirely 
 % into your available RAM.
 
-read_nwb.processing.get('ophys'). ...
+signal_subset = read_nwb.processing.get('ophys'). ...
     nwbdatainterface.get('Fluorescence'). ...
     roiresponseseries.get('RoiResponseSeries'). ...
-    data(1:5, 1:10)
+    data(1:5, 1:20);
+
+% Plot signals for a subset of RoIs and timepoints
+plot(signal_subset' + (1:size(signal_subset, 1)))
+xlabel('timepoints'); ylabel('rois')
 %% 
-% Finally, read back the image/pixel masks and display the first roi in a figure:
+% Finally, read back the image/pixel masks and display the first 10 RoIs in 
+% a figure:
 
 plane_segmentation = read_nwb.processing.get('ophys'). ...
     nwbdatainterface.get('ImageSegmentation'). ...
     planesegmentation.get('PlaneSegmentation');
 
-if ~isempty(plane_segmentation.image_mask)
-    roi_mask = plane_segmentation.image_mask.data(:,:,1);
-elseif ~isempty(plane_segmentation.pixel_mask)
-    row = plane_segmentation.getRow(1, 'columns', {'pixel_mask'});
-    pixel_mask = row.pixel_mask{1};
-    roi_mask = zeros(imaging_shape);
-    ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
-    roi_mask(ind) = pixel_mask.weight;
-end    
-imshow(roi_mask)
+num_rois = plane_segmentation.id.data.dims(1);
+
+% Replace this with the correct image size if needed
+% Example: imaging_shape = [512, 512];
+label_image = zeros(imaging_shape);
+
+for i = 1:10
+    if ~isempty(plane_segmentation.image_mask)
+        % image_mask is assumed to be height x width x nRois
+        image_mask_data = plane_segmentation.image_mask.data;
+        roi_mask = image_mask_data(:,:,i) > 0;
+        label_image(roi_mask) = i;
+    
+    elseif ~isempty(plane_segmentation.pixel_mask)
+        row = plane_segmentation.getRow(i, 'columns', {'pixel_mask'});
+        pixel_mask = row.pixel_mask{1};
+
+        % pixel_mask is assumed to have fields x, y, and weight
+        ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);
+
+        % Mark ROI pixels with the ROI index
+        label_image(ind) = i;
+    end
+end
+
+% Create one distinct color per ROI
+cmap = lines(num_rois);
+
+% Convert label image to RGB
+rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+
+imshow(rgb_image)
+title(sprintf('First %d ROIs', 10))
 %% Learn more!
 % See the <https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html API documentation> to learn what data types are available.
 %% Other MatNWB tutorials
@@ -404,4 +438,41 @@
 % Extensions>
 % * <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py 
 % Advanced HDF5 I/O>
-%%
\ No newline at end of file
+%% ... Helper functions ...
+
+function X = generateCenterCoords(varargin)
+    % Use reproducible random number generation for consistent 
+    % tutorial output and export.
+    rndGeneratorState = rng(2, 'twister');
+    rngCleanup = onCleanup(@() rng(rndGeneratorState));
+    X = randi(varargin{:});
+end
+
+function data = generateCalciumResponses(nRois, nTime)
+    arguments
+        nRois (1,1) double {mustBeInteger, mustBePositive}
+        nTime (1,1) double {mustBeInteger, mustBePositive}
+    end
+
+    % Use reproducible random number generation for consistent 
+    % tutorial output and export.
+    rndGeneratorState = rng(2, 'twister');
+    rngCleanup = onCleanup(@() rng(rndGeneratorState));
+
+    data = zeros(nRois, nTime);
+
+    eventProbability = 0.05;
+    decayTimeConstant = 3;                 % samples
+    kernelLength = 16;                     % samples
+
+    kernel = exp(-(0:kernelLength-1) / decayTimeConstant);
+
+    for i = 1:nRois
+        spikeTrain = rand(1, nTime) < eventProbability;
+        response = conv(double(spikeTrain), kernel, "same");
+        noise = 0.05 * randn(1, nTime);
+        baseline = 0.1 * randn;
+
+        data(i, :) = baseline + response + noise;
+    end
+end
\ No newline at end of file

From 2980cdc94c02b764b2a5dc824b3cc3ace12ad528 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 13:58:31 +0200
Subject: [PATCH 6/9] Improve RST generation for tutorials

---
 tools/documentation/matnwb_exportTutorials.m  |   4 +-
 .../private/generateRstForTutorial.m          | 155 ++++++++++++++++++
 .../private/generateRstForTutorials.m         | 140 ++--------------
 3 files changed, 172 insertions(+), 127 deletions(-)
 create mode 100644 tools/documentation/private/generateRstForTutorial.m

diff --git a/tools/documentation/matnwb_exportTutorials.m b/tools/documentation/matnwb_exportTutorials.m
index c021d228e..34dfc442b 100644
--- a/tools/documentation/matnwb_exportTutorials.m
+++ b/tools/documentation/matnwb_exportTutorials.m
@@ -113,10 +113,8 @@ function matnwb_exportTutorials(options)
                 postProcessLivescriptHtml(targetFilePath)
             end
         end
-    end
 
-    if ~isempty(filePaths)
-        generateRstForTutorials()
+        generateRstForTutorial(string(sourcePath))
     end
 end
 
diff --git a/tools/documentation/private/generateRstForTutorial.m b/tools/documentation/private/generateRstForTutorial.m
new file mode 100644
index 000000000..c84b6a846
--- /dev/null
+++ b/tools/documentation/private/generateRstForTutorial.m
@@ -0,0 +1,155 @@
+function rstOutputFilePath = generateRstForTutorial(sourceFilePath, options)
+% generateRstForTutorial - Generate rst for a single tutorial source file.
+
+    arguments
+        sourceFilePath (1,1) string {mustBeFile}
+        options.SourceRepoPath (1,1) string = ""
+    end
+
+    docsSourceRootDir = fullfile(misc.getMatnwbDir, 'docs', 'source');
+    tutorialStaticRootDir = fullfile(docsSourceRootDir, '_static', 'tutorials', 'media');
+    tutorialHtmlSourceDir = fullfile(docsSourceRootDir, '_static', 'html', 'tutorials');
+    tutorialRstTargetDir = fullfile(docsSourceRootDir, 'pages', 'tutorials');
+    if ~isfolder(tutorialRstTargetDir); mkdir(tutorialRstTargetDir); end
+    if ~isfolder(tutorialStaticRootDir); mkdir(tutorialStaticRootDir); end
+
+    tutorialConfigFilePath = fullfile(docsSourceRootDir, '_config', 'tutorial_config.json');
+    tutorialConfig = jsondecode(fileread(tutorialConfigFilePath));
+    rstTemplate = fileread(getRstTemplateFile('tutorial'));
+
+    [~, tutorialName, sourceExtension] = fileparts(sourceFilePath);
+    tutorialName = string(tutorialName);
+
+    assert(isfield(tutorialConfig.titles, tutorialName), ...
+        'generateRstForTutorial:MissingTutorialConfig', ...
+        'Could not find tutorial configuration for `%s`.', tutorialName)
+
+    sourceRepoPath = options.SourceRepoPath;
+    if sourceRepoPath == ""
+        sourceRepoPath = deriveSourceRepoPath(sourceFilePath);
+    end
+
+    htmlFilePath = fullfile(tutorialHtmlSourceDir, tutorialName + ".html");
+    staticHtmlPath = strrep(htmlFilePath, docsSourceRootDir, '../..');
+    title = tutorialConfig.titles.(tutorialName);
+
+    rstOutput = replace(rstTemplate, '{{static_html_path}}', staticHtmlPath);
+    rstOutput = replace(rstOutput, '{{tutorial_name}}', tutorialName);
+    rstOutput = replace(rstOutput, '{{tutorial_source_path}}', sourceRepoPath);
+
+    if string(sourceExtension) == ".mlx"
+        tutorialBody = convertMlxTutorialToRst(sourceFilePath, tutorialName, htmlFilePath, tutorialStaticRootDir);
+    else
+        tutorialBody = convertTutorialMCodeToRst(sourceFilePath);
+    end
+
+    rstOutput = replace(rstOutput, '{{tutorial_body}}', tutorialBody);
+
+    if isfield(tutorialConfig.youtube, tutorialName)
+        youtubeBadge = fileread(getRstTemplateFile('youtube_badge'));
+        youtubeBadge = replace(youtubeBadge, '{{youtube_url}}', tutorialConfig.youtube.(tutorialName));
+        title = sprintf('%s 🎬', title);
+    else
+        youtubeBadge = '';
+    end
+
+    rstOutput = replace(rstOutput, '{{youtube_badge_block}}', youtubeBadge);
+    rstOutput = replace(rstOutput, '{{tutorial_title}}', title);
+    rstOutput = replace(rstOutput, '{{tutorial_title_underline}}', repmat('=', 1, numel(title)));
+
+    rstOutputFilePath = fullfile(tutorialRstTargetDir, tutorialName + ".rst");
+    filewrite(char(rstOutputFilePath), rstOutput)
+
+    if ~nargout
+        clear rstOutputFilePath
+    end
+end
+
+function tutorialBody = convertMlxTutorialToRst(sourceFilePath, tutorialName, htmlFilePath, tutorialStaticRootDir)
+    tempMarkdownRootDir = fullfile(tempdir, 'matnwbTutorialMarkdown', ...
+        char(tutorialName), char(java.util.UUID.randomUUID));
+    mkdir(tempMarkdownRootDir);
+    cleanupDeleteTempMarkdown = onCleanup(@() safeRemoveDir(tempMarkdownRootDir));
+
+    markdownFilePath = fullfile(tempMarkdownRootDir, tutorialName + ".md");
+    export(char(sourceFilePath), char(markdownFilePath));
+
+    sourceMediaDir = fullfile(tempMarkdownRootDir, tutorialName + "_media");
+    targetMediaDir = fullfile(tutorialStaticRootDir, tutorialName);
+    safeRemoveDir(targetMediaDir)
+    if isfolder(sourceMediaDir)
+        copyfile(sourceMediaDir, targetMediaDir);
+    end
+
+    [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths(markdownFilePath, htmlFilePath);
+    mediaRelativePath = "../../_static/tutorials/media/" + tutorialName;
+    tutorialBody = convertTutorialMarkdownToRst(markdownFilePath, ...
+        "MediaRelativePath", mediaRelativePath, ...
+        "ImageNames", imageNames, ...
+        "ImageDisplayWidths", imageDisplayWidths);
+end
+
+function sourceRepoPath = deriveSourceRepoPath(sourceFilePath)
+    repoRootDir = misc.getMatnwbDir;
+    sourceRepoPath = string(sourceFilePath);
+    sourceRepoPath = erase(sourceRepoPath, string(repoRootDir) + filesep);
+    sourceRepoPath = replace(sourceRepoPath, filesep, '/');
+end
+
+function [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths(markdownFilePath, htmlFilePath)
+    imageNames = string.empty(1, 0);
+    imageDisplayWidths = double.empty(1, 0);
+
+    markdownText = fileread(markdownFilePath);
+    markdownMatches = regexp(markdownText, '!\[[^\]]*\]\(([^)]+)\)', 'tokens');
+    if isempty(markdownMatches)
+        return
+    end
+
+    imagePaths = strings(numel(markdownMatches), 1);
+    for i = 1:numel(markdownMatches)
+        imagePaths(i) = string(markdownMatches{i}{1});
+    end
+    imageNames = cellfun(@extractImageName, cellstr(imagePaths), 'UniformOutput', false);
+    imageNames = string(imageNames);
+
+    if ~isfile(htmlFilePath)
+        imageDisplayWidths = nan(size(imageNames));
+        return
+    end
+
+    htmlText = fileread(htmlFilePath);
+    widthTokens = regexp(htmlText, '<img class = "imageNode"[^>]*width = "(\d+)"', 'tokens');
+    if isempty(widthTokens)
+        imageDisplayWidths = nan(size(imageNames));
+        return
+    end
+
+    htmlWidths = nan(numel(widthTokens), 1);
+    for i = 1:numel(widthTokens)
+        htmlWidths(i) = str2double(widthTokens{i}{1});
+    end
+
+    imageDisplayWidths = nan(size(imageNames));
+    numMatches = min(numel(imageNames), numel(htmlWidths));
+    imageDisplayWidths(1:numMatches) = htmlWidths(1:numMatches);
+end
+
+function imageName = extractImageName(imagePath)
+    [~, baseName, extension] = fileparts(imagePath);
+    imageName = string(baseName) + string(extension);
+end
+
+function safeRemoveDir(folderPath)
+    if isfolder(folderPath)
+        maybeRemovePath(folderPath)
+        rmdir(folderPath, 's');
+    end
+end
+
+function maybeRemovePath(folderPath)
+    pathEntries = string(split(path, pathsep));
+    if any(pathEntries == folderPath)
+        rmpath(folderPath)
+    end
+end
diff --git a/tools/documentation/private/generateRstForTutorials.m b/tools/documentation/private/generateRstForTutorials.m
index 64330eb2f..5d62baf82 100644
--- a/tools/documentation/private/generateRstForTutorials.m
+++ b/tools/documentation/private/generateRstForTutorials.m
@@ -1,28 +1,17 @@
 function generateRstForTutorials(options)
-% generateRstForTutorials - Generate rst files for tutorial pages
+% generateRstForTutorials - Generate rst files for tutorial pages.
 
     arguments
         options.FileNames (1,:) string = string.empty
     end
 
     docsSourceRootDir = fullfile(misc.getMatnwbDir, 'docs', 'source');
-    tutorialStaticRootDir = fullfile(docsSourceRootDir, '_static', 'tutorials', 'media');
-    tutorialHtmlSourceDir = fullfile(docsSourceRootDir, '_static', 'html', 'tutorials');
-    tutorialRstTargetDir = fullfile(docsSourceRootDir, 'pages', 'tutorials');
-    legacyTutorialStaticRootDir = fullfile(docsSourceRootDir, '_static', 'markdown');
-    if ~isfolder(tutorialRstTargetDir); mkdir(tutorialRstTargetDir); end
-    if ~isfolder(tutorialStaticRootDir); mkdir(tutorialStaticRootDir); end
-    
-    tutorialConfigFilePath = fullfile(docsSourceRootDir, '_config', 'tutorial_config.json');
-    S = jsondecode(fileread(tutorialConfigFilePath));
+    tempMarkdownExportDir = fullfile(docsSourceRootDir, '_static', 'markdown');
 
-    rstTemplate = fileread( getRstTemplateFile('tutorial') );
-    tempMarkdownRootDir = fullfile(tempdir, 'matnwbTutorialMarkdown');
-    safeRemoveDir(tempMarkdownRootDir)
-    mkdir(tempMarkdownRootDir);
-    cleanupDeleteTempMarkdown = onCleanup(@() safeRemoveDir(tempMarkdownRootDir)); %#ok<NASGU>
+    tutorialConfigFilePath = fullfile(docsSourceRootDir, '_config', 'tutorial_config.json');
+    tutorialConfig = jsondecode(fileread(tutorialConfigFilePath));
 
-    tutorialNames = string(fieldnames(S.titles));
+    tutorialNames = string(fieldnames(tutorialConfig.titles));
     tutorialNames = sort(tutorialNames);
 
     if ~isempty(options.FileNames)
@@ -30,72 +19,27 @@ function generateRstForTutorials(options)
     end
 
     for i = 1:numel(tutorialNames)
-        name = tutorialNames(i);
-        [sourceFilePath, sourceRepoPath] = resolveTutorialSource(name);
+        tutorialName = tutorialNames(i);
+        [sourceFilePath, sourceRepoPath] = resolveTutorialSource(tutorialName);
         if sourceFilePath == ""
-            warning('Could not find a tutorial source file for "%s"', name)
+            warning('Could not find a tutorial source file for "%s"', tutorialName)
             continue
         end
 
-        htmlFilePath = fullfile(tutorialHtmlSourceDir, name + ".html");
-        relPath = strrep(htmlFilePath, docsSourceRootDir, '../..');
-        title = S.titles.(name);
-    
-        rstOutput = replace(rstTemplate, '{{static_html_path}}', relPath);
-        rstOutput = replace(rstOutput, '{{tutorial_name}}', name);
-        rstOutput = replace(rstOutput, '{{tutorial_source_path}}', sourceRepoPath);
-
-        [~, ~, sourceExtension] = fileparts(sourceFilePath);
-        if sourceExtension == ".mlx"
-            markdownFilePath = fullfile(tempMarkdownRootDir, name + ".md");
-            export(char(sourceFilePath), char(markdownFilePath));
-
-            sourceMediaDir = fullfile(tempMarkdownRootDir, name + "_media");
-            targetMediaDir = fullfile(tutorialStaticRootDir, name);
-            safeRemoveDir(targetMediaDir)
-            if isfolder(sourceMediaDir)
-                copyfile(sourceMediaDir, targetMediaDir);
-            end
-
-            [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths( ...
-                markdownFilePath, htmlFilePath );
-            mediaRelativePath = "../../_static/tutorials/media/" + name;
-            tutorialBody = convertTutorialMarkdownToRst(markdownFilePath, ...
-                "MediaRelativePath", mediaRelativePath, ...
-                "ImageNames", imageNames, ...
-                "ImageDisplayWidths", imageDisplayWidths);
-        else
-            tutorialBody = convertTutorialMCodeToRst(sourceFilePath);
-        end
-
-        rstOutput = replace(rstOutput, '{{tutorial_body}}', tutorialBody);
-
-        % Add the youtube badge block if the tutorial has a corresponding youtube video
-        if isfield(S.youtube, name)
-            youtubeBadge = fileread( getRstTemplateFile('youtube_badge') );
-            youtubeBadge = replace(youtubeBadge, '{{youtube_url}}', S.youtube.(name));
-            title = sprintf('%s 🎬', title); % Add emoji in the title if there is a video
-        else
-            youtubeBadge = '';
-        end
-        rstOutput = replace(rstOutput, '{{youtube_badge_block}}', youtubeBadge);
-        rstOutput = replace(rstOutput, '{{tutorial_title}}', title);
-        rstOutput = replace(rstOutput, '{{tutorial_title_underline}}', repmat('=', 1, numel(title)));
-        rstOutputFile = fullfile(tutorialRstTargetDir, name + ".rst");
-        filewrite(char(rstOutputFile), rstOutput)
+        generateRstForTutorial(sourceFilePath, ...
+            "SourceRepoPath", sourceRepoPath);
     end
 
-    % Create index
-    indexTemplate = fileread( getRstTemplateFile('index_tutorials') );
-    data.file_list = strjoin("   "+tutorialNames, newline);
-    
-    thisRst = fillTemplate(indexTemplate, data);
-    rstFilePath = fullfile(tutorialRstTargetDir, ['index', '.rst']);
+    indexTemplate = fileread(getRstTemplateFile('index_tutorials'));
+    data.file_list = strjoin("   " + tutorialNames, newline);
+
+    thisRst = fillTemplate(indexTemplate, data); %#ok<NASGU>
+    rstFilePath = fullfile(docsSourceRootDir, 'pages', 'tutorials', ['index', '.rst']); %#ok<NASGU>
 
     % Commented out because currently this index file is edited manually.
     %filewrite(rstFilePath, thisRst);
 
-    safeRemoveDir(legacyTutorialStaticRootDir)
+    safeRemoveDir(tempMarkdownExportDir)
 end
 
 function [sourceFilePath, sourceRepoPath] = resolveTutorialSource(tutorialName)
@@ -123,60 +67,8 @@ function generateRstForTutorials(options)
     end
 end
 
-function maybeRemovePath(folderPath)
-    pathEntries = string(split(path, pathsep));
-    if any(pathEntries == folderPath)
-        rmpath(folderPath)
-    end
-end
-
-function [imageNames, imageDisplayWidths] = getTutorialImageDisplayWidths(markdownFilePath, htmlFilePath)
-    imageNames = string.empty(1, 0);
-    imageDisplayWidths = double.empty(1, 0);
-
-    markdownText = fileread(markdownFilePath);
-    markdownMatches = regexp(markdownText, '!\[[^\]]*\]\(([^)]+)\)', 'tokens');
-    if isempty(markdownMatches)
-        return
-    end
-
-    imagePaths = strings(numel(markdownMatches), 1);
-    for i = 1:numel(markdownMatches)
-        imagePaths(i) = string(markdownMatches{i}{1});
-    end
-    imageNames = cellfun(@extractImageName, cellstr(imagePaths), 'UniformOutput', false);
-    imageNames = string(imageNames);
-
-    if ~isfile(htmlFilePath)
-        imageDisplayWidths = nan(size(imageNames));
-        return
-    end
-
-    htmlText = fileread(htmlFilePath);
-    widthTokens = regexp(htmlText, '<img class = "imageNode"[^>]*width = "(\d+)"', 'tokens');
-    if isempty(widthTokens)
-        imageDisplayWidths = nan(size(imageNames));
-        return
-    end
-
-    htmlWidths = nan(numel(widthTokens), 1);
-    for i = 1:numel(widthTokens)
-        htmlWidths(i) = str2double(widthTokens{i}{1});
-    end
-
-    imageDisplayWidths = nan(size(imageNames));
-    numMatches = min(numel(imageNames), numel(htmlWidths));
-    imageDisplayWidths(1:numMatches) = htmlWidths(1:numMatches);
-end
-
-function imageName = extractImageName(imagePath)
-    [~, baseName, extension] = fileparts(imagePath);
-    imageName = string(baseName) + string(extension);
-end
-
 function safeRemoveDir(folderPath)
     if isfolder(folderPath)
-        maybeRemovePath(folderPath)
         rmdir(folderPath, 's');
     end
 end

From 7b6d0d1883f6771d78ec9bbc10ff0dab95dd1510 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 14:04:15 +0200
Subject: [PATCH 7/9] Update matnwb_exportTutorials.m

---
 tools/documentation/matnwb_exportTutorials.m | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/tools/documentation/matnwb_exportTutorials.m b/tools/documentation/matnwb_exportTutorials.m
index 34dfc442b..53eb17356 100644
--- a/tools/documentation/matnwb_exportTutorials.m
+++ b/tools/documentation/matnwb_exportTutorials.m
@@ -24,7 +24,7 @@ function matnwb_exportTutorials(options)
 % cleanly.
 
     arguments
-        options.ExportFormat (1,:) string {mustStartWithDot} = [".m", ".html"]
+        options.ExportFormat (1,:) string {mustStartWithDot} = [".m", ".html", ".rst"]
         options.Expression (1,1) string = "*" % Filter by expression
         options.FileNames (1,:) string = string.empty % Filter by file names
         options.FilePaths (1,:) string = string.empty % Export specified files
@@ -34,7 +34,8 @@ function matnwb_exportTutorials(options)
 
     EXPORT_FOLDERS = dictionary(...
         '.m', fullfile(misc.getMatnwbDir, "tutorials", "private", "mcode"), ...
-        '.html', fullfile(misc.getMatnwbDir, "docs", "source", "_static", "html", "tutorials") );
+        '.html', fullfile(misc.getMatnwbDir, "docs", "source", "_static", "html", "tutorials"), ...
+        '.rst', fullfile(misc.getMatnwbDir, "docs", "source", "pages", "tutorials"));
     
     [exportFormat, targetFolderNames] = deal(options.ExportFormat);
 
@@ -108,13 +109,15 @@ function matnwb_exportTutorials(options)
         for j = 1:numel(exportFormat)
             targetFilePath = fullfile(targetFolderPaths(j), fileNames(i) + exportFormat(j));
             fprintf('Exporting livescript "%s" to "%s"\n', fileNames(i), exportFormat(j))
-            export(sourcePath, targetFilePath);
+            if strcmp(exportFormat(j), '.rst') % Custom export/conversion needed
+                generateRstForTutorial(string(sourcePath))
+            else
+                export(sourcePath, targetFilePath);
+            end
             if strcmp(exportFormat(j), '.html')
                 postProcessLivescriptHtml(targetFilePath)
             end
         end
-
-        generateRstForTutorial(string(sourcePath))
     end
 end
 

From bffefb93c44e8ccf88b2f15d866368cf2fbb6cc6 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 16:39:13 +0200
Subject: [PATCH 8/9] Update ophys.mlx

Fix ophys tutorial, removing image processing toolbox dependency
---
 tutorials/ophys.mlx | Bin 228591 -> 244502 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/tutorials/ophys.mlx b/tutorials/ophys.mlx
index a0d5602a12f911f9e91346ff84951b9f2c4e5391..cb729567ba10c07073596439cdcb739eb214eb9f 100644
GIT binary patch
delta 83511
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z!z3AhAGTV`8;TKE&1ft_c-D{N5l`SU9<;8b*dND{CzJwkqoB3ZZtu57T!xznvKRRi
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From b75622cea558a6ebacbcacfb1945ab2b4d3c23d4 Mon Sep 17 00:00:00 2001
From: ehennestad <ehennestad@gmail.com>
Date: Mon, 20 Apr 2026 16:47:12 +0200
Subject: [PATCH 9/9] Reexport ophys tutorial

---
 docs/source/_static/html/tutorials/ophys.html |  63 ++++++++++--------
 .../tutorials/media/ophys/figure_2.png        | Bin 4444 -> 4431 bytes
 docs/source/pages/tutorials/ophys.rst         |  21 ++++--
 tutorials/ophys.mlx                           | Bin 244502 -> 228653 bytes
 tutorials/private/mcode/ophys.m               |  19 ++++--
 5 files changed, 62 insertions(+), 41 deletions(-)

diff --git a/docs/source/_static/html/tutorials/ophys.html b/docs/source/_static/html/tutorials/ophys.html
index 500120066..6570bd081 100644
--- a/docs/source/_static/html/tutorials/ophys.html
+++ b/docs/source/_static/html/tutorials/ophys.html
@@ -52,26 +52,26 @@
 .S16 { margin: 10px 10px 9px 4px; padding: 0px; line-height: 21px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 14px; font-weight: 400; text-align: left;  }
 .S17 { border-left: 1px solid rgb(217, 217, 217); border-right: 1px solid rgb(217, 217, 217); border-top: 1px solid rgb(217, 217, 217); border-bottom: 1px solid rgb(217, 217, 217); border-radius: 4px; padding: 6px 45px 4px 13px; line-height: 18.004px; min-height: 0px; white-space: nowrap; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, "Courier New", monospace, Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 14px;  }
 .S18 { margin: 3px 10px 5px 4px; padding: 0px; line-height: 18px; min-height: 0px; white-space: pre-wrap; color: rgb(33, 33, 33); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 17px; font-weight: 700; text-align: left;  }
-.S19 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_17e7' ><span>MatNWB Optical Physiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_17e7"><span>MatNWB Optical Physiology Tutorial
-</span></a><span>    </span><a href = "#H_8c0c"><span>Introduction
-</span></a><span>    </span><a href = "#H_6792"><span>Set up the NWB file
-</span></a><span>        </span><a href = "#H_46e0"><span>Subject Information
-</span></a><span>    </span><a href = "#H_5e37"><span>Optical Physiology
-</span></a><span>        </span><a href = "#H_2387"><span>Imaging Plane
-</span></a><span>        </span><a href = "#H_9082"><span>Storing Two-Photon Data
-</span></a><span>        </span><a href = "#H_9054"><span>Storing One-Photon Data
-</span></a><span>        </span><a href = "#H_8c47"><span>Motion Correction (optional)
-</span></a><span>        </span><a href = "#H_68e4"><span>Plane Segmentation
-</span></a><span>        </span><a href = "#H_5763"><span>Regions of interest (ROIs)
-</span></a><span>        </span><a href = "#H_6b62"><span>Adding ROIs to NWB file 
-</span></a><span>        </span><a href = "#H_47d3"><span>Storing fluorescence of ROIs over time
-</span></a><span>    </span><a href = "#H_5d01"><span>Writing the NWB file
-</span></a><span>    </span><a href = "#H_8378"><span>Reading the NWB file
-</span></a><a href = "#T_389a"><span>Learn more!
-</span></a><span>        </span><a href = "#H_7f6d"><span>See the API documentation to learn what data types are available.
-</span></a><span>    </span><a href = "#H_6a4f"><span>Other MatNWB tutorials
-</span></a><span>    </span><a href = "#H_379a"><span>Python tutorials
-</span></a><a href = "#T_7f67"><span>... Helper functions ...</span></a></div></div><h2  class = 'S4' id = 'H_8c0c' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_6792' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'matnwb_ophys_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="53C0C548" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
+.S19 { margin: 15px 10px 5px 4px; padding: 0px; line-height: 28.8px; min-height: 0px; white-space: pre-wrap; color: rgb(192, 76, 11); font-family: Helvetica, Arial, sans-serif, Helvetica, Arial, sans-serif; font-style: normal; font-size: 24px; font-weight: 400; text-align: left;  }</style></head><body><div class = rtcContent><h1  class = 'S0' id = 'T_20f4' ><span>MatNWB Optical Physiology Tutorial</span></h1><div  class = 'S1'><div  class = 'S2'><span style=' font-weight: bold;'>Table of Contents</span></div><div  class = 'S3'><a href = "#T_20f4"><span>MatNWB Optical Physiology Tutorial
+</span></a><span>    </span><a href = "#H_70db"><span>Introduction
+</span></a><span>    </span><a href = "#H_0991"><span>Set up the NWB file
+</span></a><span>        </span><a href = "#H_3a0a"><span>Subject Information
+</span></a><span>    </span><a href = "#H_4a0a"><span>Optical Physiology
+</span></a><span>        </span><a href = "#H_1e01"><span>Imaging Plane
+</span></a><span>        </span><a href = "#H_49fa"><span>Storing Two-Photon Data
+</span></a><span>        </span><a href = "#H_651e"><span>Storing One-Photon Data
+</span></a><span>        </span><a href = "#H_2e61"><span>Motion Correction (optional)
+</span></a><span>        </span><a href = "#H_5d6d"><span>Plane Segmentation
+</span></a><span>        </span><a href = "#H_9fc3"><span>Regions of interest (ROIs)
+</span></a><span>        </span><a href = "#H_7312"><span>Adding ROIs to NWB file 
+</span></a><span>        </span><a href = "#H_26e1"><span>Storing fluorescence of ROIs over time
+</span></a><span>    </span><a href = "#H_7a45"><span>Writing the NWB file
+</span></a><span>    </span><a href = "#H_391c"><span>Reading the NWB file
+</span></a><a href = "#T_08c6"><span>Learn more!
+</span></a><span>        </span><a href = "#H_0f3d"><span>See the API documentation to learn what data types are available.
+</span></a><span>    </span><a href = "#H_80ac"><span>Other MatNWB tutorials
+</span></a><span>    </span><a href = "#H_9f53"><span>Python tutorials
+</span></a><a href = "#T_1671"><span>Helper functions</span></a></div></div><h2  class = 'S4' id = 'H_70db' ><span>Introduction</span></h2><div  class = 'S5'><span>In this tutorial, we will create fake data for a hypothetical optical physiology experiment with a freely moving animal. The types of data we will convert are:</span></div><ul  class = 'S6'><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation (ROIs)</span></li><li  class = 'S7'><span>Fluorescence and dF/F response</span></li></ul><div  class = 'S5'><span>It is recommended to first work through the </span><a href = "./intro.html"><span>Introduction to MatNWB tutorial</span></a><span>, which demonstrates installing MatNWB and creating an NWB file with subject information, animal position, and trials, as well as writing and reading NWB files in MATLAB.</span></div><div  class = 'S5'><span style=' font-weight: bold;'>Please note</span><span>: The dimensions of timeseries data in MatNWB should be defined in the opposite order of how it is defined in the nwb-schemas. In NWB, time is always stored in the first dimension of the data, whereas in MatNWB data should be specified with time along the last dimension. This is explained in more detail here: </span><a href = "./dimensionMapNoDataPipes.html"><span>MatNWB &lt;-&gt; HDF5 Dimension Mapping</span></a><span>.</span></div><h2  class = 'S4' id = 'H_0991' ><span>Set up the NWB file</span></h2><div  class = 'S5'><span>An NWB file represents a single session of an experiment. Each file must have a session_description, identifier, and session start time. Create a new </span><span style=' font-family: monospace;'>NWBFile</span><span> object with those and additional metadata. For all MatNWB functions, we use the Matlab method of entering keyword argument pairs, where arguments are entered as name followed by value.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb = NwbFile( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'identifier'</span><span >, </span><span style="color: #a709f5;">'matnwb_ophys_tutorial'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_description'</span><span >, </span><span style="color: #a709f5;">'mouse in open exploration'</span><span >,</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'session_start_time'</span><span >, datetime(2018, 4, 25, 2, 30, 3, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'timestamps_reference_time'</span><span >, datetime(2018, 4, 25, 3, 0, 45, </span><span style="color: #a709f5;">'TimeZone'</span><span >, </span><span style="color: #a709f5;">'local'</span><span >), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_experimenter'</span><span >, </span><span style="color: #a709f5;">'LastName, FirstName'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_session_id'</span><span >, </span><span style="color: #a709f5;">'Mouse5_Day3'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_institution'</span><span >, </span><span style="color: #a709f5;">'University of My Institution'</span><span >, </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % optional</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'general_related_publications'</span><span >, {</span><span style="color: #a709f5;">'DOI:10.1016/j.neuron.2016.12.011'</span><span >}); </span><span style="color: #008013;">% optional</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >nwb</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement scrollableOutput" uid="E19E188F" prevent-scroll="true" data-testid="output_0" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent scrollArea" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="802" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">nwb = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  NwbFile with properties:
 
                                              nwb_version: '2.9.0'
                                         file_create_date: []
@@ -124,14 +124,14 @@
                                    stimulus_presentation: [0×1 types.untyped.Set]
                                       stimulus_templates: [0×1 types.untyped.Set]
                                                    units: []
-</div></div></div></div></div></div><h3  class = 'S12' id = 'H_46e0' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_5e37' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_2387' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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yoQ0/CEYNHTjf8Flzk/v0HjpvLvMxBOudFURj+OgNiA0wpiNfRfHaODsIaD8g3vx638WxAbUjmY5IUQEHvqEKv2U5Nwvf2P9jc7VqLtje2+n5ww3pcI3NTWq93Vtkgnbw9HRgbGyF99mtvUuRYef5rLlvsd/97Lpd+746XVHzk0yzGT83KYjX1iW3baILT3EKuLOW6AMF3M8de9q2ok/KasAtTVGHm6sa83AHHjK78qDZXNgTXS0PaxUID4jfLKN7Y3lr6/GkM/7mhEVG4vn8IEBLfeQHnjp/JWoKo09mHCGUJALBC7DxDrI2XudJBzIENXbI/fxme14d62CxQ9Hd9XbbTyq/oB7x+Un9rvqW24nDeebvQVub9UP+OAG/2MjrECUIfVFtxqhFTHnFfJUR+fnS0I7T3E5oBu3Tz+xBhPoXFTZBirgsKzRzJDW8tzqxtQQxsVgE4dhtcx5DK0IFY3fLV/dXNHudQoKnSgeortQWwV4I8QAmJPGWKrWft5tM77iDyCl50tDoMV3Gb4q9LwPs37pFY5ev1ttSKB0qj297JlKK/6o/g+f+71fZXjB6GDgtgMXr2LOW6I2F9DPnoydBBrLWnVmgXpwz35JlU+blNutJzmGMR6I5Xz0zFmcPYGrJVXD2J7ZUQIAbGnBLEezjW1r6qkNBR+4LDqoOn9XG6f8/7yDlftuLygY2Xr9QXVcffguAJU7zfVZ7X64NZI5lQgR2ivIHbKTdWKB6oArnWjtLZnmHNQ7Vk1+UzS0M1UiPWhoZ5l+fBOdeNPgVh/okYIAbGnCLG9h/oqjCwgNNr5+K7qm9Tm9trZVrue09BnzW1wE1COhbj1+olcDoWnqT6yKbQh9yvej1VzR8YgttdfIXTQEOuNhO9fweKqEOvxt/Fdy3qTu3pD9T1PrFZVaJkrGpo8+WAZiK1CGBBCQCwQW0NstL15glaYQ1yG2CrdMYjNn3s2FWKnfFdN+Kose39bIRJALDppiPVhl2q5qXVDrN+Iyy73sWw4wRyvqDesHh87F2IJJ0AIiAVixyFW8aRynuQYU/dgbgJiV1lHdY4n1vvcqUP7Hgfb8tQCsejkITavqaqbpQqCXxZifSZoDoKPPLVe7BSIraA0TyTLjV8OU9CszmoFhgzeeZmTapkvTxeIRQiIBWKnQaz6qtakJ4+rXSfE9lbFmQqZy8bEVhPBqjw9DrbVHxETi04SYt3jqhvAVzbwZU3ihlkVYn1fbQ8I9LUDp7xzunozikDYj0XH7/GtepGDllwJE9z6CgmetoayquPQhDHfn3X5EAJigdj5EOsr3ahdzmu95qWqVoFYD3Fzh4RCynJfUKlaSjLn5w4UX0KsNYFLgKrwOd+Wl4F0D3bl3GF1AnTUEFtN7Iqbw6FVaa0DYv2Gc4ubbyr09SCxtd5eNcEr8lTDE8ereqhWdVAjqu98W67rFmQjhIBYILbdV/nIWbSjvgasg5pm568KsSHv//SinFaYXaVqndjc7yjdqu+I/L0MgmkfufT+xX/v/ZfXla9eIFCv5nYgtJcQG2DamiBVTWRS4zEWL6N9Mpy1tuuVeq3VBlSWubC3ysSp1uQxlbVVBzrG3soJ3qARSoAQEAvE/iJUrHpxgL8iXdKry32kS+21Xv+ttFrpakLY1O0+CqmlrKaMCjp4V2ucR7l99QHfrmP3FzW4N1grNFQhAlrDPL9x0ifCTXH6ILSXEHsK0hPu2MzRbcqfnMdgFyEExJ4CxJ6CFFKwT/2RO3yqV9ciBMQCjAfRkCEExAKxQOzpOTAUprDK6gsIAbEbUu+VsqdcFoSAWCAWiD3tPqD1Jk2EgNg90pQXJGxDikNmMhdCQCwQe5rqrVSwTXmcLmEECIhFCCEgFogFYhFCQCxCCAGxQCxCCIhFCCEgFogFYhFCQCxCCAGxQCwQixACYhFCCIgFYhFCQCwQixCikQRigViEEBCLEEJALBALxCKEgFiEEAJigViEEBDbhNiPP/4YwzDs5AyIBWIRQkAshmEYEAvErgyxXJcYhln7DMRiGIYBsUAshmFALIZhGBALxAKxGIYBsRiGYUAsEEt/hGEYEIthGAbEArEYhgGxbh999NHtV1555cL2vQI2Uc59OvZ33333oixvv/32TstxSNcEhgGxQCyGYSfsiV387ML2vQI2Uc59OfYAx/Pz8wv74IMPdl6e559//qJeAFkMiAVitwmxTz/99EXbEw/1++5UWXcbuYk0l7X333//oiyPPvoo/REGxK7LU7lKw3bt2rWLBnKdaa7Lnnnmmc/dpGrQtpF/NFReN4LqyD8aM25ADIgFYoHY9QFnOCvit+602CeIjf4yn4co1zbOS/Q5Xgf0R0DsUUDsOryuGWL3wSJ8IMoWjUbVmG/DC1zVTatcGAbEArGnDrHrguB9PE6VLTyg2TO7DcCuHDj0R0Dshb3xxht3vG5xoQhgKlh69dVXL/bT/i+++GL5FBRPknGxxz5h4VWM30barScs7Z9v4ChfbI/9Io3YL/JV4+agFduVRlzgkW+UVcflaeqpMp7mdCxVmq1jj7RzrKrKGn+jvJFm5BP7xrapJzY/YcbfSFfbqzKOnZsoq8pdQbO+q+rGyy4PceTHTYgBsUDsuiE22mpvu8YgNvad6o2Lvmnd4VlVv9bzKLbyV9s6B2Ijvdh/ahly3c4JI4j+wPOJPmAMYlW+Veu45cCpRiyxE4HYuLACeASs+l8A4xdMXLi+Pf7X/nFhZ1jyNHXx5ac4QZ++E6CFCSj9CczTEcDlcmofL6vSjb+60HsQWwG85x3H5GWthvvjxtI+XscVQFbnJT9dCmL9+FUH+dxEfn5uvAHRfq1hmTh3YxCrc7aPHmwMA2IPF2I1gdTbbDkmMtzFvoqLdIt9M9DF77z9VDtZgU/VL3r/pvY50guI077V0L/vG+1m7uO8nPk4Wmm6wybvn8vq6cb23E9MdUSonn3/XJ9+fuK4qvLlc1P1Rw7NYT/96U+bdaOHEl0rtHcnBrG6COXlFKD4jZbhzIHVL1QHrspTF/vqZpf3Uk9xsV2QFBek9tPvlbcgOH6vJ9lWObVv7JcbO/3WG5heKIWgLY7dy+lp6sb0/KMecl5TQgFUpw7yvTCP1rnRdg/Cj/JkL6+Ow/Nr1Y2HGvhxYxgQC8SuArH+MC14dZjMEKv+S84Jh19v87y9irYu+pVIW/t6mu5UifRjf+2n/sidDPpO/V8LYv24PE13MuS+S2lUEOt9jxww3m+7p9edOfmhYGo8qdL2dDXqqDJ4LG91btyxon7DYdXTdrhVnLA7jTL0Ku1jDzcBYhtAlC9iv2DyRVwt9ZShSBdU9roKKHtPd/LkxncCRi9PhqYWxFazJ3Vj5MZoDGJ1Q1ZP7TlNL2seMsoQPzZsUu1XQazKUO1f3dx6eIjfqQ5iP6/bHsS2AvwxDIgFYpeFWLWd0bZUE5u8vXE49P4r2jC1n+o/BKZ5FEzeWT28C3bz6FV2QPjnSNPbzRbEqj/Uvl5O7ycrWM9pyvNYeablQKrSzF7QqbP7vY+YMpGtd25UPh/ZU3+kviZ/njJPR44fQgpOCGJ7nkEPB8gXkJ6C3DIo+dOsYiyrGKCpIFQFlI9BbAVfOXZmKsT24DM3BN4Qt+B0LDa2F/9VnTMfnmqdm/ygoLqoGsMpEHsqEy0wIBaI3Q7EtpwF7knNXstqtCpDkPd1vbZX/V7V5mnFmiiLp5f7hB7EVnM9MhxOgVh3QlTzW1pp5mNvwWKLFar9Kojt9detUDSde3mUq2UlexDbyxM7coitboTek2fP8nCHD28IaP3mnDok3VtipAWx1b69eKVemi3Pao6BHYPoqeBXDXO1yuYN/NRzk5/mqxt/KsTOmayGYUAsEDsGsVNGlLSv5ly4yTnibZcPn8f31YTkHhhPdQD1IHZKOlMgNvc5rVjSsf5rrI2fAoi9UIep5yaHubX6lR7ETj0W7AghtgqGzjeXg1I0MHoqzVZBXuzvsU0eDzM1JmcZiK1uuDzkMBdiK6jM8avrgNieh7rniY0bf+q5yUH3+RxMhdhdv0kMw4DY44DYXlxjbjurSUXVJCJ/2PdJWA606o+mznJfF8RWqwDNgdiqnL00lwW/OY6hqeem4g7/XcUSQCwQ2/TEZU9oFRPbGj6Zs4iznrT0lNXy5sW+WqJqWYjtQeRciNXvqpmcOc3ezP2pELtsOMFUr6jHOLXCHwgnwIBYIHYXntieF07tjYBzzGva6ot8AnOGw7Eh6WUhtloxYRWIbYU9bApip3pi5SCZM7Sv0AZfQhKIBWJHJ3blm7harik3MPnCVLC2VgLQUlDVxZQncvmwSBVwnr2bcyA2P835qgd5ckAOWG+lmctZBdj3bqa5ENtrzP3YVF95eCmfmzwDOHs1quD81iLSzAbFgFggdhsxsT5EnmNiq/Yp2rgKGFvxmfIK9hbOj3Y0yhft6LIQ24qJ9fymQGwV91pN2l0XxC4bE1uVrzo3cX49DrZ1HRATC8Q2h8LzMh3VElt+M2pJrLhhBLx+8egpWevoKaQghxPkJVX0IoO837IQ60uV+PBRtaSLr4ZQxZ3mtWbzEidTbvipEKuGqLohvW6r2aryrurFB61z4x4M95L7On/aFvs6ULMuHwbEArHrhthqKcO8vqvaJ2/z8kt01CcpjbyCTK+d1cO5r+Hamyg2B2IFwdqucnlbrPy9vc1petvsL/HxNL2vXBVi9RDRW53AHSh+bvJLhqpRzXx+qmUg3elWOXdYneCE39ilNzV5rEoVHF4tAq39Wx7AaiHmfAHqKSzHKXnYwjIQqzdl9fKu1gVs3fRRznzslXd2HRDbg0RvILyMU8rnT/D5adjX7M2Ndz6eHmRjGBALxC67TmyegKV2sBr5ceeEXspSDUc7FEU68qpWo1r55TveplYAPRVi/QU5+aU1Vf46Hr3NMvd/uZz5RUTVSxRWGYKv1onNdesvxfG+I58bdyS1QiPco5z7I6+bDP9z30SGHclrZwVB+a0kLdjSkMqUCyb2ay2xlfNvTQ5TftV3uZzVmnpzXsk39dhbaWoJlqpu5rwecNmJU71z06vjvIxM7406hBJgQCwQu26Izeu3ykGitwhmcHEnjEYQW68391eACyrjIbxqwzzN2K9ayaAVMuevHc+gGGnoNejV69U9PEzpx29ymrme1B5rNDUfe6usCv2bElfcWtNdcKk8vIxePoGne2b10JJBPodweB+obX4tMDIIxB6V9by2h2R6ws3vqt6X+p3y+lwMA2KB2LkQe0x2LBOONDq7b/1RL5YaA2KBWIDxYBoyDANigVgg9rQcGL3JeBgQC8TugfVeKXvKZcEwIBaIBWJPtw/wCW7EwgKxR2O9+NlDPp5d36SK9SUOFgNigVgg9jQhtrdSwbYtv/IdA2IxDMOAWCAWiMUwDIjFMAwDYoFYIBbDMCAWwzAMiAViMQwDYjEMw4BYIBaIxTAMiMUwDANigVggFsMwIBbDMAyIBWIxDANiMQzDgFggFojFMAyIxTAMA2KBWCAWw7ANQixCCJ2igFggFsMwIBYhhIBYIHZliEUIIWufgViEEAJigViEEBCLEEJALBALxCKEgFiEEAJigVj6I4QQEIsQQkAsEIsQAmK3rXfeeef2a6+9dvvTTz/deF6RT9g+KY47yhT1cMj5f/jhhxfpxN9N6LPPPvvc+Yttb7755p3y77IuVb4oDwJigVggFiF0AhB7/fr1KPjG4McV+YTtk+K4o0xRD4ecfwBcpLOph4Tnnnvuc+nr2lH5d12XKg8gC8QCsYcDsZvug3bdLu2qzd5W/ps+f9GeR/rRB3nZH3744Tvt/S7r8r333rvI+8knnwRid6FPPvnk4uILT5b0wgsvrAyb1QUV+WwDloHYzZQx36RqRMLzqutom3WZjzc8wVGmsG2MLCAgFogFYoHYzZ2/Vpsu50ls12jyNusyj3pWTh4gdocKWFkFYgNoDuWEArF9xcPN448/fpF2nNd8jr3c26zL1jWmp/Z967AQEAvEIiB2ubR9dK3qZ7ZZl8o/h9bJqeP95ElBbFTCyy+/fHFiAhrib3x2D2mcSG3P0nf5JIarO7aFdzW+zxWsPGN7WB4i9rziSSjyiaeOKGP8VSykpycI1nEoRjL+z3ChWMbYHhdB/I2y5nJGGipnHFPsE+nH31VunhZ4RT4qVxxP5BP5+vnw+lN8qI7Rz522R3lzvKjnH3lGPsqvdTOozmK/OAc6x9VN7Meh+or95zZ08bvqHOucxbZWXbau7cpb6teYjm/ONeYPYbuKc0ZA7LFAbNy7c0fQ8sje2PYxT1z8bgwMpu7XeiCe81vVyaYhUuWacxxVm9ranvPXcY2do3z8YxArtpirlkNC273exupS1/CUETpdS616qPIXax1yWMFKEBuVJm+XKkFUH391ATjtO4jIK5WfAnRi1dnr/yquUTexAKCCWKUhcPG0HTC0XwaMHBMbx+P5eRnzMepYfH/VRVgF9stCrIYwVCbP02HO60/DCX4MsU2hGV5Wv8iVf/zOz7mfA7+Z/JxqmMXrJQ+ve9162fJxtKTfeAM1B2K9LnP9+LWdAXTZa8wbmanHiIBYIPbzoOL3n7dp3h75Pe9ximqT9MDt2+O+9DQqCIr7OfcH8TnHu0/Zr/VwHft4udTu5DxUvuiPvE5i33wsq0JspBV17OVSPhnAfDg9t7Gxb66bSLfig/ir/ktpxrZ8XJFmtL25363On66fKccx1vd4mfP1qPK3INb7Sy9H5cjxfsXrrAJ2N/9e/de+hU1uHGIFOrrp/WLOAc26IeNEaN/K5a6OPE6KTpgDhXtHvdKVfg4n0MWQAUUnvQLOfEHldLWfeygjTdWHg4tfjO7F03Eu+/RTNXCqd4eguJFUd1UDrMYk16Fv9zRUft/Xz5U3TDqvDqXy9IapvloPKL6tun56T6Tad0q9VdtUBk380n6+vbpmvQxqGKZcY2PlRkAsEDsOsWrX494TJPgDc9We6H6O9sg/u0NE21uOlOyU0Won/ltBzdT9esPPam/iswOMPxR7G69+wY9x2YmkVRvmzhpNWFK58kO59zGxj0OYHGH52Ko68HMVaXi6ziO+3eG1gjml48ehfKY4nHRucxuuUUV/cNLKPLkuvV+MfaMcfo69vH4d61ry0b7qoUn551HCVZxqBwuxqtT8hOIXTlVRcZJaLveWiz/29yH4qRCrYfzsOauGm6dCbOVVzvuqXH7DTamjVSA2tuUh7NbDgrbli1Y3QMtzWwFvPlf5waEX76kbKwNr5JNhNZdhbqjFVIj1c5OPzYFc9Tx2jeXZqb3hI6W9rJcEAbGnCrHRLmSniu7nPDLj7Ze3J0qjFc/oIJz7oKpP0XCtj7pM3a9qqyqYzo6VXL7Y5vWhY/R2aRWI9ZFW73vUjmbHQzWq5mDv270trkDez507p7TdnUVVHfj5ax3HHOdCbzStB//a5v1L7nvkpKqcLblPrDyrlXNoSp95tBCbnyazVcO5edi1mo1dXUSVpkKsnyQ9WfkwxJQg65xuFQKRj2+swVrVhd+L49QTnqwasvehpimgqAcQpdGr7xxjU8Hc2I2l+J7qmhoLgq+WNpkDsWPXUquRWeUaO4ZhHSAWiN0lxPaWquu1X1WYQQvIvE3IfZDAqAo3qmB7bL9eu1QNmefvVL7c93jfvS5P7Jhn3NuzVvtZheNVabQcQ+7FVNl64NYqRyVxydi+vbqZArHqO3ujiFPOW66Hsbrw6+dkINYrtGf5pOskVY3NnJtrKsTG9hwv4jYXYscupLxkRe+iXmV2ZAVjOcaoisEZy7u1PR+Hx8SO3WzVDdWr8yrmq3UccxuSORDbCvWorj2PP17mGlvHNYGAWCD25/etJqrKdH9Oucdb7doYxDpwKe2A5wpUp+zXapeqslVQ1nJUVMeyKsT6BOGqv60gduooVMvjXXkN83e90bvKuZMnbI/xzBR4nAOxvWOrHqTU72tC8ZQ+f2wU8CQhVjPoKmtdkNUwg5+kseHUqRDrJ7YKDp8LsWNlzBfKtiDWy5WHj6qLd10Q23tiVGOr31bxNhn6/YkwDwFVjU6vka3ymwOxreGj7BlYxzUGxAKxQOxqEOtxlRliffWVseHT3vYxiBXMZaCoQhzG9stllAe3NeSbR3F6bck6ITZP8FZ8sceTzoHYsTaxN7KXQ9d6dZD7xd5xTIXYXn5zILY1uTf3M85Tuu7HRl+B2AI4py5D4ReYhnfyhdgKQ4g8tDj9VIh1IMpPYtUEnbnhBNWFmm/abUFsb4hqk57YKvQjNyS9p8t8s7WOw6+3MYiVt3/ZmNgq7rUqh5Y+Wcc1lq+fQ12zD4gFYnftiZ0yzL0piM1DtGoLexOpWvvNfbhueWI3DbGtOOLW3IJ1QWzPEysI7IWY5PhiPwc51nYqxOawlbkQ25s/kkeqvVyZo4DYiRO7BJzZ46VlKny7z9qLyvdgex/ukDu+tZxJjnWpIFa/awGG7+vDSa2noFbjlferwG7XEOs3pp+PdUKs162vTqH6EVRqCZWqvsYg1p+GxzqpVSd2+bWd81L55GVuXWPe4FTXWGtSxaE2JEAsELtriK1WxXEQaS2xtS6IVRx/y3mjdmDqfnMerisnzrYgtor993Z/UxBb7ZtH9vQ5M4rXZWaKfP1Uk8Cm1s1ciJ0y10QPMbpeeiPaxMSOQGxeZkmvUdNTgF842lbN+PQJXnmNUJ8g48Pk1Q3qF2G+MHXyHEB9qZFIN1/YrXVi83ErFqiaXbnNmFhfr9WX5dDToS8Jts5wgtZSIN7Q5rVe8zInPtM1r32o3/p10FuhoBfyMRViveHScite5mopm1WusSoMAwGxQOx0iPU2yVc36a1OsE6Ibb2sJHvnpu43tvSfL8VVOVZ24YnVMeW1Xr3fXyfE+vqtPpdCdeNOEfdw5za75YnNxzG2Mk5vUvEUiPW+0o/N511UoXc67/nYvBzuIJwzennUEKuDrxZedoDtuf9142aoyGm2Fu+t1kzLE7Fy3JFOYhXr4uvNqUzVzVUdd2/pk21AbIZIrR2XQWudEKu4oVy/1TB8tah49RaR6jjUePj2ngS8eVLDVIjNjXNvUfHqGlPjUV1jvs3zPOS1+oBYIHYf1on1tii/eMQ7701ArK+nrZeZ+It2BNVT96vK6L89K16i4+3utiDWnQZn9mKYKJt7D9WurQtiWy8cyu1nnoeTr40KCvNxuNez1z73luOaCrGZLfw4W/NEvLzxW2co9+R7up7nya4Tm+M1Wq/A03fVRKje6wF7v+t91yvfMuq9vlDp9uKiWq+Nm3sMVb216nssprKVd2t7Po6cvz6PLY2W9+v9btnXMTqAtpYgy/G2vbymvt6xNUw49XxOXV4OAbFA7O2uY0VgqFdWZw+aXlVeddq97fmhM78SPe5/X1C/t/LA2H6tMurFOu6Vrd7AVZWvdSxz29b8tsHIQy9s0Kicv748yqfjaOXd2p6Pw/OPc603hfXeqqVXo+u18/G71nFoBC0fR0CfH0dL1UtuWvVWbfNy6OUPmrBV9cty+ugaUh1E/qo7dxCpnj3P6i1jJwexCO2bekH9+6Ycy4WAWCB2OYhFaJfK66Tvu3qTyYBYNFnVCyNaNhaXg37xlNp6qcY+aWxdWgTEArFALDocHYpT4lD6SCD2ADTlxRFVHCU67KfMatIJAmKBWCB2G1LIwlTDgTK9XT+E5RIPabQSiEUnq17M8j40dqvE/iIgFohFQOz+ad9n/I+9PAOIRQghIBaIBWIRQkAsQggBsUAs/RFCCIhFCCEgFohFCAGxCCEExAKxQCxCCIhFCCEgFogFYhFCQCxCCAGxB6t7F/YQ/RFCKLXPP1nYjxf2w4V9O9qJob04A2IRQjSSQOy+QOyD9EcIodQ+//2F/f7C/vrCvhXtBBCLEEJA7D7p8sIeoD9CCKX2+ZmFPb2w7y/sG9FODO3FLyD2448/xjAMOzkDYvdGlxb2lTgfGIZhskW78HcW9jsL++7Cvh7txNBeALEYhgGxQOxe6J6FXVnYIwt7dPC6/PbCfndhf3thfzDExWEYdvz2B8N9/7tDO/D9oV14ZGgn7gFiMQwDYoHYfVuhIOLdvjZ0WL+xsO8t7ImFXRsmdmAYdvx2bbjvvze0A48O7cKDd61MAMRiGAbEArF7FFLw5aGjemQYOowYuG8OEzq+jWHYSdi3hvv+G0M78MjQLnz5rlACIBbDMCAWiN0jb+ylwdNyZYh9e2DovB4a1ofEMOz47aHhvn9gaAeuDO3Cpbu8sEAshmFALBC7hzB7z9BhXR6W07l36MQwDDt+0z1/eWgH7vkcvI5B7BtvvHH7lVdeuf3BBx+stdN4++23L9J99913Lz5H+vE58jvmzjIf967so48+uihHGBCDAbFA7IHBLYZhx2/T1YLYp59+Ohr3tUNXwFOkK4iK9ONz5HesHWWA+vn5+YWt+6FgGXvmmWfuOgcYBsQCsQghdHBqQez7779/AZjhudskxEb6kU/kt+kOa1fgpgeCV1999c62F1988WLbNvIPePbjjjqPbZH/Nuodw4BYIBYhhLYGsZuyDLHbMnl8t51vgGvlaRbYbsMLXB13q1wYBsQCsQghdNAQG57CABx56iJmNT7H39gW31+7du1iaLoVzxoA9fzzz99+9NFHL/4qLtShKtKKdCO9Vhxp5BFWeQ0jjwCy+F75RHncg6yyRr6xj47Dvc7xu9iu791r6uAZpjher5+eFzTyVRiBjje2CSIzSEbeXpYov+cT9RLbo8xVLLO+GztulePY45ExDIhFCKETgtgcEyv4DFgUmAUYCcZiu/8+IErf+X6CqrGYWP+98ou/DpcBqko7vlPaytMhVvtlmIu/+k1VVodhB0/934sZDthUOg7MLYiNvPwY4n8/PoGs7+f1odhblat33O6NzecOw4BYIBYhhI4OYgU98iwKQs9saFzbPBYz9teEojGIVbxogJry93wCDr1M/tvIR+Dm3ssqjMFjQwOadUwBekrDIdGhOraPTXoTiFde3VxnXsYMrF4fDsPaV+VW/bpXuxe+oVCDSAOgwYBYIBYhhI4eYvMMewFfhsvKwzcFYisIVboBaCpTfB955v0EfT7cXsGcvLAVxFUxoyp7NYw/d4WHCmIF1KrH6js/Vi9j5fWdEoPcOqcYBsQCsQghdFQQmyHJ9xUo5c8V8LYgNjyPFeD11j2NNALotP6p8ncArWCu8uRmb7KXQ5+nzujXkP9UiHXAz6Z6y2l5aEIL/HsQu6ml1DAMiAViEUJoryC2Ar48QUifK49iBtwMsfpcwXIFmh7Dmm0MYjXcXwFeBdNz4Nr3r5YpO2uEYIxZLqvCCloeYiAWw4BYhBA6aYjtvZQg71uti5q9ky2IdSgb64CqSU8eTjDVE1vBX1WOuRCbVyboQaxDc8B/1EtlOS2PM65epgDEYhgQixBCQOxEiK0mGM2JiT1rxGnGvgF4sd0hMy8RJVAeg1jFlFZeX8XL+ioHcyF2bkzsWZq4NuVVtip/a93XMYjthTxgGBALxCKE0ElBrIAqANBB1Jez6kFsK6ZWnletVVtBrA/LVxDrXlfNzj8rYkkVauD7LwuxFZSeFaEG2j97hmOfeDDwFRR8OS2VPXu5x8JAxkIeMAyIBWIRQuikINaHxgO09OKEM1vztQexDqIBrlr8P3tNfU1Vz8OH2OO3UR6H3shbZfX1aOP/+E5lzN7guRArgKw80l7WXBc67oDzKL+O0+G2An0/RoGtw26UI7/kIXubMQyIBWIRQuhkIVbwFNDkQChIPJvwsoOALf+tgM89pnlil4A5w2kFrMo/ANe3O0Rm7+xciO1BogPnWZrglSerxXH5cmW9MIhqFQlfHq1aj7eCbAwDYoFYhBA6SIgNyAqg0jCzlrKqlpfK+1ZLYOm7gFufoNRLdyztvM8qnZnKUU2ecsCcm4+8qFPjXP0hYKy+Vymnv+2MeFgMiAViEULoaCAWW4/5m7X2Ke4ULyyGAbEIIQTEYkvHxu7CFMIRnlgmdGFALBCLEEJALLbU27u2aYQRYBgQixBCQCw2O8a1Fce6TYjtxSBjGBALxCKEEBCLYRgGxAKxCCEExGIYhgGxQCxCCAGxGIYBsUAsQggBsRiGYUAsEIsQQkAshmEYEAvEIoQQEIthGAbEIoQQEIthGAbEArEIIQTEYhiGAbFALEIITYJYhBA6RQGxCCEExCKEEBALxCKEEBCLEEJALBCLEEJALEIIAbEIIQTEIoQQEAvEIoQQEIsQQkAsEIsQQqtC7DvvvHP7tddeu/3pp5/udSekcn744Yc7L0vUVZTlzTff3Jt6+eSTTyAVhIBYhBA6HYi9fv16NO57AYc9qZwBbLvWc889d1GWfYDYOG9RlieffBJSQQiIRQghIHaXClCNMrnC0xhl3LXH+L333rsoW9Sbl21bdVjVjaB6HwAfISAWiEUIoZOFWJVp3/TZZ5/dfvjhhy/K5sP34ZHdVh1WddMqF0IIiEUIoZOD2PA4hocvhqnD4v/Y1oK78AK+/PLLF+m1PILx+9hHaeZYzvg/fi8gi//l8VTaEQOah9NVzscff/xinzzMr3QjDZU1PsdvYt/YNkUvvPDC5zyekU7kezYM6cdnP6axeoy8dZwZQP27OM5W3YSiXrKHGCEExCKE0ElBrGAtLKBJ4BQW32VA1Pe+X6TrQ//yVgr2fF+BqWDT0xCUVTGxGlqXCSbPGsP9Aslc1vh/ipS+w6aDZYZYL1/so7xzPWq/HNeqMIH426sbAa++mwrlCAGxQCxCCB0NxGqiUEBXwFUAkbyXgjXBafzVNt9X8CUo8+Fuh2XBW+zvEoxV5RTERt4OhCpTwLLycq+tA7TKEL8RmLa8zBmEY/8pDwJePi9H5FMN/efj03mI/BxKq7rJ0Ju91QgBsUAsQggdPcQKhLLHtQItDWFnCA2Ai23aLyAs0s9w5d5D99pOgVgBcDV8Xn1XAaV7nSPUoKfecH0FsSpDVY+RV/7OHwgCbgXXOcyjB7HKkwleCAGxCCF0chCrz9XyURkOBWNToSngLDyRsb9MUOawNgViKxDsAedZY6i9B8NTAbGCWD0MKEbYzb/LMcNnFupQgfUUiK3qBCEgFohFCKGjhthq2L8Fh/rt2FB8gKOgs2VzIba3bqyG4j2NFvxtCmI9frVlVZ4es1upB7HV8l8IISAWIYROyhNbxVVm4Ju6PqngN3sJPZxgLsT28vZ40l1DbBxvbK8sr0bg8N3KqwexrFCAEBCLEEInD7EVQOX40d7wdYQj5Djb8DL6cL48h8tA7LIxsatCbDXEX0GsPM85XrjnrVYcbMBoa93XM2JiEQJiEUIIiP08gMmbl4EzJh5pqFteWvd4+sQspSFPaAWxvopBC2I9zd7sfd9Pa6tmmBuD2DHYVH69iV2en+ogYDS/ZSwAP/KrYmgFyVqSLC+7VdVNBmcgFiEgFiGETg5ifTksLbPl68bmJaY89jMgKky/V6yse1wDyrRPmH6vBf0d6PzFCRUoet5RRl8GLP5OWZpqqvdSS2ZVsaoO7ZFO9mqrfLGfQNPLp/rJy2np9+79reomx9OOxSgjBMQCsQghdHQQm72uZ2kiUp7d755PB90MWA7C2ieGyn09VXk5fZvAs4LY7M11UJ46DD9nCF51Uk168zrwpcXGytdaQ1f1kL+r6mYMshECYoFYhBA6OogNmApAqt7yFGCkSUhjb4HSWrAZHqt9qqHwMamc1W+Vbi9tfd86xillaq2LOyXetVWPY/XWO+4qlGBsvVuEgFggFi2lL2DYCdp+Qyyap6nLim1T/pY1XjmLEBCLVobVexZ2aWGXF3bvYPdh2Amarv/Lwz1xz07gFohdj/T62X0CRmJhEQJi0Vrg9dLQcV9Z2FcW9sDCHlzYQwv7KoadoD003AMPDPfEleEeubRVmAVi16epKxqcWlkQAmKB2ANWdMpfjg47rhcMw+62xb3x9YU9MkDtl4d7Bog9RLVibHfhGZ4Ss4wQEAvEoq4X9r6hc/5aXC8P/o1/hGHYYEMb+t2hDf3acK/ctzVvLBCLEAJigVhU6p5hmDS8TI8CsRhWQuxvL+z7Qzv6yHDP3APEIoQQEAvE7jaU4CvDcOl3gVgMKyH27wwg+xsL+9XhnrkExCKEEBALxO5Ol4eJK99Y2PeAWAwrIfaPFva7cY8s7NeGe+YyEIsQQkAsELs73TvE+H1rYU8AsRhWQuyfLOz34h5Z2DeHe+ZeIBYhhIBYIHa3EBtLCX17YdeAWAwrIfYfLOz34x4ZHvgeAmIRQgiIBWJ3vzJBrIn5nYX9EIjFsBJif7KwP4h7ZHjg++rWVigAYhFCQCwQi4BYDANiEUIIiAVi90c3F3YViMWwI4XYjz/+GMMw7OQMiD0JvbSw28PfK0AshgGxGIZhpw6x58NsXCB2/xWe2J8t7NZQX0DsAds3r//p7X/z05/dMeoEiMUwDANip0Ps+eXLl//L4OF7Aog9KK/1rQFoHwNip1mAYtZf/Ne/vNj+9/7Jv779xE9evv0rf+vm1srz+N/9p3eVBZgEYjEMw4DYaRB7AbDXr1+//cILL9zeA5AFYufpioUYvN4JMQBiOxCb9R//0wcXHtJtlOev/t4LF/AsAyaBWAzDMCB2HGIvADbgVdoDkAViVwsxuD3U4SUgdhxi//TV/3AHHgNcs3c2vLLAHRALxGIYhu0XxH4OYKXXXnttlyD7LBC7kp5qhBgAsQXExlB+9oo6zAbI5t8H2EYaf/6fb90JQ/jxP/6Xd77/nef/xZ341n/4z/79534f++r7gOexmNgIbYj94jtBdy53TjvKFfbP/92fAeJA7Hx75ZVXLmyTebz77rsXecTfQz+WXdkydfj+++9f/ObVV1/di2N44403LsrzwQcfbCyPSDvyiLwO5brdh3v/7bffvvg/rpk9g9gmwErvvPPOrkD29QFk0fIKL+zNFGIAxE6AWE20CgCUPKwgILKl+E6/78W4BvxKAZi9mNiAUi+LK5e9FyZBmAIQO8uGxmPjnWXksWnA3Max7BI45tbhtWvXLn4TgOLbP/roo4t0nn766dvn5+d36i0+P//88xuDzEg/8tkkFEbaOpbq+6iLF1988U7dPProo7efeeaZC9DPx72t63Yf7n3VW9RHXB97ArGjAJtB9v777//hFgFsHyaXHVOIwVtDnf4fA8wCsSMQm/eRlzXDpjypFVhmUHVAzl7eFsTG7zKIupc48tAEtPD+5rKFF9gVXmZAFYj9nAWw5A45Oq9teEiB2O3WofYPQMsQ5+Aa0BLAF3+1Lb5f1Xsb+eZzEV6+uNY2CUk9iA1A92PMEB//O8ieEsSGBdzH5/i7BxA7GWAzyN53331/cwvQdT7U3zn8ufYQg78Y7CkgdhxiHU5jSD7/zj2b8X32xvrvtS1vV6hBC2IdQgNSFVogb3BAsADby6Z9WzCOAbF3DbPuqkMGYrdbhwGLgjIHxrgGBG3hicyex9hX8Lmqx1RQvIuwiwpiBbBRrjxkHp/lJY56UZ2dGsTGceu8bfrBdgRiz7/0pS/9tzkAm0H20qVL/+cWYOv2jkBP6+TeOFL7Xxf26lC/f7awHwGx8yDWvaDxv2JYfbtiWsPr6R5Tpev7KkyhBbHuzR1bKSGvrNArG3agEBudSXhD5CFrDfNqn+iEw3MWABL7R6fr8JKHT+M3ihmM/yuvlWLkIt/4vurUIo/IN/bZxrBspK1h8Mgze/TUKatcqr/4v/L+KWZU+0Wacdx5X9VzHJfXc2zPQOTnJNJS3cTfVryhlzX+VunOqUOBaI4L1fbWUHveL64Zj2fVdZOPy8+3A6HykmfP66aKY9X3LS9w5K1rOayK4awgNtKroD5fy3E8sY/qzes80o28dcwtyJtSRtVR7BfHrv1Vv15G39fDQGL/3nWt+1bXVFWn1UNfnFu1EzuC2PP777//L5cB2C2C7JWzeYv3r0OPDTGjt3Tehs/HaP9qYf/vcIx/DsRODyeQ13WKfCJYBlYH2/iut06s7ztl7dgpAmIPGGKjU/Oh3TzM6x2iYMG9Z/K0+ZCwOt8KYquOTJ1+zj8DVIYVL4N3rqtCbKTl6fuxOEzoe9VHrjsvU/zvdSXIr4bglbf++hB0jjnVPvL8xb6+fwbLfFwqs8PUnDqM43KYr6BlzMsmr73v6+EJ+VrT8QqgvC4dYquY2Pjfz4Ofg+oaElz7vjm9DLEq81jdtR6+4vfKr3c/zC1j7OPXsv+22tcfRP0c9MJF/PpyL3Nv5EK/3+QEvAbE/nerAuyWQPb1IX5z2xOfXho8sMccwkA4wQoTuxTT6lAaQ/Px28p8UpaDsH/2of0KYiNsYE48q6tVtm2tewvEbgBiBT/u/Yy/DlIVAMkjFuagMTY0mjsyAWx0lIIoH4YWGFceG897GQAbm6AUaatjdWDQMToMetlVT14m1bPgSwAmQPAO3OtZXu6wKo3WvlWMapSnejjRvl63U+tQaWZvq0IMpg7x6zgE6F7fujb9uHxfgVfOK0Oshz14XekaUl359afz4nl7/VcQq9/OnXnvx+YeZ58EVYVqTC2jwDLXh6fR2tcfdh2QtS1f17qH3CPbuh50XW9yVYsCYr+2ANj/bx0Au2GQfWqLsbBaT/XW2fhbrpjYxRJbdyZPtWJiW+YgGml6uv5GsFY4ge/vk8O0xJcv4eX7ekwsdiQQ2/KWuYdNnXEFtrnjzhAyBrGtoWiFDWi7JunkcgqCvUyrQKwfdzXs7Us2ab/c+bZAIo4le5t0/J6Ge7yroWuHGY+tHIPIDIq9lQWm1mFrP/fozYFYpeNA1wo/kMd1KsTqWvHz4g9DyjuugfhNrqeqTjPEuld52TjknrdS92LkM7eM1X2uOtL15/vme1Kwmeu9CgWoHjpbx6bj3uQErwSxv75ugN0QyN44+8UC/duAOnlfL7HEFhDrLzvwCVoVEPoKABGvGjAZ0JvT87yqNBVjOwaxHpcbMK2QhGrlA983gFaeV4dbltk6UIgd85ap41TH50PXUyFkDGLlyZnqtRIMas1J9ySvA2LHlkyqjmVKnKTDkcqueMNc1gwWPcDWvhUA5H3dy+dlCBPEjp2/bJphvirE5iH4yjucz6/qdyrEzvX4Kc7Y62kMYud6oKes8NBbLmxOGXMoQHWevS5zeEgun4+i5OtJ10V1/bWAd8o9twaI/cGVK1c+3QTArhFkzwcv4a3BE7sNsPvZALDHvhIBLztY8bWzLejLb/Vy+ZJXFfhWXtUexEZarTVic4zr2L4eg4sdGMT2PCkVTFXAlb00cyC25/WsgMJjH7OtC2IrL+rc1QkqiM2xg9kqiK1iSVsQO2XfVt5VOabWYRU6sc5wggpscgjDVIidum5sXjWhst65zkP061gRIpd91TJOgdixfR2Yp5Rjzv2yIYj9o00D7Aoge2UAq2eTl3AbujnA3bF6YBUmwWtnV4DY+C48pa34UV/iyr2krd9kuKzeANZ72UH8Ppc3YDnCCHKcbGvfKC9rxB4BxLZAQ9CYIaDXyc71xE7t8L3D9iHUKiZzFYitwhPWAbFnjbVBq0lAm4bYOEaFZmTLMcBjdViFQ8yd2OWT3vLErh54LQuxY2/X8omG/jBThQpU53pqPnHcVWzyFIj1MroXfmoZ1wmxip1t2T5A7I9+9KOLPLYBsBlk77nnnv+7s7xTtQLAtlchuH2kMbBXBu/y2EMBELtmC2jd5mSpgN2pMMpEriOC2CrutQchrVhNB95quaAeWGVQ9s440lK5WjO0q2Wc1hFOUA29Rnkc9KZ2yhourepZ8LYNiFXoxpTh9FVjYsdiqKs0qoll1XnQULUAcyrE9hbXF9j3yq0HpjFA7IVC9B4A5kBsq4wOt8tA7JTYVX91bOsczXnoa00OPBaI/eIXv/h+Z3mnZ3e8AsDrRxpGcKMROgDELrkM1SkIWD3AiV05FrKaya7YOHWc0TnntTqzR7XlScsdmcfZeQyehye4py57nXy5pHWtTpC9ghn4l4XYvCqAw2214sAciK28fnnfKn7YgcpnxM+F2AoM/bqo1tnN0OUedh+qzudBMK5jbsVx5nr08Bm/fjNEVevW5mXXdB4rQPQyxvZ83FqXOXvml4HYZcu4Doh1r2+eYKYVTvy6nJruiYYT7Eq3zo7rVba+wsJUjzYQi4DYQ4VY9y5pqai8bFPuSAV5ASY+gcM7SQfM2Ke1TqzvpwkigggHaH+Fp+/ja19qMfdVO8W8vFOAlmDfYXPO8Kivxxrp6bOvMZtjj+dAbHWsVV17WEP8Ri8+WNabPRZXndcA1ioAUY8e45wh2D2xKlts84cWh0NfFk1lrupR+8XvI88oR35o8Xsivtc+enWs8tFweQWIeT1alT/HsS4bTrCOMq4Kse7dVvqadFk9CLbSnRp+cUITu7alY3uV7Uu2wsKceGLCCTDskN/YFZ1HNekoDzt7Z5NfkFB1QBkGWx1ZdOQ5/9jfvTvVxC55b71D9xnaq3h2vHMWiOS3Rc2B2Fxngqi81usmIbY3Qa612P+UOhxbrD6/Vcstv0Ciyj+/2CK/IS57Y3v1GL/Lx6+Hp97ErvhNHIc/CGj92hYg+prBOb849/m4V53YtUwZV4XY1gSvqk2o0u29LIMltjauJ8529yrbTU1Qu7rE74BYDDtkiHXYqNZibXWkWqtybsxp6zfKv9eZKc8p+6zjDUC98ra+01JaVZxxbBtbTqxXDzlP7Vsda6/sqqOx76fUYS/WdO411gIm1ekc0OnV45TjU55T9pl6Tntp9crUOpZVy1jl2To/Y3WWJwdOuR7nrAZyoi872HTc6OtnCIjFsGOA2KnLIM0FV+y4zWNA13VtbCNOEtutyWOcQ0N47SwQC8RiGBALxA7D+Hkh9p4BF8t7rKfMyAdisbGXemwRYr8Y8aCrguyBAiwQC8RiGBB7KB3lVAMulrfW27uAWGyV9Zg3DLEXE5y+9KUv/bdlQPaAARaIBWIx7LQgdkrMKnbaYQVT4l2nDjWvK64Z2z/bdlsyArEXIHv58uX/MgdkBbD33Xff3zxQeANigVgMOx2IxTAMO0SbALGzQFYAe//99//wgOENiAViMQyIxTAMOwKInQSyAtizw39JABALxGIYEIthGHYkENsF2ddee+1YABaIBWIxDIjFMAw7MogtQTb+PyKABWKBWAwDYjEMw44QYu+A7PXr148RYIHYEYjFMOxuA2IxDMMOB2LvgOwRAiwQC8RiGBCLYRh2xBB7AbJHCLBA7AjEIoR+ISAWwzDsMCH2WAXEArEIHQ7EIoTQCTfAQCwQC8QiBMQihBAQC8QCsQgBsUAsQggBsUAsEIsQEAvEIoQQEAvEArHL6cMPP7ywzz77bKfl+OSTTy7K8emnn9KQAbEIIQTEArEXehaIBWIrvffeexfrIj/55JOUBQGxCCEExO6dXh9AFogFYu8oPK8PP/zwBTiGF1SKl3688847W/G8Rl6e93PPPXdRnnjtMwJiJ1/IccFs+qKJYYLII/7uUjFUEeV488039+7i2XQd+bne9dARQkDs1nSML3AAYldUBYwBlNuCyMgj8vL+rgXWCIjtamjktnLB7voJK578ohx60lS53B5//PGL10/GPtu8kbZRRy+//PJFHvEXISD26HU+tGvnMOx0iI22Xw/74fjQ556DwfeLv1VspxwV8V2krf1jKF2OBd/ey09pySkzp6+qhu4jT712OfrJnH+UK36nPKv+Ud9VZYkyKs34G/1s5BV5ep8X6RJWAMTuHcTGTbvroO24iXSDZnCMGyq2x42ToTaeWLfhudxGHcVxqPHYtVccISB24woP7C34dR7EytmhPiM7OTKkCf6qvqNyVMR2eRxlGlqvtnv/E/+rfNmmOidUXh+RrNIUXEa5qr7RHUJ+fBlABc1xbFH+Kh2X6oBJXkDs3kDsrhU3Q9wYYX5jtLyfcdPGDa6bKW7KY7mhAl7VGBNWgIDYo9ZLTOpaHmIFkdFmBogJ5BwW1Z5GX6FZ/gF21bC4j/xFWpGm7xt/o13Wdjkc4nMG0Ng3tue+ako8awWJkY7Sjr/uUFF9RJlje+zrx+IOEdWRANkdJzqO2N/3yw6VCrIREHvHVS+vo4ZLWhAbF1Dsp/3jxvUbMj7Hdr/BXPG0qafLyLcKGPc4TeVRSTeYyhNpz7nAFf+TfzM2hO9Pxu7B9fqMtFVHUcbcaMX2Vll1TGq0WkH1GopRHhV8RoOjc6LytPJt1QdCQOzR6LGhbX8Mfl0OYlseRd8e26r+SH1LtNd5W/QpLU9ulYb6xWjjtV/2Bqts0fZPdWJkVf2h4mQrp4c81X6M2WGkEDbfx+u4GhFUSEHV56IThdi4UPQ0pAtSN2O+eeJC9e3xv/aPC1M3jy7OPGSSn057N4fy0UWv/PwG1QWt/fwpOd8Yc548p0CsH0tuYLwcUT9evwLRXnyPN0iacJbLks+F8oi/fvNHA+bDUF62+H1ufFjOBAGxR61LQxjBTdh1eYjN/YK32ZWindW6q2rPq/C1VphBhja109quvqjVbk8ZVVUaFSBWfVAF4xXgVnCrvqsC4B7E9sqIThRidcH4khY+jOEXvj8tat+4AAWtuoH8hs4XqJ4s9QRZ3RzK2z2LykM3hc9W9JvIobzlCc43RHXjT51MlZ9+BadeR7nuouxe/gzQutHVoFVlUSMQ++j3PoyjevMg+eohoTo+B2iEgNijCyP42QCzaEmIrQCrcvpUE4TdoTDW37S2Z5irYnQr64WJZTAeK4f65DFrjfa16nEKxOJkAWKbEJYvWr8IBV0VHOY4H0FS3jfvl2+O1pOWgta1nz/RZWUIbCkPySwDsR7oX32u9pU3thXfo7rLKyWoLL1QjzhmzXLtDQ/1vus1IggBsQfrgX1paDeuwq2bh1iHtfg/+sL4neBvExAb7bnC8CrrOSZ6Q/U9T2z0V708cz/uI4i9frIHsWOhEehEILY1HO5PZf6dz0zMlme2VzEx1VB1vjl6QxStYZZcFl8OZEoaFaguC7F5eRC3PBRV1Ye82B4blcvSe2KuGqVWw9Y690AsAmKPLgb21uCBBWC3BLFy2GRnStU/rQqxvb58qpYNJ5jjFXWAV6hgdqARTgDEzobY6iLMN4V/7ln2FjqMVZ7HfHNMfTNHaymRsaGMqjFZBWLzsMicOnLo1RNyBf+rgv7cOurVC0JA7EEo1n99wryvNwkh2B7EKia02tfnnawLYj2EL+ensIax9WKnhNdVqy9UYQpaGcHz9PkwsX9rNLVXx1MdOOjEIDbPiKwg1oewNSxSmQ9XCEgVUqAnU7/g8006ddF9nzzWKsuYJ3FVT6w3VDomHyaZUkc6DoG9hlqqeNocSjHV06zlT6bWESsUoCNtgF9c2PML+5OF/W8L+18WduOILF4h+7o9oN4aPrMKwQ48sT45OfbXCkA+36QVVjcXYt35EOlrhSG9wnWK08P7+AylPtejWmIrjkvhb75Mlrb5HBAPMawmyuk41Ld7WXgpDxB7l3oTsKrhZofYKdKFHxelnqBasy91EfficgSGHjC/SoD3KhDrsT1e1tbs1TEQ1nqzVbxPK264eviI+lMjs+wQE+EE6Egb4LD/Z2H/dmH/amH/1wB5x2TPDh5Y3sS1Y4j11XPObBUdX3dVaa0DYnsvO5jquZQDo1rO0dOe8rIDh2alm/v/amkw35brWl5sXj0LxN6Rno56yzflizg/0cX+cXHG9hw4rrgX3bQZgPNN6jGhfvHmoQgHtJxnb13VDMWtCWA9iPXFrXP+DtfVygxVHflC2WcT16zVecuv//My+dNvrov4XauOVB4gFh1ZA8zqBGgliNVrX6sJUtVEptgv2vNo2/MrZDUSpv+r18m2tmvpxar91itgo6+Z+9rZXp+oFxlUoQmeZ361rq/5Xq2OoN/6sfhrbPWbnvMGnTDE+jIZeqmAr83qEOvgqOGDuOgFPdVwhT9xVhdfBWj+m/g/yqQnML/Q87qwceHreKqA8ZYXtLe4s14OIPN6if+rlzT4W1Z0c+pJtLeaQgvKe0H1Om+t85D3U+PQWmlibL1DhIBYdKoQewpqrSxEuWhDz/b1jV1xUTgQCgArmMkvR9D+reWsHHynLmUVIJjfOR155t/7GrVuem3fFLWGJ3qToiJ9X5+1guNqiCV+03qjVm/Yp+UVzscuj3cLgnN9Vk/xGgIbW54MISAWAbHHJ/X9GvXcB7VeBoGA2M9dvHk2YWtIWTGXU4YqfNiklU4FhBpmWTWPZW4OlcttbhyOpzGl7lv10KujsfNU7ddbK5CnXQTEIiD2j0/6ftknaNxHqAZi9xRiT1XEgPK0i4BYBMQCsXf3B6041m1KIXDM0QBiOQsjT3rVe5xPrQ542kVALAJi6S8RAmIP0At5quvPKSaaMAIExCIglv4SISD2gORLnnDsCAGxO9CVs5+v8XoVrgRiEQJigViEEDoEiH0srSCCgFiEgFggFiFEA7z3EHtzgNeXFvYUXAnEIgTEArEIIXQIEPvWALFXYEogFiEgFohFCKFNQmwA540hjvXKEBJwI+1zddgWHtZnh30vFd8rjOBGkcYTw29fH757rCjLE8N3lyxNj629NHh4bw5lie/Pi3R0PApxeHb4TS9O99zyb3mRL008Dq/XG6l+gViEgFiEEAJi1wCxVwfwfH2wHM/67Fn91r+fGUDeaOwj8Hur8f1LCYaV/80ExILMW410MnTeHva92Sh3VnWMbyXoPO8cx810HE819rvdgG4gFiEgFiGEjr4B/q0BUtbV8F416Ltlnlb/7rZtuzLAZwWE1YSum5b+uaX7swSpZwmi5WkVSP7M4PKSwfOtAg5vW543zIOqNK4WAPsz85Y+a7+/lMrm+T9maTpI30p1dsnA9q0EvBuF2I8//hjDsMGAWIQQ2i3EXhsa3ofWDLGVR/OlxvazBhBmiL3S8UCeFyD8egOOn7Dtl4rQAXlDczmeagD1syPgfWae6XMr6+0i/6upzOedY35s2B+IxTAgFiGETg5if7Cwby7swYXdu2aIzTGbtwwGryZ7vfCkZiB0wLtaWN6/StNB9aUijacsHCKX47wBw683AHSsjt6aeRy3ivjhrcbEAi4YtmcQi2EYdqq2aAZ/cwCnX17Y5TVD7FkjPKBnPYh9YmIaVxP8XW14hHt2a4J39WqC2AqAzxoTxaYcx6UiXMJjbDcJtEAshu07xJp+bWHfW9iPhsL8IYZh2BFatG//88L+pwGC/trC/srC7l8TEE2B2Jtppr1bL5zgsRSb2rIrIxB702Czl85ciH1iIsQ+ZR7bKcfh+T2bgPYmEIthQOzDwwzd7w0xYr+FYRh2hHZtCCH4zQFgf3WIh13XGrFXC09ma4LSmFrhBLcnAncLYm9MhM25ENs79itWjh7oT9WNYrIYEIthJwqxMZT2yMK+MRTiOxiGYUdo3x5iYK8OHtiHBsC6vKZGtxcX+lLHe6j1VC914PGSgXBeT/XSkO5jEyD2iREwfbaxOsEYxDqoX+lMXLtShD447PpxPFYcV07zfB8h9qOPPrr97rvvXti+w8gmyrlPx/7BBx9clOX999/faTkO6Zo4NIi9bxhOe3Bo1L+KYRh2hPbQ0M798tDm/dIAsOt6U9fVCdAn8Ltq0FgtF1WlcyMNpZ8PgPdWAcgtiD2z/TWkr5cTVJA8B2LzSxoeM3h/vQhpuGXQ7MeR17PVpK4rw7a53uSdeGJVF/sOI5so5z4d+7Vr1y7K8vbbb1OWI4XYLw6N572D3YdhGHaEpjbu8tDmfXHNje3YULmDYp5IdT5hGP/SWfuFCS9NDCeQx7P1soGnlgwncI9w9VKEq2m/1gSzmx3Pca6zq0Ds6vbKK69c2LK/f/HFFy9snWmu89jiHDzzzDN3tr3xxhu3n3766a3kn+smvMFRnvPz8wvPLBC7uRsYwzDs2GzTumQe1pb8dbRPdYbDe+n4a11br2A9PxtfR/XqUIYbnbxa5dCxnnfSfeqs/3pYP47HOmX1V+fe2CC8niTErmoBZNuCwjnWAsYA2m2dl8gn100F1kAsQgghhHYCsRF3GXDy6quv3om9bEFsfB/7yVvZi5EMr6H2i/8jn7F4y0g7e/lie6QRvw+L4exIr/KaKk/tq8+5nPE5vIxxjI8++uidMvY8sZGeH3uUo1VW5afPkfYc72Ueutc5irLG9qqM1bnxPFWW2Kc6r/67559//q668fpTGQ45rACIRQghhA4YYgNwBCtuATkVxAr6sgVwOSwFcAl0qrQzPAnYZOF9dGiS98/zl4cwlzO2y1MY6Xi68Z3SVZr5+xbAV/urrD7pysuaj0vwOWUyl9J2AK3yHyufn5v4qzrJAKqyCmR75db1EdcOEIsQQgihrUOsADbARt5LQWCGpPAkOogGVAUICVZ9eFnpxrYAvICn2DfyCZNHNr4TVGnfyCeDlkNVwFakL69pLqeDro7LyxkW5ZFXVWn6CgA5TQdIgXDkrzTjbwWTyj97fccAq4JEeauVtq8U4Nt1bsIEph4SEHWhcghuvR7c4+x14170CrKBWIQQQghtDWIFPtkr5yCYh5CroWjtK9Bp7aswAO2nfKqJVe79czDMIQk9j6R7h+N/lUsALPjLcZ85TcWh5nL6sXoogn6fl8USnI8tl6UHCaU5Fqus8lXnRnl6vXm9x3Y9XHi5WnWTj/tQl9wCYhFCCKEDhVhNHDor4l6rcIIKDDN05aH6AKMqbrT1u7FZ+hVQtSC22lf5CY6nQqw8mhVU5jSVf+Vx1b4VbE6tl+qctQDfAdfL7kCvY8tlGoPYqecOiEUIIYTQWiFWkFLBlg9P+/DxmAmEHJLOLAwgT27qwVfPMzsFYiuvaU5nKsS2PKuepob+p0D0GPgJLKdAbNTnlHNTTQI7S7HAy0DsoU7uAmIRQgihE4NYTfyprFoBIODOgdbz2yTEVvseCsT2hup7ntgA96nnxmOcPT52LsRW3mkgFiGEEEIbh9izmeEEyw4fa0KRp9HzOK4KsbsKJ1DdrQKxy4YTTPWKKg72bJhQd1asNEA4AUIIIYT2EmJ78OPLbmXvYAWSAW5KIzx6vuZqla7S0OfWxC6teLAMxFYz5zOMCtQ0K38dE7t68DcXYisoPStik7V/VT6dm2r/qCsP/fD8xiB26gMIEIsQQgihtUOs4MyXgpqyxFbAp14mIGjUeqQORQFQChWItPNMeQ9TqJbYyhPF5kDs2TBMHun5cTncev4BcFOW2Ipy6iUK1RJb64DY1qoNDs3xXbXElp8bXyZLEOvnK5fZ3w7Wqpsch3uor58FYhFCCKEDhtgAlTkvO+gt+O9xrT5pKFsetva8zhoTkZaB2NbLDnL87ZQXKPTKGVA5dWmqqRCr+huLVz6b8LIDXzrLz0uO7/W1fSuPvB+PHmgO+dWzQCxCCCF0wBCbYdYnALVevarXk+b9q7eBhQcv9gnYca9sL83q9az5Va4ZrFvAK2+key2rcvprZMeOPdLTsVdllXe6CqfwV+KOQVZvTVm9WjYvi6XjbZ0bnY8qTMHrQeWr6mbOUmFALEIIIYQ2CrHHZD2v7SGZJsLleF3KBcQihBBCQCwQu9emmOV9OZbwzE596xgQixBCCCEg9kQhdt+gcd+gGohFCCGEgNijsV787CEfz67fjOXxscdQr0AsQgghBMRiGBCLEEIIISAWw4BYhBBCCIgFYjEMiEUIIYSAWAwDYhFCCCEExGIYEIsQQggBsUAshgGxCCGEEBCLYUAsQgghhIBYDANiEUIIITQBYhFCvxAQixBCCAGxCAGxCCGEEAJiEQJiEUIIISAWiEUIiEUIIYSAWISAWIQQQggBsQgBsQghhBAQe6oQ+8knn9z+8MMPL/6itj777LO9qacoRxgQixBCCKGThNgXXnghoOTCrl+/Dql2FPUT9fTmm2+ebFmAWIQQQgiI3QsFCD388MMXMPvOO+9sLJ9I+5AhOWAxg/577723tWN6+eWXL0z69NNPL85bWPwPxCKEEEJA7MlAbAyLb8sD+9xzz13kdYhqAWNA5baOKfLO56kCayAWIYQQAmKbEBtexddee+0iRjKgJmAiPnuMom/vecriN7FP/r0rvpOXNMCzl67SqkDM04n/FUrw+OOP3/Wdx38qvbE4UMVohncy10P8PvKIvLx8qscpx6GyxN/YP/LJnmMvb9RRVWY/d/F9fB6LLRWAa+i+d0x+3MorLMobeeZ6qcIB9J2ON/62zpPCCjbpRQdiEUIIoSOBWIGDPHFusS2AIm8PYMygkvc5G4b2M1TpO8GM25NPPlnuW0Gme+2qvPWd9q3KVoGhx9V6PfTS8nqsIDIfh4490o1yeHkDDgWaY/WuPL3MFfQ7zGs/QWjvmNxDWp0r1V+kJQjOIKtjiTJW59w9r7rWtuWNBWIRQgihI4BYwUOAicOGhn7D+xbbBV3uiXMQDqCNNAQv8bcCOoGNvLFK18FyKsTGZ4Fc/PVZ9wFbgrsoW3znYOXSvgFkAVSKE1Ve8uiqXD6rfhmIVV4ew6vjiPqQJzi+U/04JPq5i7JH/fc8sYJEPye9Y/LtXq/KN8qewzk8TCHKr/3c06/y5tURKsgGYhFCCCEgtguxGTYFdNkrlod8AzYCngR5Fbz5EHv2bmYPqG+fCrEOhu6JjHwjvXxs8hwHcKnMgrDsDda+DmxVuZaF2BbEZS+xjtnLUcFkT1Ud9Y6p9SASkufVj1fpx/5xLAJv36c6d1PrEYhFCCGEgNjR5Y3cuzoVhHyd1rAKdFqQVg0lrwqxrVjX7HlseSl7qyCsA2IzMFegmidEeTqtczS2lFUVc1odkx5kqmNS+Emub3/4qb6fCrHhxQViEUIIISB2FGIzqLRgooLF+K3gpbIKYqeA6Tog1mG6VzZ5gnsQvG6IzXlV8ce9Ms/1Ws4pYyvWOFs1CUywnSF9CsS24BiIRQghhIDYWRCbt2cA0zD82TA8Hx5deTur4eRtQqxiMgVUXrZ9hljN3G+ZwjO2BbGalFVZTsvr1sM1pkLsFI86EIsQQggBsStDrM9e9xABX7FgVYjNICRv3RjE+iQpL1sFsR7PuQrE5qF6h/wpXuNW/awjfrQ3VH/WCSeY+iYtj4NVPG2G1anhBNtYZguIRQghhE4YYluw4kPjDk1zILaKqW1NOqvAsDVpzSdWKW3BZo5HdRiXB7SCa4F1K4Z4CsS2wF+AmNe4nQuxvaH6s2ISXmsVB3m54xx7HWh/1UEFwTrPVagBE7sQQgghtDWI9SF7LZnlHtAMQXMg1j2uCgPIS4L1wNBBWktE+Yz+DJ3+XRyXLyflx+C/zRAcxxz5qh5UB1MgNnuP/UUGFVDOBT55zSso9eOM9PLSWzq3vkyWL6floRB5xQf3hHvMbPVyBn23jdfPArEIIYTQCUOsQ91ZWgPWPYv5xQRTIDbAxyFQVr2itCqXD2/n3zugKZ3IrzqW2OZQlV8QUK3bemZLic2B2N7LDsJ7Wa3POxVifWmxalWJs2LymK9Rm60C09b14p7X/NKDHMPc8tICsQghhBAQ+7lXl2bPV2u7vzI1Dy8HsOVXpGrJLW2b+irZnLZeCBD7Vfu2yuW/j7JlGK0W3I9041jCWq+o1etyc7xo7B+/86F/1eWUsuZ9VO7ea2fneC01xF/FxSq9yC+/Vja2qU7ya2dV1lYca56QptCI/Js5k+uAWIQQQujEIRadlnwt2m28FWufywXEIoQQQkAsOiC1JqHtSgGtrUl8QCxCCCEExEJvaKfQ2JJiZLcN1UAsQgghBMSiA1PE2PbiWLcNsVqJAYhFCCGEgFggFiEgFiGEEAJiEQJiEUIIIQTEIgTEIoQQQgiIRQiIRQghhIBYhIBYhBBCCG1Z9y7soaFjvgbEIlRC7D9Y2O/HPbKwbw33zL00HwghhNBuIfbBoWN+AohFqITYP1nY78U9srBvDvcMEIsQQgjtUJcX9sDCvrGw7wGxCJUQ+0cL+924Rxb2a8M9c5nmAyGEENqdLi3sKwv7+sK+Gx02hmF32+Le+MOF/fbCfmNhvzrcM5doPhBCCKHd6Z6FXVnYIwt7dGE/GDrr8Dr97bOfT2b5MYadoP3BcA/EvfA/Luz7C/v14V65Mtw7CCGEENqRtEJBxPh9bQDZ8DbFsGnE/8VElh9i2AnateEe+N5wT/z6cI88eMbKBAghhNBeKIZFvzx0zuFlitCCiJGNyV6xasF3MOwE7dvDPfCN4Z54ZLhHvnxGKAFCCCG0F/rC0CmHdymGSSPe74Ghw46lhL6KYSdoDw33wAPDPXFluEcuneGFRQghhPYOZu8ZOumYeX3vYPdh2Amarv/Lwz1xD/CKEEIIHR7cYtipGUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIoVPU/w8W+buTamhwKAAAAABJRU5ErkJggg==" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_9082' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_9054' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_8c47' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_68e4' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_5763' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = generateCenterCoords(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_6b62' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_47d3' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >data = generateCalciumResponses(n_rois, 100); </span><span style="color: #008013;">% [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_5d01' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_8378' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="7450DA71" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
+</div></div></div></div></div></div><h3  class = 'S12' id = 'H_3a0a' ><span>Subject Information</span></h3><div  class = 'S5'><span>It is recommended to store information about the experimental subject in the file. Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Subject.html"><span style=' font-weight: bold; font-family: monospace;'>Subject</span></a><span> object to store metadata about the subject, then assign it to </span><span style=' font-family: monospace;'>nwb.general_subject</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >subject = types.core.Subject( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'subject_id'</span><span >, </span><span style="color: #a709f5;">'005'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'age'</span><span >, </span><span style="color: #a709f5;">'P90D'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'mouse 5'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'species'</span><span >, </span><span style="color: #a709f5;">'Mus musculus'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'sex'</span><span >, </span><span style="color: #a709f5;">'M' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_subject = subject;</span></span></div></div></div><h2  class = 'S14' id = 'H_4a0a' ><span>Optical Physiology</span></h2><div  class = 'S5' id = 'H_25054651' ><span>Optical physiology results are written in four steps:</span></div><ol  class = 'S6'><li  class = 'S7'><span>Create imaging plane</span></li><li  class = 'S7'><span>Acquired two-photon images</span></li><li  class = 'S7'><span>Image segmentation</span></li><li  class = 'S7'><span>Fluorescence and dF/F responses</span></li></ol><h3  class = 'S12' id = 'H_1e01' ><span>Imaging Plane</span></h3><div  class = 'S5'><span>First, you must create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span> object, which will hold information about the area and method used to collect the optical imaging data. This requires creation of a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> object for the microscope and an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> object. Then you can create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div  class = 'S15'><img class = "imageNode" src = 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" width = "527" height = "278" alt = "" style = "vertical-align: baseline; width: 527px; height: 278px;"></img></div><div  class = 'S5'><span>Create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Device.html"><span style=' font-weight: bold; font-family: monospace;'>Device</span></a><span> representing a two-photon microscope. The fields </span><span style=' font-family: monospace;'>description</span><span>, </span><span style=' font-family: monospace;'>manufacturer</span><span>, </span><span style=' font-family: monospace;'>model_number</span><span>, </span><span style=' font-family: monospace;'>model_name</span><span>, and </span><span style=' font-family: monospace;'>serial_number</span><span> are optional, but recommended. Then create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OpticalChannel.html"><span style=' font-weight: bold; font-family: monospace;'>OpticalChannel</span></a><span> and add both of these to the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >device = types.core.Device( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'My two-photon microscope'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'manufacturer'</span><span >, </span><span style="color: #a709f5;">'Loki Labs'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_number'</span><span >, </span><span style="color: #a709f5;">'ABC-123'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'model_name'</span><span >, </span><span style="color: #a709f5;">'Loki 1.0'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'serial_number'</span><span >, </span><span style="color: #a709f5;">'1234567890'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Add device to nwb object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >nwb.general_devices.set(</span><span style="color: #a709f5;">'Device'</span><span >, device);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >optical_channel = types.core.OpticalChannel( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'emission_lambda'</span><span >, 500.);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane_name = </span><span style="color: #a709f5;">'imaging_plane'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_plane = types.core.ImagingPlane( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'optical_channel'</span><span >, optical_channel, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'a very interesting part of the brain'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'device'</span><span >, types.untyped.SoftLink(device), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'excitation_lambda'</span><span >, 600., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_rate'</span><span >, 5., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'indicator'</span><span >, </span><span style="color: #a709f5;">'GFP'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'location'</span><span >, </span><span style="color: #a709f5;">'Primary visual area'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.general_optophysiology.set(imaging_plane_name, imaging_plane);</span></span></div></div></div><h3  class = 'S12' id = 'H_49fa' ><span>Storing Two-Photon Data</span></h3><div  class = 'S5' id = 'H_BEB8D2A0' ><span>You can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> class representing two photon imaging data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, like </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/SpatialSeries.html"><span style=' font-weight: bold; font-family: monospace;'>SpatialSeries</span></a><span>, inherits from </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TimeSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TimeSeries</span></a><span style=' font-weight: bold; font-family: monospace;'> </span><span>and is similar in behavior to </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span>.</span></div><div  class = 'S15' id = 'H_BEB8D2A0' ><img class = "imageNode" src = 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width = "669" height = "387" alt = "" style = "vertical-align: baseline; width: 669px; height: 387px;"></img></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_19E7751C' ><span style="white-space: pre"><span >InternalTwoPhoton = types.core.TwoPhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(200, 100, 1000), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'2pInternal'</span><span >, InternalTwoPhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_651e' ><span>Storing One-Photon Data</span></h3><div  class = 'S5'><span>Now that we have our </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImagingPlane.html"><span style=' font-weight: bold; font-family: monospace;'>ImagingPlane</span></a><span>, we can create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/OnePhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>OnePhotonSeries</span></a><span> object to store raw one-photon imaging data.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #008013;">% using internal data. this data will be stored inside the NWB file</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >InternalOnePhoton = types.core.OnePhotonSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0., </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'normalized amplitude' </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwb.acquisition.set(</span><span style="color: #a709f5;">'1pInternal'</span><span >, InternalOnePhoton);</span></span></div></div></div><h3  class = 'S12' id = 'H_2e61' ><span>Motion Correction (optional)</span></h3><div  class = 'S5' id = 'H_BE3DB7E3' ><span>You can also store the result of motion correction using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/MotionCorrection.html"><span style=' font-weight: bold; font-family: monospace;'>MotionCorrection</span></a><span> object, a container type that can hold one or more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/CorrectedImageStack.html"><span style=' font-weight: bold; font-family: monospace;'>CorrectedImageStack</span></a><span> objects.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8' id = 'H_BE3DB7E3' ><span style="white-space: pre"><span style="color: #008013;">% Create the corrected ImageSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected = types.core.ImageSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'A motion corrected image stack'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(100, 100, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 3D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'n/a'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'format'</span><span >, </span><span style="color: #a709f5;">'raw'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the xy_translation TimeSeries</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xy_translation = types.core.TimeSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'x,y translation in pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, ones(2, 1000), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;">  % 2D data array</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'pixels'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 1.0 </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the CorrectedImageStack</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >corrected_image_stack = types.core.CorrectedImageStack( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'corrected'</span><span >, corrected, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'original'</span><span >, types.untyped.SoftLink(InternalOnePhoton), </span><span style="color: #0e00ff;">...</span><span style="color: #008013;"> % Ensure `InternalOnePhoton` exists</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'xy_translation'</span><span >, xy_translation </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create the MotionCorrection object</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >motion_correction = types.core.MotionCorrection();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >motion_correction.correctedimagestack.set(</span><span style="color: #a709f5;">'CorrectedImageStack'</span><span >, corrected_image_stack);</span></span></div></div></div><div  class = 'S16'><span>The motion corrected data is considered processed data and will be added to the </span><span style=' font-family: monospace;'>processing</span><span> field of the </span><span style=' font-family: monospace;'>nwb</span><span> object using a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> called "</span><span style=' font-family: monospace;'>ophys</span><span>". First, create the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> object and then add the </span><span style=' font-family: monospace;'>motion_correction</span><span> object to it, naming it "</span><span style=' font-family: monospace;'>MotionCorrection</span><span>". </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >ophys_module = types.core.ProcessingModule( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Contains optical physiology data'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'MotionCorrection'</span><span >, motion_correction);</span></span></div></div></div><div  class = 'S16'><span>Finally, add the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> to the </span><span style=' font-family: monospace;'>nwb</span><span> (Note that we can continue adding objects to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> without needing to explicitly update the nwb):</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >nwb.processing.set(</span><span style="color: #a709f5;">'ophys'</span><span >, ophys_module);</span></span></div></div></div><h3  class = 'S18' id = 'H_5d6d' ><span>Plane Segmentation</span></h3><div  class = 'S5'><span>Image segmentation stores the detected regions of interest in the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span> data. </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> allows you to have more than one segmentation by creating more </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> objects.</span></div><div  class = 'S15'><img class = "imageNode" src = 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Kf1JsIhgZUHm88vkvA3HPo8VTdmBBYCy4kuWufj4BDENsXsvPyxWfxwL7HOd3QnBAAAgB0WWFFZi0qYV+Q0do1TddXx+X16/J+X15u+xpYd2sbUV9RX6a++G0qXxr+h9RUCC4E1XWCp+55CAtkl1LwCK/Imf/thN6FFliT20Phdni6FWnRK0ntLstzKs0pL1xjHD4F1+omWLXfFwbnnx3+FILYi+ov1387iq92Nllh3dbTCAgAAgB0WWDsmHm6p+K6SqDSr8g0ILATWx91y89hTedyomBbyN1pXSoxLIMfnmNcFmAZ6zy2msoCK9YVg8lZSLWIel08SVrHe/DZDvSWx2qc8Tp+nS8sq7bEfuZshAmt/iMGy70ZgEVsosJ6axRPdje6Ed/fnKgAAAAACa8VofJ11SCW1wAAEFgJrd4V3HtNulUQL0XVuD4G1XcQb3+5FYBFbKLB+qb9gY0yse/tzFQAAAACBtQZaXQFXsZ0pXRsBgYXA2k7UNTG3xFqVLNuH1lcIrDZxEO5DYBFbKLD+Q1/occECAAAAAgsAgYXA2mKJtY5ux7lbJAILgUUQCCwAAAAABBYAAguBBQgsBBZBILAAAAAAgQWAwEJgIbAAgYXAIhBYAAAAAAgsAAQWAgsQWAis0x7f/VNXjn7h1//gOHZ5P+bdh03vNwILAAAAEFgACCwEFiCwqA8vRWD94Z+9eUv88m9981h4hPw4LQLrkZ/7jZsn0LLX/dVnfufmsfvGS9+6bbt+XE+6rXn3YZX7jcACAAAABBYAAguBhcACBNbaBNYQv/3Hf4HAmtAqyvFpIbRESCwEFhcsAAAA7J7Aev/99wlibwOBhcACBNbWCayQHfruT//ynZvfqyVWtDRSy6zPXvrG8WfNH59jWgib+BufW0JFkaf5OmLd3/kTz942z9d+9XePpVq0ZqqmR9o0/eIvvTDYlU77E+uJ6dpuXmdMj+1qWT8OYwLLyQIrlo906phVxyu2rX2J//23iqi6B+q7WH9LYOlYx7pbxxqBBQAAAAgsBBZBILDmE1icM4QHAmsNAivkx7f/7u+Pvw95o66G+uxCxsVKq/WWutI5f/U31wdbgsX2XYR5a6YqzVkkBZJYlcjx/dG+arsukHyaugv6trVdSb+8T7F87KsLrCqtcbxcJMW2fNt+/CSvhqSZJFeeJ7aTibQjsAAAAAAQWASBwEJgEQisnRJYIY5aAkvfS8i4SPGWWy5NspjK00PWVNMluSI9Wr9aDmm6WlAJtUCK9US0WiLl/anSHQLM01LJM+2/xJ63/Aok3nS8NJ/W6WJKY2j58Yh9zsf1JAIri6tVdjFEYAEAAAACiyAQWAgsAoFFfXipAqtFJXzGxljKkid3R5RU0ucsn7JYkcCS5HFJlIVbtDAKAeSD0A8JLB9cPadb6dDn3I3PBVZ8jnVJuqlll9YrgTXW3TDSXokll3SLCqxK9GlbEpUILAAAAID5BNaLL754HJuqIJ1k2+tK+xtvvHG8nddff32l23nvvfc2+lsgsBBYnDMEAmtDAsvfqifh49+pNVHugubd8ySfXHxl+TRFoHnLJQkhF1neasll0JjA8nGvsrAS3rVPLbEqgaUWW7FPsS51o6wEVqsL5ZQxtRYVWP450xqHC4EFAAAACKzRCv5xbFAwnGjZdaT98PDwON555521HA8kFgJrWwVWyNyITf2eJ9n2utIe+URs5+233175sVj1/iCwViSwNJh4RIiOPLC3hI+LjpbAkggKOTT2JryqVZBHtV4njxuVGetC6F0n83fCx+IaaoGl6eriKDlWCaws9NYtsPJxrgbWR2ABAAAAAmv7Bda8FbAnnnhi7RVSl0rxd9nHy/cpRNkmfw8EFgIL4X2yePTRR4+3s+oWm+vYDgJrRQJrTGBUAssHOdf3kjcul3KLLrVikuDJ6451xHca2DyWy2/xcxGj+V2kSa7FsosKLB+gPQ9EPySwqnGnlH4fc0vHx8Wb3ojo69DbCH3bvoyEoy83NgaW3lSoY9t6cyQCCwAAABBYt1dKXnnllWNpIhmTK3UvvfTS0UMPPXQ8z3PPPXdb64JnnnnmeNpTTz11PG90fctd+zRPFjXRKiGW0Xp9nvg/hFRUyGL7sR6tO9IcFbYQPEpX/M3b8LRH+vL2Yz2RhlhXTI/PY62v1JIi/kowVfum7XrLiyodsX/atu+T0qLjioBCYCGwlieH1xHKI5RvxbW8SuG9CqGOwNpSgZUHcc8DnVfdFH2eaowqn65udpI3IZLif7XEChkU4sa7F0rILKMFVtXlTgKqGsR9isBy8RT75/ur8biG5vFt57cn5nnGBnGvBtRHYAEAAAC0BVbIoZAsUeHRX6+QRqVLT/Xjr+ZxIaP5ffmQVS7H4juJHhdgEj8ugLzype368vG3JbByZVrrjrRpee+OF5+jQhnTPP1DrcN8ehwHLaP0+zGLdWrbahmmaUpHzB/TQ2K1BJYEIwIKgbUNAiukdQhVP/+ztNF4dFWLyDifY1r8rbrhqiVllraxTMwfoWvDr2efnrcd/0ceIRGuNOTuuVq2GuPO16l1ZFlftYrSemLdymfydmNftV1fZ3zOx0HHN9ISea32SdPj86paYSGwtkxgtcbR8vlCyGTBkrsd5ukhbbxVkEupPMZVhLc+EpJKiwqsvF7fjyywtL/RamxIYGmZvL8hr3J3SBdXuXVVtZ4pLbByF8VqPC4EFgAAACCwaoGV5ZFkkyqkepovaRQVq6gERqXMJZAqWDFdFTRVyuJ/VTajAufzu/BSRTYLLE2Pdcff+Kx5owLnYsfT7uLMW4rFd6ogan61iMqCKock2VALFB0zSb7YlmSW0uHdApUmr2hnWaV1jFWWCQTWKgWWX4M58jXi4edtFuWeH2QpHOESSgJ8SHhLSGex7K1LXZxX165HlvXK/6p9r0Rfvrar5fJ2Y/06ZtoXF1IxXbKrWl8cT3+IgMDaUoEF0Bo0H4EFAAAACKzbBVZVAfPv9L8qS15pUsXSJVBuTaGK3lD3o1zRyhXSLHNiO2ptMCSwVFHOLRE6a/2gymLVrbDVVWdMYFXHTGnxirq6EuVKeiWwtN5NDpaNwEJg6Xz1a6q65nKLIZfOebpaHg4JYr9efV0t4S35I6Gu9Q/lF7oecxdpF/CV1PIuxYvkF1WXQkkr7aeOi+S3pPwm8gsEFgILEFgAAAAAGxNYWeBUAqsKVSpDQHmLh6iQZbk1zxv2coU0T/duP0MV0qqy6600WoJM4261xr8aqpCqpdTYMfPWGVngDVVIx8bnIhBY6xBYY/lFbimYhbe6zE4RPnk9+doYE94a228sv5Bsz9dYFmhqSenrb41PN4/wzpLe0yIRpzT69hBYOyywiPHI409Flz11TSQQWAAAALB/Asuf5rcqpFHZ1Lg0iiyGNNC6r3MZAisLnmiNMI/AypW4zlpZzCuwqhZlrQppPl75mHlXK2/BMVQhXfVbzBBYCKwxgZUFzlThLeniXQjjevJxpJaRX+QWVPMK79yayvOgZecX8whvda3M2193foHAQmARCCwAAACAjQisPLC5V6i8BYFLJI2DEzImKlW58uRjOqm7obfIyAJprELaWSsqDYKsCqMGoa8qh9q2v/FPrZ68Ncg8FdKhFhVKYx7TSi0qdMw0aHs+xl4hza3i6EKIwNoWgdVNaIE19TwNwdIVraBOIrBW3QJrWfnFPG9xVNdBtczy/JgWWAgsgkBgAQAAwF4ILG8FFP97V8A86HBU7KIVRVT0vAKlSl1M0/p80HeN2aKBhf2V8lMFlubLlT0Jt1hvHoDeRVEsK7GUK7DzVEg1KH3V3UfrkSTTm8G0jI5ZHmtHlVNtU/sU033w+9xSjkBgbUpgjY2Bla9pzR/ndhZELnwr+dulLr9j+UW+TtTKq5LD3QJjYC1LYCkPrOaJPCOOWZ4nvyQDgYXAIggEFgAAAOyNwFLlUhWkLLC8lZO+zxVEH4xc4sYrqfmtYrnyO6VLkFpH5HV7hbB6q1hsO7/1LLcGm6dCmt8q5t/l7fo24xi5tIs0eTqiwurfdekNaqt8qxgCC4E1VWDpWpdwzdecS+S4LiLi+tZb9SRn43yOabEOb42oaym2E9eQ1uctJqcIb7XSzHJI6488yK/RLOtjvth2rCe38Jwnv6gGpVd+JFGm7aqFptKoYyZxL/Gm+ZVX+D6t462lCCwEFoHAAgAAANiYwNpy2XBbhXXTodZQq6gcVqEKLPIJgbVpgeXXQJyXIVN8jCkXuCFkNI9Pi+smZItaJ+ax9HTOq1Vl/j6vz7et/CLWGYKoEktKr9IwlPZq+/m7+Fztw9BbCmO/83YlqauXVuSxrCS2tN28H0MDyyOwEFgEAgsAAAAAgbUnAit3a1rHtug+iMDaJoFFfjE9Qj5VY9qtcnurFN4ILAQWgcACAAAAQGDtkMBSl8Z1vBWw4+2DCCwE1s4L73WkK78gAoGFwCIQWAgsAAAAQGARBAILgbVlUXUx3IZQV8tVi+iqSyQCa80CiyC2KRBYAAAAgMAiCAQWAovYx0BgjQgsgG0BgQUAAAAILIJAYCGwCAQW9WEEFiCwAAAAABBYBIHAQmARCCwEFgACCwAAABBYBIHAQmAhsAgEFgKr56233jp6+eWXV7qNjz766Hgbr776KoYJgQUAAACAwCIIBNZJ8oqzs/j8LL5MfkE0BNbP9OfIvf35gsDadYF16dKl49dXXrt27fjzlStXjj8vi/vvv/+mHPvwww+PP8dfQGABAAAADAksgH0GgTWaV5yZxWdm8UUEFtEQWD/ZnyN39+cLAmvXBVbIqpBY4rHHHluawApRFevy1l3RAsu3BwgsAAAAAAQWwIkF1sU9E1gHs3hwFl/gLfREFbNz43/pz5G7+vMFgVUJrHffffdY3ERrI//79NNP35wnWjzFdyGMNF2toCSWYlrEI488cvxZ3/k6X3jhhdtaU8X8WsbXWXUddJmk5V1qRde/atsffPDB8bQsvCSt4hgoDfE371u09AIEFgAAAAACC+BEAuui6ld9vW2VIuvJLcsv7pnFp2fx/bP4oX7fH53Fl4m9jUf7c+EH+/Pinm57WjBut8AKYaXucpJAIX0iJKBEtFJymeTSSNP1OdbvksuFWKzX1+GfM5HJ5TGptF1Pl4RVEMLMtxvfx2ftZ+yzWlwpzXl8rbF0AQILAAAAEFgACKxRgXVTXql+19fnVsXlLcsz7uwFxQOz+N5ZfHYWn+tujHtE7Gd8rj8XHuzPizhHaH01RWD5WE9qjRStnkJgxTy5dZQvI5EkWaWWTbklk6RYHsvKuwO2WmHFMlp/S2CFaIo0Oy35FvPl71sCKx8fQGABAAAAAguBBTCXwLqtZ0svsVbVCuvqluUZB/2xOd/dGOcoZNa9fT5C7Gfc258Ld/XnxUFH66tpAivLIsmcEE5qyZTDW1flsaLyOn2Zal2K1hsGu2KsKxdYY+utlvMMtCWwdCyyPAMEFgAAACCwKoHVDTyU1ZAXQ0NnrFk83NKbIu778wPhXUD38oxfu5UC62I3MG7xilpine9W27pr2XkJsZ8ByxZY6u7n86jgXURg+ThV0SUw1uHRaunUFa2gPF1q9RUFbl5nq+WWdw1EYCGwAAAAAJYlsHw8WSfurZc1PMUyZI3ElY8vq/v0RdbVehiNwNpbgXVLt8ERibXMllgP75DAAoBlCSwZcY0jFaj7nQusXBC3BJZ3F5ynYJzShVACa4i4aYjtq5AbE1jqTonAQmABAAAATBVYXWMIirinXZbkWZbA8vvc+F+9L+Z5kZHqFAgsBFYSWJPPo265g7o/221fF0IAWLXAkqzS5xBVGldKUqlrdNNrySYfGD7WGSIrS7JMFKT+FsOcTt8XvUlQokyZpuYXGtQ9/qoFV1eM3RXrAwQWAAAAwBSBpfvnfA8pMZTvZfVW7rg/9vvQWE9852/5jvto3efGtFhHrDfuZ3V/7fPr5Ubea8Lvn7vGg1qlVS3JYrtKo+6x/d69eqO335tr+pDgUuu0zlqCufxQ2r0+kl/IJIHVqp/k6X68Yl+tO9st++j74n/9GHgdSf/HcduGFmobEFhf6ga6Da64O+GbyCuAPRVYyki8QPQC0psbTxVYkkcq/FRgeAafifnzTYAEVJcGcu+si6AK3ZYk64q3J+Y3LGZxBggsAAAAQGB1A4O4S3C0vtMwHVkw6X5X9+jq5aAXIUnoxHS/Z9U9ub7TdBdAnY1Jq/vulsDK28+9F/TwV+mvejPEPfY8D4a1vbhv9xc/+fHSGL0+LEmkZV6BlV8g5fsb3+V6hqbnt6xr+5KWXteINHlX0vh8Wnt1JIH1I92cLfiW3J3wendjDCwAOG0CS28ZzGIoj0cV87XGp6rGmao+twrHqRl5N7Ern9I/tk9j21fB6DcWgMACAAAAGBNYWfjkVlm5Z0CWKpUQ8jeDZ4HVFd0W84PqrvEwt3Uv7AIn7ofzUB0uKXJ6qzpGfjt4tb28vxJA1fHyh+7zCqxqfV2jxZcfS68XaHoINwm2oR4le9SFcGF5dcLuhPEGt8uzeJzsCeCUCqxdQk2Z19EiSoXUUKswQGABAAAAAqt1fx3yRcKpsy6AuQtbV7yVW7KnJZcqgVWN/9SlcWurMWWHWmC5wPGuiQptsxJu+q4beDt4TkvrwXG1rEurZQksPxYuIateK3mfYx7vYbJPw5D09/KXuwW6DQ51Ke3GuxNGSysN2H61o+UVAAJrW1Df+2VmjJvcDiCwAAAA4PQKrGix5EKl1Z2veiu3RMg8Aqtq/bOowMrDb+jeuBpTq5I56oJXjZO1SG+LqvWWthnHdRsElh+73EVxDwTWs6vY31jf2bNnf2227idTXE5ilFZXAAis7SMK9FX3HVeTZ7oOIrAAAAAAFhVYkiIaL8rvYcdkTrTeynLEu+W1uhDm++YutfqaKrB8kPRq3NkxgaXPeRDzMYHlbynXgPXr7EK4DIElQqztSyusVQqsg4OD17sbratyRBfDQ7IiAAQWAAILAAAAYAkCq6rYSy6FKPK3cruUkuSK4TP0UiWNcaWxWuN7HzNLEkjL5kHUK4EVrYViu/5ypvjOH+aqRVGkxefT9lxyqRWZ1hNpi/RouZbkkDSLv/4SKe2v3j4Y69Pb6rJAy5/92K1SYGmMs9hOzKtWWPn478FbCC+5hFwUXQ/dyQZzBwAEFgACCwAAABBYUwSWREs1rqpLn654K7d3JewaA7C7lLFK/y1vEBwTWF0xrlVOr4+BFeuVGOqspZM+S9Jo/NrOxoMaanmW31KeBdDQ8coCK46dpy+/pXEVLbB87LLW8d8DgbXJQdwBAIEFgMACAAAAWERgjQ1/oelD81Rv0h6iNRRGfF+JKR97a8q652GRYTkiHa20tI6Xvs/b8v3y6dVby/Px8d+uequipue0hnRrDYw/z++4wwLrR04isXrxiLwCQGAhsACBBQAAALAugQWwDySBda6/Pi7OI7GsxRzyCgAQWIDAAgAAAEBgAaxFYAWTW2IhrwAAgQUILAAAAAAEFsAmBNbFKRKLboMA0BRY77//PkFsRSCwAAAAAIEFcGoFlmh2J+xoeQUACCwCgQUAAACAwALYAoEV3NYSi5ZXAIDAIhBYAAAAAAgsgG0SWLd0J7RB2wEAEFgEAgsAAAAAgQWwFQLrFpHV0fIKABYVWO+9997Riy++eBy7JkF2Nd3LiNdff/3ojTfeQGABAAAAILAAdkFgdcgrADiRwHruueeOm3BKBnU3rHg5r77XfHl6CJXWsoeHh8fTNY/iiSeeOI5XXnllbgkylNbTHm+//fbxMQ0BicACAAAAQGAB7IDAAgBYTGCF/AgBpJY8LrCqlk0uiySkfPqjjz56PE9IMf/+pZdeurlsJbniu4ceemju1lTzCiwJs5OIl20SZjre0RoLgQUAAACwfoHF0BDEPgcCCwDWJrBCGD3zzDO3fA4h8tRTTx3/feedd5ryJuZx4RTzatkQK75cSKMhgSXJNUUuqcWWWo75ukLIRUuukGGRjlinpsX3MW+IN99O7EPMG8vEPK0WTdHiSfuh7cff+D5Lwfg+/sY8MT3SoeNVrT/mj+1rfp8Wx1X7HOvw5XUsY1kEFgAAAAACiyAQWABwKgVWiCZvRSWBpb9ZKLksCtnjokqCSK2tXLS4aGoJrJA3LtNyhMgJUSMJ1Vk3RJdqSremScJVAkst0OI7tWZqSbRKYHVFazM/LjGf1quIbXmLKU33fdN0F1Taduyjb0/LIbAAAAAAEFgEgcACgFMnsCqR5AIrJIqESqv7XGfd12I+zRvfS0Zl0ZPHwHKxM5Q5Slq1uhCGqIp1uxyK/711UhZUMb+3MpMAG+rK6GmQPNI2JcQk79RyqpVmyauWkFJ6cguvVvdMBBYAAAAAAosgEFgAcKoElloktQRWJVAqgSRR1VnroJBNEkd5nCYJLMkddZ/r+tZb84x3VX0X69eA9JG2nP4slGL+2G7Mr1ZOUwWW5JH2Ox9TtdRqdafsbLwxhUstHatYf6w7d+kcGzgfgQUAAACw/QJrbBzYqePExnzRa0AvTlo0Lbs4viqBwAKAUyywqjcJZoEVwsQHa68EkkRV/K83CUocaVwsb13VEi5q8VWNEaX1dCMtsLzrYFd0McwCS9vMMVVgqcVV12gxVb1dsRJYQ2n2gfW7YsB2BBYAAADAbgussXu5Kfd6eimS7h/nfTlS7vWQx2UlCAQWAGxMYI11IcyFZiW8JKqiJZJ31fMB3V1sjQmXrhhTamoLLBW2Po6Wui9WAkvp8KdTagk2VWD5etQay1uRaeD1VhfBqlvkUEiYefoQWAAAAAC7LbBOKrjyPe8i6/d7Yn/gShAILADYOYFVyRa1YAopkwdgj++0jHd9awkXfd962pPHi8qtn7LA8kHfqxZj2p63aNL6snTKxyK3Eot1als+TYO467vYN09z7uIY88W243t/+2AlE4da0iGwAAAAAHZHYOX7vXj4G9/pDdZD93q6v/SHybG8P0DWG7I1dEd+4BrLxz2r0qGH0a23cxMEAgsA1iqw1OXNC7eWwIrCq/W2O7WyyuMzSW7ldbUGcfc3Gg41jfZlPE25C6EK6s66BWqMKi3jn9XNUVKpNW5AV3Tz03Zy975Ig1qnKb25S6FLLckz3SxIaPn0vHxXtExDYAEAAADsjsDq0gNXvSk87vvG3jit+1+/D81dCP1eN+6ZY/1+T1nd+8Y97NAbwgkCgQUAaxVYUdD5Ex+9ya8aLFzTpn4f8iW+z09u9P2ig0tqYMr8v6dnnqdF1TrmDR9zIN9QaN3VMR07hr7+ahB3dVvMT+0QWAAAAAC7K7D8vnDsjdNd/zDT738rgeWtrmKa9zioBFZ83sV7TAKBBQCnVGBVBRqxWLPvatwsF1iriHiSFjEkxxBYAAAAAJsRWP5GwKF7wiywXEaNjXfaFa3xK4Hlw3RkOVUJrJjfX8REEAgsANi4wNrlVjxbkGEPjpm1SoGlsRF29dghsAAAAOC0C6y4X5NMmvKSII09Nc8bp6cKrKHWVZXAGht7iyAQWACwdoGlQmyVLYVOa4x1VYwnV6sa/HKV60ZgAQAAAJxcYC2jC6HGbF23wIrPU8aoJRBYCCwAWKvAIggEFgAAAMB2CCzJJ3+hz9CQEl3qIriIwMovCmIMLAKBBQAILIJAYAEAAAACqxRMEkedvYF6aCwqtdDyIS3mFVjR0qpLLbniBUX00CAQWACAwCIQWAgsAAAAQGDdFErVcBHV/9WQFmqF5W+49pf8VONbDa1zbNwtgkBgAQACi0BgAQAAAOyZwDppvP7660sTTiG3ovUVAotAYAHAJjg7i3sQWMQWCqxfnMVPz+JzcY725yoAAAAAAmsB8RQtsbzl1aLdGUNg7fLLgggEFgDsLmdmcTcCi9hCgfXULJ6YxWfiHO3PVQAAAAAE1oJdEU8qsJaxDgKBBQCwKAezuCsKWILYppidl/92Fj82iwfjHO3PVQAAAAAEFkEgsABgT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" width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><h3  class = 'S12' id = 'H_9fc3' ><span>Regions of interest (ROIs)</span></h3><div  class = 'S5' id = 'H_32ABACFC' ><span>ROIs can be added to a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> either as an image_mask </span><span style=' font-weight: bold;'>or</span><span> as a pixel_mask. An image mask is an array that is the same size as a single frame of the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/TwoPhotonSeries.html"><span style=' font-weight: bold; font-family: monospace;'>TwoPhotonSeries</span></a><span>, and indicates where a single region of interest is. This image mask may be boolean or continuous between 0 and 1. A pixel_mask, on the other hand, is a list of indices (i.e coordinates) and weights for the ROI. The pixel_mask is represented as a compound data type using a </span><a href = "https://nwb-schema.readthedocs.io/en/latest/format_description.html#tables-and-ragged-arrays"><span>ragged array</span></a><span> and below is an example demonstrating how to create either an image_mask or a pixel_mask. Changing the dropdown selection will update the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> object accordingly.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >selection = </span></span><span style="color: rgb(167, 9, 245);">"Create Image Mask"</span><span style="white-space: pre"><span >; </span><span style="color: #008013;">% "Create Image Mask" or "Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% generate fake image_mask data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imaging_shape = [100, 100];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >x = imaging_shape(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >y = imaging_shape(2);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >n_rois = 20;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >image_mask = zeros(y, x, n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >center = generateCenterCoords(90,2,n_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    image_mask(center(1,i):center(1,i)+10, center(2,i):center(2,i)+10, i) = 1;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >selection == </span><span style="color: #a709f5;">"Create Pixel Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    ind = find(image_mask);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    [y_ind, x_ind, roi_ind] = ind2sub(size(image_mask), ind);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct = struct();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.x = uint32(x_ind); </span><span style="color: #008013;">% Add x coordinates to struct field x</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.y = uint32(y_ind); </span><span style="color: #008013;">% Add y coordinates to struct field y</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_struct.weight = single(ones(size(x_ind))); </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel mask vector data</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask = types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, struct2table(pixel_mask_struct), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'pixel masks'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% When creating a pixel mask, it is also necessary to specify a</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% pixel_mask_index vector. See the documentation for ragged arrays linked</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% above to learn more.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    num_pixels_per_roi = zeros(n_rois, 1); </span><span style="color: #008013;">% Column vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i_roi = 1:n_rois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        num_pixels_per_roi(i_roi) = sum(roi_ind == i_roi);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = uint16(cumsum(num_pixels_per_roi)); </span><span style="color: #008013;">% Note: Use an integer </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% type that can accommodate the maximum value of the cumulative sum</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create pixel_mask_index vector</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    pixel_mask_index = types.hdmf_common.VectorIndex(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'Index into pixel_mask VectorData'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'target'</span><span >, types.untyped.ObjectView(pixel_mask) );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'roi pixel position (x,y) and pixel weight'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask_index'</span><span >, pixel_mask_index, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'pixel_mask'</span><span >, pixel_mask </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">else </span><span style="color: #008013;">% selection == "Create Image Mask"</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    plane_segmentation = types.core.PlaneSegmentation( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'colnames'</span><span >, {</span><span style="color: #a709f5;">'image_mask'</span><span >}, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'output from segmenting my favorite imaging plane'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'imaging_plane'</span><span >, types.untyped.SoftLink(imaging_plane), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #a709f5;">'image_mask'</span><span >, types.hdmf_common.VectorData(</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'data'</span><span >, image_mask, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >            </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'image masks'</span><span >) </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    );</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div><h3  class = 'S18' id = 'H_7312' ><span>Adding ROIs to NWB file </span></h3><div  class = 'S5'><span>Now create an </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ImageSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>ImageSegmentation</span></a><span> object and put the </span><span style=' font-family: monospace;'>plane_segmentation</span><span> object inside of it, naming it "</span><span style=' font-family: monospace;'>PlaneSegmentation"</span><span>.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >img_seg = types.core.ImageSegmentation();</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >img_seg.planesegmentation.set(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >, plane_segmentation);</span></span></div></div></div><div  class = 'S16'><span>Add the </span><span style=' font-family: monospace;'>img_seg</span><span> object to the "ophys" </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> we created before, naming it "</span><span style=' font-family: monospace;'>ImageSegmentation</span><span>".</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >, img_seg);</span></span></div></div></div><h3  class = 'S18' id = 'H_26e1' ><span>Storing fluorescence of ROIs over time</span></h3><div  class = 'S5' id = 'H_09424E2F' ><span>Now that ROIs are stored, you can store fluorescence data for these regions of interest. 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width = "958" height = "509" alt = "" style = "vertical-align: baseline; width: 958px; height: 509px;"></img></div><div  class = 'S5'><span>To create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object, we will need to reference a set of rows from the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table to indicate which ROIs correspond to which rows of your recorded data matrix. This is done using a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span>, which is a type of link that allows you to reference specific rows of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTable.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTable</span></a><span>, such as a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> </span><span>table by row indices.</span></div><div  class = 'S5'><span>First, we create a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/hdmf_common/DynamicTableRegion.html"><span style=' font-weight: bold; font-family: monospace;'>DynamicTableRegion</span></a><span> </span><span>that references the ROIs of the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/PlaneSegmentation.html"><span style=' font-weight: bold; font-family: monospace;'>PlaneSegmentation</span></a><span> table.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >roi_table_region = types.hdmf_common.DynamicTableRegion( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'table'</span><span >, types.untyped.ObjectView(plane_segmentation), </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'description'</span><span >, </span><span style="color: #a709f5;">'all_rois'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, (0:n_rois-1)');</span></span></div></div></div><div  class = 'S16'><span>Then we create a </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object to store fluorescence data for those ROIs.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >data = generateCalciumResponses(n_rois, 100); </span><span style="color: #008013;">% [nRoi, nT]</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >roi_response_series = types.core.RoiResponseSeries( </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'rois'</span><span >, roi_table_region, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data'</span><span >, data, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'data_unit'</span><span >, </span><span style="color: #a709f5;">'lumens'</span><span >, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time_rate'</span><span >, 3.0, </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >    </span><span style="color: #a709f5;">'starting_time'</span><span >, 0.0);</span></span></div></div></div><div  class = 'S16'><span>To help data analysis and visualization tools know that this</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object represents fluorescence data, we will store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> </span><span>object inside of a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object. Then we add the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>object into the same</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/ProcessingModule.html"><span style=' font-weight: bold; font-family: monospace;'>ProcessingModule</span></a><span> </span><span>named</span><span> </span><span style=' font-family: monospace;'>"ophys"</span><span> </span><span>that we created earlier.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >fluorescence = types.core.Fluorescence();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >fluorescence.roiresponseseries.set(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >, roi_response_series);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >ophys_module.nwbdatainterface.set(</span><span style="color: #a709f5;">'Fluorescence'</span><span >, fluorescence);</span></span></div></div></div><div  class = 'S16'><span style=' font-weight: bold;'>Tip</span><span>: If you want to store dF/F data instead of fluorescence data, then store the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/RoiResponseSeries.html"><span style=' font-weight: bold; font-family: monospace;'>RoiResponseSeries</span></a><span> object in a</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/DfOverF.html"><span style=' font-weight: bold; font-family: monospace;'>DfOverF</span></a><span> </span><span>object, which works the same way as the</span><span> </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/Fluorescence.html"><span style=' font-weight: bold; font-family: monospace;'>Fluorescence</span></a><span> </span><span>class.</span></div><h2  class = 'S14' id = 'H_7a45' ><span>Writing the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >nwb_file_name = </span><span style="color: #a709f5;">'ophys_tutorial.nwb'</span><span >;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">if </span><span >isfile(nwb_file_name); delete(nwb_file_name); </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span >nwbExport(nwb, nwb_file_name);</span></span></div></div></div><h2  class = 'S14' id = 'H_391c' ><span>Reading the NWB file</span></h2><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S17'><span style="white-space: pre"><span >read_nwb = nwbRead(nwb_file_name, </span><span style="color: #a709f5;">'ignorecache'</span><span >);</span></span></div></div></div><div  class = 'S16'><span>Data arrays are read passively from the file. Calling</span><span> </span><span style=' font-family: monospace;'>TimeSeries.data</span><span> does not read the data values, but presents an HDF5 object that can be indexed to read data. </span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >)</span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >    .roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsVariableStringElement" uid="B5CB2C7B" prevent-scroll="true" data-testid="output_1" tabindex="-1" style="width: 1137px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div class="textElement eoOutputContent" tabindex="-1" data-previous-available-width="1100" data-previous-scroll-height="123" data-hashorizontaloverflow="false" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="variable output " style="max-height: 261px; white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;"><span class="variableNameElement" style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">ans = </span></div><div style="white-space: pre; font-style: normal; color: rgb(33, 33, 33); font-size: 12px;">  DataStub with properties:
 
     filename: 'ophys_tutorial.nwb'
         path: '/processing/ophys/Fluorescence/RoiResponseSeries/data'
         dims: [20 100]
        ndims: 2
     dataType: 'double'
-</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signals = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for all RoIs</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signals' + (1:size(signals, 1)))</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(</span><span style="color: #a709f5;">'Fluorescence signals'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="25B0CDD2" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signal_subset = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data(1:5, 1:20);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for a subset of RoIs and timepoints</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signal_subset' + (1:size(signal_subset, 1)))</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="A16EF8BE" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first 10 RoIs in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >num_rois = plane_segmentation.id.data.dims(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Replace this with the correct image size if needed</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Example: imaging_shape = [512, 512];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >label_image = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:10</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% image_mask is assumed to be height x width x nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        image_mask_data = plane_segmentation.image_mask.data;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        roi_mask = image_mask_data(:,:,i) &gt; 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(roi_mask) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        row = plane_segmentation.getRow(i, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% pixel_mask is assumed to have fields x, y, and weight</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% Mark ROI pixels with the ROI index</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(ind) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Create one distinct color per ROI</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >cmap = lines(num_rois);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Convert label image to RGB</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >rgb_image = label2rgb(label_image, cmap, </span><span style="color: #a709f5;">'k'</span><span >, </span><span style="color: #a709f5;">'shuffle'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imshow(rgb_image)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(sprintf(</span><span style="color: #a709f5;">'First %d ROIs'</span><span >, 10))</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="9C6CF6E1" prevent-scroll="true" data-testid="output_4" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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NwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBGANFphGiHAATcE0B07iNkAxCAgEYA0WmEaIcABNwTQHTuI2QDEICARgDRaYRohwAE3BNAdO4jZAMQgIBG4H+WxyEAV9nUsAAAAABJRU5ErkJggg==" style="width: 314px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_389a' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_7f6d' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_6a4f' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_379a' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h1  class = 'S0' id = 'T_7f67' ><span>... Helper functions ...</span></h1><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >X = generateCenterCoords(varargin)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    X = randi(varargin{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >data = generateCalciumResponses(nRois, nTime)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">arguments</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nRois </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nTime </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data = zeros(nRois, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    eventProbability = 0.05;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    decayTimeConstant = 3;                 </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernelLength = 16;                     </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernel = exp(-(0:kernelLength-1) / decayTimeConstant);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i = 1:nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        spikeTrain = rand(1, nTime) &lt; eventProbability;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        response = conv(double(spikeTrain), kernel, </span><span style="color: #a709f5;">"same"</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        noise = 0.05 * randn(1, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        baseline = 0.1 * randn;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        data(i, :) = baseline + response + noise;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div>
+</div></div></div></div></div></div><div  class = 'S16'><span>This allows you to conveniently work with datasets that are too large to fit in RAM all at once. Access the data in the matrix using the </span><span style=' font-family: monospace;'>load</span><span> method. </span></div><div  class = 'S5'><span style=' font-family: monospace;'>load</span><span> with no input arguments reads the entire dataset:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signals = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >).nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >).data.load();</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for all RoIs</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signals' + (1:size(signals, 1)))</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(</span><span style="color: #a709f5;">'Fluorescence signals'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="FA35956B" prevent-scroll="true" data-testid="output_2" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>If all you need is a section of the data, you can read only that section by indexing the </span><span style=' font-family: monospace;'>DataStub</span><span> object like a normal array in MATLAB. This will just read the selected region from disk into RAM. This technique is particularly useful if you are dealing with a large dataset that is too big to fit entirely into your available RAM.</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >signal_subset = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'Fluorescence'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    roiresponseseries.get(</span><span style="color: #a709f5;">'RoiResponseSeries'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data(1:5, 1:20);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Plot signals for a subset of RoIs and timepoints</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >plot(signal_subset' + (1:size(signal_subset, 1)))</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >xlabel(</span><span style="color: #a709f5;">'timepoints'</span><span >); ylabel(</span><span style="color: #a709f5;">'rois'</span><span >)</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="A9F7ED42" prevent-scroll="true" data-testid="output_3" tabindex="-1" style="width: 1137px;"><div class="figureElement eoOutputContent" role="article" aria-roledescription="Use Browse Mode to explore " aria-description="figure output "><img class="figureImage figureContainingNode" 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" style="width: 560px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><div  class = 'S16'><span>Finally, read back the image/pixel masks and display the first 10 RoIs in a figure:</span></div><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span >plane_segmentation = read_nwb.processing.get(</span><span style="color: #a709f5;">'ophys'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    nwbdatainterface.get(</span><span style="color: #a709f5;">'ImageSegmentation'</span><span >). </span><span style="color: #0e00ff;">...</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    planesegmentation.get(</span><span style="color: #a709f5;">'PlaneSegmentation'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >num_rois = plane_segmentation.id.data.dims(1);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Replace this with the correct image size if needed</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #008013;">% Example: imaging_shape = [512, 512];</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >label_image = zeros(imaging_shape);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">for </span><span >i = 1:10</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">if </span><span >~isempty(plane_segmentation.image_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% image_mask is assumed to be height x width x nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        image_mask_data = plane_segmentation.image_mask.data;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        roi_mask = image_mask_data(:,:,i) &gt; 0;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(roi_mask) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">elseif </span><span >~isempty(plane_segmentation.pixel_mask)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        row = plane_segmentation.getRow(i, </span><span style="color: #a709f5;">'columns'</span><span >, {</span><span style="color: #a709f5;">'pixel_mask'</span><span >});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        pixel_mask = row.pixel_mask{1};</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% pixel_mask is assumed to have fields x, y, and weight</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        ind = sub2ind(imaging_shape, pixel_mask.y, pixel_mask.x);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        </span><span style="color: #008013;">% Mark ROI pixels with the ROI index</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        label_image(ind) = i;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >rgb_image = convertLabelImage2RGBImage(label_image); </span><span style="color: #008013;">% Local helper function</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >imshow(rgb_image)</span></span></div></div><div class="inlineWrapper outputs"><div  class = 'S10'><span style="white-space: pre"><span >title(sprintf(</span><span style="color: #a709f5;">'First %d ROIs'</span><span >, 10))</span></span></div><div  class = 'S11'><div class="inlineElement eoOutputWrapper disableDefaultGestureHandling embeddedOutputsFigure" uid="FB9D8DB9" 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" style="width: 314px;"></div><div class="outputLayer selectedOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer activeOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer scrollableOutputDecorationLayer doNotExport" aria-hidden="true"></div><div class="outputLayer navigationFocusLayer doNotExport" aria-hidden="false" tabindex="-1" role="application" aria-label="figure output "></div></div></div></div></div><h1  class = 'S19' id = 'T_08c6' ><span>Learn more!</span></h1><h3  class = 'S12' id = 'H_0f3d' ><span>See the </span><a href = "https://matnwb.readthedocs.io/en/latest/pages/neurodata_types/core/index.html"><span>API documentation</span></a><span> to learn what data types are available.</span></h3><h2  class = 'S4' id = 'H_80ac' ><span>Other MatNWB tutorials</span></h2><ul  class = 'S6'><li  class = 'S7'><a href = "./ecephys.html"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "./icephys.html"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><h2  class = 'S4' id = 'H_9f53' ><span>Python tutorials</span></h2><div  class = 'S5'><span>See our tutorials for more details about your data type:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ecephys.html#sphx-glr-tutorials-domain-ecephys-py"><span style=' text-decoration: underline;'>Extracellular electrophysiology</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/ophys.html#sphx-glr-tutorials-domain-ophys-py"><span style=' text-decoration: underline;'>Calcium imaging</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/domain/plot_icephys.html#intracellular-electrophysiology"><span style=' text-decoration: underline;'>Intracellular electrophysiology</span></a></li></ul><div  class = 'S5'><span style=' font-weight: bold;'>Check out other tutorials that teach advanced NWB topics:</span></div><ul  class = 'S6'><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/plot_iterative_write.html#sphx-glr-tutorials-advanced-io-plot-iterative-write-py"><span style=' text-decoration: underline;'>Iterative data write</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py"><span style=' text-decoration: underline;'>Extensions</span></a></li><li  class = 'S7'><a href = "https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py"><span style=' text-decoration: underline;'>Advanced HDF5 I/O</span></a></li></ul><h1  class = 'S0' id = 'T_1671' ><span>Helper functions</span></h1><div class="CodeBlock"><div class="inlineWrapper"><div  class = 'S8'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >X = generateCenterCoords(varargin)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    X = randi(varargin{:});</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >data = generateCalciumResponses(nRois, nTime)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">arguments</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nRois </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        nTime </span><span style="color: #a0522d;">(1,1) double {mustBeInteger, mustBePositive}</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Use reproducible random number generation for consistent </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% tutorial output and export.</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rndGeneratorState = rng(2, </span><span style="color: #a709f5;">'twister'</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rngCleanup = onCleanup(@() rng(rndGeneratorState));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    data = zeros(nRois, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    eventProbability = 0.05;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    decayTimeConstant = 3;                 </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernelLength = 16;                     </span><span style="color: #008013;">% samples</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    kernel = exp(-(0:kernelLength-1) / decayTimeConstant);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">for </span><span >i = 1:nRois</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        spikeTrain = rand(1, nTime) &lt; eventProbability;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        response = conv(double(spikeTrain), kernel, </span><span style="color: #a709f5;">"same"</span><span >);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        noise = 0.05 * randn(1, nTime);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        baseline = 0.1 * randn;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >        data(i, :) = baseline + response + noise;</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span style="color: #0e00ff;">function </span><span >rgbImage = convertLabelImage2RGBImage(labelImage)</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    numLabels = numel(unique(labelImage(labelImage&gt;0)));</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'>&nbsp;</div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Create one distinct color per label</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    cmap = lines(numLabels);</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    </span><span style="color: #008013;">% Convert label image to RGB</span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    map = [0 0 0; cmap]; </span><span style="color: #008013;">% black for background plus label colors </span></span></div></div><div class="inlineWrapper"><div  class = 'S9'><span style="white-space: pre"><span >    rgbImage = reshape(map(labelImage(:)+1, :), [size(labelImage), 3]);</span></span></div></div><div class="inlineWrapper"><div  class = 'S13'><span style="white-space: pre"><span style="color: #0e00ff;">end</span></span></div></div></div>
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 <!-- 
 ##### SOURCE BEGIN #####
@@ -543,11 +543,7 @@
     end
 end
 
-% Create one distinct color per ROI
-cmap = lines(num_rois);
-
-% Convert label image to RGB
-rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+rgb_image = convertLabelImage2RGBImage(label_image); % Local helper function
 
 imshow(rgb_image)
 title(sprintf('First %d ROIs', 10))
@@ -575,7 +571,7 @@
 % Extensions>
 % * <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py 
 % Advanced HDF5 I/O>
-%% ... Helper functions ...
+%% Helper functions
 
 function X = generateCenterCoords(varargin)
     % Use reproducible random number generation for consistent 
@@ -613,6 +609,17 @@
         data(i, :) = baseline + response + noise;
     end
 end
+
+function rgbImage = convertLabelImage2RGBImage(labelImage)
+    numLabels = numel(unique(labelImage(labelImage>0)));
+
+    % Create one distinct color per label
+    cmap = lines(numLabels);
+    
+    % Convert label image to RGB
+    map = [0 0 0; cmap]; % black for background plus label colors 
+    rgbImage = reshape(map(labelImage(:)+1, :), [size(labelImage), 3]);
+end
 ##### SOURCE END #####
 -->
 </div>
diff --git a/docs/source/_static/tutorials/media/ophys/figure_2.png b/docs/source/_static/tutorials/media/ophys/figure_2.png
index b0c1e0c1b2b82d7bae0d7ed78e037c5db8e9b34c..83f6fb4cdaef5cc2e0bb70ccd564b5b9a97abdd3 100644
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diff --git a/docs/source/pages/tutorials/ophys.rst b/docs/source/pages/tutorials/ophys.rst
index 8d3ec2a87..a0575fa49 100644
--- a/docs/source/pages/tutorials/ophys.rst
+++ b/docs/source/pages/tutorials/ophys.rst
@@ -503,11 +503,7 @@ Finally, read back the image/pixel masks and display the first 10 RoIs in a figu
        end
    end
    
-   % Create one distinct color per ROI
-   cmap = lines(num_rois);
-   
-   % Convert label image to RGB
-   rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+   rgb_image = convertLabelImage2RGBImage(label_image); % Local helper function
    
    imshow(rgb_image)
    title(sprintf('First %d ROIs', 10))
@@ -543,8 +539,8 @@ See our tutorials for more details about your data type:
 * `Extensions <https://pynwb.readthedocs.io/en/stable/tutorials/general/extensions.html#sphx-glr-tutorials-general-extensions-py>`_
 * `Advanced HDF5 I/O <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py>`_
 
-... Helper functions ...
-------------------------
+Helper functions
+----------------
 
 .. code-block:: matlab
 
@@ -584,3 +580,14 @@ See our tutorials for more details about your data type:
            data(i, :) = baseline + response + noise;
        end
    end
+   
+   function rgbImage = convertLabelImage2RGBImage(labelImage)
+       numLabels = numel(unique(labelImage(labelImage>0)));
+   
+       % Create one distinct color per label
+       cmap = lines(numLabels);
+       
+       % Convert label image to RGB
+       map = [0 0 0; cmap]; % black for background plus label colors 
+       rgbImage = reshape(map(labelImage(:)+1, :), [size(labelImage), 3]);
+   end
diff --git a/tutorials/ophys.mlx b/tutorials/ophys.mlx
index cb729567ba10c07073596439cdcb739eb214eb9f..bc6f7cc8eab271b3d3b51aa7df84d781f6ec0b71 100644
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diff --git a/tutorials/private/mcode/ophys.m b/tutorials/private/mcode/ophys.m
index e22a53345..2e81381e3 100644
--- a/tutorials/private/mcode/ophys.m
+++ b/tutorials/private/mcode/ophys.m
@@ -406,11 +406,7 @@
     end
 end
 
-% Create one distinct color per ROI
-cmap = lines(num_rois);
-
-% Convert label image to RGB
-rgb_image = label2rgb(label_image, cmap, 'k', 'shuffle');
+rgb_image = convertLabelImage2RGBImage(label_image); % Local helper function
 
 imshow(rgb_image)
 title(sprintf('First %d ROIs', 10))
@@ -438,7 +434,7 @@
 % Extensions>
 % * <https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/h5dataio.html#sphx-glr-tutorials-advanced-io-h5dataio-py 
 % Advanced HDF5 I/O>
-%% ... Helper functions ...
+%% Helper functions
 
 function X = generateCenterCoords(varargin)
     % Use reproducible random number generation for consistent 
@@ -475,4 +471,15 @@
 
         data(i, :) = baseline + response + noise;
     end
+end
+
+function rgbImage = convertLabelImage2RGBImage(labelImage)
+    numLabels = numel(unique(labelImage(labelImage>0)));
+
+    % Create one distinct color per label
+    cmap = lines(numLabels);
+    
+    % Convert label image to RGB
+    map = [0 0 0; cmap]; % black for background plus label colors 
+    rgbImage = reshape(map(labelImage(:)+1, :), [size(labelImage), 3]);
 end
\ No newline at end of file