Chronic-Disease-Indicators.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title: Chronic Disease Indicator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\329364451.py:1: DtypeWarning: Columns (7,10,18,19,20,21,23,30,31,32,33) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_source=pd.read_csv('Chronic_Disease_Indicators.csv')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>YearStart</th>\n",
       "      <th>YearEnd</th>\n",
       "      <th>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>DataSource</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Question</th>\n",
       "      <th>Response</th>\n",
       "      <th>DataValueUnit</th>\n",
       "      <th>DataValueType</th>\n",
       "      <th>...</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>TopicID</th>\n",
       "      <th>QuestionID</th>\n",
       "      <th>DataValueTypeID</th>\n",
       "      <th>StratificationCategoryID1</th>\n",
       "      <th>StratificationID1</th>\n",
       "      <th>StratificationCategoryID2</th>\n",
       "      <th>StratificationID2</th>\n",
       "      <th>StratificationCategoryID3</th>\n",
       "      <th>StratificationID3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>YRBSS</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>Alcohol use among youth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude Prevalence</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>ALC</td>\n",
       "      <td>ALC1_1</td>\n",
       "      <td>CrdPrev</td>\n",
       "      <td>OVERALL</td>\n",
       "      <td>OVR</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>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>YRBSS</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>Alcohol use among youth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude Prevalence</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>ALC</td>\n",
       "      <td>ALC1_1</td>\n",
       "      <td>CrdPrev</td>\n",
       "      <td>OVERALL</td>\n",
       "      <td>OVR</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>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>AR</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>YRBSS</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>Alcohol use among youth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude Prevalence</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>ALC</td>\n",
       "      <td>ALC1_1</td>\n",
       "      <td>CrdPrev</td>\n",
       "      <td>OVERALL</td>\n",
       "      <td>OVR</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>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>YRBSS</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>Alcohol use among youth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude Prevalence</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>ALC</td>\n",
       "      <td>ALC1_1</td>\n",
       "      <td>CrdPrev</td>\n",
       "      <td>OVERALL</td>\n",
       "      <td>OVR</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>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>YRBSS</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>Alcohol use among youth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>%</td>\n",
       "      <td>Crude Prevalence</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>ALC</td>\n",
       "      <td>ALC1_1</td>\n",
       "      <td>CrdPrev</td>\n",
       "      <td>OVERALL</td>\n",
       "      <td>OVR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearStart  YearEnd LocationAbbr LocationDesc DataSource    Topic  \\\n",
       "0       2015     2015           AK       Alaska      YRBSS  Alcohol   \n",
       "1       2015     2015           AL      Alabama      YRBSS  Alcohol   \n",
       "2       2015     2015           AR     Arkansas      YRBSS  Alcohol   \n",
       "3       2015     2015           AZ      Arizona      YRBSS  Alcohol   \n",
       "4       2015     2015           CA   California      YRBSS  Alcohol   \n",
       "\n",
       "                  Question Response DataValueUnit     DataValueType  ...  \\\n",
       "0  Alcohol use among youth      NaN             %  Crude Prevalence  ...   \n",
       "1  Alcohol use among youth      NaN             %  Crude Prevalence  ...   \n",
       "2  Alcohol use among youth      NaN             %  Crude Prevalence  ...   \n",
       "3  Alcohol use among youth      NaN             %  Crude Prevalence  ...   \n",
       "4  Alcohol use among youth      NaN             %  Crude Prevalence  ...   \n",
       "\n",
       "  LocationID  TopicID QuestionID DataValueTypeID  StratificationCategoryID1  \\\n",
       "0          2      ALC     ALC1_1         CrdPrev                    OVERALL   \n",
       "1          1      ALC     ALC1_1         CrdPrev                    OVERALL   \n",
       "2          5      ALC     ALC1_1         CrdPrev                    OVERALL   \n",
       "3          4      ALC     ALC1_1         CrdPrev                    OVERALL   \n",
       "4          6      ALC     ALC1_1         CrdPrev                    OVERALL   \n",
       "\n",
       "   StratificationID1 StratificationCategoryID2 StratificationID2  \\\n",
       "0                OVR                       NaN               NaN   \n",
       "1                OVR                       NaN               NaN   \n",
       "2                OVR                       NaN               NaN   \n",
       "3                OVR                       NaN               NaN   \n",
       "4                OVR                       NaN               NaN   \n",
       "\n",
       "  StratificationCategoryID3 StratificationID3  \n",
       "0                       NaN               NaN  \n",
       "1                       NaN               NaN  \n",
       "2                       NaN               NaN  \n",
       "3                       NaN               NaN  \n",
       "4                       NaN               NaN  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_source=pd.read_csv('Chronic_Disease_Indicators.csv')\n",
    "df_source.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Descriptive Statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>YearStart</th>\n",
       "      <th>YearEnd</th>\n",
       "      <th>DataValueAlt</th>\n",
       "      <th>LowConfidenceLimit</th>\n",
       "      <th>HighConfidenceLimit</th>\n",
       "      <th>LocationID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>403984.000000</td>\n",
       "      <td>403984.000000</td>\n",
       "      <td>2.736660e+05</td>\n",
       "      <td>246819.000000</td>\n",
       "      <td>246819.000000</td>\n",
       "      <td>403984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.729851</td>\n",
       "      <td>2012.756698</td>\n",
       "      <td>7.270473e+02</td>\n",
       "      <td>49.729946</td>\n",
       "      <td>62.654180</td>\n",
       "      <td>31.003374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.546388</td>\n",
       "      <td>1.515176</td>\n",
       "      <td>1.798349e+04</td>\n",
       "      <td>83.633664</td>\n",
       "      <td>95.980311</td>\n",
       "      <td>17.700259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>1.890000e+01</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>4.160000e+01</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>45.600000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>57.400000</td>\n",
       "      <td>72.200000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>3.967333e+06</td>\n",
       "      <td>1293.900000</td>\n",
       "      <td>2088.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           YearStart        YearEnd  DataValueAlt  LowConfidenceLimit  \\\n",
       "count  403984.000000  403984.000000  2.736660e+05       246819.000000   \n",
       "mean     2012.729851    2012.756698  7.270473e+02           49.729946   \n",
       "std         1.546388       1.515176  1.798349e+04           83.633664   \n",
       "min      2001.000000    2001.000000  0.000000e+00            0.200000   \n",
       "25%      2011.000000    2012.000000  1.890000e+01           13.000000   \n",
       "50%      2013.000000    2013.000000  4.160000e+01           31.100000   \n",
       "75%      2014.000000    2014.000000  7.100000e+01           57.400000   \n",
       "max      2016.000000    2016.000000  3.967333e+06         1293.900000   \n",
       "\n",
       "       HighConfidenceLimit     LocationID  \n",
       "count        246819.000000  403984.000000  \n",
       "mean             62.654180      31.003374  \n",
       "std              95.980311      17.700259  \n",
       "min               0.420000       1.000000  \n",
       "25%              20.200000      17.000000  \n",
       "50%              45.600000      30.000000  \n",
       "75%              72.200000      45.000000  \n",
       "max            2088.000000      78.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Numerical Data\n",
    "df_source.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. Understanding and Cleaning the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 403984 entries, 0 to 403983\n",
      "Data columns (total 34 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   YearStart                  403984 non-null  int64  \n",
      " 1   YearEnd                    403984 non-null  int64  \n",
      " 2   LocationAbbr               403984 non-null  object \n",
      " 3   LocationDesc               403984 non-null  object \n",
      " 4   DataSource                 403984 non-null  object \n",
      " 5   Topic                      403984 non-null  object \n",
      " 6   Question                   403984 non-null  object \n",
      " 7   Response                   79323 non-null   object \n",
      " 8   DataValueUnit              374119 non-null  object \n",
      " 9   DataValueType              403984 non-null  object \n",
      " 10  DataValue                  297817 non-null  object \n",
      " 11  DataValueAlt               273666 non-null  float64\n",
      " 12  DataValueFootnoteSymbol    188019 non-null  object \n",
      " 13  DatavalueFootnote          187853 non-null  object \n",
      " 14  LowConfidenceLimit         246819 non-null  float64\n",
      " 15  HighConfidenceLimit        246819 non-null  float64\n",
      " 16  StratificationCategory1    403984 non-null  object \n",
      " 17  Stratification1            403984 non-null  object \n",
      " 18  StratificationCategory2    79323 non-null   object \n",
      " 19  Stratification2            79323 non-null   object \n",
      " 20  StratificationCategory3    79323 non-null   object \n",
      " 21  Stratification3            79323 non-null   object \n",
      " 22  GeoLocation                401416 non-null  object \n",
      " 23  ResponseID                 79323 non-null   object \n",
      " 24  LocationID                 403984 non-null  int64  \n",
      " 25  TopicID                    403984 non-null  object \n",
      " 26  QuestionID                 403984 non-null  object \n",
      " 27  DataValueTypeID            403984 non-null  object \n",
      " 28  StratificationCategoryID1  403984 non-null  object \n",
      " 29  StratificationID1          403984 non-null  object \n",
      " 30  StratificationCategoryID2  79324 non-null   object \n",
      " 31  StratificationID2          79324 non-null   object \n",
      " 32  StratificationCategoryID3  79323 non-null   object \n",
      " 33  StratificationID3          79323 non-null   object \n",
      "dtypes: float64(3), int64(3), object(28)\n",
      "memory usage: 104.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_source.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearStart                    100.000000\n",
       "YearEnd                      100.000000\n",
       "LocationAbbr                 100.000000\n",
       "LocationDesc                 100.000000\n",
       "DataSource                   100.000000\n",
       "Topic                        100.000000\n",
       "Question                     100.000000\n",
       "Response                      19.635184\n",
       "DataValueUnit                 92.607380\n",
       "DataValueType                100.000000\n",
       "DataValue                     73.719999\n",
       "DataValueAlt                  67.741792\n",
       "DataValueFootnoteSymbol       46.541200\n",
       "DatavalueFootnote             46.500109\n",
       "LowConfidenceLimit            61.096232\n",
       "HighConfidenceLimit           61.096232\n",
       "StratificationCategory1      100.000000\n",
       "Stratification1              100.000000\n",
       "StratificationCategory2       19.635184\n",
       "Stratification2               19.635184\n",
       "StratificationCategory3       19.635184\n",
       "Stratification3               19.635184\n",
       "GeoLocation                   99.364331\n",
       "ResponseID                    19.635184\n",
       "LocationID                   100.000000\n",
       "TopicID                      100.000000\n",
       "QuestionID                   100.000000\n",
       "DataValueTypeID              100.000000\n",
       "StratificationCategoryID1    100.000000\n",
       "StratificationID1            100.000000\n",
       "StratificationCategoryID2     19.635431\n",
       "StratificationID2             19.635431\n",
       "StratificationCategoryID3     19.635184\n",
       "StratificationID3             19.635184\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features that are completely filled\n",
    "(df_source.count()/len(df_source))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_values(df):\n",
    "    for i in range(0, len(df.columns)):\n",
    "        print(\"=======start of feature \", df.columns[i], \"=====================\")\n",
    "        print (df.iloc[:,i].value_counts())\n",
    "        print (\"======end of feature \", df.columns[i], \"===================== \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======start of feature  YearStart =====================\n",
      "2013    85913\n",
      "2012    75796\n",
      "2014    75524\n",
      "2011    74593\n",
      "2015    64046\n",
      "2010    24965\n",
      "2009     2771\n",
      "2016      109\n",
      "2008      108\n",
      "2001      104\n",
      "2007       55\n",
      "Name: YearStart, dtype: int64\n",
      "======end of feature  YearStart ===================== \n",
      "\n",
      "=======start of feature  YearEnd =====================\n",
      "2013    88409\n",
      "2012    76108\n",
      "2014    75524\n",
      "2011    74556\n",
      "2015    64046\n",
      "2010    24965\n",
      "2016      109\n",
      "2008      108\n",
      "2001      104\n",
      "2007       55\n",
      "Name: YearEnd, dtype: int64\n",
      "======end of feature  YearEnd ===================== \n",
      "\n",
      "=======start of feature  LocationAbbr =====================\n",
      "NC    7779\n",
      "KY    7779\n",
      "SC    7779\n",
      "NV    7779\n",
      "NJ    7779\n",
      "WI    7779\n",
      "NE    7779\n",
      "NY    7778\n",
      "AZ    7747\n",
      "FL    7747\n",
      "IA    7746\n",
      "VT    7732\n",
      "HI    7688\n",
      "OR    7680\n",
      "NM    7680\n",
      "MI    7680\n",
      "SD    7680\n",
      "WV    7680\n",
      "WA    7680\n",
      "AR    7648\n",
      "CO    7647\n",
      "UT    7575\n",
      "MD    7575\n",
      "RI    7575\n",
      "MA    7574\n",
      "CA    7542\n",
      "MS    7533\n",
      "PA    7386\n",
      "OK    7386\n",
      "OH    7386\n",
      "WY    7386\n",
      "KS    7386\n",
      "LA    7386\n",
      "VA    7386\n",
      "MT    7386\n",
      "MO    7386\n",
      "TX    7386\n",
      "ND    7386\n",
      "MN    7385\n",
      "IN    7385\n",
      "ME    7384\n",
      "TN    7384\n",
      "NH    7383\n",
      "AK    7354\n",
      "AL    7354\n",
      "IL    7354\n",
      "ID    7354\n",
      "GA    7354\n",
      "CT    7353\n",
      "DC    7352\n",
      "DE    7351\n",
      "PR    5635\n",
      "GU    5597\n",
      "VI    5551\n",
      "US    2568\n",
      "Name: LocationAbbr, dtype: int64\n",
      "======end of feature  LocationAbbr ===================== \n",
      "\n",
      "=======start of feature  LocationDesc =====================\n",
      "North Carolina          7779\n",
      "Kentucky                7779\n",
      "South Carolina          7779\n",
      "Nevada                  7779\n",
      "New Jersey              7779\n",
      "Wisconsin               7779\n",
      "Nebraska                7779\n",
      "New York                7778\n",
      "Arizona                 7747\n",
      "Florida                 7747\n",
      "Iowa                    7746\n",
      "Vermont                 7732\n",
      "Hawaii                  7688\n",
      "Oregon                  7680\n",
      "New Mexico              7680\n",
      "Michigan                7680\n",
      "South Dakota            7680\n",
      "West Virginia           7680\n",
      "Washington              7680\n",
      "Arkansas                7648\n",
      "Colorado                7647\n",
      "Utah                    7575\n",
      "Maryland                7575\n",
      "Rhode Island            7575\n",
      "Massachusetts           7574\n",
      "California              7542\n",
      "Mississippi             7533\n",
      "Pennsylvania            7386\n",
      "Oklahoma                7386\n",
      "Ohio                    7386\n",
      "Wyoming                 7386\n",
      "Kansas                  7386\n",
      "Louisiana               7386\n",
      "Virginia                7386\n",
      "Montana                 7386\n",
      "Missouri                7386\n",
      "Texas                   7386\n",
      "North Dakota            7386\n",
      "Minnesota               7385\n",
      "Indiana                 7385\n",
      "Maine                   7384\n",
      "Tennessee               7384\n",
      "New Hampshire           7383\n",
      "Alaska                  7354\n",
      "Alabama                 7354\n",
      "Illinois                7354\n",
      "Idaho                   7354\n",
      "Georgia                 7354\n",
      "Connecticut             7353\n",
      "District of Columbia    7352\n",
      "Delaware                7351\n",
      "Puerto Rico             5635\n",
      "Guam                    5597\n",
      "Virgin Islands          5551\n",
      "United States           2568\n",
      "Name: LocationDesc, dtype: int64\n",
      "======end of feature  LocationDesc ===================== \n",
      "\n",
      "=======start of feature  DataSource =====================\n",
      "BRFSS                                                     299921\n",
      "NVSS                                                       79756\n",
      "State Inpatient Data                                        9324\n",
      "CMS Part A Claims Data                                      1872\n",
      "SEDD; SID                                                   1866\n",
      "YRBSS                                                       1497\n",
      "Death certificate                                           1248\n",
      "Statewide central cancer registries                         1248\n",
      "State Emergency Department Data                             1116\n",
      "USRDS                                                       1040\n",
      "STATE                                                        706\n",
      "CDC School Health Profiles                                   549\n",
      "PRAMS                                                        495\n",
      "APIS                                                         321\n",
      "ACS 5-Year Estimates                                         318\n",
      "NSCH                                                         312\n",
      "HRSA, UDS                                                    220\n",
      "ACS 1-Year Estimates                                         220\n",
      "National Immunization Survey                                 220\n",
      "CDC Breastfeeding Report Card                                218\n",
      "ASHW                                                         216\n",
      "Legal Research                                               215\n",
      "USDA National Farmers' Market Directory                      165\n",
      "WFRS                                                         110\n",
      "InfoUSA; USDA                                                110\n",
      "ANRF                                                         110\n",
      "mPINC                                                        110\n",
      "CDC Children's Food Environment State Indicator Report       108\n",
      "AEDS                                                         107\n",
      "NVSS, Mortality                                              104\n",
      "Current Population Survey                                     55\n",
      "CMS CCW                                                       55\n",
      "Birth Certificate, NVSS                                       52\n",
      "Name: DataSource, dtype: int64\n",
      "======end of feature  DataSource ===================== \n",
      "\n",
      "=======start of feature  Topic =====================\n",
      "Diabetes                                           67342\n",
      "Cardiovascular Disease                             62678\n",
      "Chronic Obstructive Pulmonary Disease              52536\n",
      "Asthma                                             31958\n",
      "Arthritis                                          31652\n",
      "Overarching Conditions                             30092\n",
      "Nutrition, Physical Activity, and Weight Status    29512\n",
      "Alcohol                                            27697\n",
      "Tobacco                                            24568\n",
      "Cancer                                             11605\n",
      "Chronic Kidney Disease                             11525\n",
      "Oral Health                                         7593\n",
      "Mental Health                                       5595\n",
      "Older Adults                                        4003\n",
      "Immunization                                        3838\n",
      "Reproductive Health                                 1735\n",
      "Disability                                            55\n",
      "Name: Topic, dtype: int64\n",
      "======end of feature  Topic ===================== \n",
      "\n",
      "=======start of feature  Question =====================\n",
      "Mortality from heart failure                                                                 6136\n",
      "Premature mortality among adults aged 45-64 years                                            6135\n",
      "Mortality with end-stage renal disease                                                       6135\n",
      "Mortality from cerebrovascular disease (stroke)                                              6135\n",
      "Mortality due to diabetes reported as any listed cause of death                              6135\n",
      "                                                                                             ... \n",
      "Presence of regulations pertaining to avoiding sugar in early care and education settings      54\n",
      "Prevalence of gestational diabetes                                                             52\n",
      "Life expectancy at birth                                                                       52\n",
      "Life expectancy at age 65 years                                                                52\n",
      "Oral Health Services at Federally Qualified Health Centers                                     16\n",
      "Name: Question, Length: 202, dtype: int64\n",
      "======end of feature  Question ===================== \n",
      "\n",
      "=======start of feature  Response =====================\n",
      "     79323\n",
      "Name: Response, dtype: int64\n",
      "======end of feature  Response ===================== \n",
      "\n",
      "=======start of feature  DataValueUnit =====================\n",
      "%                        280119\n",
      "cases per 100,000         49392\n",
      "Number                    24137\n",
      "cases per 10,000           8204\n",
      "cases per 1,000,000        5130\n",
      "                           3730\n",
      "per 100,000                1664\n",
      "cases per 1,000             936\n",
      "$                           321\n",
      "Number                      110\n",
      "pack sales per capita       110\n",
      "gallons                     107\n",
      "Years                       104\n",
      "per 100,000 residents        55\n",
      "Name: DataValueUnit, dtype: int64\n",
      "======end of feature  DataValueUnit ===================== \n",
      "\n",
      "=======start of feature  DataValueType =====================\n",
      "Crude Prevalence                                                    150281\n",
      "Age-adjusted Prevalence                                             128577\n",
      "Number                                                               32419\n",
      "Crude Rate                                                           31311\n",
      "Age-adjusted Rate                                                    31311\n",
      "Age-adjusted Mean                                                    10875\n",
      "Mean                                                                 10550\n",
      "Median                                                                2822\n",
      "Percent                                                               1209\n",
      "Yes/No                                                                 918\n",
      "Average Annual Number                                                  832\n",
      "Average Annual Age-adjusted Rate                                       832\n",
      "Average Annual Crude Rate                                              832\n",
      "Adjusted by age, sex, race and ethnicity                               520\n",
      "US Dollars                                                             321\n",
      "Local control of the regulation of alcohol outlet density              109\n",
      "Per capita alcohol consumption                                         107\n",
      "Commercial host (dram shop) liability status for alcohol service       106\n",
      "Prevalence                                                              52\n",
      "Name: DataValueType, dtype: int64\n",
      "======end of feature  DataValueType ===================== \n",
      "\n",
      "=======start of feature  DataValue =====================\n",
      "          23064\n",
      "1           924\n",
      "1.1         628\n",
      "No          621\n",
      "3.4         430\n",
      "          ...  \n",
      "295.25        1\n",
      "38170         1\n",
      "393.63        1\n",
      "62.67         1\n",
      "17.62         1\n",
      "Name: DataValue, Length: 20116, dtype: int64\n",
      "======end of feature  DataValue ===================== \n",
      "\n",
      "=======start of feature  DataValueAlt =====================\n",
      "1.00      991\n",
      "1.10      706\n",
      "3.60      666\n",
      "3.70      657\n",
      "3.80      647\n",
      "         ... \n",
      "266.61      1\n",
      "58.14       1\n",
      "186.56      1\n",
      "272.48      1\n",
      "17.62       1\n",
      "Name: DataValueAlt, Length: 18764, dtype: int64\n",
      "======end of feature  DataValueAlt ===================== \n",
      "\n",
      "=======start of feature  DataValueFootnoteSymbol =====================\n",
      "****     80363\n",
      "         56188\n",
      "-        26097\n",
      "~        22686\n",
      "*         1758\n",
      "#          306\n",
      "***        260\n",
      "##         138\n",
      "^^          60\n",
      "†††         55\n",
      " **         33\n",
      "^^^^        24\n",
      "~~~         24\n",
      "§           17\n",
      "*, #         6\n",
      "^^^          1\n",
      "§,‡          1\n",
      "§,‡,^        1\n",
      "§§           1\n",
      "Name: DataValueFootnoteSymbol, dtype: int64\n",
      "======end of feature  DataValueFootnoteSymbol ===================== \n",
      "\n",
      "=======start of feature  DatavalueFootnote =====================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample size of denominator and/or age group for age-standardization is less than 50 or relative standard error is more than 30%                                                                                                                                                                                                                                                                                                                                                                            80363\n",
      "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           56022\n",
      "No data available                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          26097\n",
      "Data not shown because of too few respondents or cases                                                                                                                                                                                                                                                                                                                                                                                                                                                     22686\n",
      "50 States + DC: US Median                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   1758\n",
      "US Median based on the number of participating states and/or excluding states with sample size below 50 or relative standard error above 30%                                                                                                                                                                                                                                                                                                                                                                 260\n",
      "NSCH Data provided by Data Resource Center for Child and Adolescent Health.  Http://www.childhealthdata.org                                                                                                                                                                                                                                                                                                                                                                                                  203\n",
      "Estimate is 0.0% because no cases were reported with this condition                                                                                                                                                                                                                                                                                                                                                                                                                                          138\n",
      "NSCH Data provided by Data Resource Center for Child and Adolescent Health.  http://www.childhealthdata.org                                                                                                                                                                                                                                                                                                                                                                                                  103\n",
      "State directly controlled the sale of (beer/wine/distilled spirits) at the retail and/or wholesale levels. State prices for (beer/wine/distilled spirits) combined both markups and taxes, so tax rates for this beverage type could not be determined.                                                                                                                                                                                                                                                       60\n",
      "A confidence interval for this location is not available because a census was used instead of a sample for their surveys                                                                                                                                                                                                                                                                                                                                                                                      55\n",
      "US estimate/number is based on fewer than 50 states and the District of Columbia                                                                                                                                                                                                                                                                                                                                                                                                                              33\n",
      "Data are from cancer registries that meet the data quality criteria for all invasive cancer sites combined,  for all years covering approximately 99% of the U.S. population. See registry-specific data quality information at http://www.cdc.gov/cancer/npcr/uscs/data/00_data_quality.htm                                                                                                                                                                                                                  24\n",
      "Data are not available because cancer registry did not meet the data quality criteria for all the years for publication in United States Cancer statistics (USCS)                                                                                                                                                                                                                                                                                                                                             24\n",
      "Population served by CWS exceeded the US Census state population estimate; number of people was reduced by the ratio of the population estimate to the CWS population estimate                                                                                                                                                                                                                                                                                                                                17\n",
      "50 states + DC:  Median Percent; NSCH Data provided by Data Resource Center for Child and Adolescent Health.  http://www.childhealthdata.org                                                                                                                                                                                                                                                                                                                                                                   6\n",
      "Population served by CWS exceeded the US Census state population estimate; number of people was reduced by the ratio of the population estimate to the CWS population estimate.  Complete data were not available from WFRS; state provided additional information.                                                                                                                                                                                                                                            1\n",
      "DC was not rated for this measure because the measure addresses state versus local authority and does not apply to the District of Columbia                                                                                                                                                                                                                                                                                                                                                                    1\n",
      "Population served by CWS exceeded the US Census state population estimate; number of people was reduced by the ratio of the population estimate to the CWS population estimate.  Complete data were not available from WFRS; state provided additional information.  California is developing new methodology to describe the fluoridated status of water systems that draw water from multiple sources (e.g., ground water, surface water). This report does not reflect California’s new methodology.        1\n",
      "Per capita alcohol consumption was calculated using (1) alcohol beverage sales data, either collected directly from states or provided by beverage industry sources (numerator); and (2) population data from the U.S. Census Bureau (denominator).                                                                                                                                                                                                                                                            1\n",
      "Name: DatavalueFootnote, dtype: int64\n",
      "======end of feature  DatavalueFootnote ===================== \n",
      "\n",
      "=======start of feature  LowConfidenceLimit =====================\n",
      "0.60      846\n",
      "0.70      781\n",
      "3.40      762\n",
      "2.10      752\n",
      "2.40      737\n",
      "         ... \n",
      "19.17       1\n",
      "22.96       1\n",
      "22.58       1\n",
      "13.84       1\n",
      "901.10      1\n",
      "Name: LowConfidenceLimit, Length: 10473, dtype: int64\n",
      "======end of feature  LowConfidenceLimit ===================== \n",
      "\n",
      "=======start of feature  HighConfidenceLimit =====================\n",
      "2.00      1019\n",
      "3.90       577\n",
      "4.20       558\n",
      "1.90       558\n",
      "4.00       552\n",
      "          ... \n",
      "22.85        1\n",
      "33.33        1\n",
      "27.19        1\n",
      "18.09        1\n",
      "672.80       1\n",
      "Name: HighConfidenceLimit, Length: 11159, dtype: int64\n",
      "======end of feature  HighConfidenceLimit ===================== \n",
      "\n",
      "=======start of feature  StratificationCategory1 =====================\n",
      "Race/Ethnicity    246619\n",
      "Gender             94150\n",
      "Overall            63215\n",
      "Name: StratificationCategory1, dtype: int64\n",
      "======end of feature  StratificationCategory1 ===================== \n",
      "\n",
      "=======start of feature  Stratification1 =====================\n",
      "Overall                             63215\n",
      "Black, non-Hispanic                 49324\n",
      "Hispanic                            49324\n",
      "White, non-Hispanic                 49324\n",
      "Female                              47075\n",
      "Male                                47075\n",
      "Other, non-Hispanic                 38161\n",
      "Multiracial, non-Hispanic           38160\n",
      "American Indian or Alaska Native    11163\n",
      "Asian or Pacific Islander           11163\n",
      "Name: Stratification1, dtype: int64\n",
      "======end of feature  Stratification1 ===================== \n",
      "\n",
      "=======start of feature  StratificationCategory2 =====================\n",
      "     79323\n",
      "Name: StratificationCategory2, dtype: int64\n",
      "======end of feature  StratificationCategory2 ===================== \n",
      "\n",
      "=======start of feature  Stratification2 =====================\n",
      "     79323\n",
      "Name: Stratification2, dtype: int64\n",
      "======end of feature  Stratification2 ===================== \n",
      "\n",
      "=======start of feature  StratificationCategory3 =====================\n",
      "     79323\n",
      "Name: StratificationCategory3, dtype: int64\n",
      "======end of feature  StratificationCategory3 ===================== \n",
      "\n",
      "=======start of feature  Stratification3 =====================\n",
      "     79323\n",
      "Name: Stratification3, dtype: int64\n",
      "======end of feature  Stratification3 ===================== \n",
      "\n",
      "=======start of feature  GeoLocation =====================\n",
      "(35.466220975000454, -79.15925046299964)     7779\n",
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      "(41.6410409880005, -99.36572062299967)       7779\n",
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      "(38.89037138500049, -77.03196112699965)        70\n",
      "Name: GeoLocation, dtype: int64\n",
      "======end of feature  GeoLocation ===================== \n",
      "\n",
      "=======start of feature  ResponseID =====================\n",
      "     79323\n",
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      "======end of feature  ResponseID ===================== \n",
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      "=======start of feature  LocationID =====================\n",
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      "10    7351\n",
      "72    5635\n",
      "66    5597\n",
      "78    5551\n",
      "59    2568\n",
      "Name: LocationID, dtype: int64\n",
      "======end of feature  LocationID ===================== \n",
      "\n",
      "=======start of feature  TopicID =====================\n",
      "DIA     67342\n",
      "CVD     62678\n",
      "COPD    52536\n",
      "AST     31958\n",
      "ART     31652\n",
      "OVC     30092\n",
      "NPAW    29512\n",
      "ALC     27697\n",
      "TOB     24568\n",
      "CAN     11605\n",
      "CKD     11525\n",
      "ORH      7593\n",
      "MTH      5595\n",
      "OLD      4003\n",
      "IMM      3838\n",
      "RPH      1735\n",
      "DIS        55\n",
      "Name: TopicID, dtype: int64\n",
      "======end of feature  TopicID ===================== \n",
      "\n",
      "=======start of feature  QuestionID =====================\n",
      "CVD1_4      6136\n",
      "DIA1_2      6135\n",
      "OVC5_0      6135\n",
      "DIA1_1      6135\n",
      "CVD1_5      6135\n",
      "            ... \n",
      "NPAW14_0      54\n",
      "NPAW22_0      54\n",
      "OVC4_1        52\n",
      "DIA3_2        52\n",
      "OVC4_2        52\n",
      "Name: QuestionID, Length: 201, dtype: int64\n",
      "======end of feature  QuestionID ===================== \n",
      "\n",
      "=======start of feature  DataValueTypeID =====================\n",
      "CrdPrev              150281\n",
      "AgeAdjPrev           128577\n",
      "Nmbr                  32419\n",
      "CrdRate               31311\n",
      "AgeAdjRate            31311\n",
      "AgeAdjMean            10875\n",
      "Mean                  10550\n",
      "Median                 2822\n",
      "Prct                   1209\n",
      "YesNo                   918\n",
      "AvgAnnNmbr              832\n",
      "AvgAnnAgeAdjRate        832\n",
      "AvgAnnCrdRate           832\n",
      "AgeSexRaceAdjRate       520\n",
      "USD                     321\n",
      "LclCntrlAlc             109\n",
      "PerCapAlc               107\n",
      "CommHstLiab             106\n",
      "Prev                     52\n",
      "Name: DataValueTypeID, dtype: int64\n",
      "======end of feature  DataValueTypeID ===================== \n",
      "\n",
      "=======start of feature  StratificationCategoryID1 =====================\n",
      "RACE       246619\n",
      "GENDER      94150\n",
      "OVERALL     63215\n",
      "Name: StratificationCategoryID1, dtype: int64\n",
      "======end of feature  StratificationCategoryID1 ===================== \n",
      "\n",
      "=======start of feature  StratificationID1 =====================\n",
      "OVR     63215\n",
      "BLK     49324\n",
      "HIS     49324\n",
      "WHT     49324\n",
      "GENF    47075\n",
      "GENM    47075\n",
      "OTH     38161\n",
      "MRC     38160\n",
      "AIAN    11163\n",
      "API     11163\n",
      "Name: StratificationID1, dtype: int64\n",
      "======end of feature  StratificationID1 ===================== \n",
      "\n",
      "=======start of feature  StratificationCategoryID2 =====================\n",
      "     79324\n",
      "Name: StratificationCategoryID2, dtype: int64\n",
      "======end of feature  StratificationCategoryID2 ===================== \n",
      "\n",
      "=======start of feature  StratificationID2 =====================\n",
      "     79324\n",
      "Name: StratificationID2, dtype: int64\n",
      "======end of feature  StratificationID2 ===================== \n",
      "\n",
      "=======start of feature  StratificationCategoryID3 =====================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     79323\n",
      "Name: StratificationCategoryID3, dtype: int64\n",
      "======end of feature  StratificationCategoryID3 ===================== \n",
      "\n",
      "=======start of feature  StratificationID3 =====================\n",
      "     79323\n",
      "Name: StratificationID3, dtype: int64\n",
      "======end of feature  StratificationID3 ===================== \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Exploring the values of every feature\n",
    "df_values(df_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop features that are considered meaningless\n",
    "indexes=[18,19,20,21,22,23,24,30,31,32,33]\n",
    "df_source=df_source.drop(df_source.columns[indexes],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 403984 entries, 0 to 403983\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   YearStart                  403984 non-null  int64  \n",
      " 1   YearEnd                    403984 non-null  int64  \n",
      " 2   LocationAbbr               403984 non-null  object \n",
      " 3   LocationDesc               403984 non-null  object \n",
      " 4   DataSource                 403984 non-null  object \n",
      " 5   Topic                      403984 non-null  object \n",
      " 6   Question                   403984 non-null  object \n",
      " 7   Response                   79323 non-null   object \n",
      " 8   DataValueUnit              374119 non-null  object \n",
      " 9   DataValueType              403984 non-null  object \n",
      " 10  DataValue                  297817 non-null  object \n",
      " 11  DataValueAlt               273666 non-null  float64\n",
      " 12  DataValueFootnoteSymbol    188019 non-null  object \n",
      " 13  DatavalueFootnote          187853 non-null  object \n",
      " 14  LowConfidenceLimit         246819 non-null  float64\n",
      " 15  HighConfidenceLimit        246819 non-null  float64\n",
      " 16  StratificationCategory1    403984 non-null  object \n",
      " 17  Stratification1            403984 non-null  object \n",
      " 18  TopicID                    403984 non-null  object \n",
      " 19  QuestionID                 403984 non-null  object \n",
      " 20  DataValueTypeID            403984 non-null  object \n",
      " 21  StratificationCategoryID1  403984 non-null  object \n",
      " 22  StratificationID1          403984 non-null  object \n",
      "dtypes: float64(3), int64(2), object(18)\n",
      "memory usage: 70.9+ MB\n",
      "None\n",
      "YearStart                    100.000000\n",
      "YearEnd                      100.000000\n",
      "LocationAbbr                 100.000000\n",
      "LocationDesc                 100.000000\n",
      "DataSource                   100.000000\n",
      "Topic                        100.000000\n",
      "Question                     100.000000\n",
      "Response                      19.635184\n",
      "DataValueUnit                 92.607380\n",
      "DataValueType                100.000000\n",
      "DataValue                     73.719999\n",
      "DataValueAlt                  67.741792\n",
      "DataValueFootnoteSymbol       46.541200\n",
      "DatavalueFootnote             46.500109\n",
      "LowConfidenceLimit            61.096232\n",
      "HighConfidenceLimit           61.096232\n",
      "StratificationCategory1      100.000000\n",
      "Stratification1              100.000000\n",
      "TopicID                      100.000000\n",
      "QuestionID                   100.000000\n",
      "DataValueTypeID              100.000000\n",
      "StratificationCategoryID1    100.000000\n",
      "StratificationID1            100.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#info() and non-null percentage distribution among features\n",
    "print(df_source.info())\n",
    "print((df_source.count()/len(df_source))*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(297817, 23)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_source_filtered=df_source.dropna(how='all', subset=['DataValue','DataValueAlt'])\n",
    "df_source_filtered.reset_index(drop=True, inplace=True)\n",
    "df_source_filtered.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297817 entries, 0 to 297816\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count   Dtype  \n",
      "---  ------                     --------------   -----  \n",
      " 0   YearStart                  297817 non-null  int64  \n",
      " 1   YearEnd                    297817 non-null  int64  \n",
      " 2   LocationAbbr               297817 non-null  object \n",
      " 3   LocationDesc               297817 non-null  object \n",
      " 4   DataSource                 297817 non-null  object \n",
      " 5   Topic                      297817 non-null  object \n",
      " 6   Question                   297817 non-null  object \n",
      " 7   Response                   79284 non-null   object \n",
      " 8   DataValueUnit              268822 non-null  object \n",
      " 9   DataValueType              297817 non-null  object \n",
      " 10  DataValue                  297817 non-null  object \n",
      " 11  DataValueAlt               273666 non-null  float64\n",
      " 12  DataValueFootnoteSymbol    81852 non-null   object \n",
      " 13  DatavalueFootnote          81686 non-null   object \n",
      " 14  LowConfidenceLimit         246819 non-null  float64\n",
      " 15  HighConfidenceLimit        246819 non-null  float64\n",
      " 16  StratificationCategory1    297817 non-null  object \n",
      " 17  Stratification1            297817 non-null  object \n",
      " 18  TopicID                    297817 non-null  object \n",
      " 19  QuestionID                 297817 non-null  object \n",
      " 20  DataValueTypeID            297817 non-null  object \n",
      " 21  StratificationCategoryID1  297817 non-null  object \n",
      " 22  StratificationID1          297817 non-null  object \n",
      "dtypes: float64(3), int64(2), object(18)\n",
      "memory usage: 52.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_source_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cardiovascular Disease                             52816\n",
       "Diabetes                                           44109\n",
       "Chronic Obstructive Pulmonary Disease              36815\n",
       "Nutrition, Physical Activity, and Weight Status    26507\n",
       "Overarching Conditions                             23453\n",
       "Arthritis                                          22255\n",
       "Asthma                                             21461\n",
       "Alcohol                                            20129\n",
       "Tobacco                                            16305\n",
       "Chronic Kidney Disease                              9851\n",
       "Cancer                                              6399\n",
       "Oral Health                                         5777\n",
       "Mental Health                                       4395\n",
       "Immunization                                        3553\n",
       "Older Adults                                        2829\n",
       "Reproductive Health                                 1110\n",
       "Disability                                            53\n",
       "Name: Topic, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_source_filtered['Topic'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 52816 entries, 11034 to 293242\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   YearStart                  52816 non-null  int64  \n",
      " 1   YearEnd                    52816 non-null  int64  \n",
      " 2   LocationAbbr               52816 non-null  object \n",
      " 3   LocationDesc               52816 non-null  object \n",
      " 4   DataSource                 52816 non-null  object \n",
      " 5   Topic                      52816 non-null  object \n",
      " 6   Question                   52816 non-null  object \n",
      " 7   Response                   33339 non-null  object \n",
      " 8   DataValueUnit              42434 non-null  object \n",
      " 9   DataValueType              52816 non-null  object \n",
      " 10  DataValue                  52816 non-null  object \n",
      " 11  DataValueAlt               46495 non-null  float64\n",
      " 12  DataValueFootnoteSymbol    33540 non-null  object \n",
      " 13  DatavalueFootnote          33465 non-null  object \n",
      " 14  LowConfidenceLimit         37332 non-null  float64\n",
      " 15  HighConfidenceLimit        37332 non-null  float64\n",
      " 16  StratificationCategory1    52816 non-null  object \n",
      " 17  Stratification1            52816 non-null  object \n",
      " 18  TopicID                    52816 non-null  object \n",
      " 19  QuestionID                 52816 non-null  object \n",
      " 20  DataValueTypeID            52816 non-null  object \n",
      " 21  StratificationCategoryID1  52816 non-null  object \n",
      " 22  StratificationID1          52816 non-null  object \n",
      "dtypes: float64(3), int64(2), object(18)\n",
      "memory usage: 9.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cvd=df_source_filtered[df_source_filtered['Topic']=='Cardiovascular Disease']\n",
    "df_cvd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mortality from heart failure                                                                                                   6136\n",
       "Mortality from total cardiovascular diseases                                                                                   6135\n",
       "Mortality from cerebrovascular disease (stroke)                                                                                6135\n",
       "Mortality from diseases of the heart                                                                                           6135\n",
       "Mortality from coronary heart disease                                                                                          6135\n",
       "Cholesterol screening among adults aged >= 18 years                                                                            2442\n",
       "Awareness of high blood pressure among adults aged >= 18 years                                                                 2422\n",
       "Influenza vaccination among noninstitutionalized adults aged 18-64 years with a history of coronary heart disease or stroke    2386\n",
       "High cholesterol prevalence among adults aged >= 18 years                                                                      2382\n",
       "Influenza vaccination among noninstitutionalized adults aged >= 65 years with a history of coronary heart disease or stroke    2354\n",
       "Pneumococcal vaccination among noninstitutionalized adults aged >= 65 years with a history of coronary heart disease           2246\n",
       "Pneumococcal vaccination among noninstitutionalized adults aged 18-64 years with a history of coronary heart disease           2191\n",
       "Taking medicine for high blood pressure control among adults aged >= 18 years with high blood pressure                         2064\n",
       "Hospitalization for stroke                                                                                                     1332\n",
       "Hospitalization for acute myocardial infarction                                                                                1332\n",
       "Awareness of high blood pressure among women aged 18-44 years                                                                   512\n",
       "Hospitalization for heart failure among Medicare-eligible persons aged >= 65 years                                              468\n",
       "Pre-pregnancy hypertension                                                                                                        9\n",
       "Name: Question, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cvd['Question'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Focusing on mortalities of cardiovascular diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======start of feature  YearStart =====================\n",
      "2010    4909\n",
      "2014    4908\n",
      "2013    4908\n",
      "2012    4908\n",
      "2011    4908\n",
      "Name: YearStart, dtype: int64\n",
      "======end of feature  YearStart ===================== \n",
      "\n",
      "=======start of feature  YearEnd =====================\n",
      "2010    4909\n",
      "2014    4908\n",
      "2013    4908\n",
      "2012    4908\n",
      "2011    4908\n",
      "Name: YearEnd, dtype: int64\n",
      "======end of feature  YearEnd ===================== \n",
      "\n",
      "=======start of feature  LocationAbbr =====================\n",
      "FL    481\n",
      "NE    480\n",
      "ND    480\n",
      "GA    480\n",
      "HI    480\n",
      "IL    480\n",
      "KY    480\n",
      "LA    480\n",
      "MD    480\n",
      "ME    480\n",
      "MS    480\n",
      "MT    480\n",
      "NC    480\n",
      "NH    480\n",
      "NM    480\n",
      "NJ    480\n",
      "NY    480\n",
      "OR    480\n",
      "PA    480\n",
      "RI    480\n",
      "SC    480\n",
      "TN    480\n",
      "TX    480\n",
      "VT    480\n",
      "WA    480\n",
      "DC    480\n",
      "AZ    480\n",
      "CA    480\n",
      "AL    480\n",
      "SD    480\n",
      "UT    480\n",
      "WY    480\n",
      "AR    480\n",
      "CO    480\n",
      "DE    480\n",
      "IA    480\n",
      "NV    480\n",
      "ID    480\n",
      "OK    480\n",
      "VA    480\n",
      "KS    480\n",
      "WI    480\n",
      "AK    480\n",
      "MA    480\n",
      "CT    480\n",
      "MI    480\n",
      "MN    480\n",
      "IN    480\n",
      "MO    480\n",
      "OH    480\n",
      "WV    480\n",
      "US     60\n",
      "Name: LocationAbbr, dtype: int64\n",
      "======end of feature  LocationAbbr ===================== \n",
      "\n",
      "=======start of feature  LocationDesc =====================\n",
      "Florida                 481\n",
      "Nebraska                480\n",
      "North Dakota            480\n",
      "Georgia                 480\n",
      "Hawaii                  480\n",
      "Illinois                480\n",
      "Kentucky                480\n",
      "Louisiana               480\n",
      "Maryland                480\n",
      "Maine                   480\n",
      "Mississippi             480\n",
      "Montana                 480\n",
      "North Carolina          480\n",
      "New Hampshire           480\n",
      "New Mexico              480\n",
      "New Jersey              480\n",
      "New York                480\n",
      "Oregon                  480\n",
      "Pennsylvania            480\n",
      "Rhode Island            480\n",
      "South Carolina          480\n",
      "Tennessee               480\n",
      "Texas                   480\n",
      "Vermont                 480\n",
      "Washington              480\n",
      "District of Columbia    480\n",
      "Arizona                 480\n",
      "California              480\n",
      "Alabama                 480\n",
      "South Dakota            480\n",
      "Utah                    480\n",
      "Wyoming                 480\n",
      "Arkansas                480\n",
      "Colorado                480\n",
      "Delaware                480\n",
      "Iowa                    480\n",
      "Nevada                  480\n",
      "Idaho                   480\n",
      "Oklahoma                480\n",
      "Virginia                480\n",
      "Kansas                  480\n",
      "Wisconsin               480\n",
      "Alaska                  480\n",
      "Massachusetts           480\n",
      "Connecticut             480\n",
      "Michigan                480\n",
      "Minnesota               480\n",
      "Indiana                 480\n",
      "Missouri                480\n",
      "Ohio                    480\n",
      "West Virginia           480\n",
      "United States            60\n",
      "Name: LocationDesc, dtype: int64\n",
      "======end of feature  LocationDesc ===================== \n",
      "\n",
      "=======start of feature  DataSource =====================\n",
      "NVSS    24541\n",
      "Name: DataSource, dtype: int64\n",
      "======end of feature  DataSource ===================== \n",
      "\n",
      "=======start of feature  Topic =====================\n",
      "Cardiovascular Disease    24541\n",
      "Name: Topic, dtype: int64\n",
      "======end of feature  Topic ===================== \n",
      "\n",
      "=======start of feature  Question =====================\n",
      "Mortality from heart failure                       6136\n",
      "Mortality from diseases of the heart               6135\n",
      "Mortality from total cardiovascular diseases       6135\n",
      "Mortality from cerebrovascular disease (stroke)    6135\n",
      "Name: Question, dtype: int64\n",
      "======end of feature  Question ===================== \n",
      "\n",
      "=======start of feature  Response =====================\n",
      "     24540\n",
      "Name: Response, dtype: int64\n",
      "======end of feature  Response ===================== \n",
      "\n",
      "=======start of feature  DataValueUnit =====================\n",
      "cases per 100,000    16360\n",
      "Name: DataValueUnit, dtype: int64\n",
      "======end of feature  DataValueUnit ===================== \n",
      "\n",
      "=======start of feature  DataValueType =====================\n",
      "Number               8181\n",
      "Age-adjusted Rate    8180\n",
      "Crude Rate           8180\n",
      "Name: DataValueType, dtype: int64\n",
      "======end of feature  DataValueType ===================== \n",
      "\n",
      "=======start of feature  DataValue =====================\n",
      "        4497\n",
      "20        46\n",
      "25        44\n",
      "30        44\n",
      "27        42\n",
      "        ... \n",
      "1036       1\n",
      "597        1\n",
      "8885       1\n",
      "7450       1\n",
      "1104       1\n",
      "Name: DataValue, Length: 6749, dtype: int64\n",
      "======end of feature  DataValue ===================== \n",
      "\n",
      "=======start of feature  DataValueAlt =====================\n",
      "20.0      46\n",
      "25.0      44\n",
      "30.0      44\n",
      "27.0      42\n",
      "22.0      41\n",
      "          ..\n",
      "1036.0     1\n",
      "597.0      1\n",
      "8885.0     1\n",
      "7450.0     1\n",
      "1104.0     1\n",
      "Name: DataValueAlt, Length: 6748, dtype: int64\n",
      "======end of feature  DataValueAlt ===================== \n",
      "\n",
      "=======start of feature  DataValueFootnoteSymbol =====================\n",
      "     20043\n",
      "~     4497\n",
      "Name: DataValueFootnoteSymbol, dtype: int64\n",
      "======end of feature  DataValueFootnoteSymbol ===================== \n",
      "\n",
      "=======start of feature  DatavalueFootnote =====================\n",
      "                                                          19983\n",
      "Data not shown because of too few respondents or cases     4497\n",
      "Name: DatavalueFootnote, dtype: int64\n",
      "======end of feature  DatavalueFootnote ===================== \n",
      "\n",
      "=======start of feature  LowConfidenceLimit =====================\n",
      "33.3     24\n",
      "35.1     22\n",
      "34.3     22\n",
      "42.8     22\n",
      "33.5     21\n",
      "         ..\n",
      "282.3     1\n",
      "171.8     1\n",
      "265.9     1\n",
      "217.1     1\n",
      "4.1       1\n",
      "Name: LowConfidenceLimit, Length: 3079, dtype: int64\n",
      "======end of feature  LowConfidenceLimit ===================== \n",
      "\n",
      "=======start of feature  HighConfidenceLimit =====================\n",
      "40.1     22\n",
      "40.4     21\n",
      "41.5     20\n",
      "36.8     20\n",
      "39.1     20\n",
      "         ..\n",
      "314.2     1\n",
      "372.0     1\n",
      "362.9     1\n",
      "344.3     1\n",
      "11.5      1\n",
      "Name: HighConfidenceLimit, Length: 3281, dtype: int64\n",
      "======end of feature  HighConfidenceLimit ===================== \n",
      "\n",
      "=======start of feature  StratificationCategory1 =====================\n",
      "Race/Ethnicity    15300\n",
      "Gender             6120\n",
      "Overall            3121\n",
      "Name: StratificationCategory1, dtype: int64\n",
      "======end of feature  StratificationCategory1 ===================== \n",
      "\n",
      "=======start of feature  Stratification1 =====================\n",
      "Overall                             3121\n",
      "Asian or Pacific Islander           3060\n",
      "Black, non-Hispanic                 3060\n",
      "Female                              3060\n",
      "White, non-Hispanic                 3060\n",
      "American Indian or Alaska Native    3060\n",
      "Hispanic                            3060\n",
      "Male                                3060\n",
      "Name: Stratification1, dtype: int64\n",
      "======end of feature  Stratification1 ===================== \n",
      "\n",
      "=======start of feature  TopicID =====================\n",
      "CVD    24541\n",
      "Name: TopicID, dtype: int64\n",
      "======end of feature  TopicID ===================== \n",
      "\n",
      "=======start of feature  QuestionID =====================\n",
      "CVD1_4    6136\n",
      "CVD1_2    6135\n",
      "CVD1_1    6135\n",
      "CVD1_5    6135\n",
      "Name: QuestionID, dtype: int64\n",
      "======end of feature  QuestionID ===================== \n",
      "\n",
      "=======start of feature  DataValueTypeID =====================\n",
      "Nmbr          8181\n",
      "AgeAdjRate    8180\n",
      "CrdRate       8180\n",
      "Name: DataValueTypeID, dtype: int64\n",
      "======end of feature  DataValueTypeID ===================== \n",
      "\n",
      "=======start of feature  StratificationCategoryID1 =====================\n",
      "RACE       15300\n",
      "GENDER      6120\n",
      "OVERALL     3121\n",
      "Name: StratificationCategoryID1, dtype: int64\n",
      "======end of feature  StratificationCategoryID1 ===================== \n",
      "\n",
      "=======start of feature  StratificationID1 =====================\n",
      "OVR     3121\n",
      "API     3060\n",
      "BLK     3060\n",
      "GENF    3060\n",
      "WHT     3060\n",
      "AIAN    3060\n",
      "HIS     3060\n",
      "GENM    3060\n",
      "Name: StratificationID1, dtype: int64\n",
      "======end of feature  StratificationID1 ===================== \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# A first analysis is done about mortality:\n",
    "# 1.'Mortality from heart failure',\n",
    "# 2.'Mortality from cerebrovascular disease (stroke)',\n",
    "# 3.'Mortality from diseases of the heart',\n",
    "# 4.'Mortality from total cardiovascular diseases'\n",
    "df_cvd_mortality=df_cvd[(df_cvd['Question']=='Mortality from heart failure')\n",
    "                        |(df_cvd['Question']=='Mortality from cerebrovascular disease (stroke)')\n",
    "                        |(df_cvd['Question']=='Mortality from diseases of the heart')\n",
    "                        |(df_cvd['Question']=='Mortality from total cardiovascular diseases')]\n",
    "df_values(df_cvd_mortality)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20      46\n",
      "25      44\n",
      "30      44\n",
      "27      42\n",
      "22      41\n",
      "        ..\n",
      "1036     1\n",
      "597      1\n",
      "8885     1\n",
      "7450     1\n",
      "1104     1\n",
      "Name: DataValue, Length: 6748, dtype: int64\n",
      "(20044, 23)\n"
     ]
    }
   ],
   "source": [
    "#There are 4497 rows with 'DataValue' blank, so lets drop those empty values\n",
    "df_cvd_mortality=df_cvd_mortality[df_cvd_mortality['DataValue']!=' ']\n",
    "df_cvd_mortality.reset_index(drop=True, inplace=True)\n",
    "print(df_cvd_mortality['DataValue'].value_counts())\n",
    "print(df_cvd_mortality.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20044 entries, 0 to 20043\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   YearStart                  20044 non-null  int64  \n",
      " 1   YearEnd                    20044 non-null  int64  \n",
      " 2   LocationAbbr               20044 non-null  object \n",
      " 3   LocationDesc               20044 non-null  object \n",
      " 4   DataSource                 20044 non-null  object \n",
      " 5   Topic                      20044 non-null  object \n",
      " 6   Question                   20044 non-null  object \n",
      " 7   Response                   20043 non-null  object \n",
      " 8   DataValueUnit              13362 non-null  object \n",
      " 9   DataValueType              20044 non-null  object \n",
      " 10  DataValue                  20044 non-null  object \n",
      " 11  DataValueAlt               20044 non-null  float64\n",
      " 12  DataValueFootnoteSymbol    20043 non-null  object \n",
      " 13  DatavalueFootnote          19983 non-null  object \n",
      " 14  LowConfidenceLimit         13362 non-null  float64\n",
      " 15  HighConfidenceLimit        13362 non-null  float64\n",
      " 16  StratificationCategory1    20044 non-null  object \n",
      " 17  Stratification1            20044 non-null  object \n",
      " 18  TopicID                    20044 non-null  object \n",
      " 19  QuestionID                 20044 non-null  object \n",
      " 20  DataValueTypeID            20044 non-null  object \n",
      " 21  StratificationCategoryID1  20044 non-null  object \n",
      " 22  StratificationID1          20044 non-null  object \n",
      "dtypes: float64(3), int64(2), object(18)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cvd_mortality.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20044 entries, 0 to 20043\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   YearStart                20044 non-null  int64  \n",
      " 1   YearEnd                  20044 non-null  int64  \n",
      " 2   LocationAbbr             20044 non-null  object \n",
      " 3   LocationDesc             20044 non-null  object \n",
      " 4   Topic                    20044 non-null  object \n",
      " 5   Question                 20044 non-null  object \n",
      " 6   DataValueUnit            13362 non-null  object \n",
      " 7   DataValueType            20044 non-null  object \n",
      " 8   DataValue                20044 non-null  object \n",
      " 9   DataValueAlt             20044 non-null  float64\n",
      " 10  LowConfidenceLimit       13362 non-null  float64\n",
      " 11  HighConfidenceLimit      13362 non-null  float64\n",
      " 12  StratificationCategory1  20044 non-null  object \n",
      " 13  Stratification1          20044 non-null  object \n",
      "dtypes: float64(3), int64(2), object(9)\n",
      "memory usage: 2.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Lets drop another features considered meaningless in the filtered df:\n",
    "# DataSource, Response,DataValueFootnoteSymbol,DatavalueFootnote,TopicID,QuestionID,DataValueTypeID,StratificationCategoryID1,StratificationID1 \n",
    "indexes=[4,7,12,13,18,19,20,21,22]\n",
    "df_cvd_mortality=df_cvd_mortality.drop(df_cvd_mortality.columns[indexes],axis=1)\n",
    "print(df_cvd_mortality.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>YearStart</th>\n",
       "      <th>YearEnd</th>\n",
       "      <th>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Question</th>\n",
       "      <th>DataValueUnit</th>\n",
       "      <th>DataValueType</th>\n",
       "      <th>DataValue</th>\n",
       "      <th>DataValueAlt</th>\n",
       "      <th>LowConfidenceLimit</th>\n",
       "      <th>HighConfidenceLimit</th>\n",
       "      <th>StratificationCategory1</th>\n",
       "      <th>Stratification1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>NE</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from diseases of the heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>3296</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>NM</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from diseases of the heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>3424</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>SD</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from diseases of the heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>1704</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>UT</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from diseases of the heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>3431</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from diseases of the heart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>1035</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20039</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WV</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>1069</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20040</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>133</td>\n",
       "      <td>133.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20041</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>71</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20042</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>204</td>\n",
       "      <td>204.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20043</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number</td>\n",
       "      <td>197</td>\n",
       "      <td>197.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20044 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearStart  YearEnd LocationAbbr   LocationDesc                   Topic  \\\n",
       "0           2014     2014           NE       Nebraska  Cardiovascular Disease   \n",
       "1           2014     2014           NM     New Mexico  Cardiovascular Disease   \n",
       "2           2014     2014           SD   South Dakota  Cardiovascular Disease   \n",
       "3           2014     2014           UT           Utah  Cardiovascular Disease   \n",
       "4           2014     2014           WY        Wyoming  Cardiovascular Disease   \n",
       "...          ...      ...          ...            ...                     ...   \n",
       "20039       2010     2010           WV  West Virginia  Cardiovascular Disease   \n",
       "20040       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "20041       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "20042       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "20043       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "\n",
       "                                              Question DataValueUnit  \\\n",
       "0                 Mortality from diseases of the heart           NaN   \n",
       "1                 Mortality from diseases of the heart           NaN   \n",
       "2                 Mortality from diseases of the heart           NaN   \n",
       "3                 Mortality from diseases of the heart           NaN   \n",
       "4                 Mortality from diseases of the heart           NaN   \n",
       "...                                                ...           ...   \n",
       "20039  Mortality from cerebrovascular disease (stroke)           NaN   \n",
       "20040  Mortality from cerebrovascular disease (stroke)           NaN   \n",
       "20041  Mortality from cerebrovascular disease (stroke)           NaN   \n",
       "20042  Mortality from cerebrovascular disease (stroke)           NaN   \n",
       "20043  Mortality from cerebrovascular disease (stroke)           NaN   \n",
       "\n",
       "      DataValueType DataValue  DataValueAlt  LowConfidenceLimit  \\\n",
       "0            Number      3296        3296.0                 NaN   \n",
       "1            Number      3424        3424.0                 NaN   \n",
       "2            Number      1704        1704.0                 NaN   \n",
       "3            Number      3431        3431.0                 NaN   \n",
       "4            Number      1035        1035.0                 NaN   \n",
       "...             ...       ...           ...                 ...   \n",
       "20039        Number      1069        1069.0                 NaN   \n",
       "20040        Number       133         133.0                 NaN   \n",
       "20041        Number        71          71.0                 NaN   \n",
       "20042        Number       204         204.0                 NaN   \n",
       "20043        Number       197         197.0                 NaN   \n",
       "\n",
       "       HighConfidenceLimit StratificationCategory1      Stratification1  \n",
       "0                      NaN                 Overall              Overall  \n",
       "1                      NaN                 Overall              Overall  \n",
       "2                      NaN                 Overall              Overall  \n",
       "3                      NaN                 Overall              Overall  \n",
       "4                      NaN                 Overall              Overall  \n",
       "...                    ...                     ...                  ...  \n",
       "20039                  NaN          Race/Ethnicity  White, non-Hispanic  \n",
       "20040                  NaN                  Gender               Female  \n",
       "20041                  NaN                  Gender                 Male  \n",
       "20042                  NaN                 Overall              Overall  \n",
       "20043                  NaN          Race/Ethnicity  White, non-Hispanic  \n",
       "\n",
       "[20044 rows x 14 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cvd_mortality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2.2 Study the stratification feature and see the difference the overall value and rest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearStart</th>\n",
       "      <th>YearEnd</th>\n",
       "      <th>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Question</th>\n",
       "      <th>DataValueUnit</th>\n",
       "      <th>DataValueType</th>\n",
       "      <th>DataValue</th>\n",
       "      <th>DataValueAlt</th>\n",
       "      <th>LowConfidenceLimit</th>\n",
       "      <th>HighConfidenceLimit</th>\n",
       "      <th>StratificationCategory1</th>\n",
       "      <th>Stratification1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>189</td>\n",
       "      <td>189.0</td>\n",
       "      <td>174.4</td>\n",
       "      <td>203.7</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>153.1</td>\n",
       "      <td>153.1</td>\n",
       "      <td>148.5</td>\n",
       "      <td>157.7</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>139.1</td>\n",
       "      <td>139.1</td>\n",
       "      <td>123.3</td>\n",
       "      <td>154.9</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>230.7</td>\n",
       "      <td>230.7</td>\n",
       "      <td>223.7</td>\n",
       "      <td>237.6</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>186.4</td>\n",
       "      <td>186.4</td>\n",
       "      <td>182.5</td>\n",
       "      <td>190.4</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13357</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WV</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Crude Rate</td>\n",
       "      <td>61.3</td>\n",
       "      <td>61.3</td>\n",
       "      <td>57.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13358</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Crude Rate</td>\n",
       "      <td>48.2</td>\n",
       "      <td>48.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>56.3</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13359</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Crude Rate</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13360</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Crude Rate</td>\n",
       "      <td>36.2</td>\n",
       "      <td>36.2</td>\n",
       "      <td>31.2</td>\n",
       "      <td>41.2</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13361</th>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>WY</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from cerebrovascular disease (stroke)</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Crude Rate</td>\n",
       "      <td>40.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>34.6</td>\n",
       "      <td>45.9</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13362 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearStart  YearEnd LocationAbbr   LocationDesc                   Topic  \\\n",
       "0           2014     2014           AK         Alaska  Cardiovascular Disease   \n",
       "1           2014     2014           CT    Connecticut  Cardiovascular Disease   \n",
       "2           2014     2014           CT    Connecticut  Cardiovascular Disease   \n",
       "3           2014     2014           CT    Connecticut  Cardiovascular Disease   \n",
       "4           2014     2014           CT    Connecticut  Cardiovascular Disease   \n",
       "...          ...      ...          ...            ...                     ...   \n",
       "13357       2010     2010           WV  West Virginia  Cardiovascular Disease   \n",
       "13358       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "13359       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "13360       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "13361       2010     2010           WY        Wyoming  Cardiovascular Disease   \n",
       "\n",
       "                                              Question      DataValueUnit  \\\n",
       "0         Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "1         Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "2         Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "3         Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "4         Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "...                                                ...                ...   \n",
       "13357  Mortality from cerebrovascular disease (stroke)  cases per 100,000   \n",
       "13358  Mortality from cerebrovascular disease (stroke)  cases per 100,000   \n",
       "13359  Mortality from cerebrovascular disease (stroke)  cases per 100,000   \n",
       "13360  Mortality from cerebrovascular disease (stroke)  cases per 100,000   \n",
       "13361  Mortality from cerebrovascular disease (stroke)  cases per 100,000   \n",
       "\n",
       "           DataValueType DataValue  DataValueAlt  LowConfidenceLimit  \\\n",
       "0      Age-adjusted Rate       189         189.0               174.4   \n",
       "1      Age-adjusted Rate     153.1         153.1               148.5   \n",
       "2      Age-adjusted Rate     139.1         139.1               123.3   \n",
       "3      Age-adjusted Rate     230.7         230.7               223.7   \n",
       "4      Age-adjusted Rate     186.4         186.4               182.5   \n",
       "...                  ...       ...           ...                 ...   \n",
       "13357         Crude Rate      61.3          61.3                57.6   \n",
       "13358         Crude Rate      48.2          48.2                40.0   \n",
       "13359         Crude Rate      24.7          24.7                19.3   \n",
       "13360         Crude Rate      36.2          36.2                31.2   \n",
       "13361         Crude Rate      40.2          40.2                34.6   \n",
       "\n",
       "       HighConfidenceLimit StratificationCategory1      Stratification1  \n",
       "0                    203.7          Race/Ethnicity  White, non-Hispanic  \n",
       "1                    157.7                  Gender               Female  \n",
       "2                    154.9          Race/Ethnicity             Hispanic  \n",
       "3                    237.6                  Gender                 Male  \n",
       "4                    190.4                 Overall              Overall  \n",
       "...                    ...                     ...                  ...  \n",
       "13357                 65.0          Race/Ethnicity  White, non-Hispanic  \n",
       "13358                 56.3                  Gender               Female  \n",
       "13359                 31.2                  Gender                 Male  \n",
       "13360                 41.2                 Overall              Overall  \n",
       "13361                 45.9          Race/Ethnicity  White, non-Hispanic  \n",
       "\n",
       "[13362 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# When DataValueUnit is NaN the figure expressed in DataValue is unknown, \n",
    "# thus let's drop those rows to carried out an precise analysis\n",
    "df_cvd_mortality.dropna(subset=['DataValueUnit'], inplace=True)\n",
    "df_cvd_mortality.reset_index(drop=True, inplace=True)\n",
    "df_cvd_mortality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True    13362\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Question</th>\n",
       "      <th>DataValueUnit</th>\n",
       "      <th>DataValueType</th>\n",
       "      <th>DataValue</th>\n",
       "      <th>DataValueAlt</th>\n",
       "      <th>LowConfidenceLimit</th>\n",
       "      <th>HighConfidenceLimit</th>\n",
       "      <th>StratificationCategory1</th>\n",
       "      <th>Stratification1</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>189</td>\n",
       "      <td>189.0</td>\n",
       "      <td>174.4</td>\n",
       "      <td>203.7</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>153.1</td>\n",
       "      <td>153.1</td>\n",
       "      <td>148.5</td>\n",
       "      <td>157.7</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Female</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>139.1</td>\n",
       "      <td>139.1</td>\n",
       "      <td>123.3</td>\n",
       "      <td>154.9</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>230.7</td>\n",
       "      <td>230.7</td>\n",
       "      <td>223.7</td>\n",
       "      <td>237.6</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Male</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CT</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from total cardiovascular diseases</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>186.4</td>\n",
       "      <td>186.4</td>\n",
       "      <td>182.5</td>\n",
       "      <td>190.4</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LocationAbbr LocationDesc                   Topic  \\\n",
       "0           AK       Alaska  Cardiovascular Disease   \n",
       "1           CT  Connecticut  Cardiovascular Disease   \n",
       "2           CT  Connecticut  Cardiovascular Disease   \n",
       "3           CT  Connecticut  Cardiovascular Disease   \n",
       "4           CT  Connecticut  Cardiovascular Disease   \n",
       "\n",
       "                                       Question      DataValueUnit  \\\n",
       "0  Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "1  Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "2  Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "3  Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "4  Mortality from total cardiovascular diseases  cases per 100,000   \n",
       "\n",
       "       DataValueType DataValue  DataValueAlt  LowConfidenceLimit  \\\n",
       "0  Age-adjusted Rate       189         189.0               174.4   \n",
       "1  Age-adjusted Rate     153.1         153.1               148.5   \n",
       "2  Age-adjusted Rate     139.1         139.1               123.3   \n",
       "3  Age-adjusted Rate     230.7         230.7               223.7   \n",
       "4  Age-adjusted Rate     186.4         186.4               182.5   \n",
       "\n",
       "   HighConfidenceLimit StratificationCategory1      Stratification1  Year  \n",
       "0                203.7          Race/Ethnicity  White, non-Hispanic  2014  \n",
       "1                157.7                  Gender               Female  2014  \n",
       "2                154.9          Race/Ethnicity             Hispanic  2014  \n",
       "3                237.6                  Gender                 Male  2014  \n",
       "4                190.4                 Overall              Overall  2014  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge features regarding year if possible\n",
    "print((df_cvd_mortality['YearStart']==df_cvd_mortality['YearEnd']).value_counts())\n",
    "df_cvd_mortality['Year']=df_cvd_mortality['YearStart']\n",
    "df_cvd_mortality.drop(['YearStart','YearEnd'], axis=1, inplace=True)\n",
    "df_cvd_mortality.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mortality from total cardiovascular diseases       3600\n",
       "Mortality from diseases of the heart               3496\n",
       "Mortality from heart failure                       3216\n",
       "Mortality from cerebrovascular disease (stroke)    3050\n",
       "Name: Question, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cvd_mortality['Question'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Descriptive yearly analysis of different mortalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13362 entries, 0 to 13361\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   LocationAbbr             13362 non-null  object \n",
      " 1   LocationDesc             13362 non-null  object \n",
      " 2   Topic                    13362 non-null  object \n",
      " 3   Question                 13362 non-null  object \n",
      " 4   DataValueUnit            13362 non-null  object \n",
      " 5   DataValueType            13362 non-null  object \n",
      " 6   DataValue                13362 non-null  float64\n",
      " 7   DataValueAlt             13362 non-null  float64\n",
      " 8   LowConfidenceLimit       13362 non-null  float64\n",
      " 9   HighConfidenceLimit      13362 non-null  float64\n",
      " 10  StratificationCategory1  13362 non-null  object \n",
      " 11  Stratification1          13362 non-null  object \n",
      " 12  Year                     13362 non-null  int64  \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_cvd_mortality['DataValue']=pd.to_numeric(df_cvd_mortality['DataValue'])\n",
    "df_cvd_mortality.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating into different dataframes depending on 'Question' value\n",
    "# df_cvd (1 - 4)\n",
    "df_cvd_mortality_cardio_disease=df_cvd_mortality[df_cvd_mortality['Question']=='Mortality from total cardiovascular diseases']\n",
    "df_cvd_mortality_diseases_heart=df_cvd_mortality[df_cvd_mortality['Question']=='Mortality from diseases of the heart']\n",
    "df_cvd_mortality_heart_failure=df_cvd_mortality[df_cvd_mortality['Question']=='Mortality from heart failure']\n",
    "df_cvd_mortality_cerebrovascular=df_cvd_mortality[df_cvd_mortality['Question']=='Mortality from cerebrovascular disease (stroke)']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Year evolution of mortality from total cardiovascular diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DV_overall</th>\n",
       "      <th>DV_male</th>\n",
       "      <th>DV_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>25472.5</td>\n",
       "      <td>27479.8</td>\n",
       "      <td>22961.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>24968.4</td>\n",
       "      <td>27012.1</td>\n",
       "      <td>22400.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>24836.7</td>\n",
       "      <td>26888.6</td>\n",
       "      <td>22265.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>24875.7</td>\n",
       "      <td>27135.2</td>\n",
       "      <td>22104.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>24857.8</td>\n",
       "      <td>27190.7</td>\n",
       "      <td>22008.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DV_overall  DV_male  DV_female\n",
       "Year                                \n",
       "2010     25472.5  27479.8    22961.6\n",
       "2011     24968.4  27012.1    22400.5\n",
       "2012     24836.7  26888.6    22265.5\n",
       "2013     24875.7  27135.2    22104.5\n",
       "2014     24857.8  27190.7    22008.3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the evolution of deaths by total cardiovascular diseases through the years\n",
    "# The datavalues regarding the overall, male and female count\n",
    "\n",
    "df_cvd_1_overall=df_cvd_mortality_cardio_disease[df_cvd_mortality_cardio_disease['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_1_overall.rename(columns={'DataValue':'DV_overall'},inplace=True)\n",
    "df_cvd_1_male=df_cvd_mortality_cardio_disease[df_cvd_mortality_cardio_disease['Stratification1']=='Male'].groupby('Year').sum()\n",
    "df_cvd_1_male.rename(columns={'DataValue':'DV_male'},inplace=True)\n",
    "df_cvd_1_female=df_cvd_mortality_cardio_disease[df_cvd_mortality_cardio_disease['Stratification1']=='Female'].groupby('Year').sum()\n",
    "df_cvd_1_female.rename(columns={'DataValue':'DV_female'},inplace=True)\n",
    "\n",
    "df_cvd_1_merged=pd.merge(df_cvd_1_overall,df_cvd_1_male,on='Year', how='inner')\n",
    "df_cvd_1_merged=pd.merge(df_cvd_1_merged,df_cvd_1_female,on='Year', how='inner')\n",
    "df_cvd_1_merged=df_cvd_1_merged[['DV_overall','DV_male','DV_female']]\n",
    "df_cvd_1_merged\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = pd.DataFrame({'Overall': df_cvd_1_merged['DV_overall'],\n",
    "                    'Male': df_cvd_1_merged['DV_male'],\n",
    "                       'Female': df_cvd_1_merged['DV_female']}, index= df_cvd_1_merged.index)\n",
    "ax = df_plot.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>DV_overall</th>\n",
       "      <th>DV_male</th>\n",
       "      <th>DV_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.964388</td>\n",
       "      <td>1.702806</td>\n",
       "      <td>1.832006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>-0.141268</td>\n",
       "      <td>-0.649795</td>\n",
       "      <td>0.156529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>-0.691387</td>\n",
       "      <td>-1.271018</td>\n",
       "      <td>-0.246589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-0.528482</td>\n",
       "      <td>-0.030583</td>\n",
       "      <td>-0.727344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>-0.603251</td>\n",
       "      <td>0.248590</td>\n",
       "      <td>-1.014603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DV_overall   DV_male  DV_female\n",
       "Year                                 \n",
       "2010    1.964388  1.702806   1.832006\n",
       "2011   -0.141268 -0.649795   0.156529\n",
       "2012   -0.691387 -1.271018  -0.246589\n",
       "2013   -0.528482 -0.030583  -0.727344\n",
       "2014   -0.603251  0.248590  -1.014603"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It's difficult to appreciate a trend in the latter plot,\n",
    "# Normalizing the value df_cvd_1_merged to see the evolution of the figures \n",
    "from sklearn import preprocessing\n",
    "x=df_cvd_1_merged.values \n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_cvd_1_merged_norm = pd.DataFrame(x_scaled, columns=df_cvd_1_merged.columns,index=df_cvd_1_merged.index)\n",
    "df_cvd_1_merged_norm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cvd_1_merged_norm_plt = pd.DataFrame({'Overall': df_cvd_1_merged_norm['DV_overall'],\n",
    "                    'Male': df_cvd_1_merged_norm['DV_male'],\n",
    "                       'Female': df_cvd_1_merged_norm['DV_female']}, index= df_cvd_1_merged_norm.index)\n",
    "ax1 = df_cvd_1_merged_norm_plt.plot.line(rot=0)\n",
    "ax1.xaxis.set_major_locator(plt.MaxNLocator(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_cvd_1_merged_norm_plt.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Comparison of year evolution of different mortality causes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show the evolution of deaths by total cardiovascular diseases through the years\n",
    "\n",
    "df_cvd_1_overall=df_cvd_mortality_cardio_disease[df_cvd_mortality_cardio_disease['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_2_overall=df_cvd_mortality_diseases_heart[df_cvd_mortality_diseases_heart['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_3_overall=df_cvd_mortality_heart_failure[df_cvd_mortality_heart_failure['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_4_overall=df_cvd_mortality_cerebrovascular[df_cvd_mortality_cerebrovascular['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "\n",
    "df_cvd_overall = pd.DataFrame({'Mortality from total cardiovascular diseases': df_cvd_1_overall['DataValue'],\n",
    "                    'Mortality from diseases of the heart': df_cvd_2_overall['DataValue'],\n",
    "                       'Mortality from heart failure': df_cvd_3_overall['DataValue'],\n",
    "                       'Mortality from cerebrovascular disease (stroke)':df_cvd_4_overall['DataValue']}, index= df_cvd_1_overall.index)\n",
    "\n",
    "x=df_cvd_overall.values \n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_cvd_overall_norm = pd.DataFrame(x_scaled, columns=df_cvd_overall.columns,index=df_cvd_overall.index)\n",
    "df_cvd_overall_norm \n",
    "\n",
    "ax = df_cvd_overall_norm.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = df_cvd_overall_norm.plot.line(rot=0)\n",
    "ax1.xaxis.set_major_locator(plt.MaxNLocator(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Year evolution of Mortality from heart failure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>DV_overall</th>\n",
       "      <th>DV_male</th>\n",
       "      <th>DV_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>9530.8</td>\n",
       "      <td>9734.4</td>\n",
       "      <td>9202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>9542.9</td>\n",
       "      <td>9753.6</td>\n",
       "      <td>9191.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>9458.8</td>\n",
       "      <td>9677.4</td>\n",
       "      <td>9098.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>9711.3</td>\n",
       "      <td>9974.2</td>\n",
       "      <td>9288.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>9941.6</td>\n",
       "      <td>10322.3</td>\n",
       "      <td>9416.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DV_overall  DV_male  DV_female\n",
       "Year                                \n",
       "2010      9530.8   9734.4     9202.0\n",
       "2011      9542.9   9753.6     9191.4\n",
       "2012      9458.8   9677.4     9098.4\n",
       "2013      9711.3   9974.2     9288.7\n",
       "2014      9941.6  10322.3     9416.2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In the above plot the line regarding Mortality from heart failure has a anomalous slope up during 2012-2014\n",
    "\n",
    "df_cvd_3_overall=df_cvd_mortality_heart_failure[df_cvd_mortality_heart_failure['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_3_overall.rename(columns={'DataValue':'DV_overall'},inplace=True)\n",
    "df_cvd_3_male=df_cvd_mortality_heart_failure[df_cvd_mortality_heart_failure['Stratification1']=='Male'].groupby('Year').sum()\n",
    "df_cvd_3_male.rename(columns={'DataValue':'DV_male'},inplace=True)\n",
    "df_cvd_3_female=df_cvd_mortality_heart_failure[df_cvd_mortality_heart_failure['Stratification1']=='Female'].groupby('Year').sum()\n",
    "df_cvd_3_female.rename(columns={'DataValue':'DV_female'},inplace=True)\n",
    "\n",
    "df_cvd_3_merged=pd.merge(df_cvd_3_overall,df_cvd_3_male,on='Year', how='inner')\n",
    "df_cvd_3_merged=pd.merge(df_cvd_3_merged,df_cvd_3_female,on='Year', how='inner')\n",
    "df_cvd_3_merged=df_cvd_3_merged[['DV_overall','DV_male','DV_female']]\n",
    "df_cvd_3_merged\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_cvd_3_merged.values \n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_cvd_3_merged_norm = pd.DataFrame(x_scaled, columns=df_cvd_3_merged.columns,index=df_cvd_3_merged.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cvd_3_merged_norm_plt = pd.DataFrame({'Overall': df_cvd_3_merged_norm['DV_overall'],\n",
    "                    'Male': df_cvd_3_merged_norm['DV_male'],\n",
    "                       'Female': df_cvd_3_merged_norm['DV_female']}, index= df_cvd_3_merged_norm.index)\n",
    "ax3 = df_cvd_3_merged_norm_plt.plot.line(rot=0)\n",
    "ax3.xaxis.set_major_locator(plt.MaxNLocator(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Year evolution of Mortality from diseases of the heart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>DV_overall</th>\n",
       "      <th>DV_male</th>\n",
       "      <th>DV_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>19292.0</td>\n",
       "      <td>21795.3</td>\n",
       "      <td>16557.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>18915.9</td>\n",
       "      <td>21449.9</td>\n",
       "      <td>16130.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>18850.4</td>\n",
       "      <td>21410.6</td>\n",
       "      <td>16042.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>18936.2</td>\n",
       "      <td>21585.2</td>\n",
       "      <td>16029.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>18905.0</td>\n",
       "      <td>21616.6</td>\n",
       "      <td>15933.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DV_overall  DV_male  DV_female\n",
       "Year                                \n",
       "2010     19292.0  21795.3    16557.2\n",
       "2011     18915.9  21449.9    16130.2\n",
       "2012     18850.4  21410.6    16042.1\n",
       "2013     18936.2  21585.2    16029.1\n",
       "2014     18905.0  21616.6    15933.1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In the above plot the line regarding Mortality from heart failure has a anomalous slope up during 2012-2014\n",
    "\n",
    "df_cvd_2_overall=df_cvd_mortality_diseases_heart[df_cvd_mortality_diseases_heart['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_2_overall.rename(columns={'DataValue':'DV_overall'},inplace=True)\n",
    "df_cvd_2_male=df_cvd_mortality_diseases_heart[df_cvd_mortality_diseases_heart['Stratification1']=='Male'].groupby('Year').sum()\n",
    "df_cvd_2_male.rename(columns={'DataValue':'DV_male'},inplace=True)\n",
    "df_cvd_2_female=df_cvd_mortality_diseases_heart[df_cvd_mortality_diseases_heart['Stratification1']=='Female'].groupby('Year').sum()\n",
    "df_cvd_2_female.rename(columns={'DataValue':'DV_female'},inplace=True)\n",
    "\n",
    "df_cvd_2_merged=pd.merge(df_cvd_2_overall,df_cvd_2_male,on='Year', how='inner')\n",
    "df_cvd_2_merged=pd.merge(df_cvd_2_merged,df_cvd_2_female,on='Year', how='inner')\n",
    "df_cvd_2_merged=df_cvd_2_merged[['DV_overall','DV_male','DV_female']]\n",
    "df_cvd_2_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_cvd_2_merged.values \n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_cvd_2_merged_norm = pd.DataFrame(x_scaled, columns=df_cvd_2_merged.columns,index=df_cvd_2_merged.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A1Rf+Ry0b9BJCCCGMSpKYKvZBnzkoWktyzK/w9fFfjB2OEEIIUWtIElPFWrh6E2g1DIBl5xZRoC0wckRCCCFE7SBJTEXcjoFzmyp82Dt9ZqItsCebRFacWmvwsIQQQoi6SJKY8kqKgM/aw8YXIft2hQ4N9vbARzMcgK/PLCErP6sKAhRCCCHqFkliyqtRc3BtCQXZcPKHCh/+l+7Poc1zIUe5zfIz31RBgEIIIUTdIklMeWk00GW6+jx0Gdynvfn9DGzuRf1cdTRm1dmV3M6p2GiOEEIIIUqSJKYi2owDaydIuQxXdlfoUI1Gw8zOT1KY40meksWSU0urKEghhBCibpAkpiKsHaDdU+rz0GUVPnx0sA/WaepoTMjFEOLT4w0ZnRBCCFGl/Pz8WLhwobHD0JEkpqI6F00pXdoKd2IrdKiNpTnPth9MQUYQhUoBi8IXVUGAQgghaoPJkyej0WhKPaKioowdWo0hSUxFNWoK/n1A0ULYygof/ky3xmhThgKwOXozEckRho5QCCFELfHoo4+SkJBQ4uHv72/ssGoMSWIqo8sM9c8T30BBboUObeRozagWXclPbQfAZyc+M3R0Qgghaglra2vc3d1LPMzNzfn111/p2LEjNjY2BAQE8P7771NQcLeYqkajYcmSJTz++OPY2dnRokULDh8+TFRUFH379sXe3p7u3btz+fJl3TGXL19m5MiRuLm54eDgQOfOndm588F9/1JTU3n++edxdXXFycmJ/v37c+rUqSr7+/gzSWIqo+lQcPKCrFuVKn43rZc/uTcHoyjmHLx+kCMJRwwfoxBCiDIpikJWfpZRHoboobdt2zaeeeYZXnvtNc6fP8+SJUtYtWoVH374YYn9PvjgA5599llOnjxJ8+bNmThxIi+88AJz584lLCwMgFdeeUW3f0ZGBsOGDWPnzp2Eh4czZMgQhg8fTmxs2UsnFEXhscceIzExkS1btnD8+HGCg4MZMGAAKSkpen+f5WFRLVepbcwtoOMU2P0vdYFvu/EVOrypmyO9/Jtz9HZXrFwOseD4An547AfMNJJTCiFEVcsuyKbr912Ncu2jE49iZ2lX7v1/++03HBwcdF8PHTqUGzdu8NZbb/Hcc88BEBAQwAcffMBf//pX3n33Xd2+U6ZMYdy4cQC8+eabdO/enX/84x8MGTIEgNdff50pU6bo9m/Xrh3t2rXTff2vf/2LjRs38ssvv5RIdort3r2bM2fOkJSUhLW1NQCffPIJmzZt4qeffuL5558v9/dZWZLEVFbH52DvxxB/DBJOgUe7hx9zj+k9/dm/uj+W9Y5zPvk822K2MdR/aBUFK4QQwhT169ePr776Sve1vb09gYGBhIaGlhh5KSwsJCcnh6ysLOzs1CSpbdu2utfd3NwAaNOmTYltOTk5pKWl4eTkRGZmJu+//z6//fYb169fp6CggOzs7PuOxBw/fpyMjAwaNGhQYnt2dnaJaaqqJElMZTm4QsuRcPYnOPY1jKzYnUa9ghoS1NCdmFu9sXbdwecnPmeg70AszS2rKGAhhBAAtha2HJ141GjXrojipOVeWq2W999/nzFjxpTa38bGRvfc0vLu54lGo7nvNm1R8db/+7//Y9u2bXzyyScEBgZia2vLk08+SV5eXpmxabVaPDw82LNnT6nX6tWrV75vUE9VmsTs27eP//73vxw/fpyEhAQ2btzIqFGj7rv/nj176NevX6ntERERNG/evAojraTO09Uk5sxPMPgDsK1f7kM1Gg3Tewbw1w29sGlwlPiMeNZdWsfEFhOrMGAhhBAajaZCUzo1TXBwMBcvXiyV3Ohr//79TJ48mdGjRwPqGpmYmJgHxpGYmIiFhQV+fn4GjaW8qnQRRmZmJu3atWPRooqNUly8eLHE7WRBQUFVFKGefLuBW+uifkrfV/jwEe09aWjvQHbSAACWnF5CRl6GoaMUQghRi7zzzjt88803vPfee5w7d46IiAjWrl3L3//+d73OGxgYyIYNGzh58iSnTp1i4sSJulGasgwcOJDu3bszatQotm3bRkxMDIcOHeLvf/+7buFwVavSJGbo0KH861//KnPI60FcXV1L3U5WI2k0d4vfVaKfko2lOZO6+ZF/pxOWWldSclJYfX51FQQqhBCithgyZAi//fYbO3bsoHPnznTr1o358+fTuHFjvc67YMEC6tevT48ePRg+fDhDhgwhODj4vvtrNBq2bNlC7969mTp1Kk2bNmXChAnExMTo1uBUNY1iiPu9ynMhjabc00l+fn7k5OTQsmVL/v73v5c5xVQsNzeX3Ny7tVrS0tLw8fEhNTUVJycnQ34LZcvLhE+bQ24aPLMeAgdW6PDkjFy6/3sXWttT2HqvwdbCli1jttDQtmEVBSyEEHVHTk4O0dHR+Pv7l1gvIoznfv8maWlpODs7V+jzu0bd0+vh4cHSpUtZv349GzZsoFmzZgwYMIB9+/bd95h58+bh7Oyse/j4+FRjxICVPbQvWsdyrOL9lBo4WPNEsBcF6a1xIIDsgmwWn1ps4CCFEEKI2qdGJTHNmjVjxowZBAcH0717d7788ksee+wxPvnkk/seM3fuXFJTU3WPuLi4aoy4yL39lG5frfDhUx/xBzQkXVVHcX669BMxqTGGi08IIYSohWpUElOWbt26ERkZed/Xra2tcXJyKvGodg2DIKAvoMDxivdTCnJzpG+zRhRmBeBu0YFCpZDPwz83eJhCCCFEbVLjk5jw8HA8PDyMHcbDdb6nn1J+ToUPn94zAID4K/3RoGHH1R2cvnnakBEKIYQQtUqVJjEZGRmcPHmSkydPAhAdHc3Jkyd11f/mzp3Ls88+q9t/4cKFbNq0icjISM6dO8fcuXNZv359meWOa5ymj4KTN2Qlw/mfK3z4I4ENaO7uSHZmI5rZqwuZFxxfYJA+G0IIUdfJz9Kaw5D/FlWaxISFhdGhQwc6dOgAwJw5c+jQoQPvvPMOAAkJCSXKGefl5fHGG2/Qtm1bevXqxYEDB9i8eXOFb9E2CnML6DRZfR76dYUP12g0TOuptlePudwTKzMrwm6Esf/afgMGKYQQdUtxhdqsrCwjRyKKFf9b3Fs9uLKq7Rbr6lKZW7QMJiMJ5rcEbT48vxc821fo8NyCQh75925uZeQytHcYB27+RFD9INY9vg5zsxpaK0cIIWq4hIQE7ty5g6urK3Z2drpy+6J6KYpCVlYWSUlJ1KtXr9RSkcp8fkvvJEO6t59S6Ncw8n8VOtzawpznujfm0x2XiLncFceG24i8Hcnm6M2MaDKiioIWQojazd3dHYCkpCQjRyJA7atU/G+iLxmJMbTYI7BiCFjYwF8uVKifEkBKZh7d5/1BboGWKcNi+Cl6MR72Hvw6+lesza2rKGghhKj9CgsLyc/PN3YYdZqlpeV9q/DLSExN4NMV3NrAjTMQvgZ6VGxRsou9FU909Ob7o7Fcje6Am50bCZkJhFwI4blWz1VR0EIIUfuZm5vX3DY2olJq/C3WJkejgS6V76cExcXvYPeF24xrop5r6emlpOamGixMIYQQwtRJElMV2owFa2e4HQ2Xd1X48EBXB/o3d0VRIPZqSwLrBZKWl8aKsyuqIFghhBDCNEkSUxXu7acUWvF+SgDTi263/unENaa3mgnAmog1JGYmGiREIYQQwtRJElNV9Oyn1L1JA1p4OJGTryU6tjEd3TqSW5jLlye/NHCgQgghhGmSJKaqNAyEgH6AAmEVnwbSaDTM6KWOxnxz+Cqvtp8FwM+XfybqdpQBAxVCCCFMkyQxVal4NKaS/ZQeb+uJq6M1Sem5XL3ekIG+A9EqWj4L/8zAgQohhBCmR5KYqlTcTyk7Bc5vqvDhVhZmPNfDD4Bl+6N5tcOrmGvM2RO3hxM3Thg0VCGEEMLUSBJTlcwtoNMU9fmxivdTAni6qy+2luacT0gjMdmJMUFqH6n5x+dLQzMhhBB1miQxVS34OTCzhGthcD28wofXs7PiyY7eACzfH81L7V7C1sKWUzdPsSu24rdvCyGEELWFJDFVzaERtBqlPj9Wudutpzzih0YDf1xIIi3TlkktJwGw8MRCCrQFBgpUCCGEMC2SxFSHzjPUP8/+BFkpFT48oJEDA5q7AbDiQDRTWk2hvnV9YtJi2BS1yYCBCiGEEKZDkpjq4NMF3NtAQQ6cXFOpU0wvut16/Yl48vKteKHdCwB8efJLsvKzDBaqEEIIYSokiakOGs3d0ZjQ5ZXqp9TV34XWXmrxu++PXmVs07F4OXhxM/smayIqlxgJIYQQpkySmOrS5sl7+in9UeHDNRoN03sGALD68FUUxZxXO7wKwIqzK7idc9ug4QohhBA1nSQx1cXKHjo8rT6vZD+lYW08cHey4WZ6Lr+eSmCo/1BauLQgIz+DpaeXGjBYIYQQouaTJKY6dZqm/nlpG9yOqfDhJYvfXUGDhlkdZwEQcjGE+PR4w8QphBBCmABJYqqTnv2UACZ2UYvfXUhM59DlZHp49qCbRzcKtAUsOrnIsPEKIYQQNZgkMdWtS9EC3xPfVqqfkrOdJeM6qcXvlu2/AsDsjrMB2HxlMxHJEYaJUwghhKjhJImpbk0fBWcftZ/SuY2VOsWUR/zRaGD3xZtEJaXTskFLhvoPBeCzE9IcUgghRN0gSUx1MzO/208ptHL9lPwa2jOohVr8bvmBGABe7fAqFmYWHLx+kCMJRwwRqRBCCFGjSRJjDB2eBXMruHYcrlWuG/X0Xurt1htOxJOckYuPow/jm40HYMHxBWiViteiEUIIIUyJJDHG4NAIWo5Sn4cur9QpOvvVp623M7kFWtYcjQXg+bbPY29pz/nk82yP2W6gYIUQQoiaSZIYY+miXz8ljUbDtJ5qK4JvDseQk1+Ii40LU1qpU1WfnfiM/MJ8g4UrhBBC1DSSxBiLd2dwb6v2Uwr/rlKnGNbGAw9nG25l5PHLqesATGo5iQY2DYjPiGfdpXWGjFgIIYSoUSSJMRaNBjpPV5+HVa6fkqW5GZOLit8t3x+NoijYWdrxcvuXAVhyegkZeRmGilgIIYSoUSSJMaY2Y8HGWa3eW4l+SgATuvhiZ2XOxRvpHIi6BcDooNH4OfmRkpPC6vOrDRiwEEIIUXNIEmNMVnbQ/hn1+bHK3W7tbGvJuE4+ACzbHw2ApZklrwW/BsDqc6u5lX1L/1iFEEKIGkaSGGPrXNRPKXJ7pfopAUwtKn6399JNLt1IB2Cg70DaNmxLdkE2i08tNlCwQgghRM0hSYyxNWgCTfoDSqVvt/ZtYMeQlu4ArDigjsZoNHebQ/506SdiUmMMEKwQQghRc0gSUxN0LrrdOvxbyM+u1Cmm91Jvt94Qfo1bGbnqad0709u7N4VKIV+Ef2GQUIUQQoiaQpKYmqDpEHD2hezble6n1LFxfdr51COvQMt3R67qts8KnoUGDduvbufMzTOGilgIIYQwOkliaoJ7+ylVcoGvRqNhelHxu28PXyUnvxCAoPpBjGgyAoD5x+ejKIr+8QohhBA1gCQxNUVwUT+l6yfUnkqVMLS1O171bEnOzOPnk9d022e2n4mVmRVhN8LYf22/oSIWQgghjEqSmJrCviG0Gq0+r+QCX4t7it8tKyp+B+Dh4MHTLZ4GYOGJhRRqC/UOVwghhDA2SWJqkuIFvmfXV6qfEsD4Lj7YW5kTmZTBvsi79WGmtZmGo5Ujkbcj2Ry92RDRCiGEEEYlSUxN4t3pnn5K31bqFE42lozv7AvAsv1XdNudrZ2Z0UZNkhaFLyK3MFf/eIUQQggjkiSmJtFo7na3Dl0OlZz2mfKIH2Ya2B95i4uJ6brtTzV/Cjc7NxIyEwi5EGKIiIUQQgijkSSmpmn9JNjUgztXIapy/ZR8XOx4tLVa/G75gbujMTYWNsxsPxOApaeXkpaXpne4QgghhLFIElPTWNlBh6J+SqGVu90aYFrPAAA2hV8nKT1Ht31EkxEE1gskLS+N5Wcqt4BYCCGEqAkkiamJOk1V/4zcASnRlTpFx8b16eBbj7xCLd8dvlv8ztzMnFnBswBYE7GGxMxEfaMVQgghjKJKk5h9+/YxfPhwPD090Wg0bNq06aHH7N27l44dO2JjY0NAQACLF9fB5oUNmkCTAYACYZUfLZleNBrz7ZG7xe8Aenv3Jtg1mNzCXL48+aW+0QohhBBGUaVJTGZmJu3atWPRokXl2j86Opphw4bRq1cvwsPDefvtt3nttddYv359VYZZMxUv8A3/rtL9lIa0csOrni23s/LZcOJu8TuNRsOcTnMA+Pnyz0TdjtI7XCGEEKK6VWkSM3ToUP71r38xZsyYcu2/ePFifH19WbhwIS1atGD69OlMnTqVTz755L7H5ObmkpaWVuJRKwQNvttP6eyGSp3CwtyMKY/4AeoCX632bsuBdo3aMdB3IFpFy2fhnxkiYiGEEKJa1ag1MYcPH2bw4MEltg0ZMoSwsDDy8/PLPGbevHk4OzvrHj4+PtURatW7t5+SHgt8x3f2wcHagss3M9l76WaJ114Lfg1zjTl74vZw4sYJPYIVQgghql+NSmISExNxc3Mrsc3NzY2CggJu3bpV5jFz584lNTVV94iLi6uOUKuHrp9SeKX7KTnaWDKhs5rYLbvndmsAf2d/xgSpo2TSHFIIIYSpqVFJDKjrNe5V/MH65+3FrK2tcXJyKvGoNewbQquiqbhjyyp9mslFxe8ORiVz/nrJ6baX2r2ErYUtp26eYlfcLn2iFUIIIapVjUpi3N3dSUwsectvUlISFhYWNGjQwEhRGVmXe/opZSZX6hTe9e0Y2sYDgOUHSt6y3ciuEZNaTgLgsxOfUaAtqHysQgghRDWqUUlM9+7d2bFjR4lt27dvp1OnTlhaWhopKiPz6gge7aAwt9L9lACm9/QH4JdT10hKyynx2pRWU6hnXY/o1Gg2RW3SJ1ohhBCi2lRpEpORkcHJkyc5efIkoN5CffLkSWJjYwF1Pcuzzz6r2//FF1/k6tWrzJkzh4iICFasWMHy5ct54403qjLMmk2judvdOmxFpfspdfCtT8fG9ckvVPjmnuJ3AA5WDrzQ9gUAvjz5JVn5WXqFLIQQQlSHKk1iwsLC6NChAx06dABgzpw5dOjQgXfeeQeAhIQEXUID4O/vz5YtW9izZw/t27fngw8+4PPPP+eJJ56oyjBrvtZP3NNPaWelT1M8GvPd0atk55VMhsY1G4eXgxc3s2+yJmKNPtEKIYQQ1UKj1LJbUtLS0nB2diY1NbV2LfLd9jc4vAgCB8EzP1XqFIVahb6f7CYuJZt/jWrNM90al3h985XNvLX/LRwsHdgyZgv1beobInIhhBDioSrz+V2j1sSIByjupxS1E1KuPHjf+zA30zClhzoas+JAdInidwBD/YfSwqUFGfkZLD29VK9whRBCiKomSYypaNAEAgei9lNaUenTjOvsg6O1BVduZbL7YlKJ18w0ZszqOAuAkIshxKfH6xGwEEIIUbUkiTElnfXvp+RgbcFTXX0BWLa/dIfsHp496ObRjQJtAf87+b9KhyqEEEJUNUliTEnQIKhX3E+p8k0xn+vhh7mZhsNXkjl7LbXU67M7zgbUNTIXUi5U+jpCCCFEVZIkxpSYmd9dG3Psa6jkmmyverYMKyp+t+JA6dGYlg1aMtR/KAoKC48vrGy0QgghRJWSJMbUdHgWzK0h4SRcq3zTxmm64nfXSUzNKfX6qx1excLMgoPXD3Ik4UilryOEEEJUFUliTI19A2hd1E9Jj+7W7X3q0dmvPgVahW8Ox5R63cfRh/HNxgOw4PgCtIq20tcSQgghqoIkMaao83T1z7MbKt1PCWBazwAA1hyNJSuvdM+k59s+j72lPeeTz7M9ZnulryOEEEJUBUliTJFXR/BoX9RP6ZtKn2ZQSzd8XexIzc5n/fHSt1O72LgwpdUUQG0OmV+YX+lrCSGEEIYmSYwp0mjudrfWo5+SuZmGqY/4AWp36z8XvwOY1HISDWwaEJ8Rz7pL6yobsRBCCGFwksSYKl0/pViI3PHQ3e9nbCcfHG0siEnO4o8LSaVet7O04+X2LwOw5PQSMvMzK30tIYQQwpAkiTFVlrbQ4Rn1uR4LfO2tLZioK35XdjuD0UGj8XPyIyUnhVXnVlX6WkIIIYQhSRJjyjpPAzRqP6Xky5U+zeQefliYaTgancKZ+NLF7yzNLHkt+DUAVp9bza3sW5W+lhBCCGEoksSYMpeAon5K6NVPycPZlsfaqsXvlh8oezRmoO9A2jZsS3ZBNotPLa70tYQQQghDkSTG1HW5p59SXlalT1Nc/O630wkkpJbuy6TRaHTNIX+69BMxqTGVvpYQQghhCJLEmLrAgWo/pZw7evVTautdjy7+LhRoFVYfulrmPp3dO9PbuzeFSiFfhH9R6WsJIYQQhiBJjKkzM4dO09TnoZXvpwQwvWg05vujV8nMLV38DmBW8Cw0aNh+dTtnbp6p9LWEEEIIfUkSUxt0mFTUT+kUXDte6dMMaOGGXwM70nIK+KmM4ncAQfWDGNFkBADzj89H0SNpEkIIIfQhSUxtYN9ArRsDanfrSjI30+jWxqw4GE1hGcXvAGa2n4mVmRVhN8I4cO1Apa8nhBBC6EOSmNqiuJ/SuQ2QWflboJ/o6I2zrSVXk7PYGXGjzH08HDx4usXTACw4sYDCSlYMFkIIIfQhSUxt4d0RPDtAYR6Ef1vp09hZWfB0UfG75fuj77vftDbTcLRyJPJ2JJujN1f6ekIIIURlSRJTm3Quut06tPL9lACe6+GHpbmGYzEpnIq7U+Y+ztbOTG+jjv4sCl9EbmFupa8nhBBCVIYkMbVJ6zFgWx9SYyFye6VP4+Zkw/C2noDaGPJ+JjafiJudGwmZCYRcCKn09YQQQojKkCSmNrm3n5IeC3wBphYt8N18JoHrd0oXvwOwsbBhZvuZACw9vZS0vDS9rimEEEJUhCQxtU2non5Kl//Qq59Say9nugc0oFCrsPpQzH33G9FkBIH1AknLS2P5meWVvp4QQghRUZLE1DYu/hA0SH2uRz8lgOm9iorfHYsl4z7F78zNzJkVPAuANRFrSMxM1OuaQgghRHlJElMbFd9uHf6tXv2U+jVzJaChPek5BawLi7vvfr29exPsGkxuYS5fnfqq0tcTQgghKkKSmNoocCDUaww5qXD2p0qfxsxMo1sb86DidxqNhjmd5gCwKWoTl+9UfhpLCCGEKC9JYmojM3PoXNRP6Zh+/ZSeCPamnp0lcSnZ7Dh//6mido3aMdB3IFpFy8ITCyt9PSGEEKK8JImprYr7KSWehviwSp/G1sqcZ7o2BmDZA4rfAbwW/BrmGnP2xO3hxI0Tlb6mEEIIUR6SxNRWdi53+ymF6ne79bPdG2NpriHs6m3CY2/fdz9/Z3/GBI0BpDmkEEKIqidJTG3Wpbif0kbIuFnp07g62TCinRfw4OJ3AC+1ewlbC1tO3TzFrrhdlb6mEEII8TCSxNRmXh3BM1jvfkqArrv172cTib99/zueGtk14pkWasG9z058RoG27FuzhRBCCH1JElPbdSnqpxS2Uq9+Si09nXgk8OHF7wCmtp5KPet6RKdGsylqU6WvKYQQQjyIJDG1XavRd/spXdqm16mm9wwAIORYHOk5+ffdz8HKgRfavgDAlye/JLug7LYFQgghhD4kiantLG3VO5VA7wW+fZo2okkje9JzC/gxLP6B+45rNg4vBy9uZt/ku/Pf6XVdIYQQoiySxNQFnYv7Ke3Sq5+SmZmGaUWjMSsPRlNQqL3vvlbmVrza4VUAVpxdwe2c+9/VJIQQQlSGJDF1QX0/CBqsPg/Vr0njmGAv6ttZEn87m+3nbzxw36H+Q2nh0oKM/AyWnl6q13WFEEKIP5Mkpq4o7qd08ju9+inZWJozqVtx8bsrD9zXTGPGrI6zAAi5GEJ8+oOnoIQQQoiKkCSmrggcqI7I6NlPCeCZ7o2xMjfjROwdjl998DRRD88edPPoRoG2gP+d/J9e1xVCCCHuJUlMXWFmBp0M00/J1dGGke09AVjxkOJ3gG40ZvOVzVxIuVDp6wohhBD3kiSmLunwDFjYFPVTCtXrVNN6FRe/SyAu5cHTU60atGKo/1AUFBYeX6jXdYUQQohi1ZLEfPnll/j7+2NjY0PHjh3Zv3//fffds2cPGo2m1OPCBfkNXm/39lM6pt/t1s3dnegV1BCtAqseUvwO4NUOr2JhZsHB6wc5knBEr2sLIYQQUA1JzNq1a5k1axZ/+9vfCA8Pp1evXgwdOpTY2NgHHnfx4kUSEhJ0j6CgoKoOtW4oXuB7fpNe/ZTgbiuCtaFxpD2g+B2Aj6MP45uNB2DB8QVolfvfni2EEEKUR5UnMfPnz2fatGlMnz6dFi1asHDhQnx8fPjqq68eeJyrqyvu7u66h7m5eVWHWjd4Bas9lQrzIPwbvU7Vp2kjglwdyMgt4MfQuIfu/3zb57G3tOd88nm2x2zX69pCCCFElSYxeXl5HD9+nMGDB5fYPnjwYA4dOvTAYzt06ICHhwcDBgxg9+7d990vNzeXtLS0Eg/xEJ0N009Jo9HoRmNWHox5YPE7ABcbFya3mgyozSHzCx88eiOEEEI8SJUmMbdu3aKwsBA3N7cS293c3EhMTCzzGA8PD5YuXcr69evZsGEDzZo1Y8CAAezbt6/M/efNm4ezs7Pu4ePjY/Dvo9ZpNRpsXSA1Di5t1etUozp40cDeimt3stl6rux/03s92/JZGtg0ID4jnnWX1ul1bSGEEHVbtSzs1Wg0Jb5WFKXUtmLNmjVjxowZBAcH0717d7788ksee+wxPvnkkzL3nzt3LqmpqbpHXNzDpzXqPEsbCC7up7RMr1PZWJrzTFHxu6/3R6M85NZtO0s7Xm7/MgBLTi8hMz9Tr+sLIYSou6o0iWnYsCHm5ualRl2SkpJKjc48SLdu3YiMjCzzNWtra5ycnEo8RDl0moqun9KtKL1O9Uy3xlhZmHEq7g4nYh/eI2l00Gj8nPxIyUlh1blVel1bCCFE3VWlSYyVlRUdO3Zkx44dJbbv2LGDHj16lPs84eHheHh4GDq8uu3efkph+vVTauRozej2XgAs2//w4neWZpa8FvwaAKvPreZW9i29ri+EEKJuqvLppDlz5rBs2TJWrFhBREQEs2fPJjY2lhdffBFQp4OeffZZ3f4LFy5k06ZNREZGcu7cOebOncv69et55ZVXqjrUuqdL0QLf8DWQp9+0TnHxu23nEolNfnhvpoG+A2nbsC3ZBdksPrVYr2sLIYSom6o8iRk/fjwLFy7kn//8J+3bt2ffvn1s2bKFxo3VdRQJCQklasbk5eXxxhtv0LZtW3r16sWBAwfYvHkzY8aMqepQ654mA6C+P+Smwhn9+ik1dXOkd9NGaBVYeejhozEajUbXjmD9pfVcTbuq1/WFEELUPRrlYSsxTUxaWhrOzs6kpqbK+pjyOPQFbP87uLeBF/bDfRZcl8e+Szd5dsUx7K3MOTR3AM62lg89ZuYfM9kXv4/BjQfzad9PK31tIYQQpq0yn9/SO6mua/90UT+lMxB3TK9T9QpqSFM3BzLzClkb+uCKzMVeD34dDRq2X93OmZtn9Lq+EEKIukWSmLrOzgVaP6k+D9Wvn5JGo2F6zwAAVh2MIf8hxe8AmtZvyogmIwBYcGLBQ2/RFkIIIYpJEiOgS1E/pXOb9O6nNKK9Jw0drLiemsPvZx9e/A5gZvuZWJlZEZoYyoFrB/S6vhBCiLpDkhgBnh3AqxNo8+HEar1OZWNpzqRufgAs23+lXCMrHg4eTGwxEVBHYwr1aIUghBCi7pAkRqiKu1uHrYTCAr1O9Uw3X6wszDgdn0rY1YcXvwOY3mY6jlaORN6OZHP0Zr2uL4QQom6QJEaoWo0GuwaQFq93P6UGDtY8EVxc/O5KuY5xtnZmehs1kVoUvojcwly9YhBCCFH7SRIjVJY20MEw/ZQApj6iFr/bfv4GV5PLV0hvYvOJuNm5kZCZQMiFEL1jEEIIUbtJEiPuKu6ndGW33v2Ugtwc6dusEYoCKw/GlOsYGwsbZrafCcDS00tJy0vTKwYhhBC1myQx4q76jaHpEPW5AUZjim+3/jEsjtSs/HIdM6LJCALrBZKWl8aKMyv0jkEIIUTtJUmMKKlzUT+lk9/r3U/pkcAGNHd3JCuvkO+Pla/4nbmZObOCZwHwXcR3JGaW7zZtIYQQ1SDjJlz8HY7rdyeroUgSI0pq0h9cAor6Ka3T61QajYZpPdW1MasORZNX8PDidwC9vXsT7BpMbmEuX536Sq8YhBBCVFJBLsQfhyOL4adpsLAtfBIIP0yAbX+DGlAOQ5IYUZKZGXSapj4/tgz0rKCrFr+z5kZaLlvOJJTrGI1Gw+yOswHYFLWJy3cu6xWDEEKIh1AUuBMLZ9fD1rmwbCDM84Zl/WHrm3D2J7hzFdBAo+bQaiTkZRg7aiyMHYCogdpPhF0fwI0zEHcUfLtV+lTWFuY8170xn+64xLIDVxjZ3hNNOZpMtndtz0DfgeyM3cnCEwv5ov8XlY5BCCHEn+RmwPVwiA+F+DD1z8yk0vvZuoB356JHJ/AKBhvn6o/3PiSJEaXZuUCbJyH8O3WBrx5JDMDT3RqzaHcUZ6+lcTQ6hW4BDcp13GvBr7E7bjd74vZw4sYJgt2C9YpDCCHqJK0WkiOLEpZQdYoo6Rwof5riN7MA9zZ3kxavjurygnL84mksksSIsnWeoSYx5zbBkI/AwbXSp3Kxt+KJjt58fzSWZfujy53E+Dv7MyZoDOsurWP+8fl8O/Tbco3iCCFEnZaVoo6uXAu7m7Tkppbez8lbHV0pTlo82oKlbfXHqwdJYkTZPNur/ZSuhan9lHr/n16nm/qIP98fjeWPCze4cjODgEYO5TrupXYv8duV3zh18xS74nYxwHeAXnEIIUStUpgPN84WTQkVJS0pZawjtLBVp4K8O6k/2707gZNn9cdrYJLEiPvrMgM2hqn9lB6ZDeaVf7sEujrQv7kruy4ksfJgDB+Mal2u4xrZNeKZFs/w9Zmv+ezEZ/Tx7oOFmbxthRB1VNr1e6aFwtR1LQU5pfdrEFQ0ylI00uLaEswtqz/eKiafBuL+Wo6CbW9D2jW1n1KLx/U63fSe/uy6kMS643H8ZXBT6tlZleu4qa2nsu7SOqJTo9kUtYknmz6pVxxCCGES8rIg4VTJpCX9eun9bJyLRleK17IEq2sb6wBJYsT9WdpA8LNwYAGEfq13EtO9SQNaeDgRkZDGmqOxzOwXWK7jHKwceKHtC3wc+jFfnvySxwIew9bCtOZthRDigRQFUq7ck7CEwo1zoC0ouZ/GHNxa3nPHUGdwaaKWx6iDNIqiZyGQGiYtLQ1nZ2dSU1NxcnIydjim7/ZV+KwdoMArYdAwSK/TrT8ez1/WncLV0ZoDb/bHyqJ8//HyCvMYsWkE1zKu8VqH15jRdoZecQghhFFl34Frx++uY7kWBtm3S+/n4F5y8a1ne7Cyr+5oq0VlPr9lJEY8WP3G0PRRuPS7erv10I/1Ot3wdp58vPUCSem5/Hb6OmOCvct1nJW5Fa92eJW39r/FirMreLLpk9S3qa9XLEIIUS0KC+BmxN07heJD4dbF0vuZW6tJSnFNFu/O4ORVo29xNjZJYsTDdZmuJjEnv4cB7+j1W4CVhRnP9fDjv9susmx/NKM7eJX7tumh/kNZfW41ESkRfH3ma/7a+a+VjkMIIapM+o27oyvxYXDtBOSX0Yuuvv8900Idwa0NWJRvraBQSRIjHi6gqJ9SyhU4/SN0mqLX6SZ28eWLXZGcT0jj8JVkejRpWK7jzDRmzOo4ixd2vEDIhRCebvE0Xg5eesUihBB6KciFhNMlF9+mltHw1spRTVR0C3A7gX35fvaJ+5MkRjxccT+l7X9Tp5Q6TtZreLO+vRVPdvTmuyOxLN8fXe4kBqCHZw+6eXTjSMIRFoUvYl6veZWOQwghKkRR1P5BxetY4kPVBEab/6cdNeotzd4d7460NGwKZuZGCbs2k4W9onyyb8OnLaAgG6Zshcbd9TrdlZsZ9P90LwB//KUPTcpZ/A7gXPI5Jvw2AQ0afhz+I81dmusVixBClCk3XZ0KKh5huRYGmTdL72fXsOQ6Fs8OYCOfPxUlC3tF1bGtX9RP6Vt1NEbPJCagkQMDW7iyMyKJFQei+XB0m3If26pBK4b6D+X36N9ZeHwhiwct1isWIYRAq1UX25boL3Qe+NPv+WaWann+e5si1mssi2+NREZiRPldPwlL+6j/ieec16ufEsDhy8k89fURbCzNOPTWAFzsy7+gLS49jhGbRlCgLeDrwV/TzUO/JpVCiDom81bJ25uvnYDctNL7OfuWvMXZvY1aQ0sYnIzEiKpVfOtffCgcXw199Oun1C3AhVaeTpy7nsb3R6/ySv/y16DxcfRhXNNxfH/hexYcX8APj/2AmaZuFnsSQjxEQR7cOFOyv9Dt6NL7Wdrf7S/k3VldhOvoVv3xinKTJEZUTOcZRUnMSuipXz8ljUbD9F7+zF57itWHrzKjdwDWFuVf+PZCuxf4+fLPnE8+z/aY7Tzq/2ilYxFC1BKKAqnxd29vjg9VR5ELc0vv27DZ3dubvTtDoxZ6/UwT1U/+tUTFtBp1Tz+l36HFcL1O91gbT/79+wVupOXy66kEnuxYvuJ3AC42LkxuNZn/nfwfn4d/zgDfAVjWwgZnQogHyMtUk5R7b3HOSCy9n239u6Mr3p3AqyPY1qvuaIWBSRIjKsbCuqif0nw49rXeSUxx8bv/bL3Isv1XeCK4/MXvAJ5t+SwhF0KIS49j3aV1TGwxUa94hBA1mFYLKZf/1F/oPCiFJfczswC31iXXsrgEyOLbWkgW9oqKuxOr9lNStDAzFBo11et0qVn5dJv3B9n5hayZ3pVHAitWAOrHiz/ywZEPcLFxYcuYLdhb1s6+IkLUOVkp99ziHKr2Gsq5U3o/Jy91ZKU4YfFoB1Z21R6u0I8s7BXVo56v2k/p4hb1duth/9HrdM52lozr5M3qw1dZtv9KhZOY0UGj+fb8t8SkxbD63Gpebv+yXvEIIYygsACSzpXsL5QcWXo/Cxu1Dsu9i2+dpXJ3XSUjMaJyov6A78aAtRPMiQDr8herK0vMrUz6fboHRYGdc3oT6OpYoeN3XN3BnD1zsLWwZcuYLTS0lXLeQtRoaQlFi2+L1rFcD4f8rNL7uTQpWUjOrRXI2rdaSUZiRPUJ6Kf+cEm5DGd+hE5T9TqdX0N7BrVwY/v5Gyw/EMO8MeUvfgcw0HcgbRq24cytMyw+tZi/d/u7XvEIIarIka/g0CJIiy/9mrVzyVL9Xh3BzqX6YxQmQwpriMoxM4PO09Tnx5aptzXqaXqvAAA2nIgnOaOM2yEfQKPRMLvjbADWX1rP1bSrescjhDCwUyGw9S01gdGYqV2bO06BkV/CzGPwZgxM2gj93oagQZLAiIeSJEZUXvuJYGGrzmPHHtH7dJ396tPW25ncAi1rjpbRBfZhx7t3prd3bwqUAj4/8bne8QghDCg+DH55TX3e41V4Kw5eOgDDF0KHp6FRM/WXIyEqQN4xovJs60Pbserz0K/1Pp1Go2FaT38AvjkcQ05+4UOOKO314NfRoGH71e2cuXlG75iEEAaQeg1CJqoF55o9BgP/qfc6OiFAkhihr87T1T/P/wLpN/Q+3bA2Hng423ArI49fTl2v8PFN6zdlRJMRACw4sYBatm5dCNOTlwUhT0HGDXBtBWOWyIiLMBh5Jwn9eLQD7y6gzYcTq/U+naW5GZN7+AGwfH90pZKQme1nYmVmRWhiKAeuHdA7JiFEJSkK/DwTEk6BXQN46gewrtidh0I8iCQxQn9dZqh/hq1Uaz3oaUIXX+yszLl4I50DUbcqfLyHg4eucu+CEwso1FZ8WkoIYQD7PoFzG9QKuuO+hfqNjR2RqGUkiRH6azkS7BpC+nW1AJ6enG0tGdfJB4Bl+8voNFsO09tMx9HKkcjbkWyO3qx3TEKICor4FXb/S33+2Kfg94hx4xG1UrUkMV9++SX+/v7Y2NjQsWNH9u/f/8D99+7dS8eOHbGxsSEgIIDFixdXR5iisor7KYFBFvgCTH3EH40G9l66yaUb6RU+3tnamelt1PU6i8IXkVtWB1shRNVIPAsbXlCfd3kBOk42ajii9qryJGbt2rXMmjWLv/3tb4SHh9OrVy+GDh1KbGzZt9BGR0czbNgwevXqRXh4OG+//TavvfYa69evr+pQhT46TVXrPkTvg5sX9T6dbwM7hrR0B2DFgcqNxkxsPhE3OzcSMhMIuRCid0xCiHLIuAk/PAX5mRDQF4Z8ZOyIRC1W5UnM/PnzmTZtGtOnT6dFixYsXLgQHx8fvvrqqzL3X7x4Mb6+vixcuJAWLVowffp0pk6dyieffFLVoQp91POBpkPV56HLDXLK6b3U2603hF/jVgWL3wHYWNgws/1MAJaeXkpaXppB4hJC3EdBHvw4CVJj1a7RT64EcykML6pOlSYxeXl5HD9+nMGDB5fYPnjwYA4dOlTmMYcPHy61/5AhQwgLCyM/P7/U/rm5uaSlpZV4CCPpUnS79akfIDdD79N1bFyfdj71yCvQ8t2RylXgHdFkBIH1AknLS2PFmRV6xySEuA9Fgc1zIPaw2j7gqbVScVdUuSpNYm7dukVhYSFubm4ltru5uZGYmFjmMYmJiWXuX1BQwK1bpe9UmTdvHs7OzrqHj4+P4b4BUTH+fdV+SrlpcHqt3qfTaDRMLyp+9+3hq5UqfmduZs6s4FkAfBfxHYmZZb/vhBB6OroYwr9Vp5WfXAGNmho7IlEHVMvCXo1GU+JrRVFKbXvY/mVtB5g7dy6pqam6R1xcnAEiFpViZna3+F2oYfopDW3tjlc9W5Iz8/j55LVKnaO3d2+CXYPJLczlq1NlT2MKIfQQ9Qdse1t9PugDCBpo3HhEnVGlSUzDhg0xNzcvNeqSlJRUarSlmLu7e5n7W1hY0KBBg1L7W1tb4+TkVOIhjKj9RLC0g6Tz6rCynizuKX63rJLF7+5tDrkpahOX71zWOy4hRJFbUfDTFFC00P5p6D7T2BGJOqRKkxgrKys6duzIjh07SmzfsWMHPXr0KPOY7t27l9p/+/btdOrUCUtLyyqLVRiIbT1oU9RP6Zhhbrce38UHeytzIpMy2BdZ8eJ3AO1d2zPQdyBaRcvz259nUfgi4tJl1E4IvWTfgR/GQ04q+HSFxxfAA0bZhTC0Kp9OmjNnDsuWLWPFihVEREQwe/ZsYmNjefHFFwF1OujZZ5/V7f/iiy9y9epV5syZQ0REBCtWrGD58uW88cYbVR2qMJTiKaWIXyBd/zUoTjaWjO/sC8Cy/VcqfZ7ZHWfjautKUnYSS04vYdiGYUzdNpVfL/9KdkG23nEKUacUFsBPUyE5Cpy8Yfx3as0oIapRlScx48ePZ+HChfzzn/+kffv27Nu3jy1bttC4sVp+OiEhoUTNGH9/f7Zs2cKePXto3749H3zwAZ9//jlPPPFEVYcqDMWjrfpbmbYAjuvfTwlgyiN+mGlgf+QtLiZWvPgdgK+TL1ue2MJ/e/+XHp490KAhNDGUtw+8Tb8f+/Heofc4dfOUNI0Uojx2/AMu/6FOHz/1PTi4GjsiUQdplFr2EzstLQ1nZ2dSU1NlfYwxnV4HG6aDowfMOgPm+k8FvrzmOFvOJDKukzf/ebKd3udLyEjgl8u/sClqE/EZ8brtAc4BjAocxfAmw2lo21Dv6whR65z4Fn55RX0+djW0GmXUcETtUJnPb0liRNUoyIUFrSDzJoz7Ru2vpKfjV2/zxFeHsDI34+Bb/WnkaJiha62i5fiN42yK2sT2mO3kFOYAYK4xp5d3L0YFjqK3d28szWRNlhBcPQyrh6ud6/vOhb5vGTsiUUtIEoMkMTXKH/+E/Z+CXy+Y/JtBTjn6y4OEx97htQFBzBlk+DoUGXkZbIvZxsaojZy6eUq33cXGheEBwxkVOIrA+oEGv64QJuFOLCztB1m31F9MnlylllYQwgAkiUGSmBrlThx81la99fLlo+DaXO9Tbj6dwMzvT+Bib8Wht/pjY2lugEDLduXOFTZFbeKXy7+QnJOs296mYRtGBY5iqP9QHK0cq+z6QtQouRmw4lG4cQbc28DUbWBlb+yoRC1Smc9vSaFF1annA82Gqc/DDNNPaUgrN7zq2ZKSmcfG8MoVvyuvgHoBzOk0hx1jd/BF/y/o79MfC40FZ26d4YMjH9Dvx368tf8tjiYcRatoqzQWIYxKq4VNL6oJjH0jmPCDJDCiRpCRGFG1Lu+Cb0eDlSP8JQKs9R+5WLb/Cv/aHEGgqwPbZ/XGzKz66lIkZyfz25Xf2BS1iag7UbrtXg5ejGwykpGBI/F08Ky2eISoFrs/gr0fg7kVPPcb+HY1dkSiFpLpJCSJqXG0WvhfZ7WWxGOf3q0ho4f0nHy6z9tFRm4BK6d0pl+z6r+1U1EUziWfY2PkRrZEbyEjX214qUFDV4+ujA4cTX/f/thY2FR7bEIY1NkNakVegJFfQoenjRuPqLUkiUGSmBrpyFew9S1wbQkvHTJIRc9//XaeZQei6RnYkO+mG/e3wuyCbP6I/YNNkZs4mnhUt93R0pFhAcMYHTialg1aPrBfmBA10vWT6jqYgmzo/goM+dDYEYlaTJIYJImpkbLvwPwWkJ8Fk7eA3yN6nzL+dha9/7MbrQK/v96LFh414986Pj1eV3smITNBtz2wXiCjA0fzeJPHcbFxMWKEQpRT+g34uh+kXYPAQTBxLZhV3UJ6IWRhr6iZ7u2nFGqYfkre9e0Y2sYDgOUHog1yTkPwdvTm5fYvs/WJrXw9+GuG+Q/DysyKqDtR/Dfsvwz4cQCzd89mX/w+CrQFxg5XiLLl58Dap9UEpmFTeHK5JDCiRpKRGFE9Es/A4p5gZgGzz4Gju96nDI+9zegvD2FpruHgm/1xdaqZ609Sc1PZGr2VTVGbOJt8Vre9kW0jhjdRa8/4O/sbMUIh7qEosOklOPUD2NSDGbugQRNjRyXqABmJETWXexvw6WbQfkodfOvTsXF98gsVvj1y1SDnrArO1s6Mbz6eHx7/gfUj1jOp5SRcbFy4mX2TFWdXMGLTCCZtmcSGyA1k5mcaO1xR1x36Qk1gNOYwdpUkMKJGk5EYUX3O/ATrpxm0n9LvZxJ4ac0J6ttZcuitAdhamcaQd35hPvvi97ExaiP7r+3X1ZmxtbBlUONBjA4cTUe3jrIYWFSvS9vg+/GAAkP/A11fMHZEog6Rhb1IElOj3dtPyUBN4wq1Cn0/2U1cSjYfjm7N010b6x9nNbuZdZNfr/zKxsiNxKTF6Lb7OvrqGlG62+s//SbEAyVdgGUDIS8dgp+D4Z8Z5E5CIcpLppNEzWZhrf5wBAhdZpBTmptpmNJDXU+y/EA0Wq3p5eSN7BoxtfVUfhn1C98O/ZYngp7AzsKO2PRYPg//nCHrh/DizhfZFrONvMI8Y4craqOsFPhhgprANH4Ehn0iCYwwCTISI6pXajwsbGPQfkoZuQV0/+gP0nMLWDG5E/2buxkgUOPKys9ix9UdbIzayPEbx3Xbna2decz/MUYHjaa5i/5/d0JQmA/fjYHofVDPF2bsAfsGxo5K1EEyEiNqPmfvu/2UDDQa42BtwVNdfQFYtr/m3G6tDztLO0YGjmTVo6vYPHozM9rMwNXOldTcVL6/8D1jfx3LuF/HsSZiDXdy7hg7XGHKts5VExgrB3gqRBIYYVJkJEZUv8u74dtRBu2ndO1ONr3/s5tCrcLm13rSytNZ/zhrmEJtIUcSjrAxaiO7YneRr80HwNLMkv6+/RkVOIruHt0xl3oeorzCVsBvswENTFgDzR8zdkSiDpORGGEaAvpCgyB1/v1UiEFO6VXPlmE1sPidIZmbmfOI1yN80ucTdo3dxdwuc2nh0oJ8bT7bYrbx0s6XGLJ+CJ+f+JzYtFhjhytquuj9sOX/1Of9/y4JjDBJMhIjjOPIYtj6JjRqAS8fNsgiwpNxdxj1v4NYmms48GZ/3Gpo8TtDi0iOYFPUJjZHbyY1N1W3vaNbR0YHjmZQ40HYWdoZMUJR46REqy0Fsm9D6yfhiWWykFcYndxijSQxJiMnFT5tXtRPaTP49TTIaccuPkRozG1m9mvC/w2pWwtf8wrz2B23m41RGzl8/bCu9oydhR2P+j/K6MDRtGvUTmrP1HU5abB8MNyMAM8OMOV3sLQ1dlRCyHSSMCE2ztB2nPr8mGH6KQFM6xkAwJqjsWTl1a3eRFbmVgzxG8LigYvZ9sQ2XuvwGj6OPmQVZLEhcgOTfp/EiE0jWH5mOTezbho7XGEM2kLY8LyawDi4w4TvJYERJk1GYoTxJJ6FxY+o/ZRmnQUnD71PWahV6PfJHmJTsvhgVGsmdTO94neGpCgKJ5JOsDFyI9uvbie7IBsAc405Pb16MjpwNL29e2NpgOrJwgTsfA8OLABza3UExrtjtV4+O6+QH47FcjExnUBXB1p6OtHCwwkXe6tqjUPUTDKdhCQxJmfFoxB7GPrOhb5vGeSUqw5G896v5/FvaM8fc/pgZibTJwCZ+Zlsi9nGxsiNnLx5UrfdxcaFxwIeY3TgaILqBxkvQFG1Tv8IG2aoz8csg7Zjq+3SuQWF/HA0lv/tuczN9NxSr7s72dDS04mWHk66xKaxi538361jJIlBkhiTU9xPycEdZp81SD+lzNwCus37g/ScApY924mBLU2/+J2hRadGsylqE79c/oVb2bd021s3aM2owFEMDRiKk5X8/6k14o/DyqFQmAs9Z8PA96rlsvmFWtaFxbNoVyTXU3MA9U7C4e08iU3J5Pz1NGKSs8o81s7KnBYeTrTwcKSlhzMtPZ1o5uZoMv3RRMVJEoMkMSanIK+on1KS2jG31WiDnHbe7xEs2XuFbgEuhDzf3SDnrI0KtAUcun6IjZEb2RO3hwJFXUdkbW7NAN8BjA4aTRf3LphpZPmcyUq7Dkv7QUYiNB2qroMxq9p/z4JCLRvDr/H5rkjiUtQpTHcnG17pH8i4Tj5YWdy9fkZuARcT0zh/PY3zCWmcT0jnQkIauQXaUuc104B/Q3taejoXJTfqyI2rY924E7G2kyQGSWJM0q5/wb7/QuOeMGWzQU6ZkJpNr493U6BV+O3VnrT2qn3F7wwtJSeF3y7/xsaojUTdidJt97T3ZGTgSEYGjsTLwcuIEYoKy89WR2Cuh6vlDKbvMEhxyfsp1Cr8dvo6n+2M5MqtTAAaOljzct8mTOzqi41l+UZRCgq1xCRncj4hXZfcRCSklTkVpV7DihZFCU1LD/Xh39AeC3NJvk2JJDFIEmOSUq8V9VMqhJePgGsLg5z29ZBwfj55ndEdvFgwvr1BzlkXKIrC+eTzbIzayJYrW0jPT9e91tW9K6OCRjHQdyA2FvLbb42mKOpU7dn1YOsCM3aBi3+VXEqrVdh2LpH5Oy4RmZQBQH07S17s04RJ3RtjZ2VhkOskpecQUZTYRCSoyc2VmxmU1ffV2sKMZu6OJdbZNHd3xNFGFrHXVJLEIEmMyQp5Gi78Bp2nw2OfGuSUp+PvMGLRQSzM1OJ37s7yoVtROQU57IrdxcaojRxNOIqC+uPC0dJRV3umdcPWUnumJtr3Cez6QL37b9Im8O9l8EsoisIfEUnM33GJ8wlpADjZWPB87wAmP+KPg7VhkpcHyc4r5NKNdHUqqii5iUhIIzOvsMz9Gzewo6WHmtQUJzgezjbyHq4BJIlBkhiTdWUPfDNSbUI3JwJsDPNvN27JYY5Fp/BS3ya8+WjdKn5naNczrvPz5Z/5OepnrmVc020PrBfIqMBRPB7wOA1spXlgjXBhM4RMVJ8/vgA6TTXo6RVFYV/kLebvuMSpuDuA2oh16iN+TOsVgLOtcUc7tFqF2JQs3WhNcXJTvLj4z5xtLUuM2LT0cCLQ1aHE2h1R9SSJQZIYk6Uo8L8ucOsSDPsEuswwyGm3n0vk+W+P42RjweG5A7Cvht8MazutoiU0MZRNUZvYcXUHuYXqOgULjQW9vXszOmg0Pb16YmEmf9dGceMcLBsE+ZnQeQY89olBT3/o8i3mb79E2NXbANhamvNcDz9e6B1A/Rpe7+V2Zt7dxKYouYlKyqCgjPkoS3MNga73Tkepz+vZ1ezv0ZRJEoMkMSbt6BL4/a/QqLm6NsYAw7uFWoUBn+4hJjmL90e04rkefvrHKXTS89L5Pfp3fo76mdO3Tuu2N7BpwIgmIxgVOIqAegFGjLCOybyl9kS6Ewv+veGZDQYpWwAQFpPCp9svcfhKMgBWFmZM6taYF/s0oZGjtUGuYQy5BYVEJWWUWEB8/noaaTllV/z2qmdb4s6oFh5O+NSXmjaGIEkMksSYtJxU+LSF+hvkc78ZbA7/m8MxvPPzORo3sGPXX/piLj9sqkTU7Sg2RW3i1yu/kpKTotvetlFbRgeO5lG/R3GwcjBihLVcQZ46JRt7COr7qwt57Vz0Pu2puDvM33GJvZfUVhWW5hqe6uLLzH6BtbbJqqIoXLuTrVtEfD4hlYiEdGJTyq5p42BtoUtsiu+SaurmWO67sYRKkhgkiTF5v86C4yuh5UgY941BTpmVV0D3ebtIzc5nyaSODGnlbpDzirLla/PZH7+fjVEb2R+/n0JFXWBpY27DYL/BjAocRUe3jlJ7xpAUBX59HU6sBitHmL4TXPVbA3b+ehrzd1xiZ8QNAMzNNIzt6M0r/QPxrl83u6Kn5eRzISFdN1pzPiGNizfSySujpo25mYaAhva6276Lk5uGDqY7alXVJIlBkhiTd+McfNUDNOYw+5xB+ikBfLz1Al/tuUwXPxd+fFGK31WXW9m3dLVnrqRe0W33dvBmVOAoRgaOxN1ekkq9FU/FooGJP0LTwZU+VeSNdBbsvMSWM4mAWmBuVAcvXh8QROMG9gYKuPYoKNRy5VZmiemoc9fTSMnMK3N/V0frEjVtWhTVtJERYkliAEliaoUVQ9Uh8T5vQb+5BjllYmoOPT/eRYFW4eeZj9DOp55BzivKR1EUTt86zcbIjWyN2UpmvloITYOG7p7dGR04mn6+/bA2l99SK+zybvjuCbXO0qAP4JHXKnWa6FuZfLbzEj+fuo6iqEvSHm/ryesDggh0lWnAilAUhaT0XN3i4fMJaURcTyM6OZOyPnFtLM1o7l7y7qjm7o517kYESWKQJKZWqIJ+SgCz155kY/g1AhrZM62nPyPaeUrhKyPIys/ij9g/2Bi1kdDEUN12JysnhvkPY3TQaFq4tJC6HeWRfFldyJuTCu2eglFfVXhBfFxKFp//EcmG8GsUFt2lM6SVG7MHNaW5u/wMNaSsvAIuJJYs1nchIZ3s/NI1bTQa8Gtg/6e7o5xxc7Kutf83JIlBkpha4d5+Sk+uhNZjDHLayBvpjPnqEOlFdx3YWprzeFsPJnTxIdi3fq39wVCTxaXH8XPUz/x8+WcSMxN125vVb8aowFE8FvAY9W3qGzHCGiwnFZYNVMsSeHdWF8Nbln+h7fU72SzaHcWPoXG6W4z7N3dlzqCm0qajGhVqFa4mZ5aoZ3M+IY0baWW3WHCxtyp1d1STRg5Y1oIWC5LEIElMrbHrQ9j3H4P2UwJIycxjw4l4QkLjiCoqjw4Q6OrAhM4+jO7gRQNZeFftCrWFHE04yqaoTfwR+wd5WnU9gYWZBf18+jE6cDQ9PHtgbiZ3ewCgLYTvx0HUTnDyghm7wbF83dqT0nL4cs9lvj8aS16huiC1V1BDZg9qSrCvJIw1RXJGrnp3VEJqUXKTTtTNDN1o2b2szM1o6u5AC/d71tp4OuFkYiPNksQgSUytcW8/pZcOg1tLg55eURROxN5hbWgsv55K0A3nWpprGNzSnfGdfegZ2FBqPxhBam4qW6K3sDFyIxEpEbrtrraujAgcwfAmw/Fx8MHSQNOMJmnb3+DwIrCwhalbwbP9Qw9Jzshlyb4rfHM4hpx8NXnp4u/CG4Ob0cVf/1uxRdXLyS8k8kaG7pbv4pGb9Nyya9p417ctVYnYu75tjR11liQGSWJqlbXPQMSv0GkaPD6/yi6TnpPPr6cSWBsay6n4VN12r3q2jOvkw9hO3njWs62y64v7u5hykU1Rm/jtym/cyb1T4jUrMyscrBxwsHTQ/WlvaY+jlSP2lvYltt9vH3tLe9OrLBy+Bn5+WX1ejunWO1l5fL3/CisPxpBV1E+og289/jKoGY8ENqixH2iifBRFIf52Nuf+VKzv2p3sMvd3tLEo0TeqpYcTQW4OWFsYf5RTkhgkialVruyFb0YYvJ/Sg5y/nsaPYXFsOBGvq9hppoE+TRsxvrMvA1q41oq5Z1OTV5jH3vi9bIzcyKHrh3S1ZwzB1sK2zISnvImQo5Ujtha21VP3JvYorH4cCvOg91+h/9/uu2taTj4rDkSzfH+07jf1Nl7OzBnUlL7NGknyUsulZuUTkZhW4tbvSzfSyS8s/ZFvYaYh0NWhRHLTwsMJl2puIyFJDJLE1CqKAv/rCrcuGrSfUnnk5Bey7VwiIcfidGXWARo6WPFER2/Gd/IhoJHcdmoMhdpCMgsyycjLICM/Q/dnZn4m6XnpJf4sfj0zP5P0/JKvFfd8MgQNGjXpuSfhsbeyx9GyjFGh+yRC9pb22Jg/oJvynTj1TqTMm9BiOIz9BsxKJ06ZuQWsOhTD0n1XSM3OB6C5uyOzBzVlcEs3SV7qsLwCLZdvZpRYQHw+IY07Wfll7u/uZFPizqiWnk40dqm6FguSxCBJTK1zdCn8/n/QsBnMPGqQfkoVFXMrk7Vhcfx0PJ6b6Xc/+Lr4uzChsw9DW3tga2X8oVhRMfmF+WqSc08ipPuzKCm69+uykqWMvAwKlLLXI1SGhcYCeyv7u4lQcYJjbo3D5T04ZNzEwc4Vh+6v4mDrUmLUyEpjx5bTt1m1P5GUTHXNS5NG9swe1JRhrT1kfZcok6IoJKblqCM219N0ozcxyWW3WLCzMqe5uyMtPZ2YO7SFQWvZ1Lgk5vbt27z22mv88ssvAIwYMYIvvviCevXq3feYyZMns3r16hLbunbtypEjR8p1TUliapkS/ZR+VZvaGUl+oZbdF5JYGxrH7otJFN8k4Ghjwaj2Xkzo4kMrT7k1tS5RFIXcwtzSIz55d0d+ykqE7k2Iip8rGPBHsWKBvYU9LnbOOJQx4vOgabJ7n5vceiFhMBm5BVxIuHfEJp0LCWnkFrVYsLMy5+x7QwyaHNe4JGbo0KHEx8ezdOlSAJ5//nn8/Pz49ddf73vM5MmTuXHjBitXrtRts7KywsWlfKvnJYmphX6bDWEroMUIGP+tsaMB1ArAPx2PY21YHHEpdxfQtfFyZnxnH0a09zS52xuF8SiKQlZBVsmRoOJE6Mw60iO3kmluQUbr0WTYOJKRn0F6Xjqxt2+TmH6bAnLQmOWgMS+71H1lFa8XKmvq68+J0P2mz+ws7aRPVi1RUKglJjmTc9fVKajnevgZ9Pw1KomJiIigZcuWHDlyhK5duwJw5MgRunfvzoULF2jWrFmZx02ePJk7d+6wadOmcl0nNzeX3Ny7Q/xpaWn4+PhIElOblOindBacPI0dkY5Wq3D4SjI/HItl+7kburobNpZmPNbGkwldfOjUWArpiUo6txHWTVafj1gEwZMoKNSyMfwan++K1CXQ7k42zOwfyJPBnuST/cB1QsZeL2RvaY+nvScjA0fS3bO7JDhCpzJJTJWNFR4+fBhnZ2ddAgPQrVs3nJ2dOXTo0H2TGIA9e/bg6upKvXr16NOnDx9++CGurq5l7jtv3jzef/99g8cvahC3VuDbQ+2ndHwV9Hvb2BHpmJlpeCSwIY8ENiQlM4+N4ddYGxrLpRsZrD8Rz/oT8QQ0smdCZx/GBHtLB1tRfgmnYONL6vNuL6Nt/wy/nrzGZzsjuXJL7T3V0MGal/s2YWJXX2ws1XVZtljiZKXfL3BlrRcqKwEqNWpUxnohBUW3X7FTN0/xe8zv+Dr6Mq7ZOEYFjsLZWqZiRcVV2UjMRx99xKpVq7h06VKJ7U2bNmXKlCnMnVt2Y7+1a9fi4OBA48aNiY6O5h//+AcFBQUcP34ca+vSHwAyElNHnF0PP00FBzeYdRYsqvfWv4pQFIXwuDusPRbHr6ev62pzWJprGNTSjfGdfekZ2FC61or7y0iCpf0gLR6lyQC2tf2M+buucOmGmgjUt7PkxT5NmNS9MXZWNXPdyv3WC2XkZRB2I4yfo37WJTY25jYM9R/KhOYTaNnAsIUthemolumk995776EjH6GhoWzfvp3Vq1dz8eLFEq8FBQUxbdo03nrrrXJdLyEhgcaNGxMSEsKYMQ/voSNrYmqpgjxY2BoybsCTK6D1E8aOqFwycgv47dR1QkLjOBl3R7fdq54tYzt5M7aTD15SSE/cqyAXVj0O8cfIdPRnstk8Qm+o05RONhbM6BXAlJ7+OJh4h+Os/Cw2R28m5EIIl27f/WW3bcO2TGg+gcF+g6WreR1TLUnMrVu3uHXr1gP38fPz4/vvv2fOnDncuXOnxGv16tVjwYIFTJkypdzXDAoKYvr06bz55psP3VeSmFps90ew92No/AhM2WLsaCrsQmIaIcfi2Bh+TVe/Q6OB3kGNmNDZhwEt3LCykPUBdZqioPz8MpqT35OhcWB4zvtEKx44WFsw9RE/pvUKwNm2di0YVxSFkzdPEnIhhO1Xt1OgVW9Zr29dn9FBoxnXbBxeDl5GjlJUhxq5sPfo0aN06dIFgKNHj9KtW7cHLuz9s+TkZLy8vFi6dCnPPvvsQ/eXJKYWS7sOC1oX9VM6pK6VMUHFhfTWhsZx6PKfCukFezOusw9NpJBenRTz68f4Hf+IQkXD5Pw3CTPvwHM9/HihdwD1q7l6qjHcyr7FxsiN/HjpR11Xcw0aenv3Znyz8Tzi9YgsBK7FalQSA+ot1tevX2fJkiWAeot148aNS9xi3bx5c+bNm8fo0aPJyMjgvffe44knnsDDw4OYmBjefvttYmNjiYiIwNHR8aHXlCSmlls7CSJ+gU5T4fEFxo5Gb1eTM/kxLI51YfEk3VtIz8+F8Z19GNZGCunVBcevprDzlzW8cesfmGsU/lX4LErXl3ixTxMaOda9KZUCbQH74vcRciGEwwmHddu9HbwZ32w8o4NGy0LgWqjGJTEpKSmlit0tWrSoRLE7jUbDypUrmTx5MtnZ2YwaNYrw8HDu3LmDh4cH/fr144MPPsDHx6dc15QkppaL3gerh4OlPfzlQrX0U6oOBYVa9ly8SUhRIb3Cokp6jtYWjOzgyYTOvrT2kh/atc3p+Dt8uv0S8ZEn2Wj1Dk6abMJcHsf72WW4y1opAGJSY1h7cS0/R/1Men46ANbm1jzq9yhPNX+KVg1Nc0RWlFbjkhhjkCSmlru3n9LQ/0LX540dkcHdSMvhp+PxrA2NIzblbunvVp5OTOjsw4j2XrVuXURdc/56GvN3XGJnxA2cyeBn63fw0ySS69kF66mba/Tdd8aSlZ/F79G/E3IxhAspF3TbWzdozYTmExjiNwQbCxsjRij0JUkMksTUCTWgn1J10GoVjlxJJiQ0jq1nE3WF9KwtzHisjQcTuvjS2U8K6ZmSyBvpLNwZyeYzCQBYagr5zWUhzTKPg7MvzNgFDo2MHGXNpigKp26eYu3FtWyL2Ua+Vl0k72ztzJjAMYxtNhYfx/KN3IuaRZIYJImpE3LSYH4LyMuAZ3+BgD7GjqjK3c7MY9PJa4Qci+PijXTd9oCG9owvKqRXF9dOmIroW5l8tvMSP5+6jqKoeffjbT35wOob6p1ZoU6PTtsO7q2NHapJSc5OZmPURtZdXMf1zOuAuhC4p1dPJjSfwCOej2BuJmvKTIUkMUgSU2f8NgfClteofkrVQVEUTsWnEnIsll9O3S2kZ2GmYWALN8Z38aF3UCMppFdDxKVk8fkfkWwIv6Zb5zSklRuzBzWl+bUN8Ovr6o7jv4MWw40YqWkr1Bay/9p+Qi6GcPDaQd12LwcvxjUbx+jA0dS3qW/ECEV5SBKDJDF1xo3z8FV3tZ/SrDPgXPfqSGTkFrD5tFpILzz2jm67h7MNYzv5MK6TN9717YwXYB2WkJrNol1RrA2No6Aoeenf3JU5g5qqC7RjDsA3I0FbAP3+Dn3+z8gR1x6xabH8ePFHNkZtJC0vDQArMyse9X+UCc0m0Lpha5mCraEkiUGSmDpl5TC4ehB6/xX6/83Y0RjVxcR01obGsSE8njtZdwvp9QxsyFNdfBkohfSqRVJ6Dl/uvsz3x2LJK1DXMPUKasjsQU0J9i0aCbgdo7YUyE6BVmPUCtTyoWpw2QXZbI3eSsjFEM4nn9dtb9mgJROaTeBR/0extZA7wGoSSWKQJKZOObsBfpoC9q4w+5zc0QHkFhSy/dwN1obGcSDqbmVtF3srngj2YnxnHwJdH15vSVRMckYuS/Zd4ZvDMeTkq8lLF38X/jKoKV0DGtzdMTcdlg+GpPPg0R6m/A5WMlpWlRRF4eyts4RcDGFr9FbytHkAOFk5MSpwFOObjcfXydfIUQqQJAaQJKZOKcxXK/hmJJpUP6XqEpucpRbSOx7HjbS7hfQ6Na7P+M4+PNbWo8Y2DzQVqVn5LN1/mVUHY8gsWp/UwbcefxnUjEcCG5ScttBqYe0zcHGz2sh0xu46OQ1qTLdzbrMxaiM/XvyRaxnXdNsf8XyECc0n0MurlywENiJJYpAkps7ZPQ/2/ht8e8DU340dTY1UUKhl7yW1kN6uCyUL6Y1oX1xIz0nWCVRAek4+Kw7EsOzAFdJz1F4/rb2c+MugZvRt1qjsv8s//gn7PwVza7X3l3enao5aFCvUFnLw+kFCLoRw4NoBFNT/E572noxtNpYxQWNwsXExcpR1jyQxSBJT59SSfkrVJSkth59OqIX0ribfLaTX0sOJCV18GNnOC2c7KaR3P1l5Baw6FMPSfVd0a4+auzsye1BTBrd0u38ieOYnWD9NfT56KbQbX00Ri4eJS4tj3aV1bIjaQGpuKgCWZpYM8RvC+GbjadeonST41USSGCSJqZN+fBbO/wwdp8DwhcaOxiRotQpHo1NYGxrLlrOJukWo1hZmDGvjwfjOPnT1d5Ef3kVy8gv57shVvtpzmeRMdU1Fk0b2zB7UlGGtPTB70C3t146ri9ALcuCR12HQP6spalEROQU5bIvZRsiFEM4mn9Vtb+HSgvHNxjMsYJgsBK5iksQgSUydFL0fVj9e1E8pAmykx1BF3MnKY1P4NUJC47iQeLeQnn9De8Z18uGJjl64OtbNcu65BYWsDY1j0a4oXYPOxg3seH1AECPbez28Hk9aAiztq67bChoCT/0Asuaixjt76ywhF0LYGrOV3EL1393RypGRTUYyvtl4/Jz9jBtgLSVJDJLE1EmKAl92g5sXYOh/oOsLxo7IJCmKwun4VEJC4/jl5DXdQlULMw0DWrgyobMvvZvWjUJ6+YVafjoezxd/RHI9NQcAr3q2vDYgkDHB3lial+N29fxsdQTm+glo1Bym7ag1DUvrijs5d9gUtYm1F9cSnxGv297dozsTmk+gt3dvLMxkcbyhSBKDJDF11rGvYcsb0LApzDwmdTf0lJlbwOYzCawNjeP41du67e5ONozr5M3YTj74uNS+W4MLCrVsOnmdz/+I1DXfdHOy5pX+QYzv5FP+WjuKAhtmwJl1YFtf7YnkElCFkYuqpFW0HLx2kLUX17Ivfp9uIbC7vTvjmo5jdNBoGto2NHKUpk+SGCSJqbNK9FP6GQL6GjuiWiPyRjohoXFsOBHP7T8V0hvf2YdBLd2wtjDtKRKtVuHX09f57I9IrtzMBKChgxUv9Q3k6a6+2FhW8PvbPx/+eF+tKD1pY53o71VXxKfHqwuBIzdwJ/cOABZmFgxuPJgJzSfQvlF7WUtWSZLEIElMnbb5LxC6TO1BM/47Y0dT6+QWFLLjvFpIb3/k3UJ69e0sGRPszYTOPgS5mVYhPUVR2HYukQU7InWNNevbWfJCnyY8271x5eroXPwdfngKUOCxT6HzdMMGLWqE3MJctsdsJ+RCCKdvndZtb1a/GeObj+cx/8ews6x9o5VVSZIYJImp05Ii1LUxdbifUnWJS8liXVgcP4bFk5iWo9vesaiQ3uM1vJCeoijsupDE/B2XOHdd7a/jaGPB870CmNLTHwfrSsZ+4zwsH6SOCHaaBo/PN2DUoqY6l3yOHy/+yJYrW8gpVP8/OFg6MDJwJOOajSPAWaYSy0OSGCSJqfNWPgZXD0Dv/4P+fzd2NLVeoVZh36Wb/HAslj/uKaTnYG3B8HaeTOjsQ1tv5xozvK4oCvsjbzF/xyVOxt0BwN7KnKk9/ZneM0C/GjmZyfB1P7hzFfx6qdNI5lJzpy5JzU3l56ifWXtxLbHpsbrtXT26MqHZBPr69JWFwA8gSQySxNR55zbCusnST8kIktJzWH/8GmtDY4m5p5Bec3dHJnT2YVQHL+rZGe/f48iVZOZvv8SxmBQAbC3NebZHY17o3QQXez3jKsiDb0erCXR9P7WlgJ1UfK2rtIqWI9eP8MPFH9gXvw+totZhcrVzZWzTsTzZ9ElZCFwGSWKQJKbOu7ef0hPLoc2Txo6ozlGU4kJ6cWw5k0BuUSE9KwszhrZ2Z0JnX7oFVF8hveNXbzN/x0UORiXr4nima2Ne6tuERo7W+l9AUeC32XB8JVg5wvQd4NpC//OKWuF6xnXdQuCUHDWBttBYMLDxQCY0n0Cwa3CNGak0NklikCRGcE8/pe4wdauxo6nTUrPy+fnUNX44FkdEQppuu18DO8Z19uHJYG9cnaqmkN6Z+FQ+3XGRPRdvAmBprmFCZ19m9gvE3dmA1yy+vR8NPBUCzR413LlFrZFXmMf2q9tZe2EtJ2+e1G0Pqh/EhGYTeCzgMewt7Y0XYA0gSQySxAjUKqkLW4O2AF48CO6tjR1RnacoCmeuFRfSu05Grto00dxMQ//mrkzo7EOfpo2wKE8RuYeISEhj/o5L7Dh/Q3eNsR29eaV/IN71DXy3yJU98O0YtXfXwPeg52zDnl/UShdSLhByIYQt0VvILsgGwN7SnhFNRjC+2Xia1Gti5AiNQ5IYJIkRRX58Ds5vgo6TYfhnxo5G3CMrr4DNp9VCemH3FNJzc7JmbEcfxnXywbdBxZONqKR0FuyMZPPpBADMNDCqvRevDQjCr2EV/IabfBm+7g85d6DteBi9RIosigpJy0vjl6hfWHtxLTFpMbrtXdy7ML7ZePr59sPSrO4sDpckBkliRJGYA7DqMbC0g79ckH5KNVRUUjprQ+NYf+IaKUWNFeFuIb3BrR5eSC/mViaf/RHJzyevUXRzFI+39WDWwKYEujpUTeA5qbBsENy6CF6dYPJmsKyb/aWE/hRF4UjCEUIuhLAnfs/dhcC2rjzZ9EmebPokjewaGTnKqidJDJLEiCKKAl92h5sR8OjH0O1FY0ckHiCvQMvOiBuEhMaxP/ImxT+V6tlZMqaDN+M7+9DMvWQhvbiULL7YFcn6E9d0t3YPaeXG7EFNae5ehf/3tYXw/XiI2gGOnvD8bnB0r7rriTolMTORdZfWsf7SepJz1MXoFhoL+vv2Z0LzCXRy61RrFwJLEoMkMeIexQsuGwTBK6Ey1G8i4m9n8WNYPOvC4khIvVtIr4NvPSZ09qGTnwsrD0azNjSO/EL1x1f/5q7MGdSU1l7VMOK2/e9w6AuwsIEpv4NXcNVfU9Q5+YX57IzdSciFEE4kndBtb+LchPHNxzM8YDgOVlU00mgkksQgSYy4R246fNoC8tKln5IJKtQq7Iu8ydpjceyMuEGBtvSPqp6BDZk9qCkdG9evnqBOfg+bXlKfP7kCWj9RPdcVddrFlIusvbiW3678plsIbGdhx/AmwxnfbDxB9YOMHKFhSBKDJDHiTza/AaFfQ/PHYcIaY0cjKulmei4bTsSzNjSOK7cy6eLvwl8GNaVrQIPqCyLumLrOqjAPer0BA/5RfdcWAkjPS+eXy+pC4OjUaN32jm4dmdB8AgN8B5j0QmBJYpAkRvyJrp+SWVE/JW9jRyT0oCgKaTkFONlYVO+6gNR4WNoPMpPUhHjct2Cm/+3gQlSGoigcSzzG2otr2RW7i0KlEICGtg3VhcBBT+Jm72bkKCtOkhgkiRFlWPU4xOyX355F5eRlwcpHIeEUuLWGqdvAunatRRCmKzEzkfWR6/np0k/cyla7y5trzOnv25/xzcbTxb2LySwEliQGSWJEGc5tgnXPgX0jmH1e+imJ8lMUtRfX+U1g10DtiVS/sbGjEqKU/MJ8/oj7g5ALIRy/cVy33d/Zn/HNxjOiyQgcrRwfcAbjkyQGSWJEGQrzYWEbSE+QfkqiYvZ8DHs+AjNLeO4XaNzD2BEJ8VCRtyNZe3Etv17+lawCtRmrrYUtjwc8zoTmE2hav6mRIyybJDFIEiPuY8+/Yc888OkG07YZOxphCs7/DD8+qz4f/jl0fM648QhRQRl5Gfx65VfWXljL5dTLuu3BrsFMaD6Bgb4DsTSvOQuBJYlBkhhxH+mJsKBVUT+lA+DextgRiZos4TSsGAL5WdD1RRj6sbEjEqLSFEUh7EYYIRdC2BW7iwJF7V3WwKYBTzR9grFNx+Jub/yCjZLEIEmMeIB1k+HcRnBrA+2fgib9oVFzKYInSsq4CV/3g9Q4COgHT/8E5hbGjkoIg0jKSmL9pfWsu7SOm9lqh3czjRn9fPoxvtl4unl0M9pCYElikCRGPED8cVg1DAruVoHF0UP9oGrSXy2G51D7+5OIByjIhdUjIO4IuDSBGX+AbTUV0hOiGuVr89kdu5u1F9dyLPGYbrufk5+6EDhwBE5W1fsZKkkMksSIh0i+DJe2wuVdEHMQiqpf6ri3VROaJv3U9TPS1K/uUBT4+RU4+R1YO8P0ndCoZi6AFMKQLt+5TMiFEH698iuZ+ZmAuhB4mP8wnmr+FM1cmlVLHJLEIEmMqID8HPU37su71EfimZKvW9iC3yNFSY1MPdV6h/8H295WCyM+vQ4CBxo7IiGqVWZ+JpuvbOaHCz8QdSdKt719o/ZMaD6BQY0HYWVedSUqJIlBkhihh4wkuLL3blKTkVjydUePommnfjL1VNtE7YQ1Y0HRwpCPoPtMY0ckhNEoisKJpBOEXAhh59WduoXALjYujAkaw7im4/Bw8DD4dSWJQZIYYSCKorYsKE5orh4suZYG7pl66g++3cDC2jixCv3cioSvB0BuKrR/BkYukhE3IYrcyr7FT5d+Yt2ldSRlJQHqQuDe3r15p9s7NLIz3C9zksQgSYyoIvk5EHsYrux+wNRTz7vraWTqyTRk31YTmJTL4NMVnvtVklEhylCgLWBP3B5CLoZwNOEoLjYu7Hxyp0HrzEgSgyQxoppkJMGVPfdMPd0o+Xrx1FPxXU/2DY0RpXiQwgJY86SamDp5w/O7wcHV2FEJUeNdSb1CfHo8vb17G/S8ksQgSYwwAkWBpPNwebdMPZmS39+Co1+BpZ3a1NGjrbEjEqJOq3FJzIcffsjmzZs5efIkVlZW3Llz56HHKIrC+++/z9KlS7l9+zZdu3blf//7H61atSrXNSWJEUZXPPV0eZea2Nz409STpR00vveup2Yy9VTdjq+GX19Tn4/7BlqONG48QohKfX5XaRnKvLw8xo4dS/fu3Vm+fHm5jvnPf/7D/PnzWbVqFU2bNuVf//oXgwYN4uLFizg61uwOnEIAam2ZJv3UB5Q99RS1Q30AOHoW7S9TT9Xi6iHY/Bf1ed+3JYERwoRVy3TSqlWrmDVr1kNHYhRFwdPTk1mzZvHmm28CkJubi5ubGx9//DEvvPDCQ68lIzGiRtNNPRXf9XSo9NSTR7u7ozQ+XWXqyZBuX1VbCmQlQ8tRMHaVjIIJUUPUuJGYioqOjiYxMZHBgwfrtllbW9OnTx8OHTpUZhKTm5tLbm6u7uu0tLRqiVWIStFowK2V+ujxatlTTwmn1MeBBTL1ZEi5GRAyUU1g3NvCqK/k71IIE1ejkpjERLW4mJubW4ntbm5uXL16tcxj5s2bx/vvv1/lsQlRJf489ZR+o+TUU2ZSGVNPRbdxB/QD+wZGC92kaLWw8QW4cRbsXeGpH8DKzthRCSH0ZFbRA9577z00Gs0DH2FhYXoF9ecOmoqi3Ler5ty5c0lNTdU94uLi9Lq2EEbl6AbtxsOYJfDGJXjpEAz+l5q4WNhA+nW1t8/6afDfJrCkN+x8D6L3qc0LRdn2fAQXfgNzK5iwBpy9jR2REMIAKjwS88orrzBhwoQH7uPn51epYNzd3QF1RMbD425J46SkpFKjM8Wsra2xtpY1A6IWKjX1lP2nqaezpaeeigvuBfSTqadiZ9fDvv+qz4d/Bj5djBuPEMJgKpzENGzYkIYNq+buCX9/f9zd3dmxYwcdOnQA1Duc9u7dy8cff1wl1xTCZFja3l0bA2VPPUVuVx8gU08A107AppfV5z1ehfYTjRuPEMKgqnRNTGxsLCkpKcTGxlJYWMjJkycBCAwMxMHBAYDmzZszb948Ro8ejUajYdasWXz00UcEBQURFBTERx99hJ2dHRMnyg8fIUoonnpqN1696+nGuZJ3PRVPPZ38DtD86a6nLrX/rqf0RAh5Wr37K2gwDJS1c0LUNlWaxLzzzjusXr1a93Xx6Mru3bvp27cvABcvXiQ1NVW3z1//+leys7N5+eWXdcXutm/fLjVihHgQjQbcW6uPR167z9TTSfVxYH7Jqacm/aFh09o19ZSfo96JlH4dGjaDJ5aBmbmxoxJCGJi0HRCiLkhPvGfqabc69XQvJ6+7Bff8+5r21JOiqHcinV4LNvVgxi5o0MTYUQkhHqLGtR0wBklihHiIsqaeCu+9s+nPU09dwcLKaOFW2IGFsPNd0JjDpA1qFWQhRI0nSQySxAhRYfnZaiJzeZc6WnPjbMnXLe2Lpp761fypp4tb4YcJgAJD/wtdnzd2REKIcpIkBklihNBbiamnXZB5s+TrNXXqKSkClg2CvHToOBkeX1hzky0hRCmSxCBJjBAGpdVC0r1TT4dLTz15tr9bm8ZYU09ZKWpPpNsx0LgnTNpoWlNgQghJYkCSGCGq1L1TT5d3qwnOvXRTT8V3PQVV/WhIYT58Oxpi9kO9xjBjd80ZHRJClJskMUgSI0S1Sku4O/V0ZXcZU0/ed3tDBfQDOxfDx/DbHAhbDlYOMG0HuLU0/DWEEFVOkhgkiRHCaCoy9dSkP3h30X/KJ3QZbP6Leu4J30PzYfqdTwhhNJLEIEmMEDVGXhbEHlKnne439eTfSx2hqczUU/Q++GYUKIUw4B3o9ReDhi+EqF6SxCBJjBA1VrmnnvqrtV0eNPWUcgW+7g/Zt6HNWBjztdyJJISJkyQGSWKEMAlarVqP5sruik895aTB8kFw8wJ4BsOULWpzTCGESZMkBklihDBJJaaedkHS+ZKvF089NekPUTvVTt2OHuqdSE4exolZCGFQksQgSYwQtUJaQtEoTVFSk3Wr5OsWNuoIjFdH48QnhDC4ynx+V2kXayGEqBQnD2g/UX0UTz0V3/V08wIM/VgSGCGEJDFCiBrOzAw82qqPnrOMHY0QogYxM3YAQgghhBCVIUmMEEIIIUySJDFCCCGEMEmSxAghhBDCJEkSI4QQQgiTJEmMEEIIIUySJDFCCCGEMEmSxAghhBDCJEkSI4QQQgiTJEmMEEIIIUySJDFCCCGEMEmSxAghhBDCJEkSI4QQQgiTJEmMEEIIIUyShbEDMDRFUQBIS0szciRCCCGEKK/iz+3iz/HyqHVJTHp6OgA+Pj5GjkQIIYQQFZWeno6zs3O59tUoFUl5TIBWq+X69es4Ojqi0WiMHY6oYdLS0vDx8SEuLg4nJydjhyNEKfIeFTVdVb1HFUUhPT0dT09PzMzKt9ql1o3EmJmZ4e3tbewwRA3n5OQkHxCiRpP3qKjpquI9Wt4RmGKysFcIIYQQJkmSGCGEEEKYJEliRJ1ibW3Nu+++i7W1tbFDEaJM8h4VNV1Neo/WuoW9QgghhKgbZCRGCCGEECZJkhghhBBCmCRJYoQQQghhkiSJEUIIIYRJkiRG1Djz5s2jc+fOODo64urqyqhRo7h48WKJfRRF4b333sPT0xNbW1v69u3LuXPnSuyzdOlS+vbti5OTExqNhjt37pS61ocffkiPHj2ws7OjXr165YqvPNcWtVt1vUdjYmKYNm0a/v7+2Nra0qRJE959913y8vIeGN/kyZPRaDQlHt26dTPI9y5MR3X+LC2Wm5tL+/bt0Wg0nDx58oHxGeJnqSQxosbZu3cvM2fO5MiRI+zYsYOCggIGDx5MZmambp///Oc/zJ8/n0WLFhEaGoq7uzuDBg3S9c4CyMrK4tFHH+Xtt9++77Xy8vIYO3YsL730UrnjK8+1Re1WXe/RCxcuoNVqWbJkCefOnWPBggUsXrz4ge/pYo8++igJCQm6x5YtW/T/xoVJqc6fpcX++te/4unpWa74DPKzVBGihktKSlIAZe/evYqiKIpWq1Xc3d2Vf//737p9cnJyFGdnZ2Xx4sWljt+9e7cCKLdv377vNVauXKk4Ozs/NJaKXlvUDdXxHi32n//8R/H393/gPs8995wycuTICn0Povar6vfpli1blObNmyvnzp1TACU8PPy+sRjqZ6mMxIgaLzU1FQAXFxcAoqOjSUxMZPDgwbp9rK2t6dOnD4cOHarSWIx5bVFzVed7NDU1VXedB9mzZw+urq40bdqUGTNmkJSUpNd1hemryvfpjRs3mDFjBt9++y12dnYP3d9Q15YkRtRoiqIwZ84cevbsSevWrQFITEwEwM3NrcS+bm5uuteqijGvLWqm6nyPXr58mS+++IIXX3zxgfsNHTqUNWvWsGvXLj799FNCQ0Pp378/ubm5lb62MG1V+T5VFIXJkyfz4osv0qlTp3IdY6hr17ou1qJ2eeWVVzh9+jQHDhwo9ZpGoynxtaIopbZVFWNeW9Qs1fUevX79Oo8++ihjx45l+vTpD9x3/PjxuuetW7emU6dONG7cmM2bNzNmzJhKXV+Ytqp8n37xxRekpaUxd+7cCsel77VlJEbUWK+++iq//PILu3fvxtvbW7fd3d0doFS2npSUVCqrNzRjXlvUPNX1Hr1+/Tr9+vWje/fuLF26tMLHe3h40LhxYyIjIyt8rDB9Vf0+3bVrF0eOHMHa2hoLCwsCAwMB6NSpE88991yZxxjq2pLEiBpHURReeeUVNmzYwK5du/D39y/xur+/P+7u7uzYsUO3LS8vj71799KjR48qjc2Y1xY1R3W+R69du0bfvn0JDg5m5cqVmJlV/Md2cnIycXFxeHh4VPhYYbqq6336+eefc+rUKU6ePMnJkyd1d8KtXbuWDz/8sMxjDPZ/pNxLgIWoJi+99JLi7Oys7NmzR0lISNA9srKydPv8+9//VpydnZUNGzYoZ86cUZ566inFw8NDSUtL0+2TkJCghIeHK19//bUCKPv27VPCw8OV5ORk3T5Xr15VwsPDlffff19xcHBQwsPDlfDwcCU9PV23T7NmzZQNGzZU6Nqidquu9+i1a9eUwMBApX///kp8fHyJa93r3vdoenq68pe//EU5dOiQEh0drezevVvp3r274uXlJe/ROqY6f5beKzo6usy7k6riZ6kkMaLGAcp8rFy5UrePVqtV3n33XcXd3V2xtrZWevfurZw5c6bEed59992Hnue5554rc5/du3eXiKei1xa1W3W9R1euXHnfa/05nuJjsrKylMGDByuNGjVSLC0tFV9fX+W5555TYmNjq/KvRNRA1fmz9F73S2Kq4meppujEQgghhBAmRdbECCGEEMIkSRIjhBBCCJMkSYwQQgghTJIkMUIIIYQwSZLECCGEEMIkSRIjhBBCCJMkSYwQQgghTJIkMUIIIYQwSZLECCGEEMIkSRIjhDAKRVEYOHAgQ4YMKfXal19+ibOzM7GxsUaITAhhKiSJEUIYhUajYeXKlRw9epQlS5botkdHR/Pmm2/y2Wef4evra9Br5ufnG/R8QgjjkiRGCGE0Pj4+fPbZZ7zxxhtER0ejKArTpk1jwIABdOnShWHDhuHg4ICbmxuTJk3i1q1bumO3bt1Kz549qVevHg0aNODxxx/n8uXLutdjYmLQaDT8+OOP9O3bFxsbG7777jtjfJtCiCoiDSCFEEY3atQo7ty5wxNPPMEHH3xAaGgonTp1YsaMGTz77LNkZ2fz5ptvUlBQwK5duwBYv349Go2GNm3akJmZyTvvvENMTAwnT57EzMyMmJgY/P398fPz49NPP6VDhw5YW1vj6elp5O9WCGEoksQIIYwuKSmJ1q1bk5yczE8//UR4eDhHjx5l27Ztun3i4+Px8fHh4sWLNG3atNQ5bt68iaurK2fOnKF169a6JGbhwoW8/vrr1fntCCGqiUwnCSGMztXVleeff54WLVowevRojh8/zu7du3FwcNA9mjdvDqCbMrp8+TITJ04kICAAJycn/P39AUotBu7UqVP1fjNCiGpjYewAhBACwMLCAgsL9UeSVqtl+PDhfPzxx6X28/DwAGD48OH4+Pjw9ddf4+npiVarpXXr1uTl5ZXY397evuqDF0IYhSQxQogaJzg4mPXr1+Pn56dLbO6VnJxMREQES5YsoVevXgAcOHCgusMUQhiZTCcJIWqcmTNnkpKSwlNPPcWxY8e4cuUK27dvZ+rUqRQWFlK/fn0aNGjA0qVLiYqKYteuXcyZM8fYYQshqpkkMUKIGsfT05ODBw9SWFjIkCFDaN26Na+//jrOzs6YmZlhZmZGSEgIx48fp3Xr1syePZv//ve/xg5bCFHN5O4kIYQQQpgkGYkRQgghhEmSJEYIIYQQJkmSGCGEEEKYJElihBBCCGGSJIkRQgghhEmSJEYIIYQQJkmSGCGEEEKYJElihBBCCGGSJIkRQgghhEmSJEYIIYQQJkmSGCGEEEKYpP8HG6wwZQnKL0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cvd_2_merged_norm_plt = pd.DataFrame({'Overall': df_cvd_2_merged_norm['DV_overall'],\n",
    "                    'Male': df_cvd_2_merged_norm['DV_male'],\n",
    "                       'Female': df_cvd_2_merged_norm['DV_female']}, index= df_cvd_2_merged_norm.index)\n",
    "ax2 = df_cvd_2_merged_norm_plt.plot.line(rot=0)\n",
    "ax2.xaxis.set_major_locator(plt.MaxNLocator(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Year evolution of Mortality from cerebrovascular disease (stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>DV_overall</th>\n",
       "      <th>DV_male</th>\n",
       "      <th>DV_female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4326.8</td>\n",
       "      <td>3845.2</td>\n",
       "      <td>4602.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4220.3</td>\n",
       "      <td>3742.2</td>\n",
       "      <td>4492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4142.4</td>\n",
       "      <td>3680.1</td>\n",
       "      <td>4406.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>4072.6</td>\n",
       "      <td>3679.2</td>\n",
       "      <td>4276.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>4125.7</td>\n",
       "      <td>3730.8</td>\n",
       "      <td>4330.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DV_overall  DV_male  DV_female\n",
       "Year                                \n",
       "2010      4326.8   3845.2     4602.5\n",
       "2011      4220.3   3742.2     4492.0\n",
       "2012      4142.4   3680.1     4406.5\n",
       "2013      4072.6   3679.2     4276.8\n",
       "2014      4125.7   3730.8     4330.3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cvd_4_overall=df_cvd_mortality_cerebrovascular[df_cvd_mortality_cerebrovascular['Stratification1']=='Overall'].groupby('Year').sum()\n",
    "df_cvd_4_overall.rename(columns={'DataValue':'DV_overall'},inplace=True)\n",
    "df_cvd_4_male=df_cvd_mortality_cerebrovascular[df_cvd_mortality_cerebrovascular['Stratification1']=='Male'].groupby('Year').sum()\n",
    "df_cvd_4_male.rename(columns={'DataValue':'DV_male'},inplace=True)\n",
    "df_cvd_4_female=df_cvd_mortality_cerebrovascular[df_cvd_mortality_cerebrovascular['Stratification1']=='Female'].groupby('Year').sum()\n",
    "df_cvd_4_female.rename(columns={'DataValue':'DV_female'},inplace=True)\n",
    "\n",
    "df_cvd_4_merged=pd.merge(df_cvd_4_overall,df_cvd_4_male,on='Year', how='inner')\n",
    "df_cvd_4_merged=pd.merge(df_cvd_4_merged,df_cvd_4_female,on='Year', how='inner')\n",
    "df_cvd_4_merged=df_cvd_4_merged[['DV_overall','DV_male','DV_female']]\n",
    "df_cvd_4_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_cvd_4_merged.values \n",
    "min_max_scaler = preprocessing.StandardScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "df_cvd_4_merged_norm = pd.DataFrame(x_scaled, columns=df_cvd_4_merged.columns,index=df_cvd_4_merged.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cvd_4_merged_norm_plt = pd.DataFrame({'Overall': df_cvd_4_merged_norm['DV_overall'],\n",
    "                    'Male': df_cvd_4_merged_norm['DV_male'],\n",
    "                       'Female': df_cvd_4_merged_norm['DV_female']}, index= df_cvd_4_merged_norm.index)\n",
    "ax4 = df_cvd_4_merged_norm_plt.plot.line(rot=0)\n",
    "ax4.xaxis.set_major_locator(plt.MaxNLocator(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HkpSUxIQJE6hUqRJOTk44ODjg5OTEkSNHOHDgQOa+sDvw9PSkd+/ezJ07N6WzZfny5ezfv5+XX37ZqrEfeOCBVJ00hw4d4ty5czz99NPY2f37a+nh4UHXrl3ZuHFjmsepOnXqlOrz0NBQTCZTqnfjHBwcKFeunFWPNS5fvpzWrVsTHBycan+vXr2IiYlJ056fnuvXr6e8w+jg4ICDgwMeHh7cuHEjS/fsjz/+oGXLloSGht7xuB9++IHGjRvj4eGBg4MDjo6OzJo1K91rpvdzuGLFClq3bp3yriOAvb09jz322F1jzOjvgZ+fH507d+aLL77AbDYDcOXKFX755Rd69OiBg4NDqjHvu+8+vL29sbe3x9HRkVGjRhEREcHFixcBqFGjBk5OTjz//PN88cUXaR4vAMv3z8XF5ba/w7Zw8eJF+vXrR3BwcMr3vmTJkgDpfv+7du2aoXGtuSf16tXj6tWrPPHEE/zyyy9pHqkAWLx4MS1btiQoKIikpKSUj5u/V6tWrUo51lb3Y/HixRQqVIjOnTunumaNGjUoWrRoyqprGRlLRCQvU76Zv/PNm497/nf+tO7du9/1aylXrhw+Pj4MGTKEjz/+OFUX+E2LFy/GZDLx1FNPpfp7WrRoUapXr55qFdOM5in16tVj7ty5jBs3jo0bN6aZ3mH9+vVERkbSs2fPVNc0m83cf//9bNmyJeW+3m2s9Ny4cYNNmzbRrVu3VCtB2tvb8/TTT3PmzBkOHTp013Eyy2Qy0blz51T7qlWrluoeZzR/uZfXOX78ON27d6do0aIpuVnz5s0B6/JPKVhUJJMMMZlM9O7dm6+//pqPP/6YChUqpDuPEkBERARFixZN8/y2v78/Dg4OaVqKM7tqzaBBgxg5ciRdunTh119/ZdOmTWzZsoXq1asTGxubuS/sLvr378+1a9dSWtg/+ugjihcvzoMPPmjVuP/9mm9+T9L7XgQFBWE2m7ly5Uqq/f9dvcjJyQk3NzdcXFzS7LdmctqIiIjbxnVr7HfSvXt3PvroI/r06cOff/7J5s2b2bJlC0WKFMnSPbt06VKqlvD0LFiwgEcffZRixYrx9ddfs2HDhpRkO73vR3pf482f5f9Kb9/tzs3I78EzzzzD2bNnWbZsGUDK4w83E32AzZs307ZtWwA+++wz1q1bx5YtWxg+fDhAyvexbNmy/PXXX/j7+/PSSy9RtmxZypYty/vvv58y1qVLlwgKCkqVINuS2Wymbdu2LFiwgMGDB/P333+zefNmNm7cmCrWW2X0vwPW3JOnn36a2bNnc/LkSbp27Yq/vz/169dP+b4DXLhwgV9//RVHR8dUH5UrVwZIKazZ8n5cuHCBq1ev4uTklOa64eHhKdfMyFgiInmZ8s38nW9GRETg4OCAn59fqv0Z+Rvu7e3NqlWrqFGjBm+88QaVK1cmKCiI0aNHpxSbLly4gGEYBAQEpPl7unHjxpS/p5nJU7777jt69uzJ559/TsOGDfH19aVHjx6Eh4enXBOgW7duaa45adIkDMNIefz1bmOl58qVKxiGYXUunlnp3WNnZ+dU9zij+cu9us7169dp2rQpmzZtYty4caxcuZItW7awYMECIG3+6ebmlmqREJGbNCeZZFivXr0YNWoUH3/8MePHj7/tcX5+fmzatAnDMFIlLhcvXiQpKSnN/FOZnQzx5nP/EyZMSLX/8uXLFCpUKFNj3U25cuVo374906dPp3379ixatIgxY8Zgb29v1bj//ZpvJgvnz59Pc+y5c+ews7PL1LuftuTn53fbuIC7zicWFRXF4sWLGT16NEOHDk3Zf3POsKwoUqRIqkla0/P1119TunRpvvvuu1Tf7/9OBHpTej+Hfn5+6SYud0pmbj03o78H7dq1IygoiDlz5tCuXTvmzJlD/fr1U63O+O233+Lo6MjixYtTJRMLFy5Mc+2mTZvStGlTkpOT2bp1Kx9++CEDBw4kICCAxx9/nCJFirB27VrMZvNtC2U3r/Hf71dGErG9e/eya9cu5s6dS8+ePVP2Hz169LbnZPS/A9bcE7DMAde7d29u3LjB6tWrGT16NJ06deLw4cOULFmSwoULU61atdv+N+5mQmrL+1G4cGH8/PxYsmRJutf09PTM8FgiInmd8s38m2/6+fmRlJREREREqkJZRv+GV61alW+//RbDMNi9ezdz587lrbfewtXVlaFDh1K4cGFMJhNr1qxJdz6um/syk6cULlyYadOmMW3aNE6dOsWiRYsYOnQoFy9eZMmSJSk/Zx9++CENGjRIN+6b3e93Gys9Pj4+2NnZWZWLZ5fM5C/34jrLly/n3LlzrFy5MqV7DODq1avpnqcJ+eV21EkmGVasWDFef/11OnfunOoPyn+1bt2a69evp/mfxS+//DLl9bu5+UcsvXfqTCZTmj98v/32G2fPnr3ruLe71p3eERwwYAC7d++mZ8+e2Nvb89xzz2XpOncSEhJCsWLFmDdvXqqV8W7cuMFPP/2UsgJRdrrd96F169Ypf3Ru9eWXX+Lm5paSENzunt1cvem/9+zzzz8nOTk5S7G2b9+eFStW3LG93GQy4eTklOoPYHh4eLqrW95Oy5Yt+fvvv1PeJQTLqpPffffdXc/NzO/BzZb5m5MKb926Nc3jJSaTCQcHh1QJc2xsLF999dVtY7C3t6d+/fpMnz4dgO3btwOW719cXFy6K1feFBAQgIuLC7t37061PyPfv5vf8//e808++eSu596NNffkVu7u7rRv357hw4eTkJDAvn37AMtjJXv37qVs2bLUqVMnzcfNIpkt70enTp2IiIggOTk53WumNzn07cYSEcnrlG/m33yzZcuWACkdczdlZPGdW5lMJqpXr857771HoUKFUv09NQyDs2fPpvv3tGrVqinnQ+bzlBIlSvDyyy/Tpk2blGs2btyYQoUKsX///nSvWadOHZycnDI0Vnrc3d2pX78+CxYsSPXzYzab+frrrylevDgVKlTIwHcttTv97GdUVvKX7LyOrfLPu/2uSv6nTjLJlLfffvuux/To0YPp06fTs2dPwsLCqFq1KmvXrmXChAl06NCB++67765jeHp6UrJkSX755Rdat26Nr68vhQsXplSpUnTq1Im5c+dSsWJFqlWrxrZt25gyZcpdH7+7napVq7JgwQJmzpxJ7dq1sbOzo06dOimvt2nThkqVKrFixQqeeuop/P39s3SdO7Gzs2Py5Mk8+eSTdOrUib59+xIfH8+UKVO4evVqhr7v1qpatSorV67k119/JTAwEE9PT0JCQhg9enTKPE2jRo3C19eXb775ht9++43Jkyfj7e0NWB4Fc3V15ZtvviE0NBQPDw+CgoIICgqiWbNmTJkyJeUerlq1ilmzZmX5ndi33nqLP/74g2bNmvHGG29QtWpVrl69ypIlSxg0aBAVK1akU6dOLFiwgBdffJFu3bpx+vRpxo4dS2BgIEeOHMnQdUaMGMGiRYto1aoVo0aNws3NjenTp99x9cWbMvt78MwzzzBp0iS6d++Oq6trmjm2OnbsyLvvvkv37t15/vnniYiIYOrUqWkSgY8//pjly5fTsWNHSpQoQVxcHLNnzwZIueYTTzzBnDlz6NevH4cOHaJly5aYzWY2bdpEaGgojz/+eMqcHrNnz6Zs2bJUr16dzZs3ZyiRrVixImXLlmXo0KEYhoGvry+//vprqscas8qae/Lcc8/h6upK48aNCQwMJDw8nIkTJ+Lt7Z2yFPpbb73FsmXLaNSoEa+88gohISHExcURFhbG77//zscff0zx4sVtej8ef/xxvvnmGzp06MCAAQOoV68ejo6OnDlzhhUrVvDggw/y0EMPZWgsEZH8QPlm/sw327ZtS7NmzRg8eDA3btygTp06rFu37o5vMN20ePFiZsyYQZcuXShTpgyGYbBgwQKuXr1KmzZtAEvB6vnnn6d3795s3bqVZs2a4e7uzvnz51m7di1Vq1blhRdeyHCeEhUVRcuWLenevTsVK1bE09OTLVu2sGTJkpRVPD08PPjwww/p2bMnkZGRdOvWDX9/fy5dusSuXbu4dOkSM2fOzNBYtzNx4kTatGlDy5Ytee2113BycmLGjBns3buX+fPnZ6kj6k55e0ZlNH+xVkav06hRI3x8fOjXrx+jR4/G0dGRb775hl27dmXqenf7XZUCICdWC5C84dbVhu7kv6sNGYZhREREGP369TMCAwMNBwcHo2TJksawYcOMuLi4VMcBxksvvZTuuH/99ZdRs2ZNw9nZ2QBSVl68cuWK8eyzzxr+/v6Gm5ub0aRJE2PNmjVpVrLL6GpDkZGRRrdu3YxChQoZJpPJSO/X4s033zQAY+PGjXf8XtzqTqsN3W7VmYULFxr169c3XFxcDHd3d6N169bGunXrUh1zc7WhS5cupdrfs2dPw93dPUNxpGfnzp1G48aNDTc3NwNI9b3cs2eP0blzZ8Pb29twcnIyqlevnu5qOPPnzzcqVqxoODo6plql6MyZM0bXrl0NHx8fw9PT07j//vuNvXv3GiVLlky1omZGV7c0DMtKRc8884xRtGhRw9HR0QgKCjIeffRR48KFCynHvP3220apUqUMZ2dnIzQ01Pjss89Svn+3utPP4bp164wGDRoYzs7ORtGiRY3XX3/d+PTTT++6kqJhZPz34KZGjRoZgPHkk0+m+/rs2bONkJAQw9nZ2ShTpowxceJEY9asWali2bBhg/HQQw8ZJUuWNJydnQ0/Pz+jefPmxqJFi1KNFRsba4waNcooX7684eTkZPj5+RmtWrUy1q9fn3JMVFSU0adPHyMgIMBwd3c3OnfubISFhWVodcv9+/cbbdq0MTw9PQ0fHx/jkUceMU6dOpXm3Nv9PN9JVu/JF198YbRs2dIICAgwnJycUn5mdu/enWr8S5cuGa+88opRunRpw9HR0fD19TVq165tDB8+3Lh+/Xq23I/ExERj6tSpRvXq1Q0XFxfDw8PDqFixotG3b1/jyJEjmRpLRCQvUb75r4KQb169etV45plnjEKFChlubm5GmzZtjIMHD941tzh48KDxxBNPGGXLljVcXV0Nb29vo169esbcuXPTXGP27NlG/fr1DXd3d8PV1dUoW7as0aNHD2Pr1q0px2QkT4mLizP69etnVKtWzfDy8jJcXV2NkJAQY/To0caNGzdSXXPVqlVGx44dDV9fX8PR0dEoVqyY0bFjR+OHH37I9FjpWbNmjdGqVauUr6lBgwbGr7/+muqYzKxuaRi3z9tvd4/Ty6Ezkr/cTnZcZ/369UbDhg0NNzc3o0iRIkafPn2M7du3p/kdvd21DSNjv6uSv5kM45ZeWxFJV506dTCZTGzZsiWnQxERERGRfEj5pohIztPjliK3ER0dzd69e1m8eDHbtm3j559/zumQRERERCQfUb4pIpK7qEgmchvbt2+nZcuW+Pn5MXr0aLp06ZLTIYmIiIhIPqJ8U0Qkd9HjliIiIiIiIiIiUuDZ5XQAIiIiIiIiIiIiOU1FMhERERERERERKfBUJBMRERERERERkQIv303cbzabOXfuHJ6enphMppwOR0RERPIIwzC4du0aQUFB2NnpfcTcSrmeiIiIZFZG87x8VyQ7d+4cwcHBOR2GiIiI5FGnT5+mePHiOR2G3IZyPREREcmqu+V5+a5I5unpCVi+cC8vrxyORkRERPKK6OhogoODU3IJyZ2U64mIiEhmZTTPy3dFsptt915eXkqcREREJNP0CF/uplxPREREsupueZ4m3BARERERERERkQJPRTIRERERERERESnwVCQTEREREREREZECL9/NSSYiIpIbmc1mEhIScjqMAs3R0RF7e/ucDkNERETyoeTkZBITE3M6jALLVnmeimQiIiLZLCEhgRMnTmA2m3M6lAKvUKFCFC1aVJPzi4iIiE0YhkF4eDhXr17N6VAKPFvkedlaJJs4cSILFizg4MGDuLq60qhRIyZNmkRISMgdz1u1ahWDBg1i3759BAUFMXjwYPr165edoYqIiGQLwzA4f/489vb2BAcHY2enmQ5ygmEYxMTEcPHiRQACAwNzOKK8T3meiIgIKQUyf39/3Nzc9EZcDrBlnpetRbJVq1bx0ksvUbduXZKSkhg+fDht27Zl//79uLu7p3vOiRMn6NChA8899xxff/0169at48UXX6RIkSJ07do1O8MVERGxuaSkJGJiYggKCsLNzS2nwynQXF1dAbh48SL+/v569NJKyvNERKSgS05OTimQ+fn55XQ4BZqt8rxsLZItWbIk1edz5szB39+fbdu20axZs3TP+fjjjylRogTTpk0DIDQ0lK1btzJ16lQlTyIikuckJycD4OTklMORCJBSqExMTFSRzErK80REpKC7OQeZ3gjNHWyR593TZz6ioqIA8PX1ve0xGzZsoG3btqn2tWvXjq1bt2oSPBERybPUep876D5kH+V5IiJSUCm/yB1scR/u2cT9hmEwaNAgmjRpQpUqVW57XHh4OAEBAan2BQQEkJSUxOXLl9M8WxofH098fHzK59HR0bYNXERERETuKLvyPFCuJyIiIvfOPeske/nll9m9ezfz58+/67H/rf4ZhpHufrBMGuvt7Z3yERwcbJuARUREJNdr0aIFAwcOTPm8VKlSKY/yyb2TXXkeKNcTEREpqHIiz7snRbL+/fuzaNEiVqxYQfHixe94bNGiRQkPD0+17+LFizg4OKQ7Ed6wYcOIiopK+Th9+rRNY09PVExiSkInIiKSn50+fZpnn32WoKAgnJycKFmyJAMGDCAiIiKnQ5NcIjvzPLj3uV5cYjIJSeZsvYaIiEhuoDwvrWwtkhmGwcsvv8yCBQtYvnw5pUuXvus5DRs2ZNmyZan2LV26lDp16uDo6JjmeGdnZ7y8vFJ9ZKcv1ofRZNJylh+8mK3XERERyWnHjx+nTp06HD58mPnz53P06FE+/vhj/v77bxo2bEhkZGS2XVvzU+V+9yLPg3ub6/2w9TRNJ6/gh23Z/6ariIhITlKel75sLZK99NJLfP3118ybNw9PT0/Cw8MJDw8nNjY25Zhhw4bRo0ePlM/79evHyZMnGTRoEAcOHGD27NnMmjWL1157LTtDzbBzUbFci09i7OL9xCcl53Q4IiIi2eall17CycmJpUuX0rx5c0qUKEH79u3566+/OHv2LMOHD2fYsGE0aNAgzbnVqlVj9OjRKZ/PmTOH0NBQXFxcqFixIjNmzEh5LSwsDJPJxPfff0+LFi1wcXHh66+/JiIigieeeILixYvj5uZG1apVM/Q4n9wb+THPux6fxKVr8UxfflTdZCIikq8pz0tftk7cP3PmTMDyHOmt5syZQ69evQA4f/48p06dSnmtdOnS/P7777z66qtMnz6doKAgPvjgg1yzLPjLLcvx07azhEXEMHddGH2bl83pkEREJA8xDIPYxJx5k8XV0T7Dq/5ERkby559/Mn78eFxdXVO9VrRoUZ588km+++47Vq1axdtvv82xY8coW9byN3Hfvn3s2bOHH3/8EYDPPvuM0aNH89FHH1GzZk127NjBc889h7u7Oz179kwZd8iQIbzzzjvMmTMHZ2dn4uLiqF27NkOGDMHLy4vffvuNp59+mjJlylC/fn0bfVckq/JjnvdEvRLMXHmMc1Fx/LDtNE/WL5nTIYmISB6iPC/v53nZWiTLyLxdc+fOTbOvefPmbN++PRsisp6niyND7g/h9R938+HyozxUqxj+ni45HZaIiOQRsYnJVBr1Z45ce/9b7XBzytif/iNHjmAYBqGhoem+HhoaypUrVwgICKBatWrMmzePkSNHAvDNN99Qt25dKlSoAMDYsWN55513ePjhhwFLoWT//v188sknqZKngQMHphxz060dRv3792fJkiX88MMPKpLlAvkxz3NxtOfFFmV589f9TF9+lG61i+PsYJ/TYYmISB6hPC/v53n3bHXL/KRrreJUL+7N9fgkpiw5lNPhiIiI3HO3rkj45JNP8s0336Tsnz9/Pk8++SQAly5dSpkU1sPDI+Vj3LhxHDt2LNWYderUSfV5cnIy48ePp1q1avj5+eHh4cHSpUtTdSaJ2NqjdYvj7+ls6SbbeianwxEREbnnCnKel62dZPmVnZ2JUZ0r03Xmen7YdoanGpSkenChnA5LRETyAFdHe/a/1S7Hrp1R5cqVw2QysX//frp06ZLm9YMHD+Lj40PhwoXp3r07Q4cOZfv27cTGxnL69Gkef/xxAMxmy7xOn332WZp3Be3tU8fj7u6e6vN33nmH9957j2nTplG1alXc3d0ZOHAgCQkJGf46RDJj96XdjNs4jvZ1H+WL5a7MWHGUR+qom0xERDJGed6/8mqepyJZFtUu6cNDNYvx846zvPnrPha80CjDz/+KiEjBZTKZMtwKn5P8/Pxo06YNM2bM4NVXX001X0V4eDjffPMNPXr0wGQyUbx4cZo1a8Y333xDbGws9913HwEBAQAEBARQrFgxjh8/nvKuY0atWbOGBx98kKeeegqwJGJHjhy57aMBItb6M+xPDkQe4JrHbPy9+qd0kz3VQHOTiYjI3SnPy7jcmufpcUsrDLm/Im5O9uw4dZWFO8/mdDgiIiI29dFHHxEfH0+7du1YvXo1p0+fZsmSJbRp04ZixYoxfvz4lGOffPJJvv32W3744YeUZOemN998k4kTJ/L+++9z+PBh9uzZw5w5c3j33XfveP1y5cqxbNky1q9fz4EDB+jbty/h4eHZ8rWKALxY40X8Xf05c/00NavuBGD6iqNa0VxERPId5XnpU5HMCkW9XXipZTkA3v7jIDfik3I4IhEREdspX748W7dupWzZsjz22GOULVuW559/npYtW7JhwwZ8fX1Tjn3kkUeIiIggJiYmTdt+nz59+Pzzz5k7dy5Vq1alefPmzJ07l9KlS9/x+iNHjqRWrVq0a9eOFi1aULRo0XQfCRCxFXdHd16v9zoAW6/+SBGfa5yPiuN7zU0mIiL5jPK89JmMjCxNlIdER0fj7e1NVFQUXl5e2X69uMRk2ry3itORsbzcshyvtQvJ9muKiEjeERcXx4kTJyhdujQuLloNOafd6X7c6xxCsia775NhGPRd1pcN5zdQ2q02u7d1I9DblZWvt9DcZCIikoryvNzFFnmeOsms5OJoz/AOlQD4dM1xTkXE5HBEIiIiIpJVJpOJN+q/gaOdIyditlHY/4ilm2zL6ZwOTURERLKZimQ20K5yAI3L+ZGQZGbC7wdyOhwRERERsUIp71L0rtIbAKeARWCKZ/qKY5qbTEREJJ9TkSwLouKjUn1uMpkY1aky9nYmluwLZ/3RyzkUmYiIiIjYwnNVn6OYRzGuJV3Gt9gqwqPVTSYiIpLfqUiWSYuOLaLdT+1Yf3Z9qv0hRT15qn4JAMb8up+kZHNOhCciIiIiNuDi4MKwesMASPZchZ3TBXWTiYiI5HMqkmXS7ku7uZF4gzfWvsHl2NQdY6+2qUAhN0cOXbjGvM2ncihCEREREbGF5sHNaRncEjPJeBZfRHh0LN+pm0xERCTfUpEsk16r8xrlCpUjIi6CEWtHYDb+7Rgr5ObEoDYVAHhn6WGu3EjIqTBFRERExAaG1huKi70LZudjOHjtZMaKY8QlqptMREQkP1KRLJNcHFyY0mwKLvYurDu3jq/2f5Xq9e71ShAS4ElUbCLv/XU4h6IUEREREVsI8giib/W+ALgV/Z3w61f4fqu6yURERPIjFcmyoJxPOQbXGwzAtO3T2Hd5X8prDvZ2jO5cCYCvN57kYHh0jsQoIiIiIrbRs1JPSnuXxrC/hnORpeomExERyadUJMuibuW70aZkG5LMSby++nWuJ1xPea1RucLcX7koZgPe+nU/hmHkYKQiIiIiYg1He0eG1x8OgJPPRi4mHNPcZCIiIvmQimRZZDKZGN1wNIHugZy+dprxm8anen14x1CcHOxYfyyCP/ddyKEoRUREcoewsDBMJhM7d+7M6VBEsqR+YH06lO4AJgOXoguZvvKwuslERETIX3meimRW8Hb2ZlKzSdib7Fl8fDGLji1KeS3Y143nm5YBYPzv+5VEiYhIntOrVy9MJhP9+vVL89qLL76IyWSiV69e9z4wkRzyWp3XcHf0wN71NFfs1qqbTERE8izleelTkcxKNf1r8kL1FwAYt3EcYVFhKa+90KIsAV7OnI6MZdbaEzkUoYiISNYFBwfz7bffEhsbm7IvLi6O+fPnU6JEiRyMTOTeK+JWhP41XwbA2X8J01fv1BuhIiKSZynPS0tFMhvoU7UPdYvWJTYplsGrB5OQnACAu7MDw9qHAjB9xVHCo+JyMkwREZFMq1WrFiVKlGDBggUp+xYsWEBwcDA1a9ZM2bdkyRKaNGlCoUKF8PPzo1OnThw7duyOY+/fv58OHTrg4eFBQEAATz/9NJcvX862r0XEFh4LeYwKPiGY7GOJcv2FbzefyumQREREskR5XloqktmAvZ09E5tMpJBzIQ5EHmDa9mkprz1YI4haJQoRk5DMpCUHcy5IERHJHQwDEm7kzEcWF5Lp3bs3c+bMSfl89uzZPPPMM6mOuXHjBoMGDWLLli38/fff2NnZ8dBDD2E2m9Md8/z58zRv3pwaNWqwdetWlixZwoULF3j00UezFKPIveJg58DIBiMAcCq0lQ/XL1M3mYiIWCjPA/J2nueQ0wHkFwHuAYxtPJb+y/vz1f6vaBDYgGbFm2EymXjzgco88NE6ft5xlqcalKR2SZ+cDldERHJKYgxMCMqZa79xDpzcM33a008/zbBhw1ImZV23bh3ffvstK1euTDmma9euqc6ZNWsW/v7+7N+/nypVqqQZc+bMmdSqVYsJEyak7Js9ezbBwcEcPnyYChUqZDpOkXulhn8NupR9iIXHfibW6wfmbWrFM03K5XRYIiKS05TnAXk7z1MnWWYZBhxbAYmxaV5qEdyC7hW7AzBy3UguxVwCoFrxQjxSuzgAb/26D7M5axVeERGRnFC4cGE6duzIF198wZw5c+jYsSOFCxdOdcyxY8fo3r07ZcqUwcvLi9KlSwNw6lT6j6Jt27aNFStW4OHhkfJRsWLFlLFEcrv/1RmEq70X9i7hfLTtC3WTiYhInqQ8LzV1kmXWz/1g97fQdhw06p/m5UF1BrHtwjYOXTnEsLXD+LTNp9iZ7Hj9/hD+2BvOrjNR/LT9DI/UCc6B4EVEJMc5ulne6cupa2fRM888w8svWyYsnz59eprXO3fuTHBwMJ999hlBQUGYzWaqVKlCQkJCuuOZzWY6d+7MpEmT0rwWGBiY5ThF7pVCLoX4X51XGbdpDAmev/Pp+gd5pXmdnA5LRERykvI8IG/neSqSZVapJpYi2dr3oHYvcPZM9bKzvTOTm0/m8cWPs+n8JmbvnU2fqn3w93Shf6tyTPzjIJOWHOL+KkXxdHHMma9BRERyjsmUpVb4nHb//fenJELt2rVL9VpERAQHDhzgk08+oWnTpgCsXbv2juPVqlWLn376iVKlSuHgoHRE8qZHQh5m9q5vORd3iNn7P+D5RnNwcbTP6bBERCSnKM8D8naep8ctM6v6E+BXDmIiYOPMdA8p412GYfWGAfDRjo/YdWkXAL0al6KUnxuXr8fz0Yqj9yxkERERa9nb23PgwAEOHDiAvX3qIoCPjw9+fn58+umnHD16lOXLlzNo0KA7jvfSSy8RGRnJE088webNmzl+/DhLly7lmWeeITlZj61J3mBnsmNqy7fAMJHstoO3Vy7K6ZBEREQyTXnev1Qkyyx7B2hhKYCx/kOIiUz3sC7lutC+VHuSjWSGrB7CtYRrODvYM7JTJQBmrz3Bics37lXUIiIiVvPy8sLLyyvNfjs7O7799lu2bdtGlSpVePXVV5kyZcodxwoKCmLdunUkJyfTrl07qlSpwoABA/D29sbOTumJ5B1V/StRx7czAAtOfkh0XNp5a0VERHI75XkWJsPI4jqhuVR0dDTe3t5ERUWle4NtwmyGT5rChb3QZBDcNzrdw64lXOORXx/h7PWz3F/qfiY3mwxAzzlbWH34EveF+vN5z7rZE6OIiOQKcXFxnDhxgtKlS+Pi4pLT4RR4d7of9ySHEKvlxvsUERNFy287YNhH08j3KT7pPCSnQxIRkXtAeV7uYos8L3eX8HIrOztoOdyyveljuH4x3cM8nTyZ3GwyDiYHloQtYeHRhZhMJkZ1CsXBzsRfBy6y6vClexi4iIiIiNian5s3nYP7ArA+4juOXUl/tS8RERHJ3VQky6qQ9lCsNiTGwJp3b3tYtSLVeKnmSwBM3DyR41ePU87fkx4NSwHw1q/7SEw234uIRURERORukhIsH5k0qsWT2MeXB1Mig/4akw2BiYiISHZTkSyrTCZoNcKyvXUWRJ257aHPVHmGBoENiE2KZfDqwcQnxzPgvvL4ujtx7NINvtxw8h4FLSIiIiK3tXM+fFATdn6T6VOdHe15NnQQhmHP8ZjN/Hni72wIUERERLKTimTWKNMSSjaB5ARYNfm2h9mZ7JjQZAK+Lr4cunKId7e+i7erI6+1DQFg2l+Hibgef6+iFhEREZH0xF6B6DOw7n0wZ371rb4NG+F8oyUAY9ZPIDZJk/iLiIjkJSqSWcNkgtYjLds7voaIY7c9tIhbEcY1HgfAvIPzWHFqBY/VDaZSoBfX4pJ4Z9nhexGxiIiIiNxOrR7g6gNXTsD+XzJ9upODHa/UfgFzYiGuJV1k5s5PsiFIERERyS4qklmrRAMo1waMZFj59h0PbVq8KT0q9QBg5PqRXI69yJsPVAZg/uZT7DsXle3hioiIiMhtOHtAPcsE/Kx9D7KwCPwTdcrhfq0bAF/s+4LjUcdtGaGIiIhkIxXJbOHm3GR7foAL++946IBaAwj1DSUqPoqha4ZSu6Q3naoFYhgw5tf9GFlIxkRERETERur3BUc3CN8Nx5Zn+nQnBztebdSFpGsVMZPEuA0TlN+JiIjkESqS2UJQDQh9ADBg5YQ7Hupk78SU5lNwdXBl64WtfL7nc4Z1CMXF0Y7NJyL5bc/5exKyiIiIiKTDzRdq97Jsr30vS0N0rR2MT9yjGGYHtlzYxJKwJbaLT0RERLKNimS20nI4YIIDv8K5HXc8tKRXSUY0sHSfzdw1k4sJB+nXvCwAE38/SGxC5ieKFREREREbafgS2DlA2Bo4szXTpzs52PFKs/okXLZM4j95yxSuJ1y3dZQiIiJiYyqS2Yp/Raj2mGV7+bi7Hv5A2QfoVKYTyUYyQ1YP4YkGRQjyduHs1Vg+WX37BQBEREQKslKlSjFt2rScDkPyO+/i/+Z1We4mK04R8/2Y4wtzOfYS03dOt2GAIiIi+U9uyPNUJLOlFkMs7zoe/QtObrjr4SMajKCEZwnO3zjPpK1jGdahIgAfrzrG2ataMlxERHJWr169MJlMaT6OHj2a06GJZL/GAyz/HlwMlw5l+nRHezteaVWRuAsPApbVzQ9GHrRlhCIiIlmmPC992VokW716NZ07dyYoKAiTycTChQvvePzKlSvTvUkHD+aRhMK3DNR8yrK9fOxdV0Ryd3RncrPJONg5sOzkMmJd1lGvlC9xiWYm/n7gHgQsIiJyZ/fffz/nz59P9VG6dOmcDksk+xUJgYqdLNvrPsjSEA/XKk6gUzUSo6thNsyM2zgOs2G2YZAiIiJZpzwvrWwtkt24cYPq1avz0UcfZeq8Q4cOpbpJ5cuXz6YIs0GzwWDvDCfXZWhFpMqFKzOw1kAAJm+ZTO+WLphMsHj3eTafiMzmYEVERO7M2dmZokWLpvqwt7fn119/pXbt2ri4uFCmTBnGjBlDUlJSynkmk4lPPvmETp064ebmRmhoKBs2bODo0aO0aNECd3d3GjZsyLFj/04xcOzYMR588EECAgLw8PCgbt26/PXXX3eMLyoqiueffx5/f3+8vLxo1aoVu3btyrbvhxQwjQda/t39HUSdyfTpjvZ29G9VjvgLHcHszK5Lu/jl6C+2jVFERCSLlOella1Fsvbt2zNu3DgefvjhTJ3n7++f5iblGd7FoO6zlu0MdJMBPF3paRoHNSY+OZ5PD77FI3WKAvDmon0km7VkuIhIfmIYBjGJMTnyYWTgb1JG/Pnnnzz11FO88sor7N+/n08++YS5c+cyfvz4VMeNHTuWHj16sHPnTipWrEj37t3p27cvw4YNY+tWy2ToL7/8csrx169fp0OHDvz111/s2LGDdu3a0blzZ06dOnXb72XHjh0JDw/n999/Z9u2bdSqVYvWrVsTGak3mrJbgXhiILgulGoK5kTYkLU5xR6uVZziXkWJu3QfAO9ue5ercVdtGKSIiOQWyvPyfp7nkG0jW6FmzZrExcVRqVIlRowYQcuWLXM6pMxpMgi2fWFZ5fLgbxDa6Y6H25nsGNdkHN0WdePo1aNULrMYT5cG7D8fzfdbT/NEvRL3KHAREclusUmx1J9XP0euvan7Jtwc3TJ1zuLFi/Hw8Ej5vH379ly4cIGhQ4fSs2dPAMqUKcPYsWMZPHgwo0ePTjm2d+/ePProowAMGTKEhg0bMnLkSNq1awfAgAED6N27d8rx1atXp3r16imfjxs3jp9//plFixalSrJuWrFiBXv27OHixYs4OzsDMHXqVBYuXMiPP/7I888/n6mvVTLn5hMDvXv3pmvXrhk+79ChQ3h5eaV8XqRIkewIz3aaDLSscrltLjR7Hdx8M3W6o70d/VuWZ/BP13H12c5VzjNt+zTebPRmdkQrIiI5SHle3s/zclWRLDAwkE8//ZTatWsTHx/PV199RevWrVm5ciXNmjVL95z4+Hji4+NTPo+Ojr5X4d6eRxFo0A/WvAMrxkNIB7C7c9NeYdfCTGgygb5/9eWX4z/SqUE55q8sxJQ/D9GhaiDero73KHgREZF/tWzZkpkzZ6Z87u7uTrly5diyZUuqdxSTk5OJi4sjJiYGNzdLglatWrWU1wMCAgCoWrVqqn1xcXFER0fj5eXFjRs3GDNmDIsXL+bcuXMkJSURGxt723cYt23bxvXr1/Hz80u1PzY2NlV7v2SP9u3b0759+0yf5+/vT6FChWwfUHYp2xqKVoXwPbD5U2gxNNNDPFSrGB+uOMK5cw/iVupjFhxZwEPlH6J6kep3P1lERCSbKM9LK1cVyUJCQggJCUn5vGHDhpw+fZqpU6fetkg2ceJExowZc69CzLhG/WHz53BxP+xbAFW73f2UYo3oXaU3c/bOYdWV6ZQKeI2wC/DB30cY2anSPQhaRESym6uDK5u6b8qxa2fWzWTpVmazmTFjxqQ7nYKLi0vKtqPjv2/wmEym2+4zmy0Tmb/++uv8+eefTJ06lXLlyuHq6kq3bt1ISEhINzaz2UxgYCArV65M81qeKsIUMJl9YiDH3xA1maDJq/DjM7DpY0uO5+SeqSH+7SaLxe5GXczuWxi/cTzzOs7DwS5XpeMiImIF5Xl5P8/L9X+VGzRowNdff33b14cNG8agQYNSPo+OjiY4OPhehHZnrj7QuD8sHwcrJkClLmB/9293/5r92Rq+lT2X91C25I9w4XG+WB/GE/VKUM7f467ni4hI7mYymTLdCp/b1KpVi0OHDqVJqqy1Zs0aevXqxUMPPQRY5q4ICwu7Yxzh4eE4ODhQqlQpm8YitpeVJwYgl7whGvog+JSGKydg+5fQ4IVMD/FQrWJ8tOIop8+2w6/iAQ5EHuC7Q9/xZOiT2RCwiIjkBOV5t5dX8rxsnbjfFnbs2EFgYOBtX3d2dsbLyyvVR65R/wVw84PIY7BrXoZOcbRzZFKzSbg7unPs2h5CKm4iyWwwdvF+m03EJyIiYo1Ro0bx5Zdf8uabb7Jv3z4OHDjAd999x4gRI6wat1y5cixYsICdO3eya9cuunfvnvLuY3ruu+8+GjZsSJcuXfjzzz8JCwtj/fr1jBgxImXCWMk9QkJCeO6556hVqxYNGzZkxowZdOzYkalTp97xvGHDhhEVFZXycfr06XsU8S3sHaDxK5bt9R9BUvrvet+Jo70dL7cqh5HsQdLl+wH4aMdHXI69bMtIRURErFLQ87xsLZJdv36dnTt3snPnTgBOnDjBzp07U545HTZsGD169Eg5ftq0aSxcuJAjR46wb98+hg0bxk8//ZTuJG55grOHZRJ/gFWTISn+zsf/I9gzmFENRgEQblqMk8cJVh2+xIpDF7MrUhERkQxr164dixcvZtmyZdStW5cGDRrw7rvvUrJkSavGfe+99/Dx8aFRo0Z07tyZdu3aUatWrdsebzKZ+P3332nWrBnPPPMMFSpU4PHHHycsLCxlbgzJ3Ro0aMCRI0fueEyueUO0enfwCIDoM7D3xywN8VDNYpTwdePqhVr4O5XneuJ1pm69c5FQRETkXiroeZ7JyMb2pJUrV6Y7z0TPnj2ZO3cuvXr1IiwsLOUZ08mTJ/Ppp59y9uxZXF1dqVy5MsOGDaNDhw4ZvmZ0dDTe3t5ERUXljq6yxFj4oCZcOw/tJ0P9vhk+dcTaEfxy7Bdc7Xy5ePAlSvv48+fAZjg55PoGQBER+UdcXBwnTpygdOnSqeZxkJxxp/uR63KIPMRkMvHzzz/TpUuXTJ3XrVs3IiMjWb58eYbPydH7tHYa/DUaCofAixvvujBTer7feprBP+7Gp9AFkgOnYWAwq+0s6gXWs328IiKSrZTn5S62yPOytdrSokULDMNI8zF37lwA5s6dm2oStsGDB3P06FFiY2OJjIxkzZo1mSqQ5UqOrpblwgFWT4WEGxk+9Y36b1DKqxSx5ki8i//MicvXmbv+RDYFKiIiIpJxBfKJgTrPgLM3XD4Eh//I0hAP1yxGST83rlwNoIqnZXXQ8ZvGk5icaMtIRUREJAvUknQv1HwaCpWEGxctS4dnkJujG5ObTcbRzhGz214cfTbywd9HuXgtLhuDFREREbm7rVu3UrNmTWrWrAnAoEGDqFmzJqNGWaaMOH/+fKpl3RMSEnjttdeoVq0aTZs2Ze3atfz222/prp6Va7l4Qd1nLdtr3oUsPJDhYG/Hyy0tkyEf2N8YH2dfjkcd58v9X9oyUhEREckCFcnuBQcnaDHMsr12GsRFZfjUUL9QBtW2zGvmGvAbMZxi6p+HsiFIERERkYwrsE8MNHgB7J3h7FY4uS5LQzx0s5vsuj21PZ8G4JPdn3D++nlbRioiIiKZpCLZvVLtUcv8FXFXYcOMTJ36ZOiTNCveDMOUhEux+fyw/Ti7z1zNljBFRERE5A48/KHmU5btte9laYhbu8lWbi9BzSK1iE2KZdKWSbaKUkRERLJARbJ7xc4eWr5h2d4wHWIiM3yqyWRibOOxFHEtgr3zJZz8f+XNRfvIxjUXRETExvTf7NxB90FsolF/MNnB0b/g/K4sDZHSTXYjkRDHXjiYHPj71N+sPrPaxsGKiEh2U36RO9jiPqhIdi+FPgBFq0HCtUy/8+jr4svEphMxYcLJZwu7r6zml53nsilQERGxFXt7e8AyH5PkvJiYGAAcHR1zOBLJ03xLQ+V/5lJbOy1LQzjY29G/VXkAftqYxGMhTwIwYdME4pI0/6yISF5wM5+4mV9IzrJFnudgq2AkA+zsoNVImPcIbP4MGr4EnkUzfHr9wPr0qdqHz/Z8hkvgAsYvLUubSg/j7qzbKCKSWzk4OODm5salS5dwdHTEzk7vT+UEwzCIiYnh4sWLFCpUKKV4KZJlTQbC3h9h/0KIGAF+ZTM9RJcaQXy0/AhhETG4xdxPgNsSzl4/y6y9s3ipxks2D1lERGzL3t6eQoUKcfHiRQDc3NwwmUw5HFXBY8s8z2Tks77A6OhovL29iYqKwsvLK6fDScswYFZbOLMZ6j4HHadm6vREcyI9/+jFnsu7SY4pQa/Skxl8f+VsClZERGwhISGBEydOYDabczqUAq9QoUIULVo03QQ21+cQAuSy+/R1Nzi6DGr3hs7TsjTET9vO8L8fduHr7sSYJ5J5Y93rONo58vODP1PSq6Rt4xUREZszDIPw8HCuXr2a06EUeLbI81QkywknVsMXncHOEfpvA5/MJUBnr5+ly8KuxCXfICmyFUt7TiTY1y2bghUREVswm8165DKHOTo63vGdxTyRQ0juuk9h62BuB7B3goF7MvWEwE1JyWbue3cVYRExDG4Xwu7kqaw7u45GQY34+L6P1ZEgIpJHJCcnk5iYmNNhFFi2yvNUJMspXzwAJ1ZBjaegy/RMn/7niT95bfVrGIaJqvavM//pp7MhSBERkYIjz+QQBVyuuk+3PiHQeCC0GZOlYW52k/m4OTL/xXI8+ccjJJgTeKf5O7Qt1da2MYuIiBRAGc0fNDFKTmk9yvLvrnlw+UimT29Xuh2tiz+AyWSwO34mfx48auMARUREROSOTCZo8qple+tsiIvK0jAP1giidGF3rsQksmKvwbNVnwVg0pZJ3Ei8YatoRURE5C5UJMspxetAhfZgmGHlxCwNMbH5CDztimHneI3ha0aSmJRs4yBFRERE5I4q3A9FQiE+GrbMytIQlpUuywHw6epjPFq+B8GewVyMucjMnTNtGa2IiIjcgYpkOanVcMu/e3+C8L2ZPt3VwZUPWr0DhgPxTnt5bWnmH9sUERERESvY2VlWugTYOBMSY7M0zAPV/+0m+35LOMPqDQPg6wNfc+RK5p86EBERkcxTkSwnFa0KlR+2bK8Yn6Uh6hSrTOuAPgAsvzibzWd32yo6EREREcmIKl3BOxhuXISd87I0xK3dZJ+tPk7NIg25r8R9JBvJjNs4jnw2jbCIiEiupCJZTmv5Bpjs4NDvcGZrloaY0qYfLgnVwZTMgOX/IyYxxsZBioiIiMht2TtCo/6W7fUfQHJSloa5tZvsi/VhDKk3BFcHV7Zf3M6iY4tsGLCIiIikR0WynFa4PFR/wrK9fGyWhnB0sGd8k7cwJ3pz3RzO0JVZW1lJRERERLKo5tPg5gdXwmD/wiwN4WBvxyut/+kmW3McD4fCvFD9BQDe3fYuUfFZWxhAREREMkZFstyg+RCwc4TjK+HEmiwN0Ta0DFWdXsAwTKw49zuLjy22bYwiIiIicntOblC/n2V77TTI4uORnasFUaawO1f/6SZ7qtJTlPUuS2RcJB/u+NB28YqIiEgaKpLlBj4loXZPy/bysVlOqiZ3fIjkyNYAvLl+LKejT9sqQhERERG5m7p9wNEdLuyBo39laQgHezv639JNFp9oYngDy2JP3x/6nr2XM7/Yk4iIiGSMimS5RdPXwMEFTm+CI8uyNEQJPzd6VepDUkwp4s0xvLbqdRKTE20cqIiIiIiky80X6vS2bK99L8vD/LebrG7RunQu0xkDg3Ebx5FsTrZRwCIiInIrFclyC69AqPecZXv5WDCbszTMyy1D8IjugZHsyv7IfXy4U235IiIiIvdMgxct02icXAenNmVpCMvcZOUBSzfZtbhEBtUZhKejJ/si9vHj4R9tGbGIiIj8Q0Wy3KTxq+DkCeG74eCvWRrC3dmBN9o2JO58VwDm7J3D+rPrbRmliIiIiNyOdzGo/phle920LA/Tufq/3WRfbjhJYdfC9K9lWUHz/R3vExEbYYNgRURE5FYqkuUm7n7Q8EXL9vLxkMVW+gerF6OqTxMSrjQA4I21b3A59rKtohQRERGRO2k0ADDBod/h4oEsDWFvZ0rTTfZohUcJ9Q3lWsI13t32rg0DFhEREVCRLPdp+BK4FILLh2DPD1kaws7OxJudKxN/oSPJcQFExEUwYu0IzEbWHuEUERERkUwoUgFCO1m2172f5WE6Vw+iTJF/5yazt7NnZIORmDCx6NgitoZvtVHAIiIiAiqS5T4u3tBkoGV75UTI4sT71YML0a1WaeLOdsdkOLLu3Dq+3Pel7eIUERERkdtr8qrl3z0/wNVTWRrC3s7EgJRushNci0ukapGqdKvQDYDxm8aTaNYiTSIiIraiIlluVO95cPeHK2Gw46ssDzP4/hBcCSI23PJO5vvb39ey4SIiIiL3QrHaULo5mJNgw/QsD9OpmqWbLCrW0k0GMKDWAHycfTh69SjzDsyzUcAiIiKiIllu5OQOTf9n2V41BRLjsjSMv6cL/VuXJ/FqPexiqpFkJDF49WCuJ1y3YbAiIiIikq6b3WTbvoAbWZtoP71uMm9nb16tbRl7+s7phN8It0m4IiIiBZ2KZLlVnd7gVRyunYOts7I8TO/GpSjl507U6S642RXm9LXTjN803oaBioiIiEi6yrSAwBqQFAubP8nyMJ2qBVH2n26yuevCAHiw3IPU9K9JbFIsk7dMtkm4IiIiBZ2KZLmVgzM0H2zZXvMuxGet+8vZwZ4RHSuB2Y2rYY9iZ7Jn8fHFLDq2yIbBioiIiEgaJtO/3WSbPslyPnfrSpefrz1BdFwidiY7htcfjr3JnmUnl7Hu7DpbRS0iIlJgqUiWm9XoDr5lIOYybJqZ5WFah/rTrEIR4m+UIMh4AIBxG8cRFhVmo0BFREREJF2hncG3LMRdhe1fZHmYW7vJvvinmyzEN4Tuod0BmLBpAvHJ8TYIWEREpOBSkSw3s3eEFm9Yttd9CLFXsjSMyWRiVKdQHOxMHDhQl/JeNYhNimXw6sEkJCfYMGARERERScXOHhoPsGyv/wiSslbIurWb7LM1x4mOs6xq+WL1F/F39efUtVPM3jvbJiGLiIgUVCqS5XZVuoJ/JYiPsiRWWVTO35MeDUsBdlw52Y1CzoU4EHmAadun2SpSEREREUlP9cfBo6hlrtnd32d5mE7Vgijn70F0XFLK3GQeTh68Xu91AD7f/Tmno0/bImIREZECSUWy3M7ODloOt2xvnAnXL2V5qAH3lcfX3YkT4Q4083kZgK/2f8XqM6ttEamIiIiIpMfBGRq+ZNle9z6Yk7M0TKq5yW7pJmtXsh0NAhuQYE5g4uaJGIZhk7BFREQKGhXJ8oKKHSGoJiTegLXvZXkYb1dHXmsbAsCiDYXoWvZxAEauG8mlmKwX30RERETkLmr3AhdviDgCB3/L8jAdqwam6SYzmUwMrz8cRztH1pxdw/JTy20Ts4iISAGjIlleYDJBqxGW7S2fQ9TZLA/1WN1gKgV6cS0uibiL9xPiE0JkXCTD1g7DbJhtFLCIiIiIpOLiBXWfs2yvfQ+y2O31326yqFhLN1kp71L0rtIbgLe3vE1MYoz1MYuIiBQwKpLlFWVbQ4lGkBwPq6dkeRh7OxOjO1cC4Pst4fQNHY2rgyubzm/SZK8iIiIi2al+P3BwgXPb4UTWp7voWDWQ8v/pJgPoU7UPxTyKEX4jnE92f2KDgEVERAoWFcnyCpMJWo+0bO/4CiJPZHmo+mX86FgtELMBny+/ztC6QwH4aMdH7Lq0yxbRioiIiMh/eRSBmk9btq2YQuPWbrJZa//tJnN1cGVYvWEAfLnvS45dPWZdvCIiIgWMimR5SclGlo4ycxKsfNuqoYa1r4izgx2bTkTiFNuA9qXak2wkM2T1EK4lXLNRwCIiIiKSSqP+YLKH4yvg3I4sD9PhNt1kzYOb0zK4JUlGEuM2jtMk/iIiIpmgIllec3Nust3fwcWDWR6muI8b/ZqXBWDiHwcZXOcNinkU4+z1s7y14S0lVCIiIiLZwackVO1m2V47LcvDpJqb7JZuMoCh9YbiYu/C1gtb+e1E1hcJEBERKWhUJMtritWCip0AA1ZOsGqofs3LEuTtwtmrsXyz4RKTm03GweTAkrAlLDy60CbhioiIiMh/NB5g+Xf/LxCR9Ucib85Ndi0uiTnr/p2KI8gjiL7V+wIwdctUohOirQpXRESkoFCRLC9qNQIwWRKr81mfQ8zVyZ5hHUIBmLnqKIUdy/NyzZcBmLh5IsevHrdFtCIiIiJyq4DKUOF+wIB172d5GDs7EwPuuzk32YlU3WQ9K/WktHdpIuIi+GjHR9ZGLCIiUiBka5Fs9erVdO7cmaCgIEwmEwsXLrzrOatWraJ27dq4uLhQpkwZPv744+wMMW/yD4Wqj1i2l4+zaqhO1QKpV8qXuEQzE/84SO8qvWkQ2IDYpFheX/068cnxNghYRERERFJp8qrl313zIfp8lofpUCWQCgGWbrLZa//tJnO0d2R4/eEAfHfoO/ZH7LcqXBERkYIgW4tkN27coHr16nz0UcbevTpx4gQdOnSgadOm7NixgzfeeINXXnmFn376KTvDzJtaDLVM+npkKZzalOVhTCYTozpXwmSCX3edY2vYVSY0mYCviy+Hrxzm3a3v2jBoEREREQGgRAMo0RCSE2Dj9CwPY2dnYkDrCgDMXpe6m6x+YH3al26P2TAzbuM4zIbZ6rBFRETys2wtkrVv355x48bx8MMPZ+j4jz/+mBIlSjBt2jRCQ0Pp06cPzzzzDFOnTs3OMPMmv7JQ80nL9vKxYMVE+1WKefN43WAAxvy6D1+XwoxrbOlQm3dwHitOrbA6XBERERH5j5vdZFvnQOyVLA/TvkrRdLvJAF6v8zruju7subyHn47ojWcREZE7yVVzkm3YsIG2bdum2teuXTu2bt1KYmJiuufEx8cTHR2d6qPAaDYY7J0gbA0cX2nVUK+1DcHTxYF956L5YetpmhZvSo9KPQAYuX4kF25csEHAIiIikl9oWg0bKN8W/CtBwnXY8nmWh7lTN1kRtyK8XMMy5+y0bdOIjIu0LmYREZF8LFcVycLDwwkICEi1LyAggKSkJC5fvpzuORMnTsTb2zvlIzg4+F6EmjsUCoY6z1i2rewm8/NwZsA/y4hP+fMQUbGJDKw1kEp+lYiKj2LomqEkm5NtEbWIiIjkA5pWwwZMpn+7yTZ+DAkxWR6qfZWihAR4ci0uiVn/6SZ7vOLjhPiEEJ0QzbRt06wIWEREJH/LVUUysMyRdSvjn8LPf/ffNGzYMKKiolI+Tp8+ne0x5ipN/weObnB2Gxz6w6qhejYqRdki7kTcSODDv4/gaO/I5GaTcXNwY+uFrXy25zMbBS0iIiJ5nabVsJHKD0OhEhBzGXZ+k+Vhbl3pcs7aE0TF/NtN5mDnwIgGIwD4+ejP7Li4w7qYRURE8qlcVSQrWrQo4eHhqfZdvHgRBwcH/Pz80j3H2dkZLy+vVB8Fioc/1O9r2V4xHsxZn5DV0d6OkZ0qATB3fRjHLl2npFfJlKRq5q6ZSqpEREQkS7IyrQYUgKk17B2g0SuW7XUfQPLtvxd3c3/lf7rJ4pOYtS51N1kN/xo8XN5S0By3cRxJ5qQsX0dERCS/ylVFsoYNG7Js2bJU+5YuXUqdOnVwdHTMoajygEavgLMXXNgL+3+2aqgWIf60ruhPktlg7GLLUuGdy3amU5lOmA0zQ1YPISo+yhZRi4iISAGSlWk1oIBMrVHjSXArDFGnYF/Wc7k7dZMBDKw1EG9nbw5fOcz8g/OtCllERCQ/ytYi2fXr19m5cyc7d+4ELHNR7Ny5k1OnTgGWRyV79OiRcny/fv04efIkgwYN4sCBA8yePZtZs2bx2muvZWeYeZ+bLzTqb9leMQGSrXtncESnSjjam1h56BIrDl607GswghKeJTh/4zxjNoxJeQxWREREJKMyO60GFJCpNZzcoMELlu2171k1z+z9lYtSseg/3WRrj6d6zcfFh1drWeZAm75zOhdjLmb5OiIiIvlRthbJtm7dSs2aNalZsyYAgwYNombNmowaNQqA8+fPpxTMAEqXLs3vv//OypUrqVGjBmPHjuWDDz6ga9eu2Rlm/tDgBXD1hYijsPtbq4YqXdidZxqXBmDs4v0kJJlxd3RncrPJONg5sOzkMn44/IMtohYREZECIivTakABmlqjbh9w8oSL++HI0iwPY1np8p9usnVhXI1JSPX6Q+UfolqRatxIvMHULQV8PjgREZH/yNYiWYsWLTAMI83H3LlzAZg7dy4rV65MdU7z5s3Zvn078fHxnDhxgn79+mVniPmHs+e/qyOtnARJ8VYN93KrchT2cOL45Rt8sT4MgMqFKzOw1kAAJm+ZzNErR626hoiIiBQcmlbjLlwLQZ3elu2171k1VLtbuslm/2elSzuTHSPqj8DOZMcfYX+w4dwGq64lIiKSn+SqOcnESvWeA4+ilvkstn9p1VCeLo4MblcRgA/+PsKla5ai29OVnqZxscbEJ8fz+urXiUuKszpsERERyXs0rUY2aPAi2DvBqQ1wMuvFq7t1k4X6hfJExScAmLBpAgnJCWnGEBERKYhUJMtPHF2h2T+J5uopkBBj1XDdahenajFvrsUnMfXPQ4Dl3cdxjcfh5+LH0atHmbJlirVRi4iISB6kaTWygVcgVLcUr1g3zaqh2qWam+xEmtdfqvEShV0LExYdxhf7vrDqWiIiIvmFychnM7BHR0fj7e1NVFRU/p2z4k6SEuCj2nD1FLR5CxoPsGq4bScj6TpzAyYTLHqpCVWLewOw/tx6+i7rC8B7Ld7jvpL3WR26iIhITirwOUQeke/vU8Qx+LA2YMAL6yGgcpaHWrL3PP2+3o6HswNrh7SkkJtTqtd/O/4bQ9cMxdnemYUPLqS4Z3ErgxcREcmdMpo/qJMsv3FwguZDLdtrp0FctFXD1S7pS5caQRgGvPnrvpRVqBoFNaJ3Fcu8GaPWj+L89fNWXUdEREREAL+yUOlBy/baaVYN1baSpZvs+m26yTqU7kC9ovWIT45n0uZJVl1LREQkP1CRLD+q9hj4lYfYSNg40+rhhrSviKujPdtOXmHRrnMp+/vX7E/VwlW5lnCNoWuGkmROsvpaIiIiIgVek4GWf/f+BFfCsjyMnZ2Jgffdfm4yk8nE8PrDcbBzYOWZlaw4tSLL1xIREckPVCTLj+wdoOUblu0NH0FMpFXDBXq78lLLsgBM/P0gMQmWYpijnSOTmk3C3dGd7Re388nuT6y6joiIiIgAQTWhTEswkmH9R1YN1bZSUUIDvbgen8Tna9J2k5UpVIaelXoC8Pbmt4lNirXqeiIiInmZimT5VaUuEFAV4qNh3ftWD9enaRmK+7gSHh3HzJXHUvYHewYzqoFlgt5Pd3/KlvAtVl9LREREpMBr8qrl3x1fwfVLWR7m1pUu564P48qNtCtZPl/teQLdAzl34xyf7f4sy9cSERHJ61Qky6/s7KDVcMv2pk/g2gWrhnNxtGdEx1AAPll9nNOR/66c2aFMB7qU64LZMDN0zVCuxl216loiIiIiBV7pZhBUC5LiYNPHVg3VtlJASjdZenOTuTm6MaTeEADm7JvDiai0x4iIiBQEKpLlZxXuh2J1ICkW1rxj9XDtKhelYRk/EpLMTPzjQKrXhtUbRimvUlyMucjI9SPJZ4umioiIiNxbJtO/3WRbPrNqMaZbu8nmrDuRbjdZq+BWNCvejCRzEuM3jVcuJyIiBZKKZPmZyQStR1q2t82Bq6etHM7E6AcqYWeC3/eEs/7Y5ZTX3BzdmNxsMo52jqw8vZJvD31r1bVERERECryKnSyLMcVFwba5Vg3VrnIAlQK9uJGQzOdrj6d53WQyMbTeUJztndl0fhNLwpZYdT0REZG8SEWy/K5MCyjVFJITYJX1S3tXLOrFk/VLAvDWr/tJSjanvBbqF8qg2oMAmLplKociD1l9PREREZECy87u35UuN0yHpPgsD2UymRjwz0qXc9elPzdZsGcwfar2AWDKlilcT7ie5euJiIjkRSqSFQStLRPrs3MeRBy787EZMKhNBbxdHTkYfo35W1J3pz0Z+iTNizcnwZzA66tfJyYx5jajiIiIiMhdVX0UPIPgejjssq5Tv22lO3eTAfSu0puSXiW5FHuJ6TunW3U9ERGRvEZFsoIguB6Ub2dZRnzlRKuH83F3YlCbCgC8u/QQV2P+fSfSZDIxtvFYirgW4UTUCSZvmWz19UREREQKLAcnaPSyZXvd+2BOzvJQ/+0mi0ynm8zZ3pk36r0BwLyD8/RkgIiIFCgqkhUUrUZY/t3zI1zYb/VwT9YvQYUAD67EJDLtryOpXvNx8eHtpm9jwsRPR35iyQnNaSEiIiKSZbV6gkshiDwGB361aqhU3WRr0u8ma1SsEW1LtsVsmBm3cRxmw5zucSIiIvmNimQFRWA1qNQFMGDFeKuHc7C3Y3TnygB8tfEkhy9cS/V6vcB6KXNajNkwhjPXzlh9TREREZECydkD6ve1bK99D6xYedJkMjHwn26yL9an300GMLjuYNwc3Nh5aSe/HP0ly9cTERHJS1QkK0havgEmOzi4GM5us3q4xuUK07ZSAMlmg7d+3Z9mqfAXarxA9SLVuZ54nSFrhpBoTrT6miIiIiIFUr2+4OAK53fC8RVWDdXmlm6yz27TTRbgHsCLNV4E4N1t73I17qpV1xQREckLVCQrSIqEQLXHLNvLx9lkyBEdK+Fkb8fao5dZtv9Cqtcc7RyZ1GwSno6e7L60m5k7Z9rkmiIiIiIFjrsf1O5p2V77nlVDZbSbrHtod8oVKsfV+Ku8v+N9q64pIiKSF6hIVtA0HwJ2DnBsOYSts3q4En5u9GlaGoBxvx0gLjH1ZLLFPIoxutFoAD7f8zmbzm+y+poiIiIiBVLDlyx53InVcMa6pwLaVAqgcpAXMXfoJnO0c2Rkg5EA/HT4J3Zd2mXVNUVERHI7FckKGt/SUKuHZXv5WKvmtLjppZbl8Pd05lRkDLPXnUjzertS7ehavisGBsPWDCMyLtLqa4qIiIgUOIVKQNVHLNvrbNFNZlmt/E7dZLUCavFg2QcxMBi/cTzJVqyuKSIiktupSFYQNXsdHFzg1AY4+rfVw7k7OzC0fUUAPlp+lAvRcWmOGVJvCGW8y3Ap9hIj141MM3+ZiIiIiGRA4wGWfw8shkuHrRrqvlB/qhSzdJN9ujr9bjKAV2u/iqeTJwciD/Ddoe+suqaIiEhupiJZQeQVBHUtK0/aqpusS41i1CxRiJiEZCYtOZjmdVcHV6Y0n4KTnROrz6zm6wNfW31NERERkQLHPxRCOgAGrLdunjCTycTA1pZusi83hBFxPT7d4/xc/RhYayAAH+74kMuxl626roiISG6lIllB1eRVcPKwrJB0cLHVw9nZmRjduTIAC7afZcepK2mOqeBTgdfrvg5YVknaH7Hf6uuKiIiIFDhNXrX8u+s7iDpr1VCtb+km+2xN2mkzbupaviuV/SpzPfE6U7dOteqaIiIiuZWKZAWVe2Fo8IJle/l4sMH8EjWCC9GtdnEA3vx1P2Zz2g61x0Ieo1VwK5LMSQxePZiYxBirrysiIiJSoATXg5JNwJwIG2dYNVRGu8ns7ewZ2WAkJkz8dvw3toRvseq6IiIiuZGKZAVZw5fBxRsuHYC9P9lkyMHtQnB3smfX6av8vCPtO5smk4m3Gr9FgFsAJ6NPMmHTBJtcV0RERKRAudlNtnUOxFi3KFLrUH+qFvO2zE12m5UuASoXrsyjIY8CMG7jOBKTE626roiISG6jIllB5lro38lfV0wAGyQ6/l4u9G9dHoC3lxzkenxSmmO8nb15u+nb2Jns+OXYL/x2/DerrysiIiJSoJRrDQFVIfEGbPncqqEsK11a8rcv15+8bTcZQP+a/fF18eV41HG+3P+lVdcVERHJbVQkK+jq9QX3InDlBOz8xiZD9m5cilJ+bly6Fs/0FUfTPaZO0Tr0rdYXgLEbx3I6+rRNri0iIiJSIJhM0GSgZXvjTEi4YdVwrSpausliE+/cTebt7M3/6vwPgE92f8L56+etuq6IiEhuoiJZQefsAU0GWbZXTYbEOOuHdLBnRMdKAMxac4Kwy+knbc9Xe55a/rW4kXiDwasHq2VfREREJDMqdQGfUhAbCTusWzk8M91knct0ppZ/LWKTYpm0ZZJV1xUREclNVCQTqPMMeBWD6LOwbY5Nhmwd6k/T8oVJSDYz/vcD6R7jYOfApGaT8HLyYm/EXj7c8aFNri0iIiJSINg7QKNXLNvrP7R66oxWFf2pVvyfbrLVt+8mM5lMjGgwAnuTPX+f+pvVZ1ZbdV0REZHcQkUyAUcXaPa6ZXvNO1a364MleRrVqRL2diaW7b/AmiOX0j2uqHtR3mr0FgBz9s1h/dn1Vl9bREREpMCo8SS4+0PUadjzo1VDpeom23CSy3foJivvU56nKz0NwMRNE4lLsv5pBBERkZymIplY1HwKfErDjUuw6RObDFk+wJMeDUsC8Nav+0lMNqd7XOuSrXks5DEA3lj7BpdjL9vk+iIiIiL5nqMLNHjBsr1uGpjTz7cyqmXIv91kn92hmwzgheov4O/mz5nrZ5i1d5ZV1xUREckNVCQTC3tHaDHMsr3ufYi9apNhB7augI+bI0cuXufrjSdve9xrdV6jvE95IuIiGLF2BGbDugRPREREpMCo+yw4e8Glg3B4iVVDZaabzM3RjSF1hwAwa88sTkbfPtcTERHJC1Qkk39V7QZFKkLcVdgw3SZDers58lq7EADeW3aYyBsJ6R7n4uDClGZTcLF3Yd25dXy5T0uKi4iIiGSIi7dljlmAte+CYVg1XMsQf6pnYG4ygDYl29A4qDGJ5kQmbpqIYeW1RUREcpKKZPIvO3toOdyyvXEG3LDNY4+P1y1BaKAX0XFJvLP00G2PK1uoLIPrDQbg/e3vs/fyXptcX0RERCTfa/AC2DvDmS1w0ro5Xi3dZBUA+HJD2B27yUwmE2/UfwMnOyfWnVvHspPLrLq2iIhITlKRTFIL7QyB1SHhOqx9zyZD2tuZGN25EgDzN59i/7no2x7brXw32pRsQ5KRxODVg7mecN0mMYiIiIjka55FoUZ3y7YNcrgWIUWoXtybuETzXbvJSniV4Jmqlk62SVsmcSPR+kWgREREcoKKZJKayQStRlq2t3wO0edtMmyDMn50rBqI2YC3Fu+7bSu+yWRidMPRBLoHcvraacZtGqe2fREREZGMaPwKmOzg6DII32PVUP/tJrt07fbdZADPVnmW4h7FuRhzkY93fWzVtUVERHKKimSSVrn7ILgBJMXB6ik2G3ZYh4o4O9ix8Xgkf+wNv+1x3s7eTG42GXuTPb8d/41fj/9qsxhERERE8i3fMlD5Icv22mlWD9cipAjVgwv900127I7Huji48Eb9NwD4av9XHLlyxOrri4iI3GsqkklaJhO0/qebbPsXcCXMJsMW93Gjb/OyAIz/7QBxicm3PbaGfw1eqG5ZznzcxnGERdkmBhEREZF8rfFAy7/7FkDkCauGunWly682nrxrN1nT4k1pXaI1yUYy4zbqaQAREcl7VCST9JVqAmVagjkJVk6y2bAvNC9LoLcLZ6/G3nV+iz5V+1C3aF1ik2IZvHowCcnpr4wpIiIiIv8IrGZ5KsAww/oPrR6uRYWMd5MBDKk7BFcHV7Zf3K6nAUREJM+5J0WyGTNmULp0aVxcXKhduzZr1qy57bErV67EZDKl+Th48OC9CFVudXNust3fwqXDNhnS1cmeYR1CAZix8ijnrsbe9lh7O3smNplIIedCHIg8wLTt02wSg4iIiEi+1uRVy787vobrF60a6r/dZBevxd3x+ECPQPpV7wfAO1vfISo+yqrri4iI3EvZXiT77rvvGDhwIMOHD2fHjh00bdqU9u3bc+rUqTued+jQIc6fP5/yUb58+ewOVf6reG0I6Wh5J3LlBJsN27laIHVL+RCXaObtP+5c/AxwD2Bs47GAZX6L1WdW2ywOERERkXypZGMoXheS42HjTKuHa1GhCDVudpOtuvOTAABPhz5NGe8yRMZF8uEO67vZRERE7pVsL5K9++67PPvss/Tp04fQ0FCmTZtGcHAwM2fe+Q+2v78/RYsWTfmwt7fP7lAlPa2GAybY9zOc322TIU0mE6M7V8ZkgkW7zrElLPKOx7cIbsGToU8CMGLtCC7FXLJJHCIiIiL5ksn0bzfZls8hzrpurlu7yb7edPduMkd7R0Y0GAHA94e+Z9/lfVZdX0RE5F7J1iJZQkIC27Zto23btqn2t23blvXr19/x3Jo1axIYGEjr1q1ZsWLFbY+Lj48nOjo61YfYUEBlqNLVsr1ivM2GrVLMm8frBgMw5td9JJvvPLHrq7VfJcQnhCvxVxi2dhhmw2yzWERERETynQrtoXAIxEfD1tlWD9c8k91kdYvWpVOZThgYjN04lmTz7RdsEhERyS2ytUh2+fJlkpOTCQgISLU/ICCA8PDwdM8JDAzk008/5aeffmLBggWEhITQunVrVq9O/zG7iRMn4u3tnfIRHBxs86+jwGsxDEz2cHgJnN5is2H/1zYET2cH9p6N5sdtp+94rLO9M1OaT8HVwZVN5zcxe6/1yZ6IiIhYT3PP5lJ2dtBkoGV7wwxIvHP3191ktpsM4H91/oeHowf7Ivbx4+Efrbq+iIjIvXBPJu43mUypPjcMI82+m0JCQnjuueeoVasWDRs2ZMaMGXTs2JGpU6eme/ywYcOIiopK+Th9+s7FFsmCwuWgxhOW7eVv2W5YD2cG/JNsTfnzENFxiXc8vrR3aYbVGwbARzs+YtelXTaLRURERDJPc8/mclW6gVdxuHERds2zerjmFYpQs4Slm+yTDHSTFXYtTP+a/QF4f8f7RMRGWB2DiIhIdsrWIlnhwoWxt7dP0zV28eLFNN1ld9KgQQOOHDmS7mvOzs54eXml+pBs0HwI2DnCidVwfJXNhu3RsBRlirhz+XoCH/6d/j2+VZdyXWhfqj3JRjJDVg8hOkGP14qIiOQUzT2byzk4QaOXLdvr3ofkJKuGs3STVQDg6wysdAnwWMhjhPqGci3hGu9ue9eq64uIiGS3bC2SOTk5Ubt2bZYtW5Zq/7Jly2jUqFGGx9mxYweBgYG2Dk8yo1AJqNPbsr18LBh3nkMso5wc7BjZqRIAc9aFcezS9TsebzKZGNlwJMU8inH2+lnGbhiLYaNYREREJOPuxdyzYgO1eoCrD1wJgwO/WD1cs/KFqVmiEPFJZj5eefduMns7e0Y0GIEJE4uOLWLbhW1WxyAiIpJdsv1xy0GDBvH5558ze/ZsDhw4wKuvvsqpU6fo168fYHlcskePHinHT5s2jYULF3LkyBH27dvHsGHD+Omnn3j55ZezO1S5m6avgYMrnNkCR5babNiWIf60quhPktlg3OL9dz3e08mTyc0m42ByYEnYEn4++rPNYhEREZGMuRdzz4IWabKakzvUt+TdrH3P6jc6b+0m+2bTSS5G372brFqRanStYFkIatzGcSSa7zzFhoiISE7J9iLZY489xrRp03jrrbeoUaMGq1ev5vfff6dkyZIAnD9/PtW8FQkJCbz22mtUq1aNpk2bsnbtWn777Tcefvjh7A5V7sYzAOo/b9lePhbMtlthckTHUBztTaw4dIkVBy/e9fhqRarxck1L4fTtzW9z/Ord38kUERER28vOuWdBizTZRL3nwdEdwvfAsb+tHq5Z+cLUutlNloG5yQAG1ByAj7MPR68eZd4B6+dHExERyQ73ZOL+F198kbCwMOLj49m2bRvNmjVLeW3u3LmsXLky5fPBgwdz9OhRYmNjiYyMZM2aNXTo0OFehCkZ0XggOHtZkiwbtOzfVKaIB70blwZg7OL9JCTdvQDXu0pvGgQ2IDYpltdXv058crzN4hEREZE7uxdzz4IWabIJN1+o3cuyvXaa1cNlpZuskEshXq39KgDTd04n/Eb63YYiIiI56Z4UySQfcfOFhi9ZtldMAHOyzYZ+uVU5Cns4cfzyDb7cEHbX4+1MdkxoMgFfF18OXznMu1s1GayIiMi9cq/mntUiTTbS8CXLIkxha+D0FquHa3pLN9nMVccydM6D5R6kRpEaxCbFMmXLFKtjEBERsTUVySTzGrxomQD28mHY/Z3NhvVycWRwu4oAvP/XES5du3tnWBG3IoxrPA6AeQfnseKUJv8VERG5VzT3bB7iXQyqPWbZXjfN6uFMJhOvtrF0k83bdCpD3WR2JjtGNBiBvcmepSeXsu7sOqvjEBERsSUVySTzXLwsj10CrJwISQk2G7pb7eJULebNtfgk3ll6KEPnNC3elB6VLAn4yPUj1b4vIiJyj2ju2Tym8SuACQ4uhksZy7PupEm5wtQu6ZOpbrIQ3xCeqPgEABM2TdB0GSIikquYDMPKJW5ymejoaLy9vYmKilI7fnZKiIEPasD1C9DxHajbx2ZDbzsZSdeZGzCZYNFLTaha3Puu5yQmJ/LUH0+xP2I/dQLq8Hnbz7G3s7dZTCIikv8ph8gbdJ+s9O2TliJZ9e7w0Eyrh1tz5BJPz9qMs4Mdawa3xN/L5a7nXE+4zgMLH+BS7CVeqvES/ar3szoOERGRO8lo/qBOMskaJzdo+pple/VUSIy12dC1S/ryYI0gDAPG/LqPjNRxHe0dmdxsMm4Obmy9sJXP9nxms3hERERE8o0mlsnz2fM9XLV+EYRbu8lmrMxYN5mHkweD6w4G4LPdn3E6WosxiIhI7qAimWRd7Z7gHQzXzsOWz2069ND2FXF1tGfrySss2nUuQ+eU9CrJiAYjAJi5ayY7Lu6waUwiIiIieV7xOlCqKZiTYMN0q4czmUy8+s9Kl/M2n+JCBuYmA2hXqh0NAhuQYE5g4uaJGXpTVEREJLupSCZZ5+AMzYdYtte+B/HXbDZ0oLcrL7YoC8DbfxwkJiEpQ+d1LtuZzmU6YzbMDFk9hKj4KJvFJCIiIpIv3Owm2/4F3IiwerjG5fyoU9KHhCQzMzPYTWYymXij/hs42Dmw5uwalp9ebnUcIiIi1lKRTKxT/QnwKwcxEbDxY5sO/VyzMhT3ceV8VBwfZzDhAhjeYDglPEtw/sZ53lz/pt6ZFBEREblV2VZQtBokxsDmT60ezmQyMfCWbrLwqIx1k5X2Lk3vyr0BeHvz28Qkxlgdi4iIiDVUJBPr2DtAi2GW7fUfQuwVmw3t4mjP8A6hAHyy+jinIzOWOLk7ujO5+WQc7Bz469Rf/HD4B5vFJCIiIpLnmUz/dpNt/gTir1s95K3dZB9ncKVLgOeqPUeQexDhN8L5ZPcnVschIiJiDRXJxHqVHwb/yhAfBes+sOnQ91cpSoMyvsQnmXn7j4MZD8mvMgNrDQRg8pbJHLlyxKZxiYiIiORplR4E3zKWNzi3f2n1cCaTiVfbZL6bzNXBlWH1LW+4frnvS45dzXiBTURExNZUJBPr2dlBq+GW7U0fw/WLNhvaZDIxunNl7Ezw257zbDiW8Xkznq70NI2LNSY+OZ7BqwcTl5SxZE1EREQk37Ozh8YDLNsbPoKkBKuHbFTWj7qlMt9N1iK4BS2CW5BkJDF+03hNlSEiIjlGRTKxjZAOUKy2ZW6LNe/adOjQQC+61y8BwJhf95FszljiZGeyY1zjcfi5+HH06lGmbJli07hERERE8rTqT4BHUYg+C3usn54iq3OTAQytNxQXexe2hG/htxO/WR2LiIhIVqhIJrZhMkGrEZbtrbMg6oxNh/9fmxC8XR05GH6N+ZtPZfi8wq6FmdB0AgDfH/6ev07+ZdO4RERERPIsB2do+KJle900MJutHvLWbrKZK49m+LxiHsV4vtrzAEzdMpVrCbZbNV1ERCSjVCQT2ynTEko2geQEWDXZpkP7uDvx6n3lAXhn6SGiYhIzfG6joEY8U+UZAEatH8X56+dtGpuIiIhInlW7Nzh7w+XDcOh3q4czmUy8+k832fzNpzPVTdazck9KeZUiIi6Cj3Z8ZHUsIiKSBxiGpcnm4O+wdXZOR6MimdiQyQStR1q2d3wNEbadePWpBiWpEODBlZhE3vvrcKbOfbnmy1QtXJVrCdcYsmYISeYkm8YmIiIikie5eEG9Ppbtte9a/mfFSg3L+lGvlC8JyZnrJnOyd2J4A8s8t98e+pb9EfutjkVERHIRs9lSJ9i7AJaNhi+7wJSy8F5l+PYJ+GMoJGe8ISY7qEgmtlWiAZRrA0YyrJpk06Ed7O0Y1akyAF9tPMmRCxlvw3e0c2RSs0m4O7qz4+IOLTEuIiIiclP9fuDgAme3Qdhaq4ezzE1meQJg/ubTnI+KzfC5DQIb0L5Ue8yGmfEbx2M2rH8EVEREckByElzYBzvnW4pfs9vD2yXgw1rwY2/LY/7HV0BMBJjswb8yVOkKCddzNGwVycT2bs5Ntvt7uHjApkM3KV+YtpUCSDYbvLV4f6ZWPwr2DGZUg1EAfLr7U7aEb7FpbCIiks1iIi3JlojYloc/1HzKsr3WNgswpe4my9zTBa/VfQ13R3d2X97NgiMLbBKPiIhko8Q4yxstW+fArwPhs1YwsRjMbAQL+8GmmXBqPSRcA3tnCKpledy/03vw3HJ44yy8uB4emgmuPjn6pZiMfLbGcnR0NN7e3kRFReHl5ZXT4RRc3z0NBxZBaGd47GubDn0y4gZt3l1NQrKZz3rUoU2lgEydP3LdSBYeXYi/mz8/df6JQi6FbBqfiIjYgGFAxFE4vQlObYTTm+HyIShSEV7alC2XVA6RN+g+ZZMrYfBBLcvTAM+vgqAaVg+5/thlun+2CSd7O1YNbkGgt2uGz/1q/1dM3jIZb2dvfu3yKz4uOfs/TSIi8o/4axC+B87vhvO7IHy3pTnGSE57rJMnFK0KgdUhsJrl38IVwN7xnoed0fzB4R7GJAVJy+Fw4FfLx7kdEFTTZkOX9HOnT9PSzFh5jHG/7adZhcI4O9hn+Pxh9Yax8+JOwqLDGLl+JB+0/ACTyWSz+EREJAsSYy1/L05vglObLP/GRqY9zjBDUrxlVT4RsR2fUlDlYdjzg+URmEfmWj1kwzJ+1Cvty+YTkcxceYy3HqyS4XOfqPgEC48u5PCVw0zbPo0xjcZYHY+IiGTSjQgI35W6IBZxDEin18rV959i2M2CWA3wKQ12eesBRnWSSfZZ8Dzs/g7K3QdP/WTToa/HJ9Fq6kouXotnyP0VeaFF2UydfzDyIN1/606iOZFh9YbRPbS7TeMTEZG7uHYBTv/TIXZqoyXxMv9nolYHF0s7fnA9y5yXxeuBu1+2haQcIm/QfcpG4Xvh48ZgsoOXt4Jf5vKr9NzaTbby9RYEFcp4N9nOizt5+o+nAfiq/VfU8K9hdTwiIpIOw4Br5/8tht0siEWdTv94z6DU3WGB1cGrmGUxv1wqo/mDimSSfSKPw0d1wZwEvZdAyYY2HX7B9jMM+n4X7k72rHitBf5eLpk6/5sD3/D25rdxsnNiXsd5hPiG2DQ+ERH5hznZ0oZ/a1Hs6sm0x7n7Q4n6ENwAgutbEi4Hp3sWpnKIvEH3KZt98wgcWQq1e0Hn920y5GOfbGDTiUieblCSsV0y3k0GMGrdKH4++jMVfCrwXafvcLDTgzAiIlYxDLhyInV32PldcONS+sf7lE5dECtaHTyK3NuYbUBFMiVOucOvA2DbXCjZGHr9ZtPKstls8PDM9ew8fZWutYrzzqPVM3W+YRj0X96fVWdWUdq7NN92/BY3RzebxSciUmDFX4MzWy0FsdMbLdvx0f85yAQBlS1dYsENLP/6lMrRdyCVQ+QNuk/Z7OR6mNMe7J1g4B7wLGr1kBuORfDEZxuz1E12Je4KnRd2Jio+iiF1h/BUpaesjkdEpMBIToKII/8piO2G+Ki0x5rsoHDIfwpiVcHF+97HnQ1UJFPilDtEnYUPakJyPDy1AMq1tunwO09fpcv0dQAsfKkxNYILZer8K3FX6LqoK5diL9G1fFfebPSmTeMTEcn3DMPSin9zHrHTGy0rUBrm1Mc5eUDxOpYOseD6lu1clnQph8gbdJ/ugVntLL/LjQdAm7dsMuTNbrKnGpRgXJeqmTr3x8M/MmbDGNwd3VnUZRH+bv42iUlEJF9Jird07t/6uGT4XkiKTXusvRP4V0o9f5h/JXDKv00jKpIpcco9lgyDjTMs88o8t9zmXQL/+34XP20/Q43gQix4oRF2dpkbf/P5zfRZ2gcDgynNpnB/6fttGp+ISL6SnGhJulKKYpssc1j8l3eJfx6d/OfDvxLY5+7HpJRD5A26T/fAoSUw/zHLqmSv7gXXQlYPebObzNHexKrXW2aqm8xsmHn696fZfXk37Uu1Z3LzyVbHIyKSpyXcsBTAUgpiuywFMnNS2mMd3dNZYTLknk5pkRuoSKbEKfe4fgnerw6JN+DxeVCxo02HvxgdR8upK7mRkMy7j1bn4VrFMz3GB9s/4LM9n+Hh6MEPnX+guGfmxxARyZdiIuHMFss8Yqc3w9ltad+RtHOAotUsxbCbhTGvoJyJ1wrKIfIG3ad7wGy2TOB/cT+0HgVN/2eTYR//dAMbj2etm2x/xH6e+O0JzIaZz9p+RoPABjaJSUQk14u9knZC/ctHSHeFSZdCqbvDAquDbxmws7/HQec+KpIpccpd/n4L1rwD/pWh31qbLwM7c+UxJi05iL+nM8tfa4GHc+a6FZLMSfRa0otdl3ZRrUg15t4/F0c7R5vGKCKS6xmGZVnv0xstHWKnNsHlQ2mPcyn0T4fYP6tOBtXKF+35yiHyBt2ne2TXd/Dz8+BexDI3mWPGO79ux5puMoCJmyYy7+A8SnmV4qcHfsLJvmB1QYhIAXAt/JaC2E5LQezqqfSP9SiadoVJ7+BcvcJkTlKRTIlT7hJ7BaZVt0wQ2HUWVO1m0+Hjk5Jp+95qTkbE8GKLsgy+v2Kmxzh7/SyPLHqEa4nXKFeoHO1KtaNtqbaU8S5j01hFRHKNxDg4t+PfVSdPb4KYiLTH+ZX7d3L9Eg3Ar7zN3+zIDZRD5A26T/dIciJ8UAuiTkHHd6BuH5sMe7Ob7Mn6JRj/UOa6ya4lXKPzz52JiIvglZqv8Fy152wSk4jIPWcYlpW+/9shdv1C+scXKpm6Q6xoNfAMuKch53Uqkilxyn1WTYEV48C3LLy02eZz0yzbf4HnvtyKk70dywY1o6Sfe6bHWH5qOa+vep0Ec0LKvnKFytG2ZFvalGxDOZ9ytgxZROTeunbh33nETm+CczvBnJj6GHtnKFbr37nEguuBe+EcCfdeUw6RN+g+3UObPoU/Xrf8z1n/7TbJ3TYej+DxTy3dZCtfb0mxTHaTLT6+mGFrhuFi78LCLgsp5lHM6phERLKVORkijv5TENv5zwqTuyAunRUmMUHhCmlXmHT1uddR5zsqkilxyn3ir1nmJouJgAc+hFo9bDq8YRj0mL2ZNUcu07ZSAJ/2qJOlcaLio1h+ajnLTi5jw/kNJN0y+WEZ7zK0KdmGtqXaUr5QeUxqZRWR3MqcbJnA9dai2JWwtMe5+98ywX4DSzJWwCZyvUk5RN6g+3QPJcTAtCqW3O3hz6HaIzYZ9olPN7LheESWuskMw+DZpc+yJXwLLYJb8GGrD20Sk4iITSQlwKUD/3aIhe+G8D2QGJP2WDtH8A9NPX9YQGVwynyzh9ydimRKnHKn9R/B0uGWZ6X7bwMHZ5sOf+TCNe5/fw3JZoOvn61Pk/LWdT9ExUex6swqloYtZf259STe0nFRyqtUSsEsxCdEBTMRyVnx1yyT6t9cdfLMFoiP/s9BJssqk7euOulTSnNX/EM5RN6g+3SP3XwSwL8yvLDOJv+9sLab7NjVY3Rb1I0kI4kPW31Ii+AWVsckIpJpCTFwYZ+lO+xmQezC/rRd+gAOrv+sMHnL/GFFKtr8/4fl9lQkU+KUOyXGwgc14dp5aD8Z6ve1+SXeXLSPuevDKO/vwR8DmuJgb5t5c64lXGPl6ZUsPbmU9WfXp3oks4RniZSCWahvqApmIpK9DAOiTlvmETv1zyT7F/aCYU59nKM7FK9jmUcsuB4Urwsu3jkTcx6gHCJv0H26x2KvwHtVIOE6dP8BKrS1ybA3u8m61y/BhEx2kwG8t+09Zu+dTTGPYvz84M+4Oli/sICIyG3FXrV0hN0shp3fBZcPp829AJy9UxfDilaDwuW1wmQOU5FMiVPutWUW/DbI8ojPgJ02byeNikmkxdQVXIlJ5M3OlejVuLRNxwe4nnCd1WdWs/TkUtaeXUt8cnzKa8U9itOmVBvalmxLZb/KKpiJiPWSEy0JWUpRbDNcO5f2OO8S/06uH1zP0vlh4/kf8zPlEHmD7lMO+HM4bPgISjSCZ/6wyZCbjkfw2D/dZCtea0Fxn8ytkBuTGMODvzxI+I1wnqv6HK/UesUmcYmIcP1i2vnD0puyAiwrAAfWuGX+sGrq0s+lVCRT4pR7JSXAR3Usq3ncNwaaDLT5Jb7eeJIRC/fi5eLAytdb4uueffPrxCTGpBTM1pxZQ1xyXMprQe5BKR1mVQtXVcFMRDImJhLObLWsOnlqk+UxyqTY1MeY7C0JWXADy+OTxeuBtyawtoZyiLxB9ykHRJ+DadUsjxA986elEG8D3T/byPpjWe8m+/vk3wxcORAHOwcWPLCA0t62f2NURPKxm535t84fdn6X5amn9HiXSNsh5llUBbE8QkUyJU652875sLCfZZWOAbts/vhPstmg4wdrOBh+jacalGBcl8wnXlkRkxjDmrNrWHZyGavPrCb2lv+pLepe1FIwK9mWakWqYWeyzWOgIpLHGQZEHPtncv1/usQuHUx7nIv3LStO1resQKmJXW1KOUTeoPuUQ355GXZ8BRXaQ/dvbTKktd1khmHw0t8vsebsGhoENuDTNp/qDUkRSZ/ZDJHHU88fdn6X5ZHyNEzgVy5tQczN915HLTakIpkSp9zNnAwzGsLlQ9B8KLQcZvNL3JwU1s4Ev73SlNDAe/vzEJsUy7qz61h6cimrTq8iJunfFU383fxTCmY1/GuoYCZSkCTGwbkdqVedjIlIe5xfudRFscIVwE7/rchOyiHyBt2nHHL5CHxUFzDghQ0QUMkmw97sJnuiXgkmPpz5NzVPXzvNQ788RHxyPFOaTeH+0vfbJC4RycOSE+HSIUsR7NYVJhOupz3WzgGKhKZ+XLJoFXD2vPdxS7ZSkUyJU+63byH80BOcPGHg7mypzL/0zXZ+23OehmX8mPdc/Rx7dzEuKY7159az9ORSVp5eyY3EGymvFXEtwn0l76NNyTbU8q+FvSZ0FMlfrl/8d3L905vg3M60qx7ZO1s6w4LrWR6fDK4H7tatziuZpxwib9B9ykHf94D9v0C1x+HhT2wy5OYTkTz6yQYc7EysfD3z3WQAM3fNZMbOGRRxLcKiLovwcPKwSWwikgckxlpWlPzvCpO3zBmdwsEFAqqkLoj5VwJHl3settx7KpIpccr9zGb4tJmlqt/oFWg71uaXOB0Zw33vriI+yczMJ2vRvmqgza+RWfHJ8Ww4t4FlJ5ex4tQKriVeS3nNz8WP+0reR9uSbakVUAsHO024LZKnmM1w6cC/k+uf3pj+RK/u/pZ5xILrW4pigdW0BHguoBwib9B9ykFnt8NnLS1zIg7YCYVK2GTYJz/fyLqjWe8mi0+O5+FfHubUtVM8XelpBtcdbJO4RCSXSbhhebPx1sclLx0CIzntsc5eULTqf1aYrKAFjQowFcmUOOUNh/+EeY+Cg6sl2fIsavNLvLv0EB8sP0pxH1f+GtQcF8fc06mVkJzAxvMbWRq2lOWnl3Mt4d+Cma+LL61LtKZNyTbULVpXBTOR3Cj+Opzd+u+qk2e2QnzUfw4yWd6lvHXVSZ/SmuQ1F1IOkTfoPuWwLx+E4yuhXl/oMNkmQ97aTbbitRYE+2a+m2z92fX0/asv9iZ7vuv0HSG+ITaJTURyibPb4euuEBuZ9jU3v3+LYSkrTJbWNBWSSq4qks2YMYMpU6Zw/vx5KleuzLRp02jatOltj1+1ahWDBg1i3759BAUFMXjwYPr165ehaylxymMMA2a1hTOboe5z0HGqzS8Rk5BE63dWcT4qjv+1qUD/1uVtfg1bSExOZFP4JpadXMbfp/4m6pb/0S7kXIjWJVrTtmRb6gbWxdHOMQcjFSmgDAOizvz72OSpjXBhLxjm1Mc5ukPxOpYusRL1oVgdcC2UIyFL5iiHyBt0n3LY8ZWWQpmDK7y612aPhv/bTRbMxIerZWmMQSsHsezkMmoUqcEX7b/QnK8i+cWlQzD7fkuBzN3fkmfdWhDzCtKbj3JXuaZI9t133/H0008zY8YMGjduzCeffMLnn3/O/v37KVEibYv2iRMnqFKlCs899xx9+/Zl3bp1vPjii8yfP5+uXbve9XpKnPKgE6vhi85g5wj9t4FPSZtf4pedZxnw7U5cHe1Z/lpzAr1dbX4NW0o0J7IlfIulw+zUcq7E/7vqyv/Zu+/wqMrsgePfSe8JCaQS0um99yLSBCLNLogFdNW17uq6xbKKrLr7szeUZkFQohBaIEjvNfSaDiEJIb1nZu7vj0sGIgFCmGQyM+fzPHmY3Llz7xnelJNz33teT0dP7gi+gxEhI+gb0Bd7WymYCdEgdFXq7eCGothuKMq4dj/P4CvN9Vv1Ad8OMpXfTEkOYR5knExMUdRbLjMOwuBX4I5/GOWwe1Nyueer25tNllmSSfSyaMq0Zfy7/7+ZGDXRKLEJIUwoP00tkBWeh8Bu8MgKaaov6qXJFMn69OlD9+7d+fLLLw3b2rVrx4QJE5g9e/Y1+7/66qvExsZy4sQJw7annnqKQ4cOsXPnzpueTxInM7UwGpI3Q7eH4e7PjX54RVG456ud7EvN4+6ugXx8fzejn6OhaPVa9mXtIz4lnvVp68ktvzLF2N3BnWHBwxgVOoq+AX1xsHUwYaRCmLmyPEjfq/YRS98D5/dDVWnNfTS2av+wq1ed9AwyTbzC6CSHMA8yTk3A8eVqE38nL3U2mZH+YH34291sO5tzW7PJFh5byH/3/RcvRy9WTFiBl5OXUWITQphA8UWYNwpyE9V+Yo/GgauPqaMSZqpJFMkqKytxcXHhl19+YeLEK1dynn/+eRISEti8efM1rxk8eDDdunXj448/Nmz77bffuPfeeyktLcXevuasmYqKCioqrqxcUVhYSHBwsCRO5ubcPvh2uPoH6DN7oHmk0U9x9HwB4z/bhqLA0qf60TPU+KtpNjSdXsf+rP2sS13H+tT1XCq/ZHjO3d6docFDGREygv5B/XG0lSbgQlyXokBuUs1VJy+evHY/J8/LxbDLq04GdQcH18aPVzQKKb6YBxmnJkCvg897w6WzMPId6P9noxzWGLPJqvRV3LviXs7mn2VK6ym80e8No8QmhGhk5QWwYJzaoN8zGB5bKxcmxW2pa/7QoDfq5+TkoNPp8PPzq7Hdz8+PzMzMWl+TmZlZ6/5arZacnJxr9p89ezaenp6Gj+DgYOO9AdF4WvaE1mPUlUk2vdsgp+gY5Ml9PdWvj7dWHEevN781K2xtbOkd0Jt/9v0nv9/zO/NHzeeBtg/QwrkFRVVFrEhawXMbn2PIkiG8uuVVfk/7nXJtuanDFsL0qsrVgti2j+CnB+GDSPi0Oyx/Gg4svFIg846Arg/B+I/h6d3wSgo89AsM/iuEDZICmRBCANjYwoDn1cc7PwdtxY33r6Neod4MjGyOVq/w+caz9TqGvY09/+z7TwBiTsdw+OJho8QmhGhEVWWw6H61QObSHKYukwKZaDSN0jRF84cmeoqiXLPtZvvXth3gtdde46WXXjJ8Xj2TTJihO/4Bp9fA0RgY+BL4dzT6Kf4yqg2rDl/gyPkClu4/x729zPdrxdbGlp7+Penp35O/9f4bCdkJxKfGsy51Hdml2axOXs3q5NW42LkwpOUQRoSOYGDQQJztmnY/NiGMojj7SnP99D1wIQF0lTX3sXVUe1u0uurWSSM1oBZCCIvX+T7Y+C4UXYDDS6D7NKMc9oU7o9h2Noel+8/xzLDIes0m6+HXg+iIaGITY3ln1zv8NPYnbG2azurmQogb0FXBL9MhbQc4esDUXxvkLiMhrqdBZ5I1b94cW1vba2aNZWdnXzNbrJq/v3+t+9vZ2eHjc+39x46Ojnh4eNT4EGbKvxN0mKQ+3jirQU7R3M2R5+9UV7d8f+1JCsurGuQ8jc1GY0N3v+682vtV4qfE8/2Y75nafir+rv6UaktZk7KGlza9xJAlQ3h508usTVlL6R97LQlh7soLYdXL8HFX+G8ULHkYdn6mrp6rqwTXFtB2HIx4Gx6Ph9fS4fG1MOLf0HasFMiEqKcvvviCsLAwnJyc6NGjB1u3br3h/ps3b6ZHjx44OTkRHh7OV1991UiRCqOyc4R+z6iPt3+s3oJpBD1DvRkUdXuzyQBe6vES7g7unMg9wZJTS4wSmxCigen1sOxpOB0Hdk7w4BJ1BUshGlGDFskcHBzo0aMH8fHxNbbHx8fTv3//Wl/Tr1+/a/Zft24dPXv2vKYfmbBAQ18DjQ2cWq32KWsA0/qFEt7ClZziSj7bUP/kq6my0djQ1bcrr/R6hXWT17HorkVM7zCdILcgyrRlrEtdx182/4UhS4bw0qaXWJO8RgpmwvxVFMGPU2Dvt5CXDGjAtz30eBQmfAXPHYS/nIH7f4QBz6k9xuykb58Qt2vJkiW88MIL/OMf/+DgwYMMGjSIMWPGkJaWVuv+ycnJ3HXXXQwaNIiDBw/y97//neeee46YmJhGjlwYRY/pau/GS2fh5EqjHfb54eoFzaX7z5GeW78cxcfZh+e7qbeEfnbwM3LKrm3bIoRoQhQF4v4GR34GGzu49zsIqb1mIERDavDVLZcsWcLUqVP56quv6NevH3PmzOGbb77h2LFjhISE8Nprr3H+/Hm+++47QE2eOnbsyJNPPsmMGTPYuXMnTz31FD/99BOTJ0++6fmkmasFWPY0JPwI4UNh2vIGOcXGU9k8On8v9rYa1r4wmPAWbg1ynqZEURSOXzrO2tS1xKfEc674nOE5R1tHBgQOYGToSIa0HIKbg+X/fwgLUlkCP0xRp+U7eaor5IYOAmcvU0cmzIzkELeusVcxBxmnJmfDO7DlA/X29Rkb4QYtVW7F1Lm72Xomh/t6BvPelPqtdKnT63ho9UMcu3SMceHjmD3o2q9JIUQTsXE2bP6P+njSN9D5XtPGIyxOk2jcD3Dffffx0Ucf8e9//5uuXbuyZcsWVq9eTUhICAAXLlyocbUxLCyM1atXs2nTJrp27crbb7/NJ598UqcCmbAQQ14FG3tI2gTJN75lo76GtfFlWJsWVOkU3ll14uYvsAAajYYOzTvwUo+XWD1pNUvGLeGJTk/Qyr0VFboKNqRv4G9b/8aQJUP484Y/syJxBUWVRaYOW4gbqyyFRfdd7lvhqTZ2bTdeCmRCNILKykr279/PyJEja2wfOXIkO3bsqPU1O3fuvGb/UaNGsW/fPqqqam+BUFFRQWFhYY0P0YT0eQrsnCHjICRfu3J9fb1wuT1GzIFzpF2q32wyWxtb/tX3X2jQsDJpJXsz9xotPiGEEe366kqBbMwHUiATJtXgRTKAp59+mpSUFCoqKti/fz+DBw82PLdgwQI2bdpUY/8hQ4Zw4MABKioqSE5O5qmnnmqMMEVT0SwEejyiPt7wtjr1tgH8a1x77Gw0bDiZzcZT2Q1yjqZKo9HQ3qc9z3d/npUTV7J0/FJmdJpBqEcolfpKNqVv4u/b/s7gJYN55vdnWH52OQUVBaYOW4iaqsrgp/shZSs4uKuNXYO6mzoqIaxGY6xiDrKSeZPn2vxK0/5tHxrtsD1CrvQm+2zjmXofp0PzDtzbRv2De9auWVTpLKMfrRAW49BiiHtVfTz079BnpmnjEVavUYpkQtyyQX9RmzWm74az6xvkFOEt3Hh0QCgAb688TqVW3yDnaeo0Gg1tvNvwXPfniJ0QS0x0DE91eYpwz3C0ei1bzm3hn9v/ydCfh/Kn9X/itzO/ScFMmF5VOSx+UJ214OAGD8dAy56mjkoIq9SQq5iDupJ5QUGB4SM9Pf02IxZG1/9Z0NiqdwGcP2C0w75wZ2sAYg6cr/dsMoA/d/sz3k7eJBYk8v2J740VnhDidp1crbbaAejzJxjyimnjEQIpkommyiMAes9QHzfgbLI/D4+iuZsDSRdL+G5nSoOcw5xoNBpaN2vNM12fYfmE5Sy7exlPd3maSK9ItHot285v4/UdrzN0yVCejH+SmNMx5JXnmTpsYW20FerKlYkbwN4VHloKrfqYOiohrE5jrGIOspK5WfBqBZ3uUR9v/8hoh+0R0ozBrVugu83ZZJ6OnrzU4yUAvjr0FReKLxgrRCFEfSVvhV+mg6KDzvfDqHeN1tNQiNshRTLRdA14Ub2F6sIhOBHbIKfwcLLnr6PaAPDx+jPkFFc0yHnMVYRXBH/q+id+u/s3lk9YzrNdn6V1s9ZoFS07Mnbw5s43GfbzMGasm8HPp37mUtklU4csLJ22En6eBmfj1R44D/0MIf1MHZUQVklWMRc1DFBXkuR4LOQYb/Xw6pUub3c2WXRENN19u1OmLWNS7CT+se0fbDm3RW6/FMIUMg7CTw+ArgLa3AV3fwY2UpoQTUODr27Z2GTFIwuz8V3Y/B40bwNP7wQbW6OfQqdXuPvzbRw9X8gDvYOZPal+KyhZk5SCFOJT44lPjedE7pWFD2w0NvT068nIkJEMDxlOc+fmJoxSWBxdFfz8CJxapd6O/eDPED7E1FEJCyI5xK1r7FXMQcapSVt0P5xeo/Yoi/7UaIedNm8PW05f5J4eLfngni71Pk5yQTJPr3+6xgrfHg4eDG81nNGho+kd0Bs7GztjhCyEuJ6Lp2H+aCi9BCED1ZYZ9k6mjkpYgbrmD1IkE01beQF81BnK82Hi19Dl/gY5zb6UXKZ8tRONBlY8O5COQZ4Nch5LlFaYRnxqPOtS13H80nHDdg0aevj1YETICEaEjKCFSwsTRinMnq4Klj6mziq1dYQHF0PEHaaOSlgYySHq54svvuD999/nwoULdOzYkQ8//NCwSNP06dNJSUmpsUjT5s2befHFFzl27BiBgYG8+uqrt7RIk4xTE5a2G+aNVFcpf+EweAQa5bAH0vKY9MUObG00bHh5CCE+rvU+ll7Rk5CdQFxKHPGp8eSUXVkwopljM+4MuZPRoaPp4dcD2wa4OCuEVctPh3mjofAcBHSBR1aCk/wcF41DimSSOFmOrf8Hv78FzULh2X1g2zC3Yzz300FiD2XQK7QZPz/Z74ZNh0XtzhWdM8wwO5JzxLBdg4Zuvt0YGTqSO1vdiZ9r7b1qhKiVTgu/PgHHfgNbB7j/J4i609RRCQskOYR5kHFq4uaNgbQd0O9ZGDXLaId9ZN4eNhthNtnVdHodB7IPEJesFszyKq70WfVx8mFEyAhGh42mm283bDRyK5gQt6UkRy2QXToDPlHwWJy6Oq4QjUSKZJI4WY7KEvi4K5Rkw7gPoedjDXKaCwVl3PHfzZRV6fjT0AieGhyBp4v0R6mvjOIMwwyzwxcP13iua4uujAgZwcjQkfi7+psoQmEW9Dr4dSYcXarOTLj/R2g9ytRRCQslOYR5kHFq4k6vg0X3qCsPv3AEXLyNclhjziarjVavZU/mHtamrGV96noKKwsNz/m6+DIyZCSjw0bTuXlnuZAqxK0qL4SF49Re0x4t4fG14NnS1FEJKyNFMkmcLMuuryDuVXAPhOcONth9659vPMsHa08B4OJgy709g3l8YBjB3i4Ncj5rkVmSaZhhdjD7YI3nOjfvrM4wC7mTILcgE0UomiS9Tl0W/PBisLGDe7+HtneZOiphwSSHMA8yTk2cosCXAyD7GAz7Jwz5q9EOXT2bbEqPlvzXSLPJalOlq2LnhZ2sTVnLhrQNFFcVG54LdA1kVOgoRoWOor1PeymYCXEzVWXwwxRI3QYuPvDYWmgeZeqohBWSIpkkTpZFWwGfdFfvXx/1LvR7pkFOoygKvx44zzdbkziZWQSAjQbGdAzgiUFhdGvVrEHOa02ySrJYn7aedSnrOJh9EIUrP4I6+nRkRKjawyzYPdiEUQqT0+sh9llI+BE0tnDvQmg33tRRCQsnOYR5kHEyA4d/UW+Td/GBF46Cg3EuNh5My2Pi5dlkv780hNDmxp1NVptKXSXbz28nLiWOTembKNVeWWEz2D2YUaGjGB06mtbNWkvBTIg/0lXBkqnqgh4O7jB9JQR2NXVUwkpJkUwSJ8uzfyGseA5cmsPzh8DRrcFOpSgK287mMGdLElvPXGno2jOkGTMGh3NnOz9sbSQRul0XSy+yPm098anx7M/aj17RG55r592OkaEjGRkyklYerUwYpWh0ej2sfB4OfKcWyKbMhQ4TTR2VsAKSQ5gHGSczoNPCp90hPxXGfAB9Zhrt0NPn72HTqYafTVabcm05W89vZW3KWjanb6ZcV254LtQjlNFhoxkVMorIZpGNGpcQTZJeD8v+pN4RYOsIU3+F0IGmjkpYMSmSSeJkeXRV8HlvyE2CO/4Fg//SKKc9mVnIt1uTWZ5wniqd+u0S6uPC44PCmdK9Jc4OsvKRMeSU5bAhbQPrUtaxN2tvjYJZW++2jAwZyYiQEYR6hpouSNHwFAVWvgj754PGBiZ9A52mmDoqYSUkhzAPMk5mYu+3sOpl8GwFzx0w2sJLpphNVpvSqlK2nNtCXEocW89tpVJfaXgu0ivSMMNM8hZhlRQF4v4Gu79SL3je/yO0GWPqqISVkyKZJE6WqXr6vpMnPH8YnL0a7dRZheUs3JHCD7tSKSzXAtDMxZ6H+4YwrV8oLdwdGy0WS5dbnsvvab8TnxLPnsw96BSd4bmoZlGMDFFnmIV7hZswSmF0igKr/wp7vwE0MGkOdL7X1FEJKyI5hHmQcTITVWXwUScouQgT50CX+4x26OrZZJO7t+R/9zbubLLaFFcWszF9I2tT1rI9YztavdbwXFvvtoYeZtJKQliNTe/BpnfVxxO/hi73mzYeIZAimSROlkqvh68GQPZxGPQXGP6vRg+hpELLL/vSmbs9mfTcMgAcbG2Y2C2IJwaFEeXn3ugxWbK88jw2pm9kXco6dl/YjVa5knhGekWqq2SGjJRbG8ydokDca7D7S0ADE76Arg+aOiphZSSHMA8yTmZk6//g939Di3bwpx1gY2OUwyak5zPh8+0mn01Wm8LKQjakbSAuJY7dGTXzlg4+HRgdOppRoaMIcAswYZRCNKDdc2DN5QU7Rr8HfZ8ybTxCXCZFMkmcLNeJFbDkYbB3VXuTubUwSRg6vcK6Y5nM2ZrEwbR8w/ahbVowc1A4/SJ8pIGrkRVUFLAhbQPxqfHsvLCzxpXacM9wRoSoTf+lea6ZURRY90/Y+Zn6efRn0H2qaWMSVklyCPMg42RGyvLhw45QWQQPLIE2o4126Efn72FjE5pNVpv88nzWp60nLiWOvZk1W0l0adGF0aGjGRk6El8XXxNGKYQRHf4Zfp2hPh7yNxj2mmnjEeIqUiSTxMlyKQp8MwwyDkLfZ2D0u6aOiP2puXyzJZm1xzOp/o5qH+DBjMFhjOsciL2tca6ciisKKgrYfG4z61LWsSNjB1X6KsNzoR6h6gyz0JG0adZGCmZNmaJA/Ouw4xP183EfQc9HTRqSsF6SQ5gHGSczE/86bP8YgvvC42uNdtjq2WQ2Gvj95aGENaHZZLXJKcthfep61qasZX/WfsPq3ho0dPfrzqjQUYwIGUFz5+YmjlSIejoVB4sfBEUHvZ+EMe+B5OCiCZEimSROlu3sevhhsrpSynMHwTPI1BEBkJJTwrztyfyy7xxlVWofrQBPJ6b3D+WBPq3wcDJO01pRU1FlEZvSNxGfGs/289trNM9t5d5KnWEWOoL23u2lYNaUKIp6G862/1M/H/s/6PWEaWMSVk1yCPMg42RmijLV3mS6Sng0DkL6Ge3Q1bPJJnUP4v/u7Wq04za07NJs4lPjiUuOI+FigmG7jcaGXn69GBU2ijtb3Ukzp2amC1KIW5GyHX6YBNpy6HwfTPjKaLdXC2EsUiSTxMmyKQrMvwvSdkCPR2H8R6aOqIb80kp+3J3G/O0p5BRXAODqYMv9vVvx6IBQWjZzMXGElqu4spgt57awLnUd285vo0JXYXguyC2IkSEjGRcxjtbNWpswSgHAxndh83vq4zHvQ58nTRuPsHqSQ5gHGScztOJ52L8AokbCQ78Y7bCH0vO524xmk9XmQvEF1qWuIy45jqOXjhq222ps6RvQl1Gho7ij1R14OnqaMEohbuDCIVgwDioKofVouO8Ho61mK4QxSZFMEifLl7oD5o8BGzt4dh94h5k6omtUaHUsT8jg261JnM4qBsDWRsOYjv7MHBxO55Zepg3QwlUvz74udR1bz22lXFdueK5T805MjprMmLAxuNhL0bLRXb3q0ah3od8zpo1HCCSHMBcyTmboUiJ81hMUPTy1Hfw7Gu3Qjy3Yy4aT2WY3m6w254rOsTZlLWtT1nIi94Rhu52NHf0D+zM6dDTDgofh5uBmwiiFuErOWZg3CkpzIGQAPBwD9s6mjkqIWkmRTBIn6/D9JEj8Hbo8ABO/MnU016UoCptPX+TbrclsO5tj2N47zJsZg8IZ3tYXGxu5DbAhlVaVsu38NtYkr2FT+ibDalMudi6MCRvD5KjJdGzeUW7HbAxb/gsb3lYfj3gbBjxn2niEuExyCPMg42SmfpkOx36DTvfA5G+NdtirZ5Otf2kI4S0so4CUUpDC2pS1xKXEcTb/rGG7g40DA4MGMjpsNENaDpELfcJ0Cs7BvNFQkA7+nWH6SnCSGY+i6ZIimSRO1uH8AbWJv8YGnt4FLdqYOqKbOpZRwNytycQeykCrV7/9wpu78vigMCZ3b4mTva2JI7R8l8ouEZsYS8yZGFILUw3bWzdrzeSoyYwNHyu3NTSUbR/B+jfUx8PfgEEvmTQcIa4mOYR5kHEyUxkJMGeImrP9+YBR7wAwzCbrFsT/3dfVaMdtKhLzEw0Fs+SCZMN2J1snBrcczKjQUQxqOQhnO5nBIxpJySWYPxpyToNPpNpv0K2FqaMS4oakSCaJk/VY/BCcXAnt74Z7vzN1NHWWWVDOgh0p/Lg7laJydVaTt6sDU/uGMLVfCM3dHE0coeVTFIV9WfuIORNDfEq8oeG/o60jI0NGMilqEj38esjsMmPZ8Rms+4f6eNg/YchfTRuPEH8gOYR5kHEyY99PhMQN6iItY/9ntMMePpdP9GeWN5vsjxRF4XTeaUPBLL0o3fCcs50zQ4OHMjp0NAODBuJg62DCSIVFqyiCheMh4yB4BMFjceDVytRRCXFTUiSTxMl6ZB2HL/sDCjy5BQK6mDqiW1JcoeXnvenM3ZbM+fwyABzsbJjcPYjHB4YT6WuZiV5TU1BRwMqklcScieFM3hnD9lCPUCZHTSY6MhpvJ28TRmjmdn0Fca+qj4f8DYa9Ztp4hKiF5BDmQcbJjCVvUf+4tnOCF46Am6/RDv34gr38bsGzyf5IURSO5x5nbbLawyyjJMPwnJu9G3e0uoNRoaPoF9APe2miLoylqhx+nAIpW8HZWy2QmcGdPEKAFMkkcbI2MU/AkV+MvmpSY9Lq9MQdy+SbLUkcOldg2D68rS9PDAqnb7i3zGhqBIqicCTnCDFnYliTvIYyrVq4tLOx447gO5jcejJ9A/pio5Flretszzew+i/q40F/gTv+CfK1LJogySHMg4yTGVMU+HY4nN8Pg16G4a8b7dBXzyaLf2kIERY6m6w21blLXEoca1PWkl2abXjOw8GD4a2GMzp0NL0DemNnY2fCSIVZ02nh52lwahU4uMEjKyCou6mjEqLOpEgmiZN1uZQIn/UCRQePrYNWfUwdUb0pisLelDy+2ZrE+hNZVH+HdgzyYMagcO7qFIC9rRRoGkNJVQlrktcQczqmxrLsQW5BTIycyITICfi5+pkwQjOwbx6sfFF9POAFuPNNKZCJJktyCPMg42TmTqyEJQ+Boye8eBScjDeG1bPJJnYL4kMrmE1WG72iJyE7gbiUOOJT48kpu7JgVDPHZtwZciejQ0fTw68HtjbSB1fUkV4Psc9Cwo9g6wgPL4WwwaaOSohbIkUySZysT+yf4cB3EDpIvbJhAX+IJ10sZt72ZH7Zd44KrR6AQE8nHhsYxn29gnF3kunzjeVU7imWnl7KqqRVFFUVAWCjsWFQ0CAmR01mUMtBcnX2jw58p35fAvR7Fka+YxHfl8JySQ5hHmSczJxeD1/0URt+j/g3DHjeaIc+cq6A8Z9ts8rZZLXR6XUcyD5AXLJaMMuryDM85+Pkw4iQEYwOG003324yQ15cn6LA2n/Ars9BYwv3fQ9tx5o6KiFumRTJJHGyPvnp8Gl30FXC1GUQMczUERlNbkklP+xK5budKeQUq83l3R3tuL93MI8OCCPQS1Yzaizl2nLiU+NZenopB7IPGLb7Ovtyd+TdTIqaREv3liaMsIk4+CMsfwZQoO/TMOpdKZCJJk9yCPMg42QBDv4Iy58GNz94/jDYOxnt0E8s3Mv6E9lM6BrIR/d3M9pxzZ1Wr2VP5h7Wpqxlfep6CisLDc/5uvgyMmQko8NG07l5Z2nvIWra8gFseEd9POFL6PqgyUIpr9Kh0YCjncyCFLdOimSSOFmnNa/C7q8gqCc8sd7i/igvr9Kx7OB5vt2WzNnsYgBsbTSM6xzAjEHhdAzyNHGE1iWpIIlfT/9KbGJsjauz/QL6Man1JIYHD7fOZrmHlsBvTwIK9J4JY963uO9FYZkkhzAPMk4WQFsJn3SFwvMw/mPoMd1oh756Ntm6F4fIAki1qNJVsfPCTtamrGVD2gaKq4oNzwW6BjIqdBSjQkfR3qe9FMys3dV9ZUfNhn5PmySM5JwSvtmaRMz+c1Tp9IT6uBLp60aUnxut/dyJ9HUjooUbTvZSPBPXJ0UySZysU3E2fNwFqkrhgcXQZoypI2oQer3CptPZfLMlmZ1Jlwzb+4Z7M3NwOENb+2JjI0lNY6nSVbEhfQMxp2PYeWGnYXszx2ZER0QzqfUkwj3DTRhhIzqyFH6dAYoeej4OY/8nBTJhNiSHMA8yThZi5xew9jXwDodn94ER+2PJbLK6q9RVsv38duJS4tiUvolSbanhuWD3YEaFjmJ06GhaN2stBTNrc2SpujgaCgx+Be74R6OHsD81lzlbklh3/Eqf5uux0UArbxcifd1p7acW0KJ83Ylo4YazgxTPhBTJJHGyZuvfhG0fgl9HeHIr2Fh2j4Wj5wv4dmsSKw5fQKdXv50jWrgyY1A4E7oFyRWVRnau6By/nf2NZWeWkV12ZXWp7r7dmdx6MiNCRuBsZ6G3xx79FWIeVwtk3R+BcR9Z/PefsCySQ5gHGScLUVEMH3WEsjy4ZwF0mGi0Qx89X8C4T2U22a0q15az9fxW1qasZXP6Zsp15YbnQj1CGR02mtGho4nwijBhlKJRnF4Hix8AvRZ6zYC7Pmi0i546vUL88SzmbEnkQFq+Yfvwtr7MHBxOWHNXzmQXczqriDPZxZzNKuZ0dhH5pVW1Hk+jgeBmLrT2cyPS150oX3X2WYSvKy4O0k/YmkiRTBIn61Waq84mqyiEKfOg42RTR9QoMvLLWLAjhZ92p1FUoQWguZsDU/uGMrVfCN6uDiaO0Lpo9Vq2nd9GzOkYtpzfgl5RF15wt3dnbPhYprSeQhvvNiaO0oiOL4dfHlVXmO36MER/KgUyYXYkhzAPMk4WZONs2Pwf8O8MT24x6h/hTyzcx/oTWdzdNZCPZTbZLSutKmXLuS3EpcSx9dxWKvWVhucivSINM8xCPUNNF6RoGKk74fsJoC2HTvfAxDmNktOVV+lYuv8cc7clk5xTAoCDrQ0TuwUxY3AYkb7u132toijkFFdy5nLh7Ex2EaezijmTVUTedYpnAC2bOdPaTy2cRV3+N9LXDVdHKZ5ZIimSSeJk3Ta9B5veBZ9IeHo32FrPD7qi8iqW7E1n/vYUzueXAeBoZ8OUHi15fGAY4Va+0pMpZJVksezsMn47+xvni88btnfw6cDk1pO5K+wuXO1dTRjhbTq5Cn6epl5t7PIA3P25UW+bEaKxSA5hHmScLEjJJXU2WVUpPPwrRA432qGrZ5NpNBAvs8luS3FlMRvTN7I2ZS3bM7aj1WsNz7X1bmvoYRbsHmzCKIVRXDgMC8ZBRQFEjYT7F0ED99fNLanku50pfLczldwStRjr4WTH1H4hPNIvFF+P21vYI6e4gjNZxZytLpxlF3Emq5hLJZXXfU2Ql/Pl2zWvFM+i/Nxxk+KZWZMimSRO1q28UJ1NVpar/sHe7WFTR9ToqnR6Vh+5wLdbkzlyvgBQL9AOb+vHzMHh9AptJr0lGple0bPrwi5iTsewIX2DIcl0tnNmTNgYJkdNplPzTuY1LqfiYMnDoK+6fLXxaymQCbMlOYR5kHGyMGv+Bru/hNBBMH2lUQ8947t9xB+X2WTGVFhZyIa0DcSlxLE7Yzda5UrBrKNPR0PBLMAtwIRRinq5lAjzRkHJRWjVTy1cO7g02OlSckqYuy2ZX/anU16l3nHRspkzjw8M496ewQ0+m+tScQVns4s5nV3M2csz0E5nFZNTXHHd1wR6OhHp507ry4sGRF1eNMDDyQoX6jJDUiSTxEls/wTi/wWereDP+8DO0dQRmYSiKOxOzuXbrUmsP3GlR1aXlp48MSicMR39sbOV2+IaW255LisSV7D09FJSClMM26OaRTE5ajLjwsfh6djEVys9Ew+LHwRdJXSYBJO+sapZm8LySA5hHmScLEx+urrSpV4LT/wOLXsa7dAym6xh5Zfnsz5tPXEpcezN3GtoLQHQpUUXRoeOZmToSHxdfE0YpaiTwgyYOwoK0sCvk1qwdvZqkFMdSMtjzuYk1h7PNDTj7xTkyczBTePvkrySSs5evNzz7KqZZ9lF1y+e+Xs4GRYKUFfcVPufeTpL8awpkSKZJE6iqgw+7grFmXDXf6H3DFNHZHJns4uZuy2ZmAPnqNSqiUyQlzOPDQzjvl7BMoXYBBRF4UD2AWJOx7AudR0VOvUXsIONAyNCRzA5ajI9/Xo2vdllZ3+Hnx4AXQW0vxsmz5MCmTB7kkOYBxknC/Tbn+DQImg7Du7/0aiHrp5NFt0lkE8ekNlkDSWnLIf1qetZm7KW/Vn7UVD/xNSgobtfd0aHjubOkDtp7tzcxJGKa5TmwvwxcPGkutrsY2vBzbiFTb1eYf2JLL7ZmsTelDzD9mFtWjBzcAR9w72bXq77BwWlVWrB7PKiAWezizmTVUxmYfl1X+Pn4UiUrzrbrLWfu+EWTi8X6RVtClIkk8RJAOz5Blb/Bdz84LmEBp0ybE5yiiv4fmcq3++6cu+/u5MdD/ZpxfT+oQR4Wujqi01cQUUBq5NXE3M6hlN5pwzbQzxCmBQ1ieiI6KaRXCZtgkX3qQ1d245TVyVr4H4VQjQGySHMg4yTBco+CV/0UR8/swdaGG9hm5qzyQbfsPm3MI7s0mziU+OJS44j4WKCYbuNxoZefr0YFTaKO1vdSTOnZqYLUqgqiuC7u+H8fnAPhMfioFmI0Q5fXqXjt4Pn+WZrEkkX1Wb89rYaJnQNYsbgcFr7mf/3Y0FZ1eWCWfWiAerjCwXXL561cHc0rLIZ6etmeNxMFlprUFIkk8RJAGgr4bMekJ8GI96GAc+ZOqImpbxKx68HzvPt1iSSLq8iY2ejYXyXQJ4YFEaHwCZ+u5+FUhSFY5eOsfT0UtYkr6FUWwqAncaOYa2GMTlqMn0D+mJrit5fyVvhx3tAWwatx8C934Gd/EIXlkFyCPMg42ShFj8EJ1eqKyRP+Nyoh5753T7WyWwyk7hQfIF1qeuIS47j6KWjhu22Glv6BvRlVOgo7mh1R9NvMWGJtBVqTpe8GZybwaNx4NvWKIfOK6nkh12pLNyZQk7xlQvyD/cNYXr/UPxusxm/OSgqrzLMNqtebfNsdrFhYbXaNHdzMNyyefWiAT5u1tk2yNikSCaJk6h28EdY/jQ4e8Pzh8BJvi7+SK9X2HAym2+2JrE7OdewfUCkD08MCmdo6xZNfgq0pSqtKiUuJY6Y0zEczjls2B7oGsiEqAlMjJyIv6t/4wSTugN+mKyuQhY1Eu77wWp7/QnLJDmEeZBxslDpe2HunWBjD88ngGdLox1aZpM1DeeKzrE2ZS1rU9ZyIveEYbudjR39A/szOnQ0w4KH4eYgveManE4LS6fDiRXg4AbTYqFlj9s+bNqlUuZuS+Lnfecoq9IB0trlj4ortDVnnl3+91ze9Ytn3q4Ol4tmV88+c6e5m4P8jXYLpEgmiZOoptPCF33h0hkY+ncY+qqpI2rSDp/L55utyaw+cgGdXv3xEOXrxoxB4dzdLRBHO1m50FRO550m5nQMK5JWUFRZBKi3LgwIHMDk1pMZ3HIw9jYNdNtj2i74fhJUlUDEcHVJcHvLvwoorIvkEOZBxsmCLRgHKVuh7zMw+l2jHrp6Ntn4LoF8KrPJTC6lIIW1KWuJS4njbP5Zw3YHGwcGBg1kdNhohrQcgou9tEoxOkWB5c9Cwg9g6wAP/QLhQ2/rkAnp+XyzJYk1Ry9w+c8H2gd48OSQcO7qFIC9LBJ2UyUVWhIvqitsnsku4mxWMaezi0jPvX7xrJmLvdrzzM/t8oqb6syzFu6OUjyrRZMokuXl5fHcc88RGxsLQHR0NJ9++ileXl7Xfc306dNZuHBhjW19+vRh165ddTqnJE6iVkd/haWPgqOHOpvMxdvUETV55/JKmb89hSV70ymuUJf3bu7myPT+ITzUJ0TumTehcm0569PWE3M6hn1Z+wzbmzs3Z0LkBCZFTiLYI9h4J0zfC99PhMoiNYl6YDHYS986YXkkhzAPMk4W7Ox6dcayvSu8eNSo+dqxjALGfqLOJlv3wmCiLKAXkqVIzE80FMySC5IN251snRjccjCjQkcxqOUgnO0k97htigLr/gk7PwONjdo2o934eh1Kr1fYeCqbr7ckseeqO1GGtG7BzMHh9I/wkUKNEZRWaknMLjEsGlA98ywtt5TrVXI8ne1r3K5ZPQPN18qLZ02iSDZmzBjOnTvHnDlzAJg5cyahoaGsWLHiuq+ZPn06WVlZzJ8/37DNwcEBb++6/ZKUxEnUSq+HrwdD1hEY8AKMeMvUEZmNwvIqFu9JY/72FEMDSid7G+7pEczjA8MIbe5q4gitW0pBCr+e+ZXlicvJLb+SoPQJ6MPkqMkMbzUcB9vbKGie3w/fTYCKQggdBA/+LAtgCIslOYR5kHGyYIoCXw+CzCMNMvv/ye/3sfZYFp7O9tzVyZ/xXQLpE+aDrY31/tHYlCiKwum804aCWXpRuuE5ZztnhgYPZXToaAYGDby93Maabf0f/P5v9fHdn0O3h2/5EOVVOpYnnOebrcmczS4G1J7G0V0DmTk4nLb+8nO5MZRX6TibrfY5O325cHY2u5jUSyWG2Xx/5OFkZyicXb3ipr+Hk1UUz0xeJDtx4gTt27dn165d9Omjrlaza9cu+vXrx8mTJ2nTpvZVa6ZPn05+fj7Lli2r13klcRLXdWoN/HQ/2Dmrs8nc/UwdkVmp0ulZdfgC32xN4lhGIQAaDYxs78eMQeH0CGlmFT9cm6oqXRWbzm0i5nQMOzJ2GJZe93L0YnzEeKZETSHcK/zWDppxUF3xqLwAQgao0/EdpCgqLJfkEOZBxsnCHY2BpY+pvWRfPGrU3ztJF4uZOndPjcbZfh6OjO0UyN1dA+nc0lNymSZCURSO5x5nbbLawyyjJMPwnJu9G3e0uoNRoaPoF9APe1lhu272zoVVL6mPR86C/s/e0svzSyv5cbd64TynuAIAd0c7Huzbiun9QwnwlJl+TUF5lY6ki5dnnl2+dfNMVjEpNyieuTvaEennVnPFTT93Aj0tq3hm8iLZvHnzeOmll8jPz6+x3cvLiw8//JBHH3201tdNnz6dZcuW4eDggJeXF0OGDGHWrFn4+vrW6bySOInrUhT49k44vw96Pwl3vW/qiMySoijsTLzEN1uT2HjqomF712AvZg4OZ1QHf7kia2Lni8+z7OwyfjvzG1mlWYbt3Xy7MSlqEqNCR938loULh2HheCjPh+C+8HAMOEojXWHZJIcwDzJOFk6nhc96Ql4yjP4P9P2TUQ+v1enZlZRL7KHzrDmaSVG51vBciI8L4zsHEt01kNZyO2aToSgKR3KOEJcSx9qUtWSXZhue83DwYHir4YwOHU3vgN7Y2Uhj+FodjYGljwMKDHoZhr9e55em55Yyd1syP+9Lp7RSbcYf4OnEYwPCuL93MO5OUqQ0BxXa6uJZMWezigy9z1IulRr6UP+Rq4MtkZdnnrX2czOsvBno6YyNGf69Z/Ii2bvvvsuCBQs4ffp0je2tW7fm0Ucf5bXXXqv1dUuWLMHNzY2QkBCSk5P517/+hVarZf/+/Tg6XruKWkVFBRUVFYbPCwsLCQ4OlsRJ1C5pkzozxtYB/nwAvIzYt8kKnckq4tutyfx28DyVOj0Awd7OPD4gjHt6BuMqK9iYlE6vY3vGdpaeXsqWc1vQKWpi42bvxtjwsUyOmkw7n3bXvjDzqFogK8uFlr1h6q/gKH8sCMsnxRfzIONkBfbNg5UvgkdLeO4g2DXMrXUVWh2bT10k9lAG609kUV6lNzzX1t+d8V0Cie4SSLC3tBloKvSKnoTsBOJS4ohPjSenLMfwXDPHZtwZcidtmrXBxd4FV3tX9V871yuP7V1xsXPB1saKFqI6sx5+ug/0Wuj5GIz9P/V2kJs4cq6Ar7cksvrIlWb87QI8mDk4jHGdA6UZv4Wo0OpIySnlTLZaODt7eeZZck4J2usUz1wcbA0rbKr9ztTHQV5Nu3jWYEWyN998k7feunE/p71797Ju3ToWLlzIqVOnajwXFRXF448/zt/+9rc6ne/ChQuEhISwePFiJk2aVOd4JHES11W9clL3aRD9qamjsQgXiyr4fmcK3+9KJa+0ClDveX+obwjT+4fi5yGrIJpadmk2y88uJ+ZMDOeLzxu2t/Nux5TWU7gr7C51yfWs47BwHJRegqAeMPU3cPI0YeRCNB4pvpgHGScrUFUOH3WCkmyY8CV0fbDBT1lSoWX9iSxiEzLYcuYiVborfyJ1a+VFdJdAxnYOwNddcpqmQqfXcSD7AHHJasEsryKvzq91snUyFM2qC2d//PzqItt1n2vqRbe0XWpvWW0ZdJwMk76BG8Sq1ytsPn2Rr7cksivpSq/bQVHNmTk4nIGRzS3q9jtxfZVaPamXSgwzzqoXDUjOKanx8/FqzvbVxTO3yytuqkW04GYuTaJ41mBFspycHHJycm64T2hoKIsWLarX7Za1iYqK4oknnuDVV69t3ikzycQtS98Dc0eAxhae3Qs+EaaOyGKUVepYeuAc87Ylk5xTAoC9rYboLkE8MSiMdgHyPWlqekXPnsw9xJyO4fe036nSq0VNZztnRvn3ZfLhOLoUZKEJ6ArTloOzl0njvZEqnZ6M/DJSL5WSlltKem4p5/LK0GjA3ckeD2c7PJzs8XC2x8Op+rGd+tzlx872tpLsCQMpvpgHGScrse1DWP8mNG8DT+8Cm8abtZJfWsmao5nEJmSwK/mSYQU5Gw30i/AhuksgozsE4Okit5k1FVq9lj2Ze9iQtoGcshxKqkoorSqlVFtKSVWJ4XOtor35werB2c4ZZzvnGxbSqp9ztXetsW/1fobXGKvolnkUFtyl9paNvBPu/+m6szIrtDqWJ2TwzZYkzlzdjL9LIE8MCqd9oPysFaoqnVo8U/udqYsGnM0uJuliieHOoj9ysrchooVbjRU3W/u5E+zt0qhtekx+u2V14/7du3fTu3dvAHbv3k3fvn1v2Lj/jy5dukRQUBBz5sxh2rRpN91fEidRJz/eC2fWQqd7YPK3po7G4uj0CutPZPHt1iT2ply5qjcoqjkzBoUzKEquQjUFeeV5rEhcQcyZGJIKkgzbI/U2TOr2NOPb3oeXk5fJ4lMUhYKyKtJyS2sUwtIuf2Tkl123AWld2dpo1AKasz3u1YU0p8uPnf/42K5m8c3JHjcnO+nBZ0Ekh7g1eXl5PPfcc8TGxgIQHR3Np59+ipeX13VfM336dBYuXFhjW58+fdi1a1edzyvjZCXKC+DDjurqyvcvgrZjTRJGVmE5Kw9fIPZQBofS8w3bHWxtGNy6BdFdA7mznS8uDtJioqlTFIVKfaWhYFZSVXJNEe3qz0uqSijTltV4vqSqhBLt5X0buOh29ey2Wymyudq74lJyCdelT+BSfBGXlr2xnbqs1tXJC0qr+HFPKgu2p5BdpE48cXO044HewTw6IIxAL2nGL+pGq9OTmluqFs8ur7Z5OqvohsUzBzu1eNb68qIBkb7utPZzI8THtUHya5MXyQDGjBlDRkYGX3/9NQAzZ84kJCSEFStWGPZp27Yts2fPZuLEiRQXF/Pmm28yefJkAgICSElJ4e9//ztpaWmcOHECd/eb98SRxEnUyYVD8PVgQAN/2gF+7U0dkcU6mJbHt1uTWXP0Sj+Dtv7uPDEonOgugTjYST8DU1NyzpLwwziW2lWwzs2N8su/k+xt7Lkz5E4mR02ml38vbDTGH6vq2WDVhbCri2BpuaU1GirXxtHOhlbeLoT4uBDs7ULLZi5ogKJyLYXlVRSWVV15XH75cVkVheXa6zYpvVXujnbXLarVKL5d57F8DzQdkkPcmjFjxnDu3DnmzJkDqHleaGhojTzvj6ZPn05WVhbz5883bHNwcMDb27vO55VxsiLr31RnlAX1hCfW16mPUkNKvVTCikMZxB7K4HRWsWG7i4Mtd7bzI7pLIINbt5Cf61bi6qLb9Wau1VaIq952ddGt+rnq/rHGVl10qy6i2eJITpGGC7l6qrQOoHfE1d6FXiEB9A8PwsfFvUYvtz/2dmuInFBYJq1OT3pemWHG2ZnLiwYkXiymQlt78ezQ6yMbZKZukyiS5ebmXnOF8bPPPqtxhVGj0TB//nymT59OWVkZEyZM4ODBg+Tn5xMQEMCwYcN4++23CQ6uW4N1SZxEnf08DY4vh7bj4P4fTR2NxUvPLWXe9mSW7L2yMo6vuyOP9A/loT6t8HJpmKa84iZyk2D+WCjKAN/2FD7wE2uydhNzJoYTuScMuwW7BzMpahITIifQ3Ll5nQ9/9Wyw2gphdZkN5uvuSCtvF1r5uKj/XvXRwt2xXrMSFUWhrEpHYZn2cvGsyvC40FBIq1lUK7pcdKt+fHWD59vhZG9z+RbQ6qJa7QU2uWW04UkOUXfVdwzs2rWLPn36ALBr1y769et3wzsGpk+fTn5+PsuWLav3uWWcrEhRltqbTFcB01dB6EBTR2RwMrOQ2IQMVhzOID23zLDd09meMR39ie4SSJ9wH5ltLOqstqLb1UU2Q1Gttm3Vs9sqiyjJT6VE0VFqY4Ougb78/lh0u2YmW3UPN7tri2yu9q442ztL0c3K6fQK6bmlaq+zy4sFnMkuoqhcy+a/DmuQczaJIpkpSOIk6uziKfiiLyh6mLFBbVIuGlxBaRWL9qSxYEcyWYXqtG5ne1vu6xXMYwPCaOUjK0g1mrwUtUBWeA5atIVHVoJbC8PTxy4dI+Z0DKuTV1NSpfaYs9PYMSR4CJOjJtM/sD+2NrY1ZoMZPi7d+myw2gphLZu54OzQNJvhVmh1FJVrryqkVV3nsbZGEa56e1GFcW7RuPqWUcNMtlqKadcrvrk5yi2j1SSHqLt58+bVq/fs9OnTWbZsGQ4ODnh5eTFkyBBmzZqFr69vnc8t42RlVr4E++aqPZUejjF1NNdQFIWD6fnEJmSw6sgFLhZd6ZXs6+7I2M4BRHcJpGuwl1zQEA2rohi+uxvO7wP3AJRH11DhEUBJVQkbT6fz494zHD6fDTYVaGwqiPJ3oE+kKwFemhq3kF5ddPtjQa6xZrrVWmS7/Hlbn7b08uvVdBdLELdFUZQG+1kpRTJJnERd/PYUHPoJIu5QV/ETjaZSq2fl4QzmbEniZGYRoDbEHdXBnxmDw+neqpmJI7Rw+WmwYKz6r0+UeoXe3a/WXUsqS1h2Zg0xZ2I4U3DUsN1e8campDd5mV3RVXnd8HSG2WDe6m2RIT63PxvM3On0CsVX3QpaWHa5mFZdRGvkW0ZrFs+uX1Sz5FtGJYeou3fffZcFCxZw+vTpGttbt27No48+ymuvvVbr65YsWYKbmxshISEkJyfzr3/9C61Wy/79+3F0dKz1NbJIk5XLTYZPu6sXNZ/cCgGdTR3Rden0CruSLhGbkMGaoxcovOoiUStvF8Z3CSC6SxBt/G/eQkaIW6KtgEX3QtImcG4Gj66h0rsNsYfUZvynstRc29ZGw7jOAcwYFE7HoFtbvVxRFCp0FbX3b7tBke2a20urZ8BpS9Ar9ZuV7+fix7jwcURHRhPuGV6vYwjrI0UySZxEXeQmw2c9Qa+F6ashdICpI7I6iqKw/ewlvtmaxObTFw3be4Q0Y8agMEa095dZLsZWcE4tkOWlgHcETF9FlavfNbPB0q9qmF89G8zGMRN7r73YexxEY1cKgKJoUEpb460fTKRbL0K8PQx9wpr6bDBz1pRvGb3R4gceVy1+4N7EbhmVHALefPNN3nrrrRvus3fvXtatW8fChQs5depUjeeioqJ4/PHH+dvf/lan8124cIGQkBAWL17MpEmTbikmax4nq7P0cTi6FDpOhinzTB1NnVRodWw5nUPsoQzWH8+irOrKDJw2fu5Edw1kfOdAmUEvbp9eB79MhxOxYO9KyQO/8n16C+Zvv3LXhquDLff3bsVjA8MIaiLN+P9YdLu6N9v1imwFFQVsz9hOUWWR4TgdfToSHRnNmNAxJl1wSjR9UiSTxEnU1coXYd88aNUPHl1j8qaw1uxUZhHfbk1ieUKGYRWUEB8XHh8YxpQeLWXlqNuUX1pJRnoSIbH34FqSxiWHIN7weZ+EfJdb6w3m7UJAMztKbBM4VryOk/kHDfv4OPlwd+TdTI6aTCuPVg38jsTtaiq3jNrZaK4tql3nltFmLvYMb1f7rMfbJTkE5OTkkJOTc8N9QkNDWbRoUb1ut6xNVFQUTzzxBK+++mqtz8tMMkHmEfhqIGhs4M/7wdu8Zo6UVmqJP57FikMX2Hw6myrdlV+4XYO9iO4SyLjOAfh6OJkwSmGWFAVi/wwHv0exdWBRxAfMPhVA8eXfz77ujjw6IIwH+7TC09n4jdBNoUJXweb0zcQmxrLt/DbDLaB2NnYMaTmE6IhoBgUNwt7WMt6vMB4pkkniJOqqMAM+6QbacrXXReSdpo7I6mUXlrNwZwo/7EqjoKwKAC8Xex7uE8K0/iH4uksSWZvaeoOlX9UjzLH8Iosd3iHC5gJp+hbcV/k6F/AxvP7q3mDBVzfI93Eh+AazwVILU/n1zK8sP7ucS+WXDNt7+/dmUtQk7gy5E0fb2m+jEubNFLeMers6cOBfIxrk/UgOUXfVjft3795N7969Adi9ezd9+/a9YeP+P7p06RJBQUHMmTOHadOm1ek1Mk5W6ocpcDYeej4G4z40dTT1VlBaRdyxCyxPyGBn0iWq/xKz0UDfcB+iuwQypmNAg6zsJixQ/Ouw/WP02PBs1fOs1vUCoLWfGzMGhRPdNRBHO8udzZ9TlsOa5DXEJsZyMvekYXszx2aMCRtDdGQ07b3bN4nZ6sL0pEgmiZO4FWv/ATs/g8BuMGOjzCZrIkortSzdf45vtyaTlqve2udga8PdXQOZMTic1n7W19OjoLSK1NySa4tguaVk5Jdft+DQnAIWO7xNpE0G2Ta+fB3+KW5+4TWa5bdwc8TmNm5trdJXsSV9C0vPLGX7+e0oqLF4OnoyPnw8k6MmE9ksst7HF5anPreMujrY8uXDDbPQiuQQt2bMmDFkZGTw9ddfAzBz5kxCQkJYsWKFYZ+2bdsye/ZsJk6cSHFxMW+++SaTJ08mICCAlJQU/v73v5OWlsaJEydwd6/bz3QZJyuVsh0W3AW2jvDCkev20TQn2YXlrDpygdhDGRxMyzdst7fVMKR1C8Z3CWREez+ZSS+uoSgKyctnEZ7wAQCvVM3gZ90w+oX7MHNIOENbt7C6wtCp3FOsSFzByqSVNS7aRnpFMj5iPOPCx+HrUvdFYoTlkSKZJE7iVpTkwMddoLIY7vsB2o03dUTiKjq9QvzxTL7Zmsz+1DzD9iGtWzBjUDgDIn0sJhGo0um5kF9OWm6poRh29Wywq5sA18bRzqbmLDBvFyJcyui79REc806DR0uYvhK8wxr0fVwovsBvZ3/jt7O/kVmSadjepUUXJkdNZlToKFzspQ+LaFokh7g1ubm5PPfcc8TGxgIQHR3NZ599hpeXl2EfjUbD/PnzmT59OmVlZUyYMIGDBw+Sn59PQEAAw4YN4+233yY4OLjO55VxslKKAnNHwrk9MOAFGHHj3nnmJj23lNhDGaw4lGFY0AjUFcDvbO9HdJdABrdubtGzgsTNVen0rDiUQVr8l7xQ9hkAs7UPkdFhBjMHhdOp5a0147dEWr2WnRk7iU2MZUPaBir1lQDYaGzoG9CX6Iho7mh1B852TaM3m2g8UiSTxEncqg3vwJYPoEU7+NN2kGWFm6T9qXl8uzWJtccyDT202gV4MGNQGOM6B5rFKnsFpVWG2V+puSV1ng1WrcVVvcGuviWy1tlgJZdg4XjIPgbugWqBzCeigd/hFTq9jh0ZO4g5E8Pm9M1oFbXI52rvyl1hdzG59WQ6+HRotHiEuBHJIcyDjJMVO7kaFj8Ajh7w4lFwssyCwOmsImITMog9lGGYSQ/g4WTHmI4BRHcNpG+4jyxsZEWKyqv4aU8a87en0L1oE5/af4qNRmGb/1RC7n2fYG+58FibwspC1qWsIzYxloPZV3routq7MjJkJNER0XT3646Npun//SBunxTJJHESt6osHz7uDOUFMPgVGPQy2Evvq6Yq9VIJ87Yl8/O+c4YVo/w81OakD/Q2bXPSq2eDXVMIq8NsMIereoNV9wcLuVwIa9nMue63XZTmwsJoyDoCbv4wfRU0N93tjjllOSw7u4xfz/xKelG6YXs773ZMiprE2PCxuDtY3y20oumQHMI8yDhZMb0evuwHF0/C8Ddg0EumjqhBKYrCoXMFxCZksPJwBtlFVxawaOHuyNhOasGsW7CXxcyoFzVdKChj/vYUftqdRlGFlsE2h5jr8D/s0VLRZRqOEz6RNjF1lFaYxoqkFaxIXMH54vOG7UFuQYyPGE90eDTBHnWf1SzMjxTJJHES9bHtI1j/hvrYPRAGvwzdpoKdNB1vqvJLK/lxdxoLdqRwsejKMtf39grmsQFhDXZl7erZYOpHSb1ng1XfHhlipN5gAJTlqQWyzMPg6qsWyFq0vr1jGole0bMvcx9Lzyxlfep6qvTq4gxOtk6MDB3JlNZT6NqiqyT8otFJDmEeZJysXMJPsOwp9XfbC4fB3jpumdLpFXYnX2LFoQxWH8k0LGwEEOztzPjOgUR3DaStv3xPWIITFwr5ZksSsYcy0F7OKcd7n+PDijew05VBh4kwea7c+VIPekXP/qz9rEhcwbrUdZRUlRie6+7bnfER4xkVOkou3FogKZJJ4iTqQ6+HAwthy3+h8Jy6zTMYBv8Fuj4EspRwk1Wh1RGbkMG3W5M5laX28rDRwJhOAcwYFE7XYK9bOt4fZ4PVKITVczZYdSHslmaD1UdZPnw/ATIOgktztUDm27bhzncb8svzWZG0gpjTMSQWJBq2h3uGMylqEtER0TRzambCCIU1kRzCPMg4WTldFXzcVc3Txv4f9Hrc1BE1ukqtnq1nLhJ7KIP441mUVuoMz7X2cyO6SyDjuwQS4uNqwijFrVIUhe1nLzFnaxJbTl80bO8T5s1LXarovelhNOUFEHEHPLAE7BxMGK1lKNOWsSFtA7GJsezM2GlYdMrR1pE7gu9gfMR4+gX2w85GFs+wBFIkk8RJ3A5tBRz4Drb+D4ouqNu8QmDIq9D5PrCVH5RNlaIobDmTw7dbk9h6JsewvVdoM2YMCufOdn6GWVrXzgZTm+Sn5pbUezZY9YevuxFmg9VHeQF8PxHO7wcXH3hkJfi1b/w4bpGiKBy6eIiYMzGsTVlLmbYMAHsbe4a3Gs7k1pPp7d9bekaIBiU5hHmQcRLs+griXoVmofDsfqvOy0ortfx+IpvYQxlsPnWRSp3e8FyXYC+iuwQyrnMAfh7SQqSpqtLpWXX4AnO2JHH8QiFw5ULvzEHhdHHNg3mjoDgLWvaGacvAQQqgxpZVksXKpJXEJsaSVJBk2N7cuTnjwscxPmI8rZs1jbsyRP1IkUwSJ2EMVWWwfwFs/T8oyVa3eYfDkL9BpykyxbmJO55RyLfbklhxKIMqnfqjLsTHBQ8ne1IvldR7Npj6uIFng9VHRRF8P0ld+cu5GTyyAvw7mTqqW1ZcWczq5NXEnInh+KXjhu0t3VoyKWoSEyIn0MKlhQkjFJZKcgjzIOMkqCyFjzpC6SX1lrNOU0wdUZNQUFbF2qOZxB7KYEdijmGBI40G+ob5EN01kDEd/fFykRlITUFxhZbFe9KYty2ZjIJyQF3N9L7LLUNa+bhAUaZaIMtLAd8O8OgqNccTDUZRFI5fOk5sYiyrk1eTX5FveK6ddzuiI6IZEzYGH2cf0wUp6kWKZJI4CWOqLIW938L2j9SEDKB5axj6N2g/EWxkdktTlllQzsKdKfy4K/WawlhzN0daeTsT4uPadGaD1UdFMfw4BdJ2qqt9PbICArqYOqrbduLSCWLOxLAqaRXFVcUA2GpsGdxyMFNaT2FA4ABspVgtjERyCPMg4yQA2Pw+bJwFfp3gqa3SvPwPsovKWX34ArGHMjiQlm/Ybm+rYXBUC6K7BnJnOz9cHZvYBT8rkFVYzrztySzanUbR5by0uZsDj/QL5eG+ITRzvVzELM2FBWMh+7g6a/KxteDub7rArVCVroot57ewInEFm89tRqtXx8tOY8fAoIFER0YzpOUQHGyl8GwOpEgmiZNoCBXFsGcO7PhEbYwO4NteLZa1HS/FsiaupELLhpPZONnbNt3ZYPVRWQI/3gOp28HRU52GH9Td1FEZVWlVKfGp8cSciamxhLeviy8TIycyKWoSgW6BJoxQWALJIcyDjJMA1ALChx2hqgQeioGoO00dUZOVnlvKisMZxCZkcDKzyLDdyd6GO9v5Ed0lkCFtWuBoJxedGtKpzCK+2ZrE8oTzhjscwlu4MmNQOBO7BeFkf9X/f2UJfDdBvTvAzR8eiwPvMNMELgDIK89jTfIaViSu4Oilo4btHg4ejAkbQ3RENJ2ad5KFp5owKZJJ4iQaUnkh7P4adn6q9oAC9UrmsNegzV1yNVM0nspSWHQvpGwFRw+Yugxa9jB1VA0qMT+RmDMxrEhcYZgCr0FD/8D+TG49mcEtB+NoKyvSilsnOYR5kHESBmv/ATs/g5CB6m1o4qbOZBUReyiD2EMZpF4qNWz3cLJjdEd/orsE0S/CB1tzmUnfxCmKws5EtRn/plNXmvH3DvVmxuBwhrf1vfauBW0F/HQ/JG4AJy94dI1Z9Je1Jon5icQmxrIyaSXZpdmG7aEeoURHRDM+Yjz+rjLrr6mRIpkkTqIxlOXDri9g5xdQefnKXEBXGPYPiBohxTLRsKrK1CQqaRM4uMHU3yC4t6mjajSVuko2pG1g6Zml7L6w27DdwcaBjs070s23G939utOlRRc8HT1NGKkwF5JDmAcZJ2FQcB4+7gL6Kng83qp+B94uRVE4fK6A2EMZrDycQVZhheG55m6OjOscwPgugXRv5SUzY+pBq9Oz6sgFvtmaxNHzajN+jQZGd/Bn5uBwurW6Tl8xvQ6WPgbHl4G9C0xbLl/XTZhOr2N35m5WJK5gfep6ynVqbzkNGnr79yY6Mpo7W92Ji72LiSMVIEUySZxE4yrNVa9k7vpKnfYP0LIXDPs7hA+TYpkwvqpyWPIQnF0P9q4w9Vdo1dfUUZlMemE6v579ldjE2BpX9EBNVCKbRdLdt7taOPPtToBbgIkiFU2Z5BDmQcZJ1LD8GTj4A7QZCw8sMnU0ZkmnV9iTnEvsoQzWHL1AfmmV4bmWzZwZ3yWQ6C6BtPV3l4LZTZRUaFmyN52525I5n6+u1O1kb8M9PYJ5YlAYIT43WJVSUWDF83BgIdjYw4NLIHJ4I0UubldJVQnrUtaxImkFezP3GrY72zkzImQE0RHR9PLvJSu1m5AUySRxEqZQkgPbP4Y934BW/cVIq35qsSxssGljE5ZDWwFLpsKZtepVxoeWQugAU0fVJCiKQnpROgeyD3Ag6wAHsw+SUphyzX4BrgGGglk3v25EekVK0iIkhzATMk6ihoun4fPegAJP7wLfdqaOyKxVavVsO3uR2IQM1h3PorRSZ3guyteN6C6BjO8SSGjzGxR7rFB2YTkLdqTww1WLRPm4OjCtXyhT+4Xg7VqHxu7r34RtHwIauGc+dJjYoDGLhnO++DwrElewInEFaUVphu3+rv6MDx/P+IjxhHlKj7nGJkUySZyEKRVlqSth7p0LusvT10MHqcWykP4mDU2YOW0l/PIInFoNds7w0C8QNsjUUTVpl8oukZCdwP7s/RzMOsiJ3BPoFF2Nfdwd3Onm281QOOvQvIP0NbNCkkOYBxkncY0lD8OJFdDlAZj4lamjsRhllTp+P5lFbEIGm05dpFKnNzzXpaUn47sEMq5zIP6eTiaM0rTOZKnN+JcdzDD8/4Q1d+WJQWFM7t6yZjP+G9n+McS/rj4e/zH0mN4wAYtGpSgKhy4eYnnictYmr6Wo6srCGZ2bdyY6IprRYaOlLUgjkSKZJE6iKSi8ANv+D/YvAF2lui18mFosk/4C4lbpquCX6XByJdg5qdPww4eaOiqzU1pVypGcI4bZZocuHqKseubnZfY29nRq3kn6mlkZySHMg4yTuMa5/fDtHWBjB88dBK9Wpo7I4hSUVbH2WCYrDmWw/WwO+st/QWo0ahP66K6B3NUxgGZ1mTFl5hRFYVdSLt9sTWLDySstHnqENGPm4HBGtPO7thn/jexfCCueUx/f+SYMfNG4AYsmoUJXwcb0jcSejWVHxg7DBVt7G3uGBg8lOiKaAUEDsLexN3GklkuKZJI4iaak4Bxs/R8c+F5tLgsQOUJdDTPIslciFEai00LMY3B8Odg6wgM/SZ8KI9HqtZzKO8XBrIOGwtml8kvX7BfpFUkPvx7S18yCSQ5hHmScRK0WjofkLdDnKRjznqmjsWg5xRWsPnKB5QkZ7E/NM2y3s9EwKKo50V0DGdHeHzdHOxNGaXxanZ64Y5nM2ZLE4XPq6vYaDYxs78fMweH0CPG+9YMeX65eAFX0MOB5GPFv4wYtmqScshxWJa0iNjGW03mnDdu9nby5K+wuoiOiaevdVnoAGpkUySRxEk1RXips+QASFkH17V5t7oKhr0FAZ9PGJpounRZ+mwlHY8DWAe77EVqPNHVUFuvqvmYHsw9yIOtArX3N/F396ebbjR6+PaSvmYWQHMI8yDiJWiVugO8nqq0IXjwGrj6mjsgqnMsrZeXhC8QmZHD8QqFhu5O9DcPb+jG+SyBD27So+22HTVBppZaf96Yzd3sy6bnqzHNHOxum9GjJE4PCCatvf7bEDbDoPvVuk+7TYPwnstiXFTqVe4rlictZlbSK3PJcw/aoZlFEh0czNnwsLVxamDBCyyFFMkmcRFOWmwSbP4DDi9UrRwDtxqvFMr8Opo1NNC16Hfz2FBz5WV3p6L7voc0YU0dldar7mlXPNLteX7OuLbrS3a+79DUzU5JDmAcZJ1ErRYE5Q+DCIRjyqtraQjSqs9lFxB66wIpDGSTnlBi2uzvZMaqDP3d3DaRfuA92tuZxQSm7qJzvdqTy/a5UCsrUO0GaudgzrV8o0/qF4ON2G7/jz+2DhdFQVQLt74Yp88HGfAuJ4vZV6avYmbGT5WeXszF9I1WX7z6y0djQP7A/0RHRDAsehpOd9fYAvF1SJJPESZiDnDOw+T04shS4/K3YYaJaLGvRxqShiSZAr1OXtj/0k9pn5Z6F0G6cqaMS1OxrdjDrIAkXE2rta9axeUd1tplfD+lrZgYkhzAPMk7iuo4tUxe3cfJSZ5M5upk6IqukKApHzxcSe+g8Kw5dILOw3PBcczcH7uoUQHSXQLq3anZrvbsaydnsYr7dmsSvB84bmvGH+LjwxKBwpnRvibPDbRazso7D/DFQnq/2Kn5wCdjJRTVxRUFFAWtT1hKbGMuhi4cM293t3RkZOpLoiGi6+XaT2zFvkRTJJHES5iT7JGz+Dxz77fIGDXS6R70S2jzSpKEJE9HrYcWf4eAPoLFVlwJvf7epoxLXcSt9zbr7djfMNpO+Zk2L5BDmQcZJXJdeB5/1gtxEGPUu9HvG1BFZPb1eYW9KLrGHMlh95AJ5pVWG54K8nBnXRS2YtQ/wMOkf/IqisDcljzlbEll/4koz/m6tvHhycDgj2vtja4yCXl4KzB0FxZnQshdMXSbFXHFDqYWpxCbGsiJxBRdKLhi2B7sHMz5iPOPDx9PSvaUJIzQfUiSTxEmYo8yjsGm2unohgMZGXc588F/BO8y0sYnGo9fDyhfgwEL1a2DyXOg4ydRRiVtwq33Nqgtn0tfMtCSHMA8yTuKGqlcKdA+E5w+BneWvtmguqnR6tp3NYUVCBmuPZVJSeaVtQUQLV6K7BBHdNbD+Pb7qQadXWHssk6+3JHEoPR9Q24Ld2c6PJweH0yOkmfGKd0VZMG8U5CWDb3uYvgpc6tHsX1glvaJnf9Z+lp9dTnxqPKXaUsNzPfx6EB0RzciQkbg5SNH1eqRIJomTMGcZCbDpP3B6jfq5jR10fVAtlsmy5pZNUWDVy7BvrlogmzgHOt9j6qiEEdxqX7Nuvt3o2Lyj9DVrRJJDmAcZJ3FD2gr4qLM6U+fuz6Hbw6aOSNSivErHhpPZxCZksOFUNpVaveG5TkGeRHcJZFyXAAI8nRvk/KWVWpbuP8e3W5NJy1WLDQ52Nkzu3pInBoUR0cLIhYayPFgwDrKOglcIPLYWPGQ2uaif0qpSfk/7ndjEWHZf2I1yuW2Pk60Td7S6g7sj7qZPQB9spc9dDVIkk8RJWIJz+2HTu3B2vfq5jT10nwqD/gKeQaaNTRifosCaV2HP14AGJn4FXe43dVSigdxqX7Puvt3p6ttV+po1IMkhzIOMk7ip7Z9A/L/AJwqe2QM2MkO3KSssr2LdsSxiD2Ww/WwOOr3656lGA71CvYnuEshdnQLwdr39WYE5xRV8tyOF73alkn/51k8vF3um9Q1hWv9Qmt9OM/7rqSxRV15N3w1ufvBYHHiHG/88wipllmSyMmklsYmxJBckG7b7OvsyNmIs0eHRRDaT9j0gRTJJnIRlSdutFsuSNqmf2zpAj0dh0Evg7m/S0ISRKAqs/Tvs+gLQXL76/ZCpoxKN6Fb7mnXz60YP3x7S18yIJIcwDzJO4qbKC+GjjlBeAPf9oK4gLsxCTnEFa45cIPZQBntT8gzb7Ww0DIxqTnSXQEZ28MfN0e6Wjpt0sZhvtiYTc+CcYdZaK28XnhgUxpQeLXFxuLXj1Zm2EhY/oF7wdvKE6avBv2PDnEtYNUVROHbpGMvPLmdNyhoKKgoMz7X3aU90RDR3hd1FM6dmJozStKRIJomTsEQp22Hju5C6Tf3czgl6Pg4DXwA3X5OGJm6DoqhXvHd8qn4+/hPo8YhpYxImV5++Zt18uxHVLEr6mtWT5BDmQcZJ1Mnvb8PW/0JQD3jid3VakjAr5/PLWHkog9hDGRzLKDRsd7SzYXg7X6K7BDK0jS9O9te/pWxfSi5fb0li/Yksqv/q7dLSk5mDIxjd0UjN+K9Hr4OYJ+DYr2DnDNOWQ6s+DXc+IS6r1FWy5dwWYhNj2XpuK1pFC4Cdxo5BLQdxd8TdDG45GHtbexNH2rikSCaJk7BUigLJW2DjLHXaNoC9C/SeAf2fB1cf08Ynbo2iwO9vwbYP1c/HfQg9HzNtTKLJurqv2cHsg5y4dMKQ+FRzt3enq6/0NasPySHMg4yTqJPii+psMm05TIuF8CGmjkjchsSLxcQmZLDiUAZJOSWG7e6Odozs4E9010AGRPhgZ2uDTq8QfzyTOVuSOJCWb9j3zna+zBgUTu8w74ZfSVNRYNVLsG+e2i7lgcUQdWfDnlOIWuSW57ImeQ2xibEcv3TcsN3T0ZMxoWO4O/JuOvh0MOnqso1FimSSOAlLpyiQuEEtlp3fr25zcIM+T0K/Z2W1HHOxYRZseV99fNd/1WKnEHX0x75mhy4eqrHaEUhfs1shOYR5kHESdbbqL7D3GwgfBtOWmToaYQSKonAso5DYQ2rB7EJBueE5H1cHhrfzZU9yLimXLjfjt7VhUvcgnhgURqSve+MF+vu/Yev/AA1MmQsdJzfeuYW4jrN5Z4lNimVV4iqyy7IN28M9wxkfMZ5x4ePwd7XcVj5SJJPESVgLRYEz69Ri2YVD6jZHD+j7NPT9Ezh7mTQ8cQOb3lN7zQGM/o86XkLchvr0Nevu250A1wCruIJ4M5JDmAcZJ1FneSnwSXdQdDBzEwR2M3VEwoj0eoV9qXnEHjrP6iOZ5JZUGp7zdLZnat8QpvUPwdfdqXED2/EprPun+ljuEBBNkE6vY9eFXcQmxrIhbQPlOrXYrEFDn4A+REdEM7zVcFzsXUwcqXFJkUwSJ2FtFAVOrVZ7lmUdVbc5eUK/P6uzy5zk+6FJ2fIBbHhHfTxyFvR/1rTxCItU175mfi5+dPfrbuhrFukVaZXLhksOYR5knMQtiZkBR36G9hPg3oWmjkY0kCqdnu1nc9h8+iKhPq5M6dES11ts7m8UB76H2Ms53fDXYdDLjR+DELeguLKYdanriE2MZX/WfsN2FzsXRoSM4O7Iu+nh18Mi+t1KkUwSJ2Gt9Ho4EQubZsPFk+o252Yw4HnoNQMc3Uwbn1D7j61/U31851vqwgtCNJK69jXr4tuFHn49rKqvmeQQ5kHGSdySrGPwZX9AA3/eDz4Rpo5IWKrjsfDLI6Doof+fYcTbsmCEMCvpRemsTFxJbGIs54rPGbYHugYyLmIc0RHRhHiEmDDC2yNFMkmchLXT6+DYb7DpP3DpjLrNpblakOn5ODhY1vRZs3H1FPw7/gWD/2LaeITVq2tfsw4+Hejm140evj0stq+Z5BDmQcZJ3LIf74Uza6H7IxD9iamjEZYoaRP8eA/oKqHbwxD9mRTIhNlSFIWD2QeJTYxlbcpaiquKDc91bdGV8RHjGR02Gg8H8/odLEUySZyEUOl1cOQXtViWl6xuc/WFQS9Bj0fBvpH7NFizXV9C3N/Ux0P/DkNfNW08QtTiVvqadfPtZrhN0xL6mkkOYR5knMQtS9sF80aBrQM8fxg8AkwdkbAk5/bDwvFQVQLtxsOUBWBrgls9hWgA5dpyNqZvZHnicnZm7ESv6AFwsHFgaPBQ7o68m/6B/bGzafpf81Ikk8RJiJp0Wji8GDa/B/lp6jb3ALVXQvdpYGf5t1KZ1J5vYPXlWWODX4E7/mHaeISoo1vqa3bVYgDm2NdMcgjzIOMk6mXeaEjbCf2fg5FvmzoaYSmyT8L80VCWB2FD4KFfJKcWFuti6UVWJa1ieeJyzuafNWz3cfJhbPhYoiOiaePdxoQR3liTKJLNmjWLVatWkZCQgIODA/n5+Td9jaIovPXWW8yZM4e8vDz69OnD559/TocOHep0TkmchLgJbSUk/Ahb/guFl+819wxWb/vr+hDY2ps2Pku0dy6sekl9PPAltZGrmc+4EdbtVvqaVS8G0KlFpybf10xyCPMg4yTq5fRaWHQvOLjBi0fVfq1C3I68VHWGYtEFCOoB05aDo7upoxKiwSmKwsnck8QmxrI6eTW55bmG59o0a0N0RDR3hd9Fc+fmJozyWk2iSPbGG2/g5eXFuXPnmDt3bp2KZO+99x6zZs1iwYIFtG7dmnfeeYctW7Zw6tQp3N1v/kNHEich6khbAQe+g63/U3+5A3iFwJBXoPP9Mk3cWPYvhBXPqY/7Pwcj/i0FMmFxbrWvWXXhrKn1NZMcwjzIOIl6URS1gX/2cQgfBh0mQMhAtZG//F4Wt6o4Wy2Q5SZBi7bw6Bpw8TZ1VEI0uip9FdvPbyc2MZZN6Zuo0lcBYKuxZUDQAMZHjGdY8LAmcaG0SRTJqi1YsIAXXnjhpkUyRVEIDAzkhRde4NVX1V49FRUV+Pn58d577/Hkk0/e9FySOAlxi6rKYf982Pp/UJKtbvMOhyGvQqd7wMxul2pSDv4Ay58FFOj7DIyaJYm4sApavZbTeac5kHWgTn3NqnubBboGmrSvmeQQ5kHGSdTb8eXw87Sa21x9IaQ/hAxQ//VtDzY2polPmIeyfFgwDrKOgGcreHwteASaOiohTK6gooC45DhiE2M5nHPYsN3dwZ3RoaOJjoimS4suJsv1zLJIlpSUREREBAcOHKBbt26G7XfffTdeXl4sXLjwpueSxEmIeqoshX1zYduHUHr5j1mfKBj6N+gwSRLGW3VoMfz2FKBA7ydhzHtSIBNWS1EUzhWdY3/2/ibd10xyCPMg4yRuS/oeOBMPqTvg3F7QVdR83skTWvW/UjgL6CytKMQVlaXwwyS1v51rC3hsrTobUQhRQ3JBMisSV7AiaQWZJZmG7SEeIYwPH8/4iPEEujVucdksi2Q7duxgwIABnD9/nsDAK/9hM2fOJDU1lbVr117zmoqKCioqrvxyKywsJDg4WBInIeqrohj2zIEdn6hNSAFatFOLZe2ipVhWF4d/gd9mgqKHno/D2P9JgUyIP6hPX7OOzTviZNdwK/JK8cU8yDgJo9FWwPkDkLpNLZql7VZXKLyavSu06nOlaBbYXVYGt1baSlj8IJyNB0dPmL5SLaIKIa5Lr+jZm7mX2MRY4lPjKdOWGZ7r5d+L6IhoRoSMwNXetcFjabAi2Ztvvslbb711w3327t1Lz549DZ/fapEsIyODgIArSzPPmDGD9PR04uLi6hyPJE5C3KbyQtj9Nez8FMoL1G1+nWDYa9DmLin6XM/RGIh5Qi2Q9ZgOYz+UwqIQdXArfc2+HfVtg/S2kOKLeZBxEg1Gp4XMQ2rBrPqjPL/mPraO0LLn5aJZf2jZGxzdTBKuaER6Pfw6A44uBTtnmPobhPQzdVRCmJXSqlLWp60n9mwsezL3oKCWopztnBneajjREdH09u/dYHcRNFiRLCcnh5ycnBvuExoaipPTlSssDXm7pcwkE6KBleXDri9h1xdQUahuC+gCw/4BUSOlWHa148vhl0dB0UG3h2H8p1IgE6KertfXLNQjlBUTVzTIOaX4Yh5knESj0evh4onLBbPtkLL9Sv/WahpbCOx6ZaZZq76ycqalURRY/RfY+y3Y2MEDiyFqhKmjEsKsXSi+wMqklcQmxtZoweHn4seC0Qto6d7S6Oc0y9stqxv3v/jii7zyyisAVFZW4uvrK437hTC10lzY+Rns+urKrQhBPWHY3yHiDimWnVgJvzwCei10eRDu/lwKZEIYUXVfs5zyHLr5drv5C+pBcgjzIOMkTEZR4FKiWjCrnmlWkPaHnTTg1/HKTLOQ/uDma5JwhZFseAe2fABoYPK30GmKqSMSwmIoisKRnCPEJsayJnkNrvauxE2Ow0Zj/L+jmkSRLC0tjdzcXGJjY/nggw/YunUrAJGRkbi5qdOS27Zty+zZs5k4cSIA7733HrNnz2b+/PlERUXx7rvvsmnTJk6dOoW7u/tNzymJkxANrCQHtn8Me76B6nvKg/uqxbLwIaaNzVROrYElU0FfBZ3uhYlfyaqgQpghySHMg4yTaFLy0yB155XC2aUz1+7jE1VzBU2v4MaPU9TPzs9h7d/Vx2P/B72eMG08QliwSl0l6UXpRHg1zGIYTaJINn369Fpvkdy4cSNDhw5VA9BomD9/PtOnTwfUSuJbb73F119/TV5eHn369OHzzz+nY8eOdTqnJE5CNJLibNj2kboiprZc3RY6CIa+BqEDTBpaozq9DpY8BLpK6DgZJs4BWztTRyWEqAfJIW7NrFmzWLVqFQkJCTg4ONz0jgG4kufNmTOnRp7XoUOHOp9Xxkk0acXZV27PTN0BWceAP/y55dlKzZWqC2fe4TIjvylKWATL/qQ+vuOfMPivpo1HCHFbmkSRzBQkcRKikRVegG0fwv75aqEIIHyo2rMsuLdJQ2twZ9fDTw+qy8e3nwCT50qBTAgzJjnErXnjjTfw8vLi3LlzzJ07t05Fsvfee49Zs2axYMECWrduzTvvvMOWLVvqfMcAyDgJM1OaC+m7rxTNMhLU3qVXc/OrOdOsRTtp2WBqJ1bCz9PUser7DIyaJYVMIcycFMkkcRKicRWcg63/gwPfq7cdAkTeCUP/Di17mDa2hpC4EX66X51F13Yc3LMAbO1NHZUQ4jZIDlE/t9p79oUXXuDVV18F1AWY/Pz86tx7FmSchJmrKIZze9RFAFJ3wPl9Vy4yVnPyuqqn2QDw7ywX4RpT8hb4YbI6Ll0fgujPpGgphAWoa/4gP22FEMbh2RLGfQgDXoCt/4WDP6ozrc6uh9ZjYNhr6qqYliB5C/z0gFoga3MXTJkvBTIhhLiJ5ORkMjMzGTlypGGbo6MjQ4YMYceOHdctktW2krkQZsvRTV3wKOIO9fOqcji//8otmul7oDwfTq1WPwAc3CC4z5WiWVB3sHM02VuwaOcPqDmerlK9CDr+EymQCWFlpEgmhDCuZiEQ/SkMfBE2fwCHF8PpNepH23FqzzL/uvUYbJJStsGi+9RFC6JGqTPI7BxMHZUQQjR5mZmZAPj5+dXY7ufnR2pq6nVfN3v2bN56660GjU0Ik7F3UvuThQ4A/gq6Krhw+MrtmWk7oLwAEn9XPwBsHaFlryuzzYJ7g4OrSd+GRbh4Sp1BVlms9tmVNhpCWCUpiwshGoZ3OEz8Ep7Zq674iAZOroSvBsDPj0D2SVNHeOtSd8KP90JVqXor6b3fyZVcIYRFefPNN9FoNDf82Ldv322dQ/OHvj6Kolyz7WqvvfYaBQUFho/09PTbOr8QTZqtvdqmYsBz8OBieCUZntoGY96H9neDawu1F2rqNtjyPnw/Af7TCr4ZDvGvw+m1UJZv6ndhfvLT4LsJUJYLgd3hgZ/UAqYQwupIaVwI0bCaR8Lkb2DQy7D5P3DsNzi+DI4vh05TYMir0DzK1FHeXPoe+HEKVJVA+DC47wdJnoQQFufZZ5/l/vvvv+E+oaGh9Tq2v78/oM4oCwgIMGzPzs6+ZnbZ1RwdHXF0lAsSwkrZ2IJ/J/Wjz5OgKHDp7JWZZinbofCc2tvs/D7Y/jGgUWftVy8E0Ko/uLUw9TtpuoovqgWyogxo3gYeWgqOdVtIRAhheaRIJoRoHL5t1VsTB/8VNs2GEyvgyC9wNAY63w9D/qrOPmuKzu2D7yep0+/DBsP9i8De2dRRCSGE0TVv3pzmzZs3yLHDwsLw9/cnPj6ebt26AVBZWcnmzZt57733GuScQlgcjUa9uNg8CnpMV7flp10umG1T/81NhMwj6sfur9R9mreuuYKmZ0uTvYUmpbwAfpio/p95BsPU38DVx9RRCSFMSIpkQojG5ddBnYV14RBsnK32Kju0CA4vga4PqkW0ZiGmjvKK8wcuF8iK1P4UDywBBxdTRyWEECaXlpZGbm4uaWlp6HQ6EhISAIiMjMTNzQ2Atm3bMnv2bCZOnIhGo+GFF17g3XffJSoqiqioKN59911cXFx48MEHTfhOhDBzXq3Ujy6XZ4EWZV5eCODyR/YxyDmtfuxfcPk1IVcKZiH91QuVN7jt2SJVlcGi+9VioktzmLoMPINMHZUQwsSkSCaEMI2ALmqvjfP71WLZ2Xg4+D0cWgzdp6q3Z5r6KmdGgtrro6JAvVXhgcVSIBNCiMtef/11Fi5caPi8enbYxo0bGTp0KACnTp2ioKDAsM8rr7xCWVkZTz/9NHl5efTp04d169bh7i63NglhNO7+0HGS+gFQmgtpu67connhEOSnqh+HFqn7uPlfKZiFDIAWbS17VUddldojN20HOHrA1F/VFiFCCKunURRFMXUQxlRYWIinpycFBQV4eHiYOhwhRF2l74GN70LSRvVzWwf1NoKBL4FHwA1f2iAyj8DC8VCWpy67/nCM9KcQwsJJDmEeZJyEuE0VRZC++8pMs/P7QVdZcx/nZuoFwtDLs838OlnOSo96Pfz2JBz5Geyc1FssQ/qbOiohRAOra/4gRTIhRNOSsl0tlqVuUz+3c4Kej8HAF8HNt3FiyDoGC8apKxwF9VSTJyf5eSKEpZMcwjzIOAlhZFVlaqEsdYc62yx9j7qS99Uc3KFVnyszzQK7mecK34oCa16BPXPAxk7tM9t6lKmjEkI0AimSSeIkhHlL3gIbZkH6LvVzO2foPQMGPA+uDdNUGoDsk7BgLJTmqEuAT1sGTp4Ndz4hRJMhOYR5kHESooHpqtSWE9W3Z6btUltPXM3OCVr2unKLZste4OBqknBvycbZ6mrrAJO+gc73mjYeIUSjkSKZJE5CmD9FgcQN6syy8/vUbfau6hLo/f8MLt7GPd/F02qBrCRb7Zk2bbl6u4EQwipIDmEeZJyEaGR6nTrLvnqmWeoO9WLi1Wzs1NllIQPUj1Z9mt5Fxl1fQtzf1MdjPoA+M00bjxCiUUmRTBInISyHosCZeNg4Cy4kqNsc3KHf09D3aXD2uv1z5JxVC2TFmeDfCabFGr8IJ4Ro0iSHMA8yTkKYmKJAzpnLBbPtaquMoow/7KRR86mrV9BsyDsBbibhJ1j2lPp42D9gyCumi0UIYRJSJJPESQjLoyhwarU6VT7riLrN0RP6Pwt9nqp/37BLiWqBrOgC+HaAR1aAq4/x4hZCmAXJIcyDjJMQTYyiqCtlXj3TLDfp2v2at1GLZaEDoVU/8AxqnPhOroYlD4Oigz5/gtGzQaNpnHMLIZoMKZJJ4iSE5dLr4eQKtVh28YS6zbkZ9H8Oes8ER7e6Hys3WS2QFZ6HFu1g+krTXukUQpiM5BDmQcZJCDNQeAHSdlxZQTP7+LX7NAutOdOsWZjxi1fJW+GHyaCrgC4PwN1fgI2Ncc8hhDALUiSTxEkIy6fXw7FfYdN/4NIZdZuLDwx4AXo9AQ4uN359XqpaICtIh+atYfqqxltBUwjR5EgOYR5knIQwQyWXIG3nldlmmYdB0dfcxz3gSsEsZIA68+x2CloZB2HBeKgsgjZ3wb3fg63d7b0PIYTZkiKZJE5CWA+9Do4sVVcrqp7e7+oLA1+Eno+CvfO1r8lPVwtk+angE6kWyNz9GzduIUSTIjmEeZBxEsIClBdC+p4rt2ee3w/6qpr7OHtfKZiF9Fd7nNnY1u34F0/D/NFQeglCB8FDS8HeyfjvQwhhNqRIJomTENZHp4XDi2Hze5Cfpm5zD4BBL0P3aWDnqG4rOK8WyPKSwTtcLZB5BJoubiFEkyA5hHmQcRLCAlWWqiuZV880S98L2rKa+zh6QHCfK4WzwG5g53DtsfLTYd5oKDwHAV3VXrP17VsrhLAYUiSTxEkI66WthEOLYPMHaoIE4NESBv8FIofDdxMgN1HthTF9deM1jhVCNGmSQ5gHGSchrIC2Ul3RvHqmWdouqCisuY+dM7TsqRbMQgdAUE+oKlULZJfOgE8UPBYnvWaFEIAUySRxEkKAtgIOfAdb/6euXAmgsVF7YHi1UgtkXsGmjVEI0WRIDmEeZJyEsEJ6HWQdVQtmKdvUf8tya+5jYw9OnlCao14cfXwteLY0TbxCiCanrvmDdC4UQlguO0foPQO6TYX9C9RiWUk2eAbDIyulQCaEEEIIYQ5sbCGgi/rR90+gKHDx1JWZZqnb1QuipTnqIk7TlkmBTAhRL1IkE0JYPnsn6PuU2pfszFpo1R/c/UwdlRBCCCGEqA+NBnzbqh+9HleLZnkp6gIAQT3AO8zUEQohzJQUyYQQ1sPBBTpMNHUUQgghhBDCmDQatTAmxTEhxG2yMXUAQgghhBBCCCGEEEKYmhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFZPimRCCCGEEEIIIYQQwupJkUwIIYQQQgghhBBCWD0pkgkhhBBCCCGEEEIIqydFMiGEEEIIIYQQQghh9aRIJoQQQgghhBBCCCGsnhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFbPztQBGJuiKAAUFhaaOBIhhBBCmJPq3KE6lxBNk+R6QgghhLhVdc3zLK5IVlRUBEBwcLCJIxFCCCGEOSoqKsLT09PUYYjrkFxPCCGEEPV1szxPo1jY5VK9Xk9GRgbu7u5oNBqjH7+wsJDg4GDS09Px8PAw+vGF6ckYWzYZX8sm42vZGnp8FUWhqKiIwMBAbGykI0VTJbmeuB0yvpZNxteyyfhatqaS51ncTDIbGxtatmzZ4Ofx8PCQb0wLJ2Ns2WR8LZuMr2VryPGVGWRNn+R6whhkfC2bjK9lk/G1bKbO8+QyqRBCCCGEEEIIIYSwelIkE0IIIYQQQgghhBBWT4pkt8jR0ZE33ngDR0dHU4ciGoiMsWWT8bVsMr6WTcZXNAb5OrNsMr6WTcbXssn4WramMr4W17hfCCGEEEIIIYQQQohbJTPJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFZPimRCCCGEEEIIIYQQwupZZZFs9uzZ9OrVC3d3d3x9fZkwYQKnTp2qsY+iKLz55psEBgbi7OzM0KFDOXbsWI195syZw9ChQ/Hw8ECj0ZCfn3/NufLy8pg6dSqenp54enoyderUWvcTxtOY4ztr1iz69++Pi4sLXl5eDfiuRLXGGt+UlBQef/xxwsLCcHZ2JiIigjfeeIPKysqGfotWrTG/f6Ojo2nVqhVOTk4EBAQwdepUMjIyGvLtWb3GHN9qFRUVdO3aFY1GQ0JCQgO8K9HUSJ5n2STPs2yS51k2yfMsm6XkeVZZJNu8eTPPPPMMu3btIj4+Hq1Wy8iRIykpKTHs8/777/N///d/fPbZZ+zduxd/f39GjBhBUVGRYZ/S0lJGjx7N3//+9+ue68EHHyQhIYG4uDji4uJISEhg6tSpDfr+rF1jjm9lZSX33HMPf/rTnxr0PYkrGmt8T548iV6v5+uvv+bYsWN8+OGHfPXVVzf8ehC3rzG/f4cNG8bPP//MqVOniImJITExkSlTpjTo+7N2jTm+1V555RUCAwMb5P2IpknyPMsmeZ5lkzzPskmeZ9ksJs9ThJKdna0AyubNmxVFURS9Xq/4+/sr//nPfwz7lJeXK56enspXX311zes3btyoAEpeXl6N7cePH1cAZdeuXYZtO3fuVADl5MmTDfNmxDUaanyvNn/+fMXT09PYoYs6aIzxrfb+++8rYWFhRotd3Fxjju/y5csVjUajVFZWGi1+cWMNPb6rV69W2rZtqxw7dkwBlIMHDzbE2xBNnOR5lk3yPMsmeZ5lkzzPsplrnmeVM8n+qKCgAABvb28AkpOTyczMZOTIkYZ9HB0dGTJkCDt27KjzcXfu3Imnpyd9+vQxbOvbty+enp63dBxxexpqfEXT0JjjW1BQYDiPaByNNb65ubn8+OOP9O/fH3t7+9sLWtRZQ45vVlYWM2bM4Pvvv8fFxcV4QQuzI3meZZM8z7JJnmfZJM+zbOaa51l9kUxRFF566SUGDhxIx44dAcjMzATAz8+vxr5+fn6G5+oiMzMTX1/fa7b7+vre0nFE/TXk+ArTa8zxTUxM5NNPP+Wpp56qf8DiljTG+L766qu4urri4+NDWloay5cvv/3ARZ005PgqisL06dN56qmn6Nmzp/GCFmZH8jzLJnmeZZM8z7JJnmfZzDnPs/oi2bPPPsvhw4f56aefrnlOo9HU+FxRlGu23Uxt+9fnOKJ+Gnp8hWk11vhmZGQwevRo7rnnHp544ol6HUPcusYY37/+9a8cPHiQdevWYWtry7Rp01AUpd4xi7pryPH99NNPKSws5LXXXrvtOIV5kzzPskmeZ9kkz7NskudZNnPO86y6SPbnP/+Z2NhYNm7cSMuWLQ3b/f39Aa6pZmZnZ19T9bwRf39/srKyrtl+8eLFWzqOqJ+GHl9hWo01vhkZGQwbNox+/foxZ86c2wta1FljjW/z5s1p3bo1I0aMYPHixaxevZpdu3bdXvDiphp6fDds2MCuXbtwdHTEzs6OyMhIAHr27MkjjzxihHcgzIHkeZZN8jzLJnmeZZM8z7KZe55nlUUyRVF49tln+fXXX9mwYQNhYWE1ng8LC8Pf35/4+HjDtsrKSjZv3kz//v3rfJ5+/fpRUFDAnj17DNt2795NQUHBLR1H3JrGGl9hGo05vufPn2fo0KF0796d+fPnY2NjlT8yG5Upv3+rryxWVFTc1nHE9TXW+H7yySccOnSIhIQEEhISWL16NQBLlixh1qxZxnkzosmSPM+ySZ5n2STPs2yS51k2S8nz7G77CGbomWeeYdGiRSxfvhx3d3dDJdPT0xNnZ2c0Gg0vvPAC7777LlFRUURFRfHuu+/i4uLCgw8+aDhOZmYmmZmZnD17FoAjR47g7u5Oq1at8Pb2pl27dowePZoZM2bw9ddfAzBz5kzGjRtHmzZtGv+NW4nGGl+AtLQ0cnNzSUtLQ6fTkZCQAEBkZCRubm6N+8atRGONb0ZGBkOHDqVVq1b897//5eLFi4bXVl8FEcbXWOO7Z88e9uzZw8CBA2nWrBlJSUm8/vrrRERE0K9fP5O8d2vQWOPbqlWrGuet/nkcERFR44qmsEyS51k2yfMsm+R5lk3yPMtmMXmeUdbINDNArR/z58837KPX65U33nhD8ff3VxwdHZXBgwcrR44cqXGcN95446bHuXTpkvLQQw8p7u7uiru7u/LQQw/VaYlaUX+NOb6PPPJIrfts3Lixcd6sFWqs8Z0/f/51zyUaTmON7+HDh5Vhw4Yp3t7eiqOjoxIaGqo89dRTyrlz5xrx3Vqfxvz5fLXk5GSjLg0umjbJ8yyb5HmWTfI8yyZ5nmWzlDxPc/nNCCGEEEIIIYQQQghhteTGayGEEEIIIYQQQghh9aRIJoQQQgghhBBCCCGsnhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFZPimRCCCGEEEIIIYQQwupJkUwIIYQQQgghhBBCWD0pkgkhhBBCCCGEEEIIqydFMiGEEEIIIYQQQghh9aRIJoQQQgghhBBCCCGsnhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFZPimRCCCGEEEIIIYQQwupJkUwIIYQQQgghhBBCWD0pkgkhhBBCCCGEEEIIqydFMiGEEEIIIYQQQghh9aRIJoQQQgghhBBCCCGsnhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQTQgghhBBCCCGEEFZPimRCCCGEEEIIIYQQwupJkUwIIYQQQgghhBBCWD0pkgkhhBBCCCGEEEIIqydFMiGEEEIIIYQQQghh9aRIJoQQQgghhBBCCCGsnhTJhBBCCCGEEEIIIYTVkyKZEEIIIYQQQgghhLB6UiQToh4WLFiARqNBo9GwadOma55XFIXIyEg0Gg1Dhw416rlLS0t58803az1vXaWkpKDRaFiwYIFhW/V7SklJMWxbtGgRH330Ub3Pcz25ubncf//9+Pr6otFomDBhgtHPYUwajYZnn33W1GHUUJ+vg4MHDzJkyBA8PT3RaDS3PLYajYY333zT8PmmTZuu+z0ghBCicUhOcnvMLSdpakJDQxk3bpypwzAbf8ylmkIMtX2/WbKtW7fi6OhIampqnV9jjJ91N2LM76O5c+cSFBRESUmJUY5njexMHYAQ5szd3Z25c+dek3Ru3ryZxMRE3N3djX7O0tJS3nrrLQCjJrtjx45l586dBAQEGLYtWrSIo0eP8sILLxjtPABvv/02v/32G/PmzSMiIgJvb2+jHt8a1Ofr4LHHHqOkpITFixfTrFkzQkNDb+mcO3fupGXLlrcYqRBCiMYgOUn9SE4irF1t32+WSlEUXnjhBWbMmEFISEidX9dQP+sawiOPPMJ7773H+++/b4hZ3BopkglxG+677z5+/PFHPv/8czw8PAzb586dS79+/SgsLDTauRRFoby83GjH+6MWLVrQokWLBjv+1Y4ePUpERAQPPfTQDffT6XRotVocHR0bJS5zcDtfB0ePHmXGjBmMGTOmXq/v27dvvV5XVzLeQghRf5KT1I/kJDWVlpbi4uLSKOeqqqpCo9FgZyd/ktaHsb4mG/P7zdTi4uI4cOAAixYtatDzNOb30R/Z2dnx5JNP8vbbb/Pqq6+aLA5zJrdbCnEbHnjgAQB++uknw7aCggJiYmJ47LHHan1Nbm4uTz/9NEFBQTg4OBAeHs4//vEPKioqauxXfYvfV199Rbt27XB0dGThwoWGX2JvvfWW4faK6dOnA3D27FkeffRRoqKicHFxISgoiPHjx3PkyJGbvpc/TrUeOnQoq1atIjU11XAejUaDoihERUUxatSoa45RXFyMp6cnzzzzTK3nqL6lYv369Zw4caLG7SHVz73//vu88847hIWF4ejoyMaNGwGIjY2lX79+uLi44O7uzogRI9i5c2eN47/55ptoNBoOHz7MPffcg6enJ97e3rz00ktotVpOnTrF6NGjcXd3JzQ0lPfff/+m/y9X+/7772nXrh0uLi506dKFlStXXrPPmTNnePDBB/H19cXR0ZF27drx+eef19invLycl19+ma5duxpi7NevH8uXL7/mePX5Ovij6rHVarV8+eWXhv0BMhPYEQAA9fFJREFULl68yNNPP0379u1xc3PD19eXO+64g61bt9Yay81uERg6dGitV9imT59eY+bazcZ73759REdH4+3tjZOTE926dePnn3++4bmFEMKaSU5Sk6XmJHq9nk8//ZSuXbvi7OyMl5cXffv2JTY2tsZ+S5YsoV+/fri6uuLm5saoUaM4ePBgjX2mT5+Om5sbR44cYeTIkbi7uzN8+HAAKisreeedd2jbti2Ojo60aNGCRx99lIsXL9Ya12+//Ubnzp1xcnIiPDycTz75pMbz1S0avv/+e15++WWCgoJwdHTk7NmzAMybN48uXbrg5OSEt7c3EydO5MSJE4bXf/TRR2g0GsP+V3v11VdxcHAgJycHgPj4eO6++25atmyJk5MTkZGRPPnkk4bnq128eJGZM2cSHBxseI8DBgxg/fr1NfaLi4tj+PDheHp64uLiQrt27Zg9e7bh+brmPrWpax52s6/J2hQWFjJjxgx8fHxwc3Nj9OjRnD59+pr9arvd8uDBg4wbN86QzwYGBjJ27FjOnTtn2EdRFL744gvD12KzZs2YMmUKSUlJNY5v7PFYv349w4cPx8PDAxcXFwYMGMDvv/9+w//nal9++SW9evWiTZs2NbZv2LCBoUOH4uPjg7OzM61atWLy5MmUlpaSkpJyw5911d/rBw4cYMqUKTRr1oyIiAhAzflfe+01wsLCcHBwICgoiGeeeYb8/PybxvrFF19gZ2fHG2+8ccvv/aGHHqKwsJDFixfX6f9F/IEihLhl8+fPVwBl7969ytSpU5XevXsbnvvyyy8VV1dXpbCwUOnQoYMyZMgQw3NlZWVK586dFVdXV+W///2vsm7dOuVf//qXYmdnp9x11101zgEoQUFBSufOnZVFixYpGzZsUBISEpS4uDgFUB5//HFl586dys6dO5WzZ88qiqIomzdvVl5++WVl6dKlyubNm5XffvtNmTBhguLs7KycPHnScOzk5GQFUObPn3/Ne0pOTlYURVGOHTumDBgwQPH39zecZ+fOnYqiKMrHH3+saDQa5fTp0zVi/vzzzxVAOXbsWK3/b+Xl5crOnTuVbt26KeHh4YZjFhQUGGIKCgpShg0bpixdulRZt26dkpycrPz4448KoIwcOVJZtmyZsmTJEqVHjx6Kg4ODsnXrVsPx33jjDQVQ2rRpo7z99ttKfHy88sorryiA8uyzzypt27ZVPvnkEyU+Pl559NFHFUCJiYm56XgDSmhoqNK7d2/l559/VlavXq0MHTpUsbOzUxITEw37HTt2TPH09FQ6deqkfPfdd8q6deuUl19+WbGxsVHefPNNw375+fnK9OnTle+//17ZsGGDEhcXp/zlL39RbGxslIULF97218EfZWdnKzt37lQAZcqUKTXG8uTJk8qf/vQnZfHixcqmTZuUlStXKo8//rhiY2OjbNy48ZpY3njjDcPnGzduVIAa+w0ZMqTG13y1Rx55RAkJCTF8fqPx3rBhg+Lg4KAMGjRIWbJkiRIXF6dMnz79mq9ZIYQQkpNYW04ydepURaPRKE888YSyfPlyZc2aNcqsWbOUjz/+2LDPrFmzFI1Gozz22GPKypUrlV9//VXp16+f4urqWuP/45FHHlHs7e2V0NBQZfbs2crvv/+urF27VtHpdMro0aMVV1dX5a233lLi4+OVb7/9VgkKClLat2+vlJaWGo4REhKiBAUFKa1atVLmzZunrF69WnnooYcUQPnggw8M+1XnDEFBQcqUKVOU2NhYZeXKlcqlS5eUd999VwGUBx54QFm1apXy3XffKeHh4Yqnp6dhXC9evKg4ODgo//jHP2r8f2i1WiUwMFCZNGmSYduXX36pzJ49W4mNjVU2b96sLFy4UOnSpYvSpk0bpbKy0rDfqFGjlBYtWihz5sxRNm3apCxbtkx5/fXXlcWLFxv2+fbbbxWNRqMMHTpUWbRokbJ+/Xrliy++UJ5++mnDPnXNfRTl2lyqrnnYjb4ma6PX65Vhw4Ypjo6OyqxZs5R169Ypb7zxhhIeHn5NDH/8fisuLlZ8fHyUnj17Kj///LOyefNmZcmSJcpTTz2lHD9+3PC6GTNmKPb29srLL7+sxMXFKYsWLVLatm2r+Pn5KZmZmQ0yHt9//72i0WiUCRMmKL/++quyYsUKZdy4cYqtra2yfv36Wv8vqlVUVCjOzs7KK6+8UmN7cnKy4uTkpIwYMUJZtmyZsmnTJuXHH39Upk6dquTl5Snl5eU3/FlX/b0eEhKivPrqq0p8fLyybNkyRa/XK6NGjVLs7OyUf/3rX8q6deuU//73v4qrq6vSrVs3pby83BBDSEiIMnbsWMPYvfzyy4q9vX2Nn4u3+t7btWtX4/tC1J0UyYSoh6sT0upf+kePHlUURVF69eqlTJ8+XVEU5ZqE9KuvvlIA5eeff65xvPfee08BlHXr1hm2AYqnp6eSm5tbY9+LFy9e88vterRarVJZWalERUUpL774omF7XRJSRVGUsWPHXvPLXVEUpbCwUHF3d1eef/75Gtvbt2+vDBs27KZxDRkyROnQoUONbdUxRURE1PiFqdPplMDAQKVTp06KTqczbC8qKlJ8fX2V/v37G7ZV/5L63//+V+PYXbt2VQDl119/NWyrqqpSWrRoUadfHoDi5+enFBYWGrZlZmYqNjY2yuzZsw3bRo0apbRs2VIpKCio8fpnn31WcXJyumYsq2m1WqWqqkp5/PHHlW7dul1z7tv9Orj6WM8888wN96mOZfjw4crEiROveb2xi2R/HG9FUZS2bdsq3bp1U6qqqmpsHzdunBIQEFDj60AIIayd5CTWk5Ns2bJFAa4pFF0tLS1NsbOzU/785z/X2F5UVKT4+/sr9957r2HbI488ogDKvHnzauz7008/1Vq027t3rwIoX3zxhWFbSEiIotFolISEhBr7jhgxQvHw8FBKSkoURbmSMwwePLjGfnl5eYqzs/M1hdm0tDTF0dFRefDBBw3bJk2apLRs2bLG//3q1asVQFmxYkWt/x96vV6pqqpSUlNTFUBZvny54Tk3NzflhRdeqPV1iqL+n3l4eCgDBw5U9Hr9dfe7nSLZH10vD7tR3lSbNWvWKECN4qmiqAXUmxXJ9u3bpwDKsmXLrnv86ouvf/z6Tk9Pr7UQVe12xqOkpETx9vZWxo8fX2O7TqdTunTpUuMCQW12796tADWKboqiKEuXLlWAa76Gr3ajn3XV3+uvv/56je3VhbX333+/xvYlS5YogDJnzhzDtuoiWWlpqTJ58mTF09OzRuGrPu/9oYceUvz8/K77nsT1ye2WQtymIUOGEBERwbx58zhy5Ah79+697m0NGzZswNXVlSlTptTYXj1d94/TZe+44w6aNWtW51i0Wi3vvvsu7du3x8HBATs7OxwcHDhz5kyNKeu3y93dnUcffZQFCxYYVk7ZsGEDx48fv+1VIKOjo7G3tzd8furUKTIyMpg6dSo2Nld+ZLm5uTF58mR27dpFaWlpjWP8cXWYdu3aodFoavTisrOzIzIyss4r2wwbNqxG02M/Pz98fX0Nry8vL+f3339n4sSJuLi4oNVqDR933XUX5eXl7Nq1y/D6X375hQEDBuDm5oadnR329vbMnTu31nG61a+DW/XVV1/RvXt3nJycDLH8/vvvRv2auZ4/jvfZs2c5efKkoTfMH/8fL1y4wKlTpxo8LiGEMEeSk1h2TrJmzRqA695CCrB27Vq0Wi3Tpk2r8TvUycmJIUOG1Lo63+TJk2t8vnLlSry8vBg/fnyNY3Tt2vX/2bvv6Ciq/43j791NDyQBAgkgvffeQUSkdwWR3nuVL4pYUJpYEBGQIlWkilQB6VV6R3rvhBIgCenJzu+P/RGNAaQk2YQ8r3P2nMnsnZlnsptk8tm59+Lr6xtnH4UKFaJYsWKx1rVs2ZLAwEAOHjz41GPt2rWL0NDQOENGZMmShTfffDPW+7BDhw5cu3YtVve7mTNn4uvrG+v7efv2bbp3706WLFlirmseDdL+z/de2bJlmTVrFiNGjGD37t1ERkbGyrBz504CAwPp2bNnzDAVCeF5rsP+/Z58kkfdMP891l7Lli3/c9vcuXOTJk0aBg0axOTJkzlx4kScNitXrsRkMtG6detY7xFfX1+KFSsW6z0Sn6/HvXv3aNeuXaxjWq1Wateuzb59+546o+ONGzcAyJAhQ6z1xYsXx8nJia5du/Lzzz/H6S76rP793t60aRNAnPd2s2bNcHd3j/M71t/fnzfffJO9e/fy559/xnR9hhc79wwZMnD79m2ioqJe6HxSMhXJRF6SyWSiQ4cOzJkzh8mTJ5M3b16qVKny2Lb+/v74+vrG+UObIUMGHBwc8Pf3j7X+eWeZGTBgAJ999hmNGzfm999/Z8+ePezbt49ixYoRGhr6fCf2H/r06UNQUBBz584FYMKECbz22ms0atTopfb773N+9D153PciU6ZMWK1W7t+/H2v9v2emcnJyws3NDRcXlzjrn3Xg4XTp0sVZ5+zsHPN99ff3JyoqivHjx+Po6BjrUbduXYCYsReWLFnCu+++S+bMmZkzZw67du2K+UfmcXkScrahMWPG0KNHD8qVK8fixYvZvXs3+/bto3bt2vH+nnmcf5/brVu3ABg4cGCc72PPnj0B4oxhISIiNromebWvSe7cuYPFYsHX1/eJbR79HS1Tpkycv6MLFy6M8zfUzc0t1kQPj/bx4MEDnJyc4uzDz88vzj4el+fRuv96H/3X9/Sf29epU4eMGTMyc+ZMAO7fv8+KFSto27YtFosFsI3ZVrNmTZYsWcKHH37Ixo0b2bt3b8wHlf987y1cuJB27doxbdo0KlSoQNq0aWnbti1+fn4AMeOvJeTM3s97HfasP4f+/v44ODjEuX592nvnEU9PT7Zu3Urx4sX5+OOPKVSoEJkyZeLzzz+PKVzdunULwzDw8fGJ8x7ZvXt3zHskPl+PR+/tpk2bxjnm119/jWEY3Lt374nn9ehY//7Zy5UrFxs2bCBDhgz06tWLXLlykStXLn744Yf//F790+Pe2w4ODnEmRTCZTPj6+sb52Thz5gx79uyhTp06FC5cONZzL3LuLi4uCT7JyqtKU4mIxIP27dszZMgQJk+ezMiRI5/YLl26dOzZswfDMGJdlD6q8nt7e8dq/7yfWs2ZM4e2bdvy5Zdfxlp/9+5dvLy8nmtf/yV37tzUqVOHH3/8kTp16rBixQqGDh0ac5Hyov59zo/+uN+8eTNO2xs3bmA2mxP0LqtnlSZNGiwWC23atHniJ7w5cuQAbK9Tjhw5WLhwYazz/fdAyY8k5KeXc+bM4Y033mDSpEmx1gcFBb3Q/lxcXAgICIiz/kmFrX+f26OfgcGDB/P2228/dpt/D7YqIiJ/0zXJq3tNkj59eqKjo/Hz83tiseTR6/bbb7/F3K3zNI97Xb29vUmXLh1r1qx57Db/vLMeiCliPG7dv4s0z/s9/ef78NF11rhx43jw4AHz5s0jPDycDh06xLQ5duwYR44cYdasWbRr1y5m/eMG/Pf29mbs2LGMHTuWK1eusGLFCj766CNu377NmjVrYoob/xys/nGe99rnn573OuxZfw7TpUtHVFQU/v7+sV6Dx71Wj1OkSBEWLFiAYRgcPXqUWbNmMWzYMFxdXfnoo4/w9vbGZDKxffv2x86u+WhdfL4ej94L48ePf+KM6z4+Pk88p0fbP66QVqVKFapUqUJ0dDT79+9n/Pjx9O/fHx8fH957772nfKf+9rj3dlRUFHfu3IlVKDMMAz8/P8qUKROrfYUKFWjWrBmdOnUCbJMMPLpj9UXO/d69ezg7O5MqVapnyi9/051kIvEgc+bMfPDBBzRo0CDWH4B/q169Og8fPmTZsmWx1s+ePTvm+f/y6I/O4z5dMplMcf5QrVq1iuvXr//nfp90rKd92tuvXz+OHj1Ku3btsFgsdOnS5YWO8zT58uUjc+bMzJs3D8MwYtYHBwezePHimNml7M3NzY1q1apx6NAhihYtSunSpeM8Hl2kmEwmnJycYv0x9fPze+zslk/ytPfB83jce+bo0aNxZul6VtmzZ+fMmTOxCn7+/v7s3LnzmbbPly8fefLk4ciRI4/9HpYuXTrOxbmIiPxN1ySv7jXJoy6F/y6o/FOtWrVwcHDg/PnzT/w7+l/q16+Pv78/0dHRj93+3x9WHT9+nCNHjsRaN2/ePFKnTk3JkiWfeqwKFSrg6urKnDlzYq2/du0amzZtivM+7NChA2FhYcyfP59Zs2ZRoUIF8ufPH/P8o2urf7/3pkyZ8tQcWbNmpXfv3tSoUSOmi2jFihXx9PRk8uTJsV7vf3uZa5/4vg57pFq1agAxd1c+Mm/evOfaj8lkolixYnz//fd4eXnFfG/q16+PYRhcv379se+RIkWKxGwP8fN6VKpUCS8vL06cOPHE97aTk9MT91mgQAEAzp8//8Q2FouFcuXKxcxM/+jYL3Ld/ei9++/39uLFiwkODn7s79h27dqxYMECZs6cSdu2bYmOjgZe7NwvXLhAwYIFnzmv/E13konEk6+++uo/27Rt25Yff/yRdu3acenSJYoUKcKff/7Jl19+Sd26dXnrrbf+cx+pU6cmW7ZsLF++nOrVq5M2bVq8vb3Jnj079evXZ9asWeTPn5+iRYty4MABvv322xe+TbxIkSIsWbKESZMmUapUKcxmc6yLqxo1alCwYEE2b95M69at4/Txjw9ms5lvvvmGVq1aUb9+fbp160Z4eDjffvstDx48eKbve2L54YcfqFy5MlWqVKFHjx5kz56doKAgzp07x++//x4zNkH9+vVZsmQJPXv2pGnTply9epXhw4eTMWNGzp49+0zHetr74HnUr1+f4cOH8/nnn1O1alVOnz7NsGHDyJEjxwuNYdCmTRumTJlC69at6dKlC/7+/nzzzTdxunI8zZQpU6hTpw61atWiffv2ZM6cmXv37nHy5EkOHjzIokWLnjuXiEhKomuSV/OapEqVKrRp04YRI0Zw69Yt6tevj7OzM4cOHcLNzY0+ffqQPXt2hg0bxieffMKFCxeoXbs2adKk4datW+zduxd3d3eGDh361OO89957zJ07l7p169KvXz/Kli2Lo6Mj165dY/PmzTRq1IgmTZrEtM+UKRMNGzbkiy++IGPGjMyZM4f169fz9ddf/2fR0MvLi88++4yPP/6Ytm3b0qJFC/z9/Rk6dCguLi58/vnnsdrnz5+fChUqMGrUKK5evcpPP/0U5/lcuXLx0UcfYRgGadOm5ffff2f9+vWx2gUEBFCtWjVatmxJ/vz5SZ06Nfv27WPNmjUxd7KnSpWK7777js6dO/PWW2/RpUsXfHx8OHfuHEeOHGHChAnAy137xPd12CM1a9bk9ddf58MPPyQ4OJjSpUuzY8cOfvnll//cduXKlUycOJHGjRuTM2dODMNgyZIlPHjwgBo1agC2ok3Xrl3p0KED+/fv5/XXX8fd3Z2bN2/y559/UqRIEXr06BHvr8f48eNp164d9+7do2nTpmTIkIE7d+5w5MgR7ty589QC8muvvUbOnDnZvXs3ffv2jVk/efJkNm3aRL169ciaNSthYWHMmDEDIOb34Itcd9eoUYNatWoxaNAgAgMDqVSpEkePHuXzzz+nRIkStGnT5rHbNW3aFDc3N5o2bUpoaCjz589/7nO3Wq3s3bs35q40eU52mCxAJNn750xST/PvmaQMwzD8/f2N7t27GxkzZjQcHByMbNmyGYMHD441DbBhPH0mwg0bNhglSpQwnJ2dDcBo166dYRi2GYI6depkZMiQwXBzczMqV65sbN++Pc6sO886k9S9e/eMpk2bGl5eXobJZDIe9yvjiy++MABj9+7dT/1e/NPTZpL653Th/7Rs2TKjXLlyhouLi+Hu7m5Ur17d2LFjR6w2j2aXuXPnTqz17dq1M9zd3Z8px+M86bXIli1bzPf+n+fRsWNHI3PmzIajo6ORPn16o2LFisaIESNitfvqq6+M7NmzG87OzkaBAgWMqVOnxuR/lmMbxpPfB89zHuHh4cbAgQONzJkzGy4uLkbJkiWNZcuWPdOMTI+b3dIwDOPnn382ChQoYLi4uBgFCxY0Fi5c+MTZLZ/0eh85csR49913jQwZMhiOjo6Gr6+v8eabbxqTJ09+6jmKiKQ0uib5W0q4JomOjja+//57o3DhwoaTk5Ph6elpVKhQIc7sjsuWLTOqVatmeHh4GM7Ozka2bNmMpk2bxpox70lZDMM24+bo0aONYsWKGS4uLkaqVKmM/PnzG926dTPOnj0b0+7RrHy//fabUahQIcPJycnInj27MWbMmFj7e3TNsGjRosceb9q0aUbRokVjzqlRo0bG8ePHH9v2p59+MgDD1dU1zozihmEYJ06cMGrUqGGkTp3aSJMmjdGsWTPjypUrsa5jwsLCjO7duxtFixY1PDw8DFdXVyNfvnzG559/HjMj5yOrV682qlatari7uxtubm5GwYIFja+//jpWm2e59jGMuNdSz3od9l/vycd58OCB0bFjR8PLy8twc3MzatSoYZw6deo/Z7c8deqU0aJFCyNXrlyGq6ur4enpaZQtW9aYNWtWnGPMmDHDKFeunOHu7m64uroauXLlMtq2bWvs378/wV6PrVu3GvXq1TPSpk1rODo6GpkzZzbq1av3xPfWP3322WdGmjRpYv2O27Vrl9GkSRMjW7ZshrOzs5EuXTqjatWqxooVK2Jt+6TfdU/6WTcMwwgNDTUGDRpkZMuWzXB0dDQyZsxo9OjRw7h//36sdo9+jv5p8+bNRqpUqYzatWsbISEhz3XuGzduNADjwIED//k9kbhMhvGUe0dFRP5D6dKlMZlM7Nu3z95RREREJAXTNYmIPM2NGzfIkSMHs2fPpnnz5vaOk2DatGnDhQsX2LFjh72jJEvqbikizy0wMJBjx46xcuVKDhw4wNKlS+0dSURERFIgXZOIyLPKlCkT/fv3Z+TIkTRr1ixmYPxXyfnz51m4cGHMMC/y/FQkE5HndvDgQapVq0a6dOn4/PPPady4sb0jiYiISAqkaxIReR6ffvopbm5uXL9+nSxZstg7Try7cuUKEyZMoHLlyvaOkmypu6WIiIiIiIiIiKR4r979hSIiIiIiIiIiIs9JRTIREREREREREUnxVCQTEREREREREZEU75UbuN9qtXLjxg1Sp06NyWSydxwRERFJJgzDICgoiEyZMr2SM169KnStJyIiIs/rWa/zXrki2Y0bN17JWSpEREQkcVy9epXXXnvN3jHkCXStJyIiIi/qv67zXrkiWerUqQHbiXt4eNg5jYiIiCQXgYGBZMmSJeZaQpImXeuJiIjI83rW67xXrkj26LZ7Dw8PXTiJiIjIc1MXvqRN13oiIiLyov7rOk8DboiIiIiIiIiISIqnIpmIiIiIiIiIiKR4KpKJiIiIiIiIiEiK98qNSfasoqOjiYyMtHeMFM3R0RGLxWLvGCIiIiIiIiIvRTUG+4qv+kKKK5IZhoGfnx8PHjywdxQBvLy88PX11SDJIiIiIiIikuyoxpB0xEd9IcUVyR69eTNkyICbm5uKM3ZiGAYhISHcvn0bgIwZM9o5kYiIiIiIiMjzUY3B/uKzvpCiimTR0dExb9506dLZO06K5+rqCsDt27fJkCGDul6KiIiIiIhIsqEaQ9IRX/WFFDVw/6P+wW5ubnZOIo88ei3Ud1tERERERESSE9UYkpb4qC+kqCLZI7r9MenQayEiIiIiIiLJmf6vTRri43VIkUUyERERERERERGRf1KRTOLVG2+8Qf/+/WO+zp49O2PHjrVbHhERERERERFJfuxRX1CRLJm5evUqnTp1IlOmTDg5OZEtWzb69euHv7+/vaOJiIgkrOC7YBj2TiGvupB7ep+JiEiKoPpCXCqSJSMXLlygdOnSnDlzhvnz53Pu3DkmT57Mxo0bqVChAvfu3UuwY2tgfRERsauQezCtOizpCpGh9k4jr6rjS2F8STg8z95JREREEpTqC4+nIlky0qtXL5ycnFi3bh1Vq1Yla9as1KlThw0bNnD9+nU++eQTBg8eTPny5eNsW7RoUT7//POYr2fOnEmBAgVwcXEhf/78TJw4Mea5S5cuYTKZ+PXXX3njjTdwcXFhzpw5+Pv706JFC1577TXc3NwoUqQI8+fPT5RzFxGRFMwaDYs7wf1LcHU3RITYO5G8qu5dhND7sOYjeHDV3mlEREQSjOoLj+dg7wD2ZhgGoZHRdjm2q6PlmWdfuHfvHmvXrmXkyJG4urrGes7X15dWrVqxcOFCtm7dyldffcX58+fJlSsXAMePH+evv/7it99+A2Dq1Kl8/vnnTJgwgRIlSnDo0CG6dOmCu7s77dq1i9nvoEGD+O6775g5cybOzs6EhYVRqlQpBg0ahIeHB6tWraJNmzbkzJmTcuXKxdN3RURE5F82DoXzm8DBFd6bB+7p7J1IXlUV+8Lp1XBtH6zoDa2XglmfKYuIyLNRfSH51xdSfJEsNDKagkPW2uXYJ4bVws3p2V6Cs2fPYhgGBQoUeOzzBQoU4P79+/j4+FC0aFHmzZvHZ599BsDcuXMpU6YMefPmBWD48OF89913vP322wDkyJGDEydOMGXKlFhv4v79+8e0eWTgwIExy3369GHNmjUsWrRIRTIREUkYf/0GO36wLTf+EXyL2DePvNosDtB4MkyuDBe2wP7pULaLvVOJiEgyofpC8q8v6KOxV4Tx/wPMmkwmWrVqxdy5c2PWz58/n1atWgFw586dmMH5UqVKFfMYMWIE58+fj7XP0qVLx/o6OjqakSNHUrRoUdKlS0eqVKlYt24dV65cSYQzFBGRFOfmUVje27ZcqT8UfseucSSF8M4Nb31hW14/BPzPP7W5iIjIqyYl1xdS/J1kro4WTgyrZbdjP6vcuXNjMpk4ceIEjRs3jvP8qVOnSJMmDd7e3rRs2ZKPPvqIgwcPEhoaytWrV3nvvfcAsFqtgO2WyH9XZy2W2Hnc3d1jff3dd9/x/fffM3bsWIoUKYK7uzv9+/cnIiLimc9DRETkmQT7w4JWEBUKud+C6kP4YcNZyudMS7mc6m4pCaxsVzi1Ei5th+W9oP0qMD/7dZuIiKRMqi/8LbnWF1J8kcxkMj3zLYn2lC5dOmrUqMHEiRN5//33Y/Ub9vPzY+7cubRt2xaTycRrr73G66+/zty5cwkNDeWtt97Cx8cHAB8fHzJnzsyFCxdiqr/Pavv27TRq1IjWrVsDth+Is2fPPvEWTRERkRcSHQmL2kHAFUibE96ZxsxdV/h+wxmcHcxsHvgGmbxc/3s/Ii/KbIZGE2BSJbiyC3ZPhIp97J1KRESSONUXnl1SrS+ou2UyMmHCBMLDw6lVqxbbtm3j6tWrrFmzhho1apA5c2ZGjhwZ07ZVq1YsWLCARYsWxbzpHvniiy8YNWoUP/zwA2fOnOGvv/5i5syZjBkz5qnHz507N+vXr2fnzp2cPHmSbt264efnlyDnKiIiKdi6z2x38Dilgvfm8ce5MIatPAFA3+p5VCCTxJEmO9T6/2urjcPh9im7xhEREYlPqi88nopkyUiePHnYv38/uXLlonnz5uTKlYuuXbtSrVo1du3aRdq0aWPaNmvWDH9/f0JCQuLcPtm5c2emTZvGrFmzKFKkCFWrVmXWrFnkyJHjqcf/7LPPKFmyJLVq1eKNN97A19f3sbdmioiIvLDD82DPJNtyk8nsDfah38LDGAa0KZ+Nnm/ksm8+SVlKtrN1940Oh2XdbXc5ioiIvAJUX3g8k/FoRLZXRGBgIJ6engQEBODh4RHrubCwMC5evEiOHDlwcXGxU0L5J70mIiIS4/oBmFHHVpCoOoizBfvQdPIuAkIjqVnQh0mtS2ExP9vU5i/iadcQknQk+usUeAMmloewAKj2CVT9MOGPKSIiyYL+n01anvZ6POv1g+4kExEREft7eBsWtLYVyPLW4VbJ/rSfuY+A0EhKZUvDuBYlErRAJvJEHpmg7mjb8tav4eYR++YRERGRBKMimYiIiNhXVAT82haCboB3XgLrTaTdzP1cfxBKzvTuTGtbGpfnmLFJJN4VaQYFGoA1CpZ2h6hweycSERGRBKAimYiIiNjXmo9sMwg6exDRbA49Fp3hlF8Q6VM783OHsqRxd7J3QklB1p+4RbT1X6ORmExQ73tw84bbJ2DLKPuEExERkQSlIpmIiIjYz4FZsH86YMLaZCofbg5hxzl/3J0szGxfhixp3eydUFKQ8RvP0mX2fj787SjWfxfKUqWH+t/blnf8AFf3JX5AERERSVAqkomIiIh9XNkDqwbalt/8lG8uZmfZ4Rs4mE1Mal2Kwpk97ZtPUpw8PqmwmE0sPniNISuOEWd+q4INoci7YFhts11GhNgnqIiIiCQIFclEREQk8QXegF/bgDUSCjbiZ8s7TN56HoCv3ynK63nTx9kkJDIkbtFCJB7VLpyRMe8Ww2SCObuv8OXqk3Hfc3W/gdQZwf8cbBxmn6AiIiKSIFQkExERkcQVGQYLW8PDW5ChIOvzDuGLlScA+KBWPt4p9VqcTQLCA+iwtgNf7vlShTJJUI2KZ2ZUkyIATN1+ke83nI3dwDUNNBxvW94zCS5uS+SEIiIiklBUJBMREZHEYxiw+n9w/QC4eHG08kR6/XYWw4DW5bPS841ccTYJjAik2/punPA/wbrL67gVcssOwSUlea9sVj5vUBCAcRvPxtzlGCNPDSjZzra8rBeEByVyQhEREUkIKpKJiIhI4tk3DQ7NAZOZ62/9SJuld4iIslKjoA9DGxbGZDLFah4UEUT39d057n+cNM5pmFpzKr7uvnYKLylJh0o5+LB2PgC++uMUP++8FLtBrZHglRUCrsDaTxI/oIiIiMQ7FclSoEuXLmEymTh8+LC9o4iISEpy6U9Y8xEAQVU+490NbgSERlIiqxfj3iuBxRy7QPYw4iHd13fnr7t/4eXsxdSaU8mbJq89kksK1fON3PR5MzcAn684zq/7rv79pHNqaDTRtnzwZzi73g4JRURE7O9VqjGoSJZMtG/fHpPJRPfu3eM817NnT0wmE+3bt0/8YCIiIs/iwVX4tR1Yo4gs+DbvHi3N9Qeh5PR2Z3q7Mrg6WWI1D44MpvuG7hy9exRPZ0+m1pxKvrT57BReUrIBNfLSuXIOAAYtOcryw9f/fjJHFSjXw7a8og+E3rdDQhERkeenGsPjqUiWjGTJkoUFCxYQGhoasy4sLIz58+eTNWtWOyYTERF5iogQWNgKQu5i9S1KtwftOekXhHcqZ37uWJa07k6xmgdHBtNjQw+O3DmCh5MHU2tMJX/a/HYKLymdyWTik3oFaFUuK4YBA349wppjfn83qD4E0uaCoJvwxyD7BRUREXlOqjHEpSJZMlKyZEmyZs3KkiVLYtYtWbKELFmyUKJEiZh1a9asoXLlynh5eZEuXTrq16/P+fPnH7fLGCdOnKBu3bqkSpUKHx8f2rRpw927dxPsXEREJIUwDPi9H9w8guGWjhGpPmHThYe4O1mY1aEMWdK6xWoeEhlCzw09OXT7EKmdUvNTzZ8okK6AncKL2JhMJoY3KszbJTMTbTXoM/8gW07ftj3p5AZNJoPJDEcXwokV9g0rIiLyjFRjiEtFMsOAiGD7PF5gCvsOHTowc+bMmK9nzJhBx44dY7UJDg5mwIAB7Nu3j40bN2I2m2nSpAlWq/Wx+7x58yZVq1alePHi7N+/nzVr1nDr1i3efffd584nIiISy64f4a9fwWRhfrbhzDgWjYPZxMTWpSic2TNW05DIEHpu7MnB2wdJ7ZiaqTWmUihdITsFF4nNbDbxzTtFqVckI5HRBt1+OcCu8/62J7OUhUr9bMsr34eHd+wXVERE7CeZ1RdANYZ/c7B3ALuLDIEvM9nn2B/fACf359qkTZs2DB48OGZgvB07drBgwQK2bNkS0+add96Jtc306dPJkCEDJ06coHDhwnH2OWnSJEqWLMmXX34Zs27GjBlkyZKFM2fOkDevBkkWEZEXcH4zrP8MgL35BvLxIS8ARr1dhKp508dqGhIZQu9NvTlw6wCpHFPxU82fKOStApkkLQ4WM983L05YZDQbT92m08/7+KVTOUplSwNvDIYza+H2CVj1Prz7C/xrtlYREXnFJbP6AqjG8G+6kyyZ8fb2pl69evz888/MnDmTevXq4e3tHavN+fPnadmyJTlz5sTDw4McOWyDzV65cuWx+zxw4ACbN28mVapUMY/8+fPH7EtEROS53bsIv3UAw8q1bG/T/EhRAAbWzEuz0lliNQ2NCqXPpj7s89uHu6M7U2pMobB33AsukcQQER3x1OedHMz82KoklXN7ExIRTfuZezl2PQAcnG3dLs0OcPJ3+GtRIiUWERF5caoxxKY7yRzdbBVXex37BXTs2JHevXsD8OOPP8Z5vkGDBmTJkoWpU6eSKVMmrFYrhQsXJiLi8Rd9VquVBg0a8PXXX8d5LmPGjC+UUUREUrCIYFjQCkLvE+xdlDrnG2MYJlqWy0qvarljNQ2LCqPPpj7s9duLu6M7k9+aTNH0Re0UXFK68w/O03V9V4aUH0LVLFWf2M7F0cJPbUvRbsZe9l26T5vpe1jYrQJ5MxaDqoNg80hYPRCyVwYPO91RICIiiS8Z1hdANYZ/UpHMZHqhWxLtqXbt2jFvxlq1asV6zt/fn5MnTzJlyhSqVKkCwJ9//vnU/ZUsWZLFixeTPXt2HBz0lhARkZdgGLCsJ9w+TpRrehrf7UlQlANvFfBhWMNCmP7R/SwsKoy+m/qy5+Ye3BzcmPzWZIpnKG6/7JLizTo+i9sht+mzqQ/9SvajY+GOsd6z/+Tm5MCM9mVoPW0PR64F0HLqHhZ1r0COyu/D6dVw4xCs6AOtflO3SxGRlCIZ1hdANYZ/StDultu2baNBgwZkypQJk8nEsmXLntp+y5YtmEymOI9Tp04lZMxkx2KxcPLkSU6ePInFYon1XJo0aUiXLh0//fQT586dY9OmTQwYMOCp++vVqxf37t2jRYsW7N27lwsXLrBu3To6duxIdHR0Qp6KiIi8av4cAyeWYZgd6R39PmfDPCiR1YvxLUrgYPn7siM8Opx+m/ux6+YuXB1cmfTWJBXIxO6GVBhC83zNMTAYe3AsH//5MeHR4U9sn9rFkZ87liW/b2ruPgyn1dTdXAuMhMaTweIM5zbAwZ8T8QxERESen2oMf0vQIllwcDDFihVjwoQJz7Xd6dOnuXnzZswjT548CZQw+fLw8MDDwyPOerPZzIIFCzhw4ACFCxfm/fff59tvv33qvjJlysSOHTuIjo6mVq1aFC5cmH79+uHp6YnZrGHrRETkGZ1ZBxuHAzDepRtrArOTw9ud6e3K4Or09wXXowLZzhs7cXVwZWL1iZT0KWmv1PKCXsUPQx3Njnxa/lM+KfcJFpOFlRdW0nFNR+6EPHm2Si83J+Z0Lkeu9O7cCAij5dQ9+Dlnhzc/tTVY+wncv5w4JyAiIvKCVGOwMRnGC84T+rwHMplYunQpjRs3fmKbLVu2UK1aNe7fv4+Xl9cLHScwMBBPT08CAgLivMBhYWFcvHiRHDly4OLi8kL7l/il10RE5BVx9xxMfRPCA9joXo9O/q3wTuXEkh6VyJru7zEyIqIj6L+5P9uvb8fVwZUfq/9IGd8ydgz+t6ddQ0hcf/zxBzt27KBkyZK88847z3ydd/r06Vjf3/Tp08f51PppEut12nNzDwO2DCAwIpAMbhkYV23cU2dc9QsI490pu7hyL4Rc6d1Z2KUs3ouawNXdkL0KtF0BSfwfAxEReT76fzZpedrr8azXD0nyL3WJEiXImDEj1atXZ/PmzfaOIyIiIk8TFggLWkJ4ABdci9DdvzluThZmti8bp0A2YMsAtl/fjovFhQlvTkgyBTJ5fnXq1GHEiBG8/fbbz7VdhgwZ8PX1jXk8T4EsMZXLWI4F9RaQ0zMnt0Nu025NO9ZcXPPE9r6eLsztXI6Mni6cvxNMm5kHCKz9g20g5UvbYe9PiZheREREXkSSKpJlzJiRn376icWLF7NkyRLy5ctH9erV2bZt2xO3CQ8PJzAwMNZDREREEonVCku7w93TBDmmp/n9HljNjkxsVZIir3nGNIuMjuR/W//H1mtbcbY4M776eMpmLGvH4GIvyenD0CweWZhbdy6vv/Y64dHhfLDtA8YfGo/VsD6+fVo35nYuh3cqZ07eDKTNMn/Cqn1he3LD53D3bOKFFxERkeeWpIpk+fLlo0uXLpQsWZIKFSowceJE6tWrx+jRo5+4zahRo/D09Ix5ZMmSJRETi4iIpHDbvoHTq4g2OdL6YR/u4MVXbxfhjXwZYpo8KpBtubrFViB7czzlM5a3X2axixf5MBTs/4FoKqdUjKs2jg6FOgDw09GfGLBlACGRIY9tnzN9KuZ2LkcaN0eOXH1AuyOFic5eFaLCYFkPiI5KzPgiIiLyHJJUkexxypcvz9mzT/7UbfDgwQQEBMQ8rl69mojpREREUrBTq2DLKAAGR3TgiJGbATXy0qz03x9YRVoj+WDbB2y+uhknsxPjqo2jQqYK9kosdvQiH4ZC0vhA1GK2MKD0AEZWHomj2ZGNVzbS5o82XH94/bHt8/mm5pdO5Ujt4sCeyw/4X3gXDOfUcG0f7ByXyOlFRETkWSX5ItmhQ4fImDHjE593dnaOmYXhSbMxiIiISDy7fQqWdAVgtrU2v0a/QYuyWenzZu6YJpHWSAZtG8TGKxtxNDvyw5s/UDFzRXslliTovz4MhaT1gWjDXA2ZWXsm6VzSceb+GVqsbMGBWwce27ZwZk9mdSiLm5OFZRfNzEzd3fbE5i/h1vFETC0iIiLPKkGLZA8fPuTw4cMcPnwYgIsXL3L48GGuXLkC2C562rZtG9N+7NixLFu2jLNnz3L8+HEGDx7M4sWL6d27d0LGFBERkecR+sA2UH/EQ/ZRiGERLXmrQAaGNyqEyWQCIMoaxUfbPmL95fU4mh0ZW20slTNXtm9uSXL+68NQSHofiBZLX4wF9RdQIG0B7offp/O6ziw5u+SxbUtlS8O0dqVxdjAz7FpxjrpXBGukbRy/qIhETi4iIiL/JUGLZPv376dEiRKUKFECgAEDBlCiRAmGDBkCwM2bN2MKZgAREREMHDiQokWLUqVKFf78809WrVr13LMmiYiISAKxRsPiznDvPH6m9HQL60PhLN6Ma1ECB4vtsiLKGsXg7YNZd3kdDmYHxlYby+uvvW7n4BLfUvKHob7uvvxc52dqZa9FlDWKz3d+ztd7vybKGne8sYq5vJncphSOFjOd/FsTbPEAv6Ow/endTEVERCTxOSTkzt944w0Mw3ji87NmzYr19YcffsiHH36YkJFERETkZWwaAefWE4YzncLexyOdL9PblcbNyXZJEWWN4uM/P2bNpTU4mB34/o3vVSB7Re3fv59q1arFfD1gwAAA2rVrx6xZs574Yej169dxdXWlUKFCrFq1irp16yZ69vjg6uDKt69/S26v3Px4+EfmnJzDhYALfPP6N3g6e8ZqWy1fBsa3KEmveQcZFNqOCU7jMbaNxpS3NmQuaaczEBERkX8zGU+rYiVDgYGBeHp6EhAQEOd2/LCwMC5evEiOHDlwcXGxU0L5J70mIiLJyLEl8Jtthr++Eb3Y6VaNxT0qki2dOwDR1mg+2fEJqy6swsHkwHdvfMebWd+0Z+Ln8rRrCEk6kuLrtOHyBj7+82NCo0LJ7pGdcW+OI4dnjjjtlh++Tv+FhxnvMI76lt0Y3vkwddsGjroGEhFJjvT/bNLytNfjWa8fkvzA/ZJ0Zc+enbFjx9o7hoiIJAa/YxjLewEwJaoeGxxeZ0b7MrEKZJ/t+CymQDa66uhkVSATeRlvZXuL2XVmk9E9I5cCL9FqVSt2Xt8Zp12j4pn56u0ifBbZnjuGJ6a7p2HzCDskFhERSXqSQo1BRbJkon379phMpjiPc+fO2TuaiIi86kLuwYKWmCJD2BZdhO+sLfixVUmKvuYF2ApkQ3YO4fcLv2MxWfim6jdUz1bdvplFEln+tPmZX28+JTKUICgyiB4bezDnxJw4Q480L5OVfg3KMziyMwDGzglweZc9IouISAqmGsPjqUiWjNSuXZubN2/GeuTIEfdWfhERkXgTHWXrYvngMpetGegT2YcRTYpTLV8GAKyGlS92fcGK8yuwmCx8/frX1MhWw86hRewjnWs6ptWcRpPcTbAaVr7e9zVf7PqCiOjYM1m2r5SDUjVbsSjqdUwYBC7oDOEP7ZRaRERSKtUY4lKRLBlxdnbG19c31sNisfD7779TqlQpXFxcyJkzJ0OHDiUq6u/ZlUwmE1OmTKF+/fq4ublRoEABdu3axblz53jjjTdwd3enQoUKnD9/Pmab8+fP06hRI3x8fEiVKhVlypRhw4YNT80XEBBA165dyZAhAx4eHrz55pscOXIkwb4fIiKSCDZ8Dhe2EGw40zVyAB3fKsm7ZbIAtgLZsF3DWHZuGWaTma+qfEWt7LXsHFjEvpwsTgytOJQPy3yI2WRmydkldFnXBf9Q/1jteryRi1sVv+C6kQ6P0GucnTfATolFRCSlUo0hrhRfJDMMg5DIELs84mPOhLVr19K6dWv69u3LiRMnmDJlCrNmzWLkyJGx2g0fPpy2bdty+PBh8ufPT8uWLenWrRuDBw9m//79ALGmYH/48CF169Zlw4YNHDp0iFq1atGgQYNYs1T9+/tYr149/Pz8WL16NQcOHKBkyZJUr16de/fuvfR5ioiIHRxZCLsmAPC/yB6ULFOJvtVzA38XyBafXYzZZGZU5VHUzlHbnmlFkgyTyUSbgm2YWH0iqR1Tc/D2QVqsasHpe6djtetVpyRb8g0BIM/lhexct8gecUVEJJ4k9/oCqMaQ4me3DIkMody8cvaIyp6We3BzdHumtu3bt2fOnDmxZmioU6cOt27dok6dOgwePDhm/Zw5c/jwww+5ceMGYLtQ+/TTTxk+fDgAu3fvpkKFCkyfPp2OHTsCsGDBAjp06EBoaOgTMxQqVIgePXrEvNGzZ89O//796d+/P5s2baJJkybcvn0bZ2fnmG1y587Nhx9+SNeuXR+7T80GIiKSRN04hHV6bczRYYyPasyh3L35qU0pHCxmDMNgxO4R/HrmV8wmM19W/pJ6OevZO/FLS4qzJkpcye11uhhwkT6b+nA58DKuDq6Mqjwq1ph9hmGwd0J7yvkv44aRjhON1/JWiTx2TCwiIs/q3//PJpf6AryaNYb4mN3S4YnPSJJTrVo1Jk2aFPO1u7s7uXPnZt++fbGqutHR0YSFhRESEoKbm+2HpGjRojHP+/j4AFCkSJFY68LCwggMDMTDw4Pg4GCGDh3KypUruXHjBlFRUYSGhj6xynvgwAEePnxIunTpYq0PDQ2NdYuliIgkAw/vED2/FZboMDZGl2CTbyfmtiwRUyAbuWckv575FRMmRlQa8UoUyEQSSg7PHMytO5cPtn7Arpu76L+lP72K96Jb0W4xgySX6TKBu9/tJVPkDXYu/R9b3Kbxxv+P+yciIpJQVGOIK8UXyVwdXNnTco/djv08Hr1h/8lqtTJ06FDefvvtOO3/WTl1dHSMWTaZTE9cZ7VaAfjggw9Yu3Yto0ePJnfu3Li6utK0aVMiImIPPPvPHBkzZmTLli1xnvPy8nq2ExQREfuLjiR6YVssQdc5b83I96n/x6z25XBzcsAwDEbtHcXC0wsxYWJ4peE0yNXA3olFkjxPZ08mvjWR7/Z/x5yTc/jx8I+cf3CeYZWG4ergitklNV4tp2L9uT5NzVvpPucnnNv3oEKudP+9cxERSTKSU30BVGN4nBRfJDOZTM91S2JSU7JkSU6fPh3njf2ytm/fTvv27WnSpAlg6z986dKlp+bw8/PDwcGB7Nmzx2sWERFJPNFrBmO5upMgw5UPHQbxY6c38U7ljGEYfL3va+afmo8JE8MqDaNR7kb2jiuSbDiYHRhUdhC5vXIzYs8I1lxaw5WgK/xQ7Qd83X1xyFGZ6PK9YPcEhpt/ouHP+ZjQ6S1KZUtj7+giIvKMknt9AVRjSPED9yd3Q4YMYfbs2XzxxRccP36ckydPsnDhQj799NOX2m/u3LlZsmQJhw8f5siRI7Rs2TKmAvw4b731FhUqVKBx48asXbuWS5cusXPnTj799NOYQftERCRpMw7OxrJvKgCDrL0Z0qEJ2dK5YxgG3+z7hrkn5wIwtOJQGudubMekIsnXO3nfYWqNqaRxTsMJ/xO0WNWCI3dsM3VZqn+G1Tsv6U0BfGxMo/3MvRy7HmDnxCIikpKk9BqDimTJXK1atVi5ciXr16+nTJkylC9fnjFjxpAtW7aX2u/3339PmjRpqFixIg0aNKBWrVqULFnyie1NJhOrV6/m9ddfp2PHjuTNm5f33nuPS5cuxfRPFhGRJOzqPqJ/HwDA91HNaNqqC8WyeGEYBqP3j2bOyTkAfF7hc5rkaWLPpCLJXmnf0syvP588afJwN/QuHdd05Pfzv4OjC+YmkzFMFhpYdlM1Yjttpu/htF+QvSOLiEgKkdJrDCl+dkuxL70mIiJJQJAfIRMq4xZ+hzXRZQhoMI3mZbNjGAbfH/iemcdnAjCkwhCa5W1m57AJJ7nNmphSvUqvU0hkCB9t/4jNVzcD0KFwB/qV6Idly1ew7RsCTampHvo1RiofFnWvQA5vd/sGFhGRWPT/bNISH7Nb6k4yERGRlCwqnAez3sMt/A5nrJk5X+nbmALZ2INjYwpkn5b79JUukInYg5ujG2OrjaVLkS4AzDw2k76b+/KwQg/wLYKHEcS4VD9z92EYrabu5uq9EDsnFhERebWpSCYiIpJSGQZ3f+2Ll/8hAgw3lucfTc9axTEMg3GHxjHj2AwAPi73Mc3zN7dzWJFXk9lkpm/Jvnzz+jc4W5zZdm0brdd25GqtoWB2pELUHnp47eVGQBitpu3BLyDM3pFFREReWSqSiYiIpFB3tkzC+8wCrIaJaT6f8n7z2gBMODyBaX9NA+Cjsh/RIn8Le8YUSRHq5KjDz7V/JoNrBs4HnKfFns/ZW749AB9YZ1DaK5gr90JoNW03dx+G2zesiIjIK0pFMhERkRTowcmtpNlqm6XoF/d29OjcDQeLmUlHJvHT0Z8AGFRmEK0KtLJnTJEUpZB3IebXn08R7yIEhAfQzW8jC7MUxBwRxJz0v5DJw5nzd4JpPW0PD0Ii7B1XRETklZMii2Sv2FwFyZpeCxGRxBdy5zLGr21xIJpNlkrU6/E1bk4OTDoyiUlHJgHwQekPaF2wtZ2TiqQ8GdwyMKPWDOrlrEeUEcUIh4eM8PbGcnUbyyucxjuVM6f8gmg3Yy9BYZH2jisiIuj/2qQiPl6HFFUkc3R0BCAkRIOeJhWPXotHr42IiCSsyPAQ/KY2JY3xgNNkJ1fnn/FO7cKUI1OYeHgiAANLD6RtobZ2TiqScrk4uDCq8ij6l+yPCRMLU7vR3TcDjrtH8mszH9K4OXLkWgCdZu0nJCLK3nFFRFIs1RiSlvioLzjEV5jkwGKx4OXlxe3btwFwc3PDZDLZOVXKZBgGISEh3L59Gy8vLywWi70jiYi88gyrlSMT21M64gz3jdREN59DtozpmXp0KhMOTwDg/VLv065QOzsnFRGTyUSnIp3I5ZWLQdsGsdcVWjhYmLCjP790WESL6fvYe+keXWcfYFq70rg46lpKRCSxqcaQNMRnfcFkvGL3BQYGBuLp6UlAQAAeHh5xnjcMAz8/Px48eJD44SQOLy8vfH199YtERCQRbP55KNUujiHKMHO02gxKvtGE6X9NZ+zBsQD0K9mPzkU62zekHf3XNYQkDSnxdTp7/yx9NvTgesgt3K1WvnmtLu55P6bN9D2ERETzVoEMTGpdCkdLiuokIiKSJKjGkHQ8rb7wrNcPKa5I9kh0dDSRkRrHwZ4cHR11B5mISCLZuHoRVfd0xcFk5WCBDynZ/BNmHpvJmANjAOhTog9di3a1c0r7SonFl+Qopb5O98PuM+D3luwPuYbJMOifvw35vTvRYdY+wqOs1CuSkR/eK46DCmUiInahGoN9/Vd94VmvH1JUd8t/slgsKtCIiEiKsGP/AUrs6Y+DycqJ9HUp+e7H/Hz855gCWa/ivVJ8gUwkqUvjkoafmixn1Lw3WUQA35+eQ4OI+0xo1Zuec46y6q+bODuaGd20GGaz7tAXEUlsqjG8GvRRk4iIyCvs8IUbpP29A2lND7nmko8CXaYz+8QvjN4/GoCexXrSvVh3O6cUkWfh6ODEkMa/8klAKBbD4PeLq5h1YRAjm2bDYjax5OB1Plt+TLOsiYiIvCAVyURERF5RF+88xO+XzhQwXSbQ7IVP19+Yd34p3+7/FoBuRbvRo3gPO6cUkefikYn3qo5kst9tPKKtHL17lCnn+vK/+m6YTDB3zxVGrjqpQpmIiMgLUJFMRETkFXQnKJw/fvqY2sYOorDg2HIOi279yVd7vwKgS5Eu9Crey84pReSFFH2X8tlrMP+GHzmsZm6H3GbmhQ9oU/0+ANP+vMj368/YOaSIiEjyoyKZiIjIKyY4PIoJUyfTLWI2AKHVv2R51HVG7R0FQOcinelToo9mFhZJrkwmqD+WrE6ezL1ymSrOvoRFh7H0+tfUqHgIsDJu0zkmbTlv76QiIiLJiopkIiIir5DIaCtDf/6dAQFfYTEZBBVsyR/p0zByz0gAOhTuQN8SfVUgE0nuUqWH+mNJbRiMP72f9llrA7D7/kKKllwBpnC+XnOKWTsu2jmoiIhI8qEimYiIyCvCMAyG/baHTtc+xdMUQnD6Eqwp8jrD94wAoH2h9rxf8n0VyEReFQUbQpF3sRhW/ndsEyPKDcHR7MjF0N1kLTwDk8N9vvj9BAv3XbF3UhERkWRBRTIREZFXxNj1p6l07DPyma8R7pKBNVXaMmzvlwC0LdiWAaUGqEAm8qqp+w2kzgj+52h06SAzas0gnUs67kddJl3eSVhcL/HRkr9Yfvi6vZOKiIgkeSqSiYiIvALm771C9NbR1LbsI9rkyOpqPRh6aCwArQu0ZmDpgSqQibyKXNNAw/G25d0TKR4cxIL6CyiQtgDhRiDu2afi4LGPAb8eYc0xP/tmFRERSeJUJBMREUnmNp26xablPzPA4TcAfq/Ugc9PzcLAoGX+lnxY5kMVyEReZXlqQMm2tuXlPfF1cGdW7VnUzFYTg2hcMi3GwXsFfebvY/Pp2/bNKiIikoSpSCYiIpKMHb76gNFzV/Kdw4+YTQbLi9RlyPW1GBi8l+89Pir7kQpkIilBzZHgmRUeXIF1n+Lm6MboqqPpWbwnAE7pduCQaRbd5/7JzvN37RxWREQkaVKRTEREJJm6dDeYvjO3MN70LR6mUFZkK8ZnD49jYNA8X3M+LvexCmQiKYWLBzT+0bZ8YBac3YDJZKJHsR6MeWMMLhYXHFKdwfLaBDrP+4MDl+/ZNa6IiEhSpCKZiIhIMnT3YTjtZ+xmSORYcplvstI7M5+ZAzAwaJa3mQpkIilRjtehXHfb8oreEHofgBrZavBL3V/wcfPF4nwHc+ZxtJ8/h2PXA+wYVkREJOlRkUxERCSZCYmIotOsfbwdOJu3LIf4I7Unn6R2wIqVd/K8w6flP8Vs0p94kRSp+ueQNhcE3YQ/BsWszp82Pwvqz6eodzFMljAM32m0+vVbTt0MtGNYERGRpEVX0CIiIslIVLSVXnMPkvHGevo6LGONuxuDvb2wYtAkdxOGVBiiAplISubkBk0mg8kMRxfCyd9jnvJ29WZm7RnUy9EQk8nAmnY5zZcM4PSt+3YMLCIiknToKlpERCSZMAyDT5cd4/qZg3znOIl1bq58lCE90Rg0ytWILyp+oQKZiECWslCpn2359/4Q/PdA/U4WJ0ZVGUHvYgPAMGFNtYd3l7fnr5vX7JNVREQkCdGVtIiISDLxw8az/LHvJFOdvmOXu5lBPrYCWcNcDRlacagKZCLytzcGQ4aCEHIXVvYHw4h5ymQy0a14B76u/AMmqwtW5wu0/qMVO6/8Zb+8IiIiSYCupkVERJKBhfuuMG7DacY7juecexAfZEhPFFA/Z32GVRyGxWyxd0QRSUocnKHxJDA72Lpc/vVbnCZ1c1djWo1fMEelx2q5R/dN7Vl2eq0dwoqIiCQNCVok27ZtGw0aNCBTpkyYTCaWLVv2n9ts3bqVUqVK4eLiQs6cOZk8eXJCRhQREUnyNp+6zcdLj/Ghw0KiU59jYAZvokxQN0ddRlQaoQKZiDxepuLw+oe25dX/g8CbcZqUfS0/8+vNxRKeF8MUwWe7B/LD/okY/7jzTEREJKVI0CJZcHAwxYoVY8KECc/U/uLFi9StW5cqVapw6NAhPv74Y/r27cvixYsTMqaIiEiSdeTqA3rOPUg9dlAg9XoGZPAmymSiTvY6jKw8UgUyEXm6KgMgY3EIC4AVfWJ1u3ykoG9Gfms8A0tQFQCmHZ/EgC0fEBoVmshhRURE7CtBi2R16tRhxIgRvP3228/UfvLkyWTNmpWxY8dSoEABOnfuTMeOHRk9enRCxhQREUmSLvsH03HWPnJGnaeR18+875OeKJOJWtlr8WWVL3EwO9g7oqRg6jGQTFgcbbNdWpzh3Ho4OPuxzXJn8OTXZl9j9m+GYZjZcGUtbf9ox63gW4kcWERExH6S1Jhku3btombNmrHW1apVi/379xMZGWmnVCIiIonP/2E47WbsxQi+S/c0P/BBBi8iTSZqZqvBV1W+UoFM7E49BpKRDAXgzU9ty2s/hvuXH9ssr09q5jbvh8mvG9Yod07dO0nzle9x9M7RRAwrIiJiP0mqSObn54ePj0+sdT4+PkRFRXH37t3HbhMeHk5gYGCsh4iISHIWEhFFx5/3c80/kL5px/JFeiciTSZqvFaVr17/WgUySRLUYyCZqdALspSHiIewvBdYrY9tVjizJ7NatMC43pfoMB/8w+7SYU0Hfj//eyIHFhERSXxJqkgGtimp/+nRoKH/Xv/IqFGj8PT0jHlkyZIlwTOKiIgklKhoK73nHeLI1Qe0TzOV8emDiTCbqO5Tlq+rfY+j2dHeEUVeiHoM2JnZAo0ngqMbXNoO+6Y+sWmpbGmY3qoW0dd6ERlUkAhrBB//+THfH/ieaGt0IoYWERFJXEmqSObr64ufn1+sdbdv38bBwYF06dI9dpvBgwcTEBAQ87h69WpiRBUREYl3hmHw2fJjbDp1m+oeS1mW4SIRZhPVvArwbY3JKpBJsvYiPQZAvQbiVbpcUGOYbXn953D33BObVsiVjimtKxF9sw3hd6sBMOPYDPpt7sfDiIeJkVZERCTRJakiWYUKFVi/fn2sdevWraN06dI4Oj7+HwNnZ2c8PDxiPURERJKj8ZvOMX/vVfKm2sbRjLsJN5t5wzUz39Wfi6NFBTJJ/p63xwCo10C8K90JclSFqFBY1h2ecmfYG/kyMKFlaaL9axN6/T3MOLL12lba/NGGq0H6YFpERF49CVoke/jwIYcPH+bw4cOAbcDWw4cPc+XKFcB2F1jbtm1j2nfv3p3Lly8zYMAATp48yYwZM5g+fToDBw5MyJgiIiJ29+u+q4xZf4a0bke5l3kVYWYTr5tS812TZSqQySvhRXoMgHoNxDuzGRr9CE6p4do+2Dnuqc1rFfJlzLvFiA4qTtDFrria0nDuwTlarmrJPr99iRRaREQkcSRokWz//v2UKFGCEiVKADBgwABKlCjBkCFDALh582ZMwQwgR44crF69mi1btlC8eHGGDx/OuHHjeOeddxIypoiIiF1tPn2bwUv/wsntLOas8wg3m6gSZeb7d1bg5Ohi73gi8eJFegyAeg0kCK8sUOcr2/LmL+HW8ac2b1Q8M1+/XRRrWBbunOmBt2NuHoQ/oOu6rvx6+tdECCwiIpI4TMaj+9xfEYGBgXh6ehIQEKCLKBERSfKOXntA8ym7iXA4R5psUwk3WakUFskPjZfg7FPQ3vFSFF1DPJ+HDx9y7pxtTKsSJUowZswYqlWrRtq0acmaNSuDBw/m+vXrzJ49G7D1KChcuDDdunWjS5cu7Nq1i+7duzN//vzn+kBUr1M8MQyY/x6cWQO+RaHzRnBweuomP++8xOcrjoMpkpIl13M2ZBsAzfM1Z1DZQRo3UUREkqxnvX5IUmOSiYiIpCRX/EPoOGsfEQ7n8cw2jXCTlYohofxQdbQKZJLkqcdAMmcyQYMfwDUN+B2F7aP/c5N2FbMzqHZ+MBw5eKAOldO1xYSJhacX0mN9Dx6EPUj43CIiIglId5KJiIjYgf/DcJpO3sXl4BN4ZJ1GtDmS8qGhjM/fGZc3PrJ3vBRJ1xDJg16neHZsMfzWEUwW6LwBMpf8z03GrD/DuI1nAehYI5hVft8REhVCltRZGP/meHJ55Uro1CIiIs9Fd5KJiIgkUaER0XT6eT+XH54kVdbpRJsjKRcaxrg05XGpOsje8UQkJSn8DhRqAkY0LOsBkWH/ucn7b+WhS5UcAMzc4E7HnGPInCozV4Ou0mp1K7Zd25bQqUVERBKEimQiIiKJKCraSp/5Bzl65y/cs03HMEdQNjSM8VZvXBtPtnWBEhFJTHW/A/f0cOcUbB75n81NJhMf1y1A6/JZMQz45vcAuuT6gdI+pQmODKb3xt7MOjaLV6zDioiIpAAqkomIiCQSwzD4bPlxNl08gFvW6WAOp3RoGOMDInB9bx44p7J3RBFJidzTQYNxtuWd4+HK7v/cxGQyMaxhYZqWeo1oq8FHi87TMtsImuZtioHBdwe+49MdnxIeHZ7A4UVEROKPimQiIiKJZMKmcyw8uhO3rNMxWcIoGRbGj7f9cWs6E9LmsHc8EUnJ8teFYi0BA5Z2h4jg/9zEbDbx9TtFqVc0I5HRBr3nHuWt9D0ZXHYwFpOFFedX0HFtR+6G3k34/CIiIvFARTIREZFEsGj/Vb7fvhm3rNP+v0AWziS/O7i9NRRyvWnveCIiUHsUeGSG+xdh/efPtInFbGJs8+K8VcCH8CgrXWYfIJ9bbSbXmIyHkwdH7xzlvZXvccL/RAKHFxEReXkqkomIiCSwLadvM3jVmpgCWfGIaCb63catcFOo0Nve8UREbFy9oOF42/K+qXB+8zNt5mgxM6FlCark8SYkIpr2M/bhHl2AefXmkcMzB7dCbtHuj3asvbQ24bKLiIjEAxXJREREEtBf1wLouWglLq9Nw2QJpajVwqQbN3D3KWIbA0gD9YtIUpK7OpTuZFte3hvCAp5pMxdHCz+1KU3Z7GkJCo+izYw9hIWkZW7duVTKXImw6DAGbh3Ij4d/xGpYE/AEREREXpyKZCIiIgnkin8I7eYuw5RxCiaHEIqY3Zl85RKpXNPCe3PByc3eEUVE4qoxDNJkh8BrsObjZ97M1cnC9PalKZbFiwchkbSatoc7ASZ+fPNH2hVsB8DkI5P535b/ERIZkkDhRUREXpyKZCIiIgngXnAErWYvI9x7EmaHEAo6p2fKhVOkxgzNfgavrPaOKCLyeM6poPEkwASH58DpNc+8aWoXR2Z3KEuBjB7cfRhOq2l7uPEgnIFlBjKi0ggczY5suLKBtn+05cbDGwl3DiIiIi9ARTIREZF4FhoRTevZy7nnMQ6zQzD53V5j6tnDpDYM28DYOarYO6KIyNNlqwgVetmWf+8LIfeeeVNPN0d+6VSWXOnduRkQRstpu/ELCKNR7kbMqDWDtC5pOX3/NC1WteDQ7UMJdAIiIiLPT0UyERGReBQVbaXT/JVcdhqD2SGYXO45mHbxJB7R0VC8NZTtau+IIiLP5s1PwTsvPLwFqwc+16beqZyZ16U82dK5cfVeKC2n7eZOUDjFMxRnQb0F5E+bn3th9+i4tiNLzy5NoBMQERF5PiqSiYiIxBPDMHh/6TqORH2F2eEhWd1z8vPtO3iG3IfMpaDedxqoX0SSD0dXaDwZTBY4thiOLXmuzX08XJjbuRyZPF24cCeYNtP38CAkgoypMvJz7Z+pka0GUdYohuwcwjf7viHKGpVAJyIiIvJsVCQTERGJJyPXbWVTwDDMDg/J6JqTudHueN4+Ae4ZoPkccHSxd0QRkefzWimoMsC2vOp/EHTr+TZP48bcLuVJn9qZU35BtJuxl6CwSNwc3RhddTQ9i/UE4JcTv9B7Y28CIwLj+wxERESemYpkIiIi8WDKzt3Mv/oxZocgvJ2yszBDWbxOrgKzIzT/BTwy2TuiiMiLef1D8CkCofdgZX8wjOfaPIe3O3M7lyONmyNHrgXQcdY+QiKiMJvM9Cjeg++qfoeLxYUdN3bQalUrLgVcSpDTEBER+S8qkomIiLykRUcOMf7E/zA7BuFpycLioh1Is+Vb25N1v4Ws5e0bUETkZTg4QZPJtqL/6dVwZP5z7yKvT2p+6VSO1C4O7Lt0n66zDxAWGQ1Azew1mV1nNr7uvlwKvETL1S3ZeWNnfJ+FiIjIf1KRTERE5CVsPHuCofv7YHIMxN2UmaVvDCXtiv6AAaU6QOkO9o4oIvLyfAtDtcG25T8GQcC1595F4cye/NyxLO5OFv48d5decw8SEWUFoEC6AsyvN59i6YsRFBFEzw09mXtyLsZz3rUmIiLyMlQkExEReUF7r56l/7ZumBwCcDYysrTeRNIv6wXhAZClPNT5xt4RRUTiT8V+kLk0hAfC8t7P3e0SoGTWNExvXwZnBzMbT93m/YWHiYq2Fcq8Xb2ZUWsGjXI1ItqI5qu9XzF011AioyPj+0xEREQeS0UyERGRF3D89kW6rO8EDg9wiPbht4YzybjuU7h7GlJngndn27ooiYi8KiwOtm6XDi5wYTPsn/FCuymfMx1T2pTC0WJi1V83+fC3o1ittoKbk8WJ4ZWGM7D0QMwmM4vPLqbzus7cC7sXn2ciIiLyWCqSiYiIPKfz967QelV7rJb7mKMyMLf+LLIfmQ2nV4HFGd6bA6l97B1TRCT+eeeB6p/bltd9BvcuvNBu3siXgQktS2Ixm1hy6DqfLj8W07XSZDLRrlA7Jrw5gVSOqTh4+yAtVrbg9L3T8XUWIiIij6UimYiIyHO4FnSD5r+3I8p8DyK9mVpjKgX9D8PWr2wN6n8PmUvZNaOISIIq1x2yVYbIYFjWC6zRL7SbWoV8GfNuMUwmmLfnCiNWnYw1BlmV16owt+5csqbOyo3gG7T5ow2brmyKr7MQERGJQ0UyERGRZ3Tz4U3eWdqacO5ijUjHd1UmU9YlApZ2szUo1x1KtLJvSBGRhGY2Q+MfwdEdruyE3ZNeeFeNimfm67eLAjD9z4uMWX8m1vM5vXIyr948ymUsR2hUKP0292Pq0aka0F9ERBKEimQiIiLPwC/Yj6bL2xBi3MEakY7PSo2jZlYfWNACIh5C9ipQc4S9Y4qIJI402aHWSNvyxmFw58W7Qr5bJgtDGxYCYPymc/y4+Vys5z2dPZn01iRa5G8BwLhD4xi0fRBhUWEvfEwREZHHUZFMRETkP9wKvkXzFe0IjLqFNSIt3fN+y3vFC8HizrbxeDyzQLNZYHG0d1QRkcRTqj3kqg7R4bY7aqOjXnhX7Spm56M6+QH4du1pZvx5MdbzjmZHPi73MUMqDMHB5MAfF/+g/Zr23Aq+9TJnICIiEouKZCIiIk9xO+Q2LVe2517EDawRaWmScQR9qpaBTSPg3HpwcIX35oK7t72jiogkLpMJGk0AF0+4cQj+/P6ldte9ai76Vs8DwLCVJ1iw90qcNs3yNuOnmj/h5ezFcf/jtFjVgr/u/PVSxxUREXlERTIREZEnuBNyh9ar2nM77BrWiDRUTvUpw+pWhmNL4M8xtkaNJkDGYvYNKiJiLx6ZoM63tuWtX8PNoy+1u/ffykPX13MCMHjpXyw7dD1OmzK+ZZhfbz65vXJzJ/QO7de0Z9WFVS91XBEREVCRTERE5LHuht6l3R8duRlyFWukFwVNHzK+WXXMt4/D8l62RhX7QpGm9g0qImJvRd+F/PXBGgnLekBU+AvvymQyMbhOftqUz4ZhwP8WHWHNsZtx2r2W+jXm1J3DG1neIMIawUfbP2LsgbFYDevLnImIiKRwKpKJiIj8y93Qu7T/oyNXH17CGulJxpD3md66Fk4RD2BBS4gMgVxvwltf2DuqiIj9mUxQ/3twSwe3jtnuKHup3ZkY2rAQTUu9RrTVoM/8Q2w+dTtOO3dHd36o9gOdi3QGYPqx6fTb1I/gyOCXOr6IiKRcKpKJiIj8g3+oPx3XduJy0EWskZ543O/L3PZ18XA0waL28OCybVa3d6aD2WLvuCIiSUOqDFDv/7uh//k9XNv/Urszm018/U5R6hfNSGS0Qfc5B9h57m7cdiYz/Ur246sqX+FkdmLLtS20Xt2aq0FXX+r4IiKSMqlIJiIi8v/uhd2j87rOXAy4gDXSA7NfD2a3rYOPhwts+BwubgVHd3hvHriltXdcEZGkpVBjKNIMDKtttsuIkJfancVs4vvmxXmrgA/hUVY6z97Pgcv3Htu2Xs56zKo9i/Su6Tn34BwtV7Vkn9++lzq+iIikPCqSiYiIAPfD7tN5bWfOPTiHNTI1Ude7Mb1VHfL4pIYjC2HXBFvDJpPAp5B9w4qIJFV1voFUvuB/DjYNf+ndOVrMTGhZgip5vAmJiKb9jH38dS3gsW2LpC/C/HrzKZSuEA/CH9B1XVd+Pf3rS2cQEZGUQ0UyERFJ8R6EPaDzus6cfXAWa1RqQq925fu3a1A2R1q4cQh+72trWGUgFGxk37AiIkmZW1poON62vHsiXNz+0rt0cbTwU5vSlM2RlqDwKNrM2MNpv6DHtvVx92FW7VnUyVGHKCOK4buHM3L3SCKtkS+dQ0REXn0qkomISIoWEB5Al/VdOHP/DNaoVIRe7sJnNatSt0hGeHgHFrSGqDDIUwuqfWLvuCIiSV/emlCyrW15eU8If3xB63m4OlmY0b4MxbJ48SAkklbT9nDhzsPHtnVxcOHrKl/Tr2Q/ABacXkCPDT0ICH/8HWgiIiKPqEgmIiIpVkB4AF3WdeHUvVMYUakIvdyVLuXL06FSDoiOhEXtIPAapMsD70wFs/5siog8k5ojwTMrPLgC6z6Ll12mcnZgdoeyFMzowd2H4bSatoer9x4/7pnJZKJzkc78UO0HXB1c2XNzDy1XteTCgwvxkkVERF5NutoXEZEUKTAikK7ru3Ly3kmMaHdCLnehfoHiDKqd39ZgzWC4vAOcUtsG6nfxtG9gEZHkxMUDGv9oWz4wE85tiJfdero58kunsuTOkIqbAWG0nLabmwGhT2z/ZtY3mVN3DplTZeZK0BVarm7J+svr4yWLiIi8elQkExGRFCcwIpBu67pxwv8E/H+BrNxrBfm2WVHMZhMcnA37ptoavzMV0ue1b2ARkeQox+tQtptteXkfCL0fL7tNl8qZuZ3LkS2dG1fvhdJq2h7uBIU/sX3eNHmZV28epXxKERwZzIAtA+i/uT+3Q27HSx4REXl1qEgmIiIpSlBEEN3Xd+eY/zFMVneCL3chb5o8TGlbCmcHC1zdB6v+Z2tc7RPIV8e+gUVEkrO3voC0uSDoBvzxUbzt1sfDhbmdy5HJ04ULd4JpM30PD0Iintg+rUtaptaYSpciXXAwObDxykYaLWvEr6d/xWpY4y2XiIgkbyqSiYhIivEw4iHdN3Tnr7t/YTbceXipE74u2ZnVoSweLo4Q5AcLW0N0BOSvb5vNUkREXpyTGzSeBCYzHF0AJ3+Pt12/lsaNeV3Kkz61M6f8gmg7Yy+BYU+exdLR4kjfkn1ZUH8BRbyL8DDyIcN3D6fDmg5cCNBYZSIioiKZiIikEMGRwXTf0J2jd47igDtBFzuRypSVWR3L4uvpAlHhsLANPPSD9PmhyWQN1C8iEh+yloOKfW3Lv/eH4Lvxtuvs3u7M7VyONG6OHL0WQMeZ+wiJiHrqNvnS5uOXOr8wqMwgXB1cOXj7IE1XNGXykclERj+5yCYiIq++RLn6nzhxIjly5MDFxYVSpUqxffv2J7bdsmULJpMpzuPUqVOJEVVERF5BwZHB9NjQgyN3juBocifgYkccol5jatvS5PVJDYYBqwfCtb22AfrfmwfOqe0dW0Tk1VHtY0hfAELuwsr+tt+78SSvT2p+6VSO1C4O7L98ny6z9xMWGf3UbSxmC60LtmZZo2VUzlyZSGskPx7+kXdXvsuRO0fiLZuIiCQvCV4kW7hwIf379+eTTz7h0KFDVKlShTp16nDlypWnbnf69Glu3rwZ88iTJ09CRxURkVdQSGQIPTf05NDtQzib3XlwoQNGeGa+b16ccjnT2Rrtn2EbrN9khqYzIF0u+4YWEXnVODj//x26DrYul3/9Fq+7L5zZk587lsXdycKOc/70nHuQiKj/HmssU6pMTKw+ka+rfE1al7Sce3CONqvbMGrPKIIjg+M1o4iIJH0JXiQbM2YMnTp1onPnzhQoUICxY8eSJUsWJk2a9NTtMmTIgK+vb8zDYrEkdFQREXnFhESG0HNjTw7ePoiL2Z1759tjDXuNz+oVpF7RjLZGl3fCHx/alqt/Drnfsl9gEZFXWabi8Pr//75dPRACb8br7ktmTcP09mVwdjCz6dRt+i88RFT0fxfKTCYTdXPWZXmj5TTM1RADg3mn5tF4eWO2Xt0arxlFRCRpS9AiWUREBAcOHKBmzZqx1tesWZOdO3c+ddsSJUqQMWNGqlevzubNmxMypoiIvIJCo0Lpvak3B24dwMXiTsClDljDstClSg46Vs5haxRwDX5tC9YoKPQ2VOpn39AiyZCG1ZDnUmUAZCwOYQ/g977x2u0SoHzOdPzUtjROFjOr//Ljw9+OYrU+2zG8XLwYWXkkU2pMIXOqzPgF+9F7U28+2PoBd0Pjbxw1ERFJuhK0SHb37l2io6Px8fGJtd7Hxwc/P7/HbpMxY0Z++uknFi9ezJIlS8iXLx/Vq1dn27Ztj20fHh5OYGBgrIeIiKRsoVGh9NnYh31++3C1uBF6pSMRwa/RoFgmBtcpYGsUGQoLWkHwHfApAo0mgMlk3+AiyYyG1ZDnZnG0dbu0OMHZdXDol3g/RNW86ZnQsgQWs4klh67z6fJjGM9RjKuYqSJLGy2lQ6EOmE1m1lxaQ6NljVh6dulz7UdERJKfRBm43/SvfzoMw4iz7pF8+fLRpUsXSpYsSYUKFZg4cSL16tVj9OjRj20/atQoPD09Yx5ZsmSJ9/wiIpJ8hEWF0WdTH/b47cHV4kb0zS48DMxMhZzpGN2sKGazyXbnwsr34eZhcE0L780FJ3d7RxdJdjSshryQDAXgzU9ty2sGw/3L8X6ImoV8GfNuMUwmmLfnCiNWnXyuApergysDSg9gfr35FEhbgMCIQIbsHEKXdV24Evj0IrCIiCRfCVok8/b2xmKxxLlr7Pbt23HuLnua8uXLc/bs2cc+N3jwYAICAmIeV69efanMIiKSfIVFhdF3U1/23NyDq8UVZ/9u+PtnJJ9Paia3KYWzw///I75nMhyZDyYLNJsJabLZN7hIMqRhNeSlVOgNWcpBxENY3gus/z122PNqVDwzX79dFIDpf15kzPozz72PgukKMq/ePAaUGoCLxYU9fnt4e8XbTP9rOpHWyPiOLCIidpagRTInJydKlSrF+vXrY61fv349FStWfOb9HDp0iIwZMz72OWdnZzw8PGI9REQk5YmMjqT/5v7surkLVwdX0of04upNHzJ6ujCrYxk8XR1tDS9shbWf2JZrjoCcb9gts0hylhjDaoCG1nhlmS3QeBI4usGl7bBvaoIc5t0yWRjWqBAA4zed48fN5557Hw5mBzoU7sCShkson7E84dHhjD04lparWnLc/3h8RxYRETtySOgDDBgwgDZt2lC6dGkqVKjATz/9xJUrV+jevTtguxPs+vXrzJ49G4CxY8eSPXt2ChUqREREBHPmzGHx4sUsXrw4oaOKiEgyNuXoFHbc2IGLxYXc1v7svOBJahcHfu5YloyerrZG9y/DovZgREOxFlC+h10zi7wKnndYjXz58sV8XaFCBa5evcro0aN5/fXXH7vNqFGjGDp0aPwFlqQjXS6oMcw20+X6zyFXdfDOHe+HaVshOyER0Xz1xym+XXsaq9WgV7Xctu73zyGLRxZ+qvETK86v4Nv933Lq3ilarmpJ6wKt6VW8F26ObvGeXUREEleCj0nWvHlzxo4dy7BhwyhevDjbtm1j9erVZMtm69py8+bNWIO7RkREMHDgQIoWLUqVKlX4888/WbVqFW+//XZCRxURkWTqhP8Jpv01DYBiLt3ZedwTJ4uZqW1Lk9cnta1RRIhtoP7Qe5CpBNT/XgP1i7yExBhWAzS0xiuvdCfIURWiQmFZD7BGJ8hhulfNRb/qtgkivlt/hi6z9/MgJOK592MymWiUuxHLGy2nTo46WA0rs0/M5u0Vb7Pz+tO7GYuISNJnMl6xKVoCAwPx9PQkICBAXS9FRFKAyOhI3lv1HmfunyG3W0UOHWgIwISWJahfNJOtkWHAbx3h+BJwTw9dt4Dna/YLLUmSriGeX7ly5ShVqhQTJ06MWVewYEEaNWrEqFGjnmkfTZs25d69e2zatOmZ2ut1egU9uAoTK0BEELw1FCr3T5DDGIbB/L1X+eL340REWcns5cqk1iUp+prXC+9z27VtjNg9gpvBNwFokLMBH5T5gDQuaeIptYiIxIdnvX5IlNktRUREEsrUv6Zy5v4Z3B08OXz4TQA+rVfg7wIZwI4fbAUyswO8O1sFMpF4MmDAAKZNm8aMGTM4efIk77//fpxhNdq2bRvTfuzYsSxbtoyzZ89y/PhxBg8ezOLFi+ndu7e9TkGSAq8sUPv/i6qbR8KtEwlyGJPJRMtyWVnSoyJZ07px/UEoTSft4pfdl59r5st/ev2111nWaBmtC7TGhInfL/xOo2WNWHlh5QvvU0RE7EdFMhERSbZO3TvF1KO2wZ6DrjXAiE5Fh0rZ6Vwl59+Nzm6ADV/Ylut8DdmefeIYEXk6Dash8aZEa8hTC6IjYGk3iE64mSMLZ/bk9z6VqVnQh4hoK58tO0a/BYcJDo96of25OboxqOwg5tSdQ540ebgffp/B2wfTY0MPrj+8Hs/pRUQkIam7pYiIJEuR1kharmrJqXuncAwrxr2L71ElT3pmti+Dg+X/PwPyPw9Tq0FYAJRsCw3GaRwyeSJdQyQPep1eYUF+8GM5CHsAVT+CaoMT9HCGYTBt+0W+WnOKaKtBrvTuTGpd6u+xLF9ApDWSWcdmMfnIZCKsEbg6uNK7eG9aFWiFxWyJx/QiIvI81N1SREReadP/ms6pe6ewGKm4f6U+WdO6M75Fib8LZOFBtoH6wwLgtTJQd7QKZCIiSVlqX6j3nW1527dw41CCHs5kMtHl9Zws6FoeHw9nzt8JptGEHSw9dO2F9+lodqRL0S4sbriY0j6lCY0K5dv939JqdStO3zsdj+lFRCQhqEgmIiLJzul7p5lydAoAD280wNXixdS2pfFyc7I1sFphaXe4cxJS+cK7v4CDsx0Ti4jIMyn8DhRsDEa07fd4ZFiCH7JM9rSs6luFyrm9CY2M5v2FRxi85C/CIl98ps3sntmZXms6n1f4nNSOqTnuf5zmK5sz9sBYwqIS/pxEROTFqEgmIiLJSqQ1ks92fEaUNYrIoIJEBRZlzLvFyef7j+4x27+DUyvB4gTN54BHRvsFFhGRZ2cyQb0xtpmI75yyDeSfCLxTOfNzx7L0rZ4Hkwnm773CO5N2ctk/+IX3aTaZaZq3KcsbL6dGthpEG9FMPzadd1a8w96be+MxvYiIxBcVyUREJFmZeWwmJ++dxIh2JfxmE/pVz0vtwr5/Nzj9B2weYVuuNwaylLFPUBEReTHu6WxjSALsHA9XdifKYS1mEwNq5OXnDmVJ4+bI8RuB1B//J2uP+73UftO7pWfMG2P4odoPZHDNwJWgK3Ra14khO4YQEB4QT+lFRCQ+qEgmIiLJxtn7Z5l0eBIAYX4NqZEvN/2q5/m7wZ0zsLiLbblMFyjZxg4pRUTkpeWvC8VaAgYs6wERL35H1/N6PW96VvWtQsmsXgSFRdHtlwN8ufokkdHWl9rvm1nfZFnjZTTP1xyApeeW0mhZI9ZcWsMrNpeaiEiypSKZiIgkC1HWKD7581OijCgigwqQ3aUyY5oXx2z+/8H4Q+/DgpYQEQTZKkHtUfYNLCIiL6f2KEidCe5dgA1fJOqhM3m5srBbBTpVzgHAT9su0HLqbvwCXm48sdROqfm0/Kf8XPtncnjmwD/Mnw+2fkDfTX3xC365O9ZEROTlqUgmIiLJgq2b5QmMaBcc7zdjWtsypHJ2sD0ZHgRzmoL/WfDIDM1+BoujfQOLiMjLcfWCRhNsy3t/ggtbEvXwjhYzn9UvyKRWJUnt7MC+S/epN247f569+9L7LulTkt8a/EaPYj1wMDuw5doWGi1rxPxT87EaL3fHmoiIvDgVyUREJMk7d/8cPx6eCEDErYb82PwNsnu7256MDIP5LeD6fnBNA61+g1Tp7ZhWRETiTe7qULqjbXl5bwhL/DG86hTJyO99KlMgowf+wRG0mbGHHzacxWp9uS6SThYnehbvyaL6iyiWvhghUSF8uedL2v7RlnP3z8VTehEReR4qkomISJIWZY3i/U2DiTaiiArKz8CKLXk97/8XwaIjYVE7uLQdnFJB68XgU9C+gUVEJH7VGA5e2SDgKqz92C4Rsnu7s7RnRZqXzoJhwPcbztB+1j7uBUe89L5zp8nN7Dqz+aTcJ7g7unPkzhGarWzGj4d/JCL65fcvIiLPTkUyERFJ0sbtn8alh6cwol2o5t2TLq/ntD1hjYal3eDMGnBwgZYLIXMp+4YVEZH455wKGk8CTHBoDpxeY5cYLo4Wvm5alNHNiuHiaGbbmTvUG7edA5fvv/S+zSYz7+V/j2WNlvHGa28QZY1i8pHJNP29KQdvHYyH9CIi8ixUJBMRkSTr2J3TzDwxBYD0Ee8y5p3XMZlMYBiwsj8cWwxmR2g+B7JXtm9YERFJONkrQYVetuXf+0LIPbtFaVrqNZb1qkROb3duBoTRfMoupv95MV5mqPR192Xcm+MYXXU06VzScTHgIu3WtGP4ruEERQTFQ3oREXkaFclERCRJioyOpOsfH4IpCnNoAeY074WLo8VWIFv3KRycDSYzvDMV8tSwd1wREUlob34K3nnh4S1YPdCuUfL7erC8dyXqFc1IlNVg+MoT9Jx7kMCwyJfet8lkolb2WixvvJy387wNwK9nfqXxssZsvLLxpfcvIiJPpiKZiIgkST1XjiXIuIAR7cK31YaTOY2b7Ymt38Cu/5/trOF4KNTEfiFFRCTxOLpC48lgstjuJD6+1K5xUrs4MqFFCYY2LISjxcQfx/xoOP5PTtwIjJf9ezp7MrTiUGbUmkE2j2zcDr1N/839eX/z+9wOuR0vxxARkdhUJBMRkSRn/qF97Lo3D4D6mbtTM18+2xO7foQtX9qWa38FJVrbKaGIiNjFa6Wg8vu25ZUD4KF9i0Umk4l2FbPza7cKZPZy5ZJ/CE0m7uDXfVfj7RhlfMvwW4Pf6FykMw4mBzZc2UDjZY1ZdGYRVsMab8cREREVyUREJIk5czuAL/cOxWSOIoOlGKNqdrQ9cXD237OaVfsUyvewX0gREbGfqoPApzCE3oPf+9m64dtZiaxpWNmnMm/kS094lJUPFx9l4KIjhEZEx8v+XRxc6FeyHwvqL6BwusIERQYxbNcwOq7tyMWAi/FyDBERUZFMRESSkMCwSNr+NhpcLmM2XJjZ4BvbQP3HFsOKvrZGFfvC6/Ydi0ZEROzIwQmaTLZN3HJ6NRxZYO9EAKRxd2JGuzJ8UCsfZhP8duAaTSbu4MKdh/F2jHxp8zGn7hw+LPMhrg6uHLh1gKYrmvLT0Z+IjH758dBERFI6FclERCRJiLYadF+whoeuKwEYUGogWT0zwZm1sKQrYECpDlBjGJhM9g0rIiL25VsE3vjItvzHIAi4Zt88/89sNtGrWm7mdC6HdypnTvkF0WD8n6w8eiPejmExW2hTsA1LGy2lUuZKRFgjGH9oPO+ufJejd47G23FERFIiFclERCRJ+HbtCQ6FTsFkjqJI2rK0LfwuXNwGC9uANQqKNIN636lAJiIiNpX6Q+ZSEB4Ay3sniW6Xj1TM5c3qvpUpmyMtwRHR9J53iC9WHCciKv7GEMucKjOTqk/iqypfkcY5DecenKP16tZ8tfcrgiOD4+04IiIpiYpkIiJidyuP3mD60Tk4uF3G2ezGd9VGYLp+AOa3gOhwyFcXGk8Cs8XeUUVEJKmwONhmu3RwgQubYf8MeyeKJYOHC/M6l6N71VwAzNp5iWZTdnHtfki8HcNkMlEvZz2WN15Ow1wNMTCYe3IujZc3Ztu1bfF2HBGRlEJFMhERsavjNwL4YNlGnDOsBeCjch+QMfgezHkHIh5CjqrQdCZYHO2cVEREkpz0eaH657bldZ/BvaQ1iL2DxcxHdfIzvV1pPFwcOHL1AfXH/8nm0/E7K2calzSMrDySKW9NIXOqzPgF+9FrYy8+3Poh/qH+8XosEZFXmYpkIiJiN/4Pw+kyex+k/xWTOZJyvuV5J21xmN0Ywh7Aa2XhvXng6GLnpCIikmSV6w7ZKkNkMCzrCdb469IYX6oX8GFV3yoUyezJg5BIOszcx+i1p4m2xm8X0YqZK7Kk4RLaFWyH2WTmj0t/0Gh5I5adW4aRhLqjiogkVSqSiYiIXURGW+k17yB3TJtwcLuEq4MbQ4t0w/RLYwi+DT5FoNWv4JzK3lFFRCQpM5uh0QRwdIcrO2HPJHsneqwsad34rUcF2pTPBsCEzedoPW0Pt4PC4vU4bo5uDCwzkHn15pE/bX4CwgP4bMdndFnfhauBV+P1WCIirxoVyURExC5GrDzBnqtncM6wBoCBRbqS+beuEHAV0uWBNkvBNY2dU4qISLKQNgfUGmFb3jAU7py2b54ncHawMLxxYX54rzhuThZ2XfCn3rg/2XMh/rtEFkpXiHn15vF+qfdxtjiz5+Ye3l7xNjOOzSDKGhXvxxMReRWoSCYiIonu131X+XnXRVwyLrZ1s8xQkqY7psO98+CZFdoug1Tp7R1TRESSk1IdINebtglflnaH6KRbCGpUPDMrelciT4ZU3AkKp+W0PUzach5rPHe/dDQ70rFwR5Y0XEI533KERYfx/YHvabmqJSf8T8TrsUREXgUqkomISKI6cPk+ny47hmOa3Ti4X8TVwZUvrl3GfOs4pPKxFcg8X7N3TBERSW5MJmg4AZw94cZB2PG9vRM9Ve4MqVneuxJNSmQm2mrw9ZpTdJm9n4CQyHg/VlaPrEytOZXhlYbj4eTByXsnabGqBd/t/47QqNB4P56ISHKlIpmIiCQav4Awus85QKT5Dm6+tm6WAyJdee36IVvXyjbLIF0u+4YUEZHkyzMz1P3Gtrzla7h51L55/oObkwNj3i3Gl02K4ORgZuOp29Qbv52j1x7E+7FMJhONczdmeePl1MleB6thZdbxWTRZ3oSdN3bG+/FERJIjFclERCRRhEVG023OAe4EhZI22zKsRFDG5Ma7lw6DUypovRh8Cto7poiIJHdFm0P++mCNhGU9ICrc3omeymQy0bJcVpb0qEiWtK5cux9K00m7+GX35QSZkdLb1Ztvqn7Dj9V/xNfdl+sPr9NtfTc++fMTHoQ9iPfjiYgkJyqSiYhIgjMMg0+WHuPI1Qd4ZNhPhONZXDEz9MpZzA4u0HIhZC5l75giIvIqMJmg/vfglg5uHYOtX9s70TMpnNmTlX2qUKOgDxHRVj5bdox+Cw4THJ4wY6u9/trrLGu0jFYFWmHCxIrzK2i4rCGrLqxKkOKciEhyoCKZiIgkuJk7LrH44DUcnO/jlH41AP3v3iWL1QzN50D2ynZOKCIir5RUGaDeGNvyn9/Dtf32zfOMPF0d+alNKT6umx+L2cSKIzdo9OMOzt4KSpDjuTu681HZj/il7i/k9srN/fD7fLT9I3ps7MGNhzcS5JgiIkmZimQiIpKgdpy7y8jVJwEreQquJtwaRqnQMN57GALvTIU8NewdUUREXkWFGkPhpmBYbbNdRiaPAepNJhNdX8/Fgq7l8fFw5tzthzScsIOlh64l2DGLpS/Gr/V/pXfx3jiaHdlxfQeNlzfmlxO/EG2NTrDjiogkNSqSiYhIgrniH0KveQeJthqULXqWa2F/4WK1MvzuPcwNxkGhJvaOKCIir7K639pmTvY/CxuH2zvNcymTPS2r+lahUu50hEZG8/7CIwxe8hdhkQlTtHK0ONKtWDcWN1xMKZ9ShEaF8s2+b2i9ujWn751OkGOKiCQ1KpKJiEiCCA6Pousv+3kQEknBrFFcjpoDQP/7D8jy1ggo2cbOCUVE5JXnlhYajrct754Il/60b57n5J3Kmdkdy9G3eh5MJpi/9wrvTNrJFf+QBDtmDs8czKg1gyEVhpDaMTXH/I/x3sr3+OHgD4RFhSXYcUVEkgIVyUREJN4ZhsHARUc45ReEd2onMnhPIcSIpGRYGC1K9oPyPewdUUREUoq8taBEG8CAZT0hPGHG90ooFrOJATXyMqtDWdK4OXL8RiD1xm9n7XG/BDum2WSmWd5mLGu8jLeyvkWUEcW0v6bR9Pem7PPbl2DHFRGxNxXJREQk3k3YdI4/jvnhZDHTqegaDoRexcVqZViWepirfmDveCIiktLU+hI8s8CDy7bxyaLC7Z3ouVXNm55VfatQMqsXQWFRdPvlAF+uPklktDXBjpnBLQPfV/uesW+MJb1rei4HXqbj2o58sfMLAsIDEuy4IiL2oiKZiIjEq/UnbvHd+jMAfFP2Ij/fXQNA31T5yFbnezCZ7BlPRERSIhcPaDwJzI5waiXMeQdCH9g71XPL5OXKgq4V6FgpBwA/bbtAy6m78QtI2G6Q1bNVZ3nj5byb910AFp9dTKNljVh3aR2GYSTosUVEEpOKZCIiEm/O3Q7i/YWHARhS6C5/3BxLsNlMCXMqWjZZoAKZiIjYT44q0GoROKWGS9thZh0ISLgZIxOKk4OZIQ0KMqlVSVI7O7Dv0n3qjdvOn2fvJuhxUzul5rMKnzGr9ixyeObAP8yf/239H30398UvOOG6foqIJCYVyUREJF4EhETSZfYBHoZH0eq1O3j6f8FOV2ecMTGs/i9YHJzsHVFERFK6XNWgw2pI5Qu3T8C0GnDruL1TvZA6RTKyok9l8vumxj84gjYz9jBu41ms1oS9s6uUTyl+a/Ab3Yt1x8HswJarW2i8vDELTi3AaiRc108RkcSQKEWyiRMnkiNHDlxcXChVqhTbt29/avutW7dSqlQpXFxcyJkzJ5MnT06MmCIi8oKirQZ9Fxzi4t1gqnjcplfIF4z2dAOgT4l+ZE+T284JRURE/l/GotB5PXjng6AbMKMOXHz6/ydJVQ5vd5b1qkTz0lkwDBiz/gztZ+3jXnBEgh7XyeJEr+K9WFR/EUXTFyU4MpiRe0bS7o92nH9wPkGPLSKSkBK8SLZw4UL69+/PJ598wqFDh6hSpQp16tThypUrj21/8eJF6tatS5UqVTh06BAff/wxffv2ZfHixQkdVUREXtA3a0+x9cwd8jneYrplJMNSO/DQbKZYusK0Ltze3vFERERi88oKHddA1goQHgBz3oa/frN3qhfi4mjh66ZF+bZpUVwczWw7c4d647Zz4PL9BD927jS5mV17NoPLDsbNwY3Ddw7T9PemTDw8kYjohC3UiYgkBJORwCMtlitXjpIlSzJp0qSYdQUKFKBx48aMGjUqTvtBgwaxYsUKTp48GbOue/fuHDlyhF27dv3n8QIDA/H09CQgIAAPD4/4OQkREXmi5Yev02/BYTJxlw1pRrHWEsiQ9OlwMjuxqOEicnrmtHdEkWeia4jkQa+TxKvIMFjSBU6usH1dcyRU7G3fTC/hlF8gPeYc5OLdYBzMJgbXLUDHStkxJcKYoH7BfozYPYKt17YCkNMzJ19U/IISGUok+LFFRP7Ls14/JOidZBERERw4cICaNWvGWl+zZk127tz52G127doVp32tWrXYv38/kZGRcdqHh4cTGBgY6yEiIonj2PUAPvztKN4EsNLrWwIjbvOtdzoAepforQKZiIgkbY4u0GwWlOtu+3rdJ7BmMFiT59ha+X09WNG7EvWKZCTKajB85Ql6zj1IYFjc/6Pim6+7L+PfHM+3Vb8lrUtaLgRcoO0fbRmxewQPIx4m+PFFROJDghbJ7t69S3R0ND4+PrHW+/j44Of3+BlQ/Pz8Hts+KiqKu3fjztgyatQoPD09Yx5ZsmSJvxMQEZEnuvswnK6z9+McFciS1N+SJuwqw3wzE2SCot5FaVuwrb0jikgi0NizkuyZLVD7K6gx3Pb17onwWwfbXWbJUGoXRya0LMEXDQriaDHxxzE/Go7/kxM3Ev5mApPJRO3stVnReAVNcjcBYOHphTRa3ohNVzYl+PFFRF5Wogzc/+/bew3DeOotv49r/7j1AIMHDyYgICDmcfXq1XhILCIiTxMRZaXnnIMEBNxngdtoskZeYEW6jGx3AiezE8MrDcdittg7pogkMI09K68Mkwkq9YW3p4HZEU4ss41TFprw43olBJPJRPtKOfi1WwUyebpwyT+EJhN38Ou+xPlfydPZk2GVhjGt5jSypM7C7ZDb9NvcjwFbBnAn5E6iZBAReREJWiTz9vbGYrHEuWvs9u3bce4We8TX1/ex7R0cHEiXLl2c9s7Oznh4eMR6iIhIwhq28jhHLvkxw3kMBa1nuOWelq/TegLQs3hPcnqpm6VISjBmzBg6depE586dKVCgAGPHjiVLliyxxqL9p8mTJ5M1a1bGjh1LgQIF6Ny5Mx07dmT06NGJnFzkCYo2g9aLwdkDLu+AGbXhQfL9EL5E1jSs6luFN/KlJzzKyoeLjzJw0RFCI6IT5fjlMpZjScMldCrcCYvJwvrL62m0rBGLzywmgYfGFhF5IQlaJHNycqJUqVKsX78+1vr169dTsWLFx25ToUKFOO3XrVtH6dKlcXR0TLCsIiLybObtucKC3Rf40XEc5UzHMZxSMaxgJYKiQiicrjDtCrWzd0QRSQSJMfasiF3krAod/oDUGeHOKZheA/yO2TvVC0vj7sSMdmUYWDMvZhP8duAaTSbu4MKdxBknzMXBhf6l+rOg/gIKpStEUGQQX+z6go5rO3Ip4FKiZBAReVYJ3t1ywIABTJs2jRkzZnDy5Enef/99rly5QvfutsExBw8eTNu2f49b0717dy5fvsyAAQM4efIkM2bMYPr06QwcODCho4qIyH/Yf+keQ1cc5TvHybxlOQgOLqysPoBtd4/gaHZkeKXhOJgd7B1TRBJBYow9C5qkSezEtzB0Wg/p80PQTZhZBy5stXeqF2Y2m+j9Zh7mdC6HdyonTvkF0XDCDlYdvZloGfKnzc+cunP4oPQHuDq4sv/Wft5Z8Q5Tj04l0qoiuYgkDQleJGvevDljx45l2LBhFC9enG3btrF69WqyZcsGwM2bN2ONW5EjRw5Wr17Nli1bKF68OMOHD2fcuHG88847CR1VRESe4saDULr/coDPTdNoZNmJYXbkTpOJjLpgG0uoZ/Ge5E6T284pRSSxJeTYs6BJmsSOvLJAxzWQrRKEB8Kcd+DoInuneikVc3mzqm8VymZPy8PwKHrNO8gXK44TEZU4s3k6mB1oW6gtSxouoVKmSkRYIxh3aBzNVzbnrzt/JUoGEZGnMRmvWGfwwMBAPD09CQgI0PhkIiLxJCwymmaTdtLw9kS6OKzGMJnhnen0vbWZLde2UDBdQebWnau7yCRZ0zXE84mIiMDNzY1FixbRpEmTmPX9+vXj8OHDbN0a966b119/nRIlSvDDDz/ErFu6dCnvvvsuISEhjx1aIzw8nPDw8JivAwMDyZIli14nSTyRYbCsOxxfavu6xjCo2Nc22H8yFRVtZfS6M0zeeh6A4lm8+LFVSTJ7uSZaBsMwWHlhJd/s+4YH4Q8wYaJVgVb0KdEHN0e3RMshIinDs17nJcrsliIiknwZhsHgJX9R7dYsujisBsDUYByr3JzYcm0LDmYHRlQaoQKZSAqTWGPPapImsTtHF3hnBpTvZft6/RD4YxBYE2fw+4TgYDHzUZ38TGtbGg8XBw5ffUC9cdvZfPp2omUwmUw0yNWA5Y2XUz9nfQwM5pycQ+Pljdl+bXui5RAR+ScVyURE5Kmmbb9ImqPTGOD4m21F7a+4W6AOo/aMAqBHsR7kSZPHjglFxF409qykGGYz1P4Sao60fb13CixqB5Gh9s31kt4q6MOqvlUoktmTByGRdJi5j9FrTxNtTbzORmld0jKqyigmvzWZzKkyczP4Jj039mTQtkH4h/onWg4REVCRTEREnmLbmTucXzuRIY6/2FZU+xSjXHeG7xpOYEQgBdIWoEPhDvYNKSJ2o7FnJcWp2BuazgCLE5z8HX5pAiH37J3qpWRJ68ZvPSrQprzt53bC5nO0mb6HO0Hh/7Fl/KqUuRJLGi6hbcG2mE1mVl9cTaPljVh+bjmv2AhBIpKEaUwyERF5rEt3g5k44Ru+Mn7AbDIwKvTBVHM4f1xaw4fbPsTB7MCCegvIlzafvaOKxAtdQyQPep0kSbi4HRa0gvAA8M4HrX8Dr6z2TvXSlh++zuAlfxESEU2G1M6Mb1GCcjnTJXqO43eP8/nOzzl9/zQA5TOWZ0iFIWRJrYk7ROTFaEwyERF5YQ/Do5g6fRIjjfGYTQbRJdphqjmcu2H+fLnnSwC6Fe2mApmIiKRMOarYZr5MnQnunoZpNeDmUXunemmNimdmRe9K5MmQittB4bSctofJW89jTcTulwCFvAsxv/58+pfsj7PFmd03d/P28reZdWwWUdaoRM0iIonAMODAz7B7sr2TqEgmIiKxWa0Gk2fN4rOQr3A0RROW/20sDb7HAEbuHsmD8AfkT5ufTkU62TuqiIiI/fgUhM4bIENBeOgHM+vC+U32TvXScmdIzfLelWhcPBPRVoOv/jhF11/2ExASmag5HM2OdCrSiSUNl1DWtyxh0WF8d+A7Wq5qyQn/E4maRUQSUFgg/NYRfu8L6z6BW/b9+VaRTEREYlmwbBndb3yCiymSB1nfwqXZT2C2sPbyWjZc2YCDyTabpaP58TPRiYiIpBiemaHDH5C9CkQEwdxmcGSBvVO9NDcnB75vXpyRTQrjZDGz4eRt6o3fztFrDxI9S1aPrEyrOY1hFYfh4eTByXsnabmqJWP2jyE0KnlPnCCS4l0/CFOqwPElYLLAm59C+vx2jaQimYiIxPhzx1bqHulFKlMYt7zL49VmLlgc8Q/158vdtm6WXYt2VTdLERGRR1y9oPViKPwOWKNgaTfYPsbWfSgZM5lMtCqXjSU9K5IlrSvX7ofSdNIuftl9OdEH0jeZTDTJ04TljZdTO3ttoo1oZh6fydvL32bXjV2JmkVE4oFhwK4fYXpNuH8JPLPaurBXft82m7AdqUgmIiIAXDh1hPzr2uBlCuaqeyF8uiwGRxcAvtzzJffD75M3TV46F+ls56QiIiJJjIMzvD0NKvaxfb1xKKweCNZo++aKB4Uze7KydxVqFPQhItrKZ8uO0X/hYYLDE39sMG9Xb76t+i0T3pyAj5sP1x5eo+v6rnzy5yc8CHuQ6HlE5AUE+8P892Dtx2CNhAINoPs2yFLW3skAFclERAQI8LuA28J38DYFcMkxJxl7rATnVACsvbSWdZfXYTFZbN0sLepmKSIiEofZDDVHQO2vABPsmwa/toXI5N8l0NPNkZ/alOLjuvmxmE0sP3yDRj/u4OytILvkqZqlKssbL6dF/haYMLHi/AoaLW/E6gurE/0uNxF5Dpd2wOTKcGYNWJyh3nfw7i/gmsbeyWKoSCYiksJFBfgRMq0BvsYdLpsy49VlJQ6p0gJwL+xezGyWnYt0pkC6AvaMKiIikvSV7wHNZtr+ATy1EmY3gpB79k710kwmE11fz8X8LuXx8XDm3O2HNJywg6X/1959hzdVfgEc/ybduxRoy2gLhZa9995DkSUFlaEgy4GIqOAEFAEFHD/BwRJUQBkyZSN7I3u2UEZboJTu3Wbc3x+BQmW10CRNej7PwyM3ubn3xLe0pyfnfd9jUWaJx8XOhY8afcRvz/xGRc+KxGfGM3b3WMbuHotGb9pNBoQQj6HXwY6v4NfnIOU6FA+Cof9AgyGgUpk7ulykSCaEEEVZRgJxP3ehlDaKa0pJsvuuwNO7TM7TUw5OIT4znqBiQQyvOdyMgQohhBAWpFpPGLASHD0g8uDddXesQMPyXqwb2YJmFYuTodHxzpITfLTyFJka80wtre1dm6XPLeXN2m9iq7Zlw+UNvLvjXbJ12WaJRwjxH8k3DB8W7JgMih5q9YVhO8C3hrkjeyApkgkhRFGVlUL87G74ZFwkRvHkYueFBAXd3U1my9UtbLyyERuVDRObTZRplkIIIUR+lGsGr24C97IQdwHmdoDrx80dVYEo4erAb682YmTbiqhUsPhgBCE/7yMiLt0s8djZ2PFardf4vs33ONg4sD1yOyO3jyRTm2mWeIQQt13YYpheeWU32LlAz1nQ86ecZV0KIymSCSFEUaTJJGVBH7wSTpKguLK+9k+0atI45+mEzAS+OPAFAK9Wf5VqxauZK1IhhBDCcnlXgSFbwKc6pMXAgi5wcau5oyoQNmoVoztWYv7ABhRztuP0tWS6zNjN5jPRZoupRdkWzGw3EydbJ/Ze28uIf0aQrjFP4U6IIk2bDZs/gUUhkB4LPjVg+E6o9aK5I3ssKZIJIURRo9OQ9ccA3G7sI1Vx5IcyX/Jy92dynTLlkGGaZUXPirxW6zUzBSqEEEJYAffSMGg9lG8J2amw+AU4vtjcURWY1pW8WTeyBXX8PUnJ1DLs9yNMWX8OjU5vlngal2rMT+1/wtnWmYPRB3l96+ukZqeaJRYhiqSEKzC/M+ybYThuOAyGbIUSQWYNK6+kSCaEEEWJXoduxXAcLm0mU7FjnMs43n75RdTquwtm/nP1HzZc3pCzm6W9jb0ZAxZCCCGsgKMH9PsLavQGvRZWvQ67poOV7MRY2tOJJcOa8Gqz8gDM2nWJvnMOEJ1knumO9XzqMbvjbNzs3Dgac5RhW4aRlJVklliEKFLOrIKfW8K1I4bvey8shGengZ2juSPLMymSCSFEUaEoKH+/g82Zv9AoNoxWvctbrw7EzfHuWmOJmYlMPDARgEHVB1GthEyzFEIIIQqErT30nA3N3jYcb5sI60aDTmveuAqIva2acV2r8lO/urg62HL4SgJdvt/N3ouxZomnVslazO00Fw8HD07FnmLo5qEkZiaaJRYhrJ4mA/5+B5a9AllJULYhvLYHqnQ1d2T5JkUyIYQoChQFNn+C6uiv6BQVo7Rv0uelwZQv4ZLrtC8Pf0lcZhwVPCrweq3XzRSsEEIIYaXUaujwOTwzFVDBv7/A0gGQbT3rZj1ToxRr32pOZV834tKy6T/vIN//cwG93vRdc1WLV+WXTr/g5ejFufhzDNo0iNgM8xTthLBat0JhTjvD9zOA5u8Ypph7+ps3rickRTIhhCgKdk2D/TMB+EA7lBodB9K6kneuU7ZFbGPdpXWoVWomNpso0yyFEEIIY2k0HPr8CjYOELoefusGaXHmjqrAlC/hwqo3m9GnflkUBb7ZEsbABYeJT8s2eSzBxYKZ32k+JZ1KcjHxIoM2DuJm2k2TxyGE1VEUOLYQZreGmDPgUhL6r4D2E8DG7nGvLrSkSCaEENZu/4+wfRIAn2kGkFW9L8NbBuY6JSkrKWea5cBqA6lRsobJwxRCCCGKlKrd4eXV4OgJUYdhXgeIv2zuqAqMo50NU0NqMTWkJg62anaF3aLL97s5cjXB5LEEegayoPMCfF18uZJ8hUGbBnEj9YbJ4xDCamSlwIphsPpN0KRD+Vbw2l6o2M7ckT01KZIJIYQ1O/obbPoQgK81IRzyeYGvetVEpVLlOu2rQ18RmxFLeY/yvFH7DXNEKoQQQhQ9AU1g8Gbw8If4cEOh7Poxc0dVoPrU92PVm80oX8KFG0mZvDBrP7/suYxi4k0L/N39WdB5AWVdyxKZEsnAjQOJTI40aQxCWIUbJ2BWKzi1FFQ20PZTGLAS3HzMHVmBkCKZEEJYq9N/oawZCcAsbRcWO7zA7Jfr42Rvk+u0nZE7WXtpbc40SwcbB3NEK4QQQhRNJSvBkC3gWwPSbsH8LnBhi7mjKlBVSrmzZkQzutQohVav8PnfZ3lj0VFSMjUmjaOMaxnmd55POfdyXE+7zsBNA7mcZD3de0IYlaLAwVkwt72hqO9eFgaug5bvgdrm8a+3EFIkE0IIaxS2CVYMQ4XCYm1bpun78dOA+pTxdMp1WlJWEp/v/xyAV6q+Qq2StcwRrRBCCFG0ufnCwPUQ2Bo0abD4BcNaP1bEzdGOmX3rML5rVexsVGw4HU3XGXs4ez3ZpHH4uvgyv/N8KnhUICY9hkEbB3Ex4aJJYxDC4qTHw5/9YMMY0GVDpWfhtd2GblgrI0UyIYSwNpd3w9KXQa9lla4pn2hfZXy36jQs73XfqdMOTyMmI4Zy7uVkmqUQQghhTo7u0HcZ1HwRFJ1hrZ+dUw3dG1ZCpVIxqFl5lg5vQmkPR67EpdPzx70sPWzaaY8lnErwS+dfqOxVmbjMOF7d9Crn48+bNAYhLEbEAfi5BYSuAxt7w+68Ly4G5/t/t7AGUiQTQghrEnUE/ngRtJlspz7vaV7jhYbl6N/o/i2Yd0XtYnX4alSomNhsIo62jmYIWAghhBA5bO2h58/QfLThePskWPs26LTmjauA1fEvxrqRLWgVXJIsrZ4xf53kvWUnyMjWmSwGL0cv5nacS/Xi1UnISuDVTa9yOva0ye4vRKGn18Ou6TD/WUiOAq9AGLzFsDvvf9Y3tiZSJBNCCGtx8wwsfB6yUzliU5PXMkdQO6Akn3Wrdt9C/cnZyXy2/zMAXq76MrW9a5shYCGEEELcR6WC9uPh2emACo7+Ckv6QXaauSMrUMVc7Jk/sAHvdQxGrYLlR6Lo+eNeLt1KNVkMHg4ezO44m9ola5OSncKQzUM4FmNdGycI8URSbsLCnrBtoqGztUYfGL4LStc2d2RGJ0UyIYSwBnHh8FsPyEwk3KEKA9JG4eXhzk/962Fve/+3+umHpxOTHkOAewAj6owwfbxCCCGEeLSGQ+GFhWDrCGEb4deukHrL3FEVKLVaxYi2QSwc3IgSrvacj06h28y9rDt5w2QxuNm7MavDLBr4NiBNk8bwLcM5HH3YZPcXotC5+A/83Awu7QA7Z+j+Azw/GxzczB2ZSUiRLJ+i06IJTwwnPjMend507cBCCPFQSVHwW3dIiyHGOYieSe+gs3Vm1oB6lHS7f6fKPdf2sPLiSlSo+Lzp5zLNUgghhCisqjwHL68Bp2Jw7QjM62D4YMzKNK1YgnUjW9CwnBepWVreXHyUCWvOkK3Vm+T+znbO/NDuB5qWbkqGNoPXt77O3mt7TXJvIQoNnQa2TjDMTEm7Bd7VYNgOqNPfqqdX/pdKUaxoJUggOTkZDw8PkpKScHd3L/Drf3fkO+adngeAWqXG08ETL0cvijkWM/zXoRheTl54OXjh5ZT72N3BHbVK6pJCiAKUGgPzn4G4i6S6lqdV7Bji8ODbF2rRs07Z+05PyU6h5+qe3Ey/Sf8q/RnbcKwZghaicDJ2DiEKhoyTKJJiLxh+cU2MAOcS0G8plKln7qgKnFanZ/rmMH7eaSgE1vbz5Id+de/bndtYsnRZvLvjXXZG7cRObce3rb+llV8rk9xbCLNKjIDlgyHqkOG4/qvQaTLYmebfninkNX+QIlk+fX/0e5aGLSUpKynfr7VR2RiKaneKaPcW1xyLUdyxOMUci+U85m7vft86QkIIkSMjARY8BzdPo3EtQ8ekj7isKcbQFuX5uEvVB75kwr4J/HXhL/zc/Pir21842VrPDz4hnpYUXyyDjJMoslJuwqIQiD5pmALVewEEdzJ3VEax5exN3l16nORMLZ7Odnz7Qm3aVPI2yb01Og1jd49ly9Ut2KpsmdpqKh0COpjk3kKYxbm1ht10M5PAwR26fQ/Vepo7qgInRTIjJ04avYakrCTiMuJIyEogPiOehKyE+44TMhOIy4wjJTsl3/ewVdnmKprdW0h70LGrnasU1YQoKrJS4fceEHUYvYs3L2kncDDJkxZBJZg/sAG2Nvd3re67to/hW4ejQsX8zvOp52N9n0AL8TSk+GIZZJxEkZaVAktfhvBtoLKB576Feq+YOyqjiIxP541FRzl1zdCc8FbbioxqH4yN2vi/72j1Wj7e8zHrL6/HRmXDpOaT6BLYxej3FcKkNJmw+RM4PMdwXKYehPwCxcqZNSxjkSJZIUucNDpNrqJZQmYC8ZnxOf+98+fOcaom/7u62Kntcgpm93ap5RzfM/WzmGMxXOxcpKgmhCXSZMLi3nB5F4pTMca6fcnSCDcCijuz+s1meDrb3/eS1OxUeq7pSXRaNP2q9OODhh+YIXAhCrfCmkOI3GScRJGn08CakXBiseG41QfQ+gOrXDMoU6Pji3VnWXggAoCmFYrzvxfrPHDN1YKm0+sYv288q8NXo0LFZ00/o2eQ9XXXiCIq9iIsHwjRpwzHTd+CtuPA9v7fI6xFXvMHWxPGVKTZ2djh7eyNt3Pe2oSzddmPLKL99/F0bToavYaY9Bhi0mPydA97tf39hbRHHDvZOklRTQhz02lg2UC4vAvsXZkbMI2lx51xsbdhzsv1H1ggA/j6yNdEp0VT1rUsI+uMNG3MQgghhCg4NnbQ40fwKAO7psHOLyH5mqGrzMbO3NEVKEc7G77oUYMG5bz4cMUp9oXH0eX73cx4qQ6NAosb9d42ahs+b/Y5DjYOLA1byrh949DoNfSp1Meo9xXC6E78CX+PBk0aOBeHnrMgSKYU3yGdZFYiU5tpKJxlxd831fNBhbUMbUa+7+Fo45hrqufjimuy1pEQBUyvgxXD4PRysHVke4OfGLTd8EnqrAH16FTN94Ev2399P8O2DAPgl06/0MC3gclCFsKSFNUcwtLIOAlxj39/gXXvgqKHih0M65Q5uJo7KqO4GJPCawuPcjEmFRu1ivc7VWJYi0DURp5+qSgKUw9PZeG5hQCMaTCGAVUHGPWeQhhFViqsf/9uF2q5FvD8HHAvZd64TESmW0ri9EgZ2oyHdqk96DhLl5XvezjZOuWa5vmonT+LORbD0dbRCO9UCCuhKLD2bTj6K6htCW83h2fWO5Gt0zOqfRCj2gc/8GVpmjR6ru7JjbQbvFT5JT5q9JGJAxfCckgOYRlknIT4j/PrYfmroM2A0nWg71JwNc0i96aWlqXl45WnWHX8OgDtq3jzde/aeDgbt4NOURS+O/odv5z+BYBRdUcxuMZgo95TiAIVfQqWDYK4C6BSG6Zpt3wP1DbmjsxkpEgmiVOBURSFDG1GnoppdzYtyNZn5/s+zrbOD+xOe1jXmr2N9c6XFiIXRTEsqrl/JqjUJHaZRcdNXsSkZNGxqg8/96/30E9RJ+6fyNKwpZRxLcOKbitwtnM2cfBCWA7JISyDjJMQDxB5GBb3gYx4w6Lb/VdA8QrmjsooFEVh8aEIPltzlmydnrLFnPipXz1qlPUw+n1/PvEzP574EYA3ar3Ba7Vek+VoROGmKHB4Lmz6GHRZ4FYKes2Fcs3NHZnJSZFMEiezURSFdG068RnxuaZ/PqprTavX5vs+rnauj9z587+bFthZ2RoNogjZORW2TwJA89z39DlUkWMRiQT7uLLijWa4Ojx4eckDNw4wdPNQAOZ1nEfDUg1NFrIQlkhyCMsg4yTEQ8RehIXPQ+JVwzpDfZdC2frmjspoTkUl8cbiI0TGZ2Bvo+bTrlXp38jf6EWruafm8r+j/wNgcPXBvF33bSmUicIpIxHWvAXn1hiOgzpBj5/Axbjr+RVWUiSTxMliKIpCqiY1p3j233XU/nuckJmAVsl/Uc3N3u2+nT6LORSjuFPxXMclnUvi5ehlhHcqxBPY/yNs+hAApdMUxkQ1Y9mRKDyc7FgzohkBxV0e+LJ0TTrPr3mea6nXeKHSC3zS+BNTRi2ERZIcwjLIOAnxCKkxsKg33DgOtk7Qez5UesbcURlNUrqGd5edYOu5mwB0r12ayT1r4PKQDxALyu9nf2fq4akA9K/SnzENxkihTBQukYcN07CTIkBtB+0nQJM3rXIX3LySIpkkTlZLURSSs5Pvn+r53w0KbnexJWYlolN0+bpH53KdGddkHG72bkZ6F0LkwdHfDJ/+ALT5mAW2vZmw9ixqFfz6akNaBJV86EsnHZjEn6F/yjRLIfJBcgjLIOMkxGNkpRp2wr64xbD2UJevof6r5o7KaBRFYfauS0zdFIpOr1C+hAvTe9eiXkAxo953yfklfHHwCwD6BPfh48Yfo1apjXpPIR5Lr4d9/4N/JoKiM0y/DvkFytQzd2RmJ0UySZzEbXpFT3JWcu6pnw+YCnpvkU1BoYxrGaa1nEaNkjXM/RZEUXR6heHTHxRo+hb7At9mwC+H0ekVPulShSEtAh/60sPRh3l1kyEZnttxLo1KNTJR0EJYNskhLIOMkxB5oNPA2lFw3LAjIy3fhzYfW3UXyaHL8Yz84xjRyZmoVfBaqwq83T4IB1vjLUy+8sJKxu8bj4JCj4o9mNBkAjZFaCF0Ucik3oKVwyH8H8Nxteeh63fgaNz1+ixFoSiSJSQkMHLkSNasMcyB7datGzNmzMDT0/Ohrxk4cCC//vprrscaNWrEgQMH8nRPSZzE0zp56yRjdo3hWuo1bFW2vF33bV6u9rJ8MiRMJ2wz/PkS6LVQbyCRTSfT7Ye9JKRr6FmnDN/0qfXQlv57p1n2Ce7Dp00+NXHwQlguySEsg4yTEHmkKLBjCuz8ynBcux90/R9Y8Tq9SekaJqw9w8pj1wCo7OvGty/Upkop432vWHdpHR/v+RidouPZ8s8yqfkkbNXGne4pxH0u7YAVwyD1pmGq9TNfQd2Xrbownl95zR+M+lt/3759OX78OBs3bmTjxo0cP36cAQMGPPZ1nTt35saNGzl/1q9fb8wwhcilZsmaLO26lI4BHdEqWr4+8jVv/vMm8Znx5g5NFAWXd8PSAYYCWY3epHeYytDfj5CQrqFmWQ+mPF/jkWte/O/o/7iWeo1SLqUYXX+0CQMXQgghRKGiUkGbjwyFMZUNHF8Ei18wTMe0Uh7Odnz7Qm1+6lcXLxd7zken0G3mHn7YfhGtTm+Ue3YJ7MK0VtOwVdmy/vJ6xuwag0anMcq9hLiPTmuYWvlbD0OBrGRlGLYd6r0iBbInZLQi2blz59i4cSNz586lSZMmNGnShDlz5vD3338TGhr6yNc6ODjg6+ub88fLSxZRF6blbu/O9FbT+bTxpzjYOLDn2h5C1oRw6MYhc4cmrFnUEfjjRdBmQqVnUbr/yPt/neF8dAolXB2YNaAejnYPb+E/HH2YxecXA/BZ089wsXvwov5CCCGEKELqDYSX/gA7Z8M0rAXPQspNc0dlVM/UKMWmUS3pUNUHjU5h2qZQes/az+XYNKPcr0NAB75t8y12aju2XN3COzveIUuXZZR7CZEjKQp+fQ52TwcUQ+fY0O3gXcXckVk0oxXJ9u/fj4eHB40a3V0Lp3Hjxnh4eLBv375HvnbHjh14e3sTHBzM0KFDiYmJeei5WVlZJCcn5/ojREFQqVT0qdSHxV0WE+gRyK2MWwzZPISZx2ai1ed/d00hHunmGcO27dmpUL4lhMznx90RrDt1AzsbFT/3r0spD6eHvjxdk874feMB6BXUiyalm5gqciGEEEIUdsGd4JW/wbk43DgB89pD7AVzR2VUJd0cmD2gHtNCauLmYMuxiESe+d8uft13Bb2+4Fccau3XmhltZ+Bg48DOqJ2M3DaSDG1Ggd9HCADOr4efm0PEfrB3g17zoNsMsJfNup6W0Ypk0dHReHt73/e4t7c30dHRD33dM888w6JFi9i2bRtff/01hw8fpm3btmRlPbgSP2XKFDw8PHL++Pn5Fdh7EAIguFgwf3T5g+eDnkdBYdbJWQzeNJjotId/HQuRL3HhhhbpzEQo2wBe/IN/LiYxfbOh6/bz7tWpX+7RHbUzjs0gMiUSXxdf3qv/nvFjFkIIIYRlKVsPBm+BYuUhMQLmdYRI654loVKp6F3fj43vtKRpheJkavSMX3OGl385xPXEgi9gNSvTjB/b/YiTrRP7ru/jzX/eJF2TXuD3EUWYNgs2fGBYvzgjAUrVhuE7oUaIuSOzGvkukk2YMAGVSvXIP//++y/AA9fNURTlkevpvPDCC3Tp0oXq1avTtWtXNmzYQFhYGOvWrXvg+R9++CFJSUk5fyIjI/P7loR4LGc7Zz5r+hlftfgKFzsXjsYcJWRtCNsjtps7NGHpkqLgt+6QFgM+NaDfMi4mwdt/HkdRoH9jf15q6P/ISxy5eYRF5xYBMKHJBFztXU0RuRCiiEtISGDAgAE5H1QOGDCAxMTER75m4MCB9+WNjRs3Nk3AQggoXsFQKCtdFzLi4deucP7Bv2dZkzKeTiwc3IgJXaviaKdmz8VYOn27i+VHoijofewalmrIrA6zcLFz4XD0YYZvGU5KdkqB3kMUUXHhMK8DHPzJcNz4TcO/5+IVzBuXlcl3kWzEiBGcO3fukX+qV6+Or68vN2/eP9f91q1b+Pj45Pl+pUqVIiAggAsXHtwO7ODggLu7e64/QhjLs4HPsvS5pVQtXpWkrCRGbh/JV4e+IluXbe7QhCVKjTEUyJIioXhFGLCSJFwZ9tu/pGZpaVjei3HPVXvkJTK0GYzbOw4FheeDnqdZmWYmCl4IUdTJBk1CWCjXkjDwbwjqaFgHdUl/ODzX3FEZnVqtYmCz8qwf2YLafp6kZGl5b9kJhv9+hNjUgl0/rI53HeZ0mIObvRvHbx1n2OZhJGUlFeg9RBFzajnMamWYLu1UDF5aAp0ng629uSOzOvnem7ZEiRKUKFHisec1adKEpKQkDh06RMOGDQE4ePAgSUlJNG3aNM/3i4uLIzIyklKlSuU3VCEeSavTk5KpJSVTS3Km5vbfNWRq9TQu74W3u+MDX+fv7s/CZxby7dFv+f3s7yw8t5AjN48wrdU0AtwDTPwuhMXKSIDfe0LcRfDwg5dXo3Muwdu/HuZSbBqlPRz5sV9d7G0f/VnGjGMziEiJwMfZR6ZZCiFM5s4GTQcOHMhZf3bOnDk0adKE0NBQKlWq9NDX3tmgSQhhRvYu8OIfsO4dOPobrHsXkq5Bu3FWvyNeYElXlr/WhFm7LvHd1jA2n73JkasJTOpZg87VC+57U42SNZjXcR7DtgzjdNxphmwewqwOs/BylE3pRD5kp8GGMXBsoeHYvyn0mgseZcwblxVTKQXdX3qPZ555huvXrzNr1iwAhg0bRkBAAGvXrs05p3LlykyZMoWePXuSmprKhAkT6NWrF6VKleLKlSt89NFHREREcO7cOdzc3B57z+TkZDw8PEhKSpKuMium0yuk3i5u3S1wGYpcd/6bnOu//3kuQ0uGRvfQ69uqVXSs5kP/xgE0CSz+0CnCOyN38sneT0jMSsTZ1plPm3zKc4HPGettC2uRlQq/94Cow+DqA4M2QPEKfLXxPD/tCMfRTs3y15pSvYzHIy9zLOYYr2x4BQWFH9v9SIuyLUwTvxBWSnKIvPvll18YPXr0fdMrPT09+fbbbxk0aNADXzdw4EBWrVqFvb09np6etGrVikmTJj1wHds7srKycq1Nm5ycjJ+fn4yTEAVBUWDnVNgx2XBc6yXo+n2R6U45ez2Z0UuPcz7aMB3y+TplGN+tGh5OdgV2jwsJFxi6eShxmXFU9KzInI5zKOH0+KYTIbh5FpYNhNhQQAUt34dWY8Em371OgrzneUb9v7to0SJGjhxJx44dAejWrRszZ87MdU5oaChJSYbWUxsbG06dOsVvv/1GYmIipUqVok2bNixZsiRPBTJhGXR6hdSsu0Wr5IzbxasszX2dXTnP/acQlpb98AJXfjnZ2eDmaHv7jx0anZ4z15NZfyqa9aeiqVDShX6NAuhVr+x9PzBb+bViWddlfLD7A47cPMKHuz/k4I2DfNjwQ5ztZGcR8QCaTMNCm1GHDa3SA1ZB8QqsPXGdn3aEA/BVr5qPLZBlajP5dO+nKCj0qNhDCmRCCJN6mg2aevfuTUBAAJcvX+bTTz+lbdu2HDlyBAcHhwe+ZsqUKXz22WcFFrsQ4h4qFbQeC+6lYe3bcOIPSImGF34HB+v//atqaXdWj2jGd1svMGtnOCuOXWP/pTimhtSkRVDJArlHULEg5neez5BNQ7iYeJFBGwcxt+NcfFzyvgSRKGIUBY4sgI0fGKZEu/rA83MgsJW5IysSjNpJZg7yKbBx6fUKqdn3d2bdKWg9qGsr93RGLalZ2gKLx8FWjbuTXU6By/1OscvBDncnw2N3nrtTCHN3tMP99rGroy12NvdPZzt3I5lFB6+y8ui1nIKco52abrVKM6BxOWqUzV3A0Ol1zDo5i1knZ6FX9JT3KM+0ltOo5PXw6SaiCNJpYMkACNsA9q7wyhooU4/T15II+XkfmRo9w1sF8uEzVR57qemHp/Pr2V/xdvJmZY+VuNvL9zshnpbkEIYNmh5XkDp8+DCbN2/m119/JTQ0NNdzQUFBDB48mA8++CBP97tx4wYBAQH8+eefPP/88w88RzrJhDCRC1tg6cugSQffGtBvObgVnanRR67G8+7SE1yJM+xGOaBxAB8+Wxln+4LpK4lMjmTw5sHcSLtBWdeyzOs0j9KupQvk2sKKZCYZCtZnVhqOK7SDnrMMawmKp5LXPE+KZEWIoiikZetyphs+qICV8tDpi4bzUrO0FNRXjL2t+nZRyy6neHVvR5fbf55z/0+xy83R7rHrNeWZXg/ZKYZ1orLTDIuo2zqQmqVl5bFrLDpwNacNG6BWWQ/6NQ6ga83SONnb5Dx+OPowH+z6gJiMGOzV9oxpMIY+lfo8ckdXUUTodbBiGJxeDraO0P8vKNecuNQsus3cy7XEDFoFl+SXgQ2wUT/66+V4zHFe3vAyCgo/tPuBlmVbmuhNCGHdJIeA2NhYYmNjH3lOuXLlWLx48RNNt3yQoKAghgwZwtixY/N0voyTEEZ07Sgs7gNpt8DD35CvlAw2d1Qmk56t5csN5/lt/1UAyhV35us+tagXUDDriF1Pvc6QzUOITImklEsp5naci7/7o3cxF0VI1BFYPggSr4La1rBGYJO3QF1Av/MWcVIks7LESVEU0rN1udbbSs7871TEewpaGf8tgBkKXPoCGm07G9U9Ra3chSv3/3Rt5Sp2Od19zsHW5vE3yi9NBmQkGopdmbf/m5fjzERQ9HevY+sE5ZpBhbYQ2AalZGWORCTy+4GrbDgVTbbOcK6Hkx0h9crSr5E/gSVdAUjITOCTvZ+wK2oXAB0COjCh6QTp9CnKFMXwidDRXw0/8F78A4I7otHp6Tf3IIcux1O+hAur3mz22DUwMrWZ9F7bmyvJV+hWoRuTmk8y0ZsQwvpZaw5hDOfOnaNq1aocPHgw1wZNjRs35vz5849cuP9ecXFxlClThtmzZ/Pyyy/n6TUyTkIYWfwlWBgC8eG3d9H7E/wbmzsqk9p94RbvLztJdHImahUMa1mBdzoEFcjvLzfTbjJk8xCuJF/B28mbOZ3mEOgRWABRC4ul18OBH2DrBNBrwdMfev0Cfg3MHZlVkSJZIUqcFEUhQ5O7wPXfaYq5pybe3+WVmqVFV0AVLlu16r4urdzHt4tdj5iu6GCrNl53lF5naDPNa4Hr3mNt5tPd29YJbOzhv1s0u/pChTZQoS3xPk1Yci6bxYeuEhmfkXNKs4rF6d8ogPZVfbBVq/jt7G98d/Q7tHotpV1KM7XVVGqVrPV08QnLoyiw+RPYPxNUagj5Bar1BGDc6tP8tv8qrg62rHqzKRW9H7/2xzf/fsP8M/Mp6VSSld1X4uHw6LXLhBB5VxhziMLMHBs0gYyTECaRFguLX4Br/xo64HvNhSpdzR2VSSVlaPhszRlWHLsGQGVfN77uU4tqpZ8+94rNiGXo5qFcTLyIl6MXczrOIbhY0enYE/dIi4VVr8OFzYbjKt2g2wxw8jRrWNZIimRGSpyuxqVxNS79gTsp3t/Zdffv2gIqcNnkFLgM627duxbXvV1aj5qu6GhnxALXHYpimLaY346ujMT7C1T5pVIbPvVy9DT818nzMce3H3P0BDtHQ+wx5+DSdgjfBlf2gjYj9z18aqAEtuGEQx1+vuTNpgtJOdNQfdwdeLGBPy819CdWc5H3d75PVGoUNiob3qrzFoOqD0KtkpbZImPnVNh+u9ur20yoOwCAPw9F8MGKU6hUMGdAfdpXffzirSdvnWTAhgHoFT0z2s6gtV9rIwYuRNEjxZf8iY+PZ+TIkaxZswa4u0GTp6dnzjkqlYr58+czcOBAMjIy6NGjB8eOHcu1QdPEiRPx8/PL831lnIQwkex0WP6qYS1VVPDMVGg0zNxRmdzG09F8vPIUcWnZ2NmoeLtdEK+1qoDtA9Y1zo+EzASGbxnOufhzeDh4MLvDbKoWr1pAUQuLcHk3rBgKKTfAxgE6T4H6rxo21BAFTopkRkqcJqw5w4J9V57otWoVuDrc6dLK3bX1sOmKbv9Zi8vZ3sa061tps28XsRLzX/DSa57u3vZutwtanvkreDm4Few3Fk0mRB40FMwubYcbJ3I/b+tIZumGHFDV4ueoAA6klQJU2KhVtK/iTUj9kmyOmcnGKxsBaFq6KZOaT5Ktn4uC/T/Cpg8Nf+/8JTR+HTAsDPvi7ANodArvdgjmrXZBj71Uli6LPmv7cCnpEl0DuzK5xWRjRi5EkSTFF8sg4ySECem0sP49ODLfcNxsFLQbX+TWSIpNzeKjFafYfPYmALX9PPm6Ty0q3F5u5UklZSXx+tbXORV7Cjd7N35u/zM1S9YsiJBFYabXGT5I3zXVsORPiWAImQ++1c0dmVWTIpmREqc5uy7x19GoR05XzOnc+s90RRdTF7juuHdR+scWuBJzH2enPt291Xa5u7Xy3OHlCTaPXpvJbNJi4dIOCL/daZZyPdfTmQ4lOKSuyaqkYHbra3CLYpQr4UztKmHsSZhDli6L4o7FmdJiCk1KNzHPexDGd/R3WDPC8Pc2H0OrMQDcSMqg64y9xKZm8WwNX37oWzdP3xe+O/Id807Po4RTCVZ1XyXTLIUwAim+WAYZJyFMTFFg93TY9oXhuEYf6P4D2NqbNy4TUxSFFUevMWHNGVKytDjaqRnbuTKvNCmH+jGbLj1KanYqb/7zJkdjjuJi58KP7X6krk/dAoxcFCrJ1+GvoXB1j+G4dj94dhrYu5g3riJAimTWmDgV1KL0+aYCR/d8TmG8/V87Z+tuF1UUiA0zFMvCt8OV3YZts+8RpvizU1ed3foaHHcsjnO5VaQpUahQMaTGEN6o/Qa26oLZWloUEqdXwF+DDf/umr4FHSaCSkWmRscLs/ZzIiqJyr5u/PV6U1wcHj/2p2NP0299P/SKnu/bfE8b/zYmeBNCFD1WnUNYERknIczk2CJYO9KwsHj5VvDCQsPvCEXM9cQMxiw/yZ6Lhp2Am1YozrTetSjj6fTE10zXpDNy20gORh/EydaJGW1n0KhUo4IKWRQWYZth1WuQHgf2rtDlG6j1grmjKhSytDr+PBTJrZQs3uuUt81/8kuKZIU1cTL3ovQPLWh5Przg5egBaiPsRGmNtFkQeejuembXjwN3/4llKXbsVYKYUdyFi56Gdu2aJWoxvdU0SrmWMk/MomCFbYY/XzIkkPUGwnPfgUqFoii8u+wEK45ew9PZjrUjmuPn5fzYy2Xrsumztg/hSeF0CezCly2+NPpbEKKoKvQ5hABknIQwq4tbYcnLoEkDn+rQbzm4F70cVq9XWHjwKpPXnyNTo8fNwZZxXasSUq/sE88cytRmMmrHKPZe24uDjQPftfmO5mWaF3Dkwiy02fDPZ4aNvAB8axqmV5aoaN64CgGtTs+KY9f439YLXEvMwFat4p93WxFQvOA766RIZszE6c6i9DnFrMK8KP09x3aOT3dvkX/p8benZm5DCd+OKjkq56mNLs5MKFGcNLUKR8Wed6qOom/DAeaLVTy9y7thUYihoF2jN/SclVNgnrfnMhP/PouNWsXvrzakacW8rUn3/dHvmXNqDsUdi7Oq+yo8HT2N+AaEKNqk+GIZZJyEMLPrx2BRH0iLAQ8/Q6HMu7K5ozKLy7FpvLv0OEcjEgFoX8WHKc/XoKSbwxNdL1uXzbs732VH5A7s1HZMbzWdtv5tCy5gYXrxlw0bYFw/ajhuOBw6TgTbJ/sasRZ6vcK6Uzf4dmsYl26lAeDt5sBb7YJ4ob4f9rYFv+6hFMmMlTht+hgOzjLTovSehtcVsYUyrYaiQNxFCN9OdthWuLKbGFUWY0qW4JSj4ZvkcynwikcLKjTohl1gC5mbbkmijsBv3Qzr+AU/Ay/8nrOu3p4Lsbz8y0H0Cox7riqvNi+fp0ueiT1Dv/X90Ck6vmvzHe382xnzHQhR5EnxxTLIOAlRCCRcgYW9DLmtowe89CcENDV3VGah0yvM2hXOt1vC0OgUvFzsmdSjOs/UeLIOO41ewwe7PmDz1c3Yqmz5suWXdCrXqYCjFiZxegWsfRuykg2/03f/Aao8Z+6ozEpRFLadj2H65jDO3UgGoJizHW+0rsiAJgE42hlvBpsUyYyVOG2dAHu+NfzdGhelF6aj06CPOMSlw2v59dZmVrkaCq+Vs7KZeisWP60KXZmGOFRqBxXagm8tKZAWVjfPwPxnDZ2j5VtC32U5nZtX49LoNnMvSRkaQuqVZVpIzTy14Wfrsnnh7xe4mHiRZ8o9w9RWU438JoQQUnyxDDJOQhQSaXHwx4sQdQhsHOD52VCth7mjMptzN5J5Z8lxzkenANCjdmk+61YdD+f8/96n1Wv5ZO8nrLu0DrVKzRfNvqBrha4FHbIwFk0GbPwAjiwwHPs1gl7zwNPPrGGZ276LsUzbHMqx252Xbg62DGkRyKvNy+HmaPz6iBTJjJU4pcUaplIVhUXphUmtPrWayUe/IJ1MHPUKn8bF0y01Led5xckLVWBrqNDGUDTzKGu+YMVdceHwS2fDlIOyDWDAKnAwbAeelqXl+R/3EXozhVp+niwZ1jjPn47MODaD2Sdn4+XoxaruqyjmWMyIb0IIAVJ8sRQyTkIUItnpsGIonP8bUEHnL6Hxa+aOymyytXr+908YP+0IR6+Ar7sjX4XUpFVwyXxfS6fX8fmBz1lxYQUqVExoOoHng543QtSiQMWch+WDIOYsoIIWo6H1h0W6SeZoRALTN4WyLzwOAEc7NQOblmd4y0CKuZhul1wpkkniJCxQTHoMY3d9wL83DwNQNtmXN25paaU6h7sqI/fJJYIh8HbBrFwzcHAzQ8RFXFKUoUCWFAk+NWDgWkMBHcM8+9cXHWHTmZt4uzmw9q3m+LjnbV3As3Fn6buuLzpFxzetv6FDQAdjvgshxG2SQ1gGGSchChm9DjaMgcNzDcdN34L2nxfpGRBHribw3rITXI41fODdr5E/Hz1bJU+7mt9Lr+iZfHAyS0KXAPBxo495sfKLBR6vKACKAsd+h/VjQJsBLt7w/CzD72pF1NnryXy9OZR/zscAYGejom9Df95sUxHvPP5eVJCkSCaJk7BQOr2Ouafm8uOJH9ErelzVvmRE9qFCSgotbE7R0uYUtVXhqNHffZHa1tDGW6ENBLaF0rVlR1JjS42B+c8Y1uIoXhEGbQTXu58S/m/rBb7dGoa9jZo/hzemrn/eOsE0Og0vrHuBCwkX6FSuE9NbTTfWOxBC/IfkEJZBxkmIQkhRYM838M/nhuPqvaDHT0V6cfL0bC1fbTjPr/uvAhBQ3JnpvWvRoJxXvq6jKArT/p3G72d/B+C9+u/xSrVXCjxe8RQyk+Hvd+D0csNxYBvDBl5uPuaNy0zCb6Xy7ZYw/j55AwC1CkLqlWVkuyDKFnM2W1xSJJPESVi4IzePMHbXWG6m38RObUdb78GcC6vBmWspuJNGE/VZurmep6XtadzSI3O/2NETAlsZPrkIbAPFAszyHqxWRgIseA5unjbs6vTqxlzTXzefiWbY70cAmNqrJn0a5H39gR+O/8DPJ36mmEMxVvVYhZdj/hIpIcSTkxzCMsg4CVGInfgTVr8Jei2UawEvLjIs7F+E7b0Yy/vLTnA9KROVCoa1COSdDsH5WqBcURRmHJvBnFNzABhZZyRDaw41VsgiP64fg2WDIOEyqGyg7cfQ7J0i2UkZlZDO/7Ze4K+jUehvV5m61irNO+2DCCzpat7gkCKZJE7CKiRmJvLpvk/ZEbkDgLZ+beld7l1WHUlg7YnrZGkN3WRVHGJ53T+CNrZncLu+D7KScl/Iq4KhYFahjSFhcZR/G08sKxV+7wFRh8HVBwZtgOIVcp4Ou5lCzx/2kpatY2DTckzoVi3Plz4Xd46+6/qiVbRMbzVddjISwsQkh7AMMk5CFHLh22DJAMOO397VoN8y8Chj7qjMKjlTw2drzvLX0SgAgn1c+aZPbaqXyV8B8ecTP/PD8R8AGF5zOG/WfjNPG0IJI1AUOPATbBkHeg24l4WQeeDf2NyRmVxMciYzt1/kj0MRaHSG8lL7Kt6M7lCJqqULz89pKZJJ4iSshKIoLD6/mK///RqNXoOviy9TW06lvGs1lh+JYtHBiJz1DgAaB3jwZuVkmignsb283VDMUXR3L6iyAb+Gd9czK10HbPK3PkKRpcmExb3h8i7D2mMD14NP1Zynk9I1dP9hD1fi0mkSWJzfBjfEziZvnyJpdBpeWvcSoQmhdAjowDetvzHWuxBCPITkEJZBxkkIC3DjBCzqDak3wb0M9P8LvKuYOyqz23Qmmo9XniI2NRtbtYq32wXxeusK2OYxXwT45fQvfHvkWwAGVRvEO/XekUKZqaXHw6o3IGyD4bjyc9BtBjgXrRkgCWnZ/LwrnF/3XSFTY2jeaFaxOO92rJTnpWZMSYpkkjgJK3M27izv73yfiJQIbFQ2vFH7DQZXH4wKNfvC41h44Cpbzt1Ed7u3tYSrPX3q+9G3lidlk45A+HbDJ3vx4bkv7OABgS3vTs30Km+Gd2cBdBrDp6JhG8DeFV5ZA2Xq5Tyt1ekZtOAwuy/EUsbTibVvNccrH7u1/HT8J3488SOeDp6s6r6K4k7FjfEuhBCPIDmEZZBxEsJCJFyFRSEQG2bIN19aDOWamzsqs4tLzeLjlafZeCYagFplPfi6T20qeud9Otqic4v48tCXAPSt3JcPGn4ghTJTuboP/hoCydfAxh46ToKGQ6EI/f9PydQwb89l5u2+TEqWFoC6/p6816kSTSuUMHN0DydFMkmchBVK06Qx8cBE1l1aB0CjUo34ssWXlHAyfDOKTsrkj0MR/Hk4gpvJWYDh+3WbSt4MaBxAy+CS2CRFwKXbBbNLOyEzMfdNipU3TMus0NYwNdPJ04TvsJDS62DFMMNinLaO0G85lG+R65TJ688xe9clnOxs+Ov1pvlqLQ6ND+XFv19Eq2iZ1nIanct3Luh3IITIA8khLIOMkxAWJD0e/ngJIg8YCgo9Z0H1580dldkpisKq49cYt/oMKZlaHGzVjO1cmYFNy6FW563YsixsGRP3T0RBISQ4hE8bf4paVfTWwTIZvQ52fwM7JoOiNyxn03s+lKpl7shMJlOj47f9V/hpRzgJ6RoAqpRy5/1OwbSp5F3oC7VSJJPESVgpRVFYHb6ayQcnk6HNwMvRi8nNJ9OsTLOcczQ6Pf+cu8nCAxHsuRib83jZYk70beRPn/p+lHB1MHyzv34cLm0zdJpFHjQstHqHSg1l6t9dz6xM/aI3NVNR4O9RcGSBYRfRF/+A4I65Tll17BqjlhwHYGbfOjxXs3SeL6/Ra+i7ri/n48/T3r8937T+ptD/gBHCWkkOYRlknISwMJoMQ+fN+b8Nx50mQ5M3zRtTIXEjKYMxy0+y+4IhX28SWJxpvWvmeQfA1RdXM27fOPSKnm4VuvF508+xkR3uC15KNKwYalhyBaDmi9BlOji4mTcuE8nW6llyOIIZ2y4Sk2JoxAgs6cLoDsE8W71Ungu75iZFMkmchJW7lHSJ93e+T1hCGACDqg/irTpvYae2y33erVQWHYxg+ZEokjIMFX97GzXP1PClf+MA6gcUu1uUyUqBK3tvd5ltN7TH38vBHcq3hMDWhsKZV6B1txYrCmz+BPbPNBQMQ36Baj1znXIqKomQn/eRpdXzZpsKvN+pcr5ucWcBVg8HD1Z1X5XTFSiEMD3JISyDjJMQFkivg40fwKHZhuPGb0LHL4rkDoD/pSgKCw9GMHndOTI0OlwdbBn3XFV61y+bpw9ON1zewIe7P0Sn6Him3DNMajHpvt8HxFO4uBVWDIf0WLBzhi5fQ+2+5o7KJLQ6PSuPXeN//1wgKiEDgDKeToxqH0TPOmXytZZeYSBFMkmcRBGQqc1k+r/TWRK6BICaJWsyteVUyrjev4NQpkbH2hPXWXjgKiei7u5+WcnHjf6N/elRpwxujv/5gZoYeXtq5na4tAMy4nM/7+l/dy2zwFaGxeytyc6psH2S4e/dZkLdAbmevpWSRbeZe7iRlEnbyt7Mfbl+vj5JCUsI44W/X0Cr1/JVi694NvDZgoxeCJFPkkNYBhknISyUosDe/8HW8Ybjaj0N0y9tHcwbVyFxJTaNd5ed4MjVBADaVfZmSq8aeLs5Pva1/1z9h/d2vYdWr6WdfzumtZyGnY0Uyp6KTgPbJhq+ZgF8qkPIfCgZbN64TECvV9hwOppvtoQSfsuwQVxJNwfealuRFxr44WBrmd2KUiSTxEkUIVuubmH83vGkaFJws3Pjs2af0SGgw0PPPxWVxMIDV1l94lrOTiQu9jb0qFOG/o0DqFLqAf929HqIPmHoMgvfDhEHDNsd36FSQ+m6d9czK9sALPmH84GfDJ94AnSaAk3eyPV0tlZPv7kHOHwlgcCSLqx6sxnu/y0yPoJGr6Hfun6ciz9HW7+2fNfmO5lmKYSZSQ5hGWSchLBwJ5cadgbUayCgOby40Po+aH1COr3CnN2X+GZzGNk6PcWc7fiiRw261Cz12NfuitrFO9vfIVufTcuyLfmm9Tc42EgB8okkXIW/BkPUYcNx/cHQaRLYOZk3LiNTFIXtoTFM3xTG2RvJAHg62/F6qwq83KQcTvaWWRy7Q4pkkjiJIuZa6jXG7BrDyVsnAegT3If3G7yPo+3DP31KytCw4mgUCw9czfmUAKB+QDH6Nw7gmRq+D/+kICvVsLvLnU0Abp3P/by9q2Hh/zvrmRWvaDlTM4/+DmtGGP7e5mNoNea+Uz5aeYrFByNwc7Bl1YhmVCiZ9x2JAOacnMP3x77H3d6d1T1WyzRLIQoBySEsg4yTEFbg0g74sz9kp0DJKtB/OXiUNXdUhcb56GTeWXKCc7cLFd1qlebz7tXwdH70zun7ru/j7W1vk6nLpHGpxnzf9nucbK27sFPgzq4x/B6QmWTYlbX7DKja3dxRGd2+8FimbwrlaEQiAK4OtgxpUZ7BzcvfP9vIQkmRTBInUQRp9Bp+OPYD807PAyCoWBDTW04n0DPwka9TFIX9l+JYdCCCTWei0eoN3xa8XOzpXb8s/RoG4F/8MQuIJl0zJDzh2wz/TY/N/byH3921zAJbg7PXE71Hozu9wvDJkaKHpm9Bh4n3FfcWHbzKxytPo1LBL680oE1l73zd4kLCBfr83QetXsuUFlN4LvC5gnwHQognJDmEZZBxEsJKRJ+ChSGQGg1upQ2FMp9q5o6q0MjW6vn+nwv8uOMiegV83B34sldN2lR6dN55OPowb/7zJhnaDOr51OOHdj/gYudioqgtmCYTNn8Mh+cajsvUN6xHXCzAvHEZ2bGIBKZvDmXvxTgAHO3UvNKkHK+1qkAxl0cXZS2NFMkkcRJF2L5r+/hwz4fEZ8bjZOvEhw0/pEfFHnmazheTnMmfhyP541AEN5IyAUONqGVQSQY0DqBNZW9sHrfull4PN0/dMzVzP+iy7zlBBaVr313PzK8R2BaCb8Jhm+HPlww7fNYbCM99d1+B7NDlePrOOYBWrzCmcyXeaF0xX7fQ6rX0X9+fM3FnaO3Xmu/bfC/TLIUoJCSHsAwyTkJYkcRIWNgLYkMNG0S9uMiwSZTIcSwigXeXnuBSrGHWx0sN/fmkSxVcHB6+4/zxmOO8vvV1UjWp1CpZi5/a/4SbfdHYifGJ3AqD5YPg5mnDcbO3oe2nlr10zGOcu5HM15vD2HruJgB2NipeaujPiDYV8XZ//Dp4lkiKZJI4iSIuNiOWD3d/yIEbBwB4tvyzjGsyLs+fJGl1eradj2HhwQh2hd3KebyMpxMvNfTjhQb+lHTL4zoH2ekQsc9QMAvfBjFncz9v5wLlmt9dz6xEsOmnZl7eDYtCQJsJNXobFpL9zxba1xMz6DZzD7Gp2TxXsxQzXqqT7wLX3FNz+d/R/+Fm78bq7qsp6VyyIN+FEOIpSA5hGWSchLAy6fHwZz9Drqi2g54/Q40Qc0dVqGRk6/hq43kW7LsCgJ+XE1/3rk3D8g+fmXEm9gzDtgwjOTuZqsWrMrvDbDwcPEwUsQU5vhjWvQuadHAuYfgdIKi9uaMymku3Uvl26wX+PnkdRQG1CnrVLcvIdkH4eT1m5pCFkyKZJE5CoFf0/HL6F2Yem4lO0eHv5s/UVlOpVjx/rexXYtNYfCiCpf9GkphuWKzfzkZFp2q+9G8cQKPyXvkrFiXfMEzJvLNzZlpM7ufdSt9dyyywNbgYeb2uqCPwWzfIToXgZ+CF3+/75CgjW0fvWfs4fS2ZqqXcWf56E5ztH/4J3oOEJ4bTe21vNHoNk5tPpmuFrgX5LoQQT0lyCMsg4ySEFdJkwsphcHa14bjjF9BkhOWsZ2si+y7G8v7yk1xLzEClgqEtAhndIRhHuwevIRwaH8rQzUNJyEoguFgwczrOwcuxkC55YmpZKbDuPTj5p+G4XAt4fg64P36TBEt0LTGD77deYPnRKHS3l9Z5rmYp3ukQnO+1lY3hUtIl/gr7i9iMWL5q+ZVR7iFFMkmchMhxPOY4Y3aN4UbaDWzVtoyuN5r+VfrnuwsqU6Nj3ckbLDx4lWO3F3UECPJ2pV8jf56vVzZfOzwChu3Ab565PTVzm2FqpjYz9zmlahmmZVZoC/6NC3ar8JtnYP6zkJloaO/vuwzscrcYK4rCqCXHWX38Ol4u9qwZ0YyyxfL3SYtWr+XlDS9zKvYULcu2ZGbbmTLNUohCRnIIyyDjJISV0uth00dw8CfDcaPXDTsKqi17R72ClpypYeLasyw7EgUY8vBv+tSmRtkHd4mFJ4YzZPMQYjNiCfQIZG7HuTKT4cZJWDYQ4sNBpYbWH0KLd63yay0mJZMft4ez+GAE2To9AO0qezO6YzDVSpu3szBLl8XWq1tZHracf2/+C4AKFeufX09Zt4LfyEOKZJI4CZFLUlYS4/eN55+IfwBoXbY1E5tNxNPR84mud+Z6EgsPRLD6+DXSs3UAONnZ0KNOafo1CqB6mSf8pqvJMBTKwrdB+A7D2mb3snWCcs3urmfmXeXJP2WMC4dfOhs62co2gAGrwOH+T1Jm7wpn8vrz2KpVLBzSiMaBxfN9q19O/8K3R77Fzc6Nld1X4uPi82QxCyGMRnIIyyDjJIQVUxTYPxM2f2I4rtodes6+7wNMAVvO3uTDFSeJTc3GVq3irbZBvNGmAnY26vvOvZJ0hSGbh3Az/Sb+bv7M6zQPXxdfM0RtZooCh+YYFujXZRtmr4TMg4Cm5o6swCWmZ/Pzzkv8uu8KGRrD72pNKxTn3Y6VqBdQzKyxXUq6xPKw5awJX0NSVhIAapWaFmVa0Du4N83KNMNWnb8ZO3khRTJJnIS4j6Io/Bn6J9MOT0Oj1+Dt7M1XLb6ivm/9J75mcqaGlUevsfDAVS7EpOY8XtvPkwGNA+hSs9RDW8DzJDXm7q6Z4dsNOyDdy9X37lpmga3BNY87TSZFGQpkSZHgUwMGrgWn+39g7Ay7xaD5h9Ar8Hn3arzcpFy+38KlxEv0XtubbH02E5tNpEfFHvm+hhDC+CSHsAwyTkIUAaeWw8rXQK8B/6aGBf0L687oZhSfls0nq06x/pQhP65Z1oNv+tSiovf9i/RHpUQxZPMQrqVeo4xrGeZ2nGuUbp1CKyMBVo+A838bjoM7Q4+frO7rKjVLyy97LjNn1yVSsrSA4fey9ztVollFIy9h8whZuiy2XN3C8rDlHLl5JOdxH2cfegX1omdQT6MXbqVIJomTEA91Pv487+98nyvJV1Cr1LxW6zWG1RiGzVO0GCuKwqHL8Sw8GMHG0zfQ6AzfWjyd7ehdryz9GgVQrsRTbj+tKBBzzlAwu7QdruwFbUbuc3xq3C6atQH/JmDndP91UmNg/jMQdxGKV4RBGx5YXLsSm0a3mXtIztTyQn0/vuxVI99TJHV6HS9veJmTsSdpXqY5P7b7UaZZClFISQ5hGWSchCgiLu8yLOiflQwlKkH/v8DTz9xRFTqKorDmxHU+XXWa5Ewt9rZqxnSqxKvNyqP+z470N1JvMGTzECJSIvBx9mFep3kEuAeYKXITijgIfw02fDiutoMOn0Pj161qzbtMjY7f91/lp53hxKdlA1DZ1433OlaiXRVvs/3+cSnxEssv3N811rJMS0KCQ2hepvlT/Q6aH1Ikk8RJiEdK16Qz6eAk1oSvAaCBbwO+bPEl3s557MR6hFspWSz9N5LFByO4lni3iNUiqAT9GwfQrrI3tg9oBc83TSZEHri7a2b0ydzP2zoa2qfvrGfmU82w9tiCroZpnB5+8OpG8Lj/U7SUTA09f9zHxZhU6vp78sewxjjY5v8b+ILTC/j6yNe42rmysvvKotnaLoSFkBzCMsg4CVGE3DwDC0Mg5Tq4lYJ+y8C3hrmjKpSikzIZ89fJnF3pG5X3YnrvWvftWBiTHsOQzUO4nHSZEk4lmNtxLhU8K5gjZOPT62Hvd7DtC1B0UKw8hPwCZeqaO7ICk63Vs+TfSGZuu8DN5CwAAku48E6HYLrUKHVfodQU7nSNLQtdxtGYozmP+7r48nzQ8/SsaPyusQeRIpkkTkLkydrwtUw8MJEMbQbFHIoxqfkkWpRtUSDX1ukVdoTG8PuBq+wMu8Wd7zalPBx5sYE/Lzb0w8e9ANeYSL0Fl3feLZqlXM/9vIu3Yc2x+Evg6mPoICt+f1Kg1ysM+/0IW8/dxMfdgbUjmuP9BHFeSrpE7zWGaZafN/2cnkE9n/SdCSFMQHIIyyDjJEQRkxRlKJTdOgf2bvDiQsMSG+I+iqKw+FAEk9adIz1bh4u9DZ8+V5UXGvjl6iSKy4hj6JahXEi4gJejF7M7zKaSVyUzRm4EqTGwYphh9glA9V7w3HfgaB0/N3R6hZXHrvG/f8KIjDc0JZTxdOLtdkE8X7dMwTQk5NOlxEssC1vG2ktrc3eNlW1pWGusdDOTdY09iBTJJHESIs+uJF3h/V3vcz7+PACvVH2Ft+u+jZ1NPneqfITI+HQWHYxg6b+ROS3AtmoVHav50L9RAE0qFC/YNmBFgVuhhh+M4dvgyh7QpBuec/SEQesNnWUP8M2WML7/5wL2tmqWDm9CbT/PfN9ep9fxysZXOHHrBM1KN+On9j/JNEshCjnJISyDjJMQRVBGomHq5dU9hulyPX6Emn3MHVWhdTUujXeXnuDfqwkAtK3szZfP18j1oW9iZiLDtgzjXPw5PBw8mNVhFtWKPzg3tjjh2w0FsrQYw6Zfz06FOgOsYnqlXq+w8Uw032wJ4+Lt9aBLuDrwVtuKvNjQ74lmvjyNTG1mzlpj/+0a6xXUix4VexSamTRSJJPESYh8ydJl8c2/37D4/GIAqhevztRWU/FzK9i1H7K0Ojacimbhgas5P7gBAku60L9RAL3qlcXDqeCKczm0WRB5CCIPQuUuhl0xH2Dj6Ru8ttDwDf7r3rXoVe/JFjT99cyvTP93Oi52LqzqvqrQ/HAQQjyc5BCWQcZJiCJKmwUrh8OZlYbj9hOg2SirKHwYg06vMHf3Jb7eHEa2To+nsx1f9KjOczVL55yTnJ3M61tf5+Stk7jaufJT+5+o7V3bfEE/LZ0WdkyG3d8ACpSsAr3nPzTvtySKorAj9BbTN4dy5noyYFj7+bVWFXilSTmc7E1bHAtPDM/ZoTI52xCPjcqGlmUNa42Zu2vsQaRIJomTEE9kW8Q2Pt37KcnZybjauTK+yXg6l+9slHudu5HMooNXWXn0GmnZhq2JHe3UdKtVmv6NA6hZ1tMo932Y89HJPP/jPtKzdbzarDzjulZ9outcSbpCyNoQsnRZTGgygV7BvQo4UiGEMUgOYRlknIQowvR62PIp7J9pOG44DDp/CYXsl/HCJDQ6hdFLj+cUVrrWKs3n3apRzMUegDRNGm9sfYOjMUdxtnXmh3Y/PNXO92aTGAl/DTGsVwxQbyB0mgL2zo98mSU4cCmO6ZtCcxoMXOxtGNIikMEtyuPuaITmgod4WNdYKZdSOWuN+bj4mCye/JIimSROQjyxG6k3GLt7LMdijgHQK6gXYxuOxcn2ATtFFoDULC0rj11j0YGrnI9OyXm8VlkP+jUOoGvN0kb/dCQhLZtuP+whMj6DZhWL8+ughk80l1+n1zFo0yCOxRyjSakmzOowS6ZZCmEhJIewDDJOQgj2/wCbPjL8vfJz0Gvug3c0F4BhcfeZ2y7ww45wdHqFkm4OTO1VkzaVDRt2pWvSGbl9JAdvHMTRxpHv235Pk9JNzBx1Ppz7G1a/adigy8Edun5nWIPMwp2ITGT65lB2X4gFwMFWzStNy/Faqwp43S5ymoIldo09iBTJJHES4qlo9Vp+PP4jc0/NRUGhomdFprWcRsViFY12T0VROHI1gYUHrrL+VDTZOj0A7o62hNTzo19jfyqUdC3w+2p1el6Zf4i9F+Pw83JizZvNcz5dy6/fz/7O1MNTcbFzYWW3lZRyLVXA0QohjEVyCMsg4ySEAOD0CsP0S102+DWGl/4AZy9zR1WoHY9M5N2lxwm/lQbASw39+LhLVVwdbMnUZjJ6x2h2X9uNvdqeb9t8S8uyLc0c8WNos2Dzp3BoluG4dB0ImQ9e5c0b11M6H53M15vD2HL2JgB2NipebODPiLYVC3bTs0e40zW2LGxZTuMEGLrG7qw1Vpi7xh5EimSSOAlRIA7cOMCHuz8kNiMWRxtHxjYcS6+gXkbvjopLzWLpv1EsPnQ1Z8cWgGYVi9O/UQDtq/pgV0C7tkz8+yzz9lzG2d6GFW80pbLvk33viEiOoNeaXmTqMhnXZBy9g3sXSHxCCNOQHMIyyDgJIXJc2QN/9IWsJCgeBP3/gmIB5o6qUMvU6Ji6MZRf9l4GwM/LiekhtWgUWJxsXTbv73yfbZHbsFXbMr3VdNr5tzNzxA8RexGWD4Lok4bjJiOg3XiwNV2HVUG7EpvGt1vDWHPiOooCahX0rFOWUe2D8PMyzbTRiwkXWX7B0DWWkm2Y4WOjsqFV2VaEBIfQtHRTi+gae5BCUSSbNGkS69at4/jx49jb25OYmPjY1yiKwmeffcbs2bNJSEigUaNG/PDDD1SrlredNiRxEqLgxWbE8smeT9h7fS8Anct1ZlyTcbjZuxn93nq9ws4Lt1i4/yrbQmO48x3L282BFxv681JDP0p5PHl7/V9Honh32QkAfu5fl87Vn6zzS6/oGbRxEEdjjtK4VGNmd5gt0yyFsDCSQ1gGGSchRC4x52BhL0i+Bq4+0OxtsLE3rFOmsjH8V217++/qe/5+7/N3/m77kMfvXEP9n+vZ3H7M9gHXKNx54P7wON5bdoJriRmoVDC4WXne61QJGxs9H+7+kE1XNmGjsuHLFl8abX3iJ3ZiCawbDdmp4OQFPX+G4E7mjuqJXU/M4Pt/LrDsSBQ6veGXnS41SvFOhyAqehv/961MbSabr25medjyXF1jpV1K0yvY0DXm7ext9DiMrVAUycaPH4+npydRUVHMmzcvT0Wyr776ikmTJrFgwQKCg4P54osv2LVrF6Ghobi5Pf4LRBInIYxDr+hZcGYBM47OQKtoKetalmmtplG9RHWTxRCVkM4fhyJYcjiS2NRsAGzUKtpX8aZ/4wCaVSiBWp33hOR4ZCJ9Zu0nW6tnZNuKjO5Y6YljW3RuEV8e+hJnW2dWdF9BGdcyT3wtIYR5SA5hGWSchBD3SboGi3pDzBlzR3IP1f0FtYcV7p60QHfvOTnXeND1/lMcvH29LL2KredjORKZgg41xd2ceL5+AL7FnBkXtZG1iWdQo2JiuR50K1nvCe/9iPf9oILlo4qL2Wmw/n04vshwHNAMnp8DHpaZd99KyeKH7RdZfDAiZ5mZtpW9Gd0hmOplPIx+/4d1jbX2a01IcAhNSjWx2K6xBykURbI7FixYwKhRox5bJFMUhdKlSzNq1CjGjh0LQFZWFj4+Pnz11VcMHz78sfeSxEkI4zpx6wRjd43lWuo1bFW2jKo3igFVB6BWFczUx7zI1urZeCaahQeucuhyfM7j5Uu40K+RPyH1yuLp/OhW65jkTLrO3MPN5CzaV/Fh9oB6+Sqw3SsyOZJea3uRoc3g08af0qdSnye6jhDCvCSHsAwyTkKIB8pMgl3TISkK9FpQ9KDX3f67zvB3RW841utuP6Y17Jh55/mcc/W5X3fv9XJepwOsauWiXPTA5yW8+MvNFZWiMC4unpCUNOPfWPWIIp82C7KSARW0GgutxljkzqZJ6Rpm7Qpn/t4rZGh0ADQO9OL9TpWoF2DcdfXudI0tC13G8VvHcx6/0zXWs2JPSjqXNGoM5mKRRbJLly5RoUIFjh49Sp06dXIe7969O56envz666/3vSYrK4usrKyc4+TkZPz8/CRxEsKIkrOTmbBvAluubgGgRZkWfNH8C7wcTb9YatjNFBYduMpfR6+RmqUFDDu/PFezNP0b+1Pbz/O+aY9ZWh0vzT7A0YhEKnq7svKNprg94fbJekXP4E2D+ffmvzTybcTsjrNNWjAUQhQcKb5YBhknIUShcW+B7d7i2YMKdDnn/KcYl+t1BVC4u+9+eYspS5PN2agEbiWno0ZPMUc1wT7OzLCL5w8MXUYf6tzoq3V4eBHy3vvf917uie9puPoadjMt36IABtC0UrO0zN9zmdm7L5GSafi9pZafJ+93rESzisWNulTLhYQLLA9bztpLa3N1jbXxa2PoGivdxOp/h8lr/mBrwpgeKzo6GgAfn9y7JPj4+HD16tUHvmbKlCl89tlnRo9NCHGXu707X7f6mmVhy5h6eCq7r+2m95refNnySxr4NjBpLME+bnzWvTpjOldm9fHrLDxwlbM3kvnraBR/HY2iWml3BjQOoFvt0jjb26IoCuNWneFoRCLujrbMebn+ExfIAJaELuHfm//iZOvEhKYTrP6HixBCCCGEuE2tBtRg8+S5ZGHhANRWFNacuM641WdIStFgn6HmvQ7BvOy8kt/O/soUmxSyGw5jYPWBT34jRflPcU/3iKLbfwuBOigRBHZPvh6xOWRqdCw8cJUfd4QTn2ZYMqayrxvvdqxE+yreRiuOZWgz2HxlM8vClnHi1omcx8u4lsnZodJau8aeRr6LZBMmTHhsUerw4cPUr1//iYP67xeJoigP/cL58MMPGT16dM7xnU4yIYRxqVQq+lTqQ23v2ry38z0uJ11m8KbBDK81nOE1h2OrNm0N3sXBlr6NDAv5H4tMZOH+q/x96gZnrifzwYpTTFp/jl51y+LhZMeSfyNRq2BG37qUL+HyxPeMTInk2yPfAjC63mjKupUtqLcjhBBCCCGESalUKrrXLkPjwOKMWX6SnWG3mLzhPA3KNeXFmmr+vDCfr498TZYui+G1Hr8U0kNucnf9MSun0elZ+m8kM/65SHRyJmBYHmZU+yC61iz9xEu9PE5YQhjLw5bzd/jfpGiKZtfY08j3b7EjRozgxRdffOQ55cqVe6JgfH19AUNHWalSd3eYi4mJua+77A4HBwccHBye6H5CiKcXXCyYP7v8yZeHvmTlxZX8fOJnDkcf5ssWX+Lr4mvyeFQqFXX9i1HXvxifPFeV5UciWXQwgqtx6SzYdyXnvLGdK9Mq+Mk/OdEresbvG0+GNoMGvg1kHTIhRJFjjl3MhRBCGJ+PuyMLBjXgj0ORfLHuLIevJHDmelXaNBrArtjfmXl8Jlm6LN6q85bs5v4AOr3C6uPX+G7rBSLi0wEo7eHI2+2D6FW3LLY2BV+gytBmsOnKJpaHLb+vaywkOITuFbpL11ge5btIVqJECUqUKGGMWChfvjy+vr5s2bIlZ02y7Oxsdu7cyVdffWWUewohnp6znTOfN/ucRqUa8fn+zzly8wi91/bmi2Zf0Mqvldni8nKxZ1jLCgxpHsjui7EsPHCVf87dJKReWYa1DHyqay8LXcbh6MM42TrxWZPP5NMYIUSRk52dTe/evWnSpAnz5s3L02umTp3KN998k2sX8w4dOuR5F3MhhBCmoVKp6NvIn+YVS/DeshMcuhLPut3VqFKpD1Hqpcw5NYdsXTbv1n9XCmW3KYrCxtPRfLMljAsxqQCUcHVgRJsKvNTIHwfbgu+ee1DXmK3Kljb+bQgJCqFx6cbye0o+GXU+VEREBPHx8URERKDT6Th+/DgAFStWxNXVFYDKlSszZcoUevbsiUqlYtSoUUyePJmgoCCCgoKYPHkyzs7O9O3b15ihCiEKQJfALtQoUYP3dr7HufhzjNg2gv5V+vNOvXewt3n0bpPGpFaraBVcklbBJcnW6rGzUT3VD/Nrqdf4+sjXALxd92383GWKtxCi6Lmz/MaCBQvydL6iKHz33Xd8/PHHPP/88wD8+uuv+Pj4sHjx4jztYi6EEMK0/Is788ewxvyy5zLTNodyLrQu7t4alOIr+fXsr2Tpsviw0YdFuhCjKAo7w27x9eYwTl1LAsDDyY7hrQIZ2LQczvYFW3a50zW2LGwZJ2+dzHn8TtdYj4o9KOFknMamosCoRbJx48bl2pHyTnfY9u3bad26NQChoaEkJSXlnDNmzBgyMjJ44403ctrwN2/eLJ8uCmEh/N39WfjsQr498i0Lzy1k4bmFHI05yrSW0/B39zd3eNjbPt0PcEVRGL/XMM2yrnddXqr8UgFFJoQQ1u3y5ctER0fTsWPHnMccHBxo1aoV+/bte2iR7EE7mQshhDAdG7WKoS0DaVWpJKOXHuf0tUbYZatwLLWSP0P/RKPX8GnjT7EpAuuM/dfBS3FM3xzK4SsJALjY2zC4eXkGtwjEw6lgN3QIjQ9ledhy1l1ad3/XWHAIjUtJ11hBMGqRbMGCBY/9dFFRlFzHKpWKCRMmMGHCBOMFJoQwKnsbe8Y2HEujUo34ZO8nnI07S++1vRnXZBxdAruYO7ynsixsGQejD+Jo48jEZhPlB5EQQuTRk+xiDrKTuRBCFBbBPm6sfKMZM7ddZOZ2FRl6W5xKL+OvC3+Rrcvm82afm3zzLnM5GZXItE2h7L4QCxg+iH+lSQCvtapAcdeCWzM9XZOes9bYydi7XWNlXcvSK7iXdI0ZQdH4ChZCmEVrv9Ys77qcsbvGcjTmKB/s/oCDNw7yQcMPcLZzNnd4+XY99Tpf/3t3mmVh6IwTQoiCVNh2MQfZyVwIIQoTOxs173QIpl0Vb0YvdeHKNVscy/zJ2ktrSddkMq31V9ipC7aDqjAJu5nC15tD2XTmJgC2ahUvNPDjrbZB+Ho4Fth9QuNDWRa2jHWX1pGqMaxvJl1jpiFFMiGEUfm6+DKv0zxmn5zNzyd+ZuXFlZy4dYJpraYRXCzY3OHlmaIojN83nnRtOnW969K3iqyTKISwPoVtF3OQncyFEKIwqlnWk7/fas70TSVZcMIGxzKL+SdyC4PWpfPLs9+bdT1iY7gSm8Z3W8NYfeI6igJqFfSoU4ZR7YLxL14wH/4/qmssJDiE7hW7S9eYCUiRTAhhdLZqW96o/QYNfBswdtdYLiVdou+6voxpMIbewb0tYkecvy78xYEbB3CwceDzZp/LJzdCCKsku5gLIYTIK0c7Gz55rirtq/rw9mon0jx/4UT8XrosGcyynj/j6eRi7hCf2vXEDGZsu8DSf6PQ6Q1LRT1T3ZfRHYIJ8imYddMf1jXW1r8tIcEhNCrVSH73MCEpkgkhTKaBbwOWd1vOx3s+Zs+1PUw8MJEDNw4woekE3O3dzR3eQ91IvcH0f6cDMLLOSALcA8wckRBCmJ/sYi6EEAKgcWBx/nnjdUatKs6B9OlEa47TduHL/NRhJo3KlXr8BQqh2NQsftwezsKDV8nW6gFoXakk73WsRPUyHk99/TtdY8vClnEq9lTO435ufvQK6iVdY2YkRTIhhEl5OXrxQ7sf+P3s73x39Du2XN3C2bizTG05lZola5o7vPsoisKE/RNI06RRu2Rt+lXpZ+6QhBCiUJBdzIUQQtzh6mDL3Bf6MvtQSWac+RCNfRiDNgzn5fKf8W6HWtjZWEYnVFK6htm7w5m/9wrp2ToAGpb34v1OlWhQzuupr38+/jzLw5bz96W/SdOkAYZZN+382xESHEJD34bSNWZmKuW/20tauOTkZDw8PEhKSsLdvfB2pggh4HTsad7f+T5RqVHYqmx5q+5bDKw2sFD9YFhxYQXj943HwcaBZV2XUd6jvLlDEkIYieQQlkHGSQghCrc9EUcYsf0NdKSjy/AjIPttvuvTlEq+hfcDkbQsLfP3Xmb2rkskZ2oBqFXWg3c7VqJFUImnWh4mXZPOxisbWR62/L6usZDgELpX6E5xp+JP/R7Eo+U1f5AimRDCrFKyU/h8/+dsvLIRgGalmzGp+aRC8YMiOi2anqt7kqpJ5b367/FKtVfMHZIQwogkh7AMMk5CCFH4nYk7w6ANQ8nQpaDLKIP22hDebV+HIS0CsVEXnvWIMzU6Fh2M4MftF4lLywagko8bozsG07Gqz1MVxx7VNdY7uDcNfBsUquYAaydFMkmchLAYiqKw4sIKvjz0JZm6TEo4lWBKiyk0LtXYrDG98c8b7Lm2h5ola/Jb59+wUduYLR4hhPFJDmEZZJyEEMIyhMaHMmTTUBKzE9Bl+pIRMZj6fv583bt2ge0I+aQ0Oj3L/o1ixrYL3EjKBKBccWfe6RDMczVLP3EhL12TzobLG1getpzTcadzHvd38yckOIRuFboVimaAokiKZJI4CWFxLiZc5P1d73Mx8SIqVAypMYQ3ar+Brdr0yyeuuriKT/d+ir3anmXdlhHoEWjyGIQQpiU5hGWQcRJCCMtxKfESQzYP4VbGLZRsb9KuDsFJXYyPu1Shb0N/k+9yr9MrrD1xnW+3hnE1Lh2AUh6OvN0uiF71yj7x2mnn4s6xPGw56y6vy9U11t6/PSHBIdI1VghIkUwSJyEsUoY2g6mHp7I8bDkAdbzr8FWLryjlarqdcW6m3aTn6p6kaFIYXW80g6oPMtm9hRDmIzmEZZBxEkIIyxKRHMHgzYOJTovGTl+ShPDBKFpPWgWX5KteNfH1cDR6DIqisOnMTb7ZEkrYzVQASrja80brivRt5I+jXf5njNzpGlsWtowzcWdyHr/TNda9Yne8HJ9+sX9RMKRIJomTEBZt4+WNfLb/M1I1qbjbu/N5s89p59/O6PdVFIUR20awK2oXNUvU5LdnZJqlEEWF5BCWQcZJCCEsz7XUawzeNJhrqddws/Um9sIgsjOL4e5oy8Qe1elWq7RRusoURWHXhVi+3hzKySjDbsvujrYMb1WBgU3L4eKQ/xkr5+LOsSxsGesurSNda+hGs1Xb0sG/Q07XmKk75MTjSZFMEichLF5kSiRjdo7Jmc/ft3Jf3q3/LvY29ka755rwNXy852PDNMuuywj0lGmWQhQVkkNYBhknIYSwTNFp0QzZPISryVcp7lASp7g3ORdp6CJ7toYvX/SogZdLweX5hy7HM31TKIeuxAPgbG/Dq83KM7RlIB5Odvm6VpomLWetsXu7xgLcAwgJCqFbxW7SNVbISZFMEichrIJGp+H7Y9+z4MwCAKp4VWFqy6mU8yhX4PeKSY+hx+oepGSnMKruKAbXGFzg9xBCFF6SQ1gGGSchhLBct9JvMXTzUMKTwinuWJzW7p+ycHcWWr1CCVcHvny+Bu2r+jzVPU5FJTFtcyi7wm4BYG+rZkDjAF5vXYESrg75utbZuLOGtcbu6RqzU9vR3r89vSv1pr5PfekasxBSJJPESQirsitqF5/s+YSErAScbJ34tPGndK3QtcCurygKb217i51RO6levDq/P/u7WTYMEEKYj+QQlkHGSQghLFt8ZjzDNg8jNCGUYg7FeL/WN3y/IY0LMYa1wnrXK8u4rlVxc8xft1fYzRS+2RzGxjPRANiqVfRp4MdbbStSysMpz9dJ06Sx/vJ6loct52zc2ZzHy7mXIyQ4hK4VukrXmAWSIpkkTkJYnZj0GD7Y/QGHow8D0K1CNz5u9DHOdk+/hfTa8LV8tOcj7NR2LHluCUHFgp76mkIIyyI5hGWQcRJCCMuXlJXE8C3DORN3Bnd7d75v8yMbj9gxd89lFAXKeDoxrXdNmlYo8dhrXY1L47utF1h1/BqKAioV9KhdhlHtgwgo7pLnmM7EnWF52HLWX1qfu2ssoD29g6VrzNJJkUwSJyGskk6vY86pOfx04if0ip5y7uWY3mo6lbwqPfE1b6XfosfqHiRnJzOyzkiG1hxagBELISyF5BCWQcZJCCGsQ0p2Cq9vfZ0Tt07gaufKj+1/JDvVn/eWnyAyPgOAgU3LMbZzZZzs799I60ZSBjO2XWTp4Ui0ekNZo3M1X0Z3DCbYxy1PMdzpGlsWuoxz8edyHr/TNdatQjeKORYrgHcrzE2KZJI4CWHV/o3+l7G7xxKTHoO92p73GrzHi5VezPenO4qiMHL7SHZE7qBq8aosenaRTLMUooiSHMIyyDgJIYT1SNOkMeKfEfx781+cbJ34od0PVClWh0nrzvHHoQgAAku68HXvWtTxNxSr4lKz+HFHOL8fuEq2Vg9Aq+CSvNexEjXKeuTpvmfizrAsdBnrL68nQ2soyNmp7egQYNihUrrGrI8UySRxEsLqJWYm8uneT9kRtQOAdv7t+KzpZ3g45O2HI8C6S+v4YPcH2KptWfLcEoKLBRspWiFEYSc5hGWQcRJCCOuSoc3g7W1vs//GfhxsHPi+zfc0LdOU7aExjF1+kpiULNQqeKN1RVQqmLfnMunZOgAalvPivU6VaFj+8WuEpWnSWHdpHcvDlkvXWBEkRTJJnIQoEhRFYdG5RXx95Gu0ei2lXEoxteVUanvXfuxrYzNi6bG6B0lZSYyoPYLhtYYbP2AhRKElOYRlkHESQgjrk6XLYvSO0eyK2oWd2o5vW39LK79WJKZnM271GdacuJ7r/BplPHivUyVaBpV4ZMeXoiicjTvLsrAHd431Du5NPZ960jVWBEiRTBInIYqUM3FneH/n+0SmRGKjsmFEnRG8Wv1V1Cr1A89XFIVR20exLXIbVbyqsKjLIuzU+dtBRwhhXSSHsAwyTkIIYZ00Og1jdo1ha8RWbFW2TG01lQ4BHQBYd/IG49ecpoSrA6PaB9Opms8jC1up2ak5O1Te2zVW3qM8IUGGrjFPR09jvyVRiEiRTBInIYqc1OxUJh6YyPrL6wFoXKoxU1pMoYTT/bvibLi8gTG7xmCrsuXP5/58qoX/hRDWQXIIyyDjJIQQ1kur1/LRno/YcHkDNiobJjefzLOBzwKg1yuo1Y/uGsvZofKerjF7tT0dynUgJChEusaKsLzmD7I6tRDCarjau/Jliy8NxbFDUzhw4wC91vRiSvMpNC3TNOe82IxYJh+cDMCwWsOkQCaEEEIIIUQhYKu2ZUrzKdir7VkdvpoPdn9Atj6bHhV7PLRA9qiusd7Bveka2FW6xkSeSZFMCGFVVCoVPYN6UqtkLd7b9R4XEi4wfOtwBlcfzJt13sRObcfkg5NJzEqksldlhtQYYu6QhRBCCCGEELfZqG34vNnn2NvYsyxsGZ/u/ZRsXTZ9KvXJOedO19iysGVsuLwhV9dYx3IdCQkOoa53XekaE/kmRTIhhFUK9Axk8bOLmf7vdJaELmHe6XkcvnmYTgGd2HJ1C7YqWyY2myjrkAkhhBBCCFHIqFVqPm38KQ42Diw8t5CJByaSrcume8XurL+0nuUXlnM+/nzO+YEegTk7VOZnp3sh/kvWJBNCWL3NVzYzYd8EUjQpOY+9Xut13qj9hhmjEkIUNpJDWAYZJyGEKDoUReHbo98y//R8ABxtHMnUZQJ3u8Z6B/emjncd6RoTjyRrkgkhxG0dy3WkWolqjNk1hpO3ThJcLJihNYaaOywhhBBCCCHEI6hUKt6p+w6ONo78dOInMnWZBHoEGtYaq9BVusZEgZMimRCiSCjjWoYFnRew99pe6njXwc5GplkKIYQQQghR2KlUKt6o/Qa1vWvjbOtMrZK1pGtMGI0UyYQQRYad2o7Wfq3NHYYQQgghhBAin5qWbvr4k4R4SmpzByCEEEIIIYQQQgghhLlJkUwIIYQQQgghhBBCFHlSJBNCCCGEEEIIIYQQRZ4UyYQQQgghhBBCCCFEkSdFMiGEEEIIIYQQQghR5EmRTAghhBBCCCGEEEIUeVIkE0IIIYQQQgghhBBFnhTJhBBCCCGEEEIIIUSRJ0UyIYQQQgghhBBCCFHkSZFMCCGEEEIIIYQQQhR5UiQTQgghhBBCCCGEEEWeFMmEEEIIIYQQQgghRJFna+4ACpqiKAAkJyebORIhhBBCWJI7ucOdXEIUTpLrCSGEECK/8prnWV2RLCUlBQA/Pz8zRyKEEEIIS5SSkoKHh4e5wxAPIbmeEEIIIZ7U4/I8lWJlH5fq9XquX7+Om5sbKpWqwK+fnJyMn58fkZGRuLu7F/j1hfnJGFs3GV/rJuNr3Yw9voqikJKSQunSpVGrZUWKwkpyPfE0ZHytm4yvdZPxtW6FJc+zuk4ytVpN2bJljX4fd3d3+Ydp5WSMrZuMr3WT8bVuxhxf6SAr/CTXEwVBxte6yfhaNxlf62buPE8+JhVCCCGEEEIIIYQQRZ4UyYQQQgghhBBCCCFEkSdFsnxycHBg/PjxODg4mDsUYSQyxtZNxte6yfhaNxlfYQrydWbdZHytm4yvdZPxtW6FZXytbuF+IYQQQgghhBBCCCHySzrJhBBCCCGEEEIIIUSRJ0UyIYQQQgghhBBCCFHkSZFMCCGEEEIIIYQQQhR5UiQTQgghhBBCCCGEEEVekSySTZkyhQYNGuDm5oa3tzc9evQgNDQ01zmKojBhwgRKly6Nk5MTrVu35syZM7nOmT17Nq1bt8bd3R2VSkViYuJ990pISGDAgAF4eHjg4eHBgAEDHnieKDimHN9JkybRtGlTnJ2d8fT0NOK7EneYanyvXLnC4MGDKV++PE5OTlSoUIHx48eTnZ1t7LdYpJny32+3bt3w9/fH0dGRUqVKMWDAAK5fv27Mt1fkmXJ878jKyqJ27dqoVCqOHz9uhHclChvJ86yb5HnWTfI86yZ5nnWzljyvSBbJdu7cyZtvvsmBAwfYsmULWq2Wjh07kpaWlnPO1KlT+eabb5g5cyaHDx/G19eXDh06kJKSknNOeno6nTt35qOPPnrovfr27cvx48fZuHEjGzdu5Pjx4wwYMMCo76+oM+X4Zmdn07t3b15//XWjvidxl6nG9/z58+j1embNmsWZM2f49ttv+fnnnx/59SCenin//bZp04alS5cSGhrKX3/9RXh4OCEhIUZ9f0WdKcf3jjFjxlC6dGmjvB9ROEmeZ90kz7NukudZN8nzrJvV5HmKUGJiYhRA2blzp6IoiqLX6xVfX1/lyy+/zDknMzNT8fDwUH7++ef7Xr99+3YFUBISEnI9fvbsWQVQDhw4kPPY/v37FUA5f/68cd6MuI+xxvde8+fPVzw8PAo6dJEHphjfO6ZOnaqUL1++wGIXj2fK8V29erWiUqmU7OzsAotfPJqxx3f9+vVK5cqVlTNnziiAcuzYMWO8DVHISZ5n3STPs26S51k3yfOsm6XmeUWyk+y/kpKSAPDy8gLg8uXLREdH07Fjx5xzHBwcaNWqFfv27cvzdffv34+HhweNGjXKeaxx48Z4eHjk6zri6RhrfEXhYMrxTUpKyrmPMA1TjW98fDyLFi2iadOm2NnZPV3QIs+MOb43b95k6NCh/P777zg7Oxdc0MLiSJ5n3STPs26S51k3yfOsm6XmeUW+SKYoCqNHj6Z58+ZUr14dgOjoaAB8fHxynevj45PzXF5ER0fj7e193+Pe3t75uo54csYcX2F+phzf8PBwZsyYwWuvvfbkAYt8McX4jh07FhcXF4oXL05ERASrV69++sBFnhhzfBVFYeDAgbz22mvUr1+/4IIWFkfyPOsmeZ51kzzPukmeZ90sOc8r8kWyESNGcPLkSf7444/7nlOpVLmOFUW577HHedD5T3Id8WSMPb7CvEw1vtevX6dz58707t2bIUOGPNE1RP6ZYnzff/99jh07xubNm7GxseHll19GUZQnjlnknTHHd8aMGSQnJ/Phhx8+dZzCskmeZ90kz7NukudZN8nzrJsl53lFukj21ltvsWbNGrZv307ZsmVzHvf19QW4r5oZExNzX9XzUXx9fbl58+Z9j9+6dStf1xFPxtjjK8zLVON7/fp12rRpQ5MmTZg9e/bTBS3yzFTjW6JECYKDg+nQoQN//vkn69ev58CBA08XvHgsY4/vtm3bOHDgAA4ODtja2lKxYkUA6tevzyuvvFIA70BYAsnzrJvkedZN8jzrJnmedbP0PK9IFskURWHEiBGsWLGCbdu2Ub58+VzPly9fHl9fX7Zs2ZLzWHZ2Njt37qRp06Z5vk+TJk1ISkri0KFDOY8dPHiQpKSkfF1H5I+pxleYhynH99q1a7Ru3Zq6desyf/581Ooi+S3TpMz57/fOJ4tZWVlPdR3xcKYa3++//54TJ05w/Phxjh8/zvr16wFYsmQJkyZNKpg3IwotyfOsm+R51k3yPOsmeZ51s5Y8z/apr2CB3nzzTRYvXszq1atxc3PLqWR6eHjg5OSESqVi1KhRTJ48maCgIIKCgpg8eTLOzs707ds35zrR0dFER0dz8eJFAE6dOoWbmxv+/v54eXlRpUoVOnfuzNChQ5k1axYAw4YN47nnnqNSpUqmf+NFhKnGFyAiIoL4+HgiIiLQ6XQcP34cgIoVK+Lq6mraN15EmGp8r1+/TuvWrfH392f69OncunUr57V3PgURBc9U43vo0CEOHTpE8+bNKVasGJcuXWLcuHFUqFCBJk2amOW9FwWmGl9/f/9c973z/bhChQq5PtEU1knyPOsmeZ51kzzPukmeZ92sJs8rkD0yLQzwwD/z58/POUev1yvjx49XfH19FQcHB6Vly5bKqVOncl1n/Pjxj71OXFyc0q9fP8XNzU1xc3NT+vXrl6ctasWTM+X4vvLKKw88Z/v27aZ5s0WQqcZ3/vz5D72XMB5Tje/JkyeVNm3aKF5eXoqDg4NSrlw55bXXXlOioqJM+G6LHlN+f77X5cuXC3RrcFG4SZ5n3STPs26S51k3yfOsm7Xkearbb0YIIYQQQgghhBBCiCJLJl4LIYQQQgghhBBCiCJPimRCCCGEEEIIIYQQosiTIpkQQgghhBBCCCGEKPKkSCaEEEIIIYQQQgghijwpkgkhhBBCCCGEEEKIIk+KZEIIIYQQQgghhBCiyJMimRBCCCGEEEIIIYQo8qRIJoQQQgghhBBCCCGKPCmSCSGEEEIIIYQQQogiT4pkQgghhBBCCCGEEKLIkyKZEEIIIYQQQgghhCjypEgmhBBCCCGEEEIIIYq8/wMGxbULeM9oYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's group the four images in a subplot \n",
    "fig,axs =plt.subplots(2,2, figsize=(15,10)) \n",
    "axs[0,0].plot(df_cvd_1_merged_norm_plt)\n",
    "axs[0,0].set_title('Mortality from total cardiovascular diseases')\n",
    "axs[0,1].plot(df_cvd_2_merged_norm_plt)\n",
    "axs[0,1].set_title('Mortality from diseases of the heart')\n",
    "axs[1,0].plot(df_cvd_3_merged_norm_plt)\n",
    "axs[1,0].set_title('Mortality from heart failure')\n",
    "axs[1,1].plot(df_cvd_4_merged_norm_plt)\n",
    "axs[1,1].set_title('Mortality from cerebrovascular disease (stroke)')\n",
    "for i in [0,1]:\n",
    "    for j in [0,1]:\n",
    "        axs[i,j].legend(['Overall','Male','Female'])\n",
    "        axs[i,j].xaxis.set_major_locator(plt.MaxNLocator(5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evolution of mortality per state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Question</th>\n",
       "      <th>DataValueUnit</th>\n",
       "      <th>DataValueType</th>\n",
       "      <th>DataValue</th>\n",
       "      <th>DataValueAlt</th>\n",
       "      <th>LowConfidenceLimit</th>\n",
       "      <th>HighConfidenceLimit</th>\n",
       "      <th>StratificationCategory1</th>\n",
       "      <th>Stratification1</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from heart failure</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>143.5</td>\n",
       "      <td>143.5</td>\n",
       "      <td>113.4</td>\n",
       "      <td>179.1</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>American Indian or Alaska Native</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from heart failure</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>76.9</td>\n",
       "      <td>76.9</td>\n",
       "      <td>65.8</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Female</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from heart failure</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>100.4</td>\n",
       "      <td>100.4</td>\n",
       "      <td>85.5</td>\n",
       "      <td>115.4</td>\n",
       "      <td>Gender</td>\n",
       "      <td>Male</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from heart failure</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>87.5</td>\n",
       "      <td>87.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>96.4</td>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Cardiovascular Disease</td>\n",
       "      <td>Mortality from heart failure</td>\n",
       "      <td>cases per 100,000</td>\n",
       "      <td>Age-adjusted Rate</td>\n",
       "      <td>84.7</td>\n",
       "      <td>84.7</td>\n",
       "      <td>74.5</td>\n",
       "      <td>94.9</td>\n",
       "      <td>Race/Ethnicity</td>\n",
       "      <td>White, non-Hispanic</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     LocationAbbr LocationDesc                   Topic  \\\n",
       "1434           AK       Alaska  Cardiovascular Disease   \n",
       "1435           AK       Alaska  Cardiovascular Disease   \n",
       "1436           AK       Alaska  Cardiovascular Disease   \n",
       "1437           AK       Alaska  Cardiovascular Disease   \n",
       "1438           AK       Alaska  Cardiovascular Disease   \n",
       "\n",
       "                          Question      DataValueUnit      DataValueType  \\\n",
       "1434  Mortality from heart failure  cases per 100,000  Age-adjusted Rate   \n",
       "1435  Mortality from heart failure  cases per 100,000  Age-adjusted Rate   \n",
       "1436  Mortality from heart failure  cases per 100,000  Age-adjusted Rate   \n",
       "1437  Mortality from heart failure  cases per 100,000  Age-adjusted Rate   \n",
       "1438  Mortality from heart failure  cases per 100,000  Age-adjusted Rate   \n",
       "\n",
       "      DataValue  DataValueAlt  LowConfidenceLimit  HighConfidenceLimit  \\\n",
       "1434      143.5         143.5               113.4                179.1   \n",
       "1435       76.9          76.9                65.8                 88.0   \n",
       "1436      100.4         100.4                85.5                115.4   \n",
       "1437       87.5          87.5                78.5                 96.4   \n",
       "1438       84.7          84.7                74.5                 94.9   \n",
       "\n",
       "     StratificationCategory1                   Stratification1  Year  \n",
       "1434          Race/Ethnicity  American Indian or Alaska Native  2014  \n",
       "1435                  Gender                            Female  2014  \n",
       "1436                  Gender                              Male  2014  \n",
       "1437                 Overall                           Overall  2014  \n",
       "1438          Race/Ethnicity               White, non-Hispanic  2014  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The most anamolous trend is mortality from heart failure. So, Exploring each state\n",
    "df_cvd_mortality_heart_failure.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cvd_3ov_2010=df_cvd_mortality_heart_failure[\n",
    "    (df_cvd_mortality_heart_failure['Stratification1']=='Overall')\n",
    "    &\n",
    "    (df_cvd_mortality_heart_failure['Year']==2010)\n",
    "].groupby('LocationAbbr').sum()\n",
    "df_cvd_3ov_2011=df_cvd_mortality_heart_failure[\n",
    "    (df_cvd_mortality_heart_failure['Stratification1']=='Overall')\n",
    "    &\n",
    "    (df_cvd_mortality_heart_failure['Year']==2011)\n",
    "].groupby('LocationAbbr').sum()\n",
    "df_cvd_3ov_2012=df_cvd_mortality_heart_failure[\n",
    "    (df_cvd_mortality_heart_failure['Stratification1']=='Overall')\n",
    "    &\n",
    "    (df_cvd_mortality_heart_failure['Year']==2012)\n",
    "].groupby('LocationAbbr').sum()\n",
    "df_cvd_3ov_2013=df_cvd_mortality_heart_failure[\n",
    "    (df_cvd_mortality_heart_failure['Stratification1']=='Overall')\n",
    "    &\n",
    "    (df_cvd_mortality_heart_failure['Year']==2013)\n",
    "].groupby('LocationAbbr').sum()\n",
    "df_cvd_3ov_2014=df_cvd_mortality_heart_failure[\n",
    "    (df_cvd_mortality_heart_failure['Stratification1']=='Overall')\n",
    "    &\n",
    "    (df_cvd_mortality_heart_failure['Year']==2014)\n",
    "].groupby('LocationAbbr').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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hw9O9e/dSj7RcXHzxxXnrrbfSqVOnbLnlljn66KPz5z//udRjUQ9HHHFEXnnllTqflxk9enS+853vZPXVV6+o/e7SslaKvn37ZsqUKfn000/zwQcf5IknnsjOO++cPn361GZ/5JFHMnv27NoPoVfC+2CNyV1uGpPxiCOOyM0335wPP/wwSfLhhx/mpptuKouDJTQm76qrrpoxY8ZkxIgRGT9+fMaMGVPSM08qVCzgueeey6OPPpqDDjooyRcPmAcNGpTRo0fXWW+//fbLQw89lFtuuaUUYzaZ+uatBPXNesMNN+SJJ57InXfemW7duuWqq66qc/qvclHfvK+++moOOOCAHHXUUfnBD35QilGXWU3Dcmkb0+uvvz4bbrhh7QtvX/3qV7Phhhtm3Lhxy33GplLfrPOvu6jl5XLK44bkrXQ1t1u5NYoba8G/01NOOSXTpk2rc+nVq1cJJ2y8fv361cnx61//utQjLRcL3m8fffTRTJs2LZtvvnntUV0qYbu8oAVv32nTpuWqq64q9VjLXZEeUy5JOe1jWbpf/OIXGTt2bJ5++ulSj9Ikttxyy3Tt2jVnn332Qmcn2HnnnfPSSy9lwoQJGTBgQP75z3/mG9/4Rll/QHlJeZMvjjC99tpr57jjjiurIyg31HvvvZdbb721zoc9v/vd75bt9nnNNdfMXnvtlbFjx2bMmDHZa6+9suaaa9ZZp1L2SfXJuiTluE+qb+ZFPd469thjSzBx01jW2/rLrqH5yn3/2xT33aS8XgepT+YBAwbkjTfeyJ133pn+/ftn0qRJ+drXvlb7AddyUen310VZ3H2yHPczS9OY7U853mfnt7TMkydPrrO/vffee5t5wqbT0KzTpk3LOuus08xTNr1y368uTX3yHXHEEamurs5LL72Uhx9+uOyPYrqkzEOGDMnNN99c+xy45oNG5XZ08Pkt7TYeNmxYPv/88wwbNqyZJ1v+FtzHHHrooQttp84777xSjtikFsy70047ZeWVV85Pf/rTfPrpp6Ucrckt7rZ9+OGHM3DgwBxxxBF1zixbTmqKBW3atMmOO+6YnXfeOZdddllGjRqV/v371z523nPPPTNr1qzcf//9JZ64adS8TvHXv/41Q4cOTf/+/TN06NBSj9VklvR8rhLft16W56+V+Dypxuabb57vfe97FVMWWpIPP/wwN9xwQ9q1a1fRBZLNNtss//jHP/LII4/k+9//fv7zn/9kn332KdvPjRVJjx490rt379r3PF588cVMnjy5tjxeSfvdpWWtFP369cusWbPy2GOPZfLkyenevXvWWmut9OnTJ4899lhmzZqVSZMmpUuXLtlwww0r5n2whuYuR43JePDBB+fzzz+vPThezdntam7vL7PG3qbf/OY3s8MOO2Tw4MElP/OkQsUCRo0alU8//TTrrrtuWrZsmZYtW+aKK67Irbfemnfffbd2vTPPPDPnnHNO7SkZy1V981aC+mbt3LlzNt544+y111656qqrMmjQoLI7ZXdSv7yzZ8/Ot7/97Wy++ea55JJLSjvwMth4441TVVWVZ555ZonrjR49Ov/85z9r/z9atmyZf/7znxk1alQzTbrs6pO1piG/uHWeffbZbLzxxstlvqZW39u2CJ555plssMEGtbfvs88+W+KJlq+avDXWXHPNdOvWrc5lwVMll4uVVlqpTo6111671CM1qW7duqWqqmqhv9ENN9xwodutErbLC1rw9u3WrVvWXXfdUo+13BXpMeWSLLjtorztvPPO6d+/f84888xSj9Ik1l133Tz44IOZPn16dt9994VKBiuuuGK+8Y1v5PTTT8/48eNz3nnn5fzzz8/cuXNLNPGyWVreJLXbq0p2/fXX5+OPP06vXr1q85522ml5+OGHy/ZDVTUfiBo7duwiXzSvpH3S0rIuSbnuk+qTeVGPt8rxQBjzW5bbuhw0JF8l7H+XlLfm+dLitsHPPvtsVl999bL7kH59buM2bdpk1113zdlnn52pU6fm8MMPL8ujplf6/XVBi7tPdu/eveJer2vM9qfm/6Br167Laarla2mZN9hggzr723LNmTQ8a7du3SriuUIl7FeXpD759txzz3z88ccZMmRI9tlnn3To0KEZJ2x6S8p80EEHpaqqKjfccENeeOGFPPTQQ2VxBMslWdptXHM/Lcf7a0MfF7Zv336h7dRaa63VnCMvk4bm3WKLLTJhwoRMmjQpAwcOzCeffNKc4y6Txt62X/va13LttdfmwQcfLNv3SGqKBc8991w+/vjj3HrrrenQoUOuvvrq/PGPf6x9naZdu3Z55513yjbngmpep9hyyy3z61//OnPmzMm5555b6rGa1OKez1Xq+9aNef769ttv56233irL1+Pq69xzz80TTzyR22+/vdSjLFennHJKWrVqlalTp2bChAm5+uqrSz1Sg6266qp5//33F1r+3nvvZdVVV639eoUVVqg9a/ltt92W6urqjBo1Ki+//HJzjrvM6pu3kgwZMiS33HJLZs6cmTFjxmT99dfPt771rXz22WcVt99dXNZK0q1bt6y33nqZOHFiJk6cmD59+iRJOnXqlA022CBTpkzJxIkT881vfjNJ5bwP1tDc5agxGdu3b58DDjig9gyUY8aMyQEHHFAW27NluU2/LO/bK1TM59NPP83VV1+dX/7yl3WO7vDkk09m/fXXz3XXXVdn/Z/+9Kc5//zzc+ihh+YPf/hDiaZuvIbmLWeNzdqnT5/07NkzP/vZz5p54mVT37w/+MEP8s477+Smm276UmyQGmuNNdZI//7985vf/CazZs1a6Pr33nsvTz31VB5//PFMmjSpzv/JX/7ylzz22GP5xz/+UYLJG64+WXfbbbesscYa+eUvf7nQ9XfeeWeef/75sjnFc33yFsEDDzyQp556KgMGDMhuu+2WNddcMxdddNEi162E/5P581J+OnTokF133TUjR45c5P22RqVslynWY8olse2qTBdeeGHuuuuuTJ06tdSjNIkuXbrkwQcfzJtvvpnddtstM2fOXOy6m222WT799NN8/PHHzThh02pI3ko1atSonHTSSQttn/v161d2R6qpsfvuu2fu3LmZO3du+vfvX+e6StsnLSnrkpTzPqmxmctdpeduaL5y3/8uKW/N86XLL788s2fPrnPdjBkzct1112XQoEFld0TLxvwNb7bZZkt8zvhlVen310VZ1H3ykEMOyb/+9a/ccccdC60/b968RX6QoRw0ZPsze/bs/P73v8/OO++cr3zlK80w3fJR7tvchihS1vlVeu6l5WvRokUGDx6cSZMmVUwRbnGZV1lllRx44IEZM2ZMRo8enQ033DB9+/YtzZBNqFL/hiv1ceHiNCbvV7/61TzwwAN56KGHcuCBB5ZNqWJZbtsVV1wxZ555Zn7605/mo48+aq6Rm0xNsWD99dfPiiuumCT505/+lA8++CBPPPFEnddqbrrpptx+++15++23Szx10zvnnHPyP//zP3njjTdKPcpyU/N8rgjvWyf1e/566aWXZoUVVsj+++/fPEOVQOfOnfPjH/84Z555Zj777LNSj7Nc3HfffbnqqqtSXV2drbbaKiNGjMgJJ5yQ6dOnl3q0BunRo0cef/zxhZY/9thj2WSTTRb7fZtttlmSlN3rNY3NW84GDhyYFi1a5Prrr8/YsWPz/e9/P1VVVRW5311c1krTr1+/TJo0KZMmTarzPK5Pnz65995788gjj6Rfv34V9z5YfXOXs8ZkHDJkSKZMmZK77747U6ZMKauDJZT7bVq+n6BeDu6+++68++67GTJkSNq3b1/nugMOOCCjRo3K3nvvXWf56aefXvti3Oeff15Wp4ptSN7nnntuoe/fbLPN0qpVq2aZdVk15ratcdJJJ+XAAw/MqaeeWjZHmK5P3tmzZ+emm27KXXfdlU8//TQzZsyos1779u3L6ujvl19+eXr37p3tt98+5513Xrbccst8+umnue+++3LFFVekf//+2X777bPzzjsv9L077rhjRo0alV/96lclmLzhlpb1mWeeye9+97scdNBBOeqoo/LjH/84q666aiZMmJBTTjklBxxwQAYOHFjqGPVWn7yVZM6cOZkxY0Y+++yz/Oc//8k999yTCy+8MHvvvXe+973vpUWLFrnqqqty4IEHZt99981xxx2Xbt265b///W9uvPHGvPrqqxk3blypY9Tb0vLW+OCDDxbaTrVr164sGrgN8f7772fatGl1lq2xxhrp0qVLaQZqpMsvvzxf//rXs+2222b48OHZcssts8IKK+Sxxx7Ls88+m2222SajRo2qmO1y0dXnccePf/zjEk23fNR321VJKmX71FBbbrllDj300Fx22WWlHqXJrLfeepk0aVL69euX3XbbLffee2/222+/HHzwwdl2223ToUOHPP300znzzDPTr1+/st/XLirvgtuqSrCo++jMmTPz97//Pdddd1169OhR57qDDz44Z511Vi688MLaN7jLRYsWLWqfA7Ro0aLOdZW2T1pS1hqVtk9qSOb5tWzZsuyO6D+/+uQuZw3NV+7736XlHTlyZHr37p3+/fvnggsuyAYbbJB//vOfOeWUU7LuuuuW3YFdkiVnfvvtt3PggQfmiCOOyJZbbplVVlkljz/+eC666KLst99+pRh3mVT6/XVRFnWfHDhwYG677bYcfPDBGTZsWHbdddd85StfyVNPPZVf/epXGTp0aFl+kGhJ258333wzH3/8cT744IP87W9/y0UXXZT//ve/ufXWW0swadOpT+b5dejQoeweP9Yo9/1LY1V67vrkO//883PKKaeU/dkpaiwp85AhQ/KNb3wjTz/9dE4++eSK+KBRJf8NV+LjwiVpTN4tt9yy9gimBxxwQG666aay+HzCsty2hxxySM4888xcfvnlOfnkk5tx6uVj1KhR2WuvvbLVVlvVWb755pvnhBNOyLXXXpvjjz++RNMtH3379s3mm2+eESNGZOTIkaUeZ5ks7fn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      "text/plain": [
       "<Figure size 4000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Depicting the cases per 100,000 for each state regarding heart failure and overall stratification criteria\n",
    "\n",
    "df_cvd_3_overall_year=pd.DataFrame({'2010': df_cvd_3ov_2010['DataValue'],\n",
    "                    '2011': df_cvd_3ov_2011['DataValue'],\n",
    "                    '2012': df_cvd_3ov_2012['DataValue'],\n",
    "                    '2013': df_cvd_3ov_2013['DataValue'],\n",
    "                    '2014': df_cvd_3ov_2014['DataValue']}, index=df_cvd_3ov_2010.index)\n",
    "ax1 = df_cvd_3_overall_year.plot.bar(rot=0,figsize=(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n",
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\2059158027.py:11: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  lr.fit(x[:, np.newaxis],y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>171.232258</td>\n",
       "      <td>173.003226</td>\n",
       "      <td>172.235484</td>\n",
       "      <td>180.393548</td>\n",
       "      <td>184.977419</td>\n",
       "      <td>2.671613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.793916</td>\n",
       "      <td>34.402902</td>\n",
       "      <td>32.391321</td>\n",
       "      <td>36.302864</td>\n",
       "      <td>36.802914</td>\n",
       "      <td>2.379415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>91.100000</td>\n",
       "      <td>94.600000</td>\n",
       "      <td>97.500000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>101.200000</td>\n",
       "      <td>0.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.700000</td>\n",
       "      <td>162.200000</td>\n",
       "      <td>156.200000</td>\n",
       "      <td>170.600000</td>\n",
       "      <td>174.150000</td>\n",
       "      <td>0.895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>176.400000</td>\n",
       "      <td>174.300000</td>\n",
       "      <td>181.400000</td>\n",
       "      <td>188.200000</td>\n",
       "      <td>197.500000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>193.450000</td>\n",
       "      <td>195.500000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>206.700000</td>\n",
       "      <td>213.900000</td>\n",
       "      <td>4.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>218.500000</td>\n",
       "      <td>226.900000</td>\n",
       "      <td>220.100000</td>\n",
       "      <td>234.200000</td>\n",
       "      <td>238.400000</td>\n",
       "      <td>9.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             2010        2011        2012        2013        2014      Slope\n",
       "count   31.000000   31.000000   31.000000   31.000000   31.000000  31.000000\n",
       "mean   171.232258  173.003226  172.235484  180.393548  184.977419   2.671613\n",
       "std     32.793916   34.402902   32.391321   36.302864   36.802914   2.379415\n",
       "min     91.100000   94.600000   97.500000   96.000000  101.200000   0.280000\n",
       "25%    156.700000  162.200000  156.200000  170.600000  174.150000   0.895000\n",
       "50%    176.400000  174.300000  181.400000  188.200000  197.500000   1.800000\n",
       "75%    193.450000  195.500000  194.000000  206.700000  213.900000   4.350000\n",
       "max    218.500000  226.900000  220.100000  234.200000  238.400000   9.660000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It's hard to see which state influence in the huge increase of the total figures per year\n",
    "# So, calculating the linear regression slope from 2010 to 2014 for each state to see \n",
    "# if the slope is positive is negative or positive.\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "df_cvd_3_overall_year['Slope']=np.nan\n",
    "for i in range (df_cvd_3_overall_year.shape[0]):\n",
    "    x=df_cvd_3_overall_year.columns[0:4]\n",
    "    y=df_cvd_3_overall_year.iloc[i,0:4]\n",
    "    lr.fit(x[:, np.newaxis],y)\n",
    "    df_cvd_3_overall_year['Slope'].iloc[i]=lr.coef_\n",
    "\n",
    "\n",
    "# Now we can filter to those state that have a positive slope\n",
    "df_cvd_3_overall_year_positive=df_cvd_3_overall_year[df_cvd_3_overall_year['Slope']>0]\n",
    "df_cvd_3_overall_year_positive.describe()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hplap\\AppData\\Local\\Temp\\ipykernel_12540\\1184304564.py:5: 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",
      "  df_cvd_3_overall_year_positive_half.drop('Slope',axis=1,inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               2010   2011   2012   2013   2014  Slope\n",
      "LocationAbbr                                          \n",
      "CO            144.2  149.1  153.2  157.3  160.7   4.34\n",
      "FL            129.3  123.4  129.5  133.9  139.3   1.99\n",
      "GA            161.7  160.6  159.2  168.6  171.1   1.93\n",
      "IA            185.0  197.1  200.1  203.9  216.2   5.97\n",
      "IN            201.6  216.6  215.9  231.8  230.0   8.99\n",
      "ME            190.0  187.3  202.3  217.2  215.2   9.66\n",
      "MN            176.4  185.4  184.6  193.3  201.0   4.99\n",
      "MO            192.0  195.2  187.2  202.3  201.6   2.29\n",
      "NC            173.5  169.9  175.8  179.6  188.3   2.42\n",
      "NH            183.5  187.4  181.4  191.5  199.9   1.80\n",
      "OR            216.2  226.9  219.3  234.2  238.4   4.64\n",
      "PA            196.5  200.2  199.9  211.7  213.5   4.53\n",
      "SC            194.9  195.1  191.3  210.7  223.1   4.36\n",
      "UT            151.7  163.8  148.3  172.6  182.5   4.72\n",
      "WA            187.5  195.8  191.2  196.8  197.5   2.33\n",
      "WI            185.6  185.8  184.2  195.6  200.6   2.84\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 4000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# depicting the second half of slope values >1.8\n",
    "\n",
    "df_cvd_3_overall_year_positive_half=df_cvd_3_overall_year_positive[df_cvd_3_overall_year_positive['Slope']>1.79]\n",
    "print(df_cvd_3_overall_year_positive_half)\n",
    "df_cvd_3_overall_year_positive_half.drop('Slope',axis=1,inplace=True)\n",
    "ax1 = df_cvd_3_overall_year_positive_half.plot.bar(rot=0,figsize=(40,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion of the analysis**: We can see IA, IN, and ME are the state with the highest slope. Then we can state that they contribute to the sudden increase of the figure between 2012 and 2014 in the total year figures of heart failure"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}