tutorial_integrate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: INTEGRATE using ENGRO2 model\n",
    "\n",
    "This tutorial explains how to use INTEGRATE pipeline by using ENGRO2 as test model. All the pipeline can be faithfully followed in its entirety or intermediate files may be used in order to partially execute specific steps of the pipeline.  \n",
    "\n",
    "### Step 1. getGPRsFromModel\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/getGPRsFromModel.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/getGPRsFromModel.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelineDir = 'pipeline'\n",
    "import sys\n",
    "import os\n",
    "sys.path.append(pipelineDir)\n",
    "import genericLib as gL\n",
    "import matplotlib.image as img\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "workingDirs = gL.setWorkingDirs()\n",
    "RAWDIR = workingDirs[0]\n",
    "OUTDIR = workingDirs[2]\n",
    "MODELDIR = workingDirs[3]\n",
    "FIGUREDIR = workingDirs[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/getGPRsFromModel.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced output of Step 1 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>rule</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id rule\n",
       "0     EX_lac__L_e  NaN\n",
       "1     EX_glc__D_e  NaN\n",
       "2   EX_gluIN__L_e  NaN\n",
       "3  EX_gluOUT__L_e  NaN\n",
       "4     EX_gln__L_e  NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dfOutput1 = pd.read_csv(os.path.join(OUTDIR, 'ENGRO2_GPR.csv'), sep = '\\t')\n",
    "dfOutput1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. getRASscore\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/getRASscore.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/getRASscore.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/getRASscore.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced output of Step 2 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rxn</th>\n",
       "      <th>Cellule361_Rep1</th>\n",
       "      <th>Cellule361_Rep2</th>\n",
       "      <th>Cellule361_Rep3</th>\n",
       "      <th>MDA-MB-231_Rep1</th>\n",
       "      <th>MDA-MB-231_Rep2</th>\n",
       "      <th>MDA-MB-231_Rep3</th>\n",
       "      <th>MCF-7_Rep1</th>\n",
       "      <th>MCF-7_Rep2</th>\n",
       "      <th>MCF-7_Rep3</th>\n",
       "      <th>SK-BR-3_Rep1</th>\n",
       "      <th>SK-BR-3_Rep2</th>\n",
       "      <th>SK-BR-3_Rep3</th>\n",
       "      <th>MCF102A_Rep1</th>\n",
       "      <th>MCF102A_Rep2</th>\n",
       "      <th>MCF102A_Rep3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rxn  Cellule361_Rep1  Cellule361_Rep2  Cellule361_Rep3  \\\n",
       "0     EX_lac__L_e              NaN              NaN              NaN   \n",
       "1     EX_glc__D_e              NaN              NaN              NaN   \n",
       "2   EX_gluIN__L_e              NaN              NaN              NaN   \n",
       "3  EX_gluOUT__L_e              NaN              NaN              NaN   \n",
       "4     EX_gln__L_e              NaN              NaN              NaN   \n",
       "\n",
       "   MDA-MB-231_Rep1  MDA-MB-231_Rep2  MDA-MB-231_Rep3  MCF-7_Rep1  MCF-7_Rep2  \\\n",
       "0              NaN              NaN              NaN         NaN         NaN   \n",
       "1              NaN              NaN              NaN         NaN         NaN   \n",
       "2              NaN              NaN              NaN         NaN         NaN   \n",
       "3              NaN              NaN              NaN         NaN         NaN   \n",
       "4              NaN              NaN              NaN         NaN         NaN   \n",
       "\n",
       "   MCF-7_Rep3  SK-BR-3_Rep1  SK-BR-3_Rep2  SK-BR-3_Rep3  MCF102A_Rep1  \\\n",
       "0         NaN           NaN           NaN           NaN           NaN   \n",
       "1         NaN           NaN           NaN           NaN           NaN   \n",
       "2         NaN           NaN           NaN           NaN           NaN   \n",
       "3         NaN           NaN           NaN           NaN           NaN   \n",
       "4         NaN           NaN           NaN           NaN           NaN   \n",
       "\n",
       "   MCF102A_Rep2  MCF102A_Rep3  \n",
       "0           NaN           NaN  \n",
       "1           NaN           NaN  \n",
       "2           NaN           NaN  \n",
       "3           NaN           NaN  \n",
       "4           NaN           NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOutput1 = pd.read_csv(os.path.join(OUTDIR, 'ENGRO2_RAS.csv'), sep = '\\t')\n",
    "dfOutput1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: getNormalizedRAS\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/getNormalizedRAS.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/getNormalizedRAS.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python pipeline/getNormalizedRAS.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced output of Step 3 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rxn</th>\n",
       "      <th>media_MDAMB361</th>\n",
       "      <th>media_MDAMB231</th>\n",
       "      <th>media_MCF7</th>\n",
       "      <th>media_SKBR3</th>\n",
       "      <th>media_MCF102A</th>\n",
       "      <th>norm_MDAMB361</th>\n",
       "      <th>norm_MDAMB231</th>\n",
       "      <th>norm_MCF7</th>\n",
       "      <th>norm_SKBR3</th>\n",
       "      <th>norm_MCF102A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rxn  media_MDAMB361  media_MDAMB231  media_MCF7  media_SKBR3  \\\n",
       "0     EX_lac__L_e             1.0             1.0         1.0          1.0   \n",
       "1     EX_glc__D_e             1.0             1.0         1.0          1.0   \n",
       "2   EX_gluIN__L_e             1.0             1.0         1.0          1.0   \n",
       "3  EX_gluOUT__L_e             1.0             1.0         1.0          1.0   \n",
       "4     EX_gln__L_e             1.0             1.0         1.0          1.0   \n",
       "\n",
       "   media_MCF102A  norm_MDAMB361  norm_MDAMB231  norm_MCF7  norm_SKBR3  \\\n",
       "0            1.0            1.0            1.0        1.0         1.0   \n",
       "1            1.0            1.0            1.0        1.0         1.0   \n",
       "2            1.0            1.0            1.0        1.0         1.0   \n",
       "3            1.0            1.0            1.0        1.0         1.0   \n",
       "4            1.0            1.0            1.0        1.0         1.0   \n",
       "\n",
       "   norm_MCF102A  \n",
       "0           1.0  \n",
       "1           1.0  \n",
       "2           1.0  \n",
       "3           1.0  \n",
       "4           1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOutput1 = pd.read_csv(os.path.join(OUTDIR, 'ENGRO2_wNormalizedRAS.csv'), sep = '\\t')\n",
    "dfOutput1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: rasIntegration\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/rasIntegration.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/rasIntegration.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python pipeline/rasIntegration.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As proof of concept, one of the obtained SBML of Step 4 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpplqrx5bu.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpjg3byuny.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpqkvji8ui.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp_456zmle.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpxejet_bd.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpii1orfpm.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp_y_j769o.lp\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpazyx_yfq.lp\n",
      "Reading time = 0.02 seconds\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpa92ii6qf.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpttei_r1n.lp\n",
      "Reading time = 0.04 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpsxmf98dc.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmphmfdu81z.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpb111fuen.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpsyvgm3bu.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp3ldnxve2.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpdb1c3qe6.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpdvsbsq6z.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp4jutta7y.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmppgwz0ohy.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpfe33j4pe.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpumno7iz1.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmph7xtdist.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp26b7_xby.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpzugpgme_.lp\n",
      "Reading time = 0.04 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpz0aq07kr.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpj_2lpuy1.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpzw22hsi4.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpzmlsjgue.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpgtfby9nz.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp763lrtej.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpyi5izbv4.lp\n",
      "Reading time = 0.02 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpijdwzkuv.lp\n",
      "Reading time = 0.03 seconds\n",
      ": 416 rows, 994 columns, 3844 nonzeros\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
       "                <td><strong>Name</strong></td>\n",
       "                <td>dc</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Memory address</strong></td>\n",
       "                <td>0x028c977b8850</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of metabolites</strong></td>\n",
       "                <td>410</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of reactions</strong></td>\n",
       "                <td>494</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Number of groups</strong></td>\n",
       "                <td>0</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Objective expression</strong></td>\n",
       "                <td>1.0*Biomass - 1.0*Biomass_reverse_57a34</td>\n",
       "            </tr><tr>\n",
       "                <td><strong>Compartments</strong></td>\n",
       "                <td>cytosol, mitochondrium, extracellular</td>\n",
       "            </tr>\n",
       "          </table>"
      ],
      "text/plain": [
       "<Model dc at 0x28c977b8850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cobra as cb\n",
    "model = cb.io.read_sbml_model(os.path.join(MODELDIR, 'ENGRO2_MDAMB361.xml'))\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Models splitting\n",
    "Each cell relative model needs to be converted to a mat file in order to exploit the MATLAB function to convert model from the reversible into the irreversible format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcellLines = ['MCF102A', 'SKBR3', 'MCF7', 'MDAMB231', 'MDAMB361']\n",
    "for cellLine in lcellLines:\n",
    "    model = cb.io.read_sbml_model(os.path.join(MODELDIR, 'ENGRO2_' + cellLine + '.xml'))\n",
    "    cb.io.save_matlab_model(model, os.path.join(MODELDIR, 'ENGRO2_' + cellLine + '.mat'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following MATLAB functions are then exploited to split models into their irreversible format:\n",
    "```matlab\n",
    "model = readCbModel('./models/ENGRO2_MCF102A.mat')\n",
    "[modelIrrev, matchRev, rev2irrev, irrev2rev] = convertToIrreversible(model)\n",
    "outmodel = writeCbModel(modelIrrev, 'format','sbml', 'fileName', './models/ENGRO2_MCF102A_wIrrRxns.xml')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, We want to have the same reactions for all the models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/script_uniform_models.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/script_uniform_models.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python pipeline/script_uniform_models.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Step 6: randomSampling\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/randomSampling.py nSamples\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/randomSampling.py nSamples\n",
    "```\n",
    "Because time consuming according to the number of executed steps, users are recommended to run Step 6 externally to the Jupyter notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a proof of concept, we report an example of random sampling using one batch of 1000 samples, but we recommend to increase both the number and the dimension of the batches. In output/data_of_paper_1milion_of_samples we report the results for 10 batches of 100.000 samples each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCF102A\n",
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp587wkil1.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 415 rows, 1067 columns, 4091 nonzeros\n",
      "SKBR3\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp5hk4_osm.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 415 rows, 1067 columns, 4091 nonzeros\n",
      "MCF7\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmp5lq3k6bl.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 415 rows, 1067 columns, 4091 nonzeros\n",
      "MDAMB231\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpxlri1w88.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 415 rows, 1067 columns, 4091 nonzeros\n",
      "MDAMB361\n",
      "Read LP format model from file C:\\Users\\bruno\\AppData\\Local\\Temp\\tmpuc_diww3.lp\n",
      "Reading time = 0.01 seconds\n",
      ": 415 rows, 1067 columns, 4091 nonzeros\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/randomSampling.py 1000 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As proof of concept, one of the obtained files of Step 6 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>EX_lac__L_e</th>\n",
       "      <th>EX_glc__D_e_r</th>\n",
       "      <th>EX_gluIN__L_e</th>\n",
       "      <th>EX_gluOUT__L_e</th>\n",
       "      <th>EX_gln__L_e_r</th>\n",
       "      <th>EX_asp__L_e</th>\n",
       "      <th>DM_asp__L_e</th>\n",
       "      <th>EX_co2_e</th>\n",
       "      <th>EX_h_e_f</th>\n",
       "      <th>EX_h2o_e_f</th>\n",
       "      <th>...</th>\n",
       "      <th>MTHFD2i_b</th>\n",
       "      <th>H2Ot_b</th>\n",
       "      <th>PHLACHt_b</th>\n",
       "      <th>PTRCtex2_b</th>\n",
       "      <th>H2Otm_b</th>\n",
       "      <th>GLU5SAtmc_b</th>\n",
       "      <th>G5SADs_b</th>\n",
       "      <th>r0074_b</th>\n",
       "      <th>EX_ala__L_e_b</th>\n",
       "      <th>EX_hdca_e_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.384206</td>\n",
       "      <td>0.791966</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020885</td>\n",
       "      <td>0.155698</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.376338e-07</td>\n",
       "      <td>46.698201</td>\n",
       "      <td>473.583095</td>\n",
       "      <td>193.507295</td>\n",
       "      <td>...</td>\n",
       "      <td>2.794063</td>\n",
       "      <td>31.665615</td>\n",
       "      <td>30.801369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.384299</td>\n",
       "      <td>0.792019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020891</td>\n",
       "      <td>0.155720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.583870e-06</td>\n",
       "      <td>46.694815</td>\n",
       "      <td>473.554502</td>\n",
       "      <td>193.489006</td>\n",
       "      <td>...</td>\n",
       "      <td>2.790659</td>\n",
       "      <td>31.664977</td>\n",
       "      <td>30.768632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.390353</td>\n",
       "      <td>0.795480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021057</td>\n",
       "      <td>0.156621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.569400e-06</td>\n",
       "      <td>46.758679</td>\n",
       "      <td>473.554517</td>\n",
       "      <td>193.352584</td>\n",
       "      <td>...</td>\n",
       "      <td>2.785785</td>\n",
       "      <td>31.658856</td>\n",
       "      <td>30.463332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.400356</td>\n",
       "      <td>0.801197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021362</td>\n",
       "      <td>0.158024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.587142e-06</td>\n",
       "      <td>46.881030</td>\n",
       "      <td>473.554503</td>\n",
       "      <td>193.352583</td>\n",
       "      <td>...</td>\n",
       "      <td>2.795163</td>\n",
       "      <td>31.598462</td>\n",
       "      <td>30.473736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.401309</td>\n",
       "      <td>0.801742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021364</td>\n",
       "      <td>0.158089</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.502518e-06</td>\n",
       "      <td>46.866627</td>\n",
       "      <td>473.554579</td>\n",
       "      <td>193.352637</td>\n",
       "      <td>...</td>\n",
       "      <td>2.819746</td>\n",
       "      <td>31.603203</td>\n",
       "      <td>30.901237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 531 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EX_lac__L_e  EX_glc__D_e_r  EX_gluIN__L_e  EX_gluOUT__L_e  EX_gln__L_e_r  \\\n",
       "0     1.384206       0.791966            0.0        0.020885       0.155698   \n",
       "1     1.384299       0.792019            0.0        0.020891       0.155720   \n",
       "2     1.390353       0.795480            0.0        0.021057       0.156621   \n",
       "3     1.400356       0.801197            0.0        0.021362       0.158024   \n",
       "4     1.401309       0.801742            0.0        0.021364       0.158089   \n",
       "\n",
       "   EX_asp__L_e   DM_asp__L_e   EX_co2_e    EX_h_e_f  EX_h2o_e_f  ...  \\\n",
       "0          0.0  1.376338e-07  46.698201  473.583095  193.507295  ...   \n",
       "1          0.0  2.583870e-06  46.694815  473.554502  193.489006  ...   \n",
       "2          0.0  2.569400e-06  46.758679  473.554517  193.352584  ...   \n",
       "3          0.0  2.587142e-06  46.881030  473.554503  193.352583  ...   \n",
       "4          0.0  2.502518e-06  46.866627  473.554579  193.352637  ...   \n",
       "\n",
       "   MTHFD2i_b     H2Ot_b  PHLACHt_b  PTRCtex2_b  H2Otm_b  GLU5SAtmc_b  \\\n",
       "0   2.794063  31.665615  30.801369         0.0      0.0          0.0   \n",
       "1   2.790659  31.664977  30.768632         0.0      0.0          0.0   \n",
       "2   2.785785  31.658856  30.463332         0.0      0.0          0.0   \n",
       "3   2.795163  31.598462  30.473736         0.0      0.0          0.0   \n",
       "4   2.819746  31.603203  30.901237         0.0      0.0          0.0   \n",
       "\n",
       "   G5SADs_b  r0074_b  EX_ala__L_e_b  EX_hdca_e_b  \n",
       "0       0.0      0.0            0.0          0.0  \n",
       "1       0.0      0.0            0.0          0.0  \n",
       "2       0.0      0.0            0.0          0.0  \n",
       "3       0.0      0.0            0.0          0.0  \n",
       "4       0.0      0.0            0.0          0.0  \n",
       "\n",
       "[5 rows x 531 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nSamples = 1000\n",
    "batch=0\n",
    "dfOutput1 = pd.read_csv(os.path.join(OUTDIR, 'randomSampling_ENGRO2_nSol_' + str(nSamples) +\"_nBatch_\"+str(batch)+ '_MDAMB231' + '.csv'), sep = '\\t')\n",
    "dfOutput1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: mannWhitneyUTest\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/mannWhitneyUTest.py nSamples\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/mannWhitneyUTest.py nSamples\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('MCF102A', 'MDAMB231')\n",
      "('MCF102A', 'MDAMB361')\n",
      "('MCF102A', 'MCF7')\n",
      "('MCF102A', 'SKBR3')\n",
      "('MDAMB231', 'MDAMB361')\n",
      "('MDAMB231', 'MCF7')\n",
      "('MDAMB231', 'SKBR3')\n",
      "('MDAMB361', 'MCF7')\n",
      "('MDAMB361', 'SKBR3')\n",
      "('MCF7', 'SKBR3')\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/mannWhitneyUTest.py 1000 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As proof of concept, one of the obtained files of Step 7 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Unnamed: 0</th>\n",
       "      <th>statistic_less</th>\n",
       "      <th>pvalue_less</th>\n",
       "      <th>statistic_greater</th>\n",
       "      <th>pvalue_greater</th>\n",
       "      <th>mean_MCF102A</th>\n",
       "      <th>median_MCF102A</th>\n",
       "      <th>std_MCF102A</th>\n",
       "      <th>mean_SKBR3</th>\n",
       "      <th>median_SKBR3</th>\n",
       "      <th>std_SKBR3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>672680.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>672680.0</td>\n",
       "      <td>4.386013e-41</td>\n",
       "      <td>4.290097</td>\n",
       "      <td>4.269499</td>\n",
       "      <td>1.217003</td>\n",
       "      <td>3.586463</td>\n",
       "      <td>3.481698</td>\n",
       "      <td>0.860132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e_r</td>\n",
       "      <td>634953.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>634953.0</td>\n",
       "      <td>7.267530e-26</td>\n",
       "      <td>2.532217</td>\n",
       "      <td>2.519402</td>\n",
       "      <td>0.718333</td>\n",
       "      <td>2.209427</td>\n",
       "      <td>2.144176</td>\n",
       "      <td>0.531527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.026654</td>\n",
       "      <td>0.026717</td>\n",
       "      <td>0.008533</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6508.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.030968</td>\n",
       "      <td>0.030171</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.070849</td>\n",
       "      <td>0.068016</td>\n",
       "      <td>0.016936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e_r</td>\n",
       "      <td>905584.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>905584.0</td>\n",
       "      <td>7.741797e-217</td>\n",
       "      <td>0.567066</td>\n",
       "      <td>0.564079</td>\n",
       "      <td>0.160279</td>\n",
       "      <td>0.327549</td>\n",
       "      <td>0.320059</td>\n",
       "      <td>0.075545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  statistic_less  pvalue_less  statistic_greater  \\\n",
       "0     EX_lac__L_e        672680.0          1.0           672680.0   \n",
       "1   EX_glc__D_e_r        634953.0          1.0           634953.0   \n",
       "2   EX_gluIN__L_e       1000000.0          1.0          1000000.0   \n",
       "3  EX_gluOUT__L_e          6508.0          0.0             6508.0   \n",
       "4   EX_gln__L_e_r        905584.0          1.0           905584.0   \n",
       "\n",
       "   pvalue_greater  mean_MCF102A  median_MCF102A  std_MCF102A  mean_SKBR3  \\\n",
       "0    4.386013e-41      4.290097        4.269499     1.217003    3.586463   \n",
       "1    7.267530e-26      2.532217        2.519402     0.718333    2.209427   \n",
       "2    0.000000e+00      0.026654        0.026717     0.008533    0.000000   \n",
       "3    1.000000e+00      0.030968        0.030171     0.008734    0.070849   \n",
       "4   7.741797e-217      0.567066        0.564079     0.160279    0.327549   \n",
       "\n",
       "   median_SKBR3  std_SKBR3  \n",
       "0      3.481698   0.860132  \n",
       "1      2.144176   0.531527  \n",
       "2      0.000000   0.000000  \n",
       "3      0.068016   0.016936  \n",
       "4      0.320059   0.075545  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfOutput1 = pd.read_csv(os.path.join(OUTDIR, 'mwuTest_MCF102A_vs_SKBR3.csv'), sep = '\\t')\n",
    "dfOutput1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8: RAS t-test\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/rasTtest.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/rasTtest.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bruno\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1732: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n",
      "C:\\Users\\bruno\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log2\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/rasTtest.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced outputs are:\n",
    "1. the splitted RAS scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Rxn</th>\n",
       "      <th>Cellule361_Rep1</th>\n",
       "      <th>Cellule361_Rep2</th>\n",
       "      <th>Cellule361_Rep3</th>\n",
       "      <th>MDA-MB-231_Rep1</th>\n",
       "      <th>MDA-MB-231_Rep2</th>\n",
       "      <th>MDA-MB-231_Rep3</th>\n",
       "      <th>MCF-7_Rep1</th>\n",
       "      <th>MCF-7_Rep2</th>\n",
       "      <th>MCF-7_Rep3</th>\n",
       "      <th>SK-BR-3_Rep1</th>\n",
       "      <th>SK-BR-3_Rep2</th>\n",
       "      <th>SK-BR-3_Rep3</th>\n",
       "      <th>MCF102A_Rep1</th>\n",
       "      <th>MCF102A_Rep2</th>\n",
       "      <th>MCF102A_Rep3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e_r</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e_r</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rxn  Cellule361_Rep1  Cellule361_Rep2  Cellule361_Rep3  \\\n",
       "0     EX_lac__L_e              1.0              1.0              1.0   \n",
       "1   EX_glc__D_e_r              1.0              1.0              1.0   \n",
       "2   EX_gluIN__L_e              1.0              1.0              1.0   \n",
       "3  EX_gluOUT__L_e              1.0              1.0              1.0   \n",
       "4   EX_gln__L_e_r              1.0              1.0              1.0   \n",
       "\n",
       "   MDA-MB-231_Rep1  MDA-MB-231_Rep2  MDA-MB-231_Rep3  MCF-7_Rep1  MCF-7_Rep2  \\\n",
       "0              1.0              1.0              1.0         1.0         1.0   \n",
       "1              1.0              1.0              1.0         1.0         1.0   \n",
       "2              1.0              1.0              1.0         1.0         1.0   \n",
       "3              1.0              1.0              1.0         1.0         1.0   \n",
       "4              1.0              1.0              1.0         1.0         1.0   \n",
       "\n",
       "   MCF-7_Rep3  SK-BR-3_Rep1  SK-BR-3_Rep2  SK-BR-3_Rep3  MCF102A_Rep1  \\\n",
       "0         1.0           1.0           1.0           1.0           1.0   \n",
       "1         1.0           1.0           1.0           1.0           1.0   \n",
       "2         1.0           1.0           1.0           1.0           1.0   \n",
       "3         1.0           1.0           1.0           1.0           1.0   \n",
       "4         1.0           1.0           1.0           1.0           1.0   \n",
       "\n",
       "   MCF102A_Rep2  MCF102A_Rep3  \n",
       "0           1.0           1.0  \n",
       "1           1.0           1.0  \n",
       "2           1.0           1.0  \n",
       "3           1.0           1.0  \n",
       "4           1.0           1.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRasSplitted = pd.read_csv(os.path.join(OUTDIR, 'ENGRO2_RAS_splt.csv'), sep = '\\t')\n",
    "dfRasSplitted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. the t-test for each pair of cell lines. Here, as proof of concept, one of the t-test output file is reported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Rxn</th>\n",
       "      <th>statistic_less</th>\n",
       "      <th>pvalue_less</th>\n",
       "      <th>statistic_greater</th>\n",
       "      <th>pvalue_greater</th>\n",
       "      <th>mean_MCF102A</th>\n",
       "      <th>std_MCF102A</th>\n",
       "      <th>mean_SKBR3</th>\n",
       "      <th>std_SKBR3</th>\n",
       "      <th>result</th>\n",
       "      <th>meanRatio</th>\n",
       "      <th>meanRatio_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EX_lac__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EX_glc__D_e_r</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EX_gluIN__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EX_gluOUT__L_e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EX_gln__L_e_r</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rxn  statistic_less  pvalue_less  statistic_greater  \\\n",
       "0     EX_lac__L_e             NaN          NaN                NaN   \n",
       "1   EX_glc__D_e_r             NaN          NaN                NaN   \n",
       "2   EX_gluIN__L_e             NaN          NaN                NaN   \n",
       "3  EX_gluOUT__L_e             NaN          NaN                NaN   \n",
       "4   EX_gln__L_e_r             NaN          NaN                NaN   \n",
       "\n",
       "   pvalue_greater  mean_MCF102A  std_MCF102A  mean_SKBR3  std_SKBR3  result  \\\n",
       "0             NaN           1.0          0.0         1.0        0.0     0.0   \n",
       "1             NaN           1.0          0.0         1.0        0.0     0.0   \n",
       "2             NaN           1.0          0.0         1.0        0.0     0.0   \n",
       "3             NaN           1.0          0.0         1.0        0.0     0.0   \n",
       "4             NaN           1.0          0.0         1.0        0.0     0.0   \n",
       "\n",
       "   meanRatio  meanRatio_log  \n",
       "0        1.0            0.0  \n",
       "1        1.0            0.0  \n",
       "2        1.0            0.0  \n",
       "3        1.0            0.0  \n",
       "4        1.0            0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfttest = pd.read_csv(os.path.join(OUTDIR, 'ras_MCF102A_vs_SKBR3.csv'), sep = '\\t')\n",
    "dfttest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9: Create metabolomic statistical test dataset\n",
    "\n",
    "To run the script from the Jupyter notebook:  \n",
    "\n",
    "```python\n",
    "!python pipeline/createMetabolicDataset.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:  \n",
    "```python\n",
    "python pipeline/createMetabolicDataset.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n",
      "How many reactions have all the metabolites?\t 81\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/createMetabolicDataset.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced outputs are:\n",
    "1. The dataset of statistical tests for the Metabolomic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>('MCF102A', 'MDAMB231')</th>\n",
       "      <th>('MCF102A', 'MDAMB361')</th>\n",
       "      <th>('MCF102A', 'MCF7')</th>\n",
       "      <th>('MCF102A', 'SKBR3')</th>\n",
       "      <th>('MDAMB231', 'MDAMB361')</th>\n",
       "      <th>('MDAMB231', 'MCF7')</th>\n",
       "      <th>('MDAMB231', 'SKBR3')</th>\n",
       "      <th>('MDAMB361', 'MCF7')</th>\n",
       "      <th>('MDAMB361', 'SKBR3')</th>\n",
       "      <th>('MCF7', 'SKBR3')</th>\n",
       "      <th>qc_substracts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PGI</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>FBA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>PGK</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPGM</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PGM_f</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G6PDH2r</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GND</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TKT2_f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPE_f</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "         ('MCF102A', 'MDAMB231')  ('MCF102A', 'MDAMB361')  \\\n",
       "PGI                            0                       -1   \n",
       "FBA                            0                        0   \n",
       "PGK                            1                       -1   \n",
       "DPGM                           1                       -1   \n",
       "PGM_f                          0                        0   \n",
       "G6PDH2r                        0                       -1   \n",
       "GND                            0                        0   \n",
       "RPI                            0                        1   \n",
       "TKT2_f                         1                        1   \n",
       "RPE_f                          0                        1   \n",
       "\n",
       "         ('MCF102A', 'MCF7')  ('MCF102A', 'SKBR3')  ('MDAMB231', 'MDAMB361')  \\\n",
       "PGI                        0                     1                        -1   \n",
       "FBA                        0                     0                         0   \n",
       "PGK                        0                     1                        -1   \n",
       "DPGM                       0                     1                        -1   \n",
       "PGM_f                      0                    -1                         0   \n",
       "G6PDH2r                   -1                     1                        -1   \n",
       "GND                       -1                     0                         0   \n",
       "RPI                       -1                     0                         0   \n",
       "TKT2_f                     0                     1                         0   \n",
       "RPE_f                     -1                     0                         0   \n",
       "\n",
       "         ('MDAMB231', 'MCF7')  ('MDAMB231', 'SKBR3')  ('MDAMB361', 'MCF7')  \\\n",
       "PGI                         0                      1                     0   \n",
       "FBA                         0                      0                     0   \n",
       "PGK                        -1                     -1                     1   \n",
       "DPGM                       -1                     -1                     1   \n",
       "PGM_f                       0                     -1                     0   \n",
       "G6PDH2r                    -1                      1                     0   \n",
       "GND                        -1                      0                    -1   \n",
       "RPI                        -1                      0                    -1   \n",
       "TKT2_f                      0                      0                     0   \n",
       "RPE_f                      -1                      0                    -1   \n",
       "\n",
       "         ('MDAMB361', 'SKBR3')  ('MCF7', 'SKBR3')  qc_substracts  \n",
       "PGI                          1                  1            1.0  \n",
       "FBA                          0                  0            1.0  \n",
       "PGK                          1                  1            1.0  \n",
       "DPGM                         1                  1            1.0  \n",
       "PGM_f                       -1                 -1            1.0  \n",
       "G6PDH2r                      1                  1            1.0  \n",
       "GND                          0                  1            1.0  \n",
       "RPI                         -1                  1            1.0  \n",
       "TKT2_f                       0                  0            1.0  \n",
       "RPE_f                       -1                  1            1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMet_tests = pd.read_csv(os.path.join(OUTDIR, 'resultsMetabolomic_ttest.csv'), sep = ',', index_col=0)\n",
    "dfMet_tests.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The dataset of ratio between the means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PGI</th>\n",
       "      <th>FBA</th>\n",
       "      <th>PGK</th>\n",
       "      <th>DPGM</th>\n",
       "      <th>PGM_f</th>\n",
       "      <th>G6PDH2r</th>\n",
       "      <th>GND</th>\n",
       "      <th>RPI</th>\n",
       "      <th>TKT2_f</th>\n",
       "      <th>RPE_f</th>\n",
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       "      <th>TKT1_b</th>\n",
       "      <th>TALA_b</th>\n",
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       "    <tr>\n",
       "      <th>('MCF102A', 'MDAMB231')</th>\n",
       "      <td>0.110357</td>\n",
       "      <td>-0.921459</td>\n",
       "      <td>40.217773</td>\n",
       "      <td>23.985308</td>\n",
       "      <td>0.115296</td>\n",
       "      <td>0.404931</td>\n",
       "      <td>-0.364755</td>\n",
       "      <td>0.703976</td>\n",
       "      <td>3.706489</td>\n",
       "      <td>0.703976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.904549</td>\n",
       "      <td>2.740064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086298</td>\n",
       "      <td>0.086298</td>\n",
       "      <td>2.209103</td>\n",
       "      <td>0.120630</td>\n",
       "      <td>-0.286600</td>\n",
       "      <td>4.790792</td>\n",
       "      <td>5.660982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MCF102A', 'MDAMB361')</th>\n",
       "      <td>-0.561612</td>\n",
       "      <td>-0.742250</td>\n",
       "      <td>-1.885084</td>\n",
       "      <td>-0.877235</td>\n",
       "      <td>-0.062046</td>\n",
       "      <td>-1.445562</td>\n",
       "      <td>-0.444103</td>\n",
       "      <td>1.025091</td>\n",
       "      <td>2.124243</td>\n",
       "      <td>1.025091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.662222</td>\n",
       "      <td>0.480564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.339045</td>\n",
       "      <td>-0.339045</td>\n",
       "      <td>-0.599746</td>\n",
       "      <td>-2.033999</td>\n",
       "      <td>-0.196766</td>\n",
       "      <td>-0.339337</td>\n",
       "      <td>37.721303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MCF102A', 'MCF7')</th>\n",
       "      <td>-0.209061</td>\n",
       "      <td>-0.507612</td>\n",
       "      <td>1.046164</td>\n",
       "      <td>0.502496</td>\n",
       "      <td>0.240779</td>\n",
       "      <td>-0.602154</td>\n",
       "      <td>-3.109805</td>\n",
       "      <td>-1.213429</td>\n",
       "      <td>0.322716</td>\n",
       "      <td>-1.213429</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079263</td>\n",
       "      <td>1.272005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.287947</td>\n",
       "      <td>-0.287947</td>\n",
       "      <td>-0.192781</td>\n",
       "      <td>-1.492584</td>\n",
       "      <td>-2.252194</td>\n",
       "      <td>0.529066</td>\n",
       "      <td>-0.262427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MCF102A', 'SKBR3')</th>\n",
       "      <td>1.447074</td>\n",
       "      <td>-0.733881</td>\n",
       "      <td>3.383221</td>\n",
       "      <td>1.960254</td>\n",
       "      <td>-1.409345</td>\n",
       "      <td>1.462705</td>\n",
       "      <td>-1.086992</td>\n",
       "      <td>0.366838</td>\n",
       "      <td>34.157337</td>\n",
       "      <td>0.366838</td>\n",
       "      <td>...</td>\n",
       "      <td>3.251445</td>\n",
       "      <td>37.425963</td>\n",
       "      <td>-45.060956</td>\n",
       "      <td>-0.473488</td>\n",
       "      <td>-0.473488</td>\n",
       "      <td>1.513608</td>\n",
       "      <td>-0.558289</td>\n",
       "      <td>-0.467965</td>\n",
       "      <td>4.894232</td>\n",
       "      <td>7.289487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MDAMB231', 'MDAMB361')</th>\n",
       "      <td>-0.671969</td>\n",
       "      <td>0.179208</td>\n",
       "      <td>-42.102857</td>\n",
       "      <td>-24.862542</td>\n",
       "      <td>-0.177342</td>\n",
       "      <td>-1.850493</td>\n",
       "      <td>-0.079348</td>\n",
       "      <td>0.321115</td>\n",
       "      <td>-1.582246</td>\n",
       "      <td>0.321115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.242327</td>\n",
       "      <td>-2.259500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.425342</td>\n",
       "      <td>-0.425342</td>\n",
       "      <td>-2.808849</td>\n",
       "      <td>-2.154630</td>\n",
       "      <td>0.089834</td>\n",
       "      <td>-5.130129</td>\n",
       "      <td>32.060321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MDAMB231', 'MCF7')</th>\n",
       "      <td>-0.319418</td>\n",
       "      <td>0.413847</td>\n",
       "      <td>-39.171609</td>\n",
       "      <td>-23.482811</td>\n",
       "      <td>0.125483</td>\n",
       "      <td>-1.007084</td>\n",
       "      <td>-2.745049</td>\n",
       "      <td>-1.917405</td>\n",
       "      <td>-3.383773</td>\n",
       "      <td>-1.917405</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.983812</td>\n",
       "      <td>-1.468059</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.374245</td>\n",
       "      <td>-0.374245</td>\n",
       "      <td>-2.401884</td>\n",
       "      <td>-1.613214</td>\n",
       "      <td>-1.965595</td>\n",
       "      <td>-4.261726</td>\n",
       "      <td>-5.923409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MDAMB231', 'SKBR3')</th>\n",
       "      <td>1.336717</td>\n",
       "      <td>0.187578</td>\n",
       "      <td>-36.834552</td>\n",
       "      <td>-22.025054</td>\n",
       "      <td>-1.524642</td>\n",
       "      <td>1.057774</td>\n",
       "      <td>-0.722237</td>\n",
       "      <td>-0.337139</td>\n",
       "      <td>30.450848</td>\n",
       "      <td>-0.337139</td>\n",
       "      <td>...</td>\n",
       "      <td>2.346897</td>\n",
       "      <td>34.685899</td>\n",
       "      <td>-45.060956</td>\n",
       "      <td>-0.559786</td>\n",
       "      <td>-0.559786</td>\n",
       "      <td>-0.695495</td>\n",
       "      <td>-0.678919</td>\n",
       "      <td>-0.181366</td>\n",
       "      <td>0.103440</td>\n",
       "      <td>1.628505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MDAMB361', 'MCF7')</th>\n",
       "      <td>0.352552</td>\n",
       "      <td>0.234638</td>\n",
       "      <td>2.931247</td>\n",
       "      <td>1.379731</td>\n",
       "      <td>0.302825</td>\n",
       "      <td>0.843408</td>\n",
       "      <td>-2.665702</td>\n",
       "      <td>-2.238520</td>\n",
       "      <td>-1.801527</td>\n",
       "      <td>-2.238520</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.741484</td>\n",
       "      <td>0.791441</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051097</td>\n",
       "      <td>0.051097</td>\n",
       "      <td>0.406965</td>\n",
       "      <td>0.541415</td>\n",
       "      <td>-2.055428</td>\n",
       "      <td>0.868403</td>\n",
       "      <td>-37.983730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MDAMB361', 'SKBR3')</th>\n",
       "      <td>2.008686</td>\n",
       "      <td>0.008369</td>\n",
       "      <td>5.268305</td>\n",
       "      <td>2.837489</td>\n",
       "      <td>-1.347299</td>\n",
       "      <td>2.908267</td>\n",
       "      <td>-0.642889</td>\n",
       "      <td>-0.658253</td>\n",
       "      <td>32.033093</td>\n",
       "      <td>-0.658253</td>\n",
       "      <td>...</td>\n",
       "      <td>2.589224</td>\n",
       "      <td>36.945399</td>\n",
       "      <td>-45.060956</td>\n",
       "      <td>-0.134443</td>\n",
       "      <td>-0.134443</td>\n",
       "      <td>2.113354</td>\n",
       "      <td>1.475710</td>\n",
       "      <td>-0.271199</td>\n",
       "      <td>5.233569</td>\n",
       "      <td>-30.431816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>('MCF7', 'SKBR3')</th>\n",
       "      <td>1.656135</td>\n",
       "      <td>-0.226269</td>\n",
       "      <td>2.337057</td>\n",
       "      <td>1.457757</td>\n",
       "      <td>-1.650124</td>\n",
       "      <td>2.064859</td>\n",
       "      <td>2.022813</td>\n",
       "      <td>1.580266</td>\n",
       "      <td>33.834620</td>\n",
       "      <td>1.580266</td>\n",
       "      <td>...</td>\n",
       "      <td>3.330708</td>\n",
       "      <td>36.153958</td>\n",
       "      <td>-45.060956</td>\n",
       "      <td>-0.185541</td>\n",
       "      <td>-0.185541</td>\n",
       "      <td>1.706389</td>\n",
       "      <td>0.934295</td>\n",
       "      <td>1.784229</td>\n",
       "      <td>4.365166</td>\n",
       "      <td>7.551914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               PGI       FBA        PGK       DPGM     PGM_f  \\\n",
       "('MCF102A', 'MDAMB231')   0.110357 -0.921459  40.217773  23.985308  0.115296   \n",
       "('MCF102A', 'MDAMB361')  -0.561612 -0.742250  -1.885084  -0.877235 -0.062046   \n",
       "('MCF102A', 'MCF7')      -0.209061 -0.507612   1.046164   0.502496  0.240779   \n",
       "('MCF102A', 'SKBR3')      1.447074 -0.733881   3.383221   1.960254 -1.409345   \n",
       "('MDAMB231', 'MDAMB361') -0.671969  0.179208 -42.102857 -24.862542 -0.177342   \n",
       "('MDAMB231', 'MCF7')     -0.319418  0.413847 -39.171609 -23.482811  0.125483   \n",
       "('MDAMB231', 'SKBR3')     1.336717  0.187578 -36.834552 -22.025054 -1.524642   \n",
       "('MDAMB361', 'MCF7')      0.352552  0.234638   2.931247   1.379731  0.302825   \n",
       "('MDAMB361', 'SKBR3')     2.008686  0.008369   5.268305   2.837489 -1.347299   \n",
       "('MCF7', 'SKBR3')         1.656135 -0.226269   2.337057   1.457757 -1.650124   \n",
       "\n",
       "                           G6PDH2r       GND       RPI     TKT2_f     RPE_f  \\\n",
       "('MCF102A', 'MDAMB231')   0.404931 -0.364755  0.703976   3.706489  0.703976   \n",
       "('MCF102A', 'MDAMB361')  -1.445562 -0.444103  1.025091   2.124243  1.025091   \n",
       "('MCF102A', 'MCF7')      -0.602154 -3.109805 -1.213429   0.322716 -1.213429   \n",
       "('MCF102A', 'SKBR3')      1.462705 -1.086992  0.366838  34.157337  0.366838   \n",
       "('MDAMB231', 'MDAMB361') -1.850493 -0.079348  0.321115  -1.582246  0.321115   \n",
       "('MDAMB231', 'MCF7')     -1.007084 -2.745049 -1.917405  -3.383773 -1.917405   \n",
       "('MDAMB231', 'SKBR3')     1.057774 -0.722237 -0.337139  30.450848 -0.337139   \n",
       "('MDAMB361', 'MCF7')      0.843408 -2.665702 -2.238520  -1.801527 -2.238520   \n",
       "('MDAMB361', 'SKBR3')     2.908267 -0.642889 -0.658253  32.033093 -0.658253   \n",
       "('MCF7', 'SKBR3')         2.064859  2.022813  1.580266  33.834620  1.580266   \n",
       "\n",
       "                          ...    TKT1_b     TALA_b  SUCOAS1m_b    FUMm_b  \\\n",
       "('MCF102A', 'MDAMB231')   ...  0.904549   2.740064    0.000000  0.086298   \n",
       "('MCF102A', 'MDAMB361')   ...  0.662222   0.480564    0.000000 -0.339045   \n",
       "('MCF102A', 'MCF7')       ... -0.079263   1.272005    0.000000 -0.287947   \n",
       "('MCF102A', 'SKBR3')      ...  3.251445  37.425963  -45.060956 -0.473488   \n",
       "('MDAMB231', 'MDAMB361')  ... -0.242327  -2.259500    0.000000 -0.425342   \n",
       "('MDAMB231', 'MCF7')      ... -0.983812  -1.468059    0.000000 -0.374245   \n",
       "('MDAMB231', 'SKBR3')     ...  2.346897  34.685899  -45.060956 -0.559786   \n",
       "('MDAMB361', 'MCF7')      ... -0.741484   0.791441    0.000000  0.051097   \n",
       "('MDAMB361', 'SKBR3')     ...  2.589224  36.945399  -45.060956 -0.134443   \n",
       "('MCF7', 'SKBR3')         ...  3.330708  36.153958  -45.060956 -0.185541   \n",
       "\n",
       "                             FUM_b  AKGMALtm_b   r0801_b  GLU5SAtmc_b  \\\n",
       "('MCF102A', 'MDAMB231')   0.086298    2.209103  0.120630    -0.286600   \n",
       "('MCF102A', 'MDAMB361')  -0.339045   -0.599746 -2.033999    -0.196766   \n",
       "('MCF102A', 'MCF7')      -0.287947   -0.192781 -1.492584    -2.252194   \n",
       "('MCF102A', 'SKBR3')     -0.473488    1.513608 -0.558289    -0.467965   \n",
       "('MDAMB231', 'MDAMB361') -0.425342   -2.808849 -2.154630     0.089834   \n",
       "('MDAMB231', 'MCF7')     -0.374245   -2.401884 -1.613214    -1.965595   \n",
       "('MDAMB231', 'SKBR3')    -0.559786   -0.695495 -0.678919    -0.181366   \n",
       "('MDAMB361', 'MCF7')      0.051097    0.406965  0.541415    -2.055428   \n",
       "('MDAMB361', 'SKBR3')    -0.134443    2.113354  1.475710    -0.271199   \n",
       "('MCF7', 'SKBR3')        -0.185541    1.706389  0.934295     1.784229   \n",
       "\n",
       "                          ALATA_L_b  MTHFD2i_b  \n",
       "('MCF102A', 'MDAMB231')    4.790792   5.660982  \n",
       "('MCF102A', 'MDAMB361')   -0.339337  37.721303  \n",
       "('MCF102A', 'MCF7')        0.529066  -0.262427  \n",
       "('MCF102A', 'SKBR3')       4.894232   7.289487  \n",
       "('MDAMB231', 'MDAMB361')  -5.130129  32.060321  \n",
       "('MDAMB231', 'MCF7')      -4.261726  -5.923409  \n",
       "('MDAMB231', 'SKBR3')      0.103440   1.628505  \n",
       "('MDAMB361', 'MCF7')       0.868403 -37.983730  \n",
       "('MDAMB361', 'SKBR3')      5.233569 -30.431816  \n",
       "('MCF7', 'SKBR3')          4.365166   7.551914  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfMet_mean = pd.read_csv(os.path.join(OUTDIR, 'resultsMetabolomic_mean.csv'), sep = ',',index_col=0)\n",
    "dfMet_mean.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10: Concordance data analysis\n",
    "\n",
    "To run the script from the Jupyter notebook:\n",
    "\n",
    "```python\n",
    "!python pipeline/concordanceAnalysis.py\n",
    "```\n",
    "\n",
    "In alternative, to run the script from the terminal:\n",
    "    \n",
    "```python\n",
    "python pipeline/concordanceAnalysis.py\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2022-03-10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bruno\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n",
      "C:\\Users\\bruno\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:641: RuntimeWarning: invalid value encountered in true_divide\n",
      "  k = np.sum(w_mat * confusion) / np.sum(w_mat * expected)\n",
      "C:\\Users\\bruno\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:641: RuntimeWarning: invalid value encountered in true_divide\n",
      "  k = np.sum(w_mat * confusion) / np.sum(w_mat * expected)\n"
     ]
    }
   ],
   "source": [
    "!python pipeline/concordanceAnalysis.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The produced outputs are:\n",
    "1. The dataset of Cohen coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>RPSvsFBA cohen</th>\n",
       "      <th>RPSvsRAS cohen</th>\n",
       "      <th>FBAvsRAS cohen</th>\n",
       "      <th>RPSvsFBA pvalue</th>\n",
       "      <th>RPSvsRAS pvalue</th>\n",
       "      <th>RPSvsFBA adj-pvalue</th>\n",
       "      <th>RPSvsRAS adj-pvalue</th>\n",
       "      <th>Formula</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACONT</th>\n",
       "      <td>0.793814</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.318429</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.852703</td>\n",
       "      <td>cit_c --&gt; icit_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPI</th>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.095000</td>\n",
       "      <td>ru5p__D_c --&gt; r5p_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPE_f</th>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.797980</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.020714</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.207143</td>\n",
       "      <td>ru5p__D_c --&gt; xu5p__D_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G5SADs_f</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>-0.052632</td>\n",
       "      <td>0.270833</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.555857</td>\n",
       "      <td>0.0925</td>\n",
       "      <td>0.926429</td>\n",
       "      <td>glu5sa_c --&gt; 1pyr5c_c + h2o_c + h_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VALt5m</th>\n",
       "      <td>0.578947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>val__L_c --&gt; val__L_m</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r1616_r</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>trp__L_e --&gt; trp__L_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2420</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.562500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>h_c + pi_c --&gt; h_m + pi_m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2526_r</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ser__L_e --&gt; ser__L_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2532_r</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.120879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>asn__L_e --&gt; asn__L_c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2534_r</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>thr__L_e --&gt; thr__L_c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>531 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          RPSvsFBA cohen  RPSvsRAS cohen  FBAvsRAS cohen  RPSvsFBA pvalue  \\\n",
       "ACONT           0.793814        0.100000        0.100000         0.000123   \n",
       "RPI             0.687500        0.687500        0.797980         0.001481   \n",
       "RPE_f           0.687500        0.479167        0.797980         0.001481   \n",
       "G5SADs_f        0.625000       -0.052632        0.270833         0.004568   \n",
       "VALt5m          0.578947             NaN             NaN         0.008025   \n",
       "...                  ...             ...             ...              ...   \n",
       "r1616_r              NaN             NaN        0.500000              NaN   \n",
       "r2420                NaN             NaN       -0.562500              NaN   \n",
       "r2526_r              NaN             NaN        0.500000              NaN   \n",
       "r2532_r              NaN             NaN        0.120879              NaN   \n",
       "r2534_r              NaN             NaN        0.900000              NaN   \n",
       "\n",
       "          RPSvsRAS pvalue  RPSvsFBA adj-pvalue  RPSvsRAS adj-pvalue  \\\n",
       "ACONT            0.318429               0.0100             0.852703   \n",
       "RPI              0.002000               0.0400             0.095000   \n",
       "RPE_f            0.020714               0.0400             0.207143   \n",
       "G5SADs_f         0.555857               0.0925             0.926429   \n",
       "VALt5m                NaN               0.1300                  NaN   \n",
       "...                   ...                  ...                  ...   \n",
       "r1616_r               NaN                  NaN                  NaN   \n",
       "r2420                 NaN                  NaN                  NaN   \n",
       "r2526_r               NaN                  NaN                  NaN   \n",
       "r2532_r               NaN                  NaN                  NaN   \n",
       "r2534_r               NaN                  NaN                  NaN   \n",
       "\n",
       "                                      Formula  \n",
       "ACONT                        cit_c --> icit_c  \n",
       "RPI                       ru5p__D_c --> r5p_c  \n",
       "RPE_f                 ru5p__D_c --> xu5p__D_c  \n",
       "G5SADs_f  glu5sa_c --> 1pyr5c_c + h2o_c + h_c  \n",
       "VALt5m                  val__L_c --> val__L_m  \n",
       "...                                       ...  \n",
       "r1616_r                 trp__L_e --> trp__L_c  \n",
       "r2420               h_c + pi_c --> h_m + pi_m  \n",
       "r2526_r                 ser__L_e --> ser__L_c  \n",
       "r2532_r                 asn__L_e --> asn__L_c  \n",
       "r2534_r                 thr__L_e --> thr__L_c  \n",
       "\n",
       "[531 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"outputs/df_concordance.csv\",index_col=0)\n",
    "df.sort_values(by=\"RPSvsFBA cohen\",ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Heatmap of concordance analysis (with RPS vs FFD >0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28c9f14f550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = img.imread(os.path.join(FIGUREDIR, 'concordanceHeatmap.png'))\n",
    "  \n",
    "# show image\n",
    "plt.figure(figsize=(15,20))\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Scatter plot of concordance analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28ca03f4760>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nhqr+qedG9d/3qf6N53mNY1e9tqaxa2Nu/mpdqVRCV1dX49hVf/ey6mXNDcdxGrcLuVz+wblRrfs/PTeq7aKsuSk5PtbYVT+naZ/50NyofjfdSEgIKYn7r7wpNG3alH/y5MnnXo2/RNObd8lGiOO4Uv//+/9e8t/ev6GDdSheLBZj48aNmDt3bqnT4rGxsbC2tmaeApTJZIiJiYGjo6P6NFdJUqkUsbGxqFq1KkxMTErVeZ5HYWEhEhIS4O7urr5+tGRdJBIhLS0NHh4ezFN8WVlZyM3NhZubG/MUX1paGoqKilC1alXBafHIyEiYmJhAV1cXTk5OzFN88fHxMDU1RZUqVZinfmNjY2Fra8s8bV5cXIyYmBg4OzszT39IJBLEx8fD1dVVHXpfct0LCgqQmJgIDw8P6OvrC+q5ubnIyMiAu7s7c24yMzORl5cHNzc35mnxt2/fQiaTwcXFhXn6MyEhAfr6+nBwcGDOTWxsLCpVqsQ8dSyXyxETEwM7OzvmqWHVduHi4sI8NVxYWIg3b97Azc1NcMmA6tRoSkqKert4v/7ixQvo6uqiRo0azO0iPT0dBQUFqFatGnPsqmsAnZ2dmWN/8+YNjI2NYWdnx6zHxMTAysqKeUmBap9xcHBgnv4rKipCXFwcXF1dmZcMFBQUlNpnNm/ejOjoaKxbtw4cxyEvLw9v375Vbzfvjy0zMxMikQju7u7MsaempkIqlcLV1VWwbjzPIzExEbq6uupt6f2xl7XPKBQKxMTElNpnpFIp4uPjBeNkqYinxTMzM+m0eDnE8zwOHTqE6dOno3Llyp97df5RHMcF8zzfVPDv1FyWT6zmsrxSNZe1atUq929iAJCbm1uquSzPeJ5HdHQ0XF1dBdeqljc8z5dqLv+LN+JRc6kZNZflFzWXwubyv/fuRQghhBBC/rWouSSEEEIIIVpDzSUhhBBCCNEaai7LMblcjhcvXiApKYl5Q1FxcTHCw8ORlpbGjEmRSCQICQlBdnY2s56fn4+QkBBBhItKdnY2QkJCmLFHqht0wsLCmPE2qpsNnj9/rvHu8JiYGLx+/VrjHdAvX75EfHw8sy6TyRAREYGUlBRmXSqVIiwsDBkZGcyxFxYWIjg4GLm5ucx6Xl4eQkJCmNE+qpuXQkNDUVRUxKy/ffsW4eHhGu8+T0pKwqtXrzTOTVRUFKKjo5l3CSsUCrx48QIJCQkat4unT58iNTWVWS8qKkJoaCiysrKYYy8oKEBwcDDy8vKY9dzcXISEhKCwsJA59oyMDISFhTHjbVQ36ERERGi8gzouLq7MuYmMjERsbCxzbuRyOZ49e/bBfSY9PZ1Zl0gkCA0NRU5OjsZ9Jjg4GPn5+Rrv0M7Ozta4XaSlpSE8PLzMfebZs2fMuSm5z2jaLl69eoW4uDiN+8zTp081hqRLpVK8evWK7hwnhFBzWZ49e/YM3bt3x/Dhw5k5dTdv3kSXLl005tSdOHECnTt3xtKlS5kfVn5+fujYsSM2bdrE/CBduHAhOnXqhOPHjwuWLSoqwtSpU9G5c2fcunVLUM/NzcXo0aPRtWtXZrZjRkYG+vTpg759+zJvIIiKikL37t0xePBgZGdnC+qPHz+Gr68vvvrqK8EHNc/zuHjxIjp37ow5c+YwG7yDBw+iS5cuWLNmDbOJWbduHTp16oQdO3YwP2xnz56Nzp074+zZs4KaRCLBxIkT0aVLF9y/f19QB4AlS5aga9euzGzHlJQU9OzZE/3790dycrKg/vz5c/j6+mLYsGEQiUSC+r179+Dt7Y3x48cL/u48z+PMmTPo3LkzFi5cKBg7z/PYtWsXOnfujA0bNgiaGJ7nsXr1anTq1AkHDhxgbjczZsxA586dcenSJcG6FRQUYOzYsejatSuCgoIE9fT0dAwePBi+vr7MPFRV5ufAgQORlpYmqIeFhcHb2xujR4+GWCwW1K9du4YuXbpgypQpzLEdPXoUnTp1wtKlS5n1zZs3o3Pnzti6datg7EqlEhERETh9+jROnjzJrE+ePBldu3bFjRs3BOuWn5+PUaNGwcfHB0+fPhXUU1NT8cUXX6Bbt25ITEwU1GNiYuDt7Y0vv/ySmXsZFBSErl274uuvv2Z+MQgICMD48eP/1OMfCSHlE+VclmOmpqaws7ODk5MT887TSpUqwd7eHvb29sy6lZUV7OzsmE/3AIDKlSujSpUqGu+Oc3BwQJUqVWBtbS2o6erqwsHBAfb29swn4ujp6cHe3h65ubnMCBd9fX04OTlBJpPB2NhY0CAaGxvDwcFB/eST95mbm5c5N5aWlrC3t2fG0wCAjY2NeuysumpubG1tBTXg/+fG0tJSUCs5N5oeJWZpaQlHR0fmXbaqp9no6+szn4hjYmICe3t7ODo6fnBuWGNTbResueE4DjY2NrCzs9M4N1WqVIGdnR1sbGwENY7j4OjoqI49ep+enh4cHBzUT5pijd3BwUHjIwUNDQ3VT7Bi3X1uamoKe3t79Wu8z8LCAnZ2dhrv1Le2ttY4NwBga2sLOzs7jduFmZkZTE1NmfsMgI+aG5FIxHyKlKGhIRwcHGBubs4cu5GREZycnGBvb8+sm5mZqeeGlUBhaWmpcb0JIRULRRGVU2KxGL/99hsmTJgAExMTQS4l8O5ISFZWFszMzASPc1QFMGdmZsLKyoqZzSiXy5GdnQ0bGxvmB3FxcTFycnJga2vLzCcsKipCfn4+bG1tmRl9BQUFkEqlzExO1alnnudRqVIlQRQRz797LKC+vj4zl1J1+tHY2Fjj3GRmZqJSpUrMbEalUomMjAz12P/M3PA8j+LiYohEItjY2DDnRiKRoKCggBlbkpubq36kICuTU5UpynEcMwaE5989FtDQ0JD5mE/VdmFqairI7ATenT7NyspSN2jvj12hUCAzM1Pj3MhksjK3C6lUiry8PPXYS0YRqS5JkEgkzNxJ1alnhULBfGSh6rS8jo6OxrnJysoqc7vQtM+o5qasfUZVV439/frmzZvx7Nkz+Pn5MbeboqIiiMVi2NjYMPeZwsJCFBUVlbnPKJVKjXOTk5MDPT09Zs6tKkqnrO0iPT2d+QQsFooiKt8oiqhi0BRFREcuyzGO42Btba0x51JHR0fjjsBxHHR1dWFnZ6exrq+vr7EOvDuKVNbyxsbGGj9UOI6DmZkZ8+iUqq46esM6DacauyaqZ5CXVa9SpYrG362rqwt7e3uN9bLmhuM4GBoalvn6qi8EmpiamjIbS9XyrCOiJeuso4YqZW0XwLsjq2Wtu+qos6b6h7YLIyMjGBkZAYDg1DDHcTA1NWUemVPVWUfCS9atrKzKrH9ou/jQ3JQ1trLmRsXIyIh5NP1j9pkPzQ3riGfJeln7DMdxHxy7tbX1RzeXhJDyi665JIQQQgghWkPNJSGEEEII0RpqLkm5wLoxhRBCCCH/PGouK5CioiJcvXoVkZGRzHgcsViMixcv4s2bNxpz7P6NN4BxHIdq1aqV+TMymQw3b97E06dPmddoSiQSXLlyBVFRUcwx5uXl4cKFCxrzDzMzMxEQEKAxFzM5ORkXLlyASCRi1mNiYnD58mWN2Y/Pnz/HzZs3mfmGSqUSwcHBuH//PjMySi6X4/79+wgODmaOvbi4GDdu3MDz58+ZYyssLMSlS5cQGxvLrOfm5iIgIAApKSmCGvAuHiggIEBjLmZCQgIuXrzIzH7keR5isRi3bt1iZj8qlUo8ffoUt2/fZkZGKRQKPH78GI8ePWJGRsnlcty9exdhYWHMuZFKpbh27Rozv1G1bmXtM9nZ2QgICEBqaipzblJTUxEQEICcnBxBjed5xMfH4+LFixrzUl++fIlr164xo8SUSiVCQ0Nx9+5djdvFw4cPERQUxJwbmUyGe/fu4cWLFxrzTm/cuIGYmJhS9Y+9eYPjOPX/KoqKNtaK9PdlXSddkdENPRXI/fv3MXDgQDRr1gxnzpwR3Cxz5swZTJo0CT169MDRo0dL1VS5mJ06dWJGvHxOqhslyvL06VMMGjQINWrUwPnz5wU3bdy8eRNDhgxB+/btceLECcHNNMeOHcPMmTPxxRdfYN++faVqPM9j06ZNWLt2Lb777jssXbq0VF0ul2P58uU4ePAg1q5di4kTJ5aqFxUVYc6cObh27Rr279+Pvn37lnpDlkgkGDduHOLi4nDq1CnUq1ev1PLp6ekYOXIk8vPzce3aNdSsWbNUPSoqCsOHD4ehoSHu3btX6qYMnufx5MkTDBw4EPXr18fZs2cFNwNdunQJo0aNgq+vLw4dOiS4oeTgwYOYN28eRowYgW3btgnugF67di02b96M+fPnY/78+aWWlclkWLx4MU6ePAk/Pz+MHj26VF0ikeDu3btYvXo19uzZA19f31KvLxaLMXr0aKSnp+PMmTNo1qxZqeVTUlIwfPhwKBQKXL9+HW5ubqXqz58/x7Bhw2BpaYk7d+6UutmH53k8ePAAgwYNQrNmzXDq1ClBNNS5c+cwYcIE9O3bF3v27BHsG7t378aPP/6I8ePHY/369YK5WbFiBfbs2YNly5Zh5syZpZaVSqWYN28e/P39sXv3bgwePLjU8vn5+Zg6dSpCQkJw+vRpdOjQoVQ9NzcXI0aMQH5+Pvz9/dGgQYNSr5+QkIChQ4fC0NAQ169fh7Ozc6l6YWEh3r59i8LCQnh6egr2sby8PKSlpUFfXx/VqlWDrq4u9PT0NN7sxWJlZQVdXV2NNx2WJzzPw9LSUpA+UV4pFAooFApmrFV5w/M8evfurfEG1IqImssKxMnJCXXr1kWTJk2YO7y7uztq1aqFpk0FqQIAgKpVq2rtQ0AulyMpKQlKpRLOzs6f/A2ocuXKaNCgAWrXrs28C9vFxQV16tRBkyZNmKfYa9SogZo1a6JJkybM169Xrx48PT0FjR/w7httw4YNERgYCE9PT0FdT08PjRs3RkpKClxdXZn1Zs2awdTUFA4ODoK6qakpmjRpgtzcXOad0NbW1mjQoAEqVarEvNPYzs4ODRo0QOPGjZlfHKpVq4batWvDy8uLOTc1a9aEp6cnvLy8BDUAaNCgATw9PVG3bl1BTVdXF40bN0ZERASqV68uqOvr68PBwQG1atWCi4uLoG5gYIBmzZrhzZs3zLu0zc3N4eXlBblczrxT2tbWFvXr14e9vT1z7I6OjqhXrx68vLzK3GcaN27M3Ddq164NT09PNGrUSFADgEaNGsHT0xO1atUS1PT09ODl5YXo6GhBU6wau5eXF2QyGRwdHQV1Q0NDNGvWDBkZGcw74C0sLNR/c1aeqoWFBQYNGsRcb+BdXunQoUNL/Zuurm6ZSQWElFfvf7Gt6CjnspwSi8XYuHEj5s6dq/7QU+Urqo4wvE9V19PTg46ODjPnDih9akculyMsLAyXLl1CfHw8jIyM0LRpU3Tr1k39YR8WFoZHjx6hbt26aNu2LXR0dJCUlIQuXbogLy8PN27cQO3atT/VVKjXXSaTqY9yssZW1twolUoUFxdDX1+f2USo6qrcR1YGoEwmg4GBATOfsOS3fNa6layLRCIUFBSog7xVuZo8zwuyFf/s2N/PXtT23LBOHSkUCsjlcujr6zPnJioqCo6OjsxMzo8dO4BPOjd6enpljl3T3JTcLjiOw+bNmxEdHY1169ZBR0dHPTcfs1381bnRNHZCCPkQTTmXdJFABaLKV9R0CllVZ32IqurvN5Z+fn7w9fXF6tWr8fDhQ/j7+2P8+PHo27cvnj17BgC4cuUKpkyZgmPHjoHnefX/pFKp+jrOkv8r6f1ayTqrpunLkipfkfUh+zFzo6OjAyMjozIzQ1X5hKzX19XVLTO/UPVEGU3r9qG6vr4+s8H4s2P/1HPDoqurC0NDQ4111fr/nbF/6rn50Ng11cvaLlT1z7VdAO8etzlx4kT07dtX8L+VK1cyr+X8p/E8j0uXLuGLL77Q+LhUFaVSiRcvXiA4OFh9nWlkZCT69euHw4cPf5L1O336NAYOHIhPdXDk2rVrGDRoEPMxun8Xz/NITU3Fo0ePmI/RfV9ISAgGDx6MP/7440/9ntu3b6Nfv364fv06RCIRxo4di2XLljGvMf/UeJ5HYmIiHj16hPz8/L/0Gg8fPkS/fv2Yj7CtKKi5JH8Jz/MICgrC0qVLIZFIsH79ety9exfXr1+Hr68vHj9+jAULFmD37t3YuXMneJ7H2bNn4e3tjUWLFqnf2HmeR3h4OCZNmoS+ffti165d6psT5HI5bty4gW+++Qbdu3fHuHHjcO3aNfUH2qFDh+Dt7Y0VK1Zg/fr16Nu3L+7cufPZ5oSQ8kZHRwcODg5wcXFBSkoKzp07B6lUChcXF3UQv6YveSW/7H3oZ/7OcsC7S0OcnJxgbGxc5s9KpVJMnz4dffv2RUFBAXieVz8uVRW+/1fWtax1i4mJgb+/P9LT00vN7d95zZL/f0JCAvz9/ZGUlPSXXr+snwWAkydPol27drh169YHXyc9PR3+/v7qGyM/5vcAQFJSEs6dO4fExET1Nqd6ktHHHkTQ5t9s+/btaNeuHSIiIv7S8qp9JT4+/s/vdOUEXXNJ/rKLFy8iJycHbdq0weDBg2FhYQFra2tMnz4dly5dwoMHD9CoUSP1URkdHR3B6UOxWIyFCxfCzs4O4eHhuHXrFqpWrQofHx8cO3YMU6ZMgbGxMWrWrInLly/jzJkz2L59OwYOHIi4uDjcuHEDQUFBcHZ2RuXKlZl3FBNC/hpjY2MsWbIEALB48WIEBwdj3LhxGDhwIA4ePIiOHTuid+/eePjwIXr16gUrKyv8/vvvyMjIgKWlJYYMGYJBgwahsLAQgwcPhqurKypXroxHjx6ha9eumDFjBpRKJbZs2YKrV6+C53nUrFkT33//PVxdXXH//n3s3r0bcXFxcHZ2xpIlS5CZmYm5c+eic+fOiI+Ph5WVFdq2bYuQkBD07dsXWVlZGDJkCGrWrAlzc3MEBQXB29sbkydPxvr16xEUFITCwkJ07doV7dq1w+jRoxESEoIGDRqA599dhrF582aEh4fD2toaw4YNQ9++fZGamorJkyfDyckJlpaWCAoKQvfu3fHtt9+isLAQa9asQWhoKIqKilCnTh1Mnz5dcHNdSTz/7gv3oUOHkJGRASsrKwwdOhQDBw5ERkYGJk6cCHt7e1hbW+Px48fw9fXF9OnTYWBggHPnzmHHjh2wsbEp8waqDRs24NSpU+jbty9u3rwJd3d3jBo1Cnv27EFsbCwmTJiAAQMGQKFQ4Ny5czh8+DBycnLQqlUrfPvtt4iKisKvv/4KuVyOH374Adu2bcP27dtx+vRp3LhxA3l5eXBxccE333yDtm3bqn9vbGwsJk6ciPj4eIwcORJDhw6Fjo4Orl27hr179yIlJQU1a9bEpEmTBNcjy+VyREREoKioCEqlEjk5OdizZw+uXr0KhUKBvn37YtKkSaXOpBQXF+Ps2bM4evQosrKyUK9ePSxbtgx6eno4cOAA/P39IZPJ0L59e0yaNAk2NjZYsmQJ7t69i4EDByIgIAB2dnb48ccfERoain379kEul2PChAlwd3fHb7/9hunTp8PKygrOzs54+fIlfvvtN0RFRWH37t1ISkpC9erV8c0332i8Lr+ioSOXFQDrW17JmqZ6Wd8SASAxMRHAu5seSt4o4uLiAkNDQ4hEInTr1g1fffUVAKBHjx44e/YsFi1apG4wFQoFZs+ejQsXLqB///7Iz8/HkydPIBKJsHXrVuTn56NXr14YOnQofHx8kJeXh23btkEikah/n7OzM86cOYOAgAB06NDho8b2d+sfmptP+bs/xqf+/f/mufk31z80Nx/yOdZdU5zM27dvcf/+fezbtw8eHh6wt7dHRkYG7O3t0bJlS2RnZ2PKlCkICQmBTCZDcHAwjh8/jpSUFBQUFGDZsmUICgrCw4cPsXz5chgbG6tvTMvKykJ4eDiGDh2KGzduoHHjxjA3N4dIJEJOTg4eP36MHTt2wNTUFJ6enkhPT8fjx4+RnZ2N4uJiPHnyBEePHkVOTg44jsOPP/6Iw4cPo379+rC1tYWRkRF8fX3RrFkziMViBAYGIikpCbm5ufj6669x+PBh1K1bFykpKfj6669x6dIlFBUVISQkBEeOHEF6ejpyc3OxYsUKBAcHo7i4GGKxGPXr10fdunVx4sQJ/PDDD6Xep1hznZWVBXt7e7Ro0QLp6emYPHkywsLCIJVKERoaiqNHjyItLQ35+flYuXIlHj9+jOfPn2PSpEmIjIyEjY0NLly4wIySAoA3b97gwYMHOH/+PMzMzLBz504MGzYMPM/j1atXWLBgAd6+fYsLFy5gwoQJKCgoQK1atbBz5058//33sLKyQr169aCjo4MmTZrAx8cHhoaGyMnJQfXq1dG0aVM8evQIkydPRlpamvr3XrlyBUZGRkhJScG0adPw6NEjPH78GKNHj8azZ8/QsGFDXLhwAaNHjxYccZXL5QgJCUFkZCSkUil+/PFHLFy4EAYGBmjcuDGysrJKjZfneZw6dQpjx45FXFwcmjdvDplMBolEgs2bN2P27NnQ19eHo6MjVq9ejR9++AHFxcWIjo7G3bt3cebMGVhZWeHw4cPYtm0bXFxcUL16dXAchzZt2qBLly7Q0dFBaGgoTp48icjISDRu3BjPnj3DyJEjERISgkaNGuHatWsYNWpUhT5aWRI1l+WYRCLBiRMncP/+fWaGX15eHg4fPozQ0FDBBwrP88jMzMS+ffvw+vVr5geS6s5kkUhUKmNQ9f8bGRnBxMRE3Ujq6OjAwMCg1DdOMzMztG7dGpUqVYKTk5N6vcViMZKTk6FUKnH48GHMmjULJ06cgJGREdLS0kr9viZNmqB69eowNTVVP5MaeBelEhgYyBx7QUEBjh49ikePHjHrOTk5OHjwoPq0yPtzk5aWhn379gky/lT1+Ph47N27F8nJyYLX5vl3uZX79+9HVlYWs/748WP8/vvvEIvFgjrwLlbq1KlTzHxDmUyGgIAAXLx4kXlNXFFREU6dOoXbt28zx56fn4/ff/8dT548Yf7ds7KycODAAWb+Ic/zSElJwd69e5nZjzz/LtNz3759zOxHnufx9OlTHDhwALm5ucz6w4cPcfToUWb2o0KhwLVr13DmzBnm3BQXF+PcuXO4cuUK8wO5sLAQx48fx/3795ljF4lEOHToEMLCwpj19PR0jfsMz7+7lmvv3r0a81JlMhmioqKQmZnJHHtoaCgOHTrEfH63UqnEvXv3cPz4cWZTo1AocPnyZZw/f555LZtUKlUfkWJtFyx6enqYPn06fvnlF3Tv3h1WVlZ49eoV/P39ERMTA7FYjIiICPXP16lTB5s3b8bEiRMhlUoRFxcHS0tLmJmZobCwEKamphg0aBA8PT1x9epVpKSkYO7cuVi3bh02b96Mxo0bq1+rX79+2LhxIyZNmsS8ZrVRo0bYuHEjVq9eDWNjY1y7dg0+Pj5wc3ODubk55syZgy+//LJU4/z8+XOEhYWhV69eWL9+PVatWoXCwkKcO3dO/TNNmjTBhg0b8NVXX0EikahP5erp6eHGjRu4fv06CgoKEB4eXmZzCbx7/3v16hUCAgIQFxeHnJwcvHr1Sl1v2LAh1q9fj3HjxqGwsBDJycl4+PAh0tLSMGnSJKxbtw6jRo0q83fo6+tjwYIFWLx4MQwMDFCzZk1s3LgRnTp1wtu3b5GdnQ1/f3+IRCLk5uYiOjoaSqUSt2/fhomJCbp16wYdHR0MGjQI33//PSwtLWFiYoJHjx7h4sWLyMzMFGyz/fr1w7p16zBnzhyIRCLcvHkTV69eVR91Xr9+PYYPH45Xr14hODhY47qrcmLd3d2xb98+/Pzzz5g/f36p5Aa5XI4zZ84AANavX481a9bAz88PFhYWOHv2LCpVqoQtW7bgt99+Q926dXH9+nX1e4+RkRFWrVqFVatWwdzcHJGRkfDy8lLfePrVV19h+vTpMDU1BfAuOWPHjh1YuHAhnj17htTUVHz33XdYv349xo4di5iYGAQGBpb596goqLksx8LCwjBp0iRMnjxZ8CbH8zyuXbuGr7/+GgsXLmS+CR4/fhzjx4/H6tWrmQHVHTt2hJGREYKDg9Xf3lWNiVwuR926deHq6qp+45fJZJDL5aU+uPT09NTNZskPCCMjI9jY2EBPTw8rVqzAw4cP8fDhQ1y7dg2bNm1S7+wASv3fJeXm5mL+/PnIyMgQ1O7fv49vvvkG06ZNE3zQ8jyPgIAAjB8/HkuWLGF+EB8+fBjjx4/HunXrmA3cxo0bMXHiRPX1pu9buHAhJk6ciNOnTwtqYrEYs2fPxrhx43D37l3m2LZu3YoxY8bgxYsXglpSUhLGjh2LyZMn482bN4L606dPMW7cOHz77bfM8O6bN29i4sSJmDlzpmBsPM/jjz/+wPjx47FixQpmfc+ePZgwYQL8/PwEDRzPv8u9nDBhAg4cOMBswObNm4eJEyfC399fsG75+fn47rvvMG7cOOab+Nu3bzF16lSMGTMGMTExgnpcXBzGjBmDKVOmMBt/1Wnf6dOnQyQSCeqXL1/GxIkT8f333wsaMJ7nceTIEUycOBFr1qwR1JVKJbZt24YJEyZg+/btgrpCoUBQUBCuX7+OEydOCOZGoVDg+++/x8SJE3H16lXBuolEIkyfPh1ff/01QkNDBfXExERMmTIFY8aMYW4XUVFRGDNmDGbNmsX80sOiq6sLNzc36OjoICMjA4sXL0ZsbKz6Omme50vtP3Z2djAwMFCf6VAoFGjQoAEOHTqE9u3bIzg4GKNHj8aJEyfU25bqhqX3j6B6eHiov7iyjqyqUi9U/1UoFKXmlLVfqn5GdZOTqokpuR3b2trC2NhY/UVWdUrZz88PtWvXxvTp0+Ho6AiZTFbmUeLs7GwsXLgQCQkJmDBhAry9vcFxXKkvRZUrV4aRkVGp36VaF1UsWFk3ZAHv3ldVUVs6Ojqws7ODnp4ejIyMoFAooFQqIZfLoaOjg9q1a6N58+aYOHEixowZo35vLXlEOzQ0FCtXroSxsTGmT5+O+vXrC97XWfNXcr1VN5vxPF/mFxmef5eIUDKpQlPihOrgBfD/24Pq31XbgK6uLpRKpXosurq6cHR0hL6+PvT19dXpCqrl3//72dvbq2O7FApFqTHq6emp15fQNZflWrVq1WBpaYmaNWsyM/rq1auHPn36oGPHjsx68+bN0atXL/j6+gruFOY4Dl26dMHw4cNx8OBBjBw5Eh06dEBGRgZu3boFe3t7zJ8/H5aWlnB0dISuri4uX76MqVOnolevXqWOQLBYWlpixIgReP78OdatW4eQkBBwHIfnz5+jTZs2pa7v0fTGamhoiJ49ezLzDWvWrIlevXqhQYMGzLugGzVqhN69e6Nbt27MeqtWrdCrVy907dqVeSdwly5dEBsbW+o0fUn9+vUDx3HM63OMjY3Rr18/ODg4aLxmq1mzZqhWrZr6aG9J1tbWGDJkCHR1dUsFpqu4urqif//+qFq1KjP0t27duujVqxdatmzJfCNv2rQpevfuje7duwvGznEc2rVrh549e6JTp07Muo+PD1JTU9GmTRvBa3Mch/79+8PExISZDamaG3d3d2YuppWVFfr374+srCxm7mWVKlUwePBgmJmZwdraWlB3d3dHv379UKtWLeaXlgYNGqjHxtruWrZsiZ49ewrC3oF3H4odOnTA8+fP0aFDB8Hc6ujooEaNGvDw8EDz5s0Fr62jo4MBAwbA1taWmadqamqK/v374/Xr18wnVtna2qJfv34oLCxkbhf29vYYNGhQqQ/Qj1Hyg1ipVKobpKioKI0/W3JuIiIicPbsWbi6usLV1RUSiQRJSUnw9vaGjY0N1q5di4yMDEgkEvTv31/wWpqEhoZi6dKlePXqFfLz89G+fXsYGhrC0tISeXl58PPzQ8eOHUu9Tp06dVCzZk0EBARg1apVePDgAfT19eHj41Pm71U1paqYtdzcXGaebkmq+dLT00NRURHzCVisSxKaNWsGKysr7N69G4WFheoUjr9KV1cX3t7eOHXqFLKysuDl5YXMzEykpaXBwsJCnVt66tQpFBcXw9bWVj3W7Oxs5pO5zpw5AwsLC/j7+8PU1BRt27aFrq4uNm7ciN9++w3R0dE4evQoqlWrhoYNG+LRo0fMdbOyskKnTp1w9OhRzJgxA/Xq1QPHcZg6dWqpps7X1xcBAQGYP38+evfujaysLEydOhU+Pj5Yt24d5s+fDysrKzx79gw9e/ZkvjeocBwHKysr8DyPgwcPIiMjQ51hWXKfbdeuHSwtLeHn54fk5GQcOnQITk5OaNKkCfNLf0VDzWU5Zm9vj19++YWZwcdxHGrUqIFDhw5BV1dX8EHHcRyaNm2KY8eOMfMHAcDExATr1q1Dq1atcObMGYSFhcHIyAijR4/GV199pf6A9PHxwYQJE3D37l3cuXMHtWrVQrNmzeDu7g6xWKx+k7C1tYWnpydsbW2ho6ODiRMnwsbGBkePHkVoaKi64RgwYAB0dXVhY2MDT09PjRe029jYYMaMGcx1r1q1Kvbu3atx7HXr1sXvv/+usd6qVasy56ZHjx7w9vbWeFRBdZE7q6lXnWpUKBQan5k+depU2NvbM+uVKlXCunXr1K/1vipVqmDHjh3qb/Lvc3d3x4EDBzSOvVGjRvj999+Z8Twcx6F9+/Zo1aqVxrH369cPPXv21BiRM27cOHz11VfMsenp6amPGrLqJiYmWL58OXieZ47d0tISmzZtUr/W+xwdHbFnzx6Nc1OzZk0cPnyYGdfFcRyaN2+Oo0ePMsfOcRy8vb3RsWNHjfXKlSujc+fOaNKkCbM+adIkTJgwgTl2fX19/PDDDxrnxtTUFKtXr9Y4dhsbG2zduhUcxzHHrto/VY2ntbU1atSooW7Cq1SpgkWLFsHPzw83btyAj48PMjMzYWlpCV1dXVSvXl2dzWpubg5PT09YWFjA3NwcmZmZePDgAeRyOUaOHIlRo0ahatWq2L17N3bs2IGjR4/CyckJQ4YMgZmZGWrUqFHqy4GFhUWpdQOA+vXrIyMjAzExMZg2bRrGjh0LAwMDfP3110hNTcWhQ4ego6MDX19feHp6wsbGBra2tti+fTvWrVuHP/74A5aWlli3bh369euHtLQ0uLu7w8HBARzHqX9npUqV0LVrVwQGBiIkJASVK1dGx44dkZmZCV1dXVhZWZWap5LzvWTJEmzZsgV3795F165dkZ2dDQsLC+jr68PDw0M9X5UqVVL/rkaNGmHt2rXYtm0b7t69iy5dukCpVKrvdi+pSpUqqFGjBgwNDWFgYIDq1avD3t4ewLujyKragAEDUFBQgCNHjmD37t2oUqUKevfuDX19fXTs2BGDBg1CWFiY+uaamTNnwt/fH5GRkejWrRtu3boFQ0NDmJqaokaNGmjfvj2ePHkCmUyG5cuXo127dtDV1cWWLVuwZ88eHD9+HA0aNMC0adPg5uaGFy9eqMenq6sLDw8P9d3/K1asQJUqVXDjxg1ERESgf//+pfYNjuPUT+E6evQoDhw4gLp168LAwAAzZ86Enp6e+hKhMWPGYPbs2TAyMoKDgwNq1Kihfh9T/U4A6NOnD27fvo3bt29DJBLBy8sL7u7ucHZ2Vjf8LVu2xLZt27Br1y4cO3YMnp6emDp1KmrWrInExET19l1RUYh6OcUKUf8UVNuP6rSI6oOpZN6j6lSB6nSBKpBaddpI9UGr+pmSgdU8z5d6bdWHPsdxkMvlgp+vCHJzc0uFqJdnPM8jOjoarq6u5f4xcjzPC0LU/01U+1vJ08yqL0CqdVWdYlXtq3K5XP3zJQPbVT9X8rVUp0dL7uMl93/V6U2efxf+rlq25O/V09NDWloaateuje7du+PAgQMAUOo9SbU8z/Pqf5fJZOpTryXfr94PmP+YMZQ89aqvr6/+OdYX0b8yX6rXZy1X8r2+5DhU77ElHwjwfk3186q5LvmeWnI+VGNSnXJW1VVfaFRzqTqVrpo/TX9P1Xu/prGr5qnk5wcrT1g1J6qjqqplVb+z5LKq7VmpVKrfV97/nSU/d/T19ZkPHPiY8ZT3R5tyGkLU6cgl+VtUO5mmI2yqnyl5baXK+80C62dUOzYL6+cJIZ/G+/sba/8red0bgFIfrO//e8laWQ92eH//VwXXl1Ty9QwMDNCiRQv189BZR9/ff+8p+Xqa3q/+zBhK/ntZz07/q/NV1nJljaPkON+vqRpVlvd/tqyxsx6jWvJ3sN7P33+99/8+H/P8+ffnpOTvZP37++vx/s98qK567Y8ZT0VEn8yEEELKDRsbG1y+fPlzrwYhFRo1lxVIyUsgWKdTP1T/L/u7Y1ed4tGU+aeN+seu34dq7y//Xxh7WfX3f+bPrP9/aW5YtLnd/Nm5+a8qL+Mg5L/s33VRD/mkcnJy8Ouvv+LixYvM+Ie3b99i9erVePDgATMiJiMjAwUFBcyIDdV1TEVFRerrXt6vFxcXl1mXSqUoKipiRjmUrLMiYBQKhbrOasTEYjH8/Pxw+vRpZnRQVlYWfvnlF1y7do25fFJSElatWoXHjx8z1/3Vq1dYuXIlXr58yaw/efIEq1evRkJCgqCuVCpx8+ZN9V2xrAia8+fPY+PGjcxn3SoUChw+fBg7d+5kZjsWFRVh586dOHLkiCBSiud55OXlYePGjTh//jxz7tPT0/Hzzz+rH//2vvj4eKxcuRIhISHMsT9//hwrV67UmP348OFDrF69GsnJycy5SUtLw5YtW5CdnS2oy+Vy/PHHH9i8eTMz91Imk2H//v3Yu3cvM1JKIpFg69atpaJvSq5bbm4uNmzYgIsXLzJjlVJTU7FmzRqNuZgxMTFYuXIlnj59yhx7eHg4Vq5cidjYWMGyPM/j7t27WLNmDdLS0pjbxYULF7BhwwaIRCJBXSaT4fjx44KHDqgUFxdj9+7dOHjwIHOfUCqVKCoqglQqZa67QqFAUVERiouL1XXVHdD0P/pfRfsf63OtIqMjlxXIo0ePMH/+fDRp0gRt27YV3F0YEBCAxYsXw9vbGwEBAYLld+zYgT/++AMDBgzA/PnzS9UUCgXWrFkDf39/jBs3Dl9//XWpukwmw5w5c/Do0SPMmzcPffv2LVUvLi7G6NGj8fbtWyxbtgzt27cvVc/MzMSECROQk5ODzZs3o27duuoaz/O4c+cOfv75ZxQXF+PQoUNwcHAotfzz588xe/ZseHp6om3btoI7zO/cuYP58+ejXbt2aNWqlSCi5/Tp01i8eDFCQkJw8uRJwdzs3r0b69atQ05ODtauXSsYu5+fHw4dOgRjY2PMmDGjVL2oqAg///wzrl+/DhcXFwwZMkRQ/+GHH5CYmIj69evDy8urVD01NRWLFi1Cfn4+WrduLYipiYqKUj8VqUuXLoIYjtDQUMyZMwf169dH27ZtBRE9169fx4IFC+Dt7Y0WLVoIIlZOnDiBxYsX49WrVzh48KBgbrZu3YqtW7dCIpFg+fLlpWpSqVT9iDpra2tMnDixVF0ikeDhw4dYsWIFPDw80Lt371L1goICzJ07F1lZWWjcuLEg3igpKQkLFy6EXC5H27Zt4enpWar+/PlzLFq0CObm5ujSpYv6edkqjx8/xty5c9GsWTO0adNGcPfnxYsXsXDhQvTp0wdNmjQp9aQq4F0e6k8//YQ3b95gx44dgrnZuHEj9u3bBwBYsGBBqVpRURHWrl2LgIAAODg4YPTo0aXq+fn5WLZsGUJCQlC3bl14e3uXqotEIsyePRuFhYVo1qwZmjYtfc19fHw8FixYAAMDA7Rv314QXxQaGorZs2ejUqVKOHTokGCfuHXrFhYuXIj69etjw4YNMDExgUwmw9u3bwXj1KSgoAB6enplXqdXXvA8D5FIBAsLiwpxdLW4uBjFxcXMuLPyhud5XLp0CUOGDFE/XKSio+ayAmncuDEmT56Mxo0bMzP8OnXqhAkTJpTKdCupe/fuSEpKQrt27QQ1XV1dtGnTBsnJycyMPj09PXTs2FEdmsyqd+vWDREREYIGAHgXIdOxY0ekp6fD2dlZULewsEDr1q2hr6+vzmUrydPTEzNmzICrqyuz3rx5c0yePBktW7Zk5tOp4pT69OkjqAHAoEGDIBaLMXDgQEFNX18fw4cPh6mpKXNuDQ0NMW7cONSsWROtWrVi1mfMmIGoqCjUr19fUK9cuTKmTJkCsVjMzDd0c3PDpEmTYG5uzozGqFu3LqZPny6IclFp06YNJk2ahA4dOpR6ApJK9+7dERsbyxw7AAwdOhRyubxURqGKgYEBRo0aBVtbW3Tu3FlQNzIyQuPGjTF+/HhmJqiJiQm+++47JCUloXbt2oK6g4MDpkyZAoVCwdxuPD09MWnSJFSpUoU59oYNG+Lbb79FgwYNmPtMhw4d8M0336gfi/e+Pn36ICUlBcOGDRPUgHeRVPr6+ujVq5egZmhoiDFjxsDFxUXwZQt493SXiRMn4vnz52jYsKGgbm5ujlmzZiEzMxM1atQQ1J2dndV5gap4mpLc3NzQokULVK1alTm2WrVqoWXLlmjevLm6zvM8ioqKmGN9H8/zKCgoUIdpl3c8zyM/P18dCl/eqY56V4QkD57nkZSUxDwDUFFRFFE5xYoiUp3K0tHRYd4V+LF1TW8WqlgGVv4hAPXpA03Ll1Uv+SSH92MolEolIiMjUaNGDY0ZfZ977NqcG5FIVCqKqKy5Uf1uVf2/Njc8zyMqKgouLi4wMjLS6nbzfv3vzI02xg5AEEX0d/YZ4P+fLPOhsWuq/9ntQiqVfvSzlVXNlr6+vuCIb3nE8+8eqWtra1vumy3g/5vLSpUqlfvx8jyPQ4cOYfr06cwHFJRnFEVESmV4fap6WZFEmj6kPqauqWksqaxvyJ977J9zbj6m/m+eG9X6a/rb/tvn5mPHzvqi/3e2C4DdMJf83Z9y7ISQiotu6CGEEEIIIVpDzSUhhBBCCNEaOqdRjpWMBwHYOXclM/bKqmvKyPtU+YZ/Jp+Q5c8u/1+am/d/7u/O3d8Z+4fqf3Zu3q+z/J3sxn/z3Ghah/f/XVv7zKfIBP2vXMNPCPm06MhlOZaTk4MffvgBu3btYmb0JSUl4bvvvsPJkyfVF/aXrEdGRmLq1Km4evUq80Pj8ePHmDx5ssbsxytXrmDKlCl49eqVYFmlUonjx49jxowZSE5OFtRlMhl27NiBH374ATk5OYJ6UVERfvnlF6xYsYKZb5iXl4fFixdj48aNgmxH4F1245w5c7B//37m2OPj4zFjxgycPXuWObZnz55h6tSpuH37NnNu7t+/jylTpiA0NJS5vL+/P7799ltER0cLllUoFDh06BBmzZqF1NRUQR0ADh48iIULF0IkEglqhYWFWLVqFdasWcPMN8zNzcWCBQuwdetW5t2Nb9++xffff4/ff/+due7R0dGYNm0aLl68yKyHhoZiypQpzLxU4F2EzdSpU/Hs2TNm/cyZM5g+fTrzxhCFQoG9e/di9uzZyMjIENSLi4uxYcMGLFmyhJkJKhaLsXTpUvz666/MTNCsrCzMnTuXuc8AQGJiImbOnMmMo+J5Hi9fvsS3336La9euMeuPHz/GlClTEBQUxJw7kUiEhw8fIjIykjk3x48fV98d/z6FQoGdO3di3rx5yM7OFtRV0UbLli1j7jP5+fnqfYaVCZqeno7vv/8e+/fvZ85NXFwcVq9eLdifCCEVDzWX5djLly+xbds2rF+/nhkP8ujRI2zZskVj+PaVK1ewfft2HDx4kNmgnThxAjt37sSxY8eYv3/nzp3YuXMn81FsEokE27Ztw5YtWxAYGCioi0QibNmyBb/99huzOc3KysKvv/6KzZs3IyEhQVCPjY3Fpk2bsGnTJuYHbWhoKLZu3YoNGzYwx3bnzh1s3boVu3fvZn7QXrhwAdu3b8fvv//ObNCOHj2qzgVl2b59O3bs2IHr168LaoWFhdi6dSv8/PwQHBzMXP7ixYtYv349YmJiBLW0tDSsXbsWfn5+zMY9MjISGzduxKZNm5jNaWBgILZt24aNGzcym4hr165h+/bt2Lt3L/NLy9mzZ7F9+3YcP36cWT98+DB27NiB8+fPC16b53ls2bIF27dvx507dwR1sViMzZs3Y9OmTXj69Kmgnp6ejs2bN2P9+vXM7SI5ORnr1q2Dn58fs3F//vw5Nm3aBD8/P2Zzev/+fWzfvh1+fn7MJurixYvqfeb9sSuVSpw6dQrbt2/HH3/8IVheqVQiIiIC4eHhuHLliuC1FQoF/Pz8sG3bNjx8+FBQz8vLg5+fHzZt2oQXL14I6qmpqfDz88Ovv/6KlJQUQT0+Ph7r16+Hn58fs3EPDw+Hn5+fxsD6W7du4eTJk3T0khBCp8XLswYNGqBjx45wd3cXRH1wHIcOHTrgxx9/RLNmzZj5hX379kV2dja6devGvON1zJgxMDExwZAhQwSnyTiOw6xZs1CvXj1mvqGxsTHmzZuHLl26MDP8rKyssHDhQiQkJGjMdly1ahWKi4vh7u4uOMrl6emJZcuWwcrKCra2toLlW7RogcWLF6NWrVowMDAQrLuPjw+WLFmCtm3bCuoAMHDgQEgkEvTu3Zt5x+z48eNhY2ODYcOGMU8hqoK5WfmGpqamWLBgAZ4+fcrMvQSAIUOGoH///qhVq5ag5ujoiNWrV0NHRweurq6Cet26dbFs2TI4OjoyA3/btWuHRYsWoWHDhoK7iTmOQ+/evZGRkYHOnTsLxs5xnDrT8YsvvmAuP2nSJDg7O2vcbubPn4/27dszM0HNzc2xaNEiREZGCkLBAcDe3h5LliyBSCSCu7u7oO7q6opVq1bBxMQETk5OgnqjRo2wbNkyuLm5MTNBO3fujEWLFqF58+aCu7Q5jsMXX3wBsViMbt26Ccauo6OD0aNHw9jYGEOGDBEsr6urCy8vL7x9+xb9+vUTzI2uri4WLVqEJ0+eoFOnToJ1s7CwwOLFixEfH8/MvXRycsKSJUsglUrh4uIiqHt4eGDVqlWwsrISBO0DQLNmzbB06VLUrl2bGYzdrVs3iMXich87Qwj5MMq5LKdK5lyyMuxUNF2PWbL2ofpfeW1t1lWn8GvVqlXqZz/l7/+cc5Obm1sq51Lbr/9369qcG9VpeFdXV3WT/2/Z7rQ9NzzPC3Iu/03r/jF1yrnUjHIuyy/KuaScywqprB37r9b+TXVNX5A+5e//3GP/kM+5fp97bv7N9f/y3/Vj6oQQAtA1l4QQQgghRIuouSSEEEIIIVpDp8UrkI+9Fu5j6qyf+Zz5g9r8/X93bj7lurH8G/INP2W95M99zmzHD9W1sU/9meX/TWNn1QkhFRcduaxA3rx5gy+++AK//vorMz4nIiICvXv3xt69e5kZfLdv30aPHj3g7+8vWFapVOLs2bPo0aMHM99QqVRi79696Nu3L54/f86s//zzzxgyZAgzQkYqlWL+/Pn46quvkJWVJajn5+dj8uTJmDZtGjMmJT09HcOHD8dPP/3EjF2KiYlBv379sGnTJmb8TkhICHr27InDhw8z5+bq1avo3r07rly5whzb8ePH0bNnTzx58kRQVygU2LZtG/r374/Xr18L6nK5HMuXL8fw4cOZETISiQTff/89xo8fj9zcXEE9NzcXEyZMwKxZs5i5lykpKfjyyy+xcuVKZixTZGQk+vTpg+3btzPn5tGjR+jevTtOnDjBnJsLFy6ge/fuuHXrFnNuDh06hF69eiE8PJw59sjISIwaNQpxcXGCukwmw6JFizB69Gikp6cL1q2goADTpk3DlClTmNFCWVlZGDNmDH744QfBdsHzPBISEjBo0CDmPqPKO+3duzf27dvHnJu7d++ie/fuOHfuHHNuTp8+je7du+P+/fuCZRUKBfbs2YPevXvjxYsXzLH/8ssvGDx4MBITEwV1qVSKuXPnYuzYscw4rry8PEyaNAkzZsxAYWGhoJ6WloaRI0fixx9/FGwXPM8jNjYWX3zxhcZ9hhBScVFzWYG8evUKFy5cwOnTp5lNRlBQEK5evYrTp08zl7916xauXbvGzOBTKpW4cuUKrl27hrt37wrqcrkc/v7+uHz5MjO7US6X4+TJk7h06RJevnwpqOfl5eH06dM4d+4cEhMTBfXU1FT4+/vj5MmTzAYrPj4eZ8+exZkzZ5hNxrNnz3Dx4kWcOXOGmQn68OFDXLt2DefOnRPUAOD69eu4fv06MzxbLpfj0qVLuHr1Kh48eCCoy2QynD9/HpcuXUJ4eLigXlxcjBMnTuDChQt4/fq1oJ6Tk4PTp0/j7NmzePv2raCelJSknpv3x666G9vf3x9nz56FWCwWLB8WFoZLly7h3LlzzMb8/v37uH79OvNLBwBcvnwZ169fx82bN5ljDwgIwJUrV/D48WNBXSqVIjIyElevXsXz588FdYlEgpMnTyIgIACxsbGCemZmJs6cOYMzZ84wm8/4+HgEBATgxIkTzAYrMjISFy5cwNmzZ5n7THBwMC5fvozz588zG/Pbt2/j+vXruHjxoqAGvMtLvX79OjPTU7VdXL58GSEhIcyxnz17FgEBAcztQiwWq+fmzZs3gnpaWpp6bjIzMwX1mJgYXLhwASdOnGDuE6p9RtN2QQipuOi0eAXSpk0b7Ny5EzVq1GDm1Kmy9Zo1ayaocRyH8ePHw9nZmZmxp6uri++++w5eXl7o0aOHoG5gYIAlS5agd+/e6NOnj+AUmoGBATZu3IjY2Fi0bdtWsLyNjQ38/PyQm5uL2rVrC+pubm7YvHkz9PT0mBl9DRo0wO7du2Fvbw9ra2tBvXPnzti5cyfq1KkDExMTQX3w4MEwMjJC69atBTWO4zBlyhRUr14d3t7egrq+vj7mzp2LNm3aoHfv3oK6kZERli9fjoiICPj6+grmxtjYGJs3b0ZSUhJatGghCHW3t7fH1q1bUVhYiOrVqwtev2bNmtiyZQtMTExgY2MjWPdmzZph586dcHFxgaWlpWD5bt26YceOHWjUqBEzMmbEiBGwsLBAhw4dmLmVM2fORP369dGtWzfBsgYGBli0aBG8vb3Rt29fQd3ExARt27bFhg0b0LFjR8Hrm5mZYevWrcjIyECjRo0Eyzs7O2Pbtm1QKpXMzM/69etj69atsLKyEuRachyH1q1bY8eOHfDw8GDuM3369IFSqUTTpk1haGgoqI8dOxb29vbo3Lkzc27mzZuHFi1aoGfPnoJlDQ0N8dNPP6FPnz7o2bOnYHlzc3P88ssviIuLQ6tWrQR1KysrbN++HSKRCHXr1hW8frVq1bB9+3bo6+szMz+9vLywdetW2NvbC8bOcRw6deqE7du3o3bt2hUiSogQ8vEo57KcKplz+X6Yc3mjVCqZOZfl1cfkXJYXrJzL8upjci7/7SjnUjPKuSy/KOdSmHP533v3IoQQQggh/1rUXBJCCCGEEK2h5pIQQgghhGgNNZflGM/zKCwsFNwAUrKen5/PvMtVVc/Ly9MYM6JUKpGXlwelUsmsKxSKMusymQz5+fkas/6kUikKCgo01ouKiph38KrWvbCwEEVFRczleZ6HWCz+4NywIpuA/x+7prn50NjlcnmZY//Q3BQXFzPvblaRSCSQSCQax15QUACpVFrm3JS1XXzM3PzV7aKsueF5HsXFxRCLxWVuF2XNzcduF5rqZe0zH9ouPjQ3wLu//V/ZLj40NzzPf/TcsHzM3LBiwAghFQ81l+VYQkICvvjiC3z//feCJorneYSHh6NXr15YvXo1M8fu1q1b8PX1ZeYb8jyPkydPwtvbGydPnmRm+G3duhU+Pj64ffu2YN1kMhmWL1+OHj16ICIiQlAvLCzE999/jwEDBjBzL0UiEcaNG4eRI0cycy9TU1MxZMgQTJkyhflhGhUVhX79+mH+/PnMsQcFBaFHjx5Yt24dM9/wypUr8PX1xf79+wWNAs/zOHLkCHx8fHD+/Hnm3Pz222/w9fXVGE20ePFi9OrVixnLBAArV67EoEGDkJycLKhlZ2dj1KhRGDt2LDOWKSkpCQMHDsSMGTOYETLPnz9Hr1698OOPPzL/7vfv30e3bt2wefNmZv38+fPw9fXFkSNHmHOzf/9++Pr6MjNBeZ7HL7/8gu7du4N1A59MJsO8efPQp08fREdHC+r5+fmYNGkSvvzyS6SlpQnqGRkZGDZsGCZMmIC8vDxBPS4uDv3792fuM8C7WKYePXpg1apVzLHdvHkT3bp1w86dO5n1kydPwsfHB3/88Qdz7MnJyQgICMDdu3eZmaArVqxAz5498fTpU8G6FRcXY9asWejfvz8zeigvLw9ff/01RowYwYweevv2LQYPHqwxEzQqKgq9e/dm7jMA8PjxY0yePJkyLwkh1FyWZ2lpaQgODsa9e/eYHwZv3rxBUFAQgoKCmEehXr9+jeDgYISGhjI/MJ4+fYqQkBBmNiMABAYGIiQkBK9evRLUZDKZ+nezPgglEgkCAwPx8OFDjeHYd+/exePHj5kB0ZmZmQgMDMSjR4+YR1OSkpLw5MkT3Lt3jzm22NhY9fqx6q9evUJwcDDCwsKYR6HCw8MREhLCbJyBd8HjISEhiIqKEtSkUikCAwMRFBTEzPQE3mUQPnjwgNkk5Ofn4969ewgMDGQ2l2lpaeq5YTXeCQkJCA4Oxv3795lji4qKQnBwMJ48ecJsoF68eIHg4GA8ffqU2UCFhoYiODgYL168ELw2z/N4+PAhgoODERMTI6gXFRUhMDAQjx8/ZgbK5+fnIzAwEPfu3UNOTo6gnpubi/v37yMwMJDZXL59+xaPHz9GYGAg8wheXFwcQkJCmA8KAP5/uwgJCWE2h8+ePUNISAiePXsmmDulUonk5GSkpqYiMjJS8NpKpRIPHjxASEgI4uLiBHXVPvP48WOkpqYK6iKRSD03IpFIUM/JycGDBw8QGBjIzDtNTk5GUFAQAgMDmV9KoqOjmX9TQkjFQ1FE5ZRYLMbatWvRvn172NnZoU6dOoI4iMLCQty/fx9ubm7w8PAoVVed/rt//z7q168PJycnQT0zMxNBQUFo1qwZM34hKSkJERERaN26tSBDkOd5xMTEIDY2Fu3atRNEkSiVSjx//hzZ2dlo1aqVIIZGoVAgNDQUMpkMTZs2RXR0dKkoIplMhsDAQJiamqJhw4aCWJeioiI8ePAAjo6OqFmzpmBuCgoKcP/+fdSoUQPVqlUTjF0kEuHhw4do1KgR7O3tBfX09HSEhISgefPmgmxJnueRmJiIFy9eoE2bNjA3NxfUX79+jcTERLRt2xZGRkal6rm5uXj8+DGMjY3RsmVL6OvrC+bmyZMn4DgOTZo0EURRyWQyPHjwAFZWVqhfv75g7BKJBPfv30fVqlVRo0YNQT0/Px8PHjxA7dq14eLiIhh7Tk4OHj16hKZNm6Jy5cqCempqKsLCwtCqVStBribP84iPj0dUVBRat24NU1PTUlFEPM/j5cuXSE1NRZs2bQTZkkqlEmFhYZBIJGjRogX09EpH+crlcgQFBUFfXx9eXl6C7aK4uBj3799H5cqVUbduXY37jLu7O9zd3QV11XZRv359QVRUyX1GtV28X9+4cSPu3buHXbt2MfeZ2NhYxMbGok2bNoI8Vp7n8fz5c2RmZqJ169aCfUapVCI4OBhyuRzNmjUTzM2H9hmpVIp79+7B0dGRGfslFosRFBQEBweHj4qeoSii8o2iiCoGTVFE1FyWU5RzWX5RzmX5RDmX5Rs1l+UXNZeUc0kIIYQQQj4hai4JIYQQQojWUHNJCCGEEEK0hprLCkSpVCI1NZV5Jyjw7kaQlJQUjfmIMpkMKSkpGvMRpVIpUlJSmHeeq3In3759y7z7WnX9VVpaGvMOZZ7nkZubi4yMDI0ZgFlZWcjOztaYwZeeng6RSMSsK5VKvH37VmNOn1wuR0pKisYMQJlMhuTkZI0ZgEVFRWXOTUFBAd6+fatx7Hl5eWXOTU5ODjIzMzWOPTMzEzk5ORrraWlpyMvLY9YVCgXevn2rMR9RLpcjOTmZeQcx8O4mmeTkZI3ZkBKJBCkpKRojbORyOdLT0zWOXSQSIT09XePYsrOzkZWVpbGekZGB3NxcjdtFamqqxmzJkvsMi2q70DQ3Uqm0zLkpLCzUODeqfSY1NfUv7TM8zyMrK6vM7SI9PV3jdlHy/eS/cu0+IeSfQc1lBRIeHg5vb2989913zA+7GzduoEuXLli6dKmgxvM8jh07hk6dOmHHjh2CukKhwNatW9GpUyecOnVK8GGjUCiwYsUKdO3aFbdv3xbU5XI5ZsyYge7duzMz/MRiMcaOHYs+ffowo4vS0tIwZMgQDBkyhBlNFB0djR49emDSpEnM5jooKAhdu3bF3LlzmfmGly5dQqdOnbBmzRpmvM7+/fvRqVMn7Nu3jzk3GzZsQKdOnRAQECBYXpVr6e3tzYy4KS4uxuTJkzXmXopEIowcORIDBgxgxvMkJSXhiy++wIgRIwQRNKrooO7du2PatGnMBvLBgwfo3LkzFi9ezJybs2fPolOnTli/fj1zbnbu3ImOHTvi999/Z/7d16xZg86dO+Pq1auCulQqxZMnTzBgwAAEBwcL6hKJBF9//TX69u3LjHXKysrCl19+icGDBzMjreLj49GvXz+MGTNG8MWC53k8ffoU3bp1w/fffy/4YqHKgu3cuTOWL1/ObBBPnDiBjh07YuvWrcy58fPzQ8eOHfHHH38IlpXJZFi2bBk6d+6MO3fuMMf+3XffoVu3boiIiBDUxWIxRo8ejf79+zNvsklLS8PAgQMxdOhQ5j7z+vVr9OnTB+PHj2eO/cmTJ/Dx8cH8+fM1PoyAEFIx6X34R0h5IRaLkZiYCFdXV8jlckGMS05ODpKSkpgNCvAuOzIpKYn5Ia06ypGYmMj8oFIdGUxMTGRmL6rieZKSkpgBzjKZTL1urKNERUVFSE5OBs/zzA/5wsJCJCQkwNTUlFnPz89HYmKixiOr2dnZSE5O1jg3GRkZSEpKQkZGBnPsaWlpSExMZGYvlpwbVvaiUqlEYmIikpOTmY1xcXExkpKSIBKJmEdWJRIJkpOTYWxszBybam5sbGyYR1bz8vLUc8M6QpaVlYWkpCRmtiLwrolJTk5mZnKWnBvWdqFUKpGfn4/k5GTmdqFQKJCQkICUlBRmY6yaG7lczvxCVVhYiOTkZBQVFTHHLhaLkZCQABcXF+bc5ebmIjExUePRw8zMTCQnJzMD3YEPz01qaioSExOZuZQKhQLJyclITExkHnFXzU1ubi5zn5FKpUhKSoKhoSGzOSwoKEBSUhJ0dHSYY//QdkEIqbgoiqicYkURyWQyvHr1CjY2NnB0dBQsU1RUhFevXsHZ2VmQwQe8+7B5/fo13N3dmfESubm5iI+PR40aNWBqalqqpjo1m5KSglq1agkaW57n8fbtW2RnZ6NWrVqCDD6e5/HmzRsUFRWhRo0apeKVlEolXr16BT09Pejq6sLNzU0Q46JQKBAZGQkzMzNBNiPwrgl59eoVqlSpAnt7e8HcSCQSREZGomrVqrC2thbU8/PzER0djerVq8PMzEzw+jk5OXjz5g1q1qwpiF1RNeZpaWmoVauWIHJH9eQWkUiEmjVrQiwWl4oiUmVDymQyVK9eXTB2pVKJqKgoGBgYwNXVVVCXy+WIjIyEhYWFIM9UNTcvX76Eg4MDqlSpIhh7YWEhIiMj4ebmJsitBN41ITExMahRowbMzMwE9aysLCQmJqJWrVqCTE9VdqNCoUCdOnUEmZ6qLyUFBQXw9PQUxG4plUrExcVBqVTCw8ODOTevX7+GsbExqlatKhi7XC7Hq1evYG1tzcxvlEqlePXqFZycnGBraysYW8l95v3cSuDdPhMXFwdPT0+YmJgIoogyMjLK3GdSUlKQk5PzwX3G09OTOfaYmBjo6OgwMztV+4y5uTmcnZ0FddX7SeXKlWFnZweO4yiKqAwURVR+URQR5Vx+FjzPQ6lUqo+Y6evrQ0dHR7DDqY66sY4SGBoaqt+8S/7NOI5T10qinMvyi3IuyyfKuSzfqLksv6i5FDaXdFr8H5CdnY1169bhwoUL0NXVRb9+/TBt2jRUqlSp1M/xPI8FCxbg9OnT6v+f53lUqlQJ+/fvR7Vq1TBkyBD1tWUcx8HW1hb+/v7MoyaEEEIIIf80ai4/MblcjpUrV8LPzw8DBw6EVCrFypUrwXEc5s2bV+qoIsdx6NChg/q0a2ZmJrZs2QIAsLa2hlKpRFJSEgwMDDB8+HDo6urC1NS0QnzrJ4QQQsh/AzWXn1hqairOnDmDatWq4eeff4ZcLseDBw9w5MgRTJ06tdR1WBzHoVevXujVqxd4nseuXbugUCjQp08fODo6qm/20NPTA8/zsLW1ha+vr+AZw4QQQgghnws1l59Ybm4uUlJS4OXlBSsrK3AcBwsLC6SmpiI7O5t5kT/w7prJQ4cOwdzcHMOGDVNff2VmZob09HTs3r0bqampaNCgAY4ePQp3d3cA726+iImJgUgkglgsRl5e3t+6dovn+TKvl/nU9Y9dR6lUCpFI9I9e2/N31/2vzo1YLIZEImHeWf5voY2/q0pRURHy8/MFN6z8V5U1N6rr1PLy8v6T16kVFxdrzIJlkclkWt1W/s14nodcLkdRUVGFGG9xcTFkMpnGjNfyhOd5jTm9FVX5eLf+F1MqlVAqleA4Tv2GoqOjU+bGyPM8Hj58iODgYHh7e6Nhw4bgOA6mpqbYtm0bXFxcUFRUhOnTp+P06dPw9/fHt99+C47jUFxcjNevXyMnJwehoaEYPnw4LC0tsWzZMsHdpo8ePcL27dtRs2ZNzJw5U1C/dOkSfv/9d7Rs2RITJkwQfLgfPnwYV65cgbe3N0aMGCEYx4YNGxAWFoYhQ4age/fupWpSqRRr167F69evMWXKFDRv3rxUPS8vD6tXr0ZqairmzZsHT0/PUvXs7GwsXLgQOjo6mDdvHnR0dJCfn6+e4/j4eKxcuRJmZmZYvHix4C7m8PBw+Pn5oXLlyvjpp58EdyHfuXMHe/fuRb169TBt2rRSdZ7n4e/vjxMnTqB9+/YYM2ZMqcsbVLmXt27dQo8ePTB48GDB3Pzyyy948eIFhg8fjq5du5aqFRUVYfXq1YiPj8f06dPRuHHjUvWCggJs2LABsbGxWLhwIdzc3ErV09PTsXjxYhgYGGD+/PmCu9+jo6OxevVqWFtbY9GiRTA3Ny9VDwoKwrZt2+Dk5IQlS5YIbgi7du0aDh06BC8vL0yZMkUw9lOnTuHcuXPo2rUrRowYUerLjeqI/P3799G/f3/07du31GvzPI/Vq1fj9evXGDNmDNq3bw+pVKpuLgsLC7Fy5UokJydj1qxZqFevXqnls7KysHLlSohEIixZsgQuLi6l6ikpKViyZAnMzMwwf/58wcX3L1++xC+//AI7OzssXLhQkHrw4MED7Nq1C25ubliwYIHgi1tAQABOnDiBFi1aYOLEiaXqSqUSR48exaVLl9TbRcm6QqFAWFgY7t+/jyZNmqBbt26lXlupVGL58uV48+YNxo8fj5YtW5aqFxQUYNmyZUhPT8fcuXNRs2bNUvX09HSsXLkShYWF+Omnn+Dg4FCqnpCQgKVLl8LS0hLz588XpCJERERg3bp1cHZ2xvz58wWX49y6dQuXL1/GlClTPvoLraq5rCgUCkWFyQSVy+UaI8DKG2ouhai5/MTMzc1hY2ODvLw8FBQUqO+QtLCwgLW1teDOb+DdTnno0CEolUqMGjVKfZcsx3GoW7euusnz8vLCqVOn1E8fUTWgffr0QUFBAa5du4bQ0FAYGBjA0dFREPNSuXJlxMbGwsPDA87OzoK7cS0tLREdHY02bdrA2dlZ0GSYmJjg9evX6Nu3L5ycnARj19fXx+vXr2FpaSmoq77VxsbGwtbWVlA3NzdHfn4+kpKSYG9vL6gbGRkhOzsbCoUCjo6OKCgoKBWjU1xcjJSUFFSpUgXOzs6CI8SpqamIj4+Hubk5nJycBM2ljY0NYmJiUKdOHTg7Owsaa3Nzc0RHR6Nr165wdnYWfJgaGxvj9evXGDJkCHNudHR0EBUVBWtra0FdKpVCKpUiLi4OlStXFtRzc3ORnp6O5ORk5tzo6uoiIyMDBgYGcHJyEjSXYrEYSUlJ0NPTg7OzsyAe6M2bN4iLi0OVKlXg5OQk+LtbWVkhOjoaTZo0YdbNzMwQHR2NXr16CSJseJ6HgYEBoqKiUKlSJcG6q25ii46Oho2NDZycnCCRSODo6Ah9fX1IJBIUFhbizZs36vUrSbVdZGRkwMHBgfn6aWlpkEqlcHJyEjSX2dnZSEhIgJmZGZydnQWXnNja2iImJgaurq7MeB5LS0tERUWhTZs2cHJyEjTWJiYmiI6OhomJiWB5pVIJS0tL5OfnM/cZpVIJhUKBmJgY5j5TWFiIgoICJCYmws7OTlDX19dHZmYm8vPzmXOjegIXz/NwcnISNJdpaWmIj4+HjY0NnJ2dBe8nNjY2SE5ORqVKlT66ueQ4Dvr6+oLXKq+Ki4sFN3KWV6r3sYoy3vKeZvFnURTRJyaVSjFq1CicO3cOP/74IwoLC/Hzzz/jq6++wsaNGxEWFoY5c+agT58+mDFjBgDgxYsX6NKlC6pWrYrLly/DysoKwLujbevXr0ePHj0gk8mwcuVKxMXF4dChQ+jfv3+pDyqxWIwNGzZg5MiRMDc3V5+SL0mhUCAxMRHW1tYwNzcXNAFyuRyJiYmwt7eHsbGxoK56dJ2zs7PgqCfw7sMuNTUVLi4uzHzC/Px8ZGdnw8XFRdCgqB7bV1hYyPwQV2VDKpVK2NnZCaKIVMHkBgYGzOgP1c1R5ubmsLS0ZM5NQkICbG1tBbmVqsiopKQkODg4wMjISFBXPe7RxcWFmVtZUFCA9PR0VK1alZlPKBKJIBKJmE19bm4uEhIS1I0pa25Uod2q/MH3x56SkgJjY2NYW1szx56UlARLS0tmjIhcLkdCQgKqVKkCU1NT5twkJibCyclJEJPF8zwkEkmZ24VYLEZmZqZ6uygZRaR6pGF+fj6zqVc9zlEmkzGjmlR5qrq6uqhSpQpzbpKTk2Fqavqn9xkA6rHb2dnBxMREMHZVqLvqy9z79c2bNyMsLAxbt279S/tMTk6O+osWa25U+4y9vT1zblJSUsrcZxITE1GpUiXmPqPaLoqLiz/qtC9FEZVvFEVUMVAU0WdiaGiIpUuXIi8vD6tWrYKOjg66d++OefPmQV9fHyKRCI8ePVKf3uN5HmfOnEFeXh6GDx9e6oibkZERXr9+jXPnzqGoqAh2dnaYM2cOfH19mTuvjo4OszlR0dXVRbVq1Zg11REF1bWcrLqRkRE8PDw0jt3ExKTM5StVqqTxWy3HcbCxsYGNjY3Gup2dHQAwnw6io6PDPGJYsl61alWNdVUYu6bfbWBgUObYjI2NNc4Nx3EwMzNjBoqr6paWlsxAchVVED7r785xHDMIXkW1XWiiq6sLV1dXjXU9Pb0yx25gYFDm2D+0XZibm6tP1b//5ZfjOFhZWam/cLGWZwW9l6yzHiCgoqOjIziVXlJZ+wyAD+4zhoaGZe4zwLsjv6x99mP2GWtra2bIv6qu2mc01T+0z3xou3BycvronEtCSPlFzeU/wNPTE8ePH0dqaio4joODg4P6dFvLli0REhKibiQ4jsPo0aPxxRdfCI7MeHp6IiAgANnZ2ZDJZDAzM4O9vX25udGBEEIIIf991JX8A94/GlOSmZkZateuXerfNB1V+tBRG0IIIYSQz+2/93wxQgghhBDyr0VHLiuQ4uJihIWFwc7ODlWrVhVcr1dYWIjw8HC4ubkxbwTJy8tDREQEateuLbjZQXUDzqtXr9CgQQPmTTCpqal48+YNGjZsKLiAn+d5JCQkID09HQ0bNmTeBBMVFYXCwkLUq1ePeRNMREQE9PT0UKtWLcHNDHK5HE+fPoWFhQXc3d0FY5NKpQgNDYWTkxPzBqKCggKEh4ejevXqzGv6RCIRnj17hrp168LCwkIw9szMTLx+/RqNGjVi3uiRkpKCpKQkNGzYUHDnLM/ziI+PR3Z2Nho0aCD43UqlEq9fv0ZxcTHq1q0ruF5PoVDg+fPnMDAwgKenp2BuZDIZwsPDYWNjg2rVqgnGXlRUhNDQUFStWpV5nadYLEZ4eDhq1qzJfAxpTk4OXrx4gXr16gnu2lfdZBITE4NGjRoJbhwD3m2XISEh8PLyYm4XMTExyM/PR/369QXbhVKpxMuXL8HzPOrUqcPcLp49ewZTU1N4eHgI6sXFxQgPD0eVKlUE+4zqxq3Q0FC4u7t/cJ9hXQuZlZVVap95f2ypqamIi4uDl5cXc7t48+YNMjIy0KhRI8ENQEqlEtHR0SgsLET9+vWZ28WLFy+gq6v7l/eZ8PBwODg4MPcZUn7wPI9Hjx7h5s2bAN5de+zo6IjOnTvD0dERERER8Pf3B/D/18u3aNECrq6uSE1NxcGDB+Hh4YEvvvjicw6D/IPoyGUF8uDBA/j4+GDixIkoLCwU1M+dOwcfHx/MmjVLUON5Hjt37oSPjw9++eUXQV2hUGDNmjXw9fXFvn37BHWZTIa5c+fCx8cH/v7+ghs1iouLMWbMGPTo0QMPHz4ULJ+dnY1hw4ahZ8+eiIyMFNQTEhIwcOBA9OnTR32ndEkRERHw9fXF6NGjkZOTI6jfunULPj4+mDp1KiQSiaB+/PhxeHt744cffhCsO8/z2LhxI7y9vfHbb78JlpXL5Vi6dCl8fX1x5MgRQV0qlWLmzJnw8fHB1atXBa8vkUgwYsQI9OzZE6zEhIyMDAwePBh9+vRBbGysoB4dHY1+/fphwIAByM7OFqx7SEgIfH19MW7cOGYw++XLl+Hj44PvvvuOmVl38OBBeHt748cff2TOzdq1a+Ht7Y3t27cL6jKZDAsWLICPjw/++OMPwWtLJBLcu3cPAwYMwJ07dwTLi8VifPnll+jduzeePn0qWD4lJQVffPEF+vfvj4SEBEH95cuX6NOnDwYNGiQYu+oD1dfXF9988w0KCgoEy58/fx4+Pj6YO3cuM79w9+7d8Pb2xqpVq5hzs2LFCnh7e2Pv3r2CZYuLi9U37LH2GbFYjIkTJ8LX1xeBgYGCukgkwsCBA9G3b1+8evVK8PoJCQno168fBg4ciLdv3wrq4eHh6NWrF4YNGyYYO8/zuH37Nnx8fDBt2jTmPkPKl2fPnuHgwYMICgpCeHg41q5diwULFqCgoAAxMTE4cOAA7t+/j5iYGOzZswfTp09HTEwMsrKycOjQIXVjSioGOnJZgVhbW8PDwwPVq1dn3gRkZ2cHNzc31KhRg7m8i4sLqlWrxrxbVnUnabVq1TTmOnp4eKiP8LDqNWvWhFgsZh7hMTQ0RPXq1WFqasq8W9bExAQeHh7Q1dVlxiJZWFigRo0aqF69OjOPzNbWFu7u7vDw8GDOjaOjI9zc3FC9enVBDQBcXV3h5ubGvJtWR0cH1apVg5ubmyC4Gnh3FMDDwwMeHh7MGAvVkSWe55nX2xoZGaF69erIy8tj3oFubm4ODw8PmJmZMcduaWmpnpv3j34BQJUqVdRzw7qL2cnJCdWqVdN4F7Rqm2Hdha2jowN3d3e4ubkx73DX1dWFtbU13NzcmMkBqiPVJiYmzLvrTUxMUL16dSgUCkEgOvBuu/Dw8ECVKlWYf/eP2Wfc3d3h7u7OnBvVPqMpecDd3R3VqlVjXmetq6urnhvWPqOnpwcPDw/k5OQwtwt9fX3UqlULGRkZzH3G1NQU1atXh6GhIfMRspaWlvDw8ICbmxtzbLa2turtlm4qrBh0dHQwfPhwdOnSBd988w2io6ORmZkJ4N0Ry969e2PcuHHYvn07tmzZgidPnqBNmzafea3J50A5l+WUWCzGxo0bMXfuXPUHgyoj0NjYWJA/CLw7jZabmwszMzPo6+szM/5yc3NhYWEBXV1dwSlChUIBkUikPmXOyj8Ui8WwtLRkZvBJpVIUFRUJTiur6hKJBHK5XJAvqFQqERkZqc72ez97UbV8Xl6eOlOPVc/JyYGpqakgf7Dk3JibmzMbsA/NjVwuR15ensa5KS4uRkFBATNbUXX6VSqVwsLCAiKRCAUFBepT1DzPo7CwEAqFgpm9qMoT1NXVFZySV9VFIhEMDQ0FmZ0l5+ZjtgtWkyGXyyESiWBpacnMZvzQ3ERGRsLGxoaZD6jaLmQyGTNPT5UpyvO84FKNknOjp6encbsoa59RXQ6i2i7+ztzo6Ohg8+bNiI6Oxrp168BxHGQyGfLz85l5pB+zz3xouxCLxeqHL2jaZwwMDDRuF7m5uTAxMVHvM1Kp9KOjiCjn8r9D9WStDRs2YOrUqWjcuLH6yWc7d+7EjRs3MH/+fMyYMQNjx46Fn58fduzYge+++w6tWrXCsGHD0L59e/j5+X3uoXwSlHNJOZcVmupuc010dHQ0ZuQB746klJU7qaenV2bdwMCgzAw+IyMjjU/qUOUjloX1AVpyeU3PcVfVyxr7350bfX39MuuGhobMI66qurGxscYPYFVzoIkqH7GselmZmh+am7LGDuCD20VZc6N6fVbzpFq+rO1ClSlaVv1Dc1PWPqO6vkyTPzM3rExPAwODMufuQ/vMh7YLVoJFyfqH9hlKrqhYFAoFdu3aBZ7noaenhx9++EG9//A8j2PHjuH69euIi4uDs7MzWrRo8ZnXmHwu1FwSQggh5IN0dXXx1VdfwdLSEuvXr8exY8dKNZBVq1ZF06ZN0b9/f3h5ecHOzg5JSUmfcY3J50LNJSGEEEI+ipOTE3r27Ing4GDcvHkTDx48APDuSHarVq0wceJEAP//+EdSMdHd4oQQQghR43klpPI0ZEvuI0tyD0XytzA01IeFhQX09fVhaGiIL7/8EpUqVcKFCxfA8zwsLCyYl/aoLmsp6xINUv5Qc1mOSaVSXLp0CSEhIcznbxcUFODs2bPqHMCSVDdxnDp1CvHx8cx6amoqTpw4gdTUVObvj4uLw6lTp5jRPzzP48WLFzhz5gwz4kWpVOLJkye4ePEi89uvQqHA3bt3cfPmTSgUCkG9uLgYV69exaNHj5hjl0gk8Pf3R3h4uGBsAJCfn4/Tp0/j9evXzLFnZWXh5MmTSExMZNaTk5Nx8uRJZGRkMMceHR2NP/74AyKRiFmPiIjAuXPnmJFRABAaGoorV64w42/kcjlu3bqF27dvM+dGKpXi8uXLCAoKYs5NYWEhzp07h2fPnjHnRiQS4Y8//kBMTAxz7Onp6Th58iRSUlKY9YSEBJw6dQpZWVnMsb9+/RqnT59Gfn4+sx4WFgZ/f39m/I1CocCDBw9w9epVyGQyQV0mk+H69eu4d+8ec26Kiopw4cIFhISEMMcuFotx5swZ5j4D4KP2mZMnTyI1NZW5vFwux5s3bzRuFy9fvsTZs2chFouZ9eDgYFy8eBFFRUWCumqfuXHjBuRyuaAuk8lw9epVPHz48C/tM3l5ebhx4wazRv47eF6BLMldxOSuR1L+70jOP4K43A3o2NMW58+fgY+PDwCgWbNmOHfuHJYsWYLu3bvjwoULGDJkiOD1atSogbNnz2LhwoX/9FDIZ0TNZTkWFhaGr776CuPHjxd8EPM8j6tXr2LEiBGYO3cu88Po2LFjGDFiBJYvX878oPbz88OoUaOwceNG5gfp4sWLMWLECGa2o0QiwaxZszBs2DBcv35dUM/OzsbkyZMxdOhQhIeHC+ppaWkYMWIExowZg5iYGEE9MjISI0aMwLhx49RRGSU9ePAAo0aNwjfffCNoXnmeh7+/P0aOHIkFCxYwG7gDBw5g5MiR+Pnnn5lNyvr16zFy5Ehs27aN+WE7b948jBw5kpntWFBQgGnTpmHYsGG4e/euoK56/aFDh+Lly5eCWlJSEoYPH46xY8fizZs3gnpERASGDRuGCRMmIDc3V1C/efMmRo0ahalTpwqaEJ7ncfLkSYwcORI//vgjs75r1y6MHDkSGzZsEDQpPM/j559/xsiRI7Fv3z7mdjNr1iyMHDlSHcpcklgsxpQpUzBs2DAEBgYK6mlpaRg/fjyGDRuG6OhoQT0uLg7Dhg3D119/jZSUFEE9ODgYw4YNwzfffKMx83PkyJGYOXMmc2yHDx/GyJEjsWLFCkFdqVRi8+bNGDlyJLZu3VqqruSViM/JwdXwMNx4+hS7LgRAIpOVmh+FQoHp06dj5MiRuHr1qmDdRCIRvvnmGwwbNgyhoaGCenJyMsaNG4dhw4Yx7+h+/fo1hg4digkTJjCzYh89eoShQ4di6tSpzOb23Llz+OGHH5iNKfnvkMgTkVZwHnKlCIASgBJyPh95yhswNBep48x0dXVhZWUFCwsLGBsbw9ramnnToSpOrKybx0j5Q9dclmNOTk5o3rw56tSpw4zPqV69Opo3b462bdsy6/Xr14eXlxdat27NjFFp2rQpGjdujObNmzN/f7t27RAbG8t8qoyBgQHat28PiUTCzI40NTVF+/btUblyZTg6OgrqZmZm6NSpE4qLi1G5cmWkp6eXqlepUgVt2rSBnZ0d827hatWqoXnz5mjSpAlzbLVq1ULTpk3Rpk0bZr1Ro0bw8vJCy5YtBbFKANCiRQs8fPgQTZsKEhoAAB06dEB6ejrq1q0rqBkaGqJDhw7Q0dHRmI/o6ekJU1NTZv6hpaUlOnbsqPEubUdHR7Ru3Rru7u7MO609PDzQrFkztG3bljm2unXrokmTJmjTpo2gznEcvLy81NvF+3d4cxyHli1bIiwsDI0bNxa8Nsdx6NixI/Lz81GrVi1B3cjICB06dICpqSmqVq0qqFeqVAnt2rVDeno682lBNjY26NChAypVqsS8Q97Z2RmtWrVCvXr1mB+Unp6eaNasGTp27Mi8e71BgwZo0qQJWrduLajr6Oio95kmTZqo507J87gQ9Rqr7t3G63wRdFycsDcnA3n3bmNum/Yw+9+HuY6ODjp16gSFQgFPT0/B7zY2NkaHDh1QuXJlZm6mpaUl2rZti4KCAubd/5UrV0a7du3g4ODAbARcXV3RsmVLtGjRgnmHeu3atZnbM/nv4HkeedKnUPCMs0m8BHnSpzDV9wDw34pSIv88yrksp8RiMX777TfMnj0burq60NHRYebUKRQKdY1Vl8vl6uXfrwHvTuPp6ekxP2iVSiWUSqUg9/Fj6jzPQ6lUguf5D9Z1dHQQGRmJWrVqqX9ONTaO4/7y2P/O3KjWT9O6l1UH3h2l0jT23Nxc5Ofnw8HBocy5AVDm2P8LcwO8e8KQq6srDAwM/tR28W+em5L7TKJIhBGnjyNRJILo7n3wWdmw7Ncb+rq6WNHZG1/UrqvOM/3Q3P0TY+c4jhmqrsrV/Ni7gynn8t+H53m8FZ9EpuQGs25t1A5O5l+C4z580lN1Qw8rf7a8oZxLyrmsUFQfAqwPAlW9rCdrqDIINdUAaKwD7z7AWEe+Pqau6QOMVWedhvuYsf3dellzo/qA/it1AGWOHXg3d5oa04+Zu//K3LCyHz92u/ir9X96bp68TUZiXh7e/5ovUypxMfo1+teqA93/zcuH5u7fPHby32Ci7wFIbgMofbkPB12Y6ruDjlqSj0HXXBJCyGckZdxcoyKRaa4Rom0cx8HcoDYsDBuidBPJwcygDswN65f7o5BEO+hrJiGEfEb1qtjBwtAIuUWlb7rjALRxqQod+jAn/yBdHWM4mQ+Fib4H8oufAzwPM4PasDJuAV2u7KekEaJCzWUFozrNqOnb59+plzyFWVb9U/zuf3v9vzw3H1r3f+L1y/JvnpsPvTYA1LSxxfD6DbAnLAT5AHgAuhwHL0cn9K9dR/06/7Xthvx36emYwda4E2yM2wIAOOjT35j8KXRavALJy8vDrl27cPfuXWY8TkZGBrZs2YLQ0FBmPSEhAX5+fhqzHyMjI7F582YkJycz68HBwdi2bRszGkipVOLOnTvYvXs3M99QoVDgwoULOHz4MDM2SS6X48SJEzh9+jQzNqmwsBD79+/HlStXmNFBubm52L59Ox4+fMgce2pqKjZv3oynT58y67GxsfDz80NsbCxz7M+fP8fmzZuZ+YZKpRKBgYHYsWMHMxNUqVTixo0b2LdvHzMTVC6X49y5czh27BgzE1QqleLo0aM4f/48M99QLBZj7969uHHjBvP61ZycHGzbtg1BQUHMsScnJ8PPzw8vXrwQ1AAgKioKmzZtwps3b5hzEx4eji1btiAjI4NZz8rKwoEDB5jZjwqFApcvX8bBgweZuZcymQynT5/GyZMnmZFSRUVFOHz4MC5evMicm/z8fOzevRt37txhRg9lZmZi69atGnMxExMTsWnTJkRGRjLn5uXLl9i6eTP6OTjjV+/uaOroBFcLS3zfuh3W+/ZASuRrbN26lZkJqlQqcfv2bezZs4e5z8jlcgQEBOD3339n7jPFxcU4ceIEzpw5wxx7YWEhDhw4gKtXr2rcZ3bu3IkHDx5Q/FA5xHEcdDgD6HAG1FiSP42OXFYgDx48wNSpU9G0aVNcunRJEDdy7tw5zJw5E76+vjh37lypGs/z2LNnD5YvX45JkyZh48aNpepyuRwbN27E9u3bIRaLMWfOnFJvSMXFxVi5ciX8/f1hYmKCUaNGlarLZDLMmjULkZGRcHZ2Rrdu3Uq9fmZmJmbNmoW0tDTUrVtXEGMTFxeHWbNmQS6Xo3nz5oIoloiICEyePBmenp64cuUKqlSpUqp+69YtfPvtt2jfvj3Onj0riC86efIkZs6ciX79+uH48eOCudm2bRvWrVuHhIQE/Pzzz6XqMpkMv/76Kw4ePAiFQoFp06aVqkulUixduhTXr1+HlZUVBg0aVGpuJBIJZsyYgcTERFSrVk0w9rS0NMycORP5+flo0KAB6tSpU6r++vVrzJw5E/r6+njy5Emp+CJV0z9lyhTUr18fly5dEsTUXLlyBd9++y18fHxw8uRJQXzRkSNHMG/ePAwbNgz79+8vte48z2PTpk3q5nHp0qWlli0uLsbq1atx8uRJ6OvrY/z48aXqhYWFuHfvHlauXAk7Ozv06tWr1OuLxWJMnz4dmZmZqFGjBlq1alVq+eTkZEyfPh1yuRyNGjVCjRo1StWfPXuGmTNnolKlSggKCio1dp7n8fDhQ0ydOhXNmjVDQEAAKlWqVGr5gIAATJs2DX379sXhw4cFET379+/HTz/9hHHjxmHbtm2Cufn111+xb98+iMVi/PDDD4iqURNR0MHXXk1RLJVi8vLl8Pf3h7m5OUaMGFFq+by8PCxYsAAhISFwdXVFly5dStVFIhG+/fZbFBQUoHbt2vDy8iq1bvHx8Zg2bRoMDAzg5eUFV1fXUvXQ0FDMnDkTdnZ2CAwMLDV2nudx+/ZtTJs2DW3btsXZs2fpCSyEEDVqLiuQunXrYtiwYWjatCkz+qNVq1YYPHgwevbsyVzex8cHkZGR6NWrl6Cmp6eHXr16ITc3F506dRJ809XT08PAgQNhZmaGFi1aMJf/6quvEBYWhnr16gnqlpaWGDFiBNLS0gQfggDg4OCAoUOHQl9fH1ZWVoK6m5sbRo8eDTc3N1hYWAjqDRs2xLBhw9C2bVvm3LRr1w6DBg3CgAEDBDUA6NGjBxISEtC9e3fm2Pr27QupVIr27dsL6gYGBhg8eDCqVKkiaACAd7mXY8aMwevXr5nZj9bW1hg5ciTy8vLg5OQkqLu4uGDYsGEwNzcXNEfAu+zGkSNHok6dOsxM0CZNmmDo0KHo0qUL8/FuHTt2xMCBAzFw4EBBDQB69+6N9PR0+Pr6Cmr6+voYMGAAdHV10bp1a+bY69ati0GDBjHzUo2NjTFmzBgkJiYy81IrV66MkSNHQi6Xw97eXlB3c3PD0KFDYWdnx2yO6tSpgxEjRqBx48bM7aJFixb48ssv0aNHD3W4dEldu3bF8+fP0b9/f0ENAAYMGACxWIwuXboAAHQ4Dro6OuD+N/ZBgwbBzMwMzZo1EyxramqKoUOHonbt2qhduzazPnbsWGRmZqJatWqCuoODA0aOHAkDAwNmfEqNGjXw5Zdfolq1asxcy4YNG2Lo0KFo3bo1c7sghFRclHNZTonFYmzcuBFz585Vx4+UzOBjxZn8mYw+QHidlVKphEKhYOZe/t3sx7Iy/JRKJSIjI1GjRo2Pyuj7UH6hprnRlN34T8xNybGLRCIUFBTA0dFRnX/4oXzDj80v/NRzwzq99qG5iYqKgouLC4yMjD5btuPH5Fr+lbG/v91s3rwZ0dHRWLduHXR0dD6YBftvyL0sOTdSqZT59B8Wyrks3yjnsmKgnEvySfMLgQ/nVv6d7McPZfQB0Jj7qFr+U479U8/Nvzm/8FPOjWr5v5Pp+V+ZG9YX/f/ydkMIqbjohh5CCCGEEKI11FwSQgghhBCt+dvNJcdx8RzH8SX+F/a/f2/DcdxTjuOkHMeFcBznVWIZjTWiPaprsjRdV1vymixNddUzrv/q8p+yrroeraxlNS2vjXX73HNT1vXSHzv2/9rclLzO8K9sFx+qf+65Uf1MWbW/Ojcl9wlNPnZuyqoTQoi2jlzeATD0f/+by3GcEYBTAMwBzARgB+Akx3G6ZdW0tC7kf/Ly8vDLL7/gxIkTgpw6nueRlpaG5cuX49KlS8wMv7i4OCxZsgT3799n5g9GRERg0aJFiIiIYNbv37+PxYsXIy4uTrBuSqUSFy5cwNKlS5Geni6oy+VyHD16FGvXrkVeXp6gLpVKsXv3bmzZsoWZ4VdQUIANGzZg//79zIy+nJwcrF69GqdPn2aOPSUlRR0PxKpHRUVhyZIlePz4MXPsoaGhWLRoEV6+fMms37p1C0uWLEFiYiJzbs6ePYvly5cz8w0B4PTp01i/fj0z31AikWDbtm3Yvn07M9sxPz8fv/76Kw4fPsxsBjIzM7FixQr4+/szx56QkICffvoJd+7cYY7t5cuXWLJkCTMvled5PH78GIsXL0ZUVBSzfu3aNfz0009ISUkRrBvP8zh58iRWrVrFzAQtLi7Gvn37sHHjRmYmaGFhIfz8/LB7927m3IhEIqxZswbHjh1jNlGpqalYunQpLl++zFz32NhYLFmyBA8ePGDWnz59iiVLljD3GeDddhsSEoL4+Hjm8hcvXsSyZcuQlpYmWFapVOLo0aP4+eefmfuMTCbDrl27sHnzZhQWFgrqYrEYv/32Gw4cOMDMis3OzsbKlSvxxx9/MLeb5ORkbNu2jRpMQojWbuiJAxDA83w+AHAc1x/vmsY5PM9v4TjOHsAiAB0BVCqjdl1L60PwLsPv7t27cHJyQo8ePQQxM3fv3sWyZcvQqVMndOjQQZBfeP78eaxatQpv3rxB8+bNBVErBw4cwLp16yCRSPDrr78Kfv+GDRtw+vRp2NjYYObMmaVqhYWF+PXXX3Hnzh3Uq1dPEGOjav4iIyPRtm1bQUxNRkYGFixYAF1dXXTq1ElwU0NUVBSWLl0KW1tbdOvWDQ4ODqXqjx8/xsqVK+Hh4YEePXoIolauXbuG5cuXo3v37mjbtq2g/scff2DVqlVIT0+Hl5eX4KaNvXv3YvPmzeA4DsuWLRPMzdq1a3H58mU4OTnhm2++KVUTi8X45ZdfEBQUhMaNG6N3796C5U+dOoXAwEB07NgRTZo0KVV7+/YtFi5cCBMTE3Tt2lWQ7fjixQssXboULi4u8PX1FdzdeP/+faxcuRINGjSAr6+v4O9+4cIFrFixAi9evEDr1q1LjZ3neRw7dgwrV65U526WvOmD53ns3LkTe/fuhYmJCX744YdSr83zPNasWYM7d+7A3d0do0aNKlXPy8vDmjVr8OzZMzRv3hze3t6l6mlpaVi5ciVSU1PRtWtXQaxVQkICFi5cCGtra3h7ewsiesLCwrBs2TJ4enrCx8dHkPl569YtrFq1Ci1atIC3t7fghpgzZ85g5cqVePPmDVq0aFFq7EqlEocPH8batWshl8uxYsWKUssrFAqEhIQgMDAQ58+fF+ShKhQKrFq1CkFBQahduzYGDx5cqp6bm4tVq1YhOjoarVu3FsReJScnY8WKFcjNzYWvry88PT1L1WNjY7Fw4UI4Ojqia9eugqzYJ0+eYNmyZWjYsCG6du0qiPS6cuUKdu/ezYycIoRULNpqLkcBGM1xXAaAHwCo3nWS//ffpP/91x3vjlhqqpVqLjmOmwBgAgBUrVpVS6tacdStWxdNmzZF9erVmTl1bdq0wezZs9GiRQtmTl2PHj2QmJiInj17Mu94HTp0KJRKJYYPH878/VOmTIGrqyszF9PExATffvstmjdvjjZt2gjqlpaWmDlzJt68eSMIBQcAW1tbLFq0CFKpFNWqVUNCQkKpevXq1TFnzhxYW1vD1tZWsHzTpk3x3XffoXbt2syxderUCd9//z3at2/PzC/s27cvMjMz0b9/f+YdsyNGjFDnV7JMnz4dderUYX4Qm5mZYfr06QgLC0Pz5s2Zy/fu3ZvZOAKAvb09Fi1aBI7j4OLiIqjXrl0bc+bMgaOjIzMTtGXLlvjuu+/QuHFj5th8fX3x3XffMZsrjuPwxRdfQCwWY8iQIcz6V199BUtLS2b2I8dxmDlzJpo0aYJOnToJ6pUqVcLMmTPx6tUrQZg8ANjZ2WHWrFkQiUTMbEcXFxd14/3+Fw4AaNCgAWbPng0PDw9mHmr79u0xc+ZMtGrVinmXdu/evZGamoqePXsKxq6jo4MhQ4ZAoVBg8ODBguV1dXXRqFEjJCYmokePHoLX1tXVxaxZsxAYGIi2bdsK6paWlpg1axbi4uJQv359Qd3R0RHff/89ioqKmHmobm5uWLhwIaysrAQPGQAALy8vzJ49G3Xq1BE8gAEAunTpgvT09HIfO0MI+bC/nXPJcdwCAJEAjACsBmAPYC6AtQCG8zz/O8dxEwFsAzAe745c/sqq8Ty/S9PvoZzLP0d1imvOnDll5g8qlUpmht2/oa66vqusukpkZCRq1aql/jnVa6viWjRl/GmKcvncYy+rnpubC7FYDAcHB41RMqq5+dDYy6r/G8YOANHR0XB1dYWBgUGp64g/tN18aG7K+rt/jrnheR6bN29GVFQU1q9f/6e3yw/NTcnrSDXFF5XMtfwrcyORSJiXerDwPOVclmeUc1kxfLKcS57nV5T4JY0BfIf/PxqpOq+i+poch3fNpaYa0SLVh8SnzMH7lPWysg9L1lnXeH3udf8n6mXNz8fkRv4X6u9/+WU1Ne/72O3m767bp6irfuavLP+hufmYufvUYyOEVAx/q7nkOK4+gJUALv7vtUYBkAC4CyAdwCSO4/IBjAMQD+AWAP0yaoQQQggh5D/s794tnglAF8BSvDsl/gZAf57nUwAMAiAG8BveNZODeJ5X8DxfpKn2N9eFEEIIIYR8Zn/ryCXP828BCK88f1e7A0B4VfkHauTT+TPXGQLC03N/ps6qAdDK9WKaTu997DWWn/oazL8yNx8zdlWd9bvLmpuPqf+br8EE/v86Sm1vN39mu/jUc8OirWssP+V2oalOCKm46Ak9FUhKSgqmTJmC3bt3M7MfIyMjMX78eJw6dUpQ43keQUFBGDduHG7cuMGsX7t2DePGjUNISAgzo+/kyZOYOHEioqOjBXWlUomdO3di2rRpePv2reD1ZTIZ1q5dizlz5iA3N1dQLygowJIlS7B06VJIJBJBPSsrC9999x02btzIzDdMSEjAN998gwMHDjDn5vnz5xg3bhzOnTunMdNz7NixuHfvnmBZVabn119/zcw3VCqVOHLkCCZNmsTMN1QoFNiyZQtmzpzJzASVSqVYtWoV5s+fz8y9zMvLw4IFC7Bq1SpIpVLBuqenp2PGjBnYunUrM98wLi4OEyZMwJEjR5jXt4aHh2Ps2LG4ePGixkzPsWPHIjAwkDn2M2fOYPz48Xj16hVz7LGxsZg9ezaSkpIEdblcjg0bNmD27NnMTFCJRIKlS5diyZIlzNzL3NxczJs3D2vXrhVsFzzP4+3bt/j222+xe/duyOVyQT0qKkq9z7Dm5smTJxg7diyuX7/OnJurV69i7NixCA4OFiyrVCpx4sQJTJgwgbnPyOVy9T6TmpoqqBcXF+OXX37B3LlzNe4zixYtwvLly5lZsVlZWfj++++xceNG5tgTExMxZcoUHDx4kLnPEEIqLmouK5CnT59iz5492Lt3LzNE+d69ezh06BD27dvHXD4gIACHDh3C6dOnBTW5XI4//vgDBw8exJUrVwQfdDKZDL///jsOHDiABw8eMJffsWMHDhw4gPDwcEE9JycHu3fvxq5duxAfHy+oJycnY+/evdi6dSuzyYiOjlbnK4pEIkE9JCQE+/btw/79+5nN6c2bN3H48GEcPHhQUAOAs2fP4vDhwzh79ixzbCdOnMCBAweYjblUKlXP++PHj5n1bdu2Yf/+/Xj+/LmgnpWVhV27dmHXrl3MO3XfvHmjnhvW2F++fIldu3Zh3759EIvFgnpgYCD279+PgwcPMpuQq1ev4vDhwzhy5IigBrzLBD106BD8/f0FNZlMhqNHj2L//v24c+eOoF5UVISIiAgcPnwYoaGhgnphYSG2bduGffv24fXr14J6eno6du3ahZ07dzK/tERHR2Pv3r3Ytm0bc+wRERHYs2cP9u/fz9xn7t+/j/379+P3339nfmm5ePEiDh06hOPHjwtqAHDs2DEcOnQIly9fFtSkUikOHz6Mffv24eHDh4J6QUEB9u3bh927d+PZs2eCen5+PrZu3Yp9+/YhNjZWUE9JSVHPDetLy6tXr7Bv3z5s27aNuU+EhIRg7969OHDggOBLCyGkYtNWziX5D2jVqhXWrl2LOnXqwNTUVFDv1asXRCIRM0OP4ziMHj0aZmZmzAw+PT09TJ48Ge7u7hg0aJDgFJmBgQHmzJmD9u3bo3v37oK6vr6+OjT9/cB04F2u5apVq5CdnY2aNWsK6m5ubli1ahX09fVhZ2cnqNevXx/r1q2Dk5MTbGxsBPUOHTrgl19+QaNGjZhzM2DAABQVFaFz587MuRk/fjwqV66Mvn37Cur6+vqYPn066tatKwiLB/B/7J15eE1X28bvM2UeiSTIIMQ8BBFRIhISIaiYO1BTUEVRVbS0pd621DyrokXNlNY8T4khERlknufpZDxDzrj390e+cyrWOkHRIfbvunp93+s+e5/1PFnr7Gevvda9YWJigs8//xxBQUEYMGAAkRtTU1OsXr0a2dnZ8PLyImaJHBwcsGrVKsjlcrRq1Yo4f5s2bfDdd9/BwsKCMAXn8Xjw9PTE2rVr4ebmRvV2DAwMxOrVq9GzZ0+qZcy4cePAsiyCgoKItvN4PHz00UdwcnKixm5kZIRPP/0UXl5eePvttwndzMwMvXv3xooVK9C3b1/i/BYWFlizZg2Kiorg4eFBHN+8eXOsXr0aWq0Wrq6uhN6pUyd8//33aNy4MRE7j8dDr169sGbNGrRt25bq7RgcHIzVq1ejT58+VK/YCRMmwNjYGEOHDqXmZt68eWjbti3VD9XExASLFy+Gr68vBg0aRBxvaWmJ5cuXIyUlBd7e3oRuY2ODdevWoaKiguoV6+rqih9++AEikQjNmjUj9G7duuH7779Hs2bNiDHB4/Hg6+uLH374AV26dHkjrIQ4ODien5f2ufy74HwuXwypVIpNmzZh0aJFdexBnlxjReNl9Cf7Un36q/5uhmGQnJyMtm3b1rv263XG/iz9VeamsrISMpkMzZo103/+n2z76zw/y7J1fC5f9Xe/zrY/z7mfPsfWrVuRlpaGdevWgc/n/2Nj6q/qSqWS+mTB0LGcz2XDhfO5fDPgvS6fS47/Fs8a5C+jv85zP6/+b23fvyE3f1V/ngvD6z7/6/rul9Vf9txPfoZ2o/9v7hfPo3NwcLyZcGsuOTg4ODg4ODg4XhlcccnBwcHBwcHBwfHK4B6LN2B0PnZPetHRdN2/v4iH37N8K591/POc/3k9+uqL/XmOf5Z/oe4zL9J24OVy86z3qj/d/n8q9mfpL5qbp3Uaz3pv+svG/k/l5ukcGPr3VzFmXlavzwuWg4ODg5u5bMAUFhZizJgxWLp0KdWnLikpCcHBwdiwYQOxA5llWdy/fx+BgYHYv38/9aJx9uxZDBgwAGfPnqV6+P38888ICAjAvXv3iGM1Gg3Wrl2LwYMHIzk5mdCVSiW++OILjBo1imohI5VK8eGHH2LKlClUDz+xWIz33nsP8+fPp9rnZGVlISQkBCtWrKDGHhsbi8GDB2Pbtm1E7CzL4vbt2wgICMDRo0epuTl16hQCAgJw+fJlam527tyJgQMHUv0NNRoNvvvuOwwdOhTp6emEDgCrVq3C2LFjqRYy1dXVmDJlCmbMmEH1vSwqKsKYMWOwaNEiqn1Oamoq3n77bXz//ffU2CMjIxEUFITdu3dTY7ty5QoCAgJw+vRpaqF05MgRBAYG4tatW1R98+bNGDRoEOLi4ghNrVZj+fLlePvtt5GdnU3ocrkcc+fOxXvvvUe1pKqoqMCECRMwe/Zsqu9lXl4eRowYgWXLllE9PxMSEhAcHIx169ZRYw8PD0dgYCAOHDhA1c+dO4cBAwYY9AQtLi7GuXPnqJ6gLMti7dq1GDJkCJKSkqi5Wbp0KUaOHImCggJCl0ql+Oijj/DBBx8YHDPvvvsu5s+fT7VdysrKwrBhw/DNN99QcxMTE4OZM2dynpccHBxccdmQycnJwa1bt3Du3DlqEZGYmIjbt2/jypUrVD0qKgphYWG4fv06UZwCtR5/d+/exe3bt6nff/nyZYSHh1MLKJVKhUuXLuHWrVvUC6VUKsXly5dx9epVqndjdXU1zp8/j6tXr6K4uJjQCwsLceXKFVy6dAnV1dWEnpaWps8NLbbHjx/jzp07uHr1KlWPiIhAeHg4bt26Rb2Y3r59G+Hh4VR/QgC4dOkSwsPDqZ6eCoUCly5dws2bN5Gamko9Pj4+HleuXKEW3hUVFbhw4QKuXLkCsVhM6Hl5ebh27RouXrxI9XZMTk7G7du3ce7cOWrhHB0djbCwMFy7ds3gTUl4eDju3LlDLU5v3ryJ8PBwqqcny7K4ePEiwsLCqMVlTU0NLl68iBs3biAzM5PQKysrcfnyZVy6dIlaeIvFYly8eBGXL19GeXk5oWdnZ+P69eu4ePEitcBKSEhAWFgYLly4QC2MHz58iPDwcNy4cYNqGK8bM+Hh4URuGIZBZmYm8vLyqJ6eDMPg/PnzuHPnDhITEwldJpPh4sWLuHbtGrXwLi8vx5UrV3Dx4kVq4V1cXIxLly7h8uXLVD/UjIwM3LhxAxcvXqTesMXFxVHbzcHB8ebBWRE1UKRSKVavXo1u3bqhWbNm8PLyIh5lSaVSnD9/Hu3atUOnTp3qPAZjWRbl5eW4ePEievbsiVatWhF6QUEBbty4AT8/PzRv3pxoQ1paGh48eICgoCDCW5JlWcTFxSEpKQmDBw8mPAQZhsH9+/dRUlKCQYMGER6CGo0Gt27dglqthr+/P9LT09GuXTt9G1UqFS5fvgwrKyv06dOHiF0ul+PChQtwdXVF9+7diUeAVVVVuHDhAjp37oz27dsTsYvFYly+fBm9e/eGq6sroefm5uL27dsYMGAAHB0didhTUlIQFRWFQYMGwdbWltCjo6ORnp6OwYMHEx6DlZWVuH79OoRCIYKCggiLHo1Gg+vXr4PH48HPzw9CYd3VL7ri1c7ODr169aL2iwsXLsDd3R0eHh5EbioqKnDx4kV069YNbdq0IWIvLi7G1atX4evrCycnJ0LPysrC3bt3ERgYSNh2sCyLxMRExMXFYdCgQbCysqpjRaSbOc3NzcWgQYNgZmZW53itVos7d+5AJpMhICCAyI1arcbVq1dhbGwMX1/fOjZdwJ/Fa9OmTdGzZ08i9urqaly4cAEdOnRAx44dCb2srAyXLl1Cz5490bJlS4Njxt/fH02bNiX0DRs24Pr169i7dy91zDx+/BiJiYkGx8yDBw9QVFSEoKAgwt5Hq9Xi5s2bUKlUGDBgAEQiUR1dN2YsLS3Rp08fIjdyuRznz5+Hq6srPD09idgrKytx4+YNtG1TvyXYk/FwVkQNF86K6M3AkBURV1w2UAz5XDZEdD6XTxaXDRmaz2VD5Xl8LhsKLMsSPpf/BViWhVyjwt2SbDwsyoawXIJOtk3hbG4DQT0xcMVlw4YrLt8MOJ9LDg4ODo5XTrVaiRVRF3EhLwlalRoOchZX81PxtktH+DZrBQHvv1Ekc3BwvDq4Uc/BwcHB8ZdgWRbnchJwNicBCq0GLAAWgEyjwpncBBTLyQ1lHBwcDR+uuOTg4ODg+EuoGQa3izKgZsmNX1K1CinVpf9Aqzg4OP5puOLyDYJlWVRVVUGpVFJ3ujIMg8rKSqrNyJO6Vqul2qRotVpUVFToPRqf1tVqNSorK6nfzbIslEolqqurDeo1NTWQSCQGfQBlMhnkcrnB4yUSCRQKhUG9qqoKKpWq3tzQdo4DtZsldLmhnVuj0dSbG5VKhaqqKoNtUygUBmNnWRZyuRxSqdSgLpVK681NdXV1vbnR9Yu/mpuKiopn5sbQ35VhmHpjf57cyGSyenNTU1NTb24MjZnnzY0hex5dv6Htyn9VY6a+fvGyY6a6ugpKNek08eRnODg43jy44vINIj4+HkFBQVi8eDG1gAwLC8OAAQOwevVqagF06tQp+Pv74+effyaOZVkWu3fvxoABA3DmzBlC12q1WL16NQIDAxEeHk6cX6vV4rPPPkNwcDDVZkUul2P69OkYMWIE8vPzCb2srAzvvfce3n//faqHX1ZWFoYOHYo5c+ZQLWaio6MRGBiIL7/8klokXbt2Df7+/tiwYQM1N0eOHIGfnx8OHTpEtaDZtm0bBgwYgMuXLxPn1mg0WLlyJQYOHIjIyEjieLVajXnz5mHo0KFIS0sjjpdIJJg8eTLGjBlDtd8pLi7G2LFjMXnyZML3UrdhZsiQIViwYAHVYiYiIgIBAQFYuXIlNTcXLlyAn58ftm/fTs3Nvn374Ofnh5MnT1Jzs27dOgQEBODmzZuErlKpEB0djdGjRyM2NpbQFQoFZs6ciZCQEKr9TmVlJcaPH493332Xar+Tl5eHkSNHYvr06US/0O1cHzx4MJYsWQKlUknoujHzww8/UAvI06dPo1+/ftizZw81N7t27UK/fv3wxx9/EMdqtVp8//33GDBgAO7evas/nmVZMCyLGoUCny1ahMGDByMpKYk4v0wmQ2hoKEaNGkUdM2KxGO+88w4++OADqvVQVlYWhg8fjtmzZ1Njj4mJwdDBwXhw6DR4lCLSVCiCu5Ud8e8cHBwNH25DzxtESUkJ4uPjYWFhAZVKRey+zcnJQVJSEmJjY6nHp6WlITExkWp6rtuxnZiYiIyMDKqekJCAhIQE5OXlEbpWq0V0dDQSEhJQXFyMDh061NFramoQFxeHoqIiVFZWwsnJqY5eXV2N+Ph4MAyDmpoawt6noqICsbGxUCqVUCgUhL1PYWEh4uPjYW9vD7VaTdi0ZGdnIykpCY8fP6bmJiUlBUlJSUhJSaHGnpSUhMTERGRlZVFjj4+PR3x8PNX8WqPR6K2JSkpKiN2IcrkccXFxqK6uRlVVFRwcHOrolZWViI+Ph7GxMVEkALVFRlxcHHg8HpRKJbFzNz8/HwkJCWjRogU0Gg2Rm4yMDCQlJSE+Pp6am+TkZCQlJVELY61Wi8TERCQkJCAnJ4cae0lJCRITE6l+pmq1GtHR0SgqKoJYLEaLFi3q6FKpFHFxcdBoNJBIJLCzq1vsVFRU6POuUqmIflFSUoK4uDiYm5tDrVbDxMSkjp6bm6tvv1arJWyf0tPTkZSUhISEBGpuEhMTkZSURB0zGo0GCQkJSExM1I8ZtVaL8IwcnHmchNyyckTmlUKcV4CSklK0a9euzvEqlQqPHj1CdXU1KioqiDEjkUgQFxcHY2NjSKVS2NjY1NHFYjESEhJQVVVFjb2oqAjx8fHwatIITo1dcL8iHzxowQNgzBdiYPO2aGZmTY2bg4OjYcNZETVQaFZESqUSERERaNasGdzc3Ah7CJlMhsjISLi7u1NtbiorK/Ho0SN07twZjRs3pno/Pn78GJ6enrC0tCT0/Px8ZGRkoEePHoQ/IcuyyMzMRFFRETw9PQlfS90sklQqRffu3etcxBmGQWJiIpRKJYRCITp27EjYL6nVajx8+BA2NjZo06YNYfOiUCjw4MEDuLi4EL6VunxGRkaibdu2aNq0KZHviooKREdHw8PDA7a2tkTsJSUlSEhIQI8ePWBhYUH1xczOzkaPHj2I4k43uygWi+Hp6al/zKv7GzEMg/j4eKhUKnTt2pWIXVe4Gxsbo0OHDkTsKpUKDx8+hJ2dHdzd3YnYa2pq8ODBA7i5ucHZ2Znq/fjw4UN07NgR9vb2RG7KysoQGxuLrl27Uj09CwsLkZKSAi8vL5iZmRG5iYmJgUQiQc+ePYl+wTAMUlJSUFVVhe7duxOFL8MwiIuLA8Mw6NKlC5EbjUaDR48ewcLCAm3btqXmJjIyEg4ODlTfSl1uWrduTR0zVVVViIqKQufOnYnCFgBKS0vx+PFjdO/eHVZWVnWsiHg8HvLz85Geng4vLy+YmJri14hobLgRDqlSpWsErHksfpr0Djo71f1+3U2NTCZDt27diMJXq9UiJiYGQqEQnTp1ImJXq9WIioqCjY0NWrduTR0zkZGRcHJygnVTe1wrSENEYSZMKuXo2rg5WlvZQcg3bIPGWRE1bDgrojcDzufyDYPzuWy4cD6XDZNn+VwWVkkwYf8x5FaQj7BHeHTAt0MHgs//5/oDy7JQKJW1yxNYljNRfwquuGy4cMUlWVxyay45ODg4/gOki8tQWEW39okrKIaEsuTh74TH44HP44H3//8/BwfHmwtXXHJwcHD8BzAWCiES0H+yjYUCCA1oHBwcHH833K8RBwcHx3+Adg5N0M6BfOTG5/EQ0NYdZk+tN+Xg4OD4p+CKywYMwzAoLCw06JPHMAxyc3MNegRqNBpkZ2ejpqaG0HQee9nZ2VCp6D53NTU1yM7ONuibKZFIkJuba9D/sKKiAgUFBQY99kpLS6k7iHV6UVERysrKDMaen59v0FtSq9UiJyfHoEegWq1GdnY21bpH571oKDc678WcnByD3pDV1dXIy8uj+h8CtRuIioqKDOampKQEJSUlBmMvLCw06C3JMAzy8vIM+idqNBrk5OQY9I5UqVTIzs6m7kzXbYIx1C903os5OTkG+0VVVRXy8/MNekOKxeJ6c1NcXAyxWGww9oKCAoNjRqvVIjc312C/eHLM0PRnjRmgdmMdLXYLYyN81Ks77PiA7qGzqUiEoZ3a4l3PLgBq1+MWFBQYzI1uzBjKzcuMGY1Gg6KiIoNxcXBwvDlwxWUDJjk5GUOGDEFoaKhBj76AgAAsWbKEuNixLIs//vgD/v7+WL16NbUI2rNnD/r164effvqJ6uH3v//9D35+flQPP6VSiYULF6J///64d+8eoUskEkyfPh1BQUFISkoi9LKyMowZM8agh5/O13LChAmorq4m9OjoaAQFBWHmzJlEkcOyLK5fv44BAwbgq6++ImJnWRbHjx+Hn58fNm7cSC0EduzYAT8/Pxw4cIB6If7qq6/g7++PixcvEppCocDcuXMxYMAAGNrEtnz5cgwePJhqYVNcXIwRI0Zg3Lhx1OI7LS0NgwcPxuTJkyGTyQj9/v37CAwMxNy5c4nYWJbFhQsX0L9/f3z77bdU/ddff4Wfnx927NhBFDksy2LTpk3w9/fHsWPHqP1myZIlGDBgAK5fv07NzcyZMxEYGEi1zCovL8eECRMwdOhQqrVRfn4+hg0bhnfffRdisZjQExISMHDgQEyfPp16U3Xnzh0EBARg4cKFBr1g+/fvjx9++IHq6blnzx74+flh7969RG50G9NOnz6NM2fOUI//dcNaZO/djjFNbbFskD9+fCcEK4YEwMbUBHK5HKGhoQbHTGlpKd599128/fbbVMur7OxsBAcH44MPPkBFRQWhx8TEICAgALNmzaLeVF27dg2hoaEGDeM5ODjeHDifywaMWq1GeXm5wRkqhUKBsrIygzMRNTU1EIvFBmc2pVIpysrKIJVKqd9fXV2N8vJyqmm57s0nYrGYOsOle7NJWVkZdYaLYRiUlZWBYRhq4atWq1FWVgaRSES92CmVSn1uaNTU1KCsrMzg7J0uN4ZmsHS5oRVvQK1FTXl5ucFZ4aqqKoO5AWpntwzlRqvVoqysDCYmJtTcqFQqlJWVwcbGpt7c0Mzogbq5obVdLpdDLBYbnNmUSCQoKyuj9gugdvbNUG4YhkFVVRXKysqos3+6twGJxWJq7BqNRj8zR4tdlxtDb83RxW4oN7rYDY0ZmUymzw0NlUoFhUJRb26UVRXwdW2OgB4eAP7cPPO8uamqqqLGrsuNiYlJvbmp7/eCZsbOwcHx5sFZETVQpFIp1q9fj3HjxsHKygoODg7EDk61Wo2MjAw0btyY8K0EaovPzMxMNGvWDNbWdc2QdUVEdnY2XF1dCfNpoLaAKigogJubG2HADNSaNJeVlaFly5aEP6HuEZ1MJoObmxthp6R7RKfVauHs7IyUlJQ6VkQMwyArKwvGxsZUyx6NRoPMzExYW1ujSZMmhK5SqZCZmYkmTZoQvpVA7YU0KysLzZs3h5WVFdF2mUyG3NxcuLq6Uj09KysrUVRUhJYtWxLejUCteXdlZSVatmxJ+BNWVlYiPT0dNjY2aNGiBTU3ubm5AAAXFxei7VqtFllZWTA1NUXTpk2pucnIyICtrS3VNkWpVCIzMxOOjo6wtrYmdF2/cHZ2hoWFBRG7RCJBfn4+WrRoQfX0rKioQElJib5fPGlFpHvkX11dDTc3NyI3Oj9VtVoNV1dXws6HYRjk5ORAIBDAycmJmpvMzExYWFjUO2bs7OzQqFGjescMzYJF98jfxcWFGDMsy2LLli2IiorC9u3biTHDsizKy8shFov/0pjR+akyDAMXFxdqbrKysmBkZITmzZsb7BfW1tawt7enjhndTPrz7BbnrIgaNpwV0ZsB53P5hsH5XDZcOJ/LhsmzfC7/CyiVSupbqGhwxWXDhisu3wwMFZfcY3EODg4ODg6OemEYBtXV1frlInw+H6ampvqbvurqav1yCt2Nka6olEqldZZq8Hg8mJubG7xh1Gg0+o1tZmZmMDY2bvAFakODKy45ODg4ODg46qWiogIffPABysvLIRKJIBKJ0K1bNyxYsABWVlaYNm0acnJyYGRkBD6fDzc3N8yYMQPe3t5Yvnw5bt26pS8SRSIRli5dCj8/P+J7tFotDh06hP3790OhUGD27NkYM2bM3x8wx0vBFZd/A2q1Gjdu3MCVK1cgEAgwaNAg9O7dm1gvBgBJSUnYtm1bnbs8Ly8vTJ48GTweD+Xl5Th58iTi4+Ph7OyMMWPGUN/3zMHBwcHB8arQzVxaWVlh/vz5uHLlCs6ePYv27dtj3Lhx+iUOn332GfLz87F9+3Zs2bIFXbt2hUwmg0Qiwbx589CsWTPw+Xy0a9eO+A6WZVFQUICjR49CJpPhgw8+gIeHxz8QLcfL8t9b1PMfQ2fNMnr0aBw/fhwHDx7EqFGj8Mcff1B3XObn52P37t24fPkyYmNjERsbi5ycHP0mkVmzZmH+/Pm4evUqVq5ciXfffReFhYXP1RaNRoPk5GSD3pBKpRIJCQkoLy+ntk0ulyM+Pt7gTtjq6mrEx8cb3AFdXl6OxMREg96PxcXFSE5Opu7yZVkWeXl5SE9PN2h1kpmZiezsbOouX61Wi9TUVIO+mWq1GklJSSgtLaWeW6FQID4+3uAuYZlMhvj4eIO7x6uqqpCQkGDQF1MsFiMpKcmg92NRURFSUlIMej/m5uYiIyODGjvDMMjIyEBubq5B78aUlBQUFhYa9K1MTExEWVkZNfaamhrEx8cb3CkslUoRHx9vcId0RUUFEhISDO6MV6lUSEtLM5ibgoICpKWlGcxNdnY2srKyDOYmPT0d+fn5Br0bdWPGkG+lbszQeHLM0KhvzAC11koJCQkGx4yuXzxrzBiKPTMzU//78jQvMmb+K2v3OV4eS0tL9OvXD/369QOAOr9ppqam6N27N0aPHg17e3sUFhbqfxd4PB6cnZ3h7u6OVq1awcbGhji3VqvFmjVrkJGRgcrKSuzfvx+5ubnc5Ml/EK64fM2Ul5dj8+bNMDc3x2+//YajR4+CYRhs3LjRoN2IQCDA+PHjsXnzZhw5cgRffvkl+Hw+7t69i1OnTmHQoEEICwvDnDlzEBERgRMnTjzXj/u9e/fQu3dvTJ8+nfrdZ86cQe/evbFw4UJCY1kWu3fvRq9evbB27VpC12q1+OGHH+Dt7Y39+/cT7dFoNFi8eDF69+6N8+fPE7parcbkyZPh5+eH+/fvE+evrKzE2LFj0b9/f6SlpRF6bm4uhg0bhsGDB1MLxISEBPj6+mL8+PHUAvH27dvo1asXZs+eTb3QnzhxAm+99Ra++OILqr/h1q1b4e3tjS1bthDHajQarFy5Et7e3lRvR5VKhfnz56N37964evUqoSsUCrz33nvo378/oqKiiPOLxWKMGDECAwcOpG6myMjIwODBgzFs2DCiCGJZFo8ePULfvn0xZcoUahF09epV9OrVC5988gm1ADx48CC8vb2xYsUKam7WrVsHb29v7Nq1ixr7smXL4O3tjdOnTxN6TU0Nbt++jaCgIISFhRG6TCbDmDFjEBAQgPj4eLAsq/8PAIqKijBs2DAEBwdT/VCTkpIwcOBAjBw5krBWYlkWDx48gI+PD2bMmEGMGZZlcf78efTq1YvqFQsAe/fuhbe3N1atWkXNzXfffQdvb2/88ssvxLFKpRKfffYZevXqhQsXLlBjnz59Ovr06YPIyEhCr6qqQkhICIKCgpCSkkKcPy8vT98vaDeccXFxCAgIwLhx46ix3759G3369MGcOXOoN00cDZOioiL88MMP2L17N1q1aoWAgAC9plQqERcXhzNnzqCkpAT29vZ6Nw2tVovPPvsM7777LkJDQ6m/VQKBAB999BGaN28OFxcXbNiwAT16EHtFOP4DcI/FXzMlJSVITU1F+/bt0bJlS/B4PDRu3BgJCQmorKykWvgwDIM1a9Zg7dq1aN68Ob744guMHz8ekZGRUKlU6NGjBywtLeHp6QmBQIDIyEhotVoIhUKwLAuNRgO1Wg2GYcCyrH7WwtjYGPb29rC1tdV/z5OYmprqrXcYhiHuFi0sLPQ/Fk/rDMPoLY/Mzc0JnWVZ2NjYwN7eHqampnUKAJ3euHFj2NnZwdjYmGibLm8qlQpCobCOzrIshEIhGjduDD6fDx6PRxwvFAphb2+vt1x6Wjc2NoaDg4P+bvpp3dzcXJ+bp9sO1N7N29vbw9LSkhqbLjempqbU3Nja2sLe3h4mJibU8zdp0gQVFRUwMjLS/111//H5fDRu3BgikYjIjS52Ozs7mJubU2M3MjKCvb09GjVqRI3dxMQE9vb2sLa2rtOfdOj6hY2NDbXtVlZWBnMDANbW1nBwcICZmRlVNzExQZMmTWBsbGwwN0qlEgqWwY3cDGRVV8DF0gae9rWP3+zs7KDRaCAQCIi2i0Qi2NnZ6Xd40nLj4ODwl3Oj6xc6nRa7vb09LCws6vxddeexsbGBg4ODwX5ha2sLBwcHvU3TkzqPx4O9vT2EQiG1XwgEAv144/P51Nh1uaHFZmJiAgcHhzq/J0/G8Dw8HW9D5+mbn/8aLFv79rGEhATk5OSgY8eOaNy4sT6e0tJSLFmyBCKRCB06dMBHH32kt1rj8/lYsGABmjdvDoFAgKZNm1L/7i4uLjA2NoZQKET79u31v5n/dv6rf9PXBWdF9JqJiYmBl5cXvL29cfnyZbAsi549eyI/Px/37t1DmzZt6nw+ISEB169fR4cOHfDw4UN88803sLOzw/Xr17F9+3asWrUKGzZswMcff4xLly7pZyaOHj0KkUiEiooK7N27F2KxGKWlpZg/f75+5x7DMCguLoaFhQUsLS2Jtmq1WhQVFaFRo0ZUaxC1Wo3i4mL9hf5plEolSktL4ejoSF1PKpfLUVFRgaZNm1JtViQSCaRSKRwcHKh6ZWUlVCoV4UvJsqzeGFtXaD2Nzh/R2NiY+jiGYRgUFRXBysqK8GYE/ny1nZ2dHdWzU5cbe3t76g5IhUIBsVhsMDcymQxVVVVwdHSkxl5dXQ25XA4HBwcolUqo1eo6f8Py8nJotVqqzYnusbtAINAXSU/rxcXFMDU1JfxMdbkpLCyEjY0N9WZIlxtD/UKlUqG4uBiOjo6ENyPwpzF506ZNqbZZJSUlUKvVBvtNVVUVsspKkc2qkSOthFrLQMjnw9XSBp4OzWGiVOtvXmi5KS0thZGREbVf6B49P2vM2NraEn6mwPOPGQcHB4hEIvz666/IycnB4sWLwePx9GPG0dGRmpvq6mrIZDI4OjpSHx1WVFRArVZTvVx144bP51O9XHVjxsTExGC/eHrMaDQag0tHaCiVSggEAuqYaIjI5XJqP/kvUF5ejilTpqBJkyZYt24djhw5gv3792Py5MkYM2YMpk+fDqVSieXLl8PGxgaWlpYwNjaGsbExlixZgrt372L37t1wc3MDUHvzQ+uzKpUK06ZNg0AgwNatW/8TNlUsW/t2rsWLF3NWRP/PmzGi/0FMTExgbm4OhUIBlUqlv2AYGxtTzbfbtWuH9u3bg8fjwdPTE6dPn0ZMTAxyc3P1RZPurTFSqRQMw9Qxc7axscHcuXMhlUqxZcsWtG3bts4FuX379vW2t0OHDvXqnTp1+itpeK0wDIOUlBS0bdu23rU5tAXkT/Ks3HTs2LFe/e/KTWVlJeRyOZo1a/bKzvm6c9O5c+cXbhNQOyYEAgFcXV2phSkAqLRaHA27iJNpqahzqywrxRBTPtb0DYZJPcXLs2J/lv6qxgzLsnBwcEB1dTXatGnzr/C5fNF+oXt3+vNOWug2gdBu2BoaupzQbnL+K/B4PAiFQjRt2hTjx4/HlStXcPnyZYSEhEAgEMDY2BhdunSBra0tlEql3udS98Rlx44d+icoo0eP1q/bfBKlUgmhUAiBQIDGjRv/J4pxlmX/E0Xw3wlXXL5mHB0d0aFDB6SkpCA6OhpyuRxlZWXo3bs3rK2tkZOTg9OnT6NTp07w8/PDtWvXIBAI4OHhgfj4eGRlZcHc3ByNGjWCt7c3zMzMcOfOHeTn5+PmzZtgWRa9e/fWz2rweDwIBAIIBAL9D9h/9YfseXkyzoYeK1D37/kmxKvDUKwVyhrcyM8E7cHZnYJsiBUyOFvavNa2vUp0/fh5/7YMy6JSJUGSJAcaRos2ls5oYmIDAe/vL05f5Dfn6Uf4bwr/hd8plmWgYaohV2cBAExFrjA1NcHo0aNhZWUFoVCI5s2bY86cOUhISIBcLsfw4cPBMAxMTEyI+Pz9/evcDOt8Lml5EAgEGDp0KPh8PkQi0b8+Vxx0uOLyNWNlZYVFixZh5syZGD16tH6mceHChTAxMUFycjI++eQTTJs2DX5+foiIiMD69ethbGwMmUwGhmEwe/ZsuLu7w83NDZMnT8a+ffvQs2dPyGQyDBo0CCEhIdwA5HhjUTMMVAYcBFRaLdTaf/96rb8KwzIIK32M3RnnUKwsB8sCtkaWeMfFH8HNekHE537iOV4MlmVQqYhEsfwM1NoKAICIbw1782DMnTsHPN6fyzNCQkIQEhICAPVaBtF8KhmGQVVVVR2nAx6PB2tra0yfPv0VRcPxT8H98rxmeDwehgwZgvPnz+Pu3bvg8/nw8fHRP8Jt3bo1Vq1ahS5dugAAPvzwQ3h4eCAjIwM8Hg9dunRBz5499Yvyv//+ewQHByMtLQ1NmzZF//799QvqOTjeRBqZmKJTYweEF+YQWvtGTWD3H3is9lfJl4uxNe0USpWV+n8rUVZgT8Z5uJo7wsOmFXfjyfFCKDQFKJQeh4b90zlCxZShUPobTIRNYSZyeyXfU1NTg2+//baOk4GZmRnWrFmDpk2bvpLv4Pjn4IrLvwGBQIAuXbroC8gnadGiBT755BP9/7a1tUVwcLDBc5mbm2Pw4MHP9b1qtRp3795FkyZN0KZNG+IiU1NTg4iICLi4uMDV1ZXYJCOVShEREYH27dsTGwZ0vpXR0dHo1q0bdaNIYWEhEhMT4eXlRWyGYFkWWVlZyMnJgbe3N7HmSreOsqKiAj169CDW22m1Wjx+/BgajYZ6x6zRaPDw4UOYm5ujY8eOROxKpRKRkZFwcHBAq1bkBVgulyMiIgItW7aEk5MTEbtEIkFERAQ6d+5scINRbGwsunfvTmwU0XkzpqSkwMvLi9hAxLIsMjIykJ+fD29vb+pGkJSUFOTl5cHT05PYDKHVahEbGwugdjbh6bV7arUakZGRsLa21q/vfRKFQoGIiAg0b94cbm5uhC6VShEZGYnWrVsT7zdnWRZVVVV4+PAhPDw8iPVluk0i8fHx6NGjB3Xdsc6bsWfPnsQ6Jpatfdd4cXExevbsCSMjI5gJRZje2QvpVWUolst0H4SdqTmmd+4JS9Gf+dNoNIiOjoZIJELnzp2J3KhUKkRERMDOzs7gmHnw4AFatGgBFxcXQpdIJIiMjET79u3h4OBQ75ihbaLRbRCSSqXU3GRnZyMnNwfuXduDFfJwreQRxErSX1SqrcHt0lh0tmkJAWq/g2EYxMXFQaPRoGvXrsQGIY1Gg6ioKJiZmaFDhw5EbpRKJR48eABHR0e4u7sTbZfJZHj06NEb8+7shgjLsqhWxUDDSglNy0pRpYyBqbDFK/n7mpmZYfny5XV2gvN4POrvHcd/j39+xTjHayMmJgYhISEYP3481afuypUrCA4Oxrx586g+dYcPH0ZwcDC+/vprqknzpk2bEBwcjPXr11M9/JYsWYLg4GAcPHiQOLampgZz5szB4MGDceXKFUKvqKjApEmTMHjwYMTExBB6SUkJQkJCMHr0aGRkZBB6UlIShg8fjnfffRdisZjQ7969i2HDhmHixImEPyHLsjhz5gwGDx6MhQsXUv0Lf/75ZwwZMgTffvst1bz7hx9+wODBg7F9+3bq5oYFCxYgODgYJ0+eJDS5XI4PP/wQwcHBuHXrFqEDwMqVKzF48GAkJiYSWn5+PoYNG4axY8ciJ4eczYuLi8OwYcMwYcIEVFRUEPqNGzcwdOhQTJs2jfi7syyLEydOYPDgwfjiiy+o+s6dOzFkyBCsW7eOsBBhWRbffvstgoODsXfvXmq/+fjjjzFkyBCcOXOGaJtUKsXUqVMxZMgQ3Lt3D0DtBamfkxt2BYzEOLf2YDPzILv1AF+190KgS90iKCsrC0OHDsU777yDgoIC4vwPHz7E0KFDMWnSJML3EgAuXryIoUOHYubMmdTYDhw4oB8zT+sMw2Dz5s0YMmQItmzZQsSu1Wrx6NEjnD59GkeOHCF0hmHw8eJP8NHJlfjowTp8GLEWx3NvgAV984xEIwfD/tmG/Px8jB07FkOGDEF2djbx+dTUVAQHB+O9995DSUkJod+7dw9DhgzBtGnTqIb4v//+O2bPnv2fsI3hMIyGkQEG+pS2Hu1F4fF4MDExgZmZmf4/U1PTf8VGNo6Xh5u5bMA0btwYnTp1Qrt27ahWH82bN0fnzp3RpUsXqu7m5oaOHTuiY8eO1AHftm1bdOjQweBOYg8PD8TGxqJly5aEJhKJ0KVLF5SWlqJ58+aEbmJigq5du8LExIRqLWRqagpPT0+o1WpYW1sT5uC2trbw8PDQ+2o+jYODAzp16oSuXbtSY3N2dkbnzp3RqVMnqgVMq1at0LFjR7Rv3556fPv27dGxY0fCakpH165dkZaWBldXV0LT5UYqlRp8POTi4gIAVPscc3Nz/Yzm07NfQG2/8PDwQIsWLai5adasGTp27Iju3btTY2vRogU6deqETp06EbpuqUfHjh2pu/d5PB46dOiAjh07wt3dnTg3j8dDt27dkJ+fr4/xSYyMjODh4QGGYeDg4KD/dz6Pjy52jmjh6YuCvUdQWiqDV1MXCJ5qn6WlJbp3727QcqpJkybo0qULOnToQN3B7OTkpM8NbfZG1y9os+U8Hg9t2rTR94undT6fD3t7e9jZ2entWp6EAQPbUR1gbyuFhKcAtIZnj/jgobWFE4RPrI+zsLCAh4cHZDIZNXZra2t0795d7zn6NA4ODujcuTM8PDyodluurq5o1aqVwTZx/DcwE7VAWQ0fILbI8WEqcgXAzUpzPBvO57KBIpVKsXHjRsyePRsmJiYwMjKi+thJJBKYmJgQu/J0/aKqqgoWFhZ1dp/r0Gq1kEgksLS0pBZgGo0GMpkMlpaWRBGiM3uXy+WwsrKitk3n52hhYUHVa2pq9BYQycnJaNeunf5zLFv7ukyBQEDdvah77G9kZGQwN9XV1TAzM4NQKCRyo9N1sRnKjZWVFbVAe1Zu1Go1FAoFLC0tiXNXVlaivLwc9vb21B2XutwAtUU4TZfJZBAKhTA2Nn6hfvFkbszNzYl+8azc6AyzpVIpNXag9rG9rl8A0BfhOqNwlUoFpVJJzY3O5Fmr1RrMjVwuB5/P/8v9QiKRwNTUlOgXQO3sokQieWZudH3+aX3r1q1ITEzEpk2biDGVLi3AwujtqFRJn/lYsr2lK77qNBF2xtZ1xoRuzJiZmRnsF/WNmfr6BcMwqKiooD4poKE7n0gkeiNsXHR+s//2ZQMaRoacqj2QqhPq/Lu5qDVcrEIhEpA3rDQUCoXeiujfHO+rQPfUYu7cuZzP5f/DzVw2YHg8nr4wNKTTZrZ0GkCfGdMhEAjq1YVCIdV8WXd+kUhUr25iYmLQ/47H4+lnV2iP4XSxG4LH41FNsZ/U62sbj8d7rbnRFTeGMDY2Nmjl8WRuDJ3/Wbkx1C90+l/Njc4qy9DxAOr0i6dvfnVrsgyty+LxePUWKjoLlPr0Z/WL+nLD5/Nfqt8AMGi/IlZWQaKWUzVTgRFczBzABw8drd0wvHmfOoWl7vtfZ7/g8/mwsLB47uKS49+JgGcGZ6sJKKu5BakqCUBtYdnYrB+EfMNjg4PjSbjikoODg+M/QCMjS1gITVGtkRNaW0sXLOv4AYQ8AcyExuD/Ax6XHA0DHo8HkcAGDubD0MQsEADA5xmDx/UpjheA6y0cHBwc/wFamDuiZ+P24D215s1UYIQhzXrBWmQOC5EpV1hyvBJ4PB4EfFMI+KZcYcnxwnAzlxwcHBz/AYQ8Aaa3GgZzgQnCxI+hYFSwN7bB8OY+6NuEtDnj4ODg+KfgbkfeIGQyGY4cOYLIyEiqPU5FRQV+/fVXJCYmUvWioiLs378fWVlZVAuZzMxM7N+/H8XFxVQ9Pj4eBw8eRGVlJXFulmURERGBY8eOUW1OGIbBzZs38fvvv0OpVBK6VqvF+fPncfnyZaptkkKhwMmTJxEWFkZdoymRSHDo0CFER0dTYxeLxdi/fz9SUlKoen5+Pvbt24fc3Fxq7Glpadi/fz/EYjFVj42NxaFDh6j2NwzD4N69ezh58qR+o87TsV+7dg1nz56l2iap1WqcPXsW165do9om1dTU4Pjx47h37x41N1VVVTh48CDi4uKosZeUlGDfvn1IT08nNADIycnBvn37UFBQQI09KSkJBw4cQHl5OVWvqKjAb7/9BqmU9N5jGAZ37tzBqVOnqHZaGo0Gly9fxoULF6BWqwldqVTi999/x61bt6i5kcvlOHr0KCIjI6nWQ7oxk5CQYHDM7Nu3D1lZWdTcZGZmYt++fSguLiY03Zj59ddfUVlZCR6Ph0ZGlviodQg2e36M9V1n4R2NF2RhuVArVNRNZTdv3sQff/xhsF+cP38eV65cMThmfvvtN4NjRiqV4siRI3j06BFnP8TBwVEHrrh8g7hz5w4mTZqE+fPnUy/Up06dwtSpU7F48WJCY1kWP/74I0JDQ7F+/XpC12q1WLt2LaZOnYpffvmF0FUqFb766itMnToVf/zxB3EhVqlUmD17NkJDQ3Hnzh3ieLFYjA8//BCTJ0+mejtmZmbiww8/xKRJk1BUVETosbGxmDhxIubMmYOysjJCv3btGiZPnoxPP/2U8AQFgKNHjyI0NBRffvklobEsi82bNyM0NBRbtmwhdLVajW+//RZTp07FoUOHCF2hUODzzz/H1KlTcfHiRSI3NTU1+PDDDxEaGor79+8TxxcXF2P69OmYMmUKtcBLTk7GtGnTMGXKFGKzha6onzRpEubNm4eqKtKQ++LFi5g8eTKWLFlCLeAOHDiA0NBQfPPNN9TicN26dQgNDcXOnTuJY1UqFZYvX46pU6fixIkThC6Xy3Hnzh3Mnj0bN27cIM4vkUgwffp0TJs2DdHR0cTx+fn5CA0NxdSpU6nejo8fP8a0adMQGhpKxM6yLMLCwjB58mQsWLCAOmZ+//13TJkyBV9++SX1pmf37t0IDQ3FqlWrqLn57rvvEBoair179xLHKpVKLFu2DFOmTMGZM2fAsix4PB6EfAHsTWzRmDHH14uWYcrkybh79y5x/srKSkydOhXTpk1DfHw8cf6srCxMnToVoaGhVM/PqKgoTJs2DTNmzCBiZ1lWP2YWLVpEvenh4OB4c+Eei79BtG7dGv3790efPn2oO2q7du2Kvn37GnxDUJ8+feDj44N+/foRGp/Ph5+fHxISEuDt7U3oQqEQAQEBkEql1DcVCYVCDB06FI6OjlRvSCsrKwwePBgFBQVUX0x7e3sEBAQY3IXt7OyMgQMHom3bttTdwO3bt4efnx8CAgKoO5E9PT3h4+ODoKAgQgMAX19fPHjwAH379qXGNmDAAGRnZ6NHD8KxASKRCIGBgdBqtejYsSOhGxkZYdiwYUhISKD6CNrY2GDw4MGorKys4/2oo2nTpggICICVlRU1djc3NwQGBsLDw4O6m7hjx47o168fgoKCqDvYe/bsCR8fHwwcOJDQAMDPzw8xMTHw8fEhNKFQiMDAQJSWlqJ79+6EbmxsjFatWqFv375o27YtoZuYmGDo0KHIzs6meoY2atQIgwYNgkajoVqEODs7Y8CAAXB0dKTG7u7ujgEDBqBXr15U5wIPDw/4+voiMDCQeIsUALz11lvo06cPBgwYQGgAEBAQgNTUVPTq1YvQdP1CIpFQx4ypqSmCgoJga2tL7RdmZmYYOnQoSktL4eTkROhNmjRBUFAQjI2NqW/YatGiBfr3749WrVpRY2/Xrh38/f3Rv3//ep0NODg43jw4n8sGilQqxaZNm7Bo0SK9FZHOZ+9pfz0dT+oAiM+8iE7TdP/3r+q6zzytMwyD5ORkvTH1ix7/orl52dy9bG6qqqogk8n0r1583tieJ/YXbfvr1nVLClxcXAz6Tj4rd/+V3ADA1q1bkZaWhnXr1oHP5790v/m7YtfpSqXS4BIAWuycz2XDhfO5fDPgfC45DF7g/g7d0AXqRfRn/UDRzMyf9/iGnJv/ul7fZxpSbmg3+i/bb/7pvxsHB8ebCbfmkoODg4ODg4OD45XBFZccHBwcHBwcHByvDO6x+BvAk+uiaBpAf7T25GO6+vS/cu5XqRvidX7/P52bZ/FP/m3+6dz8m/Vn5eZZ/Jtj4+Dg4NDBzVw2YKRSKbZt24azZ89SPfzKysqwfv163Lp1i+rhl5+fj9WrVyMqKopqo5KSkoLvv/8eycnJ1O9/+PAhVq1ahby8PEJjWRY3btzAunXrqNZAWq0Wf/zxB7Zv3061gFGpVDh06BB++eUXqoefXC7HTz/9hBMnTlA9/KqqqrB582ZcvHiR6tFXUlKCtWvXIjw8nBp7dnY2Vq9eTfV+ZFkWCQkJWLVqFdUaiGVZ3L9/Hz/88AMKCwsJnWEYXLlyBRs2bEBFRQWhA8D58+exc+dOqm2SUqnE/v37ceDAAaq3o0wmw44dO3D69GlqvygvL8fGjRtx9epVagFfUFCANWvW4MGDB9TYMzIysGrVKqpfKsvWenquXr2auvGD/X/7n7Vr16KkpITQGYbBhQsXsHnzZqptkkajwbFjx7B7926qPY5CocDevXtx+PBham4kEgm2bt2Ks2fPUvuFWCzGunXrcOvWLWpucnNz8cMPP+DRo0cGx8yqVauQmppKPV6hUCA2NpZqDcSyLG7evIn169dTxwzDMDhz5gy2bdsGiURC6Gq1GocOHcLPP/9MtZSqqanBrl27cPz4cYNjZtOmTQbHTHFxMX7++WfO85KDg4ObuWzIxMbGYuvWrXBycoKfnx8sLCz0GsvW+tQtXrwY/v7++O233wgrlt9++w2ff/454uPj8dNPPxF2Iz/99BPWrVsHsViMNWvW1JnNYFkWq1atwm+//QYTExPMmzevzrFyuRzffPMNbt26BTc3N4wcObKOXl5eji+//BLJycno1q0b3nrrrTp6SUkJPvnkE/D5fHh5eYHPr3uflJKSgoULF6JJkybw8fGBo6NjHf3u3bv44osv4O7uDj8/vzpWKyzL4sKFC/j8888xaNAgHDt2jLAnOnLkCD7//HNkZGRgy5YthA3Njh07sH37dshkMqxYsYKY6fnf//6HCxcuwNraGjNmzKijyWQyfP3114iIiECbNm0wZMgQPM2BAwcQERGBnj17EhY++fn5mD9/PszMzPDWW2/B3d29jv748WN89tlncHZ2ho+PD+zs7Orot27dwueff44uXbqgX79+dWJjWRa///47Pv/8c4wYMQK//voroe/fvx/ffPMNioqKsGbNGgiFwjr6li1bsHfvXjAMg8WLFxP9ZsWKFbh58ybs7e0xYcKEOm2TSCRYtmwZHj9+jA4dOiAgIKCOXlhYiM8//xzFxcXo1asXOnXqVEfPysrC/Pnz0ahRI7z11lto0aJFHT0qKgqfffYZ2rRpgz59+sDW1raOfuXKFXzxxRfo1asXrl69qndi0LX92LFj+jGzZ8+eOjrDMNizZw/WrFmDqqoqrFy5so6u1WoRGRmJsLAwnDx5EnPmzKmTG61Wiy+//BIRERFwcnLC2LFj67StsrISS5cuRWpqKjw8PAjrp7y8PHz++eeorKxEnz59CMuv1NRUfPLJJ2jWrBl69+5NWH49ePAAn332GTw8PNC7d29YWVnV0c+dO4fNmzdT7bg4ODjeLLjisgHTpk0buLu7o3Xr1lSfup49eyI0NBQ+Pj5Ub8f+/ftj8uTJGDlyZJ0CQUdISAjKysqIwlDHxIkTYWVlRRQAQK0/4eTJk9GmTRuq96O1tTVCQ0ORnp6O1q1bE3qjRo0we/ZsqFQqODs7Iz8/v47eokULzJgxA3Z2dlQPPw8PD0yePBmdOnWi+hP26dMHU6ZMwYABA6h6UFAQUlJSMHr0aGpuRo0aBZlMhmHDhhEaAEyZMgWOjo5Uz1AzMzNMnToVHh4e6Nq1K/X4AQMGwMvLC25uboRmb2+P2bNnQyAQoGnTpoTu7u6O6dOnw9nZmeoJ6unpicmTJ8PLy6tO8aPDz88PkydPxpAhQwidx+MhODgYubm5GDFiBFUfO3YsGIbB4MGDiXPzeDyEhobCzc0Nffr0IXQLCwtMmzYNjx8/JgpHoNa7cdq0aaioqICzszOhN2vWDLNnz4a5uTnVE7R9+/aYPn06WrduTfUE9fb2xqRJk+Dr60vc0ABAYGAgJk2ahFGjRhE6n8/H22+/DbFYjGHDhlH1Tp06ITU1leqLyefzMX36dHTs2JHqJWtlZYVp06YhLS2N6gnq6OiIadOmoaamhtovXFxcMGvWLNjZ2aFx48aE3qlTJ0ybNg1dunSBubk5ofv4+CAtLY17ZM7BwcH5XDZUpFIpNm7ciEWLFuktev6Kjx3DMFSLH12/MaQ/6/inv99Q23Ttqk8Hat9C065duzq2Lq/aw+9V5eZ5c2coN5WVlZBKpXqfy5fJ3cv2i9eVmye/Py0tDa6urjAyMnplsf1bc7N161akpqZi/fr1RPH5d/laPk/shvqsQqFATk4OodHgfC4bNpzP5ZsBj/O5fPPQXQRoMyxP6vUdT5u50mkADOrPOv5Z3/8iHnu0NV5/h8ffX83N8+buedpH+8w/Gfuz9OfNzZM3Ca+67c/SAUCp1SJPWrum08nCGsYCQb05f/L4lxkTT7bB0L+/ijHzunRDvzUcHBxvFlxxycHBwfH/sCyLqJICrI8KQ0J57YaiDo3sMb97H3S3b9bgZ2A4ODg4XgVcccnBwcHx/+RIqvDprXPIqP5zl/7tgizkS6vwc9BouFrZ1nM0BwcHBwfAWRG9UbAsC61Wa9AqRKcbWofLsiw0Gs1L6fWdn2EYg7purVp9xz9PbC+r/xdz81+IvT4dqI3/deeGZVlcyEpBlqSS+FyWpBIXs0n7oNcd+7P0Z42JF4m9Pt1Q2+rTOTg43ly44vINoqSkBF988QUOHz5MvSBkZWVh4cKFuHDhAvUiGhcXh08//RR3796l6mFhYVi4cCHi4+MJnWEYnDt3DosXL0Z2djZVP3z4MJYtW4bS0lKibRqNBjt37sT//vc/qoefQqHA2rVrsXHjRqq/YWVlJVasWIG9e/dS/Q0LCwuxePFinDhxgpqbtLQ0LFiwgOr9yLIsoqKisGDBAkRGRlL169evY+HChUhJSaHGfvr0aSxZsgT5+fmErtVqsX//fnz99dcoLy8n2qZWq7FlyxasWrWK6nspk8mwevVqbNu2DUqlkmhbeXk5vv76axw4cIDqb5iXl4fPPvsMv//+OzU3SUlJWLBgAdX7kWVrPT0XLFiAmJgYqn7p0iV89tlnyMjIoOYmNzcXK1euRHFxMaGr1Wrs2bMHK1eupPpeKpVKbNiwAevWraN6O0okEnz77bfYtWsXVGo1sqorwFAKLYZlkSupqqOxLIusrCx89tlnOH/+PDU3jx8/xoIFCwz6pYaFhWHBggWIj48nv5NhcPbsWSxatAg5OTnE8RqNBocOHcKXX34JsVhM6CqVCjt37sR3331H9YqtqanBmjVrsHHjRqpXbGVlJb755hv8/PPPRL9gWRZFRUX44osvcPLkSapfKgcHx5sL91j8DeLhw4dYt24dPD09MXToUMKn7sqVK9i8eTOSkpIwaNAg4vjjx49j06ZNUKlU6NWrV531ZxqNBgcOHMCuXbvQpEkTdOjQoY6uVqvx448/4uzZs+jQoQMmTZpU59wqlQpr1qxBSkoKfHx8EBwcXEfXGb4XFxdjyJAh8PT0rKPn5ORg48aNUKvVGD16NGFDk5ycjFWrVqFNmzYYNmwY7O3t6+j37t3D+vXr4ePjg8GDB9fxBAVqTcs3b96MnJwcqk3MoUOHsHnzZgiFQsJaSa1W45dffsGBAwfg4uJC+AsqFAps374d165dQ/fu3TFu3DhC15nR+/n5EbGXlJRg3bp1kEgkePvttwmLnoyMDKxfvx5CoRCjR48mPD/j4uKwevVqdOrUCUOHDiWsm27fvo0NGzYgPj4egYGBhB/q77//js2bN0MsFsPX15fIzf79+7F9+3ZYWFjAw8OjjqZUKrF7926cOHECbdq0ITw/a2pq8PDhQ2zZsgVeXl54++236+hyuRzff/89ysrKEBAQQNgXFRQUYO3atdBqtRg+fDiR++TkZGzYsAGWlpYYPXo0Wlrbgs/jEQUmHzy4WtqA/9Say+vXr2Pjxo3IzMyEv78/sev55MmT2Lx5M2QyGXr37k3kZs+ePfj555/RpEkTdOzYkciNbsx07twZEydOrKNLpVJs3rwZUVFR6NevHwIDA+vo1dXV+O677yCXyzFo0CCiX+bk5GDdunUwMjJCSEgIYWsVHx+PDRs2wN7eHqNHjyZ+L+7fv4+1a9eib9++GDRoEDFmODg43ly44vINwtvbG1999RU6depEvRAMGjQIX3zxBfz8/KjHv/feewBq/S2f3tggFAoxdepU/YXoaV0kEmHu3Lnw9PTEwIEDiXMbGRnhq6++QkJCAnr16kXodnZ2WLZsGcRiMdq1a0forq6uWLp0KUQiEVE4AkCHDh2wcuVKODk5UX0vfXx88OWXX8LT05MongDo/QlpbQeASZMmwdTUFGPGjCE0kUiEDz/8EK6urhg+fDihm5iY4JNPPkHfvn2puTcxMcGKFSuQkZFBFJYA4ODggC+//BIymQytWrUidHd3dyxbtgwWFhbU2Lt27YpvvvkGLVu2JAoIAPD398eXX36Jt956i2oZM2rUKEilUgwdOpTQAGDatGmwtbXV958nMTY2xuzZs9G+fXvihgIATE1N4e3tjcWLF1OLM3Nzc3zzzTcoKChAly5dCL158+b46quvoNVq4eLiQujt27fHsmXLYGdnBysrKwwyaoMjKXFIraz7BpxWNo0Q1KI10a8HDhyIZcuWoV+/flQv2XfeeQdqtRojRowgEwPgo48+QrNmzQhDdKD27z537lx0796dKBwBwNLSEosWLUJycrJBr9j//e9/KCsrQ/v27Qm9RYsW+OqrryASiQjDdADo0qULli5dCmdnZ+qY6NOnD77++mt069aNqnNwcLy5cD6XDRSpVIpNmzZh0aJFeuuT5/Gp+7fq9Xn8MQyD5ORktGnTxqAdyr85thfVq6qqIJPJ9D6Xz+t/+G9o+1/5u6elpcHFxQVGRkZ/6Xhdfp4nNyzLIlZchI2PwhFfVgwA6NjYAR936w0PO8fXmlsA2Lp1K9LS0rBu3Trw+fzXntsXyc3znFupVFJf60mDZTmfy4YM53P5ZsDjfC45/g0+eH9Vf5bHHwCDhuSvu23/tP5f8D98Gf1Zn/mrGk3n8XjoYueIHQNCUCCrBgA0M7eCyEDfepWx0270X3e/ed1/Nw4OjjcTrrjk4ODgeAIejwcjgQAtONshDg4Ojr8Et1ucg4ODg4ODg4PjlcEVlw0YnQ9dfT53arW6Xp87nU7TdHp9Hnt/Vde13ZDH35M6DZ0/YH2xP0t/mdw8K7aXyY1Of1Zs9eXuVcVen/5XYn8e/Xn7xV+N/Z/KjY76fCX/Lbkx1D5D45GDg+PNgisuGzAlJSWYNWsW1q5dS/WpS09Px9SpU7Fnzx7igsGyLKKjozFhwgT89ttv1Ave9evX8f777+P69evExYhlWZw4cQLjx4/Ho0ePiGO1Wi1++uknTJ48GRkZGYSusyaaOXMmSkpKCF0ul2Pp0qVYtGgRqqurCb2iogJz587FN998Q3g7AkB+fj5mzJiBzZs3E7GxLIukpCRMnjwZBw4coOoPHjzA+PHjcfbsWWpuLl++jPfffx937tyhXqgPHTqEDz74AI8fPyY0jUaDbdu2YerUqcjJySF0ANixYwfmzJmDsrIyQpNKpVi8eDG++OILyGQyQheLxZg1axa+//57qudnVlYWQkNDsWPHDurfNS4uDhMnTsTRo0epelhYGMaPH4/Lly9TYz979izGjx+PBw8eUPV9+/Zh0qRJSE5OJjStVouNGzdi2rRpKCgoIPSamhqsWLEC8+bNQ2VlJaFXVVVhwYIF+PLLL6meoEVFRfjwww+xbt06aqGUlpaGKVOmYM+ePYTGsrV+pxMmTMCpU6eo+vXr1zF+/HjcuHGDmruysjLcuHHDoCfoTz/9hClTpiA9PZ04v0ajwZo1a/Dhhx+iuLiY0OVyOZYtW4ZPP/30mWOG5gman5+P0NBQbN68mZqbxMRELF26lDNV5+Dg4IrLhkxGRgaOHTuG/fv3U02SY2JicOTIEZw4cYKqh4WF4fjx4/jjjz+oF5OLFy/i5MmTOH/+PPX7jx8/jpMnT+LOnTuEplAocOzYMRw5cgSxsbGEXl1djSNHjuDgwYPIzMwk9MrKSvzyyy84fPgwtcjIzc3FoUOHcPDgQWqRkZCQoM8NrcB6+PAhjh07hpMnT1Jjv3XrFk6cOIFz585RZ3LOnTuHkydP4sqVK4QGAEePHsWJEydw7949QqupqcGRI0dw5MgRqrk2AISHh2P//v3Izc0lNLFYrM9NUVERoWdmZuLQoUM4dOgQtciIjY3F8ePHceDAAWps9+7dw/Hjx3Hq1CnqTcm1a9dw4sQJXLhwgaqfOXMGJ0+exI0bN4hzsyyLw4cP48SJE4iIiCB0mUyGw4cP4/Dhw0hKSiL08vJyHDp0CPv376f2i+LiYuzbtw+HDx+GWCwm9LS0NH1uaIV5VFQUjh8/joMHD1KLqNu3b+PEiRP4448/CJ1hGFy+fBknTpzAlStXqHpSUhKSk5MRFhZGnJthGBw8eBAnTpxAdHQ0oUulUn1uaMVnaWkpDh06hH379lFv2PLz87F//34cPnyYataflJSkPz+tMH/w4AGuXLlS76wsBwfHmwG3oacB4+HhgVGjRsHFxYWw+uDxeBgwYAC2bNmCLl26UK1ARo8eDT6fD19fX4hEIkKfMWMGXFxcEBwcTOwa5fF4WLp0Kfr164eRI0cSx5qZmeGbb75BbGws/P39Cb1Ro0ZYvXq1Qf9Ce3t7bN++HSqVCq1atSJmP9u2bYutW7fC2tqaag3Ru3dvbNy4ES1btoSRkRHR9sGDB2Pz5s3w9PQkdKDW89Pc3Bz9+/eHUEgOo9mzZ+sN22k7apcvX45BgwYhJCSE0CwsLPDdd98hKSkJPj4+hA4AU6dORWhoKNW/sHnz5tixYwd4PB5atGhB6J07d8a2bdtgb29P9b308/PDxo0b0b59eyI2Ho+HkJAQMAyDt956i6pPmjQJdnZ2GDhwoN4G60l9/vz58PDwwPDhw6n9ZuXKlXj48CHV99LKygqrV69GZmYmvL29Cd3R0RHr1q2DVCqFu7s7obdo0QI7d+6EiYkJ1duxe/fu2LZtG5ycnKienwMHDsTGjRvh4eFBWF7xeDyMGzcOxsbG8PX1JXQ+n49p06bByckJwcHBhC4QCODl5YWKigqqV6xAIMD333+PuLg4BAQEEG2ztrbGmjVrkJeXh65duxK6k5MTNmzYAJVKBVdXV0Jv3bo1du7cCSsrK8JoH6j1yd22bRtatWoFS0tLQh86dCiEQiG3g5yDg4PzuWyoPOlzWZ9Fz5M+d4a0Z+l/5dyvUmdZFsnJyWjXrl2dz77O7/8nc1NZWVnH5/JVn/9l9VeZG5at9bl0dXXVF/n/ln73qnPDsizhc/lvavvz6JzPpWFYzueywcJyPpecz+WbyLO86v6K9m/SDd0gvc7v/6djfxb/ZPv+6dz8m/X/8t/1eXQODg4OgFtzycHBwcHBwcHB8QrhiksODg4ODg4ODo5XBldcvkHoPPbq86l7GQ+/V+Ff+Fc9/AC8Em/H58kNDV1srzM3z4rt7/B2pPG8uTH0d3uWrmvfP5UbXb/4t+XmVflavq7ccHBwvLlwxeUbRGpqKgYOHIivv/6aar8TEREBPz8/bNq0idBYlsX58+fRt29fHDlyhNB1Nil9+/al2pEwDINNmzahf//+ePjwIaFrtVosW7YMwcHBSEtLI86vUCgwe/ZsjBo1iurhV1lZiQkTJmDSpEmQSCSEnp+fj2HDhmHBggWoqakh9MTERAwYMADfffcd1XooLCwMvr6+2LlzJ9V/8PTp0+jbty/V35BhGOzduxe+vr64ffs2NfYffvgBAQEBiIuLI3S1Wo3PPvsMw4YNQ3Z2NnF+mUyGGTNm4J133qH6XorFYrz77ruYNm0aYa/Dsiyys7MRHByMJUuWUD1BdTv6aX6pQK3fad++ffHzzz9Tc3Ps2DH4+Pjg7Nmz1Nh37NiBfv364f79+9TY4+LiMHLkSCQlJRG6UqnE3LlzMWLECOTn5xNtq66uxuTJkzFhwgSqJVVRURFGjx6NWbNmEd6Ous1EgwYNwtdff03YdbEsi8jISPj5+WHLli3UG5OLFy/Cx8cHBw8epObmwIED8PHxweXLl4ljdZ6efn5+iIqKIo5XqVT48ssvMWjQIGRkZBB6TU0NZs6ciTFjxlDHTEVFBd5//31MnTqVOmby8vIQEhKCTz75hBp7YmIiBg4caHDMcHBwvLlwxeUbRHZ2NiIjI3Hnzh1qEZGUlITo6GiqLyUAREdHIzY2luo/yDAMIiIiEBMTQ/Wt1Gq1uHfvHh49eoSUlBSqfuvWLTx8+JBaQEmlUty5cwd3796levSVlZXh/v37uH37NvVCWVhYiHv37iE8PJzq0ZeRkYGHDx8azE18fDxiYmKo/oNArS9mTEwMaI4GWq0W9+/fR3R0NNU0Xa1W4+7du4iKiqIW1hqNBrdu3UJkZCTV11IikSAsLAx3796lejeWlJTg/v37uHXrFjX2vLw8PHjwAOHh4dTCOzU1FVFRUQgLC6PelMTFxSEmJgZ3794lNKD2piU2NpZqpq/RaHD37l1ER0cjISGB0NVqNfLy8hAdHU3dhaxSqXDr1i1ERERQfS2rqqoQHh6O8PBwVFRUEHpRUREePHiAW7duUWPPyclBZGQkwsPDqf0iOTkZjx49Qnh4ODU3uvFw//59QgNqPUNjY2MRExNDjf3u3bsGx4xSqURYWBgiIiKoZvs1NTW4desWHjx4QC0uy8vLcffuXYSFhaGqqorQ8/PzERERgZs3b1J9cDMzM/Hw4UOEh4dTdQ4OjjcXzoqogfKkFZHOa7CmpgbXrl2Dq6srOnbsSOz8rK6uxvXr19G5c2e4ubkRulgsxu3bt+Ht7Y2mTZsStj8FBQV48OABfH190ahRI0JPT09HQkIC/P39CZ88lmURHx+PnJwc+Pv7E9YkDMPg4cOHqK6uRt++fet4TzIMg4SEBJSVlUEoFKJXr16Ev6JKpcKNGzdgZ2eHrl27EjYvcrkcV69ehbu7O2FpBNTOjF6/fh3dunWDq6sroZeUlODOnTvo3bs3HBwciNhzc3Px8OFD+Pn5wcbGhtBTUlKQkpKC/v37w9zcnMhNbGwsioqK4Ofnh5qamjpWRFqtFhEREVAoFOjTpw/hSarRaHD79m2YmprCy8uLyI1SqcT169fh6OiILl26ELmRSqW4du0a2rVrh9atWxOxl5eX48aNG/Dy8oKzszOeprCwEHfv3oWPjw+aNGlCxJ6VlYWYmBj4+/sTtiUMw+DRo0coLCzEgAEDqP3i0aNHKC8vR79+/QhPUt1NjSFfTrVajVu3bsHKygqenp5E7AqFAteuXYOLiwsxZliW1eemU6dOaNmyJXXM3Lp1C7169UKzZs2I3OTn5+P+/fvo168fGjVqVMeKiMfjIT09HfHx8ejfvz8xZhiGQXx8PHJzc9G/f3+YmJgQemRkJCQSCdWrVqPRIDw8HEKhEN7e3n9pzFy7dg2tWrXSjxnOisgwnBVRw4WzIiKtiLjisoFCKy4bKgzDUH0uGyrP43PZUKD5XDZUnsfn8t8OV1wahisuGy5ccUkWl/+9Xy8ODg4ODg4ODo5/LVxxycHBwcHBwcHB8crgiksODg4ODg4ODo5XBldcNmBYlkVVVRXkcrlBn7ry8nIolUqqTQrDMBCLxVSfPZ0HnlgsNmhDolarIRaLqRYtLMtCqVSirKzMYNvkcjkqKysN6hKJBNXV1fXGLpPJDB5fUVGBmpoag76UZWVl9eamtLSU6hH4ZG4Mxa5SqVBWVkb1P2RZFgqFAuXl5Qb9A+VyOaqqqurNjUQiMahXVlYa7BcMw6C8vBwKhaLe3KhUKmrsWq223tw8b78wlJuamhqDudFtsqkvN9XV1ZBKpfX2i2flhtYvdHp9Y0aXm/q8IQ2dW5eb8vJyg7mRy+WoqKh45pgxpD9rzJSXl9c7Zmh2TxwcHG8eXHHZgElPT0dwcDBmzpxJ9al78OAB+vfvj2XLllFtVC5cuIB+/fph/fr11IvZgQMH0LdvX+zfv596IV2zZg18fX1x8eJF4li1Wo0lS5bA39+fat8jk8kwc+ZMBAUFIT09ndArKyvxzjvvYPTo0VSblby8PAwbNgwTJ06EVCol9Pj4eAwcOBAff/wxURyzLIuwsDD4+/tj5cqV1CLo9OnT8PPzw7Zt26i52b17N/r27YujR49SL8T/+9//4Ovri2vXrhGaSqXCggULMGDAAKpFDQB8/fXXCA4OplrQiMVijBo1CuPGjaP6XmZmZiI4OBihoaFU+53o6GgEBARg4cKFROwsy+Lq1avw8/PD6tWrqbEfPXoU/fr1w549e6j9Yvv27fD19cXvv/9Ozc3XX38NPz8/qu2TUqnEnDlzMHDgQKp1UVVVFSZOnIhhw4ZRrYmKioowfPhwvP/++1RropSUFAQFBeGjjz6iWg/dv38f/v7++OKLL6ixnTt3Dv369cPGjRup+v79+9GvXz8cOHCA6gWbmZmJ06dP49KlS9TjlyxZgv79+1PtwBQKBWbOnIlBgwYhNTWV0HW+liEhIQbHzNChQzFp0iTqTVt8fDwCAwMxd+5cqvXQ7du3MXXqVIMvIuDg4HhzED77Ixz/Vaqrq5GdnQ2BQED9wS8rK0NWVhaysrKoRUJxcTFycnKQk5MDhmGIXed5eXnIycmhei8CtUVMTk4OioqKCE2r1SIzMxPZ2dnUAkitViMzMxMZGRlU30qlUom0tDQwDEMYgwO1u+UzMzMhk8mgVCoJG5eKigpkZ2fDwsKCGntpaSmys7MN5qawsBDZ2dnIzc2lFki5ubnIycmhGnsDf+aG5tmp0WiQmZmJrKwslJeXU48vLS1Feno6tXBWKBRIS0uDsbEx1deyuroamZmZ+hlUMzOzOnpZWRmys7Ph4OBAja2kpATZ2dnIzs4m+oXOkionJwd5eXnU43V9ilb8sSyLjIwM5OTkoLS01GBuMjMzqbNkCoUCmZmZKC4upvaLmpoapKWlwdbWljBNB2qL08zMTJiamkKlUhH2PmKxGDk5OUhLS6PGVlRUhJycHGRnZ1N13Zgx1C8qKytRVVVFHTM6O6+cnBzqmFGpVMjIyEBmZia1OKypqUFmZiaqqqqoNxVyuRxpaWmQy+VQKBSwtrYm2paZmYlGjRpBrVbD2Ni4jl5SUoLCwkJqXBwcHG8WnBVRA0UqlWLdunUYMmQIGjduTPVmVCqViI2NRfPmzam+lTU1NYiNjYW7uzsaN25M6NXV1UhISECHDh2ICxFQeyFOS0tD586dqd6NBQUFyM/PR9euXQmbGZZl9RfCzp07E/6EDMMgJSUFWq0Wbdu2RWpqah0rIq1Wi/j4eJiamsLd3Z2IXffmFzs7Ozg7OxO6QqFAbGwsXFxcqL6VMpkMcXFxaNu2LWxtbQm9srISycnJ6NixI9XTs7S0FJmZmejSpQthw8KyLPLy8lBUVAQPDw8iN5WVlYiPj4eFhQU6duxIzU1SUhJ4PB7atm1LWNpoNBo8fvwYlpaWVG9GlUqF2NhYODo6onnz5oSu6xdubm5U30qpVIrHjx+jffv2sLa2JvTy8nKkpqaiU6dOsLCwIGLX3dR06dIFxsbGdayIWJZFTk4OxGIxunTpQng3MgyDtLQ0KJVKdOjQgbgh0mq1SExMhEgkQuvWrYnc6PqFra0tWrRoQe0XcXFx1DEDQN8vnmfMPG3RwrIsNm/ejAcPHmDnzp3UMVNYWIj8/Hx9bp7Ws7KyUFlZ+cwx065dO2pudGOmVatW1NzExsaiSZMm1DFTU1Oj75fPYz3DWRE1bDgrojcDzufyDYPzuWy4cD6XDRPO57JhwxWXDReuuOR8Ljk4ODg4ODg4OF4jXHHJwcHBwcHBwcHxyuA29HBwcHA8gVKrQkxlOu6VJYAHwLtxB3jYtIKx4L/xWJ5lWai0pRDLr6Ja9RgmwmZoYjYQ5qJW4PG4+QQODo7XD/dL8wah26FtyAdPo9EgPT3doD+iSqVCWloa1edOtwEoLS3NoP+hRCJBeno61RdT5y9oaHe2bhNMXl4eVQeAgoICFBYWGvTo020EoelarRYZGRkG/RHVajXS0tIMegDqdq/TvCF1G4DS0tIM+h9WVVUhIyPDoPejbge3odwUFxcjPz/fYOz5+fkoLi426E+o27VfX24M+SPq+gVtZzpQu/YqNTXVoGeoVCo12C+A2tzrHAtosel2cBvSi4qK6u0Xubm5KC0trfWR1KrwU/pZfBW3F6fzw3AqPwxfxe3FrvSzUGpJ+51njRmlUonU1FTq7mwAdcYMrW3PGjPl5eV1xgzLsqjRZCO2ZDaSy1egUHocmZWbEF08FeKaG3XaqNtUV1RUZLBfPGvM6Hbt/1fW7nNwcPw9cMXlG0RkZCQGDBiAWbNmUW1Yzp8/j/79++OLL74gNJZl8csvv8Df3x+bNm0idIZhsH79evj7++PQoUOErtFosGzZMgwYMACXL18mLkZqtRozZsxAUFAQ1feyuroa77//PoKDg5GRkUHoBQUFGDVqFEaOHAmxWEzoiYmJGDhwIKZMmUK1aQkPD0f//v0xb948qr/h6dOn4e/vj+XLlxMay7L48ccf4efnh507dxK6VqvF999/D39/f5w8eZLQVSoVFi1ahICAANy8eZPIjVKpxNSpUzF48GDExsYSx5eXl2PcuHEYNmwY1fcyKysLw4cPx5gxYwj7HpZlERsbi8DAQMyYMYNqbXT9+nX4+/tj0aJF1CLo2LFj8PPzw3fffUctHjdv3gw/Pz/s3buXOFaj0WD58uXw8/PDmTNniOMVCgXu3r2LkJAQ3L17l9DlcjkmTJiAoUOHIikpiTh/SUkJRo0ahZCQEKpNTmpqKoYNG4b33nsPEokEMZXpOFt4Dyr2z2JOxWhwruAeoivTiOLs4sWL8Pf3x7Jly6gF4K+//go/Pz+sW7fOoBdsv379cPDgQeJYtVqNL774Av7+/rhy5Qo19lmzZiEwMBBRUVH/r7PIqdoLiSoGwJ/FtkpbjMzKzdAwVfp/Kygo0PcLmu1TYmIihgwZgokTJxI3DizLIjw8HAEBAViwYAF1zHBwcLy5cMXlG4TuDR40w3SgtkCsT9dqtZDJZAZNkrVaLaRSqcGZRbVaDblcblBXqVSoqakxOAOl0w3NQCkUCoNvldHNrNJmDnW6TCYzOHum1WrrzY1Go3np3MhkMoO6UqmEQqGoNzf1xa7bufms3NDQ5eZZ/cJQ7jQaTb1/d93xhma/dP3SkK5UKuvtF7rcPbPfgMWDsiQomafi5AFKRo2o8lSwoM9KG3rjjq7fGOoXutzUp9fXL3RjSvfdGkaCSsV96mdlqlTI1Vl12l5fbhiGQU1NjcHC8Vm/JxwcHG8unBVRA4VmRaTRaJCfnw9LS0s0atSIOEatViMvLw+NGzeGpaUl1eOvoKAAjo6OMDU1pXr8FRcXo1mzZoT5tM7jr6KiAs2bNyf8CXWPxSUSCZycnAj7JN1jcZVKhWbNmtWxadFZEVlZWUEgEBC+lLrP5Ofnw8TEhGoFotFokJeXB2tra9ja2hK5UalUyMvLQ5MmTQjfSqD28WZhYSGaNm0KExMT4vxSqRQlJSVo3rw51Z+wqqoKlZWVcHJyIvwJdY8/ZTIZmjdvDolEUseKiGVZlJSUQKPRoGnTpoSFje7xp1AohL29PdE2rVaL/Px8mJubo1GjRlRP0Ly8PDRq1IjqZ6pUKpGfnw97e3vCtxKonWErKiqi9gsAkEgkEIvFaN68OdXvNDExESYmJnBxcaHmRiwWQ6FQoHnz5tTYdYbkjo6O1H5RUFAAIyMj2DVpgu1pp3Ay7zbRRgAY7dQPM9yHgf/EusUnx4yVlRVxzJNj5mmzeqDumGEZHlavWoe0tDSsX78ejewsIJFIUF5eDicnJ+qYebJfCAQCqLXViCwcixpNJvFdPIjQ3fEArE266mMvKioCn8+vd8yYmpoSnp3An78nVlZWsLGxAY/H46yI6oGzImq4cFZEpBURt6HnDUIoFMLV1dWgLhKJ4ObmZlA3MTFBy5YtDerm5uYGdR6PB2tra2pxotMbNWpELXp1ur29vcHvBlCv7yOfz4ezs7PBY4VCIVq0aGFQNzIyqjd2U1PTenULCwtq4QXUxmZjYwMbGxuDeuPGjdG4cWODuoODg8Hv5vF4aN68uUFdIBDAxcXFoP6sfmFsbFxv7GZmZvXqlpaW1IL9ye+nFd1AbWz1/ZjzeDw0bdrUoM7n8+Hk5KT/316N2uFMwT2onpq9NOEbwbNRG/BQt3+9ijHj5uaGlIQC7N5yBZev3YdMUYaFH/6Mke+9hcHDu9c7Zp7uF0K+OWxNeqFGmgU8NctqbtQaZqIWdWJv1qyZwbY9z5ip7/eEg4PjzYV7LM7BwcHx/3SxaYW3m/eG6RM7w00Fxhja7C142LR6LTMwVRVybFl9DjGRWVDUqKBWa1GQW449W64i6n76C26W4cPFehKsjbuBp5874MFE0AwtbeZCyKcXqhwcHByvEm7mkoODg+P/MREYYYpbMHrYtsWjilSwALrZtoaHTSsY8V/Pz2V8TA7Sksjd7HKZElcvxKFHb/L1pYbg8XgwFbqiU5NNENdch0yVAiOBPZqY9YeZiHzVJwcHB8frgCsuOTg4OJ7AWCBCj0Zt4dmoDQCAB95rLcoqyqRgGPrspLi4Gi+6LJ7H48FY2ATNLMagdsc4D3jNMXBwcHA8CfdYvAGj1WoRHx+P3Nxcg/6EMTExBn3uampq8OjRI5SXlxOaboNOVFQUJBIJ9fvLy8sRFRVl0OOvqKgIMTExBj3+cnJyEB8fT91JyzAM0tPTkZycbHCXbmJiIjIzMw16esbFxRn0hlQqlYiOjkZJSQlVl8lkiIqKQkVFBbXtVVVViIqKolr76Bb2P3r0iGoJBdTaxMTGxhrciZuTk4PExESDuUlNTUVqaqrB3CQkJCA7O9ugp2dsbKxBb0iFQoFHjx4Z9D+USqWIiopCVVUVoek2bkVFRVF9MXWbk6Kjo6m7lFmWRV5eHuLi4qi5YVkWGRkZSEpKMpib5ORkpKfTHzdrNBo8fvwYeXl54IEHPo9fpyhTqVSIjo426Bkql8v1Y4am08aMs1sTGBvT7/Nbt2+q/37d5qRnjZnHjx9Do9GAx+OBxxOA9/8xsGzte9qTk5Opu8+fNWbUavUzx0xSUhLnecnBwcEVl38Hup3OYWFhuHv3rsELj84aJCUlBWFhYbh16xYyMjLAMAxYloVarca9e/dw9epV/X+3b982aBUSGxuLoKAgvPvuu0SBx7Isrl27Bl9fX6q3I8uyOHr0KPr06YPly5dTL+RbtmxBnz59sHHjRqqH3+eff46+ffvi8OHDxLE1NTWYPXs2fHx8cOPGDUKvrKzEhAkT4Ofnh7i4OEIvLS1FcHAwhg0bRt2dmpqaisDAQIwZM4ZaHN+7dw8DBgzABx98QFyoWZbFuXPn4Ovri4ULFxKx63YG+vj4YNWqVVQLnrVr18LHxwc//vgj9W+9YMEC9O3bF7/99huhyeVyTJ8+HX379kVYWBihA8DXX38Nf39/JCcnE1pBQQGCgoIQEhKC3NxcQo+Pj0f//v3x7rvvUgvAmzdvwt/fH6GhoURsLMvi5MmT6Nu3L7744guigGMYBj/99BP69OmDDRs2EDrLsvjuu+/Qt29f/PLLL9R+8/HHH8PX1xfnzp0j2iaTyTB58mT069eP6odaUlKCMWPGYMCAAVQ/1KysLAQEBGDkyJFU38tHjx7Bz88PEyZMoN4YXLp0CX5+fpg5cyZRoLEsi0OHDsHHxwcrVqwgdIZhsHXrVvTp0wfbtm3T6207NEMv37bgC+r+HDs2t8HAoV2hq20ZhsGHH34IPz8/XL16lWibRCLBhAkT4O/vj5iYGEIvKCjAyJEjERgYSPVDTU9PR//+/TF27Fiq7+WDBw/g6+uLKVOmUG8Mzpw5g8mTJxu0TeLg4Hhz4B6Lv2ZYlsXDhw8xc+ZMJCQkgMfjoVu3bti5cyfat29PPKrau3cvvvnmG1RXV0OlUqFp06ZYtmwZJk+eDKlUitDQUCQlJentbBwcHHDv3j3qTmoLCws4OzvDzc2NsGgBAGtra7Ro0YJq4QIAjRs3hqurK9XeRvfdLi4ucHR0pMbu5OQEZ2dn2NnZEZpAIICTkxNcXV2pu2GFQiFcXFwgl8thbm5O6CKRCC1btoRarYaJiQlRIJqZmaFFixawt7cnLFwAwMrKCi4uLmjRogU1NltbW7i6uqJ58+bUx4l2dnZwdXWl2tsAQNOmTeHi4mJwh7uLiwtcXFyoO8AFAgGcnZ3h6upKtbfRfb+bmxvVwsXIyAhubm4QiURU6x9zc3O0aNECLi4u1NxYW1vDxcUFrq6u1Nh0/YKWG93ubVdXV6q9DfBnbmi7vHk8nj43NOcAoVAIZ2dniMVi6g5zIyMjuLi4wNTUlJob3e5tKysrwhIKqB0zutzQdqfb2trWmxtdv2jatClVt7e3h6ura51+YWwiwkefDkYz50b4+ZdkFBZXwad/e4we3wdurevm0NXVFS4uLlRnAV1u5HI51ZnA2NhYn1da7LrcODo6EpZQQO2ufldXVzg7OxNWYQDQqFGjenfmc3BwvDlwPpevGblcjrFjx+LmzZvYunUr5HI5Pv30U4wYMQI//fRTnR95lmVx8OBBlJaWwtfXF9evX8fnn3+Oli1b4s6dO+Dz+ejbty/s7OywZs0aGBkZ6S8IT18IpVIpNm7ciNDQUJiZmcHCwoLqY1daWgoLCwuYmZlRfexKS0tha2tL9a1Uq9UoKyuDnZ0dtUhRKBSoqKhAkyZNqBdqmUwGiUQCe3t7qj+hRCKBUqmk+sKxLKt/7ZyNjQ2Sk5PRrl27Oo8Qy8rKIBKJqD5rDMNALBbDzMwM5ubmVL2kpARWVlZUT09dbmgX6mflRjdDXVFRAXt7e+qFWiaTQSqVokmTJkRuKisrUVhYiCZNmlD9B3WPnoHaYoiml5WVwcjIiOpn+qx+odVqUVpaCmtra6qnp1qthlgsRuPGjam+lSqVCuXl5dR+oTM1r6qq0seelpYGV1dXGBkZ6U3L5XI57OzsqP2mqqoKWq2W6tmp84bk8/l6b8anYy8rK4OJiQl1zOhiN9QvtFotSkpKYGtrC2NjY4O5ofULhmGxYf1GJCYmYsuWzTAyFtU5Xmd2L5FIqP2CZWtfpalQKNC4cWOqXllZCYZhDOamrKwMQqEQ1tbWLzxmtFotioqKqDO+NDify4YN53P5ZsD5XP5DFBQUICIiAu7u7hg2bBgAYNWqVbh58yaqq6uJjjhmzBgIhULw+Xw0btwY3377LZRKJdRqtb6IiY+Px8yZM+Hg4IApU6bU8dnT3Szo/u+TF+CnbySe9o58WhcIBPpZSdpNiEgkqlc3NjauVzczM9MbS9P0J/0Pabpu9ubpmHU8OStIi/3J3NP0J70j/025YVkWVlZW+vhoxz9pBE/Tn5Wb+vqFznTbkC4UCuuNzcjIqF7dxMSEOuOq+6y5ubl+Npt2/JMz4TT9yRlRWuxPzrS/aOx8Pr/OTP6L5IbHA4yMhTAzN4HISKj/jJZlkVVRgYTSEpiLjNCtaVPg/9dQPs2Tfqr1jRlD+suMGd1vllQqfeF1l/+VSY5XBRdvw6Khx/dX4IrL14xcLkdFRQXatGkDU1NTsCwLc3NzFBQUoLKyss6PNY/H08/0KBQK7N+/HzKZDO+99x7s7Owgk8ng4eEBa2trKJVKnDp1Cvfv34ednR369u0LHo+H6upqnDx5EmVlZcjPz0dmZib1sW9DQjcjk5mZ2eDvkAHoX7n3JrzPWTfTCIA6w9uQ0M1sVVdX6/uyhmGQLBbjemYGKhUKCHh83LOxQaC7OxpTZk7/aTQaDXWNsyEUCgUEAoHBTX9/Bd2rVIHaG1Rdv6muroZWqwWPxwOfz4exsTHx1EH3KlAej6dfVsLn8yGXy6FUKmFpaVlntp1hGCgUCv26bBMTE+qSAx01NTXUTYANEY1GA41GY/DVpg0J3RMXjj/hisvXjEAggEgk0g8yHo8HjUYDgUBA/RHSPfpav3491qxZg+HDh2PZsmUQCASwtLTErl27YGxsDK1WCzs7O6xevRq3b9+Gj48PeDwezM3NMWTIEEgkEuzbt494VWJDRJez+t7Q05CoqqpCTU2NwbWuDQndY3RHR0fq0ouGho2NDcRisb4vn0lOwrcRD6B9YpPMg6pKhFdWYMewt9GEsh75n0SlUkGtVj/3TI6uiKuvIHtR/vjjD6xevRpCoRCrV6+Gl5cXGIbBrFmzkJaWpi8A3dzcMG7cOPj4+EAgECAxMREbN25EamoqRCIRGjdujHnz5sHLyws7d+7EyZMnsXHjRnh5eem/Kzw8HDt37kRxcTFUKhW6dOmCefPmGXzbl1qtrvdtVA0JlUqlL8gbOizLUtcpv8lwxeVrpnHjxnBxcUFRUZH+B6iiogKurq6wtbWFTCZDfn4+rK2tYW9vD5lMhm+++QY7duzAmDFj8P333+vX1VVWVqK8vBwuLi5gGAZKpRI8Hq/OjI7u/dFmZmb6O/OGPuPDMAwEAgF1/V9DRKFQQKPRUNf0NTRYloVQKISxsXGD//HWxaqLV80wuJydjUqKG0F6dTVixGIMNfBK0H8KHo9HXV9Ng2VZCAQC/Q34q0CpVOLGjRv6dZ83b97EW2+9BYZhIJfLwePx8Omnn0Imk+HXX3/F0qVLsXr1ar2zQ3JyMubMmQNbW1ukpaVBKBRCJBJBpVLpH/c/2VaJRILu3bujc+fOuHHjBk6dOgVbW1ssX74c1dXVqK6uhq2tLSQSCczNzcHn8yEUChv8uAVq1+AKBII3Il6WZRv8JM6LwmXjNWNvb4/x48ejsLAQU6ZMwZQpUyCTyTBlyhSYm5vj3r176NatG/73v/8BAH755ResX79ev9h98eLFWLhwIUpLS5GSkoKgoCCMGzcO48aNw549e+Dk5ITAwMDnGrwKhQKXLl1CYmIidWZBIpHg3LlzyMrKouplZWU4e/Ys1f+QZVkUFBTgzJkzVKsllmWRmZmJCxcuGPR+TExMxJUrV6iPFxiGQVRUFG7evEm1RWIYBuHh4bh//z71MYxKpcL169cRExNDtUqRy+W4cOECUlJSqLFXVVXh7NmzVAsXoNYa6cyZM1RfTJ0349mzZ/WbkJ7W09LScPHiRf3jvKf1x48f49q1a9RH4QzDICIiAnfu3KHaImk0Gty5cweRkZHU3CiVSly9ehWPHz+m5kYmk+H8+fNIS0uj5qaiogJnzpxBfn4+mRgAxcXFOHPmDMRiMTW27OxsnD17FtXV1dTcSCQSXL9+nfrolGEYxMTE4MaNG1TvR61Wi3v37iE8PJwau1qtxs2bN/Ho0SNq7AqFAleuXKGOGd0GmnPnzhn0hiwrK8OZM2eotkcA6oyZp9EwDCQGlj5oGQZVCgUSEhKeOWZu3bpFHTNarRZhYWG4f/8+NXbdmImNjaXqNTU1uHTpksEx809QVFSEhw8fomfPnmjXrh3u3LlT5zG0SCRC79698e677+Kzzz6DTCbD4cOHoVarUVJSUmtAb2yM1q1bY+LEiejRg9inUIegoCDMnz8fAwcOxLhx42BkZASxWAyNRoNDhw5h5MiR+OSTTzBhwgScOHHidYfPwfGvgSsuXzN8Ph/z5s3DunXrYGpqikaNGmHTpk2YMmUK+Hw+LC0t0b17d7i4uACo/fHr0aMHOnXqhNzcXCQkJCA1NRUqlQru7u6YPHky1Go1KisrMXbsWBw6dAjdunV7ruLy9u3bCAkJwcyZM6lFzG+//YYRI0ZgwYIFhMayLHbu3ImQkBD88MMPhK7VarFq1SqMHDkSe/fuJXS1Wo3FixcjJCQEv//+O3ExUqlUmDp1KkaNGoU7d+4Qx5eVleGDDz7AqFGjkJiYSOhZWVl47733MGrUKBQVFRF6bGws3n77bUydOpV6Ib927RpCQkLw8ccfUz38Dh8+jBEjRmDJkiXUImPjxo0YMWIENmzYQByr0WiwYsUKjBgxAgcPHiR0hUKBBQsWICQkBBcvXiTOX1NTg4kTJ2L06NF48OABcXxxcTHef/99jBkzBmlpaYSekpKCcePGYcyYMSgrKyPaHhkZieHDh2P69OlU38vz588jJCQECxYsoBYx+/btw4gRI/DVV19Rc7N69WqMGDEC27dvp/7dly5dihEjRuD48ePEuWtqanD79m28//77uHHjBnG8VCrVxx4dHU0cn5+fj3fffRfjxo2j+qHGx8dj3LhxeOedd1BZWUm0PSwsDMOHD8dHH31EvSk6deoUQkJCsGTJEmrhv2vXLoSEhODbb7+l5mblypUYMWIEdu/eTRzL02rBGDBrNzcygoupKWbOnInhw4fj7t27xOcqKysxbtw4jB07FgkJCcQ5MjMzMW7cOLz33nvUG4NHjx5hzJgxGD9+PGQyGTRaBiVVUpRJ5GBYVj9m5s6dSx0zfzcsyyI8PBxSqRTBwcHo27cvcnNzqT65PB4PrVu3hpWVFfLy8qBWqxEUFASpVIolS5bgnXfewdy5cxEfH19v4SwSicDn86FSqXD+/HmwLAs/Pz+IRCJotVrIZDIYGxvj+++/R2Bg4OsMn4PjXwX3WPxvwNzcHDNnzsTMmTMJzcvLC7du3dL/72nTpmHatGkGz/X5558T//a8jxycnZ3h7e2NXr16Udc4tW7dGj169EDv3r2px3fp0gU9evRA9+7dCY3P56Nbt27o0aMHOnbsSOgCgQA9e/ZEYWEh3N3dqbqvry/MzMzg7OxM6Obm5ujdu7fegudpbG1t4e3tDZFIRPX4s7e3h4+PD9zd3fW7sJ/E1dVVnxva49d27dqhR48eeOuttwgNALp16wZPT0907dqV0Ph8Pjw9PREfH4927doRulAohLe331zOWAABAABJREFUN6qqqurs/H9S9/X1RZMmTdC8eXNCt7S0RO/evVFdXU31zbSzs0OvXr1gaWlJjb1p06bo3bs3OnfuTLWEadmyJXr27KnP79N06NABnp6e8Pb2JjQA8PT0hKenJ7p06UKNzcvLCxkZGWjTpg2hi0QiODk5wcvLS38D9iRGRkbo168fsrOzqR6L1tbW6N27NzQaTZ0d9Drs7e3h7e0NBwcH6g51Z2dnvPXWW+jZsye1X7i7u6Nnz57w8vKi5qZz587o0aOHwRkwLy8vREVFoVOnTmRsIhG8LSyRLBajSvDnGBfweBjerh26Ojnhrbfe0ufoaYyNjdGvXz+IxWKq36qtrS3eeustGBsbU/1UmzZtCm9vb7i5uSG1pAIH795CYkEJBHw+vN2d4dXYFt69ehkcM383KpUK165d0y8tsLW1hUajwaVLl9CnTx/i80qlEiqVCkZGRhCJRJgwYQI6d+6M8PBw/X8mJibUG0Yduo0c27Ztw/HjxzF+/HiEhITodWNjY4waNUq/TpM2e8/B0RDhfC4bKFKpFJs2bcKiRYv0ay51HoHGxsbUC6HuMZ+pqSl13RTDMJDJZDAzM6Ou49TdqdO8E4HaGbyamhqqfyBQO7upUqmo3oq6jR1arZbwF9S90s/V1RV8Pt+gjY1cLtevZzMUu4mJCTU3z4pdp+vWVb1obnS7v2n+gcCfGyXMzMxQVVUFmUym3/Sh881kWZa67lR3AeTxePXmRiQSUYuEl+0Xz5MbnfE3re0pKSmwt7en+lICtUUCrV/ojn9WbmpqaurdYPe6x4zuRQE8Hg9bt25FWloa1q1bBz6fD41Gg6SSEhxLSkRccTFMRSIMcm+NkPbtYWlsDJVKBZVKZbDfPCs3un5BW7+r6xf5lRJ8evACMkrr7nLu5e6M5SF+sLe11udGqVRSZ4hpvEqfS5ZlkZqaiokTJ6KqqkrviapSqeDk5IRffvkFM2fOhFgsxokTJ2BpaYmdO3di9+7dmDRpEj799FM8evQIbm5usLKyQnp6Ot5//320b98eu3fvxrfffovDhw9j165d+htMHo+HmpoabNy4ESdOnMAHH3yA0NBQfSxbt27F3r17sX37dnh7e3M+lw0YzueS87l8o+HxeNRZvSf1+nb26R7jG0IgEBh8owxQO0tV3/Eikcjgwn7dBbA+aBfQJ4+nvennSf1lYn/Z3NQXO1A7Q2dodshQ0fikXt/F+9+Qm2cdb6h4AlBvv3ie3NBmc5/UX/eYqc/LVSgUolOzZmjv6Ai5Wg0hnw+TJzZI1NcvgGfn5ln9wtTUDJfDYpEpJu1zIjPzEV9Ujub25Bu4/g6eztedO3cglUoxf/58+Pn5AQCOHTuGAwcOICoqCkDtuvKlS5fqrcv69OmDCRMmAAA2b96MkpISNGvWDKWlpdBoNPD3969zc75161b962wDAwOhVqtx6NAhGBsbIz4+HosWLUKbNm0wffr0vykLHBz/TrjikoODg+NfjoDPh+UrtOt5XjQMg4ySMtAecGm1DJILxRjYhVzO8PrbpUW2rBRpkkKAB7iZ2cPMwhwffPABhg0bprfpGjduHIDaIn7YsGEoLi4Gn8+HhYUFpk+frl8uAgBLlixBZGQkioqK0L59e3Tp0gV9+vQBn8+Ht7c3MTOtW2by3nvv1dnwpHs7koeHB9599903wjKMg+NpuOKSg4ODg4OKgM9DEyv6zC2Px0NTm7/fw1DNaHC1KBZhpUlQMLW74I35IvTu0xbzRo+CiP/nZa1Vq1bUdeo02rRpQ133CwCDBg3CoEGDqFqvXr2o/+7r6wtfX9/n+m4OjoYGt1ucg4ODg4MKn8fDoC5t0MiCfHzu1sQWPm1b/K3tYVkWWdIS3C5N1BeWAKBk1AgrTUKGlL67noOD4++FKy4bMHK5HAcPHsSNGzeoPnVVVVXYu3cvIiIiCJ1lWRQXF+PHH39EQkIC1UYlKysLO3bsMLiAPz4+Xv/2iqdhWRYPHjzAnj17qPY3Wq0W169fx6+//kr1N1Sr1Thz5gx+++03qoefQqHA0aNHcfHiRaq/oVQqxf79+3Hnzh1qbsrLy7F79248evTIoKfnzp07qR5/LMsiPT0dO3fuRG5uLjX22NhY7Nq1C6WlpVQ9PDwcP//8M6qrqwkdAG7duoUjR45QrYFUKhVOnTqF33//nZqbmpoaHDp0CFevXqXGXl1djZ9//hn37t2jXqhLS0uxa9cuxMXFUWPPzc3Fzp07kZ6eTtWTk5Px448/oqCggBr7o0eP8NNPP1EtoxiGwe3bt7Fv3z6qNZBGo8H58+dx7NgxqjWQSqXCiRMncPbsWaonqEwmw4EDBwyOmYqKCuzZsweRkZHU3BQVFT1zzOzcuRPZ2dnU41UqFZKTkw32i4iICOzZs4ewTQJqc3Pjxg38+uuvVGsgjUaDM2fO4MSJE1RPUKVSiaNHj+LChQv6McPj8dDZxRFL3vZHSzsbMColhKwW3Vs0w1ejAuBg/eesZllZGU6ePEnN26skqTofKob826lYDZKrC8CVlhwc/zxccdmAiYmJwZw5czB79mziYsOyLC5fvoyZM2di2bJl1CLl6NGjmDVrFr7//ntqkbJt2zbMnj0bW7ZsoV5Ily9fjtmzZ+sXwD+JXC7HkiVL8OGHH+Lq1auEXl5ejk8++QTTp0+n+hcWFxdj2rRpmDVrFtLT0wk9OTkZM2bMwOzZs6kX6rCwMMyaNQsff/wxcaFlWRZnz57FRx99hK+//pp6IT5w4ABmzZqFdevWUYuUTZs2Yfbs2di1axe1iFi6dClmzZqF3377jdCkUik+++wzzJgxA7dv3yZ0ANi5cydCQ0Op/oX5+fmYNm0aZs+ejezsbEKPi4vDjBkz8PHHH1MLuOvXr2PWrFn45JNPiNhYlsXJkycxa9YsrFy5kqrv2bMHs2bNwpYtW4jCnmVZrF27FrNmzcK+ffuo/Wbx4sWYPXs2zp49S83NJ598ghkzZuD+/fuEXlRUhI8//hihoaFUz8/MzEyEhoZizpw5VG/Hhw8fYsaMGZg/fz61sL906RJmzZqFhQsXUm/IDh06hNmzZ2P16tWEzjAMtm/fjlmzZmHHjh2ErtVqERERgWvXruHo0aNEbrRaLT799FPMnj0bly9fJtpWVVWFuXPnYsaMGfoNLE+Sl5eHOXPmYNq0adQbwpSUFISGhmLu3Ll1vGIFfD4GebTBdA8n5P22G8JHV/HDuEB0a1H3dat//PEH1c/zVcPWUz6yeL2FLQcHx/PBrblswLRs2RJWVlZo3749ddeoh4cHhg4dioCAAOqO0969e2PQoEEYOnQo1WYlMDAQ8fHxCAoKon7/iBEjIJfL4ePjQ2gmJiYYOXIkrKys4OHhQehWVlYYOXIkkpOT0bJlS0K3tbXFO++8A5VKhWbNmhFvQHFycsLIkSP1FjZP06FDBwwZMgTdunWj7tL29PREcHAwhg0bRo3d19cXgwYNwuDBg6kWM0FBQUhPT8eAAQMIDQBGjRoFHo9HXa9lamqKUaNGwdHRkeoZCtT6I7Zo0YLq/di4cWOMGzcOAoGAupmgRYsWGDFiBFq0aEHdwd65c2cMGTIEvXv3psbWq1cvBAcH4+233yZ0Ho8Hf39/REREYODAgVQ9ODgYhYWF6NevH3FuHo+H0aNHw8zMjOoNaWZmhtGjR6Nly5Zo27YtoTdq1AijRo2CWCym+l46ODhg7NixsLS0hJ0ducvZ3d0dw4cPR8eOHak76Lt164bBgwcjMDCQaqvk4+ODQYMGYciQIYTO5/P1YyYgIICqt23bFq1ataJ6zfL5fIwdOxZNmjSh+qlaWFhgzJgxSExMpHrJNmnSRD8mHRwcCL1Zs2YYPXo0HB0d0ahRozoaj8eDR4d2GOTdDV5eXmhsTdrLeHl5Ucf6q6a1ZTPcE6dAzda9cRHyBHC3aIaGbXrDwfHfgPO5bKA86XOpu4jRfOyepD6dZgPzOvUXaZvuUWu7du30n3uR419W/7tzU1lZqfe5fNm2v279RY+lfSYtLQ2urq76G6BX1W9etO2v+/w6q5vU1FSsX7+eKD5fNHf/RG6USmXtbPlzXleqJRIYiUT12kU9jYrR4EzBQzwoS4WWrZ2pFPD48GrUCsOae8GI/++cM2GBP30u/+nG/A0olEoolUpY12PB1lBgWRYHfv2V87l8gn/nKOR4pdTn/fhXjvs79Bc5lnaD9G+O7VXo9X3un27by/xdn/7Mi/5t/82xP49e32de5tjnOf5V6EZaDZzEhc9dXBar1DDj82EpJGfI6+NDo2bwsRUinBGDBfAWvzG6GDvAqJxcAvNvgQWgkivgJFa/EcWlVKuFVKOFo4p81XBDgwULyxpyDfibDFdccnBwcHC8EngsCxM1uZGKBsuyEGkYiPg8mIDcdFcfJgB6G9mhN55Y2sAwAEOuj/63wLAshIwWxmol+A38jTUAoNIyEGoZGKvZBv+GHoZlIWBerA83dLgNPRwcHBwcHBwcHK8MbubyDUP3mNHQneTL6M+7Xu11fPe/Xf8v5+ZZbf87zl8f/+bcPOvcT5/jVZ//WW1XM1WQq7MAAOYiNwj5dTfqvGzuODg43ky44vINoqysDDt27EDnzp0xdOhQYsNAXl4edu3aBX9/f/Tr14+4YCQnJ+PAgQMYOXIkunbtSlyEoqKicOrUKXzwwQdwd3evozMMg5s3b+LWrVuYNm2afjPKk/off/yBhIQEzJgxg9itqtFocPjwYYjFYkybNo3YyatSqbBjxw6IRCJ88MEHxO54iUSCHTt2wMXFBaNGjSJ2gJeWlmL79u3w8vJCUFAQkZvs7Gzs3r0bQUFB6N27N5Gbx48f4/Dhwxg3bhw6depE5Ob+/fs4d+4cJk+ejBYtWhC5uXLlCu7fv4/p06cTO3m1Wi1+++03ZGRkYMaMGXgatVqNAwcOQCaTYerUqcT7omtqarB7925YWFjgvffeI5wBKisrsXPnTri7uyMkJITY4V1cXIydO3eiT58+6N+/P5Gb9PR07N27F8OGDYO3tzfRvpiYGBw7dgzvv/9+nU1XutzcuXMHV65cQWhoKJycnIjcFBUV4cSJEwgNDSV2eGs0Ghw7dgyFhYWYNm0a8R5vlUqFX375BVqtFpMmTSI2jshkMuzatQt2dnYYN24c4RxQXl6OnTt3omPHjhgyZEid3Oj8Tn/88Uf0798fvr6+RL9ITU3Fvn37MHLkSHTv3p3ITWRkJE6dOoWJEycSO7xZlsWNGzdw8+ZNTJ8+nRgzWq0Wf/zxBxITE/Hhhx/C1ta2ji6VSrF69WqqpVKnTp1QUlqAXPEVvD1NAr4AiLjQBOWZ7dGjR08kJSVTj/Pz80P79u1x7do15OTkQKFQoHfv3ggJCaG6Lmi0Wuz8/SwqJBJ8Mm40zP6BV1hycHD8/XDF5RvE/fv38fXXX8PT0xN+fn6EDc358+fx7bffIiIigrCJYVkWv/76K7799luUlZVhy5YtdS6kGo0GP/74I3bt2gVTU1MsXry4jq5Wq7FhwwacO3cOLi4umDRpUh1dpVJh+fLlSElJgYeHB4KDg+t8f1lZGZYvX46SkhL4+voSF+qsrCysXLkSarUagwYNgrOzcx398ePH+PLLL+Hu7g5/f3/Y29vX0W/fvo0VK1bAx8cHffv2hYVF3VfenT59Gt9++y0eP35M2MSwLIuff/4Z69evh1wux9q1a+voarUa27Ztw6+//gpbW1vMmzevjq5QKLB27Vpcv34drVu3xrhx4+rkRqFQ4KuvvkJubi48PT3h6elZ5/ji4mIsX74cEokE/v7+hH1RSkoKVqxYAaFQiEGDBtWxJ2JZFtHR0fjqq6/QsWNH+Pv7E4X9tWvXsGLFCgQGBqJ3794wMzOrox8/fhzfffcdMjMzceDAAaJ4/PHHH7Fjxw6wLIuVK1fWOValUmHTpk04efIkmjZtShTPNTU1uHfvHlatWoX27dvj7bffrnN+mUyGZcuWoaysDN7e3ujTp0+d4/Pz8/HVV19Bq9Wif//+xOv9EhIS8M0338DS0hKDBw9G48aN67T9wYMH+jHTr18/WFtb1zn+4sWLWLlyJWJiYuDt7U0Ur4cOHcJ3332nfyHB07nZunUr9u3bB2NjYyxdurTOsQqFAuvWrcO5c+fQokULTJw4sc7xEokE33//PR49egRPT08EBgbW0WtqarBz505UVFTAxsYGpaWl8Pb2hqOjI8ys5Th25DTEZWXo/64bbp6txt41sejeOxqj3umPy5dzkZ6ejrt374JlWfTq1Qt2dnYoLy/Hjh07cPHiRZiamuLRo0fYu3cvLCwsqK9HZBgGJ27eRk5xCWaNGM4VlxwcbwhccfkG0b17d8ydOxddu3alevj1798fM2fONOhbOXLkSJSXl+Odd94hZmiEQiHef/99iEQiDBs2jNBFIhFCQ0Ph5uYGPz8/4txGRkb45JNPEBcXRxRPQK1/4bx581BSUoJWrVoRurOzM2bPng0jIyOqf2G7du3wySefwNXVlep76e3tjTlz5uCtt94iiieg1rfyww8/xNtvv01oADBu3DjU1NRg3LhxhCYSiTBx4kRYW1tTL8DGxsaYMWMGOnbsSBRHOn3BggVIS0ujeoI2adIE8+bNg0QigaurK6G3atUKH3/8MSwsLIjZLaB2Fmv+/Plo06YNMfMH1Ho3zp49G35+flTLmKFDhyInJwejR48mNAAYP348gFpvz6cRiUSYMmUKHB0dERAQQOgmJibo3r07ZsyYgZ49exK6mZkZFi5ciLy8PHTo0IHQmzZtivnz50Or1cLJyYnQ27Rpgzlz5sDe3p4ae7du3TB37lx4eHhQx4y/vz9mzZqFgQMHUr1iQ0JCUFxcjPfee4/QAGDy5MkwMzPD8OHDCc3Y2BjTp083OGYsLCwwa9YsPH78mOp7aW1tjWXLlkEsFqOwsBA//vgjFi9ejKFDhyCl5EccPrIPjBa4dLwKh7aXoWMPU8xeYY1mrUvw008/oaysDKNGjUJ0dDRWrFgBX19f8Pl8xMXFYcmSJVCpVPj4449x6tQpXL9+HQMHDjS4iF+rZXD27j1cfxSDVs2aYcbbQ9DI6u9/LzkHB8ffA+dz2UB50udS9yjvn/bwe1XnflpnGAbJyclo27at/t//Sd/KFz3/i8ZeVVWl97nk8XgN2tOTZVm9z6VIJPpb+9WL6q+i32zduhVpaWlYt24d+Hz+K+s3ixYtwg8//IDTp09j2LChiC34BiMGfIesFCWMjHlo62GCReubwc5BBGerd9De7itoNBrMmDEDR48exR9//KEvcHXnZVkW3333HZYvX45169Zh1qxZgEwKJjJc/70qtRqDFn6OewmJeKtjBwj4fNyOfYwZbw/B2lkzUKxhYcbnwUbYsPaWsiwLlUaDa1HR+CPsLvJKxbA0M4VrK3d8OmwQjIRCzNmwFeKqKswbMxL9u3dFZHIKvt67H+8M8MP7Af2x/Of9eJCYDD6fDyszM3Rq2QKj+/VFG2cnal/6t1GtZVCtZdBcJPhPtPdlYFgWG38/i4mff8n5XP4/DWtEc9QLj8fT//cs3ZB/4vPqr/Pcz9P+V9n2F9X/bbl5nX/Xv0N/8nMv0/aGlpu/3jYeLIxag8fjg2UBlZKFUMSDUFj7WUsjcgb4aViWxalTp7Blyxb0798fY8eOrffzFiam2P7Jx/jl88/Q2qk5LkU8RFkVuaazoaBlGKw9chzvLv8W4Y8T0NzODiKhEKevXEV6fiHUGg3uxD3G+fsR2HDsJORKJcSVVbgcGYX0/Nr3oz9KTcf1RzGwt7UBjwdsPnEKwz//CvcTkl77KzY5OF4W7rE4BwcHxxsEj8dDE9N+MBY0gbllHvoEWeL671XY8lUxlq4egCbO5Gs5n4RhGBw+fBgLFixAjx49sG3bNjRp0gQ8Hs/gW79FIiGsLcxhLBTBwtQEpRVVUGk0DXZ2Iyk7B1tOnkbLZo44seJLtGjqCBbAveIydLCxgEqtBgAYG4kQFhePW9Gx1NwZiYT47N2xaOPUHMdu3MbUVWvx/cEjOLZ8KUSU19JycPxbaKhjm4ODg4PDACJBIwj51jAyFmLags4Y9k5L3Lsqx+7/aSGrrr9oOXbsGObMmQNzc3OMHDkS8fHxSEhIqHc2rUIixeYTp7Hn3AUkZuegjbMTmlg33NcCPs7MRllVNfp37wYXRwfEpmfipzPncTX8LlLy8vWfc2/WDF1auWH76TOoURo2nxcIBPDr1gUuDvZIyMqGuKrq7wiDg+MvwxWXDRiWZaHVasEwjEFdo9HUq6vVaoMXjZfVGYapV9dqtdBoNPXqWi39rQi62LRaLfX45439deXmWbEzDFNv7AzDGIwd+DM39cX+X8wNy7LPzI2u3xhCF7uhtulif1ZuNAwDhVYD7RN5eNnYARjMq05TqzXQaLRQKeu2s77cmJmZoVGjRhAIBNBqteDxeLC2toZ9Y1f0a7MPO9Zdx8QPJuPKpTCsXLkScrkcAGD+f+ydd3gUVRfGf7ObTe8dEkJoSei91xR6R6pgAewgHQHBgoqKICBFiiDwiSJFehVpIk1KaCGkAEkgvfdsne+PuGvCTAAVC7Dv8/A8mndn5t4z986cufec99jZ4eLigkqlMvX91KlTCIJAQUEB7777Li+//DLffffd72Ou7IUFAUdbW/w8PUjMyGDZjt3Ur16N90aOwPI36SK9oEeHHrHCdc/HD3qDARFQKZUIwJWbt5j//RZmL1nGoV9/zx2ws7bmtb69OX8jmuOXr9y3bKaAgEIQfrvPT46tzHgyYXYun2BkZWUxffp0Vq1aJXnZiqLInTt3mDhxIps2bZK8bEVR5Pr164wdO5YDBw5IXlaiKHL69GneeOMNk1zJvfz+/fsZM2YM169fl7TNYDCwceNGJkyYwN27dyW8UbdyxowZZGVlSfji4mI+++wzPvzwQwoLpbVr8/LyeOedd1i0aBHa37agyiI1NZWpU6eydu1a2b7funWLCRMmsH37dsnLXhRFLl++zJgxYzh8+LBs30+cOMGYMWO4cOGCLL9r1y7efPNNoqOjJW3T6/WsX7+eiRMnkpycLOEB1q9fz6xZs8jOzpZwhYWFzJkzh08++YTi4mIJn52dzdtvv82yZctknazExEQmTZrEhg0bZPseHR3NuHHj2LNnjyx//vx5xowZw4kTJ2T7fvjwYcaOHcuVK1dk+W3btjFu3Dhu374taZvBYGD16tVMmTKFtLQ0CW+U73nvvffIlVndyc/P54MPPmDevHmUlJRI+IyMDN566y1Wrlwp6+TFx8czfuJEvtj+PR9c+JE3T2zjk/DDXM1MxiAaiIiI4M033+TgwYMVzpkxY8Zw5swZWT43N5dTp04RGRkp4Q0GA5s3bWHW25/z+Se7+ei9baxf8zN3E7JMH5IrVqxg2rRpZGZmljt2ypQphIeHc+7cOT744AO0Wi07d+7k5MnTuDlXxcPNjw8//JABAwZgY2ODIAgolUrmzJnDxYsXad68OampqUyePJlq1aoRERHBtWvXCA8PJzw8nOnTpxMXF8cnn35Szm4qpZJ1b0/l1JeLWD5pHL+uXMLeuR/RPCiQEoWGuxZp/Ghzjs12RzhrFUmhUPJEOJkBVXxwsrPj5LUIMvPyGBzSkfdeHCGtKS5Ax0b1aRpYi40/HcVwnw+u8JhY7qSlU72SN+73SGKZYcZ/DeagjScYN27c4MSJE/j4+DBixAiJduPZs2dZsWIFUVFR9O3bVyLBc/jwYVavXk1RURGhoaESqZXt27ezZs0aHBwcaN26teT6X3/9Ndu3b6dOnToS7cWioiLWrFnDzz//TEhIiESXMjc3l1WrVhEVFUX//v0l2pKZmZksWrQIhULBoEGDJMLet27dYvny5bi7u/Pss89SqVKlcvylS5f46quvqFmzJsOHD5cIh588eZKVK1eSkJBAr169JKLsBw8eZM2aNQB07NhRIiC9ZcsWVq9ejaenp6y00urVqzl48CCNGzcmMDCwHFdYWMjq1as5d+4cXbt2lYhnAybR9cGDB0vkhVJTU1m0aBG2trYMHTpUIs4dHR3N8uXLqVKlCsOGDZNkN54/f57Vq1dz9uxZhg4dKrnvR48e5auvviIzM5Pu3buXs70oiuzdu5c1a9Zga2tLmzZtygnWi6LIpk2b+Prrr/H396dBgwblzi2KIitXruT48eO0atWKatWqlePz8/NZtWoV165do2fPnnTu3Lkcn56ezsqVK0lJSWHYsGESXcrExEQWLVqEm5sbQ4cOxd/fvxwfERHBihUrCAgIYOjQoRLbnj5zhu/CT+JczwlBXTpfBGD/nSjmtuzJ2R8P8tVXX1FUVERYWFi5vhsMBnbs2MGaNWtwdXWlRYsW5cadwWDg6tWrXLlyhZ9++kkirWQwGNj47XFyM1yxUF5DEATOnIzh5M9RvP1eP5xdVaxcuZKYmBj69etH+/btTcfa29uTnp7O2rVrycnJYfjw4RLNz7S0NL777jsqV67M66+/jq+vLw4ODiaJpitXrrBixQoaNmzIqFGjJLb94osvWLJiFalRN0yJRAM7tqdt/d/nvvVvY0mNlj02p0jU6ShS5VFomUWkZQK3LZLoV9geGx5vPcwGNarzbFgwa/cd5IWP59GpUUPCY2IRQfKssrOx4fV+vTh26bLkPDqdng0/HkZvMLD12M842tkxcfAzWKrMr24z/tswj1AZiKKIWq1GoVDIyp88LmjYsCHdunXD399fUrUFIDg4mLlz59K4cWNZ/cL+/ftTXFxMaGiobPWN0aNH4+rqSv/+/WWvP23aNJo1ayarb2hnZ8esWbO4ePEinWQ0/FxcXPjggw+4e/euxAEB8PT0ZMGCBWg0GmrUqCFZ5QoMDGTu3Lm4uLhIBNMBWrVqxccff0ytWrVk9Qm7du3Kp59+SqtWrWT7PnjwYERRpFu3bpJqPwCvvvoqPj4+FWo/zpw5k/bt29O7d28J5+DgwLvvvktERISs7iXA8OHDGTp0qMQxBfDx8WHhwoUIgoCfn5+Er1evHnPnzsXb21simA7QoUMH5syZQ/369WX73qdPH/Lz82nfvr2k74IgMGLECKytrendu7fEaRcEgTFjxlCzZk1ZTVBBEHj33Xc5c+aMrCaoo6Mjs2fPJjY2Vlb30tvbmzlz5pCXlyerh1q1alUWLVqEtbU1Pj4+Er5JkybMnTuXqlWrSpwngFadOlAjP5os2zKVeoDkojy+ijzD2/36odPqCAsLk/RdoVAwcuRIXF1d6devn8TJUCqVNG3alPT0dAYMGCC5dnZmEfZWQRQq80zPJFGE+NsZbN9yjvFTu/PBBx9w584dWd1LX19fPv74Y9Rqtey4qFmzJgsXLsTZ2VlSJQqgRYsWzJ07t0I91K5du2IFuBfmlq7QCeDuLL/CdkuVxFWrWzjpf9MeFcAgGoi0jKe2xp+G2hoI0nW+/yx0okiSWkt8iQYHpYIaNlZ8+NKL1PWvyq6Tp0sdQ3s7Rj4zgJ6tW6JUKAjw9cHF0QGloKBDwwY807E94TGxeP72QVPVy5NaVXw4cPYcdjbWhDVrwvNdw2hVp/Zj+04y4+nBU6tzqdFoSElJwWAw4OHhgZ2dHaIoEh8fz5dffsmlS5ewsLCgadOmjBw5kurVqz+ya/8TKKtzqVAoZB9G/6Z+4aPkRVEkKiqqXGnB/7J241/lc3JyyulcyuFR6SP+230HTDqXlpaWD2x72eMfZJuHufa9v4nOSaf/wXUU66WhFk6W1uzv8RJeNg4PdX45uy9btoyYmBgWLlwocT7Pn73FrGmb0Ouk2/VVq7mz8MsXsLf/fcXv77RNRbxYmI94/rTsuctir81pTllfw7XID7VFAYVWv4W+iNBSXZteRW1RPCbOZbHewJqkDDan5ZCh1aESBBo72DCtqjeBtlaIv8VfisBNjZ4AaxUCpXGZAr+vZBpEEVEUUfy26mswiOVCBIx/f1wcS7PO5dMBoQKdy6d25fLixYs8++yz2NjYsGnTJurVq0dWVhajRo3i+PHjpgfpwYMHOXjwIJs3b5ZsoT0uuJ+G3p857r/Gy30g/Vfa9nfxD8L9jv+32/5H+IdxJh/l9R90rEqhQKVQUCyTD2SpUKIU5D/kHvb89/uNylKJhVIh61yqVEqUyj+nFfqw/MMdW7EcUVlYitIVcSMsxMfntSSKIvsyc1mVlInmt7GqFkXO5BXxcVwyXwb64WBRuoJtEEUEofTeCYKAxT0r28p77KtUPtkOmRlPNp7ahJ4LFy4QHx+Pi4sLVatWNSWgnDp1ClEUTQHtxt9u2LDBLFxrhhlPObxtHWnhKd1SFoCOlWvgZCkNP3lUqFHLi2o1pCEegiDQum0ANjbS8I7/KoK0fliL97RXBCtRRaC2ymOyZgnFBpH9mXkmx7IsrhaWcK1QmlBnxp+DSQ1Bry/NxpdJiNNXwFfEyZ2zNDnOgF5f+v86vV723/1UHcx4ilcuY2NjgdK4RHt7e3Q6HYcOHUKj0SAIAkOHDmXEiBHMnTuXn3/+mdOnT1NSUiIbu2iGGWY8HbBWWjChQQfSSwqIyEpFLxpQCgqaeVTh9TptUCn+vu91OzsrXno9hMWfH+DunUxEg4hKZUGrtjXp3V+aNPZfRmW9OyHFTbhsyERDAYIoYC2qaFfSgKo678cm3lIriuTq5GWt9KJIlrZiuTAz/jjmfreZzUeP4+fpwdfTp+Dm5IgoimTnF7Dh0GEOnD1HVn4+Pu7ujO7Zje4tm1NYUsL3h4+y85fTZOTm4eflwfDOoXRr0RwLpYIl23aybv+PtKpbm0VjX0Oj0/PyZwvQGfRMGDiAd9asI7+oGINoQBRBoRAQEHijf29G9ZDGhZtRiqfWuczIyEAURXx9S+u0lpSUcPHiRaA0mWTy5Mk0btyY/Px8fv75ZzIzMx9751IURTQaDUqlUjYJxchbWFjIxmkaDAY0Gg2WlpaysT9GfUKVSiWJFyurwWc8/l7eqJNXEW/U76soyUqj0aBQKLCwsJA9XqvVolAoUCqlMUBl+35vIsbD8A9rm4r69jC2MRgMsgk2D7KNse+CIFRoG+O4+DO2Kdv3e+/7w/BGXUq5vpe9fkV9K2ubisYNcN9x8UdsU9vZk9UdB3MmNZ64/Gz8rB1o41MdV2u7+86ZB9lGru/32qZBIz8+XTCM8AtxZGUWUK2GB4FB3jg52/7hOfOwtnmYOVNRXHdFUCDQSl0XT10Rdw13sdRpqaarhJfeBSXSMfZfha1CoIaNFVcLpbJWtorSxB4zHg2y8vPZfPQYEXHxxNxN5NyNKLq2aEZBcTHjvljGzpOn6NK8Kf0btiUuNZXTEdcJbdKITzZ8z5JtOwhu3IherVuy78yvvPDxZywc+xrPdQkjJSuLq7duE5+SyuBOHWgSUIvou4no9Dqc7O0YHNyJwpJi/nfwEHEpqbzetzeV3Fypd4+ShRnl8dRuixuXxfPz8xFFkbi4OFJSUgCoXLkyAQEBCIJgkiJ5ErbE4+LiGDFiBAsXLpQVmb58+TJDhgxh3bp1Ek4URY4fP87gwYPZvXu3hDfKrAwaNIiTJ0/KavStW7eOYcOGce3aNVn+888/58UXXyQ+Pl5yfrVazbvvvsurr75KRkaGhM/Ly2PixIlMnjyZgoICCZ+SksLo0aP56KOPUMtUwoiNjeXZZ59l6dKlsrY5f/48gwYN4rvvvpPdjjl06BCDBg3i4MGDsrbZvHkzgwYN4tdff5Ucr9frWblyJcOHD+fGDWndYJ1OxyeffMKoUaNITEzkXhQXFzNjxgzGjh0rq3uZnZ3Nm2++yYwZMyS6l6IokpiYyMiRI5k7dy4ajUZyfGRkJEOHDmXVqlWyuphnzpxh0KBBbNmyRdY2e/fuZeDAgRw9elT2vm/YsIHBgwcTHh4u2/eoqCheffVVbt68KeE1Gg2zZ8/mlVdeMc3fsigoKGDy5MlMmDCBvDxpLev09HRef/113n33Xcm4MCb4vfDCCyxcuNCklyoIAm7WdvTwq017gwNrprzLju82mbbJRFFEZ9CSUnybIzHbmPzpq+w/tEfWNtu3b2fgwIH88ssvkrbp9Xq+/vprhgwZYpozgiDg4elIl+4NGDC4GcdPbOe1118mISFBcv6SkhJmzZrFG2+8UeGcGTduHFOnTpXVik1JSeHll1/mww8/lGjFiqJIbGwszz33HMuWLbuvaL0cBAQUooCdwYbG6gDaqutTWe/+SBxLgyiSrtNyOD+Pg3m5JGo06P+m57eFIDDY0wVvS4t7/g693Z2o/hiFKvzXcSEqhug7ifRo2QJbKyt2nzyDTm/g5NUItv9yktCmTVg3YypvPTuYJePH8NawwSSkprNu/0Fq+fqy+q2JvP3cML6cPA5bK2uW79hD9m/vCqVCgQh8uWM36jJj3dvVhdf79WJM/z5Ur1QJG0srhncOYcKgAbSoLVXqMON3PLXOpY+PD4IgsH//fo4fP86aNWvIyckBSiU3jNI8RoFvR0dHWcmaxwnR0dHs3r2b3bt3y4prh4eHs3//flnnEeCXX35h//79HDlyRMIZDAaOHj3Kvn37OHPmjITX6XTs37+fvXv3cunSJVl+27Zt7Nu3j6ioKAmfl5fHzp072blzp6yDlZaWxp49e9i6dausePadO3fYvn07u3btIj8/X8JHRkaye/du9uzZI+t8njt3jgMHDrB3714JB3Ds2DEOHDjAsWPHZPt26NAh9u3bx7lz5yS8Vqtl37597Nmzh4iICAmv0Wj44Ycf2Lt3rymcoyxycnLYsWMHO3fuJDU1VcInJSWxe/dutmzZIut43759m507d7J7925TZZayuHbtGnv27GHfvn2ytjlz5gwHDhzgwIEDEg5K9VIPHjzIiRMnZPv+448/sm/fPtPOQVmo1WpiYmI4cOCA7LhQq9Vs3bqVPXv2yH6UZGVlsXPnTnbs2EF6erqEv3PnjmncyPU9JiaGXbt2sWfPHlnR9UuXLrF3714OHDhgcsCK9QUcSF7N+tuz+EW9AcfuGVx12E2aWuoA/vjjjxw8eJBTp07J2sY45q5cuSLhi4uL2b17N7t27ZIdF0VFRWzdupXdu3fLFipIS0sz2UauUMHt27fZs2cPP/zwg2zfb9y4we7du9m7d6/sR8m/AYMo8mN+HsPjb/FmYgITku4wNP4W32dnof0bHExBEGjkYMP8mj70cHPE10pFfTtrJlfxZFwVT1RPeJb0PwWDwcD2n39BqVQwYfAAgqpW4dCFi2Tm5XIp9iYarY6wpo2x/60AgIVSiYOtLbFJieQVFtG4Zg1cHBwQBIGgKlXwcXcjNjGJvMLSOW+lUjEstBMnrlzj1DXpM9iMP46ndls8NDSUJUuWcOXKFXr06IFWq0Wv12NjY0OPHj1MZdKOHz8OgJ+f32O9JQ7Qrl071q1bR40aNSSC6gD9+vUzyS/dC0EQeOmll6hatSodOnSQ8EqlkokTJ9K8eXO6dOki4S0tLXn//ffp378/PXv2lGyhWVpasmTJEuLi4sqJPxvh7u7Ol19+SU5ODrVr15bw1apVY/ny5ahUKry9vSV8gwYNWL9+PV5eXri5uUn44OBg1q1bR1BQkERMHkp1Le3t7WnZsqWEM2o3BgUFERISIuFVKhXTp0+nY8eOdO/eXcJbW1szZ84cnn32Wbp06SKxjY2NDcuXLycxMZGWLVtKHDwvLy9WrlxJcXGxrLZjYGAgK1euxNbWVtJ3QRBo1qwZ69evx8fHR1bbsWvXrqxdu5YGDRrIzoERI0bg5uZG27ZtJW0XBIGJEyfSuHFjieA5lN73WbNm0a1bN3r27CnhbW1tad++PcuWLaNjx46S89vb27Nq1SoyMjJo3Lix5HgfHx9WrVqFwWCgatWqEr5+/fqsXLkSFxcXSd8FQaBNmzasW7eOatWqyc6Z3r178/XXX9OkSROsrKwwiAbOZ+3nYvaPGDCAAixtlZQIafyUsp6BVaZipbQxnX/atGm0adOGrl27Ss5tZWXF7NmzK5wzDg4OzJ8/n/j4eFq3bi3hnZ2dWb16NXl5eZIiBgD+/v6sXr0aCwsLWaH+Jk2a8NVXX+Hp6SnpuyAIdOrUibVr1xIQECA7Z/4NxGnUzElNJkn3++pTkk7LvPQUalpZ0dJOeg//KhSCQFMHWxra26IVRRSApaK0VKMZjwbJWVkcDb+Mv7cXTrZ2NA2oxard+/jlyjVTuIlOZldFIShAENAbfucMogGDKJbGThrvkQBhTRsTczeJL3fsRqPVolCY799fwVOrc1lUVMSkSZNYt26d6atbqVQycOBAVq1ahYODA7GxsYSGhpKYmMiKFSt46aWXHtn1/26U1bksGyf3V3TuHsQ/rH7ho762wWAgKiqKwMDA++rA/Z19fxD/KG0jp3P5b7bd+Jv7xdz92fMbt1+NOpd/9Nx/N39v20v0hay9PZPUEmnpSkuFNS9Wm0Nlm/IVk8qeY9myZcTGxrJgwQIUCsW/Nqf+LC8W5GM4L12FrejYZK0BW4WAs8Vf30QTRZF1WZl8nJaMXB7vCGdX3veuWBv274ZBFIlR66hlZfFUOJ6PUudy28+/MGz2xwiCgJ21NRqdjmK1muGdQxneOYSB73xAyzpBfP/eTFwdHdDqdOQWFlJQXEKncZNxsLXlwPyP8XF35+z1G/R9+z0Cqviw65MP+GzjZpZt38W2D98jr6iIUZ9+jlqrpZZvZQ4v/AwPZ2eK1WqGzf6Y0xGRHFrwKQ1qlNe9NutcmnUuTbCxsWH+/Pl06tSJ48ePo1aradmyJQMHDjR9pWdnZzNw4EAsLCxkVxYeR/z7Onh/L/9fbd9/wTZ/lv+3tSX/zmv/VV7yoSPqKdZJwy4A9KKOEr106/3eD4RH1bb/Av9PQgRyDDpZxxIgS69HhMckD90MI7Q6Hdt/Pom1pSUfvzIKP08PtHo9n2z4nuOXLvP2iKG80K0za/f/SP9Z79O4Vk0SUtOo4VOZj18exdgBfZnzzXcMff9jGtWsztFLVxBFkYmDn8HRzq7ctcKaNqFZYC2OXZKGoZjxx/DUOpeCIGBvb8+QIUMkZeiMD8xmzZrRrFkzDAaDbMC7GWaYYUZZqBRWVLKpTl6+NIHGzsIZV0tpyMaTAL1ez61bt7gbG4NzcgJBVapgqbJAFEXyi4q4kXCXguJinO3tCPKrgo1VaRa1Vqflyp07iMXF+Hl5UNXLC4VCgVanIy4llcSMDPR6A41q1sDNybHC6xsMBqLvJpIfF4cuPwdlVT8E5e+roQLQyMbG7Fj+hyCKIgbgrk5Dnl6Pt4UKd2WpS1L2oyW/qBiVhQVDQjrxXJcwHO1sMRgMZOfns+PEKdJzcvn01Zdo16Ae+8+c42ZSMlU8POjfvi0qCyXjnulPrSq+7PrlNPFp6YQ0bsiQ0E60/q2MZk2fygQ3boSrowMOtjZMGToIC6USPy8vVL+pqigUChrWrIGlygL7xzw87p/CU+tcGlHRVpMoimRkZHDq1Cm2bt1KpUqVmDdv3r/QQjPMMONxgYVgSQu3niQWR1Ogyyn39+Yu3XFUSeN9H3fo9Xr+97//8f7771NcXARaHaN6dOWdF4ZTWFzC6M8+58Tla1iqLNDq9PRp24ol48ciCALz1qxl8+GjKAUBexsbPnllNAM6tOVWUjK9pr9DUkYmOr2e3Z9+QJfmkp03oPR5fTriOiM+mkt+URHWPpWxeO1FUP4eRtHQ2oaejs7/qZXWpxmiKJKu17EoK4VjRXkUGAx4WqgY6ujKCEd3rMvcJ1dHB76ePrnc8QqFgtE9uzO65+8x7ENCOjEkpJPkWlaWKvq1a0O/dm1k23Lvebq1bE63ls3Ln0OlYvao5/9MV59aPPXOpRFGh7KgoIDz58+za9cuDh48yO3bt9FoNDz77LP/dhP/MERRRK1Wo1KpKtSxU6vVJv1CuVhGtVqNpaWlRN/QqM2o0WiwsrKqUL/wfrxOp0On02FlZVWhBp/BYKhQ+1Gr1Zo0/OT6bhTEr0jjT61WmzQ/K9In/DtsY9RmNOoXytnGmGAmZxuj7SrSRzTaBqiw7w/SBH3QuPirtjFqft7PNlZWUo1A47ioyDZlx0VFtvkr4+JBthFFkUrKAPpXnsSl3J/IUCdha+FAfacO1HVqi2iAYk2xrG2M0Ov1slvk99qmojnzd9smLi6Os2fP0qhRI5KSksjPz2f27Nn4+vqyeO6nLP34I5Zs20HXFqXO4LHwK3Rv2Zy3nxvG+MVfcuDX8ySkpXE7OYX1e/Yzqmc3RvXoyqi5n/PB+m/o2KgB7s5OfDHuDXb+cop1+3+UtZMRiekZfL3vIInpGfRr35b2LZqi9arEWXUxOlGkqa0tI1zc8JLR9jXj34FGFJmXmcy2gmxTudACrZrPs1KwE5QMdXQ1fwg85nhqpYiMEEWR4uJiwsPD+fjjjwkLC6Nfv34sXryYqKgo00tU7kX3X0dCQgI9e/Zk/Pjxsjp1ly9fplOnTsyZM0eiU2fUtWzTpg0rVqyQ1TfcunUrrVq1YvPmzbIafkuXLqVNmzay8jxarZYPPviA9u3bVyizMn78eLp27UpCQoKEz8vLY/jw4QwZMkRWwy8lJYU+ffrw8ssvy0rMREdH06VLF6ZOnSrpmyiKnDt3jg4dOjB//nxZ/scff6R169asW7dOtgzYd999R+vWrdm1a5eso7BgwQLatm0rK0Gj0WiYNWsWnTp1IjIyUsIDzJ49mx49epCUlCThsrOzGTx4MMOHD5fVvbxz5w49evRgzJgxshIz165dIyQkhJkzZ0r6JooiJ0+epF27dixZskSW3717N61bt+b777+Xtc3atWtp3bo1Bw8elLXNp59+Svv27SuUbZo6dSqhoaGsXbuWJUuWsG3bNtP4Tk1NZfDgwfTo0YOlS5eyZcsWbt68abqH6enp9O/fn4EDB/Lll1/yzTffcOrUKbRaLcXFxaxevZqwsDAmTpxoSvQTRdEk2/PZZ5/Rvn17PvjgA9kxf+TIEdq1bcfBDSfpU/lNnvN/nyF+02noHIyFYMkPP/xA69at2bZtm+zxd+/eZefOnRw/flyWnz17Np06deLy5csS26jVatOciYuLk/C5ubk8//zzPPPMMxXOmd69e/PKK6/ISlZFR0cTEhLCwIEDefHFFxkyZAiTJk3i448/Jjk5GTs7Oz779FNCmzamuETNyasR+Ht7UaNyJSLjE/jh+AkSUtNoERSIj7sbP1++ilqno2vL5tSt5k+L2kHcSUvnRsId3Bwd6d6yOR7OUvWCe3ErOYVD5y9iEEXO3YjiSkQkr7u5s7qKP2v9qjHNsxJVVPIi9Wb8O4jXqjlclCepQ68WRXYUZFMgmksrPu54aj/l1Go1d+7c4cCBA+zatYsLFy6Qm5sreRmGhoYyYMAAunV7/Mo8paWlERkZSVFRkWmlqCzu3LnDjRs3qFy5MjqdTrICGBsbS0xMDFevXsVgMEhWWq5fv87Nmze5fv267PUvXbpETEwMsbGxEokerVZLeHg4UVFR3L17l4YNG5bji4uLuXz5MlFRUaSnp0tkZIqKikyi20Z90rLIysri2rVrpKSkUFRUhN09gdtJSUncuHEDKF0purdiUXx8PFFRUVy+fFmWj46OJjY2lmvXrmEwGCQrcBEREcTGxhIZGUnfvn0l7QsPDycmJoZbt27Rrl07WdtERkaSmJhInTp1JMffuXOHq1evkpmZiY+PTzkuPz+f8PBwrK2tycvLw9XVtRyfkZHBtWvXyMvLk606lZiYSFRUFHZ2drL3/datW0RHR3PlyhUJL4oiN27cIDY2loiICFkH6dq1a8TGxhIdHS2ZV6IocvHiRaKjo4mLi6N58/LbU2q1mvDwcC5dusSECRPIz8/H19eXpk2bUrVqVe7evcuuXbuwtrYmPT2djIwMVCoVs2bN4sUXXyQ9PZ2jR48iCALx8fGmilI//vgjCoWCuXPncuvWLdPqq/Gjcvv27cyePZusrCw0Gg2enp6yjnFMTIxpzihQYmvhYOL0ej3Xr183jYt7M+wNBgPJyclkZmZy8+ZNgoODJba5cOECUVFRJCQk0KhRI1nbxMTEkJaWRrV7Kojk5+dz5coV8vLyyM/Pl2S1Zmdnc+XKFdLT0yksLMTBwaEcn5KSQmRkpMnpbtiwIQsWLGDPnj28+eabZGVlka9R49CmBQCZeXm4OznRsXED1u8/xNZjJ8gtKKR+9WpYqlRk5uYhgEmb0N7aGq1OT94fjG9vVbc2EwcP4O2VX/PeyOd4pkM7rCuo4mXGfwNpeh3qChzIFJ2GEoMBB8XjU6nJDCmeWufys88+Y9myZWRlZZlW7ZRKJW5ubgQGBhITE0N6ejqTJ0+ma9euj+WDqlGjRowbNw4vLy+JDp0gCISFhbF161aqV69uEo0vi6FDh+Ln50f9+vVly0W++eabtGrVihYtWsjqG86ZM4chQ4bQpo001sXGxoZFixZx+/Zt2rZtK+FdXFxYtWoVmZmZEscTwNPTk02bNqHT6ahevToxMTHl+MDAQDZv3oy9vb2srmWbNm3YtGkTlStXljjdgiDQq1cvtm7dSq1atWQlcF544QUCAgJo1KiR7PbmpEmT6NSpEy1btpQdO5999hkvvviibN9tbW1ZtmwZCQkJsjzAW2+9hY2Njazj6evry5YtWxAEAT8/Pwlfv359tm7dirOzM87OzhK+U6dObNmyBT8/P8l9FwSBZ555hkqVKlG7dm1Z/pVXXqFhw4Y0bdpU4nQLgsD06dPp1q2brDajIAgsXLiQ6Oho2XFjb2/PypUr2bp1K5988gk1atQgOTmZo0eP8sILL5h+16VLF9avX09sbCwjR45k1qxZpntlMBgYPnw48+fPRxAEbt68iaOjo2m1rnLlyixZsqScc9WzZ09at25tqpL06aefolAo0Gg0LF26FIBatWoRFxfH4sWL6dq1q6TvCoWCsWPH0qpVK5o3by7pu1KppEmTJhQXFzNkyBAJr1AoWLp0Kbdu3ZIdF46OjqxZs4b09HQaN26MKOpB1IBgAVjg4+PDN998g1arldX8DAgI4IcffsDOzg5PT08J36pVK7Zt28axY8f48MMPCQsLw8fHB19fX5RKJW3btmVQz+4knPwZKI2VOxVxnbX7DvJSz+5MHzGUGSvXsGz7Lnq0boGrowMiUFBcjCiKFJaUoLJQ4vgHNTNVSiV2VtYggL21NQ7/Ec1NMypGZQsVtoKCYlG6I+ZnYYWtTKiQGY8Xnlrn8urVq6SlpQGlYsONGzemZ8+ehIaG4unpSb9+/UhPT5eNq3pcoFKp6NChQ4WxXTY2NoSFhclygiDg4OBQoQSTIAi4u7vLioIbUblyZVlxZuPxNWrUkBX9htIXqZzwsxFKpdIk9i639WphYVGhYwalAtX3rgyVha2trazot7HtTk5OFa5mC4KAp6fnffkqVapQpUqVCvlatWpRq1atCttXp06dcjqXZaFUKmnRokWFx6pUKlmheiOsra1lxeCNsLe3lxXKN7bdxcXlvn339va+L1+1alWT83Pv6qAgCAQGBpKTk4MoisyaNYsZM2awc+dOhg0bZvqdra0tTk5ONGnShBEjRjBjxgyOHz9Ojx49sLOzY//+/eh0Opo3b063bt2wsrIyOZf29vam8q/Ga/r6+uLo6IiFhQU2Njb4+voiCAJarZb169dz69YtqlWrRpUqVRg5cqTsuDfOmfvtgiiVSipXriwr2C4IAtWrV6d69eoyR5byderUKbWZLgoxfzvoYkHhhmDTG8GylWyBBCMeds4Yw1SMH6T169fH09OT8PBwXn/xBdZeCMfaypI2deuQU1iIWqMlv7iY7PwCCopLUGu1lGi0tGtQj2Xbd/LT+Yv4eXpw7kYUPu7uBPpVQaPTkZqVbaqgkpadS1JGJl6uLijNjsdjD1+VJb3sndmQl0lZ99JOUDDI0RVbwXyPH3c8tc5lWXh5eTFgwAA6d+6Mv7+/bIyeGWaY8d9AVlYWBw4cIDAwkC5durBz505Onz7N7dtS4XKjQ6dQKMjJyaF69ep8+OGHLF++nB07dvDtt9/i7+/Pli1bKnT2HwbW1tasW7eO+vXrV/gx909AFEXQXkHMnQz6O/BbVJuoPoTgMA3RRroi+lfh4+PDzJkz+fDDD+nUrTt6tZrX+/amVZ3a5BYWEtK0EVuO/szuU2co0Wjo3aYVjWrWwNLCghHdurDhx8NsOnwUa0tL5rw8EncnR2LuJtJz2juk5+SgNxiYsGQ5dfyrsuuT2TjLON5mPF5QITDe1RtHpZJ9BbkUGkqliJ53cqe7vdNju6Bjxu94ap3LsludUVFRjB8/Hk9PT1q2bElwcLBsQLsZZpjx78KYbBUbG4uHhwcTJkwgOjqajIwMDh8+XG7F1piZbowJ9vPzw9LSktdee42hQ4cSFRXF0qVL2bRpE8eOHeO555770+2qUqUK/v7+ssoF/yz0iIXrQH9PEpxYiFj4NYJVCCilW95/BKGhoezevdsUrqJUKhk9ejTt27cnIToKl9RE6lWripVKhZWlim9nzSAiLo68omKc7eyoV80fO5vSVc/pr7zEoC5hGIqK8K/kTY3KlVAoFFR2d2PZxLHlkunsbGywlQnfgVL5mG1e79OopvxOiBn/LQiCgJNCyXgXb5539KBA1OOutMBWUJgdyycET61z+e6771K/fn127drF5cuXKS4uJiUlhZ07d7Jr1y7T78LDwwkKCqJy5cr/6oqEGWaYUeowbtu2DYPBQLVq1cjLy8Pb25uEhAS2b99uSnLJzs7m/PnznDlzhvXr1xMYGGjKol60aBE9e/bExcUFZ2dnlEplueQWtVrNlStXsLW1RRAEAgICyMvLIyIigvz8fNRqNefOnaNmzZp4eXkByMoW/SsQC0B3VZ7T3yn99xedS19fX3x9fcv9TalUUrt2bYKq+JYr/ygIAk72drSpJw1xEUURS5UlTQMDJeUfHWxtTVJGZXE3LZ3I+IRyWcbVKnlTy9cHP6+/1q8nDf/1akSCICAAbhYWuD2EK/Jf748Z5fHUOpcBAQG89dZbvP7661y9epUdO3Zw8OBBoqOjTVpwALNmzWLFihW8+uqrTJs27V9u9V+DwWAgIyMDOzs704uzLPR6PRkZGTg5Ocnq5Ol0OjIyMnB1dZXo4Bk19DIzM/Hw8JDEqho18nJzc/Hw8JDVNywqKqKoqAg3NzdZPj8/H61Wi6urvAZadnY2CoUCJyfptoooimRmZmJlZYW9vb2sNmN6ejoODg7Y/Ja9eq9t0tPTcXFxkZWl0mq1ZGRk4O7uLnE0jPqBWVlZeHp6olAoJHxJSQl5eXkV2qawsJCSkhJJ5reRz8vLQ6/X4+LiItv37OxslEoljo6OsnxGRgY2NjbY2dnJ2iYtLQ0nJyesra3vOy7kkp+MtvHw8JAkAD3INmXP7+3tTWpqKr/88gv169dn27ZtuLm5kZGRwdChQ7l27RrR0dGoVCp+/vlnevXqZXJ63nnnHapUqUJCQgIXL140yReJokiXLl1o164dgiBgYWHB3bt36devH4IgoFAo+Oabb4iIiOD999+npKQEURQZPHgwo0aNMkkSVeRY6nQ60tPTcXNzk7WNse/u7u6ymqBqtZqcnByTbe7ljXPG3d39tzYogYpk01SAZbnjc3JyTDHEf2bOZGRkYG9vLztn/g7cSLjD2v0/oi8TZ92vXWtq+frc56h/B0Ulag5fuIhaq0OpVOBgY4OFpzc1fb3hT9gqITWNczeisVJZ0LFRAxxsbUvVGRLuEJVwhzb16uDp4mL6/ZPmiD1p/XnS8VRHzQqCgKOjI23btmXu3LkcOnSILVu28MILL5gyZfV6PXFxcVy9WsFqwGOES5cuERwczPjx401yImVx5MgR2rdvz/vvvy/hRFHk+++/p127dqxYsULCGwwGli1bRocOHfjhhx8kvF6vZ/bs2XTs2JFjx45JEjV0Oh1jx44lLCxMVveyoKCA559/nh49ehAfHy/hU1NTeeaZZ3jmmWfIysqS8LGxsXTp0oWXX35ZNuTh3LlzdOrUialTp0o0QQH2799Pu3bt+OSTT2TlddavX0+7du1Yu3atrG0WLFhAhw4d2L17t4TXarXMnDmT4OBgTp06JTm/RqPhlVdeoWvXrrK6l7m5uQwbNow+ffrI6l4mJibSt29fhg4dSl5enqTt169fp3Pnzrzxxhuy8cYnT56kY8eOzJw5U9Y2O3fupG3btixYsEDWNqtWraJdu3Z8++23El6v1/Pxxx/TsWNHfvxRKpatVqu5cOECffr04cKFC7i4uLBt2za2bduGq6srxcXFvPbaa9y8eZNVq1bRq1cvwsPDOXfuHMeOHWPXrl2maxglrfbu3cuJEyc4fvw4Gzdu5O7du0yYMAELCwt2797NhQsXOHbsGEePHmXhwoW88cYbnDhxgsOHD3Px4kUuXrzIyZMnmTp1KqdOnSIuLo6aNWtKFBkANm/eTLt27Vi2bJmsbZYsWULbtm3ZunWr5FidTsf7779Phw4dZOdMcXEx48aNIzQ0lCtXSuslI9iBdQiyj3ZVA7D4PRkoNTWVfv36MXjwYNk5ExMTQ/fu3Rk9erRED1UURc6fP09wcDBvvfWW7PPk70BYsyZ89+4MNr0/0/RvWFjFyWf/JjLzcnnps4W8+Mk8pq1YzXNz5jJy2tus2LkHjcw8uh8MBgPLd+xm6PsfMfi9jzh5NQIoXc3bfuIkz8/5jKu34h59J8ww40/iqV25vBcKhQIvLy969+5Nt27dSElJ4ejRo+zcuZNffvnlidgSLywsJD09nbS0NFlR9NzcXDIyMkxZ9PciKyuLjIwMMjMzJZxxlSMjI0NWuFsURdLT00lPT5c4OEY+JSWF9PR0WedPq9WSlpZGWlqarPC3RqMxtVvOASouLiY1NRVnZ2eJYDyUagDezzY5OTn3tY2x73K2Ma7wZGRkyGpyGkW609PTyc/Plz3eaBs558/Yd6Nu5b0oKSkxnVuu70bbVNT3vLw8072Ty8zPzs6+r22MfZdzYIwrxunp6eTm5sryBQUFZGRkUFBQgJWVFUFBQeX4lJQUU5a5h4dHuS1uYwUZvV6PWq02rdI5OTmVO7/xHGUz9I0re3l5eeh0OgIDA8tlcYuiyOnTp7GwsJDdCYDf54ycaDlg0uKUmzNG22RkZMjOGeOKclpaWplxISDYPo+oiwP1z4hiCSJKBFUACoepIPzuAKvVatLS0rCyspJ1DouKikhPT8fa2lp2XBQUFNx3XJhRivrVq7F59ixSsrJ4fdEyZn61lpo+lans7sax8Ms0DazFraQUrC1VhDVtwpVbt4iMS6BV3drUr1EdhSCQnV/AwXPncXV0QK3Vsv3nk3Rp3lSyAlqi0XD80hXiU1LR6fXU9PWhXf162Fo/fkVAzHi8IcgJAf8X0axZM/H8+fP/6DWN25U3b94kPj6enj17/qPX/ysoKChg8eLFTJs2zeQYa7Vabty4gbu7O97e3pKXYUlJCVFRUfj6+spuPRcWFhIdHU2NGjVwcHCQbO3m5eVx69YtAgMDJdtkRuczMTGRoKAgyday0bnMzMwkMDBQkhghiiIJCQkUFxdTq1atcs6+wWDgxo0bqFQqlEol/v7+ki1EvV5PdHQ09vb2JhmZstBqtURGRuLl5YWnp6eELy4uJioqiqpVq+JSZuuprL1jYmKoWbOmZAtRFEVyc3O5ffs2QUFBkq1lo+OdkpJCUFCQZPtUFEWSkpLIzc0lICCAgoICCgsLTVJEoigSFxeHVqulZs2akr4bDAZiY2OxtLTEz89Pwut0OqKionB2dpaVN9JoNERGRlK5cuUy26+/o6ioiKioKKpVqyarm5mfn09sbCy1atWSSOwYt+wTEhKoXbu2pCyhUXTdYDBQp04d2XFx9+5dCgoKCAgIkHwEGgwGbt++jSiKVK9eXXZcxMTEYGtrS5UqVWS35G/cuIGbm5tkzhi39CMjI6lSpcoD54yjo6PENrm5ueXmzLJly4iNjWXBggUIgkBmZiZ3796lTp06suMiOTmZrKwsgoKCTCEHoiiCWATaSxg0N9CL7qjs2oGifPsMBgM3b9584JxxcHDAx8dHds7cuHEDT09P05wRC/LLxVzeD6Iokqw1YKsQJDGXTwLupKXR7OWxBPr5cujzT1GpVHx+8Ahvz53Hy7170KhmDcYuWkr1ypWwUCqJT02jfYN6ZOTkcjslFU8XZ/Z/Nocqnh4cOn+RZ2bN5tU+PbkQHUtSRgaHF31GZXd3Pv32ez7d8D3b57xPLV8fnnnnAxSCQLFGTVJGJhMHPcP04UNRKv9ZG+fpDeTpDfioHl85v4eFQRT5YtdeXnj7XUlxgicdgiBcEEVREiBtXrm8D7RaLVFRUXz99ddYWFg8Vs6lHFQqFfXr16+Qt7a2lhUsN8LOzo7GjRvLcsYVofvx7u7uuLu7V8hXqlSJSpUqVcjLCT+X5WvWrFnhQ8wYe1cRVCoVDRo0qJC3sbGRVEQpC3t7+/v23ailWhFvfEFXxPv4+Egq8ZTl763GUhYKhYKAgIAKeQsLi/tqilpaWt53XNja2lbYNwAHB4f79t3V1VU2ltQIa2trqlatKpuJbdQMrQgKhaJCLVUoHRdlV0LvhYWFBfXq1auw7VZWVvcdF/ebM0C5OSOn6fmgOSOnJSsIQun2uFVblFZtqWjPRaFQ3FdL9WHmzP2eJ2ZI4e7uhlKpJD0nF4MoIgIDO7ZneJdQuk6ZwY2EO+yd+xEbfjzMgk1buZFwh8rubuw5dQaVyoK+7dvg6eLCO2vWcfradZ7p1KHc+V0dHRg7oC9Xbt4iOz+fXRln2HP6LGMG9DFLOJnxj+Kpdi5TUlLYtm0bERERuLi40Lt3b5o1a4YoioSHh7N8+XJ2795NVlZWOYFmM8wwwwwz/l6IiOgxoEfEEiXC35jSIYoiBkCjF7FUCij/ppW2xMQkdHo9ld3dUCgEVEol9WtUw83REQcbGxxsbanl64O3mwsiUKJWk5WXx4/nLmAwiGz48TCZuXkYDAa2/XySvu3KV7Haf+ZXxi/+El9Pd2pUqoQoihQUF6PTScMazDDj78RT61xmZmYycuRIDh06hMFgQBAEvv76a7766ivu3LnDe++9R0ZGhmk14d4tIzPMMMMMM/4eaNFxRZnIeYt4igQNVQyutNXWwFt0fOROpl4UuZRdxI7EHBKLtVS2UdHPx5nGLraPxMks0Wi5mZRMfGoa327bjouDAwM7dSAyPgEEfq84JGBSKDD2UQROXr3O3fQMvF1dCI+5CYi4Ojpy8moEd9LSy13relwCaq2Wsf374u/txYXoGNN5zDDjn8RT61weOXKEn376yRSIboz5e+utt0hJSTEF2Ds5OdG5c2fGjBnzbzbXDDPMMOOpgB4Dh1Q32G95DR2lz+doZRoRyiReKmlHZdHpkTmYoihyPC2f9yKSyNX+lpSUDSfSC3i3biVCPB3+VLxg2RCHq7duEzppGhqdjkruHiydMIbWdWuXOpdl+iEgSBSKdHo9+878ispCyZppk2nfoDREY/6mrcz6ai3Hwi8bDwZKs+n/9+NPzPxqLU0CKg4TMsOMvxtPrXN5+vRpDAYDLi4uDB06FAcHB7Zt22aSenFwcGDAgAG8+uqrNGnS5D9QeeOPwxiw7+TkJJukotPpuHnzJh4eHhJ9RKNuZWxsLFWqVJFNUikuLub27dtUr14dGxsbyfXz8/O5c+cONWvWlE1GyM7OJi0tjZo1a8rqH6amplJQUCCbiCGKIomJiej1etmYO4PBQHx8PFZWVlSqVElWt/LWrVs4OzvLJqnodDpiY2Px8vLC2dlZNpHj5s2b+Pn5SbQhjfqD8fHxVK9e3VSDuSyfl5dHUlISNWrUkLVNZmYmWVlZ1KhRQ1apIDU1lZKSEqpVqyZrmzt37gDIJqkYDAbi4uKwtbXFy8tLtu+3bt3Czc1NNknFOC4qV64s0c00ajPeunULf39/2cSuwsJCEhISqFGjhmxiV25uLikpKdSoUUN2XBiz7qtXry6rDZmUlIRGo8Hf319WuzE+Ph4LCwvZJBW9Xs/t27dxdHTEw8PjD40LKE1+ut+cKSkp4fbt21SrVk1WMxRKk3y0Wq2sbbKzs0lPT6/QNmlpaeTn51OtWjVZ29y9exe9Xk/VqlUrHBfW1tZ/as5otVru3L6NH39NkzBdKOCYKrrUsTSeSIQURR4nVDEM0jRF+YicywKdgXVxmb87lr8hW6tn3e1MWrnZYWfxx5VC9MBhSx0Ok4djqdeBIOBhbcNbVRowqHJlFIJAnzatqFetKgFVfHG0s+N/M99CqVAgAAM6tKNxrZoEVPGhlq8Pr/TuScOapc9BURQZ3bMbberVobKbGyoLC4IbNaS2vx+OtrYc+vxT0nNy8a/kRXJmFgaDAWd7u0diLzPMeFg8tXu9qampiKLIsGHDWLx4MZ988gnvvPMOlpaW2NrasmjRIlauXEmrVq0kGayPCyIiIujVqxcvvviirE7d8ePH6dy5M1OmTEGtVkuO/+GHHwgNDeWjjz6SlSJZsWIFoaGhfPnll7Iafu+99x6hoaGyGn5qtZoJEyYQFhbGiRMnJHxubi6jR4+ma9euRERESPj09HT69+/PgAEDSEhIkPA3b96kZ8+ePPvss7LyP+fPn6dbt2689NJLEukiURQ5ePAgYWFhvP3227LSRt999x2hoaHMnz9fVoZl8eLFhIaGsmbNGoltAGbMmEFoaCh79uyRcCUlJbzxxhuEhYVx5swZCQ8we/ZsunfvTkxMjIRLTk6mT58+DBo0SFb3MjIykm7duvHcc8/JSh+dOnWKrl278sYbb0juuyiK7Nq1i7CwMN5//31Zfu3atYSGhrJkyRKJbURRZN68eYSGhsrqXoqiyOTJkwkLC5PVvSwuLubll1+mc+fOXLx4UcJnZGTw7LPP0qNHD9la4wkJCfTq1YshQ4bISidduXKFLl26MHLkSAoLCyX8kSNH6NKlC+PHj5ft2+bNmwkLC+Pjjz+W5ZcvX05oaCgrV66U9N1gMHDt2jV27tzJtm3bZPlx48bRpUsXjh8/LmlbYWEhI0eOpGvXrly7dk3Cp6amMmTIEHr37m36+CiLW7du0aNHD4YPHy4rqXXhwgXCwsJ49dVXKS4ulvAHDhzglVdekX1W/BEkKXIoFNTlPdTf/vuWMgMdjy5+MFOjI7ZA+uwDiC/SkFoile56EERR5IKmgKXFaQg1fbEO9Mc6oColfp7ctlZwV196PW83V9rUq4u7kxOWFhY0CahFw5o1ShO13N1oW78uHs7ONKhRnZZ1grD+7SNUEATcHB1pV78e1StXooqnB63r1cHZ3h6FQkH1ypVoWScILxcXGtWsQZOAWqgsntp1JDP+JTy1I87oMPj7+5u+8H19fVGpVLi5udGvXz/ZihqPE4yVYu6tpmOESqUqLcFWgfNsZWWFwWCQrUgDpVnEBoOhQjtZWVmZzn8vBEHA0tISURRlV4UVCoWp3feu0Bh5o8i93MqeUqk0/Uaub8a/V9R2Y5v+qm0q4o1/rygD2mg7ub4Dpko2cn1XKBQolUrTv3thYWGBQqGosG/Ga97PNve7r1ZWVuj1etkqT/DgcWG0zf3GzYPGhdEG96LsuJCLoy47LiqaM8a2yfHGMV1R342akRWNiwfZ3vj3isbNo7KNwWCgqKjINAZVKhVarZaioiKTjYy7F6IoYmNjg4WFBWq1mqISNRYWSiwUClQWFmi0WnR66QeYIAiICiUGUaSwpFRn00qlwkppUepLytT7sxYtHmnMpUoQsFEISJV1Szlr5Z/YEgcOFmeTK0qd4DSDllPqfPws5Gukm2HGk4KnVudy8ODBbN26lWeeeYawsDAAoqOj+fLLL3FwcODdd98t9wCvVasWISH/zUoQcigoKGDRokWMGjUKW1tb2fJuer2e1NRUHBwcZMu7abVaUlNTcXV1lVQfMW5/Gkse3rv1C6X6h1lZWXh5ecm+DPPy8sjPz8fb21t2Cy8nJwe1Wl1h6bvMzEwMBgPu7u5ERUURFBRk6oNRgFqlUsmWRNTr9aSlpWFraytbElGn05GamoqTk5NsSUSjcLmbm5skJKCsbTw8PGS3N4uKikxl/eRsk5ubS2FhIV5eXhLb5OTkkJiYiLu7e4XlIo2i3XLbl0bxbSsrK9mtXeO4sLe3l+iZwu+C9s7OzrLi4Wq1mvT0dNlxYdwazszMxNPTUzYkoLCwkNzcXFPfY2NjqVq1qslxy83Npbi4GE9PT9lxk5WVhV6vx93dXVbzMyMjA4VCgZubm6xtUlNTsbGxkZ0zxnHh6OhY4ZxJS0vDxcVFtiRiSUlJhXNGFEWWLl3K1atXWbp0qaxtCgoKyM/Plx0Xf3TOyNkmPT2d6OhovvvuO06fPo1Go6F69epMmjSJDRs2sHXrVr766isGDx5MTEwMw4cPx8nJiW+++YYLFy4wa+ZMirOzsLO2JqCKL3NfG80XW7az6+RpRFFEpzegUAgoFQr8vDz5bNpUUlJSmLrgCzQ6HXNfHU1oh2YsszlKguI3YXkBEEGJQH91Y0J0gY/MwdQaRN6/lsSe5FxJ0kvvSo68X68yqj+YzKkXRSZn32ZvSXlhfAHoqvIgyMqCMY6V5Q9+gmDWuXw6YNa5rADbtm1jx44dwG9yFAYDarWaiRMnlvvdsGHDHivnEn6vOlRRdSGlUinRyCsLlUqFr6+vLCcIAtbW1hXyUKp/KFcSzwhHR0dZYWnj+eXEysvyRv0/uW1pY98rglKprFBTEzDF5FUES0vLP20bQRCws7PDzq7iOKiyVWTk4OzsLBtHazz//R5wCoUCb2/vCvmHGRf3s42VldV9+25jY3Nf3t7e3iS2Lqf96OzsLCvWbuTd3NwqbJtCoahQT9TI/5Vx8SDbPGjOCIKAra2t7MqjIAg4ODjg4OBQ4bEPO2fkoFAoKCgo4I033iA5OZmXXnqJmjVrcvXqVdLT0xk5ciS7du1iyZIldOjQgXnz5nHt2jWWLFmCQqFg6tSpWCIyYWB/dHo9V2/dRqfT079DWxrVqsm127dZuHkbHRrW5/munXG0tcXexoa9p88SffcuCkHBtp9P0rNNSwaom7DJ6jxpinwMogFrUUVTnR+tddUf7cqlQuC1mh5kaHRcyi6ixCBipRBo7GLLqzU8sPgTTpECqKeyZX9JNvc+mSwQqKsyxz+a8eTjqXcuRVGUjRG692/m8mZmmGHGk45vv/2WiIgIPvjgA6ZPn45SqUSv12MwGFAoFLz88st8/vnnjB07lp9++onOnTszePBgMjIySEpKolFQII1q1aCqtxcje3TFSqXCv5I3berV5dD5CyzeuoOAKr48GxaMIAjE5OTx49lz1PWviqujIz9fvkpSeiYBlbyYUBxKjDKVQkGDr8GFKgYXLP6GNAFfGxULGvlyKaeYxCINlW1UNHKxxU6p+NMrbt1tXNhfks1VbZFpRVQB1FLZ0NTKLGZuxpOPp9a5bN269R/SrmzZsuXf2BozzDDDjH8fly5dQhAEgoODTaunZVdR33zzTQ4ePMj27dvx8PBg1qxZptCJjh07sn//fkIuXaaKpwfdWzZnxoiheFSwygwQFRNDUnIy4wb0pYqnB1OXf8WR8EuM9O6Ko2BNU33FVbkeFQRBwM5CSVv3R+P0CYJAZaUl812q8V1hOqfUeagQ6GzjQjOlPXbCU5tHa8ZThKfWuRw/frxsFm9FeNJjRswwwwwzrK2tTXGvcqhUqRKvv/46r7zyCn379qVJkyamUIbVq1eze9s2Dv+wmZ8vX2Xptp1U8fRg4uBnZM8lAgdOnUGn01HV2wsPJyeUCgU7fznNiM5hWKoe39eTIAj4K62Y4eiLRhRRCKUxo7HqP559boYZjyOe2k+oshm1CoVC8k+n05GQkEBiYiKiKD4RzqVGo+HcuXPEx8fLOtbFxcWcPn2alJQUWT4vL49Tp06RlZUlKyGTmZnJqVOnyM/Pl+WTk5M5c+aMRBbJyMfHx3Pu3DlZ6R9RFImOjubSpUuyYQyiKHL16lWuX78uG8Kg0+m4ePEisbGxsn1Tq9WcPXuWO3fuyPJFRUWcOnWKtLQ0WT43N5dTp06Rk5Mj2/f09HROnz5NYWGhLJ+YmMjZs2dlJaFEUeT27dtcuHABnU76chJFkRs3bnDlyhVZ2xgMBq5cucKNGzdkbaPVarlw4QK3bt2S7VtJSQlnzpwhKSlJli8oKODUqVOmJKJ7kZ2dzcmTJ8nLy5Pte2pqKqdPn6aoqEj2+OLiYsLDw9FoNLJ9j42NJTw8vMK+X79+nWvXrsn2Xa/Xc+nSJWJiYiq0zYPmzJkzZyqcM/n5+Zw8eZKsrCzZvmVmZnLy5ElZSaiHmTNxcXGcP39eds4YDIb7zhmj9FFkZKSp78HBwSgUCr799luys7O5ePEily5dIjU1FSgfz2tnZ8fFixe5c+cOarWalJQURgwexPq332LOSyMRgZSsbMl1jUjLzuHohXB0ej2Tlq7g+Y8/o6ikhHM3orhZwVh7nCAIAgpBwFqhwFJQ/I3FK80w47+Hp9a5NEIURSIjI5k3bx6rVq2ipKSE5ORknnvuOTp16kTHjh2ZOHEieXl5/3ZT/zJOnTpFSEgIr7zyiuyLfMeOHYSFhTFp0iQJJ4oiq1atIiQkhHnz5kl4vV7P3LlzCQkJYd26dRJeq9Uybdo0QkND2b17t+TFodFoePHFF+nWrRsnT56UHJ+VlcXQoUPp3r07N27ckPDx8fEMGDCA3r17m16EZXH16lXCwsJ44YUXZF/0x44dIyQkhLFjx8pq+G3atInQ0FBmzJgh4URR5IsvviAkJIQvvvhCwut0Oj744AOCg4PZuHGjhDcmkBm1He+1TXFxMcOHD6dHjx6cO3dOcnxaWhoDBw6kV69e3Lp1S8JHR0fTt29f+vXrJ+m7KIom/cLRo0fLjvODBw8SHBzMxIkTZZ3fb775hpCQEN577z1Z59Goa7l8+XLJsVqtlpkzZxIcHCyr7VhUVMTx48fp27cvx48fl/AFBQUMGTKEnj17cvnyZcn5k5KS6N+/P3379pXVQ42IiKB3794MHDhQ0ndRFDl16pRJ27GgoEDC7969m+DgYKZNmybr/K5evZqQkBA+/vhjWdt89NFHhIaG8vXXX0uO1Wg0TJ06leDgYPbs2SPb91deeYXQ0FDOnDkj4XNzcxkwYAB9+vQxFYcoi/j4eHr37k3//v1JTk4GYNCgQTzzzDNs3bqV0NBQQkJCCAkJYffu3ZLjjx07RlhYGG+++SZ3796lb9++dO3Tl+fnzOXTb7/H3saGjo0aSI4z4kxEJLdTUhnVoys/LZjLoc8/ZcaIYWTn53P4QniFx5lhhhn/fTy++w6PEFu3buX9999nwIABPP/886xbt45t27aZvuZXrlxJ3bp1efXVVx/rFUw3Nzdq165NYGCgbDZqpUqVCAgIICgoSPb4qlWrEhgYSLVq1SScQqGgWrVqBAYGymbDKhQKatWqRVBQkGymskKhoG7duqjVatlsXysrK4KCgnB2dpbNMLezsyMoKAiFQiGrIejk5ETdunWpVauWLO/u7k5QUBABAQGytqlcuTIBAQEEBARIOIBq1aoREBCAv7+/bN+qV69OYGCgbBa2UqmkVq1a3L59WzaTWalUUrduXZRKpWw2sLW1NbVr1yY3N9eUZV0Wjo6OBAYGYm9vL6uf6OLiQp06dQgMDJSVRfLy8qJ27drUqlVLVnnA19eXgIAAatWqJeEAqlevTq1atahaVRo/p1AoqFmzJkFBQbJZ2hYWFri7uxMQECCb6WxhYUHdunVxdHSUzSC3tbUlKCgInU4nm53v5OREYGBghZJQZeeMHO/t7U1QUBA1a9aUtY2fnx8BAQHUqFFDwgHUrFmTWrVq4efnJ+GM46KiOWNhYUFgYCD5+fm4urpKeJVKRb169cjMzKxwztSuXRsrKyuTqoOTkxOrVq1i//79HD9+HKVSiZ+fn0muDUrv97PPPouPjw8qlYqAgAC8vLxYuHAhZ06cIOX6NUKbNqZ7y+aENm38u61cXRkc0pFmgQEIgkB+cRG9OrZnZJ+etA4snVeV3N1IzMhAp9fLyVyaYYYZjwmeWp1LIwwGA4MHD2bHjh3MmTOHMWPG0L9/f44ePUrLli1JTk4mLi6OPn36sGnTpgrFj+8HY0a6cdXHysoKpbJi7S+DwUBJSYlJoLysCLqx9KBWq0WpVFZYQq6goIDFixczbdo000vPYDCQnZ2NjY2NrESQXq8nOzsbBwcHWZFonU5HdnY2zs7OsuLkGo2G3NxcXFxcZEvTqdVqCgoKcHV1ldXgKy4upqSkBGdnZ1m+sLAQnU4n0R80GAxERUVRqVIlFAqFrDajsXSepaWlrG6l0Ta2traypSz1ej1ZWVk4OTnJOmgPaxtXV1dZfcKSkhIKCwsrtE1RURFqtRoXFxeTBmblypVNYtYFBQXo9XpZbUajNqRSqZTVZjTaxuhkyNkmKysLe3t7WT3TB9lGq9WSk5MjOy6gdOU2Pz8fFxcXWdtERUXh4uJSoaZnYWEhWq1WVrNTFEVTmIacnqlRG1KlUsmOC6Nupo2NjaxupdE2Dg4Oss+GsuNCzjktaxuFApYuW8zN2Jt8/vnnKJVWDzVniouLZct0Psy4yMvLM0kcVWQbY9Wy+80Z43NILMjHcP6UpJ9yEEWRZK0BW4WAs8WTv4lmEEVi1DpqWVmgeIwXKR4WZp3LpwNmncsKYKxFrFQqqVq1KkVFRcTGxuLh4cHy5cu5cOECo0ePJjU1leLi4j/lXKanpzN37lwOHDiAQqGgb9++TJ48WXYVSqPRsG7dOtasWUN2djYtWrRg1qxZBAYGAqU10T/55BMiIyPx9fVl/Pjx9O7du8JKLmVhFI6uCEql8r46eBYWFvedOJaWlhXyRu1HOefEyN9PF9OYNHA/yL1Ayx4vt7pjxMPY5n59/6u2MTovFfH308U0OgcVwagNeT/+Qba537h4kG1UKtV9eSsrq/vOK+OKrZy6w4PGhSAIFWqpGvkHaUM+SDfzr8wZlUqFu7s7uUUJpOWsIyXrf+QUFnMjsYBKrq/iat8Ka2v58z/MnHnQuLifluqDbPOgOWOGGWY8vXjyPxcfAgaDwbQKkJqaSmZmJq6urlSrVs20XaXX6/9UgLlOp2POnDksXbqUli1bUr9+fRYsWMDixYslyRmiKLJnzx6mTp2KtbU1ffr0Ye/evYwbN478/Hzu3r3LK6+8Qnh4OIMHDyY7O5s33niDixcvPvbB72aY8bSiSJ3JzeQJiPpVWCgzEChAJRwiPecNsgvPm+e2GWaY8djhqV+5FASBSpUqodfrWblyJbt27aK4uJjq1atjY2NjSoBwdHT8U7XGk5OT2b17N9WqVeOjjz5Cr9dz/PhxNm3axIQJE8qtHIiiyMaNGykpKeGdd96hTZs23Lx5k59++okrV64QHx9PZGQkY8aM4cMPP8Tf35+xY8eya9cumjVrJrutZaw69CSjbD+f9O0X+P1jyPjvr0Kt11Gi12GjtMBS+d97JDzp4zgr/xdsVL8CBhBFRBFEEQRSScxYjpNNE4THRBtRFEUMxg481O8NiKLiib23ZSFSun1qstETjrLPqCf9A+kPDPmnBv+9N8k/DEEQ6Ny5M3v37uXXX39FEASUSiXdu3dHqVRijPP09/evcNvyfsjJySE5OZkmTZqYtpicnJxITU01xakZodPpiI2NRalU4u/vj7W1NT4+PhQUFHDnzh2ioqIQRRF/f3+USiVVqlTBwsKC6Oho9Ho9CoUCtVrNjRs3yM3N5eDBg1y9evWpcLjy8vLuu/35JEGj0aDX6//UeJRDrqaYjJIi3K3tcLKUD1v4N5Gfn4+dnd0fKnrwOCGrIByVovQjNu62jsJCyMwolRYShH3Y24zgsUlt0ekQszJAUqlbHsUGEQsEVE/mrS0PEfIMIg4KgafgkYxWBK0oYqt48jsripBaUMiL/3ZD/kN46p1LgKFDh3Lx4kUOHTqEKIqEhIQwZMgQCgsLuX37NoGBgYSEhPypl5vciprxv+/9Wi/7hScIgumf8bdGrTpjO+TOo9PpSExMJDs7Gw8PD2xsbPD29qZhw4YSJ7OwsJCjR4+asnXvRU5ODseOHaNx48aymb4pKSmcPn2aNm3ayNbxjouL49KlS3Tq1Ek25i8yMpLY2FhCQ0MlcWOiKBIeHk5GRgadOnWSrBrr9XrOnj2LVqulbdu2pKWllcvE1mq1/Pzzz9jb29OiRQtJ30tKSjh69Ci+vr7Ur19f0rb8/HyOHTtGUFCQbBZ0ZmYmP//8M82bN5fNjk9KSuLXX3+lbdu2sjF3t27d4urVq3Tq1Ek27u3atWvExcURGhoqcSKLioo4e/Yser2ejh07ShJF9Ho9p06VJlW0bdtWMm41Gg3Hjx8nzg4ihWJE7AA77gIKQSDYvSpE3aF6tWrUqVNH0rbc3FyOHz9OvXr1qF69uoRPT0/nl19+oWXLlrLZ8Xfu3OHChQt06NBBNtYzJiaGyMhIgoODcXBwIDU1FXd3d1Oyz+XLl0lKSiI4OFgSw2swGPj1118pKiqiQ4cOklhknU7HL7/8gqWlJa1atZLYRq1Wc/ToUby8vGjcuDH3orCwkCNHjhAQEGCKgy6L7Oxsfv75Zxo1avTQcyYxyxolmQCcOF5MRppI526l88GAD5Vd+2KMYLpx4waxsbEEBwfLxuBevHiR9PR0goODJXPGYDBw+vRpdDodbdu2ldhGq9Vy4sQJ7OzsaN68ucQ2JSUlHDlyBF9fXxo0kEoM5efnc/7USTrUCXjoD9ocvQErAWye0A+H8hBJ0RrwVil4bD4W/gJKDCLFooiL8im4t6LIkWvX/+1W/Kdgdi4plWL58ssvTSLRlStXxtLSEoPBwJdffonBYLhv4Pv94OjoiIeHB7m5uRQUFJiyV52dnXFxcTE5lIIgoFKp8PX15caNG6SkpODr60t6errJQTQKKRvbmZaWVlrdompV04vA1taW7t27U1hYyKZNm9i+fTt+fn7MmjVL4sDt2LGDzZs3ExoaytSpUyVOzPLly9myZQvW1tZMmDBB4sS8/fbbbNmyhapVq/Lmm29K+v7ss8+yZcsW2rZty+DBg8txRUVFrFmzhmPHjjFw4ED69u1bjs/MzGTevHlERkbyyiuv0KJFi3J8UlISU6ZMQRAE07UDAwNNL7UrV64wfvx4PDw8mDZtmkTi56effmLbtm0EBAQwa9YsSULJd999x+bNm+nZsydTp06VvKgXLFjA1q1bcXV1Zdy4cZIX9aRJk9i8eTNBQUGMGTNGYpsBAwawZ88eQkNDJbYpKCjgyy+/5MyZMzz77LN069atHJ+Tk8P69es5f/48Y8aMoWHDhuX4+Ph4JkyYgI2NDZMmTZJIR507d47x09/CdswArO+VlRIEruWWEPnZdprVb8isWbMkfVuzZg2bN29GFEWmTJlSjhdFkY8//pitW7fi6+vLm2++WS4DXBRFxowZw5YtW2jSpAmvvfZauXOLokivXr04fPgwPXr0YPDgwcTGxlK1alVUKhX5+fksXLiQK1eu8PzzzxMcHFzu+KSkJN5//31SUlIYP348tWvXLsfHxMQwduxYXF1dmTx5MlWqVCnH//LLL7zxxhsEBgYyffp0ybzfunUrW7dupW3btsycOVPigC1ZsoTNmzdjbW3N5MmTy/EGg4F33nmHrVu3UrNmTcaMGYNCoSC3qA6J6adQKfPIzFSSECfSracKEQGV6jmqew8BBPR6PV26dOH06dP079+f/v37l7t2Tk4Oc+fOJTo6mpdeeonWrVuX4xMSEpg5cyY5OTlMmjRJIo8UERHBG2+8gY+PD1OmTJFIQx05coTNmzfTuHFjpk2bJkkW2rBhA+dOneKLZQtRPqSzaMwWd1I++c6WCKZs8Se/t5BvEE3Z4k86RCDxCdDCfpR46p1LURSZMWMG9erVIyQkBG9vbxQKhWl7/H6ZoA+DSpUq0bFjR7Zt28aKFSsoLi4mPT2dl156CQcHB86dO8fEiRPp168fU6ZMoX///vz0008sWbKEkJAQjh8/Tp06dWjYsCFVqlTBx8eHXbt2Ua9ePdatW4eNjQ3dunWTrGZCqR5dx44dCQoKKidnZOx3QEAAISEhplWOe/kGDRrQsWNH2rdvL5HXEUWRVq1a0b59e9q0aSO5tiiKhIaGkpKSQqNGjSQrGVZWVoSEhAAQECBd6bCzsyM0NNTU53t5BwcHevTogUajwdPTk7S0tHIrvd7e3oSGhuLp6SkrQVO9enU6duxIo0aNZPtWp04dgoOD6dSpkyzfuHFjOnToQNu2bSWyUqIo0qZNGy5dumRyiu/lw8LCyMvLo379+pK2WVtbExISgq2tLTVq1JDwgiBQr1493N3d8fLykvDOzs6msA43NzcJ7+PjQ/uuYVyr4lWqJXgPX2CtpGnHdnRr0dY0F8q2vX79+nTq1IkOHTrISmo1bdqUDh06mFYG7+XbtWtHVFQUTZs2ldgGoHPnzmi1WsmqqTGrPjQ0FHd3d/z9/SXHOjk5ERYWRkZGBh4eHhLezc2N7t274+DggIuLi4T38/MjJCSE2rVry8rvBAUF0bFjR0JDQ8uNN6NtGjdubJoz9/IKhYKWLVvSoUMHWrRoYbKNg01trCynoNZ+DhQhIqA3WKAnGH+350zxlgqFgq5du2JpaVnuQ8oIOzs7wsLCqFKlClWqVJHwLi4uhIaGUlRUJNt3T09PunXrhqenp6zygr+/PyEhIdRvVJtCxV20akscLX2xEKwQBIG6devStGlTSb8rQtk4PLl4cR1wR62hQG/A11KFi8Vfk7QRRRERSFfrSFPrcLVU4m2tQiFz/b8D9+vvk4mnp79Pekzpn8FTr3Op1+tp3rw5165dw8/Pjy5dujBgwABatGhh0gT8qxMjNjaWKVOm8Msvv6BQKAgNDWXevHn4+vpy+PBh+vbty+jRo1m0aBGFhYV89NFHfPvtt+Tn51O3bl0++eQT2rdvjyiK/PDDD3z00UfExcXh4eHB2LFjeeONNyQrawUFBXzxxRdMmDDBpJV5L4wZ8paWlrJSRkYNQRsbG1mBaIPBQHFxMTY2NrIhA3q9npKSEtmXNJRuUarV6gp5Y2xhRVqearUaURSxtLQkKiqKoKCgcnqgJSUlKJVKiWNdtu8qlapC2xQVFWFlZXVf29ja2sr2/VHYRqPRyGor5uTkkJOTg5eXV4VxlyUlJQiCIKtXKooicdnpDD3+HRlqaaUmbxsHvm03BF8n1wr7XlRUhLW1dYXjoqioqELb/JFxAZhWLi0tLUudDp0OrVYraxv4fVxUJHv1INsYx4WcXunDzJk/Yxu9QUdhSSzzFrxL/O07zJk7G0+nVlipyscR329cGG2j0+lk54xRI/d+tikuLkapVMomL4qigeiM41zJ+45CQwoCAq5W1WnuMRov63qlx2emY3kt/KFW5irSuRRFkbsaLQuSUjlbUIjaIOJpqeI5d1cGujlj+SfDk4r0BtbFZ7InJZdcrR57CwVhHo68Wt0DRwvpR9Cjhlnn8smFWefSrHMpC2trawwGA7du3WLFihV88803BAUF0b9/f3r27ElgYCBWVlZ/eoLUqFGDb7/9loyMDARBwN3d3eQUtG7dmsuXL5uSUezt7fnwww9588030Wg0ODs7m8ShBUHgmWeeITg4mPz8fGxsbPDw8JB9icHv2pL34yvSyDPy99MQVCgUFWovQqk+4f14CwuL++pzPig737iVLZdpalzlqggP0/f7tf3fto1KparQQQDuywmCgK+zG8GVa7H19uVyqRcKBEJ9auHj4oaFouJxc7+2KxSK+9rmj/T93o9fY/iI3AeBEQ/Son2QbR40Lv7KuKnINkqFBQ42gXg5dyDfIRYf1zBZx/x+4+JBthEE4YG2ud+cyVLf5lzeMgp16aa/JRWHczxlHj18P8NB5Y2NtQ1/Ne+70GDgo7vJHM0rMI3NvBI1nyWl4mKhpJuzdCfiQRCBTXezWROXgfa3k+brDHx7pzSRakqA11OxVW2GGf8UnnrnUqFQsGLFCvbs2cP+/fu5du0a+fn5XLhwgfDwcBYuXEjLli157bXX6NWr15+6xv1EsI1bn2WhUqnw8fGpsL3u7u5/ebvejKcbKoWScfXakasp4WTqbTQGHVYKCzpUqs7rddpg8ZhI35jxz0AURW7mH6VQlyHhcjV3iS84TV3nfo/kWlHFJZzKL5TkmxcaDOzIyiHEyQGrP+hc5un07E7ONTmWRhiAn9LzGO7nio/NH5eaM8MMM+Tx1DuXgiDQoEED6tWrx5tvvsn169c5cOAA+/btIyIigszMTPbt24eLi8ufdi7NMOO/iEq2jixo3Yfr2akkFubga+9MbWcvrJXS7WAznnaI5GuTkZMYEtFTqEuT5f4MUrQ6tBWEayVqtOhEkT9aJy1PayBPp6+Qy9Xq8Xk0yl5mmGEGZufSBGNN6iZNmuDl5UWlSpVYsGAB0dHRgDlg14wnD4IgYGOhoqmHL009pHJKZpjxOwRcrKpBvsC9TqQCC5wt/XhU8jp+lpZYKxQUyoS71LS2QvUnPnxcLZW4W1qQptZJOLffODPMMOPRwbz3RWmCQWJiItu3b+ell14iODiYsWPHmhxLa2vrJyJINzc3l+XLl3P06FHZOMXU1FQWL17M+fPyJefi4uJYuHAhkZGREl4URa5fv86iRYtISEiQ5X/99VeWLFlCeno698JgMHDkyBFWrlxJnoykg16vZ9euXaxfv57i4mIJr9Vq2bhxI1u2bEGj0Uj4wsJC1qxZw/79+016oWWRnZ3NsmXLOHHihGzfk5OTWbRoEeHh4bL8zZs3WbhwITExMbJ9v3LlCosWLSIxMVHCGwwGTp06xbJly0wVoe7lf/zxR1avXk1BQYGE1+l0bN++nQ0bNpjkqspCrVazYcMGtm/fjlarlfAFBQWsWrWKH3/8UdY2mZmZLFmyhNOnT8v2/e7duyxcuJCrV69KOICoqCgWLFjArVu3ZG1z8eJFvvjiC1JSUmT59PR0vv76a3JyciTn1uv17Nu3j7Vr11JUJE1O0mq1bNmyhe+//152XBQXF7N+/Xp2794tKccKpeL8K1askJ0zRjmwxYsXc+7cOVnbxMfHs2DBAiIjI2Vtc/36dRYsWEBCQoKEM86ZxYsXy84ZvV7P4cOHK5wzOp2OnTt3Vjhn1Go1GzduZOvWrbLjorCwkK+//pr9+/djMBio4RCMs+Xv0k1GGTUrbSV8baRasn8WtWys6OLkIHk5uSiVDHRz+VPOpZ1SwUAfF6zvEfS2EKBPJWc8rMzOpRlmPEo89TOqrPZcXFyc6QWjUCjw8/MjODiYwYMH06pVq3+5pX8dJ0+eZPz48TRp0oRDhw5JdOp27drF5MmTTRWLykIURb7++ms+/vhjYmJiWLZsWTlep9PxxRdfsHr1aoqKipg+fXq5l41Go2HOnDns27cPe3t7XnzxRQk/ZcoUoqOj8fPzo3v37uXOn5GRweTJk0lLS6N+/fo0adKkHH/79m2mTJmCTqejVatWEv3Cq1evMnbsWGrWrEnTpk0lupdHjx5l/PjxtGvXjj179kiSLrZs2cKUKVPo27cvW7duldhm+fLlLFy4kLt37zJ//vxyvFarZf78+Xz77beIosiECRPK8SUlJcyePZujR4/i5ubGkCFDytmmuLiYiRMncufOHapXry7pe2pqKhMnTiQ/P5/GjRtTt27dcnxUVBSTJk1CpVLRunVrvL29y7X9/PnzjBs3jrp163Lo0CGJsPnBgweZMGECYWFhbN++XZLQ8t133zFjxgyGDRvGN998I5HnWbx4MStWrCAzM5OPPvqo3LEajYZPPvmEbdu2YWVlxauvvlqOLyoq4tSpU8yZM4dKlSrRp0+fcucvKChg/PjxZGRkEBAQQNu2bcsdf/fuXcaPH49er6dp06blBPENBgOLFi3igw8+wMPDg4sXL+Lu7s7t27eZMGECBoOBnJwczp49S506dTh48KBp3Fy7do21a9dy6tQpzp07R4sWLUxje9++fWzfvt1UWevcuXNERkayatUqiW3mz5/P+vXrKSoqYubMmeXaXlJSwocffsi+fftwcnLi+eefL3d8fn4+M2fO5NKlS1SrVo3OnTuX43Nychg3bhyFhYXUq1fPJP1kRFxcHOPHj8fS0pJmzZrh7+9fjg8PD2fSpEl4eXlx7tw5HB0qE+w9k/CsDWSUxJCfX8Cvh25RGGnHgMVW8IhCFq0EgWk+3ripLDiSm0+xwYCflSUverjR2sHOZDsjyipEyMHI963kBIj8kJhDpkaHk0pJ70pODPZ1ve/xFZ1PDnLnMIeYmPE04ql3LkVR5MCBA8TGxiIIAs7OzrRs2ZJBgwYREhKCn5/fI5Ej+i+gQYMGDB06lJYtW8pmvLZr145nnnlGImhuRPfu3blx4wb9+vWTcBYWFvTv35+srCzJSw5Kk5SGDBmCnZ2dSRfzXn7kyJGEh4fLVv9wcXHh+eefJyUlRSIKDqXajcOGDUOlUslWfalRowbPPfccNWvWlBXEb9KkCUOGDKFTp06yGbOdOnViwIABDBkyRMIB9O7dm/j4eHr37i3hLCwseOaZZygpKZGIfkNpdvOwYcPw8PCgefPmsvyoUaO4ceOGbMUcNzc3nn/+efLy8mSrBVWtWpVnn30WR0dHSd8FQSAoKIjhw4dTr1492Uzmli1bMnjwYLp16yabbRwaGkr//v0rtE3//v1JTU2lR48eEk6lUjF48GAUCgXt27eX8NbW1tSrV48BAwbIVsyxsbFh1KhRJCQkyFbM8fLy4vnnn0ev10tEwbOzs/n2228pKSkhJyeH+Ph43NzcyM/PZ+/evfj7+xMaGoparebKlSvMmjWLhQsXUlRUxAsvvEBGRgbBwcG4urqSn59PUVER58+fZ9SoUQQEBNCwYUNKSkpo1KgRzzzzjKxtBg4cSF5eHl26dJFwVlZWDB06FDs7O4kgOpTqWg4fPpzatWtTr149WS3Y0aNHk56eLltJqXLlyjz33HNYWVlJPragtCjB0KFDqVatmkn6yMM6kLDK71OkyyQlOYUz1xfQuW37+2bg/1EIgoCrhZK3Knsx2tOdYoMBd5UF1r89h/MKC5m8bBU3k5Lo0aoFk4cMBKBEo+X7w0fZc/osJRoNldxc6d++LT1ateB0xHVmrV4HQKFeRGVrx5DQjnQLaMwbn31OQmo6YlldRgTaN6zHzOeeZdvPv7Bq9z6c7e354s3X8fWseBcrKy+fed9v5mJ0LD7ubiwePwaH+6gL/N2QU1sww4x/AmadS72etm3botVq6dWrF/369SMoKKhCbcXHBQUFBSxevJhp06ZJKqRAxQ+Zv8I/SCT477q2wWAgKirKJCz9d/Ttr/KP0jY5OTkUFhZSuXJlyarNv9H2v/P8oiiW07l8VNcWRZH9+/czcOBAmjVrxsWLF5k+fTpvv/02165do0mTJqZV6qysLEaOHMlPP/3E999/j7+/Py1atGDw4MF88cUXODg4kJOTg5OTEwsWLGD27NksXbqUYcOGoVQqyc3NvW9YTdn+L1u2jNjYWBYsWIBCofjX5tSf5cWCfAznT8n+Vu5YOZ3LB+F0xHW6TXmbIrWaOv5+HF74GW6Ojmw+epxX5y+ibf161K7qR1xKCrX9/Phg9AvsOXWG/rNm07JOEK3r1mH/2XMkpWewbNKbpGXnkJ6Ty6lrEfxyNYKerVtS178qdav506dNK4Z98DEHzp5DUChYO20yz3YOkbWHQRTZ+NNRRn06n+AmjejcrAlv9OuNjVEy7R/UuTTel1RRw2VdPhoMBCntqK6w/cdE4806l08HzDqXFcAoRVSlShVcXV2f+EnwoP79Ff7vPPfD8v/V9v0XbPNn+YeZE3/3+R/1tUVRZPv27SgUCmbOnMn48ePZvXs348ePl/zW1dWVvn37sm/fPs6dO0fjxo3x8/Njy5YtnD17lgYNGjBkyBB69+5NYGAgCoWCyZMns2zZMlq3bs3IkSNxd3evsJ3329b9L4+Lh+EfNfQGA7tPnkEhCIQ2acTPl69y/kY0XZo35VLMTfR6Ay9060yPVi2wUChR68rHknZoUJ+PXxlF7ap+jFm4hIvRsXz22ssIAsz55jtOXbvOoE7tGd45FICI23GcvX6DVnVrczs5he0nTjIwuANWMlqiJ65cZcaqNegNBqIS7lDFw/0R5c//cYjAT9pMlqjvcMdQggFwE1QMtvRilJUPVmZVTzP+Zjz1CT2CINCoUSNTmTyDwUBqaipnz57lwoULZGZmyia/mGGGGY8vkpOTOXr0KLVr1yYwMJA2bdoQGRnJpUuXZH9vrNaj0+moVKkS69atY+jQoSiVSnbv3s2oUaM4ePAg3bp1Y9myZbRr147U1FRWrFjBCy+8QFJS0j/bwScUuQWFHPj1PP6VvJkybBAqlQXbT5xEFKF+9WoICoHXP19MuzETmbRsBbeSkssdr9ZqycjNJSrhDqIo4uboQEX+sSiKHDh7noLiYt7o14emgQGcirhOQmqa7O+renrSrn5dBEEgrGljerZuhaqCAhZ/N+4aSlhQEk/8b44lQKaoZb06iVO6HLP6iRl/O55a5zItLY3ly5fz5ZdfEh8fD5RukW/cuJGOHTvStWtXwsLCaN++PevXr5fNpjTDDDMeT5w6dYq4uDgiIyMJDQ1l586d5Ofns2/fPsnHZHFxMceOHUOhUFCvXj1EUSQoKIgVK1Zw4sQJ3n77bQoLC7l69SoajYaBAweyadMmTp48SZs2bYiJiSEjQyo+bsYfgyiKXIiKJubuXVrVCaKatzc1fSpzLPwyyVlZ9O/QjuWTxhHatBF5RUWs2XuA1z//gqy8fNM51u3/kRavvsm6/T/Sqk5tBnXqUOH1CoqL2XvmLO5OTgRVrUKHhvXJyMnlpwsXZZ2zqt5edGzUAIUg0K1lc/q1b4PqPlW2/k78qssjWZSqIxRh4LA28y9XUTLDjAfhqd0WP336NOPHj8fHx8eUaHD9+nWmTZtWbpUhNzeXyZMn4+fnR2ho6L/V3L8EURT/9TjBv5OvCI9LDOWf4R+EJyW+9O/gdTodW7duxdramjfffBN3d3c0Gg0rV65k7969dOvWDYBbt26xePFifv31V3bu3ElYWBjdunUjPj6e5557jg4dOuDt7c2JEyewsrKiTp06bNy4kT179tCqVSsEQSA+Ph5/f3/c3NweyjYPwr9tu786Lv8K9AYDe06fpVitYfPRn9l7+lcy8/LQ6fWcvnadkKaN6NuuDc90bE9yZhZD3v+IO2kZZJSRaQpu3JARXcJwdrCnQY1quDo4IAiC7LMkKuEu4TGxFKs19JnxLhqtDoMosufkGZ7v2hm7R5jE9KhRIOoq3JLPF+XF5M0w41HiqV25vHr1Knq9ngYNGuDj44PBYGDnzp2kpKQA4OzsTIMGDbC1tSUnJ4fvv/9eVgPwv4ycnBw++OADNmzYINv25ORk3n77bXbv3i2r4Xfz5k2mTJnCzz//LKs/eOnSJSZNmkR4eLjs9Y8dO8bkyZOJjY2VcEZ7T58+3WTzstBqtfzvf//jgw8+kNU3LCkpYenSpSxYsEBW2zE/P59PP/2Ur776SnbVOSMjg3fffZdNmzbJhj3cuXOH6dOnc+DAAVnbREVFMXnyZFntR6O8z+TJk2W1H0VR5NChQ0ydOpW4uDgJbzAY2Lp1KzNnziQtTX4LbtOmTcyZM0dW37C4uJiFCxeyePFiWdvk5uby0UcfsW7dOlltx9TUVGbOnMm2bdtkbXP79m3eeustjhw5Itv3iIgIJk+eLKuXKooip0+fZsqUKdy4cUNybmOizbRp07h7966E1+v1fP/997z77rtkZmZKeKOT+Omnn8pqghYWFvLJJ59w/PhxQkJCmD59OpMnT2bq1KkMHz7cJJmlUqkoKipi7dq1pKamMmPGDFavXo1arWb+/Pmo1WrOnz/P//73P9RqNZ9++ildu3bF1dWVI0eOsGzZMjZt2kSjRo1YunQplStXNs2ZyZMnc/nyZVmHpqCggF9//ZVbt27J2mbXrl3MmDGD5ORkCa/X6/nmm2+YPXu27JxRq9UsXbqU+fPny2qCFhQU8Mknn7Bq1SpZTdCMjAzeeeedCudMQkICCxcuRP83hRFl5eXz47kL1KhcmTkvj+TdF0cwY8QwbK2t2HbiF77csZuB737IR//7ji+37yIhNY1qlbzwcnE2nSOgii/9O7QluHFD3BwrrlFuMBjYe/osGq2OMf378N6LzzHn5ZG0qVeHs5FRku32/xrqWdhjLfN6VwCNlVINUTPMeNR4alcujULf9erVQ6VSoVar+eWXXzAYDFhaWjJ37lyGDx/O7NmzmTdvHjExMRQVFUm0If/LiIiI4MSJE/j4+NC/f/9yMjOiKHLixAnmz59Pp06dCAsLk8gT7d27l4ULF5Kamkrr1q0l2bobNmzgiy++AKBRo0aSbNylS5eyY8cOqlSpwsSJE8sdW1xczKJFizhx4gTNmzdn4MCB5fjs7Gw+//xzoqOj6dy5s0S+KCMjgw8//BCFQkHXrl3LZcQDxMbG8umnn+Lu7k7v3r0lMjTnzp1j4cKF1KhRg379+pWTUhFFkcOHD7NgwQIiIiIIDg6WSK3s2LGDRYsWkZ+fT/PmzVHdE+C/fv16li9fjo2NjaxMzBdffMHBgwepUaMGr7/+ejmusLCQBQsWcP78eVq3bi0rb7Rz505+/fVXunfvLtEvTE5O5qOPPsLGxoYePXqU03YEiIyMZO7cufj6+tK7d29JnfpTp06xcOFC6tevT+/evcvdd6Pzt3DhQm7fvk2HDh3K9V0URTZv3syiRYvQ6XQ0btwYizJbg6IosmbNGtatW4erqytvv/22ZNx8/vnnnDhxgjp16vDCCy+Ua1tBQQHz5s0jIiKCDh060Llz53J8amoq8+bNIzU1ld69e1OvXr1yfEJCAvPmzTNldjs6OgKgVCqZPXs2nTp1olevXtSsWZMjR47g4uKCIAgolaUZrxs3bmTt2rU0b96cffv2mZLIjHx8fDwlJSX06dOHr7/+GpVKZeL0ej3fffcdixYtQqVSUb9+/XLjVq/XEx4ezvnz59mzZw/jx48vZxu9Xs+8efM4f/48jRs3lkg/5ebmMm/ePGJjYwkNDZVIOyUmJjJv3jxyc3Pp06ePRLrp5s2bJj3Rnj17SmStLly4wGeffUaDBg3o3r27RNbq0KFDbNiwgTFtJImjjwQ3k5Jxd3KiZ+sWvNyrB0qlgoLiYq7HxVNQXEyTWjVJzczi18gbFJWo6dW6FWMG9MHJzg4nezua1KpJZXe3Cs/v7epK41o1cXVwoESjJTEjgw4N6zNx0AB8PT0QRRFHW1sWbN7K9bh46leXSqK5OznRuFZNnGUkvf5J1FPa00vlznZtGsZlBQFopHSgu6X7/Q41w4xHgqfWuczPL43DMTqLeXl5ppUUd3d3unXrhq2tLaGhocybN4/CwsLHLu6ybt26tGzZkho1ashqN7Zv355p06bRqlUrWZ26Xr16kZiYSK9evSTOE8CIESMQBIERI0bIXn/cuHFUr15d1jmysbFh0qRJtGnThnbt2kl4FxcX08revaLgUHqP3nvvPTQaDdWqVZNUOKlVqxZvv/02rq6uEucJoHnz5kyZMoU6derIStyEhYUxdepUOnToIMv379+fzMxMBgwYUM55MuKFF17AxsaGYcOGSTiAiRMnUq9ePYlYPJTqF06ZMoXw8HBatmwpe3z//v3p1q0bAQEBEq5SpUq89957KBQKiZg8QO3atZkxYwY+Pj44OztL+DZt2jBlyhSaNGki27cePXqQkJBA586dJU69IAgMHjyY4uJihgwZIsu/9NJLuLm5ST4ojPzUqVNp0aKFbBiKvb09b731FpGRkRIxeSjVtZw2bRo5OTkSUXAAPz8/3nvvPWxtbalSpYrJeRMEAQsLC5o1a8bMmTOpUaMG7u7ukvZ36tSJKVOmmD627nWM+/btS3p6Or169ZLImSkUCp599lkAhg0bhkJRfv1IqVTSuHFj7t69KztnlEol06ZN48yZM3ToII0VdHJyYtq0ady6dUviVEOpFuy0adNQq9Wy46JGjRq8++67uLq6yupeNm3alOnTp1O3bl3Zj+zOnTuTn5L8t22Zt6oTxPHF80EQTLnOdtbWbJg1DSi9h91btQDjivBvvxMEgfYN6nNmxWIqzN4BRvfsxqgeXU3tXzF5vOm8RgwK7sDATu2hgq30AR3a0b99239ddcQKBZOsq1JPac9BbSYlGGht4URflSeeguW/3j4znnw8tTqXo0aNYt26dYwaNcpU3u2ZZ56hoKCAjh07cuDAAaysrNi7dy+9e/emdevWpkoZjwPK6lwaX2JyGoJlcT/+r8TW/Rn+j7TNuE0dFBRUoazLXzn/g/h/2jZldS7/atv/bv6PHiv3m3t1Lh/VuPmjbf+7zy+KIsuWLSMmJoaFCxdKnM8/art/xTaFBX+7zuV/AfvP/MqCzT9gKGOTZ0ODGdWzW8Vb7f+gzqURolgqDW8AjJ9J/5Rjada5fDogmHUuy6Nu3VLJiG3btqFUKjl//jwFBQUIgmDa6hNFkWvXrgGlVVAeZRWKfxIP0tf7o8f9E/wfOfZJ1Ad82Iex3O/+7bb9lft672/+6L39L/f9Yfj7/eavHPswxz8K/vFYqvjraF47kM9ee7lcVR8vV5d/sUXyEH5bvX28XHczngQ8tc5ljx49WLRoEYmJiXz11Vemv3t5edGvXz8EQaC4uJhDhw4hCKUl8iqqEGKGGWaYYcbTA3cnJ9wfk10sM8z4N/DUftAEBgbyxRdfULt2bezs7LC1taVq1ap8+OGH1K9fH0EQuHTpEtevX8fR0ZGQEPmSX2aYYYYZZphhhhlm/I6nduVSoVDQr18/2rVrR0xMDGq1mpo1a+Lj42MK4q9Rowa7du1CqVQSFBT0L7f4r0MURbRaLUqlUpKoUJa3sLCQxHuV5VUqVYUxVxqNRpLoYITBYECn01V4vF6vx2AwmKqh3HtuvV6PKIqyPJTqFxoTM+TaptPpUCgUf6nvf9Y2BoMBrVb7SGwjd+372cbY9/+ybe7X9wcd/zDjBjBlbcv1/X62+TvnTNm+y+FhxoVer6+Q1+l09x0XD2ObB42LimxjhhlmPL14alcuodTBdHd3p379+jRs2BB3d/dyLwgvLy+aNWtG48aNZbOtHzfcvXuX119/nZUrV8rqXt64cYOXXnqJzZs3y+oTnjlzhlGjRnHo0CFZ/sCBA4wePZpz585JeIPBwKZNm3jllVeIjo6W8Hq9npUrVzJmzBjZUnlarZa5c+cyZcoUsrOzJXxhYSGzZs3i/fffp7CwUMJnZGQwYcIEFi5ciFqtlvDx8fG8+uqrfP3117K2uXr1KqNGjWLHjh2yfT9x4gQjR47k+PHjsn3fvXs3L730EpcuXZLt+zfffMNrr73GrVu3JLxOp2PJkiWMHz+e1NRUSdvUajUfffQR06dPl9W9zMvLY8aMGcyZM4fi4mJJ21NTUxk3bhxLly6V1Te8efMmL7/8Mhs2bJDVN7x48SIjR45k7969srY5cuQIL774IqdOnZK1zQ8//MDo0aOJiIgw8aIoYhB15Gvucictgs+XziIpOUFyvEaj4fPPP2fSpEmyupfFxcW89957zJo1S3ZcZGdnM3XqVD6b/xmJeclE5EYTX5iIWq9BFEUSExMZO3Ysq1atkqhFiKJomjMVaT+ePXuWF198kR9//FHWNgcPHuTFF1/k3LlzkmMNBgPff/89L730kuyc0Wq1rFixgjfffJOkpCQJX1JSwty5c3nrrbdkdS8LCgp4++23mT17tmRcQOmcmThxIgsXLpTte3x8PG+88UaFeqlmPB0oKlGTmJ5BXEoqSRmZFJWUmD5cktLTiUtJ5XZyCreTU0jPKS09KYoiJWoNd9MziE9JJTkzi8Li3zV5RVEkp6CA28kp3E1PN80tURTJzM0znS8hNY3M3Lw/ra0qiiIZubnEJadwNy1dNsbbjD+Hp3blEkpLQK5YsYLDhw+Tl5dHpUqVGDp0KIMGDXoinMl7cfXqVb755hsiIyMZMWKESePPiJMnT7Jx40YyMzMlGnoABw4cYOPGjTg5OUn0BXU6Hbt27WLjxo3UrVuXZs2alVsJ0Wq1bNq0iT179tCpUyeJhI5Wq2XNmjVERUXRv39/fHx8yvHZ2dn873//IzU1lRdeeAFXV9dyfFJSEhs2bECr1fLqq69iZ2dXjr958yZr1qwhICCA559/XiK1Eh4ezoYNG4iLi2Po0KHlNEEBjh8/zsaNGykpKaF///4S2+zevZuNGzdSqVIlOnbsKLHN9u3b+e6772jatCmNGjUqx2s0GjZu3Mjhw4fp2rUr1atXL8er1WpWr15NQkICAwcOlEjwZGRksH79evLz8xk1apRE0SA+Pp5vvvkGCwsLXnvtNcnYjoqK4uuvv6Z+/fo899xzEtueO3eODRs2kJaWxsCBAyV6qEeOHOH7779HEAR69eolsc327dv5/vvvqV69ukSvVKPRsHXrVrZu3Uq7du2oW7du6YqZoYjI7HXczt+LVlmLKiG/ci1/AS4eb2Nj4WkaW8XFxXz11VdkZGQwbNgwiexUamoq69evR6/XM2rUKMm4u3XrFhu3fI93D39uXsuiSChBpVDRwKk2I6r2IyIignXr1nEtIoJO3XtxJSWL1LwCalf2pEWNKpw+fZpvv/2W/Px8+vXrJ7Htjz/+yPfff4+9vT1dunSR2Gbr1q18//331K1bl+bNm5fj1Gq1ac6EhoZK2l5YWMiGDRu4ePEiAwcONKkHGFFQUMCqVasoLCxkxIgREj3UpKQk1q9fj5WVFaNHj5ZIN0VFRfHtt9/i6enJK6+8IlldvXTpEv/73/+4ffs2Q4cOlV35NePJhSiK/Bp5g0+/3cTF6Bh0ej02Vla0qB3IqqkTyckvYNCUGRQW5GP529gZ0KEdi8e9QVZ+PtNWrObQuYuIiNhYWtK7bWs+H/MqADq9gXfXrGftvoNUcnfjx/mf4F/JGxH4eMN3rNq1D1trayxVFrg7OTE4uANj+vfFwdbmD4WvXY69ycvzFhGfkkqgny+HF35maqsZfw1P7dOgoKCAcePGsXXrVtNX0eXLlzl+/DhZWVmMGzdOdpvrcUbbtm1ZsmQJgYGBsjp1ffr0oaSkhNatW8seP2rUKNzc3GRfkhYWFrz55pvUrl3blBBVFpaWlsycOZMuXbrQs2dPyfGWlpZ8/vnnxMTE0LZtWwnv7u7O/Pnzyc7Opk6dOhLe39+f+fPnY2lpibe3t4Rv0KABy5cvp1KlSqZSfGXRqVMnvvjiCxo0aCBxTAEGDhyIKIqy+oIAr732Gr6+vrJ9U6lUTJo0iSZNmshqO1pZWfHee+/Rt29fidMOpZqgixYtIj4+nhYtWkhWiby9vVmwYAFFRUXUrFlTcnxgYCALFizA3t5e4jhCqX7hl19+iZ+fn6zUVpcuXVi8eDFNmzaV/egaNmwYlpaWFZZHHTduHLVq1aJPnz6yfZ82bRrt2rWjX79+pr/fzNvOjZwNiKIOpVADW0clOcrTXMlcRguvdxF+e3TZ29uzZMkSUlJSJE47gK+vL4sWLUKv18vqXtatW5dXF4zjmlcceWLpyqZOr+dsVjh6Uc+oloNYunQZ2UorJm35kbvZ+YiiiFKhoFXNKoztUDpuKtKKfeGFF3BycqJr166ytpkyZQoNGjSQ/WCxtrZm5syZdO7cWXZcOTo6MmfOHG7evCmrh+rs7MzSpUvJycmR1YqtVq0aX3zxBSqVSiKYDtCkSRMWLFhA5cqVZedEp06dWLJkCXXr1n0iP8bNuD9uJibxwsfzyC0sZPrwoTSqWYOs/HzORESaVifVWg2DgjvwXJfS55qHsxOCILD71Bm2HPuZiYMGENq0CanZ2WTm/r7rkpWXx8Ffz6NQKkhITeP45StU9fYCQKPTo9PrmTFiKIF+VVi77yAfrv+WwpISZo96AYNeT3xqGnmFRVhbqvByccHV0UHyTsrOz2fLsRNcvXWboSGd6N6quTm84xHiqdW53LdvHwMGDJDdBqxevTrHjx+XrJ49Tiirc2mcMP+mNuNf5e+nwWcwGIiKiiIwMLBUeuM/1vZHzefm5pp0LgVBKub8X9Lk/DO81lDIkbuvkKOJBhGUma3Qu4SDUo2lwpkQ31U4WVZ7qPM/yDZag44PI74gMi8W7mmalcKS9+tOxEtViZfX/MDVu9KQhBfbN2Vyt/YIwl/vO8CyZcuIjY1lwYIFKBSKf21O/dlziwX5T4XO5Z/Bv6Fz+Xdi3sbNvP3VWt55fjiznn/29zEgigiCwO20dNqMmUC9KlVoGlCT2v5+9G7TGldHBxZu2cbMr9byfNcwhoUGU8OnMpXd3VD+Nua3/fwLL34yn1E9urL12AmaBwWw5YN3UCqVjF/8JWv27mf7R+/TpXlT7qSlEzZpGqIocvSL+Wz/+RfmfrcJrU6PhVJJlxZNWTF5PFb3rEjuPX2WFz6eR05BAU52doQ2bcS378xA9SdW4M06l2adSxOOHTuGVqvF1dWViRMnEhQUxIEDB/jmm29ITk7m2rVrj7VzKYd/W8Pvn9BHfBw1Pf8q/19u25/htYYiNAZp7Ggpl49Wn//Q53/QtfWinmxtrsSxBFAbNOTpCsjNzOBWujTOF+BUTDwvdWyOi538yt1/Wa/1n9JiNePJRMzd0jjf5kGl4Rqz133DkYuXsFKp+PiVUXi4ulHV2xtBEDgSfonF23Zy6Hw4q6ZMoEuzJqw/8CNr9x3kfwd/wtfDnZd6dmfC4AEoflvZtLZUMbxzKEkZmZy6FsHNxCQC/MpXlhIEATcnR/y9vbgYHUNGTi57T/+KnY0Nn746Gg8nJwrVJShldiE7NKzPmP59mLdxMx+MfoE+bVtjYV65fGR48j8XK0BiYiKiKDJs2DCmT5/OM888w9y5c6lXrx5qtVo2OcAMM8z4+2GlcMTOorIsZ2PhiY3Fo1sZsBAsqGIjfy0HCzs8LF3R6fXlKrGUhd5gwCD+uWQCM8x4nOFkXxp7nZ6bC4JAvWr+CAicuX6DnIJCnJydWPXu2+yd+yH7PptDwxrV2XfmLEmZmdSp5s+eTz9i7YypjOgSSk5BAfO+38ztpGRSsrI5fukKzvb23ExKwsHWlvScXA5fCJdth0arJbewECuVChsrS4aFBaMQBGasXMP0lauJSrgr++HmYGuLp4szgiDg4+5GFU8P8wfTI8RT61wasx+rVq1q2ja2sbHB29vbJP9hhhlm/PNQCJbUdB6ESlE+qUqBCn+HnthaeD2yaykFBV28O+BoUf5aShS0c29OJRsvanq5U8lZGqMMUM/XG2dbc7yhGU8fOjdriq21FV/vO0hccgrdW7WgRZ1AE5+WlUVOXh46vQGNTodWr0OpUKBQKLh26zbpObn0a9eGea+/TPOgQDRaHVq9nmPhl0nJyiY1K5uxC5ey/cRJBEFgxy+nKFH/FsYmglanI7+oiG3Hf+H67Xga16qJn5cnbevXYftH7/PVWxOxt7Fh7rebuJOW/i9Z6enFU7stbkRaWhoRERFAaeZqQUEBUJpJafw7gJOTk2zQ+38ZoihSUlKCSqWS1cETRZHi4mJUKpWsDp7BYKCkpAQrKytJoLMoiibexsZGNvlJr9ejVquxtraW5bVaLVqtFhsbaYafUUNPr9djbW0ty2s0pXIxcpWTRFFErVajUCgq7HtJSQkWFhYVagAWFxdjaWkpqwH4V2xjlOnQaDR/yjZGvqSkpELbqNVqBEGQ1dU09l2pVKJSqVAbdGgNOlQKC6wUpY+EhxkXf8U2FY0Lo21chRY0dX+bW/nbycu2x9GqNv7OoVR37ItOp0enUz9wXFhZWT2Ubeo7B/JGzec5mHKc24V3/s/eWcdHca1/+Jm1uEKUCO7u7lIo1hYpVqQtVGgvNQrUC79ClZbbUneB0uJW3N1dIhCixG13szq/P9LsJcwEaEuR5Dz3w6c3892ZnXN2Zufdc877fXFz6Okc0oZ7I3ug02gJ8PLg0S6teGfNdnKMZpBlNBoNNYIDGdexeakptyvbrtFoyuybsj53+J835dVced3803vmRvvmav1690xRURF6VFcZCMoZnZs04vnhQ/lk6Qp6PTeN8EqVuJCahp+XJ55ubsRcuMjT8+YTGuBPTkEhqVnZjL2nF1UqVeL7P9Yz++eFBPkXjxzGJCXRs2VzwitV4o3vfsTDYOD3ma9SKyIcu8PJlHnz2XXyFGcvJQJgczh45evv0WqKE34a16jOrEfH424wMOenXzlw9hw+np7EJicTGRyEt0g4u+VU2JHLEj7++GM6depEp06d6N69O7t3Fy9GnzVrlmt7p06dePPNN2/zmf51kpOTeeihh3j99ddVfepOnz7NsGHD+O9//6vwdpRlmT179jB48GB+/PFHVQ+/1atXM2jQIFatWqXq4ffdd98xaNAg9uzZo9jXbrczd+5chgwZwpkzZxR6UVERr732GqNHjyY5OVmhl2T7P/7446q+lxkZGYwfP54XXnhB1cMvPj6ekSNHMmvWLNW2Hz16lCFDhvDZZ58p2i7LMtu2bWPQoEEsWrRItW+WLVvG4MGDVf0NAb788kvuu+8+1JLUSjw9hw0bRkxMjEIHmDt3LmPHjlX1vczLy+OJJ55g8uTJ5OXlKfTU1FQeeughpr00gxUXD/Of/b8wbNtnTDmwgHUpJzl59jQPPvgg7777rmrbDxw4wP33388333yjqm/YsIFBgwaxfPly1b759ddfGTx4sKonKBQntQwdMpzs+Mp0DHufOv4j6Rj2LrX8hiE7dMycOZMHH3yQixcvKvY1Go288MILTJgwgczMTIWenZ3No48+yjPPPOP6IamVtDQPaMhzdSYyJXAssW8d4siXu9E7iwNtjSTRr0ld5o0eQLeoIArPHadKQRrzRg+gVuj/rI9kWWbXrl0MHjyYn376SfWeWLVqFYMGDWLNmjWqempqKuvXr2fv3r2q+ty5cxk6dCinT59WtM1qtfLaa68xatQokpKSFHpBQQFTpkxh4sSJ17xnpk6dquoJeuHCBYYPH87//d//qc7sHDlyhGeffVb1MxeUP9wNBqaNfpA178zi+eFDGNChLTMfGcuad/6PVvXq0KpRQ15/8nFG9OzOo/37suC1Gfzfo+NxdzPwYI9ufPHCM4zv25uhXTvz6bNP88XzU/Bwc6Nni+bMnvQw7RrUIzI4mKqhIbwwYhjPDR+KXqulf7s2vDHhIYZ168Lo3j344oVnWPbWGzSpUWzhNmXo/Tw95D4GdWzHmw+P5ZdXZxDkr16qs039urw2bgz1qkbfyq6rEFT4kcuioiKKiooU200mEyaTqdTfdxvJycns3LmT+Ph4XnrpJcUIX0xMDJs2bUKSJB5//HGFT92JEyfYsmULUVFRjBw5UrH/vn372LZtG61atWLAgAGK99+yZQvbt2/n2LFjCnshq9XKli1b2Lx5M7GxsQp7IaPRyMaNGzl79iwpKSmKUeOCggLWrFmDJElkZCinPC5fvszGjRsJCgripZdeUngzXrhwgS1btpCamsr06dMVbT979iybN2/G09OTRx99VDECd/ToUbZu3UrdunUZOnSoYhRqz549bNu2jc6dO6va0GzatIlt27Zx8uRJWrduXUqzWCxs2rSJvXv3cuHCBYW/IcDJkyc5ePAgly9fVlgv5ebmsnbtWjw8PMjOzsbf37+UnpKSwsZNmzB0qM32k95YKQ4Gks05HM2+RJNEiS1btpCXl8cLL7ygaPvJkyfZunUrlStXZvz48aV0WZY5dOgQW7dupVmzZgwaNKjUviUB2NatW+nTpw9du3ZV6Bs2bGDbtm2cPXOWpk2aopXc0UmeSJIGs7mQjRs3cuzYMRISEqhWrVqp/fPy8ti4cSNpaWlkZmYqMjezsrJYu3YtgYGB5Obmuiy5JEnCTWsgNy2H3Vt3kZ2WxYtTX3Rd8zqthmbR4XQJcue7TUuQ27WjSoCvYvSuxM4sOjqa0aNHl9KcTicHDhxg69attG/fnr59+5bqO6fTSUJCAomJiRw/flzhCep0Olm/fj179uzh/PnzCnshs9nMxo0bOXfuHElJSURGlk5+yMnJYePGjeTm5vLqq68qbKkyMjL4448/CA8P58UXX1R4vSYkJLBp0yays7N57rnnFL6Xp0+f5uDBg8hjRyCoGBh0OlrUqU2LOsrvKA8PDd1bt6SKXjm74eftRe9WLejdqoViv8cGlfbKlSSJjo0b0rFxQwAa1ahGn9aK5GQXDatXpWH1qqW25RmNZOXlI1Pyg00iwNublnVq01Ll3AX/nAprRTR58mTWrl17w68fOHAgc+fOvWnv/29TWFjIu+++S8uWLQkPD6dZs2aKAKiwsPhBXbt2berVq6fIXM3NzWXTpk20aNGCqlWrKvS0tDR27NhBp06dCAsLU5xDfHw8hw4dokePHooHWcnI6fnz5+ndu7fCR8/pdHLo0CEyMjLo0aMHbm5upXS73c7u3bux2Wx07tyZ2NhY6tat6zrHkuDVx8eHtm3bKtpe8iCOjIykSZMmii+//Px8Nm3aRP369aldu7ai7VlZWWzZsoU2bdoQGRmp0JOTk9mzZw9dunRRGLbLskxsbCzHjh2jV69eCm9JWZY5ceIEFy5coFevXorAODc3l+3bt2MwGOjevbsi6Lfb7ezYsQNJkujUqZMiOLRYLCzbsIYP8w5g9NEp5jCrGvwZVlCFJnXq07BhQ0XflFwXTZo0oUaNGoq2Z2RksG3bNtq3b++yS7pSv3TpEvv376dbt24K03NZljl//jynTp2iV69eeHt7ExsbS3R0NAaDAVmWOXLkCMnJyfTs2VPhr+h0OtmzZw9Go5Fu3bopAiCbzca2bdtwc3OjQ4cOiuuiqKiIjRs3EhoaSosWLRRtLywsZMOGDdSpU0dxz0DxyOjmzZuvec/s3LmTjh07EvpnJu2V+kcffcTWrVv55ptvyrxnYmJi6NmzpyL4KwnsL1++TI8ePRS+mw6Hg127drnumav7xmq1snXrVry9vcu8ZzZs2EBkZCRNmzZVvWf2b9lMF1/3G7LaEVZE5Zt8h5N8h1M1uLzV/L51Ox8sWuIaVZckGHtPb0Ug+3cRVkRKK6IKG1zm5eWpjliWhbu7u6rB9J3KlT6Xamu/Sij5/P+Jh97fOfbN1GVZ5ty5c6WCy3/7/W9n3+Tm5pbyufw7x08ozGTItvlYnMrpTV+9Bz93fJQor0q3vW9KAvGS4PKv7n+n6dfznbza57Ks/e2yE5vTjrtWj0bSKPTb1nbhc1kmIri8fTidToXjg0aSblqhFBFcCp9LF35+ftcNFksWyB84cIBLly4xYsTdOd3zd73u7jT/w7L0sn4g3c0egf/0y/h6+xu0enz1HmRYChSal86Al06Z8HGjx7/dfXMn6//kc5VlGavTwabU06xMOkqezUxtnxCGVm1Ffb/iHxq3u+0CwZ2IRqMRCSa3mArd306nk8TERPbt28fJkycpKipClmVX1uPmzZsZNWoU/fv3Z/Xq1bf7dAWCm0aQmzfdQusqtmuQ6BlWH3+Dp8pegtuJDPwYv5vXji1jV0YsJ3OTWZJ4mGcPLuRErrqXn0AgENwOKuzIpc1m49NPP2XevHlkZWVhMBjo0qUL77//PoWFhbz66qusW7fOlVEqEJQndBotk2p3xeywsiXtLEUOGx5aAz3C6jG+Rke0UoX+3XlHkmLK4beEA4qlDKnmPH6O38tbzR9AW05MgMwWCxsPHibfZKJJzRo0qBqNJEkcj4vn5IWLtG/YgKqhIciyTE5hIUfOx5KZl4ePpydNalQnvHLxkg6TxcKGA4coNJtpVqsm9aKjircXWVi5ew82uwNJAk93dxpVr0a10BB2HD9JUobSacDf25ueLZvhrmJ9JhAISlNhg8sjR47w+uuvk5ub69q2ZMkSPD09OX/+PPv27UP+088uMDCQJk2a3L6TvUk4nU7MZjM6nU6RIFOim0wm3NzcVH3sHA4HZrMZd3d3VX9Du93u8nZU8ze02+1YLBY8PT1V/Q2tVit2ux1PT88yPficTmeZ3o9msxlJksr0+DObzWg0GlWPv5K2GwwGVd/MK/vm6kSIG+kbm82GxWIps29sNhtWq/WG+uZqSkbaZVku0/9Qre1B7j680nggY6q150J2GhE+gdQKDMdNU/qzv17fXNn2q7Pu4drXxZV9o9b2K99fp9Op9s21rouSvgH+1nUhy7LrvdW8H//KdXG9vinL77SkbxKMWaQXKZcxAJzLT6PQZsHP8L8Ep5vRN9dre4kfqpqX7D/hTMIlHnrrXQpMJoZ27cz3M17AoNezdMcu3vpxAT+8NJXokGAOnD3PMx9/SkxSMgE+3tjtDprVrsmPL72Ih5sbJ+MvMPatdyg0FzGyZze+fvE59DodOYUFTHrvIxxOJ5HBQaRlZxPk58d3019g8bYdbDt2gnyjkaSMTCr7+RLs70/NiCp0aFRfBJcCwQ1QYYcnNmzYQF5eHlqtloiICKpXr45er2fBggXs27cPSZIIDw9n8uTJbNiwgf/85z+3+5T/MadPn6Zjx4688MILWK1Whb5jxw7atm3L7NmzFZosyyxdupRWrVrx7bffKnSn08lXX31Fq1atVH0vHQ4Hc+bMoX379uzevVtVf/bZZ+nWrZuqh5/RaGT8+PH07dtX1fcyMzOTBx54gKFDh5b6wVDCxYsX6d69O0888YSqh9+xY8do3749L730kmrfbNq0idatW/PBBx+o+g8uWLCAli1b8ssvvyj2dTgcfPLJJ7Rq1YoNGzYo9rfb7bzxxht06NCh2MrlKt1ms/HEE0/Qq1cvVd/LgoICRo8ezcCBA1V9L9PS0hg0aBAjR46koKB0cOKm0aFJL+C1ByfxySuzcVpsiiDhwIEDtG3blpkzZyr8UgHWrl1Ly5Yt+eSTT1T75vvvv6dly5b8/vvvqm3/4IMPaN26tarvpdVq5fDhw/Tt25cTJ04o9KKiIh555BHuueceVd/LnJwchg0bxv33369a0jUxMZF7772XcePGKezGSrKzO3fuzNSpU7FYLAp9165dtGnThrffflvV+3H58uW0bNmSr776SrVvPv/8c1q2bMnKlSsV+9rtdmbPnk2bNm3Ys2cPnjqDy+T+6uN46gwYNKUDd6PRyEMPPUT//v3LvGcGDx7Mgw8+qHrPXLhwgT59+jBx4kTVth87doyOHTvy8ssvq14XfxdZllm1Zx9Wm40qlSuz88RJ1QorOQUFPPfJZ5xNSOSDJyex479z2fLRezzav9jiSZZlVu7ei83uoErlSmw/doLkzNLXQOPqVdn60Xs8O+wB4lNS2XbsOG9NnMCWj97l9QkPoddqGdWrO5s/eo9vpj2P31WuFgKBQJ0KG1zGxcUhyzI9evRg9+7d7N27l6effhqn04lGo2HYsGFs3LiR9957jyZNmqiO2NxtZGZmcunSJc6fP6/6MEhJSSE5OZnz58+r7n/x4kVSUlKIj49XaE6nk/j4eFJSUrh06ZKqHhsbS1JSEqmpqQrd4XBw7tw5Ll26pOpbabFYiI2NJT4+XtUYvLCwkAsXLhATE6PqApCbm8vFixeJiYlRPCihuFJTYmIiMTExqkFCUlISKSkpZZqaX7hwgdTUVC5cuKDa9ri4OFJSUlTNrR0Oh6tv1IJDu93O+fPnSUhIUA2QzGazq2+uDh6h2CLm4sWLnD9/XjVwzs7OJiEhgZiYGNXrIi0tjaSkJM6fP68wnIfiAC01NbXMvim5LtSCv5LrouTaU2t7dnY2ycnJpKenq+olfaNmDG4ymYiLiyMuLk51iUteXp6rb9TaXnLPlNU3JecdGxur2jcJCQmkpKQQFxen0ADXdZGQkKDQHA4HMTExJCcnk5qaSh3fUBr6Vyn9IllGi0Tv0Pq4a5W2S+fPn+fixYuqwWNhYSHx8fHExsaq+vhmZ2dz6dIlzp07p3pPlNwzsbGxN7Vcbm6hkT/2HaBGlTCeemAQGbl5bD58VBGcn7uUxKFzMbRvWJ9h3boQHOBPVEgwvVu1RK/Vkl1QwLp9B6kdGcETgwdyOSeHrUeOlTqODMjIyDJotVrCK1XCx9OTSr6++Hh4gCThbnCjkq8P/t5eNy27WCAo71RYK6Lhw4fz22+/8X//939Mnz4dKB7NvO+++/D19WXXrl0Kn7q7iSutiEqmIq1WKwcPHiQ8PJzo6GhF20wmEwcPHqRWrVoKDz4oDlKOHDlCw4YNCQwMVPV+PHXqFM2bN8fb21uhp6amEhcXR8uWLRX+hLIsc/HiRdLS0mjRooUimC+xGzIajTRp0qTUFKPT6eTs2bNYLBZ0Oh0NGjRQPATsdjuHDx/G39+fWrVqKdpmsVg4cOAAUVFRCt9KKB4FOnjwIHXr1iU4OFjV+/Ho0aM0adIE/z9Lml157hkZGZw5c4YWLVrg5eWl0JOSkkhISKBVq1aKJQuyLBMfH09mZibNmzfHaDSWsiJyOp2cOXMGq9VK48aNFVPPDoeDY8eO4e7uTt26dVX75uDBg1SuXFnhWwnFo4MHDhygWrVqVKlSRaEXFBRw6NAhGjRooGrDkZOTw7Fjx2jWrBm+vr6Ktl++fJnz58+7rour9ePHj1NQUEDr1q1Vr4uYmBjy8vJo1qyZYurZ6XRy6tQpnE4nDRs2VO2bI0eO4O3tTe3atRV9Y7PZOHjwIKGhoaq+lSV9c6175vDhwzRq1IhKlSop+iYrK4sTJ07QvHlzfHx8SlkRSZJEamoqsbGxtGrVCnd3d87lpzH75GrO5KZicdrw1XvQJ6wBT9frhY/eXbGc4dy5c5hMJpo2bara9pMnT6LVaqlfv36Z90xAQAA1a9ZUtM1qtXLgwAEiIiKIiipey3gzrIh2HD9Jv6kzGN+3D0/dP5juz7xAs1o1Wfj6S7y7YJFrWtzbw4PBL73OuHt68+XUZxTH33z4KAOmvcJjg/rzaP++9Hz2RdrUr8tPL08nuyCfBg89isPpJCKoMskZmfRt25rPnnuagD+N9Rdv28HoWW/z7LAHmPXIuH/8LBBWROUXYUUkrIhclATVJpPJNdJWMvJhMBiw2+0kJia6Xu/l5aX6cLibMBgMiqofV+Lp6Unnzp3L1H19fenSpYuqJkkSlStXvqYeHh5OeHh4mXq1atUUFVeu1OvWVWY3X6mrGTuXoNPpFJVwrsTNzY2OHTuWqXt5eZXZNgB/f3+6du1a5rkFBwcrzNSv1CMjIxUVVa7Ua9SoQY0aNVR1jUajqNZyJVqtlubNm5ep63Q62rZtW6bu7u5Op06dytR9fHzKbDtAQEDANfsmNDRUUWXoSjw9PalXr57q7IEkSaoVjErQaDQ0atSoTF2r1dKyZdnVPvR6Pe3atSvz3D08PK57z1yrbypVquTSr/6hr3bP1PEN5ePWozmRk0SezUy0VyVq+Yagk5RethqNhnr16pX53lqt9pprya93zxgMBkXlrX+Kw+lk5a49FFmsnLqQwLsLF2G12dl76gwXU9NKvTY0MAB3g4ELqamYiorw/NM0XpZlnLLM8p27sdhsHI+L5/1fF2O12dl14jSJ6el4eRS/NjKoMk/cN5D/Ll7GgTNnSUzPcAWXAoHg71Nhg8sS5s+fz08//QQUj9CYzWZSUlLo1atXKR/FwYMH8+GHH97GMxUIBNdDlmUsThuF9iI8tW54aIsD0vIyciJJEj56d9oF1XD9XZ7Izi9g/YFD+Hp5YbHbOJNwidDAAM4nJvHH/oNcGX/XiYyke7MmbDp8lA8WLWZQx/YUmsycvHCRe9q0ZOOhI/h7e2G2Wjl7qfg4MckprD9wiPs6FQfFQf7+PDaoP75enkx690M+XrKcj595CoNK8pVAILhxKvwdlJOTo1irZbfbFesGMzOV1hQCgeDOwe50sD3jBMuT9pJhycPf4MU9YS3pE9oCg0bpfnA3U37aImOV7BQh4Yaew+djiE1OYXzfPnz09ONIkkRscgo9przA2r37aVa7JhpJQkLCy8OduU89zowvv2H+0pX8d8lydBotvVu3oJKvLxdS03hs4L289+QkJOBcYhI9pkxlzd793NO6JZJUXAZQAvq3a0ujGtVZuXsvTz0wmEbVi2dQSl4jEAj+GtcNLiVJmgcMB4KB1bIs9/9zewfgU6AOcAp4RJblw/9Eu5W4u7vj6+t7Q6+VZVlRp1cgENw5yLLMpstH+eDcEsyO4qSlJHMm5wuSMdktDI3qVG48IMsDMjJ5GiOn9emc94jDps+nuj2cGrXD2fLRu0QGB7kqDlUPD2P12/+HU5bx9/ZiaNfOVA8PK15KExbKd9Nf4NLldApMJtwNbkQGB2EqKmLbvPeICglGojgYr1UlnLXv/h8A4UGV2fjBO3i6u6HRaAjw8WbBa9PJzi+gSuXi5U/dmjVl27wPCAsMuI09JRDcndzoyOVC4OmSPyRJcgcWA2bgGeAl4HdJkmoB+r+jybKsTLX8F5kzZw4vv/zyDb/e5y5ch+N0OklLS8Pb21uRSAHFC/pTU1Px9/dXTTJxOBykpKQQFBSk8MEr8V68fPkyoaGhquvhzGYzmZmZhIaGKjwAZVnGaDSSm5tLWFiYqvdjXl4eZrOZkJAQVX/DrKwsnE4nlStXVry3LMukp6ej1+sJCAhQ9egr6RsfH58y+yYgIEDhu1ni2ZmamkpwcLDCH/F6fVPirZiVlUVYWJgiCUWWZQoLC8nPzycsLEw1Q7XERiskJETVnzAzMxNJkqhUSVkf3Ol0kp6ejpubmyL5qKTtaWlp+Pr6KhKzAFfbK1WqpJqAU6KHhIQo/BFLvBczMjLKvC5MJhM5OTmEhoaqXhf5+fkYjUZCQ0NdfWO0F7EsabcrsCzB6rSzInkvvUObE+jm40qu0mq1iqS0kr5JS0vDy8vrL98zV/ZN5cqVVe8Zm81GamoqYWFhZXpDmkwmHA6H6jVvNBrJy8srs29u9J4JCgpSvW6ud8+kpqbi4+Ojes/Y7XYyL1+mMlw3jDdJFn732k6eVYdZU4BRm0OaNofkiExGBvTAR/ZA+vMoWo2GRjX+txY7OjTE9f8lScLdYKB2ZESp4/t6eRJaKbDUNq1WS+Ma1V1/t6hTq5ReIzycGlcsCQ/09SHQ9+773hcI7gSu66sgy/LTwNyrNvcFQoD5sizPB74GqgFd/4F2SwkPD6d27do3/C8sLOxWn+I/5ty5c/Tu3Zvx48er+tTt3r2brl27MnXqVFWLmhUrVtCpUyfmzJmjarPy1Vdf0aFDB7788ktVD79Zs2bRoUMHVqxYodjXarXy7LPP0rlzZ/bs2aPQCwoKmDBhAj169ODcuXMKPSsri/vuu49BgwapWthcuHCBe+65hxEjRpCfn6/Qjx49So8ePZg4caLCYkaWZTZv3kznzp15+eWXVW1WfvvtNzp16sTcuXNxOp0Kff78+XTo0IEff/xRtSzfK6+8QseOHfnjjz8UmsViYfLkyXTp0oUDBw4o9JL9e/XqpWpxc/nyZQYMGMD999+vam0UExNDr169eOihh1Ttefbv30+3bt2YPHmy4nOXZZm1a9fSuXNnZs6cqXpd/Pjjj3Ts2JFPP/1U9bqYO3cuHTp0YNGiRar6iy++SKdOndiyZYvi2EVFRUyaNIlu3bpx7Ngx13ajo4hLJqWFFUCWNZ9MS7F9VXJyMn379mXYsGGqS11OnTpFjx49mDBhAmazWaHv2LGDLl268Nxzz6me+9KlS+nYsSPvvPOOqv7VV1/RqVMnvv76a4Vekt29ZMkSVq5cqao/88wzdOnShV27dinOzWQyue6ZM2fOKPTMzEyGDh1Kv379SElJUegJCQn07t2bkSNHqto6HT16lG7duvHYY4+p2n1t2rSJcePHqV4TV3Nel0i8LhVXC6Xif4m6dE7rlbZMAoHg7uLvrrks+RlZ8lQvMe+rDvj8TW3T3zwXQRnY7XbMZjNGo1E1wLFarZhMJlWPOygOcsxms+pDFv6XAFWWbjKZMJvNqr6SJSNUJpNJNbAtqXxiMplUgzun04nRaMTpdKo+zBwOh2t/Nd1ms7n6Ro0r+0at74qKijCZTKoPWcDVL2XpN9o3ZZlTWywWjEajattK+sZut6vqdrv9mm27Xt9YLJYyr5sSe56S9qsdv0RTazv8r2/+ynVh0OgJMHhTYFdeix5aA976Yusrh8OB0WhEr9er/ii4sm/UdKvVes17quSeKatvr3dd2Gw2VyUr+HMGQbbhkB1oZC1Go/Ev900JJfdEWdfN9dpus9mued1YrVYsReqf6dUk6TKQUR7DiUyqNgsZ2TVyKRAI7j5uyOdSkqSqwAX+XHMpSdKzwPvAKFmWf5EkaRLwGfAo4Pt3NFmWv1J534nARICoqKgWakbDAnUKCwuZO3cuDz74IL6+vqrejHa7nbi4OIKCghTTYCVTeLGxsURERCimwUqmdi9evEjVqlVVyxLm5+eTnJxM9erVVb0bc3JyyMjIoEaNGqpTw5cvX8ZoNFK1alXVKcDk5GQcDgcRERGcP3+eunXrus7R6XSSkJCAm5sbYWFhqm2/cOEC/v7+VK5cWbVcY3x8PMHBwaq+lVarlbi4OKKiolSXFJRYXFWtWlXV0zM/P5/U1FSqV6+uOm2elZVFdnY21atXV/RNbm4u8fHx+Pv7U7VqVcX0p9PpJCkpCUmSiIiIUJ3avXjxIl5eXqrT6na7nfj4eCpVqqQ6dWy1WomPjycsLEzVt7KoqIgLFy4QHR2tuqSgsLCQpKQkqlWrpljLLMsyubm5pKenU61aNfR6PbGxsURHR2MwGFzT2nl5eVSvXt11XThkJ9/Gr+fni1tw8L/ASEJiQHhr/lNnMHqNDqfTSWJiIlqtVtWz0+FwcOHCBXx8fFTvmZLrQu2egeLgMj4+nipVqlz3nlFbUvDxxx9z5MgR5s+fj86g5Wz+IY7l7aLQnkewWxWq0xR3ox/Vq9dQXVKQnp5OQUEB1apVu+Y9ExkZqXrdXOuecTgcrutO7Z6xWq0knz9HZGbKdcPCrW7HWOdxgEqmKIp0BRjd/hwplaF7UTN6FjUvd8Gl8Lksvwify5vnc1lShqRkoUuVK7b7/k1NgSzLXwBfQLGJ+t881wqLVqst9QC+Gp1OR506dVQ1SZIwGAzUr1+/TN3T07NMHYo9/spKmpIkicDAQAIDA8vUr+V9WBI4AaqjLBqNpkzPTChue61atcrU9Xr9NfvGzc3tmn3j5eVVpsegJEn4+fnh5+dXpl65cmXVtaQlhISEuEzUr0aj0RAVFVXmvlqttkzPTCjum2t5RxoMhjI9R0u8H6/VNz4+Ptfsm4CAAAICipMo1Lwf1TxDtZKGYVGdMdqL2JJ+nEK7GU+tG039wxka1RywI8taNBoN0dHRZbZNq9VSs2bNMvVrXRdQ7Jd6rbZd756RJAlfX1/0Bh17staxPm0BVrl4lPOC8TRn9YcZHvkf1TrlkiQREhJCSEiIQivRS+4ZNa53z2i12mveMwaDgapVq+LMVE65X009WxR73U4ptvvIntS3lf35CASCu4MbyRa/F2j455+RkiQ9AuwD0oHHJUkqAB4GLgJbKU7a+TuaQCAQ/G18dB5Mrj2A+yM6kGC6RIpxE3bnXranbSHALZrG/vcT6dUSSbrzS/jl23LYlbnaFViWkGfLYnfmGiI9a6KX7t6StMFOfwaY2nNAvkyyrhCNLFHZ4Uf3omaEOQLL3ailQFDRuJGRyxeAktIkjYEvgfHAUOAT4COKLYUe/TPj2yFJ0t/RBAKB4B9hsltINF9mf+Z2siz7qGTIx01jp8CeRkbReXqHvUq4Z5M7fpou05JKgV2ZVAOQbI7D4jSj19y9waWERH17ND62cHId0XhaHYQ4AvC+IktcIBDcvVw3uJRlues1ZNW6arIsb/87mkAgEPwdZFkmtSiLued+41huHA7ZAUQRoDfS3C8RP30RZkcOp/JWEObZCAn1pSJ3Cm4adzSSFmRlQpeb1hPNHX7+N4QMOllLlD0Y/+sblwgEgrsIcUdXIOx2OydPniQtLa3MTNejR4+SnZ2tqptMJo4cOUJ+fr6qzUp+fj5HjhxRzRIuSVI5duyYaqarLMukpaVx8uRJ1UxXWZa5dOkS586dK9PqJDY2lvj4eNVzdzqdnD59mqSkpDIzpI8fP056enqZ2eFHjhwhNzdXVTcajRw5coTCwkLVtufm5nLkyBGKiopU9YyMDI4fP66aHS7LMikpKZw+fVq17bIsc/HiRWJiYlTXnzqdTs6fP8/FixdVz93hcHDq1CmSk5PL7Jtjx46RmZlZZgb0kSNHyMvLU2hQnFx2+PBh1QzrksSuo0ePYrFYyrwuT58+XeZ1kZiYyKkzp/n5wkYO58TgkJ2UeNvk2Lw4XRCGQy4eDcuyxOOU/3ecEvufS5culdk3J0+eJDU1tcwM6evdM4cPH6agoEC1b/Lz8zl8+LDCcSHIvQpRnrVdbSxBQqK+b0vctZ7FAXVqKidPnizzukhISODcuXNlXhexsbFcuHChzLafOXOmzHvGbrdz4sQJLl++rKoLBIKKiwguKxB79+6ld+/ePPbYY6r2QatWraJPnz5MnTpVocmyzNdff03v3r2ZO/dq29PiB9W7775L7969+eGHHxS6zWZjxowZ9OnThzVr1igeRjabjUceeYR7772Xffv2KfbPzc1l5MiR9O/fn5iYGIWemJjIkCFDuO+++1S9HU+ePEnfvn0ZP348ubm5Cn379u307t2bp59+WtUmZvHixfTp00fVeF+WZT755BN69+7Nxx9/rNAdDgf/93//R58+fVi0aJFCt1gsPP/889xzzz1s2rRJ0TdFRUWMHTuW/v37c/iwsphVZmYmw4cPZ+DAgVy8eFGhx8fHc//99zNkyBCys7MV537kyBH69u3LxIkTVYOgDRs20Lt3b5577jnVHwa//PILvXr1YubMmarB4wcffEDv3r358ssvFfva7XZeeeUVevfuzfLlyxW62Wxm165dDBkyhJ07dyqObzQaGT16NPcOHciWSwcV+wNkWr0xOYqnkL10lYtHBP/k7NmzDBo0iAcffFDhhyrLMnv37uWee+7hySefVFgzybLM6tWr6d27N9OnT1f9YfDdd9/Ru3dv3n77bdW+mTNnDr179+b7778vpblpPOgV9CBSpgcOqxOcEgaNO039O9G+cj80kgaj0cjjjz9O37592b9/v+L4eXl5DB8+nMGDB6t6xSYlJTF48GCGDBlCWlqaQj9x4gQDBgxgzJgxqm3fvn0799xzD88880yZ1koCgaBiUuFri1ckPD098fT0JDAwULXqi7e3Nx4eHmVmcPv5+eHu7o6/v7+qHhAQgJubm2oWtEajwd/fHw8PD9VqRyXZ415eXqq2RlqtloCAAIxGo2opToPB4LLFudqiBYrLfXp7e+Pv76+aPV/S9oCAANW+8fHxwd3dvcy+8ff3d1W8UWtbSd+oZc9f2Tfe3t6qekBAAF5eXgpbIyjO7i7pc7VKSW5ubvj4+ODl5aXadg8PD7y8vMrsmyvbrrZW0dfXF3d3d1eG99UEBASUed2UfO4l56jWdnd3dzw9PfHy8lLV/f39cTNm4ZDUR89kJJxI6CQ36vrdU2pKvOS6CAgIUG27l5cXXl5eZeo3q2/U7plw92ro90Sx9/QGnn7xSZpXa0uYR1UMGrdSbS+5r6+m5J5xOBzXvGfc3d1V75mS6/FafePp6Ym/v7/qPSMQCCouN+RzeSfQsmVL+eBB9ZEJgZLCwkLmzZvHiy++6HowOJ1OsrOzXcHE1djtdnJycvD29laUroPi0cXc3Fx8fX0VZf2geAQuPz8ff39/VQ++oqIijEaj6sOqxBuyqKhINcCTZZmCggLsdrviYVYytVlSDlCtbJ/T6SQ3Nxe9Xq9a0tDhcJCTk3PdvikJJq7GarWSm5vravvVxy8qKqKgoICAgIBSNjKyLLN161bWrl2LyWSiUqVKRERE0KdPHyIjIwFIS0tj9erVnD17lqCgIMLDw2ndujW1a9fmp59+4uLFi4wePRpfX18OHz7Mnj17aNq0Kf3790ej0bim5bVarWrZPqfTSU5ODm5ubqolDR0OB9nZ2WUG/na7nezsbPz8/BR+plf2TUBAgGoQYzabKSwsVPRNSf+cO3cOf39/goODS33uRqORzz77jJSUFMx2CycNqeiq+xFYtwo5lIy0yVTS27gnNI9mAf1pFHAfuisSYUqm5XU6HUlJSSxevBi9Xs9DDz1EWFiYS79e35T8OLkam81GTk5OmX1jsVjIy8tztf2TTz4hNjaWDz74AI1G4+qbwMBA1XvGaDRisViue8+oeXKWlIsssT/6J/dMiZ+pXFiA8+BuRTvVkGWZVJsTT42Ev678B6fC57L8Inwub57PpeAuRKPRXNM7UafTXfPG0Ov119Td3NzK1Ev8D9UewCV6yShRWXpZnpklqNXJLkGj0ZQ56gjFozz/pG8MBoPCe/FK3N3dVYNSWZbZtm0b7733Hg0aNMDb25tTp07x5ZdfsnjxYlJTU3nsscdISEigevXq6PV6kpKSePXVV13B5bZt2xg2bBh79+5l0qRJhIeHM3ToUFdflIycloVGo6FSpUpl6lqt9ppt1+l012z79frmWtdFyfurjbabTCbmzp1Lfn4+zZo1IzU1leTLKUT1bEjI+BbovNwI0PswvnpXOgU1wlMXiHTVSqCSkVOHw8GPP/7I22+/jUajITg4mPHjx//jvtHr9ddsu5ubm0tX+6F/vXvG29tbdbS7RL/WPSNJUpmzEPDP7xmBQFBxEcGlQHAHoNPpmDp1Kvfddx9PPPEEP/30Exs3buTnn3/m/PnzfPrpp9x///1oNBrOnj1ban1fSS30OXPmEBQUxNdff12qWlF5p0aNGixZsgSr1cp//vMffl+ymLD6VYmsW52YFXtJ71+Td2O3odFoGD16NPv27WPPnj10796dAQMGoNPpyMvLY+3atYSHh2O1Wlm8eDFjxoxBp9ORmprKzz//TFxcHP7+/rRv357+/fuzdOlS1q5dy3333ceuXbvQarWMGzeO6tWrV5i+FwgEAjVEcCkQ3CHodDr0er1rhC4uLo7jx4/TtGlTBg4ciMPh4Ny5c+Tl5ZUazbJarUybNo2wsDC+/vprGjZsWKGCm5KKSYGBgTz++OMsWbKEgNNWetVrwPeLZpO87zzVq1dnz549LFq0iMqVK5OYmMiCBQvYtGkTjRs35uDBg8TGxjJ58mTi4uLYvn07MTEx1K1bl7feeosffviBzp07ExcXR1xcHH379uXgwYN8/fXX7Nu3j7CwMPbs2cPu3btZsmRJmdWXBAKBoCJQ/he6CAR3AXa73ZU5/Pvvv9O4cWOaNm2KxWLBz88Pg8HA0aNHGThwID179uT9998vNY1aUFCAr6/vNacxyzslZTW1Wi3GQiOyU0aSJEaNGsWCBQuoWrUqhYWF/PDDD0yZMoWcnByXtdXKlSvRaDT069ePAQMGkJmZyYYNG4Diz8bDw4MuXbowdepUZs6c6Vr/KEkSU6dOZcWKFQwcOJD9+/dz8uTJf62Nsixf0/bnduu3mjvnTO48ylvflLf2lHdEcCkQ3AGU1H1u3bo1b731FosXL6Z169YEBAQQGxtLVlYWLVq04LvvvkOn05V6wOt0OsaMGcOJEyd47LHHSElJuaMCgFtFia2SzWajZs2aaLVaV71sg8HgcioICwvD19cXWZaxWq3k5OSwfv16LBYLr732Gp999hmyLLNs2TKKioqYOnUq48aNY8OGDYwbN47HH3+czMxM1/tWqVIFvV5PSEgITqeTwsLCf9SO1NRU1WOU+Jlu27ZN1fpHlmXOnj3L7t27VW2RnE4nR48e5eDBg6q+mHa7nX379nHixIky/UZ37txJbGxsmZ6eBw4cwHkLr72KMz7/1ylvfVPe2lPeEcGlQHAHoNVqGT16NO+++y5PP/00NWrUoEqVKgwbNoyEhASef/55tm3bRmJiIrIsl5r21mq1PPfcc0yZMoVt27bx5JNPlmkGXx4pLCxkx44dfPHFF8yaNYuQkBDGjh0LFAftav+uZNeuXSQmJtK4cWNCQ0OJjo6mdu3aHD58mNOnT3Pw4EGaN2/OlClTaNy4MSdOnCA5ORkoDui++eYbli9fzrp16wgICKBmzZr/qD0rV67k119/VXx+TqeTJ554goEDB7J+/XrFfnl5eUyYMIH+/ftz5MgRhZ6cnMyIESMYOHAgCQkJCj0mJob+/fszZswYVa/YvXv30q9fPyZOnKjwvQRYvnw5kydPVjVsFwgEFQux5lIguIXIsozZYSG1KAO700GwWyA6vQ4vLy+FDY9Go2HGjBl4eXnx22+/MWHCBPR6PY0bN6ZTp04ApTwgZ8yYQVFREd9++y3Tpk3jww8/LEdr/xzIjlRwpILkA9pIJKnYuzUtLY0JEyZgMBho0KABzz77LC1atCAhIQFPT0+X/ZG7uztabbEtik6nc1kLbdiwAW9vb9599126du0KwE8//cTjjz/Opk2bcDqdfPvttxQWFqLT6XjwwQepVasWUBy8ajQaXnzxRWw2Gy+99BLR0dH/qKXBwcFUr15dsV2SJFq2bElhYSEREREK3d3dnebNm+Pp6amaxe3t7U3z5s0xGo2qnqJ+fn60bt2akJAQVcupkJAQmjdvTrNmzVx+qk5Z5nJhIafT00mUJMKqVfs7TRYIBOUM4XNZTlHzuSyvlPhc3ukZ0rIsE1+YxMb0fWRb8pCR8dJ6UF8fTU1tBMGVg1StmEr8BM1mMxqNBrvdjlarpUqVKmRkZGCxWAgNDUWv12M2m8nMzHTZ6aj5St5NyLJMbMxpokKOo5cPgWwC9KCriuw2hLQMDQ6HwxUw+vr6ujwXjUYjWVlZBAYG4unp6RrNDQ0NxWg0kp2dTWBgICaTqVQfQrEvaXp6usseKy8vD6vVik6nIzAwEIPBwIwZM3j77bfZtWsX1f4MqipXrqz4kfBX2vrJJ59w+vRp5s2bp+r5abVaMZvN+Pr6qvpaFhUVYbPZVP1MS7xkZVlW9eyUZZnCwkI0Go2rD6/W8/PzcXNzw83NDYcss/jUKT7dv5/0P6fxKwOvVq1CF1/v63o5Cp/L8o3wuawYCJ9LgeA2k2crZG3aLrKt/6vBnWcv5LDzPNUiolRHi0DpJ5ibm+ualrzaQ9HDw8Nlvl4+kEHOR7buBl1J6UkH2M8isYTwsIlIGnUfyKt9U0NDQ13//0p/SDWfSHd3d6Kiokr9fTW+vr6EhYXh4eFR6tj/FIPBoFrxpiQrXs2MvUS/ES/ZspAkSXVE80q9ZCRclmUOp6Twzo4d5F6x/jMZmJWYSvWa0US7q5+nQCAo/5T/n4sCwR3CBWMyOdZ8xXaL08aZ/As4RT6kEtmK7LgEsso6Pnts8TT5beKZZ57h9OnTNGzY8Ladw+1CBtbFxJQKLEtIstrYmV9YYdb8CgQCJWLk8hZQWFjI0qVL2b17N/7+/gwZMoRmzZqpTmsdO3aM9evXc/HiRTQaDV26dKFfv354eXmRkpLCW2+9RUFBgWufGjVq8OKLL5Y5miG4czDazZRlqGF2FBU/jMv37NHfwAFOZfKIS5PL0v59yqq6VFHIMplUt8tAll2ZjS4QCCoOIrj8l7HZbMycOZP//ve/1KxZk6ysLH755RcWLVpE69atS61FkWWZd955h7179xIUFER8fDxfffUVb7zxBi+88AL5+fksWrQId3d3atSoARRP6YkRgruDMI8gtJIWu1z6wSsBIe6V0EhiIkGBZABtWBmaF2hDbu35CIDia7ZxaCirz59XWA/pJajvWXGDboFAIKbF/3UuXLjAt99+S7169di0aRMffvghGRkZfPXVV9jt9lKvlSSJiRMnsmXLFnbu3Ml7772HLMssWbLE5UsnSRLdunXjrbfe4vPPP+fDDz8Uo5Z3CREewdTxiVYMTga7BdLIr5YYtFRFh6QJRpKuXo+qBUNH0JRd9/tuR5ZlTp06xc8//0xubq6qNdH+/fv5/fffMamMIjocDrZt28aKFSuwWq0K3WazsXbtWjZs2KDqe1lUVOSacVGzF+pcpQp+Fkup85KAzr7etPFRJgwJBIKKgxi5/JeJiYkhKyuLnj17UrlyZRo2bIi3tzdHjhzBYrGUyuaVJIkuXbq4/vb29kaSJEJDQ11f1JIk8dtvv/H777/j7+/PpEmTmDZtmssapCSj1GKx4HQ6K4TnXEnVkJKs4TsVHVp6B7ejkt6f2MJLOGQn4e5BtKrUED+d9w1/Vk6nE1mWK8RnCyBLXjg9HsLp3IXsSAfJA8nQCsnQ5s+lmOVnCrbkc3U6nVgsFl599VVWr17Nl19+yciRI0u9Nj8/n+eee44jR46wdOlSunfvXkrPzs7m4Ycfxmg0smrVKpo2bVpKj4uL49FHH8VgMLBp06ZSCUwAhw4dYuLEiYSEhLBz505Fss+pffuI+fRTQnr2pGbHjrjp9XQMCWYEVjw1Ghw3MKMiI+OEG3rt3Y785z8nVIjZJqf8v/ZSztsry7JYMX8VIrj8hxQUFLBhwwbMZrNCa9iwIfn5+ciyjIeHhyvbU6vVUlhYqBocSJLkGrF46623qFSpEs888wxarRY/Pz/eeOMN6tevT2JiIi+++CIffvghvXr1ol27dkBxJvH3339PZmYm6enpxMbG3tEB180iOzub2NjY230aN0QoPgQ6a+OUZdwcenKNmeSSef0d/6TEbuafVoK5W8jOzsZu90ej6QyyGSQ9YACURuB3O+np6eTl5RETEwPAuHHj6N27NzVq1HBtK8HhcLjKWPr7+6vqb7zxBna7HUmSFLrFYuGtt95Co9FQUFDg0h1OJ06njE6n55133sHDw4OUlBTFGvGQkBDenj6DgMBKRFSvhptBjxeQl5pInqX0rExZFDhl9BJk2sv/DyUZyLY7icNeIWYpLLKMxQlmZ/kPu2QZch3lv51/BRFc/kNyc3P54osvyMrKUmjjx4+nZs2aSJJEXl4esixjNBqx2WwEBgaq+k86nU4OHTrEo48+Sk5ODp9++imdOnVCkiRCQkKYOHEikiRhs9n4448/WLBgAfHx8a7g0t/fn6effprCwkL++9//Urt2bVVbk/JEychl7dq1K0QgnZubi8lkIjw8/Hafyr+OLMvExcURHR1913t23gghISHk5+e77tvatWtjsVjKtBeqXbs2dru9zMSiWrVqIcuya2bjakq8YfV6PXaHk91H4lmx7TSXUnOICg3gng6t6di8Ju5upfd3OJzsPxBPbLwXqbvTiY500rdvE5o0DEHKTr3hkapUmwNPjQY/bfm/b4tHLot9Lst/ayHfKVPwp89leUcGAirANfxXEMHlPyQ8PFy1VBuAm5sb6enpREVFceLECU6ePMnmzZspLCykQ4cOuLm5ceDAATZv3kzv3r1p2rQpu3bt4pFHHsFoNPLRRx/Rvn17CgsL8fX15cCBA1y6dIkOHTpw+fJljh07hru7eymPvZLydhqNptRUenmmpO/VSvuVR65sY0Vobwnlva1XX8eSJKHVasv0PwXQ6/XXDLrLCipLKFmvLcsy63adYe6PWzCai9dnJqblcux8Mg6nRO8O9VzG37Iss3XbWeZ9vJ7CQgsAqan5nDiVzBMPtaZX4I19VsXtrRjfUVDc3pJWVoT2SldMFJf39laEZQ5/FRFc/kNKpqvLIjIykqlTp/Laa6/RvXt3bDYbzZo1Y+LEiWi1Wnbt2sWMGTPw8/OjSZMm/Pe//+X8+fO4u7vzn//8B0mSqF27Nn/88QeXLl3iySefRKPRYLVacTgcjBw5krZt297CFgsEgvJGvrGI3zccdQWWJRSarPy67jAdm9fA27M4EDWZrCxZdtAVWJZgNFpYsvwI7UdUw9tdPFoEgoqM+Ab4l9FoNDz66KO0bNmSEydO4O3tTadOnVxJOt26dePjjz92TX2PHz/eVd+4hICAALRaLf379yc8PJyYmBjsdjs1atSgTZs2ZU6ZCQQCwY2QV2DmYrJyaQ9AcnoeWblGV3CZlVXI5cvKYgAAWXlFpOfbRHApEFRwxDfALUCn09GqVStatWql0Jo0aUKTJk1cf/ft27fM43h4eNChQwc6dOjwr5ynQCComBj0Ory93CiyKhNx3A06PN3/N/Xu4aHH3V19Kt6g0+BpKN9rvAUCwfUR3wICgUBwB3H8+HGSk5MV22VZZvPmzbz33ntkZirdBZxOJ8uXL+e///1vqSpeJVitVn766Se+/vpriq4q21g5wIuOzaoDcmnfSgk6t6hJgK8nubm5zJ07l/37d9C6ZTWuXkYnyzIGTQ6Vfcp/4pVAILg2IrgUCASCO4idO3eyePFiRZKAw+Hg1Vdf5eWXX2bTpk2K/XJzc3nppZeYOnUqR48eVejJycnMmDGDZ599loSE0jZOWo2Gni2qkJd8Cqe9eN2lp7ueXm3rMnZQG7RaDfv37+fFF1/ktddeY9DARnTvVh9Pz+KEIQ8PA74+Js6eWkVZJU4FAkHFQUyLCwSCW4IsyyQmJnLo0CF0Oh1du3bFx8cHq9XKrl27yM3NJTo6mpo1a7Jz504sFgtarZb8/Hzc3NyIiIhAkiSsVivbt29Ho9HQsWNH3NzcSEpK4tChQ9SuXdtlr3M1RUVFHDx4kMzMTEJDQ2nTps0dmcXasGFDevXqpTg3rVbLY489xvbt213WY1fi6+vLY489xvnz56lXr55CDw0N5dFHH8VsNlOlSpVSmiRJ1KoWwcC2ofgHV6Jtx26EVPKlRmQl3AzFI5GNGzfm4YcfplmzZkRGhvD8s/24mJBBRkYBlSt743Dks+gb0x3ZpwKB4NYigkuBQHDL2LJlC+PGjUOv1/P999/z4IMPcu7cOYYOHUpWVhYPP/wwzzzzDOPGjcNoNBISEkJeXh5BQUFMmzaNMWPGUFBQwOTJk9HpdGzbtg03Nzd27tzJmDFjmD59Om+88YbifWVZ5ssvv+Tll1/G29ubgQMH0qZNm9vQA9enc+fOqsGhJEmMGjWKUaNGqQZwOp2OJ5980vXaq/Hw8ODll18uU/fz82PO7LfK1ENDQ5k/f75L12qhTu0w6tQuqf0ezuuvv47z4O4ba6hAICi3iGlxgUBwy9FoNPz666+YTCYWL16sWhu7Xbt2bNy4kY8//hitVsvzzz/P3r17/9b77d27lwULFmCz2ejXr981E+fuZK7n5Xq7dYFAIAARXAoEgluMRqOhU6dOHDhwgN27d7N06VJ69uypCFoMBgOhoaG0aNGCSZMmkZOTw+rVq116Xl4eixcv5pdffmHXrl3XNDI+evQoMTEx2Gw29u7de9eUChUIBIK7ETEtLhAIbjldunQhOzubGTNmkJmZyTPPPFMqcLwSSZKIiopCo9GQmprq2p6ens6cOXPQarUUFBTgcDjKfL+xY8eyZ88e1q5dy9dff02TJk3ECJxAIBD8S4iRS4FAcMvx8/Pj/vvv58iRI7Rp04YGDRqU+VpZlomJicHpdBIZGenaXqNGDbZu3crRo0d5++230enK/q1sMBjQ6XRIkoSXl5er7OGdjizLrn/X0q+3/+3SBQJBxUQElwKB4Jaj0WgYOnQoPXv2ZMKECar1sc1mM/Hx8WzdupXPPvuM4OBgBg4cWOoYXl5eeHt74+7ufitP/5bgdDpZsWIFTzzxBBcvXlTodrud77//nueee4709HSFbrVa+eijj3jllVfIz1dW1DEajcyaNYt33nlH4XsJkJOTw/Tp0/nss8+w25Xm6ikpKUyZMoVff/31mqPGAoGg4iGmxQUCwb/C9Ua0atWqxerVq9FoNBw7dkyh79mzh169elFUVETVqlV55513aN68OdnZ2X/7nO6mqXCLxcKXX37JmjVraNOmDVWrVi11/oWFhXz66accOXKEPn360Lt371J6fn4+77//PiaTicGDB9OyZctSx09KSmLevHkYDAaGDx9O1apVS+knT57k008/JTg4mFGjRuHr6+vSZFlm7969zJ8/n2PHjtG/f3+K7JB86TLarCKiffV46DR3VX8LBIKbhwguBQLBTUeWZZDzwX4BWTYjaauANpw+ffqwefNmatas+aedjRaAmjVrsnnzZkJDQ4mIiGDp0qXYbDY0Gg3Z2dm0a9eO4OBgoHhK/ccff0SSJPz8/ADo3r07GzduJCoqSvV8tFotU6dO5ZFHHlEEUXcq7u7uPPfcc7Rq1YrevXsrdB8fH2bMmMHZs2dp3bq1IpDz8/Nj9uzZZGdnq1obRUdHM3PmTPR6PeHh4Qq9SZMmvPbaa0RERODp6anQO3fuzBtvvEHjJk1Zvz+WBeuPkHg5F70G6lf24MmWIdSv7CECTIGgAiLdLetlWrZsKR88ePB2n8ZdQ2FhIfPmzePFF190PcDLK06nk3PnzpVpnl3eyM3NxWg0Eh4efke2V5ZlsJ9DNv0GzhTACZIXGNohuQ9A0tz4FLYsy8TGxhIdHY3BYLihfQ4fPsyFCxdcf2s0Glq1akVERMRfbcotRZZlPvnkE2JjY/nggw/QaO78VUuyLLPvVAIvzV9NgclSSqsd6M77PaOo7KleDlKWZVJtTjw1Ev66O7+t/xSnLBNjsVPLTYfmDrxvbzb5Dif5DidV9No78nvqZuKUZT5asZqxM14lKCjodp/OLUWSpEOyLLe8ersYuRQIBDcXufDPwDLpim1GsGxB1oaBocO/+rA5ceIE27Ztc/2t0+mIiIi444PLuxGHU2bj/vOKwBIgLqeIo5dN9KzmdxvOTCAQ3E5EcCkQCG4u9jhwXlYRHGA9BIbWwI2NQv4dxo4dy9ixY/+14wv+h9PpJCvXqKrJMmSYbLf4jAQCwZ1A+Z+LEAgEtxa5CCgje1guAtl5S09H8O+h1WqoXqUSauPQWo1Edf/yl8UvEAiujwguBQLBzUUbWbzGUlWrCtK/N2pZHriWrY/D4cBisahm4suyjN1uv6Zus9mwWq1lHt9qtWKzqY82yrKMxWLB4XC4jq+RJPq2r0dEiH+p10pAh0hvGgZ5lPleAoGg/CKCS4FAcHPRhoKhA3BVIocmHMm9M5J0e752ZFnGITvueOPvLVu2cOTIEcU5Op1OPv/8c8aOHatavtJutzNnzhwmTpxIWlqaQjeZTMyYMYNnn32W3NxchZ6VlcXkyZN5/fXXVWu9JyYmMmHCBD744ANXACxJEtUjKvPmpL60qFUZuSifqn4GxjSqzHNtwvDUi0eMQFAREXe+QCC4yWiQPO4Fz9GgawjaauDWC8l7EmhCb/nZyLJMRlEy69MW8tPFd1me/CUJpnM45TvT+Pv8+fPs3r1bsd3pdPLrr7+ybNkyjh49qtALCgr4/fff+f3334mLi1PoGRkZ/Pbbb/z0009kZGQo9JSUFH755Rd+++03cnJyFPq5c+dYvHgxv//+e6ngUyNJ1K8WSusqThwnFvN536pMah5MJQ9duc8SFggE6oiEHoFAcFORJAlZ1iMZ2vyZvAP8uSrvVgcbsiyTbI5n4aUPybT+ry75sdxdDKryCE38O95xAVCvXr0YPny4YrtWq+WDDz7g5MmTqr6XAQEBfPTRR6SkpNCiRQuFHhERwfz587HZbERHRyv02rVr8/333+Pj40NoqPJHQLt27fjqq6+oVq0aPj4+pTRJkhg0cCABeh3eFcRqRyAQlI0ILgUCwU3nfwHb7Q0ynLKDHRkrSwWWAGankS3pS6jh3Qgfvf/tObkyqFWrFpUrV1YEvZIk0bx5c5o3b666nyRJdO7cuczj6nQ6+vbt63rt1bi5uXH//feXqXt5eTFq1Kgy9YCAAAYNGoTzoHLUVSAQVCzEtLhAICi3WJxFJJjOqmoZlhRyrMqa3HcykiS5/v0T/XrH/7vHFggEAhAjlwKB4B+SnJzM2rVrqVevHh06dMDpdLJjxw4uXLjAPffcQ2JiIseOHcPb25v+/fvj7e2N0Whk2bJlmEwmWrduTcOGDVm3bh3JyckAGAwGatasqaiHfSVWq5Vjx45x9uxZrFYrXbt2pUaNGi5dlmUyLmdyZHkMRkc+NbuG4+6jL86aNtlJOZHOor2LqRVSn7Zt2xISEsL+/fs5fvy4K5lGp9MxePBgAgIC/t1OFAgEgnKECC4FAsE/4vTp00ycOJHHH3+cDh064HA4+Pbbb1myZAmrV6/mjz/+4K233sLd3Z3Vq1fTrVs3tm3bxsSJEzGZTLz99tvUrVuXDz74gK1bt1KrVi3y8/Mxm8288sor9OvXT/V9k5KSeOSRR7h48SImk4kff/yxVHBpt9uZ++6HLP54G3pvLWGNAnH30WPOsbJu1mFSD+dxOCgNCYlp06Yxfvx4lixZwocffkitWrXQ6XS4u7vTpUsXEVwKBALBX0AElwKB4Jag0Wj45ZdfaNeuHQsXLkSWZcVUqq+vL99//z1Go5H777+ftWvX0qNHD9XjBQcH891337F06VLmzJlTSpNlmc2bN7Nq1SqqV69OUnoCWkmLLMscX3aRizszePPNNxnywFBXEFlCaGgo33//PcHBwXh5eeHr63vzO+MGsdlsFBUV4eXlpag1Lsuyy5fSy8tL0ZdX+lJ6enqq6mazGUmScHd3V9WNRiM6nQ43NzdVvbCwEDc3N/R6vZgWFwgELsSaS4FAcFM4ceIEH3/8MfPnz+fsWeU6xwEDBrBhwwa2bNnCtm3b6NGjB3p9aS/MkoDJbDYjyzKRkZHodOq/gb29vWnWrJki+JNlmYyMDP7v//6P0aNH06BBA9y0nvQNHU3XgAfIPyLjZnBn65ZtjBgxgjfffBOj0eiaCk9PT+fhhx9m1KhRfP3115jN5pvUQ38Nu93OvHnz6N+/P4cOHVL4XlosFt544w0eeOABYmNjFbrJZOLpp5/moYceUvW9zM7OZsKECTz22GPk5+cr9EuXLjFy5EimTp2KxVK6drgsy5w6dYr77ruPt99+u0zjdYFAUDERwaVAILgpnD59mi+++IKvvvqK8+fPK/Q+ffqg1+uZNm0aHh4e9OrVS/Ga/Px8Ro0axahRo6hSpQpTpkxRBKDXQ5Zl5s+fj06nY+LEieh0OrSSlrq+LWjn2w+DxZuCggJ8fHzo1KkTCxcuZObMmVitVurVq8cLL7zAuHHjsFqtvPTSSyxatOhv98k/wW63c+DAAfbu3avqW2m1WtmzZw+7d+92rVW9EovFwvbt29mzZw/p6crEpdzcXHbt2sWuXbtUg8u0tDT27t3L9u3bVYPHS5cusWfPHvbu3Yvdbv+brRQIBOURMS0uEAhuCkOHDmXevHnY7XYee+wxli5dWkqPjIykb9++fPLJJzz77LOEhIQojuHp6cnTTz/N9u3b2bhxI7t376Zr165/6TwKCgpYtmwZGo2G559/nn379lFQUMCbb77JzJkz8fX1RafTMWXKFGrXrs3ixYs5fvw4FouFMWPGAMWZzzVq1GDQoEHs3buXCRMm/O1++bu4ubkxe/Zsxo0bR8eOSj9OHx8fPvnkE1JTU2nXrp1C9/f3Z8GCBRQWFtKgQQPF8atWrcqiRYvQarVUqVJFoTdv3pxFixZRqVIlvLxKl/OUJIlu3bqxbNkyoqOj8fAQZR4FAsH/EMGlQCC4KWg0Gtcoo9r6O0mSGDduHKmpqYwZM4aYmBjFawwGA/369WPgwIH06NGDjz76iAYNGlCtWjXFay0WC+fOnSMpKQlZlrlw4QInTpygSpUqNG/enAsXLpCcnExRURFOp5PMzEy0Wi3dunVj06ZNbNu2jeTkZPLz82ndujV2u5358+dTt25d/P39Wb58ObIsU7169ZvfWTeAJElUrVqVqlWrlqnXq1ePevXqqfa3RqOhadOmrtdejVarpW3btmXqer2eLl26lKl7eHjQs2fPMnWBQFBxEcGlQCC4YdRqckuShEaj+bMyT7Gu0Whc2yRJQqvVAsWjYb/99hsAsbGxaLVaV2BSsg9A9erVGTZsGJ988gmrVq2idevWivdNS0tjyJAhJCUl4XQ6mTVrFgsWLGDt2rV8/fXXQHFCzOjRo9m6dSvz5s0jKiqKhx9+mOPHj/P+++8jyzL16tXjxRdfxN3dnaNHjzJnzhyKiooAGD58OGPHjr3JvXjjXC9ou9N1gUBQMRHBpUAguCFkWabAVsSZvBQyigqp5OZFPf9wmrdqxuJti3D62DmWe4gIjyhefe1VpkyZQvXq1alRowZDhgyhRo0apYKRHj16sGfPHkJDQ9Hr9cyfPx+z2Uy1atXQaDTMmDGD0aNHk5eXp3o+YWFhLFmypNR6P4PBQFBQkOt9dDodb7/9NoWFhURHRyNJEsHBwXz99ddcunQJu91OeHi4y2po7ty5TJs2DZPJhLu7O5GRkWLKVyAQCP4iIrgUCATXRZZlUs25/HJhH4nGbJzIaJAI8/Cjlr+TrLBEHLKdtOxEPLVetAnsSMOohmgkDd7e3oSFhSmO6e/vj7+/v+vvmjVrltIDAwMJCAjg6NGjfPvtt6W04OBg+vXrR8OGDa953hqNRnVK3dPTk7p16yq2+/j4KOpmCwQCgeCvIYJLgUBwXZyyzB/JJ0kwZv1vGzLJ5hzy7BYifOxo/hyUNDmMHMjeTZhHBH43oW633W7n4sWLOJ3OUtuu/Ls8UeJNqeZrabFYMJvN+Pn5qepmsxmbzYaPj4+qXlhYCBTbOKn5VhYUFKDRaFR9M51OJ/n5+RgMBjw8PBS6w+GgsCAfL253RXmBQHC7EVZEAoHguuRYjVwszFRRJMx2PTaHttRWk8NIqjnppry3v78/b7zxBm+//bbr35NPPllup6uXLFnC+vXrFetbZVlm6tSptG/fnv379yv2M5vNPProo3Tu3JnY2FiFnp2dzbBhw7jnnnu4fPmyQk9KSqJHjx5lLkU4deoUHTp04KmnnlL4XgJs376dUSNH4XA4/kpzBQJBOUSMXAoEgusiUzxSeS299N8yTrl8jiz+25jNZjIzlYG8LMskJyeTk5NDTk6OQrfb7aSmppKVlUVBQYFCLyoq4vLly+Tn57sSlq7EZDKRkZGBTqfDarUq9Pz8fLKyskhJSVENILOzs1XfVyAQVDxEcCkQ3GGYzWbi4uJUM7NDQkKQJInz58+TnZ1NdHS0q0KN0Wjk4sWL+Pr6EhERoZi2NJlMXLhwAYDo6Gi8vb3LPIeMjAwuX75MVFQUvr6++Bs8Cff0Jy/v6mo1Mm5aOwZt6WDDTeNBqIfSO1FwfQYOHMjQoUMVn59Go+GTTz4hKSmJxo0bK/bz8fHh22+/JTc3V9XXMjw8nIULF2K324mKilLotWrVYtWqVbi7uxMUFKTQW7duzdq1awkKCsLT01Oh33vvvdSJqILWpJ6AJRAIKg5iWlwguMOIj4/n3nvvpUePHrRu3ZrmzZvTtWtXevTowc8//8yxY8fo168fzZo144svvnAFocePH6dDhw688soripElWZZZuXIlrVu3pmXLlixdulQ1eC3hxx9/pE2bNuzYsQMAnaShT3hDKruVDkj9DZ7U9fdCc0UgpJP0NPVvgb8+4GZ1SYWicuXKpWqdlyBJEqGhobRs2RKDwaCqR0dH06RJE9WSmZIkUbt2berXr69YjwnFwWvDhg2pWbNmmb6XzZo1U/3hAuDu7k6DBg0Q7kQCgUCMXAoEdxjVq1dn9erVOBwO/vOf/7Bv3z6++uoratSoQXBwMLNnzwaKbXdWrFjBpEmT8PHxQZblMhNd7HY7K1aswG63o9VqWbZsGQ8++GCZpRVlWcbhcFBUVMShQ4ew2Ww0atSIx2p35VB2AqmmPEI8fGkRGI2fwcAFYwwp5kQMGjdqeNcmzCMCSaR1CAQCQYVEBJcCwR2Gh4cHDRs2xOl04uvr6xpxatCgAenp6WzYsIFGjRpRp04dli1bxpkzZ1RNxksoWau3fft2unTpgsFgYM+ePVy8eLHMUSoozg6eO3cuiYmJZGVlMXr0aN5//336hjcq9TpJkqjv25j6vo1LbRMIBAJBxURMiwsEdwmyLLN//37i4uK45557ePDBB7FaraxcufKaU9wAW7duJTMzk0GDBjFo0CCysrLYvHnzNfdxOBw0aNCAtWvX0qlTJ3788UeOHj3qqrpT8g9Q3SYQCASCiokILgWCuwS73c7y5ctxOBzk5eVx6tQpvLy8+OOPP1Szh6/cb+nSpTidTtLT00lLS0OSJJYsWYLNZitzP41GQ69evahXrx6dO3fGYrFw8eLFf6FlAjVkWSY/P5+4uLhSVYiu1LOzs7l48aJq9rYsy6Snp7vKY6rpKSkppKWlqf44cTqdXLp0iczMTFXd4XBw4cIFcnNzr/vj5no4nU5MRUUUms1Y7XbX8WRZxma3U2g2YzQXKdrplGXMFgsFJjOmoiIcTmfx8hCHA1NRkWt7efVEFQjuVMS0uEBwl5Cens6mTZvQ6XT88ssvaDQa7HY7J0+e5Pjx464kD5vNRn5+PjqdDo1Gw6VLl9i/fz86nY7PPvsMAK1Wy+HDhzl79iyNGjVSHW10Op0cPXqULl26cOLECbRaLaGhobe0zRUZm83G9OnTWbp0KV999RV9+/Yt9TmZTCYee+wxDhw4wKJFi2jZsmUpPT8/n6FDh5Kdnc2SJUuoVatWqeMnJyczcOBA3NzcWL58OcHBwaX006dPM3ToUKKjo1m8eDFeXl4uTZZldu/ezejRo+nWrRufffaZahLSDbXTYeebPzbx9eq15JtMtGtQn1fGjiIqOJj03Fxm/7iAjYeOYNDrGdatM089MBgvd3dsdjuLtmzjy1VrScvKJsjfnzcmPES7BvV5b+Ei/th3kMz8fAK8vRnfrw/j+/bGUMYaY4FAcHMRI5cCwV3Ctm3bSE5O5pFHHmHLli2cPn2ab775BoBly5a5RnXWr19Pr1696N69Ow899BCLFi0iMzOTN998k9OnT3P69GneeecdcnJyWLduXZnvp9FoWLZsGV27dmXFihXcc889tGjR4pa0VVCMRqNBo9GUudTgn+iSJLl0Na63zKFEK2t/NZyyTEZuLqlZ2RiLiricncXGA4eY/sXXRAYHMe6e3izfuZtXv/4eu8PB3EVL+GLVGgZ0aEvb+nWZ8/Ov/LZlO7Iss2bvfp766BN8PDyYOnIYQ7t2wl2vx+awk5yZxX2dO/D0A4NJz8ll2udfcSRGaSwvEAj+HcTIpUBwhyJJEk888QQDBw4kLCyMgoIC/vvf/9KyZUsCAgIIDAykR48efP755wQEBFC1alU++uijUlPd/v7+eHt7M3/+fPr370+lSpUAGDZsGAaDgejoaNX37tmzJ/Pnz6d169bExsZis9no3r07fn5+t6TtgmLrn7feeotnnnmGqKgoRZDn6enJp59+SkFBgao9kK+vL4sWLcJqtarWdg8PD2flypVoNBpVX8t69eqxfv16PDw8FL6WkiTRrl07tm3bhr+/P25ubjfUpiKrlUffncvJ+Is0ql6NwzGxBPn7UWS1Mvn+QTSvXYtNh46w7ehxLqalsfvkKQJ9fJg04F6yCwpYuHkrK3btYUjXTvywbiN6rY6+bVphsdpoXqcWLerWRq/V8sGTk3D/85yOnI/lh3UbuJyde0PnKBAI/jkiuBQI7lAkSeKee+5x/d22bVvatm1Lbm4uRqMRgICAAB566CHXax5++OEbOnZQUBCPPvooJpOJEydOlFoz5+vrS6NGjWjSpAmA67+CW4skSfj4+ODj41OmHhAQQECAup+oJEmqQeOVulrQWYJGoyEiIqJMXavVlvnjpExkKLJYSUzPYECHdgzpew8fLVyEVqMhyN8fN72eQF8f8k0mcguMRAQFcehcDH/sP0BWfgFFFivJGZnkG01cupxObmEh7y78DY0kkW808f6Tkxjdpyeef07RxyQls+/MWapXCadRjWp/7VwFAsHfRgSXAkEF5tKlSzz77LOlyv21a9eON99884ZHo24lBw4cYObMmQwaNKhUIP3zzz/z66+/uoJkSZIYNmwYw4cPZ+bMmcTFxfHOO+9QpUoV1q9fz8cff8xDDz1ERkYGa9asUbxP5cqVmT17NmazmZdffhmj0ciTTz5Jz549RTb8TSAkMIDJ9w3EUCmIn9ZtQJbB7nAU+6s6nWg1GnQ6LVNHDCM5M5P/+2EBvl6eSBoJT3c3NJKEJIFWo+HjKZMJCQig/7RX+GXTFh7o2glPNzfOJybx6LtzKTCZ+Pz5KVQNDbndzRYIKgwiuBQIKjB16tRhw4YNt/s0bpjLly+zcuVKRXLKuXPnWLVqFX369HF5dwYEBOB0Otm3bx+HDh3i1VdfBSAxMZFVq1bRoUMHqlWrRtWqVUlLS2P58uVUr16dnj17EhAQgE6nY+PGjSxcuBBZlvHz83P5hAr+GR4GA57u7jg1Eg2rV2X7vv3EJCYTVimQhLR0gvz9qVK5Mr5eniye+SqZufkcj4/nsfc+onW9uvj7eFM9LIzTFy/h5+2Fv7cXOq3WdfxTFy7y8NsfkFNYyDfTnqNrUzH6LhDcSkRwKRBUYMrbKNy4ceMYMmQIUDyte+WI7NWUjG4OGzaMPXv28Mcff9CqVSs+/PBDdDodVquVJUuWEBwcTGhoKFu2bOHy5ctERkbequbc9VzPokhC4oEunVmzZRszvvyGyOAgzl5KZMboB6ns78f6Awd5d8Fv+Hl5cfh8DNGhIUzodw9uej0P9+/L5iNHefqj+Xi6uWEqKmJIl044HE6mfvYVh8/H0KRmDb7/YwM/rNvIxAH9aN9QWXNdIBDcfERwKRAIyg3vv/8+CxcuRJIknnvuOVq2bPm3jiPLMufOnePgwYP06NGDtm3bMn36dLZs2cKYMWP+1aA8IyODoqIiRRKNLMukpqaSlJREkyZNFMsWZFkmISGBnJwcGjVqpKgvXtImu92uWl/c4XBw6tQpPDw8VCs32Ww2jh8/TlBQEJGRkQq9qKiI8ydOUF8GkJFlSEg3czQunyKrk3pR3tQId6NXqxZk5ubh4WbABNSJimDh6y/x25bt5BQU8mj/fvTv0BYJqB0RQYeG9cnIzeOxQQMY2rUT1cPDkCSJbs2asPjNV1m+aw82u50XRw6jd6uWOJwO2tSvS1ilQNe5SQhzf4HgViKCS4FAUG6oXLmyK/ApCc4kSSo1gnblusxrsX79erKzs+nWrRvNmjXDw8ODFStWMHz48H91PerKlStZtGgRY8eOLXWOTqeTyZMns2vXLr777jv69u1bar+CggLGjx/P+fPnWbFihcI2KiUlhREjRpCfn8+mTZuoWrVqKT02NpYBAwYQFhbGihUrFL6X+/fv5/7776d58+b8/vvvpXwvAVavXs1Hb/0fG96ZhUbSsO5QBj9tTCG7wIYMeLhp6NG0Ek8Mvg8PNy2yLGOyOZGQaFS9Go2qKxNuqoeH8ebD41T7SafV0qlJIzo1aXSVoue1cWNU9xEIBLcGEVwKBIJyw/jx4xk6dKjrb6vVSkBAADabjcuXL1OzZk1SUlIAXLZMaphMJpYvX47T6WTmzJkYDAYKCgrYtWsXly5dUqz5vJl4enoqAjsoDoajoqI4d+4cgYGBCl2v1xMREUFubq5qhrm7uzvh4eH4+PioGp57enoSFhZGeHi46rpSPz8/goODiYiIUIyKQnF/+vv7A5CYWcRPm1LIKvifLZbZ4mTdwUzqR3vTrUnZfS8QCO5+RHApEAjuOo4dO8ann34KQEhIiMvbc/369WRmZgLFyUrdunWjZ8+eLF++nKlTp9KsWTNWrlxJeHg4bdq0KfP4p06d4ujRo7Rv35777rvP9Z6//fYbGzduVJ02vlncf//99O7dW3F8SZKYM2cOb7zxRpnB4+eff47dbsfb21uhBwYG8uuvvwIoRh0BIiIi2LBhAxqNRjElD1C/fn127tyJwWBQDT47depEi59/Rnv2OCcvFJCVrywtanPI7D6dS9fGlRCz1AJB+UUElwLBHYTNaSOtKJ30ogw8tO5EelbBW+dd4daLybJMkcNOfF4Wl/Iyyc0yUKdSCN7e3tSpU4fk5GQ+/vhjoNiHs0mTJtSpU4ddu3axe/duAAYNGkT37t158MEHMRqN/P777+zcuZMWLVrw+OOPU69ePdf7ubu7U6tWLZfv45EjR6hSpQpTpkxxjYSeO3eOM2fOcPz4cRwOh+ro3c3AYDCoVr2RJAl3d/cyyyxeuRSgLF0t6LxSL8tTE4oTpK5loq/VavH29sZJcRBZFja7qPMtEJR3RHApENwhmB1FbE3fybmCGBxycSlHf70f3UM6U9VTWaGlvCLLMrkWMz/HHOZk9mWqFkkkFiYT6u3P6GbNOH78eKnXl5QhfOaZZ0ptLwnQPD09mTx5Mo8//jiyLKPRaNBqtaX6s0mTJuzdu9elPfzww4wfP75UAFm7dm12796NJElor7C9ESipH+WFt4eWQrOj1HaNBC1q+YlRS4GgnCNqiwsEdwCyLHMy7wyn889il+3If/4vx5bL9vRdmB3m232KtwwZWJ90nmNZqdidTmRkHLJMQmEOSy+eRNZqXFOzBoMBvV6PTqcrtc1gMLgCw5L613q93rX96kBdo9GU0kqOd+UIoiRJrverKIH+36V6mCcD2gaj1/2vnyQJWtb2o3Mj5XpRgUBQvhAjlwLBHYBNthFfeAEZ5XRiti2XtKJ0qntXvfUndhsw2a0cz0pV6QmIz8/msqmAKB/1kofljSuz3NUC2utlvt8uXafVMKJrOPUivdl+Ihuz1UmLWr50aBCAr6dOkcEvEAjKFyK4FAjuAGRZxibby1KxOZXJEeUVh9OJXVZfl2e/hlbekGWZCxcucObMGbp06aJYLynLMqdOnSIlJYXOnTsr1mI6nU4OHz5MYWEhHTt2VKwRdTgc7NmzB51OR6tWrRRT/VarlR07dlCpUiUaN26sWAdqNpvZtm0b1apVo3bt2ooA06DX0LquP63r+v/DnhAIBHcbIrgUCP5ElmV27NhBXFwcffr0ITw8HIDMzExWrVpFREQEPXr0YOvWrcTHxxMZGUn37t3R6XTY7XY2btxIdnY2/fr1w2AwsHLlSgoLCxXvU6NGDbp06cKpU6fYt28fPj4+9B/QnzD3ENKKLite765xJ8gt6F9v/52Cl96NSC9/sopMCi3Yw4vK7spM57+LLMvYnE6cJfWsNZo7ZsrbYrEwffp0li1bxvfff8/w4cNLnVtBQQFPPfUUBw4cYNWqVXTp0qWUnpuby6hRoygoKGDt2rU0aVK6BOLFixcZMWIEbm5ubNmyRVF56MiRI4wYMYLw8HB27NhRKtlHlmW2bNnC0KFD6dq1K4sWLUid8NEAAMN8SURBVFLNQBcIBBUTEVwKBFdw/PhxpkyZwttvv82zzz4LwPLly3nsscd44403aNGiBc8++yxHjx6lRo0a7Nixg7CwMKxWK++//z7Hjh2jefPmVK5cme+//56UlBRSUlLIzMwkOjoaPz8/+vXrR6dOnXjrrbdYsGABPj4+rF+/nibNGpJoSibLmu2aHtdLepoHNMHP4Hs7u+WWopUk+kTWJsmYR1aREQAJ8NTp6RNZBx/9PzcwLwkqN8TGsvjUKRLz8qhduTIPNmpE+6gotCrZ2rcanU5HmzZtXP6cV+Pm5kb79u1xd3enSpUqCt3d3Z1u3bqRmZmp6psZEBBAp06dMBgM+Poqr6+wsDDatWtHtWrVVK2HoqOj6dChA+3atUOv1//NVgoEgvKICC4Fgivo0aMHQUFBrF27lkmTJqHT6Vi1ahW+vr7cc889HDp0iHPnzlG9enWSk5PZuXOnq5b1lQQGBrJw4ULsdjuvvfYaX375JW+99RZ9+/bFYDCQkJDAzp07iYiIIDs7m9WrV/N6q9cZWKUvp/POctmSgbvGndo+NajmFY1Wuv3Bzq1CkiRq+lXmiQbt2JOWgDktg4hKfrQIjaaO/80bwV14/Djv7tyJ6U+PzPicHPYlJjK7d2961qhx20cwdTodTz31FI8++iheXl7KaWeDgVdffRWbzaaqe3h48OGHH+J0OvHw8FAcPyAggK+++splcXQ1kZGR/PLLL67kpiuRJIn69euzZMkSV5KTQCAQlFBxnlgCwXWQJImaNWvSpk0bDh48SFxcHElJSezatYumTZtSr149li5diiRJTJ8+HR8fH1asWOEy8L4SjUaDr68v/v7+rlKBnp6e+Pv74+HhwbZt20hLS+Opp56iZs2arF69moKCAgINAbSv3IZB4f24J6wHNb2ro9NUvN+AkiQR6e3PkBqNaRsSzdAaTagXEIz2Jk1bZ5lM/HTsmCuwLCGnqIjvDh/G4nCUseetRa/X4+PjU6bvpZubG97e6j6oJUGjp6dnmbqnpyceHh5l6l5eXri5uZWpe3t7YzAYbnsgLhAI7ixEcCkQXIFOp+P+++/HbDazdu1aNmzYQF5eHvfddx+5ubls2rSJhg0b0r9/fxo3bsyWLVtIS0v7S+9ht9tZvHgx/v7+DBgwgN69exMTE8PBgweLfRglDTqNDq2krdAPbUmS0EgS2j//ezP7IrmggJT8fFXtYm4uWUbjTXsvgUAgqGiI4FIguIouXboQEhLC0qVL+e233wgICKBHjx7s37+fuLg4MjIymDZtGmlpaaSnp7N58+a/ZKsSExPD/v37cTqdzJo1i71792IymVi9ejVOZ8XIhL7deBsMeKqsIwTw1OvxKEMTCAQCwfURwaVAcAWSJFGlShW6dOnCwYMH2bFjB23atKFq1aosXrwYrVZLUFAQFy5cwM/PD41GU2pq3OFwEBsby6lTpzh9+jS5ubmlji/LMhs3biQzM5OIiAiSkpKA4vVva9euJScn51Y3uUIS5edHu6uyowE0kkTvmjXxL6PE4u1GlmXXv3+iX+/4f/fYtxrhlFk25a1vylt7yjsVbzHXbaDEry4uLg5PT08aNWqEj4+PYppPlmUSExM5ceJEqe21a9d2ZYtaLBZOnTpFVlYW4eHh1K1bV1HKTvDP0Gq1DBw4kMWLF+NwOBg8eDAZGRns3LmTxo0bs27dOvz8/CgqKmLgwIHs27ePS5cuodFoyM/PZ+zYsWi1WnQ6He+9916pcoNms5mVK1cSHBzMwoULqVOnDg6HgxdffJHPP/+cvXv3cu+994rP819Gp9HwfMeOFNnt7ExIwGK3420wcE/t2jzcogW3s/fPnz9PRkYGwcHBpa4DWZY5ePAgJ0+e5L777sPf37/UfrIss3XrVpKTk7n//vsVdcbtdjt//PEHVquV/v37K5J0ioqKWLFiBb6+vvTq1Uvhe2k0Glm8eDHVq1enY8eOims0OzubrWtWMTAiBM0tun7FXVI25a1vylt7yjsiuPyXkWWZxYsXM3XqVNLT09FqtXTp0oXPPvuMsLAwxRf0H3/8waRJk/D09HR9ub/yyis8//zzmEwmXnzxRRYsWIDFYsHHx4dnn32WKVOmiGzNm4gkSfTr14+9e/ciyzLVq1dHp9OxZMkSfHx88PPzcyVLfP311+Tk5BAdHc0nn3xCQUFBqeNERkbSpUsXRo4cSbVq1dDr9cybNw8o9rssKTU4bdo0xowZo2opI7j5SJJEhK8vc/v25VxmJulGIxG+vtSsVAnDbf6xtnHjRhYtWsTkyZNLbXc4HDz33HPs378fb29vhg4dWkrPycnhmWeeISYmhqpVq9KxY8dSelJSEpMnTyY3N5cDBw5Qq1atUnpMTAwTJkwgPDycLVu2KK7FPXv2MGnSJJo0acL69evx8/Mrpa9cuZJ5s9+i//wP0Yja6wJBhUYEl/8yqampvPrqqzgcDlauXMm2bduYPXs2X375JS+//LJidACKLUY+++wzmjZtisFgcHnUrV27li+//JL77ruP5557jmeffZZ33nmHbt260bJly1vdtHKNl5cXjRs3LrWtadOmpf6WJImoqCiioqIAFA/bK7nyQV2vXj2FHhQURFBQxTFKvxOQJAlPg4Fmf5rl3ynUqVOHDh06KLZrNBpGjRpFZGQkzZo1U+g+Pj4MHz6cmJgYVV/MoKAghg8fjtlsVvW9rFKlCmPGjCEoKIjAQGX977p16zJs2DCaNm2qapjeunVrevTsIUbdBQKBCC7/bc6fP8/58+cZMGAAXbp0ISAggP/+979s3ryZ559/XvVL2maz8dZbb+Hp6Um7du144YUX8Pf3Z/369dhsNh544AFatmxJnz592L17N3v27KFFixal6vVeuTaqotTwrYjtrChthorT1q5du7qq6VxdW/yRRx5h3LhxGAwGRX/odDpeeOEFHA6Hqu7p6cmsWbOQZRm9Xq/QAwIC+Oijj1yj6VfrERERfPnll2i1WrRarUKvV68es2bOQnP84F/+rCrKZ1uCaK+gvCOCy3+I3W4nMzNTNcvX29ubtLQ0nE4ngYGBLu9DvV5Peno6DhUvvcDAQB588EGqVavGrl27+Oyzz8jMzOTHH38kJSUFSZKoVKkSkiS51lxdvnwZWZaRJIn8/HwWLVpEVlYWSUlJxMfHl/uRBFmWycnJIT4+/nafyi3BbDZjs9kwm823+1RuCQkJCaSkpKD50+PS3d3dtTShsLCQgoIC3N3dCQgIQJZlsrOzcTqdVKpUCavVSm5ubrHFk0aDwWDAx8fHVWfbZrO5Xg/FAZqvr6/Lm9ThcFBYWIjZbEaSJCpXrqw623CzyMzMpLCw8O69b60WnBb7Db+8wOFEL0lkOf5eW51OJ1l5+TicTny9PPF0JWLJmIosFP55jxh0erw93NHpdBjNZgpMJaVFJQw6HX7eXshAdl4+TpXa9V4eHvhctYb175DjcHLBemd4qP7bWJwyFlnGUgHiSlmWyXNUgIb+BURw+Q9JSkrigQceICMjQ6FNnjyZ2rVrA8WJOLIsY7PZkGUZd3d31YfHoEGDGDhwIDqdjri4ODp27MjevXvJyMhwVdkoOZbVakWW5VLVN7y9vXnggQcoKCjg22+/JSIiQtWAuTwhyzIWi4WIiIi784H8F8nPz8dkMhEaGnq7T+Vfx+Fw8P777/PDDz/g5uaGRqMhLCyMqVOnMmTIEN59913mzJlD06ZNWbp0KTqdjgkTJpCXl8eqVas4deoUEyZMcN1zvr6+dO3aleeff57o6GhOnDjB8OHDyc/Px2AwYDAY6NmzJ3PmzKFSpUrs2LGDZ555hoSEBHx8fNi+fTsRERH/WnsDAgLIysoiMjLy7ryWTUYcl5PgBkeqLkvgqZHw+ZvfUacvJjFs+qvkFhYyZej9zBgzEkmSOJtwiWmff83x2Di0Wi3+3l68MGIYw7p3Zd7y9bz53U+4GfRoNRp8PD0Z0bM7Azu049HZ75KZm4fFZsPhdOJuMKDRSDw+aADTRo/4W+f4P2QsTpkqeg1SBUhPKXQ6KXDIhOnL//pbGRlvTfn/TP8KIrj8h4SGhvL555+rVmmpUqUKBQUF+Pn5ERcXR35+PjExMRiNRho0aICbmxuZmZmkpaURHh5OQEAA586dIzw8HH9/fwoLC7Hb7a5pqBYtWrB48WKOHz9Ot27dOHPmDAaDgfr167seRFqtlsDAQNeD0mAw/KsjLXcCTqcTrVZbZiWR8oZer3eV5Cvv7bXb7djtdhwOB9OnT8fX15fp06czb948+vXrhyzLmM1mDh8+zLp16xg8eDAWiwWLxYJer3dl6A8ePJgxY8awadMmvvjiCy5evMivv/6KVqvFbDbTpEkTXnjhBT7//HN++eUX7r33XoYNG0ZAQAAjRozg999/JyEhAZ1O5xrVvNnIsuy61w0Gw135o1C2WXFKwA1cl7Iso0NCh4Tb33wwbz14iLTMTAw6Hev37ueZBwbh4+nJ50uXc+j0GWY9Mo6okGBikpPx93DHTSPhtNuxFBUxeXB/2jdswOyfFvDBL7/SrFpVPpsyGZPFwvu//s6eU6d5dcwI6leNJjokGIPEP7rfnDJoJTD8WRSgvGORJXSS/I/77W6g5LMV/A8RXP5D3N3dr5lMY7FYGDJkCD/99BMPPPAAiYmJeHh4MG7cOPR6PT///DMvvPACH3/8MY888ggzZ84kLi6OqKgoTp8+TUFBAePGjaNy5co88MADfPfdd8ydO5ft27ezc+dO2rdvT+fOncv9zSuo2Gg0Gpo1a0aDBg14//33sdlspaayK1WqxKeffkq3bt1U94+KiqJv37706NGDxMREli1bxt69e12jv0FBQfTq1Yvjx4+zbt0614/F5s2b06hRI/bs2UNCQsKtaazghig0m1mxay/VQkNo26AeS7bv4kT8BVrXq0tGXh4Op5Miq5WwSoG0qV8XP29v176SJFEvOor+7dpw8Ow59p85R0pWFvd36YjVbufnDZvQabS0qFOLdg3q38ZWCgR3J3ffT+O7DIPBwNtvv83rr7+Ou7s7rVq14qeffqJr164uw+5OnToRGhqKJEk89thjtG7dGpPJRP369Zk3bx6vvvoqGo2GmjVrsmDBAu677z6cTiePPfYYX3/9tWpmp0BQnrDZbHz88cdMnDiR7Oxsxo8fT0BAAFAcXI4dO5b4+HhWrVp1zeO4ubnRrFkz7HY7MTExru3Hjh3jySef5JtvvqFDhw507doVKA5CbtcPN1mW2bx5M6+99hopKSmKpAiHw8GyZcuYPXs22dnZiv1tNhs//PADH374IUaVcpZFRUXMnz+fr776CqvVqtDz8/N57733+PXXX7HbS6+jlGWZjIwMZs2axZo1a25LZalzlxI5GhtLh0YNGdO7J1qNhpW796HTahncsT1ajYbnPvmcLk8/z71TX2bdvgOlkhzPJiSycvde1u47gLvBQJ0opam+QCD4e4iRy38ZSZIICAhg6tSpTJ06tdR2gAceeIAHHnjAta1r166uB9vVr4ViO5z58+eXqQsE5RGn00laWhpZWVnY7XYiIyNLTRs3b96cxMRE5s+frxooXUlJIObj4+PaZjKZiI2NJS0tjWbNmpXSbhdFRUV88MEHrFmzhmrVqjF27NhSekFBAbNnz+bIkSM0b96c3r17l/ouyMvL46WXXsJkMtGxY0fFDEtCQgKvvfaaa51p1apVS+nHjh1j5syZBAcH07dvX3x9fUvpO3bs4I033qBDhw507twZ7ytGBv9tZFlm1Z59FJjMBAf4k1toxNvDnfX7DzJt5HCGd+9KvegoNh06yqZDh9l27ARv/bSAdg0bAOCUZb5cuYZfNm4mwMebV8aOolPjhrfs/AWC8o4ILm8B1wr+rtauFyiKQFJQEXFzc2PWrFn4+/szYMAA3nzzTdq1a1dKf/zxx+nfvz95eXkKn0en04nVauX06dOsWrWKkJAQWrdu7QpE27Vrx1dffcW0adP4/vvv+e2333j44Ydd+5VYe9lsNtc66H/7XnRzc2PSpElUrVrVNdNxJd7e3jz55JOcOHGC5s2bK3RfX1+mTp1KZmYmderUURw/MjKSKVOm4Obmppoc1rBhQyZPnkx0dLSi2o8kSbRt25annnqKtm3blkoqvBXkFhr5Y99BNJLE/GUrkJCw2u3kFRrZc+o0Qf5+VAkK4j9DBnN/5w70fn46xiKLy6FDo9EwY8wI7u/SCS93NwJ9fO7KNa4CwZ2KCC4FAsEdgSzLOGQZu8OBJEnoVRLRmjVrxpAhQ/jss89YtmxZqaniFi1a0K9fP37++WfFfkuWLOHw4cPExMRgs9mYOXMmdevW5fjx467XeHp68vjjj7NkyRK++OILHnjgAU6ePMn06dM5c+YMBQUFjBw5kl69evHGG2+47Iz+LTQaDf3796d///6quk6nY8yYMYD6j06DweCq8qOme3p6MmPGjDL1gIAAZs2aVaYeHh7O+++/X6Z+M5FlGZssY3PKaCSJE/EXOBF/gaHdOvP6+IeQgEPnY5j03ocs37kHq93GnlNniAyqTK7RSGZuHsO7d8HPu9hXWAIq+fkSHaI0kxcIBP8cEVwKBILbjizLXMzNZVN8HLHZWbhpdTQLC6dLdDRt27YlJCSEqlWrotFoeOqpp/D398fLy4u2bdvywgsvULNmTdzc3Jg2bRpRUVFUqlQJb29vateuzQsvvIDdbsdgMDB06FA6d+5MnTp10Gq1hISEMGXKFKpVq4ZWq6VBgwbMmTOHS5cukZ+fT6VKlejUqROdOnVynWu9evVu2QzCP53JuN36zcAhy+zNNfJLWjZnCouobNDhl5rNo/cN4sHOHahZJRxJkggJDGDaqAfx9vCgd6sW7DtzlkuX03E4nDSqUY2eLZqj02ppW78eL4wYRsNq1VTfTytp6N+uDTXCw6lSufK/3j6BoDwi3S3O+S1btpQPHjx4u0/jrqGwsJB58+bx4osvVggronPnzlG3bt0KsWwgNzcXo9FIeHh4uWlvcn4en+zbR7rL3Bo0kkTzsDA6+vhSu3p1DAbDbTzDfx9Zlvnkk0+IjY3lgw8+uCunaeXCApwHd9/Ya2WZVJsTT42Ev069rbIssyfPyNRzyWTZ/2c+rpfgobBAnqkagvYuuQecskyMxU4tN12FsCLKdzjJdzipov/3l5DcbpyyzEcrVjN2xqsVroyvJEmHZFlWWObcfd9eAoGgXOGUZXZeulQqsCzZfuLyZRJycm7TmQluNw4Zfk7NLhVYAthkWJGRzyXztZO3BALB7UEElwKB4LZidzhIzs9X1awOBymFBbf4jO5cShKL7lT9ZmN0OLlQRgCZabOTYrGJutUCwR2ICC4FAsFtRavR4FNG1RutJOFr+Hcq4typbNu2jdOnTyu2y7LMjz/+yCOPPKJq6G6325k3bx6TJ09WLUdrNpt58803mTZtGgUFyoA9NzeX5557jjlz5lBUVKTQU1NTeeyxx/j8889dWddXEhMTwyuvvoLjJnpeumsk/HXqy3o8NRp8dOV/ylUguBsRwaVAILitaCSJthERuKmsDQ718aFWpUq34axuH6dOnWLjxo2qpulffPEFP//8M3v37lXsl5+fz5dffsk333zD2bNnFXpaWhpff/01n332GampqQr90qVLfP7553z99ddkZmYq9BMnTvDtt9/y1VdfqZqy79ixgzWr19zUkUSDRmJgkB96lfixha8ntT0r1g8PgeBuQWSLCwS3EFeFEOwgy0iSDlBWgSl+nYyMHQmJklu1PI7SSJJEvaBghjRowPrYWLLMZjSSRDV/fx6o3wBZpfpMeaZTp04MGTJE8VlrtVpmzZrF4cOH6dGjh2I/f39/Zs+eTUJCAk2bNlXoERERzJkzB4vFQlRUlEKvVasWc+fOJSAggJCQEIXeunVr3n33XerUqaNqmN6vXz8ks/GmJiJJksTAYH/SrXYWX84l02bHTSPR1s+LqdVC/nZNcoFA8O8igkuB4BYhyzJ2ZyHZ5p3kWY7glG14G2pT2bMrbtpQVzAhyzJ2RzIm80as9nNIGHB3a4Wne1fA67a24d9Cp9HQpWo1moSEctloxKDVEOrtg6deT2wFCy4bNWpEeHi4YntZFbxK0Gg0DBgwoMzj6vV6Ro4cWabu4eHBxIkTy9T9/f35z3/+U6YeGhrKuLHjbjhb/Ebx1Gp4IiqIwcF+JFts+Oq0VPNww0Nz+0pzCgSCayOCS4HgFuGULSQX/Eqe5RBQPIJpMaditMVR1W8iBm0QkiThcFwmt+ATHI5k176FpgTs9iT8fMbfprP/99FIEoGengReUQ1GJGsIAHSSRJSHG1EeYhpcILgbEGsuBYJbhMkWT77lKCWBZQlF9mRyivYDxZm4Jsv2UoFlMU4s1sNYbXG36GwFAoFAIPh7iOBSILhFmGyXkFFm2YKM2Z70p2bHbr+our9MEXZ7ohjNEwgEAsEdjQguBYJbhE7jS3FVYxVN8kJCA2jQSMpkiWK0aDQ+iGVmFQeHw4HJZCrzB4XdbsdsNpep22w2LBaLqi7LMhaLBavVWqZeVFSEzWZTPbYsy5jNZux2+19okUAgqAiI4FLwr7BixQpGjBjBk08+SXp6OgAFBQVMnDiR6dOnY7PZyM/P59VXX2XcuHHs3r0bWZbJzs7mp59+4vHHH+ehhx5i+vTpbN26FafTyd69exk1ahTjxo3jzJkzABiNRl5++WWef/55PvroI9f7G41Gpk2bxogRI/jmm29cD0+j0ch//vMfnn/+eYWdSnJyMkePHsVsNv/j9suyTGpqKq+99hqjRo1izpw5uGtqYNAqS4NpJA/83JvDn8Glu1t7QFnqUKsNxaCvR0mAumjRIkaMGMGUKVPI+bOKTVZWFuPHj+f111/H6XSSk5PD9OnTmTBhAocPH0aWZdLT0/n222+ZNGkSY8eO5eWXX2b37t04nU42b97MqFGjeOSRR4iPjweKP7fnnnuOESNG8MUXX7jOJy8vj2eeeYYRI0awYMECVx/n5uby+OOP89JLL2GxWEr1yaVLlzh69Gip7X8Xq9XK1KlTmT59uqov491OifXQ0KFDOX78uCIAtFqtvP3224wZM4aEhASFbjabmTZtGo8++qiqtVBeXh5PPvkkzzzzjKq1UGpqqutauvrzkmWZmJgYRo4cybx588oMQAUCQcVEBJeCm05RURFffPEFCxcu5IsvvmDPnj3IsozVamXp0qWsW7eO/Px8ZsyYwXvvvUdwcDBNmzYlIyODcePGMWnSJE6ePInT6WTPnj288847FBUVkZiYyKJFi/jhhx/49ddfcTgcHD9+nHnz5rFmzRp27y7OUpVlmdOnT/Ppp5+ycOFCPv74Y/L/rABjtVpZtWoVa9aswWr9X+UPWZaZO3curVu35vz5865KJDfyTw1Zlvnuu++YPXs2WVlZuLm5YdAGUsVnOO66SEpuPb2mEqFeA/A21EGSirNf3QwN8fEagkZT4u+oRaetiq/XGDQafwBMJhPz589n4cKFfPbZZ67A0Ww28/vvv7Np0yaXKfa8efOIjo6mfv36JCcnM3LkSCZPnsz58+dxOBxs27aNDz74AKvVSlxcHAsXLuTbb79l2bJlyLLMvn37XO914MABV/uOHDnC559/zsKFC5k/fz6mP8s3FhUVsWzZMjZs2KAw2541axbt27cnMTHxb/dtCQ6Hgz/++IN169Zht9tveL+7BavVyvr161m3bh3Hjx9X6GazmTVr1rBq1SpiY2MVuslkYsmSJaxdu5bExESFnp6ezqpVq1i1ahVZWVkK/eLFi/zxxx8sWbJE9cfA2bNnWbNmDWvXri11LwkEAoHIFhfcdOLj49m7dy9NmzYlMTGRJUuWcO+997p0s9nMa6+9xjfffMPEiRN57bXXcHd359NPP+WPP/5g0qRJzJ49Gy8vL6xWKykpKbj9WcFFkiQCAgJYunQpTzzxBAsWLECn02Ew/G+kT5ZlVq9ejdPppE2bNpw8eZLDhw+XaeEC8Ntvv/Hrr79is9l4+OGHqV27NrNnz+app54iIiICf39/Tpw4wciRI/H19eWrr74iNDSUl156iSpVqpSyRJFlmcWLFzN//nwcDgfp6enYbDY0Gg0+unp4BkRhtiUhY8dNG4pBG8iV0+WSpMPTvTduhubY7alIkh6dLhKN5O16nzNnznD06FFatmxJTEwMy5cvL9W+wsJCXnzxRRYuXMiUKVOYOnUqer2eL774gu3bt/Pcc8/x6quv4u7ujsViITU1Fb1eDxRb2vj5+bFo0SImTJjATz/9hLu7e6lA0el0smLFCgwGAw0aNODYsWOcOnWK1q1bl9nH3333HStXrqSoqIhRo0bRoEEDXnnlFR5//HHq1KmDwWDg3LlzjBs3DoDvv/+eatWqMXToUKKjo8s8rsVi4fPPP2fz5s00btyY559/nsDAwLvepsbd3Z3/+7//Y9iwYfTr10+h+/j48NFHH3Hp0iXat2+vaK+/vz8//PAD+fn5NGzYULF/9erV+f7779HpdERERCj05s2b89133xESEqLqa9m9e3d+/vlnatWqhecVGf63EpmyFpoIylvflLf2lHdEcCm4qciyzLp168jLy+ODDz7g22+/Zdu2baSkpODlVezRGBMTQ1xcHKNHj2bmzJl4eXlhs9nYsWMHBoOB4cOH4+bmxtatW8nKysJgMFDpiiotffv2ZcuWLfzyyy+sWbOG++67j6VLl7r0wsJC1qxZQ2RkJDNmzGD48OGK4OtqwsPDCQ8PJzk5maZNm1KvXj0A9uzZg9VqpX///pw9e5ZJkybRuHFjQkJC+PbbbwkKCuL1119XPNjDwsIIDw/n8uXLNG3alFq1agHFwbFO8sbHre41+1GSNOi0wei0wap9vGbNGkwmE1OnTuW9995jw4YNpUafTp48ycmTJ3nsscd4+eWX8fDwwGw2s2vXLry9vRk6dCgajYaNGzeSl5eHu7s7wcHBrnMcOHAga9as4ZdffmHTpk088MAD/Prrr67jZ2dns27dOmrWrMmLL77IyJEjWbVqFa1atSqzTVFRUYSEhJCRkUGLFi2oW7cuDoeDnTt3sn//fvr378+xY8eYMGECTZo0wc/Pj08//RSn03nN4164cIFdu3ah0Wh47733cDgcvP3229fs37sBSZKoX7/+/7N33/FRVF0fwH8z29J77wkl9JYAUkWKiiAgRZpSBRR8FLBg5UXsIqgIKF2RJlUQpKgU6dJBWggkkARCetkkW+e8f4QdstlNAA0QkvN9Pjxx58zcuXfanp1yB/Xq1bMbF0URsbGxiI2NtRtXKBRo165dmeUrlUo88cQTZcYdHBzQs2fPMuvm4uKCvn37ltOCe4+TjbJVtWVT1dpT1fFlcVahtFotNm3ahICAALRo0QKPP/44UlJS8Ndff8njEBHMZjNycnLky2mWhwtEUYSDgwN0Oh2+//57DBs2DMOHD0dKyq2ueWrXro1HHnkEn376KfR6vVXH0ZbLtefOnUP79u3RpEkT1K5dG7///rvd9y1btG3bFm3atIEoinj55Zfx2muvyclw3bp1MW/ePPTo0QNarRavvvoqPv/8c3h4eCAuLs7mfjNBENC6dWu0bdsWarUa48aNwzPPPAOFndcb/hsFBQXYtWsXwsPDERMTg06dOiEhIcHqlYCSJEGSpHKXcWFhIb799lsMGTIEo0aNslo+DRs2ROPGjTF16lSo1WqrJMRyqfzy5cvo2LEjYmJiEBERgS1btsj3ftrTsWNHNG/eHEqlEhMnTsQrr7win5Fu2rQp5s+fj8cffxxarRZvv/02PvroIzg5OeHq1auQynlftZ+fH+bMmYPZs2cjNDQUv//+u3wbBGOMsfuPk0tWoS5evIgjR44gPT0dffr0wffffw+TyYSNGzfK923VqFEDQ4cOxebNmzF+/HhkZWVBoVCgYcOG0Ol02L9/PxwcHPD999+jTZs2NvNwcHDAwIEDkZubi86dOyMyMlKOSZKEzZs3o6CgABs2bMBTTz2Fy5cv4+LFi/L9gmUp6zKqn58f1Go1HB0dARS/Rk+tVkOhUNjcU3g/xMfHIzExEdeuXcPTTz+NJUuWQK/XY+PGjXKiW79+fQwYMACrVq3CpEmTkJubC5VKhfr160Or1eLQoUNwdXXFDz/8YPdVgc7OzhgwYAAyMzPRrVs3BAcHyzGTySRf3l62bBm6d++OlJQU/PPPP3bvDbSn9H2RAQEBUKlUcHR0hCAICA4OhkajgSiKMJvN5d5HqVKpoFar5b9ms7ncZJQxxti9xZfFWYWRJAmbNm2CTqfDgAEDEBERAQBYu3Yt/vrrLyQnJwMAXFxc8MknnwAAli1bBo1Gg2nTpuH555/Hxo0b8emnnyI9PR1hYWFITk6WH3QpqUuXLpg2bRo6dOhglXjk5ubit99+Q0hICJ577jmoVCpkZGRg0aJFWLt2LR555BF5vJ9//hlOTk5QqVTo3LkzPDw8IEkSli9fjrS0NDRq1AiAbdL5IO/ls9zrSEQYPHgwgoKCIEkSfv75Z/zxxx8YNWoUAMDd3R0zZsyAyWTCjz/+CI1Gg48++ggjR47Etm3bMHnyZCQlJSEgIAA3btyw26ann34a06ZNQ9euXZFV4hWMGRkZ+P3331GjRg0MHDgQoiji+vXr+PHHH7F+/XpMmjRJHm/58uVQq9XQaDTo0qULPDw8YDKZsGTJEjz22GOIioqy2867WcYpKSn4+OOPIUkSEhISMHjwYLi5ud3NYmWMMVaBOLlkFaawsBD//PMPmjRpgqlTpyI8PBxEhICAAEyfPh0nTpxAaGgoAgMD4enpiWnTpoGIsGvXLsydOxcTJkzA8uXLMWfOHPksXGBgIAYPHozAwEBcunQJ4eHhcHd3h7u7O/73v/8BKH5qNTAwEL6+vjh37hwkScKgQYMwdepUKJVKZGVlyWf7srKyEBwcjKSkJEyfPh1AcbIbHR2NZ555Brt27cK6detw7do1fPbZZwgNDZXvRfTw8EB4eDg0Go38EISvr2+ZiZCnpyfCwsKsHjb6r/Ly8nD+/HnExMTgww8/REBAACRJgqurKxYuXIizZ88iLCwMAQEB8PHxwcyZMyGKIrZu3YpatWphzJgxWLFiBb7//nusXr0akiQhKioKTzzxBHx8fODq6orw8HC4urrCx8cH48ePBwAcO3YMYWFh8Pb2xunTp6FSqTBkyBC89dZbUCgUSEtLQ1xcHOLi4qDVahEaGor09HT53kd3d3c0btwY/fv3x99//40VK1YgLS0Nb7/9NsLDw+HrW9xFk5eXF8LDw6FSqaBSqRAaGlrmwzmCICAoKAgeHh4oKirCX3/9hZ49e+K9996DKD68F2X0ej0kSbJpg6XHhcLCQri7u9uNFxUVwWg0ws3NzWaZEREKCgpARHBxcbEbz8/Ph0KhgJOTk914bm4uNBoNHBwcbOKSJCE/Pw/O4PvjGKvuhIel247Y2Fg6cuTIg67GQ0Or1WLmzJmYNGlShd3rdzuWTpcByF8+lvsr9Xo9VCoVTCYTBEGQ4waDAUajEQqFQr7/zmQyyV3LiKIItVoNURRhMplgMBjkxMPC0iVR3bp1oVQqYTAYoFQqoVKp5DpYvrA1Gg0MBoPVZVNBEORLsHq9HmazGQqFAmq1GjqdDqIoQqPRwGg0wmg0ynXX6XTyePa+iI1GI0wmEzQaTYWtAyJCamoqioqKEBkZKbev5LIxmUxynQHI9S69jC2X0BUKBVQq1W2XsV6vh0KhgEKhkMdRKpVyHSzrXqPRQKfTWZ1RLrnO7S1jy39blpmDgwOA4m6NEhISUKtWLZskveQ8lUoljEaj1Xp/2BARZs+ejWnTpmHhwoXo1KmTVTskScLkyZOxc+dOfPvtt2jWrJnV9DqdDhMmTMCFCxewYMECm7PC2dnZGDt2LLRaLRYuXCj/aLK4du0aRowYAX9/f3z77bc2Z3/Pnz+PUaNGITY2Fp9//rnN+ti/fz9mT/sci19+EQrF7ZN7IsJ1owQnUYCH8uH9MXCnJCJc1JtQS6OE+BBun3crzywhzywhWKV4KPfHuyER4ZuNmzH0ncnyD+XqQhCEo0Rk81Qhn7lkFUYQBPm+xJLDlEollMriTa30F5JarbYZVjqxsShZTul5ODg4QKPRyPOzF7coXceSSo4HwKqLldJ1tcSWLVuGjRs3Ws1vzJgxeOyxxyr0rKWlbEdHR5vkuOQyuxfL2HI2q+R49uplUV7XNHezjB0dHaFWqzF//nyrh8JEUcSrr74q3+ZgaVNVkJ6ejsTERJvhRIRjx47h9OnTSElJsUku9Xo9Tp48iXPnziEjI8MmudRqtThx4gRycnKg1Wptksvc3FwcPXoUwcHBKCwstEku09LScOLECfkHXOlt6urVq7h48SIID8cJC8bYvcPJJWP/UWxsLLy8vOTPgiDIXQ+xitG6dWurZEkQBPme3qqmR48e6N+/v83ZHlEU8d133+Hq1at2u2Zyc3PD4sWLkZWVhaZNm9rEg4ODsXLlSpjNZrv9htaqVQu//fYbHB0d4e/vbxN/5JFHsHXrVvj7+8s9KZSud83AACikqve2JMbY3eHkkrH/KDo6GtHR0Q+6GlVa/fr17SZMVVFAQIDd5E0QBISFhSEsLKzMe1Br164t/3dpoiiW+ZAaUHw22tJnpr24Wq1G69aty4w7OTkhJiYG0pH95TWPMVYNcHLJGGMPidvdu/ag44wxBnA/l4wxxhhjrAJxcskYY4wxxioMJ5eMMVZJFRYWIi0tDSaTySZm6ZcyPT3d7huJLP1SZmZm2n3DEREhKysLOTk5duOSJCEjIwN5eXl242azGenp6XLfmYwxZsHJJWOMVUJGoxGTJ09GTEwMdu/ebZPAFRUVYdy4cWjdujVOnTplE9dqtejXrx8ef/xxJCQk2JSfmpqKp556Cr169UJmZqZNPC4uDh07dsTzzz+PoqIiqxgR4fDhw2jZsiVee+01+f31jDEG8AM9jDFWKRER8vLyYDAYUFBQYBMvfiNOPvR6vdyZfElmsxlarRZFRUV2kz+j0YjCwkL5pQWlWcrNz8+3e2bSUm5+fj6/y50xZoWTS8YYq4RUKhU+++wzvPzyy6hTp47Nk9rOzs747rvvkJmZaTfu7u6On3/+GUVFRahZs6ZN+SEhIVi/fj0UCoXdfi0bNGiAjRs3wtXV1aZTfEEQ0K5dO2zduhV+fn42HeMzxqo3Ti4ZY6wSEgQBXl5eVh30l44HBAQgICCgzHhoaGiZ5YuiiBo1apQZVygUqFOnTplxpVKJBg0alBlnjFVffM8lY4wxxhirMJxcMsYYY4yxCsPJJWOMMcYYqzCcXDLGWCWSm5tr9+luIkJmZibOnz8Po9FoN56amoqLFy/CbDbbjSclJSExMdHu092SJOHy5ctISUmx+3S4yWTChQsXkJaWZjduMBhwOSEB3OMlY4yTS8YYq0TWrVuHdevW2SRwkiTh5ZdfxmOPPYbdu3fbTKfVajFkyBB07twZp0+ftomnpqaid+/eePLJJ5GcnGwTv3z5Mrp06YL+/fvb7ffyyJEj6NixI1544QWbfi8BYOvWrRg5YoTdxJYxVr3w0+KMMVaJqNVqODo62o25ubnB1dUVGo3GJiaKohxXq9U2caVSCTc3NwiCAKXS9tBvibu5uUEUbc87aDQauLq6lhl3cHCw6bKIMVY9cXLJGGOVyDPPPIPu3bvb9FspiiKmT5+OvLw8u/1SOjk5YeHChSgqKoKfn59N3MfHB6tWrYIkSfDx8bGJh4eHY9u2bVCpVPDw8LCJN27cGDt27ICLi4vd5LZTp05oXLMGFFfj76K1jLGqiJNLxhirRJycnKBQKGyGC4IAFxcXuLi42J1OEAS4u7vD3d29zLi3t3eZ8xUEwW5SaiGKIoKCgsqMKxQK+Pn5QeLkkrFqj++5ZIwxxhhjFYaTS8YYY4wxVmE4uWSMMcYYYxWGk0vGGKuEiAgpKSnYvXu33a5/iAiXLl3Cvn377PZ7KUkSzp49i8OHD9vtHshsNuP48eM4ffq03X4vTSYTDh06hAsXLtjt11Kv12Pv3r1ITEy0G2eMVV+cXDLGWCVkMBgwadIk9OjRA5s2bbJJ4LRaLcaOHYtevXrhwIEDNvGcnBwMHDgQzz77LM6dO2dT/tWrV9GvXz8MGDAA169ft4mfOHECffv2xZAhQ6DVaq1iRIRdu3ahZ8+emDBhgt3klzFWffHT4owxVgkpFApER0fjxIkTCA4OtomrVCpER0cjOzsbvr6+NnGNRoO6desiPT3dbtdCLi4uqFWrVpn9U3p5eSEyMhI1atSASqWyifv7+yMiIgLR0dF2+81kjFVffERgjLFKSKlU4q233sLEiRPh6Oho0++lRqPBl19+CZPJZDfu5OSEH3/8EZIkwcHBwaZ8Hx8frFu3DoIg2O23MjIyElu3boVCobDplF0QBDRq1Ah//fUXVCqV3eSTMVZ9cXLJGGOVVHmJmyAIUKvVdt/GY4nbSxpLxst6E5AlXt4bd0RRhLOzc5lxxlj1xfdcMsYYY4yxCsPJJWOMMcYYqzCcXDLG2EOCiMrt9udBxxljDODkkjHGKpWEhARkZ2fbDCcinDlzBuvWrbPpGsgSP3z4MDZt2gSdTmcTN5vN2L17N/744w+7/WIaDAZs27YN+/bts9vvZWFhITZs2IDjx4/bTTBzc3Px+++/Q+Lkk7Fqj5NLxhirRLZt24ZVq1bZJHBmsxnjx4/HkCFDsH37dpvpcnJyMHbsWAwePBjHjx+3iScnJ+OFF17AoEGDcOXKFZv4xYsXMWDAAIwZMwapqak28YMHD2LQoEF45ZVX7Ca3GzduxDvvvmM3MWWMVS/8tDhjjFUi4eHhaNy4sc1wURTx5JNPwtHREXXq1LGJOzs74/HHH0dERARCQkJs4p6ennj88cdRWFgILy8vm7ivry+6desGPz8/uLu728QjIiLw+OOPo2nTpna7NmrQoAFaNG9h0yUSY6z64eSSMcYqkS5duqBly5Y2SZogCBg/fjzGjRtnN7lTqVT44IMPYDab7XZB5Orqiq+++goA7HZf5Ovri4ULF0IURbvdH0VGRmLFihVQKpV2O01v0qQJZsyYAfH00TtuK2OsauLkkjHGKhGlUmn37J8gCGUmdpb47frFvF2/l/aS1pLx2/WLqdFowBfFGWN8zyVjjDHGGKswnFwyxhhjjLEKw5fFGWOskir5xLi9S+X/JV76afTy4v9m3oyx6ovPXDLGWCVERPj777/x7bffIiMjwyYZlCQJO3bswPz585Gfn28TN5lM+PXXX7FkyRLo9Xqb8o1GI1asWIE1a9bAbDbbxAsLC7Fo0SJs3brVJk5EyMnJwffff4+9e/dy90OMMSt85pIxxiohnU6HTz75BJs2bYK7uzuGDBliFc/Ly8N7772HY8eOITIyEp07d7aK5+Tk4H//+x8KCgrQoEEDxMTEWMUTEhIwYcIEqNVqNG/eHBEREVbx48eP47XXXoO/vz8OHz4MNzc3q/ju3bvx6quvol27dtiwYQNcXFwqrvGMsYcaJ5f3ARFBr9dDp9NBoVDAyckJCoXC7nj5+fk2ZwlEUYSrq6scL3mGQqlUwsXFhS9LMVbFaDQaDBw4EF5eXmjVqpVN3NnZGUOGDEGTJk1Qv359m7irqytefPFFZGRkICoqyiYeHByMESNGQK1Ww9/f3yZep04dDB06FBEREXafEm/atCmGDx+ORx55pNynzBlj1Q8nl/cYEeH8+fOYMmUK/v77b7i6umLIkCEYO3YsnJycbMbt27cvTp8+LQ+TJAmhoaH47bffkJubi169eiEzM1NOJps1a4bVq1fblMUYe7iJoohnn30WvXv3hkqlsvkBqVKpMHr0aEiSZLf7Io1GgzfffBNEZPfHrLOzMz788EMAsBv39vbGl19+CVEUbeKCICAsLAyzZs2CQqGAKPIdVoyxWzi5vMfy8/Mxfvx4HDp0CGPHjsWpU6fwf//3f/Dz88Pzzz9v9YUgCALGjBmD9PR0AMCpU6cwf/581KtXD66ursjKykJGRgYaN26M3r17AwD8/f3L7NeOMfZwE0XRbofnFgqFwm5iWDJeFku/meXFyzu23C7OGKu++OfmPRYXF4e//voLzZs3x5QpU/DWW29BqVRizZo1NjfZC4KAPn364MUXX8TIkSPlMwJDhgyxuuyk1Wpx8eJFSJKEli1blvsFwRhjjDF2P3FWco8lJiZCr9cjODgYKpUKAQEBcHR0xOXLl2EwGMq8Vyk5ORkbNmxAVFQUnnrqKXm4r68vCgsLsWHDBly9ehXr1q3Dzz//DG9vbwDFDwGcPn0aOTk5yM3NRVZWVpW/ZEVEKCoqsrpdoCrTarXQ6XTIysp60FW5LwoLC5GdnV0tfkQVFBTI6/ah3JaLCiGZJaDUk+tl0UoSTBAgme5s/IcZASiSCNlmCQ/hmr1rBRKh8GZ7qzqJAF3Vb+ZdqfpH63ssJSUFr732GrKzs21iAwYMgLOzM4Bb/cBZ/pbuNqQkSZLwyy+/IDU1FW+//TZ8fHwgCAJCQ0Oxfv16BAcHIyMjAwMHDsT+/ftx4MABdO/eXZ42NzcXubm5MBqNMBqN1SK5lCQJRqPx4fxCvksmkwkmkwlGo/FBV+WeIyKYzWYYjcZy95mqwmw2y+19GLdlMpmK88o7XFVmAkwEGKv+qgUBkFDc1odvzd49ExWv32qxbokg3elGX01wcvkfOTo6IjY2Flqt1iYWFhYGJycnqFQqpKamwmw2Iz09HTqdDmFhYVCpVJAkCUQkJ4CCICAvLw/Lly+Hp6cnnn32WTkmiiIiIiKgVCoREBCAyMhIHD58GHl5eVb16dSpEwoKChAXFwd/f/9qkVzm5OQgICDgQVflvsjNzUVBQYHdJ3yrIq1WCz8/v2pxf5+bmxvOnDmDgoIC1KhRwya+ceNGHDp0CC+//DICAwOtYpIkYdmyZUhISMArr7wCDw8Pq7jBYMD8+fOh0+kwduxYmyfAtVotZs2aBW9vbwwbNsxmeWdmZuKbb75B/fr18eyzz9okv0lJSVi7dCnGtY6FKNzZMcdMgJMowF1R9dMtApBrluCnFKtFcpkvEfJutreqIxRvx+wWTi7/Iy8vL7z++utlxrOzs9GiRQu5M+TDhw9Dp9OhR48e0Gg0WLFiBWbMmIG3334bffr0ARFh165dOHPmDPr164datWrJB/GtW7di3bp16NKlC65du4atW7fCx8cHDRs2lOdn72zHw3gG5G5U5zeFVPX2Vqd1a2nrkSNHsGnTJrz66qtWbTaZTJg2bRqOHDmCxo0bo3///lbT5+bmYtq0aYiPj0fHjh3Rrl07q3hKSgq++OIL5ObmokePHqhdu7ZVPCEhAR999BECAwPx1FNPISQkxCp+7NgxfP7552jUqBG6du0Kd3d3q/gff/yBn376CWNbxUC4gx+01WndAtWvvSVPX1f19laHqyp3i5PLe8zDwwNfffUV3nrrLXz88cdwcXHBhAkTMHjwYIiiiJycHFy8eBG5ubkAir9A1q5dK/dhV/Lsga+vLy5duoTt27fDaDSiZs2amDBhAurVq/egmscYq2DNmzfH008/bfOFrFAoMGnSJBw8eBCPPvqozXTu7u6YNGkSLl++bPWD0yI4OBiTJk2CTqezSRwBICoqCu+//z68vb3h5+dnE4+JicFbb72FBg0awNXV1SbepUsX5F2/VuUTCcbY7QkPS8YdGxtLR44cedDV+FeICFqtFnl5eVAqlfDy8pKTxvz8fOTk5MDT01O+P/PGjRswmUw23QxJkoT8/HwUFBRAkiQ4OzvD3d3d7mVvrVaLmTNnYtKkSeV2R1IVSJKECxcuoE6dOtXiiy0nJwcFBQUICgqq8u0lIsTHxyM8PLzcLnmqAiLC7NmzER8fjxkzZtjdry3H67LW+wOPa/MhHdlvN2avrOtGCU6iAI9qcOlUIsJFvQm1NEqIVXy/BYA8s4Q8s4RglaLKH6ckInyzcTOGvjMZvr6+D7o695UgCEeJKLb0cD5zeR8IggBXV1e7v/btDS/r3kFRFOHu7m5zOYoxVj3c7kv6QccZYwzgfi4ZY4wxxlgF4uSSMcYYY4xVGL4szhhjldTtnjB+4PdY3ibOGKue+MwlY4xVQpIkYenSpXj22WcRFxdn092J0WjEzJkzMXToUKSkpNjEdTodpkyZgnHjxtl9yUN+fj4mTJiAt99+G4WFhTbxtLQ0jB49Gp999plNh/1EhMTERDz33HOYN28eTCZTBbSYMVZVcHLJGGOVkF6vx+rVq7F+/XocPHjQJl5QUICVK1di1apVOHPmjE1cq9Xihx9+wKpVq3D58mWb+PXr17F8+XIsW7YM6enpNvG4uDisWbMGP/zwA4qKimziJ06cwJo1a/Dzzz9Dp9P9y1YyxqoivizOGGOVkIODA95//308/vjj6Natm82lZzc3N3zyySeIj49Hq1atbOIeHh6YNWsWcnJyUL9+fZvyIyIiMHPmTKhUKgQFBdnEmzZtihkzZiAoKEjuJs1CEAR06NAB33zzDRo0aAAnJ6cKaDFjrKrg5JIxxiohQRDQvHlzNG/e3G5cFEU89thjeOyxx+zGlUolnn766TLLV6vVGDBgQJlxZ2dnDB8+vMy4h4cHXnzxxTLjjLHqiy+LM8YYY4yxCsPJJWOMMcYYqzCcXDLGGGOMsQrDySVjjFUiJpPJpluhkrGCggK7cSKCwWBAYWFhmXG9Xl/uk91FRUXQ6/V2Y0SEwsJCGI3GMssvKiqC/ZozxqoTTi4ZY6wS2bp1Kw4cOGCTwEmShGnTpuGZZ56x2/WQwWDAu+++iwEDBiApKckmnp+fj3HjxmHEiBHIzMy0id+4cQNDhgzBxIkTodVqbeKXL19Gnz598MEHH9jt1/LYsWN45dVXIEnS3TSXMVYFcXLJGGOVSFJSEk6dOmUzXJIk/PHHH9i7dy/i4uJs4oWFhdixYwd27tyJ5ORkm3h2djZ27tyJbdu22e1UPSMjA9u3b8eOHTuQl5dnE09MTMSuXbuwY8cOu/1enj17FsePHy/zrCtjrPrgrogYY6wS6datGwYOHGjTb6VCocDs2bNx8eJFdOzY0WY6Dw8PzJ8/H2lpaYiJibGJh4SEYMmSJTAajYiIiLCJ165dG+vWrYOzszMCAgJs4q1bt8aaNWsQGhoKV1dXm3jPnj0R7OEOUeRzFoxVd5xcMsZYJRIWFgZ3d3eb4YIgoE6dOqhTp47d6QRBQLNmzcosV6FQoE2bNmXGVSoVOnXqVGbc0dER3bp1KzPu5uaGxx57DNKR/WWOwxirHvgnJmOMMcYYqzCcXDLGGGOMsQrDySVjjDHGGKswnFwyxlglpdPpkJ6ebrd7HyJCQUEBMjMzy+x3Mj8/H9nZ2WXGc3JykJeXV2Y8KysLWq3WblySJGRkZJTZryZjrPri5JIxxiohk8mETz/9FJ07d8bevXttEjidToc333wTTz31FM6cOWMTLygowMiRI9G3b1+7/V6mp6djwIABeP755+12TXTp0iX06tULY8eOtel4nYhw/PhxPPnkk5g8eTIMBkMFtJgxVlXw0+KMMVYJmc1mXL58GXFxcUhPT7eJm0wmxMfH48KFC8jJybGJG41GnDt3Drm5uXb7rSwoKEBcXBw0Go3dfitzcnJw8eJF6HQ6mM1mm3hmZibi4uIQGhpqN84Yq744uWSMsUpIrVZj2rRpePnll9G4cWObfi9dXFwwb948ZGRkoFGjRjZxd3d3rFu3DkVFRahbt65N+eHh4fj111+hUCgQGBhoE2/SpAm2bNkCNzc3ODs7W8UEQUCHDh2wY8cOBAQEwNHRsQJafPcIgHDbsaqnqrZsqlp7qjpOLhljrBISBAEBAQF2OzS3xMPDwxEeHm43LooiatWqVWb5oiiifv36ZcaVSiWaNGlSZlytViM2NrbM+P3AyUbZqtqyqWrtqer4nkvGGGOMMVZhOLlkjDHGGGMVhi+LsypBo9E86CrcN6IoQqVSPehq3Del7yVkrFyCADg4Fv+tRAQAGpUZglrxoKtyXygkgkoiCMp7eA7LYABMxntXPvvXOLlkDz3LvWfVhaurK1xcXB50Ne4btVpdrRLMgoICGI1Gmx9Mln4r8/LyEBAQAKVSaRPPycmBTqeDv78/RFG0iWdkZECSJPj6+trEJUlCWloalEolvL29bZa52WzGjRs34OTkBHd3d5u4yWRCxo0b8MEDvj/OwRFik+aAovJ9vUWg+lwudAXggnu7LVDyFVBi/D2cA/u3qst2zqowQRCgUCiqTQIiCAJEUawW7RUEoVq0s6S1a9fi119/tem3UpIkvPLKK2jVqhX27dtnM11hYSGGDh2Ktm3b4uzZszbxtLQ0PPPMM+jcuTOuX79uE79y5Qo6dOiA/v37IysryyZ+4sQJPPLIIxg9erRNv5cA8Pvvv2PQ4EEPvlsiUQSUSgiV8J+iEtThfv4T73H5UFaPs8APo8r3046x+ygnJweXL1+Go6Mj6tSp868TGb1ej3PnzkGhUKB27dr35TJ9dnY2Ll++DDc3N9SoUcPmTNTDRJIkxMfHIz8/H1FRUfD09HzQVXqgTCaT3eFmsxmiKNqNExHMZjMEQSjzjT5EVGZckiQIgiCPV9a8zWZzmXGFyF/29kiShEuXLiEvLw+RkZHw8vK66zJ0Oh3Onj0LtVqN6OhoSJKECxcuQJIk1KtXD2q1+h7UvHK4ceMGkpOT4eXlhcjIyAddHXYHOLlk/8qFCxfwxhtvwGi0f79LkyZN8Mknn1T6s0579+5F37590aRJE+zfv/+29SUi/Pnnnzh06BBat26Nxx57DACQkpKCTp06wcXFBbt370ZERMQ9r/uuXbvQp08fPP7441i3bh2cnJzu+TxLio+Px5o1a+Dn54fhw4f/p3VtMpkwbtw47NixAz///DP69u1bgTV9uPTs2RPPPPOMzfIURREzZ85Eeno6oqKibKZzdnbG4sWLkZ+fb3f78/f3x88//wxJkhASEmITj4qKwpYtW6BWq+Ht7W0Tj4mJwR9//AEPDw+7/Vo+8cQTqB8RDkXGtbto7b0XHx+PSZMmobCwEFFRUfjss8/g6uoKSZLw/fff49dffwUAvPDCC+jTp89ty7t48SLWrVuHwMBADBw48I7ufzaZTBg/fjy2bt2KpUuXYuDAgXfdjqSkJLRp0wahoaHYvXs3dDodunfvjvz8fJw5cwZBQUF3Xea9UlBQgB9//BFFRUUYPHhwmd1p3amVK1di4sSJGDBgAJYtW1ZBtWT3EieX7F+RJAl6vR4GgwFpaWk4e/YsHB0dERsbC4VCAYPBIJ/psJwNsXxZWs56WIZZ/lnOmJQet/Q4luElYwBs4haWuOXMTMnxYmNjsWbNGri5ucnD7Y1Xsj5btmzBV199hUmTJqF9+/YQBAH+/v5YunQplEol/Pz8bNpZXltFUSx3nNLLoWRbyzrLZHG7Otxt/UqeHT137hwmT56Mxo0bY8iQIVbruuR0JZdfWfOzDLd3Rq26cXd3t5uwCIIAT0/PMs/qCoIAPz8/efuzF7eXVJaMl/ejSKFQoGbNmmXGVSoVIiIiIFWy5DIvLw9//PEH8vLy4OTkhGeeeQadOnVCWloaZs2ahXPnzgEAOnXqJE9T+lhUcvs9f/48pkyZgtjYWDzzzDNWt+SU3B/L274lSSpzHyjv2FayDH9/fyxYsAAmk0k+E1rePl3a7ca1V5eSx9KSw0seMwEgPz8f06dPR3Z2Njp06GB1j2/JcSVJsjlulHds4HfYPzw4uWT/SnR0NDZu3AgA+PnnnzFixAgEBwdj3bp1cHV1xfz589GiRQt07twZGo0Gf/31F95//30YDAb89NNPSE5OhtFoRI0aNTB8+HD5DOC0adOwatUq9OvXD1lZWdi3bx9q1aqF9957DzVr1kR2djbmzJmDP//8E0VFRfD390fHjh3x0ksvQaVS4dy5c1i0aBEOHz4MSZLQrFkzvP/++8jMzMSoUaPg7u6Op556Chs2bEBsbCw6duyIqVOnIjo6Gu3atcPJkycxbtw4BAQEoEuXLli/fj0AYOTIkejduze++uorLF26FESExYsXY9u2bejRoweGDx+Ozz77DE5OTqhfvz4cHR1x9epVLFiwAHv37oXBYECjRo0watQoNG3aFESE5557DvHx8RgxYgQOHTqEuLg4tGvXDm+++SY8PT1x6dIlTJs2DfHx8dBqtfDz80PPnj0xaNCgO3ojChHh0qVLWLhwIQ4cOACj0YiGDRvi//7v/xAQEICLFy9i/vz5OHToECRJQkxMDEaPHo169eohJycHo0ePxvXr1zF8+HD8+eefuHLlCrp06YLXXnsNx44dw5tvvgmTyYSzZ8+iRYsWiIiIwJw5c/B///d/OHHiBAYNGoTjx48jIyMDc+fOhSAIWLhwIXbt2oWioiLUq1cPI0eORMuWLe/dhsrYTWq1GiaTCT/99BPatWuHrVu3IjExEQ4ODlb3kBqNRvz2229Yvnw5rly5An9/fwwaNAi9evXCkSNH8Prrr0Ov1+P48eNo164dwsLCsGzZMvzyyy/YsGEDrl+/DiJC/fr1MWrUKJuO5hMTE/Hyyy/j5MmTaN68OV5//XUEBweDiHDw4EEsWrQIZ8+ehYODAzp06IAXXnjB7huUsrOzMWPGDBQUFKB58+bQaDS4fv06fvjhB+zcuRNarRaRkZF4//337b6hKS8vDytWrMCmTZuQkZGBwMBAvPrqq3j00UeRl5eHn376CZs3b0Z2djYiIiIwePBgPPnkk1CpVPjiiy+watUq9O7dG/n5+di7dy+ioqLw7rvvIjAwEEOGDEFycjLMZjMGDx4MZ2dnzJ49GwkJCZg2bRo6dOgAFxcX/Pnnn5g8eTLat2+PdevWYc2aNbh27RoCAgLQr18/9OnTBw4ODhW/MbB7jpNL9q+IoijfV2h5alUQBKjVamg0GqSnp+P48eOIi4tDgwYNEBISAlEUcfToUfngodVqsXHjRuzYsQPbtm1DvXr1kJSUhOPHj+PKlSto1qwZsrKysGTJEhARFixYgBUrVuCDDz5AdHQ06tWrh8zMTGzatAnDhw9HQkIC+vfvj/Pnz6NJkyYIDw/HqVOnkJWVhaKiIpw8eRJFRUU4e/YsGjVqBCcnJ+Tm5uLEiRMAipMxrVaLkydP4ujRo7h8+TL8/PywZ88eHD58GF5eXvD394eXlxfS0tLg7e2NunXrIjAwEAaDAadOnYKrqyv0ej0yMzMxfPhw/PXXX2jSpAnc3NywaNEi/P7771i/fj3q1auHCxcu4NixY7h27RoaNWqEy5cv49ChQwgKCsL//vc/eVkEBwfD3d0dhw8fxh9//AGFQoFhw4bddh1dvXoV/fv3x4kTJ9CoUSPUqFEDZ86cQXp6OkwmEwYNGoQTJ06gZcuWUCgU+O6777Bz506sW7cOHh4eOHfuHM6ePYvk5GQ0aNAA58+fx5EjRxAZGSmv0wsXLsDBwQF16tRBUFAQFAoFLl68iMOHD+Py5cto1qwZ/P39kZubizfffBNbt25FgwYN4Ofnh+XLl2P79u1YvXp1uW+CYawieHp6IiYmBtu2bcPJkyexdOlSREVFwc3NDQcOHABQfFZtxYoVeOWVV+Dp6YmmTZvin3/+wYgRI5Cbm4sWLVogJCQE8fHxcHJyQnR0NIKCgiCKIvbs2YOcnBwEBQUhOzsbS5cuxb59+7B9+3ars8lz5sxBgwYNkJGRgW+//RYZGRlYsGABjh07hmeffRZZWVlo06YNMjIyMHXqVBw+fBhLly61aY/BYMA///yD/Px8GI1GZGdn44UXXsC2bdtQs2ZNNGzYEFeuXEFiYqJNcqnX6/H2229j/vz58PPzQ4sWLZCbm4vTp0+jVatWeO+99zB37lyEhoaibt262LZtG7Zu3Yrvv/8e/fv3l49NiYmJaNasmdxeSZIwc+ZMREZG4sCBAzAYDPI9pk5OTvL3wsWLF1GnTh0EBwdDFEXMmjUL77//vryODh8+jN9//x2pqamYMGHCvd0w2D3x8D4BwB4K0dHRWL16NZYuXYq2bdti+PDhGDduHFq1aoXWrVsjJCQEN27cwP79+62ma9euHdasWYOvvvoKoiji9OnTyMvLQ35+PgDgkUcewZgxYzB79mwsXLgQTk5OWLt2Lc6cOYP27dtj48aNWLZsGdasWWPVTZFCocBXX32FFStWYOLEiTbduVg4OztjwYIFWLNmDQYMGIDs7GysXLkSgwYNQteuXQEATz/9NH788Ue88MILUCisH2TYu3cv9u7di9q1a2PNmjVYu3YtnnrqKVy6dAk///yzPJ4oihg2bBhWr16Nl156CUSEQ4cOgYgQGxuLqVOnolOnTmjevDkaNmwIvV6P33///Y4uH2/cuBHHjx9HTEwMNmzYgGXLlmHDhg2oXbs2fvvtNzm2fv16/PLLL2jTpg3OnDlj86TyxIkTsWrVKgwdOhQGgwHHjh1DkyZN8L///Q9KpRJRUVH44Ycf8Nlnn8Hd3R1AcaLet29frFmzBnPnzkVKSgq2b9+OsLAwrFy5EmvWrMGzzz6LpKQkLF++nC+Hs3tOqVTiueeeg8FgwOTJk3Hw4EH0798fPj4+8jgGgwHff/89tFotmjdvjtjYWLRo0QJFRUVYtGgRIiIi8Morr8gP1cybNw/Tpk2Dk5MT3nzzTQwfPhyPPPIIWrduDV9fXyQmJuLkyZNW9ejTpw/WrFmDhQsXws3NDdu2bcOlS5ewZMkSpKSk4Nlnn8X69euxatUqhISEYMeOHTh8+PBt27dv3z7s2LEDYWFhWL9+PZYvX47NmzejXbt2NuNeunQJK1asgJOTExYvXoyVK1diw4YNGDJkCJKSkuTYwoULsXr1akyaNAl5eXlYvHgxioqK5HIeeeQRrF27FtOnT4eTk5Pc1ilTpsDX1xfOzs6YOnUqfvzxRzRo0ECeLiwsDKtWrcLKlStRr149/PjjjzAajZgxYwZWr16NL774ApIkYeHChfIxnz1c+Mwlu6caN26MkJAQCIKAgoICvPnmm9iwYQNUKhXUajXy8vIAALm5uVbTxcbGwtXVFb6+vlCpVDAYDJAkCf369cOJEyfwxx9/YOvWrSAidO3aFZ9//jmuXLkCIkLjxo0REBAAQRBsntr28/NDs2bN5EstZT2E4uvri/DwcLi6uqJu3boQRRHJyckQRVG+d0gUxTKT02vXrsFoNCIyMhJ+fn5wdHREdHQ0gOIuXywUCgVatGgBZ2dn+Pv7QxAE+eC9cOFCTJ06FUajEQ4ODtDr9XJfhnciMTFRvjwXGhpqtTyuXr0KIkJUVBR8fX0hSRLq1KmDXbt2ISUlRU4uHR0dERMTA0dHR/mmfMslREtCLQgClEolRFGUH/BSqVRo06YNXFxcIAgCrl+/DqPRiODgYISEhMDZ2Rn16tUDUPygAieXtiz9UiYnJ6NevXp2+728du0asrKyULduXbv9Xl65cgU6nQ61atWy+QFkeUJfoVAgMjLSprcBk8mEuLg4uLq6yvtwSUajEefOnYOfn5+87VZ2MTExaNmyJbZt24bAwED069cPR44ckeMGgwGpqakwm834/fffsWvXLgCAj48PJElCYWGhzXavVCqRnp6Ol156Cfv27YODgwOUSiVycnIgCIJNctSwYUM4Ozujbt26cHJyQn5+Pq5fv45r14rvVW3UqBGcnZ0RFhaGgIAAXLt2DdeuXUNYWFi5bUtKSoLJZEJERASio6OhUCjKfII8OzsbeXl58hlOjUYjb1/nz5+HVqtFYGAgatasCQcHBzRo0AAqlUo+rpVcnm5ubvD19YVarYZer4ckSVb3oSoUCiiVSqsfrE2bNkVwcDDUajV0Oh0yMzPl+Tg6OqJ+/fpwcHBATk6OzXcDezjwmUt2Tzk6OsoHmdTUVGzbtg0ajQaLFy/Gn3/+Kd+PVPpGbUvH2aVvRvfw8MCXX36JPXv2YPny5fD398cPP/yAU6dOISIiAoIg4Pjx47h+/Tr0ej0yMjKs7qdSq9U2X7L2pKWlISEhAXl5eThz5gwkSZITNMv0RUVFctJbWnBwMFQqFS5fvozU1FRkZ2fLDw6UfGhCEAT54Y2S7TSZTFi9ejVyc3MxceJE/PXXXxg5cqTVQzK3ExkZCUEQ8M8//+Dq1avy5fqioiKEh4dDFEXEx8fjxo0byM7OxtmzZyGKolUiUTKBtvf0MlCcZBQVFVl1jyMIAhwcHORpQkJCoFKpkJycjKtXryIvLw///PMPgOKzGA9zN0r3isFgwJtvvomOHTvit99+s1nvBQUFGD16NDp16oS///7bJp6bm4s+ffqgW7duiIuLsyk/KSkJPXr0wDPPPIMbN27YxE+fPo2uXbti4MCBKCgosIoREf766y907NgR//vf/+z2e1kZOTo6YsiQIfDx8UGPHj1Qo0YNq7hGo0FgYCBUKhUmTZqE/fv348CBA9iyZQs+++wzq87p9Xo99Ho9TCYTLl26hF27dsHHxwerVq3C5s2bUbNmTbsP3J04cQJarRZnzpxBQUEBHBwcEBQUhODgYKt4YmIirl+/DrVafUdPgoeFhUGpVCIhIQHnzp2DXq9Hdna23TN/Xl5ecHd3x/Xr13Hq1CnodDrk5eUhOzsbPj4+cHFxQXZ2NuLi4lBUVIRTp07JPw5LPmxWVvJq6YvXbDajqKgIRqPRajloNBr52ODq6gofHx/odDqcPHkShYWFcp08PT3lqyHs4cJnLtl94+bmBn9/f1y8eBEzZsyASqXCxYsX73h6IsKyZcswb948REREwGw249KlS3B3d4eHhwf69u2LFStWYM+ePejevTtCQ0ORm5uLuXPn3nVddTodRo0aBS8vLxw4cABeXl4YMGAAAMhneVatWoXExEQMGzYMDRs2tJq+bdu2ePTRR7Fz50707t0bbm5uOHjwIGrVqiWXUx6FQoEaNWrgwIEDWL16Nf7++29cvHjxrp6W7NGjB3788UccO3YMTz/9NCIjI5GRkYHvvvsOTz31FGJiYnD06FH07NkTCoUCR44cQcOGDdGjR487OgsVHBwMR0dHxMXFoV+/fnjkkUfwxhtv2B03JiYGXbt2xebNm/Hss8/C19cXBw8eRHh4OAYPHszJpR2iKCIgIAD+/v7w8PCwiSsUCgQGBiIwMNDuG5sUCgWCg4Ph4OBg9wEwjUaDgIAAODg42E0SLGfTg4OD7f4gc3V1lef/sKw/QRDQs2dP1KxZE6GhoTZne1UqFV566SWcPXsW06ZNw65du+T7iFu2bImOHTvKy/uff/7B888/j5YtW6Jfv37w8fFBWloaPv30U/kMqD1r167FuXPnkJiYiPz8fAwePBhRUVEYOnQoNm/ejNWrVyMpKQkZGRlISUlB9+7d0aJFizLLs2jTpg06d+6MLVu2oFevXqhfvz5u3LiByZMn46mnnrIaNyoqCoMHD8bcuXMxbNgwxMTEID8/H3379sXo0aPx3HPPYc6cORg5ciRq166NQ4cOwd3dHSNHjryjhwkdHBwQEhKCS5cu4bXXXkPdunXx4Ycf2h3Xx8cHw4cPx7vvvovXXnsNS5cuxYkTJ6BQKDBq1Ci4urredn6s8uHkkv1n3t7eaN68udWXUHBwMFq0aGF1v6O3tzfmzJmDmTNnIjc3Fz179kTLli2xY8cOBAYGyq9xbN68ufx0pLOzM2JjY+VfzG3btsXFixcRFxcHrVaLJ598EoMGDULDhg2hUCiwevVqLF68GIcPH0ZeXh5iY2Ph4+ODzMxMxMTEwNPT0+qXt4eHB2JjYxEdHW2VUEVFReHll1/GL7/8go4dO2LEiBF49NFHARTfMxUXF4dDhw4hJSUFeXl50Gg0aNasGZycnKDRaODl5YXFixdj0aJF2LNnDwwGA1588UWMHDkS9erVky9XOzg4yL/MfX190bx5c9SqVQuiKGLq1KlwdnbGmTNnULduXfTu3RuLFy+WL697enqiefPmiI6OtvvlHhoaip9//hk//PAD9u/fj9zcXDRt2lS+jLl8+XIsWLBAflr8lVdewciRIxEZGYnc3Fw0bNgQPj4+cHZ2BgAEBASgefPm8pnX6OhofPLJJ1i7di0yMjKQlpYGAKhTpw4MBoNVlzlubm6YO3cuFi9ejJ07d0Kn02HYsGEYMWIEYmJiYDabUadOHeTn51f7DtQtlEol/u///g/jx4+Hr6+vTcLv4OCAb775BkVFRXZf1+ji4oJly5bBaDTaXab+/v745ZdfIAgC3NzcbOK1atXCli1boNFobJ7YFQQBsbGx2LFjB1xcXCp1B97Ozs5o1qwZnJ2doVKp4OTkhObNm8vxWrVqoUWLFggICIAoiujfvz+8vb2xYsUK+QULTz/9tNz3av369TFlyhSsXbsW6enpSEtLQ40aNfDdd99h3rx5MJvNGDhwII4ePYpTp07J66Z27drIzMzEiBEjcPr0aRiNRvTq1QsTJ06ERqNBixYtsGbNGixevBhnzpxBYGAgBg0ahOHDh8PV1RU5OTmIjY2Fn58fVCoViAhNmjRBQUEBVCoVPDw8sGDBAixZsgQ7d+5EVlYWoqOj7faJqtFo8PHHH6Nx48ZWT4s3btwYarUaU6dORd26dbFp0yZkZ2eje/fuGDx4MDp37mx1nLacUXV2dkZMTIzcjZaLiwumTJmCr7/+GteuXZN7B/H390fz5s3lqypA8Y+osWPHIjg4WH5avG3btujbty969eol/8hq0aKFzZlmVnkJD0u/UbGxsVTy3hhWPq1Wi5kzZ2LSpEl3dBn4v7DXt1tZfcSVHtcyrPQ4pT+XHN/eMHt9aJY3TVn90u3btw9PPvkkatSogZ07d8pnjMrrS7P0vO31I2mvjPKWWenleyfL6nZ92d1pO+zVz9787C3X0uPZW/63q0vp6ZKSkhAQEHBHnVU/zIgIs2fPRnx8PGbMmPHQnA0sibT5kI7sv/2IKG7vdaMEJ1GAh7IC2+rsArFZSwg33y1ub18rXY/S292/2W/sDStvf7W3797tPnk3xxx77JVZ1rHRXqy842vpWwLKa3fptpc3v5LTSMmJoPgLdtt2P0lE+GbjZgx9ZzJ8fX0fdHXuK0EQjhJRbOnhfOaS/Wf2Dtj2hpU37t18LmtYyeF3Ok3p+ljuqSz50M6dzqOsz3ezHG5X7ztZNndTh7up3+3KudNhdxtj7L8oa1suL/5f9ps7/VwR++Td1Nue8pbN3cb+zfHM3vC7OR6xyouTS8ZKaNKkCfbu3Qu1Wm33UiFjjDHGysfJJWMlODs72zycwxhjjLE7x8kle+hZ3jtreUdtVWfp+qg6XCqy15VLVZecnIz8/HybLlgsr/O8fPky2rZtCycnJ5u45Q1Mbdq0sXnIRpIkHD16FEajES1atLB5UtpkMuHAgQNwdnZGkyZNbG4J0el02Lt3L4KDg1GnTh2bbU+r1eLE/n1oqQLEB7ldShJQquubykIiQKzau6zMsvjv2aZAAEp0f8YqF04u2UOPiJCYmGj3qciqSKvVoqioyOqVclWZpfP46mLLli34+eefMWrUKKsEzmw245VXXsGBAwfw448/okePHlbT5eXlYdSoUbh48SI2b95s8872lJQUDBkyBHl5efjrr79snryNi4tD3759ERgYiK1bt8qd5lscOnQIffv2RZMmTbBp0yab7o9+/fVXzPz0E+z66nOI9/ghwnIVFUE6cfgeZjX/DgFINJgRoVagctXs3tBKhAKJ4F+RD2uVZjTcu7LZf8LJJasSDIbqc5CRJMmqw3JWtfj4+KBWrVo2w0VRRGxsLHJycqy6+LLQaDSIiYmBg4MD/P39beKurq5o2rSp3bOiQHHH2k2bNkVgYKDc/VRJgYGBaNCgAWJjY+12PRQVFYXIyMg7beY9RICu6Paj3WdEBIPeBDIpq/wVBwCQzBKMZgmkUlSL9jJrnFwyxlgl0qNHD7Rv397u07eTJ0/Gu+++aze502g0+OqrryBJkt24u7s7fvzxRxCR3W6d/P39sXHjRgiCYPe1prVq1cKff/4pv86vtBYtWmDxD4uhOMldxjFW3XFyyRhjlUjJ9zKXZEn6ynqfvSAI5fYFeifx8jpDFwTB5v3mNtOr1OC3xDPGHr5eehljjDHGWKXFySVjjDHGGKswfFmcMcYqKXuvz7MXL+/1fg8yzhirnvjMJWOMVUJEhNOnT2PJkiXIycmxiUuShIMHD2LlypUoKCiwiZvNZuzYsQPr16+325uC0WjEpk2bsG3bNru9D+h0OqxZswZ79uyR+1YtKT8/H8uWLcPRo0erVVdRjLHb4+SSMcYqIb1ejylTpuCFF17A5s2bbRK4/Px8vPHGGxg5ciQOHjxoE8/JycHo0aPx0ksv4Z9//rEpPzExEaNHj8aLL76Ia9eu2cSPHTuGF198ES+99BK0Wq1VjIiwc+dOjBo1Cu+88w4KCwsroMWMsaqCL4szxlglpFKp0K1bNwDF77wvzdHRET179kRYWBhq1qxpE3dycsKzzz6LjIwMhIaG2sT9/PzQu3dvaDQaeHl52cQjIyPRo0cPREREwMHBwSZer1499OjRA23bti33KXLGWPXDySVjjFVCCoUCw4YNw3PPPQe1Wm1zX6NKpcKECRMgSRJUKpVN3MHBAR9++CGIyG73RW5ubvj6668BwG48ICAAc+fOhSiKUJR6444gCKhRowZ++uknKBQKm1dFMsaqN04uGWOsklIoFDaJnUVZnZ3fTfy/9ntZXpwxVn3xz03GGGOMMVZhbptcCoIwUxCEG4IgkCAIm0oMT7w5zPLvRIlYG0EQTgmCoBcE4ZggCM3uJMYYY4wxxh5ud3rmcmUZw/8CMPDmv0kAIAiCA4C1AFwBTADgD2CNIAiK8mL/ugWMMVaFlNetDxHJ/8qL3S5+u/Lvdt63m5YxVr3cNrkkolcAfFVGOAHAZiJaSUTbbg7riuKkcQ4RzQGwEEAkgA63iTHGWLV36tQpJCcn2yRqRIQdO3bgyy+/REZGhs10RIRffvkFM2fOtOk6CCju1/Knn37CwoULUVRUZBMvLCzE3LlzsXLlShiNRpt4bm4uZsyYgc2bN9vt9zI1NRWLFy+GxAkmY9Xef73ncgiAPEEQ0gRBGHlzWOTNvyk3/ybf/Bt1mxhjjFV7+/btw/r1622Gm81mTJkyBe+//z527txpE8/JycGUKVPw9ttv48SJEzbxlJQUvP/++3jttdeQlJRkE4+Pj8drr72GyZMn48aNGzbxv//+G++88w6mTp2K/Px8m/jWrVsxe/Zsu4knY6x6+S9Pi88HcAGAA4DPAMwVBGGHnfEs/WPY+zlbXgyCIIwGMBoAwsLC/kNVGWPs4dC4cWM8/vjjNsMVCgVefvll7N+/H61atbKJu7m54aWXXsLly5dRr149m3hAQADGjh0LvV6PoKAgm3h4eDgmTpwILy8v+Pr62q3XuHHj0KBBA7i4uNjE27dvj+vxF/lVkIyxf59cEtHHlv8WBKEpgIkAaqP4UjkAhNz8G3zzbwIAt3Ji9uYxD8A8AIiNjeVrLYyxKq9Vq1aIjo62SdIEQUC/fv3Qp08fu90TKRQKjB49GpIk2Y07ODjg9ddfl8ctzd3dHVOmTCkz7ufnhy+++AKiKNpNIKOiovDmm28Cxw/dUTsZY1XXbZNLQRC6AWhw82OoIAgvADgE4BMAW26WMQRAEYDTALIApAF4SRCEfAAjASQC2AVAVU6MMcaqvfI6JBdFscy4IAgQBKHceFl9ZlqUF7+TfjMVCgX4ojhj7E7OXL4B4NGb/90IxZfD3wGgADAVgBOAswDeJaJrACAIQj8AswF8A+AMgFFEZAZgLidWZe3btw+bN29GQkICnJ2d8dlnn8HHx8fuuCaTCbt27cL69ethMBjwxBNPoHv37nBwcAAR4eLFi1i6dCmuXLmCBg0a4Pnnn4e/vz9fimKMMcZYpXDb5JKIOpQR+rScaf4C0PBuY1XV0qVLsXPnTly9ehXOzs6YPHmy3fGICL/++itGjhwJb29vODo6Yvny5fj444/xyiuvICkpCQMGDEBiYiLq1KmDdevWYc+ePfjpp5/g7u5+n1vFGGOMMWaL39BzH7z33nvYvn07atasWe54BoMB3377LYxGI5YtW4aNGzfCz88P33//PTIzM7F27VqcOHEC48ePxx9//IHevXvjjz/+wJ49e7h/OcaqICKCJEn/uV/LexW31I2PP4yxkvjd4vdBcHAwCgoKbnvpOj09HQkJCfD390d4eDicnZ0RFRWFY8eOITExEYcOHYIgCIiNjYWjoyMaNWqEn376CadPn0a3bt0AAJIkQa/Xo7CwEAaDAQUFBeXew1UVEBGKioqg1Wqrxe0BBQUFKCwsREFBwYOuyn1RVFSEgoICGAyGB12Ve06v18NoNMrr9o8//sDOnTsxduxYhISEWI1rNpuxfv16nD17FuPGjYO3t7dV3NKvZVZWFl588UWbJ7x1Oh3mzp0LtVqNkSNH2rwnPDc3F3PmzEFoaCgGDhxocz9mWloaZs+ejdjYWHTr1g2iKIIKCiDp9CijAxAbhUYzIApQmqr2MQoAiIAivQkFZEY1OEyhwCyhUCIUSFX/HSlEBKPJzD+ySuDk8j/Kzc3F5s2bUVhYaBNr0qQJYmJi7jjh0ev1MBgMcHd3l2/c12g0MJlM0Ol0csfIDg4OEAQBGo0GgiBAq9WCiCAIAnJzc7F06VJkZGTgwIED5d6AX1UQEY4cOYLY2NhqkVymp6cjPz8fUVHVo3vYEydOoF69ejbJT1WUlpaGCxcu4JtvvgEA3LhxA1qtFj/++COcnZ2txiUiXL9+HTqdDvPnz4eDg4NVXJIkpKSkwGw247vvvoNKpbKKm81mJCUlQRAEzJo1y+ZHqMFgQGpqKjIzM5Genm6zbxUVFSE7Oxv79u1DfHx8cZwI0BfdaW6JS4lX4ObqCl9vrzub4CFGRDhy8iRiGzeuFseprJwcZGZloVY1OE4RgJNJ1zDMXKUfH7krVT/zuMfy8/OxcuVKZGZm2sSGDBmCZs2a3fGBxMXFBU5OTigoKIDJZILZbEZ+fj40Gg3c3d3h5+cHIkJOTg6ICHl5eSAi+Pj4yPPw8PDAuHHjoNVqMWvWLLz55ptV/sylyWTCRx99hLfeeuu2T8NWBceOHUNCQgL69OnzoKtyzxERpk+fjhdeeAEeHh4Pujr3XHZ2NhYtWoSJEydWiwRkzZo1iIqKQrNmzR50Ve45s9mMDz/8EG+9/361OE79888/OHXqFAYNGvSgq3LPERFmzpxZLU7m3CleEv9RUFAQli9fbvd0uFqthiAIyM/PR2ZmJkwmEyRJQmZmJlxdXeHh4YGDBw9i69at6N69O5o2bYpmzZphw4YN2LVrFwICAnD+/HnUqlULkZGR6NSpE5YuXYrNmzejfv362LlzJ9zd3dGiRQv5i8jSHYlKpUKtWrXK7bqkqlAoFKhTpw4UCkWVbytQ/AMiKChIXtdVGRGhZs2aUKvV1WLdajQa1KxZs8y+JKsSIkJwcDA8PT2rxbolItSpU6daHJOB4n5TQ0JCqs1xKioqqlpcXblTwsNyj0BsbCwdOXLkQVfjX3nrrbewePFiZGVlwWw2w9vbG23atMHy5csxb948vPbaa5g9ezbGjBmDEydOYPjw4UhMTIQgCPD09MTs2bPx5JNPIjc3F+PGjcPmzZvlX75jxozB//3f/0Gj0VjNk4hgNpuhUCiqxY5dXdoKQH6IorokINVp3Van9loeCCqvb86qpDqtW4CPU9WFIAhHiSjWZjgnl/fegQMHcP78eath/v7+ePzxx3HhwgUcPnwYjzzyCKKjowEUvwP477//htFoRNOmTeUzGUQErVaLQ4cOITU1Fd7e3oiIiIAkSXByckJERESZG7blcnpaWhqUSiWCg4Ot7tHS6XS4du0ajEYj/Pz84OHhUal2EiKCXq9HSkoKTCZTuXXMzMxEamqqzfDg4GC4u7sjKSnJ6t3IoigiLCzM5p62B4mIkJWVhYyMDKjVagQFBdn8gLCMl52djevXr1sN9/f3h7e3NwRBgMlkwrVr11BYWAgPDw/4+flVui/zknX09PSEr6+v3TpabgfJzMyEXq+Ho6MjgoKC5DMGaWlpSE9Pt5omICDA5mGX+8lS5xs3bkAURQQFBcHR0bHM/avkvujr6wtPT095XEmSkJqairy8PLi4uCAwMLDSXWI1m81yHd3c3BAQEGC3jkSEgoICZGRkoKioCBqNBoGBgfI95Tk5OUhJSbGaxsfHB35+fpXq2FRYWIhr167BbDbD398f7u7uduun1+uRkJAAc4n78pycnBAWFgaFQgEiQmZmJjIyMqDRaBAcHFzpzoQRETIyMpCZmQkHBwcEBQVBpVLZtNdsNiMhIQF6vd5quEajQUREBEwmExISEqzeQ+/s7Izw8PBKs24lSUJ2djYyMzNhNBrh7e1dbp/SRUVFuHbtmt3vJ8s+kZ+fL+8Tle0Y/G+VlVzadDdRWf/FxMQQu6WoqIj69etHPj4+pFQqqV27dmQ0Gu2OK0kSHTp0iNq0aUOurq7k4+NDw4cPp7S0NCIiSk9Pp5EjR5Kvry+5urpSmzZt6ODBg/ezOeWSJInS09NpxIgR5OPjQ66urtS+fXs6fPgwSZJkM/7MmTPJ0dFR/qdSqUipVNKqVavIaDRS3759SaVSyXFfX1/at2/fA2iZfZIk0Y4dO6h58+bk4uJC/v7+9L///Y9ycnLsjvvdd9+RUqkkBwcHuU0zZswgSZJIp9PRxx9/TKGhoeTs7Ez16tWjlStXktlsfgAts6+wsJCmTJlCISEhch1XrVpFJpPJZtzExETq3Lkz+fn5kaOjIwUGBtKrr75Kubm5REQ0depUUqlU8rJwcXGhOXPm3O8mySRJopMnT1LHjh3Jzc2NvL29afDgwXTt2jW742dmZtKYMWPkfbF169a0f/9+kiSJTCYTzZ8/n2rVqkXOzs4UFRVFs2bNIoPBcJ9bVTaDwUBz5syhGjVqyHWcPXu23TpmZWVRv379KDAwkJycnMjHx4eef/55Sk1NJSKi5cuX22zX7777bqXadq9du0aDBw8mb29vcnNzow4dOtCJEyfsjnv+/Hny8/MjtVott+exxx6j/Px8kiSJfv/9d2rWrJm8z48fP17erisDs9lM27Ztk+sYGBhIr7/+OuXn59uMm5mZSc2bN5fb6eDgQEqlkho1akTp6el04sQJ8vT0JI1GI4/z5JNPVqpt+fLly9S+fXtyc3MjpVJJr732mt1jEhFRWloaDRs2zOr76ciRIyRJEhmNRpo1axZFRUWRs7Mz1axZk+bNm1dmWQ8bAEfITs72wJPGO/3HyaU1nU5Hn3zyCU2dOpUcHByodevWZSaXOTk51L59e/Ly8qJ58+bRq6++Skqlkj744AMyGo30ySefkFqtpnHjxtG8efPIy8uL2rVrR9nZ2fe3UWWQJElOGl555RWaO3cueXh4UIcOHUir1dqMf/XqVdqxYwft2LGD1q5dS6GhoeTj40OnTp0io9FIzzzzDIWHh9PGjRtpx44dtHv3bruJ24OSlpZGTZs2pYCAAPrhhx9oxIgRpFarafbs2TZfrJIk0ezZs0mj0dCcOXPkdl+5coUkSaJNmzaRk5MTPfHEE7Rq1SqqW7cuhYeH04ULFx5Q66xJkkRr164lBwcH6t69O61evZpq165NoaGhFB8fbzN+XFwcPf/887Rs2TJaunQpRUZGkkajobVr1xIR0ZQpU8jFxYUWLlxIO3bsoJ07d1JSUtL9bpassLCQunbtSi4uLjRz5kyaNGkSaTQamjRpks2Xi9lspmnTppFaraYXX3yRFixYQD4+PtSmTRvKycmho0ePkq+vL8XGxtKqVauobdu25OXlRfv27bP7I+tBOHToEHl4eFCLFi1o7dq11Lp1a/L09KQDBw7YjHvjxg0aPnw4LViwgNasWUNNmzYlURTp66+/JkmSaOnSpSQIAk2fPl3eruPj4ytNW41GI02aNInUajW98cYbNGvWLHJzc6OuXbvaTbjOnTtHXl5eNGjQILk9R48eJaPRSKmpqdSwYUMKCgqipUuX0tChQ0mlUtH3339fadqbmppK9evXp5CQEFq6dCkNGjSI1Go1LVq0yKaOBoOB/v77b7mdb7/9NgmCQIMGDSKdTkfHjx8nFxcXevHFF+nPP/+kHTt20PHjxyvVD4ekpCSaOnUqvfTSS6RUKmnChAl2E0KTyUQffPABaTQaevXVV+n7778nDw8P6tixI+Xl5dH+/fvJ09OTWrduTatXr6bmzZuTj4+PnHw+7Di5rGIkSSJJkujcuXPk4uJSbnJ59OhRcnZ2pg4dOpDBYKB//vmHfH19qVWrVnTt2jVq3bo1eXt70+nTp0mv11OnTp3I0dGRjhw5cp9bZV9hYSE1b96c3NzcKC4ujrRaLXXq1Ik8PT3LraMkSbR+/XpSq9U0cOBAMhqNcnLp7u5OXbt2pcGDB9O6devIYDBUmh19165dpFQqqVevXmQymejAgQNyfYuKiqzGtSSXCoWCHnvsMXrmmWfom2++oezsbJIkicaMGUOiKNLPP/9MZrOZJk2aRAqFwu4XwoNgMplo5MiRpFAoaMOGDSRJEr322mukUCho8eLFNuMbDAbS6/XyGYGRI0eSKIo0d+5cIipOLtVqNXXq1In69OlDc+bModzc3AfW1jNnzpC3tze1atWKcnJyKCkpiXx8fKh+/fo2CYher6e2bduSq6srnTp1igoLC+mpp54iZ2dnOnjwIE2bNo1EUaTPPvuMJEmib7/9lhQKBU2ZMqVSrEui4jPHgiDQN998I2+bgiDQBx98YFNHk8lERUVF8rHsgw8+IADy2cmlS5cSAGrfvj317NmTPv/8c0pLS6s0bc3OzqbGjRtTYGAgxcfHU1FREbVo0YLc3Nzo7NmzNuNbksu6detS9+7dacKECfTPP/+QJEm0bds2UqvV1L9/fzKbzbR3715ycXGhp59+mgoLCx9A62xt2rSJHB0daejQoWQwGGjv3r2kUCjo6aefLjcp1Ol01KNHD3JwcKCtW7eSJElyctm4cWPq3r07vfnmmxQXF1dp1i3Rre/YFStWkEqlKjO5LCwspNjYWPLy8qILFy5QQUEBdejQgVxdXen48eM0depUUigUNHPmTJIkib744gsSRZG++OKLStXef6us5LJqXPSvgsxmM/R6vd1/ZrP5rp7As9y/5e/vD4VCATc3Nzg7O+PGjRvIz89HSkoK1Go1vLy85L86nc7mPr57hYhgNBrtttVgMCA3NxfZ2dlwdXWFk5MTlEolfHx8UFhYiLS0tDLL1el0WLJkCZRKJYYNGybf9+Xj44MWLVpAqVRi69atGDp0KDZs2HBf2goUt9dgMNhtr8lkQnJyMsxmM/z8/KBQKODl5QWVSoXk5GSYTCab8hwdHdGiRQt4eHjg9OnTeO211zB16lRIkiQ/GGa5x8fX1xeSJCE5Obn41+V9YDKZytyWLduZRqOBp6cngOJ7JCVJQlJSkk1ZKpVKvg8tISEB+/fvR1BQEFq1agWguDuvRx55BO7u7jh69CheffVVfPnll1b3ud1POTk5yMnJgYeHBxwcHODi4gJnZ2dkZ2cjNzfXaly9Xo/U1FQolUr4+vrKy6SgoABpaWnyurTcc2i5d/bKlSv3bV3ejqUuAQEBACDfO2vpb7MkhUIh3/edmpqKP//8Ex4eHnjsscfkfnxbtmwJHx8fXLx4Ee+88w7eeuutStOZvsFgwLVr1+Dg4ABPT095fZV1XBJFEXXq1EF0dDRycnIwa9YsPPfcc7h+/TqSkpJgNpsREBAAQRDg7u4OR0dHXL9+3ea+xQclNTUVOp3O6rik0Whw48YNFBUVlTndqVOnsHv3bjRv3hyPPPIIBEGAQqFAgwYNEBkZiYyMDEyfPh3Dhg1DRkbGfWxR+e70O1an0+HGjRtQqVTw9vaGg4MDPDw8kJ+fj/T0dFy5cgWiKFrtt4IgIDEx8d434gHirogqqd27d+P111+3ObCIooivv/4anTp1uuOyLE+wmc3FbxCQJAmSJEGlUkEURflmcsvTfZabrO9Xn11EhLfffhtbtmyxidWsWRPTp0+HKIpWr8GzJNjlPcxw5swZ7Ny5EzExMWjdurU8/qeffgonJycoFArMnTsXEyZMwNq1a+9bv5GJiYkYOnSo3b5Rx44dKz98UnJ9ASizrX379kXv3r3h7OyMo0eP4oknnsCmTZvwzjvvyOvQUpblC16pVN63G+d/+OEHfP311zYJkKurK+bNm2e1/VnqCpTdXiLC+fPnMWrUKGRmZuLrr79GvXr1AAAjR47E6NGj4ejoiN27d6NPnz745ZdfMHHixAfST6al25mS267lCenS7SvZRY1lGZRc9yXXZcm/lalvvdJ1tLS7rKdoiQjJycn43//+h5MnT+KDDz5A+/btIQgCnnzySXTu3BkuLi6Ii4tDp06d8NtvvyEjIwPBwcH3tV32WNZhyW23vGNnREQENmzYAHd3d2i1WvTv3x9//fUXDh06VOZyu5/76e2U9T1SXtdKZrMZS5cuRX5+PoYMGQI3NzcAQO3atbFp0ya4u7sjKysLPXr0wKFDh3DmzBl06NDhPrbqv7O0v/R3qCAIUCqVdtctULn223uharfuIVarVi28+eabNr/2BUFA7dq1y52WiOTX5bm5uSEyMhKOjo64evUq9Ho90tLSkJubi+bNm8PT0xO1atXCwYMHkZSUBHd3d1y/fh3u7u4IDQ29l020alOvXr3QtGlTm5i7uzsCAgIQFBSEv//+G2lpaXBxcUFycjLc3NwQEhICs9mMvLw8KBQKuLq6yk9IL1u2DAUFBRg6dKj8JLjBYIDJZJITa8sTfffzbIi3tzdeffVV6HQ6m1jDhg2h0+ng4OAgr6+kpCTodDrUrl0bKpUKRUVFKCoqgrOzM9RqNbRaLby8vKBQKODu7g6lUgmz2Qyz2Yz69etjy5YtiI+PR9u2bXHlyhWoVKrbvue+IrVq1QqOjo42w1UqFUJCQhAVFSW3U5IkXLp0CUqlEtHR0SAqftJakiT5Kdxjx45h1KhRSE9Px3fffYcePXpAoVDAbDZDp9PZLAuTyfTAzuz5+vrC19cXN27ckJ9y12q1qFWrFtzd3WEwGJCfnw9HR0eo1WrUrFkTKSkpuHLlClxdXXHt2jV4enoiODgYdevWBRHJTxwnJiZCkiTUqVOn0iQgdevWhSAIuHjxIiRJwuXLlwFA7nM3Pz8fJpMJrq6uUCgUuHz5Ml588UUcP34cn3/+OYYNGya/SSg/Px+enp7yulSpVNDpdA/sLHRpDg4OqFGjBuLi4nDt2jUolUrcuHEDXl5eCAwMlI9LSqUSLi4u0Gq1UKlU8hlbFxcX+apNrVq1oFarcfnyZZhMJly/fh1arVY+dlcGERERcHJyQmJiIgwGA65cuQKDwYAaNWrAwcHB6rhk6dkiKSkJGzZsQK1atdC1a1d5O7W8HEShUMDZ2RlOTk4gIrtXZiojvV4PrVYLJycnaDQa+TXNV69elbcDb29vBAQEoE6dOpAkSd5vLftE3bp1H3Ar7jF718or4z++59KawWCgxYsX06uvvkpqtZrCwsJo6tSptG3bNjKbzTRmzBgKDQ2lo0ePUmFhIQ0aNIicnZ1p5MiR1LlzZ3JwcKAFCxaQ2WymH374gZydnaljx470wgsvkJOTEw0YMKDS3OsjSRItWrSInJycqFOnTjRy5EhydHSk5557jvR6PcXHx1O9evWoV69eVFBQQETFTxVHRERQdHS01ZO5KSkp1L59exozZgy98cYbVLNmTXJ0dKSFCxdWmvtf8vLyqFu3buTq6kovvvgitWnThtzc3GjdunXyvXZ+fn60dOlSMplM9Pzzz1O/fv3onXfeoUcffZSUSiUNHz6cjEYjHTp0iAICAqhu3bo0fvx48vf3p5iYGLp+/fqDbqZs79695OvrSw0aNKAJEybID62kpqZSXl4ePf7449S4cWO6du0aXb16lRo1akSCINDjjz9OU6dOpY8++ogOHTpEBQUF1K9fPxo0aBC9++671KpVK1IqlfTKK6+QXq9/IG0zGAw0atQo0mg0NGTIEHrqqafIycmJZs6cSWazmX799Vfy9fWlDz/8kCRJomXLlpGrqyt16NCBRo0aRc7OztSvXz8qKiqiS5cuUXR0NIWGhtL48eMpKiqKoqKi6Ny5c5Vm271w4QJFRUVRWFgYTZw4kSIiIigqKorOnz9POp2OhgwZQjVr1qSTJ09SdnY2de7cmURRpJYtW9KUKVPoo48+sjqG9erVi9555x164oknSKlUUu/evSvNcclsNss9U3Tt2pWGDh1KDg4O9MILL5BOp6MLFy5QzZo1acCAAaTX62np0qXUrl07mjRpEg0bNoycnZ2pdu3adOnSJcrJyaEnnniC3NzcaNy4cdSqVStydXWV70OuDHJzc6lz587k7u5OL7/8MjVv3pzc3d3pt99+I0mS6KuvvqLAwEBauXIlERUft7/++msSRZHee+89q3Z899131LFjR3rrrbdo0KBB5ODgQA0bNqTk5OQH1TwbWVlZ9PXXX9Ozzz5LCoWCWrduTVOnTqUzZ87Q6tWrydfXl7788ksym800f/58cnZ2ps6dO9OIESPI0dGRnn/+edLpdHTu3DmKioqiyMhIGj9+PIWFhVF0dHSlejjtvwDfc1m1mM1mbN68GZs3b0ZoaChUKhWWLFmCv//+G0DxfXhubm7yr+TPP/8cw4cPx99//w2tVotp06Zh4MCBEEURzz77LL744gsUFRXh0KFDGD58OKZNm1ZpfjEDwMCBA/H555+joKAAhw8fxujRo/HZZ5/JZyBdXV3h7OwMQRBARDh06BA0Gg2GDh0Kf39/uRw3Nzc89thj+Oeff7Bx40YEBQVhxowZGDhw4ANsnTUXFxfMnDkT/fv3x969eyEIAr7++ms89dRTEAQBarUabm5u8hugunTpgry8PKxfvx5arRavv/46Pv74YygUCsTGxmLevHkIDQ3F9u3b0aFDB8ybN89qmTxoLVu2xNy5c+Hv749t27ahY8eOmDdvnnxvkrOzM1xdXSGKIgoKCiCKIqKionD58mUsWbIEP/zwA86ePQuVSoUuXbogLS0Na9euhdFoxFtvvYX33nvP5r3a94tSqcSHH34oX/bNyMjA1KlTMWLECIiiCJVKBXd3d7lvxz59+mDatGkwGAw4ePAghg4dii+//BIajQaRkZFYsGABYmJisH37dtSrVw8LFixA7dq1K82Zy5o1a2LRokVo3LgxtmzZggYNGmDhwoWoVasWgOJ+HS3HJZ1OB5PJJN93t3TpUvzwww/yMaxz584wGAxYv349bty4gXHjxmH69Ok271B/UERRxIgRI/DRRx8hMzMTx48fx9ixY/Hhhx9Co9HIxyUnJycAxVclwsLCsGXLFvz999/o1q0bFi1ahIiICLi5ueHbb79F3759sXv3biiVSsyaNQtPPvlkpVm3rq6umDVrFnr37o2dO3fCyckJs2fPRpcuXQAU92FpOS4BgFarxYEDB9CkSRMMGDDAqh3NmjWDn58fNm/ejJMnT6JPnz5YtGgRgoKCHkjb7NFqtVi1ahWOHj2KiIgI3LhxA0uWLEFiYiJUKhXc3Nzk9Tx48GB8+umn0Gq1OHLkCEaNGoXPPvsMarUatWvXxoIFC1C/fn1s374dTZs2xYIFCxAZGVlp1u29wJ2oP6To5kMhJTuhBSDf42E0GiFJkvzaPLp5v53JZJJfD1nyPhlJkmA0GuX7fCrbmwYsv4bs1VGSJBgMBjnxAoqTb6PRKF+GsrSFbt4PY7lUalkWle0VZeWtL5PJZNU2unk5ybItWMYt2WbL9mC5d68ytRUou46W7ZyIoNForD6XpFKpoFQq5XVb8p6nB/2GEMs2ZzQabbY3s9kMg8EApVIpJ8Dl7YuWdW02m+XktDKvy5J1LDnc8sNIr9fbrEvLMaz0dl0Zj0uA9foque9ZjkuWZQBAXncAbPbF2x2jKwN7dSzZUbjluGRZf5Z9tfTrW0tux4DtsqgMLOuv9PZp+Y652/3W3j5RFfAbehhjjDHGWIUpK7msXD+LGGOMMcbYQ42TS8YYY4wxVmE4uWSMMcYYYxWG+7lkjFVJZrMZ69evt3lbilqthq+vLxo1aoTQ0FCrTo5///13uR86C1EU4e7ujnr16qFOnTrywyjArQfrzp07h7NnzyI/Px9qtRo+Pj6IiIhAZGSk3IvBvXD69Gns3bu3zH48XVxcMHDgQKhUKly/fh3r168vsywHBwf06NEDPj4+yMnJwS+//ILCwkIAkB+Wc3V1RXBwMOrUqQMPD49yX2LAGKvG7PVPVBn/cT+XjLG7odfrqXnz5gTA6p8gCKTRaCgkJIS++OIL0ul0RFT8DuTevXvbjA+AVCoVeXt707BhwyglJUV+7/CNGzdo5MiR5OXlRUqlkgCQKIrk4OBAnp6eNGbMmHval93MmTNJFEW7dQZAoaGh8jvM9+zZU+Z4AMjLy4tOnjxJRERxcXEUEBBgs9xUKhW5urpSdHQ0ffLJJ5SZmVkl+upjjP07KKOfSz5zyRir8hQKBVq1agU/Pz8kJCTg1KlTSE5OxkcffYTWrVujTZs2NtPExsYiIiIC+fn5+Pvvv5GZmSm/q37OnDlQKpWYPn06Fi9eDCJCVFQU6tWrJ789Jzk5Genp6fetjcHBwfI71i18fHzsvmbO09MT7du3t+r/09XVFe7u7jbjajQadO7cGaIoIiEhAZcuXcKFCxcwefJknDlzBrNmzXogr9ZkjFVenFwyxqo8tVqN119/HU8++STy8/PRq1cv7Nu3T+702F5yOWbMGAwZMgRmsxmrVq3Ciy++CJ1Ohy1btiA5ORkeHh7YunUrJElC7dq18euvvyI8PBxA8evhzp49i9TUVABAXl4epk2bhoyMDAQGBuL111+XO9cuKirC9OnTkZKSAg8PD0yaNAmurq6Ii4vDH3/8gYSEBBiNRnh7eyM6OhotW7ZERESETR+ILVq0wLJly2zaYa8D+aioKMybN88mKbSXiFo6+A4KCoJOp8Ovv/6KN954A6mpqVi1ahWaNm2KCRMmVLo+GRljDw4nl4yxakGpVEKj0UCtViM8PBz79u0DgDLvG1QoFFCpVFCr1Wjfvj1cXFyg0+lQUFCA9PR0aDQaFBQUAChOJjMyMhAcHCy/b7hly5ZyWY6Ojrhw4QJWr14Nd3d3dO3aFc2bNwcAnD9/HtOnT0dOTg569uwJtVqN7du3Y/To0bh27ZrcibggCBBFEd26dcOKFSvk5NRCFEW5g+fbsXTkfKfjq1QqaDQaaDQaDBo0CDdu3MCbb74Jo9GIJUuWYMSIEfD09LyjshhjVR8nl4yxKo+IkJmZiaSkJFy9ehWWFzJ4enqidevWt532woUL0Ol0AG695s7d3R0BAQG4dOkSrly5gh49eqBevXpo2LAh2rVrhzZt2iAkJER+U1Dfvn2xceNG5OXlYevWrYiJiYEgCNi2bRtycnKgUCjQu3dviKKIhQsXIjk5Gc7OzujZsycCAwORmpqKM2fOlPkqy/379+Ppp5+2Gta1a1eMGTPGJoGOi4uTH/SxqFu3Lj777LPbnoEUBAFPPPEEPv/8c6SnpyMxMRFJSUmcXDLGZJxcMsaqPL1ej0mTJsHR0RFZWVnIzc1FVFQU3n//fTRq1MjuNBs3bkRSUhJycnKwceNGaLVaALfuxdRoNHjllVdw5swZ5OTkIDMzE3v27MGePXvw/fffo0aNGnj33Xfx3HPPQaFQoEOHDggLC8PFixexefNmTJgwAQCwefNmAEBYWBgee+wxSJKE7OxsAECNGjXw+eefIzAwEIIgQKvVIiMjAxqNxqa+169fx6ZNm6yGhYSE2H2SPDc3F9u2bbMalpWVdUfLUhAEeHl5wcXFBenp6TCZTMjMzLyjaRlj1QMnl4yxKo+IkJaWJr/PGwDc3d3RpEmTMi+L//LLL/jll1/kzwqFAi1atMAnn3wCjUYDQRDQu3dv+Pr6Yt68eTh8+DCSkpLkdwhfvHgRr7/+Oho3bowmTZrA29sb3bp1w9dff41z587h1KlTUKvVOHnyJACgS5cuCAwMBFB8/+SuXbtw5swZPPbYYwgJCUHdunXRtm1bdOnSxe7ZxcaNG2P48OFWwxo1amR33LCwMLz00ktwdHSUh1kS2Duh1+thNBoBFCeb9pJdxlj1xcklY6zK02g0mDFjBho2bIg5c+bg559/xvHjx/HKK69g/fr18Pb2tpmmTZs2qFmzJkRRhLe3N5o3b44OHTrA19dXTsKUSiU6dOiAtm3bIi0tDVeuXMG2bdvw/fffIy0tDRkZGThw4ACaNGkCURTRq1cvLFiwQL40rlAokJ+fD41Gg379+skP1EyYMAGSJGHz5s1ITEzElStXsHv3bsyfPx8dO3bEsmXL4OPjY1XfmjVr4tVXX72j5eHv748xY8b8q0vZRITz58/LZytdXV0RFhZ21+UwxqouTi4ZY1WeKIoIDw9Hu3btUKNGDZw5cwanT5/GgQMHsHbtWowaNcpmmpEjR2LYsGFlns0zGo1ITU2Fv78/VCoVgoODERwcjJYtW+LatWtYsGABgOKzfEDxGb6mTZuiYcOGOHDgAFauXCmfOWzUqBFiYmIAFCdvXl5e+Pjjj/H6668jMzMTCQkJmD17NjZv3ozdu3fj8OHD6Nq1671YVOUiIqSmpuKrr75CUVERAKBjx47w9fW973VhjFVenFwyxqqVwMBAjBo1CuPHj4fJZMKCBQvQp08fuLi43FU5BQUFGD58OEJCQtC9e3c0atQIrq6uiI+Px9GjRwEUn9mMjo6Wp3FxcUGvXr1w6NAhXLx4UU5ce/bsKXcLZKmTUqlEbGwsAgICEB0dLSdwkiTJ93/eDwaDAbt374abmxvi4+OxcuVK+YGoyMhITJgw4Y6fOmeMVQ+cXDLGqhVBENCnTx/MmTMH58+fx8mTJ7Flyxb07dv3rsohIqSnp+PPP//EypUrodFooFAoYDAYUFBQAEEQ0LFjRzzyyCPyNKIoonv37nKfl5azlE8//bTVKyX37NmD1atXw9HREUql0iqhDA4ORtOmTStugdxGbm4uXnjhBQDFiaYkSVAqlWjQoAGmTZuGZs2a3bPXWzLGHk6cXDLGqiRBEBAeHo68vDw4ODhYnZkMDAzEmDFj8P333wMAdu3ahaeffhpBQUHymUZ7b6spydnZGRMnTsSGDRtw5swZ5Ofnw2g0wsXFBSEhIXjiiScwceJEm47KIyMj8cwzz+Cvv/4CALRs2RJ16tSR4wqFAp07d8bVq1eRlJQEnU4HIkJgYCDq16+PN954A1FRUQCKu1KKjo6GJEkICgoqt76Ojo5y28LDw8t9L7harUbNmjXlZWB5t7inpyciIyPRtm1bdO3a9a4eAmKMVR+CvW4qKqPY2FiyXIphjLHbISKYTCa5Kx6lUik/OU1EkCRJfnLc0hel2WyWOy1XKBTlJmCWcsxmMwoKCpCRkYGioiKoVCr4+/vD1dUVoijaJF+WaSzzEUXR6s04lnfzmkwmZGVlISsrC5IkwcPDA35+flCpVHKZZrNZbkPpckqTJAkmk8mqvWUlhkQkPw1ekqUjd3vtYoxVP4IgHCWi2NLD+cwlY6xKEgShzA7HBUGwmzyWl5yVVY5SqYS7u/ttz3SWnqa8uOVMYUBAAAICAsoc904SYIu7eYOPZf6MMfZv8MtgGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYhVE+6Aqwe4OIHnQVGGOMsWpFEIQHXYVKgZPLKspoNGLWN7ORnZFz5xMJZX64D9NVdBl3MHJFHAPusowyU37hXxT2H+tyu7Iq7OfJvTrWlij3P9X1Xn4X3IOy6T+Va2dJ3UFZ/3p2/3LC4snurq53Pqtb5dpM86/Lp7uqw638o+wt93ZlCbfZ6u0flstpe5nT287ndsvizsomewPLnbbM7eKO6nUnZd8uVvZYKrUHRr7wMpycnG5Ti+qBk8sqymAwYN33v0Jx2QUQxOKDmSDcPKoJpT5b/hsAiocJpT5bx4vHF1BieMlyAEAUrT/bxEsPF2+WU2IeKH8akoeXPR+rcazixf8nf1ELdqYRrKehUmWQVV1xMyETSpRX4rO98qyGFQ+nUuNafy4vXqotlljpz6XnX9a8rNpk77NQZrz86ey1y844QqmvkLLKvYt6/6t6lGAzrr2ybYZRmdOWVT+AbNtvU2cqte3Yn29509zaxch2elgPF0qNJ9ymLEH+a1sXQbiViFnKtd196WacrKeRDwXWw0WULLNUrMRnoeTfEnUTb/cXZZRR4q94c7ncGk4l5lW6XEn+LJQo3zJcuLkM5TLl8orHlT/DMt6tuAC6WW6JuECAPNx6fpbPt8osVdbNcorHtfw3yTFY6imUqMfN8uRp7Q4v+c+6zJJ1EkuNU7w+StS7xHqwnrbkoZjkrwDx5nDFzQ3SMlwhWD4LN/9X/N/WMVEep7ism/9FAhauUKOgYDgnlzdxclmFiRChEjQQbiZuws3E0rLXlf5snWhaEkzL0bxEsiiPWyIJLTWtTeJa+q/NNDfLt0k67U9DNsNtp7ndOFQ6VnoalBp+czzbpNIyvxKfxfKTMNsE1lJ26fmWPY11UiVYlS/X0964ZZVVctoy53NnyWW5SfKdTGMZB+WMY+dzWe25k3aV255S9biT5XbzW+cu61hGMorS09hJCEuVYdtO22mEUuPalFGiHUKpmFBmWbfipZPC0tMKcrlUalenMhNLe4nnrd99tjHLZ8t8REv5sMy37ITR6u/NaVHqb+kk8lbMOkmzxEsmnmKp8kuWJcrtsi6rZNJVsg4lE0wRknWd7M335vi285GsklpLuaLVtNLN+UqQk+Ayk0vbuM18bMq3DCueT8kEs3gc4Va5VsOLPytubg+3ls2t3dxyLqJ4mFBqePFnBYQSMaFEzHq44ub3lkACHB3ASuAHehhjjDHGWIXh5JIxxqoAfoSPMVZZcHLJGGNVgHD7URhj7L7g5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkkjHGGGOMVRhOLhljjDHGWIXh5JIxxhhjjFUYTi4ZY4wxxliF4eSSMcYYY4xVGE4uGWOMMcZYheHkklUT9KArwBhjjFULygddAXbvEREEQQARAYIAWD6DAAiA/LeY5b+oeGJ5mtJ/yyyjrGlKzadUJcucj/0yCCTP92at7cyHSvy/vfkULxPLJHaGy0uEbpYi3ByXQJZpStRBnt/NmHX5uDXNzeFktUhKzse6ypb52MSs2l6iPKv52Zl/mfMpMa7NfHCbdpVY9Lct4zbTUIlp7I1TznzKak+505Y3joXN+ipjudnM1/66KbuOZF0WYDVPKlWuzS5YqgwQQEKJiYXi8K06kVWdbi34W+UXb7L2P5cuy2rPI0CQ9xXLuFRqtyW5vPJ3fbJun2U+dPNoYNP2EuPAej6W4WUfYsj6L6iMupaYr51xLOtJPmLZP5TJdSxZZ5LbRVZlkmCpVok6gKw+kyBXx/4+ZAlazcdSlnVdUaoM23FLrH/LbOy1q6z5lFVGie1D3lxK18mi5HoqvaxLzYdKzNAyvHjjtC60OCZY1c1q15fbIdzaT5mMk8sqzKDSId/hGgQIN/d2yH8F+fPN/7PsNaXGs/lrrww54SrxLS9ZdtaS8ykxL8Fq4M0y7dSj5MSlpqMSbTCZTFCqlNbjWpVnr1zrMkq3r+Ts5YPrzTKtPttMX2KaknVAyelKN+/WeGSn6ZZ6EQCJJBARFAqFbXkl5yWUFRNuxW3qUaoOdsYp3a4yyyk1vHS7rL8cyqgHbq5bpaLEtlhGOXc43G6dyqtrGdPaa6ftsiB5HLvtLV1nIpjNJiiVSvt1sDON/f8mq+G3tpNbw4Uy2n7rM9kZZpm2jJicXKLEJkI29bR8NJtNEEUBoijeKkqA5Sv9Zh3JZjrL/C3lW+/eVGrXpBKbDlnVy155gmB/uL2yrP67xDSC1bxuLSuzyQiVUimXK6JE+YKlHLLa1IunvzVMvPnf8jJC6fndKqdkmwCCWLKduFWGWKKuJcuFcKuOYqlYyTqIdupCJAGSBIVSUSpectpby028+dlyOVW4mVgLVuWWWk427cCtMqwO/yXnY/3Xep0LN8sTbs0PgCiUjJaM3xpyJs4B7BZOLquwx55ph6FDh5ZIAqsms9mMOXPmYNy4cfKXVFV25swZJCcn44knnnjQVbnniAiLFi1Cv3794Obm9qCrc8/l5eVhzZo1GD58eJXfbwFg69atCAsLQ7169R50Ve45SZIwe/ZsjB07FgqF4kFX5567ePEizp8/j6effvpBV+WeIyIkZCx/0NWoVDi5rMJ8fHxQu3btKp9wmUwmBAYGonbt2tXioF1YWAhRFFG7du0qn4BIkoSgoCDUqlULHh4eD7o691xOTg6CgoKqxX5LRDh79iwiIyMRHR39oKtzz5nNZvk4pVRW/a9ek8mEgoKCanGcIiL4+/tX+XbeDYEekpsFYmNj6ciRIw+6Gg8Ns9mM5ORkhIWFVfkNnoiQmJiIiIiIKt9WAMjPz0dRURF8fX2rfHuJCElJSQgMDIRKpXrQ1bnnjEYjrl+/jtDQ0GqxbtPT0+Ho6AhXV9cHXZ17rrodp7RaLQoKCuDn51fl20tESE5Ohr+/P9Rq9YOuzn0lCMJRIoq1Gc7JJWOMMcYYu1tlJZdV/9x8FUNEOHPmDP7880+cOHECBQUF8Pf3x/Tp02/7i0mSJFy4cAGrV6/GxYsX4ePjg6effhrt2rWTzwpZfoGtWbMGJ0+ehJOTE7p06YInnngCTk5O96OJVogI+fn52LhxI/766y+YTCa0bNkSvXv3ho+PT5m/iH/99VcsXboU9n48NWzYEG+88QYEQcCMGTNw/Phxq7ggCBgyZAi6det2T9pUHiJCXFwcVq9ejQsXLsDb2xvdunXDo48+Wu76PXjwIGbOnAmTyWQ1PDIyEpMnT4azszOICDk5Odi4cSP27NkDIkKrVq3wzDPPwNvb+143zS69Xo8///wTW7ZsQW5uLurVq4d+/fohKiqqzHVrMBiwb98+7N27F5cvX4bRaERUVBR69uyJJk2aFD/oRISVK1di/fr1NtM/+uijGDt27D09myJJEk6fPo3Vq1fjypUrCAgIQK9evdCyZctyL4nq9Xrs3LkTv/32G3Jzc1G3bl2b5WE2m3Ho0CH88ssvuH79OiIiItCnTx80atTogV1KLygowNatW/HHH3+gqKgITZo0Qd++fREcHGx3ORMRLl++jN9//x3//PMPsrKy4O7ujg4dOqBr165wdXWFIAi4cuUKPvjgA2i1WqvpPTw88MEHHyAwMPB+NdGq7levXsXq1atx+vRpuLi44Mknn0SXLl2g0WjK3K6mT5+OQ4cO2Qx/9tln0adPH7lHjwsXLmDNmjWIi4uDl5eXfIx+UGfEjEYjDhw4gA0bNiA9PR01atRA3759Ua9ePbttlSQJixYtwvbt2+2W99hjj2H06NEoLCzElClTkJSUZBVXKpWYOHEiYmNt8pV7rrCwENu3b8fBgweRmJgIIsLgwYPRo0ePcqcjIly6dAmrV6/GuXPn4OHhgW7duqFDhw7QaDTyOKmpqVi7di2OHj0KtVqNjh07olu3bnBxcbkfzbu/iOih+BcTE0OMyGw20/PPP0+CIJBSqSQAFBUVRYWFheVOJ0kS7du3j6KiokgURfLy8iKVSkVubm40e/ZsMplMJEkSxcfHU/PmzUkQBPLw8CAHBwdycHCgN954g3Q63X1q5S15eXn0/PPPk1KpJGdnZ3J1dSWFQkHdunWjtLQ0kiTJ7nTfffcdhYSEUHBwMAUHB1NgYKC8vLp160aFhYVUUFBAXbt2JUEQyMfHRx43NDSU5s+ff59bWryODh48SLVq1bJaR66urvT111+T0Wgsc9o1a9aQWq0mJycnCgoKktvyxBNPUG5uLkmSRDk5OTRgwABSKBTk4uIiL8uePXtSRkbGfWxpMYPBQB999BE5OTmRWq0mT09PEgSB6tevTydPnixz3SYkJFBYWBgplUry8fEhBwcHAkABAQG0adMmkiSJJEmid955hwCQu7u7vDyCg4Pp1VdfLbPsiiBJEm3dupWCg4NJoVCQt7c3KZVK8vLyoqVLl5LZbLY7ndFopE8++YScnZ2tlkeDBg3o1KlTJEkSmc1mWrZsGfn4+JBSqSRvb28SRZGCg4Np27Zt97RdZSkqKqLx48eTRqMhR0dH8vDwIEEQqFWrVnT58mW7dZIkiV588UUSBIHc3NzktqpUKnrhhRfk49nJkyfl/SAwMFBeh02aNKFLly7d76aSJEl0/vx5iomJIVEUycPDgzQaDTk4ONB7771HBoOhzGn79OlDAMjb29tqe5wxY4a8zR48eJBq1qxJgiBYHaNnzpxJJpPpPra0mNlsprlz55KHhwepVCry8vIiURQpKiqK9uzZY3fdmkwmevvtt63a6O/vTwqFggDQuHHjyGQyUXp6OjVo0IBEUSQ/Pz953IiICNq+fft9bysR0ZkzZ8jFxYUUCgWJokiiKNIXX3xR7jSSJNHx48epXr16JIoieXp6ysfizz77jIxGI0mSRElJSdShQwcSRZHc3d3J0dGRVCoVvfTSS1RQUHCfWljxABwhOznbA08a7/QfJ5fFJEmiNWvW0IoVK+iNN96Qd/TbJZf5+fnUtWtXAkDdu3enkydP0ocffkhKpZJCQkIoLi6OjEYjjRs3jgBQkyZN6NChQ7R48WJydnYmNzc32rFjx33/8lq6dKn8Jbp+/XrauXMnRUREkEKhoGnTppX5RZ2dnU2XLl2S/23btk3+Ip49ezZJkiQnl46OjrR8+XK6ePEixcfH06VLlygnJ+e+tpOIqKCggHr06EEA6Mknn6STJ0/Sp59+Kn+xnjt3rsxpLcll79696dy5cxQfH0/x8fGUlJQk/3BYtGgRqVQq8vf3p99++422b99OISEhJIoiff311/d93R49epR8fHxIo9HQ119/TcePH6f27dsTABo0aFCZP2auXr1KI0eOpPXr19PZs2dp48aNFBISQgDoiSeekNv7zjvvkCiK9O6778rrNj4+nm7cuHFP25qVlUWtWrUiAPTcc8/RP//8Q6+99hqJoki1atWilJQUm2ksX1C+vr6kVqvpq6++oqNHj9Kjjz5KAGjAgAFkMBjo2rVrVKdOHRJFkSZMmECnTp2i5557jgBQq1atKCsr6561yx5Jkmj79u3yj5UlS5bQwYMHqWHDhgSAxo8fbzcpkiSJPvnkE/r000/p2LFjdOLECRowYID8o/bQoUNEdCu5bNSoER07dkxej4mJieUmcveKyWSiUaNGEQBq2bIlHT58mObOnUsajYbc3Nxo//79ZU5rSS4XLFhgtT1mZWXJx6Pu3bvL+/+JEyfo448/JpVKRcHBwXT+/Pn72NJily5dovDwcBJFkd577z06efIk9e7dmwBQ586d7X7vSJJE6enpVsffpUuXyicqNm/eLI/ToEED8vX1pd9//93q+KvVau97W4mIUlNT6ZtvvqHNmzfLP/Jvl1zq9Xrq378/AaCOHTvS8ePHacaMGfL31okTJ8hkMtF7771HoihSnTp1aN++fbRy5Upyc3MjjUZDGzZsuE8trHicXFZBs2fPvuPk8vz58+Tu7k4AaOXKlUREFB8fT/7+/qRUKmnhwoV048YNqlu3LgGg999/n4iKk9J69eoRAHr77bfv669nk8lEzz77LAGgTp06UVFRERkMBho+fDgBoHbt2pFer79tOZIk0WeffUYAqEaNGnTlyhUiIjm5VKlUNHjwYBo7dix9+umndPTo0QdyluDSpUvk5eVFAOinn34iSZIoISGBgoKCSKFQ0HfffVdmUmRJLps0aULjxo2jN954g1avXi2ftTQYDPKXW48ePUin05HZbKa+ffsSAOrSpcttt6GKJEkSTZs2jQRBoPDwcDnhmzVrFgGgwMBASkpKsjut0WikoqIi+bNOp5MP7s2aNSO9Xi8nl4Ig0OOPP07jxo2j999/n/788085fq8cOnSInJycSKVSyWcTjxw5Qp6enqTRaGjdunV2l8dXX31FACgkJIRSU1OJiOibb74hQRAoNDSUEhIS6NdffyWNRkOurq509OhRIiL6/fffSRRFcnBwkJOy+8VkMtHrr79OAKhRo0ZyUvDWW28RAGrQoAGlp6fbTGdJpkquh82bN8sJyLZt24joVnIZEhJCY8eOpfHjx9PChQvp+vXrD+QsbVpaGtWoUYMEQaCPP/6YJEmi1NRU+bg5efLkMqe17H/9+/encePG0dSpU+nAgQPyFYm4uDh5/1+yZAkREV25coX8/PxIoVDQ/Pnz73ubly9fToIgkLu7u5zcrly5klQqFXl7e9PJkydvW4bJZKLRo0cTAGrTpg1lZ2cTEcnJpaurK40cOZJefvll+uqrr+j8+fNlnjS4X7RaLTVq1OiOksurV69ScHCw1YmLK1euUHh4OAmCQF9++SVptVpq1qyZ1Q+ugoICatmyJQGgF1544YG3+d8qK7nkey4rCSKCJEllxgVBkP/9G0lJSSgsLIRCoZDvU/L19YWjoyNMJhMSEhKQl5eHlJQUAEBoaCgAQKVSISAgAGfPnkV8fDzMZnOFdPcjScUdgZdFEAQUFhbK9+P4+fnJ964EBQUBAFJSUlBQUHDbe5EyMjKwYsUKAECfPn0QHBxsNR9BELBu3TqYzWYYDAZ88803mDlzJvr06VMh97Ddbt0CgCiKSE5OhlartVpHPj4+cHJygtlsRkJCAoiozG1AqVTi/PnzOH/+PAwGA5RKJfr27Ys5c+ZAo9Hg4sWLAIqXn0KhgCiKCAkJAQBcu3YN+fn5cHR0vOftFQQBkiTh8uXLICK4ubnB3d0dwK3tLisrC+np6XL9SrfTct8iESEzMxMXLlwAADzyyCNWT5UrlUrs2bMHu3fvltftG2+8gbfeeuuedQdjuQ/UyckJ3t7eEARBXo85OTm4fPmyzTRExffaAsX7pYNDcYfMlqfG09PTkZmZicTEROj1ejg7OyMgIAAAEBAQAJVKBZ1Oh8TERLRo0eKetMsek8mE+Ph4ALB6ot+yHlNSUqDVauHj42M1nSAIVvdwm0wmHDt2DEajESEhIahVq5YcUygUyMjIwOLFi2EwGAAAbdq0wZIlS+57bxipqanIzc2V91FLOyz3LJ8/f77MaQVBgFqtxsaNGyFJEvR6PWbOnInPP/8cQ4cOlcsGbi0/T09PODs7Iy0tTd5f7md74+LiQETw9PSUn+gPCgqCUqlEXl4erl+/jkaNGpVbxuXLl7Fp0yaIoojnnntO3teB4uOewWDA8uXLYTKZYDKZMHv2bCxatAht27Z9KJ4ytxy3Ld+VgiDA1dUVnp6euHLlCi5cuCAvq/9v796jo6rvfo9/fjOTZHIFAglCuIRbErAQuYmtd6mKy3s9cljUWq+teMTlUxWsN/Rp8dKlPaW21kWrj63PUrTyrGN9ulyr1dqKHlk0xULlEq4Jt0gIBEnIhcnM9/yRmV1AAhz8hQH6fq01azLZk8ne39++fGbv+f1GkkpKShQKhZSdnR2sN7W1tWpra0tLv4buQrg8QWzcuFEvvPBCl9MLCws1Y8aMAzbM/x9tbW0yM4VCoSAchsPhYONta2tTR0eHYrGYJB1wgE793NbWdkz/+2BmpjfeeENLly7t8jlnn322zj333OBgEolEgg+8p0JBKgwe6X/96U9/0ooVK9S7d29Nnz49WP5IJKJp06Zp5syZGjJkiNavX6/Zs2fr008/1ZNPPqnJkyersLDwSy9vc3OznnnmGbW2th5yemZmpm699dagvqk2cs4Fyy39sw0PZdiwYXruuec0fvx4mXUOPP7888/rjTfe0DXXXKMrr7wyeP1Dte2+ffsUj8e/9LKamRYvXnzIjjQpQ4YM0be+9a1gfvYPeft3LDvS+mbWOZTNrFmz9I9//ENjx47VPffcE0w/55xzNGbMGI0ePVqNjY164okn9M477+i5557Ttddeq9NPP/1LLGnXutrWQqGQzEzt7e1d/p2kA9o8VZtYLKaOjo7gOc65oFb7/5+u1rHulFqe/dsx9YbvaNarWCym3/72t/rJT36i7OxszZ49W4MGDZLUud97/PHHNW7cOOXn5+v3v/+9Hn/8cS1atEgvv/yyHn300W5aqq7nNR6Pf6H+qWU/3Dp79dVX68Ybb1RZWZk2b96sRx55RIsXL9ZTTz2lyy+//IDtO/XaqX1A6rUP94a8O6TWp/2PFal56+joOKr975tvvqm6ujoNHz5cV1xxRfA60WhUd955p8rKytS/f38tXbpUs2bN0rp16/Tss89qwoQJXt7sdrfUOr5/W+2/Tba1tWnfvn3BG+7929b3/vdEQrg8QWzfvr3L3s2SNGjQIH37298+5nBZWFioUCikWCwW7ABbW1uD3sW9evVSNBpVfn6+Wltb1dTUJKlz55DqqVlYWOjtneSHH36ohQsXdjk9IyNDX//614NedHv37g02zr1790rq3Dnl5uYe9v+0tLTo5ZdfVkdHhy6++GKNHDnygP9xww03SOrc0MvKyrR27Vrdc889Wrt2rXbv3u0lXLa2tur1118PzkocLCcnR1dccYV69eqlUCik9vZ2tba2ysy+0EZd1b+yslKVlZXB9O9973t66623tGnTJi1dulRXXXVVsCzNzc3BpYtUO+fn5wdnhr+sNWvW6JVXXuly+qRJkzR9+nT16tVLUmd9UjvW1PxEIpHDrutmpq1bt+ruu+/W22+/rbFjx2r+/PkaPnx48CZkypQpkhQ8vu+++/THP/5R9fX1qqmp6bK365eVasdDbWvhcDhY7oOlzmLsv66ntr3c3Fzl5OQE60AikdDevXtVVFSk9vZ2xWIxOeeOe69/51ywPPv36N5/verqyoKZKRaL6aWXXtKDDz6oeDyuH/zgB7rllluCA3NJSYnuuOOOoJ0GDBigt99+W4sWLVJVVdVxP5OXl5enrKws7dmzJ1jeWCwWhLDD1X/69OnBlZKysjJt3bpVVVVVqq+vV21trXr27KlwOKxEIhHUb/83FD73v0crtTypkw/SP/cf0Wj0iL2cd+zYoQULFsjMdN111wVXnaTOdfo73/lOsEwjRozQRx99pJ///OdasWKFWlpaTopwWVBQoIyMDO3bt08tLS2SOteJ1Juu3r17Ky8vL9gO9t//po5lBQUFp9z4mITLE8TEiRO1atWqLqeHQqEjBqmU1KXCPXv2KDc3V8XFxRo0aJD69eun2tpaLVu2TBdddJHWrVunpqYmRaNRjRkzRoWFhSorK1N9fb2WL1+ueDyunTt3atOmTQqFQho7dqy3S4k/+tGPNHfu3C6nZ2ZmKhqNqrKyUn/5y19UW1urxsZGZWdnB3WqrKxUNBpVR0eHtm3bpo6ODhUXFx+ww/vkk0/04YcfKhqN6uabbz5gA25qatKOHTs0ePBghcNhxePx4NJFVlaWt0G7+/TpoyVLlnT5xiF1aa2hoUH9+/fXhg0btHz5ck2ZMkXr16/X559/rqysrCA87tmzRw0NDcrIyFD//v3lnNP69evVt2/f4NJVY2NjsOPKy8tTJBLR+PHjtXjxYq1evVptbW0Kh8PBZbyRI0d6+3rFadOm6ZprrulyejgcVm5uriorK5WRkaHGxkbV1taqvLxcf//73yVJQ4cOVXFxsRKJhLZv367W1lb17NkzCDLr1q3TnXfeqffff1/nnnuufvGLX6i8vDw4UMViMW3cuFGDBw9WVlaWzEx1dXXBme/uPGiNGjVKBQUF2rlzp9asWaNJkyYF7Zidna0xY8Z8YZ3Nzc3VuHHjFIlE9Nlnn+mzzz5TQUGBli9frkQiEWy/I0eOVH5+vtra2rRq1SoNHjxYy5cvV0dHh3r37q3hw4d323IdSiQS0dixY7VgwQJt2rRJu3btUlFRkZYtWyZJqqioUM+ePYNtLRKJBJcF29vbNW/ePM2dO1dZWVl65plndMMNNwTbqJmptrZWeXl5Qchpbm7W7t27JSktw7eUlJRowIAB+uyzz7Rq1Sp1dHSooaFBmzdvlnNOkyZNklnnkDOtra3q0aOHCgsL1dLSom3btqm0tFSRSETxeFzbt29XIpFQOBxWdna2CgsLNXDgQG3YsEHLli3TJZdccsA+evTo0cc9XI4dO1aZmZnauXOnNm/erJKSEq1YsSL4+MLQoUPV3t4ebFv9+/c/YOid9957TytXrlRRUZGmT59+wMeMduzYoX379qlfv37B5fH6+npJUnZ29gn7bWs7d+7U559/rmg0qn79+qm0tFSnnXaaVq1apZUrVyqRSKiurk51dXUKh8OaOHGi8vLy9JWvfEWbN2/Wp59+qlgspsbGRm3ZskVS57HsVAuXae+oc7Q3OvR0SiQS9v7779u8efPs+uuvN+ec9e7d25599lmbP3++NTQ0WDwet5kzZ1o0GrVp06ZZW1ub7du3L+hdXlpaanPmzLELLrjAnHN27rnn2s6dOy2RSNgLL7xgmZmZVlhYaLNmzbLrrrvOwuGwjRgxwtatW3fcP1C+ePFiKyoqsoyMDLv55ptt5syZlpOTYz169AiGnamtrbWysjLLzc21hQsXBn+7b9++4IPk5513njU1NR3w2suXL7fy8nK76aab7KmnnrLvfve7QaenadOmHdcOLmadHVUefPBBC4fDNnDgQJszZ45Nnjw5GNYl1THil7/8peXk5NgZZ5xh9fX1FovF7MYbb7QLL7zQHnnkEXvssceCD4/36dPHlixZYolEwhYtWhQM3XP77bfbjBkzLDs72woKCuwPf/jDcW/brVu3WmVlpYVCIbvsssvs4YcfttNOO80ikYg98cQT1tHRYc3NzTZlyhSLRqM2Z84cSyQSVl9fH/Si7tGjh9133302b948mzdvnv3mN7+xlpYWa2xstEmTJtm1115rP/zhD+3++++3QYMGBZ1+Uh1mukNra6vddttt5pyziooKe/zxx+3MM88MRmpoamqyuro6Kysrs2g0am+++aYlEgmrq6uzcePGWSgUsilTpthDDz1k/fr1s3A4bE888UTQCebKK68055xNnDjRHnvsMauoqDDnnN16661H1cHNp0QiYdXV1TZkyBCLRCI2depUu//++4POS7/61a8skUjYr3/9a8vPz7eKioqgd/Szzz5r0WjUnHN26aWX2o9//OOgHaurqy2RSNjcuXNt/Pjxdt9999ncuXPt4osvtnA4bNnZ2fbaa68d93U2kUjYT3/6U8vMzLTi4mL7/ve/b1dddZU552zkyJFWW1trsVjMLr/8cotGo/bAAw9YPB639evX26hRo+yb3/ymPfnkkzZz5kwrKioKeoY3NzdbR0eHPfDAAxYKhWzQoEH26KOP2kUXXWTOOTv77LPTMlzY7t277cILLzTnnJ1zzjk2Z84cGzZsmIVCoaBjytKlS62oqMgKCgps6dKlwd82NTXZpZdeGoyacHDv/t/97nc2atQomzFjhj399NM2ffp0y87OtlAoZLNnzz7s0GvdpbGx0ebPn2/PPPOM9e/f35xzdvXVV9u8efPs3XfftXg8bg888IBlZ2fbJZdcYm1tbdbR0WFz5861SCRiJSUl9vDDDwfLPW7cuKDz2auvvmo5OTlWUFBg9957r02fPt3C4bANGDDAli9fftyX1RfRW/zUEI/H7a677jLnnDnnTJJJMuecFRcX26effmrxeNzuuOMOk2Tf+MY3gp619fX1dssttwTj7uXl5dn5559vf/vb34KddHNzc3BQy8jIsOzsbDvjjDPsnXfeSUtvto6ODnvllVesvLzcsrKyLDMz00pLS23evHnBgbSmpsYGDRpk4XDY3njjjeBv16xZE/S0fvHFF79wINq4caN97WtfC8aTS42beP3113c5Pl9327Fjh91+++3BOIa5ubl2zjnnBAHRzOyFF14IDmbbt2+3jo4OmzNnjg0cONCys7MtHA5bTk6OjR492l599dWg53ssFrMXX3zRhg8fHtRyyJAh9vzzz6dlWJdEImEffPCBTZo0yXJyciwSiVhxcbHNnDkz6FHa1NRkF154oUmyhx56yBKJhK1Zs8b69et3wLqfulVUVFhDQ4M1Nzfb1KlTrW/fvpaVlWXhcNh69Ohh559/vn388cfdPs7lpk2bbOrUqdarVy+LRCJWUFBgl156qa1atcoSiYRt27bNBg8ebJLs9ddfD8Y5/PDDD+2rX/3qAfW4++67g6GxUuMsXnbZZVZQUGCRSMR69eplU6dOtS1btnTbMh1OPB63t99+28aMGWPRaNQyMjKsf//+9uijjwbj97300ksWiURsyJAhwRvZG2+88ZBt6JyzBQsWWCKRsFdeecWGDRsWjD2YlZVlQ4cOtaefftpaW1vTEi737Nljs2fPPmAfOX78eHvvvfeCkRkuvvhik2T33nuvxeNxq6urs8mTJ1ufPn0sIyPDwuGwFRYW2pVXXmkrV64MlqOhocFuu+22A7b/8847z6qqqtKyP0okErZs2TKbPHmy5efnB8Pr3HTTTcEID1VVVZafn2+ZmZnBCAZmZh988IHl5uZaTk6Ovfvuu1947Y8//tgqKyutR48eFolEgiHX7rjjjsOOYdyd1q5da8XFxV84tjrn7LbbbrNYLGb33nuvSbILLrjA2traLJFI2K5du+yuu+6y4uLioN3OOuss++ijj4LlaG1ttblz59rAgQMtIyPDotGojRo1yhYuXHjS9hQ36zpc8vWPJxkz0+rVq7/wrQZS56XkCRMmKDc3N3hO3759NXr06KAzQSwWU3V1tXbs2KHc3FxVVFSooKAguNxiZorH49qwYYO2bt2qzMxMlZWVHfbbcLqbmWnbtm3asGGDEomEBg8eHPQSdc6ptbVVf/3rX9Xe3q7Ro0cHvWh37NihZcuWKRQKacKECV+47GvJzxxu3LhRu3btkpmpuLhYI0aMUGZmZlqWN9VGa9asUX19vXJzc1VeXq4ePXoE87NlyxatWrVKOTk5mjhxojIyMoLvkt+6dataW1uVm5ur4cOHH9BuqY0+VUszU2lpqQYOHJi2b3YxMzU2Nqq6ulptbW3q27evRowYEXRqicfj+uSTT7Rr1y4NHTpUw4YNU0tLi6qqqg7ZMSY3NzeoSXt7u2pqalRfX69YLKaePXuqrKxMeXl53d62Zp0dd1avXq1du3apoKBAFRUVys3NlXNO7e3tWrJkiVpbWzVmzBj17ds3+Gzo7t27VV1drdbW1i/UI/Xae/fu1erVq7Vnzx4VFhaqoqLisN8O093MOjtXrV27VrFYTCUlJRo6dKhCoZCcc9q6datWrlypaDSqs846S5FIRCtWrNC2bdsO+XpjxozRaaedFnwsYtOmTWpublZmZqZKS0uDb/5J1zYaj8e1fv16bdu2TVlZWSorKwtGBkgkEvrkk0+0c+dOlZaWBj3fW1patHHjRjU0NCiRSKh3794qKytTNBo9oG2PtP2nY3mbm5u1evVqNTU1qU+fPiovLw/2kXv27FFVVZUSiYTOPPPMYD+7adMmVVdXKxqNHrJzTmrbr6mp0e7duxUKhdS/f38NHTr0gA5Ex1NLS4uWLFlyyI5KJSUlGjlypNatW6eamhoVFhZq3LhxwbG1o6NDa9eu1fbt25Wdna3y8nL17NnzgLZNJBKqqanR5s2bFYlENHz48GDbP1nx3eIAAADwpqtwmZ7TFQAAADglES4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAOAN4RIAAADeEC4BAADgDeESAAAA3hAuAQAA4A3hEgAAAN4QLgEAAODNEcOlc26Ec+5959xO51yTc+6PzrlhyWlnO+eWO+fanXNLnXPj9vu7Y5oGAACAk9fRnLksST5vjqT/kPR1Sb9yzkUlLZSUL+nfJPWV9KZzLnys07wuGQAAAI67yFE85/+a2fmpB865b0o6XdJl6gyGs8zseefcaZIekXSBpIJjnPaep+UCAABAGhzxzKWZ7Uv97JybIKlQ0geShiR/vTV5vyV5P/RLTDuAc+47zrkq51zVjh07jjSrAAAASLOj7tDjnCuX9JakGkkzD/WU5L35mmZm881sgplNKCoqOtpZBQAAQJoczWVxOedGSfqTpHZJF5lZnXNuY3LygOR9SfJ+ozovfR/LNAAAAJzEjhgunXMDJf1ZnZfDH5Y0yTk3SdL/kVQvaYZzrknSreo8q/lnSRnHOA0AAAAnsaO5LD5MUpGksKQnJb0m6TUza5N0vaRmSfPUGRivN7P4sU7zumQAAAA47o545tLM/qx/fi7y4GkfSBrtcxoAAABOXnxDDwAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG2dm6Z6Ho+Kca5JUne75+BfVR1JDumfiXxS1Tw/qnj7UPj2oe/qczLUfbGZFB/8yko45OUbVZjYh3TPxr8g5V0Xt04Papwd1Tx9qnx7UPX1OxdpzWRwAAADeEC4BAADgzckULuenewb+hVH79KH26UHd04fapwd1T59TrvYnTYceAAAAnPhOpjOXAAAAOMGd8OHSOXe2c265c67dObfUOTcu3fN0qnDOjXDOve+c2+mca3LO/dE5Nyw5rcu60yb+OOeizrlq55w5536W/B2170bOuZ7Oud8453Y755qdcx8kf0/du5lz7h7nXE2yjhudczOTv6f2Hjnnfuqc257cr/z3fr8/pjrTBkfvULU/3LE2Of2Uq/0JHS6dc1FJCyXlS/o3SX0lvemcC6d1xk4dJepcB+ZI+g9JX5f0q8PVnTbx7lFJA1IPqP1x8ZKkb0p6UdI9ktZR9+7nnBsh6X9LSkj6nqQMST91zg0Ute8OC/Z/cKzrOG1wTBYc9PiQx1rpFN7nm9kJe5N0rSSTdH/y8b8nH09O97ydCjdJmQc93imp/nB1p0281n+MpFZJ9ydr+DNq3+01H5qs2X9KypQUTv6eund/7cuTdVuU/LlKUpuk66l9t9S7NFmr/04+PqZ1nDbwUvtDHmu/TLukexmPdDuhz1xKGpK835q835K8H5qGeTnlmNm+1M/OuQmSCiV9oMPXnTbxwDkXUuc7159L+ut+k6h99xqVvJ8oaa+kvc65p0Xdu52ZVUt6QNLZklZLGivpO5IGJp9C7bvXsa7jtMGXdJhjrXSK1v5ED5cHc8l7urh75Jwrl/SWpBpJMw/1lOT9oepOmxybm9X57vY36rxkIkk91HmpcH/U3q+s5H2upP8p6SNJs/TFbyuj7p4554rUuX/5u6RrJC1T59n6vIOfmryn9t3rWOtMGxyjozjWSqdI7U/0r3/cmLxPfSat5KDf40tyzo2S9CdJ7ZIuMrM659zh6l5wmGk4egMlFanzAJtyg6QNyZ+pffeoSd4vMrP/Sgaei/TPnTZ17z4XqrN2L5jZW8650ZJ+IGlVcjq1717Hul+nDTw41LE2OenUPN6m+7r8ET63EJW0XZ2FnKHOU8MblfycFLcvXd+B6vyMZYc6L1dNS966rDtt4q32oyT9j+Rtjjrfib6jzkuG1L776u4kLU+u97dLWpxc/79C3bu99hOS6/lqSbeqM1SapEpq773Wl0uanazvMkm3SRp9LHWmDbzU/txDHWuTzz8la5/2GTiKhjpP0j8k7ZP0iaQJ6Z6nU+Um6YLkBnDA7Uh1p026rR1+Ru2PS71Pl/SxOjuTrJE0nboft9p/L3lwbFPnWfr/Re27pc5/PsS+/aZjrTNt4KX2hzzWnqq15xt6AAAA4M3J1qEHAAAAJzDCJQAAALwhXAIAAMAbwiUAAAC8IVwCAADAG8IlAAAAvCFcAgAAwBvCJQAAALz5f+gmh6jjWCXGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = img.imread(os.path.join(FIGUREDIR, 'scatterplot.png'))\n",
    "  \n",
    "# show image\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Heatmap of RPS and RAS normalized means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x28ca070a790>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x16560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = img.imread(os.path.join(FIGUREDIR, 'heatmap_means.png'))\n",
    "  \n",
    "# show image\n",
    "plt.figure(figsize=(20,230))\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}