diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 8077ae1..7ae2823 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -31,6 +31,7 @@ jobs:
         run: |
           python3 -m pip install --upgrade pip
           pip uninstall -y numpy || true
+          pip install numpy
           pip install --no-cache-dir -r requirements.txt
           pip install pytest flake8
           pip3 install -e .
@@ -40,8 +41,6 @@ jobs:
         run: |
           sudo apt-get update
           sudo apt-get install -y xvfb
-      - name: Install fonts
-        run: sudo apt-get install -y fonts-dejavu
       - name: Lint with flake8
         run: |
           # stop the build if there are Python syntax errors or undefined names
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..466821a
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+dist/
+man/_build/
+plot_phylo.egg-info/
+plot_phylo/__pycache__/
+tests/__pycache__/
+.coverage
\ No newline at end of file
diff --git a/_basic_tree.pickle b/_basic_tree.pickle
new file mode 100644
index 0000000..7dd594f
Binary files /dev/null and b/_basic_tree.pickle differ
diff --git a/_big_tree.pickle b/_big_tree.pickle
new file mode 100644
index 0000000..4e321c8
Binary files /dev/null and b/_big_tree.pickle differ
diff --git a/tests/test_objects/draw_tree_scale_bar_primates.pickle b/_primates.pickle
similarity index 100%
rename from tests/test_objects/draw_tree_scale_bar_primates.pickle
rename to _primates.pickle
diff --git a/examples/big_tree_collapse.nw b/examples/big_tree_collapse.nw
new file mode 100644
index 0000000..579f0e0
--- /dev/null
+++ b/examples/big_tree_collapse.nw
@@ -0,0 +1 @@
+(Eulemur_coronatus:0.022785,((((((Eulemur_fulvus_albifrons:0.001415,Eulemur_fulvus_sanfordi:0.001102):0.002176,(Eulemur_fulvus_fulvus:0.000606,Eulemur_fulvus_mayottensis:0.001497):0.003124):0.005568,(Eulemur_fulvus_albocollaris:0.003826,Eulemur_fulvus_collaris:0.00483):0.00545):0.008709,Eulemur_rubriventer:0.019121)0.71:0.001645,(Eulemur_fulvus_rufus:0.022882,Eulemur_mongoz:0.010986):0.01093):0.007215,(Eulemur_macaco_flavifrons:0.010375,Eulemur_macaco_macaco:0.007914):0.012571)0.74:0.003619,(((Hapalemur_aureus:0.02834,((((Hapalemur_griseus:0.000184,Hapalemur_griseus_meridionalis:0.002911):0.005525,Hapalemur_griseus_occidentalis:0.001983):0.003136,Hapalemur_griseus_alaotrensis:0.008788)0.9:0.001673,Hapalemur_griseus_griseus:0.002835):0.025442):0.014357,Hapalemur_simus:0.043406)0.98:0.006411,Lemur_catta:0.034858):0.068058):0.1;
diff --git a/man/pages/examples/collapse_after.png b/man/pages/examples/collapse_after.png
new file mode 100644
index 0000000..98d299f
Binary files /dev/null and b/man/pages/examples/collapse_after.png differ
diff --git a/man/pages/examples/collapse_before.png b/man/pages/examples/collapse_before.png
new file mode 100644
index 0000000..18aa25d
Binary files /dev/null and b/man/pages/examples/collapse_before.png differ
diff --git a/man/pages/functions.rst b/man/pages/functions.rst
index 3cb7f80..d740827 100644
--- a/man/pages/functions.rst
+++ b/man/pages/functions.rst
@@ -5,3 +5,18 @@ Functions
    :members:
    :undoc-members:
    :show-inheritance:
+
+.. automodule:: plot_phylo.draw_tree
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: plot_phylo.amend_tree
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: plot_phylo.get_boxes
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/man/pages/parameters.md b/man/pages/parameters.md
index 9cadde3..9366517 100644
--- a/man/pages/parameters.md
+++ b/man/pages/parameters.md
@@ -13,6 +13,7 @@ Detailed descriptions of all parameters are provided below.
 * [ypos](#ypos) - y-axis position
 * [height](#height) - tree height
 * [width](#width) - tree width
+* [auto_ax](#auto-ax) - determine axis limits automatically?
 
 *Visualisation options*
 
@@ -31,6 +32,7 @@ Detailed descriptions of all parameters are provided below.
 * [line_col](#line-col) - set line colour
 * [line_width](#line-width) - set line width
 * [bold](#bold) - highlight tip labels in bold
+* [collapse](#collapse) - collapse clades based on a string
 
 The primate tree used in these examples is from the [10K trees](https://10ktrees.nunn-lab.org/) project and is illustrative only.
 
@@ -139,7 +141,7 @@ height_val = 5
 width_val = 20
 
 # Run the plot_phylo function with these values
-results = plot_phylo.plot_phylo("examples/primates.nw", ax, xpos=xpos_val, ypos=ypos_val, height=height_val, width=width_val, show_axis=True, branch_lengths=False)
+results = plot_phylo.plot_phylo("examples/primates.nw", ax, xpos=xpos_val, ypos=ypos_val, height=height_val, width=width_val, show_axis=True, branch_lengths=False, auto_ax=False)
 
 # Annotate these points on the tree using matplotlib functions
 # Mark the bottom left corner
@@ -187,9 +189,14 @@ Desired height of the tree, in axis units. Regardless of the height of the axis,
 
 ### `width`
 (`float`, Default 10)
-
 Desired width of the tree, in axis units. Default 10.
 
+### `auto-ax`
+(`bool`, Default True)
+
+If True, axis limits are determined automatically to best visualise the tree. If False, the user should determine the axis limits directly using matplotlib functionality. Axis limits can still be adjusted later if auto_ax is True.
+
+
 ## Visualisation Options
 ### `show_axis`
 (`bool`, Default False)
@@ -370,3 +377,40 @@ plt.savefig("examples/bold.png", bbox_inches='tight')
 ```
   
 ![Bold](./examples/bold.png "Bold")
+
+### `collapse`
+(`list`, Default `[]`)
+### `collapse_names`
+(`list`, Default `[]`)
+
+Two parameters - collapse and collapse_names are used to allow monophyletic groups to be collapsed, if all tip labels within that clade contain a specific string.
+The parameter `collapse` should be a list of strings to look for e.g.
+`['Saimiri', 'Callithrix']`
+
+The parameter `collapse_names` is a list, in the same order, of the new names to give to the
+collapsed groups, e.g. `['Saimiri species', 'Callithrix species']`.
+
+With the default settings plus `outgroup='Lemur_catta'`
+
+```
+f = plt.figure(figsize=(10, 8))
+ax = f.add_subplot()
+plot_phylo.plot_phylo("%s/examples/big_tree_collapse.nw" % path, ax, outgroup='Lemur_catta')
+plt.savefig("examples/collapse_before.png", dpi=300)
+```
+![Collapse before](./examples/collapse_before.png "Collapse before")
+
+
+With `collapse=['Eulemur', 'Hapalemur'], collapse_names=['Hapalemur species', 'Eulemur species'], outgroup='Lemur_catta'`
+```
+f = plt.figure(figsize=(10, 8))
+ax = f.add_subplot()
+plot_phylo.plot_phylo("%s/examples/big_tree_collapse.nw" % path, ax,
+                        outgroup='Lemur_catta',
+                        collapse=['Eulemur', 'Hapalemur'],
+                        collapse_names=['Eulemur species', 'Hapalemur species'])
+plt.savefig("examples/collapse_after.png", dpi=300)
+```
+
+![Collapse after](./examples/collapse_after.png "Collapse after")
+
diff --git a/plot_phylo/amend_tree.py b/plot_phylo/amend_tree.py
new file mode 100755
index 0000000..b83baab
--- /dev/null
+++ b/plot_phylo/amend_tree.py
@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+try:
+    from plot_phylo import get_boxes
+except ImportError:
+    import get_boxes
+
+
+def auto_axis(ax, textobj, xpos, ypos, width, height, depth, scale_bar,
+              branch_lengths, reverse):
+    """
+    Adjust the axis limits around the tree automatically (rather than
+    using user definted values).
+
+    Parameters
+    ----------
+    ax : matplotlib.axes._axes.Axes
+        An open matplotlib ax object
+    textobj: list of matplotlib.text.Text
+        List of text objects containing tip labels, used to ensure the
+        labels are within the limits of the plot
+    xpos : float
+        User defined desired position of the root node of the tree on the x
+        axis of ax, in axis units.
+    ypos : float
+        User defined desired position of the bottom of the tree on the y
+        axis of ax, in axis units.
+    height : float
+        User defined desired height of the tree, in axis units. Default 10.
+    width : float
+        User defined desired width of the tree, in axis units. Default 10.
+    depth: tuple(float, float, float)
+        Total height and width of the original tree in terms of number of
+        nodes, total branch length, number of tips
+    scale_bar: bool
+        User defined - True to draw a scale bar, else False
+    branch_lengths: bool
+        User defined - True to use true branch lengths, False to use
+        fixed branch lengths
+    reverse: bool
+        If True, the tree is reversed on the y-axis,
+        showing the root on the right
+        hand side. Default False.
+
+    Returns
+    -------
+    cboxes: dict
+        Dictionary where keys are tip labels and values are the boundary
+        boxes of the tip labels on the plot
+    ax: matplotlib.axes._axes.Axes
+        A modified open matplotlib ax object
+    """
+    xint = width * 0.01
+    yint = (height / depth[2])
+
+    nboxes = 0
+    cboxes = 1
+
+    # mpl adjusts the positions of the plot elments when the axis limits are
+    # changed - keep updating until the boxes stop moving
+    while nboxes != cboxes:
+        nboxes = get_boxes.get_boxes(ax, textobj)
+        if not reverse:
+            xmin = xpos - xint
+            xmaxes = [nboxes[x]['xmax'] for x in nboxes]
+            xmax = max(xmaxes) + xint
+        else:
+            xmins = [nboxes[x]['xmin'] for x in nboxes]
+            xmin = min(xmins)
+            xmax = -xpos + width + xint
+        ymaxes = [nboxes[y]['ymax'] for y in nboxes]
+        ymax = max(ymaxes)
+        ymins = [nboxes[y]['ymin'] for y in nboxes]
+        ymin = min(ymins)
+
+        if scale_bar:
+            ymin -= yint
+        ax.set_xlim(min(xmin, xmax)-xint, max(xmin, xmax)+xint)
+        ax.set_ylim(min(ymin, ymax), max(ymin, ymax))
+        cboxes = get_boxes.get_boxes(ax, textobj)
+    ax.set_autoscale_on(False)
+    return (cboxes, ax)
+
+
+def draw_scale_bar(ax, width, height, depth, left, bottom,
+                   scale_bar_width=None,
+                   appearance={'font_size': 10}):
+    '''
+    Adds a scale bar to the tree - only when branch lengths are specified.
+
+    The default length is 0.25 * total tree width, or it can be specified
+    using scale_bar_width.
+
+    Parameters
+    ----------
+    ax : matplotlib.axes._axes.Axes
+        An open matplotlib ax objectmatplotlib.axes._axes.Axes
+        An open matplotlib ax object
+
+    height : float
+        Height of the tree, in axis units.
+    width : float
+        Width of the tree, in axis units.
+    depth: tuple(float, float, float)
+        Total height and width of the original tree in terms of number of
+        nodes, total branch length, number of tips
+    bottom: float
+        The bottom position of the tree on the y axis
+    scale_bar_width: float
+        The desired width of the scale bar, if not specified
+        tree width * 0.25 is used
+    appearance: dict
+        Dictionary of parameters specifying the appearance of the tree.
+    '''
+    # depth[1] - total width of the tree in tree units
+    # width - total width of tree in axis units
+    # xint - width of one tree unit in axis units
+    xint = width / depth[1]
+
+    # Distance from x axis to start the scale bar
+    # 10% of total tree width
+    interx = width * 0.1
+
+    # Distance on y axis to extend the bracket ends on the scale bar
+    # 10% of the height of one node
+    intery = (height / depth[2]) * 0.1
+    bottom -= (height / depth[2])
+    # scale_bar_width = total width of scale bar in axis units
+    if not scale_bar_width:
+        scale_bar_width = width * 0.25
+
+    # Convert the scale bar width to tree units
+    scale = scale_bar_width / xint
+
+    # Draw the horizontal line
+    ax.plot([left+interx, left+interx+scale_bar_width],
+            [bottom, bottom], color='black')
+
+    # Bracket the ends of the scale bar with small vertical lines
+    ax.plot([left+interx, left+interx],
+            [bottom-intery, bottom+intery], color='black')
+    ax.plot([left+interx+scale_bar_width, left+interx+scale_bar_width],
+            [bottom-intery, bottom+intery], color='black')
+
+    # Add the scale text
+    ax.text(left+interx+(scale_bar_width/2), bottom+intery, "%.3f" % scale,
+            va='bottom', ha='center', fontsize=appearance['font_size']-2)
+
+
+def reverse_align(ax, ps, reverse):
+    '''
+    Realigns the text in the tip labels so that for a standard tree, the
+    text is right aligned, for a mirrored tree (root on the right), the
+    text is left aligned. Only used when tip labels are set to be aligned.
+
+    This has to happen once the whole plot is drawn as the limits
+    of the text only exist once the text exists.
+
+    Parameters
+    ----------
+    ax : matplotlib.axes._axes.Axes
+        An open matplotlib ax object
+    ps : list
+        List of lists - ordered as tip labels, tip label text objects,
+        alignment lines (if aligned). All are in the same order.
+    reverse: bool
+        If True, reverse the tree on the y-axis, showing the root on the right
+        hand side. Default False.
+
+    Returns
+    -------
+    ps_new : list
+        List of lists - ordered as tip labels, tip label text objects,
+        alignment lines (if aligned). All are in the same order. Updated
+        based on new alignment.
+    '''
+    x_extremes = []
+    ys = []
+    # Used to determine which x co-ordinates to take depending on the
+    # tree orientation
+    if reverse:
+        indi = 0
+    else:
+        indi = 1
+    for pnam, ptext, pline in ps:
+        # Get the data units of the box which encloses the text
+        box = ax.transData.inverted().transform(
+            ptext.get_window_extent(renderer=ax.figure.canvas.get_renderer()))
+        # Get the rightmost point of the text box (regular tree)
+        # or the leftmost (reversed tree)
+        x_extreme = box[indi][0]
+        x_extremes.append(x_extreme)
+
+        # Store the y axis positions
+        ys.append(box[1][1])
+
+    if not reverse:
+        # For right alignment, take the rightmost x point
+        maxi = max(x_extremes)
+    else:
+        # For left alignment, take the leftmost x point
+        maxi = min(x_extremes)
+    alis = ['left', 'right']
+
+    ps_new = []
+    for i, p in enumerate(ps):
+        # Move the text to the right position
+        p[1].set_position([maxi, ys[i]])
+
+        # Get the current left end of the dotted alignment line
+        oldline = p[2][0].get_xdata()[0]
+
+        # Left or right align the text
+        p[1].set_horizontalalignment(alis[indi])
+
+        # Get the updated text position limits
+        pbox = ax.transData.inverted().transform(p[1].get_window_extent(
+            renderer=ax.figure.canvas.get_renderer()))
+
+        # Move the dotted line to hit the new text position
+        # int(not indi) swaps 0 for 1
+        p[2][0].set_xdata([pbox[int(not indi)][0], oldline])
+
+        # Set the y axis position
+        p[1].set_verticalalignment('center')
+
+        # Store the new values
+        ps_new.append(p)
+    return (ps_new)
diff --git a/plot_phylo/draw_tree.py b/plot_phylo/draw_tree.py
new file mode 100755
index 0000000..bc497b8
--- /dev/null
+++ b/plot_phylo/draw_tree.py
@@ -0,0 +1,446 @@
+#!/usr/bin/env python3
+import statistics
+
+
+def add_leaf(tree, ax, ps,
+             posi,
+             appearance,
+             structure,
+             collapse, collapseD, countD, dots=False):
+    '''
+    Function to position and label the leaves of the tree.
+
+    Parameters
+    ----------
+    tree : ete3.Tree
+        ete3 Tree object
+    ax : matplotlib.axes._axes.Axes
+        An open matplotlib ax object
+    ps: list
+        Used internally, list of tip labels and x and y positions
+    posi: dict
+        Dictionary with position parameters
+    appearance: dict
+        Dictionary with appearance parameters
+    structure: dict
+        Dictionary with structure parameters
+    collapse: list
+        List of strings to search for in the tip labels of monophyletic clades
+        and collapse if the string is found in all labels
+    collapseD: dict
+        Dictionary showing which nodes to collapse and the replacement
+        names
+    countD: dict
+        Dictionary of how many leaves are represented by collapsed nodes
+    dots: bool
+        Not yet implemented
+
+    Returns
+    -------
+    y+posi['yint']: int
+        The y-axis position of the leaf
+    y: int
+        Previous position on y axis
+    ps: list
+        Used internally, list of tip labels and x and y positions
+    '''
+    x = posi['x']
+    y = posi['y']
+
+    # Position of the node tip
+    x_tip_pos = x - (posi['xint'] * posi['td'])
+    xmax = (posi['tot_width'] * posi['xint'])
+
+    # x_ali_pos is used to align the tips if align_tips is specified
+    # x_text_pos is the position of the text - if the tips are aligned
+    # the text is also aligned
+    if structure['align_tips'] or structure['rev_align_tips']:
+        x_ali_pos = (posi['tot_width'] * posi['xint']) + posi['x0'] + 1
+        x_text_pos = x_ali_pos
+    else:
+        x_ali_pos = None
+        x_text_pos = x
+    if structure['reverse']:
+        x_tip_pos = xmax - x_tip_pos
+        x_text_pos = xmax - x_text_pos
+        if x_ali_pos is not None:
+            x_ali_pos = xmax - x_ali_pos
+        x = xmax - x
+        hali = 'right'
+    else:
+        hali = 'left'
+    # Plot the tip label
+    if tree.name in appearance['bold']:
+        bold = 'bold'
+    else:
+        bold = 'normal'
+    adj = 0
+    if 'COLLAPSE|' not in tree.name:
+        for c in collapse:
+            tree.name = tree.name.removesuffix(c)
+        texti = appearance['label_dict'][tree.name]
+        # Plot the branch to the tip
+        if dots:
+            ax.scatter(x, -y, s=10,
+                       color=appearance['col_dict'][tree.name])
+        ax.plot([x, x_tip_pos], [-posi['y'], -posi['y']],
+                color=appearance['line_col'],
+                lw=appearance['line_width'])
+
+    else:
+        count = countD[tree.name]
+        texti = f"{collapseD[tree.name]} | {count} Seq"
+        if countD[tree.name] == 1:
+            if dots:
+                ax.scatter(x, -y, s=10,
+                           color=appearance['col_dict'][tree.name])
+            ax.plot([x, x_tip_pos], [-y, -y],
+                    color=appearance['line_col'],
+                    lw=appearance['line_width'])
+        else:
+            texti += "s"
+            if dots:
+                y += posi['yint'] * (count * 0.01)
+                markersize = 10 * count
+                xcent, adj = adjust_dots(ax, x, -y, markersize)
+                ax.scatter(xcent, -y, s=markersize,
+                           color=appearance['col_dict'][tree.name])
+                ax.plot([x, x_tip_pos], [-y, -y],
+                        color=appearance['line_col'],
+                        lw=appearance['line_width'])
+
+            else:
+                yyy = (posi['yint'] / 2) * 0.8
+                if structure['branch_lengths']:
+                    xxx = posi['xint'] * 0.4
+                else:
+                    xxx = posi['xint'] * 0.2
+                plot_kwargs = {'color': appearance['line_col'],
+                               'lw': appearance['line_width'],
+                               'solid_capstyle': 'butt'}
+
+                if not structure['branch_lengths']:
+                    ax.plot([x, x_tip_pos+xxx], [-y+yyy, -y], **plot_kwargs)
+                    ax.plot([x, x_tip_pos+xxx], [-y-yyy, -y], **plot_kwargs)
+                    ax.plot([x_tip_pos, x_tip_pos+xxx], **plot_kwargs)
+                    ax.plot([x, x], [-y+yyy, -y-yyy], **plot_kwargs)
+                else:
+                    ax.plot([x, x_tip_pos], [-y+yyy, -y], **plot_kwargs)
+                    ax.plot([x, x_tip_pos], [-y-yyy, -y], **plot_kwargs)
+                    ax.plot([x_tip_pos, x_tip_pos], [-y, -y], **plot_kwargs)
+                    ax.plot([x, x], [-y+yyy, -y-yyy], **plot_kwargs)
+
+    textpos = ax.text(x_text_pos+(adj*1.75), -y,
+                      "  %s  " % texti,
+                      color=appearance['col_dict'][tree.name],
+                      fontsize=appearance['font_size'],
+                      va='center', ha=hali, fontweight=bold)
+    # Add an extra line to the aligned tips if align_tips is specified
+    if structure['align_tips'] or structure['rev_align_tips']:
+        line = ax.plot([x, x_ali_pos], [-y, -y],
+                       color=appearance['line_col'], alpha=0.2,
+                       ls="--",
+                       lw=appearance['line_width'])
+        ps.append([tree.name, textpos, line])
+    else:
+        ps.append([tree.name, textpos])
+
+    # Store the tip label and the position of the tip on the x and y axis
+
+    return (y+posi['yint'], y, ps)
+
+
+def draw_tree(tree, ax,
+              x=0,
+              y=0,
+              x0=0,
+              ps=[],
+              dims={'width': 10,
+                    'height': 10,
+                    'depth': [5, 5, 5]},
+              structure={'align_tips': False,
+                         'rev_align_tips': False,
+                         'branch_lengths': True,
+                         'reverse': False},
+              appearance={'col_dict': {},
+                          'label_dict': {},
+                          'font_size': 10,
+                          'line_col': 'black',
+                          'line_width': 1,
+                          'show_support': True,
+                          'bold': []},
+              collapse=[],
+              collapseD=dict(),
+              countD=dict(),
+              dots=False):
+    '''
+    Plot a phylogenetic tree in matplotlib
+
+    Parameters
+    ----------
+    tree : ete3.Tree
+        ete3 Tree object
+    ax : matplotlib.axes._axes.Axes
+        An open matplotlib ax object
+    x : float
+        Current position on x axis.
+    y : float
+        Current position on y axis.
+    ps: list
+        Used internally, list of tip labels and x and y positions
+    height: float
+        Height of the tree in axis units
+    width: float
+        Width of the tree in axis units
+    depth: tuple(float, float, float)
+        Total height and width of the original tree in terms of number of
+        nodes, total branch length, number of tips
+    align_tips: bool
+        If True, the tip labels will be aligned rather than positioned at
+        the end of the branches. Default False.
+    rev_align_tips: bool
+        If True  the tip labels are right aligned
+        if reverse=False and left aligned if reverse=True.
+    branch_lengths: bool
+        If True, use the branch lengths provided in the tree, otherwise fix
+        all branches to the same length. Default True.
+    reverse: bool
+        If True, reverse the tree on the y-axis, showing the root on the right
+        hand side. Default False.
+    appearance: dict
+        Dictionary of parameters specifying the appearance of the tree.
+    collapse: list
+        Collapse nodes where possible based on strings in the list.
+    collapseD:
+        Dictionary where keys are the edited names of the leafs representing
+        collapsed nodes and values are the new name this collapsed node
+        should be given in the tree.
+
+    Returns
+    -------
+    y   float
+        Position of previous node on y axis.
+    ym  float
+        Position of current node on y axis.
+    ps  list
+        List of lists - ordered as tip labels, tip label text objects,
+        alignment lines (if aligned). All are in the same order.
+
+    '''
+
+    # This is the increment for the position of each terminal node on
+    # the y axis.
+    # The number of nodes - 1 is used because one branch will be at position 0
+    yint = dims['height'] / (dims['depth'][2] - 1)
+
+    # td is the branch length - if the branch_lengths parameter is False
+    # it is set to 1
+
+    # If the branch lengths are used, the total width of the tree is in
+    # the tree branch units, otherwise it's the total number of nodes from
+    # the root to the farthest branch
+
+    # textinc stops the tip labels from being immediately next to the tips
+
+    if structure['branch_lengths']:
+        td = tree.dist
+        tot_width = dims['depth'][1]
+
+    else:
+        td = 1
+        tot_width = dims['depth'][0] + 1
+
+    # This interval is used to scale the interval for each node
+    # so the total tree width matches the value specified
+    xint = dims['width'] / tot_width
+
+    posi = {'x': x,
+            'y': y,
+            'x0': x0,
+            'xint': xint,
+            'yint': yint,
+            'td': td,
+            'tot_width': tot_width}
+
+    if tree.is_leaf():
+        return add_leaf(tree, ax, ps,
+                        posi, appearance, structure, collapse, collapseD,
+                        countD, dots)
+
+    else:
+        # This section draws the lines for the non-terminal nodes
+
+        # For each tree, all of the children are visited and the function
+        # is recursively called
+        for c in tree.children:
+            # Scale by the branch length if branch_lengths is specified
+            if structure['branch_lengths']:
+                tdc = c.dist
+            else:
+                tdc = 1
+
+            # The position of the node on the x axis
+            x_vert_pos = x + (tdc * xint)
+
+            # returns y - the bottom position of all the labels so far
+            # cym - the middle of the previous node on the y axis
+
+            y, cym, ps = draw_tree(c, ax, x_vert_pos, y,
+                                   x0=x0, ps=ps,
+                                   dims=dims,
+                                   structure=structure,
+                                   appearance=appearance,
+                                   collapse=collapse,
+                                   collapseD=collapseD,
+                                   countD=countD,
+                                   dots=dots)
+
+            # y1 and y2 are the top and bottom positions of the current
+            # child node on the y axis, respectively
+            # Gives the extent of the vertical line for this segment
+
+            if c is tree.children[0]:
+                y1 = cym
+            elif c is tree.children[-1]:
+
+                y2 = cym
+        # midpoint of node on y axis
+        ym = (y1 + y2)/2
+
+        if structure['reverse']:
+            xmax = (tot_width * xint)
+            x = xmax - x
+
+        # Draw the lines
+        # Vertical line
+        ax.plot([x, x], [-y1, -y2],
+                color=appearance['line_col'],
+                lw=appearance['line_width'])
+
+        # Horizontal line - each node draws the one line that
+        # projects towards its parent so x is the position of the
+        # vertical line and x-(td*xint) is one increment back
+        # towards the root
+
+        if not structure['reverse']:
+            ax.plot([x, x-(td*xint)], [-ym, -ym],
+                    color=appearance['line_col'],
+                    lw=appearance['line_width'])
+        else:
+            ax.plot([x, x+(td*xint)], [-ym, -ym],
+                    color=appearance['line_col'],
+                    lw=appearance['line_width'])
+
+        # Add branch support if specified
+        # TODO - currently lands on top of the branches if branch_lengths
+        # is switched on
+        if appearance['show_support']:
+            if not structure['reverse']:
+                if tree.support == 1:
+                    ax.text(x, -ym, " %i" % tree.support, ha='left',
+                            va='center', fontsize=appearance['font_size']-2,
+                            color='#7c7c7c')
+                else:
+                    ax.text(x, -ym, " %.2f" % tree.support, ha='left',
+                            va='center', fontsize=appearance['font_size']-2,
+                            color='#7c7c7c')
+            else:
+                ax.text(x, -ym, "%.2f " % tree.support, ha='right',
+                        va='center', fontsize=appearance['font_size']-2)
+
+        return (y, ym, ps)
+
+
+def collapse_nodes(tree, collapse_list, collapse_names):
+    '''
+    Function to collapse monophyletiic groups in which all tip labels contain
+    a particular strings. Works by replacing the node with one of its
+    leaves and setting the branch length to the mean for the clade.
+
+    Parameters
+    ----------
+    tree : ete3.Tree
+        ete3 Tree object
+    collapse_list: list
+        List of strings to search for in the tip labels of monophyletic clades
+        and collapse if the string is found in all labels
+    collapse_names: list
+        Names, in the same order as collapse, for the collapsed nodes from
+        the collapse list
+
+    Returns
+    -------
+    tree: ete3.Tree
+        The edited tree object with COLLAPSE in the relevant leaf names
+    collapseD: dict
+        Dictionary where keys are the edited names of the leafs representing
+        collapsed nodes and values are the new name this collapsed node
+        should be given in the tree.
+    countD: dict
+        Dictionary showing how many leaves are represented by each collapsed
+        node
+
+    '''
+
+    cD = dict(zip(collapse_list, collapse_names))
+    collapseD = dict()
+    countD = dict()
+    for string in collapse_list:
+        keeps = set()
+        collapsed = set()
+        done = set()
+        ddD = dict()
+        xD = dict()
+        for node in tree.traverse():
+            x = 0
+            # Get all the leaf names
+            L = list(node.get_leaves())
+            dd = []
+            # Track which have the string and their branch lengths
+            for leaf in L:
+                if string in leaf.name and leaf not in done:
+                    dd.append(leaf.dist)
+                    x += 1
+            # If all the leaves have the string
+            if x == len(L) or (len(L) == 1 and leaf not in done):
+                keeps.add(L[0].name)
+                done = done | set(L)
+                if x > 0:
+                    # Pick one leaf
+                    collapsed.add(L[0])
+                    # Find the mean branch length
+                    ddD[L[0]] = statistics.mean(dd)
+                    xD[L[0].name] = x
+        # Prune to keep one leaf from each collapsed clade
+        tree.prune(keeps)
+
+        # Make the new collapsed branches
+        for leaf in tree.get_leaves():
+            if leaf in collapsed:
+                leaf.dist = ddD[leaf]
+                pnam = leaf.name
+                # Rename as COLLAPSE plus the original name
+                leaf.name = 'COLLAPSE|%s' % (leaf.name)
+                collapseD[leaf.name] = cD[string]
+                countD[leaf.name] = xD[pnam]
+
+    return (tree, collapseD, countD)
+
+
+def adjust_dots(ax, xpos, ypos, markersize):
+    '''
+    Currently not fully implemented
+
+    '''
+
+    fig = ax.figure
+    fig.canvas.draw()
+    adj = (markersize ** 0.5) * 2
+    display_coords = ax.transData.transform((xpos, ypos))
+    centre = display_coords + [adj, 0]
+    rcoords = ax.transData.transform((0, 0))
+    adj_cent = rcoords + [adj, 0]
+    # Convert back to data coords
+    x_centre, y_centre = ax.transData.inverted().transform(centre)
+    adj_new, y_new = ax.transData.inverted().transform(adj_cent)
+    return (x_centre, adj_new)
diff --git a/plot_phylo/get_boxes.py b/plot_phylo/get_boxes.py
new file mode 100755
index 0000000..0cab3bb
--- /dev/null
+++ b/plot_phylo/get_boxes.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+
+def get_boxes(ax, texts):
+    '''
+    Converts a list of text objects to their co-ordinates on the axis in
+    axis units.
+
+    Parameters
+    ----------
+    ax : matplotlib.axes._axes.Axes,
+        An open matplotlib ax object where the tree will be plotted.
+    texts : list
+        A list of matplotlib.pyplot.text objects representing the tip
+        labels from the tree and their metadata.
+
+    Returns
+    -------
+    boxpos : dict
+        A dictionary of dictionaries where the top level keys are the tip
+        labels. Within each subdictionary the key value pairs are
+        index (position in the tree starting from the top) and the minimum,
+        maximum and central position of the text box on the x and y axis,
+        as index, xmin, xmid, xmax and ymin, ymid and ymax.
+
+
+    '''
+    boxpos = dict()
+    # Iterate through the tip labels
+    for i, txt in enumerate(texts):
+        # Get the position of the boxes containing the labels, convert to axis
+        # units
+        box = ax.transData.inverted().transform(txt.get_window_extent(
+            ax.figure.canvas.get_renderer()))
+        nam = txt.get_text().strip()
+        # Build the dictionary
+        boxpos[nam] = dict()
+        boxpos[nam]['index'] = i
+        boxpos[nam]['xmin'] = box[0][0].round(3)
+        boxpos[nam]['xmax'] = box[1][0].round(3)
+        boxpos[nam]['ymin'] = box[0][1].round(3)
+        boxpos[nam]['ymax'] = box[1][1].round(3)
+        boxpos[nam]['ymid'] = (box[0][1] + ((
+            box[1][1] - box[0][1]) / 2)).round(3)
+        boxpos[nam]['xmid'] = (
+            box[0][0] + ((box[1][0] - box[0][1]) / 2)).round(3)
+
+    return (boxpos)
diff --git a/plot_phylo/plot_phylo.py b/plot_phylo/plot_phylo.py
index 526aaa1..50a5d94 100755
--- a/plot_phylo/plot_phylo.py
+++ b/plot_phylo/plot_phylo.py
@@ -1,5 +1,11 @@
 #!/usr/bin/env python3
 import ete3
+try:
+    from plot_phylo import draw_tree, amend_tree, get_boxes
+except ImportError:
+    import draw_tree
+    import amend_tree
+    import get_boxes
 
 
 def plot_phylo(tree, ax,
@@ -21,14 +27,18 @@ def plot_phylo(tree, ax,
                font_size=10,
                line_col='black',
                line_width=1,
-               bold=[]):
+               bold=[],
+               collapse=[],
+               collapse_names=[],
+               auto_ax=True,
+               dots=False):
     '''
     Parameters
     ----------
     tree : str
         Either the path to a newick formatted tree or a string containing
         a newick formatted tree. Required.
-    ax : matplotlib.axes._axes.Axes,
+    ax : matplotlib.axes._axes.Axes
         An open matplotlib ax object where the tree will be plotted. Required.
     xpos : float
         Desired position of the root node of the tree on the x axis of ax,
@@ -64,8 +74,8 @@ def plot_phylo(tree, ax,
         If True, reverse the tree on the y-axis, showing the root on the right
         hand side. Default False.
     outgroup: str
-        Leaf to use as an outgroup, must be identical to the name of the
-        leaf in the tree file.
+        Leaf to use as an outgroup, must be identical to the
+        name of the leaf in the tree file.
     col_dict : dict
         User provided dictionary with tip labels as keys and colours
         (in any format accepted by matplotlib
@@ -73,7 +83,7 @@ def plot_phylo(tree, ax,
         as values. If this is not
         specified all labels will be black, if only some labels are specified
         all others will be black.
-    label_dict : TYPE, optional
+    label_dict: dict
         User provided dictionary with current tip labels as keys and desired
         tip labels as values. If this is not specified all labels
         will be as specified in the newick, if some labels are specified
@@ -88,13 +98,21 @@ def plot_phylo(tree, ax,
         Line width. Default 2.
     bold: list
         List of tip labels to show in bold.
-
+    collapse: list
+        List of strings to search for in the tip labels of monophyletic clades
+        and collapse if the string is found in all labels
+    collapse_names: list
+        Names, in the same order as collapse, for the collapsed nodes from
+        the collapse list
+    auto_ax: bool
+        If True, determine axis limits automatically
 
     Returns
     -------
-    ps : list
-        List of lists - ordered as tip labels, tip label text objects,
-        alignment lines (if aligned). All are in the same order.
+    boxes : dict
+        Dictionary where keys are tip labels and values are the boundary
+        box on the image of each tip label.
+
     '''
     # Read the tree
     try:
@@ -105,13 +123,53 @@ def plot_phylo(tree, ax,
             T = ete3.Tree(tree, format=1)
         except ete3.parser.newick.NewickError as e:
             raise RuntimeError(f"Error in parsing Newick format: {e}")
+
     if outgroup and outgroup in set(T.get_leaf_names()):
         T.set_outgroup(outgroup)
 
+    if len(collapse) != 0:
+        assert len(collapse) == len(collapse_names), "To collapse nodes the \
+            collapse_strings and collapse_names parameters must be lists \
+            of equal length"
+        T, collapseD, countD = draw_tree.collapse_nodes(T, collapse,
+                                                        collapse_names)
+    else:
+        collapseD = dict()
+        countD = dict()
+
     # Define dictionaries for colours and labels if not provided
     for nam in T.get_leaf_names():
         if nam not in label_dict:
             label_dict[nam] = nam
+        if len(collapse) != 0:
+            for string in collapse:
+                if nam in label_dict:
+                    label_dict[nam.strip(string)] = label_dict[nam]
+                else:
+                    label_dict[nam.strip(string)] = nam.strip(string)
+
+        if len(collapse) != 0:
+            for string in collapse:
+                if nam.endswith(string):
+                    stripped = nam.removesuffix(string)
+                    cc = stripped.replace("COLLAPSE|", "")
+                    if nam in col_dict or \
+                            stripped in col_dict or cc in col_dict:
+                        if nam in col_dict:
+                            col_dict[stripped] = col_dict[nam]
+                            col_dict[f'COLLAPSE|{stripped}'] = col_dict[nam]
+                        elif stripped in col_dict:
+                            col_dict[nam] = col_dict[stripped]
+                            col_dict[f'COLLAPSE|{stripped}'] = \
+                                col_dict[stripped]
+                        else:
+                            col_dict[nam] = col_dict[cc]
+                            col_dict[stripped] = col_dict[cc]
+                            col_dict[f'COLLAPSE|{stripped}'] = col_dict[cc]
+                    else:
+                        col_dict[stripped] = 'black'
+                        col_dict[f'COLLAPSE|{stripped}'] = 'black'
+                        col_dict[nam] = 'black'
         if nam not in col_dict:
             col_dict[nam] = 'black'
     # Dictionary to pass apperance params to the plotting function
@@ -123,6 +181,15 @@ def plot_phylo(tree, ax,
                   'show_support': show_support,
                   'bold': bold}
 
+    structure = {'align_tips': align_tips,
+                 'rev_align_tips': rev_align_tips,
+                 'branch_lengths': branch_lengths,
+                 'reverse': reverse}
+    # Set basic axis limits
+    if auto_ax:
+        ax.set_xlim(0, 10)
+        ax.set_ylim(0, 10)
+
     # Calculate the total height and width of the original tree
     # in terms of number of nodes, total branch length, number of tips
     maxdist = ((T.get_farthest_leaf(topology_only=True)[1],
@@ -139,449 +206,54 @@ def plot_phylo(tree, ax,
 
     if reverse:
         xpos = -xpos
+
+    dims = {'height': height,
+            'width': width,
+            'depth': maxdist}
     # Call the main function. The second and third returns are used
     # internally when the function is called recursively but
     # are not needed by the user
 
-    _, _, ps = draw_tree(T, ax,
-                         x=xpos,
-                         y=-ypos-height,
-                         x0=xpos,
-                         ps=[],
-                         height=height,
-                         width=width,
-                         depth=maxdist,
-                         align_tips=align_tips,
-                         rev_align_tips=rev_align_tips,
-                         branch_lengths=branch_lengths,
-                         reverse=reverse,
-                         appearance=appearance)
+    _, _, ps = draw_tree.draw_tree(T, ax,
+                                   x=xpos,
+                                   y=-ypos-height,
+                                   x0=xpos,
+                                   ps=[],
+                                   dims=dims,
+                                   structure=structure,
+                                   appearance=appearance,
+                                   collapse=collapse,
+                                   collapseD=collapseD,
+                                   countD=countD,
+                                   dots=dots)
 
-    if rev_align_tips:
-        ps = reverse_align(ax, ps, reverse)
     # Hide axis
     if not show_axis:
         ax.set_axis_off()
     if scale_bar and branch_lengths:
+        # Add scale bars if needed
         if not reverse:
-            draw_scale_bar(ax, width, height, maxdist, xpos, ypos,
-                           scale_bar_width=scale_bar_width,
-                           appearance=appearance)
-        else:
-            draw_scale_bar(ax, width, height, maxdist, -xpos, ypos,
-                           scale_bar_width=scale_bar_width,
-                           appearance=appearance)
-    textobj = [p[1] for p in ps]
-    return (get_boxes(ax, textobj))
-
-
-def get_boxes(ax, texts):
-    '''
-    Converts a list of text objects to their co-ordinates on the axis in
-    axis units.
-
-    Parameters
-    ----------
-    ax : matplotlib.axes._axes.Axes,
-        An open matplotlib ax object where the tree will be plotted.
-    texts : list
-        A list of matplotlib.pyplot.text objects representing the tip
-        labels from the tree and their metadata.
-
-    Returns
-    -------
-    boxpos : dict
-        A dictionary of dictionaries where the top level keys are the tip
-        labels. Within each subdictionary the key value pairs are
-        index (position in the tree starting from the top) and the minimum,
-        maximum and central position of the text box on the x and y axis,
-        as index, xmin, xmid, xmax and ymin, ymid and ymax.
-
-    '''
-    boxpos = dict()
-    # Iterate through the tip labels
-    for i, txt in enumerate(texts):
-        # Get the position of the boxes containing the labels, convert to axis
-        # units
-        box = ax.transData.inverted().transform(txt.get_window_extent(
-            ax.figure.canvas.get_renderer()))
-        nam = txt.get_text().strip()
-        # Build the dictionary
-        boxpos[nam] = dict()
-        boxpos[nam]['index'] = i
-        boxpos[nam]['xmin'] = box[0][0].round(3)
-        boxpos[nam]['xmax'] = box[1][0].round(3)
-        boxpos[nam]['ymin'] = box[0][1].round(3)
-        boxpos[nam]['ymax'] = box[1][1].round(3)
-        boxpos[nam]['ymid'] = (box[0][1] + ((
-            box[1][1] - box[0][1]) / 2)).round(3)
-        boxpos[nam]['xmid'] = (
-            box[0][0] + ((box[1][0] - box[0][1]) / 2)).round(3)
-
-    return (boxpos)
-
-
-def draw_tree(tree, ax,
-              x=0,
-              y=0,
-              x0=0,
-              ps=[],
-              height=10,
-              width=10,
-              depth=None,
-              align_tips=False,
-              rev_align_tips=False,
-              branch_lengths=True,
-              reverse=False,
-              appearance={}):
-    '''
-    Plot a phylogenetic tree in matplotlib
-
-    Parameters
-    ----------
-    tree : ete3.Tree
-        ete3 Tree object
-    ax : matplotlib.axes._axes.Axes
-        An open matplotlib ax object
-    x : float
-        Current position on x axis.
-    y : float
-        Current position on y axis.
-    ps: list
-        Used internally, list of tip labels and x and y positions
-    height: float
-        Height of the tree in axis units
-    width: float
-        Width of the tree in axis units
-    depth: tuple(float, float, float)
-        Total height and width of the original tree in terms of number of
-        nodes, total branch length, number of tips
-    align_tips: bool
-        If True, the tip labels will be aligned rather than positioned at
-        the end of the branches. Default False.
-    rev_align_tips: bool
-        If True  the tip labels are right aligned
-        if reverse=False and left aligned if reverse=True.
-    branch_lengths: bool
-        If True, use the branch lengths provided in the tree, otherwise fix
-        all branches to the same length. Default True.
-    reverse: bool
-        If True, reverse the tree on the y-axis, showing the root on the right
-        hand side. Default False.
-    appearance: dict
-        Dictionary of parameters specifying the appearance of the tree.
-
-    Returns
-    -------
-    y   float
-        Position of previous node on y axis.
-    ym  float
-        Position of current node on y axis.
-    ps  list
-        List of lists - ordered as tip labels, tip label text objects,
-        alignment lines (if aligned). All are in the same order.
-    '''
-    # This is the increment for the position of each terminal node on
-    # the y axis.
-    # The number of nodes - 1 is used because one branch will be at position 0
-    yint = height / (depth[2] - 1)
-
-    # td is the branch length - if the branch_lengths parameter is False
-    # it is set to 1
-
-    # If the branch lengths are used, the total width of the tree is in
-    # the tree branch units, otherwise it's the total number of nodes from
-    # the root to the farthest branch
-
-    # textinc stops the tip labels from being immediately next to the tips
-
-    if branch_lengths:
-        td = tree.dist
-        tot_width = depth[1]
-
-    else:
-        td = 1
-        tot_width = depth[0] + 1
-
-    # This interval is used to scale the interval for each node
-    # so the total tree width matches the value specified
-    xint = width / tot_width
-
-    if tree.is_leaf():
-
-        # Position of the node tip
-        x_tip_pos = x - (xint * td)
-
-        # x_ali_pos is used to align the tips if align_tips is specified
-        # x_text_pos is the position of the text - if the tips are aligned
-        # the text is also aligned
-        if align_tips or rev_align_tips:
-            x_ali_pos = (tot_width * xint) + x0 + 1
-            x_text_pos = x_ali_pos
-        else:
-            x_ali_pos = None
-            x_text_pos = x
-        xmax = (tot_width * xint)
-        if reverse:
-            x_tip_pos = xmax - x_tip_pos
-            x_text_pos = xmax - x_text_pos
-            if x_ali_pos is not None:
-                x_ali_pos = xmax - x_ali_pos
-            x = xmax - x
-            hali = 'right'
-        else:
-            hali = 'left'
-        # Plot the tip label
-        if tree.name in appearance['bold']:
-            bold = 'bold'
-        else:
-            bold = 'normal'
-        textpos = ax.text(x_text_pos, -y,
-                          "  %s  " % appearance['label_dict'][tree.name],
-                          color=appearance['col_dict'][tree.name],
-                          fontsize=appearance['font_size'],
-                          va='center', ha=hali, fontweight=bold)
-
-        # Plot the branch to the tip
-        ax.plot([x, x_tip_pos], [-y, -y],
-                color=appearance['line_col'],
-                lw=appearance['line_width'])
-
-        # Add an extra line to the aligned tips if align_tips is specified
-        if align_tips or rev_align_tips:
-            line = ax.plot([x, x_ali_pos], [-y, -y],
-                           color=appearance['line_col'], alpha=0.2,
-                           ls="--",
-                           lw=appearance['line_width'])
-            ps.append([tree.name, textpos, line])
-        else:
-            ps.append([tree.name, textpos])
-
-        # Store the tip label and the position of the tip on the x and y axis
-
-        return (y+yint, y, ps)
-
-    else:
-        # This section draws the lines for the non-terminal nodes
-
-        # For each tree, all of the children are visited and the function
-        # is recursively called
-        for c in tree.children:
-            # Scale by the branch length if branch_lengths is specified
-            if branch_lengths:
-                tdc = c.dist
-            else:
-                tdc = 1
-
-            # The position of the node on the x axis
-            x_vert_pos = x + (tdc * xint)
-
-            # returns y - the bottom position of all the labels so far
-            # cym - the middle of the previous node on the y axis
-
-            y, cym, ps = draw_tree(c, ax, x_vert_pos, y,
-                                   x0=x0, ps=ps,
-                                   height=height,
-                                   width=width,
-                                   depth=depth,
-                                   align_tips=align_tips,
-                                   rev_align_tips=rev_align_tips,
-                                   branch_lengths=branch_lengths,
-                                   reverse=reverse,
-                                   appearance=appearance)
-
-            # y1 and y2 are the top and bottom positions of the current
-            # child node on the y axis, respectively
-            # Gives the extent of the vertical line for this segment
-
-            if c is tree.children[0]:
-                y1 = cym
-            elif c is tree.children[-1]:
-                y2 = cym
-
-        # midpoint of node on y axis
-        ym = (y1 + y2)/2
-
-        if reverse:
-            xmax = (tot_width * xint)
-            x = xmax - x
-
-        # Draw the lines
-        # Vertical line
-        ax.plot([x, x], [-y1, -y2],
-                color=appearance['line_col'],
-                lw=appearance['line_width'])
-
-        # Horizontal line - each node draws the one line that
-        # projects towards its parent so x is the position of the
-        # vertical line and x-(td*xint) is one increment back
-        # towards the root
-
-        if not reverse:
-            ax.plot([x, x-(td*xint)], [-ym, -ym],
-                    color=appearance['line_col'],
-                    lw=appearance['line_width'])
+            amend_tree.draw_scale_bar(ax, width, height, maxdist, xpos, ypos,
+                                      scale_bar_width=scale_bar_width,
+                                      appearance=appearance)
         else:
-            ax.plot([x, x+(td*xint)], [-ym, -ym],
-                    color=appearance['line_col'],
-                    lw=appearance['line_width'])
-
-        # Add branch support if specified
-        # TODO - currently lands on top of the branches if branch_lengths
-        # is switched on
-        if appearance['show_support']:
-            if not reverse:
-                ax.text(x, -ym, " %.2f" % tree.support, ha='left',
-                        va='center', fontsize=appearance['font_size']-2)
-            else:
-                ax.text(x, -ym, "%.2f " % tree.support, ha='right',
-                        va='center', fontsize=appearance['font_size']-2)
-
-        return (y, ym, ps)
-
-
-def reverse_align(ax, ps, reverse):
-    '''
-    Realigns the text in the tip labels so that for a standard tree, the
-    text is right aligned, for a mirrored tree (root on the right), the
-    text is left aligned. Only used when tip labels are set to be aligned.
-
-    This has to happen once the whole plot is drawn as the limits
-    of the text only exist once the text exists.
-
-    Parameters
-    ----------
-    ax : matplotlib.axes._axes.Axes
-        An open matplotlib ax object
-    ps : list
-        List of lists - ordered as tip labels, tip label text objects,
-        alignment lines (if aligned). All are in the same order.
-    reverse: bool
-        If True, reverse the tree on the y-axis, showing the root on the right
-        hand side. Default False.
-
-    Returns
-    -------
-    ps_new : list
-        List of lists - ordered as tip labels, tip label text objects,
-        alignment lines (if aligned). All are in the same order. Updated
-        based on new alignment.
-    '''
-
-    x_extremes = []
-    ys = []
-    # Used to determine which x co-ordinates to take depending on the
-    # tree orientation
-    if reverse:
-        indi = 0
-    else:
-        indi = 1
-    for pnam, ptext, pline in ps:
-        # Get the data units of the box which encloses the text
-        box = ax.transData.inverted().transform(
-            ptext.get_window_extent(renderer=ax.figure.canvas.get_renderer()))
-        # Get the rightmost point of the text box (regular tree)
-        # or the leftmost (reversed tree)
-        x_extreme = box[indi][0]
-        x_extremes.append(x_extreme)
-
-        # Store the y axis positions
-        ys.append(box[1][1])
-
-    if not reverse:
-        # For right alignment, take the rightmost x point
-        maxi = max(x_extremes)
-    else:
-        # For left alignment, take the leftmost x point
-        maxi = min(x_extremes)
-    alis = ['left', 'right']
-
-    ps_new = []
-    for i, p in enumerate(ps):
-        # Move the text to the right position
-        p[1].set_position([maxi, ys[i]])
-
-        # Get the current left end of the dotted alignment line
-        oldline = p[2][0].get_xdata()[0]
-
-        # Left or right align the text
-        p[1].set_horizontalalignment(alis[indi])
-
-        # Get the updated text position limits
-        pbox = ax.transData.inverted().transform(p[1].get_window_extent(
-            renderer=ax.figure.canvas.get_renderer()))
+            amend_tree.draw_scale_bar(ax, width, height, maxdist, -xpos,
+                                      ypos,
+                                      scale_bar_width=scale_bar_width,
+                                      appearance=appearance)
 
-        # Move the dotted line to hit the new text position
-        # int(not indi) swaps 0 for 1
-        p[2][0].set_xdata([pbox[int(not indi)][0], oldline])
-
-        # Set the y axis position
-        p[1].set_verticalalignment('center')
-
-        # Store the new values
-        ps_new.append(p)
-    return (ps_new)
-
-
-def draw_scale_bar(ax, width, height, depth, left, bottom,
-                   scale_bar_width=None,
-                   appearance={'font_size': 10}):
-    '''
-    Adds a scale bar to the tree - only when branch lengths are specified.
-
-    The default length is 0.25 * total tree width, or it can be specified
-    using scale_bar_width.
-
-    Parameters
-    ----------
-    ax : matplotlib.axes._axes.Axes
-        An open matplotlib ax object
-
-    height : float
-        Height of the tree, in axis units.
-    width : float
-        Width of the tree, in axis units.
-    depth: tuple(float, float, float)
-        Total height and width of the original tree in terms of number of
-        nodes, total branch length, number of tips
-    bottom: float
-        The bottom position of the tree on the y axis
-    scale_bar_width: float
-        The desired width of the scale bar, if not specified
-        tree width * 0.25 is used
-    appearance: dict
-        Dictionary of parameters specifying the appearance of the tree.
-    '''
-    # depth[1] - total width of the tree in tree units
-    # width - total width of tree in axis units
-    # xint - width of one tree unit in axis units
-    xint = width / depth[1]
-
-    # Distance from x axis to start the scale bar
-    # 10% of total tree width
-    interx = width * 0.1
-
-    # Distance on y axis to extend the bracket ends on the scale bar
-    # 10% of the height of one node
-    intery = (height / depth[2]) * 0.1
-    bottom -= (height / depth[2])
-    # scale_bar_width = total width of scale bar in axis units
-    if not scale_bar_width:
-        scale_bar_width = width * 0.25
-
-    # Convert the scale bar width to tree units
-    scale = scale_bar_width / xint
-
-    # Draw the horizontal line
-    ax.plot([left+interx, left+interx+scale_bar_width],
-            [bottom, bottom], color='black')
-
-    # Bracket the ends of the scale bar with small vertical lines
-    ax.plot([left+interx, left+interx],
-            [bottom-intery, bottom+intery], color='black')
-    ax.plot([left+interx+scale_bar_width, left+interx+scale_bar_width],
-            [bottom-intery, bottom+intery], color='black')
-
-    # Add the scale text
-    ax.text(left+interx+(scale_bar_width/2), bottom+intery, "%.3f" % scale,
-            va='bottom', ha='center', fontsize=appearance['font_size']-2)
+    textobj = [p[1] for p in ps]
+    # Determine the boundary boxes for the tip labels
+    boxes = get_boxes.get_boxes(ax, textobj)
+    if auto_ax:
+        boxes, ax = amend_tree.auto_axis(ax, textobj,
+                                         xpos, ypos,
+                                         width, height, maxdist,
+                                         scale_bar, branch_lengths,
+                                         reverse=reverse)
+    # Helps positions not move if mpl adjust the plot
+    ax.set_autoscale_on(False)
+    # Move the tip labels if reverse_align is true
+    if rev_align_tips:
+        ps = amend_tree.reverse_align(ax, ps, reverse)
+    return (boxes)
diff --git a/setup.py b/setup.py
index 15d3f1d..2e0adae 100644
--- a/setup.py
+++ b/setup.py
@@ -26,7 +26,6 @@
      packages=setuptools.find_packages(),
      package_dir={'plot_phylo': 'plot_phylo'},
      install_requires=['matplotlib', 'ete3'],
-     scripts=['plot_phylo/plot_phylo.py'],
      classifiers=[
          "Programming Language :: Python :: 3",
          "License :: OSI Approved :: MIT License",
diff --git a/tests/helper_functions.py b/tests/helper_functions.py
new file mode 100644
index 0000000..48fca71
--- /dev/null
+++ b/tests/helper_functions.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+import matplotlib
+matplotlib.use('Agg')
+
+
+matplotlib.rcParams.update({
+    'font.family': 'DejaVu Sans',
+    'savefig.bbox': 'standard',
+    'savefig.pad_inches': 0,
+    'figure.dpi': 100,
+})
+
+
+def compare_floats(float1, float2, tolerance=0.05):
+    abs_diff = abs(float1 - float2)
+    return abs_diff <= (tolerance * abs(float2))
+
+
+def get_plot_phylo_metadata():
+    variD = {'xpos': 0,
+             'ypos': 0,
+             'width': 10,
+             'show_axis': False,
+             'show_support': True,
+             'align_tips': False,
+             'rev_align_tips': False,
+             'branch_lengths': True,
+             'scale_bar': True,
+             'scale_bar_width': None,
+             'reverse': False,
+             'outgroup': None,
+             'col_dict': {},
+             'label_dict': {},
+             'font_size': 10,
+             'line_col': 'black',
+             'line_width': 1,
+             'bold': [],
+             'collapse': [],
+             'collapse_names': [],
+             'auto_ax': True}
+    varis = ['xpos',
+             'ypos',
+             'width',
+             'show_axis',
+             'show_support',
+             'align_tips',
+             'rev_align_tips',
+             'branch_lengths',
+             'scale_bar',
+             'scale_bar_width',
+             'reverse',
+             'outgroup',
+             'col_dict',
+             'label_dict',
+             'font_size',
+             'line_col',
+             'line_width',
+             'bold',
+             'collapse',
+             'collapse_names',
+             'auto_ax']
+    return (variD, varis)
+
+
+def get_draw_tree_metadata():
+    variD = {'x': 0,
+             'y': 0,
+             'x0': 0,
+             'ps': [],
+             'dims': {'width': 10,
+                      'height': 10,
+                      'depth': [5, 5, 5]},
+             'structure': {'align_tips': False,
+                           'rev_align_tips': False,
+                           'branch_lengths': True,
+                           'reverse': False},
+             'appearance': {'col_dict': {},
+                            'label_dict': {},
+                            'font_size': 10,
+                            'line_col': 'black',
+                            'line_width': 1,
+                            'show_support': True,
+                            'bold': []},
+             'collapse': [],
+             'collapseD': dict()}
+    varis_draw_tree = ['x',
+                       'y',
+                       'x0',
+                       'ps',
+                       'dims',
+                       'structure',
+                       'appearance',
+                       'collapse',
+                       'collapseD']
+    return (variD, varis_draw_tree)
diff --git a/tests/test_amend_tree.py b/tests/test_amend_tree.py
new file mode 100644
index 0000000..4f0c0ea
--- /dev/null
+++ b/tests/test_amend_tree.py
@@ -0,0 +1,94 @@
+from test_amend_tree_data import (test_rev_align_vars,
+                                  test_rev_align_list,
+                                  test_rev_align_nams)
+import matplotlib.pyplot as plt
+from plot_phylo import draw_tree
+from plot_phylo import amend_tree
+import pytest
+import ete3
+import pickle
+import helper_functions
+import matplotlib
+matplotlib.use('Agg')
+
+matplotlib.rcParams.update({
+    'font.family': 'DejaVu Sans',
+    'savefig.bbox': 'standard',
+    'savefig.pad_inches': 0,
+    'figure.dpi': 100,
+})
+
+
+@pytest.mark.parametrize(test_rev_align_vars,
+                         test_rev_align_list,
+                         ids=test_rev_align_nams)
+@pytest.mark.parametrize("tree, ylim", [["examples/primates.nw", 11],
+                                        ["examples/basic_tree.nw", 5],
+                                        ['examples/big_tree.nw', 300]])
+def test_reverse_align_params(x,
+                              y,
+                              x0,
+                              ps,
+                              dims,
+                              structure,
+                              appearance,
+                              collapse,
+                              collapseD,
+                              expected,
+                              ID,
+                              tree,
+                              ylim):
+    try:
+        T = ete3.Tree(tree)
+    except ete3.parser.newick.NewickError:
+        try:
+            # Allows for trees with named internal nodes
+            T = ete3.Tree(tree, format=1)
+        except ete3.parser.newick.NewickError as e:
+            raise RuntimeError(f"Error in parsing Newick format: {e}")
+    tree_stem = tree.split("/")[-1].split(".")[0]
+
+    f = plt.figure(figsize=(10, 20), dpi=100)
+    a = f.add_subplot(111)
+    a.set_xlim(-10, 20)
+    a.set_ylim(-1, ylim)
+    for nam in T.get_leaf_names():
+        if nam not in appearance['label_dict']:
+            appearance['label_dict'][nam] = nam
+        if nam not in appearance['col_dict']:
+            appearance['col_dict'][nam] = 'black'
+
+    _, _, ps = draw_tree.draw_tree(tree=T, ax=a,
+                                   x=x,
+                                   y=y,
+                                   x0=x0,
+                                   ps=[],
+                                   dims=dims,
+                                   structure=structure,
+                                   appearance=appearance)
+    reverse = amend_tree.reverse_align(a, ps, True)
+    plt.close()
+    e0 = expected.replace(".pickle", "_%s.pickle" % tree_stem)
+
+    test_dat = []
+    for v0, v1, v2 in reverse:
+        row = [v0]
+        x, y = v1.get_position()
+        row += [round(x, 2), round(y, 2)]
+        row += [v1.get_text()]
+        for w in v2:
+            x2 = [round(z, 2) for z in w.get_xdata()]
+            y2 = [round(z, 2) for z in w.get_ydata()]
+            row += x2
+            row += y2
+        test_dat += row
+    expected_obj = pickle.load(open(e0, "rb"))
+    ll = 0
+    for z1, z2 in zip(expected_obj, test_dat):
+        if isinstance(z1, str):
+            if z1 == z2:
+                ll += 1
+        else:
+            if helper_functions.compare_floats(z1, z2):
+                ll += 1
+    assert ll == len(expected_obj)
diff --git a/tests/test_amend_tree_data.py b/tests/test_amend_tree_data.py
new file mode 100644
index 0000000..56b483b
--- /dev/null
+++ b/tests/test_amend_tree_data.py
@@ -0,0 +1,41 @@
+import helper_functions
+
+
+tests_rev_align = [{},
+                   {'xpos': 1, 'ypos': 1},
+                   {'dims_height': 6},
+                   {'dims_width': 3},
+                   {'dims_depth': [2, 2, 2]},
+                   {'structure_rev_align_tips': True},
+                   {'structure_branch_lengths': False},
+                   {'appearance_col_dict': {'Homo sapiens': 'blue'}},
+                   {'appearance_label_dict': {'Homo sapiens': 'human'}},
+                   {'appearance_font_size': 6},
+                   {'appearance_line_col': 'orange'},
+                   {'appearance_line_width': 5},
+                   {'appearance_show_support': True},
+                   {'appearance_bold': ['Homo sapiens']}]
+
+
+test_rev_align_list = []
+test_rev_align_nams = []
+for test in tests_rev_align:
+    testnam = "amend_tree_%s" % "_".join(test.keys())
+    curr_dict, varis_rev_align = helper_functions.get_draw_tree_metadata()
+    curr_dict['structure']['rev_align_tips'] = True
+    curr_dict['structure']['reverse'] = True
+    for key, val in test.items():
+        if key.startswith("dims") or key.startswith(
+                "structure") or key.startswith("appearance"):
+            dnam, knam = key.split("_")[0], "_".join(key.split("_")[1:])
+            curr_dict[dnam][knam] = val
+        else:
+            curr_dict[key] = val
+
+    pass_vals = [curr_dict[v] for v in varis_rev_align]
+    pass_vals.append('tests/test_objects/%s.pickle' % testnam)
+    pass_vals.append(testnam)
+    test_rev_align_list.append(pass_vals)
+    test_rev_align_nams.append(testnam)
+
+test_rev_align_vars = ",".join(varis_rev_align + ['expected', 'ID'])
diff --git a/tests/test_draw_tree.py b/tests/test_draw_tree.py
new file mode 100644
index 0000000..cead74e
--- /dev/null
+++ b/tests/test_draw_tree.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+import matplotlib.pyplot as plt
+from plot_phylo import draw_tree
+import pytest
+from test_draw_tree_data import (test_draw_tree_vars,
+                                 test_draw_tree_list,
+                                 test_draw_tree_nams)
+
+import ete3
+import pickle
+import helper_functions
+import matplotlib
+matplotlib.use('Agg')
+
+matplotlib.rcParams.update({
+    'font.family': 'DejaVu Sans',
+    'savefig.bbox': 'standard',
+    'savefig.pad_inches': 0,
+    'figure.dpi': 100,
+})
+
+
+@pytest.mark.parametrize(test_draw_tree_vars,
+                         test_draw_tree_list,
+                         ids=test_draw_tree_nams)
+@pytest.mark.parametrize("tree, ylim", [["examples/primates.nw", 11],
+                                        ["examples/basic_tree.nw", 5],
+                                        ['examples/big_tree.nw', 300]])
+def test_draw_tree_params(x,
+                          y,
+                          x0,
+                          ps,
+                          dims,
+                          structure,
+                          appearance,
+                          collapse,
+                          collapseD,
+                          expected,
+                          ID,
+                          tree,
+                          ylim):
+    try:
+        T = ete3.Tree(tree)
+    except ete3.parser.newick.NewickError:
+        try:
+            # Allows for trees with named internal nodes
+            T = ete3.Tree(tree, format=1)
+        except ete3.parser.newick.NewickError as e:
+            raise RuntimeError(f"Error in parsing Newick format: {e}")
+    tree_stem = tree.split("/")[-1].split(".")[0]
+
+    f = plt.figure(figsize=(10, 20), dpi=100)
+    a = f.add_subplot(111)
+    a.set_xlim(-10, 20)
+    a.set_ylim(-1, ylim)
+    for nam in T.get_leaf_names():
+        if nam not in appearance['label_dict']:
+            appearance['label_dict'][nam] = nam
+        if nam not in appearance['col_dict']:
+            appearance['col_dict'][nam] = 'black'
+
+    test_obj = draw_tree.draw_tree(tree=T, ax=a,
+                                   x=x,
+                                   y=y,
+                                   x0=x0,
+                                   ps=ps,
+                                   dims=dims,
+                                   structure=structure,
+                                   appearance=appearance)
+    plt.close()
+    ytest = round(test_obj[0], 2)
+    y2test = round(test_obj[1], 2)
+    test_dat = [ytest, y2test]
+    for v in test_obj[2]:
+        row = [v[0]]
+        x, y = v[1].get_position()
+        row += [round(x, 2), round(y, 2)]
+        row += [v[1].get_text()]
+        test_dat += row
+
+    e0 = expected.replace(".pickle", "_%s.pickle" % tree_stem)
+    expected_obj = pickle.load(open(e0, "rb"))
+    ll = 0
+    for z1, z2 in zip(expected_obj, test_dat):
+        if isinstance(z1, str):
+            if z1 == z2:
+                ll += 1
+        else:
+            if helper_functions.compare_floats(z1, z2):
+                ll += 1
+    assert ll == len(expected_obj)
diff --git a/tests/test_draw_tree_data.py b/tests/test_draw_tree_data.py
new file mode 100644
index 0000000..2d3aa84
--- /dev/null
+++ b/tests/test_draw_tree_data.py
@@ -0,0 +1,42 @@
+import helper_functions
+
+
+tests_draw_tree = [{},
+                   {'xpos': 1, 'ypos': 1},
+                   {'dims_height': 6},
+                   {'dims_width': 3},
+                   {'dims_depth': [2, 2, 2]},
+                   {'structure_reverse': True},
+                   {'structure_rev_align_tips': True},
+                   {'structure_branch_lengths': False},
+                   {'structure_reverse': True},
+                   {'appearance_col_dict': {'Homo sapiens': 'blue',
+                                            'A': 'blue'}},
+                   {'appearance_label_dict': {'Homo sapiens': 'human'}},
+                   {'appearance_font_size': 6},
+                   {'appearance_line_col': 'orange'},
+                   {'appearance_line_width': 5},
+                   {'appearance_show_support': True},
+                   {'appearance_bold': ['Homo sapiens', 'A']}]
+
+
+test_draw_tree_list = []
+test_draw_tree_nams = []
+for test in tests_draw_tree:
+    testnam = "draw_tree_%s" % "_".join(test.keys())
+    curr_dict, varis_draw_tree = helper_functions.get_draw_tree_metadata()
+    for key, val in test.items():
+        if key.startswith("dims") or key.startswith(
+                "structure") or key.startswith("appearance"):
+            dnam, knam = key.split("_")[0], "_".join(key.split("_")[1:])
+            curr_dict[dnam][knam] = val
+        else:
+            curr_dict[key] = val
+
+    pass_vals = [curr_dict[v] for v in varis_draw_tree]
+    pass_vals.append('tests/test_objects/%s.pickle' % testnam)
+    pass_vals.append(testnam)
+    test_draw_tree_list.append(pass_vals)
+    test_draw_tree_nams.append(testnam)
+
+test_draw_tree_vars = ",".join(varis_draw_tree + ['expected', 'ID'])
diff --git a/tests/test_get_boxes.py b/tests/test_get_boxes.py
new file mode 100644
index 0000000..9a0d806
--- /dev/null
+++ b/tests/test_get_boxes.py
@@ -0,0 +1,19 @@
+import pytest
+from plot_phylo import get_boxes
+from test_get_boxes_data import (test_get_boxes_axes,
+                                 test_get_boxes_texts,
+                                 test_get_boxes_results)
+
+
+@pytest.mark.parametrize("ax, texts, expected_result",
+                         list(zip(test_get_boxes_axes,
+                                  test_get_boxes_texts,
+                                  test_get_boxes_results)))
+def test_get_boxes(ax, texts, expected_result):
+    boxes = get_boxes.get_boxes(ax, texts)
+    bclean = dict()
+    for b, vals in boxes.items():
+        bclean[b] = dict()
+        for v in vals:
+            bclean[b][v] = round(vals[v], 0)
+    assert bclean == expected_result
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similarity index 56%
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rename to tests/test_objects/draw_tree_structure_branch_lengths_primates.pickle
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rename to tests/test_objects/draw_tree_structure_rev_align_tips_primates.pickle
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rename to tests/test_objects/draw_tree_structure_reverse_primates.pickle
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diff --git a/tests/test_plot_phylo.py b/tests/test_plot_phylo.py
index af3f56e..7535dde 100644
--- a/tests/test_plot_phylo.py
+++ b/tests/test_plot_phylo.py
@@ -1,40 +1,22 @@
 #!/usr/bin/env python3
 import matplotlib.pyplot as plt
-import matplotlib
 import plot_phylo
 import pytest
 from test_plot_phylo_data import (test_plot_phylo_vars,
                                   test_plot_phylo_list,
-                                  test_plot_phylo_nams,
-                                  test_draw_tree_vars,
-                                  test_draw_tree_list,
-                                  test_draw_tree_nams,
-                                  test_rev_align_vars,
-                                  test_rev_align_list,
-                                  test_rev_align_nams)
-from test_get_boxes_data import (test_get_boxes_axes,
-                                 test_get_boxes_texts,
-                                 test_get_boxes_results)
-import ete3
-import pickle
+                                  test_plot_phylo_nams)
 import os
 import shutil
-import numpy as np
+import matplotlib
+from matplotlib.testing.compare import compare_images
 matplotlib.use('Agg')
 
-
-def compare_images(f1, f2, tol):
-    f1arr = matplotlib.image.imread(f1)
-    f2arr = matplotlib.image.imread(f2)
-
-    f1arr_sub = f1arr[500:1500, 500:1500, :]
-    f2arr_sub = f2arr[500:1500, 500:1500, :]
-    return np.allclose(f1arr_sub, f2arr_sub, atol=tol)
-
-
-def compare_floats(float1, float2, tolerance=0.05):
-    abs_diff = abs(float1 - float2)
-    return abs_diff <= (tolerance * abs(float2))
+matplotlib.rcParams.update({
+    'font.family': 'DejaVu Sans',
+    'savefig.bbox': 'standard',
+    'savefig.pad_inches': 0,
+    'figure.dpi': 100,
+})
 
 
 # Tests all parameters with plot_phylo with three different trees
@@ -63,14 +45,15 @@ def test_plot_phylo_params(xpos,
                            line_width,
                            bold,
                            expected_figure,
-                           ID, tree, ylim):
+                           ID, tree, ylim,
+                           collapse,
+                           collapse_names,
+                           auto_ax):
 
     tree_stem = tree.split("/")[-1].split(".")[0]
 
-    f = plt.figure(figsize=(10, 20))
+    f = plt.figure(figsize=(10, 20), dpi=100)
     a = f.add_subplot(111)
-    a.set_xlim(-10, 20)
-    a.set_ylim(-1, ylim)
     plot_phylo.plot_phylo(tree=tree, ax=a,
                           xpos=xpos,
                           ypos=ypos,
@@ -90,194 +73,26 @@ def test_plot_phylo_params(xpos,
                           font_size=font_size,
                           line_col=line_col,
                           line_width=line_width,
-                          bold=bold)
+                          bold=bold,
+                          collapse=collapse,
+                          collapse_names=collapse_names,
+                          auto_ax=auto_ax)
     try:
         os.mkdir("test_temp")
     except FileExistsError:
         pass
-    plt.savefig("test_temp/%s_%s.png" % (ID, tree_stem), bbox_inches='tight',
+    plt.savefig("test_temp/%s_%s.png" % (ID, tree_stem),
                 dpi=200)
     plt.close('all')
 
     exp = expected_figure.replace(".png", "_%s.png" % tree_stem)
     # Compare the Matplotlib figures as images
     result = compare_images("test_temp/%s_%s.png" % (ID, tree_stem),
-                            exp, tol=10)
+                            exp, tol=40)
+
     shutil.rmtree("test_temp")
     # Assert that the images are similar
-    assert result
-
-
-@pytest.mark.parametrize(test_draw_tree_vars,
-                         test_draw_tree_list,
-                         ids=test_draw_tree_nams)
-@pytest.mark.parametrize("tree, ylim", [["examples/primates.nw", 11],
-                                        ["examples/basic_tree.nw", 5],
-                                        ['examples/big_tree.nw', 300]])
-def test_draw_tree_params(x,
-                          y,
-                          x0,
-                          ps,
-                          width,
-                          depth,
-                          align_tips,
-                          rev_align_tips,
-                          branch_lengths,
-                          reverse,
-                          appearance,
-                          expected,
-                          ID,
-                          tree,
-                          ylim):
-
-    try:
-        T = ete3.Tree(tree)
-    except ete3.parser.newick.NewickError:
-        try:
-            # Allows for trees with named internal nodes
-            T = ete3.Tree(tree, format=1)
-        except ete3.parser.newick.NewickError as e:
-            raise RuntimeError(f"Error in parsing Newick format: {e}")
-    tree_stem = tree.split("/")[-1].split(".")[0]
-
-    f = plt.figure(figsize=(10, 20))
-    a = f.add_subplot(111)
-    a.set_xlim(-10, 20)
-    a.set_ylim(-1, ylim)
-    for nam in T.get_leaf_names():
-        if nam not in appearance['label_dict']:
-            appearance['label_dict'][nam] = nam
-        if nam not in appearance['col_dict']:
-            appearance['col_dict'][nam] = 'black'
-
-    test_obj = plot_phylo.draw_tree(tree=T, ax=a,
-                                    x=x,
-                                    y=y,
-                                    x0=x0,
-                                    ps=ps,
-                                    height=ylim-1,
-                                    width=width,
-                                    depth=depth,
-                                    align_tips=align_tips,
-                                    rev_align_tips=rev_align_tips,
-                                    branch_lengths=branch_lengths,
-                                    reverse=reverse,
-                                    appearance=appearance)
-    plt.close()
-    ytest = round(test_obj[0], 2)
-    y2test = round(test_obj[1], 2)
-    test_dat = [ytest, y2test]
-    for v in test_obj[2]:
-        row = [v[0]]
-        x, y = v[1].get_position()
-        row += [round(x, 2), round(y, 2)]
-        row += [v[1].get_text()]
-        test_dat += row
-    e0 = expected.replace(".pickle", "_%s.pickle" % tree_stem)
-    expected_obj = pickle.load(open(e0, "rb"))
-    ll = 0
-    for z1, z2 in zip(expected_obj, test_dat):
-        if isinstance(z1, str):
-            if z1 == z2:
-                ll += 1
-        else:
-            if compare_floats(z1, z2):
-                ll += 1
-    assert ll == len(expected_obj)
-
-
-@pytest.mark.parametrize(test_rev_align_vars,
-                         test_rev_align_list,
-                         ids=test_rev_align_nams)
-@pytest.mark.parametrize("tree, ylim", [["examples/primates.nw", 11],
-                                        ["examples/basic_tree.nw", 5],
-                                        ['examples/big_tree.nw', 300]])
-def test_reverse_align_params(x,
-                              y,
-                              x0,
-                              width,
-                              depth,
-                              branch_lengths,
-                              reverse,
-                              appearance,
-                              expected,
-                              ID,
-                              tree,
-                              ylim):
-    try:
-        T = ete3.Tree(tree)
-    except ete3.parser.newick.NewickError:
-        try:
-            # Allows for trees with named internal nodes
-            T = ete3.Tree(tree, format=1)
-        except ete3.parser.newick.NewickError as e:
-            raise RuntimeError(f"Error in parsing Newick format: {e}")
-    tree_stem = tree.split("/")[-1].split(".")[0]
-
-    f = plt.figure(figsize=(10, 20))
-    a = f.add_subplot(111)
-    a.set_xlim(-10, 19)
-    a.set_ylim(-1, ylim)
-    for nam in T.get_leaf_names():
-        if nam not in appearance['label_dict']:
-            appearance['label_dict'][nam] = nam
-        if nam not in appearance['col_dict']:
-            appearance['col_dict'][nam] = 'black'
-
-    _, _, ps = plot_phylo.draw_tree(tree=T, ax=a,
-                                    x=x,
-                                    y=y,
-                                    x0=x0,
-                                    ps=[],
-                                    height=ylim-1,
-                                    width=width,
-                                    depth=depth,
-                                    align_tips=True,
-                                    rev_align_tips=True,
-                                    branch_lengths=branch_lengths,
-                                    reverse=reverse,
-                                    appearance=appearance)
-    plt.close()
-    reverse = plot_phylo.reverse_align(a, ps, True)
-
-    e0 = expected.replace(".pickle", "_%s.pickle" % tree_stem)
-
-    test_dat = []
-    for v0, v1, v2 in reverse:
-        row = [v0]
-        x, y = v1.get_position()
-        row += [round(x, 2), round(y, 2)]
-        row += [v1.get_text()]
-        for w in v2:
-            x2 = [round(z, 2) for z in w.get_xdata()]
-            y2 = [round(z, 2) for z in w.get_ydata()]
-            row += x2
-            row += y2
-        test_dat += row
-    expected_obj = pickle.load(open(e0, "rb"))
-    ll = 0
-    for z1, z2 in zip(expected_obj, test_dat):
-        if isinstance(z1, str):
-            if z1 == z2:
-                ll += 1
-        else:
-            if compare_floats(z1, z2):
-                ll += 1
-    assert ll == len(expected_obj)
-
-
-@pytest.mark.parametrize("ax, texts, expected_result",
-                         list(zip(test_get_boxes_axes,
-                                  test_get_boxes_texts,
-                                  test_get_boxes_results)))
-def test_get_boxes(ax, texts, expected_result):
-    boxes = plot_phylo.get_boxes(ax, texts)
-    bclean = dict()
-    for b, vals in boxes.items():
-        bclean[b] = dict()
-        for v in vals:
-            bclean[b][v] = round(vals[v], 0)
-    assert bclean == expected_result
+    assert result is None
 
 
 @pytest.mark.parametrize(test_plot_phylo_vars,
@@ -303,11 +118,12 @@ def test_bad_tree(xpos,
                   line_width,
                   bold,
                   expected_figure,
-                  ID, tree, ylim):
-    f = plt.figure(figsize=(10, 20))
+                  ID, tree, ylim,
+                  collapse,
+                  collapse_names,
+                  auto_ax):
+    f = plt.figure(figsize=(10, 20), dpi=100)
     a = f.add_subplot(111)
-    a.set_xlim(-10, 20)
-    a.set_ylim(-1, ylim)
     with pytest.raises(RuntimeError,
                        match="Error in parsing Newick"):
         plot_phylo.plot_phylo(tree=tree, ax=a,
@@ -330,3 +146,4 @@ def test_bad_tree(xpos,
                               line_col=line_col,
                               line_width=line_width,
                               bold=bold)
+    plt.close()
diff --git a/tests/test_plot_phylo_data.py b/tests/test_plot_phylo_data.py
index 51bdb8a..5387d48 100644
--- a/tests/test_plot_phylo_data.py
+++ b/tests/test_plot_phylo_data.py
@@ -1,31 +1,6 @@
 #!/usr/bin/env python3
-import plot_phylo
-import copy
+import helper_functions
 
-# Default parameters values
-defaults = plot_phylo.plot_phylo.__defaults__
-# Number of parameters
-ac = plot_phylo.plot_phylo.__code__.co_argcount
-# Argument names, excluding variables defined within the function and
-# arguments with no default values
-varis = list(plot_phylo.plot_phylo.__code__.co_varnames[
-             ac-len(defaults):ac])
-
-variD = dict(zip(varis, defaults))
-
-varis.remove('height')
-variD.pop('height')
-
-defaults_draw_tree = plot_phylo.draw_tree.__defaults__
-ac_draw_tree = plot_phylo.draw_tree.__code__.co_argcount
-varis_draw_tree = list(plot_phylo.draw_tree.__code__.co_varnames[
-    ac_draw_tree-len(defaults_draw_tree):ac_draw_tree])
-varis_draw_tree.remove('height')
-
-varis_rev_align = copy.deepcopy(varis_draw_tree)
-varis_rev_align.remove('align_tips')
-varis_rev_align.remove('rev_align_tips')
-varis_rev_align.remove('ps')
 
 tests_plot_phylo = [{},
                     {'xpos': 1, 'ypos': 1},
@@ -41,29 +16,12 @@
                     {'rev_align_tips': True, 'reverse': True},
                     {'col_dict': {'Homo sapiens': 'blue'}},
                     {'label_dict': {'Homo sapiens': 'human'}},
-                    {'font_size': 20},
+                    {'font_size': 6},
                     {'line_col': 'orange'},
                     {'line_width': 5},
-                    {'bold': ['Homo sapiens']}]
-
-tests_draw_tree = [{},
-                   {'xpos': 1, 'ypos': 1},
-                   {'show_axis': False},
-                   {'align_tips': True},
-                   {'rev_align_tips': True},
-                   {'branch_lengths': False},
-                   {'scale_bar': False},
-                   {'scale_bar_width': 6},
-                   {'reverse': True},
-                   {'rev_align_tips': True, 'reverse': True},
-                   {'appearance': {'col_dict': {'Homo sapiens': 'blue'},
-                                   'label_dict': {'Homo sapiens': 'human'},
-                                   'font_size': 20,
-                                   'line_col': 'orange',
-                                   'line_width': 5,
-                                   'show_support': True,
-                                   'bold': ['Homo sapiens']}},
-                   {'depth': [2, 2, 2]}]
+                    {'bold': ['Homo sapiens']},
+                    {'collapse': ['Saimiri', 'Callithrix', 'Microcebus'],
+                     'collapse_names': ['S', 'C', 'M'], 'font_size': 6}]
 
 test_plot_phylo_list = []
 test_plot_phylo_nams = []
@@ -71,7 +29,7 @@
     testnam = "_".join(test.keys())
     if len(testnam) == 0:
         testnam = "basic"
-    curr_dict = copy.deepcopy(variD)
+    curr_dict, varis = helper_functions.get_plot_phylo_metadata()
     curr_dict.update(test)
     pass_vals = [curr_dict[v] for v in varis]
     pass_vals.append('tests/test_images/%s.png' % testnam)
@@ -79,50 +37,3 @@
     test_plot_phylo_list.append(pass_vals)
     test_plot_phylo_nams.append(testnam)
 test_plot_phylo_vars = ",".join(varis + ['expected_figure', 'ID'])
-
-test_draw_tree_list = []
-test_draw_tree_nams = []
-for test in tests_draw_tree:
-    testnam = "draw_tree_%s" % "_".join(test.keys())
-    curr_dict = copy.deepcopy(variD)
-    curr_dict['x'] = curr_dict['xpos']
-    curr_dict['x0'] = curr_dict['xpos']
-    curr_dict['y'] = curr_dict['ypos']
-    curr_dict['ps'] = []
-    curr_dict['appearance'] = dict()
-    for var in ['col_dict', 'label_dict', 'font_size',
-                'line_col', 'line_width', 'show_support', 'bold']:
-        curr_dict['appearance'][var] = curr_dict[var]
-    curr_dict['depth'] = [5, 5, 5]
-    curr_dict.update(test)
-    pass_vals = [curr_dict[v] for v in varis_draw_tree]
-    pass_vals.append('tests/test_objects/%s.pickle' % testnam)
-    pass_vals.append(testnam)
-    test_draw_tree_list.append(pass_vals)
-    test_draw_tree_nams.append(testnam)
-test_draw_tree_vars = ",".join(varis_draw_tree + ['expected', 'ID'])
-
-
-test_rev_align_list = []
-test_rev_align_nams = []
-for test in tests_draw_tree:
-    testnam = "rev_align_%s" % "_".join(test.keys())
-    if 'align_tips' not in testnam:
-        curr_dict = copy.deepcopy(variD)
-        curr_dict['x'] = curr_dict['xpos']
-        curr_dict['x0'] = curr_dict['xpos']
-        curr_dict['y'] = curr_dict['ypos']
-        curr_dict['appearance'] = dict()
-        for var in ['col_dict', 'label_dict', 'font_size',
-                    'line_col', 'line_width', 'show_support', 'bold']:
-            curr_dict['appearance'][var] = curr_dict[var]
-        curr_dict.update(test)
-        curr_dict['depth'] = [5, 5, 5]
-        curr_dict.pop('rev_align_tips')
-        curr_dict.pop('align_tips')
-        pass_vals = [curr_dict[v] for v in varis_rev_align]
-        pass_vals.append('tests/test_objects/%s.pickle' % testnam)
-        pass_vals.append(testnam)
-        test_rev_align_list.append(pass_vals)
-        test_rev_align_nams.append(testnam)
-test_rev_align_vars = ",".join(varis_rev_align + ['expected', 'ID'])
diff --git a/tutorial_blog/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/tutorial_blog/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/tutorial_blog/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial_blog/ACTB_gene_tree.nh b/tutorial_blog/ACTB_gene_tree.nh
new file mode 100644
index 0000000..7b31ba0
--- /dev/null
+++ b/tutorial_blog/ACTB_gene_tree.nh
@@ -0,0 +1,787 @@
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+ENSHGLP00000012823_Hgfe:0.070687):0,
+ENSPCOP00000004772_Pcoq:0.075458):0.001055,
+((ENSMMNP00015005967_Mmon:0.028748,
+ENSBTAP00000087576_Btau:0.046643):0.013025,
+ENSMPUP00000016509_Mpfu:0.053995):0.015982):0.007658,
+ENSCPOP00000022236_Cpor:0.079189):0.009005):0.003727,
+ENSSHAP00000034056_Shar:0.200273):0):0.002191,
+(((ENSACCP00020018922_Acch:0.019864,
+ENSSHBP00005010913_Shab:0.023298):0.003174,
+ENSTGUP00000023808_Tgut:0.026651):0.016993,
+(ENSTMTP00000019023_Tctr:0.007161,
+ENSCABP00000007785_Cabi:0.016096):0.061497):0.145065):0):0.002232):0,
+ENSSCUP00000005090_Scau:0.187447):0,
+(ENSSSCP00000035521_Sscr:0.04827,
+ENSMMSP00000023940_Mmos:0.049618):0.037395):0.00471,
+ENSP00000494750_Hsap:0.03487):0.009487):0.003595):0.001272,
+(((((((ENSCJAP00000033540_Cjac:0.038381,
+ENSNLEP00000038010_Nleu:0.043193):0,
+(((((ENSCCAP00000011957_Cimi:0.056782,
+ENSSBOP00000037313_Sbbo:0.091801):0.029646,
+ENSCCAP00000038753_Cimi:0.122894):0.002229,
+ENSANAP00000021421_Anan:0.178195):0.004755,
+(ENSPPAP00000043189_Ppan:0.020925,
+ENSGGOP00000045879_Ggor:0.044666):0.124966):0.012254,
+(ENSCJAP00000052981_Cjac:0.017204,
+ENSPANP00000049738_Panu:0.049088):0.189163):0):0.010905,
+((ENSCAFP00020021730_Cldi:0.049081,
+ENSPPRP00000010335_Ppar:0.056482):0.021372,
+(ENSBIXP00005009793_Bihy:0.018179,
+ENSOARP00020032458_Oara:0.018559):0.093201):0.017176):0,
+ENSETEP00000016080_Etel:0.511479):0.001491,
+(ENSMODP00000015077_Mdom:0.058829,
+ENSMODP00000036509_Mdom:0.072093):0.189745):0,
+(((((((ENSANAP00000008429_Anan:0.045106,
+ENSCCAP00000010133_Cimi:0.07059):0.008818,
+ENSANAP00000017912_Anan:0.040665):0.025696,
+((ENSETEP00000016532_Etel:0.430206,
+ENSDNOP00000026233_Dnov:0.464403):0.084176,
+ENSVPAP00000002728_Vpac:0.110881):0.080555):0.002474,
+ENSVVUP00000028823_Vvul:0.094543):0,
+(((ENSANAP00000022419_Anan:0.026573,
+ENSANAP00000029195_Anan:0.044895):0.018846,
+(((ENSPTRP00000070878_Ptro:0,
+ENSGGOP00000030465_Ggor:0.041237):0.079068,
+ENSPPAP00000012927_Ppan:0.002173):0.104097,
+ENSNLEP00000033007_Nleu:0.139557):0.034173):0.003101,
+(ENSDNOP00000006787_Dnov:0.018136,
+ENSDNOP00000017718_Dnov:0.036566):0.240602):0.001704):0.008384,
+ENSPCIP00000033313_Pcin:0.293105):0,
+((((ENSTSYP00000022450_Csyr:0.046392,
+ENSDORP00000021331_Dord:0.080229):0.002558,
+((ENSPCTP00005029150_Pcat:0.087929,
+ENSEASP00005050795_Easi:0.343639):0.042114,
+((ENSCDRP00005006503_Cdro:0.02146,
+ENSVPAP00000009783_Vpac:0.040746):0.037685,
+ENSCDRP00005007649_Cdro:0.088939):0.058561):0.008434):0.004315,
+ENSCHOP00000009389_Chof:0.067286):0.036089,
+ENSPCIP00000047984_Pcin:0.257347):0.000405):0):0.001895,
+(ENSMGAP00000008525_Mgal:0.092439,
+ENSACAP00000033986_Acar:0.250946):0.055086):0.012821):0.008841):0.055081):0):0.015459,
+(((((((((((ENSBGRP00000028446_Bgru:0.03077,
+ENSCWAP00000026630_Cwag:0.053528):0.02872,
+ENSRFEP00010009931_Rfer:0.087881):0.013235,
+ENSLAFP00000001069_Lafr:0.109791):0.010271,
+ENSMLUP00000003287_Mluc:0.093729):0.016258,
+ENSRROP00000013928_Rrox:0.054172):0.084806,
+ENSMODP00000028889_Mdom:0.147401):0.007712,
+ENSSPUP00000003746_Spun:0.122015):0.011924,
+ENSLLEP00000010473_Llei:0.199132):0,
+(((((ENSGFOP00000013634_Gfor:0.078452,
+ENSTRUP00000052717_Trub:0.130419):0.035882,
+(((ENSOMYP00000055957_Omyk:0.061091,
+ENSELUP00000047205_Eluc:0.065897):0.008242,
+ENSHCOP00000020840_Hcom:0.130438):0,
+(ENSSSAP00000003515_Ssal:0.07032,
+ENSCARP00000062986_Caur:0.222941):0.000468):0.133165):0.029473,
+ENSPMRP00000022946_Pmur:0.159458):0.05091,
+((((ENSHHUP00000065587_Hhuc:0.174377,
+ENSIPUP00015060894_Ipun:0.27667):0.072382,
+ENSOSIP00000022327_Osin:0.349416):0.04754,
+((((((ENSODEP00000008365_Odeg:0.034021,
+ENSHGLP00000026207_Hgfe:0.218789):0.048551,
+ENSMMMP00000007241_Mmma:0.265425):0.062063,
+ENSPANP00000011336_Panu:0.190994):0,
+((((ENSCHYP00000014058_Chya:0,
+ENSCHYP00000014456_Chya:0.012622):0.635737,
+ENSOARP00020050132_Oara:0.382089):0.051793,
+ENSBMSP00010017483_Bmus:0.271229):0.006305,
+ENSVVUP00000018607_Vvul:0.391026):0):0.041909,
+ENSPCIP00000040460_Pcin:0.257276):0.005269,
+(((((((ENSCJAP00000063529_Cjac:0.127407,
+ENSRROP00000002108_Rrox:0.181303):0.062603,
+ENSUPAP00010014141_Upar:0.178295):0.226725,
+ENSMODP00000046602_Mdom:0.142146):0,
+(ENSNNAP00000016754_Nnaj:0.333419,
+ENSACAP00000034528_Acar:0.684806):0.047356):0,
+((ENSMZEP00005017013_Mzeb:0.215092,
+ENSOSIP00000017571_Osin:0.303332):0,
+ENSIPUP00015010749_Ipun:0.29034):0.241894):0.176615,
+((((((MGP_CAROLIEiJ_P0085691_Mcar:0.099273,
+ENSMSIP00000037655_Mspi:0.374726):0.094854,
+(ENSMMMP00000006155_Mmma:0.024828,
+ENSSVLP00005014829_Svul:0.141119):0.180412):0.101063,
+ENSODEP00000010393_Odeg:0.108395):0.005886,
+(((((ENSCATP00000027886_Caty:0.0064,
+ENSMLEP00000014308_Mleu:0.019985):0.006027,
+ENSRROP00000007242_Rrox:0.040899):0.186046,
+ENSMNEP00000028540_Mnem:0.126093):0.019116,
+((ENSCATP00000036355_Caty:0.00164,
+ENSMLEP00000027131_Mleu:0.030823):0.290809,
+ENSRBIP00000028717_Rbie:0.131921):0.065394):0,
+ENSPSMP00000023186_Psim:0.173891):0.091341):0.017224,
+((ENSDLEP00000013074_Dleu:0.015765,
+ENSMMNP00015015680_Mmon:0.043572):0.232003,
+ENSVVUP00000008444_Vvul:0.045878):0.035835):0,
+((((ENSRBIP00000042021_Rbie:0.112676,
+ENSANAP00000040754_Anan:0.211866):0.007808,
+ENSODEP00000018903_Odeg:0.156322):0.050851,
+((ENSUAMP00000030750_Uame:0.013651,
+ENSAMEP00000039179_Amel:0.018348):0.058976,
+ENSCAFP00020021726_Cldi:0.078299):0.163714):0.051953,
+ENSDNOP00000024771_Dnov:0.186347):0.148826):0.211446):0.088876,
+(((((ENSNBRP00000001396_Nbri:0.012527,
+ENSACLP00000004228_Acal:0.048146):0.08168,
+ENSONIP00000079493_Onil:0.047412):0.397341,
+ENSPREP00000008026_Pret:0.263282):0.02369,
+ENSCSEP00000015222_Csem:0.137531):0.035461,
+ENSHHUP00000020645_Hhuc:0.198111):0.189342):0.148529):0.088432):0.037246,
+((((((ENSMAUP00000002345_Maur:0.042245,
+MGP_PahariEiJ_P0038219_Mpah:0.043719):0.150655,
+MGP_SPRETEiJ_P0044573_Mspr:0.378263):0,
+(((MGP_SPRETEiJ_P0032951_Mspr:0.060894,
+ENSNGAP00000007198_Ngal:0.11398):0.010662,
+ENSODEP00000011317_Odeg:0.103055):0.010889,
+ENSUPAP00010024737_Upar:0.046054):0.008891):0.126594,
+ENSETEP00000007263_Etel:0.285203):0.028025,
+ENSCAFP00845018851_Clfa:0.123115):0.007457,
+ENSCABP00000013444_Cabi:0.053588):0.056964):0.060827):0.020074,
+((ENSRBIP00000015006_Rbie:0.008087,
+ENSCJAP00000090873_Cjac:0.096267):0.199817,
+((ENSSPAP00000010111_Spar:0.15284,
+ENSCSEP00000030104_Csem:0.340673):0,
+ENSMPUP00000000536_Mpfu:0.315729):0.314971):0.078906):0.020288):0,
+(((ENSGALP00010030654_Ggal:0.043686,
+ENSPSIP00000012044_Psin:0.101719):0.075024,
+((MGP_PahariEiJ_P0060155_Mpah:0.013825,
+MGP_CAROLIEiJ_P0071942_Mcar:0.016016):0.043045,
+ENSFCAP00000061043_Fcat:0.128355):0.084841):0,
+((((ENSSMAP00000010635_Smax:0.085358,
+ENSTRUP00000034556_Trub:0.165897):0,
+ENSOJAP00000026444_Ojav:0.059555):0.077616,
+((((ENSONIP00000017925_Onil:0.09557,
+ENSPREP00000024996_Pret:0.136571):0.018539,
+ENSCGOP00000050699_Cgob:0.119623):0.00959,
+ENSOMEP00000012372_Omel:0.203609):0.026668,
+ENSAPOP00000005891_Apol:0.12419):0.116271):0.028445,
+ENSSFOP00015015826_Sfor:0.56478):0.019653):0.003952):0,
+(((((ENSLLEP00000029632_Llei:0,
+ENSLLEP00000028471_Llei:0):0,
+ENSLLEP00000028680_Llei:0):0.080439,
+ENSXETP00000104495_Xtro:0.153678):0.083696,
+ENSCJPP00005000306_Cjap:0.090556):0.023121,
+ENSOMEP00000004125_Omel:0.126614):0.011667):0.027486):0,
+((((ENSNGAP00000010045_Ngal:0.060921,
+MGP_SPRETEiJ_P0074762_Mspr:0.071463):0.024851,
+(ENSOARP00020007520_Oara:0.041326,
+ENSPTIP00000026358_Ptal:0.075114):0.048776):0.042122,
+ENSTSYP00000019285_Csyr:0.121431):0.014608,
+ENSSARP00000011001_Sara:0.108377):0.051038):0.01149,
+(((((ENSVPAP00000008805_Vpac:0.064551,
+ENSCAFP00845000805_Clfa:0.15138):0.022448,
+ENSDNOP00000031248_Dnov:0.275179):0.041589,
+((ENSTBEP00000009337_Tbel:0.062274,
+ENSFALP00000013198_Falb:0.144691):0.093463,
+(ENSHBUP00000011993_Hbur:0.053765,
+ENSONIP00000043239_Onil:0.074491):0.315846):0.063448):0.079657,
+ENSIPUP00015010233_Ipun:0.144075):0,
+ENSEBUP00000012604_Ebur:0.557613):0.059642):0.037651):0.005773,
+(((ENSLACP00000001195_Lcha:0.080689,
+ENSCMIP00000045182_Cmil:0.153401):0.051383,
+ENSBSLP00000003003_Bspl:0.134506):0.009441,
+((ENSAMEP00000030772_Amel:0.077364,
+ENSLLEP00000019823_Llei:0.174247):0.009096,
+ENSAMEP00000021512_Amel:0.082879):0.089255):0.015224):0.080379):0.001044,
+(ENSTNIP00000022061_Tnig:0.044321,
+ENSTRUP00000086165_Trub:0.093283):0.108698):0.026769):0.020015,
+(ENSLACP00000005475_Lcha:0.259192,
+T25C8.2.1_Cele:0.300188):0.032213):0.051711,
+ENSDNOP00000019480_Dnov:0.228649):0.039416,
+ENSCVAP00000024024_Cvar:0.344675):0,
+YFL039C_Scer:0.393292);
diff --git a/tutorial_blog/ACTB_gene_tree_small.nh b/tutorial_blog/ACTB_gene_tree_small.nh
new file mode 100644
index 0000000..25d8ecb
--- /dev/null
+++ b/tutorial_blog/ACTB_gene_tree_small.nh
@@ -0,0 +1 @@
+((((((ENSCJAP00000063529_Cjac:0.127407,ENSRROP00000002108_Rrox:0.181303)1:0.062603,ENSUPAP00010014141_Upar:0.178295)1:0.226725,ENSMODP00000046602_Mdom:0.142146)1:0,(ENSNNAP00000016754_Nnaj:0.333419,ENSACAP00000034528_Acar:0.684806)1:0.047356)1:0,((ENSMZEP00005017013_Mzeb:0.215092,ENSOSIP00000017571_Osin:0.303332)1:0,ENSIPUP00015010749_Ipun:0.29034)1:0.241894)1:0.176615,((((((MGP_CAROLIEiJ_P0085691_Mcar:0.099273,ENSMSIP00000037655_Mspi:0.374726)1:0.094854,(ENSMMMP00000006155_Mmma:0.024828,ENSSVLP00005014829_Svul:0.141119)1:0.180412)1:0.101063,ENSODEP00000010393_Odeg:0.108395)1:0.005886,(((((ENSCATP00000027886_Caty:0.0064,ENSMLEP00000014308_Mleu:0.019985)1:0.006027,ENSRROP00000007242_Rrox:0.040899)1:0.186046,ENSMNEP00000028540_Mnem:0.126093)1:0.019116,((ENSCATP00000036355_Caty:0.00164,ENSMLEP00000027131_Mleu:0.030823)1:0.290809,ENSRBIP00000028717_Rbie:0.131921)1:0.065394)1:0,ENSPSMP00000023186_Psim:0.173891)1:0.091341)1:0.017224,((ENSDLEP00000013074_Dleu:0.015765,ENSMMNP00015015680_Mmon:0.043572)1:0.232003,ENSVVUP00000008444_Vvul:0.045878)1:0.035835)1:0,((((ENSRBIP00000042021_Rbie:0.112676,ENSANAP00000040754_Anan:0.211866)1:0.007808,ENSODEP00000018903_Odeg:0.156322)1:0.050851,((ENSUAMP00000030750_Uame:0.013651,ENSAMEP00000039179_Amel:0.018348)1:0.058976,ENSCAFP00020021726_Cldi:0.078299)1:0.163714)1:0.051953,ENSDNOP00000024771_Dnov:0.186347)1:0.148826)1:0.211446)1:0.088876;
\ No newline at end of file
diff --git a/tutorial_blog/MT_CO1_gene_tree.nh b/tutorial_blog/MT_CO1_gene_tree.nh
new file mode 100644
index 0000000..f7c6a80
--- /dev/null
+++ b/tutorial_blog/MT_CO1_gene_tree.nh
@@ -0,0 +1,10 @@
+((((((ficedula_albicollis,
+parus_major),
+((gallus_gallus,
+meleagris_gallopavo),
+coturnix_japonica)),
+struthio_camelus_australis),
+crocodylus_porosus),
+(chrysemys_picta_bellii,
+pelodiscus_sinensis)),
+anolis_carolinensis);
diff --git a/tutorial_blog/MT_CO2_gene_tree.nh b/tutorial_blog/MT_CO2_gene_tree.nh
new file mode 100644
index 0000000..7b08f04
--- /dev/null
+++ b/tutorial_blog/MT_CO2_gene_tree.nh
@@ -0,0 +1,99 @@
+(((((((((((((ENSCARP00000000005_Caur:0.041447,
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+ENSCHAP00020000005_Char:0.160903):0.032125,
+(((((((((((ENSPREP00000000005_Pret:0.099647,
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+ENSCVAP00000000005_Cvar:0.08759):0.037526,
+ENSORLP00000025884_Olat:0.203794):0.02592,
+((ENSNBRP00000000005_Nbri:0.067413,
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+ENSMZEP00005000005_Mzeb:0.059166):0.058166):0.028812,
+ENSAOCP00000000005_Aoce:0.172918):0,
+ENSHCOP00000000005_Hcom:0.238018):0.014469,
+ENSSLUP00000000005_Sluc:0.116457):0.013909,
+(((ENSTNIP00000002153_Tnig:0.200635,
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+((ENSSDUP00000000005_Sdum:0.033219,
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+(ENSKMAP00000000005_Kmar:0.160102,
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+ENSMAMP00000052228_Marm:0.156521):0.042141,
+(ENSSTUP00000000005_Stru:0.019248,
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+ENSLOCP00000022474_Locu:0.169218):0.015277,
+((((((((((ENSGALP00010000005_Ggal:0.065304,
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+(ENSPMJP00000000005_Pmaj:0.083338,
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+ENSSCUP00000000005_Scau:0.195926):0.175862,
+ENSCPRP00005000005_Cpor:0.398208):0.036111,
+(ENSCPBP00000000005_Cpbe:0.110614,
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+(((((((((((((ENSMAUP00000000004_Maur:0.130633,
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+(ENSMUSP00000080994_Mmus:0.108518,
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+ENSTBEP00000015467_Tbel:0.194427):0.017912,
+((((ENSCPOP00000014211_Cpor:0.172438,
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+ENSCLAP00000000004_Clan:0.200148):0.060534,
+ENSOPRP00000016374_Opri:0.162826):0.020794,
+(ENSPCAP00000016135_Pcap:0.173764,
+ENSETEP00000016586_Etel:0.313178):0.067364):0.026466):0.005121,
+ENSSTOP00000032551_Itri:0.219389):0.008202,
+(((((((ENSDLEP00000000005_Dleu:0.063208,
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+ENSVPAP00000011812_Vpac:0.259583):0.042881,
+((ENSOARP00020000005_Oara:0.069526,
+ENSCHIP00000000004_Chir:0.080106):0.054026,
+((ENSBMUP00000000005_Bmut:0.019074,
+ENSBTAP00000053151_Btau:0.072717):6.7e-05,
+ENSBBBP00000000005_Bbbi:0.012226):0.105929):0.07002):0.020544,
+ENSSSCP00000019138_Sscr:0.139466):0.027322,
+((ENSFCAP00000025712_Fcat:0.093076,
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+(ENSNVIP00000032495_Nvis:0.127221,
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+ENSECAP00000023106_Ecab:0.156021):0.016067,
+(ENSDNOP00000033880_Dnov:0.153963,
+ENSTSYP00000013699_Csyr:0.188485):0.047788):0.019541):0.018492,
+(((ENSPCOP00000000004_Pcoq:0.151917,
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+ENSEEUP00000014597_Eeur:0.362463):0.060308,
+ENSLAFP00000029494_Lafr:0.287942):0.051505):0.037183,
+((ENSMODP00000026934_Mdom:0.137933,
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+(ENSVURP00010032881_Vurs:0.123266,
+ENSPCIP00000054971_Pcin:0.171324):0.043982):0.107252):0.004889,
+ENSOANP00000024994_Oana:0.28):0.04585,
+(((((((ENSMFAP00000059237_Mfas:0.03406,
+ENSMMUP00000081259_Mmul:0.041419):0.072142,
+ENSMLEP00000000004_Mleu:0.079906):0.03603,
+ENSPANP00000061110_Panu:0.080431):0.018795,
+ENSCSAP00000000004_Csab:0.091819):0.024271,
+(ENSRBIP00000000004_Rbie:0.026245,
+ENSRROP00000000004_Rrox:0.026511):0.168926):0.064622,
+((((ENSPPAP00000000004_Ppan:0.009991,
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+ENSGGOP00000020982_Ggor:0.06165):0.032735,
+ENSPPYP00000023442_Pabe:0.086443):0.066528):0.158921,
+ENSSBOP00000000004_Sbbo:0.201044):0.15478):0.078665):0.050264,
+ENSXETP00000064135_Xtro:0.219869):0.056016,
+ENSLACP00000021808_Lcha:0.213569):0.029979):0.011642,
+ENSCMIP00000000005_Cmil:0.241746):0.098425,
+ENSCSEP00000000005_Csem:0.307313):0.078772,
+ENSPMAP00000011434_Pmar:0.229274):0.301329,
+(ENSCINP00000036591_Cint:0.112853,
+ENSCSAVP00000020156_Csav:0.200594):1.18904):0.13756,
+FBpp0100177_Dmel:0.231269):0.174944,
+MTCE.31.1_Cele:1.32369):0,
+Q0250_Scer:0.753842);
diff --git a/tutorial_blog/MT_CO2_gene_tree_small.nh b/tutorial_blog/MT_CO2_gene_tree_small.nh
new file mode 100644
index 0000000..7e1ecb2
--- /dev/null
+++ b/tutorial_blog/MT_CO2_gene_tree_small.nh
@@ -0,0 +1 @@
+(((((((((((ENSMAUP00000000004_Maur:0.130633,ENSCGRP00001000004_Cgch:0.140874)1:0.060824,ENSMOCP00000000004_Moch:0.154976)1:0.021014,(ENSMUSP00000080994_Mmus:0.108518,ENSRNOP00000046414_Rnor:0.110652)1:0.057432)1:0.045149,ENSJJAP00000000004_Jjac:0.201161)1:0.040179,ENSOCUP00000026182_Ocun:0.167131)1:0.032923,ENSTBEP00000015467_Tbel:0.194427)1:0.017912,((((ENSCPOP00000014211_Cpor:0.172438,ENSHGLP00000000004_Hgfe:0.195546)1:0.029042,ENSCLAP00000000004_Clan:0.200148)1:0.060534,ENSOPRP00000016374_Opri:0.162826)1:0.020794,(ENSPCAP00000016135_Pcap:0.173764,ENSETEP00000016586_Etel:0.313178)1:0.067364)1:0.026466)1:0.005121,ENSSTOP00000032551_Itri:0.219389)1:0.008202,(((((((ENSDLEP00000000005_Dleu:0.063208,ENSTTRP00000016617_Ttru:0.108166)1:0.061716,ENSVPAP00000011812_Vpac:0.259583)1:0.042881,((ENSOARP00020000005_Oara:0.069526,ENSCHIP00000000004_Chir:0.080106)1:0.054026,((ENSBMUP00000000005_Bmut:0.019074,ENSBTAP00000053151_Btau:0.072717)1:6.7e-05,ENSBBBP00000000005_Bbbi:0.012226)1:0.105929)1:0.07002)1:0.020544,ENSSSCP00000019138_Sscr:0.139466)1:0.027322,((ENSFCAP00000025712_Fcat:0.093076,ENSPPRP00000000004_Ppar:0.094598)1:0.074473,(ENSNVIP00000032495_Nvis:0.127221,ENSUMAP00000000004_Umar:0.16904)1:0.076001)1:0.048715)1:0.020923,ENSECAP00000023106_Ecab:0.156021)1:0.016067,(ENSDNOP00000033880_Dnov:0.153963,ENSTSYP00000013699_Csyr:0.188485)1:0.047788)1:0.019541)1:0.018492,(((ENSPCOP00000000004_Pcoq:0.151917,ENSMICP00000040078_Mmur:0.164729)1:0.115858,ENSEEUP00000014597_Eeur:0.362463)1:0.060308,ENSLAFP00000029494_Lafr:0.287942)1:0.051505)1:0.037183,((ENSMODP00000026934_Mdom:0.137933,ENSSHAP00000022395_Shar:0.213116)1:0.034538,(ENSVURP00010032881_Vurs:0.123266,ENSPCIP00000054971_Pcin:0.171324)1:0.043982)1:0.107252)1:0.004889;
\ No newline at end of file
diff --git a/tutorial_blog/blog_post.md b/tutorial_blog/blog_post.md
new file mode 100644
index 0000000..8be8f22
--- /dev/null
+++ b/tutorial_blog/blog_post.md
@@ -0,0 +1,263 @@
+---
+layout: single
+title: "plot_phylo: A Python package to plot a phylogenetic tree on an existing matplotlib axis."
+excerpt: "Pandera is a a flexible and expressive toolkit for performing statistical validation checks on pandas data structures that was recently accepted into the pyOpenSci ecosystem. Learn more about Pandera."
+author: "Katy Brown"
+permalink: /blog/plot_phylo
+header:
+    overlay_image: images/pandas.jpg
+    overlay_filter: 0.6
+    caption: "Photo credit: [**Ann Batdorf, Smithsonian's National Zoo**](https://www.flickr.com/photos/nationalzoo/5371290900/in/photostream/)"
+categories:
+  - blog-post
+  - pandas
+  - data-validation
+  - dataframes
+  - highlight
+comments: true
+---
+
+Modern data engineering and analysis workflows will often involve using data
+manipulation libraries, which, in the Python universe, would be tools like
+[pandas](https://pandas.pydata.org/). One problem you may have encountered with
+this powerful data manipulation tool is that the dataframe can be an opaque
+object that's hard to reason about in terms of its contents, data types, and
+other properties.
+
+One tool that may help you with this problem is
+[pandera](https://pandera.readthedocs.io/en/latest/index.html), which was
+accepted by pyOpenSci as part of its ecosystem of packages on September 2019.
+Pandera provides a flexible and expressive data validation toolkit that helps
+users make statistical assertions about pandas data structures.
+
+## A Statistical Data Validation Toolkit for Pandas
+
+<img src="https://github.com/unionai-oss/pandera/tree/master/docs/source/_static/pandera-logo.png"
+width="250px" alt="Image showing pandera package logo.">
+
+To illustrate `pandera`'s capabilities let's use a small toy example. Suppose
+you're analyzing data for some insights in the context of a mission-critical
+project, where it’s vital to ensure the quality of the datasets that you're
+looking at.
+
+Each row in the dataset is uniquely identified by a `person_id`, and each
+column describes that person's `height_in_cm`s and `age_category`.
+
+
+```python
+import pandas as pd
+
+dataset = pd.DataFrame(
+    data={
+        "height_in_cm": [150, 145, 122, 176, 137, 151],
+        "age_category": ["20-30", "10-20", "10-20", "20-30", "10-20", "20-30"],
+    },
+    index=pd.Series([100, 101, 102, 103, 104, 105], name="person_id"),
+)
+print(dataset)
+```
+
+```
+           height_in_cm age_category
+person_id
+100                 150        20-30
+101                 145        10-20
+102                 122        10-20
+103                 176        20-30
+104                 137        10-20
+105                 151        20-30
+```
+
+You want to ensure that some columns have the correct data type, or that the
+dataset fulfills certain statistical properties. Pandera allows you to validate
+a DataFrame to ensure that these conditions are met. It allows you to spend
+less time worrying about the correctness of a DataFrame's data so you can make
+the right assumptions in analyzing it.
+
+
+### Column Presence and Type Checking
+
+The most basic type of schema is one that simply checks that specific columns
+exist with specific datatypes.
+
+```python
+import pandera as pa
+
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(pa.Int),
+        "age_category": pa.Column(pa.String),
+    },
+    index=pa.Index(pa.Int, name="person_id"),
+)
+
+schema(dataset)
+```
+
+The `schema` object is callable, so you can validate the dataset by passing
+it in as an argument to the `schema` call. If the dataframe passes schema
+validation, `schema` simply returns the dataframe.
+
+If not, it'll provide useful error messages:
+
+
+```python
+invalid_dataframe = pd.DataFrame({
+    "weight_in_kg": [44, 31, 55, 61, 55, 62],
+    "age_category": ["20-30", "10-20", "10-20", "20-30", "10-20", "20-30"],
+})
+
+schema(invalid_dataframe)
+```
+
+```
+SchemaError: column 'height_in_cm' not in dataframe
+   weight_in_kg age_category
+0            44        20-30
+1            31        10-20
+2            55        10-20
+3            61        20-30
+4            55        10-20
+```
+
+### Basic Statistical Checks
+
+If you want to make stricter assertions about the empirical properties of the
+dataset, we can supply the `checks` keyword argument to the
+[`Column`](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#column-validation)
+and [`Index`](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#index-validation)
+constructors with a [`Check`](https://pandera.readthedocs.io/en/latest/checks.html#)
+or list of `Check`s.
+
+```python
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(
+            pa.Int,
+            # height in centimeters should be between 100 and 300
+            checks=pa.Check(lambda s: (100 < s) & (s < 300)),
+        ),
+        "age_category": pa.Column(
+            pa.String,
+            # check allowable age categories
+            checks=pa.Check(lambda s: s.isin(["10-20", "20-30"]))
+        ),
+    },
+    index=pa.Index(
+        pa.Int,
+        name="person_id",
+        checks=[
+            # id is a positive integer
+            pa.Check(lambda s: s > 0),
+
+            # id is unique
+            pa.Check(lambda s: s.duplicated().sum() == 0),
+        ]
+    ),
+)
+
+schema(dataset)
+```
+
+A `Check` object specifies the exact implementation of how to validate a
+column or index. The first positional argument in its constructor is a callable
+with the signature:
+
+```
+Callable[ pd.Series, Union[ bool, pd.Series[bool] ] ]
+```
+
+Notice that the only constraint to the callable is that takes a `Series` as
+input and returns a boolean or a boolean Series. By design, checks have access
+to the entire pandas `Series` API to make assertions about the properties of a
+particular column or index.
+
+### Indexed Error Messages
+
+In cases where the `Check` returns a boolean `Series`, violations of the
+schema are reported by the index location of failure cases.
+
+```python
+invalid_data = pd.DataFrame(
+    data={
+        "height_in_cm": [91, 105, 87, 87],
+        "age_category": ["10-20", "10-20", "10-20", "10-20"]
+    },
+    index=pd.Series([200, 201, 202, 203], name="person_id")
+)
+
+schema(invalid_data)
+```
+
+```
+pandera.errors.SchemaError: <Schema Column: 'height_in_cm' type=int64> failed element-wise validator 0:
+<lambda>
+failure cases:
+               person_id  count
+failure_case
+87            [202, 203]      2
+91                 [200]      1
+```
+
+The error is reported as a stringified dataframe where the `failure_case` index
+enumerates instances of `height_in_cm` values that failed data validation, the
+`person_id` column is the index location of the failure case, and `count`
+column displays the number of instances of a particular failure case.
+
+
+### Statistical Hypothesis Tests
+
+What if we wanted to test the hypothesis that older people tend to be taller?
+We can achieve this with the [`Hypothesis`](https://pandera.readthedocs.io/en/latest/hypothesis.html)
+check:
+
+```python
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(
+            # perform a one-sided two-sample t-test of
+            # the distribution of heights by age category,
+            # with an alpha value of 5%
+            checks=pa.Hypothesis.two_sample_ttest(
+                groupby="age_category",
+                sample1="20-30",
+                relationship="greater_than",
+                sample2="10-20",
+                alpha=0.05,
+                equal_var=True,
+            )
+        ),
+        "age_category": pa.Column(
+            pa.String,
+            checks=pa.Check(lambda s: s.isin(["10-20", "20-30"])),
+        )
+    }
+)
+
+schema(dataset)
+```
+
+## Validate your Pandas Dataframes Today!
+
+Whether you use this tool in Jupyter notebooks, one-off scripts, ETL
+pipeline code, or unit tests, `pandera` enables you to make pandas code more
+readable and robust by enforcing the deterministic and statistical properties
+of pandas data structures at runtime.
+
+Hopefully this post has given you a flavor of what `pandera` can do. It
+offers a few more features that you may find useful:
+
+- [Series schema validation](https://pandera.readthedocs.io/en/latest/series_schemas.html)
+- [Coercing column data types](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#coercing-types-on-columns)
+- [Multi-index validation](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#multiindex-validation)
+- [Vectorized vs. element-wise checks](https://pandera.readthedocs.io/en/latest/checks.html#vectorized-vs-element-wise-checks)
+- [Wide checks](https://pandera.readthedocs.io/en/latest/checks.html#wide-checks)
+- [Groupby Column Checks](https://pandera.readthedocs.io/en/latest/checks.html#column-check-groups)
+- [Check input/output decorators](https://pandera.readthedocs.io/en/latest/decorators.html)
+
+## What's Next?
+
+I'm actively developing this project and have some exciting features coming
+up soon, such as [built-in checks](https://github.com/pandera-dev/pandera/issues/74), [first-class Dask support](https://docs.dask.org/en/latest/dataframe.html),
+and [yaml schema specification](https://github.com/pandera-dev/pandera/issues/91). If you'd like to contribute to this
+project, you're welcome to head on over to the [github repo](https://github.com/pandera-dev/pandera)!
diff --git a/tutorial_blog/prefixes.txt b/tutorial_blog/prefixes.txt
new file mode 100644
index 0000000..cbb005e
--- /dev/null
+++ b/tutorial_blog/prefixes.txt
@@ -0,0 +1,339 @@
+ENSLAF	Loxodonta africana (Elephant)
+ENSEGO	Erythrura gouldiae (Gouldian finch)
+ENSBBB	Bison bison bison (American bison)
+ENSCHA	Scleropages formosus (Asian bonytongue)
+ENSHCO	Hippocampus comes (Tiger tail seahorse)
+ENSFAL	Ficedula albicollis (Collared flycatcher)
+ENSSSC	Sus scrofa (Pig - Landrace)
+ENSPPA	Pan paniscus (Bonobo)
+MGP_C3HHeJ_	Mus musculus (Mouse C3H/HeJ)
+ENSZLM	Zosterops lateralis melanops (Silver-eye)
+ENSLBE	Labrus bergylta (Ballan wrasse)
+ENSNSU	Notechis scutatus (Mainland tiger snake)
+ENSXET	Xenopus tropicalis (Tropical clawed frog)
+ENSMMU	Macaca mulatta (Macaque)
+ENSACO	Amazona collaria (Yellow-billed parrot)
+ENSSSC	Sus scrofa (Pig - Ossabaw miniature)
+ENSPMG	Periophthalmus magnuspinnatus (Periophthalmus magnuspinnatus)
+ENSOAR	Ovis aries (Sheep - Hu)
+ENSCCR	Cyprinus carpio (Common carp huanghe)
+ENSLCR	Larimichthys crocea (Large yellow croaker)
+ENSBIX	Bos indicus x Bos taurus (Hybrid - Bos Indicus)
+ENSPLO	Panthera leo (Lion)
+ENSCCR	Cyprinus carpio (Common carp german mirror)
+ENSAOC	Amphiprion ocellaris (Clown anemonefish)
+ENSAHA	Apteryx haastii (Great spotted kiwi)
+ENSSSC	Sus scrofa (Pig - Jinhua)
+ENSPPR	Panthera pardus (Leopard)
+ENSANI	Accipiter nisus (Eurasian sparrowhawk)
+ENSSOR	Sphaeramia orbicularis (Orbiculate cardinalfish)
+ENSMVI	Manacus vitellinus (Golden-collared manakin)
+ENSCGR	Cricetulus griseus (Chinese hamster CriGri)
+ENSPVI	Pogona vitticeps (Central bearded dragon)
+ENSOAR	Ovis aries (Sheep - Romanov)
+ENSCHI	Capra hircus (Goat)
+ENSAPO	Acanthochromis polyacanthus (Spiny chromis)
+ENSMOD	Monodelphis domestica (Opossum)
+MGP_AJ_	Mus musculus (Mouse A/J)
+ENSCCR	Cyprinus carpio (Common carp hebao red)
+ENSTSY	Carlito syrichta (Tarsier)
+ENSBSL	Betta splendens (Siamese fighting fish)
+ENSSDU	Seriola dumerili (Greater amberjack)
+ENSNML	Neogobius melanostomus (Round goby)
+ENSUOD	Sus scrofa domesticus
+ENSLCA	Lates calcarifer (Barramundi perch)
+ENSVUR	Vombatus ursinus (Common wombat)
+ENSMUN	Melopsittacus undulatus (Budgerigar)
+ENSCGR	Cricetulus griseus (Chinese hamster CHOK1GS)
+MGP_AKRJ_	Mus musculus (Mouse AKR/J)
+ENSMCS	Malurus cyaneus samueli (Superb fairywren)
+ENSCAF	Canis lupus familiaris (Dog - Boxer)
+ENSOAR	Ovis aries (Sheep - Polled Dorset)
+ENSPNA	Pygocentrus nattereri (Red-bellied piranha)
+ENSEEE	Electrophorus electricus (Electric eel)
+ENSSSC	Sus scrofa (Pig)
+ENSPRN	Parambassis ranga (Indian glassy fish)
+ENSGAC	Gasterosteus aculeatus (Three-spined sticklebacki - Marine BAM)
+ENSBGR	Bos grunniens (Domestic yak)
+ENSEAS	Equus asinus (Donkey)
+ENSGMO	Gadus morhua (Atlantic cod - Celtic sea)
+ENSFDA	Fukomys damarensis (Damara mole rat)
+ENSUOD	Sus scrofa domesticus (Pig - Ningxiang)
+ENSPSI	Pelodiscus sinensis (Chinese softshell turtle)
+ENSPMA	Petromyzon marinus (Lamprey)
+ENSCHO	Choloepus hoffmanni (Sloth)
+ENSKMA	Kryptolebias marmoratus (Mangrove rivulus)
+ENSSMR	Salvator merianae (Argentine black and white tegu)
+ENSTNI	Tetraodon nigroviridis (Tetraodon)
+ENSLCN	Lynx canadensis (Canada lynx)
+ENSSTU	Salmo trutta (Brown trout)
+ENSDAR	Danio rerio (Zebrafish)
+ENSGAL	Gallus gallus (Chicken (Red Jungle fowl))
+ENSSLU	Sander lucioperca (Pike-perch)
+ENSONI	Oreochromis niloticus (Nile tilapia)
+ENSCPI	Chrysolophus pictus (Golden pheasant)
+ENSSSC	Sus scrofa (Pig - Hampshire)
+ENSCPV	Camarhynchus parvulus (Small tree finch)
+ENSCAF	Canis lupus familiaris (Dog - Basenji)
+ENSSAR	Sorex araneus (Shrew)
+ENSACD	Anser cygnoides (Swan goose)
+ENSMPU	Mustela putorius furo (Ferret)
+ENSGEV	Gopherus evgoodei (Goodes thornscrub tortoise)
+ENSXCO	Xiphophorus couchianus (Monterrey platyfish)
+ENSPCO	Propithecus coquereli (Coquerel's sifaka)
+ENSACU	Athene cunicularia (Burrowing owl)
+ENSCAT	Cercocebus atys (Sooty mangabey)
+ENSGGO	Gorilla gorilla gorilla (Gorilla)
+ENSPPY	Pongo abelii (Sumatran orangutan)
+ENSCLM	Cyclopterus lumpus (Lumpfish)
+ENSSSC	Sus scrofa (Pig - Wuzhishan)
+ENSOAR	Ovis aries (Sheep - Qiaoke)
+MGP_NZOHlLtJ_	Mus musculus (Mouse NZO/HlLtJ)
+MGP_C57BL6NJ_	Mus musculus (Mouse C57BL/6NJ)
+ENSCAP	Cavia aperea (Brazilian guinea pig)
+ENSMEU	Notamacropus eugenii (Wallaby)
+MGP_FVBNJ_	Mus musculus (Mouse FVB/NJ)
+ENSSSC	Sus scrofa (Pig - Berkshire)
+ENSDNO	Dasypus novemcinctus (Armadillo)
+MGP_NODShiLtJ_	Mus musculus (Mouse NOD/ShiLtJ)
+ENSJJA	Jaculus jaculus (Lesser Egyptian jerboa)
+ENSBMS	Balaenoptera musculus (Blue whale)
+ENSRNO	Rattus norvegicus (Rat - SHRSP/BbbUtx)
+ENSOAR	Ovis aries (Sheep - White dorper)
+ENSAMX	Astyanax mexicanus (Pachon cavefish)
+ENSSSC	Sus scrofa (Pig USMARC)
+ENSPCI	Phascolarctos cinereus (Koala)
+ENSBTA	Bos taurus (Cow)
+ENSMAM	Mastacembelus armatus (Zig-zag eel)
+ENSPNY	Pundamilia nyererei (Makobe Island cichlid)
+ENSDOR	Dipodomys ordii (Kangaroo rat)
+ENSRRO	Rhinopithecus roxellana (Golden snub-nosed monkey)
+ENSBJA	Buteo japonicus (Eastern buzzard)
+ENSOAR	Ovis aries (Sheep - Chinese merino)
+ENSIPU	Ictalurus punctatus (Channel catfish)
+ENSCPU	Calidris pugnax (Ruff)
+ENSOAN	Ornithorhynchus anatinus (Platypus)
+ENSRNO	Rattus norvegicus (Rat - SHR/Utx RGD_8142385)
+ENSPKI	Paramormyrops kingsleyae (Paramormyrops kingsleyae)
+ENSCWA	Catagonus wagneri (Chacoan peccary)
+ENSENL	Echeneis naucrates (Live sharksucker)
+ENSRBI	Rhinopithecus bieti (Black snub-nosed monkey)
+ENSACA	Anolis carolinensis (Green anole)
+ENSNBR	Neolamprologus brichardi (Lyretail cichlid)
+ENSTGE	Theropithecus gelada (Gelada)
+ENSSSA	Salmo salar (Atlantic salmon - North American origin Brian)
+ENSPAN	Papio anubis (Olive baboon)
+ENSTRU	Takifugu rubripes (Fugu)
+ENSEBU	Eptatretus burgeri (Hagfish)
+ENSMAL	Monopterus albus (Swamp eel)
+ENSACI	Amphilophus citrinellus (Midas cichlid)
+ENSGAG	Gopherus agassizii (Agassiz's desert tortoise)
+ENSPCT	Physeter catodon (Sperm whale)
+ENSLCO	Lepidothrix coronata (Blue-crowned manakin)
+ENSDLE	Delphinapterus leucas (Beluga whale)
+ENSCJP	Coturnix japonica (Japanese quail)
+ENSSGR	Sinocyclocheilus grahami (Golden-line barbel)
+ENSARW	Apteryx rowi (Okarito brown kiwi)
+ENSGAC	Gasterosteus aculeatus (Three-spined sticklebacki - Marine SYL)
+ENSACL	Astatotilapia calliptera (Eastern happy)
+ENSSPU	Sphenodon punctatus (Tuatara)
+ENSORL	Oryzias latipes (Japanese medaka HdrR)
+MGP_DBA2J_	Mus musculus (Mouse DBA/2J)
+ENSSCA	Serinus canaria (Common canary)
+ENSRNO	Rattus norvegicus (Rat - WKY/Bbb RGD_1581635)
+ENSNVI	Neovison vison (American mink)
+ENSSAU	Sparus aurata (Gilthead seabream)
+ENSHHU	Hucho hucho (Huchen)
+ENSSSC	Sus scrofa (Pig - NIHS-2020)
+ENSMAU	Mesocricetus auratus (Golden Hamster)
+ENSOKI	Oncorhynchus kisutch (Coho salmon)
+ENSMSI	Mus spicilegus (Steppe mouse)
+ENSVKK	Varanus komodoensis (Komodo dragon)
+ENSHBU	Haplochromis burtoni (Burton's mouthbrooder)
+ENSOAR	Ovis aries (Sheep - Kermani)
+ENSGAL	Gallus gallus (Chicken)
+ENSPSM	Prolemur simus (Greater bamboo lemur)
+ENSSBO	Saimiri boliviensis boliviensis (Bolivian squirrel monkey)
+ENSAOW	Apteryx owenii (Little spotted kiwi)
+ENSOTS	Oncorhynchus tshawytscha (Chinook salmon)
+ENSSSC	Sus scrofa (Pig - PB115)
+ENSCIN	Ciona intestinalis
+ENSMMS	Moschus moschiferus (Siberian musk deer)
+ENSPFO	Poecilia formosa (Amazon molly)
+ENSHGLF	Heterocephalus glaber (Naked mole-rat male)
+ENSRFE	Rhinolophus ferrumequinum (Greater horseshoe bat)
+ENSPMJ	Parus major (Great Tit)
+ENSVPA	Vicugna pacos (Alpaca)
+ENSPTE	Piliocolobus tephrosceles (Ugandan red Colobus)
+ENSGAA	Gasterosteus aculeatus aculeatus (Stickleback)
+ENSSSU	Suricata suricatta (Meerkat)
+ENSCAB	Chelonoidis abingdonii (Abingdon island giant tortoise)
+ENSAPE	Amphiprion percula (Orange clownfish)
+ENSOCU	Oryctolagus cuniculus (Rabbit)
+ENSSMA	Scophthalmus maximus (Turbot)
+ENSCGO	Cottoperca gobio (Channel bull blenny)
+ENSCAF	Canis lupus dingo (Dingo)
+ENSHGL	Heterocephalus glaber (Naked mole-rat)
+ENSEEU	Erinaceus europaeus (Hedgehog)
+ENSAPL	Anas platyrhynchos (Mallard)
+ENSCLA	Chinchilla lanigera (Long-tailed chinchilla)
+ENSSHB	Strigops habroptila (Kakapo)
+ENSPEM	Peromyscus maniculatus bairdii (Northern American deer mouse)
+ENSDNV	Dromaius novaehollandiae (Emu)
+ENSMMO	Mola mola (Ocean sunfish)
+ENSDLA	Dicentrarchus labrax (European seabass)
+MGP_PWKPhJ_	Mus musculus musculus (Mouse PWK/PhJ)
+MGP_LPJ_	Mus musculus (Mouse LP/J)
+ENSCAR	Carassius auratus (Goldfish)
+ENSECR	Erpetoichthys calabaricus (Reedfish)
+ENSECA	Equus caballus (Horse)
+ENSPMR	Podarcis muralis (Common wall lizard)
+ENSCSA	Chlorocebus sabaeus (Vervet-AGM)
+ENSPSN	Phocoena sinus (Vaquita)
+ENSPCA	Procavia capensis (Hyrax)
+ENSAMX	Astyanax mexicanus (Mexican tetra)
+ENSLAC	Latimeria chalumnae (Coelacanth)
+ENSSDA	Spermophilus dauricus (Daurian ground squirrel)
+ENSPME	Poecilia mexicana (Shortfin molly)
+ENSOPR	Ochotona princeps (Pika)
+ENSCRF	Cyanoderma ruficeps (Rufous-capped babbler)
+ENSCJA	Callithrix jacchus (White-tufted-ear marmoset)
+ENSPVA	Pteropus vampyrus (Megabat)
+ENSBIX	Bos indicus x Bos taurus (Hybrid - Bos Taurus)
+ENSCHI	Capra hircus (Goat - Xinong Saanen Dairy)
+ENSMLE	Mandrillus leucophaeus (Drill)
+ENSNGA	Nannospalax galili (Upper Galilee mountains blind mole rat)
+ENSSSC	Sus scrofa (Pig - Largewhite)
+ENSCDR	Camelus dromedarius (Arabian camel)
+ENSSPA	Stegastes partitus (Bicolor damselfish)
+ENSSSC	Sus scrofa (Pig - Tibetan)
+ENSMZE	Maylandia zebra (Zebra mbuna)
+ENSGMO	Gadus morhua (Atlantic cod)
+MGP_CAROLIEiJ_	Mus caroli (Ryukyu mouse)
+ENSSSC	Sus scrofa (Pig - Pietrain)
+ENSGFO	Geospiza fortis (Medium ground-finch)
+ENSOSI	Oryzias sinensis (Chinese medaka)
+ENSPCL	Phasianus colchicus (Ring-necked pheasant)
+ENSCAF	Canis lupus familiaris (Dog)
+ENSFCA	Felis catus (Cat)
+ENSCSR	Chelydra serpentina (Common snapping turtle)
+ENSPCE	Pelusios castaneus (West African mud turtle)
+ENSCPO	Cavia porcellus (Guinea Pig)
+ENSCHY	Cervus hanglu yarkandensis (Yarkand deer)
+ENSPTI	Panthera tigris altaica (Tiger)
+ENSETE	Echinops telfairi (Lesser hedgehog tenrec)
+ENSSTO	Ictidomys tridecemlineatus (Squirrel)
+ENSUAM	Ursus americanus (American black bear)
+ENSSRH	Sinocyclocheilus rhinocerous (Horned golden-line barbel)
+ENSSSA	Salmo salar (Atlantic salmon)
+ENSSSC	Sus scrofa (Pig - Rongchang)
+ENSOGA	Otolemur garnettii (Bushbaby)
+ENSPTR	Pan troglodytes (Chimpanzee)
+ENSCMM	Cairina moschata domestica (Muscovy Duck (domestic type))
+ENSCUS	Catharus ustulatus (Swainson's thrush)
+ENSUPA	Urocitellus parryii (Arctic ground squirrel)
+MGP_BALBcJ_	Mus musculus (Mouse BALB/cJ)
+ENSATE	Anabas testudineus (Climbing perch)
+ENSSSC	Sus scrofa (Pig - Meishan)
+ENSPST	Pavo cristatus (Indian peafowl)
+ENSOAB	Oreochromis aureus (Blue tilapia)
+ENSAZO	Anas zonorhyncha (Eastern spot-billed duck)
+ENSOJA	Oryzias javanicus (Javanese ricefish)
+ENSFHE	Fundulus heteroclitus (Mummichog)
+ENSCSAV	Ciona savignyi
+ENSMMN	Monodon monoceros (Narwhal)
+ENSSSC	Sus scrofa (Pig - Bama miniature)
+ENSCCE	Cyanistes caeruleus (Blue tit)
+ENSMUG	Meriones unguiculatus (Mongolian gerbil)
+ENSAME	Ailuropoda melanoleuca (Giant panda)
+ENSBOB	Bubo bubo (Eurasian eagle-owl)
+ENSNME	Numida meleagris (Helmeted guineafowl)
+ENSCHI	Capra hircus (Goat (black bengal))
+ENSOME	Oryzias melastigma (Indian medaka)
+ENSACC	Aquila chrysaetos chrysaetos (Golden eagle)
+ENSMLU	Myotis lucifugus (Microbat)
+ENSGAL	Gallus gallus (Chicken (paternal White leghorn layer))
+ENSCMI	Callorhinchus milii (Elephant shark)
+ENSORL	Oryzias latipes (Japanese medaka HNI)
+ENSCAF	Canis lupus familiaris (Dog - Great Dane)
+ENSJHY	Junco hyemalis (Dark-eyed junco)
+ENSLSD	Lonchura striata domestica (Bengalese finch)
+ENSUMA	Ursus maritimus (Polar bear)
+MGP_129S1SvImJ_	Mus musculus (Mouse 129S1/SvImJ)
+MGP_WSBEiJ_	Mus musculus domesticus (Mouse WSB/EiJ)
+ENSODE	Octodon degus (Degu)
+ENSCPG	Calidris pygmaea (Spoon-billed sandpiper)
+ENSSHA	Sarcophilus harrisii (Tasmanian devil)
+ENSMUS	Mus musculus (Mouse)
+ENSCVA	Cyprinodon variegatus (Sheepshead minnow)
+ENSCSE	Cynoglossus semilaevis (Tongue sole)
+ENSCAN	Colobus angolensis palliatus (Angola colobus)
+ENSCGR	Cricetulus griseus (Chinese hamster PICR)
+ENSLLT	Laticauda laticaudata (Blue-ringed sea krait)
+ENSRNO	Rattus norvegicus (Rat)
+ENSTBE	Tupaia belangeri (Tree Shrew)
+ENSMMD	Myripristis murdjan (Pinecone soldierfish)
+ENSZAL	Zonotrichia albicollis (White-throated sparrow)
+ENSOAR	Ovis aries (Sheep)
+ENSSVL	Sciurus vulgaris (Eurasian red squirrel)
+ENSSOC	Strix occidentalis caurina (Northern spotted owl)
+ENS	Homo sapiens (Human)
+ENSSSA	Salmo salar (Atlantic salmon - North American Atlantic Salmon)
+ENSUTT	Ursus thibetanus thibetanus (Asiatic black bear)
+ENSMNE	Macaca nemestrina (Pig-tailed macaque)
+ENSNNA	Naja naja (Indian cobra)
+ENSSSA	Salmo salar (Atlantic salmon - European origin ALTA)
+ENSABR	Anser brachyrhynchus (Pink-footed goose)
+ENSSSC	Sus scrofa (Pig - Bamei)
+ENSELU	Esox lucius (Northern pike)
+ENSMMM	Marmota marmota marmota (Alpine marmot)
+ENSNPE	Nothoprocta perdicaria (Chilean tinamou)
+ENSPRE	Poecilia reticulata (Guppy)
+ENSAPL	Anas platyrhynchos platyrhynchos (Duck)
+ENSMIC	Microcebus murinus (Mouse Lemur)
+MGP_CBAJ_	Mus musculus (Mouse CBA/J)
+ENSCCA	Cebus imitator (Panamanian white-faced capuchin)
+ENSLLE	Leptobrachium leishanense (Leishan spiny toad)
+ENSCCR	Cyprinus carpio carpio (Common carp)
+MGP_CASTEiJ_	Mus musculus castaneus (Mouse CAST/EiJ)
+ENSORL	Oryzias latipes (Japanese medaka HSOK)
+ENSCAF	Canis lupus familiaris (Dog - German Shepherd)
+ENSBMU	Bos mutus (Wild yak)
+ENSMGA	Meleagris gallopavo (Turkey)
+ENSMFA	Macaca fascicularis (Crab-eating macaque)
+ENSZCA	Zalophus californianus (California sea lion)
+ENSDCD	Denticeps clupeoides (Denticle herring)
+MGP_PahariEiJ_	Mus pahari (Shrew mouse)
+ENSLOC	Lepisosteus oculatus (Spotted gar)
+ENSMOC	Microtus ochrogaster (Prairie vole)
+ENSCMU	Corvus moneduloides (New Caledonian crow)
+ENSCHA	Clupea harengus (Atlantic herring )
+ENSGWI	Gouania willdenowi (Blunt-snouted clingfish)
+ENSFTI	Falco tinnunculus (Common kestrel)
+ENSSCU	Struthio camelus australis (African ostrich)
+ENSGAC	Gasterosteus aculeatus (Three-spined sticklebacki - Freshwater BOT)
+MGP_SPRETEiJ_	Mus spretus (Algerian mouse)
+ENSTTR	Tursiops truncatus (Dolphin)
+ENSSFA	Salarias fasciatus (Jewelled blenny)
+ENSVVU	Vulpes vulpes (Red fox)
+ENSSSC	Sus scrofa (Pig - Duroc)
+ENSSAN	Sinocyclocheilus anshuiensis (Blind barbel)
+ENSANA	Aotus nancymaae (Ma's night monkey)
+ENSSSC	Sus scrofa (Pig - euw1 (european wild boar))
+ENSCPB	Chrysemys picta bellii (Painted turtle)
+ENSPTX	Pseudonaja textilis (Eastern brown snake)
+ENSXMA	Xiphophorus maculatus (Platyfish)
+ENSPLA	Poecilia latipinna (Sailfin molly)
+ENSTMT	Terrapene carolina triunguis (Three-toed box turtle)
+ENSCCN	Castor canadensis (American beaver)
+ENSNLE	Nomascus leucogenys (Gibbon)
+ENSCHI	Capra hircus (Goat - Saanen Dairy)
+ENSGAF	Gambusia affinis (Western mosquitofish)
+ENSOAR	Ovis aries (Sheep (texel))
+ENSOAR	Ovis aries (Sheep - East friesian)
+ENSOMY	Oncorhynchus mykiss (Rainbow trout)
+ENSCPR	Crocodylus porosus (Australian saltwater crocodile)
+ENSSLD	Seriola lalandi dorsalis (Yellowtail amberjack)
+ENSNFU	Nothobranchius furzeri (Turquoise killifish)
+ENSTGU	Taeniopygia guttata (Zebra finch)
+ENSOSU	Otus sunia (Oriental scops-owl)
diff --git a/tutorial_blog/tutorial.ipynb b/tutorial_blog/tutorial.ipynb
new file mode 100644
index 0000000..b540125
--- /dev/null
+++ b/tutorial_blog/tutorial.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0a31082b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plot_phylo\n",
+    "\n",
+    "import ete3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "887f393c",
+   "metadata": {},
+   "source": [
+    "## 1. Make a blank figure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292a74f8",
+   "metadata": {},
+   "source": [
+    "The first step is to make an empty figure and subplot where we can display our phylogenetic tree. \n",
+    "\n",
+    "To do this, we use [matplotlib](https://matplotlib.org/), a plotting package, directly. \n",
+    "\n",
+    "You need to import matplotlib and, if working in a Jupyter notebook, allow matplotlib to directly plot in the notebook (`%matplotlib inline`)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "11434122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the matplotlib package\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Allow Jupyter to plot using matplotlib\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b511da69",
+   "metadata": {},
+   "source": [
+    "Now we can make the blank figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ca79c53",
+   "metadata": {},
+   "source": [
+    "* `plt.figure` generates a blank figure, `figsize=(8, 6)` means that this figure will have a width of 8 inches and a height of 6 inches.\n",
+    "* `fig.add_subplot` adds an \"ax\" to this figure - an area in which to plot data. The ax is named `subp`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "cc5c9e82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make the figure\n",
+    "fig = plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "# Add an ax\n",
+    "subp = fig.add_subplot(111)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c4deb69",
+   "metadata": {},
+   "source": [
+    "## 2. Draw a basic phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09b33249",
+   "metadata": {},
+   "source": [
+    "First, let's draw a phylogenetic tree with the default settings.\n",
+    "\n",
+    "The tree is in newick format and is a clade extracted from the gene tree for the actin beta (ACTB) gene (ENSG00000075624) downloaded from Ensembl release 113 on Feb 4 2025. It is saved as `ACTB_gene_tree_small.nh`.\n",
+    "\n",
+    "This tree doesn't have branch support included, so I will turn this off.\n",
+    "\n",
+    "* `plot_phylo` is the main plotting function in this package\n",
+    "* `tree=\"ACTB_gene_tree_small.nh\"` - the tree in newick format is in the file ACTB_gene_tree_small.nh\n",
+    "* `ax=subp` - use the ax `subp` which we previously created\n",
+    "* `show_support=False` - don't display the branch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "b2e5f31a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make the figure\n",
+    "fig = plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "# Add an ax\n",
+    "subp = fig.add_subplot(111)\n",
+    "\n",
+    "\n",
+    "# Draw the tree\n",
+    "tree1 = plot_phylo.plot_phylo(tree=\"MT_CO2_gene_tree_small.nh\",\n",
+    "                              ax=subp,\n",
+    "                              show_support=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98ff36fc",
+   "metadata": {},
+   "source": [
+    "## 3. Add more informative tip labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddcb9716",
+   "metadata": {},
+   "source": [
+    "The tip labels in this tree are not particularly informative, we can improve this by building a dictionary and passing it to `plot_phylo`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6988f99c",
+   "metadata": {},
+   "source": [
+    "The tip labels here start with the [Ensembl Stable ID prefixes](https://www.ensembl.org/info/genome/stable_ids/prefixes.html) which I have saved in a file, `prefixes.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "05524173",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pref_dict = dict()\n",
+    "for line in open(\"prefixes.txt\").readlines():\n",
+    "    pref, defi = line.strip().split(\"\\t\")\n",
+    "    pref_dict[pref] = defi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "cae585a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = dict()\n",
+    "for tip_lab in tree1:\n",
+    "    pref = tip_lab[0:6]\n",
+    "    if pref in pref_dict:\n",
+    "        labels[tip_lab] = pref_dict[pref]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "384d8a9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rD = dict()\n",
+    "for key, val in labels.items():\n",
+    "    rD[val] = key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "1e88a75a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make the figure\n",
+    "fig = plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "# Add an ax\n",
+    "subp = fig.add_subplot(111)\n",
+    "\n",
+    "\n",
+    "# Draw the tree\n",
+    "tree2 = plot_phylo.plot_phylo(tree=\"MT_CO2_gene_tree_small.nh\",\n",
+    "                              ax=subp,\n",
+    "                              show_support=False,\n",
+    "                              label_dict=labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1122b011",
+   "metadata": {},
+   "source": [
+    "## 4. Add an outgroup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "61be74ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make the figure\n",
+    "fig = plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "# Add an ax\n",
+    "subp = fig.add_subplot(111)\n",
+    "\n",
+    "\n",
+    "# Draw the tree\n",
+    "tree3 = plot_phylo.plot_phylo(tree=\"MT_CO2_gene_tree_small.nh\",\n",
+    "                              ax=subp,\n",
+    "                              show_support=False,\n",
+    "                              label_dict=labels,\n",
+    "                              outgroup='ENSPCIP00000054971_Pcin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "661ad1ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rodents = ['ENSMAUP00000000004_Maur',\n",
+    "           'ENSCGRP00001000004_Cgch',\n",
+    "           'ENSMOCP00000000004_Moch',\n",
+    "           'ENSMUSP00000080994_Mmus',\n",
+    "           'ENSRNOP00000046414_Rnor',\n",
+    "           'ENSJJAP00000000004_Jjac',\n",
+    "           'ENSCPOP00000014211_Cpor',\n",
+    "           'ENSHGLP00000000004_Hgfe',\n",
+    "           'ENSCLAP00000000004_Clan',\n",
+    "           'ENSSTOP00000032551_Itri']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "221ddf72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colours = {}\n",
+    "for rodent in rodents:\n",
+    "    colours[rodent] = 'skyblue'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "d6e2711c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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joVCICRMmMGbMGH7961/z0ksv4XA4+MMf/kDz5s3DXYfbtm3Lu+++yzfffENcXBz/+Mc/OHDgQJ15admyJSaTiX/961/ccccdbNiwgSeeeKJGup07d7Jv375afzAQQgghhDhrNN3ww8bLzs5WgMrOzm7qrJz2GppAZerUqap79+4R22bOnKkyMjLCnz///HOVlZWlzGaz6tatm1q6dKkC1MKFC5VSSr388suqR48eym63q+joaHXRRRepNWvWhI8vKChQN998s0pISFAWi0V16dJFLVq0KLz/nXfeUZ06dVJGo1G1bNlS/f3vf6+3DP/4xz9UWlqaslqtasSIEeq///1vxAQus2bNUjExMTXK6na71f3336/S0tKUyWRSbdu2Va+99lp4/5///GeVmpqqNE1TEydODB9z9913q8TERGU2m9XAgQPVqlWrwsc88cQTKisrS1mtVhUfH6/GjBmjtm/fXmd9B4NB9Ze//EVlZGSEy1s5GUtdk8BUnZhm7dq1ClA7duwIb/v000/VgAEDlNVqVdHR0apfv37q5ZdfDu+veq+qaui4X375RZ1//vnKarWqHj16qM8++yxiEpijLbtSSp1//vnqxRdfDH+ubbIZpZSaMWOG0uv16r///a8qLCxUN954o4qJiQnf882bN4fTFhQUqDFjxqioqCiVnJysHnnkEXXTTTepMWPGhNNUnwTmjTfeUJmZmcpsNqv+/furDz/8sEY+nnzySTVixIh6yyOEEEIIcabTlKpnANZpYs2aNfTu3bvRY+CEEKeHjz/+mN/97nds2LAh3IX3dOT1emnXrh3z58+PmARICCGEEOJsc0Z0ARVCnJlGjRrFli1b2LdvX3j83elo165dPPzwwxL8CSGEEOKsJwGgEOKkuvfee5s6Cw1q3759nUuXCCGEEEKcTU7fPllCCCGEEEIIIU4oCQCFEEIIIYQQ4hwhAaAQQgghhBBCnCMkABRCCCGEEEKIc4QEgEKcgSZNmsQVV1zR1NloUkOHDuW+++5r6mywadMmUlNTKS0tbeqsiFrs3LkTTdNYt27dCT93Xl4eSUlJ7Nu374SfWwghhDhZJAAU4hj4fD4SExP5y1/+Uuv+p556isTERHw+3ynOWeOdzBfjU+G9997jiSeeaOps8PDDD/Ob3/wGh8MR3qaU4uWXX+a8884jKiqK2NhY+vTpw7PPPovL5WrC3IpjVduPLsnJydx4441MnTq1aTIlhBBCHAMJAIU4BiaTiRtuuIHZs2ejlKqxf9asWdx4442YTKYmyN2ZrzGBc3x8fETQ1RT27t3Lhx9+yM033xyx/cYbb+S+++5jzJgxLFmyhHXr1vHoo4/ywQcf8NlnnzVRbsXJcPPNNzNv3jyKioqaOitCCCFEo0gAKMQxuuWWW9i2bRvLly+P2L5ixQq2bNnCLbfcQigU4s9//jPp6emYzWZ69OjBp59+Gk5b2Qq3YMECBg0ahNVqpW/fvmzevJnVq1fTp08foqKiGDlyJIcOHaqRh8cff5zk5GSio6O5/fbbIwKnTz/9lAsuuIDY2FgSEhK47LLL2LZtW3h/q1atAOjZsyeapjF06FCg9q6VV1xxBZMmTQp/fv7552nXrh0Wi4WUlBTGjh1bZz1NmzaNHj16RGx79tlnyczMDH+ubF156qmnaNasWXhNvvquUz2fmZmZPPnkk0yePBmHw0HLli15+eWXw/uXLl2KpmkUFxeHt61btw5N09i5cydQviD86NGjiYuLw26307lzZz7++OM6y7ZgwQK6d+9Oenp6xLZ58+Yxf/58/vSnP9G3b18yMzMZM2YMX331FRdeeCHASXs2KuvyySefJCUlhdjYWB5//HECgQBTpkwhPj6e9PR0XnvttYiyrF+/nmHDhmG1WklISOC2226jrKysxnmfeeYZ0tLSSEhI4De/+Q1+v7/Wujl8+DB6vZ7s7GygvFU0Pj6evn37htPMnz+ftLS0o87D0ZYNYOPGjQwYMACLxULnzp1ZunRpeF8wGOSWW26hVatWWK1WOnTowHPPPRfeP23aNObMmcMHH3yApmlomhY+vmvXrqSmprJw4cJa60EIIYQ43UgAKMQx6tq1K3379mXWrFkR21977TX69etHly5deO6555gxYwbPPPMMP/30EyNGjODyyy9ny5YtEcdMnTqVRx55hDVr1mAwGBg/fjwPPfQQzz33HCtWrGDbtm089thjEcd8+eWX5OTksGTJEubPn8/ChQt5/PHHw/udTicPPPAAq1ev5ssvv0Sn03HllVcSCoUAWLVqFQBffPEFubm5vPfee40q9w8//MA999zDn//8ZzZt2sSnn37K4MGDj7r+qqssz+eff86iRYuO6TozZsygT58+rF27lrvuuos777yTjRs3NjoPv/nNb/B6vSxfvpz169fz9NNPExUVVWf65cuX06dPn4ht8+bNo0OHDowZM6ZGek3TiImJATipz8ZXX33F/v37Wb58Of/4xz+YNm0al112GXFxcXz//ffccccd3HHHHezZswcAl8vFyJEjiYuLY/Xq1bz99tt88cUX/Pa3v40475IlS9i2bRtLlixhzpw5zJ49m9mzZ9daNzExMfTo0SMcKP3000/h/y0pKQHKg/IhQ4YcVR6OtmyVpkyZwoMPPsjatWsZMGAAl19+OQUFBUB5MJ6ens6CBQv45ZdfeOyxx/jTn/7EggULAPjd737Htddey8iRI8nNzSU3N5cBAwaEz92vXz9WrFhRaz0IIYQQpx11BsjOzlaAys7ObuqsCBHhhRdeUHa7XZWWliqllCotLVV2u1299NJLSimlmjVrpv76179GHNO3b1911113KaWU2rFjhwLUK6+8Et4/f/58Bagvv/wyvO2pp55SHTp0CH+eOHGiio+PV06nMyIvUVFRKhgM1prXvLw8Baj169dHXHvt2rUR6YYMGaLuvffeiG1jxoxREydOVEop9e6776ro6GhVUlLSUPUopZSaOnWq6t69e8S2mTNnqoyMjIjypKSkKK/XG97W0HWq5zMjI0PdcMMN4c+hUEglJyerF154QSml1JIlSxSgioqKwmnWrl2rALVjxw6llFJdu3ZV06ZNa1S5lFKqe/fu6s9//nPEtqysLHX55Zc3eOzJfDYyMjIinoMOHTqoQYMGhT8HAgFlt9vV/PnzlVJKvfzyyyouLk6VlZWF03z00UdKp9OpAwcORJw3EAiE01xzzTVq3LhxdZbxgQceUJdddplSSqlnn31WjR07VvXq1Ut99NFHSiml2rdvH74/R5OHoylbZT1Onz49nMbv96v09HT19NNP15n3u+66S1199dXhzxMnTlRjxoypNe3999+vhg4dWue5hBBCiNOJoakCT9F4+fn5LF68mMzMTKxWa1Nn55zRsWNHbDZbvWnGjx/PAw88wFtvvcUtt9zCW2+9hVKK6667jpKSEvbv38/AgQMjjhk4cCA//vhjxLZu3bqF/52SkgKUtzBW3ZaXlxdxTPfu3SPy179/f8rKytizZw8ZGRls27aNRx99lO+++478/Pxwy9/u3bvp0qXLUdREpIsvvpiMjAxat27NyJEjGTlyJFdeeWWDddWQrl27RoyZPJbrVK1HTdNITU2tUW/1ueeee7jzzjv57LPPGD58OFdffXXEOatzu91YLJaIbUopNE2r9zon+9no3LkzOp0uIk3Ve67X60lISAgfl5OTQ/fu3bHb7RF5CYVCbNq0KXzdzp07o9frw2nS0tJYv359neUcOnQor776KqFQiGXLlnHRRRfRsmVLli1bRq9evdi8eXO4BfBo8nA0ZavUv3//8L8NBgN9+vQhJycnvO3FF1/klVdeYdeuXbjdbnw+X42uy3WxWq0yuY8QQogzhgSAZ4DFixdzww03NHU2zjnZ2dn06tWr3jQxMTGMHTuWWbNmccsttzBr1izGjh1LdHR0uJtb9WCgtgDBaDSG/125r/q2ygCuIZXHjx49mhYtWvCf//yHZs2aEQqF6NKlS4MTrOh0uhoT21Qd5+VwOFizZg1Lly7ls88+47HHHmPatGmsXr2a2NjYoz5fpaov/sdyHYisM4ist8qgoWpequfj1ltvZcSIEXz00Ud89tlnPPXUU8yYMYO777671uslJibWmPyjffv2EYFFfU7Ws1FbPdRXN/UFrVW313eO2gwePJjS0lLWrFnDihUreOKJJ2jRogVPPvkkPXr0IDk5maysrOPOw9Hmq/p5FyxYwP3338+MGTPo378/DoeDv//973z//fcNngOgsLCQpKSkRqUVQgghmpoEgGeAysky5s6dG35ZEidfx44dG5XulltuYejQoSxatIivv/6aJ598EoDo6GiaNWvGypUrI8auffPNN/Tr1++48/fjjz/idrvDrcLfffcdUVFRpKenU1BQQE5ODi+99BKDBg0CYOXKlRHHV7a2BYPBiO1JSUnk5uaGPweDQTZs2BCevATKW1CGDx/O8OHDmTp1KrGxsXz11VdcddVVNfKZlJTEgQMHIl7wG7v0xNFcpyGVL+i5ubnExcXVmY8WLVqEx5H98Y9/5D//+U+dAWDPnj355ZdfIrZdf/31XHfddXzwwQc1xgEqpSgpKSEmJuakPhtHq1OnTsyZMwen0xkOxL/++mt0Ol14Qp5jUTkO8P/+7//QNI1OnTrRrFkz1q5dy6JFi8KtfyczD5W+++67cF0HAgGys7PD4wtXrFjBgAEDuOuuu8Lpq06YBOXfl+rflUobNmwIT6IkhBBCnO4kADwDVL7gZ2VlNdgiJU69IUOG0LZtW2666Sbatm0b8UI/ZcoUpk6dSps2bejRowezZs1i3bp1zJs377iv6/P5uOWWW3jkkUfYtWsXU6dO5be//S06nY64uDgSEhJ4+eWXSUtLY/fu3fzhD3+IOD45ORmr1cqnn35Keno6FouFmJgYhg0bxgMPPMBHH31EmzZtmDlzZsTMmYsWLWL79u0MHjyYuLg4Pv74Y0KhEB06dKg1n0OHDuXQoUP87W9/Y+zYsXz66ad88sknREdH11u+o71OQ9q2bUuLFi2YNm0af/nLX9iyZQszZsyISHPfffdx6aWX0r59e4qKivjqq6/q/dFlxIgR3HrrrQSDwXDXyGuvvZaFCxcyfvx4Hn30US6++GKSkpJYv349M2fO5O677+aKK644qc/G0ZowYQJTp05l4sSJTJs2jUOHDnH33Xdz4403hrteHquhQ4fy3HPPceWVV6JpGnFxcXTq1Im33nqLf/7zn6ckDwD//ve/adeuHVlZWcycOZOioiImT54MlD8b//3vf1m8eDGtWrXi9ddfZ/Xq1eGZcqH8h7jFixezadMmEhISiImJwWg04nK5yM7ODv/wI4QQQpzuZBZQIU6AyZMnR7xQVrrnnnt48MEHefDBB+natSuffvopH374Ie3atTvua1500UW0a9eOwYMHc+211zJ69GimTZsGlHd3fPPNN8nOzqZLly7cf//9/P3vf4843mAw8M9//pOXXnqJZs2ahVurJk+ezMSJE7npppsYMmQIrVq1imj9i42N5b333mPYsGFkZWXx4osvMn/+fDp37lxrPrOysnj++ef597//Tffu3Vm1ahW/+93vGizf0V6nIUajkfnz57Nx40a6d+/O008/zV/+8peINMFgkN/85jdkZWUxcuRIOnTowPPPP1/nOUeNGoXRaOSLL74Ib9M0jTfeeIN//OMfLFy4kCFDhtCtWzemTZvGmDFjGDFiBHByn42jZbPZWLx4MYWFhfTt25exY8dy0UUX8X//93/Hfe4LL7yQYDAY0UI2ZMgQgsFgRAvgycwDwPTp03n66afp3r07K1as4IMPPiAxMRGAO+64g6uuuopx48Zx3nnnUVBQENEaCPDrX/+aDh060KdPH5KSkvj6668B+OCDD2jZsmW4pV0IIYQ43Wmq+uCc09CaNWvo3bt3o8ZknY3O9fILcTp7/vnn+eCDD1i8eHFTZ0U0gX79+nHfffdx/fXXN3VWhBBCiEaRLqBCCHEcbrvtNoqKiigtLcXhcDR1dsQplJeXx9ixYxk/fnxTZ0UIIYRoNAkAhRDiOBgMBh5++OGmzoZoAsnJyTz00ENNnQ0hhBDiqMgYQCGEEEIIIYQ4R0gAKIQQQgghhBDnCAkAhRBCCCGEEOIcIQGgEEIIIYQQQpwjJAAUQpyzli5diqZpEQvdNyQzM5Nnn3220el37tyJpmmsW7euzjSzZ88mNja20edsDE3TeP/990/oOU+VU5X3wYMH88Ybb5z065wp1q9fT3p6Ok6ns6mzIoQQ4iSSAFAIccJlZmaiaRpvvvlmjX2dO3dG0zRmz5596jN2mho3bhybN29u6mycctOmTaNHjx41tufm5nLppZee1GsvWrSIAwcOcN1110Vs/+abbxg1ahRxcXFYLBa6du3KjBkzCAaDJzU/p4OuXbvSr18/Zs6c2dRZEUIIcRJJACiEOClatGjBrFmzIrZ99913HDhwALvd3kS5Oj1ZrVaSk5ObOhunjdTUVMxm80m9xj//+U9uvvlmdLoj/ze4cOFChgwZQnp6OkuWLGHjxo3ce++9/PWvf+W6665DKXVS83Q6uPnmm3nhhRfOiYBXCCHOVRIACiFOigkTJrBs2TL27NkT3vbaa68xYcIEDIbIJUh3797NmDFjiIqKIjo6mmuvvZaDBw+G91e2FL3++utkZmYSExPDddddR2lpaTiN1+vlnnvuITk5GYvFwgUXXMDq1asjrvPxxx/Tvn17rFYrF154ITt37qyR72+++YbBgwdjtVpp0aIF99xzT71d4jRN44UXXuDSSy/FarXSqlUr3n777Rrptm/fzoUXXojNZqN79+58++234X3Vu4D++OOPXHjhhTgcDqKjo+nduzc//PBDnXnYsmULgwcPxmKx0KlTJz7//PMaadavX8+wYcOwWq0kJCRw2223UVZWFt4/adIkrrjiCp588klSUlKIjY3l8ccfJxAIMGXKFOLj40lPT+e1116LOO++ffsYN24ccXFxJCQkMGbMmIh6Xbp0Kf369cNutxMbG8vAgQPZtWsXs2fP5vHHH+fHH39E07SIVuHqXUD37t3LddddR3x8PHa7nT59+vD9998DsG3bNsaMGUNKSgpRUVH07duXL774os66AsjPz+eLL77g8ssvD29zOp38+te/5vLLL+fll1+mR48eZGZmcuuttzJnzhzeeecdFixYABzp1vvmm28yYMAALBYLnTt3ZunSpRHXWbZsGf369cNsNpOWlsYf/vAHAoFAeP8777xD165dw/dk+PDh4Wetrnqreq+quu+++xg6dGj489ChQ7n77ru57777iIuLIyUlhZdffhmn08nNN9+Mw+GgTZs2fPLJJxHnGTFiBAUFBSxbtqzeOhRCCHHmkgBQCHFSpKSkMGLECObMmQOAy+XirbfeYvLkyRHplFJcccUVFBYWsmzZMj7//HO2bdvGuHHjItJt27aN999/n0WLFrFo0SKWLVvG9OnTw/sfeugh3n33XebMmcOaNWto27YtI0aMoLCwEIA9e/Zw1VVXMWrUKNatW8ett97KH/7wh4hrrF+/nhEjRnDVVVfx008/8dZbb7Fy5Up++9vf1lvWRx99lKuvvpoff/yRG264gfHjx5OTkxOR5uGHH+Z3v/sd69ato3379owfPz4iGKhqwoQJpKens3r1arKzs/nDH/6A0WisNW0oFOKqq65Cr9fz3Xff8eKLL/L73/8+Io3L5WLkyJHExcWxevVq3n77bb744osa5frqq6/Yv38/y5cv5x//+AfTpk3jsssuIy4uju+//5477riDO+64IxzUu1wuLrzwQqKioli+fDkrV64kKiqKkSNH4vP5CAQCXHHFFQwZMoSffvqJb7/9lttuuw1N0xg3bhwPPvggnTt3Jjc3l9zc3Br3HKCsrIwhQ4awf/9+PvzwQ3788UceeughQqFQeP+oUaP44osvWLt2LSNGjGD06NHs3r27zvu1cuVKbDYbWVlZ4W2fffYZBQUF/O53v6uRfvTo0bRv35758+dHbJ8yZQoPPvgga9euZcCAAVx++eUUFBQA5YHxqFGj6Nu3Lz/++CMvvPACr776Kn/5y1+A8m6u48ePZ/LkyeTk5LB06VKuuuoqlFL11tvRmDNnDomJiaxatYq7776bO++8k2uuuYYBAwawZs0aRowYwY033ojL5QofYzKZ6N69OytWrDiqawkhhDiDqDNAdna2AlR2dnZTZ6VJnOvlF2eejIwMNXPmTPX++++rNm3aqFAopObMmaN69uyplFIqJiZGzZo1Syml1Geffab0er3avXt3+Piff/5ZAWrVqlVKKaWmTp2qbDabKikpCaeZMmWKOu+885RSSpWVlSmj0ajmzZsX3u/z+VSzZs3U3/72N6WUUn/84x9VVlaWCoVC4TS///3vFaCKioqUUkrdeOON6rbbbosoy4oVK5ROp1NutzuibJUAdccdd0Qcc95556k777xTKaXUjh07FKBeeeWVGuXLyclRSik1a9YsFRMTE97vcDjU7Nmz66visMWLFyu9Xq/27NkT3vbJJ58oQC1cuFAppdTLL7+s4uLiVFlZWTjNRx99pHQ6nTpw4IBSSqmJEyeqjIwMFQwGw2k6dOigBg0aFP4cCASU3W5X8+fPV0op9eqrr6oOHTpE1KnX61VWq1UtXrxYFRQUKEAtXbq01rxPnTpVde/evcb2qnl/6aWXlMPhUAUFBY2qD6WU6tSpk/rXv/5V5/6ZM2eq1q1bR2ybPn16xLNQ3eWXX66ysrKUUkfu6fTp08P7/X6/Sk9PV08//bRSSqk//elPNerm3//+t4qKilLBYDD8d33nzp01rtVQvU2cOFGNGTMmYtu9996rhgwZEv48ZMgQdcEFF4Q/V967G2+8MbwtNzdXAerbb7+NONeVV16pJk2aVOu1hRBCnPkMtcSEQjSZ/Px8Fi9eTGZmJlartamzI2rRsWNHbDZbo9L+6le/4vbbb2f58uW89tprNVr/AHJycmjRogUtWrQIb+vUqROxsbHk5OTQt29foHxiGYfDEU6TlpZGXl4eUN466Pf7GThwYHi/0WikX79+4Za4nJwczj///IhWlP79+0fkJTs7m61btzJv3rzwNqUUoVCIHTt2RLQYVVX9PP37968x62e3bt0i8g6Ql5dHx44da5zvgQce4NZbb+X1119n+PDhXHPNNbRp06bWa+fk5NCyZUvS09PrzE9OTg7du3ePGHs5cOBAQqEQmzZtIiUlBSifoKfqmLiUlBS6dOkS/qzX60lISAjXe2V9Vb0vAB6Ph23btnHJJZcwadIkRowYwcUXX8zw4cO59tprw+VvjHXr1tGzZ0/i4+Nr3e90Onn88cdZtGgR+/fvJxAI4Ha7620BdLvdWCyWWvepOsb5KaVqtMBVrWeDwUCfPn0inrf+/ftHHDNw4EDKysrYu3cv3bt356KLLqJr166MGDGCSy65hLFjxxIXF0d8fPxx1xtEPnOV965r167hbZX3vfJ+VrJarRGtgkIIIc4uEgCK08rixYu54YYbmjoboh7Z2dn06tWrUWkNBgM33ngjU6dO5fvvv2fhwoU10tT2Yl3b9updIDVNC3cDrHxpr36equeo68W+qlAoxO23384999xTY1/Lli0bPL56/qqqmv/KfZX5r27atGlcf/31fPTRR3zyySdMnTqVN998kyuvvLJG2trKVV891Je2tjqur95DoRC9e/eOCJgrJSUlATBr1izuuecePv30U9566y0eeeQRPv/8c84///xa81NdQz8ETZkyhcWLF/PMM8/Qtm1brFYrY8eOxefz1XlMYmIiRUVFEdvat28PlAduAwYMqHHMxo0b6dSpU4P5rfq81XYfKtPo9Xo+//xzvvnmGz777DP+9a9/8fDDD/P999/TqlWreutNp9PVuO9+v79GXhq6n3U9h4WFhXX+4CCEEOLMJwGgOK1kZmYCMHfu3DpbW0TTqq3Fqj6TJ0/mmWeeCU8UUl2nTp3YvXs3e/bsCbcC/vLLLxw+fLjRz0Dbtm0xmUysXLmS66+/Hih/If7hhx+47777wtepvrbcd999F/G5V69e/Pzzz7Rt2/aoyvjdd99x0003RXzu2bPnUZ2juvbt29O+fXvuv/9+xo8fz6xZs2oNACvrb//+/TRr1gwgYoKZyjRz5szB6XSGWwG//vprdDpdOPA5Fr169eKtt94iOTmZ6OjoOtP17NmTnj178sc//pH+/fvzxhtvcP7552MymRqcbbJbt2688sorFBYW1toKuGLFCiZNmhSum7Kyslon96menwMHDlBUVBR+Ji+55BLi4+OZMWNGjQDwww8/ZMuWLTzxxBMR27/77jsGDx4MQCAQIDs7OzyuslOnTrz77rsRgeA333yDw+GgefPmQHkANnDgQAYOHMhjjz1GRkYGCxcu5IEHHqi33pKSktiwYUNEXtatW1fnONGjtWHDBsaOHXtCziWEEOL0IwGgOK1U/tqflZXV6FYmcXrLysoiPz+/zm6jw4cPp1u3bkyYMIFnn32WQCDAXXfdxZAhQ+jTp0+jrmG327nzzjvDs1W2bNmSv/3tb7hcLm655RYA7rjjDmbMmMEDDzzA7bffTnZ2do21CH//+99z/vnn85vf/IZf//rX2O12cnJy+Pzzz/nXv/5V5/Xffvtt+vTpwwUXXMC8efNYtWoVr776auMqqBq3282UKVMYO3YsrVq1Yu/evaxevZqrr7661vTDhw+nQ4cO3HTTTcyYMYOSkhIefvjhiDQTJkxg6tSpTJw4kWnTpnHo0CHuvvtubrzxxnA3wGMxYcIE/v73vzNmzBj+/Oc/k56ezu7du3nvvfeYMmUKfr+fl19+mcsvv5xmzZqxadMmNm/eHA6WMzMz2bFjB+vWrSM9PR2Hw1Fj+Yfx48fz5JNPcsUVV/DUU0+RlpbG2rVradasGf3796dt27a89957jB49Gk3TePTRR+tsWa3Us2dPkpKS+Prrr7nsssuA8mfopZde4rrrruO2227jt7/9LdHR0Xz55Zfh+3HttddGnOff//437dq1Iysri5kzZ1JUVBTu5nzXXXfx7LPPcvfdd/Pb3/6WTZs2MXXqVB544AF0Oh3ff/89X375JZdccgnJycl8//33HDp0iKysLHbs2FFvvQ0bNoy///3v/Pe//6V///7MnTuXDRs2HPePDlA+w+m+ffsYPnz4cZ9LCCHE6UlmARVCnHQJCQl1duWrnPI/Li6OwYMHM3z4cFq3bs1bb711VNeYPn06V199NTfeeCO9evVi69atLF68ONzC07JlS959913+97//0b17d1588UWefPLJiHN069aNZcuWsWXLFgYNGkTPnj159NFHGxx79fjjj/Pmm2/SrVs35syZw7x58xrVXbA2er2egoICbrrpJtq3b8+1117LpZdeyuOPP15rep1Ox8KFC/F6vfTr149bb72Vv/71rxFpbDYbixcvprCwkL59+zJ27Fguuugi/u///u+Y8lj1vMuXL6dly5ZcddVVZGVlMXnyZNxuN9HR0dhsNjZu3MjVV19N+/btw4HV7bffDsDVV1/NyJEjufDCC0lKSqoxyyaUz0r52WefkZyczKhRo+jatSvTp09Hr9cDMHPmTOLi4hgwYACjR49mxIgRDf54pNfrmTx5co2uq2PHjmXJkiXs2bOHwYMH06FDB/7xj3/w8MMP8+abb9bo0jl9+nSefvrp8KyZH3zwAYmJiQA0b96cjz/+mFWrVtG9e3fuuOMObrnlFh555BEAoqOjWb58OaNGjaJ9+/Y88sgjzJgxg0svvbTBehsxYgSPPvooDz30EH379qW0tDSiBfp4zJ8/n0suuYSMjIwTcj4hhBCnH001ZmBME1uzZg29e/c+qrFHZ5NzqfznUlnF2UHTNBYuXFhjXTZxejt48CCdO3cmOzv7qIOdnTt30qpVK9auXUuPHj1OTgabgNfrpV27dsyfPz9iQiUhhBBnF2kBFEIIcc5JSUnh1VdfrXe20HPNrl27ePjhhyX4E0KIs5yMARRCCHFOGjNmTFNn4bRSOfGQEEKIs5sEgEIIcRzOgF704gTLzMyU+y6EEOKMJQGgaJRTtUB75SLKbrf7pF1DCCGEEEKIc5UEgKJRTvUC7Tt37pRxKEIIIYQQQpxgEgCKRjlVC7Tn5ORwww03hK8nhBBCCCGEOHEkABSNcqoXaD+Z3UyFOJc1ZgmD2bNnc99991FcXAzAtGnTeP/991m3bt1JyVP1650KQ4cOpUePHjz77LNHnTYzM5P77ruP++67r85jfD4fnTp1Ys6cOWdtb4amXA7jZDyT1e9r1SVe8vLy6Ny5M+vWraN58+Yn7JpCCNEUJAAUQoijtHbtWh599FFWrVpFSUkJqampnHfeefz73/8OLwR+Jhs3bhyjRo1q6mycVO+99x5Go/GY0q5evRq73V7vMS+//DIZGRkMHDiQ2bNnc/PNN9ebfsmSJQwdOrRR+TldtGjRgtzc3LPimYf672tycjI33ngjU6dO5ZVXXjnFORNCiBNL1gEUQoijkJeXx/Dhw0lMTGTx4sXk5OTw2muvkZaWhsvlOubz+ny+E5jL42O1WklOTm7qbBw3v99f57b4+HgcDkejzlM9bVJSEjabrd5j/vWvf3HrrbcC5QF1bm5u+L/+/fvz61//OmLbgAEDGlus04Zeryc1NRWD4ez4Lbmh+3rzzTczb948ioqKTmGuhBDixJMAUAghjsI333xDSUkJr7zyCj179qRVq1YMGzaMZ599lpYtWwIQDAa55ZZbaNWqFVarlQ4dOvDcc89FnGfSpElcccUVPPXUUzRr1iy8/trevXu57rrriI+Px26306dPH77//vvwcS+88AJt2rTBZDLRoUMHXn/99YjzaprGCy+8wKWXXorVaqVVq1a8/fbbNcqxfft2LrzwQmw2G927d+fbb78N75s9ezaxsbF11sHQoUNrdH+84oormDRpUvjz888/T7t27bBYLKSkpDB27Nh66xXKJ5vKysoiKiqKkSNHkpubG963evVqLr74YhITE4mJiWHIkCGsWbOmRtlffPFFxowZg91u5y9/+QvTpk2jR48evPbaa7Ru3Rqz2YxSqkYZ6stv9bSZmZn1dh1ds2YNW7du5Ve/+hVQHlCnpqaG/zOZTNhstvDndevWMWzYMGJjY0lISOCyyy5j27Zt4fP5fD5++9vfkpaWhsViITMzk6eeeiqi3C+99BKXXXYZNpuNrKwsvv32W7Zu3crQoUOx2+30798/4pzbtm1jzJgxpKSkEBUVRd++ffniiy8iypGZmcmTTz7J5MmTcTgctGzZkpdffjm8f+fOnWiaFu6GeTTP/TPPPENaWhoJCQn85je/qTVYr2r69OmkpKTgcDi45ZZb8Hg8NdLMmjWLrKwsLBYLHTt25Pnnnw/v69+/P3/4wx8i0h86dAij0ciSJUvC5a3vvnbt2pXU1FQWLlxYb16FEOJ0JwGgEEIchdTUVAKBAAsXLqxzLbhQKER6ejoLFizgl19+4bHHHuNPf/oTCxYsiEj35ZdfkpOTw+eff86iRYsoKytjyJAh7N+/nw8//JAff/yRhx56iFAoBMDChQu59957efDBB9mwYQO33347N998c/gFttKjjz7K1VdfzY8//sgNN9zA+PHjw0usVHr44Yf53e9+x7p162jfvj3jx48nEAickDr64YcfuOeee/jzn//Mpk2b+PTTTxk8eHC9x7hcLp555hlef/11li9fzu7du/nd734X3l9aWsrEiRNZsWIF3333He3atWPUqFGUlpZGnGfq1KmMGTOG9evXM3nyZAC2bt3KggULePfdd2sdM3Ys+a3P8uXLad++PdHR0Y1K73Q6eeCBB1i9ejVffvklOp2OK6+8Mnzf//nPf/Lhhx+yYMECNm3axNy5c2tMlPXEE09w0003sW7dOjp27Mj111/P7bffzh//+Ed++OEHAH7729+G05eVlTFq1Ci++OIL1q5dy4gRIxg9ejS7d++OOO+MGTPo06cPa9eu5a677uLOO+9k48aNtZajsc/9kiVL2LZtG0uWLGHOnDnMnj2b2bNn11k/CxYsYOrUqfz1r3/lhx9+IC0tLSK4A/jPf/7Dww8/zF//+ldycnJ48sknefTRR5kzZw4AEyZMYP78+RHf2bfeeouUlBSGDBlS57Wr69evHytWrGh0eiGEOC2pM0B2drYCVHZ2dlNnpUmcDuU/VXk4HcoqREP+9Kc/KYPBoOLj49XIkSPV3/72N3XgwIF6j7nrrrvU1VdfHf48ceJElZKSorxeb3jbSy+9pBwOhyooKKj1HAMGDFC//vWvI7Zdc801atSoUeHPgLrjjjsi0px33nnqzjvvVEoptWPHDgWoV155Jbz/559/VoDKyclRSik1a9YsFRMTE94/depU1b179/DnIUOGqHvvvTfiGmPGjFETJ05USin17rvvqujoaFVSUlJHbUSaNWuWAtTWrVvD2/7973+rlJSUOo8JBALK4XCo//3vfxFlv++++yLSTZ06VRmNRpWXlxexvWoZGspv9fJmZGSomTNn1pm3e++9Vw0bNqzO/bXVX1V5eXkKUOvXr1dKKXX33XerYcOGqVAoVGt6QD3yyCPhz99++60C1KuvvhreNn/+fGWxWOq8plJKderUSf3rX/8Kf87IyFA33HBD+HMoFFLJycnqhRdeUEodeZbWrl1b5zlre+4zMjJUIBAIb7vmmmvUuHHj6jxH//79a32mqz6TLVq0UG+88UZEmieeeEL1799fKVVepwaDQS1fvjzivFOmTIkob9X7CqiFCxdGnPP+++9XQ4cOrTOvQghxJjg7Ou6Ls0blAvDVWyuEOFU6duzY4Piuv/71rzzwwAN89dVXfPfdd7z44os8+eSTLF++nK5duwLw4osv8sorr7Br1y7cbjc+n6/GTIldu3bFZDKFP69bt46ePXsSHx9f63VzcnK47bbbIrYNHDiwRje7/v371/hcveWrW7du4X+npaUB5eMbO3bsWG/ZG+Piiy8mIyOD1q1bM3LkSEaOHMmVV15Zb73abDbatGkTkae8vLzw57y8PB577DG++uorDh48SDAYxOVy1Wix6tOnT41zZ2RkkJSUdELzWx+3243FYml0+m3btvHoo4/y3XffkZ+fH2752717N126dGHSpElcfPHFdOjQgZEjR3LZZZdxySWXRJyj6v1MSUkBCD+Llds8Hg8lJSVER0fjdDp5/PHHWbRoEfv37ycQCOB2u2vUZ9XzappGampqxH2prjHPfefOndHr9eHPaWlprF+/vs5z5uTkcMcdd0Rs69+/f7jl+9ChQ+zZs4dbbrmFX//61+E0gUCAmJgYoHx838UXX8y8efMYNGgQO3bs4Ntvv+WFF16o87q1sVqtxzXWVwghTgcSAIrTys6dOwFO6aLzQlSVnZ3dqKVOEhISuOaaa7jmmmt46qmn6NmzJ8888wxz5sxhwYIF3H///cyYMYP+/fvjcDj4+9//HjGWD6gx42Bjlj/RNC3is1KqxrbGHFd1VsvKfZWBR0N0Ol2N7q9Vx3A5HA7WrFnD0qVL+eyzz3jssceYNm0aq1evrnNsYfUZOTVNi7jGpEmTOHToEM8++ywZGRmYzWb69+9fY/Kc2mZxbGjGzmPJb30SExPrDWiqGz16NC1atOA///kPzZo1IxQK0aVLl3DZevXqxY4dO/jkk0/44osvuPbaaxk+fDjvvPNO+By13c/67vGUKVNYvHgxzzzzDG3btsVqtTJ27Nga9VnbfanrOWnsc38052yMymP/85//cN5550XsqxpoTpgwgXvvvZd//etfvPHGG3Tu3Jnu3bsf1bUKCwvr/TFBCCHOBBIAitPKiBEjwuNbZC1A0RSOpQXMZDLRpk0bnE4nACtWrGDAgAHcdddd4TRVJ+CoS7du3XjllVcoLCystRUwKyuLlStXctNNN4W3ffPNN2RlZUWk++677yLSfPfdd/Ts2fOoy1WXpKSkiAlagsEgGzZs4MILLwxvMxgMDB8+nOHDhzN16lRiY2P56quvuOqqq47pmitWrOD5558PL0+xZ88e8vPzj68gVZzI/Pbs2ZMXXnihUcF5QUEBOTk5vPTSSwwaNAiAlStX1kgXHR3NuHHjGDduHGPHjmXkyJF1PieNsWLFCiZNmsSVV14JlI8JrPwB7lgd63PfkKysrFqf6UopKSk0b96c7du3M2HChDrPc8UVV3D77bfz6aef8sYbb3DjjTcedV42bNhwxi3XIYQQ1UkAKE4riYmJ9f4fuBBNbdGiRbz55ptcd911tG/fHqUU//vf//j444+ZNWsWAG3btuW///0vixcvplWrVrz++uusXr2aVq1a1Xvu8ePH8+STT4ZnB01LS2Pt2rU0a9aM/v37M2XKFK699lp69erFRRddxP/+9z/ee++9GrM3vv322/Tp04cLLriAefPmsWrVKl599dUTVgfDhg3jgQce4KOPPqJNmzbMnDkzYhH3RYsWsX37dgYPHkxcXBwff/wxoVCIDh06HPM127Zty+uvv06fPn0oKSlhypQpJ+xHohOd3wsvvBCn08nPP/9Mly5d6k0bFxdHQkICL7/8MmlpaezevbvGbJUzZ84kLS2NHj16oNPpePvtt0lNTT2m1slKbdu25b333mP06NFomsajjz56XK1wlec8lue+Iffeey8TJ06MeKZ//vlnWrduHU4zbdo07rnnHqKjo7n00kvxer388MMPFBUV8cADDwDlLcFjxozh0UcfJScnh+uvv/6o8uFyucjOzubJJ588rvIIIURTk1lAhRDiKHTq1AmbzcaDDz5Ijx49OP/881mwYAGvvPJKuEXhjjvu4KqrrmLcuHGcd955FBQURLSK1MVkMvHZZ5+RnJzMqFGj6Nq1K9OnTw93Y7viiit47rnn+Pvf/07nzp156aWXmDVrVo0Wiccff5w333yTbt26MWfOHObNm0enTp1OWB1MnjyZiRMnctNNNzFkyBBatWoV0foXGxvLe++9x7Bhw8jKyuLFF19k/vz5dO7c+Ziv+dprr1FUVETPnj258cYbueeee07YWoUnOr8JCQlcddVVzJs3r8G0Op2ON998k+zsbLp06cL999/P3//+94g0UVFRPP300/Tp04e+ffuyc+dOPv74Y3S6Y/+/8JkzZxIXF8eAAQMYPXo0I0aMaFTX5/oc63PfkHHjxvHYY4/x+9//nt69e7Nr1y7uvPPOiDS33norr7zyCrNnz6Zr164MGTKE2bNn1wg+J0yYwI8//sigQYPCy7Y01gcffEDLli3DLbVCCHGm0lT1gRynoTVr1tC7d+9Gj80525wO5T8d8iCEaJimaSxcuJArrriiqbNyTlu/fj3Dhw9n69atjV5wXpze+vXrx3333XfULYdCCHG6kRZAIYQQ4gTr2rUrf/vb3457XJ04PeTl5TF27FjGjx/f1FkRQojjJmMAhRBCiJNg4sSJTZ0FcYIkJyfz0EMPNXU2hBDihJAAUAghziJnQK9+IYQQQjQh6QIqhBBCCCGEEOcICQCFEEIIIYQQ4hwhAaAQQgghhBBCnCMkABRCCCGEOAorcp28trGoqbMh6rF8v5NPdpeelHM35v4v2lXKu9tLTsr1z1ULd5SwKs/d1Nk4K8gkMEIIIYQ4ZZ7/uZASX4jLMx10ijNH7Hslp4h8T5BRLaPolmBpohw27LxkG32SrCfsfCtynWw57GNyx7gTds7T0fS1+VzVykH7WHPDiY+D0x/ih0MeJneMjdhe5g/x7UEX2w77KPWHMOs14s16Oseb6RJvwajTTmq+Toa66nTRrlK8QcXVraObKGcn/rkemGrjjS2H6Z5gxqyXNqzjIQGgEEIIIU6paKOO9QWeiABwn9NPmT+E8TR+r1NKoQCTXgPOvGDhZAgphQZo2ulTHz8WeGhuNxBr1oe3FXuDvL65GItex5BmdpKsekIKCr1B1hd4iDLqaBdzcgNTcWyCSqHXNJKtBmJMOn4u9NLrBP4Acy6SAFAIIYQQp1SneDOr89yU+IJEm8pf0n8q8NA53syGQm9EWk8wxJJ95S0JgRCk2gxc1NxOiq38FeagK8CX+5wccAUAiDPrGNkyijSbEYCNxV5W5roo8gaxG3X0TrRwXootfP5ASLEi18UvRV5cgRDRJh3np9jonmBhV6mP+VtLuLZNNMv3u8jzBBjXJprdZf4aLRs/FnhYneemyBvEotfoEGvmkhZRDZbhpwIPXx8o79Y2fW0+AKNaRtEyysiLvxRxc4fYcFk9gRDPri9kfNtoMhwmPIEQn+11sqPUhz+ocJh09E+x1dl6ur3ExzcHXBzyBNGA5nYDw9OjiKsIlCrLe1/XeCwGXbh+Z20q5o5OccSa9fxU4OHLfU5GZzhYst9JoSfI7Z3icAdCLMt1cdAVIKQg2abnouZRpFbk/fmfCwF4b0cpUEq0ScddneNrban6Ym8ZB90BJrSLLb+HRV6+PlB+Dw06jRSrgatbR1cE4jXlFHnpkRhZB4v3lKHTNCZ2iI04LtlqoGOsOWIJncO+IJ/vdbKr1IeGRutoIxenR2Gv49eJkFIs2efkp0IvGtC9lvpXSvF9npu1+R6c/hBxZj0DU210rPgRpLLur2sbzdJ9LvI9AVJsBka1jCLBcvyv6w3d+2JvkBd/KWJMpoPsQ24OuAIkWg1cnuHAGwyxeI+TAm+AdLuR0RkObBV1savUx9L95fnVaRqJFj2XZzrYVeqv9bnulmBp8Dtd2XLYO8nKNwdcHPaF+H2PBDRNo12MmV+KJAA8XhIACiGEEOKUsht0tIo2sb7Qy8BUG/6QYmORj+vbxUQEgEop3tlWgkWv45o20Zh1OtYVeHhz62Fu6xSH1aDjf7tKSbEaGNEhBg2NPHcAXUXr3AFXgA92lHJBmo2sWDN7nX4+21OG1aALB0mLdpWy3xlgeLqdFKuBYl8QdyByPc0l+5wMa24n1qzHotfYXeaP2L/mkJuv9jkZ0sxOm2gT3mCIvc5Ao8qQFWcm3xNke4mP69rGAGDWazj9oQbrcXlu+Yv3tW2isep1FHuD+OtZC9QXUvRNtpJkMeCvCHzf217C5I6xR9WC5w8pvj3o4tIWUVgNGjaDjmJfkC7xZoan2wFYlefm7W3lZTTrdUxqH8s/NxQyqmUUraNNjZ6Eoswf4sOdpQxtbqd9jAlfSLG3Wv1X5QmEOOQJkmY78orrDoTYUepnSJqtzqCxsvxKKd7bXoJRpzGhXSwhpVi8p4z3d5aEA9LqVuW5+anQy6Uto0i06FmV52ZzsY8MhzGcZnmui83FPka0iCLerGd3mZ//7SrFZtDRsmq6/S6GNbdjM2h8uqeMj3eXcWP72q97NBp771fmurgo3U60ScfHu8r4YGcJZr2O4el2DDqND3aWsOKAixEtoggpxXs7SumeYOHyTAdBpciteO7req4b850GKPIG2Vjk5cpW0VTtmZtmM/DtQReBkMJwBnbZPV1IACiEEEKIU65bvJmv9jkZkGJlY5GXWLMu3AJQaVeZn0PuIHd3jQm/7A1rbmdzsZdNxT56JFoo8YU4L9kYbiWJtxzp9rcqz02Gw8jAVFt4X4EnyKo8N90SLBR6gmws9nFdm2gyo00AEd0GKw1Ks9GqYn9tvjnopl+ylb7Jla0SetLsxkaXwagDnQZRR9n/tcQXIsVqCLd21pb3qjpWGyc2qmUU/9xQSL4nSJK18a+EIQWXpEdF3K9MR2T9jGwRxbNFhewpC9A2xhRuMbLotaMqZ5k/RAjoEGsipqK1OLmevB72lQfOVa9R5A0Ckc8GwHM/FRCoCJh7JVq5sLmdnaV+8txB7uwcF26dHp3h4JWNxeQ6/eH7WtUPeR76p1jD9TuyRRQ7So5MEuMLKlbnuRnfLobm9iP3aq/Tz7oCT0QAOLiZLfy5f4qNt7eXNBjsfLizFE2LnPAmGII2MUfuSWPvfb8UK60rnvU+yVY+3FnKdW3tpEeV56lbvIX1FT/SeIMKb1DRNtoUbklMrNJaWdtzvbPU1+D3ASCoiGhprOQw6QgqcAZC4edBHD0JAIUQQpyx8vPzWbx4MZmZmVit0iWoqXXs2BGbzdZwQqBtjInFe8rYUxbgp0JPrd0WD7oC+EKK59YXRGwPhI681PdNtvDJ7jI2FHrJdBjpGGcOv4wWeAI1xnU1txtYfchNSCkOugNoQAtHzZf6qtJsdb8uOf0hyvyhiNaeoy3DseqZaGHhjhIOugO0cphoF2MKv6jXpsgbZEWui31OP+6AonxEY3kgeTQ96vQaJFsjX76d/hArcl3sKvPh8itCKPwhKPEdXxmTrXoyooy8mlNMq2gjrRwmOsaawl1Uq6sM6Ay1tGhW3zKxQyyK8gAqWHFcgSdItEkXDv4AEq0GzHqNfE+wRgDoCYYoC4RoVmW7TtPCXV/LzxkgoODNrYcjjg0qSKkWzCZXCaAqu5w2FOxclG6vEYAv2eekaltwY+99cpUg2W7Qas2TK1AeZFsNOrrGm3lr22EyHUYyHSay4sz1BviN/T7EmHQ1gj84cl/9obpbukXDJAAUQghxxlq8eDE33HBDU2dDVMjOzqZXr16NSqvTNDrHW1hxwEmuM8DVrWrOVqgobz24vl1MjX3miq58g9LsdI6zsLXEx/YSHysPuLg800GHWDMNvSLWEUPUUN/skA2dozFlqE1l/FK1DNXfedvEmLirczxbS3zsKvXz5tbD9EqyMqy5vdZzvrO9hGijjktbRhFl1KEUvLqxOBz81NYNNFRLl1KDTquR9qPdpbgCiuHNo4g26TDoNP67uZhgAzdBq1bG6uXUaRrXtY1mnzPAjlIf2YfcLM91clP72FpbPK0Vs0N6goqKhtEqPwhEBqOVx1e9h/Vl91jnuak85zWtY3CYIh+Y6o9A1Uet8p/19OoFyrtUx1WrC5New1ul8hu690euX7OQ1R//qkf8KsNB7yQrO0p8bCz2siLXxbi20eGWzuoa+32o6zvnCZYHn7bGfnlFrSQAFEIIccbKzMwEYO7cuWRlZTVtZgQdO3Y8qvTdEsysynOTVUeLTorVQJk/hEb93RvjLXr6Waz0S7bywY4S1hd46RBrJtFiYK8zcrzYPmeAeLMenaaRZDGggD2l/nAX0KNl1uuIMenYVeonw1HzHI0pg17TarzkV77gVh0LeNAdqHGszVg+nrFbgoX0fANL9rlqDQDdgRAFniAjW0TRoqKVcE+1sXS2ihafMn/oyCQw7sa14O0tC3BJC3u422FJLWMpdRpUH9loM+jI90Tm46A7EBkIaRrpUUbSo8q7877wcxGbD/vol1yz2TLOrMOs08j3BMJdPq0GHa0cRtbke+idZK1zHCBAokVPiS8UMUFRvjuAN6hIsNS8fxa9jiiDjv1OPy0r6jWkFAddgXAX2QSLHr0GJf5gRHfPU6Ux9/54pNoMpNoM9E+18d9NxfxS5KW53Vjrc93Y73RdDnmCOIw6CQCPkwSAQgghzliV3T6zsrIa3fIkTh+JFgP3do2vc3xTpsNIc7uB93aUMLSZnXiznjJ/iO0lPtrFmki0GFiyz0mHWBOxZj2lvhC5rgAdKsY79Uu2MmdTMV8fcJEVa2af00/2IXd4ds5Ys56u8WY+3l3G8HQ7yVYDh31BXAFFVlzjlwS4INXG4j1l2Aw6WkdXTFTi9NMnydpgGdJsRmJM5ZOoHHQFcJh0mHQaRp1Gs4oJL2JMOlxBxfJcV8R1l+c6SbUaSLIaCIQUWw/7ag1SoHzsnVWvsS6/fMmDw74gy/ZHni/OrCfaqGPlAReD0+wUeoONXng71qxjQ6GXVJsBX1Dx1T4nhmq3tTJQTrcbMWhgMejIcBj5Ps/N+gIPze1Gfi7ykO8OkmwrL8d+p5+dpX5aRRuxGXTkOgO4AiES6ggeNE0jw2FkrzMQsTbeJS2imLu5mDmbirkgzUaSVY8G5LoCFHqCpFZ0xcx0GEm26vlwZynD08snOvlsj5MWUUfGWlbXJ9nCdwfdxJn14UlgPFVa38x6HeclW/lyrxOlID3KiDeo2Of0Y9JpdD3Ja1425t4fi2JvkHUFHtrFmIgy6ij0BCn0lk8GBNT6XDfm+1CfPWV+WjVBEH22kQBQCCGEEE3GWs8v+ZqmcU2baJbnuvh4dxmuQIgog44WUUbsBh06yls3PtpVhjMQwmrQ0T7GxKC08nGIqTYDY1o5WJnr4usDLqIMOgalRS6TMKJFFMv2O/lsjxN3sHwZiP4pjRvHWKlrgoWAUqzO8/DVfic2vY4OcaZGlQGgQ6yZzYd9vLH1MN6gCk+XPyojio93lTF7UzHxFj0XNrPz1raS8HX1msayXBeHK5ZHaBFlZExmVJ11OSbTwef7nLySU0S8Wc/F6VG8UWVcml7TuDzTweI9Zby2sYhUm4HBaTbe31la6zmrGtXSwad7ypi1sZhok44haXa+2u+MSDOsuZ2v9jn5Md9DVMUyEK2jTQxMtbJ0v5OAKp8cqHO8mUOe8tZOk15jT5mfHw658QYVMSY9w5rbIyY4qa5HYvm40Aub2cJdVePMem7uGMc3B1ws3e+k1B/CoGkkWPT0S7aGlxXQNI2rWkfz+V4n87YURywDUZd+yVbK/CE+3lUGWnkZ2seaIrpgDkqzYTPo+Pagm+I9ZVj05ctZ9E89umftWDTm3h8Lo06jwBNkQ0Ep7mCofJmVJAs9KyZyqeu5buj7UJdASLGl2Me1bZtucfuzhaZUQz2Lm96aNWvo3bv3UY0tOJucDuU/HfIghBDVyd8mIUR1Sin+u/kwfZOsdIqXxd3PFtmH3Gw5fGRZCXHsTssWQJfLxcaNG8Ofc3JyAHC7G9cNQQghhBBCnJs0TWNki6hwK6I4O+g1jYvTa5/gSByd0zIA3LhxI717966xfefOnQwcOLAJciSEEEIIIc4UKTZDjXUlxZmtco1AcfxOy29Gx44dyc7ODn/OycnhhhtuCM/2JoQQQgghhBDi6J2WAaDNZqt1LIcs8iuEECfHmbqgugwREEIIIY7OaRkACiGEOLXO9AXVZYiAEEII0TgSAAohhDhjF1SXIQJCCCHE0ZEAUAghxBm/oPqZ1G1VnBwrcp1sOexjcse4Js3HrlIf87eWcF/XeCwNrGt2qp0udVQfdyDEf3KKuKl9LLF1LPZem8aUbd6WYlKsBobXs6ZfU1u0qxRvUHF1a1nr7lTKPuRmR4mfsW3OjXqXAFAIIYQQp40DrgDfHnSxp8yPN6iINuloGWXkvGQb8Za6A4Lzkm30STpxPwScCcHS0TrRdXQyfHvQTdtoU43gb2Oxl+xDbvJcQUIoYk16OsSa6Z1kwdrIQPuqVtHotJOR67PfyQ5Mfyrw8PHusvBnu0GjRZSRoc3s4Wfh+Z8L6ZtkpW/ykWe4ob8Xxd4gL/5SFE5v1mkkWPT0T7XSLubIGpHdEyx8c6D8PC2ijCeljKcTCQCFEEIIcVrYetjHwh0ltHKYuDzDQaxZjysQYmOxj+W5Tq5oVfPlUymFAkx6DZC3+9qcKXXkDyl+LPBwbbVWmGX7nXx30E3fZCtD0uxEGXUUeYOszfewodAbERDUp7GBojh5giGFvo4o3KzT+HWn8h9cCjwBPt1TxrvbS7i5Yyw6reYxR/P34rq20SRaDHiDIdbke1i4o5SbO+hJspaHQgadRqc4M9mH3BIACiGEEEKcCv6Q4qPdpbSONkW0MsSa9TSzG/EEQsCRLpbXtolm+X4XeZ4A49pEs7vMX6PF7scCD6vz3BR5g1j0Gh1izVzSorz7nycYYsm+8la+QAhSbQYuam4nxWbgpwIPXx8on1l2+tp8AEa1jKJllJEXfyni5g6x4TXmPIEQz64vZHzbaDIcphrlqq0lcXWem9WH3NzVOT5cpqX7XeR7Aug0jUSLnsszHcSYam/x3Fvm57O9ZRR4giRZDAxItfLejtJwvhpbRw1dd8thLytzXeR7gkQZdXSNtzAg1YpO08ItK/XVhScQ4rO9TnaU+vAHFQ6Tjv4pNrol1L6e2/YSHzoNmtuPvIDvd/r59qCbi5rbIwK9WLOeVtGm8HNRaUOhh+W5LrwBRetoIyNbRmHWlwd+1buAPv9zIT0SLBR5g2wq9mHRawxItUWsN1fqC/LlPic7Sv1oQHqUkeHNj7RKHU8d1uf7gy5W5bkJKugUZ+aidDv6imM2FHr4Ic9DoTeIUQcZDhMXNbdjN+rCeaq8/0v3Oyn0BGlmNzIm08EBV4Cv9jkp9YdoE21kVIYDY0VAtrHIy9cHXBR5gxh0GilWA1e3jub7PBcbCr3Ake9D5T1uqH4qWw6b2QxkH/Kg0xF+7mvQIKqiDFFGExek2vjfrjKKvEESLJEhS2P/XlSy6nVEGcv/G5xmI/uQh11l/nAACNAuxsxb2w7jD6lwnZytJAAUQgghRJPbXuLDHVCcn1J7a0718XRL9jkZVvGiadFr7C7zR+xfc8jNV/ucDGlmp020CW8wxF5nAChvEXtnWwkWvY5r2kRj1ulYV+Dhza2Hua1THFlxZvI9QbaX+LiubQwAZr2G0x/5UnkihJTivR2ldE+wcHmmg6BS5FbkszbeYIh3tpfQJrq81eOwL8SX+5y1pq2vjhq67vYSH4t2ljE83U6LKCNF3iCf7invondBmq1RZVueWx4YXdsmGqteR7E3iF+pOtPvKfOTZo18Nf25yItJp9ErqfagsepzUewNsbnYxzWto/EEFe/vKOG7g26GNLPXec1VeW4Gp9kYkGpjY5GXxXvKaBFlIMFiwB9SvLH1MC3sRia0i0GnwTcH3CzYVsItHWPRNE5KHe4u9RNl1HF9uxiKvCE+2FlCstUQDkyDCgal2Uiw6HEGQny518lHu0u5tk1MxHlW5rq4JD0Kg07jg50lvL+zBIOmMTrTgT+keG97CdmH3JyfYqPMH+LDnaUMbW6nfYwJX0ixt+J5OS/ZRoEniC+oGJXhAMCq1xqsn8qWvl2lfkw6jXFtj677qKHi+FAtj8zR/r2oFFSKHwvKg1l9tRgv1WYgqCDXGaCl4+xuBZQAUAghhBBNrsgbBCChkRN/DEqz0Sq6ZotbpW8OuumXXHW8kJ60ipalXWV+DrmD3N01JvySOay5nc3FXjYV++iRaMGoA12VFomTxRtUeIOKttEm4irKnmip+/XslyIvGnBpy/IX+0QrlPlDfLKnrEba+uqooet+c8DF+SlWula01sWa9QxKs7F0n6vRAWCJL0SK1UCazRg+R30O+0I16rvIGyTGpAu3ftVHofhVxpEWvy7xFnaV+us9pk20iV4V4yLPT7Gy+pCb3WV+EiyGirrWuLRlFFrF9X/VMoqZPxWwu8xPqs1wUurQbNC4ON2OTtNIsJTncVepLxwAdq/Sghpr1nNxup05mw/jC6qKbr7lBjezkV7RnbFbvIVluS7u6BQXvg8dYs3sKvVzfkr5MxQCOsSawq2XyVWCcYNOI6givw8bCjz11k/ls2fUlbeg19X1szYlviDfH3TjMOqIr+W5Odq/F69vLkbTIBACBcSYdHSMNUekMek1LHqNw74gIAGgEEIIcVqqXAC+ckF4cfrq2LEjNlvjAofGSLPV/Qrj9Ico84fIqONX/IOuAL6Q4rn1BRHbA6EjL5anitWgo2t8edezTIeRTIeJrDhznYFngSdIktUQDlwB0uy110V9ddTQdQ+6AxxwBfjmoCt8jFIQUOXd7xqjZ6KFhTtKOOgO0Mphol2MKRyQ1CYQUhiql1tBI2I/AGJM+nDwB2A36nAG6m+1TbYeCSA0TcNu0OH0l5fvoCtAkTfIP36q9pyo8uekVbTpuOqwrm6GSRZ9RBdRu1HHIfeR5/KAK8DKAy7yXAE8QUX5CM/yoCmxStCWXCUYtRt1GHWRQbjdqJHrCoXrISPKyKs5xbSKNtLKYaJjrKnemWwbrJ/K8lgNjQr+vEHFjB/Lu5j6Q5Bi1XNVK8dRBY51GdPKQYLZQKE3yJf7yhjRIqrWMaEGndbo5/tMJgGgEEKIM9bOnTsBzuhF7M8V2dnZ9S4xUtmCUuAN0rwRk3XUN0anocMVhLvYVWeu3i+sisp38qqvhw29K2poVO/1GKq24VcZDnonWdlR4mNjsZcVuS7GtY2OGAsXec5IdfWqbGgcU33XVaq8m2KHaq0kAAatcXXRJsbEXZ3j2VriY1epnze3HqZXkpVhzWvvkmk16PAEI08SZ9GztyBAUKkGWwGrF1erlr/aj4k8SNMIB1SK8m6Bl2c6aslrRYvXcdRho/PEkTz5goq3th2mlcPE6EwHNoNGiS/EW9tKqFZ1NeqjtnGHqsq+69pGs88ZYEepj+xDbpbnOutdjqMx9QMNP4eVTDqNmzvGogE2gy6iNbO6o/17EW3UE28p/8+kc7BwRwm3ZsWFx01W8gRC2M6ByYIkABRCCHHGGjFiBHPnziUzM1PWAjzNdezYsd79rRwmrAaN7w66ubp1zcDHEwg1el09s15HjEnHrlJ/rROzpFgNlPlDaNTdLVGv1QzcKl8Mq44FPOiue7xe+TEazkAIpVS4m9xBd81WxlSbgVSbgf6pNv67qZhfiry1BoAJFj0/F3nLW8sqXqwPNJCH+tR13RSbgUJPMPyiXbNcjasLm1FHtwQL3RIspOcbWLLPVWcAmGItL1tVnePMZB/ysOaQp9bZPo/muThaKVYDOUVebAYtomWxumOtw2NR6A3iDiiGNrMRXdFVM9flOSHn1jSN9Cgj6VFGBqbaeOHnIjYf9tEv2Ypeq/nDRWPrp/HXp9F1dTx/L1o6jCRa9Xxz0MXFVdaELPIGCSjCkxqdzc7+EgohhDhrJSYmMmHChKbOhjgBTHqNS1tE8f7OUt7ZVkKfJAuxZj3uYIiNRT5KfEHG1LIMRF0uSLWxeE8ZNoOO1tEVk1o4/fRJspLpMNLcbuC9HSUMbWYn3qynzB9ie4mPdrEm0mxGYkw6in1BDroCOEw6TDoNo06jmc3AtwddxJh0uIKK5bmuevPR0mHEtVfxfZ6bDrFmtpf42F7iC7duFHuDrCvw0C7GRJRRR6EnSKE3SJf4mq1GUD4j5LL9Lj7dU8b5KVZKfCFWHXQ3vqIrNHTdgak23tlWgqNirJSmwSF3kEPuAIOb2RtVF8tznaRaDSRZDQRCiq2HfSTUs5Zjq2gTy/a7Il7em9mNnJds5at9Tsr8IdrHmiKWgUi3Gxu9DMTR6hxv5vs8F+9uL2VQmg2HUUeJL8Smw17OS7YSUhxXHR6LaKMOvQbZhzz0TLRwyBPgmwNHf/+r2+/0s7PUT6toIzaDjlxnAFcgFB5jF2PSs6PET4EngNWgw6zXGqyf6DpmsT0RjvfvRb9kK+/vKOX8ZCuOinzuKfMTa9Kd0ID9dCUBoBBCnCXy8/NZvHjxMbWGVY6hqxxTJ0RTaB9r5sb2Or494ObDiunjo006MqJMR/3C3DXBQkApVud5+Gq/E5teR4e48tZATdO4pk00y3NdfLy7DFcgRJRBR4soI/aKwKNDrJnNh328sfUw3qBiVMsouiVYGJURxce7ypi9qZh4i54Lm9l5a1tJnflItBgY0cLOtwfcfH3ARYdYM/2SrawrKG+1Meo0CjxBNhSU4g6GsBt19E6y0DOx9lkvzXodY1tHs3hPGbM2FpNkMTAw1caHu0ob7PpaVUPXbR1tYmybaL4+4OL7g270mka8RR8xAUlDdaHXNJblujhcsaxAiygjYzKjauSlUrK1vCUtp9hLz8Qjf8MubG4n1WZgTb6btfkeVMVC8B3jzHStI1A+EYw6jQntYlm638l7O0rwBRUOo44MhxGzXiMQ4rjr8GjZjDp+1dLBslwnPxxyk2ozcGFzG+9uLz2uspr0GnvK/PxwyI03qIgx6RnW3E6bmPLvTI8EC7vL/MzZdBhfSIWXgaivfk624/l70Ta6fLKbbw66GVGxNExOkfe47s2ZRFOqnvl4TxNr1qyhd+/eDY4fOFudDuU/HfIghKjfvHnzjnss3Ny5c6VFTYgzzM+FHj7aXcb93RLO+PXLth328dV+J7d2jA13mRXiZDvkDjC/YhkYywnoznq6kxZAIYQ4S2RmZgLlQVxWVtZRHZuTk8MNN9wQPocQ4vS1vsBDrFmPw6gjzx1g6X4XWbHmMz74g/KJYwq9QUr9oZPahVCIqsr8IS7LcJwTwR9IACiEEGeNym6fWVlZx9xSLxOpCHH6cwZCrDjgwukvXzevQ6yp3sXOzzQna0yfEHWpb03Rs5EEgEIIIYQQZ5DzU2ycn3Li1lQUQpxbzo12TiGEEEIIIYQQEgAKIYQQQgghxLlCAkAhhBBCNNr0tflsLvY2nPAcsGhXKe9ur3sJCAE/Fnh4c+vhU3KtxjybPxV4mPlTwSnJT32e/7mQ1XmNX3Yn+5Cbd+pZbkSIoyFjAIUQQohz2PM/F1LiC3F5poNOcZFrqr2SU0S+JxheAw/gt13isZzkNb5W5DrZctjH5I5xJ/U657JdpT7mby3hvq7x4UXXT7RASLEi18WYTEd424pcJ19XLFyuAVFGHa2ijQxNs2MzHl8+GvNsZsWZaXMGTvjRPcHCNwdc7Cnz0yLK2NTZEWc4CQCFEEKIc1y0Ucf6Ak9EALjP6afMH6L6O3nUcbykB5VCf46v7RYMKfRnwXINlZRSKEBXy33dVOzFVLEAfFWJFj3XtY1BKcVBd5CPd5dS5gtxbduYGucIKYUGjVoTsKFnM6gURp12Ri6XYdBpdIozk33ILQGgOG4SAAohhBDnuE7xZlbnuSnxBcNrr/1U4KFzvJkNhZFd6qavzeeqVg7ax5YHiyW+IEv2OdlR6ieoFAlmA5e0sNPMbgy35PVOsvLNAReHfSF+3yOBEn+Iz/c62VXqQ0OjdbSRi9OjsBt1/FTgCbcQTV+bD8CollG0jDLy4i9F3NwhlhRb+euLJxDi2fWFjG8bTYbDhCcQ4rO9TnaU+vAHFQ6Tjv4ptnDrZXWBkGLJfic5RV68QUWazcBFze2k2Y+8YB9yB1i638mesgAAyVY9v8pwEGc+skbd9wddrMpzE1TQKc7MRen2cKD7/M+FdE+wUOQNsqXYR7tYE5dlONhY7GVlrosibxC7UUfvRAvnVZnZs8wf4pPdpewq9WM36hicZmNZrou+SdbwMgmr8tysL/BQ7Ati0etoG2PiwmZ2TBWtYId9QT7bU8ZeZ4CQUsSY9FzYzE6CRc/8reXdCZ9dXwhAl3gzl2U4UErxfZ6btfkenP4QcWY9A1NtdKz4caCy5fDaNtEs3+8izxNgXJvy+q8up8hH25ia23XakWDNYdLTJ8nKilwX/pAip8jLl/ucjM5wsGS/k0JPkNs7xeEOhFiW6+KgK0BIQbJNz0XNo0i1HXmVrfpsFnuDvPhLEWMyHazJd7PfGWBEiygAvtzn5P5uCeHjthwuvxf5niBRRh1d4y0MSLXWGtRuL/Hx7vYS7u4S2XL6+d4y8twBJrSLBWjw/lbnCYZYsq/8+xIIQWrFs5hSpXztYsy8te0w/pA6I4NYcfqQAFAIIYQ4x9kNOlpFm1hf6GVgqg1/SLGxyMf17WJqBIBV+YKKN7YcJsqo4+rW0UQZdRxwBVBV0hR5g2ws8nJlq2gq31nf216CUacxoV0sIaVYvKeM93eWMKFdLFlxZvI9QbaX+LiuokXIrNdw+kMNlmN5rot8T4Br20Rj1eso9gbxK1Vn+iX7nWwq9vGrlg5iTDq+y3Pz1rYSbu8Uh9Wgo9QXZN6Ww7SMMjK+XTRmnY69Tj+hKufcXeonyqjj+nYxFHlDfLCzhGSrgR6JR4LOVQfdDEi1MrBjLAAHXAE+2FHKBWk2smLN7HX6+WxPGVaDLhysLtpViisQ4vp2Meg0jS/3leGqVgcaMDzdToxJT7EvyGd7nCzZ7wwHOp/tKSOoYEK7GEw6jXxPEKNeI9qk48pWDhbuKOW2rDhMeo3KWGZ5rovNxT5GtIgi3qxnd5mf/+0qxWbQ0dJxJDBess/JsOZ2Ys36Ortd7nH66RxvrnVfVQadhoJwvfpDim8Puri0RRRWg4bNoKPYF6RLvJnh6eXrHa7Kc/P2tsPc1ikOcz2Ldy/dX57PlJYG9DrYUeKP2L+9xMeinWUMT7fTIspIkTfIp3vKALggrWbAlukwYtFrbDrso3vFvQopxcYiL4PSyvPWmPtblVKKd7aVYNHruKZN+XO2rmLs5G0VzyKUB4VBBbnOQMS9EOJoySQwQgghhKBbvJn1BR5UxctsrFkX0fpQm1+KvLgCiqtbR9MiykicWU9WnJnmVVrQggpGZzhItRlIthrYWeonzx3k8szybc3sRkZnONhTFiDX6a/ooneklSjKqGt0a0eJL0SK1UCazUisWU9mtIl2MbUHIL6gYm2+hwub2WgTYyLRauDSllEYdBo/FXgAWJPvwazXGNPKQZrNSLxFT7cECwmWI/ViNmhcnG4nwWKgbYyJNtEmdpX6Iq7V0mHkvBQbcWY9cWY9q/LcZDiMDEy1hc/ZO8nKqopJQQo8AXaW+rm0ZRTN7EZSbQZGtXQQqBbL9k22kuEwlZfVYWJwmo2NRUcC9hJfiHS7kWSrgViznrYxJlpGGdFpWjhosxk0oow6LHodvqBidZ6bURlRtI4uP2+3BAud482sq6iTSoPSbLSKNhFn1ocDlKo8gRDeoGqwW2aBJ8DafDdpNkM4kAspuCQ9ivQoIwkWAya9RqbDRJd4C4kWA4kWAyNbROEPEW6ZrUufJCsdYs3EmvU4jPoa+7854OL8FCtdEyzEmvW0ijYxKM3GunxPLWcr7+raMc7ML1V+GNlV6scTVHSMLW/tbOj+VrerzM8hd5Arqjxnw5rbMes1NhUfeZZM+vL7dtgXrLfMQjREWgCFEELgdpe/mOTk5DRxTsSJ1LFjR2y2xi0Y3jbGxOI9ZewpC/BToafObpNVHXQHSLHVHgBUijHpIib3KPAEiTbpwl1NARKtBsz68haqqt0vj1bPRAsLd5Rw0B2glcNEuxgT6XWMlyr2BQkpIvbrNY00m4F8TzBcvhZRxnrHLSZZ9BFdBe1GHYfckS/oadUC6QJPoEZg2txuYPUhNyGlKPAE0QGp1iPHxdXS0rar1Me3B93ke4L4goqQUgRUeXBr0mv0SbKyeE8ZO0p9ZDqMdIg1k2yt+9WvwBMgoKgxa2dQQUq146qXqbrKYNVQS9UdcgeZ8WM+SpWnaxll5NKWUeH9eq28q21VTn+IFbkudpX5cPkVIRT+UHkX5Po0lM+D7gAHXAG+OegKb6vMV11dLTvHmXn90GFK/UEcRj0/F3lpHW0Kdwlt6P5W71p60BXAF1I8tz5ydtJAqLwFvSqDTsMfqrtVW4jGkABQCCEEO3fuBOCGG25o2oyIEyo7O5tevXo1Kq1O0+gcb2HFASe5zgBXt4pu8JjaXu6rq/4CXd+ra33zfFTuq3p89ffgNjEm7uocz9YSH7tK/by59TC9kqwMa26vcb56eoZSmQ1DIyYeqf4yrwGqWimPpg7qU/W4w74gb28roUeihUFpNqx6HXucfj7ZXVbRlVKje6KFVtFGtpX42FHi59uDxQxrbqdPkrXe81/TOgaHKTKor97Ls6FWWWvFAZ5gzdLGW/SMbR0dngXUUO1cBp1WY9KXj3aX4goohjePItpUfsx/NxdTy+mPKp9KlXf17BBbs6W4rue7md1IrFlHTpGPnokWNhf7GJVxJIA92vurINyNuDpztYr3BELYTtKsreLcIQGgEEIIRowYwdy5c8nMzMRqrf3lUJx5OnbseFTpuyWYWZXnJivW1KilAZKtBn4q8OIOhOptBawq0aKnxBeKmHAm3x3AG1QkWMo/6zWtRoBW+dJbdSzgQXfN7n82Y/k4q24JFtLzDSzZ56o1AIwz69FrsLfMT0x8+XWDSnHAFaBPkiVcvvWFnhM+e2mixcBeZ+RYtH3OAPHm8tbEBIueEHDQHQxPclLkDeKtEu3kVkyGclFzezhYyqllDbxok56eiVZ6JlpZut/Jj/ke+iRZw+WpWs0JlvI6KfEHj3uMmV6nkWjRk+8JUv23BL1GxCQ6jbG3LMAlLey0qZhUpsQXxF29T+wxSLEZKPQEjzo/neLM/FzowWHUoWnQtsrSEg3d3xp5sBoo84fQgNh68lHkDRJQNNg1W4iGyBMkhBCCxMREJkyY0NTZEE0s0WLg3q7xNVpk6tIpzsy3B928u72Eoc3s5ZPAuAM4jLqIcYBVZTqMJFv1fLizlOHpUYSU4rM9TlpElY/dg/Juo8W+IAddARwmHaaKqfub2Qx8e9BFjEmHK6hYnuuKOPfyXCepVgNJVgOBkGLrYV84qKzOpNfomWhhyX4XVoOOaGP5JDD+kApP7tE7yUL2ITcf7Cilf6oVs07HfpefNJshYhzg0eqXbGXOpmK+PuAiK9bMPqef7ENuLqmYvCXBYiDTYeST3aWMaBGFTtP4ap8zokUqzlQeJP5wyEO7GBN7nf4a49a+2FtG62gT8WY9nqBiV6k/XB8xFS18Ww/7aBNtwqDTMOt1nJds5cu9TlRF91hvULHP6cek0+jaiG7BVbVyGNnr9NOX4/9RKdasY0Ohl1SbAV9Q1aiPYzUw1cY720pwmHR0jDWjaeVdVA+5AwxuVvOHg0qd4yx8fcDNNwdcdIg1RXxnGrq/1WU6jDS3G3hvR/n3KN6sp8wfYntJ+ayxld+LPWV+Yk26ow5WhahOAkAhhBBChDW2JQ/KW3nGtY3mq31O3t5WQghFgsXAJel1vzhrmsZVraP5fK+TeVuKI5aBqNQh1szmwz7e2HoYb1CFF6IflRHFx7vKmL2pmHhL+ZIGb20rOZIfTWNZrovD3iCGivXnxmTW/tINMLSZHQX8b1cpvoplIMa1iQ63floNOsa3i2HJPidvbDmMhkayVU96hqPOczZGqs3AmFYOVua6+PqAiyiDjkFpkctVXJbh4OPdpczbchi7UcfQNDv5nkB4ts4Um4Fhze18f9DNsv1OWkQZGdLMxqJdZeFzhFT5TKCl/hBmvUYrhyk8i6bDpGdQmo2l+518tLssvAzEoDQbNoOObw+6Kd5ThkWvkWI10D+1cWNJq+qRaGHWxmI8wRCWembqbIxRLR18uqeMWRuLiTbpGJJm56v9zuM6J0DraBNj20Tz9QEX3x90o9c04i368I8AdYm36EmzGch1BcJ1Wqkx97cqTdO4pk00y3NdfLy7DFcgRJRBR4soI/Yq38ecIm+D+RKiMTSl6usFf3pYs2YNvXv3PqqxDGeT06H8p0MehBD1k++pEGevEl+Q538u4rq20WTWsube6er9HSXHHECKIw65A8yvWBbieINpIaQFUAghhBDiNLOz1Ic/pEiylI8PW7rfSYypvFXoTHJhcztbDvsaTijqVeYPcVmGQ4I/cUJIACiEEMcpPz+fxYsXN/kEKpVLOFQu6SCEOHOFFCzb76LYG8Sk12huNzI603FCJ6M5FWJM+jpnHRWN1yr6zGn1Fac/CQCFEOI4LV68+LRaPmHnzp0MHDiwqbMhhDgOraNNtJaXfiHESSABoBBCHKfMzEwA5s6dS1ZWVpPlIycnhxtuuCGcHyGEEEKI6iQAFEKI41TZ7TMrK+u0mHxF1vETQgghRF1kJKkQQgghhBBCnCMkABRCCCGEEEKIc4QEgEIIIYQQtZi+Np/Nxd6Teo0CT4B/rS/AGwyd1OucTNmH3LyzraSpsyGEaCQZAyiEEEKc5Z7/uZASX4jLMx10ijNH7Hslp4h8T5BRLaPolmBpohyeu5bnuuiVZMVcsb7brlIf87eWYNZr3N0lHoPuyLIP+51+/rv5MAB/6JnYJPmtTfcEC98ccLGnzH/GrVMoxLlIWgCFEEKIc0C0Ucf6Ak/Etn1OP2X+EEZ5G2gSJb4gWw776BpvrrHPrNPYXBy5gPpPBV6iT8ObZdBpdIozk31I1iAV4kwgLYBCCCHEOaBTvJnVeW5KfEGiTXoAfirw0DnezIbCI90ci71BXvyliJs7xJJiK39N8ARCPLu+kPFto8lwmPAEQny218mOUh/+oMJh0tE/xVZnC+K8LcUkWQzoNFhf6EWvweA0O53izHy+t4xNxT5sBo2L06NoE2MK5+3LfU7u75YQPs/mYi/v7SgNt34ddAX4cp+TA64AAHFmHSNbRpFmK2+F2lvmZ1muk1xnAL1Oo5nNwJhMBxaDjud/LqRvkpW+yUdmzX1tYxHtYkwMSrPXKENly9x9XeOxGHTh68/aVMwdneKINes57Avy2Z4y9joDhJQixqTnwmb2cJmq21jsI9lqCN+PqrrEm/mp0EOniuDQH1LkFHvpnWThmwPuaufxsjLXRZE3iN2oo3eihfNSbOH909fmc1UrB+1jjwSaM38q4KLmdrolWAiGFF/uc7Kp2IsnqLAbdfRMsNA/tfwcnmCIJfucbDnsIxCCVJuBi5rbw88HQLsYM29tO4w/pDDqzqzF6oU410gAKIQQQpwD7AYdraJNrC/0MjDVhj+k2Fjk4/p2MREBYGMsz3WR7wlwbZtorHodxd4gfqXqPWZDoZfzUqxMbB9LTrGXxXvK2HzYS/sYM/1TbKw+5GbRrlLu6hLf6ADif7tKSbEaGNEhBg2NPHcAHeXHHnQFmL/1MN0SLAxvHoVOg91lfk7mSLvP9pQRVDChXQwmnUa+J4hRX3dZ9pT5SbPV/irWJd7C93lFHPYFiTHp2VTsJcakI9Uamf6AK8AHO0q5IM1GVqyZvU4/n+0pw2rQNbpL7w+H3Gw97OOKVtFEm3SU+EKU+sprSinFO9tKsOh1XNMmGrNOx7oCD29uPcxtneKwVgTDqTYDQQW5zgAtHdINVIjTmQSAQgghTrn8/HwWL15MZmamrFt4nDp27IjNZms4IdAt3sxX+5wMSLGyschLrFkX0YrTWCW+EClWQ7ilLdZcswWrumSrnoEVLUr9U6x8d9CFVa+jR2J5kDIw1cbafA957gDN7Y0LIEp8Ic5LNpJgKS9DvOVIPr7Pc5NmMzCiRVR4W5L15L72lPhCdIg1k1xxnYbq5bAvSGod9W8zaLSONrG+wMsFaTZ+KvDSLb5mQLcqz02Gwxiu23iLngJPkFV57kYHgCX+EHFmPel2A5qmEVOlRXJXmZ9D7iB3d40Jj0cc1tzO5mIvm4p94ftn0mtY9BqHfUFAAkAhTmcSAAohhDjlFi9ezA033NDU2TgrZGdn06tXr0albRtjYvGeMvaUBfip0HPMk770TLSwcEcJB90BWjlMtIsxkd7A5B/JVYIvnaZh1etIth4JNOyG8uDCFWh8G13fZAuf7C5jQ6GXTIeRjnFm4iqCroPuAB1ja+96ebL0SbKyeE8ZO0p9ZDqMEcFgbQIhMNTT2NktwcwXe510iTez3+nnylYO9pT5I9IUeAK0i4kcQ9jcbmD1ITchpdBpDbemdo238ObWw7ycU0TraBNto020ii6vu4OuAL6Q4rn1BTXyXuQNRmwz6DT8ofpbgoUQTU8CQCGEEKdcZmYmAHPnziUrK6tpM3OG69ixY6PT6jSNzvEWVhwoHxd3davoGmkq44Wqr/HV3+nbxJi4q3M8W0t87Cr18+bWw/RKsjKsec2xc0euXf1CRAQnWsW/K3uSatUzUUs+BqXZ6RxnYWuJj+0lPlYecHF5poMOseZ6A6vK81cPVeqLXbRaAqlQtW6v3RMttIo2sq3Ex44SP98eLGZYczt9kmpv5bYaNDzBui/aJtrE4pCTj3eX0TbGFO5uWVVjw62aZT2yJdVm4M7OcWwv8bOz1Mf7O0vJdBi5slU0Cogy6ri+XUyNc5qrdW/1BELYasmjEOL0IgGgEEKIU66y22dWVlajW6/EidEtwcyqPDdZsabwZCZVVb7AO/1HWuIOugM10xnLx5h1S7CQnm9gyT5XvQHg0bIZdHhDCl9QYaoINGrLR7xFTz+LlX7JVj7YUcL6Am+45W1XqZ9BaXWfv2oZvcEQxdVatCLTl+ehzB86MgmMu2b6aJOenolWeiZaWbrfyY/5njoDwBSrgXxP3dcsD9jNfJ/n5to2NYN1gESLgb3OyFbBfc4A8WZ9OMC2GbSIshZ6gvirNbSa9Tqy4sxkxZnpEOtjwbYS3IHyrr5l/hAa9XdpLfIGCSiOqUuxEOLUkp9phBBCiHNIosXAvV3jGZXhqHW/sWK2zG8Push3B9hd5md5risizfJcJ5uLvRR5gxxyB9h62EeCpeFxgEejmd2AUQfLcp0UeYP8XOhhfZXJavwhxWd7ythV6uOwL8jeMj+5rkA4H/1TbOS6AizeU0aeO0CBJ8CaQ+5wF9MMh5ENhR72lPk55A6waFdZvd0l48x6oo06Vh5wUegJsvWwj1V5kbNxfrG3jO0lPoq9QQ64Auwq9ddbL62ijexz+mu0JFY1OM3GPV3jaVXHxCr9kq3sKvXzdUW+1hd4yD7kpl+V2U0zooxk53s44AqQ6/KzeE9ZRIvsqjw3vxR5KfAEKPQE2VjsxW4oH9OX6TDS3G7gvR0l4bLtLfOzfL+TXNeRwHNPmZ9Yky7cBVcIcfqSn2mEEEKIc0xtXQmrGpURxce7ypi9qZh4S/lSBm9tKwnv12say3JdHPYGMeg0WkQZGZMZVc8Zjy2PozMcLKloRctwGLkg1cane8qA8l+w3YEQH+0qwxkIYTXoaB9jYlDakclQxrWJYVmukzmbijFUBLad4srHy/VPsVLsDfLOtvJF1wel2SomMKmdXtO4PNPB4j1lvLaxiFSbgcFpNt7fWRpOE1LlM4GW+kOY9RqtHCaGp9fdKtom2oRe09hZ6qd1dO3jFfU6DVs9s6Km2gyMaeVgZa6Lrw+4iDLoGJQWuSTHsHQ7H+8qY96WYqKMOoY3j+LAriOtqSadxncHy5eR0KGRajNwTZuYcLfXa9pEszzXxce7y3AFQkQZdLSIMmKv8hzlFHnpfoxjSoUQp5amVAPzNp8G1qxZQ+/evY9qoPvZ5HQo/+mQByFOV6fL9+N0yUdjnEl5FeJkWnPIzZbDPsa1rTnG7kxxyF2+5MZtneKw6KVzmRCnO2kBFEIIIYRoIj0SLXiCCm8whPkMDZ7K/CEuy3BI8CfEGUICQCGEEEKIJqLTNAakNm4dx9NVqzq6rwohTk8SAAohxFnC7S6fkCInJ6eJc9KwyjxW5lkIIYQQp4YEgEIIcZbYuXMnwBm1wPrOnTsZOHBgU2dDCCGEOGdIACiEEGeJESNGMHfuXDIzM8Pr7J2ucnJyuOGGG8ILwgshhBDi1JAAUAghzhKJiYlMmDChqbNxVE73QFWIE2362nyuauWgfay5yfKwfL8TZyDEpS1rXwvyRHL6Q7yysYjJHWJxmGSNQCFOBxIACiHOGvn5+SxevPiUt4DJeDZxJnj+50JKfOWLoBs0iDbp6Z5gpl+yNbzeW2PP0zfJSt8qC43/VODhy31O7u+WcMLzfbb5bZd4LPrG1/eJ5vSH+OGQh8kdY8PbFu0qZUOhFwANcBh1tIkxMSTNhqWBNSOrWrSrFG9QcXXr6PA2u1FHlzgzKw64GHUKAk4hRMMkABRCnDUWL17cpOPfZDybON0NSrPRPcFCIKTYVepn8Z4yTHqNnonnbktsSCk0OKog+HhEGZt2qYQfCzw0txuINUe2xrV2GBmV4SCkFAWeIB/vLsMTCDGmVXQdZ2q8rgkW/rupmGHN7EcVUAohTg4JAIUQZ43K8WRz584lKyvrlF1XxrOJM4VJp4UDkFiznjX5bnaU+MMBYJE3yJf7nOx3+vGHFAkWA0PTbGRWTPM/b0sxJb4QX+5z8uU+JwDj20bz8e4yoLx7I8DAVCuD0uy1dnec+VMBFzW30y3BQjCk+HKfk03FXjxBhd2oo2eChf51LItQ2cKUbjewKs9NUEGnODMXpdvRVwRwnkCIz/c62VriIxhStIwyMjw9inhLecBT2Vo5OsPBkv1OCj1BLmlh54u9Tu7uEh8RoHy+t4w8d4AJ7WIB2FvmZ+l+JwdcAawGHe1jTQxJs2OqaNEr84f4ZHcpu0r92I06BqfZWJbrimgxrV4nJb4gS/Y52VHqJ6gUCWYDl7Sw08xurLVF7Yu9ZRyskqeNRV6+PuCiyBvEoNNIsRq4unV0OE/V5RR56ZFoqbFdX+XZiDbp6RhrYn1FqyCUB8qf7i5jV5kfpz9EtElPz0RLuFwrcp3hVsTK52B822gyHCaSrQbsRh2bDvvonlDz2kKIU0sCQCHEWaOy22dWVha9evVqsusLcbpTSrG7zE+BJ0h8lZYgX1DRJtrI4DQbBk1jQ6GHd7aX8OtOccSY9FzVKprXNhbTI9ESfpG36jUuam5nZa6LX3eKA8oDzcb44ZCbrYd9XNEqmmiTjhJfiNKKbqp12V3qJ8qo4/p2MRR5Q3yws4RkqyEc1CzaXUaRN8jY1tGYdBpL9zt5e/thbs2KCweJ/pDi24MuLm0RhdWg4TDqWZnrighQQkqxscjLoDQ7AHnuAAu2lTAozcaolg5cgRCf7y3j871l/CqjvGvjol2luAIhrm8Xg07T+HJfGS5/3eXxBRVvbDlMlFHH1a2jiTLqOOAKoBpVe+UB54c7Sxna3E77GBO+kGJvmb/O9J5AiEOeIGm2+l//ir1BdpT6qRpDKsBh0nFFpgOrQcc+p59P95QRZdSRFWfmvGQbBZ4gvqBiVEV9WKucIM1mYG+ZXwJAIU4DEgAKIYQQ54il+50sz3USVBBS5WMBeycd+eEixWYgpUpwMLiZnc2HfWw97KN3khWrQYemRbYkApj1GmhH372xxB8izqwn3W5A0zRiGjFJiNmgcXG6HZ2mkWCBNtEmdpX66JFoodATZOthHze0iyE9ygjA5ZkO/r2hkC3FPjrGlbe6hRRckh4VUdaOcWZ+KfSGA5RdpX48QUXH2PLWz+8PuukUZw63eMWjZ3h6FG9sOcyIFlEc9gXZWepnYocY0mzl1x7V0sFLvxTVWZZfiry4AoqJHaKxVrQ8xpkbP1FKmT9ECOgQawrXXbK17le7wxXBdW33aethHzN+zEcpCFREoMOa28P79ZoWDoahvAV5nzPAxmIvWXFmTHoNg04jqGo/v8Oo46A72OiyCSFOHgkAhRBCnHJn0qL1Z4KOHTtis9XebbKq85KtdE2w4AqEWLbfRYbDGA6UoLxF6usDLraW+MqDC6UIhAhPHnOidY238ObWw7ycU0TraBNto020quhuWpckix5dlfF6dqOOQxWBRYE3gA5oZj/yemM16Ii36Mn3HAk+9BokWyMDrc5xZl4/dJhSfxCHUc/PRV5aR5vCXUIPugMUeYP8XOSJOE4Bxb4ghZ4gOiC1SgAWZ9bXO+HLQXeAFJs+HPwdrWSrnowoI6/mFNMq2kgrh4mOsaY6x9kFVHlkZ6hlvGOGw8iIFlH4Q4ofCzwUeoL0SYpsrVub7+bHfC+H/UECIUVQQUo9AWdVBp2GP9TYtk0hxMkkAaAQQohT7kxctP50lp2d3ahuz1aDjjiznjiznqtaOXjxlyKa2wzhMX5L9jvZUeLjwuZ24sx6jDqNhTtKCKpjf3GvfmSoyrlSbQbu7BzH9hI/O0t9vL+zlEyHkSvrmXhEVy140QBVcZX6sln1MINOqzHpSzO7kVizjpwiHz0TLWwu9jEqI+pIORT0SLTQJ6lmV+9oo45CT+2tW/XVnKGBnrJaLcdXjaF0msZ1baPZ5wywo9RH9iE3y3Od3NQ+tsYkLwBWfXlg6AkqbMbIfUadFm59vLiiZXNlrovBzcpb/XKKvHy518mw5uXjE816je8PutnvqrvLaVWeoMLWUIGFEKeEBIBCCCFOuTNp0fozQceOHY/6GItBR+8kC1/td3Kzw4imaewt89M1wUKHiglKfEEV7jZYSa9FBnGV22oLvmwGDWeVMXCFniDVh8SZ9eVjyLLizHSI9bFgWwnuQOiYWsUSLQZCwH5nINyy6Q6EKPQESWhE18pOcWZ+LvTgMJZ3dW1bpTUyxWYg3x2ss4tmgkVPCDjoDpJa0bX0/9m77/AqqvSB49/be3ovJPQECF0QkKZANEqxoCCg2FgLYsWyiuKqK7oirP50VXABRcW2ooIKgoAUaaFDQFpIgFDSk9vL/P64ySU3PRAI5Xyeh2dzZ87MnCnXnfe+pxTY3djdNYeAETolO/LsNZ6vXikn1+YfYJ20uqjYxVImkxFn9GZy+0Tp+c/uAv4qctAjour3KlgjRyOXkWtz+QbFqUmfKB3fHCymS7gWk0pBdqmTWIOKrhUC4AK7f9Bb3bNR7rTVRTOjqtp1giBcWCIAFARBEC64S3HS+stRtzAdG05a2VfWPy5Io2Bfod0X+KzOsVQJ7ALVCrLNLto53CjkMvRKOYFqBQ6PRGaJgwidEpVchkouI8GoIj3XRoxBhYTEymMWv+Bl4ykrRpWcSJ0CGTL2FtoxKGVnPU9eiFZB60A1v2aXkhpvRC2Xseq4GZNaTuug2puWArQP1rL2hJV1Jyy0DVKjrFDZqyN1fLqvkKXZpXQK1aKSy8izuzhc7GRIvJFQrZJEk4pfskpIjTcil8n4/Zi51ixfu2ANf5608t2hYgbEGLyDwFhdmFRyYg0qEkwqNpyysjPPRqxBxe4CG7lWNxF6b/B23Owks8RJ8wAVeqWcHLMLi8tTY7Ark8lIMKk4anbVORF9gklNmFbJnyesDIk3EqxRsDvfzqFiB0FqBbvybZywuAjUnAlcA9UKDhc7ybN5R0nVKGQoZN6mnycsLvrHGGo5oiAIF4oIAAVBEAThCqVXyWkfomFNWcBzXayBn7NK+eyvQvRKOT0jddg9/im7vtF6fs0q5cM9BbgleK5LGHFGFV3CtPxwuASrW/JNA3FtnIGfj5Ty+f5CjCo5g2KNnDji8u1LLZex/qR3CgM5MqL0Ska2DDynOflubGbkt6Nmvj1UjNsjEW9UMbJFoG8E0NqEaBVE65XkWFwMivMPViJ0Ssa0DmRVjoXP9xchIRGkVpAcfCaQuinBxM9ZJXy+vwiDSs6AaAO5Nhc1JTMVchl3tArg92NmvjlYjAfv1BtDyo7dIkBNnygdK4+bcUnQMURD+xANp23ea6hWyMgudbL5tBW7WyJQreDaWAMtA2sOdjuHafklq5SBMfo6r/NVEVp+zirl6kgdXcK0nLK6+CGzBPAGr13CtRwqdpzZd6iWrFIn8/YV4fBIvmkg9hc6CFAriBcZQEG4KMgk6Rwa9l8gW7ZsoVu3bvXu43C5uRjO/2KogyDUpameU/H9EAShOsUONx/sLmBUqwASTXVnIC8ESZL49K8irgrX0S6k9ixgY5m3r5Du4Vrah4gpIAThYiAygIIgCIIgCI0gs8SB0yMRrlVS6vSw8riZQLX8osp8yWQyro83+rKI55vZ6aFtkJp2wRcm2BQEoW4iABQEQRAEQWgEHglWHbdQaHejVsiINagYmmiqV/PTC6nyfI/nk0El5+rIuqcoEQThwhEBoCAIgiAIQiNoEaCmRR3zGAqCIDS1s5t5VBAEQRAEQRAEQbjkiABQEARBEARBEAThCiECQEEQBEEQBEEQhCuECAAFQRAEQWiwaVtz+avQft72//n+QpYdLT1v+6/OB7vz2XTKel6PYXV5eHdnHoV293k9jnBGod3NtK25nLSc35FPd+TZmLEj75z3c6TEwbStudhcnroLn4NTVhfv78rH4b7oZ4QTGtklMQiM1er9j3FGRkYT16RplJ93+XUQBEEQhIo+2J3PVeE6rorQNXVVGs0tzQOQX1yDZzaKP09aaRWgJkijALzByYd7CrinbdAFG5mzsR0pcfDlgeJq103sEIJRdeHyDYuOlGB3S9zaIsC3LEAtZ2KHEPTK8/tAJQdraHkJDQIUoVMSrVey6bSVPlFipNYrySXxX5rMzEwAxo4d27QVaWKZmZn06dOnqashCIIgCOedTnn5NVJyeiS259m4vWVA3YUvQm5JqnVKiwnJwagV/usN5znoqg+5TIZRdf7roZLLUJ3jrxZu6cJm41JCNSzJNtMrUof8IpuuRDh/LokAMDU1lfnz55OYmIhOd/n8ullfGRkZjB07lsTExKauiiCctdzcXJYsWXJev8ciWy4IXhtPWdmZZ6PQ4UarkNMqUM3AGIPfy/nRUiercszkmF0o5DJi9EqGJ5rQKuXVZhT/u7eA1oFq+kYbqhyvPAP0eEoI2rLA7aTFxZx9hTzYLpggjYIih5ul2aUcNbvwSBKBagUDYwy0DKw+Y/L5/kIidUoGxRkB2JVvY/MpG/l2Nyo5JJjUXBdrwFAhu3Ta6mLlcTPZpd6mfhE6BTcmmAjWKKrsD+C7Q8VoFDJuSjBVOX51mTmby8PMnfmMbhVAgkmNzeVh6VEzh0scON0SJrWcXpF6OoZqqz2nQ8UO5DKINdR/YnhJkthwysrWXBtmp4dgjYI+UXqSyiZWr60Obo/E8mNm9hXasbklDCo5XUK19CrL9tjcHlYcM7O/yIHLA1F6JdfFGnznuzrHu65buI51JywUOTw82zkUWQ2Bgl4p893/yjySty678u3IgE6hWswujy9btzPPxvJjZiZ2CEFZIYj636FiVHIZQxNNvvp0CdOy7oQVq8tDy0A1N8Qb0SrlrM7x7h+8TZQBRrcKIFCt8LuXHkni16xSjpQ6MTs9BKgVdAnT+j3v5ZnEOIOSjaesuCVoF6zhujhDjUHwjrJzeKJjqG/Z/iI7a3Is5NrcGFVyUkK09I46E2xN25pLaryBg8VOjpQ46BGho5nR+3wcNbtYddxMvt1NhE7JDc2MROjOvLrvLfTuu8DuxqCS0y1MS88Kcy7W5zvTwqTG6iohq9RJounSyV4K5+aSCADDwsIYM2ZMU1ejyV2Jwa9w+ViyZMkFy+KLbLlwpZMBg+IMBKoVFDrcLM02s+K4mdR4b/Bz0uLiywNFdAzVMijWiFwGWaVOzmePo6XZpbglGNM6ELVcRq7NjUpR/4yDW4K+0XpCtQrMLg/Lj5pZnFXC7S0DAShxuPl8fxHNjCpGtw5AI5dz1OzEcx4zKn/kWMi1ubi9ZQA6hZxCuxtnLcfLLnUSrWvYq9cfORb+KnSQGm8kRKMgq9TJT0dK0CvlNDOpaq3D5tNWDhQ5GNE8gAC1nGKHhxKH9y5LksS3B4vRKuSMbOm9XtvybCw4UMSEdsG+DGyB3c3eAjs3n2OT3PUnrezOt5PWzEiYVsHm0zb2FzpoZvIGO0nBGpYdM3OgyOELbi0uDweLHX4ZU299HNzWIgC7R+KXrBKWHjUzLNFEzwg9eTY3DrdEWllQr1PIKHH6P9kSYFLLGZFoQqeUc8zs5NfsUowqOcllxwbIKnFiVMm5s3UgBXYPP2QWE6FT0jms+gC/skPFDhZlljIozkC8UUWB3c2v2d5+rddEnwnUVudY6B9jYFCsAZkMX//QFcfNDCoL2FYdN/PdoWImtAtGIZNxwuLih8MlXBOtJzlIw1Gzk6XZpeiUct8PEHV9ZwAUchkROiVHS10iALyCXBIBoCAIl77yDPb8+fNJTk4+L8cQ2XJB8KqYyQjSKOgXLbEku9QXAG44ZSVar/R9BghvYGDSUMUOD22DNL4MRnkfuPrqVCGrFqRRMDjOwLy/inC4JdQKGVtybWgUMoY3N/kyNCHahh2joYodHiJ1SqL1Kl+9alPk8DSoP5zDLbHplJXRrQN9WcMgjYKjZifb8mw0M6lqrUNxWcYwzqBEJpMRqD6z7kipk9NWN4+mBPoybtfGGvir0M6+QocvyHFLMDTBhL4e9X5/d77fZ5NKwYR2wQCkn7bSK1JH2yBvgDU4zsDBIoevrEouo12whh35Nl8AuDvfjkkl92XEAFweuDHBSEDZuQyOM/LNwWKujTVgVMlRymW4JWq9zgqZzC+THaRRcMzsYm+h3S8A1ChlDI4zIJfJCNVCywA1R0oc9Q4A152wcHWkjpSyZzdIo6BvtJ6Vxyx+AWC7YI3f810eAF4Tpad5WZ/CmxJMvL8rn78KHSQHa9h4ykqCSeXruxeiVZBnc7PxlNUXANb1nSlnUskpcohBia4kIgAUBOGCKM9gJycn07Vr1wtyrAvlSh+oSji/kpKS0OsbNkDDkRIHf560kluWDfFIEi4J34vfSauLpKAL+2t/93AdS7JLOVziINGk8gsG6+OExcWaExZOWVzY3BIS3ixXscNNmE7JSauLeKOq1j5qja1LmJbvDxdz0uqiuUlN60A1ccaam3e6PBLKBgSAeTYXLgkWHCjyW+6WILLs2tVWh5QQLQsOFPFxRgEtAtS0ClD7AoqTFhcOj8S/d/qPWunyeLNs5QLV8noFfwBjWgehqRBYlP9lc3swuySiKzR9lctkROmVVMyXdgrVMm9fISUONya1gp35NlJCtX5NTgPUcl/wBxBj8O4jv6yJZX1tzbWyPddOkdONyyP5XdNy4VqFX784g0rOaWv9A6WTVhcnLC7WnbT4lkkSuCRvf9Dy/oLRNQz+E2M4s1ynlPuCPPA+G60DNX7lYw3eAV08koS8LEtY23emnFIuw+kRI4FeSUQAKAiCcI7EQFXC+ZSent6gH02KHG6+OVhM5zAtfaP16BRyss1OfskqLWsOKaOucTlkQOXXwdreD6vrE1a56WWnMC3NA1QcLHZwuNjJnycLuTbWQPfwun+wcbglvjpYRHOTmqGJJvRKGcUOD18dLKZ8BHtlHYGfDFk15+S9HtWfk/d/K25T+Rq0DFTzcPsQDhQ7OFLiZMGBIrqG67g2tmo/SfC+xNsaMOR+ecmRLQIxqf2Dm/I4q7Y6ROmVPNQ+mEPFTjJLHCzMLCHRpOLm5gFI4GveWFnFIK4hg5oEqeU19gGEqle68pWI0iuJ0CnYlW+neYCa01Y3t7XQUBtZlT/qllFgZ/lRM9fGGogxqNAoZGw4aeW4xelXrvKgKN7vRQPun+Rt6lme9ayo4nfwbAaOqasW9fnOlLO5PA3OyAuXNhEACoIgnKMrfaAq4fxKSkpqUPkciwuPBNfFGnyBWUal+foidEqOlDjpG139PvRKOeYK/absbk+t89aVD69f6vScGQSmmkyJd7ANHV3CdKw8bmZ7rq1eAWC+3Y3VJTEgRu/L/uRYbFXOaWe+rcaRKvVKmd85eSSJ01Y3zUzVByz6svOouM1Ja9V55PQqb5+rjqFa4nKVrDhmqTEAjNQp2F1Q/7kTQ7UKFDIodrp9feWqrWstddAovP3akoM1tA1y8PXBYqwub7PRUqcHGQ1vjttQWoUcg1LGcbOT+LLspEeSOGl1Vcm6dQrVsum0lRKnh0STyi/bB95mtyVONyaVd/kxswsZEFJ2DgpZ1R8fKssudRJrUNG1wrNXcB7mZYzUK8m3uQk+y+t73OzyNdu1uTwU2N2EljVrDtMqOWr2D1iPmV2EaLxZy3y7q87vTLnTNjdtg2sPtIXLiwgABUEQzpEYqEq4mASrFXiAzadttA5Ue/uL5fq/+PWK1PPJ3gKWZJfSJUyLQgZHSpwkBWvQK+UkmFTszLPRKlCNViHjjxxLrUPEB2sUBKjkrDlhoV+0gXy7ty9SRcuOltIiQE2IRoHNLXGkxOl7ma1LgEqOQgbpp210CdNy2uZi3Qn//XcL15J+2soPh0voFaVDI5dz3OIkWq8kVKskwaTi97JBRoI1cjadsmGvJRunKhsZ9c+TFgLVcixuiT9yLH5l/sgxE6VTEq5T4vJIHChy1HpOzQPUrDpuwebyVMmU5VcTgIRpFfSM0LH8qBlJgjijCrtb4pjZiVouIyVUW2sdNp6yYlTJidQpkCFjb6Edg1KGViEj0aQi1qDkf4eLGRBjIESjoNTp4VCxg9ZBal+fwoawuCRckv+AKzqlDIVMRrdwHetPWgnWKAjVKkg/bcPuqnr924doWHHczPY8W7WjsyrlsPhIKQNjDDg8EsuOmkkKUvuafwaqFRwudpJnc6FTyv2ymeWCNQp259s5VOwgSK1gV76NExYXgZrGnXqkT5Sebw8WY1LLSQrSIJPBaaub01YX/WKq/5GgorUnLOiUMgxKOatyLOgUctqUjZrbI0LHvH2FrD1hITlIwzGzk/TTVoaU9eutz3cGvP0Ny4Nt4cohAkBBEARBuNRJ+EZojNQruTbWwIaTVlYdNxNvVNE/Rs+iI6W+4iFaBXe0DGRVjpl5+wpRlgU77cqyAL0idRTa3Xx70DtNQt9ofa2DRChkMoYlmliSXcp/9xYQpVfSL1rPwswSXxmP5B0JtMTpQaOQ0dykZlBc3S/B4M1w3djMxKocM5tPW4nSKxkYq+e7Q2f2r1PKGd06kBXHzHyxvwgZMiJ0CuLKgoiOoVpOWd0sOlKCXAZXhetqzaoBpCUY+flIKXP3FRKi9U5b8dXBMxOeK2QyVuVYKLK7UcplxBtVDE801ri/CJ2SKL2SjEI7XcL8M58/VLhW5R5sF0zfaD16pZw/T1opzC5Fq5ARqVP6pnKorQ5quYz1J73TBMjx9rkb2TLQlxke2TKAP3Is/JxVisXlwaiUE29UYTjLORg/ziiosmxcG+8ANldH6ih1elh8pBSZDDqHepsEVw7PNAo5bQI1HCx20LqaKUKCNQraBKr55lAxNpeHFgFqv8GMOodqySp1Mm9fEQ6P5JsGoqIuYVpOWV2+a94uWEOXcC2Hih00phYBam5rGcDaExY2nLSikMkI0Sr8BmepzYAYA8uOmikomwbi1hYBKMq+6FF6JcObm1iTY2HtCQtGpZy+0WemIKnPdwa8zWGbm1RVrpFweZNJ0gWecVJosC1bttCtW7cG9wO53OogXNouxDMknlPhSuSRJGbsyOPGZibf6ImXg0/3FZJoUtUrU3IpOVjk4PfjZu5PCqpxPr0rgSRJzMooJClIXeUeLzhQRKhWweA4/2C6fB7Ae5OCL2RV621rrpV1J6w80iGkqatSLy6PxMd7ChiWaKp18CLh8iMygIIgCIJwiSp2uNmVb8dT1jzwcuDySJy2uci1uevVP/BS0zJQTX5Zs7vK/dsuZ0UON4eLnTQzqnBJEltO2yh0uGkXcuZHC6vLw+ESJ0dKnAyuZ3b4YlHscHOw2EnYeZ56pDEVOzz0itJdNv/tEOpPBICCIAiCcImas7cQnVLO0ARTg4bAv5gdKnaw6EgprQLVtA2+PCemrjhP45VCBuzMt7HimBkJCNcpGNUqkDDtmVfROfsKsZcNXBKqvbReUefsK8RU1uzyUhGiVRCivfKeRUEEgIIgCIJwyXqsY2hTV6HRtQnS8GQ1w+YLl7YAtYJxbYJqLfNw+9qbTvaNNvhN4H4xeSzl8vsuCpevy+PnQkEQBEEQBEEQBKFOIgAUBEEQBEEQBEG4QogAUBAEQRAEQRAE4QohAkBBEARBuIhNnTqVzp07N3U1LgoDBgzg8ccfb7T9XahrO27cOP75z382+n7rcz0SExOZOXNmrWVkMhkLFy6stczTTz/NpEmTGlZBQRAuSiIAFARBEK4Y69atIy0tjeDgYLRaLSkpKUyfPh23u+ZJzhuLCOTO3f/+9z9effXVpq5Gg+zYsYPFixfz6KOP+pYNGDAAmUyGTCZDrVbTsmVLnn/+eex2e5PUMScnhxtuuAGAzMxMZDIZ27Zt8yvzzDPPMGfOHA4fPtwENRQEoTGJAFAQBEG4Inz//ff079+fuLg4VqxYwd69e3nsscd4/fXXGTVqFJIk1bit0+m8gDUVahISEoLJdOkMsw/wf//3f4wcObJKvR944AFycnI4cOAAb731Fu+//z5Tp05tkjpGRUWh0dQ+8mpERARDhgzhww8/vEC1EgThfBEBoCAIgnDZM5vNPPDAAwwbNoyPP/6Yzp07k5iYyP3338+8efP49ttv+frrr4EzGZCvv/6aAQMGoNVq+fjjjwkICODbb7/12+9PP/2EwWCgpKQEgKNHjzJq1ChCQkIwGAx0796dDRs2MHfuXF555RW2b9/uy/zMnTsXgKysLIYPH47RaCQgIIDbb7+dkydP1ngumzZtYvDgwYSFhREYGEj//v3ZsmWLX5m9e/dyzTXXoNVqadeuHcuWLfNr5rdy5UpkMhmFhYW+bbZt24ZMJiMzMxOAI0eOMHToUIKDgzEYDLRv356ff/65xnrZ7XaeeeYZ4uPj0Wg0tG7dmk8++QSAuXPnEhQU5Fd+4cKFyGQy3+fyDOlnn31GYmIigYGBjBo1yndtoWqTx3M9ZmXVNakcMWIE48eP933+4IMPaN26NVqtlsjISG677bYa9+fxePjmm28YNmxYlXV6vZ6oqCiaNWvGrbfeyuDBg1m6dKlvfV5eHqNHjyYuLg69Xk9KSgpffvlllf24XC4mTpxIUFAQoaGhvPjii1V+zCgpKeHOO+/EaDQSExPDe++957e+4rPRvHlzALp06YJMJmPAgAG+csOGDau2DoIgXFpEACgIgiBc9pYuXUpeXh5PP/10lXVDhw6lTZs2VV5sn332WSZNmkRGRgY333wzo0aNYs6cOX5l5syZw2233YbJZKK0tJT+/ftz/PhxfvzxR7Zv384zzzyDx+Phjjvu4KmnnqJ9+/bk5OSQk5PDHXfcgSRJjBgxgvz8fFatWsVvv/3GwYMHueOOO2o8l5KSEu6++25Wr17N+vXrad26NWlpab5AyePxMGLECPR6PRs2bODjjz/mhRdeaPA1e+SRR7Db7fzxxx/s3LmTN998E6PRWGP5u+66iwULFvDuu++SkZHBhx9+WGv56hw8eJCFCxeyaNEiFi1axKpVq5g2bdp5PWZDbN68mUmTJvGPf/yDffv28euvv9KvX78ay+/YsYPCwkK6d+9e6363b9/O2rVrUalUvmU2m41u3bqxaNEidu3axYQJExg3bhwbNmzw23bevHkolUo2bNjAu+++y4wZM5g9e7ZfmX/961907NiRLVu28Pzzz/PEE0/w22+/VVuXjRs3ArBs2TJycnL43//+51vXo0cPsrOzOXLkSK3nIwjCxU1MBC8IQoPl5uayZMkSEhMT0el09domIyMDAKvVej6rJgjV+uuvvwBITk6udn1SUpKvTLnHH3+cW265xff5/vvvp3fv3hw/fpyYmBhyc3NZtGiR70X6iy++4PTp02zatImQEO+E1q1atfJtbzQaUSqVREVF+Zb99ttv7Nixg8OHDxMfHw/AZ599Rvv27dm0aRNXXXVVlbpee+21fp8/+ugjgoODWbVqFTfddBNLly7l4MGDrFy50nes119/ncGDB9fvYpXJysri1ltvJSUlBYAWLVrUWPavv/7i66+/5rfffmPQoEF1lq+Jx+Nh7ty5vuaS48aNY/ny5bz++uvn7ZgNkZWVhcFg4KabbsJkMpGQkECXLl1qLJ+ZmYlCoSAiIqLKug8++IDZs2fjdDpxOBzI5XLef/993/rY2Fi/HyweffRRfv31V7755ht69uzpWx4fH8+MGTOQyWS0bduWnTt3MmPGDB544AFfmT59+vDcc88B0KZNG9auXcuMGTOqfSbCw8MBCA0N9XtWy+tUfl4JCQm1XitBEC5eIgAUBKHBlixZwtixY89q28zMTPr06dPINfIqDy7Lg03hypGUlIRer6+zXE39/CRJqtI0sHLWpkePHrRv355PP/2U5557js8++4xmzZr5MkDbtm2jS5cuvuCvPjIyMoiPj/cFfwDt2rUjKCiIjIyMagPAU6dO8dJLL/H7779z8uRJ3G43FouFrKwsAPbt20d8fLzfy3uPHj3qXadykyZN4qGHHmLp0qUMGjSIW2+9lY4dO1Zbdtu2bSgUCvr379/g41SUmJjo11cuOjqaU6dOnddjNsTgwYNJSEigRYsWXH/99Vx//fXcfPPNNT57VqsVjUZTbbPTMWPG8MILL1BcXMybb75JQEAAt956q2+92+1m2rRpfPXVVxw7dgy73Y7dbsdgMPjt5+qrr/bbf69evXwDGykUCt+yinr16lXnyKDVKf/Bz2KxNHhbQRAuHiIAFAShwRITEwGYP39+jRmVyjIyMhg7dqxv2/OhvO/S2QanwqUrPT2drl271ri+TZs2gPc57N27d5X1e/fupV27dn7LKr9ogzcL+H//938899xzzJkzh3vuucf38l3fbHhF1QWetS0HGD9+PKdPn2bmzJkkJCSg0Wjo1asXDoejzm3LyeVyX9lylQe6uf/++0lNTWXx4sUsXbqUN954g+nTp/uNZlmurnOXy+VVgu/qBtap2AQSvH3TPB5PtftsrGM2ZBuTycSWLVtYuXIlS5cu5aWXXmLq1Kls2rSpSn9DgLCwMCwWCw6HA7Va7bcuMDDQlyGeP38+7du355NPPuG+++4DYPr06cyYMYOZM2eSkpKCwWDg8ccf993nc1XXM1Kd/Px84EyWUBCES5MIAAVBaLDyF6/k5ORaX7pr2/Z8SE1NZf78+Q1qmipcHpKSkmpdP2TIEEJCQpg+fXqVAPDHH39k//799ZpeYOzYsTzzzDO8++677N69m7vvvtu3rmPHjsyePZv8/Pxqs4BqtbrKdBPt2rUjKyuL7OxsXxZwz549FBUV1fjjyurVq/nggw9IS0sDIDs7m9zcXN/6pKQksrKyOHnyJJGRkYB34JiKyl/gc3JyCA4OBqgy7D94mxc++OCDPPjggzz//PPMmjWr2gAwJSUFj8fDqlWrfM0xKx+vpKQEs9nsC6yrO15DnI9jhoeHk5OT4/vsdrvZtWsXAwcO9C1TKpUMGjSIQYMG8fLLLxMUFMTvv//u11y4XPm0H3v27Kl1ChCVSsXf//53nn/+eUaPHo1er2f16tUMHz7c94OWx+Nh//79VZ6L9evXV/ncunVrX/avpjI1fWfKA9XqpkbZtWsXKpWK9u3b13gugiBc/EQAKAjCZSMsLIwxY8Y0dTWEi5DBYOCjjz5i1KhRTJgwgYkTJxIQEMDy5cuZPHkyt912G7fffnud+wkODuaWW25h8uTJDBkyhLi4ON+60aNH889//pMRI0bwxhtvEB0dzdatW4mJiaFXr14kJiZy+PBhtm3bRlxcHCaTiUGDBtGxY0fGjBnDzJkzcblcPPzww/Tv37/GgUNatWrFZ599Rvfu3SkuLmby5Ml+P3gMHjyYli1bcvfdd/PWW29RUlLiGwSmPOvTqlUr4uPjmTp1Kq+99hr79+9n+vTpfsd5/PHHueGGG2jTpg0FBQX8/vvvNQaliYmJ3H333dx77728++67dOrUiSNHjnDq1Cluv/12evbsiV6v5+9//zuPPvooGzdu9I2CerbOxzGvvfZannzySRYvXkzLli2ZMWOG30ipixYt4tChQ/Tr14/g4GB+/vlnPB4Pbdu2rXZ/4eHhdO3alTVr1tQ5B+Sdd97J3//+dz744AOefvppWrVqxXfffce6desIDg7mnXfe4cSJE1XuQXZ2Nk8++SR/+9vf2LJlC++9916Ve7l27VreeustRowYwW+//cY333zD4sWLq61HREQEOp2OX3/9lbi4OLRaLYGBgYD3x4e+ffuKH9gE4RInRgEVBEEQrgi33XYbK1asIDs7m379+tG2bVveeecdXnjhBRYsWFDvJnH33XcfDoeDe++912+5Wq1m6dKlREREkJaWRkpKCtOmTfNlYm699Vauv/56Bg4cSHh4OF9++aVv+P3g4GD69evHoEGDaNGiBV999VWNx//vf/9LQUEBXbp0Ydy4cUyaNMlvkBGFQsHChQspLS3lqquu4v777+fFF18EQKvVAt6M05dffsnevXvp1KkTb775Jq+99prfcdxuN4888gjJyclcf/31tG3blg8++KDGev3nP//htttu4+GHHyYpKYkHHngAs9kMeOfvmz9/Pj///LNvOoPGmPOusY957733cvfdd3PXXXfRv39/mjdv7pf9CwoK4n//+x/XXnstycnJfPjhh3z55Ze1ZsQmTJjA559/Xue5qNVqJk6cyFtvvUVpaSlTpkyha9eupKamMmDAAKKiohgxYkSV7e666y6sVis9evTgkUce4dFHH2XChAl+ZZ566inS09Pp0qULr776KtOnTyc1NbXaeiiVSt59910++ugjYmJiGD58uG/dl19+6Te4jCAIlyaZVNvMt8JFYcuWLXTr1q3OPi6Xex2Ei8fZPA/iGRIuF59//jmPPfYYx48fr9Kv62K1du1arrnmGg4cOEDLli2bujpXFJvNRtu2bVmwYEGVwVguJYsXL2by5Mns2LEDpVI0IBOES5n4BguCIAhCPVgsFg4fPswbb7zB3/72t4s6+Pv+++8xGo20bt2aAwcO8Nhjj9GnTx8R/DUBrVbLp59+6tdP81JkNpuZM2eOCP4E4TIgvsWCIAiCUA9vvfUWr7/+Ov369eP5559v6urUqqSkhGeeeYbs7GzCwsIYNGhQlX5hwoVzIaeqOF/q00dWEIRLgwgABUEQBKEepk6d2ij91i6Eu+66i7vuuqupqyEIgiBchMQgMIIgCIIgCIIgCFcIEQAKgiAIgiAIgiBcIUQAKAiCIAiCIAiCcIUQAaAgCIIgnAcrV65EJpP5TSTeUHPnziUoKOic61I+3+ClYvz48dXOeVfZuHHj+Oc//3n+K9TI6nt+59v//d//MWzYsKauhiAIF5gIAAVBEITLSnngVdu/uXPnnvd69O7dm5ycHAIDA8/7sS43//73v+u8Rzt27GDx4sU8+uijZGZm1nnPL+QAPh999BGdOnXCYDAQFBREly5dePPNNy/Y8evrgQceYNOmTaxZs6apqyIIwgUkRgEVBEEQLivlgVe5xx57jOLiYubMmeNbdiGCMrVaTVRU1Hk/zuXE7XYjk8nqdX/+7//+j5EjR2IymdDr9X73/O233+bXX39l2bJlvmVGo9H3tyRJuN3u8zKn3SeffMKTTz7Ju+++S//+/bHb7ezYsYM9e/ac034dDkejzz2p0Wi48847ee+997jmmmsadd+CIFy8RAZQEARBuKyUB17l/3Q6HRqNxvf5ueeeY/To0X7bPP744wwYMMD3ecCAAUycOJGJEycSFBREaGgoL774IpIk+crMnz+f7t27YzKZiIqK4s477+TUqVO+9ZWbgObl5TF69Gji4uLQ6/WkpKTw5Zdf1uucFi5cSJs2bdBqtQwePJjs7Gy/9T/99BPdunVDq9XSokULXnnlFVwuV437e/bZZ2nTpg16vZ4WLVowZcoUnE6nb/3UqVPp3Lkzn332GYmJiQQGBjJq1ChKSkp8ZUpKShgzZgwGg4Ho6GhmzJjBgAEDePzxx31lHA4HzzzzDLGxsRgMBnr27MnKlSt968ubuC5atIh27dqh0Wg4cuRInU0kPR4P33zzja/5okKh8LvnRqMRpVLp+7x3715MJhNLliyhe/fuaDQaVq9ejSRJvPXWW7Ro0QKdTkenTp349ttv/Y61Z88e0tLSMBqNREZGMm7cuFondf/pp5+4/fbbue+++2jVqhXt27dn9OjRvPrqq1XKvv3220RHRxMaGsojjzzidw8SExN57bXXGD9+PIGBgTzwwAMArFu3jn79+qHT6YiPj2fSpEmYzWYA3nvvPVJSUnz7WLhwITKZjPfff9+3LDU11W8ey2HDhrFw4UKsVmuN5yQIwuVFBICCIAiCUI158+ahVCrZsGED7777LjNmzGD27Nm+9Q6Hg1dffZXt27ezcOFCDh8+zPjx42vcn81mo1u3bixatIhdu3YxYcIExo0bx4YNG2qth8Vi4fXXX2fevHmsXbuW4uJiRo0a5Vu/ZMkSxo4dy6RJk9izZw8fffQRc+fO5fXXX69xnyaTiblz57Jnzx7+/e9/M2vWLGbMmOFX5uDBgyxcuJBFixaxaNEiVq1axbRp03zrn3zySdauXcuPP/7Ib7/9xurVq9myZYvfPu655x7Wrl3LggUL2LFjByNHjuT6669n//79fuf3xhtvMHv2bHbv3k1ERESt1wO8zT8LCwvp3r17nWUreuaZZ3jjjTfIyMigY8eOvPjii8yZM4f//Oc/7N69myeeeIKxY8eyatUqAHJycujfvz+dO3dm8+bN/Prrr5w8ebLWSdGjoqJYv349R44cqbUuK1as4ODBg6xYsYJ58+Yxd+7cKs1e//Wvf9GhQwfS09OZMmUKO3fuJDU1lVtuuYUdO3bw1VdfsWbNGiZOnAh4f7jYvXu3L0BdtWoVYWFhvvNxuVysW7fOb2L67t2743Q62bhxY4OupSAIlzBJuOilp6dLgJSenn5F10G4eJzN8yCeIaGp3H333dLw4cNr/CxJkvTYY49J/fv3933u37+/lJycLHk8Ht+yZ599VkpOTq7xOBs3bpQAqaSkRJIkSVqxYoUESAUFBTVuk5aWJj311FM1rp8zZ44ESOvXr/cty8jIkABpw4YNkiRJUt++faV//vOfftt99tlnUnR0tO8zIH3//fc1Huett96SunXr5vv88ssvS3q9XiouLvYtmzx5stSzZ09JkiSpuLhYUqlU0jfffONbX1hYKOn1eumxxx6TJEmSDhw4IMlkMunYsWN+x7ruuuuk559/3u/8tm3b5lemuntU0ffffy8pFAq/+1PRyy+/LHXq1Mn3ufxeLFy40LestLRU0mq10rp16/y2ve+++6TRo0dLkiRJU6ZMkYYMGeK3Pjs7WwKkffv2VXvs48ePS1dffbUESG3atJHuvvtu6auvvpLcbrff+SUkJEgul8u3bOTIkdIdd9zh+5yQkCCNGDHCb9/jxo2TJkyY4Lds9erVklwul6xWq+TxeKSwsDDp22+/lSRJkjp37iy98cYbUkREhCRJkrRu3TpJqVT6ntFywcHB0ty5c6s9H0EQLj+iD6AgCBdEefOijIyMJq6JcDlISkpCr9ef12NcffXVyGQy3+devXoxffp03G43CoWCrVu3MnXqVLZt20Z+fj4ejweArKws2rVrV2V/brebadOm8dVXX3Hs2DHsdjt2ux2DwVBrPZRKpV+mKykpiaCgIDIyMujRowfp6els2rTJL+Pndrux2WxYLJZqr9O3337LzJkzOXDgAKWlpbhcLgICAvzKJCYmYjKZfJ+jo6N9TVwPHTqE0+mkR48evvWBgYG0bdvW93nLli1IkkSbNm389mu32wkNDfV9VqvVdOzYsdZrUJnVakWj0fjdn/qoeB337NmDzWZj8ODBfmUcDgddunQBID09nRUrVvj1Hyx38ODBKucG3uv0559/smvXLlatWsW6deu4++67mT17Nr/++ityubfxVfv27VEoFH7b7dy5s8b6ltfnwIEDfP75575lkiTh8Xg4fPgwycnJ9OvXj5UrV3Ldddexe/duHnzwQd5++20yMjJYuXIlXbt2rXI+Op0Oi8VS67UTBOHyIQJAQRAuiMzMTADGjh3btBURLgvp6el07dr1rLaVy+V+ffkAv75X9WE2mxkyZAhDhgxh/vz5hIeHk5WVRWpqKg6Ho9ptpk+fzowZM5g5cyYpKSkYDAYef/zxGstXVF2gU77M4/HwyiuvcMstt1Qpo9Vqqyxbv349o0aN4pVXXiE1NZXAwEAWLFjA9OnT/cqpVKoqxysPcsuvX+V6VbyuHo8HhUJBenq6X6AD/gOy6HS6BgdyYWFhWCyWBg+MUjHYLj+XxYsXExsb61dOo9H4ygwdOrTaETyjo6NrPVaHDh3o0KEDjzzyCGvWrKFv376sWrWKgQMHArVf3+rqW16fv/3tb0yaNKnK8Zo1awZ4m4F+/PHHrF69mk6dOhEUFES/fv1YtWoVK1eu9OvrWi4/P5/w8PBaz0cQhMuHCAAFQbggUlNTmT9/PomJieh0uqaujnCJS0pKOuttw8PD2bVrl9+ybdu2VXkhX79+fZXPrVu3RqFQsHfvXnJzc5k2bRrx8fEAbN68udbjrl69muHDh/t+BPF4POzfv5/k5ORat3O5XGzevNmXbdu3bx+FhYW+a9C1a1f27dtHq1at6jhzr7Vr15KQkMALL7zgW1ZXf7XKWrZsiUqlYuPGjb7zLy4uZv/+/b7+ZV26dMHtdnPq1Cn69u3boP3XpXPnzoA3i1f+d0OVDzqTlZXl1yeuoq5du/Ldd9+RmJh4TiOGlmeEywdrOVtdu3Zl9+7dtd7rAQMG8Nhjj/Htt9/6gr3+/fuzbNky1q1bx2OPPeZX/uDBg9hsNl/WUxCEy58IAAVBuCDCwsIYM2ZMU1dDELj22mv517/+xaeffkqvXr2YP38+u3btqvICnJ2dzZNPPsnf/vY3tmzZwnvvvefLkjVr1gy1Ws17773Hgw8+yK5du6od5bGiVq1a8d1337Fu3TqCg4N55513OHHiRJ0BoEql4tFHH+Xdd99FpVIxceJErr76al9A+NJLL3HTTTcRHx/PyJEjkcvl7Nixg507d/Laa69VW4+srCwWLFjAVVddxeLFi/n+++8bcgkxmUzcfffdTJ48mZCQECIiInj55ZeRy+W+bF6bNm0YM2YMd911F9OnT6dLly7k5uby+++/k5KSQlpaWoOOWVF4eDhdu3ZlzZo1Zx0Amkwmnn76aZ544gk8Hg/XXHMNxcXFrFu3DqPRyN13380jjzzCrFmzGD16NJMnTyYsLIwDBw6wYMECZs2aVSWzCfDQQw8RExPDtddeS1xcHDk5Obz22muEh4fTq1evsz5n8I7eevXVV/PII4/wwAMPYDAYyMjI4LfffuO9994DvJnH0NBQPv/8c3744QfAGxQ+9dRTAFWme1i9ejUtWrSgZcuW51Q3QRAuHWIUUEEQBOGKkpqaypQpU3jmmWe46qqrKCkp4a677qpS7q677sJqtdKjRw8eeeQRHn30USZMmAB4A5C5c+fyzTff0K5dO6ZNm8bbb79d63GnTJlC165dSU1NZcCAAURFRdU61UE5vV7Ps88+y5133kmvXr3Q6XQsWLDA73wWLVrEb7/9xlVXXcXVV1/NO++8Q0JCQrX7Gz58OE888QQTJ06kc+fOrFu3jilTptRZj8reeecdevXqxU033cSgQYPo06cPycnJfs1O58yZw1133cVTTz1F27ZtGTZsGBs2bPBlDc/FhAkT/PrCnY1XX32Vl156iTfeeIPk5GRSU1P56aefaN68OQAxMTGsXbsWt9tNamoqHTp04LHHHiMwMNDXl6+yQYMGsX79ekaOHEmbNm249dZb0Wq1LF++3K/v49no2LEjq1atYv/+/fTt25cuXbowZcoUv+aoMpnMl9Esz7x27NiRwMBAunTpUqWv55dffumbYkIQhCuDTKrcEUK46GzZsoVu3bqdU5+Xy6EOwsVDPA/C5W7AgAF07tyZmTNnNnVVLhlms5nY2FimT5/Offfdd96PZ7PZaNu2LQsWLDjnzNqVateuXVx33XX89ddfBAYGNnV1BEG4QEQTUEEQBEEQGmzr1q3s3buXHj16UFRUxD/+8Q/Am2G8ELRaLZ9++mmtk7ILtTt+/DiffvqpCP4E4QojAkBBEARBEM7K22+/zb59+1Cr1XTr1o3Vq1cTFhZ2wY5f0+AtQv0MGTKkqasgCEITEAGgIAiCIFSycuXKpq7CRa9Lly6kp6c3dTUEQRCEBhKDwAiCIAiCIAiCIFwhRAAoCIIgCIIgCIJwhRABoCAIgiAIgiAIwhVCBICCIAiCIJw3O/JszNiR19TVOG9W55j5794C3+dFR0r47lDxeT/u/L8K2Z1vOy/7rnxOF6tCu5tpW3M5aXHVe5sDRQ7+u7cAMQuacCUTg8AIgiAIwhXqhMXFnyctZJc6sbslAtRymhlV9IzQE6JVNMoxkoM1tAxQN8q+BK8DRQ7MLg/tgjV+y09YXKwvu582t4RBKSdcp6BzmJZWAWpkMlm99t8zQk/3cN35qHqDHClx8OWBM8G0TikjWqekf4yBSL2SALWciR1C0Cvrd14ArQLVrM4xs7vATocQ7fmotiBc9EQAKAiCIAhXoANFDr4/XExzk5phCSaCNAosLg97Cx38kWNmRPOARjmOSi5DJa//C/qF5JYkFPUMii4mm09b6Rii9Qvo/iq080NmCYkmFTeV3U+ry8Npq5s/jluIN6jQ1jNQUitkwMVzXSYkB6NWyCh2uFl21MzXB4t4oF0wWoUco6rh9UwJ1ZJ+2iYCQOGKJQJAQRAEQbjCOD0Si7NKaBGg5tYWZwK9II2CGIMKm8sDgEeS+DWrlCOlTsxODwFqBV3CtFwV4c0OHSp28N2hYh7tEIJWeaZXyW9HSzlldTGmdRA78mwsP2bmiY6hABTY3Sw/Zua42YnTIxGqVTIgWk9iLVnC1Tlm9hc56BKmZd0JK1aXh5aBam6IN/qOK0kSa09Y2Z5nw+LyEKpVMCDGQIuy/Rba3Xy4p4DhiSa25Fo5bnaRGm+kY6iW7Xk2Np2yUmB3o1XIaBukYUi8EQCb28OKY97juzwQpVdyXaw3A1UfH+zO56pwne+aAfx3bwGtA9X0jTb4zm9nnh2zy4NOIadtsJrBccZq92dxecgscXJdrMG3zOGW+CWrlJYBam6pcD+Dy+5npzCtr8lj5fsB3uDxf4dLeK5LmN/1vjcpGPA2a7W7JeIMSjaesuKWoF2whuviDL4A2u2R+CPHwu4CO3a3hzCtkgExehJM3utvdXlYml3KUbMLq8tDsEZBr0g97UL8s5jV0StlaJVyjCo518YamL+/iONmFyEaBR/uKeCetkG++7G/yM7vx8yUODzEGFSkhGhYnFXK4ylnntHWgWqWHTVTaHcTpGmcTLcgXEpEACgIgiAIV5hDxQ6sLomrI6tv5ucLqgCTWs6IRBM6pZxjZie/ZpdiVMlJDtaQaFKhVcjYV+SgU6g3m+KRJPYW2H3BTWUOt0TLABX9ovUoZTJ25dv49lAxD7QLJlBd88t4gd3N3gIHt7UIwO6R+CWrhKVHzQxLNAGw6bQ3iEttZiRSp2BHnp1vDxVzf1KwX3PWlcfNXBtrILKZEoUctpy28vsxM/1jDLQMUGN3ezhq9vYpkySJbw8Wo1XIGdkyAI1czrY8GwsOFDGhXTA65bkPpbC3wM7mUzaGJZoI0ykwOz2csrprLH+01IlKDmEVzulwiQOrW6JnDfcTqHfzz5pklTgxquTc2TqQAruHHzKLidAp6Rzmve+Ls0opcrgZnmjCqJLzV6Gdrw8Wc1/Z9Xd5JKL0Sq6O1KNRyDhQ7OCnIyUEaeTEGFT1roeyLJvsqaYLX6HdzfeHS+gerqNTqIaTVjcrjpmrlAtUK9ArZWSXOkUAKFyRRAAoCIIgCFeYArs3wAit4+VXIZP5BXJBGgXHzC72FtpJDtYgl8lICtawJ9/uCwCPlHj7nyUFVZ/Ri9Qr/bJn/WIM/FXk4ECRg2619DtzeeDGBCMBZUHi4Dgj3xws5tpYA0aVnI2nrPSM1Pn6xQ2MVZJV6mTzaasvmwfQPVxH26AzWad1J630iKiYoVMQXRaQHCl1ctrq5tGUQF/gcW2sgb8K7ewrdPiCn3NR7PRgUMlJDFChkMkIVCtqDYiKHB4MSrlfQFfd/cwxO/niQJHv8/DEAFoFnn1fTI1SxuA4A3KZjFAttAxQc6TEew0K7G72FNh5pEMwJpW3Dj0j9RwqdrIz30b/GAMmtYKekXrf/rqH6zhc7GBvoaPeAaDV5WHNCQtquYxovRJnpShwW66NUI2Ca8uyo6FaJblWF+tOWqvsy6SSU+TwnO3lEIRLmggABUEQLhO5ubksWbKExMREdLqmH8BBaDpJSUno9fq6C9bD1lwr23PtFDnduDwSbgkidWdeH9oHa/jsdBElTjcmlYLdBXZaBKj9moRW5HBLrD1h4UCxg1KnB48k4fJAcR0v4wFquS/4A4gxKJGAfJsblRxKnR7iKgUSsQZllWxadIXg0+z0UOr0kGCqPgA5aXHh8Ej8e6f/KKYuz5mg61wlBanZfMrKh7sLaBGgokWAmtaBauQ1ZOxcHskXjNYmXKf0NeH8aE8BnnMc9TJcq/Crk0El53TZtS0fhfPjPf4jh7o93oFbwJsZXn/SSkaBnRKnB7ck4fZQr/6h7+/OB8DpgWCNnBHNTRhUcgor3YM8u5uoSk1zow3Vv+oq5bIqAaQgXClEACgIgnCZWLJkCWPHjm3qaggXgfT0dLp27Vrj+uCyTFGe3U1sLc0YMwrsLD/qbTIZY1ChUcjYcNLKcYvTVybGoCJIIyejwNtH769CB2kJ1fdfA1hx3MzhYgcDYw0EaxSo5DK+P1yMu4EBiqzKH1Q7bknlRRUDjrpacErga/ZYmUZRvyaVsrL9VFQx7ghQK3igXTCZJQ4yS5wszS5l4ykFd7YOrHaAGp1Sjs3lv8fq7qdSLvMtr1yfyhWqTxxUOSD1npd3Q6ns8/i2QVXKlV/vjaesbDpl5bo4A+FaJWqFjGVHS3HX49hjWgehUcjQK2VoFLXftMpXrKbHyuaSGjR6qCBcTkQAKAiCcJlITEwEYP78+SQnJzdtZYQmlZSUVOv65iY1OqWM9Set3NqiavbL5vKgVcrJLnUSa1DRtULTzOoyX+2CNezOt2FSyZHJoFUtA7ocLXWSEqr1NcN0uKV6NcUrdnh8WUaAY2YXMiBEo0Cj8A4QcrTUSTPjmfM5Znb5Zfwq0yjkBKrlHClx+gYrqShSp6TU6UEGZ91XTK+UY3aeOT+721Mlc6WSy2gdqKF1oIauYVpmZRRy2lo1mwUQqVdQ6vL47hF476dWUfP9rFwfu0fC4ZbKRvuEk9b6z6NXnUidNxtrcUnEG6u/3tmlTloHqn0jb0qSRIHdO1hPXYLU8hozyhWFahQcLHb4LTtRzRyBLo9EgcNd74F8BOFyI558QRCEy0R5s8/k5ORasz+CoFbIuCHeyMLMEr49WEz3cK132gC3h70FDoodboY3DyBYo2B3vp1DxQ6C1Ap25ds4YXERqPF/GW8frGXtCSvrTlhoG6SutYlikEbBvkK7L0hcnWOpMUtTkVIOi4+UMjDGgMMjseyomaQgNUaVty49I3SsybEQrFEQoVOwM8/OSauLoQmmWvd7TZSeJdml6JVyWgSocXgkjpqddA/XkWhSEWtQ8r/DxQyIMRCiUVDq9HCo2EHrIDXR+rr7riWYVOzMs9Eq0Buk/ZFj8cuS7cizIQExeiUquYzd+XaUMghUVx/wROqU6JUyjppdvj59aoWMG5oZ+SGzhG8OFtEtXEeIRoHDI3GoLCAqP2SMQYlKDqtyzHQP13Hc7GRnvr3O86hNiFZB+2ANi46UeAfY0SmxujwcKXUSrlXSMlBNsEbBX4UOjpY60SplbDplxeysXwBYX53DtGw8bWXFMTOdQrWctLrOnFuFR/KY2YlSJiO2AYPPCMLlRASAgiAIgnAFahOkYVwbOX+esPJj2TD/AWo5CUY1/WK8g2h0CdNyyurih8wSwJvp6xKu9QUV5UK0CqL1SnIsLgbFVT/6Z7nrYg38nFXKZ38VolfK6Rmpw+6pOwMYrFHQJlDNN4eKsbk8tAhQk+o3uIsWu1vi92NmzC4PYVoFt7UIqHNC+5RQLS5JYtMpG78fN6Mvm4YBvCNnjmwZwB85Fn7OKsXi8mBUyok3qjDUcwTQXpE6Cu1uvj1YjEYho2+0niLHmQxgeebud5sbDxLhWiW3tQyocYRRuUxGx1Atu/NtfoO6tC27n+tPWll0pASbS0KjkBGlVzI80eQLuHVKOUMTTKw4bmZ7ro0Ek4provT8ml1ar/OpSVqCkXUnLN4pGJwedApvgFU+DUefKD1FDg9fHyxGKYfOoVpaB6mx16cNaD0FaRTc3NzE78fMbD5tJdagoneUjiXZZpQVgu6MAgftgjUX7fyUgnC+ySTpHHsFC+fdli1b6NatW519Oi73OggXD/E8XJzEfREuV5XnpbvSmZ0eZmcUMD4pqNapMwRYd8LC1lwbj3QIAcDi9PBxRgHj2waJKSCEK5bIAAqCIAiCIFxCDCo5NzQzUuzwiACwki2nrUTrleiUco6anWw4ZaVbhek6Ch1uUuONIvgTrmgiABQEQRAEQbjEtKkwl6FwRr7dzboTVqxuDwFqOT0idPSKPDOIUYxBRUztrZQF4bInAkBBEARBEC5qfaMNfhPSC0JNBsUZGRTX1LUQhIubCAAFQRAusPM1YXtGRgYAVqu10fYpCIIgCMLlRQSAgiAIF9j5nrA9MzOTPn36nLf9C4IgCIJw6RIBoCAIdaqcsRKZpnNzviZsz8jIYOzYsb79C4IgCIIgVCYCQEEQ6lRTxkpkms7O+Z6wvTGblQqCcHYK7W4+3FPAPW2DiNSf/evW5/sLidQpGRRnrLvwedLQOhwpcfDlgWIeTwlBW8/5Es+V1eVhVkYBd7Vp/Okdzsf5NNbzcTY+2J3PVeE6roq4eP6/4vvDxcQaVPS4iOp0Obsw30pBEC5pFTNW6enpzJ8/32+5IAhXpg9257PpVNWWAKtzzPx3b0G991NodzNtay4nLa7GrJ5wBfnzpJVWAWpf8Ff+TL27Mw+72+NX9r97C1idY26Kal7xjpQ4mLY1F5vL/570idKz7oSlyr0Szg8RAAqCUKfKGavyZosi0yQIwsXGLUlNXQXhPHF7qr+3To/E9jwbnSrM91fO4ZbYWM2PFELjqune1FeETkmgWs7ufHsj1UiojWgCKgiCIAjCebcjz8aGk1YKHW4C1Qq6h2vpGu79EenDPd5s4Zx9hQDEG5WMaR1U53blzeiGJ5rYkmvluNlFaryRlBANa09Y2Z5nw+LyEKpVMCDGQIsAta8+xQ43K46ZOVzixC1JhGqUDIk3EGNQAbC/yM6aHAu5NjdGlZyUEC29o3TIZTIApm3NZUicgQNFDrJKnRhUcgbGGEgK9p+fr9DhZvkxM8fNToI1Cq5vZiS27BhWl4el2aUcNbuwujwEaxT0itTTLqTmOf6mbc3lluYmv3kAZ+zI47pYAx1Dtbg9EsuPmdlXaMfmljCo5HQJ1dIrSl/t/jySt/yufDsyoFOoFrPLg90tcWuLgGq32ZVvY/MpG/l2Nyo5JJjUXBdrwKDyzyscNbtYddxMvt1NhE7JDc2MROjOvHoeLXWy8riZExYXOqWcNkFq+kcbUCu81/iD3fl0CtVSYHezv9BB6yA1NyWYqtTnULEDuQzfda2oW7iOTadsdA3TValfQ8+nnNMjsfBwMVaXxMiWAeiU8lqfU4DjZie/ZpeSZ3MTrlXSK6ruH1DLzz/f5uavIjs6hZxBcQbiDCp+zi7lSImDILWCtAQj0foz57630PvsFtjdGFRyuoVp6RlZ/f0HsLk9rDhmZn+RA5cHovRKros11No0ddGREuxuiRi9kvTTNuRyeLh9SK3XstDu5ssDxQDM3JkPQIcQje+etg7UsKfA7nfdhPNDBICCIAiCIJxX23JtrMmxMDjeQKROyUmri1+ySlHJZaSEarm7TSDz/ipiVKsAwrRKyt7/69yu3MrjZq6NNRDZTIlCDptO29h0ykpqMyOROgU78ux8e6iY+5OCCdEqcLglvthfhFEl59YWARhVck5YXJTnMA4VO1iUWcqgOAPxRhUFdje/ZpcCcE30mRfp1TkWBsQYGBRnZFe+jR8ySwjTKQjTnnm9+uO4hYGxBkI0RlblmPkhs4QH2wUjl8lweSSi9EqujtSjUcg4UOzgpyMlBGnkvkC0oTaftnKgyMGI5gEEqOUUOzyUOGpuVrf+pJXd+XbSmhkJ0yrYfNrG/kIHzUw1H98tQd9oPaFaBWaXh+VHzSzOKuH2loF+5VYcNzOo7OV/1XEz3x0qZkK7YBQyGaesLr4+WEzfaD1pzUxYXB5+O1rKb0dLubFCkLfxpJXeUTr6JAXVWJ/sUifRuupfaZODNRwucbD2hIUh8dX3Yazv+YA3WPr2YDFKuYxRrQJRK2R1PqcOt8S3h4pJMKoYmmCiyOFh2dHSGs+nok2nrPSP0dMnSs+m01YWHSklzqCkY6iWa2MMrDhuZtGRUu5PCkImk3HC4uKHwyVcE60nOUjDUbOTpdml6JRyOoZWzZBKksS3B4vRKuSMbBmARi5nW56NBQeKmNAuGF0tfR6PlDhRy2Xc0erMDwW1XcsAtZybm5v4/nAJE5KDUStkVNx9tF7JnyctuDwSSrmsXtdHODsiABQEQRAE4aytPG7mj0r9qdwShGnPDMSx7oSFa2MNtC3LWgVpFOTa3GzLs5ESqvW9ZOoUcowVsi51bVeue7jOVwZg4ykrPSN1tCvLxg2MVZJV6mTzaStD4o3sKbBjcUnc3TbAd+xgjX99r47U+Y4RpFHQN1rPymMWvwAwKVjja3bYL8ZAZomT9NM2UisEGj0idbQK9GYe+0bpmb23kAK7m1CtEpNa4ZeZ6R6u43Cxg72FjrMOAIud3kxinEGJTCYjUF37gCjpp630ijxz/QbHGThY5Kh1m04Vrn2QRsHgOAPz/irC4ZZ82TuAa6L0NC/Lut6UYOL9Xfn8VeggOVjDhpNW2gVrfAORhKBgUJyRL/YXkRpv9AUAzUyqWrNXAEUOj99zU5EMGBBj4NtDxVwVofO7zw09H7PLww+ZJQSpFQxPNKEoq2Ndz+meAjseCdISTKjkMsJ1UOLUsSS77n6ILQPUdAnzXqM+UXq25tqIMih9mearI3V89lcRZpeEUSVj4ykrCSYVfcoyviFaBXk2NxtPWasNAI+UOjltdfNoSqDvml8ba+CvQjv7Ch10rqZZbTmVHNKaGX3XoT7XUlt2PfVKWZUBdUxqOW7Je53rem6FcyMCQEEQhMtE+bQc5dN0CMK5SEpKQq+v/cUboGeEzi8YA28WKrvUCYDF6aHY6eHnrBJ+yS7xlfFIoFHU/Ct/Q7aLrtBUze72UOr0EFcpgIo1KDlldQNw0uoiUq+oMbtx0urihMXFupMW3zJJApfkbf6nKnvhjanURC6mwjHKRVQIhMuDFLNLIhRv88v1J61kFNgpcXpwSxJuD779n42UEC0LDhTxcUYBLQLUtApQ+4KwymxuD2aXRHSFayWXyYjSK6mtR9cJi4s1JyycsriwuSWkstLFDjdhFTJxMYYzf+uUcl8wAt5rXGB3s7vA5rdvCW+z2fIsanQ9Rsh0eSSUNQSAAC0C1MQZVKzOsTAssWoT0vqez4IDxUTrlYxobvI1Ba7Pc5prcxGhU/rd1xh9/QL8CN2Z58eg9G4fUSHDbCh7hi0ubxCcZ3PROtC/CXGsQcmm01Y8kuSrd7mTFhcOj8S/d+b5LXd5oMDupsjhZnbGmQGdekXq6V0WXIbrlH7BH9T/WlZHWVY35zn2JxTqJgJAQRCEy0RmZibAeZ1kXrhypKen12uaEp1SXiWroq0QoJW/yt3QzFglq1VbmNOQ7aoNmGpZpKwjvpIkb1PPilnFcnVtW1nlF27v/r1nt/GUlU2nrFwXZyBcq0StkLHsaCnuOt5/K6/2VBj4Jkqv5KH2wRwqdpJZ4mBhZgmJJhU3N6++Px9UvVS1Hd7hlvjqYBHNTWqGJprQK2UUOzx8dbC4znr7HUOCzmFaulfT3yugQjBXn2BYp5Rjq+PgA2L0fPZXUZVpBhpyPi0D1OwrspNrc/v6Mp7t811fFZ8fWdnf1V2S8kegoaGThPeHiTtbV23uqinL2N2bFOxbVvG7XfnenOuzYSsbAVR/gaYOuZKJAFAQBOEykZqayvz580lMTBQjtArnLCkpqVH2Y1DJMankFDo8tA+pvlmXoux9T2rgdtXRlDUjPVrqpJnxzAv5MbPLl02K0CnZkWfH6vJUmwWM1CvJt7mrbS5Y0XGLi5TQCp/NrgbN6ZZd6qR1oJoOId4MqiRJFNi9g9bURK+UYXae6dOXb3PjrNTFT6OQkxysITlYQ9sgB18fLK72XLUKOQaljONmJ/Fl18ojSd4MaQ3Zmny7G6tLYkCMnoCyZno5Flu1ZY+bXb6mfDaXp6zpq/dzpF5JrrXua1wfkToFuwtqHz0yxqCiTZCaVcf9m1025HwGxOhRK2QsOFDEna0CCdMp6/WchmmV7Mq3+2WPj1ucDT3NegnTKjlq9t/3MbOLEI2i2h8jInVKSp0eZFDj/In1vUf1uZaKsjpUFw+etrkxqeQiALwARAAoCIJwmQgLC2PMmDFNXQ1BqOKaKD2/HS1FLZfRMkCNS5I4UdZErEeEDoNSjlLmHXzFpJKjkHuDk7q2q0nPCB1rciwEaxRE6BTszLNz0upiaNngIu2CNfx50sp3h4oZEGPwDgJjdWFSyYk1ePtPfXuwGJNaTlKQBpkMTlvdnLa66Bdj8B1nb6GdKL2SeIOK3QU2ciwu0prVf8L2YI2CvwodHC11olXK2HTKitlZewCYYFSRnmsjxqBCQmLlMYtfRmjjKStGlZxInQIZMvYW2jEoZX6Zm4q6hetYf9JKsEZBqFZB+mkbdlfN6ZoAlRyFDNJP2+gSpuW0zcW6E9VPs7D2hAWdUoZBKWdVjgWdQk6bsv6QV0fq+HRfIUuzS+kUqkUll5Fnd3G42FnjYC01aR6gZtVxCzaXp9aJ2vtHG5idUeC9XoENPx/w9o/zSBJfHijiztaBhGqVdT6n7YI1rMox80tWKb0jdRQ5POdtaooeETrm7Stk7QkLyUEajpmdpJf1fa1OoklFrEHJ/w57vwshGgWlTg+Hir2jrkbXs6kq1O9aBqq99+dAkYOWAWqUcpmvn2V2qZPmtQw+JDQeEQAKgnBJy83NZcmSJZdU1qu8j155nz1BuNx1CtOilMOGU1ZWHjeXDYSh8DX/k8tkDI4zsvaEhdU5FuLKpoGoa7uadA/XYndL/H7MjNnlIUyr4LYWAYSUBVaKspELfz9m5puDxXiQCNUqGRLnDe5aBKi5rWUAa09Y2HDSikImI0Sr8BvgAryDumQU2FmaXYpBJWdooqnOfk4V9YnSU+Tw8PXBYpRy6ByqpXWQGnst7eWujTPw85FSPt9fiFElZ1CskRNHXL71armM9Se9UwDI8fbnG9ky0Nd8sLKrI3WUOj0sPlKKTOatQ/MAVY3NF/UqOTc2M7Eqx8zm01ai9EoGxur57lBJlbIDYgwsO2qmoGwaiFtbBPj6jEXolIxpHciqHAuf7y9CQiJIrSA5uOYpMGoSoVMSpVeSUWj3DZhSnRCtgo6hWrblnclKNeR8yg2KMyIBX+4v5s7WgXU+p2qFjNtaBLAku5Q5+wp905J8f7jmY5ytKL2S4c1NrMmxsPaEBaNSTt9ofbUDwIC3WenIlgH8kWPh56xSb19CpZx4o8rXv7C+6nMtTeqyAZWOm1mcVeqbBsLlkdhf6OD2VjU3VRYaj0ySxIypF7stW7bQrVu3evfHuFzrIDSdyvf/YnoePv/880u2z9v8+fNFxk4QLlHVzcd3OZAkiVkZhSQFqf2ynRe7g0UOfj9u9k2HIFxa0k9b2V/kYFSrqn0RhcYnMoCCIFzSEhMTAW8wlZyc3LSVqaeMjAzGjh3rq7sgCEJTKXK4OVzs7S/pkiS2nLZR6HDXOhn9xahloJp8u5sSp8fX/0y4dChkMgbHXTo/OFzqRAAoCMIlrbzZZ3JycpNnIxvqUmmyKgjC5UsG7My3seKYGQkI1ykY1SrQbzL7S8VVtfQLFS5utc03KDS+S+/bLQiCIAiC0MSe6xLW1FVoFAFqBePaBDV1NQRBuIDEOKuCIAiCIAiCIAhXCBEACoIgCIIgCIIgXCFEACgIgiAIgiAIgnCFEAGgIAiCIAhVHClxMG1rLjaXp8Yyq3PM/HdvQZMec9GREr47VOz7/Pn+QpYdLT2nenywO59NjTxRd+V6XozcHokPd+dztNTZ1FU5b+p6fs5GfZ7b2uzIszFjR16tZRr7+TmbY1b+bjX0e3KgyMF/9xYgZqBremIQGEEQBEG4Ap2wuPjzpIXsUid2t0SAWk4zo4qeEXrfhOl16Rmhr3NS9sbWFMdsDIPiDHCRv/duy7MRqFYQZ1T5ll2s8x0uOlKC3S1xa4uGTRwunp+mO2arQDWrc8zsLrDTIUSM+tmURAAoCIIgCFeYA0UOvj9cTHOTmmEJJoI0CiwuD3sLHfyRY2ZE8/q9VKsVMrwTCVw4TXHMxqBVnP9GVx5JQgZnPRF6+mkb10TpG7dSFxnx/DTtMVNCtaSftokAsImJAFAQBEEQriBOj8TirBJaBKj9sidBGgUxBlWVZmwnrC5WHrOQa3MRqVeS1sxIaNkccatzzOwvcnBvUjBwJisTZ1Cy8ZQVtwTtgjVcF2dAURaUuDwSq3Ms7CmwY3F5CFDLuTpST6dQ7Vkdsy678m1sPmUj3+5GJYcEk5rrYg0YVPV/ud14ysrOPO8E6VqFnFaBagbGGMqCCW9zuuXHzAxPNLH8qJlip5s4g4obE0wYy45TOWNld3tYkm1mf5EdtVxGz0g9+4vsROqUDIozAt4mmX/kWNhdYMfu9hCmVTIgRk+CSe133KEJJlYcN5Nvc/O3dsFYXR5W5Vg4aXHhkSBCr+C6WCNR+ppf+05YXBTY3bQMVNVYpjJJklh7wsr2PBsWl4dQrYIBMQZaBHjrV2h38+GeAm5ubiL9tI3jZifBGgXXNzMSazhznG25NtaesGB1eWgeoCbeqGLtCQtPdAyt9rirc8zsyrcD3gwlwOhWASSY1Kw4ZuavIjslDg8GlZz2wRr6ROt9z19dz48kSWw4ZWVrrg2z00OwRkGfKD1JwWcyoAeLHCw7VkqJw0OMQUWHkLqzozaXhxXHvce2uyWCNd5r1SpQ7StzqNhR7+fn8/2FROiUKGUytufZUMi8c+n1jTact2PWpa7vCUDrQDXLjpoptLsJ0tSvpYHQ+EQAKAiCIAhXkEPFDqwuiasjq28Gp1X6B0Z/HLdwbawBvVLGr9ml/JxVWuu8cVklTowqOXe2DqTA7uGHzGIidErfRM+LjpRw3OxiUJyBSJ2SQocbq8u/nVlDj1kbtwR9o/WEahWYXR6WHzWzOKuE21sG1nsfMrzN4QLVCgodbpZmm1lx3ExqvNFXxumR2HjKyk2JRmTI+OlICb8fMzMs0VTtPn8/Zuao2cmtLQIwKOWszrFw0uImUnfm1WxxVilFDjfDE70v5X8V2vn6YDH3JQX7muk6PRJ/nrRwQ7wRnVKGXimn0OGmQ4jG24QP74v5NweLmNAuGE0NWZ3sUichGkWN66uz6bSNTaespDYzEqlTsCPPzreHirm/Qv3Aez8HxhoI0RhZlWPmh8wSHmwXjFwm42ipkyXZpQyI0dM6UENmiYM/ciy1HrdnhJ48mxuHWyItwXt9dWVBhloh48Zm3ut12ubi16xS1AoZV0fWL7P5R46FvwodpMYbCdEoyCp18tOREvRKOc1MKoodbv53uJguYVq6hGk5YXHx+zFzrfuUJImvDxbj8EgMTTARrFGQa3P75SEb+vwA7Mqzc1WEjrvaBHHM7GRxVilxBhXNA9Tn7Zi1qc/3JFCtQK+UkV3qFAFgExKDwAiCIAjCFaTA7gYgtJ4vX/1i9DQzqQjTKekVqeeY2YXLU3PHII1SxuA4A6FaJa0C1bQMUHOkxAFAvs3N3kIHac2MtA3SEKRRkGhSkxzsn0Fp6DFr0ylUS8tANUEaBbEGFYPjDBwqduJw139/V0XoSDCpffXtF61nb4Hdr4xHgtR4I9F6FVF6Jd3CtL7zrszu9rAz3861MQYSTWrCdUrSEoxIFTpcFdjd7CmwM6K5iXijimCNgp6ReuIMKnbm2/yOOyTOSJxRRahWiVohI9GkpkOIljCtkjCtkuvjjTg9kF3qqvEcixxuX+anvjaestIzUke7YA2hWiUDY71B/ebT/gOD9IjU0SpQTYhWQd8oPcUOj+85TD9tpUWAip6R3r6nXcN1tAxQV3c4H7VChlIuQyGXYVTJMarkKOTe0KZPlJ44o4ogjYLWgRp6ROjYW1D9fajM4ZbYdMpKWoKRFgHe+90xVEv7EA3b8rzXfGuujSCNgutivc94+xAtKXU0Z8wscZJjcXFL8wCal+23VaCalhUycQ15fsqF6xRcE+29bimhWqL1So6UOM/rMWtTn+8JgEklp8hxdgPmCI1DZAAFQRAuMKvV+3KUkZHRxDURLkdJSUno9Y3XjytCe+ZVobzZpNnlIVBdfQAZrlUgr9AHzaCSc9rqfdk/aXUhA+JNtTczbOgxa3PC4mLNCQunLC5sbskXZBU73ITp6vcadKTEwZ8nreSWZZ08koRL8gYM5c3bVHIIrhBUG1RyzK7qg8xCuwePBNGGM8fXKuSEVNj+pMUbrH28x3+UVbcHdMoz11chgwid/3UxOz2szrFwpNSBxSnhQcLp8Z5zTVweUDYg/rO7PZQ6PcQZ/O9lrEHJKav/cSIqZAONvvspEQrk2d20CfT/ASBar+RAsTcQKXK4mZ1x5hr0itTTu5Z+insL7Gw+baXA7sbhkfBIoFHUr89fns2FS4IFB4r8lrslfJnZXJubGL3Sr59ljKH25+ik1YVJJa91cKWGPD/lIio9v95tPOf1mLWpz/cEQCmX4TzLH3SExiECQEEQhAssMzMTgLFjxzZtRYTLUnp6Ol27dq1xffkLX57dTWw93vjlFd6dy/+sbRR3eaUBSGTgC7rqG2A09Jg1cbglvjpYRHOTmqGJJvRKGcUOD18dLKa+CcAih5tvDhaX9a/So1PIyTY7+SWrFI8k+WpY3XnXpXIZqdLfMmB826Aq+1bJ/V+mKw/6sjirBItLYlCskQC1HKVcxqd/FdZ6zjqljNO2s7jI1Zxo5UWV6w/4TQVQeXXFWphUcr/+etpagrljZic/ZJbQN1pP8wAVGrmcjEI7G+s5VUH5cUe2CMSk9n9Y6xlDVkspr3vjs3l+Ku/W+107v8esSX2/JwA2l4ReeS5HE86VCAAFQWhyubm5LFmyhMTERHS6hg3PXZ5FK8+qXQpSU1OZP3/+WZ2vINQlKSmp1vXNTWp0ShnrT1q5tUXVTJzN5anSD7CxhGuVSEB2iZPEOpr5NYZ8u7d/4YAYPQFl2cMci62OrfzllA2kcl2swRdoZRRWbdbWEEEaOXIZHLe4fPWyu73NIpuVTcEQqfNeK4tLIt7YsNe1o6UuhsQbfM39iqvpZ1lZpE7J1lwbkiTVaxRRjcLb9PJoqdNXZ4BjZhfRtQw2U1moRsFxs/+8gycsZ5qqymUyvyxVOYWMssDijKOlTgLVcr8MYVEtWc8qddEqUMig2OmmWQ1Z6jCtgr+K/JtJHrfU3LQWvBntEqeHfJu73lOsnKsLfcz6fk9cHokCh5vIBjwjQuMTV18QhCa3ZMmSc86GZWZm0qdPn0aq0fkVFhbGmDFjmroawhVKrZBxQ7yRhZklfHuwmO7hWoI0CqxuD3sLHBQ73Ayv5zQQDRWkUZASouHnrFIGxRmI0CkpcrixuKQq/QAbQ4BKjkLmnd6gS5iW0zYX60407MeiYLUCD7D5tI3WgWqOmp1sy21YEFmZRiEnJUTDimNmdArvwC1rTliQVciShGgVtA/WsOhICdeW9a2zujwcKXUSrlX69eWqLEgjZ1e+nSi9Eodb4vdjZupKuCSYVDg9Erk2N+GVmhYWOjy+JqnlgjUKekboWJNjIVijIEKnYGeenZNWF0MT6j+ISLdwHZ/vL2LjKSutAtQcKXVwqLjufmiBagWHi53k2VzolHI0Cm+gWOzwsKfATrReycEiB38V1r9Pm0Yhp2eEjuVHzUgSxBlV2N0Sx8xO1HIZKaFaOodp2XjKyvKjpXQuGwRmZ17tz0Mzk4p4o4rvDxdzbayBYI2CPJsbmQzfiKmN7UIfs77fk2NmJ0qZzG8UWOHCEwGgIAhNLjExEYD58+eTnJzcoG0zMjIYO3asbx+CINStTZCGcW3k/HnCyo9lQ70HqOUkGNX0izHUvYNzkBpvZNVxM0uzzVjd3mkgetVzhMaG0qvk3NjMxKocM5tPW4nSKxkYq+e7QyX13kekXsm1sQY2nLSy6riZeKOK/jF6Fh0pPae6XRtrYEm2mW8PFfumgSh2ePya7qUlGFl3wsLvx8yUOD3oFN4X57pe4NOamfg1u5Q5ewsJUMvpH23g9+O1j1SpU8ppE6hmd4GdAZUCwOpGuRzdKoDu4VrsZQGm2eUhTKvgthYBDco4xRlVpMYbWXvCwh/HzTQPUHNVhI7007UH6p1DtWSVOpm3rwiHR2J0qwDaBGm4KsLFb9mluCVoGaCiT5SeNSdqH1W0or7RevRKOX+etFKYXYpWISNSp6RXWVYxUK3g5uYBLD9WypZcG9F6Jf1jDPycVfvzcHNzE78fM/NjZglOj3dKhv7n+bt2IY9Z3+9JRoGDdsEav2bMwoUnk6SzaVUvXEhbtmyhW7dudfbruNzrIDSdyve/sZ+Hc9mfeDYFQbgcONwS7+/O59pYg9+ciBfSKauLBQeK+Fst00VcCL9klZBnczP2LKf+EC5OFqeHjzMKGN82SEwB0cREBlAQBEEQBOECO2FxkW9zE21QYndLrC3LUrWupWnn+RahUzIwxkCRw0OE7sIFgBtOWkg0qVErZBwsdrAz305qnLHuDYVLSqHDTWq8UQR/FwERAAqCIAiCIDSBDacs5NvdKGQyovRKxrQORH+eBuCpr5QmyD7mWFxsOGXF4ZYI0igYHGegU1jTZEGF8yfGoOI8t3oV6kkEgIIgCIIgCBdYlF7JPRWmN7iSjThPgw4JglC9pv2ZSRAEQRAEQRAEQbhgRAAoCIIgCIIgCIJwhRABoCAIgiAIgiAIwhVCBICCIAiCUI2pU6fSuXPnpq7GRSMxMZGZM2c2dTXOWV5eHhEREWRmZtZ7G5lMxsKFCwHIzMxEJpOxbdu2s67D008/zaRJk856e0EQhHMhAkBBEAThsrNu3TrS0tIIDg5Gq9WSkpLC9OnTcbvdF7QeK1euRCaTUVhYeEGPez5s2rSJCRMmNHU1ztkbb7zB0KFDSUxMBM4EdOX/goOD6devH6tWrfJtk5OTww033NBodXjmmWeYM2cOhw8fbrR9CoIg1JcIAAVBEITLyvfff0///v2Ji4tjxYoV7N27l8cee4zXX3+dUaNGIUlSU1fxkuJwOAAIDw9Hr9c3cW3OjdVq5ZNPPuH++++vsm7ZsmXk5OSwatUqAgICSEtL8wVoUVFRaDSaRqtHREQEQ4YM4cMPP2y0fQqCINSXCAAFQRCEy4bZbOaBBx5g2LBhfPzxx3Tu3JnExETuv/9+5s2bx7fffsvXX3/tK3/06FFGjRpFSEgIBoOB7t27s2HDBr99fvbZZyQmJhIYGMioUaMoKSnxrbPb7UyaNImIiAi0Wi3XXHMNmzZtAryZpYEDBwIQHByMTCZj/PjxdW4HZzKHy5cvp3v37uj1enr37s2+fft8ZQ4ePMjw4cOJjIzEaDRy1VVXsWzZslqvT3mz1o8++oj4+Hj0ej0jR470y1COHz+eESNG8MYbbxATE0ObNm2Aqk1AZTIZs2fP5uabb0av19O6dWt+/PFHv+Pt3r2bG2+8kYCAAEwmE3379uXgwYO+9XPmzCE5ORmtVktSUhIffPCBb115Zm7BggX07t0brVZL+/btWblypa9MQUEBY8aMITw8HJ1OR+vWrZkzZ06N5//LL7+gVCrp1atXlXWhoaFERUXRsWNHPvroIywWC0uXLvWda3kT0Mo8Hg8PPPAAbdq04ciRI7jdbu677z6aN2+OTqejbdu2/Pvf/66y3bBhw/jyyy9rrKsgCML5IgJAQRAE4bKxdOlS8vLyePrpp6usGzp0KG3atPG9dJeWltK/f3+OHz/Ojz/+yPbt23nmmWfweDy+bQ4ePMjChQtZtGgRixYtYtWqVUybNs23/plnnuG7775j3rx5bNmyhVatWpGamkp+fj7x8fF89913AOzbt4+cnBxfIFDbdhW98MILTJ8+nc2bN6NUKrn33nt960pLS0lLS2PZsmVs3bqV1NRUhg4dSlZWVq3X6MCBA3z99df89NNP/Prrr2zbto1HHnnEr8zy5cvJyMjgt99+Y9GiRTXu65VXXuH2229nx44dpKWlMWbMGN85HDt2jH79+qHVavn9999JT0/n3nvvxeVyATBr1ixeeOEFXn/9dTIyMvjnP//JlClTmDdvnt8xJk+ezFNPPcXWrVvp3bs3w4YNIy8vD4ApU6awZ88efvnlFzIyMvjPf/5DWFhYjfX9448/6N69e63XB/BlOp1OZ63lHA4Ht99+O5s3b2bNmjUkJCTg8XiIi4vj66+/Zs+ePbz00kv8/e9/9/vhAaBHjx5kZ2dz5MiROusjCILQqCThopeeni4BUnp6+hVdB6HpVL7/jf08nMv+xLMpVDRt2jQJkAoKCqpdP2zYMCk5OVmSJEn66KOPJJPJJOXl5VVb9uWXX5b0er1UXFzsWzZ58mSpZ8+ekiRJUmlpqaRSqaTPP//ct97hcEgxMTHSW2+9JUmSJK1YsaJKfRqy3bJly3xlFi9eLAGS1Wqt8fzbtWsnvffeezWuf/nllyWFQiFlZ2f7lv3yyy+SXC6XcnJyJEmSpLvvvluKjIyU7Ha737YJCQnSjBkzfJ8B6cUXX/Q7L5lMJv3yyy+SJEnS888/LzVv3lxyOBzV1iU+Pl764osv/Ja9+uqrUq9evSRJkqTDhw9LgDRt2jTfeqfTKcXFxUlvvvmmJEmSNHToUOmee+6p8XwrGz58uHTvvff6LSs/ztatW33n8be//U1SKBTSjh07fOf6/fff+5VfvXq1NGjQIKlPnz5SYWFhrcd9+OGHpVtvvdVvWVFRkQRIK1eurHf9BUEQGoPywoecgiAIjcdqtQKQkZHRxDURLoSkpKR69UOTaujnJ0kSMpkMgG3bttGlSxdCQkJq3E9iYiImk8n3OTo6mlOnTgHe7KDT6aRPnz6+9SqVih49etT6PDZku44dO/odG+DUqVM0a9YMs9nMK6+8wqJFizh+/Dgulwur1VpnBrBZs2bExcX5Pvfq1QuPx8O+ffuIiooCICUlBbVaXet+KtfPYDBgMpl812fbtm307dsXlUpVZbvTp0+TnZ3NfffdxwMPPOBb7nK5CAwM9CtbsbmmUqmke/fuvuv00EMPceutt7JlyxaGDBnCiBEj6N27d431tVqtaLXaatf17t0buVyOxWIhOjqauXPnkpKSUuO+Ro8eTVxcHMuXL6/yTH744YfMnj2bI0eOYLVacTgcVUaU1el0AFgslhqPIQiCcD6IAFAQhEta+VDuY8eObdqKCBdEeno6Xbt2rXF9eX+1jIyMagOBvXv30q5dO+DMC3htKgcvMpnM10S0PMgsDyjLVQwyq9OQ7Soev3xd+fEnT57MkiVLePvtt2nVqhU6nY7bbrvNN2hLfZXvt+KxDQZDvbat7frUdn3Ly8yaNYuePXv6rVMoFPWu8w033MCRI0dYvHgxy5Yt47rrruORRx7h7bffrna7sLAwCgoKql331Vdf0a5dO4KCgggNDa2zDmlpacyfP5/169dz7bXX+pZ//fXXPPHEE0yfPp1evXphMpn417/+VaVvaXlT2fDw8DqPJQiC0JhEACgIwiUtNTWV+fPnk5iYWK8XeuHSlpSUVOv6IUOGEBISwvTp06sEgD/++CP79+/n1VdfBbzZq9mzZ5Ofn19rFrAmrVq1Qq1Ws2bNGu68807A22ds8+bNPP744wC+LFrF6Sfqs119rF69mvHjx3PzzTcD3j6B9ZnbLisri+PHjxMTEwPAn3/+iVwu9wXPjaVjx47MmzcPp9NZJVCMjIwkNjaWQ4cOMWbMmFr3s379evr16wd4M4Tp6elMnDjRtz48PJzx48czfvx4+vbty+TJk2sMALt06cL8+fOrXRcfH0/Lli3rfX4PPfQQHTp0YNiwYSxevJj+/fsD3vvSu3dvHn74YV/ZigPflNu1axcqlYr27dvX+5iCIAiNQQSAgiBc0sLCwup8gRSuHAaDgY8++ohRo0YxYcIEJk6cSEBAAMuXL2fy5Mncdttt3H777YC3Cd8///lP34iX0dHRbN26lZiYmGpHiazuWA899BCTJ08mJCSEZs2a8dZbb2GxWLjvvvsASEhIQCaTsWjRItLS0tDpdBiNxjq3q49WrVrxv//9j6FDhyKTyZgyZYrfADY10Wq13H333bz99tsUFxczadIkbr/9dl/zz8YyceJE3nvvPUaNGsXzzz9PYGAg69evp0ePHrRt25apU6cyadIkAgICuOGGG7Db7WzevJmCggKefPJJ337ef/99WrduTXJyMjNmzKCgoMA3GM5LL71Et27daN++PXa7nUWLFpGcnFxjnVJTU3n++ecpKCggODj4nM/x0Ucfxe12c9NNN/HLL79wzTXX0KpVKz799FOWLFlC8+bN+eyzz9i0aRPNmzf323b16tX07dtX/HAlCMIFJ0YBFQRBEC4rt912GytWrCA7O5t+/frRtm1b3nnnHV544QUWLFjgaz6oVqtZunQpERERpKWlkZKSwrRp0+rVBLHctGnTuPXWWxk3bhxdu3blwIEDLFmyxBdcxMbG8sorr/Dcc88RGRnpy1zVtV19zJgxg+DgYHr37s3QoUNJTU2ttXlsuVatWnHLLbeQlpbGkCFD6NChg9/0C40lNDSU33//3Tfaardu3Zg1a5YvG3j//fcze/ZsX1+7/v37M3fu3CqB0rRp03jzzTfp1KkTq1ev5ocffvCN9KlWq3n++efp2LEj/fr1Q6FQsGDBghrrlJKSQvfu3auMyHkuHn/8cV555RXS0tJYt24dDz74ILfccgt33HEHPXv2JC8vzy8bWO7LL7/06/8oCIJwocikmnrKCxeNLVu20K1btzr7vlzudRDOv9zcXJYsWVKlOWVGRgZjx45lzZo19OnTh7Vr13LNNdcwf/78Wn9tr6/K+xcE4fyYOnUqCxcuZNu2bU1dlTplZmbSvHlztm7dWmUAlXPx888/8/TTT7Nr1y7k8qb5HXzx4sVMnjyZHTt2oFSKxliCIFxY4r86giD4LFmypNbBVDIzM+nTp895G3ilfP+CIAjnS1paGvv37+fYsWPEx8c3SR3MZjNz5swRwZ8gCE1C/JdHEASfxMREgCqZvfIMXfn6xh54pfL+BUEQzqfHHnusSY9f3g9VEAShKYgAUBAEn/JgLjk5udqmvuXrz9fAK2IwBEE4v6ZOncrUqVObuhr1kpiYWON8joIgCMLZE4PACIIgCIIgCIIgXCFEACgIgiAIgiAIgnCFEAGgIAiCIAiCIAjCFUIEgIIgCIIgNMjKlSuRyWQUFhY2dVWaXGJiIjNnzjynffTr148vvviicSp0gdx222288847TV0NQRDOgggABUEQBKGJJCYmIpPJkMlk6PV6OnTowEcffdTU1apT7969ycnJITAwsKmr0uQ2bdrEhAkTznr7RYsWceLECUaNGuVbVlNQOXXq1EadE/FcvPTSS7z++usUFxc3dVUEQWggEQAKgiAIQhP6xz/+QU5ODjt27GDEiBE8+OCDfPXVV9WWdTgcF7h21VOr1URFRSGTyZq6Kk0uPDwcvV5/1tu/++673HPPPRdkUnq3243H42mUfXXs2JHExEQ+//zzRtmfIAgXjggABUEQBKEJmUwmoqKiaNWqFa+99hqtW7dm4cKFAAwYMICJEyfy5JNPEhYWxuDBgwFYtWoVPXr0QKPREB0dzXPPPYfL5fLt0+Px8Oabb9KqVSs0Gg3NmjXj9ddf961/9tlnadOmDXq9nhYtWjBlyhScTicA+/btQyaTsXfvXr96vvPOO76pGSo3Ac3Ly2P06NHExcWh1+tJSUnhyy+/rPPc165dS//+/dHr9QQHB5OamkpBQQEAv/76K9dccw1BQUGEhoZy0003cfDgQd+2mZmZyGQyFixYQO/evdFqtbRv356VK1f6HWPPnj2kpaVhNBqJjIxk3Lhx5Obm+tYPGDCASZMm8cwzzxASEkJUVFSVqTKmTp1Ks2bN0Gg0xMTEMGnSJN+6ytm62spWlpuby7Jlyxg2bFid16qyP/74A5VKxYkTJ/yWP/XUU/Tr1w+AuXPnEhQUxKJFi2jXrh0ajYYjR46wadMmBg8eTFhYGIGBgfTv358tW7b49rFy5UrUajWrV6/2LZs+fTphYWHk5OT4lg0bNqxe91kQhIuLCAAFQRAE4SKi1Wp9wRjAvHnzUCqVrF27lo8++ohjx46RlpbGVVddxfbt2/nPf/7DJ598wmuvvebb5vnnn+fNN99kypQp7Nmzhy+++ILIyEjfepPJxNy5c9mzZw///ve/mTVrFjNmzACgbdu2dOvWrUpm54svvuDOO++sNutns9no1q0bixYtYteuXUyYMIFx48axYcOGGs9z27ZtXHfddbRv354///yTNWvWMHToUNxuNwBms5knn3ySTZs2sXz5cuRyOTfffHOVDNbkyZN56qmn2Lp1K71792bYsGHk5eUBkJOTQ//+/encuTObN2/m119/5eTJk1UmYp83bx4Gg4ENGzbw1ltv8Y9//IPffvsNgG+//ZYZM2bw0UcfsX//fhYuXEhKSkq159SQsgBr1qxBr9eTnJxcY5ma9OvXjxYtWvDZZ5/5lrlcLubPn88999zjW2axWHjjjTeYPXs2u3fvJiIigpKSEu6++25Wr17N+vXrad26NWlpaZSUlADeoPjxxx9n3LhxFBUVsX37dl544QVmzZpFdHS0b989evRg48aN2O32BtdfEIQmJAkXvfT0dAmQ0tPTr+g6COdfTff5fN9/8XwJV6qEhARpxowZkiRJktPplObMmSMB0gcffCBJkiT1799f6ty5s982f//736W2bdtKHo/Ht+z999+XjEaj5Ha7peLiYkmj0UizZs2qdz3eeustqVu3br7P77zzjtSiRQvf53379kmAtHv3bkmSJGnFihUSIBUUFNS4z7S0NOmpp56qcf3o0aOlPn361LuOp06dkgBp586dkiRJ0uHDhyVAmjZtmq+M0+mU4uLipDfffFOSJEmaMmWKNGTIEL/9ZGdnS4C0b98+SZK81/iaa67xK3PVVVdJzz77rCRJkjR9+nSpTZs2ksPhqLZeFe9hXWUrmzFjht91rrhPtVotGQwGv38qlUrq1KmTr9ybb74pJScn+z4vXLhQMhqNUmlpqSRJku952rZtW631cLlckslkkn766SffMrvdLnXp0kW6/fbbpfbt20v3339/le22b98uAVJmZma9zlcQhIuDskmiTkEQhMtQbm4uS5YsITExEZ1O19TVEZpYUlJSvfqGPfvss7z44ovY7XbUajWTJ0/mb3/7m2999+7d/cpnZGTQq1cvv0xcnz59KC0t5ejRo5w4cQK73c51111X4zG//fZbZs6cyYEDBygtLcXlchEQEOBbP2rUKCZPnsz69eu5+uqr+fzzz+ncuTPt2rWrdn9ut5tp06bx1VdfcezYMex2O3a7HYPBUGMdtm3bxsiRI2tcf/DgQaZMmcL69evJzc31Zf6ysrLo0KGDr1yvXr18fyuVSrp3705GRgYA6enprFixAqPRWO3+27RpA3j7s1UUHR3NqVOnABg5ciQzZ86kRYsWXH/99aSlpTF06FCUyqqvUA0pC2C1WtFqtdWumzx5MuPHj/db9u677/LHH3/4Po8fP54XX3zRd5/++9//cvvtt/tdd7VaXeX8Tp06xUsvvcTvv//OyZMncbvdWCwWsrKy/LabP38+HTt2JCEhodpBacr/O2exWKo9B0EQLk4iABQEQWgkS5YsYezYsU1dDeEikZ6eTteuXessV/6ir9friY6OrtLEsnIQJUlSlTKSJAEgk8nq/PFh/fr1jBo1ildeeYXU1FQCAwNZsGAB06dP95WJjo5m4MCBfPHFF1x99dV8+eWXfkFpZdOnT2fGjBnMnDmTlJQUDAYDjz/+eK2D1tRVz6FDhxIfH8+sWbOIiYnB4/HQoUOHeg2EU359PB4PQ4cO5c0336xSpmJTRpVKVWX78oAzPj6effv28dtvv7Fs2TIefvhh/vWvf7Fq1aoq2zWkLEBYWJivz2N161q1auW3LCQkxO9zREQEQ4cOZc6cObRo0YKff/65Sh9InU5X5XkZP348p0+fZubMmSQkJKDRaOjVq1eVa7tu3ToA8vPzyc/Pr/Is5ufnA96BcARBuHSIAFAQBKGRJCYmAjB//vyz6tMjXF6SkpLqVa66F/3atGvXju+++84vEFy3bh0mk4nY2FjCw8PR6XQsX76c+++/v8r2a9euJSEhgRdeeMG37MiRI1XKjRkzhmeffZbRo0dz8OBBv2kKKlu9ejXDhw/3/QDi8XjYv39/rd+Djh07snz5cl555ZUq6/Ly8sjIyOCjjz6ib9++gLe/XHXWr1/vG/TE5XKRnp7OxIkTAejatSvfffcdiYmJNWbh6kOn0zFs2DCGDRvGI488QlJSEjt37qw2wG9I2S5dunDixAkKCgoIDg4+q7rdf//9jBo1iri4OFq2bEmfPn3q3Gb16tV88MEHpKWlAZCdne03MA54M6RPPPEEs2bN4uuvv+auu+7y9cUst2vXLuLi4ggLCzuruguC0DREACgIgtBIyjMaycnJ9cr8CMLZePjhh5k5cyaPPvooEydOZN++fbz88ss8+eSTyOVytFotzz77LM888wxqtZo+ffpw+vRpdu/ezX333UerVq3IyspiwYIFXHXVVSxevJjvv/++ynFuueUWHnroIR566CEGDhxIbGxsjXVq1aoV3333HevWrSM4OJh33nmHEydO1BoAPv/886SkpPDwww/z4IMPolarWbFiBSNHjiQkJITQ0FA+/vhjoqOjycrK4rnnnqt2P++//z6tW7cmOTmZGTNmUFBQwL333gvAI488wqxZsxg9ejSTJ08mLCyMAwcOsGDBAmbNmoVCoajzes+dOxe3203Pnj3R6/V89tln6HQ6EhISzqkseAPA8PBw1q5dy0033VRnXapTnsV97bXX+Mc//lGvbVq1asVnn31G9+7dKS4uZvLkyX4ZWbfbzbhx4xgyZAj33HMPN9xwAykpKUyfPp3Jkyf7yq1evZohQ4acVb0FQWg6YhRQQRAEQbiExMbG8vPPP7Nx40Y6derEgw8+yH333ceLL77oKzNlyhSeeuopXnrpJZKTk7njjjt8fdqGDx/OE088wcSJE+ncuTPr1q1jypQpVY4TEBDA0KFD2b59O2PGjKm1TlOmTKFr166kpqYyYMAAoqKiGDFiRK3btGnThqVLl7J9+3Z69OhBr169+OGHH1AqlcjlchYsWEB6ejodOnTgiSee4F//+le1+5k2bRpvvvkmnTp1YvXq1fzwww++jFRMTAxr167F7XaTmppKhw4deOyxxwgMDKz3vHtBQUHMmjWLPn36+LKWP/30E6GhoedUFkChUHDvvfee01x6crmc8ePH43a7ueuuu+q1zX//+18KCgro0qUL48aNY9KkSURERPjWv/7662RmZvLxxx8DEBUVxezZs3nxxRfZtm0b4B359fvvv+eBBx4467oLgtA0ZFJ5xwHhorVlyxa6detW7/4kl2sdhPOvpvt8vu//5fJ8XS7nIQiXgszMTJo3b87WrVvp3LlzU1fnrJ08eZL27duTnp5eY6awLg888AAnT57kxx9/bOTa1ez999/nhx9+YOnSpRfsmIIgNA7RBFQQBEEQBKGJREZG8sknn5CVldXgALCoqIhNmzbx+eef88MPP5ynGlZPpVLx3nvvXdBjCoLQOEQAeAmwWq0AvmGtm0L5scvrIgiCIAhC4xg+fPhZb7dx40b+9re/MXjw4EauVe0mTJhwQY8nCELjEQHgJSAzMxPgohhePjMzs14jjAmCIAjC+ZSYmMiV3oul8pQPgiAI9SECwEtAamoq8+fPb9LJpTMyMhg7dqxvmHtBaEwXQ5a7MYhMuSAIgiAIFzsRAF4CwsLC6hyB7UJpqgBUuLxdTFnuxiAy5YIgCIIgXKxEACgIQpO7GLLcjUFkygVBEARBuNiJAFAQhCZ3MWW5G8OlHMQKgtB4xo8fT2FhIQsXLqyxTGJiIo8//jiPP/74BavXhTJ37lwef/xxCgsLfcs+/vhjXn31VY4dO8Y777xTr/OuzzVauXIlAwcOpKCggKCgoBrL/f777zz88MPs2bOn3nNBXuzq85ydjeru37m6XKZvuRB27tzJDTfcwL59+zAYDI2678vjyRcEQRAEQWhkiYmJyGSyKv+mTZvWaMfYtGnTRT+i5ty5c2sNquqruLiYiRMn8uyzz3Ls2LF6n3d9rlHv3r3JyckhMDCw1nLPPPMML7zwgi/4a6xzEy5+48ePZ8SIEU1djXpLSUmhR48ezJgxo9H3LQJAQRAEQRCEGvzjH/8gJyfH79+jjz7aaPsPDw9Hr9c32v4uZllZWTidTm688Uaio6Prfd51XSOn04larSYqKgqZTFZjuXXr1rF//35GjhzZ4LpfDBwOR1NXQainxrpX99xzD//5z39wu92Nsr9yIgAUBEEQBEGogclkIioqyu9fxeZYu3fv5sYbbyQgIACTyUTfvn05ePCg3z7efvttoqOjCQ0N5ZFHHsHpdPrWJSYmMnPmTN9nmUzG7Nmzufnmm9Hr9bRu3Zoff/zRb3+rVq2iR48eaDQaoqOjee6553C5XL71AwYMYOLEiUycOJGgoCBCQ0N58cUX/abN+OCDD2jdujVarZbIyEhuu+22as9/5cqV3HPPPRQVFfkyoFOnTgW8L7nPPPMMsbGxGAwGevbsWePUFHPnziUlJQWAFi1aIJPJyMzM5ODBgwwf8z8IcgABAABJREFUPpzIyEiMRiNXXXUVy5Yt89u2umv04YcfMnz4cAwGA6+99horV65EJpPV2lxxwYIFDBkyBK1WW2OZyoqKipgwYQIREREEBARw7bXXsn37dt/67du3M3DgQEwmEwEBAXTr1o3NmzcDcOTIEYYOHUpwcDAGg4H27dvz888/+7bds2cPaWlpGI1GIiMjGTduHLm5ub715ffxySefJCwsrM65Hmt7zupzr+bOnUuzZs3Q6/XcfPPN5OXlVTnGa6+9RkREBCaTifvvv5/nnnuuSlPOOXPmkJycjFarJSkpiQ8++KDKfg4dOsTAgQPR6/V06tSJP//802/9d999R/v27dFoNCQmJjJ9+nS/9Tk5Odx4443odDqaN2/OF198UeU5qWjq1KnMmzePH374wfccl5//sWPHuOOOOwgODiY0NJThw4f7BqeDM5nDur7Hr732GuPHjycwMJAHHngA8P7o0K9fP3Q6HfHx8UyaNAmz2ezbzm6388wzzxAfH49Go6F169Z88sknvvWpqank5eWxatWqas/rbIkAUBAEQRAE4SwcO3aMfv36odVq+f3330lPT+fee+/1C8ZWrFjBwYMHWbFiBfPmzWPu3LnMnTu31v2+8sor3H777ezYsYO0tDTGjBlDfn6+75hpaWlcddVVbN++nf/85z988sknvPbaa377mDdvHkqlkg0bNvDuu+8yY8YMZs+eDcDmzZuZNGkS//jHP9i3bx+//vor/fr1q7YuvXv3ZubMmQQEBPgyoE8//TTgzU6sXbuWBQsWsGPHDkaOHMn111/P/v37q+znjjvu8AV2GzduJCcnh/j4eEpLS0lLS2PZsmVs3bqV1NRUhg4dSlZWVq3X6OWXX2b48OHs3LmTe++9t9ay5f744w+6d+9er7IAkiRx4403cuLECX7++WfS09Pp2rUr1113ne9+jBkzhri4ODZt2kR6ejrPPfccKpUKgEceeQS73c4ff/zBzp07efPNNzEajYA3gOnfvz+dO3dm8+bN/Prrr5w8eZLbb7/drw7l93Ht2rV89NFHNda1ruesrnu1YcMG7r33Xh5++GG2bdvGwIEDqzxTn3/+Oa+//jpvvvkm6enpNGvWjP/85z9+ZWbNmsULL7zA66+/TkZGBv/85z+ZMmUK8+bN8yv3wgsv8PTTT7Nt2zbatGnD6NGjfd+b9PR0br/9dkaNGsXOnTuZOnUqU6ZM8Tufu+66i+PHj7Ny5Uq+++47Pv74Y06dOlXj9Xn66ae5/fbbuf76633Pce/evbFYLAwcOBCj0cgff/zBmjVrMBqNXH/99X5ZvPp8j//1r3/RoUMH0tPTmTJlCjt37iQ1NZVbbrmFHTt28NVXX7FmzRomTpzodx4LFizg3XffJSMjgw8//ND3jACo1Wo6derE6tWrazy3syIJQj2kp6dLgJSent7UVRHOo5rus7j/9SOukyBcXhISEiS1Wi0ZDAa/fytWrJAkSZKef/55qXnz5pLD4ah2+7vvvltKSEiQXC6Xb9nIkSOlO+64w+8YM2bM8H0GpBdffNH3ubS0VJLJZNIvv/wiSZIk/f3vf5fatm0reTweX5n3339fMhqNktvtliRJkvr37y8lJyf7lXn22Wel5ORkSZIk6bvvvpMCAgKk4uLiel2HOXPmSIGBgX7LDhw4IMlkMunYsWN+y6+77jrp+eefr3a7rVu3SoB0+PDhWo/Xrl076b333vN9ru4aPf74437brFixQgKkgoKCGvcbGBgoffrpp3WeW7nly5dLAQEBks1m81vesmVL6aOPPpIkSZJMJpM0d+7cardPSUmRpk6dWu26KVOmSEOGDPFblp2dLQHSvn37JEny3sfOnTvXeD7l6nrO6nOvRo8eLV1//fV+6++44w6/a9OzZ0/pkUce8SvTp08fqVOnTr7P8fHx0hdffOFX5tVXX5V69eolSZIkHT58WAKk2bNn+9bv3r1bAqSMjAxJkiTpzjvvlAYPHuy3j8mTJ0vt2rWTJEmSMjIyJEDatGmTb/3+/fslwO85qezuu++Whg8f7rfsk08+qfJ9stvtkk6nk5YsWeLbrj7f4xEjRvjte9y4cdKECRP8lq1evVqSy+WS1WqV9u3bJwHSb7/9VmOdJUmSbr75Zmn8+PG1lmkoMQqoIAhCI7lcJrQXhCtBUlJSvfqgTZ48mfHjx/sti42NBWDbtm307dvXl/GpTvv27VEoFL7P0dHR7Ny5s9ZjduzY0fe3wWDAZDL5shsZGRn06tXLr69bnz59KC0t5ejRozRr1gyAq6++2q9Mr169mD59Om63m8GDB5OQkECLFi24/vrruf76631NTutry5YtSJJEmzZt/Jbb7XZCQ0PrvR+z2cwrr7zCokWLOH78OC6XC6vVWmcGsCGZvHJWq7VBzT/T09MpLS2tcj5Wq9XXzPfJJ5/k/vvv57PPPmPQoEGMHDmSli1bAjBp0iQeeughli5dyqBBg7j11lt99zY9PZ0VK1b4ZXvKHTx40Hdd63uetT1n9blXGRkZ3HzzzX7re/Xqxa+//ur7vG/fPh5++GG/Mj169OD33/+fvfuO7+n6Hzj++mR/MmRPIkFEhBBbaIU2hFSMWrVTStWmYpQQ9FurqZT+qka/KGq0yre1Z+wZu4lZESVqxcgen/v7I82nPjLNFO/n4+FRn3vPPfd97r3Rzzvn3HN2AHDr1i2uXr1K7969tUMgAbKysvJMzvPoM+7s7AzAzZs38fLyIjY2ltatW+uUb9iwIZGRkWRnZ3Pu3DkMDAyoWbOmdr+HhwfW1taFXaJ8RUdHc/HiRSwsLHS2p6Wl6QzlLs7P8eP3KrfuZcuWabcpioJGo+Hy5cucPn0afX19/P39C41RrVaTkpLyxG0rjCSAQgjxnLxuC9oL8TrLHc5XFDs7Ozw8PPLdV5wlXx5PDlUqFRqN5qmPURQlz0Qnyt/v9hU2AcqjLCwsOHbsGFFRUWzZsoXx48cTHh7OkSNHij0jpkajQV9fn+joaJ0vxkC+SU1BQkND2bx5M19++SUeHh6o1Wrat29f5CQaTzMtvp2dHYmJicUur9FocHZ2zve9xtzrFB4eTpcuXVi/fj0bN25kwoQJrFixgrZt2/LRRx8RGBjI+vXr2bJlC1OmTCEiIoJBgwah0WgIDg5m2rRpeerOTYiepJ2FPTPFuVfKI++HFqagZy/3PJAzDLRevXo65R4/76Px5tZZnGe8sFiL24ZHaTQaatWqpZOk5bK3t8833tyYH/85fvxeaTQaPv74YwYPHpyn7rJly3Lx4sVixXj37l3tLxWeF0kAhRDiOXldFrQX4k3g5eX1zHVUq1aNxYsXk5mZWWgv4PPk7e3N6tWrdb4k79+/HwsLC23PJMDBgwd1jjt48CAVK1bUfhE3MDAgICCAgIAAJkyYgJWVFTt27OD999/Pc04jI6M8sxDWqFGD7Oxsbt68ydtvv/3U7dmzZw8hISHa3qekpCSdCTiepxo1ahATE1Ps8jVr1uTGjRsYGBjg7u5eYDlPT088PT0ZNmwYnTt3ZuHChdr2uLq60q9fP/r168eYMWOYP38+gwYNombNmqxevRp3d3cMDF7s1/Hi3Ctvb+98n5lHVapUicOHD9O9e3ftttwJbwAcHR0pXbo0f/zxxzOt7evt7c3evXt1tu3fvx9PT0/09fXx8vIiKyuL48ePU6tWLQAuXrxY5HqF+T3HNWvWZOXKldpJfp6nmjVr8vvvvxf4CyQfHx80Gg27du0iICCgwHrOnDlT4CRNT0sSQCGEeE5etwXthRDw8OFDbty4obPN1NSUUqVKMXDgQGbPns0HH3zAmDFjsLS05ODBg9StW5dKlSq9kHj69+9PZGQkgwYNYuDAgZw7d44JEyYwfPhwnYXNr169yvDhw/n44485duwYs2fP1s6kuG7dOv744w8aNWqEtbU1GzZsQKPRFBizu7s7SUlJbN++nerVq2Nqaoqnpyddu3alR48eREREUKNGDW7fvs2OHTvw8fEhKCioWO3x8PDgl19+ITg4GJVKRVhYWJE9pE8rMDAwz2QkANnZ2Zw4cUJnm5GREQEBAfj5+dGmTRumTZtGpUqVuH79Ohs2bKBNmzZUqVKF0NBQ2rdvT7ly5fjzzz85cuQI7dq1A2Do0KG0aNECT09PEhMT2bFjB5UrVwZyJoiZP38+nTt3JjQ0FDs7Oy5evMiKFSuYP39+nh6zZ1GcezV48GAaNGjA9OnTadOmDVu2bNEZ/gkwaNAg+vTpQ+3atWnQoAErV67k1KlTlC9fXlsmPDycwYMHU6pUKVq0aEF6ejpHjx4lMTGR4cOHFyveTz/9lDp16jB58mQ6derEgQMH+Oabb7SziXp5eREQEEDfvn2ZM2cOhoaGfPrpp6jV6kJ7wd3d3dm8eTPnzp3D1tYWS0tLunbtyowZM2jdujWTJk2iTJkyxMfH88svvxAaGkqZMmWe4ornGDVqFPXr12fAgAH06dMHMzMzYmNj2bp1K7Nnz8bd3Z2ePXvSq1cvZs2aRfXq1bly5Qo3b97UTgYUFxfHtWvXCk0Qn4bMAiqEEEIIUYDx48fj7Oys82fkyJEA2NrasmPHDpKSkvD396dWrVrMnz//hfYGli5dmg0bNnD48GGqV69Ov3796N27N+PGjdMp16NHD1JTU6lbty4DBgxg0KBB2sXUrays+OWXX3jnnXeoXLky3333HcuXL6dKlSr5nrNBgwb069ePTp06YW9vz/Tp04Gc6f579OjBp59+SqVKlWjVqhWHDh3C1dW12O2ZOXMm1tbWNGjQgODgYAIDA4s1NPdpdOvWjZiYGM6dO6ezPSkpiRo1auj8CQoKQqVSsWHDBho1akSvXr3w9PTkgw8+IC4uDkdHR/T19blz5w49evTA09OTjh070qJFCyZOnAjkJJYDBgygcuXKNG/enEqVKmmTGBcXF/bt20d2djaBgYFUrVqVIUOGYGlpqZPIPy9F3av69euzYMECZs+eja+vL1u2bMnzTHXt2pUxY8YwYsQIatasyeXLlwkJCdF5r/Kjjz5iwYIF2mU//P39WbRoEeXKlSt2rDVr1mTVqlWsWLGCqlWrMn78eCZNmqTzLu4PP/yAo6MjjRo1om3btvTp0wcLC4tC3/Hs06cPlSpVonbt2tjb27Nv3z5MTU3ZvXs3ZcuW5f3336dy5cr06tWL1NTUZ+4RrFatGrt27eLChQu8/fbb1KhRg7CwMJ0hvnPmzKF9+/b0798fLy8v+vTpo7NMxPLly2nWrBlubm7PFMvjVMrTDJgVb5xjx45Rq1atYr8zIV5NBd1nuf9CCPHqaNy4Mb6+vgWuifYmGzlyJPfv3y90SQVRfE2bNsXJyYklS5aUaBx//vknrq6ubNu2jXfffbdEY3le0tPTqVixIsuXL6dhw4bPtW4ZAiqEEEIIId4IY8eO5f/+7//Izs5+rsMs3wQpKSl89913BAYGoq+vz/Lly9m2bRtbt2596bHk9rz7+PiQkJDAyJEjcXd3L3A9y1fRlStXGDt27HNP/kASQCGEEEII8YawtLTks88+K+kwXkm5Q2I///xz0tPTqVSpEqtXr37u76cVR2ZmJp999hl//PEHFhYWNGjQgGXLlr20yZhehtzJhV4ESQCFEEIIIV4j+S1bIMSzUqvVbNu2raTDAHIm9AkMDCzpMF5ZMgmMEEIIIYQQQrwhJAEUQgghhBBCiDeEJIBCCCFEIb79/S5HbqYWWmbq8ducv5f+kiJ6+e6lZzP1+G3+SskqsMyVhxlMPX6btKwXs4bb8/J4nKfupDHz1J0SjurFupOWxezTd0jPfvn35kX+bKy78pDVfzzQfl5z+QGHi/hZFULIO4BCCCFeY9/+fpc69mrqOKiLLHvqThrbryUzrJqtzvYQTysM9QteXPhVVVB781PKSI+BVW0wNXj9rkNla2MqlDJ66eddd+Uh6dkK7co/21pjxbE7IYWa9mqM9f/5vf/x26kcu5XGvYxs9FQqLI308LY2pr6j6XM998CqNpi8pJ+fhk6m/HjhPtVtjXXaKoTQJQmgEEIIUQhTwzf7i2S2RkFfT4W54euX/AEY6qkw1Hs92wbwICObC/czeLe0mXbbyTtp7LiWTEAZc8qaG5KlUbiVms3ttIJ7eJ+WeRE/P9mKgr5K9/rnPnNPykFtgKWRHr/fTaemfdG/9BHiTSUJoBBCiDdGWpaGndeTuXA/g/RsBWtjfRq7mGGoBxvik4CcIWsADZ3UvO1slqcX8W5aNhviH5KQkoWVsT4Bj3yxznUzNYttfyZzPTkTAz0VlayMeLe0OUZ/94Tk9v44mxpw9FYq2QrUsVfTwElN1PUUTt1Jw1BPxVvOplS3NQHgxwv3sTPRp5mrufY8qVkavjlzlw4VSuFuYcSxW6kcuZXKgwwNxvoqXM0NaVsubw/TlYcZhba3uq0JienZXLiXQUUrI95yMuW7mEQ+rGSFo2nOV4dL9zPYdi2JhxkaXMwMqWpjnOc8fyZlEnU9mRspWagN9PC0MsLf2Ux7HbI0CnsSUohJTCclS0MpIz3qO/7T5tupWey4nszVpEwM9VSUszDi3TJmmBrkJBXLLtzD3sQAPRWcvpuOvgoaOZvhbW3M1j+TOHcvA1MDFU3LmFPBMv9evsd7Qvck5DwfdR3U7E5IIT1LoXwpQ5qXNdf2KimKwqGbqRy/nUZypgZrY30aOpniZZ1zDTSKwqb4JK4kZZKcqaGUkT417Ey0z9CehGTO3E3Xuf6dPXLu0/KLDxjqY4PJ3238KyWLhefu0c/bGitjfe5nZLPlahJ/JmehURQsjfRp4mJWYPvO3svAQW1AKaN/1ry7eD8DLytj7XUGsFcbAP/cQ42isPNaMqfupqMCqtuakJyl0em1zK+H/b9nE6loacTbzmba9r1fzgJPK2PupWfzXUwird0tOHY7levJWQS6mhOflEl6toKLqQHRt9LQ04P+VWx4mJHN9mvJXH6YiQooY25IQGkzrIwLXr+voqUxMYmSAApRGEkAhRBCvBEURWHVpQdkaBSC3SywNtbndlp2zhdLM0PeLW3G3oQU+nhbA2CUTw+EoiisufwAtYEePTytSNcobP8zSadMpibnPC6mBvSsZEVyloaN8Uls+TOJlm4W2nJXHmZiYahH14qW/Jmcxcb4JK4lZ+JqbkgPTyti76Wz+WoS5SwMKWWkT3VbY7b+mcw7pc0w+Du23++mY26oh5u5IQkpmWz9M5lgNwtKmxuQmqXwZ1JmvteiqPYe/iuVBk5qGnpZ5Xv8g4xsfrn8gBp2JtSwM+FGShY7riXrlLmZmsWqSw9429mUoLIWpGRp2PpnElv/TOK9v6/DuisPuZ6cRUAZMxzVBtzLyCY1SwEgKVPDsov38bU14d3SZmRqIOp6MmsvP6RLRUvtec7cTaeeo5qej1yz8/fT8bQ0xs/RlCO3Ull35SH9q9oUu6fvXrqG8/cy6FC+FGnZCmsvP+DgX6n4u+QkNbsTUjh/L4NAV3NsjPWJT8rktysPMTXQo6yFIQpgYaRHG3cL1AZ6XEvOZNPVJMwN9ahsbUw9B1PupGWTka0Q9Pe1UOur+DM5//v1qC1Xk8hWoGtFS4z0VNxOyy50iPLVpEycTXW/7pkZ6HE1KZP7GdlYGuWfTB2+mcqpu+m0KGuOnYk+h2+mcv5eBm4Wz77OWtT1nOfYsawB+noQn5TJlYeZGOmp6PR3IpypUfjx4n1czQzpWtESPRXsv5HKqksP6O1lVWAPobOpAQf+SiFLo2h/ToQQut7scS1CCCHeGHEPM0lIyeL9cqUoV8oIK2N9PCyNqGBphL6eCmN9FahyhqyZG+ppe6ker+N2WjYt3cxxNDWgrLmhNinI9fvddLI0Ci3dLLBXG+BuYUSzMub8fjed5Mx/JuEwMVDRtIwZtiYGVLc1wcZYn0yNQgMnU2xM9PFzVKOvgj+Tc4blVbLK6Z25cD9DW8epu2n42JigUql4kKHBSE9FBUtDLI30cTI1oHYB7z4W1d6yFobUczTF2lgf63x6W47fTsPKWJ93S+fEX8XGBB8bE50yh/5KxdvamDoOamxM9HN6b8qYc+bv63M3LZuz9zIIKmtOJStjrIz1cbcwovLfvWjHbqfipDbA3yXnHE6mBgSVzektupuWrT2Pgzqn9y33mhnogVpfD187E2xMcvalZivcTC3+8EYFhffczLFXG+BqbkhVGxOuPMxJzjKyFY7cTCXIzZzyfz9H1WxNqGJjzIk7aTnXV6XibWcznM0MsTLW116fs39PhmKkr8JAT/X30Nqc61/cIY8PMjSUMTPEQW2gfYbLmheclN3PyM4zDPMtZ1OM9VXM+T2ReTGJrLvykNjEdBRF0ZY5ejMNP0c1XlbG2JkY0NzVPOeZeQ5q26u199zCMOf5MtSDoLI519xebUBMYjoqVLQoa46D2gA7EwPeK2vOg4xs4gv4xQbkJN7ZCiT/yycjEqIkSQ+gEKJIqak5s6rFxsaWcCRC/MPLywtT0+JPWPFXahYWhnrYmBQ8fKwot9OyKWWkpzOczsVM93+ld9KycFAb6CRUpc0NUIA76dmY/f1l3N5EH9Uj7z6ZGaqwN/mnLj2VCrW+Hil/J40Geiqq2Bhz6k4ala2N+Ssli5up2bQrn5MwuVsYUspIj+9iEilvYUT5UoZ4Whk/1fttj/cY5XcdXEwNdOJ//Dr8lZpFYno2vyem6WxXgHsZ2dxKzel9dS2gR+mvlCyuJGUScfJ2nn2J6dna++igznvNHNT/3B+zvyeuSXmChMDSSF9nEhEzQz1tQnEnLYssBVZcvK9zTLYCjo/Ecvx2Kidvp3M/M5ssjZJn/9Oqba9m89UkLj/MwN3CkEpWxjrX4HFZGnh87h5zQz16VLLiVmoW8UmZXEvOYv2Vh5y8Y0inCqVI1ygkZeUM7c2lp1LhVMRzUVz5PV/2agOdJPivlJzn56vHZmjNUnLuf7kC6jb4+5nM1CgFlBBCSAIohChSXFwcAN26dSvZQIR4RHR0NDVr1ix2+Rc1HOzxWgv72vloWT1V3njyhKjSra+6rQkLz97jQUY2p+6m4W5hqB3CZ6yvx4deVsQ/zOTyw0z2JKSw90YKPT2ttO+TFdfzmBRFUcDXzoTa+byLVcpQj8T07HyO0j3eo5QRTfJ5x9Lskfbkd80evba5SaryBPnA43Wq+Oc+5P63Q3lLLIx0r2tuzh+bmM72v4frupgZYqyv4tBfqVxPKXyIpyqfZ0LzWODV7UwoV8qQSw8yuPwgkwN/3eOd0mb5XmcAtYGKtOz8G5/b21bLHq4mmbDswn3ikzK173kW5dHr8k+8RR+X3/P1+DYFcDI1oJW7RZ6y6kJmo037e6kL0yd85oV4k0gCKIQoUmBgIEuXLsXd3R21Wl6sF/8OXl5eT1TewcSAh5ka7qZl59sLqK8qOkmwM9HnQYaGh5nZ2qFr15KzHitjwJm76WRkK9pewGtJWagAm0ImryhWG9Q5QyFP3kkj5m46TcuY6+zXU6lwL2WEeykjGjqZEnnqDleSMrXDRx9VnPYWxM5En/OPDEUFuP7YGoGOpgbcTs3OdwgpgL1JTq/o1YeZuOezDIOjqQHn7mVgaaSXb7JcUmxN9NFXwYPMbMoW0Ht5NSmT0maGOhORPJ7w6qvyJne5y2wkZWr+mQQmNW+inDOpjJoadmqiridz8nZagQmgo9qA22mFJ9uQc08BMjVgoq+HuYEe15MztcNLNYrCXylZOsmhqYGezrDm9GwN94pI7IvLUW1AbGI6pgaqJ1rS4VZaNhaGepIAClEISQCFEEWys7Oja9euJR2GEM+krIUhruaGrLn8gHdKm2FtrM+dtGxUKihfyghLI30yNApxD3NmTcxveQB3C0NsTfRZF5fEO6XNyNAo7L6eolOmio0xexNSWB//kLecTLWTn1SxMdYO/3wW1W1N2PpnEgZ6Kjyt/kmcLt7P4F56Nq7mhpgYqLh0PwOFgpPO4rS3IL52Jhy+mcr2P5Pw/XsSmNN3dId61ndU88O5e2y5mkR1WxMM9VTcSc/i8oNMmrmaY2Wsj4+NMRvikwgoY4aD2oD7GdmkZClUtjampr0JJ++k8b+4h9RzUGNqkNNrGJOYMzFJSSWFxvp61HNQs/3PZBQlZ2bK9GyFa8k5k5j42JpgbazP73fT+eNBBlZG+py5m8aNlCwsjf+5/5ZG+lx+kMmdtJwZUo31VVgb61PKUI+9N1Jo5GzG3fTsPAubb/szifKljLAx1ictW+HKw0xsCxnWXK6UIRvjk9Aoivaabf57Qho3c0MsjPRIytSw/0YKpgYqSv89lLe2gwkH/0rF2lhfOwnM4z2JbhaGnL6ThoelESb6KnYnpDy3+1LFxphDN1NY/cdD3nY2xcJQjwcZGs7dT6eeg1pnGPajriZlUu45TFQjxOtMEkAhhBBvjLblLNhxLZlf4x6SqclZBiJ3Epcy5obUsDPhf5cfkpqtaJdFeJRKpeL9cqXYEP+QH87fw9JIn4AyZqy69EBbxlBPRUePUmz7M5nF5+7pLAPxPHhbG7P9WhJVrI11hrUa66s4dz+dvTdyZkC0NtanlbvF39P751Wc9hbE0kiftuVKsf1aEsdup+FsmjNZS+7SEpDTW9m1oiW7ElJYduE+CgpWRvraSV4AAl3N2XU9mS1Xk0nNzlkGwu/vhcgtDPXpVtGKqOvJrLz0gGxNzpIH5UoZ5hl2+7K97WyKqYEeB/5K5d7VJEz0VTiqDfBzyom9hp0JN1Oz+F/cQyDnntWwN+GPB//0mvramhCflMnic/fJ0Ch09iiFm4URrdwt2Hw1if+eTcTJ1IBGzqas/bseyBliueVqEg8zc5b6KGdhRECZgu9bhVJG6KtUxD3MpPzfPa25idvxW2mkZmtQG+hR2syADzwsUf/dc1bXQU1SpoYNV5JABdVsjPG0MiL9kSTQz1HNvfRsfr70AGN9FW87m3I/4/n0ABrqqej69/3/5fIDMrIVLAz1cLMwLHAymiyNwoV7GXT0yLv0iRDiHypFedoBIOJNcuzYMWrVqvXE79yIV4vcZyH+/R5kZDPn90R6VrJ6bpNyiNfbsVupXLifQScPy6ILFyJ3/crcdQD/baL/bucHz9hOIV538n8OIYQQ4hWQrSgkZWrYdT0FFzMDSf5EsfnamZCWrZCerXmi9+leNfqqnKVVhBCFk/97CCGEEK+AP5MyWX7xATbG+rQpl3dmRCEKoqdS0cCp+EumvKp87UyKLiSEkARQCCGEeBW4WRgxuoZdSYch3mAt3eQXD0K8Dl7fcQBCCCGEEEIIIXRIAiiEEEIIIYQQbwhJAIUQQgghhBDiDSEJoBBCCPGKaNy4MUOHDn2iY1QqFWvXri1wf1RUFCqVinv37j1TbK+buLg4VCoVJ06cKOlQnllYWBh9+/Z97vWGhITQpk2b517vi/Y0P0ePW7RoEVZWVsUun56eTtmyZYmOjn6m8wrxPEgCKIQQQrxA7u7uqFQqVCoVarUad3d3OnbsyI4dO0o6NAAaNGhAQkIClpYvd+201ynB+jf766+/+Prrr/nss8+020JCQrTPpEqlwtbWlubNm3Pq1KkSjPT1ZmxszIgRIxg1alRJhyKEJIBCCCHEizZp0iQSEhI4d+4cP/zwA1ZWVgQEBPCf//ynpEPDyMgIJycnVCpVSYfy1DIzM0s6hH+t77//Hj8/P9zd3XW2N2/enISEBBISEti+fTsGBga0bNmyZIJ8Q3Tt2pU9e/YQGxtb0qGIN5wkgEIIIcQLZmFhgZOTE2XLlqVRo0bMmzePsLAwxo8fz7lz57TlYmJiCAoKwtzcHEdHR7p3787t27cLrNfd3Z3JkyfTpUsXzM3NcXFxYfbs2XnK3b59m7Zt22JqakrFihX59ddftfseHwKaO7Rt8+bNVK5cGXNzc22ykOvIkSM0bdoUOzs7LC0t8ff359ixYzrnVKlUzJkzhxYtWqBWqylXrhw//fSTdn+5cuUAqFGjBiqVisaNG2v3LVy4kMqVK2NiYoKXlxfffvutdl9uz+GqVato3LgxJiYmLF26lPDwcHx9fXViiIyM1El8oqKiqFu3LmZmZlhZWdGwYUOuXLlS4PV9XFH3x93dncjISJ1jfH19CQ8P136+d+8effv2xdHRERMTE6pWrcq6desAuHPnDp07d6ZMmTKYmpri4+PD8uXLder7+eef8fHxQa1WY2trS0BAAMnJyQXGvGLFClq1apVnu7GxMU5OTjg5OeHr68uoUaO4evUqt27d0pa5du0anTp1wtraGltbW1q3bk1cXFyB5ypO+8+ePctbb72FiYkJ3t7ebNu2Lc8w5VGjRuHp6YmpqSnly5cnLCys0CS/Xbt2DBo0SPt56NChqFQqfv/9dwCysrKwsLBg8+bN2jIajYaRI0diY2ODk5OTTowAX331FT4+PpiZmeHq6kr//v1JSkoqMAaA3377jVq1amFiYkL58uWZOHEiWVlZ2v22trY0aNAgzz0V4mWTBFAIIYQoAUOGDEFRFP73v/8BkJCQgL+/P76+vhw9epRNmzbx119/0bFjx0LrmTFjBtWqVePYsWOMGTOGYcOGsXXrVp0yEydOpGPHjpw6dYqgoCC6du3K3bt3C6wzJSWFL7/8kiVLlrB7927i4+MZMWKEdv/Dhw/p2bMne/bs4eDBg1SsWJGgoCAePnyoU09YWBjt2rXj5MmTdOvWjc6dO2t7Pw4fPgzAtm3bSEhI4JdffgFg/vz5jB07lv/85z/ExsbyxRdfEBYWxuLFi3XqHjVqFIMHDyY2NpbAwMBCrxHkJAFt2rTB39+fU6dOceDAAfr27Vvsns+nvT+P0mg0tGjRgv3797N06VJiYmKYOnUq+vr6AKSlpVGrVi3WrVvHmTNn6Nu3L927d+fQoUPaGDp37kyvXr2IjY0lKiqK999/H0VR8j1fYmIiZ86coXbt2oXGlZSUxLJly/Dw8MDW1hbIeQaaNGmCubk5u3fvZu/evdpfBmRkZBS7zY+3v02bNpiamnLo0CHmzZvH2LFj85SzsLBg0aJFxMTE8PXXXzN//nxmzpxZYL2NGzcmKipK+3nXrl3Y2dmxa9cuIOcXFmlpaTRs2FBbZvHixZiZmXHo0CGmT5/OpEmTdH5u9PT0mDVrFmfOnGHx4sXs2LGDkSNHFhjD5s2b6datG4MHDyYmJoa5c+eyaNGiPL38devWZc+ePUVeKyFeKEWIYoiOjlYAJTo6uqRDES+Q3Gchnj83Nzdl5syZ+e5zdHRUPvnkE0VRFCUsLExp1qyZzv6rV68qgHLu3DlFURTF399fGTJkiE7dzZs31zmmU6dOSosWLbSfAWXcuHHaz0lJSYpKpVI2btyoKIqi7Ny5UwGUxMRERVEUZeHChQqgXLx4UXvM//3f/ymOjo4FtjErK0uxsLBQfvvtN53z9uvXT6dcvXr1tO29fPmyAijHjx/XKePq6qr8+OOPOtsmT56s+Pn56RwXGRmpU2bChAlK9erVdbbNnDlTcXNzUxRFUe7cuaMASlRUVIHteNTj8RXn/uR3r6tXr65MmDBBURRF2bx5s6Knp6ctXxxBQUHKp59+qijKP/9Gx8XFFevY48ePK4ASHx+vs71nz56Kvr6+YmZmppiZmSmA4uzsrPNv//fff69UqlRJ0Wg02m3p6emKWq1WNm/erK2ndevW2v1FtX/jxo2KgYGBkpCQoN2/detWBVDWrFlTYDumT5+u1KpVq8D9p06dUlQqlXLr1i3l7t27iqGhofL5558rHTp0UBRFUb744gulXr162vL+/v7KW2+9pVNHnTp1lFGjRhV4jlWrVim2trbazwsXLlQsLS21n99++23liy++0DlmyZIlirOzs862r7/+WnF3dy/wPEK8DAYvP+UUQgghcoYlbt68GXd3d9RqdUmH81S8vLwwNTV96uMVRdH2QEVHR7Nz507Mzc3zlLt06RKenp751uHn55fn8+PD8KpVq6b9u5mZGRYWFty8ebPAuExNTalQoYL2s7Ozs075mzdvMn78eHbs2MFff/1FdnY2KSkpxMfHFxlbYZO+3Lp1i6tXr9K7d2/69Omj3Z6VlZVnkpqierUeZ2NjQ0hICIGBgTRt2pSAgAA6duyIs7NzsY5/2vvzqBMnTlCmTJkCy2ZnZzN16lRWrlzJtWvXSE9PJz09HTMzMwCqV6/Ou+++i4+PD4GBgTRr1oz27dtjbW2db32pqakAmJiY5NnXpEkT5syZA8Ddu3f59ttvadGiBYcPH8bNzY3o6GguXryIhYWFznFpaWlcunSpyLbm59y5c7i6uuLk5KTdVrdu3Tzlfv75ZyIjI7l48SJJSUlkZWVRqlSpAuutWrUqtra27Nq1C0NDQ6pXr06rVq2YNWsWkDP019/fX+eYR38mIO8zvnPnTr744gtiYmJ48OABWVlZpKWlkZycrL0fj4qOjubIkSM6PX7Z2dmkpaWRkpKi/XdCrVaTkpJS2GUS4oWTBFAIIUSJyB0y9SqLjo6mZs2aT3XsnTt3uHXrlvZdOI1GQ3BwMNOmTctTtrhJSq7HhzUaGhrm2a/RaAo8Pr/yyiPDDENCQrh16xaRkZG4ublhbGyMn59fsYYGFjbkMjem+fPnU69ePZ19ucMkcz3+JVxPTy/PUMjH3xtbuHAhgwcPZtOmTaxcuZJx48axdetW6tevX2Tcxbk/RcVQ1C86IiIimDlzJpGRkdr3z4YOHaq9rvr6+mzdupX9+/ezZcsWZs+ezdixYzl06JD2OXqUnZ0dkDMU1N7eXmefmZkZHh4e2s+1atXC0tKS+fPn8/nnn6PRaKhVqxbLli3LU+/jdeUqqv2P/sKjIAcPHuSDDz5g4sSJBAYGYmlpyYoVK4iIiCjwGJVKRaNGjYiKisLIyIjGjRtTtWpVsrOzOX36NPv378+z7ENhPxNXrlwhKCiIfv36MXnyZGxsbNi7dy+9e/cu8F1EjUbDxIkTef/99/PsezQBv3v3boHXT4iXRRJAIYQQJSJ3co6lS5dSuXLlkg3mKXl5eT31sV9//TV6enraddRq1qzJ6tWrcXd3x8Cg+P97PnjwYJ7PzxJXcezZs4dvv/2WoKAgAK5evZrvZDUHDx6kR48eOp9r1KgB5Mw+Cjm9JLkcHR0pXbo0f/zxB127dn2imOzt7blx44ZOkpFfb2ONGjWoUaMGY8aMwc/Pjx9//LFYCWBx7o+9vb3OZDkPHjzg8uXL2s/VqlXjzz//5Pz58/n2Au7Zs4fWrVtrfzGi0Wi4cOGCzs+HSqWiYcOGNGzYkPHjx+Pm5saaNWsYPnx4nvoqVKhAqVKliImJKbKHUqVSoaenp+01rFmzJitXrsTBwaHQ3rcnab+Xlxfx8fH89ddfODo6Ajnv5z1q3759uLm56bwbWJyJeho3bsy8efMwMjJi0qRJqFQq3n77bb788ktSU1N13v8rytGjR8nKyiIiIgI9vZzpMlatWlXoMTVr1uTcuXM6SXV+zpw5o/0ZEKKkSAIohBCiROT2hlSuXPmpe9FeFQ8fPuTGjRtkZmZy+fJlli5dyoIFC5gyZYr2C+OAAQOYP38+nTt3JjQ0FDs7Oy5evMiKFSuYP39+nh6wXPv27WP69Om0adOGrVu38tNPP7F+/foX2h4PDw+WLFlC7dq1efDgAaGhofn2bv3000/Url2bt956i2XLlnH48GG+//57ABwcHFCr1WzatIkyZcpgYmKCpaUl4eHhDB48mFKlStGiRQvS09M5evQoiYmJ+SY5uRo3bsytW7eYPn067du3Z9OmTWzcuFGbvFy+fJl58+bRqlUrXFxcOHfuHOfPn9dJUAtTnPvzzjvvsGjRIoKDg7G2tiYsLEznvvn7+9OoUSPatWvHV199hYeHB2fPnkWlUtG8eXM8PDxYvXo1+/fvx9ramq+++oobN25oE8BDhw6xfft2mjVrhoODA4cOHeLWrVsF/gJFT0+PgIAA9u7dm2fB9vT0dG7cuAHk9BB+8803JCUlERwcDOQsWTBjxgxat27NpEmTKFOmDPHx8fzyyy+EhoZSpkyZPOcrqv1NmzalQoUK9OzZk+nTp/Pw4UNtopebtHt4eBAfH8+KFSuoU6cO69evZ82aNUXen8aNGzNkyBAMDAx4++23tds+/fRTatasWewkFnIS56ysLGbPnk1wcDD79u3ju+++K/SY8ePH07JlS1xdXenQoQN6enqcOnWK06dP8/nnn2vL7dmzh8mTJxc7FiFeBJkFVAghhHjBxo8fj7OzMx4eHnTv3p379++zfft2nUWhXVxc2LdvH9nZ2QQGBlK1alWGDBmCpaWlthciP59++inR0dHUqFGDyZMnExERUaxZMZ/Ff//7XxITE6lRowbdu3dn8ODBODg45Ck3ceJEVqxYQbVq1Vi8eDHLli3D29sbAAMDA2bNmsXcuXNxcXGhdevWAHz00UcsWLCARYsW4ePjg7+/P4sWLcp3iOOjKleuzLfffsv//d//Ub16dQ4fPqwzc6mpqSlnz56lXbt2eHp60rdvXwYOHMjHH39crDYX5/6MGTOGRo0a0bJlS4KCgmjTpo3Ou5QAq1evpk6dOnTu3Blvb29Gjhyp7QUNCwujZs2aBAYG0rhxY5ycnHQSt1KlSrF7926CgoLw9PRk3LhxRERE0KJFiwLj7tu3LytWrMgz5HfTpk04Ozvj7OxMvXr1OHLkCD/99JN2OQ5TU1N2795N2bJlef/996lcuTK9evUiNTW1wGSqqPbr6+uzdu1akpKSqFOnDh999BHjxo0D/hkm2bp1a4YNG8bAgQPx9fVl//79hIWFFXl/qlatip2dHdWrV9fG5+/vT3Z2dp73/4ri6+vLV199xbRp06hatSrLli1jypQphR4TGBjIunXr2Lp1K3Xq1KF+/fp89dVXuLm5acscOHCA+/fv0759+yeKR4jnTaU8PlhbiHwcO3aMWrVqPdP7LuLfT+6zeJnkeXt27u7uDB06NM/7Tf8GKpWKNWvW5Ol5Ei+XoijUr1+foUOH0rlz55IOJ499+/bx1ltvcfHixTzJ8uumQ4cO1KhRg88++6ykQxFvOBkCKoQQQgjxmlKpVMybN49Tp06VdCgArFmzBnNzcypWrMjFixcZMmQIDRs2fO2Tv/T0dKpXr86wYcNKOhQhJAEUQgghhHidVa9enerVq5d0GEDO+7AjR47k6tWr2NnZERAQUOgMn68LY2Nj7XBXIUqaJIBCCCHEKyouLq6kQyiQvGEi8tOjR49iT7wjhHgxJAEUQohX3Ku6oHpsbCzwz2LVQgghhHjxJAEUQohX3Ku+oHpcXNwTrdElhBBCiKcnCaAQQrziXtUF1WNjY+nWrZs2fiGEEEK8eJIACiHEK+5VX1D9VRq2Korv37xEhcjRvXt3Kleu/EKWJQgJCeHevXusXbu22McU9czExcVRrlw5jh8/jq+vb7Hq/Oabb9iyZQu//vprseMQ4nUnC8ELIYQQr7GoqChUKlWhfxYtWvTcz3vkyBH69u373Ostrri4OFQqFSdOnCixGJ7Uy4z51KlTrF+/nkGDBmm3NW7cWPtMGBsbU7p0aYKDg/nll19eeDzF4erqSkJCAlWrVi32MX369OHIkSPs3bv3BUYmxKtFEkAhhBDiNdagQQMSEhK0fzp27Ejz5s11tnXq1OmJ6szIyCiyjL29Paampk8b9ktTnLa8jr755hs6dOiAhYWFzvY+ffqQkJDAxYsXWb16Nd7e3nzwwQclmszn0tfXx8nJCQOD4g9gMzY2pkuXLsyePfsFRibEq0USQCGEEOI1ZmRkhJOTk/aPWq3G2NhY+3n06NF07txZ55ihQ4fSuHFj7efGjRszcOBAhg8fjp2dHU2bNgUgPDycsmXLYmxsjIuLC4MHD9Ye4+7uTmRkpPZzfHw8rVu3xtzcnFKlStGxY0f++usv7f7w8HB8fX2ZO3curq6umJqa0qFDB+7du6ctExUVRd26dTEzM8PKyoqGDRty5cqVfNtdrlw5AGrUqIFKpdK2JyQkhDZt2jBlyhRcXFzw9PQEchZMf3y4opWVlbZ3NLd37pdffqFJkyaYmppSvXp1Dhw4oHPMvn378Pf3x9TUFGtrawIDA0lMTARg06ZNvPXWW1hZWWFra0vLli25dOlSkTE3btw4z7DINm3aEBISov387bffUrFiRUxMTHB0dKR9+/b5XhcAjUbDTz/9RKtWrfLsMzU1xcnJCVdXV+rXr8+0adOYO3cu8+fPZ9u2bdpyp0+f5p133kGtVmNra0vfvn1JSkoq8Jy5z9DAgQO17R83blye5UJSUlLo1asXFhYWlC1blnnz5mn3Pd5Dmtu7vX37dmrXro2pqSkNGjTg3LlzOnW2atWKtWvXyozDQvxNEkAhhBBCFGnx4sUYGBiwb98+5s6dy88//8zMmTOZO3cuFy5cYO3atfj4+OR7rKIotGnThrt377Jr1y62bt3KpUuX8vQ8Xrx4kVWrVvHbb7+xadMmTpw4wYABAwDIysqiTZs2+Pv7c+rUKQ4cOEDfvn1RqVT5nvPw4cMAbNu2jYSEBJ1hjNu3byc2NpatW7eybt26J7oOY8eOZcSIEZw4cQJPT086d+5MVlYWACdOnODdd9+lSpUqHDhwgL179xIcHEx2djYAycnJDB8+nCNHjrB9+3b09PRo27YtGo2myJgLc/ToUQYPHsykSZM4d+4cmzZtolGjRgWWP3XqFPfu3aN27drFqr9nz55YW1tr40lJSaF58+ZYW1tz5MgRfvrpJ7Zt28bAgQMLrSf3GTp06BCzZs1i5syZLFiwQKdMREQEtWvX5vjx4/Tv359PPvmEs2fPFlrv2LFjiYiI4OjRoxgYGNCrVy+d/bVr1yYzM1N7fYV408kkMEIIIYQokoeHB9OnT9d+3rBhA05OTgQEBGBoaEjZsmWpW7duvsdu27aNU6dOcfnyZVxdXQFYsmQJVapU4ciRI9SpUweAtLQ0Fi9eTJkyZQCYPXs27733HhERERgZGXH//n1atmxJhQoVAAqd9dbe3h4AW1tbnJycdPaZmZmxYMECjIyMnvg6jBgxgvfeew+AiRMnUqVKFS5evIiXlxfTp0+ndu3afPvtt9ryVapU0f69Xbt2OnV9//33ODg4EBMTQ9WqVQuNuTDx8fGYmZnRsmVLLCwscHNzo0aNGgWWj4uLQ19fHwcHh2LVr6enh6enJ3FxcQAsW7aM1NRUfvjhB8zMzICcIaXBwcFMmzYNR0fHfOtxdXVl5syZqFQqKlWqxOnTp5k5cyZ9+vTRlgkKCqJ///4AjBo1ipkzZxIVFYWXl1eB8f3nP//B398fgNGjR/Pee++RlpaGiYkJgLbHOC4uTltOiDeZJIBCCCFKRO5wrNwF4cXT8/LyeuHv2z3eW9ShQwciIyMpX748zZs3JygoiODg4Hzfz4qNjcXV1VWb/AF4e3tjZWVFbGysNgEsW7asNvkD8PPzQ6PRcO7cOfz9/QkJCSEwMJCmTZsSEBBAx44dcXZ2fuK2+Pj4PFXyB1CtWjXt33PPffPmTby8vDhx4gQdOnQo8NhLly4RFhbGwYMHuX37trbnLz4+/okmNnlc06ZNcXNz096L5s2b07Zt2wKfidTUVIyNjQvsPc2Poija8rGxsVSvXl2b/AE0bNhQe68KSgDr16+vc04/Pz8iIiLIzs5GX18f0L2+KpUKJycnbt68WWhsBd2TsmXLarer1WpSUlKK21whXmuSAAohhCgRub0Jr/Ii9v8W0dHRT70EiJ6eXp73sDIzM/OUe/TLPuT05pw7d46tW7eybds2+vfvz4wZM9i1axeGhoY6ZR9NHoqzPVfuvtz/Lly4kMGDB7Np0yZWrlzJuHHj2Lp1K/Xr1y9eYwtoS+45inMdHm1bbly5iVxRS5oEBwfj6urK/PnzcXFxQaPRULVq1SInoinqHllYWHDs2DGioqLYsmUL48ePJzw8nCNHjmBlZZWnPjs7O1JSUsjIyChWIpydnc2FCxe0iXph9+1Jksr8PP7sqFQq7fUtzjGP35Ncd+/e1fawCvGmkwRQCCFEiQgMDGTp0qW4u7vLWoDPqLDhcUWxt7fnzJkzOttOnDiR54t4ftRqNa1ataJVq1YMGDAALy8vTp8+nScZ9fb2Jj4+nqtXr2p7AWNiYrh//77OMM74+HiuX7+Oi4sLAAcOHNAOP8xVo0YNatSowZgxY/Dz8+PHH3/MNwHMTWxy378rznVISEjQfr5w4cIT9xhVq1aN7du3M3HixDz77ty5Q2xsLHPnzuXtt98GyLM0QUExPx5bdnY2Z86coUmTJtptBgYGBAQEEBAQwIQJE7CysmLHjh28//77eWLJXUMvJiamWOvpLV68mMTERO0QVm9vbxYvXkxycrI2md63b1+ee/W4gwcP5vlcsWJFbe/fi3Lp0iXS0tIKHRYrxJtEEkAhhBAlws7Ojq5du5Z0GG+8d955hxkzZvDDDz/g5+fH0qVLOXPmTJFflhctWkR2djb16tXD1NSUJUuWoFarcXNzy1M2ICCAatWq0bVrVyIjI8nKyqJ///74+/vrDC01MTGhZ8+efPnllzx48IDBgwfTsWNHnJycuHz5MvPmzaNVq1a4uLhw7tw5zp8/T48ePfKNz8HBAbVazaZNmyhTpgwmJiZYWloWeh2++eYb6tevj0ajYdSoUcVKgh81ZswYfHx86N+/P/369cPIyIidO3fSoUMHbGxssLW1Zd68eTg7OxMfH8/o0aOLFfM777zD8OHDWb9+PRUqVGDmzJk6s6OuW7eOP/74g0aNGmFtbc2GDRvQaDRUqlQp3zjt7e2pWbMme/fuzZMApqSkcOPGDbKysrh27Rq//PILM2fO5JNPPtEmnF27dmXChAn07NmT8PBwbt26xaBBg+jevXuBwz8Brl69yvDhw/n44485duwYs2fPJiIi4omu8dPYs2cP5cuX1747KsSbTmYBFUIIId5ggYGBhIWFMXLkSOrUqcPDhw8LTKoeZWVlxfz582nYsKG25+u3337D1tY2T9ncJRasra1p1KgRAQEBlC9fnpUrV+qU8/Dw4P333ycoKIhmzZpRtWpV7YQqpqamnD17lnbt2uHp6Unfvn0ZOHAgH3/8cb7xGRgYMGvWLObOnYuLiwutW7cutD0RERG4urrSqFEjunTpwogRI574vUpPT0+2bNnCyZMnqVu3Ln5+fvzvf//DwMAAPT09VqxYQXR0NFWrVmXYsGHMmDGjWDH36tWLnj170qNHD/z9/SlXrpxO75+VlRW//PIL77zzDpUrV+a7775j+fLlOhPQPK5v374sW7Ysz/b58+fj7OxMhQoVaNu2LTExMaxcuVJnYhtTU1M2b97M3bt3qVOnDu3bt+fdd9/lm2++KfT69OjRg9TUVOrWrcuAAQMYNGjQS1lfcPny5ToTzQjxplMpjw8qFyIfx44do1atWs/0non495P7/HRu377N5s2bS2woY2xsLN26dWPv3r00bNjwpZ9fiOchPDyctWvXatd4Ey9WWloalSpVYsWKFfj5+b3w8zVu3BhfX1+dtSFfhjNnzvDuu+9y/vz5QnuAhXiTyBBQIYR4Rps3b/5XTGQSFxcnCaAQolhMTEz44YcfuH37dkmH8kJdv36dH374QZI/IR4hCaAQQjwjd3d3AJYuXVroumQvSm4PYG4cQghRHG/CmnjNmjUr6RCE+NeRBFAIIZ5R7rDPypUrl+jQWZlJU7zKwsPDCQ8PL+kwxAsSFRVV0iEIIf4mk8AIIYQQQgghxBtCEkAhhBBCCCGEeENIAiiEEEIIIYQQbwhJAIUQQgghHtO4cWOGDh1a0mG80e7cuYODgwNxcXFPdNy/8d7dvHkTe3t7rl27VtKhCCEJoBBCCCHeDBkZGdjZ2fH555/nu3/KlCnY2dmRkZHBL7/8wuTJk19yhOJRU6ZMITg4ON8Zjps1a4a+vj4HDx58+YE9BQcHB7p3786ECRNKOhQhJAEUQgghxJvByMiIbt26sWjRIhRFybN/4cKFdO/eHSMjI2xsbLCwsCiBKAVAamoq33//PR999FGeffHx8Rw4cICBAwfy/fffl0B0T+fDDz9k2bJlJCYmlnQo4g0nCaAQQggh3hi9e/fm0qVL7N69W2f7nj17uHDhAr179wbyDiNMT09n5MiRuLq6YmxsTMWKFbXJx6JFi7CystKpb+3atahUKp1tn3/+OQ4ODlhYWPDRRx8xevRofH19tftDQkJo06YNX375Jc7Oztja2jJgwAAyMzO1ZZYuXUrt2rWxsLDAycmJLl26cPPmzULb7O7uzuTJk+nSpQvm5ua4uLgwe/ZsnTJfffUVPj4+mJmZ4erqSv/+/UlKStIps2/fPvz9/TE1NcXa2prAwEBtMpOens7gwYNxcHDAxMSEt956iyNHjmiPTUxMpGvXrtjb26NWq6lYsSILFy4sMOaNGzdiYGCAn59fnn0LFy6kZcuWfPLJJ6xcuZLk5OQSb/+mTZt46623sLKywtbWlpYtW3Lp0iWd4318fHBycmLNmjWFxivEiyYJoBBCCCHeGD4+PtSpUydP8vHf//6XunXrUrVq1XyP69GjBytWrGDWrFnExsby3XffYW5uXuzzLlu2jP/85z9MmzaN6OhoypYty5w5c/KU27lzJ5cuXWLnzp0sXryYRYsWsWjRIu3+jIwMJk+ezMmTJ1m7di2XL18mJCSkyPPPmDGDatWqcezYMcaMGcOwYcPYunWrdr+enh6zZs3izJkzLF68mB07djBy5Ejt/hMnTvDuu+9SpUoVDhw4wN69ewkODiY7OxuAkSNHsnr1ahYvXsyxY8fw8PAgMDCQu3fvAhAWFkZMTAwbN24kNjaWOXPmYGdnV2C8u3fvpnbt2nm2K4rCwoUL6datG15eXnh6erJq1aoSb39ycjLDhw/nyJEjbN++HT09Pdq2bYtGo9GJo27duuzZs6fIeIV4oRQhiiE6OloBlOjo6JIORbxAcp+fTklft5I+vxCvmjlz5ihmZmbKw4cPFUVRlIcPHypmZmbK3LlztWX8/f2VIUOGKIqiKOfOnVMAZevWrfnWt3DhQsXS0lJn25o1a5RHv2bVq1dPGTBggE6Zhg0bKtWrV9d+7tmzp+Lm5qZkZWVpt3Xo0EHp1KlTgW05fPiwAmjbkh83NzelefPmOts6deqktGjRosBjVq1apdja2mo/d+7cWWnYsGG+ZZOSkhRDQ0Nl2bJl2m0ZGRmKi4uLMn36dEVRFCU4OFj58MMPCzzf41q3bq306tUrz/YtW7Yo9vb2SmZmpqIoijJz5sw8cT167xTlxbc/Pzdv3lQA5fTp0zrbhw0bpjRu3LjY9QjxIhiUXOophBDieUhNTQUgNja2hCMRouR5eXlhampaaJnOnTszfPhwVq5cSe/evVm5ciWKovDBBx/kW/7EiRPo6+vj7+//1HGdO3eO/v3762yrW7cuO3bs0NlWpUoV9PX1tZ+dnZ05ffq09vPx48cJDw/nxIkT3L17V9vDFB8fj7e3d4Hnf3wopZ+fH5GRkdrPO3fu5IsvviAmJoYHDx6QlZVFWloaycnJmJmZceLECTp06JBv3ZcuXSIzM5OGDRtqtxkaGlK3bl3tv0uffPIJ7dq149ixYzRr1ow2bdrQoEGDAuNNTU3FxMQkz/bvv/+eTp06YWCQ8xW2c+fOhIaGcu7cOSpVqlQi7c+9BmFhYRw8eJDbt2/r3JdHe5XVajUpKSkF1iPEyyAJoBBCvOJyp0jv1q1byQYixL9AdHQ0NWvWLLSMpaUl7du3Z+HChfTu3ZuFCxfSvn17SpUqlW95tVpdaH16enp5JpV59L29XI+/E/j4MZCTOD1+TG4ykZycTLNmzWjWrBlLly7F3t6e+Ph4AgMDycjIKDTG/OTGc+XKFYKCgujXrx+TJ0/GxsaGvXv30rt3b207CrsGue3Ir32521q0aMGVK1dYv34927Zt491332XAgAF8+eWX+dZpZ2eXZ7KUu3fvsnbtWjIzM3WGz2ZnZ/Pf//6XadOmlUj7AYKDg3F1dWX+/Pm4uLig0WioWrVqnvty9+5d7O3tnyhOIZ43SQCFEOIVFxgYyNKlS3F3dy/yS4oQrzsvL69ilevduzeNGzdm3bp17Nu3jy+++KLAsj4+Pmg0Gnbt2kVAQECe/fb29jx8+FDbWwQ5vYaPqlSpEocPH6Z79+7abUePHi1WrLnOnj3L7du3mTp1Kq6urk9Ux+PLJRw8eFB7rY4ePUpWVhYRERHo6eVMD/H4e3XVqlVj+/btTJw4MU/dHh4eGBkZsXfvXrp06QLkJMBHjx7VmUjH3t6ekJAQQkJCePvttwkNDS0wAaxRowZLly7V2bZs2TLKlCnD2rVrdbZv376dKVOm8J///EfbM/gy23/nzh1iY2OZO3cub7/9NgB79+7NN44zZ87QuHHjfPcJ8bJIAiiEEK84Ozs7unbtWtJhCPFK8ff3x8PDgx49euDh4UGjRo0KLOvu7k7Pnj3p1asXs2bNonr16ly5coWbN2/SsWNH6tWrh6mpKZ999hmDBg3i8OHDOhO3AAwaNIg+ffpQu3ZtGjRowMqVKzl16hTly5cvdsxly5bFyMiI2bNn069fP86cOVPstQr37dvH9OnTadOmDVu3buWnn35i/fr1AFSoUIGsrCxmz55NcHAw+/bt47vvvtM5fsyYMfj4+NC/f3/69euHkZERO3fupEOHDtjZ2fHJJ58QGhqKjY0NZcuWZfr06aSkpGhnVR0/fjy1atWiSpUqpKens27dOipXrlxgvIGBgYwZM4bExESsra2BnOGf7du3zzNRj5ubG6NGjWL9+vW0bt36pbffxsYGW1tb5s2bh7OzM/Hx8YwePTpPDCkpKURHRxf6ywYhXgaZBVQIIYQQb6RevXqRmJhIr169iiw7Z84c2rdvT//+/fHy8qJPnz7a5QdsbGxYunQpGzZswMfHh+XLlxMeHq5zfNeuXRkzZgwjRoygZs2a2tk783vPrSD29vYsWrSIn376CW9vb6ZOnVpgD9rjPv30U6Kjo6lRowaTJ08mIiKCwMBAAHx9ffnqq6+YNm0aVatWZdmyZUyZMkXneE9PT7Zs2cLJkyepW7cufn5+/O9//9P2uE2dOpV27drRvXt3atasycWLF9m8ebM2eTMyMmLMmDFUq1aNRo0aoa+vz4oVKwqM18fHh9q1a2t74qKjozl58iTt2rXLU9bCwoJmzZoVuibgi2y/np4eK1asIDo6mqpVqzJs2DBmzJiRJ4b//e9/lC1bVttLKERJUSn5DUAX4jHHjh2jVq1axXq3Qry65D4/HbluQoin0bRpU5ycnFiyZMkLPY+7uztDhw7VGY75KtiwYQMjRozgzJkz2qGZT+Pf0v66desydOhQ7TBZIUqKDAEVQgghhHjBUlJS+O677wgMDERfX5/ly5ezbds2nbXohK6goCAuXLjAtWvXtO88vqpu3rxJ+/bt6dy5c0mHIoQkgEIIIYQQL5pKpWLDhg18/vnnpKenU6lSJVavXp3vpDLiH0OGDCnpEJ4LBwcHnYXlhShJkgAKIYQQQrxgarWabdu2lci5c5eKeVO96e0X4nEyCYwQQgghhBBCvCEkARRCCCGEEEKIN4QkgEIIIYQQQgjxhpAEUAghhHhDubu7ExkZ+VLPGRcXh0ql4sSJEy/0PGFhYfTt2/eJjlGpVKxdu/bFBPSGWbt2LR4eHujr6xe6/EJISAht2rR5Lud8Uc9WnTp1+OWXX55rnUKUJEkAhRBCiFfM/v37CQoKwtraGhMTE3x8fIiIiCA7O/uJ6jly5MgTJ0nPytXVlYSEBKpWrfrCzvHXX3/x9ddf89lnn2m33bx5k48//piyZctibGyMk5MTgYGBHDhw4IXF8bKFhISgUqny/GnevPlzqT8qKgqVSsW9e/eKLPvxxx/Tvn17rl69yuTJkwss9/XXX7No0aLnEt+LerbCwsIYPXo0Go3mudYrREmRBFAIIYR4haxZswZ/f3/KlCnDzp07OXv2LEOGDOE///kPH3zwAYqiFLsue3t7TE1NX2C0ujIyMtDX18fJyQkDgxc3Efn333+Pn58f7u7u2m3t2rXj5MmTLF68mPPnz/Prr7/SuHFj7t69+8LiKAnNmzcnISFB58/y5ctfagxJSUncvHmTwMBAXFxcsLCwyFMmOzsbjUaDpaUlVlZWz+W8L+rZeu+997h//z6bN29+rvUKUVIkARRCCCFeEcnJyfTp04dWrVoxb948fH19cXd356OPPmLx4sX8/PPPrFq1CgA/Pz9Gjx6tc/ytW7cwNDRk586dQN4hoOHh4doeMhcXFwYPHlxgLJcuXaJ169Y4Ojpibm5OnTp18ixz4O7uzueff05ISAiWlpb06dMn32F6MTExBAUFYW5ujqOjI927d+f27dva/T///DM+Pj6o1WpsbW0JCAggOTm5wNhWrFhBq1attJ/v3bvH3r17mTZtGk2aNMHNzY26desyZswY3nvvPZ1jb9++Tdu2bTE1NaVixYr8+uuvOvuLilVRFKZPn0758uVRq9VUr16dn3/+Wbs/txdt/fr1VK9eHRMTE+rVq8fp06cLbM+TyO3dfPSPtbW1dv9XX32Fj48PZmZmuLq60r9/f5KSkrT7r1y5QnBwMNbW1piZmVGlShU2bNhAXFwcTZo0AcDa2hqVSkVISEie80dFRWkTvnfeeQeVSkVUVBSLFi3CysqKdevW4e3tjbGxMVeuXMkzBLSo65eYmEjXrl2xt7dHrVZTsWJFFi5cCOQdApqdnU3v3r0pV64carWaSpUq8fXXX+vEm3v+L7/8EmdnZ2xtbRkwYACZmZnaMvr6+gQFBb30RFqIF0USQCGEEOIVsWXLFu7cucOIESPy7AsODsbT01P7JbVr164sX75cp0dw5cqVODo64u/vn+f4n3/+mZkzZzJ37lwuXLjA2rVr8fHxKTCWpKQkgoKC2LZtG8ePHycwMJDg4GDi4+N1ys2YMYOqVasSHR1NWFhYnnoSEhLw9/fH19eXo0ePsmnTJv766y86duyo3d+5c2d69epFbGwsUVFRvP/++wX2dCYmJnLmzBlq166t3WZubo65uTlr164lPT29wDYBTJw4kY4dO3Lq1CmCgoLo2rWrtpewqFgBxo0bx8KFC5kzZw6///47w4YNo1u3buzatUvnPKGhoXz55ZccOXIEBwcHWrVqpZN0vCh6enrMmjWLM2fOsHjxYnbs2KGzQPmAAQNIT09n9+7dnD59mmnTpmFubo6rqyurV68G4Ny5cyQkJORJpgAaNGjAuXPnAFi9ejUJCQk0aNAAgJSUFKZMmcKCBQv4/fffcXBwyHN8UdcvLCyMmJgYNm7cSGxsLHPmzMHOzi7ftmo0GsqUKcOqVauIiYlh/PjxfPbZZ9pfkuTauXMnly5dYufOnSxevJhFixblGZZat25d9uzZU8yrLMS/nCJEMURHRyuAEh0dXdKhiBdI7vPTkesmXpapU6cqgJKYmJjv/latWimVK1dWFEVRbt68qRgYGCi7d+/W7vfz81NCQ0O1n93c3JSZM2cqiqIoERERiqenp5KRkfHU8Xl7eyuzZ8/Wqb9NmzY6ZS5fvqwAyvHjxxVFUZSwsDClWbNmOmWuXr2qAMq5c+e0P19xcXHFiuH48eMKoMTHx+ts//nnnxVra2vFxMREadCggTJmzBjl5MmTOmUAZdy4cdrPSUlJikqlUjZu3FisWJOSkhQTExNl//79OmV69+6tdO7cWVEURdm5c6cCKCtWrNDuv3PnjqJWq5WVK1cWq40F6dmzp6Kvr6+YmZnp/Jk0aVKBx6xatUqxtbXVfvbx8VHCw8PzLZsbe0HPX67ExEQFUHbu3KndtnDhQgVQTpw4kSfm1q1bK4qiFOv6BQcHKx9++GG+53382cpP//79lXbt2umc383NTcnKytJu69Chg9KpUyed4/73v/8penp6SnZ2doF1C/GqeHED8IUQQogX5Pbt22zevBl3d3fUanVJh/PceHl5FeudPKWA3i9FUVCpVEDO+31NmzZl2bJlvP3221y+fJkDBw4wZ86cfI/t0KEDkZGRlC9fnubNmxMUFERwcHCB71MlJyczceJE1q1bx/Xr18nKyiI1NTVPD+CjPXH5iY6OZufOnZibm+fZd+nSJZo1a8a7776Lj48PgYGBNGvWjPbt2+sMa3xUamoqACYmJjrb27Vrx3vvvceePXs4cOAAmzZtYvr06SxYsEBnKGO1atW0fzczM8PCwoKbN28WK9b79++TlpZG06ZNdfZlZGRQo0YNnW1+fn7av9vY2FCpUiViY2PzbVO/fv1YunSp9vOjQzYf16RJkzz32MbGRvv3nTt38sUXXxATE8ODBw/IysoiLS2N5ORkzMzMGDx4MJ988glbtmwhICCAdu3a6VyTZ2FkZFRoXTExMUVev08++YR27dpx7NgxmjVrRps2bbQ9jPn57rvvWLBgAVeuXCE1NZWMjAx8fX11ylSpUgV9fX3tZ2dn5zxDctVqNRqNhvT09Nfq3xzxZpIEUAgheLaEIvdLW+4XT/Hibd68mW7dupV0GM9ddHQ0NWvWLHC/p6cnkPPM5fel9+zZs3h7e2s/d+3alSFDhjB79mx+/PFHqlSpQvXq1fOt29XVlXPnzrF161a2bdtG//79mTFjBrt27cLQ0DBP+dDQUDZv3syXX36Jh4cHarWa9u3bk5GRoVPOzMys0DZrNBqCg4OZNm1ann3Ozs7o6+uzdetW9u/fz5YtW5g9ezZjx47l0KFDlCtXLs8xucMBExMTsbe319lnYmJC06ZNadq0KePHj+ejjz5iwoQJOgng421VqVTa2R+LivXMmTMArF+/ntKlS+vsNzY2LvQ65J4rP5MmTcp32G9+zMzM8PDwyHfflStXCAoKol+/fkyePBkbGxv27t1L7969tcNPP/roIwIDA1m/fj1btmxhypQpREREMGjQoGKdvzBqtbrANgLa61zY9WvRogVXrlxh/fr1bNu2jXfffZcBAwbw5Zdf5qlv1apVDBs2jIiICPz8/LCwsGDGjBkcOnRIp1xh9zzX3bt3MTU1leRPvBYkARRCCJ5PQhEXF0fDhg2fU0SiMLmzOy5dupTKlSuXbDDPkZeXV6H7mzVrho2NDREREXkSwF9//ZULFy7oTLnfpk0bPv74YzZt2sSPP/5I9+7dC61frVbTqlUrWrVqxYABA/Dy8uL06dP5JqV79uwhJCSEtm3bAjm9UnFxccVs6T9q1qzJ6tWrcXd3L7C3UaVS0bBhQxo2bMj48eNxc3NjzZo1DB8+PE/ZChUqUKpUKWJiYrQJc0G8vb2faN2/omLNndwkPj4+3/csH3Xw4EHKli0L5CSr58+fL/D+Ozg45Pu+3JM6evQoWVlZREREoKeXMw3E4+/DQc4vA/r160e/fv0YM2YM8+fPZ9CgQRgZGQE88XIjxVXc62dvb09ISAghISG8/fbb2vcpH7dnzx4aNGhA//79tdsuXbr0VLGdOXOm0F/OCPEqkQRQCCF4toQiNjaWbt266Uw5L16s3N/CV65c+Y36UmZmZsbcuXP54IMP6Nu3LwMHDqRUqVJs376d0NBQ2rdvrzMhiZmZGa1btyYsLIzY2Fi6dOlSYN2LFi0iOzubevXqYWpqypIlS1Cr1bi5ueVb3sPDg19++YXg4GBUKhVhYWFPtU7agAEDmD9/Pp07dyY0NBQ7OzsuXrzIihUrmD9/PkePHmX79u00a9YMBwcHDh06xK1btwr8OdXT0yMgIIC9e/dqZ5e8c+cOHTp0oFevXlSrVg0LCwuOHj3K9OnTad269XOL1cLCghEjRjBs2DA0Gg1vvfUWDx48YP/+/Zibm9OzZ09tXZMmTcLW1hZHR0fGjh2LnZ3dc1kQPT09nRs3buhsMzAwwM7OjgoVKpCVlcXs2bMJDg5m3759fPfddzplhw4dSosWLfD09CQxMZEdO3Zor7WbmxsqlYp169YRFBSEWq3Odzjs0yrO9Rs/fjy1atWiSpUqpKens27dugKfBQ8PD3744Qc2b95MuXLlWLJkCUeOHMm357goe/bsoVmzZs/aRCH+FSQBFEIInk9CIUODxMvQvn177XtcjRo1IjU1FQ8PD8aOHcvQoUPzDLHr2rUr7733Ho0aNdL2OOXHysqKqVOnMnz4cLKzs/Hx8eG3337D1tY23/IzZ86kV69eNGjQADs7O0aNGsWDBw+euD0uLi7s27ePUaNGERgYSHp6Om5ubjRv3hw9PT1KlSrF7t27iYyM5MGDB7i5uREREUGLFi0KrLNv37707t2b6dOno6enh7m5OfXq1WPmzJlcunSJzMxMXF1d6dOnj85i8c8aK8DkyZNxcHBgypQp/PHHH1hZWVGzZs0855k6dSpDhgzhwoULVK9enV9//VXbw/YsNm3ahLOzs862SpUqcfbsWXx9ffnqq6+YNm0aY8aMoVGjRkyZMoUePXpoy2ZnZzNgwAD+/PNPSpUqRfPmzZk5cyYApUuXZuLEiYwePZoPP/yQHj16PLdF3HMVdf2MjIwYM2YMcXFxqNVq3n77bVasWJFvXf369ePEiRN06tQJlUpF586d6d+/Pxs3bnyimK5du8b+/ft13sMU4lWmUgp6k1yIRxw7doxatWoV+X6KeLW9yff5Wdr+Jl+3kiLXXBRGURTq16/P0KFD6dy5c0mHoyMqKoomTZqQmJj43BZAFy9WaGgo9+/fZ968eSUdihDPhawDKIQQQojXikqlYt68eWRlZZV0KOI14ODgoPNurRCvOhkCKoQQQojXTvXq1Quc8VSIJxEaGlrSIQjxXEkCKIQQQgjxkjRu3LjAdRyFEOJlkARQCCGEjldhkXVZe1EIIYR4OpIACiGE0PEqLbIuay8KIYQQT0YSQCGEEDpehUXWZe1FIYQQ4ulIAiiEEELHq7TI+r91iKp4ciqVijVr1hS4GPq/efmEHTt20L9/f2JiYrTrAYo3Q+PGjfH19SUyMvKZ6qlTpw5jxozh/ffffz6BCVEI+VdKCCGEeM0cP36cDh064OjoiImJCZ6envTp04fz58+XdGhPrUGDBiQkJGBpaVnSoeQxcuRIxo4dq5P8ZWRkMGPGDGrWrImZmRmWlpZUr16dcePGcf369ed6/pCQkAIT5yeVnZ3NlClT8PLyQq1WY2NjQ/369Vm4cOFzqf/fQKVSaf+YmZlRsWJFQkJCiI6OfuK6fvnll+eyRERYWBijR49Go9E8c11CFEUSQCGEEOI1sm7dOurXr096ejrLli0jNjaWJUuWYGlpSVhY2As9d0ZGxgur28jICCcnJ1QqVb77s7OzS+TL8/79+7lw4QIdOnTQbktPT6dp06Z88cUXhISEsHv3bqKjo5k+fTp37txh9uzZLz3O4goPDycyMpLJkycTExPDzp076dOnD4mJiSUd2nO1cOFCEhIS+P333/m///s/kpKSqFevHj/88MMT1WNjY4OFhUWB+4v7M/Hee+9x//59Nm/e/ETnF+KpKEIUQ3R0tAIo0dHRJR2KeIHe5Pv8LG1/3a7bq9CeVyHGkpCcnKzY2dkpbdq0yXd/YmKioiiKkpWVpfTq1Utxd3dXTExMFE9PTyUyMlKnbM+ePZXWrVsr4eHhir29vWJhYaH07dtXSU9P15bx9/dXBgwYoAwbNkyxtbVVGjVqpCiKokRERChVq1ZVTE1NlTJlyiiffPKJ8vDhw0JjB5T58+crbdq0UdRqteLh4aH873//0+7fuXOnAmjbsHDhQsXS0lL57bfflMqVKyv6+vrKH3/8oaSlpSmhoaFKmTJlFCMjI8XDw0NZsGCBzjGPWrNmjfLo16ETJ04ojRs3VszNzRULCwulZs2aypEjRwqMe9CgQUr79u11tk2ZMkXR09NTjh07lu8xGo1G+/e0tDRl0KBBir29vWJsbKw0bNhQOXz4sHZ/UfdqwoQJCqDzZ+fOnQXGW5Tq1asr4eHhhZbZuHGj0rBhQ8XS0lKxsbFR3nvvPeXixYs6ZUaOHKlUrFhRUavVSrly5ZRx48YpGRkZOnFXr15d+eGHHxQ3NzelVKlSSqdOnZQHDx5oy2g0GmXatGlKuXLlFBMTE6VatWrKTz/9pN2f+0xs27ZNqVWrlqJWqxU/Pz/l7NmzhcYPKGvWrMmzvUePHoqFhYVy9+5dRVEU5fbt28oHH3yglC5dWlGr1UrVqlWVH3/8UecYf39/ZciQIdrPbm5uyuTJk5WePXsqpUqVUnr06KE0adJEGTBggM5xt2/fVoyMjJTt27drt4WEhCjdu3cvNHYhngfpARRCCCFeE5s3b+b27duMHDky3/25785pNBrKlCnDqlWriImJYfz48Xz22WesWrVKp/z27duJjY1l586dLF++nDVr1jBx4kSdMosXL8bAwIB9+/Yxd+5cAPT09Jg1axZnzpxh8eLF7Nixo8CYHjVx4kQ6duzIqVOnCAoKomvXrty9e7fA8ikpKUyZMoUFCxbw+++/4+DgQI8ePVixYgWzZs0iNjaW7777DnNz8yLPnatr166UKVOGI0eOEB0dzejRozE0NCyw/O7du6ldu7bOtuXLl9O0aVNq1KiR7zGP9mKOHDmS1atXs3jxYo4dO4aHhweBgYHadhd1r0aMGEHHjh1p3rw5CQkJJCQk0KBBg2K393FOTk7s2LGDW7duFVgmOTmZ4cOHc+TIEbZv346enh5t27bV6YG1sLBg0aJFxMTE8PXXXzN//nxmzpypU8+lS5dYu3Yt69atY926dezatYupU6dq948bN46FCxcyZ84cfv/9d4YNG0a3bt3YtWuXTj1jx44lIiKCo0ePYmBgQK9evZ6q7cOGDePhw4ds3boVgLS0NGrVqsW6des4c+YMffv2pXv37hw6dKjQembMmEHVqlWJjo4mLCyMjz76iB9//JH09HRtmWXLluHi4kKTJk202+rWrcuePXueKnYhnkhJZ6Di1SC/bX8zvMn3WXoA//EqtOdViLEkTJs2TQG0PRhPon///kq7du20n3v27KnY2NgoycnJ2m1z5sxRzM3NlezsbEVRcno/fH19i6x71apViq2tbaFlAGXcuHHaz0lJSYpKpVI2btyoKEr+PYCAcuLECe0x586dUwBl69at+Z6jOD2AFhYWyqJFi4psUy5LS0vlhx9+0NlmYmKiDB48WGdbmzZtFDMzM8XMzEzx8/PTttHQ0FBZtmyZtlxGRobi4uKiTJ8+vcBz5nevWrduXeyYC/P7778rlStXVvT09BQfHx/l448/VjZs2FDoMTdv3lQA5fTp0wWWmT59ulKrVi3t5wkTJiimpqY6PX6hoaFKvXr1FEXJuTYmJibK/v37derp3bu30rlzZ0VRdHsAc61fv14BlNTU1AJjoYAewNTUVAVQpk2bVuCxQUFByqeffqr9nF8P4OM98GlpaYqNjY2ycuVK7TZfX988Pa3/+9//FD09Pe3PlxAviswCKoQQ4pWTuwB87oLwbwovLy9MTU0L3K8oSrHr+u6771iwYAFXrlwhNTWVjIwMfH19dcpUr15d53x+fn4kJSVx9epV3NzcAPL0fgHs3LmTL774gpiYGB48eEBWVhZpaWkkJydjZmZWYEzVqlXT/t3MzAwLCwtu3rxZYHkjIyOdY06cOIG+vj7+/v5Ftr8gw4cP56OPPmLJkiUEBATQoUMHKlSoUGD51NRUTExM8mx//F3Fb7/9luTkZGbNmsXu3buBnB6wzMxMnbUsDQ0NqVu3rs6zXZx7VZRly5bx8ccfaz9v3LiRt99+O085b29vzpw5Q3R0NHv37mX37t0EBwcTEhLCggULtHGHhYVx8OBBbt++re35i4+Pp2rVqgD8/PPPREZGcvHiRZKSksjKyqJUqVI653J3d9d5f87Z2Vl7v2NiYkhLS6Np06Y6x2RkZOTpWX30GXB2dgbg5s2blC1b9omuUe7PT+69y87OZurUqaxcuZJr166Rnp5Oenp6oc8w5P2ZMDY2plu3bvz3v/+lY8eOnDhxgpMnT7J27Vqdcmq1Go1GQ3p6usxwLF4oSQCFEEK8cuLi4gBemQXrn5fo6OhCl+bw9PQE4OzZs/j5+RVYbtWqVQwbNoyIiAj8/PywsLBgxowZRQ5ty/VocvP4l+ErV64QFBREv379mDx5MjY2Nuzdu5fevXuTmZlZaL2PD7VUqVSFTuyiVqt1YinqS7Oenl6eJPnxmMLDw+nSpQvr169n48aNTJgwgRUrVtC2bdt867Szs8szQUrFihU5e/aszrbcxMTGxka77fGE49Htudue9V7latWqFfXq1dN+Ll26dIFl9fT0qFOnDnXq1GHYsGEsXbqU7t27M3bsWMqVK0dwcDCurq7Mnz8fFxcXNBoNVatW1U54cvDgQT744AMmTpxIYGAglpaWrFixgoiICJ3zFHa/c/+7fv36PLEaGxsXWE/udXuaCYFyk+5y5coBEBERwcyZM4mMjMTHxwczMzOGDh1a5MQu+SWIH330Eb6+vvz555/897//5d1339X+EiXX3bt3MTU1leRPvHCSAAohhHjlBAYGsnTpUtzd3d+oL0teXl6F7m/WrBl2dnZMnz6dNWvW5Nl/7949rKys2LNnDw0aNKB///7afZcuXcpT/uTJk6Smpmqv8cGDBzE3N6dMmTIFxnD06FGysrKIiIjQLovw+LuFL4qPjw8ajYZdu3YREBCQZ7+9vT0PHz7U6Yk8ceJEnnKenp54enoybNgwOnfuzMKFCwtMAGvUqEFMTIzOts6dOzNu3DiOHz9e4HuAAB4eHhgZGbF37166dOkC5CSkR48eZejQoQDFuldGRkZkZ2cXeB7IeSevsNkqC+Pt7Q3kvPt3584dYmNjmTt3rrYHce/evTrl9+3bh5ubG2PHjtVuu3LlyhOf09jYmPj4+Gfq0X0SkZGRlCpVSvvs7Nmzh9atW2t/0aTRaLhw4QKVK1d+4rp9fHyoXbs28+fP58cff8x3JtgzZ87869deFa8HSQCFEEK8cuzs7OjatWtJh/GvY2ZmxoIFC+jQoQOtWrVi8ODBeHh4cPv2bVatWkV8fDwrVqzAw8ODH374gc2bN1OuXDmWLFnCkSNHtD0fuTIyMujduzfjxo3jypUrTJgwgYEDBxa62HmFChXIyspi9uzZBAcHs2/fPr777rsX3XQgZ0hhz5496dWrF7NmzaJ69epcuXKFmzdv0rFjR+rVq4epqSmfffYZgwYN4vDhwyxatEh7fGpqKqGhobRv355y5crx559/cuTIEdq1a1fgOQMDA1m8eLHOtmHDhrF+/XreeecdwsPDefvtt7G2tub8+fNs3LgRfX19IOd+ffLJJ4SGhmJjY0PZsmWZPn06KSkp9O7dG6BY98rd3Z3Nmzdz7tw5bG1tsbS0LHTimsK0b9+ehg0b0qBBA5ycnLh8+TJjxozB09MTLy8v9PT0sLW1Zd68eTg7OxMfH8/o0aN16vDw8NA+a3Xq1GH9+vX5/kKiMBYWFowYMYJhw4ah0Wh46623ePDgAfv378fc3JyePXs+Vfty3bt3jxs3bpCens758+eZO3cua9eu5YcfftBOluTh4cHq1avZv38/1tbWfPXVV9y4ceOpEkDI6QUcOHAgpqam+f5CYc+ePTRr1uxZmiVEscgsoEIIIcRrpHXr1uzfvx9DQ0O6dOmCl5cXnTt35v79+3z++ecA9OvXj/fff59OnTpRr1497ty5o9PDlOvdd9+lYsWKNGrUiI4dOxIcHEx4eHih5/f19eWrr75i2rRpVK1alWXLljFlypQX0dR8zZkzh/bt29O/f3+8vLzo06cPycnJQM7wy6VLl7JhwwZ8fHxYvny5Tnv09fW5c+cOPXr0wNPTk44dO9KiRYs8M58+qlu3bsTExHDu3DntNhMTE7Zv387o0aNZuHAhb731FpUrV2bo0KE0bNhQ592vqVOn0q5dO7p3707NmjW5ePEimzdvxtraGijeverTpw+VKlWidu3a2Nvbs2/fvqe+foGBgfz2228EBwfj6elJz5498fLyYsuWLRgYGKCnp8eKFSuIjo6matWqDBs2jBkzZujU0bp1a4YNG8bAgQPx9fVl//79T7UG5eTJkxk/fjxTpkyhcuXK2tge/0XF0/jwww9xdnbGy8uLTz75BHNzcw4fPqztiYWcxdlr1qxJYGAgjRs3xsnJiTZt2jz1OTt37oyBgQFdunTJ897otWvX2L9/Px9++OFT1y9EcamUJ3ljXLyxjh07Rq1atYp8/0S82t7k+/wsbX/drtvr1h7xdEJCQrh3716eiSpEXiNHjuT+/fvaZTCEyM/Vq1dxd3fnyJEjef5tDQ0N5f79+8ybN6+EohNvEukBFEIIIYR4BmPHjsXNza3I9/DEmykzM5P4+HhGjRpF/fr18/3FmoODA5MnTy6B6MSbSN4BFOI1dPv2bTZv3vzEE2TkzoCWO8W+EEKIollaWvLZZ5+VdBjiX2rfvn00adIET09Pfv7553zLhIaGvuSoxJtMEkAhXkObN29+punx4+LidNalEkK8eR6dHEUI8fQaN278RGt0CvGiSQIoxGvI3d0dgKVLlz7RbGWxsbF069ZNe7wQQgghhHi9SAIoxGsod9hn5cqVn2oSjzdpXTUhhBBCiDeJTAIjhBBCCCGEEG8ISQCFEEII8cbo3r07X3zxxTPVsWjRIu1i4QDh4eH4+voWekxISMgzrSFXHEXFsW7dOmrUqIFGo3mhcQgh/t0kARRCCCFeMe7u7qhUKlQqFfr6+ri4uNC7d28SExNLOrQnFhcXh0ql4sSJEy/8XKdOnWL9+vUMGjQIgNGjR+d5Tzo2NhaVSkX37t11ti9ZsgRDQ0OSkpLo1KkT58+ff+HxPm8tW7ZEpVLx448/lnQoQogSJAmgEEII8QqaNGkSCQkJxMfHs2zZMnbv3s3gwYNLOqx/tW+++YYOHTpgYWEBQJMmTTh79iw3btzQlomKisLV1ZWdO3fqHBsVFUXdunUxNzdHrVbj4ODwUmN/Xj788ENmz55d0mEIIUqQJIBCCCHEK8jCwgInJydKly5NkyZN6NGjB8eOHdMps3r1aqpUqYKxsTHu7u5ERETo7P/222+pWLEiJiYmODo60r59+wLPlzvscd26dVSqVAlTU1Pat29PcnIyixcvxt3dHWtrawYNGqSzILpKpWLt2rU6dVlZWWmXmShXrhwANWrUQKVS0bhxYyBn6vyhQ4fqHNemTRtCQkKeKn6NRsNPP/1Eq1attNveeustDA0NiYqK0m6LiopiwIABPHz4kIsXL+psb9Kkic61KEh2djbDhw/HysoKW1tbRo4cWegyAMnJyZQqVSrPGnG//fYbZmZmPHz4EIBRo0bh6emJqakp5cuXJywsjMzMzALrvXz5Mh4eHnzyySfaYZ+tWrXi8OHD/PHHHwUeJ4R4vcksoEII8YxSU1OBnKFjr4PcduS2S/z7Xbt2jXXr1lGvXj3ttujoaDp27Eh4eDidOnVi//799O/fH1tbW0JCQjh69CiDBw9myZIlNGjQgLt377Jnz55Cz5OSksKsWbNYsWIFDx8+5P333+f999/HysqKDRs28Mcff9CuXTveeustOnXqVKzYDx8+TN26ddm2bRtVqlTByMioWMc9afynTp3i3r171K5dW7vNzMyMOnXqsHPnTj744AMAdu3aRWhoKLt27WLnzp14eHhw9epV/vjjD20CWJSIiAj++9//8v333+Pt7U1ERARr1qzhnXfeybe8mZkZH3zwAQsXLtRJYnM/5/ZYWlhYsGjRIlxcXDh9+jR9+vTBwsKCkSNH5qnzzJkzNGvWjJ49ezJlyhTtdjc3NxwcHNizZw/ly5cvVnuEEK8XSQCFEOIZxcXFAdCtW7eSDeQ5i4uLo2HDhiUdhijAqFGjGDduHNnZ2aSlpVGvXj2++uor7f6vvvqKd999l7CwMAA8PT2JiYlhxowZhISEEB8fj5mZGS1btsTCwgI3Nzdq1KhR6DkzMzOZM2cOFSpUAKB9+/YsWbKEv/76C3Nzc7y9vWnSpAk7d+4sdgJob28PgK2tLU5OTsVu/5PGHxcXh76+fp6hm40bN9b2vMXExJCamkqNGjXw9/cnKiqKPn36sHPnToyNjWnQoEGxYouMjGTMmDG0a9cOgO+++47NmzcXesxHH31EgwYNuH79Oi4uLty+fZt169axdetWbZlx48Zp/+7u7s6nn37KypUr8ySABw4coGXLlowZM4YRI0bkOVfp0qW1/24JId48kgAKIcQzCgwMZOnSpbi7u78WayjGxsbSrVs33N3dSzqUV9Lt27fZvHnzUz8PXl5emJqaFlkuNDSUkJAQFEXh6tWrfPbZZ7z33nvs3r0bfX19YmNjad26tc4xDRs2JDIykuzsbJo2bYqbmxvly5enefPmNG/enLZt2xZ6blNTU23yB+Do6Ii7uzvm5uY6227evPnE7X5STxp/amoqxsbGqFQqne1NmjThiy++4Pr160RFRfHWW2+hr6+Pv78/s2bNAnKGf9avX79Y9/P+/fskJCTg5+en3WZgYEDt2rULHQZat25dqlSpwg8//MDo0aNZsmQJZcuWpVGjRtoyP//8M5GRkVy8eJGkpCSysrIoVaqUTj3x8fEEBATw+eefM2zYsHzPpVarSUlJKbItQojXkySAQgjxjOzs7OjatWtJh/HcvQ7JbEnYvHnzM/UGR0dHU7NmzSLL2dnZ4eHhAUDFihWJjIzEz8+PnTt3EhAQgKIoeZKdRxMQCwsLjh07RlRUFFu2bGH8+PGEh4dz5MiRAt9vMzQ01PmsUqny3fboMgMqlSpP4lPYe2u59PT0Cj3uSeO3s7MjJSWFjIwMnWGmDRs2xMjIiKioKHbu3Im/vz8AtWvX5v79+5w/f56dO3fqvHv4onz00Ud88803jB49moULF/Lhhx9q7+HBgwf54IMPmDhxIoGBgVhaWrJixYo873Xa29vj4uLCihUr6N27d54EEeDu3bvanlchxJtHEkAhhBDiOcrtOV26dGmeJQaKw8vL66nOq6+vD/zz7qa3tzd79+7VKbN//348PT21ZQ0MDAgICCAgIIAJEyZgZWXFjh07eP/9958qhvzY29uTkJCg/XzhwgWd3qfcZOzRiWPyOy47O5szZ87ovIf3JPHnro8XExOjs1aeWq2mXr16REVFsXv3bkJDQ7V1N2jQgB9++IG4uLhiv/9naWmJs7MzBw8e1PbeZWVlFSux79atGyNHjmTWrFn8/vvv9OzZU7tv3759uLm5MXbsWO22K1eu5KlDrVazbt06goKCCAwMZMuWLdp3CAHS0tK4dOlSkcN9hRCvL0kAhRBCiOcot+e0cuXKxerJe1oPHz7kxo0b2iGgI0eOxM7OTvue2qeffkqdOnWYPHkynTp14sCBA3zzzTd8++23QM6i4H/88QeNGjXC2tqaDRs2oNFoqFSp0nON85133uGbb76hfv36aDQaRo0apdNr6ODggFqtZtOmTZQpUwYTExMsLS155513GD58OOvXr6dChQrMnDmTe/fuaY970vjt7e2pWbMme/fuzbNYepMmTZg5cyaAzj3z9/dn2rRp2iSxuIYMGcLUqVOpWLEilStX5quvvtKJvSDW1ta8//77hIaG0qxZM8qUKaPd5+HhQXx8PCtWrKBOnTqsX7+eNWvW5FuPmZkZ69evp0WLFrRo0YJNmzZph+kePHgQY2NjnSGqQog3iywDIYQQQryCxo8fj7OzMy4uLrRs2RIzMzO2bt2Kra0tkJPIrFq1ihUrVlC1alXGjx/PpEmTtEMZrays+OWXX3jnnXeoXLky3333HcuXL6dKlSrPNc6IiAhcXV1p1KgRXbp0YcSIETrv6RkYGDBr1izmzp2Li4uL9r3FXr160bNnT3r06IG/vz/lypXT6YV7mvj79u3LsmXL8mxv0qQJDx8+pGHDhhgY/PO7cX9/fx4+fEiDBg0wNjYudps//fRTevToQUhICH5+flhYWNC2bdtiHdu7d28yMjLo1auXzvbWrVszbNgwBg4ciK+vL/v379dO8JMfc3NzNm7ciKIoBAUFkZycDMDy5cvp2rVrsd4zFUK8nlRKYW8kC/G3Y8eOUatWrWK/myJK1tPerzf5Pr/JbX+cXItnI9fv3ystLY1KlSqxYsWKf20P2LJlyxgyZAjXr18v9pIYxXXr1i28vLw4evSodv1FIcSbR4aACiGEEOKNYGJiwg8//MDt27dLOpQ8UlJSuHz5MlOmTOHjjz9+7skf5CwM/+2330ryJ8QbToaACiGEEOKN4e/vT3BwcEmHkcf06dPx9fXF0dGRMWPGvJBz1K1bt9jrMwohXl+SAAohhBBClLDw8HAyMzPZvn27zrqKQgjxvMkQUCHEK+dZF9rOT2xsLPDPFPpCPK3cZ0ieJSGEEP9GkgAKIV45z7rQdmHi4uJo2LDhC6lbvBni4uK0/5VnSQghxL+NJIBCiFfOsy60nZ/Y2Fi6deumrVsIIYQQ4nUkCaAQ4pXzIhfafl5DSoUQ4mk1atSIfv360aVLlxKLYd26dYSFhREdHY2enkwZIcTrRH6ihRBCCPFcubu7o1KpUKlU6Ovr4+LiQu/evUlMTHxh51y0aBFWVlYvrP6XZd26ddy4cYMPPvhAZ/vx48fp0KEDjo6OmJiY4OnpSZ8+fTh//vwLiaNly5aoVCp+/PHHF1K/EKLkSAIohBBCiOdu0qRJJCQkEB8fz7Jly9i9ezeDBw8u6bCei8zMzBdW96xZs/jwww91et3WrVtH/fr1SU9PZ9myZcTGxrJkyRIsLS0JCwt7YbF8+OGHzJ49+4XVL4QoGZIACiGEEOK5s7CwwMnJidKlS9OkSRN69OjBsWPHdMqsXr2aKlWqYGxsjLu7OxERETr7v/32WypWrIiJiQmOjo60b98+33NFRUXx4Ycfcv/+fW3PY3h4OJDzrnDt2rW18XTp0oWbN29qj82v53Dt2rWoVCrt5/DwcHx9ffnvf/9L+fLlMTY2RlEU3N3diYyM1DnW19dXe+7cY8uWLYuxsTEuLi6FJsG3b99m27ZttGrVSrstJSWFDz/8kKCgIH799VcCAgIoV64c9erV48svv2Tu3Lnasrt27aJu3boYGxvj7OzM6NGjycrKAuC3337DysoKjUYDwIkTJ1CpVISGhmqP//jjj+ncubP2c6tWrTh8+DB//PFHgTELIV49kgAKIYQQ4oW6du0a69ato169etpt0dHRdOzYkQ8++IDTp08THh5OWFgYixYtAuDo0aMMHjyYSZMmce7cOTZt2kSjRo3yrb9BgwZERkZSqlQpEhISSEhIYMSIEQBkZGQwefJkTp48ydq1a7l8+TIhISFP3IaLFy+yatUqVq9ezYkTJ4p1zM8//8zMmTOZO3cuFy5cYO3atfj4+BRYfu/evZiamupMbrV582Zu377NyJEj8z0mN3m9du0aQUFB1KlTh5MnTzJnzhy+//57Pv/8cyDnvcKHDx9y/PhxICdZtLOzY9euXdq6oqKi8Pf31352c3PDwcGBPXv2FKu9QohXg0wCI4QQQojnbtSoUYwbN47s7GzS0tKoV68eX331lXb/V199xbvvvqsdwujp6UlMTAwzZswgJCSE+Ph4zMzMaNmyJRYWFri5uVGjRo18z2VkZISlpSUqlQonJyedfb169dL+vXz58syaNYu6deuSlJT0RAuuZ2RksGTJEuzt7Yt9THx8PE5OTgQEBGBoaEjZsmWpW7dugeXj4uJwdHTUGf554cIFALy8vAo917fffourqyvffPMNKpUKLy8vrl+/zqhRoxg/fjyWlpb4+voSFRVFrVq1iIqKYtiwYUycOJGHDx+SnJzM+fPnady4sU69pUuX1i5tIoR4PUgCKIQQQjxH6enpAFy+fDnPkMfXgZeXF6ampkWWCw0NJSQkBEVRuHr1Kp999hnvvfceu3fvRl9fn9jYWFq3bq1zTMOGDYmMjCQ7O5umTZvi5uZG+fLlad68Oc2bN6dt27bFOvejjh8/Tnh4OCdOnODu3bvaIZDx8fF4e3sXux43N7cnSv4AOnToQGRkpLYNQUFBBAcHY2CQ/9ev1NRUTExMdLYpilKsc8XGxuLn56czdLVhw4YkJSXx559/UrZsWRo3bkxUVBTDhw9nz549fP7556xevZq9e/dy7949HB0d8ySaarWalJSUJ2q3EOLfTRJAIYQQ4jm6fv06AGFhYS90go6SEh0dXazlV+zs7PDw8ACgYsWKREZG4ufnx86dOwkICEBRFJ1kBXSTHQsLC44dO0ZUVBRbtmxh/PjxhIeHc+TIkWLP9pmcnEyzZs1o1qwZS5cuxd7envj4eAIDA8nIyABAT08vT5KV3yQvZmZmebYVdayrqyvnzp1j69atbNu2jf79+zNjxgx27dqFoaFhnvrs7OzyzJTq6ekJwNmzZ/Hz8yuwrYVdz9ztjRs35vvvv+fkyZPo6enh7e2Nv78/u3btIjExUWf4Z667d+8+ceIrhPh3kwRQCCGEeI5yv6R///33+Pr6lmwwL0BRQxELoq+vD+T0cgF4e3uzd+9enTL79+/H09NTW9bAwICAgAACAgKYMGECVlZW7Nixg/fffz9P/UZGRmRnZ+tsO3v2LLdv32bq1Km4uroCOe8WPsre3l47BDI3ySvuO3729vYkJCRoPz948IDLly/rlFGr1bRq1YpWrVoxYMAAvLy8OH36dL5JdI0aNbhx4waJiYlYW1sD0KxZM+zs7Jg+fTpr1qzJc8y9e/ewsrLC29ub1atX6ySC+/fvx8LCgtKlSwP/vAcYGRmJv78/KpUKf39/pkyZQmJiIkOGDNGpOy0tjUuXLhU49FYI8WqSBFAIIYR4jnK/uPv6+harp+x19fDhQ27cuKEdAjpy5Ejs7Oxo0KABAJ9++il16tRh8uTJdOrUiQMHDvDNN9/w7bffAjlLH/zxxx80atQIa2trNmzYgEajoVKlSvmez93dnaSkJLZv30716tUxNTWlbNmyGBkZMXv2bPr168eZM2eYPHmyznH16tXD1NSUzz77jEGDBnH48GHtRDRFeeedd1i0aBHBwcFYW1sTFhamTV4hZ4bR7Oxs7TmWLFmCWq3Gzc0t3/pq1KiBvb09+/bto2XLlkBOz+OCBQvo0KEDrVq1YvDgwXh4eHD79m1WrVpFfHw8K1asoH///kRGRjJo0CAGDhzIuXPnmDBhAsOHD9e+U5j7HuDSpUv5+uuvgZyksEOHDmRmZuZ5/+/gwYMYGxsX2vMohHj1SAIohBBCR24PTWxsbAlH8mrKvW651/FNNX78eMaPHw/k9JTVqVOHrVu3YmtrC0DNmjVZtWoV48ePZ/LkyTg7OzNp0iTtDJ1WVlb88ssvhIeHk5aWRsWKFVm+fDlVqlTJ93wNGjSgX79+dOrUiTt37jBhwgTCw8NZtGgRn332GbNmzaJmzZp8+eWXOsss2NjYsHTpUkJDQ5k3bx4BAQGEh4fTt2/fIts4ZswY/vjjD1q2bImlpSWTJ0/W6QG0srJi6tSpDB8+nOzsbHx8fPjtt9+01+Bx+vr69OrVi2XLlmkTQIDWrVuzf/9+pkyZQpcuXXjw4AGurq6888472lk+S5cuzYYNGwgNDaV69erY2NjQu3dvxo0bp3OOJk2acOzYMW2yZ21tjbe3N9evX9eZfRRg+fLldO3a9YnfuxRC/LuplOK+XSzeaMeOHaNWrVrFfvdDlKynvV+vyn1+EXG+Km1/GZYtW0a3bt1KOoxX3tKlS+natWtJhyFeMX/99RdVqlQhOjq6wJ7Cl+HWrVt4eXlx9OhRypUrV2JxCCGeP+kBFEIIoSMwMJClS5fi7u6OWq0u6XBeObGxsXTr1g13d/eSDkW8ghwdHfn++++Jj48v0QTw8uXLfPvtt5L8CfEakgRQCCGEDjs7O+m5eg4keRZP6/HlMUpC3bp1C12zUAjx6tIruogQQgghhBBCiNeBJIBCCCGEEEII8YaQBFAIIYQQQggh3hCSAAohhBBCCCHEG0ISQCGEEOIVt2jRIqysrEo0hsaNGzN06NBCy7i7uxMZGflS4slPVFQUKpWKe/fuFVjm33AtAe7cuYODgwNxcXElHUqhVCoVa9euLdEY/o3P3ogRIxg8ePBLO58QT0ISQCGEEOJfzN3dHZVKhUqlQl9fHxcXF3r37k1iYqK2TKdOnTh//nwJRlk8R44cKdYC6yXp33Itp0yZQnBwcL7LiTRr1gx9fX0OHjz48gN7TEJCAi1atCjpMIr0sp+9kSNHsnDhQi5fvvzSzilEcUkCKIQQQvzLTZo0iYSEBOLj41m2bBm7d+/W6V1Qq9U4ODiUYITFY29vj6mpaUmHUah/w7VMTU3l+++/56OPPsqzLz4+ngMHDjBw4EC+//77EoguR0ZGBgBOTk4YGxuXWBzF9bKfPQcHB5o1a8Z333330s4pRHFJAiiEEEL8y1lYWODk5ETp0qVp0qQJPXr04NixY9r9jw9bPHnyJE2aNMHCwoJSpUpRq1Ytjh49qt2/evVqqlSpgrGxMe7u7kREROicz93dnS+++IJevXphYWFB2bJlmTdvXpFxZmVlMXDgQKysrLC1tWXcuHEoiqJT76PD8MLDwylbtizGxsa4uLjoJLWJiYn06NEDa2trTE1NadGiBRcuXMjT5s2bN1O5cmXMzc1p3rw5CQkJRca5b98+qlevjomJCfXq1eP06dP/qmu5ceNGDAwM8PPzy7Nv4cKFtGzZkk8++YSVK1eSnJyss79x48YMGjSIoUOHYm1tjaOjI/PmzSM5OZkPP/wQCwsLKlSowMaNG3WOi4mJISgoCHNzcxwdHenevTu3b9/WqXfgwIEMHz4cOzs7mjZtCuQdAvrnn3/ywQcfYGNjg5mZGbVr1+bQoUMAXLp0idatW+Po6Ii5uTl16tRh27Ztz3y94N/57LVq1Yrly5cXGbsQL5skgEIIIcQr5Nq1a6xbt4569eoVWKZr166UKVOGI0eOEB0dzejRozE0NAQgOjqajh078sEHH3D69GnCw8MJCwtj0aJFOnVERERQu3Ztjh8/Tv/+/fnkk084e/ZsobEtXrwYAwMDDh06xKxZs5g5cyYLFizIt+zPP//MzJkzmTt3LhcuXGDt2rX4+Pho94eEhHD06FF+/fVXDhw4gKIoBAUFkZmZqS2TkpLCl19+yZIlS9i9ezfx8fGMGDGiqEtIaGgoX375JUeOHMHBwYFWrVrp1FvS13L37t3Url07z3ZFUVi4cCHdunXDy8sLT09PVq1alafc4sWLsbOz4/DhwwwaNIhPPvmEDh060KBBA44dO0ZgYCDdu3cnJSUFyBnG6e/vj6+vL0ePHmXTpk389ddfdOzYMU+9BgYG7Nu3j7lz5+Y5b1JSEv7+/ly/fp1ff/2VkydPMnLkSDQajXZ/UFAQ27Zt4/jx4wQGBhIcHEx8fPwzXa9HY/s3PXt169bl6tWrXLlypdDYhXjpFCGKITo6WgGU6Ojokg5FFMPT3q9X5T6/iDhflbaLf7/n/Sy5ubkpRkZGipmZmWJiYqIASr169ZTExERtmYULFyqWlpbazxYWFsqiRYvyra9Lly5K06ZNdbaFhoYq3t7eOufs1q2b9rNGo1EcHByUOXPmFBinv7+/UrlyZUWj0Wi3jRo1SqlcubJOvTNnzlQURVEiIiIUT09PJSMjI09d58+fVwBl37592m23b99W1Gq1smrVKm2bAeXixYvaMv/3f/+nODo6Fhjjzp07FUBZsWKFdtudO3cUtVqtrFy5UltvSV/L1q1bK7169cqzfcuWLYq9vb2SmZmpKIqizJw5U2nYsKFOGX9/f+Wtt97Sfs7KylLMzMyU7t27a7clJCQogHLgwAFFURQlLCxMadasmU49V69eVQDl3Llz2np9fX3zxAQoa9asURRFUebOnatYWFgod+7cKbBtj/P29lZmz56t/fw6PXv3799XACUqKqqoyyDESyU9gEIIIcRzlJqaCkBsbCzHjh0r9E9uD0xRQkNDOXHiBKdOnWL79u0AvPfee2RnZ+dbfvjw4Xz00UcEBAQwdepULl26pN0XGxtLw4YNdco3bNiQCxcu6NRXrVo17d9VKhVOTk7cvHmz0Djr16+PSqXSfvbz88tTb64OHTqQmppK+fLl6dOnD2vWrCErK0sbo4GBgU4vp62tLZUqVSI2Nla7zdTUlAoVKmg/Ozs7Fxljbly5bGxs8tT7qJK4lqmpqZiYmOTZ/v3339OpUycMDAwA6Ny5M4cOHeLcuXM65R49n76+Pra2tjo9XI6OjgDaGKKjo9m5cyfm5ubaP15eXgA67c2vV/JRJ06coEaNGtjY2OS7Pzk5mZEjR+Lt7Y2VlRXm5uacPXs2Tw/g6/LsqdVqgGL/nAvxskgCKIQQQjxHudP2d+vWjVq1ahX6p6hhbbns7Ozw8PCgYsWKvPPOO0RGRrJ//3527tyZb/nw8HB+//133nvvPXbs2IG3tzdr1qwBcoYRPvpFOXfb43KHOeZSqVTaoXzPg6urK+fOneP//u//UKvV9O/fn0aNGpGZmZlvPPnFnl+MBR1blMevSa6SuJZ2dnY6s7wC3L17l7Vr1/Ltt99iYGCAgYEBpUuXJisri//+979Fnu/Rbbkx58ag0WgIDg7mxIkTOn8uXLhAo0aNtMeZmZkVGDP8k/AUJDQ0lNWrV/Of//yHPXv2cOLECXx8fLQTyhQW/6v47N29exfImYBGiH8Tg5IOQAghhHidBAYGsnTpUtzd3Yv8Qpzby/Kk9PX1gX96G/Pj6emJp6cnw4YNo3PnzixcuJC2bdvi7e3N3r17dcru378fT09Pbb1P6/FlCQ4ePEjFihULrFetVtOqVStatWrFgAED8PLy4vTp03h7e5OVlcWhQ4do0KABkLMu3vnz56lcufIzxZgbV9myZYGcCT/Onz9f6L142deyRo0aLF26VGfbsmXLKFOmTJ4197Zv386UKVP4z3/+o+0ZfFI1a9Zk9erVuLu7P3UdkNNzt2DBAu7evZtvL+CePXsICQmhbdu2QM47gc9rncN/47N35swZDA0NqVKlytM1SogXRBJAIYQQ4jmys7Oja9euz7XOhw8fcuPGDRRF4erVq4wcORI7OzvtF9RHpaamEhoaSvv27SlXrhx//vknR44coV27dgB8+umn1KlTh8mTJ9OpUycOHDjAN998w7fffvvMcV69epXhw4fz8ccfc+zYMWbPnp1nVsxcixYtIjs7m3r16mFqasqSJUtQq9W4ublha2tL69at6dOnD3PnzsXCwoLRo0dTunRpWrdu/cxxTpo0CVtbWxwdHRk7dix2dna0adMmT7mSupaBgYGMGTOGxMRErK2tgZzhn+3bt6dq1ao6Zd3c3Bg1ahTr169/6mszYMAA5s+fT+fOnQkNDcXOzo6LFy+yYsUK5s+fX+xktnPnznzxxRe0adOGKVOm4OzszPHjx3FxccHPzw8PDw9++eUXgoODUalUhIWFPbeevX/js7dnzx7efvvtIn8RJMTLJkNAhRBCiH+58ePH4+zsjIuLCy1btsTMzIytW7dia2ubp6y+vj537tyhR48eeHp60rFjR1q0aMHEiROBnN6eVatWsWLFCqpWrcr48eOZNGkSISEhzxxnjx49SE1NpW7dugwYMIBBgwYVuPi2lZUV8+fPp2HDhlSrVo3t27fz22+/adu0cOFCatWqRcuWLfHz80NRFDZs2JBn6N3TmDp1KkOGDKFWrVokJCTw66+/YmRklKdcSV1LHx8fateurZ3hMzo6mpMnT2oTz0dZWFjQrFmzZ1oT0MXFhX379pGdnU1gYCBVq1ZlyJAhWFpaoqdX/K+KRkZGbNmyBQcHB4KCgvDx8WHq1KnaBHLmzJlYW1vToEEDgoODCQwMpGbNmk8d96P+jc/e8uXL6dOnzzO3TYjnTaU87WB58UY5duwYtWrVIjo6+rn9Yy1enKe9X6/KfX4Rcf4/e3ce11P2/wH89Snt+16UikqlVEokVCaiQdYsWRprSLYSEmHsUpjJrkyWNGMdUbYpkUhkq7E0kjHNII01pT7n94df9+v2+bRqZHk/Hw+Pr3vuuee87/18fKd359xzPpd7J4R8HY4ePYrAwEDcuHGjTkkY+TQkJCQgKCgI165d+6BptYT8F+gbSQghhBDyifH09MSdO3fw8OFDGBgYNHY4pI5evXqF6OhoSv7IJ4m+lYQQQgghn6CpU6c2dgiknry9vRs7BEKqRHMKCCGEEEIIIeQrQQkgIYQQQgghhHwlKAEkhBBCCCGEkK8EJYCEEEIIIYQQ8pWgBJAQQgj5DJ07dw7W1taQkpISu4l5XYSGhla5Z5o4YWFhsLW1/aA+v0R1fY6kdgQCAQ4ePAgAyMvLg0AgQFZWVpX1k5OTIRAI8O+//9aqzY/t+vXr0NfXx6tXr3jl7dq1w/79+xslJvJ1oQSQEELIF+XKlSvo1asXtLW1ISsrCyMjIwwePBhPnjxp7NAa1IwZM2Bra4t79+4hJiam3u38888/WLt2LebOncuV+fr6QiAQQCAQQEpKCi1atEBgYCD3A2tgYCBOnTr1obfwSSooKMCwYcPQqlUrSEhIYNq0abW6TtxzJA2joKAAPXv2bOwwGoy1tTUcHR0RERHBKw8NDcXs2bMhFAobKTLytaAEkBBCyBfj0aNHcHd3h6amJpKSkpCTk4Pt27dDT08Pr1+/buzwasQYQ1lZWa3q5ubmomvXrtDX14eqqmq9+9y2bRucnJxgZGTEK+/RowcKCgrwxx9/4Pvvv0dUVBQCAwMBAIqKitDQ0Kh3n5+ykpISaGlpISQkBDY2NrW+rqrn2NDevn37n7b/KdLV1YWMjExjh9EgKj6/7777Dhs2bEB5eTl37ttvv8WzZ8+QlJTUWOGRrwQlgIQQQr4YaWlpeP78ObZu3Qo7OzsYGxuja9euiIyMRPPmzQEAMTExIgnTwYMHIRAIuOOrV6/Czc0NSkpKUFZWhr29PS5dulRlv2FhYWjevDlkZGTQtGlTBAQEcOdKSkowa9YsGBgYQEZGBqampti2bRuA/01VS0pKgoODA2RkZJCamoqSkhIEBARwo5idOnVCRkYGgP9NgSssLMTo0aMhEAgQExOD8vJyjBkzBsbGxpCTk0OrVq2wdu3aGp9ZXFwc+vTpI1IuIyMDXV1dGBgYYNiwYfDx8eGmzFWeAlpWVoaAgACoqqpCQ0MDwcHBGDVq1AdPTQWAhw8fYvDgwVBTU4OGhga8vLyQl5fHnXd1dRUZpevbty98fX0BAOvXr4e1tTV3ruKz/vHHH7kyDw8PzJkzBwBgZGSEtWvXYuTIkVBRUal1nOKeI2MMK1euRIsWLSAnJwcbGxv88ssv3PnafBcrnvX27dvRokULyMjIgDGG/Px8eHl5QVFREcrKyvD29sY///wjct2mTZtgYGAAeXl5DBo0SGRaZHR0NCwsLCArKwtzc3NERUXxzgcHB8PMzAzy8vJo0aIFQkNDeUloRT+xsbEwMjKCiooKhgwZghcvXoh9TowxaGlpYd++fVyZra0ttLW1uePz589DSkoKL1++BFDzdM2jR4/CzMwMcnJycHNz430/qlMxsignJwdjY2P8/PPPvPPXr19H165dIScnBw0NDYwfP56LCQAyMjLQrVs3aGpqQkVFBS4uLrh8+TKvDYFAgI0bN8LLywsKCgr4/vvvAbz7zhUWFiIlJYWrKykpCU9PT+zZs6dW8RNSX5QAEkII+WLo6uqirKwMBw4cAGOs3u34+PhAX18fGRkZyMzMxOzZsyElJSW27i+//IKIiAhs2rQJd+7cwcGDB3kJx8iRIxEXF4d169YhJycHGzduhKKiIq+NWbNmYdmyZcjJyUGbNm0wa9Ys7Nu3Dzt27MDly5dhYmICDw8PPH36FAYGBigoKICysjIiIyNRUFCAwYMHQygUQl9fH/Hx8cjOzsb8+fMxd+5cxMfHV3mfRUVFuHHjBhwcHGp8JnJyclWOPq1YsQK7du1CdHQ0zp07h+fPnzfI+1WvX7+Gm5sbFBUVcebMGZw9exaKioro0aMHSktLa9WGq6srbt68yU0BTklJgaamJveDd1lZGdLS0uDi4lLvOKt6jvPmzUN0dDQ2bNiAmzdvYvr06Rg+fDjvh/7auHv3LuLj47Fv3z7u3be+ffvi6dOnSElJwYkTJ5Cbm4vBgweLve7XX39FYmIisrKyMHnyZO78li1bEBISgiVLliAnJwdLly5FaGgoduzYwdVRUlJCTEwMsrOzsXbtWmzZskVk6mJubi4OHjyII0eO4MiRI0hJScHy5cvF3otAIECXLl2QnJzMPbvs7Gy8ffsW2dnZAN79YsTe3l7k34k4Dx48QP/+/eHp6YmsrCyMHTsWs2fPrvE64N2UywEDBuDq1asYPnw4hg4dipycHADvvns9evSAmpoaMjIy8PPPP+PkyZPw9/fnrn/x4gVGjRqF1NRUpKenw9TUFJ6eniLJ74IFC+Dl5YXr169j9OjRAABpaWnY2NggNTWVV9fR0VGkjJAGxwiphczMTAaAZWZmNnYopBbq+3l9Lp/zfxHn53LvpGZz585lTZo0Yerq6qxHjx5s5cqV7O+//+bOR0dHMxUVFd41Bw4cYO//J1FJSYnFxMTUqr/w8HBmZmbGSktLRc7dunWLAWAnTpwQe+1vv/3GALCDBw9yZS9fvmRSUlJs165dXFlpaSlr2rQpW7lyJVemoqLCoqOjq41t0qRJbMCAAVWev3LlCgPA8vPzeeWjRo1iXl5e3PGFCxeYhoYG8/b2ZowxtmDBAmZjY8Od19HRYatWreKOy8rKWPPmzXlt1Me2bdtYq1atmFAo5MpKSkqYnJwcS0pKYowx5uLiwqZOncq7zsvLi40aNYoxxphQKGSamprsl19+YYwxZmtry5YtW8a0tbUZY4ylpaWxJk2asBcvXoj0L65tccQ9x5cvXzJZWVmWlpbGqztmzBg2dOhQxljtvosLFixgUlJS7NGjR1zZ8ePHmaSkJK+/mzdvMgDs4sWL3HWSkpLswYMHXJ1jx44xCQkJVlBQwBhjzMDAgO3evZvX/+LFi5mTk1OV97py5Upmb2/Pi09eXp49f/6cKwsKCmLt27evso1169YxKysrxhhjBw8eZA4ODqx///7sxx9/ZIwx1r17dxYcHMzVB8AOHDjAGGPs3r17DAC7cuUKY4yxOXPmMAsLC953JDg4mAFgRUVFVcYAgPn5+fHK2rdvzyZOnMgYY2zz5s1MTU2NvXz5kjufkJDAJCQkeP9/8r6ysjKmpKTEfv31V14/06ZNE1u/X79+zNfXl1d26NAhJiEhwcrLy6uMnZAP1eSjZ5yEkK/ekydPkJSUBCMjI8jJydX5+orf0BYXFzdYTBVtVbRNPk3m5uaQl5evts6SJUswY8YMnD59Gunp6di4cSOWLl2KM2fO8EbmqjNjxgyMHTsWsbGxcHd3x6BBg9CyZUuxdQcNGoTIyEi0aNECPXr0gKenJ3r37o0mTZogKysLkpKSNY4uvT9ylJubi7dv38LZ2Zkrk5KSgqOjY43fz40bN2Lr1q24f/8+iouLUVpaWu1qnRXfe1lZWZFzR44cgaKiIsrKyvD27Vt4eXlh/fr1IvWePXuGf/75B46OjlyZpKQk7O3tq13M4v3RneHDh2Pjxo0idTIzM3H37l0oKSnxyt+8eYPc3Nwq237f+yNO33zzDW7evAk/Pz+sXr0aOTk5SE5ORtu2bWs12lQVcc8xOzsbb968Qbdu3Xh1S0tLYWdnV6f2DQ0NoaWlxR3n5OTAwMAABgYGXJmlpSVUVVWRk5ODdu3aAQCaN28OfX19ro6TkxOEQiFu3boFSUlJPHjwAGPGjMG4ceO4OmVlZbypr7/88gsiIyNx9+5dvHz5EmVlZVBWVubFZ2RkxPuM9PT08OjRoyrvx9XVFVOnTsWTJ0+QkpICV1dXNG/eHCkpKRg/fjzS0tJqvfhOTk4OOnTowJs26+TkVKtrK9dzcnLiRlhzcnJgY2MDBQUF7ryzszP3/HR0dPDo0SPMnz8fp0+fxj///IPy8nK8fv0a+fn5vHarGmGXk5MTeTdZTk4OQqEQJSUl9frvIyG1QQkgIeSjS0pKwvDhwz+4nby8PN4PyR/aFoAGiYv8dzIzM9G2bdsa62loaGDQoEEYNGgQli1bBjs7O6xevRo7duyAhISEyPTQylMbw8LCMGzYMCQkJODYsWNYsGAB4uLi0K9fP5G+DAwMcOvWLZw4cQInT57EpEmTsGrVKqSkpNT6B7j3f8isiO39H2gryiuXvS8+Ph7Tp09HeHg4nJycoKSkhFWrVuHChQtVXqOpqQng3TS89xMMAHBzc8OGDRsgJSWFpk2bVjkFtoK4eKvz/jL+lROKCkKhEPb29ti1a5fIuYp4a/N5urq6YvPmzUhNTYWNjQ1UVVXRpUsXpKSkIDk5Ga6urtXGWhNxz7Ei+U1ISECzZs149SsWNKlN7AD/+wFU/V2o6TtScU4gEHDxbdmyBe3bt+fVk5SUBACkp6djyJAhWLhwITw8PKCiooK4uDiEh4fz6lf+brzfvjhWVlbQ0NBASkoKUlJSsGjRIhgYGGDJkiXIyMhAcXExOnXqVOX1le+5IVU8o+qeZUW5r68vHj9+jMjISBgaGkJGRgZOTk4i05Mrf34Vnj59KvKLpadPn0JeXp6SP/KfogSQEPLRVaySt3PnTlhYWNT5+pycHAwfPrxBV9vz8PDAzp076z0qST4Oc3PzOl8jLS2Nli1bclsYaGlp4cWLF3j16hX3g5m4PcXMzMxgZmaG6dOnY+jQoYiOjhabAALvfmvfp08f9OnTB5MnT4a5uTmuX78Oa2trCIVCpKSkwN3dvVbxmpiYQFpaGmfPnsWwYcMAvEsKLl26VO2oSGpqKjp27IhJkyZxZTWNkrVs2RLKysrIzs6GmZkZ75yCggJMTExqjFdFRQU6Ojq4ePEiOnfuDAAoLy/HlStXqh19rE3bbdu2xd69e6GtrV1lkqilpYWCggLuuLy8HDdu3ICbmxtXVjHi9Msvv3DJnouLC06ePIm0tDRMnTq1xliqI+45WlpaQkZGBvn5+VWOANf2u1iZpaUl8vPz8eDBA24UMDs7G8+ePeP9f2p+fj7++usvNG3aFMC7xVUkJCRgZmYGHR0dNGvWDH/88Qd8fHzE9nPu3DkYGhoiJCSEK7t//37ND6QGFaOyhw4dwo0bN9C5c2coKSnh7du32LhxI9q2bSsy6lsVS0tLkfdN09PTa3Vteno6Ro4cyTuuGJ21tLTEjh07eJ/NuXPnuOcHvPs3FxUVBU9PTwDv3kesy3YzN27cwMCBA0XKavNLLkI+BCWAhJCPriLBsrCw+KD/0DVkoqapqVnlD0Hk83HkyBHExcVhyJAhMDMzA2MMv/76K44ePYro6GgAQPv27SEvL4+5c+diypQpuHjxIm8fveLiYgQFBWHgwIEwNjbGn3/+iYyMDAwYMEBsnxUrcFa0GxsbCzk5ORgaGkJDQwOjRo3C6NGjsW7dOtjY2OD+/ft49OgRvL29xbanoKCAiRMnIigoCOrq6mjevDlWrlyJ169fY8yYMVXeu4mJCX766SckJSXB2NgYsbGxyMjIgLGxcZXXSEhIwN3dHWfPnv2gFTunTJmCZcuWwcTEBObm5li/fj2KioqqHY2qDR8fH6xatQpeXl5YtGgR9PX1kZ+fj/379yMoKAj6+vro2rUrZsyYgYSEBLRs2RIREREiK11WjDjt2rULhw4dAvAuKZw5cyYAiIw2VSRhL1++xOPHj5GVlQVpaWlYWlqKjVPcc1RSUkJgYCCmT58OoVCITp064fnz50hLS4OioiJGjRpV43exKu7u7mjTpg18fHwQGRmJsrIyTJo0CS4uLrzphrKyshg1ahRWr16N58+fIyAgAN7e3tDV1QXwbqQ7ICAAysrK6NmzJ0pKSnDp0iUUFRVhxowZMDExQX5+PuLi4tCuXTskJCTgwIEDNcZXG66urpg+fTrs7Oy45L5Lly7YtWsXZsyYUet2/Pz8EB4ejhkzZmDChAnIzMys9b6YP//8MxwcHNCpUyfs2rULFy9e5Fbo9fHxwYIFCzBq1CiEhYXh8ePHmDJlCkaMGAEdHR0A7/7NxcbGwsHBAc+fP0dQUFCt/7uUl5eHhw8fivxiKDU1Fd27d6/1/RNSL4307iH5zNACGZ+XT30RmA/th76PpCq5ubls3LhxzMzMjMnJyTFVVVXWrl07kcVSDhw4wExMTJisrCzr1asX27x5M7fwRklJCRsyZAgzMDBg0tLSrGnTpszf358VFxeL7fPAgQOsffv2TFlZmSkoKLAOHTqwkydPcueLi4vZ9OnTmZ6eHpOWlmYmJiZs+/btjLH/LQJTebGK4uJiNmXKFKapqclkZGSYs7Mzt7hHhcqLwLx584b5+voyFRUVpqqqyiZOnMhmz57NW6xFnMTERNasWTPeohOVF4GprPIiMG/fvmX+/v5MWVmZqampseDgYDZo0CA2ZMiQavuujYKCAjZy5EjuWbRo0YKNGzeOPXv2jDH2boGciRMnMnV1daatrc2WLVvGWwSmwoABA5ikpCR3nVAoZOrq6szBwUGkTwAifwwNDauNU9xzFAqFbO3ataxVq1ZMSkqKaWlpMQ8PD5aSksLVqe67yJjos65w//591qdPH6agoMCUlJTYoEGDeIuTVFwXFRXFmjZtymRlZVn//v3Z06dPee3s2rWL2draMmlpaaampsa6dOnC9u/fz50PCgpiGhoaTFFRkQ0ePJhFRETwFq4RF19ERESNz+v69esMAAsMDORdB4AdOXKEVxfVLALDGGO//vorMzExYTIyMqxz585s+/bttVoE5scff2TdunVjMjIyzNDQkO3Zs4dX59q1a8zNzY3JysoydXV1Nm7cON5iQZcvX2YODg5MRkaGmZqasp9//pkZGhqyiIgIsbG/b+nSpczDw4NX9ueffzIpKSnewj2E/BcEjDXw5GnyRbp8+TLs7e1r/f4NaVz1/bw+1uf8of3Q95GQhsMYQ4cOHTBt2jQMHTq0QdoUCoWwsLCAt7c3Fi9e3CBtfur+i+f4IcLCwnDw4MFaTSklH1dJSQlMTU2xZ88e3nvsQUFBePbsGTZv3tyI0ZGvAe0DSAghhHzFBAIBNm/ejLKysnq3cf/+fWzZsgW3b9/G9evXMXHiRNy7d497h/Fr0BDPkXwd7t+/j5CQEJFFzLS1tb+aX5iQxkXvABJCCCFfORsbG9jY2NT7egkJCcTExCAwMBCMMVhZWeHkyZP1WuTpc/ahz5F8HSoWmKosKCioEaIhXyNKAAkhhBDyQQwMDHDu3LnGDoO8JywsDGFhYY0dBiHkE0RTQAkhhBBCCCHkK0EJICGEEEIIIYR8JSgBJIQQQgghhJCvBCWAhBBCyCcsOTkZAoGA29w8JiYGqqqqjRrTx3L69GmYm5tDKBR+lP4CAwMREBDwUfoihJDGQgkgIYQQ8h8zMjKCQCDg/dHX169XW4MHD8bt27cbOMLac3V1xbRp0z5KX7NmzUJISAgkJP7340ppaSlWrlwJGxsbyMvLQ1NTE87OzoiOjsbbt29r1W5eXh4EAoHIHnmzZs1CdHQ07t2715C3QQghnxRKAAkhhJCPYNGiRSgoKOD+XLlypV7tyMnJQVtbu4Gj+/SkpaXhzp07GDRoEFdWWloKDw8PLF++HOPHj0daWhouXryIyZMnY/369bh58+YH9amtrY3u3btj48aNHxo+IYR8sigBJIQQQj4CJSUl6Orqcn+0tLQAAIwxrFy5Ei1atICcnBxsbGzwyy+/VNlO5SmgV69ehZubG5SUlKCsrAx7e3tcunSpyuv//fdfjB8/Hjo6OpCVlYWVlRWOHDkCACgsLMTQoUOhr68PeXl5WFtbY8+ePdy1vr6+SElJwdq1a7mRzLy8PLHTUg8ePAiBQFDvOOPi4tC9e3fIyspyZZGRkThz5gxOnTqFyZMnw9bWFi1atMCwYcNw4cIFmJqaAgASExPRqVMnqKqqQkNDA7169UJubi7XjrGxMQDAzs4OAoEArq6u3Lk+ffrw7pkQQr40tA8gIYQQ0ojmzZuH/fv3Y8OGDTA1NcWZM2cwfPhwaGlpwcXFpcbrfXx8YGdnhw0bNkBSUhJZWVmQkpISW1coFKJnz5548eIFdu7ciZYtWyI7OxuSkpIAgDdv3sDe3h7BwcFQVlZGQkICRowYgRYtWqB9+/ZYu3Ytbt++DSsrKyxatAgAuES2IeMEgDNnzmDo0KG8sl27dsHd3R12dnYi9aWkpLj2Xr16hRkzZsDa2hqvXr3C/Pnz0a9fP2RlZUFCQgIXL16Eo6MjTp48idatW0NaWpprx9HREQ8ePMD9+/dhaGhYq3sjhJDPCSWAhBBCyEcQHByMefPmccdLly7FmDFjsGbNGpw+fRpOTk4AgBYtWuDs2bPYtGlTrRLA/Px8BAUFwdzcHAC4UTBxTp48iYsXLyInJwdmZmZcfxWaNWuGwMBA7njKlClITEzEzz//jPbt20NFRQXS0tKQl5eHrq5une6/LnEC797Ta9q0Ka/szp07vNG6qgwYMIB3vG3bNmhrayM7OxtWVlZc0qqhoSFyH82aNeP6pwSQEPIlogSQEEJItZ48eYKkpCQYGRlBTk6uscP55Jibm0NeXr7GekFBQfD19eWONTU1kZ2djTdv3qBbt268uqWlpWJHucSZMWMGxo4di9jYWLi7u2PQoEFo2bKl2LpZWVnQ19fnkr/KysvLsXz5cuzduxcPHz5ESUkJSkpKoKCgUKtYGipOACguLuZN/wTeTZd9f1ppVXJzcxEaGor09HQ8efKEW0U0Pz8fVlZW1V5b8R1//fp1jf0QQsjniBJAQggh1UpKSsLw4cMbO4xPVmZmJtq2bVtjPU1NTZiYmPDKKhKThIQEbuSpgoyMTK36DwsLw7Bhw5CQkIBjx45hwYIFiIuLQ79+/UTq1pTAh4eHIyIiApGRkbC2toaCggKmTZuG0tLSaq+TkJAAY4xXVnlFzrrECbx7XkVFRbwyMzMz5OTkVBsLAPTu3RsGBgbYsmULmjZtCqFQCCsrqxrvAwCePn0KoPZTWwkh5HNDCSAhhJBqGRkZAQB27twJCwuLxg3mE1QxpbE+LC0tISMjg/z8/FpN96yKmZkZzMzMMH36dAwdOhTR0dFiE6s2bdrgzz//xO3bt8WOAqampsLLy4tL+IVCIe7cucP73KWlpVFeXs67TktLCy9evMCrV6+40cLKWyzUJU7g3QIt2dnZvLJhw4Zh7ty5uHLlisgIaVlZGUpKSvDmzRvk5ORg06ZN6Ny5MwDg7NmzvLoV7/xVvg8AuHHjBqSkpNC6dWuxcRFCyOeOEkBCCCHVqhg1srCwqNVIF6k9JSUlBAYGYvr06RAKhejUqROeP3+OtLQ0KCoqYtSoUdVeX1xcjKCgIAwcOBDGxsb4888/kZGRIfIOXAUXFxd06dIFAwYMwJo1a2BiYoLff/8dAoEAPXr0gImJCfbt24e0tDSoqalhzZo1+Pvvv3kJoJGRES5cuIC8vDwoKipCXV0d7du3h7y8PObOnYspU6bg4sWLiImJqXecAODh4YEdO3bwyqZNm4aEhAR88803WLx4MTp16gQlJSVcunQJK1aswLZt29CmTRtoaGhg8+bN0NPTQ35+PmbPns1rR1tbG3JyckhMTIS+vj5kZWWhoqIC4F0S3LlzZ5ruTAj5YtE2EIQQQkgjWrx4MebPn49ly5bBwsICHh4e+PXXX7mtCqojKSmJwsJCjBw5EmZmZvD29kbPnj2xcOHCKq/Zt28f2rVrh6FDh8LS0hKzZs3iRsJCQ0PRtm1beHh4wNXVFbq6uujbty/v+sDAQEhKSsLS0hJaWlrIz8+Huro6du7ciaNHj3JbR4SFhX1QnMOHD0d2djZu3brFlcnIyODEiROYNWsWNm3ahA4dOqBdu3ZYt24dAgICYGVlBQkJCcTFxSEzMxNWVlaYPn06Vq1axWu7SZMmWLduHTZt2oSmTZvCy8uLO7dnzx6MGzeuxmdPCCGfKwGrPGmfEDEuX74Me3v7Wr/rQhpXfT+vj/U5f2g/9H38uOh5k8Yya9YsPHv2DJs2bfoo/SUkJCAoKAjXrl1DkyY0SYoQ8mWiEUBCCCGEfJJCQkJgaGgo9l29/8KrV68QHR1NyR8h5ItG/w9HCCGEkE+SiooK5s6d+9H68/b2/mh9EUJIY6ERQEIIIYQQQgj5StAIICGENIAvebP0in3XiouLGzkSQgghhHwoSgAJIaQBfA2bpefl5cHZ2bmxwyCEEELIB6AEkBBCGsCXvFl6Tk4Ohg8fzt0jIYQQQj5flAASQkgD+Bo2S//SprZ+LZKTk+Hm5oaioiKoqqo2djgf3ce6/23btmHv3r04fvz4f9ZHY8rLy4OxsTGuXLkCW1tbXL9+HT179sStW7egoKDQ2OERQuqAFoEhhBBCPgIjIyMIBAIIBALIy8vDysqqwfe3c3V1xbRp0xq0TVKzkpISzJ8/H6GhoVxZWFgYbG1tGy+o/5i1tTUcHR0RERHR2KEQQuqIEkBCCCHkI1m0aBEKCgpw7do19O3bF35+fti7d29jh1Uvb9++bewQeEpLSxut73379kFRURGdO3dutBgaSl2e43fffYcNGzZ8tH0aCSENgxJAQggh5CNRUlKCrq4uTExM8P3338PU1BQHDx4EAAQHB8PMzAzy8vJo0aIFQkNDeUlWxYhSbGwsjIyMoKKigiFDhuDFixcAAF9fX6SkpGDt2rXcSGNeXh53fWZmJhwcHCAvL4+OHTvi1q1bvNh+/fVX2NvbQ1ZWFi1atMDChQtRVlbGnRcIBNi4cSO8vLygoKCA77//HuXl5RgzZgyMjY0hJyeHVq1aYe3atdU+g+TkZAgEAiQkJMDGxgaysrJo3749rl+/ztUpLCzE0KFDoa+vD3l5eVhbW2PPnj28dlxdXeHv748ZM2ZAU1MT3bp1AwAcPXoUZmZmkJOTg5ubG+8ZvP8c3xcZGcl7xzU5ORmOjo5QUFCAqqoqnJ2dcf/+/SrvKS4uDn369Kn2vit7+PAhBg8eDDU1NWhoaMDLy4sXq1AoxKJFi6Cvrw8ZGRnY2toiMTGRO5+XlweBQIC4uDh07NgRsrKyaN26NZKTk7k6tfl8fH190bdvXyxbtgxNmzaFmZkZAODixYuws7ODrKwsHBwccOXKFZF78PDwQGFhIVJSUup074SQxkUJICGEENJIZGVluSRPSUkJMTExyM7Oxtq1a7FlyxaR6XW5ubk4ePAgjhw5giNHjiAlJQXLly8HAKxduxZOTk4YN24cCgoKUFBQAAMDA+7akJAQhIeH49KlS2jSpAlGjx7NnatYxTYgIADZ2dnYtGkTYmJisGTJEl7/CxYsgJeXF65fv47Ro0dDKBRCX18f8fHxyM7Oxvz58zF37lzEx8fXeO9BQUFYvXo1MjIyoK2tjT59+nDP4s2bN7C3t8eRI0dw48YNjB8/HiNGjMCFCxd4bezYsQNNmjTBuXPnsGnTJjx48AD9+/eHp6cnsrKyMHbsWMyePbsOnwhQVlaGvn37wsXFBdeuXcP58+cxfvx4CASCKq9JTU2Fg4NDrft4/fo13NzcoKioiDNnzuDs2bNQVFREjx49uBG4tWvXIjw8HKtXr8a1a9fg4eGBPn364M6dO7y2goKCMHPmTFy5cgUdO3ZEnz59UFhYCAC1/nxOnTqFnJwcnDhxAkeOHMGrV6/Qq1cvtGrVCpmZmQgLC0NgYKDIfUhLS8PGxgapqam1vndCyCeAEVILmZmZDADLzMxs7FBILdT38/pYn/OH9vMpfh8/xZgaypd8bx+ToaEhi4iIYIwx9vbtWxYdHc0AsKioKLH1V65cyezt7bnjBQsWMHl5efb8+XOuLCgoiLVv3547dnFxYVOnTuW189tvvzEA7OTJk1xZQkICA8CKi4sZY4x17tyZLV26lHddbGws09PT444BsGnTptV4n5MmTWIDBgyo8nxFPHFxcVxZYWEhk5OTY3v37q3yOk9PTzZz5kzu2MXFhdna2vLqzJkzh1lYWDChUMiVBQcHMwCsqKiIMfbuOdrY2PCui4iIYIaGhlwsAFhycnJNt8oYY6yoqIgBYGfOnOGVi+unwrZt21irVq14cZaUlDA5OTmWlJTEGGOsadOmbMmSJbzr2rVrxyZNmsQYY+zevXsMAFu+fDl3/u3bt0xfX5+tWLGiyngrfz6jRo1iOjo6rKSkhCvbtGkTU1dXZ69eveLKNmzYwACwK1eu8Nrr168f8/X1rbI/Qsinh1YBJYQQUq2KDeArNoQnoszNzSEvL19jveDgYMybNw8lJSWQlpZGUFAQJkyYAAD45ZdfEBkZibt37+Lly5coKyuDsrIy73ojIyMoKSlxx3p6enj06FGtYmzTpg3vOgB49OgRmjdvjszMTGRkZPBG/MrLy/HmzRu8fv2auzdxo1wbN27E1q1bcf/+fRQXF6O0tLRWi584OTlxf1dXV0erVq2471h5eTmWL1+OvXv34uHDhygpKUFJSYnIapOV48nJyUGHDh14o3Xv91Mb6urq8PX1hYeHB7p16wZ3d3d4e3tzz6yyin8fsrKyte4jMzMTd+/e5X2WwLuRz9zcXDx//hx//fWXyL6bzs7OuHr1Kq/s/ftr0qQJHBwceP9Wa/P5WFtbQ1pamjvOycmBjY0N7ztd1XOUk5PD69eva3fjhJBPAiWAhBBCqlXxXtKXvtH9h8jMzKzV9h9BQUHw9fWFvLw89PT0uEQlPT0dQ4YMwcKFC+Hh4QEVFRXExcUhPDycd72UlBTvWCAQQCgU1irG96+t6LfiWqFQiIULF6J///4i172f2FROwOLj4zF9+nSEh4fDyckJSkpKWLVqlchUzdqqiCs8PBwRERGIjIyEtbU1FBQUMG3aNJEFSirHwxirsQ8JCQmRepUXtImOjkZAQAASExOxd+9ezJs3DydOnECHDh1E2tPQ0IBAIEBRUVGt7hF497zt7e2xa9cukXNaWlrc3ytPO2WMVTsVtfJ1tf186vMcKzx9+hQtW7asdX1CSOOjBJAQQki1PDw8sHPnThgZGdFegFUwNzevVT1NTU2YmJiIlJ87dw6GhoYICQnhyqpbdKQq0tLS9VqRsW3btrh165bY2KqTmpqKjh07YtKkSVxZbm5ura5NT09H8+bNAQBFRUW4ffs29xxTU1Ph5eXF/dJBKBTizp07sLCwqLZNS0tLblGd9/t5n5aWFv7++29eMpWVlSXSlp2dHezs7DBnzhw4OTlh9+7dYhNAaWlpWFpaIjs7G927d6/Vvbdt2xZ79+6Ftra2yChvhaZNm+Ls2bPo0qULV5aWlgZHR0eR+6uoU1ZWhszMTPj7+wOo/+djaWmJ2NhYFBcXc//mKz/HCjdu3MDAgQNrbJMQ8umgBJAQQki1NDU14ePj09hhfNFMTEyQn5+PuLg4tGvXDgkJCThw4ECd2zEyMsKFCxeQl5cHRUVFqKur1+q6+fPno1evXjAwMMCgQYMgISGBa9eu4fr16/j++++rjfunn35CUlISjI2NERsbi4yMDBgbG9fY56JFi6ChoQEdHR2EhIRAU1MTffv25drdt28f0tLSoKamhjVr1uDvv/+uMQH08/NDeHg4ZsyYgQkTJiAzMxMxMTG8Oq6urnj8+DFWrlyJgQMHIjExEceOHeMSsXv37mHz5s3o06cPmjZtilu3buH27dsYOXJklf16eHjg7NmzInswFhcXiySXioqK8PHxwapVq+Dl5cWt9Jmfn4/9+/cjKCgI+vr6CAoKwoIFC9CyZUvY2toiOjoaWVlZIqOGP/74I0xNTWFhYYGIiAgUFRVxC/zU9/MZNmwYQkJCMGbMGMybNw95eXlYvXq1SL28vDw8fPgQ7u7u1bZHCPm00CqghBBCSCPz8vLC9OnT4e/vD1tbW6SlpfE2Fa+twMBASEpKwtLSElpaWsjPz6/VdR4eHjhy5AhOnDiBdu3aoUOHDlizZg0MDQ2rvc7Pzw/9+/fH4MGD0b59exQWFvJGm6qzfPlyTJ06Ffb29igoKMDhw4e599BCQ0PRtm1beHh4wNXVFbq6ulxyWJ3mzZtj3759+PXXX2FjY4ONGzdi6dKlvDoWFhaIiorCjz/+CBsbG1y8eJG3wqW8vDx+//13DBgwAGZmZhg/fjz8/f25dzXFGTduHI4ePYpnz57xym/fvs2NJFb8GTt2LOTl5XHmzBk0b94c/fv3h4WFBUaPHo3i4mIuEQ0ICMDMmTMxc+ZMWFtbIzExEYcPH4apqanIc1yxYgW3GuehQ4egqakJoP6fj6KiIn799VdkZ2fDzs4OISEhWLFihUi9PXv2oHv37jV+TwghnxYBq8tEb/LVunz5Muzt7Wv9ngtpXPX9vD7W5/yh/XyK38dPMSZCPkXJyclwc3NDUVERVFVVGzucBuPt7c1NGf0Y8vLyYGxsjCtXrtRq0Z2GVlJSAlNTU+zZs0dksRpCyKeNRgAJIYQQQj7QqlWroKio2NhhfDT3799HSEgIJX+EfIboHUBCCCGEkA9kaGiIKVOmNHYYH42ZmRnMzMwaOwxCSD1QAkgIIYSQj8bV1bVO2wwQ8YyMjOg5EkLqhRJAQsgHe/LkCZKSkmq9TUDFJsUVGyjX1ae4MfmH3hMhhBBCyMdACSAh5IMlJSXVa5PwvLy8er0/8ilvTF7feyKEEEII+RgoASSEfDAjIyMAwM6dO2vcpwt4N1o2fPhw7rq6+hQ3Jv/QeyKEEEII+RgoASSEfLCKJMzCwqJOWyDUN3n7lDcm/1QSUkI+BTExMZg2bRr+/fffxg6lSmFhYTh48KDIhu0f261bt+Di4oI7d+5ASUmpUWOpSmNvPfEh6vNddHV1ha2tLSIjI8WeDwwMRGlpKdatW9cwQRLykdA2EIQQQsgXxMjICAKBAOnp6bzyadOmwdXV9aPGMnjwYNy+ffuj9llXgYGBOHXqVGOHgZCQEEyePFls8teqVStIS0vj4cOHjRDZ/xgYGKCgoABWVlaNGkd9/BffxVmzZiE6Ohr37t1r0HYJ+a9RAkgIIYR8YWRlZREcHNzYYUBOTg7a2tqNHUa1FBUVoaGh0agx/Pnnnzh8+DC+++47kXNnz57FmzdvMGjQIMTExHz84P5faWkpJCUloauriyZNPr8JZP/Fd1FbWxvdu3fHxo0bG7RdQv5rlAASQgghX5gJEyYgPT0dR48erbZedHQ0LCwsICsrC3Nzc0RFRfHOX79+HV27doWcnBw0NDQwfvx4vHz5EsC7xZ9kZWVFptQFBATAxcUFwLtpd6qqqty5q1evws3NDUpKSlBWVoa9vT0uXbrEnd+3bx9at24NGRkZGBkZITw8nNe2kZERli5ditGjR0NJSQnNmzfH5s2bq7y/TZs2oVmzZhAKhbzyPn36YNSoUQDeTQF9fzpjcnIyHB0doaCgAFVVVTg7O+P+/fvc+Q0bNqBly5aQlpZGq1atEBsby2tbIBBg69at6NevH+Tl5WFqaorDhw9XGSMAxMfHw8bGBvr6+iLntm3bhmHDhmHEiBHYvn27yNYPRkZG+P777zFy5EgoKirC0NAQhw4dwuPHj+Hl5QVFRUVYW1vznjMApKWloUuXLpCTk4OBgQECAgLw6tUrkXZ9fX2hoqKCcePGIS8vDwKBgDdd9ubNm/j222+hrKwMJSUldO7cGbm5uQCAjIwMdOvWDZqamlBRUYGLiwsuX778wc+rPvdc+btY8bnHxsbCyMgIKioqGDJkCF68eFFlv4mJiVBRUcFPP/3ElfXp0wd79uypNl5CPjWUABJCCCFfGCMjI/j5+WHOnDkiyU+FLVu2ICQkBEuWLEFOTg6WLl2K0NBQ7NixAwDw+vVr9OjRA2pqasjIyMDPP/+MkydPwt/fHwDg7u4OVVVV7Nu3j2uzvLwc8fHxVb6j6+PjA319fWRkZCAzMxOzZ8+GlJQUACAzMxPe3t4YMmQIrl+/jrCwMISGhoqMeoWHh8PBwQFXrlzBpEmTMHHiRPz+++9i+xs0aBCePHmC3377jSsrKipCUlKS2BjLysrQt29fuLi44Nq1azh//jzGjx8PgUAAADhw4ACmTp2KmTNn4saNG5gwYQK+++47XvsAsHDhQnh7e+PatWvw9PSEj48Pnj59KjZGADhz5gwcHBxEyl+8eIGff/4Zw4cPR7du3fDq1SskJyeL1IuIiICzszOuXLmCb7/9FiNGjMDIkSMxfPhwXL58GSYmJhg5ciSXPF6/fh0eHh7o378/rl27hr179+Ls2bPcZ1th1apVsLKyQmZmJkJDQ0X6ffjwIbp06QJZWVmcPn0amZmZGD16NMrKyrj4R40ahdTUVKSnp8PU1BSenp4iSVZdn1d97lmc3NxcHDx4EEeOHMGRI0eQkpKC5cuXi60bFxcHb29v/PTTTxg5ciRX7ujoiAcPHvB+SUDIp+7zG8MnhBDy0dV1r0fy3zA3N4e8vHyt6s6bNw/R0dHYtWsXRowYIXJ+8eLFCA8PR//+/QEAxsbGyM7OxqZNmzBq1Cjs2rULxcXF+Omnn6CgoAAA+OGHH9C7d2+sWLECOjo6GDx4MHbv3o0xY8YAAE6dOoWioiIMGjRIbEz5+fkICgqCubk5AMDU1JQ7t2bNGnzzzTdcomFmZobs7GysWrUKvr6+XD1PT09MmjQJABAcHIyIiAgkJydzbb5PXV0dPXr0wO7du/HNN98AAH7++Weoq6tzx+97/vw5nj17hl69eqFly5YAwFvZePXq1fD19eX6nzFjBtLT07F69Wq4ublx9Xx9fTF06FAAwNKlS7F+/XpcvHgRPXr0EPtc8vLyYG9vL1IeFxcHU1NTtG7dGgAwZMgQbNu2jddXxTOZMGECAGD+/PnYsGED2rVrx30OwcHBcHJywj///ANdXV2sWrUKw4YNw7Rp0wC8+xzWrVsHFxcXbNiwAbKysgCArl27IjAwkBfn+3788UeoqKggLi6OS+TNzMy48127duXV37RpE9TU1JCSkoJevXrV+3nV557FEQqFiImJ4d67HDFiBE6dOoUlS5bw6kVFRWHu3Lk4dOiQyLNv1qwZ92wMDQ2rjJeQTwklgIQQQmpU370eScPKzMys9Uq7WlpaCAwMxPz58zF48GDeucePH+PBgwcYM2YMxo0bx5WXlZVBRUUFwLutTWxsbLjkDwCcnZ0hFApx69Yt6OjowMfHB05OTvjrr7/QtGlT7Nq1C56enlBTUxMb04wZMzB27FjExsbC3d0dgwYN4hKtnJwceHl58eo7OzsjMjIS5eXlkJSUBAC0adOGOy8QCKCrq4tHjx5V+Rx8fHwwfvx4REVFQUZGBrt27cKQIUO49t6nrq4OX19feHh4oFu3bnB3d4e3tzf09PS4GMePHy8S49q1a3ll78eooKAAJSWlamMsLi7mkq73bdu2jffvbvjw4ejSpQv+/fdf3nTG9/vT0dEBAFhbW4uUPXr0CLq6usjMzMTdu3exa9curg5jDEKhEPfu3eOSXnGjku/LyspC586dueSvskePHmH+/Pk4ffo0/vnnH5SXl+P169fIz8/n1avr86rPPYtjZGTEW3RHT09PpN99+/bhn3/+wdmzZ+Ho6CjSRsUvxF6/fl1tvIR8SigBJIQQUqO67vVI/hviRrmqM2PGDERFRYm821cxLXTLli1o374971xFYsQY46Y+VlZR7ujoiJYtWyIuLg4TJ07EgQMHEB0dXWU8YWFhGDZsGBISEnDs2DEsWLAAcXFx6Nevn9j+xE3fq5xsCASCKqe5AkDv3r0hFAqRkJCAdu3aITU1FWvWrKmyfnR0NAICApCYmIi9e/di3rx5OHHiBDp06MC79/djrFxW1xg1NTVRVFTEK8vOzsaFCxeQkZHBW9CnvLwce/bswcSJE8X2VxGLuLKKGIRCISZMmICAgACRWJo3b879/f3kX5yaZgP4+vri8ePHiIyMhKGhIWRkZODk5ITS0lJevbo+r8rX1Oaea2qjqn5tbW1x+fJlREdHo127diKfdcVUVS0trWrjJeRTQgkgIYSQGtV3r0fSuBQVFREaGoqwsDD07t2bK9fR0UGzZs3wxx9/VPm+nqWlJXbs2IFXr15xicC5c+cgISHBm+Y3bNgw7Nq1C/r6+pCQkMC3335bbUxmZmYwMzPD9OnTMXToUERHR6Nfv36wtLTE2bNneXXT0tJgZmYmdrSutuTk5NC/f3/s2rULd+/ehZmZmdjplu+zs7ODnZ0d5syZAycnJ+zevRsdOnSAhYUFzp49y3sHLC0t7YN/KWJnZ4fs7Gxe2bZt29ClSxf8+OOPvPLY2Fhs27aNlwDWVdu2bXHz5k2YmJjUuw3g3Sjcjh078PbtW7GjgKmpqYiKioKnpycA4MGDB3jy5MkH9fmxtWzZEuHh4XB1dYWkpCR++OEH3vkbN25ASkqKm6ZLyOeAFoEhhBBCvmDjx4+HioqKyEqFYWFhWLZsGdauXYvbt2/j+vXriI6O5kbHfHx8ICsri1GjRuHGjRv47bffMGXKFIwYMYKbXldR7/Lly1iyZAkGDhwodioj8G6ao7+/P5KTk3H//n2cO3cOGRkZXPI0c+ZMnDp1CosXL8bt27exY8cO/PDDD7x30OrLx8cHCQkJ2L59e7VTme/du4c5c+bg/PnzuH//Po4fP47bt29zMQYFBSEmJgYbN27EnTt3sGbNGuzfv/+DY/Tw8MD58+dRXl4OAHj79i1iY2MxdOhQWFlZ8f6MHTsWmZmZuHr1ar37Cw4Oxvnz5zF58mRkZWXhzp07OHz4MKZMmVKndvz9/fH8+XMMGTIEly5dwp07dxAbG4tbt24BAExMTBAbG4ucnBxcuHABPj4+n+U7xGZmZvjtt9+wb98+7r3JCqmpqejcufNneV/k60UjgKRWiouLAbx7/4F8+io+p4rPjRDy9ZKSksLixYsxbNgwXvnYsWMhLy+PVatWYdasWVBQUIC1tTX3A668vDySkpIwdepUtGvXDvLy8hgwYIDI9ElTU1O0a9cOGRkZiIyMrDIOSUlJFBYWYuTIkfjnn3+gqamJ/v37Y+HChQDejUrFx8dj/vz5WLx4MfT09LBo0SLeAjD11bVrV6irq+PWrVsiz+F98vLy+P3337Fjxw4UFhZCT08P/v7+3GIjffv2xdq1a7Fq1SoEBATA2NgY0dHRcHV1/aD4PD09ISUlhZMnT8LDwwOHDx9GYWEh+vXrJ1LX1NQU1tbW2LZtG9atW1ev/tq0aYOUlBSEhISgc+fOYIyhZcuWIu+K1kRDQwOnT59GUFAQXFxcICkpCVtbWzg7OwMAtm/fjvHjx8POzg7NmzfH0qVLGyShbwytWrXC6dOnuZHAii1K9uzZw32HCflcCFh16+MS8v927dpFC0B8hnbu3Fnl9C5xLl++DHt7+zotNFGf6+rbz6fsS7yn933p90dIY4uKisKhQ4eQlJTU2KGQWkpISEBQUBCuXbuGJk1oTIV8PujbSmrFw8MDO3fupCXgPxM5OTkYPnw4t3AHIYSQT9v48eNRVFSEFy9e8FamJJ+uV69eITo6mpI/8tmhbyypFU1NzTqNJJFPAyXrhBDyeWjSpAlCQkIaOwxSB97e3o0dAiH1QgkgIYQ0gC/9PVl6r5QQQgj5MlACSAghDSAvLw8Avvh3ZfPy8rgFHgghhBDy+aEEkBBCGsCX/p4svVdKCCGEfBkoASSEkAbwtbwn+yUmt4QQQsjXhDaCJ4QQQgj5j7m6uopsIl5ZTEwMVFVVP0o8dVVYWAhtbW1uuntDqM0zqY+8vDwIBAJkZWU1eNu1ceTIEdjZ2UEoFDZK/4TUhBJAQgghhDQIgUCAgwcPipRPmzbtgzdL/9zt378fixcv5o6NjIwQGRnJqzN48GDcvn37I0dWO8uWLUPv3r25aeAVSVbFHzU1NXTp0gUpKSmNG+gnoFevXhAIBNi9e3djh0KIWJQAEkIIIaRRlZaWNnYI/5m3b98CANTV1Wvc309OTg7a2tofI6w6KS4uxrZt2zB27FiRcydPnkRBQQFSUlKgrKwMT09P3Lt376PExRhDWVnZR+mrtio+7++++w7r169v5GgIEY8SQEIIIYR8VK6urvD398eMGTOgqamJbt26AQDCwsLQvHlzyMjIoGnTpggICOCuETe6qKqqipiYGADvkkh/f3/o6elBVlYWRkZGWLZsWZUx+Pr6om/fvli6dCl0dHSgqqqKhQsXoqysDEFBQVBXV4e+vj62b9/Ouy44OBhmZmaQl5dHixYtEBoayv3QX3EPtra22L59O1q0aAEZGRkwxnjTHV1dXXH//n1Mnz6dG0EDRKeAvt9W8+bNoaioiIkTJ6K8vBwrV66Erq4utLW1sWTJEu4acdMf//33XwgEAiQnJwMAioqK4OPjAy0tLcjJycHU1BTR0dFVPqtjx46hSZMmcHJyEjmnoaEBXV1dtGnTBps2bcLr169x/PhxAEBKSgocHR0hIyMDPT09zJ49u9qEbefOnXBwcICSkhJ0dXUxbNgwPHr0iDufnJwMgUCApKQkODg4QEZGBqmpqVW29/vvv6Njx46QlZVF69atufuvkJ2dDU9PTygqKkJHRwcjRozAkydPuPOJiYno1KkTVFVVoaGhgV69eiE3N5c7X/Gs4+Pj4erqCllZWezcuRMA0KdPH1y8eBF//PFHlfER0lgoASSEEELIR7djxw40adIE586dw6ZNm/DLL78gIiICmzZtwp07d3Dw4EFYW1vXur1169bh8OHDiI+Px61bt7hVeatz+vRp/PXXXzhz5gzWrFmDsLAw9OrVC2pqarhw4QL8/Pzg5+eHBw8ecNcoKSkhJiYG2dnZWLt2LbZs2YKIiAheu3fv3kV8fDz27dsn9j20/fv3Q19fH4sWLUJBQQEKCgqqjDE3NxfHjh1DYmIi9uzZg+3bt+Pbb7/Fn3/+iZSUFKxYsQLz5s1Denp6rZ9VaGgosrOzcezYMeTk5GDDhg3Q1NSssv6ZM2fg4OBQY7vy8vIA3o2CPXz4EJ6enmjXrh2uXr2KDRs2YNu2bfj++++rvL60tBSLFy/G1atXcfDgQdy7dw++vr4i9WbNmoVly5YhJycHbdq0qbK9oKAgzJw5E1euXEHHjh3Rp08fFBYWAgAKCgrg4uICW1tbXLp0CYmJifjnn394m7u/evUKM2bMQEZGBk6dOgUJCQn069dP5N2+4OBgBAQEICcnBx4eHgAAQ0NDaGtrV5ugEtJYaBVQQgghNfrSN7onNTM3N+d+wG8IJiYmWLlyJXd89OhR6Orqwt3dHVJSUmjevDkcHR1r3V5+fj5MTU3RqVMnCAQCGBoa1niNuro61q1bBwkJCbRq1QorV67E69evMXfuXADAnDlzsHz5cpw7dw5DhgwBAMybN4+73sjICDNnzsTevXsxa9Ysrry0tBSxsbHQ0tKqsl9JSUlupKs6QqEQ27dvh5KSEiwtLeHm5oZbt27h6NGjXNwrVqxAcnIyOnToUOM9A++elZ2dHZfU1ZQo5+XloWnTptXWefXqFebMmQNJSUm4uLggKioKBgYG+OGHHyAQCGBubo6//voLwcHBmD9/PiQkRMcgRo8ezf29RYsWWLduHRwdHfHy5UsoKipy5xYtWsSNGlfH398fAwYMAABs2LABiYmJ2LZtG2bNmoUNGzagbdu2WLp0KVd/+/btMDAwwO3bt2FmZsZdW2Hbtm3Q1tZGdnY2rKysuPJp06ahf//+Iv03a9asQRfNIaShUAJICCGkRl/LRvekapmZmWjbtm2DtVd5RGnQoEGIjIxEixYt0KNHD3h6eqJ3795o0qR2P6r4+vqiW7duaNWqFXr06IFevXqhe/fu1V7TunVrXiKio6PD+8FeUlISGhoavGmIv/zyCyIjI3H37l28fPkSZWVlUFZW5rVraGhYZfJXV0ZGRrx3B3V0dCApKSkS9/sx1mTixIkYMGAALl++jO7du6Nv377o2LFjlfWLi4shKysr9lzHjh0hISGB169fQ09PDzExMbC2tsaCBQvg5OTETW8FAGdnZ7x8+RJ//vknmjdvLtLWlStXEBYWhqysLDx9+pQbacvPz4elpSVXrzajkQB4U1abNGkCBwcH7pdYmZmZ+O2333iJZYXc3FyYmZkhNzcXoaGhSE9Px5MnT3jxvP89qSoeOTk5vH79ulaxEvIxUQJICCGkRl/6RvekZubm5jXWUVJSwrNnz0TK//33X6ioqPDKFBQUeMcGBga4desWTpw4gZMnT2LSpElYtWoVUlJSICUlBYFAAMYY75r3371r27Yt7t27h2PHjuHkyZPw9vaGu7s7fvnllyrjlZKS4h0LBAKxZRU/+Kenp2PIkCFYuHAhPDw8oKKigri4OISHh1d7bx+irjFWJIbvP6v3nxMA9OzZE/fv30dCQgJOnjyJb775BpMnT8bq1avFxqCpqYmioiKx5/bu3QtLS0vuPbkKjDFe8vd+TJXLgXcjiN27d0f37t2xc+dOaGlpIT8/Hx4eHiKLBH3I863oWygUonfv3lixYoVIHT09PQBA7969YWBggC1btqBp06YQCoWwsrKqdTxPnz5tsF8EENKQKAEkhBBSo69lo3vyYczNzZGRkYFRo0ZxZYwxZGZmomfPnjVeLycnhz59+qBPnz6YPHkyzM3Ncf36dbRt2xZaWlq8d+Xu3LkjMrqirKyMwYMHY/DgwRg4cCB69OiBp0+fQl1dvUHu79y5czA0NERISAhXdv/+/Xq1JS0tjfLy8gaJ630VCUdBQQHs7OwAQOx7iFpaWvD19YWvry86d+6MoKCgKhNAOzs7bnGTygwMDNCyZUuRcktLS+zbt4+XCKalpUFJSQnNmjUTqf/777/jyZMnWL58OQwMDAAAly5dqvmGq5Geno4uXboAAMrKypCZmQl/f38A735hsG/fPhgZGYkdZS4sLEROTg42bdqEzp07AwDOnj1b677fvHmD3Nxc7jMg5FNCCSAhhBBCGkRgYCBGjRoFc3NzdO/eHcXFxdi8eTNyc3MxefLkaq+NiYlBeXk52rdvD3l5ecTGxkJOTo57l69r16744Ycf0KFDBwiFQgQHB/NGwiIiIqCnpwdbW1tISEjg559/hq6uboNurG5iYoL8/HzExcWhXbt2SEhIwIEDB+rVlpGREc6cOYMhQ4ZARkam2kVY6kJOTg4dOnTA8uXLYWRkhCdPnvDeWwSA+fPnw97eHq1bt0ZJSQmOHDkCCwuLKtv08PDAnDlzUFRUBDU1tVrFMWnSJERGRmLKlCnw9/fHrVu3sGDBAsyYMUPs+3/NmzeHtLQ01q9fDz8/P9y4cYO3b2J9/PjjjzA1NYWFhQUiIiJQVFTEvWc4efJkbNmyBUOHDkVQUBA0NTVx9+5dxMXFYcuWLVBTU4OGhgY2b94MPT095OfnY/bs2bXuOz09HTIyMmJXTiWksdEqoIQQQghpEN7e3oiJicGOHTvQrl07dO/eHbm5uUhNTa1xURZVVVVs2bIFzs7OaNOmDU6dOoVff/2Vm1YYHh4OAwMDdOnSBcOGDUNgYCBvURpFRUWsWLECDg4OaNeuHfLy8riFUhqKl5cXpk+fDn9/f9ja2iItLQ2hoaH1amvRokXIy8tDy5YtG3ya4Pbt2/H27Vs4ODhg6tSpIitvSktLY86cOWjTpg26dOkCSUlJxMXFVdmetbU1HBwcEB8fX+sYmjVrhqNHj+LixYuwsbGBn58fxowZI5KMVtDS0kJMTAx+/vlnWFpaYvny5VWOSNbW8uXLsWLFCtjY2CA1NRWHDh3iEu2mTZvi3LlzKC8vh4eHB6ysrDB16lSoqKhAQkICEhISiIuLQ2ZmJqysrDB9+nSsWrWq1n3v2bMHPj4+DbpwEiENRcAqT6gnhHz2Ll++DHt7+zov2vCxrqtvP4QQQhrH0aNHERgYiBs3bjRoUv0levz4MczNzXHp0iUYGxs3djiEiKApoIQQQgghpFqenp64c+cOHj58yL2jR8S7d+8eoqKiKPkjnyxKAAkhhBBCSI2mTp3a2CF8FhwdHeu0hyUhHxslgIQQTn03+66oX3E9IYQQQgj5NFECSAjhfOhm33l5eXB2dm7AiAghhBBCSEOiBJAQwqnvZt85OTkYPnw4jIyM/rvgCCGEEELIB6MEkBDC+dDNvuuSNBLytcnLy4OxsTGuXLkCW1vberfj6+uLf//9FwcPHgQAuLq6wtbWFpGRkQ0SpzilpaWwtLTEjh07PptR/nbt2mHOnDno379/Y4dCCCGfFFrHlxBCyEdlZGQEgUAg8mf58uWNHRqpwubNm2FoaMhL/gQCAZeEvs/X1xd9+/b9eMFVITQ0FLNnz4ZQKGzsUAgh5JNCCSAhhJCPbtGiRSgoKOD9mTJlSmOHRaqwfv16jB079qP0xRhDWVnZB7fz7bff4tmzZ0hKSmqAqAgh5MtBCSAhhJCPTklJCbq6urw/CgoK3PmjR4/CzMwMcnJycHNzQ0xMDAQCAf79918AQFhYmMg0ysjISN57qK6urpg2bRqvTt++feHr68sdR0VFwdTUFLKystDR0cHAgQOrjfvcuXNwcXGBvLw81NTU4OHhgaKiIgBAYmIiOnXqBFVVVWhoaKBXr17Izc0VaeP3339Hx44dISsri9atWyM5OZk7V15ejjFjxsDY2BhycnJo1aoV1q5dW21MlYkbmVNVVUVMTAyAd9M5/f39oaenB1lZWRgZGWHZsmVVtnf58mXcvXsX3377bZ3iqFBSUoKAgABoa2tDVlYWnTp1QkZGBnc+OTkZAoEASUlJcHBwgIyMDFJTU3H16lW4ublBSUkJysrKsLe3x6VLl7jr0tLS0KVLF8jJycHAwAABAQF49eoVd15SUhKenp7Ys2dPveImhJAvFSWAhBBCPikPHjxA//794enpiaysLIwdOxazZ89u8H4uXbqEgIAALFq0CLdu3UJiYiK6dOlSZf2srCx88803aN26Nc6fP4+zZ8+id+/eKC8vBwC8evUKM2bMQEZGBk6dOgUJCQn069dPZApiUFAQZs6ciStXrqBjx47o06cPCgsLAQBCoRD6+vqIj49HdnY25s+fj7lz5yI+Pr7B7nvdunU4fPgw4uPjcevWLW7hp6qcOXMGZmZmUFZWrld/s2bNwr59+7Bjxw5cvnwZJiYm8PDwwNOnT0XqLVu2DDk5OWjTpg18fHygr6+PjIwMZGZmYvbs2ZCSkgIAXL9+HR4eHujfvz+uXbuGvXv34uzZs/D39+e16ejoiNTU1HrFTQghXypaBIYQQshHFxwcjHnz5vHKjhw5AldXV2zYsAEtWrRAREQEBAIBWrVqhevXr2PFihUNGkN+fj4UFBTQq1cvKCkpwdDQEHZ2dlXWX7lyJRwcHBAVFcWVtW7dmvv7gAEDePW3bdsGbW1tZGdnw8rKiiv39/fn6m7YsAGJiYnYtm0bZs2aBSkpKSxcuJCra2xsjLS0NMTHx8Pb2/uD7xl4d9+mpqbo1KkTBAIBDA0Nq62fl5eHpk2bij03dOhQSEpK8spKSkq40cJXr15hw4YNiImJQc+ePQEAW7ZswYkTJ7Bt2zYEBQVx1y1atAjdunXjxRkUFARzc3MAgKmpKXdu1apVGDZsGDfCa2pqinXr1sHFxQUbNmyArKwsAKBZs2bIz8+HUCiEhAT9zpsQQgBKAAkhjaC+G86TT5+5uTnk5eVrrBcUFMSbigm8+2EdePe96NChAwQCAXfOycmpQeMEgG7dusHQ0BAtWrRAjx490KNHD/Tr16/K+LOysjBo0KAq28vNzUVoaCjS09Px5MkTbuQvPz+flwC+fy9NmjSBg4MD79/Cxo0bsXXrVty/fx/FxcUoLS39oFVDK/P19UW3bt3QqlUr9OjRA7169UL37t2rrF9cXMwlVJVFRETA3d2dVxYcHMyNiubm5uLt27e8xWOkpKTg6Ogo8u/fwcGBdzxjxgyMHTsWsbGxcHd3x6BBg9CyZUsAQGZmJu7evYtdu3Zx9RljEAqFuHfvHiwsLAC8W5lYKBSipKSEVikmhJD/RwkgIeSj+9AN58mnKzMzE23btq2xnqamJkxMTMSeY4zVeL2EhIRIvbdv39apjpKSEi5fvozk5GQcP34c8+fPR1hYGDIyMqCqqirSZ00JRO/evWFgYIAtW7agadOmEAqFsLKyQmlpaY33U5HsxsfHY/r06QgPD4eTkxOUlJSwatUqXLhwocY23m+ruvtu27Yt7t27h2PHjuHkyZPw9vaGu7s7fvnlF7HtaWpq4vr162LP6erqinyOSkpK3LuaFXG8n8xXlFcue/8dUODde57Dhg1DQkICjh07hgULFiAuLo6bVjthwgQEBASIxNS8eXPu70+fPoW8vDwlf4QQ8h5KAAkhH119N5wnn76K6XofwtLSUmQRk/T0dN6xlpYW/v77b14ikZWVJVKnoKCAOy4vL8eNGzfg5ubGlTVp0gTu7u5wd3fHggULoKqqitOnT4vdO65NmzY4deoUb4pmhcLCQuTk5GDTpk3o3LkzAODs2bNi7y89PZ1717CsrAyZmZncu2upqano2LEjJk2axNUXt5BMdSrf9507d/D69WteHWVlZQwePBiDBw/GwIED0aNHDzx9+hTq6uoi7dnZ2WHDhg1ik7aamJiYQFpaGmfPnsWwYcMAvEtGL126JLJAjzhmZmYwMzPD9OnTMXToUERHR6Nfv35o27Ytbt68WeUvESrcuHGjVr+QIISQrwklgISQj+5DN5wnn78XL17g77//5pXJy8tDWVkZfn5+CA8Px4wZMzBhwgRkZmZyK1hWcHV1xePHj7Fy5UoMHDgQiYmJOHbsGG+hkq5du2LGjBlISEhAy5YtERERwY1MAe/eOfzjjz/QpUsXqKmp4ejRoxAKhWjVqpXYmOfMmQNra2tMmjQJfn5+kJaWxm+//YZBgwZBXV0dGhoa2Lx5M/T09JCfn1/lwjU//vgjTE1NYWFhgYiICBQVFWH06NEA3iVMP/30E5KSkmBsbIzY2FhkZGTA2Ni41s+2a9eu+OGHH9ChQwcIhUIEBwdzi6cA76Zt6unpwdbWFhISEvj555+hq6srdtQTANzc3PDq1SvcvHmTN5W1NhQUFDBx4kQEBQVBXV0dzZs3x8qVK/H69WuMGTOmyuuKi4sRFBSEgQMHwtjYGH/++ScyMjK4dyeDg4PRoUMHTJ48GePGjYOCggJycnJw4sQJrF+/nmsnNTW12umthBDyNaI3ogkhhHx08+fPh56eHu/PrFmzALybwrdv3z78+uuvsLGxwcaNG7F06VLe9RYWFoiKisKPP/4IGxsbXLx4EYGBgbw6o0ePxqhRozBy5Ei4uLjA2NiYN/qnqqqK/fv3o2vXrrCwsMDGjRuxZ88e3sIu7zMzM8Px48dx9epVODo6wsnJCYcOHUKTJk0gISGBuLg4ZGZmwsrKCtOnT8eqVavEtrN8+XKsWLECNjY2SE1NxaFDh6CpqQkA8PPzQ//+/TF48GC0b98ehYWFvNHA2ggPD4eBgQG6dOmCYcOGITAwkPdeo6KiIlasWAEHBwe0a9cOeXl5OHr0aJWLpGhoaKB///689+3qYvny5RgwYABGjBiBtm3b4u7du0hKSoKamlqV10hKSqKwsBAjR46EmZkZvL290bNnT270tU2bNkhJScGdO3fQuXNn2NnZITQ0FHp6elwbDx8+RFpaGr777rt6xU0IIV8qAavNyxaEkM/K5cuXYW9vX+v3sT63/sjXJzk5GW5ubigqKqpypIr8d65fvw53d3fcvXsXSkpKjR1OrQQFBeHZs2fYvHlzY4dCCCGfFBoBJIQQQki1rK2tsXLlSm4Bp8+BtrY2Fi9e3NhhEELIJ4feASSEEEJIjUaNGtXYIdTJ+3sMEkII+R9KAAkhhHzyXF1da7U9BCGEEEKqR1NACSGEEEIIIeQrQQkgIYQQQgghhHwlKAEkhBBCCCGEkK8EJYCEEEII+aQJBAIcPHiwscOol9OnT8Pc3BxCobCxQ6mTmJgY3pYrYWFhsLW15Y59fX3Rt29f7tjV1RXTpk2rdftHjhyBnZ3dZ/dcCPkSUAJICCHki2BkZASBQACBQAA5OTkYGRnB29sbp0+fbuzQyAcqKChAz549G6y9uiYrH2LWrFkICQmBhAT/R67i4mKoqalBXV0dxcXFHyWWDxEYGIhTp041WHu9evWCQCDA7t27G6xNQkjtUAJICCHki7Fo0SIUFBTg1q1b+Omnn6Cqqgp3d3csWbKksUMjH0BXVxcyMjKNHUadpaWl4c6dOxg0aJDIuX379sHKygqWlpbYv39/jW29ffv2vwix1hQVFaGhodGgbX733XdYv359g7ZJCKkZJYCEEEK+GEpKStDV1UXz5s3RpUsXbN68GaGhoZg/fz5u3boFACgvL8eYMWNgbGwMOTk5tGrVCmvXruW1k5ycDEdHRygoKEBVVRXOzs64f/8+8vLyICEhgUuXLvHqr1+/HoaGhmCMITk5GQKBAAkJCbCxsYGsrCzat2+P69evc/UrT6cDgMjISBgZGdUYgzh5eXkQCATYv38/3NzcIC8vDxsbG5w/f55Xb9++fWjdujVkZGRgZGSE8PBw7tycOXPQoUMHkbbbtGmDBQsWcMfR0dGwsLCArKwszM3NERUVJRJHfHw8OnfuDDk5ObRr1w63b99GRkYGHBwcoKioiB49euDx48e8frZv387FpqenB39/f+7c+1NAa3uv586dg4uLC+Tl5aGmpgYPDw8UFRXB19cXKSkpWLt2LTdinJeXJzLlEQAOHjwIgUDAHV+9ehVubm5QUlKCsrIy7O3tRb4L74uLi0P37t0hKysrcm7btm0YPnw4hg8fjm3btomcFwgE2LhxI7y8vKCgoIDvv/+e+95s374dzZs3h6KiIiZOnIjy8nKsXLkSurq60NbWFvmFx5o1a2BtbQ0FBQUYGBhg0qRJePnyJa9OTEwMmjdvDnl5efTr1w+FhYW88+K+s9UpKirCyJEjoaamBnl5efTs2RN37tzh1enTpw8uXryIP/74o9btEkI+HCWAhBBCvmhTp04FYwyHDh0CAAiFQujr6yM+Ph7Z2dmYP38+5s6di/j4eABAWVkZ+vbtCxcXF1y7dg3nz5/H+PHjIRAIYGRkBHd3d0RHR/P6iI6Ohq+vLy9ZCAoKwurVq5GRkQFtbW306dOn1qM41cVQnZCQEAQGBiIrKwtmZmYYOnQoysrKAACZmZnw9vbGkCFDcP36dYSFhSE0NBQxMTEAAB8fH1y4cAG5ublcezdv3sT169fh4+MDANiyZQtCQkKwZMkS5OTkYOnSpQgNDcWOHTt4cSxYsADz5s3D5cuX0aRJEwwdOhSzZs3C2rVrkZqaitzcXMyfP5+rv2HDBkyePBnjx4/H9evXcfjwYZiYmNT7XrOysvDNN9+gdevWOH/+PM6ePYvevXujvLwca9euhZOTE8aNG4eCggIUFBTAwMCgVp+Lj48P9PX1kZGRgczMTMyePRtSUlJV1j9z5gwcHBxEynNzc3H+/Hl4e3vD29sbaWlpYpOgBQsWwMvLC9evX8fo0aO5a48dO4bExETs2bMH27dvx7fffos///wTKSkpWLFiBebNm4f09HSuHQkJCaxbtw43btzAjh07cPr0acyaNYs7f+HCBYwePRqTJk1CVlYW3Nzc8P3339fqmVTF19cXly5dwuHDh3H+/HkwxuDp6cn7N2BoaAhtbW2kpqZ+UF+EkDpihJAvTmZmJgPAMjMzv8j+CBHH0NCQRUREiD2no6PDJk6cWOW1kyZNYgMGDGCMMVZYWMgAsOTkZLF19+7dy9TU1NibN28YY4xlZWUxgUDA7t27xxhj7LfffmMAWFxcHHdNYWEhk5OTY3v37mWMMbZgwQJmY2PDazciIoIZGhrWKobK7t27xwCwrVu3cmU3b95kAFhOTg5jjLFhw4axbt268a4LCgpilpaW3HGbNm3YokWLuOM5c+awdu3acccGBgZs9+7dvDYWL17MnJycqoxjz549DAA7deoUV7Zs2TLWqlUr7rhp06YsJCSkyvsDwA4cOFDrex06dChzdnausj0XFxc2depUXll0dDRTUVHhlR04cIC9/6OSkpISi4mJqbLdylRUVNhPP/0kUj537lzWt29f7tjLy0vk/gGwadOm8coWLFjA5OXl2fPnz7kyDw8PZmRkxMrLy7myVq1asWXLllUZV3x8PNPQ0OCOhw4dynr06MGrM3jwYN7zqPydHTVqFPPy8uKO33+mt2/fZgDYuXPnuPNPnjxhcnJyLD4+ntePnZ0dCwsLqzJWQkjDa9IIOSchhPynnjx5gqSkJBgZGUFOTq6xwyENwNzcHPLy8vW+njHGGz3buHEjtm7divv376O4uBilpaXc9DZ1dXX4+vrCw8MD3bp1g7u7O7y9vaGnpwcA6Nu3L/z9/XHgwAEMGTIE27dvh5ubG2/6JgA4OTlxf1dXV0erVq2Qk5NTq3hriqEqbdq04f5eUffRo0cwNzdHTk4OvLy8ePWdnZ0RGRmJ8vJySEpKwsfHB9u3b0doaCgYY9izZw+3WMrjx4/x4MEDjBkzBuPGjePaKCsrg4qKSpVx6OjoAACsra15ZY8ePeLi++uvv/DNN9/U6tnU5l6zsrLEvnf3oWbMmIGxY8ciNjYW7u7uGDRoEFq2bFll/eLiYpHpn+Xl5dixYwdv2vHw4cMxffp0LFy4EJKSkly5uNFDIyMjKCkpccc6OjqQlJTkLTLz/vMFgN9++w1Lly5FdnY2nj9/jrKyMrx58wavXr2CgoICcnJy0K9fP14/Tk5OSExMrMVTEZWTk4MmTZqgffv2XJmGhobYfwNycnJ4/fp1vfohhNQPJYCEkC9OUlIShg8f3thhkAaUmZmJtm3b1uvawsJCPH78GMbGxgCA+Ph4TJ8+HeHh4XBycoKSkhJWrVqFCxcucNdER0cjICAAiYmJ2Lt3L+bNm4cTJ06gQ4cOkJaWxogRIxAdHY3+/ftj9+7diIyMrFUsFUmohIQEGGO8c5Wnh1YXQ1Xen45Y0VfFMvuVk+CKsvcNGzYMs2fPxuXLl1FcXIwHDx5gyJAhvHa2bNnC+8EeAC9pqSqOymUV7dX3lzTV3Wt92qzNZxIWFoZhw4YhISEBx44dw4IFCxAXFyeSPFXQ1NREUVERrywpKQkPHz7E4MGDeeXl5eU4fvw4b7VTBQUFkTYrTzkVCARiyyqexf379+Hp6Qk/Pz8sXrwY6urqOHv2LMaMGcPdX+X7/lBVtSfuO/j06VNoaWk1aP+EkOpRAkgI+eJUjMTs3LkTFhYWjRsMaRDm5ub1vnbt2rWQkJDg9ixLTU1Fx44dMWnSJK7O+++9VbCzs4OdnR3mzJkDJycn7N69m0u+xo4dCysrK0RFReHt27fo37+/yPXp6elo3rw5gHcLYty+fZu7Dy0tLfz999+8H4izsrLqFENdWVpa4uzZs7yytLQ0mJmZcQmcvr4+unTpgl27dqG4uBju7u7cCJ6Ojg6aNWuGP/74g3snsCEoKSnByMgIp06dgpubW4O02aZNG5w6dQoLFy4Ue15aWhrl5eW8Mi0tLbx48YIbFQPEfyZmZmYwMzPD9OnTMXToUERHR1eZANrZ2SE7O5tXtm3bNgwZMgQhISG88uXLl2Pbtm0Nut0FAFy6dAllZWUIDw/nRgkr3netYGlpyXtnEIDIcV1YWlqirKwMFy5cQMeOHQG8+0XM7du3ef+f/ObNG+Tm5sLOzq7efRFC6o4SQELIF6fit/8WFhb1HjUin6cXL17g77//xtu3b3Hv3j3s3LkTW7duxbJly7hFRUxMTPDTTz8hKSkJxsbGiI2NRUZGBjdCeO/ePWzevBl9+vRB06ZNcevWLdy+fRsjR47k+rGwsECHDh0QHByM0aNHix1xWrRoETQ0NKCjo4OQkBBoampySairqyseP36MlStXYuDAgUhMTMSxY8egrKxc6xjqaubMmWjXrh0WL16MwYMH4/z58/jhhx94q3gC7xY6CQsLQ2lpKSIiInjnwsLCEBAQAGVlZfTs2RMlJSW4dOkSioqKMGPGjHrHFhYWBj8/P2hra6Nnz5548eIFzp07hylTptSrvTlz5sDa2hqTJk2Cn58fpKWl8dtvv2HQoEHQ1NSEkZERLly4gLy8PCgqKkJdXR3t27eHvLw85s6diylTpuDixYvcAjnAu+mcQUFBGDhwIIyNjfHnn38iIyMDAwYMqDIODw8P3gI5jx8/xq+//orDhw/DysqKV3fUqFH49ttv8fjx4wYdEWvZsiXKysqwfv169O7dG+fOncPGjRt5dQICAtCxY0esXLkSffv2xfHjx+s9/RMATE1N4eXlhXHjxmHTpk1QUlLC7Nmz0axZM9405PT0dMjIyPCmSxNC/nu0CighhJAvxvz586GnpwcTExOMGDECz549w6lTpxAcHMzV8fPzQ//+/TF48GC0b98ehYWFvNFAeXl5/P777xgwYADMzMwwfvx4+Pv7Y8KECby+xowZg9LSUm51xsqWL1+OqVOnwt7eHgUFBTh8+DCkpaUBvEsgo6Ki8OOPP8LGxgYXL15EYGBgnWOoi7Zt2yI+Ph5xcXGwsrLC/PnzsWjRIvj6+vLqDRo0CIWFhXj9+jWXsFYYO3Ystm7dipiYGFhbW8PFxQUxMTFc8lxfo0aNQmRkJKKiotC6dWv06tVLZMuAujAzM8Px48dx9epVODo6wsnJCYcOHUKTJu9+7x0YGAhJSUlYWlpCS0sL+fn5UFdXx86dO3H06FFYW1tjz549CAsL49qUlJREYWEhRo4cCTMzM3h7e6Nnz55VjjIC797ty87O5rYg+emnn6CgoCD2fceK7SViY2Prfd/i2NraYs2aNVixYgWsrKywa9cuLFu2jFenQ4cO2Lp1K9avXw9bW1scP34c8+bN+6B+o6OjYW9vj169esHJyQmMMRw9epQ3XXXPnj3w8fH5oPd7CSF1J2ANPfGbENLoLl++DHt7+w96b+pT7u9zi4d8mZYsWYK4uDje/n7Au/373NzcUFRUJLKvHPn6zJo1C8+ePcOmTZsaO5RPyuPHj2Fubo5Lly598C8QCCF1QyOAhBBCSB28fPkSGRkZWL9+PQICAho7HPKJCwkJgaGhocg7h1+7e/fuISoqipI/QhoBJYCEEEJIHfj7+6NTp05wcXGpcvonIRVUVFQwd+5ckZVSv3aOjo4iK6ESQj4OWgSGEEIIqYOYmBje4iCVubq6Nviy+oQQQkhDoQSQEPLBiouLAaDWm1z/1yriqIiLEEIIIYS8QwkgIeSD5eXlAcAnt/l6Xl4enJ2dGzsMQgghhJBPBiWAhJAP5uHhgZ07d8LIyEjsfmgfW05ODoYPH85tCE8IIYQQQt6hBJAQ8sE0NTXh4+PT2GGI+BSSUULqysjICNOmTcO0adMAAAKBAAcOHBDZk+9T5Ovri3///RcHDx6ssW5eXh6MjY1x5coV2Nra/uexAcCtW7fg4uKCO3fuQElJ6aP0+b6wsDAcPHgQWVlZH9TOkSNHEBoaiszMTEhI0Hp+hJC6of/XIIQQQuroypUrGDRoEHR0dCArKwszMzOMGzcOt2/fbvC+CgoK0LNnTwDvkiaBQPDBCcR/Ze3atdUukFNXMTExDbqXYkhICCZPngwlJSX4+vpCIBBU+6ehBQYG4tSpUx/cTq9evSAQCLB79+4GiIoQ8rWhBJAQQgipgyNHjqBDhw4oKSnBrl27kJOTg9jYWKioqCA0NLTe7ZaWloot19XVhYyMTL3b/RjKy8shFAqhoqLSoAlbQ/rzzz9x+PBhfPfddwDeJasFBQXcHwCIjo4WKautimdQHUVFRWhoaNTvBv7f27dvAQDfffcd1q9f/0FtEUK+TpQAEkIIIbX0+vVrfPfdd/D09MThw4fh7u4OY2NjtG/fHqtXr8amTZsAvEsGxowZA2NjY8jJyaFVq1ZYu3Ytry1fX1/07dsXy5YtQ9OmTWFmZia2T4FAwE2prNg0287ODgKBAK6urgAAoVCIRYsWQV9fHzIyMrC1tUViYmK19/LLL7/A2toacnJy0NDQgLu7O169eoUzZ85ASkoKf//9N6/+zJkz0aVLFwD/G5k7cuQILC0tISMjg/v373P3VEEoFGLFihUwMTGBjIwMmjdvjiVLlvDa/eOPP+Dm5gZ5eXnY2Njg/PnzAIDk5GR89913ePbsGTciFxYWBgDYuXMnHBwcoKSkBF1dXQwbNgyPHj2q9n7j4+NhY2MDfX19AO/259PV1eX+AICqqip3vHv3blhbW0NBQQEGBgaYNGkSXr58ybVX1TNITk6Go6MjFBQUoKqqCmdnZ9y/fx/Auymglae7RkdHw8LCArKysjA3N0dUVBR3rmLENz4+Hq6urpCVlcXOnTsBAH369MHFixfxxx9/VHvfhBBSGSWAhBBCSC0lJSXhyZMnmDVrltjzFaNfQqEQ+vr6iI+PR3Z2NubPn4+5c+ciPj6eV//UqVPIycnBiRMncOTIkRr7v3jxIgDg5MmTKCgowP79+wG8G80KDw/H6tWrce3aNXh4eKBPnz64c+eO2HYKCgowdOhQjB49Gjk5OUhOTkb//v3BGEOXLl3QokULxMbGcvXLysqwc+dObvQMeJcML1u2DFu3bsXNmzehra0t0s+cOXOwYsUKhIaGIjs7G7t374aOjg6vTkhICAIDA5GVlQUzMzMMHToUZWVl6NixIyIjI6GsrMyNyAUGBgJ4N1q6ePFiXL16FQcPHsS9e/fg6+tb7bM7c+YMHBwcanzGFSQkJLBu3TrcuHEDO3bswOnTp0U+98rPQF1dHX379oWLiwuuXbuG8+fPY/z48VVOJ92yZQtCQkKwZMkS5OTkYOnSpQgNDcWOHTt49YKDgxEQEICcnBx4eHgAAAwNDaGtrY3U1NRa3xMhhAAAGCHki5OZmckAsMzMzMYOpVF87fdP/jsrVqxgANjTp0/rfO2kSZPYgAEDuONRo0YxHR0dVlJSwqtnaGjIIiIiuGMA7MCBA4wxxu7du8cAsCtXrvCuadq0KVuyZAmvrF27dmzSpEliY6n4N5KXlyf2/IoVK5iFhQV3fPDgQaaoqMhevnzJGGMsOjqaAWBZWVm860aNGsW8vLwYY4w9f/6cycjIsC1btojto+Jetm7dypXdvHmTAWA5OTlcPyoqKmKvf9/FixcZAPbixYsq69jY2LBFixZVef795yxOfHw809DQ4I7FPYPCwkIGgCUnJ4ttY8GCBczGxoY7NjAwYLt37+bVWbx4MXNycmKM/e8ZRUZGim3Pzs6OhYWFVRkzIYSIQ6uAEkK+OJ/axvTk82Fubg55efkqzzPGat3Wxo0bsXXrVty/fx/FxcUoLS0Vmf5nbW0NaWnp+oYLAHj+/Dn++usvkT0vnZ2dcfXqVbHX2NjY4JtvvoG1tTU8PDzQvXt3DBw4EGpqagDeTU+dN28e0tPT0aFDB2zfvh3e3t5QUFDg2pCWlkabNm2qjCsnJwclJSX45ptvqo3//Tb09PQAAI8ePYK5uXmV11y5cgVhYWHIysrC06dPuXfv8vPzYWlpKfaa4uJiyMrKVhvL+3777TcsXboU2dnZeP78OcrKyvDmzRu8evWKew6Vn4G6ujp8fX3h4eGBbt26wd3dHd7e3tx9ve/x48d48OABxowZg3HjxnHlZWVlUFFR4dWtauRSTk4Or1+/rvU9EUIIQNtAEEK+QJ/qxvTk05eZmYm2bdtWeb7iPb3ff/8dTk5OVdaLj4/H9OnTER4eDicnJygpKWHVqlW4cOECr977CdWHqjzNkDFW5dRDSUlJnDhxAmlpaTh+/DjWr1+PkJAQXLhwAcbGxtDW1kbv3r0RHR2NFi1a4OjRo0hOTua1IScnV+1KmbXdhkVKSkrkHqpbTOXVq1fo3r07unfvjp07d0JLSwv5+fnw8PCociEd4N12NUVFRbWK6f79+/D09ISfnx8WL14MdXV1nD17FmPGjOEWYQHEP4Po6GgEBAQgMTERe/fuxbx583DixAl06NCBV6/iHrds2YL27dvzzklKSvKOq/qePH36FFpaWrW6J0IIqUAJICHki/OpbUxPPh/VjToBQPfu3aGpqYmVK1fiwIEDIuf//fdfqKqqIjU1FR07dsSkSZO4c7m5uR8cX8VoYXl5OVemrKyMpk2b4uzZs9wiLQCQlpYGR0fHKtsSCARwdnaGs7Mz5s+fD0NDQxw4cAAzZswAAIwdOxZDhgyBvr4+WrZsKTLCWBNTU1PIycnh1KlTGDt2bJ2urSAtLc27V+Bd8v3kyRMsX74cBgYGAIBLly7V2JadnR2ys7Nr1e+lS5dQVlaG8PBwbp+9yu9v1tSXnZ0d5syZAycnJ+zevVskAdTR0UGzZs3wxx9/1Gsf1Tdv3iA3Nxd2dnZ1vpYQ8nWjBJAQ8sX5VDemJ58/BQUFbN26FYMGDUKfPn0QEBAAExMTPHnyBPHx8cjPz0dcXBxMTEzw008/ISkpCcbGxoiNjUVGRga3imd9aWtrQ05ODomJidDX14esrCxUVFQQFBSEBQsWoGXLlrC1tUV0dDSysrKwa9cuse1cuHABp06dQvfu3aGtrY0LFy7g8ePHsLCw4Op4eHhARUUF33//PRYtWlTnWGVlZREcHIxZs2ZBWloazs7OePz4MW7evIkxY8bUqg0jIyO8fPkSp06dgo2NDeTl5dG8eXNIS0tj/fr18PPzw40bN7B48eIa2/Lw8MDYsWNRXl4uMsJWWcuWLVFWVob169ejd+/eOHfuHDZu3FhjH/fu3cPmzZvRp08fNG3aFLdu3cLt27cxcuRIsfXDwsIQEBAAZWVl9OzZEyUlJbh06RKKioq4RLwq6enpkJGRqXYkmhBCxKFVQAkhhJA68PLyQlpaGqSkpDBs2DCYm5tj6NChePbsGb7//nsAgJ+fH/r374/Bgwejffv2KCws5I0G1leTJk2wbt06bNq0CU2bNoWXlxcAICAgADNnzsTMmTNhbW2NxMREHD58GKampmLbUVZWxpkzZ+Dp6QkzMzPMmzcP4eHh3IbzwLtVMH19fVFeXl5lAlOT0NBQzJw5E/Pnz4eFhQUGDx5c43YN7+vYsSP8/PwwePBgaGlpYeXKldDS0kJMTAx+/vlnWFpaYvny5Vi9enWNbXl6ekJKSgonT56ssa6trS3WrFmDFStWwMrKCrt27cKyZctqvE5eXh6///47BgwYADMzM4wfPx7+/v6YMGGC2Ppjx47F1q1bERMTA2tra7i4uCAmJqZWvyjYs2cPfHx8qn1nlRBCxBGwurzRTgj5LFy+fBn29vY1vs9ECCHVGTduHP755x8cPny4sUNpEFFRUTh06BCSkpIaO5QP8vjxY5ibm+PSpUsfPKpMCPn60BRQQgghhPA8e/YMGRkZ2LVrFw4dOtTY4TSY8ePHo6ioCC9evICSklJjh1Nv9+7dQ1RUFCV/hJB6oRFAQr5ANAJICPkQrq6uuHjxIiZMmICIiIjGDocQQkgDohFAQgghhPBU3vKBEELIl4MWgSGEEEIIIYSQrwQlgIQQQgghhBDylaAEkBBCCCGEEEK+EpQAEkIIIeSLZ2RkhMjIyGrrhIWFwdbW9qPEQ2onLy8PAoEAWVlZAN69nyoQCPDvv/9We93p06dhbm4OoVD43wf5Can8vP5Lrq6umDZtGnf8+vVrDBgwAMrKyrX6jIDaf57VGThwINasWVPv679GlAASQggh5KMwMjKCQCCAQCCAvLw8rKyssGnTpo/Sd0ZGBsaPH88dCwQCHDx48KP0TRpOx44dUVBQABUVlWrrzZo1CyEhIZCQ+N+PuqWlpVi5ciVsbGwgLy8PTU1NODs7Izo6Gm/fvv2vQ28UFQnhf2H//v1YvHgxd7xjxw6kpqYiLS2tVp9RQ5k/fz6WLFmC58+ff5T+vgSUABJCCCHko1m0aBEKCgpw7do19O3bF35+fti7d6/YuqWlpQ3Wr5aWFuTl5RusPfLh6pN0SUtLQ1dXt9qkJi0tDXfu3MGgQYO4stLSUnh4eGD58uUYP3480tLScPHiRUyePBnr16/HzZs363UPH9OnlqSqq6vz9tPMzc2FhYUFrKysavyMGlKbNm1gZGSEXbt2fZT+vgSUABJCCCHko1FSUoKuri5MTEzw/fffw9TUlBuJc3V1hb+/P2bMmAFNTU1069YNAJCSkgJHR0fIyMhAT08Ps2fPRllZGddmxXX+/v5QVVWFhoYG5s2bh/e3On5/CqiRkREAoF+/fhAIBNxxhdjYWBgZGUFFRQVDhgzBixcvuHOMMaxcuRItWrSAnJwcbGxs8Msvv/Cuv3nzJr799lsoKytDSUkJnTt3Rm5uLhfr+9PmAKBv377w9fXljqOiomBqagpZWVno6Ohg4MCB1T7Tc+fOwcXFBfLy8lBTU4OHhweKiooAACUlJQgICIC2tjZkZWXRqVMnZGRk8K4/evQozMzMICcnBzc3N8TExPCm5YmbGhsZGSny3KKjo2FhYQFZWVmYm5sjKiqKO1cxEhUfHw9XV1fIyspi586dNV5XWW2mDMbFxaF79+6QlZXlxXvmzBmcOnUKkydPhq2tLVq0aIFhw4bhwoULMDU1/SKfV2X3799H7969oaamBgUFBbRu3RpHjx6tsn5138X3v8uurq4IDw/HmTNnIBAI4OrqCgDYuXMnHBwcuH/3w4YNw6NHj6rsr7i4GN9++y06dOiAp0+forCwEEOHDoW+vj7k5eVhbW2NPXv2iFzXp08fseVEPEoACSGEENJoZGVleSMbO3bsQJMmTXDu3Dls2rQJDx8+hKenJ9q1a4erV69iw4YN2LZtG77//nteOxXXXbhwAevWrUNERAS2bt0qts+KH+ijo6NRUFDA+wE/NzcXBw8exJEjR3DkyBGkpKRg+fLl3Pl58+YhOjoaGzZswM2bNzF9+nQMHz4cKSkpAICHDx+iS5cukJWVxenTp5GZmYnRo0fzEtbqXLp0CQEBAVi0aBFu3bqFxMREdOnSpcr6WVlZ+Oabb9C6dWucP38eZ8+eRe/evVFeXg7g3VTIffv2YceOHbh8+TJMTEzg4eGBp0+fAgAePHiA/v37w9PTE1lZWRg7dixmz55dq1jft2XLFoSEhGDJkiXIycnB0qVLERoaih07dvDqBQcHIyAgADk5OfDw8Kj1dXVx5swZODg48Mp27doFd3d32NnZidSXkpKCgoICgC//eU2ePBklJSU4c+YMrl+/jhUrVkBRUVFs3bp8F/fv349x48bByckJBQUF2L9/P4B3I6+LFy/G1atXcfDgQVbpb44AADzoSURBVNy7d4/3y473PXv2DN27d0dpaSlOnToFdXV1vHnzBvb29jhy5Ahu3LiB8ePHY8SIEbhw4QLvWkdHR1y8eBElJSW1eg5fPUYI+eJkZmYyACwzM7OxQyGEEI6hoSGLiIhgjDH29u1bFh0dzQCwqKgoxhhjLi4uzNbWlnfN3LlzWatWrZhQKOTKfvzxR6aoqMjKy8u56ywsLHh1goODmYWFhdi+GWMMADtw4ACvrwULFjB5eXn2/PlzriwoKIi1b9+eMcbYy5cvmaysLEtLS+NdN2bMGDZ06FDGGGNz5sxhxsbGrLS0VOwzcHFxYVOnTuWVeXl5sVGjRjHGGNu3bx9TVlbmxVCdoUOHMmdnZ7HnXr58yaSkpNiuXbu4stLSUta0aVO2cuVKLl5xzw4AKyoqYoy9ey42Nja8tiMiIpihoSF3bGBgwHbv3s2rs3jxYubk5MQYY+zevXsMAIuMjOTVqe11V65cYYwx9ttvv/FiE0dFRYX99NNPvDI5OTkWEBBQ5TWMfZnPqzJra2sWFhZW7XOoUNN3sfJ3eerUqczFxaXaNi9evMgAsBcvXjDG/vd5/v7778zGxob179+flZSUVNuGp6cnmzlzJq/s6tWrDADLy8ur+cYIa9IoWSchhJCv3pMnT5CUlAQjIyPIyck1djjkA5mbm9fqHbvg4GDMmzcPJSUlkJaWRlBQECZMmMCdrzxyk5OTAycnJ977RM7Oznj58iX+/PNPNG/eHADQoUMHXh0nJyeEh4ejvLwckpKStb4PIyMj3ntNenp63JS17OxsvHnzhpuaWqG0tJQbWcrKykLnzp0hJSVV6z7f161bNxgaGqJFixbo0aMHevTogX79+lX5bLOysnjvur0vNzcXb9++hbOzM1cmJSUFR0dH5OTkAHj3fMU9u7p4/PgxHjx4gDFjxmDcuHFceVlZmchCIO9/vnW5ri6Ki4t50z+Bd1N3a3on7Wt4XgEBAZg4cSKOHz8Od3d3DBgwAG3atBFbt67fRXGuXLmCsLAwZGVl4enTp9yqrPn5+bC0tOTqubu7o127doiPj+f9ey0vL8fy5cuxd+9ePHz4ECUlJSgpKeFGbCtU/Dfk9evXtY7ta0YJICGEkEaRlJSE4cOHN3YYpIFkZmaibdu2NdYLCgqCr68v5OXloaenJ/JDeeUf7MT94M7+/92+/2KRicqJm0Ag4H5orfjfhIQENGvWjFdPRkYGAGr8ZYaEhATv3USAv7iHkpISLl++jOTkZBw/fhzz589HWFgYMjIyoKqqKtJedf1V9Zzef6aVY6lPzBXPZcuWLWjfvj2vXuXk+/3Pty7X1YWmpib3DmQFMzMzLomrytfwvMaOHQsPDw8kJCTg+PHjWLZsGcLDwzFlyhSRunX9Llb26tUrdO/eHd27d8fOnTuhpaWF/Px8eHh4iCzw9O2332Lfvn3Izs6GtbU1Vx4eHo6IiAhERkbC2toaCgoKmDZtmsj1FVN0tbS0avUcvnaUABJCCGkUFQsi7Ny5ExYWFo0bDPlg5ubmtaqnqakJExOTWrdraWmJffv28X4IT0tLg5KSEi8JS09P512Xnp4OU1PTKn8wlpKS4t6Tq0ssMjIyyM/Ph4uLi9g6bdq0wY4dO/D27Vuxo4BaWlooKCjgjsvLy3Hjxg24ublxZU2aNIG7uzvc3d2xYMECqKqq4vTp0+jfv7/Y/k6dOoWFCxeKnDMxMYG0tDTOnj2LYcOGAXiXiFy6dIlbvMPS0lJkO4zKz1JLSwt///037zN4f585HR0dNGvWDH/88Qd8fHzEPhdx6ntdTezs7JCdnc0rGzZsGObOnYsrV66IvAdYVlaGkpKSr+Z5GRgYwM/PD35+fpgzZw62bNkiNgEE6vZdrOz333/HkydPsHz5chgYGAB4916hOMuXL4eioiK++eYbJCcnc6ODqamp8PLy4n5ZKBQKcefOHZH/Zty4cQP6+vrQ1NSs9XP4mlECSAghpFFUjFxYWFjUauSIfJ0mTZqEyMhITJkyBf7+/rh16xYWLFiAGTNm8PZ4e/DgAWbMmIEJEybg8uXLWL9+PcLDw6ts18jICKdOnYKzszNkZGSgpqZWYyxKSkoIDAzE9OnTIRQK0alTJzx//hxpaWlQVFTEqFGj4O/vj/Xr12PIkCGYM2cOVFRUkJ6eDkdHR7Rq1Qpdu3bFjBkzkJCQgJYtWyIiIoK3ouWRI0fwxx9/oEuXLlBTU8PRo0chFArRqlUrsTHNmTMH1tbWmDRpEvz8/CAtLY3ffvsNgwYNgqamJiZOnIigoCCoq6ujefPmWLlyJV6/fo0xY8YAAPz8/BAeHs49u8zMTMTExPD6cHV1xePHj7Fy5UoMHDgQiYmJOHbsGJSVlbk6YWFhCAgIgLKyMnr27ImSkhJcunQJRUVFmDFjRpXPtL7XVcfDw0NkUZRp06YhISEB33zzDRYvXoxOnTpBSUkJly5dwooVK7Bt2zbY2tp+8c9r2rRp6NmzJ8zMzFBUVITTp09X+Qu4un4XK2vevDmkpaWxfv16+Pn54caNG7x9AytbvXo1ysvL0bVrVyQnJ8Pc3BwmJibYt28f0tLSoKamhjVr1uDvv/8WiTk1NRXdu3evVVwEtAgMIV8iWgSGfA7oe/r1qbwQS2XiFkhhjLHk5GTWrl07Ji0tzXR1dVlwcDB7+/Yt77pJkyYxPz8/pqyszNTU1Njs2bN5C3VU7vvw4cPMxMSENWnShFucozaLdwiFQrZ27VrWqlUrJiUlxbS0tJiHhwdLSUnh6ly9epV1796dycvLMyUlJda5c2eWm5vLGHu3qMjEiROZuro609bWZsuWLeMtApOamspcXFyYmpoak5OTY23atGF79+6t9rkmJyezjh07MhkZGaaqqso8PDy4BUmKi4vZlClTmKamJpORkWHOzs7s4sWLvOt//fVXZmJiwmRkZFjnzp3Z9u3bRRZa2bBhAzMwMGAKCgps5MiRbMmSJbznwhhju3btYra2tkxaWpqpqamxLl26sP379zPGql+cpC7X1WYRmKdPnzI5OTn2+++/88rfvHnDli1bxqytrZmsrCxTV1dnzs7OLCYmhvs+fWnPqzJ/f3/WsmVLJiMjw7S0tNiIESPYkydPxNat6btYm0Vgdu/ezYyMjJiMjAxzcnJihw8frvHznDJlCtPT02O3bt1ihYWFzMvLiykqKjJtbW02b948NnLkSObl5cXVLy4uZsrKyuz8+fNi74OIEjBWi8nMhJDPyuXLl2Fvb1/rd3IIaQz0PSUNxdXVFba2ttw+f+TDJCcnw83NDUVFRbV61+tTNGvWLDx79gybNm36z/v6Ep7X5+zHH3/EoUOHcPz48cYO5bNB+wASQgghhJAvSkhICAwNDev8nif5/EhJSWH9+vWNHcZnhd4BJIQQQgghXxQVFRXMnTu3scMgH8H48eMbO4TPDiWAhBBCCPmsJScnN3YIXxRXV9dabXdA3qHnRT43lAASQkgNaMPy/0bFnlzFxcWNHAkhhBDy9aAEkBBCakAblv+38vLy4Ozs3NhhEEIIIV8FSgAJIaQGtGH5fyMnJwfDhw/nni8hhBBC/nuUABJCSA1ow/L/Fk2rJR9KIBDgwIED6Nu3b2OHUqWYmBhMmzaNt+n7l+ZT2g5hxIgRsLCwoIVgGlFgYCBKS0uxbt26xg6FVELbQBBCCCHkk2JkZASBQIC4uDiRc61bt4ZAIEBMTAxXVlBQgJ49e37ECIk4HTt2REFBAVRUVBo1jmvXriEhIQFTpkzhylxdXSEQCLB8+XKR+p6enhAIBAgLC/uIUdZdWFgYbG1tGzuMWps1axaio6Nx7969xg6FVEIJICGEEEI+OQYGBoiOjuaVpaen4++//4aCggKvXFdXFzIyMvXq5+3bt/WOkfzP27dvIS0tDV1dXQgEgkaN5YcffsCgQYOgpKTEKxf3nfrrr79w+vRp6OnpfcwQv2jl5eUQCoXQ1tZG9+7dsXHjxsYOiVRCCSAhhBBCPjk+Pj5ISUnBgwcPuLLt27fDx8cHTZrw32ARCAQ4ePAgd/znn39iyJAhUFdXh4KCAhwcHHDhwgUA/xtF2b59O1q0aAEZGRkwxpCfnw8vLy8oKipCWVkZ3t7e+Oeff3j9HD58GA4ODpCVlYWmpib69+/PnSstLcWsWbPQrFkzKCgooH379mK3pzh48CDMzMwgKyuLbt268e7P19dXZBrrtGnT4Orqyh3/8ssvsLa2hpycHDQ0NODu7o5Xr16JfYbJyckQCARISkqCnZ0d5OTk0LVrVzx69AjHjh2DhYUFlJWVMXToULx+/Zq7zsjICJGRkby2bG1teSNkAoEAGzduhJeXFxQUFPD9999z/VVMc42JiYGqqiqSkpJgYWEBRUVF9OjRAwUFBVw7rq6umDZtGq+vvn37wtfXlzuOioqCqakpZGVloaOjg4EDB4q9XwAQCoX4+eef0adPH5FzvXr1QmFhIc6dO8eVxcTEoHv37tDW1ubVLSoqwsiRI6GmpgZ5eXn07NkTd+7c4c6LG42LjIzkvdOcnJwMR0dHKCgoQFVVFc7Ozrh//z53/tdff4W9vT1kZWXRokULLFy4EGVlZVXeW00ePnyIwYMHQ01NDRoaGvDy8kJeXh53vuL7tXTpUujo6EBVVZXrMygoCOrq6tDX18f27dt59/D+ZwoAWVlZEAgEXNsVn/ORI0dgaWkJGRkZ7j779OmDPXv21PueyH+DEkBCCCGEfHJ0dHTg4eGBHTt2AABev36NvXv3YvTo0dVe9/LlS7i4uOCvv/7C4cOHcfXqVcyaNQtCoZCrc/fuXcTHx2Pfvn3IysoC8C7pePr0KVJSUnDixAnk5uZi8ODB3DUJCQno378/vv32W1y5cgWnTp2Cg4MDd/67777DuXPnEBcXh2vXrmHQoEHo0aMHL2l4/fo1lixZgh07duDcuXN4/vw5hgwZUutnUlBQgKFDh2L06NHIyclBcnIy+vfvX+MedGFhYfjhhx+QlpaGBw8ewNvbG5GRkdi9ezcSEhJw4sQJrF+/vtZxVFiwYAG8vLxw/fr1Kj+X169fY/Xq1YiNjcWZM2eQn5+PwMDAWvdx6dIlBAQEYNGiRbh16xYSExPRpUuXKutfu3YN//77L++zqSAtLQ0fHx/eKGBMTIzY2H19fXHp0iUcPnwY58+fB2MMnp6etR4xLisrQ9++feHi4oJr167h/PnzGD9+PDc6WrG6dEBAALKzs7Fp0ybExMRgyZIltWq/stevX8PNzQ2Kioo4c+YMzp49yyXcpaWlXL3Tp0/jr7/+wpkzZ7BmzRqEhYWhV69eUFNTw4ULF+Dn5wc/Pz/eLyZq2/+yZcuwdetW3Lx5k0uoHR0d8eDBA17iSz4BjBDyxcnMzGQAWGZmZmOH8kWg5/nfoOdKqmJoaMgiIiLYwYMHWcuWLZlQKGQ7duxgdnZ2jDHGVFRUWHR0NFcfADtw4ABjjLFNmzYxJSUlVlhYKLbtBQsWMCkpKfbo0SOu7Pjx40xSUpLl5+dzZTdv3mQA2MWLFxljjDk5OTEfHx+xbd69e5cJBAL28OFDXvk333zD5syZwxhjLDo6mgFg6enp3PmcnBwGgF24cIExxtioUaOYl5cXr42pU6cyFxcXxtj//s3k5eWJjaOy3377jQFgJ0+e5MqWLVvGALDc3FyubMKECczDw4M7rnj+77OxsWELFizgjgGwadOmie2vqKiId893797l6vz4449MR0eHO3ZxcWFTp07ltePl5cVGjRrFGGNs3759TFlZmT1//rxW93zgwAEmKSnJhEIhr7yin6tXrzIlJSX28uVLlpKSwrS1tVlpaSnv/m7fvs0AsHPnznHXP3nyhMnJybH4+HjG2LvvkY2NDa+PiIgIZmhoyBhjrLCwkAFgycnJYuPs3LkzW7p0Ka8sNjaW6enpVXlv4vqssG3bNtaqVSvefZeUlDA5OTmWlJTEGHv3/TI0NGTl5eVcnVatWrHOnTtzx2VlZUxBQYHt2bOHMSb6mTLG2JUrVxgAdu/ePcbY/z7nrKwskbiePXtW7XMgjYNWASWEENIoKjaAr9gQnnwdzM3NIS8vX6u63/5fe3ceVmPe/wH8fdq1KW2izdpCq5iJUZaoaVCWMBLJNkYYS2JMi2ow1GN7HjN4nqeaycTPkGvITibL2KJoahqyFGosWUvH1Ll/f7i6H8dpYxB6v67rXFf39/5u92LmfM73e3/vTz7B5MmTkZGRgf/+97/1jv4BT6enOTs7o0WLFrXmsbS0hJGRkbidl5cHc3NzmJubi2l2dnbQ09NDXl4eunbtiqysLEycOLHG+s6cOQNBENCxY0e5dKlUCgMDA3FbRUVFbmTKxsZGbKNbt271HpujoyP69u0Le3t7eHl5oX///hg2bBj09fXrLOfg4CD+bWJiAk1NTbRt21Yu7eTJk/W2/7yaRtmep6mpiXbt2onbpqamuHnzZoPb6NevHywtLdG2bVt4e3vD29sbgwcPrvUeevz4MdTV1Wt9DtHBwQEdOnTATz/9hPT0dAQGBkJVVVUuT15eHlRUVPDBBx+IaQYGBrC2tm7wf69atGiBoKAgeHl5oV+/fvD09MTw4cPFZw0zMzNx6tQpuRG/qqoqVFRUoLy8vMH/RqplZmbi4sWLCs89VlRUoKCgQNzu1KkTlJT+NwHQxMQEnTt3FreVlZVhYGDwQtcIeDq6+ux9Vq16lednpxhT42MASEREjaL6+ZHRo0c3bkfojcrMzGzw61RUVFQQGBiIyMhInDhxAqmpqfWWachrRZ5fREYQhBoDhmfT66pXJpNBWVkZmZmZUFZWltunra0tt11TO9VpSkpKCtM5n51yqKysjH379uHYsWPYu3cvVq9ejQULFuDEiRNo06ZNrf17NsCRSCQKAY9EIpGbIltfP6o9fx7ra7u6rWfrrq8tHR0dnDlzBocOHcLevXsRERGBqKgonDp1qsZXTRgaGqK8vBxPnjyBmppajX0KDg7Gv/71L+Tm5tYY+D7fn2fTG3qtACAhIQHTp0/H7t27sWnTJnz11VfYt28fPvzwQ8hkMixcuFDuOdJqGhoaNbZfF5lMhi5dumDDhg0K+579saOm61HX/VAdLD57rDXdC82aNavx3i4tLVXoAzU+BoBERNQovLy8kJycDCsrK74LsAmxsbF5ofzBwcGIi4sTF7eoj4ODA/7973+jtLS0zlHAZ9nZ2aGwsBBFRUXiKGBubi7u378PW1tbsd4DBw5g3LhxCuWdnZ1RVVWFmzdvomfPnrW2U1lZidOnT4ujffn5+bh37554ToyMjJCTkyNXJisrSyGA69GjB3r06IGIiAhYWloiNTUVs2bNatCxNoSRkZHcQi0PHjx4bUv5P99WVVUVcnJy0Lt3bzFNRUUFnp6e8PT0RGRkJPT09HDw4MEag6fqhVlyc3NrfWXCqFGjMGfOHDg6OsLOzk5hv52dHSorK3HixAl0794dAHDnzh388ccf4v1gZGSEkpISuaCw+nnSZzk7O8PZ2Rnz58+Hm5sbfvzxR3z44YdwcXFBfn4+2rdv36DzVB8XFxds2rQJxsbG0NXVfSV1Av8L3IqLi8V/fzUdZ21ycnKgqqqKTp06vbI+0d/HAJCIiBqFoaEhAgICGrsb9JaztbXF7du3Gzwl7tNPP8WiRYvg5+eHxYsXw9TUFGfPnkWrVq3g5uZWYxlPT084ODggICAAK1asQGVlJT7//HN4eHiI0xwjIyPRt29ftGvXDiNHjkRlZSV27dqFuXPnomPHjggICMCYMWMQHx8PZ2dn3L59GwcPHoS9vT18fHwAPB19mTZtGlatWgVVVVWEhITgww8/FAPCPn36YNmyZfj+++/h5uaG5ORk5OTkwNnZGQBw4sQJHDhwQFy18sSJE7h165YYlLwqffr0QWJiIgYOHAh9fX2Eh4crjGy+yrZmzZqFtLQ0tGvXDsuXL5dbcXLHjh24dOkS3N3doa+vj507d0Imk8Ha2rrG+oyMjODi4oIjR47UGgDq6+ujuLhYYeSrWocOHeDr64uJEydi7dq10NHRwbx589C6dWv4+voCeLp66a1bt7B06VIMGzYMu3fvxq5du8Tg6/Lly1i3bh0GDRqEVq1aIT8/H3/88QfGjBkDAIiIiMCAAQNgbm4Of39/KCkp4dy5czh//jxiY2NrPV+PHz9WCMC0tbUREBCAZcuWwdfXF9HR0TAzM0NhYSG2bt2K0NBQmJmZ1VpnXdq3bw9zc3NERUUhNjYWFy5cQHx8fIPLHz58GD179uSPfG8ZBoBE9Mbcvn0be/bseedGfKqf+ah+Zo2I3qxnn6Orj5qaGvbu3YvZs2fDx8cHlZWVsLOzw7/+9a9ay1S/RmLatGlwd3eHkpISvL295VbG7NWrFzZv3oyYmBgsWbIEurq6cqtRJiQkIDY2FrNnz8b169dhYGAANzc3MfgDnj4PFxYWhlGjRuHatWv46KOP5Jbc9/LyQnh4OObOnYuKigoEBwdjzJgxOH/+PABAV1cXGRkZWLFiBR48eABLS0vEx8fj448/bvD5aYj58+fj0qVLGDBgAJo3b46YmJjXNgIYHByM7OxsjBkzBioqKpg5c6bc6J+enh62bt2KqKgoVFRUoEOHDkhJSalzRGnSpElITExESEhIrXlqmj76rISEBMyYMQMDBgzAkydP4O7ujp07d4pBo62tLdasWYNFixYhJiYGQ4cOxZw5c7Bu3ToAT6/177//jqSkJNy5cwempqYICQnB5MmTATy91jt27EB0dDSWLl0KVVVV2NjYYMKECXX2648//hB/EKjm4eGBQ4cOISMjA2FhYRgyZAgePnyI1q1bo2/fvn9rRFBVVRUpKSmYMmUKHB0d0bVrV8TGxsLf379B5VNSUrBw4cKXbp9eD4lQ20RnInpnnTlzBl26dHmhZ23ehA0bNrzTz3slJydzxIqI6C1XUVEBa2trbNy4sdZRX3r90tLSEBoainPnzim8u5MaF68GEb0x1S/ITU5OfuVTll6nvLw8jB49Wu4Fv0RE9HbS0NDA999/j9u3bzd2V5q0srIyJCQkMPh7C/GKENEbUz3t09bW9q0amWyod2naKhFRU+bh4dHYXWjyhg8f3thdoFoo1Z+FiIiIiIiI3gcMAImIiIiIiJoIBoBERERERERNBANAIiIiIiKiJoIBIBEREdE7KioqqtYXnhMBQGBgIBYtWvTG2qt+r+ar1KtXL3zxxRevtM6azJkzB9OnT3/t7TQ2BoBEREREjczKygoSiUThs2TJkjrLzZkzBwcOHHhDvaR3zblz55CWloZp06aJabUFU4mJidDT03tznXsLzZ07FwkJCbh8+XJjd+W1YgBIRERE9BaIjo5GcXGx3OfZL+7PEgQBlZWV0NbWhoGBwRvu6fvhyZMnjd2F1+6f//wn/P39oaOj09hdeScYGxujf//++O677xq7K68VA0AiIiKit4COjg5atmwp99HS0gIAHDp0CBKJBHv27IGrqyvU1dVx+PBhhSmgQUFB8PPzQ1xcHExNTWFgYICpU6fir7/+EvMkJyfD1dVVbG/UqFG4efOmXF9+++03fPLJJ9DV1YWOjg569uyJgoICcX9CQgJsbW2hoaEBGxsbrFmzRtxX3dd79+6JaVlZWZBIJLhy5QoA4OrVqxg4cCD09fWhpaWFTp06YefOnbWemydPnmDu3Llo3bo1tLS08MEHH+DQoUPi/pqmwq5YsQJWVlYK52bx4sVo1aoVOnbsCAA4f/48+vTpg2bNmsHAwACTJk3Co0ePFMotXLgQxsbG0NXVxeTJk+UCyN27d+Ojjz6Cnp4eDAwMMGDAALnzBQDXr1/HiBEjoK+vDwMDA/j6+ornA6h5ZM7Pzw9BQUHi9po1a9ChQwdoaGjAxMQEw4YNq/WcyWQybN68GYMGDao1T322b9+OLl26QENDA23btsXChQtRWVkp7r9w4QLc3d2hoaEBOzs77Nu3T6GOY8eOwcnJCRoaGnB1dcW2bdsgkUiQlZUl5snNzYWPjw+0tbVhYmKCwMBA3L59W+F45s6dixYtWqBly5aIioqS219YWAhfX19oa2tDV1cXw4cPx59//imXJzY2FsbGxtDR0cGECRMwb948hftm0KBBSElJebkT9o5gAEhERET0jpg7dy4WL16MvLw8ODg41JgnPT0dBQUFSE9PR1JSEhITE5GYmCjuf/LkCWJiYpCdnY1t27bh8uXLckHG9evXxS/1Bw8eRGZmJoKDg8Uv/uvXr8eCBQvw9ddfIy8vD4sWLUJ4eDiSkpIafBxTp06FVCpFRkYGzp8/j2+++Qba2tq15h83bhyOHj2KjRs34ty5c/D394e3tzcuXLjQ4DYB4MCBA8jLy8O+ffuwY8cOlJeXw9vbG/r6+jh16hQ2b96M/fv3IyQkpMZy6enpSElJQWpqKhYuXCjuLysrw6xZs3Dq1CkcOHAASkpKGDx4MGQyGQCgvLwcvXv3hra2NjIyMnDkyBFoa2vD29u7wSORp0+fxvTp0xEdHY38/Hzs3r0b7u7uteY/d+4c7t27B1dX1xc6R9X27NmD0aNHY/r06cjNzcXatWuRmJiIr7/+GsDTgGzIkCFQVlbG8ePH8d133yEsLEyujocPH2LgwIGwt7fHmTNnEBMTo5CnuLgYHh4ecHJywunTp7F79278+eefCi+ST0pKgpaWFk6cOIGlS5ciOjpaDDgFQYCfnx9KS0vxyy+/YN++fSgoKMCIESPE8hs2bMDXX3+Nb775BpmZmbCwsMC3336rcNzdunVDUVERrl69+lLn7Z0gENF7JzMzUwAgZGZmNnZX5Lyt/arPu9pvInp3WFpaCmpqaoKWlpbcJz09XRAEQUhPTxcACNu2bZMrFxkZKTg6OorbY8eOFSwtLYXKykoxzd/fXxgxYkStbZ88eVIAIDx8+FAQBEGYP3++0KZNG+HJkyc15jc3Nxd+/PFHubSYmBjBzc1Nrq93794V9589e1YAIFy+fFkQBEGwt7cXoqKi6jwn1S5evChIJBLh+vXrcul9+/YV5s+fLwiC4nkQBEFYvny5YGlpKW6PHTtWMDExEaRSqZi2bt06QV9fX3j06JGYlpaWJigpKQklJSViuRYtWghlZWVinm+//VbQ1tYWqqqqauzzzZs3BQDC+fPnBUEQhP/85z+CtbW1IJPJxDxSqVRo1qyZsGfPHkEQBMHDw0OYMWOGXD2+vr7C2LFjBUEQhC1btgi6urrCgwcPajtVclJTUwVlZWW5NqvbUVVVVbjX1NXVhebNm4v5evbsKSxatEiu7A8//CCYmpoKgiAIe/bsEZSVlYWioiJx/65duwQAQmpqqnieDAwMhMePH4t51q9fLwAQzp49KwiCIISHhwv9+/eXa6eoqEgAIOTn54t9/uijj+TydO3aVQgLCxMEQRD27t0rKCsrC4WFheL+3377TQAgnDx5UhAEQfjggw+EqVOnytXRo0cPhfvm/v37AgDh0KFDwvtKpVGiTiKid8jjx48BAHl5eY3cEyJ6F9nY2EBTU7PefKGhoXIjcQDQunVrue2GjOZ06tQJysrK4rapqSnOnz8vbp89exZRUVHIyspCaWmpOEpVWFgIOzs7ZGVloWfPnlBVVVWo+9atWygqKsL48eMxceJEMb2yshLNmzevt2/Vpk+fjilTpmDv3r3w9PTE0KFDax3RPHPmDARBEKdsVpNKpS/8/KO9vT3U1NTE7by8PDg6OopTbQGgR48ekMlkyM/Ph4mJCQDA0dFR7hq6ubnh0aNHKCoqgqWlJQoKChAeHo7jx4/j9u3bcue0c+fOyMzMxMWLFxWexauoqFCYKlqbfv36wdLSEm3btoW3tze8vb0xePDgWu+tx48fQ11dHRKJRGFfQEAAFixYIJe2detWudVCMzMzcerUKXHEDwCqqqpQUVGB8vJy5OXlwcLCAmZmZnLn5Vn5+flwcHCAhoaGmNatWze5PJmZmUhPT69xBLigoEC87s/fH6ampuLU5by8PJibm8Pc3Fzcb2dnBz09PeTl5aFr167Iz8/H559/LldHt27dcPDgQbm0Zs2aAXg6avu+YgBIRFSP6mc0Ro8e3bgdIaJ3UmZmJlxcXOrNZ2hoiPbt29eZ59lApTbPB24SiUQMSMrKytC/f3/0798fycnJMDIyQmFhIby8vMSpiNVfgGtSXc/69evxwQcfyO2rDjqVlJ4+YSQIgrjv2WcQAWDChAnw8vJCWloa9u7di8WLFyM+Pr7GRW9kMhmUlZWRmZkpF9gCEIMGJSUlufZqahNQPH+CINQYIAGoNb2mPAMHDoS5uTnWr1+PVq1aQSaToXPnzuI5lclk6NKlCzZs2KBQh5GRUYOOQUdHB2fOnMGhQ4ewd+9eREREICoqCqdOnapx9U5DQ0OUl5fjyZMnckEvADRv3lzhXjM2NpbblslkWLhwIYYMGaJQt4aGhkJfnz0f1Wo6v8+Xk8lkGDhwIL755huF+kxNTcW/67qva7uOz6fX1xcAKC0tBfC/6/I+YgBI9B56W0esqvtT3b93hZeXF5KTk2FlZVXnFyMioprY2Ng0dhdEv//+O27fvo0lS5aIoyWnT5+Wy+Pg4ICkpCT89ddfCl+6TUxM0Lp1a1y6dAkBAQE1tlH9xbm4uBj6+voAILfgRzVzc3N89tln+OyzzzB//nysX7++xgDQ2dkZVVVVuHnzJnr27FlrmyUlJXJf+Gtq83l2dnZISkpCWVmZGBwePXoUSkpKciOO2dnZePz4sfj/gOPHj0NbWxtmZma4c+cO8vLysHbtWrF/R44ckWvHxcUFmzZtEheRqe0YiouLxe2qqirk5OSgd+/eYpqKigo8PT3h6emJyMhI6Onp4eDBgzUGadWLm+Tm5r7UuyJdXFyQn59f648SdnZ2KCwsxI0bN9CqVSsAwK+//iqXx8bGBhs2bIBUKoW6ujoAxfvNxcUFW7ZsgZWVFVRUXi40qe5LUVGReF/n5ubi/v37sLW1BQBYW1vj5MmTCAwMFMs93xcAyMnJgaqqKjp16vRSfXkXMAAkeg+97SNWV65cQY8ePRq7Gw1maGhY6xcdIqJX5eHDhygpKZFL09TUrDVgeBkWFhZQU1PD6tWr8dlnnyEnJwcxMTFyeUJCQrB69WqMHDkS8+fPR/PmzXH8+HF069YN1tbWiIqKwvTp06Grq4uPP/4YUqkUp0+fxt27dzFr1iy0b98e5ubmiIqKQmxsLC5cuID4+Hi5Nr744gt8/PHH6NixI+7evYuDBw+KX9Sf17FjRwQEBGDMmDGIj4+Hs7Mzbt++jYMHD8Le3h4+Pj7o1asXbt26haVLl2LYsGHYvXs3du3aVe+5CwgIQGRkJMaOHYuoqCjcunUL06ZNQ2BgoDj9E3i6cM748ePx1Vdf4erVq4iMjERISAiUlJTEVT3XrVsHU1NTFBYWYt68eQrtLFu2DL6+voiOjoaZmRkKCwuxdetWhIaGwszMDH369MGsWbOQlpaGdu3aYfny5XIrqe7YsQOXLl2Cu7s79PX1sXPnTshkMlhbW9d4bEZGRnBxccGRI0deKgCMiIjAgAEDYG5uDn9/fygpKeHcuXM4f/48YmNj4enpCWtra/G6PHjwQGFa6ahRo7BgwQJMmjQJ8+bNQ2FhIeLi4gD8bzRu6tSpWL9+PT799FOEhobC0NAQFy9exMaNG7F+/XqFUd+aeHp6wsHBAQEBAVixYgUqKyvx+eefw8PDQ5w2PW3aNEycOBGurq7o3r07Nm3ahHPnzqFt27ZydR0+fBg9e/Z8r3/wZQBI9B56W0es8vLyMHr0aLlluYmI6KmIiAhERETIpU2ePPmVvpPMyMgIiYmJ+PLLL7Fq1Sq4uLggLi5O7lUBBgYGOHjwIEJDQ+Hh4QFlZWU4OTmJP9xNmDABmpqaWLZsGebOnQstLS3Y29uLrzBQVVVFSkoKpkyZAkdHR3Tt2hWxsbHw9/cX26iqqsLUqVNx7do16OrqwtvbG8uXL6+13wkJCYiNjcXs2bNx/fp1GBgYwM3NDT4+PgAAW1tbrFmzBosWLUJMTAyGDh2KOXPmYN26dXWeD01NTezZswczZsxA165doampiaFDh+If//iHXL6+ffuiQ4cOcHd3h1QqxciRI8XXECgpKWHjxo2YPn06OnfuDGtra6xatQq9evWSaycjIwNhYWEYMmQIHj58iNatW6Nv375ikBocHIzs7GyMGTMGKioqmDlzptzon56eHrZu3YqoqChUVFSgQ4cOSElJqXOkatKkSUhMTFRY1bQhvLy8sGPHDkRHR2Pp0qVQVVWFjY0NJkyYIB53amoqxo8fj27dusHKygqrVq2Ct7e3WIeuri62b9+OKVOmwMnJCfb29oiIiMCoUaPE5wJbtWqFo0ePIiwsDF5eXpBKpbC0tIS3t7c4nbg+EokE27Ztw7Rp0+Du7g4lJSV4e3tj9erVYp6AgABcunQJc+bMQUVFBYYPH46goCCcPHlSrq6UlBS5FV7fRxKhpsmvRESvwZkzZ9ClS5cGPw9DRETU2IKCgnDv3j1s27atsbvywioqKmBtbY2NGzcqLNDSWDZs2IBx48bh/v37jf4jdb9+/dCyZUv88MMPAIC0tDSEhobi3LlzLz0d9V3w/h4ZEREREVETpqGhge+//17hpepv0vfff4+2bduidevWyM7ORlhYGIYPH/7Gg7/y8nJ899138PLygrKyMlJSUrB//365l9eXlZUhISHhvQ7+AAaARERERETvLQ8Pj0Ztv6SkBBERESgpKYGpqSn8/f3lXi3xpkgkEuzcuROxsbGQSqWwtrbGli1b4OnpKeZ5/uXz7ytOASWiN4ZTQImIiIgaV8OerCQiIiIiIqJ3HgNAIiIiIiKiJoIBIBERERERURPBAJCIiIgaXfV7vN5VgiBg0qRJaNGiBSQSCbKysmrN+y4d63/+8x/079//hcpYWVlhxYoVr7QfQUFB8PPze6V1NpRUKoWFhQUyMzMbpX2iV40BIBERURPzOr6gv43e5HHu3r0biYmJ2LFjB4qLi9G5c+da8xYXF+Pjjz9+I/36O6RSKSIiIhAeHi6mRUVFQSKRKHxsbGwasaevTlRUFJycnOTS1NXVMWfOHISFhTVOp4heMb4GgoiIiOhvKigogKmpKbp3715rnidPnkBNTQ0tW7Z8gz17eVu2bIG2tjZ69uwpl96pUyfs379fLu19f29aQEAAQkNDkZeXB1tb28buDtHfwhFAIiIikvPtt9+iXbt2UFNTg7W1NX744QdxX3BwMBwcHCCVSgEAf/31F7p06YKAgIAGlQeACxcuwN3dHRoaGrCzs5N7EXO18+fPo0+fPmjWrBkMDAwwadIkPHr0SNxfPSUwLi4OpqamMDAwwNSpU/HXX38BAHr16oWrV69i5syZ4igVANy5cweffvopzMzMoKmpCXt7e6SkpNR5PuorExQUhGnTpqGwsBASiQRWVlZiH0JCQjBr1iwYGhqiX79+ABSngF67dg0jR45EixYtoKWlBVdXV5w4cQLA08DS19cXJiYm0NbWRteuXRWCLysrKyxatAjBwcHQ0dGBhYUF1q1bJ5cnLCwMHTt2hKamJtq2bYvw8HDxXNVm48aNGDRokEK6iooKWrZsKfcxNDSstZ779+9j0qRJMDY2hq6uLvr06YPs7Gxxf/Wo29q1a2Fubg5NTU34+/vj3r17CnXVdr0BIDk5Ga6urtDR0UHLli0xatQo3Lx5U9x/6NAhSCQSHDhwAK6urtDU1ET37t2Rn58PAEhMTMTChQuRnZ0t3jOJiYkAAAMDA3Tv3r3ee4XoXcAAkIiIiESpqamYMWMGZs+ejZycHEyePBnjxo1Deno6AGDVqlUoKyvDvHnzAADh4eG4ffs21qxZ06DyMpkMQ4YMgbKyMo4fP47vvvtOYWpdeXk5vL29oa+vj1OnTmHz5s3Yv38/QkJC5PKlp6ejoKAA6enpSEpKQmJioviFfevWrTAzM0N0dDSKi4tRXFwMAKioqECXLl2wY8cO5OTkYNKkSQgMDBQDrprUV2blypWIjo6GmZkZiouLcerUKbFsUlISVFRUcPToUaxdu1ah7kePHsHDwwM3btzAzz//jOzsbMydOxcymUzc7+Pjg/379+Ps2bPw8vLCwIEDUVhYKFdPfHw8XF1dcfbsWXz++eeYMmUKfv/9d3G/jo4OEhMTkZubi5UrV2L9+vVYvnx5rccMAIcPH4arq2udeeojCAI++eQTlJSUYOfOneJ7YPv27YvS0lIx38WLF/F///d/2L59O3bv3o2srCxMnTpVrq66rjfwdIQ1JiYG2dnZ2LZtGy5fvoygoCCFPi1YsADx8fE4ffo0VFRUEBwcDAAYMWIEZs+ejU6dOon3zIgRI8Ry3bp1w+HDh//W+SB6KwhERG9IZmamAEDIzMxs7K4QNWmWlpbC8uXLa9zXvXt3YeLEiXJp/v7+go+Pj7h97NgxQVVVVQgPDxdUVFSEX375pcHl9+zZIygrKwtFRUXi/l27dgkAhNTUVEEQBGHdunWCvr6+8OjRIzFPWlqaoKSkJJSUlAiCIAhjx44VLC0thcrKSrl2RowY0aDjfJaPj48we/bsevPVVWb58uWCpaWlXB4PDw/ByclJoeyzx7p27VpBR0dHuHPnToPbtrOzE1avXi1uW1paCqNHjxa3ZTKZYGxsLHz77be11rF06VKhS5cute6/e/euAEDIyMiQS4+MjBSUlJQELS0tuc/48ePl+lN93g8cOCDo6uoKFRUVcvW0a9dOWLt2rVhnTfeEkpKSUFxcLAhCw673806ePCkAEB4+fCgIgiCkp6cLAIT9+/eLedLS0gQAwuPHj8W+ODo61ljfypUrBSsrq1rbI3pXvN8TtonorfL48WMAQF5eXiP3hOj9ZWNjA01NzZcun5eXh0mTJsml9ejRAytXrhS33dzcMGfOHMTExCAsLAzu7u4NLp+XlwcLCwuYmZnJ1fd8HxwdHaGlpSVXh0wmQ35+PkxMTAA8fRZNWVlZzGNqaorz58/XeXxVVVVYsmQJNm3ahOvXr0MqlUIqlcq19SrKVKtvBC0rKwvOzs5o0aJFjfvLysqwcOFC7NixAzdu3EBlZSUeP36sMALo4OAg/i2RSNCyZUu56Y8//fQTVqxYgYsXL+LRo0eorKyErq5urf2q/u+1hoaGwj5ra2v8/PPPcmk6Ojo11pOZmYlHjx7BwMBAof6CggJxu6Z7ovp6Vz8zWd/1Pnv2LKKiopCVlYXS0lJxFLWwsBB2dnZivmfPlampKQDg5s2bsLCwqPEYqjVr1gzl5eV15iF6FzAAJKI35sqVKwCA0aNHN25HiN5j1VPs/o7q5+WqCYIglyaTyXD06FEoKyvjwoULL1ReEIQXbq+2vKqqqgr7qr/01yY+Ph7Lly/HihUrYG9vDy0tLXzxxRd48uTJKy1Trb4gsVmzZnXuDw0NxZ49exAXF4f27dujWbNmGDZsmELbdZ2L48ePY+TIkVi4cCG8vLzQvHlzbNy4EfHx8bW2a2BgAIlEgrt37yrsU1NTQ/v27evsdzWZTAZTU1McOnRIYZ+enl6t5aqvc0Ovd1lZGfr374/+/fsjOTkZRkZGKCwshJeXV53nqrr++u4bACgtLYWRkVG9+YjedgwAieiN8fLyQnJyMqysrOr90kNEL+fvLsdva2uLI0eOYMyYMWLasWPH5FY+XLZsGfLy8vDLL7/Ay8sLCQkJGDduXIPK29nZobCwEDdu3ECrVq0AAL/++qtcH+zs7JCUlISysjIxgDp69CiUlJTQsWPHBh+Lmpoaqqqq5NIOHz4MX19f8YcomUyGCxcu1Lmy48uUaSgHBwf8+9//RmlpaY2jgIcPH0ZQUBAGDx4M4OkzgdU/pjXU0aNHYWlpiQULFohpV69erbOMmpoa7OzskJub+8LvAXyWi4sLSkpKoKKiIi6OU5Oa7okXud6///47bt++jSVLlsDc3BwAcPr06Rfub033TLWcnBw4Ozu/cJ1EbxsGgET0xhgaGsqtFEhEjef69esKLyu3sLBAaGgohg8fLi7UsX37dmzdulVceTIrKwsRERH46aefxKmdM2bMgIeHB9q2bVtveU9PT1hbW2PMmDGIj4/HgwcP5AIT4OmS+5GRkRg7diyioqJw69YtTJs2DYGBgeL0z4awsrJCRkYGRo4cCXV1dRgaGqJ9+/bYsmULjh07Bn19ffzjH/9ASUlJncHcy5RpqE8//RSLFi2Cn58fFi9eDFNTU5w9exatWrWCm5sb2rdvj61bt2LgwIGQSCQIDw9v0GjV8/0vLCzExo0b0bVrV6SlpSE1NbXecl5eXjhy5Ai++OILufTKykqUlJTIpUkkkhqvjaenJ9zc3ODn54dvvvkG1tbWuHHjBnbu3Ak/Pz9xiqyGhgbGjh2LuLg4PHjwANOnT8fw4cMb/MoMCwsLqKmpYfXq1fjss8+Qk5ODmJiYBpV9lpWVFS5fvoysrCyYmZlBR0cH6urqAJ4G4y9TJ9HbhquAEhERNUFxcXFwdnaW+/z888/w8/PDypUrsWzZMnTq1Alr165FQkICevXqhYqKCgQEBCAoKAgDBw4EAIwfPx6enp4IDAxEVVVVneUBQElJCampqZBKpejWrRsmTJiAr7/+Wq5vmpqa2LNnD0pLS9G1a1cMGzYMffv2xT//+c8XOsbo6GhcuXIF7dq1E6fuhYeHw8XFBV5eXujVqxdatmwJPz+/Out5mTINpaamhr1798LY2Bg+Pj6wt7fHkiVLxGfdli9fDn19fXTv3h0DBw6El5fXC0/x9fX1xcyZMxESEgInJyccO3ZM7uXutZk4cSJ27tyJ+/fvy6X/9ttvMDU1lftYWlrWWIdEIsHOnTvh7u6O4OBgdOzYESNHjsSVK1fkAsb27dtjyJAh8PHxQf/+/dG5c2dxZdmGMDIyQmJiIjZv3gw7OzssWbIEcXFxDS5fbejQofD29kbv3r1hZGQkvvbh119/xf379zFs2LAXrpPobSMRapqMT0RERERN3vDhw+Hs7Iz58+e/tjaioqKwbds2hRHpt4m/vz+cnZ3x5ZdfNnZXiP42jgASERERUY2WLVsGbW3txu5Go5JKpXB0dMTMmTMbuytErwQDQHojLly4gO7du6Njx47o1q0bcnNzFfJcuXIFvXr1QvPmzWtcNjsuLg6dO3eGk5MTPvzwQ/FFu7m5uXBychI/VlZWtS6nTURERA1naWmJadOmNXY3GpW6ujq++uorLl5G7w1OAaU3ok+fPhgzZgyCgoLw008/IT4+XmHVt9LSUuTm5uL+/fuIjIyUW70rOzsbgwYNwm+//QZtbW0kJydj1apVOHnypEJbISEhkEgkWL169Ws/LiIiIiKidwlHAOm1u3nzJs6cOSMunz106FBcvnxZYRnrFi1a4KOPPqr1nUl//fUXysrKAAD37t2Te2FsNalUih9//BHjx49/tQdBRERERPQe4Gsg6LUrKipCq1atoKLy9HaTSCSwsLBAYWFhne8EepajoyNmzZqFNm3aoEWLFlBXV0dGRoZCvq1bt6JNmzZwcnJ6hUdARERERPR+4AggvRESiURu+0VnHl+9ehU///wzCgoKcO3aNcycObPG98n997//5egfEREREVEtGADSa2dubo5r166hsrISwNPgr6ioCBYWFg2uY/PmzejcuTNMTU0BAOPGjUNGRgaqqqrEPFevXsWxY8cwatSoV3sARERERETvCQaA9NoZGxvD2dkZycnJAIAtW7bAysqqwdM/AaBt27Y4cuQIHj16BADYvn07bG1txRflAkBCQgIGDx4MPT29V9l9IiIiIqL3BlcBpTciPz8fQUFBuHPnDnR1dZGUlIROnTphwoQJGDRoEAYNGgSpVIp27dpBKpXi/v37MDY2RmBgIBYvXgxBEPDll18iNTUV6urq0NHRwerVq+Hs7Azg6ahimzZtkJCQgN69ezfy0RIRERERvZ0YADYyQRBQXl4OANDU1FR4Vo7oWbxfiIiIiOjv4CqgjaysrAw6OjoAgD///LPWVyAQAU/vFxMTEwDAw4cPoa2t3cg9IiIiIqJ3CQPARlY9mgNA/GJP1BDl5eUMAImIiIjohXARGCIiIiIioiaCI4CNTFNTU/ybU0CpPs9OAX323iEiIiIiaggGgI3s2UU8tLS0GABSg3EBGCIiIiJ6UVwFtJFxVUd6EbxfiIiIiOjvYABIRERERETURHARGCIiIiIioiaCASAREREREVETwQCQiIiIiIioiWAASERERERE1EQwACQiIiIiImoiGAASERERERE1EQwAiYiIiIiImggGgERERERERE0EA0AiIiIiIqImggEgERERERFRE8EAkIiIiIiIqIlgAEhERERERNREMAAkIiIiIiJqIhgAEhERERERNREMAImIiIiIiJoIBoBERERERERNBANAIiIiIiKiJoIBIBERERERURPBAJCIiIiIiKiJYABIRERERETURDAAJCIiIiIiaiIYABIRERERETURDACJiIiIiIiaCAaARERERERETQQDQCIiIiIioiaCASAREREREVETwQCQiIiIiIioiWAASERERERE1EQwACQiIiIiImoiGAASERERERE1EQwAiYiIiIiImggGgERERERERE0EA0AiIiIiIqImggEgERERERFRE8EAkIiIiIiIqIlgAEhERERERNREMAAkIiIiIiJqIhgAEhERERERNREMAImIiIiIiJoIBoBERERERERNBANAIiIiIiKiJoIBIBERERERURPx/+fFHEmPBJoRAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make the figure\n",
+    "fig = plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "# Add an ax\n",
+    "subp = fig.add_subplot(111)\n",
+    "\n",
+    "\n",
+    "# Draw the tree\n",
+    "tree3 = plot_phylo.plot_phylo(tree=\"MT_CO2_gene_tree_small.nh\",\n",
+    "                              ax=subp,\n",
+    "                              show_support=False,\n",
+    "                              label_dict=labels,\n",
+    "                              col_dict=colours,\n",
+    "                              outgroup='ENSPCIP00000054971_Pcin')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "5ff0cff4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Mesocricetus auratus (Golden Hamster)': 'ENSMAUP00000000004_Maur',\n",
+       " 'Cricetulus griseus (Chinese hamster PICR)': 'ENSCGRP00001000004_Cgch',\n",
+       " 'Microtus ochrogaster (Prairie vole)': 'ENSMOCP00000000004_Moch',\n",
+       " 'Mus musculus (Mouse)': 'ENSMUSP00000080994_Mmus',\n",
+       " 'Rattus norvegicus (Rat)': 'ENSRNOP00000046414_Rnor',\n",
+       " 'Jaculus jaculus (Lesser Egyptian jerboa)': 'ENSJJAP00000000004_Jjac',\n",
+       " 'Oryctolagus cuniculus (Rabbit)': 'ENSOCUP00000026182_Ocun',\n",
+       " 'Tupaia belangeri (Tree Shrew)': 'ENSTBEP00000015467_Tbel',\n",
+       " 'Cavia porcellus (Guinea Pig)': 'ENSCPOP00000014211_Cpor',\n",
+       " 'Heterocephalus glaber (Naked mole-rat)': 'ENSHGLP00000000004_Hgfe',\n",
+       " 'Chinchilla lanigera (Long-tailed chinchilla)': 'ENSCLAP00000000004_Clan',\n",
+       " 'Ochotona princeps (Pika)': 'ENSOPRP00000016374_Opri',\n",
+       " 'Procavia capensis (Hyrax)': 'ENSPCAP00000016135_Pcap',\n",
+       " 'Echinops telfairi (Lesser hedgehog tenrec)': 'ENSETEP00000016586_Etel',\n",
+       " 'Ictidomys tridecemlineatus (Squirrel)': 'ENSSTOP00000032551_Itri',\n",
+       " 'Delphinapterus leucas (Beluga whale)': 'ENSDLEP00000000005_Dleu',\n",
+       " 'Tursiops truncatus (Dolphin)': 'ENSTTRP00000016617_Ttru',\n",
+       " 'Vicugna pacos (Alpaca)': 'ENSVPAP00000011812_Vpac',\n",
+       " 'Ovis aries (Sheep - East friesian)': 'ENSOARP00020000005_Oara',\n",
+       " 'Capra hircus (Goat - Saanen Dairy)': 'ENSCHIP00000000004_Chir',\n",
+       " 'Bos mutus (Wild yak)': 'ENSBMUP00000000005_Bmut',\n",
+       " 'Bos taurus (Cow)': 'ENSBTAP00000053151_Btau',\n",
+       " 'Bison bison bison (American bison)': 'ENSBBBP00000000005_Bbbi',\n",
+       " 'Sus scrofa (Pig - euw1 (european wild boar))': 'ENSSSCP00000019138_Sscr',\n",
+       " 'Felis catus (Cat)': 'ENSFCAP00000025712_Fcat',\n",
+       " 'Panthera pardus (Leopard)': 'ENSPPRP00000000004_Ppar',\n",
+       " 'Neovison vison (American mink)': 'ENSNVIP00000032495_Nvis',\n",
+       " 'Ursus maritimus (Polar bear)': 'ENSUMAP00000000004_Umar',\n",
+       " 'Equus caballus (Horse)': 'ENSECAP00000023106_Ecab',\n",
+       " 'Dasypus novemcinctus (Armadillo)': 'ENSDNOP00000033880_Dnov',\n",
+       " 'Carlito syrichta (Tarsier)': 'ENSTSYP00000013699_Csyr',\n",
+       " \"Propithecus coquereli (Coquerel's sifaka)\": 'ENSPCOP00000000004_Pcoq',\n",
+       " 'Microcebus murinus (Mouse Lemur)': 'ENSMICP00000040078_Mmur',\n",
+       " 'Erinaceus europaeus (Hedgehog)': 'ENSEEUP00000014597_Eeur',\n",
+       " 'Loxodonta africana (Elephant)': 'ENSLAFP00000029494_Lafr',\n",
+       " 'Monodelphis domestica (Opossum)': 'ENSMODP00000026934_Mdom',\n",
+       " 'Sarcophilus harrisii (Tasmanian devil)': 'ENSSHAP00000022395_Shar',\n",
+       " 'Vombatus ursinus (Common wombat)': 'ENSVURP00010032881_Vurs',\n",
+       " 'Phascolarctos cinereus (Koala)': 'ENSPCIP00000054971_Pcin'}"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3bb17b5",
+   "metadata": {},
+   "source": [
+    "## 4. Highlight rodent species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bca4c0a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ENSPCIP00000054971_Pcin"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial_blog/tutorial.md b/tutorial_blog/tutorial.md
new file mode 100644
index 0000000..dc2cfe5
--- /dev/null
+++ b/tutorial_blog/tutorial.md
@@ -0,0 +1,263 @@
+---
+layout: single
+title: "Pandera: A Statistical Data Validation Toolkit for Pandas"
+excerpt: "Pandera is a a flexible and expressive toolkit for performing statistical validation checks on pandas data structures that was recently accepted into the pyOpenSci ecosystem. Learn more about Pandera."
+author: "Niels Bantilan"
+permalink: /blog/pandera-python-pandas-dataframe-validation
+header:
+    overlay_image: images/pandas.jpg
+    overlay_filter: 0.6
+    caption: "Photo credit: [**Ann Batdorf, Smithsonian's National Zoo**](https://www.flickr.com/photos/nationalzoo/5371290900/in/photostream/)"
+categories:
+  - blog-post
+  - pandas
+  - data-validation
+  - dataframes
+  - highlight
+comments: true
+---
+
+Modern data engineering and analysis workflows will often involve using data
+manipulation libraries, which, in the Python universe, would be tools like
+[pandas](https://pandas.pydata.org/). One problem you may have encountered with
+this powerful data manipulation tool is that the dataframe can be an opaque
+object that's hard to reason about in terms of its contents, data types, and
+other properties.
+
+One tool that may help you with this problem is
+[pandera](https://pandera.readthedocs.io/en/latest/index.html), which was
+accepted by pyOpenSci as part of its ecosystem of packages on September 2019.
+Pandera provides a flexible and expressive data validation toolkit that helps
+users make statistical assertions about pandas data structures.
+
+## A Statistical Data Validation Toolkit for Pandas
+
+<img src="https://github.com/unionai-oss/pandera/tree/master/docs/source/_static/pandera-logo.png"
+width="250px" alt="Image showing pandera package logo.">
+
+To illustrate `pandera`'s capabilities let's use a small toy example. Suppose
+you're analyzing data for some insights in the context of a mission-critical
+project, where it’s vital to ensure the quality of the datasets that you're
+looking at.
+
+Each row in the dataset is uniquely identified by a `person_id`, and each
+column describes that person's `height_in_cm`s and `age_category`.
+
+
+```python
+import pandas as pd
+
+dataset = pd.DataFrame(
+    data={
+        "height_in_cm": [150, 145, 122, 176, 137, 151],
+        "age_category": ["20-30", "10-20", "10-20", "20-30", "10-20", "20-30"],
+    },
+    index=pd.Series([100, 101, 102, 103, 104, 105], name="person_id"),
+)
+print(dataset)
+```
+
+```
+           height_in_cm age_category
+person_id
+100                 150        20-30
+101                 145        10-20
+102                 122        10-20
+103                 176        20-30
+104                 137        10-20
+105                 151        20-30
+```
+
+You want to ensure that some columns have the correct data type, or that the
+dataset fulfills certain statistical properties. Pandera allows you to validate
+a DataFrame to ensure that these conditions are met. It allows you to spend
+less time worrying about the correctness of a DataFrame's data so you can make
+the right assumptions in analyzing it.
+
+
+### Column Presence and Type Checking
+
+The most basic type of schema is one that simply checks that specific columns
+exist with specific datatypes.
+
+```python
+import pandera as pa
+
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(pa.Int),
+        "age_category": pa.Column(pa.String),
+    },
+    index=pa.Index(pa.Int, name="person_id"),
+)
+
+schema(dataset)
+```
+
+The `schema` object is callable, so you can validate the dataset by passing
+it in as an argument to the `schema` call. If the dataframe passes schema
+validation, `schema` simply returns the dataframe.
+
+If not, it'll provide useful error messages:
+
+
+```python
+invalid_dataframe = pd.DataFrame({
+    "weight_in_kg": [44, 31, 55, 61, 55, 62],
+    "age_category": ["20-30", "10-20", "10-20", "20-30", "10-20", "20-30"],
+})
+
+schema(invalid_dataframe)
+```
+
+```
+SchemaError: column 'height_in_cm' not in dataframe
+   weight_in_kg age_category
+0            44        20-30
+1            31        10-20
+2            55        10-20
+3            61        20-30
+4            55        10-20
+```
+
+### Basic Statistical Checks
+
+If you want to make stricter assertions about the empirical properties of the
+dataset, we can supply the `checks` keyword argument to the
+[`Column`](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#column-validation)
+and [`Index`](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#index-validation)
+constructors with a [`Check`](https://pandera.readthedocs.io/en/latest/checks.html#)
+or list of `Check`s.
+
+```python
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(
+            pa.Int,
+            # height in centimeters should be between 100 and 300
+            checks=pa.Check(lambda s: (100 < s) & (s < 300)),
+        ),
+        "age_category": pa.Column(
+            pa.String,
+            # check allowable age categories
+            checks=pa.Check(lambda s: s.isin(["10-20", "20-30"]))
+        ),
+    },
+    index=pa.Index(
+        pa.Int,
+        name="person_id",
+        checks=[
+            # id is a positive integer
+            pa.Check(lambda s: s > 0),
+
+            # id is unique
+            pa.Check(lambda s: s.duplicated().sum() == 0),
+        ]
+    ),
+)
+
+schema(dataset)
+```
+
+A `Check` object specifies the exact implementation of how to validate a
+column or index. The first positional argument in its constructor is a callable
+with the signature:
+
+```
+Callable[ pd.Series, Union[ bool, pd.Series[bool] ] ]
+```
+
+Notice that the only constraint to the callable is that takes a `Series` as
+input and returns a boolean or a boolean Series. By design, checks have access
+to the entire pandas `Series` API to make assertions about the properties of a
+particular column or index.
+
+### Indexed Error Messages
+
+In cases where the `Check` returns a boolean `Series`, violations of the
+schema are reported by the index location of failure cases.
+
+```python
+invalid_data = pd.DataFrame(
+    data={
+        "height_in_cm": [91, 105, 87, 87],
+        "age_category": ["10-20", "10-20", "10-20", "10-20"]
+    },
+    index=pd.Series([200, 201, 202, 203], name="person_id")
+)
+
+schema(invalid_data)
+```
+
+```
+pandera.errors.SchemaError: <Schema Column: 'height_in_cm' type=int64> failed element-wise validator 0:
+<lambda>
+failure cases:
+               person_id  count
+failure_case
+87            [202, 203]      2
+91                 [200]      1
+```
+
+The error is reported as a stringified dataframe where the `failure_case` index
+enumerates instances of `height_in_cm` values that failed data validation, the
+`person_id` column is the index location of the failure case, and `count`
+column displays the number of instances of a particular failure case.
+
+
+### Statistical Hypothesis Tests
+
+What if we wanted to test the hypothesis that older people tend to be taller?
+We can achieve this with the [`Hypothesis`](https://pandera.readthedocs.io/en/latest/hypothesis.html)
+check:
+
+```python
+schema = pa.DataFrameSchema(
+    columns={
+        "height_in_cm": pa.Column(
+            # perform a one-sided two-sample t-test of
+            # the distribution of heights by age category,
+            # with an alpha value of 5%
+            checks=pa.Hypothesis.two_sample_ttest(
+                groupby="age_category",
+                sample1="20-30",
+                relationship="greater_than",
+                sample2="10-20",
+                alpha=0.05,
+                equal_var=True,
+            )
+        ),
+        "age_category": pa.Column(
+            pa.String,
+            checks=pa.Check(lambda s: s.isin(["10-20", "20-30"])),
+        )
+    }
+)
+
+schema(dataset)
+```
+
+## Validate your Pandas Dataframes Today!
+
+Whether you use this tool in Jupyter notebooks, one-off scripts, ETL
+pipeline code, or unit tests, `pandera` enables you to make pandas code more
+readable and robust by enforcing the deterministic and statistical properties
+of pandas data structures at runtime.
+
+Hopefully this post has given you a flavor of what `pandera` can do. It
+offers a few more features that you may find useful:
+
+- [Series schema validation](https://pandera.readthedocs.io/en/latest/series_schemas.html)
+- [Coercing column data types](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#coercing-types-on-columns)
+- [Multi-index validation](https://pandera.readthedocs.io/en/latest/dataframe_schemas.html#multiindex-validation)
+- [Vectorized vs. element-wise checks](https://pandera.readthedocs.io/en/latest/checks.html#vectorized-vs-element-wise-checks)
+- [Wide checks](https://pandera.readthedocs.io/en/latest/checks.html#wide-checks)
+- [Groupby Column Checks](https://pandera.readthedocs.io/en/latest/checks.html#column-check-groups)
+- [Check input/output decorators](https://pandera.readthedocs.io/en/latest/decorators.html)
+
+## What's Next?
+
+I'm actively developing this project and have some exciting features coming
+up soon, such as [built-in checks](https://github.com/pandera-dev/pandera/issues/74), [first-class Dask support](https://docs.dask.org/en/latest/dataframe.html),
+and [yaml schema specification](https://github.com/pandera-dev/pandera/issues/91). If you'd like to contribute to this
+project, you're welcome to head on over to the [github repo](https://github.com/pandera-dev/pandera)!