CONTROL_R10_BF10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# control experiment\n",
    "\n",
    "# 10 reps, 100 iterations \n",
    "# samples from ONLY WT!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC0[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 0)\n",
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:17: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC1[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 1)\n",
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC2[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 2)\n",
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC0['Ntrue'] = [(CONTROL_R10_bayexpress_FC0.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC0))]\n",
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:24: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC1['Ntrue'] = [(CONTROL_R10_bayexpress_FC1.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC1))]\n",
      "/var/folders/dl/6r82c34d3pbdmnyfpk1l0ff00000gn/T/ipykernel_43982/4096341775.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  CONTROL_R10_bayexpress_FC2['Ntrue'] = [(CONTROL_R10_bayexpress_FC2.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC2))]\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "0      15S_rRNA  False  False  False  False  False  False  False  False   \n",
       "1      21S_rRNA  False   True  False   True   True  False   True  False   \n",
       "2          HRA1  False  False  False  False  False  False  False  False   \n",
       "3          ICR1  False  False  False  False  False  False  False  False   \n",
       "4          LSR1  False   True  False  False  False  False  False  False   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  tY(GUA)J2  False  False  False  False  False  False  False  False   \n",
       "7122  tY(GUA)M1  False  False  False  False  False  False  False  False   \n",
       "7123  tY(GUA)M2  False  False  False  False  False  False  False  False   \n",
       "7124   tY(GUA)O  False  False  False  False  False  False  False  False   \n",
       "7125   tY(GUA)Q  False  False  False  False  False  False  False  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "0     False  ...  False  False  False  False  False  False  False  False   \n",
       "1     False  ...   True   True  False  False  False  False   True  False   \n",
       "2     False  ...  False  False  False  False  False  False  False  False   \n",
       "3     False  ...  False  False  False  False  False  False  False  False   \n",
       "4      True  ...  False   True  False  False  False  False   True  False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  False  ...  False  False  False  False  False  False  False  False   \n",
       "7122  False  ...  False  False  False  False  False  False  False  False   \n",
       "7123  False  ...  False  False  False  False  False  False  False  False   \n",
       "7124  False  ...  False  False  False  False  False  False  False  False   \n",
       "7125  False  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "0     False      4  \n",
       "1      True     39  \n",
       "2     False      0  \n",
       "3     False      0  \n",
       "4      True     36  \n",
       "...     ...    ...  \n",
       "7121  False      0  \n",
       "7122  False      0  \n",
       "7123  False      0  \n",
       "7124  False      0  \n",
       "7125  False      0  \n",
       "\n",
       "[7126 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "0      15S_rRNA  False  False  False  False  False  False  False  False   \n",
       "1      21S_rRNA  False   True  False  False  False  False  False  False   \n",
       "2          HRA1  False  False  False  False  False  False  False  False   \n",
       "3          ICR1  False  False  False  False  False  False  False  False   \n",
       "4          LSR1  False  False  False  False  False  False  False  False   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  tY(GUA)J2  False  False  False  False  False  False  False  False   \n",
       "7122  tY(GUA)M1  False  False  False  False  False  False  False  False   \n",
       "7123  tY(GUA)M2  False  False  False  False  False  False  False  False   \n",
       "7124   tY(GUA)O  False  False  False  False  False  False  False  False   \n",
       "7125   tY(GUA)Q  False  False  False  False  False  False  False  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "0     False  ...  False  False  False  False  False  False  False  False   \n",
       "1     False  ...  False   True  False  False  False  False  False  False   \n",
       "2     False  ...  False  False  False  False  False  False  False  False   \n",
       "3     False  ...  False  False  False  False  False  False  False  False   \n",
       "4     False  ...  False  False  False  False  False  False  False  False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  False  ...  False  False  False  False  False  False  False  False   \n",
       "7122  False  ...  False  False  False  False  False  False  False  False   \n",
       "7123  False  ...  False  False  False  False  False  False  False  False   \n",
       "7124  False  ...  False  False  False  False  False  False  False  False   \n",
       "7125  False  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "0     False      4  \n",
       "1     False     10  \n",
       "2     False      0  \n",
       "3     False      0  \n",
       "4     False      1  \n",
       "...     ...    ...  \n",
       "7121  False      0  \n",
       "7122  False      0  \n",
       "7123  False      0  \n",
       "7124  False      0  \n",
       "7125  False      0  \n",
       "\n",
       "[7126 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "0      15S_rRNA  False  False  False  False  False  False  False  False   \n",
       "1      21S_rRNA  False  False  False  False  False  False  False  False   \n",
       "2          HRA1  False  False  False  False  False  False  False  False   \n",
       "3          ICR1  False  False  False  False  False  False  False  False   \n",
       "4          LSR1  False  False  False  False  False  False  False  False   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  tY(GUA)J2  False  False  False  False  False  False  False  False   \n",
       "7122  tY(GUA)M1  False  False  False  False  False  False  False  False   \n",
       "7123  tY(GUA)M2  False  False  False  False  False  False  False  False   \n",
       "7124   tY(GUA)O  False  False  False  False  False  False  False  False   \n",
       "7125   tY(GUA)Q  False  False  False  False  False  False  False  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "0     False  ...  False  False  False  False  False  False  False  False   \n",
       "1     False  ...  False  False  False  False  False  False  False  False   \n",
       "2     False  ...  False  False  False  False  False  False  False  False   \n",
       "3     False  ...  False  False  False  False  False  False  False  False   \n",
       "4     False  ...  False  False  False  False  False  False  False  False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "7121  False  ...  False  False  False  False  False  False  False  False   \n",
       "7122  False  ...  False  False  False  False  False  False  False  False   \n",
       "7123  False  ...  False  False  False  False  False  False  False  False   \n",
       "7124  False  ...  False  False  False  False  False  False  False  False   \n",
       "7125  False  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "0     False      0  \n",
       "1     False      0  \n",
       "2     False      0  \n",
       "3     False      0  \n",
       "4     False      0  \n",
       "...     ...    ...  \n",
       "7121  False      0  \n",
       "7122  False      0  \n",
       "7123  False      0  \n",
       "7124  False      0  \n",
       "7125  False      0  \n",
       "\n",
       "[7126 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all bayexpress results\n",
    "# 10 replicates\n",
    "# BF_21 > 10\n",
    "\n",
    "CONTROL_R10_bayexpress_FC0 = pd.read_csv(f'DGE_results/CONTROL_R10_1_bayexpress.csv').iloc[:,1:2]\n",
    "CONTROL_R10_bayexpress_FC1 = pd.read_csv(f'DGE_results/CONTROL_R10_1_bayexpress.csv').iloc[:,1:2]\n",
    "CONTROL_R10_bayexpress_FC2 = pd.read_csv(f'DGE_results/CONTROL_R10_1_bayexpress.csv').iloc[:,1:2]\n",
    "\n",
    "for i in range(1,101):\n",
    "\n",
    "    data = pd.read_csv(f'DGE_results/CONTROL_R10_{i}_bayexpress.csv').iloc[:,1:]\n",
    "\n",
    "    # criteria 0 for significance: (BF_21 > 10) & (abs(FC) > 0)\n",
    "    CONTROL_R10_bayexpress_FC0[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 0)\n",
    "\n",
    "    # criteria 1 for significance: (BF_21 > 10) & (abs(FC) > 1)\n",
    "    CONTROL_R10_bayexpress_FC1[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 1)\n",
    "\n",
    "    # criteria 2 for significance: (BF_21 > 10) & (abs(FC) > 2)\n",
    "    CONTROL_R10_bayexpress_FC2[f'{i}'] = (data.BF_21 > 10) & (abs(data.FC) > 2)\n",
    "\n",
    "CONTROL_R10_bayexpress_FC0['Ntrue'] = [(CONTROL_R10_bayexpress_FC0.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC0))]\n",
    "\n",
    "CONTROL_R10_bayexpress_FC1['Ntrue'] = [(CONTROL_R10_bayexpress_FC1.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC1))]\n",
    "\n",
    "CONTROL_R10_bayexpress_FC2['Ntrue'] = [(CONTROL_R10_bayexpress_FC2.iloc[i].value_counts().get(True, 0)) for i in range(len(CONTROL_R10_bayexpress_FC2))]\n",
    "\n",
    "display(CONTROL_R10_bayexpress_FC0)\n",
    "display(CONTROL_R10_bayexpress_FC1)\n",
    "display(CONTROL_R10_bayexpress_FC2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Set global font size to 12\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, sharey=True, figsize=(6, 4), dpi=300)\n",
    "\n",
    "# scatters\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC0.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#332288', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 0, total = {sum(CONTROL_R10_bayexpress_FC0.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC1.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#44AA99', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 1, total = {sum(CONTROL_R10_bayexpress_FC1.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC2.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#CC6677', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 2, total = {sum(CONTROL_R10_bayexpress_FC2.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.legend(loc='upper left')\n",
    "\n",
    "# Use tex in labels\n",
    "axs.set_xticks(np.arange(1, 101))\n",
    "axs.set_xticklabels('')\n",
    "\n",
    "# formatting y axis ticks\n",
    "plt.gca().ticklabel_format(axis='y', style='plain', useOffset=False)\n",
    "\n",
    "axs.set_xlabel('100 iterations')\n",
    "axs.set_ylabel('# putative DEGs')\n",
    "\n",
    "# Set y limit 3500\n",
    "axs.set_ylim(-100, 3500)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmwAAARICAYAAAAPof69AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAuIwAALiMBeKU/dgABAABJREFUeJzs3XdYU+f7P/A3G2UrKCKCFHFvUVHBvfceVT7ipq4O66yT2qq1rQOrWG3VtrgHiopbEJx1oHUDAiKCIlOWrPz+8AdfwwmQhEASeL+uy+syd87znJsRcnLuZ2iIRCIRiIiIiIiIiIiIiIiISGk0lZ0AERERERERERERERFRZceCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESsaCDRERERERERERERERkZKxYENERERERERERERERKRkLNgQEREREREREREREREpGQs2RERERERERERERERESqat7ASoeOnp6QgNDUVUVBSio6Px/v17ZGRkoEqVKjAxMYGFhQVatmwJW1vbMs3j1atXuHPnDsLDw5Gamgo9PT3UrFkTTZs2RcuWLaGpWba1v9DQUAQHByMqKgppaWmoUqUKrKys0Lx5czRp0qRMz01EREREREREREREVNZYsFEhubm5uHPnDq5evYpr167h/v37CAsLQ15eXoltzc3NMXLkSEyaNAnt2rVTWD67du3Cb7/9huDg4CKPq169Ov73v/9h3rx5qF27tkLODQAZGRnYtm0bvLy8EBISUuRx1tbWmDJlCr766iuYmpoq7PxEREREREREREREROVFQyQSiZSdBH309OlTNGrUqNT9DB8+HJs2bYK1tbXcfTx58gRjxozBf//9J3UbQ0ND/Prrr5g2bZrc58139epVjB8/HpGRkVK3sbCwwM6dOzF48OBSn5+IiIiIiIiIiIiIqDxxD5sK6OjRo2jRogVu374tV/tr166hffv2MhVrACA1NRXTp0/HwoUL5TpvvqNHj6Jbt24yFWsAIC4uDkOHDsXWrVtLdX4iIiIiIiIiIiIiovLGGTYqpPAMGw0NDTRq1AhNmzZFvXr1YGNjAyMjI+jq6iI1NRXR0dG4d+8ezp49i9TUVEF/xsbGuHXrFho0aCB1DqGhoXB0dERycrLguW7duqFXr16wtbVFcnIyHj9+DG9vbyQmJgqO3bBhA7766iupz5vv2rVr6Nq1K7Kzs8XimpqaGDhwIJydnVGnTh3ExcXh/v372LdvH9LT08WO1dDQwJEjRzBs2DCZz09EREREREREREREpAws2KiQZ8+eYciQIejXrx/69OmDDh06wMTEpMR2GRkZ2Lx5M1auXInMzEyx5zp16oTAwEBoaGiU2E9eXh6cnJzw77//isUtLS1x5MgRdOzYUdAmLS0N8+bNw/bt28XiOjo6uH37Npo3b17ieT/tq3Hjxnj58qVYvEGDBjh27JjE5eISEhIwadIknDhxQixuYmKCp0+fwtLSUurzExEREREREREREREpCws2FUhgYCB69uyJrKwssbi/vz+6dOlSYvsdO3Zg+vTpYrFq1arhzp07qFu3brFtv/nmG2zYsEEs1rlzZwQEBEiXPIDvvvsOP/74o1jMwcEBN27cQLVq1Ypsl5ubizFjxuDIkSNi8f/973/Ys2eP1OcnIiIiIiIiIiIiIlIWFmwqmEWLFmHdunVisVmzZmHLli3FtsvNzYWDgwPCw8PF4t7e3vj8889LPG9WVhZat26NR48eicUDAgLQuXPnEtsnJSXB1tYWKSkpBTFNTU0EBATA2dm5xPbx8fFo0qQJ3rx5UxDT0tLC8+fP8dlnn5XYnoiIiIiIiIiIiIhImTSVnQAp1oQJEwSxx48fl9juzJkzgmJNs2bNpCrWAICuri5WrVoliG/btk2q9t7e3mLFGgAYMGCAVMUaAKhevTrmz58vFsvNzcWOHTukak9EREREREREREREpEws2FQw9vb2glhMTEyJ7fbv3y+Iubu7y3TuIUOGCPaMOX78ONLT08vl/G5ubtDT0xOL7du3T6Y+iIiIiIiIiIiIiIiUgQWbCiYzM1MQ09XVLbaNSCTCuXPnBPERI0bIdG5tbW0MGTJELJaRkVHiPjbJycm4fv26WMzQ0BB9+vSR6fzVq1dH165dxWKRkZF48uSJTP0QEREREREREREREZU3FmwqmH///VcQkzTr5lPPnj3D27dvxWIODg6oWbOmzOd3cXERxAIDA4ttc/36deTm5orFOnToAC0trXI5PxERERERERERERGRsrFgU8GsX79eEOvdu3exbe7cuSOIdejQQa7zd+zYUar+Ven8RERERERERERERETKxoJNBZGdnY1Zs2bhwoULYnELCwu4uroW2/bp06eCWL169eTKw8bGBtra2mKxZ8+eldv5Jc0mKun8RERERERERERERETKxoKNmouIiMBvv/2Gpk2bYuvWrWLPaWhoYMeOHTAwMCixj8JsbW3lykdLSwu1a9cWi7169QrZ2dnlcv7atWsLllJ78eKFXH0REREREREREREREZUX7ZIPIWXr2LEj0tPTCx6LRCKkpaXh7du3eP/+vcQ2urq68PLywpAhQ0rsPzY2VhCrU6eO3PnWqVMHkZGRBY9zc3Px7t071KpVq8zPr6WlhVq1auHVq1cFsTdv3sjVFxERERERERERERFReWHBRg08ePAAaWlpUh/fvXt3/PLLL2jZsqVUxyckJAhihoaGUp9Pmrbx8fFFFmzK+vxZWVlITU0tVZ/53r59i7i4OJnapKSk4Pbt2zA2NoapqSnq1KkDPT29UudCRERERERERERERIrz4cMHREVFFTzu0qULTE1Ny+38LNhUIM2bN8eWLVvg4uIiUztJxaAqVarInYektp/OEFLW+RVRsNm6dStWrVpV6n6IiIiIiIiIiIiISLX5+PhItYqVonAPmwrkwYMH6NatG0aPHo379+9L3U7S/jL6+vpy5yGpYJKVlaWy5yciIiIiIiIiIiIiUjYWbNRAamoqRCJRwb/s7GzExcUhODgYO3bsQO/evaGhoQHg434xhw4dgqOjI3744Qe5z5nfn6LaikQitTo/EREREREREREREVF54pJoakhbWxvm5uYwNzdHixYtMHXqVAQHB8PNza1gZk1OTg6WLl2K+Ph4/Prrr8X2p6OjI4hlZGTInZ+ktrq6usWe/8OHD4I+5F3CTNbzy2LmzJkYNWqUTG0eP36M0aNHFzz28fFBvXr1FJIPVS7JSRn49cdLiH2VIncfltbG+GZJd5iYyr/sIBERERERERERUUUUGhqKoUOHFjyuU6dOuZ6fBZsKomXLlggKCkLPnj1x8+bNgviGDRvQoUOHYosMVatWFcQUXbAxMDAo9vxlXbAp7vyyqFGjBmrUqFGqPurVq4cmTZooJB+qfBr91RgL5/jgZXiizG1t7MywznMozKoLX/NEREREREREREQkTk9Pr1zPxyXRKhBDQ0McOHBAsIfL/PnzkZOTU2S76tWrC2Kpqaly5yGpraRzlNf5dXV15S7+EKkas+pVsc5zKGzszGRqx2INEREREZHi5OTkqVQ/REREVDGwYFPB2NrawtXVVSwWGRmJs2fPFtmmZs2agtirV6/kziEqKkrssaamJszNzcvl/Lm5uYiJiSmxfyJ1JmvRhsUaIiIiIiLFCbgQgjluB5EYn16qfhLj0zHH7SACLoQoKDMiIiJSdyzYVED9+vUTxPz9/Ys83s7OThCLjIyU69x5eXmIjo4Wi1lbW0vcJ6cszv/69WvBbCJJ/ROpO7PqVbHYo7dUxy726M1iDRERERGRAgRcCMG6FecQHhaPhXN85C7aJManY+EcH4SHxWPdinMs2hAREREAFmwqpLp16wpiL168KPL4Bg0aCGKhoaFynfvly5fIzs4WizVs2LDYNoo8f1hYmCBW0vmJ1FFifDrWLD8n1bFrlp8r9eg/IiIiIqLKLr9Yk/f/VzF7GZ4oV9Emv1iTvy9lXh5YtCEiIiIALNhUSPr6+oLY+/fvizy+TZs2gtj169flOve1a9cEsdatWxfbRtnnJ1I3hT/glUTeD5JERERERPRR4WJNPlmvtYu6lmfRhoiIiAAWbCqkt2/fCmLF7SHTsGFDWFhYiMWeP38usZ+SBAUFCWKdO3cutk3Hjh2hpaUlFrt+/Tpyc3PL5fxE6kTWYk0+Fm2IiIiIiOSTk5OH/bvvCIo1+aS91i7pWj4vD9i/+w5ycoo4EREREVV4LNhUQDdu3BDELC0tizxeQ0MDvXsL98I4evSoTOfNycnB8ePHxWL6+vro0qVLse1MTEzg5OQkFnv//j3Onz8v0/kTEhJw+fJlsZiNjQ0aNWokUz9EqkreYk0+Fm2IiIiIiGSnra2JHzcNho2dWZHHlHStLc21vI2dGX7cNBja2rxVQ0REVFnxKqCCEYlE2L9/vyDeoUOHYtuNHTtWEPPy8pLp3L6+vnj9+rVYbMiQIahateTNzhVx/j179iAzM1MsNm7cOJn6IFJV0n7A2/b3mFJ9kCQiIiIiIiGz6lWxznOoXNfa0l7Lr/McCrPqJX9+JiIiooqLBZsKZseOHbh3755YTE9PD/369Su2Xd++fVG3bl2x2P3793HgwAGpzpuVlYUVK1YI4l988YVU7SdMmAAjIyOxmK+vr8Q9aSRJTEzE+vXrxWJaWlqYNm2aVO2JVJksH/Ds6pnL/UGSiIiIiIiKJk/RhsUaIiIikgULNiri3r17+Omnn5CeLv8N1EOHDmH27NmC+OTJk2FoaFhsW21tbSxatEgQnz17NiIjI0s895IlS/Dff/+JxZydnUtcDi2fqakpZs2aJRbLy8vDpEmTkJhY/PJPeXl5mDFjBmJiYsTi48aNg729vVTnJ1JV8nzAK83oPyIiIiIiKpos19rhoe9YrCEiIiKZsGCjIpKTk7Fw4ULY2dlh/vz5uH37ttRtHz58iM8//xyjR49Gdna22HM1a9bEjz/+KFU/U6dORevWrcVi7969Q4cOHXD9+nWJbdLT0/HFF1/gl19+EYtra2vD09NT6q8BAL777jtYW1uLxZ4/f46OHTvi6dOnEtskJiZi+PDhOHTokFjc2NgY69atk+n8RKqmNKPxWLQhIiIiIiob0l5rf+F6gMUaIiIikomGSCQSKTsJAvz9/dGtWzexWM2aNdG6dWu0bNkStWrVgqmpKapWrYrU1FQkJibi4cOHuHnzJh4+fCixTxMTE5w5cwZOTk5S5/H8+XO0bdsWKSkpgue6d++O3r17w8bGBsnJyXjy5An++ecfJCQkCI79+eefMW/ePKnPmy8wMBDdu3dHTk6OWFxTUxODBg2Ci4sLateujXfv3uH+/fvYt28f0tLSBP0cOnQII0eOlPn8ZeHRo0do2rRpweOHDx+iSZMmSsyI1EFOTh7muB1EeFh8kcdI8wFPmqKPnX11eO4ezc1NiYiIiIhkIM21dlFYrCEiIlJNyr6Xy4KNipBUsCkNa2trHD16FG3btpW5bWBgIPr374/U1FS5zj1v3jz8/PPPcrUFPhZbPv/8c0HRRhoaGhrYuHEj5s6dK/f5FU3ZL3JSXwEXQrBuxTnk5Qmfk+UDXnEfJDU1gYWreqNLTwdFpExElUxOTp5Cir2K6oeIiKi8yVO0YbGGiIhIdSn7Xi4/GasIfX19aGlplbofHR0dfPXVV3jy5IlcxRoAcHFxwY0bN8R+MaVhYGAALy+vUhVrAGDUqFG4dOkSbGxsZGpnbm6Oo0ePqlSxhqg0uvR0wMJVvaFZ6C+1rB/wilqygcUaIiqNgAshmON2sNTLKibGp2OO20EEXAhRUGZERETlR5rl0T7FYg0REREVhwUbFeHk5IS3b99i3759mDJlCpo0aQLNwndpi1ClShV06tQJmzdvRkxMDDZs2ABDQ8NS5dOkSRPcu3cPv//+O1q0aFHssdWrV8eXX36JZ8+eYcaMGaU6bz4XFxc8efIE69evR7169Yo9tnbt2li2bBmeP3+OoUOHKuT8RKqicNFG3g94hT9IslhDRKWRPwMwPCy+VHth5Y9KDg+Lx7oV51i0ISIitWRWvSoWe/SW6tjFHr1ZrCEiIqIicUk0FZaWloaQkBCEh4cjNjYWqampyMzMhIGBAYyNjWFqaoqGDRuiUaNGCpmdU5yXL1/izp07iIiIQFpaGnR0dFCzZk00bdoUrVu3lrq4JK+QkBDcu3cPUVFRSE9Ph76+PqysrNC8eXM0a9asTM9dWsqeRkcVQ8CFEOzffQc/bhpcqg94ifHpWPLlCYx1a8NiDRHJRdJyjfIUkyUtIcNiMhERqSNZlkXjDBsiIiLVpux7uSzYEJUxZb/IqeLgXhFEpGzcW4uIiEgc97AhIiKqWJR9L5d37IiI1ISiiiws1hCRPHJy8rB/9x2JxRoAeBmeKNXyaCXd2MrLA/bvvoOcnCJOREREpCLkKdYA0r9nEhERUeWjrewEiKj8iEQi5OXlgRPriIhIHt//OgArF57Cq8gkic+/jU3Bsm9PYOW6ATCtVkXwfFJCBlYuPIW3sSnQryJ5OVdrW1OsXDcAQB6LNkREpLKkeU8rTknvmUSqSkNDA5qamtDQ0FB2KkRKwdVPqKyxYENUgeXl5SE1NRXv379HWloacnNzlZ0SERGpuRnfNEP0yyRkZeUUecztfx+gto2p2AeQnJw8RL9MQu/BtQDUkthOV1cbtW1MERf/CnHxis6ciIhIMaR9T7OsbYzY6BSZ3zOJ1IGWlhYMDAxgZGQEQ0PDMt/bmEgVcH9hKg/8a0pUAWVmZiI6OhohISGIjo5GSkoKizVERKQQ2tqaqG1jCl3dosf9ZGXlIPplUsEMmfwbW8XdsMov1vCGFRERqTJZ3tP09LRlfs8kUhe5ublISUkRu/eQmZmp7LSIykz+fp7hYfGlWtYyfznN8LB4rFtxDgEXQhScKak7zrAhqmAyMjLw8uVL5BW1yQAREVEpaWtrwsbODFlZucUus5mbmwdNTQ3k5uahppVRkcdpaGhAV1eLS2sQEZFKE4lEMr+nyfKeqaWlwfdCUkt5eXlISUlBamoqbGxsUKUKl/mjiiW/WJN/qy1/L7J1nkNlmmlTeO+zvDxg3YpzAMCZNlSAQxiJKhAWa4iIqLxIU2QRiUTIysop9gYVizVERKQOPr6nFV90Keo9Tfr3zOL7J1J1eXl5ePnyJTIyMpSdCpHCFC7W5Msv2kg706ZwsSZfftGGM20oH2fYEFUQmZmZRRZrqlatCmNjY1SpUgVaWrwpRkREipOTk4vXr5KRnSX7YAEdXU1YWZtAW1v2zZqJiIjKi0gkwqvIJGih6GWmpXlPk+Y9U1dXC9a2pvzMRirr40yzXGRkZCAlJQXp6eI3q/OLNra2ttDX11dSlkSKkZOTh/277wiKNfmknWlTVLEmX14esH/3HXTqas8lookFG6KKIj4+XlCsMTAwgJWVFbS1+VInIqKyoa2tDZu61fHqZRKyPki/X5qunhasbUxZrCEiIrVgXsMIsa9TIGkCjLTvaSW9Z2pofDyPjo6OotImKhM6OjrQ19eHmZkZcnJy8Pr1a6SlpRU8n5eXh/j4eNSuXVuJWRKVnra2Jn7cNLjYYktJRZuSijUAYGNnhh83DWaxhgBwSTSiCiEvLw/v378XixkYGMDa2prFGiIiKnPa2h9vVOnqSVd8YbGGiIjUjZGxPiytjFF44ous72lFvWdqaACWVsYwMuaMBFIv2trasLa2hoGBgVg8NTWVy7VThWBWvSrWeQ6FjZ1ZkccUtTyatMUaWffCoYqNBRuiCiA1NVWw1rGVlRU0NfkSJyKi8qGtrYVaVsZSHVvLypjFGiIiUjuFizbyDkAoXLRhsYbUnaamJqysrMRieXl5SE1NVVJGRIolT9GGxRqSF+/mElUAhWfXVK1alTNriIioXOXk5CLmdYpUx8a8TkFOjvTLpxEREamK/KKNnp52qWaL5hdt9PS0WayhCkFbWxtVq4rfdC58r4JInclStAkPfcdiDcmNBRuiCuDTtWIBwNhYuhHOREREipCTkyvTHjZZHz4ez6INERGpIyNjfdjYmZV6tqi2thZs7MxYrKEKo/C9iML3KojUnbRFmy9cD7BYQ3JjwYZIzYlEIuTmit/wqlKlipKyISKiykbWYk0+Fm2IiEidaRTezEbJ/RCpgsL3InJzcwXLtxOpO2mKNsVhsYZKwoINkZqTtImflhb3BSAiorInb7EmH4s2RERERBWHpH10Jd2zIFJ38hZtWKwhabBgQ6TmJI1W4SgtIiIqa9IUa3T1tGBrZ1awqbIkLNoQERERVQySCjacYUMVlaxFGxZrSFos2BARERGRTKQt1ljbmEJPXwfWNqYs2hARERERUYViVr0qFnv0lurYxR69WawhqbBgQ0RERERSk6VYk78Zs7a2Fos2RERERERUoSTGp2PN8nNSHbtm+TkkxqeXcUZUEbBgQ0RERERSkadYk49FGyIiIiIiqigS49OxcI4PXoYnSnX8y/BELJzjw6INlYgFGyIiIiIqkUgkQvTLZLmKNfmkLdpEv0zmeudERERERKSSZC3W5GPRhqTBgg0RERERlUhDQwPVzKtCQ0Py8yUVa/KVVLTR0MD/P08RJyIiIiIiIlISeYs1+Vi0oZKwYENEREREUjEy1oellbGgaCNtsSZfUUUbDQ3A0soYRsb6ikqZiIiIiIhIIaQp1tjYmWHb32NgY2dW5DEs2lBxWLAhIiIiIqkVLtrIWqzJV7how2INERERERGpKmmLNes8h8KunjnWeQ5l0YbkwoINEREREckkv2ijp6ctV7EmX37RRk9Pm8UaIiIiIiJSSbIUa8yqVwUAmFWvyqINyYUFGyIiIiKSmZGxPmzszOQu1uTT1taCjZ0ZizVERERERKRy5CnW5GPRhuTBgg0RERERyUWj8GY2Su6HiIiIiIhIUXJy8rDkyxNyFWvySVu0WfLlCeTk5JU6Z1J/LNgQERERESnY7t27oaGhIfYvIiJC2WkRESlc165dxf7Wde3aVdkpERERKYS2tibGurWBZhF30Esq1uQrqWijqQmMdWsDbW3eqidAW9kJEBERkeJERUXh0aNHePXqFZKSkpCVlQUzMzOYmZmhYcOGaNasGbS0SreEFZEipKen49atW3j27BkSExMhEolgYmICBwcHtGvXDiYmJspOkSRIT0/Hf//9h4iICMTGxiI9/ePSDaampjAzM0OtWrXQqlUrGBsbKznTyis5ORm3bt1CSEgIkpOToaGhATMzMzRo0ADt27dHlSpVlJ0iEZHc4uPjERYWhqioKLx58wZpaWn48OEDDA0NYWJiAmtra7Ru3RrVq1cv0zxSUlJw9+5dREREICkpCampqTA0NISpqSlsbW3Rpk0bvhcSVRBdejoAANatOIe8TybASFusyZdftCm8vJqmJrBwVe+C8xCxYENERGUiIiICdnZ2MrXR1NSEoaEhjI2NUbt2bbRo0QJt27bF8OHDUa1aNblzqVu3LiIjI+VuL0liYiJMTU0V2qc8nj59irNnz+LSpUu4cuUKkpKSij3ewMAAXbt2hbu7O/r37w/NooYKySEmJgZ37tzB7du3C/69efNG7BhbW1vOMlCirKwsPHz4UOxn9PDhQ2RnZ4sdt2vXLri5uZVJDv/++y/Wr18PX19fZGZmSjxGW1sbvXr1wrx589CjR48yyUNV+fv7o1u3bgrtc8iQIfDx8ZG7fUhICPbu3QtfX18EBwcjNze32OM1NDTQoEEDdO7cGePHj4eLiwuXvSsHFy5cwC+//IILFy4gJydH4jH6+voYPHgw5s+fD0dHx3LOkIjk4ebmhj179iikrz59+uDMmTMK6UuSjIwMtGzZEs+fPxc8N3HiROzevVum/tLS0nDt2jVcvXoVN27cwIMHDxATEyNVWwcHB4wfPx5ubm6wtbWV6bxFSU5Oxt9//41du3bh3r17EIlERR6roaGBli1bYtKkSXB1dZXrc4M8n6eKc/36dTg5OSmsP6LKpHDRRtZiTb7CRRsWa0gSFmyIiEhl5OXlISUlBSkpKXj16hVu3ryJ33//HbNmzcKwYcOwbt06hX3gUmcZGRn4+eefcfDgQTx8+FCmtmlpaTh16hROnTqFJk2aYPfu3XLftLtx4wbOnz9fcOP/9evXcvVDZSc1NRUHDhwo+Bk9ePAAWVlZSsnlw4cP+Prrr+Hl5VXsDQ4AyMnJgZ+fH/z8/DBq1Cjs3LmTo1SV4OHDh1iyZAlOnjxZ4s/sUyKRCE+fPsXTp0/x+++/w9bWFrNnz8acOXOgp6dXhhlXTsnJyZgyZQqOHDlS4rGZmZk4ePAgDh06hFmzZuGXX36Brq5uOWSpOIVvXqvrQABJxdnLly9zOTFSa999953EYo28/v77b3zxxRdytQ0JCcHKlSvxww8/4Ntvv8XSpUtRtapsN1Y/tX//fnz55Zd4+/atVMeLRCLcu3cP9+7dw/fff48NGzZg/Pjxcp+fiJQvv6iyf/cd/LhpsMzFmnz5RZslX57AWLc2LNaQABfGIyIilZeVlYUDBw6gcePG+Ouvv5SdjtK9efMGy5cvl7lYU9ijR4/QoUMHbNmyRa72a9euxfLly3HixAkWa1RUaGgopk6dCi8vL9y+fVtpxZq0tDT07t0b27Ztk+nGPwAcOnQIHTt2RFxcXBllR4VlZ2dj4cKFaNmyJXx9fWX+mRUWGRmJ+fPno379+jh8+LCCsiQAePv2LTp27ChVseZTIpEIW7ZsQZ8+fQqWtSMiKo2goCBs2rRJ2WkIZGdnY82aNXBxccG7d+/k6mPRokUYN26c1MWawuLi4jBhwgTMnz9frvZEpDq69HSA5+7Rchdr8plVrwrP3aNZrCGJOMOGiIjKjYGBAerVq1fk89nZ2UhOTkZMTAzyPl0c9v9LT0+Hm5sbcnNzMWnSpFLl0qJFi1K1V9V9YOrVq4cuXbrAwcEBNWrUgIGBARISEhAcHIzTp08jKipK7PicnBzMmTMHurq6mD59upKypopMJBJh7NixuHLliuC52rVrY/z48XBwcICenh7Cw8Ph4+ODe/fuiR336NEjDBw4EIGBgWo3G0ARzMzMYGNjI3d7WZZTSUxMxKBBg3D16tUij2nYsCG6d+8Oa2trmJubw8zMDBkZGXj79i2eP3+Oq1ev4tGjR4J2L1++xJYtWzBy5Ei5vg4S9+HDB/Tv3x+PHz8WPFe/fn2MHj0a9vb2EIlECAsLw4EDBxAaGip2nL+/P8aPH49jx46VV9pEVEo6Ojpo3LixXG3t7e0VnM1H6enpmDRpksTrd0XS09NDy5Yt0bBhQzg4OKBmzZowMjKChoYGUlJSEBYWhlu3biEgIECwfOfdu3fRo0cP3Lp1S6bZnj///DPWrVsn8bkOHTqgR48e+Oyzz2BkZIT3798jJCQEFy9exK1btyT2ZWFhgQULFsj2hX+iTp06ci8VbWBgIPd5iej/aGsrZv6DovqhiocFGyIiKjeOjo7w9/cv8bj09HTcuHEDf/zxB/bt2yc2ulskEmHOnDno3r17qZZHCw4OlrutqmncuDHc3Nzw+eefo3bt2kUel5OTg127duGbb75Bamqq2HNz5sxB165dUb9+/VLnU6VKFbRo0QKOjo5yz96hsmdjYwNHR0fExcUhMDCwzM6zZcsWnDx5UhD38PDAokWLoKOjIxZfvnw5fHx84OrqKvZ7euvWLSxbtqzImyaqxs3NTWH7AA0ePFjmdf/lkZiYiM6dO0ucvWdgYICvvvoKM2bMQJ06dUrsKyIiAv/88w9+++03xMbGlkW6ld6SJUtw584dsZiOjg5+++03TJ06VbBvkIeHB3bu3IlZs2aJ7XHj4+ODbdu2yb3sEBGVLysrK5W7jl20aJFYQbh9+/a4efNmqfvV1tZGhw4d0K9fP/Tq1QutW7eWauBGTEwMvvvuO+zatUss/uDBA6xatQo//vijVOePiIjA8uXLBfGGDRti9+7daN++fZFtg4KCMGnSJEGhfMWKFRg5ciQ+++wzqXIozMPDo8z2GSQiItXAUh4REamcqlWronv37vD29sapU6egr68v9nxaWhrWrFmjpOxUR7du3RAQEIBHjx5h/vz5xRZrgI8feqdNm4arV68KNj7NysrCvHnzZM5BT08Pjo6OcHd3x86dOxEcHIyUlBRcv34dnp6eMvdHZcPKygqDBw+Gh4cHTp8+jbi4OERGRuLIkSPo3r17mZ333bt3WLp0qSC+efNmLFu2TFCsyTd06FBcunRJcFNmw4YNCl0bn/5PTk4Ohg0bJrFYM3ToUISGhmL16tVSFWsAoG7duli6dCnCw8Pxww8/cFSvgj158gSbN28WxA8fPoxp06YJijUAoKmpienTp0tclm7JkiVITEwsk1yJqGILCAgQG6Dj4OCAFStWKKTvqVOn4tq1a1i2bBmcnJyknmVbq1Yt/Pnnn1i7dq3guc2bN0u9FOS2bduQkZEhFrO3t0dgYGCxxRoAcHZ2RmBgIOrWrSsWz8zMxLZt26Q6PxERVU4s2BARkUrr168fPDw8BHEfH58yX3ZBVZmYmMDf3x+XLl1C586dZW7fvHlz7Ny5UxD38/OTaW1vT09PvH//Hv/++y+2bduGKVOmoEWLFtDW5gReVVG/fn3ExMQgOjoax48fx7Jly9CvXz+Ym5uXy/k3b96MlJQUsVivXr0wZ86cEtu2bdtWMKo1Oztb4s0XKr3Vq1cjICBAEP/iiy9w5MgRWFpaytWvvr4+lixZgnv37qFNmzalTZP+vzVr1ojNkgGAadOmYfDgwSW2HTJkCKZOnSoWS0pKYpGdiGSWlpaGyZMnF8yG19DQwM6dO1GlShUlZ/bRwoUL4eTkJBZLS0vD6dOnpWovabnIX375RerrKEtLS/z888+C+PHjx6VqT0RElRMLNkREpPJmzJghGIn/5s2bSrvRvZmZGbp06VKqPkaMGIHmzZuLxXJzc+Hn5yd1H3Xq1ClyhoSqi4iIQIsWLfDzzz9X6N+jqlWryn2jvbRyc3OxY8cOQVyWgsu3336L6tWri8X27duH5OTkUudH/+fZs2f44YcfBPExY8Zg69at0NQs/UcGBwcHBAUFYdSoUaXuqzh///03evfujb///luw9GNFkZiYiIMHD4rFdHR08P3330vdx/fffy/4+/37779X2oEQRCSf+fPn48WLFwWPZ86cKddgorI0fvx4QUzS3l+F5ebmIiwsTCxmbGyMAQMGyHT+wYMHw8jISCwWFhYm2GOHiIgoH4fAEpFay8nJU8hGbYrqh8qGsbEx6tevL9jEOjY2FtbW1krKSv3169cPDx48EIt9+qG7onvw4AHmz5+PhQsXonv37nB1dcXw4cNhaGio7NQqBH9/f8HeJW3btkXr1q2l7kNPTw9ubm745ZdfCmKZmZk4duwY129XoGXLlglma1hZWSl8yRZ9fX3MmjVLoX0Wlpubi/Pnz+P8+fMwMDDA0KFD4erqip49e0JLS6tMz11ejh49ig8fPojFhg4dipo1a0rdh6WlJQYPHowjR44UxKKjo3HlyhV07dpVUamqvZcvXyI4OBhxcXGIi4uDvr4+LCwsYGVlBScnJ5WZRVCU3NxcvHjxAk+fPkV0dDRSUlKQm5sLMzMzmJmZoWHDhmjWrJlCirJU+Vy6dAleXl4Fj21tbVVyFqy9vb0gFhMTU2K7uLg4QRG7Xr16Ms8k19HRgb29vdi+Q3l5eYiLi1PaoBoiIlJtLNgQkdoKuBCC/bvv4MdNg2FWvarc/STGp2PJlycw1q0NuvR0UGCGpEjGxsaCGEcCl46NjY0gVhk3B8/Ly8OFCxdw4cIFfPHFFwU3eHv16lVhbvAqw5kzZwSxkSNHytzPyJEjxQo2wMfl+1S9YLN7925MmjRJLBYeHi5Yy17ZoqKicPToUUF87dq1MDMzU0JGipOWlgZvb294e3vD0tIS48aNg6urK1q1aqXs1EpFka+tTws2wMfXlioWbOrWrYvIyEiJz0VGRkrcs6ewy5cvS/W1JSUl4ZdffsHRo0eLHYWvr6+Pzp07w93dHcOGDSuxXwBYuXIlVq1aVeTz3bp1K7GPiRMnYvfu3UU+HxISgqNHj+LSpUu4evUq0tLSiu3PxMQEvXr1wrffflvinhxE+d6/fy+2FBrwcZaeKg56yczMFMSk2QtH0jHS7qFTmJ6eniBWeI9OIiKifBxKQ0RqKeBCCNatOIfwsHgsnOODxHjpNo4sLDE+HQvn+CA8LB7rVpxDwIUQBWdKiiJpbxULCwslZFJxSNpwVdVHC5e19PR07N27F/369UPt2rXxzTff4O7du8pOSy0FBgYKYs7OzjL306ZNG1StKl6Uv3Llitx5kbg9e/YIlmUxNzfH6NGjlZRR2YiNjcWGDRvQunVrNG3aFOvWrcOrV6+UnZZcFPXacnFxkarvysTT0xOfffYZVq9eXeKSSZmZmTh37hyGDx+Ojh074r///iunLCWLj49H69atUb9+fSxatAjnzp0rsVgDAMnJyTh8+DCcnJwwZMgQJCUllX2ypPa+/fZbsSLq5MmT0bt3byVmVLR///1XEJM066YwMzMzmJiYiMWioqLkyqFwu2rVqsHU1FSuvoiIqOJjwYaI1E5+sSZ/csXL8ES5ijb5xZqX4YkAgLw8sGijomJjYxEaGioWMzExUbmR6uqm8PcUAGrVqqWETMpf9erVMXnyZMEH8U+9efMGGzZsQJs2bdCkSROsXbtW7g/qlU1OTg7u378vFtPR0YGjo6PMfUlqFxsbK9VyJlSyEydOCGJubm4SRwOrA0dHRwwYMKDY/bUePXqERYsWwcbGBt27d8euXbuQkpJSjlnKLzo6Gm/evBGL2djYwMrKSua+ateuLZhpef/+/Uq5r0Jubi5mzJiBuXPnIjExUeb2169fh7OzMy5dulQG2Unn/fv3uHfvXqn6OHHiBNq1a6e2xUwqH+fPn8fvv/9e8NjKykowE1ZVJCQk4I8//hDEpSkuaWhoCGa9RUdH4+HDhzLlcP/+fcF+iT169JCpDyIiqly4JBoRqZXCxZp8+UWbdZ5DpVoerXCxJl9+0QYAl0dTIWvXrhVbcgH4uF6/NEugkGQ5OTk4fvy4IN62bVslZFP+jIyM8Mcff2Dr1q04efIkvL29cfr0acG+EPkeP36MxYsXY8mSJejSpQtcXV0xcuRIiUv10ccligovQVKnTh25lxKxt7cXzKp59uxZpSkwlpX379/jzp07gnj37t2VkI1iNG3aFCdPnsS7d+9w8OBBeHt749q1axKPFYlEuHz5Mi5fvoxZs2Zh8ODBcHV1RZ8+fWTeo6C8PH36VBCrV6+e3P3Z29vj5cuXBY/T09MRFRWlcgMiGjduXDAa/eXLl2JFFR0dHTRu3LjEPopbqmn69On4888/BXF9fX306dMHnTt3Rq1atZCRkYGIiAgcP35csAdcSkoK+vXrh0uXLqFTp04Sz2NpaYkWLVoAAFJTUwUbmtvb25e4pJSk5UwlMTQ0RNu2bdGoUSM4ODjAxMQERkZGyMrKQmJiIh4/fozLly/jyZMnYu1CQkIwZswYBAQEqOzroLJLT0/HmjVrEBQUhKdPnyIuLg4fPnxAtWrVUK1aNdSvXx+dO3dG9+7dC37fFCUlJQVTpkwRi23btk0lZ4vExcVh6NChePv2rVi8X79+aNiwoVR9zJw5Ez4+PmKxJUuW4Pjx41J9DhGJRFi8eLEgPmfOHKnOL4m/vz9u3ryJmzdvIiYmBvHx8ahatSqqVauGmjVron379ujcuTN69uzJ61QiIjXFKzAiUhs5OXnYv/uOoFiTT9qiTVHFmnx5ecD+3XfQqas9tLU5EVGZRCIRfv31V2zatEksrquriwULFigpq4rhxIkTgv1qqlWrJteyOupMT08PI0aMwIgRI5CUlIRDhw7B29sbV65cERQJgY+/k/7+/vD39xe7wdu3b1/e2PpERESEIGZrayt3f5JuUL548UIl99pQJ8HBwRL3AmvXrp0SslEsc3NzzJw5EzNnzkR4eDj27t0Lb29vwc3pfBkZGThw4AAOHDgACwsLjBs3DhMmTFC5InZ5vbZUrWBz+vTpgv+7ublhz549BY+trKzENvOW1YEDByQWa4YMGYKtW7dKnL3k4eGBkydPwt3dHdHR0QXxrKwsTJgwAcHBwRJncLq7u8Pd3R3Ax5uuhUfv79y5s1R/10xNTTFhwgSMHDkSHTt2LHamWb5r167hyy+/xO3bt8ViGzduxLfffit3LuVB0l5h5cXW1lbi67E8xMXFYcmSJYJ4bGwsYmNj8fjx44IiQ8eOHbFw4UIMHjxYIef++uuvxWYbjx07VmF9K0JOTg4ePXqEw4cPY8uWLYIl/szMzLBt2zap++vVqxdGjx6NgwcPFsR8fX0xc+ZMbNy4sdjZqBkZGZg9ezb8/PzE4pMnT5a4JKW0Pv37ly85ORnJyckIDw/HjRs3sGnTJhgbG2PGjBn45ptvYGlpKff5iIio/PFOJBGpDW1tTfy4aTBs7IreBLmk5dFKKtYAgI2dGX7cNJjFGiXIzs5GQkICbt26hY0bN6JVq1YSbxZs3rxZqtG0JFlmZqbE0X6TJk2q1EUHU1NTTJs2Df7+/oiMjMS6devQvHnzIo/PzMzEwYMHMWjQIFhZWWHu3Lm4detWOWasugoXA4GPM2zkJalt4WWhSHbPnj0TxOrWrYvq1asrIZuyY2dnh++++w6PHz/GnTt38M033xS7hFhcXBw2b96Mdu3aoWHDhvjhhx+UdmO2ML62FCs1NRVffPGFID5t2jQcO3as2N+TgQMHIjAwELVr1xaLR0REYOnSpQrPtSRWVlZ4/fo1PD090aVLF6mKNcDHG/qBgYHo27evWHzz5s3Iyckpi1SpHF27dg1DhgzB6NGjS730o5+fn1hx09zcHJ6enqVNUWaPHz9Gy5Ytxf41a9YMdnZ2MDAwQMuWLbF69WpBsaZmzZq4cOGCzEXuP//8E507dxaLeXl5oUmTJvj5559x9+5dJCcnIzc3F0lJSbh9+zbWrVuHxo0bC4rBAwcOxNatW+X6umWVkpKC9evXo0mTJjh58mS5nJOIiBSj8t6VISK1ZFa9KtZ5Di226FLUTBtpizXSLqtGsgsICCjVMmbW1tbw9PTE0KFDS51Ly5Yt5Wr36ehYdbV48WI8f/5cLGZmZoaFCxcqKSPVU6dOHSxYsAALFizAw4cP4e3tjb1794otHfSpuLg4eHp6wtPTE/Xr14erqysmTJigcqPUy0tCQoIgVtIyP8WR1DY+Pl7u/tTNiRMn5P6bdfr06SJvOkvak6mij8Jt3bo1WrdujfXr1+Py5cvw9vbGkSNHiryR+ezZMyxduhTLli2Ds7MzXF1dMWrUKKUt/8PXlmLt3r1bsGdN27Zt4eXlJdX1ip2dHQ4dOoROnTqJzcrctWsXPDw8YGZW9CAjRZN3yUng49Jve/bsgZ2dHdLTPw56ioqKwrlz59C/f39FpUgKlr9xvb6+PhITE/Hu3TtkZ2dLPPbQoUO4d+8eAgMD5fo7n5SUhGnTponFPD09YW5uLlfupZGeni7YJ684Ojo6cHV1xdq1a2FhYSHz+QwMDHD+/HmsWLECGzZsKFg+NywsDPPnz5eqD2NjY3z33Xf49ttvoalZ+kGBurq6sLCwgImJCT58+ID4+HhBgSpfQkICBg8ejJ9++knlZ80REdFHLNgQkdqRp2jDYo16c3Z2xjfffINBgwYpbAaILB/0PiVpdLM6OXLkCDZu3CiI//zzz3J9iK0MmjZtijVr1uDHH39EUFAQ/vnnHxw+fFjijVMAeP78OZYtW4bly5ejU6dOcHV1xejRo1VyffeykpaWJohVqVJF7v4ktc2/qVgZJCYmyrUROvBxiabi+i2ssvyeampqokePHujRo4dgLytJ3zORSITAwEAEBgZizpw5GDRoECZMmID+/ftLPZNBEfjaUqwtW7YIYr/99ptMN1Q7dOiAiRMnYvfu3QWxtLQ07Nq1C998840i0iwXNWrUQN++fXH06NGCWFBQkEoXbKpVq6bwPVqkVdzsq7LSrFkzDBw4EL169ULz5s0FsyE/fPiAf//9F0ePHsWOHTuQmpoq9nxoaCgGDhyIgIAAGBgYyHTuL7/8Umz5vyFDhmDs2LHyfzHlZNiwYfj1119LPYBGV1cXa9aswezZs/Hnn39i+/btYt+PolhaWmLx4sX43//+V6r3V0NDQ/Tt2xf9+/dH+/btUb9+fcFnoqioKFy+fBlbt27FzZs3xZ4TiURYsGABrK2t1eLnRkRU2bFgQ0RqSZaizWKP3liz/ByLNWosKCgIoaGhCA4Oxty5cyvccj3l5e7du5g4caIgPmLECEyePFkJGakXDQ0NuLi4wMXFBZ6envDz84O3tzd8fX2RmZkpOF4kEiEoKAhBQUGYO3cuBg4cWHCDtzQjodWBpBG++vr6cvcn6aZycYUIkk5GRoYgJmnfjYpOX18fI0eOxMiRI5GYmFiwl1VgYKDEvaw+fPiAw4cP4/Dhw6hevTrGjBkDV1dXODk5lXmufG0pTlRUlGBZQEdHR7n2LZo5c6ZYwQYAzp8/r1YFGwBwcHAQe3zjxg0lZSKdwYMHq9T+KWVlwIABmD17NhwdHYs9Tk9PD87OznB2dsZ3332HSZMmwdfXV+yYO3fuYNGiRTItZXby5En89ddfBY9NTU1l2gdGmY4dO4aLFy9i6tSpWLhwIWrUqCF3X0lJSfjrr7/g7e0tVbEG+DjQ6/vvv0dYWBjmzZsncd+w4hgaGmLbtm0YP348jIyMij22Tp06+N///of//e9/OHjwIKZPn47k5OSC50UiESZNmgRnZ2dYW1vLlAcREZUvbtBARGorv2hT0p42X7geYLFGRRgYGKBFixbF/rO3t0e1atUEbWNjY+Hh4YFGjRpxHWY5vHjxAgMGDBCMzm7YsCF27dqlpKzUl66uLoYMGYKDBw/izZs32LVrF3r27FnkqOwPHz7gyJEjGDZsGGrVqoWwsLByzlj5SrMcoqS2km6kk2wkfQ9L83OqCMzMzDB9+nQEBAQgMjISa9euRbNmzYo8Pj4+Hlu3bkWHDh3kXrautPjaks/Vq1cFsZEjR8rVV9u2bWFnZycWu379utK/l9HR0Th06BCWLVuGMWPGoG/fvujYsSNatWol2AOkZcuWgqJTUcuAUvkaNWpUicWawqpXr44TJ05IHJCzfft2vHjxQqp+EhMTMX36dLHYr7/+ilq1asmUjyI5OjpCJBKJ/cvMzERsbCwCAwPx008/if3dTklJwa+//oomTZrg1KlTcp3zwIEDsLe3x5IlSwSFXm1tbVhbW6NJkyaoU6cO9PT0xJ5/9+4dNm/ejIYNG2Lz5s0yndfc3Bzu7u4lFmsKGz16NAIDA2FsbCwWz8zMxMqVK2Xqi4iIyh8LNkSk1qQp2hSHxZry5ejoiODg4GL/hYaGIj4+Hq9fv8Y///wDZ2dnsT7i4uIwdOhQ7N27t1S5FP6gJ+0/dfyQEx0djZ49ewqWc7O2tsaZM2dk/hBI4oyNjeHm5obz58/j1atX+PXXX9G6desij09ISMD79+/LMcPyJ2mJKEmzOaQlqW1Fn6X0qYkTJ8r9N6u4ZWAkza4oag38yqhOnTpYuHAhHjx4gAcPHmDhwoXFjo6OiIgo85z42lKcu3fvCmKy3hQvrm1ycrLSivOHDx9G9+7dYWNjg9GjR2P16tU4ePAgzp49i+vXryM4OBj3798X/Hvz5o1YP/x7oP62b9+ONm3aiMWys7OlLhzMnj0bMTExBY979+6NSZMmKTRHRdDT00PNmjXh7OyM+fPn48GDBzh48KDYHjvv3r3D0KFDxZb9k8aaNWswduxYwVK4Q4YMwdmzZ5GcnIyoqCg8fPgQL1++REpKCgIDA+Hq6gotLa2C4zMyMvDll1+W28y7Zs2aCYqwwMe9uyrTXmVEROqIBRsiUnvyFm1YrFFttWrVwvjx4xEYGIjNmzeLjQLOzc3F5MmT8fDhQyVmqB7evn2Lnj17Ijw8XCxes2ZNXLx4Eba2tkrKrGKqVasWvv76axw7dgxubm7KTkdpqlYV/l1V9E1lWdffJyFJsxl5g1ayZs2aYe3atTh8+DDat2+vtDz42lKcd+/eCWKNGjWSu7/GjRtLdY6y9Pr1a/To0QOjRo3C5cuXkZeXV6r+Pl1OidSTtrY21q5dK4j7+fmV2NbHx0dsgJShoSF+//13heZXlkaNGoUrV66IFW1ycnLg5uYm9QyjgwcPYsmSJWKxqlWr4ujRo/Dx8UHv3r0Ff5d1dXXh7OyMv/76C5cvXxYsw7Zhwwbs3LlTzq9KNsOGDUOnTp3EYrm5uTh37ly5nJ+IiOTDgg0RVQiyFm1YrFEvc+bMwfz588ViHz58wNy5c5WUkXqIj49Hz5498fTpU7G4ubk5Ll68iPr16ysps4opISEBv//+O7p06YK6detKHNVYWUjaZ6rw5seykNSWe1mVnqQ17AuPsCcgLCwM33//PRo2bIh27doJNnMuT3xtKU5ionC53NJsCm5mJrwGLTwivyxFR0eja9euuHTpksL6zMnJUVhfpDw9evRAzZo1xWLPnz/Hq1evimwTHx8Pd3d3sdi6devUbqBPo0aN8Ntvv4nF3r9/j+XLl5fYNjU1FV988YUgvnfvXgwbNkyq87u4uMDX11cwO/Kbb74ptwESn3/+uSB28eLFcjk3ERHJhwUbIqowzKpXxWKP3lIdu9ijN4s1ambJkiWCEWyXL1/Gf//9p6SMVFtiYiJ69eol+P5Uq1YNFy5cQJMmTZSUWcWSkZGBgwcPYsiQIbC0tMSMGTNw5cqVIvctMDExwZQpU2TedFbdFL4xBKDYG0MliYqKkuocJJsGDRoIYuHh4eV6k1lVvX37Fp6ennByckK9evWwfPlywb4Fn6pbty6+/vrrMs+Lry3FKbw0pYaGhsQZTNKSNDOpPJe/dHNzQ0hIiCDesmVLLF68GMeOHcPdu3cRGxuLlJQUZGVlCZZQXLFiRbnlS+VHQ0MDnTt3FsSL26No2bJlYgX8zp07SyxeqIPRo0cLBikdOHCgxILJrl27BO+HgwYNwpAhQ2Q6f7t27TBjxgyx2Pv37/Hnn3/K1I+8unbtKohxfyoiItWmrewEiIgUJTE+HWuWSze9e83yc5xho2ZMTEzg4uKCs2fPisXPnTtX7IbQlVFycjJ69eqFe/fuicVNTU1x7tw5tGjRQkmZVQy5ubm4ePEivL29cezYsRJvyOno6KBv375wdXXFoEGDoK+vX06ZKk/hzbcBIDIyUu7+JN1YkHQOkk2rVq2goaEhKDDeunULffv2VVJWypOamgofHx94e3vj/PnzyM3NLfZ4U1NTjBw5Eq6urnBxcRFburOs8LWlOIX3bxOJREhPT5e7aJOWllbiOcrKqVOncOHCBbFYjRo18Ndff6FPnz5S91Oa5fWU4cSJE1LNlCgLVlZWOH36tFLOLY9atWoJYnFxcUUe//r1a7HHERERaNWqldTnkzR778SJE2jZsqVYrLy+j/369cPz588LHufk5ODq1asYMGBAkW1OnToliE2cOFGu80+cOBFbtmwRi509e7Zc9rOR9WdPRETKx4INEVUIifHpWDjHBy/DhctbSPIyPBEL5/iwaKNmHBwcBAWb+/fvKykb1ZSSkoLevXvjzp07YnFjY2OcPXtWsPEsSe/ff/+Ft7c39u/fL9WyUe3bt8eECRMwduxYsfXTKwNbW1vo6+sjMzOzIBYVFYWsrCy5NjSXtHF3w4YNS5UjfbyZ3KZNG9y+fVssfunSpUpTsMnOzsbZs2fh7e2NEydOID09vdjjdXR00K9fv4ICrJ6eXjll+pGkWVGhoaFy91f4tVWlSpUKPwMwn6QlzJKSkuQu2EgarS9pn6iysG/fPrHHWlpa8PX1Rbt27WTqR91m1yUkJCjtOlDd9vuSNANMlgLdy5cvSz0rIzExUbAUYXl9H+vWrSuIlbSPzYMHDwQxJycnuc7funVr6OjoIDs7u9j+y0Jpf/ZERFT+uCQaEak9WYs1+fKLNonxxd+cIdVhbGwsiJX3hr6q7P379+jTpw9u3bolFjcyMsLZs2dlvnFDH9d4X7lyJRwcHNCuXTts2rSp2GLNZ599hmXLluH58+e4ceMGZs+eXemKNcDHTY6bN28uFsvKyhIUEqWRk5ODf//9VyxmaWkpccQoyW7w4MGC2O7du5GVlaWEbMqHSCRCUFAQZs6ciVq1amHQoEHYv39/scUaJycnbNmyBTExMTh+/DhGjhxZ7sUa4OO+Q4U3sI6MjERMTIzMfb1+/VowO6dFixbQ0tIqVY7qwsLCQhB78uSJ3P09fvxYECuvv//nz58Xe9y3b1+53vOl3Yid1I+kGRWV6fpE0uzmkmZIx8fHC2KS/m5IQ1NTU7A/WHkVSCv7z56ISB2xYENEak3eYk0+Fm3US3JysiBWWW4slSQ1NRV9+/bFjRs3xOKGhobw8/OTe0RgZRQbG4tNmzahXbt2aNCgAVatWlXsCHYzMzPMmDEDQUFBCAsLg4eHBxwcHMoxY9Xk4uIiiAUFBcncz927dwU30iWtxU/ymThxIjQ1xT8SxMXF4dChQ0rKqOw8evQIS5YsgZ2dHVxcXLBt2zaJN+TyffbZZ1i+fDlCQkJw/fp1zJo1S3DDTRkU9dqS1KYyvbZat24tiBWebSaLwoVlU1NT2Nvby92ftLKysvD27VuxmKTfkZLk5uYKBnxQxSGpGClv8UEdFX6NACUXLSQVeUozM6XwtUxp9sySRWX/2RMRqSMWbIhIbUlTrLGxM8O2v8fAxk647EU+Fm3Ux9OnTwUxS0tLJWSiWtLS0tC/f39cu3ZNLG5gYIDTp0+jU6dOSspMfbx//x579uxB7969YW1tja+++kpw8+1Turq6GDZsGI4cOYLY2Fh4eXnx+1yIpCW1jhw5InM/hw8flqpvko+NjQ2GDx8uiC9atEjtlvyRJCoqCj/99BNatGiBpk2bYs2aNcXu+VKtWjW4u7vj6tWrCAsLw6pVq1CvXr1yzLhklf21pa0tvqp3SXsNFUXS32xJ3xNp3LlzB+Hh4WIxJyenYvc1Kvx1APJ9LZJmGsuzFNvp06cl7juiytzc3CASiZTyLyIiQtlfvtQSEhIEA3r09fVRv379Itv4+PiU6vtz+fJlQZ8TJ05U2vex8NcPlPwZQlJRo/DrXFrx8fFISUkpsf+yIGmPIO5nSUSk2liwISK1JG2xZp3nUNjVM8c6z6Es2qi5xMREXL16VRBv1KiRErJRHenp6Rg4cCACAwPF4lWrVsWpU6fkGmVbWWRlZeH48eMYM2YMatasCTc3txI3Gu/YsSO2bduG2NhYHD16FMOHD5drT5bKoFu3boKbITdv3kRwcLDUfWRlZWH37t1iMT09PQwbNkwBGVK+1atXC24ev3r1CrNmzVLoeTIzM7F161aF9ilJYmIiduzYga5du8LW1hYLFy4sdq8APT09DB8+HEePHkVMTAy2bduGjh07lnme8pL0d+fYsWMSR5AX5e3bt/Dx8RGLWVlZoUuXLopIsUwZGRmJPZa3yGBtbS24hrh9+7ZcSzdK+r3u3bt3sW0Kfx2AfF+LpP0p5Fku9tdff5W5DamHn3/+WXBt07VrV1SpUkVJGZWv2NhYQQFJU1MT7du3L7adpNnSkoof0pDUrriCmaK8e/cOf/zxhyDer1+/Mj83ERHJjwUbIlI7shRrzKp/nGpuVr0qizZq7vvvvxfbwDyfpP0XKovMzEwMGTIE/v7+YvEqVarA19dXLW68KUtsbCwsLS0xdOhQHDx4sNglLhwcHLBq1SqEhYXh6tWrcHd3l7hZNYnT0tLC1KlTBfHFixdL3cevv/4qWHt93LhxMDU1LW169IkGDRpgyZIlgvjevXsxd+5c5OXllfocISEhcHZ2xsGDB0vdV3FOnjwJS0tLTJ8+HQEBARCJRBKP09DQQKdOneDl5YWYmBgcOXIEw4YNU4sCbLVq1TB69GixWFZWFlasWCF1H8uXLxfb/BoApk+fLlgeTxUV/vublJQk2MhcWpKKkrNnzy7y90aSW7duCQrLBgYGmDRpUrHtJL2PyLOHjImJiWBppXPnzsnUx86dOwXXElQx3L59Gxs3bhTER44cWf7JKMm8efMEnyE6dOiAmjVrFtuuT58+gtjmzZtlLqzm5ORgzZo1UvWvSCKRCDNnzhTM7LGxsUHbtm3L9NxERFQ6qn9FTkT0CXmKNflYtFFfGzduxIYNGwTx7t27q9xSNeUlKysLw4cPx4ULF8Ti+cWa7t27Kykz9ZCZmVnsDT5zc3PMmjULN27cwPPnz7F8+XJ89tln5ZhhxTB37lzBKPIzZ87gt99+K7HtnTt3sHLlSrGYtrY2Fi1aJFMOGhoagn/qtJRNeVm6dKnEIq+npydGjx4t0+yNT2VmZuLHH39Ey5Yt5Zq5IKt3794hKyuryOfr168PDw8PhIWFISgoCDNmzFDLAuyiRYsEe7ht374dJ0+eLLGtr68vtm/fLhYzMTHBnDlzpD5/RESExNdWeWjWrJkgJu+odzc3N8HyYTdu3MDs2bOlah8ZGYmRI0cKippTpkwpsbBcp04dmJiYiMXk/TqcnZ3FHvv7+0vd15kzZzB37ly5zktlKzw8HNu3b8eHDx/kav/vv/9i4MCBgkEp9evXx8SJExWRYpny9vbGoUOHZCqgfiovLw/z5s3D3r17Bc9J8zs/bNgw6OjoiMXevHmDzz//HDk5OVLlIBKJMH36dME+Mnp6ehg6dGixbXfu3IlHjx5JdZ7CsrKyMHPmTIn70X3//fdqUZwnIqrM+FeaiNRGTk4elnx5Qq5iTT5pizZLvjyBnJzSjygm+cXExOCff/5Bp06d8PXXXwue19HRgaenpxIyU76cnByMHj0afn5+YnF9fX34+PigR48eSspMvenr62PUqFE4fvw4Xr9+jS1btpS4XAYVz8LCAh4eHoL4nDlzsHr16iJveJw4cQLdunUT3KT66quv0KBBgzLJtbLT0dHBsWPH0LRpU8FzR44cgb29PZYvX45Xr15J1V9kZCRWr14NOzs7fPfdd4LNlsuTubk5Zs+ejRs3buDZs2dYtmwZ7OzslJaPIjRp0kRQVBCJRBg+fDj++OMPiTc4RSIRduzYgREjRgie++GHH+Ta90QZnJycBDcb582bh+PHjwtmDZXEwMAA27ZtE8S3bt2KUaNG4c2bN0W2PX36NJydnREVFSUWr1u3rsS/e4VpaGigQ4cOYrELFy5g8eLFMhdIC8+4AoAxY8YUuydPZmYmPDw8MGTIkIIb+sbGxjKdl8pWcnIy3N3d8dlnn2HZsmW4f/++VO3i4uKwbNkydOrUSfA7rKmpiY0bN0rcQ0nVhISEYPTo0WjWrBnWr19f7P5jn8rLy8P58+fRvn17iUv99e7dW+JrpjBbW1u4u7sL4r6+vnBxcSl2qc38/Pv27Ytdu3YJnps1axbq1KlTbPuTJ0+iWbNmGDx4MPbt24f379+XmDPwcYZdx44d4eXlJXiuY8eOmDBhglT9EBGR8miI5B2uQERSefTokdjNj4cPH6JJkyYK6z8nJwchISFiMQcHB7W4CJdHwIUQrFtxDpJWZympWPOp4mbqaGoCC1f1RpeewnWLSXoRERGCG2IGBgYlzohJS0tDQkICEhISijxGW1sbe/fuxahRo6TKpW7duoIPeer89uft7S3xw5apqSlsbW1L1bejoyN27twp9fEtW7Ys9vnCNxd0dHTQuHHjYtvIsseJvPJ/PzU0NODi4gJXV1eMGjVKMNq5IvDy8pL4oT1fbGys4IZOnTp1ir156+7uLvEmhiQikQgDBgwQFBiBj3tIjB8/Hg4ODtDT00N4eDh8fHxw9+5dwbFt2rTB1atXoaenJ9V580ka9R8eHo66devK1I+sdu/eLVgSSZrz+vv7o1u3bmKxiRMnCpZcKisJCQkYNGgQrl27VuQxjRs3Rrdu3WBtbQ0LCwuYmpoiMzMTb968wbNnz3Dt2jU8evRI4t/ZLl26lOnSS/nfd319fQwePBgTJkxA3759BaOkK4LMzEx06NBB4t/MBg0aYMyYMfjss88gEonw4sUL7N+/X3DNCACDBg3C8ePHZZohI+k9Hii/99YBAwZInEGiq6uLOnXqwMDAQPD17Ny5E46OjhL7mzJlCv78809BvEqVKujXrx9cXFxgaWmJzMxMhIeH48SJExK/7zo6Orh8+TI6deok1ddx6NChIm8c16pVC9WqVRNc0w8ePFhQEMrOzkajRo0QFhYm6Kd169YYNGgQ6tWrBx0dHbx9+xZ37tzByZMnER8fX3Bc48aNMWjQIKxbt06svTQ/065duyIgIKDgcVm/ziuL4OBgtGrVSixmbW2N1q1bo3nz5qhVqxZMTEygp6eHxMREREdH49q1awgMDJS4jDDwcdaktDPISkMR72UrV67EqlWrxGL16tVD69at0bRpU5ibm8PU1BTa2tp4//493rx5g/v37yMoKAjR0dES+2zatCkuXryIGjVqSJVDYmIinJ2d8fjxY4nPd+rUCV27doW9vT2MjIyQmpqKyMhIXLlyBf7+/hKXFG3WrBkCAwNLvOYcOnQojh8/XvBYT08PLVq0QIsWLdCgQQOYmprC2NgYHz58QEJCAh48eAB/f3+JfweAj+8LQUFBMDc3l+prl1dluz9BRBVTWd/LLQn/YhKRWskvohQu2shSrAH+b6ZN4aINizVlKy0tTerRgUWxtLTEjh07MHDgQAVlpX6KGkGclJSEpKSkUvUt694gsv48s7OzS/07oAgGBgb44YcfMGHCBNjY2Cg7nTIVGxsr8/c8KipKMGq8cJ/S0tDQwMGDB9G3b19cvXpV7LlXr14Jbg5K0qhRI5w+fVrmYg3Jrlq1avD398d3332HX3/9VbBRNQA8fvy4yJtXxalfvz7mz5+viDSLZG9vj507d2LUqFEVfraAvr4+/Pz80LVrVzx79kzsuWfPnkk1y8PFxQX79u0rt+XMFGX9+vUICAhAWlqaWDwrK6vIm5XF7Tvx+++/Q0tLCzt27BCLZ2Rk4OjRozh69GiJORkbG+Po0aNSF2sAYMSIEejRowcuXrwoeC4mJgYxMTGCuKSBEjo6Ojh06BCcnZ0Fs9nu3r0rsQj+qdq1a+PUqVPlVhgm+b169QqvXr3CiRMnZGqnr6+Pn3/+WeK+TeokNDQUoaGhcu2H1qlTJ/j4+MhUsDAzM8O5c+fQtWtXhIaGCp6/evWq4NqmOA0aNMDZs2flGiD04cMH3Lp1C7du3ZK5bceOHbF///4yL9YQEZFicEk0IlI7XXo6YOGq3shfDUPWYk2+wsujsVij2szNzfHtt9/i6dOnlbpYQ4phYWGBJUuWVPhijaowNDTEhQsXMGPGDJlvDI8YMQLXr1+XejTsp+Li4gQxCwuLEjcarux0dHTw008/4d69exg4cGCpb+Y7ODjgt99+w6NHjzBgwAAFZSmZi4sLpkyZUuGLNfksLS1x48YNDBs2TKZ2Ghoa+OKLL3D+/HkYGBjIfF5Jr63yHHXYuHFjnD9/XmH72GlpaeH333/Hpk2b5NrTyMnJCUFBQTIvSaqpqYnDhw/j888/l/mchbVq1Qpnz55FrVq1ZGrn5OSEGzdulPmsQ1IeZ2dn3LlzR+2KNfL8bZKkevXq8PLyQmBgoFwFi9q1a+PevXtwd3eX+/1QQ0MDM2fOxN27d2V+jZaGiYkJfvjhB1y5cqXEJdiIiEh1sGBDRGopv2hjZ19drmJNvvyijZ19dRZrVISuri7Mzc1hb2+PPn36YPHixfD19cXr16+xfv36CrlkFVFloK+vDy8vL9y4cQMjRowodraMtrY2+vTpg3PnzuHw4cNyv+4vX74siC1YsABVqlSRq7/KplmzZvD19cWzZ8+wYsUKtGrVSrDRvSSamppo3LgxZs6ciWvXruH58+eYOXMml0MpI6ampjh69CjOnTuHPn36FPsz0tPTw8iRI3Hz5k1s3bpV7llrkl5bK1eulKsveXXo0AFPnz7F6dOnMXPmTDg7O8PKygqGhoZyb6g9d+5chIWFYenSpWjYsGGxx+rr66NXr144cuQIrl+/jmbNmsl1TlNTU3h7e+Pp06dYuXIlBg4cCHt7e5iZmcm8lJ+zszPu37+PBQsWlDhj1tHREXv27MHVq1dhbW0tV+5Utpo2bYorV67Aw8MDvXv3hoWFhdRtbW1tMXXqVNy5cweBgYElLkeriubPn48XL17gt99+w6hRo2QaZFOtWjUMGjQIBw8eRHR0tFwDRj5laGiIbdu24fnz55g/f77USxDXrVsXCxYsQGhoKH777TdUrSr9Z9atW7fir7/+wuTJk9GsWTOp/x5UqVIFLi4u2LZtG6Kjo7FkyRKp3ruJiEh1cA8bojLGPWzKVk5OHrS1S197VlQ/REQknfT0dNy8eRPPnj0r2LPKxMQE9erVQ/v27WVenk8Sd3d3bN++veCxpaUlwsLCZLphQuLS0tLw33//ITw8HG/evClYfsnMzAxmZmawsrJCq1atYGRkpORMK6+kpCTcvHkToaGhSE5OBvDx5mWDBg3Qvn17hfz+9+3bF2fPni143KJFC9y7d0/tllYrSWRkJIKDgxEXF4d3795BV1cXNWrUgJWVFZycnFT6b0lubi5u376NR48e4d27d8jJyYGRkRHs7Ozg6OgIS0tLZadIcoiJicGLFy8QFRWFuLg4pKenIzs7G0ZGRjAzM4OFhQVat25dYWeSvnv3DqGhoYiIiEBcXBzS0tKQk5MDY2NjGBsbw9zcHM2aNSv1no7SyN8PKjY2FklJSUhLS4OBgQFMTU1haWmJNm3ayDU7uCgfPnxAaGgoXr58iVevXiElJQUZGRnQ0dGBqakpzMzMYG9vj+bNmyt137bKfn+CiCoGZe9hw4INURljwYaIiEg5GjRogOfPnxc83rhxI7788kslZkSk/rKzs2FmZia2f4yPjw+GDBmixKyIiEgV8P4EEVUEyi7YcDg5EREREVU40dHRYsWa2rVrY8aMGUrMiKhiuHXrllixxtHRkcUaIiIiIiIFYcGGiIiIiCqcwntsLFmyBPr6+krKhqjiKPzaWrVqlZIyISIiIiKqeFiwISIiIqIK59KlSwX/t7GxwdSpU5WYDVHF8elry8nJCf3791diNkREREREFQsLNkRERERU4Xw6C2Dp0qXQ1dVVYjZEFcOHDx9w/fr1gsceHh5KzIaIiIiIqOLhrl9EREREVOGEh4crOwWiCkdPTw8ZGRnKToOIiIiIqMLiDBsiIiIiIiIiIiIiIiIl4wwbIiKiUvDy8oKXl1eZn8fd3R3u7u5lfh4iIiIiIiIiIlIOFmyIiIhKITY2Fvfv3y+X8xARERERERERUcXFJdGIiIiIiIiIiIiIiIiUjAUbIiIiIiIiIiIiIiIiJeOSaERERKWwcuVKrFy5UtlpEBERERERERGRmuMMGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiIiIiIiIiIiVjwYaIiIiIiIiIiIiIiEjJWLAhIiIiIiIiIiIiIiJSMhZsiIiIiIiIiIiIiIiIlIwFGyIiIiIiBdu9ezc0NDTE/kVERCg7LSIihevatavY37quXbsqOyUiIiIitaWt7ASIiIhIcaKiovDo0SO8evUKSUlJyMrKgpmZGczMzNCwYUM0a9YMWlpayk6TCOnp6bh16xaePXuGxMREiEQimJiYwMHBAe3atYOJiYmyUyQJ0tPT8d9//yEiIgKxsbFIT08HAJiamsLMzAy1atVCq1atYGxsrORMiYgqLl7vERERVVws2BARUZmIiIiAnZ2dTG00NTVhaGgIY2Nj1K5dGy1atEDbtm0xfPhwVKtWTe5c6tati8jISLnbS5KYmAhTU1OF9imPp0+f4uzZs7h06RKuXLmCpKSkYo83MDBA165d4e7ujv79+0NTU3GTbWNiYnDnzh3cvn274N+bN2/EjrG1teUsAyXKysrCw4cPxX5GDx8+RHZ2tthxu3btgpubW5nk8O+//2L9+vXw9fVFZmamxGO0tbXRq1cvzJs3Dz169CiTPFSVv78/unXrptA+hwwZAh8fH7nbh4SEYO/evfD19UVwcDByc3OLPV5DQwMNGjRA586dMX78eLi4uEBDQ0Pu81PJUlJScO/ePbHXdlhYGEQikdhx4eHhqFu3rnKSJCK5KfN6r2vXrggICJC7/admzJgBLy8vhfQlydu3b9GkSRO8e/dO8NyKFSuwcuXKMjs3ERGRorBgQ0REKiMvLw8pKSlISUnBq1evcPPmTfz++++YNWsWhg0bhnXr1sHW1lbZaSpdRkYGfv75Zxw8eBAPHz6UqW1aWhpOnTqFU6dOoUmTJti9ezccHR3lyuPGjRs4f/58wc3B169fy9UPlZ3U1FQcOHCg4Gf04MEDZGVlKSWXDx8+4Ouvv4aXl5fgJnJhOTk58PPzg5+fH0aNGoWdO3dyxoYSPHz4EEuWLMHJkydL/Jl9SiQS4enTp3j69Cl+//132NraYvbs2ZgzZw709PTKMOPKIyIiAj4+PgWv7efPn8v0M1Jnbm5u2LNnT8FjdR0IIKk4e/nyZS4nRgVU5XpPnXzxxRcSizVERETqhHvYEBGRysvKysKBAwfQuHFj/PXXX8pOR+nevHmD5cuXy/zhvbBHjx6hQ4cO2LJli1zt165di+XLl+PEiRMs1qio0NBQTJ06FV5eXrh9+7bSijVpaWno3bs3tm3bJvNN5UOHDqFjx46Ii4sro+yosOzsbCxcuBAtW7aEr69vqQsBkZGRmD9/PurXr4/Dhw8rKMvKzcfHB19//TW8vb3x7NmzSlOsIapMVOV6T13s3bsXR48eVXYaREREpcYZNkREVG4MDAxQr169Ip/Pzs5GcnIyYmJikJeXJ3g+PT0dbm5uyM3NxaRJk0qVS4sWLUrVXlXXBa9Xrx66dOkCBwcH1KhRAwYGBkhISEBwcDBOnz6NqKgoseNzcnIwZ84c6OrqYvr06UrKmioykUiEsWPH4sqVK4LnateujfHjx8PBwQF6enoIDw+Hj48P7t27J3bco0ePMHDgQAQGBkJXV7e8UlcZZmZmsLGxkbu9LMtTJiYmYtCgQbh69WqRxzRs2BDdu3eHtbU1zM3NYWZmhoyMDLx9+xbPnz/H1atX8ejRI0G7ly9fYsuWLRg5cqRcXwcREX2kzOu9kq7ni1OnTp1SnbsosbGxmDNnTpn0TUREVN5YsCEionLj6OgIf3//Eo9LT0/HjRs38Mcff2Dfvn1iI4dFIhHmzJmD7t27l2p5tODgYLnbqprGjRvDzc0Nn3/+OWrXrl3kcTk5Odi1axe++eYbpKamij03Z84cdO3aFfXr1y91PlWqVEGLFi3g6OhY4UdzqjMbGxs4OjoiLi4OgYGBZXaeLVu24OTJk4K4h4cHFi1aBB0dHbH48uXL4ePjA1dXV7Hf01u3bmHZsmVYt25dmeWqSG5ubgrbB2jw4MHYvXu3QvoqTmJiIjp37ixxNLeBgQG++uorzJgxQ6obbhEREfjnn3/w22+/ITY2tizSJQm0tLTQsGFDODo6IiAgQC2XCiMiyVTlek/a6/nyNGPGDCQkJBQ8bt++PW7evKnEjIiIiOTHJdGIiEjlVK1aFd27d4e3tzdOnToFfX19sefT0tKwZs0aJWWnOrp164aAgAA8evQI8+fPL/bDO/BxI/dp06bh6tWrMDU1FXsuKysL8+bNkzkHPT09ODo6wt3dHTt37kRwcDBSUlJw/fp1eHp6ytwflQ0rKysMHjwYHh4eOH36NOLi4hAZGYkjR46ge/fuZXbed+/eYenSpYL45s2bsWzZMkGxJt/QoUNx6dIlwWyaDRs24Pnz52WSa2WXk5ODYcOGSSzWDB06FKGhoVi9erXUo6Pr1q2LpUuXIjw8HD/88AMMDAwUnXKlp6GhgQYNGmD8+PHYsGEDAgMDkZKSgocPH2L37t3c842oglCF6z1V9tdff+HEiRMFj11cXDBjxgwlZkRERFQ6nGFDREQqrV+/fvDw8MCCBQvE4j4+Pti6dSs0NSvf2AMTExP4+/ujS5cucrVv3rw5du7cKViWyM/PD+/evYO5ublU/Xh6esLS0rLIm+6kfPXr10dMTAwsLS2Vcv7NmzcjJSVFLNarVy+pli1p27Ytli9fLlbwyc7Oxtq1a/Hnn38qPNfKbvXq1QgICBDEv/jiC2zZskXuv7X6+vpYsmQJRo0ahXHjxpU2Tfr/xo8fjylTpsDIyEjZqRBRGVGV6z1V9vr1a3z55ZcFj6tUqYI//vij2GU9iYiIVF3lu8tFRERqZ8aMGYKiwJs3byrtRvdmZmZyf3jPN2LECDRv3lwslpubCz8/P6n7qFOnjtoWayIiItCiRQv8/PPPFfr3qGrVqkor1uTm5mLHjh2C+Nq1a6Xu49tvv0X16tXFYvv27UNycnKp86P/8+zZM/zwww+C+JgxYxRWGHdwcEBQUBBGjRpV6r6K8/fff6N37974+++/BUsBVSQWFhYs1hBVcKpyvafKpk2bhqSkpILHHh4ecHBwUF5CRERECsAZNkSk1nLz8qClgBtJiuqHyoaxsTHq168v2MQ6NjYW1tbWSspK/fXr1w8PHjwQi7148UJJ2ZS/Bw8eYP78+Vi4cCG6d+8OV1dXDB8+HIaGhspOrULw9/cX7F3Stm1btG7dWuo+9PT04Obmhl9++aUglpmZiWPHjilsfxgCli1bhpycHLGYlZUVtm3bptDz6OvrY9asWQrts7Dc3FycP38e58+fh4GBAYYOHQpXV1f07NkTWlpaZXpuqnhevnyJ4OBgxMXFIS4uDvr6+rCwsICVlRWcnJxQpUoVZadYrNzcXLx48QJPnz5FdHQ0UlJSkJubCzMzM5iZmaFhw4Zo1qxZpZytXJlU1Ou9P//8E6dPny543K5dO3z99ddKzIiIiEgxWLBRA1FRUXj06BFevXqFpKQkZGVlCS6yK/oH0NDQUAQHByMqKgppaWmoUqUKrKys0Lx5czRp0kTZ6ZGSXHsdgWMhD/GdUw+Y6sn/gTnpQwZ+uHERwxyaoqNVXcUlSAplbGwsiOXl5Skhk4rDxsZGEKuMm4Pn5eXhwoULuHDhAr744ouCG7y9evWq8O+vZenMmTOCWOFlWaQxcuRIsYIN8HE5F1Uv2OzevRuTJk0Si4WHh6Nu3brKSagIUVFROHr0qCC+du1amJmZKSEjxUlLS4O3tze8vb1haWmJcePGwdXVFa1atVJ2aiSjunXrIjIyUuJzkZGR0NDQKLGPy5cvo2vXriUel5SUhF9++QVHjx7F48ePizxOX18fnTt3hru7O4YNG1ZivwCwcuVKrFq1qsjnu3XrVmIfEydOxO7du4t8PiQkBEePHsWlS5dw9epVpKWlFdufiYkJevXqhW+//Rbt27cv8fykfiri9d6rV6/wzTffFDzW1dXFn3/+yes2IiKqEFiwUUFPnz7F2bNncenSJVy5ckVsiq8kBgYG6Nq1K9zd3dG/f3+5R0h17dpV4trl8pgxYwa8vLxK1UdGRga2bdsGLy8vhISEFHmctbU1pkyZgq+++kqwqSJVXNdeR8Dz7lXkiUT4/voFLOvQU66iTdKHDHx//QJevU+G592Pax2zaKOa3r17J4hZWFgoIZOKIz09XRBT9dHCZS09PR179+7F3r17UbNmTXz++eeYMGGCTLNC6KPAwEBBzNnZWeZ+2rRpg6pVq4r9vl65cqVUudH/2bNnD3Jzc8Vi5ubmGD16tJIyKhuxsbHYsGEDNmzYgCZNmsDV1RXjx4/nLE0S4+npiRUrViAxMbHEYzMzM3Hu3DmcO3cOHTp0wPbt29GsWbNyyFKy+Ph49OrVC/fu3ZOpXXJyMg4fPozDhw9j8ODB2LNnDz9TVTAV8Xpv6tSpYsujLl26lAM5iYiowuDcZxWRkZGB77//Hs2aNUOjRo3w1Vdf4cSJEyUWa4CPowdPnTqFQYMGoXnz5rh9+3bZJ1zGrl69ikaNGmHevHnFFmuAj6NrVq1ahfr16+PEiRPllCEp06fFGgB49T4Z31+/gKQPGTL182mxBgDyRCJ43r2Ka68jFJ0ylVJsbCxCQ0PFYiYmJio3Ul3dFP6eAkCtWrWUkEn5q169OiZPngwTE5Mij3nz5g02bNiANm3aoEmTJli7di2ioqLKMUv1lZOTg/v374vFdHR04OjoKHNfktrFxsYiJiamVDnSR5Kundzc3KCnp6eEbErP0dERAwYMKHZ/rUePHmHRokWwsbFB9+7dsWvXLqSkpJRjlqRqcnNzMWPGDMydO1eqYk1h169fh7OzMy5dulQG2Unn/fv3MhdrCjtx4gTatWuHV69eKSgrUgUV7Xpvx44dOHv2bMHjFi1aYNGiRUrMiIiISLE4w0ZFvHnzBsuXLy91P48ePUKHDh2wYcMGzJ49WwGZlb+jR49i7NixyM7OlqldXFwchg4dii1btmDmzJlllB0pW+FiTb78oo20M20KF2vy5RdtAM60USVr166FqNDPfOjQoVItgUKS5eTk4Pjx44J427ZtlZBN+TMyMsIff/yBrVu34uTJk/D29sbp06fx4cMHicc/fvwYixcvxpIlS9ClSxe4urpi5MiREpfqo49LFGVmZorF6tSpA11dXbn6s7e3F8yqefbsmVrfcFIF79+/x507dwTx7t27KyEbxWjatClOnjyJd+/e4eDBg/D29sa1a9ckHisSiXD58mVcvnwZs2bNwuDBg+Hq6oo+ffpAW5sfk1RJ48aNC2Z9vHz5UqyooqOjg8aNG5fYR3H7k02fPh1//vmnIK6vr48+ffqgc+fOqFWrFjIyMhAREYHjx48L9gRJSUlBv379cOnSJXTq1EnieSwtLdGiRQsAQGpqKsLCwsSet7e3L3EfNUnLW0liaGiItm3bolGjRnBwcICJiQmMjIyQlZWFxMREPH78GJcvX8aTJ0/E2oWEhGDMmDEICAjg66ACKIvrvbdv32LlypUICgpCaGgo3r17h9zcXFSrVg3Vq1dHkyZN4OLigp49e6J+/fqlSV/g5cuXmDdvXsFjbW1t/Pnnn8UW6YmIiNQNr8DUQL169dClSxc4ODigRo0aMDAwQEJCAoKDg3H69GnBaN+cnBzMmTMHurq6mD59utznNTAwQL169eRqW6dOHbnaXbt2TWKxRlNTEwMHDoSzszPq1KmDuLg43L9/H/v27ROb4i0SiTB79mzUqlVL6rWkSX3k5uXhWMhDQbEmn7RFm6KKNfnyRCIcC3mI9pY20OImrEolEonw66+/YtOmTWJxXV1dLFiwQElZVQwnTpwQrF9erVo1uZasUmd6enoYMWIERowYgaSkJBw6dAje3t64cuWKoEgIfPyd9Pf3h7+/v9gN3r59+/LG1iciIiIEMVtbW7n7k3SD8sWLF1LtR0FFCw4OlrgXWLt27ZSQjWKZm5tj5syZmDlzJsLDw7F37154e3sLbk7ny8jIwIEDB3DgwAFYWFhg3LhxmDBhQqUpYqu6TzcWd3Nzw549ewoeW1lZITg4WO6+Dxw4ILFYM2TIEGzduhVWVlaC5zw8PHDy5Em4u7sjOjq6IJ6VlYUJEyYgODhY4gxOd3d3uLu7AwD8/f0Fe9bs3LmzVH/XTE1NMWHCBIwcORIdO3aU6ib2tWvX8OWXX4qt0nDt2jVs3LgR3377rdy5lAdJe4WVF1tbW4nvdaqmLK73njx5InEvptevX+P169f477//sH//fgBAv379sGjRInTu3Fnu8+UTiUSYMmUK3r9/XxD79ttvuWwtERFVOLyzoKIaN24MNzc3fP7556hdu3aRx+Xk5GDXrl345ptvkJqaKvbcnDlz0LVrV7lHtTg6OsLf31+utvJIS0vDuHHjBMWaBg0a4NixY2jUqJGgzU8//YRJkyaJLechEokwadIkdOjQAZaWlmWeN5UfLU1NfOfUo9hiS0lFm5KKNQBgbWSC75x6sFijBNnZ2Xj//j1CQ0Nx7do17N69W7CsEgBs3rxZqtG0JFlmZiYWL14siE+aNKlSFx1MTU0xbdo0TJs2DVFRUdi3bx+8vb0Fo6jzZWZm4uDBgzh48CAsLCwwduxYTJgwoULc7C4tSZsZyzuYo6i2b968kbs/+ujZs2eCWN26dVG9enUlZFN27Ozs8N133+G7777D3bt34e3tjf379+P169cSj4+Li8PmzZuxefNmNGjQoGC/Gy7DWfGkpqbiiy++EMSnTZuG7du3FzuTd+DAgQgMDISLi4tY0SYiIgJLly6Fp6dnmeRcFCsrK7x+/VrmvUk6duyIwMBADBs2DGfOnCmIb968GV999VWlvi5Qd6pwvefn5wc/Pz/Mnj0bv/zyi9wzbQHAy8sLFy5cKHjcsGFDrFy5UgFZEhERqRZefamYbt26YeXKlVKPQNHW1sa0adPQvn17dOnSRWzPm6ysLMybNw++vr5llK1i/fjjj3j58qVYzMHBAdeuXUO1atUktqlWrRqOHj2KMWPG4MiRIwXx5ORkLFy4UGz0HVUMpnpVsKxDT7mKNtIWa6RdVo1kFxAQUKplzKytreHp6YmhQ4eWOpeWLVvK1e7T0bHqavHixXj+/LlYzMzMDAsXLlRSRqqnTp06WLBgARYsWICHDx/C29sbe/fuFbxP5YuLi4Onpyc8PT1Rv359uLq6YsKECZX2Bm9CQoIgVtIyP8WR1DY+Pl7u/tTNiRMn5P6bdfr0aYkzBABI3JOpog92ad26NVq3bo3169fj8uXL8Pb2xpEjR4rcw+bZs2dYunQpli1bBmdnZ7i6umLUqFHclL2C2L17t2DPmrZt28LLy0uq6xU7OzscOnQInTp1EpuVuWvXLnh4eMDMzEzhORelNDfC9fX1sWfPHtjZ2RWsXhAVFYVz586hf//+ikqRyllZX+9ZWFjAxMQEOjo6SEhIKFgaTZItW7bg9u3buHDhAgwMDGQ+V3h4uNjsek1NTfzxxx9qu98aEamOnJw8aGuXfrCuovohAliwURkmJibw9/dHly5d5GrfvHlz7Ny5EyNHjhSL+/n54d27dzA3N1dEmmUmKSkJW7ZsEYtpamrizz//LLJYk09LSwvbt29HUFCQ2Ghbb29vrFixAp999lmZ5EzKI0/RhsUa9ebs7IxvvvkGgwYNUtiIQEkzd6QhaeaAOjly5Ag2btwoiP/888+wsLAo/4TUQNOmTbFmzRr8+OOPCAoKwj///IPDhw9LLEoAwPPnz7Fs2TIsX74cnTp1gqurK0aPHl2pbvCmpaUJYrKO+i6p7adLolZ0iYmJcm2EDnwcwFNcv4VVlt9TTU1N9OjRAz169BDsZSXpeyYSiRAYGIjAwEDMmTMHgwYNwoQJE9C/f3/unaDGCn/+AIDffvsNmjLMsu7QoQMmTpyI3bt3F8TS0tIKVkFQFzVq1EDfvn1x9OjRglhQUJBKF2yqVatWsCdQeSuqEK4qyuJ6z8nJCf3790ePHj3QtGlTwT5+aWlpuH79Ovbt24d//vlH8Lf0xo0bGDt2LHx8fKClpSX1eUUiESZPniy2osicOXPQsWNHub4OIqJ8ARdCsH/3Hfy4aTDMqleVu5/E+HQs+fIExrq1QZeeDgrMkCorFmxUhJmZmdzFmnwjRoxA8+bNxZZuyc3NhZ+fH1xdXUubYpny9vYWjGwcMGCA1GvrVq9eHfPnzxdbZzk3Nxc7duzAmjVrFJorqQZZijZzWztj890gFmvUWP6mpsHBwZg7d26FW66nvNy9excTJ04UxEeMGIHJkycrISP1oqGhARcXF7i4uMDT0xN+fn7w9vaGr68vMjMzBceLRCIEBQUhKCgIc+fOxcCBAwtu8JZmJLQ6KLy8KfBxBLe8JBVsiitEkHQyMjIEMUn7blR0+vr6GDlyJEaOHInExMSCvawCAwMl7mX14cMHHD58GIcPH0b16tUxZswYuLq6wsnJSQnZk7yioqIEywI6OjrKtW/RzJkzxQo2AHD+/Hm1KtgAH1c3+NSNGzeUlIl0Bg8ejMGDBys7DZWj6Ou9iRMnYvv27WjQoEGxxxkYGKBnz57o2bMnli9fjnHjxuH69etix5w8eRIbN27EvHnzpD7/li1bxJZqt7Ozww8//CDT10BEVFjAhRCsW3EOeXnAwjk+WOc5VK6iTWJ8OhbO8cHL8ESsW3EOAFi0oVLjXK0Kpl+/foLYixcvlJCJbPI3JfyUrEsOubm5CaZE79u3r1R5kWrLL9pYGxV9c+nV+2QsCDjFYo2KMDAwQIsWLYr9Z29vL3FmXWxsLDw8PNCoUSOcPHlSCdmrtxcvXmDAgAGCmQ8NGzbErl27lJSV+tLV1cWQIUNw8OBBvHnzBrt27ULPnj2LHJX94cMHHDlyBMOGDUOtWrUQFhZWzhkrX2mWQ5TUVtKNdJKNpO9haX5OFYGZmRmmT5+OgIAAREZGYu3atWjWrFmRx8fHx2Pr1q3o0KGD3MvWkXJcvXpVECu8WoG02rZtCzs7O7HY9evXlf53Kjo6GocOHcKyZcswZswY9O3bFx07dkSrVq3QsmVLwb/CRaeilgEl1VUW13uTJk0qsVhTmK2tLQICAtC7d2/Bcz/++COSk4v+bPapsLAwLFq0qOCxhoYGdu7cKdeyakRE+T4t1gDAy/BELJzjg8R42Wbwf1qsAYC8PGDdinMIuBCi6JSpkuEMmwrGxsZGEFP15XuSk5MFI28MDQ3Rp08fmfqpXr06unbtirNnzxbEIiMj8eTJEzRq1EghuZLqkWamTXFYrClfjo6OYiPkihMTE4NLly7By8sLQUFBBfG4uDgMHToUf/31Fz7//HO5c1H2TZTyFB0djZ49ewreD6ytrXHmzBkYGRkpKbOKwdjYGG5ubnBzc0NMTAz279+Pf/75B3fv3pV4fEJCAt6/f1/OWZYvSUtESZrNIS1JbSv6LKVPFV5uSVEkzVz6dD/Eyq5OnTpYuHAhFi5ciP/++w/e3t7Yt29fkTexIyIiyjdBKhVJf6MdHR3l7s/R0RHh4eEFj5OTkxEWFoZ69erJ3ae8Dh8+jK1btyIgIAB5+Xej5MC/B+pF1a73dHR0cPjwYTRu3BivXr0qiCckJGDXrl346quvim2fl5eHSZMmiS2BOm3aNHTv3r2sUiaiSqBwsSZfftFG2pk2hYs1+fKLNgBn2pD8OMOmgpG0nntp1owvD9evXxdsTtihQweZ1rXN5+LiIogFBgbKnRupB2lm2kjCYo1qq1WrFsaPH4/AwEBs3rxZbNR3bm4uJk+ejIcPHyoxQ/Xw9u1b9OzZU+wmEgDUrFkTFy9ehK2trZIyq5hq1aqFr7/+GseOHYObm5uy01GaqlWFH3IUXbDh6NrSkzSbkTdoJWvWrBnWrl2Lw4cPo3379spOhxTg3bt3glhpBnk1btxYqnOUpdevX6NHjx4YNWoULl++XKpiDQCpZ0GQ8qnq9Z6RkRFWrFghiPv5+ZXYdtOmTWKf5a2trbF+/XqF5kdElUtOTh72774jKNbkk3amTVHFmnx5ecD+3XeQk1O692GqvFiwqWBCQ0MFsVq1aikhE+nduXNHEOvQoYNcfUnaeFBS/1TxyFq0YbFGvcyZMwfz588Xi3348AFz585VUkbqIT4+Hj179sTTp0/F4ubm5rh48SLq16+vpMwqpoSEBPz+++/o0qUL6tatWyYzItSFpH2mPt0sWFaS2nIvq9KztrYWxN68eaOETFRbWFgYvv/+ezRs2BDt2rXDzZs3lZ0SKUBiovAmi6mpqdz9mZmZCWIJCQly9yer6OhodO3aFZcuXVJYnzk5OQrri8qOql/vjR49Gtra4ou7XLlypdjfr5CQEHz33Xdise3bt8PY2LhMciSiykFbWxM/bhoMGzvhe3a+koo2JRVrAMDGzgw/bhoMbW3edif58DenAsnJycHx48cFcXk2zgQ+jtJZuXIlevbsibp168LQ0BBVqlRB7dq10bx5c4wbNw5bt27F8+fPS5V34QtLAHIvHWBvby+IFd5MlCouU70qmNvaWapj57Z2ZrFGzSxZskQwav/y5cv477//lJSRaktMTESvXr0E359q1arhwoULaNKkiZIyq1gyMjJw8OBBDBkyBJaWlpgxYwauXLlS5JJ7JiYmmDJlisQlTCuSmjVrCmKfLociq6ioKKnOQbKRtCdBeHh4ud5kVlVv376Fp6cnnJycUK9ePSxfvrzYa8q6devi66+/LscMqbQKL02poaEhcXagtCTN+ivP5S/d3NwQEiJcM79ly5ZYvHgxjh07hrt37yI2NhYpKSnIysqCSCQS+ydpJgSpNnW43jM2NkarVq3EYpmZmXj79m2Rbb788kux2bWurq7o379/meVIRJWHWfWqWOc5VK6ijbTFGmmXVSMqCvewqUBOnDghWK+2WrVqcHaW7gZ2YU+ePMGqVasE8devX+P169f477//sH//fgBAv379sGjRInTu3Fnm80ha71veKdu1a9eGlpaW2BJrL168kKsvUj9JHzKw+W5QyQcC2Hw3iDNs1IyJiQlcXFzE9qkCgHPnzhW7IXRllJycjF69euHevXticVNTU5w7dw4tWrRQUmYVQ25uLi5evAhvb28cO3asxBtyOjo66Nu3L1xdXTFo0CDo6+uXU6bKU3jzbeDjvnLykrRniKRzkGxatWoFDQ0NQYHx1q1b6Nu3r5KyUp7U1FT4+PjA29sb58+fFyzZW5ipqSlGjhwJV1dXuLi4iC3dSaqv8H4eIpEI6enpchdtCm/yLukcZeXUqVO4cOGCWKxGjRr466+/ZNoXtDRLVyrDiRMnsHz5cqWc28rKCqdPn1bKufOp0/WepFU/4uLiYGVlJfH4169fiz2+fv06WrZsKfX5JA088PLygo+Pj1jM0dERO3fulLpfIqoY8os2xRVfCu9pw2INlScWbCqIzMxMLF68WBCfNGmSYPpxWfDz84Ofnx9mz56NX375RaaNgAsXmYCPm7zKQ0tLC7Vq1RIbxavIpT3evn2LuLg4mdpIWqaOFC/pQwa+v34Br95Lt9b2q/fJ+P76BRZt1IyDg4OgYHP//n0lZaOaUlJS0Lt3b8FykMbGxjh79izatGmjpMzU37///gtvb2/s379fqveW9u3bY8KECRg7dizMzc3LIUPVYWtrC319fWRmZhbEoqKikJWVJdM1Qr6wsDBBrGHDhqXKkT7eTG7Tpg1u374tFr906VKlKdhkZ2fj7Nmz8Pb2xokTJyTuB/kpHR0d9OvXr6AAq6enV06ZkqJJWsIsKSlJ7oKNpP2fJO0TVRb27dsn9lhLSwu+vr5o166dTP2o2+y6hIQEpV0HKnu/L3W73pM0A02WAqEiPlO/efNGcP1WmmUQiUi9yVK0WezRG2uWn2OxhsoNCzYVxOLFiwVLk5mZmWHhwoWl7tvCwgImJibQ0dFBQkIC3r17V+SIwy1btuD27du4cOGC1JsBS/pgYGhoKHe+hdtmZWUhNTW1VH3m27p1q8RZR6RcshZr8rFoo34krVtd3hv6qrL379+jT58+uHXrlljcyMgIZ8+elfnGDQHPnz/H3r174e3tLdXNgs8++wzjx4+Hq6srHBwcyiFD1aStrY3mzZuL/S5mZWXhzp07Mu9Tl5OTg3///VcsZmlpqfJ79KmLwYMHCwo2u3fvxurVq+UqrqkDkUiEq1evYu/evTh48CDi4+NLbOPk5FRQgOX+SRWDhYWFIPbkyZMiR/yX5PHjx4JYeRXrz58/L/a4b9++cr3nc2UC9aCO13uSBj1WtsEsRKR6pC3afOF6oNh+WKwhReMeNhXAkSNHsHHjRkH8559/lvhBpCROTk7w8PDA1atXkZycjLdv3yIkJASPHz9GbGwskpOTcf78eUyePFniB/kbN25g7NixJS4jkU/S8gFVqsh/81xS25JGS5L6krdYky+/aJP0Qb2WgKiskpOFP2ctLS0lZKJ6UlNT0bdvX9y4cUMsbmhoCD8/Pzg5OSkpM/UTGxuLTZs2oV27dmjQoAFWrVpVbLHGzMwMM2bMQFBQEMLCwuDh4VGpizX5XFxcBLGgIOmWrfzU3bt3Be/j8izBSpJNnDgRmpriHwni4uJw6NAhJWVUdh49eoQlS5bAzs4OLi4u2LZtW7HFms8++wzLly9HSEgIrl+/jlmzZrFYU4G0bt1aECtcvJRF4cKyqampxP01FS0rK0uwF4ikv78lyc3NFRQASPWo6/XekydPBDF57lUQESmaNHvaFIfFGioLLNioubt372LixImC+IgRIzB58mSZ+po4cSKePn2K69evY9myZejYsaPE0ewGBgbo2bMn/vjjDzx//lziSNmTJ09KLCJJkp2dLYiVZn1/SQWbrKwsufsj1SVNscbayAQ/dRkAayOTIo9h0UZ9PH36VBCztLRUQiaqJS0tDf3798e1a9fE4gYGBjh9+jQ6deqkpMzUx/v377Fnzx707t0b1tbW+OqrrwQ33z6lq6uLYcOG4ciRI4iNjYWXlxe/z4VIWlLryJEjMvdz+PBhqfom+djY2GD48OGC+KJFi5S+5I8iREVF4aeffkKLFi3QtGlTrFmzptj9lKpVqwZ3d3dcvXoVYWFhWLVqFerVq1eOGVNJCi/3LO0gscIk/c2W9PdGGnfu3EF4eLhYzMnJqdh9jSQtWy3P1yJpprE8S7GdPn0aqampMrdTJjc3N4hEIqX8k7QPa1lT1+u9Bw8eIDo6Wixma2sLE5OiP58FBweX6ueza9cuQZ8rVqwQHOfv76/oL5eI1JC8RRsWa6iscEk0NfbixQsMGDBAMEOlYcOGEi9QSjJp0iSZ29ja2iIgIAADBw7EuXPnxJ778ccfMXXq1GIvxIpSmk1bJbUtvJmuvGbOnIlRo0bJ1CY0NBRDhw5VyPnp/0hbrMlf7mxZh57FHs/l0VRfYmIirl69Kog3atRICdmojvT0dAwcOBCBgYFi8apVq+LUqVNyjbKtLLKysuDn54e9e/fC19dXqrXUO3bsCFdXV4wZM0bi/gf0f7p16wZLS0uxvepu3ryJ4OBgqTcOzsrKwu7du8Vienp6GDZsmAIzpdWrV8PHxwc5OTkFsVevXmHWrFnw9vZW2HkyMzPx559/YubMmQrrU5LExEQcPnwY3t7euHLlSonXgXp6ehgwYAAmTJiAAQMGVNil4CoKIyMjscfyFhmsra3RqFEjsZH/t2/fxp07d2Te/2Pr1q2CWO/evYttU/jrAOT7WiQtQy3PcrG//vqrzG2o/Kjz9d7atWsFsX79+ikhEyKiokmzPNqnWKyhssQZNmoqOjoaPXv2FLsJAnz84HHmzBmJHwDKio6ODg4fPgxra2uxeEJCglSFIx0dHUFMlg0IpWmrqA/eNWrUQJMmTWT6x1GZiidrsQZAQdGGM23U1/fffy+2gXm+wYMHKyEb1ZCZmYkhQ4YIRgdWqVIFvr6+6NKli3ISUwOxsbGwtLTE0KFDcfDgwWLfdxwcHLBq1SqEhYXh6tWrcHd3Z7FGClpaWpg6daogvnjxYqn7+PXXXwXr3o8bN46bBCtYgwYNsGTJEkF87969mDt3LvLy8kp9jpCQEDg7O+PgwYOl7qs4J0+ehKWlJaZPn46AgIAiizUaGhro1KkTvLy8EBMTgyNHjmDYsGEs1qiBwn9/k5KSkJhY8o0VSWbNmiWIzZ49W6bBXrdu3RIUlg0MDEocDCfpfUSePWRMTExQtar4zaLCA+lKsnPnTs40UGHqfL3n6+uL/fv3C+IjR45UQjZERMUzq14Viz2KH3CRb7FHbxZrqMywYKOG3r59i549ewqm3desWRMXL16Era1tuedkZGSEFStWCOJ+fn4lti38AQNQfMFG0sgzUk/yFGvysWijvjZu3IgNGzYI4t27d6+0RdGsrCwMHz4cFy5cEIvnf3jv3r27kjJTD5mZmcXe4DM3N8esWbNw48YNPH/+HMuXL8dnn31WjhlWDHPnzhUMIjlz5gx+++23EtveuXMHK1euFItpa2tj0aJFMuWgoaEh+KeMpWxU3dKlSyXe9PP09MTo0aMFe2RIKzMzEz/++CNatmyJO3fulDbNEr17967YpXDr168PDw8PhIWFISgoCDNmzGABVg4RERESX1vloVmzZoLY6dOn5erLzc1NsHzYjRs3MHv2bKnaR0ZGYuTIkYKi5pQpU0osLNepU0ewEoG8X4ezs7PYY39/f6n7OnPmDObOnSvXeansKfN67/bt2zhw4IDcRXs/Pz+MGzdOUADt1q0bevTooYgUiYgUKjE+HWuWSzfoYc3yc0iM537ZVDZYsFEz8fHx6Nmzp2AfB3Nzc1y8eBH169dXUmbA6NGjBWsxX7lyRWx5DUkkbd5amvWTC7fV1dWFoaGh3P2R6sjNy8MPNy7KVazJJ23R5ocbF5GrgBHFJL+YmBj8888/6NSpE77++mvB8zo6OvD09FRCZsqXk5OD0aNHC4ri+vr68PHx4YdgOenr62PUqFE4fvw4Xr9+jS1btqB9+/bKTkutWVhYwMPDQxCfM2cOVq9eXeQ1wokTJ9CtWzd8+PBBLP7VV1+hQYMGZZJrZaejo4Njx46hadOmgueOHDkCe3t7LF++HK9evZKqv8jISKxevRp2dnb47rvvkJ6uvA+05ubmmD17Nm7cuIFnz55h2bJlsLOzU1o+VDpOTk7Q1BT/GDtv3jwcP35c4t6YxTEwMMC2bdsE8a1bt2LUqFF48+ZNkW1Pnz4NZ2dnREVFicXr1q0r8e9eYRoaGoK9QC9cuIDFixfLXCAdPXq0IDZmzJhi9+TJzMyEh4cHhgwZUjDgTdL+paQ8yr7ee/XqFcaOHYuGDRti7dq1CAkJkapdZGQkZs6cKXH5dn19fS6/R0QqKTE+Xerl0ADgZXgiFs7xYdGGygT3sFEjiYmJ6NWrF/777z+xeLVq1XDhwgU0adJESZl9ZGxsjFatWolt0pyZmYm3b9/CysqqyHY1a9ZEaGioWOzVq1dyfZDOzc1FTEyMoH+qGLQ0NTHMoSk8715FnoSlKkoq1uQraU8bTQ0NDHNoCi1N1rQV7fbt2yXuXZGWloaEhAQkJCQUeYy2tja8vb3RuHFjBWeoHg4cOIDjx48L4vr6+liwYAEWLFggd9+Ojo7YuXOn1MdLuxdJvtevX5fYJjg4WKY+S0NDQwMuLi5wdXXFqFGj5Np3TdV5eXnBy8uryOcLL68KAMuXL8fGjRuLbOPu7g53d3epzv/ll1/i3LlzYjecRCIRli1bhu3bt2P8+PFwcHCAnp4ewsPD4ePjg7t37wr6adOmDVavXi3VOUk+ZmZmCAgIwKBBgwSbWqempuL777/H999/j8aNG6Nbt26wtraGhYUFTE1NkZmZiTdv3uDZs2e4du0aHj16pLA9BOWhr6+PwYMHY8KECejbt6/EJXjV3dSpU3H79u0iny98fQ0A/fv3L3bZt507d8LR0VEh+ZWVWrVqoW/fvmIzSN68eYOhQ4dCV1cXderUgYGBgWDGT1Ff2+jRo3H27Fn8+eefYvHDhw/j1KlT6NevH1xcXGBpaYnMzEyEh4fjxIkTEt+rdHR08M8//0j9XjJ58mScOXNGLLZ27VqsXbsWtWrVQrVq1QQD4gYPHiwoCP3vf//DmjVrEBYWVhBLTU3FqFGj0Lp1awwaNAj16tWDjo4O3r59izt37uDkyZOIj48vOL5x48YYNGgQ1q1bJ1XuVPZU5XovJCQEixcvxuLFi2Fvb49WrVqhWbNmqFGjBoyNjaGjo4OEhARERkYiKCgI169flzggQ0tLC/v375f52pGIqKzJWqzJl1+04V42pGgs2KiJ5ORk9OrVC/fu3ROLm5qa4ty5c2jRooWSMhNXq1YtQSwuLq7Ygo2dnZ1gI/HIyEi5Nk58/fq14OKQIygrlo5WdQFAULSRtliTr6iijaaGBua07lRwHlKstLQ03L9/v1R9WFpaYseOHRg4cKCCslI/RY0gTkpKQlJSUqn6lnVvEFl/ntnZ2aX+HVAEAwMD/PDDD5gwYQJsbGyUnU6Zio2Nlfl7HhUVJRg1XrhPaWloaODgwYPo27ev4P3+1atXUt0cbNSoEU6fPg09PT2pz0vyqVatGvz9/fHdd9/h119/RW5uruCYx48f4/HjxzL3Xb9+fcyfP18RaRbJ3t4eO3fuxKhRoyr8bIHQ0FCZX9tPnjwp9vnSzHIvT+vXr0dAQIBg9H5WVpZY0eJTxX1tv//+O7S0tLBjxw6xeEZGBo4ePYqjR4+WmJOxsTGOHj2KTp06SfEVfDRixAj06NEDFy9eFDwXExMjGIgGSB4ooaOjg0OHDsHZ2Vkwm+3u3bsSi+Cfql27Nk6dOiXYi4eUS5Wu9/KFhYUhLCys2NlbRZ1v586dGDJkiFznJSIqK/IWa/KxaENlgcPH1UBKSgp69+4tWPfb2NgYZ8+eRZs2bZSUmZCkvWJK2o9G0tImkkYESkPSB7SGDRvK1Repro5WdTGndSdo/v+Rk7IWa/IVXh6NxRrVZm5ujm+//RZPnz6t1MUaUgwLCwssWbKkwhdrVIWhoSEuXLiAGTNmyLzPxYgRI3D9+nXUqFFD5vPGxcUJYhYWFpx9WwIdHR389NNPuHfvHgYOHFjqvUkcHBzw22+/4dGjRxgwYICCspTMxcUFU6ZMqfDFGmWT9Noqz9n+jRs3xvnz5xW2j52WlhZ+//13bNq0Sa49jZycnBAUFCTzElWampo4fPgwPv/8c5nPWVirVq1w9uxZiQPoiuPk5IQbN26gbt26pc6BqDANDQ0MHDgQ//33H0aMGKHsdIiIxEhTrLGxM8O2v8fAxq7o6wMuj0aKxoKNinv//j369OmDW7duicWNjIxw9uxZtGvXTkmZSSbpw5u5uXmxbSQVnK5fvy7X+Qsv3wEArVu3lqsvUm35RRsbY1O5ijX58os2NsamLNaoCF1dXZibm8Pe3h59+vTB4sWL4evri9evX2P9+vUVcskqospAX18fXl5euHHjBkaMGFHsbBltbW306dMH586dw+HDh+V+3V++fFkQW7BgAapUke89o7Jp1qwZfH198ezZM6xYsQKtWrWClpZWie00NTXRuHFjzJw5E9euXcPz588xc+ZMwdJOpL4kvbZWrlxZrjl06NABT58+xenTpzFz5kw4OzvDysoKhoaGgj1upDV37lyEhYVh6dKlJQ760tfXR69evXDkyBFcv34dzZo1k+ucpqam8Pb2xtOnT7Fy5UoMHDgQ9vb2MDMzk3kpP2dnZ9y/fx8LFiwocQaFo6Mj9uzZg6tXr8La2lqu3Kli69u3L86dO4clS5agS5cuUs/K0dDQQP369fHVV1/h6dOn8PX15e8YEakcaYs16zyHwq6eOdZ5DmXRhsqNhkiZi0tTsVJTU9GnTx9BEcLQ0BBnzpyRabp9ebG2tkZ0dLRYLCkpqdgbLcnJyahevbrYkhtGRkZITEyU6qbAp/r37y/YlPHx48do1KiRTP0o0qNHj8Q28H348KFCRyDm5OQINoB0cHCoNDdFcvPyFLLXjKL6ISIi6aSnp+PmzZt49uxZwZ5VJiYmqFevHtq3by/3ci2fcnd3x/bt2wseW1paIiwsDFWrcrkCeaWlpeG///5DeHg43rx5U7D8kpmZGczMzGBlZYVWrVrByMhIyZlSWerbty/Onj1b8LhFixa4d+9eqWdjqZrIyEgEBwcjLi4O7969g66uLmrUqAErKys4OTmp9N+S3Nxc3L59G48ePcK7d++Qk5MDIyMj2NnZwdHREZaWlspOkdTQy5cvERERgaioKLx79w7p6enIy8uDsbExzMzMULNmTTg6Oso1U40qhsp+f4LUgyzFmk+XOZO3Hamfsr6XWxIWbFRUWloa+vXrh8DAQLG4gYEB/Pz85Nrfpaw9ePBAsJeOra0tIiIiSmzr7OwsWNfez88Pffv2lfr8CQkJqF27NjIzMwtiNjY2iIyMlLqPssCCDRERkXI0aNAAz58/L3i8ceNGfPnll0rMiEj9ZWdnw8zMTGz/GB8fH+5NQUREvD9BKq+0RRcWbSoHZRdsOJxcBaWnp2PgwIGCYk3VqlVx6tQplSzWAMDatWsFsX79+knVduzYsYKYl5eXTOffs2ePWLEGAMaNGydTH0RERFQxREdHixVrateujRkzZigxI6KK4datW2LFGkdHRxZriIiISOXl5ORhyZcnSlVsMateVarl0ZZ8eQI5OXmlzpkqJxZsVExmZiaGDBkCf39/sXiVKlXg6+uLLl26KCexEvj6+mL//v2C+MiRI6VqP2HCBMHSGb6+vhL3pJEkMTER69evF4tpaWlh2rRpUrUnIiKiiqXwHhtLliyBvr6+krIhqjgKv7ZWrVqlpEyIiIiIpKetrYmxbm1Q1Gr40s6MKaloo6kJjHVrA21t3nYn+fA3R4VkZWVh+PDhuHDhglg8v1jTvXv3Mjnv7du3ceDAAeTlyVf59fPzw7hx41B4db1u3bqhR48eUvVhamqKWbNmicXy8vIwadIkJCYWXfnOP27GjBmIiYkRi48bNw729vZSnZ+IiIgqlkuX/h979x3fdLX/cfydLgqFDsoehQpl76GAIENANgiIqHAFvSoO3IqgouC9KHe4UETFC9yfDNlDUREZMh3IlllKmWWUlkJLadPm9wc3kTRpm6Rpk7Sv5+PRB+T0+z3nE+hIvu/vOWed5e9RUVH661//6sFqgOLj5u+tdu3aqU+fPh6sBgAAwHGdu8do3KSeNqGNs8uY5Rba+PlJ4yb1VOfuMe4qGSUQgY2XMBqNGjZsmL799lur9uDgYC1fvtzh4MMVp06d0vDhw9WgQQO98847NuuN5iY+Pl5PPPGE+vbta7UsgnSj7nfffdepOl599VXVqFHDqu3w4cPq0KGDDh48aPecpKQkDR48WIsWLbJqDw0N1dSpU50aHwAAFB83zwJ47bXXFBQU5MFqgOLh+vXr2rZtm+Xx5MmTPVgNAACA83KGNq7uOZMztCGsgbuw65eX+Oqrr7RixQqb9uDgYL388st6+eWXXe67TZs2mjlzZr7HHTlyROPHj9f48eNVp04dtWzZUk2bNlWlSpUUGhqqwMBAXbp0SfHx8dq8ebO2bdsmo9Fo04+/v78WLFigFi1aOFVn2bJlNW/ePHXr1s2q34MHD6px48bq37+/OnXqpOrVq+vixYvavXu35s+fbxMWSdIXX3yhatWqOTU+AAAoPuLi4jxdAlDslCpVSteuXfN0GQAAAAViDlUWzN6hKR8McDqsMTOHNhOeWanho1oT1sAtCGy8RGZmpt325ORkJScnF6jv8PBwp8+JjY1VbGysFi9e7PRYM2fOdHnj0U6dOmnevHm6//77rUKb7OxsrVixwm6odTODwaD333/f4b1zAAAAAAAAAJQsnbvH6PYudQq810xEZBlNmz2MPWvgNgQ2cAuDwaC+ffvqk08+sVnWzFn33HOPqlSpohEjRujEiRMOn1ehQgV9/vnnGjRoUIHGBwBnzJgxQzNmzCj0ccaMGaMxY8YU+jgAAAAAAJQE7gpZCGvgTgQ2UK9evbRmzRpt2LBBW7Zs0e7dux2a1WMwGBQTE6M+ffro8ccfV7169dxWU6dOnXTgwAFNnz5dn376qY4ePZrrsdWrV9dDDz2k5557ThEREbkeBwCFISEhQbt37y6ScQAAAAAAAFB8Edh4iVGjRmnUqFEeGTs4OFg9evRQjx49LG0nTpzQ8ePHdfLkSV28eFFpaWnKzs5WaGioIiIiVLlyZbVp06ZQA5IyZcroxRdf1IsvvqgjR45o586dOnnypNLS0hQcHKxq1aqpWbNmatq0aaHVAAAAAAAAAABAUSCwgV1RUVGKiorydBkWMTExiolh4y4AAAAAAAAAQPFEYAMAQAG8+eabevPNNz1dBgAAAAAAAHwcOyIBAAAAAAAAAAB4GIENAAAAAAAAAACAhxHYAAAAAAAAAAAAeBiBDQAAAAAAAAAAgIcR2AAAAAAAAAAAAHgYgQ0AAAAAAAAAAICHEdgAAAAAAAAAAAB4GIENAAAAAAAAAACAhxHYAAAAAAAAAAAAeBiBDQAAAAAAAAAAgIcR2AAAAAAAAAAAAHgYgQ0AAAAAAAAAAICHEdgAAAAAAAAAAAB4GIENAAAAAAAAAACAhxHYAAAAAAAAAAAAeBiBDQAAAAAAAAAAgIcR2AAAAAAAAAAAAHgYgQ0AAAAAAAAAAICHEdgAAAAAAAAAAAB4GIENAAAAAAAAAACAhxHYAAAAAAAAAAAAeBiBDQAAAAAAAAAAgIcR2AAAAAAAAAAAAHgYgQ0AAAAAAAAAAICHEdgAAAAAbjZ79mwZDAarj+PHj3u6LABwuy5dulj9rOvSpYunSwIAAPBZAZ4uAAAAuM/Jkye1f/9+nTp1SsnJycrIyFBERIQiIiLUoEEDNW3aVP7+/p4uE1BaWpp++eUXHTp0SElJSTKZTAoLC1NMTIxuvfVWhYWFebpE2JGWlqa9e/fq+PHjSkhIUFpamiQpPDxcERERqlq1qlq2bKnQ0FAPV1rynDt3Tvv27VN8fLySk5OVnp6u0NBQRUREqG7dumrZsqWCgoI8XSaAAkpLS9PRo0d18uRJnT59WleuXNG1a9dUunRphYWFqWLFimrRooVq1arl6VIBAIALCGwA+ASTySSDweA1/SB/x48fV3R0tFPn+Pn5qWzZsgoNDVX16tXVvHlztW3bVoMHD1b58uVdrqV27dqKj493+Xx7kpKSFB4e7tY+XXHw4EF9//33WrdunX766SclJyfneXxISIi6dOmiMWPGqE+fPvLzc99k27Nnz2rHjh367bffLB/nzp2zOqZWrVrMMvCgjIwM7du3z+r/aN++fcrMzLQ6btasWRo1alSh1PDrr7/qn//8p1atWqX09HS7xwQEBKhHjx564YUXdOeddxZKHd5qw4YN6tq1q1v7HDhwoJYvX+7y+UeOHNG8efO0atUq7dq1S1lZWXkebzAYVL9+fd1xxx164IEH1KlTJ373FoL4+Hh99913WrdunTZu3Gjz8zanUqVKqX379nr00Uc1ZMgQwhvAB2RlZWnHjh3asmWLtm7dqt27dys2NlbZ2dn5nluhQgUNHTpUo0eP1q233loE1QIAAHcgsAHg9a6kpOvSxTRVjwpTQIDrMwOMxiydPnFZ5SuUUbnQYDdWCHfJzs5WSkqKUlJSdOrUKf3888/67LPP9OSTT+ruu+/W1KlTuVtQ0rVr1/Svf/1LCxcu1L59+5w6NzU1Vd98842++eYbNW7cWLNnz1abNm1cqmP79u364YcfLBf+z5w541I/KDxXr17VV199Zfk/2rNnjzIyMjxSy/Xr1/Xcc89pxowZMplMeR5rNBr17bff6ttvv9U999yjmTNnMmPDA/bt26cJEybo66+/zvf/7GYmk0kHDx7UwYMH9dlnn6lWrVp66qmnNHbsWJUqVaoQKy4Z3n33XS1cuFA///yzU+ddv35dGzZs0IYNG1SzZk3NnDlTPXv2LKQqC8+oUaM0Z84cy2NfvRHAXji7fv16lhODlSNHjui2225z6dyLFy9qxowZmjFjhgYPHqwPPvhANWrUcHOFAADA3djDBoBXu5KSroQzKbp+3ahTJ5JlNOZ9V29ujMYsnTqRrOvXjUo4k6IrKfbv6oZ3ysjI0FdffaVGjRrpv//9r6fL8bhz585p4sSJToc1Oe3fv1/t27fXRx995NL577zzjiZOnKiVK1cS1nipo0eP6q9//atmzJih3377zWNhTWpqqnr27KlPPvnEqQv/krRo0SJ16NBBFy5cKKTqkFNmZqbGjRunFi1aaNWqVU7/n+UUHx+vl156SfXq1dPixYvdVGXJ9cILLzgd1uR08uRJ3XXXXXrllVfcVBUAb7Z06VI1b95cv/32m6dLAQAA+WCGDQCvZQ5rzNeJMq7fCF1qRIU7NdPGHNZkXL8R9phMUsKZFElipk0RCwkJUd26dXP9fGZmpi5fvqyzZ8/aXeohLS1No0aNUlZWlkaPHl2gWpo3b16g8711H5i6deuqc+fOiomJUaVKlRQSEqJLly5p165dWr16tU6ePGl1vNFo1NixYxUUFKRHH33UQ1WjODOZTBo+fLh++uknm89Vr15dDzzwgGJiYlSqVCnFxcVp+fLl2rlzp9Vx+/fvV79+/bRp06YSuYxTRESEoqKiXD7fmeUpk5KS1L9/f23ZsiXXYxo0aKBu3bqpRo0aqlChgiIiInTt2jWdP39ehw8f1pYtW7R//36b806cOKGPPvpIQ4cOdel5IG9RUVHq1KmTGjdurIoVKyosLEzJycnav3+/vvvuOx06dMjmnKlTp8rf319///vfPVAxAGcZDAY1bNhQTZo0Ud26dRUVFaVy5copKChIV69e1enTp7Vz5059//33unr1qtW5ly5d0p133qlffvlF9evX99AzAAAA+SGwAeCVcoY1Zs6GNjnDGjNCG89o06aNNmzYkO9xaWlp2r59u7744gvNnz/f6u5uk8mksWPHqlu3bgVaHm3Xrl0un+ttGjVqpFGjRun+++9X9erVcz3OaDRq1qxZev75523exI8dO1ZdunRRvXr1ClxP6dKl1bx5c7Vp08bl2TsofFFRUWrTpo0uXLigTZs2Fdo4H330kb7++mub9smTJ+uVV15RYGCgVfvEiRO1fPlyjRw50urr9JdfftHrr7+uqVOnFlqt7jRq1Ci37QM0YMAAzZ492y195SUpKUl33HGH3dl7ISEhevbZZ/XYY4+pZs2a+fZ1/Phxffnll/r444+VkJBQGOVCN5YDe/DBBzVy5Mg8b4gwmUxatmyZHn/8cZ0/f97qc1OmTFGPHj1YigvwQuY9wXr37q277rpL7du3V1hYWL7nXbt2TR9++KHefPNNq/3iUlJS9PDDD2vTpk3sLQYAgJdiSTQAXsdkMunSxTSbsMbMHNrktzxabmHNn+Pof+MUbKkXuF+ZMmXUrVs3zZ07V998842Cg61DtdTUVL399tseqs57dO3aVRs3btT+/fv10ksv5RnWSDc2cn/kkUe0ZcsWhYeHW30uIyNDL7zwgtM1lCpVSm3atNGYMWM0c+ZM7dq1SykpKdq2bZumTZvmdH8oHNWqVdOAAQM0efJkrV69WhcuXFB8fLyWLFmibt26Fdq4Fy9e1GuvvWbT/uGHH+r111+3CWvMBg0apHXr1tnMpnnvvfd0+PDhQqm1pDMajbr77rvthjWDBg3S0aNH9be//c2hsEaSateurddee01xcXH6+9//rpCQEHeXXKK1atVKK1euVFxcnCZNmpRnWCPduOg7ePBg7dixw+5srbFjxxZWqQAKoH79+jp48KDee+899erVy6GwRrpx88y4ceO0Zs0am9+lW7ZssTvrFQAAeAcCGwBex2AwqHpUmIJK5T6DJr/QJr+wRpKCSvmrelQYd5d5ud69e2vy5Mk27cuXL7e7bFpJEBYWpg0bNmjdunW64447nD6/WbNmmjlzpk37t99+q4sXLzrcz7Rp03TlyhX9+uuv+uSTT/Twww+refPmCghgAq+3qFevns6ePavTp09rxYoVev3119W7d29VqFChSMb/8MMPlZKSYtXWo0cPhy4Ot23bVhMnTrRqy8zM1DvvvOPWGnHD3/72N23cuNGm/fHHH9eSJUtUpUoVl/oNDg7WhAkTtHPnTrVu3bqgZUI3fv/t2LFD/fv3d/o1TI0aNbRkyRKb8/bt22ezFCEA39epUyc999xzNu2LFi3yQDUAAMARBDYAvFJAgL9qRIW7FNo4GtY4uxcOPOexxx6zuRP/3LlzJXaj+4iICHXu3LlAfQwZMkTNmjWzasvKytK3337rcB81a9bMdYaEtzt+/LiaN2+uf/3rX8X666hMmTIuX2gvqKysLH3++ec27c4ELi+++KIiIyOt2ubPn6/Lly8XuD786dChQ3b3MLn33ns1ffp0+fkV/C1DTEyMNm/erHvuuafAfeXl//7v/9SzZ0/93//9n83Sj8XFwIEDC3R+mzZt1K9fP5t2e0sXAvB9I0aMsGn7448/PFAJAABwBLfAAvBa5tAmr/Dl+rVMqz1tXA1rTFlZMnjpJvKQQkNDVa9ePZtNrBMSElSjRg0PVeX7evfurT179li1HTt2zEPVFL09e/bopZde0rhx49StWzeNHDlSgwcPVtmyZT1dWrGwYcMGm71L2rZtq1atWjncR6lSpTRq1Cj9+9//trSlp6dr2bJlbtsfBtLrr78uo9Fo1VatWjV98sknbh0nODhYTz75pFv7zCkrK0s//PCDfvjhB4WEhGjQoEEaOXKkunfvLn9+z1v06dNHq1atsmorST//HXXixAnt2rVLFy5c0IULFxQcHKyKFSuqWrVqateunUqXLu3pEvOUlZWlY8eO6eDBgzp9+rRSUlKUlZWliIgIRUREqEGDBmratKlbQll4rzp16ti0nT171gOVAAAARxDYAPBqeYU2xthYZez6XabefXVKUtVqoTp7JsXpsCbzylXFfT5fle68XeHNGxXWU0EBhYaG2rSV1CXR3MXePgYlcXPw7OxsrV27VmvXrtXjjz9uucDbo0cPLvAWwHfffWfTNnToUKf7GTp0qFVgI91Yvs/bA5vZs2dr9OjRVm1xcXGqXbu2ZwrKxcmTJ7V06VKb9nfeeUcREREeqMh9UlNTNXfuXM2dO1dVqlTRfffdp5EjR6ply5aeLs3jfO3nf+3atRUfH2/3c/Hx8Q4tDbd+/Xp16dIl3+OSk5P173//W0uXLs1zFkJwcLDuuOMOjRkzRnfffXe+/UrSm2++qUmTJuX6+a5du+bbx4MPPqjZs2fn+vkjR45o6dKlWrdunbZs2aLU1NQ8+wsLC1OPHj304osv6rbbbst3fPie9PR0m7ac+9oAAADvwa00ALyeveXRjLGxSl//o7IvXVL6N18rPemK4uOSXAprjn02V+kJ53Vi3nIl72Z5AG9lb2+VihUreqCS4iMtLc2mzdvvFi5saWlpmjdvnnr37q3q1avr+eef1++//+7psnzSpk2bbNo6duzodD+tW7dWmTJlrNrYLNl95syZo6ws69+dFSpU0LBhwzxUUeFISEjQe++9p1atWqlJkyaaOnWqTp065emyPIaf//ZNmzZNt9xyi/72t7/lu2RUenq61qxZo8GDB6tDhw7au3dvEVVpX2Jiolq1aqV69erplVde0Zo1a/INayTp8uXLWrx4sdq1a6eBAwcqOTm58ItFkfr1119t2uzNugEAAN6BwAaAT7g5tDGHNTKZJEnZyUlK/+ZrZdu5+GCWV1hz/dz/ggCTidDGSyUkJOjo0aNWbWFhYV53p7qvyflvKklVq1b1QCVFLzIyUg899JDCwsJyPebcuXN677331Lp1azVu3FjvvPOOTp48WYRV+i6j0ajdu3dbtQUGBqpNmzZO92XvvISEBJZzcZOVK1fatI0aNUqlSpXyQDUF16ZNG/Xt2zfP/bX279+vV155RVFRUerWrZtmzZqllJSUIqzS80ryz397srKy9Nhjj+npp59WUlKS0+dv27ZNHTt21Lp16wqhOsdcuXJFO3fuLFAfK1eu1K233lqiw8zi6J///KdNW8+ePT1QCQAAcARLogHwGQEB/iqXfFZJN4U1ZubQJrhvP/nluBPbobDG7H+hjSSWR/Mi77zzjkw5/s8HDRrk0BIosM9oNGrFihU27W3btvVANUWvXLly+uKLLzR9+nR9/fXXmjt3rlavXq3r16/bPf6PP/7Q+PHjNWHCBHXu3FkjR47U0KFD7S7VhxtLFOVcgqVmzZouL8FSp04dm1k1hw4dKtEXmN3hypUr2rFjh017t27dPFCNezRp0kRff/21Ll68qIULF2ru3LnaunWr3WNNJpPWr1+v9evX68knn9SAAQM0cuRI3XXXXQoIKN5vkxYvXmzT5s0//xs1aqTw8HBJN/aVuTlUCQwMVKNG+b9my2t/skcffVT/+c9/bNqDg4N111136Y477lDVqlV17do1HT9+XCtWrLDZAy4lJUW9e/fWunXrdPvtt9sdp0qVKmrevLkk6erVq4qNjbX6fJ06dfLdR83ecnb2lC1bVm3btlXDhg0VExOjsLAwlStXThkZGUpKStIff/yh9evX68CBA1bnHTlyRPfee682btxY7L8PirvMzEw9++yzWrt2rVV7xYoVNXLkSKs2k8nkltfV7uoHAICSjFdgAHyGKStLiRu2yc/foKwsk2R9/d5uaONUWGMZyKTzP25RWJP6MrB/hUeZTCa9++67+uCDD6zag4KC9PLLL3uoquJh5cqVNvsVlC9f3qUlq3xZqVKlNGTIEA0ZMkTJyclatGiR5s6dq59++skmJJRufE1u2LBBGzZssLrA26tXLy5s3eT48eM2bbVq1XK5P3sXKI8dO+bQfhTI3a5du+zuBXbrrbd6oBr3qlChgp544gk98cQTiouL07x58zR37lybi9Nm165d01dffaWvvvpKFStW1H333acRI0Z4dYjhqt9//90mqPPz81O/fv08VFH+Vq9ebfn7qFGjNGfOHMvjatWqadeuXS73/dVXX9kNawYOHKjp06erWrVqNp+bPHmyvv76a40ZM0anT5+2tGdkZGjEiBHatWuX3RmcY8aM0ZgxYyRJGzZssNmzZubMmQX6uRYeHq4RI0Zo6NCh6tChQ54zzcy2bt2qZ555Rr/99ptV2/vvv68XX3zR5VqKgr29wopKrVq17P6u8wbHjx/XN998ow8//FCHDx+2+pzBYNDnn3+ukJAQS9uVlHRdupim6lFhVu+ZnGU0Zun0icsqX6GMyoUGu9wPAAAlHUuiAfAZBn9/RT9yn4IrV5C/n/0fXzmXR6taLdS5sEZSqcoVFP3IfYQ1HpCZmalLly7pl19+0fvvv6+WLVvavVjw4YcfOnQ3LexLT0/X+PHjbdpHjx5dokOH8PBwPfLII9qwYYPi4+M1depUNWvWLNfj09PTtXDhQvXv31/VqlXT008/rV9++aUIK/Ze9jYvr1mzpsv92Tv33LlzLveHGw4dOmTTVrt2bUVGRnqgmsITHR2tV199VX/88Yd27Nih559/3u5FeLMLFy7oww8/1K233qoGDRro73//u9demHWWyWTS888/b9N+9913q0KFCh6oyLOuXr2qxx9/3Kb9kUce0bJly/L8OunXr582bdqk6tWrW7UfP35cr732mttrzU+1atV05swZTZs2TZ07d3YorJGkDh06aNOmTerVq5dV+4cffiij0VgYpcINOnTooBYtWlg+mjdvrrp16yo0NFTR0dF66qmnbMKaoKAgffHFFxo4cKCl7UpKuhLOpOj6daNOnUiW0Zj7fqB5MRqzdOpEsq5fNyrhTIqupKTnfxIAALCr5F6VAeCTAsuVVa2H7tOhj/9PunTJ7jE3z7Q5e+bPGTaOhjW3PPqAAsvlvRwFXLNx48YCLZNQo0YNTZs2TYMGDSpwLS1atHDpvJvvjvVV48ePt3kTHxERoXHjxhXJ+L6w7EbNmjX18ssv6+WXX9a+ffs0d+5czZs3TydOnLB7/IULFzRt2jRNmzZN9erV08iRIzVixIgSu8/SJTs/n/Nb5icv9s5NTEx0uT9fs3LlSpd/Zq1evTrXi8729mSqUqWKS+P4ilatWqlVq1b65z//qfXr12vu3LlasmRJrnvYHDp0SK+99ppef/11dezYUSNHjtQ999xjWZ7L13z44YfauHGjVVtgYKDeeustD1XkWbNnz7bZs6Zt27aaMWOGQ79foqOjtWjRIt1+++1WszJnzZqlyZMnKyIiwu0158bVJSelG0u/zZkzR9HR0Ur7301PJ0+e1Jo1a9SnTx93lQg32rNnj1JTUx0+vlu3bvr3v/9t9bvEHNaYv3Qzrt8IXXKuTpAfc1iTcf1G2GMySQlnbvxMZaYNAADOI7AB4FOMxiwlJGWqVO++N2bSJNvfGNYc2qhvP52SVCUiUPH/mU9Y46M6duyo559/Xv3793fbDJCcG6I7yt7MAV+yZMkSvf/++zbt//rXv1SxYsVCH99kMulEXJJPLbvRpEkTvf3225oyZYo2b96sL7/8UosXL7YbSkjS4cOH9frrr2vixIm6/fbbNXLkSA0bNsxnL/C6wt5FpNKlS7vcn71zzRcVS4KkpCSXNkKXbizRlFe/OZWUr1M/Pz/deeeduvPOO232srL3b2YymbRp0yZt2rRJY8eOVf/+/TVixAj16dPH4ZkMnrZ9+3a7y4m+8soratiwoQcq8ryPPvrIpu3jjz+WXy4zue1p3769HnzwQc2ePdvSlpqaqlmzZtmdzeStKlWqpF69emnp0qWWts2bN3t1YFO+fHnLnkBFLa/ZV96kWbNm+uijj9SpUyer9pxhjZmzoU3OsMaM0AYAANcR2ADwGTe/IfArU0bBffs5FNqY7rxTh+atk1KSldu9koQ13m3z5s06evSodu3apaeffrrYLddTVH7//Xc9+OCDNu1DhgzRQw89VCQ1ZBlNlmU3nL2D0+zmnwVFeTHAYDCoU6dO6tSpk6ZNm6Zvv/1Wc+fO1apVq5Sebrv0h8lk0ubNm7V582Y9/fTT6tevn+UCb0HuhPYFmZmZNm3Bwa7/H9kLbPIKIuCYa9eu2bTZ23ejuAsODtbQoUM1dOhQJSUlWfay2rRpk929rK5fv67Fixdr8eLFioyM1L333quRI0eqXbt2HqjeMfHx8Ro0aJDN9027du00ceJED1XlWSdPnrRZFrBNmzYu7Vv0xBNPWAU2kvTDDz/4VGAjSTExMVaPt2/f7qFKHDNgwAANGDDA02V4tT179qhr164aPHiwXn31VTVv3lwmk0mXLqbZhDVmjoY2uYU1ZiaTdOlimsqWK1VoM6IBACiOCGwA+AR7bwgcDW2uLVl844FB8vf3swltCGuKTkhIiOrWrZvnMVevXlVSUpLN7IWEhARNnjxZn3zyif7zn/949ebI3ujYsWPq27evzcyHBg0aaNasWUVej68vuxEUFKSBAwdq4MCBSklJ0dKlSzV37lytW7fO7ibu169f15IlS7RkyRKVL19ev/zyi+rUqVNk9XqDglyssXeuvQvpcI69f8OSflEtIiJCjz76qB599FGdPHlS8+bN09y5c7V37167xycmJmr69OmaPn26mjdvrl27dhVtwQ5ITExU7969bfZ9qly5shYtWlRi9y7bsmWLTdvQoUNd6qtt27aKjo5WXFycpW3btm2FunSnI06fPq2tW7dqz549Onz4sC5fvqyUlBRdu3bN7vd/zlnEuS0DCs+7evWq1WOj0ajk5GSdPn1av/76qxYtWqQffvhBJpNJWVlZWrRokZYtW6Y333xTr776qqpHheUZtuT3Oi2/sEaSgkr5q3pUWIn/vQIAgLNK5qtzAD4lrzcE5tDm+rffSJeTlZWdLeV2Dc8kZWVlW4U2hDVFq02bNtqwYYNDx549e1br1q3TjBkztHnzZkv7hQsXNGjQIP33v//V/fff73ItJeli7+nTp9W9e3ebCzE1atTQd999p3LlynmkruKy7EZoaKhGjRqlUaNG6ezZs1qwYIG+/PJL/f7773aPv3Tpkq5cuVLEVRYte0tE2ZvN4Sh75xb3WUo3y7nckrvYm7mUnJzs9nF8Vc2aNTVu3DiNGzdOe/fu1dy5czV//vxcL2IfP368aAt0QEpKinr16qUDBw5YtYeFhenbb79VjRo1PFSZ59n7Gd2mTRuX+2vTpo1VYHP58mXFxsbme6NKYVi8eLGmT5+ujRs32r2JwFH8PPAdAQEBqlChgipUqKDmzZvrr3/9q3bt2qVRo0ZZlgE2Go167bXXlJiYqHfffVc1osJdCm0cDWtcnUkNAEBJ5/jivADgAY68IQiOKKf6T45UcJUK8vf3U67rnkmW0MYkwhpvV7VqVT3wwAPatGmTPvzwQ6u787KysvTQQw9p3759HqzQN5w/f17du3e3uogk3biz+scff1StWrU8VNkN5osBRmPu3+OS48tueDqIq1q1qp577jktW7ZMo0aN8mgtnlSmTBmbNncHNiEhIS73hxvKly9v08YFWvuaNm2qd955R4sXL9Ztt93m6XIckpqaqr59++q3336zag8JCdHq1avVsmVLD1XmHS5etN3XsCB7+TRq1MihMQrTmTNndOedd+qee+7R+vXrCxTWSDdCJ/iuFi1aaPPmzTY/s957773/za67EaoElco9VMn5Oo2wBgCAwkdgA8BrOfOGIDgiVLc8+oCCKzsW2ig0XLUeuo+wxkeMHTtWL730klXb9evX9fTTT3uoIt+QmJio7t276+DBg1btFSpU0I8//qh69ep5qDJr+YU2vrLsxqVLl/TZZ5+pc+fOql27dqHMiPAV9vaZyrl8izPsncteVgVnb3ZFzmWzIMXGxuqtt95SgwYNdOutt+rnn3/2dEn5unbtmgYMGGA1Q1W6Mavq66+/VocOHTxUmfdISrJdTjc8PNzl/iIiImzaci7vWphOnz6tLl26aN26dW7r02g0uq0veEbZsmX11Vdf2cyofOmll2Q0Gp0Kba6nZxLWAABQBAhsAHglV+7eCixXVlH33y2DlG9oE9S1mxKSMvO9qx/eY8KECTZ37a9fvz7XfQVKuqSkJPXo0cPm36d8+fJau3atGjdu7KHK7MsttPH2OzmvXbumhQsXauDAgapSpYoee+wx/fTTT7nO9AkLC9PDDz+sqKioIq60aFWuXNmm7dSpUy73d/LkSYfGgHPq169v0xYXF1ekF5m91fnz5zVt2jS1a9dOdevW1cSJE202qL9Z7dq19dxzzxVhhbm7fv267r77bpsL96VKldKKFSvUpUsXzxTmZXIuTWkwGOzODnSUvVl/Rbn85ahRo3TkyBGb9hYtWmj8+PFatmyZfv/9dyUkJCglJUUZGRkymUxWH2+88UaR1YuiU6tWLY0cOdKqLT4+Xt9//70kORzaxMclee3rMQAAihP2sAHgdUwmk06fuOz0G4LMK1d1Yt4ySbKENllZ9ve0uf7jjzL07afTMigqOoLNMH1AWFiYOnXqZHlzabZmzRo1bdrUQ1V5p8uXL6tHjx7auXOnVXt4eLjWrFmj5s2be6gy5Rmk5lwr3VvDmqysLP3444+aO3euli1blu8FucDAQPXq1UsjR45U//79FRxc9PvsFLXo6Gibtvj4eJf7s7dniL0x4JyWLVvKYDDYBIy//PKLevXq5aGqPOfq1atavny55s6dqx9++EFZWXnf1BEeHq6hQ4dq5MiR6tSpk1e8lsjIyNDgwYNtflcGBQVp2bJl6tGjh4cq8z45928zmUxKS0tzObRJTU3Nd4zC8s0332jt2rVWbZUqVdJ///tf3XXXXQ73U5ClKz1h5cqVmjhxokfGrlatmlavXu2RsV3Ru3dvffbZZ1ZtGzZsUN++fSX9Gdrk97orN4Q1AAC4D4ENAK9jMBhUvkIZJZxJkb2b1HMLa459NlfXz/25VnheoU12cpLSV3+tyg/f5xUXWOCYmJgYm4tQ5o1UcUNKSop69uypHTt2WLWHhobq+++/V+vWrT1U2Q0BAX4KKuWf7wa3VauF6uyZFK8Ka3799VfNnTtXCxYscGjZqNtuu00jRozQ8OHDVaFChSKo0HvUqlVLwcHBSk9Pt7SdPHlSGRkZCgoKcrq/2NhYm7YGDRoUqEbcuJjcunVrmz1O1q1bV2ICm8zMTH3//feaO3euVq5cqbS0tDyPDwwMVO/evS0BbKlSpYqo0vxlZmbqnnvusbmIHBgYqMWLF6t3794eqsw72VvCLDk52eXAxt7+T/b2iSoM8+fPt3rs7++vVatW6dZbb3WqH1+bXXfp0iWPvQ70tf2+ateubdN27Ngxq8euhjaENQAAuBeBDQCvVC70xh3oOUMbR8Maszxn2qQk6/z8xSr76APsZeMjQkNDbdqKekNfb3blyhXddddd+uWXX6zay5Urp++//97pCzeFJb+LAeZlN/JSVBcHDh8+rHnz5mnu3Lk6evRovsffcssteuCBBzRy5EjFxMQUam3eLCAgQM2aNbP6WszIyNCOHTvUvn17p/oyGo369ddfrdqqVKmiqlWruqXWkm7AgAE2gc3s2bP1t7/9zaVwzReYTCZt2bJF8+bN08KFC5WYmJjvOe3atbMEsN64f1JmZqaGDRumlStXWrUHBgZq4cKF6t+/v4cq814VK1a0aTtw4ICqVavmUn9//PGHTVtRhfU//PCD1eNevXq59Ds/5wV8FB/2ZvfamyHsbGhDWAMAgPuxhw0Ar1UuNFhVqoXKPAHG2bDGzN6eNn7+BvkZDLp+7qKOfTZXmVdc3wwbRefy5cs2bf7+vEGUbizl06tXL23fvt2qvWzZsvr222/Vrl07D1Vmy5G10vNS2BcHEhIS9MEHH+jWW29V/fr1NWnSpDzDmoiICD322GPavHmzYmNjNXny5BId1ph16tTJpi3nBuiO+P33321mPdxxxx0u1wVrDz74oPz8rN8SXLhwQYsWLfJQRYVn//79mjBhgqKjo9WpUyd98skneYY1t9xyiyZOnKgjR45o27ZtevLJJ70yrDEajRo+fLiWL19u1R4QEKAFCxZo0KBBHqnL27Vq1cqmLWd46YycwXJ4eLjq1Knjcn+OysjI0Pnz563a7P38zU9WVpbNDR8oPnJ+jUi5B4oBAf6qWs32Jil7qlYLJawBAMDNCGwAeDVzaFOqVIBLYU2pyhUU89wjCq5cQf7+fjIY/gxrzAhtfMfBgwdt2qpUqeKBSrxLamqq+vTpo61bt1q1h4SEaPXq1br99ts9VFnuXA1tCiusuXLliubMmaOePXuqRo0aevbZZ20uvlnVERSku+++W0uWLFFCQoJmzJjhlf/OnmRvSa0lS5Y43c/ixYsd6huuiYqK0uDBg23aX3nlFZ9b8seekydP6h//+IeaN2+uJk2a6O23385zP6Xy5ctrzJgx2rJli2JjYzVp0iTVrVu3CCt2TlZWlh544AEtXbrUqt3f319z5861+3/r6wICrBeJyG+vodzY+5lt7+eNI3bs2KG4uDirtnbt2uW57G7O5yG59lzszTR2ZSm21atX6+pV33otPGrUKJlMJo98HD9+3NNP3yk5b+iRcn8NbTRm6eyZFIf6PXsmRUaja9+DAADAPgIbAF6vXGiwoqIjXAprbnn0AZWuWkm3PPqAJbTxs/PmmdDG+yUlJWnLli027Q0bNvRANd4jLS1N/fr106ZNm6zay5Qpo2+++calu2yLirOhjbvDmoyMDK1YsUL33nuvKleurFGjRuW70XiHDh30ySefKCEhQUuXLtXgwYOL7bJRBdW1a1ebi0E///yzdu3a5XAfGRkZmj17tlVbqVKldPfdd7uhQpj97W9/s7l4fOrUKT355JNuHSc9PV3Tp093a5/2JCUl6fPPP1eXLl1Uq1YtjRs3Tnv27Mn1+FKlSmnw4MFaunSpzp49q08++UQdOnQo9DoLKjs7WyNHjtTChQut2v39/fXll19q2LBhHqqscJUrV87qsashQ40aNWxeQ/z22282e8A5wt7Xdc+ePfM8J+fzkFx7LiEhITZtriwX++677zp9DnyDyWTSggULbNrtLVFqNGY5tYeNee9BQhsAANyHwAaAT7j5DkVnwhrz3jSB5crqlkcfUKnKua8lTmjj3d566y2rDczNBgwY4IFqvEN6eroGDhyoDRs2WLWXLl1aq1atUufOnT1TmBM8texGQkKCqlSpokGDBmnhwoW6du1arsfGxMRo0qRJio2N1ZYtWzRmzBi7m1XDmr+/v/7617/atI8fP97hPt59911duHDBqu2+++5TeHh4QcvDTerXr68JEybYtM+bN09PP/20srOzCzzGkSNH1LFjR5twwd2+/vprValSRY8++qg2btwokynnBnY3GAwG3X777ZoxY4bOnj2rJUuW6O677/aZADY7O1ujR4+22Wzez89Pc+bM0fDhwz1UWeHL+fM3OTlZSUl573uWG3uh5FNPPZXr1409v/zyi02wHBISotGjR+d5nr3fI67sIRMWFqYyZcpYta1Zs8apPmbOnGnzWgLFx+eff66dO3datZUqVUq9e/e2anM2rDEjtAEAwL0IbAD4FFfCGjNCG9/1/vvv67333rNp79atm1cvVVOYMjIyNHjwYK1du9aq3RzWdOvWzUOVOcdTy26kp6fneYGvQoUKevLJJ7V9+3YdPnxYEydO1C233OKWsUuSp59+2uYu8u+++04ff/xxvufu2LFDb775plVbQECAXnnlFadqMBgMNh++tpRNUXjttdfshrzTpk3TsGHD7O5/4Ij09HRNmTJFLVq0cGnmgrMuXryojIyMXD9fr149TZ48WbGxsdq8ebMee+wxnwtgTSaTHn30Uf33v/+1avfz89Ps2bP1wAMPFHoNx48ft/u9VRSaNm1q07Z69WqX+ho1apTN8mHbt2/XU0895dD58fHxGjp0qE2o+fDDD+cbLNesWVNhYWFWba4+j44dO1o93rBhg8N9fffdd3r66addGheFa+fOnfrHP/5hs4+bMxYtWmT36/mhhx5S2bJ/vldyNawxI7QBAMB9CGwA+AxTVpbiPp/vUlhj5mhoE/f5fJlcXBMd7nH27Fl9+eWXuv322/Xcc8/ZfD4wMFDTpk3zQGWeZzQaNWzYMH377bdW7cHBwVq+fLnuvPNOD1XmHG9bdiM4OFj33HOPVqxYoTNnzuijjz7SbbfdVihjlRQVK1bU5MmTbdrHjh2rv/3tbzIajXbPW7lypbp27arr169btT/77LOqX79+odRa0gUGBmrZsmVq0qSJzeeWLFmiOnXqaOLEiTp16pRD/cXHx+tvf/uboqOj9eqrrxbogmNBVahQQU899ZS2b9+uQ4cO6fXXX1d0dLTH6imop556Sl988YVVm5+fn7744guNHDnSQ1UVnXbt2snPz/pt7AsvvKAVK1YoMzPTqb5CQkL0ySef2LRPnz5d99xzj86dO5fruatXr1bHjh118uRJq/batWvb/bmXk8FgsFmSau3atRo/frzTAam95e/uvffePPfkSU9P1+TJkzVw4EDLLNPQUMdmvKJoXL58WePGjVN0dLReeukl/fbbbw6fu2/fPt1///0aNmyYzfdF5cqVNWXKFMtjR16PBZXyV63oiDyXsSW0AQDAPQwmZ+Z7A3Da/v37rS5+7Nu3T40bN3Zb/0ajUUeOHLFqi4mJsbuRaXGQvPsPnZi3XLLzoyu/sOZmec7UMRgUdf8ghTdv5IaKS67jx4/bXBALCQnJd0ZMamqqLl26pEuXLuV6TEBAgObNm6d77rnHoVpq165ts8m0L//6mzt3rkaMGGHTHh4erlq1ahWo7zZt2mjmzJkOH9+iRYs8P797926rx4GBgWrUqJFMJikzM0umbNv/hxXL1uXZpzv2sjF/fRoMBnXq1EkjR47UPffcY3O3c3EwY8YMzZgxI9fPJyQk2FyUrFmzZp6bVo8ZM0ZjxoxxaHyTyaS+ffvaBIzSjT0kHnjgAcXExKhUqVKKi4vT8uXL9fvvv9sc27p1a23ZskWlSpVyaFwze3f9x8XFqXbt2k7146zZs2fbLInkyLgbNmxQ165drdoefPBBmyWXCsulS5fUv39/bd26NddjGjVqpK5du6pGjRqqWLGiwsPDlZ6ernPnzunQoUPaunWr9u/fb/fnbOfOnQt16SXzv3twcLAGDBigESNGqFevXgoMDCy0MYvSli1bbGZTSDf2LYuJiSlQ39WqVXN4Voa93/FS0f1u7du3r91ag4KCVLNmTYWEhNh878+cOVNt2rSx29/DDz+s//znPzbtpUuXVu/evdWpUydVqVJF6enpiouL08qVK+3uxxUYGKj169fr9ttvd+h5LFq0KNe9hqpWrary5cvbvKYfMGCATSCUmZmphg0bKjY21qafVq1aqX///qpbt64CAwN1/vx57dixQ19//bUSExMtxzVq1Ej9+/fX1KlTrc535P+0S5cu2rhxo+VxYX+flxT2fh9UrlxZrVq1UosWLVS1alWFh4erTJkyunr1qpKSkrRv3z79/PPP2rdvn90+w8LC9N1336ldu3aSHA9rzK+7nD0eJU9Juz4BoHgq7Gu5+eEnJgCfYg5RcoY2zoQ10p8zbWxCG8KaQpWammpzAd9ZVapU0eeff65+/fq5qSrfk9sdxMnJyUpOTi5Q387uDeLs/2dmZmaBvwbMd3AW5GJASEiI/v73v2vEiBGKiooqUD3eLiEhwel/85MnT9rcNZ6zT0cZDAYtXLhQvXr10pYtW6w+d+rUKZuLg/Y0bNhQq1evdjqsgfPKly+vDRs26NVXX9W7776rLDuzTf/44w/98ccfTvddr149vfTSS+4oM1d16tTRzJkzdc899xTL2QK5/fxPS0sr8M/Wgv7+KEr//Oc/tXHjRqWmplq1Z2Rk2A0tJOnq1dyXu/3ss8/k7++vzz//3Kr92rVrWrp0qZYuXZpvTaGhoVq6dKnDYY0kDRkyRHfeead+/PFHm8+dPXtWZ8+etWm3d6NEYGCgFi1apI4dO9rMZvv999/thuA3q169ur755psiC4bhunPnzunbb7+1exNEfmrUqKGlS5eqbdu2kpwPa6Qbew/WiArP8zx3vE4DAKAkY0k0AD4nvHkjRd0/SPrfnZPOhjVmNsujEdZ4tQoVKujFF1/UwYMHS3RYUxIUxbIbFStW1IQJE4p9WOMtypYtq7Vr1+qxxx5zep+LIUOGaNu2bapUqZLT4164cMGmrWLFiqpcubLTfZUkgYGB+sc//qGdO3eqX79+Bd6bJCYmRh9//LH279+vvn37uqlK+zp16qSHH364WIY13sTe91ZR3nXYqFEj/fDDD27bx87f31+fffaZPvjgA5f2NGrXrp02b97s9JKkfn5+Wrx4se6//36nx8ypZcuW+v7771W1alWnzmvXrp22b99e6LMO4bzg4GD5+xc88AgMDNSzzz6rAwcOFCisMTOHNiyPBgBA4SCwAeCTzKFNcJVKLoU1ZubQJrhKJcIaLxEUFKQKFSqoTp06uuuuuzR+/HitWrVKZ86c0T//+c9iuWQVrNWIClep4EAuBhQzwcHBmjFjhrZv364hQ4bkOVsmICBAd911l9asWaPFixe7/H2/fv16m7aXX35ZpUuXdqm/kqZp06ZatWqVDh06pDfeeEMtW7Z06OKhn5+fGjVqpCeeeEJbt27V4cOH9cQTT7AcSjFi73vrzTffLNIa2rdvr4MHD2r16tV64okn1LFjR1WrVk1ly5a12ePGUU8//bRiY2P12muvqUGDBnkeGxwcrB49emjJkiXatm2bmjZt6tKY4eHhmjt3rg4ePKg333xT/fr1U506dRQREeH0Un4dO3bU7t279fLLL+c7Y7ZNmzaaM2eOtmzZoho1arhUOwpXu3btdP78ec2fP18PP/ywGjdu7PDXdunSpXX77bfrww8/1NmzZ/Xee++pbNkb75dMJpNOn7hcoGXNHA1tTp+47NPLEAMA4AnsYQMUMvawKVymrCwZ3HDnmbv6AWCfq3dyslZ68ZWWlqaff/5Zhw4dsuxZFRYWprp16+q2225zenk+e8aMGaNPP/3U8rhKlSqKjY1VmTJlCtx3SZWamqq9e/cqLi5O586dsyy/FBERoYiICFWrVk0tW7ZUuXLlPFwpClOvXr30/fffWx43b95cO3fuLPBsLG8THx+vXbt26cKFC7p48aKCgoJUqVIlVatWTe3atfPqnyVZWVn67bfftH//fl28eFFGo1HlypVTdHS02rRpoypVqni6RLggNTVVR44cUVxcnBISEnT16lWlp6crJCREoaGhCg8PV4MGDdSwYcM8A/YrKelKOJNib1tQp15X5fU6zWCQqlQLVbnQYKeeI3xbSb8+AaB48PQeNgQ2QCEjsAFQ0hU0dCG0gavq16+vw4cPWx6///77euaZZzxYEeD7MjMzFRERYbV/zPLlyzVw4EAPVgXAWfZCG1deT9l7nUZYU3JxfQJAceDpwIYl0QAAQKFh2Q14yunTp63CmurVq+uxxx7zYEVA8fDLL79YhTVt2rQhrAF8ULnQYFWpFmreFtTlm19yvk4jrAEAoGAIbAAAQKExGAwqX6GMclslx9GLA/mFNgaD/jdO8VqOB67LucfGhAkTFBzMxSPkzl2Bb3EPjnN+b02aNMlDlQAoKHNoU6pUQIFmKptfp5UqFUBYAwBAARHYAACAQpXzDk4zZ+/kzC204U5O2LNu3TrL36OiovTXv/7Vg9XA211JSdeJuCQZjbnPBnSE0ZilE3FJupKS7qbKvM/N31vt2rVTnz59PFgNgIIqFxqsqOiIAi8rGxDgr6joCF6PAQBQQAQ2AACg0LHsBorazbMAXnvtNQUFBXmwGngz8z4O168bdepEssuhjXkfh+vXjUo4k1IsQ5vr169r27ZtlseTJ0/2YDUA3MVdM5SZ6QwAQMGx6xcAACgS5lDl0sU0VY8KK/CyG6dPXFb5CmUIa2BXXFycp0uAD8i56XbG9RuhS0E33TaZpIQzKZJUrH5GlSpVSteuXfN0GQAAAECxRWADAACKTLnQYJUtV6rAd2Cal93gTk4ArsoZ1pg5G9rkDGvMimtoAwAAAKDwENgAAFAAM2bM0IwZMwp9nDFjxmjMmDGFPk5RYNkNAJ5mMpl06WKaTVhj5mhok1tY8+c4N2YVuiOoBgAAAFD8EdgAAFAACQkJ2r17d5GMAwBwD4PBoOpRYXmGLfmFNvmFNdKN/bqqR4UVKKwxmUxuCXvc1Q8AAACAwuPn6QIAAAAAoKiZ98MKKpX7DBpzaGM0WocyjoY1zu6Fk9OVlHSdiEuyGd9ZRmOWTsQl6UpKeoH6AQAAAFC4CGwAAAAAlEiuhDZFGdYknEnR9etGu6GRo8z1Xr9uVMKZFEIbAAAAwIsR2AAAUABvvvmmTCZToX+8+eabnn6qAFAsORPaXE/PLNKwxrzHTm4zffKTM1wymURoAwAAAHgxAhsAAAAAJZqjoU18XFKRhzU3j+9MaJPbTCBCGwAAAMB7EdgAAAAAKPEcCW3y4o6wxmQy6dLFNJuwxszR0Ca/ZdtMJv1vnFwGAgAAAOARBDYAAAAAINdDG3eENZJkMBhUPSrMqT11cnJ0j53qUWEyGAwFqhcAAACAexHYAAAAAMD/OBvauCuscWb83EIbR8Mad9YLAAAAwH0IbAAAAADgJgEB/qpaLdShY6tWC3V7+OFKaENYAwAAAPg+AhsAAAAAuInRmKWzZ1IcOvbsmZR895RxhTOhzfX0TMIaAAAAoBggsAEAAACA/3FkpsrN8ttTpiAcDW3i45IIawAAAIBigMAGAAAAAOR8WGPm6dAmL4Q1AAAAgO8gsAF8nMFgsGkzmUweqAQAAMB3uRrWmHljaENYAwAoStnZ2TZt9q5ZAAByR2AD+Dg/P9tv46ws918oAAAAKK4cCWuCSvmrVnSEQ3vKeENoQ1gDAChq9gIbe9csAAC546cm4OMMBoP8/a3fiF+7ds1D1QAAAPgWR8OaGlHhKhUc6NCeMoUZ2lStFurQsVWrhRLWAACKVM5rEf7+/sywAQAnFevAxmg06tNPP1XPnj1VpUoVlS5dWlFRUerfv78WLlzo6fIAtwkJCbF6nJKS4qFKAAAAfIczYY05/HBkpkthhTZGY5bOnnHsdd7ZMymFEhoBAJCbnNcicl6rAADkL8DTBeTnypUrevvtty2PDQaDXn/9dQUHB+d53smTJ9W3b1/t379f0p97epw6dUqnT5/W6tWr9cknn2j+/PmqUqVK4T0BoAiUK1fO6oVRWlqajEajAgK8/lscAADAI1wJa8zMoU1e55tDG3ctS+bsHjvuHh8AgLwYjUalpaVZtZUrV85D1QCA7/L6q7mrVq3SO++8Y5lCeccdd+Qb1qSlpalHjx46fPiwpe3mKZjm8Oann35Sr169tGnTJn6JwKeVLVtWBoPB8rUtSWfOnFGNGjVYLxYAACAHk8mk0ycuuxTWmDka2pw+cVlR0REFWhLG2bDm5vEJbQAAhS07O1tnzpyxavPz81PZsmU9VBEA+C6vv5K7YsUKSX+GLI899li+57z55ps6fPiwDAaD5cNkMlk+bm7bu3evnn322cJ8CkCh8/PzswkdU1NTderUKRmNRg9VBQAA4J0MBoPKVyij3DKU/MIas/yWRzMY9L9xij6sMSvMPXUAADAajTp16pRSU1Ot2suWLcsNpADgAq+fYbNlyxZLuOLv76/evXvneXxiYqKmT59uM6OmRYsWatCggc6ePatNmzZZghuTyaQ5c+bomWeeUbNmzQr76QCFJjIyUlevXlV2dralLTU1VUeOHFGZMmUUGhqq0qVLy8/PjxdNAACgxCtdJkAVK4fofMIV3TRJWYFBfqpSrawkk8M3vlSpVlZnTl1WZsafr8MMBqli5XIqXSbA5RtojMYsm35zCgzyU5Wq5ZRw9kqux11Pz9aJ44mqViOMmTYAgALJzs5Wdna2rl27ppSUFJtl0KQbN5VGRkZ6oDoA8H1eHdicOXNGZ86cscyIadOmjcLCwvI8Z8GCBUpLS7OEMQEBAZozZ47uu+8+yzG///67+vTpowsXLki6EejMnDlTH374YaE+H6AwBQcHKyoqSidOnLAKbaQbywTaexEFAABQ0mVlZSsz88bsE4PBoCz5Ky4u0el+TCaTMjKyLCsDBAb662xCkpTgWl05+7PHXG/8icT8j78upRw8p6Ag/wLN+AEAIC9+fn6KiorKdzsDAIB9Xn2bfWxsrNVjR2bAfPXVV5JkmUHz6KOPWoU1ktSqVSvNnDnTapbNokWL3Fc44CGlS5dWVFQUM2gAAAAc5O/vp8BAf/kZDAUKMwz/O9/PYFBgoL/8/V1/PeZoWHNzvTkfu9ovAACuMoc1pUuX9nQpAOCzvHqGTXx8vKQ/w5cGDRrkeXxqaqq2b99utfl6bvvT9OvXTw0aNNChQ4ckSefPn9eJEycUFRXlvicAeEDp0qVVq1YtJSYm2iyRBgAAAFv+/n7y8zMUeOaJwWBQYAFnsBiN2Tp9IlkZGbkvoxYUFKDqUeE24xgMBvn7+zl8fkAAN/kAAArOz89PZcuWVWRkJDNrAKCAvPoVelJSktXjiIiIPI/funWr1frQTZs2Vd26dXM9vkePHlZ3l+3Zs8fFSgHvEhwcrOrVqysmJkbVq1dXaGio/P1ZrxwAACA37lomrCD9mEwmnT5ZsLAlIMBP1aPCFRSU+715GRlGnT6ZzEwbAIDL/P39FRoaanXtgbAGAArOq2fY5Nxzo1y5cnkev337dkl/zsjp06dPnsc3bNjQ6vHZs2ddqBLwXn5+fgoNDVVoaKikG98b2dnZvDkHAADwUhfOHtNn766TvUnSNWqF682pfRVePv+lZqpWvqY3x32jU/HJNp/z85OeGd9N9erd4oaKAQAlicFgkJ+fH/uhAUAh8erAJisry+pxZmZmnsf//PPPVo87deqU5/Hly5eX9OddcCkpKc6WCPiUG8tkMNMGAADAW3W+s55kMmjqG2usQpuo6Ai99a8Biogs41A/FSqV01v/GqBxY5frRNyfKxf4+UnjJvVU5ztj3F06AAAAgALy6iXRcs6oSU5OzvVYk8mkrVu3Wm262aFDhzz7z3nh+vr1664VCgAAAABu0rl7jMZN6im//71bi4qO0NRpgxwOa8wiIsto6rRBioq+sbS0JazpTlgDAAAAeCOvnmFj3rPGHMIcPnw412N//fVXJScnW46tX7++wsLC8uzfHACZl1ArU8a5N0AAAAAAUBjMocqC2Ts05QPHZ9bkZA5tJjyzUsNHtSasAQAAALyYVwc2N+8xYzKZtGHDhlyPXbp0qeXvBoNBHTt2zLf/xMREq8fmfT4AAAAAwNM6d4/R7V3qKCCgYAsjRESW0bTZwwrcDwAAAIDC5dWv2Bs3bqzAwEDL4127dmn79u02x127dk2zZ8+WwWCwbKbetWvXfPv/448/rB7XqlWrgBUDAAAAgPu4K2QhrAEAAAC8n1e/ag8ODtZdd91lWbLMZDJp5MiRiouLsxyTnZ2tJ598UufPn7c6r2/fvvn2v3PnTssSapJUp04d9z4BAAAAAAAAAAAAB3j1kmiSNHr0aH399deSbix1Fhsbq4YNG6pLly6KjIzUzz//rLi4OEugYzAYNGTIEJUrVy7Pfi9cuKD9+/dbHoeGhqp27dqF+VQAAAAAAAAAAADs8vrA5u6771anTp20adMmy2yYjIwM/fDDD5JkWQLN/LmAgAC9/vrr+fa7YsUKZWdny2AwyGAw6LbbbiukZwAAAAAAAAAAAJA3r14SzezLL79UjRo1LDNozOFMzseS9PbbbysmJibfPhcuXGjpQ5LuuOOOQqgcAAAAAAAAAAAgfz4R2NSsWVObNm1Sly5dZDKZLB+SLH8PDg7We++9p+effz7f/g4cOKC1a9daBT19+vQptPoBAAAAAAAAAADy4vVLopnVqlVL69at05YtW7RixQodO3ZMV65cUWRkpG677TYNHz5clStXdqivL774QmFhYZbHVapUUYsWLQqpcgAAAAAoXozGbAUEFPz+P3f1AwAAABQHBpN5qgqAQrF//341adLE8njfvn1q3LixBysCAAAAXLdx7REtmL1DUz4YoIjIMi73k5SYpgnPrNTwUa3VuXv+y1oDAAAAhc3T13K5lQkAAAAA4JCNa49o6htrFBebqHFjlyspMc2lfpIS0zRu7HLFxSZq6htrtHHtETdXCgAAAPgen1kSrSQ7efKk9u/fr1OnTik5OVkZGRmKiIhQRESEGjRooKZNm8rf379Qazh16pR27NihuLg4Xb16VaVKlVLlypXVpEkTtWjRQn5+hZv9HT16VLt27dLJkyeVmpqq0qVLq1q1amrWrBmzVQAAAIAiYA5rsrNvPD4Rl6RxY5dr6rRBTs20MYc1J+KSJEnZ2dLUN9ZIEjNtAAAAUKIR2HihgwcP6vvvv9e6dev0008/KTk5Oc/jQ0JC1KVLF40ZM0Z9+vRxW3iSlZWlWbNm6eOPP9auXbtyPS4yMlJ/+ctf9MILL6h69epuGVuSrl27pk8++UQzZszQkSO533FXo0YNPfzww3r22WcVHh7utvEBAAAA3JAzrDFzNrTJGdaYEdoAAAAA7GHjNa5du6Z//etfWrhwofbt2+dyP40bN9bs2bPVpk2bAtVz4MAB3Xvvvdq7d6/D55QtW1bvvvuuHnnkkQKNLUlbtmzRAw88oPj4eIfPqVixombOnKkBAwYUeHx38vS6hwAAAEBBGI3ZGjtqoeJiE3M9Jio6It/QJrew5mbRdSI1bfYwBQSwejcAAACKnqev5fIq2EucO3dOEydOLFBYI934gmrfvr0++ugjl/vYunWrbrvtNqfCGkm6evWqHn30UY0bN87lsSVp6dKl6tq1q1NhjSRduHBBgwYN0vTp0ws0PgAAAIA/BQT4acoHAxQVHZHrMeaZNrntaeNIWBMVHaEpHwwgrAEAAECJ5fNLov30008unxsZGekTMx3q1q2rzp07KyYmRpUqVVJISIguXbqkXbt2afXq1Tp58qTV8UajUWPHjlVQUJAeffRRp8Y6evSo+vTpoytXrth8rmvXrurRo4dq1aqly5cv648//tDcuXOVlGT9pusf//iHqlatqmeffdbp57p161YNHz5cmZmZVu1+fn7q16+fOnbsqJo1a+rChQvavXu35s+fr7S0P98UmkwmPfXUU6pataruvvtup8cHAAAAYCsisoymThuUZ+iS2/JojoY1zu6FAwAAABQ3XrEk2oULF/Trr79atVWoUEG33nprvuf6+fnJYDC4NG7ZsmV16NAhValSxaXz3en48eOKjo62PG7UqJFGjRql+++/P899YYxGo2bNmqXnn39eV69etfpcUFCQ9u7dq3r16jlUQ3Z2ttq1a2fzf1GlShUtWbJEHTp0sDknNTVVL7zwgj799FOr9sDAQP32229q1qyZQ2Ob+2rUqJFOnDhh1V6/fn0tW7ZMDRs2tDnn0qVLGj16tFauXGnVHhYWpoMHD3rF/62np9EBAAAA7uJs+EJYAwAAAF/i6Wu5XjHX/MUXX1T//v0tHwMHDlRqaqpTfZhMJqc/rl69qtdff72QnpVrunbtqo0bN2r//v166aWX8gxrJCkgIECPPPKItmzZovDwcKvPZWRk6IUXXnB47C+++MImrClfvry2bdtmN6yRpJCQEM2YMUPPPfecVXtmZqbGjh3r8NiSNGXKFJuwJiYmRlu3brUb1pjrW7p0qYYMGWLVfvny5QIvzQYAAADAmnmmjSPLo8UdvUhYAwAAADjB4zNsjhw5okaNGik7O1smk0kGg0EvvfSS3nnnHYfOL8gMG5PJJH9/f+3atcvjMx6SkpK0Z88ede7c2eU+lixZoqFDh1q1+fv7KyEhQRUqVMjz3KysLMXExCguLs6qfe7cubr//vvzHTsjI0OtWrXS/v37rdo3btyoO+64I9/zk5OTVatWLaWkpFja/Pz8tHHjRnXs2DHf8xMTE9W4cWOdO3fO0ubv76/Dhw/rlltuyff8wuTpVBYAAABwN0dmzuSHsAYAAADextPXcj0+w+add95RVlaW5XF0dLQmTZrkdD/Ozq4xy87O1ieffOKW51IQERERBQprJGnIkCE2S5BlZWXp22+/zffc7777ziasadq0qUNhjXRj+TV7/2+O/tvOnTvXKqyRpL59+zoU1kg39iN66aWXrNqysrL0+eefO3Q+AAAAAMc5MtMmL4Q1AAAAgK0ATw6ekZGhJUuWyGAwWGbXTJ48WaVKlXKpv/Lly6t///75HpeWlqZFixZZxv3qq6/03nvvKTAw0KVxvUnv3r21Z88eq7Zjx47le96CBQts2saMGePU2AMHDlSVKlWUkJBgaVuxYoXS0tJUpkzeb8TcMf6oUaP06quv6vr165a2+fPn6+2333aqHwAAAAD5M4c2zs60IawBAAAA7PNoYLNmzRqlpKRYljSrXbu2wzM67ImKitKsWbMcOvbkyZPavn27pBsb169atUqDBw92eWxvERUVZdN2c4Bij8lk0po1a2zac+4Lk5+AgAANHDhQn376qaXt2rVr2rhxo3r37p3reZcvX9a2bdus2sqWLau77rrLqfEjIyPVpUsXff/995a2+Ph4HThwINc9cAAAAAC4ztnQhrAGAAAAyJ1Hl0RbtmyZJFlm1zz22GNFNvbzzz8vSZaw6JtvvimysQtTWlqaTVvp0qXzPOfQoUM6f/68VVtMTIwqV67s9PidOnWyadu0aVOe52zbts1qWTxJat++vfz9/YtkfAAAAACui4gso/GTezp07PjJPQlrAAAAgFx4NLDZvn27JTCRpKFDhxbZ2H369FFwcLCkG4HRr7/+WmRjF6ajR4/atFWtWjXPc3bs2GHT1r59e5fG79Chg0P9e9P4AAAAAFyXlJimtyfazti35+2Ja5SUaHuTGQAAAAAPBjbp6ek6fPiw5XGdOnV0yy23FNn4ZcqUUfv27WUymSRJBw8eVHp6epGNXxiMRqNWrFhh0962bds8zzt48KBNW926dV2qISoqSgEB1ivtHTp0qMjGr1Onjk1bfuMDAAAAcE1SYppTe9iciEvSuLHLCW0AAAAAOzwW2Ozdu9eyDJbBYFC7du2KvIZGjRpZ/p6VlaXdu3cXeQ3utHLlSpv9asqXL6+OHTvmed7x48dt2mrVquVSDf7+/qpevbpV26lTp5SZmVkk41evXt1mKbVjx4651BcAAACA3Dkb1pgR2gAAAAD2BeR/SOE4efKk1eP69esXqL+bl1ZzVM5ZHDnDDl+Snp6u8ePH27SPHj3aZsZLTvaed82aNV2upWbNmoqPj7c8zsrK0sWLF3Ndms2d4/v7+6tq1ao6deqUpe3cuXMu9WXP+fPndeHCBafOsbdMHQAAAODLXA1rzMyhzdRpg9jTBgAAAPgfjwU2ly9flnRj/xiDwWAzK8NZ5qXNnBEWFmb1ODk5uUA1eNL48eOtlpiTpIiICI0bNy7fcy9dumTTVrZsWZdrsXduYmJiroFNYY+fkZGhq1evFqhPs+nTp2vSpEkF7gcAAADwVY6ENVHRERo/uafenrgm1+MIbQAAAABrHlsSzRzYmJUrV86lfgYPHqzBgwdryJAh6t69u1Pnmi/gm2fn+Gpgs2TJEr3//vs27f/6179UsWLFfM9PTU21aStdurTL9dg7Ny0t9+UOPD0+AAAAAMc4GtZMnTZI0XUraOq0QYqKjsj1WJZHAwAAAP7ksRk2OS/S57XHSV4WL17scg1Go9Hq8dWrV13uy1N+//13PfjggzbtQ4YM0UMPPeRQH/b+7YODg12uyV5gkpGR4bXjAwAAAMifM2GNecZMRGQZTZ02KM/zmGkDAAAA3OCxwKZUqVJWjxMTE4u8BvOY5mXZctbk7Y4dO6a+ffvahF8NGjTQrFmzCtS3K3sC5XWus0vWeXr83DzxxBO65557nDrn6NGjGjRokFvGBwAAADzBlbDGjNAGAAAAcIzHAhvzEmjmi+ueDGzM3LHHSVE5ffq0unfvroSEBKv2GjVq6LvvvnNqibnAwECbtmvXrrlcm71zg4KC8hz/+vXrNn24+v/h7PjOqFSpkipVquSWvgAAAABfYDRma8IzK10Ka8wcDW0mPLNS02YPU0CAx1bvBgAAADzGY6+Cq1evbvX4yJEjRV5DzjFz1uStzp8/r+7duysuLs6qvXLlyvrxxx9Vq1Ytp/orU8b2TZW7A5uQkBCvHR8AAABA7gIC/DR8VGv55fLuMb+wxswc2uS2p42fnzR8VGvCGgAAAJRYHnslHB0dbfm7yWTShg0biryG9evXWy2fdcsttxR5Dc5KTExU9+7ddfDgQav2ChUq6Mcff1S9evWc7jMyMtKmrSD7+dg7194YRTV+UFCQT82eAgAAALxN5+4xGjepp01o42hYY5ZbaOPnJ42b1FOdu8e4q2QAAADA53gssGnYsKHVzIozZ87o8OHDRTb+4cOHdebMGcvj4OBgNWzYsMjGd0VSUpJ69OihvXv3WrWXL19ea9euVePGjV3qt3LlyjZtp06dcqkvSTp58qTVYz8/P1WoUKFIxs/KytLZs2fz7R8AAACAc3KGNs6GNWY5QxvCGgAAAOAGjwU2fn5+uu2226w2g58+fXqRjf/xxx9b/m4wGHTrrbfKL7c5/l7g8uXL6tGjh3bu3GnVHh4erjVr1qh58+Yu933zbCez+Ph4l/rKzs7W6dOnrdpq1Khhd5+cwhj/zJkzMhqN+fYPAAAAwHnm0Ca6TqRLYY2ZObSJrhNJWAMAAAD8j0cTigEDBki6EZiYTCZ9/vnnSkhIKPRxz549q88//9wy7s21eKOUlBT17NlTO3bssGoPDQ3V999/r9atWxeo//r169u0HT161KW+Tpw4oczMTKu2Bg0aFNn4sbGxNm35jQ8AAADAcZ27x2ja7GEuhzVmEZFlNG32MMIaAAAA4H88Gtjce++9VjMv0tPT9fjjjxfqmCaTSWPGjFF6erqlLSAgQPfdd1+hjuuqK1eu6K677tIvv/xi1V6uXDl9//33uvXWWws8hr3AZ9u2bS71tXXrVpu2Vq1aefX4AAAAAJwTEOCet5Lu6gcAAAAoDjz66rhKlSq65557ZDKZLLNdVq5cqRdffLHQxnzxxRe1atUqy3gGg0FDhgxRlSpVCm1MV129elW9evXS9u3brdrLli2rb7/9Vu3atXPLOA0aNFDFihWt2g4fPqzz58873dfmzZtt2u644448z+nQoYP8/f2t2rZt26asrKwiGR8AAAAAAAAAAE/z+O1Mb731loKCgiT9uTTae++9p4cfflipqaluGyc1NVUPPfSQ3n//fRkMBkt7YGCg3nrrLbeN4y6pqanq06ePzYyRkJAQrV69WrfffrvbxjIYDOrZs6dN+9KlS53qx2g0asWKFVZtwcHB6ty5c57nhYWF2YRPV65c0Q8//ODU+JcuXdL69eut2qKiotSwYUOn+gEAAAAAAAAAoKh5PLCJjo7WhAkTLHvJmEOb2bNnq0WLFlq6dKmys7Nd7j87O1tLly5Vy5YtNWfOHMs45tk148ePV506ddzyXNwlLS1N/fr106ZNm6zay5Qpo2+++UadOnVy+5jDhw+3aZsxY4ZTfaxatUpnzpyxahs4cKDKlMl/bWt3jD9nzhyrpe4kee1SdwAAAAAAAAAA3MxgMicYHpSdna3u3btrw4YNltkvNwc4VatW1ejRo9WlSxe1bdtWoaGhefaXkpKiX3/9VRs3btSsWbN05swZq/7MOnXqpHXr1snPz+O5lUV6err69++vtWvXWrWXLl1aX3/9tbp161Yo4xqNRsXExOj48eNW7QsWLNC9996b7/kZGRlq06aN9u7da9W+YcOGfGfYSFJycrKioqJ05coVS5ufn582bdqkDh065Ht+UlKSGjdurLNnz1ra/P39dejQIY8Hcvv371eTJk0sj/ft26fGjRt7sCIAAAAAAAAAQE6evpYbUGQj5cHPz0/Lly/XHXfcoT179shgMFgFN2fOnNGUKVM0ZcoUGQwG3XLLLapUqZLCwsIUHh4uSbp8+bKSk5N14cIFxcbGWs2kkayDGpPJpGbNmmnFihVeFdZkZGRo8ODBdsOaVatWFVpYI0kBAQF65ZVXNGbMGKv2p556Su3atVOtWrXyPH/ChAk2YU3Hjh0dCmskKTw8XE8++aTeeecdS1t2drZGjx6t7du3KyIiItdzs7Oz9dhjj1mFNdKN2TWeDmsAAAAAAAAAAHCEV8ywMbt8+bIGDRqkjRs3WgUs0p/Bi1nOzzt6nMlkUqdOnbRixQpL2OMNjEajhg4dancPmBUrVtjdY8bdsrKydOutt+r333+3aq9ataqWLFmi9u3b25yTlpamF154wWb5soCAAP36669q0aKFw+NfvXpVDRs21KlTp6zaGzRooGXLlqlBgwY25yQlJWn06NE2/26hoaE6cOCAqlWr5vD4hcXTqSwAAAAAAAAAIH+evpbrVYGNdCM0ePPNN/XOO+8oKyvL4WDGLK/j/f39NW7cOL355psKCPCKyUUWc+fO1YgRI2zaw8PD853dkp82bdpo5syZDh17+PBhtW3bVikpKTaf69atm3r27KmoqChdvnxZBw4c0JdffqlLly7ZHPuvf/1LL7zwgtO1btq0Sd26dZPRaLRq9/PzU//+/dWpUydVr15dFy9e1O7duzV//nylpqba9LNo0SINHTrU6fELg6e/yQEAAAAAAAAA+fP0tVzvSi10Y9+Rt956Sw888IAmTJiglStXKjs7W5J1GJNbMHMzc6jj5+enAQMGaMqUKWrYsGHhFF5AmZmZdtuTk5OVnJxcoL6dmUlUr149ff311+rTp4+uXr1q9bl169Zp3bp1+fbxwgsvuBTWSDf2FZo3b57uv/9+q9AmOztbK1assJlJk5PBYND777/vNWENAAAAAAAAAACO8J4NXHJo0KCBli5dqtjYWE2aNEmtW7eWdCOEceRDklq2bKk33nhDR48e1bJly7w2rPE2nTp10vbt262SREeEhIRoxowZ+te//lWg8e+55x6tW7dOUVFRTp1XoUIFLV26VE8//XSBxgcAAAAAAAAAoKh53QybnGrVqqXXX39dr7/+uq5cuaKdO3fq8OHDOnXqlJKSknTt2jWZTCaVLl1aERERqlmzpmJiYtSqVSuVK1fO0+X7rMaNG2vnzp2aNWuWPv74Y+3evTvXYyMjIzVixAi99NJLql69ulvG79Spkw4cOKDp06fr008/1dGjR3M9tnr16nrooYf03HPPKSIiwi3jAwAAAAAAAABQlLxuDxt4pxMnTmjHjh06fvy4UlNTFRgYqMqVK6tJkyZq1aqV/PwKd7LWkSNHtHPnTp08eVJpaWkKDg5WtWrV1KxZMzVt2rRQxy4oT697CAAAAAAAAADIn6ev5Xr9DBt4h6ioKKeXKHOnmJgYxcTEeGx8AAAAAAAAAAAKk9fuYQMAAAAAAAAAAFBSENgAAAAAAAAAAAB4GIENAAAAAAAAAACAh5XoPWy+/vprXbp0yfL4L3/5iwerAQAgd0ZjtgICCn6fhbv6AQAAAAAAgHt5NLApX7685e/NmjXThg0bHD73yJEjunbtmtX5znr99de1Z88ey2MCGwCAN9q49ogWzN6hKR8MUERkGZf7SUpM04RnVmr4qNbq3D3GjRUCAAAAAACgoDwa2CQnJ1v+npKS4tS5w4YNs4QtBoNBRqPRpRpMJpOlDwAAvM3GtUc09Y01ys6Wxo1drqnTBrkU2iQlpmnc2OU6EZekqW+skSRCGwAAAAAAAC/i8TVRChKUmEwmy4cnxgcAoDDdHNZI0om4JI0bu1xJiWlO9XNzWCNJ2dnS1DfWaOPaI+4uGQAAAAAAAC7yeGBTEIQtAIDiKmdYY+ZsaJMzrDEjtAEAAAAAAPAuPh3YAABQHBmN2Vowe4dNWGPmaGiTW1hjlp0tLZi9Q0ZjLgMBAAAAAACgyBDYAADgZQIC/DTlgwGKio7I9Zj8Qpv8whpJioqO0JQPBigggJcDAAAAAAAAnsYVGgAAvFBEZBlNnTbIpdDG0bBm6rRBiogs47aaAQAAAAAA4DoCGwAAvJQroQ1hDQAAAAAAgG8isAEAwIs5E9rEHb1IWAMAAAAAAOCjCGwAAPByjoY2j4/8irAGAAAAAADARxHYAADgAxwJbfJCWAMAAAAAAODdCGwAAPARroY2hDUAAAAA4B5GY7ZX9QOgeCGwAQDAhzgb2hDWAAAAAIB7bFx7RGNHLVRSYlqB+klKTNPYUQu1ce0RN1UGoLggsAEAwMdERJbR+Mk9HTp2/OSehDUAAAAAUEAb1x7R1DfWKC42UePGLnc5tElKTNO4scsVF5uoqW+sIbQBYIXABgAAH5OUmKa3J65x6Ni3J64p8N1fAAAAAFCSmcOa7P+tYnYiLsml0MYc1pyIS5IkZWeL0AaAFQIbAAB8SM4X+Plx9Y0EAAAAAMA2rDFz9r1Wbu/lCG0A3IzABgAAH+FsWGNGaAMAAAAAzjMas7Vg9g6bsMbM0fda+b2Xy86WFszeIaMxl4EAlBgENgAA+ABXwxozQhsAAAAAcE5AgJ+mfDBAUdERuR6T33stR97LRUVHaMoHAxQQwKVaoKQL8HQBZkePHlW3bt2cOv5mzpybWx8AAHgjR1/gj5/cU29PXJPrceY3ElOnDVJEZJnCKhcAAAAAio2IyDKaOm1Qnu/Jcnuv5eh7Od6jATAzmEwmk6cG9/Pzk8FgkMlkksFgcPr8m0t35fyb+zEYDMrKynK5DyA3+/fvV5MmTSyP9+3bp8aNG3uwIgC+xNkX+LwhAAAAAAD3470ZUDJ4+lqu18yzM5lMTn8U9HwPZlUAAOTLlRf45ru/CjJlHwAAAABgzZn3WnFHLxLWAHCJVwQ2BoPBYx8AAHijgtyNRWgDAAAAAO7n6Hutx0d+RVgDwCUeD2xcnRnjzg8AALyJ0ZitCc+sLNALfEffSEx4ZqWMxuwC1wwAAAAAJYEj77XyQlgDIC8Bnhw8Li7Ok8MDAOCVAgL8NHxUa019Y42y7WQpjr7Az29zTD8/afio1goI8Pj9GwAAAADgM/J7r5UbwhoA+fFoYFOrVi1PDg8AgNfq3D1GkmxCG2df4Of2RsLPTxo3qadlHAAAAACA45wNbQhrADiCW2oBAPBSnbvHaNyknvL7329rV1/g55yyT1gDAAAAAAUXEVlG4yf3dOjY8ZN7EtYAyBeBDQAAXswc2kTXiSzQ3Vjm0Ca6TiRhDQAAAAC4QVJimt6euMahY9+euEZJiWmFXBEAX+fRJdEAAED+OneP0e1d6hR4r5mIyDKaNnsYe9YAAAAAQAElJaY5tYfNibgkjRu7nGXRAOSJKzYAAPgAd4UshDUAAAAAUDDOhjVm5tCGmTYAcsNVGwAAAAAAAABwgKthjRmhDYC8ENgAAAAAAAAAQD4cCWuioiP0yf/dq6joiFyPIbQBkBuf2cNm+/bt2rp1q37//XcdO3ZMp06dUkpKiq5duyaDwaDSpUsrNDRUNWvW1C233KJWrVrp9ttvV9u2bT1dOgAAAAAAAAAf5mhYY96jZuq0QXkez542AOwxmEwmk6eLyE1sbKymTZum+fPn6+LFi1afy61sg8Fg9bhy5coaMWKEnnzySdWqVavQagVys3//fjVp0sTyeN++fWrcuLEHKwIAAAAAAICjnA1rCnoeAM/x9LVcr1wS7fLly3ryySfVsGFDTZs2TRcuXJDJZLL6MBgMdj9yHpeQkKB///vfqlevnp5//nlduXLF008PAAAAAAAAgA8oSOhinmnD8mgAHOV1gc3vv/+uli1basaMGTIajbmGM7nJLcTJzMzUBx98oNatW2vv3r1F+IwAAAAAAAAA+BqjMVsTnllZoBkyjoY2E55ZKaMxu8A1A/BtXhXYbNmyRV27dlV8fLxVUHOznDNocvu42c3BzdGjR9W5c2ft2LGjKJ8aAAAAAAAAAB8SEOCn4aNayy+XK6iOLmeWX2jj5ycNH9VaAQFedakWgAcEeLoAs/j4eA0YMEBXrlyxCWrMAUyFChXUrl07tW3bVpUqVVJ4eLgiIiKUnZ2t5ORkJSUl6dy5c/rll1/0888/KynpRvpt7sv8Z3Jysvr06aPff/9d1atXL+JnCgAAAAAAAMAXdO4eI0ma+sYaZd80AcbZvWfMoU3O5dX8/KRxk3paxgFQsnlNYHP//fcrKSnJJqgJCgrSAw88oGeeeUbNmjVzqs8dO3bo/fff18KFC5WZmWnV94ULFzRixAitX7/ebc8BAAAAAAAAQPGSM7RxNqwxyxnaENYAyMkr5tn997//1bZt22zCmu7du+v48eP64osvnA5rJKl169b6v//7P8XGxqpLly6WmTrmcX766SfNnz/fPU8CAAAAAAAAQLHUuXuMxk3qqeg6kS6FNWbm0Ca6TiRhDQAbBlPODV+KmMlkUv369RUbG2t5bDAY9OKLL+rtt9+WX26LRDopOztbL774ot5//31LYGMe+8CBA24ZA7Bn//79atKkieXxvn371LhxYw9WBAAAAAAAAFcYjdlu2WvGXf0AcC9PX8v1+E+Fb7/9VkePHpX0Z1gzbNgwTZ061W1hjST5+fnp3Xff1ZAhQ3RzRnX48GF9//33bhsHAAAAAAAAQPHkrpCFsAaAPR7/ybBgwQKrx7Vr19bnn39eaON98cUXql27tlUby6IBAAAAAAAAAABP8mhgYzKZtGrVKhkMBsvsmpdeeklly5YttDFDQ0P14osvWsYzmUz6+uuvC208AAAAAAAAAACA/Hg0sNm7d68uX75seRwWFqa//OUvhT7ugw8+qNDQUMvjpKQk7d27t9DHBQAAAAAAAAAAsMejgc327dstfzcYDLr77rtVpkyZQh83JCREgwcPttrL5uZaAAAAAAAAAAAAipJHA5uDBw9KkiU4ue2224ps7Pbt20u6ERRJ0h9//FFkYwMAAAAAAAAAANzMo4HN4cOHrR4XZWBz6623Wj0+cuRIkY0NAAAAAAAAAABwM48GNmfPnrXMcJGkJk2aFNnYN49lMpl05syZIhsbAAAAAAAAAADgZh4NbM6fP2/5e0hIiPz9/YtsbH9/f5UrV87y+MKFC0U2NgAAAAAAAAAAwM08GthcuXLF8vewsLAiH//mMVNSUop8fAAAAAAAAAAAAMnDgc3169ctfw8PDy/y8W8ObDIyMop8fAAAAAAAAAAAAMnDgc3NIUlgYGCRjx8QEGC3FgAAAAAAAAAAgKLk0cDGZDJ5cngAAAAAAAAAAACv4NHABgAAAAAAAAAAAAQ2AAAAAAAAAAAAHkdgAwAAAAAAAAAA4GEBni7A7OrVq/rpp5+KfEwAAAAAAAAAAABP85rAJjY2Vl27dvV0GQAAAAAAAAAAAEXOawIbk8nkkXENBoNHxgUAAAAAAAAAADDzisCG0AQAAAAAAAAAAJRkHg9sPDWzBgAAAAAAAAAAwFt4NLB58MEHPTk8AAAAAAAAAACAV/BoYDNr1ixPDg8AAAAAAIBiwmjMVkCAn9f0AwCAs/jtAwAAAAAAAJ+2ce0RjR21UEmJaQXqJykxTWNHLdTGtUfcVBkAAI4jsAEAAAAAAIDP2rj2iKa+sUZxsYkaN3a5y6FNUmKaxo1drrjYRE19Yw2hDQCgyBHYAAAAAAAAwCeZw5rs7BuPT8QluRTamMOaE3FJkqTsbBHaAACKHIENAAAAAAAAfE7OsMbM2dAmZ1hjRmgDAChqBDYAAAAAAADwKUZjthbM3mET1pg5GtrkFtaYZWdLC2bvkNGYy0AAALgRgQ0AAAAAAEAhctfFfkKDPwUE+GnKBwMUFR2R6zH5hTb5hTWSFBUdoSkfDFBAAJfQAACFj982AAAAAAAAhWTj2iMaO2qh03uq5JSUmKaxoxayPNdNIiLLaOq0QS6FNo6GNVOnDVJEZBm31QwAQF4IbAAAAAAAAAqBeY+VuNhEp/ZUyckcLsTFJrKnSg6uhDaENQAAb0VgAwAAAAAA4GbmsMa8x4qje6rklDNcyM4WoU0OzoQ2cUcvEtYAALwWgQ0AAAAAAIAb5QxrzJwNbXKbCUJoY8vR0ObxkV8R1gAAvBaBDQAAANgMGQAANzEas7Vg9g6bsMbM0dAmv2W7srOlBbN38Lv3Jo6ENnkhrAEAeBqBDQAAQAnHZsgAALhPQICfpnwwwKk9VXJydI+VKR8MUEAAl3Zu5mpoQ1gDAPAG/FYHAAAowdgMGQAA93NmT5Wcv3sdDWsIF3LnbGjDvycAwFsQ2AAAAJRQbIYMAEDhcSW0Iaxxn4jIMho/uadDx46f3JN/TwCAVyCwAQAAKIHYDBkAgMLnTGgTd/QiYY0bJSWm6e2Jaxw69u2Jawq8NCwAAO5AYAMAAFDCsBkyAABFx9HQ5vGRXxHWuIkjM5Vu5uosYwAA3I3ABgAAoIRhM2QAAIqWs3uq5ERY4zhnwxozQhsAgDfg3TMAAEAJxGbIAAAULVdDG36nOs7VsMaM0AYA4GkENgAAACUUmyEDAFC0nA1t+J3qOEdfo3zyf/cWaJYxAACFicAGAACgBGMzZAAAilZEZBmNn9zToWPHT+7J71QHOHNDSXTdCi7PMgYAoLAR2AAAAJRwbIYMAEDRSUpM09sT1zh07NsT1xAa5MOV2b8FWRoWAIDCRGADAAAANkMGAKAIOLvHCqFB3gqyVCuhDQDAG/lsYHPhwgX997//1cMPP6xWrVopKipKISEh8vf3V0BAgKfLAwAA8DlshgwAQOFxNqwxIzSwz2jM1oRnVhZo9q+joc2EZ1bKaMwucM0AAOTH5wKbU6dO6cknn1StWrU0evRozZ49W7t27dKpU6d07do1mUwmmUymPPt44IEHFBoaavkYP358EVUPAADg3dgMGQAA93M1rDEjtLEVEOCn4aNayy+XK1uOvkbJ77WPn580fFRrBQT43CU0AIAP8qnfNt98842aN2+uGTNmKD093RLOGAwGy4cjnnrqKV29etXyMXv2bGVnc6cEAACAxGbIAAC4k6PLdn3yf/eyPJeTOneP0bhJPW1CG2dvKMkttPHzk8ZN6qnO3WPcVTIAAHnymcBm5syZGjhwoJKSkqxCGkkOzaq5Wfv27dW+fXvL+efPn9eaNY5t+AcAAFDcsRkyAADu4cweK9F1K7Cnigtyhjauzv7NGdoQ1gAAPMEnApv169friSeeUHZ2tiWoMZlMKlu2rAYMGKBnnnlGlStXdqrP++67zxL8SNJ3331XGKUDAAD4FDZDBgDAPZwJa8zhgqN7qvC715o5tImuE1mgpVrN//7RdSIJawAAHmEwOTM1xQMyMzNVr149xcfHW4KawMBAvfHGG3r++ecVHBwsSWrZsqX27NljCWGysrLy7DchIUE1atSwzM5p1KiR9u3bVxRPCSXM/v371aRJE8vjffv2qXHjxh6sCAAA+wqyvj572QAA8CdXwhp3nl9SGY3Zbtlrxl39AAB8j6ev5Xr9b5+ZM2dahTVBQUH65ptvNGHCBEtY44oqVaqofv36lscHDhxQWhp3pwAAgJKJzZABAHAPozFbE55ZWaCwxdGZNhOeWSmjkT15zdwVshDWAAA8xet/A33xxReSZJk5M3nyZHXv3t0tfbdu3dpq75uDBw+6pV8AAABfwmbIAAC4T0CAn4aPam3ZUyUnR2fG5Bfa+PlJw0e1JlwAAKAY8erf6pcuXdLOnTst+8xUrFhRzzzzjNv6zzmV6ciRI27rGwAAwBewGTIAAO5n3lMlZ2jj7DJmuYU2fn5ijxUAAIohrw5stm/fbpkBYzAY1KdPH5UqVcpt/ZcvX97q8aVLl9zWNwAAgLdjM2QAAApPztDG1T1ncv7uJawBAKD48urA5ty5c5JkCW3atm3r1v7Dw8MlyTKD58qVK27tHwAAwFsVZDNjQhsAABxjDm2i60S6FNaYmX/3RteJJKwBAKAY8+rA5uLFi1aPK1So4Nb+MzMzrR7fvJ8NAABAccVmyAAAFJ3O3WM0bfYwl8Mas4jIMpo2exhhDQAAxZhXBzb+/v5Wj41Go1v7Ny+BZg5qIiMj3do/AACAN2IzZAAAipa7fhfyOxUAgKoEg1AAAOoESURBVOLNq3/TV6xY0eqxu/eY2b9/v9VjAhsAAFBSsBkyAAAAAADexasDm0qVKkn6c4+ZPXv2uLX/DRs2WPqWpOjoaLf2DwAA4M3YDBkAAAAAAO8R4OkC8tKmTRtLoGIymbR+/Xq39b1p0yYdOnTI0n9YWJiaN2/utv4BAAB8gTlUWTB7h6Z8MKDAmyFPeGalho9qTVgDAAAAAICTvHqGTWRkpFq3bm15HBsbqx9//LHA/ZpMJr3++uuWxwaDQZ06dbKabQMAAFBSsBkyAAAAAACe59WBjSQNGjRIJpNJBoNBJpNJzz33nIxGY4H6fPXVV/XTTz9Z+pSkkSNHuqNcAAAAn8RmyAAAAAAAeJbXv6MeO3asIiMjLY/379+ve++9V1lZWU73ZTQa9eSTT2rq1KmW2TQGg0H16tXT0KFD3VYzAAAAAAAAAACAM7w+sClXrpxee+01q1k2y5cvV9u2bbVx40aH+khNTdWsWbPUoEEDzZgxwzKrxvzn3//+90KrHwAAAAAAAAAAID8Bni7AEc8884y2bt2qRYsWWUKbXbt2qVu3boqOjlaHDh109uxZSwAjSdOmTVNcXJz27NmjLVu2KCMjw/J5cx8Gg0FPPfWUBg8e7KmnBgAAAAAAAAAA4BuBjST997//1dmzZ7V582bLcmYmk0nHjh1TXFyc1bEmk0nPPvus1WNJlvPM7rrrLr377ruFWzjc4ujRo9q1a5dOnjyp1NRUlS5dWtWqVVOzZs3UuHFjT5cHAAAAAAAAwA2Mxmy37I3prn6AouQzgU2pUqW0bt06vfDCC5o2bZoMBoNVcJPTzW03BzXm9rFjx+q9996Tn593f9NeunRJv/32m+Vjx44dOnHihM1x9v4NnNWlSxeHl5nLz2OPPaYZM2YUqI9r167pk08+0YwZM3TkyJFcj6tRo4YefvhhPfvsswoPDy/QmAAAAAAAAAA8Y+PaI1owe4emfDBAEZFlXO4nKTFNE55ZqeGjWqtz9xg3VggULp8JbCQpICBAH3zwgfr06aPJkydr27Ztls/lnD2TkznQaNGihd566y317du3UGt11b59+/TNN99ox44d+u2332xmD5UUW7Zs0QMPPKD4+Ph8jz116pQmTZqk6dOna+bMmRowYEARVAgAAAAAAADAXTauPaKpb6xRdrY0buxyTZ02yKXQJikxTePGLteJuCRNfWONJBHawGd49/SSXNx1113asmWLNm3apGeeeUYtWrSw7Etj76NWrVr661//qlWrVun333/32rBGkmbOnKlXXnlFixYtKrFhzdKlS9W1a1eHwpqbXbhwQYMGDdL06dMLqTIAgK8wGrO9qh8AAAAAQO5uDmsk6URcksaNXa6kxDSn+rk5rJGk7Gxp6htrtHFt7qv3AN7Ep2bY5HT77bfr9ttvlyRdvXpVZ8+eVWJiopKSklS6dGlVqFBBlStXVsWKFT1cqW8KCQlR3bp1XTq3Zs2aLp23detWDR8+XJmZmVbtfn5+6tevnzp27KiaNWvqwoUL2r17t+bPn6+0tD9/cJtMJj311FOqWrWq7r77bpdqAAD4NqbQAwAAAIDvyBnWmJlDG0dn2uQMa8zMoY3ETBt4P58ObG5WtmxZxcTEKCameH7TBQUFqUmTJmrTpo0WLlyo5OTkQh+zTZs22rBhQ6GPY5aamqr77rvPJqypX7++li1bpoYNG9qc849//EOjR4/WypUrLW0mk0mjR49W+/btVaVKlUKvGwDgPZhCDwAAAAC+w2jM1oLZO2zCGjNHQ5vcwhqz7Gxpwewdur1LHQUE+OSiUygh+Or0QgEBAWrWrJkeeughTZ8+Xb/88ouuXLmiHTt26NNPP1VYWJinSywUU6ZM0YkTJ6zaYmJitHXrVrthjSSVL19eS5cu1ZAhQ6zaL1++rHHjxhVarQAA78MUegAAAADwLQEBfprywQBFRUfkekx+7+3yC2skKSo6QlM+GEBYA6/HV6iXGTdunFJSUrR792598cUXevzxx9W2bVsFBQV5urRClZycrI8++siqzc/PT//5z39Uvnz5PM/19/fXp59+qsqVK1u1z507V8eOHXN7rQAA75PfFHpHQ5v8ptAT2gAAAACAe0VEltHUaYNcCm0cDWtcXX0BKGpeH9gcOHDA0yUUqapVq6p06dKeLqPIzZ07VykpKVZtffv2VceOHR06PzIyUi+99JJVW1ZWlj7//HO31QgA8E6OTqHPL7RxdAq90ZjLQAAAAAAAl7gS2hDWoDjy+sCmcePG6tChgz7//HNduXLF0+WgkCxYsMCmbcyYMU71MWrUKJUqVcqqbf78+QWqCwDg/ZhCDwAAAAC+z5nQJu7oRcIaFEs+ccXh559/1pgxY1SlShX95S9/0bp16zxdEtzo8uXL2rZtm1Vb2bJldddddznVT2RkpLp06WLVFh8fX+JmaQFAScQUegAAAADwfY6+t3t85Fe8h0Ox5BOBjSSZTCZdu3ZNc+fOVY8ePRQdHa3JkycrPj7e06WhgLZt26asrCyrtvbt28vf39/pvjp16mTTtmnTJpdrAwD4DqbQAwAAAIDvc+S9XV54Dwdf5jOBjcFgkMFgkMlkkslkUnx8vCZNmqQ6deqoe/fumjdvntLT0z1dJlywY8cOm7b27du71FeHDh0c6h8AUDwxhR4AAAAAfJ+roQ3v4eDrvD6weeSRRxQaGmoJaszBjTm8yc7O1vr16zVy5EhVrVpVjz/+uH7++WdPl10snD9/Xm+++aa6d++u2rVrq2zZsipdurSqV6+uZs2a6b777tP06dN1+PDhAo1z8OBBm7a6deu61FedOnVs2g4dOuRSXwAA38QUegAAAADwfc6GNryHQ3Hg9YHNp59+qoSEBH355Zfq0aOHJaiRbGfdXL58WZ999pk6dOigxo0b69///rfOnTvn4Wfguw4cOKBJkybpxx9/VHx8vFJTU5Wenq4zZ85o7969WrBggZ588knVr19fffr00U8//eTSOMePH7dpq1Wrlkt9Va9e3WYptWPHjrnUFwDAdzGFHgAAAAB8X0RkGY2f3NOhY8dP7sl7OPg8g8mcfviIU6dOac6cOZozZ46OHj0q6UZwY3bz0zEYDPL391evXr308MMPq2/fvgoICCjymt2tdu3aNnv3uOO/sUuXLtq4cWOB+njqqaf073//W0FBQQ6fExMTY/m/NDt27Jiio6NdqqFmzZo6deqU5XFQUJCuX7/uUl85nT9/XhcuXHDqnKNHj2rQoEGWx/v27VPjxo3dUg8AIG+O7FGTE2ENAAAAAHgHZ97T8V4O7rB//341adLE8rior+X6XGBzs82bN2vWrFlavHixrly5Isl+eGNuq1ChgkaMGKHRo0db/aP7mqIObCpWrKiwsDAFBgbq0qVLunjxorKysnLtp127dlq7dq1CQkIcGjcyMlKXLl2yajt//rwqVqzo3BP4n4YNG9oss3blyhWVLVvWpf5u9uabb2rSpEkF6oPABgCKFi/wAQAAAMD3cAMePMHTgY3XL4mWl44dO+qLL75QQkKCZs+erS5dukhSrkumXbhwQe+//76aN2+utm3b6pNPPlFycrLnnoCXateunSZPnqwtW7bo8uXLOn/+vI4cOaI//vhDCQkJunz5sn744Qc99NBDdmfSbN++XcOHD88z1LlZamqqTVvp0qVdrt/euWlpaS73BwDwbUyhBwAAAADf4kpYI93Yr3Tc2OVKSuRaIHyTTwc2ZqVLl9Zf/vIXrVu3TseOHdPEiRNVq1YtS1BjDm5uDm927Nihp556StWqVdP999+vNWvWePppeNyDDz6ogwcPatu2bXr99dfVoUMHhYaG2hwXEhKi7t2764svvtDhw4fVvn17m2O+/vprvf/++w6Nm5mZadMWHBzsdP1m9gKbjIwMl/sDAPi2pMQ0vT3Rsd/zb09cwwt7AAAAAPAgV8MaM0Ib+DKfXhItPxs2bNB//vMfLVu2zDKLI7f9bvz8/GQ0Gou8RlcU1pJorsrMzFS/fv1sQq/y5cvr2LFjCgsLy/N8f39/ZWdnW7UZjUb5+/u7VE/Hjh21ZcsWq7YTJ06oZs2aLvV3M/awAQDfwhR6AAAAAPAdjryHi4qO0PjJPfX2xDX5Hsd7OzjL00uiBRTZSB7QpUsXdenSRVevXtVXX32lOXPmaPPmzZbPm8Mb86wbuCYwMFCLFy9Wo0aNdOrUKUv7pUuXNGvWLD377LP5nn/9+nWrtmvXrrm858y1a9ds2uwt3eaKSpUqqVKlSm7pCwBQuAo6hZ4X9gAAAABQdBwNa8zv1aZOG5Tn8by3gy8qFkui5ads2bJ6+OGH9dNPP+nIkSOaMGGCqlevTkjjRuXKldMbb7xh0/7tt9/me26ZMrY/MO2FLo6yd25ISIjL/QEAfA9T6AEAAADAdzgb1kiyhDZR0RG5nsN7O/iaEhHY3Oz8+fM6d+6cUlJSrJZHQ8ENGzZMAQHWk7Z++umnfJeai4yMtGm7evWqy3XkPDcoKMjl2ToAAN/j6Av9T/7vXl7YAwAAAICHuRLWmBHaoLgpEYHNmTNn9M4776hBgwbq2LGj/vOf/+jq1avMsHGz0NBQtWzZ0qotPT1d58+fz/O8ypUr27TdvLSaM7KysnT27Nl8+wcAFE/OvNCPrluBF/YAAAAA4EFGY7YmPLOyQHvROBraTHhmpYzG7FyPAbxBsQ1sMjIytHDhQvXu3Vu1atXSq6++qsOHD1vtV8MMG/erWrWqTduFCxfyPCc6OtqmLT4+3qXxz5w5YzOjx17/AIDihyn0AAAAAOBbAgL8NHxUa/nlcpU6v7DGLL/3dn5+0vBRrRUQUGwvh6OYKHZfob/99puefPJJVa1aVffdd5/WrFmjrKwsmUwmGQwGy4c5uClfvrzGjh2rHTt2eLr0YsHeXjH57UdTv359m7ajR4+6NH5sbKxNW4MGDVzqCwDgO5hCDwAAAAC+qXP3GI2b1NMmtHE0rDHL7b2dn580blJPde4e466SgUJTLAKb8+fP69///reaNGmi2267TTNmzFBSUpIllDGHNJJkMpnk5+en3r17a+HChTpz5ow++OADtWjRwrNPopiwN5umQoUKeZ7TunVrm7Zt27a5NP7WrVtt2lq1auVSXwAA38AUegAAAADwbTlDG2fDGrOc7+0Ia+BrfDawMRqNWrp0qQYMGKAaNWro5Zdf1h9//GET0tw8myYmJkZTpkxRfHy8vvnmGw0dOlSBgYGefirFyoEDB2zaKlasmOc5HTp0kL+/v1Xbtm3blJWV5fT4mzdvtmm74447nO4HAOA7mEIPAAAAAL7PHNpE14l0KawxM7+3i64TSVgDnxPg6QKctWvXLs2ePVvz5s1TYmKiJNndk8bcVrZsWQ0bNkyjR4/W7bffXvQFlyB79uzR6dOnrdpq1aqlsLCwPM8LCwtTu3bttGXLFkvblStX9MMPP6hXr14Oj3/p0iWtX7/eqi0qKkoNGzZ0uA8AgG8yvwCf+sYaZd80AcbVKfQ5l1fjriwAAAAAKHydu8fo9i51CnyjXERkGU2bPYwb7uBzfOIrNjExUR988IFatmyp1q1ba9q0abp48WKuS55JN2ZVzJo1SwkJCZo5cyZhTRF45513bNp69+7t0LnDhw+3aZsxY4ZT48+ZM0fp6elWbffdd59TfQAAfBdT6AEAAADA97krZCGsgS/y+hk2Q4YM0TfffKPMzExLGCPZn01Ts2ZN/eUvf9Ho0aN1yy23FHmtJdmqVau0YMECm/ahQ4c6dP6IESM0YcIEXblyxarPrVu3qkOHDvmen5SUpH/+859Wbf7+/nrkkUccGh8AUDyYQ5UFs3doygcDCjyFfsIzKzV8VGvCGgAAAADwYkZjtlsCGnf1A7jK67/6li1bpoyMjFz3pQkKCtK9996r77//XsePH9dbb71FWOOk3377TV999ZWys13bRPnbb7/VfffdZxWoSVLXrl115513OtRHeHi4nnzySau27OxsjR49WklJuW8ibT7uscce09mzZ63a77vvPtWpU8eh8QEAxUfn7jGaNnuYy2GNmXkKPWENAAAAAHivjWuPaOyohUpKTCtQP0mJaRo7aqE2rj3ipsoA53l9YCPJZskzk8mk1q1b6+OPP9bZs2c1f/589ejRw2rWDRx36tQpDR8+XA0aNNA777yjI0cc+6EUHx+vJ554Qn379lVqaqrV54KDg/Xuu+86Vcerr76qGjVqWLUdPnxYHTp00MGDB+2ek5SUpMGDB2vRokVW7aGhoZo6dapT4wMAig+m0AMAAABA8bdx7RFNfWON4mITNW7scpdDm6TENI0bu1xxsYma+sYaQht4jNcviWZmMplUsWJFjRgxQqNHj1aTJk08XVKh6dOnj86cOZPr5+19rkWLFnn2uXr1alWrVi3PY44cOaLx48dr/PjxqlOnjlq2bKmmTZuqUqVKCg0NVWBgoC5duqT4+Hht3rxZ27Ztk9FotOnH399fCxYsyLemnMqWLat58+apW7duVv0ePHhQjRs3Vv/+/dWpUydVr15dFy9e1O7duzV//nybsEiSvvjii3yfLwAAAAAAAADfZA5rzIsGnYhL0rixy53ey9Qc1pyIu7HKT3a2NPWNNZLEigsocl4f2Pj7+6tPnz4aPXq0+vXrp4AAry+5wP744w/Fx8c7dc7u3bvz/HxGRoZT/cXGxio2NlaLFy926rzw8HDNnDlTAwcOdOo8s06dOmnevHm6//77rUKb7OxsrVixQitWrMjzfIPBoPfff9/hvXMAAAAAAAAA+JacYY2Zs6FNzrDGjNAGnuL163ycOnVKK1as0KBBg0pEWOOrDAaD+vXrp71792rIkCEF6uuee+7RunXrFBUV5dR5FSpU0NKlS/X0008XaHwAAAAAAAAA3slozNaC2Ttswhozc2iT3/JouYU1ZtnZ0oLZO2Q0urbvN+AKrw9sKleu7OkSir1evXppzZo1mjBhgjp37qzw8HCHzjMYDKpXr56effZZHTx4UKtWrbLZg8ZVnTp10oEDB/TPf/5TdevWzfPY6tWr6/XXX9fhw4c1aNAgt4wPAAAAAAAAwPsEBPhpygcDFBUdkesx+YU2+YU1khQVHaEpHwxgb1MUKYPJZDJ5ugh4nxMnTuj48eM6efKkLl68qLS0NGVnZys0NFQRERGqXLmy2rRpo4iI3H8wutORI0e0c+dOnTx5UmlpaQoODla1atXUrFkzNW3atEhqcNX+/fut9lzat2+fGjdu7MGKAAAAAAAAAN/maOiSc3k0V89DyeDpa7msMQa7oqKinF6SrDDFxMQoJob1IgEAAAAAAABIEZFlNHXaoDzDl5x72hDWwNsxnwsAAAAAAAAA4HPMoY0jy6PFHb1IWAOvR2ADAAAAAAAAAPBJjoY2j4/8irAGXs8jS6JNnjzZbvvEiRMdPrYw2BsfAAAAAAAAAOC9HFkeLS+ENfAWBpPJZCrqQf38/GQwGGzas7KyHD62MNgbHygoT29UBQAAAAAAAJQEjuxRkxNhDW7m6Wu5Hl0SzWQyWT6cObYwPgAAAAAAAAAAvsuR5dFuRlgDb+PRwMZgMDg8e8Z8bGF8AAAAAAAAAAB8X0RkGY2f3NOhY8dP7klYA6/iscCG2TUAAAAAAAAAAHdKSkzT2xPXOHTs2xPXKCkxrZArAhznkcAmOzvb5iO3/WPsHVsYH+xfAwAAAAAAAAC+y9k9bE7EJWnc2OWENvAaHl0SDQAAAAAAAACAgnI2rDEjtIE3IbABAAAAAAAAAPgsV8MaM0IbeAsCGwAAAAAAAACAT3IkrImKjtAn/3evoqIjcj2G0AbegMAGAAAAAAAAAOBzHA1rpk4bpOi6FTR12iBCG3g1AhsAAAAAAAAAgE9xJqyJiCwjSYqILENoA6/m9YGNv7+/5aN169Zu7btly5aWvgMCAtzaNwAAAAAAAADA/VwJa8wIbeDNvD6wMZlMVh+F2T8AAAAAAAAAwHsZjdma8MxKl8IaM0dDmwnPrJTRmF3gmgFHeX1gI0kGg0EGg6HQ+gYAAAAAAAAAeL+AAD8NH9Vafrlc2c4vrDHLL7Tx85OGj2qtgACfuISOYsJnvtqYAQMAAAAAAAAA6Nw9RuMm9bQJbRwNa8xyC238/KRxk3qqc/cYd5UMOMRnAhsAAAAAAAAAACTb0MbZsMYsZ2hDWANPCvB0AZ6Unp5u+Xvp0qU9WAkAAAAAAAAAwBnmUGXB7B2a8sEAp8MaM3NoM+GZlRo+qjVhDTymRAc2Z86csfy9XLlyHqwEAAAAAAAAAOCszt1jdHuXOgXeayYisoymzR7GnjXwqBL71bdjxw5duXLF8rh69eoerAYAAAAAAAAA4Ap3hSyENfC0EvkVePr0aT311FOSJJPJJIPBoCZNmni4KgAAAAAAAAAAUFJ5fEm0hx56yOFjT5w44dTxN8vKytLVq1d17Ngx7du3T9nZ2TIYDDKZTJKkbt26udQvAAAAAAAAAABAQXk8sJk9e7YMBkOex5hDlaSkJM2ZM6dA45n7kmQZt1y5choyZEiB+gUAAAAAAAAAAHCVxwMbs5uDlIIck5+bwyFzf++9955CQkIK3DcAAAAAAAAAAIArvCawyW2Wjb0ZMa4ymUxW/ZUvX17vvfeeRo4cWaB+AQAAAAAAAAAACsLjgU1UVFSeQUx8fLxlr5mAgABVq1bNpXECAgJUrlw5RUREqGnTpurQoYMGDhyoUqVKuVo6AAAAAAAAAACAW/w/e3ceJ0V17n/8W909+wzbsIOACrgALoAbioALiTsmGg0mRo0xMYkx+ZkbjVm8mrgl3iTGm8QsJiZqNPG6JjGKuCAKRgUXUBBQENmZBZh9errr98dM9VR3V3dX9fQ2M5/360UiRVfVOVV16pyqp+qpvAdsNm3alPTffT5f5L+nTJmilStXZrlEAAAAAAAAAAAAueVL/ZPC0NN0aAAAAAAAAAAAAIUq72/YuGH/7gwAAAAAAAAAAEBfU/ABm40bN0b+u7i4OI8lAQAAAAAAAAAAyI6CD9iMHz8+30UAAAAAAAAAAADIql7zDRsAAAAAAAAAAIC+ioANAAAAAAAAgKzo6AgX1HL6KrYz0DcQsAEAAAAAAACQcUsWr9dVl/xd9bXNPVpOfW2zrrrk71qyeH2GSta3sJ2BvoOADQAAAAAAAICMWrJ4vW6/YZE2flCra696PO1gQn1ts6696nFt/KBWt9+wiGBCDLYz0Lf0yoBNOBzW008/re9///s65ZRTNHnyZA0fPlwlJSXy+/1p/QkEAvmuFgAAAAAAANDrWUGEcFd2rc0b69MKJlhBhM0b6yVJ4bAIJtiwnYG+p1dFKcLhsH7xi1/ol7/8pT7++OPIdNM081gqAAAAAAAAAFJ8EMFiBRNuv2uBBleXp1xObBDBYgUTJGnOKZMyVu7ehu0M9E295g2bbdu2ac6cOfqv//ovbd68WaZpRgI1hmH06A8AAAAAAACAnunoCOuhe1fEBREsbt8ASRREsITD0kP3rlBHR4IV9XFsZ6Dv6hUBm71792revHlatmyZTNOMCrTYAzf2v9v/2KX6dwAAAAAAAADeBQI+3XLn2Rq3/+CEv0kVTEgVRJCkcfsP1i13nq1AoFfc2sw4tjPQd/WKlGhf+MIXtH79+kigxjRNlZeX65Of/KQmTZqke++9V7t27YoEc374wx+qpaVFdXV1+vDDD/Xaa6+psbFRkiLzDxw4UFdeeaVKSkryXDsAAAAAAACgbxhcXa7b71qQNBiQKG2X2yCC23RffRnbGeibDLPAXzF57bXXdOyxx0YCLYZh6BOf+IT+/Oc/a9iwYZKkI488Uu+8807k30OhUNQywuGw/vWvf+lnP/uZlixZEnk759BDD9W//vUvjRs3Luf1Qv/x7rvvaurUqZG/r169WlOmTMljiQAAAAAAALLLa1CAIEJ62M5AZuX7Xm7Bv892xx13RP7bMAzNnDlTjz/+eCRY44bP59NZZ52lF154Qb/73e9UWloqqXPjz549W9u3b894uQEAAABEy1T+c/KoAwBQ+Kw3QNyk7dq4oYYgQprYzkDfUtABG9M09eyzz0berpGkO++8U8XFxWkv8/LLL9cjjzyioqIiGYahjz/+WOeff36migwAAADAwZLF63XVJX9P+fHbVOprm3XVJX/XksXrM1QyAACQLW6DCVd+/m8EEXqA7Qz0HQUdsFm1apX27t0b+fshhxyiY489tsfL/eQnP6nrr78+EgRavny5HnjggR4vFwAAAEC8JYvX6/YbFmnjB7VJP36bipXCY+MHtbr9hkUEbQAA6AXcBBOSIYjgDtsZ6BsKOmCzdu3ayH8bhqF58+a5mi/2GzZOrr32WlVXV0fe3vnlL3+ZdjkBAAAAOLOCNeGuLGZWSg6vQZvYfOvhsAjaAADQS6QbTCCI4A3bGej9CjpgU1/feTFmvQlz6KGHOv7OMIyov7e2tqZcdklJic4888zIst944w3t2rWrJ8UFAAAAYBMbrLF4Ddok+jguQRsAAHoPr8EEggjpYTsDvVuvCNhYBg92PtGUlZVFAi+S1Nzs7sJvxowZUX9/4403PJYQAAAAgJOOjrAeundFXLDG4jZokyhYYwmHpYfuXaGOjgQrAgAABWNwdbm+e9N8V7/97k3zCSKkie0M9F4FHbDx+/1Rfy8uLnb83YABA6L+vm3bNlfLHz58eNTfN27c6KF0AAAAABIJBHy65c6zU378NlnQJlWwRup8KvSWO89WIFDQlzYAAECdffutP1zk6re3/nBR2t+96+/YzkDvVdBXNbGBmIaGBsffDRw4MOrvH330kavlB4NBSd0p1RItHwAAAIB3blJyJArauA3WkMIDAIDewU3fbpfud+/6O7Yz0LsVdMBmzJgxkroDKnv37nX83eTJk6P+/tprr7la/oYNGyR1fyMnEAikVU4AAAAAztIJ2hCsAQCgb/EaRLAQTPCG7Qz0fgUdsDn44IOj/r5+vfPHRA877DBJnYEd0zT173//29Xy//GPf0SCQZI0dOjQNEsKAAAAIBEvQZuNG2oI1gAA0IekG0SwEExwh+0M9A0FHbA54IADVFFREfn7mjVrHH83a9Ys+XzdVXnrrbe0ePHipMt+5JFHtHLlyqhpU6ZM6UFpAQAAACTiNmhz5ef/RrAGAIA+wu1bs7+574Ieffeuv2M7A31HQQdsfD6fjj/+eJmmKdM09frrrysUCsX9bvTo0ZozZ45M04y8ZfP5z39eK1ascFzu008/rUsvvTTq7ZpBgwZp5syZWasLAAAA0N+5CdokQ7AGAIDew0uK0/0nDk37u3f9HdsZ6FsKOmAjSSeddFLkvxsbG7Vs2TLH31155ZWR/zYMQzt37tRxxx2ns88+Wz/96U91zz336Pbbb9fJJ5+sM844Q42NjZIUCfJcccUVUQEcAAAAAJmXbtCGYA0AAL1HOt+jS+e7d/0d2xnoewzTNM18FyKZDRs2aPLkyZFgylVXXaVf/OIXjr89+eST9cILL0R+awVjYtmnm6ap0aNH6+2331Z1dXV2KoF+7d1339XUqVMjf1+9ejXp9wAAQL/nJc86wRoAAHqPdIIImZy/v2A7A9mR73u5Bf+GzcSJE3XEEUdE0qLdd999amtrc/zt/fffr4kTJ8qKQVnp0WL/2IM15eXl+vvf/06wBgAAAMihwdXl+u5N81399rs3zedGAQAAvUBHR1jXX/1kj4IAbt8Auf7qJ9XREe5xmXsjtjPQdxV8wEaSFi9erI0bN2rjxo1auXKl/H6/4+9GjRqlF198USeffHJUcCb2j/VvBx54oF5++WXNmjUrxzUCAAAA+rf62mbd+sNFrn576w8XkZIDAIBeIBDw6cJLZsiX4I6j2zc2UgUTfD7pwktmKBDoFbc2M47tDPRdvaK1DRkyROPHj4/8CQQCCX87evRoPfvss/rnP/+piy66SMOGDYt6u6asrEzz58/XH/7wB61Zs0ZHHHFE7ioCAAAAwFM6NIk86gAA9CZzTpmka2+cHxdM8JpeK1EwweeTrr1xvuacMilTRe6V2M5A31Tw37DJhGAwqJqaGlVUVGjAgAH5Lg76mXznPQQAACgkXoM1duRRBwCg91iyeL1uv2GRwuGe9eH2sQNBhHhsZyCz8n0vt1e8YdNTRUVFGjVqFMEaAAAAII96EqyReNMGAIDexHoDZP8Dq3v0wIX1Bsj+B1YTRHDAdgb6ln7xhg2QT/mOygIAABQCN8GacfsP1ndvmq9bf7ioRx/RBQAAhaOjI5yRb6Bkajl9FdsZyIx838ul9QEAAADIKrfBmtvvWqD9Jw5N+vFbiTdtAADoTTJ1858gQnJsZ6BvCOS7AKlcdtllkf8eN26c/vu//ztjy/7v//5vbd68WZJkGIbuueeejC0bAAAAgLdgjfXGjJWSI9l8VtCGN20AAAAA9BUFnxLN5/PJMAxJ0uGHH66VK1dmbNlHHnmk3nnnHZmmKcMwFAqFMrZswJLv1+gAAADyJZ1gTSbnBwAAAAAv8n0vt9e845atuFKBx6sAAACAXqmjI6zrr36yR8EW602bVOnRrr/6SXV0hHtcZgAAAADIp14TsMkW6+0dAAAAAJkTCPh04SUz5EtwxeH2zZhUQRufT7rwkhnkWwcAAADQ63FVAwAAACAr5pwySdfeOD8uaOM1jVmioI3PJ11743zNOWVSpooMAAAAAHnTrwM2HR0dkf8uKirKY0kAAACAvik2aJPuN2digzYEawAAAAD0NYF8FyCf6urqIv9dWVmZx5IAAAAAfZcVVHno3hW65c6zPQdrLFbQ5vqrn9SFl8wgWAMAAACgT+m3AZvdu3dr+/btkW/YDBkyJM8lAgAAAPquOadM0vFzD+zxt2YGV5frrns/wzdrAAAAAPQ5/fYq5+c//3nkvw3D0CGHHJLH0gAAAAB9X6aCLARrAAAAAPRFeX/D5qWXXnL928bGRk+/twuFQmpsbNSHH36op556SosXL5ZhGDJNU4ZhaObMmWktFwAAAAAAAAAAoKfyHrCZO3duJC1ZIqZpSpI++OADzZs3LyPrtQI1lgsvvDAjywUAAAAAAAAAAPAq7wEbixWU6elv3LKCNYZhaOHChZo0iQ+WAgAAAAAAAACA/CiYgE2it2zsQZpUb+J4YS335JNP1q9+9auMLRcAAAAAAAAAAMCrggjYuH1zJlNv2AwcOFDHHXecLrvsMn3605/OaCAIAAAAAAAAAADAq7wHbF544YWE/2aapk466SQZhiHTNDVx4kT9/ve/T2s9gUBAVVVVGjx4sPbbb790iwsAAAAAAAAAAJBxeQ/YzJkzx9XvDMNQZWWl698DAAAAAAAAAAD0FnkP2LiRqVRoAAAAAAAAAAAAhajgAzY33HBD5L9HjhyZx5IAAAAAAAAAAABkR68K2AAAAAAAAAAAAPRFvnwXAAAAAAAAAAAAoL8jYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAngXyXYBULrvsspysxzAM3XPPPTlZFwAAAAAAAAAAgF3BB2zuvfdeGYaR1XWYpknABgAAAAAAAAAA5E3BB2wspmlmZbnZDgYBAAAAAAAAAACk0msCNpkOrGQrAAQAAAAAAAAAAOBVwQdsxo0bl3awJhgMqq6uTq2trZFp1rJKSko0cuTIjJQRAAAAAAAAAACgJwo+YLNp06YeL2Pr1q1asmSJfve73+mll16SYRgKBoNauHChbr755p4XEgAAAAAAAAAAoAd8+S5ALowZM0YLFy7Uiy++qMcff1yDBw+WaZq67bbbdNFFF+W7eAAAAAAAAAAAoJ/rFwEbu7PPPlvPP/+8KisrZZqmHnroIV133XX5LhYAAAAAAAAAAOjH+l3ARpIOO+ww/exnP5MkmaapO+64Q6+99lqeSwUAAAAAAAAAAPqrfhmwkaRLL71U48aNk2EYMk1TP/rRj/JdJAAAAAAAAAAA0E/124CNz+fTGWecIdM0ZZqmnnnmGe3ZsyffxQIAAAAAAAAAAP1Qvw3YSNK0adMi/x0KhbRs2bI8lgYAAAAAAAAAAPRX/TpgU11dHfX3Dz74IE8lAQAAAAAAAAAA/Vm/Dtg0NzdLkgzDkCQ1NDTkszgAAAAAAAAAAKCf6tcBmzVr1kiSTNOUJFVVVeWzOAAAAAAAAAAAoJ/qtwGbUCikRx55JPJ2jSQNHz48jyUCAAAAAAAAAAD9Vb8N2PzoRz+K+2bNjBkz8lQaAAAAAAAAAADQnwXyXYBca2ho0Pe//3397//+b9TbNQceeKAmTpyYx5IBAAAAAAAAAID+quADNi+99FKP5g8Gg9q3b58+/PBDvfbaa/rXv/6llpYWmaYpwzAi///tb387QyUGAAAAAAAAAADwpuADNnPnzo16E6anTNOUpKhlHn300br88ssztg4AAAAAAAAAAAAvCj5gY7ECLT1lD9SYpqmpU6fqqaeeks/Xbz/nAwAAAAAAAAAA8qzXRCkMw8jIH9M0ZZqmfD6fvvnNb+o///mPBg8enO/qAQAAAAAAAACAfqxXvGGTqbdrKisrdcQRR+i0007TJZdcolGjRmVkuQAAAAAAAAAAAD1R8AGbF154oUfzFxUVacCAARo0aJDGjBmT0e/hAAAAAAAAAAAAZELBB2zmzJmT7yJA0pYtW7RixQpt3LhRjY2NKikp0YgRIzR16lQdccQRWf8G0IYNG/TWW2/p448/VlNTk8rKyjR69GgddthhmjJlSlbXDQAAAAAAAABAthV8wKa/q6ur0xtvvBH5s2LFCm3evDnud5lKG2cXCoX0pz/9Sb/61a/01ltvJfxddXW1Lr74Yl1zzTUaM2ZMxtbf0tKi3/zmN7r77ru1fv36hL8bO3asvvjFL+qb3/ymBg0alLH1AwAAAAAAAACQK4aZjTv9SNvq1av1r3/9SytWrNAbb7yhjRs3upov07txzZo1uuCCC7Rq1SrX81RWVupnP/uZvvSlL/V4/a+88oouuugiffTRR67nGTZsmP7whz/o7LPP7vH6M+ndd9/V1KlTI39fvXo1bwUBAAAAAAAAQIHJ973c7Oaxgmd/+MMfdN111+nhhx92HazJtGXLlumYY47xFKyRpMbGRl1xxRW69tpre7T+Rx99VPPmzfMUrJGk3bt3a8GCBfr1r3/do/UDAAAAAAAAmdLRES6o5QAoXKREQ5QNGzbo9NNPV0NDQ9y/zZs3T6eeeqrGjx+vvXv36r333tMDDzyg+vr6qN/95Cc/0ahRo/TNb37T8/qXLVumCy+8UMFgMGq6z+fTmWeeqRNOOEH77befdu/erbffflsPPvigmpubI78zTVNf//rXNWrUKJ177rme1w8AAAAAAABkypLF6/XQvSt0y51na3B1edrLqa9t1vVXP6kLL5mhOadMymAJARQSAja9RHFxsaZOnaqZM2fq73//u/bs2ZPxdYTDYS1cuFB79+6Nmj5y5Eg98sgjmjVrVtw8t912m6655hr99re/jZr+ne98RyeddJIOO+ww1+tvamrSZz/72bhgzUEHHaTHHntMhxxySNw8P/nJT3TppZfqySefjEwzTVOXXnqpjjvuOI0cOdL1+gEAAAAAAIBMWbJ4vW6/YZHCYenaqx7X7XctSCtoU1/brGuvelybN9br9hsWSRJBG6CP6vUBm61bt+rVV1/VihUrtGvXLu3Zs0cNDQ2qqqrSoEGDNHz4cM2YMUPHHnusxowZk+/iuhIIBHTooYdq5syZkT+HH364iouLJUnPPPNMVgI299xzj15//fWoaUOGDNHy5cs1YcIEx3kqKip09913q7y8XD//+c8j04PBoK666iotWbLE9fpvueUWbd68OWrapEmTtGzZMg0ZMsRxniFDhujRRx/VBRdcoEceeSQyfe/evbr22mv15z//2fX6AQAAvOjoCCsQ6HmG4UwtBwAAAIXDHqyRpM0b69MK2tiDNZIUDougDdCHGWamv1afAw0NDbrvvvt09913691333U935QpU3TllVfqc5/7nKqqqrJYwvRt375dgwYNUllZWcLfTJgwIe77Lj3djaFQSJMmTYr7bs4DDzyghQsXppy/vb1d06dPj9sfS5Ys0Yknnphy/j179mj8+PHat29fZJrP59OSJUt0wgknpJy/trZWU6ZM0c6dOyPT/H6/1q1bpwMOOCDl/NmU7w9VAQCAzCO1BQAAABKJDdbYjdt/sOugTWywxs7nk669cT5jSCDD8n0vt9c9ynffffdp//3311VXXaXVq1fLNE3Xf1avXq2vf/3r2n///XX//ffnuyqORo0alTRYky1PP/10XLBm2rRproI1UmfKthtvvDFu+m9+8xtX8z/wwANRwRpJOuOMM1wFaySpurpa//Vf/xU1LRQK6fe//72r+QEAANyyLsA3flCra696XPW1zalncmBdgG/8oFa337BISxavz3BJAQAAkGsdHWE9dO8Kx2CN1P2mTaoxZLJgjdT5ps1D965QR0eCFQHolXpNwKatrU3nnnuuLrnkEtXV1UXeKDEMw/UfqfNNlLq6On3hC1/Qpz71KbW1teWzWgXjoYceipv2la98xdMyzjnnnLhvxjzxxBNqbk59EyMT67/kkktUUlISNe3BBx/0tAwAAIBkEqW28Bq0SZTagqANAABA7xYI+HTLnWdr3P6DE/4m1RgyVbBG6nxT55Y7zya1LtDH9IoW3draqrPOOktPPvmkTNOMCsI4vUkjyXF67HxPPPGEzj777H4ftDFNU4sWLYqb/ulPf9rTcgKBgM4555yoaS0tLSm/Y7N3714tX748alplZaU+8YlPeFp/dXW15s6dGzXto48+0po1azwtBwAAwEmi1BZegzaJLsAJ2gAAAPQNg6vLdftdC9IK2rgN1nj9Fg6A3qFXBGyuuuoqLV68WJKi3pQxTVPHHXecfvrTn+q5557T1q1b1djYqFAopMbGRm3dulXPP/+8fvrTn+q4446LCuhYQZvFixfrqquuylvdCsH777+vXbt2RU2bNGmSRowY4XlZs2fPjpu2dOnSpPMsX75coVAoatpxxx0nv9+fk/UDAACkQmoLAAAAeJFO0IZgDYCCD9i89NJL+uMf/xgJ1EidwZq5c+dq1apVeuWVV3TNNddo3rx5GjVqlMrLO09W5eXlGjVqlObOnatrrrlGr7zyilatWqV58+bFBW3uueeefn1Tf8WKFXHTjjvuuLSWNWvWLFfLL6T1AwAApEJqCwAAAHjlJWizcUMNwRoAhR+wue2226LSnEnSddddp+eff15TpkzxtKwpU6boueee0/XXXx9Zln09/dXatWvjpk2cODGtZY0bN06BQCBq2vvvv5+z9R944IFx01KtHwAAwA1SWwAAAMArt2PIKz//N8aKAAo7YFNfX6/nnnsu8iaMYRi67LLLdMstt/RouT/+8Y91+eWXR5ZppUarr098UuzLNm3aFDdt/PjxaS3L7/drzJgxUdO2bNmiYDCYk/WPGTMmLpXahx9+mNayAAAAYpHaAgAAAF65GUMmw1gR6D8KOmCzfPnyqBv9AwYM0M9+9rOMLPuOO+7QwIEDI3/v6OiI+/B9f7Fjx464afvtt1/ay4udNxQKqaamJifr9/v9GjVqVNS0nTt3prUsJ7t27dK7777r6c+GDRsytn4AAJB/pLYAAACAV+kGbRgrAv1LIPVP8mfr1q2R/zYMQ+ecc46qqqoysuwBAwbonHPO0V/+8hfH9fUndXV1cdMqKyvTXp7TvLW1tXGBlFytv729XY2NjT1apuXXv/61brzxxh4vBwAA9G7WBXeyYIyV2iIZLsABAAD6DzdjSDvGikD/U9Bv2OzevVtS97drZs6cmdHlH3XUUVF/T/YWSF/W1NQUN62srCzt5TnN29zs/PHdQlg/AABAOkhtAQAAAK8GV5fruzfNd/Xb7940n7Ei0M8UdMAm9sb7iBEjMrr84cOHS+p8e0eSSktLM7r83sLp+zI92RZOAZP29vaCXT8AAEC6SG0BAAAAL+prm3XrDxe5+u2tP1wU+S4igP6hoFOixX7HZM+ePRldvrU80zRlGIbGjh2b0eX3ZlYQK1PzWm9J9Zb1J/LVr35V559/vqd5NmzYoAULFmRk/QAAoPCQ2gIAAABu1Nc2ux4zSt3fRWTsCPQfBR2wmTFjhqTuG/Br1qzJ6PLXrl3ruL7+pqioKG5aS0tL2stzmre4uDjp+tva2uKWke43Z7yu34vhw4dH3swCAACwWKktUn2zRiK1BQAAQH/kNVhjIWgD9C8FnRJt//3317Rp0yR1viHx6KOPZuxNCdM09cgjj8gwDBmGoUMOOUQHHHBARpbd25SXx5/sMx2wqaioKNj1AwCQSx0d4YJaDjKD1BYAAABIJN1gjcUK2jCGBPq+gg7YSNI3v/nNSMqyjz/+WL/4xS8ystw777xTmzdvjgSAvvWtb2Vkub1RdXV13LTGxsa0l+c0r9M6crX+4uLitN/WAQAgk5YsXq+rLvl7jy+06mubddUlf9eSxeszVDL0RLqpLbjgBgAA6PvcjBXH7T9Yv7nvgqTfRWQMCfQPBR+wufTSS3XCCSfINE2Zpqnvfe97+ve//92jZT799NO6/vrrI2/XzJo1S1/84hczVOLeZ8SIEXHTtmzZkvbyPv7446i/+3w+DR06NCfrD4VC2r59e8rlAwCQa0sWr9ftNyzSxg9qe3ShZV3wbfygVrffsIigTZ71NLUFF9wAAAB9l9tgze13LdD+E4fq9rsWELQB+rmCD9hI0iOPPKKDDjpIktTa2qoFCxboxz/+sdrb2z0tJxgM6uabb9aCBQvU2toq0zQ1efJkPfroo9kodq+x//77x0376KOP0lpWOBzW1q1bo6aNHTvW8Ts52Vj/tm3b1NHRkXL5AADkkhWsCXdlMUv3Qiv2gi8cFkGbPCK1BQAAABLxEqyxvk0zuLqcoA3Qz/WKgM2wYcO0fPlynXHGGZI6Ay833HCDJk6cqO9///t6/fXXEwZv2tvb9cYbb+j73/++Jk6cqB/+8Idqb2+XYRg644wztHz5cg0bNiyX1Sk4VjDMbsOGDWkta/PmzQoGg1HTDj744Jyt/4MPPoiblmr9AABkU2ywxuL1QivRBR9Bm/wgtQUAAAASSSdYYyFoA/RvgXwXIJWTTjop6u+lpaVqa2uTaZrasmWLbr31Vt16660KBAIaN26cBg4cqIqKCjU1NWnv3r3avHlz5I0L63s1hmGotLRUTU1N+tSnPuWpPIZh6LnnnstM5QrEjBkz4qYtX748rWUtW7Ysbtr06dMLev0AAGRLR0dYD927Ii5YY7EutJwu1OxSXfCFw9JD967Q8XMPVCDQK57H6dW8XoDffteCpL93exwAAACg8HV0hHX91U+mFayxuB1DXn/1k7rr3s9wDQD0IQUfsHnxxRdlGEbcdGuaFYQJBoORtysMw4hMd5pH6kyttmTJEk9lMU3TsSy93cEHH6xhw4Zp9+7dkWnr1q3Trl27NHz4cE/Levnll+OmnXjiiUnnmTVrlvx+v0KhUGTa8uXLFQqF5Pf7s75+AACyJRDw6ZY7z+7RzXq3wYFb7jybC7Uc6ElqC4I2AAAAfV8g4NOFl8xwfMteSh2ssaQaQ/p80oWXzOAaAOhjelWLThSEif2TbHqq5fVHhmFo/vz5cdO9ftuno6NDTzzxRNS00tJSzZkzJ+l8AwcO1LHHHhs1raGhQc8++6yn9dfV1emFF16ImjZu3DgdcsghnpYDAEAm9SSlQU9SKSDzSG0BAAAAN+acMknX3jhfvpg7r17H7onGkD6fdO2N8zXnlEmZKjKAAtFrAjZWcMU0zYz98bq8vuzCCy+Mm3b33Xd7WsY//vEPbdu2LWraOeeco/Ly1J1QJtb/5z//Wa2trVHTPvvZz3paBgAA2ZDOzXqCNYUlk6ktUh0H11/9pDo6EuTRAwAAQK8QG7RJd+weO4YkWAP0bYZZ4JGIuXPnFlwasti3OHJtwoQJ+uijj6Km9XQ3dnR0aNKkSdq0aVPU9IceekgXXHBByvnb29s1c+ZMrVq1Kmr6iy++mPING0nas2ePxo0bp4aGhsg0n8+npUuXatasWSnnr6+v15QpU7R9+/bINL/fr/fff18HHnhgyvmz6d1339XUqVMjf1+9erWmTJmSxxIBAPLFbRDmuzfN160/XESwpsAsWby+x6ktpOTHARfgAAAAfcuSxev10L0rdMudZ/do7F5f26zrr35SF14yg7EikEX5vpdb8AEbxMtGwEaSfvvb3+orX/lK1LShQ4fqjTfe0Pjx45PO++1vf1v/8z//EzXthBNO0NKlS12v/7vf/a5uu+22qGmTJ0/Wq6++qsGDEz+JGg6HdeGFF+rhhx+Omv65z31O9913n+v1Z0u+GzkAoLC4CdqkQrAmf5yCNunsD6fjgGANAABA39TREc7It2YytRwAieX7Xi4tHBGXX365pk+fHjWtpqZGxx13nJYvX+44T3Nzs6688sq4YE0gENBdd93laf3f+973NHbs2Khp69at06xZs7R27VrHeerr6/WpT30qLlgzYMAA3X777Z7WDwBALrhJi5UMwZr8IrUFAAAAvMpUkIVgDdD3BfJdAMQ7/fTT474FY+f0b0cccUTSZT711FMaPXp00t/4/X49+OCDOuqoo7Rv377I9O3bt2vWrFk66aSTNH/+fI0bN0579+7VmjVrdP/996uuri5uWbfddlvKMsWqrKzUX//6V5100knq6OiITF+7dq2mTJmis846S7Nnz9aYMWNUU1Ojt99+Ww8++KCamprilnXPPfekrC8AAPli3az3+qYNwZrCYAVVeprawjoOSG0BAAAAAJBIiVaQnFKe9dTGjRs1YcIEV79dunSpTj/9dDU2Nqa1rmuuuUZ33HFHWvNK0sMPP6yFCxdGBW3cMgxDv/jFL/SNb3wj7fVnWr5fowMAFC4v6dEI1hQeUlsAAAAAQN+S73u5XBkizuzZs/Xqq69GHZhuVFRU6O677+5RsEaSzj//fD3//PMaN26cp/mGDh2qRx99tKCCNQAAJDO4ulzfvWm+q99+96b5BGsKDKktAAAAAACZxNUhHE2ZMkVvvvmmfve73+nwww9P+tvq6mpdffXVev/99/XlL385I+ufPXu21qxZo5/+9KeaOHFi0t+OGTNGP/jBD7Ru3TotWLAgI+sHACAX6mubdesPF7n67a0/XKT62uYslwgAAAAAAOQLKdHgyubNm7VixQpt2rRJTU1NKioq0ogRIzR16lRNnz5dPl92Y3/r16/Xm2++qY8//ljNzc0qLS3V6NGjddhhh2natGlZXXdP5fs1OgBAYfKSDs1CWjQAAAAAALIn3/dyAzlbE3q1cePGeU5RlkmTJk3SpEl8iBcA0DekE6yRpM0b63XtVY8TtAEAAAAAoA8iJRoAAEAOpRussVhBG9KjAQAAAADQtxCwAQAAyBE3wZpx+w/Wb+67QOP2H5zwNwRtAAAAAADoewjYAAAA5IDbYM3tdy3Q/hOH6va7FhC0AQAAAACgHyFgAwAAkGVegjXWt2kGV5cTtAEAAAAAoB8hYAMAAJBF6QRrLARtAAAAAADoPwjYAAAAZElHR1jXX/1kWsEai9ugzfVXP6mOjnCPywwAAAAAAPKDgA0AAECWBAI+XXjJDPkSjLhSBWssqYI2Pp904SUzFAgwtAMAAAAAoLfiqh4AACCL5pwySdfeOD8uaOM2WGNJFLTx+aRrb5yvOadMylSRAQAAAABAHhCwAQAAyLLYoI3XYI0lNmhDsAYAAAAAgL4jkO8CAAAA9AdWUOWhe1foljvP9hyssVhBm+uvflIXXjKDYA0AAAAAAH0EARsAAIAcmXPKJB0/98Aef2tmcHW57rr3M3yzBgAAAACAPoSrfAAAgBzKVJCFYA0AAAAAAH0LV/oAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDzrFQGbI488Un6/X36/X4FAIN/FAQAAAAAAAAAAyKheE/0wTTPfRQAAAAAAAAAAAMiKXhOwMQzDddBm5cqVamxsjPz9xBNPzFaxAAAAAAAAAAAAeqzXBGy8+OIXv6h33nlHUmegp6OjI88lAgAAAAAAAAAASKxPBmwkUqgBAAAAAAAAAIDeI68Bm8MPP1xz5szRnDlzdOKJJ2rYsGEZW7aXFGoAAAAAAAAAAAD5lNeAzapVq7R69Wr96le/kiQdfPDBmjt3biSIM2LEiHwWDwAAAAAAAAAAICcKIiWa9SbMmjVrtHbtWt19992SpEmTJmnOnDnatWtXPosHAAAAAAAAAACQVXkP2FjBGsMw4qatW7dO69evj5vnwQcf1OzZszV27NjcFBIAAAAAAAAAACCL8hqw2bVrl1588UW9+OKLeuGFF7RmzZrIvzkFcCyf+9znJEkTJkyIfP9mzpw52n///XNTcAAAAAAAAAAAgAzKa8Bm6NChOu+883TeeedJknbv3h0J3rz44otau3Zt3Dz24M3GjRu1adMm/fnPf5YkjR49WnPmzNGOHTtyUwEAAAAAAAAAAIAM8OW7AHbDhg3T+eefr1//+td67733tH37dv31r3/VyJEjZRhG1Fs39r+bpinTNLV161Y9+OCDcd+8ueeee/TBBx/ktC4AAAAAAAAAAABuFVTAJtaIESN04YUXasSIEZI6AzOGYehrX/uapk6dGpkmKS6gY38T54orrtDkyZM1btw4feELX9C9996rzZs357AmAAAAAAAAAAAAieU1JVq67rrrLklSfX29li5dqiVLluill17SW2+9pVAoFPVbe+Bmy5Ytuv/++3X//fdLksaPH6+TTjpJ8+bN07x58zR69OjcVQIAAAAAAAAAAKBLrwzYWAYPHqyzzz5bZ599tiSpsbFRS5cu1RVXXKFt27ZJig7Y2FOoSdKmTZv0pz/9SX/6059kGIY6OjpyXAMAAAAAAAAAAIACT4nmVWVlpU477TQNHTpUUncKte985zs6+uij5ff7HVOoWd/AAQAAAAAAAAAAyIe8vmEzZ84czZ07V3PnztWsWbNUUlKSlfXcdtttkrrfwHnxxRf1wgsv6M0334xLoQYAAAAAAAAAAJBreQ3YLF26VC+//LJ+/OMfq7i4WEcffXRWAzjWGzinnXaaJKmhoUFLly7V888/ryVLlmR0XQAAAAAAAAAAAG4VxDdsTNNUW1ubXn75ZccATn19fVbWW1VVpdNPP12nn356VpYPAAAAAAAAAADgRt4DNvZvytin2QM4sR5//HGdeOKJGjJkSM7KCQAAAAAAAAAAkC15Ddi8+eabeumll7RkyRItXbpUu3fvjvxbbADHPu3Tn/60DMPQ1KlTNXfuXM2bN09z5szRoEGDclp+AAAAAAAAAACATMhrwObwww/X4YcfrquuukqS9N5772nJkiV66aWX9NJLL2n79u1Rv7cCN9Z/v/POO1q1apXuuusuGYahww47THPnzlVtbW1O6wEAAAAAAAAAANATeU+JZnfooYfq0EMP1ZVXXilJWr9+vV588UX94Ac/iLx9kyiFmmmaeuutt/T222/HLfef//ynZs+erYEDB+agFgAAAAAAAAAAAN748l2AZCZNmqQvfelLGjVqlKTOwIxhGFq4cKFGjx4dCdRInQEcK4hjTbf+fs4552jo0KGaOXOmvv3tb+tf//qX9u3bl59KAQAAAAAAAAAAxCioN2zcuv/++yVJGzZs0IsvvqgXX3xRS5Ys0datW6N+Z0+hFgqFtHLlSr355pv6+c9/Lp/PpyOOOELz5s3T3LlzNXv2bFVVVeW0HgAAAAAAAAAAAFIvDdhYJk6cqIkTJ+ryyy+X1BnAeeGFF1ylUAuFQlqxYoVWrlyp//mf/1EgEFBbW1vuKwEAAAAAAAAAAPq9gk6J5tXEiRMdU6hdccUVmjRpUtIUah0dHXkrNwAAAAAAAAAA6N969Rs2bt19992SpO3bt+uFF16I/Pnwww/zXDIAAAAAAAAAAIB+ErCxjBo1SgsXLtTChQslSVu2bNELL7yg559/Xi+++GJ+CwcAAAAAAAAAAPqtfhWwiTV27Fh9/vOf1+c///l8FwUAAAAAAAAAAPRjveYbNta3ZwAAAAAAAAAAAPqaXvGGzU033aS6urp8FwMAAAAAAAAAACArekXA5qyzzsp3EQAAAAAAAAAAALKm16RE88I0TVKoAQAAAAAAAACAXqNXvGHj1b///W+1t7fnuxgAAAAAAAAAAACu9MmAzahRo/JdBAAAAAAAAAAAANf6ZEo0AAAAAAAAAACA3oSADQAAAAAAAAAAQJ4RsAEAAAAAAAAAAMgzAjYAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDwjYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAnhGwAQAAAAAAAAAAyDMCNgAAAAAAAAAAAHlGwAYAAAAAAAAAACDPCNgAAAAAAAAAAADkGQEbAAAAAAAAAACAPCNgAwAAAAAAAAAAkGcEbAAAAAAAAAAAAPKMgA0AAAAAAAAAAECeEbABAAAAAAAAAADIMwI2AAAAAAAAAAAAeUbABgAAAAAAAAAAIM8I2AAAAAAAAAAAAOQZARsAAAAAAAAAAIA8I2ADAAAAAAAAAACQZwRsAAAAAAAAAAAA8oyADQAAAAAAAAAAQJ4RsAEAAAAAAAAAAMgzAjYAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDwjYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAnhGwAQAAAAAAAAAAyDMCNgAAAAAAAAAAAHlGwAYAAAAAAAAAACDPCNgAAAAAAAAAAADkGQEbAAAAAAAAAACAPCNgAwAAAAAAAAAAkGcEbAAAAAAAAAAAAPKMgA0AAAAAAAAAAECeEbABAAAAAAAAAADIMwI2AAAAAAAAAAAAeUbABgAAAAAAAAAAIM8I2AAAAAAAAAAAAOQZARsAAAAAAAAAAIA8I2ADAAAAAAAAAACQZwRsAAAAAAAAAAAA8oyADQAAAAAAAAAAQJ4RsAEAAAAAAAAAAMgzAjYAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDwjYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAngXyXQDAjS1btmjFihXauHGjGhsbVVJSohEjRmjq1Kk64ogj5PMRewQAAAAAAAAA9F4EbBDlkksu0Z///OeMLOsTn/iEnn766bTnD4VC+tOf/qRf/epXeuuttxL+rrq6WhdffLGuueYajRkzJu31AQAAAAAAAACQL7yWgIK0Zs0aHXnkkfrSl76UNFgjSbW1tfr5z3+ugw8+WL///e9zU0AAAAAAAAAAADKIgA0KzrJly3TMMcdo1apVnuZrbGzUFVdcoWuvvTZLJQMAAAAAAAAAIDtIiYakioqKdOihh6Y174EHHuh5ng0bNuj0009XQ0ND3L/NmzdPp556qsaPH6+9e/fqvffe0wMPPKD6+vqo3/3kJz/RqFGj9M1vfjOtcgMAAAAAAAAAkGsEbJDU6NGjU6Yky5RwOKyFCxdq7969UdNHjhypRx55RLNmzYqb57bbbtM111yj3/72t1HTv/Od7+ikk07SYYcdltUyAwAAAAAAAACQCaREQ8G455579Prrr0dNGzJkiJYvX+4YrJGkiooK3X333frWt74VNT0YDOqqq67KWlkBAAAAAAAAAMgkAjYoCKFQSLfeemvc9LvuuksTJkxIOf9tt92mKVOmRE176aWX9NJLL2WqiAAAAAAAAAAAZA0BGxSEp59+Whs3boyaNm3aNC1cuNDV/MXFxbrxxhvjpv/mN7/JSPkAAAAAAAAAAMgmAjYoCA899FDctK985SuelnHOOedo5MiRUdOeeOIJNTc396hsAAAAAAAAAABkGwEb5J1pmlq0aFHc9E9/+tOelhMIBHTOOedETWtpadGSJUt6VD4AAAAAAAAAALKNgA3y7v3339euXbuipk2aNEkjRozwvKzZs2fHTVu6dGnaZQMAAAAAAAAAIBcI2CDvVqxYETftuOOOS2tZs2bNcrV8AAAAAAAAAAAKSSDfBUBha25u1q233qqXX35Za9eu1e7du9XW1qYhQ4ZoyJAhmjx5sk488USddNJJOvzww9Nax9q1a+OmTZw4Ma1ljRs3ToFAQB0dHZFp77//flrLAgAAAAAAAAAgVwjYIKndu3fr+uuvj5u+Y8cO7dixQ++9954ef/xxSZ1vt1x77bU6++yzPa1j06ZNcdPGjx+fTnHl9/s1ZswYffTRR5FpW7ZsUTAYVFFRUVrLBAAAAAAAAAAg2wjYIGOWLVumc845R+eff77+8Ic/aMCAAa7m27FjR9y0/fbbL+1y7LffflEBm1AopJqaGo0aNSrtZVp27dql3bt3e5pnw4YNPV4vAAAAAAAAAKBvI2ADV4YMGaJBgwaptLRU9fX1qqmpUTAYdPztww8/rDfffFNLly7VyJEjUy67rq4ublplZWXaZXWat7a2NiMBm1//+te68cYbe7wcAAAAAAAAAADsCNjA0bRp03TmmWfq1FNP1WGHHabq6uqof29ra9Prr7+uRx99VL///e/V2NgY9e8bNmzQmWeeqSVLlqiioiLpupqamuKmlZWVpV12p3mbm5vTXh4AAAAAAAAAANnmy3cBUFjOOOMMvf7663rnnXd0yy23aN68eXHBGkkqKSnRCSecoJ/97GfatGmTzjrrrLjfrFixQtddd13KdTq9qVNaWppeBeQcsGlvb097eQAAAAAAAAAAZBtv2CDK+eef73me6upqPfnkk/riF7+oP/7xj1H/9tvf/lbf+ta3dMABB3hapmEYnsuRbF7TNNNent1Xv/pVz9tow4YNWrBgQUbWDwAAAAAAAADomwjYIGN++9vf6u2339aKFSsi04LBoH75y1/qF7/4RcL5ioqK4qa1tLSkXQ6neYuLi9Nent3w4cM1fPjwjCwLAAAAAAAAAAALKdGQMYFAQLfddlvc9H//+99J5ysvL4+blumATarv6AAAAAAAAAAAkE8EbJBRJ598skaMGBE1bd26ddqyZUvCeZy+kdPY2Jh2GZzmdVoHAAAAAAAAAACFgoANMsowDJ144olx0zdv3pxwntgAj6SkAZ5UPv7446i/+3w+DR06NO3lAQAAAAAAAACQbQRskHGjRo2Km7Z79+6Ev99///3jpn300UdprTscDmvr1q1R08aOHev4nRwAAAAAAAAAAAoFARtknNP3YpJ9k+aggw6Km7Zhw4a01r1582YFg8GoaQcffHBaywIAAAAAAAAAIFcI2CDjnN6mSZaSbMaMGXHTli9fnta6ly1bFjdt+vTpaS0LAAAAAAAAAIBcIWCDjFuzZk3ctGHDhiX8/cEHHxz37+vWrdOuXbs8r/vll1+Om+b0TR0AAAAAAAAAAAoJARtkVF1dnV599dWoaaWlpZo8eXLCeQzD0Pz58+OmP/roo57W3dHRoSeeeCJu3XPmzPG0HAAAAAAAAAAAco2ADTLqjjvuUCgUipo2d+5clZWVJZ3vwgsvjJt29913e1r3P/7xD23bti1q2jnnnKPy8nJPywEAAAAAAAAAINcI2CBj3njjDf3iF7+Im37eeeelnPeTn/ykJkyYEDXt7bff1t/+9jdX625vb9cNN9wQN/3KK690NT8AAAAAAAAAAPlEwAaSpI0bN+q3v/2t2tra0pr/9ddf15lnnqmWlpao6ZMnT9YXvvCFlPMHAgFdd911cdO//vWv66OPPko5//XXX69Vq1ZFTTvhhBNIhwYAAAAAAAAA6BUI2ECStHfvXn3lK1/RAQccoB/84Ad6++23Xc23e/du/eAHP9Dxxx+vnTt3Rv2bz+fTL37xCwUCAVfLuvzyyzV9+vSoaTU1NTruuOO0fPlyx3mam5t15ZVX6n/+53+ipgcCAd11112u1gsAAAAAAAAAQL65u5OOfmPbtm368Y9/rB//+McaO3aspk+frsMOO0yjRo3SwIEDVVJSovr6em3dulXLli3T0qVL1dra6risO++8U6eddprrdfv9fj344IM66qijtG/fvsj07du3a9asWTrppJM0f/58jRs3Tnv37tWaNWt0//33q66uLm5Zt912m4444gjP9QcAAAAAAAAAIB8I2CChLVu2aMuWLXryySc9zVdaWqo77rhDX/va1zyvc/LkyfrnP/+p008/XY2NjVH/9vzzz+v5559PuYxrrrlG11xzjed1AwAAAAAAAACQL6REQ0adcMIJWrFiRVrBGsvs2bP16quvaurUqZ7mq6io0N1336077rgj7XUDAAAAAAAAAJAPBGwgSZo6dapeeukl3XTTTZo/f76GDRvmet7x48fr8ssv14oVK7R06VIdeuihPS7PlClT9Oabb+p3v/udDj/88KS/ra6u1tVXX633339fX/7yl3u8bgAAAAAAAAAAco2UaJAkBQIBzZ49W7Nnz45M2759uz788EN9/PHH2r17t5qbmxUMBlVVVaXBgwdr2LBhmj59ukaMGJG1Mn3pS1/Sl770JW3evFkrVqzQpk2b1NTUpKKiIo0YMUJTp07V9OnT5fMRewQAAAAAAAAA9F4EbJDQqFGjNGrUqHwXQ5I0btw4jRs3Lt/FAAAAAAAAAAAgK3gtAQAAAAAAAAAAIM8I2AAAAAAAAAAAAOQZARsAAAAAAAAAAIA8I2ADAAAAAAAAAACQZwRsAAAAAAAAAAAA8oyADQAAAAAAAAAAQJ4RsAEAAAAAAAAAAMgzAjYAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDwjYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAnhGwAQAAAAAAAAAAyDMCNgAAAAAAAAAAAHlGwAYAAAAAAAAAACDPCNgAAAAAAAAAAADkGQEbAAAAAAAAAACAPCNgAwAAAAAAAAAAkGcEbAAAAAAAAAAAAPKMgA0AAAAAAAAAAECeEbABAAAAAAAAAADIMwI2AAAAAAAAAAAAeUbABgAAAAAAAAAAIM8I2AAAAAAAAAAAAOQZARsAAAAAAAAAAIA8I2ADAAAAAAAAAACQZwRsAAAAAAAAAAAA8oyADQAAAAAAAAAAQJ4RsAEAAAAAAAAAAMgzAjYAAAAAAAAAAAB5RsAGAAAAAAAAAAAgzwjYAAAAAAAAAAAA5BkBGwAAAAAAAAAAgDwjYAMAAAAAAAAAAJBnBGwAAAAAAAAAAADyjIANAAAAAAAAAABAnhGwAQAAAAAAAAAAyDMCNgAAAAAAAAAAAHlGwAYAAAAAAAAAACDPCNgAAAAAAAAAAADkGQEbAAAAAAAAAACAPCNgAwAAAAAAAAAAkGcEbAAAAAAAAAAAAPKMgA0AAAAAAAAAAECeEbABAAAAAAAAAADIMwI2AAAAAAAAAAAAeUbABgAAAAAAAAAAIM8I2AAAAAAAAAAAAOQZARsAAAAAAAAAAIA8I2ADAAAAAOgzQuFwQS0HAAAAcIuADQAAAACgT1i2bZOuW/qU9rS19Gg5e9padN3Sp7Rs26bMFAwAAABwgYANAAAAAKDXW7Ztk+5a+Yo279ujHy1fnHbQZk9bi360fLE279uju1a+QtAGAAAAOUPABgAAAADQq1nBmrBpSpK2NOxNK2hjBWu2NOyVJIVNk6ANAAAAcoaADQAAAACg14oN1li8Bm1igzUWgjYAAADIFQI2AAAAAIBeKRQO67H1q+OCNRa3QZtEwRpL2DT12PrVCoXDPS4zAAAAkAgBGwAAAABAr+T3+fS9Y0/W2KqBCX+TKmiTKlgjSWOrBup7x54sv49LaAAAAGQPo00AAAAAQK81qKRMPzjulLSCNm6DNT847hQNKinLWJkBAAAAJwRsAAAAAAC9WjpBG4I1AAAAKDQEbAAAAAAAvZ6XoM1H++p7RbAmU9/M4ds7AAAAvQMBGwAAAABAn+A2aPOdJf8q+GDNsm2bdN3SpxJ+e8etPW0tum7pU1q2bVNmCgYAAICsIWADAAAAAOgz3ARtkimUYM1dK1/R5n17HL+945aV9m3zvj26a+UrBG0AAAAKHAEbAAAAAECfkm7QppCCNWHTlBT/7R23Yr/REzZNgjYAAAAFjoANAAAAAKDP8Rq0KcRgjcVr0CY2WGMhaAMAAFDYCNgAAAAAAPqkQSVl+sb0E1z99hvTT8hrsCYUDuux9avjgjUWt0GbRMEaS9g09dj61QqFwz0uMwAAADKLgA0AAAAAoE/a09aiX6582dVvf7ny5bS/FZMJfp9P3zv25KRvBKUK2qQK1kidbxJ979iT5fdxOwAAAKDQMEIDAAAAAPQ5boIXdul+KyaT3KRxS1ROt8GafKd9AwAAQGIEbAAAAAAAfYrXYI2ltwZtCNYAAAD0DQRsAAAAAAB9RrrBGktvC9p8tK+eYA0AAEAfQcAGAAAAANAnuH3T5CdzzujRt2JywW3Q5jtL/kWwBgAAoI8gYAMAAAAA6PW8pAUbP2Bw2t+KySU3QZtkCNYAAAD0LgRsAAAAAAC9WjrfcEnnWzH5kG7QhmANAABA70PABgAAAADQa6UTrLH01aANwRoAAIDeiYANAAAAAKBXCoXDuvnV53r0DRe3QZubX31OoXC4x2VO16CSMn1j+gmufvuN6ScQrAEAAOiFCNgAQA9l6sI9nzcAAAAAeiO/z6dzJ02VzzAc/93tmyapgjY+w9C5k6bK78vfJfSethb9cuXLrn77y5Uv5/WNIAAAAKSHgA0A9MCybZt03dKnenxBvKetRdctfUrLtm3KTMEAAAD6iVmjJ+iq6cfHBW28pgVLFLTxGYaumn68Zo2ekKkie+Ym7ZtdIaRxAwAAgHcEbAAgTcu2bdJdK1/R5n17enRBbF2Ab963R3etfIWgDQAAgEexQZt0v+ESG7TpjcEaC0EbAACA3oeADQCkwQrWhE1TUvoXxLEX4GHTJGgDAACQBitoM27AoLSCNRYraDNuwKBeGawxu8anUs+CNqTrBQAAyD0CNgDgUWywxuL1gjjRBThBGwAAgPTMGj1Bt80+Pe1gjWVQSZlum316wQdrxlYN1E/mnBF5I6g91KGGYFvUODWdoA3pegEAAPKDgA0AeBAKh/XY+tVxwRqL2wviVBfgYdPUY+tX82QjAACAR35fZi5zM7WcdLgN1vzguFM0fsBg/eC4U1RVXKKmYFChsKnG9vSDNqTrBQAAyB8CNgDggd/n0/eOPTnuY7R2qS6I3V6Af+/Yk3N+oyBTASICTQAAAOnxEqyx3iR6r3anGtrb5Pd1fsMnZKYXtCFdLwAAQH4RsAEAj2I/Rusk0QVxOhfgubJs2yZdt/SpHn+YlhQaAAAA6UlnrGil65WkqqKStIM2pOsFAADIPwI2AJCGdII2hR6suWvlK9q8b0/aH6aVSKEBAACQrlA4rJtffc7TWDE2Xa9hGK6CNje/+lzUG9Gk6wUAACgMBGwAIE1egjYf7asv+GCNdSGfzodpJVJoAAAA9ITf59O5k6bKZxiO/+40VnRK15sqaOMzDJ07aWok9W466XpJowsAyCb6GfRnBGwAoAfcBm2+s+RfvSJYY/EatCGFBgAAQM/NGj1BV00/Pi5ok2ys6DQeTRS0kaSrph+vWaMnSEo/BRtpdAEA2UI/g/6OgA0A9JCboE0y+QrWxKbQiOU2aEMKDQAAgMyJDdq4GSu6CdqETVMDikt0aPUIST37Xg5pdAEA2UA/AxCwAYCMSDdok69gjeScQiNWqqBNOik0AAAAkJwVtBk3YJDrsWKqoE1FUbH2tbelna6XNLrRSNcDAJlFPwN04u4ZAGSI16BNPoM1Fi/f4YkdJKXzVCYAAADcmTV6gm6bfbqncVSioM2A4lIV+f2S0kvXSxrdaKTrAYDMop8BuhGwAYAMGlRSpm9MP8HVb78x/YSCCGSkE7QhWAMAAJB96byhnOl0vaTRjUa6HgDILPoZIBoBGwB50VdTCOxpa9EvV77s6re/XPlyj5/KyxQvQZt0Umig5/pqmwF6M9olgEKVyXS9pNHtRroeAMg8+hkgGkcogJzrqykE3AwQ7NK9wMsWt0Ebryk00HN9tc0AvRntEkChy2S6XtLokq4HALKJfgboRsAGQE711RQCXoM1lt4YtEmGQVDmpdtmYp/YT7fN8OQ/EK+v9mUA+p5Mpuvtz2l0SdcDANnXn/uZfCFjQGEiYAMgZ/pqCoF0gzWWvhK0YRCUeem2mdgn/9NtMzz5D8Trq30ZgL4p0+l6+2saXdL1AEBu9Nd+Jh/IGFC4GAUAyIm+mkLA7YXXT+ac0aMLvFzLZAoNpCfdNhP75H+iQWyqNsOT/0C8vtqXAeibspWut7+m0SVdDwDkRn/tZ3KJjAGFjYANgKzrqykEvFx4jR8wOO0LvHzJZAoNeJNum4m9mfzxvj26+vkntHnfHsflJGozhfjkP69qI9/6al8GoG/Kdrre/ppGN9fpehj/AOiv+ms/kwtkDCh8BGwAZF1fTCGQzoVXT57Ky4dMp9CAe+m0mdhBl2maagi2qTkYVGN7m+NNZqc2U4hP/vOqNgpBX+zLAPRNuUrX21/T6OYqXQ/jHwD9XX/tZ7KJjAG9A1eCAHKiL6UQ6El5ekvQJlspNOCel2OltqUp6sl/K1gTCnf+PWSacUEbp2O0EJ/851VtFJK+1JcB6Jtyna63v6bRzXa6HsY/ANCpv/Yz2UDGgN6DgA2AnMl1CoF0pOpQQuGwbn71uZTluf6YkxKWx+12uPnV5/LSwWU7hQbcc3us3PKf5/X1I4/X2KqBccEaiz1ok06wRsr9k/+8qo1C1Bv6Mok0OoUqne3pNE+mloO+JV/pevtrGt1speth/NM70M8CudNf+5lMI2NA78GWA5BTuUohkA43aQf8Pp/OnTRVPsNIWJ5vTD9Bt732QtKLoVTbwWcYOnfS1Jx3cLlKoQH33LaZX658WZdOPUrt4VBcsMYSMk0Fw6G4QWwhPvnPq9ooZIXcl0mk0SlU6ewXp3nS2S/sy74vn+l6+3Ma3Uyn62H80zvQzwK51Z/7mUwjY0DvQMAGQM5lO4VAOrykHZg1eoKumn58XNDGCtb8cuXLrtIOJNoOPsPQVdOP16zRE3pSJc9ynUID7nkZVBX5/PInCCj6fYaKff6oQWwhDrp4VRu9QSH2ZRJpdApVOvvFaZ509gv7su/LZ7pe0uhmLl0P45/egX4WyC36mczrLRkD+jMCNgDyIlspBNKRTtqB2KCNPVjjJe1A7HYo9GBNplNowD23bcZnGKosLokL2vh9hqqKSmQYRl6f/HeDV7XRWxRSXyblPo0O6WDcSWe/OM1zwyuLdMMrz3gaZ5ASyb3eejy7Tdeb7FyTbrpe0uh2y0S6HsY/mZHNtky6OiC36Geyp9AzBvR3fbOHBtArZDqFQDp6knbACtqMGzAoLlhj8RK0GTdgUMEHazKdQgPepBu0sQdrLPl48t8LXtVGb1EIfZmU+zQ6pINxJ5394jSPaZpaV79b6+pqoqYnG2eQEsm93nw8u0nX6+Zc4zVdL2l0o2UqXQ/jn57JZlsmXR2QW/Qz2VeoGQNAwAZAnmUqhUA6MpF2YNboCbru6HmOwRqLm7QDg0rKdNvs03tFsMZC0CY/vAZtyouK4oI1bhTCoItXtdFb5LMvk3KfRuflLRtJB+NCOvvFaR7TNNUQbFMobCpkmmpsb4sL2sSOM0iJ5F5fSG+ULF2vl3ON23S9pNGNlul0PYx/0pPNtky6OiC36Gdyp9AyBqATARsAeZeJFALpyFTagVv+83xG0g7kOi1BPlNo5EJvTW3ihts24zMM/fiET2q/AYM8Ld/LoCvb25lXtXOnL7eZXMhXXyblNo3O/AmT9au3lpEORqmPdbf75aZlz0a2X+w89mBNZL0xQZvYcQYpkdzrS+mNnNL1ptPfpUrXSxrdaNlK15Pr8U86fbfTPJlajlc9bctWGRK15f/s2Oz5fB6LczPgDv1M7hVKxgB0oxcAkHeZSiGQjv6cdiBfKTRyoTenNnHDS5v50+rX9Y3pJ2Tlyf9cbWde1c6+vt5mciGffZmUm/7sk/sfpD+uep10MHLfZlLtF9M0taZul/7rxX9GlmXNM7KiKi5YY7GCNiMrKqPOa719bJJLfTG9kT1db0/2caJ0vaTRjZbtdD25Gv+kMwZwmiedMUAmxg09bctWXRIFvqy2/F7tTs/ncwvnZsAd+pn8yXfGAEQjYAMgrzKdQiAd/TntQK5TaORCX0htkkw6beaXK1/WpVOPcvV7t0/+53o786p29vT1NpMLhdCXSdntz64/5iQt2rSOdDDy3mYS7Rf72zOb9+1xuMlnSs6bu5MR+R9JfWdskgt9Ob3RrNETdNvs03u8j2PT9ZJGN1qu0vVke/yTzhjAaZ50xgCZGDf0tC1bdflob72ufv4Jbd63x/F3VluuKirxfD7n3Ay4Qz+Tf/nMGIBoBGwA5E22Ugikoy+mXXJ7cyBVCg23y0mVQqOn3JTDTToEN8sphNQmbsrl1uZ9e/T9l5+WmeBi1s7Nk//5SiFTiK9q9/Y0Yn0pHVC+5KIv83J8pNufJTo/WO2nuqyCdDBKv83E7pfYVGemFLnJZ+2XHU2Nqiwukd/hTVi/z1BVUYl2NDX0qrFJochlGsF8HM+ZWp+1nL6eRterXKfrydb4J53zmdM8N7yySDe88oynMYDbcUM2Uk/G1iUUDqsh2KbmYDDuG2EWe1tO53zupp912k+5SjvX28ez2ShDIdSlP3HqZ4yYN4zT7Wfsy+kt/Uy+5DtjALoV5pUQgD4v2ykE0tGX0i55TW2QKIWG1zQFiVJo9JSb+rhJh+CmPoWU2sRNuVIJd6XNaQ4G1RBsSxm0cfskYr5SyBTSq9q9PY1YvvdlX5CLviyd48ypnZimqX3trQqGQpF1W/1Ze6hDDcH4m1ReU0709XQwPW0zqVKdWTf5vvX8k5Ht5DOMuKCNFawxuqb1lrFJoenPaXG96stpdL3KV7qeTI9/0jmfOc1jmqbW1e/WurqaqOnJxgBuxw3ZTD0ZG6yxzsex3wiTnLel1/O5237WLldp53r7eNauL9Wlv4ntZybu2KfP/GeTyto6JKXfz5S1degz/9mkiTv2Seod/Uy+FErGAHTiCAWQc7lKIZCOvpB2Kd30RrEpNNJNUxCbQqOn3NTHTToE6+m/ZPUpxNQmbsolObcZK1gT6touobDZo6BNoaSQKYRXtXt7GrFC2Ze9WS76sp4cZ/b+zP70b1OwPRK0kaT2UIeagkGFwvEfsveScqKvp4PJVJvplDjVmSmpJRSMmmYP2sQGa9wo9G2bT/05La5XfTGNrlf5TteTqfFPOuczp3ns532nQIfTGMDtuOHlLRuzlnqytqUpUi6nYIu9LqmP79Tnc6/9rJS7tHO9fTxr15fq0l9Z/cyknQ06dfU2VTe26ZwVm3VgUXla/cyBReU6Z8VmVTe26dTV2zRpZ0PB9zP5UkjZb9CJgA16jd27d+upp57SXXfdpZtvvlk/+clPdM8992j58uUKBoOpF4CCkOsUAukoxLRLUm5SgllPmnhJb+S0vHSeWHFajttUDanSIcQ+/edUnz1tLbpp2bMZSW2SyZvT6baZ2GBNd9nSD9oUSgqZfL+qnY80YplKf2FN87Iva1uaEpY/nTbTF9J7WPtu6949CedL1Jc5tb2te+NvLqQ6ztzUz7q51x4ORaVqsYI21k2kSF0TfMjeabnppINJta0y2X9mev9n8vznJtVZLJ9haPKQoZo8eFjWgjWFmkYm2+XKZVrcQt3GbqVKo5uKVe6eptHNx/m/ENLCZWr8k875LHae2PO+FP92SuwYwO0YcP6EyfrVW8uylnryupee0hemzIzqG2OFTFPBcChh4Mvt+bzEF/Dcz/Y07Zx17KQaZ/Z0nOF2Palkor32lhS/Xuua6PeZWk6uuSnHobubtWBtjYyupjm0JaiLVu1URXv3w0ZullPRHtJFq3ZqaEtn+zNMacHaGh26uzm9wudBpq//EinE7DcgYINe4P/+7/80e/ZsjRgxQmeccYa+8Y1v6Pvf/76uvfZaXX755Zo1a5aqq6t1+eWXa926dfkuLpLIVwqBdBRS2iWpcFOCZfO1c6+pGtw8WWe/kLTXZ09bi7794j+1pm5XwqcN3e7jTL5Cn26b+cb0ExQMh+KCNZZQ2FR7OJRW28p3Cpl8v6qdjzRi6bQzN2k03OzLj/ft0RefeVjPfhTdv6bbZvpCeg9r35Wu/zgqVYNdor6sqrgkLiWKlaqhdP3HkWMo1XH20b56V9vMurlX5PNH3UwyJTUG29QUbI+fKeZD9omkk97LSTb6z2zt/0ye/9ykOrMbWzVQNx7/Cd14/PysjE0KNY1MrsqVi7S4hbqNvUqURjeV2Pqnm0Y3X+f/fKeFy/T4J53zWarzvpQ4IOF2DPjJ/Q/SH1e9nvXUkz98+Zm4vtHO7zNU7PM7Br7cns9LfAE1dzg8WJqkn+1p2jnr+E8UXLbm+eOq13o0znC7HjfjlJ6ez3pLil+v56ZEv/e6zfLdZ1jc1H/P2+9p818fV7HPp4qiYvl9PlUVlSi0u04f/u4BBRsaXdUn2NCoD3/3gEK761RVVCJ/1/KKfT5t/uvj2vP2e1moYWZl6/ovltNxH5tGOV/Zb/o7AjYoWFu3btXcuXN1/vnn6+WXX076NHhDQ4PuueceTZs2TT/60Y9cfVgbuZXvFALpKIS0S1LhpgTL5mvn6aaeSfVknaS4oM3D77+tb7/wD23etycuVYHFS7AmU6/Qp9tmrBu0xT6//L4EF6GGoSKfX39a/bq+Mf2EnARt8hGsSVaHdOQjjVg67cxLGo1k+9JqP83BoH72xkuRoI0VrPHaZvpCeg97sMaeqsEetEl0HL9Xu1MN7W2SFNlmZW0dUakaStd/rJuWPauH338n4XH28b49uvr5J7Rpb33SbWY/znyGoYqi4si/mTJlyjmLS0VRceRD9u62nbf0XnbZCtZkMyVKJs9/9v2SKtWZNc7IxtikUNPI5Lpc2UyLW6jbOF2xaXRTSVR/r2l083n+l/KXFi5b45/0r7ESn/clxQUk3I4Brz/mJC3atC5nqSedAi1S9Pk43fN5ZaBYbeH4BzqkxP1sT9POWcf/R3vrdfXzT2jzvj2O6w+Fw/rHB++lPc5wu55UaXEzcT7rLSl+vZ6bEv3e6zYrlD7DTf3NUEi7nntF6tqXxX6/qopLIufbtp01Wnf3ffrJC/9OWh8rWNO2s0ZSZ9usKi5Rsd/ftSJTu557RaYtPXChyfb1nyVRsMaeRnlAcUles9/0Z4bJnW0UoHXr1mnu3Lnavn17WvN/9rOf1X333Se/dVLOo3fffVdTp06N/H316tWaMmVKHkuUeR3BoAJFRQn/PRQO67qlT0UN5oywKdN2M3ls1UB9d+ZcDa2sSriOxnBHXIcSu5xxAwbpttmny+/zOZYrVVnNUEhG13FjdWBb9+6JWkcsI2xqzMDopwzty0lUn9hyOM1jf2LIqqv9otC+nNgO175trI5XplQVKJbh98VdFO5pa9GPX3lWHzfti6uftRxr3e/V7kxaLrf1t5fZWo5VrkOrRySsj2VseZV+cML8uAvkPW0t+u+lz+j9vTVxT9b5wqbCPkN+w9ABg4aopSOoLQ374v6tsmuAuF/FAH3/+FMTXoS7qUvshXeqYzPdNhNbhrChuIBVkSmVl5ZGBr/jBgzSdUfP0y3/eV5b9tRL/uhnOZz2r319bo6Zb0w/Qb9c+XJ32w2F49aTajvXNzfpx689n7T9R3FYx9iqgfr+0SdpcHmF4yxuzhnJ2llkPQmOy8j8Ly/SluaGqOnptDOn8jqdM0ZWVEkytaOpMeGxGbsv7YP1SLvwGfrK4cfqHx+siRybbttMuuWK3Q+p2pmXc2ay/e+0HkPSgJJSDftol05dvS2SqkGS6iqK9cSMcaoeWu3Yl0U9/RkKa1+oXeVtIX1u1U4Naep+y8U0pGenjlbzxDEyZGhr476o48O+X6y2HPD54rZZ7P60UiSaobDCXceZz5AMGZE38ez72bpRlazN2NdhT8FoLUdKEIToapvp9BmppvVk/9uX6eZ8nuj8J0WfN2PPf/a2bm03n2mqoqQ0YbDGPs6Q5GpsolBYYwcNTjk2yWabcWrPudqXVl299M3J5km0ja1+JtnxnM+6pJpm53ZsmurfYpeTqv6ZGDP29PzvZT32OpmhcNw1gNs2I0Vfaxj++HO5lMb4R4obA6Ua/6QzngubpppbWxWMKUaRFDmfxZ0DHcZmUvQYMBPjLK99k70u9n9LNZ5NdT6PTU/spp+11980TTW1tSr2sQf7WD52zBgKhyPz2MdmbuuSapxhHftu15OobWSyz8jl2Nw+f7b6s0S/t+8zN9ssk9emTrJR/9hgi12469isKSvSk0fup+ayorj6BBsateHu+xXcXZuw3CUjhuqAKy5SUVVlTurvdZ5U28vtPKmus5yOj9jrP8Pv04CSUl0944So8W+i4Gay+wa9Ub7v5fKGDQpObW2tTj31VMdgzYwZM3TTTTfpvvvu0+9+9zt9+9vf1ujRo+N+9+CDD+qaa67JRXH7vVdfeFGLfnSHamviO1VLbAqBiTv2RaWRGVs1UF+fPF2v/eR/9eoLLyZcR6ihKSqqb6WRmbij62a7LYWAU7lqa2q06Ed3OK5D6nwFd/2df4y8avuj5YtVs7tGn3r1Q03YtsdxHqsutTW1kacJgg2NWn/nHxO+autUNqd57B2vfZtZTy5s3LY1qj72p+Tsv7d3vKVtHTrvPxt1wI69cSnB/vjw3zRr8dtRT4rbt7HTTWSnctXW1Liq/8ZtWyOdvX09Vrneq93pWB9LaWtQxyx+S//71/vjnuJoWvW+Tlnynsrbop+cqWgP6dI3t+uQ3U2SIW1t2BcJ1hyyu0mXvrldFe2hyBNs4/0luuzN7dLajT2qi/1pFjfHZjptxj54sn5f3h7qeg28czlVwbAue2uHJu/svCCx2kx1WYXOCpbp/Nc2qaS1+7LQXhfHJ2bWbtRlb27XgUXlUfvF3mZiU8iM31avT/9nY9R6DiwqT7qdt77xlpbd9kvV1nQPvmPXY39V22kdndu5Vstu+6W2vvFWwn2Z6pyRqJ3Zy5XouNzT1qL//ev9OmbxWwm3s9t2Ftv+JedzRmlrMJJGo6Q1mPjYtO1L+znD3mZCYVO/enOZNu+rl+S+zfSoXHLfzuzpPdxuM6f9b29L9vWYkgZv2qFTYoI1kjSkqV2feXubvjrmkLi+LLb+F7z2kca3mvrsO9s1qLEt6gFgw5ROXb1NF2igfjjr1KjjrLQ1GLVfLl65TWO31KkjHI7aZrHnc+uGUWlbR2Rf+n2GBhSXRp4sjtqXtu9cJWozseuwnlKuCoaj1hEbrLHa5nh/SeRCLp0+06lteu0zE63H9fnc4fwnRZ+bYs9/9nJZ+6U8GNJlb+7QpJ0NcmIfZ9zwyjO64ZVFqq2pjRr/xLK28+7dNUnHJj3ZZm7aTOw2y9W+tMZztTU1rvtm+xjQ/rHiRNvZ3s/Yb0zEbud818WSqbFpsnVI8fszVf0zNWbsyfnfy3rsb9pcXTFGF6/YouunHhe5KZXsWsNpGw8qKdP1U4/TxSu26OqKMXE3Ub2OfyTncVay8Y+ktMZzk3c26LK3dqgq2P3GQVVHOOp8Zp8n0dgsdgzY03FWOn2TVZeh4e5/i73OTOd8bn+Dx20/a0/pNmZrnS5esS3q2x32/t9KO2cP1tjnscZm9nGW0/f13I4zrHbmZT1W28hmn5Grsbn95nO2+rNEv7feetq8b4+rbZbpa9NY2ap/UVWlDrjiIpWMGBq1PitYEw6bGtLYrsuXrNehH9dH1SfY0Ki3bv+VGj78SKGQ85tQTsGabNbf6zyptteethZX86S6znr2o3Vx9w1i2/9lb23XEfWdmQHcvGme8r4BPOMNGxScT3/603r00UejplVVVem+++7TOeecE/f7YDCoW265Rf/93/8d929PPfWUTjvttGwV1ZV8R2Wz6dUXXlTr44tlmFLTwHIdc/UVqh46NOHvl23bpKee+rdOWbVVhtn5RPLrc6bqS4cdozW/+bMq9jbLNKTSBafo2HlzE67DX1Whn7zwbx21ZLWGNLXLNKTF08bo9NNP06zRExznkaT/3Pk7x3VI3flSZZryDxuiB6aN0NaGvTprxUeqbgrKNKRnpozSptGDIvNM3LEv8pS19XT16MoBumjVToV210mGoXELF2jQ4Ycm3WYDSkq7nyTpmqdq2sGRNyyc1mOaps5euVnVTUEppj5b33hLH97/qMLhsOoqinX/YSPUEPCpoj2khe/s0NDmzvo8O220Phw5UKMrqzR2a71mvL4hah2SdM6KzRrS1C6fz6cDPvcpjZx+WNJySdJn3t6m0a1h+Xy+hPWXKdVWFOmJ6ePkM4zIeqynyzeMHBB5W2rHynei6mPV375vlh05QV9f+DkNKimLr/+0EWoocqj/1NF6q7pEobCpQ3Y36Zy1u2WYUk15kf562Ej5fIYue7dWY9oyV5fXl7zk6dh022aKzzlJj/mbE+6X5mK/Ai1tOv+tbUnbjJe6NKxa67nNjN9Wr0++u0NG13qenDFeY6sGJm0zsfsz1f5/8dnFCv/jhah1tJUWRVJP2Y/nMTOPSNguU50zvJbLuiCc9eamqLLZt7PbdubU/o+ac2LcPDKlmvKAHpg2UpJ00aodGtrcIcXsz+8O2F9bHnpCMk2Z1YN0+4QS1fjMuDbz+MFDtWZY5xOgh+5udtVmnM5lbstlbzOpjs31I6oUDIdU7A94Pmfa93/jgHItmTVJH4Xaoo4Z05AWTxml6ZvqNLSpPS71qs9nqMJfrLa2VrUbkukzEu4XIywFwmF1+AyFDUmmOp/k7VpWRVGxBowZpUlXX6Ztb67Sh/c/qlA4HLXN7PvlHwcP15axQzRh4GBdd/Q8/fmRhzXddj6/f9oIhU0zap7F00brg5GdF1oH7NirU1dti9qXTcV+VXWE9bl3dsa1GeuG2HSHPuPsFZs1uLEtbh2x7b9pYLmO++aXHfu/VH2mU9t03M4u938653N7m7HOfx8Em1XaGkx4Dowt1/3TRkiGItvYvg5LOvO4Pc/2ZJzhts3Yt9m5HRVqf/K5rO9LazwXDoe1rdSnvx/e+WCX2/7MupkjSe/9+s/as3V73HaO3cYHXXmxpo0/IPrJYMPQ2AvP0a37NhZEXby2s2Rt0z5mtt/8qq2pidqfqcYmUmbGjD05/6fT/u3b2TRNlXZtg31trQnHDYmumaxjpnVnjYyY7ZzNcYY1/ondn+leA6U6Nzmdl9pKi3RgUXnCMWA69U+nb7LXpWr0CP3fEWO0rXGf4/5Pt/7p9LO///uDmvH6B1HzSNH9/4qjJuqy8z6j2157QR/trdeYrXU6a82uuHmscZbfYf9b2znVOOPjMYMVMsMq8vk9ryebfUauxub2bZatsUmyc6Z1PV8VDEfezk60zbJ5bZrN+tvXY+9P7cEamVJpMCSf2ZlJ4unDxui9/QZrUnGFzl66TsbuzofKTJ+hovJy+W1v9CUK1mSr/l7nSdVntpQENN5fqjnL3lflvpaE86S6znLqM71cm1nfV4sNvCY7zmK/ydZb5PteLgEbFJRnn31W8+fPj5pWXFysV155RTNnzkw675133qlvfvObUdMOPPBArV27VoFAINNFdS3fjTxb7J2OJVXQZs/b72nDfY+oqSuHv89naMDw4drWtFflDa2R31mdnCTHdcy8/HPa/fd/qW7rts6OW1JFcYkmfv7TWlu3K26e5spSmTJV0dgWt45j582NulCxBgR1JQGFzLAGt3REzWNdsNg70Uj9yoskUxrS1qGqoq58q7aLD6dt1lJVprFVAxWus71S2jWPDt4/6sIjsp6yoqiyGYYRGeQcPGR45OK+ob1NHWZYNWVFevzgYVqwdreGNnc/OWQa0mszD5Df8OmoNz5wrMuglqB8XU+iWRfTCcuVZB57/TtTSncOuOrLiuQ3DA1qiS7XyqMm6rLzL5DWbuyuT9dALbb+1jzLjpygzxx8uHb+7Z8K2/IL11UU69GDhursNbs66290pgGSIT0zdZRagx06a+2uqLrYy5Wpuqxd9p+0jk23babm5Jl6a9f2SHDHXv+3jz1I566t0b7tO1K2GVPSnrKADMXXxQryHLq7OdJmIv8+aIA+3FurQTHHmNVm7Bfrlr3lRZowcIh8e2xPltvajP3CK3KcOex/o+vmuKS4/VJbUaRnp43RJ1dvj0o9ZV2AfdywJ+1zhtvj8vOfPk/3PfJ/kQtCS7LjLK32P+uYyDzRx2ZApikNaY2eZ+VRE3XBwUeo9uGnos5/NWVF0W3GVp8nDx4mQ4anNmOvi9ty2duM23b26bPP0SNPPuFpm1nHjNG5eJkyVVvufMyYhrTq8Ak6qTaolh271RRs7zqWOoM1Hc3NnakADKmlyB8J2tj3ixHuvtC0fmcFbaz891WjR+iAKy5S04ebtfmvjysUDmtvW6tM04zbZla5np4yUhd/+nwtevrpSP1NKX4e2/nv2amdN3+tCzXrkqCm3NZntARldM4hn8+nERecqX9+8F7C8/+A5va4dWwYOSC+/RtSa2WZ9hswyLH/S9RnJmubUdvZ5f4/dt5cz+dze5ux+IcN0UMTB+nw/7zfeePBNs8zU0Yp4PNFxgzd+8V5HdY2s48z4vZlW0dkv5jJtrO66+I0NknnPCN5azMrj5qo6SPGSv98Mev70vHcbBubxJbLPs6w78/i6sHqMMOq37Ez0mda2zkYDsVt47rKYh1x2UXqePK56DQuhqHq80/X39a+lde6SFJ7bX1UudIem0px6ygZMVSDzztNb/zhflXsbY4qm86cq5U7t2R9zJjO+T+d9p9oO/uGDNTHDXs8X8/U/9+/446ZcQsXqCnY7mn8Y6+LaXY1TjnXJSowYLsGipQ7xXjO6Rqosz1LFY3R9U/UZmorivT2MZN14Ya9ncGamPpL8jzOctz/Kfqm2LokG2cnPzcnPp9L6fez9nkS9eUrj5qo+Z/8pP7yyMMO5//ueQzD0MCSUvlt7cza/27HGZ8662w9+o8nPa3Hfp2RrT4jV2PzXIxNYs+Z1jaLvZ63HvSJ3WbZvjbN1dhM6gzarLv7vu57Pmb3GNoSNqQ3p++vIz+ql69mj+zsQZtUwZps1d/rPIn6zLqKYj0zdZROWbVV1U3R+98+j9vrrGmzZ2nJs8/plFVb4+aRpCGtociDZPZjxikNvf1Bantd7A+H9lb5vpdLwAYF5cQTT9TSpUujpt188826/vrrXc0/f/58Pfvss1HT/vznP+viiy/OWBm9yncjzwanTseSKGhjvyBoD4XUGupQub9IHc3Nkmmqpcgv2V5N94XDkoxIruEIUyo2TZWWlMrwGWoMtqvUH1Cx36/29na1dnQoZJ/HNFUa7HyNvDVmHaYhlR11mMpWbYi6WRkOhSPztAR8MmPmMY48WIG310ddRFkDCEOddfH5fVE3RlqmTVTL6+9EbzNrHkNxT4DIMFR93AztXvaGGtpbIzcM7PWxl80wDPnDYZUFilRcXNxZl/Y2dYTDMiQFQp1Pc9vrIkMKhM3ObxhEbbPuurSVBFRV3P29k8Tliq6/DCNqkGvV3z4gMCSVOewbn89QVXGphs2aqdrlKyIXkWHTVGNbm4rag477xh82O/NFOxwzgXBYQUNd36HofpLdOs46ojeLyoLhjNal7bBJDvvf/bHpts0U+wNqNaNfAfcbhsrlk78oINNw02bUVS4zrlwVxSUadcLRUftFksLhsNqbmmWapmObeXvMQB2+dW9U/SPbOcHxX3H04dq29D8x7Sz++LcGrLHnDOsCx+ja/yGfL6ouUucxIyntc4aU+rhMXv/O9bQXB1RV4qadJW7/6ipbyTvr1dDWqg4zHHds2ucJ+Hyq9AVkGIZ8AX/c02uRNuNwzvDaZiJ1cVmuqpLuNuP1nOH1nGkdM9bxYq3H6Zix1rPfp05TzSuvq2Hbzqh2adi+FWUP2lj7pbGtVUVtHVEXmvagjd/wqXrsaE3+yucjwRrrxkMobEqm6bjNZHTWZ/XYQZq6ZU/UTaSo7Vzkkwxf5PxXZEqlgYCazJDCYTNqnqg+o+vGkNXOEvUZkhm3DtOQ3ho9QIclOP4NGSqqiG//zn1m6rZZ8s56T22meeoBKl/9oevjrNLobjN24XBYLa2tapMZ3WYU3TdF75foNmPpbDPTVbt8pcLhsPO+DPii+jOfz6eOwyfLfHNNwvNMq8PYJJ1xhtc2UypDbR0hz+dZr/vSOjfbb9Y5jU2scjmNM9S1rTuaWhQyw2oJxNQnFJZpSKGYfWxIKgqbKistUyDm+xzhjg6ZkhrDHXmpS6ipMx2Jv7xUhs9NO0s8Ng13dEhKfPy3G4rrZ33hsEoDRWpVOOtjRq99ptf2n2g7W2OgdK9n/DHHTCjYoZaOYPS3YZKMf0xbXew3eA3TuS6d9fFp9Oxj1PTa257Gc6vGDNLh26LP537DUFkwLMlUc5FP9k9HJmszJaYc6x97nLkZZzmVK1XfZO8z7fOUdYTkN4y4usTO4+Z8nol+1pSi+/8EdZmyZY/sBXbq//0xY7Pa5Ss8jzO8rKcjGIxcZ2S7z8jF2DwXYxO/7ZwZCpvR2yzmet4wOr+HZG2zDp+R1WtT+3kmW/WPenDBHgxobI8L1tj3myTF/0vntX/5+DE66GuXpAzWZKP+Xudx7DO75vGHw+owove/YZ/HDLu+zho2a6a2v/yamtrbFHZ5zEy46Nyot2Alh6CajfVwaOw8vUm+7+USsEHBeO+99+IO/qFDh2rLli0qKSlxtYwVK1bEvYlz7LHHavny5Rkrp1f5buSZ1hEMatGP7lDFnqaEv4kN2pihkNbf+Ue17tgV+U0oHFawqfsGl3XTSkbnx84inUWx7e0o0/ZkctcTEz5/54A1bJpqb2ySaUqtxV0Xkl2doa/rLBc2YjpF0+z8MGZpuUxFB2vs80QNJCQVSyovLXN8NddeF+vGiCQ1t7YoaL9YiZ0n5rVd0zRldoTkKwrEBZIcy2aaKu8wZRhScWWFrWzd2yDq94a6LuTCXdvZ71gun9+nQHlZ5CLfuVymY/2tC/DKQLFa2loVNIy4AUHsvrG2mSFF1mOxLiKtYya+/lZdEhwzhtF1IdG9/0u76t9S7I+66MpkXZz3v/tj05qUrM1YdTEMyVdeFnnyP3IRHQ7L8PnkLy+VfMnaTPT+t5eroqhYRT6ft/3SVZeAKXXYLrzitnPM8d/58Vf32yzxOSP6+LdvY8d50tgv6de/ez2mEX3T2nP7V8y5yRasjT02rRu9ARmd5wxJvgrn81myc4aXNmOEwlLA765chk+VRT07Z7g/Z3afM5zWY9//kZuJhqHSkcM14bILtPGeB9W8fVdUu7Qzu9ZXZBiqKClTR3OLwqGQ8++K/KqrLNHrc6bqv078hHbf/Vc1b9+ZsJ+xbzNDhgzTlD9sKuT3xd3gdzqefYZUHnTa/z3rM5zamT9sejr+vbZ/69/T6c8DXdvMzXFWGSiW2dz5xHWgsvs7D/ZzgFP7t46zluJAv2szlYFiBZuaC3Js5jTOsII1VjtNNDZtLuoOWMTWv6iiIhK0sQdMIu0sh3UJNbXI7HrowRoDWOM553aWeGxqr0ui4z9qXBLZ1+HosWmWx4xe+kwv7d/t2DTd6xn7+c/r9YxpmpG62A6ArI1nwlHrkSpD4e4b+D5DjX7DRZsJO9bf6ThLt1zJ+ibZ+sxE8/j8vkhdYueJ7mcTnM9z1M8mLleC/j+2n8nWOENSSTAUOZZz0Wdkc2yek7GJ7Xo+UFGufe1tcdvZ/nvDMFRVVKxQc4tMU2ou8jnWJRPXpj09z7juzw1DzYMqdPR3vq5b33hRWxr2anBDqz7/yocq7oj/Lo1P3YGa+JG4FDYM1VdX6qhvfTlyb8rxflYW6u95Hqc+M9k5Q93nU8OQ2ooDrseMVvvf196mkMM8seMM6/pn0tWXyfB3ts2EaevUmd652O+Pm6e3yfe93N6ZSA590oMPPhg37dJLL3UdrJGkGTNmaMaMGVHTXn31VW3cuLHH5UOnQFGRjvnGl9Q0sDzhbyr2Nus/d/4u8kE1w+/X/l/6bOTjceGYG8+Sup8CMzufRK0dUqG6Id1PQcQOIo2wqWBzs8JdH5TzGZ1PT9RXd80T0xlKkq9rGdbTN02DKjTx219WYPgQxwGENU9ZR1hGV7mau+YpGzms82PuhhH3tIdVl3Co84mlouHVmvjtL3dvs5i62OtjfSCvdOQwHfSdr6hkxNDONDn+4qRlk2Fo28ASBcaOjBl4hyPzRH6vzrdqDMPQjoGl2jGo1LFcPsOQwqZCza2RC/7YclUFilXW4Vx/mabCYVPby/yqvHKhaiqKHC+I7fumwl/cNSDoXo91zLTHHDOx9d86oEQ7Btpe0409ZqwLR6ustvob6uwQyxLsy57UpWzksJj97/7YLB3prs3IMFRXXaHy8WNU7Peroqg4KlgjSWY4rFBza+Tv8W0m/ri0ylUeCHQNujzuF0m1lcW677gJqq0o6iy303aOOf7LRg6TLj9P9ZUlSbdZ6nNG9PFv38adx3+ZagZX9Gi/pF//7vUYZg/bf9d6Kq9cqG2lPoVN03Y8x8/jM6WwpF2Dy+SPO2fYzgFJzhle2kzVVy9yXy6z5+cM1+fMAaWqGVKR8GadtQ6fFLlYKRkxVPt/6bMqHjRAg887Ta2trY7BGmtbFMvQuC98pvOJ4XC4+0nNmN8FwqaemTZaHwSb9ePXnlfRhad3brMEKSDs28yQVFdZovuPP0B1FcWON1Ei8wRD8hlSVXGpKsaPUfn4MZ3n8kh/5tBnmJ1lMA0l7TPs67DaTH1lie6ftX/q478puv335Jzppc0ErviM6+PM7/OpvGubWWLPAbHrsPdN0fslvs34jM7jrHzUcB30na+oaHh1wm0WWU/XeotHDI1sM+sp7UR1scYmgeHVWW8z1jZLZ2zmdV9Gzn8J2ozRNU9lgnFGbLDGvm+sfmbHwDJtHVDa/e8O9Q82Namj61g2uvpZq53lsi72YI3UPQawpsW3s+RjU3tdLLHHf9S4pHMDRMYmuRozZqv9ux2bpns9E7Jdzxijh2nnoK7xbIrxjyQ1D6rQqG9eFrMvw0nbWX1VqXT5eZ7HMw/M2l91FcVRdbGuxSQpHAq7bDPO9Y89znpSrmR9k73PTDRPJOOCwzyuzuc56mft5Yoem8lxnrixWRrjDFfrkVQzuFy1OewzvBwzXsfmORmb2K7nmzraHctmH5tLUnMoKP/YEdo+sCRhXTJxbRp/nslSf15RpP2+eEEkWFPW1qFPrNrW9WaRolhvCxs+n8IO4+uwYai1yK+yhpaoe1Nx97OyVH/P88T2mUUl8suImid2e5mSaoeUq3ZwhafrLKv9mwnmiR1nWNc/TsEaa19YbdgK1sTOA+8I2KBgPP3003HTzjvvPM/LcZrHadlIX/XQoTrm6is8BW2Kqip1wBUXyTdkYNwgymINcpurSnXM/7tSR/+/r3Suw2EQKUUP8ktGDNVBX7tER33ry2quLI3rDC1Wp9hUWaJjrr5CxSOH6YFpI1RXEkg6T1lHWC1VpTrm6is0fPw4HXDFRSodOiTqqSqnutSVBPTAtBEqHjlMx1x9hVqqyhzrYq+Pb8hAHXDFRSobNSKyzTqam5OWrb4soMdnjtcvDxqomrKihNuscyDd2cnXVRTrsaPG67GZ47WnvCg+WNPFusgvrh4cVa7i6sEKN7fK71Auq/57yor098NH6+YP3tQT08epvqwobkBgL1tHgvonOmbs9X/iqAl67KjxnRdfiY4Z24VEfP3DjuXKRF2Gjx+nY66+wtOxaR1nbtvM0d/6ig762iUqGTFURT6fym3BGov9ho29zSQ7Ln2mZLa0pb1f/jFjvPYOLNeTM8Yn3Waxx/+sw4/U8EvPsx2bifeN23OGfVBcX1miYZd/Rsdec2XG90sm6u+1/T85fZxu/uBN/f3w0UmPZ5/teP7bkWP188lVPThnuGsz16991VO5MnHOcHXOPGq8/nbkWNWWFzle4Fq/L2kPyQzHf1z7jT/cr3ZDcReQFtNnqKy0VHV//6d8AX/kyfbYoE3YMNTh8+kTq7aprK1DWxr26roVL9i2WeK2aW2zJ2aMU21lie6fNkL1ZYHEx5kplQfDKh06RAd+9WId+NWLVTJiaNxTwrHbyzA786f/5YiRenTGuKTlsu//1+dM1fVnnafXTpyS/PjvCtpk6pzpts38fPNqT8eZfZs53RSK3Wa1FUV6bOZ4/eXwka73ywFXXKS2IQM6xyZlga79krgudaXR44zWqrKk85TaxiZtQwZkvc2kMzZLZ19Gn/+cj02/KZktzuOZ2GCNfd9Yx/N/5k3TpK9drLrK4qT1t4I2JSOGRo6ZXNYlNlhjiR3PWe3M7djUzfFvH2c1DSyPjE1yOWbMVvt3Owbq6fXMEd+8QkO/eL778c/VV+jASRNT7kurne0pK9LwS8/TrMOP9DyeqR9Qpicc+oCw2ZlaOnZ/PnbUeD1+1HjVViQ+Z8TW395meloup21m9U3fPfPTen3O1JT9rLXN8t3P+gwjMn6w1+WJGeNUX1Vqq386YzN34wzP65m+nx46ckxO+4xsjs1zMTaxX89bb1w5bTNrbF5TVqS7DhqkR2cmr0smrk2t80y2+/Mfv7Ms7kP2MozOVMNdQ+hIW/D5FKgsV1FFuUzbG1NWsMbKlxZ7b8q6n5Xt+nudx349L9NUcbDDef/bxpl/O3Ks/jZ9rKfrLDfXZrHjDCulXGywJrIOw1BVcUkkWBP7zSB4R8AGBaGpqUkrV66MmlZeXq4jjzzS87Jmz54dNy32uzjouXSCNvvaWvVxw57uJ++cGIb2qxqkASWlqh46VDMv/5yKTdNxECl1DqRa21o1+LzTIh2C6fhCbMx8MhQyw7r51ee0tWGvQmb8hW10sbrLFb2c+JtvFlOdr7dva9ynm199TpVFxRpbNTA2vXf0PKa0pWGv9rV1pl1xtc3U+e0DwzDUUhLQM9NGKxB2DiRJnR229TR3S4mVCkqRJ3US1ScZp3ms+tt/40+x7FzU30hRf8dyZaAunctxd2xavLaZoqpKjVu4QOFgh+PNGqnzhk24I6RxCxeoqKpSA0pKs35cSults4MGD5PfSDFU8XjO8JmSPxxW1afma9akQzrXm439op7XP531dC7I/fGciXNGqnWkUy4pN9usrbTzw7ed9Xf+rWFG9zO1NTX6z52/6/y4dswFZKRcPkPFFeXydQVpYtMRWedM+wXlkKZ2nbNis8raOmwLct5m1vzWNjNNU43BNoVNM2nVfYYRdSzbzxmJ3gCy9v/jBw9TQ8Cnxo72zkf6UuzLkkBA18w8UeMHDNa1R89TaSD5E3amTH28b0/2z5nqWdssqqrU4PNOU0uSt6t8phQwTT07dbRq/GFP+yUc7hybbGvcl7LNKI1xhiSFTFNbGvbq5lefU31rc9bbjJSbfdm1oqRtxkmHGU46DrQfz9PGH6AjLrtIRWEzYf19ptTS2qLA2SerqKoyp3XxyusYwM3xb5hSsSnNvPxzcd+0lApnzJjV9fTwesbr+Edyty+lzm8YHDR4mPu6qGfjDFfnjJj696RcXvqma2aeqGK/P8V5tmvePPSzTtdmsf1/TAWTXs95GZtlaj2F3GcU1PWcenY9n6u6pDNPOvsmKlgTWaih9uJAVLDGX1HW+S0fv09F5d1Bm7YiX9xxG3tvKp265GIeL+PMxdPGqLW0yFpJxq7NEo0zEgVruusggjUZxDdsUBBeeeUVnXDCCVHTTjzxRC1ZssTzstrb21VZWalgMBiZNnnyZL3//vs9Lmc68p33MNuiblwl0DSwXAddfL7e/8vDnb8zO1NTGTFnH/sNrpIRQzVu4bna/NfH1Lxtl4LNzk/LWDe7mgbFr8PpKQZ7ntCmgeUyz5yrlscWqbop2JkyqyN+MJmoXG07a6LeWAjbTqf2m3D1lSUaccGZGrL4NbXtrFEoFHasj30et9vMXp+6imItmjZa81dt05DGNsff29dTV2n7fVN71DrsnbR9MJSs/pIi28Bel6hyda3Hed9ktv6JnmJxU/9E5cpUXdwcmz1pM63bd0XtFztrf5aOHBa1L9M9LlPVJZvbLL1zhrrOGRVZ2y/ZqH866/F0POewzeSqnWX6nBHXz9jZ1hcbrIn6mf2c6fOppcinUMxQPNU28xlG59PMMdvsX1NG6BOrtmtos3N/5utKG+F0Po89Z0T3Z515rHeXF+mJQ4bp3LU1GtLUnrDPDBuGgiUBVRaXqMx2nmnZsVsNba0qbo9/WtBah2lIzQMqdPAXekHb3NOU5JhRV5spSblfJMnn9ytg2y91pxytnX/7pwY3trlqM+7HGV3bWZ1pR8rOPVXGP5fkrs3kcF/GthnJeTzz4f2PdH4styuVk9N2djqem7btUrCpKeH+bynyq7ayWJM+f762PPh4TuqSaGxmSTaP5zHAnqaE40zrHFg2anjvGDNmeD25uJ5Jdm2SrP0na2fZGM9l4pzhZV8OamxLr29qb1VxW/K+aU9FeufzdPrZT71fq+oE12ZO/X+y6zm37czNOKNH6ynwPiOf13OZvp7PVV1ysm9sOr8L1fnNLHt/Zmed/0xTkfrGymf9Pc2TpJ+1jzMzfW3mZpyRSF8L1uT7Xi4BGxSEe+65R5dffnnUtMsuu0z33HNPWss74IADor5b4/f71draqkAgkGSu7Mh3I88FN0GbODEdQ7IbXJIcLz5iX3V1Woe9U4z7EJ71M3U+NSUpbvCdqlyS4oI2ca/gduXxTFaflHXpqo99myWqT6Lfu1pPzOA70WAoUf0lKWRILYHUdYneN5mtvylJ4XDchZR9PT6focqiks6n5azjymEd1sdDExYtzbq4OTaT1T/Vsel0wybV/vR6XDpt59i6JNrOsfV3s50L5ZyRrFxu58nm8R81T0c4koImWdvMdZtxWy77PFnfZg4XUq7WY5u/2JTKSktT9hnhrg9Xm1LURzqT1qVrmzn1M5Gfybk/c3M+d3oAwX6DX5IMw5D90iG2z7TfEHN6Oi9smnFBm9h1dD4AGHP8FGDbTNxmYj6iXFSihq6PunrZZu2hkJqC7Qnqks44I35fxlUrF20mh/vSfp5xOv5jP5LrtJ2THc8doXBc0Cbl8ZylusTNEjMGcDOP57GpwzjT69ikYM7/GVpPtsYmXtbhtJ5sjOcSzWMfA8SNGxzPGSnGDS72pb3vk7z3TSHT1L7WFsexqf2cWVVcoqZgu+fzeaScLvvZ2P4v2fnfUcz1nNt25mac0aP1FHifkY/rOS/X806BtHzXJSf7Ru6CNRY3QZtM1SXr8zjez3ExTxrXWV7HGbH6WrBGyv+9XFKioSBs2rQpbtr48ePTXt64ceOi/h4KhbR58+a0l4fk3KRHi2N0p5Fxc+ER+5qrq4FH12/CRvKOzZAinZFpGGoJ+BR2WS5JUalufH6/giWBpDdRYuvj+oZgzDYLVJQnHxBEfu9tm1nrCBmSr7w0ZUdtr7/h8ylQXu6qLoE0659qf1oXbfZ9KVnz+CI3nq2Pf1YVlchn5by11d8qlynJTPYic5p1cXNsOtXfbZuJTcHkZuDl5bh02s5OF/eJtrO9/i0ut3OhnDOcyuV1nuj1xL+2n2g9rtt/1zyB8vKUbTNxm4kO1mS6zeTqnOHlnNnq9Zxp0zSoQhO+erGrPmPi1y9R6chh8hmdN7NSMgz5y8oUMpKXK1F/5uZ8bvh8MspKFbLt/7hgTcxejW3/weLEN6qkrvzWJaVqLw7Etf/uhSr+6MnAOdNLm0l1nCXvZ7qDNVabGVDc+SHW+G3WuY6QFTiw3XAs9vtVUVQcKVdbSUA+f/rjjJTBmnS2WTptJsf70jrPxPZ/pqTGYHt0sDTmfJbsxqskBfw+FVVURO3/lMdzFuriOEvM2MzNPJ7HpkZ0SkivY5OCOv9naD3ZGpt4WUfserIynjMMx3ns47nIR6jt4wZb+++sv4sxYIpxljVvVJ089E1hszPVmWkYaimKr4tdS0cwrfN5pCou+tm4EsbV39uYMWRI/vIyV/O4GWf0aD057jNyMTbPen/W9Xufvyulrt/napvl6to0J/25aboO1kjd5z/DsL5dmnzxPalL1udx6GfdzJPudZaXMYNdXwzWFILcv24AONixY0fctP322y/t5TnNu3PnTh1wwAFpL1OSdu3apd27d3uaZ8OGDT1aZ29hBW08vWljGKqtrtR+VYMUrtub8ud+v08qL1drW6vaDcPdTTTDUO2QChkyVN7YmvhnXb81ZUqGT3XV5a7LJXUOwMvHj+ksZ02dGoPtKvUHHG+i2OtTNH5M50f1GlpcrcfaZqMrBmrfrl1Jf2p2/WkuMlQU7vyotdtttn1g54B7SGt75MIs6Sxd9e8ww6rfsdNVVZrDQVXtN0o7mhpc1d+qz9YBJfIbPg1qCTr/xnaBZF1IBcKmOvw+mYYUsF1sSopcUEaesrXqL2lAc3tkwaZhSrFPfadZF0muj824eTy0GevGiPU0v5uBl5vj0mk7x+4X6+I+2XbeMbBMIdPUYGtfptjOhXTOiNRdqY/LROuJ1L812Hmj3U3ZXLZ/S1OoXYPHjZIkx7bp2GaKfCoKK3LOcLMvvbaZVOVyknY787LNDENtxX4Fwqb7Y0ad6RVmXv451f/fv139ftsTz2jcwgX68P5HtXfrtpS/NyU1dLRr74BSyZAGNSc+zmL7sx0DS1ydz8OmqcZQu8LWOTPSZ5i2NwScgzZbqkpkGIaGtcV/tD2WFbTZVmmqtSPU3f6j6uBQxh6cM720mVTHWcJ+xjQV8vmigjX2NjOguET72ts6v5Vi2y/WvgyHO2/y2efrHEMUqzXUocqiYpWOHiIpvXFGaUOLovdlhrZZOm0mR/vSOs8EDJ/aa+ujFyep1B9QUzg65Yp1PisJBDSy3Uw5/gn4fVJFhVpaWxT0OWePj9viGa5LIvaxqdt5PI9Nu24muXm7MLZc2RwzWmXLdPtPtp6sj008rMNaTzbGc91lUldfb/UB0uCWju4F234ePW7ovEnpD4fV4fN1t5kE44ZU46ye9E32t+wMSabh09YBnd+DiNRF3YGUUn8g7fO5tb7U/Ww0q/6Rbdza4XrMaI3NBra027aVMy/jjJ6sJxd9Rs7H5tnuzwxDzUWGyuVXi8KSiyRJOb02zWb9u94wCZud6QbdBhKs82ywuVmlwZDroFXO6u9lHls/G/L55CYClc51VjrjDIlgTTbxhg0KQl1dXdy0ysr0G7zTvLW1tWkvz/LrX/9aU6dO9fRnwYIFPV5vb+H1TZumgeU6+ltf0UFfu0QlI+I/SuqkfPRwHfhfX1bTIPfrOOb/Xamj/99XUparc1BmqDmNcpWMGKoDv3qxDvzqxSobOUxVxSVJb6JY8xz0tUt01Le+7GmbHfy1S/W36WNUU1aU8Hf2C7Dd5cW6++ixqqssjvqNz2doQElp95PyXeoqivXYUeP12MzxqikrSviUWGxdhn3xM/rrEaOTlsuupqxIf58+Vgd97ZKU9bfqU1tRpCeOmqDHjhqvuoqY+hhdH1uMGYztrijWb44aq93lRQnHN9aFpM/XmTv3kZnj9JfDR6qm3FYXM/HTf17qYvFybNrn8Xpslo4aroOvvVKlI4e5+n2q4zL24l5S3H5JdHEfu50fO2q8Hj9qvGor3G3nQjtnuDouU7Szx48ar9ryouRvpNjK5qb929WUFekvh43QXw4bGTeP076Uos8ZbvdlOm0mUbmS1SWdduZ5m1UU66mTp6jRwzqsYE2yvM52bTtr9OH9j+rhyUNSlsvaT7vLAvrLESP16IxxKY8zqz+rd3k+t9+0qqss0X3HH6C6ymIZknzqTm0zsKQ08mSxpaa8SH89crQeP2q8asrd9RllI4fpmP93pV6dOyW6/av7xlisdM+Z6bSZRMdZojZTU1ms+2cdoLrKkqRtZkBxiQKGL2q/2PeldZMv9k2bqq5vLvRknNE8sDyyL5PJVZvJ1b786xGjNeyLn3HsM6LeYupSV1Gs/8ybpqO+9WWVuew3K0YP18hvXqbamHGW5Hw8Z6MuTuxjUy/zeB6bDqrQgf/1ZZWNGu56HdkcM0bKleH2n2w9uRibpHNtkunxXNj29oihzjF3TXmR7j9ilO47fFRkDGAYRtRvpZhxQ2WJ7pt1gGpizv+x4wa346x0+qa4lIiS6iuK9cRRE/TAkaOj6mIo+m3GdM7nbvtZe19u1T9qG5dFjxmTjTOtsdnuskDScWY644x01hPZN1nsM/IxNs9Jf1ZerF/PHN15Pevm9zm8Ns1a/W3pwMJdD5TF7o+SEUM16Vtfcjwven3TJpf19zpP48ByPXXyVO2OPWcmkO51VjrjDII12UPABgWhqakpblpZWVnay3Oat7nZw/dVkLbqoUN10MXnu/rtQRefr+qhQ1VUValxC891Nc+4hedq+PhxntfhtlxGD8pVVFUZmcfNw0LWPF622djPLtD/rlupD4LNWjRttONvYi/AHj9kmHaWF+mxg4dGDe4d0xtJWjRttFpKAmopCWjRtNGON5BiDT7vNN2yennScsVaNG20Pgg263/XrdTYzy5I+Dt7fZ6dNkatpUWRslnsF5LWBaS9/rsri/WPQ4Y7XkDal1FVVKI3j5mk3UZIDUU+PX5IzM0ap1Q9Hupi5/XYtM/j9dgsGzXC87HsVC6ntBNS9H5559iDkj7Fb23nd449SC0lAbWVFunZaWOif5RgOxfSOcPVceminUXqnyS4YXHT/mM9M3W0VjXv1aqWPVo0tXueRPtS6j5n/GvKCFW62JfptJlE5UomnXaWzjZbNG201gXCemX6eFdZFCadd6anYI3Uea6q27pNh7/6vl46aETC39mPs8cPGaaGgE+7fKGobZboODPk7nwee9Nq0bTRqq8qTXgs29PBSNI/DhkuX2W52kqLXPcZ4xaeq+qhQ3X9yWdq5dGTItMTpupSeufMdPe/03GWrM08O22M6geUuTv/FXef/2LPGVLnTb7GYHvUmgwV1jgjltc2k+t9ecvq5Rp83mmOv4kN2rxz7EH6zrzTVD10qKd+5sBJEzXp8+dHjT8SHc/ZqotTuezHjJd5vI5Nho8f53od2Rwz2mWy/SeTy7FJpvZlOuM5U9FpBK0HA/5x6HA1FfvVVOyPjLWt4z72fGYfA+4ZUBY//utakTUe8jLO8tI3xdbFsmjaaLWWFslfUa5npo1yDNZE1u3hfO61n7WnnZM6+39rGz9+yLDINnJMO9flmamjo8dmCcaZPRlneFlP7HbORp+Rj7F5LvozU9JjBw3Vx6W+qOv5ZHJ9bZrx+sd8u6WtyBeXbtAKFpSNGq4DrrgoYdAmUFYmX9e3Y5JtvFzV3+s8pqRXpu+vdYFQj6//kunJOAPZQcAGBSEYjH9NtbS0NO3lOQVs2tvbHX6JTKutqdH7f3nY1W/f/8vDqq2pUbChUZv/+pireTb/9THt+miz53XkolzBhsa05nFbNlPS+vseVs3uGpW1dWj+qvg0Ok5Pyy1Ys1vDGtt11nu7ogb3semNrAHr/FXbVNbWEbWOZDfgwqapN/5wv2prahOWy4m1nt27a7T+vocdx0+x9Tl11VaVtgaj1uPzdV4wlQW6noSTooI2C9bs1oimoM59vybqAjJRfY55Y6PKgyFVtIe0YE18CkSn2y9u6hIrl8dmy/adGTkuI9s2hrVfDiwq13nr6lKmkPEZhs5bV6cDi8pV0hrUqau2xv3GaT2Fcs5wfVy6aGdR9U9yYeim/TvNc8qqLSpt61Bpa4dOXrUlsuxE+1LqbjNnvLuz80PBKd6W8NpmkpUrGa/tLJ1tZq1n0L4WTX9tfeqnK01TNb//m1q2u0u3I0UHSIY0tum81z9yvICMPc4WrNmtivZQ1DZze5wlOp87PWE8f9U2DW5oTXosR26MGdK579eooj3kus+Qus8zoYYmHb/yI8lIHqyRvJ8ze7L/nY6zZG3m1FVbNbnD7/n8l6hcpf5A3JoKZZzhxFObUe73ZW1Nrd74w/0Jj0craOP3+XTeujpVtIfS6me2PPh4ZPyR6HjOdl1iy5XtY0bq3J+7Ptrsah3ZHDPaZbr9J5PLsUmm9mU64zkrJVjscs5dW6OqjrCqguGosbYl9nxmnQMnd/gcx3/qKls64yy3fZNpmnF1kbr3/8TiCl21tUMBny/hd8LsdUl2Pi/x+z33s7YNIam7/48aZ8UcnPb+3z7Oip3H6Q0mi9dxhtv1OG3nTPcZ+Ribuy1bbDm9nJusep21ZlfU9XyqbZbra9OM1j8mWCNJJcGwZHa3Yf+wIVFvdhRVVToGbUKhsDpaOlPCpQra5KL+Xuex9v/019Zp8L6WHl//JdOTcQaywzCd3usHcuzkk0/W888/HzVt8eLFOvnkk9Na3ve//33dfPPNUdP+8pe/6POf/3zaZZTS/4aNPS3a6tWrNWXKlB6Vo1DV1tR4+4aNpOaqUk/5mEOhsLecz5KaKku85RVNo1ySVFw9WJL7POGS5Bsy0FWecPsgtL4s4JiP1xp8dpjhqIGIYZrd33CRFPD5VOXw4U37jbs9ZUWO+YsTpkZIMU9lUUnnjd+YJ9n2lBV1f8PEUFT+ZqfgU2z9Y8vTHgqpKdjePb9MGWFTRaYcv+Fjnz+2LlHfVuni79p2XuuSSC6OTTMc9vQNGyn1cem0b/aUF+mAgdUy9uxztQ5JCg+q0qa9dRoYc8wku3Gb73NGOselnZvjLFlbSNj+Y9qZfZ660kB3bnnbsn0+QyW+gJo62uPOGfY2k6g+6bQZt+VK55xhl842657ZjM6tn2g9tgvKRB+CLhnR+ZT+5r8+pradNdEBkphUD1Z+bZ/PUHmgWA3tbXHHmn2bGUZXGhgXx5njudl2Ho+tl5Vb3+dPcSy3dwZl3PYZUf/msv+zc3vO7NH+l/e+yTBNlchw/Q0PKXH9k90czPs4o6dtxiaX+9I6NpMdj9a6DHnfzm77mVzWxZLNYybCNF19wyabY8ao4ih77T+RXIxN0rk2ycZ4zj7WtjRVlsqQ4tpzovOZVf82I/7eqWEYPR9nueybOsLhuLrYt7N1Tkgl0XYuLypSa6gj7hhL1s/a24l1LSMzZsyk7rFy7LYImab2trXKNM24eSyGYaiyqLjz7ac0xxlu11NVXKLmjvas9hn5GJu7LVuicro5N4XCZuT3sdfzVnn8GT6XpXNtmtH6G1J5MKxwKBw3vzVWrqss1utzpuo7807ToJLoB7WDDY368HcPqG1njUKhsILNzTJitot9zJ1uXXIxj317GaYZ9a1ES7rXf5kcZ/TltGjvvvuupk6dGvl7ru/lErBBQfjkJz+pZ555JmraP/7xD5155plpLe+aa67Rz372s6hpDz30kC644IK0y5iufDfyXEknWGPdrJLhfKMrlr3TTdbRxq6jNNj5ocnO37sYdnssl9R5UzzU3Nnhuv0YnlUf01TSusR21mUd4bj62Af59sG99XufKYWN7nmS3oBtb1VJW4dMOW9j6was6XDjMXaeRAGRzop175uWIn/U4FO2OtjZ6x8sLlJlSXwd7BeSPkkl7SEZZuJjxu/zqbKo2LEu9ly5fp9PA4q91yXhkZCDY9M6Ls1wWIbP5+rY7MlxaXhoM/b12Ldz0qfs83zOsLctOzfHpV3YNNXQ1qri9s4L28gxE7XQzpzmrtp/bDtrb1NHOBw3j/3YDBjdgdu2UIcag+0O54zoIEKiGwqu24zHcqXbztLaZra6lAZDkXNmwvU4PP0XG7SxX8AEGxq17u77VLd1W1ywJnJcGIaCJQFVFpeoMdiujnA46niL3WYyOi/Uk9+0cj6fR45ldR5n3ZVIHYRyuw6L1WfYJ7s9z0Rxec7s0f6PWU+i/e+0Dp/LbSbZzs3qPP+FupaVLFiT93FGT9tM1Apzty9jxyYpgzYet7PbfqY31MVeH9dt0+U5I1GfkYkxY8bO/x7W47QNsj028Xptks3xXNRYu+sGq7rGAKnOZ7H1jx0DZmyc5bJvCjrUxZDhus0kOp+XFxWrLdShUDjmxnOSNmNKamhvi5rH7FyJyoLR/b+9ZLHXZqGwGTePfZPa65XOOCPsYT0Bh+usTPYZ+Ribuy1b1M/k7dxkD4jFXs9bZbMH1PJ5bZqx+htSedCUuo5/pzc82gM+3Xf8AaqvKtXYqoH6wXGnOAZt3v/VvWr+aGtcsMYSNgy1Fsecn7JYf6/zJBpnJrqfk6nrLK/jDEtfDdrk+14uKdFQEMrL4z+21dLi/olLN/NWVFSkvTwk15NgjWFKRthUe1OzwrEDWpvYJyTc5CG1Dwh9pvWxuRQxao/lkqJvipvhsEJNLY5P+SSqT7K6JOqs7fVJlBLMUPfvJWue6NeJYwdChml2XqiYibdxqT/gGKyJnSfpa+cx+6YsGOocPJr/v707j5KiOvg+/uvu2TdgNvZ1GAVBBCIiCIIkijvggooLi7txiTHRHH2V+Bjj9pCoMWqMBhMFNFEQXKJoRJ4gCDoiiiCIyKYg68DMMMzW9f4xzNDVXd1TVT3T3eD3c47n2Je+W9Xdqu501aGbiMHLyeD6p9TUWp7PxkebeDzKqPWrYSkWrj6mRxUE1SW9xi+PzDeendbFsiXEoG0GtsvGz020TbvtUjrYzjzmdma3zwTn03Cc7WzWxHPMaOhbpuNgs12asvEbSq22aDOB6YbZrLHs/2EeoxEcJ7BtBkr1JSkrOUVeKWjMOHTMwj5Gy2afcVMuN/3MzTFrzEdqzKO+/mHysdhskcztMvjCpSLFpxnHtq1/8WeY+IHHr+ERMo2PVwo5ZvXjdKRHj0Uazw/NE9abNZLkMyT//gMRxwy7c4bVZo2dcSawbHbGzKjOv4N2ZjX+SS7GZn/9jS6fx2Nrsyau64xo+owpw9idS6u1SaQ+4/Q4251nDoe6BNfH7pwZOGaEa/+R5ozmWDM2y/gfxXouFmsTp9cmLb2eM621a/yS37A1nlnVP3gN2BzrLCdzk1Vd7PaZSON5qs8X+ti1JubZhvk/pP41ofN/cF0Cr81C4xxaZzYcZ7frDL+LfFpyzoj12txJ2Rq/Jmdjk98wVF5TdTDP0Ov5wLI1PLo4XtemzVr/lDT5Ug+9Uy74GsLwSLVej0YffHTXlrK9um/JeyqtMt/721d1QFvK9kYsfk1S0K3wFqy/0zjhjpd0KI5P0V//RbvOCFT1w06tf2YGj0drZmzYICHk5eWFhJWXu+/sVnGt8kD07GzWVLTKUKebJqqi1cGNOYubVcEXBqlt81V869VKbZsf9uesgZOJVR6BC8L675snxWjLJYXeFG8MC1jkB8exqk/wxURFqwy1uW6CdmUe3IQJmqwD65PpS6mfcDu0VcqV52tPVqppAR3umO1MT9bcYcXyFeSa6iK/cehCNqhcGe0K1OXmSfo+zRv2r8S9hqH02jplJ9WXK7D+Xo9Hmb4Uy3MTuJjYlZms3OsmqCwnPWz9I52bZI+n/q+9Gi4ILeqzJytVtRPP1Y6M5Ih1yaj1Kyc5urq0uW5CTNumVbtsPMcH26bbdhlYLqt21lSfCZdP4AVoIo8ZnW+a6LpdmupvGKZ8TBdfOenKddj/U9vmq93PL6/vm4ZRn6ZFnPSaOnlVf8H9fZpX7X5+uVLb5ivF41VWndHkmDH7pCJ9lxq5/wf3mWjK5aSfpV55geNjdqjPHLpQN9f/UD67MpL1wdBipRgKuVkSeP4rDxxQmwvOaNysKa2q1H1L3tM3Nfv1zrEdlOQ3LON7PR7J75e/plZHX3+FMtoVyNNQhpC6mC+mAo+ZZG88z2xXoNSDc0bwuWx8yXSEMcPunNHnV9dFNc7Y7ZtO58xox/Nw82zwxl1Tawb5/cqo8Ss54JdZibrOcNNn5o/srfKc2J7LLF9K/a8EXPQZu8fZ7jxzONTFSZtp7JsW478U2v4bx/8WXjNGM/5Hs56LxdrE6bVJrNZzwWttSRHHs0j1b1gDNsc6y83cZFWXpvqMnfE88NrM7jzb51fXKaNdgen8h5v/G+pidW0Wbp3lORjnuNtvcLzOyLx6vON8Go5zS84ZsVqbd47B2sRXkKu5w4q1Mz05wvX8oQ21wOv5WF+btkT90zu1U+eLzpXHV9+HGvuJ59CvvnIrqjWmZJPlpk3Dvan0skodSPbJb/HLkH2t0tX+lkkxqb/TOOU56Zo/8hjtyky2PF4NcRo2IZvjOsvNOsMKmzbNjw0bJIS2bduGhG3ZssV1eps3b7aVB6Jjd7Nm8C3XqKi4WINvuUb7s9MsL/CkQ4spb24r9bjmUqW3L1SbC87QgaoDYX/O6jUMpRiGjr/qssY8KrJSQybDQ9+vnxT3Z6VFVa4e11yqlLw2ljfFpUOL75S8NqY43txWlhcrDXVJq6lTZXa6jr/5Kr2wY73mDeyiPelJlpN1Q31q9x8q25DjBqjworOV5A+98RqYT5Lfr3eO7aBV3hrN6tdOybmhdQm8yEmvqVNGQZ76/XyyHtnwuf55XAeVpieHPWYNfzFmVf/a/fvD1iW9pk570pM1d2AXvbBjvU78xTWqyEoLW3+rc9Nlwlj5a+saf05tVZ8kv19548/U2+XbY1aX42++KiZts6H+Vu1Sqm+b/ppadZkw1nG7tCqX3fMSbT6JNGacdOt1jttl0/WvbzP7s9I0+BfXOO7/3a66JKRvhq+PX6UZyfrncR30yIbP1enicw/2mUM3N4KPWZLfr3f6ttfHdRX6W588230mmnJ1u+oS2/3stQGd9dC3nzkeM01tJsxfj3kNKcnv13t9O6jf55tU5/PK8IZ55INHqvbo4Eu1d6rO79f9H/2n/tn2VbUa/cX3qvV6Qy4gG467x+uVN8mnrfPe1XE3TFZGQW6EY1bfZ4KPmdXcZDWe97/pStOcEXwTqbFOFmOGkzyi6f92+6bbOdPteD7gyglKMRR2ng3cuItmzZCI6wwnfebdYzvqm2RD/z3pqJidy5S8NjIqD8gXZZ+JdJztzjMDrpyQ8HVx0zePv+oyWxvXOeePjsmaMZrx3+16LhZrEzfXJrFazzW0s+C1thRdn2mov9t1lpu5yWldnI7NbubZ/jdd2Tj/B8aJ5tqs4SZ/YP0D82lqnfFyv/b61ZcfusqnJeeMWK3Nh956XcuvTdq00qx+7bTKW9P4Bz5Oruc9bWJzbdrS83nhKUPU5fLzD23a+LyqSk0yPToseNPm/o/+o+07tpvvTR18fFjgmntfq3T1v/3nUd03ask4/z2pl75J9uu9YzsqyQh//iPNs06us9ysM9i0iR02bJAQunfvHhK2ceNG1+lt2rTJ9Nnn86lLly6u00MoJ5s1efn1g3pOav1LHCM+29MwtLmsVPuqDmjXzp365NkXVe3xWP51hCQZXo/SUtO055V/N04MTb9OT6YfAzstl1t2fp7r8UidslupTVqGxhX3lc/rlc/TxFAdULaasnLlvbdMqampMrwe0+K+8esBPyfOrK7TmT16ha269+DP2b2e+seueb1ejSvuW5+mRzIsjnW4Z5zaqb8k+Q4+73hccV+1SctQt1ZtIj9DOqj+m2a+Jm9ykjwWz932ejwyvB6lpqaq8D+faFyHopjVxefxtnjbbKr+0qEbwptmvqaasnJH7TInNc1VuSRn7d9tPrEcM5y0S8lm/eVR11ZtXPX/8prqJvvmoXwMyahv3+M6FGnLS/NMbSbsmLFyq/LqPMrwJdnuM3bGDMtyFfdVeU217X6W7PPpnKJjHI+ZdtqM3yPVeb0a/eU2rezcRmnJKUrJzAjZtAn867/Mvfu19LFnVLp7t8YV91VmdZ3GlGxSbkV1yAVk4GaNLyNNHp9PhT89SR6fV9kp9S/x9sh6PAo+ZpHeaxA8nkuynDNC8ggaMyKxykNy1//r62evb/o8Xldzpt2ySQHjeYci7Xv1HaWnpdneuIuVWKwznPSZ01duVWZ1nX7WtTgm5zJYtH3GipN5Zt+r72hch6KErYvkvG/WlJVrzyv/ttX+lz83U6MLOrf4Osvt+O80n8C1aUuvTRJ1neWG2/o7XWdFEm5uigW382y4+d/ttVm4+ttdZ/i8Xp1TdIzjfFp6zmjQ0mvzmKxNyks1snMPZVbXhf0Dn8avB13Pj+zcQ9+Vt/y1qRSbtVn+iQPV5fLz5UtLVVJG+sF2as63YdMms7pOows665PHnw29NxWw5m7YrGm4N+WkLuYkWy7Oz7r2rK/Pyq2q83rlDxPNyTx7qAyR1wx21hmprXLU45pL2bSJEY/R1IM5gRhYtGiRhg8fbgobMWKEPvjgA8dpVVdXKysrSzU1NY1hRx11lNasWRNtMV2J94uqWoKbzZqasnKtf2aGqn7YKb/fr+qK0L9kCbzZVZGVKo88yig/uAg3Gp55eyiO4fUoOSNDvoa/vshtVf9XzGWVlj85lcwvaqtolaHjr7pMe175t/1yBcUxDKP+Z+xBf2HVeOPN61Vq23y1ueAMffLsi/XHzKIuwfVJbZuvLhPGauX0l7R/246w9Qks2/7s+osu/+699f/mN1S3/1DZ/IZh+r4kZRTkKjslTdW79oSti7xeJQXUxVwuc11Cbjw6qH/gucloV6C+ky/Wppmv2T43IfW3qI/H65U3I61xgZKS10Zl1VXav2NXi9al66XjAuK0TNu0W/+GukjBfcZ+u3RyXipaZejoK8ZrzT/+6bj9O8ongccMW/X3eJSc6b7/N5Rt44w5ln3TKk5g/7dqM+HGDMnjqM9EGjMsyxXSZ+yPGY6OmY02E5iHVP94j+6jhuv7OW/LX1fXeP6Dj1ODkPMSwOdpeASL37RZ02XCWGX26NI4Z1qdF6tjFjhm2hnPU/LaSNKh8x80ZwSfS0mmOHbnDDfznzmO/b55qP3b6zN2+qapnQX1GasxILgtBI7NdtcMgeNfIq0z3PSZ1PxclVZVOh5nnZ7LSGuzaPpM4HE2zRlh6h88z5jXGYlTFzdtJnjMsNv+26RmqGrn7hZfMzqdMx33fxdrUzdrk8DjnFDrLBfXQG77jNP62ymX1fWM0z7jNE6s5lk712ZO6h9pnHGSj53z31xzRizW5rFYm5jGzKBj1vB+m+Bx1nzMWu7a1HzMWm4+D7yHVF26T98+N6v+3BoB70MLYKp/GBU5GRr8i0Ppmu5ntWD9ncYJPf+hcZzMs1ZxmlozhOv//W+6svFxz4H39sIJfp/n4Sje93LZsEFCqKioUE5OjunFhpmZmSotLVVSUlKEmKEWL16sk046yRR28cUXa9asWc1SVqfi3cmbW21Njebf97/KLK0I+53gidaoq9PXj/1NB7Ztb/xO8GIqcEIwjPqfeUvSgZSA8x8wAQUv7v2GoeryChmGdCDl4Esugya44AWhDEPJkjLTMhqDIpUrXJzgSS544e03DO0/UKmawBt5QZNpcH0Mw5BRWydvcpKq6+pUUVMdUh9T2QxDaTV+eTxSalaWddm8Hu1P9jY+njkzKVneA9WSpKSsDFt1sS6XlF5b1/hTWjf1Dzw3mckpSvZ6G/Ox02bs1N+qLnUV9Qs7f3pqi9Ulyeu1iNPMbdNF/a37jP12aeu8HIyT5DdU5wv4K55mzieRx4xo62+r/weVrcZv3TfrPFJlkre+bVr0/+A2E2nMsNtn7IwZpnKF7TP2xww7x8xWm/F4VJXiU8PKpOElymntClVwyhBtfmme/HV1qjxwQNUeWf+FstV5kRpfLuuR5K+plTfJ17hZ06rv0SFzZvDYHHzMrMbMSON54PgX7vxHGjPtzhlu5j/rOPb75qH2H7nP2OmbpnYWps8EjgFW/TJ4bHYyz9o5l26Ps9Nxxk2fyUhKllF5wPU4a/dc2lmbRdtnLOeM4PpbHOPQdUb86+KmzYQbM+y2f29GekzWjHbnTMf938Xa1M3axOo4J8I6q+F8OrkGirbP2K2/m2szN+Os0zixmmfdXJvZqUukccZOPrbOfzPPGS25No/J2sRqzJT5D3yC1+bWx6z5r02tj1kLzOeSKlpn6rS7f6Wk5Pp335j+8Ncwb9pY1j9I8L0py/tZLVB/x3HCnX95lFJT63qedbNmCI7TcP1TfMsUeXy+kPMSTnCcw0287+XySDQkhMzMTA0YMMAUVlFRoeXLlztOa9GiRSFhJ598suuywSwpOVm5pwyRYXFfSgqdECU1Pt4lcCL2er2Nj5QJnng8Ho+qk7yqTgoa2A9OaLXJPtMCSjr4XN/UFNUkBQxrHs/Bn8BaLCBUvxDzDT5OHq+9coWL4/F45MtMl8frDVkQN6TpG3yc+Zh5Dv08N3hBKNUveAtGnCh5PErx+ZSZnCKfz6fkzEzrsnk8qk6qPwaBa6+GsnkDfk7s83rrJ96kJHnTUuRNSw35fri6WJer/phFVf/MTPkOppfi85nysXVubNQ/pC4eT2P9W7Iu1nGauW26qL91n7HfLm2Vqz4j7evf03X7t5NPIo8Z0dbfVv8PKlu49lyfjy9s/284lnbGDLt9xs6YYSpX2D5jf8ywc8xstZnMDGWnBtTfVx+/8Kcnqc2AvuoyYawyOrRT0a+vVUXrTFmxPC9er7KTU+X1eJTWrkC97rhe6R3aqcuEsWp93DGWc2bw2Bx8zKz6TKTxPHD8C3f+I42ZdvKQ3M1/0Y6ZdvuMrb4Z2M7C9JmGMaA22Reah8XY7GSebcnjHIs+k5qUFNU462b8C3fMou0zlnNGcP2Dj7HlOiP+dXHTZsKNGXbbf6zWjC3W/12sTd2sTayOcyKssyTn10DR9pmWKle4+jd3nFjNs26uzezUJdI4YycfW+e/mecMu23GTZ+JydrEasz0epWVkqqkMGtz62PW/Nem1sesBeZzj5R7ypDGzRpJSs7OanwMl9dT/wdPEesfwOrelOX9rBaov+M44c5/ampU86ybNUNwnIbrn8CNl8DzYskiDpxhwwYJ4/TTTw8Je/XVVx2n88orr9hKG+6deMpIpY39WcimjdWE2KD1cceoy4SxIRcGGV07aldeVshEnXz+aUo+/9TQPFpnqOjX1yqjQ6H5Hzwe9Zx8kZKC43g82p2bpV25mSF5pI39mQZfNt5RucLF8Xg8yujaURldO5oWxPJ41GXCWA2+bHzoMfNIe/KylNG1o2lB2BCn49jRjfmk+HzKTklVki/yMes5+SLzJC413hBMa1/Y+Ezhhom328QL1W3iBY7qYlWuhmPmtv5JPq+pXMH52Dk3dupvElT/lqyLdZzmbZtu6m/dZ+y3SzvlShv7M5159ZSo2r/d+ifqmBFt/e30/+CyWbVnX1DbtOr/kv0xw26faWrMCC5Xc4wZdsfMptpMcP0bNlWk+nmt+JYpKuzapf6Foq0yTOlEPC8eT+OjAtLbt1XxLVMa021IO9I8Y3XMrPpMpPHczvk3cTlnuJn/oh0z7faZpvpmcDsLd8zS2xdabtyFG5udzLMteZxj0WeiHWfdjH9Wx6w5+oxlXRR5nrEaMxOhLm7aTLTtP1Zrxpbq/7Fam1gd50RYZ7m5Boq2z7RUudz2mUSdZ91cmzVVl6bGGTv52Dn/zT1nNGiJPhOLtYnlmOnxRFybN3U911zXptbHrPnn8xNPGalgIZs2TdVfke9NWd7PaoH6O40T7vxHM8+6WTNYxQm8TrE6LyYR4sA+NmyQMC655JKQsOnTp6u6utp2GsuXL9fHH39sChs8eLC6d+8edflgFjzJRZoQGwTfgEptm6+jfz5JJ9x6XeONrsCJOlwehV27mCeGgAnBKs4Jv7xOg395vWUebsoVLk7RDVeo6IYrLMsV7pgNuvVaHf3zSWHjBObjsVE2q3I13BBsOGaeoHzc1MWqXNHWP7hcbs6NnfoHly1WdYlF23RT/2jbpZ1y2TlmzZFPIo8Zsej/4eIEtmc7/d/JmGG3zzgtl51j1tSYYeeY2WkzVvk0aPirsbz8fNOmjZ3zEvhcZ6u/PmtqbLY7ZkY6N9GOmXbysHMu7cZx0jft9hmn7SzcMQveuGtqbHbSZ1ryOMeiz8TqXMaiz7iZZxK1Lm7aTLTtP1Zrxpbq/+GOc3OvTRJ1neWmncWq/rEaZxN1no12bWaVj91+Fu35b+45w26bSdS1Sbg1Y6S1eayuTWM1n1sJ3BywU/+m7k3Fqv5O44Q7/9HMs27WDJGuf8KdF9mMA5sMIIEMGzbMkGT678EHH7Qd//TTTw+J//zzz7dgiZu2cuVKU3lWrlwZ1/I0tyXvLzDevPv3xs4dO2zH2fPZl8aaac8Y1fvKGsN27thhvHn3740l7y+wnUf1vjJjzbRnjD2ffWkrTqQ83JQrXJxI5QpXtqbiOC2b1febysdNXX7s9XdTl1i0TTf1j8V5iVU+iTxmxOo4O23PP/Yxw02bseLmvDQlFsf5xz5muilbuGPmdGz+MfWZRF6bOY3jpv6JWpdw9UmE9v9jX5skal3Cxfkx9ZlEnWcTNU4izxmJujaJ1fWcm7rE6txYcdPOIolV/Z3GSeR1phU3cRJdvO/legzj4JuogATwzjvvhDy+LDU1VYsXL9bAgQMjxn3iiSd00003mcK6d++uNWvWKDngGZixFu8XVcVCbU2N6Tmjdhh1dSF/URwpnXD/ZpVOpDhNldVpucLFiVSucGk2Fcdp2cKlFykfN3X5sdffTV1i0Tbd1D8W5yVW+STymBGr4+y0Pf/Yxww3bcZJOm7TCxenuY/zj33MdFO2cP/mdGz+MfWZRF6bOY3jpv6JWpdw5UiE9v9jX5skal3Cxfkx9ZlEnWcTNU4izxmJujaJ1fWcm7rE6tzYTSea9GJVf6dxEnmd6aS8h6t438tlwwYJZ+zYsZo7d64pLCcnRy+++KLOOeeckO/X1NTowQcf1NSpUxXcnF9//XWdffbZLVrepsS7kwMAAAAAAAAAmhbve7lJMcsJsOnZZ59VSUmJtmzZ0hi2b98+nXvuuTr++OM1ZswYde/eXZWVlfr66681Y8YMfffddyHp3HjjjXHfrAEAAAAAAAAAwA42bJBw8vPzNX/+fI0aNUrbtm0z/dsnn3yiTz75pMk0xo8fr0cffbSFSggAAAAAAAAAQPPyxrsAgJXevXvr448/1vDhwx3FS05O1m9/+1vNmjVLviPo2YkAAAAAAAAAgCMbGzZIWJ06ddLChQv18ssva+jQofJ4PGG/m5WVpcmTJ+uLL77Q1KlT5fXStAEAAAAAAAAAhw8eiYaE5vF4NH78eI0fP17bt2/XsmXLtH79eu3bt09JSUnKz89X7969NWjQIKWkpMS7uAAAAAAAAAAAuMKGDQ4bhYWFOvvss+NdDAAAAAAAAAAAmh3PjQIAAAAAAAAAAIgzNmwAAAAAAAAAAADijA0bAAAAAAAAAACAOGPDBgAAAAAAAAAAIM7YsAEAAAAAAAAAAIgzNmwAAAAAAAAAAADiLCneBQCOdFVVVabP69ati1NJAAAAAAAAAADhBN+7Db6329LYsAFa2ObNm02fx44dG5+CAAAAAAAAAABs27x5swYOHBiz/HgkGgAAAAAAAAAAQJyxYQMAAAAAAAAAABBnHsMwjHgXAjiSlZaWauHChY2fO3furNTU1DiWKLbWrVtnegzca6+9pp49ezZrnFjkcaTFSdRyxSpOopYrVnEStVyxipOo5UrkOIlarljFSdRyxSpOopYrkeMkarliFSdRyxWrOIlarljFSdRyJXKcRC1XrOIkarliFSdRyxWrOIlarkSOk6jlilWcRC1XrOIkarmOJFVVVaZXXIwYMUKtW7eOWf68wwZoYa1bt9aYMWPiXYyE0bNnT/Xp06dF48QijyMtTqKWK1ZxErVcsYqTqOWKVZxELVcix0nUcsUqTqKWK1ZxErVciRwnUcsVqziJWq5YxUnUcsUqTqKWK5HjJGq5YhUnUcsVqziJWq5YxUnUciVynEQtV6ziJGq5YhUnUct1uIvlO2uC8Ug0AAAAAAAAAACAOGPDBgAAAAAAAAAAIM7YsAEAAAAAAAAAAIgzNmwAAAAAAAAAAADijA0bAAAAAAAAAACAOGPDBgAAAAAAAAAAIM7YsAEAAAAAAAAAAIgzNmwAAAAAAAAAAADijA0bAAAAAAAAAACAOGPDBgAAAAAAAAAAIM7YsAEAAAAAAAAAAIizpHgXAMCRraCgQFOnTjV9bu44scjjSIuTqOWKVZxELVes4iRquWIVJ1HLlchxErVcsYqTqOWKVZxELVcix0nUcsUqTqKWK1ZxErVcsYqTqOVK5DiJWq5YxUnUcsUqTqKWK1ZxErVciRwnUcsVqziJWq5YxUnUcqH5eAzDMOJdCAAAAAAAAAAAgB8zHokGAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAAAAAAAAAHHGhg0AAAAAAAAAAECcsWEDAAAAAAAAAAAQZ2zYAAAAAAAAAAAAxBkbNgAAAADQzEaOHCmPx9P438iRI+NdJCSIDz74wNQ2PB6PPvjgg3gXCwAAAAkgKd4FAAAAAHD42bJli0pKSvTtt9+qvLxcqampatu2rfr27av+/fvL623Zvw1bt26dPvvsM23evFkVFRVKT09Xhw4d1K9fP/Xp06dF8wYAAACAlsCGDQAAAHCY2L17tz755JPG/0pKSrRp06aQ7xmG0SL519XVafr06frzn/+szz77LOz38vLydMUVV+i2225Tx44dmy3/yspKPfXUU3r66af19ddfh/1ep06ddOWVV+oXv/iFWrdu3Wz5AwAAAEBL4pFoAAAAQIJauXKlHnroIY0fP149evRQXl6eRo8erbvuuktz5syx3KxpKatXr9aAAQN09dVXR9yskaRdu3bpj3/8o3r16qW//vWvzZL/hx9+qN69e+u2226LuFkj1f/6595779VRRx2lefPmNUv+sdKtWzfTo7ImTZoU7yL96G3YsCHkEWbPP/98vIsFAACAIxC/sAEAAAAS1LPPPqvHHnss3sXQ4sWLdfrpp6usrMxRvPLycl1zzTVat26dHnroIdf5z549WxdffLFqamocxduxY4fGjh2rJ554QjfccIPr/AEAAAAgFtiwAQAAABDWunXrdOaZZ1pu1pxyyik69dRT1bVrV+3du1erVq3SjBkztGfPHtP3Hn74YbVv316/+MUvHOe/ePFiy80ar9ers88+W8OGDVPnzp21Y8cOrVixQrNmzdL+/fsbv2cYhm688Ua1b99e48aNc5w/AAAAAMQKGzYAAADAYSYlJUV9+/bV8ccfr3/+858qLS1tkXz8fr8mTJigvXv3msLbtWunV199VUOHDg2J8+CDD+q2227TX/7yF1P47bffrlGjRqlfv36286+oqNAll1wSsllz9NFHa86cOerdu3dInIcffliTJ082PQrNMAxNnjxZQ4YMUbt27WznH40PPvggJvng8DNy5MgWe88UAAAADm+8wwYAAABIYElJSerXr5+mTJmiJ598UsuWLVNZWZlKSkr0l7/8Ra1atWqxvJ977jl9/PHHprDc3FwtWbLEcrNGkjIzM/X000/r1ltvNYXX1NTopptucpT/73//+5D39BQXF2vx4sWWmzUN5Zs9e7bOP/98U/jevXt1xx13OMofAAAAAGLJY/CnPQAAAEBC2rp1q1q3bq309PSw3+nWrZs2btxoCmuOJX5dXZ2Ki4v17bffmsJnzJihCRMmNBm/urpaAwcO1JdffmkKX7hwoU4++eQm45eWlqpr167at29fY5jX69XChQs1bNiwJuPv2rVLffr00Q8//NAY5vP5tHbtWvXo0aPJ+PESfD4nTpzIC+7jbMOGDerevbspbPr06Zo0aVJ8CgQAAIAjFr+wAQAAABJU+/btI27WtKS33347ZLPm2GOPtbVZI9U/tu3ee+8NCX/qqadsxZ8xY4Zps0aSzjrrLFubNZKUl5enX//616awuro6/fWvf7UVHwAAAABijXfYAAAAAAjx0ksvhYRdd911jtIYM2aM2rVrp23btjWGzZ07V/v371dGRkaL5z9p0iTdddddqqqqagybNWuWHnjgAUfpHKlqamr0ySefaPPmzdqxY4f27t2r1q1bq6CgQMccc4z69OnT4mXYv3+/li1bpq+//lq7d+9WbW2tWrVqpVNOOcVW/pWVlVq7dq2++uor7dy5U/v27VNSUpLatGmj/Px89e/fX926dWvxesRbdXW1li1bpi1btmj79u2qqKhQXl6eCgsL1adPHxUXF8ekHJs3b9bHH3+sjRs3av/+/crNzVXbtm110kknqW3bts2WT1lZmb744gutXbtWpaWlKi8vV3JysjIyMpSfn6+uXbuquLhYBQUFzZYnAABATBgAAAAADltdu3Y1JJn+i5bf7zcKCwtD0t22bZvjtK699tqQdN56662IcUpLSw2fz2eKk5WVZdTW1jrOf/To0SH5r1q1ynE6To0YMcKU54gRIyy/9+2334aUz81/TsydO9c499xzjezs7Ihptm/f3rj++uuNjRs3Oq7/1KlTI5Zx8eLFxrhx44zU1FTLvKdOnWqZrt/vNxYtWmTcfvvtxgknnGAkJSU1eWw6depk3HzzzY7qMX369KjPSbhzvmDBgpDvLliwwHbZAr377rvGueeea2RlZUUsS48ePYxf/vKXxtatW13lEzzOTJw40fTvs2fPNk488cSw+Xs8HuOEE04w3nzzTVf5G4Zh1NbWGs8//7xxyimnGF6v19Y56N69u3H55Zcbc+bMMSorK13nDQAAECs8Eg0AAACAyZo1a7R9+3ZTWHFxsau/kB8+fHhI2H//+9+IcZYsWaK6ujpT2JAhQ+Tz+WKS/5Fq2bJlGjJkiMaMGaN58+aprKws4ve3bt2qp556SsXFxbrzzjvl9/ujLkNNTY1uvPFGnXTSSZozZ47p109NWbhwobp27aphw4bp4Ycf1rJly1RbW9tkvC1btujxxx9XUVGR7r777mapR7ytX79eo0eP1qmnnqp58+apvLy8ye//4Q9/UM+ePXXfffc12zEoLS3Vueeeq/POO08fffRR2O8ZhqFly5bprLPO0sSJE22dt0Cff/65Bg4cqEmTJmnBggW2y//tt9/qhRde0Lhx43gXFAAAOCywYQMAAADApKSkJCRsyJAhrtIaOnSorfQTKf8j0cyZM3XyySdHvKkeTnV1tR544AGde+65TW4MRFJXV6cLLrhAf/7zn2UYhuP43377rTZv3uw6/9raWv3ud7/TmDFjHG8YJJKSkhINGTJE8+fPdxy3oqJC99xzj8aPH68DBw5EVY5du3Zp+PDhev311x3F+8c//qFJkybZ/n5JSYlGjBihzz//3GEJAQAADj+8wwYAAACAyVdffRUS1rNnT1dpdenSRUlJSaYb5GvWrIlZ/kVFRSFhTeUfSykpKTruuOMaP69atUo1NTWNn9u0aaMuXbpElcdTTz2lG264ISQ8MzNTp556qgYNGqT27dsrOztbe/fu1ddff613331Xn376qen7b775piZNmqRXXnnFVTnuuecezZs3r/Fzbm6uzjjjDA0aNEiFhYWqrKzUli1b9O9//1sej6fJ9Nq1a6ef/OQn6t27t7p166acnBxlZGSooqJC27dv1xdffKG333475Ndib7zxhu688049/PDDYdPOzc1tPC/V1dVavXq16d87d+6s3NzciOVz22YjWb16tUaOHGm5cVZcXKxx48apZ8+eysnJ0bZt27Rs2TLLX+C8+uqrqqqq0rx582wd62C1tbUaN26cVq5c2Rg2YMAAjR49Wj169FDr1q21e/duLV26VLNnz9bevXtN8WfMmKGxY8fqggsuiJhPVVWVLrvsMpWWlprCPR6PhgwZomHDhqmoqEg5OTny+Xzat2+fdu3apVWrVmnFihVasWKFq81BAACAuInzI9kAAAAARKEl3mFz2WWXhaT597//vdnK6PP5jOrq6rDfHzZsWEj+CxcudJV3bW1tyPtwOnfu7LYqttl9h02wpt4V4tTSpUuNlJQUU5rp6enGAw88YOzduzdi3A8++MAoKioKORePP/54k/lavcOm4Tz4fD7j7rvvNsrLy8PGt3rfyPTp040ePXoYv/vd74wVK1Y0XXmj/vy/+OKLRrt27ULeqbJs2TJbaVi9Z2j69Om24lpx+w6bAwcOGMcdd1xI3NzcXOOFF14IG2/37t3GpEmTLN/x8uijj9oqc3C7TEtLa/z/o446yvjPf/4TNu727dst3yXVq1evJvN94YUXQuINHDjQWLlypa1yb9261fjLX/5iDBgwwHjqqadsxQEAAIgnHokGAAAAwGTbtm0hYZ07d3adXnDcuro67dy5Myb5+3w+tW/f3hT2ww8/uErrcFNVVaWLLrpI1dXVjWGFhYVaunSpfvOb3ygnJydi/BEjRqikpET9+vUzhd97772qqKhwXJ66ujp5vV7NmjVL//M//6PMzMyw301LSwsJO//887Vu3TrdddddIWUKx+fz6dJLL9XSpUtNv1QyDEN//OMfHdchnqZNm6YVK1aYwnJzc7VgwQJddtllYeO1adNG06dP15133hnyb3fccYe+++47x2VpeJzaoEGDtGTJEo0aNSrsdwsKCjR37lz17dvXFP7VV19p0aJFEfOZO3eu6XN+fr7mz5+vPn362Cpnu3btdM011+jTTz/VFVdcYSsOAABAPLFhAwAAAMBk9+7dIWFZWVmu07OKu2vXrrjlX11dHdW7WA4X//jHP7Rhw4bGz16vV3PmzNGxxx5rO41WrVppzpw5SklJaQzbtWuXnn32WVdluvXWW3XhhRe6ipudne3q8V1S/aP5nnzySVPYv/71r5BHdSWqmpoaPfHEEyHhzz//vO3Nq/vvv1+nn366Kayqqkp//vOfXZWpdevWeuWVV5p8NJwkpaamWj6C7p133okYb/369abP48aNU15enrOCHpSRkeEqHgAAQCyxYQMAAADAxOrXE+np6a7Ts4q7f//+hM3/SGAYhh555BFT2KWXXqqhQ4c6TqtHjx66/PLLTWFz5sxxnE52drZ++9vfOo7XXM444wzl5+c3fq6trdXSpUvjVh4nXn31VW3dutUUduaZZ+qcc85xlM4TTzwhn89nCnvmmWdM702y65ZbbnH0fqXTTjtNBQUFprCSkpKIccrKykyf3W7WAAAAHC7YsAEAAABgYnXz1uoRVXZZbZgEPqYr0fI/EqxYsUJff/21Keyqq65ynd5ZZ51l+rx06VJVVVU5SuOiiy6K6pdS0fJ6vSoqKjKFffTRR3EqjTPvvvtuSNgNN9zgOJ2ioiKNHj3aFLZr1y59+umnjtO6+uqrHX3f5/PpJz/5iSlszZo1EeMEb9A09Qg1AACAw11SvAsAAAAAIPG5fRRVuLiGYRxW+R9uFi5caPrs8/l0wgknuE6ve/fups8HDhzQ6tWr1b9/f9tpnHLKKa7zD2flypX6+OOP9fnnn2vjxo3at2+fysrKwm4mrVu3zvR506ZNzV6mlvDhhx+aPmdmZoY83syu8ePH66233gpJf/DgwbbTKCoqUseOHR3nHbxh1tQj6QYPHmzaVFu0aJHuuusu3XvvvUpK4nYGAAA48rDCAQAAAGCSnJwcElZZWek6Pau4ge9Esco/+IZ7ZWWl619nOM3/SBB8g9/j8ejEE090nZ7VL5J27tzpKI2BAwe6zj9QVVWV/vSnP2n69OlatWpVVGmVlpY2S5laUkVFhdauXWsKGzBgQMijzewaNGhQSJjTX9gUFxe7yrtVq1amz01t2EyaNEmPP/64aYP197//vWbOnKnJkydr3Lhxjt7JBAAAkOjYsAEAAABgYvVy7ubesMnMzIyYf0tv2ETK/0iwZcsW0+fa2lqtWLGiWfPYtWuXo+8XFhZGneeHH36oiRMn6ptvvok6LanpDYNEsGvXrpBfhPXu3dt1er169ZLX65Xf728Mc7r5lpub6yrv4M3g2traiN/v37+/br75Zj322GOm8A0bNmjq1KmaOnWqCgsLNWzYMA0aNEhDhw7V4MGDlZqa6qp8AAAA8caGDQAAAAATqxd7l5eXu07PKm6kl4fn5eVpz549IWkEv7Dcbf4pKSlxfZdKLDjdTHHD6SZeTk5OVPktWLBAZ599tvbv3x9VOoGs3peUaIL7giS1bt3adXper1c5OTmmXxft3r3bURpWv8JrKdOmTZPP59Mf/vAHy3/fvn27Zs+erdmzZ0uqf9/VySefrIsvvlgXXHCBsrOzY1ZWAACAaHnjXQAAAAAAiaVt27YhYcG/2HBi8+bNps9er1f5+fkxyb+urk5bt25tMv0jjdVN/niL5p0jpaWluuiii0I2a7xer0477TQ98MADevvtt/Xll19q586dKi8vV11dnQzDMP03YsSIaKsRc2VlZSFh0f5CLDi+VR6Jwufzadq0aVq2bJnOOeecJtvRgQMHNH/+fE2ZMkXdunXTAw880OQveQAAABIFv7ABAAAAYBL8gnlJ2rhxo6u0/H6/vvvuO1NYp06dIv6Ffvfu3UPewbJx40YNHz7ccf7ff/99yM1aq/odadLT002f+/fvr+XLl8epNNG7//77tWPHDlPY8ccfr5kzZzp6n0o0j/aLF6tfiFRUVESVZnD8w+FXKIMGDdK8efO0fft2vfHGG1qwYIE+/PBDffvtt2Hj7N69W3feeafmzp2rt99+O6pfJgEAAMQCv7ABAAAAYHL00UeHhK1bt85VWps2bQp57FSvXr1ilr/Vu06ayv9IEPwLpkg3tQ8HL730kulz586d9d577znarJGcP/orEbRp0yYkLPBxZk75/X7t27fPFOb2nTTxUFhYqClTpuiFF17Q+vXrtW3bNr3yyiu6+eabw7aHpUuXavz48TEuKQAAgHNs2AAAAAAw+clPfhIStmTJEldpLV68OCRs4MCBCZ3/kSD4sW979+7Vpk2b4lSa6KxevTrkkXg333yzWrVq5SidmpqaqB7tFy/5+fnyeDymsNWrV7tOb82aNfL7/SF5HK7atm2r888/X4899pjWrl2rkpISXXzxxSHfe/fdd/Xvf/87DiUEAACwjw0bAAAAACa9evVSQUGBKWzt2rXavn2747QWLVoUEnbyySdHjDN06FD5fD5T2JIlS1RXVxeT/I8EJ5xwQkjY4XqzOvgdSJJcPR5v+fLlOnDgQHMUKaYyMjJCfnX22WefueoPkvTxxx+HhFltkh6uBg4cqFmzZun+++8P+bdXX301DiUCAACwjw0bAAAAACYej0ennXZaSPjs2bMdpVNbW6u5c+eawtLS0pp88XurVq104oknmsLKysr07rvvOsp/9+7dWrBggSmsS5cu6t27t6N0Yin4hepub8qfeuqpIWEvvviiq7TibefOnSFhbh7h9fLLL7vK3+ol927Pi1tDhw41fS4vL9fbb7/tKq1//etfTaZ/JLjjjjuUl5dnCvv888/jVBoAAAB72LABAAAAEMLqkUJPP/20ozRef/11ff/996awMWPGKCMjIyb5//3vfw/5RcUll1ziKI1YC375e3l5uat0Bg8eHPJYtEWLFum9995zXbZ4yczMDAmz2sSJpLS0VH/7299c5R98TiT358Wt0aNHh4Q57Q9S/buMgjd68vPzj8jHBPp8vpB32uzduzdOpQEAALCHDRsAAAAAIU4//XR169bNFLZixQrbv1Korq7W1KlTQ8Kvv/56W/Evu+yykBvlr7/+uuU7aazs2bNHjzzyiCnM5/Pp6quvthU/XoJfML9+/XpX6SQlJemOO+4ICb/yyiu1Y8cOV2nGS/v27UPC5s+f7yiNG2+8UaWlpa7yz87ODvmVjdvz4ta4cePUoUMHU9gbb7yht956y1E6N910k2pra01h1157rZKTk6MuYyLaunWr6XPwox4BAAASDRs2AAAAAEIkJSXpN7/5TUj4jTfeqI0bNzYZ/84779QXX3xhChs2bFiTj0Nr0Lp1a/385z83hfn9fk2ePFl79uyJGNfv9+vaa68NuVl7ySWXqKioyFb+8XLssceaPq9cudLyHS52XH/99erUqZMpbNOmTTrjjDO0ZcsWV2nu2LFD/+///T/Hj6eLxoABA5SVlWUKe+yxx2zX4b777tOMGTNc5+/1enXMMceYwt555x35/X7XaTqVnJysG2+8MSR84sSJWrVqla007rnnHr355pumsLS0NN1www3NUsbmVl5erl/96le2xhsrc+fODYl73HHHNUfRAAAAWgwbNgAAAAAsXXXVVSGPStq5c6eGDBmiJUuWWMbZv3+/rr/+ek2bNs0UnpSUpD/96U+O8r/rrrtCNhzWrl2roUOH6quvvrKMs2fPHp133nkh7+nIycnRQw895Cj/eAh+l4jf79eFF16oTz75xHFaaWlp+uc//6nU1FRTeElJiQYOHKgnnnhClZWVTaZTXV2tN954Q1dccYW6du2q+++/P6aPlkpOTtbYsWNNYXv27NFPf/rTiO8k+f777zVhwgTdc889jWE5OTmuyhB8XtasWaOrrrrK9WaCG7fddpv69+9vCtu5c6dGjhypl156KWy80tJSXXXVVbrvvvtC/u2hhx4K+eVOoqitrdW0adNUVFSkcePGacaMGbband/v1/PPP6/LLrss5N+swgAAABJJ6NsTAQAAACSMM888M+Q9MIGs/i34pm6wt956y9ZNWp/Pp1mzZmnQoEHat29fY/jWrVs1dOhQjRo1Sqeddpq6dOmivXv3avXq1XrxxRe1e/fukLQefPDBJssVLCsrSzNnztSoUaNMj3H66quv1KdPH51zzjkaPny4OnbsqJ07d2rFihWaNWuWKioqQtJ67rnnEvbGdKAxY8YoNzfXdAyXLl2qQYMGKTs7Wx06dFBaWlpIvM8++8wyvSFDhuiZZ57RpEmTZBhGY/iOHTt000036e6779aIESN04oknqrCwUDk5OSovL1dpaak2bNigkpISLV++3PKYxtLdd9+tl19+WTU1NY1ha9eu1YABA3T66adr1KhR6tSpk2pra7V161Z98MEHeu+991RVVdX4/SlTpuibb77RwoULHec/ZcqUkHfGTJ8+XdOnT1dBQYEKCgpCHit2/PHH69lnn3WcVzgpKSmaOXOmTjjhBNM7dHbs2KFLLrlE9957r8aOHauePXsqOztbP/zwg5YuXap58+aprKwsJL2zzjpLN910U7OVr6XU1dXptdde02uvvabk5GT169dPAwYMUK9evdSmTRu1atVKNTU12r59u7788ku9/fbb2rRpU0g6l156qYYMGRKHGgAAANjHhg0AAACQwFatWuX4r/hXrFgR8d+rq6ttp3XUUUfpjTfe0JlnnhnyovX3339f77//fpNp3Hbbbbrtttts5xlo+PDhmjlzpiZMmGDatPH7/Zo7d67mzp0bMb7H49Gjjz6qCy64wFX+sZaWlqY//vGPmjhxYsi/lZWVac2aNY7TvOKKK5SXl6fLL7885HFypaWlto5jvB111FF64okndO2115rC/X6/3nrrrSbf5TJq1Cg9+eSTGj16tKv8Bw0apMmTJ2v69Okh/7Zjxw7L9wK1bt3aVV6R9O7dWwsWLNBZZ52l7du3m/7tq6++0oMPPmgrnfPOO08zZsyQx+Np9jK2pJqaGpWUlKikpMRRvBEjRujJJ59soVIBAAA0Hx6JBgAAACCi4cOH66OPPlLfvn0dxcvMzNTTTz+t//3f/40q/wsvvFDvv/++unTp4ihefn6+Zs+erZtvvjmq/GPtiiuu0LPPPqvs7OxmS/Oss85SSUmJxo0bF9VN+qSkJJ199tnq169fs5XNrmuuuUZPPvmkUlJSHMWbMmWK3nrrrZBHwzn11FNP6ZZbbpHXG9/L6OOPP15LlizRz372M8dxMzMzde+99+pf//qX5S+1EklSUpLy8vKiSiM5OVm//OUv9c4777h+HB4AAEAssWEDAAAAoEl9+vTR8uXL9cwzzzT54u68vDzdcsstWrNmTcgvItwaPny4Vq9erUceeUQ9e/aM+N2OHTvq7rvv1tq1a0PefXK4uPLKK/Xdd99p+vTpuvzyyzVgwAAVFhYqPT3ddZrdu3fX7Nmz9eWXX+q6665TUVGRrXi5ubk6//zz9fTTT2vLli16/fXXddRRR7kuRzSuv/56ffrpp7roootCHkEWKCUlReecc47+7//+T88991zUmzWSlJqaqkcffVQbNmzQww8/rPPOO09HH3208vLyHG8iRatHjx569913NX/+fJ1zzjnKzMxs8vu33nqr1q1bp3vuuSfum052ZGVlafv27Vq0aJHuvPNOnXzyycrIyLAVt2vXrrr99tu1evVqTZs2rVnOPwAAQCx4jMAHGQMAAACADZs2bVJJSYk2bNigiooKJScnq23bturbt68GDhzY4jeEv/76ay1fvlybN2/W/v37lZaWpg4dOqhfv3469thjWzTvI8mWLVu0YsUK7dy5U7t27VJlZaWysrKUk5OjLl26qFevXurYsWO8i2mpoqJCixcv1vr167V79255PB7l5uaquLhYgwYNUlZWVryLGDPV1dVaunSpNm/erB07dqiiokJ5eXkqKChQ375947bB1txqa2v1zTff6JtvvtGWLVu0b98+VVZWKiMjQ9nZ2erSpYuOPfbYhG2zAAAATWHDBgAAAAAAAAAAIM4S/3fQAAAAAAAAAAAARzg2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiDM2bAAAAAAAAAAAAOKMDRsAAAAAAAAAAIA4Y8MGAAAAAAAAAAAgztiwAQAAAAAAAAAAiLP/D9o40/ExAgvrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoom in \n",
    "\n",
    "# Set global font size to 12\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, sharey=True, figsize=(6, 4), dpi=300)\n",
    "\n",
    "\n",
    "# scatters\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC0.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#332288', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 0, total = {sum(CONTROL_R10_bayexpress_FC0.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC1.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#44AA99', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 1, total = {sum(CONTROL_R10_bayexpress_FC1.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.scatter(np.arange(1, 101),\n",
    "            CONTROL_R10_bayexpress_FC2.iloc[:, 1:-1].sum(axis=0),\n",
    "            c='#CC6677', s=30,\n",
    "            label=f'BF_21 > 10, iFC > 2, total = {sum(CONTROL_R10_bayexpress_FC2.iloc[:,1:-1].sum(axis=0))}',\n",
    "            alpha=0.9, marker='x')\n",
    "\n",
    "axs.legend(loc='upper left')\n",
    "\n",
    "# Use tex in labels\n",
    "axs.set_xticks(np.arange(1, 101))\n",
    "axs.set_xticklabels('')\n",
    "\n",
    "# formatting y axis ticks\n",
    "plt.gca().ticklabel_format(axis='y', style='plain', useOffset=False)\n",
    "\n",
    "# Set axis labels\n",
    "axs.set_xlabel('100 iterations')  # Font size is now 12 by default\n",
    "axs.set_ylabel('# putative DEGs')  # Font size is now 12 by default\n",
    "\n",
    "# Set y limit 3500\n",
    "axs.set_ylim(-10, 300)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmwAAAR4CAYAAAAL2/kQAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAuIwAALiMBeKU/dgABAABJREFUeJzs/XvYZFdZ543fdXjOnU6TTnfnnEDOdAgQBjEx4RAHdDhM8IAv8HMERiOiqGhQUIdhcPgBAo4oCkEGvJxXiL7+jAZ8ZxydQTklKmRCRMmBQMiBHLo7HZL0c36eqt8f3dVdVU/t/f2ute619tpV9+e6uEjXU1W7atfaa6913/f3eze63W5XDMMwDMMwDMMwDMMwDMMwDMMwjMpoVv0BDMMwDMMwDMMwDMMwDMMwDMMwJh1L2BiGYRiGYRiGYRiGYRiGYRiGYVSMJWwMwzAMwzAMwzAMwzAMwzAMwzAqxhI2hmEYhmEYhmEYhmEYhmEYhmEYFWMJG8MwDMMwDMMwDMMwDMMwDMMwjIqxhI1hGIZhGIZhGIZhGIZhGIZhGEbFWMLGMAzDMAzDMAzDMAzDMAzDMAyjYixhYxiGYRiGYRiGYRiGYRiGYRiGUTGWsDEMwzAMwzAMwzAMwzAMwzAMw6gYS9gYhmEYhmEYhmEYhmEYhmEYhmFUjCVsDMMwDMMwDMMwDMMwDMMwDMMwKsYSNoZhGIZhGIZhGIZhGIZhGIZhGBVjCRvDMAzDMAzDMAzDMAzDMAzDMIyKsYSNYRiGYRiGYRiGYRiGYRiGYRhGxVjCxjAMwzAMwzAMwzAMwzAMwzAMo2IsYWMYhmEYhmEYhmEYhmEYhmEYhlExlrAxDMMwDMMwDMMwDMMwDMMwDMOoGEvYGIZhGIZhGIZhGIZhGIZhGIZhVIwlbAzDMAzDMAzDMAzDMAzDMAzDMCrGEjaGYRiGYRiGYRiGYRiGYRiGYRgVYwkbwzAMwzAMwzAMwzAMwzAMwzCMirGEjWEYhmEYhmEYhmEYhmEYhmEYRsVYwsYwDMMwDMMwDMMwDMMwDMMwDKNiLGFjGIZhGIZhGIZhGIZhGIZhGIZRMZawMQzDMAzDMAzDMAzDMAzDMAzDqBhL2BiGYRiGYRiGYRiGYRiGYRiGYVSMJWwMwzAMwzAMwzAMwzAMwzAMwzAqxhI2hmEYhmEYhmEYhmEYhmEYhmEYFWMJG8MwDMMwDMMwDMMwDMMwDMMwjIppV/0BjHpw//33y8033yx33323HDp0SGZmZmTPnj1y0UUXyTOe8QxpNuPm/u666y75yle+Ivfdd58sLi7K3NycnHLKKXLxxRfL3r17ox7bMAzDMAzDMAzDMAzDMAzDMGJjCZvMeO1rXyt/+Id/qPJe3/d93yd/9Vd/5f36zc1N+YM/+AP5vd/7PfnKV75S+LydO3fKj/3Yj8k111wjp556qvfxhlleXpYPf/jDcu2118rXv/71wueddtpp8uM//uPypje9SXbs2KF2fMMwDMMwDMMwDMMwDMMwDMNIhVmiGSO57bbb5JnPfKZcffXVpckaEZFHHnlEfuu3fksuuOAC+ehHP6py/C9+8Yty4YUXyjXXXFOarBE5rP55xzveIeedd5586lOfUjm+YRiGYRiGYRiGYRiGYRiGYaTEEjbGFm688UZ5znOeI1/96ledXnfo0CH5yZ/8SXnLW94SdPzrr79eXvCCF8g999zj9Lr9+/fLy1/+cvnQhz4UdHzDMAzDMAzDMAzDMAzDMAzDSI1ZomXO1NSUPPWpT/V67dlnn+38mrvuukte/OIXyxNPPLHlby94wQvkhS98oZx55pny2GOPyde+9jX5xCc+IY8++ujA89773vfKySefLG9605ucj3/jjTfKK1/5SllfXx94vNlsyktf+lK5/PLL5fTTT5f9+/fLrbfeKtddd50sLS0dfV6325U3vvGNcvLJJ8sP/MAPOB/fMAzDMAzDMAzDMAzDMAzDMKqg0e12u1V/COMYwz1szjzzTPnWt76V5NidTke++7u/W770pS8NPH7SSSfJn/3Zn8lll1225TWLi4tyzTXXyEc+8pGBx6empuTLX/6yXHzxxfTxFxcX5alPfarce++9A4+ff/758ud//udy4YUXbnnNwYMH5XWve90WK7Tjjz9ebr/9djnppJPo4xuGYRiGYRiGYRiGYRiGYRhGVZglmnGUj33sY1uSNSeccILcdNNNI5M1IiILCwty7bXXyi/8wi8MPL6+vi4/+7M/63T8d73rXVuSNeeee67ceOONI5M1vc93/fXXyw/90A8NPP7YY48FW7MZhmEYhmEYhmEYhmEYhmEYRiosYWOIiMjm5qa8+93v3vL4Bz/4QTnrrLPg69/znvfI3r17Bx773Oc+J5/73Oeo43/nO9+R3/3d3x14rNlsysc//nE54YQTSl/barXkIx/5iOzZs2fg8U984hPyzW9+kzq+YRiGYRiGYRiGYRiGYRiGYVSJJWwMERH5q7/6K7n77rsHHnva054mr371q6nXT09Pyzve8Y4tj3/4wx+mXv+JT3xCHn/88YHHXvKSl8jll19OvX7nzp3yS7/0SwOPbW5uykc/+lHq9YZhGIZhGIZhGIZhGIZhGIZRJZawMURE5I//+I+3PPZTP/VTTu9x1VVXbekZc8MNN8jS0lKS47/2ta+VmZmZgceuu+46p/cwDMMwDMMwDMMwDMMwDMMwjCqwhI0h3W5X/vqv/3rL48N9YRDtdluuuuqqgceWl5fls5/9bOnrHnvsMbnpppsGHtu2bZt83/d9n9Pxd+7cKc9//vMHHrvnnnvktttuc3ofwzAMwzAMwzAMwzAMwzAMw0iNJWwMueOOO2Tfvn0Dj5177rlbesIwXHHFFVse+/znP1/6mptuukk2NzcHHrv00kul1WolOb5hGIZhGIZhGIZhGIZhGIZhVI0lbAy5+eabtzx26aWXer3XZZddRr1/Tsc3DMMwDMMwDMMwDMMwDMMwjKppV/0BjHKWlpbk3e9+t3zhC1+Q22+/Xfbv3y+rq6tywgknyAknnCDnnXeePPe5z5Urr7xSnv70p3sd4/bbb9/y2DnnnOP1XmeccYa0223Z2Ng4+tgdd9yR7Phnn332lsfQ8Q3DMAzDMAzDMAzDMAzDMAyjaixhkzn79++XX/3VX93y+EMPPSQPPfSQfO1rX5O/+Iu/EJHD6pK3vOUt8m//7b91Osa3vvWtLY+deeaZPh9XWq2WnHrqqXLPPfccfez++++X9fV1mZqain78U089VVqt1oDF2je/+U2v9zIMwzAMwzAMwzAMwzAMwzCMVJgl2hhx4403ylVXXSU/8iM/Io8//jj9uoceemjLY6effrr35xh+7ebmphw4cCDJ8Vutlpx88skDjz388MNe72UYhmEYhmEYhmEYhmEYhmEYqTCFTU044YQTZMeOHTI7OyuPPvqoHDhwQNbX10c+90//9E/llltukc9//vNy0kknwfc+ePDglse2bdvm/VlHvfaRRx7ZkkhJdfy1tTU5dOhQ0Hv22Ldvn+zfv9/pNY8//rh8+ctflu3bt8uOHTvk9NNPl5mZmeDPYhiGYRiGYRiGYRiGYRiGYeixuroq991339F/P+95z5MdO3YkO74lbDLlaU97mrz0pS+VF77whXLxxRfLzp07B/6+uroqX/rSl+T666+Xj370o3Lo0KGBv991113y0pe+VD772c/KwsJC6bEWFxe3PDY3N+f92Ue9dmlpqfLjayRsPvShD8k73vGO4PcxDMMwDMMwDMMwDMMwDMMw8uYv/uIv5Kqrrkp2PLNEy4yXvOQl8qUvfUn+6Z/+Sd71rnfJC17wgi3JGhGRmZkZufzyy+W//Jf/It/61rfkZS972Zbn3HzzzfLWt74VHnOUUmd2dtbvC8johMna2lq2xzcMwzAMwzAMwzAMwzAMwzCMqrGETWa84hWvkH/1r/6V02t27twpn/rUp+Tf//t/v+VvH/nIR+Sb3/ym8+doNBrOryl7bbfbrdXxDcMwDMMwDMMwDMMwDMMwDCMlZok2RnzkIx+RW2+9VW6++eajj62vr8vv/M7vyAc+8IHC101NTW15bHl52ftzjHrt9PR06fFXV1e3vIevhZnr8V346Z/+aXnFK17h9Jqvfe1r8iM/8iNH//0Xf/EXcs4556h8nnHkM//zDvnrT99e+PcTTpyXX/5PLww+zr13H5QP/ebnS5/zll9/oTzphPngY5VxYN8hef+v/+/S5/zsW54np56+Q+V4//PTX5O//Z9fL/z7nlOOk1/41StVjoVYX9+Ut/3CXzq/7t+8/KnyvH99boRPlBf/8IVvyZ//8a2Ff5+Za8s73vcSERG58bPfkE/96T8XPnfhuCl527tfrP4Ze/yX/+//ln0PHir8+/f+m/PlhS+5wPl9lxbX5Nff8j9Kn/Pjb7xUzr1g99F//9HH/lH++ZYHC5+/9xknyb/7iec4f5aPfvAL8o07Hin8+3d9z5nyg696BnyftbUN+Y+/+P+WPufHXv9d8tSnDfZ9+7PrbpEvffHewtecc/6J8hM/+z3w+GV88Df+Tr5932OFf3/uvz5bXvzyi4KO4cOf/Leb5ZZ/vL/w7xdctFte+1OXRjv+r/3Cp2Rzvbjw40f+3TPlkuecEe34IiL/67/fLv/rv99R+PcTdy/Im//jvxYRkf/2+/8gX/unhwqfe/Elp8ir//2ztzz+P274F/ns39zl/RlTnId+/vnWB+WPPvqPpc955wdeJu22W30aM++U8ap//yx5+iWnwecdPLAo7/1P/6v0OT/95ivkjLNO8P4s/XzwvX8n3763+Pq+4nufIi/5gafR7/ex3/2ifP32A4V//1eXni4//P+5xOkzulD1vKDBf/+Lf5bP/a9vwOedfNp2+fm3viDBJxL5y+u/Kl/4THGx36lnHC8/+8vP93rvL/ztN+Qv/6x4rbJt+7T8zJufJ7/xH/+m9H3e8IuXy5lP2epAMU74rH9ceOD+x+R33vN3pc+55m1Xyq49x8H3+va935EPvvezXp/j2d9zhvzQq55Z+Pf/8w/3yv/zf99S+PdmW+T8C/fIbV99uPA5T/9Xp8qrXltcEHvr/7lfrvv4zYV/FxF59wf/bVBBaSre9R/+pzz+nZXCv2vtoVZX1+Xt1/z30ue89qeeI//to/8gnc3i5/xfr7lEnvns04M/Tyjv+Y9/Ld85WBz/+v6rnipfveXbpffQqtbIGnzq//dPcuPf3V349zOe8iT56V987si/ff4zd8n/e/2/FL52+45Z+dV3fl/wZxwXHj24BO9xP33NFXLGk3XWfkYYd911l7z85S8/+u/TT087X1nCZoxot9vynve8R174wsFg9v/4H/+jNGEzP781IK2dsCnrozM/Px89YYP6+LDs3r1bdu/2Wxj3OOecc2Tv3r0qn2ccufuOruzY/kTh3590/ILK+Ztu7ZMd28vVZ0+98Klywok6Y6eIh570uOzYXpygEhE579wL5KyzdTamX/3yiuzYXtxTas+uJyUbn51OV3ZsL96EFfGUJ58ne/c+NcInyosDD07Lju3FwbCZ2fbR3+r+bzZkx/ZHC5+7fcds1N/1pN3fkLXF4uOfesqTvY7/xOMrsmP7P5U+59xzL5C9e48lN0475YDc/43iAO1Ju073+iy7dt4nj2wvtus87dSnUO+7vrYpO7Z/pfQ5Zz/lfNm7d3BBeOOe78iO7cW73pNOOiX4N969625ZfKw4KXXaKWdXcv/atXO/7NhenDA57dQzon6unTtukbXV4nN/5pnnyt69cQsx/uX/rMqO7cVJ0ZNPOjZ3P/msg/LAt1qFz92x/dSR5+sfPvu47Njuv/479dSnyN6953u/3pWlx7bJju3fLn3ORRftlVbLLWFz6IlVOO+Ucc5Tzpe9e8+Czzuw/5Ds2H5b+Xudfb53QHaYnU/6hix+p3htfdaZ5zldRyef9KDsf2Cm5O9+8z59/D2PyN0l88LJJ52W/Xr7tlvX5J+2FwdXe+zZdUKy7/LVLy3LP29fLfz7rhN3en+WO/5pvXStctIp2+XpT3+a7Nj+tdL3OeOMc2Tv3lO8PkNd8Fn/uHD8cd+RHduLC7hERM455wI57Ywd8L1m2vtlx3a/ZP/unWeWjqf9D0zLju37io8925ZTTzlFHrynOKx10u6zSo/x+CNzsmN7cZFDo9mQiy6qRyD+lJPulGbn8cK/n7yn/FywHL5PFheViYicd94FsnPHA7K+3il8zllnnit7954d/HlCOf64fxbZKJ6Lz37KebL/21Oy+J3ifdkpJ3P7gBz58hcOyY7txS0ETt69u/C7ffvupuzY/p3C127fHncPWjceeuBxeI+78MKnqsV+DF1mZorXvTEwS7Qx43u/93tlz549A4/deeedcv/9xRVoo3rkHDpUHBRAjHrtqGOkOv709LR38sdID6qE3dgoKdNxoNPBNnnNZvxKKuYYmpZ+XfC9m46BrRCazYY0PM6xa7V0XUFBxs7msQ1Q1a6PaNxsbhRv1spgrtPW0BhCFZAdz5OFrh12LDMFmqO+NzoXTYXKz1jnLpT19fJ5v90uTk5ogK5F3/HtwtraRunfp6aOnYPZua3K6X5WV7b2DhQRWV4K6/dXltSqCp+K6NBLiV07MNcsMweybIBx6npvbYKnozkzlA647OpQDd9/3ZaR9LuAY4X8rktgjpmbn5LpGVxPmuNcow21TwkYF60Wfu3mJndvY583CnR/R/u+druJ79Hg821ugvVVjbYd0zPlc8rqSvlagoX5zdvtFt4fBIwdTTZKkkoiIu2ppjTBNdPJ5Lv40AE31LI9TgusHVKskesEWouJTE6sw8DYSBgzGo2GPPe5W+WK995bbKMynOARkdIED+K+++4b+Hez2ZQTTzwxyfE3NzflwQcHrXBGvb+RLzBhAxZULMyG0yeZ4Aqz19KMkW6CBZlG0NeFNrFh3PKaCVnEoKDfpkPQJPbvin7HDc9NTAdsokW2JovQefMNNqFkBX2OieeN+ow42Rr+G8MAbEUJGzTvT03HnRNgMAhFjhWA52Dq2GecnS0PeC4vj07YLB4KTNiApJI2zHj0mfpCizXYtQNTIKGZ9EBjCAVdhkHfUzPZNBqQRI98dA1yTNig8R/yu64sjZ57eswvTFPnZG017VxTBbH3KYzykA20MgHIImDChgmko/UyWIOie0nqvVEIM+D+v6qU7GTGRquNfxtmnZ8CNA6nplrSAotkl31ZbqDlVNl8EZownTSoZCe5NjDGn8mIek0YJ5+8VRq9f//+wuc/+clP3vLYPffc43XsTqcj3/72oD3FaaedNrJPTozjP/DAA7KxMbiIH/X+Rr6gYLzWTZ/ZcOZSnZlWYZP2O7sGiHxfU0dcNjlojMQeyijw6LshYwLhw+cJBxG9PgrczNBV9Z4BFlwBqvAjR6ysDqFqhQ2uqox/XmAwYdpFYTM62BmesElb9c4ovvwUNoEJG/L1zDWrGQBiKtVdQEHM2Akb9POnKLoJpT/RWkbKCv9YRQ8iIksgYTM3NyXNZmNgPhvF6gQkbGI7ATDrfVphE5SwKX8tSgZNTbWgWgjdo6GCOaH7QCgzQKGmVVjBKWyIhE3VFgFHYBSoaP+ZS/LJB6QOKrvdw9iNKWwG2ADreRFOAWlMBvW5+xg0o/q1lPWkOf/8rX7jd93l50N77733yvr64GL8ggvKG01rHv8b39jauBMd38iLVLJaZoGYwhKNCexormVRsHrYXio2rr0FfF9TR5jv2dtkwjESOWMTqyKXCQwNHxt/Fr85BH4WPYHNyPkJVoAqXLv43AUfwgscsKlWYRNfSXC491EZLgmbIoXN0mKoJVriIGqmsRHN5K2qwkbZEq1qhU2K6y42U9NkO9mEBUQxrTGR7eLcwuG5CwadJyFhE0lB2KPNKGzIAHRIMd0GuLeh4CZluwXmChRoT7Ef1AJZCmpdO4yqqtWuh41Yp9OFY6A91QpWcuUMdBEoqRqA7ig1TmTFACWpReIXohn1YTKiXhPGKDVNmSXZs571rC2P3XTTTV7HvvHGG7c8dskll5S+purjG3mR7KZPvE2K/XHqClC0mEz9edoegdZJUdgwv0Vvo4MCV7HHMgpo+9plMMEC94SNpyUaqsCke9j4BWnRtauSsEEKm4oi5Chgw9oK+YIq3ULsYFgYu44eyBKtSGGD+ksgUitsEN73s8BLiZ1vKYWNYgAIWwu5XUdVJzJTJLFjwybJUn4XpOYJ+V2XFoEl2vy0iKSzdcoZn4IVFxjVSAqFDbpvUMoHh56Po0BrmzrMJT1QslNLnUZZorWa2EYsg2A+o3iYmmqNda8WnLQs/hu6/rqd7lgUWGjB9GNGKlNjcpiMqNeEcdttt215bNeuXYXPv+CCC7b8/c4775R9+/Y5H/sLX/jClsdG9dTp57LLLpNWa3BSuummm2Rz030x7nN8Iy9S3fRjWw2wMEfQtERD3zu1esUUNsUwcmj2Woht74cDd56qFqbC1DVh47k51PQ49/k5oJ2hiiVa2GeIBbREi7yxKassFElTVQkVNlNulmjDc0e32w1W2KDPqE2sAEDofKmqsFH8itqWaOg8RZ8vlGwqq2SanLtS9tCAic6AnxUlheeOJGxQ4/SJUNhEtm6m1pjkvS1uDxswb1HN4MMUNrlYZDMghU1RwYYrbOP0qpWYDOx3QS4UOSSffIGFfyXfnSmkrHMySxvmXExKv14DYyNhzDh48KD8/d///cBjs7Ozct555xW+ptFoyIte9KItj19//fVOx97Y2JAbbrhhy7Gf97znlb7u+OOPl+/+7u8eeOyJJ56Qv/mbv3E6/sGDB+Vv//ZvBx4744wz5MILL3R6H6NamApPphIGUa8eNprv5b8gi4GPWmZSFjFMsOnoOK74d2214yRJmM3P8AYKBdd9E6Ahm5mtT3bfwGK7gviWaFVZja+vYQ/7mODxnYHCxsESTWRrwHNtbRMqMFzfMzaxeneFBsfZucBpjldA2xINKjEiTxi59D4Iga6iTbg0Q2tfprdcESugh828WaIdhRneIX0nmeInNgAdlLAJVdhMtWAgfQPco3EPmzz2gwxIYZvSEo3q+5JBwgatr0SO9OOJVJyWA2g9VaaUYpK/dbaL04ayE5yQ4lQDYyNhzHj/+9+/RZny/Oc/X+bm5kpf98pXvnLLY9dee63TsT/96U/LAw88MPDYVVddJfPz8/C1Gsf/wz/8Q1lZWRl47FWvepXTexjVk+qmzwRt8+lho6iwQbZKiZNUPsmXSUnYcJvpI5ZoFQeusOVBpL4xstXWAw1h3ybe6BS7zBfoM45M2CTwWIe9C3JV2ESeE+D4TnBekG1Mv981CtiIiCwPBU6XDmF1zY4nla8lUzcC17wmNWHvo1TCRinIwXr0uxBLzciCE3b5B1nZhE3KsYyC3yHLDaiwOZJshiqBSbBEo3rYxE7YkJZoAfMUur/D+3+LCKQjhQ1K2NRgLumBrp2VhAqbVqsJz10OgXzmu0xNt2CMIvY9LyZwfV/y1ZniyxTWwXWB7f9kGCKWsBkrvvzlL8sHPvCBLY//8A//MHzt93//98tZZ5018Nitt94qf/Inf0Ide21tTd7+9rdvefwNb3gD9fof/dEfleOOO27gsU9/+tMje9KM4tFHH5X3ve99A4+1Wi25+uqrqdcb+ZDqps9sOFNs9plKXM1YfG5VZGaJVgzVw+bI7wkDl5HHMho33j1siGq1LT1swGfxtelBr3OZL3yCb/jaDb8u0JjTTB67gKycYitsQu1WNEDX0LSjwmY4ubJI2KHtOKE8YZPaEg3hO+uFTpe0JRpxzWolSWNYcOBq49gJm/K/18ESjW0snDT5hBL3ngHWjY2OrIFEy9xCzxINqATWxl9hQ/XwC0nYKNoYxUzYQIXNNFbYIOWDqoK6YlD/J3QNsjC/OWNXV5XVbj+McwfTKylEfVg1IZbpbcXk7ySAxluz1ajF+sVIw2REvWrA3XffLR/5yEdkdXXV6/Vf+tKX5KUvfaksLy8PPH7eeefJa17zGvj6drstb33rW7c8/sY3vlHuuece+Ppf/dVfla9+9asDj11++eXQDq3Hjh075Gd+5mcGHut0OvK6171OHn300dLXdjodef3rXy8PPvjgwOOvetWr5Oyzz6aOb+QDs3nV8EFlNpwpFujUXithD5vUCwTXil6Ryak6QRtQkf6ETRxrIJZYzaeZQPiWhE0kv2zUlNYloIaeOer3hAEFhR8ZBX8qS9jAZumRFTaBDY01WAcBSldLtOWhSnemf82OE8oV00gFpE208ZjIEo05jFrChgmuKfewiZ6wySDQFwrbwyZlwBhVkftedivL5XZoIiLz80cs0cB5mQRLNOZEh4yLZrPhpfYdRci+LLiHDWG7hZJfOFhdn+BpKjtBJsnRajXhucuh78s6YQfbbrdg8mlzo/rv4kvIHsN62LihbU9rjDc2GjLhsccek5/6qZ+SpzzlKfK2t71Nbr31Vup1+/fvl7e97W3yPd/zPfLwww8P/K3ZbMoHPvABabexNYaIyE/8xE/IJZdcMvDYgQMH5NJLL5Wbbrpp5GuWlpbkDW94g/zmb/7mwOPtdls++MEPUsft8Wu/9mty2mmnDTx25513ymWXXSa33377yNc8+uij8oM/+IPyp3/6pwOPb9++XX7jN37D6fhGHjA3qVQKm1yqG5IqbFInbDw2QXXaOIXAVF8fXQAjJXvkjA22CfC7ZplgwZaETaQgItpUOp1inx42Ca5d2JOioo01CgxE72GDFGRJEjagj8+0myXacONhJmHzJJSwycwSzTeYmUph02g0kjVkjmHBgXteVauwqUNVPDt3pbRkQusF30QZM8fMzXMKm9T2i1XAWG2G3ve11NEhQfeN0B427SbsXYgVNuV/r8Nc0mN6pnxO0bp2mN9c47dJAVJxixxeY6HvUmcVSUjSUtNecRLAc1rcPY1RL7hIvpGMBx54QN75znfKO9/5TjnttNPkkksukYsvvlhOPvlkOf7442VmZkYeffRR+fa3vy033nijfP7zn9/St6XHb//2b8u/+Tf/hj52q9WS6667Tp797GfL448/fvTxBx98UC677DK58sor5UUvepGcccYZ8thjj8ltt90mf/RHfyQHDx7c8l7vec975BnPeIbTd9+2bZt88pOflCuvvFI2No4tJm6//XbZu3evvOxlL5MrrrhCTj31VDlw4IDceuutct1118ni4uKW9/rYxz4mp5xyitPxjTxIlbBhen6k2B8n72ETIHmOgc+iZFIUNlR/gy6nsIndsBgll3w38z69ptBn8b6ekP2OQyLRJ8iCgmQadoY5WqJ1u11Y/Rg/YQPGd4KqSlSF3H8OKIXNULX74qFyhXej2ZDtx8+WPic3hY1vojo0we1mjyhSFsdQS9hQli/j1sMm6uFVYHvYZOSI5t0zD/WvERGZJy3RhhPO40iKXputVlM2S4LVbJA1ZF+2vt6RbrdbOG/iHna4twi6R8O9EapmyYjp6TTJTkYx0Wo34RjNQWGDVNwih+MTKEZR56QEtH0u+R2th40bUDUY2TXAqBeWsMmY+++/X+6//3751Kc+5fS62dlZef/737/FYozhvPPOk7/8y7+UF7/4xXLo0KGBv33mM5+Rz3zmM/A9rrnmGrnmmmucjy0icsUVV8gnP/lJefWrXz2QtOl0OnLDDTfIDTfcUPr6RqMhH/jAB6i+PUaeJFPYQOlvoh42xCE0l7JoMZm6Sa9P8mVSKk+4htRcD5uqLdF8NzGUh/vQeYLBJs/NoaYlGXrqqPkJeWNrVGBji6PgQzgTo/eGK7hXRwqFDUrYHPuMKNgp4q6wmZ+fgpW76RM25X/3vSRCLyWX1zcbDdksucunVNioW6LFVtiAv6dez/jABmZSVvhD5ZTnmFxZwpZoc0eSzUglOAmWaMxaJXSIH167hSdsQgPVG+udwuQlSrZMTeM+KWj9hIPVpX/OCmSJ1tnsysb6ppcldT94P3l4Lok1n2hCFTRMtaJZLudAiIqfUthYwuYoZolmuGCjYcy4/PLL5eabb/ZK1vS44oor5O///u/loosucnrdwsKCXHvttfL+97/f+9giIq94xSvkM5/5jJxxxhlOrzvxxBPl+uuvl5/7uZ8LOr5RLZQPqkIFSy4NJlMrbNDCOLXdmI+iZ1IWMsy1cKyHTfnzYlvd4R4fngobxhJk6NjQfsHzekKvc0mY+AQ6Uyhs8HdIvxmlAs0VW6KlqBB1Udg0mw2ZnSsP2iwPBU8XUcJm2zSs3E0eRM1UYeMy3+JkYLqEDav26AGtMCvuYVOHhA2rDkyqsIkUlFxiEjYLhxM22BItbXK4ClIpbMpg126hwdi1kh5tjMIGrvtgD5vSP2djkc0ww1iiKtyr0T2l1W5Ko9HA66csLNG4gga0zk7RzzAWIUVpVLFtjc+NNmaJZrgwGVGvGnDRRRfJ5z73Ofn1X/91edGLXiS7du2iX3vmmWfKT/zET8jNN98sn//85+WpT31q8OfZu3ev3HLLLfL7v//78vSnP730uTt37pSf//mflzvuuENe//rXBx9b5HDS6LbbbpP3ve99cs4555Q+99RTT5W3ve1tcuedd8rLX/5yleMb1cHc9DWqNHLr5VKKag+b8r+n9mluta2HTREt4rfoJS+ragbfI1aPD2Yjt8USLVYfFhQcdrh2fAKJKCmgMmcpNSDWhFFtuAaaXcnBgx0GrYYCvyhos7IyGDxFCpuFhWmssEmcsIHJ14puFS5zAa7Y1RlbbINoF2Bg3yzRINPk3JWyhw0ck76WaGCOaTQbR9UBuc01VcCc5tA1u1YPm1Dng7LXo/4ijFUVCqTDHjZ1mEyO4KOw9YFVCcCihAws0dD6SuSwGrIdyf45B8IUNsR+1RQ2R0HnwizRjH7MEi0T2u22XHHFFXLFFVccfezBBx+Ub37zm3LffffJ/v37ZWlpSdbX1+W4446TJz3pSbJr1y655JJLZM+ePdE+09VXXy1XX3213HvvvXLzzTfLt771LVlcXJSpqSnZs2ePXHTRRXLJJZfAYIYP8/Pz8uY3v1ne/OY3y9e//nW55ZZb5L777pOlpSWZnZ2VU045RS6++GJ52tOepn5sozqYgEEKH9RUS3PKEk21hw2wVUqdsPFQ2KTus1MVzEb8mCUaCFxFHtG46sxT1eJhiRarYh1txFz289C2bcRnRL+xRkAvR7sHJtAcvYcNSCzH3ohubnbgtTAc+J2bm5bHHh3d41BEZGWoh83SIaCwWSAUNokt0RAh816jwQVMR79WM2GTryUaKiqIXUiALfHyD7LSCpuUlmjgvPlaGC2DHjbz81NHfzNk6zQJCRvm2g+970OFDflbh94Dyyw/UX+R9hTRJwV8j1oV8QFmQLJTROdejRJpvbGFxmgONmJMD5upqRbRr7O+SYkQFT9liVbjZJY2ZolmuGAJm4w5+eST5eSTT676Y4iIyBlnnOFsUabJueeeK+eee25lxzfSQTWuIwJ4COi9m5Ulmt7xctuU+Mh+ffre1BFmAcxWusYezyhp77uJoQIWwwmbWH0VwOtcEonouh+1cYLXrkIiE17/Fey3qMrHyHOCVlDLF+YcDKuMoMJmeTDgCS3RFqY5b/yNTrLNZtTeXQEZG0Yd2SOVvz9nLej4u1UciIMBphoEWdn1TMrvUpUl2tzC9NH/Nks0v/WPK2j8sYmY0EB12T0OzV2HA+lhRRU4WF2ffcfM7BR8joYlGjqnvbGFxlgWCRuQfDrajydScVoOhNg+U3b2prA5CpvsNAwRs0QzDCMzKEs0hQqWqnt+uBxHVWEDFpOpFwltL0u0ybh1MWOjtwDG3sMqH6kQdN2mTNigYJNvAFSzgbZPA1t07erMWdU2ER/FOln5GBM058RWfVLVn1sUNuVBGx9LNMZ6br2kF4E2MXuYBCd72KeiynClABDVVNmxgKLqHjaQ/PM10mg0ZIpIlKVM2MRKIi6jpPD8sTkLKmwSzjNVwZzn0GIcaPFEWjKG9qcoU3yggoVWqyktaFtafi6RAielJWEoyE5QREehBnvYHBlb+B5XfSAfrTPbvX484LvUuU9LmCVamtjNuLC+ZpZoBo+NBsMwsoJS2CgEx2CgJ6PKTFWFTaQmzb60PBQ2kyIVZprI9xbYVVvDxLL18VLYRPosmkle9HuM+oyw+k1hzkLuplX0SkKVaCIpethUG5hmgivDSSussHFL2Mxvm6b6baS0RYNWkAG3ipAp0+VaRIEOrWuOCSS53luxEiNugAauZ+qQsRGRKWA1eJiEChukAPV8X6SwmZ/vU9iAuWZjvTP2AUCmQCL0vo+VKdyvzRQVlFFqiaahsAFjJcQOKjdmUylswDntBZ1RkqPyxL4QfZKOzEdQLVRnhQ2yfS75HZkEQwo7+7qArp3YRWhGvZiMqJdhGLWBqfDUkNWGSH9VSbwHQAtjpnGgJj7Jl4lR2BBj8OjvWXHz5VibGKbCdNiqIlYTb1zNz78Xeu6onzOFwsbHqi02TCAoviUassGIrLAh7nnDG7y5eaSwGQzYHGJ62BDNjFP2loiZqA55rcu1CHsvqCls9K+jqnsTVK0s1WJqmlHYJPggR0DnzXdMoh42cwvH5qzc5poqYJK1ocU42O4zjSVamQJwEyhs2lPNYNtSNKbrYK/YgymsWF1RUNiAe8rRHjYVr58YYJ+kI/dGZP8cu0ghJiguUpZ4Q0o9EUvY9AMThB7FrMb4MhlRL8MwagOTMEihsEm1OOd62OgFPVCyK7WyyCthMyEKG+Z79jY6cIhEjlzFsglgggCpFDYwOOgSpPX4PTSP7/seVexFKSunyNVoyL9+I3JVJaNa2aKwAQHPYYUNsitaWKifwiaERI5o0Swch6F62LgmbOBnd3o7deoSZGWqaVOqn2FRjOd1t7TIK2xmgUJQZPz72DCFLqHDAu256B42gfuykB427XYTfw9wLrtAN1YnS7Sp6RYcFxrJTqiw6fWwCbSrSwGy3esF0LH9c/XfxReUOCtbq1A9bDJIzOUCTHZ62MUb48tkRL0Mw6gNjUYD3qhUEjbQSiVRDxsqYaN3PPS9XZola+Cqlmk0G7UJwoRC9bDpWaKBzWbsM6bVuHYYnyRFrCCiqiVZBEs0jWsXnrtKetgwvTdiK2yQgix2Dxt3W7hZpLBZPhaw6XS62K6ohgqbqoJsTpZoSGGjlCVlxpBrMUSs5vQsMJlVk6UCk3BOWkxDKEB97gVLSGHTb4mW2VxTBcw5DlWcw/5s5L0tNBhb1kOECaajoopup1s6H4XYQeVGo9GA14+Gwgat63vrslR92kJgbPdEGEVsfZMSIT1sGo0G/p1NYXMUnIQ2hY1xDEvYGIaRHehGpbEgQgvEnIqpNIOkKar0XXBtrNeukY90KEzQrxewwtYwVfewiZOwGVVVGSuICFV5Lo3GwVNHJmwCqt9Y0DtUk7Ah7MAi97DBCZu454VJWm1J2AAf+5WVYwmaleV1OL4Xts1kp7BBhEx7IdeTy2vhfKVliYYCBFNN5/sE7E0Qeb6o+r6nBXNdpSxUQRXxIn6FRCswKWyWaP1Qa5XAYYHtqrgfOjQYu7ZW/Fui956ablEFK6UJm0xcF7SYnimfUzR62KBk3lGFDRhjVVjtDgMt0aZYe7fqv4svaH2P7ODQ/rzOySxtsCWaheiNY9hoMAwjO2DFV4IqDbQw0YKJJ2jGPFL0wXDBtTpwUuzQRLhzwy6AYw/nWAFtn4Ac7qvg2cMGFXMr9q0YeXyYvAr/kaE9UwV7USpZEdkSDQUcfC3/WHws0WbnkMLmWPB0CdihifAKm7Lm0drABGJVPWwcXgv7IylddIytkCs42VRtgKYuNkaUJVqCz3H0WMTBfAofoMKmb86aAQFnEZ2gc84wlz6TXAt5PbvGDN2XFQXMu90uLNpot5tQYSNSPh/hYHU95pIeM6BgY03BTnBzg1uTQlVKBn1fsIqLs3fL4bv4EtoTLpbTwjiCrh3XYlZjvLHRYBhGdqAblcZNP5tmtYkzNijwxwTkNHFNwGgEpesCs0HsjWNsDRNZYRMpoO2jsIEVcLF62Dg1rnCvONxMMGflaPeAEgDNVnybRDSmYm9EmSTIcJU+6gHRb4m2yCZsmGbGGVmiBSlsgl7rorZL1cOmfAz53FvRa2IneKG1bU1irFTCJmHAmDmWz7hcBgqbuQVXS7R81HwxYBKewT1slHpyhK4NihQ2zPGZHjYiQGEDPn/tEjYpFDbontLuqVKqVSgzoO9y1BKt4rVgTJBdNPruWvaKkwBW2JglmnGMyYl8GYZRG1IobHKRv6dW2CALCSYgp4kpbIqhetgc2ehUoXzoJ57CBm2itx4X97DxtERTrMCElmgjjgUt2TQs0dB7VDDOWG/xmCDFZexeHUz/kWGFBFTYrKwfHdPLRMJmYWGasp5LqbDJ1RJLU22n5e/PWr5o4qtmZIH3vZpkbNrEdZVyLFPFIo6Ljk6nO6DqG8X8fL/CBidsTGGDg+EItHZLpbApum9Q976pFrWXKPuMcE9YMzvm6WnQw0bh2kFjoxd0xgqbHBI2rL1btWvBmCB1EPod0f4crUEmCaTomqTiVANjo8EwjOyAN31QmcBQtSLh2GGISsYxVti42rBM0iKG2Yj3xjFsvh05ARmrKbuPhR+6pnwthjRVeT5WPTigoGCJBox3YvekGAUK2KTwekbHiK08Qpu7RmPrfRMlbDqb3aMb6EOHCIXNtmlpNBoyBQL7OQVRU6lkhnGZb5lm2RrEaHIL7dwiB6/GxcYIXVMiab8LtS51/G1XltfhOmVu/pjChkkOj30PG+J+G66w0bE1DL0HFtmeMYmg9lTTSZHu+jeR+tgr9kAJz9UVBYUNCMD3xhZUYmaQ5NgAe+P2kcIgvBas/rv4govCQA+bitfJdQJdO1PTkxPrMDA2GgzDyA5409dQ2ICNENPAUgNKYaN4vLLGniJcVaMmzgkbU9gM0FsAd8EoiT2acY+POEmSUVWP6Lz5Fn1rJsV8bJBSBBSQHX4V1YN5KGyqDUyjRHt7qrVlTM3O4bl8ZeVwxTvqYdOeah5VX6KkfkqFTUxPtGQJG3AcLU98XA0do4dN3OsiG2vbQFA1vEhahU2MHjaof43IYdvFHq1WE6q+ckoOx4AJZIeOC9zDhvudQ6vniwozOHVpK7iHDZxLapL87YHu0xrJTnRPoXvYZGAjhteZ3Hfpdrq1VdmEOo9ge8Xqf+dcYNVphiFiCRvDMDKkncASDXufj5/CptPpElUdeVuipaimzwm6HwuMW8bu8YEr6HzGsU+SgtlQ+XwWFDxx62FT/udR3zuFZQe2REu/EUUJAMZOKBS0EY0dmEYKm1FWlrOg6bCIHLUoQgmb+YXpo+MbBYJSBlFR4UVVPWxcQNetmsImgiUaTGRGb2JT/ueqLPFcYdY1Kb8Ksy5zDUqugP41IiJzQ6pAqBIY84RNiiSCVpA1XGEz+h6H7n0ifA+bMust3C+xXnuPGdDDTqeHDVcEUPl9ggB+l6M9bPTnxlwItV2G9ooZJOZygb12DEPEEjaGYWQIChyoKGxAgK2R0+yotPZjKqqSK2wcg0QomTduIKVXb2MANwiRgz3MZtbHKsAnYMFUt7vuD5kEj2bfilGkaIoL7eQq2G+hgE2KjQ26DmM3U4WJ9hEqI2SJJiKysnz4nrAILNHm+6yKUJ+zlAobKLCJri0cDRM87BFLETgMbBDtcW9FFimxrW6gsrQmCRumUCapvRvVW9FVYUMkbBYG5yysEkio5qsAXPEefgzcQytNwqZIRcqsG6emuR42ZWsoXJAD3z4rZmbK5xSNawftxY8mbFDhWQbKC3adydzbc/g+PqBeQugaQPvzOtvFaYPtBCcr1mGUY6PBMIzsQAtvjeBY1T0/+kGBXi2FDVNRlbvCZtIWMaiai90kxw5cxdrE4KpHv4SN62dhKuZcLMnQU0dVHGI1gYIlGrJqq6KHTQaWaLAKOXLlILKyHKUycrNEWy193sK2voQNCqKmtEQDhNzHQ64nl9eia66jlLFBASmf6wjOY9F72JT/vS5BVurcp1TYEJkAZ0s0oOITGUwMi+AConHvYQN7NCkMcHxv435n9nlFFCpsiPtJq9WkFMZlAWO0vqpLP6weM0Bhq3HtoLVZb3+nZbsXE6xAPaKwCeyVlDPBChvYf7ieiawYoAIaU9gY/aQtpTYMwyBAN6pxskRj0IqRMhuf7HvYTJjCBlZfH9noVN18OdYmBiWkRlV4M5YFzgobxkvepYcNiL6N7mFT/p4uVf1FoGmvioRNDgobrIKoWmGz9RxoW6L1QAqblEHUmAqOIDs1F7Uda3sZCAqo+lxHVTeTHp8eNnkpbKjeio4/7TLoYdNobFUFTgOVgFmiKSRslBQ2oYV0RfsTFNgUOXz/Y+aasu+CXBfqlrBB186KRg8b0kYM9jrLIMGBxlkvqc4UDdY1MYFtl8u/O7REq6nyKAZmiWa4YKPBMIzsaINqQ41q5qoD3P2kCpJmqbBxTdi067VpCgXaVRwJFMcOjCFibWJ8Kr6YYJNrI2/mEnSZM1wDLd1uF1t2aFiiZbix9unfog3aiMbuYYNUKyMt0YCHvUifJRpI2PQrbNA9IqXCBt4bQy6JgNe6VL7jZKBSDxtU0RlBYYMsVoJBP39NgqzMuddQU7Aw581VpYos0WbnprZcCykap+dMaMU7g1oPm8B92UbBfZ5ZN7anWpTCpuw+HRqszo2ZmQQKGzA2ekVEqJgoh4QNWmeyaiGR+iYmoMoM3IPQ/tx62ByDVXQZhoglbAzDyBBoiaZw00cS7LwSNjrHyVFh46qYabcnaxGDNtO9DT0aIrEDV0xvIZ9NDN5Ee/awcdwgMk1RXeJprlZCzBzAbCQR6NxV0Rt2A8xbKTY2KBgU29KjKJjVY1TSqj3Vgj3ClnsKG9DDZm6gh00+QdSY1qZBlmhO/axAMlAtYcPZ17hQtcKmTkrpMqhCmYTfhbqHOr7nMpGwGQatR1fHvYcNGt8K6zo0/1TdwwYFNkUOV6NTfRRLCnWwWq8ec0kP1MNmdSWBwqbdsxFD97jqA/lQxTx9JGFDFKfFLuCJRWhRGNqf11V5FAOzRDNcsNFgGEZ2JKnSyGijn6pyklHYpKhW7wcFFIeZuB42YGxskpZosUcYU33os4mBCZsR54cJNrkG2ClLNKceNm6JEaaaWUVhgz5XBZWQuIdN/DmhaqsHpFopSlohW7TVIz1sXBQ2yGolrcKm/O9BtmYhChunhE3537WuOVzR6X4d4f57cW0UcV+vaIdWhelhk7aISF9hgyzR5he2zlVwrhlzhY3P+scVtN9i122xFDZI+SDSS9hEVtjURK3XI4U6DRYBHNmvod8mix42ZK9Ean+RQQLKh9BCVi213iRglmiGCzYaDMPIjhQKmxQNvLXQCngwC3S0yNeGUWYMPj+f3yUFbH8DHLisvoeNzybGR2HDJC5crymmyl3VEm3o82kfvwiYsHGuqw4H9rBJoLDBlmhxN6LYFm70vD07Vz6fL5M9bBb6etjk1Ag85nhEfaZKX+uitoM9JBJZonkECJiAcUxVHnbEq8d6gUk6p1ySUgobx991abFcYdOv4uuBFTbjnbBJYYmG1uBaChs0z60XJJSZ/d7UVIsqGir7jPBc12zvMQMsUTWuHfSb9+4p0Go3g4QNbYkWmBjMmVCreLQ/N0u0w3Q6XThGzBLN6McSNoZhZAeW1YZX8OJqquBD8DhW2/vCVD4nt0Rz7WFTMx/pUNAm9GiguOJKY2YT47NYR4HwUeeHsQZztRlST9ig41WWsCn/e449bFLYJKLxvVGxJVpRwBcrbA4HbVDCZj7THjawh0mIrZnnSxsNt+Oi+UqrYIMNrrkQo9eJEwkso1LAKWzSrX2Ye4nrvQAqbEYkbCa9hw206UrSw4b7nVFiBRUPrK2N/i3RvU/kcHCzFaisDu3fkRu4sGIzeD2HVJvHFDZgH1OF1+4QtMKGcRPIoCePD8EKm4qV6HWB2Qubwsbox0aDYRjZgaw5NjfCF0MpGniz4B42aRQ2jYafLUoIrgmYSbNEQ5vQTVpho/WJRsP8jj6bGNS0etQmmlLYxNhQOfWwcbRES5SwgT1sKthvsd7iMUGb9G6nG9X6CfUfK0qiQIXN0rpsbnZkZbn83jDvoLBZLwi8xSBqD5OQjI3L06HCRueii9HkNkZg3wXcdyLaoVVhzn3K7xJDpbrkY4kG+mWNvcIG9ehSGBRqPWxQwgYUD/gqbBrNhjSbDdKW17+HzbhZoolwdnNloLHR2xegc5eD8mITFcUcWWMxe53a9moJVNig/Xltz4syzJxqCRujHxsNhmFkR4oqDeTOlLKaKtVGAFU+T023klvBuS5KJk1hg60EDg9kGLiMPMY4Owr3wJ2PTUWMICKVMHG4dmDfCh+FjYJlBxxvk6qwqTgwXRTM6lHYw2ZEI+9+Vlc3YDNwkUFLNKSwSdkIPMceNq5CCKTe0kous5YvLlSdsKnaClSL1L0DEcy9zFlh42WJhnrYJFTzVUCKdZ1GD5tOpwuvxbn58nvRRsH+BN//uaSASPmYRd+zLmq9Hqj/k8gxha0vrM0mLgSqXpGC1li9+zTlJlBDJUm3i69hdA2YwoaDSVxNWnGqUY6NBsMwsgMrbMJv+lErc5XRWswihU1qOzQR90VJagVQ1aDNQS/xWHWPSybx5rNY95HoV5awcdnQI4VNxyNho1FtC96jCusKbFVRvcJGJG6VaJFdTI8iSyVU1by8tC6HnliFx+9X2KDgMlIDaYIVFiGWaH6vdb0O0XG0rrkY1xHzVWMqz0IrgnMBJUFF0ga7OIWN23tChc2IgD5SCYy7wgatfzS2KciScYMYd8y9DytsRt830Ljv3ftC16BonmWKNnKC2c+FWgoit4v2UUs05BRQfSAfJZ+OWqJVbQMaCY09BrRXVHBHGQeYNXKKQjSjPkxW5MswjFqAmmAm6WGTsMGkqz2SL6jymQkaaGMKm3KQXUXn6Ean2gQks4nxCWjDJpgjvleMhA2TNHWpwMQ2iIP/TtbDBr1HFQkb2L+l+h42InGVBOgcFCVRkCXa6soG7F8jIrKwbebof0/lZFMUsXeXtyOa43WIrlutBsZo/vUJEDD3Yx9lJUsOvQ80YNZeyNJOE2bsuwYll4CSb27BethsAa1/FNbDOMiKf2dmT4YUNkUOACi42SviotagZT1swHium8KGSdisRFbYHLVEg704q5/HUUFDT8XMFBnGvOfFgvkNUFzEFDYczHyZohDNqA82GgzDyA4UxNfwQc1KYYNipKizMgna3DKex9q4KmwmTSYMK9PIjUFsiz/md/EJaPskVqMkbIjAoEvCBP0eWxI2xEZHI3iDhkmWlmgJEs3M+I7pz43sLIst0bYGQftZXl6Dle8iwz1s8lHYIKroYeOaOEVjS+uawwEpD4VNhF4nLlTdu00LJlmWMtjFJOJcf9VlkBgepbCZdEs01MNPY3zD/iKMwoZYh84Be86iogQ4b7W5pICI9bAZJlxhw91ToHK7Yku0breLe7z11EKE52nV38cHpvgBXQMp3FHGAWa+9OkpaIwvkxX5MgyjFqRoXIcq5lNWUzVcupUHABM2VShsHIPMrs+vO9Au54jCBm4QIg8xxi7CJ+CEEhWjNukxgojMZsYlOIxVdUOWaMoJI9/3qKKgfX2Ns0SJSdWbdGxnNfoczMwSCptDRMKmL5iak01RjgoL17UDnuO1Ejb6PWyq7u0EFZg1CbIya6/cGja7KGy63S5W2IzoYZPTXFMFaHwz9yUEKpBjKu+ZsYnUnkWFGThhw1tVlSXA8FxSr73HLLj3i4QnbDbA2DimsMk7YcOoF3sBdMZ9o46WaIyLAEzYIHeUGp6XGCDFvMjkuYkY5dhoMAwjO9CNSmPjigI9sRUJ/aSqakdV2lUobFyrSCZtEcNWX1fdfJmrbnQfx6jCdKQlGvFdXS0LKEs0l1MMnccGj6exmWKAiaQcFTYJVHdVb9LR3D01PfocIBua5eV1WQQJm+mZ1sA8zfSwidq3pI+Y857vGsD1ddASreYKm6h2N/nl67xgks5MkEcLxgLS5RJfXd2A9475EZZoyNZpfW2z8mBvTOD9VkNhA1XcjMIGP2cGKGyK7nEomN679zWbDTgfld2jcb/E0j9nB7OfC0l4djpdOD6P9bBBScFqA/nM+O1ZVDF70DomJjRsl6ElWmZFB1XBxLBS7GuM+mCjwTCM7ICyWoXFEFpoplycw6CS0n4UVVMxnsfaMIGBfnyCSnUG2lVs9BI28a0zyuD8wz162HhYolEKG8cgD7OZcQkOuypZmASTTsKm/O9VKBrYZrAxqXqTvoGS7QV9ZdCcvrqyIYvAqqi/f40IFwhK1W8DV0X7v7d3DxvXhA24B2olSbHli/t1xMw5MZN3aF6uS98JqodNwmAXM4ZdEiUrQF0jMtoyCykERUTW1sZXZYN6sWvc8zX6TjCB2NnZ8oRNZ7M7ckyhgo3+zx9kIQyGs4blbEo4SzT/JDDzm/cKzuA+puKkKxpjIn09bKjinfolkak9DkrYoH5YNUxkxYBK2ExYrMMox0aDYRjZARU2CsEgWJmb0hLNsdreF6ywqcASzTFINGkKGxzYPzI2KlbYMNVAPot1XPW49XtRGyrHa4p5OqPC6OFqg8TMARpzFj3eEgIDzQk2NlVv0qHKqOAcQIXN0rosgx428wuD78HYNyWzKkKnPGDe850zXeYBEazI0UoEovnXp6JTO7DvCpqO6mKJxsxhKYNd2raiyA5NRGRuYetcFTvonDswIa2wroPqB+L6ZQKQ6F4kMvo+51KwEdKPZxNkx+rSD6tHu92UVrv8Q4fcp5n5qE0mbKpOcDB995x6JdVQ9UcpbMBFoJH8nQQYtaxPAY0xvkxW5MswjFqAAgcaN30UCI4d4B48WJrDZKmwcQwSuT6/7rAL4KoVNkwizdWGTMSvRwEVRHScQ6iEiVMPG3jAgX8y547pJ4HAlmjBh3AGbW5SKGyq3qRDSzTPHjbra5vyxOOrpc8ZtiqaKlDz9BPqjc9SRQIR4TrXZqOw8Uh8MgmRqAmbMfFEoyzREipsmESAy++6BFR8IiLzI3rYMGvSVHNNFaRQkMH9FjHumOeMUlANMypwjnvY9Cts/PvxYNeF+u09YvaAYuajFmmJJlJtkoP5Lr05mlln59ZvjEHFEg0kCHuOEJMOU4RjlmhGPzYaDMPIDtTXRMcHNZ9mtamaDq/CPgjpKzoY3+l+TGEzSG9sYMVI7B42cXp8oOSsr8LGNc6rUX3WD0yMDP9bwa6AAb1H6gB5t9uVdeRhn4slWqRNerfbhUmrItULEyQ7eGCx9O9bLNGI+wRKMGkRVWHh+dJce9jgXlAelmjMvB8zYePR4yxHmGsqqSWasq0oUvGJjO5hk5WarwLQtaOxT2HsWdHnYIroZn0TNjDR7KCwKVHR+Ki5c2eWsET1hbJ1OrJu4nqdVZfkcOkpwvXrrGHChvjM6Lu3Yf/h8VVDusC4xFjCxujHRoNhGNkBLdEUNq6wci3hRh9boukcZx1sbBn7iRi0HSxkJm0Rg60EuGshdnEgs5n1sfbxqXqMYdPDXIMuCRPXJC1j4aaRzMythw2TnE8xJzBJwFh2D5ubXTj+ioLtM6BvgIjIwUeWSv++MBRIZe4TjMWIBtDaNOC9fQN0rolTlFTQSni4VKqzMAmRmEnenKxtQ8hOYaOsnGIs0UYF9DlLtPFN2KD1j8b4ZlTr6D5MWaIxCZtRlmgw0dynsAHfJUxhU4+5pB90/YRcO8x655jCptrEPsKlhw1TZFhHSzTmE2OFTbhabxKgLNESFKIZ9WGyIl+GYdSCFJZoKSrXWBqJPNGQwoapZoyBi80Zs/AfJ1AFc68qEAauIicgG40G/Kw+PtWoGemo8RDDpoeqPnNK2IAnDP2gzPE1fmIUgNWyZ2LhmnMmsESrsEKU2dwVqSOZvgFIYTM3ZFU0Q/Q6S1X1jiyxQuY935e6rh1Q1apG8KfT6cJr1ythQ3xXHytMlhwt8Xxg1kDMPKAFNd85nPtlkLCZmm6NHH+MJdrqGPewQec4RQ8bEdzfhblPz84RybcRexS03+tPdiK7qrK5FJ7rGiZskCVqkCUa1YeD62EjUrElGrPG6rMMRfvQWlqiKewx0BpCqx9f3aHsBCcs1mGUYwkbwzCyA9701zvBG3VYuZaRwkaL9bU8FTYuygDrYTNI7zpIHUgfRchmufA1qNfUiGPGCK5rVJ/1g63HBv+t4S/N4Pq5YsNYa6WwcqSqkCMFpkPOARPwXDxUble0sM29h00qhQ28MKvY86pboin07IuU+NS2znJlXKrimfVm0r6KBJqWaPMLoxPL00RyeKwVNjCJEH4MKukK5g8tS7RRlknoXtLfewslv8sCpWitWZe5pB/YwybAEo3qq9jie9hU2ZCes0TjrffqqLDR6WGDFDb1Oy8xgGrnqWZ293ujWiYr8mUYRi2I1cC8H1xNFfT2bqCqdqUoKapEnCECcTFwaXZsPWwG6S38crCGgYt1jw2ZT9Uj5ZftaommvAGD1mPDlmjMZkrFEi2vjShX+ZggYUPcEGKdG8auo7CHDaGwQQwnbBiFzRooDtAiprWpr/I1xx42jHe8T0Un1Uy6QhVMnYIeJ+5eKP37i1++N9En0bcwWlosV9gMq/h6UAqbgKBz7kBVsobChihGgD1stCzRRvWwcbByhCrvkuQ3dF2oYcU7un5Ckp3M2qw3tmKsyTXhLNH6FTbIeq9+ShLmNo32GOi8VJmUy4kY9rTGeGMjwjCM7GAC+KE3/qotpAaPVf53rYUsWpynqFQfhZPCZuISNkBh0+lZoqHApdpHKgR9Vp9r1qeCOkYQUVvhguYXL4WNwo+MGxCn3VRTG+kEmxuquXqkzSijVilKWiFLFIbhZuCMwmYtE5uioEsilSUauuYUhlUsa0Hm/MYMXlWZDNJm79NPKfxboyFyzvm7kn0WZv3rcuqhwqYgYdOeasG5N5X9YhWgc4xUzQzMe6RS2HglbAYs0QJ62CSwn0sNUqiFXDvMb95bmzFjrFpLNPxd+tdY6J6NrJxzhPk90TVgCRsOVEAzaXEOA2MjwjCM7GBuVqF+3j5WS7FwDd76ghI2TDVjDFxsziat8gRVuvY2BtAZKMFms9UGn9XD1xkt8H1tKlwVM0yiwuUU483/4PGYz6tRAZoqecyy7riRjgV1T6owYVMUbJ+ZaQcna4eDqe12E94fk/WwgYnqkB42fq9tON6i0LlE/SMYmIBUrB42MXMq6NTUycbo5T9ysRx3/MzIv/3wjz5Tth03+m8x0O45seRpiSZCqAQSqfmqANkhaqzrKLtPBYUNlbAZsa9De71+qyq4Bi3Z90G1Zo3mkh7o2gnqYcP04ej1sGFcMyrs+8JZovUpbKD1V/0SE8x9Gl0DsP/wRrid/TiA1mMuriPGZGAjwjCM7GACB6GWaNgbOh+FjRbIEq0qhY1LoGjSKk+gxUPvOkCBS60PVAKqbvS5ZrHCZusxte1c2Oc7WZI5JkaYij2NOQsrbIIP4QRj5ZSkhw0xpmJt0hmV0VTBBq/RaASrbIYt0USwLVqqHja4F53/e/u+1vU6RNXHGnaMzHXkk7DJ3eqmTkXxJ+7eJr/89hfKUy8+6ehjx22fkf/rNc+Sl/3Q05J+Fk5h49LDptwSrSyYj1QCuaj5YoBOsUaRBmVBDe5tTMCbKQgbdd9ARRsDPWyQwqbMEi1ScVCVYEs0/2uH6ot2JJlGJYArDOS72LuJEJZoNVTYMEpY2MMG9lyt57nRBluiVROLMfKlmnJqwzCMEpiKr1CFTczKXGfQsRQWshsbHRj4qUxh4xDonrTKE6TE6G1A4RBJMJ6xf7j7OEaJilGB9Cg9bJjqM4f3c1XVMUFbjTkLfq7kPWziKANcoWz2KuxhU6YympubkpVl/0ra+REJm+mZdul7pqp6h2e8CoWN4+ugwiawOEUkniUaYxMUM0CDE3b1CrKedfZO+ZX//H1y8MCidDpd2X78LGwcHgPqHuowLlHCZth2sR/YOH2MLdFi9ujqodHDBs0vrXbjSCPt8rXUSIUNSDYPKB+QIr3kc6KEQd3mEpG41w6jKO79NlTCRuE+54trE3g0zpj7bW4wCTOYsGGKbTc6E1d4OQya01K4Bhj1whI2hmFkB6ewCVsQwQaTGSlsNCqPmOaSqJIxFi6Boklb6CHVRm8c56AYQ5/VR4HgY10Yo0G8dg8b114xzHzn0zR8GCZQ1+12kwUvmMS8T6DZFcrSI1LAgUnYlAVmZuamRGTZ+/gLI4Kp00DVlEvVe0jfgWQKG1SVqqGwiWWJxiQyI1kFUjaVNayKFxE54cSFSo/PWd05WKItlluizc2XWKKBnlkhjdNzBxaWJephgwLQaH3Sah0OeLenWqXqy1FqGnTswd4iYA1aMpcie0WN9VVqkLo25NrhFDZHetgoJAVjgm33Bj9/iJIrV5iEGVbYEEp062NDJbgNo5/JinwZhlELmABcaHAsq4QNqM3XUIqvERY1VSls2g4bIZd+N+MAa4mWg8y8jRI2HpsYlKwcFWygkg6u50s5OAhFdUOfT8NfmoEJcKd0rmCSFSkUNlyFaJU9bIrPAdM7oIy5EQ3BkQ0dc7/RIOq8l0phk6BgI5YlmnavExe0VY/GMZgh7DIuocJmxBzTwxQ2xYQkpHtQVfHg3gYTNkeOgRL9o3vYcO8twqyXR79Xt9vFar0aJn9hD5uVyD1sjuwJmHFaZSAfrTOHLapoq+oaoVGUxs0l9Ts32sAeNmaJZgwxWZEvwzBqAdXgOdASDXmppJS/pzgUp7CpKGHjsDiZNIUNqn48mgRBXucJNpuoKshnE4OC4KM20TGCiMzz3eYMkKT1OD6jLEJQgbqEyUHkXy+SpodNs9kgrKuqS9iUWSjMBvSwaTRG2xWhQNB6qkbg0MbG/619X+o616LnawR/olmiVdibgFLY1NDGKAcajYZzUUEZS0vlCptyS7R6qPligHv4hR+DuYaRmgL9vZcMRlY/o3vY8PZBuGho9PlkpiiN9VVq0J4utsKmF8Bnei2lttvtB6u4Bn97aL1Xw6QEcz9FcwW6/kTqaRenjYvNo2GIWMLGMIwMYW5WjH9uGdivOOjtncD9LBJZoiUIfI7CRTUzaQsZFCSmg3kJxjO0o/C4Zn2UcDGSDhr+zoPPLf/78DXPqDc0KkCjqJMCCO3foklVm3SkVmm2GqWJ7BCFzez81MhxjZJkq4mCqOiyDLkmUvWwYW0vQ3Cxr3GB+aqx5gtKYWP5Gm/g2sPhd10OsUSLGHTOHdjDRsMSTcGuCt37eoHcNlLYjLjXoXVj20lhM/pzplpfpQZZoq2E9LAB95RGs3F07cAU2qF+lTGBKq6hNSb6PrW0RFMoStNQ600CmxtgvpywOIeBsRFhGEZ2MD6ojCd7GTAQnNKvWLGSsQjGoqYqhY2LambSFjKoYqk3jmECMkHGJqThaxHoOh01dhoNrIZwDYIy16BLcBAmaYeOp91Dpwgm2KyRQGZB6pJmq5HMvhLNU7E2oi4e/qOYnfVP2IzqXyMiMo36SiRS2MAeDyEKG+8eNo7PBwfSCP642Ne4wLwmmiUaMyfXMMiaC7jPGvc+62ubUClZZomGgs7jbIkGexNqWKIRExaaP3BPhp7CpvxYowo0oF3VVH/Cxu8ezYzllDbZWsRMdsJEWt9+oEpLWQas4hrqYROhX2fVMPdTFBehEnM1PDfaQIVNoiI0oz5MVuTLMIxawFhkhcpqoV9xRpZoKj1sslbYOPSwmTBLNNaGKWbgkgUGtD0Cd0hBVLQRRDGIGJZoLht612s+VcKmSoujUYQmKzSpyrcc2YvBhM2cfyK+yKooF5siqLAJmPh8X+qaJIDjSiHhwVmieShsKu1hY5ZoMUHnjv1dl5fL+9eIiMwtFCeVsa3T+FqiZaOwCe1h0+Is0UYVlm2Aoo3+/SKyEC76Hkx/xXFM2GxudL330k69hRjbvQptxNA5GI5JoL6roQ4gVcDszzR62JglGrb0n7Q4h4GxEWEYRnYwgYPQauYUzTxZUgQVmCrEOihsXOzTxgEsvT88jnHgUusTFePb8LX0NZ4BC3T9uqpEOPsdJ4mN0/FSBG5EyHGScF+NNjYpFXeoEpkJ+viAqtORPVmIJVpRwiaXHjYwUR3y5p6TpuvaASoZNBI2hLWgT6Nb5rtGS9gQz7F8jT9YYcP9rkvADk0EKGzMEq2QVEUaaL8Fm2gf2dOhorBRlecbIJA/NaCw8bMt1VZQ5wIqrBDxv36wVd2xYzNjLKVyexio4mq7KWxiFe/EhLsGTGGjgWvPJMOwEWEYRnYwN30mAFFGDooE9lg6Cpt8LdFcAkWMXd44ARtS9xbZaDwnqA7EAW0PhQ34XkUVleqWaEw1t1MPG/D5hnvYQGs4rYRNdQHYUdRJYRNrI+rSdHkUMSzRYA8bwoIzBVUoLFyPiZ6v4e3PWaK5n6sqe15pBJiMYtCpY4OSy0tEwqZgnhHBQeextkRLkLBhih6Q+oFV2CCrn2EL1M3NDjwHAwobz35gjLqjjlXvKNkp4p+wwTZ4fZZomQfyUcJxeL1D78tqBFMEi9YI3FxiCRuo6DJLNGOI+t19DMMYezQ2EAjcwybd9Ij6i2hUHqFNbaPZqKw/TNvFEm3CFDasDVOFxWlHQb+NVw8bsLgvVNhAa4wIlmgOwUH4TMeEjVZgkrJES7gZRT1sUBNjTXyDQaGgczA1Xf65QizRFo6bGfk46mGzniiICue9gMvCNxjqmhxH910VhQ1R0ekzh+Q2Xwxj+Rp/mqAAo0tKLZeWCEu0+QBLtERqviqAvQk1etgwwXRkiYaC962ewgbcN4aKE1ytHH37zGkX5OQCU4S3shInYTOYSMv7PgF7igzdo6vqZxgTykUAXANMDMEs0ZhrZ7LiHAbGRoRhGNnBBOVDq3FysJA6drDyP2skbFDQb4aQzsei5aCw8bFtqTMoINazYUoV0C8jRkAbBSuLjomCTa59WNQbXCtboqkpzzKLSbhaVcQEJiQj2WAgNekUCIKFWKIVBVJhD5tECpuY857vK10TPWje6HbD1wDQM93zOqoyYcO8ryls/IEKG7aHDZOwKZmjZsD8tjrGPWxSKGy4HjaBCpt2T2FTfqzhfQrat4gMrgHgerngczJjGfXHyREmYRPLEq1/bZ6bcnsYbL07uN6pqngnJhpFaRrJ30kAq9MsPG8MYiPCMIzsaDYbcLOIKmIQHdBvIGU1VQ49bKqyQxNxC7pOWuUJTDyQQeIUPZliWEYhO6CiDTpW2Lh9FiZe6hI8cbVUSDVfIVs7kbQbrpws0VDAJtZ5QckPNCfOzgYobAqsilCldKqq95jWpr73ZddrMUUfGNemyi5o20+yUAGmGgZZcwHdz1nlF7JEa7UbpRaLMDk8xpZoqC2axn2fmeZgDxvUz+SowsbdEg3RvwZAhStF62VmHV1HhQ1z7/e1FETr+f7kXO5WWa49Raqyx42Jxv00RbHtOJBTb06jHtiIMAwjOxoNbM8VWs2MArApAtw9UvSwQZVqdUnYTFoPG7gB7fQs0arvyYQD2h49bMBrChM2KNjk+FEYRY7LOcbXvJslmtZ8xbxNSvu9nBQ22J6wmh42KAgWorAp6i0Be9hkUvVeRcDe9VpE40okvIlxTAsO9h5VCZO1XFAFJSzZ33VpsTxhMz8/XXos1Idj1dPSqQ6gdZ3G/NZoNAb6jYwCzR+oB8gxhQ1I2Ay9D7r39b+3CLayLioAYtZ3dUz+Mvs634QNVAn0K2wyt85E43d43FZVvBMTJgGPri9mf17Hc6ONWaIZrtiIMAwjS1ClBqpQQMAAaEYKG5UeNqDiuVJLNId+QXVs/BkCa/GAezmkUNjo+zrjXlOjv5eWnUsP7QbX8LlDh4PWKEqJzCqbiI8iNFmhCVIfxbJEgz1sQBAsJGGzsG10DxsURF1PpbCJORY9LylCpDb0fCKYFWqJ5lAN7Qr69BrrF9/3NUs0f1Dikf1ZkSXa3PzopHCPGaASWFvdiDbGqgZaPiodBym5YQ8b0h4L3au29LABgfTh94RWVUU9bMZUrYeuHRGRNc/iCqiw6dvDM3ZyVSZsXAuD8F6nfvPRJpLzCd5XUQqbGp4bbWIqno3xZLIiX4Zh1Aa08EYSfAjqYZM0YVP+d42g1DpYlKM+CDFxCRZNmrcrug66OSlsPDfLpa8Bm4iioBK0knO8pmDgxLVvhWOSFm1ytIIJzPukDI5tIDuwlJZoUPVZjcIGqV1CLNGKFDa59LAhQvbe753MEo0JZgWOLdxU2f86QsGrSi3RLGHjjZbV3RKwRJtfKE8oI5VAt8sF9usIVNgoFTChim7Ywwb2ZDg8lqAl2nDChrBPGuhh42uJNqYJG8Yy1tdSEN1TXJRPIuEq0hBwQYNjD5s6qkiImAhaE7WJ3zm02HYcgJZoAQU0xnhiI8IwjCzBCpuwBRGqJkm60U/RwwYpbBJWqg/jopqp46YpBOiVvNlL2JS/T4pKYySHR/1oRuGbKHHtERP6OVyr6qECaOgHxdYoOss5qjlsyoSNo7d4THBfpEgKG5SwiaiwKUzYoB42ifpK4OvC/7197zWucy1znNCqVNzk1v/+UNV1wWD5Gn+07qFLi+UKGzQ/ITWfiL+tU+6ga0drfEMlN9gvsQobaIm2NpywwcHd/vdESo6iQj9mLNexh02zWd4fSiSghw0Ym/3Be6pPEqHwiIVrAD3GXqdq0DXM/IZMQWVwse0YYJZohis2IgzDyBJ0w2Iqr8qAAe6UChvwd434KAqgTVXYw4ZVzbSnmhNncYKVIj1LtAwUNoFVmqNA6rKiZB+uDtbdNLgmeF37VqWycKR62GRkiZaVwiZSwGHY138YFJCZC0jYbNvm18Nmc6ObxOIkh0T1MK7XImVDGGqJhjz6QxQ2aK6NZYnGjK8JWy9ogsYlex9YWS5P2BQlhXswfThSJYhTk6pQA97bUA8b0uInhiVa20HJEaSwqelcghS2vj2gcBFAf8KmgdfkFSb20RprOB4RY69TNeg2zdjaNRoNXGQYGLsZB8wSzXDFEjaGYWQJumGF2s+gzWZOa/MuYfyCQH0Q6qCwmbT+NSJMDxvWEi1BD5sIjTg3PK3A0ObCNYYIN/SuCRu0+RlW2ChbshW+D/E9UrYLwN7iKXvYoI1onBODbOFQEIzxsS+iKJjKVL0nCaJGHIy+U6brXIt6I4mE24phSzT/eytW2MQJ0DA/fU7ruLqBAtRsIm5pEViizSNLNMLWKZEFY2rQOdYa39DiCcw/uJ/J4Q86NV1+nOHfkbFPclHYFBXqUAmbmu4/UMLT9z696XhPQaqUKnvYoPvj8Borxl6naqDChtxjoLnEetgQ86VZohlD2IgwDCNLkEVHaJVGqop1Bm37plEg2XulPWzIYJElbLbCVl4nsUQLrNIcBUpUFJ0f9H1dg4joGkSb0WHg5xv6eMhigal+Y6D6aSTcWKMKWxQA0gQ3mo2zSUfBSLS5i2GJhhQ2ImlsiqIqZT3nzBgKm9CKXWwt6J/4hEqMSNMFc++bNAtVTeDag7wPLIMeNnPz5QqbSbZEg+sfx3VHEaFrN9YSDVlpbgb2sMF95goUNsS9u65zCSrYWPHuYQPWpEO/Rc5JDtf7I05K1C9h47vXGgbt501hE7eAxhhPbEQYhpEl1StsElqiJTjU2ipQ2BBVjLGgEzYBPvt1BXol0z1stD5RMVCB4GgZFdIIFjafdZw+tOcLbIlWkcIms6BEVgobaIMRyxKt/BygIFir1fTq9dNoNgqDPah5tAhWdWoQc97zfamzwoYIuobaiqHEJ2tLOgqoxIiU4GXOyaRZqGrSAEOC7mGzFN8SzdfWKXfQOkVrfOO1W/lvzdpjoeKCLQobJmHT956shfCWx8c4+RtLYeMadNZKAMcArbHqlHzyBV0DrP3iOCaztHGxEzQMEUvYGIaRKeiGxXgbl4EXJwkX5zB6G34IVIHIbIpjwSpn2pOosAHfudvpSrebpl8Ewtc/vPD5IQkbGER0mz+QLaFzo3Hw/OGjaSt8imDeJanCBlY+ppsTQoNavqDEB3MOZoHt0Ci2bZsuHNdUX4kkCZt4hRe+r3VtK8EcJzQZ6Gpf4wLuFxZLYhPnbY3DYHUv9z7LwBINKQCzsV+sALhP0UrYJFLYuPawYZL+/UUbsMCpQBXCrE3rmrCZhQkbv/s0+5sX/XuYWL3OGKCSe9gSDRWE1dD2C92n2akGFVaG9h8eB2L2FDTGk8mLfhmGUQtQAAFV9yBq1cNGYSGLNrRMxXQsWL/WVsIG47nAbMg7nS4MXqXYbMKAtmPQkam4K0oSYYWNbvLIeb5ACZuh4+Hjj6slWlj/Fk3wJr0ahQ1jZzkz656wKat8p4Koawks0cDfQ66KVD1sUlxzuMmt/3awqt4EZokWlwa4etiih+VlpLAJT9hMqiWaliI2VDEAA5BTbMKmM3Bdw74ajcH5BzY8Lxiz4zyXoOKK1ZXy67MI1LNveF8Hx1iFgXzcw2bwu6DiwVjFOzHRsl9EiTlL2MRdjxnjiY0IwzCyBC6IAitYYA+bhGoO3MMm/Bio2tkUNnnCSKM7m1284UxhiebpH174fGLgFypslO0XtPyde7haoiXrucUEmxNWQkJLtISJ5qqaqcKEDZHwnvPoY1OWsGF62PhW7roAr+MghY3n6xyvRSYQEnrJwQBBzB420RI2+Dk5Fd7UDTQumfPf6XRlZbk8mTIPeti0p5rwdxxbhU2i+z68tyVS2HQ73YH7KLZEbQ4kyKElWlEPmwA1d+5MA7vr1UQKG+0iKk1cLUOrsseNCVq/suMf7wPrd2406XS6cE2UshDNqAeTF/0yDKMWYEu0QIVNBj0/jh4rwTGgwqbChI31sCmGWSRvdjrEeE6gsEGWB649bAJsKtD3dbVfgAoX1yCto90M2gBqBRNyU9isr8Vrlu5KVd7cWGGDzwFqPDyKhW3FgVRGkZlCYYMImva8LdF07RFFwsdWzIpOaD8ZKcHLzEPWw8YfeA8lzv8SsEMTEZkDdo2NRgOrBBIkh6tAu1CkCHhvA58DBu+PzC/Mvap/b4eVO4Pvh+axonXUOM8lSKHm2/8J7cG39n1B+4MqEzZuSm70XeqYlEBFf2o9bCZcYcPErqyHjTGMjQjDMLIELbxDF0RwcZJwcY42Aqh/BgPygp4BVVgxYRcnrBJnnGA25N1Ol+ixovWJioF2FMBCYRiq6rHgmFi15qiwQZZzjicYXvPDlmgRe3X0Q403lSNxMBW2qYD++JE26Ruwhw2eu7UVNlQPmwRB1JhWgcks0Yj7WqhKJWaTW9jDpsLglZZl1CSioVJdXsIJm7J5pgdSCYyrwka7d14ReO0GFDbIHqvFJ2z67/mwsb2r7VbBmOXsd+s5l6B7te+1g9Y7w2szvH6qJmHT7XaJXomD4zbX7xKClpovduym7jCWcGaJZgxjI8IwjCyBCpvAKg3tivmoKKz9kMd3HSzRJjJhQ2wSNze7WGGTQMfVAhVYG44LdSphUxCwwEFE14SN8nyBLNGGLnp0LtAGkoWJ/6QMwGJv8ZQ9bPzsVkLodruy7tgQdxQ+CptSSzTimCmCqPC6DLgsfIOhzgobSkUZmLCJmPjEPWy837oUjd5+RjEaKtWlJdwfYw5YoolglcC4JmxQ4FerEAfZDaP1B1rb9eYXRpnZX1yGlYFuzeCLEk/M/FpXSzR07/ft/wR/m6ExBdfksW4UAGb/5GzvVsOEjVa/LJiwmXSFjSVsDA9sRBiGkSXohhU7YZNycY4XsmGLv263CxU2zEYqFsNVckVMokwYJUFEjlQsaScUPND2dQ7xFW+ha0rbEs3x9CJFzlZLtDyaD4skbWFDNzNOARpTMRrNou8vwlUtI9uhUSyUJGyazQbsnYP6pqkQ0QrS95UxEjaxFTYhiU9snRUnQBOS0DcwaOnBKWxwwmaemJsm1RINzW9aqo/QAjkUhD1qicYk+vvuG1BhO3QPQuvlojmDGcvjaonma13qqtr0/W1iw1hUDa+xxtESDa1f2XspKqx0LdwbN5jxFtJT0BhPJi/6ZRhGLYguq01kMcQQ+1Ab67jHSaU9bEjlDPu8cYIJxHc73Ur9n3tAGzLHqjMmwVOketBuhA0t0RyDtOiaH/49UYKJSewxMPNeyrHm6i0eE6SwiVE5iAJWIqTCZsbDEq2kh40IYbWSoIcNVtiEWKL5vdbZEi1B36ioPWwc+3FpwbxvVkrpmqFRSKRniYYSNjgxVEc2QbJTa58S2n+QbUDPBCKdetgM90nxtC1l9pNaKubUIDtB32QnTNjURJVCKR6GEoO+vZJyRqtfFkr+ulpjjxvMXGMKG2MYGxGGYWQJrPgiKo/LQNUkKYupYD+LwIgHI3mvUmFD97CZwEUMs0nc7GBJfwrFGPR1dqy0ZlQwhQqbQIuPrc8HgRPnhA30RBs8fiKFDZUgTCSx0bID0wKOqQibdK2EjY/CBgVS0T0DqTo1iDoSvXvYuD0/ScLGsUG0CzhRH80TLc77GiLirgIdxdJiecKm0WxQdo2z0BJtPBU20KpJS1kb3MOGVdjgeWbNwRJta28RT4XNGCd/kcJmdcUv2Ql72Dj2F8pZYTNcLIj3OvW7N0HXETJhia7BOqqPNEF7GpHJjHUY5diIMAwjS4a9iYcJvem7etZWSWhcgvH3th42eUIF84hrIUUCUruyiqm4Kzo/rgoWBEyIOZ5gqAAaOmAqC0fmXVLFSRnFSspKtFY7/SadSXowlmizc+7ze5kl2uHjxvHGdwElD5WEZ0642hTVXWGj0evEhxDLTAOjYXWHLNHm5qYolQhW2IxnDxuoBFZa2AX3sCHVFkxxQX+RAuphN7wn8G0Gz8wlWirm1OD+T54KGxB43qKwQZayFQXymaKYYWVYFWrr2MA9BmuJFtsdpeZQFnxmiWYMUc+7j2EYYw/0QQ1cEOGNUNDbOwGL7QMDHkwvgUp72NAKm8kLvjBJqs3NLm4YmSBjE2qrsfX5RECuYIOOPKadEzbK5xcmlIaueRyYTtfDJlUlJGdVkVBhg3zLq7JEYxI2s2OqsInZwyYnS7TAIIdr824XUJA01nzBvGtN205kAUo8UgobYIk2v8DNS8jWiSlKqiPo2tGy6UK/Nexhg9QWR9b4TGFY/z1vfQ0obIZ7i4A1aJF9MDNH1VZhAxRsa6sbXntM9jfvgfYHoX3afClK4vUzPM5gj8waKmzgHoOca9qBar1xh9rXmMLGGMJGhGEYWYKC8yGWaMziNGWz2tjB9OwVNmzCxhQ2I+l2u9KNaw5EAaXwjgv1kKbSGg2TXT5Lw3FoNkBGeHiKQgFbNYUN8z6JJDZMoplJVmgBPdgjbNKpc0AkrWbn3BM2C9tmSv+eRdU7zNj4v7XvNaWtthMJH1to7h22r3EB9jqJ1JtgdQWPr5Tzw7gBFTZEEnFpsVxhw85LWCUwngmbVIU4oTZGcH5p93rY4Hmmf2/nnBTwnEuZ9WBd1XroPt3t+hVXsDZ4PdD5q6oZPfPdt/bjGT8VCUpcsXONWaKVYz1sDB9sRBiGkSUxLdFys9KI3cOGCfqhDXFM2MVJSBVwXUEbA5EjCps0zhmlQMsD5SSJSPH50WiY3I92jyAYpHW1RFOqtGU2ZckUNplZB2gnJBkoSzQiCDZL9IkYJlRhk6KvRFyFjefrHOcCphAh1FYMWQuFBAg0ep34wDS0n/NIVBqHQfc05mddWS5P2KA5pgdODo9nDxt03Wut66CVWImdbYfooehkidZ3z0MK0+E9AbNeHpVoZPor1lWtx/SI8imuwMm04d8GzCcVqVK4PoH16McTAjr/7B4Hqd5D3VHqDlNsbD1sjGFsRBiGkSUogIACEGUwC8Oc5O+hAQ9mMV5lJSq7OGlPpCUaZ5eTyjKr9BhIgbDZdUo+MhW8Rd8L2Ve5JkGRnZu6JVrHMWGjFE2g7JkSKWwob/GEGxuckNTfiFJJK9BLRsRXYQMSNiCIur4Wv+odKt8quGW4zrWcJVrcHjYhAQKcHI8ToEH9UURE5ua5hICxFdzDBo/JpUVgiTZvlmhlaAVRESF2tkyhQm9+abWacL5Y67tvoODmsGKHWi97KGwazUYSW+EYMMV4Pgkb175o0FK2Mks0PH6HE41o3VlH2y+0fmXnGtxHqn7nRhMmdmUKG2MYGxGGYWQJXhD5L+6YeGPKxTk8VGCAlKnSrlRhQ1qdTaIlGq14yKCgi/kdXSrPgpRw4LQxvtX9qCtsHKvSsSVbwmBCorG2TlSiJVXYgHtSDOsnraSVT8JmHgS7YRA1QQ8bBLIeRK/2epXjy1L0jYKBz6AeNrr9wlhQwmZ2rl1bG6Mc0LAVRSooNqGG1qdJ7BcrINV9HylTyoLzTACyP4gLe5/1zVXovYfv/8weYdR3QetBdC3kDGN37aqG7Xa7tA1eD6agqwqY/fGwagSqD7v1U9kwSUsGtE4Oid2MA7n15jTqQY1vQYZhjDNo4R3id8tUQie1RCMWfyEwm9kqe9iwi5NJlAmzFYO40jyFwoaxo9BN2BSdH+0Gp9rnF84vjpZoWsnMFMFjFiYQlFIZCC3RIpwXJmmFAmAi7pZoU1NNeG6hJVqChA064yHTXipLtCQJGxAkYGz1itC2n2RZBnZbZocWhsbvugSSanqWaGOasEmknA5JujLFL/1rd1Rk0a/MdFVx+M6lUKFeU3WNCKmwWcFqxX5cbPB6QBuxqnrYeBTFMHvRqr6PLygswvSHEiFiNwHuKOMAY4lmChtjGBsRhmFkCWpOuUkssgqhFDb+b+8KOlZoDxtUQdSealZaicokJQ4/b/JuWVwPG8J/O8Hvy/yOLnJ4JiBU9L1QAsXZvgp5ySuf3+FATaqEHDMPhM5HLLltbLDPv/4GfY2wFWMS3q4Km3lghyaCrdhS2BTBxGvAdeF7T3QN7sVO2HQ6XXieQhQ26PtWprAh7baM0eDeROGWaOy8hILOE2uJpnTfbwG74bJ7G1Ux3reORYUA/e+HLEG3qjj8Aum4R2B99x5MQYdrDygXG7weTSBTSmW1OwyTcPRRclVl8eYLms/ZPQZW2NQrkaUNSlg1GmkLho16UN87kGEYYw3sYRNQvRJktVQBsRU200QPhJiwwSKUxBtHmKqmTqebrDltGb52FEVQ12nBF0PBdWeFDTq/Tu/GJGmHjg8VNko/MPE2qfbVTP+WlNYBsIdNhIpKxsOfuVe5JmwWts3A52ShsMlg3hvGuYcNMW+GNGRmrqMQ9aqGdZYP0G5rzvrXhKChsFkBKqj5Ba0eNuNZtZ3KEg3Z2ZYFn5n7novCpv++ge9/w4F0omhoxHdByu+c9oOuzBDqWleFDbOOH/4tsrVE87g/cn3n6pWYwElL7hqAc0nNzos2jGqwrv2yjHhMXvTLMIxagAIILoHfYUIq92Pg2s/CFaSwYSqwYsIGiyZRYcOMw84m7mET1suBg/l9nHrYEAv7omAnDjbRH+PI83UTJtgDe0hhAzazWvMVagwrks4STat/ixbQNiZCwAEpbNgePq6WaIxVEbIpyqHqPSTQlsoSjTlOSJCDqoAPuI6ytUQzhU0Q0MKIsURbBAkbsocNKioaV0s0qLBJ1MOmVGHDBLwHFDblx+q/76Nq9OEiLmoNOmIu7YIFdK0TNhF62DD3oy3JNFjwkmcPm2arseX3Z5IXVX0fX9B+i70GsJ19vc6LNq5JaMMQsYSNYRiZAvsFBCRsGCuHlJ7F+FhhCxyosKmwf43I4YUgE+SazB42jPS+k0WlObOJcak6C1HCwWCTYxZU23oJJmmHTlMqL3vma6RL2OCxklcPm/QKG/b7T023nBIJTCAVKmwSVL1HHYuek6br2qHRwPe/kO8ZO2GDkryxLBSR3ZYlbMKARQVgTHa7XbWkGgo6dza7QUVcuYKV06l62BSfWyYw3XZQ2Gz0BdDRGmC4mt83kA4LYmpc8d6easHz4prwZOxqh8cUvsdVc/3i/m5bxytSkYjUT0mC9xjcGgHa2Y/hPO0CTEJPYJzDwNioMAwjS3DjujGyRHO0R3IFVTpXrbAREWkTGy1mkTxucNJ73AA0RcZG29c55DrFCRFHSzQw3TjPF459q1JV2jJB/VgWR8MwChtWYaJBFZYeyFaM3dw1Gg2ZneMT8wtEDxuosCH67wQD86j5K2xEsK1YUMImsrUg+r6xKo2R3ZYlbMJA1w4K8K2ubMB7BaPkE8GWaCJ5KPq0UV93FID7s5VYojn2M0FzzYAlGgpuTg+rOPwC6Vp2ULkCe0A53qsphc3Q2gStVVIVAg3jE0DneiXVS0mC9xjc+8DCppolsrRB6yFL2BijsFFhGEaWoCqNw0FqvwUR9bKMKqpCK1RzV9iIcOqZSbREo1QrHXwtJFHYEMEDl+oqyhu94Pxo9xuBlhnKjcaHf070efUUNkTCJlETGx+riphUsRFFwXaX/mOzs3wAe55I2MyAICr6/TRA12VYwsbvtT4vQ/NHSDDLJ7jmgna/MJblJZCwcezbZAzieo8aBimgRPjfiLN1Gr+EDTrJegkb/56h3Drt2PujArEBSzSksBnuLUKpvEcobFCwOqP9oA/aPaB8VJvoflpVIJ/pEzgMtdepWWJCq18W2stPvMIGrOlbZolmjGDyol+GYdQCphG9742fsWVSa+JNELuHDVqMM9WLsWGSMRNpiUb2FEFBsRSWDszv4xJ4ZMZ90SYCbdxdg4jw/ConDtwVNjrXBmWJlihh42NVERNYhRyhohKpjFBhQz9OChui8n0KJIuQOkgDNBRDpj3fl/oUFqCK3diWaGH3Vl37SRZst8WpN4zRQAsjMN8tgYSaiMgcrbAhGqePWcKm28XKaa11B1aPlvSwcQzeQ0u0gR42KGEzpLAhLYSH0QpW58rMTHlidGUFX6v9+CRsqkrsI1AAfdR4ZeID45awYZOWprApB85pE1iYamBsVBiGkSVMxaevLRonsEnYw8axAbkr60Du7lKlHQsmYDSJUmHKk5u4DlIoEZjNsss1ywQpixJasGLd8ZqCmxnH84s+3/A1j6zklPI1h/tpxG2pRYM20qnnAzS+u52uuq0HSnq4JK2cFDZEIBX3sNmIrsaK+v4ZrQFC/P0ZpVNI8hMFr2JZ3UCFjVmiBRG6Ll1ewgqbefI3ohI2K+OWsMHP0VJ+hARZnRM2UGHT6ftvtzUAE0j3UdikLOCLgbbChlJVbVE/oTFWlSWaW1JQhNvrVGXx5otW0hKty5n+R+OM65xmGCKWsDEMI1OiJmyIhVRKBXzsAOlqDRQ2zO89iQobqodNFxkDpYHbLDskbIjnFipsAhsmuz7fdb5Am5/hYE1ShU9EeyYXclPYUL7lyucGbe5c+o+5BLAZhQ06drcbf3PejdjjwXcN4HNMnLCJq7AJCRKEKjF8QQkBS9iEgUYxGpOUJRqpgpqdJSzREij6UkKtf5Ru+zDpWnINuwbv0X27v58KKkYaTv4w9+jRPWzKj1N3hQ26flYdFTZMkdgWu7qI97gQfALo3DjLYVfGg5OW3BoBK9EnO2GDrp3hvlyGIWIJG8MwMoWStnve+JmFYUqFDSK0ihh5ezP+4LGxHjajaTQaRECsk0U1l7avM/Odio6p3QgbPVt7vhj+7nAzpSWxkbgN0F1AyoDUGxvthCTDBlLYOJyDGSLo2YNS2DB9JRybGasTYonmeU3HSNiE2MXE7mEDA3ERVFCdTldWlsvH1rxZogWBgpJYYYODwMw8IzKplmj4OUzgmAGtv0t72DCWi61EChtqDeqhsFFcX1UBun5QUd8wrn2LRPT7SmoBe9iMGK/M/bJuiQktW+2QuWQSwIques81RhxsVBiGkSWMN7+vwoYKBCeVwINgTeC7o8pDZjMcG2aRMqkLGbRX3NzEXudJLNGoTQw/mqFEv1H8vdDG3TUJij+LoyUa+j26bgkbzQrQXBrs5mYdUEWjWdzDxkFh49CEfX5buMJGxN1qxRV0HYckUn1f6nMtaieY+2FUTiHqVdiDL0KCdwX0rxERmXUY78ZW0PiHChuggJqZbdPrEkYFjgqT6saoXivD6ClsQA+thAqb/iIF1+AmpYId8Xmxghm+bdagPZ7rteNzT0F9FivrYbPhobBhnA9qlphAhRVszhLaK3rGbcYF62Fj+GCjwjCMLKEUNhFv/CkVNuhQsRU2LrY6sWB+77r7SPuCNjqcYkzr0xTDVCG6qDNQIKAsyAlVScqWaK4JXhjkHPp3Uo91s0QbiXZCkkGzh82MQw8byhItA4UNCjKEzHu+awCfl2knmPtBASmRyAqbCPPFMpGwMUu0MKBtJ7REK/+N2P41IlyfxXFT2DCVWlqFOEE9bJjgfd/7o2K83j2v0+lCO8XhggUqkD5i3KLcWIqCp5hASzTHa8dHYQOtsiqzRHNXPFC9RetmiQY+L3sNhMwlkwDszUkUKxuTh40KwzCyhAmO+SpsmAVDyipz3Nw17P3roLBhFoOjmj9OAkxTZy05ewjMJsal6gyN+7Igp3YQkVH7aLLVEi2dx3pos2ktclPYMJVv2ptRtLlz6mHjorBRStgwDe9DQEOxEeSJ5vcyn7VDVIUN028gIPmJ5v0YldOM3ZbLeDe2gsYxSpYiFdQcaYcmcvieNIUC/ZHVfKlJad2M+04EKmz63h8l33oJZqYgb7hgwdeqCq6vMlEd+6KusKHuKcPqp2p6nSFQQcOoohimOK1uiQk037D2i+ga3NzoJttD5IhZohk+2KgwDCNLqIW3Z8KGWStk1WQytsKGsJvIgRDbljqDFTbEdZBCYUMs6F38i2MqbFw3DOj5rkFa1yQt3EwpBhSg4i9VDxvFZIUGnN2K7rmB1Z9OChs+Mb+wbQY+h7JEq7gReJDCxnPS9Fk7wH4hAdccUwEfpLABJ5mxdnJlGdhtiZjCJpTQQqKlxfLfyEVhI6IfdM6dkB5+rjDzT9HnQQHIVrsxkPCAibcj9wx0/xfZmmjibEvde9jUXWGDkmQrKxESNsM9bKByO9MeNiPGK1ecVq+khJbtM7MPzKHnalWgfe2kFqYa5Uxm9MswjOyhgr+E1ccomOBHygU6tkQLe3+osCHsJnJgUi3RYPUjsXlKUSGonWSFNmRlChsURHTcTMFKfsf5wrWHTRdZdiheG9BOLlF13AaYt0JUAT5QwSDloAMKWrlYorkEsJnncj1s4gZR4b28gspon7VDqJqhDJj4LukFxpCtwmaeV3AYW4H3AXAPRUk1198HN04fr4QNU1SiVVhGBVkL5hE0vwy/N+xhc+SexyQFht8rVg8bVl2QKzOgKM9dYUMk04b2A6jgrqogvs8ai7JsrygB5YtW0jKmO8o4gFTnZolmjMJGhWEYWcLctHxv+kzwI2lFFepnEZqwqYHChkkoTKrCRsMuJ4XFn69/eOFzYRPM4vGgHUTUqj5jGf7qaPOnOV+hsZLKzQD3sEk7HzAJY+2+amhz56IyYhU2s3NtKiAxRRw7dhAVBTXDetj4vs4jYQOD4/7jirHgCJm/0GtjxK2YHjazZokWBL4PgB42IKnG2C72M4MSNo4qgdyhFDaJLNFEitXRMGEztG5HhRa9AjNkByqy9R7EFVVsPa+of0pKi+wYoHu/q50gWuc0W40t9wW8j6lIYeNhURXTAaQqtPp0VrFOrhNmiWb4YKPCMIwsYWSh3pZomTRp7xG7ZwRM2NREYcP0kBhHUPA0l2olpgrRRdmCru+yjQG6plwbnKI5w1X9xVTG9l/3WG2kd23A+SgTS7TkCpsKrB40q/HYnh5sIBUFUEWq72ETpBzxfK2Xwgb2KfP6KCLCjKGw66isl5hIHEUeUthMz7Qs8BEIWgOjuQ5Zorkm1HDQebwSNtQ+JanCZvTnQeu04esQFYitOyhshj93s9kglGE+Cpt6J2y01WnQBm/EeIL3iYoUNjCAPuL+qF2clgPoPs1axDKFlXXr76MJtpCsvoDWyA9bzRqGkSXMZtulH0Y/Kb2hNQhZ9nU6XdgHIQ+FDX7OpCpsYPKBuA5SJCA5/3D+moWqljJLNJTAcAwi4qCjoyUa8YP0HxIlujTnKxjwyCVhk3hjQ9lgqPewAXYdTgob3YRNq92EYyW6TRFU2AQ1sfEiiiVaQMYG2deEJjZ8AqShwIb21r8mGNjXBFx76DeaX3DtYVM+1606qgRyJ20PG/+1G7rnDRdaQUs0hx42o+2q3BXpKddXVYCKK1yTnbAPx8i+L8ASraKeL8h6d9QY0y5OywE037BFaUxh5SQrbGCC2yzRjBHYqDAMI0tiSo4pb+iEEht4pIAK1bU1vBBnKqVjw3xFJmA6jqDNNNXDJsGG07e6sQg0Jsoq9tDl697DBm3ond6Oon8DFdLPx5XQymotUDPYqem08wF1T1IOTqP+Y049bMiK9m3bZqjnNRoNaMlWtcKmEjwuRaywCelhAwKqgQkbaOcWYb5A6o25OetfE0q4wgYkbBx72MCgM7HWrRMprZupIGvBPIIrxt0SNuvrHel2u1xj+1GJAY8Cpy4oixt3SzTXwgoYdPZQ2Liq3rXwKWigFDY1U5GghBm7h2QUu77FtuNA7AIaYzyxUWEYRpYwwXnvHjYJrQYoIvaMYAJmLlXaVTKpCpsWyAZwCps047ntUd1Y/FzQLLvkGkXzh6utF3q+6/ml5pe+Cx/380nXwyYVuSlsqtikIx//GD1s5hwq31HVO0o4hYICbWE9bPxejObrUUA1Q0AwC42hVqAlGlIzhlq6jmIJNrQ3hU0oob/rsvJvhGydJtISTelWHWJjBHvYDCtsiHvWxnqH6mEzKriJ1n6jAtMoWD3ulmgb6x2nYhOfIoAqlJgMyIGiKGGTa08eX9AeAyXcjj7PetiUAi34JrQw1SjHRoVhGFnCbCB8EzZM/CBl0DJmDxumcioHhQ1liTahCxkVhU2i4YwCjy6bGOwrXnwsWB3saokGPrZrgpcJ/vd/RBhQ0EwwQ3umRAobMK6nElsHcLYxiS3RXBQ2ZIB0waEZOOp/Fj1hA053SKLa96U+r0PrjZDq49gBgioUeSugh40lbMIJ+V273S5MqrHWiz3QXBPdfjExVGGZ0sKO62Ezeh5BSthhFQxTQb6+vskpbEYUbeD+hVvfF9rvZlLE4guzx3NJeMIiAI9EWqreiMNAxUNBgtHHei9nYFEanbAZP7s4TfB8WY8CWiMtkxn9Mgwje6gKlnFR2ABCClTXCF9vVH2VC5MqFUbBPEZenmrDCZNLLlV8KGFT8p2gTY/jhiGlwmXUMdGcpVkBWoXF0SiQOtAlWaEBo5zQrqqEKiOHc8Am5hdISzQRXC0du+odFTOETHu+RRs+awd0/QYpbBybgruCP3vQ249kGfWwcWxob2wFFhKV/K7ra5uyuVE+Zl1/oxmk5huzHjZUYZnSfT+kGMFVYYMSbyKH73tUD5sR9x9U7Ddq7Yctb/PZD/rA3PtdekChPYdfIq2ihI2HwkYEJyZqp7BRsl2mim2Ja3tcib0eM8YTGxWGYWQLslfyVtgktBpgQMcKUdjUpYcNc8LZpofjBlbY5FOthILaLokSZJFQdl5CGyZveb5yBSansOlP2IBzoThhwYBzosYhvpWPsWA2opqNczudLry2XewsWdWBS+U7smSLblMUUWHji09wD/ddiGeJFtrk1qeiPZRlU9hEB1oYldwTUUJNRGTOVWED1qnjprBJ2WuTUdkV7bdQ4ZyPJdrhhB+jsHHvlTJq3MK1Zs0TNsi6VERkdQVfsz1w36Kt5wv2aatIdeFrvZvr9/EF7TF0FTb1SmZpYj1sDB9sVBiGkS2oethbYUNshFLab8FNV2SFTV162KTuWZELGjZjqeKWozZq/bgs1FGcryyBhxIYrioR7QpM6vfot0RTsitg0LaT82V9DVmi5dfDRjM4TVUYuyhsZiMkbGAj8LiVlPA6DrksfBU2Hq9D81VI0Qauhg5N2KS3ukEJgdk5t2SAsRV4Dy0ZkyihJiIybz1sSmGStGxfCQRaY4oE9LBpeyRs1jdhbxGRgv4i4LuMSjakVDBXAXPvd1GouSbpRKpJ7DP4Wu/C3qIVfR9f4DVArmuY9YRZohVjlmjGKCxhYxhGtqCkCapUKCJGE9wgTGFDwVS4jyNoU84lbBJZoiGFjUPwDlZ8lXynkOpgn+fHOL39x0RBT80EMw4eqx2qFFz5mHY+oDaiis1UkSWcCFa49NNsNqhK24VtfLAb3TuqDqLWpocNqtYNsUSDli9hAYKY6qAiUEP7+QVT2ISC7qFl96SlxfLfR0Rkbt4tqTY7i+aaMbPZYW60aj1s8PsU9rBx7JHFFBmsr21S+7tRewI8l3okbGrfw4ZQ2Lj0sPFI2GTbw8YzgD5uChtkXcomLZm9iK87yjhglmiGDzYqDMPIFnTj8g0EMAvDlBL4BsjYhCz7qB42NVHYTKolmkYvp1TjGSXVXALaIZ7KuIeN44YBFfI7nl9XSzTk760ZT0BB7lQbaxSwyVJho7hJZxI2rtV4s0TfCBeFDexhE1lhg2KaIdeFb7LHJ3mqPl/1UbUlWowML7ZEM4VNKCG9zJZAQk3EbZ4RmTxLNKanh9a6LqRROLbHGk7Y4GOtr28SgfTmyDnax5Y3pYK5Cpg+pS7XD1rHj7qnwMR+RUF838IgdM2k6vWoRcoeNmaJVowlbIxR2KgwDCNbUCDBt0qDWUglTdjEVNgQi/AcLNGYsz2pChvkL04tflNZoik2FkWJgTLrCxhscrymkL2BawUmEwDo/4gpFTYN8FapNqJMwCYljG1MSGB9GKbC2DXZPktYoyw4BFKRwoZJOoWA7o2oGKL0tb4v9VHYQHuVAIVN5IpOdJ5iNJNGlmjWwyackETc8iLRw4ZIHveD1IFVq/m0Ydb9aRM2npZo3gobz2bw0JbXPWGT0iI7BoyLgsv146Ow0V6Ta4Et0UaPV1RAWDcVCTr/qpZoNTs3muBkZ/XxGCM/6n0HMgxjrIlniYafk7KiCids/N8bVU1NTbcqaczsA9MYdRxBYzGnjYGmwgb1XihLDuHqYPpjiAi+BmMkeHsBm263i5UEisdH3yXVvhopA1IrbBiFn2ZwmlGnuJ6D2TkcuHFpBl69wiae8sz3vuhjnwPyNUGqNhRQjW2Jpq3I63S6soIUNo7JAGMrIYk4pLCZmmo6Fwrlbr+oDWOppLV0Z+yOitZusJ/JUAKFCUgeVth4NoOHtrxbPy+ao+qyRyqCUtisOChs0D1lxG8MFSkVWIh1Ol342xclIGCRRc1UJKjYiN1jmMKmmG63a5Zohhc2KgzDyBZUQb254bfAoxQ2CRfocDMQUWFTFzs0kclV2KBAMbP4TTWeQ2xUhoGb6ICEjWsQET7f8fQyv0fvsk89X0GLxgQZm263C5sOp07YNBoN3BtJcSOKrDpE3NWRjCWaSw8bdP+oOogalMj0fKlP8hbaXgYEs2Ir1ZDyTFuRt7a6AZdEprAJByfuSxQ2KKHmaIcmgoPO6+ud2tkQlZGbwqbo3LoqbJg9x/p6x19hg9bLI94XqgtqvvVoNhvQik7VEm3Eb8AUAqW+fpk1VlGCESm5NNeCKdBS8VM9bMCaZFzpdHDxXd3VfEYcbFQYhpEtsRQ2zKIwaUFVRIUNqnBmKq9yoe5Vbr7ASi4icZnq3OFr1qWHDThWyQaQCdS6bA61/J1dnt8L2DCVh2xDUAbYbDpBwoZRYlVRiTYqENFP+h42budA2xKt6r4SMQM8vnZqPkki1Hch5JpD66TQAAG6tWj/RlRDe1PYBIPWDGX3JfQbzXv0GNK2dcqdlE4AlI1RQQAaJ1YGA96MwmZtbQMrbAoSPz72kmiNldIiOxboXu1y7fioNplzmDphg8aYSHHPJXTPjmEFGhOtPpmUEr1mySwtuAShheaNrdioMAwjWzSDv/0w1fVMvwItYJPvkIQNWIQzm2CjWlAwfpTFwzCpcl1YYeOSsPGX6DOKEye1D/h7DAVTb55ivL01AwqaKilfQiofY4LuC5obUSZho22J1mg2ZIZI6vRA1dLxe9iU/z0kUe370jgKG/9xFduCAwVItRO8qH+NiJ+CwxgErTvKflZkieajgGKUGbETxClJqazletgUKGzg/DL4GZvNBu5PyihsCj4zXC+PmEthQc4YVL3PzKLiCv5eDXvYjLinMOdQ2z4TwcQQitZYeJzVK2GD7tPsuqbRaMBzk5ONd0oYZVFq5wCjHtT/DmQYxtiCLdE8EzZEACGlmAM7ogVYokGFTR6Lg2c++7SqP0K2oIUytfhNpbABwT+XTUxII1hGceKyOdTyd3Z5fu+ypxLMigkbqLBJkrAhNjYV2DmicaWasCGSVq6WlsgSbX5+ymksQYXNWuQAqlKQQROfJBGqSg255rAlWtweNpqqMxFstyViChsNQqwxoSWaT8JmwhQ2VMJGaX4L6mHjaIkmgoOS62ub8P5XtD9EvS5HKmzGvIeNiK7CxqcIgBmrqZUXzBqraE+De/LUKymB9mYuexx0biZVYcN8b+thY4zCRoVhGNkSS2HDVKynXKD7Wq8wwB42mShsLn3uU0r//szvmtyEDg6I5aOwgZtlxYRNSA8bEW4e6IGeGtMSjepRpJmwAX93OW++cFYV6RM2aHxrVlVSSStXhQ2osp136F8jQihsHKp2fYg5En2vKS+FDbKfCrFEg/Y1oQqbtBaKK4zCxnrYBIOVvWkt0dDcJeKmEsgdJkmrZYkWEkz3UVugYrz19U2s3FFUPmhb3uYIun5WV/R62Iz6zRmrrPSWaP5rLPR90H03N3AfJ/4aQGuKSU3YMDGrSe3Va5Rjo8IwjGxBlZ++N/2QZuYxiKmwQRtY1wrtWOw5+Ti5YO+ewr8//4XnJfw0eQEXv0QPm1QbTk0FAqpQK+thQyVEXCzRKqjA7G2gUnrZi9TIEq2CjU1KhQ2TtHLd3G3fMVf69+OPn3V6P1i1G90SLeJY9LykfKYCeM0FJAJhL4jICRtT2NQTmEQsuQ9Ahc2CKWwQKa1QGRsj74SNh8JmjVHYFMxbPralaH2n2SOwKjTVsD6/uXZfSQ2YAHpxYjB8X5YT8BoA1qf9QIXNxFqi5bmvMfLHRoVhGNkCGzz7KmzgwsTrbf2J2MNmHSzCc1HYNBoN+dm3PF9OP+tJA483Ww35kX93iVzyXadX9MmqBwVOcqpW0uzxgX3Fi88Lk0Bx+SxdUMvvmrChnn/kkMznRA1QXYAb6wT70BjqEg1gQ2PF4DRKdkxNNZ3HXVlSXETkqRef7PR+uIdN3AAqaokVEtD0TcL6JE9D1AyIqi3RtC0UUX+UqalmJf2txo0Q5dQiUth49BiatIQNZ92sl0jANkaePWxGqGmmp8t/y431TbgGKApslhXyiBT0sEHqggmwRHNS2HjY4CF1MvO+2oQE0DX7deYAWrvqKmzqlczSgkoQtm3tYmwlj0idYRjGCNCNy9cSDdobJV6cR+1hUxOFjYjI9uNn5Z2/9TL5ypfvl/vueVRaraZ89xVnyYm7tlX90SoFSe+ZxGUqiz+UZHXxdYYJm5LvRPWwcbistJO8LpZoKa1RRPB8lKIKcmODsESrYO7CPZrS9bCZAkGvUZxz/i65YO8euf1fHt7yt4Vt0/Lc7z3H6f3Qb7C+3pFOp1uZpUwVcTaf9QN6TVDCxqPfgAtcP66u2j0IqzfckwHGVkLuA0uHyhM2Cx6/0QzRb3F1jBI2jKpOc15ttRpSdmUV3du8gveMJRp4X99m8KOCxSHFQXVhVrOHDSwC8FTYJA7kcwqbgl5JY5aUQHEGl7kGrZN9Yzd1J2S8GZONJWwMw8gWdOOKpbBJHeWJGUxHVdq5KGx6NJsNueS7Tp9oRc0wSLXCeCWnGtJYYZOoh422wgY8NYaNYu/7M8Faxh+cBZ27BC1sKG/xKqwDUPXuqIbGvqwjhc20+/dvtZryc299vvze+z8n/3Lrg0cf333ScfL6N10ue07e7vR+M0zV+9qGzM5GsqgCgzHk3ur7Up8gKpo3gxI2sXvYECeq0+mqzVHLQGFjdmg6YIVN8d9QD5uFbTPOn4dJUKMCpTrBFGrpJmz81pkwYTNifkHq2PX1TXj/K9ofwmbwI+ZS2HB9LBQ2wIbOIWHjk6RDv4tImv6I/YT0SkRq67opbDT7OGG1Xr3OjRZmiWb4klekzjAMow+4gSCqsEeBNkKawU+GmBXtaBHOBNyMasE9AvJR2Gh81h5o3JfND5yChf4oOHnkeH5dPh9z/Wv+vui9ovYNOQK1sanA9ghVDmp6c6Nz4KOwERE5bvusvPXXXyTf+uYjcmDfoszOteWpTzvZK/jHqJzW1zajJWzgUAy4LLyVtj4Km4i2YpuoF0SoJRrTTHqzKy2ly3V5GShs5i1howG8D3S6I5VT3W43iiVau92UVrtR2htirBQ2zDWvuKzD6tEiS7TyzznKCgsqM9c6hDKwqBm8e7AYJQpQoUYdmAH34BUHSzS09x6VTGPWF5oKZQamV2LReGq14/WdqwLcw8ZFYRPHzr7umCWa4YtF6gzDyBYoq/VcEFXRQDzoeAHrPrSBrcJWyHADJkEoSzStT1MOqg5y2cSEJElQxfrhz+KQPNL2OHd4OhO40ay0RWqhFJZozEa6iko07FuetodNCGc9Zaec9ZSdQe/BJPxXVzfkuKCjjCZ6jwfP1/oUfMA+ZQHVurhBdNjcQQXiFJO8yBJt1hQ2KrBFBcNDd3VlA66xfRI2IocV4csbxb//WuSeWSlhLhnN3nW+VfE+Cj6osFnbwEkBz94iIxM24DvEUFCnBits+AJInyQdk9hPbSNG9Uos2COja69OKpJutwvnG5drwBQ2ozFLNMMXGxWGYWQLVNgQQb1RoOBBcvk7zNcEKGzABtYUNvmDG53no7CBC3WHwCPaRJcFGpmv6xRERHOG44ae+T1cLNE0PdZDmk1rEbKRjglWfSbsYZNBY3VWYRMDZhhW0SzaZ65FSZMQhY1vpToL831DPv8wK6awSQL1u464CA8dWoWvW9jml7CZAapCl8bpucMUlGhOb7BXYqHCxsMSDfY+24S2qIWBdNhH0aOHzRgkbNBeb3W1fF7txydJx1pnpoQpdtPslZQr1B7DYbKB/X0mVWGTaSGakT82KgzDyBZ04/INjuW2OG9o+hoMsQqqpkxhkz9wIw2q3UTSVQjCTQzxWXug3E7ZsajqYIfNofacwWx+esEw7c0UAgr+MlHYVJGwwAqbyUrYcD1sYiVs4o7DhI5ocH4OCf6gIEFoRSelsFGcM5YWywOL8/N+yQBjENbqbpilQ+V2aCIiC54Km5lZFHQen4QNlZAmlMQsvoVBPv1M0L1rbW0T3v+KiidgH8URcxFuuF7/cBnqV4r2iv34JOko1XvihA0aY41mo/D+plFIlwuM84FLUZhvP6xxh1nHmSWaMYr634EMwxhbUCDBt0oDWqIlnhlj9rBZN4VN7cGVXPksfjU3MSj4XZakYIKILkFQTX9nES6o69LDRjNwA3sXqB2pGKTKaLaKN9Ix8fX59wGdgxwSNkzC36WZsQtMQDPMEc3vxT7jEvVICElOIevY0HFEBfYVA3HLy+UJgVlT2Kjgq7BZAv1rRMIs0cpwsXXKHUYBrKqw8ew74RO8nwb3jY0N3MOmyBIUBYtHKZfQWnAM8jVwr+dyn0Z2daN+A8Z6M/VeBiVsyopGsZIrn30ZgllfuKxrsMKmPuojTRiFDdpjGJOJjQrDMLIllg8q9GpN3sOm/O8hhcRoA4t8jY3qQeoJRmmWakRjWw2XhA3YRJfMD5oJDBHdHgwiQkVaekkiZuOn2sMGfLYUVZA4WFPNvDXKm70fTRsMqLDJQB05DSyKRCJWvTMBzYDrwvea8jkmmq98r7nNzQ5MNqN1FiK11Q3qYWMKGx18lVOLTMLG1xIN9uEYI4UN7OGnu1fx7cmBe2Rtfd827GGz6b0GQAnkkUUVYHrSXk9WgWbCBvawGZFM01a9a4D2I2XrTBRYHzdLNJe5Bp+b+iSzNEFzWpmiy5hs6n8HMgxjbEHSUN+bPlqcoGpXbeBCKCRhAxQ2TMDNqBaNxW+qRSCsbnSxIYNVjyUKG2Jz4TJ/oNiwsyUa1dD5SMJGufoNv1f531NsqqGNU0VVaCnVbutrfh7+KWES/lX2sEnej0787Amh1Z5n8CdFk9vUlmiwh82cKWw04FSgIxI2wBKt1W54K7uxrdP4JGzgNaM8t/kGoNE9b9S9GhVcHO5hA1QcBZ8X7d9G2TGh3opV9ELTBt2rV1c3aSWnj8KGU70nVtiAtUnZvVFzr1M1lIrfYY+Bez2OjxLShRBFlzHZ2MgwDCNbkEQfNaUsAi5KUy/OIx1uY6MDK6HMEi1/UMU2FcxLNKZxdaODwgZcp2XBWKbK3SXxwFS7ukAFw478P+UvramwiWjPxJKrwgb64ysGHFDACtnKpIBJ+Ne1h40vPkkimLDx/K4pmtxqz7UIpLCZM0s0FXwTccgSbX5h2juRii3RJidho23ThX7vwh424F49MmEzXf7h19c24f6uWGHjbluKbbLrn7CZmS2fF7udLr2n9ulbxCg50/ewcR+7PXC/zvqoSDjbZUWFTY3OjSYwuR1YPGOMLzYyDMPIFqSwYapHR6HdQDyUWBZEqH+NSB5V2kY5GoqvVDlIZBmF+in0g5I7ZQldqprPRe0TYM82Cqo/QM8STbn6Db4XnI/UDlUIrHysaN5C16KmbzlKdORQjddsNuAmM1oPG+I5IdeF75zpEtg4+poqFTaBTW6pwL5Scq3b7coSSNjMmsJGBSoRN+JnRQmbhYUZ349E2DqNT+U2Skhrqz5wf7atc0mn04VKx1GBeqiwWduEjcmL7n/wHj1iAYN72IxBwoZQwzIKtW63iy3RRvw22n0lNQiyREMJzhQLZSWoPYbDfJPSOrhO+CahDaP63Z5hGEYBuHGdp8Ims2oq7Ijmt7hhKptNYZM/Gv7ZqayBcHUjf82GbKK1/bKx2od+K/r5vYCNdkNQRA4Km1ytA1L6luMeNnnM3SjpH01hE7ka13fO9HkdVFH69rDJxRJNKZG5uroBf3ffhvbGIL69iRYPrZa+JuT3wbZOk6Ow0d6n+ARZfZtoo3vG+vqmbEC7qtHv4XOPRnPKWCRsgMJGhLt+mDXOyIQNsY9J3cMGrjNL7o0+Sq5c0d5jmMJmND79vgxDxBI2hmFkDGpg7q2wSVy5hmgATzTf+ChT2WwKm/ypk8IGLTjd+sb4X6fafRXght7xBFMKmyOHZDbIqgmbSIo/F7K1RIsUWB8FTNhkYp8wAxJHsXrYMITMe94KG49rEVaF+1qiEWuk0CBByh42K0BdI2I9bLSIZom2LSRhMzk9bFInEXx6JTLruVH7OKqHjYfVmohIE3jFjVLYwD3hGCRsmH5zzJ6R6T8yaixxqve0gXyYFCxRn2KFTX0SNkxyySVBDGM3iXsV5QK6dswSzSjCRoZhGNmCrDp8+wVoNxAPJVYwfZWwhzCFTf5oVFKmUtighbqLAiHEhkw9YYPaXjn+Rk4KG+JzalbbonOXQmGzBuwcq1LYQNWn4kYUJTpysU+YqqjqnRmHIfOev8LG4zWRrPY4S7TAhA1TOa00ZSwvEwkb62Gjgm9vokWQsNkWkLCZnaQeNkjVq7xP8elhw6znRgXvUf+1wwkbT4UN7C2y9TOjgPU4JGyYvd7qCqGw8bynoN9FJL0qBSUOyopixklFwhSEmMImHGSJloPNsZEnNjIMw8gWdNNn5PijgMGP1Gtz6InmaYlGbF6ZqiujWuokk0b9G1w2ZCGbaM5/Xy955BrcdekPwFQdao4ROB1loLBBAZ9Y+DZm9gEFrHKxREO/RawgKpNwDcpTp1TYgOvX95pj1khFgU8W5ttqKWyWCYWN9bDRgbIVHXEPXTpUnrCZmzeFDQMsWEnew2aEJRoTvB8xt6FivPW1AIUNStiMUtjA4qAJSdhQChu/9Siz5k2h3u4HB9CLx6mPkitXmIIQl3VNrGLbuoO+d2g/QWN8yWO3ZxiGMQJUbeDSwLwf3CQzL4WN7yKW6R0wnUnQzyhGYzymqhBEmxiXhXqITQUTzHBR+0B7NmeFDR8MoxQ2ij8vtERLoLDx9a+PDVIToGa8LqD5e2o6j0QuuodE62FDnOpqFDYePWzAS3ztVZjgWqi1HqOw0QrELS2VJwNETGGjBTOOR/ewKf+NFiJaoq0RqvK6kNwSzUthQwTvPXrYrK1twoKdIvsgGEgf1cMmM5vsGKBrR4QrruD6om39fVvgdxFJn7AJ6WHTarsruXKFOe9OChuwJvC1s687ufbmNPLHRoZhGNmCbl7dTtdrgQetBhJLbNDGOGYPG1PY5I+OJZrCByFA16yLtQ9U2JQkspiKSJeqdaywod/q8POJ5/Sue2aO01TYYEs0tUMVgnvYVLN8RedZs6oSJa1ysURDlbtV9rAJuZWn7GEDeyN5Fqdk08NGqaKW6WEzH6DgMI7hq7BBlmgLC/6/zwxYr46VJRpacyTuYTNqLmEUfKPmF5SwYea7omp0nzVo6nNdBWqWaJ59i5g1eWqrLHS8ssIglIBK3Y8nBGZNzxRm9MDJrPqcG02QogvNwcbkYiPDMIxsYW5ePjd+FKhNvTiPFUynEjamsMkepjItF6B/uEOSBG14ShU2yg1OtfteufTYoayfFOesHCzRcOVjNckKzR5NCKiwySRhg4Jvde1h44vPMVEwxLfyOIUlGjWXKWV5UQ+b9lQTjkeDgxnGo8blEkjYzEdU2IyTJRpccyjPbdjiaesHYu53oxIoGveuoqINaFs64nughPI49LBpTzXhOpG5fqjffMTvm/I+wRKieNC0f64aao/h8H6jbBD7mVRLNFyIZmsXYzT1iQIZhjFxMPJQnxs/9CtOnrCJ1MMGBPwazUap5NvIAw3/7FRJSGwZ5ZAkgddp8bGooKnDZQWTvM4SG+bz8QkbzeANGisp9tR4I11VD5t0G1F0DnIJTMMG0lVaogW8v+815TPXomP5KreoHhOBVZ1UYF8peIV62MxZ/xo1mIrq4WtwY6MDq/TngxQ22BLNpTddzqDvob2ma3tUxTPruVGFdxo96IoK+mAPmxGfGboujIElWqPRgAo1qoeNp6qq0WjAMaulxGQJWWdCtXWNkhLUHsNJYWOWaKPAPWwsHmOMxkaGYRjZwihsfG78uSlsEL5VR0hhMzPTGouNyLijYXeV6nf28UEvfm5ADxtGYUN+FqqS37mHDXPcw//PBDs1m+Jm0cMGWAdU1b8FWT1obtLX0TnIpBoP9pVYq05hE1R84TlnelmioWrdiD1sQoMEvtZZPqAeNrPWv0YNZhQPj8tlosfQtm0znp+I7MNRpQWjIiHrHx9gsc1IhY2f5aKGQrbo/ocU6aPmUpQPT93XNBYaPaA2iN+86J6SUqHMELLO1HQTqBpm3arZw2ZiFTYBPZOMycZGhmEY2YJktSJctc8wKHaQemkeSWBDNK3OI+BnlKORa0k1plGS1WVDFtJ0lwsicp8jhsLFxR4iemB6CNhTKwdLtIoUNtC3XCngsLnZgec5m4QNaiAdqRF47Lyh77zr8zrYN8rzmvNtEO0CU3mr1Uwa9bAxhY0eLradPRYP4YRNiMKG6bk4Nn1sEitsYJB1VA8bz4SwRg+6oqQAVNgMBYu73W7tivh80bAUpFRVBb8BLAZKnORA47dsnanpJlA1TBGWU8IG7QM36pPM0gSNN81epMZ4YSPDMIxsYQIJTLXPMCG9MWIQS/2AFTbWv6YO1ElhA5tnO1j7xO5hw24OmWCpu8KGr0qHDXEbur8vDB6nUNhAr+eqFDZpKgcZG7FcEu5ToA9atL4SlCda+n50Ptcieo1vte7GBrZFDV3vMMlqrUDcEkjYzM/7JwOMQZh72vC9EfWvEYlriSYyPn1s0DWvPbX5WDz5J2zC711F+0P4PYbOK3MbqVMfyTJmocKGsESjfvOi3yavhE1IDxtNN4GqoVT8prAJJiRBaEw243EHMgxjLGFk6EjSPBKksEmesCn/u2+AdBVUNucS8DPK0Uggpopb4k0/P5bRc8sqKamEDW2Jhp8TJcnbs0RLXP2JxkqKTTVKWFSlLknVaBYFEkTysU9AvvixethQyreAa8MrCeo50aK1jq/CBgYIFKx+GDtGLVUe6mFjlmh6+DQJXzy0Cl+zsC1EYUNYokVS9KUGrfu1kwjI7nNUkJWyRBvVw0ahWEzLdotZB5rC5hi+fYtEcA9A315tvoQ0gdd0E6gabRU/sllFhSTjCopX5bKmN/LDRoZhGNnCVBv4VGqgQE9qhQ2i67mGXQe9A0xhUw9cmj0WkWrDiRI23S4f8Ecy/VKFjUfDZN/PIeIep3VRAKHzhSr9XEGbapTw1gBt6NoVJZvRufZRfI6CSXJMA2VLKqrqYcMQkqj2WQf49jpA83O361e4AQMECtcRk9jS8vNfWS5XcJglmh6UCnTYEo1Q2MwFqKCYNeu4WKLBJKe2wsbD7pOzxxrVwyZ8TVsUTEdrv26nO7CmqqwgpwJmZkHCZkVLYVOUsElT8MIS0lNE002gapg9mcseEq2D1tfqc240gfuawH6CxvhiI8MwjGxBFSwi3OJxGLRAd+1HEQoO1sRR2GhUuRnxqdNmkblmWW9n3MOm+FjMJYws19jPcfizuFqiEcdlLdFSK2wSWKKhDV11Chs9BVkZTOPsqmzhhqmshw3xnFRWkEeP5/k6plreR9mWIkDA9QtLo7CZM4WNGj62osgSbXauHTTm0FwjMj6WaGh54pscLnw/D7tPZv816n1VLNGKFDaO45ZZB9ZpDV4GUsMyyU5fVZWIe3+h2KCChlKFTaK1YAq0+3Qi28ulxTVZWS6/l48jZolm+JLHbs8wDGMEzMbOp7EfqnxJb4kWqYcNqGxmNr9G9WhszFNtOJnjsIkSZClQdqxGo4GtBlmlD1N95ngNcz1suONr/7a4cjBBwibAWzwm2B9fJ+DABMJQ75hUIGvNWAobLZstTXzXDszrfAJAKQIEnP2kUsIGBHlC1BvGIMwtbUvC5hBQQAX+Ppwl2ngkbFCSs6EssfEJpvsqbDT2HkU9bBh1db8NGtWjcDzyNdgSjVHYEDbkvsm0BLVAA4QUNGi6CVQNVZTmsA/dtec4+Jx9Dz1Bv9+4YJZohi82MgzDyBYqYeNRkQMVNtn1sPF7X7RxNYVNPdDpYZPKEk0veIcCFui8oCAo6zEdo1cG1dD5qMKmfI6DFmauKCW6QkAb6aoUNuiepOVbjuwsRfJR2CCbolg9bBjVRsi05/Na37nap18IAwoQMIpIhI8Sw5clU9gkg7MVHfxdD4GETUj/GhHOEg0py+tCamVtGzUK3xhhicaoLUasCTUSxb49bESGFDbEPRsF5+sCtEQj7tXMb150T0BzSm4Km6KkoAiXwGD7ZFYNY1nqsrbZvWcbfM7+fYfo9xsXzBLN8MVGhmEY2UJZohHVPsPAjVBm1VS+diLIVscUNvVAo4dNKpjPym7K0EYabcxxNZ9i4sFxzmCePskKm1wr0dC50Qo4rBP3taqSVsMghc3q6qbuteZASKLa57W+QVTKVqzGlmhac8YKSNjMW8JGDZd7VA9kibYArHIQ7akmvMZWV8bDaif5fR8pbEYUjiAFX6vdGDmPxuxhwyRX+j83lfgfE0s0rLDB1w5UbU41C++d6DymthGDSu6ScUqNs5okbLjiF/4aOP5Jc7CgaP/Dk5ewQfsDs0QziqhPFMgwjImDWRD5KWz8e2PEAHnDeidsTGEzFmg0lU+VhOSuWTyeu90uDFCizR/cHCpaorn2veIs0Q4fF/byUfayh/OR6tFGg5rBVtfDBgS1PCw6R8HYiKFESSqmgTVbt9NVOy/9cFaF/u/vExD17X9HWUl6rHWwJVr4WsdlLguh2+3K0lJcyy3jGK7WUiI4YTMfqLBpNBpEH47xUNiErn9cQWu3UXM4mpOK3rPRaAQrRAv7pDgmkJn7iMYaPAdmwZ6PuXZ8f3MRrOJK0R+xnxDL0JRWoLFhPqfLeqjRaMjuk8tt0R5+0CzRhjFLNKMIGxmGYWQLE0xgvP6HyU5hE+mAqPkqCrQZeaChsMnLEg1fs8y+DTXqRhsMtmKdCTZGsUQ78vlgLx/t3xYlbCIrbLrdLlSYVJWwQWNOS0nAKEdzUdhMgwCqCFZ7+sDMEanmvR7elmjEvOkTy0KJsjLLFxbOGib8ulhf24TvMztnChstmEtneEwuRlbYiBAqgTHpYYOC19r3fZ8m6jB4X7KHC+nB1mw1vG23RAbXoMzcNDEKG+LaCSkCaICfJkZhRxlBPWyI+ERdetjEsH1GfWz2PzyBCRukSKyRm4aRFhsZhmFkC5WwAZXYo0hduYaI1cMG9Q5AlYpGHmhYX6Tqy6SlimOSOsEKG/LCirGhdwmGwebDiS3RYttbMZv2qryefRoz+8D0fclFYcP0lYiRsGEIujY8Xup7PJQIFMlXYROr/84wy8vYrsd62Ojh87sugR428wkSNow6sQ5gJwDd46Fim1HzD0wIlyZs/L9AeTN4Qq3Yr7CJUJCTK9gSDV876DcvS2TAgpeECptOpzuyL1M/ZWssRnWVuiePLzESNqiPzb4JTNhACz7rYWMUYCPDMIxsoXrYeCyIslPYAHwDpFBhY5ZotaBOm0Wm2pqxRNPYQKAKVLbqO4rCxsFGKLSXjyvoo8WuGkSbGhEdZYAPsApZ6dygc9BoNrKpxmMSR8ie04fYiUOvHjael6KL4s4FmLBRUm/CohOF62IZ9K8REZkzhY0a1D1q6HeFPWy2zQR9JhFcaDQulmhofaJuiQb2W6PWIWhtUja/hChEy17L3Bf7C4KYualOa/Ay8LUTrrApO/9of5DSQiy0MIiJT6RWDPlShcLmwMOHaqNA0gIrnvNY0xv5YdE6wzCypdFoSLPVKF3EMdYxw6BAD1PtqomWddMwqEp7OpMKbaMclR4lifabTACQUthoJGzQ5pDtYaPckPPw8/njwutfOcOMAkGxA+XIDk2kOnUJqoBjkpEMKGGT09zNWGtWZokW8P4+r/W1KaKsJD3WAbCiU2kcNZoN6ZaMfY1K42XQv0ZER8FhHMa1F4iIyOLiaunzVRQ2YL4ZF0s0NL9pW6Kh3p2jrmEYvC+5X4YUXZS9r2vRUIxgda5AdRpx7YQobJBdHaOsH8XioVW55Uv3y/LSmpz5lJ1y7gW74Joc2aGJlK/3fObHXKGSlo7FHbtPKk/YrK935LFHl+VJO+ed3rfOoPkyF5tjIz8sYWMYRta0201Z2yxeWPkEAmCgJ/HaPJbPvilsxgONBKJ6n5Oi41DVjXhzwFzXKMiJNlTsZorZzLieXuqaP3JYlDDSboiLxkps1wrG5rKqjQ2TXO90usEBHpRsz2ljR/WwqUhhE/I7eClsPI/HHMsn+BNiWeRCs1leWKMxZzAKG+tho4drQLLb7coisERb2BaesEEWjDHmmirodMqvXe19A5oLRgXTUdC7TG0RUnRQqrBxtJekEjaZqFlDmZktv3bW1ztw/RLS9wWtLTc97nFf+NtvyH/76D/I8uKx+8PZ550ob/6P/1q2HVes6KMKgwKVXLWxRKOK0tzeE1miiRy2RZushI3/fGlMNjYyDMPIGizT17dEy62ayjfWgawhcqrSNorRUNikar7NXDubIBAhQlbPI4UN2LizShHmaT5zBqtkQVWH2tYotbBEq6yHjZvdii/oHExlZJ1AKWyiJGyIJwXMez4v9V07xKrWTdHDRgTfXzTmDKaHzbz1sFGDsunruwhXVzdgMUaSHjZjkrCBChtlK1Q0B22MskQDPUDKLH5iWaIx56XjqLDJa0foD9VvDlw/ITZ46LdxdZO464798l9/94sDyRoRkW/ceUB+612fKV3jM4VBZSowaq+T0OItBNjXt+G+hzyRSNjsf/iQ03vWmW4X90wySzSjCCuvNijuv/9+ufnmm+Xuu++WQ4cOyczMjOzZs0cuuugiecYzngEDY6Hcdddd8pWvfEXuu+8+WVxclLm5OTnllFPk4osvlr1790Y9tlEtKKDgZYlWt4SNR3lqt9uFNjSmsKkHGlU3qfoyUQ1fCV9nSmED7jvoO7ObKWpD75OwaZQnY3vHRfkt9R426LtE3oOGVj7GhG1oHDqzorlby8pKA6qHTQRLtOhSL59J03OipYKMPgmbRE1uW62GlKVTVBI2QGHTbDUqs0ocRxhVbv8luATUNSIiCyoJm/LfeIVonF4HcK/NtD1sRqlH0TqtbO0acg9vtYu/O6V86EyowobY862ubpQqFdE9pWwcITW4a4Ljf//VHYVB8Du/tk/u/dajcuaTTxj599DCoFTFOylA17HP/mZ2dkq275iVx7+zUvicfQ894fy+dYXrmWTrF2M0Fq2rMd1uV17wghfIZz/72S1/e97znid/93d/F/T+m5ub8gd/8Afye7/3e/KVr3yl8Hk7d+6UH/uxH5NrrrlGTj311KBj9rO8vCwf/vCH5dprr5Wvf/3rhc877bTT5Md//MflTW96k+zYsUPt+EYeoBuYl8IGBHpSqRGOHa/874QgYQsb6x2YmLKETT1QSSAmGtNUw1dig0zZkMEeNjgAwcAkTGM0KO8dNrU1Sopq+TIYb/GqArOuDY19qVMPG6pqN0LCJnbvAZ/LyteekAmOZ62wgfaT8XvYzM1NJV+7jTNMkK5/rltaTNNjaBbYOiFleV1A6w7twjJm7upsdqTZPHbvgf1MyhI2sSzRXBU2xP06tyI+X5g9H1LYbIDzFZLkcL1P3HX7/tK///NXHihM2KB7o0j5OGPunT4Wb1WAtji+65rde7ZZwuYIzHiryjnAyB8bGTXmgx/84MhkjQa33XabPPOZz5Srr766NFkjIvLII4/Ib/3Wb8kFF1wgH/3oR1WO/8UvflEuvPBCueaaa0qTNSKH1T/veMc75LzzzpNPfepTKsc38gFVfXkpbBJvhBAxjre2hqsMcwr6GcVojI9kChtmE0MsXJngJOxhgxIPZIU+FRj2OMHsa1JbOKLNGXvefGHm9Oos0dLYYGzUqIdNe6oJ55d14n7kSmRHNM8krK/CRifRPQxuCq4zjuBcqxC4WgIKmzmFZIBxDOa+0n8rOEQobOYVethMiiVabgobka0BaKiwKXnPEPufUOXDQA+byL3QcoJS2ACFGrJ1KvvN0Xl0XTstgST+I/sXC/9GBdBLxqiWm0AOwLnGc/zv2nNc6d/375scS7TQ8WZMNjYyaspdd90lv/IrvxLlvW+88UZ5znOeI1/96ledXnfo0CH5yZ/8SXnLW94SdPzrr79eXvCCF8g999zj9Lr9+/fLy1/+cvnQhz4UdHwjL8r8cEU8FTZgUajdEwIBN14eAVKmytAUNvVAJ2GTj8KGqTqjbMjAd2IaxDNo9NPxoXcOoIWjtl1HhPnIhVBv8Zig+5GITqNZpLCp6vuPotHAVlSrFVW9J1fLel6KVA8bj3GFAkZaAQKtubYMZIk2V2LjY7jDXDr91lKMwmbbtuIm4Cxo3bo6JgkbbN2sezyqJ8fQfAIVfCX3S6b3WeH7liSaGTXA5oDCZoISNkCdJoJ7haH1TVkyDe0PXO2/UXHPIweKEzbrhOo32BKtJgqbWEVhu08CCZtJUtgw+5oxsV409LFoXQ3pdDryute9TpaWltTf+6677pIXv/jF8sQTWyfRF7zgBfLCF75QzjzzTHnsscfka1/7mnziE5+QRx99dOB5733ve+Xkk0+WN73pTc7Hv/HGG+WVr3ylrK8PLhiazaa89KUvlcsvv1xOP/102b9/v9x6661y3XXXDZyHbrcrb3zjG+Xkk0+WH/iBH3A+vpEfZV7FIlzVwjCwmWdmi3Of+KgpbMYHjYB8qrilz6Z/5HOYHjZIYQP+zm6mmGCjz5yBkjy9zStKcPmoe0rfT7kK0pVQb/GYUNW7ClWVqI9PTgobkcPBt7IigSgKm8jBEJ9r2nftQCVsvBQ2aXrYYEu08N9qBQQS5+YtYaNJo9E43Get7Kfr72EDEjaNZoMKGCOQSoBZ+9aBWFXvRTAKm+HPhBV8ZT1sAhQ2Ja917S3CJAly2xP6wlgSLgKlHLqnlCVlUJLRde2EPsvBkoRNqCUalxisi8JGv4eNiMiuPdtK//7owWVZW92YiOJRTmGT17reyIfxv0LGkA984APyhS984ei/n/Oc58g//MM/BL9vp9ORV7/61fLYY48NPH7SSSfJn/3Zn8lll1225TXvec975JprrpGPfOQjA4//8i//slx55ZVy8cUX08dfXFyUV73qVVuSNeeff778+Z//uVx44YVbXvPe975XXve61w1YoXW7XXnd614nl156qZx00kn08Y08QT1smKqFYWAPG+d3DIQ4YLfbdaoWNoXN+KDRVD6ZwsZj0z8KJkmJNua4NxRpiRZpQw+r0o8cFits0iZsYitsULJCpMoeNnEC68OgBEd2CZuZlkhJsWQMhQ3XW0r9sOB4uSVsUDW0zjhCwSuNKYPpYWMoAzI2/WNy8dBq6VstLEyrrEOmZ/JU82kDrZuVJzemunt4PoGWaKU9bAIUNiX3P9e5NHYvtJyYX5iWRrNRuqY89ET5dRyi2sQ9bNxuFGitWKawYXollo4zKjE4Jgobz7lmD1DYiBy2RTv19B1e718nzBLNCMFGRs2488475T/8h/9w9N8nnHCC/M7v/I7Ke3/sYx+TL33pSwOPnXDCCXLTTTeNTNaIiCwsLMi1114rv/ALvzDw+Pr6uvzsz/6s0/Hf9a53yb333jvw2Lnnnis33njjyGRN7/Ndf/318kM/9EMDjz/22GPB1mxGHqAKUNQAcRQoAJraEo1ZDLkGPBhbiBmw8TXyIDTIkTJoqVV1xjwHXTdNUM6nqbCJQe+4SNGSOpgQu4cNo7CpKmFBWf6pWKKVv0du6khkbxOjrwRlVRgw+fm8NDeFDbbWS6Ow0bgmkFWPKWz0QQnq/jGJFDZMdT9DFXNNFWSpsBm6jlHwvswhIUhhU9Ybh+nF09/DZoISNs1mQxbAdYgSNlBVVaqw0VNidjpduDZ/4rHVwvkgtFci5SZQE4VNrKKwXUzC5uHJ6GNDJWyUCmiM8cMSNjWi0+nIa1/7WlleXj762G//9m/L7t27g997c3NT3v3ud295/IMf/KCcddZZ8PXvec97ZO/evQOPfe5zn5PPfe5z1PG/853vyO/+7u8OPNZsNuXjH/+4nHDCCaWvbbVa8pGPfET27Nkz8PgnPvEJ+eY3v0kd38gXtIlADRBHkbqJNyKG+oHx5zWFTT0Itq1JmLHRqjpjkiQoYAE3h+RmKtaGHlqiHfGbSV1pyyp/YoHmrmarUVkAhdukKyhsatTDRgRXva8R9yNXclTY5JawQesjLUu0JD1sFlHCRichYBwDjcr+a3ARJGwWtun8PtASbUISNtr3QJ8AdEjwPqTooiyw6XqP1lhr1oltx5X3kXri8ZXSv0OFTUAPG5cEB2ufdvCR0e0DQnsluiYGcyZWUdiTTpiHtvb7H56MPjaUoqsiq2cjf2xk1Ij3v//9ctNNNx3990te8hL50R/9UZX3/qu/+iu5++67Bx572tOeJq9+9aup109PT8s73vGOLY9/+MMfpl7/iU98Qh5//PGBx17ykpfI5ZdfTr1+586d8ku/9EsDj21ubspHP/pR6vVGvqAbmM+CKDeFDYNrM0ZOYWMJmzoQOh5TBi2bzQY8HqWwITbRsIeNUuIhVmAYJVp6h4WBG2VLNPRdYiuOUBCoSjswV398X1DSCiVIUoOS/1F62ERX2HgkYWNaonmMK9zDRmccwR42CllepLCZN4WNOi69iZIpbAhLNNf1co7AqvcKFDbDgV3cgL4k4B2gEtXsYcOsNcdFYSMism17ecImpsIG3R9dihIYJbZIsS0a+h6NRvkeI1XxTg4gt4Li1zVk1+5ylc3DD01IwiZQ0WVMNjYyasJtt90mb3/724/+e/v27XLttdeqvf8f//Efb3nsp37qp5ze46qrrtrSM+aGG26QpaXR1Q3ax3/ta18rMzODC5HrrrvO6T2M/EAVOUzVwjBoM9dI3MWGifG4BkkZhU1VfSAMNxibsTJSV5mjjT8jDaeqHqElGrDpAY02j34WhX46PvTOAQrUagcTUjQQLwNVPla5qaGqKlV62JSfg+x62IB7SQyFDUNIsttLNec52TKf06uHDQgSaF1LLSX7yTKWl8oTNrOWsFHHRTmFmpWrKWxmy3/nbqdLBcdyB/baVF7YoWtYZGuCBqstyizR/O9hZa+lAul941ZjrVknkMIG9rCBSboyhY3e2pLZR4iIPLJ/dMIGqpjbzdLf3cdCMFfQbxoy/Hft2Vb6d7NEO4YlbIwibGTUgM3NTXnta18rKyvHZKrvf//75bTTTlN5/263K3/913+95fHhvjCIdrstV1111cBjy8vL8tnPfrb0dY899tiAckhEZNu2bfJ93/d9TsffuXOnPP/5zx947J577pHbbrvN6X2MvEAe6z4bMxRQ02jy7gITrNHuYdNqN6h+DEb1hCYDtC2z4PEUAv6MCgddp9ByjNwcMp/X5wxjBdCRhA2yRPOsfit+v2ot0XJW2DD3Btaqo4xUvUe0gAmbKD1s4lyXIS/2vRSZe7GPSgVdS1oBggZ4G43my8tL5QmBuTlL2GjjopxaAgkbLYUN03uRUZjnTpYKm6H5BK3Tyua1kD5sqE8KHLf9ChuFtWadQAkblHiFCpuyvi8oYeNwn2AszUREDhYpbFAxA1hnuiYGcwb3sPFfJ+wGfWz2TYrChrFEy6wQy8iHvHZ8xkje+973yj/+4z8e/feVV14pV199tdr733HHHbJv376Bx84999wtPWEYrrjiii2Pff7zny99zU033SSbm4MT2aWXXiqtlvvE5XN8I280PW+PggIfidfmMaq31oAFDWrcauRD3Yr70DXLVJ1RfWPAcXDiCB6C/ywxetj0LNGgv7TzoUtBCsPYdjNIXRJipRIKZbeiobABwYjcFDZTqBF4lB426m8ZjK+ih5njfZIe2BItTQ+bFJZo1sNGH1Ts0T/XwR42apZoeO06Dn1s0PpE27rZp48WCnqXBe+DFDbT5fMWSrAM9LBhrDXHyRItsIdNSBEAKi7qsIty4nP0KLZECyuKaTTcEoM5gxJLIUV/jMJmHCwsEWaJZoRgIyNz/uVf/kX+03/6T0f/vbCwIP/1v/5X1WPcfPPNWx679NJLvd7rsssuo94/p+MbeYNuYOyirR+0JszRr9h1QbO2Wq8eCEYxzMYAvIHehyFA18+GVsIGWaLBYBM5dzCV/BHmjN45QOdC+9g4kRQ5YZOxJRpjT+hzTxoGJThyS9igqnd0P/KBGYch93KfQgrfwAalsPFIBELLIqVxhM5VaN+r9bVNGPAwhY0+LpZosIeNliUak7CpyIJREzS/ae9TmPvq8L0NreXK5rUQS2bUewva4fZ9bmZeZezi6kKwJVqIwgb+Likt0fz7Lx19jkNiMGdiqvl27ylX2KyubMgTj5ePuXHALNGMEGxkZMzGxoa85jWvkbW1Y4vgd73rXfLkJz9Z9Ti33377lsfOOeccr/c644wzpN0eXEzfcccdyY5/9tlnb3kMHd/IG3QD87GfgRZDiQPcVHWtY5AUWUIwVYpGPqCNQRmpE5DIWoOpFGcCfMheAf2dvaRiKWzYeQYFbrSDCehjRe9hk7ElmmtDY1/QOQixk4kBVNjEsERjxmHAvdznlb5qWSZxwvSl66fb7SazRNOwwSwDqWtEROYWLGGjDasCFSESNgkVNuNgiQYLNZT3KT4KG5wQjqOwQfMWVnkf+x6U5W2GRXy+xO9hU/y7ot/FJbGPCnt6HHwkjsJGBK8HvRxAKgBdA2gvVcauk8oVNiKTYYvGWKIxtpTGZGIjI2Pe/e53D6hDLrvsMnnjG9+ofpxvfetbWx4788wzvd6r1WrJqaeeOvDY/fffL+vrxZstzeOfeuqpW6zUvvnNb3q9l5EHaIHnU82MFoWpF+fUxssx3oGCO7kF/IxyQsZkaks1uFlmetgoJElgEJHcTDHJUp/gCatkQVV6qa1RQqvlEcjOscoqNJ+glg/r6BxkprBBis2qKt5D5j4vhY1nYKPdbsLXugahNze7MCmtlrBRDMSNAvWvETGFTQxYS7ROpyvLS+VJtW3bygPFLIw6fBws0VIrbFL3sAlK2IBgOlRy9KmrqeKgCUrYrK1ulu4fQ35zVFvEqO+Pfg4Hhc2oawkpNpnxie7ZtUnYgLkmZB2FFDYiIvsfnoCEDbSPbESxxzfGA0vYZMo//dM/yX/+z//56L9nZ2fl4x//uHpzYRGRhx56aMtjp59+uvf7Db92c3NTDhw4kOT4rVZLTj755IHHHn74Ya/3MvIABaZ8FDapN0KIGPdoqLCxHja1grHMKSL1IhD5hzNJViaZgr4X+jurWmM29DFOcS8Yho6v3RAX2hupHm0rOatLmAC3xiZ9fQ2cg8wsLZFNEUrC+UD1HghR2Hi8NOR4aFy7nsOUTW5RYD+0+TKlsLEeNuqwRQVIXSMiMjevk1BjLNHGQmGDCjWU1xzMGnOz45iwKethE3AfR8F0F5X38HcaxTjFUVHCRqS8jw0KPJcl0zQT+2yx5sryxsj5ScN6F6nbffrOVQG2RPPff84vTMsCsMPc9/Ah7/evCyHKNMOwiF2GrK+vy2te85oBVcrb3/52Of/886Mc7+DBg1se27YNSxiLGPXaRx55ZEsiJdXx19bW5NChQ0Hv2WPfvn2yf/9+p9fcddddwcedZGAPG4/gWGqrAQ1c+0YghQ2z6TXyISiJmHg4o8U9E9BmVApobsAKG+6aimaJRtrNoIBCamuU6JZoYO6qUl1CBbUUNulQYZPZ5g4F31ztvBg45VvAASKo5sqYnmnLynLx7+7aByhlk1toPxk4Z6wA9YaIyLxSQsA4BroEenPd4iHcg2BBTWFD9LCJ0DMrNbiwTLeAkypGGFbYbJR/xnbJ/ZKxnCp8LbJEc1jDMI4LOe4JfWESNoeeWJUTTlwY+bcQhQ36XVwS+6wlmojIwQNLW+YfnLDBa6xWezwUNmjNGlrEuvuk4+Tuux4p/PtkWKKlsac1xhOL2GXIO9/5TvnKV75y9N/Petaz5Jd+6ZeiHW9xcau/59zcnPf7jXrt0tJS5cfXSNh86EMfkne84x3B72PwRLFEy05hg4/n2ufbetiMFyEewo3EGRu0iWHUMxq+4uicsYmHWJX8MLjb5RQ22vMVm0iKBe5hU6ElGnEd+qg+++l2u7Ahbm6WlkixGaWHTSSrwmOvdX9NSP87dE92VdgwwSKtIAEb2PdliUjYzFrCRh3Ys8tBYYOqrFmYuW8cLNFi2hSNgllHDF/HqGCubH4JUfmjYDoat/1rDBys5j9XHTiOTNgUARU2Jb85+l1c+v9tOCRsHjmwKKef9aSBx1CykVlnoqRp7OImLWLvMSxhg4uWrH+NUYaNjsy45ZZb5F3vetfRf09NTcnHP/7xLX1ZNBnVX2Z2dtb7/UYlTNbWihfzVR/fyBtUheUTHMMKG+e3DII5nuvCD21Ycwv4GeWENJZP3sMGKWyIsUypWsAXQ39nVWvM82IkeXsBG+TYEZLMGwnKI3W6zoo/F2DlY4UKGyqoRVislEEpI3LrYQPtvOpX8e7VwyZgVzWj3AeIKWbRChKgOb8baKSIetg0mg1TDUcAXQK9e9QikbCZX9BJ2DQaDWgJOQ6WaDCIqnzf9+phA+aYUku0EIUN6mEDi3X6etigAr4xUteIcAqbxUPF1zNW2BSfLy3V++HPwT/3kQNbC4M11pnomtFQW6cAJYdD9zcn7i4vmD6wb/wt0UxhY4RgoyMj1tbW5DWveY1sbBxbaP7Kr/yKXHzxxck/S1hV4tbXugZ3qj6+kQ9YYeMeCOqCOIZ2E29EjOOh4E5uPRCMckLGSGo7B7hZJjYxzHPQcdA5Y5OglNrHJ7gLE0q945dPWNoBBeb9Yt5SNawqYtFoNPC4CtykM/e0KlVGo4DqkBgKm8i9pbx62ATM01PKKiWmAllNYRP5mkAN7efmpsbKtigXYI+GI9fgUkmAt8e8Yo8h2DNrDBI2KO+f2gpVxKOHTcn+Dc13ZcAeNg69RdD6DqrMasb0TBuuH554oqSHDQw8F/82MGHjsLB0s0TbmrBB90euh814WKIxtoAh7D6pPGHzyIElJ8VUHUFjAc1pxmRj5UgZ8Y53vEO++tWvHv33RRddJL/2a78W/bhTU1ttBJaXl73fb9Rrp6eLF+pTU1Oyujoov11eXva2MHM9vgs//dM/La94xSucXnPXXXfJy1/+cpXjTyKogsXHEg0u0BMnbGIAFTZWjVorQiqhG4n3myjJyqjimI0buk61erFQgeEoPWx6Cpu0AQXOorErsZojIYXJ1HS1AZR2qyHrJb9JqA0Go6QIsZOJAVLYrK93pNPpqt5bY1kVhrw2JHk6o6xSYtZGWsnP2H2vYMLG7NDiQKgtRcor8kUO97gKaTI/zOH1a7Ft0+oY9LBBayAULHal0WhIs9UoTa4Or93Qvbps3RqSLEZrTNxbpC9hE7l/R45s2z4rjz5SbFVfaokWoKrSKOY6+jlcLNH2b03YaATQ0fcJtcdNRYoeNmV0O105sH9RTjple9BxcgZaCWZWhGXkRV47vgnmy1/+srz3ve89+u9WqyUf//jH1RINZczPz295TDths7Awunld7/ixEzZlx3dh9+7dsnv3bpX3MjjQot7LEi0zCTwfIOWBChuzRKsVIWMydeUxtAkgLKOQl3Wjgb+XWsKGeFqMTX0vGJY6oMAkn7qdrkikKSRnhY3I4fFd1mMmtKqSqRzVDH5qwBQArK9vqtpWcT1s1A5HEaSwUVYNUAkbpSAB+t6hKvflZaywMfRh+5khS7RtSv1reiCF+DgobGChSITJrdUsT9gMr5lCFDYhexB0/8O9Rfos0YBd41gmbI6biZawKduzY7tkhx42Dnv/UQkb1FOEuTeyCsTciW0LuHtPecJGRGT/w0+Md8IGKOdREtqYbGx0ZMDq6qq89rWvHbBC+8Vf/EV59rOfneT4O3fu3PLYoUP+fpKjXjvqGKmOPz097Z38MaoHW6J5BMdQ8CB1woZ4jnoPG1PY1Ar1PiURgTYBoNmnCFHxRZwPrYQNo/bxs08q//tRSzTYfDi9NUpMSzQ0p1dtB4aCQaEJG66HTV7Ld8ZiEwVIXGH84UM2wV7XdESFjev5Y4pZtCzRUEAntsJm1hQ2UWB/V9RjaF45YTMZlmjpEwmw2Ga4hw2415XNLyFFB2jeKuujIjL4PSZSYQP62BQlbDqdLkwklo0hTetMl73/gZE9bMJ7ikCFTU0s0bCKP+waOOHEBfjb73t4vPvYhFgJGkZeO74J5bd/+7flX/7lX47++9xzz5Vf//VfT3b8PXv2bHns/vvv936/++67b+DfzWZTTjzxxCTH39zclAcffBC+v1EfUGDKR2ETW/7rSozjrQFLCFPY1Au0AS0jtWIMWXShniwiOhVfsDpYyRKNUfuMgq1eRpupkLExCuaruHiNu4IC01V7PbsEg3xggo1Vn4NhGIs27SAq1aMl4DzFuKbLQD0dXBupM0otNUs0cE2wc20RMCGg2B/FOAarnEKWaPML2gobdK3U3xINXTNREjbIznZYYROgtgi5h6FgOiyq6PseaH2VuqdpCnwTNqFFAOh3c0nsu1iiPfrIorM6jFk7oOsltHdbKmInh9vtpuzcVe50s/+hJ4KOkTtmiWaEYCXWGfDAAw8M/PuJJ56Q7/7u76Zfv7a2daH85S9/WZ7xjGdsefwrX/nKlsee/OQnb3nsnnvuoY/fT6fTkW9/+9sDj5122mkj++T0H/+LX/ziluNfccUVzsd/4IEHBpRKvfc36ksbbSB8KlhQMDjDBbprfBQFd0xhUy/QBrSM1LZAGgFtjb4tOHFEKmwqsCYR6ethAzZ92gEFyqIxotUDsg5oV5xsxlWVYeeGCbTnlnBnPo9rDxYEOk/NViP5vTxIYQNtniL0sFEKEqDzHHpNQEs0U9hEAQ3n3u+KEjYLyRM25eOlDiCrrhhWty49OTqdLtyXlAW0gxQ2IJiOe6UMfo/S90q9gE4AStg88XhBwobYb5f95ng+4ffzSCEz8L4bXXn8sRXZ8aS5vteHW+9q2D/nACqQ05hrdu/ZJgdKVDTjr7BB480SNkYxFrHLkIceekgeeuihoPdYXFyUW2+9lXru+eefv+Wxu+66y+u49957r6yvDy6UL7jggmTH/8Y3vrHlMXR8I2/QwtzHEg1bDDm/ZRjMAV172ICEjWYvASM+QYHH1D1sHKs0R6FhU4GeoZWw8c2loU3Q0YRN8ubD+DmhPSnKWF8LbwYbE1hVGZjMYgIRVZ+DYZgCAFeFCCLES5/BZ84NyKurK2woBZKWJVrsHjbAEs0SNnGAhRFHftcl0MNmYVt5gNiVWWiJVn+FDXYC0D8mKpDrv7cx80tZQLvVakqj2fAq/kCJZljoN+EKm+O2l1+Pi4dGJ2xCiwDQ2sllLLgWaz6y/9Bgwkalh024/XMOoJ6hGoUvu/YcJyLFsc39D4+5wsYs0YwALJ1nyLOe9awtj910001e73XjjTdueeySSy7J+vhG3qCAwuZGxzkYgJ6evioXP8e5hw2yFcqsQtsoJ2RMpk5AulQ3Fj5HQQWnpbCBx/E8wbQlGjhf2gEF5tzGbKaKKx8r7mEDbTBCe9jEtfqKAaOw0e5hg3sdBZ6j1D1sgMLGuYcNoWrRChKg7x3q5W8Km2qARQ9HEzbFTcpFROaULetQzyzt5HAlIGFvhH0Kurf1z7mc2qL8M/r2o0PzFl6D8gkbbcvZHPC2RCN+87JkmaY6men118/BA0tDxwpfP2jYP+dA7B42IiK7TyrvJb3voTFX2JglmhGAjQ5DLrjgAtm1a9fAY3feeafs27fP+b2+8IUvbHnsuc99bulrLrvsMmm1Bm+MN910k2xuum/ufY5v5A2syOm6Bw9TWwwhtBNE3W4XBndMYVMvQjaNMawzytBQIGhUfGn1sKnKEq133NTNh5n5L6LABloHVK0uQdW7G4HBacY6LDdLNKYAQDuIiu5xyK4E4dXDJmCexjZP+gqb0HN09H3Q9w6cL1aQwmbOEjYxQL9r7950CFmibUtriabdL6sKkJ1SjHUdVEdv9ids8EWN5hffeznsYePwPVBQfRwVNkjxduiJtZGFkIzCpuw3R9bOLgkOxjq2n0cOLDq9nikMgvbP49LDRsUS7bjSvy8trhUmCscBs0QzQrDRkQEf+MAHpNvtev/v7rvv3vKez3ve80Y+dxSNRkNe9KIXbXn8+uuvd/oeGxsbcsMNNww8Njs7K8973vNKX3f88cdv6dnzxBNPyN/8zd84Hf/gwYPyt3/7twOPnXHGGXLhhRc6vY+RF0xAwbXJM2xonuEC3WXZx1TiWg+besH0bCki9YYTbfqZTR+0AyECo7Dqm9wcovnCN5kG55kjx8Xzle5SjuphEzFjk3slmkv1rg9Us/jMFDZMAUDtFDYehARR0T3Z9fyh89No6vX40Zpri1haKk8IaCs4jCMg284Oa4mm+/ug+WYcEjawP0wUhQ3fw4ayxwJrQd/CA1QggM6NSw+bVgzvuYpBCptupzvymmb22mXrf81eZygAPsyB/YMKDmQ9y6wz0do7VFmaihS2gLtPKk/YiIgc2De+Khs0FixhY5Rho8MQEZFXvvKVWx679tprnd7j05/+tDzwwAMDj1111VUyPz+f5Ph/+Id/KCsrKwOPvepVr3J6DyM/mGCoax8b2MPGxwslAO0AKVWhDSwljLyoU+NTXHWGr1eNJAX6HOwlBRV5nr8N7mFz+P9Te9kzgaCYlmhIGVC1wgYHHcI26Vg5ohdo14JR2GgHUWNXLPqc4aCEDTiHrucvdo+ffrTUjEUsg4SAWaLFAf2unc7hezVK2MwvpFXYrI5BDxt4zURYEzr1sCEC5qjgru2ZsMEKGz4xgM5zYwwjZaiHjchoW7RwhY1erzPX/jBbLNEUCj5a7XD75xzAtoDhF8GuPeWWaCIi+x4a3z42uBDNYjJGMWN4GzJ8+P7v/34566yzBh679dZb5U/+5E+o16+trcnb3/72LY+/4Q1voF7/oz/6o3LccYPZ909/+tMje9KM4tFHH5X3ve99A4+1Wi25+uqrqdcb+cLcxFwTNmiBruHXqo1LwIOxTpkGDY6NvAixrkmd7ME9PvBYxkkKQmEDqyy5a6oL9G2xLOd6iWU8Xykv5agEsu4hj71vF1Y+Vp2wgX3VAhU2uSesRtFqNWHwgikkcAEmJAKVWD5JsZBEGrR5clbYhDdVZoGqs4C41cb6JpwTzBItDmjt0LPfRcGoBfWEjW5yM0eq6K2CruOBHjaBagsRf4UNuge72PKiuSm34ggNkMJGZHTChvnNy34bTQsxZ0u0/YOWaGidxVmipemTGRuYtFS4BLYdNyOzc+VrnH0Pj6/CBq1XNZJixvhio8MQEZF2uy1vfetbtzz+xje+Ue655x74+l/91V+Vr371qwOPXX755dAOrceOHTvkZ37mZwYe63Q68rrXvU4effTR0td2Oh15/etfLw8++ODA46961avk7LPPpo5v5AuzaHK1RIPy38QBbu3DcZZo+QX9jGJCJOmpxTlIobFJbGJw1SPRwwZ8caS0O/q8SJsZttoQnS/thBwToAitmC8iNCCQgtg2GCgQwahZqgAVAaytKStswH0ONaaG+PSwCUnYEAobtwrkdIlPtGYKab68soLHjSls4gCLHjpdWQT9a0T0FTYTYYlWwT4FB6Ddetige7XvHI0K+Vx68cAeNjVSt7P4JmwoVVWZJRrqSdsptu8fZtPREu2gcw8bQmGjYP+cA2gvpJG0bDQa0BZtnBU2Gj2TjMnFRodxlJ/4iZ+QSy65ZOCxAwcOyKWXXio33XTTyNcsLS3JG97wBvnN3/zNgcfb7bZ88IMfdDr+r/3ar8lpp5028Nidd94pl112mdx+++0jX/Poo4/KD/7gD8qf/umfDjy+fft2+Y3f+A2n4xt5wlQduHrZwor1xBVVnCUa/36MwobpOWDkQ4hfeeoNJ2z4Smxi0AaCOR9YYUP2sIk0X8CkU5c7vnaPIma4sMkuV+rQvwX3sAnbpCMlRY4KGxGm6l1ZYQPO81QFvY5i9rDpdrGtRj9IlaJZ0YmTz/7vvQz614hYD5tYYEu0riwCOzQRkQUiQOwCulZWxiBhkyKIOgxScverkimFDXg/3+IDdA90sS2FDdfHUGGzsG0GrvNGKmyYJF3JfZcpLmJVKa7JkMe+szygqoE98IixycyPdQC5DWhdA7v3lCds9j88vgkbNF9W3ZvTyBuL2BlHabVact1118mzn/1sefzxx48+/uCDD8pll10mV155pbzoRS+SM844Qx577DG57bbb5I/+6I/k4MGDW97rPe95jzzjGc9wOv62bdvkk5/8pFx55ZWysXFssX377bfL3r175WUve5lcccUVcuqpp8qBAwfk1ltvleuuu04WFxe3vNfHPvYxOeWUU5yOb+QJcxNztaCJFWz0hYnxOPWwITaruVZpG6MJsulLrbCBlmh4o4UWt8z5QPYL7GYKXXraCZMevc+X2hqFSiBH2oiiILNI9XMX8vlnFGRloKRVrnaWU6kVNsgTPFBh45N7CeknxdgDra1t0OM/qSVaxL5OS0vr8DmzZokWBXRv63a7VEJtXlkBZQqbOMppdG/rT5KHqi1E/IoPGk3cww2u/Rx62KhbzmZAs9mQ+YXpUnXcSIUNU1BTcr4Ya+fOZldaxLBwKV4QObyOf/Tgkuzac5x0u11i/RDeJ3NcethoXQO7TirvY7Pvocm1RMu1EMvIgzx3fUZlnHfeefKXf/mX8uIXv1gOHRqcOD/zmc/IZz7zGfge11xzjVxzzTVex7/iiivkk5/8pLz61a8eSNp0Oh254YYb5IYbbih9faPRkA984APywz/8w17HN/IDbSBE3CttkDtHDG/oMkxhYyBaAZHA5JZooJcFE9DWqC5lgk0McDPjeYLZqvTUlh2UJVqkpDcTEKh6Y+NShewDsrTM1TphJrXCBllMBCYkfK6rkOQtUg2IHD6HC7h3r4gwCS3NhA22uvFlmUjYaCcEjMMwPWwYS7SFbboKG7R+3dzoysZGJ9u5kgFasUYoFMHq0T6FjYIlms+9nPlNocq779ymtpzNhYVtMyBhs/VvSNUqUq6AZtaWm52OTAnTu9Z9PfHI/kXZtec4znqXGJtwnAWuBVOB1vNaewyksDmw/5B0Ot2xVLWh8Wo9bIwybHQYW7jiiivk7//+7+Wiiy5yet3CwoJce+218v73vz/o+K94xSvkM5/5jJxxxhlOrzvxxBPl+uuvl5/7uZ8LOr6RF8yiyXnhlmhxQqN8OCYwVnWVuuFGiMIm9eIXJZeYzRK2ISM27bCvAquwiRM4Qa/qSlUKG/ycWFYPlCVa5T1s4qkJROrbwya5wgZZTASOE59lQJAlGqmwYUHjMLjHTx9oOg5Rna0sEwkb5R4pxmHQ79rZ7Mrioa2V+P00GvoKKKYH47ryfJOaSizRHHq/MIVyaA5m5rwt70kk4l1U3lhhM37BYxHcx2akJRpjg1dyvjT7I/r0h3nkSB8bjbEr4na95EwqW8Bde8qrTTqb3S29hsYFuF41SzSjBBsdxkj27t0rt9xyi/z+7/++PP3pTy997s6dO+Xnf/7n5Y477pDXv/71Kse/4oor5LbbbpP3ve99cs4555Q+99RTT5W3ve1tcuedd8rLX/5yleMb+cAEI5lFZI9ut1uZxVHh8SiFjYMlGrFRzdVWxxhNnSqO0AaX2cSgyjTmfEAFCzltwEpXz58GXfe9jStSbKj3sKEUNqqHPApliVa1wgaN78BGs+tr4d7qVTADPhfqzeMK3gCHWqK5X1ch1diswoYlpcIGqhmDFDblCo5Ggzt3hjuMSnUJ9LCZm59WX78wCvFVZUVfamAiIUJhmcu9jVnHITWqzxzN3P81lUJ1Wnu7gBM2K1se41RVJQobZj9PqlJcLdFEHBM2RADdZZzlTLIeNieVK2xERPY9NJ59bGJb+Brjja1wx4Czzjorij1Ju92Wq6++Wq6++mq599575eabb5Zvfetbsri4KFNTU7Jnzx656KKL5JJLLqGqnV2Zn5+XN7/5zfLmN79Zvv71r8stt9wi9913nywtLcns7KyccsopcvHFF8vTnvY09WMb+cDIRF0qbZhLJbUEnjmayyWOgjpTU82x3YSMKyFy6UbiJjYallFYYRNuibaJvBF7nwV8XF9/ZxwMO/z/qStttRPILjBqyaoTFrFtMKDCJlOv6ykQREVWb66g81TFBjgkeYos5UTcFDbQgkPVEi1e82XUw2Z2bsrWM5FgVKqLIGEzv01f/cQlN2uusKnAEs1FMcCpLYAl2rT7HMSshVvg3PRbe/WUzEWMqyXacdsjKWxK7iuMtTN7r/CxRDt4YElEuLUIs85yGWc5g/YYWq4jJ+7aJo1G+Z5q38OH5KkqR8sLrHg2DYVRjCVsDIozzjjD2aJMk3PPPVfOPffcyo5vVAdTgeVS0cwsBsddYWPVqPUjZEymHs9oE8MkStBzmAAd+hxs1Xes5r/odb1rXsMezgUmQBHLEo2pmqx6Y4OD02GbdNSbpeqEVRHI3kY7gAorFoN72Li/JiRxgCzlRBwVNpEt4/phep34gnrYaNttGcdgigpQD5uFCHZ1k5Cw0ShaccWlGIEplENJYZ/iA05hwxcNQXWBWaIdBSVJGs1G6bhk9iIdMsnBqLGHeWT/4d7MTLKHskSL3M8wFalsl6emW7LjhHl59JGlwufsf3hCFTZmiWaUYKPDMIysYRYKTgobItCYulqTCQxpKmwsYVM/UPKhjNQFgi6bZd/nMJtotDlkkw5Q4eJ5gllLtNSWHVVaoqFkhUi41VUoaCMfuklH1mG5KmyQzeaqdsIGBF1Cz5NPVWnIXKuvsAHWeorjCM75ES3R5hcsYRMLdA10OtgSbSGCwoa5VrTnm9SkqnrvJ73CJlIPG4eiCmx5O6EJmxGJWLQWbYN1ObOfj6mweeSIwoazRCMSg6BYKrR4JxWwKE1xj7EH2KLte+iQ2rFyAinCQ1w0jPHHRodhGFnDLJpcEjZoEyRSgWexcsYGKmwyrdA2ivG13RJJv+HUaMSJrlPGWgF9Dj2FjWfCBlUv9/4/uSUafk5IT4oy0KZGpHqFDRzfgZt0bImW59IdNQJPbYkWugH2U9j4H1NdYYMs4xTHEVSdBSQxkcJmzhQ20UAB1m6nK0tAYTNfkcJmdaXmCRtwG4mxT0G/d7/6gephA97PJ2msoXzoTzykXl/lAkzYPL61hw26p6DzzpxLZlyJiGxuuN9Teg3t1SzRYL/O8VDYaNoCnrhnW+nfx1Zhk1DxbIwfNjoMw8ga5ibmYonGBBpTF1SZwsZAhNgypB7P6JplNjEowKdR8bXJKmxg9Znf8dE36AUSUitsmPdjEt8+MDYXVVuCoWvRJ5DQDwommMLmMMhiwqc/Qj+pFTbtdhOOLZdziAIEmhWdUIkRYom2DCzR5vUTAkYP/LuiHjYxLNFmGEs05QRxcipIJKCA+4aDJVqr3YDzgl/CRlf5gCy4JjVhs7K8sSVBg3qyoHsKc89hFTZMcc8wi4fWZGV5nVPYMIlBqCyth8ImZb+s3SBhs28MEzadThfGnnJd1xt5YAkbwzCyRrMiR4TzUk+tSFDvYQOCOoydhJEXQRVOicczumYphY2CfztTHcyALj3fDT1riZbay157PnKB2YRXvbFBwSCX+9EocA+bPBPuSLmprbDBFYuB4yRxDxsRog+QoiVa8PnpAwmLQizRVkxhUxnM74os0eYjWKJRCpsaW6J1u1247ojRmxAGoB0UNkxw3kftzyhMofKhr6gCrjUntIeNyFZbtNA+HFQPm4iWaCIijxxYVEvYxC7eSUWqHjYiIrv2lFuiPfHYqqyAIo26wRQVV231bOSNJWwMw8iaRqMBF4EulmhMdX+WPWwc3g/2QMg04GcUE6SwUfwcDOizMvY4GjYVWj1s0GfxTfCiYFi3y33GKhQ20SzRwNzVbJU3tU0BtsEIS9jg+TvPpTsKompXvKOATajFhJfCJjRhg86hkyVauia3yLIzZL5ACpv5eUvYxALNtd0OobDZhgPDrjSbDRi4R4VLOcPUQ2jaFPWAdp8bDgkbYv71CVJSvUXQPdqhh02M85wDVMLmidWBf4cm6ZgkHmsjhu5vRRw8sMgVBhHJRGTNjBRJuYCUQKo9bE4uT9iIiOx7aLxUNkxyUTMpZowfee76DMMw+kCLPJeEDUOMyrXS45nCxgAwPVuKSN7DRkGBgCqSmKA97KtAXlMaap+RIIVNt1tJwkbbotGFlI3SfcE2GIGWaLCHTfXnYBRYHaLdwyZuQsLnqgoN7uGkFx+ERvOspmc6+t6s/eQokIJj1hI20YB91rqCe9hE+n3QtVJnhQ2y6RKJpbDhr2O0RmPmFx97U0r5gCzRNnmFTer9YCqohM1QH5tQ1SZlt0smOXz3/Y/sX4QqZhEdhQ37XaoGuggo7iGRwkZEZP++Q2rHywFK0ZXput7IA0vYGIaRPWjh5CKNpgKgOVZUOcQ70EbVFDb1I0RhE5Dr8QI3fFWwRCPOB9q0s1XfcEPvOV+gl3U63GdE1e2uaNpWuAIbpWfQmDP2Jh32Zsl0Y4eCb9oV7yhgWMV5Cg3uoWIKl6QXvpb0zg8M7EdV2FgPm1jARNxmR5aXQMImQg8bEV01Wm5QCpsKLNEGFTblH7JNrEv8etgQyh3wnP57NLaDqn7NEQMvhQ3Rt6gMzf6I/pZoS6QlGqGwgRaCNbFES9gn8/gds3CtuP/hMUvYEGqwHPY2Rr7Y6DAMI3tgANih0obrYUO/nQpcRbuLwqZ8IWsKm/oRtGDOrIcNE+xH451RHKGvzVovoOiJb5AWJ4a7A9Ydhe+jbYlWYQ+bOihsUCBqI3CTjlQUuVbioUbg2gmb2Mk9n+sq9FpExRQu55BpCq6FxpxfBEoIzJnCJhro3ra4uAaTCzEs0UTSzzcpYe77MZZ10EqsL9GB5l/GEs2rhw3xGthHsW8+Qsnk1Ar1VMzMtqEK1dUSDSU5GNsn1jLZtz/MwUc4SzRGoYsVafVQ2KRUmTUaDdl90rbS5zz84ORZolnCxijDRodhGNmjaYnGLAa1K9YRnCUa/35oo2oKm/pRpyo/tPBkEiXoOcwGAp0zNumAKv58FXnouu92ucp07XgCMx/FUtigHjZtjwCPNuj+EKqwQcEEnyBXCqDCRr2HTVjwCOJxYYUmbGYUz2H089MH0+vEl+WlcoXN3JwlbGKB7gXDAd1RLGyLpbApH791tkRjFPVRFDYO6mjUn4NZs/oUHzDvi5vB9ylsYL9E7nPVjUajIdtAMvXQkN0hVtiUnyxmb82sLX3714j0LNFwMQOzDkbftzYKG1ggpzvXIFu0/Q+PW8LGLNGMMMb0NmQYxjiBKl1cmjxPhMIGBHXQRtfIj5DNeeom7UzwDm3KUICPqdTTUtjgHjbU22wFWqJ1qc+oncxjkmEhAdgyULIihyo0lypkH+pqiYYq3jubXdV+c6jC26c/Qj9VFFZPKaoGUFWn5jjS6hc2zOZmByqGrYdNPNBcN9zjYhRVWaKtrtQ3YcMErWMoP1Bgtn89ghQOjMJmyqPPGBPYRArs/vOb0g4qN5AtmnsPG5CwIcYs1eMyYI3F9LBh743Idjl0LZgKVGSk3cdp955yhc2+cUvYmCWaEYiNDsMwsgcqbByqbZjAQWoJfGqFDQqsGfkR0sMm9XaT2aijDQKyEqCq31Aig7yoYHLCc76Aia1ul0swK2+muASy6iGPMg6WaLTVXgEoaRWaiIgF87nWgd0bS7eLkz9VbIBjK2yQAq0ftC5iLF9YYMLG85pA6hoRU9jEpAFWDyvL+HpeiJSwGWdLNKrXZsCasAgXhQ22x4rUw4aYt1yKKnC/xPENlcGEzZCCDqmq0NqIGbNMMVBI4ccj+w8RhUHcuISWaIoFKjFB63ntojCksDnw8KFoKv4qMEs0IxQbHYZhZA+2WHJQ2DAbodQVVcqHwwobS9jUDVTJVUbqBCSzuN8E16FG1SNshN3llGvIhtp3voC/S5cL3GjbFWg2hnUF9m/JYFODgg4hNhidThcrbDJN2DCFAFq2aJubXRhkCB0rPvNm7B42LjZPsIeNYhAGzbW+wZeVZZywiaXgMDglKyJWDxtsiaZrwZgSzglAf12H1pmdAYVN+Pzicy9j5nXYDL5fYTOhPWxEPBI20Eqs/LwzYwIlhUTcChe2vHa9I995dLn0Oazyy6VXUs50YIGc7vF2n1SesFlf78hj4DeqE2aJZoRS/c7XMAwDgG5kLlUsVOVa4oQNp7BxsEQzhc3YERQ4SbzfpAL+YFOGrlNGxcPYLzDzQRcYyvv3sAHH7Xap4L++wkZ3PnIBbWxy6N8S0wYDWXWI5KEyGgUTfNOqeqcqFgPPk89lHXotzoAgNLIH6wedI83kJ5oDfecLU9hUTOCtZWqqGS3BDBU2Smq+KqD2KRESCbA4ru/+jOYXyhLNK2FDWKJBhQ2fsBlnS7QFx4QNWtughAxzLjmFTVgyFjW1b5FrB5gY3ORU8lWT+hpAlmgi42WLxsSocihGM/LFRodhGNnTBotvF3k0s3bSDoDC4xHPcbJEA9VHuVZoG8WELJhTVwgytgfINkqj6pG5jpmECNpA+s4X6Dt0ul1KyaK9maIs0SJVDm6AuSuHKjS0sULJyDIYBYqP738KmGSaVtU75QkeeJ585s3QqVZVYZPSEg2pzjzni2VCYTNnCptohN5bYv42SCleZ0s05v4aY58CEx2dfku08s+I7LFE/IoPmNcgG7P+PoroXMewnssFqLA55KawQfcUZj5hLGVDe+E9/ODjpX9ng+eMYqgG+Rq4D9Kea04kEjb7Hz6keswqYcartu2cMV7Y6DAMI3tQRRUjoe5BWQ3Q76aDusIGVBaawqZ+hAROUlcIMht1VKmnUfGlZe0Vq/oMK2y44L/270udt1gJG9jDpvpla0wbDGZjl6vChrHa1OphwyiRmHlImxDrShGssHGxgsE9JvTGEe5h4xdgW15ag88xhU08QlUcC9siJmwUk5u5UZUTAEp0bG4c+1zQcpEIevsoZplEM2MT2zvHaA0YQ8mUCyhh88TjugobJijNjH2XvrWjQAobdo3FjPE69LGB14DyXDM7OyXbd8yWPmffQ+OjsEE9k0R0C2iM8cNGh2EY2YMWRS6LN6Z6J3WTSc0m30wPhBxshQw3mI1BLmhsYjSqHrXsF6DCxnMvg3vYdCsJ3HAJZNVDHgU2g80gWQEbMwds0JmERq4KSea+otXDJoUnuJclWuCliJJemj1sNC04GLWgD4wl2qwlbKIRWlm9EFFho2kfmBtVKGtFmN4v/Qqb8B42PkljZt5i9nC9JDIsyBljhc1x28sTNouHVgeKBaENHjjvnOod39tDLdHQfYW9N1KKIdQEMwPgfiuwEGUUqI/NOCVsKEW4YgGNMX7UJwJkGMbEghaBmy6LN6qZJ/92KihmbBgbCKYS2siLkCq/1ONZQ6GBNjnM+dBSiqBn+FrO4Ubd3GWvnWBmNtWxfLlhwiaDTQ2q3g3pYbNObOzqrLDR62GTpyVaaBAVnUNWYdPt4sIN1R42xFzmwxJQ2MzOtce6x0TVhJ7b+YXygHAIMLm5Ul+FDWWJFmHYQ0u0/h42sAE9/oA+xQfM/Y/p+XhUYQOK+MZ5fkEKm82N7sB11K+wGoWGJRqlsImsWmHXDtQ4I4pEq6aKPk6oj83+feNjiYb2BI1GYJ9aY+yxhI1hGNmDFk8bYBHZT1VWA2VoKmyYClxT2NSPkKB86h42TGUl2nDBTbSSwoaZDzT66Yx+Xfnfu90uFfzXtuxg3i+WwgYFgaamq1+2QtuYgA06Y52Qa8I9qcKGsZgITO75KWwCEzbgHLIJL2YMaqrV0Fzr2/MKVUKbHVpcQhU28xX2sDFLNHfQ2m2jb17RUNj4JGwoSzSHNahZopXzxOMrR/879DdvNhvwvprCEg3BFsUw+7KQAp5UYIWN/jWwCyhs9o+TwoZQO6fepxv1ovqdr2EYBgBuIhwUNozVQOobp2YPG6YCN9eAn1FMyII59XjWSJSgv7cIiT5lv6BgieZrmYF+ly5xbBH9hqBa582HOihssJogxBKNSUTkuXRngm8uPVhK34dK2IyfwoZNeHEJrXQKGxG/OWN5uTxhMztvCZuYBPewOS5ewmZ2FlwrNU7YMMt97fu+CF7LdDvdo3sRjR5ZPmpR5n1d5iNkwRXjPOfCwjacsDn0xLE+NsjulUmmoTHGJDhCLdEQ7DqT6ZUUUsCTCtR3McYWctfucoXNoweXaz2H94PWY3WyPDeqwUaIYRjZgwILLhUsTAA0S4UN+V6msBlPQuTSqQt3mMUn2iRDVQtxjbo0ng36LJF62HQ7XaqBvbaUXqv3jw9oYzOVQWNOdD8KscBgEhq59rBpNBrws2lVvTOWKMHnyeOyCk7YEAobpngjhWVcP7ESNitQYRMvIWCEj+eYPWxgcrPGwT7mWolRiNOmFANHEjawapxTQTPWaYPvm7iHzRgnbFAPG5HBhA1KlDDjByWBmbHPWMeGQFuiKex1cgCt5xnFmit7gMJGZHxs0bDCJs81vZEP1e98DcMwALjJMx8IoCrXEq/PNRU21sNmPAmp8kuesGE2/aEKGyJJQfViYRQ2kSwzGEs0zsteWWFDWTTGUtgAS7QM+reg8d3t+iuQGOVIDuegCC1LLwTXxDW9wia0Ghvdm7td7rtTCRvFIAwTIPVJ8iKFzZwpbKKStyVa+Vyzvt6JpgSNDfO5mYIUV1wSHRqWaCLu9zOtHja9NShc30VouJ4LrgqbDVCMwtxz0X6eGfsoWRgKOyapvU7mCZtutwvjIjFUZsgSTURk/8OTkrAZ3znG0MFGiGEY2YNuZk6WaBVYDKlA7jsZyxRT2NQPxgKskOQ9bIjNMuphA2ylmGuUqvomEg+xNjNQYUMG/rUVNkygOlYgDFZwZjB3MRZ4vgGF2idslCy9EMw9Pzhh4/GaUAsp5t7MqJSo86M4jpivvelhFbi0uFb6d0vYxCV06RBTYTNDFB6trdVUZVNRZRm1djsSgEaJY9bmx/V+xrwvE0g/lnhCCRvuc9WRdrsJ51AXSzTmt0Hr5Tws0bgfnVoLZm6Jxkw1Mfo4PemEeahk2v/wePSxgQmbDJwDjLyxEWIYRvYguahLcIypDM+xyaQpbCYb3z4pIukVNhqNOKFNBfGlqMQDsTmMZZnBNOquKsGM3jOawmat/gobEb/gtAjZwybjzR0Kour1sCEs0QLHipfCJvBSZO7NzDlMoUDqh5oDPaaMFaCwiangMMLVBXEVNkTCZjVuYDcWVVihinAB914AekNJYeOaOGZsUZ0C6eBUM+vZOrNwXLnKxsUSjfnNUfGZhiVa6JqYvTcyhXS5q/yYPVCUflnNhpy4q7yPzcMPjUnCpga9OY28Ge+7kGEYYwHyOGbsP3owi6fUnsVUzwhFhQ1TmWjkRYiHcAyv8zKYzQ4KOqI+IMyGXKuvArbM8G1ig49bVYIZvWWsTShSmORgHUCNK8+qSvT9p6aaya9nF1L1sGGKNEIVJD6nOTS4xyhsGNUAZYmWOGHjYw2zDHvYmMImJqFTzfy2ihU2Ne1jw9z3q+thc0SZolQ17qr4Z4KbTCD96PcAxRU5FvBpcpxDwgbZjzMJP7R2Z9ZOKHE0O9eWuQX/ewOr5Gb2Ibn3sKkyJrIb2KKZJZphHMZGiGEY2YM2ES6BAKonROKEjWbPCLRBbTTyrtA2RhOyZ0y94ZwlgmgrK+XjFFWYMhtyZjPFXFZ4Q+NpiQZe1xXOTiFGBShU/0QqGkQb8RwUNtQmPVIPmxws4cpAwTc9hU35+zSajfAgQ6YKG0Y1wKyJVC3RFItO+llaKrdEmzVLtKiENpuOaYnGBPrROiNXmKB1jGUdpY7eqLaHDbN/4QLpR3rYgHt1lhbZimxTVNhQPWzA+dToYTM11ZKdJy7A9ymCVtgQYxwp0aqmyoTNrj3lCpt9k6KwsZiMAbARYhhG9qCqHcb+o0eOChumUk5LYTM13cq6QtsYDesHPpLEP/f0dAsGE1ZXyiunNTbRTKJKR2ED38LrdZ1Ol0owx5iv0DvGskRDc3kOGxuXKmRXUDA+h4RVGal62KRQYvncJkPvrTOgkboIqbAheiFpWioxc63PNbFiCptawzQ192VmdrIVNjHu+y49bGDChu1h46yw0elh01PW4B42471fwgmbY0lzjSSdRg8bZInWajUDEzbcmGSuF1+1dSpyTtjsf/hQtL1GSlDSzhQ2BsJGiGEY2YNuZk6WaBVZDaQCbVDNDq2e1MmWodFowGDKynL5OEWbCGajxFSLUgkblDzy/G3g67pdqhdKlISNQhWkDyjQnEPCgvL5d7gn9YPuZa72MalBn08rgIqCOhrjpIoeNlPTOgobZk2keS1Rtq4ec8Yy6GGDGmYbYYTeW2L2sGHWsnd87eFox48JE6OMofygEh1HAtDIHospbBBxT9gw85ZLIB3NS+OesEFJ1SceXzn63xoFNWhdztwnoNJnqik7d/knbNh7o0a/zqqhEjYR+mWJiOzeU26JtrqyIU88vlr6nDqALdHyXtcb1WMJG8MwsgfdzJwkx8xGKPH6nLMT4YIdqyCYw1iuGPkRYntVxYYTJWxQL4sOSFQwwVTmOmb8pVHOxHszg/M1lSlsqrBE63a7sHIyh4RNrH4dIiLrQD2haWMVg1Q9bJC1WlVKrFALKS2FDVIgieiOJa3k+PDzUWJ/fj5eQsDIO2GzfcccXGd84W+/Ee34MaGCqBE2KlzC5vC9Tatq3N0SDT/fJZCOivjGPWFz3HagsDnU18MG/ebEeUeWaMgOWQQnjqamWnJCiMKGXD+4JDhzpap+WSIiu08uT9iIjIctGhqvQQ4axkRgI8QwjOxBNzOXamYmkBYadHGFWQrRlmggIOZazWbkQYh9TRWKsdnZ8srnFWCJpqGwYa5jRnGHNjS+5xcFAjqdbmV2Beg7+VTLIxhryxysA2Ju0lHCKneFDap61+phk6KJq9dlnYnChlkTaV5LzBzEzLX9rAB1jQjXL83wJ0TFMTc/FTXY3Ww25MKnnVT6nHvvflTuuftgtM8Qi6ru+y69X9AcQ1uiOSbXNfqkiBy7RyPLqnFP2Lj0sEG/OZNMg+ptYo/O3P/T9LBhrpf6K2yY68mHXbvLLdFERPY/XP+EzaZC7ydjsrERYhhG9qCbmUs1MxUzSK2wUQyomyXaeBKyaazCTS3YEg1soplAEqewwRMC2tB4W6KBiaZLHFukooQNI1V0BNlciOShMKESgZ4JLaSeyEFhVEaqHjYpLCZ8ruvQqvd2uwmDpoxKibFE0yxMiWGJhuzQRMwSLTYhw3lhW3z10+UvOBs+p44qG+Zaqc4S7YjCBtyv2fnF9Z7OBDcpxV9PYYMs0SLZQeUCStgsL673qao0CqlQoRJ8C2rsnZjCEo0pVIhkH6wFsweKMdeIHFZgovvEvocPRTl2SlIUGBnjjY0QwzCyB1VqMZXZPahmnhn2C2EXfSggZgqbehIWlK9AYQMqn1dXAnvYNIlNu5LVIHqO728Dp5lulQqb8r/H2IQidYlIHvMXV4Xs2cOmBpZwZaTqYQPP07TO9sZ1KaBxLcJzSFiiUQkbxSABc55cVWfLS0TCxhQ2UWkS99kiYtqh9bjk2afD49z42W9m30diGEaNFuO+zwTce+cS9bBh5xdX1ShlieYQSIfruwz3g5qghI2IyOKhNel0ujCRyBRKoDkF2SGLiKyvoft/TpZoec89Vc01PXafVG6LNgmWaDkUohl5YwkbwzCyB1UfMJXZPZhAY2oLKap6hbQTMYXNeBLSw6aK/WZsSzQmaK7VVwErbOBbeL2w0+lS1W8hY6PwPVEPmyiWaHgezyFhwWzSnfqq9YF6j7QzSFiVgRJqWgobdJ7U1COOF7dGJSpUKRGWaOhaarYaqkEYZg5i+/D1WF5ag89JkRSYZELWDin6C01Nt+Q5l59V+pzHv7Mi//yVB6J/Fk2q6ivhYveJAtHsHOzcw4ZIBDHP2SQVNrHUBbnAJGwOPbFK2WwySTqUFGQS+7CXTrspTzphHr5P8eu5MTkWPWwqKgrrMREJG7NEMwKxEWIYRvagRZGTJRqzOEksgWf2XWysYxUEc6aJpsZGfoQEIKvw4EbjDPUnQAoF5jsxlZGUUgQ8xbcKGSZFulz1W4yEHApSOMZeKahG6RlsbDjfcs8eNkgh6ej3n5p0PWzQearmPqeisIG2cuEKG+3rSG2u7YNR2FgPm7iErDtSWKKJiFxx5fjZolGFGjEUNsS80FubMUFzBqZv18D7KilsjvWw0Uk81ZXjtnMJG6YwklkboUQjo7DBAfCWTE235PgnzcL3GgW7zmJiBS49dqugKhV/j117yvvYHNhnlmiGYSPEMIzsgT1sNrp09SbztOQKG8XjrYNgjils6kndGp+iQBqqEkeJCuaaYSojmQQu/izwLbxe1+1iC4pGsxFlvkIB2Mos0TJQ2DBqAl8bDJS0mnYMbqUGKWyY/isMaAOsNU5cLy2Na3EGJLsZlRIOpupeR1pzbT9LhMLGetjky3yihM055++SPSeXV2nf/Pf3yuKh1dLn5ARl3VxRD5uNzY50u13cgJ4MQrL2Uz2YpIBTD5sY1Sc1YmEbqbAhkoiUwgY8h1lbIku03pg6YaefLRp7f6QSg0xTngqhXEdiKmz2lM/djxxYotT3OWOWaEYolrAxDCN7OHk7t+iuqmI9FHZTsQqCOah618iTkM15FZYOwZZoCs1Nuc1UuCWafw8bpGLpwsBrQIuBUqoYM0wFZw49bGLaYNRdYZNLDxut/iyu17ZGEBVVnDPnEAcIdMcR1fvCMWGzAhQ20zOtsa9+r5qQ8ZzKrq7RaMjlLyhX2ayvd+RLN96T5PNoUFUQlbqONzqH+5mAj8hemy49bKammlRS3GXth+LpzDmpM3PzU1Ap8sTjK1TQnOthAyzRQG8kEd5iaucuz4QNeX9sNBrwOvRVW6eCUthEDIrsOqlcYdPtdOXA/sVox08Bskg2hY2BsBFiGEb2MMEXto9NVRXrpcekLNG4Rd86CObkEPA03AnZNFaRgJyZLQ86llmidbs4GMBUUDKbdqbqGz7HW2GDbcfgeYj046K3jdFIFQWZRfLY2HCWaHEUNq72ManBdl5KlmjoPCklJFyvLo3LcUahDxDshaR8HTFrJleFzTKwzTR1TXxCEjYLCfsLfc/znwKf8/ka2aIxy/0Y9362GEGrn4mImxqSfc9mkwmkd2gV8zjTaDRgH5tDT6xSffmY+wq2AtbpYSPin7BxGZNtsB6MoUbXhErYRCyMQAobEZH9D9e7j41Zohmh2AgxDCN7mJsZE+wTidhAPAAqQcT2sAHBnJnMA37GaEIWzKkTkCIisyBhs7pSnFjUqvhigk3MseIpbMr/TilsIm2kmP462lAVnBlYB3D++H4JGxSMz31jx/SwcW0+P4rUll8sGtfjFEp6MQobGCDQPT9ac20/qIfN3Fy6hMCkEhKsZqyWtNi15zi5YO+e0ufc+bV98vCDjyf6RGEwCf8YyzrWSoy5v7FFRi73dKfkDtHcnrkVtWLJmDOCStgoFdRo9KRF9rm9MbXzRF9LNP43R0nEGMVNmjBzTUxL7hNOXID3mX0P17uPzQZY12spwo3xxUaIYRjZwwQX2EVRrOBrKFqbL9TDZgr44xt5ElJNWcWIRj1syizRqIa7SpZoTBARPcP3t4EJmw6uSo82XyH1T4SqQUY5kEPCIqYlGgq0u9jHVAGj4ES2bwzoPdQsvxyvbY2rESlsmPMH+0soW6LFSNigHjamsIlPyLojlSVaj8uvLLdFExH54t99M8EnCYdS2EQo1mCSLBubHbV+JiJu9zSX+z+03trscImxMVfYiOCEzeKhNTJJF66wYe4TqLjnqMLGN2HjkBhsgoRe7gkb5q4cMy7SbjehEmr/Q6awMSYbGyGGYWQPs/BnJPoiWG5dhRrhyIFL/8wGO1ZBM3dT2NQTJkFRSAVjGlXalypslBruMhttKmETyTIDva7b7cJzEWu+QoG6GI160aZGJA9LRyYo4WuDgRLu7Qy+fxlM8E3DFg1W2CptgF0vrxQ9bFZVFDbpEzauyirUw8YSNvEJCVanTth812VnQivEL/zdN1QUfrGpqtcm25ODUsOSCSWX5HHLSWEDmtuTCpuqivhSghI2Tzy+Qq3PmN8SJmyIBAf6LD0l1gneCRuHMYks0XLvYVORmq+fPSeV26LVXmEDzrGLctCYTCxhYxhG9jDBhborbBDsJhPZpUybwqaWhDRXrmJMz87hhE3RmGbUG8x3YqqDq7VEw7ZjaLMX67fV8Bl3BfXdEMljY6NZQDDM+lq9N3aoh40IZ+mFgJZoSufJ+fpSiGzMgHv0GijKEME9/ULuJ6PgbAJ1e9jMz5slWmyCethsS/v7zM1Py7O++8zS5+x/6JDcedu+RJ/IH1gk0ohXrIF6cmxudqh+JrzChi8iU1XYdDqy2anWDioXGEs0qm8Ro7BR6PnCFiT4W6I5KGzA92ESXVVCWVBHvgZ27dlW+ve697DZRIqwzNf1RvVYwsYwjOxhFv7sogjFGasS2CCbZDY+ChM2prCpJXXbNM7Mllc/d7vFlfbMtUwlbAhVEpMcwsGTOJZonW4XJ4tClFdlQLu2CAkboLxothpZXAfMz+1rg4GSVrlboqVT2CBLlGrOE9u3oQwVhQ30+Nfd/jFKDNc5A/WwmTWFTXSCEjaJFTYiIlcQtmhf+NtvJPgkYcAgasSNCu7J0VUL3ou4zUVOPWzA9+hsdtWKg+rOcUzChljTUD1swGZXwxKt99sf/6Q5abXdfz+3xGD496kSKmETqVdmD5Sw2fdQzRU2YL7UWDca440lbAzDyB5m4c80RBSJZ28Uila1HLJEM4VNPQnZNFaRhJyZxYnBlYIKaka9wWwgqL4KxLG6wOXZ99qFCiAmYRPLEg0qbPSPydpcVE2j0YCJMu+EDUhm5HIOikDJhv8/e+cdJkWVtfG3OkzODA55QJRgAhGVLGAClKBiBgXzmtbPHBbzKuYMuKCiK6IiqGBOgAQRJakYCJLjAMPk2F3fH+wM013dde6teGu4v+dh16murqrurnDvOed9D2CNwsaphATvtW3Fc5xS2LD0sKErkK09j6y61zaEUtgkE33SJOYxczo7bYkGAEd3aY6s7GTddX5cuNGSe5Cd0Kpe+/bN0pODRS3HGvS2S2HjZ+iVwvI5DoWETWoa0cOGVWHD8PvQYyeWhA3bWNHnU5DdhF9lw2O9SycGG4HCxuZJ5GF5+pZo5WXVKC2psvUY7MTp8Zik8SETNhKJRHgstUQjggZ2D0ziQe2VJYhdWxsmE1JSYeNNzFjYuJGETGJI2MTrY8MygaAm48D//NgtUIrYFjxhsURzKcHMov6xGtZGsiLAEgwyAqkcETxhQyUbAGsUNpTll2XnivOOaKStHIvChrSMs/haYklU8StsqnVflz1s7MdMAjLFYUs04MA4qdcph+uuU1Feg+VLtzh0RMagxvt2zlMoRUI4FLYseA+A7DvUEJ5EPFXUU1sbZppXuVXE5yQslmgsyncmSzQrethwPN9yDdii8TwfqV5NIcEVNkwqM5sVIIcRPWwAYM9u76psyLmNxYpnSeNDniESiUR4WAZPzJZonu1hQ6/DUjUoFTbexMykUeGNOloAZYkG6CRsGCrsmL8PIrDBkuhViVWM/jZkUiRMK2zsktKT/XVsmIR6RWEDsNnGGIFK2PAEt9yApYcNi0KEwqlzhffeacX4gfoOmRQ2DgcIWO5DvKqz8jJCYSN72NiO0UCdP6AgkeFeYAeNwRbNrUINgLasCoVUMmEOsBcZ8dyredale6WELSsO8jpUwqamJozyMv0EOsBoiUacFyzFQDw9QXJyU8jtRWPleWa0n6FTsFwDdvXLqoMlYbN7p3f72NQS8wGRitEkYiLPEIlEIjyWJmyIwaBrlmgWNPlmSdi4NYmWmMNrlmhJyQyWaJWxA3IsEzbW74MKJLIkQsngiVFLNOozqCrCRFNcu+5XbliiUYHogED9W8igA0Mz42hCoTCZrBRdIcnSw4ZFIUJBJbZYq7speC9tK65H6jtkec7XUJZxFltwMClsOO8Z8Swz65AKG/sx+mxLSU2wPcgXj1b52cg/PEd3nV9WbMf+wgqHjogfNwvLqHtnba21lmg8qlGe+xbZKyWkWlsc5GHSM/QTNgBQtJ++XliSdNT3GaqlfxP6+bivLiMAAQAASURBVHbwOHJsVtiQiiHBFTZWzreMkpqWQD7Pd+/ypsImFKKdT6QlmoRCJmwkEonwsARfWCq+ALry2TVLNAv2y2I1w1IBLREPn4+294qHKwkbUwobOtjNOoGgriuWqm/q3mLUro5UsTBYolFBCaNQx2bHJJRuJC/OkNWOqkrq8wN83upuwNLDxhKFDaOHvVl4n8tWjB+oZzTLc94xy7j/wdTDhuOeEQ6rdMJG9rCxHaPqgtRUOgBsJ30G6Kts1LCKH77/26Gj4YcKotqZRGBRprAUyLHeY3ieaTzKQMraLRRShQhWiwClsAHAlOBkmauTdrJM9t/sz7cmTQ0kbDjGD9T432g/Q6dgSVrafQ0oioKmeWm663hVYcN0rxRcOS9xH3mGSCQS4WHqYWOR7Ng1hY0FvTbYLNFkwsarGLUncaPSlUXJFS8gZ2UjWCsUNlQgnUVVYISwSleAKjaN4ljUP1ZD24GJk6xgsY3hhUpCAOJP7AIBH3mfskJhQwVsrLKO4711WmKJxqCwoRS3VJWy1eeR1QmbA59Rfx2psHEAEwobN+nZrx05lhfZFo3sm2dnDxsqAF0bZgpCs6ocecZPfMoHWgXL1L9DJmwA0AkbxacwfVdUbyGWgi1qvt8w4dKEU2Hj87N9jvr1KcWQ6AkbQa6Bw/L0bdH2FpTZfgx2QNnTAmIVo0nERJ4hEolEeFjkoqwBMtreiGkzjsPUw4ZFYSN4hbYkPoYn6S6c04EgHbitjKOwYbH/oyZ9dVBBGxbrKkoRYDSRwBTkpJoP2zSRIhU2LiRsRJrUkAobA5N0loS7SEmreFDPGCd62FhlicZ777TEEo1IdqsqndyjK5CtPY9Y7sc8fa9Y+iVIhY39GH2+pKS5m7DJzEpGlxNa6q6zeUMhNm3Y59AR8eGqJRr1bAurTEFIt3vYkJ+jlq2HzaGQsElNY7BEIxI2rOMzUsFFzOVVVbXVEo13nEmNNVgULG7CNN9y4BrIzEnWfb0qjoW26LCpEcUf10vcRZzZr0QikcSByRKNYQIBMFSuGbQ3MgvZwwZSYXOoYzQI6YbCRlEU0hYtXqU9S/KV1aqFSnKxTNipRKhRmyoWVR0duLHnfkVt1g5LNGpiI1Kyma4S5f9+mCzRvJCwIZ4xlihsbEqiRsN777TiXmtFHyAyoWXxOIflY4c4+jpVEHZoAJDssorjUMBooC7N5YQNQNuiAeKqbMjCMlsTNiwKG4Yxmg2WaDxjYGpsFAqrbAkbl+aEThJM8CMxSf+5TSlsmBM2lE0x8Zzg7Z/Eq7DhHTtQcxEjamsnYbkGnJhDkspiCwp93IDF/UV05bzEfeQZIpFIhMfnU+hGhYwVzVQA1C2FjWKBDIIlECZS0FPCh9FJult9magJYDxLNJZKbNbvggo4UftiawRvNGFDJ5PInlt2BW6o/jp2JGyICRmPr7jdBKigFkdwug6qahTwxv2bSthYorAhrgu31FiWWKIxFFVQ3yGlwLE6QKAodI81nntGRTmdsEmRlmi2Y3TokJzifsKm24mtSWu2xfP/FtKyiCp6d9MSLRxSmYKQbitsaCUHm8Km8etrDkDZojmlsKGeEyz9ahuOFVPTEsi5SOR7rVXYiHh/aQhTDxuDdtw8JBD9D60YN7oBi8LG6gIaSeNDniESicQTBIgBA8tDEWCwGHIpuE0GO1gs0aoYLNGkwsazGG0w71YSMimZqLSPY4nGYrfFOsClE736+2JRPRi9pliSTm5Zo1DbNZCPIKHu4Vb1JbECsqEx0UMkFkxe1wIlreJhd6VkKET3HrDKYoJ3PGDFvdYahY3z9oJm77UNiZfMb4gISYHGjtHnS6oACptggh8n92mru07x/kr8umK7MwfEAWVTZKvChiEAzdTDhjHIy5OE4blv0UUVUmHTEDJhU1Sp+zrrmNxs/z/eXn+KoqBJU3aVDe/YwYqePG7CMt9ywhKNUtp5VWHDZol2aNxjJMaRZ4hEIvEE1CSCZRAHMEyEBG1iw1KdSgVxfH5FDgw8jBNVTlaSSFiiVcbxJGaZ4LBepqzWafFgqeoyWq1OfYawqpJKDdsSNpTChsGikReyh41AyQqWhsa8MCUHvaCwoSbeJi3RWL4nqxQkvMMBURQ2VEDVDms9s/fahrD0sEniqJqWGMNoYiBVELu6vgNpW7RF88SzRXNNWQs2iye6h5jCPJfiuVfzzF9YeqWwjDUPhR42AJ2wIYskGH9H8nchFTYMAfCoJApPHxvLLdFsUKNbCYvriAiWaCzjPhFhG6+KP66XuIuM3EkkEk9AVb2wyo4p+a9bQXFqQMTSGJAK4ngh2CeJj9GAmJ3VmHokUb0s4ihsWCqxWSfRZq0UWRI2lJQ/HpQNIksPG9t+WwvtjVghEzYCNeZkaWjMC0sFodF+SU5CJRzMVkqyFGdYlpBwoYdNYqIFChviOzLaD00Pq2xrAbqHTTDok0EOBzB6PqcIoLABgCM6NkVe83TddZYt2Yyy0iqHjogRjytseCx+FEVhVs/yXPOU8iEUCgujLhCB9Az9hA0F629OfZ/UuWVEidykSQp9YHXv5Xw2sljviYyb/bIaQo3ZzBb6uAVTDxtZSCshkGeIRCLxBJQFDaslGjU+d62HjQV3YyqII+3QvI1Rawa3VGNGLdFYkpNWTQ6pxANLcNlwDxuGiRAl1LCyqr0h5PdmQ9EgFWQOJogzZGVpaMwLS3LQDmWE1dCWaOYm3iJbTIiisKGTn9Z/P2bvtQ2hetgkC6LgaOwYfb5QvWOcQlEU9Bmgr7KpqQlj6aJNDh0RG9Tzw84hHUuig0zYcN5fAoxjKB41DnXuhhn6EwKHTsImlVDYULD+NtTYnRr/Mz3/o46FyxKNt4cNeb2IrbChEjZ2zTGioYqRvKqwEXm8KvEOjfoMqa2txauvvoozzjgDzZo1Q3JyMtq0aYOhQ4fi/fffd/vwJBIJB1R1NXvCRkxLNFphQ2+DVNjIhI2nMVrp5F4PG8ISLU4VtaMKG2KywmaJZjBhQ1mihVXSWsuu6jfqfsTi/c6LpxQ2RAGBkarKxtLDhvQiZ+i1pgdv02Ez8N47rRg/sCSAqaQXNR5yo4cNSzV7HRXl+pZoycSzRWIRBs/ntDRzwV8r6d3/cHKdhYLZolHzFKP9DFmg+oWGasNk1Tjv/YW1EIFnDEApH0Ihlak46FBJ2FCWaBSWKWyI/n9sAfDI84THEo137EAnbLytsHFqAklaolWHmK5X0Wgs43qJuwgfvSspKcHjjz9e/7eiKBg3bhySkpJ037dlyxacddZZWL16NYCDg5+tW7di27Zt+OyzzzBx4kRMnz4dzZo1s+8DSCQSS7BqUEQNTkQdnLMESKuIIA6L1YpEXKgmqvFwKwlJJQgr4ylsGM511kQFFdig9mVnXxEyScvQFJfHeoQH6vu1Y+JETWxYbVOcwGzj3FhQ55riU5gbObtJImmJZlJhw9J02KKEBO94wIoEapDBYpFKepEBVRsCBFTfK5Zq9jooS7TkFJmwcQKj9xtRFDYA0DQvHZ2OzsOfq3fFXWfN77uxa0cx8ppnOHhk8SHHQK4qbFTy+cY7VmUdQ/Hc16mxUZhhfAWIOye0GrNJVuaEjdkeNiwB8IAJhQ2vJZqFVqBuQM8xnFLY6I97VPXAuMZryQ2psJFYgfAJmzlz5mD8+PH1gY1+/fqRyZry8nKcfvrpWLNmTf2yhoGRukDD999/j0GDBmHBggVIT9f3uJVIJO5CyZRZgjiAuAkbK2LqlMer0V4bEjEwbJvnlsKGaApdVRlHYcPQsJ11ckh9Z9T9gMU32WjChuVe45a/NBV8tSNhU0NZogk0UWPx+eeFst8LBn2uJV95sL2HDZMlijs9bKwYPwQCPvj8im6Cg+xh44LChgrE8dwzKEs0Sr0psQbDPWwEStgAQJ+B7XUTNgCwaN7fOPfirs4cEAFphWpjEJWlOI4KmnNbojEqZ7gs0Vh62MiETT2mFTaMvzllJ0upyo1YouU2TaMPrP69nAob4nPzFCq4gVsq/mhYCrKqq0MyYSM5JBH+DPn4448BHBzoX3vtteR7HnzwQaxZswaKotT/U1W1/l/DZb/++ituueUWOz+CRCKxAOqBxhwgE9QSjQqQskAFwowGliViYNQGw4pzywikJZoZhQ3jR6Im25RND4vCxq4JRFilK0Dt+m2pzfL0o2CFsrpi9bl3AvK8MtLDhlQYifP59SC9yE02jzVSYesUVl2OZvsAUdeSHco8utKY/ZqIZ5dZh2gJgcaK0fM5NU2s3+ekXvnkfWnB3PXCWO6Q1s02VuFQyaBwKIxaqocN5/2F+m3q1+N4BrIoH6xUc3sdswkb1mcudX5Rzwm253/keZLdJIU+sP/Bq+SmerwY6WfoJG7NMaKhlNmAN/vYUGpnQFqiSWiET9gsWrSoPoDq9/sxePBg3fX37t2LCRMmaBQ1Xbt2xUUXXYRTTjml/rW6pM2bb76JX375xb4PIZFITENNAFh87QGWZp4uDc4tqGgnFTayh42noSqYRSORUNjES9iwBPaYFTYMtmN6UKoPgG2iEQu6hw19fHadE7QlmvX7rKn2kMKGweefF0ol6pWEO3WcVY4obKyZ3vAMBxTFuvEDqVLSsURTVZXsA2DV99MQqnKaJxheXkZYokmFjSMYVRckp4iVsElOSUD3Hm1019mzqxRr/tjt0BHpQwZRXVbYUPcXXoUN67OdJxFEJ55UpmC6VNiwwZqwofsjWqCwiTqWxMQA0jPYPh9vr0TKQtBIP0MncWuOEQ1L0oKlp6hosFgdHyr3GIlxhE7YbN++Hdu3bwdw4AbfvXt3ZGZm6r7n3XffRXl5OYADkwO/349p06Zh+fLleOeddzB37lwsXboUTZs2rX+PqqqYMmWKfR9EIpGYhrSgYQyQUUEDUYPiLBXb1GDGKwE/SWyoiUHc97k0GExK0g+qxbNEs9KmwmzVN4slmtHgJ8tnoI7Prt/WDgUJBTWxEck2gA5q8X8/lGrCL1DCSg8q2VBjuocNPWm3KrnHU8luZSU2bSsX/ztkqujkDEqxQOWqeK6Jiopq3ddFSwg0Voyc00nJAaHu1XX0GdCeXGfBd+sdOBIa0grVxsIy2u5TJR0NeH9/1rkJqxIHYOjFE1YZ1dxizgmtRhSFDdnDxmDBRk4uWx8bboUNmbDxtsLGqfOf5do2a6frBqQ9raAxJ4lYiDeiasD69ZEDp+OOO458z3vvvQcA9dZn11xzDS6++OKIdbp164YpU6bUr6OqKmbMmGHdgUskEsuhmliyDOIAuppEUIENU0U75WsvFTbexrA03aVzmlTYVMQ+Xy1N2BD3DVphY19fERb1D5lgbkQ9bCiVpEgKG7oxs1TYxKNKRx3CAsuz3irLL57ry8prMTGRskSL/x2yfD+8QSkWyIICnh42lMImRSpsnMDImEPUZNoxXVsgKztZd52lizYyFWnYDTlPsbEIh6mHjcWWi6x2pzyJIDqQHmZ6TjvVdN1tTPewYfzNaUWK9ZZoAJCTy2aLxt3Dhjg/KPtAt6Eey5Tlm1U0Vks0L1k9S8RF6ITNpk2bABwMDHTq1El3/bKyMixZsiQiCBKvP83ZZ58dsb3du3dj8+bNJo9YIpHYBVXFzpqwoZp5utXvw4pgj55NCuCdgJ8kNkbVX25VCFIJm5rqUMzkDEvVI6vaiLqeqR42ZCP4BBNJUIafhQoo2KaesiCBzAuVsLDDxskoLD7/vJDnmkAJKz0SiGvCrMKGZdLOU4ltFVZeidR9RS+ozJTQskEBQd1rebz8K4geNtISzRmMPF9E619Th8+noHf/w3XXqSivwfKlWxw6ovhQ4xI75ynUODMcUkkVH78lGtv6PAkbls/BMo45VHrYpKQmmPqsrIkOKgFgVmGjKLHHZ01y0+iDA786jDrPjNjjOgk1x3Dq/GcZ34uQTOeF+v1FVKNKxEPos6SwsDDi7+zsbN31Fy9ejNragxfzscceiyOOOCLu+qeffnpElajsYyORiAv1ULPKEs21wbkFEhupsGncGK10cks1RlmiAbEH4CEqqwr2z0RNpkiFDZmwMT6MYgm6uJWwccMSjaqcFClhQVYhG/h+yM/vkYR7AqUOMauwYegrZdkkmOPyMmpZGYtE4rfWVdgYrEA2C3XPYEnE11FRLhU2ImBkPJyaKmbCBvCOLRqtrLVv36SbQShM2htS24iG9dnOo36gxsu1oTDTOMZv55ctED6fgjQTyVZWJRJ1T6HGvKTFVCC26r1JU1ZLNM4eNsT5YcdY2UroHjbOnP9UoQ/gzR429PnqjXG9xF2EfgrV9aKpIz09XXf9JUuWADhohzZkyBDd9Tt37hzx944dOwwcpUQicQIWmT4LZDNPQaupqIo7gK4+kQkbb2N04MzTh8FKkgiFDQBUxqikZrEDYVUNme2rQCdsjF9TLJ+Bau7rVsLGaks0VVVRQwTihUrYEAkBI77lh4rChurVQ0FZTCg+xTJLNJ5kt5WJ8SDVw8akwsaOqk6yop0xcKWqKqmwSZIKG0cw8nxJEVRhAwCt8rPRtn2O7jq/rtyO/YUVDh1RbKjnh52FZSyKAeoew6+wYexhw/EMZLHeYrknHSoKGwBINWGLxqywMWlTTBUkxDv3mrBaonGeu2QxqeCWaG6q+RrC4gDiyR42xLzGDrWzpPEh9FkSCkVemDU1+gP4H3/8MeLvvn376q6fk3Ng0FYXNCkuLuY9RIlE4hDUYJC5hw2lsHFJjmBFDxtqMCMt0byN0eC8WxNOlqBaZWUMhQ0RrOApeKSqI6n7gZ3XFMutJkyojexLMDursHFUNWEB1PduZJJO90vyxv2buiZCtXTTaj2oxJ6VTVx5xgNW3mcphY1eItloU2azUF8V6z2jqqqWDNqlCKziaEwYGQ+LrLABgN799VU2aljFD9//7dDRxDkG4lKxM4jKUhxH3b95E+as6lGuHjakSjjMZr97CCVszPSxYe5hQyqfzPaVjH0uNWnKaInGrbBxXo1uJaIUsbLcA7ypsCF62Ag0r5GIi9BnSbSiZv/+/XHXVVUVixcvrh9cKoqCXr166W7f74+8OVRVVRk7UIlEYjtkYz/mHjZiDE6isUIFIRU2jRurqsadguphAwCVldpCDPoaZf8eqCAqtS+q34aZIDpLgJe6r9llV0B9xVYrbKhJDcA/kbYTMqhlwLectoTzxvXP8pwxM/F2sokrT1zUyiAqpdzTsz9lU9jYYYlmLjleRyVhhwbIHjZOYeSUFj2Z1rNfO1JF4rYtGlWoYWdhGfVsC4dUhqpxvuNjtkTjSdiQygfVUvvdxoAjCRsGm2K9ZwVpxxfnd89hVdhwjrPsUFs7Ca3mc+Y4/H4fOScyq852AxYLP4mEQuizpK5nTd3AZM2aNXHX/emnnyISOh07dkRmZqbu9uvWr3swpKSw3cwlEonzNHaFjRUWRFQQLJHoLSARG6NW2m5NOBMZAreVFdoBOJVEYfXKBsxXv5HVfKYUNuZ72Nh1v6K2y9OPggVKNQGI1cOFVNgwBIKioe33xPn8erCozqh+a3pQwUIrJ8A8hRRWFntQz2q9PkBMyU87LNFI1RnbPYOyQwOA5FSZsHECI0UiqWnGg75OkJmVjC4ntNRdZ8vGQmzasM+hI9JCKmwsVBFqts3Q+8VqhQ2rUpmnaIPuLUIrbHjsdxsDphI2jEk6lp5Aeuc/NU7yxzlHsnNSmIqkeIsZWHoliQxpieZgESs17mGZJ4gGOV4VqBBNIi5CJ2wa9phRVRXz5s2Lu+6sWbPq/1tRFPTp04fc/t69eyP+zsjI4D9IiUTiCFZVNJMN9kS9KzLEOqggmFTYeBujagq3VGMslmhVMRU2RJKC4/OYTYS6bolG2sPZlLAhE13W7o+lUbpIlmBkvw4DVZVkDx+vJGxsV9g42MSVp4eNhdeiKYWNS/aC5L2WMclbIRU24tAIFTYA0IewRQOAhXPdU9lQhSS2KmwYFANUwoa7DwhzDxsOhQ3Zi0e11H63MWAmYcP63GVJNoZ1zi+jFlN+vw9Z2cnkvnnPXWpeZkRt7SR0TMS5i4Aa41OOByIiLdEkViD0WXL00UcjGDw4KF+5ciWWLFmiWa+iogJTp06Foij1wZcBAwaQ2//9998j/s7Pzzd5xBKJxC6ohA1LVSlAV6651mDSpP+7qqq6VbcAm+JBIi6Ge9i4VCHIYolWFaOHDWmJxtNXglhXb2IIMKgezFiiWaCwsavS1grFHw+UkgkQa2IToCbpBhRIlN2DSAkrPVgSS2aax1LnipX9Wbh62Fh4KZKVpqZ72Fh/LlHfFavqrLy8mlwnOUX8pEBjgKUaPprUNPF/m+NPbE0mlhbP/9u1huHU89XOIhzSEi0cJoPQvM9q9h42HAobKmETDtPf8yGkrgHMJmys6WED6I+fzFhMNWmaSu6btzCGVvGLnbARySaeGpeYGTe6hbREk1iB0GdJUlISzjzzTKiqWp+MGT16NDZs2FC/Tjgcxg033IDdu3dHvO+ss84it79ixYqICUb79nTFjUQicQcqCBOqZQuQkRVVLg3Qze6WparWKxXakth4rYeN3+8jKyIrYyVsqGuUxxKNUkIQkxVqgmDOEo1eh+xhY5clGvG69QkbhvuXQAkL0h/fjh42Hrl/sxQGUP3W9KC+WyvPE64eNhZWolIqJf0eNi5ZojH0JmChokwqbEShMfawAQ7cS3v0bau7TvH+Svy6YrszBxSFmwob6t4Qqg2T4xLesSpzDxuOZDxpiRZS6WC1x8bcZknPMNPDhu2cZEkA6D0rqLmu3rmUy5Cw4X02Up9b9B42LLaATkGNHVkKu0SDTNgINK+RiIvwT6KxY8fW/7eiKFi/fj06d+6MQYMG4dJLL0WHDh3w5ptv1id0FEXBeeedh/T0dN3tFhQUYPXq1fV/Z2RkoG3btnZ9DIlEYhKrFDaUt5hbChu6ol3//Sw9AaTCxtsYV9hYfCAcJCbpB9ZiW6IRPWw4AqN297AxZ4nGYA1BBhTcsUSzOF8T8zyIRqSEBYs/Pi9kctAjEzuWa8JWhY1LFYtW3mephI2uwkZQSzTWS4LqYRMI+oS6FzRmjIyHUz2QsAGAPgPoIs1F89yxRSNtilzsYRMKq2TSnCpoiIbV6oznGUjd48KhsGuWs6KSZqL/FKv6iSWZpxfkpub6eudeThMbEjZUglPwhA2lBneyiJVKyJqx0nULq9WIkkMT4aN355xzDvr27YsFCxbUBzeqq6vx9ddfAzhY5Vn3WiAQwLhx48jtfvzxxwiHw1CUAw3lTj75ZJs+gUQisQJSYcNoXeBm5ZoZVCLRxFKxLIMc3sZrlmgAkJQcQGlJVdzXjViiWdvDRv/9dlqiMVlDUAobu3rYUFZyBiy/9Ni3p5xcx4xdh9Ww+OPzQiUiPJOwYVHYmPAid7KHDc/1ZeW1SCW99J73TJZodiRsiHsGqyqvgrBEk+oa5zByTntBYQMAR3Rsirzm6di1oyTuOsuWbEZZaRVSTQSyjUDPU+zbN4t6lGqkzq2wIXp2GdkuS+KJmlcdagmbVBNjLNYkHZPCRudZYcZiKic3hdw37ziLxUJQZNxMDkeTQNwHpCWa5FDFE2fJ22+/jVatWtUraOoCCdF/A8Djjz+OI488ktzm+++/X78NAOjXr58NRy6RSKyClumzBQPI6n0HBycNUQgTImpQxTKQkQobb8NbtViHuwob/XMupiWahdcoOWknAg9kXxHGQENMGD4G2cPGph/XqgbirOzdU6b7elJyQKjeCCz++LxQtnBeSdgw9bAh+q3pQVuiWDi14elhY2XChnhW6z3vWdTGRp8lepi1n6yDUtgkpciEjVMYKfZIEeg+rYeiKKTKpqYmjKWLNjl0RAdxs7eKn2HMRFaNc96DWe/ZXJZoZFGFVNhEI0wPG53fhQqA642TmjRNI/fNa1FFJWyM2OM6CWkT7+A1QBWqeFFhQ1kdy4SNhAVPnCWtW7fGggUL0L9/f6iqWv8PQP1/JyUl4bnnnsOtt95Kbu+PP/7AN998EzEQHTJkiG3HL5FIzEM1eWa1RKMDjS4N0E3uVipsGj9GlTKuKmwIS7TKmJZo+hMcSxU2pCWa/rHYbYlGTqZs8linjs3qHjZ7Ckp1X8/JTRVK/UhWVRqwwai1sV+Sk7Aklsz0sKGe9VZ6gvOccVYGUVkUNvGuQSqh5Q8otlxLpCqPUQVdWa6fsJEKG2fhTUSasVVymt79DyfXWeiCLZqVKmNeWBQ2VCGJHT1seO9blHVuOEz3sHHLItst0h1Q2LCsp/esIAPgOkm9JgwKG94AOouSS2RIlZmD425qjOvFHjbUHFImbCQseKbcOj8/H9999x0WLVqEjz/+GH///TdKSkrQpEkTnHzyybjooouQl5fHtK3XXnsNmZmZ9X83a9YMXbt2tenIJRKJFVBBGNYqljBVuebSs9OsdZNU2DR+jKq/3Jx0JhHBtcoKAwobjouUDCISF1YNpbBJNGGJxpSwcccSjfqKqe+Nl70F+gqbJrm097iT2FFVSVqieSRh4/MpCAZ9uhNVM9YWolYsWpkEoRQ2qnogMRPrnKDuGVZaxjWE7GHDeM8oJxI2KSneUHA0Fnw+gDHXBsWnkKpakWial45OR+fhz9W74q6z5vfd2LWjGHnNMxw7LrJ3nY1jOqYeNlTChreHDcOzjVdhSipsQmH6exaoSMQJnFDYmO3dSCls9MZmuYfRChteJTepLA2p9Y5AIkKqzBx0HaHuA2YKfdyCvld6Y1wvcRfvjKr+R+/evdG7d29T23j66afx9NNPW3REEonECahgNYtvO8Dg1ypoRRVV0V7FMJBh6S0gEReq0Xk83JwnJBAJjVjN5qkJhMLxNZBBRGJfdjaCZ/ldXOthYzKBzAuZsGkqWsLGeks0O881p0lIDKCmJn4vEnMKG+cqFnmCLFYGNliUe1VVtTEDHG55pltlo1hBKWxSpcLGSXyKghBRhV1HamqCsIHJePQZ2F43YQMAi+b9jXMv7urMAcHdeQqVbAmHVNKCmnJEiIYlYcOr2qF7i0iFTTQJiQEEE/yGrKdYfx+WwjO938VMr7/0jCS0bZ+Djev3xX5vgh/57XLI42sISwFZOKy6ZrdOIVJf30PREs1L43qJe0gdlkQi8QTUJII1YUPF0dwaoJMWRJR1E8NAxox9k8R9RB3w60FZosVKNFqpsKGCqFTigbKpMnVNCaywMXs/4oXqYSNawoayouO1RFNVlZzYeen+TU68TVhbkD1sLPyeeHLkVsY1WIor4j3zqfPIjv41gHU9bCor4if6AGmJ5jQ8Y+KUVO+pn07qlU/eMxbMXW+5DagelBrNziAqy/iKKhDjTQqzBC35m8FTChs6YePFMbdZjKpsmHvYMCR29H4XMllI9Dk6c+hRcV8bNOwo/h42TBZv4tqikf2yHIyJkAobLyZsKEVY4NC7x0j4kQkbiUTiCSgbD2aFjYsTIf39mnu/VNg0fowmE920dbDDEo2nkp367FS/HFL1YCI4zNR8lbiv2ZVgpr836yagNdUhFO+v1F0nl6FZrJOwNGbmIRRSyeShl7yuAzZOvKlkD28lti4c905LLdEYFTaxMNOU2QxW3TMoS7RkaYnmKDwBO147IRFITklA9x5tdNfZs6sUf/2+26EjElthA9AKSW5LNIZ7EhWIj4ZKDKgM1m6HmsIGANIzjCVsmBU2LONevR42VA87IlbQZ0B7XDy2O4INzifFp2DAmR0w8tLjyWOLhmkczzkedBKqt5yTCZuEBP0YhScVNqTi2TuFWBL3kNE7iUTiCSyzRBM2YWNOCUANZAJBn7B2bxI2WCY6MXExYUP52VdWxLJEs24CQSohiMCI25ZotURlnuFzgoA6NiurjSl1DSBgDxuqMTNnRSWligC808MGsFlhQ02AXbKYsPL5akph45IlGhXcZE3YUJZoVBGAxFp4xsReVNgAB4K4P3y/QXedhXPXo9PRbL1yzeKmEwCLqoRM2HAmzVkS1LyBTZaxEaXW5FFzNxYMK2wYE2os566eIoUaO7A834aMOBr9Tj0CvyzfBp9fQdvDm6BZC2M9qlj2J3TCxsICObNQ55A3FTZ0bEYioZBniUQi8QRUEEZl8CMG3G3maQaV8BCnFDZU5YpEfFisBGLhpqU8lbCJaYlmYVKVWlVvYmi3TRXL56Aqbe0K3FgVfGWBKWEjmiWaxQoblomol7yug8TzhrIa1H0vFbCxcALMc++0cuyQSPT+AnQUNkQQ0q4AAdkvjDVhEyOJ35DkFJmwcZLGrrABgGO6tkBWdrLuOksXbXSs6bWbNkUs6pgaKtHBmRRmSbLzJppZxstUMJWnX2JjITXNZoUNiyWazvlv1fMtLT0RvU45HD36tDOcrAHYrkUrx8tWQyWHnXRoIBU2Jgp93IK08POQcl7iHvIskUgknoBlkEfZBwHiJmyo4C2pBCAmkiwBIInYGK10crMJMNXDJqbCxkJfcR9RIUlNDCkhCRWY1sOKn8VS+6cGkA3ELZx/7i3QT9goCpCdk2LdDi2AbGgcUrlUSCwTUS8pbII2Vko6qSDhSw6LorBx0DKuAeQ9gzVhU6bfwyZFJmwc5VBQ2Ph8Cnr3P1x3nYryGixfusWR43GzEbgVqhLeewzLs42y2dQeA/0dUc9dUQv47MT2HjYmrYBFs5hiSQzyKq6dhHQdcdQSjVBme1BhQyvCvDOul7iH50uuv//+e8PvbdKkCY4++mgLj0YikdgFq+w4COLhR4ybvNrDhu614fnb/SGP0UonNxU2SZTCplKbaKQqkngmEFTsQS8wwhREN1GtbsW9xq7flrRotFJhU1Cq+3pGVpJwyQqWgJSqsv8+LBNRLyls7KyUpN5r5ffEc8/1Sg8buwIEpMKGIYGpqiqDwsabSQGvwlMokurRhA1wwBbt0w9X666z4Lv16NG3ne3HIrrChoI3ac7Uw4ZzmyzPaOpeSRX8NEYM97BhTdgw3E/0xuV0DxtnfzOWxCBl8+wmlBrcyaQlpbSrrnZG4Wglop2vEm8iRASvoKAAP/30U8Sy3NxcnHTSSeR7+/fvb3iSlJaWhr/++gvNmjUz9H6JROIcLDJnlj42IUL/69b4nLyNEbEOSmGTIBU2nsfoRNpVhQ3RbyBW0JEKVvBUgFLJHb3EA4sKIJGhEj4eVvwsdk2myIQNdUPiYO+ect3Xm+SmWbYvq2AJOoRqw/AxJppYeti41ZvFCFQi00ylJBVgcKti0Uqvd5YCi7gKG1Et0RiqjGuqQ+R6soeNs/A8p1I8aokGAK3ys9G2fQ42rt8Xd51fV27H/n3lyLJZ8UldA3YGUa3YNm8Qkq2HDaclGksPG+I55Gaxk1vYrrBh+FL1EzbuPN/iweT+IXDChiq+cjJhQzmBeNESza3xmKRxIcRZcvvtt2Po0KH1/4YPH46yMtrTvCGqqnL/Ky0txbhx42z6VBKJxEoCFlRLAbSVj5Py38gdUxZEsofNoY6TXsJWQSU0amvCmmC1lU0wqeSOrsKGoZrLTBDdinuN0b5GFGTvHwd72IjWvwZgnKRT5uANYOthI8SQnQnqujClsKl2cALMcYlaeXsOBHzkfS7eM59OaNl0z7Cghw2lrgFkDxun4Rl3eNUSrY7e/dvrvq6GVSz+foPtx0HaFNk4FrTi/sBricbUw4ZzrMXWw4Yo4LNpfCUyRhM2rAVlLOvpKVKoALjTSmSm4h2BLdEo5auTCRvqt/OiJZqoBUYSb+H6k2jt2rV45513ABwcoNx2220YMGAA13YUReH+p6oqpk6ditWr9SXQEonEfVgGeSxVLGQTb0Et0ahEU3UV0RxdKmw8j9EKbjd9uBMJSzQAqIyyRaPsA3g+j5nEA9VYFzDXV8SKe41dSTy6H4V1+6Is0cRM2JjzYY+GpdjAW5Zo9iVsnLSY4LlGrb4WqWR3PHsQ+vuxyRLNZB8+4ECfEArZw8ZZeAoL0gw2LBeFnv3akeOshXPX234cIXKeYt++rRgv8qrB/X6FPM94CxYs6WHjwSIpsxhX2LA9V5h62HjJEo1B8S8VNmxQ8ykzvQ/dwsmei5LGi+tnyfjx4xEKHbwA27Vrh4ceeoh7O7zqmjrC4TAmTpxoyWeRSCT2weRHzBAEIhuauxTcJi2IiFgHpQaQChvvY7RZtKs9bBjsa6IrxemKL/bvgaqQ1LsfsPglm7JEM/zOg1hpw9QQ0kqOoR8FC6qqYm8BobDJFS9hw1J5y6NCYuqXJFgfHz3srJSkJsBWJrZ47p1Wq3PJ4EWcIg3XLNGIa4LlnlFRXk2uI3vYOAtPwM7r6qfMrGR0OaGl7jpbNhZi04b4tmmWQI2BbHruA9b0sGFJljREURQycMk7/mVZn0zY2Pg9i4rtlmgM9xO9JAL1/Lfi/OWBTTEksMKGyCU56TpCxSnCIVXo5Fc0qqrS4zGZsJEw4GoEr7q6GjNnzqxXuyiKgocffhiJicYeFjk5ORg6dCi5Xnl5OWbMmFG/3/feew/PPfccgkFvDzQlksYMy0ONzRKN8kRzKWFDvE71jKiSCptGj9FKJzd72LAobKo0ChsqYcO+f7Kvgs5shSWobCb4aUXlmn09bPRfZ2kgzkJZaTWpDswVUWHDUiXKMUlnOde8pLCherCYStg4mJDgUthYfC0mGFbYuBMgsKKHTTmDwsbrSQHPwXENpHpcYQMAffq3x4qlW3XXWfDdOuRfSffZNQoZRLVxTGe0MChiGwbuMQkJft3nAu/zjyXZQj1L3FSnu4VhSzTG5JbZni9U3yHHLdGYFEPiJhn05kCAsyozlqKkmuoQ/MneSHKwzAFkwkbCgqsJm6+++grFxcX1A4+2bdvikksuMby9Nm3a4I033mBad8uWLViyZAkAYN++fZgzZw7OPfdcw/uWSCT2wuJfzGJBQ/bHEFRhQ/X4riZ62JhRAkjEwHC1n5sKGxZLtKi+BdQgl6fii7T20tmV7UF0CyZCdgVuKBWTRfka7CHs0ABBLdFM+rBHw6IO9dLELpigf6xmrC2oqmgrLb+4FDaWW6IZswdxyzKG+vwsSV4WS7RkBtWmxDp4VOepad5XPx1/YmukpCagvCy+2uuH7zfg4jHdLUluxIK6Vux0AuBVx8SCpedoNNRYijcRz2JVRfawkZZozLBeCyxzbL15ei0xP3DcEo3hehFbYUOp+Zz7PlkSNtXVISbnBhFgGtd7qBBL4h6uzv4+/PBDAKhX11x77bWO7fvWW28FcHCC8emnnzq2b4lEwg/LoIhNYaP/umvjc7KHjf6BUwEwqlpXIj5eVNgwWaJFKWyoc50nSGKm6pslqGzKEs2Cn8WK4EosyPwxh92XHpQdGgA0aZpmyb6sxGyVaDTUs0tRnLf6MANpiWaqh42DChIXFTaUSilekQatQLKphw3xtbNYBEYn7zX78CuesgZsDPCMH1JSvZ+wCSb40aNvW911ivdX4tcV2207BvL52ggVNgHiuuZNxLMUOElLNC2paYmGTi/W5wqTIiXOuDwcVslrw+km7laPBZ2GdjRw0hKNQWFjYuzoNCwxKS8VYkncw9WzZMmSJREDwZEjRzq27yFDhiApKQnAgeDQTz/95Ni+JRIJP0wKG4ZBESn/5fFbshCzPWwohY20RPM+RifSblYJsiQ0KiujFTbW2VRQahy9ICKbJZrx68qKiZBd/tK0lZwzCZtg0If0DPFsdlgCOTyWaFRyMBDwuZp45YUKqlNJhXiwBGws7WHDs67VChuDDXhJj3+bKmbJvlcM9wyqh01yctBT10FjgCdondJI+gv1GdCeXGfh3PW27d9NJwBretgYs0TTgzewydT3lFLYHIKWaD6fYijxylo8xKLYiPesYAnW29WjLR4sn4fF/cMtqAI5J5+3rJZoXkEmbCRW4dpZUllZiTVr1tT/3b59exx++OGO7T8lJQU9e/asv1H9+eefqKysdGz/EomED6YqFg8rbMiKdrMKG6JaVyI+Xpw8sii7NAobC4MVVLJKz3okXo+IOhQf3ShX9/0W3GxYbD+MQFs0WpOw2bdHP2HTpGmakAFalu+dT2FDJGw8piqgkibUtRUPloCNlUokvh42lu0WABCketjEU9i4ZIlGXRMsSV6qh01yI1BweA3WSyCY4G806qcjOjZFXvN03XWW/7gZZaVVtuzfyjEQL1YkdI3cY6hnBm8insmVgZg3iTj2cAIjtmjWKmxij51EtI5lOc9CFhU42QF1bE7WsLJc41RxqkhQYzFAWqJJ2HAtYfPrr78iFDpwIiuKgh49ejh+DEcddVT9f4dCIaxatcrxY5BIJGywDMJYqhm82sNGKmwkxi3RLD4QDnw+BYlEH5vKqIQN5d9uqcJGJ6heQ6gAWOT7dmOXwoY6Z1j6UbCwl0zYiNe/BmCrOufxLaeqBp1upGsW6towamvBErAJOlxhW4fV1yKlsIl3ztCWaO4obFgSNlQPG9m/xnlYg9ZpjaB/TR2KopAqm5qaMH5ctNGW/atE00o7EwlW2KwaSZq3apOl/3q+/uvRsCgfpMImNkYSNlxWxcQ5Fu9ZwaZYcHasxNSTR2BLNCo5bNccIxaNzhKNQUkuFTYSFlw7S7Zs2RLxd8eOHU1tz8jg5Ygjjoj4e+fOnaaOQSKR2IdVCRuRBicR+6V2SylsiISNmV4bEjEwWvno1jldB5mwiepbYKWnMhV80LusaggVgNkguhXBALsCCk5Zou0hLNFETdgwKT4J+82GUMlBryVsyB42Bm0tnA7Y8FSXWm09SfWwqYrzzKeUXXYFtKy4Z1A9bJJTZMLGaVifMSmNKGEDAL37064fdtmiUXaadla9W9Fk3MhYtWv3VnFfCwR9OOq45nzHwHDeyh42sTGWsOEYlxt8VjA9/x0u2GBJTvLY4zoN9Vy2S8UfC2rMA0hLNMmhiWtnSVFREYCDNj8tW7Y0tT3KLigWmZmZEX/v37/f1DFIJBL7sMKPGGCo3hfUE426w1GWaI3FquJQxrjCxt1JZ1KSfpAtOvBISvQ5ggHUZ9fraWX3NWXFz2JXwoZU/FlUMEj1sGmSK2rChsEGg8O3nFKOeC1hQ9k8GFbYOB2w4bhILVfYEKrY6ipjPWzsChDQCRv6t6N62KTIhI3jMCdsGpldXdO8dHQ6Ok93nbV/FGDXjmLrd05ZN9tpiWbBto3cY07u0zZukuyK63tyjwVYxolkwkZaojHhDyh89qHEb+MpSzSGhIZVBU52QB2baAobal4mEiy2yNISTcKCayXXdQmbOtLT9b1i43HuuefWPyTatWvH9d60tDQABwMTMmEjkYgLWxUL/XCkJKquKWyI18keNlJh0+jxoiUawK+wsbSHjYmqb2oyb9YSzYpEmm0JG6qBuAWWaLW1YezfV667jpcVNjyTdOpcc7pq1CzUtRGqVREOq9znL0t1pZXJLZ5L1OrEuFGFjVuWaNRvyXLLqCAUNknJjSsp4AVYz+vURpawAYA+A9vjz9W7dNdZOPdvnHdJV0v3S6kz7bTqskJhYyRorigKrrqpN47p2gLfffkXqipr0aRpKoYMPxqdjmnGvT2W70jU+aDb8CZseFWb5Lg8jiKFUiIbORazMMUmOIp3nIYay1thkcgKUw8bDyVsWBKMTn6/Eu/iWgSvrCyyqrKmRn+QHo8PPvjA8DHU1kZOdkpLSw1vSyKR2IvPp0DxKbrBXJaHI1nBmepOBaeZHjbhsEpb6kiFjecxOpF2W2FDJ2yiFDZE4tXShI2OVQHZV8RswsYKSzSbBvtO9LAp3FtGBnFFTdiwXIt8CptGZonGVClZS6rvohHZYsLqiTelsIl3fyKTf25ZojHYwlSUSUs00WB93qam8dsoic5JvfLx5qs/6o4FVi3bannChnou2qn8YJlrURjpYQMcuHf3GdCe7B/Eui0Kqim4URtir8OtsOH8nshnRZwLgKUo0+m5LsulyGOP6zTUc9nJpGUg6IOi6N//WOI8oiBiglHiTVx7EiUmRj4M9u7d6/gx1O2zLrscfUwSiUQsqAE4FcyprQ1rgsPRuGXrYKaHTTXRawOQCpvGgFcboCYRjaKjz19aYWNhc1Pd68pemyorfk27AjfUds0Ec+qg7NAAj1uicXxH1LnmNYUNS9CEpSGr5j1EgA2w1mKCL9lt7bWYQDyzjfewsedcogI7LIqzcmmJJhysAbvklMansElOSUD3Hm1019m6qdByyyPy+WpzEU7AZPJZhEQHy3iZCqi6XezkFtwKG87xCTkuN2GJ5rRiQVEU8vOIrLARySZeURRy/BZv3CMibONV9++VEvFx7Syps0Crexi6mbCpo84iTSKRiAn1YKMCFeVl+sEAAEhzqUqQGhPpN0dnsImRChvPY3Qi4vacM4nTEo3sYWNhI3C9wIi0RIuPBQIb7GFI2OQIm7BhsERjqAatg5rYJTA0YxUJJmsLAxNvliSPlQkJnmCF1b15qYRNvOe+a5ZoVL8whpsGaYkmEzaOw66waXwJGwA4sWe+7us1NWEU7CqxdJ9kI3Cbg9JmbdGMKmyshOUzUImxQ9WuyG6FDdX3JeQhSzSA7vskdA8bQmHjdKEgNa8y2v/QDVgSdV5Tz0vcwbUnasuWLSP+Xrt2rePHEL3P6GOSSCRiEaAaFRIPR5aEjWuNU0lLtPiDKpaKE6mw8T5GpeluVwnSCZvI89dKib6Zqm/SEs3kNWXFz2KF33wsqHPGignovr36CZv0jERh71ssVnTxgg6xoCpH3bL5MgrLJNTIxJup6bCVE2COa9TqwAYVuIiX8HLL5oe81zIkMCvLCUs0Qq0psR7WpGVjTdi0bptNrrNtSxG5Dg9UIsHuMZ3Ze4RXFDYUbhc7uUVaBmfChnN8YnRczqJYcKM4kfr8PGNBp6HG8k73cSITNl7qYcOQsBHhXikRH9fOknbt2tX/t6qqmDdvnuPHMHfu3IhBz+GHH+74MUgkEnaooBU1KCorrSL3Iaolml5xanUVPYChqnUl4kMlLOPhtpVaItGnoqoyMkhHVWJT1XkNIRth60xWqOsqaLJS3ZIeNjb9tuR2LZDYUJZoovavAdjOQRa/9TrIHmQeq8JjUZ+xVMtGI3IPG6sDG9QzO56NHvUd2fX9UNXoLLcMSmHTGG23RIf1vHat2Mlmmualkc/6bZv3W7pP0qbI5jGdWWWJCMqUul48prZxiAZTuS3ReBU2xDMoXhKBRbHgxvOfsmkOi9zDhpxvOXstBwk1OWUfLBJMCRuPFWNJ3MG1s6Rz585ISUmp/3v79u1Ys2aNY/tfs2YNtm/fXv93UlISOnfu7Nj+JRIJP9SDjaq+ZbJE4xyoWgVVMaebsJE9bA4JDE8+BbdEi1aIUZXYLOqGOqjrSs9+jbqfmLWpEtkSjYLF3oiCTNgIaocGsE2yWJqs10E2ivdYwobleGsYnlvRMCVsLLT84rlGra56Z1HYRCtvw2GVPO/sOpdoVZ7+b1dTHSLt3KTCxnmYLdEaacLG7/ehWctM3XW2bdlv6T5phY2lu9NgpurbH1BcV3XXYTZxJMrncBpuS7QA3/dEqfbiFbuwWaI5H9qkPr/IPWzIe43jCRv9389TChtiXO/zK64XVEq8gWsJG5/Ph5NPPjlisjFhwgTH9v/KK6/U/7eiKDjppJO4GhlLJBLnobxpqWBOWSmdsEl2ySOdfmQbVwIA5vttSNzHq9LpRF5LNLKHDfsAl6z61lPYUJZopnvYmHo7APsSNtT3ZkXBoJcVNiy/HY/ChgpUU5NY0bBLYcMyWbfSw57nGrXcEo0oslBV7XnjZgUyfc/Qv69T6hoASE6VCRunYb0GGqvCBgBats7SfX2r1Qobl4OoPEUx0Yg0TjV7Tz5Ug6ncChvOZy51fsUbl7NYormhWDDak0cErJxvWQGlsPFUwsYltbOk8eHqmTJs2DAABxImqqpi8uTJ2Llzp+373bFjByZPnly/34bHIpFIxIWqYqklAmRlhMImKTng2mTDboWNtETzPkYHzjyNs+0giaiKjrZE0+vXBND2Aw2hAhuqGn9/1HVl1qbKiupNuwI35LFZobDZQyVs0kzvwy4URaGrKjkSNtS55kYjXTOwJDNZnlvRUN+polhrxyOywgbQqhNZzjm7ggSkwoYIWlWUM/QYlJZojsP6vE11SZ3uBK3ys3Rf375lv6WNxV23RDNxjxDJ4sfsfM5M4srLJCYGuJSqvN8z9YyOl+CgClvcUndR54mV9waroZSvTn+fpLK4USVsvDWul7iHq0/VCy+8EMHgwUBOZWUl/vGPf9i6T1VVcd1116GysrJ+WSAQwMUXX2zrfiUSiXmoQSE1mCsnFDapaS5OOMn4qFTYHOoYnTy6betAKmwqo4OOVLCCfd8svUbiXVqkJVqi+wobu/ylzSS6WCgvq0YF0WBcZIUNQAcyeaoqqYmd2X5JTsOSzKSe17EgreMCPkvvdyIrbABttSmbZZw9YwEyaEXcL5gUNi4poA9lmBU2jfi3adlK3xKtpiaMgl0llu2PsimyuwiHp09gNCJVjZtW2ByilmiKoiCNYz7Ma0Nq1D6THie5M8+leh2JbIlGJZOc7kdF/YZGrHTdgppDWmnfK2ncuHqmNGvWDOeffz5UVa1Xu8yePRu33367bfu8/fbbMWfOnPr9KYqC8847D82aNbNtnxKJxBqoagSqurSstEr3dTctHUwpbKroAYxZ+yaJ+xidRLs956T6J1VWRvZioCqxeRrBsnz2eD1zSEu0RqywYQlUmBHZUHZogNg9bAAGCyguSzR7zzWnYXneGLG2oJI81icjvKWwYbOMs2fqR90zqCB0RRlDwkb2sHEc1meMqwVPNtMqP5tcZ9uWIsv257YlGm9PkobwNqC3E7Nqn0NVYQPw2aLxK2yMFbtQlmhuOWRQ53zICg9hm6DmW073sKEK4aiiHZGQlmgSq3D9THnkkUeQkHAgSFqXRHnuuedw5ZVXoqyMntCzUlZWhiuuuALPP/98xKQqGAzikUcesWw/EonEPkhLNEphQ1R0u9k0lXYgij+oig7aRBNM8LuuspCYx/DAWXBLNDWsRly7pB0Ix+dhSe7EC47YnbCxoiLfrgkqy1dsRmFD2aEBXkjYUJN09u+HVI54LOHu9/vIYJcRSzQqYGN1xSKfwsbSXRtU2LiXsKGeT1b0sElqxCoOUWF5TikK/Zz3Mk3z0kiV4zYL+9iQChu7EzYmxhVCWaLJHjaG4UnY8D5TqO81bg8bsmDDnXOPVJcK3MOGGsY73sOGmFdJSzTJoYjrT9V27drh3nvvrZ/41yVtpk6diq5du2LWrFmkv6Ie4XAYs2bNwvHHH48333yzfj916pp77rkH7du3t+SzSCQSe7FdYZMmrsIGJhQ2lMJB4g2MBtrcztVRlmgAUNmgjw2lTOCpemSZbMQLJFLV6qb7Qlnwu9j127IkB81MQqmEjT+gIDM72fD2nYCepLOPXWuqxbT6MAOlEKkxYInm9ASY5/qyuhI1kcFyUdvDhr4mbbNEMxiEq6Oc6DGo+BQ5lnEBlgKJ5JSERh3c9vt9aNZS3xZt25b9lu2PKlqxuwDLVMJGIIUNjxo75vvdHjy7SFoGh8KGc25C9rDxmiUace/j6WfoNFSM1elrgBw3eihhQ1nhSUs0CStCnCn/+te/0L9/f03SZv369Tj//PPRpk0bjBs3Dt9++y2Ki4vJ7RUXF+Pbb7/F/fffj/z8fJx//vlYt25dfZKmbh99+/bFuHHjbP1sEonEOqiANZ2woXrYeFNhQyoBPFadLYmN0XGz24GUpCS68rayQXU1FdjjCQiYUYpQqgezfUWsCLrY9duyHJsphQ1hiZbdJNX185aCtMHgSGjR55r37uF2eJGTSiTLFTYuWqIZUdgw2IXYp8qjgnD610MlobBJTg5KpbALsCQi3Sx2coqWrbN0X99qocKGqlO1X2FjfPsiKWzMWpo5bQclEnYqbEg1pkFLNLcUC3RsQlyFjdv2i9EEE/THPY1LYSPOvVIiNkKUKvl8Pnz00Ufo168ffvnlFyiKUj8oV1UV27dvx2OPPYbHHnsMiqLg8MMPx2GHHYbMzExkZWUBAIqKirB//34UFBRg/fr1EUoaIHIioaoqjjvuOHz88cdk01iJRCIO1ESAClZQFZxu9rChyu31hlRSYXNoIFLlIg9JDAqbqsqD5zAV2OOJ2bF8Z/EmU1Qll9lEqBWVa2YrSONul0VhYyphU6r7eq7gdmgA/d2zNIA/uK69yUE3oJQchhQ2lCWKxRNgrnuNxYENIz1sWM45u4IEVJCX7GFDWNYmSzs0V2B5FrhpJ+wUrfKzgAXxX9++ZT/CYdWaZAplCyuwJZpIQUijfR/rEL1oxE7sTNhQv0u8sSX1/DfTe8kMVCzRjFOQ3VhZIGcFtDLbSwkbMXsuSbyHMFG8jIwMzJ8/HyNGjMD8+fMjlDAAIhIw69atw/r162NuJ7riM7oaS1VV9O3bFx9//DEyM/XlzRKJRCzMVrGQChuP9rChKk6oJn4Sb2B08uh2VTKbJdrBwKOV/u0s1WHxri2qx0YCUQlGYcXPYpddAdNmTRQNUgqbJk29kLChenbwWKKJWTlqBjsm3o5bonB5oll7LVKVpkCsHjYMCRubkn/UvVZVEeF0EA3Vwya5EfdIERmW09pNdbpTtGylHzOoqQmjYFcJ8ppnmNqPqqpkXwm7q97NFIKIFIQ0G8A/lIt6U9M4LNE4f3OjPV+ookzXLNE83MPGygI5K6AKfagCVZGgrI6lJZqEFaHOlMzMTHzzzTe477774PP5IoIodaqbun8HBjTaf9Hr1aGqKnw+H+699158++239cociUTiHaiEjVmFDc8A1WrohE3816gBjOleGxIhMDqJdttFhqUZccMeNlTiled7YEloxLIECIdVhGr1j8O01aAlChv3LNEoKwU9qB42XkjYkFWiHJN0MhHhQVtL2hLNSMLG2YpFnkvU6mrsQMBHXt8ahQ1DEsyu5B9LNbveOIZU2KTKhI0bsJzX7qrTnaFVfja5zrYtRab3wyJctbuvhBlbM6Es0UwrbCw6EA+SbmMPG2oMH6//Xy1he+6Wuouyx6WO202o+41U2BiHsun3YiGWxB2EexT5/X488sgj+PXXXzFixIiI5ExDohMz0QkaABFJnOHDh+OXX37Bo48+ikBABi8lEi9CDRz0gl6qqqJMYEs0MkCqM6qqqtIfwEhLtMaBVxU2LD1sGlqiUcoEHushlkrUWIF1lkkBi2WRHlYEeO2y7GCyRDOYsAmHVezdU667jicSNkT1LqtvuaqqtNWXByvxqCSTkYQNVbFodWKL595px7VIPbujVYAsgSG7glosX1W8QBwAVJTrj8+Skxt/UkBEWK6BQ8ESrWleGmlNuc2CPjZ610gdditszNg7UsFrJzHTiwewz3LWC9hpiUYlHOOpPmhLVKmw4UFVVdLRgHBrtxwyYeOpHjZiKsIk3kPYKF6nTp0wa9YsbNq0CW+99RZmz56N5cuXMze5VRQFxx9/PIYNG4bLL78cbdu2tfeAJRKJ7dCWaPEHc5UVNeTARGRbB71bH9W82YvV2RItxhM2Fh8IJ4GgD4pP0b3+GjactrIJJsuEPda4gmVSYPa6suJ3sS0ZZ+M5s39fOXkvbuKBHjZUAQFVXVcHFYQAvDmxo64PI81j6abD7gXY7LgWgwl+XeVJdVSxBsu5ZFfyjyW4qXdvpyzRUqTCxhVYVJwpAo+drcLv96FZy0xs2VgYd51tW/ab3g+TwsbuhI1U2Bx4v9uDZxfhSdjwqjCoYpd440NKQepWYQv17GMdCzoNy73GbB8oXkhLNC8lbATtuSTxHsImbOrIz8/HuHHjMG7cOJSUlGDFihVYs2YNtm7disLCQlRUVEBVVSQnJyM7OxutW7fGkUceiW7duiE9Pd3tw5dIJBZCTQT0Ho6UugZwWWHD4P8ej+igTTRSYdM4MFot6LbCRlEUJCUFdAOPDa19rGyCadTai8Un2WwQ3YrfxWwFaTyMWsmxQNmhAUCTpmmGtu0kRm09omFRc3kyYUNZotnQw8bqCluea9SO2yxlZ6pR2LhoicakytMZyFCWtSzWmhLrYbkGDgVLNABo2TpLN2Gz1QKFTYih95ndQzozCSGRetiYtYy1W8kkMlwKG85ECamwiWeJRj7/3Tn3KEUa61jQaURQ80VD9dz1kiWa0+NVSePFU1G89PR09OvXD/369XP7UCQSiQuQPWx0Bh9lpXTCRuweNvEDHVTFiVTYNA4M97ARYNJJJWwqKw4GHq1sgmnU2oulisusJRpw4LMwCodjv99FSzRWxXM0ewsYEja5KYa27STUJJ3Vt7zRJmyI64MlucD7HqsrbLnuNTYkTxOJ4IVGYUMECAD7krws3xWryjIWySkyYeMG0hLtIK3ys4AF8V/fvmU/wmHVnAJGAIWNmcC3SFXjZu3Z7P6eRcZOhQ1Z7BJPYUMFwF0aJ1HFpKz2uE7DUnTl9DWQkKAfmm5MlmhuKsIl3kKeKRKJxDOQgyKdwVwFg8LGzUknNSnWG1ZRagCpsGkceNmeIZHoY1NVeTBYZ6XCxmjChiWYHCQmFkyY/E3tsitgSQSR3tdx2EMkbFJSE5CcIn4A0CrfcpaEjSd72NhgbUEFbKxObPEpbKy/P1PBC43Chgxo+WxTXLLcl/UCV3oJfQBIlgobV2B5hh4yCptWmbqv19SEUbCrxNQ+WIKodqumzahkGpPCRiZs2OBVClBFA/HGTmTBhksBcOo8MapGtxs9xWsdTl8DdKFPWNjvM5pQrf5xenFcL3EHeaZIJBLPQA0K9YIVpQwKG6F9uHUGVlVEwoaSGEu8gVFvcBGmnEnJ+oHHqsoD53A4rJKKEx5VCcu6RhU2VANiFszGXdxU2Bi2RCMSNk2ait+/BqCTZazfD1MPGw+qJMnmsXYobFysWLQjoU59h9H3KVctY1gUNjo3d6qHjReSuI0RlmeBm+p0J2mVn02us21Lkal9MFW926SSq8NMHxqRqsaNqtLr338IJ2z8fh+SGfuG8aqqqO81ni2gqJZoZvrruglL0ZXjCRuGohuv2KLRChvvjesl7iDOU1UikUgIzAyKykqr9Lcd9FlicWQUUmGj18OmmkjYWKEEkLiO0XigCJPOJEJhU9kgYUNBWVHxrmvcEs38dWX2t7Hrt2Wp4DVa47Z3T6nu655J2FAWnQz2VIC7fUfshLInMWJt4XTAhueea0fyNEj1sKmKVtjof6d2Vr+zqP307u+UClpaorkDkyWayMVOFtI0L40s1Nhmso8Ni9Wo3QobM+MKkRI2PGPFWIgwdnYT1kQs7/iEVKTEU9gIaolGJqAETdiwWLU5bonGUGDqFVs0KrEkkhpRIjbyTJFIJJ6BDJDpVCpTdhspqQmuNmc3s+uqKv1BgVTYNA4URTEWFBTASo1qnl3Xv4Cp4otjkGvU2ovNEs2CHjYm329fwoZex6glWqNR2JA+7GyTdKfUXE5DVUoaU9gQARuLiy547rd2XIuJlEopWmFDfT82nkdmbBRra0KoIY5dWqK5A4vr5qFiieb3+9Cspb4t2rYt+03tw+qiFSOYUdiIFIQ0bYlms5JJdNIZbdF4k3SGe9iQCluXEjbE56GssdyCKTksoMKGsoEXBTfHY5LGxSFddv3JJ59g37599X9fdtllLh6NRCKhoCYCelUspYTCxu0JJ5Us0pvE1UiFzSGD36+gljNQLkC+htkSLZ4VQkN4AqMswYNYPs5sChsLJocmfhw7J1JMChuGyV4sqIRNbq43EjZW9bBhUeJY3ZvFCUhLNEMKG2ct0RSOlKodCRuqT1a0HarTPX4aQvUlAOKPYyg7NMD9MdqhCstzxs3+j07TsnUWtmwsjPv6VrMKG5bxnd09bEzcy8wke6zGbPLIy30jrYC1jw3Lvb8hRnu+UEl9Xms2q6CuF9biHacRITkcDcu8yiuWaJSyyovjeok7uBrFy8nJqf/v4447DvPmzWN+79q1a1FRURHxfl7GjRuHX375pf5vmbCRSMSGCsboBXPKiR42aYJbOugFR6ulwuaQwUhQ0E3lWB1JRHV05f8CjyzBCqs/TjjGoJqyGQSssV8wE+RlqXw2vm36uFjsFKKprKhBGXEvzvFIwoaayLLaYLBMPr3Yw4Y6ZiMJGypg40XrOD0SiWd39LOfOufs/H5YnjPxAkTlhB0aIC3R3EL2sImkVX4WsCD+69u37Ec4rBp+tjP1sLE5iGqm94tIChuzx+K0ukA0mBM2nEk6o8WXVMGGWwFwUmFjYKzsBCxFRY4rbBgKTL1iiSZqzyWJ93A1YbN///76/y4uLuZ67wUXXFCfbFEUBbW1xuRxdUFQEQJaEolEHzJhoxPMoQICKanuTjjJCZjsYSOB0YSNDQfCSSKjJRrLxIZnEm606ptFym5F0MTMb2Nn9addk7S9e/TVNYB3LNGsmqSzTD69WIlHJmyMWKJRChuLLSZ4LjHeKmMWeBU2pGe6jRXILPfDuAobwrIWAJJTxC6qaaxQz5lg0OfJhLJRWrbSt0SrqQmjYFcJ8ppnGNp+LMVvNHYnbMwkOkSy+TH7PYmUfHID1oQNbyGAUUcJNwsS9CDtcQXtYcOikne6hw2L/S+LA4IIUPNIkdSIErFx/UwxkyhRVbX+nxv7l0gkzkIlbPQGc2WCW6IZbWZRWxsm/XGpYLnEOxiZQIrwnEtMYrNEY6ku5UkmMPVViLFLKglqVYDKzG9jpgqWguWoWH6raPY1ooSNGYvOhrD0S/JiJR6VZDIy6aYmwFYntnguTzvus5TChruHjY0BLbsTNilSYeMOxHmd7PbY2WFa5WeT62zbUmR4+26ojKMxk3wWKclhNokuwNDZVeyyRKMKB+JaolWL2ROE+jwhg/0e7YZpvuXwRcBSYOqZhI3DFr6SxounzxQRglASicQ5qGqEkI78tIxQ2KS6bIlGeeXHq7qj+tcA0hKtMeHVJqikJVrlgYCd1Z7KLCqUWPukbAatCgybStjYWPlmJviqB9W/RvEpyM5J4d6uG1BBCtaqSso2QVG8WYlHVUoas0QjFCQWBwt5rk87VGkJRLGFtoeNewECtuR4HEu0cgZLtEMsMSAK1DnjerGTwzTNSyPvbdtM9LERwRLNzH1UpGeV2aIWp9UFopGewaiw4RwPU99rvLm8qAFwP+FPrBebcBMR7jXRsBTDsRQ5iYC0RJNYhTxTJBKJZyAt0XQsaChLNLcTNhTxAh1szdGlwqaxYGTwLMKkk1J51StsGILcPJNwlnVj7ZOaELA0xmTBlCWanb8ry4EZUDfvIRQ22TnJQlXo6kEWEDBaolH38EDA58kCJcrOy5glGqWwsfjc4fja7bAopBI20UkvSm1rZ4CAJZEe75qgFDaKT7Hsnivho1WbLN3X2x6eo/t6Y8Pv96FZS31btG1b9hvePstj1e6+EmaSLgGBnt+yh405WHtT8X7PPiLBEW++K2oAnCqkY1VbOw1TwsbhIkGW57xnFDaktbYc00jYEOepKpFIJATUoFANq/Gb2hKNrt2uEqRiPfEmcdVVUmFzKEFNdGIhQqyXUtjUJ2ws9lRm+ewxFTbEhEAIS7RGqLDxih0aQH9HrN8PWTXq0UA1lTyxwxLN6gmw6wob4rePfv7TPX7sO5eYFDbxxmdUj8GUoCeTlo2Bzsc2Q1Jy/MRh9575Dh6NGLRsnaX7+labFTZ2XwumetgIVDVuNthMKScaO+w9bPi+J0qdHC+xL2oAnO5h411LNDt7ZcaC5TekLKtFQVRFmMR7yDNFIpF4BpYHebzK+FIiYZPqcsLGaPC1irBuAqTCpjFhR2NrJ0hi6GETDqtM/u081wpL4CFWkohM2FhliWZiFGZn9SfLd2xkCkombHLTDGzVHcgeNow2GJQ1mNV9WZyCeu6oYZW78tTpCTBfDxtLdw2AVthE36eoCmQ71WtMSd44CfmKCn2FjbRDc4/MrGRce0vfmOf3gDM74KReh17CplV+lu7r27fsN1TQAFhvC2sEUz1sBApCmv2eDnWFjV0JG6rwLBz2liUaafEW5/O4DZvCxtnv1OdTyGIfKnEnCqIqwiTeQ0bxJBKJZ2CZRMQKANVUh8igGKv02zYohU2cgZVU2BxaGFLYCDDpTCQSNsCBc5nFRspqhU2sa4vqDUUFUlkxUylrd9CGgiW5Fg2dsPFG/xqAfh7VMiYjaojJp1cTNiwqtJrqEPzJ7Pc06ruyPGHD4YlmRzKERWGjqmr9fYSuQLaxhw1Lv7A410QFg8JG4h7de7TBYy8Ox6cf/oY9u0uRlBTAKacdiW4ntzkklU8tW+lbotXUhFGwqwR5zTP4N87kiSauwkYkS1PZw8YcrD1s+C3R9F+Pq7ARNABOJSlFVdiwjOHduL0HEvy6Yz0vKGzCYZX83aUlmoQVmbCRSCSegaVyK9aAjq2hrdgBATOWaFT/EIl3MDKBFCGgkpREX1+V/1PZUPAkoJgUNrESNg4F0c38NG4rbHgriMNhFfv2NiJLNItsMKh+SV5N2LAkB2pqQqRdYh0sCjyrvyu3b51UYlhVDyRp6pJjVAWynecSS0FNvHFMOdHDJiVFKmzcplWbLFz7zz5uH4YQtMrPJtfZtqXIUMImxKKwsVlpbcZKrDEpbA71hA2zwoazEIAaO8V6zrMEwN0aK5Fqa4NqO7thsaB2IwGbkOBHRVn8MUE1g7OI27Ao7KXCRsKKPFMkEolnCATowVish2QZYYcGAGkuK2yooHq8JowsfQCs6rchcR9/wEDCxobj4IVFYVNVWcNmB8IxgWBJasRM2BAVXFZNDM34Q9tpVcBkicZSCdyA4qJKUgHQpKmHLNHIHjasChuq74g3h+osVpyU8rUhVGILsKFikeP6tCO4x9KAt6pB0QZlMWdngMDovRYAKoiiGtELaiSHFk3z0kjbnm0G+9iwPFftLsIJNBKFjdnk0aGesElKDjIl73h/c2rsFCvBwRQAdy1hQ3weRntcp4mneG2IG0UrQWLsyDNudAsWu1+ZsJGwIs8UiUTiGVgebjEVNoTdBgCkprncw8bgqEgqbA4tjEzUhVDYMFTRV1bWMk0geCbRLNdVrCAilQi1zGbQTMLGxt+VqYE4Z9Hgvj366hrAWwobKhjEYu8H0DZWXlXYUAFNgK3goA7KDgVwt4eNLQkbhmd3w+AFpQy0s/rd6L0WAMp1qmkBIJlRhSWROIHf70Ozlvq2aNu27De0bRZlpt2JBDPFICIFIY1YCEe836M9I61CURQmlQ3vc4VWJ2ufY5R6FHCvxyd1PbIW7zgNUw8bF5KWVKEKVeQkAizjVZHUiBKxkWeKRCLxDEYTNmWlVeT7kl223KBiHUYVNopPkYOCRoSR6kUB8jVIYlLY1DJJ9Ll62LBUfccIkFATAhEs0eycSLFsmVdhQ/WvAYAmuR5K2FBBB8ZJOnUP96rChqqSBNhUM3WwTNKtt0Sz9l7DC6/Chvo+7QymMtkoxknIU7a1yanSEk0iFi1bZ+m+vtVWhY2hTTNjJvAtUsLGbABfhGInt2HpY8Pfw4ZIcMQck9PjKVEt0YTtYcNwWHYq+eNB/Y4shapuw6ICYnGNkUgAmbCRSCQewnjCRj8YoChAiuABAaM9bBIT/XLS0Ygw1MNGAFsHlkrxyooay6tLmYKIMS4uarBtlc2gmaSLndWfdvSw2UsobJKSA64rHXmgvv9QLaPChqgcZbEWExGWZAOPwobNEsVFhY0Nz1lehY2bTZnNWKJVyh42Eo/RKj9L9/XtW/ZzPyMBtiCq3WM6M7ZmIlmimR0jme2B0xhIZbAL532uUIm0WGNyVyxRGaHOs1oG5wA3YOoZ6sBxREPNr7yhsGFJ2Ihzr5SIjTxTJBKJZ2BRisTyDaUs0ZJSgu57FdvUw4Yl4CPxDm5J/s3CrLCxWKLP1IvFiCWaVUF0L1uicQaj9hSU6r7epGmqp5LLfsJuhXWS7qYqwk5Yql15Jt4s67r5Xdlx6iYyWC9G9LAhEzb2BbRYArXxFJSUwiYlRVqiScSiZSt9S7SamjAKdpVwb5dpDGTzc9JUwkag55VZSzQRip3chsUSjfe5S32vsZ5jbliisuJVhQ2TBbULCViq2Ien0MctWCyRZX9hCSsykieRSDwDy2As1kCvjEjYsFQQ2Y3RhBGlsGGpcpZ4ByMTSBGC4IGgH4GgT7dfR1WV9Qkb4MB3ppdciLVPWmFjzSTGlCWajck7lnOGW2FDWKI1yU3j2p7bUN8/y2QYoK0+PNvDhuHZw5OwYQvYWGyJxpMctiGwYbnCxkZ7PZZ7Wbz7cAWhsEmSCRuJYLTKzybX2balCHnNM7i2K0JfCTNJF5GKiswG8F0v5BMAOxI2VLFLTIWNpxM29ilswmEVy3/cjHlfr0VVVS3ymqVj8IijScvGuvdSuHENUGNeFrsxt2FRhImkRpSIjUzYSCSHEKqqIhwOc/ceEAcViUn6D7iamlrU1kYmMSorqnXfl5WdqHmP0/j9+p/NH0DMY6ypqdV9X2p60PXPJrGOxEQ/eQ1Eo6phIc6B9IwEXbVbVWU1QrX65zMAqFC5Pk9yik/XniocDmm2pyKkexxJSX5LvlMjv2f9e5N8tv2uqhomjysU43vTo3h/ue42m+alCHGeshIIKLqfJxBQmD6Pquqfa4nJ1pxrTuMjnmkAUF1dw/zZqqtryO3Fe04axe8D+/WpWH+f9fvp/VdWVdfvV/HpX7fBINs5aQSj94xwWCWvgZSUgCevAUnjJSc3GWnpAd2E+/athTiuW3Ou7YZD+tcCAIRtHtOx3Lvjv9fae7AZWO6fuih8Y83GSGZmou53GA7xFw/RxS4es0Qjkhq1NilsVFXFa68sxvffrKtf9udvu/D9d+tx278GossJrXTfb3XPUKtoHJZo4iYYJd5DJmwkkkZMOBxGaWkpSkpKUFZWhlBI/IecHqFQGOeOaqe7TlnlbqxdWxixrEW+qvu+pOQA1q5da8kxGqV5G+geYzDBH/MYD2sZ0n1fQmLs90m8yYl9M3Fsd77G7Fu2bhCikufs81vrDmJTMitQUVNDXuM7dm7C7gL2zzPi4ra6vvAJKWWaa6Tv6U1RW9Mk7nuym8CS62rgkMNQU5Nr6L2JNl7bLPfamtA+rF3LbvlyUr8snNA7vo1Mdk6ip+5VWU2rdb8jn19h+jzHnJCK9p3jbyctw1vfSx2qqv9MA4AwCrF2bTnT9qoqa8nt7d23DUUl1gVt8lrT10EdqrKf+bOwoqr6YxcACKmFWLv2gHrtlDMPQ63O/SS7iWrbuRQO8x1rw/edc6n++xJTtfdoicRtzrvscFRXxZ9XpaVXcp+3lbX0GGj7jk3Ytdu+MV0Y9DHEozbMNy6wk4wmVYY/BwCUVezG2rX7LDwi79H6CB/5HW7ZugHBXQGkpqYiPT0daWlpunZ0RhQpIgfAKUWaGlahqqrlbgc/fL8hIlnTcH8vPzUfz00eqauQYrE1diNh0xgs0ZjOVxsVz5LGhUzYSCSNkMrKSuzduxelpaUIh8VsdmcEpmFDjMhsiBiUuOHRahVUgYzdXtcSZzEy4BflDKDshVRVBewoRFNAbFf7InVdCXFZ2dnDhmnb7D+Wqqqkp7Nb1ZG2wfj1UIpXEU41IygKyGuPR+zLogx20/7Rjl0risLwHaoN/pvanjXHZeW2RbVlkUgogkG/bsLGUGBRAAMEU/cJgS5Vs4ci0EdxDRaLOwVAKBRCcXExiouL4fP5kJaWhiZNmiApKUm7PrHJWHN2oRM2DM+ncFi13C5w+dItcV+rrKjFn7/tRPee+brHROGOwkY/PN1YLNHs7CkoaVwIk7BZt24dBg4cyLV+Q3jeG28bEkljoKKiAps3b25UiZo6FJ+CFq31G33G8j7NyUlBVnZy3PewDLbsJj0zCSlpCXFfjzfAzcxKQlpG/AoambBpXDRpmsrdO0SUxqmHNUvXDbr6/T74GK5x3glE85b624s1yWvWPF03bmLVxPCw5ulcQeuG2HltKwoM3Wv1sHp7bpOaloDEJPPD6NymabrWFCL1BOClBdGYm+c6SkgM0PcGi7+r9IwkpKTGfy43xK7zl+c7dOq+FQtFYRifxdi/3+C4TiJxmya5qcjUmVsYITGJ4T5n85iO5V4bD5Gu1ZS0BLQw8YwW6bO4RWpaon4vNVXVzDHC4TCKi4tRWlqKNm3aIDk58hqhFDaxlB9CW6IxFH2GQyr8Fh/epg366q9NGwpNJ2zcmD9S1111tfg2hSInGCXeQ5iETVlZGebPn8/1nrrAj6qq3O+VSBojjTlZAzBWz6oqouuiyCGJh5Ma9Gdz4igkEhpa6KK9dh3Zb6xDsfwoDm1YklIevg2bgmHK7MBR2IORa08SCc936MXvmuWYD9V7g0Rs2KYk1lshSSROQZ26etZn4XAYmzdv1iRtqISjqh5IJjRcj6VviWsKG4ZCkVAojCCszdhUV+knLqjXmRQ2Lty7EhOpHjbix7hkwkZiJcIkbMw2QTf6fjmIkjQWKisr4yZrUlJSkJGRgeTkZPj9fs+e96qqYtNG/YqSpk3TkRqlVNm2eT9qdPr3ZGXpK3CcYP++Cuwviu9/Hwz40bJ1lmb57l0lKC+P38g9LTURuYelWXGIEgEo2FWCMp3fOxZt8rN1J1VOwXKupqQlYveuYt3ttMnP4aow3bKxECE1/uC5SU4q0jMP2jaoqorNG/fpBhJj3WeMsH1rEaprjFWLJScFkdc8w/QxxCIcVrF5k/699rC8DKSkBJm2V1pShT17SuO+rgBozfm7uk1ZaRUKCvQ/U367+H2Q6ti+dT+qdYIRmZkpyM5x9/lklK2bC1Ebw4++juzsVGRmaS1TYlFeVo3du/V7I+Tn51haEVpUWIHC/Wx9aZo1y0BSMtv1wAM1fsnOSqmv8t+8YR/COneu3Nw0XU97Mxgdn1WU12AXcc9v3SZbiD5sEklDampC2LZ1v+46PM9JgO164B0D8VJdVYPt2/WPIR4tW2UiGBQjvFRWWo2CAuP9dET6LG6yt6AUJaVV2hdUoOlhqUhMCqCiogLFxcUoL498XtYlbfLz8+vt0ZgUKVEJG8pSF3BPEcXybGIJ4PNCJbEoS8Yww3dqtWqZhQDRw6bGCwobgROMEu8hxFPIq8FjiUQk9u7dq0nWpKamokWLFggEhLjULcGv+HUDqT6fL8bnVeBT4g8AEoIB178jf8Cve4w+nz/mMSrw6b7P73f/s0msw+cLwKfw+fcGAgEhEjYBP3Hsig9+n/75DADBYIBr3ODz+6GG4q/v80deW2pYhaL4dXUNgUDs65EXn88Pn2Ks4CTePcEKwmGV/B38Me+1sVHVat3tBfw+JCRYH+y2E78/RH9HDAUSiuKHXuwtaNG55gZ+fwDhcPxrPvbzOt669Pcd4Lw3UPj9+s/liHUD9jxr/QE/dHJeUBrcBxTFB707fcCmY6yDGp/5/dpzWVHo3zUhISjnihLh8Pv9CPgCupaW4VqV65rz+cP0fS4QsDVhEwqB+b4XTcAfFKY3gz9A31v0EOmzuEnTvEwAJSgrO1hw5VMUZDdJQWbWgWKBpKQkZGdno7a2Ftu3b0dZWVn9uuFwGHv37kXLli0PvJel50soDDQIaLMEwP0uBcCZPg+nlTULVC8XyjpM775VhxtFVAlEwsZQbzCHYUnQuXW+SryH6zNAs8oaiURyYDBUUhJZRZSamopWrVoJEai1EkVRuO4bqqqSAyWWah9RIZsMe/ejSWJgLGYlRqCLqnyP5VttyX4513eyubmp3r4297CxklrCwsCLlWZWfUfU+eblQDV17LzPcj18iuLqd2XXnlm/Q1VVSXsxt0+lWL8hOT5z+XeVSOKhKAqCQT+qdAKjeurJmDCNP/g2yYupzQt0qZo+FIE+i5v4fArymmeguqoWlZW1UBQgOSUh5rgtEAigVatW2Lp1a0TSprS0FOFwGD6fj8lCLPq5QKlJFJ/imkKbrYeN9QobKnFBWaJRcy5FcWf8mZCgH56uqfJ+wiYQ9MlxjYQZVxM2GzZscHP3EkmjobS0VDMRbtGiRaNL1gD/m6jojDGi5zrhMB3EEMGGx+gRsASxJBIRoM7FcFhl7HXCeU5Tq0ffM+w4hrgbsmYzolNbqz/B8nuxipXhHFBVejUy6e7hc8TsZ49c2dSheBYyYROuS9gwbcyCI9LbPF9BDcCQsBFgfCaRxCOYoJ+woargRcTM+Eao55VQB+N9EhIDSEikQ4c+nw8tWrTA2rVr65eFw2GUlpYiIyOD6Z4eikpwUAHwYNC9eAdLwVHI4oK0UChMWppVme1h49L1EyB+S5Z+Rm5DJmw8WKQmcQ9XEzb5+flu7t4TlJeXY926ddiyZQu2bduGkpISVFRUIDk5GZmZmWjatCm6du1q+3e5detWLFu2DBs2bEBpaSkSExORl5eHY445Bl27drU9MbBu3TqsXLkSW7ZsQVlZGZKTk9GiRQscd9xxOProo23dtxeIVtekpKR41kKFhMjYRAcLmJrqeTggQFfIePezSbR4+eckFTYslaVG9ku8Hr1XpuMQYaztdrU8x7os1WZeg+3rV8k1SWWZhy96JxU2tjzreDZp089EN2hW6/6D3JaIZ1KsvosNccNDXyJhhbLvqakOQVVV5vuTEHlpE5ecSHMOs0cizifxHoFAACkpKRE9bUpKSg4kbBgUKdGPMzoA7l7RD7PFm4WwJIKrCSUKFR9hUULZAaWwoZRDIkAWqXnY2UXiPI00outNQqEQli1bhkWLFmHx4sVYtWoV1q9fT05mACA3NxcjR47E2LFjcdJJJ1l2PG+88QZeeeUVrFy5Mu56TZo0wWWXXYbbbrut3p/UCioqKjBx4kRMmjQpokojmlatWuHKK6/ELbfcgqysLMv27yUayo4BICPDnkbUIkDOBWIobCg8ERCI8zHIIJaHk1GSWPD/nqLMn6m8vhpW6QCuoQ/DV+bvpCWaGew8AkVRQIgZmSNLqqoi1BirzRh+AJZ8hOg2VmYgFTYcMQx3vif2jdpniab/en2+xoJt2U2s60EqbCReJkgkbMKqitqaMLlePdQQCPaPPxrLFWf6a2osX4RLZGRkRCRs6mIVfiMKG0JV4WbRD0svkhChhuGFRWVC9rARtOCTTILXhLmS4G5A2kB7sEhN4h7ybBGItWvX4uSTT8att96KDz74AGvXrmVK1gDAnj17MGnSJJx88sk477zzsHXrVlPH8scff+D444/H1VdfrZusAQ40u3/uuefQqVMnTJ482dR+61i0aBE6d+6M2267TTdZAxxQ/zz00EPo0KEDZs+ebcn+vYSqqgiFIh/cycnJLh2N/ShUtXK0woahqsXL1nGNuDhbEgMv/560JRq9DTta+GgVNgyb9PDv4DThsEo2N/ViwobpFCDOJVWlk5QiT0oprFDV1a8raHDBbqjPVXdtiZBoNrJ58nf18PhM0vihqsEBA31s3MaUJVpjug83ps/iPNGxiFDogNqMredL5HNBZIsplgQUSxN6Hqj+NYB3FTYsyW0qIeI2IivCJN5DjoIbIbNmzUKXLl3w888/G3r/4sWLcfLJJ+PXX3/lel9paSmuueYa3HXXXYb2W8esWbMwYMAAbNq0iet9BQUFGDFiBCZMmGBq/14jVlLP72/EDwLO4KtnLNF4pUN1S2V1qsQjMAVv3fADidqnk4FPM5txPzDC9mOxTFS9mLBh6mFDfEeNPTloqSUauTPmTTHDtUmbfigre9jYfy7xj2OkwkbiZQJBH1mMUsNh4WOPypgPc+MS647DNCYPRqjP4kFiFUOGw2G2HjZhvh42rlqiMSWgnLdEo3rY0MUSLiVsgvRvSamH3IayRPPknEfiGtISTWAURUHnzp1xzDHH4IgjjkCbNm2Qnp6OhIQElJaWYtu2bVixYgW+/PJLlJaWRrx33759OPXUU7F06VJ07NiReZ/r1q3DkCFDND1RAGDAgAE4/fTTkZ+fj6KiIvz++++YNm0aCgsLI9Z78skn0bx5c9xyyy3cn3nx4sW46KKLUFNTE7Hc5/Ph7LPPRp8+fdC6dWsUFBRg1apVmD59eoTcVlVV3HjjjWjevDnOOecc7v17kViDe/cDefbBGwAigwGK4omAQKxP0dirsyUx8PDPSV1nYVUlA9xGTmfe97DYNFl3XYn7g1INxFlj7ZSVBeDNajOrFDbkfjx8D2e182JB+B42NkH1yzrYwkaAjI2B31smbCReRlEUBIN+VOkEEK1U2MirgR35XblLrISNqqpM9/ToZAKlqAgmuBcAZ+phw1A8ygNLHxcqqUMp36lEtF1QlmjAAVs0kWmMfTsl7iETNgKhKAo6duyIwYMH48wzz0TPnj2RmZlJvq+iogIvvvgiHnzwQVRWVtYvLy4uxpVXXokFCxYwTWTD4TAuueQSFBUVRSxv1qwZZs6ciV69emneM378eNx222149dVXI5bfeeedGDhwII477jhyv3WUlZXh4osv1iRrOnbsiA8//BCdO3fWvOfJJ5/E2LFjI6zQVFXF2LFj0bNnTzRr1ox5/xJvwCtEoXxjvRIMUMPq/yqTDj7kWQQJHo71SWJAWQLGfI8gJwHTc4j0eba+h010kogl8CnEbUOEY2CAmrj4FMUbfcSiYLmsqDOJLcbuve+mDksVNu4Xnuvv36btUkGT+u9Q/HxNTGTCRuJ1ggn6CRuWaniRMHqfcKK/Dg9mD0WcT9K4YBnvRc/dRW7izmIdFt2TxyxMPWyIpA6l+nHr2ctiiVYjusKG6mHjwSI1iXvI9J5AdOzYEX/++Seee+45DBo0iClZAxzwCL3rrrvw1VdfISEhIeK1RYsW4fvvv2fazmuvvYaffvopYllOTg5++OGHmMkaAEhNTcWkSZPwf//3fxHLa2pqcNNNNzHtt47HHnsMmzdvjlh25JFHYvHixTGTNXXHN2vWLJx33nkRy4uKikxbs0nEhMzXRM39qT5QogQKqaMIhVXs3lkaEeBq7NXZkhhw/pwi/fqWVKEZUdhQK7hoiWYG94+ADSph4w/4hPg+ubEgY8N2rjEej4B4XmHDg027J3vY1FuiMZxLdt81DDi7yoSNxOuQTbKrQ+zJaTdsYaMwep9w/R4cjczYCImfoS9Z9HOBSlC4GQBnSRZZrbCxwhKNOia3LNGY+oIJngSXlmgSK/HM2bJkyRI8++yzGDVqFHr16oU2bdogKysLiYmJSEpKQnZ2NvLz89GnTx9cdtlleP755zXJh8ZO3759NYkTAJgxYwb53lAohMcff1yz/KWXXkLbtm3J948fPx5HH310xLLvv/+eOVm0f/9+vPzyyxHLfD4fXn/9deTk5Oi+1+/349VXX0VeXl7E8mnTpuHvv/9m2r/EQ1DVplGzHToYIMZtkCVxVF5ejb0FZfUTP6YAjQx2NCq8/GuynIvkBMLQjvlWJyv5DR9InG259G7TMM4/QwI3izUDy7dP3qNZVBEevoczq0NYEFxhYxdMvb8gSg8bfWIdokzYSLwOVREeVlXmJtlu9OqybB+CXarmD0ewD9RIYJoLRKk/QrX6V0bQRYspP8MYlhoH88KSsKASxdSYwS+0wkbshA11vnp13iNxB6HPlvXr1+OWW25BXl4eevfujTvuuAPTp0/Hjz/+iK1bt6K4uBg1NTWorq5GUVERtmzZgh9++AHTpk3Dbbfdhh49eqBFixa48847uRvYe5VRo0Zplv3+++/k+7744gts2LAhYtmxxx6LSy65hGm/CQkJeOihhzTLJ06cyPT+adOmobi4OGLZWWedhT59+jC9v0mTJrjjjjsiloVCIUyePJnp/RLvwFuxS1ksiRIMSExkc6gsLq5E0f4D1ocsvTYE+XgSq/Dw78nih2yHwoYieo8slfyWVZKa2Yzd5wJ1r2XcDF1p5lFrAEss0VhUER7GUks0sRU2dqlXyDHP/8YBTN+kzd8R+R3E+A1lwkbidZgqwpn72NhQtMKJohi7m7l9D9ZgVmAj2MdpLLAEq0PRPWyocSRDo3q7YFHYRH8es7BYoqmqvjWXuAoblh42YidsRD5fJd5DyIRNUVERbrjhBnTu3BkvvfQSCgoK6ptr1/2rC5hE/4teb+fOnXjmmWfQoUMH3HrrrSgpKXH749lK+/btNct27NhBvu/dd9/VLLvuuuu49j18+HBNz5iPP/4Y5eXljux/zJgxSExMjFg2ffp0rm1IxIecEEQFBLwSDEhIDCAtPZFeEcC+vWUoLanyjHWTxDpst7SxEap5NsCisDHSw4dYgdMSTZRLyuV8DTNk802PVpoxnYuWWKIJcsIZgHq88lmi6b9ux9fEtUmbfiZqjHKwh40A9nqc2w+HVfIaEGWMJpHEIxD0kQUpNQxNwgEIIrGBoZuFaFeql8fLjRmWUytaYVNLBOjd7GHD8oyyWmHDqjDRs0UTNT7CorCh7N7cRuTzVeI9hDtbli9fjuOPPx6TJk1CbW1t3ORMPOIlcWpqavDCCy/ghBNOwK+//urgJ3KWyspKzbLovjbRqKqKr776SrM8ui8MRSAQwPDhwyOWVVRUYP78+brvKyoqwg8//BCxLC0tDWeeeSbX/ps0aYL+/ftHLNu0aRP++OMPru1IBIdXYeORHjYAkJubikSGSj0AKNhdivLyGnI9L9vpSGLA+3MKFOx1q4cN9aZoG0VKuSbMNWX7YRhoSBG9hqrSlmguWlmYgilfo/8decHGygyknRdH1SmZ3PLyF6UDlbA7WKRmflt2E32ILL7+MmEjER1FURAkKqZZFTbC5GuMvEe0S9W0wka0D9Q4MNLzhSr8cdMSjSWOYHUPG9YeLtW6CRv979StuQ51LwX0lUMiQBaqeXXeI3EFoc6WRYsWYcCAAdi0aVNEoqYh0QqaeP8a0jBxs27dOpxyyilYtmyZkx/NMWL17YmlumnIX3/9hd27d0csO/LIIzU9YVjo27evZtmCBQt03/PDDz8gFIp88PTs2RN+P79c0Mj+Jd6CDCFyW6KJcxv0+X3Ia57OVHGuqir27S0j15MTjsYFd77GlqMwBsu5aPWkBuD/DkS3XqrDA/ka1NaGydW8q7BhwAqFjYcD1rQglidhY25fhhDgqye/Qxz4btjUWtYck+HtR6sZZcJG0kigqsKt6rng2NXQCDI2IjvOHsoYKd6iA+AuWqIxxBFCIYsVNowJYL3EDjXfYvlcdsCisNFLRIkAnWCUlmgSdthKuR1g06ZNGDZsGEpKSjSJmrpJSG5uLnr06IETTzwRhx12GLKyspCdnY1wOIz9+/ejsLAQu3btwtKlS/Hjjz+isLAQwMHgSt3/79+/H0OGDMHy5cvRsmVLhz+pvTz11FOaZWeccYbue2Ilr3r27Glo/7169WLavkj7l3gLsto0KiJA+cb6BVLYAAcGnc2aZ2D7tiJLgtcC5aMkViDYhJgHRVHgUxSEdQKLZMWXoSAC8Xp0ktfBwLC55I/d/Sj0Ybk7UZMWwMs9bOjvn+5hY8luhIVWh7Bvi1rVHvsb9m3a9TOx3CNYFDbOnEZ84zPqfg+IVVQjkcSD6rtQ1wDcdMGHYwobBeyd6v73HtGeVaIdjwTAgeJEilBUsSVlMeVm4Y+fYd9U8SgvrAlgXYUNcUwsNtZ24Pf74PMrusfHqjByi8ZqBS1xB2ESNpdccgkKCws1iZqEhARceuml+Oc//4njjjuOa5vLli3D888/j/fffx81NTUR2y4oKMCoUaMwd+5cyz6Dm9TU1OCWW27BN998E7G8adOmGD16tO57//zzT82yI444wtBxtGnTBoFAALW1Bx8Qf/31l2P7j6UmovYv8RZWV3CKWL2ZkBhAXrN07NxezDld0iKKGkBiDV7/NRWfAugMwukkpfXfQPQevaKw8cLJQE2yAbbJrogwnQbEucSkivDCDx0HVjsvpmuK6m/l9tdk0/5ZFFax3AU02yEspa2Ad+ssRSleVphJDh2oqvCwqqK2Jkyux5PEthMjtwrRnlVmjkeYcV4jhElhE93DRuAAuM+nQFH0r12rFTbV1WwKE73EBnWvcTM+kpDgR2VF/M/IMrdwE+p89eq8R+IOQpwtb731Fn744QdNsua0007Dxo0b8dprr3EnawDghBNOwH//+1+sX78e/fv3r5/M1O3n+++/93xT+o0bN+KVV17BMcccgwkTJkS8pigKJk+ejNTUVHIb0eTn5xs6Hr/fr1Etbd26FTU18XttWLn/li1baqzU/v77b0PbkggKQwCojnBY1a3mB8RM2ABAckoCcg9LM7UNBXLS0ejw+M9JNealEqzGWtgweAo1/NMjCRvXLdEYoPrX+H0+Ye/BVkBNiJlUER7+ejgvPVPrun9d2rN/lstDDYO3GN4eeNWM0hJN0khIYOg/ydbHhhoDidzExvKjMIdoxyMBwJiwiRocUT1L3LREA+g+NtYnbNgSFlUmeti4qW6lEtvCK2wEVoRJvIfrZ4uqqnj00UfrJ1p1gZI77rgDX3zxBZo1a2Z6H61atcI333yDW265JSJpo6oqHn74YdPbt5tevXqha9eu9f+6dOmCI444AhkZGWjXrh1uvPFGrFmzJuI9CQkJeO211zB8+HBy+zt37tQsa926teHjjX5vKBTCnj17HNm/3+9H8+bNI5bt2rXL0LYkYsKjsPG63UZ6RhKyc1IMv19WpjY+vP6Lkk3IyQ0Y2Ce5RuReVeK24XpcuA6Xj4OlErgxN95kOQ/++/Zb9cqGun8Ni1REUEXYCaudFwv0d8W0Gdtw3RKNCvS6/QVBe3+nEjYHik5sOxyJxfTv3z/iXte/f3+3D8kxAkEfWZBSw9J3wXmRcZzd8O9IhHtMQwR2nD2kYbEijy72qa0lAuAujyWpWILV/TlrGRMWetZp5PPXxWuASoALn7ARWBEm8R6uW6J9/vnnWLduXX0CRVEUXHDBBXjiiScs3Y/P58Ozzz6LLVu2YObMmfWDijVr1uDLL7/EmWeeaen+rOSXX35BWRndXLyOgQMH4plnnkHXrl2Z1t+3b59mWVqa8cr+WO/du3evJpHi1P6rq6tRWlpqapt17N69GwUFBVzvWbdunen9Sg5CTSIaBi1YPGOpqhi3ycpORm1NGCUlldzvpSaPEg/C8Jtu37ENa9f+iZ07t6OkpBhp6UFkZ2cjOzsbnTp1wrHHHqtRIjqFiNXS3JZoVn4GU5uy297I/PapSUt1dSXmzZuHv/76C4WFhVBVFZmZmTjyyCNx0kknITMz0/Qx2AeL9IGyRDO/CzcoLy/Hr7/+io0bN2Lnzp0oLy8HgPreks2bN8fxxx+PYCCJ3JYaBlP5GJnYseF5x7VFFy3RwjF62JSUFGPVquXYuOlvlJQUw+/zoV37lujYsSNOPvlkJCcnW3+s1AqcChufzydcEFgiiYWiKAgG/ajSsSpiUdiIIJQDYFNxjLXs3bsX69evx5YtW7Br1y6UlZWhqqoKaWlpyMzMRIsWLZGb0xbZ2Tnc2+b5LMXFxVi+fDk2btyI/fv318ccsrKykJ+fjxNOOAEZGRncx9BYYelhE/08IwPgDNu0E79fQXwvGVptzosVChvSMt7F7zRIKKZqGC3h3IJO2Hi0d6fEFVxP2Lz77rsRf7dt2xaTJ0+2bX+vvfYali1bhk2bNtUvmz59utAJG1aOO+44vPzyy+jbty/X+2Ilg8xM5GK9t24y7+b+rUjYTJgwAQ899JDp7UiMQ1qsRChsvG+3oSgKcg9LRW0ojIryar73uvzZNm7ciHbt2nG9x+fzIS0tDRkZGWjZsiW6dOmCE088Eeeeey5ycvgnXXW0bds24r5vBYWFhcjKyrJ0mxSxftH169fi+wXfYcmShVj60w8oLi7S3UZqair69++P6667DkOGDLFUZbZjxw4sW7YMP//8c/2/aJVjy5at8f285Ya2byh4x9v3ykFLNDcKQKurq/Hbb79F/Ea//fabxrr0madexogRF8bfEIMyIt6k5ZdfVmDylJfx3dyvUFkZOxkdCARw+umn47bbbsOpp55K7stprDgN4p1rS35chEtHjTC/gwYMHz4cH330keH3r127Fu+88w7mzJmDlStXIhTSDxgoioIOHTrg+K4nY/iw83DiiT1jXjvsChv9122J6wswPGBS2IQPfj+LFs3HlNcnYPHi7yP6STYkKSkJw4YNwx133IHu3btbebD6xxl1syUTNoIX1EgkDQkm6CdsWBuFx+OOO2/ErA/fM7WNOs4880x88cUXcV83dD9t8J6Kigp07dpV4wACAJdffjmmTp3KtemysjIsXrwYixYtwpIlS/DLL79gx44dTO9t2/ZwDB82EuedexFatmR18ND/AoqKivDf//4Xb7zxBlasWKH7HFMUBV27dsXYsWMxevRoQ/MGI/MpPX744Qf06NHDsu3xwFJMGG0hVkNZTLlsieYnkhshixU2rAmbap2EDXVMbhZ9JiQSCRvCIs9tSEs0D7sLSJzH1YSNqqqYM2dOhLrmjjvusCSwHo+MjAzcfvvtuPHGG+v3+8knn9i2Pyf55ZdfMGDAAJx77rm477770KVLF6b3xeovk5REV0XGI1bCpLo6fqDZ7f1LPIbFCRu/4Akb4MBgPy8vDTu2FetOBrXvs/GgbCIcDqO4uBjFxcXYunUrfvzxR/znP//BDTfcgHPOOQdPPPGE4R5XjYL//aaVlRWYPGUCPvv8Y6xZ8wfXJsrKyvDpp5/i008/xdFHH42pU6caDtotWbIEX3/9dX3gf/v27Ya2Yye8lwHZiFOU64rxOEpLS/Hee+/V/0a//PKLY8/EaBuLqqoq/PuxcXhn+lQyUF9bW4vPP/8cn3/+Oc4//3xMmTJFqCpVRTmgQdL7FNQTSJQG03r89ttvuPfee/HJJ58wJ1eAA2P8v/76C3/99Rfefe8ttGzZGqNHXYnLRl+FxMTEBuuxb08Pt5UYdu2dqYeNqqKoaD9uuPEafPElPaeprKzE+++/jxkzZuCGG27AM888g4SEBAuOlg9aYRP/w48ZMwZvvvlm/d/5+fkxe2KKzrx58zBgwICIZXPnzj2k7MQaCwlE34Wa6lB9vCMugjwTzFqi3XfffTGTNUb573//i3/84x+G3rtx49944cUnMWHic7jqyutxw/W3IjmZsJvW+fjvvvsu/vnPf2L37t1M+1dVFStWrMCKFSvwyCOP4LnnnsOll17K8QkaFywN16MdMkK1+heG2wFwqrggbHEPGyohUIdeYodW2Lg3pqIUNtUeV9hQCT6JpCGuni2//voriooOVgJnZmbisssus32/l19+ecSkv7CwEL/++qvt+zVKaWnpAX/q//2rqalBQUEBVq5cicmTJ+OMM86oHySFQiHMmDED3bt3x7///W/D+zQz8TVTQSnK/iXiQlqiNfitqSZ/CtxXobDi8/uQ1zydy/fU7QCWlVRXV+O9997DUUcdhbfeesvtw3GdPXsK8PwL47mTNdGsXr0aPXv2xMsvv2zo/ePHj8f999+P2bNnO5KsMXZGU/eM6L/FDgzXwXoU69atw1VXXYVJkybh559/tixZQz1VQ6FwREC2vLwMY8aej2nvvMH9TJ4xYwZ69erFbUnqOsTHFHlsUlNTg7vuugtdu3bFnDlzTB/rtm1bMP6JB3HaGT3w+eez65czb9cNhQ0PLlqi7dq9C2cOOpUpWdMQVVXx8ssv48wzz9RVwrPC02MQMJewkUhEg2qUHVZVsnm6ME8EEwqbhQsX4oUXXrD0cKygpqYGEye9gAsvHop9+/bqrhvv49999924+OKLmZM10RQUFGDUqFG44447DL2/McDyrA6FOXvYuGwx5SecCkIMFu086FmdNURPYSPy85e2RPN2Dxvq80kkDXFVYbNkyZL6/1YUBeeccw5SUow32GYlNTUV5557boQcd8mSJTj22GNt37cVBAIB5ObmIjc3F126dMFVV12FlStXYsyYMVi1ahWAA5Wp//rXv7B37148++yzutsLBoOaZRUVFYaPL9Z79Sr3gsEgqqqqNNswqrTi3T8P119/Pc4//3yu96xbtw4jRoywZP8SvqAMy2BElOArC4GgH82aZ2D7tiI2uzcBP1tqaiqOOOKIuK/X1NSgqKgIO3bsQDisHfCUl5djzJgxCIVCGDt2rKljYVUhxsONPjAs52t+fjucfFIvtM0/HLlNmyK/bR727duHlStX4rPPPsOWLVsi1q+trcVNN92EhIQEXHPNNXYdujUYcUTjbKzgbHNzExtz+/ombkENPbtVVcU/b7kGS3/6QbNey5Ytcemll+LII49EYmIiNmzYgI8++ggrVqyIWG/16tU4++yzsWDBAlfUALGoU2obhee92dnZaNOmjeF98dipFBYWYujQoVi0aFHcdTp16oSBAweiVatWyM3NRXZ2NioqKrB7926sWbMGixYtwurVqzXv2759K/779msYPHgYAB5LNNETqfbsn1JyVVVV4dJRI/DXX9rkfbt27TFk8HC0adMWgYAP+wp34L333tP0Vpw3bx4uvfRSfPjhh9Z/gAZo+oUJHDCSSHihGmUDB/rY6Cd22J8JwWAQRx11FPP6DWnfvr3u60bdZ8vLyzF27NiY43crSUxMRNeuXdGpUycceeSRyMvLQ3p6OhRFQXFxMdavX4+lS5di/vz5GvvO1at/wejLz8OsD76MUHtqPkwUTz/9dNweyz179sSpp56Kww8/HOnp6SgpKcHatWvx7bffYunSpTG31bRpU9x5553cn72O1q1bG7aKTk1NNbxfsyiKAsWn6N7/o18jLdFcbuJOqYaoAlJeWBU2eomdMDGmctcSTf9eymoJ5xZUzyK3FWESb+FqwubPP/8EgHp58Mknn+zYvnv27ImpU6fWT/B+//13x/ZtB127dsXChQtx2mmn4ccff6xf/txzz6Fnz566SYZYSTKrEzZ6A4OUlBTbEzZWDUwOO+wwHHbYYZZsS2IQyiO9wQCE9kf33gMzITGAvGbp2Lm9mJzWiage6t69O+bNm0euV15ejiVLluC1117D9OnTI35XVVVx0003YeDAgabs0VauXGn4vaJx5BEdce65F2HY0PPQrFnz+uV+vw/57Q5O6Gpra/HGG2/g1ltvRWlpacQ2brrpJvTv3x8dOnQwfTzJycno0qULunfvbli9ExMbJhDaICJ1CBYeg5l8jQW7b9OmDbp3746CggIsWLDA0h00rDB7679T8N3crzTrPPjgg7j33ns1hSP3338/PvroI4wePTriPF26dCnGjRsXN2jiOIQn2iWXjMZ1/7g67us8uZ5hw4Zx+/4bobCwEP369cNvv/2meS01NRW33HILrr32WrRuTfcCWLduPSa8MgVvT3sdBQWxK5JZrd1d6WEjCHrBrWee/TdWrIzsCRYMBvHgA0/gwgtG1d+vkpICaNEqCw8//DCmTJmCG264IaLHzUcffYSJEycath1iQipsJI2YQNAHn6LoBkJrqmqBVJ2CA45nQosWLWwcxxqzRLv77rsjEsInn3xyRFzCKIFAAD179sTgwYNx+umno1u3bkyFG0t/WI2nnvk3Ppg5PWL5n3+uxksvP4Xbb/tXzPdFf/qNGzfi/vvv16zXqVMnTJ06VTeGtXDhQowdO1aTKH/ggQcwcuRIHH744eTniMXDDz+MMWPGGHqv2/j9Cmp17v/RihRKmeZ2AJy2RHOnh42eEoU6JjdjCEHi92RNWLkFrQjzXvxJ4h6uni3R3qZOJmxOOumkiL/Xrl3r2L7tIi0tDe+9956mh8sdd9wRt/EoADRp0kSzLDqQx0Os98bah1P7T0hIsLUvksRZqKBMRA8bYjDi1WBAckoCcg+jz2nKT1tkUlJSMHDgQEybNg2ffvqppq9VWVkZHn/8cZeOzj2iz/8ePfpg+rSP8cXnC3HN1TdGJGtiEQgEcPXVV2PRokWaxqfV1dW47bbbuI8pMTER3bt3x3XXXYcpU6Zg5cqVKC4uxg8//ICXXnqJe3t6GLpiOW16nKzkN7Ulzje3aNECw4YNw8MPP4zPPvsMBQUF2LRpE2bOnImBAwdy74CaftZNWPbt24tnn9Neqw+Mewz3339/TJUvAIwYMQLfffedJijz3HPPWeqNbwaGdvD6rwpmiVZbW4tzzjknZrJmxIgRWLduHR599FGmZA1wQNVz4w23Yf7cZbjt1ntjq+gtU9gwbcY27Nx/vHvOunVr8OZbkzXLX37pdVx04eiI99XZyfp8PlxzzTX44IMPNO+79957UVhYaPlxHiTyN6Sq8EUsOpFI4qEoCt17gQg0ivJEMHI/++GHhREFOkceeSQeeOABS47nqquuwuLFizFu3Dj06NGDWWV7WF4zPDH+Rdx5+zjNa2++NRkVFXGsIKO+gIkTJ2oKQtu3b48FCxaQ8as+ffpgwYIFaNu2bcTyyspKTJw4kf4QjRBq/h2dzKcUKsJbolmsOGNN2OgqbAQumAgSakVWSzi3IHvYyISNhANXz5YdO3ZEDO6POeYYx/bdcF+qqgrZKNkI+fn5GD16dMSyTZs24csvv4z7nry8PM2yrVu3Gj6GaLsdn8+H3NxcR/YfCoWwY8cOcvsS70IGBHgUNh4OBqRnJCE7R99CMlmvis9DDB48GA8//LBm+UcffWS77YKopKdn4J23P8K0/36Ik07qFXe9eGf4cccdhylTpmiWf/7559izZw/zcbz00ksoKSnBTz/9hIkTJ+LKK69Ely5dEAi4KuCNgPcqJwPDHhtnd+jQATt27MC2bdvw8ccfY9y4cRg8eLDuc5kN/e+priLyzbcmo7S0JOK1Pr3748orriPv5yeeeKKmqrWmpgbjx483cLw2QCo+9d8uWL4Gjz76KObPn69Z/o9//AMzZ85Es2bNuLZX9/smJibh+n/8H2Z//B2OOSbSipJFYaOqKh3ItCFj4nYSqI541iQTX31BU5B14YWjcdqpg7QrR21i+PDhuOqqqyKW7d+/3/Ike0Oif8PGPEaTHJpQfWxE771glPLyMvzz/26oHz8pioIpU6Zoikidpu4Ocu21N+P4rt0jXisvL8e8ed8wbSeWXeQzzzzDPI5q1qwZnn76ac3yjz/+mOn9jQ0yYROVoBHfEk3/84RqrR3ssSpMqqvir0fNddx8/iYkEvdRQnHlNqQizOUEo8RbuHp3a9iwLTU11dF+AH6/H+np6fV/e66RrQ6DBw/WLNOzIIrla75p0yZD+w6Hw9i2bVvEslatWsWtoLV6/9u3b9dMXnl82yXiw6WwIYL5PqIiRnSyspORmRV7MpSVnYzk5PjXnde49tprNfeRXbt2NZpkOyt1AdDMzCycfHJvU9s677zzcNxxx0UsC4VC+Pzzz5m30bp1a937u9UYC6DGftPWrZtx1tD+mDDpxYjziAogu98r4wCsh5GSksIdaAcYEl3E91RbG0YoFMJ77/9X89odd/yLeYJ9++23a5S406dPR1FREdP77cT0mSBQxuavv/7Cv//9b83yCy+8EBMmTDD0vIw+R9u1bY/33/2kvn8NwKYyYvmaGl6X//3vf3HGGWfgv//9rynFtl19aXiJlSQuKtqPzz6LDPYFg0HcesvdsbcR44bxyCOPaO7f//nPf4wXQnCqGemEjbfHaJJDD0rZXlMd0r/nCfJI4B3njH/iIWzatKH+7+uvvx79+vWz+rD4afA5hg07T/PyunWx1boNP30oFML69esjXs/IyMBZZ53FdSjDhg2LiD0BwPr16zU9dg4F/IQleTjKBptKeLhuiUY8q6wuLmRV2FR7VmGjfx+tFTzxTSls3E4wSryFqyWwJSUHKy4zMzMd339mZmb9RK64uNjx/dtFtOQWAP7++++463fs2FGzLNpnlZXNmzejpqYmYlmnTp1032Pl/qMHVCz7lzQuGg4/SEs0wnNWdBRFQZPcVCQnB1FcVImamhCCQT/S0hOQmhankaZHycjIQIcOHTRNrHfu3IlWrVq5dFQegDjFBw8ejF9++SVimd7zwpPofAd//rkajzwyDv/+9wMYOHAgRo8ejROOPwXJyfH7nonSw8b2YLIFPWyW/LhI07vkuOOOxzFHd4GfcYKdmJiIMWPG4JlnnqlfVllZiQ8//NB9/3aOAgIjrzvJuHHjNAUvLVq0MGXZoigKFEWJCFAmJiZh9Kgr6/+mGs8fWIleJzrA9vXXX+Prr79GamoqRowYgdGjR+O0006zpThMgb2J3Fjb/vKrT1BdHdn/8fTTBiM3N3afxViH16xZMwwbNgwzZ86sX7Zt2zZ8//336N+/P/9xcq7vFdvazZs3Y+XKlSgoKEBBQQGSkpLQtGlTtGjRAj169HBdRUARCoXw999/488//8S2bdtQXFyMUCiE7OxsZGdno1OnTjj22GNlgswCqEBjWFVRWxOOux6DltAZOC69xT8swDvTp9b/nZ+fL4wKtuHHaNOmreb13QW7yDcWFBRoAu5HHHEEt5I8GAyiffv2EX2HwuEwCgoKDBXVeBnq3t6waTvVwB1wX7HgJ+IJLJ+BByss0aixl7s9bPR/T5Et0cJhlfxuZcJGwoOrCZuGjeajvfSdIDMzs14NUl1d7fj+7SK61wMQmRyL5oQTTtAs++GHHwzte/HixZpl3bp1032P2/uXeAsqKKKqKlRVhaIoZPWIX5BggFlSUhOQ0kjsz/TIyMjQLDvULNGsjgm2adNGs2znzp3W7sRCjARFWd4RDofxzTff4JtvvkFycgpOP20wRow4H31699cEeEXJ17h996LCSqHaEL7//jvN8kFnDgXAN2EZOXJkRMIGOGDf53bChjof35n2Fm646bqIZRs2bKgvrBGlh82WLVswa9YszfLx48cjOzvb1LYVRT/fwvIVsClsYi8vKyvDtGnTMG3aNDRr1gwXX3wxRo8ejeOPP57eqCDEOs9iXluDhnJtAzhwbTVM2AAHri0jCRuKhr+jqqq6zdkBbVCvbdu2cRX4mzZtYno+zJ07l+mz7d+/H8888wxmzZqF33//Pe56SUlJ6NevH6677jqcc8455HYB4MEHH8RDDz0U9/UBAwaQ27j88ssxderUuK+vXbsWs2bNwnfffYdFixahrKxMd3uZmZk4/fTTcfvttzvaU7axkUD0XgAO9LGhEjtuwzq+KC0txd33/DPiWfaf//xHnN6xDT5Iw7hTHfEV4gffGKtfDmsPnWgSE7XFdLHiNo0dqmCy4fydUisA7gfAqWR3iKUwhYOaaraEhZ4FY0jggglSqchoCecGLHZ1ASIhJZE0xNW7W8MkiZOWKnU0rIxoTAmbhlZzdeh5rHbq1AlNmzaNWLZmzZqY26FYuHChZhklie7Vq5cmIPbDDz8Ykggb2b/EW/AES6kmf6JUb0rYiNVbJfreJeGjvFzbcFX0amFuOC/ziopyzJ4zE1dceRF69TkW/35sHH5bverg5gSxRHM9Y6ODqqqorQ3j55+XaF7r3v1AMJCnIvKEE07QNKz//vvvzR2kA1BTdEHyNXjzzTc1Y67c3FxccMEFprfNUmRBwbIOy3W5c+dOPPfcc+jWrRuOOeYYPPHEE6Z6NjbYuflt6BBrrPJTrGvrhB7xNxLnEPv27atZtmDBAuZji9gF+T2w9xgE3BujvfTSSzj88MPx6KOP6iZrgANqv6+++grnnnsuevXqhV9//dWho4zN3r170a1bN3To0AF33303vvrqKzJZAwBFRUX44IMP0KNHDwwfPhz79++3/2AbIYGgL27PqTpq9KrDBXkmsI5zHh//ALZtO9i79oorrsAZZ5xh12Fx0/Bz/PrrCs3rsVQ3QOTtMjs7W+MEE92vl5Xo9+Xk5LhStOw2tIUYZ8LGbUs0KgEVsra4sKaabXu6ChuBe9h4uReYFxKMEm/h6tkiSmVhY2PJEu0kTk9qqyhKzMFVrGpLPWprazXN85KSknDKKafovi8zMxM9ekROMktKSvD1119z7X/fvn2YO3duxLI2bdqgc+fOXNuRiA3LJKLu1kL6s3rcEu1QYufOnRqrxMzMzJgWkI0azsCgQkT1Y9lPNm/enGsfjmLhJZuVlYPzR14SU7lVx549BXj9jUkYPuI0DBrcB5NefQHbtlsQ4K3Hw/cgndtrqDaMmtpa/PFnpIVhMBjEscd0BcA3YQkGg+jePbJh8M6dO7Fjxw7mbdgB2VONeL8o4+DZs2drlo0ZMyZmNTAvPH3n4q7DuZ/u3bvjrLPO0i0GW716Ne6++260adMGAwcOxBtvvKG1R2a8PO2+iqO/w507d2DPnsjemy1btkJens5YP85RtmzZUqO0XLVqle19FURM2IRCIVx77bW4+eabUVhYyP3+H374AX369MF332nVT05RUlKCFSu0gWkeZs+ejZNOOsmaZOYhhqIopJ2PyNXh9TBcegsXzsO7771V/3ezZs01SlhR2L+/EO/PeEezvG+fOGq2Bp9fURSN6m3btm347bffuI5h1apVmr6bp556Ktc2GgukhVgD9QdLcN5tS7QA0ZPHLYVNdZWewkbcolZKqchqCecGMmEjsRpXLdEk1qOqKt59913N8p49e+q+76KLLsK0adMilk2aNAnXXXddnHdomTNnjmYgMnz4cE1VbLz9L1q0SLP/QYMGMe//zTffRGVlZcSyiy++mPn9ksbDAVs02dC2MTF+/HhNcHPEiBHiqB0cwspPGyvJDgAnnniihXuxFiOfP16gMi0tDeMffwFPjH8Wy1cuxLRp0/DZZ5/FtM0AgLXr/sJTTz+Kp5/5N0455RSMHj0aI0eO1E342In7p378+2ttbRjbtm1BVVXkM7l5s5b1ViK8E5b27dtrVDV//fWXqwlGKiFKIUK+pqSkBMuWLdMsHzhwoCXbd0ph0/CCOOaYY/DJJ59gz549eP/99zFt2rSYlrl12547dy7mzp2LG264AcOGDcPo0aNx5plnCpNOjf4O1/+9VrNOu7aHE9uI/1r79u2xefPm+r/Ly8uxZcsWywsiIizRDCRsjjrqqPpq9M2bN0ckVYLBII466ihym3pWTddccw1ef/11zfKkpCSceeaZ6NevH5o3b46Kigps3LgRH3/8saYHXHFxMQYPHozvvvsOvXv3jrmfZs2aoUuXLgAOWEpF999s3749aSkVy840FmlpaTjxxBPRuXNnHHnkkcjMzER6ejqqq6tRWFiI33//HXPnzsUff/wR8b61a9fiwgsvxPz587l7dRzqBBP8qNIJqOpZEfE8EsrLy/H4449j4cKF+PPPP1FQUICqqirk5OQgJycHHTp0QL9+/TBw4MD6840V6t5XUlKCu++9JWLZM0+/IJxaRFGAvXv34LrrL8PevZFJ7lNOORXt2x/JtJ3rr78eH330UcSye++9Fx9//DFjIaGKe+65R7P8pptuYtp/LObNm4cff/wRP/74I3bs2IG9e/ciJSUFOTk5yMvLw8knn4x+/frhtNNOc22cGg+qP0pDu+vaWpaEjbvzeT+xf6pfGw+qqqKmhk1hU62X2CEOyVWFDaGYElphIy3RJBYjR2CNjMmTJ2sqqxITEzF48GDd9w0aNAht27bFxo0b65etWrUK7733Hi688EJyv9XV1XjggQc0y//xj38wHfeoUaNw7733RvTamTNnDhYvXoxevXqR7y8sLMRTTz0Vsczv9+Pqq69m2r/kwMNv1874vY5EoaqiGrt3l+quU1fpsmtHse564VAYScne7f2S1yxdeB9ss6iqimeffRYvvPBCxPKEhATceeedLh2Vi/COn3XWnz17tqZfTU5ODvr06cN/XI5hYAJBvCUhIRHnnXcezjvvPOzZsxf/efUtzJ79AZb+9EPMQLGqqpg3bx7mzZsXEeAdNGgQf2DL1HzI3XCy3lyvtjaMrdu0liEtWraq/29eC4tYAcq///7bll4bzLA7QMV+WYCMzcqVK2P2AjvppJMs2b41CRuW/WiX5ebm4vrrr8f111+PDRs24J133sG0adM0wek6Kioq8N577+G9995D06ZNMXLkBTjjtOE47jh3+91Ef4fbtm3WrNOyZWuubTQk3rXFm7DhSSIbUdh89tln9f89ZswYvPnmm/V/t2jRIqKZNy/vvfdezGTN8OHDMWHCBLRo0ULz2sMPP4xPPvkE1113XX1PVODAnGjUqFFYuXKlxkoJAK677rr6grh58+ZpqvenTJli6r6WlZWFUaNGYeTIkejVqxeT7fjixYvxz3/+Ez///HPEsueffx6333674WNxgqlTp2Ls2LGu7Ds/Pz9i7gzQgU7dc5/jkVBQUIB7771Xs3znzp3YuXMnfv/99/okQ69evXDXXXdh2LBhbBsnLuZ/P/Yv7Nhx8Jw/+6xzMGTwWczHbje1tbVYvXo1Xn/tbUx9czKKi4siXs/MzMIjDz0V593a++Xpp5+OCy64AO+//379sjlz5uD666/H888/r6tGraiowI033ojPP/88YvkVV1wR05KSlYb3vzqKiopQVFSEDRs2YMmSJXjhhReQkZGBa6+9Frfeequu44qT+Dks0aheKwBtoWU31DVPqVl4qGVM1gCEwoawjXezIDJIKGxEVimy/NZuJxgl3kImbARhxYoV+Prrr3HjjTcyKVJiMWPGDNx4442a5VdccQVZqRUIBHD33XdrFDU33ngjevTogfz8fN3333vvvRrf5j59+pB2aHVkZWXhhhtuwPjx4+uXhcNhjB07FkuWLNFtehsOh3HttddqrFEuvvhitG/fnmn/EmDXzhLcc5O22l40wmGVfFDXNaujJLPBBD/pNS0yj780HK3aZLl9GJZSU1ODkpISrFu3DosXL8bUqVOxatUqzXovvvgiUzVtY8Oqs7WysjJmtd/YsWPFrqa1Pl8TQWZGFi66cDQuunA0tu/YhjlzZmH2nJn4M8raq47Kykq8//77eP/999G0aVNcdNFFGDVqFHOwW+R8jZnN19aGsadA2wevefMDQU+/T+FWOLZurQ1I79q1y9gBWoTZx4cICZu//vpLs6xt27Zo0qSJJdsnLdEYYg9W9LBp164d7rvvPtx3331Yvnw5pk2bhnfffVejDK+joKAAEye+gokTX8Hhhx+BEcPPx/BhI9GqVQxlg93XYtSlUhDz2mqpvw2dY7Tu2iKScw3+myVhQ1VhW0VpaWnMArOrr74ar776qu65dfbZZ2PBggXo27dvRNJm48aN+Ne//oWXXnrJlmOOR4sWLbB9+3buXnS9evXCggULcM455+CLL76oX/7iiy/illtuEXtcIBhU8FZfXWbPM2Hx4sUYPnw4zj//fEyZMoVUXOh9gnnzv8GMDw7ai+VkN8ED9z/u2PVax++//45LLrkkYlkoFEJpaSm2b98etz9xbm5TvDZlOpnkjub111/Hzp07I5S+kyZNwtdff43rrrsOAwcOrFfH1c1jvv32W0yaNEmT1Dv77LMxYcIErv0bpbi4GE899RRee+01vPnmmzj77LMd2a8edM8XPks0P2FJZjekxRuDTRYren1poqnW62FDupC4aYmmn4Crrg5BVVUhXTZY1E+UIksiaYgwo6/S0lLHG8iWlupX6TtJUVER7rrrLjzzzDO47LLLcOGFF2r82uPx22+/4bHHHsP06dM1r+Xl5eGxxx5j2s5VV12F//znP1i+fHn9sj179qBnz56YOXNmTFu18vJy3HbbbZg0aVLE8kAgwD1Jue+++/D2229HeCavWbMGvXr1wocffohOnTpp3lNYWIixY8dqbH0yMjLwxBNPcO1f4hEYn81MnvemDkRihPnz55saYLVq1QovvfQSRowYYfpYunbtauh9DatjHceiwek999yDNWvWRCzLzs7GXXfdZcn27cKWa7ahTU+DwHCL5i1x7TU34dprbsJfa/7A7NkfYPacWdgep4dNQUEBXnrpJbz00kvo0KEDRo8ejVGjRtnWZ8n2+5cJ9UhtbQj7i7Q9IFJTUgEAfgN+47EKT/bu3cu9HWuhAtT6TyKefM3s2bMN37M+++yzmAoBIHbzZCurcKmiCKaklUGFTTy6deuGbt264amnnsLcuXMxbdo0zJw5U9vD5n/8/fc6PPvc43ju+fHofsLJGDHiAgwZPAwZGQfUE3Zfi9HfYVHRfs06dddWXHQO0rJri7xnHPwhqYSNAucCRlOnTtX0rDnxxBMxadIkpvFKu3btMGPGDPTu3TvifH7jjTfw8MMP6xadWU2d5aQRkpKS8Oabb6Jdu3YoLy8HcOD+8NVXX2HIkCFWHWKjx4zCxki6pq5xfVJSEgoLC7Fnzx7U1NTEXHfGjBlYsWIFFixYoH+fj/MRiouLcN99t0Ysu//+x5GTY02Cn4fy8vKYBV3xCAaDGDH8fNxx+zg0aZKru26sj5+amoqvv/4aDzzwAJ577rl6+9z169fjjjvuYDqGjIwM3Hfffbj99tstseVOSEhA06ZNkZmZiaqqKuzduxf79++Pue6+ffswbNgwPPnkk66r5niuERZLNMpCy258VA8bCy3RWCy36tArXCUENq72+aX6gKlhFaGQikBAvEgOkyWaTNhIOBAmYbN+/XqNJPxQZPfu3Xj66afx9NNPIy8vD926dUPXrl3RvHlzZGVlISUlBaWlpSgsLMRvv/2GH3/8MW7Tu8zMTHz00UfMfrJ+vx/Tp0/HiSeeGDFp3bFjB3r16oWBAwfijDPOQJs2bVBUVIQ//vgDb7/9Nvbt26fZ1vjx47kDC2lpaXjnnXcwcOBA1NYerAj4888/cfTRR2Po0KHo27cvWrZsiT179mDVqlWYPn06ysrKNNt67bXX4gYnJIcIAlQuS6yjT58+uPXWWzF06FDLKj15JnoNibYRcxIrhqYzZ87E888/r1n+9NNPo2nTphbswUaMNbHRpeGdIl7wuGOHzrjj9nG4/bZ/YfOW1Xj3ven44IMPYj7/gAPFBuPGjcP999+P3r17Y/To0bjggguE83c3A2WJVlFRrlmemHSg6tvIZCVWxXhdUNEtyFiuhZZohYWFhhqhA4hbaVy33WgsPU+panOWfI0FCptY+Hw+nHrqqTj11FMxYcIEfPLJJ/W9rGJ9Z6qq4qefl+Cnn5fgoYfvwakDz8Dw4efj1AFncO+bh+jPFuu8T0xK4tpGQ6y6tnh+ATJh41Mcq559+eWXNcteeeUVroBqz549cfnll2Pq1Kn1y8rKyvDGG2/g1ltvjf9GwTjssMMwaNAgzJo1q37ZwoULhU7Y5OTkcPdosYpYc026P4c5S7Rjjz0WZ599Nk4//XQcd9xxGjVkVVUVfvrpJ8yaNQuTJ0/WFKiuW7cOZ599NubPn4/U1NiJ3njX3sOP3ouduw46Wpx22mAMPfsc3feIwBmnn4X77n04tkIyFnE+SkJCAh5//HHceOONeP311/Hqq69GKOvi0axZM9xzzz247LLLTD1f09LSMGjQIAwZMgQnn3wyOnTooJkTbdmyBXPnzsWECRPw448/RrymqiruvPNOtGrVChdddJHh4zALj4UYUxN3l3uC0Ako6xQ2lHtI5LrxFTbUMbnpQJKQSP+eNdW1CATEs7VnOV+phJRE0hBhEjZu2UKIPLjYtWsXPv/8c43nKQutWrXCrFmzuJtHd+jQAZ988gmGDBmiGeB99913+O6778ht3Hbbbbjtttu49ltH37598c477+CSSy6JSNqEw2F8/PHHMRtkN0RRFDz//PMYOXKkof1LxIf1imVS2Ih7+UuiWLhwIdatW4eVK1fi5ptvtsyux3NwnrPRqy9fvhyXX365Zr3zzjsPV1xxhfHjcggjTd7p9xy8W1BDEUVR0LdvXwwY2B8vvfQSPv/8c0ybNg1z5sxBZWWldsuqioULF2LhwoW4+eabcfbZZ2PUqFEYMmTIgUpoE/cg18cvOt9VqCaMmhrtRDEx4YDPO2//GiB2UFkvEeEFRKgrqKio0CyL1XfDKKQlmo09bHhISkrCyJEjMXLkSBQWFmLGjBl46623sXjxwpjHWF1dhc+/mIPPv5iD7OwcXHzxRRg9ejR69Ohh7kBiEB0Arq3VVs/XXVtxt6HzmmPXVoOvkQwWOaSu2bJli8YWsHv37tzzJ+BAY/KGCRsA+Prrrz2VsAGAI4+MbMS+ZMkSl46EjWHDhrH3ZnEAMnhr8MY/YMDpuOaa63DmoP666yUmJqJPnz7o06cP7rvvPowdOxZz5syJWGfZsmW4++6747phxPoE3333FT788GAPl4yMTDzy0JMH3yPwnOqrrz/F4h++x4UXjMI119yE3CbGi5P279+Pt956C9OmTWNK1gAHCr0eeeQRrF+/HrfddlvMvmF6pKWlYeLEibj00kuRnp6uu27r1q1x2WWX4bLLLsP777+Pa665BkVFB3v4qKqKsWPHok+fPmjVqpXOluyDUqQ0tOti6dnitmKB2n/YQoUNT8KmtiaMcFiNeU+ibkPUb2QnLAmN6uoQko11kbAV2cNGYjVCnC2Korj2TxSSkpLg95vPtgaDQdxyyy34448/DE02gANJkyVLluCYY47hel9qaiomTZqEp59+2tB+6zj//PPx3XffcQ9mcnNzMWvWLNx8882m9i/xPmr9/1CIcw84VEhNTUWXLl10/7Vv3x45OTma9+7cuRMPP/wwOnfujE8++cSFo3cf7oRFg9X//vtvnHXWWRpVYqdOnfDGG29YcHTch+TMm8mAccP/Zu+rkJCQgOHDh+P999/Hrl278MYbb+C0006LW5VdVVWFmTNn4pxzzkHz5s2xfv165o/gBkaSY3XEs7CoG3cZmazEGrO53QOGHEdSChuGPh52E+s7tHJ8bIUlGtN1aeHzPDs7G9dccw2+/PIbfD9/Be68fRw6dozfM62wcB8mTJiAnj17Grat04Pp5yBW0qv6t+za4rgcKIWNUwmbRYsWaZYZLfo68cQT0a5du4hlP/zwg+v3qW3btmHGjBkYN24cLrzwQgwaNAi9evXC8ccfj65du2r+RSedNm/e7M6BexSyh42qxj3/9c6UIYOHo2uXblzH0qRJE8yePTtmQc6rr76Kv//+O/Yboz5CUdF+3DcuMvF43z0P47DD3Gti3717d6iqGvGvsrISO3fuxIIFC/Dkk0+ic6eD9+3S0hK89vpEDB7cF3PnfqW77XjPwPfeew/t27fHvffeq0n0BgIBtGrVCkcffTRat26NxMTIJPqePXvw4osvolOnTnjxxRe5Pmtubi6uu+46MlkTzQUXXIAFCxZoehZVVlbiwQcf5NqWlfiJa6Q2QmEjvsUU1UMnZKHChscSDYjfx4ZKLLjawyaR1hRQ/YzdgiXBKHvYSHhwXWHj9iBWFHr06IHdu3fjq6++wjfffIMlS5bgjz/+YJJQJicno1u3brjwwgtxySWXWFJ5fvTRR2PFihV444038Morr+haBzVp0gSjRo3CHXfcgZYt9ZuestK3b1/88ccfmDBhAl599VWsW7cu7rotW7bEFVdcgf/7v/9z1Cda4m0EytceUnTv3h3z5s1jWnfHjh347rvvMGnSJCxcuLB+eUFBAUaMGIG33npL03CUB08+fwyet9u2bcNpp52msXNr1aoVvvjiC+5JoFvYkK+JgCV+HuvekZGRgTFjxmDMmDHYsWMH3n33Xbz99tsRPeEasm/fPpSUlCArI4/j6KIPxPhbrSFOwElVEQqrCAa1Q8zKqgMqpICBHjaxlCBm+jVYgrl8DVe/gmi7JauIpa6I54FvBFphQ2+D6VZtw/Wg4H+9rK69GddeezP++ut3fDz7A8z55MO4vayim0tbQXTgJBAIataprNReHw3R+3qsurZ41Ix0wsaZgEasezRrD9FYdO/eHRs2bKj/u6ioCOvXr8cRRxxheJtG+eCDDzBhwgTMnz/flCWPlfeDQwGWQKcaVmPbRdIyX0PH9Oqrr2LVqlVYtmxZ/bKamhq8+OKLMS1yo6/lBx+6G7t376r/u2+f/hg5MnL8LUIhbGJiIvLy8pCXl4c+ffpg1KXXYubMGXjggbuwr/BAX659hXtx3fWX48XnJ+PMM89m3vbjjz+Oe++9V7N8+PDhuP7669GnTx+kpBws+a+ursbSpUvxn//8B++88w5CoQMB5oqKCvzzn//Exo0b8eyzz5r8xDTHHnsspk6dinPPPTdi+dSpU/HEE0+44lhA2QZGKGy8YIlG9HsJ1bqjsAGAqqpaJCVrxwxUwZCb13MwgcUSTdCEjQcSjBJv4WrCJpYty6FMTk4OLrroonpP0bKyMqxduxYbNmzAzp07UVpaisrKSqSmpiIjIwNZWVno1KkTOnfubIk6J5pAIICrr74aV199NTZv3oxly5Zh48aNKCsrQzAYRF5eHo455hh069bNlolVSkoKbr/9dtx+++1Yu3YtVqxYgS1btqC8vBxJSUlo0aIFjjvuOBx77LGW71vicVSPBuMlETRv3hyXXnopLr30Urz00kv45z//Wf+7hkIhXHHFFTjuuOO41YCHFgp2796N0047LSKIBAB5eXn49ttvkZ+f7/QhiQWvwoaYxDRv3hz/93//h/POOw8PPPAAEWQ3/mW4/TXG+6bqArFJSdpEQNX/gspGKvdiBZXj+e87hZnfoK4i2G1iqRmtTdjYr7BxSjXfseNRuPOO+zHozKF46OF7sHLVMvpNFhD92WIl2WJdH1EbifuSZdcWR3JOFIXNnj17NMs6d+5seHtHHaVVYu3Zs8fRhM327dsxevRoJhtrFhraKUloqGA0cOD8NzJzN3pVBAIBjB8/HqeffnrE8s8//zx2wqbBjr76+jPMnjOz/u/U1FT8+1FtokGAfI0GBQeUSR2O7IyLLxlWn7Spra3FnXfdhM6dj0GbNm1jv7EB77//viZZk5KSgrfffhvnnHNOzH0nJCTUW9NdffXVGDlyJHbv3l3/+nPPPYejjjoKV111lZmPyMQ555yD3r17RygKQ6EQvvrqK1x88cW27z+aAKlI8ZYlGhUHs7aHTfy+NLGIl9gIkc9grt1YSgJDwoY3ceUUXkgwSryFqwkbJy1YvEhqamq9PN1t2rRpw21RZiVHHnmkxlNZcojCNCGwp0GxxD1uuukmbN26FU8+edAvu6qqCjfffLNlQQkvwHvaFhbuw6WjR+DPP/+MWJ6bm4tvv/0WHTp0sPDoHMDIdUsFjBv2sKEqzqB/79i3bx8++OADTJs2DQsWLKCD0Y77w1m4+TgfrS4Qm52lTQSUlR+w4zMSjI3uqwfA/V5WJpIRAuRqACCmh/2uXbtirGkMKxQ25D7Mb4Jk06YNmD1nFmbP+QB//x1f9W0H0d9hVpZWTR5tdUltoyFWXVtcakZBEjaFhYWaZWaagsdS+u/bt8/w9njZtm0bBgwYgLVr11q2zYY9RSU0LOduvD42dj4WTj31VOTl5UXc39esWYOtW7dqnwP/+wiFhfsw7v7bI16684770bJla+0ORJxX/e+QjjiiAx58cDxu/ufV9S+VlpXi+ReewLPPTIz3tgPrlZbiH//4h2add955B8OHD2c6jL59+2LOnDno06cPamoO9iC79dZbMXLkSFP3HFYuueQSjQXkt99+60rChkpqNuz5QikWFJ9CWpLZjZ9Q2LAE8VnhVZZUxbFEI+c7LlqisfSwEVZh44EEo8RbuG6JJpFIxCCvWToef4lt4Okm4VAY27bu112n6WFpKCutRnl5/Ka5yckJyD0szeKjc5a8Zt6wsbKKe++9Fy+//DLKy8vrl82dOxe//vrrIaS0Yx9AFxXtx2WXn4ffVv8asTwnJwfffPMNjj76aKsPjhFnVSU87yHzKzEmMBUVFZgzZw6mTZuGzz//PGIyHovMzEyMHDkSbdq0gWpmDidgbAQ4GIjNzdU29d25cwcAY8HYLVu2aJbl5ZmwlLMAswobEejYsaNm2YYNG7Bv376Y6htenFLY2MHu3bvx5lv/xezZM5nUNG3btsWYMWMsP47ozxf72trOtY2GOHZtNVQzEk2YnUrYlJSURPytKEqErREvsZRJ0fuwkzFjxsRM1nTt2hWDBw/GSSedhPz8fLRo0QIpKSlISkpCMBhpl/Pggw/ioYcecuqQGx1MCZt4wVLqdmjislAUBf369cOMGTMilm/evFmTsKm7Xzz3/Hjs2VNQv/ykE3vi0kvGWn1ottHwmM4aMgLPPT8eGzYc7B/46Wcf4cEHxiMjIzPuNt544w1N0nXo0KHMyZo6TjrpJFx77bV4+eWX65eVlJTg9ddfx6233qrzTmvo37+/Zplb/amoBEfD66OGCIAHiG05Aa2wsW68x5uoiNfDhjomv4sSGy9bosVLkNWhKO72B5J4D5mwkUgkAA48HFu1yXL7MEjCYTWiGWEs8ppnoKSoUjdhk56eiKZ5h1bCw+tkZmaib9+++PLLLyOWf/XVV4dMwoY1LllSUozLxozEb6t/iVielZWFr776Cl26dLHh6BzAjowNhyVaXRAjFArh22+/xbRp0/Dhhx+SAblgMIhBgwZh9OjRGDp0KJKSkgAA+/boV8XrHovhd9pLXdVeq1ZaVe62bQcCw0YmK7ECC9ENvh3HoAoJEEdhc/zxx0NRFM25v3TpUgwaNMj09q1J2FD74DkifUpLS/HRRx9h2rRp+Prrr+v7DsQjIyMTQ4YMwz/+cRX69u1rS/IoOlHcqpXWxnJbnJ469dvQOSzLri2Onk6iKGyi+7epqory8nLDSZtYSienesR9+umn+OabbyKWHXbYYXjrrbdw5plnMm+HtNcTjNmzZ+P+++93Zd8tWrTAZ599FrGMuYeNAcxeFc2bN9csKygoiLHmAXbtjux7uHXbFgwdNiDmuoGgX/PZY6n3Zs+erXEPifU9WkH0/fiUfqdGJGxqa2uxbNmPGDDgjLjv+/TTTzXbNWrrf/nll0ckbADgyy+/dCRhw/vb2wmPhRilsBHBXsofoBRDVlqicSZs4qxPzndcTCqwWaKJqfzctUN/PpiUHJQuLxIuZMJGIpF4CqZnnKoiRAyOZHWDNznyyCM1CZtVq1a5dDRiUlJSgsvHnI/ffov8XjIyMvDll1/ihBNOcOnIDmDmyrPjqm04ZaECw7/8shzPvfAJ3n33XSbbqJNPPhmjRo3CRRddhNzcXHMHGo3ttzDKSi42dYHYli1bIzExCVVVlfWv7dyxHdXV1YYmguvXr9cs69SpE/d2rIRqsq53OomisElPT8cJJ5yAn3/+OWL5d999Z1HCRv91FpWZ3QqbmpoafPnll5g2bRpmz54doeKMRTAYxCn9TsWI4edj4MAzkZaWgtb5Wissq4i+XA5vp+2HsmnTBs2yCHS+o+hrKzk52aANMnsGk0rYOBUsimVhtn//fsMJm1j9n6xQqrEwffr0iL/9fj/mzJmDk046iWs7Tlq4WcG+fftcGwfG+r0V5cCTQe8MN15xb+66iKUAi5Wgi3e72L59K7YTyWGKwsJCjRWhlX3T9GgVw8pt85ZNuu/55ZdfNMt69OhhaP/dunVDMBiMUGLH2r4dsP72TuCjFDYNFJghwk4sEHTfXopSo4QIRSkPTils3IyTeNkSbctG/edni1bx1XwSSSxkwkYikXgOaiKkqiwDEfcHeBJ+MjIyNMtiNQ1urFBxydLSUoy94gKs+mV5xPL09HR8+eWX3IEbW3A4Y0PGchsqbGLcNzZsWI/Zc2bi49kf0EFRAIcffjguvfRSjB492tbea3ZPpejvLfY9tu7eGwgE0KnjURHnYnVNNX5bvQodO2srPfWora3FTz/9FLGsWbNmMStGnYTOE+hJbKw8EnMMGzZMk7CZOnUqHn30USQkJJjaNhV4t0JhY+RiUFUVixYtwjvvvIP3338fe/fuJd9zfNfuGD58JM4+6xxkZx8MwttdLBmdkGrevAWaNGmKvXsPVkdv27YFu3fvxGGHNYu9jTjb3r59OzZtigxYdunSBX4/f9Uyz+UgSrCoaVOtvdwff/yBFi1aGNre77//rllmebI+Dl9//XXE34MGDTL0zP/777+tOqRDEkVRoPgUXRVNvPPf3r53sRUVsc/PxlHUFn1vTkxM0qxTVqZVATX8+LGeDbHuGyz4fD40adIEO3ceVC45lSBl/+3th7q/N0xwkJZoAREUNlTCxsIeNjW8PWxirx8WxJY0Fv6Aj7yH8iqNnGLzJm1fvIa0bmtfcY+kcSITNhKJxFMoihLTvqUhqqrSwQABPG8l/BQVFWmWGQkseZf4521ZWSnGXnkBVqyMDLympaXh888/N1wRKBKUoiHeu/Q5eK+ou68UFOzCJ59+hNlzZuKXX1aQe8jOzsYFF1yA0aNHo3fv3tYdmu57xbyHNbz3dj+xhyZ5uGzZjxh5/mCubS5fvlyjeujXr5/xg7QKU5Zo4mRsLr/8cjz44IMRNiQFBQWYMWMGLr30UlPbFq2HzerVqzFt2jS88847mkRFLNq0bovhw0dixIjz0Tb/cOb9WEmspNeJ3U/GF19+ErHs52U/Ysjg2H0V4n1HCxcu1CwzfG0xWqIdGKOJoYLu1q2bZtnPP/+MU0891dD2ohPLWVlZaN++vaFt8VBdXY3du3dHLOvbty/3dkKhEJYuXWrVYR2y+HyK7jzEqMLG7FXxxx9/aJbFSj4IOrwwQOQH2btXW+CVnd1E911JSUmoro602K6oqDBczBA9ljHTM4sH1t/eCaj7e8NnPmWJRiVLnIBHMWQW3kRFTRzrsDAxrnIzYaMoCoJBH6rjJJsA/sSVE1RW1KBgZ4wEcAPayISNhBP373ASiURiNSrtFyst0bzJn3/+qVnWrFnsiuLGSLxJdHl5Ga686mIsXx4ZLEpJScVnn33Gl0QQGRsu27o5S0lJCaZPn4bLx5yP3n274NF//0s3WZOQkIBzzjkHM2fOxM6dOzFp0iQD33Pjuw81DESd0neg5vUvo4LMLHzwwQeaZVbYdZnFRL5GqIRNmzZtcO6552qW33333aatakhLNIavwWwPmy1btuDJJ59Ely5dcMwxx+Dxxx/XTdbk5OTguuuuw3ffzsPc737CLf+8i0jW2Hsdx0q29OunvbaiEziR24i93Mpri/VbUFVaYEaN0QKByJpDqtdQPGLds2N9JywsW7YMGzZEqjB79Oihm1CM/hyAsc8SS2lsxIrts88+i9l3RGTGjBkDVVVd+bdx48aYx0Sdv3ETNjY+Fvbt24clS5ZELEtKSkKHDh3ivufViW9h/doCpn+VlTWa72fu3LmabV5++eXM36NZoi+9Fat+1qzTtOlhsd7Z4HVtUiP6Omdl7969KC4ujtq/M0mTWD2C3Opn6fezK1JqCYVNUAhLNEIxRBQo8BAvARMPLypsACAhQV9XEM/qzU22EOoaALba50oaJ+7f4SQSiYQTKjgTCqt0MIAYLErEo7CwEIsWLdIs79y5swtHIw4VFeW46ppL8dPPkRPx5OQUvDX1PUNVto0JvftFdXU1vvr6M1x44YXIy8vDjTdfi4WL5ukGzHr16oWJEydi586dmDVrFs4991zTtlFGcDvVQ/WwAYAePfpogiErVi7DypUrmfdTXV2NqVOnRixLTEzEOeecw7wN2zBRhixQvgYA8Oijj2qCx1u3bsUNN9xgarvRweqqqkq8Pe31+r/rAna6GFDYFBYWYvLkyejfvz/y8/Nx11136fYKSExMxLnnnotZs2Zhx44dmDhxInr06Kl/XHX7Z1rLOLHiJmeecTYSgpH3na+/+gx79sZuIh3rVN29ezc++uijiGUtWrTAKaecYuxAGW0UWdQFVLAoPT094m+jSYZWrVppxhA///wzli1bxr2tCRMmaJadccYZMdY8SPTnAIx9llj9KYzYxT777LPc75Fo8VHKwjgBXPLKMHGzefrppzVjm/79+yM5OVm7GwPPNiFVOQ2OqaBgF5YsiZxD+Hw+dO2iVdk1fF8sa9tYyQ8WYr1PL2FmFXv27MFrr72mWT54MJ/a2Sp4Epq1VA8bASzRqJhCqNbCHjZEAiuaeIkNUrnscsImmKD/u/J+D07AkrBpJRM2Ek5kxFIikXgPYlbA4hXrduWIhJ9HHnkElZWVmuXDhg1z4Wjcoa6ZbR1VVZW49rrL8OOPkZPQpKRk/OfVt9GzZx9nD5ABUVzACgp2oUevo3HtPy7D+++/r9t8tW3bw3HLzXdhyeKVWLRoEa677rqYzap5MfVx7O6bQa0Qr0C4wUTb7/fjgvNHada55557mI/j2Wef1XivX3zxxcjKymLehl2Y+QlEUtgAQMeOHXHvvfdqlr/zzju4+eabSQureDS8ZjdsXI8LLjobn332cf0ytf5/4sOrsPnkk0/QrFkzXHPNNZg/f37c71pRFPTu3RuTJk3Cjh07MHPmTJxzzjkNErCMv7Dd12KM8UpWVjaGDIm0P6uuqcYLLzwZZyPabdx///0Rza8B4JprrjHR448IUv/v/1nOJeoYou+/+/fv1zQyZyVWUvLGG2/kukaXLl2qSSynpqZi7Nixuu+L9Rwx0kMmMzNTY6301VdfcW1jypQpmDdvHve+JVqoYKfTlmg///wznn/+ec3ykSNHxt6PkX6BrpeRaGl4RI89/gCqqiLnEMcf3x25uVqFTcP3nXnmmZrXX3zxRe7Eam1tLR5//HHN8ljbtxJVVXH99ddrlD1t2rTBiSeeaOu+40EmbBoqbAhLtIAAlmiUYsjo+CkW1ZwKm3gWaiHKOt7lDGwCmbARzxJt80b9MUh2kxSkpSc6dDSSxoL7dziJRCLhhBpDUHZoAC1flojF888/j+eee06zfODAgTjiiCNcOCL3qa6uxj9uGINFi+dHLK9L1vTqKaiyRpAsRVVVFYqK9sd9PSe7CUaPugIzZ3yBb7/+ETfddDvatm1n2f4BmPo4IgZHAG0g6vLLrkZaalrEsi+++AKvvPIKua1ly5bhwQcfjFgWCARw9913cx1TXe+zhv8ssWAxYfclWL4GAPCvf/0rprripZdewgUXXKDpkcGCoiioqqrEhInPYeiwgfjtt1WadajAOJ2wifwh9uzZo+k50JAOHTrg4Ycfxvr167Fw4UJce+21sROwglxi0Yn6Oq699mZND7fp776J777TBuqjx01z5szBq6++GrEsMzMTN910E/Nxbdy4MeKays5JQfsjdex9/vc7WqGwOfbYYzXLjFa9jxkzRmMftmTJEtx4441M79+0aRNGjhypCcpdeeWVZGK5devWyMzMjFhm9HP06RNZoDFv3jzmbX3xxRe4+eabDe1XosWwJVqM7PWWLZvwzvQ3UVVVBSM3pZ9++glnn322piilQ4cOuPzyy2O/yVjGxhamTZuGGTNmGCtyUBSEw2E89vj9mD1npublyy+7Os77Dv7nOeecg2AwGPHyrl27cMkll6C2li14rqoqrrnmGk0fmcTERIwYMUL3vVOmTMHq1auZ9hNNdXU1rr/+esyYMUPz2iOPPGIiOW8Oqu9Mw1spZYkWEMESjehhE7Kwh00NZw+buAob4jnsusImqJ+wEdISjUjYyP41EiO4f4eTSCQSTiipPsvASCpsxGfHjh14++230bt3b/zf//2f5vVgMIiXXnrJhSNzF0VRUFtbi5v+eRXmz/824rXExCRMmvAmevcSoCF7XIxfe0beyWPtkZSUhCGDh+HVSf/F4kW/4sEHnkDXricY2pbtuHwo8S3RIifXTZrk4pZ/3qVZ76abbsKjjz4aN+Axe/ZsDBgw4H9BqoPccsst6Nixo6Fjtho6aRb/WSSawgY4cE/98MMPccwxx2hemzlzJtq3b4/7778fW7duZdrepk2b8MSTj+OUASfgmWcfQ0VFecz1qPg9+V0xXAu5ubm48cYbsWTJEvz1118YN24c2rWzJgHrxKUYK3jS4chOGD3qyohlqqri+hvH4P0Zb0d8b3X3LlVVMXnyZJx33nma7f373/821PeElbqjoQJFAD1G69GjhybYeNttt+Hjjz/WqIYoUlNTMXHiRM3yCRMm4Pzzz8euXbvivvezzz5Dnz59sGXLlojlbdu2xcMPP0zuW1EU9OwZab33zTff4J577uFOkF5wwQWaZRdeeKFuT57Kyko8/PDDGD58eH1APyMjg2u/Ei1UIDxewibWra6kpBjj7r8dA07tjsfHP4xVq7RJ71gUFBRg3Lhx6N27t+Yc9vl8eP7552P2UAKMjrUMvImBtWvX4oILLsCxxx6Lp556Srf/WEPC4TDmzfsO5448E6+9rr2++/bpj7OGjCC3k5+fj+uuu06zfM6cOejbt6+u1Wbd8Q8aNAhvvPGG5rUbbrgBrVu31n3/J598gmOPPRbDhg3D9OnTUVJSQh4zcEBh16tXL0yaNEnzWq9evTBqlFb97BT0PL6hwsYDlmhUDxuGQlJW4ilm4q4fJ7FBFU5QSSi7oS3RxFLYqKpKWqK1lgkbiQH0uzlJJBKJgFBDCCZLNJcHIocqP//8M7p27aq7TllZGfbt24d9+/bFXScQCGDatGk46qijLD5Cb/Dppx/hm28+1yxPTEzEE08+hCeefAjAgSAfVaUUTffu3TFlyhTm9anfM5odO7bj7KH9ddf5ZM682C/YcNkqioK+ffti9OjR6NvnTAQDWj/3hutaum9LtxafSZMmxZy017Fz507Nsiee+jcmTno57nsuv/xK3HW3NpEaaxI4Zsy1WLBoXkSCUVVVjBs3Dq+++iouvfRSHHnkkUhMTMSGDRvw0UcfYfny5ZrtnHDCCXj00UfjHpPjGM/XCKmwAQ5YNM2fPx9Dhw7F4sWLI14rLS3FI488gkceeQRHHXUUBgwYgFatWqFp06bIyspCZWUldu3ahb/++guLFy/G6tWrmRJTphU2cX6IpKQkDBs2DKNGjcKgQYM0VdIUzNenA4ncA/ce7Rdx5x3jsHTpYvz+x2/1y2pqanDPvf+HyVNewVlDRqB163zkNEnBpk0b8e6772Lt2rWa7QwdOhTXX3+9nR/BUoVN8+bNMWjQoAgFya5duzBixAgkJCSgdevWSE1N1dyzp0yZgu7du2u2d8EFF+DLL7/E66+/HrH8gw8+wKefforBgwejb9++aNasGSorK7FhwwbMnj07Zj+uYDCIt99+W6OciccVV1yBL774ImLZ+PHjMX78eDRv3hw5OTmawPqwYcM0CaHLLrsMjz/+ONavX1+/rLS0FOeffz66deuGoUOH4ogjjkAwGMTu3buxbNkyfPLJJ9i7d2/9+kcddRSGDh2KJ554gunYJbGhzl+WpGU0u3btxDPPPolnnn0SrVq1Qrdu3XDcccehefPmyMzMRGJiIgoLC7Ft2zYsXrwYCxYsiGkjDAAvvPCCbv8SQwIbm++Dq1evxp133ok777wTRxxxBLp164ZjjjkGubm5yMrKQiAQQElJCXbt2oVVq1Zh4cKF2LZtW8xtdejQGU8/re05VUf0M+Whhx7Ct99+i99//z1i+ZIlS9ClSxf07t0b/fv3R/v27ZGeno7S0lJs2rQJ33//PebNmxfTEuvYY4/F/fffz/TZVVXFnDlzMGfOHCQmJqJLly7o0qULOnbsiKysLGRkZKCqqgr79u3DL7/8gnnz5kXcBxrSsWNHfPzxx66pawA6GRDZw0Y/MM87x7EDUjFkocKmljNRUVUVe33Kps3tAjXKEo03cWU3e/eUoaJMv1hEKmwkRpAJG4lE4j1M9rBR4P5A5FClrKyMuTowHs2aNcPkyZNx9tlnW3RUHkMBampjDwqLi4tQXFxkavO8vUF4f8+amhr88acxewcjxLvUk5NTcNut92LE8PPRu28XKIqC7VuLUFkZf8CtCKRL5rmD7dy5k/t32rZtK7YhvpJi9+7YVeexAlGKouClF6Zg7JUXYtmypRGvbd26lSk42LlzZ3z22WdITBTH/9nMY0REhU0dOTk5mDdvHu677z48++yzmkbVAPD7779rglcstGvXHldfFdUzhFLYECtE/w7t27fHlClTcP755zcatYBPURArPJGYmITXX3sXl4wagb//Xhfx2t9/r8NLLz9Nbrtv376YPn26A+OiA78jlbDxKQqTHctTTz2F+fPno6ysLGJ5dXV13GClXt+J//znP/D7/Zg8eXLE8oqKCsyaNQuzZs0ijykjIwOzZs1C7969yXXrOO+883Dqqafi22+/1by2Y8cO7NixQ7M8VqFEMBjEjBkz0KdPH5SXR6rZli9fHjMJ3pCWLVvi008/1fTikfBD9rAxef/funUrtm7ditmzZ3O9LykpCU8//XTMvk1eYt26dVi3bh3ef/997veecMJJmDThLeTkNIm/UtTPl52dja+++gr9+/fHunXrNKsvWrQIixYt0iyPR8eOHfHll18yJ3UbUlVVhaVLl2Lp0qX0ylH06tUL7777LnJzc7nfayWkAq2hwoawRPMH3J/P+4nPE7Kwh00VpxVYvJ431C3IdYVNon6Ymtcazm4oOzT8P3vnHR9F8f7xz15ySUhCGgEhlIAQQBABDRCa0pTeJBSFaLAAFrArIiKiX0X9KQqKqCigBhASBAKhSm8iYKQoPQmhBwiE9Fz5/RFz5nJlZsvtzt3N+/Xi+zVzuzOzs7uzM/OZ53kA1G/oOgtmjufC0NIDh8Ph0EFaUyC5RNPpdFywcUMiIyPx6quv4vjx494r1oDd2CVqoOR7W6NGJJ595iVERdWzpJEW0ZnqNxiqSmUcLUQFBQXjp0UpSHz8SdHtOGzYMOzduxe1atkGByaRk5Njk1azZk3ccccdovOqCulddCY0MKzXAChf/P3444/x559/YsCAAbKf/YYN78S70z/C+rRd6N79IavfiIuXImPYdO3aFU8++aRqYo06LtEc/1az5h1IWb4eDz3YX1yegoBnnnkGmzZtQlBQkOg62Xu3YmKaOzy+4jaSBBta3/ktWrTApk2bFItj5+Pjg2+//RZffPGF/ZhGBOLi4rBr1y707NlT1Hk6nQ7Jycl49NFHRZdZlbZt22LDhg2oU6eOqPPi4uKwb98+NGzYUHYdONJj2Ljyu9ClSxccPHiQSqyR0t+7anwkpW+yR3h4BN6b8X/4Zcka52IN7PfpdevWxZ9//okJEyZIvlZBEPDss8/i0KFDot9ROYSGhuJ///sfduzYQXTBpgbEmC+V3g+S6ysmXKKRLIaUjGFDELCqUurIwoZQJ63nO3pCbCJH16UV5wiCja9eh9pRnrGBiKMuXLDhcDhuB2kQQVp05e7Q2MbPzw+RkZFo3LgxevfujTfffBOpqam4ePEiPvnkE0k70jwK/viKg2LSUdFlkIObK1AfxTJk80Fwthjr7x+AWbPmYN++fRg2bJhTaxlfX1/07t0bGzduRHJysuT3fuvWrTZpr7/+OqpVc+z6jhpZLtEYV2z+pVWrVkhNTcWJEyfwzjvvoG3btjaB7u2h0+nQokULTJjwDJYvS8Nvm37HmNFP2I2ZQHaJJs7CRnVUKJ807gkJCcXXcxdi0YLl6Nq1u9N75O/vj/j4ePz++++YO3euZKs1e+/WCxNfc3xChUs04qYa+gbt2LEjjh8/jrS0NDz77LPo0qULoqKiEBwcLNnlz6RJk3DmzBlMnToVzZs7FqCAcouFBx98ECkpKdi7dy9atWolqcywsDAkJSXh+PHjmD59OgYMGIDGjRsjPDxctCu/Ll264K+//sLrr79OtJiNjY3FokWLsHv3btSrV8/psRx6iIKNiAXcpk3vwtLFq/HiC5PRvXtP1KxZk/rc6OhoPPXUUzh48CB27txJ70ZYkks08efQ8Nprr+Hs2bP46quvMHz4cDRo0ID63PDwCPTs0RtzZs/H7p2H8egjj8taiA4ODsbXX3+NkydP4rXXXkN0dDTVeQ0bNsTrr7+O06dP46uvvkJgYCB1mXPnzsWPP/6IJ554Aq1ataLuD6pVq4auXbvi66+/xoULFzBlyhSqb7caEC3QKr0fJK8ZpIV9NfDxIVjYKBjDxiA2ho0DCxuipavGayV+fs4tbMS6hnM15wnxa6LqhcKX4DqPw7GHYHaXGSOH46YcO3bMKoDv0aNH0bJlS8XyNxgMNv7IY2JiHAaS9AQunb+FIidui0j4+/uibv0w5SrE4ajIuYwbMFAO/qtV06NOXbYELqPRhKwMx/GJnHFH7eoICha3uFhYUIrLl/KcHtPwzgjodDpkZ+U63c0XHhGI8Aj6iTaJm7lFuHG9gHygHaIbRRAniXK4lpOPvFv2/d8DQECAHlH1bJ+tzDPXnVpMRNYMRkhoAACgsLAQv//+O06cOGGJWRUaGoomTZqgQ4cOot3z2WPChAn45ptvLH/Xrl0bZ86cEbVg4oi8m0W4ds3x/fPR6RB9p30XCKR77+urQwNG3ScUFBTgyJEjyMjIwJUrVyzul8LDwxEeHo6oqCi0bdsW1atXh8FgwrlM5+97nbqhqFbN8SIUyVVhaGgAatQMlnYxTiguLsPF82QXk2r0s6Q2qEpe3i2k/3UQWVkZuH07D+ERgahRowaaNWuGDh06KPL89+nTBxs2bLD83arVPfg1ZbPDBVGdIKBh4xq4nlOAW7eKHObL2hgtKysL6enpyMnJwbVr1+Dn54datWohKioKcXFxirSlqzAajThw4ACOHTuGa9euwWAwoHr16mjUqBFiY2NRu3ZtravokeTfLsHVK46DwwsAGjauYfOunD+X6zQ2Q3h4IMJrBOLSpUs4e/YssrOzkZOTg8LCQpSVlaF69eoIDw9HzZo1ce+990q2JC0pLsMFir6vMo3sXI+ruHbtGk6fPo3MzEzk5OSgoKAABoMBISEhCAkJQWRkJFq1aoXQkFqSxlhh4dUQUYPOsqciHtTly5dx8+ZNFBQUICgoCGFhYahduzbuu+8+SdbBjigpKcHp06dx7tw5nD9/Hnl5eSgqKoJer0dYWBjCw8PRuHFj3HPPPaLFXiVxtj6xdOEBrP3VsWvkhk1q4L1Pyz0pfDR9E47+edHhsff3aoKnJ9K7oHQFe3ecxdxPdzr8XdAJ+PHXxxQpa9ora5Bx+jr5wH+5u00dvPHuQzbpT49KQnGRY/dqTz7XEd0eaiqpjkrwzRe7sGuLfdemAHDPvXXx2ju9VKyRc954fiUuZjvuM7t0vxPjX+yqYo04SuHqtVwSnruiy+FwPBeZ8wGdCxc5ORyXo/VucpnoNN8ObweLhY26LgIYbAkLUlzvmc1monuryjuPAwMD0b17d3Tv3l10WbRUtQKYPHmycgusxOfBmUs0tt1ROCMoKAhxcXGIi4sjHktzGfItbNhtK6UQY3UClFvc3N+1B9C1vJ9p1ETZmAVlZWXYtWuXVdqUN6c6vRe0LtHEXquriY6Opt5Jzxo+Pj7o0KEDOnTooHVVvArSM2xGeb9W9X0hbqP99/A6deq41qWWyD5V7digkZGRiIyMJH6Dbt10LAw7Q8z4p1atWujbt6+kcqTg7++Pli1bqrpgqDSkebh1DBvnlhSu3LhEC+l6zCYzTCazIt82kou4qjgSgJVyTeoq9Hrn1mCOLIe0oKzUiEsXnG8M5PFrOFLRvofjcDgckcidFPgwthjA4YjB3WPYCDoZVyDl3adZMK74f4LhkuJdh4z8tH8KbCd7ZsIEEFB3MfbChQs4efKk5e+6deti/PjxiuVPehydLb55iwhBcx3EEDaUi5hKQ5utGvdKThGuqN/+/ftRUPDfzvXY2FgMGDDQ+Un/3kh3E2w4HLHQLHYqGddCacS+gZ7yvbLgYZfDGmJivhgMbuASjeZ9pxgf01Am1iVaiX1hgzRe11oI8/N3LtiIbQdXciH7JrE9GzQUHxePwwG4YMPhcNwQufMCvhjAcWtEPL6sTqKlvoNSzhJzDnERnaW+w9VVkSBG0ExI1WzDqtY1U6ZMQUBAgGrlO0P1eEkaQWVhQ3huvEXccoasa3RB81R9t959913qgkwm5wtwfIzGcXdonmGSNao91HozRPc3jL6yUrtNRi/HYyBZ2hsrfSNIgo2vr/ZxeWg8d5gUimPjzGWiPUocCDak/kfrYRXJwkaspZErOZfpPH4NANTngg1HIlyw4XA47ofMUQRfDOC4M57w9KorfNDv8Gc+uLkVrq2MlNxpBBs1+98tW7ZY/rtBgwZ46qmnFM2ftKjl7HnyFhFCEATi4gwxnKaXtJUz5FyjK6wyK79bcXFx6NevH1nkxb9uE7mFDcfDoRJs7L0HGlkTyi2H3T6Y1Xp5Nz6E4OuVrd1JLtF8WbCwIVgMAcpZ2JDaoyqOLFEI+yY0/w77+ZFcorEk2DiP0xgSFoDQsGoq1YbjaWjfw3E4HI5I5A4hSKbYHA7TeMDjKzWOjaTTqM4xw2w2k9dKGIphw+L6CM2OYTUngZWtAKZOnQo/Pz/Vygb+W6C2+5uXWNgAkGStJeZ317UVZcYq3CtBxoxN6fYpKSnB3r17LX/PmDGjvBzK80kWVYKOT0857g3Nd87ee0AxCpFYI3GIdonmklrIR3Lf51EfYPYgzQEMRjEWNtp/L0gCFEC+DlqUsLAxm80U32GNY9j4OQ+1zpJLtPNZN53+Xj+aW9dwpOP8TeBwOBwGke8STfvBHYcjFXePYQOoOxGg1GvIu/zB8i5SF0C6VIku0dQUbDIyMlyav6zHwYusRsqvRbq1kWaWb+zoNZJFbkD5Z8nf3x9FRdKCeZvN5H6CxxnkuDs075yUHfeqfRZEW9i4phpa4WnXwxqkjZOVxQR3cInmQ7GuYNTIJZo9YYPGGyPNNbkSkoUNKy7RzGYzzmU4t7Dh7tA4cuCrlhwOx+2Qu/jALWw4bo0HPL6SFx+lnEcTQwNk9wBSi3eeoYxTNV5RsDffI+3YA+QtPDMHxbU4mhh7k4UNaf2dLNg4P1/rd0ENWIthY78c2hg2bO/s5XDkotMJxHdWkks0lRAfw4bNd9Ybvg3uCCmgfeV4L0Y3cIlGs65gMsp/uQ0GE9U4uzIlJUabMRZNPB2tv8OkGDalDmLzqM2tm8W4nVfi9JgGXLDhyMBtLWxycnKwbt06bN++HX/++SeuXbuG69evo7i4GIIgwGBg4yXmcDguQOYYgu/e5CjJvHnzMG/ePJeXM2HCBEyYMEHcSYw+6lInAi7ziAatLGzUawflS7FtL2JsCkHQfBKoJHRXYrZ7pLfEsAFAXMwzE9YOaN5NTVHhXsl5b9R6lmhKMZvNRNeJWvvO53CUQCcIMDp51u19Lxnv6RzC7OeK1Xp5OaQ+3ijKwkZ7wYYkQAGAkWZXGAGx8WuA8o1UhjIT9JUsVmiGVFp/h/VuYmGTTYhfA3ALG4483E6wOX/+PD788EMsWLAAJSXlaqbYidzo0aORmppq+fu5557Dhx9+qGg9ORyO65DrEkrrQQjHs7h8+TL++usvVcoBxC2+sfqkS34HJZ1GaWJDkxMrfYcaC8QSzvG2nfM0t4Fb2JCtqpyN47WILWXJV+Hj5CDn1WHpWWLNbSKH4yp0PgKcbWS3u1Oe9F2QVyVqRBvYMDra5CFs2ITUx1e2RiEtzLPgEo3mm6WEhY29eDQ0lJYarAQQGvFI6+8wySWa0WCGyWTWvJ7nMnOd/i7oBNStF6ZOZTgeifaStAjWrl2L1q1bY968eSguLi6fxJnNEATB8o+G559/Hvn5+ZZ/CxcuhEkB1ZvD4agDj2HD4bg3UhdYXafXkHd9A8pP4iUvJihaC2mF2GsuooWNNy7EOhRsvMfChnQpzpqCZk+WBzWVQ9zCwoaiGJpFK+62luMJkIRq+99LVpR80YoNm0huL1YvyDMg9fGV3w0jwcKGZImhBj4UVj5SYlZVRapVSWmJ9Xk0btW0HlfR3NfSUu09KmUTBJuoeiFMPKMc98VtVi3nz5+PwYMHIzc310qkAWARbmjp2LEjOnbsaDn/6tWr2Lhxo0vqzeFwXACPYcPxYrQeRCuBdAsb8edRnWHWyCWaB9zLynidYEMTw8bBApw3WdiQ3hvn754WrgpFokLxcq6RpUeJZoOcR8W54ngtJJGVZZdo4kPYsPnOcrmGTUgbJyu+E2az2WNcopGugwZDmbQ8qlrmGCk2TtBckyuhETnKSrV3i5ad5VywqR/N3aFx5KF9D0fB1q1b8eyzz8JkMlmEGrPZjODgYAwaNAgvvPAC7rjjDlF5PvLIIxbhBwDWr1/viqpzOBwXIN/Chg/FOcoxffp0y8YBV/6bPn261peqGAKDow9SHA2AoUUJNRaIJZzjbYKNPJdoXmRhQ7jvzl2iUeTvqqaizFeNOyXrGhnyo2TkLtE4XgLR7RPTLtHEOTlj9Y3lBjZs4kPYOFkhKBiNZuIYQGthobwONC7R5As2Ul2iVRU2qDaoae4SjRy5Q2vBxmAw4UL2TafH8Pg1HLlo38MRKCsrwxNPPAGDwWARanx9ffH+++/jypUrWLlyJWbNmoXatWuLyjc+Pt6i7pvNZmzevNkV1edwOC5AzhBCJ8J9IofDIqKeX0afdak7qF0ZwobOwkZKBZSHiWrYW2vythg2Ms4lPm+sPGwKIMslGpXrDs9pK0fIsrBRyyUaxTHcJRrHW5Ak2JBQ89UQNdZ0XTU4ngfpm1RhYWMwkBfkWXA3RbPJwKiAYCNVoKgq9ND0PT6aCzY0LtG0FWwuX7gFo8F5WzZoGKFSbTieClm61Jj58+cjKyvLItb4+flhzZo16NWrl6x8a9eujWbNmuH48eMAgH/++QeFhYUIDAxUotocDseFyFn04wsBHG+C1add8jss4TSqfaJauUSTihr1IJZh217eZmFDZ2LjIJnwuHlSU8lxiUazpKn9a+n6CsiLYaNgRWRC2mUsgKF+lsORAel7RyNGa4kAehdtzL6zUjcHMXo5ngLJjVmF50waF2Du4hKNxg0ZCamCTWkVwYaq79H4JfDVs+8S7Rwhfg3ALWw48tG+hyPw/fffA4DFfdmMGTNkizUV3HfffVaTxArxhsPhsI0sCxuC31wOh3k8YCIpdeFenJMOy0kUkN0ulC8kii/eFajigonwu73m8jbBhuZqHIkRXuUSTY6FjYY+0aj7GxVulZxXR7VniaIYmj7Ck559jvdCjGFjp28TE5PX5XiATzTPiPzleRDdpJrMVPFrADYEG5qxrSSLuiqUlkpziVbVEoXKwkbjDa7+/hSCTRnbgk1gkB8ianBjAI48tO/hnHDjxg38+eefloF7zZo18cILLyiWf8uWLa3+PnXqlGJ5czgcFyJjMq+1iS+HIxdPeIIlL8i56OJpXKJVxNBTEtbaQW4ZxMVYT1uIlW5gQxYIPaipZFnYsL8RVKUYNuxb2NAUQ4ph42miLsd7cYVLNDXFTDEbZFh9a7X+NnDsQzMXN5nMVC7RaCwxXI0PhWikRAybMgqLI3tUtbCh6Xu03jihp7ivVa9LbbIJgk2DhuGatyPH/WFasNm3b59lEicIAvr16wd/f3/F8o+IsPYpeOPGDcXy5nA4rkPOt4+7ROO4O54w+FNzUY7SwIZCsFGkOp6D3Rg2zieTnrYYS+duz4GFDWnC7EEPnBzBhkax8YQ+kYSs+E+qKTbyAy8L3Aqa4yG4JIaNiogLl8hoH8xdojGJjsKFmMlo9iiXaAZFYtioZ2Gj9XidJjaR1hY22VnOBZt60dwdGkc+2vdwTrhy5QqA/yZy7dq1UzT/sLAwAP8NMm7fvq1o/hwOhz20HoBwOKrC6OMudfFR0lmUVhBkiwd2GlOSaziF4S7RIM/ChpS1BzUV0SWak3UMKgsbcdWhhzZjVUJKsW9hQ4PX9REcr0VsDBuz2UyOGaPm6yFKsHFdNeTAaLW8Hpp+3mgyuY1LNBr3YSYFYthUFV7ozxNvYaP1Blc/P3KodantoQT5t0uQe73Q6TH1G4apUxmOR6N9D+eEa9euWf0dGRmpaP5lZWVWfzPlN5bD4ThEzsIFj2HDcXs8YAYq2TWWpPPoVtVJFg+ydrg7zFTl81xYhNlsJk4CXdKGGkJrvWWTZDZ7WQwbOS7RKCxsNH6u1ChdloGNSh8NmlJIi1ZcsOF4CkQLG7OZLvh3JdTVa7w4iI0HfX9ZhCrmi9F9XKJRWQwpYFFXJlWwKbE+j2pcpfE74Ksnt6nU9lCC8wTrGgBo0DCCeAyHQ4LplUsfH+sO2GBQ1k9hhQu0ik6rRo0aiubP4XBcA3eJxvFmPGAKLXlRTsq7T3eOGVp4qGL1/gAgV65Ke5nNZKsRj1uMpXgo7LWJO8RlURKBMNuQ6RGNgffI9TWQI0qp9ixRlGP0MreJHO+F5p01Ve7gWNs36hEWNipac3OooZmLm0xu5BKN4l2nsRYiIdUFWEmVWC9GCmsfrWP+CoIAPUG00dLC5hwhfg0A1GsQ5vqKcDwe7Xs4J9SsWdPqb6VjzBw7dszqby7YcDjugTwLGz4M57g5rM6MRcDaJZS7RHMfiwd1qiJOsXEHn9hKQ3Uf7DxX7rC7UUlcbWGjeYeikks0ycWo1D40NeQWNhxvgcqKoNJ3k0qvUbGvE1UUq68tq/XycnwovF0YjSYYKAQKFgQbupg88gUbyS7Rqgg2NJZ9WlsuA4Ce4BZNyxg2JMGmVu3qCKimV6k2HE9G+x7OCbVq1QLw30Tv8OHDiua/bds2q0lko0aNFM2fw+G4BlkWNgwMQDgcOYh7gtl83tWMYUPrtsqdBBsW7mvV1qKZAHpa/ytRr6G0sPGctpIl2FDlL7JClDAUwqa8HBUtE12FifDwe1ofwfFeaFy/Wm90oFhElVEf8dCXxur3intEYxMaCxuzyQwDhcjBhEs0nUB8ZrR0iVbVwob0HQYAHwoRytX4+Tm/t1q6RMsmCDYNGoarVBOOp6P9m+iE2NhYywDAbDZj69atiuW9c+dOnDhxwvJ3aGgoWrdurVj+HA7HlUgfSbMwAOFwZOEBE0nJi3IuU2ycBz4HXDSBl5ipGo8AsWpV5nsmgqsjwANjiEm8f3QWNpKyZhLStTh1iUaKiwR2FwuVRup1suQSjQQXbDieAs2zLDaGjZqI6TeYfWuZrZh3Q/NuGN3IJRpAFqEUcYkmUaCoeh6VhQ0D744vQbApLVU2XAYtJpMZ5885F2zqc8GGoxBs9HAOqFGjBu677z7L32fOnMFvv/0mO1+z2Yy3337b8rcgCOjatavXTPg4HHeHW9hwvBlRTzCjj7sgCFS7T+2c6ZJz3M0lGov3lWb3IAsuFpSEzsLGnokNRd4e1Fakd8dkNtO5PpOQtyqoVAVpfSYjbUQJH6NxPAXRLtEY027EjTXZfG8lO5Jk9Ho8BZrNkyZal2gMWNgA5A1JSljYKOUSzV1cGPv7kwQbbSxsrl6+jdIS52XXj+aCDUcZmBZsAGDIkCEwm80QBAFmsxkvvfQSDAZ5aupbb72FHTt2WPIEgISEBCWqy+FwVIALNhyvRsQLwPLTLmURUcq7T+cSjbxg7IpFT5bvj1jcZQKoKBIvh8rCRlrWTELz6jhqEvJ7KaFCtFDnrVKMGImzNrXWHpUoxpOESo53Q/Msi13EVVVHEDPWZPW1VdGYm0MPlYWN0UxlleJD4V5NDUiWPkrEsCmTaFFSUkVccJcNVnqCGGfQSLAhuUMDuIUNRzmYF2wmTpyIGjVqWP4+duwYRo4cCaNR/AtqMBjw3HPP4aOPPrIsvAiCgKZNmyI+Pl6xOnM4HBcjY2ZA4zeXw2EZT3mCVfOOResSjTB/kbpY6gpY2DFftblIE8By11Uuq44m0ASCtx/DhsYdhec0Fs21OGoT4nvJQDup5nFMsgtFtRQb+eXQBG/mcNwBGktiJXbduwpPcInGwOeBYwcqd4FmsmDjq9cxMQYAyNdk1NDCpqrQQ7XBioF21RNdomkj2JwjCDZ+/j6oVbu6SrXheDrMj4qrV6+OqVOnWlnZrFy5Eu3atcP27dup8igoKMCCBQvQvHlzzJs3zzIprPj///3vfy6rP4fDUR55FjbMd3scjnO0H0MrgtjdW66MVWE2u5lLNBUgx7Cxbi+ST+zyoKwe2IaEa7L3XNG4vvGkpqJ51x1b2BDydmk7sXUTJL8/bmRh43FWeByvhvQ8i3aJxuiHgYXd+PZhu8/0VmiEeaPRTHSJxkr8GgDwUcPChsJFnD1sLGwo6sLC5gk/P1+nv2sl2GRn3nD6e/3ocD6W4SiG87eAEV544QXs2bMHy5cvt4g26enp6NGjBxo1aoROnTrh0qVLVpPiOXPmICMjA4cPH8bu3btRWlpq+b0iD0EQ8Pzzz+Phhx/W6tI4HI4EpO4WFQSB1bkOhyMCD9j2CPV2b1VYQZDWQjRZGGZ6LUFcKaQde54qltM8W1WhsrDxoMkenUs0RxY2mio2dKgVw0biM6GaUKpAMXyRg+NJ6HQC4GRN0XqjA1vWNp6wwYJxjdtroXFjZjKaiAKFry8b8WsAwIdkYWOU/36XKRTDhkYcZuFbTLKwoYlx5ArOZTm3sOHu0DhK4haCDQD8+OOPuHTpEnbt2mUZQJjNZpw9exYZGRlWx5rNZrz44otWfwO2A4/evXvjs88+c23FORyO8kgcQ3jsDm+OV+EpT7BoCxsXvrtmihg2rhCYJOfIwEMg2iUaA5M/l0BQbOy7RKPI1oOay11dotHmrJ5LNHXP0wIWFok4HKUgffesvps03wWZ9RGFmL1B7tTJ0OBp18MYNM+LyWQmihwsWdiQLFJo4vGQkGpRUvU8o4nCwoaBbzEphk1Jiby45lIoLipDzuV8p8c04IINR0HY6eUI+Pv7Y8uWLZg4caKVAFNhLVN1oleRVmFJU1nkMZvNmDhxItasWQMfH3aUeQ6HQ4fUiQELgw8ORzYe8hir+T7KWTAWk4cnIdYqgmxh45ntR/Fk2abQWNh4yosO2mdJ7A/0eUuGsVsgOYaNataM8vPw1H6C452IcolGk6GKr4eYolgdHtHEmbN7nuI14VSGysLGROESjWCBoSakGLkmCpGEhFSXaDYWNhQxbFh4p/0YtLDJJljXAOUu0TgcpXAbwQYAfH198cUXX2DdunWIi4uzEmoqRBl7/4D/hJo2bdogNTUVX3zxhce65+BwPB2pgwiaASKHwzqiJtEuq4V8RC8iuvhizIS5FAuTlwpYFI+8VbAhx7ChS7PK0vI/noG7WthQo6J7R2knKlsPVxbksf0ExysRI9iwBhN9qxJ4ynV4EHQxbChcojEQZ6UCH8K6okkBl2hVhRfq86pY2ND0Oz4MtC3JJZoWMWxoBJt6XLDhKIjbuESrTO/evdG7d2/s3r0bycnJ2L59Ow4fPuxQuW7YsCF69eqFwYMHo3///irXlsPhKI3UsTcXaTkegagXgN2JqtiFOVdeidlMYWHjkoVEdu+PWEwkl3IeuhAr5XtEY83lMYtloBRsHAim5LaSUiNlUc0lmsQhjLtYa+k87LnncIiCjZlhl2giYPm9lRJnjtmG9hBI8V6Afy1sCG7EWHKJ5uOrQgwbhSxsaAQbFtwYk1yiaSHYnMt0LthERAYiuLq/SrXheANuKdhU0LlzZ3Tu3BkAkJ+fj0uXLuH69evIzc1FtWrVEBkZiTvuuAM1a9bUuKYcDkdZuEs0jvfC8LxYFOInA9IvXKCYsWviEk1qlmo8A1RWEf8dZiZMRj21/yVdlaQYNh7WVGLd61mnK5C5q1GpClLjaKnVRHLL8dQ+guO9kMY5NK6JrDNU05Wsa45VHQmKjbuI3O4KzfjfZDTDSBJsGHKJRrJIUcYlmrQ8pAg2rojbKRaSS7QyLSxsCIINj1/DURq3FmwqExwcjJiYGMTExGhdFQ6H42IkW9hwl2gcb4PhR170ZEDWtZBm7GYK10tyyneQp8rnubIMkoUNC7v1XALpwbDTLt4WL0meSzT2LWzUQnoMG4Ur4iI8to/geC1kl2j/LcCaxduBuBYPUWwk1Yzdy/EIaNxtGU0ULtEYsrAhuXkjiU80SHWJVlZmgslktvRHNEIxC2smej/nS9VSLY6kYjabiYJN/YYRKtWG4y2w08txOBwOJVKDSPLdmxxPgOF5sSjELs7J1mucYDZTCA5MNTwrdfmvzbw2hg0BSTFsPKyp6CxsHKST8hZdG3po81bPJZpUwYbxGDv/4q19BMdzERXDhjGXaJ7hfBee90H1AGj6epORwiUaA3FWKiC5eTMYlbCwkS5QlJb+J/ZQuURj4L0hWdio7RLtek4BigrLnB7DLWw4SsNOL8fhcDgikDKQ4DFsOJ4B/bOv/XDbMaJfRxkXQ3RbRTV5kV6+0jBTl0rN5q2CDelbZK9VvNHChmRRJ93CxoVtxZhiI/kVUvFxklOUp/YRHO9FlGDDGp5hYCOpT2L4cjwCGusNqhg2DLlEI12TSYkYNjIEitKS/86lconGwPdYT3SJJs3iSCqk+DUAUC+aCzYcZWF+9fKff/7RugocDodFJIwjaIIccjisw/LEWAxiF1pl+RQnnEqyrgFc5K6H5XtJYxVR+b+9VbBx8ltyyhLUiAwqtwr9919mZqbXCTaAJM9x//4gL191UMmChXELm38Lk3yqp/YR3kK3bt2s+rpu3bppXSXNIT3TZpPZ8j2gWs5VVXzlig3HNfhQ7NgymcwwuJFLNNI1yRVny9tDupWOGAsbVtyT+vmTBBv5VktiyM5yLtj46nWoUzdEpdpwvAXmY9i0bNkScXFxGDt2LEaNGoXq1atrXSUOh8MAgoQokiz4Y+VwXM3FSxdw6tRxXL58EWVlRfDVA+Hh4QgPD0fz5s3RqlUr+PhovytN9OKcC19fM8WYn6lFdBWqIqYIk8lMFL3s3e/CwkLs378fJ06cQG5uLsxmM0JDQxETE4P27dsjNDRUZK01QMK9cHeXaIWFhThy5AgyMzNx+fJlFBYWAgDCwsIQHh6OOnXqoG3btggJ+W/iWv7+OL5wJi1sGFu1kxzDRuF6kMqSuizFraA5noZAeKbNKO/jaN/tqiJKdnY2jh07hvPnz+PmzZsoLS1VbLwnKoSNpBLUQVq/yfIVuT9UFjZGE9nChiXBhlAXo0yXaHLjtYixsPFhZL1Er2fLwoYUv6Zu/TCq+EwcjhiYF2wA4Pfff8fvv/+OF198EcOGDUNiYiJ69OihdbU4HI6GSBl/892b6pKZmYlGjRqJOken0yE4OBghISGoW7cuWrdujXbt2uHhhx9GRIT0QH4NGzZEVlaW5PPtkZubi7CwMEXzlMKZM6ewY+cW7Nu3C/v/2Iu8vFtOjw8KCkK3bt0wYcIE9OvXT9FFskuXLuHgwYM4cOCA5d+VK1esjomOjkZmZqaqO7hIO0VJi8KAaxbRtfBwVFpaiqNHj1rdo6NHj6KszNov8zfzvkOvnkOo8qRpv8r97x9//IFPPvkEqampKC4utnu8r68vHnzwQbzyyivo2bMnVT20QMpzQWqvvft2YXiDgRJrZJ/Bgwdj5cqVks8/deoUFi9ejNTUVKSnp8NodL54IAgCmjVrhvvvvx+jR49Gw/p3Oz3ekYWWO4hbatWBVM7t27fx99+HceToXzhyJB1Hjqbj3Dlbi66MjAw0bNjQdRWVCB+jcTwNkitIAP8GAwe5swNw/MRxbNmyGVu2bMGOHTtw8+ZNp8fLGu9Vqfqjowfj9/176M93wvjx4zFv3jxF8rLH1atX0bJlS1y7ds3mt0kTX8MLk153ej4L3xVPhqavN5rMRJHC11f7zWcVuNr9oRx3aABQWvKfuOEuVt5El2hlJlGCt1zOZd5w+nt9Hr+G4wLcQrAByjuWoqIiJCUlISkpCQ0aNMDYsWPx+OOPIzo6WuvqcTgclZEm2PBdD6xjMpmQl5eHvLw8nD9/Hr///ju+/fZbPPfccxg6dCg++ugjr+/zBUFAcXERvps/F2nrVuHkSXGuQwsKCrB27VqsXbsWLVu2xMKFCxEbGyupLvv27cOmTZssC/8XL16kPpdmIaMyssbjJJdoNP6cXTIhcL1kk5+fj19++cVyjw4fPozS0lJFyqiY81EFMNUJKCkpwUsvvYR58+YRJ4wGgwHr1q3DunXrMHz4cMyfP9/KYoMdxN9DogghsSau4OjRo5gyZQrWrFlDJcxVYDabcfz4cRw/fhzffvst6tWtjzFjnsRjCU/B39/fzvGO83EKIwsLalB1UeL8+XPYtCmtXKA5mo6MjDOi7pFLkGFiI0awSUxMxKJFiyx/V2wEcDe2bduG7t27W6Vt3bqVuxPzEKiCq5ucu0TTarwnyw2txjzzzDN2xRoOGwiCAEEnOHWlazaZYTQ4/5j46tmZ1xMtbAjWQiRkCzal9BY2rGye8PMjL1WXlRrh5+/6Je3SEgMuX7zt9JgGXLDhuAB2ejkCFf5wzeZyX69ZWVl499130bhxY/Tq1QuLFy92uEuTw+F4IBIWabhLNPeltLQUv/zyC1q0aIEff/xR6+poiwBcu5aDz7+YKXryXpVjx46hY8eO+PLLLyWdP3PmTEybNg2rV68WJdYAUnwku+79pbOwYcfERkxVTp8+jaeeegrz5s3DgQMHKMUacXWjEWyKigrx0EMP4euvvxa9qLx8+XJ06tQJOTk5os5TA0mPhRvsbiwrK8Mbb7yBNm3aIDU1VbYQcP5CNmZ+NB29HorDunWrbX537BLNeb4MNJVqVO0zN21Kw/sfvI1Vq5Nx9uxp7cUayOul+RiN42nQLHyS4r9pNt5z09dx8eLFWLFihaw83PTS3QrSHkqDwQSDwblIQXKZpSakOLlGo7zvc6lM91+VLWxMhLqwItjoKQS5UplCFi0Xsm8S+2puYcNxBcxb2Dz99NNYtmwZbt0qd/FSeRJbId5s3boVW7duxXPPPYdRo0YhMTERHTp00KrKHA5HBbhLNPckKCgITZo0cfh7WVkZbt26hUuXLsFkst2NVFhYiMTERBiNRowdO1ZWXVq3bi3rfK3iwJCe4ujoRujQvhMaRt+Jeg2icMcdEbhx4wbS09ORlpaG7Oxsq+MNBgMmTpwIPz8/jBs3znUVr4JoCxsZZZGKorIQ8bLug+5yy9uN1H5msxkJY0Zjx44dNr/VrVsXo0ePRkxMDPz9/ZGRkYGVK1fizz//tDru2LFjGDBgAHbu3Ak/Pz/Kq2AT4rq6ncYPDw9HgwYNJJcpxj1lbm4uBg4ciN27dzs8pnnz5ujRowfq1auHyMhIhIeHo6ioCFevXsXJkyexe/duHDt2zOa8ixfP46efv0ffvoOs0qVa2LhS3GLtlXePIYx0Exs+RuN4GjQbUyzfTwmvTZMmTfDAAw8gJiYGtWrVQlBQkGLjPVLXGhgYiOgG5d8VvZ+vqDFS/fr16Q8WweXLlzFx4kT5GfGuyOXodDoY4Xix3Wwmu0RjKV6IjlAX+TFs5J1vZWEjIeakFugpLWzU4Bwhfg0A1I/mgg1HeZgXbL755hvMnj0bKSkpWLRoEX777TeYTCaLxQ3w32Tu1q1b+Pbbb/Htt9+iefPmeOKJJzBmzBjccccdWl4Ch8NxAWJN9QWwMwDxZmJjY7Ft2zbicYWFhdi3bx++//57LFmyxGrRzmw2Y+LEiejRo4cs92jp6emSz9UUO7PimCbN8PDDozBo4DDUrl3Hkh4eHojwGoGWvw0GAxYsWICXX34Z+fn5VnlMnDgR3bp1Q9OmTWVXsVq1amjdujViY2Md7uYUbWEj6/UlxbChyMEF/YfkHBWqSoMGDRAbG4ucnBzs3LlTfAYV60wEwebHn+Zjbdpam/QZM2Zg8uTJ0Ov1VunTpk3DypUrkZCQYPWc7t+/H2+//TY++ugj8XV1Ec4Eg/hhj2BsYiJq3lHdKv3KpTzReQ4aNAgLFy6UVEcx5Obm4v7778fRo0dtfgsKCsKLL76I8ePHUy24ZWZm4ueff8ZXX32Fy5cvOz3WoTCjpYUNZd5qWUTRluPj44M774xBq7tbY/8fe3H+/DkX16wSMpqCj9E4noYSLtGq0qJFCyQmJuLRRx9F3bp1HR4nd7xHmme1ursNFietAlC+UMmCtcP48eNx48Z/cSY6dOiA33//XUJOvC9yNT4+Asqc/G40mmEgiBRMuUQjWIiaZAo2lS1k5J5Pqgsr32I/QgwbAERRTymys5wLNiFhAQgNq6ZKXTjeBTu9nBP8/f3x6KOPYsOGDcjMzMR7772Hxo0bWyxsKsSbyi7T/vnnH7z++uuoX78+Bg0ahFWrVsFgkNfRcTgchhC71qsTmHAzw6EjMDAQPXr0QFJSEtauXYuAgACr3wsKCvDhhx9qVDttqfwUx8V1wZKkVVi/bhfGPf28lVhjD19fXzz99NPYvXs3wsLCrH4rLS3FK6+8Iro+/v7+iI2NxYQJEzB//nykp6cjLy8Pe/fuxZw5cxyeJzaklAtD2LidhY0U3/JRUVEYNGgQZsyYgbS0NOTk5CArKwspKSno0aOHvUKIVLSas/a7ceM6Pptl+67Onj0bb7/9to1YU8GQIUOwZcsWG2uaWbNm4eTJk+TKqQWhnezpEKzGsDEYDBg6dKhdsWbIkCE4ffo03n//ferd0Q0bNsTUqVORkZGByW+8jcDAQIfHOnaJxr77OLWwJxoLgoA772yCwYPiMXXKe/hlSSr++vMs1qftxCcff4m6UfXUraOMc1lZJOJwlEIQRIw/CN+FuLgu2LJlK44dO4bXXnvNqVgDKDDeE/E6stAN//jjj1i9+j93m127dsX48eMl5cXC9Xg6pLiyJpOJaJXCgkhYAcnax0gxz3CGXGGipJJgQxyDMvItphFs1HKJdj7rptPfuXUNx1Uwb2FTlXr16uGtt97CW2+9hV27dmHBggVITk7G7dvlQaCqukwzGAyWYHuRkZEYM2YMxo4di7vvvlurS+BwOAogdpHGR+zqMIcZ+vbtixkzZuD111+3Sl+5ciXmzp1LHPR7ItWrh2DxzyvRoUNn5wc6eE3uuecezJ8/H/Hx8Vbp69atw7Vr1xAZGUlVjzlz5qB27doOF92dVk3sjFjODJq4qK5RDBupiKhK06ZNcenSJdSuXdtl1XEm2Cz68Tvk51sH6nzwwQep3Ja0a9cO06ZNw9SpUy1pZWVlmDlzJn744QfpFVYQaSFs2BQh3n//fWzfvt0m/ZlnnsGXX34pua8NCAjAiy+8il69+uPFl+wvoDl2ieY8byZeS5XqUPW5GDQoHsOHj0FwcLCTk1xcKQXLY2WRiMNRCkEQoNMJThdr/7NQtX9M5fFedKMI0XWQOt4T07dqPT66ePEiXnjhBcvf1apVw/fff+/UrSdHW0gCvclIdonm68vO/I8Ug40UN4aEXMHGysKGIB6R4vGohS+FIFcmM7YPDWazGecybjg9hsev4bgKdno5CXTp0gXff/89Ll++jIULF6Jbt24A/psIV7W6ycnJweeff47WrVujXbt2+Prrr3Hz5k3tLoDD4UhG7NyAB7N1b8aPH28jCly5ckV0oHtPQBCA0NAwslhDYNiwYbjnnnus0oxGI9atW0edR/369SWJNcC/CxkaTvLPnz+H/gO74bv5X+HSJefPkQAXLUhIzFLMaYGBgS4VawDHkz+j0Yhflv1kkz5z5kzqvF999VXUqFHDKm3JkiWW2IZaQ3ou7IkzUmLYuJoTJ07gf//7n036yJEjFRHGBUFAo4aNsWzpGpv4NYAMCxs7jfXTTz/hoYcewk8//WTjCsgVqHW7qq6h1KgR6VysAaD2w8QtbDgca8iWBM77OO3Ge+7zPj799NNWazozZsxATEyM5Py0FqC8AaLAYTLDYHBuYePDkmBDeM/lxrApLZFrYVMphg3J2oeR558VC5tbN4txO6/E6TENuGDDcRFuZ2Fjj2rVquGxxx7DY489hqysLCxYsAA//vgjMjMzAdha3QDAwYMHcejQIbzyyisYMmQIEhMT8dBDD2lRfQ6HIwWRgwm+EODehISEoGnTpjZBrC9fvox69dR1+aI99M8y6ci+ffvi8OHDVmlnz56VUCdpCDoBoNx15goDm+PHj2Hm8WP4+JMZ6BjXFUOGDEfvh/ojKMh6EZRP3h3w760zmexPRPf9vhs5OVet0tq1a4d7772Xugh/f38kJibi008/taQVFxfj119/RWJiougqq429p5tsYeOaujjj7bfftnEdHBUVha+//lqR/CveIX//ACSMedLmd4khbOy+3EajEZs2bcKmTZsQFBSEIUOGICEhAb169YKPD70LFdbeeykWKKpfgYw2Y3Wcdu7cOaSnpyMnJwc5OTkICAhAzZo1ERUVhbi4OFSrxrbfeqPRiLNnz+L48eO4cOEC8vLyYDQaER4ejvDwcDRv3hytWrXySmtlNSC9t5YYNvI24BMRO95zFwubH374AWlpaZa/27dvj5deeklyfmz2Qp4HyYrDZCLHsGHJJRrJ2keuYKOqhQ0jm1z9/MlL1WrEsMnOdG5dA3ALG47r8AjBpjLR0dGYPn06pk+fjm3btuGHH37Ar7/+ioKCAgC24k1xcTGWLl2KZcuW8Rg3HI4bIXYowepCAIeekJAQmzRHC8WejJLz4gYNGtikkYKDK4lOJ8CohvthQqOZTCbs3rMdu/dsx7R3XseDvfpiyJDh6NK5G3x8fFzmqkdKLJp/T2QKR5O/HTu22KRVdctCQ3x8vJVgA5S7c2FBsHH2aCWnLMEbkydZpWVkZMBXF0rKVX7FRJCdnY0VK1bYpM+cORPh4cpMQkn9ltnOM1RhIe88X+cZFxQUICkpCUlJSahduzYeeeQRJCQkoG3btsQ6U6PSYqUglPcYLl7X1QySYNCwYUNkZWXZ/S0rK4tq0Xjr1q0WjwzOuHnzJj799FOsWLECf//9t8PjAgICcP/992PChAkYOnQoMV8AmD59Ot59912Hv3fv3p2Yx+OPP46FCxc6/P3UqVNYsWIFtmzZgt27d1vmwY4IDQ3Fgw8+iFdffRUdOnQgls+hh+j6SYRSI6erceV4Tyu95vz583j55Zctf/v5+eGHH34QJcxztEFHivliNMFgcCOXaJTCrFTKZFqSVLZEIc2dWXFPqqewsJHbLjScy8x1+rugE1C3XpjL68HxTtjp5VxAt27d8OOPP+LSpUv47rvv0KVLF8vkz2w2W1ymAXT+6zkcDjuIdonGdw66PdeuXbNJq1mzpgY18RwKCwtt0tTcLSzGJZpaOziLigqxOjUFTzw5Cp26tML/PngbR4/9pUrZtEgWekSVQcb879KxvcV2ADhwYJ9NWpcuXUTX5b777rMJWL9jxw7R+bgG8feCNORUe668aNEiGKsop5GRkRgxYoRiZUhxHUejTIjpFi5fvoxZs2bh3nvvxd13342PPvoI58+fp8/AUR1k5yCiLEYWUpSm3O2k1rUoZ86cObjzzjvx/vvvOxVrgHJrv40bN+Lhhx9Gp06dcOTIEZVqaZ/r16/j3nvvRdOmTTF58mRs3LiRKNYAwK1bt5CcnIy4uDgMHjyYuwxXEGUXcqW/JGLHe6xZGNrjqaeesnKPOnXqVLRs2VJWnu5w3Z4A0fLMSHaJ5qtnZ25PcvFmJFwLiVKZsVoqW9iQxqCsxP2lsaBSwyVaNkGwiaoXQiUucThSYONtdDHBwcF48sknsWPHDpw6dQpTpkxB3bp1uUjD4bgxYgfUPIaNe3P58mWcPn3aKi00NBQNGzbUpkIaIurRJxxbtU0BoE6dOuLVjDL5AAEAAElEQVQqJAO1Fh/ttVlYWASGxz+K6tVtLbcquHYtBz8smIcBA3ugZcuWmDlzJrKzsxWsmMrnKY3FJZrteMpgMOCf49YuDPV6PWJjY0UXY++8y5cv49KlS6LzUhopazvE8afKC0arV6+2SUtMTIS/v79iZQiEGYd9vYY8Trc3FoiNjUX//v2dxtc6duwYJk+ejAYNGqBHjx5YsGAB8vLyiOVpjejFRJX7CqmPrk4naL5QajQaMX78eEyaNAm5uc4XaOyxd+9edOnSBVu22FoWqsXt27fx559/yspj9erVaN++vSJiJodesHH1qoTY8R7t6+iyGH8EvvvuO2zYsMHyd+vWrTF58mTV68GRhi/BwsZkJrtE8/VlZ5GcdD1GmRY2cmPYWLlEI7hnI43X1EKnE4iinBoWNtlZzscD9aO5OzSO6/A4l2gkrl69iitXriAvL0/ziQGHw5GByNeX5CuXwzYzZ860WeQcMmSIl/bjyig2BoMBq1atsklv166dhDpJQ5Rgo/CtDg4OxswPv8C70z/G1q0bsWp1CrZt34zSUvuBJf/++2+8+eabmDJlCh544AEkJCQgPj7erqs+V6PKUy+iEHuCzYUL2SgpKbZKq1+/Pvz8/CRVp3HjxjZWNSdOnFBVYLSLJMGGkKWK3drt27dx8OBBm/QePXooWg6pr7bnFohmX5W9bO+++26sWbMG165dw7Jly5CUlIQ9e/bYPd9sNmPr1q3YunUrnnvuOQwaNAgJCQno3bs3fH3ZmybpBAFqeJGUjrSHl8YKukWLFggLCwNQHlemsqii1+vRokULYh7BwcEOfxs3bhx++OEHm/SAgAD07t0b999/P+rUqYOioiJkZmZi1apVNjFB8vLy0LdvX2zZsgWdO9sPFF+7dm20bt0aAJCfn48zZ85Y/d64cWOn9QTsu7eyR3BwMNq1a4e77roLMTExCA0NRfXq1VFaWorc3Fz8/fff2Lp1K/755x+r806dOoWRI0di+/btTL4H7gRpnGOxUKXo8KR+G1wx3rt+/Rq+mP0xDh78HecvZOHatWswGo2IiIhAjRo10LJlS3Tt2hW9evVC06ZNJZdjj3PnzuGVV16x/O3r64sffvjBqUhPjTdOKzSAJAoYDSayhQ1LLtFIApTMGDYGuTFsrFyiOT+WJTfyer2PU+FOruURCYPBhAvZN50ew+PXcFyJV4zALl68iB9//BELFy7EqVOnLOkVbtE4HA5QZjLiWiHZbQIrXC8qREGJ/YVVexiKTSjK96w4VZGBQdDr2Nld5ArMZjM+++wzfPHFF1bpfn5+eP311zWqlcYo9NlavXq1jf/yiIgISS6rpKKhXmPB398fffoMRJ8+A5GXdwtp61Zj9epk7P9jr11LCLPZjG3btmHbtm1WC7x9+vTxuoWtitaxJ9icv2BriRQdHS25LHsLlGfPnqWKR+FKpLinc+RCrnKuapGenm7Xn3n79u0VLYc43nbwrsnJNzIyEs8++yyeffZZZGRkYPHixUhKSrJZnK6gqKgIv/zyC3755RfUrFkTjzzyCLo/MAD33OM83o2aUwmxO1/VnuVILY9mgahyYPHExEQsWrTI8ndUVBTS09Mllg788ssvdsWawYMHY+7cuYiKirL5bcaMGVizZg0mTJiACxcuWNJLS0sxZswYpKenIzTUNl7VhAkTMGHCBADAtm3bbGLWzJ8/X1a/FhYWhjFjxiA+Ph6dOnWiWsTes2cPXnjhBRw4cMAq7fPPP8err74quS5qsHDhQowdO1aTsqOjo5GZmen0GFfHtqBByniP1GefPnMSs+d8YpN+8eJFXLx4EUeOHMHSpUsBAH379sXkyZNx//33S6i9NWazGU8++SRu375tSXv11Vdx7733ys4b4HqNWpDcbhmNJuJYiS3BhuASTaZgI9f1l5WFDSmGDUPro3o/HxQVljn83dUWNpcv3ILR4Pw5bNAwwqV14Hg3HruyUFpaipUrV2LBggXYvHkzTCaT1cSvcvwaDocDXCsswHv7NmtdDWoMZSZRgx/9eR+Pc4v2dlwv1AlWf3e/KykrK8Pt27dx+vRp7NmzBwsXLsRff9nGD5k9ezbVblpPRJR9jYODi4uL8eabb9qkjx07VlXRQa1dXLTf+5CQUIwamYBRIxNw8dIFpKauwOrUFByv4tqrguLiYixbtgzLli1DzZo1MWrUKIwZM4Z6sVvy1aswfhEjRNhbcLqWc9UmrX79+pLrY+/cK1euSM5PMaRY2JCyVPFTdeLECZu0hg0bokaNGoqWQ7ome+syVJ6LKduqUaNGeOutt/DWW2/h0KFDSEpKwtKlS3Hx4kW7x+fk5GD27NmYPXs27ryzCYYMHo7Bg+JRrx6dZYOrYH7uIsMlmlbk5+fjmWeesUl/+umn8c033zht8wEDBmDnzp3o2rWrlWiTmZmJqVOnYs6cOS6psyOioqJw8eJF0bHoOnXqhJ07d2Lo0KFYv369JX327Nl48cUXvW4zgpKQYvW52iWa5PGegq/kunXrsG7dOjz//PP49NNPJVvaAsC8efOwefN/89XmzZtj+vTpCtSyAsb7WA+BNCcvKSFvsvSliHGiFiRPHkajTJdoMoWJEisLG+d1YcnCxo8QG8bVgs05QvwagFvYcFyLx42+Dhw4gAULFmDp0qWWgIkVQk3lAXdFWo0aNfDoo49qtjOHw+FwvInt27fLWnCqV68e5syZgyFDhsiuS5s2bSSdV3l3rCYoMI5+8803cfLkSau08PBwvPHGG/IzF4EYl2hqL1RG1amL8eMmYvy4iTh37jQ2bFqFxYsX49y5c3aPz8nJwZw5czBnzhw0bdoUCQkJGDNmjEviLDGzZvvvWMreLsibt2wnOSQ3P86wd+7169cl56cUYm+F2WwmWo7Yu7+rV6+W3GelpaXZtRAAYDcmU+3atSWV4wzS++vImk1uvva49957ce+99+KTTz7B1q1bkZSUhJSUFIcxbM6ePY3PZn2IWZ/PROx9HTBkyAj06zsIISEV1hPqvZDiY9iw0lk4R9BwQ83ChQttYta0a9cO8+bNo2rvRo0aYfny5ejcubPVM7tgwQLMmDED4eHqLebIWQgPCAjAokWL0KhRI0uA+uzsbGzcuBH9+vVTqopeB2lh2rJ4SnKVCWn9ndTxHm1JNWpEIjw8DHq9Hjdu3LC4RrPHl19+iQMHDmDz5s0ICgqiLOE/MjIyrKzrdTodvv/+e0XjrXG9Rh1ILsRoYrawZGHjQ6iLSaZgI9clWlklAYxkuUS6N2pCEuXkClkkSIJNYJAfImoEurQOHO/GIwSbq1ev4qeffsKCBQssbg6qWtNUYDab4ePjg4ceeghjx47F4MGDlfF3yuFwVMVD1yw4DujSpQtefvllDBw4ULGdnvYsd2io6lZCbaS4YKpMSkoKPv/8c5v0//u//0PNmjVl5S0W0s5TxZBZzF13tUC3HnH44IMPsGvXLvz8889ITk7GjRs37B5/8uRJvP3225g2bRo6d+6MhIQEjBgxwhJ/Qal6uRSZMWyKigpt0sTu+iadW7GoqCki76FUq5Hc3FxJgdCBcqtzR9jL0+Y5VQDiq26vXSTGsKFFp9OhZ8+e6NmzJ+bOnYs1a9YgKSkJaWlpdtvMbDbjjwP78MeBfXh3xpvo2eMhDB48HI88OgzVoM5cgqWdr/aQej+0vK4vv/zSJu2rr76iiqtTQceOHfH4449j4cKFlrSCggIsWLAAL7/8shLVVIVatWqhT58+WLFihSVt165dTAs2ERERlphAauNICK8M6dkmu8iUjqzxnoOXuW2bWDzwQC906tQVTWPuQkR4KOpXcglUUFCAvXv3YsmSJfj5559t+tJ9+/Zh1KhRWLlyJXx86C0kzGYznnjiCeTn51vSJk6ciE6dOlHnQQPbPaznQBr/l1JY2OgZsrAhfS9IbshIKGphQxiIqjY3o4BkYeNqwSabINg0aBjOvuUzx61xW8HGYDBg9erVWLhwIdavXw+j0ehUpAGApk2bIjExEY899hjVAIvD4XA4bLBr1y6cPn0a6enpmDRpkuLuetwKGePCQ4cO4fHHH7dJHzZsGJ544gkZlZKGGAsbOdctdyhdMaYQBAFdu3ZF165dMWfOHKxbtw5JSUlITU1FcXGxzXlmsxm7du3Crl27MGnSJAwYMABjxoxBv379/t0JLa1mrEwNzCi/RnuTv7Iy28l2QECA5LLsCTbOhAi1ECugms1m4hlyRVkxFBUV2aTZi7shF9KE1vSv5ZG98bucfGkJCAhAfHw84uPjkZubi+XLlyMpKQk7d+60W4/S0hKsW5+KdetT8dbUlzBy5EgkJCQgLi5Okfo4gv11AWkV1Eqwyc7OtnELGBsbKykY+7PPPmsl2ADApk2b3EqwAYCYmBirv/ft26dRTegYNGgQBg0apHU1HEIa51T0fcT+TuTLL3e8V7W4h4eOxPvvfYo772zi9MCgoCD06tULvXr1wrRp0/DII49g7969VsesWbMGn3/+OV555RW6i0G5sLpt2zbL340aNcL//vc/6vOpYb6P9QyUcYnGkCUIwcLGaJAn2JSVyovDW/l8sks0WUUpCkmwkWt5RCI7y7lgUy+au0PjuBa3E2zS09OxcOFCLF682OIKw5nLs+DgYIwYMQJjx45F586d1a8wh8NhAr77gQ2CgoLQpEkTp8fk5+cjNzfXxnrh8uXLmDFjBr7++mv88MMPGDBggCuryixSn+SzZ8+if//+KCgosEpv3rw5FixYIL9iEhCzSKflG2yv+/Dz88PgwYMxePBg5OXlYcWKFUhKSsKWLVvs7qQrKSlBSkoKUlJSEBERgf379yM6upHECkk7TXHM4gImy+mH7Z1Ls6DvasRektlMvn1qfq/staEryqcRZ81m6/akub2uaKnw8HCMGzcO48aNw66dfyF1dQpWpabgxIm/7R5//fp1zJ07F3PnzkXr1q2Rnp7uglqVI/besNJVkNBKsNm9e7dNWnx8vKS82rVrh0aNGiEjI8OStnfvXhshUm0uXLiAPXv24PDhwzh58iRu3bqFvLw8FBUV2X3/q1oRO3IDyqGDZrc6zXdUzBOkxHivannx8Y/aP85JxaKjo7F9+3YMGDAAGzdutPrtgw8+wFNPPUW1QeDMmTOYPHlypTIFzJ8/X5JbNRLu0me6O6SYLzQu0XwYct1F+oYZZVrSlZXJE3xKKrUnyT2bqM10Lkbv53y52pUWNvm3S5B73bklfwMev4bjYtxCsLl+/Tp+/vlnLFy4EIcPHwbg3OWZIAi4//77MXbsWAwfPhyBgdyvIIfD4bBAbGys1Q45Z1y6dAlbtmzBvHnzsGvXLkt6Tk4OhgwZgh9//BGPPmp/AkkDC4u9kpBglHLhwgX06tXLZiGmXr16WL9+PapXr65c/UQgbhFL2cV+Jc8PCQlBYmIiEhMTcenSJSxduhQ///wzDh06ZPf4Gzdu4Pbt2zJ2y7MzmXK00KTX2w4x7Vlz0GLvXDnxGhRDtGBDVmzsPRdV3S0phT3LpYoYkEpC86yX98naWNg4IqpOXYwfPwnjx0/CiRN/Y9XqZKSu+RUXL563e3xmZqZL68P65hN3c4lmr4+OjY2VnF9sbKyVYHPr1i2cOXOGuFHFFSQnJ2Pu3LnYvn27LHc8rugPvAmaZ1vMxgcSio33KF9mkkWoXq9HcnIyWrRogfPn/+s3b9y4gQULFuDFF190er7JZMLYsWOtXKA+/fTT6NGjB1X9RMN4H+spkEQBt3OJRrAYkmthUyrTwqZye5JcovkwZGJDusdyLY+ccf1aAfGY+lyw4bgYdt5GBwwbNgx169bFyy+/jL/++stiMiwIguVfRVq9evXw1ltv4dSpU9i2bRsef/xxLtZwOJzysTcff7sdderUwejRo7Fz507Mnj3baqHKaDTiiSeewNGjRzWsoTaIcpcklMd569Wrl9UiEgDccccd+O233xAdHa1wDekRMyfQcg4tZpG0Tp06eOmll/Drr78iMTHRRfVxSbaiMcOx//2AAFshQGnBxhW7a8Ui2n0ZVQwb9W5wRESETZprBBsaCxtzlb9pMpZYIQk0a9YCr782DV/N+QFtWt+nXsGVEMTO3BjpK0hoJdhcu3bNJu2uu+6SnF+LFi2oynAlFy9eRM+ePTF8+HBs3bpVduyEW7duKVQz74Tm2TabzHTfBgJKjveo30iKA6tXr4533nnHJn3dunXEc7/44gvs3LnT8ne9evXwySef0NaOwygk6xh3c4lGEjnkirJlMi1JrAQbN7Kw8fMnCDYyLY+cUZhPdrtcI1L7eQjHs2HewubXX3+1/Lc9l2f+/v4YMmQInnjiCfTq1Yv5nWccDkcLeL/g7kycOBHnz5/Hxx9/bEkrKSnBpEmTsGXLFg1rpgEiHucbN65j0OB+OH78uFV6ZGQkfvvtNzRt2lThyolDtW+2zGJoF0lv3LiB5ORkp7EvPA1Hk9DwMFshoHKwYLHYO5eJWFZSLGyUzVIW9erVs0m7cuWK4uXQWdhU+ZsqX3VaKysrA6tTV2B1ajLOnj2tSpn2YCkYsF0k1o8UsNlV5Oba+qcPCwuTnF94uO1u26ruXV3JhQsX0L17d5w6dUqxPA0G1+1g9gZoFj9NJjO5vyNkc/36dfTq1Uu58R7lq0zbB48YMQLPPPOM1fO0Y8cOGAwG+PraX5I6deoU3nrrLau0b775BiEhIXSVkwDjPazHQBIyaSxKSHFj1IRoYWOUa2HjXLCpFqRHUUGZw99LSoyWTe+kcahWGyjsQbKwkWt55IyC/BLiMYHBDFj6czwa5gUbwL5Qc9999+GJJ57AI488ImtgzeFwyokMDMLbcb20rgY1t/OKkZtLt1tbr9ehTpTyQZS1JjLQu3Z1TJkyBV9++aWVW4StW7fiyJEjaNWqlYY1UxfaYfStWzeR+MRwHDlyxCo9IiICmzdvRsuWLZWvnEhETQpkzB/kTj2cLUgUFRUhNTUVSUlJWLduHcrKHE+YgPKA7vHx8WjQoIHMWrkWqjUYsxmONg1GRta0SavsDkUs2dnZNml33HGH5PyUQnQMGwoZQs01+WbNmtmkZWRk4MaNG3atb6QixcKGxsTGlW119epVLPrxe6xenYL0vw4Sj2/YsKHLrOoqYH1jmtTaabVAdPv2bau/BUGQ5Z3BntVf1TJcSWJiol2xpk2bNujbty/at2+P6OhoREVFITAwEAEBAdDr9VbHTp8+He+++65aVfZ46F2iEXa9O/ktNzcXDz74oKLjPdq+hvbNDQkJQdu2bfHHH39Y0oqLi3H16lVERUXZPeeFF16wsq5NSEhAv379KEuUCNtdrMfgQxA4Sihi2Pj6suMSjWQx5GoLm6Bgf6eCjdlkhtFggq/eh1gXlixs9H4kl2iui2FTUODcwsZXr4MfoX4cjlzcQrAByidxNWvWxJgxYzB27FjcfffdWleJw/Eo9Dof1Al23Y4lpQky+sG3kG5nTUCA3q2ujWOf0NBQdO3aFRs2bLBK37hxo1cJNjSTydu38/BYYjyOHv3LKj0sLAwbN25E69atXVQ5cYiZFIh2PVXlbDlUXbgwGo347bffkJSUhF9//ZW4IKfX69GnTx8kJCRg4MCBCAgIAACYJO64Y2nR1tHEr149W0EqKytLcjn2Al83atRIcn7KobxLNDVvb9u2be3uuNy/fz/69OmjWDk0liG2eg15AVPpdyE/Px8rV65EUlISNm3aBKPR+WJASEgohg0bhsTEx9G1a1eXv5tiF1LY6Smco9UCUdV4HmazGYWFhZJFm6pB3u2V4SrWrl2LzZs3W6XVqlULP/74I3r37k2djxzXlVqwevVqTJs2TZOyo6KikJaW5vQYasFGoonNrVu38OCDD+LPP/+0SldtvCfi1a1Tp45NWk5OjkPB5uLFi1Z/7927F23atKEuz5512+IlC7Fpk/U9a9WqDT784PN//3KXXtO9IfX5NLFJWHKJRrL2MRLckJEgCRPB1f1x7YpzK/bSUiOVYMOSJa+fn/PlapLlkRwKCYJNYJAfU/MxjmfCvGDj4+ODfv36YezYsRgwYIBDk1kOh+NdiPk+smTay5FHTEyMjWDz119/OTjaU3H+PN++fRuPJw63EWtCQkKwYcMG3HefNrEX7CFqUqCdXoOKLuSPP/5AUlISli5dSuU2qkOHDhgzZgxGjRqFyMhIxevFAo5i2NStWx/+/gEoKSm2pGVnZ6O0tBR+fuJdCJw5c8YmrXnz5qLzURrRFjY0bvJUnABWr14d9913Hw4cOGCVvmXLFkUFG5pnveqzRGoqpSbKZWVl2LBhA5KSkrB69WorK0576PV6PHB/TwwZPBw9evRG4yZ3wM9fnfmJ+OGMyp2MxOK0GqfZc2F28+ZNyYKNvfhPSlqqOWPJkiVWf/v4+CA1NRXt27cXlY+aLtyU4MaNG5qNA2nifQmCAJ0gOA30bZboEi0vLw8PPfQQDh60tgBUYrxH272K6YftWaCJEQhPn5bvjvLatRxcu5ZjlRYS8p8XBr7+qg4ki5RSKgsbdgQb0jfMbDLDZDJL/taRhIlgCtdcpSUGBAb5ORy3V0By76Ymej/n99ilFjaEGDaBQdwdGsf1MK9+nD9/ngmXFxwOhy3ETBC4YOM52PNbrXZAX61x9ujn5+dj7BMj8NfhQ1bp1atXx4YNG0Qv3LgacRY2MsqRcW5Gxhl8Nz8VySm/UC0W3HnnnRg9ejQSEhIQExPjkpqxsqBgNsPhIpSvry+aN2th9SyWlpbi4MGD6Nixo6hyDAaDlRsVAKhdu7bd3bqsw5heAwAYNGiQjWCzcOFCvP/++5LENXtIcYlGFmyk18dsNmP37t1YvHgxli1bhuvXrxPPadsmFoMHx2NA/6EID6+0CK/i/RJtieIeeo1m47SaNW1dN/7zzz8Od/yT+Pvvv23S7Ir1LmDTpk1Wf/fp00fSN//s2bNKVYnzLzqd4DTQtzMxp4Kqb8jt27fRu3dv7N+/3ypdqfEetVWziFc3JyfHJk2t94PDFqQ+v6SEJoYNO+6odAQBCii3qtfppNW5rIxsYUOiok1J1j4srZuQLWxcGcPGuWBDI5JxOHJhXrDhYg2Hw7GHKAsbhnaKcORx69YtmzQfH3YG7FpSUJCPsU+OwJ/p1guvwcHBWLduHeLi4jSqmWPUimEj9tycnCtYs3YlVqem4PDhP4nHh4eHY8SIEUhISEDnzp0lVtI9cbYIFdsuzkY83LVrl2jB5tChQzZWD/fff7+oPFyFWCsPGgsbee7/xPP4449j+vTpMJn+c9GXk5OD5cuXY/To0YqUQfOqi3aJJkGxOXbsGJKSkrB48WIqF30N6jfE4MHxGDJkOBpG3ym6PKVh3/2GtPpptUB077332qQdOHAAPXv2lJRfVWE5LCwMjRs3lpSXGEpLS3H16lWrtK5du4rOx2g02ggAHPkIOgFwst5qMprJc5VKP+fn56NPnz7Yt2+f1SGKjveU12vwzz//2KTZE005no9OR4j5QuFCzIchCxtSTB4AMJrMkhdfSwkCVhCFYPOfNYr7CDbkGDbSXEvTUFhQ4vT3wCBym3M4cmFesOFwOBy5kAaFHPfh+PHjNmm1a9fWoCbaIQjly7mVh9uFhQV48qlHcOiQ9WJRUFAQ0tLSmBURxC0+unYCkZ+fjw0b12D16hTs3beTGLvCz88P/fv3x5gxYzBgwADFLBGoUGXRlq4MZ76wH+jaA99/P9cqLSUlBa+99pqomiQnJ9ukKequSw6iXaJRZKfyXLlBgwZ4+OGHbdp58uTJ6N+/P8LCwmSX4QoLG9p2ys7OxpIlS5CUlITDhw8Tj4+IiLAIsLVrNSUer6bAxrpeI7V+YheIqrrIJvXXjrD3bUxOTsYbb7whOq+DBw8iIyPDKi0uLs7ps2/P1beUa7FnaSzFFVtaWhry853HQWCNxMREJCYmal0Np5Ceb5PZDMFM9w4UFBSgX79+2LNnj1W60uM9pV2iHT58GBcuXLBKi46ORmhoqIMzgPT0dLpKOGDhwoUYO3asVdqkia/hhUmvOzyHfVHcM6AROJyh8xGYEhaoYlVJjFsJUFjYBHuqhQ1JsHGdhQ1NDBsOx9VwwYbD4bglYgbUcgeFHDbIzc3F7t27bdLvuusuDWqjMYJgWc0sKirEU+NG448D1jstq1ULxPJlv0raZasWggAb8cnhsXLKcXB2aWkptu/4DatTU7Bly0YUF5N9qXfq1AkJCQkYOXKk3fgH4uql7nlKY4ZzwSYurgtq1boDV6/+F+/n999/R3p6OnXg4NLSUixcuNAqzd/fH0OHDpVQY+URey9o4rJosWD0/vvvY+XKlTAY/pv8nj9/Hs899xySkpLkF/Dvu15cUozlyYsxZvQTNocoaWGTm5uL5ORkJCUlYceOHcS8/P39LQJs//79LQLs2dMULjcZdonGSl/hDJ2EZ7569epWf0sVGerVq4e77rrLauf/gQMHcPDgQdHxP+bOnWuT9tBDDzk9p+p1ANKuxV5sECnuYj/77DPR53DIEAUbkxk+PoT+DgIKCwsxYMAA7Ny50+q3wMBArF27VuHxHt07Sfvqzpw50yatb9++YirE8SDker/Q69nyrkBj7UMSShxhNpuJsVpoXKJV5EGMYcOQYEO6z6TYPnLIJ7hEC+Iu0TgqwLedczgct0SUSzSGBh4c6bz33nsoLi62SR80aJAGtdGWiie6pKQY4yc8ht9/txayAgKq4dtvfkaXLuyKNcC/i9O076fCLtFycq4grlNLTHjmMaSlrXIq1jRp0gTvvvsuzpw5g927d2PChAmyxRpH9WIFqqqZzU597/v4+GD0o4/ZpL/55pvU9fjss89s/N4/8sgjilh9KIJol2ik/KRXRQ7NmjXDlClTbNIXL16MSZMmWblLk4IgCMjMOosRowYgLW2V3WOUimGzZs0a1K5dG+PGjcP27dsdijWCIKBz586YN28eLl26hJSUFAwdOlRdazmRiBbz1H6eJJQnZYxWtf+9efMmcnNzxRcO4LnnnrNJe/7556ncF1awf/9+G2E5KCjIZnd/Vex9R6TEkAkNDUVgYKBV2saNG0XlMX/+fGzbtk102RwyNAHJSTtXikuKMXjwYJt7VK1aNaSmpuKBBx6QWUtr6Lsa8oGpqalYunSpTXp8fLy4SqkAw8Myj0Inc2OKL0Pu0ADAh8Kbh1GihY3RYCKOh2jEA4uFDWE8x5KVGcklmisFm6JCLthwtIetno7D4XBoETGY4IKN+/P5559j1qxZNuk9evRAkyZNNKiR9pSWluKZ5xKxe892q/QKsaZTR7bFmgpoJ21Kh7ApKSnBrVs3HZ4TEV4DCWOeQMry9Thy+G9MmzYNd96pfQwLgCmPaMSdek8/9YzNLvL169fjq6++IuZ98OBBTJ8+3SrN19cXkydPpqvcv1RYrVT+l5mZKSoPh3mLPN5MWJXTcqI8depUu4t+c+bMwYgRI2xiZNBSXFyMDz74AP0HdsfRo385PM5mgVyihc21a9dQWup4ot20aVPMmDEDZ86cwa5duzB+/HhlBFgVYGk4k5mZafNe3VE7RHQ+UnZat2rVyiYtLS1NdD5AuUutqu7D9u3bh+eff57q/KysLMTHx9uImk8++SRRWK5fv76NSyip19GlSxerv7dt20ad1/r16zFp0iRJ5XLIkDammEzOvwylpaUYP+ExbN682Sq9Qqzp0aOHArW0psL97uEj6Viz9leHoj3pk7Vu3To88sgjNv179+7dJceKcikM9bGejM5H3jIkS/FrALr60MTlsQfJHRoABFG4RCstcT8LG5JLNEOZUdTmCjEU3OYu0Tjao4lLtBkzZthNnzZtGvWxrsBe+RwOh03EWdiwNajj0HHp0iX89ttv+Prrr218dQOAXq/HnDlzNKiZ9hiNBkx84Sls3/6bVbq/fwDmzV2Ezp3KA7IztEnKIaRgvP8d6PqL8fcPQM8eD2Hw4OF44P6e0Ov1/xbtBg2pEc5cogFAzVo1MWPGDLz00ktW6RMnTkRubi4mT55sN47D6tWrMWbMGJSUWAf9fPHFF9GsWTP5FVcI0Y+GRKsRNdDr9fj1119x//334+jRo1a/paSkYMOGDXjppZcwbtw41KtXj5hfVlYWfvrpJ3z11Ve4fPky8XiReo2otoqMjMSoUaMwZswYdOjQgf5ECtS8ZZ7YF0lZHIqLi4NOp7NaRH7llVcQHByMfv36WfpuGoKCgvD1119j5MiRVulz587F1atX8eWXX+KOO+6we25aWhrGjx+P8+fPW6U3bNiQag4rCAI6duyI9evXW9I2b96MN998Ey+99BJq1apFfR0jRoywsaoZOXIkFixY4NCSobi4GB9//DH+97//WUTOkJAQ5OXlUZfLIUPjEs0RBkP5eG/rNmuxJiAgACtXrnS56HH58kW88OI4zPp8JobHP4revQegUcPG/x3g4NKysrLw0UcfYd68eTYLqgEBAdz9npfjaS7RaL5jUi1saKxIAgJ84eMrwGhw3JdUWNiQDKbl3hsl0fs5X642m8stkHxd8DwU5Jc4/T2ICzYcFdBEsJk+fbrdCYc9wcTRsa6ACzYcjvsgpldgaeDh7Rw4cIAYu6KgoAA3btzAjRs3HB7j6+uLpKQktGjRQuEaugdr1q7E5s3rbNL9/f3x0cfv4qOP3wUA+Op9RC+GxcbGYv78+dTH08YiqeDixYtW55SVGW12e61J3WZznqy32MnJgiCgXWwchgwZjn59B6N6ddsd4oKLNF/J4xuR582bNw/z5s1z+Lu9xfR33nkHwUGfOjzn0UcS8eJLE4musnQ6AS+88AI2btyIdev+e2bNZjPefvttfPPNNxg9ejRiYmLg7++PjIwMrFy5EocOHbLJ67777sP777/vtDz1EesSjV0LG6DcRdP27dsxcOBAG6E8Pz8f7733Ht577z20aNEC3bt3R7169VCzZk2EhYWhuLgYV65cwYkTJ7Bnzx4cO3ZM1M7Hqv0Asa0IbR8QEIBBgwZhzJgx6NOnj6hFfFFoGMPmzSkv4siRdIfHn8vOtEnr16+fU7dv8+fPR2xsrNQqikbKppo6deqgT58+VhYkV65cwZAhQ+Dn54f69esjKCjI5n1ydG0jRozAhg0b8MMPP1ilJycnY+3atejbty+6du2K2rVro7i4GBkZGVi9erXdoOh6vR4///yz02DqlXniiSesBBugPN7HzJkzUadOHURERNiI2oMGDbIRhB577DF8+OGHOHPmjCUtPz8fw4cPx7333ouBAweiSZMm0Ov1uHr1Kg4ePIg1a9bg+vXrluNbtGiBgQMH4qOPPqKqO4cOkiWxyYlLtLUOxnsBAQF4/fXX8frrr0uuF2m8V/n9ycw8i0/+73188n/vo0GDhmjZohWaNW2BuvXrICqqJvR6PW7cuIGsrCzs2rULe/futYqJVoGPjw+WLl0qeuyoFlp/g70FH5lWHMy5RKNYa5Aq2JDi1wCAn78v/Px8UWQoc3hMaem/gg1hbCXXXZ2S6PXk+1xaalRcsCkrNaKszPn9orFq4nDkoolgU0HliRjp4+gqUzfa8jkcDluIeWdZMu31dgoKCvDXX45d4tBQu3ZtfPfddxgwYIBCtXI/DA4G5Hl5t5CXd0tW3mJjg4i9n2VlZdKeAYUVm2rVAvHKy1MwZPBwREU5txZw5RhBANHowu45Yrh8+bLoNs/Oznb6e861cvdYJAsbna7cVdKyZcvQp08f7N5tHW/p/PnzVIuDd911F9LS0uDvz9YESeyjwbpgAwARERHYtm0b3nrrLXz22WcwGm0XC/7++2/8/fffovNu1Kgxnn7KNmYIIN7CxtGL0LhxY8yfPx/Dhw9HSIh4F13iUe+eVX0+srIy8M/xY6Ly+Oeff5z+LiXovRyo45hV4ZNPPsH27dtRUFBglV5aWmolWlTG2bV9++238PHxwXfffWeVXlRUhBUrVmDFihXEOoWEhGDFihXo3LkzxRWUM2zYMPTs2RO//fabzW+XLl3CpUuXbNLtLXbr9XosX74cXbp0QWFhodVvhw4dsiuCV6Zu3bpYu3atTSwejnzIMWxMDscBZQ7Gezdv3sTNmzdl1Ys43nNQ7XPnMnHuXCbWrU8VXd78+fMxePBgUedxPA+p/X4FvhQL+WpC4+KNNF52BI2Fjd7PB37+PigqdCLYULpEk3tvlMSPYGED0LmME0tBgXN3aABQLchFm4A4nEpo2tNV+DsWc6wr/nE4HDdExKsrdxcPhw0iIyPx6quv4vjx414t1gDwSh/bSsewqVEjEs8+8xJRrAHcw7WcVtAINgAQHByMzZs3Y/z48aLHXsOGDcPevXtFuQeqICcnxyatZs2aDl0ciUa0YEPIjpFnTa/X4+OPP8aff/6JAQMGyB4vx8TE4H/vf4L1abvQvftDdo+pKmZJjffTtWtXPPnkkyqJNereM5aGM/berebN7xKdj9RNNS1atMCmTZsUi2Pn4+ODb7/9Fl988YWkmEZxcXHYtWuXaBdVOp0OycnJePTRR0WXWZW2bdtiw4YNqFOnjqjz4uLisG/fPjRs2FB2HTi20LlEc+3mVCmQrBip8xEEDBgwAEeOHMGwYcMUydNlMNTHejK+MmPY+Pqy5RLNh+J6jC6MYaPX+xDdh1VY2JAsfVja6KonxLABgNISW0s+uRRSCDbB3MKGowKaCTZms9nyT8yxrvjH4XDcD2qxF2ztFOGQ8fPzQ2RkJBo3bozevXvjzTffRGpqKi5evIhPPvmE2s2IJ+OVT7TSio2Y01lZRa+AkeqYzWZRwUsDAgIwb9487Nu3D8OGDXNqLePr64vevXtj48aNSE5Olvzeb9261Sbt9ddfR7Vq1STlVxXRIWzcwMKmMq1atUJqaipOnDiBd955B23btoWPD3kCrdPp0KJFCzz77LPYs2cPTp48icTHn7Ybr6gCG8HGTcQtNWFpPGPv3XrttSmi85GzONSxY0ccP34caWlpePbZZ9GlSxdERUUhODhYcvzCSZMm4cyZM5g6dSqaN2/u9NiAgAA8+OCDSElJwd69e9GqVStJZYaFhSEpKQnHjx/H9OnTMWDAADRu3Bjh4eGiXfl16dIFf/31F15//XWiBUVsbCwWLVqE3bt3U8Wl4kiD9N46c4mmKQLwwP09sGjBcjzzzIvo0L4TQkLovsWCIKBp06Z48cUXcfz4caSmprrFM8ZOD+vZyLaw4S7RrPDz84G/v/OxGa2FDUuxf2kEG5LrMimQ4tcAQFAwj2HDcT2auEQj+TuXeiyHw/EeaId5Fe54OOrTsGFDZkTxzMxMraugKMOHj8aQIaOIx91Ru7rLfezKvcc5V/Jx+3YxxZHS32O5PYBLXaIJgug2FFub6dOnY/r06SLPAjLPXHfq69psJtlAAIKdiV/79u2RnJyMwsJC/P777zhx4oQlZlVoaCiaNGmCDh06iHbPZ48tW7ZY/V27dm08++yzsvO14OTZiB/2COKHPYJ6DcIsbh1yrubjdp7j510QgG7dujHTd1YQExNjeY4KCgpw5MgRZGRk4MqVKxb3S+Hh4QgPD0dUVBTatm2L6tWrW+VBeo1sLtnNxC01EATByo3i4qRVTo+PqheKgADXuO2o+m61bt0agwYMQZ6T59secnfz+vj4oG/fvujbt6+sfCoTHh5uideUlZWF9PR05OTk4Nq1a/Dz80OtWrUQFRWFuLg4BAYGKlZus2bN8M4778jOp2bNmvjoo4/wwQcf4MCBAzh27BiuXbsGg8GA6tWro1GjRoiNjUXt2rVtzpX6vdi2bZvsensqRJdocBxXouI74u/vi7r1w5SvnBMEAfD3D0CXLt3QpUs3S/rFi+dx/vw5XLp0ESWlt2GGASaTCSEhIQgPD8cdd9yB2NhYSZZqSpKYmIgRwx/F5Ut5mtaDYwuNRYoz9C4IMi8Hmu+YSaJgQ2NBovfzIboPK/k3HyNRsKGvm6vx9ycvV1dYDilJQT6FS7RALthwXI+mMWw4HA5HMpQLNTQ+ZTkcjnYIlK+orLVZmQu7zK0LM1IhE4V7B2eT2MDAQHTv3h3du3dXslpWVLUCmDx5sqILrFR3onIzeYAIERQUhLi4OMTFxYk6T2y8SnexsFG7HoJOIO6QtRzrov3iZWVl2LVrl1Xau+++K2nXNEvuV+wRHR2N6OhorashCR8fH3To0AEdOnTQuipeDd1iLlsiPeC4/4iKqmdxJ1sjMgihYcpYrLoCsf2zO3yDPQHZQj1rFjYU9XGlS7RyCxvnS7sWSx3SOJShbzJNrCIaCySxkFyiCQIQGMQFG47rYaun43A4HEpox9OsLwRwOFLwpPkk6++ojsF4d6zUhiaAqpb398KFCzh58qTl77p162L8+PHKFkJxeSL0Go96t6si1sLG3dzHqYWY63ZVE+3fvx8FBQWWv2NjYzF48GBJfRPr3wAORy5Ugg0pELgW/R1/NTkuQkfhQswZzLlEozBLoRkz26PClZkz9Hof+BEEG4uFDUE4YumbTLIaAoBSFwg2JJdoAYF6ptqJ47mw1dNxOBwOJVyw4Xg3nvNc61RYhJBThJeuCZdDuHaSWwVAnfvriKrWNVOmTEFAQICiZVBZMFQSHrxZhCBdW1WXQESvcMw0lboVEfNOuep5qvpuvfvuu5Lz4uM0jqdDExNC6mKuK6HpPpj/Zok2sXFNNTjWyO33mXOJRhPDxiAxhg3BwkbQCfDx1cGPEMOmwhKFNA7VctxeFT+aGDausLApLHP6u6vdjXM4FXDBhsPhuCW0bj5YCpzH4SgF9VCaoUG3I2hN77VaFGByMUKlKpGKkesSzdVUjrHRoEEDPPXUU8oXwi1sqCG7RKv6t3uIW2rXgtaNZPnBrqlD5XcrLi4O/fr1k1weF2w4ng7NOIfNuL3kejPSDTtEbPUYvxyPQe78nMZVlprQxOQxSnzHSRYker0OgiAQxY0KCxuSOMySO3m9RoINycImiLtD46gEO28jh8PhiIHWwkamyTWHwyQe9FirsVgnL/yNi+snIXtXxaUQC80Ck5a+sCtbAUydOhV+fspPsKgej0pzY3cRIVwBUWgwibOwYaap1I5ho7FLtJKSEuzdu9fy94wZM/4rT0JjcMGG4+koEcNGE49oHvBqir8GD7hoN8BH5vycRiBRE5rrkWxhU2pw+nuFtRHJJVpphWDjRi7RfHx0xHlEKaF9pFCQ7zyGDY9fw1ELslNAjfHx+U9VbdOmDQ4ePKhY3m3btsXhw4cBlE9+DAblX3YOh+MaBKF8WYC0v9uHoUEHh6MUtE+1Ozz9tIuPshYOZJwsaje7lPxVO0lqQY57WRqf+1pO/DIyMlQohXx9ZrP9/7abmyeskDlArEs0YmBct+jhlEecYKN8G/n7+6OoqEix/LglNMfTEQTS15RRl2g0x7D+zeIu0ZhE7tjQlzmXaK5ze1hW5lzoqRBqSBY2FZY6ZsLqCUuCDQD4+/uguMjxOi2pfaRQWOBcsAkK5oINRx2YHyGbzWarf67Mn8PhuBc0kwTWBh0cjiKwPkEWAevvKPOLEa6EcOlEtwpe0HZ0lygmho28+rAM6dpE6jUe3VbOENNnqt5EUiwGGf8GcDhyodm8YCNYswBFJ8t6P8xdorGJfMGGrWVMmg2iRgo3wvYgWZCItbAh1YO1d5oUr4hkgSQFbmHDYQXmLWwA1y6WCILAxRoOx12hMLFhyQ8rxzOZN28e5s2b5/JyJkyYgAkTJgDwrAkl9TdejpWMjAZzvWBDYytoe4YayI1h4w0uKak8oolwicbcTFlByDFsqrhEI7wXrCz0qy3qiipO7bpJOId10Z7DUQJBJ9i4fRR1vgbfBqoiWf9mccWGSXx8ZcawkXm+0lBZ2BilukRzHqOlwrLGnyjY/Gth40YxbACyNRUpxo8UuIUNhxXcQrABKCa4HA7H6yifvLiXWS/H87h8+TL++usvVcqx4EGPNa03HK0uWRW9Ro1zpEC0sHE++fQGCxuaB6TyV4o0nPXkT5ZowYawtuHBTeUUrWPYKFmg1m4TORy1KHf9p7zrHq1h/e31VteZrCN3fOjry5hLNJ0AQSc4FUOkukQjCRL6fwUbPcElWkmFhY2bWceThCiSoCWFAqJg4694mRyOPdiSTzkcDkcENOMJvhDA8Wrc4PGn3iWv0bWw6RKNjTqRpp7e0P9SPR5mMS7RPLfNRLtEIz1hHtxWzqCO+yXiWKUQWxprC0Mcjqtwx+8hTf/B/CssOoQN6xfkGci14mDNJRoA+BCsyg0GiRY2ZXSCDdnCplywIVnYsGK9XAHpXpPaRwoF+SVOfw8M1CteJodjD/Z6OhUpLi62/He1atU0rAmHw5ECzaDaG1zycLwPMQt2rEO7YCfnWuQsWrp6MUKSgY1KN1ZuMaxN+rRCjIUN84tfMiBa2FRdRPDitnKGQGuVqEUDiSySj9E43oJswUaLV8UDXKKJrh3bl+MxyH0fSHFNtIB0TUYXu0SjjWFDsvQhCU9q4+fn/LoqLIeUwmQyo7iwzOkx3MKGoxZu4xLNFVy8eNHy39WrV9ewJhwORxIU4wkfWn9LHI5Epk+fjunTp2tdDbeFflFfmwmEy0UHtuZFVZBXOXfcUSwWQRDIUYgqx7AhucRgfPFLDmQLG2tLJFIQbk+2RnIGtcjtBs3jDX0EhwPIH0swq9e4vBYyEW1hw1EDuaKAD2NxVoCKOjkWV0hxHx1REXvGERUWNn4El2hlZSaYTGayhQ1jgwfSdRnKlHU1WVhQStxcFRjEY9hw1IG9nk4lDh48iNu3b1v+rlu3roa14XA4UuAu0TjeCmNjaVlQv6OyTGxknMpgY6tWJZnleE3/KyI2C2m6zuDjphikBUszxMWs9OS2cgZ1n6RFkHKRx3tNH8HxetzR/Z8nuEQT7eKM8evxFOQKmL6+7C1jEi1sCHEfHWEguPyqsEAhWdgA5e7DSJthWPsuk2LzlJYqa2FTVOg8fg0ABAVzwYajDuz1dCpw4cIFPP/88wDKJ4aCIODuu+/WuFYcDkcsNBMJ7m6D45mwbZUiBkEQ6BYF5JQh51wmm5DJStnA2qTPVdBepdls9vIYNuRrq2geGuHGk9vKGbQLXZo0j8hCvaWP4HDku0Rj9F1htV4VMF49b0WuBwzSIr4W+BBEJMkWNgSXaPp/Y7z4+ZPbpLTEQKwHa99l0r0muYwTS0E+jWDDXaJx1EFzl2hPPPEE9bHnzp0TdXxljEYj8vPzcfbsWRw9ehQmkwmCIFgmhD169JCUL4fD0Q7ScEJHuRDM4bgb1I+1mzz+OgEgzmM0uhZX74r15AC3rE36XAbBJ9p/IgRFVh7cZDSPQ/m4XPD6tnIGtVGiGzSQ1/QRHK9H7rPOqv7Kejcjtn6ePCZjCbkbKlm0sCG5aSPFjnEEyYJE/6+FjT+FhU1pqZFYD9Y2u5JcopEELbEUFJAFG+4SjaMWmgs2CxcuJAch/XfWlpubi0WLFskqr/KOvYpyq1evjmHDhsnKl8PhaACh72BtwMHhcOwj6ASA5FNZTv5yVhQYXI1Qq0pyy3F5/B9GIEexKf/N661GqCxs6NuKxXdTDagtbFxcD7tlil0c9ZI+gsNxT3GSxvqZ9eviLtFYRG4MGjZj2Dh/eAwGV7lE+zeGDY1gU2Igjq9Yc9+otoVNIY2FDRdsOCqhuWBTAc3ETIxfa0dUnghX5Ddr1iwEBQXJzpvD4agLaTyhk2luzeGwCnUIA9dWQzGoFjI0mkC4fI3FXW6SBNxzgUoChMsUZ2HjuW1GZ2Fj/f/O8OCmcgrtM+IOz5LX9BEcr0e2OKnBq0LVhTD+Cou3sOGogdzPE4su0UjrDkajNMGGZEHiq/9XsKFoExoLG9Y2UlTE6HGE8i7RSpz+rtfrmHz+OJ4JM4KNo0mFPYsYqVT1HR4REYFZs2YhISFBVr4cDkcbSH0CXwjgeC6e9Wy7OoaNHFibuAhQcyFWXjne0gcTb4eouCzy68MqVDFsKhYSvFzccoYnucTkG2s43oI7ukSjKdRLu2GOTEjxXkiw6BKN5NnD5CLBRqyFDakerI3dffXO77XaLtF4/BqOmmgu2DRo0MDphCsrK8sSa8bX1xdRUVGSyvH19UX16tURHh6OVq1aoVOnThg8eDD8/fkLx+G4LSQLG+4SjeOpeNij7erJgYd5RFMNuZfO2qTPVZD1GhEihAe3GZVgI0bcklshN4XaJZoGnZfYMr2lj+Bw5D/rGrzPNC7RGB8kCQLZaWmVE1xYG04Fct1usSjYkFyiGSXGsCFZkFQINjQxbEpKDDARxlesvdNECxuCyzixFBIEm8Bg7g6Nox6aCzaZmZlOf6+886ply5Y4dOiQi2vE4XDcBV+C/1oWB3McjhJQD6XZGnM7hGoBUqNrcfXERWzuqk6kZBblNYuxpHvCRQgAdOtgYmLYsLaooBb0LtFcXBEF8Jo+guP1yH7WWXWJ5g4IAp2fTXjQNTOOTmYMmgo3YCxBiqtjMkoUbAiCRIV7Lho3XaUlRpgIhj4k4UltSK7eFHeJRhJsePwajoq4zWqmt07KOByOYwKq6Z3+HhjIP6gcD8XDPok0O+1kBbaVMYZw+fjDw+5lZTzZWqQyRAsbUS7RPLfN6CxsKgQbmvzk1sg9oX2tNHmWRBbJBRuOtyD3e8iuSzT232H2a+h9yBUFWNyUSRKhJLtEKzE4/d0i2FCIWKUlhv9czzqAtbE7SZwrLXXePmIpuO1csAnmFjYcFWGvp7ND1dgzHA6HAwBBwX6o5kC0CQz0Q7VA54IOh+Ou0IoXbA25HeNqCxs57eAGaxHM4jWLsYSHpGIE6+0ihEAx6/hP3KLJ0IMbywnUC6Ts6zXe00dwvB53fNbpXKKpUBG5uEMdvQy57wOLgo0vQYQySBRsSBY2FS7DdDqBaGVTXEwWN3wYiy3n76+uhQ3RJRq3sOGoiOYu0UhkZGRY/tvPj78cHA7nPwRBQK3a1ZF7oxD5eSUwmc3QCQKCQ/wRUSPQLXZ9cTiS8LBHm87CRhtc34+Iy19Vj2jcJRoVxKsU4+bLg9tMnIUNjTWS7Cq5JdQxbFxcDyXwlj6Cw3FHl2hu0YlQIOYyvPW7ojayBRsGXaKRLWzEb0A3mcwwGpyfV1mk8fPzcSpgFBeVEctk7R0gWQ4pH8OmxOnvgUE8BjpHPZgXbKKjo7WuAofDYRgfHx0iawYjokYQzCYzBJ117CsOxxOhH0szNup2gMtj2MixzmFuQVHN+shwJQf3cJWiCHQhbLzewgYAMfizxcKGJi9PbywH0Mew0SBIucgyuWDD8RbkPuss6jVu850XSF8eq4NdWRPOv8iOYcOghQ3pHTdKsLChcfdVWdDw8/dFQb5jC5HiYrJgI/feKA3JaqikRFnBJt9J+wHlHl44HLVg623kcDgcieh0Anx8dVys4XgHtPNJN5l30r228sQDyeeyFsLGbe6p4B4LOQpAvE4ewwZA+bWRrs8swieaBzeVU+hj2Li2HkrABRuOtyAIApU1sZMcFKsLdYmE+rrL90qUhY3LasGpjI8HukTzIQgdRgkWNoYyssjjV8llmL+/8/34xUVkAYi173KFyzdHmE1mSWKYI0gu0bhgw1ET9no6DofD4XA4TqGNYeMuULlE00h1cfmChMjs3cO+hkXLJO0wo8LNl/PjvKHFaAUbmrZyl8VCpaF2icZ4+wjg/QTHu5D1vLNqYuMOcMWGOeRacZDcZGkBSbAxmSRY2JSItbAhxbBxQ5doBAsbQLk4Nmazmcew4TAFF2w4HA6Hw3E3GBtMy4XlRTt5O2JdgJsoNt5k7Uh8RCgtbGgsUNwdgfBYmEW0lbciCJSSvQZNJOa2CF5khcfhAPJ2rmvxppDqy9pOfEfwfoY95D47Pgxa2Oh8nF+TlBg2pRRChJ+ftUs0Z5RQxLAhCU9qQyPY0LQTDaWlRqJVUxAXbDgqwtbbyOFwOBwOh4iHeUSjmrjJmW+z7BJNLIxVxyHuspCjBtRevrygyYgWNiZKCxsvaCtn0IjcrC9S8j6C42242zMfEKCX9bs7wni36TH4EMQNEkxa2Lggho2hjCxE6Cu5DPMjiBvFxWSLHdbGoqRrAoAyinaioZAQvwYAgoL9FSmLw6HBuQTLKCaTCRs3bsSuXbuwb98+nDt3Djdv3sStW7dgMFB0QnYQBEHyuRwOh8PhqApjg2m5kHZz6eRaHsg51cWzd/ExbNzj5rvbwpQcyM9IhQjBrUZIl2iijffjBW3lDIEiiLYmLSTivnhTH8HhADItdjV4XXz1OgQG+qGw0HYRUwBQPTRA/UpJQFyz835JDeRa1rMYw4bk5k1KDBsayxG9/r9ySRY2xTQWNoxZyKvpEq2A4A4NAAJ5DBuOiriVYGMymfD5559j9uzZyM7OtqTTBHHlcDgcDsdT8LQYNn7+vtAJAkwOvuf+Ab6yFrPltBfJhZNsWL6VMtrcqxZjCZdKa2HjDRoE8T2mjWHjBW3lDJ0ggLQ8oYUAKKZEb3KbyOEAgCDLqkCD91kQUPOOYFy9dBtFlWJf6AQBNWoGoVo1d7Gw8fIPBoPIFQV8WbSwIcWwkWBhQ+cS7b8lXX+SYENhYcOam2oaayqaWD80kOLXANwlGkdd3EawuXjxIkaOHIk9e/ZYCTRK+Pvmgg+Hw+Fw3AoP84mm0wkIC6+GGzcKbX4TAIRHBMorgGELG7GVc5MQNsxN+FwJ7ZVyCxvyNZoo1S1vaCtn0AjJrDeRV4m6HA7kWdho9bb4+OhQp14oiovKUFpqhCCUB91mLc6FM0TF1uLdkiqQ4r04Q9AJTH4/SG7epLhEKyslCxGVLVBI1ijFhTQxbNhqWxqXaErFsCnILyEeE8QtbDgq4haCza1bt9C9e3ecPn0aZrPZMkkzm82WyW/ltKpUntRxcYbD4XA47g5bQ2llCA2vBrMZuHWzyLJo6+urQ43IIARotItTAIMLw4xVxxEsTqZdBTEui5m7RKuA0sCGW9gQoHpWtGgkEUV6Ux/B4QAyn3mNX5eAanrNxmIcz0OO4MeiOzQA8CHUS4pLtLIysshTWdAgWthQWKKwNhatHKPHETSxfmgoIMSwEXQC/D0wdheHXdxCsHn88cdx6tQpizWN2WxGYGAg+vTpg5iYGCxcuBBXr161iDnTpk1DUVERbty4gbNnz2L//v3Iz88HAMv5oaGheOaZZ+Dvz4NGcTgcDsfNoBxMu5PrNEEQEF4jECFhAZZdnP5+vopYakjNgbVJSzks1skWWb76PYyKKToXIbi4pRQ018+4XsMFG47XwZ95bRD1vfDyb4tayGlmXz2bgg3p/TaZJMSwoRBYKruH8/N3bo1SQhHDhrV+isbCpkQll2iBgXrm2ofj2TAv2Ozfvx+rV6+2CC2CIKBPnz5YtGgRatasCQBYv349rl69ajnnnXfescrDZDJh7dq1+Oyzz7B9+3YIgoC8vDykpqZi7dq1aNCggarXxOFwOBwOxz4+PjpUq6bwZEzi2FqNRWGxJai6liCjLDnuLtwN4j2xWI1wEUIxCxtlquO20CwYaPI8iSiTL3pwvA05zzx/W6Qjpu14O6uDHAsbmpgmWkC6JqNBgks0guWIr15n1a/4EaxRit1QsKG53zSWSDSQXKIFcndoHJVhU56uxP/93/9Z/lsQBMTGxmLlypUWsYYGnU6HgQMHYuvWrfj2228REBAAADh27Bi6du2KS5cuKV5vDofD4XBchRes6yqMtAajiRMhG4bvpZyqsTbhcy20ViOEXLygyagtbMDFLWew6hFNDN4U54rDAWQ+8/x1kQ5vO+aQM0ZkNX4S6ZqMJuUFm6oxa0gWNjTCBmvfZl+9jjieoYn1Q0MBwcImKJh7Z+KoC5u93b+YzWZs2rTJYl0DAF988QX8/KQrm0899RRSUlKg1+shCAKys7MxfPhwparM4XA4HA47sDXm1gypC5csLgqzVyP7sDbhcyXUjwm3sCELNv+6DDGT1hQ8v6mcwuqzwl2icTiOkffM8/dFMiL6S0a7Vo9DJyeGDasu0QiW5SYJMWxKSwiCTRXrE5KFDQ0+jH2bBUGwcvtmj9JSZWLYkFyiBQVxCxuOurDZ2/3LkSNHcOvWLcvfd911F+Li4mTn26dPH0yZMsUiAu3duxdJSUmy8+VwOBwORw1YXazzNJhsZzXrJKMsr4ph4+BSk1OWoHFMTUTVC4MgCKhVOwTnz59znI0XNBnJas3iEo1b2DiFLoYN20FsuGDjWXTr1s0Sb1YQBHTr1k3rKjGHLJdo/HWRDG869pDzLrDqEs2X5BJNQgybMoIQ4efvW+Vv+W3D4oYrUhwbUjvRUphPiGHDBRuOyjAdw+b48eOW/xYEAd27d6c6z2g0wsfH+Uv9xhtvYM6cObhx4wbMZjNmz56N0aNHy6ovh8PhcDhac/HSBZw6dRyXL1+EzqcMRqMB4eHhCA8PR/PmzdGqVSviN5JTjioLJAyvwrjaJVphYSH279+PEydOIDc3F2azGaGhoYiJiUH79u0RGhoqowbqISi0HOQuIkRhYSGOHDmCzMxMXL58GYWFhQCAsLAwhIeHo06dOmjbti1CQkJszqV1iUbQa1h+bVTBkfB17dpVnDh5HBcvZMOMEpjMBoSEhCA8PBxNmjRB27ZtZXkqINZLxLvABRuOt6HkM19YWIjTp08jOzsbFy5cwO3bt1FUVIRq1aohNDQUNWvWRJs2bRAdHa1YmW6LqCA2vF9SA51OgCCQXcXaw9eXzT3nPoR6SYlhQ7Ic0VexNlLEwoZBl3N+fj4ocPI7yXUcLQUEwSaIx7DhqAzTgk1ubi6A8smbIAho0aKF3eOqTv6Ki4sRFBTkNG9/f38MGDAAixYtAgAcOHAAV69eRa1atRSoOYfD4XAyMzPRqFEjUefodDoEBwcjJCQEdevWRevWrdGuXTs8/PDDiIiIkFyXhg0bIisrS/L59sjNzUVYWJiiedJS+bN35swp7Ni5Bfv27cL+P/YiL++W4xMBBAUFoVu3bpgwYQL69esHnU65gfmlS5dw8OBBHDhwwPLvypUrVsdER0cjMzNTsTJpkDr/VsNKRGwJcqtUWlqKo0ePWt2jo0ePoqzMOhDpggUL0K/PMMnlOFuY+uOPP/DJJ58gNTUVxcXFdo/x9fXFgw8+iFdeeQU9e/aUXA9VUOgxqbi327Zto96kRMvgwYOxcuVKyeefOnUKixcvRmpqKtLT02E0Op8cC4KAZs2a4f7778fo0aPRtWvXf3feOy/Hotdw93FOqeibLlzIxvYdW7B3307s378H167lOD3P398fHTt2xLhx4zBs2DCXijckuGDD8Tak7lw3Go04ePAP/HX4IPbs2YO//voLZ86cgYkiJkZkZCTi4+MxduxYtG/fXlL57o4ovcZlteBURdAJMEtwE8aioACAOJ+ieV+rQorNUlWg8fdnenlXMr4ECxulXKIRY9hwCxuOyjD9RlcINhWEh4fbPa5atWpWE7vCwkKiYAMA9913n0WwAcpFm379+kmsLYfD4XDkYjKZkJeXh7y8PJw/fx6///47vv32Wzz33HMYOnQoPvroI75bEEBRURHmfPkp0tatwsmT/4g6t6CgAGvXrsXatWvRsmVLLFy4ELGxsZLqsW/fPmzatMmy8H/x4kVJ+bgeyYqNstXQgPz8fPzyyy+We3T48GGUljqfkCiBvcXYkpISvPTSS5g3bx5xQd5gMGDdunVYt24dhg8fjvnz59u12GABpZ4SFkWIo0ePYsqUKVizZg3xnlXGbDbj+PHjOH78OL799ltER0fj+eefx5jRTxLPK/9/5/kz2FSqMvfrOUhJSUb6XwdFnVdSUoJt27Zh27ZtqF+/PubPn4+HHnpIuYqJcokmfdEtMTHRag6nxUYAJbAnzm7dupW7E/NQpIqUmZln8VCfBySde+3aNcybNw/z5s3Dww8/jC+++AL16tWTlJe7wuK3lVMeK0VKXBc9YfFeK3wIMWyMBgku0QiWI1XbQgmXaKTr0ALVXKIRBJtAbmHDURk25el/qeqyxdEusKoTeNoFo6rWNBkZGSJqx+FwOBy1KC0txS+//IIWLVrgxx9/1Lo6mpOTcxWffzFTtFhTlWPHjqFjx4748ssvJZ0/c+ZMTJs2DatXr2ZYrJG+oM7kHF9kpU6fPo2nnnoK8+bNw4EDB8SJNTKuv+pO4oKCAjz00EP4+uuvRS38A8Dy5cvRqVMn5OQ4tx7QDIUeFJYWlcrKyvDGG2+gTZs2SE1NFX3PqpKVlYXXXnsN7dq3xrp1qx0e959gwxUbZ7w97U3RYk1VsrOz0bt3b0yePFmhWonrMriFDcfb0PqZX7FiBVq3bo0DBw5oWg+m4d2SaugkWsqw6hJNRxJsJFjYkF2iWa+XKmFhw2IMGz3B1ZtSgk1BfonT34OC/RUph8OhhWkLm6pCzO3bt+0eV9XHeVZWFlq3bk3Mv8L9R8UE2VH+HA6Hw1GGoKAgNGnSxOHvZWVluHXrFi5dumTXdLywsBCJiYkwGo0YO3asrLrQfCecwWocmOjoRujQvhMaRt+JmGbRCAmpjhs3biA9PR1paWnIzs62Ot5gMGDixInw8/PDuHHjNKq1i2HYJZpY1KyRUjFszGYzRo0ahR07dtgcV7duXYwePRoxMTHw9/dHRkYGVq5ciT///NPquGPHjmHAgAHYuXOnpm6c7KGchY3j38LDw9GgQQPJeYtxT5mbm4uBAwdi9+7dDo9p3rw5evTogXr16iEyMhLh4eEoKirC1atXcfLkSezevRvHjh2zOe/8+Wz89PP36Nt3kN18zf92+1yvkUZUVD20i41DTEwz3Nm4PiIjI3Dz5k0cO3YM69evx4kTJ2zO+eijj+Dj44P//e9/8isgysKG30SOd6HUMy8IAu666y7cfffdaNKkCRo0aIDq1avDz88P+fn5uHDhAv78809s2LAB+fn5VufeuHEDPXv2xP79+9GsWTNF6sM6Yr4XvFdSD5LA4QhfXzbnX0SXaBKsiUhCRFXLEyWsj1ic+5AsbJRwiWY0mlBc5NwFHXeJxlEbpgWbunXrAvhPULl1y75f/qZNm1r9vX//fgwaZH8iWJnTp08D+C9Gjq8v083B4XA4bk9sbCy2bdtGPK6wsBD79u3D999/jyVLlljttjabzZg4cSJ69Oghyz1aenq65HO1pupYOqZJMzz88CgMGjgMtWvXsaQ3aBhhtRPNYDBgwYIFePnll20m8RMnTkS3bt1svqlSqFatGlq3bo3Y2FjJ1jssoMqcRcN5UYMGDRAbG4ucnBzs3LnTzhHSKqcTBCtrkS+//BJr1qyxOW7GjBmYPHky9Hq9Vfq0adOwcuVKJCQkWD2n+/fvx9tvv42PPvpIUr1chaPnJH7YI4gf9gj0eh/Ujw7H+XO5zieVTh64QYMGYeHChfIqSkFubi7uv/9+HD161Oa3oKAgvPjiixg/fjzq169PzCszMxM///wzvvrqK1y+fJmqfFoLG5askbSmbt36eHjoSAwZMhwNo+/8L71+mNVuW7PZjF9//RXPPPMMrl69apXHBx98gAcffFABV1z094ULNhxvQxAECADELtsKgoCYmKbo378fevfujY4dO9psWLVHUVERZs+ejenTp1vFi8vLy8OTTz6JnTt3eklfKkax8Yb2YAOpwoCvnk0LG5Llj8noAgsbP+UtbKRaPrmSqpZEVSHF+qGhqLCMeEwgF2w4KsPe21iJ5s2bW/196tQpu8fdc889AMoHM2azGevWraPKPzU11WqQEhkZKbGmHA6Hw1GSwMBA9OjRA0lJSVi7di0CAgKsfi8oKMCHH36oUe3YIS6uC5YkrcL6dbsw7unnrcQae/j6+uLpp5/G7t27ERYWZvVbaWkpXnnlFdF18Pf3R2xsLCZMmID58+cjPT0deXl52Lt3L+bMmSM6P1dQsUgi5TxXI7YEqVWKiorCoEGDMGPGDKSlpSEnJwdZWVlISUlBjx49pGXqgMoLsdeuXcPUqVNtjpk9ezbefvttG7GmgiFDhmDLli021jSzZs3CyZMnFa2vbEg3hTIui9br1waDAUOHDrUr1gwZMgSnT5/G+++/TyXWAEDDhg0xdepUZGRk4H//+x9VfEmLUMOIhU1YeCDh92rqVMQOLVveg2/n/YztWw/ixRfesBJrANu+RRAEPPzwwzh48KBda62JEyfKrhPtbdEJApNuVzgcVyJIfO7vvLMJDh74C7NmzUKfPn2oxBqgfPPMG2+8gY0bN9p8S3fv3m3X6tUT4RY2bOIj0bUZqy7RiDFsTMrHsKlqeeKnhEs0Bl8CUmweJSxs8m87d4cGcMGGoz5s9nb/cuedd1pN7v75x76v/k6dOlmZIKanp2Pz5s1O805JScGhQ4es0lq2bCmjthwOh8NxBX379sWMGTNs0leuXGnXbZo3EBIahsU/r0TST7+ifftODo9zNOa+5557MH/+fJv0devW4dq1a9T1mDNnDm7fvo0//vgDX3/9NZ588km0bt3aYyxWPWHnadOmTXHp0iVcuHABq1atwttvv42+ffvSbVKR6kqu0oLU7NmzkZeXZ/X7gw8+SLU43K5dO0ybNs0qraysDDNnzpRWMRdB1Gsq/p9xq5H3338f27dvt0l/5plnkJKSgtq1a0vKNyAgAFOmTMGff/6Jtm3vdXqsGeXtZCYoNtIkWPFUC7QvKP73uzaT96VLlmP1yt/Qs2dvh8+No/R69eohJSXF5vejR4/auCIUDeVt4WINx1vRwrKsa9eueOmll2zSly9frnpdNIF3N0wi1cKGZG2hFSTLFKNB/JyV5BKtaluQXIfR4OOOFjYEYYuGwgJyjM/g6jyGDUdd2HsbK6HT6dC5c+fyiZvZjD/++ANGo+3LGBUVhQceeMDi2sxsNiMhIQEHD9oPxrl+/XqMHTvWaqISFhaG2NhYl10Lh8PhcKQzfvx4m534V65cYTrQvSuJCA9Hhw6dyQc6mQsNGzbMYqFagdFopLZSBYD69es7tJBgjaqLk+fPn0P/gd3w3fyvcOWKfXdNTOo1IisVGBgoeaFd6uVXLMYajUZ89913Nr+LEVxeffVV1KhRwyptyZIlDt3ksojFaIQRqxF7nDhxwm4Mk5EjR2Lu3LlE3+w0xMTEYPOmrQ7j11RQPu53npeztvrpp5/w0EMP4aeffrJx/SiWatX0CA2zb0UTGlYN1app0/8NpHD97OwFjo2NxYABA2zS7bkudAXcHRrHW9Hq2R8zZoxN2t9//61BTdRHVIvzrkk1pMawkWqZ42p8CO+2UZJLNOeuvvR+1hvkPNbChhTDpkQdwYa0iYfDURrmt8D26NEDGzduBADk5+djz5496Nq1q81xzzzzDLZu3QqgfFHmypUr6NixI/r06YOuXbsiIiIC165dw8aNG7Ft2zYrcUcQBIwbN07znY0cDofDsU9ISAiaNm1qE8T68uXLqFevnka10hCFPld9+/bF4cOHrdLOnj2rTOZuwPHjxzDz+DF8/MkMdIzriiFDhqP3Q/0RFBQMQKVd4CLHHu4wUqlYkNq2bZtN7JJ27drh3nudW1lUxt/fH4mJifj0008tacXFxfj111+RmJioSH3lQryFFS7RSO4wNByHvv322zAYrBcGoqKi8PXXXytaTrVqAUgY86TTY8xmCmskJ++m0WjEpk2bsGnTJgQFBWHIkCFISEhAr1694OMjfvdpRI1A+Pv74tbNIphMZuh0AkLDqiEoWDvXGDQ7k0kLw/369UNqaqpVmtz+n/YRdifB5ty5c0hPT0dOTg5ycnIQEBCAmjVrIioqCnFxcahWTTu3eDQYjUacPXsWx48fx4ULF5CXlwej0Yjw8HCEh4ejefPmaNWqlSKiLIeM1Gdf7jpF48aNbdIuXbokK0+3QUTbqWW9yZEeK4VVl2hECxujFJdozkUeV7hEY9LCxs/5dSlhYVNAIdgEBXMLG466MC/YPPzww5g8ebJlkJKSkmJXsImPj0f37t2xdevWcv+wggCDwYC1a9di7dq1VsdWiDQV1KlTB6+++qprL4TD4XA4sggJCbFJ81aXaEpNJ+3FMaANDu5JmEwm7N6zHbv3bMe0d17Hg736YsiQ4Rg0qL/Ly2Z6aUCmS7T169fb/BYfHy86v/j4eCvBBih338eKYOOooZJTluCNyZOs0rZvPYh69WzfO0A7vSY7OxsrVqywSZ85cybCw8MVLYtm0dFsorCwoSyvoKAASUlJSEpKQu3atfHII48gISEBbdu2pcyhvM7B1f2ZcoXh5+8LnSDA5KCh9Hof4qKLa/p/ujsjZdG6YcOGyMrKsvtbVlYW1bO1detWdOvWjXjczZs38emnn2LFihVOrRACAgJw//33Y8KECRg6dCgxXwCYPn063n33XYe/d+/enZjH448/joULFzr8/dSpU1ixYgW2bNmC3bt3o6CgwGl+oaGhePDBB/Hqq6+iQ4cOxPI50tFqg2hxcbFNWtW4Np4Kt7BhE5JFiiOYdYlGuB4pc9YyooWNdVv4+uog6ATyBiEnsOiylGRhQ2onGgryncew8Q/wZVYs5HguzD9xTZo0QZs2bSxu0X766SeUlNh/mX7++Wc0adLEsiuvwoKm6r+KgZLZbEZgYCCWLVtm426Dw+FwOGxhL7ZKzZo1NagJAyg04S8sLLRJY323sGQom6yoqBCrU1PwxJOj0KJlY7z88ss2Me+0xB2MgSsmrTt37rT5rUuXLqLzu++++xAYaB38naVgyUrdE60W8hYtWmTjcjgyMhIjRoxQvCyaS6SxsJHS6JcvX8asWbNw77334u6778ZHH32E8+fPi86HBXQ6wamFD424pGX/z7KFzZw5c3DnnXfi/fffJ7qMKi4uxsaNG/Hwww+jU6dOOHLkiEq1tM/169dx7733omnTppg8eTI2btxIFGsA4NatW0hOTkZcXBwGDx6Mmzdvur6yXorkZ1/mK/PHH3/YpNmzuvFI2O1uvBqpwgCri+akepkkWNiUkmLY2BEy/P3lCVpSYwu5El+CSEdqJxoK851b2AQGeYfAzWELNnu7KmzevBkZGRnIyMjAoUOHHLozqFOnDrZt24aePXtaiTNV/1X81rhxY+zatQudOjkO2MzhcDgc7bl8+TJOnz5tlRYaGoqGDRtqUyGNoR1Kk46r2qZA+bfUE6naFmFhERge/yiqV7e13Krg6tWrmDVrFu677z60bNkSM2fORHZ2tmsrSkS9iZRU1yA6XbmV819//WWVrtfrJcULtHfe5cuXPc6di1Zz5NWrV9ukJSYmwt9feYsSKgsbsxmQodfExsaif//+TuNrHTt2DJMnT0aDBg3Qo0cPLFiwAHl5ecS6sUSNmsEICLC9xuBgf4SFk4UXV/T/7uwSzWg0Yvz48Zg0aRJyc3NFn79371506dIFW7ZscUHt6Lh9+zb+/PNPWXmsXr0a7du3d1sxk3Wkup6T+8Z88sknNmkPPfSQzFzdAzFjGfZ6Js/Fx8Ni2JC+a2YzYBJp+VJGiM1iz/JErls0t7SwUcElWhAXbDgawLxLNACIiIhAREQE1bFRUVHYtGkT0tLSsGTJEmzatAlXr161/B4YGIguXbpgxIgReOyxx+Dr6xZNwOFwOF7NzJkzbXZcDxkyxHtjjymg2BgMBqxatcomvV27dtLqxDqCgMqrwMHBwZj54Rd4d/rH2Lp1I1atTsG27ZtRWmrfivfvv//Gm2++iSlTpuCBBx5AQkIC4uPj7brqcylqPvJSNwLrdMjKyrJxwVK/fn3JLlgaN25sY1Vz4sQJJgRGpfohLfqz27dv4+DBgzbpPXr0cEl5tIINMYaNk3zuvvturFmzBteuXcOyZcuQlJSEPXv2OCxr69at2Lp1K5577jkMGjQICQkJ6N27N/NzBJ1OQJ26ISgqLENxURkgAIGBfgioRhcUNzk52SZNrf5fimDTokULhIWFASiPK1NZVNHr9WjRogUxj+DgYIe/jRs3Dj/88INNekBAAHr37o37778fderUQVFRETIzM7Fq1SqbGHB5eXno27cvtmzZgs6dO9stp3bt2mjdujWA8visZ86csfq9cePGTusJ2HdnZ4/g4GC0a9cOd911F2JiYhAaGorq1aujtLQUubm5+Pvvv7F161b8888/VuedOnUKI0eOxPbt25l/D9wNtcXKsrIyvPjii9i8ebNVes2aNZGQkKBqXTRDTJN76bRCC6SKl6y6RKMRkkxGE3Q6+vqThAh7FjZyBRsWN1SQXaIpINiQLGw0jFvI8V48dgTWr18/9OvXD0D5QOXatWsICgpSf2GFw3ETTAYDynJvaV0Njgj04aHQefhE2mw247PPPsMXX3xhle7n54fXX39do1ppjxJBUVevXm0TryAiIkKSyyp3wFGL+fv7o0+fgejTZyDy8m4hbd1qrF6djP1/7LW7aGw2m7Ft2zZs27bNaoG3T58+0ha2RN5K9qZRtuh0AjIzM23So6OjJedpb4Hy7NmzVPEoXI4bu0RLT0+361e9ffv2LimP5hJNZAMbqnwiIyPx7LPP4tlnn0VGRgYWL16MpKQkm8XpCoqKivDLL7/gl19+Qc2aNfHII49gzJgxTIvYgiAgMMhPtKuOQ4cO2Qh1Op0OAwYMkF0fa2ncwXESFurS0tIs/52YmIhFixZZ/o6KikJ6erroPCv45Zdf7Io1gwcPxty5cxEVFWXz24wZM7BmzRpMmDABFy5csKSXlpZizJgxSE9PR2hoqM15EyZMwIQJEwAA27Zts4lZM3/+fFn9WlhYGMaMGYP4+Hh06tTJqaVZBXv27MELL7yAAwcOWKV9/vnnzMd5XbhwIcaOHatJ2dHR0Xa/dc6QvHtd5PchMzMTa9euxezZs3Hy5MkqWQn47rvvEBQUJK0uboa4lnOHUZZnINXChlWXaKS4cQBgNJlFLcASXaLZEa9ku0RjULBRwyVaUSHBwoYLNhwN8OyVvn/R6/VM7MDkcFimLPcWzn67WOtqcERw57hH4V/Ts+JvlZWV4fbt2zh9+jT27NmDhQsX2rhVAoDZs2dT7ab1WKjH0vYPLC4uxptvvmmTPnbsWK/eTRsSEopRIxMwamQCLl66gG3b12L58l9sdlFXUFxcjGXLlmHZsmWoWbMmRo0ahTFjxoha7BY9LVJxHiW1KJ1OsBu8vH79+pLrYu/cK1euSM5PSZS6JVoYDJ44ccImrWHDhi6L7UizWEnjMkSsuNWoUSO89dZbeOutt3Do0CEkJSVh6dKluHjxot3jc3JyMHv2bMyePRvNmjVDQkICRo8e7RFuOM1mM15++WWb9KFDhyIyMlKVOkgNNu0K8vPz8cwzz9ikP/300/jmm2+cPmsDBgzAzp070bVrVyvRJjMzE1OnTsWcOXNcUmdHREVF4eLFi6JjEXXq1Ak7d+7E0KFDsX79ekv67Nmz8eKLL3r1uEBppC6G2jurU6dOVrGozGYzCgoKcPXqVdy+fdtuPn5+fpg3bx4GDx4sqR5uiYjvhbca7muB1E0qpMV7raB5t40GE0DpbdZsNhMtR+xZnuj9pPfXLLpDAwA/ggilhIVNPsHChrtE42gBH31xOBwORzW2b98uaxd5vXr1MGfOHAwZMkR2Xdq0aSPpvMq7Y92VN99802bHZXh4ON544w2NaqQCIh+7qDp18crLr2Lq1Ck4evQokpKSsHjxYpw7d87u8Tk5OZgzZw7mzJmDpk2bIiEhAWPGjPGIBV6x6HQCbty4YZNOcvPjDHvnXr9+XXJ+SqKYSzQnE+XVq1dL7rPS0tLsWggAsBuTqXbt2pLKoYHKwsZoa/EjJR9H3Hvvvbj33nvxySefYOvWrUhKSkJKSorDGDYnTpzA1KlT8fbbb6NLly5ISEjA8OHDLe653I3Zs2dj+/btVml6vR7vvfeeMgUIQrmzfiewtIN34cKFNjFr2rVrh3nz5lG9240aNcLy5cvRuXNnK6vMBQsWYMaMGQgPD1e8zo6Q6nISKHf9tmjRIjRq1MgiAmRnZ2Pjxo0WrxUc+Sj57B8+fBgFBQXUx/fo0QOffvqp5G+Ju8JFGDaRGouGVQsbHYXFkJgYNoYy8ljInjjjT3Af5gypVk+uxo8gQikRw6aQ5BKNCzYcDeCCjRuQnZ2NY8eO4fz587h58yZKS0sRHh6O8PBwNG/eHK1atYKPj2t3Gpw/fx4HDx5ERkYG8vPz4e/vjzvuuAN333032rRpI9kHKS2nT59Geno6srOzUVBQgGrVqiEqKgr33HMPWrZs6dKyORyO9nTp0gUvv/wyBg4cqNhOT3uWOzTYsxxQGznD6ZSUFHz++ec26f/3f/+HmjVrysiZbaS4katYrLv77rvx4Ycf4oMPPsCuXbvw888/Izk52a4oAQAnT57E22+/jWnTpqFz585ISEjAiBEjHCzwinXWwb6JjU4n2F1EErvrm3Ru5Z3FnoCz5s7NzZUUCB0od9HkLN+quFKIoIphQ7OgocAKnE6nQ8+ePdGzZ0/MnTsXa9asQVJSEtLS0uy2mdlsxs6dO7Fz505MnDgRAwcOxJgxY9CvXz8qt1MssG/fPrvuRCdPnoy77rpLkTLoXKKxsyj05Zdf2qR99dVXouY2HTt2xOOPP46FCxda0goKCrBgwQK71kysUqtWLfTp0wcrVqywpO3atYtpwSYiIsISE0htHAnhzpAs2Mh4Ze655x58+eWX6Nq1q/RMvAR2eibPR+q7wKpg40vjEo1iQ0oFpaUG4jF6vW2ZcmLYsPr824vVUxlDmQkmk1mWIF5YQHKJRmkaxeEoCPOCzRNPPGH57wYNGmD69OmK5T19+nTLTllBEPD9998rlrccjh8/jg0bNmDLli3YsWMHbt686fT4oKAgdOvWDRMmTEC/fv0UE0+MRiMWLFiAr776yqlf6Bo1auCxxx7DK6+8grp16ypSNlDuS/zrr7/GvHnzcOrUKYfH1atXD08++SRefPFFt93tyOFwnLNr1y6LcDtp0iSXuetxGygXK6sedujQITz++OM2xw0bNszqe+uRSBjDV20/QRDQtWtXdO3aFXPmzMG6deuQlJSE1NRUFBcX25xvNpuxa9cu7Nq1C5MmTcKAAQMsC7yWndBi66XqbEqi6xadgLKyMpv0gIAAyTWxJ9g4EyLURKndu1rEsCkqKrJJsxd3QyloYpzQuURTrEoAyp/N+Ph4xMfHIzc3F8uXL0dSUhJ27txpN5ZVSUkJkpOTkZycjBo1amDkyJFISEhAXFycshVTkKysLAwZMsTmvYmLi8O0adNUrQsrFjbZ2dk2bgFjY2MlxS169tlnrQQbANi0aZNbCTYAEBMTY/X3vn37NKoJHYMGDcKgQYO0rgY1SrpEo+Xw4cPo3r07Hn74Ybz11luaCVxaIep7wUbX5BVIFmxYdYlGI9gY6AWbMgoLG3874ow9N2m00FyDFtiL1VOVsjKj3fagpSC/xOnv3MKGowXMCzYLFy60TGBbt26tqGCzatUqHD58GGazWXPBpqioCP/3f/+HZcuW4ejRo6LOLSgowNq1a7F27Vq0bNkSCxcuRGxsrKz6/PPPPxg5ciSOHDlCPPb69euYNWsWvvvuO3z22Wd4+umnZZUNALt378bo0aORlZVFPPb8+fN49913MXfuXMyfP9+tBu0cjrcRFBSEJk2aOD0mPz8fubm5NtYLly9fxowZM/D111/jhx9+kB0c2Z2Rslh59uxZ9O/f38byoXnz5liwYIFCNfMsnC2g+/n5YfDgwRg8eDDy8vKwYsUKJCUlYcuWLXaDuJeUlCAlJQUpKSmIiIjA/v370bhxY/F1En2GdOTEsLGbn4xVdnvn2ltI1waFXKJpsFBkrw1dLRwJguD03rkiho0YwsPDMW7cOIwbNw7Z2dlYvHgxkpKSHI6Jr1+/jrlz52Lu3Llo3bq1001OWnH9+nX07dvXJu7THXfcgeXLlysbo4TCxIbGdYwa7N692yYtPj5eUl7t2rVDo0aNkJGRYUnbu3evZZ6pFRcuXMCePXtw+PBhnDx5Erdu3UJeXh6KiorsvodVrYgduQHlSENJ67L8/Hyrvw0GA27evIkLFy7gjz/+wPLly7Fp0yaYzWYYjUYsX74cv/76K6ZPn4633npLsXqwD1dsWMRHojjAqoUNjQAlxiVaGYWFjT3xSo6FDSubKapCimEDlMexkSrYlMf/IlnYcMGGoz7MCzYVuGpSzspk/8qVK4rsbjt27Bg6duyIWbNm4fnnn5eUx549e9CnTx+HwQodkZ+fj3HjxuH06dP46KOPJJUNACtWrMCoUaPs7s51Rk5ODoYMGYIvv/wSzz77rOTyORyO64iNjcW2bduojr106RK2bNmCefPmYdeuXZb0inf9xx9/xKOPPiq5Lqz0/2pw4cIF9OrVy2Yhpl69eli/fj2qV6+uUc3UQ8oUhHZhJSQkBImJiUhMTMSlS5ewdOlS/Pzzzzh06JDd42/cuGH5xoquF5tzKSt0OsGuiyh71hy02DtXTrwGJVHMwsbJ81bV3ZJS2LNcIll2y0UgxDihEmyUrJAT6tevjzfeeANvvPEGjhw5gqSkJCxZssThInZmZqZKNaMnLy8Pffr0wT///GOVHhoainXr1qFevXqKlkdzb3SMBJWw10fL2fQWGxtrJdjcunULZ86cIW5UcQXJycmYO3cutm/fbncTAS2u7g+8Deku0cjn+fr6IjIyEpGRkWjdujWeeuoppKenIzEx0eIG2GAwYOrUqbh+/To+++wzaXVxM8R0N4x0TV6Bx7lEo6iXOJdo5Lgs9lyFybKwYVSwobGwKS0xANWluS0rKTbAZHQ+9uQu0Tha4DaCjasg7fJjgSZNmuCBBx5ATEwMatWqhaCgINy4cQPp6elIS0uzCRhrMBgwceJE+Pn5Ydy4caLKOn36NPr162dXrOnevTsefPBBREdH49atW/j777+RlJRk4//8448/Rp06dfDiiy+KvtY9e/bYFWt0Oh0GDBiALl26oH79+sjJycFff/2FJUuWWPmwN5vNeP7551GnTh0MHTpUdPkcDocd6tSpg9GjR2P06NGYM2cOXnjhBUt/bTQa8cQTT+Cee+7B3XffrXFN1UfMhPLq1avo1auX1SISUL6z+rfffkN0dLTCtWMUBVyi0VCnTh289NJLGDZsGN555x0XLLK7RwybwMBAm3SlBZugoCDJ+SmKYi7RlMlHDBERETZpLhdsdACcrFlobWHjiFatWmHmzJkYNmwYJk6ciN9//131OoiloKAA/fv3x4EDB6zSg4KCkJaWhrZt27qgVLKJDSuLQteuXbNJkxPLp0WLFnbLUFOwuXjxIhISErBlyxZF8rt165Yi+XDKUfvZb9OmDXbt2oVevXpZ9VmzZs1Cx44dMXz4cFXrw+FU4HEu0ZS2sCkjCzZ2XaJ5ooUNhQhFI3A5ghS/BgACA90jViHHs/B6wYZVWrRogcTERDz66KNO48IYDAZLQMuqZtETJ05Et27d0LRpU6oyTSYTHn30UZuBee3atZGSkoJOnTrZnDNz5ky88sor+Oabb6zSX3/9dfTo0QP33HMPVdlA+aTykUcesRFrmjVrhl9//dXuBOrjjz/G2LFjsXr1akua2WzG2LFj0bFjR9SuXZu6fA6Hwy4TJ07E+fPn8fHHH1vSSkpKMGnSJMUWJdwLugH1jRs30KtXLxw/ftwqPTIyEr/99hv198EzED8JEbsofOPGDSQnJzuNfSG3Wm7hEk0Q7MaZqjpOEYO9c1mJZaXUPdFChLBnXVHVbZbSkK5Tixg2JM6cOWNxjVY15gmrFBUVYdCgQVYWqkC5VdWaNWvsjusVgeLesOISreqmMwCy4mGGh4fbpFV17+pKLly4gO7duzuN/SkWg4HslodDjxYxbIKDg/HLL7/grrvustr88Nprr2Ho0KHKukRkEDHfVi3dF3obUr8Dvno2LWwUj2FDIUDYaws5go2SLhuVRO9HviYDhcDliIJ8smDDLWw4WuDZX2cClQeg9lx3aEH37t0xffp03H///VTH+/r64umnn0aHDh3wwAMPWO2KLC0txSuvvILU1FSqvL7//nv88ccfVmkRERHYu3cvGjZsaPecoKAgzJs3D4GBgZg1a5YlvaysDBMnTsT27dupygaADz74wMbFRExMDPbs2WN3B2hF/VasWIGRI0ciJSXFkn7r1i288cYbWLRoEXX53o4+PBR3jpPuXoqjPvpw1wWGZpEpU6bgyy+/tLKq27p1K44cOYJWrVppWDP1oRlO5926ifgRI2ziLkRERGDz5s1o2bKlayrHKFLm4DQT96KiIqSmpiIpKQnr1q0juvMMDQ1FfHw8GjRoIL5CAPMu0XSCAEEn4I477rD57fz585LzrWpNDMBuGZqgwAKPVre1WbNmNmkZGRm4ceOGw7GXXJQQbNRQbK5evYpffvkFSUlJVNY0DRs2RGJiosvrRUNJSQmGDh1qs6HB398fq1atQrdu3VxWNunOCGBnUbSqRwFBsG8dSIs9qz+xLqblkJiYaFesadOmDfr27Yv27dsjOjoaUVFRCAwMREBAgM0cePr06Xj33XfVqrLXIfnZl/nKREdHIyEhAd9++60lLSsrCxs2bED//v3lZc46lG3HRq/kPfjoJMawkRj7xtVQuUQTYWFDYzHiZ0fIkOUSjZFvc1X0FCKdHAubgvwS4jGBPIYNRwO8WrCpvOMpODhYw5qUL+Bs27YNDzzwgKTz77nnHsyfP98mUOa6detw7do1REZGOj3faDTiww8/tEmfM2eOQ7GmMjNnzsTGjRtx7NgxS9qOHTuwY8cOKvHp5s2b+PLLL63SdDodfvjhB+KCgY+PD7755hvs2rXLaldoUlIS3nnnHdx5553E8jmAztcX/jXZ2K3M4dgjNDQUXbt2xYYNG6zSN27c6HWCDWlWeft2HhIS43H06F9W6WFhYdi4cSNat27twsp5Do7mLUajEb/99huSkpLw66+/Ehfk9Ho9+vTpg4SEBAwcOBABAQHS6yT5THUKq9id16hRI5vfsrKyJFfFXswQe2VogRL3RBAETRax27Zta9c98P79+9GnTx+XlEm6TC0tbPLz87Fy5UokJSVh06ZNMBqdLwCEhYUhPj4eCQkJ6Nq1KxNCRGlpKR5++GGbb6Wfnx9+/fVXPPjgg66tAKEJdDptnnV7VI3fZjabUVhYKFm0KSgoIJbhKtauXYvNmzdbpdWqVQs//vgjevfuTZ2PHNeVWrB69WpF4sBKISoqCmlpaaLOqXj+tXDJ3rdvXyvBBgC2bdvm8YING70NpypSrTnc2SWa0hY29sQZT7SwsSdMVUWWS7RCcuzsoCAu2HDUx2sFm5ycHFy6dMkyYXDVLkJawsPDJYs1FQwbNgz33HMPDh8+bEkzGo1Yt24dEhISnJ67fv16m/gGrVq1og7o7efnh3fffddGMPr666+pBJukpCTk5eVZpfXv3x9dunShKr9GjRp47bXX8Oqrr1rSjEYjvvvuO7tCFIfDcU9iYmJsFqEqAql6E84Wu27fvo3HE4fbiDUhISHYsGED7rvvPldXzyPQ2VlA/+OPP5CUlISlS5dSuY3q0KEDxowZg1GjRjncOCGw7BNNQmEVE9bo6GgEBASguLjY8lt2djZKS0vh5yd+0nPmzBmbtObNm4vOxxUosvis0Ry5evXquO+++2xinGzZssWFgo3zizVTBEhXcsG/rKwMGzZsQFJSElavXm1lxWkPvV6Pvn37WgRYf3923GSUlZVh+PDhNovIer0eycnJ6Nu3r8vrQLozLPnIt+fC7ObNm5IFG3vxn9SaYy5ZssTqbx8fH6SmpqJ9+/ai8lHThZsS3LhxQ7NxoNR4XzpBgFGkYKPEW2NvE+bZs2cVyJlxKL8XrAjJ3oKPx7lEUziGDYUAobcj2Pj7Sxe0pN4TV2PvOqsizyWacwsbnY8A/wCvXTrnaAibvZ0KVHbfJQiCrACTLGFvIkYzEFu6dKlN2oQJE0SVPXjwYJuYMatWrSJOfJUqPzEx0WbSXHXywuFw3JuQkBCbNHtBg70Be0Pq/Px8jH1iBP46fMgqvXr16tiwYYPohRtPQvRE/N/DT548ienTpyMmJgbt27fHF1984VSsufPOO/H222/j5MmT2LdvH55//nmilaukiqmAlLWLisVYX19fmzh2paWlOHjwoOg8DQaDjcvW2rVro06dOuIr6AqU0Gs0XCgaNGiQTdrChQtRWkr26S0FFixszGYzdu3ahWeffRZ16tTBwIEDsXTpUqdj1ri4OHz55Ze4dOkSVq1ahfj4eObEmhEjRljFdQTKxZply5Zh4MCB6lSEcHMEiW5wXEHNmjVt0v755x/J+f399982acr2/47ZtGmT1d99+vSR9M33igV8jZEUu0OBb4Q96141XfZpBZtL0Byp4r2vL5sWNjQu3oxGegub0lLn8cMEnQAfO+7hPNPChnzP5blEcz7eDQry44IuRxM0lwl37NhBfWx+fr6o4ytjNBqRn5+Ps2fPIi0tDZs3b7aYIwuCgNjYWEn5soY9n/iXL192eo7ZbMbGjRtt0ocNGyaqbF9fXwwePBjffPONJa2oqAjbt293uqPv1q1b2Lt3r1VacHCwKPN9oNzKplu3bla777OysvDPP/94jCDH4Xg7t27dsknz8WFz4O5qqrrUKCjIx9gnR+DPdOud8sHBwVi3bh3i4uLUrqLbkpNzBWnrVmPd+l9thAJ7hIeHY8SIEUhISEDnzp3FFSZWR2J8vlB5At61a1fs37/f6vddu3ahY8eOovI8dOiQzUI6baw/d0HL+/r4449j+vTpMFWybMnJycHy5csxevRoxcuTG8NGTgyUY8eOISkpCYsXL6Zy0XfnnXdizJgxSEhIQJMmTSSVqQYGgwGjRo3CypUrrdJ9fX2xdOlSDBkyRJN62YMlC5t7773XJu3AgQPo2bOnpPyqfi/CwsLQuHFjSXmJobS0FFevXrVK69q1q+h8jEajTZ/NUR6t4kRUfUYA9QRFLaFubna6Jq9AJzEWDU2sGC2gsrAxKmdh40jE8Jch2LAaw4bGDR5J4HJGYQFBsAlmZ3MOx7vQXLDp1q0b2TXCvwtSZ86cQffu3RUpt0KoqWDUqFGK5Ks19nYGVqtWzek5J06csBnAxcTESArm27VrVyvBBgB27tzpVLDZu3evjY/wjh07SlqEtRffYufOnVyw4XA8hOPHj9ukVbXs80YKCwvw5FOP4NAh68WioKAgpKWliRcRPBDSFCQ/Px8bNq7B6tUp2LtvJzF2hZ+fH/r3748xY8ZgwIABktx8eSKVF2P79OmDTz/91Or3lJQUvPbaa6LyTE5OtklzlbsuKSjiEU3DSXKDBg3w8MMP27Tz5MmT0b9/f4SFhSlaHnHcL/P8qmRnZ2PJkiVISkqychvsiIiICIsA26lTJ1FlaYHRaMTo0aOxYsUKq3QfHx8kJSXh4YcfVrU+arhE8/W1nsKS+mtH2Ps2Jicn44033hCd18GDB23cS8fFxTl9XqteByDtWuxZGktxxZaWlob8/HzR52lJYmIiEhMTta6GKKS8A0p8Ifbt22eTxsfQ/8HmUrXnItnChlGXaPasXapipHD5WkEZwcWXI8HGEy1sdDoBer0OZWWO24/GhZwjSC7RqgXqJefN4chBc8GmAprAe0oG56sYPAuCgEcffRQxMTGK5a0lp0+ftkkjuQyx555E7O7XCuxNbEnuT7Qun8PhuAe5ubnYvXu3TbrXCrICADNQVFSIp8aNxh8HrCfi1aoFYu3atZJ22XokduYgpaWl2L7jN6xOTcGWLRtRXEwOttypUyckJCRg5MiRduMfKFAtRY+Xg5SyKk/2unfvjtq1a1tZ+v7+++9IT09HmzZtqPIrLS3FwoULrdL8/f0xdOhQCbVzFfLvitauFt5//32sXLkSBsN/OxTPnz+P5557DklJSYqVU1xcjIWLvsPw+MekZ0LRVLm5uUhOTkZSUhJ27NhBnEP4+/tbBNj+/fu7jQBrMpmQkJCAZcuWWaX7+Pjg559/xogRI9SvFOH+KCHYVK9e3epvqSJDvXr1cNddd1m5QTtw4AAOHjwoOt7b3LlzbdIeeughp+dUvQ5A2rUEBQXZpElxF/vZZ5+JPocjHkmLogq4gbTnglzqnNut4CY2TEIjcNiDVZdoNJY/RgO9YFNS4lyAcGR1QuM+zBGsxrABAF8/H+eCjYwYNkQLm+rcwoajDczI08K/wX2r/qM5Rso/s9kMs9mMHj164KuvvtLoqpXFYDBg1apVNunt2rVzep69HetS3T40aNDAZrfYiRMnVCvfntsBUvkcDsc9eO+996wCmFdgL/6CNyAAKCkpxvgJj+H3362FrICAapj/7c944IEHtKmcG5CTcwVxnVpiwjOPIS1tlVOxJiYmBu+++y7OnDmD3bt3Y8KECYqINZJQc2FfRgwboHzR+KmnnrI55s0336TO77PPPkNOTo5V2iOPPKK41YcclLGwkZ+HHJo1a4YpU6bYpC9evBiTJk2ycpcmlVOnTqFLly5YnfqrrHxI4taaNWtQu3ZtjBs3Dtu3b3co1giCgM6dO2PevHm4dOkSUlJSMHToULcSa8aOHWsTr1Gn02HRokUaeg9wfn+UEGyq9r83b95Ebm6upLyee+45m7Tnn39e1EbB/fv32wjLQUFBGDt2rNPz7H1HpMSQCQ0NRWBgoFWaPXfXzpg/fz62bdsmumyOeLRwC/jdd9/hzz///H/27jxOjqre//+71+nZJ5nJvpOFQAKELJAEAgE3QBEuiAKiAqKICuIX5HLVK+KCKLj9UIgIilcQFUEBUUCWBMgCJGRfyb5nMplJZt+6+/fHMGE6M9N1qrunq7r79Xw8eGiqazldPd1Vpz7n8zkxy/Ly8uJWwcgWxmfbvc+qs1Lic9i45hFmDJP3E7ZREq3dIgDRW+mzpDJsnL4ZjcMqENVqEeCKp8EiYFNUlBn3hcg+rvi16wye9PSf6Xp2/ispKdF5552nv/71r3rxxRd7nMQ6Ez3zzDPd5qvp37+/zjzzzLjbbd++vduyUaNGJdQGn8+nYcOGxSzbvXu32tra0nL8YcOGdSulxuSZQOb7xS9+oZ///Ofdlp977rmunlegL7W2temGr1ythYsWxCwPhfL14G8e1exZ2TXHR7KO7YS0tLToyJHDva7fv3+5vvKVr2jJkiXatGmTvvOd7+i4447rg4alfpepY79xx3ZYb7rppm6jyJ9//nmjwTLLli3Td7/73Zhlfr9ft99+u6029TRwp6d7j0Rlekm0Tt/+9rd7DPLed999+uQnP9nj/Acmmpubddddd2nKlCkpyXq2OlVVVVVqbe298z1hwgR973vf05YtW/TGG2/o+uuvdy4Am6BoNKovfvGL+r//+7+Y5V6vV4888kifzD10rO3bt/f43bJ6aJXQhOvHOOmkk7ot+9e//pXQvq6++upu5cOWLFmir371q0bb79ixQ5/4xCe6BTU///nPWwaWR4wYodLS0phlib6PY/t78+fPN97X888/r5tuuimh48I+Ow+q165dpd88eJ+aGq0zgHvzxBNP9Pj3fO2116qoqCjh/WYK08ur81fh3JJtJdFM5uSJhFNXEq2385BMho2b5pg7ViAYPxCVTEm0xvr4AZv8AgI2cIbjJdFeffXVXl/rzIDpzIgZN26cfvvb3yZ0HL/fr+LiYvXr108jRoxItLmu1dzc3OOI1WuuuabH+shdHRvkkZTUORoxYkTMRK7hcFhVVVW9lmZL5fF9Pp+GDBmi3bt3H1124MCBhPYFwFn79u3Tyy+/rAceeECLFi3q9nogENB9993nQMuc197erq/eeK0WLHg5ZnleXkjz7v+DzphNsCYReXkhfeDcD+uiiy7TeR85TyNHu28yXhc814/r2AlLBwwYoO9973v6+te/HrP8xhtvVE1NjW6//fYe71OeeeYZXXXVVWppia0rffPNN+v4449PfcOTkoqSaCloRpICgYD+/ve/66yzztKaNWtiXnvyySf1wgsv6Otf/7q++MUvavjw4Zb727Fjh/74xz/q17/+dY/3eolKJLhVUVGhyy+/XFdddZVOP/30lLXFKV/96lf18MMPxyzzer16+OGH9ZnPfMahVnXIy/Orubn3gVqhUPK14GfOnCmv1xsTJLnllltUVFSkCy64QIGA+TEKCwv1wAMP6FOf+lTM8vvvv1+VlZX61a9+1eu8nv/61790/fXXx/Q7JGn06NH63ve+Z3lsj8ejWbNm6fnnnz+67KWXXtL//M//6Otf/7oGDhxo/D4++clPdsuq+dSnPqXf//73+sQnPtHjNs3NzfrJT36iH/7wh0eDnCUlJaqtrTU+Luyz81C0rr5OP7nne3rkD/P02c9+Vp/61Kc0ffp0o23XrFmju+66q1sWniQNGjRId911l3E7MhoRG1dKNHjv1pJoRhk2EfMMm5aW9rivB3oriZZEho2rAzYWgbrWpOawsSiJRoYNHOJ4wMa0XIvH41FRURHlXXrxP//zP9q0aVPMsn79+hlNmlldXd1tWTKjbXra9tChQ70GbPr6+K2traqvr0/JCKLKyspupVms9DSvEJCrli5dajl3RUNDg6qrq3v8bejk9/v12GOP6cQTT0xxCzPDX/7yF734n393W56Xl6cf/+RO/fgnd0oeT0KjrKZPn66HHnrIeH3TuUg67d2713KbFStW2NqnkV76IB6PRzOmz9TFF1+mC86/SMXFHVm36SuL1Pedo3nz5mnevHm9vt7Tw/TvfOc7+tlPf6729p47QFdecbWuvPLqbst76oB/7Wtf04svvqh///v9v9loNKr//d//1W9+8xt9+tOf1vjx45WXl6dt27bpH//4h955551u+5k2bZp+8IMf9Po+nJItGTZSx73jggULdOGFF3YLlNfX1+v73/++vv/97+vEE0/UOeeco+HDh2vAgAEqKytTc3OzDhw4oI0bN2rRokVau3Ztj2WlPEn+zZueqlAopI9//OO66qqrdN5559l6iO9mCxcu7HG+lFAopF/84hf6xS9+kfC+hw4dmnCGR6eikjzV1jb3+NkHAj6F8pP/HIYMGaLzzjsvpq0HDhzQxRdfrGAwqBEjRqiwsLDb9+qhhx7q8WH3Jz/5Sb3wwgv63e9+F7P8b3/7m5577jmdf/75mjNnjgYPHqzm5mZt27ZNzzzzTI/XqkAgoEcffbRb5kxvrr322piAjSTdfffduvvuuzVkyBD179+/W1D74x//eLeA0Gc/+1n96Ec/0pYtW44uq6+v12WXXaapU6fqwgsv1Lhx4xQIBFRZWally5bpn//8pw4dOnR0/RNPPFEXXnihfvzjHxu1HYlJZA6byspK3Xvvvbr33ns1aNAgTZ06VVOmTNGQIUNUVlamgoIC1dfXq6amRmvWrNGbb77ZLfDeqbS0VP/4xz9cVVq0LzGDjTv5vPYzZTwe986z4vV65PF6FI0TlLGTYdMeZ74WKV5JtMQDWm65F+1J0DLDJn6AKx6rkmiFhQRs4AzHAzYm7NQQzkVPPvlkj52ze++9VwMGDLDcvqGhoduy/Pz8hNvT07aNjY2OHz8VAZv7779fd955Z9L7AXJVQ0ODVq5cmdQ+Bg8erN/+9rf62Mc+lqJWZZ7eykzW1h5Rbe2RpPZttwNv9/Nsa2tL+m8gEcd2QfLzC3TL//umLr7oMg0d2j1bIF19lnQcZv/+/bbP+a5du7Rr165eXz9Y1XN5rJ46ex6PR3/961913nnnaeHC2PmWdu/ebfRw8IQTTtC//vUv5eVl58Sfbuoj9+/fX/Pnz9e3vvUt/exnP1M43D1ot27dOq1bt872vidMmKAbb7w5qfZZBXzGjh2rhx56SJdddlnWlD3uqrff/8bGxqR/Ww8fPpzU9lLHQ6SKAYU6dLBBkS59OL/fq8FDSlI2gveee+7RggULuvUjWltbY4IWXdXX1/e6vwcffFA+n69bNYempiY99dRTeuqppyzbVFJSoqeeekpnnHGGwTvocOmll+oDH/iAXn755W6v7du3T/v27eu2vKdBD4FAQE888YTOPPPMbv2ud955p8cgeFfDhg3Tc889120uHqRest+BAwcO6N///nfMIAhTw4cP11NPPWU5x21WMY7YuOhCnAMSCVz6/V5XBxV8Po/a4wRs7Mxh02oRgPD3MijPKrARj1uDYZIUsBiEaFVCLp5Gi4BNARk2cIg7C0B2cccddxz97/rrr3e6Oa7zzjvv6HOf+1y35ZdeeqmuvfZao3301PELhUIJt6mngEm8WuJOHx9AZqioqNCtt96qDRs25HSwBqlRXl6hL9/w9R6DNZJ7R5m5tV2deqvhXVRUpJdeeknXX3+97fdw6aWXavHixbbKA3XqKSt2wIABvZY4SoTHk2zOiPs+10AgoJ/85Cdavny5PvaxjyXdvvHjx+vXv/611q5dq/M+ktwk11ZNmTNnjj7/+c9nZbDGTXr6bk2aNEmSVFwS0ohR/VReUah+/Qs0cFCRRozsZ/nAxY4TTzxR//nPf1I2j53P59ODDz6oX/7ylwnNaTRz5ky98cYb+sAHPmBrO6/Xq7/97W+68sorbR/zWKeeeqpeeOGFXqsa9GbmzJlasmSJRo8enXQbYM1OwCYvmNdtftZEBAIB3XzzzVq/fn1uBWuUfFYn+kYiwQF/L2XA3MJnMY9N2M4cNhYlvnqropBUho2LS6JZVY1ItCRae3tELc3xg2MFZNjAIa7PsLnjjjucboJrbd26VR/96Ee7jSybOHGifv/73ye172Q65j1tazdLyunjA3BOMBhUSUmJSktLNW7cOE2dOlWzZ8/WRz7ykawpaQMH2LyupO0Bunv7Rgk5dg6brkKhkObNm6drr71WP/nJT/TPf/6z2xw1nfx+vz7wgQ/olltu0Yc+9KGE29PTXIm33XZbUpm8PemcbzHx7VPYmBQ66aST9Oyzz+rdd9/VY489pmeeeUarVq3qMeumK6/Xq4kTJ2ru3Lm66qqrNGvWrKOveTy9z29ixK0nK8f09N367ne/e/T/+/xelZal9nt2rFmzZmnDhg168cUX9c9//lOrVq3S1q1bVVtbq8bGxpg5bkzddNNN+sxnPqOf/exn+tvf/qYNGzb0um4oFNKcOXP0pS99SZdccknC76OsrEyPPfaYvvOd7+jPf/6zli5dqvXr16u6ulr19fW9ZlX15Mwzz9TKlSt177336sEHH4ybNTV9+nTdeOONuuqqq+RNoDwREmMnYHPqqdP11pL1Wr/xbb366itasmSJ1q9fb/S3nZ+fr6lTp+pTn/qUrrzySpWXlyfT7MzFFDaulMhvjt/v7t8pq3l57ARsrAIQvQZsksmwcfF1oLc5ezpZBbh6Y5VdI0lFRdmZ3Q/3c33ABj3bs2ePPvjBD3arOz98+HA9//zzKi4uNt5XTw9Am5qaEm5bT9vGmwsgEAh0e2DT1NSUcAkzu8e348tf/rIuu+wyW9ts3rxZF198cUqOD2SS0aNHuyZYun37dqebkFJXX321PnjuRXFv5n1er0Yd17/P2+KWz9iK3Y64x6V9lkSeVX/3u9+NeZBqqq62WQcrey8h1BOTB1GnnXaa/va3v6mxsVFvvvmmNm7ceHTOqs5A7emnn56S+vqvvPJKzL8HDx6sL3/5y0nv18QnLr1Cn7j0CqN1uwYI586d67rv1fjx44/+HTU0NGj16tXatm2bDhw4cLT8Ur9+/dSvXz8NHTpUp556aq/3osmO4Mz1eI1b/j6O/W6dcsopuvTSS9PeDp/Pp/PPP1/nn59c5lZX/fr1Ozpf044dO7RixQodPHhQVVVVCgaDGjhwoIYOHaqZM2eqoKAgZcc9/vjjUzJYccCAAfrxj3+su+66S0uXLtXatWtVVVWl9vZ2FRcXa8yYMZo+fboGDx7cbdtErxfz589Put25wm5JtLKyfvrUpy7Xpz/dkYXV0NCgd999V9u2bdP+/ftVX1+v5uZmFRYWqqSkRGVlZZo4caJOOOGElGTnZDrjs53j15Z0SyjDxuUBG6uAR8RGSTSrAERvGater0eBgFdtFnPg9MTNGTZWGbqJZtiYBGzIsIFTCNhkoMrKSn3wgx/Utm3bYpYPGjRIL7/8skaNGmVrfz11NFIdsCksLIx7/L4O2MQ7vh0DBw5MqCQLAKSexU21e++5M0K6Mmzc/DElcg7sPIgqKCjQOeeco3POOcf2cUwdmwVw++23p/QB61EeSUk8Q3dbSbR4CgsLNXPmTM2cOTOh7ZN9q5l0rrJVW1ub3njjjZhld955Z1Z+NqNGjbLdt3ILn8+n008/XaeffrrTTUEXiTwU7frVKiws1JQpU3qcywg9IGLjSol8D3wuL4mWygwby4BNnHMRzPOrrc3+lACpmmOuL1iVREt0DpuG+p4z/bsiYAOnuDtEjW4OHTqkD37wg93S8ysqKvTyyy9rwoQJtvfZU3p0vEk5rfS0bbwU7L4+fjAYTDj4AwBulYXPxfqU3fPl2gePbm3Xe9w0Om/Pnj3atGnT0X8PGzasz+ZDTLZGvss/1pRK9ruVS+fKrd56662YkszTp0/XRRdd5GCLgMwRr3QoUs/0+szHkl5W8730xPUZNhbvKRIxH9ljlTESL+MkmJfYuHw33cMfK2BR6i3RkmgN9QYl0YopiQZnuPsXDzFqamr0oQ99SKtXr45Z3r9/f7300ktHJ/q0q6eJd3fv3p3QviRp165dMf/2er2qqKhIy/HD4bD27dtnuX8AyHbuveV2ir0zkrY+i91AUt+0ImUHc9PovGOza775zW8qFAr1ybGSftCTQ0+Kkg/Y5M65cqtjv1t33nmnQy0BMo+brpM5gdPtSol8D9wfsLHKsLFREs0iYyTeXDVW2Si9cXMw2WoOm9bW9oT2a1ISLb+AOXThDNeXRLv22mvTchyPx6OHH344LcdKxJEjR/ShD31Iy5cvj1leVlamF198UaecckrC+x4zZky3ZTt27EhoX5FIRHv27IlZNnz48LgThY8ZM0YLFy7sdvw5c+bYPv7evXvV3h77Y93T+wOATMdDy76VvlFm7v0cbc/7I3fFHbrOsTFy5Ehdd911fXewJN93Lj2/S74kWmragcR1/W7NnDlTF1xwgYOtATJLIg+q+d1LHOfOnRL5Hlg9tHea12IOm9SWROv9WIlm2FiVdHNSXp5FSbREM2wsAjahfH9C2WBAKrg+YPPII4/0+UOpaDTq6oBNbW2tPvzhD2vZsmUxy0tKSvTCCy9o2rRpSe3/+OOP77Zs8+bNCe1r586damtri1k2ceLEtB1/y5Yt3ZZZHR8AspJ777kdYb8kWt+0I1lubZfU0VF1UyCxaxbAt7/9bQWDfVeDOvmSaO45b30t6feaQ+fKjVpaWrR48eKj//7e977nYGuAzJPYgBB+9xJnWhKNc5xOVtkoPW7j9gwbf/z3FLERsLHKGIlXIizhDBsXjx7yOzSHTWER5dDgHNcHbDpFo0nM5BqH2y/MdXV1+shHPqK33norZnlxcbFeeOEFnXbaaUkfo6eAT9eOmB2LFi3qtmzq1KmuPj4AZCKXX74yXrruD7Lpc3RbR2/btm3pOxgxCGPMYZPZ8vLy1NTU5HQzgIzl8Xjk9XgU6aPnG4jFNcOdvNk4h41lhk0qS6Klfg6bTC6J1tKSWMCm0WIOm4LCvhvsBVhx9y9eFx6PJ6X/ZYL6+nqdd955WrJkSczyoqIi/fvf/9bMmTNTcpyJEydqwIABMcs2bdqkyspK2/t64403ui0766yz4m4ze/Zs+XyxP8CLFy9WOGz/RzeR4wMAcoDtDBuX3iuks102j+W2gE06Jf3O3fr31geSTrBhpDmADGf3eplDlwjHcIrTK5HggOtLolnOYZPKkmi9nwur8mG9SV85aPvyLIJQ7X1UEq2QgA0c5PoMm5EjRyb80KStrU3V1dVqbm4+uqxzX3l5eRo8eHBK2tgXGhoadMEFF3TLGCksLNS//vUvnXHGGSk7lsfj0Yc//GE99thjMcufeuopfelLXzLeT3t7u55++umYZaFQSGeffXbc7UpLSzVz5syYeWzq6ur0n//8R+edd57x8aurq7tNgjpy5EidcMIJxvsAALvmzZunefPm9flxvvSlL8X8JltdG917y+0Muw953fpwJJ3Nsj2HjYs7en2OrBFjyf6d5NK5ApCdvF6PlNjzRdjk8XTcAVrmNnBtSatE5kvxx5m3xQ2s5jqJ2MiwabUK2MTJsIlXLi2eRMrUpYvVZ594STSLDJsiAjZwjusDNtu3b096H3v27NGCBQv04IMP6rXXXpPH41FbW5uuvPJK/fCHP0y+kSnW2Nioj33sY3r99ddjlhcUFOi5557TnDlzUn7Myy+/vFvAZt68ebYCNs8++6z27t0bs+yiiy5SQUGB0fG7Bmw6j28nYPOHP/whJjgnSVdccYXx9gCQiP3792vlypVpOY497r3pzgTuzbBxugG9y+kMm6RLouXOueNcAch1OT3AwQkej0QJOldJZCJ3t0/+bhXwCEfMMmzC4YhlcCdeSbSEM2xcfH8VtAhCtbbEn/OnN41WGTYEbOAgd//ipciwYcN05ZVXav78+frHP/6hfv36KRqN6u6779anP/1pp5sXo7m5WRdddJHmz58fszw/P1/PPvusZbZKos477zyNHj06ZtnKlSv1l7/8xWj71tZW3XHHHd2W33DDDUbbX3XVVSouLo5Z9uyzz/Y4J01PampqdM8998Qs8/l8+sIXvmC0PQBkHKt7avfeczvDpSXRbGex9EkrUiOXAzbJcnEfOeWYwwZArrNzvfTI3Q9SM4HJ2eMcp1ci94zuL4lmMYdNu1nQ0KocmhR/npqE57Bx8X18vACVJLW1RRKa99yqJBpz2MBJORGw6erjH/+4XnnlFRUVFSkajerPf/6zbr/9dqebJakj6HHJJZfopZdeilneGaw599xz++zYfr+/x/Pw1a9+VTt27LDc/pvf/KZWr14ds+zMM880DjCVlZXpK1/5SsyySCSia665RjU1NXG3jUQiuv7667Vv376Y5VdccYXGjh1rdHwAyDTuvaV2J/vlvfqkGT0cqM83SJjdZxdu7uj1teSDELlz7jhXAHJdLl8vHcHpdp1EvgNuL4lm9Z4ihhk2JuW94p2LeOXS4nFz5p/Je2pvM58jqFOjRUk05rCBk9z9i9dHTj75ZP3sZz+TJEWjUd1777166623HG1Te3u7PvnJT+rf//53zPJQKKR//OMf+sAHPtDnbbjuuus0derUmGVVVVWaNWuWFi9e3OM2jY2NuuGGG/TTn/40Zrnf79d9991n6/jf+ta3NHz48JhlmzZt0uzZs7Vhw4Yet6mpqdEll1yiJ554ImZ5SUmJfvzjH9s6PgAk4rvf/a6i0Wif//fd73439sA8tLTHpRk29hvWN61IBR5AJS6XghBJv9XcOVUAspSt62UOXR/6itEZ5DSnVSLlzfx+d2fY+P0WGTaGc9hYzV8jSXlxsmjivRaPm+/jTbKrWlvtl0VrqG+J+3phUZ7tfQKp4vo5bPrKNddco+9///vatWuXotGovv/97+vZZ591rD1/+ctf9PTTT3dbHgqFdNttt+m2225LeN/Tp0/XQw89ZLmez+fT448/rhkzZqi2tvbo8n379mn27Nk699xz9eEPf1gjR47UkSNHtH79ej366KOqrq7utq+7775bU6ZMsdXOoqIi/elPf9K5556r9vb3f2w3bNigSZMm6cILL9ScOXM0bNgwVVVVaeXKlXr88cfV0NDQbV8PP/ywhg4dauv4AJBJ3HtL7Vb2zphbn4+4tFmS3D0yr68lPy9LatqRCYwngI6zPQBkMjvXS37xUsBjfdXhPKdXIveMVgERp3m9VgEbwwwbg4BNvABGLpZEk8zOW1eRSFSNjW1x16EkGpyUswEbr9erj370o3rggQckSS+88IIOHz6ssrIyR9rT1tbzD8Xhw4d1+PDhpPZt5z1NmDBB//znP3XBBReovr4+5rVXXnlFr7zyiuU+brnlFt1yyy12mylJmjNnjv70pz/pyiuvjAnaRCIRPf300z0GtbryeDz6xS9+oU984hMJHR8AMobFPbV7b7mdYbskGg+FbUcR3NzR63PJlvnKsXPn8XgSqjXesW2KGwMAaZbT10sHcLbdx+fLvpJoPr9FSTTDgE1ri3WmiD9OwCYrM2xMAjYGpeS6amluUzQS/160sIiADZzj7l+8PnbSSScd/f/hcNh4gvtsN2fOHC1ZskSTJ0+2tV1hYaHmzZune++9N6njX3bZZXrllVc0cuRIW9tVVFToqaee0k033ZTU8QEgE1jeUrv3ntsZLi2JZvswLv5c3dzR62tU+bInmQAVwVQAmS6Xr5eOMDjdXFvSK5HvgElZLCf5LDJsIhbBgU4mgYe8vHgZNomdJ6sMIScFg9ZBqJYWewGbBov5ayRKosFZ7v1GpkF5eXnMv7ds2eJQS9xn0qRJWr58uR588EGdcsopcdctLy/X1772NW3cuFHXX399So4/Z84crV+/Xvfcc4/GjRsXd91hw4bpf//3f7Vp0yZdfPHFKTk+ALgfHUs77J4ttz5L8aTxc7d9ztx60tIh6ZJouXXuknm7OXaqAGQhO9dLfvOSl2vX2EyQyBw2iWyTTl6LrKH29jSVRDMIbvTEzV8TkwybdpsZNg0N1gEbSqLBSTlbEk2SGhsbJb1/Aa+rq3OsLVdffbWuvvpqx47fE7/fry984Qv6whe+oJ07d2rZsmXavn27GhoaFAgENGjQIE2ePFlTp07tk2h8QUGBbr31Vt1666169913tXz5cu3atUuNjY0KhUIaOnSoTj755JhMKQDIFdY31S6+63aC3QwbtwYf0tksm8fK5YBN0hk2OXbqknl4xoM3AJkul6+XrsVHklYJzWHj8pJoVs/ETDNsWk0CNnECGCbzvfTEKuDkJJOAjcl566rRJMOGgA0clNMBm/Xr10uSotGoPB6PiouLHW6Re40cOdJ2ibJUGj9+vMaPH+/Y8QHAddx7T+1STPDb11wb5EqDZIMIuXbukjpfuXWqAGQhj63BjvzoJcvkksNZTi9fNpZEswh4mM5hY1ISLV4WTTAL57AxCUK1tlrP/dNVQ32L5ToFzGEDB7k7RN2HwuGwnnzyyZgO48CBAx1sEQAA5qxuqd17y+0MO8+HPR6Pa0fxuzjBxtUdPbdz6Z9bn0muJFqOnSwAWcfW9ZKfvBQwOYmc6HTyJlDezO939+NLn0X7wmHDOWwMMkXiZRslnGHj4vsrk2Bde5tZQKyTVUk0f8Cb8LkEUsHdv3h96Pvf/363OWumTZvmUGsAALDLvTfVmS6dD4RtHyqtnSl7x8rlgE2yH0uuBSGSK4mWwoYAgAPsPBjlJy95RqebE51WidwzWgVEnGY1x06qSqIFAt6491EJZ9i4eI4go5JoLfYybBotAjYFhcGcuz+Hu+RcSbS6ujp9+9vf1q9+9auYL9/YsWMtJ7cH3Kini0g0anYzACBzWd4/cn8Zw87p8KS1v2Lvg0rrx8ocNjYkEYBIYSsyRXIV0XLxjAHIJmTYuA+nOXUike6ZDsc+s0jkntHtJdGs5oAJt5tlgFiV9grEKYcmSXlZWBLN5/PK6/MoEidLye4cNlYZNgXMXwOHuT5g89prryW1fVtbm2pra7V161a99dZbeu6559TU1HR03prO/7311ltT1GIgvXqa3C4cDisQCDjQGgDp4rfotLi9U5N2NkuiuZZLm+aRy89bH0u2xFeunbtk5uzJsVMFIAvl2rxlTjO6xvKRpExPAZtjn1kkki3j+pJoFt/rdsM5bKxKe1mV6TLJRumJ2+9Fg0Gfmpt6D2aZlJLrqqEufsCmiPlr4DDXB2zmzp2b0h+OzsyDrvs87bTTdN1116XsGEA6eTwe+Xw+hcPvX6CampoUCoUcbBWAvpafH5BHUm/jjEL5BG1j2Sg/4vIOS7rYy0rKvaBDjGTeeg6etuRKouXgCQOQVeyMZCerMHmcwfRqamqK+bfP5+ueYZPAtdzvd/dgNKuSYvGyQ7qyyrDxWwRkEs+wSWiztAlYBWza7AVsmhqtMmzybO0PSDWXfyXfF41GU/Jf11GM0WhUkydP1r/+9a8esxSATFFYWBjz79raWodaAiBdAkGf+pUX9vhafkFQxSXcZHZlK/iQxp693WO59Vm1m8sopEMyD9RyMQCR1FvOvdMFIMt4PDZ+yvjNS55Rgg0nOlWOfRZx7LMKybp8WE/8AXc/s7PKsOkp86gnVqW98vLiB2yCFq/3xs1z2EhS0KIUnO2SaPUtcV+nJBqc5u5vZBedgZZk/+sM3Hi9Xt18881688031a9fP6ffHpCU4uLimH83Njaqvd3epGsAMk9pWUgDBxUrL88vn9cjv9+r/v0LNGhwUU4+BI4rW0qipfOBgo1D5XrAJpmPxdV/bn2EDBsAuczj8XDdTCdKoqVNe3u7GhsbY5Yd+6xC6piTxC7Xl0SzaF/YMMOm3SLwYFX22iqw0ZtEsp7Syep9t1lkJh2rvj5+hk0hJdHgMNeXRJNSN4F6UVGRpkyZovPPP19XX321hgwZkpL9Ak4rKio6GpDstHfvXg0fPpzsMSCLeTweFRXnqaiYbJpUcnN/JZ1NszPaNNevNUkljLj5D66PJDfnT+raAQBO8Xq9CkesR4Tzk5c8k3PIeU5eJBLR3r17Y5Z5vV4VFRV1WzeRgKXrS6JZ3AubZti0tMT/XbCax9Qf8Mrj9Sgasfcc1e1za1llDtnNsLEqiUbABk5zfcDm1VdfTWr7QCCgkpISlZWVadiwYTnZKUb283q9Ki4ujkk/bmho0O7duzV06FD5/a7/qgNAn7Jz+Xd1h8WlTcv5kcJkjNiSzCjOHDxdALKQ8b0Gv3nJI2LT59rb27V37141NDTELC8qKuoxkJFQwMbtJdH88d9TuN0sgGI1F4vVHDUej0fBoE8tzfYyTtx+L2+ZYWNzDpuGOqs5bAjYwFmuf4p79tlnO90EICOUl5ervr4+ZuRGQ0OD3n33XRUUFKikpET5+fnyer05PxIaQO4JhyOKRA1v5KORtJWVDEdstEsdHeJ0PeBvD4eN2xZV+s6ZG0Ui5ufqWNGoJ+fOnd2/+5htw2HjUaoA4F5mv4ORSO5dI1ItanCNDofDnGcbIpGIIpGImpqaVFtb260MmtQxqLS8vLzH7RPLsHH3MwyfxTOW9rDZvYtVaa+AQeAqmJd7AZtWi8ykYzU0WGTYELCBw1wfsAFgJhQKaeTIkdq5c2e3BxmNjY093kQBQK4IhyNqbzfrKDU1eVRzJD1lFyKRqK0RYZs3V/dha2JFo1Hj8gJNTR4dTtM5c6Nwe8S4I36sxkaP6hsrU9wid0vmfKXzOwAAfaWtLayIQckij8ejhqbcukakmsm5bmr2quqQuwMCmcTr9WrkyJEKhUI9v57AHDZWD+ydFgjGb19LU5vRfqzuvQMGc9Tk5flVpxaj43Vye8AmaJFZZKc/1dYaVpvFeS4souQ4nMUVCcgi+fn5GjlyJBk0AHAMd3dBsgB1qmAHfy4AAGSlzmBNfn5+r+v4fImURHN3wMZqTtH6uhajIG27ReAhaBEYkqyDRz1xe8DGKrPI6rx1ZZVdI0n5hQHj/QF9gQwbIMvk5+dr1KhROnToULcSaQCQq5qa2lW5v85o3ZKykMorCvu4RR2am9t0YK9ZuzweafTYnktL9IVwOKq9u44YrVvWP1/9+hf0cYvcq76+RdVViWWy5hcENHhoSYpb5G4N9a06dLDBesVjeL0ejTqufx+0CADS60hNk+pqrUfA5+X5NHREWd83KIsdrm5UvcV8Ff3KC1TWr/cAA6x5vV4VFRWpvLy818yaTomU900kyJNOJaXx33M0KtXVNqu0LP7fmVWGjUngymqem554XX5+rTKLWlrMS8A1GgRsisiwgcMI2ABZKBQKadiwYYpEIqqvr1ddXZ0aGhoUDidWLx4AMp2dfmE6J4G3daQ096PsHM7to/L6mieJDyedf29ukfBbzr1TBSBLmV83+eFLWg5eZ9PF5/OpsLBQxcXFKioqMq70kch8NG4viWYVsJGkuiPJB2xMMmysyof1JJl72XQI5sV/321t5gOVG+qtg+WFRcxhA2cRsAGymNfrVUlJiUpKOkbuRqNRRSIRRaPWqbgAkE1Wr9irpx5dZbTuxy6ZrNNnju/jFnVYv3q/cbtC+QH94qGZfdyi99UeadYvfvCO0bpXXXeaps8Y28ctcq8FL72rpx5dk9C202eN0plnTU1xi9zt7UXbEzpfZf0LdPd9p/dBiwAgvf65erX++eQ2y/WOG1+h2747PQ0tyl5vvfa2Xn8l/rn+ryumaMZp6bn3ywYej0derzfhQSeeBAb6uL0kmknApvZIs+U6VnOrGAVsEimJ5voMG4uATYozbPILCNjAWRkfsNmzZ4+WLFmiZcuWqbKyUocPH1ZdXZ2Ki4tVVlamgQMHatq0aZo5c6aGDRvmdHMBR3k8Hvl87r7RAYC+4PP61NJsNvLK7/fL70/PLZLH67XRrmja2tVxPL9x20L5wbS2zW28HvO/r27ber05d+4CgUBC5ysSUc6dKwDZKRTKM/odbG9P77U/G0XlsTzXXk/uXYud5EskYJNAVk46FZekKGBjMReLVWkwKbEMG7dny1tlWFllJnXVUB8/YOPxSAWFBGzgrIy8ItXV1emPf/yj5s2bp7Vr1xpvN2nSJN1www266qqrVFxc3IctBAAAbmKnE5LOEXx2RiamuyNl53j5+bk9MWcyoxL9/twbSJHIZLiS+8uhAICpkOF100s5r6SZBAdysTypk7w++8EXtwdsAkGf8gsCamps63Udk4CNVeAhELQ+D4lk2Lj9O2D1nqwyk7qyKokWKgi4PoCF7OfuX7we/PGPf9SYMWN04403as2aNYpGo8b/rVmzRl/96lc1ZswYPfroo06/FQAAkCZ2Si9Y1UhOJTsPYhIpH5EuuT4KzZfAg4dOiXSqM12igRe3P6wBAFP5BWYBGzdf+zOFSXCAh7PplUhswJcB9wBWZdFqa01KosUv7RXsowwbX6aXRLPITOqqMU5QTZIKi/KM9wX0Fff/4r2npaVF//Vf/6Wrr75a1dXVR+fg8Hg8xv9JHXN4VFdX63Of+5wuueQStbRYTzYFAAAym53SCyYdoVSx02FNpHxEMsiwMZdMICEXgxCJZtjkYjYSgOxket0kkJA8k/snznN6eTweW8FIn9/j+gwQSSq2CtgcNgnYxC/fZ3IPlUhfxu3n1+o92SuJFv85cGGOD0SDO2RED7G5uVkXXnihnnnmGUWj0ZggTE+ZNJJ6XH7sdk8//bQ+/vGPE7QBACDL2ekU+gNpvD1ycYaNnYcXIcORwtkqmQc9/lzMsEk0YJPO7yYA9CHTDBsCCckzycxw+4Tr2chv45xnSklUqwybuhRk2Jici7wEqgW4/bcmYHEPaKckWqPFHDa5XjkA7pARvZ4bb7xRL730kiTFZMpEo1HNmjVL99xzj15++WXt2bNH9fX1CofDqq+v1549e/TKK6/onnvu0axZs2ICOp1Bm5deekk33nijY+8NAAD0PTudkHRm2NhpVyL1vpNio9+W6xk2yXw2mfIQIpUSfc+5eK4AZCfT66bbR71nAq/XIGDDeU47Wxk26b4HTpBlSTSLOWyi0aja2uJn2JiU0k2kJJrrAzaWGTbxA11dNTTED9gUFhGwgfPS90QiQa+99pp+97vfxdyoRKNRzZ07V/fdd58mTZrU43YFBQUqKCjQkCFDNHfuXN1yyy1au3atbrrpJr366qsxmTYPP/ywPvOZz2jOnDnpelsAACCN7DzwSOecInaeD6T7YYKd4+UX5HbHJpm637kYhEj0O5YpD2wAwIppZqrbH6JmApNrNHMFpV/HNd0sKyJTMmwtAzYWJdHaLYI1kmFJtEQCNi6/x7K6d2xrixytrGSlgQwbZAB3fyMl3X333TFlziTp9ttv1yuvvNJrsKY3kyZN0ssvv6xvfvObR/fV9TgAACA72XngkWi5pkTYCSSl+6GNadvyQv6cf6CUzPu3KvGQjRLPsMm9cwUgOxln2OT49TUVTMqdMSAg/Wzdm2fI4JbikuRKorW0WGeJGGXYJNCXcfu9vNXfQDQSVTgcjbtOp0YybJABXH1Vqqmp0csvv3w0E8bj8ejaa6/VXXfdldR+f/CDH+i66647us/O0mg1NTUpajkAAHATO7XJ09kptJVhk+b66qYPiXK9HJpkVh+/N35/ZjyESCV/gt+xRLcDALcxzUylUlfyTEqicZ7Tz859babcK1ll2DTUt6q9vfcsmvY264wjk35KIhk2bv8OBA3m5bGa/6eTdUm0PKP9AH3J1QGbxYsXq62t7ei/S0pK9LOf/Swl+7733ntVWlp69N/t7e1avHhxSvYNAADcxU55r/SWRLORYZPukmiGARvTsi7ZzJ/MHDZp/Htzi0S/Y/4kAmMA4CZer8foAaTPINiA+EyuHWTYpJ+djA6f3+XRhPeUlMUP2EhSfZwsm9ZWg4CNwT1UnsFvy7EyPcNGMjt/ktRoVRKNvg1cwNVXpT179hz9/x6PRxdddJGKi4tTsu+SkhJddNFFMaXRuh4PAABkDzt1mdNbEs183bRn2Bgejgyb5Op+Z0pd9lQiwwYApHyTh4LufoaaEYweRHOe084k86lTppREK7EoiSZJR44kF7AJBq2zZwIG6xzL7eUXTbKG2gzOXyQSVVMjGTZwP1f3EA8ePCjp/blrpk+fntL9z5gxI+bfVVVVKd0/AABwBzuBkbSWRLPROXJiDhuT88bEnMnOYZMZDyFSyev1JBSoysXgFoDslZ9vff0k8yN5RnPYkMmUdj5bJdEy4/OxKokmxZ/HxqSkV19l2Lj9O2DyvtsMSso1NbYqajHVDX0buIGrv5H5+fkx/x40aFBK9z9w4EBJ75cjCYWsf1wBAEDmsfPAI5G6z4myUxLNzropY3BMMmySe5CQiwEbKbH3nSkPbADAhEmGjdvnlcgEJg+i3Z5dkI3sZNhkyhw2RQYZNrWH4wRs2nqf36aTSVnZrMywMXjfJhlKjRbz10hSYREBGzjP1b2eESNGxPz78OHDKd1/5/46M3iGDx+e0v0DAAB3sJVhk86SaDbWtTMSMVVM+tLMYUOGTSIS+Z5lygMbADBhFrBx90PUTGCSYeP2+TuykZ1znikZtn6/1/Jhf23ckmgOZtg40M+ww+R+2aQkWoPF/DUSJdHgDq7+1Zs2bZqk929S1q9fn9L9b9iwocfjAQCA7GKnU5jOB+h22uXEyDcvGTZGmMPGvkS+Z7ka3AKQnUIG108CCckzybLmPKefz599JdEkqdiiLFr8kmjWAQeTgE0i1QLcHhw2ed+tLdYBrwaDDBtKosENXP2rN2bMGJ100kmSOrJgnnrqqaPZMMmKRqN68skn36vP7tEJJ5yg4447LiX7BgAA7mL6QN0f8Ka3026jc+RIbWmTgA0ZNsmVREtjRpebJFQSLUeDWwCyk8mAB7eXKcoEJpkDBGzSz06AwJ9BAzas5rE5Eq8kmknAxuBcmJQPO5bbvwMm78lkDptGkwwbAjZwAdf3em6++WZFo1F5PB7t2rVLv/jFL1Ky31/+8pfauXPn0QDQ17/+9ZTsFwAAuI9pFyTdI/jtDGZzJMPG4Jj5BXRqkunk5mqZr8RKorm+6wIAxkyun25/iJoJTAbtmJRNQ2rZmV8yk67/VgGbeBk2VnOweH0eo/OWSIaN239rTPporS0GJdEsMmwCAW/ODqaCu7j+V++aa67RmWeeqWg0qmg0qm9961v697//ndQ+n3/+eX3zm988ml0ze/Zsff7zn09RiwEAgNuYZtik+wbdzuhCJzpSJs0jw0byJZNhk6NZIwll2GTQAxsAsMIcNunhM7h/MikBi9SyNYdNBl3/rQI28eawscqwMc2cyUukJJrLAzY+v9eyjSYZNg31LXFfZ/4auEVG/Oo9+eSTOv744yVJzc3Nuvjii/WDH/xAra3WqWxdtbW16Yc//KEuvvhiNTc3KxqNasKECXrqqaf6otkAAMAlTDuFiZQQSIadzqojARuTDBvmsDF6GNSbXJ2XJZHvGiMeAWSTUL71Q1W3j3rPBEYZNpzntLMzyX0mlUSzmsOmNl5JNIuAg+l9UCIZNsncy6aDx+OxHORkVBLNIsOmoIjKAXCHjAjYDBgwQIsXL9ZHP/pRSR2BlzvuuEPjxo3Tt7/9bb399tu9Bm9aW1u1dOlSffvb39a4ceP0ne98R62trfJ4PProRz+qxYsXa8CAAel8OwAAIM1MO+Ju7hA68TDBZMQpGTbmGVw9ydUghD+B922nfAoAuJ3JgAcCCckzCQy4PbsgG9k555lUPra0D0uiBYNmgRiv12N/3r8MyDKzev+tLe2W+7AqiVbA/DVwCfth1zQ799xzY/4dCoXU0tKiaDSq3bt360c/+pF+9KMfye/3a+TIkSotLVVhYaEaGhp05MgR7dy5U+3tHV/azvlqPB6PQqGQGhoadMkll9hqj8fj0csvv5yaNwcAANLCrRk2dvpGTtRXNynFQoZNcg/UcjXDJpFScLl6rgBkJ5M5bCiJljyTYD+BsfTzec3vA3z+zPl8ikviB2yaGtvU1hruccBOW2v8gIOd+6Bg0K/2NvOqRHYynpxiNciprS1iuY+GuvjnpIgMG7iE6wM28+fP7/EmpXNZZxCmra1NW7ZsOfpa5/KetpE6SqstWLDAVlui0Sg3TAAAZCC3BmxslURz4B7EY9CXzmckWlK11d2c1dWXTEeJdpVJNewBwIpJhqqNZ9rohcm9FgGb9LMzECmTBmxYzWEjdWTZ9K8o7LbcKsPGTlZ2MOhTY4Px6hnxrNOqn9ZqEfCSDEqi0a+BS2TU5b+3IMyx/8VbbrU/AACQfUzLLqT74bmdzpET5TrIsDGTaKkujyczRjT2hUQybHI1uAUgO5lcPynVlTzmsHEnO+VkM6kkWklZvuU6tUd6LotmNQeLnYFlduexyYT7UavAXZtFwEuSGhta4r5eUJhnq01AX3F9hk2nzuBKqoMsBG0AAMh+ptkpiYz6T4qNvpET83cwh42ZRMvV+f3ejBjR2BcCCXzXEgnyAIBbhZjDJi1MHkQnMxcdEmMnc9z2fCwOKi6xfuDfW8CmtcUiw8ZWSTR7Qa5MCA5bZRhZZShJUn19/AybQkqiwSVcH7A566yzcrYjCwAAUsP0gXraS6LZybBxpCSa9TFDIQI2iT5QSyRokS0SyrDJoBG2AGDFpPSOE+VQs43RHDac57SzMy9NJpVELS4JyeOR4o0NTzjDxkbWjN0Mm0z4Dlj106zOn2RdEo2ADdzC9b3E+fPnO90EAACQ4UwfqLu5JJoTpQqszlsg4LVVTztbeTweebweRSP2MrczacRoqiUSrMrl8wUg+5hk2CgDHqK6ncmgnUzILsg2tjJsMihg4/V6VFScp7ra3ktv9RawabcIONgZ7BLMs3d/ngnZfFZ9DquSaNFolDlskDEy51cPAAAgQaaBEbudm2TZeQ7jyBw2FscMUQ7tKH8CAbVMmkQ31RLJZsukBzYAYMWkpGgmzCvhdoUGD2ALuJ9JOztl6DJtcFBJWSju670FbFpa2uNuZ2ewi90yz5kQtLQsiWZRUq6tNaz2tkjcdUx+L4B0oNcDAABygklHJN0dQjudIydGvlkFlPLz6dR08iUQTMjlgE0i7z2XzxeA7BMM+izvAygPn7yKgUUq69f7RPDlAwrVv6IwjS2CZC8Y6cQ8jskoLokfsKmr7a0kWvxggp2BZXYHoWXCObYKQrW1xg94NVjMXyNJhUXWcxAB6eD+byQAAEAKmHQM0/1A2M6DGCcCNlblKkxGB+cKrzeBOVlyuMRXIsHRdJcsBIC+5PF4lG9RFi0TyhS5ncfj0QX/NanX1z8a5zX0HTv3wJmWYVtSmliGTZtVho2N+6A8m3PYZEJs2LIkmkXAq8GiHJokFTCHDVwis371AAAAEmTSMUykTFMy7HSOnMmwsQjYmNTfzxGJlK3J5YyRhAI2GfbABgCsWA18yISHqJngvI+fqI/+16SYie79Aa8u/MRJ+uAFEx1sWe6yc9+UafdLJaW9Z3RJUu3hngM2rRZzsNi5dwraDNhkQoaN1d9Bq0WGjdX8NRIl0eAe9mf7BAAAyEA+n0dtFuukvSSarQyb9HekPBaHzC8kYNPJZFLjY+V0hk0CD18I2ADINgMGFenQwYY4rxensTXZy+Px6PKrp+vDHztBG9YekMcjTZw0WP3KC5xuWs6yM4dNpmXYFpfEL6tV22tJtPgBGzsDy2zPy5kBwWGr99RmEfBqqG+xPEYhGTZwCQI2AAAgJ5hl2KT31shehk3ftaP3Y5JhYyqRgFq6/97cJJFstkx7YAMAVk4+dZg2rDnQ42sej3T8iQPT3KLs1r+iULPPPs7pZkD2MscTyWJ2UkmZxRw2vZVES2GGjd2SaD4nOho2WQ32sQp4Wc1h4/F6lBeibwN3cP83EgAAIAVMOoaBNGc82MmwsZqYuC94LIbb5RcwCq2TL4Hsj1zOGEkk+JLL5wtAdvrQRydq1HH9e3ztkiumkGGDrGUnYJNpJdFKLUqitTS3q6WH+WosS6LZOA92qwY40c+wy2qwT2tL/PNnVRKtoCDAvGFwDXo9AAAgJxgFbNKc8WBrdKEDI9+sSnaFyLA5yp9A7e9MewCRSokER8mwAZBtQvkBfeOOD+oD5x9/9AFr+YBCXX3DTH38spMdbh3Qd+xkJmfa9d+qJJrUc5aNVYaNnexkuxk2mRCosOqnWWXYWAZsKIcGF8ndOgwAACCnmIwcS6RMU1Js9I2cGPk2ckx/bd9S3evrY8dXpLE17pbYHDaZ9QAileyWg/N4Mq8kCgCYKC3L19VfmqnPfOE0eTyejHhwCiTLzjU90zJsS8riZ9hIUu2RZlUMLIpZZhVwsDOwzG6GTSb87lhm2LR2z1rqqt5iDpvCIutAG5AumfWrBwAAkCCfUUm09D5At1MSzYmH1TNmjer1tYLCoE48eXAaW+NuiXR0A8HcvRW3+yDB7/fa+r4AQKbx+bwZ8dAUSAU7A10yLmBTGn8OG6kjYNNVNBo1KIlmfh7sZNhkQjk0yWAOG4vzZ5VhU1hIhg3cI7N+9QAAABJl8LA3mJfegI3X5XPYTJk+XB+7dHK35T6/R1+7fS4j0brwJVASze/P3Qwb2wGbHM5GAgAg29i5B860e4CCwqDlffuxAZtwOKpoJBp3GzvZyUEbARsHqi4nxOre0Srg1VhvURKNgA1chJJoAAAgJ5hkqKQ/w8Z8XTsd21T61GenaeppI/TKC5vU3NSmQYOL9cELJnYr45DrEgnYpL0En4vY/a5l2uhaAADQO5+N63qm3QN4vR4Vl+Sp9nD3eWo6HRuwabcohybZG+xi5x7TqT6GXVbvKRKOKhyO9HpP3mARsClkDhu4CAEbAACQE0wyVOyO+k+ajQ5SInOkpMr4iQM1fuJAx46fCRKawybDHkCkkp2yHlLmja4FAAC9s5M5non3SyWlobgBm7pjAjYtLfHnX5HsBWFsZdgkMOjICSb9tLbWsHz5Pb+fxkZKoiFzZMa3EgAAIEk+g3x/O52bVLBTq5669u7mT6QkWi5n2Ngo6yHZG4kLAADczWRuyU6ZOGijtCw/7uu1R5pi/t2W4gwbO3PYZEofwyhgE+c8WmXYFJBhAxeh5wMAAHKCx+Cux9Ul0TKlwHSOSiTDJt1/b25iN8PG7voAAMC97GR1ZOI9QHFJ/Hkea4+0xPy7zWL+FclewMbOup6MKYlmHYSKdx4b6lt6fU0Sc3PCVTLvVw8AACABJqPH0t0htNNBypTRb7kqkXISuRywsTNxriT5/bl7rgAAyDa2Mmwy8B6gpDQU9/Xa2tiSaK0GARs7907ZmGFjUhKut/MYDkfU3BS/7Bwl0eAmBGwAAEBOMAmOpLskmmSeZZMpnalc5U8owyZ3b8X9duewoSQaAABZw84cNplYFrXYKmBz+JiSaCYZNjbunezMd+PkPJl2mJTG6+08NjW2WW5bQMAGLpJ5v3oAAAAJMMuwcWAEn2HEhoCNuyWSYZOJI0ZTxePx2HrwYDfAAwAA3MtneN/k9Xky8h7YKsOm7kizotHo0X+bBGzsDCyzs643Q0qi5eWZZNj0nEVjVQ5NImADd6HnAwAAcoLJA3U7o9FShQyb7JDI52Onvng28tupxZ7D5eMAAMg2pvdNmZphW1KaH/f1traIWprfDy70Fmjoys69kJ2SaHaynZxk8v57C3w11LdabltUzBw2cI/M/OUDAACwyWr0mMfjTMkF0w5rppQryFWJPFDI9awROw8ecjkbCQCAbGN6X5up13+rDBtJqj3y/jw2ZiXRbNw3BbxZNyjMZKBTb3PYNDZYB2zIsIGb5HYvEQAA5AyrjqE/4DOa5ybVTI+YKeUKcpVpaY+ucj1rxE5GW64HtwAAyCbGGTYZev23HbBpix+w8XjsnQuPx2OcyZ0pGTbBoHXWUG/nsYGADTJMZv7yAQAA2GQV73CiHJok45pomdKZylWJZEDlesDGZPLYTrl+rgAAyCamA10ytSRacYl1ea2uAZveMkM6JTKwzLQsmi9D+hgmAaveS6LFn8MmL+TP2L81ZCf+GgEAQE7weePf9jgVsDEdYZhIBgfSx2vx99WTXA9C2MqwoRMNAEDWMJ/DJjPvlQoKg/L5479HOyXREumnZFuGjcfjUcAiaNN7SbS2uNuRXQO3yYiez6mnniqfzyefzye/33ziLAAAgE5WnRE7E6CnkulgOSqiuZsvgQybTC3zkSr25rDJ7XMFAEA2MQ3YBIKZef33eDwqKc2Pu07tkaaj/9+qJFoiAZugaYZNBs2TGbAoi9ba2t7jcqsMm0ICNnCZjPnli0ajR/8DAACwy6pj6FSGjWl5AzJs3C2hDBunyvC5hL3Jc3P7XAEAkE1M72sz+f7XqiyanZJoVoGKnpiWRHNiDs9EWfXX2tsiPS5vtJjDpqCIgA3cJWPSVTwej3Gw5p133lF9ff3Rf5911ll91SwAAJAhrAI2iXSEUsG0k5Qp5QpylVXZi55kapmPVLETsMr1bCQAALJJtpdEk6SSsnxJNb2+XmejJFoimUam91mmn4UbWFVE6C3DxipgU0jABi6TMQEbOz7/+c9r1apVkjoegrS39/yFBQAAucMqMOJcho3Zet4MGv2Wi/wJjAC1qsOd7WzNYZPBI2wBAEAs45JoGXyvVGKVYVP7fpmu3gINnfoywyaTAjZ5efHvHXfvONzj8vp6i4ANJdHgMpn7y2eBEmoAAKArr0V9ZqcmgDfNsLFqP5zlTShgk7mjRlPBVoZNjpePAwAgm5jeN2VySdTi0lDc12sPd5nDprXnUl6dEpvDxmybTCqJZnXvvPi1bXrnrV3dljdaBGwKCNjAZRwN2Jxyyim66aab9OSTT+rgwYMp3Xcm/eAAAIC+Z10SzaGAjeHdWCaNfstFiQTUcn4OGzsBG3/WjjMDACDnmN7XZvIcNqWl+XFfjy2JZpFhk0DgyjTDxpdBg8IqBhZZrvPQrxbqSJdgmGRSEi1+NhSQbo7+8q1evVq//vWv9clPflKDBw/WpEmT9JWvfEV//etfdeDAASebBgAAsoxVRycUcvccNgRs3M2XwOeT8xk2Nt5/JtewBwAAsUyDBJlcEs0yw6a2+WhVoFaLOWxMs2VitzHr22TSPJkzZo2yXKfuSIseum9hTMWlhoaWOFuQYQP3ccUvX2fpsvXr12vevHm64oorNHToUE2cOFHXX3+9KisrnW4iAADIcKOP6x/39THjKtLUkljGc9hkUGcqF/kSyADJ5DIfqUCGDQAAuck0SJDJ90olFgGbcHv0aOZHW1v8gE0ig3xM77MyaZ7MmXPGaMKJAy3XW7F0j159cZOkjmfODVZz2BQRsIG7ON7z6Yx4ejyeoyNMOwM4mzZt0kMPPaT9+/fHbPP4449r9+7daW8rAADIXFNPH9nrKL1AwKupp41Ic4s6mHaSMmn0Wy7yeu3dVns8mVWCoi/YefiQ6+XjAADIJn7TOWwyeMCGVcBGkupqOzI/WltSH7DJC2Zfho3X69GXbj5ToXzr9/bYw29r/95atTS3KxKOP785JdHgNo7+8lVWVuqvf/2rvvzlL2vixIlHAzVSzwGcTldddZVGjRqlsWPH6tprr9Ujjzyibdu2OfIeAABAZhg6vFTXfmV2t06Jx+vRF792pgYPLXGmYYYBG5/NgADSy+4DBb/fm/NzLtoricbfPwAA2cI4wyaDr/8mAZva9+axscqwMS1vlsg2mTZP0IBBxfrsF0+3XK+1Jaz7f/qa6mqbLdctKAikomlAyjhTrP09FRUV+sQnPqFPfOITkqSDBw9q/vz5evXVVzV//nxt2LCh2zZdAzfbtm3T9u3b9Yc//EGSNHToUJ199tndMnIAAAAk6cxzxmrCCQP1n39tUH1di0pKQvrgBcdrwKBix9pk+sw+k0a/5SK7JesChqMes1mQkmgAAOQk0yBBNpdEk6Taw02STEqi2b8PMr3PysQ+xpnnjNU7b+3S0sU74663bfMh/en3Sy33R4YN3MZVPcUBAwbosssu02WXXSZJOnDggF599VX9v//3/3TgwAFJsSXUOnUu27Nnjx5//PFu+3344Yc1d+5cjR07tq/fAgAAcLmBg4v16WtnON2Mo0wf9DOHjbt5bZY382fwJLqpYuchTCKlQAAAgDuZ3tdm8v1SXsivQMCrtrZIr+vUvpf90drSHndfiQz0CeZl3xw2nTwejz7/ldnavOGgDtc0xV3XKqgjSQXMYQOXcfUv36BBg3T55Zdr0KBBkjoCMx6PR1/5ylc0efLko8uk2BJqXZdL0he/+EVNmDBBI0eO1Oc+9zk98sgj2rnT+gsLAADQ5ww7SQRs3M20FnsnAhDmDxKkzB5hCwAAYpkOdLF7f+UmHo9HJWX5cdcxLomWwFx+eYYl0TK16nJRcZ6+cNMZKdlXYSEBG7iLqzJsTN13332SpJqaGr3++utasGCBXnvtNa1YsULhcOyPXNfAze7du/Xoo4/q0UcflSSNGjVK5557rs455xydc845Gjp0aPreBAAAgMxLohGwcTcvARvbmMMGAIDcZJrVkekDNopLQzp0sKHX1+s6AzatqQ/YBLK4JFqnk6cO04c+OlH/ea77lBqmvD6P8kIZ+XgcWSyjez79+vXTxz/+cf30pz/V22+/rZqaGj333HMaNmxYt4wb6f0yatFoVNFoVNu3b9fvf/97ffazn9XIkSOdeAsAACDHmXZYCdi4m93PJ5NLfKQKARsAAHKT6UCXTL9fKimJP49NZ4ZNq0XAJpHAlWmGjS9TU2zec/nnpmnoiNKEty8sDHZ7fgw4LbO/lccoKirS+eefr4qKCknvl1C77bbbdNppp8nn8/VYQq0zgAMAAJBuph2ETB79lgvsBhTIsDEf+Wl3XQAA4G4+05Jo/sy+/peUxp/MvtY0w8ZGGdlOuZBhI0nBPL9u+Poc2/NJdiosiv8ZAU5wNGBz9tln64477tCrr76qlpaWPjvO3XffrSVLlqi6ulrPPfecvvGNb2jatGnyer0EagAAgLMM+xa+DO9MZTsybOyzE7TyZXANewAAEMv0vinTM2xLSlM1h439kl3GGTYJBjrcZPTYcl165ZSEts0vCKS2MUAKOFqk7/XXX9cbb7yhH/zgBwoGgzrttNM0d+5czZ07V7Nnz1ZeXmqjnJ0ZOOeff74kqa6uTq+//rpeeeUVLViwIKXHAgAAMGHaYc300W/ZzmfzgUIiHe9sY6ceOxlJAABkD+OATYYPcCkutS6JFolE1d4WibteIuchaBiwMR495nIfu+QkrVy2R5vWVdrarrCYDBu4jyt6itFoVC0tLXrjjTd6DODU1NT0yXGLi4t1wQUX6IILLuiT/QMAAFgxLZlMhoG72f18Mn3EaCrYqcee6Q9sAADA+0zvmzK/JFr8gE1dbYtaW9st95PIQB/TgE02ZNhIHUHAL918pr75tWfU3GR9TjsVFQX7sFVAYhzv+XSdU6brss4Azg9+8APt2rUrZpt//OMfqq6uTms7AQAA+oLHcFQbc2G6m92SaGSM2JuXhgAXAADZI3dKosUP2EQjUR2ubrLcj52s5E55hvPeZFMW/4BBxfrsF0+3tU1+AQEbuI+jv3zLly/XL3/5S11yySWqqKhQNBrtNYATjUaPLrv00ks1cOBATZkyRTfffLOefvppHT582Im3AAAAkBTTTpLdgADSy+5Ep3ayS7KVnYcPmT7CFgAAvM9rmmGT4Rm2VgEbSao6WG+5TiL3jaYZNtnWxzjznLGaMXuU8fqFZNjAhRwtiXbKKafolFNO0Y033ihJWrdunRYsWKDXXntNr732mvbt2xezfmcwp/P/r1q1SqtXr9Z9990nj8ejk08+WXPnztWhQ4fS+j4AAAASZZo5Y9qxhTP8Nj+fQJDP006WkZ1sHAAA4G7mGTaZff03CdgcOthguU4iGTam22RbwMbj8ejaL8/Su+srdbjGOnupsJCADdzHVT3FE088UTfccIMef/xx7dmzRxs3btRvfvMbDRw4UB6PJybrpuu/o9GoIpGIVqxYoV/+8pfas2dPzH7/+c9/6siRI2l9LwAAACY8hhEbLzXRXM1ns2RHpj+ASAVKogEAkJtM500JZHiGTbGDAZuA4bw32RawkaSi4jx98WtnGK1bQIYNXMjVv3zjx4/XF77wBQ0ZMkSSjpZFu/LKKzV06NBuJdS6BnC6llC76KKLVFFRoenTp+vWW2/Vc889p9raWmfeFAAAQBfGJdGyZELQbGW3s5tIxzvb2MmwIWADAED2ML3/tTsgxm3y8vzKC8UPnByqsg7YJJJp7Pd75fNbn+dsmsOmq5NOHaYPfXSi5XrlFYVpaA1gj6Ml0RL16KOPSpI2b96s+fPna/78+VqwYEG3zJquJdTC4bDeeecdLV++XD//+c/l9Xo1ZcoUnXPOOZo7d67mzJmj4uLitL4PAAAA0y5SNo5+yyY+myXRCEDYC9hk+gMbAADwPp/XcA6bLMhILi4NqaW593lqTDJs7NwzdRUM+tXU3hZ3nWzO4r/8c9O0dtU+7d3Vc9Ulf8Cr4ycNSnOrAGsZ3fMZN26crrvuOj366KPatWuXNm3aZFxCLRwOa9myZfrpT3+qCy+8UBUVFU69DQAAkMNMS6KZrgdn2A2o+cmwMZ5I2B/w8vcPAEAWMc0cz/SSaJJUUhK/LJpRSbS8xMbbB/Os7zezOYs/mOfXV245S4W9lD371GenKRQKpLlVgLXM/+XrYty4cT2WUPviF7+o8ePHxy2h1t7e7li7AQBA7jIcYGhc6xvO8NrMsEl0pGQ28Xg8Rg9iyEYCACC7mA508WfB/VJJmVXApvfsm06JlETr2M460JPtWfwjx/TXt354no4/ceDRZSVlIX3u+tP1kQtPcLBlQO8ysiSaXfPmzZMk7du3T6+++urR/7Zu3epwywAAQM4zzbDJ8s5UprMbVCBg0yEQ9KutrTXuOtlQDgUAALzP4/HI4/UoGonGXc9uyVk3KimNH7Bpa4tY7iPR+8Y8kwybHMhiHjG6n779o/N15HCTopGoikpCDAiCq+VEwKbTkCFDdOWVV+rKK6+UJO3evVuvvvqqXnnlFc2fP9/ZxgEAgJxk2kcyrfUNZ9jNgMqGEh+pEAh6JYtKIJwrAACyj8/nUbtFwCYb7gGKLUqiWfEHvAlnwQQNMmxyaVBYaVm+000AjORUwOZYw4cP12c+8xl95jOfcbopAAAgR5mOasulzlQmslsSjayRDialOnxkIwEAkHVM5qfLhpJopRYl0awkk5WdZzD3TbaXRAMyUcaEqjvnngEAAMgmppOp05lyN5/NzyfRWuTZhjlsAADITSbZydlwD5Bshk0y94xBo4BN5p9jINtkRIbN9773PVVXVzvdDAAAgNQzfM5PwMbdfDYfKPizoMRHKpiU6siGhzUAACCWSaAgG+4BrOawsRJMKmBjMIdN5p9iIOtkRMDmwgsvdLoJAAAAfcIkEEM5NPezG1BLprxFNjEZNcq5AgAg+xhl2GTBPUCyAZtkzoFJsMduWV8AfS8rv5XRaJQSagAAICOYlESzO6E90s9ns7NLEKKDyXkgGwkAgOxjMiCJDJtkM2ysx+kbVmcGkEYZkWFj17///W+1trY63QwAAABLJp0k03lu4BwCNokxCtj4OVcAAGQbq3snj9dj+/7KjZKdwyaZgE0ec9gAGSkrAzZDhgxxugkAAABmyLDJCl6bnxFZIx1MHkIQ3AIAIPtYlZP1Z8n9byDoU35BQE2NbQlun/ijW5PSs8yTCbgPPUUAAAAHeQ0CNsxh436257BJYrRkNjGpy+7z8/cPAEC2sRrskg3z13RKpixaMgNXzDJsuM8C3IaADQAAgINMqp2ZBHXgLI/HYyvLhjJfHYJ5ZNgAAJCLfBaluLIpG7k4iYCNyb1Sb8iwATJT9vz6AQAAZCCjgE2WlITIdj4bHd5AFj2ESIbZHDacKwAAso1lhk0WDW5xc4YN48IA96H3AwAA4CCTcmeMfMsMPhuBBbJGOpiMns2mkigAAKCDVQZ5NmXYJBWwSaKMbtCkJJove84zkC34VgIAADjIYzCszWtRMgLuYOdzYg6bDkGDiXTJsAEAIPtYZ9hkz/U/mYCNyb1S79salEQjxQZwnez59QMAAMhAJl0kMmwyg89vpyQaARvJrDQc5woAgOxjldmRTSXRikuSybBJ/NGtWYYN/QzAbQjYAAAAOMikJJrJOnCe1eS5XVHmq0OADBsAAHKS1dx/2XT9dyzDJs8gw4Z+BuA62fPrBwAAkIFMSqLZmcwezjEdoejxSD5GM0oyy7AhuAUAQPaxGpCUVXPYlDkzh02eQYaNSV8EQHplz68fAABABjLpI5FhkxlMJ231+710jt9j8hAim0bYAgCADv4cKolWkkxJtCQGrhiVRKOfAbgOvR8AAAAHmXSSyMbIDFYPHjqZlAHLFSZlPsiwAQAg+1gNSMqmOeySK4nWtxk2BGwA9yFgAwAA4CiDOWzIxsgIPr/Z55RNJT6SZXIuyLABACD7WA1IMr2vygRFyWTYJBGwMdmWgA3gPvR+AAAAHGQSi6EjlRm8XsMMmywaMZosswwbuiwAAGQbq/umbCqJ5vd7VVgUTGhbMmyA3EPvBwAAwEEmnSTTuVHgLNPSdQRs3hcwCMZwvgAAyD5WGTTJBCrcqDjBsmhJzWFjcA59ZDIDrsO3EgAAwEFGGTaURMsIphk2ZIy8z+QhhI+AJQAAWWfkmP5xXx91XPzXM01pogGbJAJXwTy/Bg8ribuO1ecAIP3o/QAAADjJIBjjNczcgLNMPycyRt5n8hAimQcVAADAnabOGCFPL5nmPr9Hp0wbnuYW9a1EM2xMysfGM2PWqF5fGz22v8orCpPaP4DUI2ADAADgIKOSaNSWzgh+w0wQMmzeZxK88lOqAwCArHPc+Apd/rlpPb529Zdmaejw0jS3qG+VOJBhI0mXXH6KJk4a1G15UXGebrxtblL7BtA3kgvTAgAAICkmoRhKomUG0xrgyY6UzCZBg8lwCdgAAJCdLrh4ko4/caBe/Od61de1qrRfSB+58ESNysIyXQnPYZNkwMYf8Om/7/yQlryxXW++sU3RqDRmXIU+cuEJKirOS2rfAPoGvUUAAAAnURIta5hmQhGAeJ9JtpGfEnIAAGStsRMG6Ib/N8DpZvS5ROewCaagNKw/4NOZ54zVmeeMTXpfAPoevUUAAAAHURIte/iYw8a2ouK8XuvXdyoty09TawAAAPpGcYkzGTYAMg8BGwAAAAeZVDvzerllywRe4zls6Hh3CoUCGju+vNfXi0vysq6GPQAAyD0Jz2HDfSOQc+j9AwAAOMhjUhKNO7aM4DMM2ASCfKBdXfrpU3vNsrnsqqlkmAEAgIxXkmDGcCpKogHILPQWAQAAHGQSsLEqGQV3MC2J5vfT8e5q8ilD9ZVbz1LFoKKjy4pL8vTpz8/QOR+Z4GDLAAAAUqO4JC+h7QJBph8Hcg3fegAAAAeZlETzkWKTEUwzbBgp2d3pZ4zW9JkjtWt7jaLRqIYML1UoFHC6WQAAAClRXBKSxyNFo+bbeLwe4wFBALIHARsAAAAHmQRsyLDJDF7jDBsCcD3x+bwaPbb3+WwAAAAyldfrUVFxnupqW4y3CQZ9Rtn4ALILvUUAAAAHmQRjGFmXGbyGmVB+MmwAAAByTklZyNb6gQD3jEAuImADAADgIKM5bBhZlxFMM2fofAMAAOSe4hKbARsG+QA5iYANAACAg0xiMV5KomUE08+JgA0AAEDuKSm1F7Bh3kMgNxGwAQAAcJDXIGJjOjcKnGX6OQUC3IIDAADkmpLSfFvrM8gHyE30FgEAAJxkErChJFpG8PsoiQYAAICeFZfk2VqfkmhAbiJgAwAA4CCjkmiGgQA4y2c4h42fgA0AAEDOKSmjJBoAa/T+AQAAHGQy7wlz2GQGr9c0YMMtOAAAQK4ptVsSLc/fRy0B4Gb0FgEAAFzOQ0m0jOAznsOG0ZIAAAC5xnZJNAb5ADmJbz4AAICDPAbZM6aBADjLS8AGAAAAvSgps5dhEwySYQPkIgI2AAAADvIaZM+YBHXgPNOSaARsAAAAck9Jqb05bALMYQPkJAI2AAAALucjYJMR/H7msAEAAEDPCgqDtgZiMcgHyE30FgEAABzkNei0kWGTGYznsGG0JAAAQM7xej0qKTWfxyaYxz0jkIsI2AAAADjIJBhjEtSB87w+wwwbP51vAACAXFRcYl4WjQwbIDcRsAEAAHA507lR4CzT0nUBSqIBAADkpNKyfON1g2RlAzmJ3iIAAICDvB6TDJs0NARJM82wYbQkAABAbiouMS+JFgj6+7AlANyK7j8AAICDjEqiGQYC4CzmsAEAAEA8JaV2SqLRBwByEd98AAAAlzPJwoHzfGTYAAAAII5iGwGbIBk2QE4iYAMAAOAgr0GGjUkWDpxnGrDxE7ABAADISbYybMjKBnISARsAAAAHmQRsTEttwVleg8/J4+HzBAAAyFUlpfnG6wYJ2AA5iYANAACAy3koiZYRTDJs/H4vnycAAECOIsMGgBWKIcLI7t27tWzZMm3btk319fXKy8vToEGDNHnyZE2ZMkVeb9/G/jZv3qwVK1Zo165damhoUH5+voYOHaqTTz5ZkyZN6tNjAwDQl0ye3ZORkRlMPqcAtcgBAAByFgEbAFboMbpcdXW1li5devS/ZcuWaefOnd3Wi0ajKT92OBzW73//e/3617/WihUrel2vvLxcn/3sZ3XLLbdo2LBhKTt+U1OTHnjgAc2bN0/vvvtur+sNHz5cn//853XzzTerrKwsZccHACAdmMMme3hNMmwCJLgDAADkquKSPON1gwz0AXIS33yXWbNmjZ577jktW7ZMS5cu1bZt2xxpx/r16/WpT31Kq1evtlz30KFD+vnPf67f/va3+tnPfqYvfOELSR9/4cKF+vSnP60dO3ZYrrt7927deeeduv/++/XQQw/p4x//eNLHBwDATUyCOnCez+BzCgQYKQkAAJCrCgqD8vk9CrdbD7wOMNAHyEl8813moYce0u23364nnnjCsWDNokWLdPrppxsFa7qqr6/XF7/4Rf33f/93Usd/6qmndM455xgFa7o6ePCgLr74Yt1///1JHR8AgHQyyZ7p69KjSA2f3/pzImADAACQuzwej0pK843WpZQukJv45iPG5s2bdcEFF6iurq7ba+ecc44+9KEPadSoUTpy5IjWrVunxx57TDU1NTHr/eQnP9GQIUN088032z7+okWLdPnll6utrS1mudfr1cc+9jGdeeaZGjFihA4ePKiVK1fq8ccfV2Nj49H1otGovvrVr2rIkCH6r//6L9vHBwAg3UyyZ8iwyQwmnxMl0QAAAHJbcUmeag41Wq4XZA4bICcRsMkQwWBQkydP1vTp0/XXv/5Vhw8fTvkxIpGIrrzySh05ciRm+eDBg/Xkk09q9uzZ3ba5++67dcstt+g3v/lNzPLbbrtN5557rk4++WTj4zc0NOiKK67oFqw5/vjj9fe//10nnHBCt21+8pOf6JprrtEzzzxzdFk0GtU111yjWbNmafDgwcbHBwDArQjYZAYybAAAAGClpCxfUo3legRsgNzEED8X8vv9Ovnkk3Xttdfq/vvv11tvvaW6ujotW7ZMv/nNb1RaWtonx3344Yf19ttvxyzr37+/Fi9e3GOwRpIKCws1b948ff3rX49Z3tbWphtvvNHW8e+66y7t3LkzZtn48eO1aNGiHoM1ne176qmndOmll8YsP3LkSNKl2QAASAevhwybbOEzKF1Hhg0AAEBuKynJM1rPz0AfICfRY3SZ//7v/1Ztba1Wrlyphx9+WDfccINmzJihYDDYp8cNh8P60Y9+1G35fffdp9GjR1tuf/fdd2vSpEkxy1577TW99tprRsc/fPiwfvWrX8Us83q9+t3vfqf+/fvH3dbn8+k3v/mNBg0aFLP8scce09atW42ODwCAU8zmsCFgkwm8PuvPKUgtcgAAgJxWXBoyWo8MGyA3EbBxmSFDhig/32zysVR6/vnntW3btphlJ510kq688kqj7YPBoO68885uyx944AGj7R977DHV1tbGLPvoRz+qM88802j78vJyfeMb34hZFg6H9dvf/tZoewAA3IyATWbw+QwybAzKpgEAACB7lZaaPfcLELABchI9RkiS/vznP3db9qUvfcnWPi666KJuc8Y8/fTTamy0nkgtFce/+uqrlZcXm1b6+OOP29oHAADpZhKMIWCTGXwGGTbMYQMAAJDbTDJsAgGvPAalkwFkHwI2UDQa1Ysvvtht+bHzwljx+/266KKLYpY1NTVpwYIFcbc7cuSIFi9eHLOsqKhIH/nIR2wdv7y8XHPnzo1ZtmPHDq1fv97WfgAASCeTjpjXIHMDzjPKsCFgAwAAkNNKTAI2lNEFcha9f2jjxo2qrKyMWTZ+/Phuc8KYmDNnTrdlr7/+etxtFi9erHA4HLNs1qxZ8vnsP9BI5PgAALgdGTaZwSSwFghy+w0AAJDLzAI23DMCuYpvP7Rs2bJuy2bNmpXQvmbPnm20fzcdHwAAJ5kEY6iGkBlMSqL5/WTYAAAA5DIybADEQ8AG2rBhQ7dl48aNS2hfI0eOlN8fe1HZuHFj2o4/duzYbsusjg8AgJOYwyZ7mHxOQSaPBQAAyGkmARvuGYHcRcAG2r59e7dlo0aNSmhfPp9Pw4YNi1m2e/dutbW1peX4w4YN61ZKbevWrQntCwAAt/AQsMkIRnPY+Ln9BgAAyGV5Ib8Cgfj3hAHmPQRyFj1GaP/+/d2WjRgxIuH9HbttOBxWVVVVWo7v8/k0ZMiQmGUHDhxIaF8AAKSDSTDG5+WWLRMYBWwYLQkAAJDTPB6PSsry464T4J4RyFkURISqq6u7LSsqKkp4fz1te+jQoW6BlHQdv7W1VfX19Unts1NlZaUOHjxoa5vNmzcnfVwAQPbyGkxQQ4ZNZvAZZM8wWhIAAADFpSEdOtjQ6+uURANyFwEbqKGh+wUiPz9+pD+enrZtbGx0/PipCNjcf//9uvPOO5PeDwAAdphMZg/nGcTeCNgAAABAJSXx57EhYAPkLuproMf5ZUIh6wnQetNTwKS1tdW1xwcAwEkm2TMek0gAHOfxeOS1CK5Z1SsHAABA9hswKP6g4rL+BWlqCQC3oceIHiXzYKinbaPRaEYdHwCAdDG55HkpiZYxrOax8fsZLQkAAJDrpkwfHvf1EyYPTlNLALgNJdGgQCDQbVlTU1PC++tp22AwGPf4LS0t3faRaAkzu8e348tf/rIuu+wyW9ts3rxZF198cUqODwDIPiaDFKyyNuAeg4YUa/eOw72+PnhoSfoaAwAAAFeaPGWoJp0yRGtX7uv22sRJg3TaGaPT3ygArkDABioo6J5mmeqATWFhYdzj93XAJt7x7Rg4cKAGDhyYkn0BACCZZc94KYmWMSadMqTXgE1eyK9xxw9Ib4MAAADgOn6/Vzf/zzl69KG3tGjBVrW1ReT1eTTnnLH61Oemye+nKBKQqwjYQOXl5d2W1dfXJ7y/nrbt6RhdX6upqem2jwEDEnugcezxg8FgwsEfAADcwGtRZgvucekVU7TqnT3at7u222tfvOkMBZhAFgAAAJJC+QFdd+MZuuKa6WpqbFN+QUCFRXlONwuAw+j9Q4MGDeq2bPfu3Qnvb9euXTH/9nq9qqioSMvxw+Gw9u2LTSftaf8AALgFc9hkl/yCoL75/Y/oIxeeoKLiPIXy/Rp/wgDd+p0PUNoCAAAA3RQW5aliYBHBGgCSyLCBpDFjxnRbtmPHjoT2FYlEtGfPnphlw4cP73GenK7HX7hwYbfjz5kzx/bx9+7dq/b29m77BwDArYxKohGwyShl/Qt01XWn6arrTnO6KQAAAACADEKGDXT88cd3W7Z58+aE9rVz5061tbXFLJs4cWLajr9ly5Zuy6yODwCAkzwGKTYEbAAAAAAAyH4EbKBp06Z1W7Z48eKE9rVo0aJuy6ZOnerq4wMA4ChKogEAAAAAABGwgToyUAYMGBCzbNOmTaqsrLS9rzfeeKPbsrPOOivuNrNnz5bPFzsB7+LFixUOh9NyfAAAnOQlwwYAAAAAAIiADdRRiuXDH/5wt+VPPfWUrf20t7fr6aefjlkWCoV09tlnx92utLRUM2fOjFlWV1en//znP7aOX11drVdffTVm2ciRI3XCCSfY2g8AAOlESTQAAAAAACARsMF7Lr/88m7L5s2bZ2sfzz77rPbu3Ruz7KKLLlJBQUFajv+HP/xBzc3NMcuuuOIKW/sAACDd+pXHv04GAl4VFeelqTUAAAAAAMApBGwgSTrvvPM0evTomGUrV67UX/7yF6PtW1tbdccdd3RbfsMNNxhtf9VVV6m4uDhm2bPPPtvjnDQ9qamp0T333BOzzOfz6Qtf+ILR9gAAOGXQkGKV9cvv9fWxxw+QP+Dr9XUAAAAAAJAdCNhAkuT3+3X77bd3W/7Vr35VO3bssNz+m9/8plavXh2z7Mwzz7Qsh9aprKxMX/nKV2KWRSIRXXPNNaqpqYm7bSQS0fXXX699+/bFLL/iiis0duxYo+MDAOAUj8eja748q8fXAkGfPv35GWluEQAAAAAAcAIBGxx13XXXaerUqTHLqqqqNGvWLC1evLjHbRobG3XDDTfopz/9acxyv9+v++67z9bxv/Wtb2n48OExyzZt2qTZs2drw4YNPW5TU1OjSy65RE888UTM8pKSEv34xz+2dXwAAJwy9bQR+uptZ2vI8JKjy8YdX6FvfOeDGn1cuYMtAwAAAAAA6eJ3ugHo7oILLug2F0xXPb02ZcqUuPv817/+paFDh8Zdx+fz6fHHH9eMGTNUW1t7dPm+ffs0e/ZsnXvuufrwhz+skSNH6siRI1q/fr0effRRVVdXd9vX3XffbdmmYxUVFelPf/qTzj33XLW3tx9dvmHDBk2aNEkXXnih5syZo2HDhqmqqkorV67U448/roaGhm77evjhhy3fLwAAbnL6GaM1Y9Yo1dV2zMdWUhqSx+NxuFUAAAAAACBdPNFoNOp0IxBr9OjRRmXI7Ni2bVu3OWp68/rrr+uCCy5QfX19Qse65ZZbdO+99ya0rSQ98cQTuvLKK2OCNqY8Ho9+8Ytf6Kabbkr4+Km2du1aTZ48+ei/16xZo0mTJjnYIgAAAAAAAADAsZx+lktJNHQzZ84cLVmyJOYP00RhYaHmzZuXVLBGki677DK98sorGjlypK3tKioq9NRTT7kqWAMAAAAAAAAAgAkCNujRpEmTtHz5cj344IM65ZRT4q5bXl6ur33ta9q4caOuv/76lBx/zpw5Wr9+ve655x6NGzcu7rrDhg3T//7v/2rTpk26+OKLU3J8AAAAAAAAAADSiZJoMLJz504tW7ZM27dvV0NDgwKBgAYNGqTJkydr6tSp8nr7Nvb37rvvavny5dq1a5caGxsVCoU0dOhQnXzyyTrppJP69NjJcjqNDgAAAAAAAABgzelnuf60HQkZbeTIkbZLlKXS+PHjNX78eMeODwAAAAAAAABAX6IkGgAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAwwjYAAAAAAAAAAAAOIyADQAAAAAAAAAAgMMI2AAAAAAAAAAAADiMgA0AAAAAAAAAAIDDCNgAAAAAAAAAAAA4jIANAAAAAAAAAACAw/xONwAAAAAAgL4WjUa1pmq/DjTWS5KmDRqufqF8h1sFAAAAvI+ADQAAAAAgq9W2NOu3q9/UlsOHji57ZvNafXTsCfrI6OMdbBkAAADwPkqiAQAAAACy2l82rogJ1khSezSipzev1dqq/Q61CgAAAIhFwAYAAAAAkLU2H67S8sq9vb7+t02rFI1G09giAAAAoGcEbAAAAAAAWeutfbvivn6gsV6bag6mqTUAAABA7wjYAAAAAACyUnskouUH9liu99rurWloDQAAABCf3+kGAAAAAADQFzZUV6qhvdVyvZWV+3S4pUlleflpaFXy9tQf0bNb1mn9oUpJ0riycn183CSNKunncMsAAACQDDJsAAAAAABZaen++OXQOkUU1aI92/u2MSlS2Vive99eoFUH96ktElZbJKz11ZX6yVvz9crOzczHAwAAkMEI2AAAAAAAsk5LuF0rD+4zXv+NPdsVjkb6sEXJi0ajemTN22oJt3d/TVH9bdMq/XXjSte/DwAAAPSMgA0AAAAAIOusPri/x8BGbw63NGn1wf192KLkbTl8SNtra+Kus2D3Vs1bsUTN7ebvHQAAAO5AwAYAAAAAkHVMy6F19drurX3QktQxbd/aQ/v1s6ULVNPc1MctAgAAQCoRsAEAAAAAZJWGtlatPXTA9nYbqitV2VjfBy1KXm1rs5ZX7jVef3f9Ef3krVe1s/Zw3zUKAAAAKUXABgAAAACQVVZU7k14Hhe3Ztks2rPD9ns60tqsny1doFU25vIBAACAcwjYAAAAAACyytsJlEPr9ObenWoNh1PYmuRFolEt3LMtoW1bI2H9ZuUSvbJzs6LRaIpbBgAAgFQiYAMAAAAAyBo1zU16t6Yq4e0b2lu17MDuFLYoeWur9utQc2PC20cV1d82rdJfN65MOPMIAAAAfc/vdAMAIFsdaWnWM1vWak/dEbVFIpo6aJjmDB+jkmDI6aYBAABkreWVexRVcpkkr+3eqllDR6WoRcl7bXdi2TXHWrB7q6qaGvX5k05TyM/jAAAAALchwwYA+sC7NVX6wZKXtHjvDu2sO6x9DbV6but6/XLZG2poa3W6eQAAAFnLqhyaz+PVoIKiuOvsqK3RjtqaVDYrYVVNDVp36EDK9rf20H79bOkC1TQ3pWyfAAAASA0CNgCQYnvrazVv5eIeAzP7Gmr1yJqlDrQKAAAg+1U21lsGWiaVD9K5I8db7uu13VtT1aykvLF7m2XG0Hmjj9eUAUON97m7/oh+8tar2ll7OMnWAQAAIJUI2ABACh1uadKvly9UU3tbr+usPbRfG6sr09iq1IhGo6ppbtSO2hpqnwMAAFdaapFdI0nTB4/QjMEjFPLFLwm2dP9uxzOj2yJhLdq7I+46Po9Hc0eM1XUnn64PjrIORHU60tqs+5a/oYON9ck2EwAAAClCwAYAUqS5vV33L1+kmhbr8hL/2LxW0WhytdXTafuRat2x6EV9643n9eO3XtVtC57TSzvezaj3AAAAsls0GrUsh5bn8+vkAYMV8vt1+pCRcddti4T15r6dqWyibcsP7FF9W0vcdaYMHKaSvJC8Ho8uGX+Srph4qrzyGO2/oa1Vj657h3s6AAAAlyBgAwApEI5E9NDqN7W7/ojR+jtqa7S8ck8ftyo11h06oJ8ve11VTQ1HlzW1t+mpd1fr2S3rHGwZAADA+3bXH9EBi2yRUwYMUfC9zJqzhh9nuc/Xdm91NJjx+u5tlusc+z7mDB+jL5862zKDqNO7h6u07EDq7kvD0Yi2HanWoj3bOzKzI2RmAwBSpzXcrs01VVq0Z7v21B9h0AGyjtkdHACgV9FoVH/esML2ZLDPbFmnUwYMlc/r3th5VVODfr/6bbVFwj2+/vz2jRrXr0Inlg9Kc8sAAABiWWXXSB3l0DoNKSrR+LIKvXu4qtf1KxvrtbHmoCb2H5iSNtqxu+6Ithw5FHedIYUlGldW3m35ieWDdMuMs/XAisWqbm60PNbf312tkwcMPhrMStSBhjr9avlCHepyzBHFZfriyTNVnl+Q1L4BANhRW6MHVy6JqWwyprS/vnTKLBUH8xxsGZA67n1KCAAZ4oXtG7Vw73bb21U21mtRAtulS1skrIdWvamG9vi12/+4dpnj9d0BAEBui0SjWrp/d9x1CgNBnXBM4GWOQZbN67u3JtW2RJkc96zhY+Tx9Fz+bFhRqW6bMVejSvpZ7qempUkvbt9ku41dVTU16N6lC2KCNZK0q+6w7nl7vmqarcsGZ4MjLc1atGe7nti4Sgv3bM+Z9w0AfW1HbY1+tvS1bmXotx2p1s+Xvqbm9naHWgakFgEbAEjCm/t26pkkyoL9c+t6195U/HXjSu2sO2y53pHWZv1pPbXPAQCAc7YcPqTDFvMITh04rFtm85SBQy1H5K6s3Ge571Rram/TWxYZQ0GvT6dZzMNTkhfS16fN0ckVQyyP+Z8d78aUwLUjEo3qD2uX9jqIp7a1WQ+vflPtWV4ebWP1Qf1gyUt6dP07enXXZj22/h3dteRlrT9U6XTTACCj1be26Ler3uy1+sf+xjr9bdOqNLcK6BsEbAC4ysHGev393TX6/955Q4+ue0erDu5zukm92lBdqUfXLUtqH3WtLXp11+YUtSh1Fu3droV7thuvv7xyr+OT8uaqPfVH9OcNK/T/vfOGfr18kZ7YuFLzd23R2qr9OthYr3A0ux+MAG4UjUb11r6d+t3qt/Tr5Qv1j81rVNva7HSzgKxmUg5tRpdyaJ38Xq/OGDo67nYRRW3dF6XCW/t2qSUcf1DPaUNGKt8fsNxX0OfXZydNU1EgfmCqLRLW399dbaudnf6zY5O2HI5fvm3rkeqE958JdtTW6NfLF3YLWjW0t+r+FYu07Ui1Qy0DgMwWiUb1+zVvW5b4XLR3u1ZW7k1Tq3JDazisBbucnc8vFzGHDTLGwYMH9fbbb2vLli2qra1VIBBQeXm5TjzxRE2fPl2BgHVnBe626uA+PbLmbTV36Zwu2rtdc4aN0acmTpG3l3IPTthbX6sHV76pcAouWv/ZvklnDhvjmnqru+oO6y8bVtre7q8bV2pcvwpV5Bf2QavQk6X7d+v/1i5Ve5egzNpjnpX4PB6V5xdqQH6RBhYUamBBkSryO/63f36BfB7GbgCp1BoO65E1b2vFwfc7i2sPHdCiPTt07UkzHJkHA8h27ZGIlh/YE3edfnn5Oq6HuV4k6YxhY/Ti9k2KqPf7uoV7tuu80cenZe7BaDRqXA7NVEEgqI+PO1F/Wr887nrLK/dqY3WljrfxW7Wz9rD+aZhx/uquLRpd2r/H4Fkm6xz53d7LQJlwNKLfrnpTt59+jkqCoTS3DgAy23Nb12t9tVmm4mPrl2tMaX+V5PFbm6za1mb9ZuUSbTtSrfq2Fn30uBOcblLOIGAD1/vb3/6mX/7yl1q4cGGvEd3i4mJ98pOf1G233aYJEyakuYVIhXdrqvRQL52c1/dsk8/r1WUTTu61Rnc61TQ36VfLF6o53Ga5boE/oKDPH7eMRnO4Xc9v26jLjj85lc1MSENbqx5cuaTXNON4msPt+sPapfr6tLNcFVzLVusOHdAja96O+3BJksLRqCob61XZWN9jMGdUST/NGX6cThs8whXfLyCTdZQEig3WdKpva9EDKxbrhimzCNoAKbahutJyzr3pg0f0en9Snl+gyRWDtaqq98zuwy1NWlW1T6cOHJZUW01sOXxIextq465zXGl/DS8us7Xf2UNH6/Xd27TLouTtExtX6X9mnms0qKM1HNYja9+2NYjpsXXvaFhRqYYWlRhv42amI78PtzTpd6vf1o1Tz2DADAAYWlO1X//etsF4/fq2Fv1x3TJ9ecps+rdJ2NdQq/uXLzo6L91zW9erIr9Qp1uUYkVqcJcA19qzZ4/mzp2ryy67TG+88Ubc9Lu6ujo9/PDDOumkk/T973+fVL0Ms6+hVr9ZubjXEWmSNH/XFr26a0saW9Wz5vZ2PbBikVEdc7/Hq+tPmaWLxk2yXPe13VsTrhmeKpFoVI+sWdptolg7thw+pP/sSG7CWljbXXdYD6160zJYYyUcjWrrkWr9Ye1S/Wn9ckUy9LczGo0q8t5/gJOeene1lscpw9AWCeuBFYu1wXCEIAAzZuXQhsd9fY5Btsrru7cZtykZrxlk18wZfpzt/Xo9HqMBQnsbavWG4Xv9x+Y12t9QZ6sdrZGwHly1RE3t1oOfMoGdkd+bag7q2c2Jz395rM77H/q/ALJRVVODHlnztu3t1h46oNf3pOeanY02VFfq3rcXdHs29Ni6d/RuTZVDrcotBGzgSps2bdKMGTO0YMECW9u1trbqO9/5jj796U8rHLafIYD0q21p1v3LF6nRoMP25KbVesei3EVfCkciemj1m9pdf8Ro/c9Mmqbx/So0Y/AIDSsqjb/vaMS4lERfeX7bRq09tD/p/fxzy3rtrD2cfIPQo+rmRv16+aKY0oGpsHDvdj22/p2MCnqEoxG9vnubvv3G8/rqy3/X1155Wn/esCLtE0MDkvTKzs16Zaf1nGQEbYDUagm3W855OLig2PJe7ITyQZZlXTdUV6qysd52G+2obW2OG/iVpMJAUFMHJZbpM66swqgc2bNb1qm+tSXuOusOHdD8BAdUVTbW69F1yzI+0LD64D5bI78l6cUdm5KeY2Fffa0eXLlEX3357/rqy3/Xj958RatdPPcnANjVGg7rwZVLjJ4V9eTJTat1wOaAAnRMS/Dr5Qt7HFTRHo3owZVL+vxeCARs4EKHDh3Shz70Ie3b1/2Gc9q0afre976nP/7xj3rwwQd16623aujQod3We/zxx3XLLbeko7lIQnN7u+5fscg4oyOqqP6wdqm2Wkxo2hei0age37BC6w4dMFr/4nGTj3aGvR6PLjbIsnl7/27ttihR0VfWVu3Xc1vXp2Rf4WhEj6x9W60ETVOusa1Vv16+SEf6aPLyxXt3ZEzQprm9XfNWLNbjG5ar5r0ATTga0Wu7t+pHS15hYl+k1YrKvXpyk/lE2k4HbRraWvVuTZWqmhoy/mFpNmkNt2v7kWqtPrjP9n/bj1Tn7HV39cH9arEYxDDdoOyn1+PRnGHWWTYm2S/JWLRnh8Jxss6ljtJmAa8v4WNcPG6yghbbN7a36dk4g4ka2lr1x7XLEm6D1DFfzks73k1qH06qamrQH9YuTWjbP6xdlvADr6X7d+vut16NKb+5u/6IHli5WE9sXJUR93HZLBqNqqqpQZtqDqqysZ7rLJCAaDSqv2xYYTxYtidtkbAeWbtU4Uj8ayo6RKJRPb15rR5d907cMqcN7a26f/kiNbTFL0WL5DCHDVzni1/8onbu3BmzrLi4WH/84x910UUXdVv/rrvu0l133aXvfve7Mct/+ctf6iMf+YjOP//8vmwuEhSORvS7NW9pp80ARVskrAdWLtY3ZszVwIKivmncMaLRqP61bYMW7d1utP6cYWP0oVHjY5adWD5I48sq9O7h3tNHo+q4QH7l1DOSaa5tHWnGSxU1KK913ujjVdfaooUW52J/Q53+sXmNPnn8KSlqJdojHZPV7rOoaZ+sxXt3SJI+fcJU185FVNPcqPtXLNaeXm7g69pa9Itlr+tzk6YnPAIZMLXtSLV+v+Zto9/QrjqDNuma0yYcjWjVwX1asGuLNnUpZVCRX6i5I8Zq5pCRKggE+7wdiFXZWK81Vfu17tABvVtTldAccp2Kg3n62HEn6MxhY3KqZvtSg3Jo0y3KoXWaNXSU/rl1fdzP4c29O/XxsScq6Et9VzoSjeoNgxIuZw4bndRx+oXydd6Y4/WMRXb3wj3bNWf4mG5z5USjUf1p/TspGUDy9Oa1GlXaTxP6DUh6X+mU7Mjv5nCbHly5RN84ba7yDP+WotGoXti+Me7n9uquzTrU1KCrJ89QyM/jnnRpDbdrU02V1lbt19pDB2JKXQ8sKNLZw4/TzKGjlO8PONhKIHO8sWe7Fu/bkfR+dtTW6F/bNujCsSemoFXZqzUc1v+tXap3Ks0q2lQ21etP69/RF06e2ccty11cweEq//nPf/TUU0/FLAsGg3rllVc0ffr0HrcJBAK64447VFZWpptvvjnmtRtvvFEbNmyQn5tVV4lGo/rrhpVaU5VY+a2Gtlb9avlC3TrjbJUEQyluXfdjPbbunR4nkO7J5IrB+uTEU7o9KPF4PLp4/GTd8/b8uNuvPXRAG6srdXyaJqRui4T10Ko3LSfqlaSJ/QfqY2NPVGs4rE01B3XQYs6d+bu2aHLFYJ1YPihVzc1Z0WhUj657RxtrDqbleG4O2uyordG8FYstHxK1RcJ6aPWbuqhpkj48akJOPbxE+lQ21uuBFYsTfsiejqBNQ1urFu3ZrgW7t/Y4IXZVU4P+tmmVnt2yTqcPGamzRxynIYXJTwR+sLFeO2oPq7G9VaNL+mtEcSnfQ8U+1Ft36IDltdSOutYWPb5hhdZU7denT5za5/dIbtDQ1qq1FtnPo0r6GQ/yKQrm6dSBw/TW/p29rtPQ3qqlB3Zr9tDRdppqZG3VfsuJ6yeVD9KAFAxa+sCo8Vq0d0fcORQjiuqvG1fp69PmxHx/39y307JsmySFfH7LEq4RRfXw6rd0+2nnql8o3/wNOCgVI7+ljrmCHl+/XJ+bNN3y97E9EtGf1r+jJft6/9vstKpqn36+7DV96ZRZGXNO0yESjWpnbY0qG+vVEg6rLC+kAQVFqsgvlN9rr/hMNBpVZWO91h46oLVV+7X58KFe7wUqG+v1xHvX2ZlDR2nuiLFpG3gIZKIdtTV6YuNKo3VNrjMvbNuoSeWDdFxZeSqal3VqW5v1m5VLbFWoGFJYokvGW8+Jh8R5ouRnwkXOOussvf766zHLfvjDH+qb3/ym0fYf/vCH9Z///Cdm2R/+8Ad99rOfTVkb7Vq7dq0mT5589N9r1qzRpEnW5bGy2YvbN+kfm9ckvZ/RJf1187Qz+2SEoyRtrK7UH9YuM54PY2RxmW6edlbc0Wy/XbXEsoM7qqSfbpsxNy0Pth5b/44W7tluuV6/vHzdfvq5Kg7mSZK2Hj6kny19zXLS+9JgSN+e9UEVMmo7Kc9uWWdcH700GFJda4vlZ2Ni1tBRrgrarDq4T79b/ZZabT4cnz10tK6YOEU+m51xIJ761hbd+/YCVTYlX8M54PWlPGizu+6IFuzaorf277IdUJrYf6DmjhiryRWDjb//bZGw3q2p0tqqA1p7aH+3Uj/Di0p16YSTdXz/zBpFnwqpzKIxVRzM02dPnKZJFYP7/FhOw0IS+QAATh5JREFUemPPNv1p/fK463xiwsk6d+Q4431uPXxI9y6NP4/myOIy3Tpjru2HvFZ+vXyR5XyCXzpllk4eMCQlx1tZuVe/WbXEcr1rJ592NEupqqlBdy152fIBWdDr03+fdo7+vGFF3AzzTseV9tfN085K+TntC6/v3qbHN8T/u7PjU8eforNHjO319Ya2Vv121ZKY7EgTZXn5+vKUWd0ypHJJfWuL1h068N5/lapv6z4vk1ce9Qvla0BBkQa+99+A/MJuwZzOgPuaqv1aW7XfuKx3TyaVD9LcEWN1Qvkg19xnA25Q39qiu9961XLwgiSdMXS0pg4arvuWv2G5bkV+ob55+gfIPDzGvoZa3b/cfJoCSTqh/0Bdd/LpWZ8x6PSzXAI2cI1169Z1++OvqKjQ7t27lZeXZ7SPZcuWdcvEmTlzphYvXpyydtrl9Jfcbd7ev0u/X/N2yvZ3yoCh+sLJp6f0Rrc9EtGzW9bppR3vGpe4KQ8V6Bsz5qokL/5o1gMNdfr+4pcsH6Zfd9JpmjrIrHxHohbt3a5H171juZ7f49X/m36WRpf2j1luGkQ4deBQXXfS6Y6PrI5Eo6pubtTBxgZVNtar6r2HrCXBkCb0H6ARxWWu7DAt2rNdj663/pyk978P0WhUh5obdbCxXgebGnSwsV6VjfU62NigqqYGW8GcRIM24UhEaw8d0LYj1WqPhDW6tL9OqhicUIA1Go3q1V1b9OSm1bbLTnWa2H+grjvpNEo+JaDzQfzmmirVtbVoYH6Rju8/UMOLS135nUmH1nBY/987r2trCudKSkXQpreyZ4myKpd2sLFe6w4d0JqqA9pUc9AoEDFlwFD91/jJKckQcKtoNKptR6r19v5dKc+iseucEeN08fhJSc134ma/WPa6NsXJPvXIo7vmnK9Si/uzrqLRqH705iuW2RMD84t0yYSTdFLF4JTc41Q1NeiOhS/Gvc71DxXoe2d8JGW/vdFoVPctX2g5n1a/vHx9Z/aHFPD69POlr2nLEev5JK+YeKrmDB+j2pZm/ejNV4zKp80dMdb15XR31Nbop28vULvFPEOSNG3QcC07sNtyPZ/Hq69Pm9Pj6O+DjfW6f8UiHUhwvpuQz69rTzpNkxMM3ta3tmhV1T7trD2s5vb4Qbpj+TweDSkq0YnlgzS0KPnMTRORaFQ7amu07r3Mlx21hxO+d5TeD+aU5IW0u+5IygPulEsD3heJRvXr5Qu13mCOx5HFZbplxtkKeH16YuMqvbprs+U2s4aO0mdOnJaKpmaFDdWV+u2qN9Vko7TnmcPG6FPHn5ITAyGdfpZLaBGu8fjjj3dbds011xgHayRp2rRpmjZtmpYte38CzCVLlmjbtm0aM8Z6ElH0rXdrqownJy30B43KdK08uFd/27RKl004OSWd5QMNdfr9mrdtza1T4A/oy6fOtgzWSNKgwmLNHjbasj75M5vX6ZQBQ/vsQrir7rD+ssEszfiy40/uFqyRpAvGTNS6Qwe0o7Ym7vbLK/fqzX07NXPoqITaasexQZmDTfVHgxVVTY1xJ/EtDuTphPJBmlQxSCeWD3JFVtC6QwcsRw53Gl3SX9dMnt7xEMfjOTpC8FjhSESbD1fpNyvfVHPY+ubMbnm0utYWLdyzTa/t3tYtO63QH9TsYaN11vDjVJ5fYPS+wtGInti4KulJnjdUV+repQv05SmzVZFfmNS+ckFVU4PWVu2P+yC+OJinyeWDdWLFIE3sP9AV35l0iESj+sPat42DNRP6VRgFT5Ipj2ZV9ixRx5ZLO3PYaNW2tvSaRWNixcG9WlO1X+eMHKfzxhyfVQ+o2iJhLd2/Wwt2bbE9R19feXXXZm2qqdQ1k09L2wPTdKlpbtK7Ft+tCf0qbAVrpI4ytnOGH2eZQVHZVK95KxdrYv+BunTCSRpWVGrrOMd6Y/c2ywfLZw4bndJAucfj0WUTTtYPl7wcdzBHTUuTXty+SQGvzyhYc1LF4KPz7JTkhXTdyafrF8teizuJsdRRTndMaX/NGDzC1vtIl/rWFv121ZtGwZozho7Wp0+cqsJA0PIeJhyN6KHVb+n208+JKWW45fAh/Wblkh6zQkw1h9s1b8ViXXb8yXGzeI61u+6wXt21RUv37046SPHUu6s1oV+Fzh4xVicPGCKfJ7X9G5MsmkRF1DEIKplsmngolwa877mt642CNYX+oK47+fSjg1EuGjdJG6orLed6Xbx3h06uGKJTBg5NSXsz2aK92/X4+uWW1+VOHnl08fhJ+uDI8Y4PxM0VZNjANWbMmKGlS5fGLHvzzTd12mmn2drP3Xffrf/5n/+JWXb//ffrhhtuSLqNiXA6KusW+xpq9dO3FxhNzNkvL1+3zpirpzevjVtDvKtLx5+kD4wan3D7otGoFu7drr9tXGWr3JLf49WNU8/U+H4VxtscaWnWHQtfsDzO5ROn6KzhxxnvN562SFhVTQ062NiRbTF/1xajjsfpQ0bqsydO6/WiXNlYr7uWvGz5XkI+v7458wMpf1B+uKVJ6w4d0PpDldpTd0RVTQ1GnWgrXnk0qrSfJpUP0qSKwY5k3+yuO6yfLX3NsuSIJA3IL9StM+YeLVlnYtuRat33zkKjoI1knWmzs/awFuzaoqUHrDv2Xnl0ysAhOnvEWI0vq+j176u5vV0Pr37Tcn4CO4qDefrSKbM0pocgZKqEoxFtP1Lz3sjOGrVFIhr23gjTCf0HGE8unE5W5ayseOXRmNL+7wU8B2d19s3fNq3SKzutR/FJ0nmjj9eFY0/Uk++uNt4mXqZNNBpVXVvL0Yy5g431OtBYpzVVB9JSZivVioN5unDsiZo9NLUPodOtprlJr+/eqjf2bE/pg8JUCnh9umT8ZJ01/Lis6Wi/snOz/rZpVdx1rjphqma/Fziwo7m9Xd98/V9G12Cp4zfwjGGj9bGxJ9q6Fndqi4T1rdefj/v34/N49MMzzzcaIGSXye9awOtTJBqxfLhTHMjTt2Z9oNscSiafl9RRSu22085xXYAx0ZHfbZGwfr70NW23GOAkSRP6DdCNU8+Qz+PV0v279cd1y1L6237OiHG6dMJJvf7ehqMRrazcp/m7tmizQRm7RPQPFejs4cdp9rDRCQ/0SHUWjdsMKSyRz8bvtM/rVf/Okm75RRpQUKQBBYUqDYZs/9533mccHfz2Xrb+4ZYm+TwelecXakB+4dGBYRX5RRlRYiocjWjb4WqtfW+wYTgS0bDi0o57834VfVZiHebWVO3X/SsWWa7niUpfGj1Jw2qa1Lhzj9obGhXsV6qmQf11/6HtagrGDwgXBfL07Zkf6JNraSaIRKN6dss6vbB9o/E2Aa9PV0+erlMHDuvDlrmP089yCdjAFRoaGlRSUqJI5P0HrQUFBTp8+LACAXsjLxcuXKgzzzwzZtkVV1yhP/3pTylpq11Of8ndoLalWfe8Pd8oQJDvD+jW6WdrSFGJ2iMR/Xr5QuOJ1hMtI9bQ1qrH1r2jFQetJ0/tyiuPrp4842hNbzue2bxWz1tcJIuDebpz9keMb4KPDcpUdimHVd3cZLsjM6yoVN+YcbblDezru7fq8Q0rLPc3tqxcX592VlIP5Tpvttcc2q91VQeSnuzVVLqzb6qbG3XPW/ONyocUBoL6xoy5CY3GSzZoE45EtOLgXs3fucVoxG1PhhWVaO6IcZoxeHjM31pNc6PuX7FYe/rgMw54ffrcpOmaOih1N521Lc1a+97ozvWHDvQanA54fRpbVq7JFYM1qXyQBhYUOfbw1CSLJlHZmn1j+sBRkmYMHqGr35tIOhqN2g7aXD5xiiR1+z03fYCcSYYVleoTGTa/TTQa1dYj1Zq/a4uWH9iTknnD0mFS+WB9ZtLUbg/TM9GP33o1bpav3+PVj866IOHfn79sWKEFNrM78/0BXTBmos4eMdbWXCxv7dupR9YujbvOtEHD9fmT7A1kM9XY1qo7F/1HdSkIOPY2x040GtXv1rxtVCJsYEGR/vu0c1yVgWdaCrjQH9R/n35OzCClmuZG/ejNV40Cuh8eNUEhv1/PbFmXVHt7c3LFEF09eUZM/6K+tUVv7Nmu13ZvNZ67M1kBr0+nDR6hs0eM1fBi6+y0vsyiyVZ5Pv97c/EUxgRzOu491S0o03m/Yfc+oyQY0oCCwqNz/wwseC9o5HAwp3Ng39qqA9pQXdlr2aeA16fx/Sp0YvkgTa4YrAH5hVkzsCFTVDU16O43X+m1/+QLRzSgul4DD9XrpCapf2vP91y1rc3aHpQqy4t0oKJYNaX5ivbwWU4qH6QvT5mdc59zazis/1u7VO9U7jHepjiYpy9Pma1RJf36sGXu5PSzXAI2cIWegixnnXWWFiyIP+FnT1pbW1VUVKS2tvd/7CdMmKCNG80jyKnk9Jfcac3t7frFsteMSoP4PF599dTZOr7LqOKm9jb99O0F2muR3ip13GzdNPVMje2h/nNvNlRX6v/WLrPdOSkO5ulzk6brxPJBtrbr1NTepu8sfEENbfHLvl049kSdP2bi0X+3RcKqamzQwabuN9iJBGV6k+8P6PbTzjGaYyAajeqBlYu1pir+JLlSxzwio0v6He0sDCgoVHEgL+7NkunNdrp49X65saMdoPcmJe0fKkh6lHhjW6t+uvQ1y5RuqeNv/mtTz+yx5rmpRII2F42bpEV7tvdY9ixRXculNbS16oEVi4wCVp3GlpZre21N3LJ3x7p43GR9aFRiad1ds2jWHTqQcPmj8lCBJr0XvLHKvglHI6ppbjr63a/sUvqvprlJEVu3dFHjFPhUsFv+JOjzqaLLKM6K9x4EDMwvUnEw/m9GV+2RiA51/l6+97+d56+xva3Lw4wu3+n8jv899rNYUblXv131Zo+/s4G2sAqbWlXY2KKixlaNjPp1SrBY7UfqFA2H5S8qVLCsVOvDjVrRUqeGgqAaCoJqDAWkHOssxpPq+W3C0YgONfU8n1ddW4sCXt/Rh1mdI4U7/w56e1DcWfZs/q4t2uWSsmd2FQfz9NkTp2lSD3NaxBv80fmdGXjMw7+O701hn45Qjkajqm1t6WhTU732N9TppR3vxt3m5AFD9KVTZiV8zMrGev1g8UsJZe5azW/T0Naqqi73cm/t32WZ2fj1aWfZyui2640924zLsPbmjGGj9ekTpvb6enN7u+55e77RPc7wolKN7zfg/b+3giL1C+UbX08i0aiOtDQf/d3ver4PtzTJ4/GoIr9QFV2yBTqvB0WBYMzntvrgPj2w0npOVI88+vKUWT1+tzZUV+pX7yxMeXC30B9UWSjf1gCXEcVlumHKLNW3tqSs7FkyeiqXFolGtbO2RmuzNIsmVxQH82K+X30ZzOmaRbO2an/CA/sq8guPVlnoi+ybzv58ZVN9l7lVO/63rrXF5v285PV41C8v/2i/tOt5Lg8VuG6ukbZIWIeaGmPe99qq/bGDe6NRFTW2auChOg2qqldFTYM80ajy/QENyC9Sb3fOUUmVjXVHg45tfp8OlhfpwHsBnNbg+5/lp46forNHpKaaSV9rDbd3ef4Te39W39pi/MsYjUZtXYOGFpbohimzjUuZZxunn+USsIErPPzww7ruuutill177bV6+OGHE9rfcccdp23b3p8jxOfzqbm5WX4HRng4/SV3QqS1Ve31HfOFPLbuHeMMmU+MP1mn9jDi/Uhzk+atWqJag4e3Bb6APn3iVBVZlKOIRqNadmCP3thjXS/8WBPKBuiSCZNVnOTo1IV7tutf29bHXSfP69cpA4boUHOTDjU36EhLc1o6K1edMFUn2AhG1bU261fvLFS9wbxDx8rz+lWRX6D++YUqDxWof36BigJB7ag9rE3VldrXWGd7n07xy6t+oYL33k+ByvMLVZ5foJJgyCiQE30vRdkkW8Ujjy6fOCXhSWS72ll7WI+sWaqWiFnQxiNPn/0deuWRz+NVW9T8wcGZw8boI6OP1/baav1p3XI1GQafJGnawOGaM3yMUQAgHI1od90RvVtTpc01VbaOYyLg8WlUST9N6D9AA/ILVN3cpKqmRlU3N+hQU6NqmpsUVvIl/zJdntev8vwC9Q8VqCK/UP3y81UeKlRLe1vHb2VTw9HzdqSlOeGHYyXBUMdvUqhAJXl5emP3dqmt7WhQprCxVYVNrSpqbFWg7f3RqEGvT4MKinv9zte0NB29nkU9HjXkB48GcOoL8tSQH1RTKKBoH8ZxyvLyNWXAUG09csg1c6108surWcNG69RBQ40fzIYjER1paTr6+R9qalB1U5NqWhoVTvDzLwoEVR7q+A3v+Dso1IHGOr29f5fRHHt2hXwBjSsr1/j+FRpfOkD5hhnmLe1tembLOq2rtl868vTBI9U/VNBxzpqbVN3coMNJ3GeUBEMd179QgSpCheqfX6j+oZACNh52RaPRjmDGe797h5oaVf1e+1oj9kZ9X378FJ3UQ6aHHUv27tQ/t65L+JyMLa3QqQOHqqalUYeaOu7lDjU1GJUI7mpgfrFumnpGn44GjkSjmrdikfYYBFN6UpFXqBtOnW35ELaysV7zVixWi83PU5J88qpfKF/9Qx33WJ33Wz6Pt+N7f/Q3oFHVzY227iW6CvkCR68z5aECvblvp9E1/9yR4/SBkb2XaJ6/c7P+szN+oNGOirxCfWbyNBUH8/TEhpVaX2Ndrq1T0Ou3/Z3qa2V5+R3fl/fmp+qL39p0GFpYogn9BqgsFNLS/Xu0u/6w001ypaJA3nvX2EKVh/KP9pvsBEjCkYh21R/WpuoqbTl8yHgAmqmAx6fRpf00vt8AHVfaz9b1rD0Sfu83qfH936c09ueljn5V/7wC9c/vOL/93+unlubl93kgJxyJqLq58ejvstW9uScqFTa2atChOg2sqldBc+z33+/xakhhiWWfuj0a0b762h6PcaQ4X5XlRaosL1I4lKfPTZ6eUBnTvtIWbteh5ibVNDWqqqnh6PxZJs/BUm1caYWuOGGKQr0MYPIXF8kbyO5Sgk4/yyVgA1f43//9X/3gBz+IWXbnnXfqO9/5TkL7mzt3brfsnC1btui449IfQXf6S+6Euk1btfuJ51Td3GhcWqEsL1+lcQIgrZGwDjTUOVpyxCOP+oXyVRxIzUU9qqj21temZM6VVCoNhlSWl297u6b2NlU22Z+AGonpl1egkhTeYLaE21XZWJ8xZX2knr+TbZGwDjY1ZOR8Hsh8Po9XgwuL5bcINHQN2qRTyOdXcTCkfH/g6OjE1khYda0tamhrZfRymgW9PuX7A8r3BxT0+XsdMWqivq2lI9OOz1BSx0Oq4cWl8iR1Vjs0hdtU09zk6HWlf6ggZfef8bSE27U/gYEyHnk0qKDIeI62xvY2Hcyye0arkd9Sx+jvqqZ62wG7noR8fg3ILzr68DKqjqx0J64tmSiVg4+8Ho/yfQGF/AHl+/3dBhu0hNtV19aqRq6zQEI88mhwYbGCXp/R+p0DP5CY4kCe+oUK4l7PRl99mfKHJT9w1M2cfpab3eEwZIz9+7uXUhoxYkTC++tp2wMHDjgSsMlVR1qbjYM1RYG8uMEaqeOhRkVBoQ42Njhyoxv0dpTnCRjeJJjwyKOyvHxX3Uzk+wIqTSBYI3V0VIsCedSUToOSYCilwRpJ75W5KcqYoI1XHlUUFCrfFzvqJ/BedsPBpnq1ZOFcH3CvznKJVsEaSer33u9sOh6seeRRYSCo4mBejx3doNen8lCByvJCqm9rVX1rS9IDCYJen0L+gEI+vxra29TAdUFSx0O9kC/wXpCm+0O9ZBQF8pTn8+tQU2NCmQvZJj8QTEmwRuq4NwoVBlTX2qIjrXbLTybP+953OB3yfH4VBoKWJXuPVZoXMg7WSFKBP6CSYChrggt+j1cVoULLvziPpPJQodoa65IKABYG8lQeyo/5G/eo49ri93pVk8IyydnCI4/yfH7l+/3K9wfk9/oUjkbUHomoLRJWeySi9khYbZGOZVbnL+h9f19WAfc8n195/397dx4dZX3vcfzzPLMnIZmEJWwSEBARUEQFRTbxUhVstWrBYk+rrVxta6ut2MXWo57e2ltq1db11gXqkYuIaN2wwG0BQwFbFgGRRYSwVDBA9n22+0dIZDKTZGYymWci79c5nMN88/yWSWZ5nt/3+f1+NrsCJ79nKxvq41rCFzjd5bkzYk7WSI17vNb6fV12lp5Vmsankj3OgMSQsEFaKCkpiYhlZSW+fnm0sidOJLYh9qmKi4t17Fhsy3s12bs3tg2Gv0h2lRyLeV8Lj92hPHdsa2J6bI3HnkhxgiPb6ZbX5U7ahf+pMhxOORvq1JAGswHshqnunvYvNtuS6/aoPuBP+9kNhgzZTt6RmG4znNqTYXcmNAMqFl0laWM3TPXKyGo1gWozGu/0PVFbw4l6kpmGkfLByq7AUONeCPFcTHZ20sZumOrmdCnL4YppSUabYSrH6Va2060af2PiJtaNh00ZJ+8sdshtt4clrTx2h7o5nCqtrz0tk6imYSjL4VJGEmbRtMdh2pSf2Y277CVlJnmzekNSttOlTIdT5fW1qkrhnfKZDqfMTn3lhPO6PKr1+WI+D3DZ7MpOYJlgr8ujhoA/7g3O040hQz0zsmLew9A8uXfO0erKhF5D7a1K0M3hkt0wdby2Ounnci6bvfn8uT0hNe5ZZGXiyG6YJ7+XGr+bWr6P7IYpu82Uu0WyMSRFJHOCoZBMw5DDtCWccG/5PVvZUH9afi8C8chyuJSVwE0Lee4M1Vf7u9y1vlWarmUyknz+hMSRsEFaqK6OHID3eBIfkIxWtqamJsqR8Xnqqaf04IMPdrieL7KPS49rRdFuXRDDsU2zVuK5BM1yOOUPBlXekJyNzttiM0x1d2e0uvFwMhhqvPCyeikxh2lTr4ysmC/CWmPKUHd3hj6rqbL8zj5Dhuym2XgxZpqymzY5zM//f+pyQHV+n2r9PtUHApb3uy0uW+N+P505bJPMpI3H5mjeIL6yoV61fl+Hf79Nm8S3d6FsyFB3T6bs9baUfF58UX0+EG+X2+5o3F8oGFCt36e6LvCeSZU8tyeh74rOSNpEW/YsHoYaN7HOtDvbXC6taRaNx+6QK4a7i3tndFO1v0FldbWnxcWzw7Sp28kB/lQOtjfdZe+xO3Sitvq0+F23ZDPMVtdc73jdRuPyZE6XSutqk76XWTTt7cuYbHbDVLbLHdPNV43nfYnd7GOocXPvI9WVXXq2Qbx3fkufz2yMZ5a9cfIcO5bZVh67Q/mZ3XSspqrDnwGGDGWdnKUZ70oDwVBIVb56VSZh5mYsWs6iSXRlBEOtJ3OS4dTvWZZLA1rnMu3Kcyc2LmgajdeCn3Wh/XCtYjt5M2S832XoXCRskBZ8vsiLHbc78Q3doyVsGhq4y7qzhUIhvbNvZ0wXXU13yCcyiOF1uRUIBTt16S2P3aHu7oykLlfSVltumz1ldxgaMsISF25b40BssoaTXDa7cmK80E8WQ4bcdrvcNrscpk120ya7Gdury2na5HTalO10K6iQ6vz+5sHodBrocpi2k2ujd/7AX0eSNk1Lt7S8sHd77PKHgqpqqFeVryGhwZkMu/Nkwiq230FjQtQtu2mqpK7Gkgthu2HKNMy029i3Le0NxDe9Z3KcbgVDIdUFfKo9+b7pyoNuicpxupXVgb0lOpK0sRnmyc/zxoR0Rwaoovl8uTSP6gI++YNB2Q1TrhazaGKVaXcqI8uhioZ6VbSy6WxXZsiQx96YMOuMQb54uG129cnM1om6GtWcZjMNvS5Pp39TNt3oUuv3qbS+8/a3yXJEX8qws2U7Xar2NbT7vLxujxwd2LjaZpjq6clMixt9EpHond9S48ypxsH69q9nbIapHp7MuD5XnKZNvTO7qbimOqFzkHhnaUZjGoaynW51c7pV6/epsqEu6dc77c2iSXenLpdWH/CrIRCU4nwvBEIh+ZuWdUvyedip5xmhUKh51lFX/v52nLxxLx1WuEDrbIapHhmZbV732TwuObvnqu7IMYUCkX9P98kZoKf7rOO2OE2besa4pDNSi4QN0pbRgTv9o5UNsYRLpzMMQ7efd4kWHy2VdLDV40zDODmbI/EvhTx3hgLBYNLvbIy2iXkqeF2ehDZ5bU3LpIzdNE8mMkzZjM6/lMlxumVIKu/EATlH06CyzS633Z6URIYpQxl2R/NU4HSZfdM4oNHxGVDxiDdp03hh71aWw9nqhb3dMBuX8nC5Ve1rvHCP9WKpcWnCxAbhshxO2U1Tx2qrOn05L0NGcxLUY7c3D54HQkHV+v2Nr6eAL62WFWs5iyaeE3bTMJRhdyrD7lRIOu1m32Q6XElZorBp34Hy+rqIpNepgyWnfpbbTTNlA1M2w1CmPTl7aBgylON0K9PhVFl93Rdif5umZc+6OV1pdcFrGoZ6ejJV6bOfFntaNJ3DJTqAnoimgeJk72/TtPdUoncWJ6N9r8ujY23MAM+wO5Jyvuyy2ZWfkaXjXWxGWEfu/G6S6/aoIRhoc1mspht2EkmM2QxT+ZlZOlFbrRp/bNdM7pOzoxOdpRmNITWfX7c1czO2upIziybd2Azz5PlUx+oJKnRyP55T9+RpO5kTeZ7x+f9bO8/4fMm4U+pP02RO0419Hlvj53XTeymdz81Pd40rsURPIrh791TWkIHKHFwgT998GaapYINP1QcOq/qTA6r65IB8ZRXNx3tdbtX5fSTooshyuJTr9nS5RPfpgoQN0oLDEblsQW1t4nfnRyvrdHb84u173/uevva1r8VVZu/evbr22ms73HZX4bbbde2QEdq+YWfUGTCGjJMXHR07uTYk9cjI1GfVlUn78m1aos2KE3+Xza4cp1vlcdz9YXVSpj3ZTreynC7V+f0Rm3kmchd+ayfbnanl7Jt6vz/swsQXDCgQCnXqIJipxgG3VDzflmJJ2jQtexbPLK2m5TWyHE7VBfxtLpeWrCSq++SSTMdqq5N+J3TT3Z0eu0OuVu7utBlm83MOSWoINM3k8ie0QXjzkn8nl/1zmLb474A1GvuerH01DEXOvmkIBhQIxv9+94eCHf7MiOyfETYQYTNMBUPhgw3xDDJ4bI0zMVuyeVxy5HrlzM2RM88r0+WUr7RcDaXlaigpk6+iUgpGttPN4VKm3dk42BEKyZbipEyqNW7SnZGy/W3C3jOmTXbDVPCUu5ITGWSyatmzeHVzuOS22XWitiauz5v23jO+GDbm7iyRy53aLLu7vqP729ian8vJ8zjDlDvBPTKSKcPuUJbDFfV83mHaYt6DMhYum119s3LUEPCrIRCQLxQ+CJyM11nja+bz5XFDUtgeJfF8z8Ry53csmvYM+Ky6MupguttmV09P7PvjRGPKUA9PVpt7W3Vk2bN4Nc3czHV5Yl4uravPokklU0bjuZhpkxQ+xtKUzGk6L+vIeYbNMGWzmXJFebmkQzLHcXIViaa99aK9V1s7N69N4eB+tOv5eP8eTX9XX5I/M1Oh5XmG3TTlstnDZpbaPC5lDhqgzMEFyjpzgOxZmRH1mE6Hug0dpG5DBykUCqmhpKwxebO3SDUHP238nK2p+kKsBGA2n89+/n0W73eRaRhy2GxpdZMRIpGwQVrIyIg84U92wiYzM/KDPV69evVSr169OlzPF51pNK6xbDfNiGWxurszkrZMiClD+ZndVFpXqxpfQ8IngKYMdXO6leNypWS5qdbkuDwyDUMVDfXNJxPpnpRpT9OMldYuGD6/8yv6wGxnzKJJlCmjcVC+RTx0yp1syUzmGDLktDVe1Fp596Dr5LI6JXU1qgs0bh7beBegIykX9m6bvdXl0lymXbluj1xJ+sxwmDb1zuim0vrahO/slFqfRRN7+c+XwJCr9Tv8oiVlTv0sSPv3v9H4e1ISXr7BsPdZ6wngaBd+8XxmBpoHoyPvTG36jjFlKCMrS3379pUrzytn07+TCRqbp+0lXYN+v3zllY3Jm5NJnIaSMjWUlstXUSlXCmfSpYNT97cpr6/rUEK1ZVLG0byPWWzvmfBBpvDP86a/fzotexYPh2lTfmY3VTTUqbLFeUZH3jOfJ1cj35sdHTCKlpRJ9eyyeJy6v01ZfV3YjQjRkjJN/+/IYHxnyzu5PHClr07BUOjk69+hPLcn6QmlsO/GUzQlVk59P576ujv1ddYyKRPPgFa0Qc+WyZymm4fy3BlJG+iyG6Z6Z3ZTSV2Nav2N51mmDGW73Mp2JufapGlvK5fNptJT9hFzmLaTA9aJL3uWqMjl0upVf/I884s6i8ZqTckcdfLvM95kTtP/O5LM6eiNfad+/nhdnqTOvkn19XxIUiAYlC8U+VlmRTInkfOMaLNoYm7PMOTqnitX91zljR3dPPsmc9fH+mTbhwpVVqd9QisyKfP57Derb+ZA6nSdqwx8oXXv3j0iVlWV+Cbs0cpGawOdI2NAPw269UZJ0tbiI3rzkw8VCIV0+YAhGtn/zE5pMxAKqaS2WoE4T6Ycpk1el0c2M30ulgPBkGoDDQoEpW7O1peX+qKq9zcuqeS2NyZpujJ/MJjwAGSuK0Mue3pdoDYEAqrx+RqTm530ugyFQqpoqJfTtMkTZfZlsviDQZXW1cT9mWE3THndjctXdYZgqHEmV30goG5OV1p9NqWren9AVb562c3G9fY74zMzFAqpytcgv0Ly5ubKk5WVcF2m3d58IdlS0O+Xv6JKwSh7+50OGt//DapNYM8V18l1yjvjPRMKhVTtb1C9P6AshyvtPpvjFQg2Pp9QqPPOMzryt7QZhnJcHjltXfv33LQnnttm79Tvs1TwB4Oq9fsaZzqk0XMJBEOqaKhTKBRSN6dbDlvnfDen6m9Z6/PJFwoq0+HstGVwQ6GQqn0NkqEO7b/WGer9AdX4GxqXl7RgZjms0/Q9W+1r/G6KR2dfzwdDIZXX1yU0E9hz8ga3dLmeD4RCqkjwuSTCbXMo2xX78zdMU/bsbrK5kre06amzb868eppqKytVfPx40upPtgyHU90czk671k4WZ67X6i584XXtkTB8YeTn50fEDh8+nHB9hw4diqkNdA6b2yWbu6ckaVx+T+X0660Pjx/Vfwwd1alfPB2fQ5U+vkjPJV5uSTlWdyKJEh/WTT9uSdkpaCdVK/an698meYvMnB5S9ZmRitelabfLmedNQUvpy5odO9qXrv1KVCrOM75ov7N4uSV5re5EEqXrd2YqXsup+lu2PT8zedL1vflFuwZAfDySeljdiVZ8kc7NT+dxBsMwlJGdrYHZqbiiBTqGhA3SwqBBgyJiBw4cSLi+gwfDN7y32WwaMGBAwvWhY87O66Wz81hKDgAAAAAAAABaQ8IGaWHYsGERsb179yZUV0NDQ8QMm8GDB8tu0dJK9fXhG3Um+rwAAAAAAAAAAJ2n5dhty7HdzkbCBmnh/PPPl2maCgY/3zh448aN8vv9cSdaNm7cKF+Ltd/HjBmTlH4momXy6Nprr7WmIwAAAAAAAACAmB06dCilY8vs5Ia0kJmZqfPPPz8sVl1drS1btsRd19q1ayNikyZNSrhvAAAAAAAAAAB0NhI2SBtXXnllRGzp0qVx1/Pqq6/GVDcAAAAAAAAAAOnCCIVCIas7AUjSjh07NHLkyLBYr169dOjQITmdzpjq2LJlS8QUtXHjxmnDhg1J62e8ysrKtGbNmubHZ5xxhlwul2X96Qr27t0btnTcX/7yFw0ZMsS6DgEAAAAAAABo1RdlPK++vj5si4vJkyfL6/WmrH32sEHaGDFihCZMmBC2pFlxcbEeffRR/fSnP42pjnvvvTci9t3vfjdpfUyE1+vVNddcY2kfurohQ4ZoxIgRVncDAAAAAAAAQAy68nielfuhsyQa0sovf/nLiNj999+vzZs3t1v2iSee0F//+tew2KBBgzR79uyk9Q8AAAAAAAAAgM5AwgZp5YorroiYjVJfX6/LLrtMb731VtQyPp9Pv/rVr/TDH/4w4md//OMf5XA4OqWvAAAAAAAAAAAkC0uiIe0899xz2rRpkw4fPtwcq6io0Fe+8hVdeOGFuuaaazRo0CDV1tbq448/1sKFC/Xvf/87op477rhDV199dSq7DgAAAAAAAABAQkjYIO306NFDK1as0NSpU3X06NGwn23cuFEbN25st46ZM2fqscce66QeAgAAAAAAAACQXCyJhrQ0fPhw/etf/9LEiRPjKudwOPTAAw9o0aJFstlsndQ7AAAAAAAAAACSi4QN0lb//v21Zs0aLV68WOPHj5dhGK0em5WVpVtuuUXbt2/X/fffL9PkpQ0AAAAAAAAA6DpYEg1pzTAMzZw5UzNnzlRxcbH++c9/at++faqoqJDdblePHj00fPhwXXTRRXI6nVZ3FwAAAAAAAACAhJCwQZfRq1cvXX311VZ3AwAAAAAAAACApGPdKAAAAAAAAAAAAIuRsAEAAAAAAAAAALAYCRsAAAAAAAAAAACLsYcNgLTTs2dP3X///WGPAQAAAAAAAKQnxvOSwwiFQiGrOwEAAAAAAAAAAHA6Y0k0AAAAAAAAAAAAi5GwAQAAAAAAAAAAsBgJGwAAAAAAAAAAAIuRsAEAAAAAAAAAALAYCRsAAAAAAAAAAACLkbABAAAAAAAAAACwGAkbAAAAAAAAAAAAi5GwAQAAAAAAAAAAsBgJGwAAAAAAAAAAAIuRsAEAAAAAAAAAALAYCRsAAAAAAAAAAACLkbABAAAAAAAAAACwGAkbAAAAAAAAAAAAi5GwAQAAAAAAAAAAsJjd6g4AAAAAAAAAAICu5fDhw9q0aZP279+vqqoquVwu5efna+TIkRo9erRMs3Pni+zdu1cffPCBDh06pOrqank8HvXt21fnnnuuRowY0altdxYSNgAsVVJSoo0bNzb/27Rpkw4ePBhxXCgUsqB3AAAAAAAAQHqxcjwtEAho/vz5evLJJ/XBBx+0elz37t31zW9+U3fffbf69euXtPZra2v19NNP65lnntHHH3/c6nH9+/fXd77zHd11113yer1Ja7+zGSFGQQGk0Icffqh33nlHmzZt0saNG7V///6YyvFRBQAAAAAAgNNRuoyn7dy5U7NmzdL27dtjLpOVlaVHHnlEc+bM6XD7//jHP3TTTTfpwIEDMZfp2bOnnnvuOX3lK1/pcPupQMIGQErddddd+sMf/hB3OT6qAAAAAAAAcDpKh/G0devW6corr1RlZWVC5X/yk5/ot7/9bcLtv/baa7rxxhvl8/niLmsYhp544gl973vfS7j9VGFJNAAAAAAAAAAAENXevXs1ffr0qMmayy67TNOmTVNBQYHKy8v10UcfaeHChSotLQ07bt68eerTp4/uuuuuuNtft25d1GSNaZq6+uqrNWHCBJ1xxhk6duyYtm7dqkWLFqmmpqb5uFAopDvuuEN9+vTRV7/61bjbTyVm2ABIqdbuCHA6nRo5cqQuvPBCvfLKKyorKwv7OR9VAAAAAAAAOB1ZOZ4WDAZ18cUX61//+ldYvHfv3lq6dKnGjx8fUaa6ulp33323/ud//ics7nA4tHHjRp177rkxt19dXa1zzjknYo+eYcOG6fXXX9fw4cMjypSUlOiWW27Rm2++GRbPycnRrl271Lt375jbTzVm2ABIObvdrnPOOUcXXnhh87/zzjtPTqdTkrR8+fKILxgAAAAAAADgdGXVeNrzzz8fkazJy8vT+vXrNXDgwKhlMjMz9cwzzygjI0OPPvpoc9zn8+kHP/iB1qxZE3P7Dz30UESyZujQoVq3bp3y8vKilsnLy9Nrr72mWbNmaenSpc3x8vJy/fSnP9Wf//znmNtPNWbYAEipI0eOyOv1yuPxtHrMwIEDIzYP46MKAAAAAAAApyOrxtMCgYCGDh2q/fv3h8UXLlyo2bNnt1u+oaFBY8aM0Y4dO8Lia9as0aRJk9otX1ZWpoKCAlVUVDTHTNPUmjVrNGHChHbLnzhxQiNGjNBnn33WHLPZbNqzZ4/OPPPMdstbwbS6AwBOL3369GnzywUAAAAAAADA56waT/vrX/8akawZNWpUTMkaqXHJtgcffDAi/vTTT8dUfuHChWHJGkmaMWNGTMkaSerevbvuueeesFggENCzzz4bU3krkLABAAAAAAAAAABhXn755YjY7bffHlcd11xzTcSeMW+88YZqampS0v7NN98sl8sVFlu0aFFcdaQSCRsAAAAAAAAAANAsFAppxYoVEfHrr78+rnrsdruuueaasFhtbW27+9iUl5dr/fr1YbGsrCxdccUVcbXfvXt3TZkyJSx24MAB7dy5M656UoWEDQAAAAAAAAAAaLZ7924VFxeHxYYOHar8/Py465o4cWJErLCwsM0y69evVyAQCItdcsklstlsKWnfKiRsAAAAAAAAAABAs02bNkXELrnkkoTqGj9+fEz1p1P7ViFhAwAAAAAAAAAAmu3atSsiNmTIkITqGjBggOx2e1hs9+7dKWt/8ODBEbH22rcKCRsAAAAAAAAAANCsqKgoIlZQUJBQXTabTf369QuLHT58WD6fLyXt9+vXL2IptX379iVUV2cjYQMAAAAAAAAAAJodPXo0InbGGWckXF/LsoFAQMePH09J+zabTX369AmLffbZZwnV1dlI2AAAAAAAAAAAgGYlJSURsaysrITri1b2xIkTlrXf0NCgqqqqhOvrLCRsAAAAAAAAAABAs+rq6oiYx+NJuL5oZWtqatK2fauQsAEAAAAAAAAAAM2i7S/jdrsTri9awqShoSFt27cKCRsAAAAAAAAAANAmwzCSWjYUCnWp9lOBhA0AAAAAAAAAAGjmcDgiYrW1tQnXF62s0+lM2/atQsIGAAAAAAAAAAA0y8jIiIglO2GSmZmZtu1bhYQNAAAAAAAAAABo1r1794hYVVVVwvVFKxutjVS173Q6lZWVlXB9nYWEDQAAAAAAAAAAaJafnx8RO3z4cML1HTp0KOyxaZrq0aNHStoPBAI6cuRIu/WnAxI2AAAAAAAAAACg2aBBgyJiBw4cSKiuYDCof//732Gx/v37R92npjPa//TTT+X3+9utPx2QsAEAAAAAAAAAAM2GDRsWEdu7d29CdR08eFA+ny8sdvbZZ6es/U8++SQi1l77ViFhAwAAAAAAAAAAml1wwQURsfXr1ydU17p16yJiY8aMSev2rULCBgAAAAAAAAAANDv77LPVs2fPsNiePXtUXFwcd11r166NiE2aNKnNMuPHj5fNZguLrV+/XoFAICXtW4WEDQAAAAAAAAAAaGYYhr70pS9FxF977bW46vH7/XrjjTfCYm63W5MnT26zXE5Oji6++OKwWGVlpVauXBlX+yUlJVq1alVYbMCAARo+fHhc9aQKCRsAAAAAAAAAABDmxhtvjIg988wzcdXx1ltv6dNPPw2LXXPNNcrIyEhJ+3/+859VV1cXFvv6178eVx2pZIRCoZDVnQCAUw0cOFAHDhwIi/FRBQAAAAAAAETXGeNpfr9fQ4cOVVFRUVj85Zdf1qxZs9ot39DQoAsvvFDbt28Pi69evbrdGTaSVFZWpgEDBqiysrI5ZpqmCgsLNX78+HbLl5aWasSIETpy5EhzzGazaffu3Ro8eHC75a3ADBsAAAAAAAAAABDGbrfrZz/7WUT8jjvuiEgORXPvvfdGJGsmTJgQU7JGkrxer77//e+HxYLBoG655RaVlpa2WTYYDOq2224LS9ZIjbNr0jVZI5GwAQAAAAAAAAAAUdx6660aM2ZMWOz48eO65JJLtH79+qhlampq9N3vfle///3vw+J2u12PP/54XO3/4he/UP/+/cNie/bs0fjx47Vr166oZUpLS3XddddpyZIlYfHs7Gz99re/jav9VGNJNAApN3369Ii1K0/10UcfyefzhcXOO++8NutctmyZ+vbtm5T+AQAAAAAAAOnEyvG0PXv26KKLLlJFRUXEz6ZOnaovfelLGjBggMrLy7Vz50699NJLKikpiTj24Ycf1t13391uey0VFhZq6tSp8vv9YXHTNPXlL39ZEydOVL9+/XT8+HFt3bpVixYtUnV1dUQ9S5Ys0Q033BB3+6lEwgZAykVbU7Oj9u/fr4EDBya1TgAAAAAAACAdWD2eVlhYqOnTp6uqqiqhtu6++249/PDDCZWVGpMts2fPjkjaxMIwDD322GP64Q9/mHD7qcKSaAAAAAAAAAAAoFUTJ07Uhg0bNHLkyLjKZWZm6plnnulQskaSvva1r+nvf/+7BgwYEFe5Hj166LXXXusSyRqJhA0AAAAAAAAAAGjHiBEjtGXLFv3pT39qd7m17t27684779Tu3bt12223JaX9iRMnaufOnfrd736nIUOGtHlsv379dN9992nPnj269tprk9J+KrAkGgAAAAAAAAAAiMvBgwe1adMmFRUVqbq6Wg6HQ/n5+Ro5cqTGjBkj0+zc+SIff/yxtmzZokOHDqmmpkZut1t9+/bVueeeq1GjRnVq252FhA0AAAAAAAAAAIDFWBINAAAAAAAAAADAYiRsAAAAAAAAAAAALEbCBgAAAAAAAAAAwGIkbAAAAAAAAAAAACxGwgYAAAAAAAAAAMBiJGwAAAAAAAAAAAAsRsIGAAAAAAAAAADAYiRsAAAAAAAAAAAALEbCBgAAAAAAAAAAwGIkbAAAAAAAAAAAACxGwgYAAAAAAAAAAMBiJGwAAAAAAAAAAAAsRsIGAAAAAAAAAADAYiRsAAAAAAAAAAAALEbCBgAAAAAAAAAAwGIkbAAAAAAAAAAAACxGwgYAAAAAAAAAAMBiJGwAAAAAAAAAAAAsRsIGAAAAAAAAAADAYiRsAAAAAAAAAAAALEbCBgAAAAAAAAAAwGIkbAAAAAAAAAAAACxGwgYAAAAAAAAAAMBiJGwAAAAAAAAAAAAsRsIGAAAAAAAAAADAYiRsAAAAAABppaioSIZhhP1bsGCB1d0CAAAAOhUJGwAAAAAAAAAAAIuRsAEAAABgiWizKKZMmWJ1twAAAADAEiRsAAAAAHQpU6ZMIcmThlom3x544AGruwQAAAB0KSRsAAAAAAAAAAAALEbCBgAAAAAAAAAAwGJ2qzsAAAAAAMCpBg4cqFAoZHU3AAAAgJRihg0AAAAAAAAAAIDFSNgAAAAAAAAAAABYjIQNAAAAAAAAAACAxdjDBgAAAABaEQwGtWXLFhUVFenYsWMqKSlRdna2evbsqSFDhuj888+XaXbufXANDQ3auHGjdu/erePHj6u+vl7Z2dkaN26cxo0bF1P5vXv3ateuXTp69KgqKiokSXl5ecrLy9OoUaM0bNiwTn0O6SAQCGjz5s3av3+/jh07pvLycuXl5alnz54666yzNGrUqJT049ixY9qwYYP27dunqqoq5eTkqFevXho3bpwKCgqS1k5tba127NihnTt3qrS0VJWVlbLZbMrIyFBubq4KCgo0ePBg9evXL2ltAgAAoGOMEDs5AgAAALBAUVGRBg0aFBabPHmyVq9eHXGsYRgdbm///v0aOHBgTMcWFhbqySef1MqVK1VSUtLqcXl5eZo+fbp+/vOf65xzzomrPwsWLNAtt9zSah937NihefPmaenSpaquro4o/61vfUsLFiyIWvcHH3ygv/zlL1q1apXef/991dfXt9mXnj176uqrr9bcuXNjfh6rV6/WZZddFtOxrSkoKFBRUVFEPNprY/78+br55pvjbmPjxo36/e9/rxUrVrT5t+zbt69mzJihn/70pxo8eHDc7UyZMkVr1qxpftzytbx69Wo99NBD+tvf/qZgMBi1jnPOOUf33nuvZs+enfBr/vXXX9cLL7yg5cuXy+fztXt83759demll+qaa67Rl7/8ZWVnZyfULgAAADqOJdEAAAAA4KQ9e/Zo+vTpmjRpkhYvXtzmAL8klZSU6KWXXtKoUaN06623qq6uLin9+K//+i+NHj1aL774YtRkTWt27dqlYcOG6fzzz9eDDz6o9957r91kjdQ462P+/PkaOXKk5syZE1OZdHf8+HHdeOONGjt2rF5++eV2/5affvqpnn32WQ0fPlx33nln0n4H9fX1mjNnji677DKtXLmy1WSNJH300Uf6xje+oSuvvDKuv7skHTx4UJMnT9Z1112nt99+O6ZkjdT4vJcsWaJvfOMbeuihh+JqEwAAAMlFwgYAAAAAJP3tb3/TuHHj9O6778ZdNhgM6vnnn9fkyZP12Wefdagfd9xxh+677z75/f64yx49elR79uxJuO1QKKTnnntOkyZNal46rSvat2+fxo8fr8WLFyveRSV8Pp/++Mc/atq0aSotLe1QP+rq6nTVVVfpueeei6vcihUrNH36dAUCgZiOLyoq0oQJE/Tee+8l0k0AAACkCfawAQAAAJD2zjvvvOb/7927N2z2QWZmpoYMGdJuHU6ns9WfvfXWW7r++usjZiU4nU5NnTpV48aN0xlnnKGcnBxVVVWpqKhIf//731VYWBh2/D//+U9de+21eu+99+RwOGJ9es2effZZPfnkk82Ps7KyNG3aNF166aXKz89XMBjU4cOHtWrVKtlstnbry83N1UUXXaThw4dr8ODBys7OVlZWlmpra3X8+HHt2LFDK1as0MGDByOex6233qpXXnml1bqzsrLC/i5bt24N+3l+fr569+7dZv/69u3b7nOIV3FxsSZMmKAjR45E/Kx///667rrrNHz4cOXl5am4uFhbt27V66+/rhMnToQdW1hYqP/4j//QunXr5HK5EurLt7/9ba1atar58bBhw3TVVVfp7LPPVl5ensrLy7VlyxYtXbo0ItH33nvv6dFHH9XcuXNjaufQoUMR8dGjR2vKlCk666yz5PV65XA4VFlZqdLSUu3atUvbtm3Txo0bY04MAQAAoJOFAAAAAMAC+/fvD0kK+zd58uR2y02ePDnuMm3Zt29fyOv1htVpt9tD99xzT6i4uLjNslu2bAldcMEFEc/jxz/+cbvtzp8/P6KczWZr/v/tt98eOn78eKvla2trI2KrVq0K9e7dO/Szn/0s9P7774cCgUC7/QgGg6F33nknNHTo0Ij+LFmypN3yTVqWvf/++2Mu21K018b8+fNjei5XXXVVRFmPxxN65JFHQn6/P2q5mpqa0D333BMyTTOi7F133RVTn1u+Lt1ud/P/e/fu3ebvsrKyMvTNb34zom2v1xuqqalps93CwsKIcmeeeWZo7dq1MfW7pKQktHDhwtCkSZNCP/vZz2IqAwAAgM7BkmgAAAAATms33XSTysrKmh9nZGRo+fLlmjdvnnr27Nlm2dGjR2vdunWaNm1aWPzxxx+POuOhPU0zHX7/+9/r6aefVvfu3Vs91u12R8TGjh2rQ4cO6Te/+Y3Gjh0r02z/ks8wDE2fPl3vv/++zj///LCfPfLII3E+A2stWrQoYkk7t9utt956Sz/60Y9anZXk8Xg0b948Pf300xE/+8Mf/qCNGzfG3Zem/YzOPPNMbdiwQTfccEOrx2ZlZWnBggW64oorwuJlZWVaunRpm+288cYbYY8dDoeWL1+uSy+9NKZ+5ubmavbs2VqzZo3uu+++mMoAAACgc5CwAQAAAHDaWrlypdavXx8We+GFFzR16tSY63A6nVqyZIl69OjRHPP5fAknO66//nr9+Mc/TqhsRkaG7PbEVr7Ozc3Viy++GBZbv369Pvroo4Tqs0K03/m8efN0+eWXx1T+P//zP3X77beHxUKhUMJ/S4fDoVdeeUUFBQXtHmsYRtR2li9f3ma5ffv2hT2eMmVKTEsERpORkZFQOQAAACQHCRsAAAAAp63f/va3YY8nTpyoWbNmxV1PTk6O7rzzzrDY66+/Hnc9pmnq4YcfjrtcsowcOVJjxowJi61du9ai3sRnw4YN2rRpU1hs1KhR+v73vx9XPb/5zW+Um5sbFnv11Vcj9piJxezZs3XBBRfEfPw555wT8ftv+ZxaqqysDHvc1qwsAAAApDcSNgAAAABOSyUlJfr73/8eFrv11lsTrm/GjBlhjw8cOKADBw7EVcfUqVM1cODAhPuQDEOHDg17vGHDBot6Ep+VK1dGxG677baYloU7ldfr1de//vWwmM/n0+rVq+Pu05w5c+IuM3bs2LDHe/bsafP4lgma999/X36/P+52AQAAYL3E5soDAAAAQBdXWFioUCgUFhs/fnzC9Q0aNCgitmXLlpiWw2py2WWXJdx+az755BNt2LBB27Zt0yeffKKKigpVVFSovr4+4vlL0sGDB9t8nK7+8Y9/RMSuv/76hOqaOXOmnnrqqYj645l95fF4IpIvsRg8eHDY40AgoKqqKmVlZUU9fty4cXr55ZebH+/fv19z5szRk08+yRJnAAAAXQwJGwAAAACnpdYG+A3DSFobx48fj+v4lsthJSoYDOr555/Xs88+q3/9618dqqusrCwpfepsmzdvDnvcv39/9e7dO6G6LrjgApmmqWAw2Gr97SkoKJDD4Yi77ZycnIhYeXl5qwmbG2+8Uffee69qa2ubYwsWLNCyZct0880367rrrtNFF10U90wjAAAApB4JGwAAAACnpcOHD0fEtm3bltQ2Tpw4EdfxvXr16nCbO3fu1E033aQtW7Z0uC6pMVmQ7kKhkEpKSsJiw4cPT7i+rKwsnXHGGWFL2sWbfMvLy0uo7WhJHp/P1+rxvXv31kMPPaQf/ehHYfHi4mLNmzdP8+bNk9fr1fjx4zVu3DhdfPHFGj9+fKsJIAAAAFiHhA0AAACA01K8yZREnDrrIRbZ2dkdau/DDz/U1KlTdezYsQ7Vc6q2kgXpoqKiQoFAICzm9Xo7VGdubm5YwqZlQqg9icyuSdRdd90lv9+vn//851H3rykrK9OyZcu0bNkySZLdbtfFF1+sWbNm6cYbb1SPHj1S1lcAAAC0jjnRAAAAAE5LpaWlVnchgt2e+D11Pp9PM2fOjJqsufTSS/XAAw/o7bff1tatW1VcXKzKykr5/X6FQqGwf9/61rc68hQsUVlZGRHLzMzsUJ0ty0drI53MnTtXH374oW666Sa53e42j/X7/Vq7dq1+8IMfqKCgQPfcc4+qq6tT1FMAAAC0hhk2AAAAAE5LHo8n7LHX603LJE6s/vSnP2nnzp1hscGDB+vll1/WhRdeGHM98c4KSgfdunWLiHU0AdGyfLQ20s2wYcP00ksv6cknn9Q777yjVatWae3atdq9e7dCoVDUMjU1NXr44Yf15ptvasWKFSooKEhxrwEAANCEhA0AAACA01LLZaDKyspUVlbW4aW0rLJo0aKwx926ddP//d//aeDAgXHVE+/SX+kgOztbNpstbFm0srKyDtXZsnyie9JYIScnR7Nnz9bs2bMlNc4mW7dunQoLC7Vs2TJt3749osyePXs0Y8YMbd68WU6nM9VdBgAAgFgSDQAAAMBpKj8/PyK2bds2C3rScVVVVVq/fn1Y7Jvf/GbcyRpJ2rdvX5J6lTqGYah79+5hsZazjeJRXV2tgwcPhsW68j4vubm5mjFjhv77v/9b27Zt0+7du/Xd735XNpst7LgdO3bo+eeft6iXAAAAIGEDAAAA4LQ0duzYiNi7775rQU867tNPP1UwGAyLTZw4Me56iouLu2TCRpLGjBkT9vjw4cP67LPPEqpr06ZNEb/PCy64IOG+pZuzzjpLTz31lF588cWIny1dutSCHgEAAEAiYQMAAACgi7Hbw1d2PnUZrHhMmzYtIrZ48WL5/f6E6rPS8ePHI2KJLOG1ePHihPvQcrZGon+XRI0fPz4i9uqrryZU15IlS2Kqv6ubPXu2Ro8eHRbrqrPMAAAAvghI2AAAAADoUlpu/l5VVZVQPf369YuYNbF//34tWLAg0a5ZJjMzMyIWLYnTFp/Pp8cffzzhPiTr75KoK664IiL2pz/9KWKmTHvKy8v1v//7v2Exh8Ohyy67rEP9S1dnn3122OPy8nKLegIAAAASNgAAAAC6lNzc3LDH+/fvVygUSqiuX/ziFxGxuXPnas+ePQnVZ5U+ffpExFasWBFXHQ8++KA+/vjjhPvQ8u+S6qXVxo4dqwsvvDAstm3bNj3zzDNx1fOLX/xCJSUlYbGZM2eqV69eHe5jOjpy5EjY4549e1rUEwAAAJCwAQAAANCljBo1KuxxeXm51q1bl1BdX/3qVyMG+cvLy3XVVVdpx44dCdVZWVmp3/3ud3rppZcSKp+IXr166ayzzgqLLVy4UFu3bo2p/Pz58/Wb3/ymQ31o+XdZs2aNqqurO1RnvH784x9HxObOnas1a9bEVP6FF17QU089FRYzDEM/+tGPktK/znD33Xfro48+Sqjs5s2bVVhYGBY777zzktEtAAAAJICEDQAAAIAuJdpeIt/+9re1atWquJe/kqRFixZF7Peyb98+jRs3Tr/+9a9jWiIqGAxq1apVuv322zVgwAD95Cc/0dGjR+PuS0fMnDkz7LHP59OVV16p1atXt1qmrKxMd955p77zne80/+6ys7MTar/l36W8vFyzZs3Szp07E6ovEV//+tc1ffr0sFhtba1mzJihJ554otXXR11dnX7+859rzpw5EbO17rrrroil89LJ888/rxEjRmjatGl69tlnVVxcHFO5t99+W1dddVXE7+Qb3/hGZ3QTAAAAMTBCia4dAAAAAAAdUFRUpEGDBoXFJk+e3GaCocmIESOizirweDzq37+/MjIyIn62bNky9e3bN2p9f/vb3zR9+nQ1NDRE/CwzM1MTJkzQpZdeqj59+sjr9aqmpkZlZWU6dOiQNm/erM2bN6usrCys3O9+9zvNnTu31eewYMEC3XLLLWGx/fv3a+DAga2WaUtJSYkGDx4c0Q9JmjRpkq644goNHDhQpmnq6NGjWrdund59992wvWYuv/xy9evXTy+++GJzrKCgQEVFRe22f+TIEQ0YMEB+vz/iZ7m5ucrPz5fL5QqL9+3bV8uWLYs4PtprY/78+br55pvb7UdxcbFGjx4dsdSXJA0YMEDXXXedhg8fLq/Xq+PHj+uDDz7Q66+/HnXPnzFjxmjdunUR/Y5mypQpYTN5Yn0ttxTv68Lr9YYlFU3T1IgRI3T++efrnHPOUffu3eX1ehUIBFRSUqKdO3dq5cqV2rVrV0RdEydO1OrVq2Wa3NsJAABgBbvVHQAAAACAeD3++OP60pe+pEAgEBavra1tdR+WaMmYJpdffrkKCwt1ww036NChQ2E/q66u1vLly7V8+fKOd7wT5eXlaeHChfrKV74S8Xt577339N5777VZfuTIkVqyZEnCy3/16dNHv/zlL/XAAw9E/Ky0tFSlpaUR8WjJpY7q1auX1q5dqyuvvDLitXDw4EE99thjMdUzYcIEvfnmmzEla9JJMBjU9u3btX379rjKjRw5Ui+//DLJGgAAAAtxJgYAAACgy5k6dapef/115efnJ63OsWPHavPmzbrlllvkcDgSrscwDE2ZMkUTJ05MWt9iNX36dC1ZsiTuZc2uvvpqFRYWKjc3t0Pt33ffffr1r38tp9PZoXo66swzz9Q//vEPzZw5U4ZhxFXW4XDoBz/4gVauXNnh30cq9O7du0PlDcPQt771La1du7bVGWgAAABIDRI2AAAAALqkL3/5yyoqKtIrr7yi73znOxo7dqx69+6tzMzMuAfpm/To0UMvvPCC9u7dq7lz52rEiBEx1dWtWzfNmDFDjz76qPbv369Vq1Zp3LhxCfWho7761a9q27Ztuu222+TxeFo9zjRNTZkyRW+88Ybeeusteb3eDrdtmqbuvfde/fvf/9YTTzyhWbNmaeTIkerRo4fcbneH649Hz549tXjxYv3zn//UrFmz2k2+9OnTR3PmzNHOnTv1xz/+MeX9TdSuXbu0adMm/epXv9K0adNiTtb16tVL3/ve97R582YtWLBAOTk5ndxTAAAAtIc9bAAAAACgDceOHdOmTZt07NgxnThxQlVVVcrMzFS3bt3Uv39/nX322SooKEg4SdSZ6uvr9f7772v37t06ceKEgsGgvF6vBg8erIsuukh5eXlWdzFlAoGANm7cqKKiIh07dkwVFRXyer3q1auXzjrrLJ177rlWdzEpgsGgioqK9Mknn+jAgQOqqKhQTU2NXC6XsrOz1adPH5177rkJ75UEAACAzkPCBgAAAAAAAAAAwGIsiQYAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGAxEjYAAAAAAAAAAAAWI2EDAAAAAAAAAABgMRI2AAAAAAAAAAAAFiNhAwAAAAAAAAAAYDESNgAAAAAAAAAAABYjYQMAAAAAAAAAAGCx/we3LuEXdkMPWAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# just a different visualisation\n",
    "\n",
    "# Set global font size to 12\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "\n",
    "fig, axs = plt.subplots(1,1,sharey=True, figsize=(6,4), dpi=300)\n",
    "\n",
    "\n",
    "series = CONTROL_R10_bayexpress_FC0.iloc[:,1:-1].sum(axis=0)\n",
    "series.plot(kind='line', color='#332288', alpha=0.8, ylim=[-5,500], linewidth=2, label=f'BF_21 > 10, iFC > 0, total = {sum(CONTROL_R10_bayexpress_FC0.iloc[:,1:-1].sum(axis=0))}')\n",
    "\n",
    "series = CONTROL_R10_bayexpress_FC1.iloc[:,1:-1].sum(axis=0)\n",
    "series.plot(kind='line', color='#44AA99', alpha=0.8, ylim=[-5,500], linewidth=2, label=f'BF_21 > 10, iFC > 1, total = {sum(CONTROL_R10_bayexpress_FC1.iloc[:,1:-1].sum(axis=0))}')\n",
    "\n",
    "series = CONTROL_R10_bayexpress_FC2.iloc[:,1:-1].sum(axis=0)\n",
    "series.plot(kind='line', color='#CC6677', alpha=0.8, ylim=[-5,500], linewidth=2, label=f'BF_21 > 10, iFC > 2, total = {sum(CONTROL_R10_bayexpress_FC2.iloc[:,1:-1].sum(axis=0))}')\n",
    "\n",
    "axs.set_xlabel('Iterations')\n",
    "axs.set_ylabel('# putative DEGs')\n",
    "\n",
    "axs.set_xticks([1,100])\n",
    "axs.set_xticklabels(['1', '100'])\n",
    "\n",
    "axs.legend(loc='center left')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find genes that got hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>snR44</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6782</th>\n",
       "      <td>snR49</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6807</th>\n",
       "      <td>snR70</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3286 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "0      15S_rRNA  False  False  False  False  False  False  False  False   \n",
       "1      21S_rRNA  False   True  False   True   True  False   True  False   \n",
       "4          LSR1  False   True  False  False  False  False  False  False   \n",
       "41       RDN5-1  False   True  False  False  False   True  False  False   \n",
       "46       RDN5-6  False   True  False  False  False   True  False  False   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6777      snR44  False  False  False  False  False   True  False  False   \n",
       "6782      snR49  False  False  False  False  False   True  False  False   \n",
       "6807      snR70  False  False  False  False  False  False  False  False   \n",
       "6821      snR83  False  False  False  False  False   True  False  False   \n",
       "6824      snR86  False  False  False  False  False   True   True  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "0     False  ...  False  False  False  False  False  False  False  False   \n",
       "1     False  ...   True   True  False  False  False  False   True  False   \n",
       "4      True  ...  False   True  False  False  False  False   True  False   \n",
       "41    False  ...  False   True  False  False  False  False  False  False   \n",
       "46    False  ...  False   True  False  False  False  False  False  False   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6777  False  ...  False  False  False  False  False  False   True  False   \n",
       "6782  False  ...  False  False  False  False  False  False  False  False   \n",
       "6807  False  ...  False  False  False  False  False  False  False  False   \n",
       "6821  False  ...  False  False  False  False  False  False   True  False   \n",
       "6824  False  ...  False  False  False  False  False  False   True  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "0     False      4  \n",
       "1      True     39  \n",
       "4      True     36  \n",
       "41     True     26  \n",
       "46     True     26  \n",
       "...     ...    ...  \n",
       "6777  False      7  \n",
       "6782  False      1  \n",
       "6807  False      1  \n",
       "6821  False      6  \n",
       "6824  False     20  \n",
       "\n",
       "[3286 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All replicates, bayexpress results\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>-5.206545</td>\n",
       "      <td>-0.564787</td>\n",
       "      <td>-240.338654</td>\n",
       "      <td>False</td>\n",
       "      <td>-258.581365</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>76.746125</td>\n",
       "      <td>0.504048</td>\n",
       "      <td>-194.077441</td>\n",
       "      <td>False</td>\n",
       "      <td>-196.467262</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>-7.312143</td>\n",
       "      <td>0.861839</td>\n",
       "      <td>-270.299503</td>\n",
       "      <td>False</td>\n",
       "      <td>-281.214963</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>-7.406574</td>\n",
       "      <td>0.022303</td>\n",
       "      <td>-262.736078</td>\n",
       "      <td>False</td>\n",
       "      <td>-275.253246</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>-7.727890</td>\n",
       "      <td>0.183767</td>\n",
       "      <td>-271.177492</td>\n",
       "      <td>False</td>\n",
       "      <td>-284.863866</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>-7.622253</td>\n",
       "      <td>0.347266</td>\n",
       "      <td>-270.291196</td>\n",
       "      <td>False</td>\n",
       "      <td>-282.682788</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>-8.273720</td>\n",
       "      <td>-0.138161</td>\n",
       "      <td>-275.349735</td>\n",
       "      <td>False</td>\n",
       "      <td>-289.991744</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "0      15S_rRNA  -3.710645  0.274047 -107.138056   False    53.627445   \n",
       "1      21S_rRNA  -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "2          HRA1  -5.206545 -0.564787 -240.338654   False  -258.581365   \n",
       "3          ICR1  76.746125  0.504048 -194.077441   False  -196.467262   \n",
       "4          LSR1   0.451012  0.120243  251.787125    True   478.066414   \n",
       "...         ...        ...       ...         ...     ...          ...   \n",
       "7121  tY(GUA)J2  -7.312143  0.861839 -270.299503   False  -281.214963   \n",
       "7122  tY(GUA)M1  -7.406574  0.022303 -262.736078   False  -275.253246   \n",
       "7123  tY(GUA)M2  -7.727890  0.183767 -271.177492   False  -284.863866   \n",
       "7124   tY(GUA)O  -7.622253  0.347266 -270.291196   False  -282.682788   \n",
       "7125   tY(GUA)Q  -8.273720 -0.138161 -275.349735   False  -289.991744   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  \n",
       "...        ...  \n",
       "7121     False  \n",
       "7122     False  \n",
       "7123     False  \n",
       "7124     False  \n",
       "7125     False  \n",
       "\n",
       "[7126 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>snR44</td>\n",
       "      <td>44.784503</td>\n",
       "      <td>-0.377448</td>\n",
       "      <td>-69.135666</td>\n",
       "      <td>False</td>\n",
       "      <td>-160.634165</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6782</th>\n",
       "      <td>snR49</td>\n",
       "      <td>3.810074</td>\n",
       "      <td>-0.194770</td>\n",
       "      <td>-128.891351</td>\n",
       "      <td>False</td>\n",
       "      <td>-181.780529</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6807</th>\n",
       "      <td>snR70</td>\n",
       "      <td>50.106839</td>\n",
       "      <td>0.523481</td>\n",
       "      <td>-171.219260</td>\n",
       "      <td>False</td>\n",
       "      <td>-175.092799</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>-5.961317</td>\n",
       "      <td>-0.049198</td>\n",
       "      <td>-97.702962</td>\n",
       "      <td>False</td>\n",
       "      <td>-164.859374</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3286 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "0      15S_rRNA   -3.710645  0.274047 -107.138056   False    53.627445   \n",
       "1      21S_rRNA   -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "4          LSR1    0.451012  0.120243  251.787125    True   478.066414   \n",
       "41       RDN5-1   13.774601  0.316040  246.914584    True   521.186933   \n",
       "46       RDN5-6   15.230943  0.469612  172.850686    True   347.304031   \n",
       "...         ...         ...       ...         ...     ...          ...   \n",
       "6777      snR44   44.784503 -0.377448  -69.135666   False  -160.634165   \n",
       "6782      snR49    3.810074 -0.194770 -128.891351   False  -181.780529   \n",
       "6807      snR70   50.106839  0.523481 -171.219260   False  -175.092799   \n",
       "6821      snR83   -5.961317 -0.049198  -97.702962   False  -164.859374   \n",
       "6824      snR86  272.275536  0.372995  120.984196    True   160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "...        ...  \n",
       "6777     False  \n",
       "6782     False  \n",
       "6807     False  \n",
       "6821     False  \n",
       "6824      True  \n",
       "\n",
       "[3286 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RUF5-1</td>\n",
       "      <td>-1.191579</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>232.720615</td>\n",
       "      <td>True</td>\n",
       "      <td>4.230186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>YPR190C</td>\n",
       "      <td>48.045047</td>\n",
       "      <td>-0.216275</td>\n",
       "      <td>135.716958</td>\n",
       "      <td>True</td>\n",
       "      <td>-136.905959</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>218.989419</td>\n",
       "      <td>0.279871</td>\n",
       "      <td>251.986788</td>\n",
       "      <td>True</td>\n",
       "      <td>-38.705868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6761</th>\n",
       "      <td>snR30</td>\n",
       "      <td>20.556922</td>\n",
       "      <td>0.159709</td>\n",
       "      <td>2.616569</td>\n",
       "      <td>True</td>\n",
       "      <td>85.355966</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1602 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "1      21S_rRNA   -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "4          LSR1    0.451012  0.120243  251.787125    True   478.066414   \n",
       "41       RDN5-1   13.774601  0.316040  246.914584    True   521.186933   \n",
       "46       RDN5-6   15.230943  0.469612  172.850686    True   347.304031   \n",
       "56       RUF5-1   -1.191579  0.080133  232.720615    True     4.230186   \n",
       "...         ...         ...       ...         ...     ...          ...   \n",
       "6730    YPR190C   48.045047 -0.216275  135.716958    True  -136.905959   \n",
       "6731    YPR191W  218.989419  0.279871  251.986788    True   -38.705868   \n",
       "6752     snR17a    7.383833  0.329308   28.421292    True   239.879652   \n",
       "6761      snR30   20.556922  0.159709    2.616569    True    85.355966   \n",
       "6824      snR86  272.275536  0.372995  120.984196    True   160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "56        True  \n",
       "...        ...  \n",
       "6730     False  \n",
       "6731     False  \n",
       "6752      True  \n",
       "6761      True  \n",
       "6824      True  \n",
       "\n",
       "[1602 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>RPR1</td>\n",
       "      <td>102.505781</td>\n",
       "      <td>-0.973507</td>\n",
       "      <td>-29.530821</td>\n",
       "      <td>False</td>\n",
       "      <td>-163.636890</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>RUF20</td>\n",
       "      <td>58.301953</td>\n",
       "      <td>0.467352</td>\n",
       "      <td>-87.667016</td>\n",
       "      <td>False</td>\n",
       "      <td>-105.505837</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>24.026871</td>\n",
       "      <td>0.572910</td>\n",
       "      <td>-40.011620</td>\n",
       "      <td>False</td>\n",
       "      <td>-18.211398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>YAL001C</td>\n",
       "      <td>510.650373</td>\n",
       "      <td>0.634130</td>\n",
       "      <td>-70.980470</td>\n",
       "      <td>False</td>\n",
       "      <td>-69.042919</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6768</th>\n",
       "      <td>snR37</td>\n",
       "      <td>2.932227</td>\n",
       "      <td>-0.229684</td>\n",
       "      <td>-57.966295</td>\n",
       "      <td>False</td>\n",
       "      <td>-50.162245</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>snR44</td>\n",
       "      <td>44.784503</td>\n",
       "      <td>-0.377448</td>\n",
       "      <td>-69.135666</td>\n",
       "      <td>False</td>\n",
       "      <td>-160.634165</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6782</th>\n",
       "      <td>snR49</td>\n",
       "      <td>3.810074</td>\n",
       "      <td>-0.194770</td>\n",
       "      <td>-128.891351</td>\n",
       "      <td>False</td>\n",
       "      <td>-181.780529</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6807</th>\n",
       "      <td>snR70</td>\n",
       "      <td>50.106839</td>\n",
       "      <td>0.523481</td>\n",
       "      <td>-171.219260</td>\n",
       "      <td>False</td>\n",
       "      <td>-175.092799</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>-5.961317</td>\n",
       "      <td>-0.049198</td>\n",
       "      <td>-97.702962</td>\n",
       "      <td>False</td>\n",
       "      <td>-164.859374</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1684 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT  BF_k1_Snf2  \\\n",
       "0      15S_rRNA   -3.710645  0.274047 -107.138056   False   53.627445   \n",
       "51         RPR1  102.505781 -0.973507  -29.530821   False -163.636890   \n",
       "52        RUF20   58.301953  0.467352  -87.667016   False -105.505837   \n",
       "53        RUF21   24.026871  0.572910  -40.011620   False  -18.211398   \n",
       "61      YAL001C  510.650373  0.634130  -70.980470   False  -69.042919   \n",
       "...         ...         ...       ...         ...     ...         ...   \n",
       "6768      snR37    2.932227 -0.229684  -57.966295   False  -50.162245   \n",
       "6777      snR44   44.784503 -0.377448  -69.135666   False -160.634165   \n",
       "6782      snR49    3.810074 -0.194770 -128.891351   False -181.780529   \n",
       "6807      snR70   50.106839  0.523481 -171.219260   False -175.092799   \n",
       "6821      snR83   -5.961317 -0.049198  -97.702962   False -164.859374   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "51       False  \n",
       "52       False  \n",
       "53       False  \n",
       "61       False  \n",
       "...        ...  \n",
       "6768     False  \n",
       "6777     False  \n",
       "6782     False  \n",
       "6807     False  \n",
       "6821     False  \n",
       "\n",
       "[1684 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among the genes that produced 0 hits, where there any genes that would be identified as variable by BF_k1 > 1?\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [locus_name, BF_21, FC, BF_k1_WT, CIG_WT, BF_k1_Snf2, CIG_Snf2]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R10 means number of replicates in 100 bootstrapping iterations is 10\n",
    "# FC > 0\n",
    "# got hits (any)\n",
    "\n",
    "# let's find out which genes we found to produce hits in the control and see if we can identify them with Bayes factors for consistency\n",
    "\n",
    "print('Find genes that got hits in the control experiment')\n",
    "display(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 0])\n",
    "\n",
    "# importing results from differential gene expression anaylsis to check what they are\n",
    "RALL_bayexpress = pd.read_csv('RALL_bayexpress.csv', index_col=0)\n",
    "\n",
    "print('All replicates, bayexpress results')\n",
    "display(RALL_bayexpress)\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 0].index)])\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 0].index)].loc[(RALL_bayexpress.BF_k1_WT > 1)])\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 0].index)].loc[(RALL_bayexpress.BF_k1_WT < 1)])\n",
    "\n",
    "print('Among the genes that produced 0 hits, where there any genes that would be identified as variable by BF_k1 > 1?')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue == 0].index)].loc[(RALL_bayexpress.BF_k1_WT > 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find genes that got more than 10 hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6745</th>\n",
       "      <td>YPR204W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1525 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "1      21S_rRNA  False   True  False   True   True  False   True  False   \n",
       "4          LSR1  False   True  False  False  False  False  False  False   \n",
       "41       RDN5-1  False   True  False  False  False   True  False  False   \n",
       "46       RDN5-6  False   True  False  False  False   True  False  False   \n",
       "53        RUF21  False  False  False  False   True   True  False  False   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6731    YPR191W  False  False  False  False  False  False   True  False   \n",
       "6745    YPR204W  False  False   True  False  False   True  False   True   \n",
       "6752     snR17a  False   True  False  False  False  False  False  False   \n",
       "6757     snR190  False  False  False  False  False  False  False  False   \n",
       "6824      snR86  False  False  False  False  False   True   True  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "1     False  ...   True   True  False  False  False  False   True  False   \n",
       "4      True  ...  False   True  False  False  False  False   True  False   \n",
       "41    False  ...  False   True  False  False  False  False  False  False   \n",
       "46    False  ...  False   True  False  False  False  False  False  False   \n",
       "53    False  ...  False   True  False  False  False  False   True   True   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6731  False  ...  False  False  False  False  False   True  False  False   \n",
       "6745  False  ...  False  False  False  False  False  False  False  False   \n",
       "6752   True  ...  False   True  False  False  False  False   True  False   \n",
       "6757   True  ...  False   True  False  False  False  False  False  False   \n",
       "6824  False  ...  False  False  False  False  False  False   True  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "1      True     39  \n",
       "4      True     36  \n",
       "41     True     26  \n",
       "46     True     26  \n",
       "53    False     13  \n",
       "...     ...    ...  \n",
       "6731   True     25  \n",
       "6745  False     14  \n",
       "6752   True     18  \n",
       "6757   True     12  \n",
       "6824  False     20  \n",
       "\n",
       "[1525 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All replicates, bayexpress results\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>-5.206545</td>\n",
       "      <td>-0.564787</td>\n",
       "      <td>-240.338654</td>\n",
       "      <td>False</td>\n",
       "      <td>-258.581365</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>76.746125</td>\n",
       "      <td>0.504048</td>\n",
       "      <td>-194.077441</td>\n",
       "      <td>False</td>\n",
       "      <td>-196.467262</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>-7.312143</td>\n",
       "      <td>0.861839</td>\n",
       "      <td>-270.299503</td>\n",
       "      <td>False</td>\n",
       "      <td>-281.214963</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>-7.406574</td>\n",
       "      <td>0.022303</td>\n",
       "      <td>-262.736078</td>\n",
       "      <td>False</td>\n",
       "      <td>-275.253246</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>-7.727890</td>\n",
       "      <td>0.183767</td>\n",
       "      <td>-271.177492</td>\n",
       "      <td>False</td>\n",
       "      <td>-284.863866</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>-7.622253</td>\n",
       "      <td>0.347266</td>\n",
       "      <td>-270.291196</td>\n",
       "      <td>False</td>\n",
       "      <td>-282.682788</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>-8.273720</td>\n",
       "      <td>-0.138161</td>\n",
       "      <td>-275.349735</td>\n",
       "      <td>False</td>\n",
       "      <td>-289.991744</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "0      15S_rRNA  -3.710645  0.274047 -107.138056   False    53.627445   \n",
       "1      21S_rRNA  -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "2          HRA1  -5.206545 -0.564787 -240.338654   False  -258.581365   \n",
       "3          ICR1  76.746125  0.504048 -194.077441   False  -196.467262   \n",
       "4          LSR1   0.451012  0.120243  251.787125    True   478.066414   \n",
       "...         ...        ...       ...         ...     ...          ...   \n",
       "7121  tY(GUA)J2  -7.312143  0.861839 -270.299503   False  -281.214963   \n",
       "7122  tY(GUA)M1  -7.406574  0.022303 -262.736078   False  -275.253246   \n",
       "7123  tY(GUA)M2  -7.727890  0.183767 -271.177492   False  -284.863866   \n",
       "7124   tY(GUA)O  -7.622253  0.347266 -270.291196   False  -282.682788   \n",
       "7125   tY(GUA)Q  -8.273720 -0.138161 -275.349735   False  -289.991744   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  \n",
       "...        ...  \n",
       "7121     False  \n",
       "7122     False  \n",
       "7123     False  \n",
       "7124     False  \n",
       "7125     False  \n",
       "\n",
       "[7126 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got more than 10 hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>24.026871</td>\n",
       "      <td>0.572910</td>\n",
       "      <td>-40.011620</td>\n",
       "      <td>False</td>\n",
       "      <td>-18.211398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>218.989419</td>\n",
       "      <td>0.279871</td>\n",
       "      <td>251.986788</td>\n",
       "      <td>True</td>\n",
       "      <td>-38.705868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6745</th>\n",
       "      <td>YPR204W</td>\n",
       "      <td>814.266672</td>\n",
       "      <td>0.541523</td>\n",
       "      <td>-1.746145</td>\n",
       "      <td>True</td>\n",
       "      <td>37.268866</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>-6.562044</td>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-49.290008</td>\n",
       "      <td>False</td>\n",
       "      <td>-63.762229</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1525 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "1      21S_rRNA   -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "4          LSR1    0.451012  0.120243  251.787125    True   478.066414   \n",
       "41       RDN5-1   13.774601  0.316040  246.914584    True   521.186933   \n",
       "46       RDN5-6   15.230943  0.469612  172.850686    True   347.304031   \n",
       "53        RUF21   24.026871  0.572910  -40.011620   False   -18.211398   \n",
       "...         ...         ...       ...         ...     ...          ...   \n",
       "6731    YPR191W  218.989419  0.279871  251.986788    True   -38.705868   \n",
       "6745    YPR204W  814.266672  0.541523   -1.746145    True    37.268866   \n",
       "6752     snR17a    7.383833  0.329308   28.421292    True   239.879652   \n",
       "6757     snR190   -6.562044 -0.001634  -49.290008   False   -63.762229   \n",
       "6824      snR86  272.275536  0.372995  120.984196    True   160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "53       False  \n",
       "...        ...  \n",
       "6731     False  \n",
       "6745      True  \n",
       "6752      True  \n",
       "6757     False  \n",
       "6824      True  \n",
       "\n",
       "[1525 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got more than 10 hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RUF5-1</td>\n",
       "      <td>-1.191579</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>232.720615</td>\n",
       "      <td>True</td>\n",
       "      <td>4.230186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6727</th>\n",
       "      <td>YPR187W</td>\n",
       "      <td>39.001492</td>\n",
       "      <td>-0.117950</td>\n",
       "      <td>100.066660</td>\n",
       "      <td>True</td>\n",
       "      <td>-108.538938</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>YPR190C</td>\n",
       "      <td>48.045047</td>\n",
       "      <td>-0.216275</td>\n",
       "      <td>135.716958</td>\n",
       "      <td>True</td>\n",
       "      <td>-136.905959</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>218.989419</td>\n",
       "      <td>0.279871</td>\n",
       "      <td>251.986788</td>\n",
       "      <td>True</td>\n",
       "      <td>-38.705868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1457 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "1      21S_rRNA   -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "4          LSR1    0.451012  0.120243  251.787125    True   478.066414   \n",
       "41       RDN5-1   13.774601  0.316040  246.914584    True   521.186933   \n",
       "46       RDN5-6   15.230943  0.469612  172.850686    True   347.304031   \n",
       "56       RUF5-1   -1.191579  0.080133  232.720615    True     4.230186   \n",
       "...         ...         ...       ...         ...     ...          ...   \n",
       "6727    YPR187W   39.001492 -0.117950  100.066660    True  -108.538938   \n",
       "6730    YPR190C   48.045047 -0.216275  135.716958    True  -136.905959   \n",
       "6731    YPR191W  218.989419  0.279871  251.986788    True   -38.705868   \n",
       "6752     snR17a    7.383833  0.329308   28.421292    True   239.879652   \n",
       "6824      snR86  272.275536  0.372995  120.984196    True   160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "56        True  \n",
       "...        ...  \n",
       "6727     False  \n",
       "6730     False  \n",
       "6731     False  \n",
       "6752      True  \n",
       "6824      True  \n",
       "\n",
       "[1457 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got more than 10 hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>24.026871</td>\n",
       "      <td>0.572910</td>\n",
       "      <td>-40.011620</td>\n",
       "      <td>False</td>\n",
       "      <td>-18.211398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>YAL037C-A</td>\n",
       "      <td>1.234011</td>\n",
       "      <td>-0.338439</td>\n",
       "      <td>-16.174943</td>\n",
       "      <td>False</td>\n",
       "      <td>-127.606812</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>YBL026W</td>\n",
       "      <td>76.856290</td>\n",
       "      <td>0.281821</td>\n",
       "      <td>-60.714558</td>\n",
       "      <td>False</td>\n",
       "      <td>-103.152154</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>YBR162C</td>\n",
       "      <td>5986.100458</td>\n",
       "      <td>0.547220</td>\n",
       "      <td>-11.212930</td>\n",
       "      <td>False</td>\n",
       "      <td>42.095771</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>YBR215W</td>\n",
       "      <td>161.651123</td>\n",
       "      <td>0.400619</td>\n",
       "      <td>-6.054872</td>\n",
       "      <td>False</td>\n",
       "      <td>-76.262040</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>YPL242C</td>\n",
       "      <td>209.314218</td>\n",
       "      <td>0.408458</td>\n",
       "      <td>-24.378333</td>\n",
       "      <td>False</td>\n",
       "      <td>39.478586</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>YPR023C</td>\n",
       "      <td>1970.133813</td>\n",
       "      <td>0.799128</td>\n",
       "      <td>-8.107800</td>\n",
       "      <td>False</td>\n",
       "      <td>14.670405</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6562</th>\n",
       "      <td>YPR037C</td>\n",
       "      <td>117.783455</td>\n",
       "      <td>0.268815</td>\n",
       "      <td>-4.448602</td>\n",
       "      <td>False</td>\n",
       "      <td>25.339151</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6745</th>\n",
       "      <td>YPR204W</td>\n",
       "      <td>814.266672</td>\n",
       "      <td>0.541523</td>\n",
       "      <td>-1.746145</td>\n",
       "      <td>True</td>\n",
       "      <td>37.268866</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>-6.562044</td>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-49.290008</td>\n",
       "      <td>False</td>\n",
       "      <td>-63.762229</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name        BF_21        FC   BF_k1_WT  CIG_WT  BF_k1_Snf2  \\\n",
       "53        RUF21    24.026871  0.572910 -40.011620   False  -18.211398   \n",
       "103   YAL037C-A     1.234011 -0.338439 -16.174943   False -127.606812   \n",
       "211     YBL026W    76.856290  0.281821 -60.714558   False -103.152154   \n",
       "489     YBR162C  5986.100458  0.547220 -11.212930   False   42.095771   \n",
       "549     YBR215W   161.651123  0.400619  -6.054872   False  -76.262040   \n",
       "...         ...          ...       ...        ...     ...         ...   \n",
       "6474    YPL242C   209.314218  0.408458 -24.378333   False   39.478586   \n",
       "6547    YPR023C  1970.133813  0.799128  -8.107800   False   14.670405   \n",
       "6562    YPR037C   117.783455  0.268815  -4.448602   False   25.339151   \n",
       "6745    YPR204W   814.266672  0.541523  -1.746145    True   37.268866   \n",
       "6757     snR190    -6.562044 -0.001634 -49.290008   False  -63.762229   \n",
       "\n",
       "      CIG_Snf2  \n",
       "53       False  \n",
       "103      False  \n",
       "211      False  \n",
       "489       True  \n",
       "549      False  \n",
       "...        ...  \n",
       "6474      True  \n",
       "6547      True  \n",
       "6562      True  \n",
       "6745      True  \n",
       "6757     False  \n",
       "\n",
       "[68 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R10 means number of replicates in 100 bootstrapping iterations is 10\n",
    "# FC > 0\n",
    "# got more than 10 hits\n",
    "\n",
    "# let's find out which genes we found to produce hits in the control and see if we can identify them with Bayes factors for consistency\n",
    "\n",
    "print('Find genes that got more than 10 hits in the control experiment')\n",
    "display(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 10])\n",
    "\n",
    "# importing results from differential gene expression anaylsis to check what they are\n",
    "RALL_bayexpress = pd.read_csv('RALL_bayexpress.csv', index_col=0)\n",
    "\n",
    "print('All replicates, bayexpress results')\n",
    "display(RALL_bayexpress)\n",
    "\n",
    "print('Display only the genes that got more than 10 hits in the control experiment')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 10].index)])\n",
    "\n",
    "print('Display only the genes that got more than 10 hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 10].index)].loc[(RALL_bayexpress.BF_k1_WT > 1)])\n",
    "\n",
    "print('Display only the genes that got more than 10 hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 10].index)].loc[(RALL_bayexpress.BF_k1_WT < 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Find genes that got hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SCR1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>YAL026C-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>YAL037C-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>YAL037C-B</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>YBL077W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>YBR190W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>YBR191W-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>YCR021C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YDL158C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>YDL228C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>YDR133C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>YDR183C-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>YDR406W-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>YDR504C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>YDR523C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>YDR524W-C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>YEL033W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>YER091C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>YFR032C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>YFR052C-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>YGL184C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2453</th>\n",
       "      <td>YGR025W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>YGR142W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4049</th>\n",
       "      <td>YKR080W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>YLR062C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>YLR076C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4314</th>\n",
       "      <td>YLR154W-E</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>YLR154W-F</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4330</th>\n",
       "      <td>YLR162W</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>YLR162W-A</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>YLR342W-A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5496</th>\n",
       "      <td>YNL303W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5980</th>\n",
       "      <td>YOR169C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>YOR309C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>YPL130W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370</th>\n",
       "      <td>YPL142C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>YPR044C</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "0      15S_rRNA  False  False  False  False  False  False  False  False   \n",
       "1      21S_rRNA  False   True  False  False  False  False  False  False   \n",
       "4          LSR1  False  False  False  False  False  False  False  False   \n",
       "41       RDN5-1  False   True  False  False  False  False  False  False   \n",
       "46       RDN5-6  False   True  False  False  False   True  False  False   \n",
       "53        RUF21  False  False  False  False   True   True  False  False   \n",
       "58         SCR1  False   True  False  False  False   True  False  False   \n",
       "89    YAL026C-A  False  False  False  False  False  False  False  False   \n",
       "103   YAL037C-A  False  False  False  False   True   True  False  False   \n",
       "104   YAL037C-B  False  False  False  False  False  False  False  False   \n",
       "269     YBL077W  False  False  False  False  False  False  False  False   \n",
       "519     YBR190W  False  False  False  False  False  False  False  False   \n",
       "521   YBR191W-A  False  False  False  False   True   True  False  False   \n",
       "740     YCR021C  False  False  False  False  False  False  False  False   \n",
       "996     YDL158C  False  False  False  False  False  False  False  False   \n",
       "1070    YDL228C  False  False  False  False  False  False  False  False   \n",
       "1236    YDR133C  False  False  False  False  False  False  False  False   \n",
       "1290  YDR183C-A  False  False  False  False  False  False  False  False   \n",
       "1538  YDR406W-A  False  False  False  False  False  False  False  False   \n",
       "1637    YDR504C  False  False  False  False  False   True  False  False   \n",
       "1657    YDR523C  False  False  False  False  False  False  False  False   \n",
       "1661  YDR524W-C  False  False  False  False  False   True  False  False   \n",
       "1725    YEL033W  False  False  False  False  False  False  False  False   \n",
       "1893    YER091C  False  False  False  False  False  False  False  False   \n",
       "2125    YFR032C  False  False  False  False  False  False  False  False   \n",
       "2149  YFR052C-A  False  False  False  False  False  False  False  False   \n",
       "2345    YGL184C  False  False  False  False   True  False  False   True   \n",
       "2453    YGR025W  False  False  False  False  False  False  False  False   \n",
       "2581    YGR142W  False  False  False  False  False   True  False  False   \n",
       "4049    YKR080W  False  False  False  False  False  False  False  False   \n",
       "4212    YLR062C  False  False  False  False  False  False  False  False   \n",
       "4226    YLR076C  False  False  False  False  False  False  False  False   \n",
       "4314  YLR154W-E  False   True  False  False  False  False  False  False   \n",
       "4315  YLR154W-F  False  False  False  False  False  False  False  False   \n",
       "4330    YLR162W  False   True  False  False  False   True  False  False   \n",
       "4331  YLR162W-A  False   True  False  False  False  False  False  False   \n",
       "4525  YLR342W-A  False  False  False  False   True   True  False  False   \n",
       "5496    YNL303W  False  False  False  False  False   True  False  False   \n",
       "5980    YOR169C  False  False  False  False  False   True  False  False   \n",
       "6127    YOR309C  False  False  False  False  False   True  False  False   \n",
       "6357    YPL130W  False  False  False  False  False  False  False  False   \n",
       "6370    YPL142C  False  False  False  False  False  False  False  False   \n",
       "6569    YPR044C  False  False  False  False  False  False  False  False   \n",
       "6752     snR17a  False   True  False  False  False  False  False  False   \n",
       "6757     snR190  False  False  False  False  False  False  False  False   \n",
       "6821      snR83  False  False  False  False  False  False  False  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "0     False  ...  False  False  False  False  False  False  False  False   \n",
       "1     False  ...  False   True  False  False  False  False  False  False   \n",
       "4     False  ...  False  False  False  False  False  False  False  False   \n",
       "41    False  ...  False   True  False  False  False  False  False  False   \n",
       "46    False  ...  False   True  False  False  False  False  False  False   \n",
       "53    False  ...  False   True  False  False  False  False  False  False   \n",
       "58    False  ...  False   True  False  False  False  False  False  False   \n",
       "89    False  ...  False  False  False  False  False  False  False  False   \n",
       "103    True  ...  False  False  False  False  False  False  False   True   \n",
       "104   False  ...  False  False  False  False  False  False  False  False   \n",
       "269   False  ...  False  False  False  False  False  False  False  False   \n",
       "519   False  ...  False  False  False  False  False  False  False  False   \n",
       "521   False  ...  False  False  False  False  False  False  False  False   \n",
       "740   False  ...  False  False  False  False  False  False  False  False   \n",
       "996   False  ...  False  False  False  False  False  False  False  False   \n",
       "1070  False  ...  False  False  False  False  False  False  False  False   \n",
       "1236  False  ...  False  False  False  False  False  False  False  False   \n",
       "1290  False  ...  False  False  False  False  False  False  False  False   \n",
       "1538  False  ...  False  False  False  False  False  False  False  False   \n",
       "1637  False  ...  False  False  False  False  False  False  False  False   \n",
       "1657  False  ...  False  False  False  False  False  False  False  False   \n",
       "1661  False  ...  False  False  False  False  False  False  False  False   \n",
       "1725  False  ...  False  False  False  False  False  False  False  False   \n",
       "1893  False  ...  False  False  False  False  False  False  False  False   \n",
       "2125  False  ...  False  False  False  False  False  False  False  False   \n",
       "2149  False  ...  False  False  False  False  False  False  False  False   \n",
       "2345  False  ...   True  False  False  False  False  False  False  False   \n",
       "2453  False  ...  False  False  False  False  False  False  False  False   \n",
       "2581  False  ...  False  False  False  False  False  False  False  False   \n",
       "4049  False  ...  False  False  False  False  False  False  False  False   \n",
       "4212  False  ...  False  False  False  False  False  False  False  False   \n",
       "4226  False  ...  False  False  False  False  False  False  False  False   \n",
       "4314  False  ...  False  False  False  False  False  False  False  False   \n",
       "4315  False  ...  False  False  False  False  False  False  False  False   \n",
       "4330  False  ...  False   True  False  False  False  False  False  False   \n",
       "4331  False  ...  False   True  False  False  False  False  False  False   \n",
       "4525  False  ...  False  False  False  False  False  False  False  False   \n",
       "5496  False  ...  False   True  False  False  False  False  False  False   \n",
       "5980  False  ...  False  False  False  False  False  False  False  False   \n",
       "6127  False  ...  False  False  False  False  False  False  False  False   \n",
       "6357  False  ...  False  False  False  False  False  False  False  False   \n",
       "6370  False  ...  False  False  False  False  False  False  False  False   \n",
       "6569  False  ...  False  False  False  False  False  False  False  False   \n",
       "6752  False  ...  False   True  False  False  False  False   True  False   \n",
       "6757   True  ...  False   True  False  False  False  False  False  False   \n",
       "6821  False  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "0     False      4  \n",
       "1     False     10  \n",
       "4     False      1  \n",
       "41    False     13  \n",
       "46     True     24  \n",
       "53    False      9  \n",
       "58     True     19  \n",
       "89    False      1  \n",
       "103   False     13  \n",
       "104   False      1  \n",
       "269   False      1  \n",
       "519   False      1  \n",
       "521   False     10  \n",
       "740   False      1  \n",
       "996   False      1  \n",
       "1070  False      1  \n",
       "1236  False      1  \n",
       "1290  False      1  \n",
       "1538  False      2  \n",
       "1637  False      4  \n",
       "1657  False      1  \n",
       "1661  False      3  \n",
       "1725  False      2  \n",
       "1893  False      2  \n",
       "2125  False      1  \n",
       "2149  False      2  \n",
       "2345   True     35  \n",
       "2453  False      1  \n",
       "2581  False      1  \n",
       "4049  False      2  \n",
       "4212  False      2  \n",
       "4226  False      1  \n",
       "4314  False      4  \n",
       "4315  False      5  \n",
       "4330   True     16  \n",
       "4331   True     13  \n",
       "4525  False      5  \n",
       "5496  False      7  \n",
       "5980  False      4  \n",
       "6127  False      2  \n",
       "6357  False      1  \n",
       "6370  False      4  \n",
       "6569  False      4  \n",
       "6752   True      5  \n",
       "6757   True     12  \n",
       "6821  False      1  \n",
       "\n",
       "[46 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All replicates, bayexpress results\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HRA1</td>\n",
       "      <td>-5.206545</td>\n",
       "      <td>-0.564787</td>\n",
       "      <td>-240.338654</td>\n",
       "      <td>False</td>\n",
       "      <td>-258.581365</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ICR1</td>\n",
       "      <td>76.746125</td>\n",
       "      <td>0.504048</td>\n",
       "      <td>-194.077441</td>\n",
       "      <td>False</td>\n",
       "      <td>-196.467262</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>tY(GUA)J2</td>\n",
       "      <td>-7.312143</td>\n",
       "      <td>0.861839</td>\n",
       "      <td>-270.299503</td>\n",
       "      <td>False</td>\n",
       "      <td>-281.214963</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7122</th>\n",
       "      <td>tY(GUA)M1</td>\n",
       "      <td>-7.406574</td>\n",
       "      <td>0.022303</td>\n",
       "      <td>-262.736078</td>\n",
       "      <td>False</td>\n",
       "      <td>-275.253246</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7123</th>\n",
       "      <td>tY(GUA)M2</td>\n",
       "      <td>-7.727890</td>\n",
       "      <td>0.183767</td>\n",
       "      <td>-271.177492</td>\n",
       "      <td>False</td>\n",
       "      <td>-284.863866</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7124</th>\n",
       "      <td>tY(GUA)O</td>\n",
       "      <td>-7.622253</td>\n",
       "      <td>0.347266</td>\n",
       "      <td>-270.291196</td>\n",
       "      <td>False</td>\n",
       "      <td>-282.682788</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7125</th>\n",
       "      <td>tY(GUA)Q</td>\n",
       "      <td>-8.273720</td>\n",
       "      <td>-0.138161</td>\n",
       "      <td>-275.349735</td>\n",
       "      <td>False</td>\n",
       "      <td>-289.991744</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7126 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "0      15S_rRNA  -3.710645  0.274047 -107.138056   False    53.627445   \n",
       "1      21S_rRNA  -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "2          HRA1  -5.206545 -0.564787 -240.338654   False  -258.581365   \n",
       "3          ICR1  76.746125  0.504048 -194.077441   False  -196.467262   \n",
       "4          LSR1   0.451012  0.120243  251.787125    True   478.066414   \n",
       "...         ...        ...       ...         ...     ...          ...   \n",
       "7121  tY(GUA)J2  -7.312143  0.861839 -270.299503   False  -281.214963   \n",
       "7122  tY(GUA)M1  -7.406574  0.022303 -262.736078   False  -275.253246   \n",
       "7123  tY(GUA)M2  -7.727890  0.183767 -271.177492   False  -284.863866   \n",
       "7124   tY(GUA)O  -7.622253  0.347266 -270.291196   False  -282.682788   \n",
       "7125   tY(GUA)Q  -8.273720 -0.138161 -275.349735   False  -289.991744   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "1         True  \n",
       "2        False  \n",
       "3        False  \n",
       "4         True  \n",
       "...        ...  \n",
       "7121     False  \n",
       "7122     False  \n",
       "7123     False  \n",
       "7124     False  \n",
       "7125     False  \n",
       "\n",
       "[7126 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>24.026871</td>\n",
       "      <td>0.572910</td>\n",
       "      <td>-40.011620</td>\n",
       "      <td>False</td>\n",
       "      <td>-18.211398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SCR1</td>\n",
       "      <td>10.530223</td>\n",
       "      <td>0.125605</td>\n",
       "      <td>3463.535578</td>\n",
       "      <td>True</td>\n",
       "      <td>5939.249536</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>YAL026C-A</td>\n",
       "      <td>34.843922</td>\n",
       "      <td>0.889168</td>\n",
       "      <td>-177.688653</td>\n",
       "      <td>False</td>\n",
       "      <td>-174.081566</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>YAL037C-A</td>\n",
       "      <td>1.234011</td>\n",
       "      <td>-0.338439</td>\n",
       "      <td>-16.174943</td>\n",
       "      <td>False</td>\n",
       "      <td>-127.606812</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>YAL037C-B</td>\n",
       "      <td>-0.026307</td>\n",
       "      <td>-0.114575</td>\n",
       "      <td>402.523899</td>\n",
       "      <td>True</td>\n",
       "      <td>305.562530</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>YBL077W</td>\n",
       "      <td>8.840953</td>\n",
       "      <td>0.374675</td>\n",
       "      <td>-138.677913</td>\n",
       "      <td>False</td>\n",
       "      <td>-165.974434</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>YBR190W</td>\n",
       "      <td>-6.162852</td>\n",
       "      <td>0.068453</td>\n",
       "      <td>-127.176368</td>\n",
       "      <td>False</td>\n",
       "      <td>-127.193474</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>YBR191W-A</td>\n",
       "      <td>-2.073965</td>\n",
       "      <td>0.242930</td>\n",
       "      <td>-41.687820</td>\n",
       "      <td>False</td>\n",
       "      <td>-37.177753</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>YCR021C</td>\n",
       "      <td>6285.979055</td>\n",
       "      <td>-2.312448</td>\n",
       "      <td>3075.033017</td>\n",
       "      <td>True</td>\n",
       "      <td>-200.094307</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YDL158C</td>\n",
       "      <td>63.381742</td>\n",
       "      <td>0.722901</td>\n",
       "      <td>-134.671667</td>\n",
       "      <td>False</td>\n",
       "      <td>-110.514422</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>YDL228C</td>\n",
       "      <td>-6.080882</td>\n",
       "      <td>-0.076954</td>\n",
       "      <td>-97.659005</td>\n",
       "      <td>False</td>\n",
       "      <td>-132.975270</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>YDR133C</td>\n",
       "      <td>633.970158</td>\n",
       "      <td>-0.796620</td>\n",
       "      <td>1321.491205</td>\n",
       "      <td>True</td>\n",
       "      <td>364.619590</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>YDR183C-A</td>\n",
       "      <td>11.146833</td>\n",
       "      <td>-0.700943</td>\n",
       "      <td>-142.305452</td>\n",
       "      <td>False</td>\n",
       "      <td>-207.136546</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>YDR406W-A</td>\n",
       "      <td>-1.338486</td>\n",
       "      <td>-0.434386</td>\n",
       "      <td>-163.390623</td>\n",
       "      <td>False</td>\n",
       "      <td>-218.090466</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>YDR504C</td>\n",
       "      <td>73.845677</td>\n",
       "      <td>0.858922</td>\n",
       "      <td>-105.658685</td>\n",
       "      <td>False</td>\n",
       "      <td>-111.919707</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>YDR523C</td>\n",
       "      <td>12.770130</td>\n",
       "      <td>-0.403779</td>\n",
       "      <td>-50.576683</td>\n",
       "      <td>False</td>\n",
       "      <td>-223.229099</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>YDR524W-C</td>\n",
       "      <td>2433.134417</td>\n",
       "      <td>0.599017</td>\n",
       "      <td>6596.560889</td>\n",
       "      <td>True</td>\n",
       "      <td>7411.509121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>YEL033W</td>\n",
       "      <td>23.839270</td>\n",
       "      <td>-0.219443</td>\n",
       "      <td>530.392958</td>\n",
       "      <td>True</td>\n",
       "      <td>191.684677</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>YER091C</td>\n",
       "      <td>16720.982384</td>\n",
       "      <td>1.209366</td>\n",
       "      <td>11172.902928</td>\n",
       "      <td>True</td>\n",
       "      <td>208.046987</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>YFR032C</td>\n",
       "      <td>-2.313784</td>\n",
       "      <td>-0.167096</td>\n",
       "      <td>10.849885</td>\n",
       "      <td>True</td>\n",
       "      <td>-174.178322</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>YFR052C-A</td>\n",
       "      <td>518.610442</td>\n",
       "      <td>-0.985185</td>\n",
       "      <td>945.154728</td>\n",
       "      <td>True</td>\n",
       "      <td>54.584141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>YGL184C</td>\n",
       "      <td>19.779086</td>\n",
       "      <td>0.267701</td>\n",
       "      <td>1232.526242</td>\n",
       "      <td>True</td>\n",
       "      <td>-187.281082</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2453</th>\n",
       "      <td>YGR025W</td>\n",
       "      <td>-0.381789</td>\n",
       "      <td>0.266660</td>\n",
       "      <td>-130.960566</td>\n",
       "      <td>False</td>\n",
       "      <td>-157.582010</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>YGR142W</td>\n",
       "      <td>-2.131331</td>\n",
       "      <td>0.076279</td>\n",
       "      <td>473.880712</td>\n",
       "      <td>True</td>\n",
       "      <td>-51.428615</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4049</th>\n",
       "      <td>YKR080W</td>\n",
       "      <td>11373.906779</td>\n",
       "      <td>-2.199797</td>\n",
       "      <td>5857.539836</td>\n",
       "      <td>True</td>\n",
       "      <td>-79.673667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>YLR062C</td>\n",
       "      <td>-5.711755</td>\n",
       "      <td>-0.087536</td>\n",
       "      <td>-82.288570</td>\n",
       "      <td>False</td>\n",
       "      <td>-140.545094</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>YLR076C</td>\n",
       "      <td>52.271901</td>\n",
       "      <td>0.295462</td>\n",
       "      <td>447.226573</td>\n",
       "      <td>True</td>\n",
       "      <td>230.348063</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4314</th>\n",
       "      <td>YLR154W-E</td>\n",
       "      <td>-3.151336</td>\n",
       "      <td>-0.176792</td>\n",
       "      <td>-60.248108</td>\n",
       "      <td>False</td>\n",
       "      <td>-149.662561</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>YLR154W-F</td>\n",
       "      <td>5.995940</td>\n",
       "      <td>0.364397</td>\n",
       "      <td>-92.559879</td>\n",
       "      <td>False</td>\n",
       "      <td>-177.842838</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4330</th>\n",
       "      <td>YLR162W</td>\n",
       "      <td>119.338212</td>\n",
       "      <td>-0.268336</td>\n",
       "      <td>5959.704092</td>\n",
       "      <td>True</td>\n",
       "      <td>5323.348914</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>YLR162W-A</td>\n",
       "      <td>-2.223327</td>\n",
       "      <td>-0.059773</td>\n",
       "      <td>2858.237808</td>\n",
       "      <td>True</td>\n",
       "      <td>3645.853301</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>YLR342W-A</td>\n",
       "      <td>99.672642</td>\n",
       "      <td>0.714200</td>\n",
       "      <td>53.416615</td>\n",
       "      <td>True</td>\n",
       "      <td>194.250092</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5496</th>\n",
       "      <td>YNL303W</td>\n",
       "      <td>-5.296007</td>\n",
       "      <td>0.079900</td>\n",
       "      <td>102.367316</td>\n",
       "      <td>True</td>\n",
       "      <td>-47.590184</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5980</th>\n",
       "      <td>YOR169C</td>\n",
       "      <td>-6.487829</td>\n",
       "      <td>-0.051168</td>\n",
       "      <td>-139.559004</td>\n",
       "      <td>False</td>\n",
       "      <td>-175.662102</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>YOR309C</td>\n",
       "      <td>-5.763854</td>\n",
       "      <td>-0.040480</td>\n",
       "      <td>124.276754</td>\n",
       "      <td>True</td>\n",
       "      <td>-4.400978</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>YPL130W</td>\n",
       "      <td>7.958170</td>\n",
       "      <td>0.425944</td>\n",
       "      <td>-94.156259</td>\n",
       "      <td>False</td>\n",
       "      <td>-173.879273</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370</th>\n",
       "      <td>YPL142C</td>\n",
       "      <td>-5.468159</td>\n",
       "      <td>0.088311</td>\n",
       "      <td>-42.710655</td>\n",
       "      <td>False</td>\n",
       "      <td>-95.295908</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>YPR044C</td>\n",
       "      <td>-5.552581</td>\n",
       "      <td>0.073819</td>\n",
       "      <td>67.995942</td>\n",
       "      <td>True</td>\n",
       "      <td>-30.565124</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>-6.562044</td>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-49.290008</td>\n",
       "      <td>False</td>\n",
       "      <td>-63.762229</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>-5.961317</td>\n",
       "      <td>-0.049198</td>\n",
       "      <td>-97.702962</td>\n",
       "      <td>False</td>\n",
       "      <td>-164.859374</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name         BF_21        FC      BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "0      15S_rRNA     -3.710645  0.274047   -107.138056   False    53.627445   \n",
       "1      21S_rRNA     -1.302642  0.147177    362.115507    True  1052.203968   \n",
       "4          LSR1      0.451012  0.120243    251.787125    True   478.066414   \n",
       "41       RDN5-1     13.774601  0.316040    246.914584    True   521.186933   \n",
       "46       RDN5-6     15.230943  0.469612    172.850686    True   347.304031   \n",
       "53        RUF21     24.026871  0.572910    -40.011620   False   -18.211398   \n",
       "58         SCR1     10.530223  0.125605   3463.535578    True  5939.249536   \n",
       "89    YAL026C-A     34.843922  0.889168   -177.688653   False  -174.081566   \n",
       "103   YAL037C-A      1.234011 -0.338439    -16.174943   False  -127.606812   \n",
       "104   YAL037C-B     -0.026307 -0.114575    402.523899    True   305.562530   \n",
       "269     YBL077W      8.840953  0.374675   -138.677913   False  -165.974434   \n",
       "519     YBR190W     -6.162852  0.068453   -127.176368   False  -127.193474   \n",
       "521   YBR191W-A     -2.073965  0.242930    -41.687820   False   -37.177753   \n",
       "740     YCR021C   6285.979055 -2.312448   3075.033017    True  -200.094307   \n",
       "996     YDL158C     63.381742  0.722901   -134.671667   False  -110.514422   \n",
       "1070    YDL228C     -6.080882 -0.076954    -97.659005   False  -132.975270   \n",
       "1236    YDR133C    633.970158 -0.796620   1321.491205    True   364.619590   \n",
       "1290  YDR183C-A     11.146833 -0.700943   -142.305452   False  -207.136546   \n",
       "1538  YDR406W-A     -1.338486 -0.434386   -163.390623   False  -218.090466   \n",
       "1637    YDR504C     73.845677  0.858922   -105.658685   False  -111.919707   \n",
       "1657    YDR523C     12.770130 -0.403779    -50.576683   False  -223.229099   \n",
       "1661  YDR524W-C   2433.134417  0.599017   6596.560889    True  7411.509121   \n",
       "1725    YEL033W     23.839270 -0.219443    530.392958    True   191.684677   \n",
       "1893    YER091C  16720.982384  1.209366  11172.902928    True   208.046987   \n",
       "2125    YFR032C     -2.313784 -0.167096     10.849885    True  -174.178322   \n",
       "2149  YFR052C-A    518.610442 -0.985185    945.154728    True    54.584141   \n",
       "2345    YGL184C     19.779086  0.267701   1232.526242    True  -187.281082   \n",
       "2453    YGR025W     -0.381789  0.266660   -130.960566   False  -157.582010   \n",
       "2581    YGR142W     -2.131331  0.076279    473.880712    True   -51.428615   \n",
       "4049    YKR080W  11373.906779 -2.199797   5857.539836    True   -79.673667   \n",
       "4212    YLR062C     -5.711755 -0.087536    -82.288570   False  -140.545094   \n",
       "4226    YLR076C     52.271901  0.295462    447.226573    True   230.348063   \n",
       "4314  YLR154W-E     -3.151336 -0.176792    -60.248108   False  -149.662561   \n",
       "4315  YLR154W-F      5.995940  0.364397    -92.559879   False  -177.842838   \n",
       "4330    YLR162W    119.338212 -0.268336   5959.704092    True  5323.348914   \n",
       "4331  YLR162W-A     -2.223327 -0.059773   2858.237808    True  3645.853301   \n",
       "4525  YLR342W-A     99.672642  0.714200     53.416615    True   194.250092   \n",
       "5496    YNL303W     -5.296007  0.079900    102.367316    True   -47.590184   \n",
       "5980    YOR169C     -6.487829 -0.051168   -139.559004   False  -175.662102   \n",
       "6127    YOR309C     -5.763854 -0.040480    124.276754    True    -4.400978   \n",
       "6357    YPL130W      7.958170  0.425944    -94.156259   False  -173.879273   \n",
       "6370    YPL142C     -5.468159  0.088311    -42.710655   False   -95.295908   \n",
       "6569    YPR044C     -5.552581  0.073819     67.995942    True   -30.565124   \n",
       "6752     snR17a      7.383833  0.329308     28.421292    True   239.879652   \n",
       "6757     snR190     -6.562044 -0.001634    -49.290008   False   -63.762229   \n",
       "6821      snR83     -5.961317 -0.049198    -97.702962   False  -164.859374   \n",
       "\n",
       "      CIG_Snf2  \n",
       "0         True  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "53       False  \n",
       "58        True  \n",
       "89       False  \n",
       "103      False  \n",
       "104       True  \n",
       "269      False  \n",
       "519      False  \n",
       "521      False  \n",
       "740      False  \n",
       "996      False  \n",
       "1070     False  \n",
       "1236      True  \n",
       "1290     False  \n",
       "1538     False  \n",
       "1637     False  \n",
       "1657     False  \n",
       "1661      True  \n",
       "1725      True  \n",
       "1893      True  \n",
       "2125     False  \n",
       "2149      True  \n",
       "2345     False  \n",
       "2453     False  \n",
       "2581     False  \n",
       "4049     False  \n",
       "4212     False  \n",
       "4226      True  \n",
       "4314     False  \n",
       "4315     False  \n",
       "4330      True  \n",
       "4331      True  \n",
       "4525      True  \n",
       "5496     False  \n",
       "5980     False  \n",
       "6127     False  \n",
       "6357     False  \n",
       "6370     False  \n",
       "6569     False  \n",
       "6752      True  \n",
       "6757     False  \n",
       "6821     False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SCR1</td>\n",
       "      <td>10.530223</td>\n",
       "      <td>0.125605</td>\n",
       "      <td>3463.535578</td>\n",
       "      <td>True</td>\n",
       "      <td>5939.249536</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>YAL037C-B</td>\n",
       "      <td>-0.026307</td>\n",
       "      <td>-0.114575</td>\n",
       "      <td>402.523899</td>\n",
       "      <td>True</td>\n",
       "      <td>305.562530</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>YCR021C</td>\n",
       "      <td>6285.979055</td>\n",
       "      <td>-2.312448</td>\n",
       "      <td>3075.033017</td>\n",
       "      <td>True</td>\n",
       "      <td>-200.094307</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>YDR133C</td>\n",
       "      <td>633.970158</td>\n",
       "      <td>-0.796620</td>\n",
       "      <td>1321.491205</td>\n",
       "      <td>True</td>\n",
       "      <td>364.619590</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>YDR524W-C</td>\n",
       "      <td>2433.134417</td>\n",
       "      <td>0.599017</td>\n",
       "      <td>6596.560889</td>\n",
       "      <td>True</td>\n",
       "      <td>7411.509121</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>YEL033W</td>\n",
       "      <td>23.839270</td>\n",
       "      <td>-0.219443</td>\n",
       "      <td>530.392958</td>\n",
       "      <td>True</td>\n",
       "      <td>191.684677</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>YER091C</td>\n",
       "      <td>16720.982384</td>\n",
       "      <td>1.209366</td>\n",
       "      <td>11172.902928</td>\n",
       "      <td>True</td>\n",
       "      <td>208.046987</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>YFR032C</td>\n",
       "      <td>-2.313784</td>\n",
       "      <td>-0.167096</td>\n",
       "      <td>10.849885</td>\n",
       "      <td>True</td>\n",
       "      <td>-174.178322</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>YFR052C-A</td>\n",
       "      <td>518.610442</td>\n",
       "      <td>-0.985185</td>\n",
       "      <td>945.154728</td>\n",
       "      <td>True</td>\n",
       "      <td>54.584141</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>YGL184C</td>\n",
       "      <td>19.779086</td>\n",
       "      <td>0.267701</td>\n",
       "      <td>1232.526242</td>\n",
       "      <td>True</td>\n",
       "      <td>-187.281082</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2581</th>\n",
       "      <td>YGR142W</td>\n",
       "      <td>-2.131331</td>\n",
       "      <td>0.076279</td>\n",
       "      <td>473.880712</td>\n",
       "      <td>True</td>\n",
       "      <td>-51.428615</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4049</th>\n",
       "      <td>YKR080W</td>\n",
       "      <td>11373.906779</td>\n",
       "      <td>-2.199797</td>\n",
       "      <td>5857.539836</td>\n",
       "      <td>True</td>\n",
       "      <td>-79.673667</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>YLR076C</td>\n",
       "      <td>52.271901</td>\n",
       "      <td>0.295462</td>\n",
       "      <td>447.226573</td>\n",
       "      <td>True</td>\n",
       "      <td>230.348063</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4330</th>\n",
       "      <td>YLR162W</td>\n",
       "      <td>119.338212</td>\n",
       "      <td>-0.268336</td>\n",
       "      <td>5959.704092</td>\n",
       "      <td>True</td>\n",
       "      <td>5323.348914</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4331</th>\n",
       "      <td>YLR162W-A</td>\n",
       "      <td>-2.223327</td>\n",
       "      <td>-0.059773</td>\n",
       "      <td>2858.237808</td>\n",
       "      <td>True</td>\n",
       "      <td>3645.853301</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>YLR342W-A</td>\n",
       "      <td>99.672642</td>\n",
       "      <td>0.714200</td>\n",
       "      <td>53.416615</td>\n",
       "      <td>True</td>\n",
       "      <td>194.250092</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5496</th>\n",
       "      <td>YNL303W</td>\n",
       "      <td>-5.296007</td>\n",
       "      <td>0.079900</td>\n",
       "      <td>102.367316</td>\n",
       "      <td>True</td>\n",
       "      <td>-47.590184</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6127</th>\n",
       "      <td>YOR309C</td>\n",
       "      <td>-5.763854</td>\n",
       "      <td>-0.040480</td>\n",
       "      <td>124.276754</td>\n",
       "      <td>True</td>\n",
       "      <td>-4.400978</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6569</th>\n",
       "      <td>YPR044C</td>\n",
       "      <td>-5.552581</td>\n",
       "      <td>0.073819</td>\n",
       "      <td>67.995942</td>\n",
       "      <td>True</td>\n",
       "      <td>-30.565124</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name         BF_21        FC      BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "1      21S_rRNA     -1.302642  0.147177    362.115507    True  1052.203968   \n",
       "4          LSR1      0.451012  0.120243    251.787125    True   478.066414   \n",
       "41       RDN5-1     13.774601  0.316040    246.914584    True   521.186933   \n",
       "46       RDN5-6     15.230943  0.469612    172.850686    True   347.304031   \n",
       "58         SCR1     10.530223  0.125605   3463.535578    True  5939.249536   \n",
       "104   YAL037C-B     -0.026307 -0.114575    402.523899    True   305.562530   \n",
       "740     YCR021C   6285.979055 -2.312448   3075.033017    True  -200.094307   \n",
       "1236    YDR133C    633.970158 -0.796620   1321.491205    True   364.619590   \n",
       "1661  YDR524W-C   2433.134417  0.599017   6596.560889    True  7411.509121   \n",
       "1725    YEL033W     23.839270 -0.219443    530.392958    True   191.684677   \n",
       "1893    YER091C  16720.982384  1.209366  11172.902928    True   208.046987   \n",
       "2125    YFR032C     -2.313784 -0.167096     10.849885    True  -174.178322   \n",
       "2149  YFR052C-A    518.610442 -0.985185    945.154728    True    54.584141   \n",
       "2345    YGL184C     19.779086  0.267701   1232.526242    True  -187.281082   \n",
       "2581    YGR142W     -2.131331  0.076279    473.880712    True   -51.428615   \n",
       "4049    YKR080W  11373.906779 -2.199797   5857.539836    True   -79.673667   \n",
       "4226    YLR076C     52.271901  0.295462    447.226573    True   230.348063   \n",
       "4330    YLR162W    119.338212 -0.268336   5959.704092    True  5323.348914   \n",
       "4331  YLR162W-A     -2.223327 -0.059773   2858.237808    True  3645.853301   \n",
       "4525  YLR342W-A     99.672642  0.714200     53.416615    True   194.250092   \n",
       "5496    YNL303W     -5.296007  0.079900    102.367316    True   -47.590184   \n",
       "6127    YOR309C     -5.763854 -0.040480    124.276754    True    -4.400978   \n",
       "6569    YPR044C     -5.552581  0.073819     67.995942    True   -30.565124   \n",
       "6752     snR17a      7.383833  0.329308     28.421292    True   239.879652   \n",
       "\n",
       "      CIG_Snf2  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "58        True  \n",
       "104       True  \n",
       "740      False  \n",
       "1236      True  \n",
       "1661      True  \n",
       "1725      True  \n",
       "1893      True  \n",
       "2125     False  \n",
       "2149      True  \n",
       "2345     False  \n",
       "2581     False  \n",
       "4049     False  \n",
       "4226      True  \n",
       "4330      True  \n",
       "4331      True  \n",
       "4525      True  \n",
       "5496     False  \n",
       "6127     False  \n",
       "6569     False  \n",
       "6752      True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Display only the genes that got hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15S_rRNA</td>\n",
       "      <td>-3.710645</td>\n",
       "      <td>0.274047</td>\n",
       "      <td>-107.138056</td>\n",
       "      <td>False</td>\n",
       "      <td>53.627445</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RUF21</td>\n",
       "      <td>24.026871</td>\n",
       "      <td>0.572910</td>\n",
       "      <td>-40.011620</td>\n",
       "      <td>False</td>\n",
       "      <td>-18.211398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>YAL026C-A</td>\n",
       "      <td>34.843922</td>\n",
       "      <td>0.889168</td>\n",
       "      <td>-177.688653</td>\n",
       "      <td>False</td>\n",
       "      <td>-174.081566</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>YAL037C-A</td>\n",
       "      <td>1.234011</td>\n",
       "      <td>-0.338439</td>\n",
       "      <td>-16.174943</td>\n",
       "      <td>False</td>\n",
       "      <td>-127.606812</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>YBL077W</td>\n",
       "      <td>8.840953</td>\n",
       "      <td>0.374675</td>\n",
       "      <td>-138.677913</td>\n",
       "      <td>False</td>\n",
       "      <td>-165.974434</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>YBR190W</td>\n",
       "      <td>-6.162852</td>\n",
       "      <td>0.068453</td>\n",
       "      <td>-127.176368</td>\n",
       "      <td>False</td>\n",
       "      <td>-127.193474</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>YBR191W-A</td>\n",
       "      <td>-2.073965</td>\n",
       "      <td>0.242930</td>\n",
       "      <td>-41.687820</td>\n",
       "      <td>False</td>\n",
       "      <td>-37.177753</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YDL158C</td>\n",
       "      <td>63.381742</td>\n",
       "      <td>0.722901</td>\n",
       "      <td>-134.671667</td>\n",
       "      <td>False</td>\n",
       "      <td>-110.514422</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>YDL228C</td>\n",
       "      <td>-6.080882</td>\n",
       "      <td>-0.076954</td>\n",
       "      <td>-97.659005</td>\n",
       "      <td>False</td>\n",
       "      <td>-132.975270</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>YDR183C-A</td>\n",
       "      <td>11.146833</td>\n",
       "      <td>-0.700943</td>\n",
       "      <td>-142.305452</td>\n",
       "      <td>False</td>\n",
       "      <td>-207.136546</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>YDR406W-A</td>\n",
       "      <td>-1.338486</td>\n",
       "      <td>-0.434386</td>\n",
       "      <td>-163.390623</td>\n",
       "      <td>False</td>\n",
       "      <td>-218.090466</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>YDR504C</td>\n",
       "      <td>73.845677</td>\n",
       "      <td>0.858922</td>\n",
       "      <td>-105.658685</td>\n",
       "      <td>False</td>\n",
       "      <td>-111.919707</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>YDR523C</td>\n",
       "      <td>12.770130</td>\n",
       "      <td>-0.403779</td>\n",
       "      <td>-50.576683</td>\n",
       "      <td>False</td>\n",
       "      <td>-223.229099</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2453</th>\n",
       "      <td>YGR025W</td>\n",
       "      <td>-0.381789</td>\n",
       "      <td>0.266660</td>\n",
       "      <td>-130.960566</td>\n",
       "      <td>False</td>\n",
       "      <td>-157.582010</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212</th>\n",
       "      <td>YLR062C</td>\n",
       "      <td>-5.711755</td>\n",
       "      <td>-0.087536</td>\n",
       "      <td>-82.288570</td>\n",
       "      <td>False</td>\n",
       "      <td>-140.545094</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4314</th>\n",
       "      <td>YLR154W-E</td>\n",
       "      <td>-3.151336</td>\n",
       "      <td>-0.176792</td>\n",
       "      <td>-60.248108</td>\n",
       "      <td>False</td>\n",
       "      <td>-149.662561</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>YLR154W-F</td>\n",
       "      <td>5.995940</td>\n",
       "      <td>0.364397</td>\n",
       "      <td>-92.559879</td>\n",
       "      <td>False</td>\n",
       "      <td>-177.842838</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5980</th>\n",
       "      <td>YOR169C</td>\n",
       "      <td>-6.487829</td>\n",
       "      <td>-0.051168</td>\n",
       "      <td>-139.559004</td>\n",
       "      <td>False</td>\n",
       "      <td>-175.662102</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>YPL130W</td>\n",
       "      <td>7.958170</td>\n",
       "      <td>0.425944</td>\n",
       "      <td>-94.156259</td>\n",
       "      <td>False</td>\n",
       "      <td>-173.879273</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370</th>\n",
       "      <td>YPL142C</td>\n",
       "      <td>-5.468159</td>\n",
       "      <td>0.088311</td>\n",
       "      <td>-42.710655</td>\n",
       "      <td>False</td>\n",
       "      <td>-95.295908</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6757</th>\n",
       "      <td>snR190</td>\n",
       "      <td>-6.562044</td>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-49.290008</td>\n",
       "      <td>False</td>\n",
       "      <td>-63.762229</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6821</th>\n",
       "      <td>snR83</td>\n",
       "      <td>-5.961317</td>\n",
       "      <td>-0.049198</td>\n",
       "      <td>-97.702962</td>\n",
       "      <td>False</td>\n",
       "      <td>-164.859374</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      BF_21        FC    BF_k1_WT  CIG_WT  BF_k1_Snf2  CIG_Snf2\n",
       "0      15S_rRNA  -3.710645  0.274047 -107.138056   False   53.627445      True\n",
       "53        RUF21  24.026871  0.572910  -40.011620   False  -18.211398     False\n",
       "89    YAL026C-A  34.843922  0.889168 -177.688653   False -174.081566     False\n",
       "103   YAL037C-A   1.234011 -0.338439  -16.174943   False -127.606812     False\n",
       "269     YBL077W   8.840953  0.374675 -138.677913   False -165.974434     False\n",
       "519     YBR190W  -6.162852  0.068453 -127.176368   False -127.193474     False\n",
       "521   YBR191W-A  -2.073965  0.242930  -41.687820   False  -37.177753     False\n",
       "996     YDL158C  63.381742  0.722901 -134.671667   False -110.514422     False\n",
       "1070    YDL228C  -6.080882 -0.076954  -97.659005   False -132.975270     False\n",
       "1290  YDR183C-A  11.146833 -0.700943 -142.305452   False -207.136546     False\n",
       "1538  YDR406W-A  -1.338486 -0.434386 -163.390623   False -218.090466     False\n",
       "1637    YDR504C  73.845677  0.858922 -105.658685   False -111.919707     False\n",
       "1657    YDR523C  12.770130 -0.403779  -50.576683   False -223.229099     False\n",
       "2453    YGR025W  -0.381789  0.266660 -130.960566   False -157.582010     False\n",
       "4212    YLR062C  -5.711755 -0.087536  -82.288570   False -140.545094     False\n",
       "4314  YLR154W-E  -3.151336 -0.176792  -60.248108   False -149.662561     False\n",
       "4315  YLR154W-F   5.995940  0.364397  -92.559879   False -177.842838     False\n",
       "5980    YOR169C  -6.487829 -0.051168 -139.559004   False -175.662102     False\n",
       "6357    YPL130W   7.958170  0.425944  -94.156259   False -173.879273     False\n",
       "6370    YPL142C  -5.468159  0.088311  -42.710655   False  -95.295908     False\n",
       "6757     snR190  -6.562044 -0.001634  -49.290008   False  -63.762229     False\n",
       "6821      snR83  -5.961317 -0.049198  -97.702962   False -164.859374     False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among the genes that produced 0 hits, where there any genes that would be identified as variable by BF_k1 > 1?\n"
     ]
    },
    {
     "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RUF5-1</td>\n",
       "      <td>-1.191579</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>232.720615</td>\n",
       "      <td>True</td>\n",
       "      <td>4.230186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>RUF5-2</td>\n",
       "      <td>-3.139384</td>\n",
       "      <td>0.077677</td>\n",
       "      <td>105.653391</td>\n",
       "      <td>True</td>\n",
       "      <td>-93.026862</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SRG1</td>\n",
       "      <td>365.733360</td>\n",
       "      <td>-0.939803</td>\n",
       "      <td>1.190075</td>\n",
       "      <td>True</td>\n",
       "      <td>-141.263713</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>TLC1</td>\n",
       "      <td>777.763124</td>\n",
       "      <td>0.472738</td>\n",
       "      <td>284.895344</td>\n",
       "      <td>True</td>\n",
       "      <td>178.488220</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>YAL003W</td>\n",
       "      <td>6673.475968</td>\n",
       "      <td>-0.594133</td>\n",
       "      <td>954.663405</td>\n",
       "      <td>True</td>\n",
       "      <td>259.131618</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6727</th>\n",
       "      <td>YPR187W</td>\n",
       "      <td>39.001492</td>\n",
       "      <td>-0.117950</td>\n",
       "      <td>100.066660</td>\n",
       "      <td>True</td>\n",
       "      <td>-108.538938</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>YPR190C</td>\n",
       "      <td>48.045047</td>\n",
       "      <td>-0.216275</td>\n",
       "      <td>135.716958</td>\n",
       "      <td>True</td>\n",
       "      <td>-136.905959</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>218.989419</td>\n",
       "      <td>0.279871</td>\n",
       "      <td>251.986788</td>\n",
       "      <td>True</td>\n",
       "      <td>-38.705868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6761</th>\n",
       "      <td>snR30</td>\n",
       "      <td>20.556922</td>\n",
       "      <td>0.159709</td>\n",
       "      <td>2.616569</td>\n",
       "      <td>True</td>\n",
       "      <td>85.355966</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1578 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name        BF_21        FC    BF_k1_WT  CIG_WT  BF_k1_Snf2  \\\n",
       "56       RUF5-1    -1.191579  0.080133  232.720615    True    4.230186   \n",
       "57       RUF5-2    -3.139384  0.077677  105.653391    True  -93.026862   \n",
       "59         SRG1   365.733360 -0.939803    1.190075    True -141.263713   \n",
       "60         TLC1   777.763124  0.472738  284.895344    True  178.488220   \n",
       "63      YAL003W  6673.475968 -0.594133  954.663405    True  259.131618   \n",
       "...         ...          ...       ...         ...     ...         ...   \n",
       "6727    YPR187W    39.001492 -0.117950  100.066660    True -108.538938   \n",
       "6730    YPR190C    48.045047 -0.216275  135.716958    True -136.905959   \n",
       "6731    YPR191W   218.989419  0.279871  251.986788    True  -38.705868   \n",
       "6761      snR30    20.556922  0.159709    2.616569    True   85.355966   \n",
       "6824      snR86   272.275536  0.372995  120.984196    True  160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "56        True  \n",
       "57       False  \n",
       "59       False  \n",
       "60        True  \n",
       "63        True  \n",
       "...        ...  \n",
       "6727     False  \n",
       "6730     False  \n",
       "6731     False  \n",
       "6761      True  \n",
       "6824      True  \n",
       "\n",
       "[1578 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R10 means number of replicates in 100 bootstrapping iterations is 10\n",
    "# FC > 1\n",
    "# got hits (any)\n",
    "\n",
    "# let's find out which genes we found to produce hits in the control and see if we can identify them with Bayes factors for consistency\n",
    "\n",
    "print('Find genes that got hits in the control experiment')\n",
    "display(CONTROL_R10_bayexpress_FC1.loc[CONTROL_R10_bayexpress_FC1.Ntrue > 0])\n",
    "\n",
    "# importing results from differential gene expression anaylsis to check what they are\n",
    "RALL_bayexpress = pd.read_csv('RALL_bayexpress.csv', index_col=0)\n",
    "\n",
    "print('All replicates, bayexpress results')\n",
    "display(RALL_bayexpress)\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC1.loc[CONTROL_R10_bayexpress_FC1.Ntrue > 0].index)])\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment + where identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC1.loc[CONTROL_R10_bayexpress_FC1.Ntrue > 0].index)].loc[(RALL_bayexpress.BF_k1_WT > 1)])\n",
    "\n",
    "print('Display only the genes that got hits in the control experiment + where NOT identified with bayes factors for consistency of replicates in the WT')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC1.loc[CONTROL_R10_bayexpress_FC1.Ntrue > 0].index)].loc[(RALL_bayexpress.BF_k1_WT < 1)])\n",
    "\n",
    "print('Among the genes that produced 0 hits, where there any genes that would be identified as variable by BF_k1 > 1?')\n",
    "display(RALL_bayexpress.iloc[(CONTROL_R10_bayexpress_FC1.loc[CONTROL_R10_bayexpress_FC1.Ntrue == 0].index)].loc[(RALL_bayexpress.BF_k1_WT > 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>locus_name</th>\n",
       "      <th>BF_21</th>\n",
       "      <th>FC</th>\n",
       "      <th>BF_k1_WT</th>\n",
       "      <th>CIG_WT</th>\n",
       "      <th>BF_k1_Snf2</th>\n",
       "      <th>CIG_Snf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>-1.302642</td>\n",
       "      <td>0.147177</td>\n",
       "      <td>362.115507</td>\n",
       "      <td>True</td>\n",
       "      <td>1052.203968</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>0.451012</td>\n",
       "      <td>0.120243</td>\n",
       "      <td>251.787125</td>\n",
       "      <td>True</td>\n",
       "      <td>478.066414</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>13.774601</td>\n",
       "      <td>0.316040</td>\n",
       "      <td>246.914584</td>\n",
       "      <td>True</td>\n",
       "      <td>521.186933</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>15.230943</td>\n",
       "      <td>0.469612</td>\n",
       "      <td>172.850686</td>\n",
       "      <td>True</td>\n",
       "      <td>347.304031</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RUF5-1</td>\n",
       "      <td>-1.191579</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>232.720615</td>\n",
       "      <td>True</td>\n",
       "      <td>4.230186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>218.989419</td>\n",
       "      <td>0.279871</td>\n",
       "      <td>251.986788</td>\n",
       "      <td>True</td>\n",
       "      <td>-38.705868</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6745</th>\n",
       "      <td>YPR204W</td>\n",
       "      <td>814.266672</td>\n",
       "      <td>0.541523</td>\n",
       "      <td>-1.746145</td>\n",
       "      <td>True</td>\n",
       "      <td>37.268866</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>7.383833</td>\n",
       "      <td>0.329308</td>\n",
       "      <td>28.421292</td>\n",
       "      <td>True</td>\n",
       "      <td>239.879652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6761</th>\n",
       "      <td>snR30</td>\n",
       "      <td>20.556922</td>\n",
       "      <td>0.159709</td>\n",
       "      <td>2.616569</td>\n",
       "      <td>True</td>\n",
       "      <td>85.355966</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>272.275536</td>\n",
       "      <td>0.372995</td>\n",
       "      <td>120.984196</td>\n",
       "      <td>True</td>\n",
       "      <td>160.053022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1633 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name       BF_21        FC    BF_k1_WT  CIG_WT   BF_k1_Snf2  \\\n",
       "1      21S_rRNA   -1.302642  0.147177  362.115507    True  1052.203968   \n",
       "4          LSR1    0.451012  0.120243  251.787125    True   478.066414   \n",
       "41       RDN5-1   13.774601  0.316040  246.914584    True   521.186933   \n",
       "46       RDN5-6   15.230943  0.469612  172.850686    True   347.304031   \n",
       "56       RUF5-1   -1.191579  0.080133  232.720615    True     4.230186   \n",
       "...         ...         ...       ...         ...     ...          ...   \n",
       "6731    YPR191W  218.989419  0.279871  251.986788    True   -38.705868   \n",
       "6745    YPR204W  814.266672  0.541523   -1.746145    True    37.268866   \n",
       "6752     snR17a    7.383833  0.329308   28.421292    True   239.879652   \n",
       "6761      snR30   20.556922  0.159709    2.616569    True    85.355966   \n",
       "6824      snR86  272.275536  0.372995  120.984196    True   160.053022   \n",
       "\n",
       "      CIG_Snf2  \n",
       "1         True  \n",
       "4         True  \n",
       "41        True  \n",
       "46        True  \n",
       "56        True  \n",
       "...        ...  \n",
       "6731     False  \n",
       "6745      True  \n",
       "6752      True  \n",
       "6761      True  \n",
       "6824      True  \n",
       "\n",
       "[1633 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>locus_name</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "      <th>Ntrue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21S_rRNA</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LSR1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>RDN5-1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>RDN5-6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RUF5-1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6731</th>\n",
       "      <td>YPR191W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6745</th>\n",
       "      <td>YPR204W</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6752</th>\n",
       "      <td>snR17a</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6761</th>\n",
       "      <td>snR30</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6824</th>\n",
       "      <td>snR86</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1633 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     locus_name      1      2      3      4      5      6      7      8  \\\n",
       "1      21S_rRNA  False   True  False   True   True  False   True  False   \n",
       "4          LSR1  False   True  False  False  False  False  False  False   \n",
       "41       RDN5-1  False   True  False  False  False   True  False  False   \n",
       "46       RDN5-6  False   True  False  False  False   True  False  False   \n",
       "56       RUF5-1  False   True  False  False   True   True  False   True   \n",
       "...         ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6731    YPR191W  False  False  False  False  False  False   True  False   \n",
       "6745    YPR204W  False  False   True  False  False   True  False   True   \n",
       "6752     snR17a  False   True  False  False  False  False  False  False   \n",
       "6761      snR30   True   True  False  False  False  False  False  False   \n",
       "6824      snR86  False  False  False  False  False   True   True  False   \n",
       "\n",
       "          9  ...     92     93     94     95     96     97     98     99  \\\n",
       "1     False  ...   True   True  False  False  False  False   True  False   \n",
       "4      True  ...  False   True  False  False  False  False   True  False   \n",
       "41    False  ...  False   True  False  False  False  False  False  False   \n",
       "46    False  ...  False   True  False  False  False  False  False  False   \n",
       "56     True  ...  False   True  False  False  False  False   True   True   \n",
       "...     ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "6731  False  ...  False  False  False  False  False   True  False  False   \n",
       "6745  False  ...  False  False  False  False  False  False  False  False   \n",
       "6752   True  ...  False   True  False  False  False  False   True  False   \n",
       "6761  False  ...  False   True  False  False  False  False  False  False   \n",
       "6824  False  ...  False  False  False  False  False  False   True  False   \n",
       "\n",
       "        100  Ntrue  \n",
       "1      True     39  \n",
       "4      True     36  \n",
       "41     True     26  \n",
       "46     True     26  \n",
       "56    False     31  \n",
       "...     ...    ...  \n",
       "6731   True     25  \n",
       "6745  False     14  \n",
       "6752   True     18  \n",
       "6761   True     10  \n",
       "6824  False     20  \n",
       "\n",
       "[1633 rows x 102 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparison whether bootstrapping BF_k1 for consistency identify the same genes as the control experiments\n",
    "\n",
    "# Importing the results from explore_clean_yeast_consistency.ipynb\n",
    "\n",
    "RALL_bayexpress_CIG_WT_union = pd.read_csv('RALL_bayexpress_CIG_WT_union.csv', index_col=0)\n",
    "\n",
    "display(RALL_bayexpress_CIG_WT_union)\n",
    "\n",
    "# all genes identified by boostrapping BF_k1 for consistency are producing hits, but not all hit producing genes are identified by bootstrapping BF_k1 for consistency\n",
    "display(CONTROL_R10_bayexpress_FC0.loc[CONTROL_R10_bayexpress_FC0.Ntrue > 0].loc[RALL_bayexpress_CIG_WT_union.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bayexpressbase",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}