{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "27ef9ff2",
   "metadata": {},
   "source": [
    "# Morphology: Trees (Notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afd6e86",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Poirazi-Lab/DendroTweaks/blob/main/docs/source/tutorials/Morphology_trees.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d51c8808",
   "metadata": {},
   "source": [
    "This tutorial will guide you through creating a neuronal morphology representation step by step. First, we will read data from an SWC file using the `SWCReader` class. Next, we will use factory functions to create a `PointTree` i.e. the graph-tree representation of our morphological reconstruction. Then, we will split the point tree into sections to obtain a `SectionTree`. Finally, we will build a `SegmentTree` that divides our cell into computational segments for simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebfae05",
   "metadata": {},
   "source": [
    "<div style=\"\n",
    "  border-left: 6px solid #f39c12;\n",
    "  background-color: #2a2514;\n",
    "  color: lightgray;\n",
    "  padding: 12px 16px;\n",
    "  margin: 16px 0;\n",
    "  border-radius: 4px;\n",
    "  width: 95%;\n",
    "  font-family: 'Segoe UI', sans-serif;\n",
    "\">\n",
    "  <div style=\"font-weight: bold; color: #f1c40f; margin-bottom: 4px;\">Warning</div>\n",
    "  Note that for most use cases, you won't need to follow the manual morphology processing described below. You can simply use the <code>load_morphology</code> method of the <code>Model</code> class. Only follow the steps below if the method fails to read the morphology automatically or if you want to manually intervene in the process.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b2fc29",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install dendrotweaks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bab762",
   "metadata": {},
   "source": [
    "If you are using Google Colab, you might also need to restart the session as the installation downgraded some packages (numpy). You can do it manually or programmatically as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d89844c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.kill(os.getpid(), 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da45c1d7",
   "metadata": {},
   "source": [
    "Let's begin by importing the standard libraries and the dendrotweaks library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1af247be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b39e390e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.6'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dendrotweaks as dd\n",
    "dd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2fa1409",
   "metadata": {},
   "outputs": [],
   "source": [
    "dd.apply_dark_theme() # Set the theme for the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbab1947",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('examples', exist_ok=True)\n",
    "if not os.listdir('examples/'):\n",
    "    print(\"Downloading example data...\")\n",
    "    dd.download_example_data('examples')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8757ef",
   "metadata": {},
   "source": [
    "## Reading an SWC file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8cfe7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path to file: examples/Park_2019/morphology/Park_2019.swc\n"
     ]
    }
   ],
   "source": [
    "path_to_swc_file = os.path.join('examples', 'Park_2019', 'morphology', 'original.swc')\n",
    "print(f'Path to file: {path_to_swc_file}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d36e4dd",
   "metadata": {},
   "source": [
    "The first step in neuronal morphology analysis is reading the SWC file. DendroTweaks provides a straightforward method to load SWC data into a pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "660ac1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import SWCReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f58fd7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = SWCReader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44b55884",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = reader.read_file(path_to_swc_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aee78bd",
   "metadata": {},
   "source": [
    "For initial data exploration, you can use standard pandas operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93ec40f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Type</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Color</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>R</th>\n",
       "      <th>Parent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>soma</td>\n",
       "      <td>#E69F00</td>\n",
       "      <td>11.621167</td>\n",
       "      <td>104.434833</td>\n",
       "      <td>-6.694167</td>\n",
       "      <td>10.218962</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>soma</td>\n",
       "      <td>#E69F00</td>\n",
       "      <td>11.621167</td>\n",
       "      <td>94.215871</td>\n",
       "      <td>-6.694167</td>\n",
       "      <td>10.218962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>soma</td>\n",
       "      <td>#E69F00</td>\n",
       "      <td>11.621167</td>\n",
       "      <td>114.653796</td>\n",
       "      <td>-6.694167</td>\n",
       "      <td>10.218962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>axon</td>\n",
       "      <td>#F0E442</td>\n",
       "      <td>5.260000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>-9.100000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>axon</td>\n",
       "      <td>#F0E442</td>\n",
       "      <td>5.290000</td>\n",
       "      <td>91.490000</td>\n",
       "      <td>-9.100000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index  Type Domain    Color          X           Y         Z          R  \\\n",
       "0      1     1   soma  #E69F00  11.621167  104.434833 -6.694167  10.218962   \n",
       "1      2     1   soma  #E69F00  11.621167   94.215871 -6.694167  10.218962   \n",
       "2      3     1   soma  #E69F00  11.621167  114.653796 -6.694167  10.218962   \n",
       "3      4     2   axon  #F0E442   5.260000   92.000000 -9.100000   0.295000   \n",
       "4      5     2   axon  #F0E442   5.290000   91.490000 -9.100000   0.295000   \n",
       "\n",
       "   Parent  \n",
       "0      -1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1dd7562f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2214 entries, 0 to 2213\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Index   2214 non-null   int64  \n",
      " 1   Type    2214 non-null   int64  \n",
      " 2   Domain  2214 non-null   object \n",
      " 3   Color   2214 non-null   object \n",
      " 4   X       2214 non-null   float64\n",
      " 5   Y       2214 non-null   float64\n",
      " 6   Z       2214 non-null   float64\n",
      " 7   R       2214 non-null   float64\n",
      " 8   Parent  2214 non-null   int64  \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 155.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a801d5",
   "metadata": {},
   "source": [
    "## Creating a Point Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828bb076",
   "metadata": {},
   "source": [
    "While the DataFrame provides a convenient way to manipulate data, a tree structure is more suitable for morphological analysis. The `PointTree` class represents a tree structure of neuronal points, with each node containing spatial information. To create a point tree from the DataFrame, use the `create_point_tree` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7e06f0bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import create_point_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "116bd383",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_tree = create_point_tree(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "188de5dd",
   "metadata": {},
   "source": [
    "Once we have the point tree, we can perform various postprocessing steps to refine and standardize the morphological data. We will start by removing overlapping nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e54ed50",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_tree.remove_overlaps()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c6cbe9",
   "metadata": {},
   "source": [
    "### Soma notation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b4f81c",
   "metadata": {},
   "source": [
    "Next, we will check and standardize the soma notation. The soma can be represented in three ways in SWC files:\n",
    "\n",
    "- Three-point soma: soma represented by three points\n",
    "- One-point soma: soma represented by a single point\n",
    "- Contour: set of points defining the soma boundary\n",
    "\n",
    "NeuroMorpho.org considers the three-point soma notation the standard representation. For more details, see the [Soma format representation in NeuroMorpho.Org](https://neuromorpho.org/SomaFormat.html) \n",
    "\n",
    "To check the current soma notation we can use the soma_notation property:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c18d9434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3PS'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_tree.soma_notation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7baf3f4",
   "metadata": {},
   "source": [
    "We can change the soma notation to the three-point soma using the change_soma_notation method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9735014",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_tree.change_soma_notation('3PS')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b28521",
   "metadata": {},
   "source": [
    "### Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e01d90",
   "metadata": {},
   "source": [
    "We now want to make sure that the nodes are properly sorted. For this we will perform depth-first traversal of the tree and update each node’s index as we visit the node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5370a32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted PointTree(root=Point(idx=0), num_nodes=2214).\n"
     ]
    }
   ],
   "source": [
    "point_tree.sort(sort_children=True, force=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7c4c1e",
   "metadata": {},
   "source": [
    "Sorting the trees is performed based on ther topological structure only. That is tree graph traversal is performed depth-first and children are selected in a way that the child with a smaller subtree (in terms of the number of bifurcations in the subtree) is traversed first.\n",
    "This ensures deterministic traversal and indexing of a neruonal morhology."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3004e303",
   "metadata": {},
   "source": [
    "### Shifting and aligning the tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb70885f",
   "metadata": {},
   "source": [
    "We can also shift the tree to the soma center and align the apical dendrite with the vertical axis.\n",
    "\n",
    "The `align_apical_dendrite` method should be used only for cells with an apical dendrite. The round_coordinates method rounds the coordinates to the specified number of decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e49e009",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_tree.shift_coordinates_to_soma_center()\n",
    "point_tree.align_apical_dendrite()\n",
    "point_tree.round_coordinates(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5efd62",
   "metadata": {},
   "source": [
    "Let's now validate the tree structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d6c56ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import validate_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c666ab01",
   "metadata": {},
   "source": [
    "The method will automatically detect the tree type and run the required validation steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9eb864f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for unique node ids...\n",
      "Checking for unique root node...\n",
      "Checking for duplicate children...\n",
      "Checking tree connectivity...\n",
      "Checking for loops...\n",
      "Checking for bifurcations with more than 2 children...\n",
      "Checking for NaN values...\n",
      "Checking for bifurcations in the soma...\n",
      "Checking if the tree is sorted...\n",
      "***Validation complete.***\n"
     ]
    }
   ],
   "source": [
    "validate_tree(point_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b22de1",
   "metadata": {},
   "source": [
    "## Creating a Section Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "902db678",
   "metadata": {},
   "source": [
    "Now we are ready to create a section tree from the point tree. The create_section_tree function splits the point tree into sections based on the bifurcation points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "85fc4b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import create_section_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f4c563a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extended 44 nodes.\n",
      "Sorted PointTree(root=Point(idx=0), num_nodes=2258).\n"
     ]
    }
   ],
   "source": [
    "sec_tree = create_section_tree(point_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c753e772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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96c09///ld/4q33nycXbuvIqvfe0BhoaGeMtbbuIXfvGX+Pf/+A8A3vkrv8oDX/sKL3jBC/jGN77BK17xIzznOZfyMze8gRMnTvDYY9/m9tvfz2/91m/ygQ98ULIWlhXJ4NCw9Mp1S2A4CXjde+Cffgcz8cyaLvd2EwWYnlfHq4dl4EgvtLhFgqnJg1QqlTgQXWF0NP8McWn56W+Io1O3Xp/jwvpnwfhTvX2Vs6RUsuk0fuDTjiRX3KT8TnpALvJbr4BcHiYPisxTFAis3x4HqE89II8XScJ0y8TM/vpZL4Tx78tEb5c1qvWCX9noZJbmjpfRuOwV6KEN8s2xJ3qlguaTD+qe/s5vkqEa1wUU+q73djzB3WTSBpVdzPZBV44DBnyt477q0iEq1apkftO5JWtHmX2sgQ7VUEYvkmONNIonD6Ecl8IyX5uX75GdhuHhIQDK5QoAu3buJJVK8a//+m+d33nqqac4dOgQL3rhCwF40QtfyONPPMGJEyc6v/OlL/8Lw8PDXHbZc+Z9nFQqxeDgYM8/y/LDcVwZ3jFamps37IC3/xV4FQkQssOrqtx7rijlhKL0YaBS3AxX3yhZkMpRyBUwxtDwvKU90AXi1esYYyT4O1mWMBX+rDkdl8IHi/Bz/0s2JhMHSDqKYrFIbmCQ4eECxUKBpOugghaO0SR9DwViUZrOSctBJPmyZzf8/S/DXe/FcRwymbTItXT/PJKJ2bNbgono6+/KBpnoIjh5EGU0hXx+WV9QLPPjFzYz/ZK3Ur7hQ9Rf9Cb00AZUc4bMt+8jf+ghXEUs/9N9XkTnyl23ilNO4KPKY2TTKQr5PIX8MLl0ivT0cZutnEVP5UJD4CTxA40fBHFf9c/+qfRQKyX9rEvUjhLJDEUZ0yA9BBjIFaESZqevvlE2Fjogk04v+/d5xWQuu1FK8Qfvex8PfP3rPPnkkwBs2LCBZrNJrVbr+d0TJybYsGF953e6A0v5+YnOz+bj1371V3jXu27p91OwLAKO41IojlCuVDDjT8E//U7v0ApqRWfjFoNsLkej1cLsf1CCygfujF+v7DCBbiz1IS6IRrMB2bz0kp4sS9iagVIjzlxGmUavAtonmUzO8UN2HHfO9zoSMfVJnEymM+FrUDjT47jpVEfyJZfrmgpG4cxM4KTTMi0+c6L36/C+tNa4qYEzki+yLC2Rr3cQBjCJdRehX/52ggt3dX7HLY+RefJLpJ9+AOW3AMgPD5/0vOicSzMTuNnsnPPADnfNRUrKJToDUVe+Wobwdl4L3/osFLv6V/2uPuvZ7SjRcGjlCG463ffj7BbA19rEGdNHPg/7H5L1a3hDXFUqHUI5zoropV2RweUH3n87l132HK7/8Z9Y9Mf6yP/4U/7iL/9n5/+Dg4M89ujeRX9cy8KIhlbK5ZJkMHOFuKfQaMzkQVse7yJ+vcoy5BO9XsMboHqMltEEWX/ZyFucjqi0ZLSGxnQ8VbnnZgkoo8xg4QJpBdABHPueSA4s0FVkPqvSfvyuZWXR6ZFTKvTY3kx78hB8+f/Am95P6viTZJ78Eonj32G2zLU9LxbObP3PdCZDpVIJvdAHxF0o6l3125DM9lpUJpLx/89TO0q0TjWazcgGZJYT0sY4sKyNi6f7xDOAWvbl8IiVccXo4vbb/5BXv/rHeN1PvJ6jR492vj8+Pk46nWZ4eLgne7l+/TrGx090fuf5P/D8nvuLpsnHx8fnfbxWq0Wr1er307AsIo7jko0sIjdfJs3wW6+Q3V92GM+rkclkl2UT9FIQ9WBWKhVMvQrZIWked5OYoE2lWl10Yd5+oHVApVqVwBIAI0M4mVCuozQGSLY2ygwaDO12W/7GGCiN4boOg0N5e35YzhhjtHg9Q+zD3ZFEu5nMnl9hILE8nFNWOrN7EzVKZMDCjWHdN/JZTqTiLGTUu7rludJe4LjxZjJcFySY3CRfaw0JV9pRHIdCoX8BXc865biiHrH3Hmh5ccY08nuvHuvo5irlUFhBus3L+2oxi9tv/0Ouv+46fvL1P8XBg73+mY/s3Uur1eLlL/8hPvvZzwGw45JL2LZtGw89/DAADz38MP/9v/8a69atY2JiAoAf+eEfplar8Z3vfPf8PhnLonLKcm84Db1cp+yWgijALJcmMV5NFrdr3iaL257d1KoViiPrlvowT4oM8FREZHrH1eJMUj0mFxLdlmwSyr7nlr7TEyxkBuPsE4T9ehdgZsZhcHhpD3QVEPUmaq3DAZxB6Znu0v/k4/9dhuvqVfkjHUg/q+OG/ucJWRcSSaluKUU+X6DdahEsYjtKFBR7DU+OZcfV0vsdCeCjJdCNMqbVYyggFTTmbYdY7qyYI/3A+2/nDTfcwC/84i8xPT3Nhg3r2bBhPZlMBoCpqSk+8Yk9/P7v/S4vu+Yadl51FX/6p/+DBx98iG984xsAfPnL/8J3vvNd/uIv/l+e97zLecUrfoT3vOfd/PXf/K3NTq4yYk3HQBaQ9GBcHh/dBphVIbfTT5RypEQzehG84ufFR7ijF7q8XWI9ry5SIaMXSqa6ehR+8jaRZ0kNiAVeOm0DS0tfiPzmp6aqlMtl+dxccJmVj1pkGp6HDoJYUP6Sl8zV/8zlwZuS0jKEQ3EO+E3QmqRjSCZc0o4ilxtgZGSURCJJNjfA4OCwCMbnBhkcHCabG+hLQBc5f3mNBqDidap0SILcymHxbH/JGyWwnDwY+r0XGRzq33GcT1ZM5vLnfu7tAPzfT3+q5/u//M5f4c47PwnAbb/5W2ij+du//WtSqTRf/vKXeddvxBqWWmtuestb+eAH3889n/8c9XqdT37y/+P2299/3p6H5fxxyvJ4rgD1ih3wmYWjFLq7HynSC8VgjF5Wr1N3r1Wr1ZIBntJYLNvy6d+X6cqZSdxkktyA7WmznDsdRx0d9vS1WhIsvO49HXvQ7n49RykrH9Un/GQWtCO9iFFvYjSkF61X3pSoq9VOSEnZq0HQJpvNkc0trm1lEPjUqhW0MThKMZwvoNyE6GSCZCuPPNm7TnUL4Bc2Q+UobjLF8Aqf/Fej6zYs75TEMuNMTdstywNjxKHHwFzLw8wg1CskEwmGhq3EC8jiKJpvqme4Ba3J5rLLZhBK3tcqRod+u41pKXflN4pLTuECsV00hmwua72QLX3BGM1UrUpbG5GyiaZ6m9PSZ5ktwKf/AI5Lm9X5CGjWAsHQBuq7Xk/rxMFYn/bNH4ztV2vHO57rrqMYHhqi2Wh0hnwWu6RsjGZmeppmO2zBKW6VISFjpMXoW5+F9RfDpS+Dr90hwXHPOqVJJJO4jovrLv7xngtnGgOtmMylxbIQTjkNXRuHbJ62V6NcLpNJp9f8hcB1EyijMbgyDDO0Hn7qd+D//gHt6hGWi8FwfWZGyuBuQnrcmnXAhJuGIVmwMWQymWUTEFtWNj29lVEptnuq945bYrkYN0GxUFjTa0k/0Nk89ateR/PZL5PN4+bnwb57YLo0tzdx6niP7Nf5mr7vZLLbLVmPbvpw1zDXbnjsi3LdibKVI1vl63ROBoaUQ6E4uupadmxwaVn1dIZVymWZGp4dYO66DrPvPrEDbLVIJlwSbmJZ7x4XE8d1CYyCN/5hl87bUcwyGXY1RtNoeOKSE03lHnkSPvluMAEE0j/tuAlbCrf0hZ6BsV3XiWbifFO9EwdQSlGwgeVZ0T0BroBEZgDzohtoXvlaSIi+ZHJsH7lv/SNu0uCl0/K71SMkM+klqUzEckINjHJE4ig/a5iruAUmDoLfkp/dfZsExDoAr0Y2u3qrKja4tKwJeuV2KtKfFw34XPM2OPw45Ddh9j9Iy83Sak7jNZsU8mtPkiaVTOE1m7HdWGkMjCaV6r+I8ELouAYVN8cL+ebL5P+lMdL4K3K60rJ86QyMrdsu68XYt+XzsQqmepeaaNgFkN7D4mb88hF49IvwvFeTGH+K3Df/geT49zq/kxsYXNIiSs/UukHWSb/Zq58Z9qsrBY7rEFSPSyvW5KFwA7K6lStscLkGmS06u1YWwyjAnKpVaXs1yUBEcjulMemHqRyFTZdKNnPP7jU58JPN5Wi22+jAh9oJ2WUPFMmmlvrIhCAIJJtRPty7kFePkUgkGbSSL5Y+onWA53nxwFj1GLzpdvjKx+CRz9me7XMgyghjzBw/b/bcTOof3stguzZHdH4pMUZTq1Zlar3b/ef6d8Fn/miOGUO+UMRxnPCau3Y2IDa4XGPIjquGNjoWnW00SLouYESOxmiU46zK0rBSDkPDeemR2XcvHNwnF4viFumHeeBOeM4PyQI3sg0z8cyac/SJ+lQbnkc7laX9wp+Cq16L/vwf4E6fOP0dLDJBEEC0sO/ZDYUtIuWhNUPF4unvwGI5Q4LAp1IpAwqaM3N7Kx3XBpYLJBqOMkaLVFw0AQ4dP29VHkMto82inA+VXvefKJP9uQ9AfgOUj8LEARzHYThf6JhPrDUHJvuJWGN4Xl0Cy5s+DD/7Z3KrFO0goB1oWkbJrVbUGw1K5TLVSmlV6UEq5ZAfHiabTqHKYxD4Ih9y160ywLLr+liCJztMew1qoEYN8cMJSG65DDKDeFdet9SHBYAf+OK+oTU4SZg8INlVo1d1mclyftE6CANLZIBnZKu00qQHRTtXBxTyNrBcCNEQTNsPJCPcmk8f9PCy0geNNxqA48SOOlEm+8pXQ+UYyWSSkdF1FEfWLXtXs8Vk7T7zNUq71ZJd96wdIpOH4PnXS3p/57Xx7b778APpiXGUWDCjFKlkakVPVivlkMsNks3mpFwxfYKmDmSh+OS7Y8mi6nHMynyKfSO79//SXn8JzdJRTKNFQreXLKNtTGjTGPiw87XibpFIwt57UayeDZDl7JlPY/BcLu5TkY1wrgiVI3DD+2DsMTj+FDz1AFlrIbtgpERspDVp333xcGWPPqizbPRBpXwfBpbpARhcxxxHncoRHDfJ0ArXp+wXNrhcYxiA8pHeXrXKUblA++2wMbnrdnSbaIntf1B6TJJpCHy8ZhOv0SCbyZDNSWv1Suzj7JasUNNTNJoNyUpEE+UYUsnlMciyVCROPAUf/zWYnqRV3EyrNLZkw05evR5+ZWQDFA0cYXBde6Ffq8T6rA6MbEWXx6hUqxTy+QUHmL7flnUgkhq6+7aucrjTWfcsZ4cxmmarKVqQUUm5NCYZYa8Kk4dkinqZJC+iHkuMic+HymHZbBz4Fnz3K+Kok0gynLeBZYQNLtcYJ5sERhsJMEtjsOXy3ttLXyaZvFYD6mXJGD36BQkyGw25Px0gtlZboXIMr9nEwayoLGduYICW70sQHbTD5wTtdgutgzWbpWh4HjSrPf69SzHsZIyWwYqRLVCbkPeoNi5ZTGBouHBejsOy/KhVKxJYztIYrFUrFEfWnfX9GaPli8aUbK4j04WJZ8BAYWRk2a9ny5GOJmSg5ZrSMxz1eZLJ5LLpYdU6oFatSIbVaEhm4o1GbRzuem+csUykbGA5C/tKrDE6QV7gQzW8MBsjArV77wFUfLvvvnjQpXxYpF66M5vpAbGzUo78c8LbN/wBBgjSQwRG4TWbIj4cLdjLlGiQJZPJSPM+wMhWfAPlSoUgDGLWGoEOemV/omEnY2JZoPNA57Gq43N8w5VizQb/ax2tA5GEKW6dozGozdkb0EVDjx2qx2Stq1cAcBxlz7WzxBhNfWaacrkkwdqbbpdryB23yL999+Iu8dR9fIyTTE6MUy6XCbSW69rohXLdcxJx323QhsmDZFJJCjawnIN9NdYYSjkUC3my2QyJoIHrOCRch5SrSLoOKQcSjiOaXTqIB12cBLzwpyWTGWU4W3Xx6x7dJkGm48r3xx4Tq8VLXiLZLsdFB0E8GbiMUcoh8NuyoLz1T+A//b+doadKubyqBpvOFNdxY9kf6Bl2CoLz93oEQQDZYckifPr3pYRWL4PjkElnzttxWJYPHSkbiDUGoXOOOursRWy8ejj0+DO/D8qVwbHmtPQUGcgXrCLB2aB1QKlcxms2MQZJWGy+TALMq2+EmRKugvwS9ipGupVeo4E2QEf8SElgeek18i0dgJuStQdIpzMMDAzZwHIebFl8DRINs5xKhVbrgKmpGv7kIUBJKeBTvyuL7d57ASM7z0i+59KXyQ6/dkIa3suHuyR9ZMqy3axTrdWWdBE5EwKtobhtHqeFA5TLZVLJ5IqVa5LScp12q4WBM2pZyGSzcmHYs7vXn716jFZ4n+fj+WsdQHs6LkvVTnRK4rb/bW3SETcf2Sq947M0BofzhbO6v+jzwbqL4KKd8F/+N9z3ETj0KCgoFkds1vIs6AzCGHo1IaOe/+0vgK/dQTqdPq9raOSu02w1MVpjjAkrcEqSJIProDUjgWRpDH70F+F7X5WvHReUg+O6DAyuHYm6s2VlXBEt5x3Hccnni4yMriOXy5Gul0g4Csf4YrGntezioib3rVfIB685A0890CvpUxoT2YZd1xHo81tKXQiu48ybBSGZBQytRJa2NqFcU5NqrbasM7LGaOr1aSrlEqXJCbx6Hd8oAl/EoUvlyimPP2oXQGvpOUsPSHCX34RBnbf3U+cvEMvHqQmRL2nOgHJIJhMrJri39A8JBBux29br3hNueqTdJ51MnvUwj+eFA2PR0ONgEV7+n0EpHGXL4WeL59XFiz2dk43pNW+T68Udt8BH3wl7duM66pynwqU/sky5PEmteuoKkzGaciXMUmqDQQFKjjE1IMdZ3AzNuvRYDq2X65z8MbRmyGYzVobqNNjMpeWUdE9TR0S7e698RL4RtKW5uTtAqR6TBaRyVL4f2SweepRg+vh5fAZnz+DQsNiRdWdBlCvPM1eAZ71YJDTuuAV2vpZg371M1arLphG9m7mi+UfCAa5AdukA2qc+M8PA4NBJ78dxXFKpJC03K88/v1E2D3e+i2Bqcd9PA3hXXEfga/jaHXDVa3okiJKJ5KI+vmV50gkEI3Hzz31AznHdRrmJBWWVWq2WbGCMnjv0uIAS+1rGGE2j0RBN2ma9VxOyx93o7CtZ3S5zGGi22/L+FLehy2OUKxWKhcK8mwEJeIPe91MpSYAAlBrwk78plsDah+pxEUyfPBjaNtrs9Zlgg0vLWdOtEel5ddq1YxgFqVS6U570vDrN8hhaGwkGXv52WVjKh3HSy8RH8CQ4jkshn6dSLossUTIrgWV+Y5ix2xiX+/02FLfQnjxIuVJZdl7kDc+TYYdIE3LblTKoRbgRMAZQNBoemWz2lJmehJug1ZyRwLrLO7dlzKKVxg2K+otupPHcV0LLQ+39PGbffZJZKB/BdZePFp7l/NJqtUQ5wElIAJgZks8rhkJxYdPcRofKGUrJBiyyPzWGtO3rPSukZYFO6wo66NGEXKi7USw7peS+Jg9JyX2WUsD0VI3h/Nz+2FarJVnK7sLt4Cig5b50AJ/6PRheL5txQDWmyGSWjzzSSsAGl5YFc6rezSj4rFRr6G/fL7vAylEwBs9rYLQhNzCwbD+orpugODJCpVLB+E0ZJKkeE1mS7nL/lsvlee26HrPv3mXnRe4Hviy8j35Byj2lQ9Iz5KRlcTYGghb4rbA3ypAIpzZnB8nz9l4Ob8RUj1GtVkin0n3pP9U6YHqqRqANZvRCzLYfACD3yKfIpKARpAimj+OmUyuq39XSH6J+ucD3oXIM3vAHPeLmjuOcwwZPxbMcxsT/sH29Z0MnazmyRXoZJw9JW43rwuRBUA6FBayTHdckx5Vg8ulvwH98dF6lgKA8Nu/fB74PQb03c1lpxLqV3/l3KI2hKsfI5HJkszagXAg2uLQsGko5pJMJvEZDsnzP/RHpzbzrVhrNBs22T7Gw/ErJEY7jUiwWpfwSNGR4pXo8LPcfkYzJvvvikv/YtzETz/Q10DpXOpmYWbt6/KYs+iYcgU1m5HsofG0ol8sUi8Wei7RSDsmEK6XxTZfKwNau6+GOWwhmStSbTZrt9jkNbGktblBS4toqF6K/+QVyl7+M7JF9ME+bhmXt0NFJ9H1ASbn67tvi8rVSpFMLMz3QOuiVLnITHUmyTCaz5J/llUTD82RpqR4/idj4wtb96alQImr0QlnPHvm8VJaiHvmuiorryP1Hm5FGU5Iand1D93utg1i3snoMJ5Gy8kLniA0uLYuKNlqkil737vibI9tg4gBGB9Sq1QUvNOeD7p7TTp/P9Dhto9HtBjz/xyWwDEv+ZIcJdEC92cRrNkkm3CWdKHccNy7jl4/CZ26HIACMDGYpR4LkwJfBiERa5H0Cn6mpGvlZZaV5S+PVYyIpsv0FBHt2Uy6XyKQzCyohTU3VJLCcFQy3nvhXsvOUuCxrB2M0U7Wq6CTuuFrO58iC7xyVAzqSRkrFagQJcSNDKXIDdkNzNgQ6kHVfOXHwXzmKctxzWu8DrWWdKoXBZH6jtCw57hylgMGhfLxZjTLQChi9CDCSTXXCXnoDShmc6hFS6ZTNVvYBG1xaFpUejcSuXSW5Avgt/Ob0ipAngrmBZrlSwey7V+zLInme2jj84Jth+wswe3bTcrO0mtN4jQaZdPq89+y4rit9Q99/ED7zR3LxXHeR7PS1BlxZdPMbJUjctl2a12vjBJGQfBeZbJZmu02w52bxpK8ckQvHrushlYXiFszkIbwwi3m2E5WB78viP7vENXmwT6+IZSUSZywDyVpt3AFPPzyP3/fCMoySaTOw/mJ44x/BI5+TrNv3v04q8Jb92rTsyBWlXaj7/TnxzDlngF3HQUeuSXfcIplGjASIiZT03CpFoVDEGBP6gau4BSibP4lPfIbcgJUV6if2E2NZVDLZrBQh9uyGj/2qLAhD60XioVWH0QsJtF4RAuvdRPI8yhhZ0FI5uRhFgVboYiNC8h/GKAev0aBcLlOfmT5vzzWTzeI6Cj79vjgj+LN/KreOA6YttnbVYzIlGwnlN+sYY+ZIeijlkB8eJpdOkagelnLSK39JAsvIp37XdR3h/M5E7ymQstUM09NTGDc5vwyUndRd03heXbJWu66T4GDrFZKluvs2eOJfYP+DuInEgvsiAx0K9JfG5LPw4hvksZrTJBboTb6WCX74v8Xvz5P/CvsfOqf3J2JguABOUt6jVE7aZnRAJpMm7UIuN8DIyChAGFgi0mmpAQk+G9OxtNB3/h32PwhK2X7aRcB+aiyLShSElctl0UjMDksQRtjvcuk18L2vdqat08kkBoPruMuiZ/FUdPdktlrT+Eb3BlrdQvKj20QXcv9DeI0GLd8/L9naKBgsTU7M2/TOxIHQ1k5BfkSmJLsC3/kGlKIMbiablWzSXbfKfVWOyu01b5MBruo4jUb9lCWmTkZKGwnGTzwtQe+sEtdC++gsK594OGRbp7eZu28LS6I+qjxGJpM5p1Km67gSeOQ3yga4uAVKh1Bg1QjOktYFl+NvfyFc8Bwyd/w6po/Dd/6FPwAvugnu+RDOkcdxEwkGh4Z7esODwKdSLsmGNFfsuOlIWVyFPvGxskAmbftpFwP7iloWnSgIU0pBvRoPkoxug4ueH09bgwz6DKyn3mxSLpepVkpMTVXx6jPLMrMZBVrD+QJuIiFN4aE48Bwh+S6rzPOZrVXKwTmJMLzjOCRTKXlPykdCy89A+jDTg6f0D48C16SjJINw5atFw656TJ5vW5r6TyWyLlp1RmxC3/YRuPH9Uq4PAgksgwBle97WNJ3hkMrRWCfxyldDaYxkwqVYHCGXGzynAEFkuBzZ+KYHJfDQmnTSaqieDUYp6i98IwCZ/Q8w4BoGB4fJ5vqjDNK85KUwWCTz3B+iWBxlON87dNiZJgcpgTempIc2Gt4xRtY3vwWAchJ2bVkkbObScl6QAHMEz6vTaDQkbxlNekbZrrFvy6L+3FeEPYs34wcBqAStdmPJ+hbPhCjQangewdRx0X6MhOSrx2IXo8gqs3yY9uSh89ZvOpwviDbcPPZ4rWaTth/Arh+Pxcn33QdtD7JD+EHjlM97aDgvklP77oWD+3qF83VAMDMx798ao2m2WnI8UUZ182UwuhW3epREIoGbXh5T95alI9BBrGcZCZtPHuqce/04N6LPr+d5eH5TMlwjW2iUj9JeIT3hS0nHTtFJoe/5E9i2k8yhh/r6GDo9SHvrlQCkv//VeX+nM02ezEpgmd8kmcrscFihCTfbQYNU9tyy3ZZTY4NLy3mjW3x9qlalXRqTzF73tHWuINmDrlIyTz8MO6/F7LvvvJaUz5bZAz+eV6dRHguFhNu9VpkP3Cl2mPvupVqpoBwlskFKnZHf99niugkK+Ty1agU9eRBHKcm2ugkyWUf0K/fdGw7phMFhODXbMhqtg5NqB0rrwzCVcgk9eVAGLi69RrLSd92Kdub2SxqjqdRqIvA+e+Crcox0Om0lhyxAWLKerWfZh+GQ2SjlSH+434a3/HHnfAz23EzD8+z52EWknOEHPjoI8IPQwUi1JWP4zU9TU6qvcj7N7S8BJ4E78QyJ6pF5fycIwmnyxrRsRqrHpATuVZHBHofiyPLRIV7N2ODSct6JMg4nnbbOb4xLyZe+TBYIv90JNoP9Dy37xX62i1GjckSCTO33ZmsP7iOYKUG7IY3qgY8Xyhi5ir4GmyIMv67ne1HGwQS+SK9EU9m5gnh45zdB9dhJ3S66n28imaSllbxf3/86PPQP4CRQjun5Xa0DqpUyWrkymXv/X0pGKr8JKkdwHeu6Y4npKBTc9V7JYJYOoxbpHOlkSXt6kzcve8va80nHUlYHMgQYhK09jgtv/mAnKNd7dvd1nW5e8lIA0t//yrzH1HEjizKW1ePgpiRjqRRc9sPw5L9QKZdIJlO47vLv61/J2FfVsiTMP219TDJmj38pbqrfeoUEmYlk3LdY3CwXgRVAFGQWiyMkEy6UDvf2JlaPyYT2TR+W577ztbJIG0OQHiIwCq/ZpFLtf3+mXCSqeE0RTydoyW4/V5Ap9x98s2RwRrbKpO5pSLihXuaVr4aNz5bboI3RunPske6cNkbe382XyWtx9Y0wU8JVLMustGXpUMpheGhIsooTByEzgDGGam2q75+JHuk0CHuTj8j3LQB49broF++8VtasHVdLn+rItt6gvLClL+u01gGVukfwmffD3b9F8qnekng0FFhvtmSYEsKM5SD4DQksE1n43lcgmUErh+bQRurNFtVabVn28q8GbObSsmT0OODoBk4mjTGGZpTlC9pw162g3NgJJywpL3d/8tlE2dpqrUaw7z44+OhcjcjIq3xkq4hCX/ISkUO54xZ04Pc9W9vwPHQQSDCbzoobiQmgOT3HPzxyuzgVnQxTx/v7MLhJ2oFPtVZjeGiIaqUsw0M7rxdLyqgcvv0F8LU7SKfTNrC0zKHR8DAYeOuf9GTGPK8uFrR9IpPNUs8UQw3FzeG5r2wmPUTafTxYd2G4Vm2TDf/3vw6lxqz2lsO46YWrPBijqc/M0Gi1wsnvUTj8GFVjKBYKnTadjkxVZAf51T1yTM1pyWAe+x4ETQmGH/1Cj0GDbXlYPGxwaVlSuvsUI3K5AQk4ZyYIHIXvtwAlU35hSdl/yc9gvv4xyXyuELqHflrVw/h6lnRR5FVeGpPnGjnrjGyF2jjNVoNAB32TafIDH9I5KR3NlCX70JySicp53C7O9PlN1aq0Jw9JgBr20waf2C3ZV63lwhQNcEVCyGEAay/ilvlozx78Wr8dCptpV49APyUKE2n4mQ/CvntIfevTJNawf30nuGs2OjI+CgUYUZbYdiWUDsmGP7Lf3HOzfJ4rR3DUwj/PWgeyXgS+2HDOcuyK3MO0DvDqYbC7frsEk9/5dxj/vmwOxp+S4y1ujYNh2/JwXlh7nxjLsicKOAeHhsnni4yMjJLLZknPjJNafxG84X20L3050y/9OcwKE9eeV7ro739ZgjkU7L1XAsvhDb0yRs06gTY0BzeKTFOlMkfg/GzQOqDVakGzLj1JiaRMh+c3yuMHgbQrBD6FfP6kwzzzPT/HcaU/9hU/L4FzfhO4rigEXHCZSCJ1S8pMHsQx2pbDLSfFgAQ03eXqylH6vbUM8pshM4C66jUMZtN9k9BZaRijKZUrNJrN2CLWTYmLUTYv5fB998nP7ro1lDDToH3c6hGy6YV7c0dWnAZkwzuPPm/g+9RnpimXQj3LylE5J1JZ2bDrACYPSNXLaKkSJZISDNuWh/OCzVxalj092c12lebXPsr0D/8CrUt+kGm/ifvlv6LdamJgUSatF4Me6aLpcQJHYbT4GAfayGDTHbfEk9sgwy+bL4MTT2NCD+9sJnsOHt5OOETUlF2948B0CQZGOrIdmXQG9ywdSrQO4r61/Cb4xK+LrtzoRfC698Df/kJvZlQ55AuFZf+eWZaOVDIlvcGRFFFpDIwm1WdxfX9kKwBueYyVtW3tL169LgGaUtI2c+WrYe89ska06rH/upOGdhNKh8nmsn2R9vG8uvRBrtsuusiRPm9Xm44xBq/RAIycD7NkqqRv3UUFLdLZLK12gN57r2RB9+zuqGLYlofFwwaXlhVH+tC34N/+F9NX/yytr/8DNMOSWfnIgj2tl4L5WgIgljFqV49glGRtdHGrBJYQW0tOHFiwNFPHw/uiXfCNTwGh966JBYYxLEhgWDmO3N8dt0jmoVGTLOyjXwCvAm//K/E5P/okjuOQLxTPODNqWZtkczma7bb0CNdOQODjuG7fbfuC4jYAEuVDfb3flUSnr9J1IZmB4Y2y+UwNwOAoKCS4z+bDSWwoFkf68hkOAl/K3Lm8PMZzXg5PfHlOmw4QbzLmkamiuBmqx8hmcmRzA+S6pJMMCmd6vG+uQZb5scGlZUWSPvhNWsf30yofEXeXaFcbDr/0u9H/fBJNmEe9ZF59hnrlyJzdO7kCbL6MYP+DTNWqZyQoHUl2GGMkI1DcDCoBxo/t0ZrTgEI5akELb8JN0Go3ZfL96W+KgPF8PZbJlC2FW86ISF2ilr8If2AdbnuG/Ikn+37u+GFw6ZbG+nq/KwmvXgeMtMboupSStzwXWjNQacAN74sDuaceIJvJ9iWw7LjrKCXDhcMbxJdcuTLcWTosUm4gmcrKUdGwbE5LL34UbCoFpcM4iWQnK3myjbxl8bDBpWXFoiYPzm3yH9l6Rp7WK4lMNisi53t2S8ayWxN0447Q7ecglWrtlH1OnSZ5Y+hkKp96IAwqHVmUUzkpexnx3F0I6UyGeqMhZTSQ+416LL/yMXjk8yQTLkM2sLScBUo5pDdejP+D/wnn0F7Ul7/b1/s3QFCUsvhazVx2spbZPHih240OpBdcufL1Xe+Nh3bcRN+yxx13ndELwwBxTILHmTJgJLCM1gulJKmQ3wQf/zXRtKyOixQakMlmyK3Rftnlgn3lLSsW0aSb5Zd9hp7WK4leTdBnpGm9Nh5LM4Xe7DoURJ8PrQPK5bI0ye+8FlmsA+nnjIaijOn8U87CS47NRgNQsOES+cbwRslYfvLdsO9elNM/2z7L2kJNTcKDd+PvvRevPtNXjUI9MIJJD4RDKUf7dr8ric6a2ZyBwqZ4TdA+BC1cBS6GxDkO7cxHoEN3ncoRePnPydrmdnm7Ownp+dxxtQSg67fLWvjWj0B2CNWaIZvNMDIywsDAkF1flhibubSsWDLZLI1WG93VoN3xtDaaYObEUh9i34g0QcWbfeqkbj/e5KE5Az4yfRnu/kdDfTo3Kf67kfRQIg1+E5rTJBIJhs+hZzUIAiheANOT8l7c+AF45HOSXZgp4TSn7cJvOWuM0dSfegj8r2JGtlJvtmi2231rrYj6Ld3qMVRUfl1jBDqQNpbmtDh0DRQ7DjeZdIaBwaFFe2zXcdCNKRgcgX/8bQkkCxeEP1XSD75uu1Rrnn44bhOqHoPmDNlszpa+lxF2hbesWCJP64RSYltYuABe8kZ41TugepxAG6ana33PcCwV3W4/SjHX7adyFDBM1XrdfLx6XUrhuWLsdqR96aHKb5JFPJEMvYEdhvPnNrkdBL7IxkyX5LZ6DF58g+heNutxptRiOQsanodut8Ri8K0fgZs+RKBN3yoUfjGeFF+rOI4T9zsaLb2OAMpZ0HDf2TA4NCzVlNqErElv+EPJViolQ0TR+rX1CtlQ33FLR8ZNwZqe+hYb35lldb2zmUvLikYph+F86HxTOQrf+5p4WisHPwjwh2XCsJ8ZjqVGKYdMOiPSLI9+AQ4/3pFmIZun3ahRLpfJpNMyZdtsSLmpMSUXjX33iQSRCaS8nhkCrwpAoVA859fIRINBV70GHvvnRZePsawNJCO+eZaQ+gUE0+P9uf81PilujKbZagNGJvIzg+DVZBN/HqTClFLxxnP0QhkaKh+WQLIxFa9fd98miYTAl6SCcigUz33dWgmYsPWp1W6hgGQqRSaTpTo1hdYGRrZAaXlc72xwaVnx9GpGHsNP5wgaMz1T5MGe3VTKJZTjrBgtzFORzeUkuAx8uRBEwutTE7Dresy++zoyRcYYaEyLQHptXC4aM+X4zuoVlONQ6JMkkFIKClvg5W+HI09IUFkbXzT5GMvaoEc/tWMxeATt9CcT3tG4LK3N4LLheWiD6Ol2TYNn0ulFkwqLXICaraa4d4GoYJTG5PFHtklZfvb6NXkQjAwPDgwMrui1/EwxRlOpVtFay2BTcTN++QiedmUared6t/S2lja4tKwKuqUmpqdrBCNzreL0TAnaTbxmE6/RIJNOkxtYmROFSjkUCwVxsmhOS2ZyakIyhNe8TbKZ+U0E+x8MxaBNnKUMe6jYfLm48lTHMc3pcIp84UQyR9pNxeXwGz/QmRBPJFyG7SCPZYEo5YCjejPhyu1Ll4VJpNBDGwBIrNGyeKADeV03XxZr6n70nehFskc0RlOuVMJKhwOJhNw2pqRd5/tfl//nN8qa1b1+AcpRDA4OL8qxLSd6bDhRIgT/5g/GG6yP/3dYd9Gys7W0q7xl1eE6rvQjdk+RV4/BC39adnfhLrzRbFAql8/JRnEpiYZ8komEyIZc9Zq4/7I0Jo3vhc2yeJtwOjwIBdLdFFx9o/xeSybMK5Xygnt1jNFUazXqzZZcpI0W4eM73iVl+NFtpJ/3IzawtCwYrcMewCtfDRufLbfaR/ehv8wvbAXloLwqTqPWh6NdWRijCQIt68F5skdseB4mCF2AbvqwmDr4bemzrB4TAwYdyFpuTNwDqhSgFiyVtpKIAvBGK1y307m5/ujZ4fP6vp0pNnNpWXVkslma7TbBnpvDKfIjsiPfdb1IV4xslVJycwaMoVKpkEmn0VqjdYByHBJuYkW4Nyglsj7VWo1g371wcJ8szMUt0vj+wJ2gHNLpFEEQ4DdEIJ2hUfjU78XWkm4SgjZevU5u4MzE57v7f7TWGOXGpZkjT8In302iehgufj7+tTfTmJkkPbYP1XdHaMtqR+sA32+LY8yjX4gzl05CbFPPATmPp+Gf/xyl5P/L/XPfT6KNYRDo82qP6Ae+uABF3uHPeiGc2C8BpZvq9IHLQXZ9Ycyybq+JKjiBDnAdd8HXEWO0DGcGgbxOO68X7eDIHz3KXDamUEph9twsPcnlI8vC1tIGl5ZVR3cPZqt6GF9reOUvSWAZaWHqQHp7gjamOSM+taOhQDmKVlg+V+FihlKkU+ll2asZPV/Pq+NNHiK8QsYyRUEbv3aMQmGEmekpGo0GlI+GU+Phqh34kEzjefUzeo5z+n9SObGGi3bUmy+D0W2408fJ1cepYAiKW2ld+AOkD35jkV8Ry2oh2sB4jXAiPPDF+clvw5bLRdz7HHou5TyuoStV8KbRpcNUlOqrfuNyRwIhA2/5sJSjz4PRQRD4tJpNQMVaxS94PTzxLyJZ5rixYLoxpDNpHMdBax83m122G/+OUQXI0FHpMF7DI5PJkM1KMOzV6zRbzVBP2CGZTKKUIggCdBAQBFoqSMoBTByAX/M2OPSoXL/23BwL2Tsu+eEhmo0GwfTxZWNrufzeHYulD0Q9mMP5Ao6bgLtuDWUrbo6HXxpTUhLODokw78/+mZRnjIadrxVP7/QQWrloA15jbhndGE29Pk21UqJSKVGfmV4SGYiOZWR0nS1ulrL3K38JqsclCES8wpXrSgDqpuI7cJPgSwaoUq2d9DlEkhfVakV8np0wW/nCnxYppHlKM07bI/PEFwHwdr4OQ38GMCyrmyjw8yJR/lwRMJK5PP49ucWQSqZOc08nx/PqUla/6UPwtj+Fmz4kZgTe/GYEq5GeKfxUFl7x8zCyFcdxFy2wFN1dZN0xRrKld75L+ioBhteHesUGpWBwcJhcbpDBwWGyy9B5R/oip2OjikhGyU1gRrbhNZqUJicplUp4jQbagFYuQf4CGs0WXqNFKwDfD+TvlRI5pl3Xy/WqPBb3sF/xY9IeMHmwI2TvOC7Z3MCyen2W/ggslkUk0sLMplIweUg+lOkBKYsbHQaZM9KfCLGFpN+W3pZLXhL3aYYZwXK5xNRUlfrMtCwWnoefGiTQ4DWbocXiEumMRTJATz8M3/0PyV4aLd8nHgRSGBFKNlqCTKMlMN146bxOP1oHVCslSuUy9WaLIJHt7f/Zdb1kSffcDH//Tthzc09pJvPEF1Gteid7abGcjk7gt+NqqTI0p+XzqgP5/IZWf+dSHm23WnMtZAub5ftrhJ4pfIin8BehFz02dAAGRgADz31FOJA4GbuGGS3tTI5DvlDs+3H0kyDwKZXL4SaIXhmlN39QAsLhDfHGX6l4U/7cV8jXO18b2luC9AAouZ9r3tYJsjsB+Le/CI5LcWSUXG75Tsovz6OyWPqIUg65gUFGRkelNOFNySKWHgx3ykb6EyEumyeSsjjkN8YBZyon/4yh5WbxogvQyDbJgOY3gnLQQdA3Yeducdx6fZr6zDTTUzVq1TJTtSrVaplKeZJKpUStVo3+KL4A6yDc/ccfdaUcCsWR0E88nNI0WnbaP/leKGzG8+pMTVXx6jMEgU+5UsEPwlLNTR+CF/4UtLy4/yeVlSypDkhUD5NLp3p01pxW3WYvLWdFJ/DbuCPWOJyaiB1kQDZK53BxNSDKBj2B1dG11RWcSMtn/45bxKf7jlvASaCc/ocHDc+T9albd/c7/ybvqd/oDFsycQClAwr5PK67PLv3jNFMT9UkWFYq3gR1yyit3y7uZLVxQEliIzUQ/6x6XL722/J9JyHZ3GhIp3oM3hwGoYEPEwdIOIpiobBo8lD9Ynm+axZLn5ivuRoFrWYL3ajJRcQY2RGmBkSax01K43R+k2Tkuvs0jYFESjKau66T3aRXgxveJ1nCzBD4LXGpWeDxel6ddquFMaHfrnIkuC2PyeMXQ6tLR0Ggw2EcnyBowUmCNmN0z6BCNGleLpdEQ+6Cy+B17wGvEjr9QMvN0mpOU/c8CT4veYlc3Ndvl9fmyX+d2//jJhg+Sb9a5okv0njuq8Ls5fNJH/zmgl4jy9qgE/htvSIe4EnnRJc1FPY+1wtsKpkSvdg1KvRvnATBc35YWgyuek1PL2si2f/XINCB2M5GckORbuXEM/KGJ7NgGjiuS+EcLGgXm07PuR9Or49eKJug/Q9JwBzJKJ14WgLIVE7W6WhIqdSQn+U3whNfhi3PFce0qMqktfzsjltkg1U6BI7LyAoSi18ZR2mxnAWdbN+UONXUG02agxupN1uUKxW8RouguAXjuPGu3XHDsoQC3Q77XA7D3/w8fGJ3HFiC7CCjjGZhM9SrUgYpbhENtladdrt1xqXxqG+zUilRmpzEq3v4+QsICB0rhtfDje+XflDHlV1spzf02k6PqJRStspgUnznYAzGmDm9lI7jks1k5T5P7IdP/a4sZkbLDvySl4SP6chxHP2uiBdHmcpXvQO0xjEBieqRTv/PyRa/3uzlT9jspeWUpJJhu8bdt8VSNPUaCTdBsdgfwf9sLieWh0HQEfqHtWElaFBMv+y/oF/2s7KO7bsPjj4J++7DdZ2+vgbRmtxODoLf6pIbGpA10wDKhbZHMrG8A0sIWzaCsG0gm49tKUe2ynmUHpBrxp7dEmg2Z+JMbVRZ2nMzPP4l+XrvPfKadE/F18alujZxAHRAJpVa1q/JbFbOkVosZ0C33mJzaBNGKQkEb/yAlHOVkkBs1vBOJ7jcea0scsoR2ZNG2JeoddwLlMrB5a/slNBIJKUMUjoEGHASaK0plUuUShOnHPLROqBcLuN5HkFqUI7DCcvVb/gD+X9tXEorUXm+enxWb2h4myvI8bzqHfDSt8C2K6NXBdKDaKOpVSs9x9LpV5vt9NOc6WoJ2AYoaNQkyxB5+t51K24ySaE4Qr4wckb9P5knvgjlIwRf+t+Up6epVVeuzqhlccnmcqhESs7N6Qlp73ATDPcx8IiUFkhlxPc+lwcUtampZeHPvJjUn/9TtC5+EbhJhtZvIZdOkZ4+Pqel5VyJsnz1ZgudyklxJfBF9qhekbXuBT8J7/gEjG5btEGifmGMFsWNdC7Owg6tj/vbdQDeFJlMmmw6RSrwwn59Hdtq6gCCNm71GJl0imwmTcqFRMIFlFxvdCCBOOC4iUX3du83tixuWVV0ZDW6rLC44xYJzl58g2QX/bb88uwArXZCvh7dJiWbpx+WYHPfvfJhH71QynRtD/73z4k3d8T3vio7z/wmCfBC3UiD7HK9RoNsNkMmkxXJCB3gKAfP82SxzRXDUlFocVYak2xodFzV43F5/tKXxV9vuTy+bUzJrviu94Y6dYfDRQ3Y8CyoV/AnD1IqlUi4oo/pOG6X08+M9PrUxntbAsqHJXD1W/Iv8GHyENls9uylmRpT8NF3AgZT3Eq7PEa5UlkRPUSWJUCHItthHqQfbjyzaTYacl6/9U+WlX3eYhC1CbUGN+B7dWh5DD64h9TEU9Dn5xo9VqPZQBMKpXdp4NJuSmD5xj8S6bI+23kuFg3PkyJWywNUnIXNDEllBygUiz29oj2e4O06yVyObPbka2fczrW8pZdOhQ0uLauKQHfJakBohbUlDs7Kh+OM3uwATQeShYwCuOoxCTYLmyXIe92742A18MNhmJRMoBsjmc+WF2Yf3bCMHupkjmzBqxzFa4TabiNb5FgcRzKDlSOSMYwszoJ2mA0dk8f6/tfh8fvlvh7/Enxtj2RYo3LK3nvlsYbWyX1MHohflEQaXvwGEU0PS9F+oHuCumKxKItZ0KBpjDz3T75bjnFovdxnOMnpOA754siCgsHpqZocZ3ShOfE07NnN9FSN4fzyngq1nF/kIm7gLX/cOVf0IgR9HdvDZWaf1286mp7GwICBr92BeuSzpNJOrCnZ98fSsrnttuPdfJm83pMHZf28+7YeUXzlLO9xKjlftshrFq3PiUTHVrdQKM4ZQoqGSs9U16DbznilsrJCYYvlNLiOO3f6s3RIgrM9N0twtvfeUPNyN6Dk/zqQYG3ffbG7TTQ1Xjk6V6ooPSDTgVHfo+NIkNeckayfDuJyu1Jxn+TssrzjwmUvF8mKqbBkUq/I/Tz1QCdzkwo8sukU2XSa9MwESUeRciDhOOA34+n3qUkcN4GKUjxRW8Cnfz/upYyOXSkqlTJefQZAdNKGhikWi/K7E89Ib2X1eOzk4yRIJJMLzjIGWseOHNHrWdwi7iAWSxfzbxQ3y/f7iKwZh+fVaF1NzKfpabzaomh6xo/1YVlv5pnIB2Td6rbzDNoklul0eITjOHH70UveKIFmu0kikWRkZHTZTrefb+yrYFlV9Fg/hlZYCkgGHol0inQmEzoZjBM4SqzjlAzOBEEbUJI1vOvWMCN4j9zxbKkiHUjAGWU3R7ZJ4/XO62TyMjPUW26P+iTnK8t3/2zimfCZKBznzFyB4hJKEzeT7jTil8plCaZnynEv5dSEBIobd0D5MKZ8WPpT2+1On5XjuBQKRSrlkgwrRSiRLPLbCw8EXcdBR44cUeayfBg9a5rdYnGUEwclnXPlCG564aLp85HOZKiTkM3nMrLP6zetk2h6tqpH6LeTYqvZ9VjrLobD354zkU8iJb8T2XlOSs/6cn7djdF4jRZg5BpR3BLqcS6em9FKxQaXllVFt/XjyaywTlZu6ARpMxNoR6Ecg5tM02i2MXfd2vHbxWhZDCPv7i2Xd/UltsMg8UBcYteBZA9PVpbv9FAeAiCTzoiTzhkuVCcrocS9lNNhL+UJwMTH/rU9sPVKePl/Jtizm6lalaFhGZbo7L6VI883mZWg201hgoULTA8ODVOuVCRrXAxbA5QLmFXZ42ZZGFoHIkqtVK/Ulepv0GeMpjY1JTaomSGYPIRSiuGhc9PPXI4YrecG65WjfR9c8v22SLFFj3Xtbvjr/xYODYYT+cZIufyVvyS95cefghPPwDLfYHpePdQEdmPppm1Xwt57aDYadv3qQo2u27C8GxyWGYODgzzz9Pe5ePslTE9PL/XhWM4DkfZkq9USq7Soh6h6TLKbUVawNi4Lzr57w35LRXfPJZVj8YDCyFYJrLp0KxVQyOf7OtjSOfZmpL0ZyhVVjsqxv/2vYLAoQzaTB1GO2zmGWrVMOwjL/n5bguW995BwHfLn0B9Zq1VoG0f6Q4fWw2t+Df7xt0hPH2dwcLhvz92ycqlWy/jaiI1eFHw89QCu61AojPTtcbz6DPVmq3cAcM/N5NKpVRUoGKMplyYxypHAKMog6gBHQbE42pfH0TqgXJpEBl26HmviIGgfpRSJREJ8tFFzjiXhKPJ9fH/7Rcfj3qtL69LwRhkAi/j7XyY9M74m1q8zjYFs5tJiOQ2Rb3cuF2c3/ZkTtHQg+ni5YQk0I/F1kCASJDs3OCq9i+Uj0n/UmCJVOyblPQN65gRuOr0oE4Hdx651wPRUDb80JrHv639bAsuoB2rX9Zh991KpVCgWi2RzA7SnpiRYjrKMyDDQuZSwk4kk7WYLrr9lUcudlpVL4PswepEMf2y+TL750Xeiw2ncvj2ODuaWilfZME8kzyYyvXPdu9LpTN8ea2qqJl+MbpNNdGksttpVDsWREZRyCHCk7aY7mwkMDRf6diz9QutAqi0AKJGsipzJOhngI7jptSG8f6bY4NJiOQu6S9CdMnrQRLsOyjEkkplO2a5cqWBqx6W36OiTsSOFcmi1W4wMjZ7XEpDjuAzni52LTfCp3w17ho7K7TVvg7FvYyaeYXp6SvqmlJJm++Y0bNghGaTweS80s9PTFzu6Ley1AmOM7bu0hChxpJrVm3syB6qF0jPMs0o3Oh15thvfD1/887DnUSoqjuuekzd7N8Zo/HZb9IErR+ZknROu0/lsN6+8Fp79I/D5D+IcfRI34TI4NLys5Mi0DpiqVfGDAFCiy3nFj0mPaCSCHrZrKNaG8P7ZYINLi2WBnE4uopDPi73ixAHpx6yNSwlIi07kUvUYRn2pU7Uq7cmDomd5zdsk+1o+DJkhWl6UgbgQ3vyB+I8//mtQGyfQC7O37H58z6vjVY51eli98hFafq2vAs6WlYnjKAJjentzjcHpswZiOpOh3mjOeZx0pn/ZvKWmM3W/+TIxk3jkc/DwP+K6Dvk+CdKb0KABlGiGusl44KUsertDw3kAdHqQxvNeC6ksgxdcTLoxfs6P32+CwKdSrcbtTekBaX16+dvhyBOyIXZU2ErkUCisHFvG84V9NSyWRcJxXBJuuBPffBn84Julgb1yFJJZ2pkCS9XwrJSIqCvHlbL3J98t05xD68Gbkl9KpOaRdRqDZv2cZVqUclDRZP5b/jiUZvoQgZbBHsvaxnUTMuilAzkHdQA4fZd5aTYa8sVVr4ULLpNbVPz9VYDjdE3dp7Kw/QXQbpJOpfsWWJYrVXzfh5HNsfavk5CNdeCTH46zkt5VP45JZXEnD5B6+qHT3r/WAdVKidLkCcqncTzrB1oHVMplqdrsuFoSA62wFF49JgH6rusgaJNMJikuUPN3tWMzlxbLIjI0nJd+nf0Pyg4+Ej8P2rQvfyW1C69g4Bt34ZzYz/RUjUBrXMc5LyUipZz5s6tOeMHx2/J1p/wTTsr3aWI3CObRMSxcQDC9/DIZFiGWvQpwHXfRnEN0ZLW66/qeYTLd591YR0D9FT8ff/PQo6um51Km7pvyn56p+/55h9frMxgdlo6r4/D634KH/zF0M2uiHEUikQQgGFxH49IfASD3zbtRp9heG6Opz8zQaDZDuTgXkxnE86o0mg0KhXPzl+/0zwc+Rks7jtZBWAY3ose7cQfsfyge2Ox+Dd2klR86BfZVsVgWESecvkYHEsA5CQh81EABrnw1/qbLqP7wL1OuTdHWBl3cRlsbypXKefHcdhyXbCa8yETZ1WQmnmo34QBA9ZhkI7SmWOiPTIvWwVzx6soR6zW+TIkGG+rNJs3BjdSbTSrV2qJkkbTWUlp99Atw/Hty6yTk+31kNQuoG6OpVCryOX7jH8lne2QraE06mejbZ7jheeKznSvIhuDTvw9eFeplcBwyXQND9ef/FLgJkkceI3X0iVPeb7lSEQ9vEEm0/EbJII5ehDFQLpeo10+fxZRJ7xmmp2t49RmM0fG53GjQ0op2oGkZhR9oQIn0WnlMJNtGtoo+cHpA1sDJQ2RTKQp5G1ieCpu5tFgWGddNMDIyGmd8khkyKdCffx/ertfTfOQ+WZRnWSJOTdXOSfLnTMnmcjSaTcz+hySIbM6E+pahfaVypI8KKI70rwSklCN9S93iyspdFP9oy8KIJFiarSY60OC6Peep3rMbz6uTyw32+4FlgnjnayVzueXy2OK0j2SyWRpaoe+4RTJVpUOrRkDd8+oSeK3bPnfqvk+Z2c50eLMuskJRhq92IrTIVZ2BIX/0IlrbXwJGk/vGP8x7fx3Jn4YX319rBtyUOJjd8D64/y9luCa/Ea90GM/zcF03PDUMynFwHRfHcTAYms0WRqmw//MIjVYLowORZdp5rWxcotsdV4v3uVeTx7/rVslU6gC8KbLZ3GlNLSyCfYUslvNANPwzODhMNicC6e5MicGv/A3O0SfmtUT02+1F7S3qPrZMOi0L6NB6cLskNRKp0PpRkUqn+67BSdDutX/T/nl5zpbTY4ymXK3iNZtoHW405nF4abcWLqp/UlSoEduducTQ752HUg7Zrc8WubDqURwTMDQ4uCqCh1arBdm8bNp6MrOH+5aZDXxfHgNknaiFFrZN0T+MqhwGmPmBGwBI7f86ifKhOfcVqVh4jQaix3uhtM0062KJO7JVps/LhyXIdBISALopAm0IDAROEl8bmoPr8ZpNGo2mBJE3fbjT162DQDzrRy+M3dWi2407oDElWVIdgJOU4R1jKBSL5AZWx7lxPrCvksWyxLgmiGVXoEd25XwNt2RzOZTjSH9R0ITiBXDRLskcXPR8KF5A4Pe3XK0cZ27p003axXuJicqI1WoVEwRyAd9xNSTS83pEL8ZQWjqVjlsyujUZU/3VEtQ6YGb/o5IVG96IVg6VanVVtGZoraExLRvGO26Bj/2qTMUb3bfMrDFGAsn8Rnm/HEcsY42hWJSqS7VSolwu43/mA/Dvf0fm4bvx6jNMTVWpVkpUyiUq5Ukq5RKB1vEQTWkMXvjTskZgYPKgyBqNbIuDzKteI+objitZ7qgC9NxXxOft6IW9GyI3GQfdiaQM6kS3URm8Ng6ZAfAbKMelODJiPcPPEvtqWSxLzLyWiE4ChjfSmjq+aEMT3SjlkEy4tILwcarjcP27elxLgsBH66Bv2cuEm6DVbs4qfd5DG6ze5RIQlSQbzTDbk8xAwogbycYd8NQDkjmc5RGd6nPAB7LZabbbYY9l6HIFfZcImp6qiXTmrJaU6akaw+ehJWWxMEaL3aOjwkBpSHq+MSil+v/Zqo2LxaxX7fRqG2Pink+QMvlD/0hNOfI9JyG2mwYJSo0v51X3EM2nf18C18lDoLWcg44LxzfGGcfUAAyvj7OP67fDI5+PM5FPP9yrY+q3YkvefffJ/e29R47nrltFj1gHqMYUmWyWbNaWwReCfcUsliXGcVwyqZS4/bQa4vf9E++F2jh+oCmVS0xOjFOtlBY1o5JwZdhIvH+1BBEf/zW5DUvV5XKJmempvpSuJWhGpJCOfVcyl/lNmPOYsbUIxmgqVSlJShnxQ/DCn4KWF2d0RrfFvZBVcVWRdo/+iHB3o5TD8NCQ/Kc5I4GLUn3PKgaBnrclJQhWdmuG59WBcCAvaj9RClA9AzYLReuAWrUc/Uceozkj/zcAShx4QHq2h9dLhjOyn9x5rWQclSMZQowoA1SO9GYPU1kojYktbaFANpNBmTDInDwoGcfWTG/28cTTEjhG521xi6xhf//LsoGPWi6ioLvdFBtM3SbpKNIzE+SyWYrFEXI5WwZfKPZVs1iWAbmBAdxkEmYmYXpSduwjW+FNt4t0kXLwtaFcLi9agCmlsjDLMCuIIJreBRrNBpVq9ZwDzChbSnoQNl0KV98ompcjW0QixnLe8Ly6yP90lxF3XR9OFwdw13s7pWmMwQ0aZLMZioX+iHDPx/T0lAQCb/0I/OJH5dxQSrKNfcOcpCVlqRRo+0O71YLRrbIhiAizv+e6GYgmrdtRAB4GrSTTchsFbyBT5JmhUOKsq0wdZRwzQ1K6H71QjByKW+Du2yTwDLOH2WyWYrGI6ybIDQyKNW0UZEYZRx2EA19KAsjHvxSet7fKfQU+qjxGNp2iWCyK9qcOIBAXMieRpFAcYThfZHAo7ou3LBxbFrdYlgGRa021WiGYKUmgtet62bmPbpNSzdMPQ+AzVauRL/S/ZKeUQ7FYoFyajL8ZNuaDksU4V4B6FR0EfXEYcl1XHmPXdT02f84qst9bCbRbLbmwzy4jvuodcOdv4DgGp3aMZO78lAk7VoLrLp6bVSyP9e1xXDeBDoI5Dj1uYmVfGo0BKsfgDX/QY8Ho9KEkPlULS99XvRa+9VkZuhlaL32rG58NU+MwFWYtGzPAtAz5RO0VTz8MW54rGUdj4v7K6jHZTH/lY/DI50gmEmL0MOt4lXLIDQyKykWoU6lRobiFL9PiMxM46bQsWzMncLPZnvaiQj4fqnf4c35m6Q8r+xNksawilHJIp9LUm01x0UhlY1ecS18mi29tHL8xTbk0QSKZJOEm+rowOo5LcWSUcrkspS43FZfU8htF7y1XgKB1ThaQESbMhPX28Um/lmXxiPorW+0WCghwZVinu4yY3wSVI7jJ5Hm35OyUdefxF3ed/hyHMVoEs42RoKg5DRt2wLHvkQxFv1c0RksWsCPzpVDnOG1vjBYnnnUXS/YxmZV16fpbenpWGVoPleOhpJmWie/uMvW++6QiY9rSp5nfJOdccQuUDqEcd97AspvT2e+einP5W8uZYYNLi2UZkclmabbbBHt2Q2GzLNzFLbIoP3CnlKhRaK1pBdBqN/CaTQr5fN8GbRzHZWRkRPTmvLp8M5uXwDLSsdMBbh8mTtvttmQ+nvsKqB6XIPrxL9GuHTvn+7bMj/RXVmVYRjny+k8clKGKu2+T804HMHmIRCLB8BK4kLRaLbESnJqck1UcHMqf031HzizNVhODgsImOLFfHmP8KdQq0LkMdCAbQ+3H0/bKJdDtc7pfrx6uB+Ux2HZFaPPo9r5HWktwWT0uA0UXvxgOPCLHctd7Y5kfo3FcV1yXqsc6Q0dKKQp9MmqwLB323bNYlhFReTybTkm2IfBlAY963tykXHRBFuud12Kg772YUempODIq/uNeVfqnqsc7F4V+XICN1lK+2/4C+LF3yG31eN+dWCyCMZqpmrQ14LgyuPOzfwY3vj/us52eAGNwEgmG84vXU3nK49Ra+n1/4r0y4NZqyMDbOZ7jkZZivdkiSGRgZIv0cl59o/T9pgdJJtyVH9hEMk47r4Vnv1Ruje70XS4ErQM8z5Og1Rjpd3QTcYAZ+ohjQtkgQi3JC54jj33Fj0nGM3xts9kcxeIoI8UiuUyGtG6EgzTnZutoWR6sqE/QD/7g1Xzi4x/jsUf3MnHiONdee23Pz//sz/4HEyeO9/z75Cfv6PmdQqHA//zLv+Dp/U/x/ae+y0c+8icMDPR/2tFiWShKOeRyg4yMjIj3bmlMNAa3PA9+8jYJ8JJZSOVi+Q1E9qPfAuSO40oDfTYLA0XI5XESKdLJPpUNlZp3Mt1obcXU+0wUWLX9QDYKkWwLiHtLcQuu65DGJ5fNUliiwBKIz4tZVoLAgq1Ro8A60Dqchv9pqByVrNmLb5C+3+aMqCasUMSPO+qT7q8I/VStFr8vOJKhDNry/3boX+648LaPwCUvgVxR1q5o+vvxL4HflFaLRKIzWDSfwYRl5bOi3sVcLsdj3/427/qNd5/0d754//1c/rwrOv9+/ud/sefn//N//gXPuew5/MwNb+Smt7yVl/7g1fzxhz+82IdusZw1SjkUiiOyYPsNuch+6nelVyloi89uIimlqFwBY8yiSPgo5ZDN5uQC4tXQwxvwWq2++J+fTCz7fArIrxU8ry6B1a7rYomh7inp6lHSqfSyuMif9Ly46rVnPTEeBVzlcpm278fSQ7uul1Lunpvh798Je25e0daP3XJS4Tckk1gb72QUFypDpHWA77clSByVzSzJTG8mWTmw7iJ5bfMbxelmaL20Whgtx1A6RDadOu89vJbzz4raot1//5e4//4vnfJ3Ws0W4+Mn5v3Zs5/9bF71ylfyqle9mkf27gXg3e+5lTvv2MNv//bvcOx4f/xWLZZ+4TguhXyeSqUsZadkGnxfFIOchDTGR32Q2WGCoLEox+F5dWi2JeMTNu6bPbupVMoUiyMLvlBkczm5GHaX60a3gdEEM/N/ji1njzGaRqMh2cpr3gaHHpWs0p6bxTu5cgRHOcsmsOqIqAcBHVmb0W3w8rfDkSfOeGI8ytYGvh/rKz76hXhI6JW/BJ98N4nqYVLp9IqeGu6RkyofFVHy0phsCsPPV25gYUMsEswr0aF8wx/2TKBjtEyDu6nYanLX9fDt+8NeykGYPBT2UtqS91phRQWXZ8I117yUJx7/NtVqhX//9//gD//odpl8BV70ohdSqVQ6gSXAv/7rv6G15gde8AN8/vP3LNVhWywnxXUTjIyM4nl12q0WxoEg6j8bKEpgObwBqsdoGkOujy46ER2pmlm+0mby4DlJEinlkM1kJMDcdqVIlWy9Au66Fe0o69TTJxqeF8rThGXgGz8A//a3sO9enPIY6UxmWTmRKOXIpqpcQjdnJFi55m1y7OXDolN4Bnj1emwpWD0u9zH2bWm/KFwg0+dLNLTUb+bISd3wvp4gMJvJnNVzjAaf/MCXwbuRrTLUd9et8fCOUjC6HSYPgNOUdSia+q4eA61xmzOkrdzPmmNVBZdfuv/LfO6zn+fAwYNcfPHF3Pbe9/DJO+/gtddeh9aaDRs2MDEx0fM3QRBQLlfYuGHDvPeZSqVIpWLNvcHBwUV9DhbLfER9mITtwVoHsmmqV0QaqDYuch7VY5TL5b43xRuIfaUjyZHKUXBTtFrNc7pwZHM5Wr5PsP8huWB9bQ84CdqBT7VWsyW0c8QYTavVlODAcWPZp0mRfCkUi8vy9VXKIV8oijXqvnsl2xpOjPuBOe3GozOAsu7COODq0VL8PMmEy9AqOb86n9FI7ufu22I5qa4ex1Peh9HMTE/TbDakzK1UxzyB6jF4/W/Dw/8ojxMEMLwOnn21BJdGSwCfGYCJZzoJ57yd/F6TrKrg8p8+9anO10888QSPP/4433j4Qa655hr+/d//fUH3+Wu/+iu861239OkILUtNtBsPggCtA5RyOhepnlvH6buGZD9xHJdUKknLzcKzXiw9Truuh0++Gyae6XuAmUqm8JrNOb7SaI2vFOVKZcFySNGE/FStSnvykPQEhlmqYM9uPK8ugbXlrOmUhQMtm4FuUe0Tz5A5y2zW+cZxXNLJJM1mM7ZGfeFPw6d+F69eJzdw8vNiaiocQKkcnV+/M5FYNYElQDKdJfDqC5aTMkZTKpWRqFDJANVVr5U2AjctZfZP/a68jjOT8vPRi+AFr4cn/kVUHxJJqFclIA3aJBKJVfP6Ws6OVf2uHzhwgImJCZ61/WIAxsfHWbduXc/vuK5LsVjg+Pj4vPfxkf/xp1y8/ZLOvyuu3LnYh21ZIFoHVKtlSpMnKJUmejywjdHU69OUyyXqnkdzcD1tbWgZRTvQc2+1ot5oUC6Xqc9ML8vJ5YSbiN1tXnxDp2RIrkC/p8ezubBk2m0JaYxkhd50OwbO6fGUcnBcV/rqXvHzIiAflt49r7EsX/+VgLiQGMnWjWyVwOOJf4H9D55xNmupabVacp791/8NP/O7cNFOKGym0Tx1f3Hg+/KcoyxeNOAyeXBVDZVEPt8N7UgQqIMFyUl5Xh1M14BOcUusRjE0Iq/fhh1y/xt2yP+f+Sbc+S6pnCjAb8n6YzSEQuiWtcnK/2SdggsuuICRkRGOH5fA8aGHHqZQKLDzqqs6v/NDP/QyHMfhm9/45rz30Wq1mJ6e7vlnWX5Efre+NpjRizDKodFqUZqcpFIpUSpNSpBS3CLabE5CGtONjjXg5rk1ysFbpkFmJpsVJ/A9u+FjvypZmaH14tXb5+lxpRyKhTzZbAbVmpHX8Oo3Se/e5stgZFtH6mWhr5HruBIc90wwi5h6R8zdclYEOhCR9M2XSYB59Y0wU8JVrIjgyphQkipqyYBOS4Y5hRaq/J2RAZRX/pI872KoD6sUudzgsn/uZ0LH51sb6b8OZHgnZdpnLSfVaonPNukcJFLymieSoirww/9Ffjb+lLRWjD8FIG5JEwdElui6W+Ci54tsmtYojB3eWcOsqLL4wECO7du3d/5/0YUXcsUVz6NcrlCplLnl5pv5zGc/x/j4OBdffDG/89u/ydNPP82XvvxlAL73ve/xxfvv50/+5MPsvvldJJMJ3n/7H/FP//QpOym+wpmOSmA3fbjXhgykJJgId/ROQhrd775NyoMjW8Pd+UluQ19vs/8hvEaDRqtFMuEui5J5NPRQLpelxynqveyeHtf9mx6P+j4VSiwqd1wdW1SWD0M2T9urUqnWKOTPPnDJZLPUGw2ZYB7dJqX34hYI2uLYs/yTbMsOx3HigH39dhGp/9oeUunUigiuOpujSAu1uyXjFJqN8d+ZOQMomVT6PBz54mOMplopS2/krHXPaE126OyG7HQQiqw3w42cUqJG4bjw2dtheL2UvicO4DgOw4UCjuNQKpXkd+/7iLw/oSZpJr06XmfLwlhRweWunbv49Kf/qfP/973v9wC44847ueWW3+Dy513Om970JvL5YY4dO8a//Mu/8ke3v192ZCG/+Iv/jwSU/3g3Wms+89nPceutt57352I5dzr9kzqgbZA+o+5p5uIW2X0/60XSzH/Va6R/KAoqjz8lF6otl5/6NvL1zm/C7H+Qlpul1Zyh2W4vefYnEjmfM9wTTo+3jCbI+rh9FIbOZLN4zSZmz24pmZUPS8a0Ng67rkfvu5datXrW7i5KOThKsjHkN8nrvvUKuOu9mHOzRF67GOb1bo+GLZY7gQ4gMyztH92ajUD6FMGL76Zg3Xop85bG4gEUFi7Hs5zosfBcd+Gcde90Uk3da6fruKTS6bkVB2Pk9XNTkhGtjpPN5XpUBeoz0xJYRhva2gl5f5RaES0XlsVjRQWXX/nqV1m3fuNJf/7GN9542vuoVCr8wi/+Uj8Py7IEaB2EPX4GcnloTUmQc+RJKQFG2TQdyKRo9VickTz+FEweghPPSOP53nskozn7dt998vuRr/elL5PhgE2Xwq7rCPbsplqtkE4trT5eFGBWKhVMvQrZIXm+bhITtKlUqxTy+b4FmB2ZmEoFM/GM+I7XxuW1uuZtcHAf/uTBBWUw06m0DA5Fk70P3AlGk1ol2abzjdZ6Xu92PTNx+j9eBriOC40ZyZrVxrs0Gw1qnsylMRqvFdBK5+Xz3z3AtAA5nuWK59VFA1Qp8fnuVnEoH5Zy9UmIxNa1DiA9CPUq9foMoLoceGJUu04ml50jVWWMjqfxb/wAPPI5Oce+/wBuc3pVvM5LgdYB01M1Aq1xHYfBoeEV2V6wooJLiwVkUStXKnKRSQ+AV4t3zp98t5RUQ8mSnuBwy+WdoFIphes6OA4Y5ciaGt5q5eD7LUCJE87dt0kWNLqf5/xQZ9gkmClRbzaXPIvZyWCWJjFerVcXcM9uqpWy+IT36fjijGkpfLx4wpvKURi9EF05ctaT3rF4dpil0gEUNpPOJKA105djX0toHcj7sf0FcfDxtT1oZ2WkgjutElOTYidYr8BAAVJZ2lPxEKYxGq9ep9FsYZSCdEGCpE5J/MzleJY7HUH8dA4GRqF2XFqAotK/MQwOnXyQxvPqcl4Mb5Bg0HHlcza6TYLL0phYyzbl81Ycmd8kQfqgjWSFIwvNE0+LzeM52EyuRaJMcqvdwg/Clo/iNnR5jHKlQrFQWHEBpg0uLSsKYzS1alUayB0HBtdB5bAEIulBVHMKJg9KRlP+QC4wYUZSAZlMJp5+PsXjeF6dRuWIJEq0L/czHEr+RMMmV98I219AsOdmpmpVHNfFddwlyWQq5Ui1c/QimbiGTpnMTBzou2akUg6ZdEYyjY9+AQ4/HvfDXXoNfPcrtKpHOJvreZQVjUp2rV3XYV76Vupjexn6j//Tl+NeSyjHAVRvWdxJoJyVURePWyW0VCQ6IvvvJQh8qpUSynHwA/Gjx3FjF6kjT4r7TmXlu+900xHEb3ngH4Wf/M0e7clMJnPKQKTVbEFmKJzwVvKabXy2DOnMct9JuM5JX7N2qyVVi+b0nH5YW2k4Nd1tCY5yaLZ9OcdNeF2b1UM7PVVjOF9c6sM+K1b+J82yZog0+/xIAsdxRf7ihvdJU7tXI5vJMjK6npHRdeRyA6RnJkg6ipRjyGUyFItFcgOnnxSNhleKxRFy2Szp6RMoY2Snf8e7ZOikuEUCzfXbobiZth/QHNxIvdlcsulyp7tMBnF7wNB6Aq37PnWdzeVkaCSSKNKBXGQuej5UjhL4wVm/Bko5ZHMDDA4OMTx9DBJpWs+6mtbm5/X12NcCCTch782Vr5YA4spXQ+DL91cI6VRagqCnH4bv/kfsVY3CDzRtX8umasfVMNrVf7j5MhjdFmYsl9YrvZ/4gQ8jW+RzpgP41O/B9CTMTOIkkqftKQ10INWeVA5SA9I3/br3yAb8rlvhyX+F/Q+CUqeUEjIGyW4Ob4g94EOJstWQIe43kl2foVarUCqXpeI1uBGv0RDbzp2vlfO6uHVuD+0plBGWK6vj02ZZlUQfxqmpKuXSBKXJCYJ2Cy77EenxefMHJXAae0ys3KDjjdwJUIaGGc4XGRrKL+gC030/xWKRXCZNohr2cr7yl3qnpXddBz/7Z3DThzsSRtVa7bwGmMP5gqz6e3bDR98pt8ZIAF7YTKPRX83IKNOYcN1OGQ1UVwBA2M+1MBKlA2SevB+AmZe8FeOmTvMXlm4y2SwqNyyZ5WPfhX334brLx0P8TMjmcrFGZe1EvIFZd5EElEZLgLRxh8jm9Gysjkjf5irCb7el1eFV74CXvFHWvsmDEPin7XE2KNHCxMjntTUjr5lXgbf/lehXThzAMfqUpVhjdBjwGLGEzObDz78hk06vmkC+X0SSUfVGg7avY2WTn/0zOYcjTdFkdt7kwKl6aJcrK2f7alkzdErSjYbsjke2yGKaTEkP5BNfhmPfgTd/KB7QqRwhu8hlryjQzGSz4npy13tlWKJ8WHb917xNfnH99o6EUbD/wdM6ifQT101QyOepVsqYiQMyxX3D+8BvQPUYxrCgSe5ToZTDcD5PqTQpQcD0ZBwA6IBGaYzcOWSOco98itZFP4AeWk995+sY+OY/9OW41wJKOSRe8Xba5eMkvv8AqXRqxZWHT+o/f/dtcNnLpfxdGpvfhcdZWYH06TBGS/k0mZLnP7JV2nOSGVTQOnVg6bhMX/Nz8PBnxEpTIZ9XHUglpnABVI7gJFKnDVI9ry7VomjQKmjLD5SzKqbx+0nHqtdxREP5kc/Duovj7OTGHbD/ITm3g7ZU5M6ih3a5snJWGMuqJ3LRKZVKeHUPk8zKDk85vYLnUdP5Vz4mAzpPPYCjnPNWionsCnPpFOnp4yQdFZaFRfCbE0/L8W3cETrMeNJAf55w3YQM7ziuDEB87v1ywS1ugeJmfN+nUl242Pl8KOWEdTJkkXzpWySzUhX92HMRc1d+k4Gv74GWR6NWpjw1TaVSWnai9ssVvf5Z8OIbyG7YtmLLw9lcDjeRkHLtE//SO2TXmJLhlrtuld7owEeVx1aVC09UxalWK3QGDXtaHdpSPTjZ3ydSTL3inbS2vwRe86sSwBgTD/MEbdzKYbLp0weWHc/2kS3w1o/IZ/3ZL4XsMKnk2rN7NEZTn5E1qVKepFotM1WrUq2UKJcmKZdLgJGWDb8tcnGlruzk1ivk2rb3HjGniALMiQOggwXb6i41NnNpWRZ0PJC1jqe9dSCBZWkMDnwrFjaPdnSPfE6GSs5gQKffRFnMnmPfs1u0NqvHeqfLlTrvDdkybJOWbE+3ZuTdt4HjoIOAhud1nkM/SCQS+NrMkREikZY+r3Mgeehb8LFfgekSurgZykfwwin9s3EhWWsYJ0EwvAkAt3RoiY9m4UQbOs+r0yiPyT4maMfDeo0ZMAGq5ZHNZldcdvZUaB3IZhAgmYHhvEzP77s3XguBwaHh+f8+NcDUK38Ff/0l0G4y9ODHSRTysdxNMnnGcjfGaCqVChB6tvdMid9PYo0Jp/fojaI6nuo4DmgTbrhVHFBuuVw2Q8Mb4g1/+LnMZtIEQYBJuDiOwg2rZCv1PLbBpWVJiabmms0mQRDAJS+GC54TB0JXvUb0Jr/7FVnItlwui2kQkEwmGBpe+sCi+8LnTR4KteJMnF3RPkH1/DtAZXM5Gs0mZv9DcbBX3CLHNjVBq9Xo6+I1NBy6BXWLXTsyUOKmMud03w3Pg2YrngQ+8TTccQs68M9a7mgtERS2gOOiGlM4XmWpD+eciIbsstkcXr2OVxoDlLhv+S1wU2QvuoLssceX+lD7Rk9J9aYPw9PfkM/xKSbEI1mmVrsFTgJ9+Ysw+c2o5gxD9/8PkhP7wXEXtNmVSfUwC6e6VAgmD6GMWVUtCDB3qttgaDabcZXGSWB0IBnHK18tvc07rw21kl3Y/kJp24gCysjxqHoMMoMwcQClFIUVKDV0OmxwaVky4myliXfgB/fCM9+UBauwWTKVUcN6MiMfWkC57rIILCOiC1+7FeqUFTdLP9jWK+CuW9FbrsB71k4y3/4CypyfEvkps5eBjw+UK5W+lV0cx6VQKFCplMXfPJkBv41yFOnMuQWXHY/s7inKka1QHcfz6nMEni2CP7IVALc8xmpRHlTKEQF1JyETtn5bPLD33ktw6DFIro7zIM4SmngKPr8JvvPvMiFe2AwzJZxEAuUopqaq6CCIdRKVI9Jpj34BDu5lKF8kOXVum1w/8CE7LJ7tN7wvli068QyZFShQP9upKAqOG56HH/i0/UCGoAqboBRqJzuO/CtuhckDorU8vDE06dgmt6kB6UeN+ikjHebMIMyUAUUqaJBYZVn2bmxwaVkSjHKY0Y4ElrOyUR2bRh3AtitkIQNoN1COQzqVJjewPHvHhobzIvC+/0EJlr+2Rxb51/536oNFmttfQu6rf4d/6DHarRYGSCVTi1bWF1FyH73/QSnLfO0OKVWPboNXvQNz161UKmWKxfmFks8W101QLI5Qrk1JNik7jGlMUa3Vzql87Tpur0d21Ncalttt9nJ+guI2ABLllVsSn49AB9LzF+m5AhzcR7M0xsBJRL9XGp5Xl57iXDHu0Vu/XVQqPvlunPIhjDFo5eA1mmFJNuxBdlxR0+hoJd6M3yiTPIc2mCDwxUpZ+RLkRpWZ0iGUc/563vuB9EnO0Gi1QsHyrVA+jGcSoMA0WxII+lNyfXr6G/DVTwAmnvRevx2+/L9kQKd0CLY8N75tzUCpIZv5SP8znYN6BRVuwldbpnI2Nri0LDrGTeEXtxCMXIhf3BbeboEv/x9IPDU3G+W3ZfEqHYa990hGbHTdirhgOI5LsVCQfqbyGK7jMDA0hP+tf2DmRW8iGBhlavI4NNsd5xCv2cRrNMiGvaPAnN30Qp+7SAVFJfuDkEjBi98IL3i9yCgVNmMmD/Y1OGt4ngSwb/2TzsVN79l9TlPzmWyWRquNjvpaK0dDCRqZSG/XjsHKubadN/wwuHRLp/aaXmnMu9kIB+rOpzrDYiEZtYZI/MzXoxcGlYBsXqMN+Y6r4dBjkjXrXlcLmwlmFp611DqgEpXn85viLNzEMwAU+rQ57Tezy9ooCIKAVtsPBcvdHsFy84lfl9fzpg9J0Hj8e/KzRz4v2ci2J+9B9Npe8zZ49J9lkGzvvXJ/e+8BFQ5K3fVeyG+Ur70pstm5NpqrFRtcWvqKTg/gj1zYE0gGw5tkUZrN4KjICs3ORm25PJQRgXQ6s+LKBs48/Uzu0w+QPPIotaFtBOUjc7O1gY/XaNBs+4BBG0KZIwk+kwmXhJtY0GsRlexbrRaBMXIBivQ5K0fBTeF5jb4tes1mQ8pDsy5ujfLYgi/6UZBcLk1iJg9KmfDSa0Ss/a73oo3GGL2izpPFxgBBUcriqy1z2bGF3HOzZOFLY3LRD9p4pcPnfcCvn3QGeIyW9pL8xt5gzoTZM6XgkpeIIkNUkt24A77/dcmada+rlcO4Cxy26SnPj2yDG9/f5SP+dVSjtiRZuO7eUgUkU6meNayn7aqwSfpTlRJ3olYVUL2i++u3y7o4uC5sQdgYX5/yGyUbaXSvl3v1GGgfZTSO64LxUa6DoxRaK4wxqNoxktnMmgkqI2xwaTkjtA7i6ULHYWBoGIY2hIHktk5AqQdG5v175dVIlA7ilg6SKB0kUTqEqh2jpiDo0lgTm8Z7cV1n1ciIRDjNGRKTDxLM1ztYOwHbrkTvf0gWsLf8cbyb3rOblpul1Zym3miQTLgkE8mzDjRTyZRYNc6yaoua0/uVvTQm9BvuubgdxWiN1sGCL0SiDJCV51A9JhfRh/4BjEZr03d7y5WOHhjBpAdA+7jVo0t9OH2lYwsZiPQQ6y4SAfDH/hkwlEuT4e8p0unzryaxUKJAzigln9HK0TCwHBJZsfgXpZS7cYeoM0Ql2e4ybLd2pVq43mdniCcS+J49IX4KCaRzIco6+oGP0bJx1DrAmFDv0xB6mCvIDOJ7VRqNZqfkLBlLE5e1H7hTWgUe+Tx876vSRtDdbnDiaWjWoR1+b9f18O37RXOycIG85obYpKJrUr9QHFn1Ze6zxQaXllMS7Q49z5MP8shWdOUIldoM3PBuGJw7cejUxuNAsnyIROkgjlede+dKkR8elkVgehztKJRjSCRXjw/wbE7ZO7hxhwRl06VZwec22HSpOADt2U3b17R9keE5m2Aqm8vRaLUxkVVj4MsPRreBDmjVjp2VD/hJn6Pr4ms9N4gFKpUKxWJxwe+t9JC2Jajotpt81TsI7npv3+WVVjJRv6VbPYbS/hIfTf9JJVM0dFM2pcUt8O2wPKkcCc5QmMwgnlel0WxQKBRP6TjTr1aUsyUIfGrVSihnE7LhWZKVfOCTgBZ5G6Ug+p1kRgK9SDh+331Szr3rVilbaw1K41aPkEqnzilrFgSBDPE0ps+bwHcn6xhomcRut+X5RcEkDqDDoaUNMHUCRrZhJg9RLk2SSCYxhS0w6MZl7aiakt8IrXr8t93tBkrhKAe952apHFWPozCkpk/gZDLxtLjWMHkQN5FgaGhl6lAuNja4tJwUrQPZRRsDudDey3FF0PyuW+HeP8H90f/alZE8hFs+hNNunPFjdOtFrgVO2Tu49QoZuNFBb/BZPgzP+aE40Jw4ALuuI9h331kFU0o5FAt5KS03p+eUloPAP6fMYsS8kkRuEoqbMaUxKuUS+VNc6E/3HAr5PNVqhUAhAs67rpdyVnEzwfT5l3xarvhhSXwl61ueEkUs0bN+u0i+3Pmu8Gdh4FA5BjgYHVAuTeK4LgqFUnEZFaBSrYm/c+ECKB2m7nlkw1ImcMryazfRZrzZErka5TikTvH7vt+mWqvJ8a4L9X1RYg7xo78oGbbSWOiko2OZM78tQdddt8ox6wBMm0QiiTszgZvL9SVANkbTarcluMtvklK4m5Q1CEWhuLDP8enoZB13vhYeuScMKpHrz5WvlmAxPSDHMnVCpJn+7x+A60JxK355DCbH5PyIytpRiXvX9ZLhro3L88kMwMQzsrYUCiilwoTHcdzM3ETHwMBQ35/vasQGl5Y5xNnKunwjV4TmtCzWpTGRnyhuwTn8GIXP/t7SHuwKo3vAplU9IlOvBgkw73ovjlLguvHOuXxYZC52XR8HmrmCXFxGt9EYGoXNl5I6/Ci6dIipargZAFKpNINDQz0L46lKy6DOObMI0nOaSadpNBrSBL/x2fDCn4ZP/z5k82ivSrlSOaV38elew3QqTb3ZhO0v6PJ3P4KvDF59ZtVmvs+G1TopDl0DL+u6eubGHpPbdA7cVBhYRv2JCXBctPbl//lN+KXDeHUP5ShM5AJ2/19K0JbfiFc6HFZsovNo/vJr9zGVK1XRPVRKhjoKm/HKh2m2en2/e9ZYNzErQP4N+fs7fwNywxI4NqdRSomQeq4IjZp8301KIIr0p59MSH2heF5dHjMa4smG5XnlUFykwBLAV65UVPwwY2sIJX82xC43XlUC7fUXi+an9nuGc/jEr8ud7blZei51EGddayfAGFxHoVozJGcN2qylhMdiYYPLVUTkyd1utTo934575oMg0d97Xph5HN0mC3Sj1qXTNSS6ZuXDuM5qUc47v0QDNrlcVzlu5gRu6PsM4c596jhNrSUI/OS7JbAcCr18E9IvpC99GfWd11OfqsL+fYCSRbjl0Wq3KU1OgFIo6Mg4ZbJZ0b7slMfb8jdOAlOv9EzbLrRcmBsYoNluY2Ym5WL/6d+Pj33X9bDv3nNyLcpkszTbbenXHdkSatBpgsJm6pWjNFrt09rYrXa6NS5XGx070e7e3qPfkXJxsw6EZc/I4vDKV8fC1je8T4JIxwXHxbQbkjUce0w+Y9HP3QSkcuDV5HejNTBXxNTLlEslstks2mhcx5Whsiiw7JYCOvIk+s7f6PSBmkgrMdThprhVfq/lwRf/XH6mHAmIJg91ysEGZP2NNvq1cTlGX34+MNj/CflWqyVVlRs/0DXE8wBuc7rvgWXc12/QkfzSlsslaMwVZGo+kvqJJuirx0QDeWA0fh2h08uuSofIZrIEMydw0mlQoE+SkbT0F/vKrhIiJwfP8/BTgwQGfG1oBVBvNCiXyyf1Yo68UUuTk3j1uixgjivDNW/4A/k6mlasV+CpB8Jem/7uktciUVvA4OBwx/e5872hYckigpShUllZTB0X9t6L4yZIN8rwf/4r7P2cLMCOIzt0x41LSUhmRmuD12hQmZomNVQMp06R93l4g1y03CSeV6dUmmBmZopypUq92aQ5uJF6s0m5Ujkjn/SofK2MkQnXVE7OoZGtIt9R3EK73WZyYpzJiXF5vOmpM/YK7/Z3dyOr0Df+EfynP4ebPowOM0NrFZNIoYc2AKszc9npAzRhb+/Hfw2+/6BoqzoJwEhQks71CluPdAWRV70m1Hy9UILU40/1/vzNH5S+x/SArIm18fhzUtwCgOd5NNsB9fqMBLzpXPw43QGjUhjkX0fgfMfVcqzR9PEjn5OAynFl3Y16DB1HXF9AWpOGN8DUhDz/5jQAxUKh74GSMVpe5/KReIhn13USvKv+Jha0DihXKrS1QRe3wkxJPtN77wVUHExG31OOvB/pQWkXqI3HryN0qjwJ1+2sr7mBQXK5wZ611rJ42MzlCifONnqItMJF0uAeyVdoH3Zei9l3H16jQaPV6nFkEW/UsN9oXbjIzi6BR9PM4bRiInTHsU3Mi4/juBSLRckeBk2063SGntKZDJVviWMR67fL+xWVryLbTAK5iD39cEdw3AQ+ree9WqYn243eUtKe3fI7xtBoNOW+33Q7bL6sM7leLpconsF0ZHTs5XIJ41UlY3nN2+RCFU5ZShDsYkxAo9Wi0WiE117Jsp5qyjcKwlutpmTZN18WvxaFzXiTh1bMlHC/8QtbQTmoegWnMbXUh9NXOn2AtOMMXu0EYMQKMvDBSUHQlEBotrB1FERG8j2XvBi+/v9Ji4hy4PjGucMfxsjmLerv+8ffiQOsKFtpTBx4RVJAT39DPpfdwZhBAtqNO+Cpr8v39uyGREY2kMMbZd2N7C1HLwyP9ULZDJbGeqbHF6s87Xn1uGVn1nBeKtVfD/GpqVqvOHlY1nbQqIQr/ZdRMOlVRQEhkSBh2jiZDNpomq32eRk2spwZNrhcQfSUvYFkMkmr7aMjr9fKUfmA3vA+cU/IDEnJM+zPI78Js/9ByjMNEtf+CuRGCJ78D8zez/d+qO+4JeyvyctCXBoLJ4vVovbZWOZnvqEnYzRTtbC/qztou+MWed+7bTM37pCf1U7EdzB1Qi4c3YLAYVDG5EGIMizGyIVu82U9Py+Xy51zoTPE0GxgjMF1XQbzBRi+AH9kK6b8RVAa9t0Lhx7tLPwddLtTKgckw2PAazTwms3T9mYamFf6CFiT0+PyfkzDP/95+BauLv3PhudJBjDaQGeHY5mewI8tIZsz8N2vdGW7QmHrpx6QTU0k3/OjvyCDM5OHABP/vHv4o3oM6mU5vx7+RzBB1/Sy/FlPH6AOpNcvkZaAsbtI6CZkTd16hWzoJw7KHTSn5T5Lh2TdTQ3Ev7vlcqgc7rVcfOoBspnsoqzHWgeSsBjdGh7TmKwfocJEv914At+XxMissraZPEixMBJP1DdqOI7DcL6A6/aGL7morB6aVwzaKe4lxQaXy5CT+Z12Moyhs0vQbMlC1pVZ4o5bOgM3TByQxS/sz+PSl8lFd+IA/sRhePEPwqNflJ61+XQXoxJ4KNi70AEMS3/pEQcevUicOca+LedB9L4nkpLBzg7JReyBOzuZS4wht/9r1DFzZZEiq81cQTYmxsiFDOKgLZGCoE25XArFgrvkUfw2vjZUqlPwMx8Wqapv3ifDSUPrJajdeiVMjUufZGZIggA/diziWS+KM63GnHbI6KT6ndlhgqC5uG/GMqNTiahUwZtGlw5TUWpV9Z92rB9n9QFSrwJGPg/ReYCJz3tEJsugZB3de48MxETyPQAmwHVdtNaYnuEPJGsY9fclUvIvChrdhJRu8xvleJQjn5+g3RuEQjzJfvdtsgHEgDHSR9zyZcP41APyu1Gv57775Ou7bu2oTLiJxKJYLsai6Uqey5yAtv8e4rJBHJujkhFtQV03QXFk3SnvYz7zCsvSYYPLZUZP4BBOC3vNJonhomQoZ2cYA783szSyNcw2hrtwlZCFKZK6eeBOyA2T+N5/kGlM0hr/Lq3SPLqLgTjFOGdQnrScX7x6XXb6O66WrOTWK+RC9ZWPdbTa2HuPBIaZIbEg6xJLd1zZsCRTKarVaigSvEUyI1rD0Dq5UEd/8/2vS09bFLTpsERYr4qQcdSfG4TyKDqQr+/9YxI//HZ8lGR+rr9lTvmdSP80kZRAN9L77Mq0mubMKTOQ2VxOgsvAl7/RQadc2mL1Ze5OhefVJXDqWif0nt2ryntdtGKPzBHzBiXnoe46DxJpUi4MzSqP9gh0o3BmJnCz2c6QR2eD3zX8YYwR55zauASagR8HjVHAWBsX2bZworpQKFCbmkIH3X3KOpbAmTwYSuBIFSCXi6WMtDbyeYt+t14FpUhNHycRDv8txnnt1UNP89GL5PndfZtcPyYPoZRaHA9xUUafU9buqXBYVhQ2uFxm9LgKdDm0tKdqULxgboaxOt6bWZo8BCeeETFYx0EHrXBqUssiMbQeqsdJmTbpZ6ZJGU1Fqbm6i0pRXKAWoWVx6OmvVQrKRyXDV9wiGZNHPgfKEavIRAJjDO2pcQk2Qs297o1CIuGQz+epVSuYyQPyIKkBmKkQZVMACTi7RdfzG2WgIFeQTc3TD3d6e9l3r2SDXBfn8GPk7/lDppIJWr4/98IR4SQlGIbeTVCUccoOE+iTa6eKfmdBtDWb02Fv3IS0gVSPr6nSeLvVmrfVoV09smq819OZDPVGs/d80qHYdnQe+m3ZsOy9h0QqM+c+TqevO9/PlZLBmZnpaRHSVnSdx91BYEWcgVJpPK9OOpnEJBO0Wi2M1hhtAAP1augglBKZoR4fbIUm1LZMJMOsrCKVTC3YBvZM6JTDc3mpYnRnLU88Q2YxspbREJ/W8pqWj4RVC3FYsqxMbHC5zAh0IBnL2Q4tEwdEFHa+DONTD8BH3wnlwyhjyGRiu7OOEPrkIelNqh7HdWMrsG7dxXb1CEaJPqLNVC4v4ox2OHhVOSoLcdRfG/gkEwmGhvNn9b4lEklGRtd3AtdWq4lBNheRr3vD8/A8mUrtBG5RlqY7yxiVticOgO/gJmRjMjg0RGmyBASh+HJE2NMZtLqfaG+mNb8RaidwM6ceIHAcl1QySSuRhWe9WP5u1/Vwxy0E0+Nn/HqsdE7Wf2pW0TW60QhliK4Keyu3XSmbk6AtG5t99/ZY8y3U9nA+jDE0Wy3AgJuWwC/wpSWkUYPAx71wJ/rY92i0W7KWlw6Ly0syKQLdreh8V5j0AA2vSsPzUG5CYlU3AYGWisBNH5KS/Sd+HWrHaQ1volU+ctbuXGfKdDRYE02l331bx71GOU7fs5bSxhFWLyK/9GRGhqgwNrhcwdjgcpkxrz1g+bAEhl4tLGHGGUbluGRSSdHuSs/V7uqZNtaNefW9It3F1ZLZWG1EwzuBH4gUSPfwzthjkN+EKo+ddWDZTbf25mxyA4NkslnZpHhVORcjWaHuLGNU2g6JpKqUchgZHekZRkslUz0bmE5wWz1GEGVIc0UJLN0z80U2Rkvmctd1PT2kwRrSY00mk9KLPav/NJlKLfWh9QVjtIjzj2yDV/x8/INDj8rGJQr0wuGcTLo/mTaRa5uh0Qwz6AMjcq694X1drR43w4t+hgDg8OM9gaGpHac5uCFsVwp1MIfWhVqVSUgPiGvWztdKz+iOqyUTun47PHi39Cp3tToEe27Gq9dRSoWZTkc0HLXu+fpsrSwDHbqFOW48lT5xADAUiiN9D2Y9rz6rZaAXZw19dlcbNrhcZmSy4p5i9uyWBTQSzq4eJ5uV8s7ZZhjXmsXiaqLHgjM7LP2z0fBO1F9bObIo5apuok2KlOVDaZugHWcZnUSnz9NxnDn2jqfbwMwWlq9sezE6CEiVnmGwMXlGz005DqB6Ayvl4vutNdN3qaJssA7ivkNj5PurgIbnSXavclQCukRGsmtTJ+L2H9cFH1ALy7RF6geR3WMimQwHbbo2PZHuYpcvtVKKbKNEY+IwemTr3MDw6W/AVz4e2xbWxmOx9Uc+D8e/F8sjbdwh7SYnnpYgM7o/kNviFrzpSWg1pV0qkjsqhK5eSnUG5M4my+k4DrpyRNyKuoZ4Eq7b9xYp2VA2RBvUKKlgRK8xgJPEdmWtXGxwucyIxKcrlQpm4kBPKbtjT2UzjKueufqlF0oPVKQ/+pWPdfpr3USi44G8mEQBYDabkyxk7RgGIz2duk3CdRkcGj7ni5BSDoktl9GqThBMPE3D884o+5JwE7TajbjnbsvlodanWjN9l9pokR277IclKAk9lfXMidP/8QogCMJJcScBH/91aQ1RCtZdLNPGRkFjWjKLXo3a1NQZBVbdnuBaR5aRDhQ3409GIvQqdorpOJYNiklA5LP9zINQn6HebM0NDB/5fK9cUWRnGOlpPvFlOWdLh6QiUNwiwWsqK2Xqnpao0Lnnpg9J0PrAnRKkdn/dleU83fkfZWb9ti+i7XfdGrcWKMXQcP/1IkVHs0sbVDlz+2Xduf2ylpWBDS6XIWdSyrasXnqzlXm5sHTrl6YH4JHPhT7hmfPeH7vYbRTGaNr/+nfgTREUt1BvNs/IzjGytTT77pMAqzQmF3btE6yS4Op0OI4jPZfbXxAHIl/bg5NeHWXxQAeStXzDH8Knfh/a3lwTgKH18N/++owDq8gdBogHdLrtGz9zOxzaJ37lswPLeoXZ+r891qTpASmfn3haAshu28LmjASJkZ7mt++fK48U+ODVUI6L2XNzqCASyoVFEnKPfD4Wfe/+GuQ+0gN4jRrtdgvHcXHd3lJ5u92iVq1KoO668PrfFi3P8hEIAjKZzKIMdrZaLclaBr5UQdCyGSxcAJUjOF0yfJaVhw0ulym2lL02iRrcjVKw7iIJkLrdkopbYOKZBQ3vrBQ8r45ptnsUE85ETkcph0w6Lb7p+U2i67r1CrjrvbirJLg6LfM6qpjYx3qFY7QWdYFwiI3RC+eUi5ma6Pr/ZoLp4ye/v0jT0RgZpNn+Qjj0GAyvj+93445QWL152sASYmtSkTryaIMEhoVN8t7UxiE9JIFmJLZeuACqx1BAUhmM0yuPBMTSSOkUBoMXSchFWc85X4eDQI0pjJuirX35efkIDQ25Zz0fXxsa3/lqLKk0eiFctFP+AXz0nZhTvH4LRetA5NSiwaUrXw1HvwczEzB5kEQiyfAq0mZdi9jg0mJZJmgdUK2U5QJ6wWXwuveAV4ldd0L9UqWcVRtYwsnldJrlsbg15CRkczlavk+w/yFpI/jaHSgM6czaKK9poyWjddmPSEn20petqrI4qNiF57H75xXeJluQXw3/75ximEl6OLtK1Bt3iK5rZN+4frtsUGCW5mQl1Kec31iiOznQkRiaOYGTTofDNi2cbBaDod1uo6pHSGbSpzy/u5MNYoFZk+xoYVMYpO6WIDX6Oj0oWdOd18qQULf26R23MD3yLHj4n+QOoz7Q0tzX002fndXjfCYg3c9J60Bkw6I+YKMla1u4AJozKDfBcH71rm9rBRtcWizLgCDwqVTK8p9cQZr7//YX4O1/Fcv7PPUAypEL2mpeeE8mp6O1oVqrdXroOhexIEDrAOU4JNwEQ4ODVKtVzMRByOUx3hSVapVCfvXbwYnaxNFZZfE7Vk/mNtKWfPQLIgUUtObqp05PdIn+GzyvTiY7v02iH/jS196Yinsdo4xvlFGsHEE5LulUQgLB1gzJbPa0G53OIS9CFao7O9oTtHZ93WxOoYtb4yGhngzvVtT4fkyrLq03jalYCL57SImzk3LqSKYFWgLWeo16fYZEIinDQjrA9wN5H9ddBBjpHXdUx3a2MNL/qXTL+ccGlxbLEtPRenNcKUuVxqSkVT0Gn/mjjttSNpNdE/qjJ7VzvOo1BN/+ogQLmWzvFH17CnBptRvUG54IMt/4gY4tqtmz+7Q2kquBTDZLIwC95+Yw4BrDUWcm5bQSMMbELjzBDBQ2ym5kakJ6LTESTG18tmRtH/8SlMbmvPedifBmExw/Lnff9V4pHWsNaNzqEVLp1BkHkueT0wrBo6iXj4gOaNTb2TUQlJ05jmc0pruPNMrMTjzTycye6fPWyQwz7gBBpSptBbVxCVgdB19r8XYPNGAkuzxbpD20llztG8C1wvL6tFgsa5BG5Lhz04fhrR+RQYKpE1IKP/okCigWR8gNDC67C9xikM3lZDAl8OUCpQMJMl/+dihsxqt7lEuT4sEcDTzlw9LgzmtjL+cv/jm0vI4RQTSBv9oxRkswPj0ZBkmrpOESYpmlnddKZqw2Aa97N7zjDrmdmhSdyx97h2Rvq8chkcIYE5bANTPTU5TKZenNRUk5eGoCMsNyzpUO47gOI6OjFAoj5HIr83OXyWZxHQV775XPxJ7d8Pe/DHtuxnUUmWyW4XxBfrk2LmV07Ysb0MAoyWSCZqMRO+gQBeUzTE/X8OozaOXSvPAFTP3wL1J+wx/TKl4o61YUWGYGACPvl9Gi35lIy0T/0Hrpnf3Ov8P+B2GxrCUtS4LNXFosS4y4Mm2ZO5gw8QzOGiiDzyaS45qqVWk3Z2SSNhKOrx4LZUvC7Eaj1jVkMRSXACcOSMbzkc9JkBEaEXj1Ko1Gg3QqTW5gYNW9rg3PwwQ+vOWPu4ahTj8xvWJQKi6L48wzvKTlvf7or4S2kL78fjJNs9mg3miEuqwuXPISOPKklISvek2PBE6/xNeXkt7BotBDfXoct8uXPJGQ36lVQ4MENylZx6kJWoVNtCbHqHt1XNfFaIOWO+5oaNazSXjpfxa5JEClc5h6Je7hrFdg/cW9+p1PPSD3EX1mQ5H21fCaW2JscGmxLDGOcqTv6DO3y+K79YqwFA75wuou456MaGipXC6LtNDhxyV4KG6R4GDykDjxPPoFuUhlh+VCFrkE5Qrgt0RS5YE7O0YEksXSNFotmu02xVUWuM9rH3uaiemVRDoVqgHoANBxiTwSjHcScls+HP7fDfsHx+S1UaHQ/uiF8lnb/5BsTh79Qo8Y+mppIziTfs/IAhagXp/Bm5qQcvX9fymvn3IIoiDdcWZJP90MX7uDzOgFpJ95EGfyABWlMM0ZufNcQT633fqdo9vk82sC+YwCykmQG1gFmx9LBxtcWixLiNYBjWZT/nP4cfj+g4ABYyjMcrlZa5xUWuju28LgsS0Zq9oJCSyVKzp5USZTBxKIDozI/zHwop+R0tye3ZggOK280UpDBnpmDUOVj6yagZ5sLifngzGiyaj9XuHtffdBLi+lXTcRB0Jf/EvYd4/oKropCXi6h3fSuQX1Ga42tA7E5WfsMQnQr3qNaG86rnwGlZql4nAB7t7PMlAYke+Fr1+5UpHPnleVv9t3n9xHpN9pjHQkBA1S2cyy7Gm1nBs2uLRYloh5NS3zG6F6jGw2g+vaj2c2l6PRamH2Pyji2VEWsjYuwURpTPrkDGB8CSyqx4Fw8CNoQbspgeboNnjB66WEV9wCpcN4XmNVXdg6At5RqXjyUKe/brXgprMETQ8uej4c+Bbsuze2yo0EyqG31WT8KUikxA0m0qu8+7ZYuserkT2LCfDVimxODsuAzcg2CdpTA1IqV464Ec1ScZjtLOo4LiPFonixt1ryfmSGJOBXkJo5QSKXs8Ygqxx79bJYlgBjNFO1qmha7rou7ikMNS21biz1IS4L5tqhDsnr5CRkUAGx6kunUjRbTRnwGdoAGy8R7TzloNp1DApe9Q4JLCM9RBMAilq1ump09aI+u9rIJfiZAm7xAvIT31kVzy2WuTESKD7zjXhWaeKAZLNr472l8SgQajclUFKqV68ylL8pFkfWdJUgorM5eeoByTRueS60ZkT38/IfhRP75/S4plJzdTCVchgYHCLX0bxs4NqAck2hRtdtWEWjhIvP4OAgzzz9fS7efgnT09NLfTiWFUjnIqk1FLeKJEdxC7zpdvjku2HiALlsdnUMYPSJbk3LQAcSlCtFKpnqTJiWy2WMcmLrx+IW0D6p6XFabb9rECHUQyxultvSGG4ydUYe1CuFxrNeyszLfo7E0SfI//OHl/pw+oIXeXbf9CEprX7lY/Ctz0owufO1XQM590q53HHj93ziYOxCY4wMmwQtUDawnE0k09RoNiV2j/paCf2/tQ+pHLTqOK5LYZVszCxnxpnGQDZzabGcZ7x6XazPRrbKRe7yH5Us21c+tuoGCuD0jh1nwpkMJpzM+jGRTjEwMEi5NBlmuPJwxY/Bs14suoajFxJUjq6eiWrAKY/Bg3fjTx7Cq8+sioxREMwaVnrFz8MT/wqtugzkRNm0KJ35/7P33uGRnfXZ/+c5Z7qkKdJqtdX22uuCcQ3GmGaKCQk4hRADxgbyJnmTkORN+cUFgg15gyEQWhLIm5BCSAJeAyYESIxNcY3BBbdd13XZXXt3tdKqTNFozpmZc57n98czo5FWWm+TNCPp+7kuX7JGo5lH0szOPd9y3xogaGwjty4mEoWg1ggkWNlzzXOhlEOqq9uOpDQ2zTWqEbseoiIuLsFUNOVSf1wJC4OIS0FYRIzRDX89YHLczoDtf86KzIf/G+VGltVCQatK26gU5gfxqlWcRju7WXmcj593RvRjccjOZyoHz/eo12vE4wmq9br1vtz1EDz0XdsiPeXV8PSPCSaWx0a1MZrJXY/Cjq3Qu55KtYZfq5Nd4lnN+sBW98jOVrKMDlsb46ahIh0FZ/9Cq6L5yM0oZYhFHNxYlwijQ7AQyULCykHEpSAsIl6lYttzjgvpAWvPoa0lx1LytGwaktdrNbv4qcBxXSJuxFZdlYPuXkWlHhIWSraV2RAEZssVhPEeqE7iVat41Sq5g2Q0HwnTff1qtYpNBcFgeo+jnt8DYd0KjzCAwrD9PN4FkwXI76WmbYzkUq9keV7FCrHpWdJbrljym/FKOVYwTp/5Q9ks9Ze8wc5RZgamUnnAzKxoKkjEE6S6lu7vQBCWCiIuBWGRMEbjV33bnjvzza0lni1XgusSjTgLKiznak8Dc7asp1/XcRyMMQT1OgaIRqNUawEGM2WmbJNgoFavUYml4R1/AT198MP/B/Vgpn1J70ZYc4pdZLrhKgiDeYtmbFZb6sUxu8V62eemefJd0fBHBOqeFfjdq+DxH059f3miRDqTO6YztJtarTbblD+7jlpxkKUcgKKNtvGBTeuh9afbmcvC0KwsdRy3JSpLI/YNBUgCjCAsEiIuBWERaLaHjXLsi96jP4A9j9slnuxaGHsBN75wLShjNIXSBFrrRnt6L57v26xm5djqT36Qar1OuqeH0sTEtFb2tAWYwhBhtWY/f9cnp7K7ueGqlufgoz+AJ2+Hl/0yKhLFjB2Qa5zfC6e+tiE0rU+lqU7O68yjXZbaOEfq0fOtxY53/EXr/FuugGiCMKzNy/23E6P1bK/Lwr4ZMX5LEaO1HR9pViNHX2h+YXZKjw6tO8C03GrHUUuiKyAIywERl4KwgDQrgLValTDUswXZj78ChUGU4yzoEo/veVZYzmhPX2nteDIDcOmnoDhEeMNVFGJ9GDMx47rccJVtPW562VS1kT2P2Z9lmkgkqENuHbFHv0f30z+EsE7RgXDLlS2hmh6wkY4jO60Y0CEk09Rq5Xmbg3MdB53fM1vURpMQNEzrp5+/sVGsl0MOd9Nu50DB1RTVSxZlH3fNzfBKyeanN/PGD5y5/Oa101riivgcljmCICwMIi4FYYGYucyywYqbW//eCsxGugWP3IRyXLILHPMY6tC+0E6v5PVttJvVOx+wGdznXwK9GzC1im1dz2hlb7AzbVP/v99WhGCmSIxEYXwvkXgMFXVg2hxkODFMzRhM08+zsM+KnkaiTqCtqfx8WJt096RtSsiWK1r2Q8q1c5bNaMjp58/vtW30ZaAtY7EYvl+dJbhi8SUurhStXPHsOiiP2hhHpRozlo2vo+xjOz9ofS/DAMd1pSUuCIuI9AgEYQFomqSHQWAj1C79S1sJzO+1Qq4haKKRyLwssxwK13HtC/DITntBUxAObLbiqzhsLxvbjRv41lz6wOtmBlr/X6vAs/fCv/0fOzPazHXeesusRJjmHGR3T5pcLkcyHrfZws2FmtJ+2+50I2htPfaOFcex/nuEoW2FO1F7xsyA3TCeOv8fNGYxNRiD4y7tZR4AdWBkyiEuXzIY0zJILwzaywqDtv39qsth45n2smgC3v0ZohtfSpyAVDIpXoyCsMhI5VIQ5pkZFcu+42bOV2bWwAPfgntuwHUcehbJuHsqeWPLFbbqUxyyonLDGXDPFpjMwxO34TqKdMRQctTMVrYxdgv3nhvAaJTjEo9FCIqDGEA5CseFSCz+oq3t6R56+fw4xi/PTCjaciW1eo35qDG5boRcb69N9wl8SKanoiGV42DCAMaet4LFGLsE8uY/wDzwdZQJ5+EE7UFrPecGtZ4cbffRjhFlheXZb4GdD9nHizE2r3qqOq0g1YNT9+jxRlHd6XYfWhBWJCIuBWGeqUxOEtYbVTl/Ak69EJ66c2q+0lUQj7+4CJtvmjY9nlfBz++x3UMdwI3XoIBo6BGJx6bONNXKLg/jxGN2W7w0hFEQi8WP2ZtSKQfXcQiy660ZNkyNCqji4Lz8zGArmLlcrmGbNIlxIBa159daUyoW0CZEReOYd38avep4yokuuu/+Z5RZmj1yrUM7cjBjg3oL2lnilcvpbXGlWrOVAydDtQyrN8PQ05BZS/LR76F00NbjCsJKRsSlIMwjWof4tRq4LvSsgvweuzmtXHj4JtxotG0xg0o5pFLdJJM2eSOcHMGdJigPvO5CGyhHYzGCWVvNg0TjsXm9n+bPfWA51HUdcr2rpj6vbf02E2/4fWrrz6KYPRF2/hRgXo3eF4M5/SCdCMpZmmJ5CmPs8yhobPS7MbuQtv9ZW7nc/6wVnRe+j/j3P93eswrCCkfEpSDMI+WJkn2Bm2ZgzZYrIKzjOKoj8qs7JXkjmUzhO8ZurTfa745ySCbbs3gR2/soXXf+A5MvPEXYzHtvJArNl9H7QmOMph40KnbT/SC33kIkurQWerQOmSgVCYJpFUgHa51ljF0Ge8kb4PmHYWLMztcmM6S23ylVS0FoMyIuBWEeaFoO1YMAkhk7Wwktm5vxvUSjkbYLy05CKYfoq99JbbKEu+O+RR8VmAuz/W6o1mbbMM2j0ftC4nseBmXnLB/9QeOxtxulWFJ59VqH5PN5+0lTTDYiQznpFbD3CTtysv2uadVZB7wC/sPfI9EBb+IEYSUjzz5BOEaaCzyVas0u8FTLcP2f2Azrps2NDnGXwSbyfGKMJtz9OBSHcWKJtgtLaFg25dbNtmGKd01FXnYy1nJqPVz+ObjgUpuEFO8m6rpt/90eCROlkv0fpRr/Nb6QW28dDvyytbBq2i2FAWCFp9ad/3cShOWOVC4F4RixMYlmZrVryxXwL79jhaYxKNddUpWjhcYYTaFYQhfuht711Mf3UlCKbKa9FSfXce2bgelzoNOM3n1/gmSyc+cv7fkH7Sb1+ZfY8z9xK3VjMEZ37LmnE4YBQVCHRDdT9Y+6Z6uX+b3W4SCZthZWyTRUiva61QqsPRUK+6gXB2fN2AqCsHiIuBSEY8RWu2ZnOTP2AspxiMfipLq6lsQL+2LheRWbFT1tNlVvuQLPq9jlmzaRSCbxa3V007JputH7xCjGMK8xlfNNIpnEq1YxW66wRviNRCRTHO7oczfROqRQaLTDq5VWqpDR1uDecaz1UGYAKnmoFKzArFascfqGM+CeGzBLfDFeEJY68monCMeAMdrGOo69ALf/Y6sVXthHKtVFb+8qurp7RFgeQL1Wm1OQ12rtzfZWyiGbSZOMxezfNAwgkYaJUdsez63D8ypUJssdmdWtlEM04kKix7bEL7jUtsh719s3QR3OxESjHZ7M2o/GNP7DCkvdEJnje1tfq5Qa4tPYyEejiUXn13FAEIQjQyqXgnCUzDJL33YLPPYjCANcd2Gzwpc6Bmz7doYN0T7CMEDrsK1b2U2jdwDP92H9S+yc34Yz4MZrMCi7Qe77ZLNZXLez/hmNuBFq1UlrTj+Vqz6IO88WTwtB2FyIq05Cur9het/I5dQhoFAmxI1ECXWIyW1oxT+Wxxqzl0jUoyC0GdW3avUSNz9bXLq7u9m18zlO2HQS5XK53ccR2kQz3rEehAckzFxB1FH0pCVu7sWoTJbxqlWbitPc9tUhaI1ynI7Yym69edAzU40u+l1bIQsDwJCIJzpq7MEYzThJK8yy1uLJ7RAbrLloOi1Ua1UrLh3Xui2U9ttZykoBgFyuF8dxbeu8WMRoba/7jo/DnsdsVvyz95JMJKbeHAiCML8crgbqvH9pBKHDscsoDWEZT8HWm+FrV9sXxNx6nCW2mdsOkqmUbWOGgd321aEVmauO65it7GZSkauAyXHbYn7XJ2HdaVMb5AC+7zOezzNZnuiIVrmJ98C7PwMXXEqsMkoqHutIYWmMpjJZJp/PU/F9wmBa2744ZH+/DWEZj1mPzkJ+jPz4uK18v+uT9u/wzWvhyTtgx/24kYhULQWhA+isf20EYQngVSo2v9mNQHrAVk/G98BdX7btxw432u4ElHJIJhL2k41nwqsuhzf9vl2gSabxfb8jhJpSdiGLum/jFGPJ1gZ5rWJbuOnVoBR+1YpM3cbZRq1DSsW8dSvY9RBdiTjJVOdUVZs03QI838coxxq+0xgvya2zM5TVSTs/YUwjiz5v55sV9nrrTrMC84JLYXIcV9GRIloQViKdNSwkCB1Ms33nV337onfJx+wL3MhO2HIlbPs+bjQqs5aHSTKVwq/VMDvut6Ly3q9BTz+U9mMMTJSKHTFe8KIb5KX9sHGTrWCPPg/GtM1sXeuQfKFgZxCTWdj3FHljOjJZaMotYPMFtn0f1MGJQGEQ3vEXM9rcGChGs0DBfnMyY8V9c15308vgni3E4/G2P1YEQbDIM1EQDoNmYkjF8zCJtE0NufXv7XZ4/ybIru2YeMelgt3MztiW+OjzEO+2Yq13g/W+DEKKpVLbK5jNDfJELNrYIK+3zupG4by3W8ufVLZhtm7wPW/RzzkjevR9n7cflbKXdxBah3iVSssQfXw3RKKt7e8bPwRP3Qk77m8YqDuYiVEr4CMxa6De02+Tk77yR7ZKa4y8qROEDkJeBQXhEDRnLI1S0H+CbYdmBmz15JGbGpvOg8QTCRGWR4jjuCQTDVGw7jR45bvtwkxxGM55K6Fuj1A7EKUcurp6yPX2EYlGwStaW5x0P3znOit2/IlGq7yHMFz81ngYashtmGnvlFtvL+8QwjCw1VWwbgEbzrAic9v37ZhJ02po9Hn78bLPQd9GVFi3XpZhHTBW2Me77fV0SCIWk+eeIHQQ8mwUhBfBGE2pVLKbqWf9PLzzE/DuT8PEiK2k/PRbsOUKHOWQTMoiwdGQTKVwIxFbqXrqTrugkVtvN/Cza9si1A6G47hkMjlyvX0ox7FvMGKNzexmq9wvt2nu0kC+0S6GVvQonWEIYoy2BulK2eeS0Q1fSmP/P6jarxkN5/4C/N71dr4yv4e4M+OGbLU7sJ6oyomQ6upsc3hBWGnIzKUgHASbFlLAGGNTQLZ9H/Y8bpcIcuthdBeu6xKPxzsiF3up0tzKLuTH0WMvwDkXt6ydCoOETufFrTiOSy6Xo1DIoyuNtnMqa83W3SjKWXxB50Yi6CC0beLceissjbHCvQPwKhUrDHMb4MJfh8EnG/6Uo/byZtJOZgAe/YF9ro3vRgGpri6SqRQTpSJBEKCUwgl8YslER8dxCsJKpTP+1RGEDmNGK3zV8fZFML3afvzxV+yLnnLIZLLywjYPKOUQiUapaWWFxd4n7O9auQRBrSNzsZVyiMfjeH7VbjsHdTs7uPVm3Gh80c8TiUSo1wMYONlm2q/eDENPE+kAcTllLxVN2OpqcQgu/ZR9Lj1yEyjXCkvHtV9L2Ja3Uops1j7HlIJMtrfdP4ogCIdB+//VEYQOxKtUbCt88wWtdJZvXmu99x65CeW4Uy96wvwQcSPU6n5LqK0/3VaLUZ2bi90sUD76g5YZ/PTLF/Moxti28sgOe5aRHY028+Kf5UC8SsO3NKjb2crp1VUUZPqhMAxBQCQSxQ2ruMmkdAQEYYki4lIQptGssFgTbwUvPArP3WdfrLNrYewFopFIR1jkLDcSyaT1Pdz2fejbaIVa7wYIA2qlfR0pNLTW1pfxJW+wc5envAaeuA09Obqo5zBG4/tV6F0/8yxP3o6eHFnUs8x1Ns9reIJWy3Ze0o3aZZwGTmk/bsSluyfXcbZJgiAcOSIuBaFB09hZG21b4fm9oOvWdmh8z1Q0oQjLhUEph0Q8bvO8M2usOGrkeQdaM57Pk4jFOipqUevQ+l5uelkrx/ueLYs+JzpVGSwOzzqLG1/8Fn2TMAwoFvL2k+pkyxvUjUDg2FlLDLlcX9vOKAjC/CPiUhAaTBk7X/bZ1ovzlivhpW+Cx38EhNIKX2CSqRS1ICDccb8V9Pd+zVYvL/pduPFD+FUfv1rFcRTxWJxkqr3LHMpxAGU9F5ttceUS1KtoHS5KFc5WBj3o22CNyJtnGdu9qP6PYRhQKhbQWtuFG8clNI2efLNqOTFq/79SoBG/g+tKpVIQlhvyKikIDeq1mp0Dm+4TmF0L+5+F7FrcSERadgvMi+d5b8R604DWBq9aZXx8nMpkuW1G6xE3Yr0Xz3yzXaQ5883WSkc5DaeBhT/XVA57YciK8AsutZVfHeIoFkV8B0GdQrGIRkE0gWkKS6Xs9n+tYquWRje8KpmaBU1nsgt+PkEQFpclJS5f+coLuP6rX+GxR7cyOjLMW97yllnX+eAHrubxx7ax+4Vd/Mc3b+TEEzfN+Ho2m+WLf/937NzxLM89+zR//dd/RVeX+BMKYFDW2Hm6T2BhH9SrUBgkFou194ArhIPmeU9LwWHzBXazGPB8m+mdz48tutBMJJNW7m77Pgw/01rs6dvYmjVcIIzRVCbLtmqZTLd8I5++G3Y+AMpWdxeS5hmKTf/Ks99iz3HZZ+04SW6DtZXq3WCrlokeW8FstMMzWZmxFITlyJISl6lUiscef5yrP/DBOb/+B3/wf/it3/rfXHnl1fzcz7+VSqXCN77+deLTZo6++MW/49TTTuVXL3knl13+Hl71ygv43Gc/u1g/gtDBqPUvsS+MN1wFX/1j+9FoGNstJumLzJRo23KFjfi74aqZKTgDm61gaQpN5UxVMwvF4qIJzOacKDq01cILLrXVw8I+SKbxPH9BztK0yvJ8H1CtyqAOoTQCYQDYMYOFojmjbM+AFZJB3VaY+zfZpZ2m7dC7Pgln/ZxNNjKGSMS1aUeR6IKdTxCE9rGkxOWtt97GJz7xSb73vZvn/Pr7f+e3+dzn/oqbb7mFJ554gt/7/f/DmjUDvLVR4Tz55JN500UX8f/98Z/w0EMPcd999/PBP/0Qv/Irb2PNwMBi/ihChxF2ryJ4y5VT28mU9tuPxpBMJslmJDN8MWnmjitjYHTXzBSc3g120Wd8T0to9m2cqmbqMGTSSWHcmZVmrUNKRVvhLBXz85aik0yl7Ozljvth+//Y6mFPv/VthAWpXnqVCtoY+zP3Hd+qDCYzdnEGQ26B54OnZpQ3X2BnPfN7rM/n+G5baT7zzbZCueUK+NrVsO0WUA7xeIJMtrdjK5bGaLzKJOVyCa8y2fZse0FYiiybV8vjjz+egYEB7rzrrqnLJiYmeOihhzjv5ecB8PKXn0ehUOCRrVunrnPnnXehteZnXvYzc95uLBaju7t7xn/C8sIAk+dfDqks7hv+N8lkkjgBqWSS3t5eUl3dIizbQDMFJ5lM4tYmbVVOa/vxxmvsx+lCc1o1s7rmJYy/+wsUfuEjlC94L5XjXk6+UKSuDTq3kbo25AsFwkaF71hoCmF0aO114t32zUnvBsitw/e8eRMpTYHseRU7HzywGQp7bbX0le+2s6lAMpFccPFWa84oD2xuRDga2HqzbYdvucK25nVo36SNvTBVTe3q4H9Dm9XYSrVKtXuASrVKoVgSgSkIR8iy2RZfvbofgJGRmZ5u+0dGGFi9unGd1YyOzvSfC8OQfL4wdZ0D+eM/+kOuvvqqBTix0CnUjn8Z9Q1nQlin58Fv4HaiWfcKRSmHVKqbVKrll1grDRHq0L4r0GEri3zDGXa7XIeQSoPjEvYeR9h7HDx6m90FmuEEcAWFQp7e3r5jfvPgOC6OY1vzrDutZbx/4zUYN0qlWqNar5NJH30FXOuQfKFgZxuVY+eDN5xhf/ZvXmvb8oVB3EhkQdvhYP8WYRC2zrDqOBh9wf6O67ZNHivvx02mQFk/UNdxO9KrdDpzOUboLVfgeRVSqc4VxYLQaSwbcblQ/PXffJ6/+/svTn3e3d3NY49ufZHvEJYSOppk8uXvBiD52M24peE2n0g4GHMJTS8/aL8Y1m0102gcxyHz7O2YfdsI+k4gWLUJP7/HzgROdwLIrYfR5+ct/Scei+NVq7ZiVxyyQtdoOP+dsPkCwi1XHtN9lSdKVlhe9lm440uw93ErKrPrrKAe243jKDLphbfLmvLVbC4RZddZYak1kUiUdGZpesHO7RixjlpxkAXW64KwrFh6z/6DsH+/rVj29/fPuHx1fz/D+/c3rrOfVatWzfi667rkctmp6xxIrVajXC7P+E9YPnjnvg2TyuKUhkg++r12H0c4TJpCs7e3l2QyQaQ0hKsMyXicbCaDoxzcSp747ofpevhbRL2CnQmc7gSQ3wvRBH7Vn5e2dTKVwnEc2/4tNmZ2ezfAy97WELPrbMX1KAlD3RLIb7nCzjmGAZRHbUtaKTLZ3KKIOr/qW1/N3g1W2JbHGmdgyQpLaLgjzeEYEQZBW9wIBGGpsmwql88//zzDw8Nc+NrX8thjjwO2yvgzP/MzfPnL/wbAT3/6ANlslrPPOout27YB8NrXvgbHcXjowYfadnahPQR9J+Cf+gYAuu67HqWPff5OWFyaIpNDVJW6e9K2pTw901q5ENTQyTSVahmvWiXd08NkuUwQWC9G13VJZ7KHNb/YnL30PY9azScAeNPvT7NSGsSNH7mdlV0wqdioyaZA7t8Ev3wt/MdHwC8TjUbp7kkvmmm70dr6ar7j47DnMRh+Fp69F4xZssISIBaN2erzdFN8owE15UZQrdftstkS/jkFYaFZUuKyqyvFpk0t38rjjzuOM854Kfl8gb179/LFf/hH/uRP/j927NjJ8y+8wJ9+8AMMDQ3zvZvtdvkzzzzDj269lb/6q89yxZVXE41G+MtPfoL//M9vMzQs7dCVhFEO5Ve+D5RD7Ll7iO17st1HEhYQx3HJZjIUCnm7dBNNQFCzJvmXfw6KQ5gtV1AsFAAzlSgTGsiPj+M4CuU4xKKxF00FUsohmeoikUxSLJUIb7zGipT8Xsitx+3th/Hn5/zeuTBG29sJAjtnqdyZAtlxyGUPT/zOF5XKZONwjZb4VDKRsqbyS5hkKmWtlcLAWjo1l8ZQNkN+5wPoMJi3UQpBWK4sqbde55x9Dnfcfht33H4bAB/72Ee54/bb+OAHrwbgC1/4W/75n7/EZz/3GX74g1vo6uriXe+6lGq1OnUb73//7/HMM8/yn9/6Jl+7YQv33nc/f3LFFW35eYTFZ8qOplwhvP1LML6Xrge/0e5jCYuA60bo7e0jlerC0XVrPH7552xlsX+T9WdsLstUy9Y3soF2Y4RG2VSg/KFtjJpJQ6l4jPjEEM5LLoRL/5KJV/4ahYlJxsdGGB8fZbI88aJtVt/zCHXDcqj/BPiNf4QNZ1rR40SIRtxFFZZah/ieB73rW235abZdPenMop1lIVDKIZlI2E82ngmvutxWnwuD09wIUsc03iAIKwHVt2q1afchlhLd3d3s2vkcJ2w6SeYvlxhah+TzeftJPGV9CJVDLpvpWM89YWHwKpNUqtWZ2+M3XGVthNad1rLRaSbKZNbAxVfZ64QBYMhmc7iHWakzkRj51/4u5tvXgQ7s7GR+T6ONrA7qSVmeKFHtGYDTXmcXhN796Rm596l4bFEraKVinnq9bg3S3/YReOBbdkaxtB/lKHp7Vx36RjqcqWqx1nZRqThkK8UX/a6t1IbWpkwql8JK5HA10NLuYQjCETBRKtrKlONCesCaPeuQiVKRTLa33ccTFpFEMolXrWK2XGErlvm91vi8tN9WqIpDti3qFe2WR09/o7q5wV5eLVMo5A9bYKqghvvdjxPoYJYdkgnDg7ZZtQ7t2ZqWQzdc1bIcchwSyeQC/HYOTqj11MgA37nO/j4qeXAaSUXLgGbV2fMqeON77IXT3AhUoptEUqJgBeHFWFJtcUE4FoLAtu7YdB6c8mq45GOgHMJQWlwrjTkTgJrG501T9mpjttCJwM/9sRWDzVSgVBaAQiF/2NvDOqjObYfkuAQHMXNXjmPv/5vX2sduQ2y6mGPyzDxaXGfayMD0qEljFtxbczGZy41ApTJw3q9gXvI6JicnJb1HEF4EqVwKK4Lm9i9Gw3P3w7P3WePnzACqMNjewwltoZkA5HkVPG/CXjitQoUx2LJlCP/xYZsXbrQVVs2oxUqBiVKRnvSht4ddx0FP3/Zu2iHpkFo1RHeFs8YzIm6EWr0KZ/+8ze3eeAZsvZlYPN6WbeXunjT50oQV4okeW9kFsotkgbTYTHcj0E6U/HP3QWGIam495Pfg1+oSDSsIcyDiUlj2hGFAsVQCN9KadQObKIIh3hzgF1YcTfGQTKbwvAr10hBGQSwWn6rEeZUK3tgL9htSWSss06tt6zyaoB7YiMBDiYw57ZCMsfOLYY18Pk8ul5shMBPJJH6tjt72fbvZXthnz+RXSSxCxOOBOI4L7/wL+PH1OEPbcSORRbNAajd+OQ/VOlz2GUnvEYRDIG+3hGVPqVhoJZu87/P2o1I2UQSWVTtPODqaIjOT7SWbbeXJK+WQ6uomm+u1jxmvCPEuKyyb29LnXIw2mlKx+KJtUsdxScRi9nsmxmzl8yVvAAx05QBDPj/O5OQElcky5XIJ3/OIRdyWJc4r3w3v/AQoRaFQWPS2rI53w5pT4Ff/nEzfatKZ3IoQltDMUl83O72nVmvvwQShAxFxKSx7tDFzz7rpkGQyIS0t4ZC4boRsNmc/qRRstVGH0LcRXv1eyK4jCOqM5/OUSoWpeTxjNJVKmWJhnEJhHADlRiDwrRXS9rtsFdQvN+Y4Fb5ftWbdXf1UqlX8Wg1ya+EXPwjnX2K32Xs3TkVgLiZB/0n291EYxKkt7n23G6P1nOk9RsvcpSAciLTFhWWPo9Tcs25AMilVS+HwaPpk5vPjmKAG57zVCsvikG1X9x0HhUHqgaYe+Hi+j2nEMloj9UHCWq2RMdhYJEpl7fxierX9mMq2rJBe8gbY9DLbRs8PwuBT9mvNx28yjedNkEwe3NR9vgn6TwQgMvLcotxfR6GUnbk9ML1HqXafTBA6DhGXwrInnclSKBZnzbotVg6zsHxQyiERT9iIwEd/AHufaImMU15tIxBHn4dz3orZ9n0wobUOuvRTVoQ2fDIdpWxFvVKYLTCbVkjF4Za5++gu+PoHYNUJM22TYFFn/uqNymVkZMei3F8nEY/FbXpPc0teh2AM8bjMbAvCgcgrq7Dscd0I2UwGx2gYewHHaLKZDJFItN1HE5YgyVQKx3Hs7GRxf2se8vhzrfBLZe1md99Gm6wzMQKP3NTyyYylrH9lbp29wUrBmvofaIWUGZhRpcQYK1zj3a3r5hZv5s8oh6DPjpasxMplMpXCcd2Gi4DFcV2Z2RaEOZDKpbAicN0IuWWQHiK0n6ZHprUwmmRqM+yb17YqipGoFYinvMa2zIvDLZ/MMLAt9F/7W9vq/voHoFKy7VUdwI0fsgLmqTttKk9Pv/3+JutOs9XNDWfAjdcs2lJPmFsP0TiqVsEt7luU++wkmn/3inbxN5wDPavIPP0DFNIWF4QDEXEpCIJwhDS3yxOJpN3aHtsNyZ7WFvm277eqkPdsgclxeOI22z4H20KHqeUcymNQ82B8D67j2uzqsd2tljmmecd2HrM4ZIXnIs78NZd5IqM7UJhDXHt5opRDKgL+6/83ROOYoW1QGj70NwrCCkPEpSAIwlHSNGL3PY8wrBK6DkFQA5pVyIYhe6UIkRiEDTF4/Ln2BkZ2WqF4waVTyzthGDBVDQ1qU7N9QCulZ5Fn/rQOqTz9U3jyJ4SAZrbh+0pBGYNbHCRctYkguwFXxKUgzELEpSAIwjGglDMjF9wYbcXm5AhuPEY8kaDq+4Q6xIklqdbq6Buvgew6KAzaCuc5F9sIyt6Ndq7SaEDBcWfZFvjjt4LjgHJsa93eEbDwPq1hGNiFOFWE3AZ0fg95Y8hlsytWYLojOwh3PIg3tBNdmSSRTMpyoCBMQ8SlIAjCPHKg2ARmfJ5MWvFZK+4l0Bou+l0rLJvLO6ms9dGMJay3Zc2D+78JdR/O/Dk4+VVWgD57LwqzoKLGGE2hkAfHteEDTSuvLVdQniiRzuQW7L47FWM09bv+HfwyYW59w4tUYiAFYToiLgVBEBaRpvhMJJPkCwXMjR+yFcvpFkMbzoA9j9mFn1v/3mae922Ex39oZzp1CEqhFnje0vc8WyGdI4QgbMaorjA8r4Kp1maIbb3lCkrFIunMoTPmBWElIM8CQRCENtDcPlamYageS7Yshs57u22Nf/2Dtg1+2WfgfV+wgkYH9j9jiMfiC3rGMAwhmoBmCAFMVVhdZ2W+fNRrNeuXe0AMZBDUyefzVCbLix7LKQidhlQuBUEQ2kRzIcjaGk3YC8M6fPujtmLoRuxs5gwhsx7GnseJRBZ+3lKHEIS2TX9ACEF3T2ZB77tTMdCKgWyOCTQSmkxhH57v49dqRCMuETci85jCikTEpSAIQhtp2holkyk8r0KtNGRFnWFm1XBKyNjo0kx6YWf8jNEE9QCicevN6UbtrKdyiETcFbvME4vGbELTgTGQp7wanrsPMmswO+6n5iapVct4vk8iHieZWryYTkFoNyIuBUEQOoCmyEylWhvnlUrJbogfUDUE8H1vQWMfPa/SMHYP4eyft6lDkShsvZlYNLZg99vpJFMpqvU6eq6Epp/+R8s4f80pNgZ0y5V4vk8tCBb8DYEgdAryKBcEQegwmks/ud4+WxULA1s1DAMwysY+Vhcu9tGKWx9611vh9OgPYPgZaw4PJJLJBbvvTqc5K5tMJlD1Sst79JvX2jcAG86wbwIyA7ba3IgBDbWxC1KCsAIQcSkIi4QxGq8ySblcwqtMytC/cEgcx0U5DqAgPWCrY2//MyiNEIZBw3B9fjFGky8U7eOzMGStki64FDJrQIc4ihVffWtWmXO5XisyC4NW+DfjO3v6rXdpM/JzYDNk19oFKUFYAUhbXBAWAWM0xVKR0GhYk4OhPBXfIxGLk+rqWvEv1sLBScQTdsavkodEF3znuimT9UIhT29v37w+fjyvgtGhbccbbStyzdlCpRZ8Q30pMX1e1vc8wvIINWMwxSE7k1kcalUz79lCuEhRnYLQbkRcCsIi4HueFZYfvhy1sR+zewSuux6/6lML6mQzWRGYwpwkUyk837eVsWbsY+8GK/zGduN73izT9mOhXqtBPAXd/YC2orI0Yu+fhU8EWopMN843RpMfH8Pk7eIVxliBrlyCoIoxWp7rwrJHHuGCsAiEOoQ1OdTGfgD7cW2vjaDWIROlorTJhTlRyiHiNv6p3ngmvOpyeNPv26WRaMI+tuYRA1Ct2K30N/2+vb+NZwKQTCREGB0CpRwc1wUUnP0WWHsqnPlm+6YAJXOXwopAKpeCsAi4jgtDeczukVblcjgPPSkIQuqVKsVSkUxaEj6E2fSkM+QLBdhxv10WuWcLKBeCOm40Ma/3FYvG8ELfiqEbr4HsWigMohxHqpaHSTQaJazW7CJUc6QAA8k0oa62+3iCsOCIuBSERSCRTOLVfMx112PW9lph2dsDoyXbNrvoHMI7ts17i1NYHjiOSzaTsTnfo89DNAlhHTfizuvmttYhtXrdRkyGdYi4MPYCynHIZnPyxucwUbYlYZ/bzVGG9Goo7cdNzO+bAUHoRORfCkFYBJRyyKazKG1gzygkYjBStC8+a3rh7a+Bgdy8tziF5YPrRujt7SORG4DTLoRXXkrPqrXzJvi0DskXCoTGQHaN9bgM6iSTKXK53hVrmn40aKOhdx2kMlCdhGQGSiMoVraNk7BykMqlICwSNuqvF8+rUC37aK3h9WfBO19nheb+PBqHcrmE67gSGyfMQimHLtdQf9kvEvYeRz2YxH3ux/Ny2xMTJSsoL/tsKw1oyxXUgzopeRweEa7jWoP1d3wc9jwGw8/Cs/eSkJlVYYUg4lIQFpHp1iXFUpHw7sfhuX22Te441MMQVmVgKE+1XpMZTGFOYs8/hNd7HLXjX0ZinsRlGATQd/zMHPPcesKxF+bl9lcSiWSSar1OeOM11oh+fC/Kmd8RBkHoZORVSxDagFIOmXSGVCxBfGQCpzdjZ7Q+/B7UR38NPnw5odE2gk8QDiD2woMA1Neejo4eu2AxRmOMaeWYg/2Y32urmcIRoZRDuqcHZTSMvgCJLowxFEsT4gohrAikcikIbWK6N165UKK6pneGVZEZyOHvGyeZTEn1UphBpLgPNfgE5p6vU8iPEQmrdPekj3oucsoex5hZOeax2MrNET8WfN/DKAWXf25qzEBvuQLPqyxoJrwgdALyiiUIHYDruLBv3FoUgf24v4BB8oiF2WgdYm78MOx5FNO9iro25AsF9FEuhIU6tBnYuXV2szk/CGEIOkRJ5fKoqNVqVqRPHzPIrrOXC8IyRyqXgtABJJJJvKpnrYrW9cHQuI2JDDXh+GS7jyd0GOWJEmBmLd9MTJTIZHJHfHuu41pT9nf8RWsB5bn7IN6F75dJpSSi9EgxWluRPrKz9Tcq7JO2uLAiEHEpCB2AUg6JWAKv6kF/Bl5+Cpy6ET59I05MfPGEmYRaQ27jrOWbYPT5o4oXtG9uqpgbPwS9G21LPLMGSvsBpJV7NChlIzpvuKplpG60zLAKKwJ5KyoIHUIylcJ1I7B1B9z3FHzmRliTw7zlvHYfTegwXMeZe/kGdVRLYEo5ZDMZ2xIffR7i3VZY9m6A3DpqVWnlHimxWMzOsOqwZaRuDPFYvN1HE4QFRyqXgtAhNDfIfc8jHC1jjl9L7f97G9VYlOhggdjTg+0+otAhdPekbRzk9OUbJwLp1XiFfSQSySNe7lFKWTGkFKw7DQY2w4Yz4MZrxNz/CDFGU60FzU+m/lOOKxGawopAKpeC0EE0N8i7u9P05KskHtkFjmLy3a8hzEospGBxHJdELGaXbmo+bDgTfukamBgFFPl8/oiXe6YWx5SCnQ/A03fDN6+1rVxj5v+HWMZ4XgWDgXd9El79HjjuLADisYjMrgorAnmUC0IHk/ruT3F3j2K64pTfcyHGlaesYEl1daEcBybHwSvCd66zVcy+jYChUCgc0fJIEAagnDlbuTIneGRMbYqvOw3OvwR+8YPQu4GgXm/30QRhUZBXKkHoYFSg6f7qXahKlfC4VVR+UeYvBYtSDol43ArAzBq44FK46Hft1ncqizkCE35jtBVETTFqDGCmKpaJuCyVHS7GaMIwbG2KQ2tTvL1HE4RFQ2YuBaHDcfOTdH3tbsq/cRHVV51KZNd+4o/savexhA4gmUrh12qYHfdbUXnv16Cn3y7jJNN4Xumw5i99zwMDROIQNJZ3av6UuEx1yUjG4WCMZqJUtL/LOTbFY7LMI6wQpHIpCEuA2FODJH60DYDJS15JMJBp84mETqC15a1hdBfEu6ywTK+GagVQh9UeD3UIqYwVRGf9nF3mya0DBclkUuYEDwNjNMVSiXrQMKTv3dAaLwgDMEaWeYQVg/yLIQhLhOQPtxF5eh/EIpTf+zpMXBoPgl3uyeUaxumVIiR6WjZCfRsPqz3uOi74ZStKn7gNdACFQdluPgI8r0KoDZzzVigOwZt+H151OWw8E4BkIiEiXVgxyCNdEJYIyhi6b/gfnMIkenWGyUteKTNcAmAFZjKRtJ+sOw1e+e7W/GUyje/7L1q9TCSTOI4DxWGIpWB0F8pospmMCKJDYIymMlnG8zxb7X31e+0yzzevhafuhB3340YiItKFFYX8qyEISwhnskrXV++CUFM7+wSqrz6t3UcSOoRkKmW3x3fcD9v/x4qbnn6oVjCGF82oN8agw8C2xXUAKHEfOgyarXDP9wFlxXxxyFoQnflmGHuBqKPIpNMi0oUVhfTVBGGJEX1hlNR/P0jll19O5RdehrtnjOjzI+0+ltBmmvOX+fy4TdlJZVvtcR0Qlg/+GJmYKIHjzMoqL0+USB9FVvlKwatUbBTn5gsgvw8UrSWesd0o5dCTluqvsPKQR7wgLEHiP36K2NZd4DqU33MhukusYoQXb4/X6vU5W+PGaOu/mNswK6s81Ifvk7nS0DpstMLX2wWowl77u77gUmsNpUMS8bgIS2FFIo96QViCKKDrm/fgDBcxmRTly16DEaNrgYO0x9MDGNScrXG77KPmzCp3HXmJOBgTEyVrLl/YZ2Mym3OWT94hc5bCikf+5RCEJYqqBvR85U6o1glOXov35rPbfSShA1DKIRpxIdENa06xlbTLPwe9G2ZlhBujqfo+9K6z2eRbroB//wP70Ri6e9Jt+ik6GxOJE8TTtv3dFJXGWMuhsd0k4zGZsxRWNPLIF4QljLu/SNd/3AuAf9GZ1E5b3+YTCZ1AxI2AP2ltcc6/xC6ZFAYJ172UQIfkx0cZG93P+NgYWmso7rfZ5BvOtObpYYjCHNJ8fSUSZNZSfOs10HccFAZbrfDcOgAiEZdUqluEpbCikUe/ICxx4o/sIv7jpwCYvPTVhLnuNp9IaDeJZBLXUbDlSluJvOEqyK0neNX7KBYKaOXAquPBdSGZtmbf37nOZpRX8uA4Evk4B9VNr6D41msJs+vgdf/LJvHc+KEpyyGUoictAQeCIOJSEJYBqf9+EPeFUUwqTvm9F2Ii8tReySjlkEmnScVjxMvDpGJRuo47Hf7rE3ZO8LLPwvu+YD/W/ZbALO2HMMBxHJkXnIZxIpRf8R7Kr/0tiMaJ7nuC3G2fJ5fNEHUUTn4PUUeRy2al2isIiBWRICwLVKjp/uqdlP7oYsINfVR+6eV0feu+dh9LaCNKOSRT0zLBn/8pkxOj0HvcrK1wVp8IsSQ8chPRSETsc6YRdq9i4nW/S9h3PBhNcttNJLd9F2UMOK5YNQnCHIi4FIRlgluo0L3lbiZ+8yKqF5xC5PkR4g/uaPexhA7CAXRzK7zpZzm+GyoF8CdAfBltXGalQq1ew8RS6E2nQ89qlD9B993/TGzw8XYfURA6HhGXgrCMiD6zj+SPtuG9+Wwm3/4K3L3jRIYK7T6W0CGkM1kKxaLdBs+uswspKJjMg1Jks7kVLywLxaJdclIOpHLw0HfgmR+T7u4m4hfbfURBWBKs3H9FBGGZkrh1G9HteyEaYeJdr6JS8ymXS3iVyRfNlxaWP64bIZvJcHwYEB97wVrnhHUi0Si5XC+uu7LrDb7nocMQHBcu+0xjLvUzMDFCfXyw3ccThCWDiEtBWGYoA103/Bg1lEf/4014XoXqqm4qNZ98MY8+wOtQWFk4jkME+4//2UAccLVGiQm/9QGNp6B348y51Oz6WR6hgiAcnJX9NlUQlilOpUr0C/9FdTQP116G2tiP2T2Cue56CoU8uVzvim5/rmR8z2MvcB9wFrANOD8M8T1v5gLQCsMYbauWQc3OoU6fSy3sxY3Hj/n2vUqFaq1q70cpFOBGIvT0pA+5ZW6Mxvc8Qh3iOi6JZFKew0LHIuJSEJYr+QkYyKE29gNYgbm2F7NndMULiZVMqENOwwpLGh9PBp5ZwZU5YzTFUokwNOBGQQfWIzS7FgqDOMohkUwe8W02xaCjHPxaHaNDm+TTEJIm0UPgFcnnx8lmc3OOJTRFqV+t2ojX3HrID1Kt1yUFSOhYRFwKwjLFdVwYGsfsHpmqXDKch3SKwEgLdKXiOi5PYSuWzcrlM43LVypWBBq4/LOQWQN3fRm2fR8nv4d4IkEymTpsETdbDG6AsRfsF5XCOq9j76e03y4NVfJTHQXHcaeEaRAG1IMQo3VrDrRRTQ23XClvEoWORcSlICxTEskkXs3HXHc9Zm2vFZa9PTBWonbxK/BqYO7chg4DabOtIBLJJJNVnwtCW8F8AsB1j7gyt5wIw9DGNzbnLN/0ezD4JNHyMKnU4SdeGaMplEroIJgpBv/rk7B7G+BA3YN4lxWW6dUtgTk5Tn58nGgsShBqDMpeLyjD5gugOHyAP+k6wvLw/P8yBGEekFcSQVimKOWQTWdR2sCeUUjEYKQIvWm48Ewqjz6DV/OpruqhUvMploqyTb4CUMqhK5vj4kiElwNviUToWsEWRFqHVGs1yO+185VgP+YHj7ia63seWhsrBvummdUPbIZqBWqTtuXuFSHR0xKY/gREE+C61CMpW9u87DNw0ivs7Qxsbs2BHsP5BGGxkMqlICxjHMcll+u1bb9aiBtPEg9dyv94C/Xhmcs+4XXXS5tthaCUQzqZ4h8mSjxhDBeuUGFpjKZQKNh2dXpgKoOd8d0oOKxq7nTT9RAXejdYMbjzgdZS0IYzmle2XXGFzXBP5WBiBM76OXj0BzaO85HvwfAz9vsyA/Dk7fb7c+vt+TJroDCI6xz5HKggLBYiLgVhmTMrBhBwdg3D6tnLPtV9edlGXSH8TzQGwOlhyGodsn8FVsF8z8MYA/0nwDs/AY/cZNvPk3mioXfIx/8s0/V40lYY5xCDjusSi0ao1Wp2WxxlBWb/JgjqLfujpqAc2QnnXAyP3wo3fsguF4UBKr+HxBHOgQrCYiOPTEFYgUxf9gHsx33jhFpPeWJKm3x5M+44bGtsJ7+2Vm/zadpDqENIZWF8DxSH4PxL4Jy3QrVM5DAM5WeZrv/GP9gK6I0fsu3vhhhMxmNkMxm6unrI5froW7WaXG8voOyyTyTaanufczH09NsUpa9dDcVhlDHEyyOkkklyuV5SqW4RlkJHo/pWrTbtPsRSoru7m107n+OETSdRLpfbfRxBOCqs9UqR0GgYyNllH2PgA+9CbV5nxeZ115OKJaRNvoz588kJft/z+Go8wR/3pNt9nEWn1LOe+guP2bnHiZEZLfFcNjtDwE3f4DZao1yXeiyNqfmQ7of3/JW9Ys2Df/kdnOoEiXjiRTsAWocUikVbPW1aIGXXQX4QhSEWjeK60kUQOofD1UDyaBWEFYhSDpl0hlQsQXysjIuCNb2ozevs1zf2w0BOUkmWOXc2WuOvq9fsm4sliJ15nGRiokipmKc8cXhRp/XVJ1N/y1V2RrI4DLEUjD6PMoZsJjNLWBZLJSp+lZpW1LWh1r0GUx6Dannmsk1xCKplEnH7xuzFRKHjuOSyWVKJBDHHEHUU8fJ+Uok4uVyO7p70IW9DEDoRmbkUhBXK9FlMrzJJZTg/yxMzQOFVJqVysky5NxqjBmzUmk06ZOcSyxbXOqScH0cZQxSoA1nq7Mc+pqPxOBE3QjyRwPc8PK/S+uaJSfiZEaLnv53I7V9Eax83mZzzsT7lg3n2z7cWb/o3weBT8PUPgg6P2nR9rploQVjqLK1/SQRBWBASySTVeo2w6Ym5bxyMIRzIUtlfoFqvkUlnRGAuMypK8UAkyquCOhfW6uxMLp2XBGM0pfw4EWP4HeAfgU3A80AXVmieUq3yNFUmK5PUwS7dOI5tPY/vga/+MU4ySTI193JMsxXuV33IbZy5eAOw7jToXY9TGEQpgyoOEo3HZNlGWPHIo18QhBltcnek1Jq//Nj/gg9fTth4kRWWH3fFbGv8tfVam09yeDTb4MViAWMMpwM+0Au8APw2EAL3Y9OHfqfxuQErLN/xF3YBx3EhmabqVRgfG6VYyDNRarXWK5NlCsUSlWoNnchAfs/MxRto5I4PkYgnyGZ7yWRl2UYQQCqXgiA0aLbnwnKJcF3fjPlLM5AjHJMFtuXIXdEoH8SKS2WMjSzsQIzRVCYnCXwPB8gAUeAp4LXAMDbO0gdeQis73W9cNw+Y3AbY85itWvassibmTgTcCEFYB+WCNtYOaGx3aws8swau/xPYerNdvNlyha1+FvbhOkr8JgXhAJbV26urr7qS0ZHhGf/d85O7p74ej8f5y7/8BE9vf5Jdu3bw5S9/if7+/jaeWBA6D9dxYd8BNkXDeXQYijXRMuShSJQh4J+MITpRPKxlmMXGGE05P07N93CBnwJXAUXgOGxL3MVGWSaAJ7FVSxqfF2lULvN7YPjZVjqO49o5SqPh7LfYj5d9Ft73BZuy04yEjCXh8s9BogcV1lqLN/EYmXRaKpWCcADLrnL55JNP8auXXDL1eRC0tl0/dt1H+dmffRO/+Zu/RalU4pOf/AT/+q//wsUX/2I7jioIHcmM+cs1ORjKg+tQD0KKpaLMXi4z6hheDQwCp9RqjNdqlKs+3W2OhGzOO4Y6JBKEaK1RwOnYquSJwNewQjKHrVy6wBexFc3zgZOBp7GBOAowWsOz99o7iHdZC6LmHOWB85QDm2HHT1spO8UhqE6STCRlAUcQDsGyE5dBGLB//8isy3t6erj88sv4nff/Lv9zt61m/sEf/hH33vNjXvayl/Hggw8u9lEFoSNpzl9OlIrUB8fgjefA218DI0WJiFyGeF6FSeyM4lnYit/5YYjnVUiluttypuYWuGMMpwGPYVvbCWwbfFvjrF8EXgOMRSKkYnHiiQRV36fe8KJ8xnGJuO4c2+IKqpN2fnL9S2Z+nB7ZaLRtgefWN7K8pQUuCIfDshOXJ246kcce3YrvV3nggQe47mMfZ+/evZxz9tnEYjHuvPOuqes+++yz7N69m5efd95BxWUsFiPWGHgHayAqCMsdpRwc14WBDOqyN9oLZfZyWVKv1WbMKJ6Frfg9VatBanHP0szprnoVorQE7x8B/w+oAMcDFwCnYquWjlL0ZFqG5wd745Pq6ibVZf/91jokXyhYC6Gtt9j2+Nab7fzltHlKx3GIx6Lo8jBuPCaWXIJwmCwrcfngQw/xB3/4hzz77HMMDKzmqiuv5L//6zu89rWvY/Xq1VSrVUql0ozvGRkZZfXqg89d/vEf/SFXX33VQh9dEDoOGxE52/vSjSXafTRhHjHGto6b1cBtwDMsvqd6s1qpG3fcbH8DfBz4Z6y90E5sG3wboJQines9YsHnOC69uRxepUKtXgMToFwHR4ExCqe8X8SkIBwDy0pc3nrrbVP//8QTT/Dggw/xyMMP8stv+2V8zz+q2/zrv/k8f/f3X5z6vLu7m8ce3XrMZxWETmfG7OW6PhgaR2W6SBA79DcLSwalQGOrgadh2866cflioXVIcXyMKPBLwD3MbH/vAALARCJoYAyIx47NT1Ipx1Yz5+UnEARhOstKXB5IqVTiueeeY9OmTdx5x53E43HS6fSM6mV//6o5ZzSb1Go1arWl4f8mCPNJc/bS9zzqfkD9LS/HGEP5f54gUg2lqrNMcFyXSBBQw/pE1oEYELjuotx/0wwdbLXyPOAmZre/jVKkM1l5zAnCEmBZP0u7ulKccMIJDA8P88jWrdRqNS688LVTX9980kls3LiRnz7wQBtPKQidS9P7sid04CdPwi0PUOtJUKn5FEvFjrOsEY6ciBshAH4P+NXGx6Bx+WLgex7GGAaw1crXYwXl80AaW70MlKL7KNrfgiC0h2VVufzz//tnfP8HP2D37j2sWTPAB66+mjAM+da3/pOJiQmuv34L1330zynkC0xMTPCJT/wF99//U9kUF4RD4Hse1Hz48OVT85fhddczUSrSI9ZES5p4IkG9Msk/AKdg5y9N4/LFINQh/ViT8+OBNwKbsRXUESCeSkmcoiAsMZaVuFy3bh3/+A9fJJfLMTY2xn333c/Pv+WtjI2NAXDthz+CNpovf/lLxGJxbr/9dq7+wAfafGpB6HxCHcKaHGqjXX5TG/sxa3LU946J9+USp+r7KGxMog+8AWvxU/X9RbGcch2XPK1c8BzweONrmd4+HGdx2vOCIMwfqm/V6kXeCVzadHd3s2vnc5yw6STKZbFkEVYGXmWSygGVSz6+BS48E+7YRjIWR6EIdYjruDKPuYQol0uc4vtTiTYAZwLPxBN096QX/P7DMGAyP47CCsvmBHw614u7SK15QRAOj8PVQPLMFQThkMxK7dlfgIGcNVd/ei/eaBHqAQz0wtA41XpNqplLBNdx57QiCmpVjNEL+jc0RjNRyOMC78dWThPYymmtWiWZkpcoQViKyDNXEIRDMiO1Z+8YXHTOVGoP+8ZAG/jIe2bMY0qSz9IgkUwy4VU43xheAmwHTgB2GrOgKT3GaCZKRYwxvBT4m2lfuw14RocH+U5BEDodEZeCIBwWSjn0pDMUS0XCO7bBk7thOG8zm9f0zpzHlCSfJYNSDtpx6A1DzgfeBfwf4JUsXEqPMZrJQp4wDFnNTE/LZuXUlVlLQViyHHa/Y83AwEKeQxCEJUCzgpmKJYiPlUnFEiSicRi2ST6A/bg/L+JgSaEoAL8PfBBrWv4Mdmt8IfA9D8KQ/wMUgOOwnpbnAucDWkmGtyAsZQ67cnn33XfxgQ/8Kf/xrW8t5HkEQehwmt6XTYzR1Er1xjxmL+zPw0AvkZ4MjJRe5JaETsAYjQmDOVN6YtH5T2MyRlOrVTkdG+t4J7YVn+HYIh0FQegcDvvZ+/G/+ASf/eyn+dKX/olsNruARxIEYSkxo5o5OoH78tPgQ5dSederMYuZISgcFb7nEcHOWdaxKT01bNUymZq/nrgxmspkmYnxMUwQsB1bIb0b+G3slrgbjYr9kCAsAw5bXH75y//Kha97A725Xn784//h59785oU8lyAIS4hmNbO7O0163wSOgfC4VVRfeUq7jyYcglCHnAo8BFyHTelZBTjx+LxVD5szljWvgmMMd2MrpBdgZzv/AYi6rhjyC8Iy4YgWel544QV+5e2/ym/+5m/wr//6Lzz99DOEYTDjOm+86Gfn9YCCICwtnJJH8uaHqLz9AipvOZfo47txi5V2H0s4CE0roh3YecttwL8B7jxmizdnLN8G7MSKyruBvwU+BQSRCBnJDReEZcMRb4tv2LCBX7j4YgqFIjffcgthEBz6mwRBWFHE73uG2rknEmxaTeVXzqf7X+9AGuSdSSKZpFz1OT8MORm7yKMB4/uYY4xdNEbjVSr4vscZwHnA92hthr8V+L9APDZ/VVJh+WKMxvc8CWtYAhyRuHzve97DRz/6f7nzzrt4zWsvnIpVFARBmI4y0PUf91L844upn76R+pnHEXv0hXYfS5gDpRzcWJyaV+FU4HLg9cDrtT4mn0tjNOX8OEZrHGxm+euBr2ErlydhhSyuK5vhwiExRlMoltBGQ2495Pfi1+pkM2kRmB3IYYvLr3/9Bn7m3HP5wAf/lG9848aFPJMgCMsAd3+RxO2P4f/s2Uz+8suJPLMPx6+3+1jCHGijOR345rTLTgae8DySR1m9tBUmTQxriv5+4I3YxaEa8IRSxJPJo759YWXh+VUrLC/7LPRvgpGd6C1XLKjRv3D0HLa4dB2XC1/3Bvbt27eQ5xEEYRmRvP0xamefgF6dwXvrz9D1rfvafSRhDuaKgHwOUMYcddJSqEMyWDF54IwlkQhZmbEUmLvVTSROkNtI0Hc8Yd8JBH0nEH7vc5CsWWEJ9mN2HfXi4IIY/QvHxmGLy0ve8c6FPIcgCMsQFWi6/uNeJn7356hecAqxh3YS3bW/3ccSDmB6BOTpWJ/LU7AVxufCo5urdx2XAlaoXoKdt3w9MmMptARlEAbUgxCDgt4NML6XSiIHv/opSMxRjcwPwsjOqcolhX0YGebuSCT+URCEBSW6cz/x+57BP3Utpfw4amwElCIei5Pq6hKR0QEo5WBclyAIOBF4J425S6BeraK7wiPyntQ6pFKZJAK42HnL/wZCrEl6l8xYrliM0RRLJUJtINEFQRku+0xLMN5wFWy7BXXmzxIZ20Vk7Hkio7uoe2P4Rtuv926A8T1gNLFYvN0/kjAHIi4FQVhw4t+9H39sFLTBrO+DoTx+rUq1XiOXzYnA7AAc1yUaBHwPa0v0USCKrV6W8uNkevsO6++kdcjEuF32dIH7abXaXw4ESsnfewUy5RxQ9THKsbOTj3wPhp+Z1eqOPfwdup+6ZYbDRDTqUHMcdBhAaT+EAY7rzqvRvzB/yDNcEIQFpzI6BtrAhy9HffTX4MOXg6MwOrQeiELbibgR6sBvYUXgbwEBsAYwxuB5h+dVWp4oTWWSn4oVljQ+ngpos1CJ5UKnYje9i3jVKsYAuQ1WSGYGbAVyZKe94shOyO8lYoJZ1mVKOWQzGVLJJHECUskk2YyY7ncqUrkUBGHBCbWGtTnUxn4A1MZ+zJocDOUJdIhXmRTvujbTnLv8B2N4CTbv+3jgeaAfGKvVXnRxwsY7TlKv1+kFytjZzd/Fpv9UgCdB/E5XGMZoJkpFdBiCG4Ez3wyP/sAKyXMuhsdvhS1XNOyFBnEddVBrqmYSmND5iLgUBGHBcR0HPZTH7B6xwnL3CAzlIdTUkoqa50NDbFbrNTISA7joKOXQneulOD7GNmAAKyyPx6bqvFjBUeuQ0vgYCisei9i2mAa+1Lgsh80rdxrXl/zw5YsxmslymWrVB+UABuJdkB6AV78X9jxuZyeza6E4hAJiE8O48Zi8uVwmiLgUBGHB6e5Jky/m4brrpyqWaAPdCfBrtl3eEJ3hddczUSpKznQbcBwX5bqYMGQ/sBorMA2gDlJy1DqkOD6GAWLA94FfBHysqHSxAvUF4AzgcWMojo8RT6XE43KJM5eNkDGG/Pg4OI4Vlo4LZ/0cbL0ZxndDcQje9Un48VfgkZuIRiLyXF+GiLgUBGHBcRyXXCbHxESJcO/Y1LZ4GEJ9Te+sdnl97xjFUlEqmG3AjUSIhCE17DJPgBWN9cbfYbqgcJRDzbczszHsTOUbsR6ZL8MKzARWWDaN1GPAZuDpSoXJapUuWehakmgdUigW7Xxtdi2M7aFSmWy8CzH240mvgOKwrVbuftTOV2650l6/MIjjRulJz3/CjsREth/5bQuCsCg4jksmk6O3r5/e3lV0dfcQxYF947ZNDvbj/gJcdA5h4wVCWFwiboQA+D3gVxsfAyCs1ygW8kyMjxFWJjnF96l7FYwxDAB17JzmNuwS0CXY9vgwcBpwB9aS6F7gJ8DvAPUwZKJUxBi9uD+kcEwYoykUClZYvuMv7IXNSiXKfsxtgIHNrWrlpZ+CM37Wbnnn95CMxxYkutFaHRWp1Hyqq3qo1HyK8hhbdKRyKQhC20gkk1TrNcJmu3x/AQZy8PbXwJO7CcfK7T7iiiORTFKqTPIPMLXYswk7dxkL6tRp2Qtdgk3eyTeu8zxwPtaAfXvj9gzwBPBA4/ZOBF6DFZpnANvrdSYLealgLiE8r2LF2qpN8PzDML7XLutsOg8Gn4LJccjvgQ1n2EWdG66CzBooDOJGo2QWoFo5/Wyh0bNGbY42aUo4OuSZLAhC21DKIZPOEFUO7B2DC8+EP70URoownMeVpY9FRymHALuAcz7wEawwPB3Y0PjYtBc6Dyssjwf2Nr6nCjwK6EiUnlwvsUSSEPg28DhwDa0K5lasUCUUS6qlRK0WQDJj29xP/xjiKejdaCuV/gSk++0G2I0fAh1AGMDYbhKxhRWWxmh83y4HTh+1YSBHqMMFuU9hbqRyKQhCW1HKoSedoVgqEt6xDZ7eC0PjEIsShAFeZVJmphYZpRR5Y/h9Wgbo24ELgbtoZZC/HluZ3IkVliOAqxTpXO/UNnhXdw+mqwuvUiHwPf7WGM7AVjA/2fjeLJA/yphJYXEJe1YTkrTVyswAjO2285Xju22lspmeE01CtQxju3Ech3Q2h+surOTwPQ+DgeHCTGeK4TxuLLGg9y3MRMSlIAhtp1nB9D2Pmh8QJONQ8akN9FIbGhd7okXEGI0xhhBbuTwZeAY4AfhXrKCcfrlyHNxEgoLWJA6yPKGUQ6qrG5NKMVEq8lS9znnYRZ+TgVHAVKtU3LJskHcwtfVnUX7t/4YHvwP3fcMm5UTiENZAh3DjNVZw6hBqkyQX2REg1CGs7QNHwce3YNb0wuAYypiDemcKC4OIS0EQOoIpg+TyJEG9CtfKzFQ78D2Pxr4vGngMOz+1E/jZxsdQKZ6JxXHdI9vEnapSj4+xyxjuwCYBKSANFCoVqpWK2BR1GAaFd/Yv4p39SwC4x52JfuBbmKAOsRgExrbBIy6M70E5Dtls7+J7mWZ7YCQPV74Dtu+GXcOwZ4REXDofi42IS0EQOopQhzBwQJrPQI7acEHa44tAGIasAgpYj8pV2I3vOvCk6xKPJ+g+hr+DUg6RWJxTqj7fx7bbI43bj2GXgcSmqHPQ0STl1/xv6hvPBiD+1G10PfB1SPe0LKmSSVCgtcaNdS3689QYjVevUT1hDfgV+PSNsDpr2+FuRPLH24CIS0EQOgrXcWFofNbMVKC1eF8uAlqHFLFt8J1Yr8tm8k5mnsSe67o8DXwTWxX9beCfsMs9J2KXfv62YVMkBtvtI8isY+INv4dOr4GwTte9XyHx3E/sFzskitEYTaFURGNgpABlH2UgNlrGjSXkDWmbEHEpCEJHMcOeaKBhT7S2F977JsJPfUPa4wuMUg5RYDc2U3w/DVuRaGzeXqQTySSTVZ/Hw5AzsGbrYlPUfqabj5ve46i96U+gK4szOUbPHX9HZOz5dh9xFl7Vt8Ly2sum3oya667HdV35d6KNiLgUBKGjaC73FIsFwkIZfukCuOhcVCKGGciJ9+UCY4ymjk3TaSbsfBHsFu48oZRDVzZHWCzydFDnDcCTtGyKbsOarj8AfDsM8SoVUl3d83b/wmxmJO7kNsD+XfCtP8N93a+Tvudfcaqd97zTiSjVDWsgX5o1RiP/TrQXEZeCIHQcSjnEY3EqVR/OOtEKS7EUWRSalct/wibrPAVEacU/zuf9pDMZyoU8XwxDosDfYiuY78eKzFMa1616FVDIks8CMZW4oxRc9lno3wQjO2HLlcRu/hROsvNmFsNsF+XfeCP6we3wX7vEeqjDEHEpCEJHMqM9vq4PhsZR2W4SJtruoy1r9AGVy9diK5d6AeLzlHLozubwPY96GEC9zlNaE6OVRR4FMsBIpULN82Z4aArzg1etY4yBVcdbYQn2Y24dujzc3sPNQbChl4lffyOmJ4l6+Wmomx9AN8dohvO4yhHroTYjbwEFQehImu3xVCxBrFKDX3gF5kOXUk7HKZdLeJVJyQteAIzWU5XLnzY+RhuXLwRNC6qengzZXC9GKTZj2+Lbsek/JeBsIGIM5fy4/N2PEWM0XmWScrnERDyLt/4sSGWt+fnITnulkZ2Q39sxKVnNM5cimuIJveioizs4TvaffkQ22U0qliA+ViYVS8jSXwcglUtBEDoWNW0jdTIax/+rb1Ebytv8cTFXXxiUmrNyiVKLcNcO8WSSpysVHgB6sUbr9zJti9wY2SI/CprLOkEYUA9CDAr6NsLwLiiMgF+G9GqbA55bD+O7UdARFcDWRriGeBd87374yRP0uEmcetgxm+tCCxGXgiAsCdRN90PVn7EVKubq808sGiMIghkzl6Zx+WKQTKaYrFb5dhhisBXL6VvkLwGeqtcpjI8RTyZlDvNFmFNQJrohmIDLPtOarbz+CpRSmOKw/fro8yilyGazHfG79WrVxkZ4K1iB666nGqvLc79Daf+jRhAE4TDQQTDLXJ2BHH7Vlxb5PJJMpcg0qpcv0PC5dJxFM6JubpLHGkskTzBzi7zpuZk1hlqlIm3yg2CMplgqUanWqEVSdgv8ss/AiedD33EzZyt71xONOKQSceLaJ5VMksvl2jrb2myDTwQ+XjoO63pnPfdDHbbtfMKLI+JSEIQlwXRzdWBqK1R3J6jUfPLFPBMTRRGax4hSDpcnU1wHvMxxiKe66M71LmoFq5lFnuntQyvF32IzyJuJPlFgI1ZkBlpTKhblb96gKcqKxSKhNrMFZWZgjtnKQSJuhGSqi+7uNMlUV1srls02eKXuU8skYGgc9o7Neu53yjyoMBtpiwuCsCSYZa4+nIdVafjIe2CkiLnuemrxCLWKj1+vEYtECOr1qZZuMiXt08OlH/ggsCEa48E2th0dx6Wnt4+JUpHt9fpUos/0TfLNwPZAzNahVa0MtYFoArp7W4LyydutkDznYnj8VthyhZ2tzA/iOqojZisBjKMop5PoUqHVBn92ED75dZCN8CXDyn0WCoKwpJi+Pe4M5SEVh4+8B5WI2TbZ2l449yT48OVoo/GrPsGaLCEGr+qTLxakunWYpI01TC8twhLPoVDKoSedAdflcaz35R3YNvm9wDbsVjthiO95bTtnuzFGM1GaVq0871egsK8lKHv6raD82tVQHEYZQ3ximFQ8RiadbrsoN66Df/5milf9MrX1uZlt8M3rYCCHi5KN8CWCVC4FQVgyTN8er1R8GCnCNONkzj91KqGDNTnU7/+S/drHtmDCEM+rkEpJ0suhSDdEeNHpjBfvAxN9HsAu9pzV+PqJQBYYqfoAKyZPetbCjtbT2t9rYPv/wJYrIbvOCkoMsYlh3ES8Y35HJupSPf9kvNedjsna57ZKpzCDYzON0UcKxGMJWeBZIoi4FARhyXHQFvlF57aE5gWnAY04uLW9MFqiXqtB54WNdBwZ3TmVyybTE32+HYZEsFXLE4HzgALw0jDk6cokk1V/2bbI7UxlhWqtijZYi6hENwRlOOet8OgPbLWyfxNc9Lvw9Q8SKe4l1iGCciq/HIM+cQ3Br10EfWkAVLFC8q4niN2/ixLOjOe3tMGXFiIuBUFYcjRb5L7nEY6WqWmDGSnCx2+wwlIbOHUj0Bj+3zcOYYiRBYDDoqeD2uLTaSb6eJUKge9xvjFkscLyfqZ5YYbhsvTCNEZTKBbRWoMx4Li2Bf7I92D4GXj1e2HP49arMrvWmqBHIqQ7oO09df5yCa1DWNsHT78An/82zvt/keRPthN/4DlUaKvmU8/vsTJuLNERwlg4fERcCoKwJJneIp+qhoyVcWJxPN+HT32jVdVsiKWoVD4Oi2ZbvNSBL+bNTXKTSuF7HiNVn5eG4QwvzDOA7fXlt+Tjex46DMGNwKbzoDg8c2GnOATv+iT8+CvwyPeIRlx62igsp56XOkR1pagf14d+vDDTr/Jj1xP/i2+QOCC/fPrzW1h6iLgUBGHJc+ALUSKRpFDIY/aOQjQCoYZ1fYSXXIjZcjfKtPGwHY7WIX8SBIwCY5VJdDTSkVne0//mT1cmp7ww78XOYm4Dzm8s+SwXkRLqEOIpSA/AwGbY+cDBN8AjkbYLy2KpSGjsc4+hcXi6Amtn+lWa1Tn0WLktZxQWDhGXgiAsOxzHJZfrnaqa0JejevUl1KMRSuszuE/txnVcabUdgNYh5fExfgqcCpTCAD0+RndvX0cKTLDzt5NVn78NQ87ACssy8D3seG2pVl0Wf2djNGEQQliD8d2w4QwrJG+4yi7vFIdQYBd24rG2/8x2rlLDh6dVKf/8q1N+lWraIp4bS7TtnMLCIOJSEIRlyYxqZgCRb/+UyWefJxjOEwzkYJ9kkx9IeaKEg51fnKr+NS5PZ3JtPdvBaG6SN70w78V6YD6N9cB8OgiWdHu8ucDjV6t2G1wp0CHceI1th4cBjO8m2UFRmCbiUD1+FVS8mVXKgRxqfx4jizrLnvY/CgVBEBYBc9+TtjV3zWWoP38ffPhywsZMmGAJteZUWhY/Z2F9JUPd2f6g070wX4PNQ296YN4BBGFIqVhYculNWofkCwU838coB05+JfQdD694J6w6AWJJSPYQi8VIpbo7QliGuW5Kv/fzhKdvhH0HJGqNFEjEEqRiCfGrXOZI5VIQhBVBqMNZ2eRmTY5wVOa9mjiOw/YwZButyuXTjcs7nWYFs1QscEoQTLXH3499oTslCGwVs0NtipoVylq9ZhfQlCIMNTgObL7ALu805yw3XwCvfLedt9xyJZF4rN3HB6B26jom3/0aTCqOSkZRtzyIPqBKKUlZKwMRl4IgrAhsNnl+5rzX/gKs6oVau0/XGURcG5l5PrZi+TQQAq7bmfOWB6KUQywW5+kgYBt27vJpZtoUfT4MqY2PoZSD6zp096TbOk/aFJWe79uWNwrcKASNB2XfcS1ReeCcZWEQ12l/W9ko8N50Fv5FZ4GjcF8Ypfurd+IkuvCNI3ZCKxARl4IgrAhmGK+vycFwAdbkqP7eL8AX/hvGSyt+ycdg2AwcDzwPvB7YBTxTrWK69JL4vTQXfM4PQ1LYucumTdFT2DxyxxhONSHbGwtM7VpYMkZTKJXQQWAvcFw4883WCH3zBTD4FIzvaYnKb14L6dUQBqj8HhKJRNvnLHUqxuSlr6F+2noA4vdsJ/XdB6xfpdgJrVg6/18KQRCEeWB6Nnl8tEwykcT9ldfA3/0X1dFxqqt6qNR8iqXikprLm09cx2Un8CngycbHFwBlzJKZTW22x91UF6VIhKdhyqboZTC1sLS18dHBLiy1A9/z0NpYIRnvht6NENTtx4HN4E/YTPBvXgtG2+Wd/F6SyQS5XG/b5yyD9b2U/vBiKyzrAV1f+zFd/3n/lBG6sHKRyqUgCCuGWcbMX/4Rnu/BtZdNtcrD665flukuh0MimWTCq3C+MZyOrfSdgp0a2BGG7T3cEdD8OyeSSSYL+SmbonGYc2GpXK/zLq/CN2Jxhqo+oQ4XpYod6hB611sh+dx9MO7D+pe0rIZ6N9jKZTwFYy+glEM2m+sIWyj//M1Ufvl8iLo4oyW6v3IXkX35dh9L6BBW1r+cgiAI09D1OgxkZyz5sCZHPQhWZAVTKQftugTYVvJHgC8CO2gIoSXGVBUzGmU70Atsxy4qQWth6RTgmsky3fkxwsokOd/Hr0xSHB+zUYULgDHapu2M720JSR3C1lsABTd+CMI66BDlT5BMJsnl2i8sTcSlfMkrqVzySoi6RJ/YTfrz3xNhKcxAKpeCIKxYDrrkc9E5hHdsw/MqKNSiVbI6AWMMSewyzA7go9gXinqH2xEdjKZN0WQhz08b1dfpC0saODGZ4hrf52mjOQE7CnAG8LgxFMfHiKdShz3bOD3y0FEOKNBaz3j82PSaEmGgQWGFZHYtaA0YHAyO6+BOjuImk2193M3YYndd9KtegjnrOJTWJL+/lcQdj0nilTALEZeCIKxYEskkfr1m7VLW5KywHMjB218DT+7GGxwD14E1ORjK49drxCNRtNHLVmwaY6hjLXx84LXY6qUxS1dBNCuYvudRD+rUg4DHUVPb4t9wXMpGk/N9XgBuw/78UaALGKtU8CoVHMchncniunO/dE6JRm0guwbyg3YDPLcB8nvxnDjRV72TYOhZdPEOOOctsO37cNbP2VnL486GrbeQSMQ7YhHGGE2hWEBrDY6C/h74n0dh+266168htnOk3UcUOhQRl4IgrFiCILCtcYC9Y3DROVZYjhRhuNHmmxZfp6+7Hq/qNbKS88sy4cdogwH+CTgNO3dpAKM1xiyNjfG5aM5hHsy0x3Vc9gNnY43XtwNrgZ3KsV6TuQ3o/B4KxSLZTAbXjcyo6il7J4QauOwzsPNBuPdr8O5PQ/8mGNmJueEqapWy9e5pLu/0HQdv+O3WQXY/SlgeXshfxWFjF45CcN0Zc8lcdz1hwYcOEMBCZyLiUhCEZcdUazIM0TpEOQ4RN0I8kcD3Peq1GlprNAYiLqzOwlAe7tgGT75gK5iuA/HIrPg61uRQv/9LU8s/nlchlepu6887X9j5Qisu69j2cH3a18ulPN3pzjMgnw8SySRVr8ITxvAAdj5zF9jK41k/b4XgxjNg682UigXi8UTLm1I5kFsHY7utWOzfBI98zwrI/k32Dvo3QW497s6f4jgO9bEXWss7IzunBCj5QdwOMEUP1ubwgyTsHoa+9KznQTgm4QPCwRFxKQjCgmKMxvMq1Gs1DBCLxkimUgBTs2nNFvNclx1MyEyfbZs9z1YkNHqqnQ1Qq3pUqg07nbW9MDhmK1LNyuSzg/DJr9kK5npbmaQezJzHHM7DBacBrRdZb3AUDMsieWSiVDrolue7Xwv/cU+I73kd0bKdb5RySOd6KefH+baxAhsAJwJbb4ZYF9QmwYmgg5oVlmC9KZvVydv/EbbdYkViZgCevH2mcBzfQzweI5FMUnQU4dZbwI3Alisguw4K+3Adtaim6DNmRN0I7rknU339SwlOHMDcdB88vRsGx2c9D9xYYtHOKCw9RFwKgrBgWJPoInqa0POqPj4hKIWp1a3Q2zeOrzQGM/MyR5N66eZW/KAxYAwmCJl8aie6VrPXHRrHj0DigjOo7xkmLOVntLP5+BZ4w9m2Mvn6s1CXvRHz/74Lw/lWRWbzOsy6PtgzCvvGcdwIGDMjvg5t4NSN9ijN5R+l8Koefr1K1I0QcSNLdhYzDANegrUReapxWVNkreuDE9fAztGltzV+uDiOS09vH16lQtWr2J89qNmlm7pnvSaDGmBg8yth92OQ7m9VJ1/9Xnj0Rw2xuNZuf2+5whqg5wenhKP1XE3jex5BGGBQOOX9uA3huViPnRlvxNb22jdUz++Bda9BhZpYfx/1eBTj12Ha88BR7U8FEjobEZeCICwYvudZYTld6H1sC6Zas+3Ej7ynNc/4seuteJt+2ce3UN6QQV38ihm3a266Dx59euY85Me3UOmJQn8aJvsOyBDvhXpoBW69IY5OGICtO2ZWZBpVzmQ8ObO6OlbGicWtJ+anvmFF6NC4vT1jYCCH2bqDWtylVvGX7OKPUornjOEO7NzhA8B3gBzw1B7YMQSRWPs9FhcSpRxSXd0kkkny42P2cdrI+sYYQIFyZ3pTNquTxSHQdaKugzM5ghOP223x8vAs4TjLc7UNeJ5HyOznZ+Qfb6Fnoo5TrGCSPXimMVc6XCAai7c9FUjofERcCoKwYITaCroZQm9tL4yV7Gv0gfOMhfIsUaie3487OG5vUCm7NLF7xH7tgO93nhlEdSUJD2zjDY3DKeuteDzNVh45dSOYn8yoyKAN6UyWaLQ18zZDABjsQk9/Bl5+ir2Nz9wIrzjNVjFPXAtvPGfW4o9X86eqmvFEgqrvt+ZBG6385lxoO4VoLB4n8DxeD7wEu9RyErATuOUhmzG+UipWM/wkHccKy1XHQ36vtQwaOLllcr7lSlupLAza6mc63ZHia0YLPBGnekI/TFZmPo9WZ3Gf2o3TnbaXNcR2qp0HF5YcIi4FQVgw5vSR3DcODb/BWVVDrWdeNjhGcnSS5K6bZtyuV5mkUvNn3W5irGLn2VA2Q7wpGh0Hbt9qv/n2rZgnd9vLjYFEDBpzk5lsjkgketCfJ5lKUQvqhFt32PN+915bvTx1I/zXvXD+qXMu/pjrrp+qalZ8z4qTdAoma/Zf4TAEx86FelWPRCzRlhnOVKqLku9TN4ZtQD9WWHanIJZKUDPtjRtsG9PnKp/fCv/xEfiPD0M0blvfSuMWB4nFYx1b1dM6pFAq2Fb/mhwMF2BvAJWqzFMK847qW7V66ZqXtYHu7m527XyOEzadRLks23KC8GLMmLlsCj1jUJEIKmxsa0+b48KYGZe5jTzwA1+sZ8yKzXHdF90W9zw8r2JvqCcJlSqu4x62pVBzQcn3fbtXPdALIwVYlYaPvMfaGF13PfzyK6fa+ebP/n2qqsl110NXAryqFbYTFThpLTy/H153lp0L1baSmU1nFz2R5UB7nV97fcgHf9Xwh/+a5f7n2r/FvJiMj49iYilID8B7/grKefjy70C9agWnDgCIRmNEo9GOHX8wRpPPj2McNbMFft31KMfB6LmfR4JwIIergaRyKQjCgqGUQzadwfMq1IYL6P4s5vxTcM44kfQ//pDq5CThWBk3lpi5LT7tsrle5FTjBfBg132xebZUVzfJVMp+bz3EjR9ZAopSDqlUN8lk4zbGJ6lpgxkpwsdvmHvxZzjfqmqu7Z0pNHtSVlg6Dvg120rvz2C27qBQyJPL9S7qC/2BbdCLz8/TlaizOrN8F3kORiKewPMrLbugu/61ISwdwEA0ATqgnh6gPr4Xz/dIJBIdU700ShGuz1EJqpj8GKxbNWuUJDo6QSQWO+RzThCOBBGXgiAsKE0xlkqBTqUovull6K44/sUvI/XfD866/uEuORzLQsR8LFNMv42pSmlj8adaq6E/9Y1WW35VGi4699BCc2gc9o1BOgnxKGbSp5DPAwatDUqBG4kQi8Zas5sLHE05XHSBOgOZpRn/eCwkUyn8Wg2jQztXaYytWCqs9+WjP4B3/AXc+vfgRjCZAbzxvfi+3xaRaQC9Ok1981rqm9dYO6FUHPNvP7RjGEOzLYUisUTbF4uE5YeIS0EQFg2nWKHrxp9Q/l9vwL/wdCLP7CO2fbDdxzpmDhSryWRDbI6WZ1c1DyY0B3Lw3D6IRWH7XhjIQrVuo/eUgg19mKE8QRgQ1OszPTsXMC1ouGhvbyWKS6Ucctns1JiA1qGNwVx1gjVV790Iex6zSz6XfKwlMrNr8fJ7qdUDMse43HOgn+uBbyqia1YRnrKO+uY11DevwaRnrt4or4YTGsKyB6sy8PEt9rG2bxwFK2ZBS1hcRFwKgnDMzGVoDnMbosee2EP8x09RffVpTL7r1UT+6r9xJrw2/wTzy5xVzcMRmgYI9ay5uClvzubnRtsq2hvPmbo8vO56ioUCjqPmdfO8JS5XXlscZo4JaB1ae6L8HpvWM74bhp+dKTLf/WnIrIEff4Xwke8xUSrScxSivzn76td8jFJ2CWffuH1T4ShY22eX4+oV+KXzUInGPGw9ILJrhOizQ0Sf3Ye71y7QFZVLOFK08757RlFKkc0uz7Qlof2IuBQE4aiZtdyyphf2F/DrtdZyTsM8fXplLXXTgwSbVhP0dlM8Pkv03uEl5Qd5JByu0HSUQsNs66Zp3pxTrfQ9oxCNzLx8IEdYKBNWbWBjrerNSzVzf9EuFK1egZXLA3Ecl3QmS2liwqb2uFF49l7bKh8esCIzswa+/kFrUZTooe4VGR8bI5lMHpYDwAxRaczMFKktt9mFr2tnBgQ437yb+JpVRJ8bIvL8CCo44G81fUa5FuImU8vyuSZ0DiIuBUE4YuYUlSNFm8d99TvRn/6GraxNM0QPP7aF0gl9xE5Yi1OoEL/zCYKndqCH81QbbbqFau12Cgeb02wuUnheBe9A66Zp3pxTFc6eJJQqEHVbl+8vwC9dAGedOJVIFN6x7agrZ01Wclt8LqLRGNlMhlKxgK43IiCNaonMH3/FCsueVVDabwVobh3e+B78apVYNDrlF9r8mzQfC0EYUA8Du73tOHDOSTBanHqz0QwCONCXMvqTJ0k1fCkPRieYtgsrBxGXgiAcEVqHFAp5W1VpWPngOnDVO6yh+Pbd1tbkQEP0tb0E3XHCi18GNFJ2hvNwzWUtAXrd9cs2u/pA5nqxTyZTVOv12ZGTTW/O/XnoS1shDzM9O9f2wkXnohKxGYlE9b1jjOfHbP554vCqZ9MZLlgB29etiTiGQKt5+x0sVVw3Qq531dTnU9XGahXzyE2QzFhh2fTHzKyBr/4xpjhMVUXAq+DXamQzGYCWXVdXAiYDOHczjBZtitSjO6febBCdwzd2SHwphc5jeZYHDoPf+I1f56EHf8qe3c/z/Vtu5txzz233kQShI7EvnJOUyyUqlTL5Yt765W3st9Y5q9J2y3n7blvB3DVsq20Nc2ZoVNb2jREpecQe3klkxzBqzyisnZmyw0DOpvqsUJrWTcloHPaOQhBCKmYF5t5RSMStsGwYvqfiSSLDBWvK/t43WWHZTCSKuraaedE5Desc8LwK44Vx9BH8jvMVRT2wN7EqLdXLuWjOZeZyOaKRCHhFiKVsm7x/Ezz47ZbYdKJ2ZCSoUyoWmDSBHR/58OW2Urmuz4rKfePWzmogZyNTP/xvNgjAGJsq9eF/g+uux5Wcb6EDWZGVy7e97Ze57qN/zpVXXc2DDz7E+3/nt7nxG1/jgle+mtHR0XYfTxA6hhlm5Y3ZSQzwgXeiNq+bmvkilbCicnAM9ozguBFw1IwKnKsc0i/kUTfcDRwkZUfSQaaEypQXZxDiJJLU6zV0w/C9uyeN47hEIta4u1gqEk63PnIduHObFftvfw1s32PnNBv52Efin6m14VP/qShOGjyvgjFdy3Zs4VhRyqEnnSGfz2Oqky1/zB0PAGpaqzwC2TUE43sJShPQFbeRoqvScM8TVlTevx0+faPd8A5D1L5xEokEiUTSbouLL6XQwazIhJ7v33IzDz/yMB/84IcAUEqxbevD/NM/f4nPf/4LL/q9ktAjLHea85T1Wg2ttW3X/em7W2JyrvSZPaM2K1ypqehCmHtbfPr9vFjKjnD4TE8kqtft3403nWuF5UjRvgFIxOwIQzwKlSqpZOqQ4wfGaMqlPA4hJ6+DpwfB4NKdli3jF0PrkHyhYD9RjhX1sRT4E7Z62bQtGt8D8S5b6exKwDXvhr/5TxgtQX8WhsdRqI4yZhdWNpLQcxCi0Shnn30Wf/03n5+6zBjDnXfdxcvPO2/W9WOxGLFYK/Ksu7t7Uc4pCO1gRlxjs1KpHfi3H2KueXfLj3HXsL1+MyscSMZnz/O9mHg5VMqOcPgcuCg0Xhi3W8Xb99hKZm+PFZna2FGGniRh7dCtcd/zwIT890fg9I3wxG5460fDFTMXe7Q4jktvLtfyxwwDjFeEeDekV1vbovE99v8nRmzrfOwF+PC/gw5RKGLjk7gJ2eoWliYrTlz29fYSiUQYGRmZcfnI/hFO3nzyrOv/8R/9IVdffdViHU8Q2opX9a2wnO6z+LEtVkDe+jDmrBOtWNk3Zme+hvMoIJvrPaoMbNlgnX/s3GaWQiFvK8o9yak5TTDQl4GxEm780HN6oQ45aY0VlmA/nrgGdo6u3LnYw2W6P6YxmvH8ODRb5cPPQqLHCstpvpg8chOOUmSyuUXPlBeE+UTeDh2Cv/6bz3PCppOm/jvjzLPbfSRBWBCCtTn809bNXrJZ22tbqjc/ANddj6MckvEk8bEyqViCXEZeCDsN142Qy9k30mrCs8bsWkN30grLaUb3L3o7jstzQ7ZiCfbjjiF7uXD46IEcnH1i45PQ2hZVCtC7oeWL+egPoO94tHIoTUxgjCxPCUuXFVe5HBsfJwgC+vv7Z1zev7qf/fv3z7p+rVajVqst1vEEYdExjsJ//Uvx3nQW5vsPwNbnZi7Z7LMJH67jEpfW9ZLBcVwymRwwbSazHuLGk4f9N0wkk5TrPhdfF3LyWnh2H6AOT5gKltpp6yhf9lq49a7Qy14AAFNtSURBVGHYtgMSUZj07Rzm2Au2YtlM9unfBINPEX79g5SKBWKxuDzfhCXJihOX9XqdrVu3ceGFr+Xmm28G7BLCha99Lf/8pX9p8+kEYeGZHtWoerqo/+bPoU9eC0B0bT+h487yWbRCJSsvckuM6ctZBohFY0ckVpRy6E7nyCYmOWeTx+vOUNxw36GXeZq+j7V6DQVEY7EVt5BiAP/C0/He+jPgKCInrkNHImi/BumUNcHXGh65CfqOt8Ky5tlFH+UQxLoJvBKe75PNZqU7ICwpVpy4BPj7L36Rv/3C53nkkUd46KGHef/v/DapVIobbvhau48mCAvGrFSdtX0wUoCv/hD1x2+n65ZHiD28E7rTVpAMFzAoYvH4ERtvC+1nruUsr+pTrdfIHsEbBaUckskUn/pfHvXA8PWfKsyLeIwYoykUC3Zj3bGZ2MFQnmq9TnaFOAEY12HyVy+gdt5JAMTve4bUt++H7nQrgjHVRTyRoDxRop7fYy2Ldj5oF30yA41FnxMwYy9QKBbJZeXNnbB0WJHi8tvf/g59fX188ANXs3r1ah577HHe+a53z1ryEYTlwgzbn3W9MFywL/xXvgM+fSOJj3+duNNwRVAOqVQ3pNp6ZOEY8T1vzuUsHR75tvdoyUFrG2eeSxnGJw+e0uN7njVpd124tpW+pFdI+pLuTlB+3+sITlgNWpP67gPEf7IdBTDHAltPOkOxVCLcciVE49aa6IBFH/PITccc4ykIi8mKFJcAX/rSv/AlaYMLy5jp7W8dhjb5ZlrWNx/fYlN1VmfRY2Xojh36RoUlQxAGkIrDbY9gVqXhonPtctZo6YhTkAKtGCs79Kc1A9mQ8cmDC5xQh5CMw6p0KxO7PwOpOP6kzeJebnOEU9ngiQj100/ArMnieDW6v3oX0Wf2vej3WksuW9Gs1nzCMLQt8uaiT34v9G6kPr6HfH5cPC+FJcGKFZeCsJyZmazTa+MAI659kaeR9d2MapRUnGWHMZpaUId6DR58Grwa/M9jMD4BWuMmjrwsvb9oxeXqtObJvQe/nuu44FVgcNy+ienPwEe/CpM+uitBZaKC53tkl7jdzpSgDAPqYWCdnvr74EcPwcPP0pPOEhk7vKCNpiVXIpm06T7TF32ahutuBJNdi5ffS60ekEmnRWAKHYuIS0FYBkyvUrqOiwZCDmiJXnc9fOtuuOyN9vNGVKPrRmT7d5nhVSp2o8R1oS8Ng+M29UVrnMjR/b2Hiw4v3QgD2ReveiaSSfxaFR1q+5hLxe12dH/GituBHGZonPz4GI7jkM5kcd2l81LUXFbyaz5GKZusMxnMeq7Vi1UiRzgCoJRDNpulUCximos+ex6buU0+spNwy5UrYsRAWLosnWe0IAhzMiv/ezgPsSisyc3wqzQDObj1EcyTu635uTEk5kjVEZY+tXrNztROm3nkuutRxjmiZZ7p7C/ZKuNA5sX9F5Wy9zGVTjPhYXqSVli+/xfg/33XVtHX5ND7xikU8sSiMSIN0buQj0WtQ8oTJUKtcR2H7p40SqmpCqTRGsdxcd3ZcaUw7bkWBuA48OHL4bZHYOfQrOdaeJhVywNxHJdcNstEqWgXfYafhex6u+zzyPfssk92DeGk7AgInYuIS0FYghxynvJj18Pe8Zl+lcN5opEIjkQtLnsUzPnmwh0uHPXffKhgv+9Q4hJmptN4lUkqExXY2A9fv8P6O3748la7fLREbXUPtaE81XrtmLLlp7eqpwvFeCLRqDZWrehem0MP5ckXxnHicXS9Zqu8RttK79A4nu8Rj8dRSqG1xnVcjDH2Tdy5m2G0aH+vq9JwzxOznmvHMmqilNNa9HnmHitk77nBpvpUCoBBxWWURehcRFwKwhJjVqVyKD97nnKg17a9p/lVuo0XLBGUyxtjtBVwe8cwW26Dt7/Gxj8O54nG4kd9u8PFprg8smWgRDKJ53uYwTGIOFOi19x0n61mTmsnhx/9KhOlIo7rzkgRmj7ycbCK4lSrGuzzwWj7nBgap1LzIWxssE9vX//5V9D1Orz+bLjrUbj6nfCVH4HrYvoz+EP5hp1SL+wbs9ns6/vghAF4dKe9jYvOhR8/Puu5dqyjJs1Fn1KxSKD1tBzyE2DsBar1Oimj5fksdCQiLgVhCaF1SLGQtx6Cm9fC7/0STHiz5ymHxkkmEigUoVQqVwwtj8lwalOcOx+1s5aOSzJ59P5S+4u2Lb46fWSxhHaOMEehVMD4dRjK28foaAnW9M7cKI+41EMNqzIwlLeVRqXsz9OVgDmWgWa1ql9/lhWKTRG55Ta4Y5utSCZiM6u5yTjkuqEe2k367XZkhGsvs2k63723NVqw5Tb7+xwuwKkb4f7t8LEtsDoLI0WUgdjo/D7XlHJwIy6BM82eqDF3abZcIXOXQsci4lIQlghah+SLjUrKulWwaz/86b/AJ34DDpindBvG1yImVw7GaIqFAjoM7AVBYCuYoSbiOKSPsWo9mFd84SbYORziVSaPSEA5jksuk2OyXKZar7UWfSrVqXYy37obghDecLYVe6dtRN/6iH28N5eBNvZj9o6SL4wTj8ZxHId6UJ/Rqm4KxSnRWg9thT/bBU/tmdm+9qpQrcNpG23M6a7hqe81tz0C6w68nV5wHfjMjVZUhiEMjpJMphbs+Wa370u2Ytm/yV7Yvwly6wnLw/N+f4IwH4i4FIQlQnmiNDWvNmMD/O++K/OUKxybxlNAKwMbVtlRiboG14pLA1OPh7kiIQ+11GWMZvuuEtt3wclrIahNUq77dKcPHQXZRCm7QJMMA0rFArpUsZXGZjt57yjEo3DnNlulLFUAA90pKyyvuWzGnGa1r8v+nFrbn7nRqubk9bCvNW9M1LXXu/T18Mygvb/mOAngKIW+fattmT/8LDiOfW6tSsNPnph5O/sLtnW+fbcVooNjJBMpGzqwQNixAt/aE43snKpckt+HG48u2P0KwrGg+latfpEgL+FAuru72bXzOU7YdBLl8tFtAwrC0ZCfLKL7elAf/bWpy8xH/g32jOJGo8e0CCEsbSqVMl6tOvuNR6ghFiESGjLZXozR5IsFTNPo3KuCAcd1X3SL3KtMEtQm+d5H4PSN8MRueOtHIRLrOqa27PTFtHqthla0qpRremHPiLVU2tiP+r/vtXOa/3WvFZrbdsC3f2JvSKnWvOS+cStatbYV/aHx1nX6MrB/HAxEolF6DtgW12FIaLS1GOrP2ha5o2bfzrTZysV43mkdWnsigNx6KOyD3HqSJ51L6tm7F/S+BWE6h6uBpHIpCB2OAaqvPg397POw/YC23lAex1mcFzihMzFGU/V9SMZg2w5Mf6ZlPbW/ANU60ZSdtaxMTmKCRtu8HtgHlwIdBi86vxfqkJPWwKoeeOen4Mk9Vrv5Vf+YquRN83Cwlflq3JmqUqqN/ZhnB+ETX4PBsVlzmua2R2w1sR7aKuNnbrSCUGsIQiLRKO5oGTeeJJ5IUPV9wmIFN9k168zTf+4pwZufxIkn7O9n7IDbWeQOQdOeyPc8wvIweuNLqV/8QTytCXc9gioMHnTZSRDagYhLQehgjKOovO18qhecAoWXwAe+NLOtZwyZ7OG3JoXlRXOZRTvKLqZ89164fzvmvW+yVbegtdldnihRrVXtzODaXvv4cZTdgHbdF42EdB2XZwbhvKshCB3A2KDxap3x8TGy2dwxG6G7rgsNy6KppZvN6zCrczBSmDWnyao0+HXbEv/Qu+HWh634nKigyj6ZTG7G7R9uhXW64J2Ldi3QTD+XmdhL+bmfUHvgu9TyeyG7DvJ7qdbrktwjdATyCBSEDkUnY0z85kVWWGpD6u6nyPZkiCoHZ1+eqHLIZZZ2hJ5wbHiVit2SPvtEeMVpcNU7rAXVJ742Q1h6lQrVmGotx3zo3dZr0nGsKWYQUqtWKRXzdjP7ABLJJKGBIFT2+hHXttw3rALXaW2oHwO26qZaVUqwH8eKJOMJUrEEsWpoPTyvux5+8qT9xqG83da++BXwxnOgUiXiLu/nhALcu/6lldzzvs/DZZ8h1Abf89p9PEGQyqUgdCLhqh4mfv2N6P40VOt0b7mb2JN7wHFJH1CREVYmxmi8akNIbN8NjzwHq3NWQBoDqURjptLY9veEZzecx0pw68Ooi1+BWdtrq30Vn0zaYaJcp1QcI53pm/GmRSkHpVxMMmo3pAPdMkL/1t1w6yMUC3myud5japFPWRYd4Bk5feFoetuaeGJq+3x6Nb87nT7G327no8O6rVjO2CBfJxvkQkcg4lIQOoz6SWsov/dCTCqOky/T/eXbiQwV2n0socOYyg9Xyl5gsEbfjmM3n1c1MsVDDWhQjp3BzHTBaMlWBfeNQxiiFDiug1IaYwwTpSKZbO+M+3NdB+1VbRt9wyorLD/xNdt+X9+HHspTLBWPaf63aVnke95B5xoPbFunmpGO+/I20jGdXhHVfNdxbeWyuUE++BSM7SZ0nSO2ihKE+UbEpSB0EP4rTqbytvPBdXCfH6Hn3+7AKfvtPpbQgfjV5uPC2Da1o0Bjk2ky3dCThD96G/z1f8JAnzX/Nspa/Dz8rE2V0QaMwTlAgwTNpZ9pdPekyRcaG9NDeVuxHM63LIK+dTfhrY8wUSoeUxLUoWYeD8RZodX8RDJJtV4n3HIl5NbBuO1sBNl1BPlBmb8U2oo86gShAzCOYvIXz6PyqxeA6xB7aAfpf/iBCEthToyxFUYUdsllwoN0qhEqrqAWwJO74QvfsfY7sag1Em98jztZxUWB1rgORKMOG9cniESUnXucA7ux3IvTtPm59RHbZm9WMO96FNb3UW8sGRlzZEk+wpHRjIdMxWO4xX22Mv2Oj8Npr4MTX04YBLa6LQhtQCqXgtBmdCLK5GWvpX7aegCStzxM4rbHmPslXljpGKMpFYsNoejYLXE/D/kJ2yJPxWHSt3GHYyXr6/i6s+HuxwAaAtG2jcfHRom4hh999zzOOL2bx54oc9Ev/JTwINUux3HJ9a5qxZAeUMGcygi/7nqJJlwEmlXesFwizAzArX9vW+W9G8Fx8avVQxrkC8JCII84QWgjYa6bid/7eSssawHdX7mTpAhL4SA0rYcCWpvaoOCCl9iZS4O159HG2vek4vay/3kUtLWtmj6P6LgOJ52Y4ozTbcLMGad3c+KmFI774i8NjuOSzfXiKmeqgjkjIzwVx6/6eJVJqWAuAq7jwthuKywv+Ric8mo48eUYY6R6KbQFqVwKwiLT3Hatp2IEp63GZFM4xQo9/3Y7kT3j7T6e0KE0K5ZhUIf1ffCyk+GlJ1jz8NKkrVoqbISiV7XzlRN2m9wxkM5kZ3lRxmIxnt1R4bEnypxwfILr/nIH25+ZxHGjGKNftOKlGuk0E6Ui9aG8XRBqxjNWqui1OSpDear1mpj8LzA2ItLDZAbgR//Pzl/GbNXY832pXgqLjsQ/HiES/ygcC83KU4iGdX12W7c3TTbRhVuutvt4QocylR1ujJ2dHMrb6uRADiY9u6SjlK1kru9rbYlrTa6376Db08ZoyhMFMAHRqEM9MGw+McWzOzzAobvn4JGQ02+jWCoSGj1lcj4jf/vhZ0nGk6S6Fi5/W2hEgFY86xbguLY1Pr4bdEgyHpffvzAvSPyjIHQQzWpltVYl1Bo++C6bPtLIgK7FqjKfJhwUr1KxwnJ6dvif/bu1HgIrKrW29kMfeBeMFK3RuOZFbXmUsgJyolQkCEP++8af4cf35Hl42wQ3fX8Ur1I5pChpVjB9z8Of9NEDWZvxPZy3SUCOg1+T6tlCk0ym8DzPzuFe8jHY8xgMPwvP3ku1VhVxKSwqIi4FYYGZUdlZ0wtD4/CVH2H+9NKpDOhwTKrgwtwYo/F9z1YFG9nhxCKNCpVqVTIdx85ZNg3SB3KofYces1DKwXFdTjwhxp/86Xae21HhlJO7iLhQr/sYc2hRON0+qDI0bn02r20t+BhZ8FlwlHJwXZcws2ZWa1xrc8gxB0GYT0RcCsIC43ueFZbTq04f2wK3Pow560SbQhJLtPuYQgfSfGNiXGdGdjgTFcDA2SfBCQNw6kb41Dfs8k7TIH04TyJ+eI8r13F5+pkK0ajiu18/11YvNya46fsjh1W9bDI1+zfQWvCRN1CLRywWwxvb02qNp/th3AcdHtHfURCOFRGXgrDAhDqEdX0zX2xXZ+HmB+A79+Aqh0Qy2d5DCh2J73n28fOGs6Eewmkb4daHG19V8NwgbNsBa7Zbz8nBsSmDdEcpkqnUYd1PIpnE9yuccFzigOqlOuzqJdjqWSKRwJu+4POtu2FwDB2JSPVsgUkmU3buUjk2c7x/k03w2XIltXqNw3s0CMKxI+JSEBYYM5CDfSOY3SOtyuVwHhdFfI54O0FoEoSBTd+561E7vzg4ZoWDo+xC2NC49bPcN25nLoFYNSRyhI8rpRziiSTPPFchGnVm+F6+8Rd+ekQt7WQyRa1eJ/zoV+3ZQ23N1echHlJ4cZRycCMu4YGZ49m1qOJgew8nrChEXArCAmJiEYLf+Xn4+/+Cj12PWZ2zwrKxBCEvssLBMEYT1OvWLP2ic+Dtr7FVwNu3wuvPspXMU9bDndvsPGapQiabIxKJHtX9JZMpqr7HiSckOeH4BH/1t7t4frdPNhOhUJodB3kwZlgUhTPHQcRcfeGJxWJ40zPHR3ZCYZBoPNbuowkrCBGXgrAATG2H9/aiH3wa9es/T+Kvvo0eK+NKtVI4BM1ZS+0ouwR216OwfQ+sbWRo3/aIjXuc9O18XalCIpk8amEJ06uXk7zhrQ+wZ6/PyZtTFIoBxqgjamk3l4RYlZlprt6VwG9EmspzYGFIJlP49RDz1f8P4imoVlCOSzIpTXFh8RBxKQjzzIzt8GoV/ute1PcfJBnvQsVltlI4NHMvgV1v2+BqWks8GYeyB0qRmodqYDKZolj12fVChd947waqVc0rz8/yL1/Ze8QVR9dx4UBz9UkfPZCjMjSG53skEgmSSbEomm+UMRjHhZ5VUN970Lx4QVgoRFwKwjwzawnj5PXo27fia0fagcIhMUZTrVUhEZ2yHlIb+zHJhkH5dMF53fWgFNFIZF4EmrWziRKN1Pn3LYOccnIXTz8zSTSq7PznEZBIJqnWa4TXXT/TXP3ffggoTDKG53lUazWymUObtQuHh+951hP1ss9MtcX1litlHEFYVOTZLAjzzIwljF1D9qPrHvGLs7DyaCU4Gcg2rIc+8TXMs4M2ynFNbobrAGt7bVKPmZ+gNWM0QVDH8zXvu2wdN33zXH743fMIAoPRR5YR3py9TMUSOJNV68f5+C5bcXUda/juumitJf96Hgl1CLkDFnpy6+zlgrBIiLgUhHnGaG03ZK+5DPVn74VrLgOtj/jFWVh5+J5HGAZwwmr7GDq+H/aOwie/hnJUq80MU64D9CRfNIXncGlGQUYjhlNOTvGlf9/Lma/4Cf/9/f1sOv7o7qNprp6IJ6zR+wNP27b+tY3nxrWXgaOo1WvHfH7B4jou5AftIg/Yj/lBe7kgLBLSFheEeUYl4pBJzDKRdkbFRFp4cepB3YqvXfttpW/XfgCUAXXcaszQGHz0q5h1fVZY9vbASBF3HmZ5fc9DKc1/feNn+JM/3U4koli7Js5f/e0LhKEhGj36+5hqke8dgw2rZj031HDhmM8vWOzvuk645UpbwcwP4jpKvHSFRUUql4JwjBij8SqTlMslJgmovfJU2F+YVWFyXakcCC9OGARWXH74ctRHf83OKKIwroM2GsJG+3v3CCRiVlg67rwIh1CHbD4xxY/vyfPcjgpf/9ezWDsQZ83qGGFo7P0fJc0WeSQSnbP66gz0HvP5BYv9XadJxWPEy8Ok4jEy6bTMtAqLilQuBeEYmJUbPpyHJ56HXBquu94aqDd8LaVyIBwSpWbMVbJ9NyhavpYnr4fbtxKNRnFqGjeenDdLH9dxeea5Cg9vm+CEE5Jc9hvbqAeGk09KEY0qwqCK1uFRt+CVckhnMhRKRfS05wZrctT+zy9S/pvvQn5iSiyLGDp6pme9C0I7EHEpCMfAwXLDE6kUTixBKL6WwiEwRuN5FWq1mvW13DvK/9/ence3Vd35/3/deyVZ8iJbTmxnIRDC1tIABZIpy1Cg0Ha6TIFpmdIwnc5Mf23pArTzDVshQBM6IdACLR2gUzqlhYS10LK004WEtWxJSkISGuLsENvxIq/a772/P66tRLGzy5Zkv5+PBw8T6do6Og9Z/uhzzvl83MZtGEdOgvVNuR16mjrAsjAMk8rKcF7HEQyF6O1J8swf2nAcF7/fYPHTM3M69fT2dBOujhzwYxiGSU242vu9ae/FDIZIXXAG9n8/SbItCg0RaO4gmU6pyYBICVNwKXIQbMeGibW77CGrwW3rJZTnP/4y+riuQ0dnFBwbLAsyNrh4J8Qn1kJTu1ckfaeyVixZgTsMMZdhmFRW1RCPxYjHY9RU+7j3l+9y2JQgX/n3QzhyWjl/W5fIy+PkZNV++Wfi8bh3yEedfERGBQWXIvthoPOO7diYZWWkTz4C3lib2ze8OYoVCBZ6qFLkXNehu6sTbNvrFV5fA9s7vdJCjoPR1OFVGBoqczlM2+W9wK+cdDpOZ1eG5Su6efjXzSx6tJlNW2JY5oF3ANodJ5WGhprcD2gTa8l0H3wgKyKFoeBSZB8Nub9ycwuMrx7UN1z7K2VPHMemszPqlafy+7yMpWF4B3hueQQwCQQCOLZNOmOPSOZyQCIexzQZtCRu21Bdk/9s/M6dfLIf0FqipD75dyRsk7JX38HITxlPERkhCi5F9tHu9leW1VRj+bW/UvaN6zpEox2A62UlM/3F9ZvavSLjEyLwbhuWaeE6rvcuPUKZSxg4NV7B1MOC3P6TTWzemqA67KOz28hLPc1d7dzJJ3vIZ1wYPnYysWCA1PRDqXjsFaxoX94fW0SGh/4CiuyjjAlMGpfbIaWhBnr6CJVXUFkZJlReocBS9ijW1+dlKS3LCyQtCwzTWw5f+o5XbNzw6hI6jg3pDBx3OMw4GmZf6BXkP4iyQHvjnRrv4+xPLuWWOzaxfEU3Xd0ZcDPD8rg7d/Ipa++lPBAkYltU/N+bkMqQOWoiXf/5j8RPnkasv+RXPNY3rHMgIgdHmUuRfZA+vJ50fQX8aZn2V8pBSaaS3h7LXXuEY8J73gGempoIjuOQGchqrt4EK9bDxHFQV4PZMXxZvGAoRDIZZ/PW+C5L40uH7ZDNUKVzgn9Zi3/tNvr++VTSEyPE1qyHdFInykVKgIJLkT1wDUicNZ34x0/ATWVg+TrVr5SD47owYZcKA/1L4aZpUl3jlfqJdka9ZfMJES+baZqwrR1cFytYPmzDMwwTny/AkYdbTD+2EoDpx1Zy5LQQ6zeNbH9qq72Hqnv+RE9diHRLVCfKRUqEgkuR3XAqyuj7/Omk3zcZgLLVWyl3AyRVv1IOglFbhbvrAZb+pfCaSC2GYdLdFR06u+mzMNL2sH+gsSyLxg1xVq3pzWYuGzfE8flG/oOU4bqYG5qhPpIbkNfVEG+JkkgmsEyTisoqkokEqXQKA/AHAoRC5fr9FCkABZciQ0hPraN31hm4NRWQzlDxxOsElq7HAGVK5IDZNRU4X/403Hi/lwEfyEo6LjU1kWwgZDsOTIwMmd0Mlg9/wBQMhUjEUnz0M0s56shy/vZODChclt47Ud6xIyBfsxlaorimgTshgtPUQWdn1LvYAEJlZOJxEqkUkeoaBZgiI0zBpchOXAMSZ36A+Mc/CJaJ2dJF5cIX8DV3FnpoUuJcA/ouPBWjIYL5zc9g3PkUTlMUyzSprAnnnsQeFx5cnqc/uxkKDd+S+ADDMBlfH+Hfv1DJ5q0JNmyCQFmwYEHaoBPl29q8g1ADmd1Fi+HZv3pbB0wTxodhWweu7RCPxSivqCzIuEXGKgWXMuYNFEbPmGCfdAT2R6ZjWCaB5RuoePw1jFSm0EOUUSB5ytFkjpoIqQxVv3sTq7pmyOtSxx2K848nwzX/u8fs5nBLpXYUl7TyX4FovwycKB9oG5nCy1hme7CnbfD5vP2sO+3LZN5CUukUwx+Oi8jOFFzKmJZTGH3SOHh1DWzYRujYIwgu34hR6AHKqGCPqyL2qZMBKP/dcqy2npz7Bz7gpMv9pGsCEAwQ+KcP4zz2wu6zm8PIdR2iHZ3cckc7Rx9VQTweAxJUVhVuiXnnE+XddpT0Tpld/JZXsumQ8VBXjfvMa9DWDeVl0JcsyHhFxjIFlzKmDVkYfd5C6FiFob2VkgeuYdD3z6dBwIdvXRNlr6zNvd916Ozq9GpammXw5Kvw4moqCGBWRwoy5kQ8jus6/OmpGSNSimh/VVaFiXZFd2R2mzq8O5o6YO4D0NHjddGKJXEMb46171Jk5Oi3TcYk13WIxXqJJ+Leqdzl63ATqf7C6BFsZ2RLrsjolTjj/WQOr4dEiopHXxnUyjAei+E4jrf2PC7s7Rns6CHRV7iONF6XnvJBpYiK5ffCNC0i1RH8honZFMVvWl4JJ8f1MpbXzsK48Ysw52JcvGBZREaOgksZc1zXobO7i3gqiTup1vuD9PRr8P0HcRu3efUrR2j5UUYnx7Hp7ooS7Y4S+9tG3M5eKp5ahtU5OGBMpVPeCeczj4OpE7yvRv/tBWKZFo0bYqxa0wvA68u6WLsuhp3JFE13HNO0CFdHiETGEa6O4PP5KQuUwYTILl209GFRZKRpWVzGnEQ8jjNEj3Ca2uHmh7AsnwqjywFzHNtbsjUMr5vMO+/C1T/HV1kNQ31ocft7jO/SPxy7cAFcMBSityfJRz69lCOmhWhsjOGz4MgjgqxbH6O3J1nQ/Ze7Y1lW9pQ9ddXw+EuwrR3H59PSuMgIUnApY47t2IOyG+7EWmjrxpfKEFZLOTkIvT3dXmC5yz7evp5uwkPtoTQM7zDKiUfC1AY4Zgrc8giGWbjXoGGYVFbVkIjHWdeYxLIMni3S/Zc7y5YsmvuAF7DbDkweR7o5Sld3l9pFiowQ/ZbJmOMVZO7PboD3takDEkkCgTL98ZGDYjvO4KXZCRHv9l04jo2dyXhbM97aCE+8DL/8E9TVFHxrxsDpbMvnK+r9lzsbKFnkN/sDyyv/GT70PjhhGradIR6LFXqIImOCMpcy5gRDIRLpFM5OPcJxXEzT0nK4HDTLNHGGKIBu7ZKJdF3HWz43Te9QWX2NV8+yqQNcBytUHFlBy7RYtz5WFK0g94VhmJiWBbVhuP/P3u/3xFowTRKpBKER6HAkMtYpuJQxxzBMasLVxOMx0i2duBgEysr0R0fyorIqTLSzA753P26oDOJencXKmpqc6xLxuLff0rIGFf7GNormg87A/stzP7OUo48sZ+26wraC3BcD7SJz5rZxG+78B+mMdmCapnqPiwwjBZcyJhmGSXl5JWrdIflmGAamZeG4LkQqIZnGNAwMI7ckv+3YECqD8eHc/b8NEayWzqIJegb2Xx5zBCRTSQ49JMS7Te7ev7GAgqEQ8UQct6HGm9NEyttuYJg4poEzoYZMc5RkOk2N9mGK5J2CSxGRPErE417dyrNP8NoSvm8KzpIVgw7AWKYF8ZjXA3vnJfSWKIFAWQGfwdBWv91FOm1z9JHl2Jk4vT2pojwxDl5AHAwGiQ/srV65of8UvpmTyXRufojurk4CgTKCoVBRPheRUqTgUkQkjzJ2ZsjSQhl7R49613VwDaAsAIkUzH0Ad0IttEQxDYNQqLhS6ol4nEzGZvHTxX9ifEAoVE4qncaetxACfggFslliN5Hy9mMaBpnyAJmeGPFEnJqayIi12BQZzRRciojkkes4MERLUbd/VTzbz57+fvbb2sFxsZqj3t7fItwHaDs206aG+NPiNu795bscNiXItKlBNm4pvhPjAwZOjificZKppLcNoT9LnM1k1lV7rSKn1OFua6ezu5NIdaTo5l+k1Ci4FBHJI9O0oK560D5Ks83rdhOPxbAzaS+wNE04fTq8+BZlZcGizQKahknjhhgL7tjEMUdV8NCvm8lkXPz+4j3UAzvKKQVDIa9/u+14B6YCfigv8wLLa2dBVQjuehK3sYnOaAfVymCKHBR9PBMRyRPXdfozZO24ixbjJlLZfZSWZeG6DvFkHAwTWjq94ukvrQLLzFk2LzoGGIbLl2ZN4sTjq/jSrEkYhuu1rSwBhmFSU11DqCyIhYGZSEFPHCbUeoHlNf8Lm7bDIeNxTINoVxSnCOt4ipQKZS5FRPIgu9zt9i93P7cSXlwFGRurv4ZqIh73Lt75YEl22bzw/bp3x7Zt/H6TXy3axtFHVfDOuj78fpOMXToBmGGYlFdUUk5/jdFoB+62drjrSa/g+i7bGHp311FJRPZKmUsRkTyIx2LYdgZOmOZ1hbnyn8F28JtWtu1gtvzQpNrcDj4Nxb0M6zoOmYzLn56cwfO/n8mfnpxBJuN6+0tLkGGY1NREvMRrY9M+d1QSkX2j4FJE5CA5jk08EQcXWL8NnnzFO41cV4NpWRiGies6OLYNfQnY2obbuA0gZ9m8WJmmNagF5BGHlxd1QLw3pmkRqY5gmubgdrDNUdxx1cTiMXp7u4nH+nBdBZsi+0rL4iIiB8F1HTq7O72l7knjvM4w48LeaWTHwQqWe9d0dXr7+CqCXtee+Q955YdaOzGNIu94Y1k0bojntIBcv7F4W0DuK8Mwqa6JeG045y3EnRDxWnCaBq7PIJ5KQEMEmjtIplPZDLSI7JmCSxGRgxCPxbzl4ROPhKkNcMwU+MGjUF6G0ZsgGAoR6+vzspYG4LcgaYLjYjR3EAyFirL80M4GWkB+5NNvUBvx09qWAkwqKoOFHtpBG8hg9vZ0Yzf194CfOJ50U2vOvlh73sKiruspUkwUXIqIHCDXdUikEl5JobYueGsjvL4W6mpgW3s2G5lIJXMzm+PD0NqFaVpeG9IiZxgmFZXVpBKdRDszvP+YSt5pjNHX21W0XXr2h2laOYd3eluiUB+BumrcZ16Dtm4oLyOTLOIT/SJFRMGliMgBSsTjuIaRe9L4pkVg2xj9nXYS8TiYu1zz/UVQEcSIpQr9FPZZMpHAzrg8W0Jdeg6UZVrQ1A5zH/BqYU6ohViSNN4HilIPpkWGm35DREQOkO3YMGGXk9/1NeDinUYeOCG+62nkhgj0xPEHAgUc/f6xHZsjdjnUc+S0kPf8RplgKITh4mUsZ18IM4+GE6bhOg7xWKzQwxMpegouRUQOkGVa0NKRe9K4JUooFMqepLZMa/Bp5KaObGazVFimReOGGKvWeJ2GVq3ppXFD3Ht+o4xhmAT8Ae+Dwv1/hqde9bY9mCaJVEInx0X2YlQtiy9f9gaHHnpozm1z593Ej398Z/bfxx57LAsWzOfED36Q9vZ27r3359z5k/8e6aGKyChQVh4iFgjC9xfh1nunii3DzAkag6EQsUp//zU10BzFYEdms1QMHOo559NvEIn4aW1NgTE6DvUMxbIsb3+sZcF1s7w+5I+/hPvsm/R0d1Glk+MiuzWqgkuA+fNv5v4HHsj+u7e3L/v/lZWVPProw7zw/AvMnn0lx77//fzoR7fT1dXNr+6/vxDDFZES5boOsZAFU8dDdQWBja34AkFvSbU/6HBdhxgOHDUZunrxb+3Av8s1pWLgUE862UlrW5pw2Ed3j01vTydV4dIKlPdFMBQinojjNtR4geX8h6AlCpPHkW6O0tXdpdJEIrsx6oLL3r4+tm9vHfK+Cz/3WQJ+P5dd/m3S6TRr165l+nHT+frXv6bgUkT2WbbVY7cLlgvbOrAxqKyozgkss7UtV8QglsI2TaqqS/d0dSIeJ5n09ljG4g6u62LbNvFYjPKK4j/1vj8MwyQYDBJvjsLjL3mB5bUqTSSyL0rzHW4PLrvsUt5Z+zaLF/+Zb33zGzldL2bMmMErr7xKOp3O3rZk8RKOOuooqqurh/x5gUCAysrKnP9EZGzLtno8/nCYcTRccSG26+zoHd5/jWPbeMUtPU5/IFaqEskEpmng8xkcOS2Ez2dgmgaJZKLQQxsWoVA5lmHCs29CfU3uoayJtaPyMJNIPoyqzOXPfnYvK1e+RbSzk7+bOYPrrruWhoYG5lx/AwD19fVs2bIl53u2t7Zm7+vq6hr0M799+WVceeUVwz94ESkJe6ptaUd3bMPZEXC54LO8ckQOJFPJ0s3yuS6mCYufnrlTOaI3sG230CMbFoZhUh2upqe7i3T/oaxsOamWKJmZ78fZEsWMl05JKZGRUPTB5Zw513H5ZZfu8ZpTTj2dxsZG7r7np9nb1qxZQyqd5oc/uJV5N32fVOrAfvnv+NGPuevue7L/rqysZNVbKw7oZ4lIaXL7s5K2Y2PbNi4MWdvSCpZnr3dhyMLpuKUdiB11xK7liMp5e23pZmP3xjBMqsLV3jaIeQtxGyKwPQoNEeyLz6I7nqL8V89jr9uK7dhYplWSe2pF8qnog8u77rqbBx98aI/XbN68ecjbly1bjt/v59ApU2hcv57t27dTV1eXc019/7+3b98+5M9IpVIHHJiKSOnL7q90Ha+YdnOHl4ms87bSGFPqcOtrMJo6dnTkGSWF03dl+XysWx/L6THeuCGG5Sv6PyUHZSCDmYjHsdt7sfxBfJFa+noT2JVBetrbIT3QhzyqPuQy5hX9O0J7ezvt7e0H9L3HTZ+Obdu0trUBsHTpUr773Wvw+XxkMl4brzPPOpN169YNuSQuIpLdX7lz7/BbHvEOecz6SHaJNBgM4rouPd1R0pkMBHxQ5QWbxpQ6L+P1bhuB/gC0FFVVhenp7uAjn36DI6eV07ghhuNAVThc6KENO8Mwcw/vNHVS/aPf0XVYDfb2KFx3sQ77iPQbNR+rZsyYwde+9lU+8IFjOeyww/jcZz/LvHlzefSxx7KB42O/fpxUOs2P7ridY445hvPPP4+vfuUr3H33T/fy00VkLPL2VyZ37K986lWvqHZdDTz7Ju6cX8K8hViGSVlZkGhnB2k7A+VlkM7AVffidvaWbOH0XZmmRVW4Ftf18bd1CVzXR1W4Nlswfqwxkml8y9dDg9eByU2kYOUGCPhJppIqti5jVtFnLvdVKpXkgvPP58orZhMIBNiyZQv3/PSn3L3Tfsmenh4uvPDzLFgwn2f//Ec6Ojr4wQ9/qDJEIjKkeDKBazDk/kq/z4fZ3ovVX7eyu6sTMLx9luPDsK0DbAeu/jk4bkkWTh+KaVqEqyOFHkbR8PqQd+A2bvM+eDRHoaEGu6mDaLSDgD+AYRhk7AyO42CZJpVV4TEbkMvYYIwbX1/au8tHWGVlJZs2rmfq4UfQ29tb6OGISJ5lD+/gkgoHcf0mxve+tOP+Ob/EaOogEqnNCRQ72ltxLTM3EJ23EDI25eUVOuQxSmX35NoZL8M952JvP+7cB7ze5OOrYXuntwd3grcnE9clUh1RgCklZ19jIL3TiYj0GwgUYqkEyfGVuO1dsK0/KwU5+yt3DhSzp8PL/LByA24i5dVCbIhgmt5ePQWWo9PAYR+facEEb3mcZ/8KHT1eoDm+asfhrrlf8m4zDHp7ugs9dJFhM2qWxUVEDobrOnR3dWFn0jB5HJx4BPzLOd7hnZsfwp04Dlqig3qHO45NZ2fU+4dlwm9fgdfX4n7xXGiJUlY2Ontvyw6GYRIIlJHpr4VJWzdMqPUy2O09O4JO+g93TYiQ6ezDtUwMW/syZfTRR2kRGfNc16Gzu4sMDhwyHlo64anX4Bd/hLoafKZFWXsv5YFgTokZ7/s6cU0DptRBItW/37Idbn4I0zAIlZfuAR7Zd8FQyOvmM28h/LURtrV7gea4KhgIOunPfjdHcafW0/Wf/0jq2EPQ3jQZbZS5FJExLxGP47jO4P2SzR2ASyBUMaisjOs69HR34doOnPNB+Ke/94qk99ez9MVShMOl20dc9s/OtTAzyQxpwJ23EMZVg+PCvIW4A3suAT5/Nk5dmN5/Oxvf+maCj79GonEztg79yCigdz0RGfMyA+WDFr+J+8xr3oGMibX99xrZ4ugDXNehs6tzR9mhJSvg+w9639cQgZ44gUCZAssxZqAWZlVVNZHqCOWBIGVdMYKBMnyGidkUxW+YRMI11N7zR4LPvgXpDOlxlfRs2EzadXAmRki7DtGuKI56l0uJUuZSRMY013W8IDGWgY3N8MoaeHk1tHdDVQgrlhoUJMb6+nD6GzGQzniZqaZ2eOT5bD3LXQNSGVsGFV3fVTJD+R/epOy1d+hKxbwM+C6Z896ebpV9kpKkj9UiMqbFYzFcx4ETpsHMo2H2hd6BjLIA9CUIBAI517uuQyIRBwOvDWQ6A7jgAs+tHDX1LGVkWJ0xjPfaBx36YUIE29FhHylNevcTkTHL68CTGLoDT18c07QGddSJ9fV511te6RksCwwTXBfDMFS/UPabZZq5h34at3kHglyXeKxPnX6k5GhZXETGrEQ87p3UPet4SNtw1GR4biU4Dn6fn6qdToYPSKaSO+oW7nz4BxPTNJWxlP1WWRUm2hX1Dv3U13iHfkwTd2ItsaYOkulUTpUCkWKn4FJExqyMnfFqUy5ZAaEAxFNeVtJlyMASANfN1jCEHXULebeNQFA1LWX/maZFpDpCb083maaOQZ2e7HkLScTje97DKVJE9DFIRMYsO5PxMpau6+2fNAywHSzL2m2WyPL5hqxbiGEMWkIX2VcDPdsDZWW72X+pk+NSOpS5FJExx3Ud4vEYtm17S9yTxnn9n8eHvVqVe6hqXVUVJtrZAd+7HzdUBvEkuDrEI/lhmVb2w0t220VLFMuvrLiUDgWXIjJmDASViUQCF9cLKlu7vIzl7AvhB49CRRAjltrtzzAMA9OycFwXIpWQTGMaBqapwFIOXjAUIplOYc9biDuhFrZHoSGCP1QF0b5CD09kn+jdUETGBMexiUY7iMfjuJXB/hPfJlxxIbREYe3WbAF0/y7lh3bmdfNxvT1xc78Ecy7GcV0S8fgIPhsZrQY6/ZQHgpS19WCe+gH4z8/SO6GK3t5unR6XkqDMpYiMagPZyni8vzbl5J2WwJs7vKByQi1saskWQN/T3knbsQftiXMbItjtvSPzhGTU27kAe6a5h67bHsfeHsVuiIBOj0sJ0CtTREatgTaN8UTca9OI4e2nnH0hdPRAedALKre1w18b96kA+s574oAde+JU21KGQXprCzS3w4ePg2kT4azjsR1bmXIpaspcisio5LoOXZ2dOHZ/m8ZMxstcNnfA6k3eEvi7bV5QaRgEy0KEysv3mg3asSfuAdyKEPTEMDAoUxkiGQZeuSwLXnjL63ff1AGW5d0uUqSUuRSRUcd1HTq7O7ENFw4Z39+m0fFOhrsuLH3H+yMNhMpCRGpqKa+o3KdlRsMwCVeFMUwLYgmYPB7XNOju6dZeOMk713HAduDaWRg3fNHLumcyZNJp7b+UoqXMpYiMOjmHbgbKudy0CGwb/D54rx3DZ1ETqT2gVo3JRALXtuHsE7w6mcccgr1khQpdS96ZpgV11d7rOJHy2pOaJs6ECLHmqPZfSlFScCkio86Qh24m1kJ7N/Ql8Pn8hKsP/A9yxs542VAtVcows6yd9viu3OAV7Ff3HilyCi5FZNQxff7BhaibOrzMpWEcVGAJ/UuVrjOov7hr5PFJiJBb95KAHxpqcj80TRpHojaMz1eGf1NrgUcr4lFwKSKjjvuJGfDySi/ga4h4dSwdN2+ddAaWKqmrxn3mNWjrhvIyjIRa9El+DdS9TMTjJFNJ7F0/NDV34Mw8mp5PfQjfO9sI/XEF/i1thR62jHEKLkVkVLHHV5H86Alw9nT8Nz+K/e52XNfFsiyqwtUHtMdyV5ZleZnQuQ94JY0m1EIsSRrvMJH2v0k+DdS9DIZCdHV3ed17+j80mT4f/nAVKdshc/Qkeo6ehP9v7xH64wp877YXeugyRim4FJFRwwX6zvs78FkE1jdTGXMwasfn/XGCoRDxRBy3rTtnadzV/jcZRjtnMe32XqxAkGAohPHUMuyX1hI/5zhSJ08j/b7JpN83Gf+arV6QuS1a6KHLGKPgUkRGjfRxh5I5ZhKkbcp/8wbDtQXSMEz8Pj+poA8Wv4k7PgznnAjq1CPDbOfuPTuzor1UPvYK9pJVXpB50uGkj51C+tgp+N/aQuhPK/A1d478gGVMUnApIqOCW+aj7x9nABB8bhVWe8/wPZbrkHYyEMvAxmZ4ZQ28vBpau7DKQsP2uCJ7Y7X3UPnIX3YEmR88nPRxh5I+7lD8KzcT+tNKfC2dhR6mjHIKLkVkVIifezxuTQVmew+hJauH9bES8TguDDotbrjekrlIoVmt3VQ+9DL24lXEzz2e1PGHkT7+MNLTDyWwchOhP6/EbOn0ltgdG8u0vCV27ReWPNCrSERKXqahhsTfvx+A8t++jpEZ3lPbtmPDpHE5JWFoiBDwB/THWYqKtb2LykUvEr7jafwrN4NpkPrg4XR+4x+IGmliqQTJ8ZXEUgm6urvU8UfyQplLESlpLhC74O/AMvG/tYXA37YN32O5Dol4nHTAgvfacBu3YRw5yctctkSxAuovLsXJ19xJ1QMvkJkYIf7RE0ht3obb3ZdbkH3uA/R0d2FaljKZclAUXIpISUudNI3MtAZIZSh/6o1hexzXdbwyMK4D48d5fcUXPIxbH4HWTizD1JK4FD1fU5SqXz1Hj5MiNaE2m32nrhp8FmnbgfE10Nyh1pJywBRcikjJckIBYp86CYDQn1didcaG7bES8bgXWO6yz9Jq7aJsoCSM/ghLifAl0qS6e7IF2Xn8JbCdQZnM7u4uDMPAzmQAA8syqawK56VerIxeCi5FpGTFP/5B3KoQZksnwRffHtbHsh0bJtbmtt5riOBr71VdSyk5O7eVdBsisK0dJo8blMnMpDNgGGAaMCGC0xwl2hUlUh1RgCm7pY/ZIlKSMpNrSZ5yNAAVv3kdwx6+gwiOY5NOp2FrK+5/LcLt7N2xz1J/YKUEDRRkLw8EKWvvxe/zQX9rSWBHJnPaBC+4nHMxxtwvwZyLwTDo7eku7BOQoqbMpYiUHNcw6LvgQ2AaBP66Ef/6lmF7LMexiXZFvT+wh4yHTdvhip+B62JZPu2zlJK1c0H27J7iXTKZ9CVhQiQ3Yz8hgt2krj+yewouRaTkJP/uSOxDx2PEU5Q/vWxYH6u3p3tH5manvZamiw47yKixa2tJx+cj3RyFqfWwaXt2b6a7tRWao1imXveyewouRaSkOBVlxD9xIgChP67A7IkP6+PZjgMTB2duaIoqsJRRZchM5oZm78PVvIXe6745Cq5LZThc4NFKMdM7o4iUlNgnT8ItL8N6r4OyV9YO++NZppmzF02ZGxkLsnsyg+X4LR+m42Jui+I3TB3mkb1S5lJESkb6sDpSM48EoPyJ1zAcd9gfs7Iq7O25VOZGxpiBTKZ2Fcv+0kdvESkJrml4nXiAstfX4d/SNiKPa5oWkeoIfsPEbFLmRkRkb5S5FJGiNtByMVVfReav6zCCxxL63V9HdAymaRGujozoY4qIlCoFlyJStFzXIdoZxXVseDcBm1tw/7AMo6wCdJhGRKQo6d1ZRIpWrK8P13XBsmBCBCwTYklifX2FHpqIiOyGMpciUrSSqaTXdm6XGpPJVJIKqgo9PBERGYIylyJSlFzXwXUcKPPDyg24iZRXa3JCBNzhPyUuIiIHRplLESk6ruvQ2dXpZS0BnvgLvPo33C991Ksx6dNbl4hIsVLmUkSKTjwWw3Ecb6/l+LC317KpA+Y/CK5LVZVqTIqIFCt9/BeRopNKp7ys5XWzcvZaGrZBjWpMiogUNWUuRaToGAATcvt50xDBsiwFliIiRU6ZSxEpOv5AgExzFLdxG6zdCptaoKkdX1mw0EMTEZG9UHApIkUnFConkUrh3vywtzw+IQKmSSqTodx1MFRAXUSkaCm4FJGiYxgmwUCAeCqZU+PSmbeQRDxOqLyi0EMUEZHd0Md/ESlKjuMMue/SduwCj0xERPZEwaWIFCXLtKA56p0UB+9rS9S7XUREipaWxUWkKJUFg8TtFO737setKofeOJZpEQyFCj00ERHZA2UuRaTouK5Dd083ruPC5PHQl8AwDMJVYR3mEREpcspcikjRScTj2K6Tc5jHnbeQZCKhwzwiIkVOKQARKTq2Y8OEWh3mEREpQcpcikjRMQ0TtrXh/veTMLUBjpniHeYJqIi6iEixU3ApIkXFdR2SmTSYJrREYcUGcP6CaZo6zCMiUgIUXIpIUUnE4zi77Ldk3kLKAgEd5hERKQF6pxaRopKxM1ARhMVv4j7zGtRVQ0PEK6ouIiJFT5lLESkaruuQtjPQl4GNzfDKGnh5NbR2YZVpSVxEpBQocykiRSMei+E6DpwwDWYeDbMvhLZuDBfttxQRKRHKXIpIUXBdh0Qq4R3kaeuCtzbC62uhroZAR5/2W4qIlAgFlyJSFBLxOK5h5B7kuWkR2DZWsLzQwxMRkX2kVICIFIWMnYHystyDPPU1GBhaEhcRKSHKXIpIwbmuQyqThnQKlr0D8RS8uArauwkGg1oSFxEpIQouRaTg4rEYOC4YhneDC7R2ARAKaUlcRKSUKB0gIgWXTCW9wNIyYVzY+2oYmIaprKWISIlR5lJECs91wTTgulk5XXlw3EKPTERE9lPJpAS+851v87tnnmbL5o2sb3xnyGsmT57Mg4seYMvmjby9ZjU33nA9lmXlXHP6aaex+Nk/8d67W3j99Ve56KLPj8TwRWQPDNOECRGMKXXev6fUQUPEu11EREpKybxzB/x+nnzyKe6775dD3m+aJg8uWojfH+CTn/o037r0Ui666PNcffVV2WsOPfRQFi1ayEsvvcxZZ5/DT3/6P9xx+22cffZZI/IcRGRogUAAmqNexhK8ry1R73YRESkpJbMsvuCWWwF2m2k8++yzOOaYo/ns5y6ktbWVVatWc/PNC7j++jnccsutpNNp/u1L/8qWLVu4/oYbAVi3bh2nfOhDXHLJ11iy5LkReR4iMliwspK4PwQ3LcStj0BLFNMwdZhHRKQElUzmcm9mzpjBmrffprW1NXvb4iXPEQ6Hed/7jvGumTmD5194Ief7Fi9ZwswZM3b7cwOBAJWVlTn/iUj+uK5DzAccPgGOmESgrYfyQJCacLUO84iIlKCSyVzuTX19fU5gCWT/XV9fv8drwuEwwWCQRCIx6Od++/LLuPLKK4Zp1CJjm+s6dHV3YeOAH9jWgY1BZWVIgaWISIkqaHA5Z851XH7ZpXu85pRTT6exsXGERjTYHT/6MXfdfU/235WVlax6a0XBxiMymiTicWzXyWn5aM9bSCIeJ1ReUejhiYjIAShocHnXXXfz4IMP7fGazZs379PP2r59OyeedGLObXV1ddn7Br4O3LbzNd3d3UNmLQFSqRSpVGqfxiAi+8d27EGnxN2GCHZ7b4FHJiIiB6qgwWV7ezvt7e15+VlvLF3Kd77zbcaPH09bWxsAZ515Jt3d3axd65UueuONpZx77rk533fWmWfyxtKleRmDiOwfy7Syp8Sz9S1boliBYKGHJiIiB6hk9lxOnjyZSKSGQyZPxrIspk//AAAbN26kry/GkiXPsXbtO9x110/43vfmUl9fzzXXXM3P//cX2czjfb/8FV/+8pe54fo5LFz0IGec8fecd95n+MKsiwv51ETGrGAoRDKdwr5pIe7EcdDUgWWYBEOhQg9NREQOkDFufH1JtMC4884f8YWLLhp0+3nnXcDLf/kLAIcccgi33rqA0087jVgsxsMPP8LceTdh23b2+tNPO415N83lmKOPZtu2Jn5422089NDD+zyOyspKNm1cz9TDj6C3V0t3IgfLdR36QhbJIxowKoJEXtugwzwiIkVoX2Ogkgkui4WCS5H8s8t8dJ4yDdq6Cf11EyF/QAGmiEiR2dcYqGSWxUVkdHJdh57trfDkdphUSzzWR8qIU606lyIiJUnBpYgUlMoRiYiMLkoLiEhBDVWOiIaId7uIiJQcBZciUlA7lyMCdpQjMq0Cj0xERA6ElsVFpKCy5YjmPYBbEYKeGAYGZUHVuhQRKUXKXIpIQRmGSbgqDD4fxBIweTyuadDd043rOoUenoiI7CdlLkWk4JKJBGRsHeoRERkFFFyKSMHZjg31EVi5AXfxmzA+DHU12NG+Qg9NRET2k4JLESk40zChuQOefBUm1cJf1oDtYJZp36WISKlRcCkihWcApgHXzcouizNvoXe7iIiUFB3oEZGCcxxnyFqXjqMDPSIipUbBpYgUnGpdioiMHloWF5GC21HrciHupHHQ1IFlmARDoUIPTURE9pMylyJScIZhUh2uJhQqh6kN8JlTqJrYgGHoLUpEpNQocykiRcEwTMrLQqQ+8SGchmrspl6sd7YVelgiIrKflBYQkaLia+oAwJ4UKfBIRETkQCi4FJGiYr3XH1xOri3wSERE5EAouBSRouLbFgUgo8yliEhJUnApIkXF6g8unXFh3IC2hYuIlBoFlyJSVMy+BEZXDEyDzERlL0VESo2CSxEpOr5tOtQjIlKqFFyKSNEZWBq3J+lQj4hIqVFwKSJFZyBzqUM9IiKlR7vlRaTomO+24z7zGpntncTiMULBoLr1iIiUCAWXIlJUXNehZ9O7sHkrTKwlnoyTSiWpDlcrwBQRKQF6pxaRopKIx3FcB66dBVd9Hs46HjuToae7C9d1Cj08ERHZCwWXIlJUbMeGCRGoq4b5D8ELb8HkcaRdhy4FmCIiRU/L4iJSVCzTguYoPP4StETh2lkYU+pwt7Ziz1tIIh4nVF5R6GGKiMhuKHMpIkUlGAphGSY8+ybU12BMqQPwvjZEvMymiIgULWUuRaSoGIZJdbianu4u0k0duIsWQ9oGvwXNHVhloUIPUURE9kDBpYgUHcMwqawKE+3uhOdWenswm73C6mXBYGEHJyIie6TgUkSKUjKRAAO47uLsnkvmLSSZSGjPpYhIEdOeSxEpSunyAEwapz2XIiIlRsGliBQV13WIJWKkgxa824bbuM27fWsrtES90+QiIlK0tCwuIkXD7a9laeOAVQ6GATc/jNsQgdZOLMMkGNKBHhGRYqbgUkSKRiIex3YdmJO7z9Jq7aIsECQYCqkFpIhIkdO7tIgUBdd1SKaSEPTDyg24iVR2n6XP5yNUXqHAUkSkBChzKSIF57oOnV2dOI4Nrg+e+Au8+jfcL33U22cZUPkhEZFSoTSAiBRcPBbDcRywLBgfBsuEpg6Y/6D2WYqIlBhlLkWk4FLpFJgGXDcrZ6+l6XjderQcLiJSOhRcikjBGQD1Nd5ey8VvetnLuhrM1i4FliIiJUbBpYgUnOXzkWnqgCdehnA59CbAcfGVlRV6aCIisp+UEhCRgnJdh2Q65aUvy8ugJw61VWCAYRiFHp6IiOwnBZciUlDxWAxcvMM848JgmtDeDeVl3iEfEREpKVoWF5GC2t1hHnriWOUVhR6eiIjsJ2UuRaSgDIAJEa9gOmQLpwMqQSQiUoIUXIpIQfkDAWiOehlL8L62RAmFynVSXESkBGlZXEQKKhQqJ5lO48xbiNsQgZYopmESKi8v9NBEROQAKLgUkYIyDJOacDWJeBy7vRcrECQYCilrKSJSohRcikjBGYZJSId3RERGBaUGRERERCRvFFyKiIiISN4ouBQRERGRvFFwKSIiIiJ5o+BSRERERPJGwaWIiIiI5I2CSxERERHJGwWXIiIiIpI3Ci5FREREJG8UXIqIiIhI3ii4FBEREZG8UXApIiIiInmj4FJERERE8kbBpYiIiIjkjYJLEREREckbBZciIiIikjcKLkVEREQkbxRcioiIiEjeKLgUERERkbxRcCkiIiIieaPgUkRERETyRsGliIiIiOSNr9ADKFWVlZWFHoKIiIjIiNnX2EfB5X4amNhVb60o8EhERERERl5lZSW9vb27vd8YN77eHcHxjAoTJkzY46SKp7KyklVvrWD6cSdovg6Q5vDgaQ4PnuYwPzSPB09zePAOdg4rKytpbm7e4zXKXB6AvU2q5Ort7dWbwEHSHB48zeHB0xzmh+bx4GkOD96BzuG+fI8O9IiIiIhI3ii4FBEREZG8UXApwyaVSnHLLbeSSqUKPZSSpTk8eJrDg6c5zA/N48HTHB68kZhDHegRERERkbxR5lJERERE8kbBpYiIiIjkjYJLEREREckbBZciIiIikjcKLiUvvvOdb/O7Z55my+aNrG98Z8hr2lpbBv13wfnn51xz+mmnsfjZP/Heu1t4/fVXueiiz4/A6IvDvszh5MmTeXDRA2zZvJG316zmxhuux7KsnGvG8hzuavmyNwa95i677NKca4499lieeuq3vLt1MyveXM6l3/pmgUZbvP7jP/6d5cve4N2tm/nD//2eE088sdBDKlpXXjF70Gvulb+8lL2/rKyMBQvm887at9m0aQO/+MXPqaurK+CIC+/UU09h4QP3s+qtFbS1tvCJT3xi0DVXX3Ulq1etZOuWTfz6sUeZNu3wnPtramq45+672LihkfWN73DHHbdTUVE+Uk+h4PY2h3fe+aNBr8uHH34w55p8zqGCS8mLgN/Pk08+xX33/XKP133r0ss49gPTs//97ve/z9536KGHsmjRQl566WXOOvscfvrT/+GO22/j7LPPGtaxF4u9zaFpmjy4aCF+f4BPfurTfOvSS7noos9z9dVXZa8Z63M4lPnzb855zd1778+z91VWVvLoow/z7tZ3Oefcj3HjjXO54orZ/OsXv1jAEReX888/j3lzv8etP/ghHznno6xevZpHH3mI8ePHF3poRevtt/+W85r71Kc/k73vpnlz+fjHPsaXv/wVzvvM+UxomMB99/1vAUdbeOXl5axavZorr7p6yPsvvfRbfOUr/x+zZ1/Jx//hk8RiMR55+GHKysqy19xzz10c875j+Ozn/plZF/8Lp516Crf98Icj9RQKbm9zCPDnZ5/NeV1+9auX5NyfzzlU+0fJiwW33Aqw1yxZV1c327e3Dnnfv33pX9myZQvX33AjAOvWreOUD32ISy75GkuWPJfP4Ralvc3h2WefxTHHHM1nP3chra2trFq1mptvXsD118/hlltuJZ1Oj/k5HEpvX99uX3MXfu6zBPx+Lrv826TTadauXcv046bz9a9/jV/df/8Ij7Q4ff2SS7j/gQd48MGHAPh/s6/gox89l1mzvsCPf3xngUdXnDJ2ZsjXXFVVFRdfPIuvXfJ1XnzJy2ZeetnlvPrKy5x88sksW7ZspIdaFJ59djHPPrt4t/df8rWvctttt/P7//s/AL7xzW/x9ppVfPITn+CJ3/yGo446inPPOYdzz/0Yb65YAcDV13yXhx5cxA033EhzS8uIPI9C2tscAqSSqd2+F+Z7DpW5lBF1y4L5rP3bGv74h/9j1qwv5Nw3c+YMnn/hhZzbFi9ZwswZM0ZyiEVr5owZrHn7bVpbd7w5LF7yHOFwmPe97xjvGs3hIJdddinvrH2bxYv/zLe++Y2cbQQzZszglVdeJZ1OZ29bsngJRx11FNXV1YUYblHx+/2ccMLxPP/8i9nbXNfl+RdeGNOvqb2Zdvg0Vr21gqVvvM49d9/F5MmTAfjgCScQCAR4/vkdv6ONjY1s3bpV87kbhx12GA0NDTnvaz09PSxfvpwZM705mzlzBp2dndmgCOD551/AcRxOOvmkER9zsTr99NN4e81qXn3lZW69ZQGRSCR7X77nUJlLGTHz59/Miy+9RDwe56yzzuKWBTdTUVHBz352LwD19fU5gRNAa2sr4XCYYDBIIpEoxLCLxu7mZ+C+PV0zVufwZz+7l5Ur3yLa2cnfzZzBddddS0NDA3OuvwHw5mvLli0537N9pznt6uoa8TEXk3G1tfh8vsGvqe2tHHXkUQUaVXFbtnw5l152GY2N62loqOeK2bN5+qnfcsYZZ1JfX08ymaS7uzvne1pb26ivH9v7LndnYF52fQ1ub22lYaf3vba2tpz7bdsmGu3MXjPWLX52Cc88/Ts2b9nC1KlTue7aa3j4oQf5h098Esdx8j6HCi5lt+bMuY7Ldzn8sKtTTj2dxsbGffp5P7zt9uz/v/XWKirKy/nWN7+RDS5Ho3zPoezfnN59z0+zt61Zs4ZUOs0Pf3Ar8276vtrHybDYeWlyzZo1LFu2nDf/uozzzj+PRHxsfbiT4vHEb36T/f+3337be20ufZ3TTz+dF198cfffeIAUXMpu3XXX3dl9VruzefPmA/75y5YvZ/bs/0cgECCVSrF9+/ZBpybr6uro7u4u2YxbPudw+/btnHhS7indgfnavn179utom8NdHcycLlu2HL/fz6FTptC4fv2Q81W/y5yOZe0dHWQymcGvqfo6zc8+6u7uZv369Rx++OE8/9zzlJWVEQ6Hc7KXdXXjd7sXbqwbmJe6ujpaWna85urr6nhr1er+a7YPOmBmWRaRSA0tep0OafPmzbS1tTHt8Km8+OKLeZ9DBZeyW+3t7bS3tw/bz58+fTrRaDSbQXrjjaWce+65OdecdeaZvLF06bCNYbjlcw7fWLqU73zn24wfPz67fHHWmWfS3d3N2rVe6aLROIe7Opg5PW76dGzbprV//pYuXcp3v3sNPp+PTCYDwJlnncm6devG/JI4QDqdZsWKlXz4w2fw+/7KDoZh8OEzzuDen4/tE877qqKinKlTp/LIo4/x5ooVpFIpPvzhM3j66WcAOPKII5gyZcqo+h3Np82bN9PS0sKHzziDVf3BZGVlJSeddBK/+IVXWeONN5ZSU1PDCccfz4qVKwE444y/xzRNli9bXrCxF7OJEydSW1ubDdjzPYcKLiUvJk+eTCRSwyGTJ2NZFtOnfwCAjRs30tcX4+Mf+xh19XUsXbqMZDLBWWeeybcvv5y77ror+zPu++Wv+PKXv8wN189h4aIHOeOMv+e88z7DF2ZdXKinNaL2NodLljzH2rXvcNddP+F735tLfX0911xzNT//319kA/SxPoc7mzFjBieffBIvvfQSvb19zJwxg3nz5vLoY49lA8fHfv04s6+YzY/uuJ0f3/kT3v/+9/HVr3yFOXOuL/Doi8fd99zDT+78MW+++SbLl/+VS772VcrLy/eaPR6rvnfjDfzhj39k69Z3mTChgauuvBLbtnn88Sfo6elh4cJFzJv7PTqjnfT09DB//n/x+utvjNmT4uAF4IcfvqNu5WGHHsr06R8gGu3kvffe456f/g//+Z/fYcOGjWzesoVrrr6K5uaWbCm7devW8ednn+X223/I/5t9JX6/jwU3z+eJJ34zJk6Kw57nsLMzyhWzZ/PU08+wfft2pk6dyo03zGHjxo0sXrIEyP8cGuPG17t5e3YyZt1554/4wkUXDbr9vPMu4OW//IWPfORsrrvuWqYdfjgYBhs3buS+X9zHr+5/ANfd8RI8/bTTmHfTXI45+mi2bWvih7fdxkMPPTyST6Vg9jaHAIcccgi33rqA0087jVgsxsMPP8LceTdh23b2+rE8hzs7/vjjuGXBAo466kgCgQBbtmzhkUcf4+6778nZb3nssceyYMF8TvzgB+no6OBn9/6cO+/8SQFHXny+/OX/4Fvf/Ab19fWsWrWaa757LcuXKyM0lJ/9z0859dRTiEQitLe389prr/P9//ovNm3ytmqUlZUxd+6N/NMFFxAIlLFkyRKuvOqqMb0sfvppp/Hb3z4x6PYHH3qISy+9HPCKqH/xX79IdTjMa6+9zpVXXsX6DRuy19bU1LDg5vl8/OMfw3Ecnnr6Gb773e/S1xcbsedRSHuawyuuuIpf/eo+jpt+HNXVYZqbm3nuueeZf/OCnINS+ZxDBZciIiIikjeqcykiIiIieaPgUkRERETyRsGliIiIiOSNgksRERERyRsFlyIiIiKSNwouRURERCRvFFyKiIiISN4ouBQRERGRvFFwKSJSxEzT5HfPPM19v8jt5V1VVcWKN5fz3e9eU6CRiYgMTcGliEgRcxyHSy+9jI985Gw+99nPZm+/ef5/EY12cuutPyjg6EREBvMVegAiIrJn6zdsYN5N32f+/O/z4ksvctKJJ3HBBefz0Y99nHQ6XejhiYjkUG9xEZES8ZsnHse2bd7//vdz789/zm233V7oIYmIDKLgUkSkRBx55JG8+srLrF69ho+ccy62bRd6SCIig2jPpYhIibh41hfo64tx2GGHMmnSpEIPR0RkSAouRURKwMyZM7jkkq9x8cX/wvLlf+VHd2hJXESKk4JLEZEiFwqF+MmdP+YX9/2Sl15+mcu//R1OOulE/v3fvlTooYmIDKLgUkSkyF133bUYhsG8eTcBsHXrVm644UZuuOF6pkyZUuDRiYjk0oEeEZEidtppp/L4rx/jvPMv4LXXXs+575FHHsJn+finz36uQKMTERlMwaWIiIiI5I2WxUVEREQkbxRcioiIiEjeKLgUERERkbxRcCkiIiIieaPgUkRERETyRsGliIiIiOSNgksRERERyRsFlyIiIiKSNwouRURERCRvFFyKiIiISN4ouBQRERGRvFFwKSIiIiJ58/8DIO+UVK5oGNMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sections_to_highlight = [\n",
    "    sec for sec in sec_tree \n",
    "    if sec.domain_name == 'apic' and sec.path_distance() < 50\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "sec_tree.plot(\n",
    "    ax=ax, \n",
    "    show_domains=True, \n",
    "    show_points=True, \n",
    "    show_lines=True, \n",
    "    highlight_sections=sections_to_highlight # in red\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f0166a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parent |   idx\n",
      "---------------\n",
      "    -1 |   •0\n",
      "     0 |   ├─•1\n",
      "     0 |   ├─•2\n",
      "     0 |   ├─•3\n",
      "     0 |   ├─•4\n",
      "     0 |   ├─•5\n",
      "     0 |   ├─•6\n",
      "     6 |   │ ├─•7\n",
      "     6 |   │ └─•8\n",
      "     0 |   └─•9\n",
      "     9 |     ├─•10\n",
      "    10 |     │ ├─•11\n",
      "    11 |     │ │ ├─•12\n",
      "    11 |     │ │ └─•13\n",
      "    10 |     │ └─•14\n",
      "    14 |     │   ├─•15\n",
      "    14 |     │   └─•16\n",
      "     9 |     └─•17\n",
      "    17 |       ├─•18\n",
      "    18 |       │ ├─•19\n",
      "    19 |       │ │ ├─•20\n",
      "    19 |       │ │ └─•21\n",
      "    21 |       │ │   ├─•22\n",
      "    21 |       │ │   └─•23\n",
      "    23 |       │ │     ├─•24\n",
      "    23 |       │ │     └─•25\n",
      "    18 |       │ └─•26\n",
      "    26 |       │   ├─•27\n",
      "    26 |       │   └─•28\n",
      "    28 |       │     ├─•29\n",
      "    28 |       │     └─•30\n",
      "    30 |       │       ├─•31\n",
      "    30 |       │       └─•32\n",
      "    17 |       └─•33\n",
      "    33 |         ├─•34\n",
      "    34 |         │ ├─•35\n",
      "    34 |         │ └─•36\n",
      "    33 |         └─•37\n",
      "    37 |           ├─•38\n",
      "    37 |           └─•39\n",
      "    39 |             ├─•40\n",
      "    40 |             │ ├─•41\n",
      "    40 |             │ └─•42\n",
      "    42 |             │   ├─•43\n",
      "    42 |             │   └─•44\n",
      "    39 |             └─•45\n",
      "    45 |               ├─•46\n",
      "    46 |               │ ├─•47\n",
      "    46 |               │ └─•48\n",
      "    45 |               └─•49\n",
      "    49 |                 ├─•50\n",
      "    49 |                 └─•51\n"
     ]
    }
   ],
   "source": [
    "sec_tree.topology()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72c58ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for unique node ids...\n",
      "Checking for unique root node...\n",
      "Checking for duplicate children...\n",
      "Checking tree connectivity...\n",
      "Checking for loops...\n",
      "Checking for bifurcations with more than 2 children...\n",
      "Checking for NaN values...\n",
      "Checking for bifurcations in the soma...\n",
      "Checking the extended tree for geometric continuity...\n",
      "Checking if the tree is sorted...\n",
      "***Validation complete.***\n"
     ]
    }
   ],
   "source": [
    "validate_tree(point_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272b1d9f",
   "metadata": {},
   "source": [
    "## Creating domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b481132b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import create_domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f04157ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "domains = create_domains(sec_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8fbd28d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apic': <Domain(apic, 4, #0072B2, 43 sections)>,\n",
       " 'axon': <Domain(axon, 2, #F0E442, 1 sections)>,\n",
       " 'dend': <Domain(dend, 3, #019E73, 7 sections)>,\n",
       " 'soma': <Domain(soma, 1, #E69F00, 1 sections)>}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b76ede5",
   "metadata": {},
   "source": [
    "## Creating a Segment Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6fa22a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dendrotweaks.morphology.io import create_segment_tree\n",
    "from dendrotweaks.utils import calculate_lambda_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f27ed90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for sec in sec_tree.sections:\n",
    "    sec.create_and_reference()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b29159",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_lambda = 0.1\n",
    "f = 100\n",
    "\n",
    "for sec in sec_tree.sections:\n",
    "    lambda_f = calculate_lambda_f(sec.distances, sec.diameters, sec.Ra, sec.cm, f)\n",
    "    nseg = max(1, int((sec.L / (d_lambda * lambda_f) + 0.9) / 2) * 2 + 1)\n",
    "    sec._nseg = sec._ref.nseg = nseg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "70fa7869",
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_tree = create_segment_tree(sec_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c4fa5db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for unique node ids...\n",
      "Checking for unique root node...\n",
      "Checking for duplicate children...\n",
      "Checking tree connectivity...\n",
      "Checking for loops...\n",
      "Checking for bifurcations with more than 2 children...\n",
      "Checking if the tree is sorted...\n",
      "***Validation complete.***\n"
     ]
    }
   ],
   "source": [
    "validate_tree(seg_tree)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dendrotweakstest",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "name": "python",
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