{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Week 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This week, I continued my approach from last week.  I wanted to investigate - once COVID-19 got into the US, how did it initially spread?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: biopython in c:\\users\\alex\\anaconda3\\lib\\site-packages (1.76)\n",
      "Requirement already satisfied: numpy in c:\\users\\alex\\anaconda3\\lib\\site-packages (from biopython) (1.18.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install biopython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import yaml\n",
    "import urllib\n",
    "from Bio.Phylo.TreeConstruction import DistanceMatrix\n",
    "from Bio.Phylo.TreeConstruction import DistanceTreeConstructor\n",
    "from Bio import Phylo\n",
    "import matplotlib\n",
    "import random\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Given Data - aligned sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, I started with the data given by my professor.  Below is a table of all of the COVID-19 genomes made available by the galaxy project.  Specifically, this table contains the aligned sequences corresponding to the spike protein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_table = pd.read_csv('../../data/position_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>seqid</th>\n",
       "      <th>S_1_1</th>\n",
       "      <th>S_1_2</th>\n",
       "      <th>S_1_3</th>\n",
       "      <th>S_2_1</th>\n",
       "      <th>S_2_2</th>\n",
       "      <th>S_2_3</th>\n",
       "      <th>S_3_1</th>\n",
       "      <th>S_3_2</th>\n",
       "      <th>S_3_3</th>\n",
       "      <th>...</th>\n",
       "      <th>S_1270_3</th>\n",
       "      <th>S_1271_1</th>\n",
       "      <th>S_1271_2</th>\n",
       "      <th>S_1271_3</th>\n",
       "      <th>S_1272_1</th>\n",
       "      <th>S_1272_2</th>\n",
       "      <th>S_1272_3</th>\n",
       "      <th>S_1273_1</th>\n",
       "      <th>S_1273_2</th>\n",
       "      <th>S_1273_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MT007544.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MT019529.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MT019530.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MT019531.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MT019532.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>MT334544.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>MT334546.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>MT334547.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>MT334557.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>MT334561.1</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>677 rows × 3820 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          seqid S_1_1 S_1_2 S_1_3 S_2_1 S_2_2 S_2_3 S_3_1 S_3_2 S_3_3  ...  \\\n",
       "0    MT007544.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "1    MT019529.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "2    MT019530.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "3    MT019531.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "4    MT019532.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "..          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "672  MT334544.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "673  MT334546.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "674  MT334547.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "675  MT334557.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "676  MT334561.1     A     T     G     T     T     T     G     T     T  ...   \n",
       "\n",
       "    S_1270_3 S_1271_1 S_1271_2 S_1271_3 S_1272_1 S_1272_2 S_1272_3 S_1273_1  \\\n",
       "0          A        C        A        T        T        A        C        A   \n",
       "1          A        C        A        T        T        A        C        A   \n",
       "2          A        C        A        T        T        A        C        A   \n",
       "3          A        C        A        T        T        A        C        A   \n",
       "4          A        C        A        T        T        A        C        A   \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "672        A        C        A        T        T        A        C        A   \n",
       "673        A        C        A        T        T        A        C        A   \n",
       "674        A        C        A        T        T        A        C        A   \n",
       "675        A        C        A        T        T        A        C        A   \n",
       "676        A        C        A        T        T        A        C        A   \n",
       "\n",
       "    S_1273_2 S_1273_3  \n",
       "0          C        A  \n",
       "1          C        A  \n",
       "2          C        A  \n",
       "3          C        A  \n",
       "4          C        A  \n",
       "..       ...      ...  \n",
       "672        C        A  \n",
       "673        C        A  \n",
       "674        C        A  \n",
       "675        C        A  \n",
       "676        C        A  \n",
       "\n",
       "[677 rows x 3820 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get location and date data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last time, I wanted to get extra info about these genomes, like when and where the sample was taken.  \n",
    "After rooting around on the galaxy project website, I found a script called _fetch_sra_acc.sh_.  In this file, I found out that they get their data from the NCBI.  Specifically, they pull info on the genomes from a file called [ncov-sequences.yaml](https://www.ncbi.nlm.nih.gov/core/assets/genbank/files/ncov-sequences.yaml).  I loaded the data into a dictionary to look up in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with urllib.request.urlopen(\"https://www.ncbi.nlm.nih.gov/core/assets/genbank/files/ncov-sequences.yaml\") as response:\n",
    "    text = response.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alex\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "lookup = yaml.load(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_to_description = {}\n",
    "\n",
    "for description in lookup[\"genbank-sequences\"]:\n",
    "    name = description[\"accession\"]\n",
    "    name_to_description[name] = description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I swept over my genome table to add the extra location and date info.  They changed their format since last time I used the data, so I had to update my code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_table[\"name\"] = position_table[\"seqid\"].str.replace(\"\\.\\d\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 169)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_location(name):\n",
    "    if name in name_to_description:\n",
    "        description = name_to_description[name]\n",
    "        location = description[\"country\"]\n",
    "        splits = [fragment.strip() for fragment in location.split(\":\")]\n",
    "        country = splits[0]\n",
    "        state = \"\".join(splits[1:])\n",
    "        if state == \"\":\n",
    "            state = np.nan\n",
    "        \n",
    "        return country, state\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "position_table[\"country\"], position_table[\"state\"] = zip(*position_table[\"name\"].apply(get_location))\n",
    "position_table[\"country\"].isna().sum(), position_table[\"state\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Australia', 'China', 'USA', 'South Korea', 'Taiwan', 'Nepal',\n",
       "       'Sweden', 'Brazil', 'India', 'Italy', 'Viet Nam', 'Spain',\n",
       "       'Pakistan', 'Peru', 'Israel', 'France', 'Iran', 'South Africa',\n",
       "       'Turkey', 'Greece'], dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_table[\"country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Victoria', 'Hubei, Wuhan', 'WA', 'CA', 'Hangzhou', 'WI', 'MA',\n",
       "       nan, 'IL', 'Yunnan', 'TX', 'Guangdong, Guangzhou', 'Guangzhou',\n",
       "       'Beijing', 'Snohomish County, WA', 'Kerala State', 'Shanghai',\n",
       "       'MN', 'CA, San Diego County', 'Ho Chi Minh city', 'Valencia',\n",
       "       'Gilgit', 'CT', 'Zhejiang, Hangzhou', 'San Francisco, CA', 'KPK',\n",
       "       'RI', 'GA', 'OR', 'FL', 'Anhui, Fuyang', 'Wuhan', 'North Carolina',\n",
       "       'AZ', 'NH', 'NY', 'MI', 'NC', 'VA', 'KwaZulu-Natal', 'DC', 'IA',\n",
       "       'IN', 'KS', 'LA', 'MD', 'MO', 'NE', 'NJ', 'NV', 'OH', 'PA', 'SC',\n",
       "       'UT'], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_table[\"state\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like all genomes have a country, but many don't have a state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_date(name):\n",
    "    if name in name_to_description:\n",
    "        description = name_to_description[name]\n",
    "        date = description[\"collection_date\"]\n",
    "        return date\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "position_table[\"date\"] = position_table[\"name\"].apply(get_date)\n",
    "position_table[\"date\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the genomes also have a date.  I had to get rid of one malformed date, though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_table = position_table[position_table[\"date\"] != \"2020\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_table[\"date\"] = pd.to_datetime(position_table[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem and Sequence Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I wanted to take a look at how COVID-19 started spreading inside the US.  To do this, I selected the first 100 recorded COVID-19 genomes in the US (by date)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_table = position_table.set_index(\"seqid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MT233526.1', 'MT246667.1', 'MT020880.1', 'MT020881.1', 'MT044258.1',\n",
       "       'MT044257.1', 'MT027062.1', 'MT027063.1', 'MT027064.1', 'MT039888.1',\n",
       "       'MT039887.1', 'MT106052.1', 'MT106053.1', 'MT106054.1', 'MT159705.1',\n",
       "       'MT159706.1', 'MT159707.1', 'MT159708.1', 'MT159710.1', 'MT159717.1',\n",
       "       'MT184911.1', 'MT184912.1', 'MT159713.1', 'MT159714.1', 'MT159718.1',\n",
       "       'MT159719.1', 'MT184907.1', 'MT184910.1', 'MT159709.1', 'MT159711.1',\n",
       "       'MT159720.1', 'MT159721.1', 'MT159722.1', 'MT184908.1', 'MT184909.1',\n",
       "       'MT118835.1', 'MT152824.1', 'MT159715.1', 'MT159716.1', 'MT184913.1',\n",
       "       'MT159712.1', 'MT276324.1', 'MT163716.1', 'MT276325.1', 'MT276328.1',\n",
       "       'MT163717.1', 'MT276323.1', 'MT276329.1', 'MT276330.1', 'MT163718.1',\n",
       "       'MT276326.1', 'MT276327.1', 'MT276331.1', 'MT304484.1', 'MT304489.1',\n",
       "       'MT163719.1', 'MT304482.1', 'MT304483.1', 'MT304487.1', 'MT304488.1',\n",
       "       'MT304490.1', 'MT304491.1', 'MT322394.1', 'MT322395.1', 'MT322396.1',\n",
       "       'MT322397.1', 'MT322398.1', 'MT322400.1', 'MT322401.1', 'MT322402.1',\n",
       "       'MT322403.1', 'MT322404.1', 'MT322405.1', 'MT322406.1', 'MT322407.1',\n",
       "       'MT322408.1', 'MT322409.1', 'MT322410.1', 'MT322411.1', 'MT322412.1',\n",
       "       'MT322413.1', 'MT322414.1', 'MT322415.1', 'MT322416.1', 'MT322417.1',\n",
       "       'MT322418.1', 'MT322419.1', 'MT322420.1', 'MT322421.1', 'MT322422.1',\n",
       "       'MT322423.1', 'MT322424.1', 'MT325571.1', 'MT325573.1', 'MT325574.1',\n",
       "       'MT304477.1', 'MT304478.1', 'MT304485.1', 'MT304486.1', 'MT325565.1'],\n",
       "      dtype='object', name='seqid')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_seqs = position_table[position_table.country == \"USA\"][\"date\"].nsmallest(100).index\n",
    "subset_seqs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select our distance metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I got a distance matrix among all of the selected sequences.  I decided to stick with a simple distance metric - just the number of differences between the two.  This seemed appropriate because the sequences were relatively short and well-aligned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distance(seq1, seq2):\n",
    "    return sum(seq1 != seq2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>MT020880.1</th>\n",
       "      <th>MT020881.1</th>\n",
       "      <th>MT027062.1</th>\n",
       "      <th>MT027063.1</th>\n",
       "      <th>MT027064.1</th>\n",
       "      <th>MT039887.1</th>\n",
       "      <th>MT039888.1</th>\n",
       "      <th>MT044257.1</th>\n",
       "      <th>MT044258.1</th>\n",
       "      <th>MT106052.1</th>\n",
       "      <th>...</th>\n",
       "      <th>MT322419.1</th>\n",
       "      <th>MT322420.1</th>\n",
       "      <th>MT322421.1</th>\n",
       "      <th>MT322422.1</th>\n",
       "      <th>MT322423.1</th>\n",
       "      <th>MT322424.1</th>\n",
       "      <th>MT325565.1</th>\n",
       "      <th>MT325571.1</th>\n",
       "      <th>MT325573.1</th>\n",
       "      <th>MT325574.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MT020880.1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT020881.1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT027062.1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT027063.1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT027064.1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            MT020880.1  MT020881.1  MT027062.1  MT027063.1  MT027064.1  \\\n",
       "MT020880.1           0           1           3           3           4   \n",
       "MT020881.1           1           0           3           3           4   \n",
       "MT027062.1           3           3           0           1           2   \n",
       "MT027063.1           3           3           1           0           2   \n",
       "MT027064.1           4           4           2           2           0   \n",
       "\n",
       "            MT039887.1  MT039888.1  MT044257.1  MT044258.1  MT106052.1  ...  \\\n",
       "MT020880.1           3           4           4           3           3  ...   \n",
       "MT020881.1           3           4           4           3           3  ...   \n",
       "MT027062.1           3           3           4           2           2  ...   \n",
       "MT027063.1           3           3           4           2           2  ...   \n",
       "MT027064.1           4           4           5           3           3  ...   \n",
       "\n",
       "            MT322419.1  MT322420.1  MT322421.1  MT322422.1  MT322423.1  \\\n",
       "MT020880.1           4           4           7           7           7   \n",
       "MT020881.1           4           4           7           7           7   \n",
       "MT027062.1           4           4           7           7           7   \n",
       "MT027063.1           4           4           7           7           7   \n",
       "MT027064.1           5           5           8           8           8   \n",
       "\n",
       "            MT322424.1  MT325565.1  MT325571.1  MT325573.1  MT325574.1  \n",
       "MT020880.1           4           4           4           4           3  \n",
       "MT020881.1           4           4           4           4           3  \n",
       "MT027062.1           4           4           4           4           3  \n",
       "MT027063.1           4           4           4           4           3  \n",
       "MT027064.1           5           5           5           5           4  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances = {}\n",
    "for i,seqid1 in enumerate(subset_seqs):\n",
    "    distances[seqid1,seqid1]=0\n",
    "    for j in range(i+1,len(subset_seqs)):\n",
    "        seqid2 = subset_seqs[j]\n",
    "        distances[seqid1,seqid2] = get_distance(position_table.loc[seqid1], position_table.loc[seqid2])\n",
    "        distances[seqid2,seqid1] = distances[seqid1,seqid2]\n",
    "distances = pd.Series(distances).unstack()\n",
    "distances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seqid\n",
       "MT233526.1    WA\n",
       "MT246667.1    WA\n",
       "MT020880.1    WA\n",
       "MT020881.1    WA\n",
       "MT044258.1    CA\n",
       "              ..\n",
       "MT304477.1    AZ\n",
       "MT304478.1    FL\n",
       "MT304485.1    NH\n",
       "MT304486.1    NY\n",
       "MT325565.1    FL\n",
       "Name: state, Length: 100, dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_table.loc[subset_seqs][\"state\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select colors for each state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I knew that I would want to visualize my results, so I assigned a color to each of the states in my subset of genomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WA', 'CA', 'IL', 'MA', 'WI', 'TX', nan, 'Snohomish County, WA',\n",
       "       'OR', 'RI', 'FL', 'GA', 'NH', 'VA', 'AZ', 'NY'], dtype=object)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = position_table.loc[subset_seqs][\"state\"].unique()\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_to_color = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['red',\n",
       " 'yellow',\n",
       " 'green',\n",
       " 'cyan',\n",
       " 'blue',\n",
       " 'magenta',\n",
       " 'black',\n",
       " 'maroon',\n",
       " 'olive',\n",
       " 'lime',\n",
       " 'aqua',\n",
       " 'teal',\n",
       " 'navy',\n",
       " 'fuchsia',\n",
       " 'purple',\n",
       " 'silver',\n",
       " 'gray',\n",
       " 'pink',\n",
       " 'salmon',\n",
       " 'orange',\n",
       " 'gold',\n",
       " 'tan',\n",
       " 'brown']"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_colors = [color for color in Phylo.BaseTree.BranchColor.color_names\n",
    "              if len(color) > 1 and color not in [\"white\", \"grey\"]]\n",
    "all_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#colors = random.sample(all_colors, len(countries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = all_colors[:len(states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "for state, color in zip(states, colors):\n",
    "    state_to_color[state] = color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'WA': 'red',\n",
       " 'CA': 'yellow',\n",
       " 'IL': 'green',\n",
       " 'MA': 'cyan',\n",
       " 'WI': 'blue',\n",
       " 'TX': 'magenta',\n",
       " nan: 'black',\n",
       " 'Snohomish County, WA': 'maroon',\n",
       " 'OR': 'olive',\n",
       " 'RI': 'lime',\n",
       " 'FL': 'aqua',\n",
       " 'GA': 'teal',\n",
       " 'NH': 'navy',\n",
       " 'VA': 'fuchsia',\n",
       " 'AZ': 'purple',\n",
       " 'NY': 'silver'}"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_to_color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also made a legend for my graph at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "patches = []\n",
    "\n",
    "for state in state_to_color:\n",
    "    patch = mpatches.Patch(color=state_to_color[state], label=state)\n",
    "    patches.append(patch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use Biopython to construct a phylogenetic tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I used Biopython to contruct a phylogenetic tree from my distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.tril(distances.values).tolist()\n",
    "for i in range(len(matrix)):\n",
    "    matrix[i] = matrix[i][:i+1]\n",
    "distance_matrix = DistanceMatrix(list(distances.index), matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_constructor = DistanceTreeConstructor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neighbor Joining tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I tried out a Neighbor Joining tree and an Upgma tree, and the Neighbor Joining tree worked better in the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "nj_tree = tree_constructor.nj(distance_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "nj_tree.ladderize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "for clade in nj_tree.get_terminals():\n",
    "    state = get_location(clade.name.split(\".\")[0])[1]\n",
    "    clade.color = state_to_color[state]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2sXAgQOLVW4dMmQIly5deuj5QgghhKgMKY/9eoqiEBAQgJ+fH2fPniUuLo4VK1aQklJwblhYGF26dGHXrl0V3DZRHUlnT4giKjJnz8bGhvr165OdnY2lpSVHjx4lLS0NgC5dutCyZUusra35448/uHbtGgBHjhzhueeeo0mTJnh7e2Ntba3m7JmZmWFtbU1eXh4xMTF8+umnQMF/1E+cOEF0dDSenp6cOHFCbf++ffto0aIFACkpKWg0Gqytrbl06ZJ6vhBCCCFqjyNHjmBpacmLL76obtPr9fTu3ZuzZ8+SkZHB22+/TVhYWBW2Ujwu0tmrQDqdTp22V5nWr1+Ph4cH7u7urFu3Tt0+ZswYtUOi0+lMisGI0qmJOXv79+8nISGBhIQEPvnkE2bOnFliO4YOHSrZi0IIIUQtFxsbe9+CfYXLRHr37s3vv/+uxnmJ2ks6ezVIXl4esbGxhISEEBkZya+//srevXtJSEgA4Msvv1Q7CiNHjlQD1kXp1cScvd27dxMYGIhGo8HX15f09HQuX75c7DhfX18cHR1L3UYhhBBC1C7h4eGMHTsWMzMzRowYwbZt26q6SaKSSTXOcsjIyLhvzt6pU6do0KABN2/epE6dOri7u2Nubo7BYKBBgwakp6eTm5tLu3btsLe3R1EUzp07x40bN8jPz6dFixa0aNGC9PR0jEYjderUISMjA51Ox59//smQIUMAOH/+PP7+/rRs2VJth6Io/Pzzz3h6enL48OHH/bLUGLUlZ+/ixYsm8R5arZaLFy9Kx04IIYR4Arm7u7N9+/Zi22NiYkhISGDAgAEA3L17FxcXF2bNmvW4mygeIxnZK6OMjAzy8vLuuz8rKwsnJye6dOmChYWFyfRORVHw9vamdevWnD9/HijIVbOwsMDb2xtvb28uX77M7du3gYKphTqdji5dulCvXj1u3LhBTk4OeXl5pKWlkZ2dbXLvGzduYGlpWe6MuCdRZeTsnTp1imPHjnHs2DG14xgUFMTRo0fp1KkTR48eLVfOnqIoxbZpNJpytVsIIYQQNdPTTz9NdnY2ISEh6raoqCjmzJnDkiVLMBqNGI1GLl26xMWLF9X3oqJ2kpG9MrK1tQVMR4d0Oh27d+8mIyODAQMGEB0dDcA777xDTk4Ob7zxBn5+fixfvpyePXuSkpJCz549iYiIYNSoUcTExJCeng6Ag4MDy5Ytw8rKiqVLl3LkyBH1PqGhoWzcuBFbW1vGjx9P3bp1Wbt2rbp/5syZ/PWvf+WVV155DK9E7VPTcva0Wq26zhAgOTlZLcoihBBCiKrUjIqtyNnsoUdoNBp27drFyy+/zKpVq7C2tkan0xEREUFwcLDJsQEBAYSHh7No0aIKbKOoTqSzV0nq1Kmjfm9ubq6O0hXdZ25uTm5uLlAwOrNhw4ZiIzkRERHFyulPnTqVqVOnAvDaa6+h1WrVfbm5uezcuZNffvmlYh/oCRIQEMDly5fx9/dXYwvKIjc3l/T0dBwcHNScvb/85S9AQc5eo0aNMDMzu2/O3sqVK0t1n2HDhvHBBx8wduxYfv75Z+zs7GQKpxBCCFEtXKmSu7Zo0YKvvvrqoce99957j6E1oirJNM5qwt/fn+DgYHJycgA4c+YMmZmZJR5bWDnpwoUL7Ny506SIyKFDh+jQoYNJB1A8mpqWszdkyBBcXFxo06YN06dP58MPP1TPKVqRdeHChWi1WrKystBqtSxZsqQsL48QQgghhKghpLNXTUybNg03Nze8vb3x8PBgxowZ6qjfvUaOHImbmxtDhw5l48aNNGzYUN0XHh5eqgqSomTVOWdv6NChODk54ezsTO/evdVOXVJSEnFxcTRo0ACNRmNSRtlgMKjfp6amcvfuXdzc3EhOTpbOnhBCCCFEbacoSo396ty5s1JV7OzsFDs7O5NtrVq1Uq5du1bp9163bp3i7u6uuLm5KWvXrjXZ9/777yvt2rVT3NzclAULFlR6W2qTVq1aKV5eXsqFCxcURVGUuLg4xcvLS3F3dzc5rm/fvkpUVJT6c0ZGhnLs2DElODhYmTVr1gOPLTRq1Chl06ZNiqIoyvfff69MnDix2DFpaWlKw4YNlczMTEVRFOX5559XwsLCFEVRlBkzZigffvihoiiKMn36dPX706dPK61atSrx+Y4ePar88ssvxZ5HCCGEEBUjLi6uqpsgarmSfseAaOU+/SUZ2atBHpazd+TIEXbv3k1MTAynT59m/vz5Vdzimqe65uwpisLhw4cZNWoUAJMmTVJH/DQaDTdv3gQKKrHerzhLnz59aNSoUanbKIQQQgghajYp0FIO1S1nLy4uDkdHx1KV6xc1K2cvLS0Ne3t7NZ6hMEsPYMmSJQwcOJANGzaQmZnJoUOHytVeIYQQQghRO8jIXhlVx5y9rKwsbty4wYkTJzAYDOpojyi96pqzpzwgSy8sLExdK7hv3z7++te/kp+fX652CyGEEEKImk9G9sqoOubseXh48PTTT7N+/XqioqIYM2YMv/zyiwRsP6LqmLPn4OCgjgZbWFiYZOmFhoZy4MABALp3786dO3dITU2ladOm5W6/EEIIIcqueVBzUjIrLmevmU0zrsx/cJyDra0tGRkZGI1Gnn32WWJjYyvs/qLmkZG9SnJvzl7RypoPytkzGAwYDAYSExMZOHAgQIk5eydOnOCHH36gUaNGtG3bFiiY2jdixAg0Gg1du3bFzMzMZERRlE5AQAALFy4s93TY3Nxc9fUvzNnz8PAACipjFo6+3S9nr+h6QY1GQ79+/di+fTsAX3zxBcOHDwegZcuWfP/99wDEx8dz584dmjRpUq62CyGEEKL8KrKjVxnXE7WfdPaqiYrI2Xvuuec4fPiwev7du3dxcHB4DK2vXaprzt4777zDe++9R5s2bUhLS2Pq1KkArFmzhpCQELy8vBg3bhybNm1Co9Fw6dIldW0nwLhx4+jevTu///47Wq2W0NDQ8rxMQgghhBCimpPOXjVRETl7U6ZM4dy5c3h4eDB27Fi++OILmcL5iKpzzp5Go1G/zMzM1H/bkJAQMjMz0Wg0ZGVlMXr0aABatGjBvn371GdxcXHBwsKCOnXqkJycrHYWhRBCCCFE7aQpqfBDTeHj46MUrot73Ozt7QHUNXai5tPpdNjb2/PNN9/w1FNPER8fz7hx48jNzTWZ7+7n50dQUBA+Pj4AZGZmcvLkSWJjY4mNjeWDDz6477GFnn/+eZ599lkmTZrE4cOH+fzzz4tV/fzzzz9p06YNycnJ1KtXj9GjRzNixAjGjh3Liy++iJeXFzNnzjQ5Z8OGDZw8eZLPPvus2PP99NNPtGrVirZt25KRkVHu10sIIYQQpuLj43F1dVV/1iyt+A/dlX8++L27rNmr3e79HQPQaDS/KIriU9LxMrJXgXQ63WNZI7d+/Xo8PDxwd3dn3bp16vYlS5bg5OSkjkAVHdURpVMTc/aKelCbfX19cXR0LHUbhRBCCCFEzSbVOMvhfjl7hRUUK5qiKGRlZREXF4e3tzdmZmYsXbqUf/3rX9SrVw+j0Yi1tbU66rh69WpWr15dKW2pDWpLzl6h8+fPk5iYyNNPP12u9gohhBBCiNpBOntl9KCcvTt37lRqqHqDBg0wNzcHCqaTpqam0rJly8f5+LVWZeTsOTk5cevWLUaOHMmWLVsIDAwkKCiI2bNns2nTJvr06VOunL1C4eHhjBo1Sv3dEEIIIUTVambTrMKjFx5FYVG2QmvXruX555+vsPaI6k86e+Vgbm5+35y9Nm3asH//fvR6PaNHj2bYsGFMnDgRPz8/OnfuzJo1a9i3bx/vvfcehw4d4pNPPuHq1au88cYbZGdn07NnTz7//HPOnz/PM888w8mTJ3F2diY+Pp7hw4ezY8cO6tatS//+/fHx8WHDhg0sWbKETZs28eeff+Lj48OaNWvU4i2i9Gpazl6h8PBwNm7cWO42CyGEEKJiPCwTrzIUrsvX6XRqlXfx5JI1e5XE2dkZvV4PQOfOnTEajeq+ESNGFNt+8OBBNm/ejF6vp1u3bqSlpZGQkABA165dcXZ2BsDV1ZVFixYxYMAABg0ahJeXlzoiNHPmTM6ePYvBYMDR0ZFXXnnlMT1t7VLTcvag4JO769ev071793K1WQghhBBC1B7S2SsjW1tbbG1t77u/KkLVmzVrhrm5OWZmZkyfPp3IyMiKedgnTE3L2YOCzuHYsWOLTe0s/MABYOHChWi1WrKystBqtSxZsuRRXhYhhBBCCFHDSGevmqiIUPXLly+rx+zatUsdRRKlVxNz9gDc3NzYs2cP7u7ujB8/Xt1uMBjU7y0tLdFoNNSrV4/k5GTp7AkhhBBC1HKyZq+amDZtGkajEW9vbxRFoUmTJiWW1oeCUPW0tDQsLS1NQtUXLlyIwWBAo9Gg0+n4+OOPH+cj1Aq3bt0iKSlJzdkr5O/vr07rLClnb9myZWrO3r22bt1aLGdv/vz5BAYGqjl7r776Klu2bKFfv35qB60wZ69whHfRokXMnTtXzdkLDQ1l5syZJCQksHLlSv773//SsGFD9cOAew0dOpTZs2erI8FCCCGEEKJ2k1D1MpJQ9dpHp9Pxt7/9DSsrK+bPn8+bb76JjY0NW7ZseWCoeqFNmzYRHR1dqlB1d3d3vv32W7RaLYqiYGdnx82bN02O+eSTTzh69Chbt25VPwC4cuUKFhYW/PjjjyxZsoRvv/2WhQsX0q5dO6ZNm1aq5ywMWxVCCCFExSop8FqIiiSh6kKUw6hRo9Qqmd988w1Dhw4t9zVfeOEF9Ho9y5YtUyMUCnP2AJOcvaLCw8PVKboPytk7c+YMZ86coWfPnvj6+nLgwIFyt1kIIYQQQtR8Mo2zHO4NVRc1S0mh6jUxZy83N5eEhAQiIiJITk6md+/exMbGqqPPQgghhKgazYGKS9mDZsDDwhw0Gg0TJ05ky5YtQMH7BEdHR7p168bevXvV44YPH87Vq1f58ccfK7CForqRkb0yKilU/aeffnoseSbJyclERUURFRVFcnJysf1JSUkcPXpUslXKqDBnr2j0QVmVlLMHqDl7J0+eZPny5QClztkDTHL2tFotw4cPx9LSEmdnZ9q3b6/GdgghhBCi6lRkR6+017OxsSE2Npbbt28D8N1336nvRwqlp6dz4sQJ0tPTSUxMrOBWiupERvbK4X6h6g4ODpVyv7y8POLj4xk7dizXrl3DysqKQYMGERwcrBbdSEpKYtq0aeTm5lZqW2qzgIAALl++jL+/P5cuXSrzdXJzc0lPT8fBwUHN2fvLX/4CFOTsNWrUCDMzs/vm7K1cuVL9uWjO3tixY01y9p577jnCwsKYPHkyqampnDlzBhcXlzK3WwghhBA12+DBg/n3v//NqFGj1OzeY8eOqft37NjB0KFDadasGeHh4bz66qtV2FpRmWRkrxIYjUZcXV2ZPn067u7uDBw4UP10xc/Pj0WLFtG1a1fatWun/uHl5eWxYMECunTpgqenp1pJMyIign79+jF+/Hg6duxIfHw8vr6+1KtXDwsLC/r27cuuXbvUe8+dO5fVq1cXy1sTpVfTcvb8/f1p3Lgxbm5u9OvXj3fffZfGjRsDkrMnhBBCPInGjh1LeHg4d+7cISYmhm7dupnsL+wAjhs3jrCwsCpqpXgcpLNXSRISEpg1axanT5/G3t5eLcYBBSM+kZGRrFu3jqVLlwIQGhqKnZ2dOj0zJCREHVaPjIxk+fLlxMXF4eHhwQ8//EBaWhpZWVns27ePpKQkAPbs2YOTkxNeXl6P/4FriZqYs/fFF1+wZcsWrKysMDc3N6m0KTl7QgghxJPH09MTo9FIWFgYQ4YMMdmXkpLCH3/8Qa9evWjXrh0WFhYlRkeJ2kGmcZaRra3tA/c7OzuroyqdO3fGaDSq+0aMGFFs+8GDB4mJiWH79u0A3Lhxg4SEBKysrOjatSvOzs4AuLq6smjRIgYMGICtrS1eXl5YWFiQlZXF8uXLOXjwYAU/6ZOlJubsQcE6w6KRDyWRnD0hhBDiyTFs2DDmz59PRESEScXvL7/8kuvXr6vvLW/evEl4eDhvv/12VTVVVCIZ2askderUUb83NzdXC2sU3Vd0u6IobNiwAYPBgEXvD7EAACAASURBVMFgIDExUX2Tb2NjY3LtqVOncuLECX744QcaNWpE27ZtOXv2LImJiXh5eaHT6UhOTsbb25srVx5Ws0kUNXr0aL788kvg/6Y4PIyNjQ29evXC2tq61PeJi4ujf//+APTr14/du3cXO2b79u0MHjyYevXqoSgKhw8fZtSoUQBMmjRJHfErLV9fXxwdHR/pHCGEEELUTFOmTOHNN9+kY8eOJtvDwsI4cOAARqMRo9HIL7/8Qnh4eBW1UlQ26exVE/7+/gQHB6sVNM+cOUNmZmaJx169ehWACxcusHPnTsaNG0fHjh25evWq+oer1Wo5ceIEzZs3f2zPUBvUxJw9KFho7enpyahRo9RpvUIIIYSoWs2q8HparZY5c+aYbDMajVy4cAFfX191m7OzMw0aNODnn3+uoFaK6kSmcZbDvTl7V65cYfjw4eTl5ZGYmKjuS0pKIi8vj4iICAwGAzNmzKB+/frk5ORw5coV/Pz8UBSFxMREGjZsiKIoWFpa4uHhQUZGBklJSSb3OXnyJLm5uWg0Glq3bl1snVnRthSW7hfF1ZacvaFDhzJu3Djq1KnDRx99pE4NFUIIIUTVqor5VUXX7hfy8/NT30sW/bC40IkTJyq7WaKKSGevjErK2Sv8lMTS0pIuXbqo25966in1+6LVES0tLdVzNBoNLi4uxUrm29vbFwvH7tSp00PbV/QTG/FoCnP2Nm3aVO5rlZSzFxgYqObsQcHv0o4dO0qds2dhYWGSs1dYeRNg+vTpLFq0qNztFkIIIYQQNZ909srh3pw9UTvUtJy9y5cvq2vx9uzZg6ura5nbLIQQQgghag9ZsyfEPWpazt7777+Pu7s7Xl5evP/++yYjkpKzJ4QQQgjx5NKUtBaopvDx8VGio6Or5N6F66uKVtkUQgghhBBPrvj4eJlhIypVSb9jGo3mF0VRfEo6Xkb2KpBOpyM1NbXS77N+/Xo8PDxwd3dn3bp16vb/+Z//UUeRBg4cWK4piELUNDqdjt69e5ts0+v1eHh48O2336LX69Hr9dja2tK+fXv0ej2BgYGkpaXRr18/bG1tmT17tsn5YWFhdOzYEU9PTwYNGlTs7zsoKAiNRqNuv379OgEBAXh6etK1a1eT3MUDBw7Qvn172rRpw6pVq9Tthw8fxtvbGw8PDyZNmnTfD5AGDRqEvb09zz77bLleJyGEEEI8OaSzV4Pk5eURGxtLSEgIkZGR/Prrr+zdu5eEhAQAFixYQExMDAaDgWeffZa33nqrilssxON169YtNXoiPj5e3e7v769mWPr4+LB161YMBgObN2/G2tqaZcuWERQUZHKt3Nxc5syZw5EjR4iJicHT09MkuD4pKYnvvvuOli1bqttWrFiBXq8nJiaGzZs3qyWv8/LymDVrFvv37ycuLo6wsDDi4uLIz89n0qRJhIeHExsbS6tWrfjiiy9KfLYFCxawZcuWCnuthBBCCFH7SWevEhiNRlxdXZk+fTru7u4MHDiQ27dvAwWlbxctWkTXrl1p164dx44dAwreDC5YsIAuXbrg6enJxx9/DBSs7erXrx/jx4+nY8eOxMfH4+vrS7169bCwsKBv377s2rULgAYNGqhtyMzMVEvzC/GkGD16NF9++SVQMCpXmFP4IDY2NvTq1Qtra2uT7YqioCgKmZmZKIrCzZs31QqoAHPnzmX16tUmf2dxcXH0798fgA4dOmA0GklJSSEyMpI2bdrg4uKClZUVY8eOZffu3aSlpVGnTh3atWsHwIABA9T8xXv179+f+vXrP9oLIoQQoko1bw4aTcV9lSY+ee7cuSYzv/z9/Zk2bZr68yuvvMJ7772Hh4dHZTyyqGakGmclSUhIICwsjJCQEEaPHs2OHTuYOHEiUDBiEBkZyb59+1i6dCmHDh0iNDQUOzs7oqKiyM7OpmfPngwcOBCAyMhIYmNjcXZ2Jj4+ntdff520tDTq1q3Lvn378PH5vym6r7/+Ops3b8bOzo4jR45UybPXFEWzC0XNU1Il3FGjRjF58mTmz5/PN998w9atW8s8GmZpaUlwcDAdO3bExsaGtm3bsnHjRqCg6qmTkxNeXl4m53h5ebFz50569epFZGQk58+fJzk5mYsXL5pEsGi1Wn7++We1Umt0dDQ+Pj5s375dHZkUQghR86WkPP7r9ejRg23btvHyyy+Tn59PamoqN2/eVPcfP36cdevW8dlnn1Vs40S1JCN7ZWRra4utre199zs7O6uVEDt37ozRaFT3jRgxotj2gwcPsnnzZvR6Pd26dSMtLU2dntm1a1ecnZ0BcHV1ZdGiRQwYMIBBgwbh5eVlEsa9fPlykpKSmDBhgsmUM2HKz88Pg8FQ1c0QFaxRo0Y0bNiQ8PBwXF1dqVevXpmvlZOTQ3BwMCdPnuTSpUt4enqycuVKsrKyWL58eYnTpBcvXsz169fR6/Vs2LCBTp06YWFhQUmFsDQaDRqNhvDwcObOnUvXrl2pX7++yd+zEEII8ah69uzJ8ePHATh9+jQeHh7Ur1+f69evk52dTXx8PA0bNqziVorHRd5VVJI6deqo35ubm6vTOIvuMzc3V4sxKIrChg0b8Pf3N7lOREQENjY2JtumTp2qlt1/7bXX0Gq1xe4/fvx4nnnmGZYuXVoxD1QL6fV6yUmshcaMGcOsWbNMIijKovDDgNatWwMFU0RXrVrF8OHDSUxMVEf1kpOT8fb2JjIykubNm/P5558DBX/Tzs7OODs7k5WVZTJil5ycrE4J7d69uzqd++DBg5w5c6Zc7RZCCPFka9GiBRYWFly4cIHjx4/TvXt3Ll68yI8//oidnR2enp6PFC8lajYZ2asm/P39CQ4OJicnB4AzZ86QmZlZ4rFXr14F4MKFC+zcuVNdl1Q4EggF08w6dOhQya0WovoJCAhg4cKFxT44eVROTk7ExcVx7do1AL777jtcXV3p2LEjV69exWg0YjQa0Wq1nDhxgubNm5Oens7du3cB+PTTT+nTpw8NGjSgS5cuJCQkkJiYyN27dwkPD2fYsGHA//09Z2dn88477/Diiy+Wq91CCCFE4eheYWeve/fu6s89evSo6uaJx0hG9qqJadOmYTQa8fb2RlEUmjRpwtdff13isSNHjiQtLQ1LS0s2btyoDsUvXryY33//HTMzM1q1asVHH330OB+hxtPpdERHR+Pg4FCp90lPT2fatGnExsai0Wj47LPP6N69O9u2bWPJkiXEx8cTGRlpshZTlF79+vVZtGjRI52j0+m4efMmd+/e5euvv+bgwYO4ubnxz3/+kz59+mBpaUmrVq0eOloYHx9PYGAg5ubmuLm5ERoaChTkcn7wwQf4+/uTl5fHlClTcHd3B+Ddd99l79695OfnM3PmTJ5++mkAoqOj+eijj/j0008B6N27N7/99hsZGRlotVpCQ0PL3aEVQghRO/Xo0YPjx49z6tQpPDw8eOqpp1izZg0NGjRgypQpVd088RhJZ68CFa6/c3BwMMnXmj9/vvp90WmDDg4O6jlmZmasWLGCFStWmFzTz8+vWCGRwilf97pfFT9RPeTl5WFubs6cOXMYNGgQ27dv5+7du2RlZQHg4eHBzp07mTFjRhW3tOYKCAgw+ft47rnnAPj222/VDuAff/zBhAkTqFu3Lp6enqxduxZnZ2eioqKYPHmyyVpXOzs7dQ1dTk5OsbV3QUFBnD9/Xv25Q4cOeHh4cPbsWbUwS+GHMWZmZuo6PXNzc/WcQYMG8f3335Ofn8/27dt59tlnadOmDT4+PmpHLy0tDQsLC27fvs3UqVNlPa4QQogH6tmzJ2vWrMHFxQVzc3MaNWpEeno6p0+fJiQkhIyMjKpuonhMZBqnEPeozOiMmzdv8sMPP6hrLq2srLC3twcKiu+0b9++Cp649qhpOXsAM2fOVNszfvx43n777WLPdb82CiGEqN6aNaua63Xs2JHU1FR8fX1NttnZ2VX6DCZRvcjInriv2hxNYDAY1GqpJams6AyDwUCTJk144YUX+PXXX+ncuTPr168vVoRHlE1hzt78+fPVnL2HRS8U5uz98ccfJtuL5uw1btyYmzdv0qZNG3V/Yc7e8OHD1W1xcXG8+uqrgGnO3rlz59ScPUDN2XNzc0Oj0aglsW/cuGGS5fewNgohhKjerlypmvuam5ubxC0AJksRdDqdySw0UXtJZ0+UqDCa4EEdoprOYDCYdGivXLnC8OHDycvLw8rKipdffhkoKIQTFRXFp59+isFg4MaNG/j5+XH37l1OnjyJn58fp0+fJjMzk1WrVgEFIznDhw/HzMwMS0tLXnjhBaBg5OnEiRPk5uZiZ2fHgQMHcHNzU6M1Cts1Y8YMCdB+iNqQswcFhVyGDBlC3bp1adCgAT/99FOZ2iuEEEIIcS/p7In7qs3RBPb29g+cr67RaEy+z8/PV382MzNTtxddw9WmTRsaNWpkcp309HST9Vl16tShTp06NGjQAChYtykh2hWnsnL2XFxc+Pvf/87KlSuZN28ey5cv5+DBg8XOWbx4MXPmzEGv19OxY8eH5uwBrF27ln379tGtWzfeffdd5s2bp67VE0IIIYQoD+nsiSeWra2tSWdWp9Oxe/duMjIyePbZZ9V9QUFBZGRksGTJEvz8/AgKCsLHx4fU1FR8fHyIiIjgk08+Yd++fWzbtg1LS0vOnDmDk5MTUVFRBAUFsXfvXvU+vXv35uOPP6Z9+/YsWbKEzMxM3n33XXV/0XuIR1eTcvauXbvGr7/+Srdu3dS2Dxo0qFztFkIIIYQoJJ09ISrAo0RnbNiwgQkTJnD37l1cXFzUzsGuXbv4+9//zrVr13jmmWfQ6/V8++23j/MxaoWAgAAuX76Mv78/ly5dKvN1iubsNWnSpFjOXqGikR3p6enUq1cPKyur++bsOTk5ER4ezr/+9S8aNmzIjRs3OHPmDO3atVPvIYQQQghREaSzJ8Q97l20XNHRGTqdTr3H77//zm+//Ub37t3p27cvrq6u1K1bF51OR3h4eIU/25OgpuXshYSEMHLkSMzMzGjYsCGfffYZULAuMDo6mrfeeuuBbRRCCCGEuB+JXhDiMcnLywNQc/Z+++03fv31V3UkZ9WqVfTv35+EhAT69++vFnsRpRcQEGDyc9GcPb1ej16vJzo6mgkTJqDX6wkMDCQtLQ1nZ2fu3r3L5MmTSU5OVjtR4eHh5OfnY2ZmRlJSkvpveP78efr374+npyc6nY47d+4AcOfOHWxsbLC2tubcuXM4OjqqI7xbt25Fo9FQt25dzp49S05ODgBZWVnq+s+7d+9y69YtAIYNG6Z29KDgQ4dGjRqRmZmJwWCQjp4QQgghHko6exVIp9ORmppa6fdZv349Hh4euLu7s27dOnX7ggUL6NChA56engQEBJCenl7pbamNqipnb/fu3UyaNAmASZMm3XcaqLi/iszZK1R4rMFgoGnTpkBBxyswMJCYmBjefPNNNW6hX79+6rGHDx+mXr16agTHhAkT+O233zh16hS3b99Wi7A4Oztz9OhRYmJi+J//+R/+9re/ldiOnj17cujQIVq1alUxL5YQQojK1xzQVOBX8wffLi0tTf1ws3nz5jg5OaHX62ndujXOzs78+eefAFy/fh1nZ2fOnz9fwQ8sqhvp7NUgeXl5xMbGEhISQmRkJL/++it79+4lISEBgAEDBhAbG0tMTAzt2rVj5cqVVdzimishIYFZs2Zx+vRp7O3t2bFjh7qvMGdv3bp1LF26FMAkZy8qKoqQkBASExOBgpy95cuXExcXx7lz59ScvU6dOjFt2jQyMzMBSElJwdHREQBHR0eTdWGidApz9gA1Z+9hCjPsrK2tS32fuLg4+vfvDxR08Hbv3l3smO3btzN48GC1IuiQIUPQaDRoNBq6du1KcnIyAD169KBhw4YA+Pr6qtvv1alTJ3Q6XanbKIQQohpIebzXa9y4sfqh44svvsjcuXMxGAycPXuWmTNnsnjxYqCgevTf/vY3+QDxCSBr9iqB0Whk8ODB9OrVi+PHj+Pk5MTu3bupW7cufn5+dOvWjSNHjpCenk5oaCi9e/cmLy+PxYsXExERQXZ2NrNmzWLGjBlERESwdOlSHB0dMRgMLF26FF9fX/UNZN++fdm1axcLFy5URxCg4E3j9u3bq+olqPb0en21y9nLyMgwac+9PwtTlZ2zV+iFF17A3NyckSNH8sYbb6DRaPDy8mLHjh3MmTOHXbt2cevWLdLS0mjcuLF6Xnh4OPPmzSt2vZycHLZs2cL69euL7QsNDWXw4MHlaq8QQghRkrlz59K5c2fWrVvHf/7zHzZs2FDVTRKPgYzsVZLKGhny8PDghx9+IC0tjaysLPbt21diTttnn30mbxofwGAwPFLOXtGctAfl7Pn4+ODj40O3bt3UzL2H5ewVtsPKyors7GwAsrOzsbS0LO9jPnEqMmcPCqZwnjp1imPHjnHs2DG14xgUFMTRo0fp1KkTR48excnJCQuL//vs7PLly5w6dQp/f/9i13zppZfo06cPvXv3Ntl+5MgRQkNDeeedd8rVZiGEEKIklpaWvPvuu8ydO5d169ZhZWVV1U0Sj4GM7JWRXq9/4H5nZ2f1mM6dO6tVGwFGjBhRbPvBgweJiYlRR+Nu3LhBQkICVlZWdO3aFWdnZwBcXV1ZtGgRAwYMwNbWFi8vL5M3mQDLly/HwsKCCRMmVMSj1lrVLWdvwYIFNG7cmMWLF7Nq1Sp69+7N6tWrH/OrUvNVVM4eFMQvQEGFz/HjxxMZGUlgYCAtWrRg586dQMEI7I4dO7Czs1PP++qrrwgICCjWYV+6dCnXrl1T13QWiomJYdq0aezfv99kdFAIIYSoSPv378fR0ZHY2FgGDBhQ1c0Rj4F09ipJnTp11O/Nzc3VAh9F95mbm5ObmwsUBDBv2LCh2EhAREQENjY2JtumTp2qFvh47bXX0Gq16r4vvviCvXv38v3335uMTomHS05OJi0tzeTfrrQeJWfv7Nmz6gcBderUUUdwn332WYYNG8Y///lP6tevT3R0dNkf5glWUTl7ubm5pKen4+DgQE5ODnv37uUvf/kLAKmpqTRq1AgzMzNWrlzJlClTTM4NCwsrtmb2008/5dtvv+X7779XR4ehYJrwiBEj2LJlC+3atStze4UQQogHMRgMfPfdd/z000/06tWLsWPHqrUCRO0l0zirCX9/f4KDg9Vy7GfOnFELd9yrsHDHhQsX2Llzp1qE4sCBA7zzzjvs2bOn3NPXnkRarZbGjRuXmLO3ZMkSoKDz7ePjA5Scs3fq1CliY2M5cuQIdnZ2+Pn5qaN6hWX7raysSEpK4vbt26Snp6vFOebPn8+ePXvIzs5m9erVakabeDSFOXuPMj1Fp9Mxb948Nm3ahFarJS4ujuzsbPz9/fH09ESv1+Pk5MT06dOBgt+D9u3b065dO1JSUnj99dfVaxmNRpKSkujbt6/JPV588UVSUlLo3r07er1ejVV46623SEtL46WXXkKv16u/X1BQ1KWww/r++++j1WpJTk7G09OTadOmlfk1EkII8WRRFIWZM2eybt06WrZsyYIFC0xyhEXtJZ29amLatGm4ubnh7e2Nh4cHM2bMUEf97jVy5Ejc3NwYOnQoGzduVDsLs2fP5tatWwwYMAC9Xs+LL774OB+h1qjM6IUH+f333+nTpw9QUFm16DpPUToVmbNnY2ND/fr11fWTR48eJS0tDYAuXbrQsmVLrK2t+eOPP7h27RpQsO7uueeeo0mTJnh7e2Ntba2O8I4ZMwYLCwtyc3Px9vZW4xrmz59Phw4diI+PZ+LEiSYjuvv27aNFixZAwQcK1tbW5OXlERMTo0Y3CCGEqMaaVY/rhYSE0LJlS3Xq5ksvvcRvv/3G0aNHK7BxolpSFKXGfnXu3FmpKn379lX69u1bZfevbLX9+ezs7BQ7OzuTba1atVKuXbumJCYmKubm5srJkycVRVGU559/XtmyZYuiKAWvy7x58xRFUZR///vfSv/+/RVFUZSPP/5YWbZsmaIoinLnzh2lc+fOyrlz55QjR44o9erVU86dO6feR6fTKZ06dVK8vb2Vjz/+WN3evXt35euvv1YURVHWrFmj2NraVtLT106tWrVSvLy8lAsXLiiKoihxcXGKl5eX4u7ubnJc3759laioKPXnjIwM5dixY0pwcLAya9asBx5baNSoUcqmTZsURVGU77//Xpk4cWKxY9LS0pSGDRsqmZmZiqIU/L7k5+cr+fn5ytixY5UPP/xQURRFSUlJUSIjI5XXXntNeffdd+/7fCdOnFASExPV31MhhBDVT1xcXFU3QdRyJf2OAdHKffpLMrInnkiFozz3U5YCO5s3b0av19OtWzfS0tLU/MOiBXYA/vvf/3LixAn279/Pxo0b+eGHH4CCCqobN26kc+fO3Lp1S6pklUFNzNlr2rQpXbp0eWj1VcnZE0IIIcSjkgIt4on1oJy9S5cuqfuSkpLIy8vj0KFDGAwG/vGPf1C/fn1ycnJITk5Wc/YcHR2xt7cHwN7enhUrVpCenk5SUtJ98/Ju3LjBCy+8wFNPPaVuq1+/Pv/+97/JycmRnL0HqI05e0IIIYQQFUk6e+KJVFLOXnZ2Njk5OSaVEkurYcOGXLp0CXt7e8zMzMjKyiqxqmdeXh5RUVFqXMbt27dxd3cH4Pr16yQmJpKfn092drZUyCqDysjZc3Jy4tatW4wcOZItW7YQGBhIUFAQs2fPZtOmTfTp06dCcvaEEEIIISqadPbEE+venD0LCwu2bNlCnTp1HjlnLz8/nzfeeINvvvnGJHrh5MmTas5eXl4e58+fp0OHDrRq1QqA8ePHq5UcO3ToQP369bGxscHLy4tr166VOHolHqwm5uwJIYQQQlQG6ewJ8f8VRi9kZGSQl5fH9OnTOX78OE5OTiZrsrZt28ZLL71Eenq6OkVQURRycnKwtrYmOzubsWPHqm/+MzMzGT9+PAaDgbi4OFq0aMHRo0dxcHAwuX+rVq2YMmUKY8aMISwsjG+++ebxPXwtUtNy9oQQQgghKou84xCiBAkJCcyaNYvTp09jb29vEoOQm5tLZGQk69atY+nSpQCEhoZiZ2dHVFQUUVFRhISEqGHpkZGRLF++nLi4OAA0Gg0DBw6kc+fOfPLJJ+p1161bx4IFC3jqqaeYP39+sc6CKJ2alrN35coVtFot7733Hm+//TZarZabN28CkrMnhBBCiPKRzp54IlXHapzBwcGsXbuWpKQk1q5dy9SpUyvykZ8INTFnr1mzZowYMYKmTZvSsmVL9uzZQ4MGDQDTnL2UlBQ0Gg3W1tZcunRJcvaEEKIGaN68uVqJuSK+mjdvXtWPJGoY6ewJUYKixVXMzc1NAu4L9xXdrigKGzZswGAwYDAYSExMZODAgUBBaf+iCt+8N23alICAACIjIwH44osv1I7k888/r24XpXfr1i2SkpIAiI+PV7f7+/ur/zY+Pj5s3boVg8HA5s2bsba2ZtmyZQQFBZV4zcJjDQYDTZs2BQqC0AMDA4mJieHNN99UO279+vVTjz18+DD16tVTfw8mTJjAb7/9xqlTp7h9+7baWdu/fz8JCQkkJCTwySefMHPmzBLbMXToUPmdEEKIGiYlJaVaX0/UftLZE6IC+Pv7ExwcTE5ODgBnzpwhMzOz2HGZmZncunVL/f7gwYN4eHgAqGv5AA4fPkzbtm0fU+trj5qYs7d7924CAwPRaDT4+vqSnp7O5cuXi13P19dXKrQKIYR4KKPRiKurK9OnT8fd3Z2BAwdy+/ZtQkJC6NKlC15eXowcOZKsrCwAJk+ezD/+8Q969OiBi4sL27dvr+InEBVJCrSIJ9aDcvYSExOL5exFRERgMBiYMWOGmrN35coV/Pz8UBSFxMREGjZsiKIoWFpa4uHhQUZGhknO3u3btzl9+jRQMBrYtGlTVq1axapVq7C2tmbo0KEoioKZmRlt27aVnL0HqC05excvXjTJWdRqtVy8eFE6dkIIIcosISGBsLAwQkJCGD16NDt27GDEiBHq2vM33niD0NBQ/v73vwMFkUH/+c9/+O233xg2bBijRo2qyuaLCiSdPfFEioiIKNaR8vX1BcDS0pIuXbqo24u+ES+6zs/S0lI9R6PR4OLigouLi8k17e3t1aB1gLp16+Lj41Nim+zs7OjcuXPZHkgANTNnT1GUYsdoNJpytVsIIcSTraTaA7Gxsbzxxhukp6eTkZFh8v+o5557DjMzM9zc3GSqaC0jnT3xxJIMu9qppuXsabVadZ0hQHJysrquUwghhCiLe2sP3L59m8mTJ/P111/j5eXFpk2bTN4HFT2+pA8hRc0la/aEELVKQEAACxcuLHFU7VHk5uaSmpoKoObsFa6vTE1NJT8/H+C+OXv3rhcszNkLCwszydkbNmwYmzdvRlEUfvrpJ+zs7GQKpxBCiAp369YtHB0dycnJYevWrVXdHPGYSGdPCFGr1LScvSFDhuDi4kKbNm2YPn06H374oXpO0WnDCxcuRKvVkpWVhVarZcmSJWV5eYQQQjxGzZo1qzbXW7ZsGd26dWPAgAF06NChAlslqjNNTR6q9fHxUaKjo6vk3oXrvWrrVMDa/nxCiOpFp9Px1FNPcezYMXWbXq8nNzeXNWvWsGjRIgD++OMPnJycqFu3Lp6enqxdu5ZRo0YRFRXF5MmT+eCDD9Tzw8LCWLFiBRqNhhYtWvC///u/ODg4qPuDgoJYsGAB165dw8HBgevXrzNlyhTOnj2LtbU1n332mTqaO2XKFPbu3UvTpk2JjY1Vr/Hnn38yZswYjEYjOp2Or776ioYNGxZ7vkGDBvHTTz/Rq1cv9u7dW+GvnxCieoiPj8fV1bWqmyFqsZJ+xzQazS+KopRYFEJG9oQQQlQLFZmTmJuby5w5czhy5AgxMTF4enqadASTkpL47rvvaNmyKnvNzgAAIABJREFUpbptxYoV6PV6YmJi2Lx5M3PmzFH3TZ48mQMHDhRr86pVq+jfvz8JCQn079+fVatWlfhsCxYsKHdlWCGEEOJRSWdPCCFEtVCROYmKoqAoCpmZmSiKws2bN00K38ydO5fVq1ebVD4tmp/YoUMHjEajWpWuT58+NGrUqNj9d+/ezaRJkwCYNGkSX3/9dYnt7N+/P/Xr13/o8wghhBAVSapxilLT6XRER0ebTIOqDOvXryckJARFUZg+fTovv/wyANu2bWPJkiXEx8cTGRl53wiD0pIMOyGqTmXnJFpaWhIcHEzHjh2xsbGhbdu2bNy4EYA9e/bg5OSEl5eXyTleXl7s3LmTXr16ERkZyfnz50lOTn7gGpmUlBS1oI6joyNXr14tU3uFEEKIyiAje6LayMvLIzY2lpCQECIjI/n111/Zu3cvCQkJAHh4eLBz50769OlT7nv5+flhMBjKfR0hRMWpyJzEnJwcgoODOXnyJJcuXcLT05OVK1eSlZXF8uXL1QI5RS1evJjr16+j1+vZsGEDnTp1MslPFEIIIWoa+b+YeGRGo5HBgwfTq1cvjh8/jpOTE7t376Zu3br4+fnRrVs3jhw5Qnp6OqGhofTu3Zu8vDwWL15MREQE2dnZzJo1ixkzZhAREcHSpUtxdHTEYDCwdOlSfH191Td5ffv2ZdeuXSxcuLDCFzzr9XopQCNENVNROYmFH+a0bt0aKJgiumrVKoYPH05iYqI6qpecnIy3tzeRkZE0b96czz//HCiYBurs7Iyzs/MD79OsWTMuX76Mo6Mjly9fpmnTpuVqtxBCCFGRpLNXDgaDodZOBTQYDCZl3++VkJBAWFgYISEhjB49mh07djBx4kSgoDBCZGQk+/btY+nSpRw6dIjQ0FDs7OyIiooiOzubnj17MnDgQOD/sXfv8THd+R/HX5MREUkkiEskmLgEuY6IoCqCxbZN3asXu6ErSstWqaLbdt1Wy8pWW7XaRS+s0paiLJqfS9BVTYKhRAiSSoQgBEnIZXx/f0xzNiMJEQkSn+c+PHbmnPM953smaWa+8728ISYmhsOHD+Pp6cnRo0d56623yMjIwN7enk2bNt3zcE0hRNUxcOBAzp49S9++fUlLSyv3edzd3YmPj+fChQs0aNCA//u//6Ndu3b4+flZDbUsOjw9MzOT2rVrU7NmTZYsWUJISAh16tS57XX69evHl19+ydSpU/nyyy/p379/uesshBBCVDRp7JVTdHR0tW3olYWnp6fWGOzQoQPJycnavkGDBhXbHhUVxaFDh1i9ejUAV65cITExkZo1axIcHKx9e96uXTumTJlC7969cXR0JCAgQIZRCfEIKcxJvBsGg4GrV6+Sl5fHunXriIqKwtvbm2nTphESEoKtrS3Nmze/Y2/h0aNHCQ8PR6/X4+3tzdKlS7V9zz//PNHR0Vy8eBEPDw9mzJjByJEjmTp1KkOHDmXp0qU0a9aMb7/9FoC4uDg++eQTlixZAkC3bt1ISEggKysLDw8Pli5dSt++fe/uxRFCVDmRjRuT/dtCTxXBoVEjJp07d9tjZs+ezVdffYVer8fGxoZPP/2UTp063fW1pk+fjqOjI5MmTSpvdUv0/fffEx8fz9SpU0vc/8UXXxAXF2e1gnJJ8vPzeeedd1izZg12dnbUrl2bGTNm8MQTT1RYXZOTk9mzZw8vvPBCucoPHDiQ4cOHM2DAAADatGnDH//4R95++20ABg8ezLBhw7TPzuPHj2f16tWkpKRgY1Mxs+3kU/Q9qM5DAO/UkLWzs9Me6/V6rl+/XmyfXq+noKAAsAyJWrBgQbEPN9HR0Tg4OFhtGzlyJCNHjgTgL3/5Cx4eHuW+j7txPxagOXbsGM8++6z2/NSpU8ycOZPXXnsNk8nEmDFjuHHjBjVq1OCf//wnwcHBlVaX6uZhz2nbsmUL48ePx2w2ExERob3JdevWjWvXrgFw/vx5goODS1zRsbrntBX9wqiQwWCwyrSDkv/ullQWLEH2Y8aMKfN1u3Tpos0RvtXKlStL3F6/fn22bdtWbHtQUJDW0AOsfi+FEI+OimzoleV8P/30Exs3bmT//v3Y2dlx8eJF8vLyKrQO96pfv37069fvns/zzjvvcPbsWQ4fPoydnR3p6ens3LmzAmr4P8nJyXz11Vflbuw99thj7NmzhwEDBpCRkYGjoyM//fSTtv+nn37SFg+7efMma9eupWnTpuzatavCOpVkgRZxX/Tt25dFixaRn58PwPHjx8nOzi7x2MIhVqdPn+a7774r0/LrVYHZbKZNmzZaXti+ffuoXbs2AwcOBGDy5MlMmzYNk8nEzJkzmTx58gOucdXzsOa0mc1mxo4dy+bNm4mPj2flypXEx8cDlkZAYd26dOmifbt3K8lpE0IIcSdnz57F1dVV++Ld1dVVi50xGAxMmzaNwMBA/Pz8SEhIAODSpUsMGDAAf39/OnfuzKFDh7TzxcfHExoaSosWLfjoo4+07e+//z6+vr74+vrywQcfAJaGUdu2bYmIiMDX15dhw4axdetWunbtSuvWrYmJiQEsPXfjxo0DLCut+/r6EhAQYLUAX1paGr///e9p3bp1iZ+HcnJyWLx4MQsWLNDutVGjRgwdOhSwfEHn5+eHr6+v1WgRR0dH7fHq1asZMWIEYMlSffXVV3nsscdo0aKFNhJt6tSp7N69G6PRyPz58+nWrZvVAn9du3a1er1u1bVrV/bs2QPAnj17CAsL48KFCyilSEpKwt7ensaNGwOwY8cOfH19efnll0v9grE8pLEn7ouIiAi8vb0JDAzE19eX0aNHa71+txo8eDDe3t48/fTTLFy4kLp16wKwdu1aPDw8+Omnn3jqqacqbQhUcnIy7dq1Y9SoUfj4+NCnTx+t5zI0NJQpU6YQHByMl5eX9m292WzmjTfeoGPHjvj7+/Ppp58Cll6IHj168MILL+Dn52d1nW3bttGyZUuaN28OgE6n4+rVq4BlmGvRTDBRNg9rTltMTAytWrWiRYsW1KxZk+eee47169dbXe/atWts375dG+pxK8lpE0IIcSd9+vQhJSUFLy8vXnnllWI9Xa6uruzfv5+XX35Z+5Jz2rRptG/fnkOHDvHuu+8SHh6uHZ+QkMAPP/xATEwMM2bMID8/n3379vH555/z888/s3fvXhYvXsyBAwcAy+iZ8ePHc+jQIRISEvjqq6/48ccfiYyM5N133y1W35kzZ/LDDz9w8OBBvv/+e227yWTi66+/5pdffuHrr7/WvsgtdOLECZo1a1bivOq0tDSmTJnC9u3bMZlMxMbGlpqBWtTZs2f58ccf2bhxozb6Zs6cOVoDb8KECURERGhTAo4fP05ubi7+/v6lnrNDhw4cPnyYvLw89uzZQ5cuXWjTpg1Hjx5lz549dO3aVTu28HPLwIED2bhxo9ZBcq9kGKco1a0L0BgMBoYMGQJY/ljc2r1c+Lzo2G6DwWB1XP369QHLB+miCxkUPUav12sr2s2aNYtZs2Zp+1q1akWrVq0AyM3NLXcXd+G3MkXLnzt3jv79+2M2m0lISMDBwYEGDRpgMpkIDAykUaNGmEwmTp48ScuWLbl+/Tr9+vUjICCAtLQ08vPzad68Ofb29kyaNInPPvuM3NxcfvnlF4KCgmjYsKHV9Y4dO4ajo6O2LTs7m+HDhzN8+HCUUrRv3/6Rnhd6J1Upp+3MmTM0bdpUO87Dw4Off/7ZquzatWvp1avXHRcEEUIIIUrj6OjIvn372L17Nzt27ODZZ59lzpw5Wg9W0XUVvvvuOwB+/PFH1qxZA0DPnj3JyMjgypUrADz11FPY2dlhZ2dHw4YNSU9P58cff2TgwIHaNJxBgwaxe/du+vXrh6enp/blto+PD7169UKn0+Hn51fikPuuXbsyYsQIhg4dajWypVevXjg7OwPg7e3Nr7/+avU+ejuxsbGEhobSoEEDAIYNG8auXbtK/TK10IABA7CxscHb25v0UobLPvPMM8yaNYt58+bx2Wefaa9raezs7PDx8WH//v3s3buXyZMnc+rUKfbs2cOBAwd47LHHAMjLy2PTpk3Mnz8fJycnOnXqRFRUFE899VSZ7vl2pGdPlMhkMpGVlfWgq1FpsrKybnt/9vb2Wle/o6MjN27c0PYVztlycnLStl++fJn09HTi4uLYv38/BQUFWm+gk5MT9vb2Vue/efMmFy9e1P4QgeUbpZYtW9K5c2datmzJsWPHKuZmHyEPa06bUqrYsUV7BKHsPZFCCCHE7ej1ekJDQ5kxYwYff/yx1pCD0tdVuFXhe9StazQUFBSUePyt5wewsbHRntvY2JQ4ouuTTz7hb3/7GykpKRiNRjIyMkq9blGtWrXi9OnT2pz3om5Xv6LvvUU/2916zdLOUbt2bXr37s369ev55ptvyjSX77HHHmPXrl1cu3aNunXr0rlzZ/bs2WPVs7dlyxauXLmCn58fBoOBH3/8scKGckrPniiVo6NjtV2ExsXFBbDuHTIYDKxfv56srCzCwsK0fZGRkWRlZTF9+nRCQ0OJjIwkKCiIixcvEhQURHR0NIMHD+all14qcQGayMjIYgtqrF+/noULFxIVFaVtc3Z2JiUlBZ1Oh1IKZ2fnavv6V6aHMactJyfHaghKamqq1ZDQjIwMYmJiWLt27T3VWQghxKPt2LFj2NjY0Lp1a8DyXlY4XaQ0ISEhrFixgnfeeYfo6GhcXV1vO8okJCSEESNGMHXqVJRSrF27ttyjaE6ePEmnTp3o1KkTGzZsKDZcszS1a9dm5MiRvPrqq3z66afUrFmTs2fPsm3bNnr16sX48eO5ePEidevWZeXKlfz5z38GLPP6jh49Sps2bVi7du0dp0c4OTkVa1BGRETw9NNP061bN+rVqwdYYsQ+/vhjli1bVuwcXbt25fXXX9dGa/n7+7N3717S09Px8fEBLF/4LlmyRPvSNzs7W/v8cC9fXIP07AlRIe5mARoouRenSZMm2tj67du3a3+oxd0ZOHAgkydPvuc5nUVz2oBiOW3JyckkJyfj4eHB/v37ady4MZmZmdqqZ0Vz2jp27EhiYiJJSUnk5eWxatUqq5XIvv32W8LCworNGxRCCFG1OTRqdF/Pl5WVxfDhw/H29sbf35/4+HimT59+2zLTp08nLi4Of39/LTP0dgIDAxkxYgTBwcF06tSJiIgI2rdvf7e3AlgWHytcSCUkJKTY9Ijb+dvf/kaDBg3w9vbG19eXAQMG0KBBA9zc3Hjvvffo0aMHAQEBBAYGalOH5syZQ1hYGD179sTNze2O1/D396dGjRoEBAQwf/58wDIEtk6dOrz44ovacadPny42iqvQY489xqlTp+jSpQsANWrUoGHDhgQFBWFjY0NOTg4//PCD1ZDNwvUENmzYUObXo1SFixBUxX8dOnRQonI4OzsrZ2fnB12NSlPS/TVv3lxduHBBJSUlKR8fH237vHnz1LRp05RSSnXv3l3FxsYqpZS6cOGCat68uVJKKbPZrN58803l6+urfHx8VGhoqMrMzFQ7duxQTz31lNV1srOzVb169VRmZqbV9t27d6vAwEDl7++vgoODVVxcXAXfdfVW+PMr6tafpVLWP8OiZevWrascHByUu7u7OnLkiFJKqUWLFqm2bdsqPz8/FRYWpi5evHjb6+7Zs0e1atVKtWnTRg0cOFBdunRJO+4///mPat26tWrRooX629/+VqxOmzdvttoWGxurRo4cqT1//PHHlaurq6pVq5Zyd3dXW7ZsKetLI4QQ4j6Jj49/0FUQlezMmTOqdevWymw2a9smTZqkDh48eF+uX9LvGBCnSmkvSc+eELe4Ndtr0qRJ2rdi0dHRBAUFAZa5e4WTjW1sbHj33Xf55ZdfOHz4MDt27MDZ2ZnQ0FCrIZzHjh3jscceo2nTpnTv3p06depoSxYvWLAAs9mMTqcjPT1dyxoUZVcYY1GocDL2Dz/8gNFoxGg0EhcXx7BhwzAajYSHh5ORkYGnpyd5eXmMGDGC1NRUvL29AVi1ahU3b97ExsaGlJQUzGYzAL/++iu9evXC398fg8Ggjfu/ceMGDg4O1KpVi1OnTuHm5qatAHbq1Clu3rzJqVOnGD16tFbHy5cvU7duXSZPnkxwcLD2u3drTtuzzz6Ls7MzN27cwGQySSC3EEIIcZ8tW7aMTp06MXv2bKvQ83nz5t12Vc4HSebsCXGfFM3ZK3zu7u6uNVAKIwMAXn/9dW0VKlF2hTl7TZs2LZazV9g4KjrvEizj4mfNmsXhw4eLBXgDrFixQju20KRJkwgPD2f48OFs376dN998k+XLl9OjRw/t53vp0iVatWpFnz59AMuY/bCwsGIrrBZm861du5aEhATGjh1bYkh3aeWFEEIIcX+Eh4dbRVNUBdKzJ8QtHlTOXiGlFN98842szFgOFZmzdztF8/R69OhRLDMPLGGtTzzxhDaxun379hgMhtueq2g2361KKy+EEEIIURrp2ROPrKysrIcqZ69QZmYmmZmZjBo16v68EFVUZefsFXrxxRfR6/UMHjyYt99+G51OR0BAAGvWrGH8+PGsXbuWa9eukZGRoeVIgmUI6MSJE+94/tKy+RpV8KR+IYQQQjx6pGdPiBI8iJy9QufPn9dC5cXdqcicPbAM4fzll1/YvXs3u3fv1hqOkZGR7Ny5k/bt27Nz507c3d2pUeN/352dPXuWX375pUzz6krL5hNCCCGEuFfyiUI8sm7NEXzQOXsABQUFuLu7s3PnTjw8PCrlvqu7isrZA0v8Alga7C+88AIxMTGEh4fTpEkTvvvuO8DSQ7xmzRqrOZbffPMNAwcOxNbW9o7XqFOnTonZfEIIIYQQ90oae0JUgMKcvZ49e2Jra8vx48e1hkJJSptPtnXrVtq2bSsNvXswcOBAzp49S9++fUlLSyv3eQoKCsjMzMTV1ZX8/Hw2btzI7373OwAuXrxIvXr1sLGx4b333uNPf/qTVdmVK1fy3nvvlek6mZmZ1K5dm5o1a1pl8wkhhKj6IiMbk51dfB52eTk4NGLSpHN3PC41NZWxY8cSHx/PzZs3CQsLY968eezZs4f+/fvTokULrl+/TlhYGJGRkRVWP/HwkWGcQvwmNTWVjIyMcpWNiIjA29ubwMBAfH19GT16NAUFBcWOO3bsGP7+/qxevZr333/fKnoBYNq0aSQkJODj48PkyZPLfS+PMicnJ6ZMmULNmjXLXMZgMDBx4kS++OILPDw8iI+PJzc3l759++Lv74/RaMTd3V2bRxkdHU2bNm3w8vIiPT2dt956SztXcnIyKSkpdO/e3eoaH330ER4eHqSmpuLv709ERAQAR48excfHh7Zt27J582Y+/PBDrcyTTz6pNVhLKy+EEOLhVZENvbKeTynFoEGDGDBgAImJiRw/fpysrCztvapbt24cOHCAAwcOsHHjRv773/9WaB3Fw0V69oT4jYeHB/Xr18fV1bVYzl6hosM+S8rZe/fdd63OGRoaqi3AUhi9cOjQIe150eiFHTt24OTkxOnTp7Gzs+P8+fOVcJfV28CBA7UVUsE6Z2/KlCkAnDhxgmHDhmFvb4+/vz/z58/H09OT2NhYRowYwccff6yVd3Jy4uzZs9jb27Nz504yMjJo2LAhHTt2pFmzZly4cIETJ05w4cIFPDw82LFjBxMmTKBBgwYEBgaSkJDAqlWrGDBgADY2NtSqVQuz2cyhQ4e0uZ/e3t60bduW06dPc+zYMdatW8eLL74IwKZNm7S6pKeno9PpqFWr1j31WAohhKjetm/fTq1atbT3Er1er73X9ejRQzvO3t4eo9HImTNnHlRVxX0gPXtC3OJBRS8sWrSIqVOnYmdnByCLtJRDYc4eUCxnz2QyYTKZCAoKYsWKFZhMJpYtW0atWrWYNWtWqcNYCo81mUzaz6QwZ+/QoUP89a9/5c033wTQcvZMJhPbt2+ndu3aVjl7W7duLRa1sXDhQry9vTl48CDR0dG8/vrr5OXlFavH008/TUxMzL2/SEIIIaq1I0eO0KFDB6ttderUoVmzZpw4cULbdvnyZRITEwkJCbnfVRT3kTT2hChBYmIiY8eO5ciRI7i4uLBmzRptX0FBATExMXzwwQfMmDEDgKVLl+Ls7ExsbCyxsbEsXryYpKQkAGJiYpg9ezbx8fFW11i1apXVvL3jx4+ze/duOnXqRPfu3YmNjb0Pd1q9VMWcPZ1Ox7Vr11BKkZWVRb169UpcjbNz5864ubmVuY5CCCEeTUopdDpdqdt3796Nv78/jRs3JiwsjMaNGz+AWor7RRp7QpTA09MTo9EIQIcOHbThmgCDBg0qtj0qKoply5ZhNBrp1KkTGRkZJCYmAhAcHFxsdcW8vDy+//57nnnmGW1bQUEBly9fZu/evcybN4+hQ4eilKrEu6x+hgwZoq2SuWHDBp5++ul7PueLL76I0Whk1qxZ2s+jMGcPsMrZK+rWxnxpxo0bx9GjR2nSpAl+fn58+OGH2NjIn2YhhBDl4+PjQ1xcnNW2q1evkpKSQsuWLenWrRuHDh3il19+YdGiRZhMpgdUU3E/yJw98UgyGo2YTKZSQ9XT0tK0fSkpKZjNZrZu3YrJZOLVV1/FycmJ/Px8UlNTCQ0N5ciRI7i5ueHi4gKAi4sL7777LpmZmaSkpBQLTr948SI3b97k2Wef1balpaWxa9cubTz9uXPn6Nq1610tNPIoKSlUvTJy9tzd3bl27RqDBw9m+fLlhIeHExkZybhx4/jiiy8ICQm5p5y9H374AaPRyPbt2zl58iS9e/emW7dusiKnEEKIcunVqxdTp05l2bJlhIeHYzabef311xkxYoTV+6KXlxdvvvkmc+fOZeXKlQ+wxqIyydfH4pFkMpnIysqqsPPVrVuXtLQ0bt68CUBOTg5ms7nU40sKTnd1deXy5ctaeaVUmXLahLXCnL2y9KrdSUk5e4CWs3fgwAFmz54NUO6cvc8//5xBgwah0+lo1aoVnp6eJCQk3HPdhRBCPHgODo3u+/l0Oh1r167l22+/pXXr1nh5eVGrVq1ii8gBjBkzhl27dmlTT0T1Iz174pF1a6h6jRo1+Oyzz7Czs7vrUPWbN2/y9ttvs2HDBpRSNGjQgHXr1nHgwAGrUPVjx47xzDPPcOnSJRo1asT+/fuZOXMmr732Gu+88w7/+Mc/OH/+PDqdjmnTpmkLf4iyq2o5e82aNWPbtm1069aN9PR0jh07RosWLcpdbyGEEA+PsmTiVYamTZuyYcOGYtuLrhIOlhU5ZTXOak4pVWX/dejQQYnK4ezsrJydnR90NSpNSffXvHlzdeHChUq7ZkFBQbHnjRo1UsnJyUoppaZNm6bmzZtXadev7kr6+SUlJSkfHx+rbd27d1exsbHFytatW1c5ODgod3d3deTIEZWVlaUCAwOVn5+f8vb2Vq+++qr2M/z2229Vq1atVOvWrdXIkSPVjRs3rK7ZpEkTZTabra7x4YcfKnd3d6XX65Wbm5saOXKkUkqpM2fOqN69eytfX1/l4+Ojli9frpUJCAjQHr/xxhvK3d1d6XQ65e7urqZNm1b+F0sIIUSliI+Pf9BVENVcSb9jQJwqpb0kwziFuMWDil4Q964ws7BQ0Zw9o9GI0WgkLi6OYcOGYTQaCQ8PJyMjA09PT/Ly8hgxYgSpqal4e3vj4OCAk5MTubm52Nraajl7gJazV6tWLS1nDyxZiQMGDNBy9mrVqsW6desAiuXsLVmyBLAMCY2KiuKzzz7j6NGjVquCFp00b2tri06no3bt2qSmpjJ9+vRKex2FEEIIUT1IY0+IEjyI6AWAjz/+GH9/f/70pz9p8/dE2VXFnD2wfFkwZcqU2y7oIjl7QgghhLhb0tgTogQPInrh5Zdf5uTJk5hMJtzc3Hj99dcr8Q6rp6qYswewYMECBg8eXGzRnqIkZ08IIYQQd0sae+KRVDikrzR2dnbaY71eT0FBQbF9RbcrpViwYIHWq5OUlKT16Dg4OBQ7/+bNmwkMDKRRo/+tqtWoUSP0ej02NjaMGjVKenHKoSrm7J05c4a1a9cyZsyYe66rEEIIIURRshqneGTdLmcvKSmpWM5edHQ0JpOJ0aNHazl7586dIzQ0lLS0NIYNG4a3tzc2Njbk5ORgZ2enDSu8NWcvPj6eevXqWW3Pzc3VGpKpqalcvXq1WDnxP9UlZ++1115j7ty56PX6e6qrEEIIIcStpLEnHkkl5ezl5uaSn5+Pjc3dd3i7ublx48YN9u/fr+Xj+fr6FjsuJyeH+Ph4srOzycnJ4cSJExgMBjw8PDh16hTZ2dnk5+eTl5dHhw4dyn1/j7LCnL0vvvjins9VUs5eeHi4lrMHkJWVxZo1a8qdsxcXF8dzzz0HWCIdNm3aRI0aNbTFZYQQQlRdjWlMOukVdr5GNOIct49z0Ov1+Pn5UVBQgKenJ8uXL8fFxYXk5GTCwsI4fPhwhdVHPPyksSfKzGAwEBcXh6ura6Ve58MPP2Tx4sUopRg1ahSvvfYaAJcuXeLZZ58lOTkZg8HAN998Q926dct9nVtz9gwGA5s2baq0+zObzVa9N2azGXd3d7Zt26Yt2pGSkkJERAQJCQls2bKl0l/r6qiq5ewVDbIdMWIEYWFh0tATQohqoiIbemU9n729vbaa8/Dhw1m4cCFvvfVWhdZDVB0yZ088NMxmM4cPH2bx4sXExMRw8OBBNm7cqC10MmfOHHr16kViYiK9evVizpw5lVKPBxm9MGHCBP7+97+j0+kq5d4eBU5OTkyZMoWaNWuWuYzBYGDixIl88cUXeHh4EB8fT25uLn379sXf3x+j0Yi7uzujRo0CLD/XNm3a4OXlRXp6utWbaHJyMikpKXTv3t3qGh999BEeHh6kpqbi7+9PRETEHetVdF7p5MmT8fDwICcnBw8PD4leEEIIcUddunSR0PRHXWnnCa0XAAAgAElEQVQBfFXhn4SqV57bhY4nJSWptm3bqoiICOXt7a169+6tcnJylFKWwOrJkyerjh07qtatW6tdu3YppSwB4pMmTVJBQUHKz89PffLJJ0oppXbs2KFCQ0PV888/r9q1a6e++eYbLWxaKaVmzpyp5s6dq5RSysvLS6WlpSmllEpLS1NeXl6Vdn96vV4dOHBAKaXUM888owVdd+/eXU2cOFEppdR//vMf1atXL6WUUp9++qmaNWuWUkqpGzduqA4dOqhTp06pHTt2qNq1a6tTp04Vq8OLL76oFixYoD1fv369evXVV63qIoQQQoiq49bAayrhf3fi4OCglLJ89hoyZIjavHmzUkqppKQk5ePjU/E3Le4rCVUX90Vl5dD5+vqya9cuMjIyyMnJYdOmTVpuWnp6urb0vJubG+fPny93/e+0Guf9jl7Iyclh9uzZzJw5s9z39KgzGAx069bNapvRaMTX19cqVN3R0ZE2bdpYhar36NEDR0dHxo0bZ1U+NDRUO9ZoNGq/c7/++iu9evXC39+f0NBQUlNTAUuoeuGxRqPRKlT9448/plWrVuh0Oi5evKhdY/369VrvYVBQED/++GOJ9/fWW2/RtGlTHB0dK+w1E0IIUf1cv34do9FI/fr1uXTpEr17937QVRIPkMzZE+VSnsbQoUOHWL16NQBXrlwhMTGRmjVrWjWG2rVrx5QpU+jduzeOjo4EBARYrXJ4v9wavVA4jLPovpKiF25dfTE6OrpM0QsnT54kKSmJgIAAwLIaZ2BgIDExMTRu3Lhib64aK1z9tGnTpsVC1Qt/NqGhoURGRhIUFARAdnY2s2bN4vDhwyVOWl+xYoV2bKHCUPXhw4ezfft23nzzTZYvX66FqoNljmmrVq2sQtXDwsKKrbDaq1cv+vXrh06n49ChQwwdOpSEhIRi9Xj66acZN24crVu3Lv8LJIQQotornLN35coVwsLCWLhwIa+++uqDrpZ4QKRnT5ToQebQjRw5kv3797Nr1y7q1aunfbht1KgRZ8+eBSxL298ugPp+69u3L4sWLSI/Px+A48ePk52dXerxtwZ++/n5cf78eZKTk0lOTsbDw4P9+/dLQ+8uVcVQdUdHR22OZnZ2dqnzNSVUXQghxN1wdnbmo48+IjIyUvt8Ih490rMn7ovCxlDPnj2xtbXl+PHj2rL2tzp//jwNGzbk9OnTfPfdd/z0008A9OvXjy+//JKpU6fy5Zdf0r9//3uqU0Xm7CmlSEpKom7dulbRC1lZWcVy9sxmM3v37iU1NZUvv/yyxLoV1qUsS/c/qkrK2RsyZAgjRoxg0qRJbNiwgRUrVrB8+fJ7us6LL76IXq9n8ODBvP322+h0Oi1Uffz48Vah6vXr19fKrVq1iokTJ5bpGmvXruXNN9/k/Pnz/Oc//7mn+gohhHh4NKJRhUcv3I327dsTEBDAqlWrik11EI8GaeyJ+yIiIoLk5GQCAwNRStGgQQNtLtOtBg8eTEZGBra2tixcuFCLV5g6dSpDhw5l6dKlNGvWjG+//bbc9YmOji42nK5z584A2Nra0rFjR21706ZNtcdFezttbW21MjqdjhYtWtCiRQurc7q4uODi4mK1Ta/X07Vr19vWr/C84u5UxVB1sMRFDBw4kF27dvHOO++wdevWe6q3EEKIh8OdMvEqw605whs2bNAeS8beo0cae6LMCuffubq6Wv2xmDRpkva4aG+Lq6urVsbGxoZ3332Xd9991+qcoaGhxRpdhXEGt6pfvz7btm0r/w3coqSeIVH1VbVQ9aJCQkI4efIkFy9elIxFIYQQQtwzmbMnhKhWBg4cyOTJk8vcq1aagoICbdXMwlB1X19fwBKqfvPmTYBSQ9XLMl8Q4MSJE1hWTYb9+/eTl5dnNRxUCCGEEKK8pLEnhKhWqlqo+po1a/D19cVoNDJ27Fi+/vprbZEWCVUXQgghxL2Qxp4QotoYOHCg1fMBAwYAWOXsxcXFMWzYMKucPU9PT/Ly8hgxYgSpqal4e3vj4OCAk5MTubm52NrasnPnTjIyMgDo2LEjzZo1o1atWpw4cYILFy4Alpy9AQMG0KBBAwIDA61y9mxsbKhVqxZms5lDhw6xZMkSAPr370+dOnU4evQogwcP5vHHH9fqXxjjAGjXU0phMpmksSeEEEKIO7rvc/Z0Ol1TYBnQGLgJ/Esp9aFOp6sHfA0YgGRgqFLq8v2unxCi6qqKOXv16tXjo48+KnXBokKllRdCCCGEKM2D6NkrAF5XSrUDOgNjdTqdNzAV2KaUag1s++25EEKUWVXM2WvYsCEdO3a842IupZUXQgghhCjNfe/ZU0qdBc7+9viaTqc7CrgD/YHQ3w77EogGptzv+olHh/SQVG3VKWdPCCFE9dQYKjBlDxrBHcMc9Ho9fn5+2vN169aRnJxMZGQkGzdurMDaiKrggUYv6HQ6A9Ae+Blo9FtDEKXUWZ1O17CUMi8BL4FlDosQ5REaGorJZLJaAGPv3r106NCh0oPM9+7dq2Wy6XQ6OnToAMDJkyfJyMjQ5na1bdvWKrtN3FlVzdkTQghRPVVkQ6+s57O3t7ea8w3/i88Sj54H9klSp9M5AmuA15RSVwtXn7sTpdS/gH8BBAUFqcqroajujEajVe+QwWBg/fr1lZZvZjab0ev1GAwG4uLiil0nKiqKnj17UqNGDaZMsXRqz507t1LqUp1V5Zw9IYQQQoiK9EBW49TpdLZYGnorlFLf/bY5XafTuf223w04/yDqJkRycjLt2rVj1KhR+Pj40KdPH65fvw5YegSnTJlCcHAwXl5eWgC82WzmjTfeoGPHjvj7+/Ppp58ClqGGPXr04IUXXrAaUlGSPn36aL1DnTt3JjU1tRLvsvqqajl7QgghREW6fv26tgL1ratUi0fPg1iNUwcsBY4qpd4vsut7YDgw57f/L77igbivTCZTtZ3XdusQzlslJiaycuVKFi9ezNChQ1mzZg1/+MMfAEsjICYmhk2bNjFjxgy2bt3K0qVLcXZ2JjY2ltzcXLp27aqtwhgTE8Phw4fx9PQELEM3+/Tpg06nY/To0bz00kvFrv/ZZ5/x7LPPVsKdV3+FOXt3w2AwcPXqVfLy8li3bh1RUVE0b96cvn37kp+fj9ls5ne/+51Vzt6bb76JTqcjJCSEhQsXaue6Xc7e3//+d86dO4e/vz9PPvkkS5Ys4dy5cwQFBXH16lVsbGz44IMPiI+Pp06dOtoxTZo0KbW8EEIIUVRJwzjFo+tB9Ox1Bf4I9NTpdKbf/j2JpZHXW6fTJQK9f3suHhCTyURWVtaDrsYD4+npqTUGO3ToYDXWfdCgQcW2R0VFsWzZMoxGI506dSIjI4PExEQAgoODtYYewH//+1/279/P5s2bWbhwIbt27bK69uzZs6lRowbDhg2rxDusnh7mnL2nn34ad3d3PD096datG//85z8ByM3NpU2bNhgMBoxGI3v37qVOnToAbNq0iSZNmgCQnp6OTqejVq1apKWlSUNPCCGEEHf0IFbj/BEobYJer/tZF3F7jo6OJa54WB3cqcfSzs5Oe6zX67VhnEX36fV6CgoKAFBKsWDBgmJDB6Ojo3FwcLDaVvjhvWHDhgwcOJCYmBhCQkIA+PLLL9m4cSPbtm2jrPNYxf88zDl7U6ZMYcKECTz33HOMGTOGpUuX8vLLL5d6rls9/fTTjBs3jtatW1fMiyWEEEKIau+BzNkTorrp27cvixYtIj8/H4Djx4+TnZ1d7Ljs7GyuXbumPY6KitLmgW3ZsoW5c+fy/fff3/Mqko+qhzVnTynF9u3bGTJkCADDhw/XevzKci6wzON0c3Mrcx2FEEI8eI0eovNt27YNDw8P7d9PP/1UYfUSDy9Z1108sm6dk3ju3Dn69++P2WwmKSlJ25eSkoLZbCY6OhqTycTo0aNxcnIiPz+fc+fOERoailKKpKQk6tati1IKW1tbfH19ycrKIiUlRTvX9evXOXLkCGDpDWzYsCFz5sxhzpw5/PzzzyiltCGfderUwcvL636+JFVKVcrZy8jIwMXFRVuAx8PDgzNnzgCU6VxCCCGqpjtl4lWGkqbhhIaGWo1SEo8OaeyJR1J0dHSxoZydO3cGwNbWlo4dO2rbmzZtqj0uuqiLra2tVkan09GiRQtatGhhdU4XFxdcXFy05/b29sWGBBbq1KlT+W5GaB7WnD2liqfEFA7TvdO5hBBCCCHKSz5RiEdWdZ2P+Kh7GHP2XF1dyczMpKCggBo1apCamqrN3bzTuYQQQgghykvm7AkhqpWHMWdPp9PRo0cPVq9eDVgW4unfv3+ZziWEEEIIUV7S2BNCVCuFOXs1a9YscxmDwcDEiRP54osv8PDwID4+ntzcXPr27Yu/vz9GoxF3d3ernL02bdrg5eVFeno6b731lnau0nL25s6dy/vvv0+rVq3IyMhg5MiRdzxX0WHDkydPxsPDg5ycHDw8PJg+fXp5Xh4hhBBCPEKksSeEqDYe5py9TZs2cenSJU6ePMmiRYu0CI/c3Fzs7e2pVasW8fHxJCQkaPUvGopbeD2lFCaTSRp7QgghhLgjmbMnhKg2Huacva5duxIWFlZsYSBPT0927txJ3bp12bx5My+99BI///xzsXqUVl4IIYQQojTS2BNCVBuFOXuTJk3S5s3dKXqhMGfvxIkTZb5OfHw88+fPByzZeIU9iEUVzdkDaN++fYnneuyxx7THnTt3JjU1tcTjSisvhBDi4dU4MpL0EnJ3y6uRgwPnJk2643Hp6elMmDCBvXv3UrduXWrWrMnkyZO1ETDjx49n9erVpKSkYGMjA/2qM2nsiVJlZWVJL4J4aFWlnL2yWrp0KU888cQ91VcIIcTDoyIbemU9n1KKAQMGMHz4cL766isAfv31V77//nsAbt68ydq1a2natCm7du2Sz3rVnDTlRYmysrIwm81W2/bu3Ut+fn6lXzs1NZXY2FhiY2OJj4/XViq8fPky+/btIzY2loSEhBKzy8SjrTJy9n755Rd2797N7t27tYZjZGQkO3fupH379uzcufOOOXtlsWPHDpYuXcrcuXPvqc5CCCEebdu3b6dmzZqMGTNG29a8eXP+/Oc/A5b3G19fX15++WVWrlz5oKop7hPp2ROl0uv1Vr0nBoOB9evX4+rqWinXM5vNnDt3jscff5yLFy9ib2/P0KFDefLJJwkPD6d58+bs27cPLy8v/vrXv9K8eXNtRUMhCj2MOXt3cujQISIiIti8ebNV76AQQghxt44cOUJgYGCp+wunOfTv35+//OUv5Ofnl/n9SlQ90rMn7lpycjLt2rVj1KhR+Pj40KdPH65fvw5YFr+YMmUKwcHBeHl5sXv3bsDSkHvjjTfo2LEj/v7+fPrpp4BlKF6PHj144YUX8PPzAyz5ZtevX6egoICcnByaNGlCRkYGdnZ2eHl5AdC7d2/WrFnzAO5ePOwexpy92zl9+jSDBg1i+fLl2u+3EEIIUVHGjh1LQEAAHTt2JC8vj02bNjFgwADq1KlDp06diIqKetBVFJVIGnuiXBITExk7dixHjhzBxcXFquFVUFBATEwMH3zwATNmzAAsc5GcnZ214ZmLFy8mKSkJgJiYGGbPnk18fDzu7u5MmjSJZs2a4ebmhrOzM3369MHV1ZX8/Hzi4uIAtEnFQtzqYc3Z++ijj/Dw8CA1NRV/f38iIiIAmDlzJhkZGbzyyisYjUarlT+ffPJJ0tLSblteCCGEKMrHx4f9+/drzxcuXMi2bdu4cOECW7Zs4cqVK/j5+WEwGPjxxx9lKGc1J409USJHR0ccHR1L3e/p6akFPnfo0IHk5GRt36BBg4ptj4qKYtmyZRiNRjp16kRGRgaJiYkABAcH4+npCVjm5a1fv56kpCTS0tLIzs7m3//+NzqdjlWrVjFhwgSCg4NxcnKymiMlBJQvZw9g9OjR1KtXD3d3d5YuXYq3tzd6vR69Xo9OpwPAxcUFvV4PWOYGOjk5UbNmTfLz87XtV65c4c9//jOurq74+fnx+eefa3VJTU3F2dmZ1q1bM2TIEBYvXsy1a9eIi4ujefPm2jGPP/64VmbTpk1az/batWvJzMxkzJgxpKWlsWTJkkp6FYUQQlRlPXv25MaNGyxatEjblpOTA1hGnixZsoTk5GSSk5NJSkoiKipK2y+qH2nsiXIpDIQGy9y+goKCYvuKbldKsWDBAkwmEyaTiaSkJC1/zMHBQSu7detWPD09adCgAba2tgwaNIg9e/YA0KVLF3bv3k1MTAwhISG0bt260u9TVC2FOXtAsZy9wt+9oKAgVqxYgclkYtmyZcTHx7Nq1SqOHDnCli1beOWVVzCbzdjZ2bF9+3YOHjyIyWRiy5Yt7N27l5s3bzJ8+HBWrVrF4cOHad68OV9++SVg+fbU29ubgwcPEh0dzeuvv05eXh579uzhv//9L4cOHeLw4cPExsayc+dOnJyctHqZTCaaN2+ufVlSVK1atZg1axaRkZH354UUQghRIRoV+Yxzv86n0+lYt24dO3fuxNPTk+DgYIYPH86MGTP44YcfeOqpp7RjC+OHNmzYUKH1FA8P6RoR90Xfvn1ZtGgRPXv2xNbWluPHj2uLXxTVrFkz9u7dS05ODvb29mzbtk0b1nb+/HkaNmxIbm4uc+fOtRo6JwSUL2dv/fr1PPfcc9jZ2eHp6UmrVq2IiYmhS5cuWu92fn4++fn56HS6EuePvvfee4wcORKdTse1a9dQSpGVlUW9evWoUaMGOp2OGzdukJeXh1KK/Px8GjVqZFWPxMREzp8/T7du3YrVsTxZgEIIIR68smTiVQY3NzdWrVpVbPvw4cOLbStccExUT9LYE6XKyoKi0SvnzkH//mA2Q1LS//alpFi2RUeDyQSjR4OTE+TnW8qEhoJSESQlJVO3biBKKWxtG+Dru46sLEv5/12nE9nZQ3B1DUSnq4GjY3uSkl5izRo4eXIely5tRKmbNGnyMjNn9mTmzPv4goiHSgkxe+XK2Ttz5gydO3fWnnt4eHDmzBnAsrBQhw4dOHHiBGPHjqVTp05aYy0uLo6goCCr+aPjxo2jX79+NGnShGvXrvH1119jY2NDly5d6NGjB25ubiilGDduHO3atbOqx8qVK3n22We1YaNCCCGEEPdKGnuiRJacPettnTsnA2Br60rHjoe17U2b/u9bK6MxWntsa+uqldHpbGjR4l1atHjX6pwuLqG4uIRabTMYZmAwzChWp5Yt59Gy5by7vxnxyChPzl5JeY2FDS69Xo/JZCIzM5OBAwdy+PBhfH19tfmjubm59OnTR5s/Wjg3cPv27Zw8eZLevXvTrVs3zp8/z9GjR0lNTQUsvYG7du0iJCREu+aqVavuOQBeCCGEEKIoaeyJUun1JfeeCPEwu9ucPQ8PD6uVXVNTU2nSpInVMS4uLoSGhrJlyxZ8fX21+aNgWXzo+PHjAHz++edMnToVnU5Hq1at8PT0JCEhgZ07d9K5c2dtWOgTTzzB3r17tcbewYMHKSgooEOHDvd6+0IIIYQQGlmgRQhRrdxtzl6/fv1YtWoVubm5JCUlkZiYSHBwMBcuXCAzMxOA69evs3XrVtq2bQtY5o8C2vzRMWPGAJY5p9u2bQMgPT2dY8eO0aJFC5o1a8bOnTspKCggPz+fnTt3Wg3jvJtcPiGEEEKIspKePSFEtVKYs1dWPj4+DB06FG9vb2rUqMHChQvR6/WcPXuW4cOHYzabuXnzJkOHDiUsLAyAefPmsXHjRm7evMnLL79Mz549AXjnnXcYMWIEfn5+KKWYO3curq6uDBkyhO3bt+Pn54dOp+P3v/89Tz/9tFaHb775hk2bNlnV6/vvvycuLo6Zv01MNRgMXL16lby8PNatW0dUVBTe3t73+nIJIYQQohqTnj0hRLVRnpy9jIwMtm7dSnp6Or179+aJJ54AwN/fn8mTJ6OUwsbGhj179nDx4kXA0tg7evQoo0aNYsKECdp2e3t7HBwc0Ol01K5dW8ui1Ov15Ofnk5GRgY2NDe+//75Wx3feeQdHR0eee+45+vTpo4Wo9+vXT2voAbRt25abN28SGhpKamqqNPSEEEIIcUe6khYnqCqCgoJUXFzcg65GtVS44ETR/DwhHmYGgwEXFxc2bNhA06ZNOXr0KM8//zwFBQUcPvy/BYVCQ0OJjIzUIj2ys7M5cOAAhw8f5vDhw3z88ceA5Xe/SZMmxMfH4+rqyuTJk6lduzbTp08HICUlhYiICBISEti3bx+urq688cYbODo6Mm3aNBISEhg7dqw2rHPXrl04OjoSHh5uVZ+rV69Sp04dAD766CPi4+P55JNPit3ftm3byMnJ4dNPP2Xjxo2V8hoKIYS4N0ePHrUapt+4cSTp6dkVdv5GjRw4d+72cQ46nY6JEyfyj3/8A4DIyEiysrKYPn0606dPx9HRkUlFIiEMBgNxcXG4urpWWD1F5bn1dwxAp9PtU0oFlXS89OwJIaqNwpw9KPs8uMIMu1q1alltV0qhlCI7OxulFFevXrVauGXChAn8/e9/t4pKiI+Pp1evXoClJy45OZn09HQAQkJCqFevXrHrFzb0wNLwLC16oVevXjg5Od3xfoQQQjw8KrKhV9bz2dnZ8d1332mjTsSjTebsiUdWaNEQQVHlRJewVGx5cvZKY2try6JFi/Dz88PBwYHWrVuzcOFCwDKfzt3dnYCAAKsyAQEBfPfddzz++OPExMTw66+/kpqaWixA/VZvvfUWy5Ytw9nZmR07dpSrvkIIIQRYRme99NJLzJ8/n9mzZz/o6ogHTHr2RJkZDIb78i3R/Pnz8fHxwdfXl+eff54bN24AlmFsgYGBGI1GHn/8cU6cOFHua4SGhmIymSqqyuIhUZ6cvdLk5+ezaNEiDhw4QFpaGv7+/rz33nvk5OQwe/Zsq/l0haZOncrly5cxGo0sWLCA9u3ba0Oib2f27NmkpKQwbNgwbRipEEIIUV5jx45lxYoVXLlypdi++fPna/PYjUajNldcVE/SsyceGmazmXPnzmnzluzt7Rk6dCirVq1ixIgRvPzyy6xfv5527drxz3/+k7/97W9lzlIridFoLLF3SFRtd5uzV5rCLwNatmwJWIaIzpkzh/79+5OUlKT16qWmphIYGEhMTAyNGzfm888/ByzDQD09PfH09CzzNV944QWeeuopZsyYcU91F0II8WirU6cO4eHhfPTRR9jb21vtmzBhQrE5e6L6kp49cdeSk5Np164do0aNwsfHhz59+nD9+nXA0mM2ZcoUgoOD8fLy0oKnzWYzb7zxBh07dsTf359PP/0UsAzF69GjBy+88AJ+fn6AZWGM69evU1BQQE5OjjZPSqfTcfXqVQCuXLlSLPhaCLj7nL3SuLu7Ex8fz4ULFwD4v//7P9q1a4efnx/nz58nOTmZ5ORkPDw82L9/P40bNyYzM5O8vDwAlixZQkhIiNWcvJIkJiZqj7///nsty08IIYS4F6+99hpLly4lO7ti5w2KqkUae6JcEhMTGTt2LEeOHMHFxYU1a9Zo+woKCoiJieGDDz7QeiiWLl2Ks7MzsbGxxMbGsnjxYpKSkgCIiYlh9uzZxMfH4+7uzqRJk2jWrBlubm44OzvTp08fwPLh+cknn8TDw4Ply5czderUCr2n+zVM1WAw4Ofnh9Fo1FaELLRgwQLatGmDj48PkydPrvS6VEeFOXs1a9YscxmDwcDEiRP54osv8PDwID4+niZNmjBt2jRCQkLw9/fHZDLxl7/85bbnOXr0KD4+PrRt25bNmzfz4Ycfavuef/55unTpwrFjx/Dw8GDp0qWAZeinr68v/v7+REVFaWXi4uKIiIjQynfr1o1nnnmGbdu24eHhwQ8//HA3L4sQQohHTL169Rg6dKj2fiMeTTKMU5TI0dHxtvs9PT21DLEOHTqQnJys7Rs0aFCx7VFRURw6dIjVq1cDlp65xMREatasSXBwsDbU7fLly6xfv56kpCRcXFx45pln+Pe//80f/vAH5s+fz6ZNm+jUqRPz5s1j4sSJLFmypILvvPKYzWb0ej0AO3bsKLbE8Y4dO1i/fj2HDh3Czs6O8+fPP4hqVllFfwcLGQwGq5gDKHlhl5LKAowZM4YxY8aU+bpdunSx6qkrauXKlSVuL/pFSVFBQUFWv9+FveTVlcFgoGnTplb3aTQaKSgo4B//+AdTpkwB4MSJE7i7u2Nvb4+/vz/z589nyJAhxMbGMmLECKs5j6GhoZw9e1YbwhQVFUXDhg359ddf+dOf/sSFCxeoV68e//73v/Hw8GDHjh1MmDBBK5+QkMCqVasYMGAAH3/8MR988AEnT57kwoULVv/9RkdH89prr5Gfn4+rqys7d+4sdn+3Ky+EqL4aNXKo8OiFu/H666/LXPBHnDT2RLnY2dlpj/V6vTaMs+g+vV6v5fQppViwYEGxoXXR0dE4OPzvD9fWrVvx9PSkQYMGgKXhuGfPHvr27cvBgwfp1KkTYJmX9fvf/75S7i05OZknnniCxx9/nD179uDu7s769euxt7cnNDSUTp06sWPHDjIzM1m6dCndunXDbDYzdepUoqOjyc3NZezYsYwePZro6GhmzJiBm5sbJpOJ+Pj4Uq+7aNEipk6dqr1+DRs2rJT7E+Jhde3aNVJSUrScxEJ9+/bV/naUlJM4a9YsLSfxVitWrCjWgz5p0iTCw8MZPnw427dv580332T58uX06NFDm6t56dIlWrVqpY0s6Nq1K2FhYcVW8c3MzOSVV15hy5YtNGvWrNQvaUorL4So3u6UiVcZsrKytMeNGjUiJydHe16YFVtUaV94iupBGnuiVFlZWVYfTM6dO0f//v0xm80kJSVp+1JSUjCbzURHR2MymRg9ejROTk7k5+dz7tw5QkNDSUtLY9iwYXh7e2NjY0NOTg52dnbah7vCc129epVjx45x/PhxbPRKNhQAACAASURBVGxsOHbsGE5OThw5coTTp08THBxM7dq1OXv2LJcuXSr3ByeTyaT1TJYkMTGRlStXsnjxYoYOHcqaNWv4wx/+APxvmOqmTZuYMWMGW7dutRqmmpubS9euXbUPiTExMRw+fFjrvdTpdPTp0wedTsfo0aN56aWXADh+/Di7d+/mrbfeolatWkRGRtKxY8dy3Z8QVVFhTuKkSZO0nMQ7RWcU5iTezeq88fHxzJ8/H4AePXowYMCAYsesXr2aJ554QlvRtX379iWe66uvvmLQoEE0a9YMKP1LmtLKCyGEEJVJGnuiRFlZWZjN5go7n5ubGzdu3GD//v0opbC1tcXX17fYcXXq1KFBgwbs27cPnU6Ho6Mjbm5u6HQ62rRpo/WM1ahRgzZt2txTnUwmU6mN2Zo1a/Laa68BcPr0aWJjY1myZAkmk4krV64QGhpKXl4eBw4cIDQ0lCNHjpCdnc2cOXMAy5DN/v37Y2Njg62tLS+++KJ2ncaNG2NnZ0deXh4TJ07kn//8Jy4uLiQmJrJ06VJatmzJtWvXCAkJITg4uNSQbSGqssrOSSz04osvotfrGTx4MG+//TY6nY6AgADWrFnD+PHjWbt2LdeuXSMjI4P69etr5VatWsXEiRPveP7jx4+Tn59PaGgo165dY/z48YSHh99TnYUQQoiKIo09USq9Xl9towlcXFyshjncqmgDS6fTcfPmTe25jY2Ntl0ppW1v1aoV9erVszpPZmamNk+vUOEwzZo1a+Lq6sq1a9dwcXHBzs4OV1dXdDqdtoJjfn7+XS00IkRVVpE5iWAZwunu7s61a9cYPHgwy5cvJzw8nMjISMaNG8cXX3xBSEgI7u7uVnmIZ8+e5ZdffinTiq4FBQXs27ePbdu2cf36dbp06ULnzp3x8vK6p7oLIYQQFUEae+KR5ejoaNWYNRgMrF+/nqysLMLCwrR9kZGRZGVlMX36dKv5QhcvXiQoKIjo6Gj+9a9/sWnTJr799ltsbW05fvw47u7uxMbGEhkZycaNGwHL/KKbN2/i5OREdnY2vXv35q9//Su///3v+eSTT0hLS2PmzJkcP36cXr168d///ld69sQjpaJyEsESnwGWFVpfeOEFYmJiCA8Pp0mTJnz33XeAZRTDmjVrcHZ21sp98803DBw4EFtb2ztew8PDA1dXVxwcHHBwcCAkJISDBw9KY08IIcRDQaIXhPhNamoqGRkZ5SobERGBt7c3gYGB+Pr6Mnr0aG1xmqLS09OpX78+9vb21K9fn19//VVbaObMmTNERkZib2+P0WjklVdekYaeeORUVE5iQUGBFqWSn5/Pxo0btaHjFy9e1Hrr33vvPf70pz9ZlS2cL1gW/fv3Z/fu3Vou6M8//0y7du3uqe5CCCFEhVFKVdl/HTp0UKJy6PV6pdfrH3Q1Ko2zs7Nydna22ta8eXN14cKFSrtmQUHBba8zbdo0NW/evEq7vhAPs5L+u0hKSlI+Pj5W27p3765iY2OLla1bt65ycHBQ7u7u6siRIyorK0sFBgYqPz8/5e3trV599VXtv8Fvv/1WtWrVSrVu3VqNHDlS3bhxw+qaTZo0UWaz2eoaH374oXJ3d1d6vV65ubmpkSNHavv+/ve/q3bt2ikfHx81f/58bfsTTzyhzpw5c8fyQojqIz4+/kFXQVRzJf2OAXGqlPbSA2+w3cs/aexVnke5sZeUlKTatm2rIiIilLe3t+rdu7fKyclRSlk+aE6ePFl17NhRtW7dWu3atUspZWnITZo0SQUFBSk/Pz/1ySefKKWU2rFjhwoNDVXPP/+8ateundV1biWNvXvTvHlz9fjjj1ttCwgIUD4+PmrLli0qICBABQQEKAcHB+Xl5aUCAgLUH//4R3Xx4kUVGhqqHBwc1NixY63Kd+/eXTs2ICBApaenK6WUSk5OVj179lR+fn6qe/fuKiUlRSml1Pbt27VjAwIClJ2dnVq7dq1SSqkFCxaoli1bKsDq579jxw5Vp04drcyMGTNKvL/SygshhHh4FPsg3khV7KffRneuQ/fu3dWWLVv+n707D6u6zPs4/j6AICKKhhiCgkvIDgKCFubRQnDJxNwpHzPSHHtaHFxmrJmsQa1sNLW0lDLLdNJUyFEkF9RHh4DkSLiBCgbhko4Lm2z+nj+O5xdHwAXFBb6v6/KKc/+2+z6ScJ97+RiVzZ8/X5k0aZKiKIryz3/+U7GwsFAuXrxYlyaK++x2O3syjVOIGmRlZTF58mQOHjyIjY2NUfC1IXphwYIFzJo1C8AoeiElJYVly5aRnZ0N6KMXoqOj1Z1EDdEL/v7+fP7550bPXbx4Md7e3owfP54LFy7co9Y2HIYoD6BaTptOp0On0xEQEMCqVavQ6XSsXLmSpk2b8t577zFv3rwa72k4V6fTqdvqG3La0tPT+dvf/sZf/vIXADWnTafTsWPHDpo1a2aU07Zt2zacnJyqPaNXr17qdX/7299qrMeNrhdCCPGAOnPv7zd69GjWrFljVLZmzRp1evrq1avp3r07GzZsuMuVEw8i6eyJRsnX1/eGOXsdO3ZUj/v7+xsFjg4dOrRaeUJCAitXrsTX15egoCDOnz9PVlYWAIGBgWrGHsDevXvZv38/W7Zs4ZNPPmH37t0ATJo0iePHj6PT6bC3t+fPf/7z3Wxyo2DIaYNbX3dlyGlr2rTpLT/n0KFDPPXUU4C+gxcbG1vtnJpy2pydnW/5Gde70+uFEEI0DsOGDWPTpk2UlpYC+tD0/Px8goODOX78OIWFhfzjH/9g9erV97mm4l6Qzp4QNTDEI4A+gqLqZiuGY1XLFUVh0aJF6uhMdna2OqJjZWVldO927doB+vDl8PBwkpOTAWjbti2mpqaYmJjw8ssvq+Xi1g0bNkzdZfGHH37gmWeeueN7vvjii/j6+vLee++pURuGnDbAKKetqqqfot7Mf/7zH3x8fOjfvz8HDx684zoLIYRovB555BECAwOJj48H9D+PRo4ciUajUT8I7dWrF0ePHuXs2bP3ubaivkn0gmi0rg9VP3nyJAMGDMDExITs7Gz1WG5uLpWVlSQmJqLT6Zg4cSLW1taUl5dz+vRptFot+fn5RERE4O7ujomJCcXFxVhYWKjTCg33qqysJCUlRc30KikpwcPDQ/0EzsLCgtzcXE6cOMEjjzxiVD9hrKYMyIcxp83Pz4+TJ0/SvHlzNm/ezJAhQ9RRYSGEEKIuDFM5n332WdasWcMXX3wB6Dt+GzZswMTEhKFDh7J27VomT558n2sr6pOM7IlGSafT3TBU/XbZ29vTrFkz9u/fT0pKCpmZmUaB66Af/SsrK6OsrExdNNuhQwc1iP3EiRMkJydz8uRJTExMZMpeHRly2m51VO1GasppA9SctrS0NKKjowHqnNPWokULmjdvDsCAAQMoLy9XIwOEEEKIuhgyZAjbt29n//79lJSU4OfnR3p6OllZWYSEhODs7MyaNWtkKmcjICN7otGqKVR98+bNFBYW0r9/fx577DH27dtH586diY2NxdLSEq1WS1BQEDt37uTixYv8+OOP9OrVi8rKSmbMmEFiYiKlpaVMnjyZiRMnkpiYyKxZs7C3t0en03Ho0CGcnZ1JTU3F1ta2Wp2GDRvG22+/zbPPPkt8fHyN54gbCw8P59SpU4SGhpKfn1/n+1RUVHDx4kVsbW3VnLann34a0Oe0tW7dGhMTk1pz2ubMmXNLzzl9+jRt27ZFo9GQnJzM1atXeeSRR+pcbyGEEKJ58+ZotVrGjx9vtDHLO++8o24qBvo9Ck6ePCmbfzVgMrInRA3ux26ccXFxODg44OPjcw9b2vBYW1szffp0zM3Nb/kaZ2dnpkyZwooVK3B0dOTQoUOUlpYSGhqKt7c3vr6+ODg48PLLLwP6KaRdu3bFxcWFM2fOMHPmTPVeOTk55Obm0rt3b6NnLFy4EEdHR/Ly8vD29iYyMhLQb+Ti6emJj48Pr732GmvWrEGj0QD6kT5Dh7W264UQQjzA2t6/+40ePZoDBw4watQoQD+FMzw83Oic8PDwajt3ioZFRvbELbvRiNTdNH/+fJYvX45Go8HLy4svv/ySpk2b0qtXLwoKCgA4e/YsgYGBbNy4sV7qUJfdONPT01m3bh0Aly5dIisrC3Nz8xp342zXrh1nz54lJCQEV1dXAgICiI6OJiEhoV7a01iEh4ezZ88e9fWQIUMA2Lp1K9OnTwfg2LFjREREYGlpibe3N/Pnz6djx46kpKQwbtw4Fi9erF5vbW3NqVOnsLS0ZNeuXZw/fx47Ozt+/fVXmjRpgpmZGdnZ2Zw+fVr9VPTTTz/FxsYGDw8PQkJC+PjjjykpKWHr1q00b96crl278swzzzB37lwAXn31VV599VXWrVvH8OHDjTqpmzdvVr/W6XSUlZXh6upKRkZG/b2JQggh7p7T9+/R4eHhRktKDB9CV/XPf/7zXlZJ3AcysiceGJWVlfz2228sXLiQ1NRUMjIyqKysVD9x2rNnj7rbZc+ePdVOV13cLHrhXu/Gefz4cbKzs/Hx8cHZ2Zm8vDz8/Pw4ffo+/pR4CN2rnL1u3bqRmppKeno6w4YNY9q0aQDs27ePvXv3kp6eTkZGBikpKezatQvQZ/MdOXKEtLQ09u7dy5YtW4zqvXDhQoKCgmpt27hx49Sd1YQQQgghboV09sRty8nJwc3NjZdffhkPDw/69etHSUkJAFqtlunTpxMYGIiLi4s6ylJZWcnUqVPp3r073t7efPbZZ4B+OlyfPn0YM2YMXl5egH6aZElJCRUVFRQXF6udI4OCggJ27Nihjto8CEJDQ1myZAnl5eUAZGZmUlRUVO28oqIidXSyqKiIhIQEPD098fLy4uzZs+Tk5JCTk4OjoyP79+/n0UcfvafteNjdq5y9Pn36qDt99ujRg7y8PEA/RffKlSuUlZVRWlpKeXk5bdu2pVmzZvTp0wcAc3Nz/Pz81GsA3n77baZNm3bDOjz55JPqZj5CCCGEELdCOnuiTuprTZuDgwNRUVF06NABe3t7WrZsqY6QGWzYsIGnnnqKFi1a3LsG30RkZCTu7u74+fnh6enJxIkTjUYDDc6cOUNwcDA+Pj4EBgYycOBAwsLC7kONG6Z7lbNXVUxMDP379wegZ8+e9OnTB3t7e+zt7QkNDcXNzc3o/IsXL/LDDz+ooexpaWnk5uYyaNCgO66rEEIIIURVsmZP1MiwFXxt6mtN24ULF4iNjSU7OxsbGxuGDx/ON998w/PPP6/ef/Xq1Xdlc4rTOh0rquTYvePszKZhwwCIsrVVj9le+7NCq2UckBEVRUaVawznuQB/NuyiqChsePZZAIZdu9bgzVat/qjEjz+y4scfq9Wtal1Ezcbdx5w9g2+++YbU1FR1quaxY8c4fPiwOmoXEhLC7t27efLJJwH9ByGjR4/mtddeo1OnTly9epU333yTFStW3FE9hRBCCCFqIiN7ok7qa03btm3b6NixI23atKFJkyYMHTqUffv2qcfPnz9PcnIyAwcOvKP6JyYmMuMGa/bEw+te5OyB/ns1OjqauLg49Xt+w4YN9OjRg+bNm9O8eXP69+9PUlKSes2ECRN47LHHeOONNwD9lOSMjAy0Wi3Ozs4kJSUxePBgUlNT77juQgghhBAysifuCcOatr59+9KkSRMyMzPVX6Sr6tChA0lJSRQXF2Npacn27dsJCAhQj69du5ZBgwbd1vqq2tQ0MiQefvciZy8tLY2JEycSHx+vbtoC+u/fZcuW8Ze//AVFUdi1a5fasXvrrbe4dOkSy5cvV89v2bKlUYC6Vqtl3rx5Rt/zQgghhBB1JZ09cU9ERkaSk5ODn58fiqLQpk2bGmMTgoKCGDZsGH5+fpiZmdGtWzcmTJigHl+zZg0zZsy4l1UXDxlDzt7tcHZ25vLly5SVlbFx40YSEhJwcnIiNDSU8vJyKisrefrpp9WcvalTp1JYWMjw4cMBfScvLi6OYcOGsWPHDry8vNBoNISFhfHMM8+Ql5dHdHQ0rq6u+Pn5AfrIhRtNR87PzycyMlKNXxg9ejSJiYmcO3cOR0dHZs2axUsvvVSXt0gIIcQ9Mu/ReRSdqb5hW11ZtbUi6nTULZ27YcMGhg4dyuHDh3F1deWTTz5h2bJl6vGKigoOHjzIoUOHqq0vFw2HpqYNBx4WAQEBikx3qh82NjaAfjMJIYQQQghxc4cPHzbqOM3SzLrrz/i78vdbOm/EiBGcOnWKp556infeeafa8b/+9a/8+uuvfPPNN3e5hqI+Xf89BqDRaH5WFKXGaUGyZk8IIcR95+zsTK9evYzKfH198fT0ZOvWrWo2piGY3tfXV90sZ86cOXTp0oWuXbuydetWAHJzc+nTpw9ubm54eHjw8ccfq/cdOXKkej9nZ2ejzM2a7gX6D76GDRuGq6srbm5u/Oc//zGq67x589BoNEbTcqsKCwvDxsZGdl0VQtwThYWF7N27l5iYGDWvuKrdu3fz3Xff8emnn96H2ol7SaZxCiGEeCAUFBSQm5tL+/btOXz4sFoeGhpKaGgoUH1d46FDh1izZg0HDx4kPz+fp59+mszMTMzMzPjoo4/w8/OjoKAAf39/QkJCcHd3V7MYAf785z/TsmXLG97L1NSU119/nbCwMNatW0dZWRnFxcXqPXJzc/nxxx/p0KFDrW2bOnUqxcXFasaoEELUp40bNxIWFoaLiwutW7dm//796jKCixcv8uKLL7Jy5coHKsZK1A8Z2RNCCPFAGDFihNoRW7169S3tqBobG8uoUaOwsLCgY8eOdOnSheTkZOzt7dVfbKytrXFzc+O3334zulZRFL777jv1ObXd6/Lly+zevVtdI2lubq5OdQd48803+eCDD9BoNLXW86mnnsLa2vr23hAhhKij1atXM2rUKABGjRrF6tWr1WOTJk3i+eef54knnrhf1RP3kIzsCdFgae93BYS4gcRqJcOGDWPcuHFERUXxww8/sGrVKr7++usb3uW3336jR48e6mtHR8dqnbqcnBzS0tIICgoyKt+zZw9t27blscceu+G9LC0tadOmDS+++CIHDhzA39+fjz/+GCsrK+Li4nBwcMDHx+d23wAhhKgX58+fZ8eOHWRkZKDRaKisrESj0fDBBx+wcuVKcnJybvpvq2g4ZGRPiAZIq9Wi1erudzWEuC2tW7emVatWrFmzBjc3N5o1a3bTa2raZKzqCFthYSHPPfccCxYsqDZd6frRw9ruVVFRwf79+5k0aRJpaWlYWVkxd+5ciouLiY6O5t13372dZgohRL1at24dY8eO5eTJk+Tk5JCbm0vHjh3ZvXs3M2fOZNWqVZiZyXhPYyF/00I0WL7UNHoixINs5MiRTJ48mRUrVtzS+Y6OjuTm5qqv8/LyaNeuHQDl5eU899xzREREMHToUKPrKioqWL9+PT///PNN7+Xo6Iijo6M6Mjhs2DDmzp3L8ePHyc7OVkf18vLy8PPzIzk5mUcffbRO7RdCNCxWba3uevTCzaxevbpaTNVzzz3HihUrKCoqqvbv4aJFi6ptkCUaDunsCdFIODs7k5qaiq2tbb0+Z/z48WzatAk7OzsyMjLU8rfffpvY2FhMTEyws7NjxYoV6i/lQhiEh4dz6tQpQkNDyc/Pv+n5gwcPZsyYMUyZMoX8/HyysrIIDAxEURReeukl3NzcmDJlSrXrtm3bhqurK46Ojje9l6mpKe3bt+fo0aN07dqV7du34+7ujpeXF2fPnlWvv1f/jwkhHh63mol3NyUmJlYre+211wD48ssv73FtxP0m0ziFEHdFZWUlAOPGjSM+Pr7a8alTp5Keno5Op2PQoEEy9U3UyNramunTp2Nubn5L53t4eDBixAjc3d0JCwvjk08+wdTUlL179/L111+zY8cONWbBEFAPsGbNmmobwNR2L9B/8h0REYG3tzc6nY6//vWvN6xXamoqkZGR6utevXoxfPhwtm/fjqOjo1GsgxBCCFFfJFRd1EhC1R9uWq0WMP50zzDqUFhYSP/+/QkODmbfvn04ODgQGxuLpaUlWq2WoKAgdu7cycWLF4mJiaFXr15UVlYyY8YMEhMTKS0tZfLkyUycOJHExERmzZqFvb09Op2OQ4cOAfoNMQYNGmQ0slfVnDlz+PXXX1myZMlda7OzszPt27dnz549apmvry8VFRV89NFHTJ8+HYBjx47h4OCApaUl3t7erFy5kjlz5hATE4OpqSkLFy4kNDSU3Nxcxo4dy+nTpzExMWHChAm8/vrrgH6q4dGjRwH9/yM2NjbodDq1bdffy3BeZGSkumD+iy++oGfPnrc84hkWFkZSUhLBwcFs2rTprr1vQggh7p6aAq+FuJtuN1RdpnGKRsvQIWqIdDqdUVD09bKysli9ejXLli1jxIgRfP/99zz//POAfi1TcnIymzdvZtasWWzbto2YmBhatmxJSkoKpaWlPPHEE/Tr1w+A5ORkMjIy6Nix403rNXPmTFauXEnLli3ZuXPn3WlsFQ9jTtvUqVN57733AFi4cCHvvvsuS5curdY2yWkTQgghxO2SaZyiUdJqtepITGPUsWNHtTPo7+9PTk6OesywcLtqeUJCAitXrsTX15egoCDOnz9PVlYWAIGBgbfU0QOIjo4mNzeXiIgIFi9efPcadM3DmNNWdYfIoqKiWrPaJKdNCCGEELdLRvZEo+Xr61vjIuaGQD9qqcM4a+808CxQiYVFvnrM1DSXkpJKYBugw8LiNcAaU9NyKiryAC2KcpBFi+wJDTUESdsAs0lMvIiVVS7VM/2uANk1lOuNGXOFgQN/YdasXXfQysRqJQ9jThvU/4inEEIIIRonGdkTogHS6XTodIVGZXl5pZw/X16n+4WGtmLJknzKy68CkJlZTFFRZY3njh9/lICAnzl2rMSoPDLyKK6uyXh7pzJ4cAadOjWtU11u5GHLaTOo7xFPIYQQQjROMrInRIPVHOPRLzPga8ACGFTl2DygEHgH/UjcPCAAOHftv4lERl4lJ+ct/Px+QFEU2rRpw8aNG4G0a+dvorKyElNTU06efAo4QGnpeRwdjzFr1ixeeukljhwJxtT0AmBKUVExjo79gE/veqsfppy2640ZM4aBAwcya9asW26vEEKIB9e+ffsoL6/bB601adKkCY8//vgNz9FoNEyZMoWPPvoIgHnz5lFYWMjjjz/O3//+d/bt24dGo6GyshJ/f38+/fTTm95TPLxkZE+IRsLR0ZFHHnkE0MckvPzyy3h4eJCQkKDuVAmwdu1aAgMDefzxx9UpkIqiUF5eTtOmTTExMWHUqFHqpiRFRUWMGTMGLy8vALZv305qaioeHh7k5eWp69T+7//+j4MHD5Kens6HH37IpUuX6qWd4eHhTJs2Td2Q5WYGDx7MmjVrKC0tJTs7+45z2mq616OPPqrmtAFqThugrn0EiIuLw9XV9U6aL4QQ4gFyNzt6t3o/CwsL1q9fz7lz54zK+/Xrh5OTEzExMYA+UqZ79+7S0WvgZGRPiEbofu3GafDFF18wcuTIemmbIaftVlXNVjMzM1Oz1f7v//6Pr7/+Gi8vL3Uzm9mzZzNgwADg5jltVe8Ff+S0lZWV0alTJzXYdsaMGRw9ehQTExOcnJzUnThTU1NZunQpy5cvB/Q5bUeOHKGwsBBHR0diYmJuuUMrhBCi8TAzM2PChAnMnz+f6Ohoo2Pz588nODiYnj17snjxYpKTk+9TLcW9IiN7QjRAhhDp2tyv3ThBvz7NzMyMiIiI22zVzYWHhxu9HjJkCABbt25V35PU1FQiIiLw9fVl7NixAJiYmKjr9ExM9P8sOjk5odVqKS8vp7y8nBdffFHt6I0cORKdTsfSpUtxdnY2eq9ruhfocwCdnZ0pKyvj6NGjHDlyBIBOnTpRUVEB6H9AGzZtCQgIUDt6AI899hgajYbOnTuTl5cnHT0hhBC1mjx5MqtWrao2i8be3p433niDnj178tZbb9G6dev7VENxr0hnT4hGyMLCQv3a1NRU7WxUPVa1XFEUFi1adG3jFx3Z2dnqyJ6hc3IrvvrqKzZt2sSqVatqjRi4E4acPaBazp6h7gEBAaxatQqdTsfKlSuNsvHi4+P505/+RGVlpZqzd/jwYZKSkvjkk0/U0Ph//etf6v2ee+45tYNc270ANWfvyJEjHDhwQA1EDQkJISMjg/T0dFxcXJgzZ06NbRs3bhzx8fF3/T0TQgjR8LRo0YKxY8eycOHCascmT55MZWUl48aNu/cVE/ecdPaEEDcVGhrKkiVL1LUCmZmZFBUV3dY94uPjef/994mLi7ulXTLr4mHM2evXrx9mZvoZ9T169CAvL6/Gej755JPyCawQQohb9sYbbxATE1Pt53XVGSii4ZM1e0Jc4+zsTGpqKra2tvX2jKNHjxqtVTtx4gTvvvsub7zxBm+//TaxsbGYmJhgZ2fHihUr1F0h66b2nD3jDLzca2WJ166ZCFgD5deu0RIZqZCTk42fX6tru3E2YeNGT/S7eBrn7I0efYjExEucO1eOo6MFs2Y589JL9rz66k+UliqEhOinfPbo0YKlS13uoH2J1Uoe1pw9g/pcyyiEEKJxad26NSNGjCAmJobx48ff7+qI+0Q6e0LcI5WVlXTt2hWdTqe+dnBwUNeZTZ06lffeew+AhQsX8u6776qbddwufVi81qgsJ0ffCbG1bUJGRne1PCqqfZXr/lh7ZmvbRL3GxETD7NmdmD27k9E9tVobtFobo7LVq91rrNOxY0E1lt9ND3rO3qJFiwgKCuL1119n7ty56t831O9aRiGEEPdHkyZN7nr0wu3485//LPmtjZx09oS4Tk5ODv379yc4OJh9+/bh4OBAbGwslpaWaLVagoKC2LlzJxcvXiQmJoZevXpRWVnJjBkzSExMpLS0lMmTJzNx4kQSExOZNWsW9vb26HQ6dc0X6LffGneXpAAAIABJREFU79y5M05OTgBGHYmioqK7MMUi8Q6vfzg9jDl7hrWM27dvl6k1QgjRgNyPWIPCwkL167Zt21JcXHzDc0TDJp09cUNarfZ+V6Fe6HS6G+5WeS+iCWraun/mzJmsXLmSli1bsnPnzrvc6sYhPDycU6dOERoaSn5+/k3PHzx4MGPGjGHKlCnk5+ffcc5eTfcyNTVVc/a6du1qlLNnWMu4a9euelvLKIQQQojGSTZoEbUqLCxUpxwCJCUl3fVw0Jrk5eWRkpJCSkoKhw4d4urVq4B+ilx2djbJycmkpKTUupHF3VDf0QRlZWXExcUxfPhwo/Lo6Ghyc3OJiIiQaRd1ZMjZMzc3v6Xzq2bjhYWFqdl4e/fu5euvv2bHjh1qbMPmzZvV626Ws1f1XvBHzp63tzc6nY6//vWvALz66qsUFBQQEhKCr68vr7zyCgD5+flq1APA6NGj6dmzJ0ePHlVz9oQQQgghbkRG9sQN+fr6Xlv/pd/AJDY2tt42MKmsrOT06dMEBwdz7tw5LC0tGTFiBAMGDGDcuHF8+eWX7Ny5kxUrVmBiYsLZs2exs7Or07NuNmJ5fTRBSUlJtWM1RRNcn32WmJhYYzTBli1b8PPzo23btjU+f8yYMQwcOJBZs2bdUnuEfvqts7Mz7du3Z8+ePYD+e9bMzAxPT08++ugjNWxdq9Xi4OCApaUl3t7evPDCC6xfv56ysjLeeustLCws6Nu3L5cvX6ZXr17qM8aOHcvzzz/PggULWLFiBd999x3u7u5oNBp8fHz49ttvef7551m/fj2VlZVERUVx8uRJXnnlFVxcXGjbti3Hjx8nKyuL999/n7lz53Ls2DH1/uvWrWP48OFERkYSEBBg1LlcvXo148ePZ9OmTdjY2Kg7ewohhHiwKIoiU/JFvahpb4CbkZE9cdtycnJwc3Pj5ZdfxsPDg379+qmdIa1Wy/Tp0wkMDMTFxUX9pbuyspKpU6fSvXt3vL29+eyzzwB9Z6hPnz6MGTMGLy8vQD9NsqSkhIqKCoqLi9X1U0uWLOFvf/ubGlRd145efbjdaIKaYgEMI4EAcXFxuLq61k9lG7i6ZO3Z2tryww8/8Msvv/DVV1/xwgsvAPpRQsM1Op0OJycndWQ3KyuLOXPmsHfvXg4ePMiCBQsAfWDtvn370Ol0/PTTT8ydO1edThoVFcWRI0dIS0tj7969bNmyxajeCxcurLbjZ1WStSeEEA+2pk2bcv78+Tr9Ui7EjSiKwvnz52natOltXScje6JO6nNNW1RUFB06dMDS0pJ+/fqp5x0/fpx//etfbNiwgTZt2rBw4UJ1y/v7LTIykpycHPz8/K5FE7Rh48aNNZ5bXFzMjz/+qHZ4DWbMmMHRo0cxMTHBycmpzjtxNnaGrL2oqCi1U32z+IVu3bqpX3t4eHDlyhVKS0uNRnizsrI4e/asOtK3bNkyJk+eTKtWrYA/PnyoOn20tLRUnYbcrFkz+vTpo57j5+dnNBX57bffZtq0acybN6/Wej755JNGU4qFEEI8WBwdHcnLy+P333+/31URDVDTpk2N9gq4FdLZEzXy9fU1Wq93vbqsaUtPT2fdunUAXLp0iaysLMzNzY3WtF24cIHY2Fiys7OxsbFh+PDhfPPNNzz//POUlpbStGlTUlNTWb9+PePHj1dHDutOq36Vk+MMDMPWFjIybNVjUVF/nKuf0aovsLU1XKPFxARmz4bZsx+5dq4CPItWC/oZo388p1kzOH/eC33m3R++/x7AMEW2AJAt+G8usVpJXbL2qvr+++/p1q2bUUcP9KOxI0eOVKfmZGZmAvDEE09QWVnJO++8Q1hYGAC5ubkMHDiQY8eO8eGHH1bLS7x48SI//PADr7/+OgBpaWnk5uYyaNCgG3b2hBBCPNiaNGlSbZ2+EPeTTOMUdXL9mjbD2rWqx2pa02aYDpedna2O2FVd07Zt2zY6duxImzZtaNKkCUOHDmXfvn2A/tOy5557DtDvuJienn5HbdDpdGi1tXdoxcOpLll7BgcPHmT69OnVRl2h+oYsFRUVZGVlkZiYyOrVq4mMjOTixYsAtG/fnvT0dI4dO8ZXX33FmTNnjK4bPXo0r732Gp06deLq1au8+eabfPTRR3fQaiGEEEKI6mRkT9wThjVtffv2pUmTJmRmZuLg4FDtvA4dOpCUlERxcTGWlpZs376dgIAAAIYMGcKOHTsYP348u3btwsXF5S7UzJfGmkfXkN1u1h7od4ENDw9n5cqVdO7c2ejYgQMHqKiowN/fXy1zdHSkR48e6qe4Xbt2JSsri+7d/wisb9euHR4eHuzZs4dhw4YBMGHCBB577DHeeOMNQL9WLyMjQ9006PTp0wwePJi4uDj1e18IIYQQoi5kZE/cE5GRkbi7u+Pn54enpycTJ040Gg00CAoKYtiwYfj5+eHl5cXVq1eZMGECoF/T9v333+Pl5cVf/vIXli9ffq+bIR4S4eHhTJs2rdruqLW5ePEiAwcOZM6cOTzxxBPVjte0oc6QIUPULMRz586RmZlJp06dyMvLUzcsunDhAnv37qVr164AvPXWW1y6dEndzAWgZcuWnDt3jpycHHJycujRo4d09IQQQghxdyiK8tD+8ff3V0T96N27t9KyZUuld+/e97sq9aK29jk5OSm///57vT77yJEjio+Pj/rH2tpamT9/vqIoihIVFaV07dpV8fLyUoYMGaJcuHChXuvS0NT095edna14eHgYlfXu3VtJSUlRX7/33ntKs2bNjP5ezpw5ox7v2LGjcvjwYaN7XL16VXnzzTcVNzc3xdPTU1m9erWiKIqSkJCgeHl5Kd7e3oqXl5fy2WefKYqiKLm5uQqguLq6qs9YtmxZtTZUrdtvv/2m9O/fXz02atQo5dFHH1XMzMwUBwcHZfny5XV5m4QQQgjRgACpSi39JRnZE+IeqqyspGvXruraxZ9//plmzZoRHh4OQEhICBkZGaSnp+Pi4sKcOXPuc40fPob30mDIkCEAbN26VQ1HT01NJSIiAl9fX8aOHcukSZMIDAzk2LFjBAcHo9Pp1N01Z86cSXl5ebWRttzcXNLS0rCwsECj0dCiRQtAHz/SrVs3rl69Snl5OefPnwfA1taW7t27Y25uTnl5Oc8++yyRkZGAPlLBsOnRxYsXMTPTz7Bv166dUdbehQsXKCkpITQ0lLy8PMnaE0IIIcQNSWdPiBrUd5agwfbt2+ncuTNOTk4A9OvXT/1Fv0ePHkZb84tbU5ecvaZNm/Lee+/VuBPmM888Q3JycrXyf/zjH4wYMYK0tDTWrFnDn/70JwDWrl1LaWkpv/zyCz///DOfffYZOTk5WFhYsGPHDg4cOIBOpyM+Pp6kpCT1fh9++KFaP8NOt9ebOnXqbe0sKoQQQojGTTp7QtQiKyuLyZMnc/DgQWxsbPhen40A/JEluGDBAmbNmgVglCWYkpLCsmXLyM7OBvRZgtHR0Rw6dMjoGdfv8FjVF198Qf/+/eupdQ2XIWcPal5rVxMrKyuCg4NrDCrt0aMH9vb21co1Gg2XL18G9FEihngFjUZDUVERFRUVlJSUYG5uTosWLdBoNDRv3hyA8vJyysvL1RiHW/XUU09hbW19W9cIIYQQovGS3ThFo1VYWAjoqJqBB6fR599V0rGjOb6++h0T/f1/JScnBVgO6Bg69BKgxd+/jJycNEBLQsJB0tOLWLduLgCXLlWSlfUs5uYmBAY2oWPHF42eX1Z2lbi4/zBnzhFgndGx6OiTmJkVEBFx+dozRc0Sq5Xcac7erXrnnXfo168fixYtoqioiG3btqnPj42Nxd7enuLiYubPn0/r1q0B/eivv78/x44dY/LkyQQFBan3mzlzJu+++y5PPfUUc+fOrZbzJ4QQQghxu2RkTzRKifp0dHS6wlrPsbD4Y9TF1FRDRYVS5ZhJtXJFgUWLuqDTBaDTBZCdHUS/fvpf8q2sTKvdf8uW/+LnZ03btuZG5V99dZpNm86zapXbbY/8iDvL2bsdq1evZty4ceTl5bF582ZeeOEFrl69SnJyMqampuTn55Odnc1HH33EiRMnAH32pE6nIy8vj+TkZDIyMgCYM2cOR44cISUlhf/+97+8//779VJnIYQQQjQuMrInGi3DlDrj0SFnIBYoBAZVOTbvWtk76EcC5wEBwLlr/00kNPRzlizZTN++a6/LEky5dv4mo+evXj2K0aP/DPwx4hcfH8/7709h167jNGvW5m41tdGpS87e7YqJiSE+Ph6Anj17cuXKFc6dO8e3335LWFgYTZo0wc7OjieeeILU1FQ6deqkXmtjY4NWqyU+Ph5PT091mqiFhQUvvvhijWsHhRBCCCFul4zsCXGX3GqWIEBxcTE//vgjQ4cONSp/9dVXKSgoICQkBF9fX1555ZV7UfUG53Zz9uqiQ4cObN++HdBvBHPlyhXatGlDhw4d2LFjB4qiUFRURFJSEq6urvz+++9cvHgRgJKSErZt24arqysAp06dAvRROBs3bsTT07Pe6i2EEEKIRqS2TIaH4Y/k7NWfhp6zpyiK0rJlS6Vly5ZGZfc7Z++7775T3N3dFY1GY5QDJ25NXXP2DNe2atVKsbKyUhwcHJSDBw8qiqIoU6dOVRwcHBSNRqM4ODgof//73xVFUZSDBw8qjz/+uOLt7a34+PgoW7duVRRFUQoKCpRhw4Yp7u7uipubm/LBBx8oiqIoBw4cUHx9fRUvLy/Fw8NDmTVrlvrsPn36KJ6enoqHh4cSERGhFBQUKIqiKCkpKcpLL72knhccHKzY2toqTZs2VRwcHJT4+Pi78K4JIYQQ4mHGDXL2NPrjD6eAgAAlNTX1flejQdJqteoW8Ib1bQ2NjY0NgDraAuDs7Exqaiq2trb18szKykpMTU2NXjs4OPDTTz/h5OTE4cOHMTExYeLEicybN69atpsQDZmzszPt27dX40wAfH19qaio4KOPPmL69OkAHDt2DAcHBywtLfH29mb+/PkMGzaMlJQUxo0bx+LFi9Xrf/75Z8aNG0dJSQkDBgzg448/VtfCLlq0iMWLF2NmZsbAgQP54IMPAP0aypiYGExNTVm4cKE6Quzs7Iy1tTWmpqaYmZlh+Pnz9ttvExsbi4mJCXZ2dqxYsULdnbWqsLAwkpKSCA4OZtOmTdWOCyGEEHWh0Wh+VhSlxl8aZRqnEDW4Xzl7bm5udO3a9R62VIgHy93OSZw0aRKff/45WVlZZGVlqessd+7cSWxsLOnp6Rw8eJCoqCgADh06xJo1azh48CDx8fH86U9/orKyUr3fzp070el0VP2gcerUqaSnp6PT6Rg0aBDvvvtujW2TnEQhhBD3mnT2hKjF/c7ZE6Ixups5iadOneLy5cv07NkTjUbD2LFj2bhxIwBLlixhxowZasSFnZ0dALGxsYwaNQoLCws6duxIly5dSE5OvuHzW7RooX5dVFRU6y66kpMohBDiXpPdOEWj5evry4Oas6ev10RAfjEUDVlitZK7mZP422+/4ejoqL52dHTkt99+AyAzM5M9e/Ywc+ZMmjZtyrx58+jevTu//fYbPXr0qPEajUZDv3790Gg0TJw4kQkTJqjnzZw5k5UrV9KyZUt27txZp/oKIYQQd5uM7IlGSz8l7MHL2ROiMbubOYk1rUk3jLpVVFRw4cIFkpKS+PDDDxkxYoS6mL22a/bu3cv+/fvZsmULn3zyCbt371bPiY6OJjc3l4iICKM1g0IIIcT9JCN7opFrzoOUs/eHqs8QonG5WzmJjo6O5OXlqa/z8vLUjVMcHR0ZOnQoGo2GwMBATExMOHfuHI6OjuqaweuvMfzXzs6O8PBwkpOTefLJJ42eOWbMGAYOHKhO7xZCCCHuJxnZE6KKvLw8zp8/X6drbzVn7+jRo3h7e7Nu3Tr++c9/0qJFCxYsWACgbjaxa9cuHn/8cfr27XtH7RHiYXS3chLt7e2xtrYmKSkJRVFYuXIlzz77LABDhgxhx44dgH5KZ1lZGba2tgwePJg1a9ZQWlpKdnY2WVlZBAYGUlRUREFBAaBfl5eQkKDmIWZlZanPjIuLU/MThRBCiPtNRvaEqMLR0ZFHHnkEW1tbMjIy1HLDTn2AURSFra0tOTk5AJiYmDB79mxmz55tdE+tVotWqwX0O3Z27dqV9PR09bWDgwPh4eEAZGRk8M477zBjxgzmzp3LhQsX6qGVQjzYrK2t1ZiFW+Xs7Mzly5cpKytj48aNJCQk4O7uzpIlS9Tohf79+9O/f38Axo8fz/jx4/H09MTc3JyvvvoKjUaDh4cHI0aMwN3dHTMzMz755BNMTU05c+aM+v9pRUUFY8aMISwsDIAZM2Zw9OhRTExMcHJyYunSpQCkpqaydOlSli9fDkCvXr04cuQIhYWFODo6EhMTc8cdWiGEEOKGagvgexj+SKh6/WnsoerZ2dmKq6urEhkZqbi7uyshISFKcXGxoij692batGlK9+7dlccee0zZvXu3oiiKUlFRoURFRSkBAQGKl5eXsnTpUkVRFGXnzp2KVqtVRo8erbi5uRk9b+vWrcrjjz+uvnZxcVHy8/MVRVGU/Px8xcXFpd7a3xA5OTkpwcHBRmU+Pj6Kh4eHEh8frwbZW1lZKS4uLoqPj4/ywgsvKOfOnVO0Wq1iZWWlTJ482ej61NRUxdPTU+ncubPyv//7v8rVq1fVYwsXLlRcXFwUd3d3ZerUqWr57Nmzlc6dOysuLi5GwedOTk6Kp6en4uPjo1T99ysqKkrp2rWr4uXlpQwZMkS5cOFCje178cUXlTZt2lQLiRdCCCFE48UNQtVlGqcQtbgf0QtnzpzB3t4e0E9BO3v2bH03s8F5GHPaQkJCyMjIID09HRcXF+bMmVNj28aNG6c+XwghhBDiZqSzJxotX1/fa/ELNevYsaN63N/fX52uCTB06NBq5QkJCaxcuRJfX1+CgoI4f/68upYnMDCQjh07Gt2/rKyMuLg4hg8ffhdbJR7GnLZ+/fphZqafVd+jRw+jTUWqevLJJ2nduvVN2yOEEEIIAbJmTzR6tefsWVjkq8dMTXMpKakEtgE6LCxeA6wxNS2noiIP0KIoB1m0yJ7QUJtr97IBZpOYeBErq9zrngNbtpzDz+8qbduOVMvati3j1Kme2NtbcOpUKXZ2pdWuE1UlVit5WHPaDL744gtGjhxZrVwIIYQQ4nZJZ080WjqdDn2cwh/y8ko5f75czdG7HaGhrViyJJ++fW1o0sSEzMxiHBwsajz34sUKXn01i4oKcHNL4YsvXOjZsyU9elgTFJRGy5ZmVFRcJSysVV2a1qjdj5y2lJQURowYwYkTJ26a09auXTvOnj1LSEgIrq6uRlv3R0dHY2ZmRkRERJ3rLIQQQghhIJ090chdn7NnBnwNWHC7OXuRkVfJyXkLP78fUBSFNm3aXJvyl4YhZ6+yshJTU1MmT47gv/9NIz8/H0tLS4qLiwEb0tO7YWvrQEFBAWZmZjRpMvDateJ2PIw5bV999RWbNm1i+/btaudQCCGEEOJOyJo9IaowRC+APhbh5ZdfxsPDg4SEBKOt4NeuXUtgYCCPP/64OkVQURTKy8tp2rQpJiYmjBo1ipYtWwL6XK4xY8bg5eXF5cuX2bdvH4WFhbRs2RJzc3NsbPRTP48fP87PP/9MVlYWCQkJbN68+R6/Aw3Dw5bTFh8fz/vvv09cXNwdjUQKIYQQQlQlnT0halFfu3GeOHGCNm3a8OKLL9KtWzciIyMpKioCwNPTk7i4OEDfoaw6QiRunSGnzdzc/JavcXZ2ZsqUKaxYsQJHR0d159QlS5YQGRlJly5d6Ny5s1FO24kTJ/D09GTUqFE15rSFhYUZ5bQFBwfj4+NDYGAgAwcOVHPaXn31VQoKCggJCcHX15dXXnkFgPz8fAYMGKDWcfTo0fTs2ZOjR4+qOW1CCCGEELWRzp5otO7XbpwVFRXs37+fSZMmkZaWhpWVFXPnzgX0m3N88skn+Pv7U1BQcFudFaFnCL42GDJkCABbt25V/85TU1OJiIjA19eXsWPHAjBx4kRat26Ng4MDMTExuLu7AxAQEMCBAwdo0aIFOTk56hRLc3NzvvnmG/r06UNmZiZ9+/YF4J///CerVq3CysoKR0dH9T6dOnUiIyMDjUZDkyZN+Omnn9Q6fvbZZ7Rp0waA5s2bqzEO7dq1U0d3z58/z+nTpykoKGDixInk5eXx0ksv1ct7KIQQQoiGQdbsCVELw5b6AKamppSUlFQ7ZmpqSkVFBaCfxrlo0aJqUwcTExOxsrJSXzs6OuLo6EhQUBCg3z3S0NlzdXUlISEB0E8N/Pe//10PLWvYDDl77du3r5azZ/i70Wq1zJs3j4CAAMA4Gy8/P5+nn36azMxMTE1NAfj4449xc3Pj8uXLRs9KTU3l4sWLRmXdunUjNTWVZs2asWTJEqZNm6ZGQVhaWl7bGMjYpEmTiI2Nxc3NjU8//ZR//OMf1dYbGrIAMzIyyMjIuLM3SQghhBCNgozsCXGXhIaGsmTJEsrLywF9Z80wPbOqRx99lPbt23P06FEAtm/fro7+GELUr169yj/+8Q91Op+4dXXJ2btRNl5eXh7//ve/iYyMNLqmsrKSqVOn8sEHHxiV9+nTR113d6PMvKo0Go3akbx06ZK6kUtVtWUBCiGEEELURkb2RCNXe84eZFc5lnutLPHaNRMBa6D82jVaIiMVcnKy8fNrdW03ziZs3OiJfhdP45y9RYtKiIgIoKxMoVOnpnz5ZVdAy+rVeXzyST4AQ4fa8uKLJ4GV9dHwBiKxWkldcvZulI33xhtv8MEHH6ibqxgsXryYwYMHY29vX+t9Y2Ji1DV+AFeuXCEgIAAzMzNmzJihTjFdvnw5AwYMwNLSkhYtWpCUlHTTlgshhBBC3Ix09kSjlZiYyPWB5Tk5+l/4bW2bkJHRXS2Pimpf5bo/1vnZ2jZRrzEx0TB7didmz+5kdE+t1gat1saozNe3Oamp/tXq9Prrjrz+umO1cnHr6pKzV1s23qZNm7Czs8Pf3//a94tefn4+a9euNSq73jfffENqaiq7du1Sy3799VfatWvHiRMn6Nu3L15eXnTu3Jn58+ezefNmgoKC+PDDD5kyZQrLly+/rXYLIYQQQlxPOnuikUu83xUQ9eB2c/Zqy8aLi4sjLi6OzZs3c+XKFS5fvszzzz/P6NGjOXbsGF26dAGguLiYLl26cOzYMQC2bdtGdHQ0u3btMlr7aZie2alTJ7RaLWlpabRo0YIDBw6oazhHjhyp7tIphBBCCHEnZM2eEKLBud2cvdqy8ebMmUNeXh45OTmsWbOGvn378s033zBw4EBOnz5NTk4OOTk5NGvWTO3opaWlMXHiROLi4rCzs1OfceHCBUpLSwE4d+4ce/fuxd3dnVatWnHp0iUyMzMB+PHHH3Fzc7vL74gQQgghGiMZ2RNCNDiGnL1bVTUbz8zMTM3Gq4upU6dSWFjI8OHDAejQoQNxcXEcPnyYiRMnYmJiwtWrV5kxY4a6Mc+yZct47rnnMDExoVWrVnzxxRcAxMXFkZqayrvvvgvoswAvX75MWVkZGzduJCEhQb2HEEIIIcT1ZGRPCNFg5OTkYGtri7OzM7169QL0HaSMjAx8fX3x9PRk69atXLx4kcjISJo3b07Xrl3x9fXl6NGjJCcn065dO4YPH86rr75qdG9ra2tOnjxJly5deO2114zW+c2ZM4euXbvi4eGBn58fZ86cQafTMXLkSA4dOkTXrl0pKCjgl19+4cCBAxQUFLBgwQJ8fX0JCAggPDxcPZaYmMj69evRaDQ8/vjjakfP0L7//ve/BAcHU1hYyLRp0+7NGyuEEEKIh5KM7AkhGqS65O0VFRXVmmU3adIkPv/8c3r06MGAAQOIj4+nf//+7Ny5k9jYWNLT07GwsFDjM26W3bdz505sbW2r1Ts3N5cff/yRDh061Nq2qVOnUlxczGeffXZnb5IQQgghGjQZ2RNCNEh1ydurLcvu1KlTXL58mZ49e6LRaBg7diwbN24EYMmSJcyYMUPdiMWwTu9G2X038uabb/LBBx+g0WhqPeepp57C2tr6pvcSQgghROMmnT0hRIM0bNgw1q9fD8APP/zAM888U+d7/fbbbzg6/hGJUTWHLzMzkz179hAUFETv3r1JSUlRr2nfvn2N12g0Gvr164e/vz+ff/65ek5cXBwODg74+PjUua5CCCGEEAYyjVM0Xtr7XQFx1yRWL6pL3l5tasvhA6ioqODChQskJSWRkpLCiBEjOHHixA2v2bt3L+3atePs2bOEhITg6upKQEAA0dHRJCQk1LmeQgghhBBVycieaJS0Wi1anfZ+V0PUM0Pe3q1M4bwRR0dH8vLy1NeGHD7DsaFDh6LRaAgMDMTExIRz587Vmt0Hf+Tt2dnZER4eTnJyMsePHyc7OxsfHx+cnZ3Jy8vDz8+P06dP31HdhRBCCNF4ycieaLx8MRoRcnZ2JjU1tcZNM+4mZ2dnrK2tMTU1xczMjNTUVADefvttYmNjMTExwc7OjhUrVqidAlE34eHhnDp1itDQUPLz8+t8H3t7e6ytrUlKSiIoKIiVK1fyv//7vwAMGTKEHTt2oNVqyczMpKysDFtbWwYPHsyYMWOYMmUK+fn5anZfUVERV69exdramqKiIhISEvjb3/6Gl5eXurkL3LvvRyGEEEI0XDKyJ26Ls7Mz586dq/fnzJ8/Hw8PDzw9PRk9ejRXrlwBYNy4cXTs2BFfX198fX3R6XT1Xpe7pbKyUv16586d6HQ6taMH+h0W09PT0el0DBo0yGjLfVE3hrw9c3PzW77G2dmZKVOmsGLFChwdHTl06BCg34glMjKSLl260LlzZ/r37w/A+PHjOXHiBJ6enowaNYqvvvoKjUZjlN0XFhamZvedOXOG4OBgfHx8CAwMZODAgYSFhd2wTqmNKPuWAAAgAElEQVSpqURGRqqve/XqxfDhw9m+fTuOjo5s3bq1Du+OEEIIIRo8RVEe2j/+/v6KqB+9e/dWWrZsqfTu3duo3MnJSfn999/r7bkVFRVKXl6e4uzsrBQXFyuKoijDhw9XvvzyS0VRFOV//ud/lLVr197xc3r37l1r27KzsxVXV1clMjJScXd3V0JCQtS69O7dW5k2bZrSvXt35bHHHlN2796t1jsqKkoJCAhQvLy8lKVLlyqKoig7d+5UtFqtMnr0aMXNzc3oOTcye/Zs5ZVXXrnjdgohRH1xcnJSgoODjcp8fHwUDw8PJT4+XvHx8VF8fHwUKysrxcXFRfHx8VFeeOEF5aefflKPeXt7K+vXr1ev37Jli+Li4qJ07txZmTNnTrVnvvrqq4qVlVW18rVr1yqAkpKSoiiKopSVlSljx45VPD09FVdXV2X27NnquQsWLFA8PDwUd3d3Zf78+TW27fDhw0qPHj0Uc3Nz5cMPP6zT+yOEEPcKkKrU0l+SkT1RJzk5Obi5ufHyyy/j4eFBv379KCkpAfTr4aZPn05gYCAuLi7s2bMH0I9sTZ06le7du+Pt7a1mhCUmJtKnTx/GjBmDl5cXoN/0oqSkhIqKCoqLi+/5dMasrCwmT57MwYMHsbGx4fvvv1ePVVRUkJyczIIFC5g1axYAMTExtGzZkpSUFFJSUli2bBnZ2dkAJCcnEx0drY4Q1bYTI8DMmTNp3749q1atkpE9IcQDz5BnCVTLs9TpdOh0OgICAli1ahU6nY6VK1fi6elJamoqOp2O+Ph4Jk6cSEVFBZWVlUyePJktW7Zw6NAhVq9erf67CfoR7osXL9ZYh4ULFxIUFKSWrV27ltLSUn755Rd+/vlnPvvsM3JycsjIyGDZsmUkJydz4MABNm3aRFZWVrV7tm7dmoULFxIVFXU33y4hhLjnZM2euC2nT5/m2WefpbKykiNHjmBlZUWbNm3Q6XT4+fnRtm1bdDodx48fp3PnzpSUlDB48GB8fHzIz8+nvLwcJycnLC0tiYqK4osvvlB/IAcEBGBnZ0dERAQWFha0adMGU1NTWrVqxezZs5k9ezZHjhzhu+++Y9y4cdjY2NCpUydMTG7/MwudToevr2+txw1TRQH8/f3JyclRjw0dOrRaeUJCAunp6axbtw6AS5cukZWVhbm5OYGBgXTs2FG9vqadGJ988kkAoqOjiY6OZs6cOSxevFjtTAohxIPIkGcZFRWl5ll+/fXXN7ym6s64V65cUXepTU5OpkuXLnTq1AmAUaNGERsbi7u7u/ph4bfffsuGDRuM7vf2228zbdo05s2bp5ZpNBqKiorUDw7Nzc1p0aIFKSkp9OjRQ61D79692bBhA9OmTTO6p52dHXZ2dvz73/+u+5sjhBAPABnZE7UqLCw0eq3VaikrK1NfW1pa0rx5cwCaN2+urqsD1E0lrK2t1fILFy5w5swZUlNT2b9/v/pD2HCepaUlAOXl5Zw/f56goCB69OhBZWUlZ86cAfSdsO7du+Pn50dFRQW//vprvbTdEJANYGpqSkVFRbVjVcsVRWHRokXqJ9nZ2dn069cP0Ad1V1XTTozXGzNmjNFoohBCPIjqmmf5008/4eHhgZeXF0uXLsXMzOyG2ZSLFy9m8ODB2NvbG90nLS2N3NxcBg0aVK1eVlZW2Nvb06FDB6KiomjdujWenp7s3r2b8+fPU1xczObNm412zRVCiIZGRvZEjRITE7GxsalWbm5uTmxsLIWFhQwaNIjExEQA5s2bR2FhIe+88w5arZZ58+YREBDAuXPnCAgIIDExkeeee44JEyYQGhpa7Vnz5s1j06ZNgH76TXx8PDExMQCsXLmSpKQkPv300xtedzu0Wu1tX3MjoaGhLFmyhL59+9KkSRMyMzNxcHCodl5tOzGCfuroY489BujDtV1dXetWGW1dWyGEEDeQWL2ornmWQUFBHDx4kMOHD/M///M/9O/fv9Zsyvz8fNauXav+vDG4evUqb775JitWrKh2XXJyMqampuTn53PhwgV69erF008/jZubG9OnTyckJITmzZvj4+ODmZn8KiSEaLhkZE/U6kbTHOvC0CEqLy8HIDMzk6KiomrndejQgaSkJIqLi1EUhe3bt+Pm5gbAqVOnAP1I2saNG/H09LyrdayryMhI3N3d8fPzw9PTU12Dcr0b7cQ4Y8YMPD098fb2JiEhgY8//vi261FTfqBzkjPnyu/BDqp58/FI8cAzxZPRh0Zz5eqVm18khHjo3UmepZubG1ZWVmRkZNSaTZmWlsaxY8fo0qULzs7OFBcX06VLFwoKCsjIyECr1eLs7ExSUhKDBw8mNTWVb7/9lrCwMJo0aYKdnR1PPPGEuvvxSy+9xP79+9m9ezetW7dWP2QTQoiGSD7OEvdMZGQkOTk5+Pn5oSgKbdq0YePGjdXOCwoKYtiwYfj5+WFmZka3bt2YMGECABEREfz+++8oioKvry9Lly69a/UzrL+ztbUlIyNDLa+6QL/qJ8u2trbqNSYmJuq6wqq0Wq3RKGKnTp04cOBAjc+/a9M2r8sPxBmIBeoprq2yspLTp0+zMHghh84dwtLSkhEjRrBmwBrGjRtXPw8VQjwwbjfPMjs7m/bt22NmZsbJkyc5evQozs7O2NjYkJWVRXZ2Ng4ODqxZs4Zvv/0WDw8PTp8+rV7fvHlzjh07BmAUBVR1Vsn27dvZsWMHzz//PMXFxSQlJfHGG28AcPbsWezs7Pj1119Zv349//nPf+7yOyKEEA8O6eyJ29KjRw9sbW3rtUMEMGvWrBo3J/l/9u48rOoy///487CKKyouKNhBc2E/Aor+1DxUiktuRY5LGRZuA+U3Q2WyZmQ0l0pb1LHGIU1ztMWKbMzdo6Y5QHpkEFNITonggomyKJuf3x/H84kjqIigKe/HdXFd8Fnvz0fq4j73fb9fO3fuvPOHqKNMJhMDBw6kd+/e7N+/n7Zt2xIfH4+TkxN6vZ7g4GB27dpFbm4ucXFx9OnTh7KyMmJiYjAYDBQVFREZGcmkSZMwGAzExsbi6uqK0Whk27Zt6hpMe3v7e1JBVQhxb1jyLKvq+++/Z8GCBdjb22NjY8M//vEPdZ330qVLCQ0NpaysjOeffx5vb+9qtSkyMpLx48fj4+ODoiiMHz8ePz8/AJ566inOnz+Pvb09y5Yto2nTpgDqh4eTJ0/m9OnTBAUFcenSJWxsbHj33XdJTU2lcePG1WqPEELcK9LZE3WW0Wis8bV799qtqoympaWxbt06VqxYwciRI9mwYQPPPPMM8HukxKZNm4iNjWX79u1WkRJFRUX06tVLLTyTkJBASkqKWmk0Ojqadu3a4eTkRP/+/dXjhBAPpvJVii20Wq3VB4FAhbV2zz77LM8++2yl1xw0aBCDBg266X2vLx5W2X0aNmzI559/Xulxljig602ePFn9vnXr1mRmZt60HUIIcT+QNXuiTjIYDDW+JvF+UJ1IidWrV6PT6QgODub8+fNqJlX5SIkLFy4QHx9PRkYGWVlZFBQU8Mknn9yVZ9JqtfTp08dqm06nw8fHhy1btqDT6dDpdDRs2JDOnTuj0+kYN24c27ZtIzAwEF9fXwIDA9VR47y8PPUcnU6Hi4uLOv0L4LPPPsPLywtvb2/GjBkDwC+//EJgYCA6nQ5vb2+r6cUDBgzA398fb29vJk+eTFlZGWAuROTt7Y2NjY26lqgyzz//PC1btvzDrE8VQgghxP1DRvZEnXX9p80PgluNVF4fKWGJvii/r7JIicoqqJaPlNi+fTseHh60aNECMHcc9+/fr44a1jZLsLO7u3uFYGdL28uv5wFzyfaNGzfSpk0bUlJSCA0N5dSpUzRq1Aij0aheIzAwUO0Ip6WlMX/+fPbt20fTpk05e/YsAK6uruzfvx9HR0fy8/Px8fFh6NChtGnThs8++4zGjRujKAphYWF8/vnnjBo1Ch8fH7788ksmTZp002cLDw8nKiqKcePG1eg7E0IIIcSDT0b2hBA3VBMVVO8GS7AzoAY730rXrl3VdYXe3t5cuXKFoqIiq2PS0tI4e/asOnK4YsUKIiMj1TU+LVu2BMyRJJbOclFREVevXlWvYVnjU1paSnFxsRog7enpSefOnW/ZzkceeYRmzZrd8jghhBBCiOvJyJ4QDxoj1ll7p4FhQBmQUW7fyWvbDNfOmQQ0AkqunaOHCCUCU4aJgKbXKqjat+Brn68h/9r5164VTDBhBWEEuARgp7Gja8OuTMyYCLWRC2+ouCksLIzw8HCio6PZuHEja9euZc2aNVW+5IYNG+jatavVyCeYO45/+tOf1A7a8ePHAejVqxdlZWXMnj1bjc44efIkgwcPJj09nbfeesuqQE1oaCgJCQkMHDiQsLCw23xgIYQQQojqkc6eEA8Qg8FQIVTd1MMEgIu9CyndylVQdS9XQVVnUL93sXdRz7HR2DCv/Tzmtb+ugqqzHr2z9Y1itbHEaitWUL0bqhvsDHDkyBFmzpzJ1q1bK+xbv369VaextLSUtLQ0DAYDmZmZ9OnTh5SUFJydnXF3dyc5OZmsrCyGDx9OWFgYrVq1AmDLli1cuXKFsWPHsnPnTvr163fnDy2EEEIIcQvS2RPiQWO41w24NyzBzqtWraryOZmZmYwYMYLVq1fToUMHq32HDx+mtLSUwMBAdZubmxs9evTA3t4eDw8POnfuTFpaGt26dVOPadOmDd7e3uzdu9dqFK9evXoMHTqU+Ph46ewJIYQQ4q6QNXtCiAfCiBEjmDFjRoViMjeSm5vL4MGDmT9/Pr169aqwv7K1f8OHD2fXrl2AOcz5+PHjtG/fnszMTLXYzYULF9i3bx+dO3cmPz+f7OxswDwquGnTJrp06XInjymEEEIIUWXS2RNCPBAswc4ODg5VOn7p0qWkp6czZ84cNWbBUl0TzBEL13f2QkNDad68OV5eXoSEhPDWW2/RvHlzjh49SnBwMP7+/vTt25fo6Gh8fX0pKChg6NCh+Pn54e/vT8uWLdUsr6+++go3Nzd++OEHBg8erHZSs7KyrHLGRo8eTc+ePTl27Bhubm7ExcXd6asSQgghRB0hnT0hxH3NZDLh4uJita2yYGeTycTLL7+s/vzaa6/RsWNHSktLWbhwIQD9+/dX8/gaN27MvHnzrPL4goKCeOKJJ0hNTWX//v0sWLAAnU7H9OnTycrKIiQkhOTkZCZOnMhnn31GSEgIhYWF+Pj4cOTIEaKjowkODkan0/Haa6/x2muvUVRUREZGBnZ2dnTp0oV+/frh5+entnPhwoV06dIFHx8fmjVrhqura6XvQfL4hBBCCHE9WbMnhKgz/qh5fM7OzkRHRxMSEkJxcTGPPfYY3333HQMHDmTu3LmMHDmSKVOmkJqayqBBg9TQ+/Ikj08IIYQQ15ORPSFEnfFHzeOrX78+ISEh6jEBAQFkZmYCoNFouHTpEgAXL160inQoT/L4hBBCCHE9GdkTAli1Sn+vmyBqWHi4ocK2P3oeH5gLx2zcuJGpU6cCMHv2bPr378+SJUsoKChg+/btVW6vEEIIIeo2GdkTdZ5er2fBAuOtDxT3vZrI4/vwww8r7Fu/fr3VKGH5PL5169YRERFBbm4ugJrHl56ezscff8yZM2eszhs9ejQvvfQS7du3B8wdyfDwcDIzM9m0aRPPPvusOiIohBBCCHEzMrInBNC6ta7SkSDx4Pkj5/FNnDiRjh078n//93/qcXFxcWzevBmAnj17cuXKFXJyctSpoUIIIYQQNyIje0JUQqvVkpOTc1fu4+vri06nUwuCgLlDYokD0Gq16HS6Wm9LXfFHzOMDc3XQixcv8u6771pdq127duzYsQOAo0ePcuXKFVq0aHF7Dy2EEEKIOkk6e0LcA2VlZer3u3btwmg0kpSUpG779NNPMRqNGI1GnnrqKbXKo7hzf8Q8vszMTN544w1SU1MJCAhAp9Pxr3/9C4BFixaxYsUK/P39GT16NKtWrUKj0UgenxBCCCFuSTp7QtyEyWTC09OTCRMm4O3tTf/+/dWRGb1ez8yZM+nevTudOnVi7969gLkjN336dLp164afn5+6xstgMBASEsKYMWPw9fWt0v0VRam0MyHMtFqtWgHTQqfT4ePjw5YtW9TOWcOGDXF0dOTxxx9n3LhxanbekCFDcHR0ZOfOnYA5miE3N5eIiAh0Oh0uLi7k5ORQUFCA0Wjk1Vdfpbi4WP13BHNnfezYseh0Ory9vfnggw/QaDTMnTsXDw8PSkpKmDNnDjExMfTr14/k5GRefvllsrOz+cc//oFOp2Pz5s0oisLRo0fVTn5ERASzZs0iNDSUw4cPc/jwYYxGI/379wfM00A3bdqkPve6devIzs6mpKSEzMxMXnjhhbv0ryCEEEKIP6p7tmZPo9HYAknAKUVRntBoNB7AeqAZcBB4VlGU4nvVPnH36fX6e3Jfo9F402mSaWlprFu3jhUrVjBy5Eg2bNjAM888A5gLaiQkJLBp0yZiY2PZvn07cXFxNGnShMTERIqKiujVq5f6B3pCQgIpKSl4eHgA5rL6/fv3R6PRMGnSJCZOnGh1771799KqVSs6duxYS09//7sfs/PAPFV36dKlN322IUOGEBUVJf/+QgghhKiWezmyNxU4Wu7nhcA7iqJ0BC4A8rF0HaLX663+yP4j8fDwUDuDgYGBVoHWlo5A+e1bt25l9erV6HQ6goODOX/+PGlpaQB0795d7egB7Nu3j4MHD/Ldd9+xbNky9uzZY3XvqmbB1WX3Y3ZeVfXo0QNXV9fbOkcIIYQQwuKejOxpNBo3YDDwBjBNYw6nehQYc+2Qj4HZwPJ70T5xb+h0OgwGw12/761GFMtnqtna2qrTOMvvs7W1pbS0FDBPvVyyZEmFAiAGg4EGDRpYbbN0OFq2bMmIESNISEjgkUceAcyjhl9++SU//vjj7TxM1Y+9H1Xy+3E/ZudZ7rtnzx46derEO++8g7u7e5XbLIQQQghRFfdqZO9dYAZgCYtqDuQqilJ67edMoG1lJ2o0mokajSZJo9EknTt3rvZbKsRtCg0NZfny5ZSUlADmTkJBQUGF4woKCsjLy1O/37p1Kz4+Pur+7du306VLF9zc3Kp0X71ej/660VHtgQPkXGtHbXr+2DFa7t+PT2Ki1fbPz53DOzERm927Sbr2rDXtfszOGzJkCCaTieTkZB5//HGee+656j6+EEIIIcQN3fWRPY1G8wRwVlGUHzUajd6yuZJDlcrOVxTln8A/AYKCgio9Roh7KSIiApPJREBAAIqi0KJFC77++usKx505c4YRI0YA5g7BmDFj1JEiqNjZqBKdznr0S6uF+HhwcanGk9xaWVkZtra2hO/ZQ1TDhowbN87q/j5Hj/KljQ2TJk2Ct9+GcvESNel+y85r3ry5+v2ECROYOXPm7T6yEEIIIcQt3YtpnL2AoRqNZhBQD2iMeaTPWaPR2F0b3XMDsu5B24QAUNffubi4kJKSom6Pjo5Wvy8/5dTFxUU9x8bGhnnz5jFv3jyra+r1eqspo+3bt+fw4cM3bMPtdFxuxWQyMXDgQHr37s3+/ftp27Yt8fHxODk5odfrCQ4OZteuXeTm5hIXF0efPn0oKysjJiYGg8FAUVERkZGRTJo0CYPBQGxsLK6urhiNRlJTU3nkkUes1jJaeHp61tgz3MyIESPIzs4mNDSUrKxb/6+jutl569atIzw8vEJ2XvPmzXFyclKz86ZNmwb8np1niVGwyM7OVtfiffPNN3ftPQkhhBCibrnrnT1FUf4C/AXg2shetKIoYzUazedAGOaKnM8B8Xe7baLuMhqN96waaE25l1VF7zVLdl5Vlc/OmzNnDmAurGMpuvLZZ59ZxRqAeXru1q1b8fLywtbWVs3O27ZtG6+88goajQZFUSpk53Xp0oWAgAAAoqKiiIiI4P333+ebb77Bzs6OZs2aWXXsdTqdWqxoxowZ/Pvf/6awsBA3NzciIiKYPXv2HbwpIYQQQtQlf6ScvZmYi7WkY17DJ4nA4q4wGAw37SQ9KGqzqui9ZJkKazF8+HAAq5y9pKQkNQtv3LhxTJkyhe7du5Oenk7v3r0xGo1qR6+4uJjHH3+coUOH0qVLFzZs2ADAhx9+yLZt23BwcKBJkyb4+fkBcPbsWWxsbNBoNNjY2DB58mSMRiNubm68+uqr5Ofnk56ermbnAcyfP5+//e1vlJSUcPbsWf7+97+r7S9fldbe3h6NRkP9+vXJzMyUjp4QQgghbss9y9kDUBTFABiufX8C6H4v2yPqrntRBbSm3cuqovdSdXL2CgoKmDNnDikpKVbTdAHeeOMNWrZsyfHjx7l69Sq//fYbAGPGjGHy5MmAeerltGnT2Lx5M2PHjmXs2LEA/O9//2PYsGFqp/pGOXk3yuy7nuTsCSGEEOJO/JFG9oQQfyBVrSp6r1UnZ69Bgwb07t2bevXqVdj30Ucf8Ze//AUwr790uVbcpnHjxuoxBQUFaiRDedff/0Y5eTfK7Lue5OwJIYQQ4k7c05E9IW5Gq9WSlJSk/rFdW9555x3+9a9/odFo8PX1ZeXKldSrV4+lS5fy7rvv8vPPP3Pu3Llab0eNMBqts/ZOn4Zhw6CsDDIyft938qR5m8FgPmfSJGjUCEpKzOfo9UQoCqaMDAKaNjVXFbW352sfH8jPN59f7j6jU1MxXLxITkkJbo6OxGq1vODqylc5ObyYlsa5khIG/7//h65hQ7Zcm/5YLbWQs1eeJUrh9ddfx2Aw0KFDB5YuXUqrVq0AWLZsGYsXL6a4uJidO3dWOP/TTz8lPv7Wy41vltknhBBCCFFTpLMn6qyysjJOnz7N+++/T2pqKk5OTowcOZL169cTHh5Or169eOKJJ+6bwi0Gg6FCqLqpRw8AXOztSSkXERBdLsDbUG69oou9vXqOjUbDvPbtmXctG85C7+yM3tnZats6L69K2zTCxYURtdxJvpOcveuVlpaSmZlJr169WLx4MYsXLyY6OlrtPEZGRhIZGcm///1v5s6dy8cff6ye+9///pf69etbZSXe7D6WzL7MzEz69OlDSkoKzte9VyGEEEKIOyGdPfGHV5uxAdu2baO0tJTLly9jb29PYWEhbdq0AaBr1673+Mmr4QFYe1gd1cnZq0zz5s2pX7++WvTl6aefJi6uYq2oUaNGMWXKFKttt5OLWJXMPiGEEEKIOyWdPVFj7mQE7F7GBkRHR9OuXTucnJzo37+/epy4f9xuzt6NaDQahgwZgsFg4NFHH2XHjh14XRu1TEtLUwul/Oc//7EqmnL16lU+//xz9uzZU6X73CizTwghhBCiJkmBFlEj9Hq9Vcn4mlZbsQEXLlwgPj6ejIwMsrKyKCgo4JNPPqm15xC1w5Kz5+DgUOVztFot06ZNY9WqVbi5uZGamgrAwoULmT17Nn5+fqxZs4ZFixYB5mw+b29vdDodixcvtprCuWfPHtzc3Cp02GbMmIGbm5uak2eJTggNDaV58+Z4eXkREhKiZvYBVh963Oh8IYQQQoiqkJE9UWN0Ol21IwzuVWzA9u3b8fDwoEWLFoC547h//3511FD8MWi1Wtzd3dm7d6+6TafT0bBhQ3788Uc1UD09PZ22bdvi5OTEuHHjeOeddwgLCyMpKYlVq1ap0QtgLpISFRWFwWDAxsaGo0eP4uXlxXfffceFCxewtbWlqKiI/Px8wPwhwe7duwHzhwS+vr4cPHgQnU7Htm3bOHXqFA0bNlSPB/Mavx9//JFmzZrRrFkzNWdPo9GoawIBZs2axfTp07lw4YLV+W+++SZvvvlmLb1VIYQQQjzoZGRPPJCqGhvQrl07Dhw4QGFhIYqisGPHDjw9Pe92c0UVWPL0gAp5ekajEaPRSFBQEGvXrsVoNLJ69Wrq1avHnDlzePvttytcr3yeXmpqKn379gXMeXr/+9//MBqNzJgxg2nTpgEwduxY9T5r1qxBq9Va5eklJCRUuEd0dDTjxo0jOTmZv/71r2qkw/VudL4QQgghxJ2Qzp54IEVERODl5UVAQAA+Pj5MmjRJHfUrLzg4mLCwMAICAvD19eXq1atMnDgRgPfffx83NzcyMzPx8/NTR2XEvXE/5umlpqby2GOPARASEnLDWAbJ0xNCCCFEbZBpnOIPy7L+zsXFhZSUFHV7dHS0+n35aaMuLi7qOTY2NsybN4958+ZZXVOv16OfPdsqoiAWiG3d2vzDyZNwbernS8BLDz8MDz9s3peeXiHaQNSSByRPz9/fnw0bNjB16lS++uor8vLyOH/+vLo+TwghhBCiNsnInqhT9Ho9+losJCNqT23l6R08eJCePXtafYgQGRnJzz//zMKFC5k7d67VubeTp/f222+ze/duunbtyu7du2nbti12dvIZmxBCCCHuDvmrQ9Q9Ol2dzaO7391veXpt2rThyy+/BCA/P58NGzbQpEmTO2q7EEIIIURVycieEOK+MWLECGbMmFGhyurtKp+nB1TI07O4UZ7eqFGjqnSfnJwcrl69CsD8+fN5/vnn76jdQgghhBC3Qzp7Qoj7xv2Wp2cwGOjcuTOdOnXizJkzzJo1Sz1H8vSEEEIIUduksyeE+MMzmUxqtUwLrVZrVbgHzJ2rsLAw+vTpY3Vuu3bt0Gq1xMXFMWbMGHQ6Hd7e3pw5cwYbGxvatm1LgwYNCAkJIS4ujpCQEIxGI7t27cLb25vi4mImTpzIxIkTyc3NZcOGDQB88MEH+Pr6snXrVrU9mZmZdOzYEZ1Ox9y5c2nQoAHp6elERUXh6OjIrFmzcHd3Jz09XW3jm2++yf79++nbty8tWrTgyy+/ZNOmTZW+i+eff56WLVtWac2gEEIIIeo26eyJOk+r1ZKTk1Pr98nNzSUsLIwuXbrg6enJDz/8AMDs2bNp27YtOp0OnU53wz/yRdXdj5l8c+fOZeTIkRw6dIj1675ojBIAACAASURBVNfz5z//udJnCw8PZ/PmzXf2goQQQghRJ0hnT4haVlZWBsDUqVMZMGAAP/30E4cPH7YKb3/55ZfVzsGgQYPuVVMfGPdjJp9Go+HSpUsAXLx4kTZt2lTazkceeYRmzZrd8nmEEEIIIaQap7gjWq2WpKSkWr/PO++8w7/+9S80Gg2+vr6sXLnS6o/yF198kZUrV5Kfn1/te5hMJgZ6e9O7cWP2X7pEW0dH4r29cbK1RW80Ety4Mbtyc8ktLSWuUyf6ODtTpijEnDiB4eJFiq5eJbJNGya1aYMhN5dYkwlXR0eM+fkc6NqVPT/+yCqTCT75BAfA4fcbg60tfPvtHbyhOuwByeSbPXs2/fv3Z8mSJRQUFLB9+/ZqtVcIIYQQwkI6e+IPw2g0or8utFxRFIqLi9Vpd7a2tuzYsQOdTkfra0HoeXl5ZGZmcvny5QrnV3aP8oUxyhs1ahQ/FRayztOTFZ07MzI1lQ05OTxz7Y/6UkUhISCATefPE/vLL2x3diYuO5smdnYkBgRQdPUqvQ4don/TpgAk5OWR0rkzHk5OGPPzaWFvz/hjxzhcUEBgw4a89/DDNLC1BWDpqVOsPnOGoEaNWNS+PU3t7e/gTYrayuRbvHgxixcvJjo6Wu08RkZGEhkZyb///W/mzp1rVdDldjL51q1bR3h4OK+88go//PADzz77LCkpKdjYyAQMIYQQQlSPdPZEjbhy5Qr/+9//mDBhAvv376dt27bEx8fj5OSEXq8nODiYXbt2kZubS1xcHH369KGsrIyYmBgMBgNFRUW0bNkSMI+kmEwmHB0dyc/Px8/PD0VRuHr1KjY2NpSVlanVGBVF4eeff8bT07NG1t05OTmhuzZSGbhwIaaSEnjtNdDrefKNN6BXLwLPnMHUqxcYDGwNCyM5OZkvro3+XHRxIW3OHBwcHOgeG4vHrl0AlCYlcbBHD5Zs20ZwcDBTp05lQePGzJkzhylnzvC6iwsajYbXX3+dV7Kz+eijj+74Weq6+y2TLy4uTl2L17NnT65cuUJOTo7634UQQgghxO2Szp6oMYWFhURGRrJixQpGjhzJhg0beOaZZwDz6EhCQgKbNm0iNjaW7du3ExcXR5MmTUhMTKSoqIhevXqxcuVKfvnlFwYPHsyhQ4fw8PAA4L333mPWrFk4OTkxYMAA1q5dq24fPnw4L7/8Mg0bNlRz027kViN/5ddc2dracvnyZfVnR0dHdXtpaSlg7mwuWbKkQu6bwWCgQYMG6s9ubm64ubkRHBwMmKcZLliwAECdDggwYcIEnnjiiZu2UVTNiBEjyM7OJjQ0lKysrGpfp3wm36OPPlohk8+Sw3ejTL49e/ZU6T7t2rVjx44dhIeHc/ToUa5cuUKLFi2q3W4hhBBCCJkfJG7KMrVSr9djNBpveqyTk5M6RTIwMBCTyaTue/LJJyts37p1K6tXr0an0xEcHMz58+fVQOvu3burHb0LFy4QHx9PRkYGWVlZFBQU8Mknn5CVlcXnn3/Oiy++WMNPXXWhoaEsX76ckpISAI4fP05BQUGF41q3bo27uzvHjh0DrEO8s7Oz1eO++uorKalfQ+63TL5FixaxYsUK/P39GT16NKtWrUKj0ZCVlWVVtGf06NH07NmTY8eO4ebmVukooxBCCCEEyMieuAmDwXDLkbDyamtUbPv27Xh4eKijHE8++ST79++nadOmpKen8/DDDwPmkcWHH37YKr+stkVERGAymQgICEBRFFq0aMHXX39d6bFLlixh7NixFBcX0759e1auXAmY//g3Go1oNBq0Wi0ffvjhXWv/g6j8hwwWer0ed3d3q225ubmEh4ezaNEiZs6cCUBOTg5t27bFyckJPz8/WrVqRUhICImJiYSHh7N06VL1/OLiYi5fvkxJSQllZWVERUXh7e3NBx98wLJly7C1taVhw4akpqbi5eXF2rVreeuttwBwcXEhKyuLb7/9Fp1Ox6xZs1i9ejUXLlywKjL08ssvs+vaVOBOnTpx9uxZtWBMeTc6XwghhBB1nKIo9+1XYGCgIu6evn37Kn379rXa9tBDDynnzp1TgoODlfr166vb33rrLeVvf/ubel5iYqKiKIpy7tw55aGHHlIURVE+/PBDZdiwYUpxcbGiKIpy7NgxJT8/X9m1a5cyePBg9VoHDhxQvLy8lIKCAuXq1avKuHHjlPfff79C+xo0aFCtZ6jK89W2CxcuKE899ZTSuXNnpUuXLsr+/fut9r/11lsKcFfa8iB66KGHFH9/f+XXX39VFEVRUlNTFX9/f8Xb29vquPK/q4qiKPn5+crevXuV5cuXK5GRkVbH/vWvf1VmzZqlKIqilJWVqf82Fy9eVI+Jj49XQkNDK7QnOTlZ8fDwUH/+4YcflKysrJv+Dr///vvK+PHjK91XlfOFEEII8WACkpQb9JdkGqe4ZyIiIvDy8iIgIAAfHx8mTZqkjvqVFxwcTFhYGAEBAfj6+nL16lUmTpx4D1pc86qSwXfy5Em2bdtGu3bt7lUzHwj3Y/bezc4pryrnCyGEEKLukWmc4o5YpszVq1ePbt26qdujo6PV78sXTXFxcVHPsbGxYd68ecybN8/qmpY1guXFxsYSGxt707bU6PQ1y/1Pn4ZhwzCVlTHwf/+7+xl8wMtHjvDmQw8x7MwZGDYMJJbh1h6Q7D2LX375hYyMDB599NFqtVcIIYQQdZOM7Ik6p3zRmfJflgI0er0efSXFaNIKC4ls25Yj3brhbGfHhnJRD5YMvnc7dCD2l18ArDL4EgMCWJGdTca1dYwJeXm8odWS2q0bJ65cUTP4uv74IxHHjlFwbcTvm5wc2jo64t+wYW2/lgdebWXvHTx4kJ49e1p9wBEZGcnPP//MwoULmTt3rtW5t5O9Z7F+/XrCwsKwvZbLKIQQQghRFTKyJ+qUKhed0enMo0NaLcTHQ34+Hv363dUMvr/85S+8ERLC1n37oEmT39tybbqguH33W/Ze+XOWLVtW/QYLIYQQok6Szp6oc26UxXerTqCloijcnQy+n3/+mYyMDPz9/QHIzMwkICCAhIQEWrduXbWHFVbut+w9gGPHjnHhwgV69uxZ7fYKIYQQom6SaZxC1JI7zeDz9fXl7NmzmEwmTCYTbm5uHDx4UDp6d+B+y94Dc2GWUaNGVSj0Ysm0vNX5QgghhKi7pLMnRC2parVR+D2Dz8/PD6PRyKuvvnqXW/tgM5lMBAUF0adPH3WbVqvFzs4OHx8ftmzZgk6nIzc3F71eT+fOndHpdIwbN44VK1bg4eGBh4cHrVq14vTp0wA89NBD7Nmzh+TkZBo0aKAGn7/33nscOXKEF154gaysLEaOHMmMGTMA8+jxP//5T3r27Im3tze+vr5cuXKFN998kxMnThAREUH9+vVZv349GzZsAODixYts3rwZnU5Hp06dcHZ2BlDXmAK8+eabPPfcc7Rt25bc3Fzp7AkhhBACAI05muH+FBQUpCRdW0Mlap9lmmNl0yBvtu9+cf00zvv5WURFWq0WZ2dnNm7ciLu7O0ePHmX06NGUlpaSkpKiHqfX63n77bcJCgoC4NChQ7Rq1Yo2bdqQkpJCaGgop06dUo//8ssv+eKLL0hOTlavs2vXLt544w3+85//4OjoyNmzZ2nZsiWlpaUEBASwZs0a/P39OX/+PM7Oztja2vK3v/2NsrIy5s6dy9WrV/ntt9/UOAeLJUuWcOjQIT766KMKz3fgwAEeeughOnbsKMHqQgghRB2i0Wh+VBQlqLJ9MrInRBVotVpyylXfrC25ubmEhYXRpUsXPD09+eGHHwBziX8/Pz90Oh39+/e/o/VmdVl1sva6du1KmzZtAPD29ubKlSsUFRUB5riPxYsX89prr1mds3z5cmJiYtS1nC1btgRg69at+Pn5qeswmzdvrlbYvFFuX3mStSeEEEKI2yGdPSH+AG4Vrj59+nSSk5MxGo088cQT/P3vf7+Xzb1vhYWF8eWXXwKwceNGhgwZclvnb9iwga5du6qduNdff51XXnmlQozD8ePH2bt3L8HBwfTt25fExER1u0ajITQ0lICAAN58803AOrcvICCAp59+mjNnzlhdU7L2hBBCCHG7pBqnELfBZDIxcOBAevfuzf79+2nbti3x8fE4OTmh1+sJDg5m165d5ObmEhcXR58+fSgrKyMmJgaDwUBRURGRkZFMmjQJg8FAbGwsrq6uGI1GDhw4wJ49e9RYAAcHB7WQSOPGjdU2FBQUVCjWUS3XTVvVHjhAUmAgLrUc2v78sWN8e/48Le3tSenWTd3+W0kJf0pNxVRUhNbRkc+8vGh6J22pZBrunWTtHTlyhJkzZ7J161bAvGYuPT2dd955B5PJZHVsaWkpFy5c4MCBAyQmJjJy5EhOnDhBaWkp33//PYmJidSvX5/HHnuMwMBA/P391dy+xYsXs3jxYqKjo61C3yVrTwghhBC3S0b2hLhNaWlpREZGcuTIEZydndVCGmD+Iz8hIYF3332X2NhYAOLi4mjSpAmJiYkkJiayYsUKMjIyAEhISOCNN94gNTWVEydO0KJFC8aPH0/Xrl2JiIiwqt45a9Ys3N3dWbt27R2P7N0oOL42lV1bHxzeqhWbfX0r7F/w66881rQpad2781jTpiw4ebJW2mHJ2rudrLvMzExGjBjB6tWr6dChAwA//PADP/74I1qtlt69e3P8+HF13aebmxtPPvkkGo2G7t27Y2NjQ05ODm5ubvTt2xcXFxfq16/PoEGDOHjwYKW5fQcPHrRqQ3Xy+YQQQghRt0lnT4hyjEajVZXDynh4eKhl7wMDA61GdZ588skK27du3crq1avR6XQEBwdz/vx50tLSAOjevTseHh6AuaN48OBBpkyZwqFDh2jQoAELFixQr/3GG29w8uRJxo4dy9KlS+/8YS3B8Zav1q0hPh7TqlV4njnDhI4d8T53jv4ODlz+7jswGNADM4OD6V5YSKesLPbOmQMGA2U7djC9Wze6FRTg99tvfDh6NBgMGGbPJkSjYUybNvieOwcGA48YjTTbsAE8PKzuH1+/Ps/t2wcGA8/t28fXTk7W7bvdrxsYMWIEM2bMqJB/eCO5ubkMHjyY+fPn06tXL3X7lClTyMrKwmQy8f3339OpUye1qM/w4cPZuXMnYJ66WVxcjIuLC6GhoSQnJ1NYWEhpaSm7d+/Gy8vLKrcPsMrtA8naE0IIIUT1SGdPiGsMBoNVdtmNXB+uXj5O4Wbh6paOZEZGBv379we4Zbj69aM7AGPGjLEaTawNtTV6eTNnzpxRC4y4urpy9uzZWnm2283aW7p0Kenp6cyZMwedTodOp7tl255//nlOnDiBj48Po0aN4uOPP0aj0dC0aVOmTZtGt27d0Ol0BAQEMHjwYODGuX0gWXtCCCGEqB5ZsydEOQaDQZ2KVz6K4fTp0wwbNoyysjIyMjLUfSdPnqSsrAyDwYDRaGTSpEk0atSIkpISTp8+jV6vJysri7Fjx+Ll5YWNjQ2FhYU4OjqSl5fHyZMnre5z4cIFunfvTv369TGZTJSVlaHX6yksLFTXl506dUrNg6suo9F4045tdUYvk5OT+eKLLwBzNlxaWhoODg5Wo5f32ogRI9i7d6/68/DhwwHYsmULM2fOBCA9PZ2xY8fi5OSEn58fzz77LF26dKG4uBgHBwfeeusttbrmgAEDyM7OprS0VF2faWtry5w5c0hOTsbOzg4XFxe6dOkCmH+/IiMj1ffRsGFDwPx7FB4ezrlz57CxsWHo0KG0a9dObWfz5s1ZunQpGzduZPDgwWphl/Kj0Dk5ORQXF+Pl5WUVJSGEEEKIuks6e0JUwvJHdFVG+m7F1dWVK1eucPDgQRRFwd7eHh8fn0qP7dixI0ePHkVRFOrVq0fnzp0ByMjIoLCwEI1Gg6OjI506dbrjdt3M9aOXly9frrCvstHL66dGGgwGq9HLm2nVqhXZ2dm4urqSnZ2tdqhqkqWDbcnZswgNDVXbXlnO3saNGyvN2fvss89o3LgxiqIQFhbG559/zqhRo5g+fTpz5swB4P333+fvf/87H3zwAQB9+vTh22+/tWqXnZ0dixYtIiAggLy8PAIDA+nXrx9eXl7s2rWL+Ph4kpOT1cy+yoSHhxMVFcW4ceNq9qUJIYQQ4r4lnT0hbkCn0z2wwep3MipYmdDQUJYvX86jjz6Kvb09x48fp23btrd1jaFDh/Lxxx8TExPDxx9/zLBhw2q0jfB7zl50dLSaWVe+4mVlunbtqn5fPmfP0dFRrZJaWlpKcXGxOs3ydqunurq6qlNYGzVqhKenJ6dOncLLy+uGmX3Xe+SRRypUBRVCCCFE3SZr9oQQdywiIgIvLy8CAgLw8fFh0qRJVmsZyxs9ejQ9e/bk2LFjuLm5ERcXB0BMTAzbtm2jY8eObNu2jZiYmBpvZ03n7IG5o9uyZUsaNWpEWFiYuv1G1VN/+OEH/P39GThwIEeOHKlwD5PJxKFDh9S1mzfK7BNCCCGEuBUZ2ROiirRaLUlJSbi4uNTqfXJzc4mIiCAlJQWNRsNHH31Ez549mT59Ohs3bsTBwYEOHTqwcuVKnJ2dq38jo9Eqa8+k1UJYGC5AiouLui/acoBejwEg2rzFxXKOXo8NMA+Y17y5+VhFgWHD0AP6a+darAPo3Nn8BbBmDaxZQ3NgB0DbtlBWBtfWBlZbLefsWWzZsoUrV64wduxYdu7cSb9+/QBz9dQ33niD+fPns3TpUmJjYwkICOCXX36hYcOGbNq0ieHDh6uVWQHy8/N56qmnePfdd61GDSvL7KuRrEUhhBBCPNBkZE+IP4iysjIApk6dyoABA/jpp584fPgwnp6eAPTr14+UlBSSk5Pp1KkT8+fPr/a9DAYDhhpYj3g/qqmcvfLq1avH0KFDiY+Pr7CvfPXUxo0bq0VZBg0aRElJCTk5OQCUlJTw1FNPMXbsWLUIDtw4s08IIYQQ4lZkZE+I22QymRg4cCC9e/dm//79tG3blvj4eJycnNDr9QQHB7Nr1y5yc3OJi4tTqzTGxMRgMBgoKioiMjKSSZMmYTAYiI2NxdXVFaPRyIEDB9izZw+rVq0CwMHBQY0IsMQ1APTo0UOtfFltD+h6xFsZMWIE2dnZhIaGkpWVdcvjb5Szl5+fT15eHq6urpSWlrJp0yb69OkDmKMrOnbsCMA333yjVuM8ffo0rVq1QqPRkJCQwNWrV2nevDmKovDCCy/g6enJtGnTrO5vyezT6/VWmX1CCCGEELciI3tCVENt5dCdOHGCFi1aMH78eLp27UpERAQFBQUV7v/RRx8xcODAu/OwD5iaytkrKChg6NCh+Pn54e/vT8uWLZk8eTJgXn/o4+ODn58fW7du5b333gPgiy++wMfHB39/f1566SXWr1+PRqNh3759rFmzhp07d6r32LRpE3DjzL6srCwGDRqktvNGayGFEEIIUXfJyJ4QlcjPz7/p/trKoSstLeXgwYMsWbKE4OBgpk6dyoIFC9Qy/mBeC2ZnZ8fYsWNr6nHrhMoqVWq12gqZdNdXYH3ttdd47bXXKr3mjYql3Cj0PioqiqioqArbe/fujaIolZ7j4ODAJ598UmF7mzZt1A4hmIPXhRBCCCHKk5E9Ia5jMBjUdVU3cn0OXfnKkzfLoTMajRiNRjIyMtRpmeVz6Nzc3HBzc1MrMYaFhXHw4EF1/8cff8y3337L2rVrpUDHbdJqteo0SwudToePjw9btmxRR9QaNmxI586d0el0ambd/Pnzefjhh+ncuTNbtmwBzEHoISEheHp64u3trY7eAfz222/069ePjh070q9fPy5cuACYO/lDhgzB398fb29vVq5cqZ4zY8YMvL298fT05KWXXlI7fz/++CO+vr48/PDDVtvL++mnn+jZsyeOjo68/fbbNfvihBBCCHHfks6eEJWoiTD18iw5dCUlJYC5nH5l0zNbt26Nu7s7x44dA2DHjh14eXkBsHnzZhYuXMg333xzW1Ukxe8soepAhVB1S0c8KCiItWvXYjQaWb16Nampqaxfv54jR46wefNm/vznP1NWVqYGoR89epQDBw6wbNkyUlNTAViwYAGPPfYYaWlpPPbYYyxYsACAZcuW4eXlxeHDhzEYDLzyyisUFxezf/9+9u3bR3JyMikpKSQmJrJ7924ApkyZwj//+U/S0tJIS0tj8+bNFZ6rWbNmvP/++0RHR1fYJ4QQQoi6Szp7QtwFt5NDt2TJEsaOHYufnx9Go5FXX30VME8BzMvLo1+/fuh0OnV9mKg6S6g6oIaq30p8fDyjRo3C0dERDw8PHn74YRISEnB1dSUgIACwDkK3nPPcc88B8Nxzz/H1118DoNFoyMvLQ1EU8vPzadasGXZ2dmg0Gq5cuUJxcTFFRUWUlJTQqlUrsrOzuXTpEj179kSj0TBu3Dj1WuW1bNmSbt26YW9vXyPvSQghhBAPBlmzJ0QVWdZ8ubi4WK3zKj+aUn69l4uLi3qOjY0N8+bNY968eVbX1Ov16Mtl0IF5VDEpKanC/dPT0+/sAa533X0fOJVUGw0LCyM8PJzo6Gg2btzI2rVrWbNmzU0vc+rUKXr06KH+7ObmpnbqLK4PQj9z5gyurq4AuLq6cvbsWcDcYR86dCht2rQhLy+PTz/9FBsbG3r27ElISAiurq4oikJUVBSenp4kJSXh5uZ203sLIYQQQtyIjOwJUQfp9Xr0RqPVNu2BA+Rcm2Zam97LzMQnMRHvxETezcys9fuVV51Q9crWyJVfL1lZEPqNWNYGZmVlYTQaiYqK4tKlS6Snp3P06FEyMzM5deoUO3fuZM+ePbe8txBCCCHEzcjInhB1lU5nPfql1UJ8PNRShltZWRlHjx5lxahRJJw7h4ODAwMGDGDw8uVqJt3dYAlVt2QZ3oqbm5u6zg/MAett2rQBbhyEbpmC6erqSnZ2Ni1btgRg5cqVxMTEoNFoePjhh/Hw8OCnn35i9+7d9OjRQy0MNHDgQA4cOMCzzz5LZrkOcfl7CyGEEELciozsCSGsmEwmPD09mTBhAt7e3vTv35/Lly8D5hHBmTNn0r17dzp16sTevXsBc0du+vTpdOvWDT8/Pz788EPAPK01JCSEMWPG4Ovry9GjR+nRowf169fHzs6Ovn378tVXX93V5xsxYgQzZswgNDS0SscPHTqU9evXU1RUREZGBmlpaXTv3v2mQehDhw7l448/BswVVIcNGwZAu3bt2LFjB2Ce6nns2DHat29Pu3bt2L17N6WlpZSUlLB79248PT1xdXWlUaNGHDhwAEVRWL16tXotIYQQQohbkZE9IW7AaDRWWE/3oDAajTetOJqWlsa6detYsWIFI0eOZMOGDTzzzDPA76HxmzZtIjY2lu3bt1uFxhcVFdGrVy81WiIhIYGUlBQ8PDw4evQos2bN4vz58zg5ObFp0yaCgoLuyjNbWELVq8rb25uRI0fi5eWFnZ0dy5Ytw9bWlu+//541a9bg6+urvst58+YxaNAgYmJiGDlyJHFxcbRr147PP/8cgNdff53w8HB8fX1RFIWFCxfi4uJCWFgYO3fuxNfXF41Gw4ABAxgyZAgAy5cvJzw8nMuXLzNw4EAGDhwIwAcffADA5MmTOX36NEFBQVy6dAkbGxveffddUlNTbzmtVAghhBAPNhnZE6ISBoOhxuMX7ifVCY1fvXo1Op2O4OBgzp8/T1paGoBVaLynpyczZ86kX79+DBgwAH9/f+zs7t5nTiNGjLD6efjw4QBWOXtJSUmMHTvWKmfPxsZGXStnY2P+3+ZDDz2EXq+npKSEkpISxo8fz6BBgwDzujrLceXPbdCgAY6OjtjY2KAoihrFYWtrS5MmTdTtpaWl6nq9r776iosXL3LmzBmWLl2qXmvy5MlqRVZ7e3s6duzI1atXeeaZZ8jMzJSOnhBCCCFkZE+IGzFUUs3xQXGrEcvrQ+Mt0zjL76ssNP76qZEGg8EqNB7ghRde4IUXXgDg1Vdftao2WdssOXvu7u4VcvYsbdfr9bz99tvqiGP5nL2srCwef/xxjh8/rubsBQQEkJeXR2BgIP369cPLy0vN2YuJiWHBggUsWLCAhQsXqjl7Gzdu5Ny5c3Tu3JmxY8eSlJSk5uwB9O7dm927d6PX6xkyZAhRUVE3XddYr1495syZQ0pKilWlWCGEEELUbTKyJ4S4Y1UNjQfUGIJff/2VL7/8skpZdzXlfsvZA+jRo4ca43AjDRo0oHfv3tSrV696L0YIIYQQDyQZ2ROirjIarbP2Tp+GYcOgrAwyMn7fd/KkeZvBYD5n0iRo1AhKSszn6PVEKAqmjAwCmjZFURRa2NvztY8P5Oebzy93n6cOHeJ8aSn2Gg3LOnSg6XVTK2vMA5CzJ4QQQghxJ6SzJ0QdZDAYKoSqm651aFzs7Unp1k3dHu3u/vt55dYxutjbq+fYaDTMa9+eee3bW11T7+yM3tnZatverl1r4hGq5Y+Ss7dz505+/vln+vXrR58+fTh79qyaswfQr18/9uzZwyOPPHKbTyiEEEII8Tvp7AlRVz3AaxJv5n7K2ZPOnhBCCCHuhKzZE0LUKfdTzp4QQgghxJ2Qzp4Qok6x5Ow5ODhU6fjyOXsDBgxQc/b27dvHmjVr2LlzpxrbsGnTJgBiYmLYtm0bHTt2ZNu2bcTExADmnL39+/fj6+vLY489ZpWz16FDB3x9ffH398ff31/N2ZsxYwZubm4UFhbi5ubG7NmzAfjmm2/461//qrZTq9Uybdo0Vq1ahZubG6mpqTX41oQQQghxP9JUth7lfhEUFKQkJSXd62bUGZZy/ZVFEtxsnxA1RavV4u7uzt69e9VtOp2O0tJSFi1apIalp6en07ZtW5ycnPDz8yMqKoqJEycC5jV4s2fPZsSIEZw8eZJx48Zx+vRpbGxsmDhxIlOnTgVg9uzZrFixghYtWgC/B6abkEjBagAAIABJREFUTCY8PT3p3LkzYK6W+cEHH5CXl0efPn3UdmVmZvLMM8/w7rvvqtu++OILnn76aRITEysNk3/++ef59ttvadmypUQoCCGEEKJKNBrNj4qiVPzDAlmzJ4S4z1QnK6+wsJCkpCTs7OzIzs5WR85ulpUH8PLLLxMdHV2hDR06dMBoNFpta9SokdW2wMBAq3V8eXl5vP/++2rFzsqEh4cTFRWlhrkLIYQQQtwJmcYphLivVCcrr379+tjZmT/bunLlilpN82ZZeXciLS2Ns2fPWo30vf7668yYMeOmWXiPPPIIzZo1u+P7CyGEEEKAjOyJOkpfxUqM4t4yhIdX2FadrDyA//73vzz//PP88ssvrFmzRu38WVyflQewdOlSVq9eTVBQEIsWLaJp06YAZGRk0LVrVxo3bszcuXOtOnVg7oT+6U9/UjuVhw4d4uTJkzzxxBO8/fbbt/sahBBCCCGqRUb2RJ2j1+sxLlhgte1AdDQleXm1fu/SwkKOLFtGwl/+QuKrr3IxPV3dd2r7dvP2WbP4+bPPar0t96vqZOUBBAcHc+TIERITE5k/fz5XrlxR91WWlTdlyhR+/vlnjEYjrq6uvPLKK4B5NPDXX3/l0KFDLF68mDFjxnDp0iWre61fv14dcbx69Sovv/wyixYtqonHF0IIIYSoMhnZE3WSrnVrq1Ej7ezZxI8ejYuLS63cr6ysDFtbW5577jnCJ00iIiKC4uJiCgsLcXZ2ZteuXbxx+jQpJhOOjo6cPXtWzWYTFd1uVl55np6eNGjQgJSUFIKCgm6alWcxYcIEnnjiCQAcHR1xdHQEzOvyOnTowPHjx9X1gYcPH6a0tJTAwEDAvFYvJSVFLWJ0+vRphg4dyjfffFNpkRYhhBBCiJoiI3tClGOptDhhwgS8vb3p378/ly9fBswjgjNnzqR79+506tRJrQhZVlbG9OnT6datG35+fnz44YeAuTJpSEgIY8aMwdfXl0uXLrFnzx5eeOEFABwcHHB2dgZg+fLlxMTEqJ0I6ejd3O1m5WVkZFBaWgrAL7/8wrFjx9BqtTfNysvOzla//+qrr/Dx8QHg3LlzlJWVAXDixAnS0tJo3769euz16wibNGlCTk4OJpMJk8lEjx49pKMnhBBCiLtCRvbEfc8yYlJVRqMRnU53w/1paWmsW7eOFStWMHLkSDZs2MAzzzwDQGlpKQkJCWzatInY2Fi2b99OXFwcTZo0ITExkaKiInr16kX//v0BSEhIICUlBQ8PD4xGIy1atGD8+PEcPnyYwMBA3nvvPRo0aMDx48fZu3cvs2bNol69erz99tt069at2u/kQWfJyquq77//ngULFmBvb4+NjQ3/+Mc/cHFx4fvvv2fNmjX4+vqqvxOWiIUZM2ZgNBrRaDRotVq1E79nzx7++te/Ymdnh62tLR988IFVUZXPPvtMzdu7laysLCIiItTjR48ejcFgICcnBzc3N2JjY9UPB4QQQgghbleVR/Y0Gk1LjUbTzvJVm40SDwatVktOTk6t3kOv1/Pf//6XxMREEhMTSU1N5erVqwAcO3aMpKQkkpKSOHLkiDoacyseHh7qH/6BgYGYTCZ1n2WaX/ntW7duZfXq1eh0OoKDgzl//jxpaWkAdO/eHQ8PD8DcUTx48CBTpkzh0KFDNGjQgAXX1g6WlpZy4cIFDhw4wFtvvcXIkSO5nzMwa4vJZKow1Var1VbIpDMYDAQFBaHVaunTpw/PPvssR44cwWg0cvXqVV577TW2bNlCVFQU/v7+nDhxQh3BXb9+Pdu2bVNDye3t7fm///s/XF1dycvLY86cOdjb26PRaPj111/ZsWOHet/PPvuMevXq8dRTTzFmzBjAPJIYGBiITqfD29ubUaNGERQURGFhIRMmTODEiRN4e3sTExPDunXryM7OpqSkhMWLF7No0SK8vb3Va11v1qxZuLu707Bhwxp7x0IIIYR4cNxyZE+j0QwFFgFtgLPAQ8BRwLt2mybEzZWVlVFUVARATk4OTk5OjBw5kkGDBhEeHs6lS5fUYhvTpk2jZcuWxMTE3HIk0DKVEsDW1lbtBJTfZ2trq04LVBSFJUuWVJhSaDAYaNCggfqzm5sbbm5uarXHsLAwtbPn5ubGk08+iUajoXv37tjY2JCTk6MGeovqq04u36FDh9i4cSNt2rQhJSWF0NBQTp06ddMsvbS0NObPn8++ffto2rQpZ8+eBcwFXfbv34+joyP5+fn4+PgwdOhQnJ2diY6OJiQkhOLiYh577DG+++47Bg4ceMNrXW/IkCFERUXRsWPHWnl3QgghhLi/VWVkbw7QAziuKIoH8Biwr1ZbJR4otbkODsydrcuXL1NaWkphYSFt2rQBUDt6lv2WMvg1LTQ0lOXLl1NSUgLA8ePHKSgoqHBc69atcXd359ixYwDs2LFDDe8ePnw4O3fuVM8vLi6utWIxdU11cvm6du2q/h55e3tz5coV9YMFi+uz9FasWEFkZKQaz2BZd+ng4KB+SFBUVKSOPNevX5+QkBD1mICAADIzM296rev16NEDV1fXKr4JIYQQQtQ1VVmzV6IoynmNRmOj0WhsFEXZpdFoFtZ6y8QDpbbWwen1etzc3GjXrh1OTk70799fPQ5g/PjxbNq0CS8vL6vS98bTp62y9k7n5zNs3TrKiorIyM1V951MTKSsqAjDqlUYT59m0saNNEpJoSQvj9P5+ehXrUKxsyOjtJSmHh4ogH2jRvi8+CL5v/7KycxMq/tc7tePoAEDUEpLqdeiBZ1feAH9qlVctbPj2LZtLProI2xsbWk/ejQhH39ca/8eD6qazOWz2LBhA127drUa8YWKWXrHjx8HoFevXpSVlTF79mwGDBgAwMmTJxk8eDDp6em89dZbakfSIjc3l40bNzJ16tRbXksIIYQQoqqq0tnL1Wg0DYE9wFqNRnMWKK3dZok/KqPRWOk0yFsVPanOOrjk5GS++OILAC5evEhaWhoODg5W6+BKSko4f/48GRkZODs78/TTT/PJJ5+oHcmVK1dSVlbGiy++yKeffsr48eMxGAwVQtV7XAu6tm/UiG5z56rb3QcOVL/XxcSo39s3aqSeo7GxoX1YGO3Dwqyu6dylC85dulhta9iuHYF/+1uF92NjZ4fnpEk3fH+i+qqbywdw5MgRZs6cydatWyvsW79+vVWnsbS0lLS0NAwGA5mZmfTp04eUlBScnZ1xd3cnOTmZrKwshg8fTlhYmBrtUFpayujRo3nppZfUqp43u5YQQgghRFVVpbM3DLgCvAyMBZoAf6/NRok/JoPBcNuVLy1qax1cbm7u/2fvzuOqLPP/j79uOIALpjloKajgDrKc4LhNZicdXCpJRrNFR0UZtZ806oTLNN9JbRHNJSsdLbdySUpNRTNzxcoZhZMeDVGBghSFyUwFRFkO9++P47njCCoKhMDnOQ8fcu77nPu+rjtx/HAtb+rUqaOtbfvzn//Mf/7zH63Ys133ueeeY+7cuYSFhVmvU8oIkKi57iWXLz09ndDQUFavXk2bNm3szt2cpQfWdZfdunXDyckJLy8vOnToQHJyst2uqs2bN6dTp0588803DL7xw4ExY8bQrl07Jk6ceFfXEkIIIYS4k7Ks2WulqqpFVdVCVVU/VlX1PcCvshsm7k+xsbGl/rrdqN69KOs6OBcXF7KyssjNzUVVVfbu3Yu3tzeqqpKSkgJYC8dt27bR8aZRNlF73G0u3+XLl3nqqaeIiori0UcfLXG+tLV/AwcOZP/+/YB1w6CkpCRat25Nenq69sONS5cucfDgQTp06ADA//3f/3HlyhUWLlxYpmsJIYQQQtyNshR7nymKMlWxqqsoyvtAVGU3TNRu4eHh+Pj4EBgYiK+vL2PHjtVG/Yp74IEHaNKkCYGBgfj5+VFUVMSYMWNQVZURI0bg5+eHn58fGRkZvPbaa1XQE3E/sOXyOTs7l+n9ixYtIiUlhTfeeAO9Xo9er7fbEfOzzz4rUez17duXP/zhD/j4+PDEE08wd+5c/vCHP3Dy5Em6du1KQEAAjz/+OJGRkfj5+ZGens5bb71FYmIigYGB6PV6li9ffttrAXY/WJkyZQoeHh7k5ubi4eHBjBkzyvmkhBBCCFGTKHfK8lIUpT4wBwgCGgDrgDmqqhZVfvNuz2AwqCaTqaqbIfgt2Dw2NrbK7+vp6YnJZKrU3SxPnz7Nc889p73+8ccfef3115k4cSLHjh1j3Lhx5OTk4Onpybp167SdQYWojjw9PWnRooW2Wy5Yi87CwkLmz5+vBdynpKTg7u5O3bp18ff3JyIigjFjxgDWEfYZM2YQGhrK2bNnGT58OJmZmTg4ODBmzBhtc5oZM2awbNkybWq2LeTetquvbVS0W7duLF26lOzsbG1HVLBOvx02bBgLFy5k6dKlLF68GEdHR1xdXfnwww+1HXCLGzVqFNu3b6dp06YlMhuFEEKI+52iKN+pqmoo7VyZduMErgF1gTpA6v1Q6AlRVSwWCx06dNDy1iwWC+7u7oSGhgLWUcl58+bx+OOPs3LlSubOncsbb7xRlU0WotzuJa8wNzcXk8mETqcjIyODgIAABgwYgE6nY/78+QQGBpKdnU1QUBDBwcFaITZp0iQiIyNLtKFNmzZ2OYfAbbMPX3zxRcaNGwdATEwMf//739m5c2eJ644cOZKIiAiGDx9enkckhBBC3HfKMo0zHmux1xnoAbygKMrGSm2VEBWgsvP9bPbu3UubNm1o1aoVYB3169mzJwDBwcFs2rTp9+qyEJXmXvIK69Wrh05n/Zni9evXtZiKZs2aERgYCFiLNW9vb86dO1fuNt6cfVh8RP3q1au3zNrs2bMnjRs3Lvf9hRBCiPtNWUb2RquqapsrmQk8oyjKXyqxTaKWuNedPYu7U+RDZeX7FRcdHW33D19fX19iYmJ45pln2LBhA2fPni13P8X95ebojpqmIvMKDx8+zKhRo/jpp59Ys2aNVvzZpKWlcfToUbp27aodW7RoEatXr8ZgMDB//nwtXD41NZVHHnmEBx54gDfffNNu+iaUzD4EWLx4MQsWLCA/P599+/bdzWMQQgghqr07juzZCj1FUZoqitJSUZSWwIFKb5mo0YxGY4npWJXhXvL9Vq9ejV6vp2vXrly8eJHk5GQAu3w/m/z8fGJiYnj22We1YytXrmTx4sUEBQWRnZ1d5k1BRPVgNBoxz55td+xQZCQF2dmVet+iggKOvP46ptdeI/6f/yRt8+ZKvd/N7jWvsGvXrpw4cYL4+HiioqK4fv26di4nJ4dBgwaxcOFCbRTupZde4ocffsBsNtOsWTNeeeUVwDoaeObMGY4ePcqCBQt48cUXycrKsrvXzT94ARg/fjw//PADc+bM4c1iGZpCCCFEbXDHkT1FUQYAC4DmwM9AK+Ak0KlymyZqOr1eX+4NXe40OlhZ+X42X375JYGBgVpANkDHjh21EO6kpCS++OKLu+uUuO/pH37YbvTLc8YMtr7wQqVtCmSxWHBwcODqCy/g6upKQUEBPXr0YHbHjnTr1q1S7lmae8krtPH29qZ+/fokJCRgMBgoKChg0KBBDB06VPvBC2D3vfTXv/6Vp59+GrB+v9q+Z4OCgmjTpg1JSUna+sDSsg+Le/7553nppZfuut1CCCFEdVaWNXtvAt2AJFVVvYDewMFKbZUQVaSs+X42pa1dsm3RX1RUxJtvvqltECFqvspcJ6ooCq6urgAUFBRQUFBwyzVoleVu8wpTU1O1H6T89NNPnD59Gk9PT1RVZfTo0Xh7e/P3v//d7jMZGRna15s3b8bX1xeACxcuYLFYAOvut8nJyXbZg6V9L9pG5QG++OIL2rVrdxe9FUIIIaq/Mu3GqarqRUVRHBRFcVBVdb+iKHMqvWVCVIHw8HDS0tIIDAxEVVWaNGnCli1bSn1vbm4uu3fv1v5xbrN+/XoWL14MWKeKhoWFVXq7b6Ui1kUKe1W5TtRisRAUFERKSgrjx4+3W+f2e7DlFZbVt99+y+zZs3FycsLBwYF///vfuLm58e2337JmzRr8/Py0Z2mLWJgyZQpmsxlFUfD09NS+v77++mtee+01dDodjo6OLF261G5Tlc8++4wdO3bY3X/RokXs2bMHJycnHnzwQT7++GMAzp8/T3h4uPb+F154gdjYWH755Rc8PDyYOXMmo0ePLtezEkIIIe4HZRnZu6woiivwNbBOUZR3scYxCHFfSktLw83NDU9PT7vMrMjISC10OjY2Vpv+5ebmpq3Zc3BwYNasWXz//fckJCSwf/9+GjZsiNFoZPv27Xb3qVevHhcvXqRhw4Z2xydMmEBSUhJJSUnMnj37dx99sfm91kUKe5W5TtTR0RGz2Ux6erpWCP5ebNEiNgMHDgTgq6++0oLnTSYTQ4cORa/XM3z4cDp06ICTkxNgLVRtua6tWrXCaDRqI5RhYWE8+eSTgDVe4ddff8XBwUFbowfWZ/bjjz+iKApFRUXa9Ojs7Gz0ej0PPPAAzz//PG5ubkycOBGAQYMG4eLiQkJCAuPHj6dTJ+vqg+bNm9sVhq1bt0an0+Hi4kJ6eroUekIIIWqMsozsHQNygUnAUKAh4FqZjRJCVIyKWBcp7FX1OlGARo0aYTQa2blzpzbNsbJVx5y9li1b8tFHHzFv3rzb9m3AgAFERETINE8hhBA1TllG9p5QVbVIVdVCVVU/VlX1PayZe0IIIcqprOtEL1y4wOXLlwG4du0ae/bsoWPHjr9bO6tjzp6npyf+/v44ONz+/+q6detGs2bNyn1/IYQQ4n5zy5E9RVFeAv4f0EZRlOPFTjVANmgRQtRi5sxMu6y9zJwcnlm/HkteHqmXL2vnzsbHY8nLI/ajjzBnZjJ22zYaJCRQkJ1NZk4Oxo8+QtXpSC0s5EEvL1TAqUEDfF9+mZwzZzibnq5dK+fsWU4vX45aVGRdT9q5M/N++YV5lZD5V5Ny9oQQQoja7HbTOD8BvgSigGnFjmerqvprpbZKCFEpPD09MZlMlRYRYDNq1Ci2b99O06ZN7daVHTt2jHHjxpGTk4Onpyfr1q3T8tWqi9jY2BKh6t1uTBN0atCAzsWy3Fr07699rZ/221+jTg0aaJ9RHBxoPXgwrQcPtrtmo44daVRs5M61RQuCZs6ssH7crfLm7J08eZIRI0bQv39/6tSpA9w6Z+9f//oXiqLwr3/9i1deeYWVK1dqOXt/+MMf+O677xg4cCAnTpyw+/MTHR1dpgJUCCGEqC1uWeypqnoFuALcea6OEEJg3YTD0dGRkSNHEhERwfDhw+3Oh4eHM2/ePB5//HFWrlzJ3LlzeeONN6qotfeutJGv2qA65+wJIYQQtVFZ1uwJIWqYysyDA+jZs6fdtvg2p0+fpmfPngAEBwezadOm36O7ooJUt5w9IYQQorYry26cQtzXzGaz5MmVoirz4G7F19eXmJgYnnnmGTZs2MDZs2crrsOi0lW3nL34+HhCQ0O5dOkS27ZtY/r06Zw4cQKw7lRr28VzypQpfPLJJ+Tm5uLh4UF4eLgW0yKEEEJUZ1LsiWotNjZWCr17dC95cMePH2fjxo0AXLlyheTkZJydnUvkwd3KypUr+dvf/sbrr79OSEgIzs7OFdspUSmK/9mwuTnHEigR8/GXv/yFv/zlLyU+26NHDy1z72a3WnM3aNAgBg0adMs2/vjjjyWOde7cmfT09BLtbtGihV1cw65du2jUqBErVqxg6tSpbNmyhXnz5uHu7k7dunXx9/dn9erVREVFsWLFChwdHXnvvffsRjgtFgsGgwF3d/cSmZwvv/wyq1atIicnB4AFCxawfPlydDodTZo0YeXKlbRq1QqwxnLYRshbtmxJTEwMAI899hjZ2dkA/Pzzz3Tp0oUtW7aU6HO/fv04dOgQPXr0KNEOIYQQtY8Ue6Lakxy50t0PeXA369ixI7t27QKsEQO2YGwhfk/3khmYmJhIdHQ0J06c4Pz58/zpT38iKSkJR0dHAN599128vb3Jysqyu5fJZNIiM2weeeQRTCYT9erVY8mSJUyZMkWLtahbt26JLEFAm04N1sL3mWeeKbVvkydPJjc3VxsRFUIIUbvJmj0hRJmUNQ/udn7++WcAioqKePPNNxk3blyFt1OIO7mXzMCtW7fy/PPP4+LigpeXF23btiUuLg6A9PR0vvjiC8LDw+0+Y1vn+vbbb9sdf+KJJ7TdTLt161Zi9PF2srOz2bdvHwMHDiz1fO/evWnQoEGZryeEEKJmk5E9IUSZhIeHk5aWRmBgoDXnrUkTtmzZwsSdEzmbfhbjR0btvYlLE7ly6goFOQW4NHbBc6AnzXo2I31XOuf3nQfALciNn5SfWP3R6irqkagNYkfGljh2L5mB586do1u3btprDw8PLQh+4sSJvP3229o0S5tFixYREhJy28D2FStW0L9YRMf169cxGAzodDqmTZtWoqjbvHkzvXv3rnaRJUIIIaqGFHtC1CK2tVdubm52660iIyO1r4tPi3Vzc9M+4+DgwKxZs5g1a5Z23mg0kpaZhn7abxvBHIo8RND0IJwaOJW4v0cfDzz6eJS7H7kZuSQuSdReX79wHc9Qzwq5tqj57iUzsLQ1hoqiaHmSQUFBdt8758+fZ8OGDbedZr527VpMJhMHDhzQjp05c4bmzZvz448/0qtXL/z8/GjTpo12fv369SVGEIUQQohbkWJPCFEu+of1dqMnnjM82frC1koLbrdl+fGP3167u7uzN2qvtsmFEHdyt5mBHh4edrvHpqen07x5c2JiYoiJiWHHjh1cv36drKwshg0bxgsvvEBKSgpt27YFIDc3l7Zt25KSkgLAnj17eOuttzhw4IDd+tnmzZsD0Lp1a4xGI0ePHtWKvYsXLxIXF8fmzZsr4hEIIYSoBWTNnhCiUlR2lp/N3r17adOmjRR64q7cbWZgSEgI0dHR5OXlkZqaSnJyMl26dCEqKor09HTS0tKIjo6mV69erF27lqeeeorMzEzS0tJIS0ujXr16WqF39OhRxo4dS0xMDE2bNtXucenSJfLy8gD45ZdfOHjwID4+Ptr5DRs28PTTT1OnTp0KfBJCCCFqMhnZE6IGq+wMwvshyy86OlrCtMVdu9vMwE6dOjFkyBB8fHzQ6XQsXrxY24nzbk2ePJmcnByeffZZ4LeIhZMnTzJ27FgcHBwoKipi2rRpdsVedHQ006ZNs7uWyWRi6dKlLF++HLBGNJw6dYqcnBw8PDxYsWJFmQtaIYQQNY+M7AlRQ8XGxt62EPs93EuW3+rVq9Hr9XTt2pWLFy+SnJwMUGqWX35+PjExMdo/moW4HVvGXvEpxnq9nqeffpr58+ej1+vR6/W4urqSkZFBeHg4w4cPByAqKopVq1ah0+l477336N+/P9evX6dLly4EBATQqVMn9u/fr2Xb7du3j8DAQHx9fRkxYoQWv3DlyhXq1q3Lww8/TEFBARMmTNCy9LZs2UJRURH5+fk88cQTjBo1CrCu0/Pz8+PXX39l4cKF/PLLL1r7DQYDy5cv59SpU3Tv3p24uDimTp3KtWvXSE9Pl0JPCCFqORnZE6IGq+wMwqrO8vvyyy8JDAzkoYceutcuiFqmIjP2XFxc2LdvH66urhQUFNCjRw/69+9Ply5dGDFiBHv37qV9+/a89tprfPzxx4wePZrFixfj4+PDtm3buHDhAh06dGDo0KGYTCYOHjzI8ePHAWvw/IEDB+jRowcTJkwgMTERNzc3pkyZwqJFi5gxY4Zdvxo3bsx7771XatC6EEKI2ktG9oQQ9427zfIra0aaEDYVmbGnKAqurq4AFBQUUFBQgKIoXLx4ERcXF9q3bw9AcHAwmzZtAqw7eGZnZ6OqKjk5OTRu3BidToeiKFy/fp38/Hzy8vIoKCjgoYceQlVVVFXl6tWrqKpKVlaWtolLcU2bNqVz5844OZXcBVcIIUTtJSN7QohyMQPGYq8zgWcAC5Ba7NzZG8dib3xmLNAAKLjxGSOghoeTmpbGgzey/JyaNMF3yxZybny++H0subkc2r2b9A8+4OPK6Zqo5mJLOVbRGXsWi4WgoCBSUlIYP348Xbt2RVVVCgoKMJlMGAwGNm7cqO3kGRERQUhICM2bNyc7O5tPP/0UBwcHunfvzhNPPEGzZs1QVZWIiAi8vb0BWLJkCX5+ftSvX5927dqxePHiCnk+Qgghaj4Z2RNC3LPY2Fj0N00V7ZaWhpObG3U8PelcLMuvRWQknjemnuljY2lwY4qck5sb3W6s2VMcHGg9axaG77+nc0IC+v370TVsSCOjEb8ba6FsHOvV49GLF9E1bFhp/RM1T0Vm7IF1GrLZbCY9PV3bREhRFKKjo5k0aRJdunShQYMG6HTWn61+9dVX6PV6zp8/j9lsJiIigqysLFJSUjh58iTp6emcO3eOffv28fXXX1NQUMCSJUs4evQo58+fx9/fn6ioqIp9KEIIIWosGdkTQpRLbFU3QIi7VFEZe8U1atQIo9HIzp078fX1pXv37lqkyK5du0hKSgJg1apVTJs2DUVRaNu2LV5eXpw6dYoDBw7QrVs3bVpo//79OXToEHXr1gXQsvaGDBnC7Nmzy9V/IYQQtYeM7AkhhKhVKipj78KFC9oum9euXWPPnj107NgRgJ9//hmAvLw85syZw7hx4wBrzMLevXsB+N///sfp06dp3bo1LVu25MCBAxQWFlJQUMCBAwfw9vbG3d2dxMRELly4AMDu3bu16Z1CCCHEncjInhBCiFqlojL2MjIyGDFiBBaLhaKiIoYMGcLTTz8NwNy5c9m+fTtFRUW89NJL9OrVC4B//etfjBw5Ej8/P1RVZc6cObi5uTF48GD27duHn58fiqLQr18/BgwYAMD06dPp2bMnTk5OtGrVShuRXLp0KQDjxo0jMzMTg8FAVlYWDg4OLFy4kMTERB544IEKfHK/+2JOAAAgAElEQVRCCCGqG6W0tQjVhcFgUE0mU1U3Q/DbFvxl3er/bt8vxK3YstNsU+bAmp1WWFjI/PnztX/Up6Sk4O7uTt26dfH39yciIoIxY8YA1jVZM2bMIDQ0FICdO3cyYcIELBYL4eHhJYKsX375ZVatWkVOTo7d8Y0bN/Lss88SHx+PwWCgoKCA8PBwjhw5QmFhIcOHD+cf//gHp0+f5rnnntM+9+OPP/L6668zceJEu+udOnWKsLAwjhw5wltvvUVkZGTFPTghhBBC1AiKonynqqqhtHMysieEqPbuJTstNzcXk8mETqcjIyODgIAABgwYgKIojB8/nt27d+Ph4UHnzp0JCQnBx8cHAJPJpE3du7kN7733Hl27dtWObdiwgby8PL7//ntyc3Px8fHhhRdeoEOHDpjNZsC6m6O7u7tWaBYn2WlCCCGEKA9ZsyeEqPbuJTutXr162g6J169f13ZXjIuLo23btrRu3RpnZ2eef/55tm7dClgLs8mTJ/P222+XuN6//vUvpkyZQp06dbRjiqJw9epVCgsLuXbtGs7OziWm1e3du5c2bdrQqlWrEteU7DQhhBBClIcUe0KIam/w4MF8/vnnAGzbtk1b63Qnhw8fplOnTvj5+bF06VJ0Oh3nzp2jRYsW2nuKZ6otWrSIkJAQmjVrZnedo0ePcvbsWW29VvF21a9fn2bNmtGyZUsiIyNp3Lix3Xuio6MlGF4IIYQQlUKmcYpa56OPjFXdBFEOI0fGljh2L9lpAF27duXEiROcPHmSESNG0L9//1tmqp0/f54NGzaUWGdaVFTEpEmTSt3GPy4uDkdHR86fP8+lS5d47LHH+NOf/kTr1q0ByM/PJyYmRnLThBBCCFEpZGRP1CpGo5HZs81V3QxRCWzZafcySubt7U39+vVJSEi4Zaba0aNHSUlJoW3btnh6epKbm0vbtm3Jzs4mISEBo9GIp6cnhw4dIiQkBJPJxCeffEK/fv1wcnKiadOmPProoxTfVOrLL78kMDCQhx56qEKegRBCCCFEcTKyJ2qdhx/Wlzo6JKq30NBQMjIy6Nu3L+fPn7/j+1NTU2nRogU6nY6ffvqJ06dP4+npSaNGjUhOTiY1NRV3d3eio6P55JNP6NSpE5mZmdrnXV1dSUlJAeCXX37RjhffCGbv3r3s27ePYcOGkZuby6FDh+x23Czr+kIhhBBCiHshxZ4Qoka42+y0b7/9ltmzZ+Pk5ISDgwP//ve/cXNzA6xr8/r27YvFYmHUqFF06tTpnto0fvx4wsLC8PX1RVVVwsLC8Pf3B6y7ge7evZsPPvjA7jOSnSaEEEKIiiLTOIUQ1VpaWhoGg4HHHntMO+bp6YlOp8PX15evvvoKvV7P5cuXMRqNdOjQAb1ez+7du1m1ahVgXXc3ffp0Nm/eDMCTTz7Je++9h06nY9WqVcyePbvEfcPCwnB1dS1xPCIigs6dO2MymXB1deWTTz7BYDDg6OjIypUrtfV5Dg4OtGnThp49e9KpUyemT58OWIu8cePGAfDwww/zySef0LZtW3Jycli4cKEUekIIIYQoMxnZE0LUCNUta69Vq1bs27cPV1dXCgoK6NGjB/3796dbt25212zZsiUfffQR8+bNq/BnJoQQQoiaTUb2hBA1QnXL2lMURRsZLCgooKCgQLt/cZ6envj7++PgIH9dCyGEEOLuyL8ehBA1QnXM2rNYLOj1epo2bUpwcLDdiKAQQgghRHnJNE5R63l6emIymbTNOSrL5cuXCQ8PJyEhAUVRWLlyJd27d8dsNjNu3DiuX7+OTqfj3//+N126dCnXvYw1PEswtoZk7Tk6OmI2m7l8+TKhoaEkJCTg6+tbpnYLIYQQQtyJjOwJUcksFgsAEyZMoF+/fpw6dYpjx47h7e0NwJQpU5g+fTpms5nXX3+dKVOmlOt+RqMR801ZgociD1GQXVCu65aVWqTy3fTv+H7h99qxc3vOcXjqYQ6EHajUdlTHrD2ARo0aYTQa2blzZ7mfgRBCCCGEjYzsCXFDWloa/fv3p0ePHvznP//B3d2drVu3UrduXYxGI127dmX//v1cvnyZFStW8Nhjj2GxWJg2bRqxsbHk5eUxfvx4xo4dS2xsLDNnzqRZs2aYzWYOHTrE119/rY38ODs74+zsDFhHjLKysgC4cuUKzZs3L3df9A/r7Ua/PGd4svWFrZU2emmxWHB0dARgwYIFuD/qTlZWFttHbgfgaMBRHnzzQYxGY6W2ozpl7V24cAEnJycaNWrEtWvX2LNnz11FRwghhBBC3IkUe+K+ZDQaK+W6ZrMZvV5/y/PJycmsX7+eZcuWMWTIEDZt2sSwYcMAKCwsJC4ujh07djBz5kz27NnDihUraNiwIfHx8eTl5fHoo4/Sp08fwDp9LyEhAS8vL8xmM02aNCEsLIxjx44RFBTEu+++S/369Vm4cCF9+/YlMjKSoqIi/vOf/1RK36FyC9rExETS09P54osv+Oc//8mCBQu0+z7yyCOV1qfiqlPW3vHjxxkxYgQWi4WioiKGDBmirfd77bXXMBgMhISEEB8fT2hoKJcuXWLbtm1Mnz6dEydO3FNbhBBCCFG7KKWtTakuDAaDevN0KFE1bMXZzWuZ7uX9RqPxjkXZvSrtuocOHSIoKAiLxcLx48e19XJnzpxBVVVatWqF2WzGy8uLhg0bkp+fz9GjR7W1XlevXtV2SrRYLLRr1w4HBwfS0tK0e2VnZ3PkyBEeeeQRHnjgAVJSUnB0dMTLy4uUlBQaNmxIkyZN+Pnnn7UIgPL2sfizta1LzMnJoW3btphMJvR6PUOGDCEkJIRhw4ZhNBoJCgpi/vz57NixgwULFrBnzx4+/PBDfv75Z/7v//5PK2g3bNjATz/9xFNPPaUVtGDdjOQf//gH2dnZzJs3j+3bt9u17fdaHymEEEIIUVsoivKdqqqG0s7JyJ64L91crFQUWyFpu8fNim99rygKRUVF2mtbQacoit0GHm3bttV2V7S5fPmyNq0RwMXFBRcXFy0Q283NTVsTlpmZSZs2bQBo0qQJSUlJ5erjnXh5eWl9DwoKIi0tTTv35z//ucTxXbt2cfz4cTZu3AhYp5omJyfj7OxMly5dtEJv+/btNG3alKCgoEr5b3c7np6etGjRgm+++UY7ptfrKSwsZP78+dpoX0pKCu7u7tStWxd/f38iIiIYM2YMAKqqMmPGDEJDQwHYuXMnEyZMwGKxEB4ezrRp0+zu+fLLL7Nq1SpycnLsjm/cuJFnn32W+Ph4DAYDBQUFhIeHc+TIEQoLCxk+fDj/+Mc/OHv2LMOHDyczMxMHBwfGjBnDhAkTSvTt1KlThIWFceTIEd566y0iIyMr9NkJIYQQouaSYk/USsWLSU9PT7Zu3UpOTg5PP/20dnzevHnk5OQwY8YMuzVYv/zyCwaDgdjYWD788EN27NjBhg0bcHJyIikpCXd3d+Lj40uMbD322GN88MEHdOjQgRkzZnD16lXmzp2Lt7c3M2fOxGg0snfvXqZMmVKuYulOU2BdXFy0rx0dHbl27VqJc46OjhQWFgLWIuj999/XgsltYmNjqV+/vvb64MGDxMTEsGPHDq5fv05WVhbDhg1j7dq199yXu1HdQtVdXFyYP38+gYGBZGdnExQURHBwsHYPm8aNG/Pee++xZcuWCn9mQgghhKjZZDdOIcohPDwcHx8fAgMD8fX1ZezYsVqRdLP333+foUOH4u/vj9ls5tVXXwVg2bJlvPLKKwQEBPDqq6/y4Ycf/p5duKO+ffuyZMkSCgqsu2gmJSVx9erVEu+LiooiPT2dtLQ0oqOj6dWr1+9W6EH1C1Vv1qwZgYGBgHWtobe3t5blV1zTpk3p3LkzTk5Od/lEhBBCCFHbycieqPVs0xXd3NxISEjQjhefLld8pM3NzU37jIODA7NmzWLWrFl21zQajSVG2PR6fYkt9wF69OjBd999V75O3MScabbL2svMyeSZ9c9gybOQejlVO3c2/iyWPAuxH8VizjQzdttYGiQ0oCC7gMycTIwfGVF1KqmFqTzo9SAqKk4NnPB92ZecMzmcTT9baqbf5VOX7c6l707n7Jdnyb+Sj3s7dxr7NabDqA733L/ScvYGDx7MyJEjiYyMZNu2baxbt441a9bc8VqHDx9m1KhR/PTTT6xZs+aWoeqHDx8GyhaqPm/ePLt2bd26lWbNmpGbm8s777xTYtpvWlqatg5UCCGEEKKiSLEnRA0TGxtbogDrNq8bAE4NnOj8ZmfteIv+vxU0+mm/rWF0auCkfUZxUGg9uDWtB7e2u2ajjo1o1LFRqW24+ZxHsAcewR731qEyqo6h6gA5OTkMGjSIhQsXams6hRBCCCEqghR7QtRApY181Qa2UPXSiq47udtQdUALVf/uu++0UHWwbroTEhJCTEzMLUPVW7duTUFBAYMGDWLo0KHa5jhCCCGEEBVF1uwJgXWTluKh2JXl8uXLDB48mI4dO+Lt7c1///tfwFqk6PV69Ho9np6elRI7URuEhoYyZcqUEpvJ3Epqaqq2xrJ4qHrnzp21UPX8/Hyio6MJCQnhqaeeIjMzk7S0NNLS0qhXr54WnfHLL79ox7t160ZMTAwGg4GWLVuyb98+VFXl6tWrHDp0iI4dO6KqKqNHj8bb25u///3vlflYhBBCCFFLycieEL8Di8WCo6MjEyZMoF+/fmzcuJH8/Hxyc3MBtI1FAF555RUaNmxYVU2t1qpTqPq3337LmjVr8PPz04r7WbNm8eSTT7J06VIAxo0bR2ZmJgaDgaysLBwcHFi4cCGJiYky5VMIIYQQdyQje0IUk5aWhre3N3/961/p1KkTffr00aIJjEYjU6dOpUuXLrRv317LdLPtzti5c2f8/f354IMPAOvauSeeeIIXX3wRPz8/srKy+Prrrxk9ejQAzs7ONGpkv+ZNVVU+++yzMu0kKezZ8vFsBg4cCMBXX32ljZqaTCaGDh2KXq9n+PDhdOjQQdvl0mKxaGv1zp49y9y5c3F0dKROnTq4urpq1508eTIdO3bE39+f4ODgEhEMZ86cwWQyaev6XF1dadCgARcuXMDBwYHJkycD1o15IiMjyc/Pp6ioCC8vL/74xz8C1iJv3LhxADz88MP06dOHOnXq4OHhQXp6uhR6QgghhCgTKfZErXNzCPbNkpOTGT9+PCdOnKBRo0Zs2rRJO1dYWEhcXBwLFy5k5syZAKxYsYKGDRsSHx9PfHw8y5YtIzU1FbBuzvHWW2+RmJjIjz/+SJMmTQgLC+ORRx4hPDy8RITBN998w0MPPUS7du0quNc1ny1nDyiRs2c2mzGbzRgMBtatW4fZbGb16tX4+vpiMpkwm83s3LlTi87Q6XTMnz+fkydPcujQIRYvXkxiYiIAwcHBJCQkcPz4cdq3b09UVJRdOyZNmkT//v3tjo0cOZKdO3eWaPOdrnWnzwshhBBC3I4Ue6JWiY2NtRulKY2Xl5c2rS4oKEiLWQC0TTSKH9+1axerV69Gr9fTtWtXLl68SHJyMgBdunTBy8sLsBaKR44c4aWXXuLo0aPUr1+f2bNn2927rPlwoqSKzNm7XQZenz59tM9069aN9PR07XpbtmyhdevWJaZ89uzZs0Tcwp2uVZbPCyGEEELcjqzZE7XOnTY/cXFx0b52dHTUpnEWP+fo6Kht7KGqKu+//36JTUFiY2OpX7++9trDwwMPDw8tS23w4MF2xV5hYSGff/55xWTuGct/iftabMlDFZmzV9ztMvBWrlzJc889B8DVq1eZM2cOu3fvtsvZK6vi1xJCCCGEqAgysidEOfXt25clS5ZQUFAAQFJSUonpmWBde9WiRQtOnz4NwN69e/Hx8dHO79mzh44dO+LhUb48OqPRiNFsLNc1qqPy5uzFx8cTFRXF9evXtXO3y8B766230Ol0DB06FIDp06czadKkO44cl+bmawkhhBBCVAQZ2ROinMLDw0lLSyMwMBBVVWnSpAlbtmwp9b3vv/8+Q4cOJT8/n9atW7Nq1SrtXHR0dMVN4dRT6uhXTVdROXsGg+G2GXgff/wx27dvZ+/evdrUz8OHD7Nx40amTJnC5cuXcXBwoE6dOkRERNz2vqVdSwghhBCiIkixJ+4bnp6emEymSr9Peno6GRkZ+Pr64ufnx6pVq0hLS2Po0KGYTCacnJwYNWoUH3zwAZGRkdrnbLsrAri5uWlr9hwcHJg1axazZs2yu4/RaNRCtm1sO0KW5l4KFGEvNDSUjIwM+vbty/nz5+/4/tTUVFq0aIFOp7PL2btdBt7OnTuZM2cOBw4csBs9tO3OCjBjxgxcXV3vWOjd6lpCCCGEEBVBpnGKWsNisXDu3DnOnTtHYGAgCQkJWCwWoqOjARg6dCinTp3i+++/59q1ayxfvryKWyzuli1nz9nZuUzv//bbbwkICECv1xMaGqrl7B08eJA1a9awb98+LbZhx44dAERERJCdnU1wcDB6vV6LSLidF154ge7du3P69Gk8PDxYsWLFba91/vx5nnzyyTt+XgghhBDitlRVrba/goKCVHF/ePzxx9XHH3+8XO9v1aqVeuHCBbVr165qvXr11PDwcNXHx0cNDg5Wc3Nztc9NmTJF7dy5s9quXTv166+/VlVVVQsLC9XIyEjVYDCofn5+6tKlS1VVVdX9+/erRqNRfeGFF1Rvb281PT1ddXFxUf/4xz+qBQUF6lNPPaV+9dVXJdq3YMEC9dVXX723h1HFbvdsK1urVq1UX19fNSAgQC3+/fnZZ5+pPj4+qqIoanx8fKW3Q1QvrVq1Unv06GF3LCAgQO3UqZO6c+dONSAgQA0ICFDr16+vtm/fXg0ICFD/8pe/qIcPH9bO+fv7q59//rmqqqp65swZ1Wg0qh07dlR9fHzUhQsXateNjIxUO3TooPr5+akDBw5UL126ZHffn376Sa1fv746d+5c7VhYWJjapEkTtVOnTnbvLeuf61t9XgghhKgIgEm9Rb0k0zhFhTGbzSWmLd7uvbfbFTM3N5fx48ezbNkyhgwZwqZNmxg2bBjwW9bdjh07mDlzJnv27LHLusvLy+PRRx+lT58+gDXrLiEhQYtA8PDw4NChQzRr1ow+ffpo77MpKChgzZo1vPvuu/fwFGoni8WCo6MjAPv378fNzc3uvK+vL59//jljx46tiuaJasCWk9iiRYsSOYm2nW6NRiPz5s3DYDAA1r8nTCYTOp2OjIwMAgICGDBggJaTGBgYSHZ2NkFBQQQHB+Pj40NwcDBRUVHodDqmTp1KVFQUc+bM0e53q5zEiIgIhg8fbne8rH+ub/V5IYQQorLJNE5RIWJjY+8YaXA36tatWylZd5cuXeLixYt07dqV8+fPc/XqVdauXWt37//3//4fPXv25LHHHquw/twv0tLS8Pb25q9//SudOnWiT58+WrSE0Whk6tSpdOnShfbt22tr0CwWC5MnT6Zz5874+/vzwQcfANb/5k888QQvvvgifn5+t72vt7c3HTp0qNzOiWqtOuYklvXPteQkCiGEqCoysicqTPENTO7kTiOAxXclrMisuz179lCnTh0SExMJDg4mMzOTqVOnauvz0tLSyMnJoVOnTmUepbzfmM1m9Ojts/YygWcACySfSmZ9/fUsa7KMIeYhbArcxLCHhoEZCn8oJK5NHDuu7WBmyEz2BOxhxfkVNCxoSHyrePLq5vFo5KP0WdkH8iDu+zgSDAl4NfUCIyj/U+jj1QcFhbHNxjKm+ZibGgeMBRr8Tg9D3J9iSx6q7jmJQgghxP1Iij1RI9iy7nr16oWTkxNJSUm4u7uXeF/Lli1p3LgxjRs3RlVVLl++TIMG1sojIyODS5cu4e/vX623wM/JycGM+Zbnvep6oXe9MWrqGkTa9TTt3J/dboyaNvjt+K5Luzh+9TgbL2wE4IrlCsnXknF2cKZLgy541fXSPn9Qf5DmLs35Of9ngo8H07FeR3o26lnBPRQ1UXlzEk+ePMmIESPo378/derUAX6/nEQhhBDifiXFnqgRypp117VrVwYPHsynn36KTqcjODiY5cuX4+Ligk6no1WrVmRnZwPW6aKvvfba792VcmvUqJH1i9hiBz2BrUAOuDztop1znOfItZxrMAMwgss8FzCA4y+OFBoKIRbUQSrvj7nFqOm8+rD9t2PNaQ5AU5oSOiOUONc4ekYWK/aMwDzAUEGdFTVKdcxJFEIIIe5nUuyJ+4Zt/V2dOnXo3Lmzdryis+5mzpzJzJkzS9zfNiVU2CvrqOnVq1cpKiqiQYMGXL16lV27dlXLYllUneqWkyiEEELc72SDFiHEbYWHh+Pj40NgYCC+vr6MHTu21ML4f//7Hz169CAgIIAuXbrw1FNP0a9fPwA2b96Mh4cH//3vf3nqqadKjBIKAdUvJ/FWf64lJ1EIIcT9QrFGM1RPBoNBNZlMVd0McQ9so22lbepyu3PizmzTOC9fvqwd8/T0xGQylYhEqGienp40aNAAR0dHdDodtu/PX3/9leeee460tDQ8PT357LPPePDBByv0vi1atLAbndHr9RQWFjJ//nymTp0KQEpKCu7u7tStWxd/f38iIiIYM8a6iYyqqsyYMYPQ0FDOnj3L8OHDyczMxMHBgTFjxjBhwgQAJk+ezLZt23B2dqZNmzasWrXqt6mzwJkzZ/Dx8WHGjBnaqPTOnTuZMGECFouF8PBwpk2bBsC+ffuIjIwkPz+foKAgVqxYUWKDEYB+/fpx6NAhevTowfbt20ucF0IIIUTtpSjKd6qqlrpIpkpG9hRFaaQoykZFUU4pinJSUZTuiqI0VhRlt6IoyTd+r7h/CQohKpXFYtG+3r9/P2azmeI/iJk9eza9e/cmOTmZ3r17M3v27Apvgy2nDSiR02Y2mzGbzRgMBtatW4fZbGb16tX4+vpiMpkwm83s3LlTG7W05bSdPHmSQ4cOsXjxYhITEwEIDg4mISGB48eP0759e6KiouzacXNOm8ViYfz48Xz55ZckJiayfv16EhMTKSoqYsSIEURHR5OQkECrVq34+OOPS+3b5MmTy7QzpRBCCCFEcVU1jfNdYKeqqh2BAOAkMA3Yq6pqO2DvjddCiApUVTl7W7duZcSIEQCMGDGi1M1zyut+zWmLi4ujbdu2tG7dGmdnZ55//nm2bt3KxYsXcXFxoX379oC1iNy0aVOp7ezdu7e2a6wQQgghRFn97sWeoigPAD2BFQCqquarqnoZawqY7cfaHwMDf++2CVFj5GDd+dL2y5az97w1Z2/80fGcaHKCRuZGbArcZH2PGQo/KSSuXhwLXRYyM2QmGGGF9woabmxIfP144hvFsyxyGaldU2EixH0dx1s/vEVi00S7nL2gBkF82P5D7f7/++F/NHuhGRih2QvN+PnHn+3bd7e/SjF48GA+//xzALZt28aAAQPK9KgOHz5Mp06d8PPzY+nSpXed02YbxbPltE2fPt3uPefOnaNFixbaaw8PD86dO4ebmxsFBQXaCOjGjRu1kUkhhBBCiIpQFbtxtgYuAKsURQkAvgMmAA+pqpoBoKpqhqIoTUv7sKIoY4AxYM1ME0LcnZqas3e/5rSVti5aURQURSE6OppJkyaRl5dnN2IohBBCCFERquJfFjogEHhZVdXDiqK8y11M2VRV9UPgQ7Bu0FI5TRSimnPlvsrZe6jDQ2Ssz6BZs2ZkZGTQ1NjUvn0V5H7MaQsKCrIbsUtPT6d5c+tz6t69uzZddteuXSQlJd1jz4UQQgghSqqKNXvpQLqqqodvvN6Itfj7n6IozQBu/P5zFbRNCHETW85eQUEBAElJSVy9erXE+65evaoF0tty9nx9fQEICQnRNh/5+OOPeeaZZyqlraGhoUyZMqXM0Q6pqalajMTd5rTFxMSUyGlLS0sjLS2NiRMn8uqrrxIREUHnzp1JTk4mNTWV/Px8oqOjCQkJAeDnn61/zeXl5TFnzpwyxQAIIYQQQpTV717sqaqaCZxVFKXDjUO9gUQgBhhx49gIrOMQQogqVhE5e9OmTWP37t20a9eO3bt3a9EDFe1+zGnT6XQsWrSIvn374u3tzZAhQ7QNXObOnYu3tzf+/v4MGDCAXr16AWAymQgPD9eu8dhjj/Hss8+yd+9ePDw8+Oqrr+7l8QghhBCilqmq3ThfBtYpinIc0AOzgNlAsKIoyUDwjddCiArk6elJQkKC9joyMpIZM2YA1mmZBoM1osXNzY20tDQAHBwcmDVrFt9//z0JCQns37+fhg0bYjQa7TLfWrduzbFjxzhy5AjOzs7897//1c4dO3aMS5cu4eLigoeHR4n1bxUhNDTU7vXAgdY9nr766iutYDOZTAwdOhS9Xs/w4cPp0KEDTk5OgHXXUdv6ulatWmE0GikoKKCgoICwsDAtJDs0NJT69etTVFSEl5dXiRiJM2fOMG/ePLtjDg4O2jo9R0dH7Xi/fv2oW7cudevWZePGjaSkpABgMBhYvnw5ABcvXkSn03Ht2jVGjx5Nenq6hNILIYQQokyqpNhTVdWsqqpBVVV/VVUHqqp6SVXVi6qq9lZVtd2N33+tirYJIe5e8Zy9d999F29vb+313eTJlUd1y9kDeOmll7T2vPjii7z55psl+lWnTh3eeOONEgWkEEIIIcSdVNXInhCiClR2zl56ejpffPGF3RTEu8mTK4/qlrMH1l05s7KyALhy5Yq2cUtx9evXp0ePHtoOoUIIIYQQZSX7fAtRE9ly9mxsOXsWa87e+vrrWdZkGUPMQ9gUuIlhDw2z5uz9UEhcmzh2XNvBzJCZ7AnYw4rzK2hY0JD4VvHk1c3j0chH6bOyD+RB3PdxJBgS8GrqBUaYeGIib7d8m+y/Z8NZaxvcVDcK0gswBZkwNDCwMWUjZy+fvWVeXpnEljw0ePBgRo4cSWRkJNu2bWPdunWsWbPmjpc6fPgwo0aN4qeffmLNmlJQuNgAACAASURBVDV3nbP33HPPAb/l7O3evdtuFK60nL3Dh637Uy1fvpwnn3ySunXr8sADD3Do0KGy9F4IIYQQokxkZE+IWuZecvZW/281epOerke6crHwIsnXkgHscva2X9xOU+emBDUIsrufoihEe0czKWUSXY50oYFjA3RKxf+cqbw5e/Hx8URFRXH9+nXtXGXm7AG888477Nixg/T0dMLCwkrs/CmEEEIIUR4ysidETVQFOXsH/3GQmDUx7EjbwfXr18m6lsUwj2GsXbuW7nTnG4rlyS1Pgs8qvtvVKWfvwoULHDt2TBsxfO6557TdS4UQQgghKoKM7AkhbqusOXtRUVGkp6eTlpZGdHQ0vXr1Yu3atcDvlydXnXL2HnzwQa5cuaIFqe/evdtuYxshhBBCiPKSkT0hxG2Fh4eTlpZGYGAgqqrSpEkTtmzZclfXmDt3Ltu3b6eoqIiXXnpJy5OraLacvbL69ttvmT17Nk5OTjg4OGg5e99++y1r1qzBz88Pvd465XXWrFk8+eSTREREkJeXR3BwMGDdpGXp0qW3vEfxnD2LxcKoUaO0DVyWLVvGoEGDcHBw4MEHH2TlypUAxMTEYDKZeP311wFrZEZWVhb5+fls2bKFXbt24ePjc0/PSAghhBC1h4zsCVGLVGbOno3FYmHSpEl2x26VJ1eRqmPO3oIFC3B0dERRFJKSkrRRxJCQEK3QA+jYsSNFRUUYjUbS09Ol0BNCCCFEmUixJ4Qot9vl7EHZ8uTKqzrm7H3zzTda27p3715ibaDN5MmTy7SzqBBCCCFEcVLsCVGLVEXOHpQtT668qmPOnk12djb79u3TRiNv1rt3bxo0aFCm5yCEEEIIYSNr9oSoie6jnD2A5XWX82SPJ6nrUJcHdA9w6JFDkrNXzObNm+ndu3eJeAchhBBCiPKQkT0hapnfO2cP4J1z77DDbwfp3dMJeziMv/9Q8Xly1TFnz6asI5FCCCGEEHdDRvaEqInuo5y9d955h2PdjtH1yI08uTM38uRiqXDVKWfP5uLFi8TFxbF58+Z767QQQgghxC1IsSeEuC1bzl6vXr1wcnIiKSkJd3f3Eu+LiorSNiuJjY1l3rx5rF27lsLCQi1Prn379pWaJxcaGkpGRgZ9+/bl/Pnzd3x/amoqLVq0QKfT3XXO3oEDB0rk7NnMmDEDV1dXIiIiKCws1HL23N3diY6O5pNPPtHeu2HDBp5++mnq1KlTAU9ACCGEEOI3UuwJIW6rvDl7Op3ulnlyFa265ewBREdHM23aNLvPmEwmli5dyvLlywF47LHHOHXqFDk5OXh4eLBixYoyB8cLIYQQovaSNXtC1CJVlbN3qzy5ilQdc/bA+ty/+OILu7V+BoNBK/QuXryITqfj2rVrjB49mvT0dCn0hBBCCFEmMrInhCg3i8WiFTG2nD1b1ALYT3EcNGgQzzzzTIW3wZaz16JFixI5e7biyGg0Mm/ePK2ozc3NxWQyodPpyMjIICAggAEDBmg5e4GBgWRnZxMUFERwcDA+Pj4EBwcTFRWFTqdj6tSpREVFMWfOHO1+t8rZ2717Nx4eHnTu3JmQkBAtGN1kMnH58uVb9qtOnTq88cYbJCQk2BXqQgghhBB3IiN7QtQiVZWzZ3OnPLnyqI45e7Zn+/bbb9+yjfXr16dHjx6ypk8IIYQQd01G9oSoie6znD2bzZmb6a3rzQMh5cyTiy15qDrm7C1atIiQkBCaNWtWll4LIYQQQtwVKfaEqGXuJWfv+NXjbLywEYArliskX0vG2cH5ljl7sZdjS733+p/XE96s9FG/8ipvzt7JkycZMWIE/fv310bRKjNn7/z582zYsIHY2Nh76K0QQgghxJ1JsSdETXQf5eytXbsWuJEn1z6Ozd9thkqakVidcvaOHj1KSkoKbdu2BazrB9u2bUtKSsq9PwAhhBBCiGKk2BNC3FZ5c/Zsfo88ueqUs9epUycyMzO1z7i6ukqhJ4QQQogKJcWeEOK2ypuzZ1NanlxFq445e6WJiYnBZDLx+uuvA9bIjKysLPLz89myZQu7du3SdvMUQgghhLgV2Y1TiFqkqnL2VFXl0Ucf5W9/+xve3t689957Fd636pizN3r0aAICAvD396dfv37k5OQAEBISohV6Fy9exMvLi/z8fEaOHEl6eroUekIIIYQoEyn2hBDlZrFYtK9tOXvFffTRR5w9e5ZTp05x8uRJnn/++Qpvgy1nDyiRs2c2mzGbzRgMBtatW4fZbGb16tX4+vpiMpkwm83s3LmTsWPHUlhYqOXsnTx5kkOHDrF48WISExMBCA4OJiEhgePHj9O+fXtt6qrNrXL2vvzySxITE1m/fr12rXfeeYdjx45x/PhxWrZsyaJFi0r0y5azd3MBKYQQQghxJ1LsCVGLVFXO3pIlS3jttddwcLD+ldO0adMK71t1zNmz7fCpqirXrl3T7l+c5OwJIYQQ4l5JsSdELZOcnMz48eM5ceIEjRo1YtOmTdq5wsJC4uLiWLhwITNnzgRgxYoVNGzYkPj4eOLj41m2bBmpqamAtZB56623tJGqiRMn8vbbb2tFnc0PP/zAp59+isFgoH///iQnJ1d4vwYPHsznn38OwLZt2xgwYECZPnf48GE6deqEn58fS5cuveucPdsoni1nb/r06XbvKS1nz1Y4AoSFhfHwww9z6tQpXn755bJ1VgghhBCiDGSDFiFqGL1eD2ZuGaru5eyFfuKNnL0zQaTFp8FywAx/vvJnMEJQfhBpR9PACLtO3MjZm10sZ++ZGzl7Tl3wCiuWs/drU4JeuZGzVyxUPe9yHnVW1sHUwsTnZz5nlGEU3zzyDfcstuSh6pazZ7Nq1SosFgsvv/wyn376KWFhYWVqtxBCCCHEnUixJ0QNYzabIefW510UF+1rR8WRa0XXfjvn4KIdL1QLAVBReb/t+/RtfFPO3uVY6jvW114fvHKQmF9i2HFxB9eLrpNlyWLYyWGs9V6Lh4sHg5oMAiDULZSw05VT0FSnnL3iHB0dee6555g7d64Ue0IIIYSoMFLsCVET3SZUnaeLnZt349gMrKNw8wAD8MuN32Oh74d9WbJjCb023JSzF3/j/Tc25Iy68T8olrO33ZqzN3DaQPa138eoUaM4EHuA9pPblzo6V17VKWdPVVV++OEH2rZti6qqbNu2jY4dO1bcwxBCCCFErSfFnhDitioiZ2/atGkMHTqUd955B1dXV5YvX14pba1OOXtFRUWMGDGCrKwsVFUlICCAJUuWAJKzJ4QQQoiKIRu0CFGLVFXO3sSJE0lMTMTR0ZFr166Vuo6tvKo6Zy8uLk67z+bNm2nTpo3WluTkZJydnalTpw7161unvjo4OODh4YGjoyM6nY6DBw/Ss2dPwD5nD6BXr17odDo8PT0lZ08IIYQQZSYje0KIcrNYLFpYuC1nLysry+49c+fOZfDgwZXWBlvOXosWLUrk7PXta11vaDQamTdvnlbU5ubmYjKZ0Ol0ZGRkEBAQwIABA7ScvcDAQLKzswkKCiI4OBgfHx+Cg4OJiopCp9MxdepUoqKimDNnjpbZd/O1Tp06xbJly4iLi8PZ2Zl+/frx1FNP0a5dOy0qAuCVV16hYcOGpfZt5MiRREREMHz48Ep7fkIIIYSoeWRkT4hapKpy9n4PVZ2zd6trnTx5km7dumnnH3/8cTZv3mzXDlVV+eyzz27Z5p49e9K4ceOyPwwhhBBCCKTYE6LWqYqcPYB//vOf+Pv7M2nSJPLy8iq8X1Wds3era/n6+vL1119z8eJFcnNz2bFjh93unGDd3OWhhx6iXbt2d9ttIYQQQohbkmmcQtQw92POXlReFA8//DD5aj5j1o9hzuY5vOb52r13MrbkoarO2bvVtby9vZk6dSrBwcG4uroSEBBQoqAs60ikEEIIIcTdkJE9IWoYs9mMOcd8y/M35+zZ8vTg9jl7ZoMZs8FMatdU+jTuA1Bqzp7nIU+eT3yefZf3MezkMACauTRDURRcHFwIeziMuOy4iutwMbacvXspnIrn7AFlytlbt26dXUD6ra41evRojhw5wv9n797joizz/4+/Bhg8YaIiCow2kCfOIwexh4cGW0VTSVwyTddTWLbamhsedrNNf6WYmVraamuWaaUbWqJGpoZTZquAOSpaAi20TGgmX0kBOQzM7w+cO0ZAEcETn+fj4eOL9z33fV/XHe6Xi+u6Pu+vv/6adu3a2czgmc1mPvnkEx5//PEbbrMQQgghxLXIzJ4Q96I7LGfvzJkzuLm5YbFY2D5rO37N/WBJw3f7dubs1XYvgHPnzuHq6sr//vc/PvnkE/7zn/8o1+3bt4+ePXui0Wga5iUIIYQQQlwhgz0hxDU1RM7euHHj+PXXX7FYLOh0umvm0t2M25mzV9u9AP74xz+Sl5eHWq3mrbfeom3btkobtmzZUm0mMjc3l5iYGBITEwEYO3YsBoOB8+fPo9FoWLhwIU8++eRNvSshhBBC3PtUjZF3dauEhIRYUlNTb3czRD3o9XqgcgboRs6J63N2dgYgPz9fOabVaklNTVUGH42pvLyckJAQPDw8quXwPfvss7z33nsUFBQ0ejuEEEIIIZoClUp1xGKxhNR0TvbsCSFuWnl5ufK1NWfvaqmpqTYD0Iak1Wrp37+/zTGdToefn59NqLqTkxM9evRQQtWrBqEHBgYqkQg5OTmEh4fj7e2Nr68vb7zxhnLf2bNn07NnTwICAoiKilL6VNu9oPKd+Pn54evry8qVK23auWrVKnr06IGvry9z5sypsX9TpkzB1dUVPz+/BnlfQgghhGgaZLAnRBNyu3L2rPdYunRpo/XNGqoOVAtVNxqNGI1GQkJC+PDDDzEajWzcuFEJQjcajezevZunn34as9mshKp///33HDp0iLfeekuJlxg0aBBpaWkcP36c7t27ExdXuU+xtnulpaUpoerHjh1j165dZGRkALB//34SEhI4fvw4J0+eJDY2tsa+TZo0id27dzfauxNCCCHEvUkGe0I0MbcjZ2/16tVERkbi5ubWaP26G0PV16xZw7x582jWrLIKqqura43tlFB1IYQQQtSHFGgRd4VbtedsxYoVvPPOO6hUKvz9/Xnvvfdo3rw5WVlZjBkzhv/7v/8jKCiITZs24ejo2Khtqa87LWcvtySX+FPxGHSGyoqgl69qW30Yqh+Kjo5m0qRJxMbGsnPnTj788EM2bdp03VsdPnyYKVOm8NNPP7Fp06YbDlWvGplQ0738/Px44YUXyMvLo0WLFiQmJhISUrmsPj09nQMHDvDCCy/QvHlzli1bRmho6I29CyGEEEKIWsjMnhBULjP8+eefefPNN0lNTSUtLY3y8nK2bNkCwNy5c5k1axYZGRm0bduW9evX3+YW1+5Oy9k7WnCUzMuZdD3cFe0hLUUVRXQ93LVB+ww3H6qekpJCXFwcxcXFyrn6hqpXvVfVUPUhQ4bYhKqbzWYuXLjAoUOHeO211xg9ejR3c9EsIYQQQtxZZLAn7iqNvefMbDZz+fJlzGYzRUVFuLu7Y7FYSEpKIjo6GoCJEyfecPTALWfN2bP+6UTlrNoWwLPK8WeASVe+1gFvX/k64co1BohYFMEarzWU7S0DA6T/K53CzwphJfDg7/eK+18cphIT2cXZbNmzhYFDB/LBLx8w7MQwzpaeJbs4m+zibFq2aknm5Uzb9t3on1rcbaHqGo2GUaNGoVKp6N27N3Z2dpw/f/6G2y6EEEIIURMZ7Im7TmPtOfPw8CA2NpYuXbrg5uZGmzZtGDx4MHl5eTg7OyuzMRqNRtm/dbcwmUzk5eXV69qYmBh8fHwICgrCz89PKTxSm/LycqZOnUpKSopy7MknnyQwMJCAgACKi4sbLXohKiqKOXPmEBERUafPZ2VlKX250VD1HTt2VAtVr+leUBmqDiih6tbB6MiRI0lKSgIql3SWlpbekngMIYQQQjQNsmdP3DZGo1HJ1Lv6uDXIuiaenp7K+eDgYLKzs5Vz1hmYqsf37NnD8ePH2br1yp6z334jIyMDR0dHevfujadn5Z6zCxcukJCQQFZWFs7Ozjz22GN88MEHNQ4caprNuZNpNBrat2+Pi4uLMtsE2FR/rJpr6OLiorw/Ozs7Fi9ezOLFi23uqdfrlf9+5eXl2NvbA5UxA6GhoVy8eFH57IoVK5RlkH/9619ZvXo18+bNa8guAndfqPqUKVOYMmUKfn5+ODo68v7776NSqSRUXQghhBANQmb2xG1hMBiuOaC7FmvlQgB7e3ubWSbruarHLRYLq1atUsrvZ2VlMXjwlT1nrX7fc7Zv3z48PT3p0KEDarWaUaNG8e233+Li4kJ+fr5yP5PJhLu7e73afrvdrugF60DPYrFw+fLlRhksR0VF2fx95MiRADY5e6mpqYwbN07J2evRowdqtVrpp3W/3P33349er6esrIyysjImT57MI488ojynVatWVFRU4OnpyZIlSwBqvRdUFo+xs7PDbDZz4sQJ5fjixYvZv38/Dg4OVFRUKPsF3d3dlYEeQIsWLSgvL6dHjx6YTCYZ6AkhhBCiTmSwJ24bg8FQ45/6DgJrExERwZo1aygrKwMql8sVFhZW+1yXLl04dOgQRUVFWCwWvvzyS7y9vVGpVISHhyszg++//z6PPvpog7bxVrod0QsAkydPplOnTvzwww88++yzDd6vuzFnD2DWrFlK+6wDyqtJzp4QQggh6kMGe+KeV9c9Z2FhYURHRxMUFIS/vz8VFRU89dRTALz66qssX76crl27kpeXd0fPrFhnsWpTn2WwGzduRKfTERYWRl5enjJYqboMdteuXbi6uhIcHFzjc9977z1yc3Px9vZW8vAa0t2Ys1dXkrMnhBBCiPqQPXvirmAdeDTWnjOrhQsXKjNaVXl5eZGcnHxznbiVrpGz1yy3mXLOPseey+WXYV/lNc3+0gxag32ZPWaTGfRgOWlhldsqIpyv7F10BhaDId9Aq5xWyr0O/vcgO37ZQeK7iRRXFHOx/CLjO47nA+8PlGbYY8/j+Y/z2r9fY/L7k+vfP0P1Q3djzh5UBs5v3LiRkJAQXn/9dWU/nxBCCCHEzZKZPSHuMTXl7JlKTOSV1a8aZ0TbCNbkrqGs4soy2KJ0CsurL4ON84rD9KCJH8N+pLV9a1rbt+YD7w+wWCxEpkXSI7kHfil+TM+YTrcW3erVlmu5G3P2nnnmGX788UeMRiNubm48//zzDfAmhBBCCCEqycyeEPcia86elQOwCWgGDK9ybhlQACygcoZuGRACnL/yfw0QUxFD9vxsgnYGYbFY6NChQ2XO4NErn991VTXO5W/QxaFL5QysASwVFjL9MlHbqbFYLBQWFtL5L53BNtGgQVhz9jZs2HDD11bNxgsJCalTzt6XX3553Zy9kJAQnnzySWXp79///nc0Gg0AHTt2VK6ZOnUqw4cPv+F2CyGEEELURgZ7QjQB1uiFgoICJQfv22+/xcPDg4SEBOVz8fHx/PnPfyY/P19ZAmmxWCgrK6N58+aUlJQwZswY2rRpA0BhYSFPPPEERqORU6dOKdU4X3jhBZYvXw5ULqO1FjeByhiGxgoOj4qK4syZM0RERJCbm3vdz2dlZdG5c2ccHBxuOGfvq6++qpazV9O9oDJnz9XVVcnZ+89//gPAmTNncHNzA+DTTz/Fz8+vgd6EEEIIIYQs4xSiybld1TgBysrK2LRpE0OGDGmUvllz9hwdHev0+W+++YbAwEB0Oh1RUVFKNt7BgwfZtGkTSUlJSsEbaxTCjBkzuHTpEoMGDUKn0zFt2rRr3gsqc/Z8fHwYMWKETc7enDlz8Pf3JyAggP3797NixQoAcnNzbSpzjh07lgcffJDTp0+j0WhYv359g70zIYQQQty7ZGZPiHvM9aIrGiuUvmo1zqrFcqr685//zIABA+jfv/9N9LC6qn2w0mq1NsV8gGrt+tOf/sSf/vSnatf269fPJievqszMzBqP13YvQMksvFptBWSuztnbvHlzjZ+7V2i1Wjp37mzznnQ6HWazmddff525c+cCle/ew8ODFi1aEBAQwIwZM5SKuRaLhQULFhAVFUVOTg4TJkzg7Nmz2NnZ8dRTTzFz5kwAZs+ezc6dO3F0dOSBBx7gvffew9nZmeTk5BrvBfDGG2+wbt06LBYLU6dO5bnnnrNp/7Jly5g9eza//vqrMsCvasiQIRw6dIh+/fqxa9euhn+BQgghRC1ksCdEE3N1KL01VL3quZpC6SMiImzuYzAYbELpDx48yI4dO0hMTKS4uJiLFy8yfvx4PvigshrnwoUL+fXXX5VQdiGqsuYkdu7cuVpOovV7T6/Xs2zZMqWaaVFREampqTg4OHDmzBkCAwMZMWKEkpMYFBTEpUuXCA4OZtCgQfj4+DBo0CDi4uJwcHBg7ty5xMXF8eqrryo5iVff64cfflByEh0dHRkyZAjDhg2jW7fKIkM5OTns3buXLl261Nq32bNnU1RUJN/7QgghbjlZximEuKa6htLHxcVhMpnIzs5my5YtDBw4UBnovfPOO3zxxRds3ry51iWeomm7W3MSZ82axdKlS2ss1GP18MMP07p167q/DCGEEKKByMyeEPeia+TskVXlXM6VY4Yr1zwNtAbKrlyjhxhLDNlZ2QS1vVKNU92B7X7bK6t45lz1HKt823PTvprG/c3v58EODwIwymUU/9D+o0G6Ku5ChuqH7sacxB07duDh4UFgYOCN9V8IIYS4RWSwJ8Q9xmAwVBuAZffJBsBF7UJaaJVQ+s5VQul1BuVrF7WLco2dyo7FXotZ7HVVKL2zHr3zVQ+q5Zz5IfMN9kI0NTebk/j9998zceJEhg4dSvPmzYH65yRWvVfVnEQnJyclJ7GoqIhFixaxZ8+ehnsJQgghRAOTwZ4Q9yLD7W6AEDfubspJ/PHHH8nKylJm9UwmE0FBQSQnJ9OpU6cb77wQQgjRCGSwJ4QQ4o5wN+Uktm3blnPnzinXa7VaUlNTa6zGKYQQQtwuMtgTQghxR7DmJNbVN998w5IlS1Cr1djZ2SnZht988w2bNm3C399fiRlZvHgxjzzyCDNmzKCkpIRBgwYBlUVa1q5dW+u9oDInMS8vD7VabZOTWJvU1FTWrl3LO++8A0D//v354YcfKCgoUHISr65uK4QQQjQGGewJIYS4rRozJ9Ga4WcNqc/MzKyW4afT6ZQMP7VajZ+fH61atSI4OJjS0lIcHR1ZvXo1AwcO5NKlSzZZliaTifHjxyt9+Pjjj1mwYAEqlYonnniCjz76iA8++IBRo0ZRXl5OWVkZWVlZQGV0xGOPPcaPP/6Ivb09I0aMYMmSJQCsXbuWt956C3t7e5ycnPjXv/6Fj49Ptb5OmTJFybi8+n0JIYQQqtqCg+8GISEhltTU1NvdDNHA9Ho9UP0HOyGEuFFarRZnZ2d27typZPiNHTsWs9lsMzi6OsPv6NGjdOzYEXd3d9LS0oiIiFDiG6oKDg5mxYoVDBgwgIyMDEaPHk1SUpKyzNPV1ZXS0lIsFgvNmjWjoKAAPz8/vv32W5ydnTl8+DDh4eGUlpby8MMP8/e//52hQ4dy8eJFpajMjh07+Oc//8nu3burPf/rr7/GycmJCRMmyGBPCCGaKJVKdcRisYTUdE4Cr4QQQtzT6pPh16tXL9zd3QHw9fWluLiYkpISm89kZGRw7tw5+vfvD8C6deuYPn26sszT1dUVAEdHR5o1awZASUkJFRUVQGW2X3h4uPKZoKAgJfevavXQwsLCWnP8BgwYQLt27erwFoQQQjRFsoxTCCGq0NcYHCjuFoYaStHWN8PPatu2bfTq1UsZsFlt3ryZxx9/XBmIpaenA9C3b1/Ky8tZsGABQ4YMASAnJ4dhw4aRmZnJa6+9pgwkrfLz89m5cyczZ85Ujr311lssX76c0tJSkpKS6txeIYQQwkpm9oQQ4gq9Xo9Rb7zdzRANrL4ZfgAnT55k7ty5vP3229XObdmyxWaW0Gw2k5GRgcFgYPPmzcTExJCfnw9A586dOX78OJmZmbz//vv88ssvNteNHTuWv/zlL3h5eSnHp0+fzo8//sirr77KK6+8Up+uCyGEaOJkZk8IIarQoatxdkjc3eqT4WcymYiKimLjxo088MADNueOHTuG2WwmODhYOabRaOjTpw9qtRpPT0969OhBRkYGoaGhymfc3d3x9fXlwIEDREdHA/DUU0/RrVs3nnvuuRrbMWbMGJ555pkb6K0QQghRSWb2hBDiGrRaLefPn2/050yZMgVXV1f8/PxqPL9s2TJUKtUtacu9KCoqijlz5tQ58iA/P59hw4YRFxdH3759q52vae/fyJEj2b9/PwDnz58nPT0dLy8vTCYTly9fBuDChQscPHiQHj16ADB//nx+++03Vq5caXOvjIwM5evPPvuMbt261b2zQgghxBUy2BNCiNuovLwcgEmTJtVYbREq93vt3buXLl263Mqm3VOsGX6Ojo51+vzq1avJzMzk5ZdfRqfTodPpbELUP/7442qDvYiICNq3b4+Pjw/h4eG89tprtG/fnu+//56wsDACAwN56KGHiI2Nxd/fH5PJxKJFizh16hRBQUHodDolm2/16tX4+vqi0+lYvnw577//PgC5ublKjATA2LFjefDBBzl9+rSS4SeEEEJYyWBPCCHqIDs7G29vb6ZOnYqvry+DBw9WZmv0ej1z586ld+/edO/enQMHDgCVA7nZs2cTGhpKQECAsu/LYDAQHh7OE088gb+/P3DtqoqzZs1i6dKltVZkFNcWFRVl8/eRI0cC8MUXXygDudTUVMaNG4dOp2PChAmEhYXRs2dPysvLsbe3Z/ny5bi6uio5e/fddx9jxozBxcVFWX6pUqno06cPABUVFezYsQOA7t27o1arUalUlJeXK9U4NRoNERERODo6UlZWRp8+fZg8eTIAxfCU6AAAIABJREFUL730Eu7u7hQWFuLg4KAUdHF3dycxMVHpy4ULF7h8+TIRERGYTCaefPLJRnyTQggh7jayZ08IUS/WPMR7idFotAnMvlpGRgabN29m3bp1jB49mm3btjF+/HigsshGcnIyiYmJLFy4kH379rF+/XratGlDSkoKJSUl9O3bl8GDBwOQnJxMWloanp6e12zTjh078PDwIDAwsOE62sRcunSJnJwcJWfPKiIiQlnWWVPO3s6dO6vl7LVu3Rqj8fciPsHBwYwaNQqo/P6Ii4vj4MGDSs4egJubG99++61Nzl5kZCTu7u58/PHH3HfffVgsFqKjo4mPj2fMmDEsWbKEhx9+mHnz5rFkyRKWLFnCq6++Wq1vs2fPpqioqMYCMkIIIYTM7Akhbpher7f5gbep8PT0VAaDwcHBZGdnK+esP/BXPb5nzx42btyITqcjLCyMvLw8ZS9W7969rzvQKyoqYtGiRfy///f/Gr4zTcidmrMHv+fpmc1mSktLldnbhIQEJk6cCMDEiRPZvn17je18+OGHad26dR3eghBCiKZIZvaEEPWi0+kwGAy3uxkN6nqzlVVz1uzt7ZVlnFXP2dvbYzabAbBYLKxatapaURCDwUCrVq2u254ff/yRrKwsZVbPZDIRFBREcnIynTp1qlOfrqav11V3D0MNx+70nL2IiAiSk5MZOnSoUqHzl19+wc3NDaicGay6X1AIIYSoK5nZE0KIRhIREcGaNWsoKysDKgcDhYWFdb7e39+fc+fOkZ2dTXZ2NhqNhu+++67+Az29HuNVA9pDWi1lt6jCp6W8nCO9enFi+PBb8jyrOz1n74svvuDMmTOUlJRIeLoQQogGJTN7QgjRSGJiYsjOziYoKAiLxUKHDh1qXY43duxYDAYD58+fR6PRsHDhwkYptqHDdvZLCyQALg3+pErWAicAy994Aw9vby5evMiuRnpebe7knD2A5s2bExkZSUJCAoMGDaJjx46cOXMGNzc3zpw5oywJFUIIIW6EzOwJIcQ1ZGdn4+LiglarJS0tTTkeGxvLggULgMplmdbCHi4uLsqePTs7OxYvXsyJEydIS0tj//79tGnTBr1ez65dtsOdzZs3c+bMGcrKymqtqmhtS2P1szGrjZpMJj777DNiYmIapf3Xcyfm7BUUFHDmzBmgclYwMTGRnj17AhAZGanELbz//vs8+uij9eu4EEKIJk1m9oQQogqj0XhPVhqF21tt9LnnnmPp0qVcunSp8TtaA2vOXl1Vzdl7+eWXgcqCO9YZto8//tgmAgEql+3u2bMHHx8f7O3tlZy9vXv38vzzz6NSqbBYLErO3i+//EJkZCQlJSWUl5czcOBApk2bBsC8efMYPXo069evp0uXLsTHxwOQmprK2rVrlTy+/v3788MPP1BQUKDk7NV1QCuEEOLeJzN7QghxhcFguOZg6F7XWNVGd+3ahaurq82Sx1vpTs3Za926Na6urpSWlgLQqlUrHBwqfwdbUFAAQIsWLSgvL6eoqAiAkJAQZaAHlfmMzZs3x97eHpPJJAM9IYQQNmRmTwghqrjXKoxWdbuqjR48eJAdO3aQmJhIcXExFy9eZPz48XzwwQc326U6uVNz9pydnYmNjSU8PJzS0lIefvhhPv/8c4YOHUpsbCwTJkxg4sSJJCUl8be//a3GCqIjRoxgxowZdOvWrdHenxBCiLuXzOwJIYSol7pWG42Li8NkMpGdnc2WLVsYOHDgLRvowZ2bs9eyZUvCw8OVzwQFBWEymQA4deoUDz/8MADh4eEkJCTU2M4+ffooEQ1CCCHE1WRmTwghmhAjtll7Z4FHgXIgq8q5nCvHDFeueRpoDZRduUYPWGJiyMrOpu2VaqPqDh3w276dgivXV32OVf41zjUEQw3H7vScPagsCLNz505mzpwJQGBgINu2bWPmzJl8+umnXLp0iby8PNq3b1/ndgshhBAy2BNCiCbCYDBUG2T1ubL/Tu3iQmiVaqOdY2OVr3VVlraqXVyUa1R2dngtXozX4sU293TW63GuZcnotc41lobI2duzZ0+1c1u2bLEZNFbN2TOZTPTv35+0tDScnZ2VnL3c3FxGjhxJdHQ0HTt2VK4bO3Ysf/nLX/Dy8gJg2bJlzJgxgw0bNjBgwAA8PDyU/XxCCCFEXcn/5xBCiCbEcLsbcJvcyTl7Tz31FN26dVMKvVg/98knnwCVxVq2bdtGmzZt6tN1IYQQTZjs2RNCCHHPuxNz9gDmz5/Pb7/9xsqVK23udf78eWVvX1xcHFOmTLmxDgshhBDIYE8I0UC0Wi3nz59v1GcUFxfTu3dvAgMD8fX15aWXXlLOJSUlERQUhJ+fHxMnTlQqRgoBv+fsOTo61unzVXP2rPEM1uqaUJmzd/VgLyIigvbt2+Pj40N4eLiSs/f9998TFhZGYGAgDz30kJKzZzKZWLRoEadOnSIoKAidTqfEKhgMBnr06EH37t355ZdfeOGFF5TnVI0HmTNnDhqNhqKiIjQaDQsWLLiJtySEEOKeY7FY7to/wcHBFnHveeihhywPPfTQ7W6GuIaa/hvdf//9ll9//bXRnmk2my0VFRWWS5cuWSwWi6W0tNTSu3dvy3/+8x9LeXm5RaPRWE6fPm2xWCyWF1980fLOO+80WlvEnev++++39OvXz+ZYYGCgxdfX17J7925LYGCgJTAw0NKqVStL9+7dLYGBgZY//elPlsOHDyvnAgICLJ988oly/eeff27p3r275YEHHrDExcVVe+aMGTMsrVq1qnY8Pj7eAlhSUlIsFkvl9+yECRMsfn5+lp49e1oWL15s83mz2WzR6XSWYcOG1di3r776ytKrVy+Lvb29JT4+/obfjRBCiHsTkGqpZbwkM3tCiAaVnZ2Nt7c3U6dOxdfXl8GDBytL2PR6PXPnzqV37950796dAwcOAFBeXs7s2bMJDQ0lICCAt99+G6ic3QgPD+eJJ57A398flUqFk5MTAGVlZZSVlaFSqcjLy6NZs2Z0794dgEGDBrFt27bb0HtxJ7Dm6gHVcvWMRiNGo5GQkBA+/PBDjEYjGzduxM/Pj9TUVIxGI7t37+bpp5/GbDZTXl7O9OnT+fzzzzl16hSbN2/m1KlTyj1TU1PJz8+vsQ1vvvkmYWFhyrH4+HhKSko4ceIER44c4e2337YJrn/jjTfw9vautV9dunRhw4YNPPHEEzfzeoQQQjQhUqBFiCbkeqHadWU0Gm2Wkl0tIyODzZs3s27dOkaPHs22bdsYP348UFl5MDk5mcTERBYuXMi+fftYv349bdq0ISUlhZKSEvr27cvgwYMBSE5OJi0tDU9PT6ByYBgcHExmZibTp08nLCwMi8VCWVkZqamphISEsHXrVuWHfdH0WHP1YmNjlb1114taqFqhs7i4WIlTSE5OpmvXrkqVzDFjxpCQkICPj4/yS4qPPvqITz/91OZ+L774InPmzGHZsmXKMZVKRWFhIWazmcuXL+Po6Mh9990HVBaD+eyzz3jhhRdYvnx5jW3UarUA2NnJ72mFEELUjQz2hGgi9Hr9dQdpDcXT01N5TnBwsM3sxahRo6od37NnD8ePH2fr1q0A/Pbbb2RkZODo6Ejv3r2VgR6Avb09RqOR/Px8oqKiSEtLw8/Pjy1btjBr1ixKSkoYPHjwdcvU6/UbGq7D4rYxGCZVO1bfXL3Dhw8zZcoUfvrpJzZt2oSDgwM///wznTt3Vj6j0Wg4fPgwULmvLzIyslqo+dGjR8nJyWH48OE2g73o6GgSEhJwc3OjqKiIFStW0K5dOwCee+45li5dyqVLl+rzGoQQQogayWBPiCZEp9NhqJKZVl/XmyGsGj5tb2+vLOOses7e3l4pomKxWFi1alW1SokGg4FWrVrV+AxnZ2f0ej27d+/Gz8+PBx98UFkWumfPHiXgurb2G41n0enmKccOHYolOPgl1OrW1+zbzTp9ej15ecdQq+8jNPQV5XhBwf9IT3+fiooyVCp7unX7E/fd59WobblX1TdXLywsjJMnT/L9998zceJEhg4dSuVWCFsqlYrc3Fzi4+Or/XuqqKhg1qxZNUY8JCcnY29vT25uLhcuXKB///784Q9/4NSpU7i6uhIcHNwg/z6FEEIIKxnsCSFuu4iICNasWcPAgQNRq9Wkp6fj4eFR7XO//vorarUaZ2dnLl++zL59+5g7dy4A586dw9XVlZKSEl599VWb6oU10ek62cwKabULSEgYi4uLS4P2zaq8vBx7e3u+/toLJycnJkyYYPP8wYMH8+9/r2bo0KEkJiaydOlSDIb/1yhtaQrqk6tn5e3tTatWrUhLS0Oj0dgsCTaZTLi7u3P06FEyMzPp2rUrAEVFRXTt2pUjR46Qlpam/ELk7NmzREZGsmPHDj766COGDBmCWq3G1dWVvn37kpqaytGjR9mxYweJiYkUFxdz8eJFxo8fzwcffNAQr0IIIUQTJgv/hRC3XUxMDD4+Pkp0grU4xtXOnDlDeHg4AQEBhIaGMmjQIIYPHw7Aa6+9hre3NwEBAYwYMYKBAwfWqy2NWWAGYMCAAcrSvapUKhUXL14EKpexuru716v9otKN5uplZWUp33M//fQTp0+fRqvVEhoaSkZGBllZWZSWlrJlyxYiIyMZNmwYZ8+eJTs7m+zsbFq2bElmZiZt2rTh/PnzyvE+ffqwY8cOQkJC6NKlC0lJSVgsFgoLCzl06BA9e/YkLi4Ok8lEdnY2W7ZsYeDAgTLQE0II0SBkZk8I0SCs++9cXFxIS0tTju/atQv4felnbGysck6r1dosCW3fvj1Quazz0UcfVY5X/UybNm2Ur5OSkkhKSlL+3rFjRwC2b9/O9u3ba23r7SwwU5uVK1cSERFBbGwsFRUVfPvtt9f8vLg2a65eXX3zzTcsWbIEtVqNnZ0d//znP5VZ3tWrVxMREUF5eTlTpkzB19e3Xm2aPn06kydPxs/PD4vFwuTJkwkICLjmNf/4xz8ICQkhMjKSlJQUoqKiuHDhAjt37uSll17i5MmT9WqLEEKIpkFm9oQQjcZaFOZuU58CMxs3bkSn0xEWFkZeXh4ZGRkA1QrM1GbNmjWsWLGCnJwcVqxYwZNPPtmwnbpHabVa+vfvr/w9OzubP/zhD/j5+fHFF1+g0+kYOXIk2dnZ9OjRA51Ox4QJE3jhhRd4+umn8ff3Jzg4GA8PD06ePMmBAweoqKhgwYIF6HQ6XFxclD2gP/74I926dcPHxwdfX18lAuGnn35S7u3r68vatWuByqWdrVq1Yvz48fj6+vLKK68QHx/PyZMniYiI4MMPP0Sn09G9e3ecnZ2Byn8z1l+QAEr0g5OTE6GhoZhMJgoLC8nLy5OBnhBCiOuSmT0hRKNqqKIwDelOKDBztffff5833ngDgMcee4yYmJg6XSd+z9Xr3LlztVw9638TvV7PsmXLCAkJASorZu7cuRN3d3fS0tKIiIjg559/pnXr1ja/oAgODlYG+BkZGcTFxXHw4EHatm3LuXPnAHBzc+Pbb7+lWbNmFBQU4OfnR2RkJM7OzsTGxhIeHk5paSkPP/wwn3/+OUOHDmXFihXKM1atWsXRo0dr7NuIESOYMWMG3bp1a9iXJoQQokmQmT0hhLhJ1gIzZWVlAKSnp1NYWHhD93B3d+err74CKpenyg/3dWfN1QOUXL3r6dWrl7Iv0tfXl+LiYkpKSmw+k5GRwblz55SZw3Xr1jF9+nTatm0LgKurKwCOjo7KLwFKSkqoqKgAKrP7wsPDlc8EBQVhMpmqteVabe7Tp0+1aAchhBCirmRmTwjRJBnPnkVfpVLj2YICHt28mfKSErLy85VzOSkplJeUYNiwAePZszy9cyet09Iou3SJswUF6DdswOLgQJbZTFtPTyyAunVr/J59loL//Y8ck8nmOafWruW3H36grKCAZu3aoR05ErcBA2geFcWISZOwVFRgp1bT7U9/srlOVDJMmlTtWH1z9ay2bdtGr169bGZ0oXIQ9vjjjysB69Y4j759+1JeXs6CBQsYMmQIADk5OQwbNozMzExee+21agV28vPz2blzJzNnzrQ5/tNPP5GVlVXvgkJCCCHEtchgTwjR5BgMhmoDqT5Xwq/VrVsT+srv+Xedhw5VvtbN+z2XT926tXKNys4Or+hovKKjbe7p3LMnzj172hzzmTatxja16d6d4AULbrgvov65egAnT55k7ty57Nmzp9q5LVu22AwazWYzGRkZGAwGTCYT/fv3Jy0tDWdnZzp37szx48fJzc1l5MiRREdHKwWDzGYzY8eO5S9/+QteXl7VnhEdHY29vX09ey+EEELUTgZ7QogmqaYZInH3qk+unslkIioqio0bN/LAAw/YnDt27Bhms5ng4GDlmEajoU+fPqjVajw9PenRowcZGRmEhoYqn3F3d8fX15cDBw4QfWXw/9RTT9GtWzeee+65am3YsmULb7311g32VgghhKgb2bMnhBDirnejuXr5+fkMGzaMuLg4+vbtW+18TfvoRo4cyf79+wE4f/486enpeHl5YTKZlCI+Fy5c4ODBg/To0QOA+fPn89tvv7Fy5cpqzzh9+jQXLlzgwQcfvKG+CiGEEHUlgz0hxC2l1Wo5f/58oz6juLiY3r17ExgYiK+vLy+99JJy7ssvvyQoKAidTke/fv3IzMxs1LaIW8Oaq+fo6Finz69evZrMzExefvlldDodOp1Oqa4J8PHHH1cb7EVERNC+fXt8fHwIDw/ntddeo3379nz//feEhYURGBjIQw89RGxsLP7+/phMJhYtWsSpU6eU77l33nlHud/mzZsZM2aMsifQqmoG5Jw5c9BoNBQVFaHRaFggS32FEELcAFnGKYS4p5SXl9OsWTOSkpJwcnKirKyMfv36MXToUPr06cMzzzxDQkIC3t7e/POf/+SVV165oaV/4s5SNQPRSqvVkpaWZvP3zp07K7ELAFu3bsXT05NXX31VCV/38vLCw8ODFi1a0K9fPy5evKgMvCwWCwsWLGD58uUsX76c3bt3M3PmTObPn09MTAzHjx+3acOzzz7Le++9h8VisTm+detWVCoVKSkpLFiwgLKyMiZOnMh3332H2WxmwoQJSvSDVquldevWuLi40KlTJ1JTU6v19YcffmDy5Ml89913LFq0iNjY2Pq9SCGEEPckGewJIW6L7Oxshg4dSr9+/fj222/x8PAgISGBFi1aoNfrCQsLY//+/eTn57N+/Xr69+9PeXk58+bNw2AwUFJSwvTp03n66acxGAwsXLgQNzc3jEYjp06dwsnJCYCysjLKysqU2ROVSsXFixcB+O2336pVTRT3pvpk8RUVFZGamoqDgwNnzpwhMDCQESNGoFKpmD59Onv37kWj0RAaGkpkZCQ+Pj4ApKamkp+fX2Mb3nzzTcLCwpRj8fHxlJSUcOLECYqKivDx8WHs2LFotVoA9u/fj4uLS639ateuHW+++Sbbt2+/6XckhBDi3iPLOIUQt01GRgbTp0/n5MmTODs7s23bNuWc2WwmOTmZlStXsnDhQgDWr19PmzZtSElJISUlhXXr1pGVlQVAcnKysmQOKmf4dDodrq6uDBo0SPkB+5133uGRRx5Bo9GwadMm5lWpsCnuXfXJ4mvZsiUODpW/Ey0uLlZ+YZCcnEzXrl3x8vLC0dGRMWPGkJCQAFR+382ePZulS5dWu9+LL77InDlzaN68uXJMpVJRWFiI2Wzm8uXLODo6ct9999W5X66uroSGhqJWq+t8jRBCiKZDBntCiHoxGo3o9fpr/rEuR6uNp6enskwuODjYZkneqFGjqh3fs2cPGzduRKfTERYWRl5eHhkZGQD07t0bT09P5Xp7e3uMRiMmk4nk5GRlWd+KFStITEzEZDIxefJk/vrXvzbUKxF3sOjoaD755BMAdu7cyYgRI+p03eHDh/H19cXf35+1a9fi4ODAzz//TOfOnZXPaDQafv75Z6ByL2BkZGS1IPSjR4+Sk5PD8OHDq7WrVatWuLm50aVLF2JjY2nXrh1QORAcPHgwwcHB/Otf/6p334UQQjRdsoxTCHHDDAYDer3+pu9TNcTa3t5eqWhY9Zy9vT1msxmo3De1atWqahUXDQYDrVq1qvEZzs7O6PV6du/eTceOHTl27Jgyy/f4448rodhX0+s31Ltf4vYyGCZVO1bfLL6wsDBOnjzJ999/z8SJExk6dGi1fXhQOTDLzc0lPj4eg8Fgc66iooJZs2bVuDc0OTkZe3t7cnNzuXDhAv379+cPf/gDXl5eHDx4EHd3d86dO8egQYPo2bMnAwYMqFO7hRBCCJDBnhCinq7+gbYmDTEgrCoiIoI1a9YwcOBA1Go16enpeHh4VPvcr7/+ilqtxtnZmcuXL7Nv3z7mzp1L27Zt+e2330hPT6d79+7s3bsXb2/vGtttNJ5Fp5MlnveS+mTxWXl7e9OqVSvS0tLQaDTk5OQo50wmE+7u7hw9epTMzEy6du0KVO7569q1K0eOHCEtLU3593D27FkiIyPZsWMHH330EUOGDEGtVuPq6krfvn1JTU3Fy8tL2U/q6upKVFQUycnJMtgTQghxQ2SwJ4S4a8TExJCdnU1QUBAWi4UOHTrUWJjizJkzTJw4kfLycioqKhg9erSyfG7dunX88Y9/xM7OjrZt2/Luu+/W+CydrlONM0Ti7hUVFcWZM2eIiIggNzf3up/Pysqic+fOODg48NNPP3H69Gm0Wi3Ozs5kZGSQlZWFh4cHW7Zs4aOPPsLX15ezZ88q1zs5OSnRHlXjRqoWgvnyyy9JSkpi/PjxFBUVcejQIZ577jkKCwupqKigdevWFBYWsmfPHv7xj380/EsRQghxT5PBnhDilrLuv3NxcbEpj1+1ZHzVWUMXFxflGjs7OxYvXszixYtt7mndI2gVEBDA0aNHa3x+VFQUUVFRN9cJcVeyZvHV1TfffMOSJUtQq9XY2dnxz3/+U6mMuXr1aiIiIigvL2fKlCn4+vrWq03Tp09n8uTJ+Pn5YbFYmDx5MgEBAfz3v/9Vvk/NZjNPPPGEsuR47dq1AEybNo2zZ88SEhLCxYsXsbOzY+XKlZw6deqGirwIIYS4d0mBFiGEEPe07OxsQkJC6N+/v3JMq9Xi4OCAn58fX3zxBTqdjvz8fPR6PT169ECn07F3717ee+89oHLf3UsvvcSnn34KgL+/Px4eHjg6OvLRRx/xxhtvKPdesGABHh4edO3aFZ1OR2JiotKOFi1akJ+fT0xMDNOmTcPJyYn4+HiOHj1Kv379WLduHT179uTo0aMcO3aMWbNmce7cOeLj45VQ9mnTpjFt2jQAOnXqhMlk4tlnn6V169bk5+fLQE8IIYRCZvaEEEI0CQ2Ztefg4MDrr79OUFAQly5dIjg4mEGDBilZe7Nmzaox4PyBBx6osUrtokWLcHV1JT09nYqKCv7v//5POff444+zevXqa/ZtxIgRzJgxg27dut34ixFCCHHPkpk9IYQQTUJDZu25ubkRFBQEVC4P9fb2VuIX6uPdd9/lb3/7G1C5XPlaQeo16dOnT7W4ByGEEEIGe0IIUQdardamyEZjmTJlCq6urvj5+dkcf/zxx9HpdOh0OrRarZJPKOquIbP2qsrOzubo0aNKpAdU7ukLCAhgypQpXLhwQTmelZVFr169eOihhzhw4AAA+fn5QGXoelBQEI899hi//PKLcs22bdsICAggOjrapgqoEEIIcT2yjFMIIe4A5eXl2NvbM2nSJGbMmMGECRNszltnpACef/552rRpc1PP09/U1Xc+Qw3HGjJrr3nz5gAUFBTwxz/+kZUrVyp75Z555hlefPFFVCoVL774Is8//zzvvvsubm5u/O9//6N9+/YcOXKEkSNHcvLkScxmMyaTib59+7J8+XKWL19ObGwsmzZtYsSIEYwdO5ZmzZqxdu1aJk6cSFJSUgO9JSGEEPc6mdkTQogbkJ2djbe3N1OnTsXX15fBgwcrYfB6vZ65c+fSu3dvunfvrszclJeXM3v2bEJDQwkICODtt98GKquOhoeH88QTT+Dv7w/AgAEDaNeuXa3Pt1gsfPzxx3VaglgbvV6P8aoMxENaLWW3YObykFZLqr8/qTodR67si7uVrFl79Xl/VbP2AMrKyvjjH//IuHHjGDVqlPK5jh07Ym9vj52dHVOnTiU5ORmAZs2a0b59ewCCg4N54IEHSE9Pp3379rRs2VKpvvnYY4/x3XffAdC+fXuaNWsGwNSpUzly5Ej9Oy+EEKLJkZk9IUSjMhqNDR6u3tiMRuM1l0lmZGSwefNm1q1bx+jRo9m2bRvjx48HKsvkJycnk5iYyMKFC9m3bx/r16+nTZs2pKSkUFJSQt++fRk8eDAAycnJpKWl4enpWae2HThwgI4dO950IQ4dtrNfWiABuLGdYnVnnbnUAqn799/wnrSG0lBZexaLhSeffBJvb2/++te/2lxz5swZZf/cp59+qizJ/fXXX2nXrh329vb897//JSMjAy8vL1QqFSNGjMBgMDBw4EC+/PJLpdBL1Xvt2LEDb2/vhnwdQggh7nEysyeEaDQGg+Ge3Fvm6emp9Cs4OFjJAQSUGZ6qx/fs2cPGjRvR6XSEhYWRl5dHRkYGAL17967zQA/qXlikvhp75vJ2s2btOTo61unz33zzDYGBgeh0OqKiopSsvYMHD7Jp0yaSkpKUvZTWiIU5c+bg7+9PQEAA+/fvZ8WKFQB8/fXXBAQEEBgYSHR0NGvXrlVmcV999VUWLFhAQEAAmzZt4vXXXwfgzTffxNfXl8DAQN588002bNigtK3qv605c+ag0WgoKipCo9GwYMGCBnhbQggh7nYqi8Vyu9tQbyEhIZbU1NTb3QzRwKyzQFWDtcXNk/dad3q9vtrs3qFDhwgODqa8vJwTJ04QGhoKQE5ODuXl5Wi1WoxGIw888ACtW7emrKyMI0c1KWLyAAAgAElEQVSO0KdPH06ePImbm1u15Zn5+fnk5ORUGwgVFxfbPMPKYrHwn//8h+DgYGVpX31Y+1b1e0Gr1ZKamkpBQQFdu3YlNTUVnU7H6NGjiYyMZPz48ej1eoKDg3n99ddJTExk+fLl7Nu3j3/961+cO3eO+fPnKzOX8fHx/PTTTwwbNsxm5tLT05O2bduiUql4+umneeqpp+rdDyGEEEKASqU6YrFYatwbITN7QghxFaPRSEFBQYPdr23btuTm5lJRUQFUZreVl5ff8H0uXLhAy5Ytb2qgVxeNOXN58OBBvvvuOz7//HPeeustvv7660bti5VWq7UJVYfKmbGqoeo6nQ4nJyclVH3ChAkkJycr5wIDA5VQdYDdu3fTo0cPunbtypIlS6o989lnn8XJyana8a1bt6JSqbD+svLDDz9UnqHT6bCzs1Oy+DZv3qzMEg4ZMqTGirA//PADDz74IM2aNWPZsmU39Z6EEELcW2TPnhBC1MDJyanazFdCQgIFBQUMHz5cObds2TIKCgpYsGCBTSD3+fPnCQkJwWAwUFFRwfz589m5cycWi4UOHTqwfft2jh49yrJly9i1a5fynLFjx2IwGCgtLSUzM5OFCxfy5JNPAjBp0iT+/Oc/M23atJvq2/X2UFYdTNrb2yvLOKues7e3x2w2A5UzjqtWrVKCya0MBgOtWrWyOebu7g6Aq6srUVFRJCcnM2DAgHr35UY0ZKi6SqVi+vTp7N27F41GQ2hoKJGRkcpeu9TUVCVS4eo2vPnmmzYxDePGjWPcuHEAnDhxgkcffRSdTofZbGbmzJmcOnUKFxcX5syZw+rVq6st0WzXrh1vvvkm27dvb9D3JYQQ4u4nM3tCCFEH2dnZuLi4oNVqlWqMALGxscoP3waDQRkkuLi4KDNfdnZ2LF68mBMnTpCWlsb+/ftp06YNer3eZqAHlTM5Z86coaysDJPJpAz0ADZs2HDTA73GEBERwZo1aygrKwMgPT2dwsLCap8rLCzk0qVLytd79uyplifYmBoyVD05OZmuXbvi5eWFo6MjY8aMISEhAfh9D+PSpUur3e/FF19kzpw5SnTD1aq2y2KxYLFYKCwsxGKxcPHiRWWwXJWrqyuhoaGo1eo6vAUhhBBNiczsCSFEE2TENmvvLPAoUA5kVTmXc+WY4co1TwOtgbIr1+gBS0wMWdnZtA0KwmKxoO7QAb/t2ym4cr31Xpd/+YWTV+IFLGYzrk88wZIhQ6i+APLmGWo4Fh0dzaRJk4iNjWXnzp18+OGHbNq06br3Onz4MFOmTOGnn35i06ZNODg48PPPP9O5c2flMxqNhsOHDwOVgeqRkZFKFU2ro0ePkpOTw/Dhw2tdbvnvf/9bGTSq1WrWrFmDv78/rVq1olu3brz11lt1ewFCCCEEMrMnRJOm1Wpr3APUGMrLy+nVqxfDhw9XjmVlZREWFka3bt14/PHHKS0tvSVtqY9b9a6mTJmCq6trjTNeq1atokePHvj6+jJnzpx6P8NgMKC7qlBPn+xs1C4uNNdqCa0yc9k5NhbtlZlLncFA6yszl2oXF/pcmblU2dnhtXgxISdOEJqWhm7/fhzatMFZr8e/ysxlCy8vQo4dI+TYMUJPnuT+F16odx/q42ZD1VNSUoiLi6O4uJiaipupVCpyc3OJj4/n2WeftTlXUVHBrFmzlCqbNTl8+DAtW7ZU/tuXlZWxZs0ajh49Sm5uLgEBAcTFxd1Aj4UQQjR1MrMnhGg01mw1gDfeeANvb28uXryonJ87dy6zZs1izJgxTJs2jfXr1/PMM8/crubeVtZ3NWnSJGbMmMGECRNszu/fv5+EhASOHz9Os2bNOHfu3E09z3BTV9+9rKHqVSMM6qpqqLpGoyEnJ0c5ZzKZcHd35+jRo2RmZtK1a1egcs9f165dOXLkCGlpacp+ybNnzxIZGcmOHTuUpb9btmyxWVpqLdLywAMPAJXLUGsqBCOEEELURmb2hBCNnq1mMpn47LPPiImJUZ5psVhISkoiOjoagIkTJ94VBSYa+10NGDCgWkQDwJo1a5g3b55SIMXV1fVWdPeeExUVxZw5c6oVk6lNVlaWUoimaqh6aGgoGRkZZGVlUVpaypYtW4iMjGTYsGGcPXuW7OxssrOzadmyJZmZmbRp04bz588rx/v06WMz0KuoqCA+Pp4xY8Yoz/bw8ODUqVP8+uuvAOzdu1dC1YUQQtwQGewJIQDIyMhg+vTpnDx5EmdnZ7Zt26acM5vNJCcns3LlShYuXAjA+vXradOmDSkpKaSkpLBu3TqysrKAyuIVixYt4tSpUwA899xzLF26FDu73/8nJy8vD2dnZ6X4hUaj4eeff75V3b0pjfmuapOens6BAwcICwvjoYceIiUlpfE6eA9rqFB1BwcHVq9eTUREBN7e3owePRpfX996t+vrr79Go9Hg5eWlHHN3d+ell15iwIABBAQEYDQa+fvf/w7A2rVrWbt2LVA5S6jRaFi+fDmvvPIKGo3GZgZdCCFE0yXLOMUdyWg0Xrc8vLgxV4eEX60+2WrHjx9n69atAPz2229kZGTg6Ohok622a9cuXF1dCQ4OtokyqG3P053gWu8JGu9dXYvZbObChQscOnSIlJQURo8ezX//+9875p3d6bKzs9FqtXTu3FmZcdVqtTg4OODn58frr7/O3LlzgcoZWg8PD1q0aEFAQADvvfceTz31FBUVFbz00ktYLBaioqJ45JFHsLOzY+bMmbz33nvY29szb948m+dOnjwZJyenarmNM2bMIDQ0lJSUFEJCQujbty89evTA398fs9nMhAkT+Nvf/sa0adMYM2YMMTExpKWl0a9fP959912bqqydOnVi3759TJ48me+++4758+dz3333NfIbFUIIcTeQwZ644xgMBhno3QaNla128OBBduzYQWJiIsXFxVy8eJHx48ezadMm8vPzMZvNODg4KHue7gaNmUNXG41Gw6hRo1CpVPTu3Rs7OzvOnz9Phw4dbrY7TcqdmrUXHx9PSUkJJ06coKioCB8fH8aOHYtWq2XmzJkMGTKErVu3UlpaSlFRUbV7StaeEEKImshgT9yRDFdVChQ3r6EH0NZstYEDB6JWq0lPT8fDw6Pa5+Li4pQKggaDgWXLlvHBBx8AEB4eztatWxkzZgzvv/8+jz76aIO28U5R13d1LSNHjiQpKQm9Xk96ejqlpaW4uLg0UovvXdasvdjYWCXT7nrxC1WrdtaWtQcoWXs+Pj7KPs2PPvqITz/91OZ+1qy9qvELKpWKwsJCzGYzly9fxtHRkfvuu4+LFy/y9ddfKwVlHB0da1yC6urqiqurK5999lm93osQQoh7kwz2hBD1EhMTQ3Z2NkFXstU6dOhww7MKr776KmPGjGH+/Pn06tXLJkD8TmCthghQWlrKiRMnuHz5MsXFxcq53NxcioqKMBqNFBQUcPr0aRwcHLhw4QKlpaUYjUZCQkI4fPgwPj4+WCwW2rZty/Lly8nMzOTixYs2z5k3bx5HjhwhPz+fjh07Mm3aNKKioggODmbnzp107doVtVrNiy++yLFjx275O7mb1LQc907N2ouOjiYhIQE3NzeKiopYsWIF7dq1w2g00qFDByZPnsyxY8cIDg7mjTfeqPOMsBBCiKZNBntCNGHWPWUuLi6kVclWi42NVb6uOsvq4uKiXGNnZ8fixYtZvHixzT31en2ts4hXn/Py8iI5Ofmm+tBYjEYjMTExvPPOOwAkJibyyCOP8OGHHyp77wCbiATrZwHatm1LYmIiUPmunn322WrZayEhIcpSQaslS5awYMECvv76a1q3bk3UlRBytVpN586dSUlJoW3btrz++uvMmDGD/v37N2zH73E3m7X3/fffM3HiRIYOHXrdrL2rVyhYs/Zqin1ITk7G3t6e3NxcLly4QP/+/fnDH/6A2Wzmu+++Y9WqVYSFhTFz5kyWLFnCyy+/XJ/uCyGEaGJksCeEELVwcnKymR1ydHTE39+/0ZZPWrP2/vrXv/KPf/yDCRMm2Dy/U6dOzJkzx2YwLm7cnZi199FHHzFkyBDUajWurq707duX1NRUBgwYgEajUfb3RUdHS9aeEEKIOpPoBSGEuEG3K2tPNIw7MWuvS5cuJCUlYbFYKCws5NChQ/Ts2ZNOnTrRuXNnTp8+DcCXX36pFIARQgghrkcGe0IIUYOrS+Vf7XZk7UHlXrCAgACmTJnChQsXbqKHTdedmLU3ffp0CgoK8PPzIzQ0lMmTJxMQEADAqlWrGDdunGTtCSGEuGEy2BNCiKsYDAacnJyu+Zn6ZO1t3LgRnU5HWFgYeXl5ZGRkANQ5a++ZZ57hxx9/xGg04ubmxvPPP1+P3jVt1j2QViNHjgTgiy++QKfTodPpSE1NZdy4ceh0OiZMmECPHj1Qq9VA5Qxt1b16dnZ2qFQqVCoV9vb21Z539T5Nq61bt/LVV18pf09ISCAjIwO1Wo2joyNz587FaDRy6dIlJk2ahNlsxs7Ojm+++Ub5BcK0adOUvD21Wk23bt2oqKhg/PjxmEwmydoTQgghe/aEEKIm1wtWvx1Zex07dlS+njp1KsOHD6/TdeJ3d2rO3rhx4xg3bhwAJ06c4NFHH1W+B6tWaw0ODlZ+mVBV8+bNefnll0lLS7MptiSEEKJpk5k9IYS4BaxZe2VlZQCkp6dTWFh4Q/c4c+aM8vWnn36Kn59fg7axKbDm7AFKzt71tGzZEgeHyt+N1paz5+joqOTswe97NJcuXVrtftacvebNm9f4vNralZGRwblz52qswNqqVSv69etX6z2FEEI0TTKzJ4QQtThrPMsG/Qbl7wVnC9j86GZKykvIz8pXzqXkpFBSXsIGwwbOGs/y/9m797go6/z//48ZThIY1KKloA6ZnA+zMKJt2o75Q9LMRE0ry1Nk7spu9QkPt9r9CO6Glrqa1lfLrFW3dFPzuH484lhuqzBtk+IhsZiExESTFEVkhuv3B86VI0fljK/73ro1c11zXdf7Pe0NffG+rtdz8wubyW6fzcWyixSfLubvxr/jqrhiy7UReFcgKNDerT1/iPgDJ4tPkp+X73SdJUeWcOznYxSXFXO3x90M1Q3loU4P8d7R9zh56SQaNPi182Nsj7FOxwln40zjKm1rqTl71/vnP/+pFo3XW7VqFaNGjVKLTSGEEKI2UuwJIUQVTCZTpUJqbu+Kv5y3d2vPX3v+Vd0+sMtA9fV0/XT1dXu39uoxWo2WEfeNYMR9I5zOGeIbQohviNO2SWGTqhzTxNCJNz8R4aSl5uw5HDhwgDvuuKPKVdvVq1fXqTAVQgghHKTYE0KIalS1MiRav5aYs+d4PnD16tVV3sL59ddfY7PZiI2NvfkJCyGEuG1JsSeEEOK2kpiYSEFBAQkJCZw6darWz+fm5tKlSxdcXV2dcvZ8fX3VnD1/f39Wr17Nxx9/THh4OKdPn1aP9/b25sSJEwCcPXtW3X5jI5jy8nLWrFnDZ599VmkMdX2+UAghhLieFHtCCCFuK46cvbrat28fs2fPxs3NDa1Wq+bsAWrOnt1uZ8KECbecswfw2WefERAQwH333Vdp3yeffMLWrVudtm3atAmz2czMmTMB0Ol0XLhwgatXr7JhwwZ27NghAexCCHGbk26cQgghGpVOp6vUQVKv1xMREeGUb+ft7U1wcLCab5eZmanui46OZv369erx27ZtIzg4mPvvv5/Zs2dXuuYf/vCHSlmJVqsVk8mERqPBbDYD4O/vT2xsLJGRkYSGhjJr1ixMJhMGg4H58+cTHh7OG2+8QVRUFPv37+e///2vms0HFat23t7efP/99wQHB1c5/+Li4iq3O67jYDQa2b9/f5Wf/e677wgJcX62c8iQIWqh55jfTz/9RHFxMfn5+VLoCSGEkJU9IYQQja+l5tutWbOG0tJSDh06xOXLlwkLC+Opp57Czc2NhQsXcuTIETw9PRk5ciSrV69m3LhxTufs2rUrf//736vtrCmEEEI0J1nZE0II0ehaar6dRqPh0qVL2Gw2SkpKcHd358477wRQt9lsNi5fvkznzp0rnVOn0xEVFYVWK3+cCiGEaHlkZU8I0aIYMTb3EEQ9mTBV2tZS8+1GjBjBxo0b6dSpE5cvX2b+/PncfffdAKSkpNC1a1c8PT0ZMGAAAwYMuJWvQwghhGg28qtIIUSLYTQasRgtzT0M0Qjqm2+XlZXFrFmzuHLlSq35dn/4wx+c9jny7ebNm1fpuMzMTFxcXDh16hS5ubnMmzeP7777jvPnz7Nx40Zyc3M5deoUly5d4h//+MetTV4IIYRoJrKyJ4RoUfToq1wZEq1fS8y3+/jjj3nkkUdwc3OjY8eOPPjgg5jNZjQaDYGBgXTo0AGAYcOG8cUXX/DMM8/U+3sQQgghmoqs7AkhWjSdTueUTdYYrly5QlxcHNHR0YSHhzNjxgx1n6IovPbaawQFBREaGsrChQsbdSxtWWJiIlOnTlUbstQmNzcXm80G4JRv17NnTzXf7urVq6xevZohQ4bw6KOPcvr0aaxWK1arlTvuuIMTJ07g4+PD2bNn1e29e/dWg8y7du1KRkYGiqJw6dIl9u/fT0hICF27dmX//v1cvnwZRVHYvXs3oaGhjfn1CCGEEA1OVvaEELc1u92Oh4cHGRkZeHt7U1ZWRp8+fRg4cCC9e/fm73//O3l5eRw7dgytVsuZM2eae8itVkvMt5s8eTLjx48nIiICRVEYP348UVFRQMXzfDExMbi6uvLrX/+aiRMnAvC///u/GAwGhgwZQlZWFomJiZw/f57NmzczY8YMDh8+fEtjEUIIIRqaFHtCiFbBarUycOBA+vTpwxdffIG/vz8bN27E09MTo9FIr1692LNnD0VFRSxbtoy+fftit9uZPn06JpOJ0tJSJk+ezAsvvIDJZCItLY1OnTphsVg4cuSImslWVlZGWVmZ2vlx8eLFfPzxx2q3xY4dOzbbd9Da6HQ6unTpgtVqVbfp9XpsNhvz5s3D1dUVvV7PiRMn8Pf3x9PTk4ULF5KcnKwWVq6ursyYMYPExESgIl/vxRdfxG63k5SUxPTp052u+Yc//IEPP/yQ4uJip3y7tWvX8sQTT5CVlYXJZOLq1auMHz8es9mMVqvlnXfeUW/zfO2111ixYgXnz5+vMiNv5syZnDt3jn79+pGVlcW4ceN4++23G/jbE0IIIeqvWYo9jUbzMpAEKMAhYDzQCVgN3A38F3hWUZSrzTE+IUTjcPxlujoWiwW9Xl/t/pycHFatWsXSpUsZOXIk69atU5+hstlsZGZmsnXrVtLS0ti1axfLli3Dx8eHrKwsSktLefDBB9WOipmZmWRnZxMYGAhUrPDFxsZy4sQJJk+erGaxffvtt/zzn/9k/fr1dOjQgYULF9KjR48G+DZuDy01X2/p0qUAHDp0iDNnzjBw4ECysrLQarU89thjJCcn1/jfuV27dvzlL38hOzub7Ozs+n9RQgghRCNo8mf2NBqNP/BHwKAoSgTgAjwJvAHMVxSlB3AeeK6pxyaEaDxGoxGLpX6dNgMDA9ViMDY21mnFaNiwYZW279ixgxUrVqDX6+nVqxfnzp0jJycHgLi4OLXQA3BxccFisZCfn68WggClpaW0a9cOs9nM888/z4QJE+o1h9tNS83XO3LkCP379wcqVmt9fX0xm80A9O7du1J0w428vLzo06eP0zmFEEKIlqa5buN0BTw1Gk0ZcAdQADwMPH1t/3IgFVjcLKMTQjQKvV6PyWSqdn9tK38eHh7qaxcXF0pKSirtc3FxUZt6KIrCokWLKjUEMZlMeHl5VXkNX19fjEYj27ZtIyIigoCAAIYPHw5UNBgZP358jWOskfHWD20VTJU3tdR8vejoaDZu3MiTTz5JXl4eX375JXl5ecTFxd3a3IUQQogWqMlX9hRF+QGYC5ykosj7GfgSKFIUxXbtY/mAf1XHazSaiRqNxqzRaMyFhYVNMWQhRCuVkJDA4sWLKSsrA+D48eNcunSp0ucKCwvV2/9KSkrYtWsXISEhAAwdOpSMjAwA9u7dS1BQ0C2NxWg0YrQYnbbp9us4W9a4nUYBimxFjDg8gpDMEEKzQvnPz/9p9Gs6tNR8vQkTJhAQEIDBYOCll17iN7/5jbqaKIQQQrQVTf4nm0ajuQt4HAgEioA1wMAqPlr5T3VAUZT3gPcADAZDlZ8RQgiApKQkrFYrMTExKIpChw4d2LBhQ6XPFRQUMHbsWOx2O+Xl5YwcOZLBgwcDMH36dEaPHs38+fPx9vbm/fffv/UB6XFe/dIBGwG/Wz9lTex2Oy4uLrw49kUeGfcIa5PWcvXqVS5fvgy+jXPNqrTEfD2DwcD8+fPVc/3mN7+RZzGFEEK0Oc3xa8z/D8hVFKUQQKPRfAr8BvDVaDSu11b3AoBTzTA2IUQL43j+zs/Pz6kRRkpKivr6+ltD/fz81GO0Wi3p6emkp6c7ndNoNDrdMhoVFcVXX31V5fV9fX3517/+Vb9J1KIxO43u37+fzz77TC203N3dcXd3b9T53CgxMZGCggISEhI4dar2H+25ubl06dIFV1dXp3w9X19fNV/P39+f1atX8/HHHxMeHs7p06fV4729vTlx4gSAU0bj9Y1gHPl5Xl5e7Ny5E1dXV7XRixBCCNFWNEexdxLordFo7gBKgP6AGdgDjKCiI+dYKn7fLYS4zVgsllqf3WuNmqvTqMVioUOHDowfP56vv/6a2NhY3nrrrWqfWWwMLTFf78yZMyQkJKDVavH393d6jnDq1Kl8/PHHXL58mYCAAJKSkkhNTWXTpk2YzWZmzpwJVERLXLhwgatXr7JhwwZ27NghBaMQQogWpTme2TsArKUiXuHQtTG8B0wD/kej0ZwAfgUsa+qxCSGal8lkqrEgassaq9OozWbjv//9L7/73e/46quv8PLyYvbs2U02L0c+nsPQoUMB2L59O3q9Hr1ej9lsZvTo0ej1esaMGUNwcDBubm5Axa2o1z+rp9Vq0Wg0aDQaXFxcKl3vxuf2HNauXcvevXvV9507d1af0yssLCQ3N1fdN2rUKO666y7uu+8+hg0bxowZMwAYMmSIWugdO3aMTp06cenSJVJTU8nPz5dCTwghRIvTLE+jK4oyA5hxw+bvAGmDJsRtrqZuna1Zc3UaDQgIICAgQM2YGzFiRJMWe60xZ+93v/sd7733Hr1792bQoEFs27aNgQOdHy2/++67WbhwYZXPgAohhBAtRZOv7AkhhKi/unYavffee+nSpQvffPMNALt3727SFajWlrNXUFDAhQsXeOCBB9BoNIwZM6bKgq5jx4707NlTXYEUQgghWiLpMy2EaDY6nQ6z2aw+j9UYrly5wkMPPURpaSk2m40RI0aQlpYGQN++fbl48SJQ8QxXXFxc467UWHDO2jtNRW9iO5B73b68a9tM1455AWgPlF07xghJShLWXCsxd13rNOrWgQ0RG6D42vHXXWdRySJGG0ZzVbnKfe3u48PgDxsn889UeVNry9nTarUEBAQ4XeOHH364yS9CCCGEaBmk2BNCtFl2ux0PDw8yMjLw9vamrKyMPn36MHDgQHr37s3nn3+ufnb48OE8/vjjjTYWk8lUqcCy9rYC4OfmR3bP6zqNdrmu06jepL72c/NTj9FqtKTfl076fTd0GvU1YvR1vpDeW4851lzPGdya+ubsHT16lLFjxzJw4MBac/ZuvAXYkbNXVeTDhAkTOHr0KAaDgW7duqnP71V3DSGEEKI1kmJPCNHsGjN64MiRI3h7ewNQVlZGWVlZpb+8X7x4kYyMDD788MPGnaipcU/fUrWmnL277rqL/Pz8StcQQgghWiMp9oS4jTRnrEFzRQ9AxQpfbGwsJ06cYPLkyU6NOgDWr19P//79ufPOOxtp9re31paz1759e/bv30+vXr1YsWJFtR0+hRBCiJZOij0hbhMmk6lF59fdSvTAwYMHWbt2LQA///wzOTk5uLu7O0UPQEUXS4vFQlFREYmJiWRnZxMREaHuX7VqFUlJSY08w9tXa8vZW7x4MePGjaOkpISBAweqnTiXLFkCwKRJkzh9+jQGg4ELFy6g1WpZsGABR44ckV8YCCGEaFGk2BPiNtKcsQbNFT1wPV9fX4xGI9u2bVOLvXPnzpGZmcn69etvek5tmU6no0uXLk7PNer1emw2G/PmzVOLtxMnTuDv74+npydRUVEkJyczceJEAHx8fPj8889JTEwkLy+PMWPGcPr0abRaLW+99RYvvvgiAD179uSZZ57B3d2d7t278+GHH/Lss8+q1z158iTe3t6kpqaSkpJCZGQkY8aM4R//+Acff/wxEydOVM8FUFxczNy5c5kyZQqFhYVqoXj9//8nTZrEjz/+SJ8+fdiyZYvT3A0GA9nZ2dxo0qRJ6ut7773X6XZPIYQQoiWS6AUhRKtU1+iBwsJCNXutpKSEXbt2ERISou5fs2YNgwcPdmrLLyo4MvKAShl5FosFi8WCwWDgo48+wmKxsGLFCiIiIjCbzVgsFrZt28YLL7yAzWbD1dWVefPmcfToUfbv388777zDkSNHAIiPjyc7O5uDBw8SFBTErFmznMbx8ssvO+Xc1XQugLy8PHbu3EnXrl2rnduUKVPq1BVUCCGEaM2k2BNCtEpJSUmEhYURExNDRESEWlTcqKCggH79+hEVFUXPnj2Jj49n8ODB6v7Vq1fXKfvtdtSQGXmdOnUiJiYGqLitMzQ0VI00GDBggHpM7969nVbMNmzYwH333ed0u2ZN54KK4vDNN9+ssYtm//79ad++fe1fghBCCNGKyW2cQohm43j+zs/Pz+m2uZSU66IHrrv1zs/PTz1Gq9WSnp5OevoN0QNGo9Mto1FRUXz11VfVjqHOt7Yaa/1E62aqvKkhM/KuZ7Va+eqrryo1ygH44IMPGDVqFACXLl3ijTfeYOfOnTSurKIAACAASURBVE4ZeTWda9OmTfj7+xMdHV3rOIUQQoi2Too9IYSohdFoBItz5t3toCEz8hy3yRYXFzN8+HAWLFhQqZnJ66+/jqurK6NHjwZgxowZvPzyy2p0xo1uPNfly5d5/fXX2bFjRz1mLYQQQrQdUuwJIURd6Lktc/IaKiPPYDBQVlbG8OHDGT16tNph1WH58uVs2bKF3bt3q7dfHjhwgLVr1zJ16lSKiorQarW0a9eO5OTkKs/17bffkpubq67q5efnExMTQ2ZmJvfee2/9vgghhBCiFZJiTwghRLUaKiNPURSee+45QkND+Z//+R+nY7Zt28Ybb7zB3r17nVYPr+8Empqaire3N8nJydWeKzIykjNnzqjvdTodZrNZ7cYphBBC3G6kQYsQQohqOTLy3N3d6/T5ffv2ER0djV6vJzExUc3I+/e//83KlSvJyMhAr9ej1+vZunUrAMnJyVy8eJH4+Hj0er1TxEFVajpXdcxms1OWYt++fXniiSfYvXs3AQEBbN++vU7zE0IIIVoTKfaEEOIW6HQ6zp4926jXuHLlCnFxcURHRxMeHs6MGTPUfePGjVOD6PV6PRaLpcGvn5iY6PR+6NChAGzfvl29rtlsZvTo0ej1esaMGUNwcDBubm4A2O12FEUBoFu3bhiNRsrKyigrK2P8+PEMGjRIvY6Xlxfl5eUEBgYye/Zsp+uePHnSqUFLTef66aefiI+Pp0ePHvTo0QMXFxegIjvv/fffV8/h5eVFWVkZ/fv3Jz8/v1JeoxBCCNEWSLEnhBAtkN1ux8PDg4yMDL7++ms1t27//v3qZ+bMmaPm3en1+gYfQ2vM2Zs9ezb9+/cnJyeH/v37VyocHSRnTwghxO1Aij0hhKgHq9VKaGgozz//POHh4QwYMICSkhKgoovntGnTiIuLIygoSH0GzW63M2XKFHr27ElUVBTvvvsuUBED0a9fP55++mkiIyPRaDRqJ0rHKlZN2XENrTXm7G3cuJGxY8cCMHbsWDZs2FDlOCVnTwghxO1Aij0hhKinnJwcJk+ezOHDh/H19WXdunXqPpvNRmZmJgsWLCAtLQ2AZcuW4ePjQ1ZWFllZWSxdupTc3FwAMjMzef3119WVKrvdjl6vp2PHjsTHxztl07322mtERUXx8ssvU1pa2uDzGjFiBJ9++ikAmzdv5rHHHqvTcQcOHCA8PJzIyEiWLFly0zl7jlU8R87e9bev3ujGc/3444906tQJqCgKr2/YIoQQQtxupBunEKLJWCwWp8Dz1sJisaBH7xysfhp4HLBDoHsg+pcqbqOMPRmLNcsK7wMWGPbzMDBC7NVYrF9ZwQg7Du/g4KWDrJ29FoCf7T+T83gO7lp34tziCBwfqF7GBRcsvhaK9EUkvptIdkY2EV4RzCqdxb333stV5SoTV03kjfVv8L+6/731SZoqb2ptOXtCCCGEcCbFnhCiSZhMplZZ6EFFUWGh+gYoHhoP9bWLxoWS8pJf9mk91O02xQaAgsKi+xeRcLdzUxBTkQkvF68qr+Hr6ovR18i2n7YR4RVBJ49O6rXH3zueuXlzqzyuvlpTzh7APffcQ0FBAZ06daKgoICOHTvWa/5CCCFEaybFnhCiyZhMpuYewi3x9fWteGG6bqMO2AgUA4Ov2zf32rZUKlYC5wIG4Oy1f5sg4b0EFm9dzMNrHsbNzY3jx4/j7+8PWdc+v6XiVIWFhbi5ueHr60tJSQm7Buxi2rRpMBi1oFEUhQ0vbyCiXQRU3YukXlpTzh7AkCFDWL58OdOnT2f58uU8/vjj9fwGhBBCiNZLij0hhGhiSUlJWK1WYmJiUBSFDh06VNlIpKCggLFjx2K32ykvL2fkyJEMHjwYgNGjR1NYWIiiKOj1epYsWdIoY3Xk7NXVvn37mD17Nm5ubmi1WjVnb9++faxcuZLIyEi1c2h6ejqDBg0iOTmZ0tJS4uPjgYomLTXNx5GzV9W5pk+fzsiRI1m2bBldu3ZlzZo1QEXO3pIlS9T4hb59+3Ls2DGKi4sJCAhg2bJlEr8ghBCizZEGLUIIUQ86nY7s7Gz1fUpKCqmpqUDFSqbBYADAz88Pq9UKgFarJT09nUOHDpGdnc2ePXvw8fHBaDSyZcsW9VxBQUG4ubmh0WhQFAW73a7uy8jI4NChQ/Tr148NGzZU+1xbfbTUnL0+ffowfvx4Tp8+jc1mw2KxqOfKz8/n8uXLtGvXzqkz6I05ez169ECj0dC9e3fJ2RNCCNFmSbEnhBAtUF1y9sxmM0VFRY02hpaaswcVofLbtm2rNOakpCRmz57NoUOHSExMZM6cOVXOrbrjhRBCiLZEij0hhKiH5srZc5zjzTffbLS5tdScPYCHHnqIu+++u9L1v/nmGx566CGgooi8PgajLscLIYQQbYkUe0IIUU/NkbP39ttvM2TIEDVTrjG0hpy9G0VERLBp0yYA1qxZo65MCiGEELcjadAihBB1UUyLydm72/Vu1hxZg0lvqugIWnLD2G6FqfKmlp6zV5UPPviAP/7xj8ycOZMhQ4bg7u5e52OFEEKItkaKPSGEqKemztkLvSOUEyUnuP/A/QBcLr/M/Qfu50SvEw06L2i5OXvVCQkJYceOHQAcP36cf/3rXzc9biGEEKKtkGJPCCHqwpsWk7P36OBHOc3pX4bm7c2J4oYv9KBl5uzV5MyZM3Ts2JHy8nL++te/MmnSpJucsRBCCNF2yDN7QgjRxJKSkggLCyMmJoaIiAi1Y+WNCgoK6NevH1FRUfTs2ZP4+Hg1Z6+pOHL26no75L59+4iOjkav15OYmKjm7Dmy8TIyMtTYhq1btwKQnJzMxYsXiY+PR6/X16lAe+qpp3jggQf45ptv1Jw8qGgkExQUREhICJ07d2b8+PEAnDp1So1nqOl4IYQQoi3RODKQWiODwaCYzebmHoYQoo3z9fUFaNSYAyGEEEKIW6HRaL5UFMVQ1T5Z2RNCiFug0+k4e/Zso17jypUrxMXFER0dTXh4uFNXyueee47o6GiioqIYMWIExcXFDXptnU5H3759nbbp9XoiIiKcQtW9vb0JDg5WQ9UzMzPVfdHR0axfvx6AvLw8+vXrR2hoKOHh4bz11lvqeadMmUJISAhRUVEkJiZWKqpPnjyJt7e3U7D6hAkT6NixIxEREU6ftVgs9O7dG71ej8FgIDMzs8r5PfLII/j6+jb5SqkQQgjRlKTYE0KIFqi2UPX58+fz9ddfc/DgQbp27crbb7/d4GNojaHqU6dOZcaMGVgsFmbOnMnUqVOrnNuUKVNYuXJlvb4fIYQQoqWTYk8IIeqhuULVHbEFiqJQUlKibm9IrTFUXaPRcOHCBQB+/vlnOnfuXOU4+/fvT/v27WudjxBCCNGaSTdOIYSoixpy9nKO5bDKaxVLOyxlpGUk62LW8cw9z4AFbN/ayOyeydaSraQNSWNX9C6WnVqGT5kPWd2yKPUs5cGUBxnwwQAohcxDmWQbsgnsGAhGsCt2Yr+M5UTJCSb7T6bXtF+CyMcfG8/Wn7YSdkcY8yLnwf/VY36myptGjBjBuHHjSElJYfPmzXz00Ud1Wg07cOAAEyZM4Pvvv2flypU3Hao+atQo4JdQ9Z07dzrdwlmTBQsWkJCQQEpKCuXl5XzxxRd1Ok4IIYRoi2RlTwgh6inQMxC997VQde9YrFes6r5hfhV5crHtf9m+4/wOVvy4Ar1ZT6//9uKc7Rw5JTkAxLWPI9DzulB1jQsWg4X8B/LJvJBJ9qVsdd+HIR9y6oFThHqF8s/Cfzb4vOobqp6VlcWsWbO4cuWKuq+xQ9UXL17M/PnzycvLY/78+Tz33HN1PlYIIYRoa2RlTwgh6qKGnD2PwR7qPpe5LpQUl6g5ex5zPcAALmddsBlsYAJluMKiiYtISLghVN1kwmuul5qzdz1ffDGmGdnmtY2IlF+akrjgwqi9o5gzZw7jt4xvsOk6tLZQ9eXLl6vNX5544gmSkpJuetxCCCFEWyEre0II0cQSEhJYvHgxZWVlABw/fpxLly5V+lxhYaHambKkpIRdu3YREhKCoiicOFERoq4oCps3byYkJKRRxpqYmMjUqVMrFabVyc3NVTMDbzZUfdOmTZVC1a1WK1arlZdeeolXX3211lD1zp07s3fvXgAyMjLo0aPHzUxXCCGEaFNkZU8IIZpYUlISVquVmJgYFEWhQ4cObNiwodLnCgoKGDt2LHa7nfLyckaOHMngwYMpLy9n7NixXLhwAUVRiI6OZvHixY0yVkeoel3t27eP2bNn4+bmhlarVUPV9+3bx8qVK4mMjESvr7jlNT09nUGDBpGcnExpaSnx8fFARZOWJUuW1Hidp556CpPJxNmzZwkICCAtLY3nnnuOpUuX8uKLL2Kz2WjXrh3vvfceAGazmSVLlvD+++8D0LdvX44dO0ZxcbEaql7XglYIIYRoLWRlTwgh6kGn05Gd/ctzdCkpKaSmpgIVt2UaDBUZp35+flitVgC0Wi3p6ekcOnSI7Oxs9uzZg4+PD0ajkS1bfrmHMygoCDc3NzQaDYqiYLfb1eN1Oh1Xr14FwMPDA09PzwafW2JiotP7oUOHAjjl7JnNZkaPHq3m7AUHB+Pm5gZUdB1VFAWAbt26YTQa1a6i48ePZ9CgQep1vLy8KC8vJzAwkNmzZztd9+TJk5UatHh6emK32wkODiY/P199Nm/RokXY7XY0Gg0//vijut1gMKiFHkCPHj3QaDR0796d/Px8KfSEEEK0SVLsCSFEC1Rbzt7o0aM5duwYhw4doqSkxKmQaSitMWfvn//8pzq24cOHV3o2sLbjhRBCiLZEij0hhKiH5srZGzRoEBqNBo1GQ1xcnFM2XUNpjTl7Doqi8Mknn1Q75tqOF0IIIdoCeWZPCCHqogXm7AGUlZex8quVvNX9Lfh3PeZnqrypNebsOXz++efcc8890qBFCCHEbU2KPSGEqKdbydk7eOkgawvXAvCz/WdySnJw17pXm7NXZCsiMTuR7EvZRHj9Er3w+5zf85DPQ/T17dvg86pvzt7Ro0cZO3YsAwcOpF27dkDj5+w51HUlUgghhGjLpNgTQoi6aIE5e2lpaRT6FvLup+822k35rS1nD8Bms/Hpp5/y5Zdf3vSYhRBCiLZEij0hhGhijpy9hx9+GDc3N44fP46/v3+lzxUWFuLm5oavr6+as+eIQXj//ffZvn07u3fvRqttvMevExMTKSgoICEhgVOnTtX6+dzcXLp06YKrq+tN5+zt3bu3Us6eQ2pqKt7e3rUWeoCaRxgQEHATMxVCCCHaHmnQIoQQTSwpKYmwsDBiYmKIiIhQO1beqKCggH79+hEVFUXPnj2Jj49n8ODBAEyaNIkff/yRBx54AL1ez8yZMxtlrI6cPXd39zp9ft++fURHR6PX60lMTFRz9v7973+zcuVKMjIy1NiGrVu3ApCcnMzFixeJj49Hr9czadKkWq/z1FNP8cADD/DNN9+oOXkOq1evrnQL56lTp9Soh9qOF0IIIdoKWdkTQoh6qCpnz8FkMqmvq8rZS09PdzqX0WjEaDSq7x05e+Xl5U45ewALFixgwYIFfP311xQWFuLn59ewE6NiVe/61bXrc/YcK4wnTpxg9OjReHp6EhUVRXJyco05e6dPn0ar1TJx4kSnnL3NmzdTXl5O9+7dq83Zc+QXgnPO3vXfP0BsbCzz589n0aJFPProo7z55pt07txZLS5rO14IIYRoK2RlTwghWqDacvYefPBBdu3aRbdu3RptDK0xZ2/Pnj1s3LiRgwcPcvjwYafiuy7HCyGEEG2JFHtCCFEPzZWz9+tf/xqdTteoc2uNOXuLFy9m+vTpeHh4ANCxY8cqxyk5e0IIIW4HUuwJIURdOHL2HP84cvaerMjZm/zVZA53OIyvxZd1MesqPmMB28c2Mu/IZIHHAtKGpIERloUuw2etD1leWWT5ZrE0ZSm5vXLhJcj8LJPXv32dIx2PVOTs/daO3ltPx/YdiT8bX5GzV9U4jPX8pwojRozg008/BWDz5s089thjdfqqDhw4QHh4OJGRkSxZsuSmc/Ycq3iOnL0ZM2bU6boAx48f5/PPP6dXr1789re/JSsrq87HCiGEEG2NPLMnhBD11Jw5e42pNebs2Ww2zp8/z/79+8nKymLkyJF899136gqjEEIIcTuRYk8IIeqiBebsOY2j4fuzAK0vZy8gIIBhw4ah0WiIi4tDq9Vy9uxZOnTocNPjF0IIIVo7uY1TCCGamCNnr6ysDKi49fDSpUuVPldYWEhRURGAmrMXEhLSpGNNTExk6tSplQrT6uTm5qoxEjebs7dp06ZKOXtWqxWr1cpLL73Eq6++WmvO3tChQ8nIyAAqvterV682SqdSIYQQojWQYk8IIZpYQ+TsLVy4kICAAPLz84mKiiIpKalRxtracvYmTJjAd999R0REBE8++STLly9Ho9FIzp4QQojbksaRgdQaGQwGxWw2N/cwhBBtnK+vL4C6ynY70Ol0dOnSxSlnT6/XY7PZmDdvnlPOnr+/v1PO3sSJEwFQFIXU1FQSExPJy8tjzJgxTjl7L774IgBTpkxh8+bNuLu70717dz788EP1O4eKnL2wsDBSU1PVKIUJEyawZcsWOnbs6JSTl5qaytKlS9XbNtPT052KPIfqjhdCCCFaG41G86WiKIaq9snKnhBC3AKdTsfZs2cb9RpXrlwhLi6O6OhowsPDnbpS5ubm0qtXL3r06MGoUaO4evVqg1+/NebsOT7vGF9VhV5txwshhBBthRR7QgjRAtUWqj5t2jRefvllcnJyuOuuuxrlNsTWmLNXV5KzJ4QQ4nYgxZ4QQtRFNTl71t5WQr1Ceb7T84R7hTPg7gGU9C0BIxh9jUzrOo24O+MIuiOIz/Wfq9l5U7pMoeedPYnyjuLdoHfBCCa9iX6+/Xj6nqeJvDMSTT8N3oO9wQhlD5dRdqQMze81KL9VyFifwYjFI8AIYw+MZcNrGyRn75q3336bqKgoJkyYwPnz52/qWCGEEKItkWJPCCFqUVxcTLG9uNr9OZdzmOw/mcM9D+Pr6su6s+vUfTbFRmZMJgu6LyDt+zQAlhUsw8fVh6yYLLJislhasJTcklwAMi9m8rrudY70rLjF0a7Y0Zv1dPyiI/F3xdPrzl6cs53D19UXV01FERXgEcAPpT80+Lzrm7OXlZXFrFmzuHLlirqvsXP2fve73/Htt99isVjo1KkTr7zySp2PFUIIIdoaydkTQohaqMWG6bqNOtScvcD4QPTma6Hqb8RiLbPCnwAjDHt9GDwIsT/GYn3QCibYMWIHBw8eZG3RtVB1v5/J+UsO7u7uxKXFEbjnulB1XLBgoaioiMTERLIXZXPPPffAA9eNJw80gzTO42sgrS1n75577lFfP//882r3UiGEEOJ2JMWeEELUk4eHh/raxcWFkpKSSvtcXFzUeAVFUVi0qJpQdS+vKq/h6+uL0Whk27ZtvPLKKxQVFamNT/Lz8+ncuXNDTwuoyNkrKCggISGBU6dO1fr53NxcunTpgqur603n7O3du7dSzp5Damoq3t7etebsFRQU0KlTJwDWr19PREREjZ8XQggh2jK5jVMIIZpYfUPVNRoN/fr1Y+3aipXB5cuX8/jjjzfKWFtbzt7UqVOJjIwkKiqKPXv2MH/+fADJ2RNCCHFbkmJPCCGaWEOEqr/xxhv87W9/4/777+fcuXM899xzDT7OxMREp/dDhw4FYPv27WrBZjabGT16NHq9njFjxhAcHIybmxtQ0VHUkeXarVs3jEYjZWVllJWVMX78eLX4SkxMxMvLi/LycgIDA5k9e7bTdU+ePMncuXOdtnl6emK32wkODiY/P1+d/8qVKzl06BBjxoxh8+bN6lg6d+6sFpcA58+fp6SkhISEBKfjhRBCiLZEbuMUQoh60Ol0TqHcjtBvqLgt08HPzw+r1QqAVqslPT2d9PR0p3MZjUaMRqP6/q677sLX11cNIvfx8VH3Xbx4ERcXFzw9Pbly5QqlpaVOt5M2BEfOXpcuXSrl7DluQTUajcydOxeDoSLL9fLly5jNZlxdXSkoKCA6OprHHntMzdmLiYnh4sWLxMbGEh8fT1hYGPHx8cyaNQtXV1emTZvGrFmzeOONN9TrVZezl5yczJgxYyqNOy8vj507d9K1a9dq5zZlyhQuX77Mu+++W6/vSAghhGjJZGVPCCFaILvdXmMQeVJSErNnz+bQoUMkJiYyZ86cBh9Da83Ze/nll3nzzTfVa1elf//+tG/fvtb5CCGEEK2ZrOwJIURdOHL2HBw5e3YrAw8NpM+dffjiwhf4e/izMXwjni6eGC1Get3Ziz1FeyiyFbEsaBl9fftiV+xM/246pp9NlJaXMrnzZF7o/AKmIhNp1jQ6eXTCUmzhSM8jdKKi2Uh72hP6Uyg/PPkDYXeH8c1X3/DQ/z4EGoi/Ek/CoQT+8vlfbn1+psqbRowYwbhx40hJSWHz5s189NFHrFy5stZTHThwgAkTJvD999+zcuXKm87ZGzVqFPBLzt7OnTsr3cZZnU2bNuHv7090dHSdPi+EEEK0ZbKyJ4QQtWjOnD0H6xUrXxV/Ra87KwqkCK8INp3bBMCawjXkleY16Jyh9eXsXb58mddff52ZM2fWcYZCCCFE2yYre0IIURcuNEvOHlwrkH47nAUfL+DOYRUF0gfHPuCPf/wjM8/NZMiYIbgvdL/tc/a+/fZbcnNz1VW9/Px8YmJiyMzM5N57773p8QshhBCtnRR7QghRi9pWlhozZ6+6AikkJIQdO3YAFdEN//rXv25hZrVrTTl7kZGRnDlzRn2v0+kwm834+fndzJSFEEKINkNu4xRCiCZW15y9mgokR1FTXl7OX//61zpl092K1pazVx2z2UxSUpL6vm/fvjzxxBPs3r2bgIAAtm/fXqf5CSGEEK2JFHtCCNHE6pqzV1OBtGrVKoKCgggJCaFz586MHz++wcfZGnP2/vznPxMVFYVerycoKIirV68CYDAYeP/999Xjvby8KCsro3///uTn51daZRVCCCHaAo3jD+LWyGAwKGazubmHIYRo43x9fQEoKipq5pE0HZ1Oh6+vL5s3b1Zz9p566ilsNptTrmBVOXvu7u5OOXunTp2isLCQgoICp5y9DRs2EBYWxo4dO3j44YfVnD3AKWdv+PDhaLVaevXqpeYYfvbZZ3h7ezNmzBin8Vy4cEFt/LJw4UKOHDnCkiVLKs1v9+7das7eli1bGv4LFEIIIZqIRqP5UlEUQ1X7ZGVPCCFElVpjzt71HT4vXbpUbdae5OwJIYS4HUiDFiGEqIsbcvZ0+3WYY834uTVe84+8K3mMOTaG02Wn0aJlYqeJvBjwIgCWYguTjk/iSvkVXDWu/L8e/4+4O+Nu/WKmyptaY84ewGuvvcaKFSvw8fFhz549dT5OCCGEaGtkZU8IIWpRW85eY7Ardlw1rszrPo+jPY+y/9f7eefUOxy5VJG/N/W7qczoNgOLwcJM3Uymfje1wcfQ2nL2rj9PXl4eo0eP5u23376pY4UQQoi2RFb2hBCiLqrJ2bMWWxk4cCB9+vThiy++wN/fn40bN+Lp6YnRaKRXr17s2bOHoqIili1bRt++fbHb7UyfPh2TyURpaSmTJ0/mhRdewGQykZaWRqdOnbBYLBw5coROdAKgPe0JfTyUH5J/ICw+DE2ChgsTLsAo+HnVz3Te3Bk+bvhpt6acvRs9/fTTPProo6Slpd302IUQQoi2QIo9IYSop5ycHFatWsXSpUsZOXIk69at45lnngHAZrORmZnJ1q1bSUtLY9euXSxbtgwfHx+ysrIoLS3lwQcfZMCAAQBkZmaSnZ1NYKBzsPqNtz4uWLCAhIQEUlJSKC8v54svvmiUubWmnD2o+G/Ro0cPADZt2kRISMjNTFcIIYRoU+Q2TiGEqIW3t3eNtxIGBgai1+sBiI2NxWq1qvscK1jXb9+xYwcrVqxAr9fTq1cvzp07R05ODgBxcXGVCr2qbn1cvHgx8+fPJy8vj/nz56vRAw2tteXsTZ8+nYiICKKiotixYwdvvfUWIDl7QgghblOKorTaf2JjYxUhhGhsPj4+io+Pj9O2bt26KYWFhUpubq4SHh6ubp8zZ44yY8YMRVEU5be//a2SlZWlKIqiFBYWKt26dVMURVGGDRumbNu2rdJ19uzZozz66KNO265evaoMGDBAmTdvntP2O++8UykvL1cURVHKy8uV9u3b12uOQgghGka3bt2UPn36OG2Ljo5WwsPDlW3btinR0dFKdHS04uXlpQQFBSnR0dHKs88+qxw4cEDdFxUVpXz66aeKoijKyZMnFaPRqISEhChhYWHKggUL1POmpKQowcHBSmRkpDJ06FDl/PnzTtf9/vvvFS8vL2XOnDm1nutPf/qTEhkZqURHRyvx8fHKDz/8UOX8EhISFB8fn0p/XonmA5iVauolWdkTQogmlpCQwOLFiykrKwPg+PHjXLp0qdLnlBpufezcuTN79+4FICMjQ711UQghRPO7ePEieXl5ABw9elTdnpCQgMViwWKxYDAY+Oijj7BYLKxYsYKIiAjMZjMWi4Vt27bxwgsvYLPZcHV1Zd68eRw9epT9+/fzzjvvcORIRbOu+Ph4srOzOXjwIEFBQcyaNctpHC+//DIDBw5U39d0rilTpnDw4EEsFguDBw9m5syZVc5typQpderMLFoGKfaEEKKJJSUlERYWRkxMDBEREeof6Deq6dbHpUuX8sorrxAdHc2rr77Ke++919TTEEIIUY2WmlNa07kkp7RtkgYtQghxCxzP3/n5+ZGdna1uT0lJUV+bTCb1tZ+fn3qMVqslPT2d9PR0p3MajUaMRqP6vk+fPlTcnVFZnz59+PLLL+s3CSGEaArG5h5AIzNV3tQackqrOpfklLY9srInhBBCCCEahdFoxGgxipNcxwAAIABJREFUNvcwmlxLzymt7lySU9r2yMqeEEIIIYRoPHqqXP1q61pqTmlN53KQnNK2Q1b2hBDiFuh0Os6ePduo18jLy6Nfv36EhoYSHh6uxghAxV8iHM/x6XQ6NfpBCCFauub++dlUEhMTmTp1KgkJCXX6fG5urvr89s3mlG7atKlSTqnVasVqtfLSSy/x6quvkpycXOO5HBFAIDmlbYms7AkhRAtkt9vVrmkxMTFcvHiR2NhY4uPjCQsLUx/8B3jllVfw8fFpxtEKIUTLUdvPz6biyCmtq3379jF79mzc3NzQarVqTum+fftYuXIlkZGR6i/20tPTGTRoEMnJyZSWlhIfHw9UNGlZsmRJtddwNP6q6lzTp0/nm2++QavV0q1bN/U8ZrOZJUuW8P777wMVOaXHjh2juLhYzTmta0Ermp6s7AkhRD1YrVZCQ0N5/vnnCQ8PZ8CAAZSUlAAVz6pMmzaNuLg4goKC+Pzzz4GKv4hMmTKFnj17EhUVxbvvvgtUNHTp168fTz/9NJGRkTV2TXNQFIVPPvmkTp3ehBCiJWnun5+NKTEx0en90KFDAdi+fbt6V4bZbGb06NHo9XrGjBlDcHAwbm5u6jwdDbq6deuG0WikrKyMsrIyxo8fz6BBg9TreHl5UV5eTmBgILNnz3a67smTJ50atNR0rvvuu09dWXR1dcXLywsAg8GgFnoAPXr0QKPR0L17d/Lz86XQa+E01XV6aw0MBoNiNpubexhCiDbO19eX4uJi+vTpo27bv38/sbGx2O12Dhw4QGxsLN7e3hw5coRf/epX3HPPPVgsFtq3b0/37t05d+4c+fn5REdHc+rUKcrKyujWrRvl5eV89dVXhIWFUVpayqFDhzAYDHh6ejqN4cqVK2ou0/Xd2YqKivj222+JjY1tsu9DCCHqymKxoNfrnboT63Q6zGYzxcXF3H///ZjNZvR6PSNHjmTIkCE888wzGI1GYmNjmTdvHlu3buVvf/sbu3bt4r333uPMmTP86U9/orS0lAcffJA1a9bw/fff8+ijj5KdnU1gYKDTGKxWKw899BDZ2dmVGps0Bp1Oh6+vL5s3b6ZLly4cPXqUp556CpvN5tS92Wg0MnfuXAwGAwCXL1/G3d0dV1dXCgoK1D8vCgsLKSgocFql3LBhA2FhYezYsYOHH34YV1dXdRXxjTfeUK8xfPhwtFotvXr1IiUlhYKCgls+l8Nnn32Gt7c3Y8aMcZqPaD4ajeZLRVEMVe2TlT0hhKgnT09PteOZt7e3U/c0Pz8/oOI3y47t58+f58cff8RsNvPf//4Xm82m/ja7ffv2lQo9u93O4cOH6d69e6U23GfOnKFjx46NNjchhGhMgYGB6u2EsbGxakQNoDYPuX77jh07WLFiBXq9nl69enHu3Dn1WbO4uLhKhV5NHSwbU2vM2avpXNd76KGHuPvuu+v4TYjmJs/sCSFEHXh7e1f6zfTGjRspLi5m8ODB6r65c+dSXFxMamqq029tz549i8FgwGQyMXz4cCZOnFjp1heTycTcuXPZsmWLuq2srIzBgweTlpZW6WF6m82Gv78/e/fuJSAgoNHmLoQQt8poNIIF56y908DjgB08Tnmo+1zyXCixl8AuwAIef/SA9uBS5oIt3wZGUA4rLOq0iATfaz8/fYF0MBWZ8MrzcrpOWXkZw7OHM/qu0QxbOAwWNtIkTZU3tdacvarOJVo3WdkTQogmlpCQwOLFiykrKwPg+PHjXLp0qdLnauqaBrBr1y5CQkKk0BNCtFgWiwVLscVpW35pPufKzt3S+RLuSmDxqcWUlV/7+Xn5OJfsVf/8DM0K5fOfP+eD0x/c0rXqo7Xm7FV1LtG6ycqeEEI0saSkJKxWKzExMSiKQocOHdiwYUOlz9XUNQ1g9erV0phFCNHyeeO8+uUKrAQ8gMHX7ZsLFAOpVKzQzQUMwNlr/zZBUnkS1j9Zidl8w8/Pr659fkvFre//+c9/+Lbvt9x///18m/8t+iK908/PptAac/aqOpdo3aTYE0KIW+B4fsTPz8/pAfWUlBT19fW3ffr5+anHaLVa0tPTSU9Pdzqn0WisuOXpmj59+lBTE61b+QuEEEI0t4CAAH71q19RXFyM3W7n+eef54svvsDf35+NGzeqn1uzZg2///3vKSoqUm+BVBSFsrIy2rVrR2lpKU8++aQaPXPp0iWefvppLBYLR44cQVEUrFYrgwcPxmKxVDmWxpSYmEhBQQEJCQmcOnWq1s/n5ubSpUsXXF1dbzpnb+/evZVy9hxSU1Px9vauNWevunOJ1k1u4xRCCCGEEM0iJyeHyZMnc/jwYXx9fVm3bp26z2azkZmZyYIFC0hLSwNg2bJl+Pj4kJWVRVZWFkuXLiU3NxeAzMxMXn/9dY4cOdIsc7mRI2fP3d29Tp/ft28f0dHR6PV6EhMT1Zw9x10eGRkZamzD1q1bAUhOTubixYvEx8ej1+uZNGlSjde4lXOdOnXKaUX0qaee4oEHHuCbb75Rc/ZEyyXFnhBCCCGEaBaN3Y2zubTGnL1Zs2Zx5513cvDgQd5//301VL1z585qQQgVHajtdjvBwcHk5+fz3HPPNeRXJxqYFHtCCCGEEKJROAqb6nh4eKivXVxc1FDv6/ddv11RFBYtWlTR+MViITc3lwEDBgCoIeAtwcWLF8nLywPg6NGj6vaEhAR17AaDgY8++giLxcKKFSuIiIjAbDZjsVjYtm0bL7zwAjabDVdXV+bNm8fRo0fZv38/77zzjrp6GR8fT3Z2NgcPHiQoKIhZs2Y5jePll19m4MCB6vuazhUREcGnn37KQw89VOPcxo0bx7Zt2xrkexKNT4o9IYQQQgjRKtS1m3Fza405e6GhoQQHB9c6TsnZa12k2BNCiFug0+k4e/Zso14jLy+Pfv36ERoaSnh4OG+99ZbT/kWLFhEcHEx4eDhTp05t1LEIIURLkJSURFhYGDExMURERKirX1VpzmfLRowYwaeffgrA5s2beeyxx+p03IEDBwgPDycyMpIlS5bcdM6eYxXPkbM3Y8aMaq9V07lE2yHdOIUQogWy2+3q7TYxMTFcvHiR2NhY4uPjCQsLY8+ePWzcuJGDBw/i4eHBmTNnmnvIQghRtRtC1a06K4wAP/zI9stW96VwrZuxEUyYcLz1w6/iGCNo0ZJOOum/utbNWAEeB+O1/11/nVWsgmAq/oGKuIfac81vnqnypvrm7B09epSxY8cycOBA2rVrBzRdzp5oW2RlTwgh6sFqtRIaGsrzzz9PeHg4AwYMoKSkBKiIUpg2bRpxcXEEBQWprbDtdjtTpkyhZ8+eREVF8e677wIVUQ39+vXj6aefJjIyssbbbRYvXsz06dPVZ1o6duzY1FMXQohamUwmTHpTcw+jWThy9m4lD/X6nD2gTtl4H330kVPO3tSpU9HpdCxYsID09HTefvvtWs8l2h5Z2RNCiHrKyclh1apVLF26lJEjR7Ju3TqeeeYZ4JfW4Vu3biUtLY1du3Y5tQ4vLS3lwQcfVBsMZGZmkp2dXamj3I232xw/fpzPP/+c1157jXbt2jF37lx69uzZtBMXQoi6MDX3AJpHa8vZE22TrOwJIUQ9NXbr8Kput7HZbJw/f579+/czZ84cRo4cWWMAuxBCiKbV2nL21q9fT0BAAP/5z3949NFHSUhIACRnr7WTlT0hhKhFTW3DoXLrcMdtnNfvq6p1uOMPUgeTyVSpdXh1t9sEBAQwbNgwNBoNcXFxaLVazp49S4cOHW5tkjfQ6XR06dLF6bfDer0em83GvHnzmDZtGgAnTpzA398fT09PoqKiSE5OZuLEieo8U1NTSUxMJC8vjzFjxnD69Gm0Wi0TJ07kxRdfBGDKlCls3rwZd3d3unfvzocffoivr6963ZMnTxIWFkZqaiopKRUP8UyYMIEtW7bQsWNH9TYngJ9++olRo0ZhtVrR6XR88skn3HXXXZXm98gjj7B//3769OnDli1bGuQ7E0IIwOkXfg46nc7pZxVU/My/3rPPPsuzzz5b6dg+ffpU+8u8EydO1Dqe1NTUOp0rMTGxUj4gVM7ZW7VqVa3XFC2HrOwJIUQTq2vr8Jputxk6dCgZGRnq8VevXsXPz69Bx9lSc6Kg+pyn2bNn079/f3Jycujfv3+lgGGHKVOmsHJlY3RqEEIIIVoOKfaEEKKJ1bV1eE2320yYMIHvvvuOiIgInnzySZYvX64+mN9QWmpOFFSf87Rx40bGjh0LwNixY9mwYUOV4+zfvz/t27evdT5CCCFEaya3cQohxC1w3Kbj5+fndGuO4zZDcL5Fx8/PTz1Gq9WSnp5Oenq60zmNRiNGo1F9X9PtNu7u7vzjH/+o3yRqMWLECMaNG0dKSgqbN2/mo48+qtNq2IEDB5gwYQLff/89K1euvOmcqFGjRgG/5ETt3LmTuXPn1mnMP/74I506dQIqCkyJpBBCCHE7k2JPCCHq4oacqDbHVHlTS8+JEkIIIUTNpNgTQohaWCwWKG7uUTQPR07U3//+95s+9vqcKIPBUKecqN27dzvlRK1du5apU6dSVFSEVqulXbt2JCcnV3vNe+65h4KCAjp16kRBQYHkDwohhLitSbEnhBB14c1tmRXVEnOiajJkyBCWL1/O9OnTWb58OY8//vhNzlgIIYRoO6RBixBCiGq1xJwoqD7nafr06ezcuZMePXqwc+dOpk+fDoDZbCYpKUk9vm/fvjzxxBPs3r2bgIAAtm/ffrNfjRBCCNHiaVpzCK/BYFDMZnNzD0MI0cY5Mt+KiorUbTqdDrPZ3OBxB9erKZsuNTWVpUuXqrl66enpTqG3QgghhLg9aDSaLxVFMVS1T1b2hBCiBbLb7TVm00FF/pwj766hCz2dTkffvn2dtun1eiIiIti+fbu6Ouft7U1wcDB6vZ4xY8aQmZmp7ouOjmb9+vVAReHar18/QkNDCQ8P56233lLPO2XKFEJCQoiKiiIxMdGpqIaKUHVvb2+njpzbtm0jODiY+++/3ylLb9y4cQQGBqpjsFgsVc7vkUcewdfXl8GDB9f7uxJCCCFaKin2hBCiHqxWK6GhoTz//POEh4czYMAASkpKgIoohWnTphEXF0dQUJD6DJrdbmfKlCn07NmTqKgo3n33XaAiqqFfv348/fTTREZG1phN1xRaaqi63W5n8uTJ/N///R9Hjhxh1apVTkXwnDlz1PHp9foq5yah6kIIIW4HUuwJIUQ95eTkMHnyZA4fPoyvry/r1q1T99lsNjIzM1mwYAFpaWkALFu2DB8fH7KyssjKymLp0qXk5uYCkJmZyeuvv+5UvEDV2XRvv/02UVFRTJgwgfPnzzf4vFpqqHpmZib3338/9913H+7u7jz55JNs3LjxpuYmoepCCCFuB9KNUwgh6qIY55y908DjgB0C3QPRv1SxghR7MhZrlhXeByww7OdhYITYq7FYv7KCEXYc3sHBSwdZO3stAD/bfybn8Rzcte7EucUROD7Q+dL2YoZbhrOg6wLuHFKRTfe7q7/jz93+jAYNf979Z14Je4UPgj+49fmZKm9qqaHqP/zwA126dFHfBwQEcODAAfX9a6+9xsyZM+nfvz+zZ8/Gw8Oj1jELIYQQbZGs7Anx/7d3/9FV1Oe+x99PfpDY4AEk4MIECdCoUDARItYjaKoHI9xWRaqXmnulEFBqOaW6ELi1RaUVsLVV0AoqKMixtaKCiC60yMVjdHlI8ITKjxS0SSFXFE48CljEJDz3j72zSQghv7OZ8HmtlZW9ZyYzz+xZLPLkO/P9iLRQgh1rJmItlkqvPLYuJqHOcsd55JuPUJRVRFFWESWXlHD1WVcDkBSbVGvfFUcrGLttLLk9c7mhx7FsurM7nU2sxRJjMUzuNZlNBza1+nm1NFS9oKCAefPm8dVXX0XWtUao+okmFqseQZw3bx7FxcUUFBTw2Wef8cADDzTpnEVERDoSjeyJiDTG8Tl7acDLhEb8vltj3YPhZfcSGgl8EMgC/iv8fSPkPJHDotcWceXKK4mPj2fnzp2kpKRAQXj7taFduTt54/MYMGQAdz5cO5uuOjgcYNVDqxiUNgiea9UzBk7NUPWhQ4dGniUEKCsr45xzzgGIfCYJCQlMmDCh1oigiIjI6UbNnohIO5s0aRKlpaUMGTIEd6dHjx6sXr26znbV2XSDBw+OTDRSHbEwY8YMioqKMDPS0tIik7y0tlMxVL2yspJdu3ZRUlJCSkoKzz33HH/4wx+AY02wu7N69WoGDRrUSp+EiIhI8KjZExFpgbS0NLZu3Rp5P3369MjrjRs3Rl4nJydTWloKQExMDHPnzmXu3Lm19pWdnU12dnbkfZ8+fcjOzq6Vs1cdsVD97NyDDz7IXXfdxVNPteB5vZOoDlVvrPz8fObPn098fDwxMTGRUPX8/Px6G9epU6dy5MgRRo4cCYQmaVm8eHG9x4iLi+PRRx8lJyeHqqoqJk6cGJnAJTc3l/379+PuZGZmRvZTWFjI4sWLWbJkCRAKVS8uLubQoUORUPacnJxmfUYiIiKnKoWqi4g0IBqh6lVVVezbt4+9e/cyZMgQDh48yNChQ1m9ejUDBw4EQtl1kyZNori4mM2bN7dpwLsES1paGr179641OpqZmUllZSW//e1vIw38hx9+SEpKCmeccQYXXnghU6dO5dZbbwVCtxHfe++9jBkzhj179nDLLbfU+sPDtGnTgFCMxSuvvEKnTp3o378/Tz/9dOTfDIRyEgcOHMi9994b+WPIunXrmDZtGlVVVUyaNIlZs2ZFjvnzn/+clStXEhsby49+9CN+8pOf1Dm/a665hvfee4/hw4ezdu3atvkQRUQCQqHqIiJtJJo5e3fccQe//vWvI8+4idQUxJzEZcuWsWfPHoqLi9mxYwfjxo074bkpJ1FEpHHU7ImItFA0cvbWrFlDSkoKGRkZ7XSWEjRBzElctGgRs2fPJiYm9OtJz549T1inchJFRBpHz+yJiDTGKZSz94+qf3D/lvt548I3QjVV1xLfhucvp7aNdRcFMSfxo48+4k9/+hOrVq2iR48eLFy4kPT09MZ8AiIicgIa2RMRaaH2ztn76PBHlHxVQkZhBmnvpVF2pIwhm4fwydeftOl5SrAEMSfxyJEjJCYmUlhYyOTJk5k4cWKTzllERGrTyJ6ISGOcQjl7gxnMPvYdK6WNJ4uR4ApaTmJqaipjx44FQrEfEyZMaOaZi4gIqNkTEWl3rZGzJ9IYQctJvP7669mwYQMTJ07krbfe4rzzzmulT0JE5PSkZk9EpAWilbP3i1/8gpdffpmYmBjOO+88vv7669Y/OQm8oOUkzpo1i9zcXB566CE6d+4cyUVUTqKISPMoZ09EpAGnYs7egQMHIs9MLVy4kO3bt5/0F+ymCmpOW25uLoWFhcTHxzNs2DAef/xx4uPrzlyjnDYREekolLMnItJGopWzV3NyjC+//LJNsvaCmNOWm5tLcXExH3zwAYcPH46MBB1POW0iInI6ULMnItJC0cjZA7j77rvp3bs3zz77LHPmzGn18wpiTtvo0aMxM8yMYcOG1dpXTcppExGR04Ge2RMRaUBmZiYUccrk7FW7n/u5v//9zNs9j0cvfpT70u5r/klurLsoiDlt1SoqKlixYgULFixosF4REZGOSiN7IiINKCoqouhQUb3r2ztn73g397yZF/e/eMJ1LRHEnLZqt99+O5dffjkjRoxoVM0iIiIdkUb2REQa4xTK2YPQraPp6ekArHlkDRe8dQG80KpnDAQvpw3gvvvuY//+/ZFnIUVERE5XavZERNpZa+TszZo1i7/+9a/ExMTQp0+fVp2Js6ag5bQtWbKE119/nTfffJOYGN28IiIipzc1eyIiLRCtnL1+/fqxbds2OnXqRFxcHElJtW//bC1By2mbMmUKffr04dJLLwXghhtuYPbs2cppExGR05L+7CkicgqqqqpqUVxBaxgzZkyt99dffz0Ar7/+OpmZmWRmZlJYWEhubi6ZmZnccsstnH/++ZFcu6qqqsjzddWNa0VFBRUVFUyYMCHSuI4ZM4akpCSOHj1K3759mT9/fq3j7t69u9YELRBqmKtn3YyNjY0sf/jhhzEztmzZwvr165k9ezYAWVlZtWIYkpKSqKio4KqrrqKsrEyNnoiIdEhq9kREWiBaOXsniytoLUHM2bvssstYv349ffr0Oem5KWdPREROB2r2RERaKFo5e9WeeuqpWs1Qawlizt5FF11EWlpag3UqZ09ERE4HemZPRKQBp2rOHsD9f7+fuINx5B7IDR2zuTbWXRTknD0RERFRsyci0qCioqJQnEI9js/ZO3z08LF1J8nZyzmr9nNiGz/f2KScveWfLGdt+VrezHizTs5ca2hpzt6OHTsYP348o0aNIjExEWi/nD0RERFRsyci0jinWM7eunXreODOB3jro7f4Ro/GNWHNEcScPREREQlRsyci0gxlZWWUl5eTkJDQ8MbHaWzO3ksvvcSKFStISEhg8eLFdO/enSeffJLRo0fzwx/+kPLycnr27El6ejpXXnllm2TtBS1nT0RERGpw98B+DR061EVE2lqXLl28S5cutZb16dPH9+/f32bHrKys9I8//tg3b97s7u4HDhzw9PR037Ztm7u7b9++3YuLi/2KK67wgoKCVj/+ic6vpKTEv/Wtb9Vadvzxn3nmGR84cKBnZGT4RRdd5KtWrXJ397ffftsBHzx4sGdkZHhGRoa/+uqr7u7ev39/T01NjSy/7bbb6tRzzz33+G9+85vI+1dffdXT09O9X79+/qtf/SqyfMGCBZ6SkuKxsbHeq1cvz8vLc3f3goKCyGt39+HDh3tycrInJiZ6SkqKr1u3rrkflYiISFQBhV5Pv6TZOEVEWiBa0QsDBgzg/PPPb9NzC2LO3ve+9z1SUlLo27cvI0aM4LHHHgNq5+yVl5cTFxfH4cOHycvLU86eiIh0WGr2RERaKNrRC20liDl7M2fO5I477mDXrl1069aNpUuX1jmvxMREfvnLX9ZpIEVERDoaNXsiIg2oHsWqT9++fSPrhw4dSmlpaWRd9UQkNZe/8cYbPPPMM2RmZnLJJZdQXl7Orl27ABg2bBh9+x4XvXCSGSzbUtBy9tydDRs28P3vfx+A8ePHn/BZyKSkJIYPHx6ZIVRERKSj0gQtIiKNcZKcvYSPEyLrYvfEcrjqMKwP/UzCTxLgTIitiKWyrBKywbc5j/R6hJyu4VsHuwJzw9ELe5JqHafiaAVjt44lt1suNyy8ARaeoK7bgJbmg2+suyhoOXvl5eV07do1crzU1NRIQykiInI60sieiEgDioqKKDpUVGtZ2ZEyyivKm7W/nG45LPp4ERVHKwDY+Y+dfFn1ZZ3tdh/eTe/3elNwsIClnyxlQdmCyLrPKj5j5JaRbDq4iR/v+jH/XfHfzarlZFqas1dQUMC8efP46quvIuvaMmevvuUiIiKnK43siYg0xvE5e3HACiCBJufsTTo6idKflzLkleOiF/6TSM5eVVUV77/yPp+O+ZTBgwfj7szYOYOE6QlMmTKFvDF5bNq6iaq4KrYd3caQj4dEnvtrTUHK2UtOTubzzz+PPCOo/D0RETndqdkTEWmG1NRUunfvzqFDh6iqqmLy5Mm8++67pKSk8PLLL0e2W7lyJbfffjuff/555BZId6eiooLExESOHDnCuHHj6NKlCxC6dfHmm2+mqKiI7du31xqtuu666+jfvz8A27dvp7i4mF69erF3716ys7Pb5DyDlLNnZnznO9/hhRdeYNy4cSxfvpzrrruu9T4MERGRgNFtnCIiLRSN2Tg//fRTevXqBYQmP9m3b1+bnNuZZ57JzJkz6dSpU6O2z8/PJyMjg8zMTMaMGcNjjz1GcnIy77zzDitWrGDDhg2RCW9ee+01AKZOncrBgwcZOXIkmZmZTJky5aTHiIuL49FHHyUnJ4cBAwZw0003RSZweeCBB/jd737HN7/5TcrLy8nLywNgzZo1zJ49O7KPtLQ07rzzTpYtW0Zqamqdz1tERKQjULMnItKA03U2zmjn7G3atClynFWrVkVGNSHUYHfq1InExESSkpIiyw8cOEBMTAydO3empKSELVu2AHDttdcyZ86cyHYXXHABR48eJTs7m7KyMgYOHNjaH5+IiEjUtVmzZ2ZPmdk+M9taY9lZZvZnM9sV/t4tvNzMbKGZfWhmfzGzIW1Vl4hIa0tISIi8jo2NpbKyss66msvdnUceeSSSVVdSUsLVV18NUKtxAep9zu3ss89m7969AOzdu5eePXu2+nlFO2evvn1t3bqVJ598kk2bNrFlyxbWrl0baZZnzJjBPffcQ1FREXPmzGHGjBknPLe77rqrUTOLioiIBFlbjuwtA645btks4E13TwfeDL8HGAWkh79uBRa1YV0iIlGVk5PDokWLqKgIz8a5cydffll3Ns6TPed27bXXsnz5coA2ezYt2jl79e1rx44dfPvb346sv+KKK1i1ahUQmn3zwIEDAHzxxRf1TtBy1VVXceaZLc2rEBERObW12QQt7v7vZpZ23OLrOJYgtZzQ/HUzw8uf8dD9Pu+ZWVcz6+Xue9uqPhGRJjlJzh4lNdbtCS/bSO0MvIrwz2TDJJ9EaUkpQ7qFZ+OM78HqQatDs3juObavd754hxVFKxicNJjMJaHbROf2ncvo7qOZVTGLm7bfxNI5Szk34VxWDlxZu76m2lh3UbRz9urb16BBg7j77rspLy/njDPO4LXXXiMrKwuAhx9+mJycHKZPn87Ro0d59913m/Y5iIiIdCDtPRvn2dUNnLvvNbPq+45SCP2KU60svKxOs2dmtxIa/ePcc89t22pFRICNGzfWaaRKv10KQHJ8MlsvjtytzvTe04/9XObGyOvk+OTIz8RYDHP7zWVuv7m19pndNZvsrscONLzLcPyKutlxAN3ydt7zAAAJO0lEQVTju/NmxptNPZUmaWnO3o4dOxg/fjyjRo0iMTERaFrOXn37GjBgADNnzmTkyJF07tyZjIyMSEO5aNEiHnroIcaOHcvzzz9PXl4e69evb6VPREREJFhOleiFE6XenvA3HHd/AngCICsr68S/BYmItLaN0S4gOqKZs3eyfeXl5UVm2vzZz35GampqZD8LFoTC52+88UYmTZrU5LpFREQ6ivaejfNTM+sFEP5ePVd4GdC7xnapQMOBTiIi0qbGjBnDjBkzyMnJadT2JSUlkYlompqzt2bNmlqjh/XtC4hETezevZuXXnop8jzhOeecw1tvvQXAhg0bSE9Pb/7Ji4iIBFx7j+ytAcYD88PfX66xfKqZPQdcAnyh5/VERKKvOmevsfLz85k/fz7x8fHExMREcvby8/NZsWIFgwcPjsRUzJ07l9GjRzN16lSOHDnCyJEjgdAkLYsXL653XwBjx46lvLyc+Ph4fv/739OtWzcAnnzySaZNm0ZlZSWJiYk88cQTABQWFrJ48WKWLFkCwIgRIyguLubQoUOkpqaydOnSRje0IiIiQWHVGUitvmOzPxJ6yiUZ+BS4B1gNPA+cC+wGbnT3zyx0z86jhGbv/Acwwd0LGzpGVlaWFxY2uJmIiIiIiEiHZGab3T3rROvacjbO+ubovuoE2zrw47aqRURERERE5HTT3s/siYiIiIiISDtos9s424OZ7Qf+HsUSkoH/iuLxpWV0/YJN1y/YdP2CTdcv2HT9gk3XL9ja4vr1cfceJ1oR6GYv2syssL77Y+XUp+sXbLp+wabrF2y6fsGm6xdsun7B1t7XT7dxioiIiIiIdEBq9kRERERERDogNXst80S0C5AW0fULNl2/YNP1CzZdv2DT9Qs2Xb9ga9frp2f2REREREREOiCN7ImIiIiIiHRAavZEREREREQ6IDV7zWRm15jZX83sQzObFe16pPHM7Ckz22dmW6NdizSdmfU2s/9rZjvMbJuZTYt2TdJ4ZpZoZpvMbEv4+t0X7Zqkacws1sz+08zWRrsWaTozKzWzD8ysyMwKo12PNJ6ZdTWzF8ysOPx/4KXRrkkax8zOD/+bq/46YGY/bZdj65m9pjOzWGAnMBIoAwqAH7j79qgWJo1iZpcDh4Bn3H1QtOuRpjGzXkAvd3/fzM4ENgPX699fMJiZAUnufsjM4oF8YJq7vxfl0qSRzOxOIAv4J3f/brTrkaYxs1Igy90Vyh0wZrYceNvdl5hZJ+Ab7v55tOuSpgn3Ef8PuMTd/97Wx9PIXvMMAz5097+5+9fAc8B1Ua5JGsnd/x34LNp1SPO4+153fz/8+iCwA0iJblXSWB5yKPw2PvylvzoGhJmlAv8DWBLtWkROJ2b2T8DlwFIAd/9ajV5gXQV81B6NHqjZa64UYE+N92Xol02RdmdmacBFwH9EtxJpivBtgEXAPuDP7q7rFxwPAzOAo9EuRJrNgTfMbLOZ3RrtYqTR+gH7gafDt1EvMbOkaBclzTIO+GN7HUzNXvPYCZbpL9Mi7cjMOgMvAj919wPRrkcaz92r3D0TSAWGmZlupw4AM/susM/dN0e7FmmRy9x9CDAK+HH40QY59cUBQ4BF7n4R8CWgOSMCJnz77bXAyvY6ppq95ikDetd4nwp8HKVaRE474We9XgSedfeXol2PNE/4FqSNwDVRLkUa5zLg2vAzX88BV5rZv0W3JGkqd/84/H0fsIrQoyly6isDymrcCfECoeZPgmUU8L67f9peB1Sz1zwFQLqZ9Q136OOANVGuSeS0EJ7gYymww91/F+16pGnMrIeZdQ2/PgP4F6A4ulVJY7j7/3H3VHdPI/T/3gZ3/19RLkuawMySwhNbEb4F8GpAM1MHgLt/Auwxs/PDi64CNDFZ8PyAdryFE0JDwtJE7l5pZlOB14FY4Cl33xblsqSRzOyPQDaQbGZlwD3uvjS6VUkTXAb8b+CD8HNfAD9z99eiWJM0Xi9geXg2shjgeXfXFP4i7eNsYFXob2bEAX9w93XRLUma4F+BZ8MDDX8DJkS5HmkCM/sGoZn8b2vX4yp6QUREREREpOPRbZwiIiIiIiIdkJo9ERERERGRDkjNnoiIiIiISAekZk9ERERERKQDUrMnIiIiIiLSAanZExGRwDGzNDNr13wwM9toZlkNbNMmdZlZtpn9c433y8zs+619HBER6VjU7ImISIcVzvPrCLKBf25oIxERkZrU7ImISFDFmdlyM/uLmb0QDqzFzErNbLaZ5QM3mtlkMyswsy1m9mKN7ZaZ2UIze9fM/lZzpMzMZpjZB+GfmV/jmDea2SYz22lmI05WnJnFmtlvwsf+i5ndFl6eHR4lfMHMis3sWQunXJvZ6PCy/HBta80sDZgC3GFmRTWOe/mJahcREammZk9ERILqfOAJd78QOADcXmPdV+4+3N2fA15y94vdPQPYAeTV2K4XMBz4LjAfwMxGAdcDl4R/5tc1to9z92HAT4F7GqgvD/jC3S8GLgYmm1nf8LqLwvsYCPQDLjOzROBxYJS7Dwd6ALh7KbAYeMjdM9397fpqFxERqUnNnoiIBNUed38n/PrfCDU+1f5U4/UgM3vbzD4AcoFv1Vi32t2Puvt24Ozwsn8Bnnb3fwC4+2c1tn8p/H0zkNZAfVcDt5hZEfAfQHcgPbxuk7uXuftRoCi8rwuAv7l7SXibPzaw/xPVLiIiEhEX7QJERESayU/y/ssar5cB17v7FjP7IaHn36odqfHaanw/ft/Hb19Fw/+HGvCv7v56rYVm2ccdt3pfRtOcqHYREZEIjeyJiEhQnWtml4Zf/wDIr2e7M4G9ZhZPaGSvIW8AE2s823dWM+t7HfhR+LiY2XlmlnSS7YuBfuFn9AD+Z411Bwmdh4iISKOp2RMRkaDaAYw3s78AZwGL6tnuF4Ruo/wzoYbqpNx9HbAGKAzfgjm9mfUtAbYD74fjGB7nJKOB7n6Y0HOH68KTy3wKfBFe/Qow5rgJWkRERE7K3Ou7U0VERETak5l1dvdD4dk5fw/scveHol2XiIgEk0b2RERETh2Tw6OJ24AuhEYDRUREmkUjeyIiIiIiIh2QRvZEREREREQ6IDV7IiIiIiIiHZCaPRERERERkQ5IzZ6IiIiIiEgHpGZPRERERESkA/r/bdBJsHg0zLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "axes = fig.add_subplot(1, 1, 1)\n",
    "plt.legend(handles=patches)\n",
    "Phylo.draw(nj_tree, axes=axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This phylogeny is interesting because of where all of the unknown states show up - they are all clumped together near the top of the tree.  I wonder if they all come from the same place.  The graph also shows many of the genomes with the same color clustered together.  This makes sense, because strains of the virus within a state should be more similar.  This could be some indicator that people are following quarantine/shelter in home guidelines.  However, whenever we see different colors close to each other, it indicates that someone probably traveled to spread the virus from one state to another.  For example, Florida, Rhode Island, New York, New Hampshire, and other states on the east coast seem to be garbled together.  This indicates that many people were probably traveling around to spread the virus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}