Final_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  1. Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proteins are essential biomolecules composed of amino acid chains, ranging from 44 to 27,000 amino acids in length. While much is understood about proteins, ongoing research continues to unveil new ones. They play crucial roles in structural, regulatory, and enzymatic processes within the human body, and studying them has been pivotal in comprehending various bodily functions and degradation pathways. One significant challenge in elucidating a protein's function is its classification. Identifying the protein family to which a novel protein belongs aids in understanding evolutionary relationships and assessing its overall function. By leveraging amino acid sequence and residue data, the modeling process aims to classify proteins into their respective families, facilitating the unraveling of their functions. With potential application in various fields, including drug discovery, biotechnology, and personalized medicine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Necessary Packages/Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.dummy import DummyClassifier\n",
    "import Bio\n",
    "from Bio import SeqIO\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data utilized in this project was downloaded from Kaggle and sourced from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). The dataset included 2 dataframes with 141,401 and 467,304 entries, respectively. After extensive data manipulation 144,378 entries were left including target proteins. As mentioned above the input features were residue count and amino acid sequence. Files can be directly downloaded from the following link: https://www.kaggle.com/datasets/shahir/protein-data-set/code?datasetId=11797&sortBy=voteCount\n",
    "\n",
    "- files names: pdb_data_no_dups & pdb_data_seq\n",
    "- files can be acessed at same level as notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId         classification experimentalTechnique macromoleculeType  \\\n",
       "0        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION    DNA/RNA Hybrid   \n",
       "1        101D                    DNA     X-RAY DIFFRACTION               DNA   \n",
       "2        101M       OXYGEN TRANSPORT     X-RAY DIFFRACTION           Protein   \n",
       "3        102D                    DNA     X-RAY DIFFRACTION               DNA   \n",
       "4        102L  HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION           Protein   \n",
       "\n",
       "   residueCount  resolution  structureMolecularWeight  \\\n",
       "0            20        1.90                   6360.30   \n",
       "1            24        2.25                   7939.35   \n",
       "2           154        2.07                  18112.80   \n",
       "3            24        2.20                   7637.17   \n",
       "4           165        1.74                  18926.61   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1                            NaN                   NaN             2.00   \n",
       "2                            NaN                   NaN             3.09   \n",
       "3  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "4                            NaN                   NaN             2.75   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              38.45                                                NaN   \n",
       "2              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "3              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "4              55.28                                                NaN   \n",
       "\n",
       "   phValue  publicationYear  \n",
       "0      7.0           1994.0  \n",
       "1      NaN           1995.0  \n",
       "2      9.0           1999.0  \n",
       "3      7.0           1995.0  \n",
       "4      NaN           1993.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing data containing protein info\n",
    "df1 = pd.read_csv('pdb_data_no_dups.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141401, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shape of df1\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureId</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount</th>\n",
       "      <th>macromoleculeType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId chainId                                           sequence  \\\n",
       "0        100D       A                                         CCGGCGCCGG   \n",
       "1        100D       B                                         CCGGCGCCGG   \n",
       "2        101D       A                                       CGCGAATTCGCG   \n",
       "3        101D       B                                       CGCGAATTCGCG   \n",
       "4        101M       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "\n",
       "   residueCount macromoleculeType  \n",
       "0            20    DNA/RNA Hybrid  \n",
       "1            20    DNA/RNA Hybrid  \n",
       "2            24               DNA  \n",
       "3            24               DNA  \n",
       "4           154           Protein  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing data containing protein/sequence info\n",
    "df2 = pd.read_csv('pdb_data_seq.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(467304, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shape of df2\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Data Frames 1 & 2 have 3 common columns structureId, residueCount, & macromoleculeType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several steps were taken to adequately prepare the raw data for modeling. Both data frames were merged using an inner join and outer join. Ultimately, the inner joined dataframes were used for modeling because it represented a common cross section between both dataframes without generating additional rows containing null values. The remaining null values were also dropped in addition to any columns not pertinent for modeling. Finally, the data was subsetted to only contain proteins in the 'macromolecule_Type_x' column. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Inner Join - Outputs datafame with intersection of both dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType_x</th>\n",
       "      <th>residueCount_x</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount_y</th>\n",
       "      <th>macromoleculeType_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "      <td>165</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>103D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>SOLUTION NMR</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7502.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>GTGGAATGGAAC</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId         classification experimentalTechnique  \\\n",
       "0        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "1        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "2        101D                    DNA     X-RAY DIFFRACTION   \n",
       "3        101D                    DNA     X-RAY DIFFRACTION   \n",
       "4        101M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "5        102D                    DNA     X-RAY DIFFRACTION   \n",
       "6        102D                    DNA     X-RAY DIFFRACTION   \n",
       "7        102L  HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "8        102M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "9        103D                    DNA          SOLUTION NMR   \n",
       "\n",
       "  macromoleculeType_x  residueCount_x  resolution  structureMolecularWeight  \\\n",
       "0      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "1      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "2                 DNA              24        2.25                   7939.35   \n",
       "3                 DNA              24        2.25                   7939.35   \n",
       "4             Protein             154        2.07                  18112.80   \n",
       "5                 DNA              24        2.20                   7637.17   \n",
       "6                 DNA              24        2.20                   7637.17   \n",
       "7             Protein             165        1.74                  18926.61   \n",
       "8             Protein             154        1.84                  18010.64   \n",
       "9                 DNA              24         NaN                   7502.93   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "2                            NaN                   NaN             2.00   \n",
       "3                            NaN                   NaN             2.00   \n",
       "4                            NaN                   NaN             3.09   \n",
       "5  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "6  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "7                            NaN                   NaN             2.75   \n",
       "8                            NaN                   NaN             3.09   \n",
       "9                            NaN                   NaN              NaN   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "2              38.45                                                NaN   \n",
       "3              38.45                                                NaN   \n",
       "4              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "5              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "6              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "7              55.28                                                NaN   \n",
       "8              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "9                NaN                                                NaN   \n",
       "\n",
       "   phValue  publicationYear chainId  \\\n",
       "0      7.0           1994.0       A   \n",
       "1      7.0           1994.0       B   \n",
       "2      NaN           1995.0       A   \n",
       "3      NaN           1995.0       B   \n",
       "4      9.0           1999.0       A   \n",
       "5      7.0           1995.0       A   \n",
       "6      7.0           1995.0       B   \n",
       "7      NaN           1993.0       A   \n",
       "8      9.0           1999.0       A   \n",
       "9      NaN           1994.0       A   \n",
       "\n",
       "                                            sequence  residueCount_y  \\\n",
       "0                                         CCGGCGCCGG              20   \n",
       "1                                         CCGGCGCCGG              20   \n",
       "2                                       CGCGAATTCGCG              24   \n",
       "3                                       CGCGAATTCGCG              24   \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...             154   \n",
       "5                                       CGCAAATTTGCG              24   \n",
       "6                                       CGCAAATTTGCG              24   \n",
       "7  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...             165   \n",
       "8  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...             154   \n",
       "9                                       GTGGAATGGAAC              24   \n",
       "\n",
       "  macromoleculeType_y  \n",
       "0      DNA/RNA Hybrid  \n",
       "1      DNA/RNA Hybrid  \n",
       "2                 DNA  \n",
       "3                 DNA  \n",
       "4             Protein  \n",
       "5                 DNA  \n",
       "6                 DNA  \n",
       "7             Protein  \n",
       "8             Protein  \n",
       "9                 DNA  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inner Join(df1+df2)\n",
    "inner_df = pd.merge(df1,df2, how='inner', on='structureId')\n",
    "inner_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(471149, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Outer Join - Maintains integerity of both data sets fills in blank spaces with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType_x</th>\n",
       "      <th>residueCount_x</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount_y</th>\n",
       "      <th>macromoleculeType_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>103D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>SOLUTION NMR</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7502.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>GTGGAATGGAAC</td>\n",
       "      <td>24.0</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId         classification experimentalTechnique  \\\n",
       "0        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "1        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "2        101D                    DNA     X-RAY DIFFRACTION   \n",
       "3        101D                    DNA     X-RAY DIFFRACTION   \n",
       "4        101M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "5        102D                    DNA     X-RAY DIFFRACTION   \n",
       "6        102D                    DNA     X-RAY DIFFRACTION   \n",
       "7        102L  HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "8        102M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "9        103D                    DNA          SOLUTION NMR   \n",
       "\n",
       "  macromoleculeType_x  residueCount_x  resolution  structureMolecularWeight  \\\n",
       "0      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "1      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "2                 DNA              24        2.25                   7939.35   \n",
       "3                 DNA              24        2.25                   7939.35   \n",
       "4             Protein             154        2.07                  18112.80   \n",
       "5                 DNA              24        2.20                   7637.17   \n",
       "6                 DNA              24        2.20                   7637.17   \n",
       "7             Protein             165        1.74                  18926.61   \n",
       "8             Protein             154        1.84                  18010.64   \n",
       "9                 DNA              24         NaN                   7502.93   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "2                            NaN                   NaN             2.00   \n",
       "3                            NaN                   NaN             2.00   \n",
       "4                            NaN                   NaN             3.09   \n",
       "5  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "6  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "7                            NaN                   NaN             2.75   \n",
       "8                            NaN                   NaN             3.09   \n",
       "9                            NaN                   NaN              NaN   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "2              38.45                                                NaN   \n",
       "3              38.45                                                NaN   \n",
       "4              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "5              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "6              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "7              55.28                                                NaN   \n",
       "8              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "9                NaN                                                NaN   \n",
       "\n",
       "   phValue  publicationYear chainId  \\\n",
       "0      7.0           1994.0       A   \n",
       "1      7.0           1994.0       B   \n",
       "2      NaN           1995.0       A   \n",
       "3      NaN           1995.0       B   \n",
       "4      9.0           1999.0       A   \n",
       "5      7.0           1995.0       A   \n",
       "6      7.0           1995.0       B   \n",
       "7      NaN           1993.0       A   \n",
       "8      9.0           1999.0       A   \n",
       "9      NaN           1994.0       A   \n",
       "\n",
       "                                            sequence  residueCount_y  \\\n",
       "0                                         CCGGCGCCGG            20.0   \n",
       "1                                         CCGGCGCCGG            20.0   \n",
       "2                                       CGCGAATTCGCG            24.0   \n",
       "3                                       CGCGAATTCGCG            24.0   \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...           154.0   \n",
       "5                                       CGCAAATTTGCG            24.0   \n",
       "6                                       CGCAAATTTGCG            24.0   \n",
       "7  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...           165.0   \n",
       "8  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...           154.0   \n",
       "9                                       GTGGAATGGAAC            24.0   \n",
       "\n",
       "  macromoleculeType_y  \n",
       "0      DNA/RNA Hybrid  \n",
       "1      DNA/RNA Hybrid  \n",
       "2                 DNA  \n",
       "3                 DNA  \n",
       "4             Protein  \n",
       "5                 DNA  \n",
       "6                 DNA  \n",
       "7             Protein  \n",
       "8             Protein  \n",
       "9                 DNA  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer Join(df1+df2)\n",
    "outer_df = pd.merge(df1,df2, how='outer', on='structureId')\n",
    "outer_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(471811, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outer_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Column \"inner_df\" will be used for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType_x</th>\n",
       "      <th>residueCount_x</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>publicationYear</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount_y</th>\n",
       "      <th>macromoleculeType_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId    classification experimentalTechnique macromoleculeType_x  \\\n",
       "0        100D    DNA-RNA HYBRID     X-RAY DIFFRACTION      DNA/RNA Hybrid   \n",
       "1        100D    DNA-RNA HYBRID     X-RAY DIFFRACTION      DNA/RNA Hybrid   \n",
       "2        101D               DNA     X-RAY DIFFRACTION                 DNA   \n",
       "3        101D               DNA     X-RAY DIFFRACTION                 DNA   \n",
       "4        101M  OXYGEN TRANSPORT     X-RAY DIFFRACTION             Protein   \n",
       "\n",
       "   residueCount_x  resolution  structureMolecularWeight  \\\n",
       "0              20        1.90                   6360.30   \n",
       "1              20        1.90                   6360.30   \n",
       "2              24        2.25                   7939.35   \n",
       "3              24        2.25                   7939.35   \n",
       "4             154        2.07                  18112.80   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "2                            NaN                   NaN             2.00   \n",
       "3                            NaN                   NaN             2.00   \n",
       "4                            NaN                   NaN             3.09   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "2              38.45                                                NaN   \n",
       "3              38.45                                                NaN   \n",
       "4              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "\n",
       "   phValue  publicationYear chainId  \\\n",
       "0      7.0           1994.0       A   \n",
       "1      7.0           1994.0       B   \n",
       "2      NaN           1995.0       A   \n",
       "3      NaN           1995.0       B   \n",
       "4      9.0           1999.0       A   \n",
       "\n",
       "                                            sequence  residueCount_y  \\\n",
       "0                                         CCGGCGCCGG              20   \n",
       "1                                         CCGGCGCCGG              20   \n",
       "2                                       CGCGAATTCGCG              24   \n",
       "3                                       CGCGAATTCGCG              24   \n",
       "4  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...             154   \n",
       "\n",
       "  macromoleculeType_y  \n",
       "0      DNA/RNA Hybrid  \n",
       "1      DNA/RNA Hybrid  \n",
       "2                 DNA  \n",
       "3                 DNA  \n",
       "4             Protein  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning new variable name for \"inner_df\"\n",
    "protein_class_df = inner_df\n",
    "protein_class_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Removing Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 471149 entries, 0 to 471148\n",
      "Data columns (total 18 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   structureId               471149 non-null  object \n",
      " 1   classification            471145 non-null  object \n",
      " 2   experimentalTechnique     471149 non-null  object \n",
      " 3   macromoleculeType_x       435379 non-null  object \n",
      " 4   residueCount_x            471149 non-null  int64  \n",
      " 5   resolution                449486 non-null  float64\n",
      " 6   structureMolecularWeight  471149 non-null  float64\n",
      " 7   crystallizationMethod     314754 non-null  object \n",
      " 8   crystallizationTempK      317697 non-null  float64\n",
      " 9   densityMatthews           389645 non-null  float64\n",
      " 10  densityPercentSol         389768 non-null  float64\n",
      " 11  pdbxDetails               385468 non-null  object \n",
      " 12  phValue                   340694 non-null  float64\n",
      " 13  publicationYear           413513 non-null  float64\n",
      " 14  chainId                   471139 non-null  object \n",
      " 15  sequence                  471121 non-null  object \n",
      " 16  residueCount_y            471149 non-null  int64  \n",
      " 17  macromoleculeType_y       435379 non-null  object \n",
      "dtypes: float64(7), int64(2), object(9)\n",
      "memory usage: 64.7+ MB\n"
     ]
    }
   ],
   "source": [
    "protein_class_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Irrelevant columns will be removed from dataframe\n",
    "* The classification and sequence data are pertinent for further analysis. I will remove any missing or abherrant information from these 2 columns as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType_x</th>\n",
       "      <th>residueCount_x</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount_y</th>\n",
       "      <th>macromoleculeType_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100D</td>\n",
       "      <td>DNA-RNA HYBRID</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "      <td>20</td>\n",
       "      <td>1.90</td>\n",
       "      <td>6360.30</td>\n",
       "      <td>VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.78</td>\n",
       "      <td>30.89</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, HANGING DROP</td>\n",
       "      <td>7.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CCGGCGCCGG</td>\n",
       "      <td>20</td>\n",
       "      <td>DNA/RNA Hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7939.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>38.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCGAATTCGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>2.20</td>\n",
       "      <td>7637.17</td>\n",
       "      <td>VAPOR DIFFUSION, SITTING DROP</td>\n",
       "      <td>277.0</td>\n",
       "      <td>2.28</td>\n",
       "      <td>46.06</td>\n",
       "      <td>pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>B</td>\n",
       "      <td>CGCAAATTTGCG</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "      <td>165</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>103D</td>\n",
       "      <td>DNA</td>\n",
       "      <td>SOLUTION NMR</td>\n",
       "      <td>DNA</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7502.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>GTGGAATGGAAC</td>\n",
       "      <td>24</td>\n",
       "      <td>DNA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  structureId         classification experimentalTechnique  \\\n",
       "0        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "1        100D         DNA-RNA HYBRID     X-RAY DIFFRACTION   \n",
       "2        101D                    DNA     X-RAY DIFFRACTION   \n",
       "3        101D                    DNA     X-RAY DIFFRACTION   \n",
       "4        101M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "5        102D                    DNA     X-RAY DIFFRACTION   \n",
       "6        102D                    DNA     X-RAY DIFFRACTION   \n",
       "7        102L  HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "8        102M       OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "9        103D                    DNA          SOLUTION NMR   \n",
       "\n",
       "  macromoleculeType_x  residueCount_x  resolution  structureMolecularWeight  \\\n",
       "0      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "1      DNA/RNA Hybrid              20        1.90                   6360.30   \n",
       "2                 DNA              24        2.25                   7939.35   \n",
       "3                 DNA              24        2.25                   7939.35   \n",
       "4             Protein             154        2.07                  18112.80   \n",
       "5                 DNA              24        2.20                   7637.17   \n",
       "6                 DNA              24        2.20                   7637.17   \n",
       "7             Protein             165        1.74                  18926.61   \n",
       "8             Protein             154        1.84                  18010.64   \n",
       "9                 DNA              24         NaN                   7502.93   \n",
       "\n",
       "           crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "0  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "1  VAPOR DIFFUSION, HANGING DROP                   NaN             1.78   \n",
       "2                            NaN                   NaN             2.00   \n",
       "3                            NaN                   NaN             2.00   \n",
       "4                            NaN                   NaN             3.09   \n",
       "5  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "6  VAPOR DIFFUSION, SITTING DROP                 277.0             2.28   \n",
       "7                            NaN                   NaN             2.75   \n",
       "8                            NaN                   NaN             3.09   \n",
       "9                            NaN                   NaN              NaN   \n",
       "\n",
       "   densityPercentSol                                        pdbxDetails  \\\n",
       "0              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "1              30.89             pH 7.00, VAPOR DIFFUSION, HANGING DROP   \n",
       "2              38.45                                                NaN   \n",
       "3              38.45                                                NaN   \n",
       "4              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "5              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "6              46.06  pH 7.00, VAPOR DIFFUSION, SITTING DROP, temper...   \n",
       "7              55.28                                                NaN   \n",
       "8              60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "9                NaN                                                NaN   \n",
       "\n",
       "   phValue chainId                                           sequence  \\\n",
       "0      7.0       A                                         CCGGCGCCGG   \n",
       "1      7.0       B                                         CCGGCGCCGG   \n",
       "2      NaN       A                                       CGCGAATTCGCG   \n",
       "3      NaN       B                                       CGCGAATTCGCG   \n",
       "4      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "5      7.0       A                                       CGCAAATTTGCG   \n",
       "6      7.0       B                                       CGCAAATTTGCG   \n",
       "7      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...   \n",
       "8      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "9      NaN       A                                       GTGGAATGGAAC   \n",
       "\n",
       "   residueCount_y macromoleculeType_y  \n",
       "0              20      DNA/RNA Hybrid  \n",
       "1              20      DNA/RNA Hybrid  \n",
       "2              24                 DNA  \n",
       "3              24                 DNA  \n",
       "4             154             Protein  \n",
       "5              24                 DNA  \n",
       "6              24                 DNA  \n",
       "7             165             Protein  \n",
       "8             154             Protein  \n",
       "9              24                 DNA  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping publication year column(Column not relavant for assessing protein classificaion)\n",
    "protein_class_df.drop('publicationYear', axis=1, inplace=True)\n",
    "protein_class_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structureId\n",
       "False    471149\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for nulls in \"structureId\" column\n",
    "protein_class_df['structureId'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are no null values in the \"structureId\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequence\n",
       "False    471121\n",
       "True         28\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for nulls in \"sequence\" column\n",
    "protein_class_df['sequence'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are 28 null values in the \"sequence\" column that need to be removed from the dataframe in addition to the associated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_class_df.dropna(subset=['sequence'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sequence\n",
       "False    471121\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming that nulls were removed from \"sequence\" column\n",
    "protein_class_df['sequence'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structureId\n",
       "False    471121\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming that the necessary columns were removed\n",
    "protein_class_df['structureId'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis(EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking object('string') type in 'sequence' columnb\n",
    "protein_class_df['sequence'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "macromoleculeType_x\n",
       "Protein                       346322\n",
       "Protein#RNA                    57898\n",
       "Protein#DNA                    21343\n",
       "DNA                             3794\n",
       "Protein#DNA#RNA                 2712\n",
       "RNA                             2410\n",
       "Protein#RNA#DNA/RNA Hybrid       304\n",
       "Protein#DNA#DNA/RNA Hybrid       159\n",
       "DNA/RNA Hybrid                   141\n",
       "DNA#RNA                          125\n",
       "RNA#DNA/RNA Hybrid                74\n",
       "Protein#DNA/RNA Hybrid            68\n",
       "DNA#DNA/RNA Hybrid                26\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Frequent macromoleule Types\n",
    "protein_class_df['macromoleculeType_x'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346322, 17)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subsetting dataframe so that protein is the only macromolecule type displayed in the dataframe\n",
    "protein_classification = protein_class_df[protein_class_df.macromoleculeType_x == 'Protein']\n",
    "protein_classification.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>structureId</th>\n",
       "      <th>classification</th>\n",
       "      <th>experimentalTechnique</th>\n",
       "      <th>macromoleculeType_x</th>\n",
       "      <th>residueCount_x</th>\n",
       "      <th>resolution</th>\n",
       "      <th>structureMolecularWeight</th>\n",
       "      <th>crystallizationMethod</th>\n",
       "      <th>crystallizationTempK</th>\n",
       "      <th>densityMatthews</th>\n",
       "      <th>densityPercentSol</th>\n",
       "      <th>pdbxDetails</th>\n",
       "      <th>phValue</th>\n",
       "      <th>chainId</th>\n",
       "      <th>sequence</th>\n",
       "      <th>residueCount_y</th>\n",
       "      <th>macromoleculeType_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18112.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>102L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>165</td>\n",
       "      <td>1.74</td>\n",
       "      <td>18926.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.75</td>\n",
       "      <td>55.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...</td>\n",
       "      <td>165</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.84</td>\n",
       "      <td>18010.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.20</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>103L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>167</td>\n",
       "      <td>1.90</td>\n",
       "      <td>19092.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>54.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNSLDAAK...</td>\n",
       "      <td>167</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>103M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.07</td>\n",
       "      <td>18093.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.09</td>\n",
       "      <td>60.30</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>104L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>332</td>\n",
       "      <td>2.80</td>\n",
       "      <td>37541.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.04</td>\n",
       "      <td>59.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSAA...</td>\n",
       "      <td>332</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>332</td>\n",
       "      <td>2.80</td>\n",
       "      <td>37541.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.04</td>\n",
       "      <td>59.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSAA...</td>\n",
       "      <td>332</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>104M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>1.71</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.87</td>\n",
       "      <td>34.30</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, UNBUFFERED, pH 7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>153</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>105M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>153</td>\n",
       "      <td>2.02</td>\n",
       "      <td>18030.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.83</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, UNBUFFERED, pH 9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...</td>\n",
       "      <td>153</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>106M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.99</td>\n",
       "      <td>18181.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05</td>\n",
       "      <td>59.70</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>107L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>164</td>\n",
       "      <td>1.80</td>\n",
       "      <td>18825.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.81</td>\n",
       "      <td>56.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKGEL...</td>\n",
       "      <td>164</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>107M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.09</td>\n",
       "      <td>18208.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.08</td>\n",
       "      <td>60.10</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>108L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>164</td>\n",
       "      <td>1.80</td>\n",
       "      <td>18881.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.79</td>\n",
       "      <td>55.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKIEL...</td>\n",
       "      <td>164</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>108M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18208.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.07</td>\n",
       "      <td>60.00</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>109L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>164</td>\n",
       "      <td>1.85</td>\n",
       "      <td>18897.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>55.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKKEL...</td>\n",
       "      <td>164</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>109M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.83</td>\n",
       "      <td>18133.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.07</td>\n",
       "      <td>59.90</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>10GS</td>\n",
       "      <td>TRANSFERASE/TRANSFERASE INHIBITOR</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>418</td>\n",
       "      <td>2.20</td>\n",
       "      <td>47830.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.61</td>\n",
       "      <td>52.86</td>\n",
       "      <td>pH 5.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>A</td>\n",
       "      <td>PPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKAS...</td>\n",
       "      <td>418</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10GS</td>\n",
       "      <td>TRANSFERASE/TRANSFERASE INHIBITOR</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>418</td>\n",
       "      <td>2.20</td>\n",
       "      <td>47830.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.61</td>\n",
       "      <td>52.86</td>\n",
       "      <td>pH 5.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>B</td>\n",
       "      <td>PPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKAS...</td>\n",
       "      <td>418</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>110L</td>\n",
       "      <td>HYDROLASE(O-GLYCOSYL)</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>164</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18881.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.78</td>\n",
       "      <td>55.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKLEL...</td>\n",
       "      <td>164</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>110M</td>\n",
       "      <td>OXYGEN TRANSPORT</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>Protein</td>\n",
       "      <td>154</td>\n",
       "      <td>1.77</td>\n",
       "      <td>18118.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.06</td>\n",
       "      <td>59.90</td>\n",
       "      <td>3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>A</td>\n",
       "      <td>MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...</td>\n",
       "      <td>154</td>\n",
       "      <td>Protein</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   structureId                     classification experimentalTechnique  \\\n",
       "4         101M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "7         102L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "8         102M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "11        103L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "12        103M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "15        104L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "16        104L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "17        104M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "22        105M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "27        106M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "30        107L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "31        107M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "34        108L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "35        108M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "38        109L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "39        109M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "40        10GS  TRANSFERASE/TRANSFERASE INHIBITOR     X-RAY DIFFRACTION   \n",
       "41        10GS  TRANSFERASE/TRANSFERASE INHIBITOR     X-RAY DIFFRACTION   \n",
       "46        110L              HYDROLASE(O-GLYCOSYL)     X-RAY DIFFRACTION   \n",
       "47        110M                   OXYGEN TRANSPORT     X-RAY DIFFRACTION   \n",
       "\n",
       "   macromoleculeType_x  residueCount_x  resolution  structureMolecularWeight  \\\n",
       "4              Protein             154        2.07                  18112.80   \n",
       "7              Protein             165        1.74                  18926.61   \n",
       "8              Protein             154        1.84                  18010.64   \n",
       "11             Protein             167        1.90                  19092.72   \n",
       "12             Protein             154        2.07                  18093.78   \n",
       "15             Protein             332        2.80                  37541.04   \n",
       "16             Protein             332        2.80                  37541.04   \n",
       "17             Protein             153        1.71                  18030.63   \n",
       "22             Protein             153        2.02                  18030.63   \n",
       "27             Protein             154        1.99                  18181.84   \n",
       "30             Protein             164        1.80                  18825.51   \n",
       "31             Protein             154        2.09                  18208.89   \n",
       "34             Protein             164        1.80                  18881.62   \n",
       "35             Protein             154        2.67                  18208.89   \n",
       "38             Protein             164        1.85                  18897.64   \n",
       "39             Protein             154        1.83                  18133.80   \n",
       "40             Protein             418        2.20                  47830.70   \n",
       "41             Protein             418        2.20                  47830.70   \n",
       "46             Protein             164        1.70                  18881.62   \n",
       "47             Protein             154        1.77                  18118.77   \n",
       "\n",
       "   crystallizationMethod  crystallizationTempK  densityMatthews  \\\n",
       "4                    NaN                   NaN             3.09   \n",
       "7                    NaN                   NaN             2.75   \n",
       "8                    NaN                   NaN             3.09   \n",
       "11                   NaN                   NaN             2.70   \n",
       "12                   NaN                   NaN             3.09   \n",
       "15                   NaN                   NaN             3.04   \n",
       "16                   NaN                   NaN             3.04   \n",
       "17                   NaN                   NaN             1.87   \n",
       "22                   NaN                   NaN             1.83   \n",
       "27                   NaN                   NaN             3.05   \n",
       "30                   NaN                   NaN             2.81   \n",
       "31                   NaN                   NaN             3.08   \n",
       "34                   NaN                   NaN             2.79   \n",
       "35                   NaN                   NaN             3.07   \n",
       "38                   NaN                   NaN             2.76   \n",
       "39                   NaN                   NaN             3.07   \n",
       "40                   NaN                   NaN             2.61   \n",
       "41                   NaN                   NaN             2.61   \n",
       "46                   NaN                   NaN             2.78   \n",
       "47                   NaN                   NaN             3.06   \n",
       "\n",
       "    densityPercentSol                                        pdbxDetails  \\\n",
       "4               60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "7               55.28                                                NaN   \n",
       "8               60.20  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "11              54.46                                                NaN   \n",
       "12              60.30  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "15              59.49                                                NaN   \n",
       "16              59.49                                                NaN   \n",
       "17              34.30         3.0 M AMMONIUM SULFATE, UNBUFFERED, pH 7.0   \n",
       "22              33.00         3.0 M AMMONIUM SULFATE, UNBUFFERED, pH 9.0   \n",
       "27              59.70  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "30              56.17                                                NaN   \n",
       "31              60.10  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "34              55.90                                                NaN   \n",
       "35              60.00  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "38              55.47                                                NaN   \n",
       "39              59.90  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "40              52.86                                             pH 5.8   \n",
       "41              52.86                                             pH 5.8   \n",
       "46              55.71                                                NaN   \n",
       "47              59.90  3.0 M AMMONIUM SULFATE, 20 MM TRIS, 1MM EDTA, ...   \n",
       "\n",
       "    phValue chainId                                           sequence  \\\n",
       "4       9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "7       NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAAKSE...   \n",
       "8       9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "11      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNSLDAAK...   \n",
       "12      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "15      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSAA...   \n",
       "16      NaN       B  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSAA...   \n",
       "17      7.0       A  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...   \n",
       "22      9.0       A  VLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDRF...   \n",
       "27      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "30      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKGEL...   \n",
       "31      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "34      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKIEL...   \n",
       "35      7.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "38      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKKEL...   \n",
       "39      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "40      5.8       A  PPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKAS...   \n",
       "41      5.8       B  PPYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKAS...   \n",
       "46      NaN       A  MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKLEL...   \n",
       "47      9.0       A  MVLSEGEWQLVLHVWAKVEADVAGHGQDILIRLFKSHPETLEKFDR...   \n",
       "\n",
       "    residueCount_y macromoleculeType_y  \n",
       "4              154             Protein  \n",
       "7              165             Protein  \n",
       "8              154             Protein  \n",
       "11             167             Protein  \n",
       "12             154             Protein  \n",
       "15             332             Protein  \n",
       "16             332             Protein  \n",
       "17             153             Protein  \n",
       "22             153             Protein  \n",
       "27             154             Protein  \n",
       "30             164             Protein  \n",
       "31             154             Protein  \n",
       "34             164             Protein  \n",
       "35             154             Protein  \n",
       "38             164             Protein  \n",
       "39             154             Protein  \n",
       "40             418             Protein  \n",
       "41             418             Protein  \n",
       "46             164             Protein  \n",
       "47             154             Protein  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing final dataframe\n",
    "protein_classification.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Duplicates are still present in the dataset\n",
    "* Difference between duplicates are found in the \"chainId\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classification\n",
       "HYDROLASE                            46336\n",
       "TRANSFERASE                          36424\n",
       "OXIDOREDUCTASE                       34321\n",
       "IMMUNE SYSTEM                        15615\n",
       "LYASE                                11682\n",
       "                                     ...  \n",
       "STRUCTURAL PROTEIN, CELL CYCLE           1\n",
       "KINASE (GLYCOGEN METABOLISM)             1\n",
       "SURFACE GLYCOPROTEIN                     1\n",
       "IMMUNE SYSTEM,HYDROLASE INHIBITOR        1\n",
       "antimicrobial                            1\n",
       "Name: count, Length: 4468, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the protein type counts to determine the top 5 most frequent proteins in data set\n",
    "protein_classification['classification'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4468"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique proteins in 'classification' column\n",
    "protein_classification['classification'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating variable for top 5 proteins\n",
    "top5_proteins = protein_classification['classification'].value_counts().head(5)\n",
    "\n",
    "# Plotting the histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "top5_proteins.plot(kind='bar', color=['blue', 'green', 'red', 'purple', 'orange'])\n",
    "plt.title('Top 5 Proteins')\n",
    "plt.xlabel('Classification')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.savefig('Images/top_5_proteins(1).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  The 5 most frequent proteins were used as target features primarily to simplify the modeling process. There are 4,468 unique values for proteins in the 'classification' column which would increase the overall complexity of the modeling process. In addition to this there were a number of unique proteins with a value of one which would not be adequate for training due to a massive class imbalance between the top 5 occuring proteins and the single count proteins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jedia\\AppData\\Local\\Temp\\ipykernel_12980\\1300708135.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  protein_classification['sequence_length'] = sequence_lengths\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the length of each sequence\n",
    "sequence_lengths = protein_classification['sequence'].apply(len)\n",
    "protein_classification['sequence_length'] = sequence_lengths\n",
    "\n",
    "# Plotting the distribution of sequence lengths\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(protein_classification['sequence_length'], bins=30, color='skyblue', edgecolor='black')\n",
    "plt.title('Distribution of Sequence Lengths')\n",
    "plt.xlabel('Sequence Length')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.savefig('Images/Sequence_Length_Distribution(2).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Most amino acid sequences are 500 and below in length. This gives an overall picture of the length and complexity of the amino acid sequences in the dataset and helps to assess how to incorporate these sequences in the modeling process. Amino acids can be handled as text and can be analyzed using classic NLP methods, such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. Approaches such as one hot encoding can be utilized as well, but based on the size of the amino acid sequence lengths in the above chart it could generate far too many columns and be difficult to deal with for analysis. The most practical option at the moment is Label Encoding which assigns a unique value to each sequence present in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experimentalTechnique\n",
       "X-RAY DIFFRACTION                                            311210\n",
       "ELECTRON MICROSCOPY                                           21641\n",
       "SOLUTION NMR                                                  12356\n",
       "SOLID-STATE NMR                                                 440\n",
       "ELECTRON CRYSTALLOGRAPHY                                        147\n",
       "SOLUTION SCATTERING                                              93\n",
       "NEUTRON DIFFRACTION                                              85\n",
       "SOLID-STATE NMR, ELECTRON MICROSCOPY                             78\n",
       "NEUTRON DIFFRACTION, X-RAY DIFFRACTION                           50\n",
       "X-RAY DIFFRACTION, NEUTRON DIFFRACTION                           39\n",
       "POWDER DIFFRACTION                                               32\n",
       "FIBER DIFFRACTION                                                28\n",
       "ELECTRON MICROSCOPY, SOLUTION SCATTERING, SOLID-STATE NMR        24\n",
       "ELECTRON MICROSCOPY, SOLID-STATE NMR                             16\n",
       "SOLUTION SCATTERING, SOLUTION NMR                                15\n",
       "X-RAY DIFFRACTION, EPR                                           13\n",
       "SOLUTION NMR, THEORETICAL MODEL                                  11\n",
       "SOLUTION NMR, SOLUTION SCATTERING                                 9\n",
       "SOLID-STATE NMR, SOLUTION NMR                                     7\n",
       "EPR, X-RAY DIFFRACTION                                            7\n",
       "SOLUTION NMR, SOLID-STATE NMR                                     5\n",
       "INFRARED SPECTROSCOPY                                             4\n",
       "ELECTRON MICROSCOPY, SOLUTION SCATTERING                          4\n",
       "SOLUTION SCATTERING, X-RAY DIFFRACTION                            2\n",
       "SOLUTION SCATTERING, ELECTRON MICROSCOPY                          2\n",
       "SOLUTION NMR, EPR                                                 1\n",
       "FIBER DIFFRACTION, SOLID-STATE NMR                                1\n",
       "X-RAY DIFFRACTION, SOLUTION NMR                                   1\n",
       "NEUTRON DIFFRACTION, SOLUTION NMR                                 1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimental_tech = protein_classification['experimentalTechnique'].value_counts()\n",
    "experimental_tech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/nT592t3hQU6/xjdr1kzS1WTqWhs3btSuXbt03333SZLq1asnf39/zZ8/36NcXFxcrpuwSVeThoxtyfDTTz9lGRurIERFRenWW2/VRx995NHBy/nz57VgwQJ3j5IFKT4+3j2MwNq1a9W+fXv16tVLBw4cyFJ2+fLlHklyWlqa5s+fr4oVK7oT7NjYWO3YsUMVK1ZU3bp1s/xlTtrsEhsbq/3796tEiRLZbldOTWqvJ6fvcEYimvl79fbbb+dt4/9Abvctowlp5h8oCkPTpk119uzZLB31fPTRR1nKli9fXnv37lVKSop73qlTp7R27VqPcrGxsTp16pTS0tKyPQ5RUVFZ1u3t7a169erpzTfflCT973//s2L3ANiEJ20AkA/t2rVTdHS06tatq1KlSunQoUN6/fXXVa5cOXfvgBlN8N544w11795dvr6+ioqKUlRUlJ544glNmzZNXl5eat26tbv3yLJly+rZZ5+VJHd37+PHj1fx4sXVsWNHHT16VKNGjVLp0qU93hG7ntjYWL3yyisaMWKEGjdurD179mj06NGqUKFCtj0vWsnLy0uTJk3SI488otjYWPXp00cpKSmaPHmyzpw5owkTJuRr/fv27VNcXFyW+WXKlFGZMmWUlpamhx9+WC6XSx999JG8vb01e/Zs1apVS507d9aaNWs8mpqVLFlSzZo100svveTuPXL37t0e3f6PHj1ay5YtU8OGDdW/f39FRUXp0qVLOnjwoBYvXqyZM2dmeYJqh4EDB2rBggVq1KiRnn32WdWsWVPp6ek6fPiwli5dqsGDB6tevXo3tM6cvsMNGzZU8eLF9eSTT2rEiBHy9fXVhx9+qG3bthXEruV63+69915169ZNY8aM0fHjxxUbGyt/f39t2bJFgYGBeuaZZwpk+7Lz6KOP6rXXXtOjjz6qsWPHqnLlylq8eLG+/fbbLGW7deumt99+W127dlXv3r116tQpTZo0KUsPqg899JA+/PBDtWnTRgMGDNDdd98tX19fHT16VCtXrtT999+vjh07aubMmVqxYoXatm2r2267TZcuXXI/pWzevLkt+w/AGiRtAJAPTZs21YIFC/Sf//xHycnJioiIUIsWLfTSSy+537tp0qSJhg8frvfff1///ve/lZ6erpUrV7qbKlasWFHvvvuu3nzzTYWGhqpVq1YaP368x/szY8eOVVBQkGbOnKlZs2apatWqmjFjhl588UUVK1YsV9v64osv6sKFC3r33Xc1adIkVa9eXTNnztTChQuzdE5RELp06aKgoCCNHz9enTt3lre3t+rXr6+VK1eqYcOG+Vr3Cy+8kO38F198UWPGjNGIESP0ww8/aNmyZe53iYoXL6558+apUaNGGjp0qF5//XX359q3b6877rhD//znP3X48GFVrFhRH374oUc37aVLl9amTZv0yiuvaPLkyTp69KiCg4NVoUIFtWrVypbmntkJCgrSDz/8oAkTJuidd97RgQMH3ONzNW/ePE9P2q73Hf766681ePBgde3aVUFBQbr//vs1f/583XXXXYW6b7Nnz9Zdd92ld999V7Nnz1ZAQICqV6+e43eloAQGBmrFihUaMGCAhg0bJpfLpZiYGM2bNy/L9/6ee+7R+++/rwkTJuj+++/X7bffrhEjRmjx4sUeddTb21tfffWV3njjDc2dO1fjx4+Xj4+PypQpo8aNG7uT7Fq1amnp0qUaMWKEEhISVLRoUUVHR+urr75STEyMnYcBQD65jMnFQEQAAMc5cOCAqlatqhEjRth+I3ozc7lc6tevn6ZPn17Ym4Kb2MGDB1WhQgXNmjUrSw+yAJAZT9oA4E9g27Zt+vjjj9WwYUOFhIRoz5497mZTmQeGBgAANxeSNgD4EwgKCtKmTZv07rvv6syZMwoNDVWTJk00duzYHLv9BwAANweaRwIAAACAg9HlPwAAAAA4GEkbAAAAADgYSRsAAAAAOBgdkdgsPT1dx44dU3BwsFwuV2FvDgAAAIBCYozR2bNnFRkZKS+vnJ+nkbTZ7NixYypbtmxhbwYAAAAAhzhy5IjKlCmT43KSNpsFBwdLuvofExISUshbAwAAAKCwJCcnq2zZsu4cISckbTbLaBIZEhJC0gYAAADgD1+boiMSAAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwn8LeAPw/o1yj8vS5EWaExVsCAAAAwCl40gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAOVqhJ24wZM1SzZk2FhIQoJCREDRo00DfffONebozRyJEjFRkZqYCAADVp0kQ7d+70WEdKSoqeeeYZlSxZUkFBQWrfvr2OHj3qUSYxMVHdunVTaGioQkND1a1bN505c8ajzOHDh9WuXTsFBQWpZMmS6t+/vy5fvuxRZvv27WrcuLECAgJ06623avTo0TLGWHtQAAAAAOAahZq0lSlTRhMmTNCmTZu0adMmNWvWTPfff787MZs0aZKmTp2q6dOna+PGjYqIiFCLFi109uxZ9zoGDhyohQsXat68eVqzZo3OnTun2NhYpaWluct06dJFW7du1ZIlS7RkyRJt3bpV3bp1cy9PS0tT27Ztdf78ea1Zs0bz5s3TggULNHjwYHeZ5ORktWjRQpGRkdq4caOmTZumKVOmaOrUqTYcKQAAAAB/VS7jsEdFt9xyiyZPnqyePXsqMjJSAwcO1PPPPy/p6lO18PBwTZw4UX369FFSUpJKlSqluXPnqnPnzpKkY8eOqWzZslq8eLFatmypXbt2qXr16oqLi1O9evUkSXFxcWrQoIF2796tqKgoffPNN4qNjdWRI0cUGRkpSZo3b5569OihEydOKCQkRDNmzNDw4cN1/Phx+fv7S5ImTJigadOm6ejRo3K5XLnav+TkZIWGhiopKUkhISEey0a5RuXpmI0wI/L0OQAAAACF53q5wbV8bNym60pLS9Onn36q8+fPq0GDBjpw4IASEhIUExPjLuPv76/GjRtr7dq16tOnjzZv3qzU1FSPMpGRkYqOjtbatWvVsmVLrVu3TqGhoe6ETZLq16+v0NBQrV27VlFRUVq3bp2io6PdCZsktWzZUikpKdq8ebOaNm2qdevWqXHjxu6ELaPM8OHDdfDgQVWoUCHb/UpJSVFKSop7Ojk5WZKUmpqq1NRUj7JeAXl78Jl5PQAAAACcL7f38YWetG3fvl0NGjTQpUuXVLRoUS1cuFDVq1fX2rVrJUnh4eEe5cPDw3Xo0CFJUkJCgvz8/FS8ePEsZRISEtxlwsLCssQNCwvzKJM5TvHixeXn5+dRpnz58lniZCzLKWkbP368Ro3K+gRt6dKlCgwM9JhX8+Oa2a7jjyxevDhPnwMAAABQeC5cuJCrcoWetEVFRWnr1q06c+aMFixYoO7du2vVqlXu5ZmbHRpj/rApYuYy2ZW3okxGy9Lrbc/w4cM1aNAg93RycrLKli2rmJiYLI9AJ4ROyHE91zMsaViePgcAAACg8GS0wvsjhZ60+fn5qVKlSpKkunXrauPGjXrjjTfc77ElJCSodOnS7vInTpxwP+GKiIjQ5cuXlZiY6PG07cSJE2rYsKG7zPHjx7PEPXnypMd61q9f77E8MTFRqampHmUynrpdG0fK+jTwWv7+/h5NKjP4+vrK19fXY176xfQc13M9mdcDAAAAwPlyex/vuHHajDFKSUlRhQoVFBERoWXLlrmXXb58WatWrXInZHXq1JGvr69Hmfj4eO3YscNdpkGDBkpKStKGDRvcZdavX6+kpCSPMjt27FB8fLy7zNKlS+Xv7686deq4y6xevdpjGIClS5cqMjIyS7NJAAAAALBKoSZtL7zwgn744QcdPHhQ27dv14svvqjvv/9ejzzyiFwulwYOHKhx48Zp4cKF2rFjh3r06KHAwEB16dJFkhQaGqpevXpp8ODBWr58ubZs2aKuXbuqRo0aat68uSSpWrVqatWqlXr37q24uDjFxcWpd+/eio2NVVRUlCQpJiZG1atXV7du3bRlyxYtX75cQ4YMUe/evd1NGLt06SJ/f3/16NFDO3bs0MKFCzVu3DgNGjQo1z1HAgAAAMCNKtTmkcePH1e3bt0UHx+v0NBQ1axZU0uWLFGLFi0kSUOHDtXFixfVt29fJSYmql69elq6dKmCg4Pd63jttdfk4+OjTp066eLFi7rvvvs0e/ZseXt7u8t8+OGH6t+/v7uXyfbt22v69Onu5d7e3vr666/Vt29f3XPPPQoICFCXLl00ZcoUd5nQ0FAtW7ZM/fr1U926dVW8eHENGjTI4301AAAAALCa48Zpu9kxThsAAAAAKffjtDnunTYAAAAAwP9D0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADkbSBgAAAAAORtIGAAAAAA5G0gYAAAAADlaoSdv48eP1t7/9TcHBwQoLC1OHDh20Z88ejzI9evSQy+Xy+Ktfv75HmZSUFD3zzDMqWbKkgoKC1L59ex09etSjTGJiorp166bQ0FCFhoaqW7duOnPmjEeZw4cPq127dgoKClLJkiXVv39/Xb582aPM9u3b1bhxYwUEBOjWW2/V6NGjZYyx7qAAAAAAwDUKNWlbtWqV+vXrp7i4OC1btkxXrlxRTEyMzp8/71GuVatWio+Pd/8tXrzYY/nAgQO1cOFCzZs3T2vWrNG5c+cUGxurtLQ0d5kuXbpo69atWrJkiZYsWaKtW7eqW7du7uVpaWlq27atzp8/rzVr1mjevHlasGCBBg8e7C6TnJysFi1aKDIyUhs3btS0adM0ZcoUTZ06tYCOEAAAAIC/Op/CDL5kyRKP6VmzZiksLEybN29Wo0aN3PP9/f0VERGR7TqSkpL07rvvau7cuWrevLkk6YMPPlDZsmX13XffqWXLltq1a5eWLFmiuLg41atXT5L073//Ww0aNNCePXsUFRWlpUuX6ueff9aRI0cUGRkpSXr11VfVo0cPjR07ViEhIfrwww916dIlzZ49W/7+/oqOjtbevXs1depUDRo0SC6XqyAOEwAAAIC/sEJN2jJLSkqSJN1yyy0e87///nuFhYWpWLFiaty4scaOHauwsDBJ0ubNm5WamqqYmBh3+cjISEVHR2vt2rVq2bKl1q1bp9DQUHfCJkn169dXaGio1q5dq6ioKK1bt07R0dHuhE2SWrZsqZSUFG3evFlNmzbVunXr1LhxY/n7+3uUGT58uA4ePKgKFSpk2aeUlBSlpKS4p5OTkyVJqampSk1N9SjrFZC3B5+Z1wMAAADA+XJ7H++YpM0Yo0GDBunvf/+7oqOj3fNbt26t//u//1O5cuV04MABvfTSS2rWrJk2b94sf39/JSQkyM/PT8WLF/dYX3h4uBISEiRJCQkJ7iTvWmFhYR5lwsPDPZYXL15cfn5+HmXKly+fJU7GsuyStvHjx2vUqFFZ5i9dulSBgYEe82p+XDPbY/NHMjcXBQAAAOB8Fy5cyFU5xyRtTz/9tH766SetWbPGY37nzp3d/46OjlbdunVVrlw5ff3113rggQdyXJ8xxqO5YnZNF60ok9EJSU5NI4cPH65Bgwa5p5OTk1W2bFnFxMQoJCTEo+yE0Ak57s/1DEsalqfPAQAAACg8Ga3w/ogjkrZnnnlGX331lVavXq0yZcpct2zp0qVVrlw57du3T5IUERGhy5cvKzEx0eNp24kTJ9SwYUN3mePHj2dZ18mTJ91PyiIiIrR+/XqP5YmJiUpNTfUok/HU7do4krI8pcvg7+/v0Zwyg6+vr3x9fT3mpV9Mz3nHryPzegAAAAA4X27v4wu190hjjJ5++ml9/vnnWrFiRbbNCzM7deqUjhw5otKlS0uS6tSpI19fXy1btsxdJj4+Xjt27HAnbQ0aNFBSUpI2bNjgLrN+/XolJSV5lNmxY4fi4+PdZZYuXSp/f3/VqVPHXWb16tUewwAsXbpUkZGRWZpNAgAAAIAVCjVp69evnz744AN99NFHCg4OVkJCghISEnTx4kVJ0rlz5zRkyBCtW7dOBw8e1Pfff6927dqpZMmS6tixoyQpNDRUvXr10uDBg7V8+XJt2bJFXbt2VY0aNdy9SVarVk2tWrVS7969FRcXp7i4OPXu3VuxsbGKioqSJMXExKh69erq1q2btmzZouXLl2vIkCHq3bu3uxljly5d5O/vrx49emjHjh1auHChxo0bR8+RAAAAAApMoSZtM2bMUFJSkpo0aaLSpUu7/+bPny9J8vb21vbt23X//ferSpUq6t69u6pUqaJ169YpODjYvZ7XXntNHTp0UKdOnXTPPfcoMDBQixYtkre3t7vMhx9+qBo1aigmJkYxMTGqWbOm5s6d617u7e2tr7/+WkWKFNE999yjTp06qUOHDpoyZYq7TGhoqJYtW6ajR4+qbt266tu3rwYNGuTxzhoAAAAAWMllMnrSgC2Sk5MVGhqqpKSkLB2RjHJl7WUyN0aYEVZsGgAAAAAbXS83uFahPmkDAAAAAFwfSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4WKEmbePHj9ff/vY3BQcHKywsTB06dNCePXs8yhhjNHLkSEVGRiogIEBNmjTRzp07PcqkpKTomWeeUcmSJRUUFKT27dvr6NGjHmUSExPVrVs3hYaGKjQ0VN26ddOZM2c8yhw+fFjt2rVTUFCQSpYsqf79++vy5cseZbZv367GjRsrICBAt956q0aPHi1jjHUHBQAAAACuUahJ26pVq9SvXz/FxcVp2bJlunLlimJiYnT+/Hl3mUmTJmnq1KmaPn26Nm7cqIiICLVo0UJnz551lxk4cKAWLlyoefPmac2aNTp37pxiY2OVlpbmLtOlSxdt3bpVS5Ys0ZIlS7R161Z169bNvTwtLU1t27bV+fPntWbNGs2bN08LFizQ4MGD3WWSk5PVokULRUZGauPGjZo2bZqmTJmiqVOnFvCRAgAAAPBX5TIOekx08uRJhYWFadWqVWrUqJGMMYqMjNTAgQP1/PPPS7r6VC08PFwTJ05Unz59lJSUpFKlSmnu3Lnq3LmzJOnYsWMqW7asFi9erJYtW2rXrl2qXr264uLiVK9ePUlSXFycGjRooN27dysqKkrffPONYmNjdeTIEUVGRkqS5s2bpx49eujEiRMKCQnRjBkzNHz4cB0/flz+/v6SpAkTJmjatGk6evSoXC7XH+5jcnKyQkNDlZSUpJCQEI9lo1yj8nTcRpgRefocAAAAgMJzvdzgWj42btMfSkpKkiTdcsstkqQDBw4oISFBMTEx7jL+/v5q3Lix1q5dqz59+mjz5s1KTU31KBMZGano6GitXbtWLVu21Lp16xQaGupO2CSpfv36Cg0N1dq1axUVFaV169YpOjranbBJUsuWLZWSkqLNmzeradOmWrdunRo3buxO2DLKDB8+XAcPHlSFChWy7FNKSopSUlLc08nJyZKk1NRUpaamepT1Csjbg8/M6wEAAADgfLm9j3dM0maM0aBBg/T3v/9d0dHRkqSEhARJUnh4uEfZ8PBwHTp0yF3Gz89PxYsXz1Im4/MJCQkKCwvLEjMsLMyjTOY4xYsXl5+fn0eZ8uXLZ4mTsSy7pG38+PEaNSrrE7SlS5cqMDDQY17Nj2tmKZcbixcvztPnAAAAABSeCxcu5KqcY5K2p59+Wj/99JPWrFmTZVnmZofGmD9sipi5THblrSiT0bo0p+0ZPny4Bg0a5J5OTk5W2bJlFRMTk+UR6ITQCdfbpRwNSxqWp88BAAAAKDwZrfD+iCOStmeeeUZfffWVVq9erTJlyrjnR0RESLr6FKt06dLu+SdOnHA/4YqIiNDly5eVmJjo8bTtxIkTatiwobvM8ePHs8Q9efKkx3rWr1/vsTwxMVGpqakeZTKeul0bR8r6NDCDv7+/R3PKDL6+vvL19fWYl34xPdt1/JHM6wEAAADgfLm9jy/U3iONMXr66af1+eefa8WKFVmaF1aoUEERERFatmyZe97ly5e1atUqd0JWp04d+fr6epSJj4/Xjh073GUaNGigpKQkbdiwwV1m/fr1SkpK8iizY8cOxcfHu8ssXbpU/v7+qlOnjrvM6tWrPYYBWLp0qSIjI7M0mwQAAAAAKxRq0tavXz998MEH+uijjxQcHKyEhAQlJCTo4sWLkq42ORw4cKDGjRunhQsXaseOHerRo4cCAwPVpUsXSVJoaKh69eqlwYMHa/ny5dqyZYu6du2qGjVqqHnz5pKkatWqqVWrVurdu7fi4uIUFxen3r17KzY2VlFRUZKkmJgYVa9eXd26ddOWLVu0fPlyDRkyRL1793Y3Y+zSpYv8/f3Vo0cP7dixQwsXLtS4ceM0aNCgXPUcCQAAAAA3qlCbR86YMUOS1KRJE4/5s2bNUo8ePSRJQ4cO1cWLF9W3b18lJiaqXr16Wrp0qYKDg93lX3vtNfn4+KhTp066ePGi7rvvPs2ePVve3t7uMh9++KH69+/v7mWyffv2mj59unu5t7e3vv76a/Xt21f33HOPAgIC1KVLF02ZMsVdJjQ0VMuWLVO/fv1Ut25dFS9eXIMGDfJ4Zw0AAAAArOSocdr+ChinDQAAAICU+3HaCrV5JAAAAADg+kjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMHylLQdOHDA6u0AAAAAAGQjT0lbpUqV1LRpU33wwQe6dOmS1dsEAAAAAPj/5Slp27Ztm2rXrq3BgwcrIiJCffr00YYNG6zeNgAAAAD4y8tT0hYdHa2pU6fqt99+06xZs5SQkKC///3vuuOOOzR16lSdPHnS6u0EAAAAgL+kfHVE4uPjo44dO+qTTz7RxIkTtX//fg0ZMkRlypTRo48+qvj4eKu2EwAAAAD+kvKVtG3atEl9+/ZV6dKlNXXqVA0ZMkT79+/XihUr9Ntvv+n++++3ajsBAAAA4C/JJy8fmjp1qmbNmqU9e/aoTZs2mjNnjtq0aSMvr6s5YIUKFfT222+ratWqlm4sAAAAAPzV5ClpmzFjhnr27KnHHntMERER2Za57bbb9O677+Zr4wAAAADgry5PSdu+ffv+sIyfn5+6d++el9UDAAAAAP5/eXqnbdasWfr000+zzP/000/1/vvv53ujAAAAAABX5SlpmzBhgkqWLJllflhYmMaNG5fvjQIAAAAAXJWnpO3QoUOqUKFClvnlypXT4cOH871RAAAAAICr8pS0hYWF6aeffsoyf9u2bSpRokS+NwoAAAAAcFWekraHHnpI/fv318qVK5WWlqa0tDStWLFCAwYM0EMPPWT1NgIAAADAX1aeeo8cM2aMDh06pPvuu08+PldXkZ6erkcffZR32gAAAADAQnlK2vz8/DR//ny98sor2rZtmwICAlSjRg2VK1fO6u0DAAAAgL+0PCVtGapUqaIqVapYtS0AAAAAgEzylLSlpaVp9uzZWr58uU6cOKH09HSP5StWrLBk4wAAAADgry5PSduAAQM0e/ZstW3bVtHR0XK5XFZvFwAAAABAeUza5s2bp08++URt2rSxensAAAAAANfIU5f/fn5+qlSpktXbAgAAAADIJE9J2+DBg/XGG2/IGGP19gAAAAAArpGn5pFr1qzRypUr9c033+iOO+6Qr6+vx/LPP//cko0DAAAAgL+6PCVtxYoVU8eOHa3eFgAAAABAJnlK2mbNmmX1dgAAAAAAspGnd9ok6cqVK/ruu+/09ttv6+zZs5KkY8eO6dy5c5ZtHAAAAAD81eXpSduhQ4fUqlUrHT58WCkpKWrRooWCg4M1adIkXbp0STNnzrR6OwEAAADgLylPT9oGDBigunXrKjExUQEBAe75HTt21PLlyy3bOAAAAAD4q8tz75E//vij/Pz8POaXK1dOv/32myUbBgAAAADI45O29PR0paWlZZl/9OhRBQcH53ujAAAAAABX5Slpa9GihV5//XX3tMvl0rlz5zRixAi1adPGqm0DAAAAgL+8PDWPfO2119S0aVNVr15dly5dUpcuXbRv3z6VLFlSH3/8sdXbCAAAAAB/WXlK2iIjI7V161Z9/PHH+t///qf09HT16tVLjzzyiEfHJAAAAACA/MlT0iZJAQEB6tmzp3r27Gnl9gAAAAAArpGnpG3OnDnXXf7oo4/maWMAAAAAAJ7ylLQNGDDAYzo1NVUXLlyQn5+fAgMDSdoAAAAAwCJ56j0yMTHR4+/cuXPas2eP/v73v9MRCQAAAABYKE9JW3YqV66sCRMmZHkKBwAAAADIO8uSNkny9vbWsWPHrFwlAAAAAPyl5emdtq+++spj2hij+Ph4TZ8+Xffcc48lGwYAAAAAyGPS1qFDB49pl8ulUqVKqVmzZnr11Vet2C4AAAAAgPKYtKWnp1u9HQAAAACAbFj6ThsAAAAAwFp5etI2aNCgXJedOnVqXkIAAAAAAJTHpG3Lli363//+pytXrigqKkqStHfvXnl7e+uuu+5yl3O5XNZsJQAAAAD8ReUpaWvXrp2Cg4P1/vvvq3jx4pKuDrj92GOP6d5779XgwYMt3UgAAAAA+KvK0zttr776qsaPH+9O2CSpePHiGjNmDL1HAgAAAICF8pS0JScn6/jx41nmnzhxQmfPns33RgEAAAAArspT0taxY0c99thj+uyzz3T06FEdPXpUn332mXr16qUHHngg1+tZvXq12rVrp8jISLlcLn3xxRcey3v06CGXy+XxV79+fY8yKSkpeuaZZ1SyZEkFBQWpffv2Onr0qEeZxMREdevWTaGhoQoNDVW3bt105swZjzKHDx9Wu3btFBQUpJIlS6p///66fPmyR5nt27ercePGCggI0K233qrRo0fLGJPr/QUAAACAG5WnpG3mzJlq27atunbtqnLlyqlcuXJ65JFH1Lp1a7311lu5Xs/58+d15513avr06TmWadWqleLj491/ixcv9lg+cOBALVy4UPPmzdOaNWt07tw5xcbGKi0tzV2mS5cu2rp1q5YsWaIlS5Zo69at6tatm3t5Wlqa2rZtq/Pnz2vNmjWaN2+eFixY4PFuXnJyslq0aKHIyEht3LhR06ZN05QpU+gdEwAAAECBcpl8PCo6f/689u/fL2OMKlWqpKCgoLxviMulhQsXqkOHDu55PXr00JkzZ7I8gcuQlJSkUqVKae7cuercubMk6dixYypbtqwWL16sli1bateuXapevbri4uJUr149SVJcXJwaNGig3bt3KyoqSt98841iY2N15MgRRUZGSpLmzZunHj166MSJEwoJCdGMGTM0fPhwHT9+XP7+/pKkCRMmaNq0aTp69Giue8pMTk5WaGiokpKSFBIS4rFslGvUjRwytxFmRJ4+BwAAAKDwXC83uFaeeo/MkPH0q1GjRgoICJAxxvJu/r///nuFhYWpWLFiaty4scaOHauwsDBJ0ubNm5WamqqYmBh3+cjISEVHR2vt2rVq2bKl1q1bp9DQUHfCJkn169dXaGio1q5dq6ioKK1bt07R0dHuhE2SWrZsqZSUFG3evFlNmzbVunXr1LhxY3fCllFm+PDhOnjwoCpUqJDt9qekpCglJcU9nZycLElKTU1VamqqR1mvgLyNdZ55PQAAAACcL7f38XlK2k6dOqVOnTpp5cqVcrlc2rdvn26//XY9/vjjKlasmGU9SLZu3Vr/93//p3LlyunAgQN66aWX1KxZM23evFn+/v5KSEiQn5+fRy+WkhQeHq6EhARJUkJCgjvJu1ZYWJhHmfDwcI/lxYsXl5+fn0eZ8uXLZ4mTsSynpG38+PEaNSrrE7SlS5cqMDDQY17Nj2vmdCiuK3OTUQAAAADOd+HChVyVy1PS9uyzz8rX11eHDx9WtWrV3PM7d+6sZ5991rKkLaPJoyRFR0erbt26KleunL7++uvrdniS+Ylfdk//rCiT0bL0ek8Xhw8frkGDBrmnk5OTVbZsWcXExGR5BDohdEKO67meYUnD8vQ5AAAAAIUnoxXeH8lT0rZ06VJ9++23KlOmjMf8ypUr69ChQ3lZZa6ULl1a5cqV0759+yRJERERunz5shITEz2etp04cUINGzZ0l8lueIKTJ0+6n5RFRERo/fr1HssTExOVmprqUSbjqdu1cSRleUp3LX9/f48mlRl8fX3l6+vrMS/9YnqO67mezOsBAAAA4Hy5vY/P00tU58+fz9K0T5J+//33bBMUq5w6dUpHjhxR6dKlJUl16tSRr6+vli1b5i4THx+vHTt2uJO2Bg0aKCkpSRs2bHCXWb9+vZKSkjzK7NixQ/Hx8e4yS5culb+/v+rUqeMus3r1ao9hAJYuXarIyMgszSYBAAAAwCp5StoaNWqkOXPmuKddLpfS09M1efJkNW3aNNfrOXfunLZu3aqtW7dKkg4cOKCtW7fq8OHDOnfunIYMGaJ169bp4MGD+v7779WuXTuVLFlSHTt2lCSFhoaqV69eGjx4sJYvX64tW7aoa9euqlGjhpo3by5Jqlatmlq1aqXevXsrLi5OcXFx6t27t2JjYxUVFSVJiomJUfXq1dWtWzdt2bJFy5cv15AhQ9S7d293E8YuXbrI399fPXr00I4dO7Rw4UKNGzdOgwYNsrzzFQAAAADIkKfmkZMnT1aTJk20adMmXb58WUOHDtXOnTt1+vRp/fjjj7lez6ZNmzySvIx3v7p3764ZM2Zo+/btmjNnjs6cOaPSpUuradOmmj9/voKDg92fee211+Tj46NOnTrp4sWLuu+++zR79mx5e3u7y3z44Yfq37+/u5fJ9u3be4wN5+3tra+//lp9+/bVPffco4CAAHXp0kVTpkxxlwkNDdWyZcvUr18/1a1bV8WLF9egQYM83lcDAAAAAKvleZy2hIQEzZgxQ5s3b1Z6erruuusu9evXz910EdljnDYAAAAAUgGO05YxLtrbb7+dbVf2AAAAAADr3PA7bb6+vtqxYwfvcQEAAACADfLUEcmjjz6qd9991+ptAQAAAABkkqeOSC5fvqz//Oc/WrZsmerWraugoCCP5VOnTrVk4wAAAADgr+6GkrZff/1V5cuX144dO3TXXXdJkvbu3etRhmaTAAAAAGCdG0raKleurPj4eK1cuVKS1LlzZ/3rX/9SeHh4gWwcAAAAAPzV3dA7bZlHB/jmm290/vx5SzcIAAAAAPD/5Kkjkgx5HOINAAAAAJBLN5S0uVyuLO+s8Q4bAAAAABScG3qnzRijHj16yN/fX5J06dIlPfnkk1l6j/z888+t20IAAAAA+Au7oaSte/fuHtNdu3a1dGMAAAAAAJ5uKGmbNWtWQW0HAAAAACAb+eqIBAAAAABQsEjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBCjVpW716tdq1a6fIyEi5XC598cUXHsuNMRo5cqQiIyMVEBCgJk2aaOfOnR5lUlJS9Mwzz6hkyZIKCgpS+/btdfToUY8yiYmJ6tatm0JDQxUaGqpu3brpzJkzHmUOHz6sdu3aKSgoSCVLllT//v11+fJljzLbt29X48aNFRAQoFtvvVWjR4+WMcay4wEAAAAAmRVq0nb+/Hndeeedmj59erbLJ02apKlTp2r69OnauHGjIiIi1KJFC509e9ZdZuDAgVq4cKHmzZunNWvW6Ny5c4qNjVVaWpq7TJcuXbR161YtWbJES5Ys0datW9WtWzf38rS0NLVt21bnz5/XmjVrNG/ePC1YsECDBw92l0lOTlaLFi0UGRmpjRs3atq0aZoyZYqmTp1aAEcGAAAAAK5yGYc8KnK5XFq4cKE6dOgg6epTtsjISA0cOFDPP/+8pKtP1cLDwzVx4kT16dNHSUlJKlWqlObOnavOnTtLko4dO6ayZctq8eLFatmypXbt2qXq1asrLi5O9erVkyTFxcWpQYMG2r17t6KiovTNN98oNjZWR44cUWRkpCRp3rx56tGjh06cOKGQkBDNmDFDw4cP1/Hjx+Xv7y9JmjBhgqZNm6ajR4/K5XLlaj+Tk5MVGhqqpKQkhYSEeCwb5RqVp2M3wozI0+cAAAAAFJ7r5QbX8rFxm27IgQMHlJCQoJiYGPc8f39/NW7cWGvXrlWfPn20efNmpaamepSJjIxUdHS01q5dq5YtW2rdunUKDQ11J2ySVL9+fYWGhmrt2rWKiorSunXrFB0d7U7YJKlly5ZKSUnR5s2b1bRpU61bt06NGzd2J2wZZYYPH66DBw+qQoUK2e5HSkqKUlJS3NPJycmSpNTUVKWmpnqU9QrI24PPzOsBAAAA4Hy5vY93bNKWkJAgSQoPD/eYHx4erkOHDrnL+Pn5qXjx4lnKZHw+ISFBYWFhWdYfFhbmUSZznOLFi8vPz8+jTPny5bPEyViWU9I2fvx4jRqV9Qna0qVLFRgY6DGv5sc1s13HH1m8eHGePgcAAACg8Fy4cCFX5RybtGXI3OzQGPOHTREzl8muvBVlMlqWXm97hg8frkGDBrmnk5OTVbZsWcXExGR5BDohdEKO67meYUnD8vQ5AAAAAIUnoxXeH3Fs0hYRESHp6lOs0qVLu+efOHHC/YQrIiJCly9fVmJiosfTthMnTqhhw4buMsePH8+y/pMnT3qsZ/369R7LExMTlZqa6lEm46nbtXGkrE8Dr+Xv7+/RpDKDr6+vfH19PealX0zPcT3Xk3k9AAAAAJwvt/fxjh2nrUKFCoqIiNCyZcvc8y5fvqxVq1a5E7I6derI19fXo0x8fLx27NjhLtOgQQMlJSVpw4YN7jLr169XUlKSR5kdO3YoPj7eXWbp0qXy9/dXnTp13GVWr17tMQzA0qVLFRkZmaXZJAAAAABYpVCTtnPnzmnr1q3aunWrpKudj2zdulWHDx+Wy+XSwIEDNW7cOC1cuFA7duxQjx49FBgYqC5dukiSQkND1atXLw0ePFjLly/Xli1b1LVrV9WoUUPNmzeXJFWrVk2tWrVS7969FRcXp7i4OPXu3VuxsbGKioqSJMXExKh69erq1q2btmzZouXLl2vIkCHq3bu3uwljly5d5O/vrx49emjHjh1auHChxo0bp0GDBuW650gAAAAAuFGF2jxy06ZNatq0qXs6492v7t27a/bs2Ro6dKguXryovn37KjExUfXq1dPSpUsVHBzs/sxrr70mHx8fderUSRcvXtR9992n2bNny9vb213mww8/VP/+/d29TLZv395jbDhvb299/fXX6tu3r+655x4FBASoS5cumjJlirtMaGioli1bpn79+qlu3boqXry4Bg0a5PG+GgAAAABYzTHjtP1VME4bAAAAACn347Q59p02AAAAAABJGwAAAAA4GkkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADgYSRsAAAAAOBhJGwAAAAA4GEkbAAAAADiYo5O2kSNHyuVyefxFRES4lxtjNHLkSEVGRiogIEBNmjTRzp07PdaRkpKiZ555RiVLllRQUJDat2+vo0ePepRJTExUt27dFBoaqtDQUHXr1k1nzpzxKHP48GG1a9dOQUFBKlmypPr376/Lly8X2L4DAAAAgOTwpE2S7rjjDsXHx7v/tm/f7l42adIkTZ06VdOnT9fGjRsVERGhFi1a6OzZs+4yAwcO1MKFCzVv3jytWbNG586dU2xsrNLS0txlunTpoq1bt2rJkiVasmSJtm7dqm7durmXp6WlqW3btjp//rzWrFmjefPmacGCBRo8eLA9BwEAAADAX5ZPYW/AH/Hx8fF4upbBGKPXX39dL774oh544AFJ0vvvv6/w8HB99NFH6tOnj5KSkvTuu+9q7ty5at68uSTpgw8+UNmyZfXdd9+pZcuW2rVrl5YsWaK4uDjVq1dPkvTvf/9bDRo00J49exQVFaWlS5fq559/1pEjRxQZGSlJevXVV9WjRw+NHTtWISEhNh0NAAAAAH81jk/a9u3bp8jISPn7+6tevXoaN26cbr/9dh04cEAJCQmKiYlxl/X391fjxo21du1a9enTR5s3b1ZqaqpHmcjISEVHR2vt2rVq2bKl1q1bp9DQUHfCJkn169dXaGio1q5dq6ioKK1bt07R0dHuhE2SWrZsqZSUFG3evFlNmzbNcftTUlKUkpLink5OTpYkpaamKjU11aOsV0DeHnxmXg8AAAAA58vtfbyjk7Z69eppzpw5qlKlio4fP64xY8aoYcOG2rlzpxISEiRJ4eHhHp8JDw/XoUOHJEkJCQny8/NT8eLFs5TJ+HxCQoLCwsKyxA4LC/MokzlO8eLF5efn5y6Tk/Hjx2vUqFFZ5i9dulSBgYEe82p+XPO668rJ4sWL8/Q5AAAAAIXnwoULuSrn6KStdevW7n/XqFFDDRo0UMWKFfX++++rfv36kiSXy+XxGWNMlnmZZS6TXfm8lMnO8OHDNWjQIPd0cnKyypYtq5iYmCzNKieETrjuunIyLGlYnj4HAAAAoPBktML7I45O2jILCgpSjRo1tG/fPnXo0EHS1adgpUuXdpc5ceKE+6lYRESELl++rMTERI+nbSdOnFDDhg3dZY4fP54l1smTJz3Ws379eo/liYmJSk1NzfIELjN/f3/5+/tnme/r6ytfX1+PeekX06+7rpxkXg8AAAAA58vtfbzje4+8VkpKinbt2qXSpUurQoUKioiI0LJly9zLL1++rFWrVrkTsjp16sjX19ejTHx8vHbs2OEu06BBAyUlJWnDhg3uMuvXr1dSUpJHmR07dig+Pt5dZunSpfL391edOnUKdJ8BAAAA/LU5+knbkCFD1K5dO9122206ceKExowZo+TkZHXv3l0ul0sDBw7UuHHjVLlyZVWuXFnjxo1TYGCgunTpIkkKDQ1Vr169NHjwYJUoUUK33HKLhgwZoho1arh7k6xWrZpatWql3r176+2335YkPfHEE4qNjVVUVJQkKSYmRtWrV1e3bt00efJknT59WkOGDFHv3r3pORIAAABAgXJ00nb06FE9/PDD+v3331WqVCnVr19fcXFxKleunCRp6NChunjxovr27avExETVq1dPS5cuVXBwsHsdr732mnx8fNSpUyddvHhR9913n2bPni1vb293mQ8//FD9+/d39zLZvn17TZ8+3b3c29tbX3/9tfr27at77rlHAQEB6tKli6ZMmWLTkQAAAADwV+UyxpjC3oi/kuTkZIWGhiopKSnLU7pRrqy9TObGCDPCik0DAAAAYKPr5QbX+lO90wYAAAAAfzUkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgJG0AAAAA4GAkbQAAAADgYCRtAAAAAOBgPoW9ASgco1yjbvgzI8yIAtgSAAAAANfDkzYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkjYAAAAAcDCSNgAAAABwMJI2AAAAAHAwn8LeANz8RrlG3fBnRpgRBbAlAAAAwJ8PT9oAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2gAAAADAwUjaAAAAAMDBSNoAAAAAwMFI2vLgrbfeUoUKFVSkSBHVqVNHP/zwQ2FvEgAAAICbFEnbDZo/f74GDhyoF198UVu2bNG9996r1q1b6/Dhw4W9aQAAAABuQiRtN2jq1Knq1auXHn/8cVWrVk2vv/66ypYtqxkzZhT2pgEAAAC4CfkU9gb8mVy+fFmbN2/WsGHDPObHxMRo7dq12X4mJSVFKSkp7umkpCRJ0unTp5Wamuq5/iKX87Rdp06duuHP5CVWXuLYGWtqmak3/BlJGnR0UJ4+BwAAAOTH2bNnJUnGmOuWc5k/KgG3Y8eO6dZbb9WPP/6ohg0buuePGzdO77//vvbs2ZPlMyNHjtSoUaPs3EwAAAAAfyJHjhxRmTJlclzOk7Y8cLlcHtPGmCzzMgwfPlyDBv2/Jznp6ek6ffq0SpQokeNnMktOTlbZsmV15MgRhYSE5H3DHRTrZtynmzXWzbhPdsa6GffpZo11M+6TnbFuxn26WWPdjPt0s8a6GffJzlh/hn0yxujs2bOKjIy8bjmSthtQsmRJeXt7KyEhwWP+iRMnFB4enu1n/P395e/v7zGvWLFieYofEhJS4F84u2PdjPt0s8a6GffJzlg34z7drLFuxn2yM9bNuE83a6ybcZ9u1lg34z7ZGcvp+xQaGvqHZeiI5Ab4+fmpTp06WrZsmcf8ZcuWeTSXBAAAAACr8KTtBg0aNEjdunVT3bp11aBBA73zzjs6fPiwnnzyycLeNAAAAAA3IZK2G9S5c2edOnVKo0ePVnx8vKKjo7V48WKVK1euwGL6+/trxIgRWZpZ/plj3Yz7dLPGuhn3yc5YN+M+3ayxbsZ9sjPWzbhPN2usm3GfbtZYN+M+2RnrZtoneo8EAAAAAAfjnTYAAAAAcDCSNgAAAABwMJI2AAAAAHAwkra/qKNHjxb2JvypcfwAIHtXrlzR4cOHC3szAOCmQtL2FxUdHa25c+cW9mYoPj5eTz/9dGFvxg1zyvH7s1q0aFFhbwL+pD777DNb4ly4cMGWODdjrJ07d6pChQoFHgfOMm3atMLehD81px0/u861kn3nwI0bN9oSp6Dubek90kFGjx6dq3Ivv/xyvmO99dZbGjZsmFq0aKF33nlHJUqUyPc6c/Lzzz9r5cqV8vX1VadOnVSsWDH9/vvvGjt2rGbOnKkKFSro559/LrD4GT7//HONHDlSP/30U77XZefxS09P186dO1WjRg1J0syZM3X58mX3cm9vbz311FPy8srfbzAVKlSQy+W6bhmXy6X9+/fnK450tVvcrl276o033lDRokXzvb7rsate2Vl/V69enatyjRo1yncsLy+vXH0vrly5ku9Y0tWnNHv27JGvr6+qVKninv/ll1/q5Zdf1u7du5WSkmJJrOxcunRJb775piZPnqyEhIQCi3Mzx9q2bZvuuusupaWl5Xtddp3/csPKa4iddfh6rNynW265RXXq1NGsWbNUpkwZC7YuZxcvXtTy5csVGxsrSRo+fLjHecHb21uvvPKKihQpku9Ydl0b7Tx+UuGfa6WCOS+dO3dO3t7eCggIcM/bunWrXnrpJS1evNiS85JUSPe2Bo5Rq1atHP9q165tAgMDjZeXl2Xxfv31V9O0aVMTHh5uvvzyS8vWe61FixYZPz8/43K5jMvlMhUrVjQrVqwwJUuWNE2aNDGLFi2yNN4777xj/vGPf5iHH37YxMXFGWOMWb58ualVq5YJCAgwTzzxhGWx7Dh+xhjz4YcfmkaNGrmnixYtasqUKWPKly9vypcvb4oWLWr+85//5DvO66+/nuPfwIEDTUBAgGXfv61bt5ratWub8uXLm++//96SdebErnplZ/3NqE/Z/Xl5eRkvLy/j7e1tSawvvvgix7+hQ4eagIAAU6RIEUti7dy501SoUMG9Dx07djQJCQmmUaNGJjQ01AwePNgcPnw433FSUlLMCy+8YOrWrWsaNGhgFi5caIwx5r333jOlS5c2kZGRZty4cfmOczPHup6tW7da9l236/yXwa5riJ112K59+u2330zbtm1NsWLFzJw5cyxZZ05mzpxpYmNj3dNFixY19erVM02aNDFNmjQxERERZurUqZbEsuvaaOfxs+tca4x956UjR46Yhg0bGi8vL+Pr62ueffZZc/78edOtWzfj4+NjHnzwQbN27dp8xzHG/nvbDCRtfwJbtmwxLVu2NL6+vqZPnz6Wr3/atGnGx8fH1KhRw9SuXdvjL7/q169v+vfvb86ePWteffVV43K5TJUqVcyqVass2HJPkydPNr6+vqZOnTomMDDQBAYGmrFjx5oSJUqYkSNHmpMnT1oe05iCPX7GGNO8eXPz0UcfuaeLFi1q9u/f756eMWOGadKkiSWxMjt16pQZOHCg8ff3N40aNTLr1q2zbN2pqalmxIgRxt/f3wwaNMicOnXKJCUlefwVpIKuVwUZ58yZM9n+HTt2zDz//PMmICDA3HHHHZbEys6uXbtMhw4djLe3t3n00UfNoUOHLFlvu3btTLNmzcyiRYvMQw89ZFwul6lcubIZNWqUSU5OtiSGMcYMHz7chISEmAcffNBEREQYHx8f88QTT5gqVaqY2bNnm8uXLxMrH6xM2uw8/9l5DbGrDhfGdXHWrFmmePHipmPHjmbz5s1m27ZtHn9WuPfee83nn3/uns78vZg7d66pX7++JbGyU5DXRjuOn13nWmPsOy898sgjpmbNmmbatGmmSZMmxsvLy9x1113mscceM7/++qslMTLYeW97LZI2B/v111/NI488Ynx8fEynTp3M3r17LY9x8OBB06RJExMWFmb++c9/mpEjR3r85VdoaKjZs2ePMebqTbq3t7dZvHhxvtebnapVq5p3333XGGPMypUrjcvlMvfdd59JTEwskHjGFPzxM8aYW2+91WzdutU9nfni9PPPP5vixYtbEivDhQsXzJgxY0xoaKi58847zddff23p+q/17bffGm9vb/cvfl5eXu5fmwuCHfXKzjjGGJOWlmb+/e9/mzJlypjbbrvNvPfeeyYtLc3yOL/99pt5/PHHja+vr4mNjTXbt2+3dP3h4eFm8+bNxhhjEhMTjcvlMu+8846lMYwxpmLFiu4bvq1btxqXy2Ueeughk5qaSqxcyHwTmflv/vz5ltVfO89/hXENyVBQdbiw9mnZsmXu83rG+dzK83p4eLjZsWOHe7pkyZLmwIED7uk9e/aYkJAQS2Jdy65rox3Hz45zrTH2nZciIyPNmjVrjDHGxMfHG5fLZcaPH29pjAx23ttei6TNgU6ePGmefvpp4+fnZ5o1a2Y2bNhQIHHeeecdExwcbDp27GhOnDhRIDFcLpc5fvy4e7po0aLml19+KZBYAQEBHr/4+/n5uZuCFAQ7jp8xxvj7+3scsxMnTnhczPft22f8/PwsiXXlyhUzY8YMExERYcqXL2/mzJlj0tPTLVl3dhYsWGDCwsJM06ZNzXfffWe+//57jz8r2VWv7IqTYcGCBSYqKsrccsstZvLkyebSpUuWxzhz5oy7KWSDBg3M6tWrLY9hzNXzRUJCgns6KCjIfWG0kp+fnzly5Ih72t/f32zZssXyODdrrGtvIrNr2mflzaWd5z+7ryEZCrIOF8Y+vfrqqyYwMNB0797d/PLLL+bgwYMef1YoUqSI2b17d47Ld+3aZfz9/S2JZYy910Y7jp9d51pj7DsveXl5mfj4ePd0YGCg+fnnny2PY4y997bX8rH2DTnkx/nz5zVlyhRNnTpVlSpV0qJFixQTE1MgsVq1aqUNGzZo+vTpevTRRwskRoaff/7Z/YKpMUZ79uzR+fPnPcrUrFkz33EuXbrk8dKxn5+fSpUqle/1ZsfO4xceHq49e/aoYsWKkpRln3bt2qWIiIh8x/nkk0/0z3/+U0lJSXrhhRf01FNPyc/PL9/rzc6ZM2fUt29fffXVVxo7dqwGDBhQIHEk++qVnfVXklatWqXnn39e27dv14ABA/T8888rNDTU8jiTJk3SxIkTFRERoY8//lj333+/5TEyuFwujw4lvLy85Ovra3mc1NRUj++2r69vgRy7mzXWgQMHLF9nTuw6/0n2XkMke+qwnfv066+/6tFHH9X+/fv10UcfFei5okyZMtqxY4eioqKyXf7TTz9Z1pmHXddGO4+fXedayd5zoLe3t/vfXl5elnREkxO77m2vRe+RDhIREaGzZ8/qmWee0cMPP5xjb0VWfAlatGhhSw9FGT3PZfc1y5jvcrks6c3Hy8tLY8aMcfdG+Pzzz+u5555TyZIlPcr1798/37HsOn6S1LNnT+3Zs0c//vhjlmXGGN1zzz2qWrWq3nvvvXzF8fLyUkBAgB5++GGFhITkWG7q1Kn5iiNJkZGRKlu2rObMmZPjRdcqdtUrO+tvmzZttHz5cj322GMaOXKkZTet2cn4XjRv3tzjgpjZ559/bkms0NBQ97E7c+aMQkJCsvQMePr06XzHeeKJJxQYGChJevPNN9W1a9csNxJWfNdv1lh2sev8J9l7DbGrDtu5T0WLFlXLli319ttvZ1m/1QYMGKDvvvtOmzdvznJjfvHiRdWtW1fNmzfXG2+8ke9Ydl0b7Tx+dp1rM2LZcV7y8vJSdHS0fHyuPo/66aefVLVq1SwJ9v/+9798xcmIZde9rce6Sdqc49rKkvnLUBBfgk8//VRffPGFUlNT1bx5cz3xxBOWrPdahw4dylW5cuXK5TtW+fLlc9Ut76+//prvWHbav3+/7rrrLlWtWlVDhgxRlSpV5HK5tHv3bk2ZMkV79uzR5s2bValSpXzFadKkSa6O34oVK/IVR5LGjBmjF154wZZuuu2qV3bWXy8vL/n4+CgoKOi6/2dWXHB79Ojxh98LSZo1a1a+Y73//vu5Kte9e/d8xbHzu34zxpo0aZKeeeYZd5faq1evVr169eTv7y9JOnv2rJ5//nm99dZb+Yoj2Xf+k+y9hthVh+3cpw8++EBdu3ZVSkqKrly5oqCgoHyvMyfHjx9XrVq15Ofnp6efftrjezF9+nRduXJFW7ZsUXh4eL5j2VWv7Dx+dp1rJfuO36hRo3JVbsSIEfmKI9l7b3stkjYHsfNL8M477+jJJ59U5cqVVaRIEe3YsUNDhw7V+PHj873uv4IHHnggV+WsePogSRs2bFCPHj20e/du98nPGKOqVatq1qxZqlevniVx7HLhwgU999xzHj8a/Otf/yqQXxftqld21l87L7hAZt7e3oqPj1dYWJgkKSQkRFu3btXtt98u6eoNdWRkpGU/MN5s5z/p5qzDv//+u7p3766lS5cqPT1d9erV0wcffOD+XljtwIEDeuqpp7Rs2TL3j2Qul0stWrTQW2+9VWBxC4rdxw9/PiRtf1E1atRQhw4d9Morr0iSZs+erWeeeUZnz561NM7hw4dzVe62226zNG5Be+yxxzymP/roI7Vr107BwcEe8614+nCtLVu2aN++fZKkypUrq3bt2pau/1q///67XC5XgQwc/txzz+mtt97SI488ooCAAH300Udq0qSJPv30U8tjwTpnzpzRL7/8IpfLpYoVK6pYsWKFvUnI5OzZs4qLi1NqaqruvvvuAvkhxMvLSwkJCe6kLTg4WNu2bSuwpC2Dnec/3LjevXtr0aJF6t+/v4oUKaKZM2eqXLlyWrZsWYHGPX36tH755RdJUqVKlXTLLbcUaLyCujbaffzsaG11syqse1uSNgfauHGjPv74Y+3du1cul0uVK1dWly5dVLduXctiBAUFafv27e6LbFpamgICAnT48GFL29Zf+w7Mtb+EXTvPqiZjc+bMyVW5gug4JPNNy5/VmTNn9OKLL2r+/PlKTEyUJBUvXlwPPfSQxowZY9lNesWKFTV27Fg99NBDkq7+kn7PPffo0qVL131vKj/sqFd2xpGuvruxbNkyd6wqVaqoefPm7mZrVjl48KD69eunb7/91qMet2rVStOnT1f58uUtiZPb+pPfplyjR4/OVbmXX345X3HsjvXTTz+pdevWio+Pl3T1Cdhnn32m5s2b53vd1yqspK2gFcY1pKDrsJ37dNttt2nmzJlq06aNJGn37t2Kjo7WxYsXC6yTC7vYcW208/jZ2drKrnNgs2bNclXOiqbodt7bXoukzWGGDh2qKVOmqGjRorr99ttljNGvv/6qCxcuaMiQIZo4caIlcTJfdKWCSTx8fHxUpkwZ9ejRQ+3atXO/IJrZnXfeme9YXl5eKlq0qHx8fLJ9OVS6WqmseM8ns4JM2uw64Z0+fVoNGjTQb7/9pkceeUTVqlWTMUa7du3SRx99pLJly2rt2rUqXrx4vuJIV3swO3DggG699Vb3vICAAO3du1dly5bN9/ozs6te2RVHkr766is9/vjj+v333z3mlyxZUu+++67atWtnSZwjR47ob3/7m3x9fdW3b1+P78WMGTN05coVbdy40ZJOeby8vFSuXDl16dLF49yUWX57G/Xy8lJkZKTCwsKue66w6oV1u2K1adNGiYmJevXVV1WkSBGNGjVKe/bs0e7du/O97mvZmbTZmfTafQ2xow7buU8+Pj46cuSISpcu7Z4XGBioXbt2Wf5uT8+ePf+wjMvl0rvvvpvvWHZdG+08fna1tpLsOwdmXD/atm173ST3tddey1ccyd57Ww8FMY4A8mb27NmmSJEiZtq0aR4jxF++fNm88cYbpkiRIub999+3JJbL5TJjx441b7zxhvuvSJEi5qWXXvKYl1/x8fFmwoQJpmrVqiY8PNwMHjy4wMbNqF69uilRooQZMGCA2bZtW4HEyEnmAV+tVKtWrRz/ateubQIDAy0ZE2nAgAEmOjraY+yWDPHx8aZGjRpm4MCB+Y5jzNXxVDKPbVe0aFHz66+/WrL+a9lVr+ysvz/++KPx9fU1Dz74oFm7dq1JTEw0iYmJ5scffzQPPPCA8fPzM2vXrrUk1mOPPWYaNWpkLl68mGXZhQsXTKNGjUzPnj0tiTV//nzTqlUrU6RIEdOxY0ezaNGiAhkkvHXr1qZIkSLm/vvvN19++aW5cuWK5TEKI1apUqXMxo0b3dO///678fLyMmfPnrU0TubrR+Zrx5gxYywbp82u858x9l5D7KrDdu5Tduf14ODgAjmvd+jQIce/du3amYCAAMu+F3ZdG+08foGBgR73LFeuXDG+vr4e45xZxa5z4MSJE021atVMWFiYefbZZ8327dsLJI4x9t7bXoukzUH+9re/malTp+a4/NVXXzV/+9vfLIlVrlw5U758+ev+VahQwZJYGX744QfTs2dPExwcbOrVq2feeecdy2/I4uLizBNPPGFCQ0NNnTp1zFtvvWWSkpIsjZGdgkzacrJlyxbTsmVL4+vra/r06ZPv9ZUrV84sWbIkx+XffPONKVeuXL7jGHP1pq9NmzamY8eO7j8fHx8TExPjMc8KdtUrO+tv69atzRNPPJHj8ieeeMK0bt3aklilS5c2P/zwQ47LV61aZUqXLm1JrAxHjx41Y8aMMZUqVTKlS5c2zz//vNm7d6+lMY4dO2bGjRtnqlSpYiIiIszQoUOvO1jvnyFW5gFfjSmYH0Nyc/0oX768pTEzs/r8l8Gua4idddiufXK5XKZGjRqmdu3a7j9vb29zxx13eMwrSF988YWpXr26KVasmBk/frwl67Tr2mjn8cvpXFFQ9zF2nm/Xrl1rHn/8cRMSEmL+9re/mRkzZhTofaAd97YZaB7pIJnfM8vs119/VY0aNbIM3vdnc/z4cT388MNatWqVTp48WSAvDV+8eFGffvqpZs2apQ0bNqhDhw5677333F1S59dXX33lMf3www/r9ddfz9K9cPv27S2Jd60DBw7opZde0vz58/XAAw9ozJgxqly5cr7X6+/vr/379+fYzO3o0aOqVKmSLl26lO9YmTtyyYkVHbnYVa/srL/FixfX6tWrVaNGjWyX//TTT2rcuLH73Yv8yM33omLFikpJScl3rOysWrVKI0eO1OrVq/X7779b0jw3s9WrV2vWrFlasGCBatSooe+++87y9wLtiOXt7a29e/e6B082xqhs2bJas2aNx3uH1xtryukK6vyXWUFfQ+yswxkKep/s7HI9sx9//FHPP/+8tmzZoqefflrDhg2z7Fxh17XRzuOXefw+Kfsx/KwYvy8zu863Fy5c0Keffqo333xTP//8s44dO1ag5z477m150uYgwcHBZteuXTku3717twkODrZxi6z1448/ml69enn8+lFQv0ZkWLVqlWnSpInx8vIyp0+ftmy9LpfrD/+sapqR4eTJk+bpp582fn5+plmzZmbDhg2Wrj8yMvK6T1RWr15tIiMjLY1pB7vqlZ31t0iRIubgwYM5Lj948KAJCAiwJFb58uVtewJ7rYsXL5q5c+eapk2bmoCAANO5c2dz6dIly+MYc7WZ5/vvv2/uvvtuExAQUKC/yhZkrIzzzrV/184riPOSXQr6/JeTgrqG2FmHMyuofSoMO3bsMLGxscbHx8f07NnTHDlyxPIYN+O1sTBaW2Ww63z7ww8/mMcee8wULVrU1KtXz1y4cKFA4th5b0vS5iBNmjQx//znP3Nc/uKLL5rGjRtbFi81NdVMmjTJ1K5d2wQFBZmiRYua2rVrm8mTJ3u8k5Mfx44dMxMmTDBRUVHudsY7duywZN05OXr0qBk7dqy7adVzzz133Ztppzt37pwZOXKkCQkJMXfddZf59ttvCyROz549TaNGjUxKSkqWZZcuXTKNGze27N2layUmJpqNGzeaTZs2mcTERMvXb1e9srP+1qxZ07z33ns5Ln/33XdNjRo1LIk1YMAAU6NGjSzvWhhjzPHjx03NmjXNgAEDLIllzNWmXL179zYhISGmdu3aZtq0aQV2Y3ltM5q6deuaN998s0C+g3bF+v7773P1Z4W0tDTz7rvvmrZt25o77rjDREdHm3bt2pn333/fpKenWxLDGPvOf9ey4xpiZx02xv7r4rZt28ynn35qPvvsswJ5l+7w4cOmR48exsfHx3To0KFA3ycqjGtjQR+/wmDHOfC3334zY8eONZUrV3a/a7Zz505LYxhTOPe2xpC0OcqiRYuMt7e3ee655zxeeI2PjzdDhgwxPj4+ZtGiRZbEunDhgrnnnnuMl5eXiYmJMQMGDDD9+/c3MTExxsvLy9x7773Zdjxwo3x9fU25cuXMyy+/bDZt2mS2bduW7Z8VMjoxCAgIMB06dCjwl/6NuXrCPnfuXIHGCA8PN4GBgeb55583W7duLbBjeOTIERMeHm5uu+02M3HiRPPll1+aL7/80owfP96ULVvWhIWFmcOHD1uwR1cdOHDAtGnTxnh7e7ufBHh7e5u2bduaAwcOWBbHrnplZ/2dOnWqueWWW8zXX3+dZdl///tfU6JEieu+X3cjTp8+bSpXrmyCg4PNU0895e5sok+fPiY4ONhUrlzZnDp1ypJY1atXNyVLljT9+/cv0BuViRMnmqpVq5pSpUqZgQMHmp9++ummiGWX9PR007ZtW+NyuUytWrXMQw89ZDp37mxq1qxpXC6Xuf/++y2LZdf5zxh7ryF21WG7r4vr16830dHR7ie7GU93a9SoYenT0YCAAPf3IuNald2fFey8Ntp1/Oxk1zkwo8OT9u3bmy+++MKkpqYWSBxj7L23vRbvtDnMtGnTNGTIEF25ckWhoaGSpKSkJHl7e2vSpEkaOHCgJXFefvllvf/++1q0aJFq1qzpsWzbtm1q3769HnvsMY0cOTJfcby8vNz/zhjDIvNXzqqxLLy8vHTbbbfpkUceyfJu2bWsaKP9+++/q3v37lq6dKnS09NVr149ffDBBwXS5X/mY3jt8cuYtuoYHjhwQH379tXSpUs9xh5p0aKFpk+frkqVKuU7hmRvN/KSffXKrjjp6enq3LmzFixYoKioKFWrVk2S9PPPP2vfvn3q0KGDPv30U4/vTn4kJibqhRde0Pz583XmzBlJUrFixdSpUyeNHTvWskFmvby8FBQUJB8fH48xbzLLb/fkGeeK2NhY+fn55Vhu6tSp+Ypjd6xPPvlEHTp0cMc5ePCgypYt6x5T6MKFC5o+fbqGDh2arzizZs3SgAED9OWXX6pp06Yey1asWKEOHTpo+vTploz9Zef5z85riF112M59+vnnn1WvXj1Vq1ZNzz77rMd5/bXXXtOePXsUFxen6tWr5ztWbo6LleNk2XFttPP4tWnTRh9//LH7OjV27Fj169fPPd7cqVOndO+99+rnn3/Odyy7zoFeXl4qXbq0wsLCrnv9sGoolwwFfW/rsU6SNuc5evSoPv30U+3bt0+SVKVKFT344IOWjl9VpUoVjR8/Xg8++GC2yz/99FO9+OKL2rt3b77iHDp0KFflrBiDpHz58tetqNLVSpTfgXklqXfv3lq0aJH69++vIkWKaObMmSpXrpyWLVuW73VnZucxzJCYmOj+/lWqVMnyF2p79uyp/fv369tvv1WRIkU8ll28eFGtWrVSpUqVLBljJ4Md9crOOJI0f/5890DeGbEeeugh96DlVjPG6OTJk5KkUqVK/WF9u1Hvv/9+rsp17949X3GaNGmSq3OFFYOw2hnL29tb8fHx7vHTQkJCtHXrVsvHT4uJiVGzZs00bNiwbJePGzdOq1at0rfffpuvONLNew3JUNB12M59+r//+z+lpaVpwYIFWWIaY/TAAw/I19dXn3zySb5jFZaCvDbaefzsOldI9p0D7ezIpTDuyySStr+sIkWKaN++fTneSB45ckSVK1e2pKfAm9Ftt92mmTNnqk2bNpKk3bt3Kzo6WhcvXrzuoI64KjIyUp988on+/ve/Z7t89erVeuihh3Ts2DGbtwz487Jr0OuIiAgtWbJEtWrVynb5li1b1Lp1ayUkJOQrDv5cSpUqpW+++UZ169bNdvnGjRvVpk0b9w8/8GTn8bPrXAFrZT+ENwpF5m7kc2JFN/IhISE6ceJEjklbQkKCJV2jHj58OFflbrvttnzHstOxY8dUu3Zt93TVqlXl5+enY8eOWf7Lyk8//ZSrcpmbud6oBx54IFflPv/883zFka42vbi2C/LMbr/9dp06dSrfcST76pWd9Tc5OTlX5ayow7Vr187VEzUrmpzgz+H06dPXbWoXHh5uWVf1dp3/7GZnHbbL2bNnr/u9iIiI0NmzZy2JZef51q5ro53HD/lTWPe2JG0O0qFDhz8sY1Ub2aZNm2rcuHFasGBBtssnTJigJk2a5DtOTk0zMt5DkK7u05UrV/Ida86cObkqZ8V7FsYY+fh4Vh8fHx+lp6fne92Z1apVK8u7HJlZ8b0ICQmxvLlbTiIjI7Vz584c31nbsWOHSpcubUksu+qVnfW3WLFi1/2/svI9n9zsl1UqVKiQq2Y0+/fvz1ec0aNH56rcyy+/nK84dseyS1paWpbz37W8vb0tOadL9p3/JHuvIXbVYTv3qXz58tqwYUOOPwavX7/esh817Tzf2nVttPP4uVyuLPtUUPto1zmwadOmubp+LF++PF9xJHvvba9F88i/qIwXXu+44w4NGjRIVatWdc9/7bXX9PPPPysuLk533HFHvuJs27Yt2/nGGM2bN0//+te/VLRoUZ04cSJfcSRddyBNl8ul8+fP68qVK5a9sB4dHe1x4/LTTz+5n7hlsOLpQ2G1nS5IAwcO1IoVK7R8+XL3QMAZTpw4oRYtWqhp06Z6/fXXC2cDHW7VqlW5Kte4ceN8xzp8+LDKlCljWacm1/PGG2/kuOzgwYN6++23lZKSku86fO1T8sxcLpf27NmjS5cuWXKusDOWl5eX3n//fXfnAg8//LBef/1196/3Z86c0WOPPZbvWF5eXmrdunWOgzKnpKRoyZIlluyTnec/O68hdtVhO/dpxIgRmj17tr7++mtFR0d7LNu+fbvatWun7t275/rdo78aO49f5jq8aNEiNWvWTEFBQZKsrcN2nQOfffbZHJclJyfr448/tuT6Idl7b3stkjYH6dmzp9544w0FBwfbEi8uLk69evXSrl27PHq/qVq1qv7zn/+oYcOGBRL3u+++07Bhw7R3714NGjRIQ4YMUdGiRQskliTFx8dr1KhReu+999SsWTMtWbIk3+u084VXu2R+MbkgJSYmql69ekpISFDXrl09fjT46KOPFBERobi4OEte8rarXtlZf+fMmaPOnTvneNNsJTu/F9k5ffq0XnnlFc2YMUP16tXTxIkTVb9+/QKJtXXrVg0bNkwrVqxQz549NXPmzAKJU1Cx7OpR77HHHstVuVmzZuUrjlMUxDXEzjqcnYLYp0uXLum+++7T+vXr1aJFC48eMb/77jvdfffdWrFiRZbOpwrKhQsXFBgYmO/12HUOtPP4OaEO23G+vXLlit58802NHTtWoaGheuWVVwqsoy5b7m0tH0QAeebl5WWOHz9ue9wtW7aY+fPnm/nz55stW7YUWJxNmzaZ5s2bG39/f9OvX78C39fk5GTz4osvmqJFi5p69eqZFStWFGi8wrJgwQJLBmF1uVy2fv9Onz5tnnzySVO8eHH3eDTFixc3ffr0Mb///rtlceyqV3bWXztj2f29yHDhwgUzZswYExoaau68885sx7Oyyq+//moeeeQR4+PjYzp16mT27t17U8T6K7Dq/JedgryGFNb1vqCviykpKWbChAnmzjvvNAEBASYgIMDceeedZvz48ebSpUuWxsrJxYsXzZQpU0x4eLgl67PzHOiE41fQ7DoHfvDBB+b22283pUuXNm+++WaBjdtm570tSZuD2H1zlJSUZNLS0rLMT0tLM0lJSZbF2bdvn+nUqZPx9vY2Dz/8sNm/f79l685OSkqKefXVV02JEiVMVFSU+fTTTws0XmanT582//rXv8ydd95p2Trfeecd849//MM8/PDDJi4uzhhjzPLly02tWrVMQECAeeKJJ/Ido7BuztPT083x48fN8ePHTXp6uuXrt2u/7Dx+N2ssY4y5cuWKmTFjhomIiDDly5c3c+bMKZDvhTHGnDx50jz99NPGz8/PNGvWrEAHr7Uzlp0SExPNxo0bzaZNm0xiYmKBxLDj/HctO64hdterwr4uWi0lJcW88MILpm7duqZBgwZm4cKFxhhj3nvvPVO6dGkTGRlpxo0bZ0mswro23mzsOgd+88035s477zQhISFm9OjR5ty5cwUSx+57W2NI2hzF5XKZEydO2BLr888/N5UrVzbnz5/Psuz8+fOmSpUq5quvvsp3nKeeesr4+fmZli1bFuhTPGOu3vzPnj3b3HbbbSYyMtK8/fbb5sqVKwUa81rLli0zDz30kClSpIgpU6aM6d+/vyXrnTx5svH19TV16tQxgYGBJjAw0IwdO9aUKFHCjBw50pw8edKSOC6Xy8yZM8d8+eWX1/2zwoULF8yXX35pkpOTsyxLSkoyX375pWW/KtpVr+ysv3bHGjt2rHnjjTeu+2eF+fPnm8qVK5uwsDDz+uuvm5SUFEvWm9m5c+fMyJEjTUhIiLnrrrvMt99+WyBx7I5lpwMHDpg2bdoYb29v4+XlZby8vIy3t7dp27atOXDggGVx7Dr/GWPvNcSuOmznPmX8YJndj75nzpzJcVleDB8+3ISEhJgHH3zQREREGB8fH/PEE0+YKlWqmNmzZ5vLly9bEscY+66Ndh4/O9l1Dly/fr1p0qSJKVKkiBk4cKCl54bM7Ly3vRbvtDmIl5eXQkND/7D3m9OnT+c7VkxMjDp16qTHH3882+Xvvfee5s+fn+/BUb28vFSkSBH3O0s5saLDjpo1a2r//v165plnNHDgwBzbslvZhfLhw4c1a9YszZo1S+fOnVNiYqI++eSTHActz4tq1arpueeeU8+ePfX999+rWbNmatasmT777DMVK1bMsjh2vQ8jXe1w4quvvsqxF6fmzZurY8eO6tevX75j2VWv7Ky/f9QRRAYrhmfw8vJSmTJl5O3tnWMZqwbn9fLyUkBAgB5++OHr1tOpU6fmK05G19nPPPOMHn744Rz/z6zoRt7OWHY5cuSI/va3v8nX11d9+/ZVtWrVZIzRrl27NGPGDF25ckUbN27MsXfYG2HX+U+y9xpiVx22c59eeeUV/fTTT/r000+zXd6pUyfdeeedevHFF/Mdq1KlSpo8ebI6duyobdu2qXbt2urcubPmzp173Z5N88Kua6Odx89Odp0DM64fffr0ue6QQv37989XnIxYdt3bXoukzUG8vLz0+uuvu3v+ykn37t3zHSsyMlKrV69WpUqVsl3+yy+/qFGjRvke3NjODjuuPbFerytWK5KOTz75RP/5z3/0448/qk2bNuratatat26toKAgbdu2TdWrV893jAyBgYHavXu3e7wPf39/rV69WvXq1bMshpR1sM2CdPfdd+ull15Su3btsl3+3//+V6NHj9aGDRvyHcuuemVn/fXy8lKnTp0UEBBw3XJWvERu5/eiSZMmueqyecWKFfmKk/lcce1lMGPaqnOFnbHs0rNnT+3fv1/ffvttlk4RLl68qFatWqlSpUp699138x3LrvOfZO81xK46bOc+1apVS6+++qruu+++bJcvX75cQ4YM0ZYtW/Idy9/fX/v373f/MFCkSBHFxcXlOOB7fth1DrTz+NnJrnNgTt3wX8uqHxgLqzM6xmlzmIceesi23vuuN35EamqqJYOj2tl74sqVK22L1aVLFw0dOlQLFiwo8N4CL1265HFj5Ofnl6WbfCvYNUabJO3bt0933nlnjstr1qypffv2WRbPrnplVxxJ+te//mVLLDu/F99//70tcQ4cOGBLHLtj2WXJkiX65JNPsu3FLiAgwNIe2uw6/0n2XkMke+qwnfu0f/9+Va5cOcfllStXzvcYixlSU1M9htfx9fX9wx/M8squc6Cdx89Odp0DDx48aEscqfB6BidpcxA7b47Kly+vTZs25fhod9OmTX+qcb8ka8akyq2ePXvqrbfe0qpVq9StWzd17tz5uuPh5Nd//vMfd9exV65c0ezZs1WyZEmPMvl95G/nQ/crV67o5MmT7l/PMzt58qRlg1LaVa/srL92xroZG2PYeW77s51Hc+PUqVPXbX50++2369SpU5bFs+P8J9l7DbGrDtu5T97e3jp27FiO5/Vjx45ZOt7jyy+/7G7uefnyZY0ZMyZL4pbfptSSfedAu4/fH1m9erXuvPPOfCfDN+M5sLCQtDmInTdHDzzwgF588UW1aNHCPfBqhoSEBP3zn/9U165dbdueP5t33nlHb7zxhj755BO99957GjhwoFq2bCljjNLT0y2Nddttt+nf//63ezoiIkJz5871KONyufJ909K9e/c/bKpjlTvuuEPfffed6tSpk+3yZcuW5Xtg9wx21Ss766+dsUaMGFGg4yjCXhUqVFCzZs00evRo3XrrrXlaR2RkpHbu3JnjO2s7duxQ6dKl87OZbnad/+x2M/4YUrt2bX3xxRc5jqO4cOHC6w60fCMaNWqkPXv2uKcbNmyYpdmbVYmxXddGO49fbjRp0kTFixfXCy+8oMGDB9sWF9dhW5cn+EOpqalm27Zt5sKFC1mWnT9/3mzbti3bLvrzIjk52dxxxx0mODjYPPXUU+b11183b7zxhnnyySdNcHCwqV69erY9+yF7e/fuNcOGDTORkZEmJCTEPPzww2bBggWFvVk35LfffjODBw/OseeqIUOGmISEBEtivf322yYoKMgsWrQoy7KvvvrKBAUFmbffftuSWHbVKzvr7/fff19gY85ktnfvXvPQQw/l+L2wq6tjWGPEiBGmR48e5vbbb8/zOgYMGGBq1KiRbe+Hx48fNzVr1jQDBgzIx1be/Oysw3b57LPPjI+Pj5k2bZpHD5VXrlwx//rXv4yvr++fcqgBu66NTjt+Bw8eNCtXrjTDhg2zLSauj6TNQWbNmmXq1KmTbXe8V65cMXXq1DFz5861LN6ZM2fMU089ZW655Rb34Ma33HKLeeqppwpsvJ2bXVpamvnqq6/M/fffb/z8/Ap7c27IkCFDTO/evXNc3qdPHzN06FDL4j3yyCPG5XKZatWqmQ4dOpiOHTuaqlWrGi8vL/PQQw9ZFseuemVn/V2+fLmpVq1ajjcR1atXN6tXr7Yk1hNPPGGee+65HJcPHTrUPPnkk5bEwp/D6dOnTeXKld0/+mUM+9CnTx8THBxsKleubE6dOlXYm+lodtZhO73wwgvG5XKZkJAQU6tWLVO7dm0TEhJivLy8zPPPP1/Ym5cndl4bb8bjB+uQtDnIPffcYz7++OMcl8+fP9/ce++9lsdNT083J06cKLDBjf+KLly4YCZNmmTJulq3bm3OnDnjnh4zZoxHUv3777+batWq5TtO9erVzQ8//JDj8h9//NFUr14933GuNX/+fHP//feb6tWrm2rVqpn777/fzJ8/39IYdtUrO+tv+/btzdSpU3Nc/sYbb5gOHTpYEqtKlSrXHQR106ZNpkqVKpbEwp/H6dOnzZNPPmmKFy/u/tGvePHipk+fPub333+3LI5d5z+72VmH7bZ+/XrTv39/06ZNG9O6dWszYMAAs379ektjVKtWzeOHgd69e3s8+T1+/LgJCAiwJJbd10Y7jt8fOXbsmOnXr5+tMfHH6PLfQcLCwrRhw4YcX/A+cOCA7r77bp08ebLAtmHVqlU6f/68GjRoYEnHGnPmzMlVuUcffTTfsf6IMUYnT560rLeu33//XevXr5evr6/uu+8+eXt7KzU1VW+99ZYmTJig1NRU/f777/mO4+3trfj4ePd2h4SEaOvWrbr99tslScePH1dkZGS+u8sNCgrSrl27cnwJ+vDhw6pWrZrOnz+frzh2s6te2Vl/y5UrpyVLlqhatWrZLt+9e7diYmJ0+PDhfMcKCAjQ7t27c3yZ/NChQ6pWrZouXLiQ71iTJk3SM888435/JKNr94yxrM6ePavnn39eb731Vr7inDhx4rrngStXruh///uf7r777nzFsdvo0aNzVe7ll1+2LGbGeVWSSpUqZXkHG3ad/+xmZx2+Hquvi3bJ3A1/dt+L0qVLW/KO+c16bfz555+1cuVK+fr6qlOnTipWrJh+//13jR07VjNnzlSFChX0888/5zvOzXi+Lax7WzoicZDz588rOTk5x+Vnz5615MZIkiZPnqxz5865x5owxqh169ZaunSppKs3oMuXL893ZxADBgzIcZnL5dL58+d15coVS77YgYGBOnTokLs76FatWmnWrFnuF+JPnDhh2cV97dq1atu2rZKSkuRyuVS3bl3NmjVLHTp0UHp6ul588UX17Nkz33GkrC+sF9TvLAEBATp48GCOF6aDBw9a9jJ2enq60tPTPQZBPX78uGbOnKnz58+rXbt2uvfeey2JZVe9srP+Hj9+XL6+vjku9/HxsezHndDQUO3fvz/HpO2XX36xbMD64cOHq0ePHu7vWWxsrMeN2IULF/T222/nO2krXbq0RyJQrVo1ffvtt+7v/qlTp9SgQQNLzhVNmzbN1dhBOQ00fyMWLlx43Rh79uzRpUuXLE3aXC5Xgd7w23X+k5Src7bL5bJk/Dm76rCd18XTp0/rwoULHh3U7Ny5U1OmTNH58+fVoUMHdenSJd9xspPd98KqHxDsujbaefz++9//6sEHH1Rqaqqkqz+Y/fvf/1anTp0UHR2tTz/9VLGxsZbEsut86+XllatzrRU9U9t5b3stkjYHqVy5stauXZvjqPBr1qy57hgeN+Ljjz/W888/757+7LPPtHr1av3www+qVq2aHn30UY0aNUqffPJJvuLkNNZbfHy8Ro0apffee08tWrTIV4wMly5d8jhx//jjj7p48aJHGasu+C+99JJatmypf/7zn3rvvff0+uuvKzY2ViNHjlS3bt1s7ZLdKvXq1dPcuXPVqFGjbJfPmTPHsl/CevXqJV9fX73zzjuSriY0f/vb33Tp0iWVLl1ar732mr788ku1adMm37Hsqld21t9bb71V27dvV6VKlbJd/tNPP1nWe1+jRo00bdo0NWvWLNvl//rXvyxLsO26Qc+83qNHj2a5kFsV+3qD/SYnJ+vjjz9WSkqKJbFyGnR369atGjZsmHbs2KHevXvnO05O34XM8jsIut2uNzZpWlqavvvuO6WkpFiStNlVh+28Lvbr10+lS5d2d7N/4sQJ3XvvvYqMjFTFihXVo0cPpaWlqVu3bpbEs4td10Y7j9/YsWP15JNPauzYsXrnnXc0ZMgQPfnkk1qwYEGO+5lXdp1vr/ej1dq1azVt2jTLvut23tt6sLMtJq5v4sSJpkSJEmbbtm1Zlm3dutWUKFHCTJw40ZJYxYoVMz///LN7ukePHqZr167u6XXr1pkyZcpYEutaycnJ5sUXXzRFixY19erVMytWrLBs3S6Xyxw/ftw9XbRoUY9e7RISEoyXl5clsUqUKGF27NhhjLnaM6CXl5f55JNPLFl3Zl5eXh5t9YsWLWp+/fVX97RV+7VixQrj7e1tBg8e7NETVkJCghk0aJDx9vY2y5cvz3ccY4ypXLmy+fbbb93T06dPN6VLl3a/uzJ06FDTpEkTS2LZVa/srL9PP/20iY6ONhcvXsyy7MKFCyY6Oto888wzlsT63//+Z/z9/c2DDz5o1q9fb86cOWPOnDlj4uLizAMPPGD8/f3N5s2bLYllVx2281yRndTUVPP666+bUqVKmUqVKl33Xcj8+PXXX80jjzxifHx8TKdOnczevXstWa/L5TLly5c3/fr1MwMHDszxzwp2nf+u54svvjDVq1c3xYoVM+PHj7dknXbVYTu/6+XLlzcrV650T0+ePNlUrFjR3Uvm5MmTTb169SyJZef3wq5ro53HLzQ01OzZs8cYc/V85O3tbRYvXmzJujMrzPPtrl27TIcOHYy3t7d59NFHzaFDhwokTkHe216LpM1BLl++bJo0aWJ8fHxMq1atzMCBA82zzz5rWrVqZXx8fEzjxo3N5cuXLYkVFBTkUWmioqLMW2+95Z4+dOiQKVKkiCWxjDEmJSXFvPrqq6ZEiRImKiqqQLqttfPEkF2sffv2WbLu7GK1adPGdOzY0XTs2NH4+PiYmJgY93SbNm0s26+ZM2caf39/4+XlZYoVK2aKFy9uvLy8jL+/v8f3I78CAwM9LrAdO3Y0Tz/9tHt6586dplSpUpbEsqte2Vl/ExISTGRkpClbtqyZOHGi+eKLL8yXX35pJkyYYMqWLWsiIyMtG57BGGMWLVpkSpUqZby8vDz+SpUqZb788kvL4vwVkrYPPvjA3H777aZ06dLmzTffLJBu30+ePGmefvpp4+fnZ5o1a3bdjmTyYuLEiaZatWomLCzMPPvss2b79u2Wrv9adp7/MluzZo255557TGBgoBk6dKg5ffq0Zeu2qw7b+V0vUqSIOXjwoHu6devWZsiQIe7pPXv2mFtuucWSWC6Xy9SoUcPUrl3b1K5d23h7e5s77rjDPV2jRg1Lvxd2XBvtPn6Zvxe//PKLJevOTayCPt/+9ttv5vHHHze+vr4mNja2wM5RdtzbXovmkQ7i6+urpUuX6rXXXtNHH32k1atXyxijKlWqaOzYsRo4cOB128DfiEqVKmn16tW6/fbbdfjwYe3du1eNGzd2Lz969KhKlCiR7zjGGM2ZM0cvv/yyrly5onHjxqlXr17y9vbO97ozc7lcHs0SM09bHevs2bMqUqSIjDFyuVy6cOFClnearHjX59FHH/XYj+wGPbeq3XSfPn0UGxurTz75RL/88ov7+/ePf/wjx4F086JIkSIeTXTi4uI0efJkj+Xnzp2zJJZd9crO+hseHq61a9fqqaee0vDhw91NPlwul1q2bKm33npL4eHhlsSSrr5bdujQIS1ZssTjexETE6PAwEDL4kjSf/7zH/dg3leuXNHs2bNVsmRJSVeb0Vohu/p77tw5d/293ruJebVkyRINGzZMBw4c0JAhQzRo0CAFBQVZGuP8+fOaMmWKpk6dqkqVKmnRokWKiYmxNIYkDR06VEOHDtW6dev03nvv6Z577lFUVJR69uypLl26WPaOo2Tv+S/Dzp07NWzYMC1ZskSPPvqo5s2bZ+n5T7KvDtt5XQwJCdGZM2fc779u2LBBvXr18ohtVVPgESNGeEzff//9Wco8+OCDlsSS7Lk22nn8pKsdkSQkJEi6eq+2Z8+eLJ2p5NTc/0bYeb5NSkrSuHHjNG3aNNWqVUvLly+3rPn+tey8t80cGH8iW7ZssWQ9M2fONEFBQaZnz56mevXqpmHDhh7LX3nlFRMbG5vvODVq1DCBgYHm+eefN/Hx8SYpKSnbPyu4XC73L2AZ3VCHhoa6p4sVK2bpk7ZrnzjkNH0z+emnnywbMLdp06buATtXr15tvLy8zLFjx9zLly5daipWrGhJrNywql4VRpzTp0+bDRs2mPXr13s8CbBj4N5rxyW0Qrly5Uz58v8fe2cel1P6///Xudt3SVMMJZSyli3LBxVZsowlZE+G7EL2JUZ2IvvWxgzKaCQMxr5kp7KUJZU1a4UWVO/fH327f93Vnca57quYno/HeTw65zqP63U697ne1/5+V//qIRae5ffy5ctkZ2dH6urq5OHhQa9fv2aSb1EYGRlJ7W1kZCRFRUUVebAmLS2NAgMDqWnTpqSlpcXMpvPm8ePH5OrqSsrKytSjRw+ZLQSKRJFlmGe92LVrV3Jzc6Ps7Gzau3cvqaqqyvw/Bw8eJEtLSyZaiYmJlJ2dzSQvsbCqG3m+vzwblxeuI//B2gbysrfLli2jihUrUp06dWj//v0Mnlw+PNu2+Sl3+f8dkJqaij/++APbt29HVFQUM9fGfn5+OHjwIIyNjeHl5QVjY2Np2pgxY+Do6IiePXuK0pBIJNK/ixrdo/8bdWHxPwUFBZXovqFDh4rWOnPmTInuyz97+a0UdHnNkzxHCX5+frh27RoaNGiAyMhI0fmeOnUKTk5OqFKlCl68eIH+/fvLbO4fM2YM0tLSSvybfguKKlelpZPH3bt3sX37dvzxxx94+fKlQjQePHgAf39/BAUFITk5GR07dsT+/fsVoqUIeJZfiUQCDQ0NuLu7yw0HAQATJkxgopWHIAgym+7zzlnZ2/ycP38e/v7+2Lt3L+rWrYtTp04x8zTL0/5pampCEASMHz8eLVu2lHtf9+7dFfocLMswz3oxMjIS7du3x4cPH5CVlYVZs2Zh4cKF0vTBgwdDS0sLmzdvFq1VmvUioJi6kef7S0xMLNF98rwG/xt42ds8W9u+fftiZ7xCQ0NF6eRp5aHotm1+ypdHlmFOnjwJPz8//PXXXzA1NUXv3r2ZeK3KY/jw4TJT7/kR61I7j1OnTjHJpySYmZmhZcuWMm7kFQWLxlxJKY1xlTNnzsDPzw/79u1DZmYmpk6dil27dsn1dPZvsbe3x/Xr1/HPP//A2NgYffr0kUm3trZWWMwWRZcr3joA8PHjR+zZswd+fn64evUqmjdvjhkzZjDVyMjIQEhICPz8/HDp0iVkZ2dj9erVcHNzky5nFIuDgwNCQ0NRoUIFJvnJIzExEf369ZPGf1MkJiYmEAThq+74WXTa4uPjRedRUp4/f47AwEAEBgbi/fv3GDRoEC5fvow6deow1eFp/zIzMwHkuj+XhyIaYoDiyjDPetHa2hoxMTGIiIiAsbExbG1tZdJdXFyYfR+lNd+gyLqR5/tj0RkrKbzsbcGl1IqEZ9s2P+UzbWWMp0+fIjAwEP7+/khLS0Pfvn2xefNmREVFMa8MAeDZs2fYt28f7t+/D0EQYGFhgV69euHnn39mrqVoSnPk7c6dOzIVuZKSkugYd3kUDCKqKF68eIGAgADpt9e/f38MGDAALVq0UNj3xwte5Yp3+T1//jy2b9+Offv2SQOhnjlzBq1atWKmceXKFWzfvh3BwcGwsLDAoEGD4OLigqpVqzL/v3h966U9Sv+94+TkhFOnTqFDhw5wc3NDly5dFNYp4PVNlBaKLsM/6rfO87v4EevGkgZtlxeb7t/wo36DpUH5TFsZwsnJCefPn0fXrl2xbt06dOrUCUpKSkymwoti48aNmDx5Mj5//gw9PT0QEd6/f4+pU6fCx8cHY8aMYabFo3PIc/zh3LlzmDx5Mq5evQoAaN68OdLT02U2kx89ehTt27dnonf06FHo6ekVe4/YJTtmZmbo06cPNmzYAEdHR5npf0Wxd+9e7N69W/pdmJubY8CAAXB2dmamwatc8Sy/y5cvh7+/Pz5+/Ij+/fvj/PnzaNiwIVRUVKCvr89Uq2XLlhg/fjyuXLmC2rVrM827tPjRxyqvXr1aZLlq0qQJk/yPHDmCypUr4/Hjx1iwYAEWLFhQ5H03btxgosfD/vGGVxnm/a1nZWVh9erVRX5/EydOZOaMCZB1WiQPFjPYPOtGXu/PzMxM+nf+dkv+a6xmlUvD3qakpODhw4cQBAE1a9ZU2OoN7hMfzHfJlfPNKCkp0aRJkwrF0lFWVqY7d+4w1Tp48KA07kh+BxDPnz+nSZMmkbKyMh06dIiJ1oYNG0hNTU26IVpPT48EQSA1NTXasGEDEw2i3M2u+eO2KBIXFxfy9fWVnmtra9OZM2coISGB4uPjadKkSdSrVy8mWkVtFC5q47BYLCwsqHr16jRr1iyKiYmRXlfE95ednU19+/YlQRCodu3a9Msvv1D37t3JwsKCJBIJ9evXj3Jycpho8SpXPMuvkpISzZo1i7KyshSu5ejoSDo6OjRgwAD6+++/pb+LIrQEQaCHDx/K3dTNanM3T1tBlOtQYvny5WRjY0NaWlqkra1NNjY2tGLFCmZhIPKYOnUqCYJAOjo61LBhQ2rQoAFpa2uTRCKhadOmMdGYP39+iQ4W8LJ/+QkJCaGePXtS3bp1qV69etSzZ0/mrrx5lWGe33p6ejq1atWKJBIJdejQgSZOnEgTJkygDh06kEQiodatWxcZl+5bEASBqlWrVqzDIjMzMyZavOpGnu9PSUmJTE1NycvLi65du0aRkZFFHizg+Q3Gx8eTk5MTKSkpSR2dKCkpUZcuXSg+Pp6pFq+2bX7KZ9rKEOfOnYO/vz+aNGkCS0tLDB48GP369VOI1vLlyzFjxgx4e3vLXK9cuTJ8fHygqamJZcuWwcnJSZTOoUOHMGHCBHh4eGDKlCmoXLkygNzlBitWrMDEiRNRvXp10Tp5zJ0796suyH18fETrXL16FRMnTpS5VrVqVek68cGDB6NLly6idfLgsQzk3r17uHDhAvz8/NC0aVPpUjig6I22YlizZg2OHz+OAwcOoGvXrjJpBw4cwLBhw+Dr6wsPDw/RWrzKFc/y+9tvvyEwMBA7d+5E//79MXjwYNSrV08hWseOHcOTJ08QEBCA0aNHIyMjQ/p/KWL/gIWFhdw0Yjj66+rq+tU9Fiw2rGdkZMDR0REXL15E+/bt0aZNGxARYmNjMX36dBw4cADHjh2Durq6aK2goCCsW7cOa9euhbu7u3RU/suXL9i0aROmT5+OunXrinaRX9DduqLhtQwuJycH/fv3x969e2FhYQFLS0sQEe7cuYN+/fqhT58+2L17N5PvnmcZ5lUvLlmyBE+ePMHNmzcLuYqPiopC9+7dsXTpUsyfP1+0FgBcu3aNy3fBq27k+f6ePn2KoKAgBAYGYvPmzRg0aBCGDx8OKysr0XkXBQ97++TJEzRv3hwqKipYuHAhrKysQESIiYnBpk2b0KJFC1y9epVJiAbebVspCukKliOKtLQ08vPzo1atWpGKigpJJBJas2YNvX//npmGjo4OxcbGyk2PjY0lbW1t0Tpt2rSh2bNny02fPXs2tWnTRrQOUe5oTsuWLcnOzk7uYW9vz0SrYBDMffv2UVpamvQ8ISGBVFVVmWhJJBKZwJQ8+PDhA23dupWaN29OgiCQnZ0dbd26ldloWf369cnPz09u+vbt26levXpMtPLgUa546hARnT59moYMGUJaWlrUoEEDUlJSovPnzzPXyc+xY8fIxcWF1NXVydzcnGbOnEnXrl1jkrcgCBQaGkqnT58u9mCh069fP3J1dS32YMHcuXPJxMSkSFf7kZGRZGJiQl5eXky0mjZtSj4+PnLTV61aRU2bNmWixQue9m/VqlVUsWJFCg8PL5QWFhZGFStWpNWrVzPVVHQZ5lkvmpub059//ik3PSQkhMzNzZloFQzYzAtF1o08319+zp07R25ubqSjo0O2tra0detWpuEUeNnbYcOGUZs2bYqcjUxPT6c2bdqQm5ubaB0ivm3b/JR32so4sbGxNHXqVDI2NiZ1dXXq1q0bk3y1tLRkItIXJC4ujrS0tETr8OocEvE14oaGhnTq1Cm56adOnaJKlSox0SqtyimPu3fv0pQpU+inn34iZWVlJnmqq6tTYmKi3PSEhARSV1dnolUUiipXpaXz/v172rRpEzVr1oyUlJSoRYsWtGrVKoVo5fHu3Ttau3YtWVtbM43nw+Nb51mmeDbENDU1v2rXNTU1mWjxgudvVRqDSXkoqgzzfH9qamr0+PFjuemPHz8mNTU1JlqlXS8Ssa8beb6/okhKSiJ7e3uSSCT09u1bZvny+q0qV65M586dk5t+5swZqly5MhMtnm3b/Cje00A5oqhduzaWL1+Op0+fMluWAQB169ZFWFiY3PT9+/cz8X6Yk5NT7MZZFRUVZptUebl6BQBbW1vs2LFDbnpgYGAhd73fytChQ5nFPPoWrKyssHLlSjx79gyLFi1ikqeGhgZSUlLkpr9//16h/7OiylVp6ejo6GDUqFG4fPkybt68iWbNmmHp0qUK0cpDX18f48ePx82bN7FkyRKFauUnKytLdB48bcXjx4+LDV/RvHnzEnty+xpKSkr4/Pmz3PQvX74UG7+oLMLT/j148KBY51Ht27fHw4cPFaKtqDLM81vX1dXFq1ev5KYnJSVBV1eXiVbbtm2L/dZ5wLpu5Pn+8hMREYFff/0VFhYW+PjxIzZs2MDUcQevb/Dt27fFxsKsUaMG3r59y0SLZ9s2P+Wdtu8EJSUlZGdnM4vDM2bMGMyePRsbN26UaQRlZWVhw4YNmDNnDkaPHi1ah1fnEPi6h6K3b99izZo1TLQmT56MoKAgTJ06VcbIvnr1ClOmTMHvv/+OyZMnM9HasWMHMjIymORVEj5+/FhILzIyEr169cLMmTOZaLRo0QKbNm2Sm75hwwa0aNGCiVZxsC5Xpa0DAPXr18e0adPQt29fZnlmZWXhzp07uH//vsz1sLAwWFtbY+7cuUx0TE1N5XYq7t69i8mTJzPxyvU1W3Hr1i0m+ykBvg2xxo0b448//pCbvnPnTjRq1IiJFi942r/SHkzKg2UZ5lkv2tvbY/HixXLTly5dCjs7OyZaZ8+ehaqqKpO8Soqi60ae7+/FixdYtmwZLC0t0bNnT+jq6iIiIgJXrlzBqFGjmHrI5GVvq1Spgjt37shNv337tnTvmVh4tm1lYD53V44otm7dSs7OztS/f3+6dOkSERGdOHGCrK2tSUNDg9zd3ZlpTZkyhQRBIF1dXbKxsSEbGxvS1dUliURCHh4eTDQCAwNJQ0ODNmzYQF++fJFe//LlC61fv540NDQoICCAmVZmZqbMtZycHDpy5Aj16dOHVFVVmS1ZJMr1HKSqqkoSiYQqVKhA+vr6JJFISFVVldatW8dMh9fSgidPnlDLli1JIpGQiooKTZo0idLS0mjw4MGkrKxMvXv3poiICCZaFy5cIBUVFerTpw9dvnyZUlNTKSUlhS5evEjOzs6koqLCdF8Hr3LFs/zeuXOH1q9fT1u2bKHk5GQiInr9+jV5eHiQhoYGWVlZMdMxMzOTeuLq2bMnJSUlUZs2bUhPT4+mTJlS7JIeMXz48IG2bdtGzZs3JyUlJWrVqlWxe7ZKyunTp2XsERFRamoqbd68mZo2bUqCIFDDhg1F6xAR9e3bt1hPsr169aI+ffow0QoPDyclJSWaOnUqJSUlSa+/ePGCPD09SVlZucj9Wqx5/PgxDRs2jElePJfBOTk50ahRo+Smu7u7k5OTEzM9HmWYZ714584d0tbWJltbWwoODqaoqCiKioqi3bt3U7NmzUhbW5tu377NRIvnd8GrbuT5/lRUVMjU1JTmzZtH165dk2oVPFjAy95OnDiR6tevX+T+wpcvX1KDBg1o4sSJonWI+LZt81PeaStDrFixglRUVKhx48akqalJmpqatGjRIjIwMKD58+fT69evmWtevHiRJkyYQJ07d6bOnTvTxIkT6eLFi0w1eHQOCxIfH09z586latWqkUQiocGDB9M///xTyL2yWB4/fkw+Pj40evRoGj16NPn4+DBvwPJylztw4EBq0KABrVu3juzs7EgikVCjRo1o2LBh9OjRI+Z6oaGhVKlSJWlnIO8wMDAodg/Qv4VXueJZfsPDw0lVVVXq8rxmzZp08uRJqlSpEtnZ2TFtmHfr1o0cHBwoPDycXFxcSBAEMjc3pwULFijEuQpR7sb4oUOHkra2NtWvX1+hDlZOnz5NgwcPJk1NTZJIJDR9+nR68OABs/x5NsSIiNauXSsdTNLX15cOJqmoqDB3oiGPyMhIpvscebkL5zmYxLMM58GjXrx48SLVqVNHGopBIpGQIAhkZWVFFy5cYKbDKzwIEd+6kef7yx8yI09HkaE0iBRrb9+9e0fm5uako6NDo0ePJl9fX/L19SV3d3fS0dEhc3Nzpnv1SqNtW95pK0NYWlpKN0GfOnWKBEGgdu3aSUfgvmd4dA4zMzNp165d5ODgQOrq6tLYOoqIJ8WTvBggeQ0weYdYqlSpIm2QvHjxggRBoCVLlojOtzjS0tIoNDSUli1bRsuWLaO//vpLxgsnC3iVK57lt3nz5jRhwgT68OEDrVq1igRBIAsLCzpz5gxzLSMjI7p+/ToRESUnJ5MgCLR161bmOkREy5Yto9q1a9PPP/9Mnp6e0jhBrMvw8+fPadGiRVSzZk0yNjamSZMm0dWrVxVmK3g1xPJ48uSJzGDS6tWrFTYbWhSsO2087F8evAaTeJXh0qoXb968ScHBwRQcHEw3b95knn/+slTUwbLTURp1o6LfX0JCQokOFvC0t+/evaNRo0aRvr6+tPOpr69P7u7u9ObNG6ZaRHzatvkRiEohVHk5RaKpqYnY2FiYmJgAANTU1HD27FlmDi3+DaGhoZg/fz6io6O5a38rlSpVQp06dTBo0CD06dMH+vr6AHI3hEZFRaFOnTrMNffu3Yvdu3fj/v37EAQB5ubmGDBgAJydnZlpSCQSrFmzBnp6esXeN3ToUFE6SkpKePbsGYyNjQEAWlpauHbtmsLitnyNZ8+eMdm/xKtc8Sy/FSpUwJUrV2BhYYGsrCyoq6sjPDwcnTt3Zq4lkUjw4sULGBkZAQC0tbVx48aNYuOpfSvKysqYPn06fvvtN5m9bazLsLq6Ovr06YNBgwbB0dFRun9DkbYCAG7evIkHDx4AyI1HZ21trRCd0iYqKgqNGjViEk+Pl/3LT3p6Oo4ePSrzW3Xo0OGrsc7+DbzKcGnUi8Vx9epVNG3aVHQ+EokE+/btQ8WKFYu9r23btqK1ylLdyOr98aQ07C0R4fXr1wAAQ0NDrg55FEl5cO0yRGZmpkyAVVVVVRgaGipMb9u2bTh27BhUVFQwceJE2Nra4uTJk5gyZQru3buHwYMHi9YoaaevYCDJbyE7OxuCIEAQBIV7SOMZhBUAXFxcuAQRzf/eJBIJk4C//5akpCQsWrQI27dvZ+KAgFe54ll+379/L/XupaysDA0NDYV0ooBcz1/5N6VLJJJivWaJgVfAYVNTU5w/fx4mJiYwNTWFpaUlc42isLGxgY2NjcLyP3DgQInu6969u8KeQRHwsn95aGpqomfPngrV4FWGedaLeXz8+BFKSkoyTlsiIyMxd+5cHD58mElnHgBatWrF7bvgWTfyeH8l9VibNwgphtKwt4IgKPTb4Nm2zU95p62MsX37dmhrawPI9dgWGBiISpUqydwzYcIE0TorV67ErFmz0KBBA8TExCAsLAyzZ8+Gj48Pxo8fj7FjxxbS/Rasra0hCEKx3oMEQWBihF68eIF9+/bBz88PEydOROfOnTFo0CCFjLCsWbMGx48fx4EDB9C1a1eZtAMHDmDYsGHw9fVl4hGJ1wgREaFdu3ZQVs41CxkZGejWrVshD103btwQrZWSkoKxY8dKBw1mzJiBcePGYf78+Vi5ciXq1q0Lf39/0Tp58CpXvHSAXG+KSUlJAHJ/u3v37iEtLU3mHhYVBhHBwsJC+h1+/PgRNjY2hbyLvXv3TrTWrFmzMGvWLJw5cwb+/v5o3rw5atasCSJCcnKy6PzzuHfvHi5cuAA/Pz80bdoUFhYWGDRoEAD25a2kXmR9fHxEa/Xo0eOr97Cwt7169So2vTgPjP8WniPka9euLdF931MZ5lkvPn36FP369cOlS5egpKSEcePGwdvbG6NGjcLu3bvxyy+/4Pz588x15ZGVlSWtz8TAq27k+f6qV69e5DdARNLrgiAwCbHCy97a2NiUKD8WbRiebVuZPMuXR5Yd5BWi/AiCgEePHonWsrKywtSpU+Hm5obTp0/DwcEBDg4O+PPPP5nG50hMTCzRfaampsw0ASAuLg4BAQEICgrCs2fP0L9/f7i6usLBwYHJaGODBg3g4eEBNze3ItP9/PywZs0a3Lp1S7SWRCJBUlJSkaNGycnJ+P333+Hn54fIyEhROgsWLCjRfV5eXqJ0gNyQE+Hh4ejXrx+OHDmCmJgYdOzYEZmZmfDy8mKypCUPXuWKZ/mVSCRyK4y866wqjKCgoBLdx3J5Wh4fPnzAH3/8gYCAAFy/fh3NmjWDs7Mzs3AaQG4ndPfu3fD398fly5fRtm1bDBgwAD169GAyU2pnZ1ei7+LkyZOitXgxbNiwEt0XEBAgWouX/QMAMzOzr97zPZbhPBRdLw4aNAi3bt3CiBEjsG/fPpw9exbW1tZo2LAh5s6dW6L3W1LMzMxw7do1GBgYFEq7e/cutm/fjj/++AMvX74UrcWrbuT5/qKiooq8TkTYs2cP1q5dC21t7WLDlXwLirS3PNswpdW2LXdE8h9FQ0ODEhMTpeeqqqpSF+UsWbBgAXPHEvIICgoq5No4OzubDh8+TL179yZVVVUyMDBgoqWuri7z/gqSkJBA6urqTLSK4p9//iEXFxdSV1enqlWr0oQJExSmpQhMTEzon3/+ISKiuLg4EgSBmSve/wI8N5HzxMzMTO5m8ejoaJo4cSIZGhqK1pFnl+7evUtTpkyhn376iZSVlUXr8GbYsGEK8+hZlvje7R8RvzLMs14sDYcdeSgqPAhPSvP9EeWWq8aNG5OOjg55eXnRhw8fmOTLy94mJiZSdna26HxKAs+2bX7KO23/UQrGONHW1qa4uDjmOhKJhFssla9pvXr1ilatWsVES19fv9gYJtHR0Uw9mhHlGqT58+eTqakpGRgYkEQiYerN7MSJE4ViqSgKZWVlevbsmfRcQ0ODbt26xUW7nH+Hn59foUafoihJ7KXPnz+L1vmarfjy5Qvt27dPtA5R8R1R1vC0t/LIzs6mAwcO0C+//MI0X0XbPyIie3v7H8Jbc3541osSiYRevHghPdfU1KS7d+8yyVsePMKD8KobS+P9ERFdu3aN2rdvT2pqajR27FjmNoSXvS1L7U2F6bKdtytHDCYmJnj79q30fP369Xj//r3C9LZv3461a9di7dq10v03eed5h1iI4+rbr2kZGhoyW1bVokULbNq0SW76hg0b0KJFCyZaISEh6NChA6ysrHD79m34+vri+fPnkEgkTL1XOTo6yuxLat68OZ49e8Ys//zk5OTIOLNQUlKClpaWQrR4lSue5XfIkCH48OGD9DwqKgpfvnxRiNaIESOQmpoqPa9SpQoSEhIUolUSWDhB+ZqtUFZW/uq+rZKSkJDAfF+DPHja24I8ePAAM2fORNWqVdG3b19m+fKyfwBw+vRpfP78mWme8uBVhnnWiwA/hx3Lly+HpaUlXFxcYGhoiPPnzyM6OhqCIEg9ZLKCZ93I0+HJw4cP0a9fP9ja2sLQ0BB3797F+vXrmTvw4GVvy1J7U1GU72krQxRcu6+rq4vIyEjUqFGDuRav/TcSiQQvX75UqBfM0tCKiIiAnZ0devToAU9PT6n3yJiYGKxatQphYWE4deoUWrVqJVpLWVkZ06ZNw8yZM6GjoyO9ztpdbsHvT0dHB1FRUQr5/iQSCTp37gw1NTUAQHh4OBwcHAp13EJDQ5lo8ShXPMuvkpISXrx4wUWL93dx8uTJr7rxFuucgbddkrcnSxFavP4vINchQ0hICPz8/HDp0iVkZ2dj9erVcHNzkzrkEQsv+wfw/a14lWHe33q9evWkDjuio6NhaWmpEGdWvMKDAPxsIM/3N2bMGPj5+cHe3h5Lly5VaPgRXt/gj2xr8yj3HlmGUWR/mudIeX6vS/JgYYQAwNXVVdoRkAeLjkDLli0RHByMkSNHYt++fTJp+vr62L17N5MOGwC4ublh48aNOHPmDAYPHox+/foxH0nkzZAhQ2QGDfI8SfGA1ziVInUK5v0jjb21a9eOi3MGnnYpv5dAebByDZ3f06c8xHr6vHLlCrZv347g4GCpJ7i9e/eiatWqaN++PbMOG8Df/n348OGrsxu6urqidXiWYV71YkEHD7/88ovoPOXBKzwIT3i+v82bN0NdXR2vXr2S61ANYGcDednb/B6c5cHK+yvPOiSP8k5bOQqnY8eOTCvx4tDR0ZGJbaJIevbsiY4dOxYZhFVFRQWPHz9mEuNk69at8PX1RUhICPz9/eHh4YGOHTuCiJCTkyM6/zzyYvnIO2dJYGCgQvIthz08vwsAuHz5MpfRS552iVdHFMj1oPa1QNRiadmyJcaPH48rV66gdu3aCtXiZf/yKC5WGuvfihe86sVhw4ahatWqhcKBKAJe4UEAfjaQhVfDsqgF8LO3mzdvLtYTqiAIzDptPOuQPMqXR5YhJBIJvL29pR/B9OnTMXXqVIXEeTp58iTGjRuHS5cuFRo1TE1NRcuWLbFp0ya0adNGlA7v6WpeWl8jKioKjRo1Ukjl/uDBA/j7+2PHjh34+PEjunTpAmdnZ9FrwnkuzcjOzsadO3dgbm5eqDGRnp6Ohw8fol69ekwqf17limf5LbiMsGXLlggJCUHVqlVl7mMxeyORSKCnpydtpKSkpEBXV1chcdp4lWHedunKlStf7YiycA3N6//q0KEDLl26hG7dumHw4MHo2LEjBEFQyNK0gijK/gG572/fvn1fXZ7LIiQJrzJcmks+eaLI8CC86sbMzEwcO3YM9vb2MkuBgdxg7KdPn0bHjh2/Omta1vhR7XpptDfLO21lCJ5xnrp37w57e3tMmjSpyPS1a9fi1KlT+Ouvv0Tp8DTipVlhFESRnbY8cnJycOjQIfj5+eHvv//Gp0+fROXHM8ZJYGAg1q9fj8uXLxcaFcvOzoatrS08PDyYLJssj9MmDp5x2nhVhDxtxY/aaH7y5AkCAgIQEBCAjIwM9OvXDxs3bkR0dDRzByFFwdr+AfwbfTzK8I/6rRfHrVu34Ofnh127djGJM8arbvT19cWBAwdw4sSJItPbt2+PHj16YNy4caJ0eMPrG/wvtDfLO23/UUxNTXHkyBG5lWtsbCw6dOiAx48fi9L5L4x8FAWPTlt+Xr16VSb+75LSunVrjB07Fi4uLkWmh4SEYP369Th79iznJ/s+KLXAngrG3t4ef/31FypUqKBQnR/VLpWWDfznn3/g7++P/fv3o1q1anB2doazszMaNWrERZ+V/eP5/niV4f/C9yePL1++MPE2y4tmzZph7ty56NatW5HpBw8exG+//YYrV65wfjJxlM+0saN8T9t/lJcvXxZrzJSVlfH69WvROvHx8dy865w6deqry1q+R65fvw5PT0+EhYUVuZS1R48e8PX1FW08Tp48iTZt2nx1Yy0L7t27h+bNm8tNb9q0KWJiYhT+HN8rPDtj/v7+GDhwIJclOadOnSp0LTMzE8HBwUhLS4OjoyPMzc1F68izS1lZWcjMzGS6T6Ft27aFllEpis+fP+Pu3btF7mFivew4P46OjnB0dERycjJ+//13+Pv7Y9myZUwGrXjZPyC3XBW3H4YlvMow73qRlyOIkmzx2Lx5M1q3bs1Ei0fd+ODBAzRs2FBueoMGDaT7578neNlbLy8vbnvMeLZtZVBsGLhy/g3VqlWTCcK6bt06Sk1NVYhWjRo1KDQ0VG76vn37yMzMTLTOsGHDijw8PDxo06ZN9OHDB9EaeVy6dIkOHz4scy0oKIiqV69OhoaGNGLECGZBgqOiooo9goODSSKRMNHq378//fbbb3LTvb29aeDAgaJ1CgaLtLW1padPn4rOtyg0NTWLDU4eFRVFmpqaTLR4lSue5Xfw4MH0/v176XlkZCSToNNFUfC7qFy5MsXHxytEy9PTkyZMmCA9//TpE1lbW5OKigrp6emRlpYWRUREiNY5dOgQ7dixQ+aat7c3qampkZKSEjk6OtK7d+9E6/AmICCAGjduTFlZWYXSsrKyqHHjxrRz504uz3L9+nUm+fCyf7zhVYZ51ouCIFC1atWoevXqcg8W7Qoiom7dupGPj4/cdF9fX+rRowcTLV51o7a2Nl27dk1u+rVr10hbW5u5rqLhZW/PnDlT5BEZGUkfP34UnX9+eLZt81PeaStDCIIgYxh0dHQoLi5OIVrjxo2jevXqUUZGRqG09PR0qlevHo0fP160To8ePYo87OzsyNDQkKpUqcLsf+zUqRMtXbpUeh4dHU3Kysr066+/0qpVq8jY2Ji8vLyYaAmCQBKJhARBKHTkXWfVaatRo0axHZzo6GgmFWHB709bW1th31/Dhg1p06ZNctM3bNhADRs2ZKLFq1zxLL8FGxGK1OL5XdStW5fCwsKk5/7+/qSvr08JCQmUk5NDrq6u5OTkJFrH3t6e1q9fLz2/cOECSSQS8vb2pn379pGlpSVNmjRJtA7R/7cVBY8KFSqQra0t7du3j4kOEVGrVq1o9+7dctODg4OpdevWonXu379PLi4uRQ5KpKSkUP/+/Zl9I7zsHxFJOxUFD2tra+rXrx9dvXqViQ4RvzLMu17M/z8pEhMTE7p7967c9JiYGKpWrRoTLV420NbWVua3KsiSJUvI1taWua6i4WVvi2uXKSsr0/jx45kNjPBs2+anfHlkGYYUuN1wzpw5CA0NhYWFBcaNG4fatWtDEATExMRgw4YNyM7OxuzZs0XrFOfIJCMjA0OGDMGMGTMQEhIiWisyMhILFy6Unu/Zswe2trbYtm0bAKBatWrw8vLC/PnzRWvFx8eLzqOkPHv2rJAnqfxoa2vjxYsX3J6HBQMGDMCcOXPQsmXLQt7RoqKiMG/ePEybNk0h2oosV7x0CubN639SNI8fP5bxPHjs2DE4OztLl5JNnDgRTk5OonVu376NVatWSc///PNPODo6Sm2euro6Jk6cCB8fH9FaoaGhRTqoSUlJwZUrVzBo0CAEBQWhT58+orXu37/PZdnxihUrUK1atSLjlenp6aFatWpYsWIFNm3aJFqLp/3z8PAo8npKSgquXr2KFi1aSL37iYVXGeZZLyoyFEhBeG3x4ImbmxsmT56MunXromvXrjJp4eHh8Pb2ZmKTAGDHjh0lum/IkCGitXjZW3mhHvJs7dSpU2FsbIxZs2aJ0gH4tm3zU95p+49iZGSEiIgIjB49GjNnzpRWGIIgoGPHjti4cSOMjIwU+gwaGhqYPn06E1fNQG6Bzf/MZ86cQadOnaTnTZs2xZMnT5hofW0/QnJyMsLDw5kYPENDQ9y7dw9mZmZFpsfGxhZyK/8t8IzHNWnSJPz9999o3Lgx2rdvD0tLS+mgwfHjx9GqVSu5nk3L4QvP70Iikcg0Xi9duoS5c+dKzytUqMAkBtOHDx9gYGAgPT9//jycnZ2l53Xr1sXz589F6wBAjx495KYNHToUderUwcqVK5l02tLS0vD+/Xu56R8+fEB6erponbNnz2Lnzp1y0/v27YsBAwaI1gH42T8gd1CgOBYuXIj58+cz6bTxgme9yHPw6Oeff8atW7dQq1atItOjo6NRuXJlJlq8bODIkSNx9uxZdO/eHZaWljKD6ffv30ffvn0xcuRIJlrFfeuCICAtLQ1ZWVlM2jC87K28+JR6enowNTWFqqqqNL6fImHdts1PeaetjJF/E29WVhYCAwMVEucJyO14HD58GMnJyXj48CGICObm5tDX12eSf0moWLEiUlJSmORlZGSE+Ph4VKtWDZ8/f8aNGzdkXPV++PCBmyepx48fY9iwYUwMXvv27bFo0SKZijYPIsLixYvRvn170TpEhHbt2kk3W6enp6Nbt24KidOmoqKCY8eOYfXq1di1axfOnj0LIoKFhQUWLVoEDw8Ppr8Vr3LFs/zevXsXSUlJAHJ/u9jYWHz8+FHmHhZx2vJ+l7xGysePH2FjY6OQOG2WlpYIDw/H5MmTcefOHTx+/FimgZyYmMhkMKlKlSqIiYmBiYkJPn78iKioKKxevVqa/vbtW2hqaorWKQkdOnTAnDlzmORlbm6OiIgIub/7+fPnmThySUxMLNbxR6VKlZh1BHjZv5Lg7OwMX19fZvnxKMM860WejiCcnJwwb948dO7cGerq6jJpGRkZ8PLyKjRb9a3wrBt///13dO/eHbt27cL9+/dBRKhduzYWLFiAvn37is4/D3mDXy9evMCCBQvg7+8PR0dHJlplxd42bNiwxF5bxcKybZuf8k5bGcLExES6ZAEAjI2NC41msozmnoe+vj6aNm3KNM+SEhERgZo1azLJq1OnTpgxYwaWLVuG/fv3Q1NTU8ZzVHR0NDMtnsyZMweNGzeGra0tpkyZIjP6tmrVKty/fx8BAQGidQrGmPnll19E51kcKioqmDZtmsKWQebBq1zxLr/t2rWTGdnOa6CwjtPG4tsqKVOnTkX//v1x6NAh3LlzB05OTjIzLIcPH0azZs1E6zg7O8PDwwOzZs3C4cOHYWxsLLOs8Nq1a6hdu7ZonZKQkZFRqNH5rfBadqynp4e4uDi5Kw4ePnxY5NLJb4GX/SsNeJRhnvXigAEDMHz4cGzZsqVIj46jR4+Gt7c3atSoIVqL1xYPgH/d2LdvX6YdtJLw4cMHLFu2DL6+vqhbty6OHj3KbEa5rNjb58+fc3PRz7Jtm5/yTlsZIiEhgZtWSadtQ0NDRelER0cXeT01NRVXr17F4sWL4e3tLUojD29vb/Tq1Qtt27aFtrY2goKCZEbC/P390aFDByZaPKlZsyaOHz8OV1dXuLi4SGc8iAh16tTBP//8I3eJyL+BRdDssgivcsWz/PLcU8kiaHZJ6d27Nw4fPoxDhw6hQ4cOGD9+vEy6pqYmxowZI1rHy8sLz58/x4QJE2BsbIzff/9dxtX77t275cZKYs22bdtgY2PDJC9ey47btGmDdevWwcHBocj0tWvXMnG1DvCzfyXhzz//RL169ZjkxasM86wXV65cyW2vI88tHrzqxuKWNueH1YAIkBsmZP369Vi8eDEqVaqEgIAAmaWLLCgL9vbVq1eYM2eOXJv1b+HZts1PeXDt/yjDhg0r0X1iRzAlEol01LAghoaG8PT0xNSpU0VpFCQ1NRXa2tqF4u28e/cO2traXGImKSq4dmRkJB48eCBdsmZtbc00f17o6+uXaE8AiyV35ZRTmkyePLnI66mpqbh27Rri4uJw7tw5Zh23L1++SJcd57cVAwYMgIeHBxP7d/PmTbRo0QJdu3bFtGnTpKPksbGxWL58OQ4dOoSIiAjmwbUVbf/Wrl1b5PW8htjff/+No0ePMmv48YRHvVi7dm38/vvvclfuXL9+HQMGDMC9e/dEa+WnNLd4sCSvvSQPlisoiAg7duzAvHnzkJWVBS8vLwwfPpxbnEJFYGNjU+T7S01NxdOnT2FlZYVjx44xmW0rjbYtUN5pK1PIqzAKwnp5pCKRt35YT08PFSpUQFpaGq5fv442bdooRH/37t3o3r07tLS0mOb7td/q2bNnWLlyJfNOW35YB6aUZ/AKwmLdflBQUInuYzHLw6tc8Sy/8kb5CsJiT1tpd7Dr16+Pw4cPo1q1agrJP4+lS5di1KhRqFChAtN85S0x0tXVhaWlJcaMGcM1WHpkZCSTzs7Bgwfh5uaGt2/fylw3MDDA9u3b0b17d9EaxaGIQOjynJ3k/VaTJ0+Gra0tEy2eZbggiqoXNTQ0EBsbK/d7TkxMhJWVFRNnOPJITExEWloaLC0tmQWR51U3njlzpkT3tW3bVpQOkPtdxcXFYfz48fDw8JC7p4zlrF5+FGFv8+/VzE9e+e3QoQOzTmlptW3LO21lCHkVRn4EQcCjR484PA0fFDUjlYeuri4iIyOZrKHPT0l+K4DNEpjDhw/j7du3GDx4sPTaokWLsHDhQmRlZcHBwQHBwcGiRxflGbyCfG/LKHmVK57lt7hRvvxaLMoVzw52Uejo6CAqKop5GS6IomzF13jy5Am8vLzg7++vMI3U1FT88ccf2L59O6KiopjZ24yMDBw5ckQ6y2FhYYEOHTowdSrAy/6VhJcvX2LLli2YN2+e6Lx4luGCKOpbNzY2xq5du+TORJ44cQIDBw6UOl8RQ1BQEJKTk2XCNIwcORJ+fn4Acmf9jh49ymSw50esG/N3aIvqkLKc1SuK0rC3MTEx6NKlC5c2tKLatuWdtv8obm5uJbpPkQ0JQPGdNl4NPkXi4OCA3r17Y+zYsQByN7i2bt0av/32G6ysrDB79mx07tyZWfyWcso+JfWAxXMGR1HwKsOlZSsUaQNPnjwJPz8//PXXXzA1NUXv3r3Ru3dvZksxeVCW7B/L36o0y7CivvW+ffviy5cvcmNY/fLLL1BVVcXevXtFa7Vo0QIjR46UbvU4cuQIunXrhsDAQFhZWWHcuHGoU6cOtm/fLlrrR4TnrF5RlIa9VXR7k4dWuSOS/yiBgYEwNTWFjY3NDxOYtyzx9u1b7Ny5U26w1n8Dz0DAebx58wYJCQkQBAHVq1eXibHCgpIa6h9pVpklpdEZIyJcv35d+l2YmZmVeNmQGFq3bg0NDQ2FavxIPH36FIGBgfD390daWpq0Ib1v3z6ZwOVi4LkUuDTsHw9+hAGVgsycORMtWrSAs7NzkXsdjx49ioiICCZa9+/fR5MmTaTnYWFh6N69OwYOHAgAWLx4cYn37v8bFFk3lnTpHouOgKI6Y+UolvJOWxkjJycHgYGBCA0NlWkcOTs7Y/DgwcwaSKNGjcKePXvw6NEjuLm5YdCgQahYsSKTvMsSf//9N37++WcuWkSEY8eOwc/PD2FhYdDV1WXSaeMZCPjOnTsYPXo0Lly4IHO9bdu22LRpEzO3vAkJCTA1NcWAAQO4uODlVa546eTx4MEDhIWFyWj16NGD+ejlqVOnMHz4cCQmJsp4aTMzM4O/v7/C9qQCucvjeHD37l1utkJRODk54fz58+jatSvWrVuHTp06QUlJCZs3b2aqkz/GkjxYhbfgaf9KA15lOD+KqhdtbGzw559/ws3NrdBsm4GBAUJCQpg5p8nIyJDZbxURESGzgqhGjRpMlmHmwaNuJCKYmppi6NCh3GbDnz17hn379uH+/fsQBAEWFhbo1auXwm3hj2BvS4PyTlsZgojQvXt3HD58GA0bNkT9+vVBRIiJiYGrqytCQ0Oxf/9+JlobN27E6tWrERoaCn9/f8ycORNdunTB8OHD0aFDB2aNywMHDhSbrii3x3mjYRoaGvj48SPU1NQUogPkdkD8/f0RGBiIZ8+eYeDAgTh06BCzGCe8AlMmJSWhbdu2MDQ0hI+PDywtLUFEuHv3LrZt24bWrVvj9u3bTDpZe/bsQUBAAHx8fNC5c2e4ubnBycmJ2cbx/PAqVzzLLwAsWbIE8+bNQ05ODn766ScQEV6/fo0ZM2Zg8eLF8PT0ZKLz8OFDdO3aFba2tli9erXMd7F27Vo4OTkhOjqaaSPz5MmTRXZ8WXcOS2v2UFEcO3YMEyZMwOjRo5kE0ZYHz5ATZSUwryLgVYbz4FEvdu3aFYmJiQrf62hqaorr16/D1NQUb968wZ07d/C///1Pmp6UlAQ9PT0mWrzqxsuXL8Pf3x++vr4wMzODm5sbBg4cqLD9mhs3bsTkyZPx+fNn6OnpgYjw/v17TJ06FT4+PkzCq+TnR7K3pdW2BZVTZvD39ycdHR06efJkobQTJ06Qjo4OBQUFKUQ7ISGB5s+fTzVq1KBq1arRhw8fmOQrCMJXD4lEwkSLiOj27dvUunVrkkgkMoe9vT3FxsYy08nMzKRdu3aRg4MDqaurU8+ePWnv3r2krKxMd+7cYaZDRDRt2jSytLSkHTt2kIuLC5mYmFBWVpY0fcuWLdSqVSsmOo0aNaKMjIxCaenp6dSoUSOaMWOGaJ38PH36lLy9valWrVpUuXJlmj59Ot2/f5+pBq9yxbP8njx5kiQSCXl5edG7d++k19++fUtz584lJSUlOnPmDBOtsWPHkoODQ5FpOTk55ODgQOPGjWOiRUTk7u5OgiBQxYoVqXnz5mRra0sVK1YkiUTCVOfkyZNkZmZGEolExhbVrFmT2bsjIurZs2exh729PTMbGBERQb/++ivp6upSs2bNaN26dfTq1Svmdsne3p6Sk5OZ5VccvOwfEdGkSZOKPQYNGsTst+JZhnnVizxZvHgxGRsb02+//UZ2dnZUt25dmfTVq1dTu3btmGjxrhszMjJo586d5ODgQJqamtSvXz86duwYs/yJiA4ePEhKSko0ZcoUev78ufT68+fPadKkSaSsrEyHDh1ipsfD3laoUIH09fXlHjo6OszKL++2bR7lnbYyhKOjIy1ZskRu+qJFi6hDhw4K0U5MTKQFCxaQmZkZ/fzzz8w6bTx58eIFGRgYkKWlJa1Zs4aOHDlCf//9N61atYosLS3J0NCQXr58yUTLwMCAWrduTVu2bJGpcBXRaUtLS6NBgwZRhQoVyNLSks6ePSuTbmdnR0uXLhWtY2NjQ8HBwXLTd+/eTTY2NqJ15HH69Gmys7MjiUQi807Fwqtc8Sy/ffv2pZEjR8pNHzFiBLm4uDDRqlu3Lh04cEBu+oEDBwo1mL6V0NBQUlVVpYCAAMrJyZFez87OJj8/P1JVVaWwsDDROg8ePCBNTU2yt7en/fv3U2xsLMXExNC+ffuobdu2pKWlRXFxcaJ1iIhcXV1LdLAkLS2N/Pz8qFWrVqSiokISiYTWrFlD79+/Z5K/IAjMbOnX4GX/8vIqycECXmWYZ73YuXNnSklJkZ57e3vLdO7fvHlDVlZWTLSys7Npzpw5ZG1tTZ06daK7d+/KpDs7O9P27duZaJVm3fjo0SPpwM7bt2+Z5dumTRuaPXu23PTZs2dTmzZtmGjxsreBgYElOr5nyjttZQgjIyO6efOm3PQbN26QkZERM7282aL27duTuro6OTs706FDhyg7O5uZBk94joZVqFCB2rRpQ1u3bqXU1FTpdUV02nihp6dHDx48kJv+4MED0tPTY66bN6pob29PGhoa1K9fP8rMzGSWP69yxbP8Vq9enc6dOyc3/ezZs1S9enUmWjo6OhQfHy83/dGjR6Strc1Eq1u3bsWW0WnTplH37t1F6/CePSxNYmNjaerUqWRsbEzq6urUrVs30Xny7LT9qPAqwzzrRYlEIvNd6OjoyDTGk5KSFDL7oGhKo2588uQJLVy4kGrWrElVqlSh6dOn05cvX5jlr6OjU+wsa2xsLDO7/l+yt4qmvNNWhlBRUZGZpi7Is2fPSFVVlYnW6NGjSV9fnxo2bEhr1qyhN2/eMMlXHiEhIdSzZ0+qW7cu1atXT7qckCU8R8MyMjLo999/l3Y0evXqRaGhoaSiovLddtoKVrgFSUpKIiUlJWZ6ly5dohEjRpCuri7Z2NjQunXrmM6w5cGrXPEsvxoaGvTkyRO56U+ePCF1dXUmWl9roLNsiP388890+fJluemXL1+mn3/+WbQOz9nD/CQnJ9PVq1fp2rVr3JYX5pGVlUV//fUXk06vIAj08OFDSk1NLfb43nn9+rXC6kZeZZhnvVjQVmhra/8QnTZedeOnT59oz5495OjoKN12ER4erpCB9K/NbsXFxZGWlhYTLd72Nj09ncLCwmjFihW0cuVKOnDgAKWnpzPLPz882rb5KXdEUobIzs6GsrL8n0RJSQlZWVlMtDZv3gwTExOYmZnhzJkzcmN2hIaGitLJyclB//79sXfvXlhYWEg38N65cwf9+vVDnz59sHv3biYbUR89elSsZ6omTZowcyGvrq6OgQMHYuDAgYiLi0NAQAAmTJiArKwsLFq0CK6urnBwcCixC9+ywocPH6Curl5k2vv375mFh6hbty5evXqFAQMG4Ny5c2jQoAGTfIuCV7niWX4zMzOhqqoqN11FRQWfP39mogXkevqS54ntzZs3zHTevHlTrEexn3/+GW/fvhWt8/jxY9SvX19uer169UocR6skJCQkYOzYsTh69KiM981OnTph/fr1qF69OjMteSgpKSE7O5vZBnkLCwu5aaTgwLyKJCUlBbNnz0ZwcDCSk5MBAPr6+nBxcYG3tzcqVKjARIdXGeZZL/7I8KgbK1euDB0dHQwdOhQbN26UOjb5+PGjzH35vWZ+K3Xr1kVYWBgmTZpUZPr+/ftRt25d0ToAX3t74MAB/Prrr4XqpUqVKsHPzw/dunVjosOzbZuf8k5bGYKI4OrqKtej06dPn5hpDRkyhIvHnjVr1uD48eM4cOAAunbtKpN24MABDBs2DL6+vsxc4xdnzHR0dAoZv29lx44d6NevH9TU1FCzZk14e3vjt99+w9GjR+Hn54euXbtCR0eHaYNW0dD/efkqLp3VNxMTEwMtLS3s2LEDO3fulHvfu3fvRGvxKlc8yy8AbN++Hdra2kWmffjwgalWu3btimyUCILA9Lv4/PlzsQ1ZZWVlJg3Zjx8/FuvJTlNTE+np6aJ1AODJkydo3rw5VFRUsHDhQlhZWUm9im7atAktWrTA1atXUbVqVSZ627Ztw7Fjx6CiooKJEyfC1tYWJ0+exJQpU3Dv3j0MGTKEic6ff/75w4WJeffuHVq0aCH1Apz/twoMDMSJEycQERHBzJsfjzLMs14UBKGQLfgePQMWhFfdmJycjOTkZCxcuBDe3t5ydVgMhowZMwajR4+GmpoaRo4cKR1wzMrKwpYtWzBnzhxs3LhRtA7Az95GRETA2dkZ3bt3x5QpU2BlZQUgd9Bx1apVcHZ2xunTp9GiRQvRWjzbtvkRiNXQeTmiKWkgyICAAAU/CTsaNGgADw8Pmfgp+fHz88OaNWtw69Yt0VpKSkq4f/8+DA0Ni0x/+fIlLC0tmRg8JSUlvHjxQq6L39evX2Pnzp2YPHmyaC1eyJttLQiLoJxBQUElum/o0KGitXiVK57lt3r16iVqJLCYVSnpCCiLYMESiQQjR46UW8Gnp6dj27ZtosuwRCLByZMn5XY63rx5A0dHRya2ws3NDXFxcTh69GihkfqMjAx06tQJtWrVgp+fn2itlStXYtasWWjQoAFiYmIAALNnz4aPjw/Gjx+PsWPHolKlSqJ1JBIJkpKSuMRY5ImHhwdOnDiB48ePw8jISCYtKSkJHTp0QLt27UoUp+5r8CrDPOtFiUSCzp07SweuwsPD4eDgAC0tLQC5A1dHjhz57mZgedWNPOtgAPD09ISPjw90dHRQs2ZNAEBcXBw+fvyICRMmMPnOAX721snJCdWqVcOWLVuKTHd3d8eTJ0+YxP7k2bbNT3mnrRyFoqGhgXv37sHExKTI9MTERFhaWiIjI0O0lkQiKbYSZDlK9aM2WsoppzSxs7MrUUP21KlTonTybMXXZg9Z2IoqVaogJCREJoZUfs6ePQsXFxcmAaKtrKwwdepUuLm54fTp03BwcICDgwP+/PNPZsv6gB/X/lWvXh1btmxBx44di0w/cuQIRo0ahYSEBL4PJgKe9eKPOPD8o3Pp0iXs3r0bDx48AJC77NnFxQXNmzdnpsHL3urr6+Ps2bNyl2JGR0ejbdu20mXPYuDZts1P+fLIchSKhoYGUlJS5H7Y79+/h4aGBhMtsQ25fwuvZR87duwo0X2slj2VU05pcfr0aS46PINDv337ttg9azVq1GCyTw/IbSi0b98eQG4HWEVFBYsWLWLaYQNyZ1V57dflaf9evHhR7D6eevXqyd3bWVbhWS/y7Iz99ttvJbpv3rx5Cn6S75vmzZsz7aAVBS97m5mZWexSYD09PWbbFHi2bfNTPtNWjkLp0qULTExMsGnTpiLTR40ahSdPnuDQoUOcn0wcBZeByEOsIxcAxe6fEAQBaWlpyMrK+u6WnJRTzn8BMzMzbN68mcvsTcEZMB0dHURFRaFGjRqi887Pq1evip1ly8rKwo0bN9CsWTPRWjzt388//4zg4GC5s6Lnzp2Di4sLnj17JlrrRyUxMRHHjh3Dly9fYGdnhzp16ihEx8bGRm6aIAi4d+8eMjMzy+tFOURHR5foPkU6CWNNw4YN4eHhIXfG19/fH2vWrCnx/14cpdW2LZ9pK0ehzJ49G3Z2dnj79i08PT2lHnZiYmKwatUqhIWFMRsJfP/+fYnuY+F5CchtECliJKUg8qbyX7x4gQULFsDf3x+Ojo4Kf45yylE0Jd0D6uPjI0qHZ4Pll19+wdSpU9GoUaNC+4pevXqF6dOno0ePHqJ18sjv3CIrKwuBgYGF9rFNmDBBlEblypVl9vRaWVnh6NGj0lHnt2/fokWLFkwazDztX6dOnTB79mz8888/hRzifPr0CXPnzkWnTp2YaPGCZ7149uxZODk5SZ1KKCsrIygoCP379xedd0Fu3rxZ5PXIyEjMmDEDt2/fxogRI5jr/ihYW1vLXbKYB6tls7zsraurKzw9PWFkZAQnJyeZtEOHDmHatGmYPXu2KI08eLZt81M+01aOwvnrr78wcuTIQp4A9fX1sWXLFvTu3ZuJzn9lT9uHDx+wbNky+Pr6om7duliyZAns7e25P0c55bCmpN+xIve05cHKViQnJ8PW1hZJSUkYNGgQLC0tAeR6NNu1axeMjY1x6dIlJp4YS+LcQhAE0S7evzaj9/LlS1SuXBk5OTmidIpCkfbv6dOnaNKkCdTU1DB27FiZ32rjxo349OkTrl27hmrVqjHR4wHPerFt27bQ1dXFli1boKGhgZkzZ+LQoUN48uSJ6Ly/Rnx8PObOnYvg4GD06tUL3t7eMDc3V7ju9wpvB1M87G1OTg769euHffv2oXbt2jLeIx88eIAePXpg7969kEgkonTy4NW2zU95p60MER4eziyGxNc4e/Zsie5r06YNE7309HQcPXpUZrNrhw4dinUD+2/h6Xnpa94jFcHnz5+xfv16LF68GJUqVYK3tzecnZ256X+v8CxX/0UyMzOxfv16eHp6lvajlBieDRYgt+M2a9YsBAcHIyUlBQBQoUIF9O3bF4sWLYKBgQETHV6UpNNWpUoVpkvTeNm/+Ph4jBkzBseOHZOJqefo6Ij169ejVq1aTHSePn3KLMxDcfCsFytWrIizZ8+iXr16AIC0tDTo6urizZs3zMIkFOTNmzdYsGABtm7div/9739YunQpmjZtylSjd+/e2Lp163dXTovjt99+g6enJ9M2mDx429vg4GDs3r0b9+/fB/D/nau4uLgwyT8/PNq2+SnvtJUh1NTUMGjQIPj6+sqN3cKK4kYa8kblBEFgFgy4LJCWlobr168z6YjynGkjIuzYsQPz5s1DVlYWvLy8MHz4cOaOAOS5rs2PIAhMXJPz3ETOq1zxHggpjhs3bmDevHk4ePAgk/zevHmDy5cvQ0VFBe3atYOSkhK+fPmCjRs3YsmSJcjKyuISkzAuLg4jRozAyZMnFaqTnJyM8PBw5s59iAivX78GABgaGn63Max4dtp42b+CJCcnSxtitWrVYh6TrkKFCli3bh0GDx7MNN9/i6LrRR0dHURHR8PMzEx0/vlJS0vDypUr4ePjg1q1amHJkiXo0KEDU408WrZsiUePHmHbtm0KHQDkWQeXxsCzPBRlb39EyjttZYioqCgMGzYMycnJCAwMZBaLoyhSU1OLvJ6eng5fX1+sXbsWNWrUwO3bt0XpXL58Ge/evUPnzp2l13bs2AEvLy+kpaWhR48eWLdu3VcderAgKioKjRo1YtKQOHPmDFq1aiUNSKlIGjRogLi4OIwfPx4eHh5yR3DE7kno2bOn3LTs7GwcP34cnz59YvL+eG4i51WueA+E/PPPP9Igyr/++itq1KiB2NhYzJgxA+Hh4XB0dMSRI0dE60RERKBLly5ITU2FIAho0qQJAgIC0KNHD+Tk5Ehj1fAYsWVZhktb58yZM0hLS0OLFi2YzUKYmJjg5s2b0hmB9evXY8iQIcz28eaRP/YXEaFatWo4f/681Esmy9hfvOxfcSQmJiItLQ2WlpbMllZt3LgRM2bMgKOjY6nO4rD81ouKx9WyZUuEhITIzCqy2CtqbGyMDx8+YPz48ejfv7/cARAWWkSElStXwsvLC/3798eaNWugo6MjOt+C8KyDy1LYDlbfYE5ODnJycmTaZS9fvsTmzZuRlpaGbt26oXXr1mIfF0Aptm2pnDLFly9fyMvLi9TU1Gjy5Mn09u1bSk1NlTkUQXZ2Nm3bto2qVq1KJiYm5O/vT9nZ2aLz7dSpEy1dulR6Hh0dTcrKyvTrr7/SqlWryNjYmLy8vETrlITIyEiSSCRM8rp06RIdPnxY5lpQUBBVr16dDA0NacSIEZSZmclESxAE6SGRSAodedcVxf79+6lOnTpUoUIFWrJkicJ0iIhu3rxJHTt2JBUVFXJ3d2eWL49ylZKSUuTx/Plzmj59OmloaFDdunUZ/DdEgYGBJAgCGRgYkCAIZGhoSDt37iQdHR1ydXWlW7duMdEhInJwcKB+/frRrVu3aNKkSSQIApmZmVFQUBDl5OQw0ykJLMswL53ly5fTvHnzpOc5OTnUsWNHaZk2MjKi27dvM9ESBIFevnwpPdfR0aG4uDgmeRfUKcoGKcIm8bR/gYGBtHr1aplrI0aMkGpZWVnR48ePmWgRET169Ijs7e3JyMiIwsLCmOX7b2D5ref9Fvl/s/y/nSK/C0Vq5RETE0PNmzcnU1NTWrVqFfn6+socikIRdbAgCPTq1SsmeYmF1Tfo6upKI0aMkJ6/f/+eqlWrRoaGhtSgQQNSVlamQ4cOidYhKr22bXmnrYxy9OhRUlJS4tI437dvH9WuXZsqVqxIK1asYNbZICIyNjamq1evSs9nzZpFrVq1kp6HhISQlZUVM73iYFk58Sywp0+fLtHBmvPnz1OrVq1IU1OTpk2bRu/evWOukcejR49o4MCBpKysTH379qX79+8rRIdnuVLUQAgRUcOGDaWVd3BwMAmCQI0aNaKHDx8yyT8/BgYG0k5FWloaSSQSCgkJYa5TEr7HTpuNjQ3t2bNHeh4SEkIaGhp0/vx5evv2LXXp0oX69OnDRKtgp01bW1shnTaeNomnVvPmzcnf3196/vfff5OysjL9/vvvdP36dWrRogUNHz6ciVZ+1q1bR8rKylS/fn2ysbGRORQNy289ISGhRMf3ppWfbdu2kZKSElWtWpWqV68uPczMzJhrKbIOFgShyO+N9/dHxO4bNDc3p6NHj0rP169fT5UrV6aUlBQiIpo2bRrZ2dmJ1iEqvbZtucv/MkhoaChGjx6NNm3aYPbs2QpbgnfmzBlMnz4dt27dwsSJEzF9+nTo6ekx1UhOToaRkZGMZn6XyU2bNuXiWYo1kZGRWLhwofR8z549sLW1xbZt2wAA1apVg5eXF+bPny9aS5HLZIvizp07mDFjBo4cOYIhQ4Zgz549CtswX3ATeUREBPNN5HnwKld5WrNmzcLr168xc+ZMjB8/nukyibi4OPTr1w8A4OzsDCUlJfj4+KBmzZrMNPJ49+6d1FW9pqYmNDU1i13eWo4s8fHxMku0Dh8+jN69e6NVq1YAgDlz5qBPnz6l9XjfBE+bxFPr/v37aNKkifQ8LCwM3bt3x8CBAwEAixcvlhsD6ltJTEzEvn37ULFiRfzyyy9cltwrClaOJMqaFpC7zO7XX3/F+fPn4efnh6FDhypMi1cd3LFjR4X7T+DJs2fPZDyGnjhxAr1795a2a4cOHcosAHxptW2/X+vwA5KSkoIxY8bgwIEDWLRoESZOnKgwLScnJ5w4cQLDhg3D/v37YWxsrBAdIyMjxMfHo1q1avj8+TNu3LiBBQsWSNM/fPgAFRUVJloHDhwoNj0+Pp6JDlA6BfbZs2fYt28f7t+/D0EQYGFhgV69euHnn39mkv+TJ08wb948/P777+jatSuio6OlLnNZU3ATeXh4uMI2kfMsVzwGQoDc96elpQUgd2+Curq6wtyQC4KADx8+QF1dXeoePD09vVD8JxZ7imxsbIp1zpEX/0ksa9euLTadZfDkL1++yHTYL168KPMNVqlShakTFx5x2r4Ga0c4gOLtHwBkZGTIfMcREREyziFq1KiBpKQkZnrbtm3DlClT0L59e9y+fbtQHD8W8KwX3717h/T0dJkOxp07d7By5UrpPp8BAwYw0wOAq1evSj0FCoIAc3NzDBgwQKbzLZY9e/Zg3LhxsLGxQXR0tMJsLc86GACmTp3KZU8bL3urrq6OjIwM6fmlS5ewYsUKmfSPHz8y0eLZts1PeaetDFGnTh2YmJjg+vXrqF27dpH3vH79molhP3LkCJSVlREcHIyQkBC59xWMP/Fv6dSpE2bMmIFly5Zh//790NTUlNkIGh0dzWx2oCQBall5a+NdYDdu3IjJkyfj8+fP0NPTAxHh/fv3mDp1Knx8fDBmzBjRGrVr14YgCJgyZQpatmyJBw8eSL2n5ad79+6itWrWrFloE3lRAThZbCLnVa54DYTkcfToUWmHMCcnBydOnCjkOIjFb0VEsLCwkDnPP9NGDOM8sQwyXRyrV6/+6j15gaLFUqtWLZw9exY1atTA48ePcf/+fZnZo6dPnzJzQmFiYiKd7QdynTXs3LlT5h5BEJh02kriCIcVPOwfkDt7c/36dZiamuLNmze4c+cO/ve//0nTk5KSmA3CdOrUCVeuXJE6i1EUPOvFsWPHonLlyvDx8QGQGzy+devWqFKlCmrWrAlXV1dkZ2cz85g5bdo0rFy5Etra2qhRowaICKdPn4avry88PT2xbNkyJjrDhw/H0qVLMX78+CLT3759i507d8LDw0OUDs86mKfnWl72tmHDhti5cyeWLFmCc+fO4eXLl3BwcJCmx8XFoUqVKqJ1AL5tWxmYL7gs55tZuHAhZWVlFbqek5NDhw4dop49e5KqqioTrcDAwBIdYnn16hX973//I0EQSEdHh0JDQ2XSHRwcaNasWaJ1eDNy5Ehq0aIFnT17liZPnkwGBgb06dMnafrvv/9OTZo0YaJ18OBBUlJSoilTptDz58+l158/f06TJk1itrm2qM3jRW0mZwHPTeS8ypUgCKSiokIVKlQgfX19uQcLeP5WpbWn8kdh8+bNpKWlRW5ublSnTh1q2bKlTPrChQupa9eupfR03wZPRzi87B8R0eLFi8nY2Jh+++03srOzK+Q4aPXq1dSuXTsmWu3bt6cnT54Umfbu3Ttau3YtNWzYkIkWL6pXr06nTp2Snq9YsYJq1qxJX758kZ7b2toy0QoMDCR1dXVat24dff78WXr98+fP5OvrS+rq6hQUFMREq6g91jk5OXTkyBHq06cPqaqqUqVKlUTr8K6D8+9//RE4efIkqaurU40aNUhDQ4Pc3Nxk0kePHk1DhgxholVabdtyl/9lmEePHsHf3x9BQUH4+PEjunTpgt69exfrFraskpqaCm1t7UKxdd69ewdtbW2oqqqW0pN9G69fv0avXr1w4cIFaGtrIygoSOZ3adeuHZo3b45FixaJ1mrbti1at24Nb2/vItPnzJmDc+fOlTiIalmAd7DN/CiqXAUFBZXoPkXuhfiRiI6OllnyxGLWtbTw8/PDwYMHYWxsDC8vL5lZ2DFjxsDR0fG7suvW1tZwcXHBjBkzEBISAhcXF9jY2CAkJIT56DJP+5eTkwMvLy/pb+Xj4yOzPK1Pnz7o1KkThg8fLlqrKI4fPw4/Pz/s378flSpVQq9eveDr66sQLUWgoaGB2NhYqd12cnJC3bp1pUvU7t+/jxYtWuDt27eitZo1a4b+/ftj0qRJRab7+Phgz549uHLlimit/CQkJMDf3x+BgYF49uwZBg4ciCFDhsDe3l7hsQNZkpiYCBMTk+82VqQ87t69i3/++QfGxsbo06ePTIiOrVu3olmzZrC2tmamx71ty7wbWI4oMjIyaOfOndS2bVtSU1Ojrl27kpKSEtORy/ykp6dTWFgYrVixglauXEkHDhyg9PR0hWjxIiQkhHr27El169alevXqUc+ePWnv3r0K0UpJSSlyFuft27cyM29i0NHRodjYWLnpsbGxpK2tzUTrR4V3ufqRCA4OlvmW4+PjZb75tLQ0WrZsGTO9y5cvU7169WRmYCUSCdWvX5+uXLnCTOfLly+0fPlysrGxIS0tLdLW1iYbGxtasWKFzMj990S1atXozZs30vN169YpJEyMtrY2PXr0iIhyvaQqKysrbLb1R7d/iYmJNH/+fDI1NSUDAwOSSCT0559/MtfhUS/+9NNPFBkZKT03MDCQ+V/u379PWlpaTLQ0NTWL9YwaFxdHmpqaTLQyMzNp165d5ODgQOrq6tJ3p6ysTHfu3GGiwZthw4YVeXh4eNCmTZvow4cPTPV+RHtbGpR32soQo0ePJn19fWrevDmtX79eWvkqyjCEhYWRoaFhoel3Q0NDOnDgAHM9RZOdnU19+/YlQRCodu3a9Msvv1D37t3JwsKCJBIJ9evXj1lcqV69esk0jhSJlpbWVysnVhUhEd9O75UrV2jSpEnUpUsX6tq1K02aNEnGjS4LeJcrHgMho0ePlqlUd+zYIXOenJxMnTt3ZqIlkUiKjf2VlJTEbMnOnTt3SFtbm5o2bUq7du2imzdv0o0bN+iPP/6gJk2akI6ODpPfLD09nVq1akUSiYQ6dOhAEydOpAkTJlCHDh1IIpFQ69atKSMjg8F/xBeecdp4hBYg4m//eBEcHEyOjo6kqalJzs7OtH//fvr06RNzu8SzXuzatSu5ublRdnY27d27l1RVVWXc1B88eJAsLS2ZaOno6FBMTIzc9NjYWNLR0WGiZWBgQK1bt6YtW7bI/D+KqkN41ME9evQo8rCzsyNDQ0OqUqUKszL9o9rb0qC801aGUFJSolmzZtH79+9lrivCMFy4cIFUVFSod+/eFBERQcnJyZScnEwXLlygXr16kaqqKkVERDDVVDSrVq2iihUrUnh4eKG0sLAwqlixYqHAqd9KixYtyMjIiEvntlmzZuTj4yM3fdWqVdSsWTPROjwrdyKiqVOnSteDN2zYkBo0aEDa2tokkUho2rRpzHR4liteAyE8O1Jfa6Cz1HJ2dqaePXsW+Z3l5ORQjx49mMQ0mzt3LpmYmFBUVFShtMjISDIxMVFIYFRFw6szJQgC7dixg8LCwigsLIw0NTVp69at0vO8gwW87B9vlJSUaObMmQq3SzzrxZs3b5KBgQGpqqqSRCKhOXPmyKQPGjSI3N3dmWjZ2dkVyj8/s2fPprZt2zLRqlChArVp04a2bt0qM3P9PXewiyM9PZ2cnZ2ZxY/8Ue1taVDeaStD/PHHH9S+fXvS0tKivn37Unh4OH358kUhjcvOnTvTyJEj5aaPHDmS2Ug9L+rXr09+fn5y07dv30716tVjopWTk0PLly+XbnYtWPGyJDAwkDQ0NGjDhg3SDd1EucsN1q9fTxoaGhQQECBah2flznMTOa9yxXMghGdHiqdWpUqVip1pvXLlCpMN/+bm5sUuQQsJCSFzc3PROrzh2Wnj5TCBl/3jzYgRI0hPT49atmxJmzZtks7gsLZLPOtFolwHDfv376dLly4VSjt48KB0Wa1YwsPDSUlJiaZOnUpJSUnS6y9evCBPT09SVlYusi77FjIyMuj3338ne3t70tDQoF69elFoaCipqKh8tx3sr3H16lWqVq0ak7x+VHtbGpR32sog8fHxNG/ePDIxMaFKlSqRRCJhPjVeoUIFio6OlpseFRVFFSpUYKqpaNTV1SkxMVFuekJCAqmrqzPVjImJoebNm5OpqSmtWrWKfH19ZQ5WTJkyhQRBIF1dXbKxsSEbGxvS1dUliURCHh4eTDR4Vu5Nmzb96uh506ZNmWjloehyxXMg5EfttKmpqdHjx4/lpj9+/JjU1NS+Gx3eCIJAixYtktofdXV1mjt3rsLsEi942L/SID09nQIDA6lNmzakpqZG3bt3Z77XtjTqRV6sXbtWOquX551XIpGQioqKwjo3Dx8+pNmzZ1PVqlVJEAQaMGAAHTt2rMi97f8W3h3s4oiLi2O2vPRHtbelQXmnrQyTk5NDf//9N/Xp04fU1NTo559/pvHjxzPJW11dnRISEuSmJyQkkIaGBhMtXujr6xc5/Z5HdHQ0M5fr+dm2bRspKSlR1apVqXr16tLDzMyMqc7FixdpwoQJ1LlzZ+rcuTNNnDiRLl68yCx/npU7z03kBVFUueI5EMK701bcUrigoCBmWrVr1y52RHbv3r1kYWEhWsfQ0JCuXbsmN/3KlStkaGgoWoc3pqamMjaoqIO1XeKFou1faXP//n2aMWMGValShXR1dal///60b98+0fmWVr3IiydPnpCPjw+NHj2aRo8eTatXry62g8CK7OxsOnz4MPXu3ZtUVVWpYsWKovMsSx3snTt3krW1NZO8flR7+zXOnDlDKSkpTPMs77R9J7x9+5ZWr17NLG5LgwYNyN/fX266n58f1a9fn4kWL5ycnGjUqFFy093d3cnJyYmZXlJSEnXt2pUqVKjAJKZdacOzcue5ibw4WJYrngMhgiCQu7s7TZo0iSZNmkSqqqrk5uYmPXd3d1dITD1FL4XLmwktaqYhOjqaTE1Nad68eaJ1+vbtS7169ZKb3qtXL2b7OUrCsGHDaMeOHdz0WMHTaVFZYcGCBXTmzBmF5Z+dnU0HDhygX375hUn8SN714n+R169f02+//SY6H551cFRUVJHH2bNnadWqVWRgYECbNm1iolWW7G316tXJzc2Nnj59qnAtQRCoYsWKtHLlSmZ5KrMNIFCOoqhYsSJat26N48ePM8nP1dUVnp6eMDIygpOTk0zaoUOHMG3aNMyePZuJFi9mz54NOzs7vH37Fp6enrC0tAQRISYmBqtWrUJYWBhOnTrFRGvPnj0YN24cbGxsEB0djWrVqjHJtyiio6NLdJ/YOFYtWrTApk2bsGnTpiLTN2zYgBYtWojSyKNx48b4448/sHDhwiLTd+7ciUaNGjHRKg6W5crCwgInT57EsGHDikw/ceIEatWqJVoHANq0aYN79+5Jz1u2bIlHjx4VuocFOTk5TPIpCTNnzsTx48dhbW0NR0dHaYysu3fv4vjx42jWrBlmzpwpWsfLywu2trZo3rw5Jk+eDEtLS6nO6tWrcffuXVy6dEm0Tkl59OgRTp06hZUrVyIqKoqb7reSk5OD/v37Y+/evbCwsJDa2jt37qBfv37o06cPdu/ezSQGFC/7V1ICAgKwdOlStGvXDuHh4czzl0gk6NatG9q3b4/169eLzo9nvciTAwcOlOi+7t27K/Q5kpKSsHjxYmzbtg1z584VlRfPOtja2hqCIICKCNVsaGiI6dOnY9SoUUy0ypK9HTp0KBITE9GmTRvExcUpVCs+Ph7x8fE4evQou0yZdf/KYcKxY8fI09OTZs6cKV3uFBMTQ7/88gtJJBLq2LEjE53s7GxydnYmQRDI0tKSevbsST179qTatWuTRCKhXr16UXZ2NhMtnoSGhkr3K+U/CsaLEYumpiatXbuWWX7FkTeToeiZjjxHGn369KHLly9TamoqpaSk0MWLF8nZ2ZlUVFTo/PnzDP4jvpvIifiUKx8fH6pYsSIdOnSoUNrBgwfJwMCg2H185eTy6dMnWrp0KTVs2JA0NDRIQ0ODGjZsSEuWLKHMzExmOhcvXqQ6depIy09eGbOysqILFy4w0/k3FBePrCQU3Lsm7xALT4cJvOzfvyEjI4OOHDkiOp/Xr1/TwYMH6ejRo9I9UZ8/f6Y1a9aQsbExGRgYiNYg4lcv8oTnCoDk5GQaMGAAVapUiSpXrky+vr6UnZ1Nc+fOJQ0NDWrSpAnt2rVLtA7POjghIaHIIzk5mYiIPn78yHRGuSza2+8RgaiIbnY5pUJQUBCGDRuGihUr4t27d6hUqRJ8fHwwZswY9O7dG1OmTEG9evWYagYHB2P37t24f/8+gNzZAhcXF7i4uDDV4Ul6ejqOHj2KBw8eAMj9nzp06ABNTU1mGg8ePIC5ubnc9NDQUMyfP7/Eo8TFkZiYWKL7TE1NRWv99ddfGDlyJN69eydzXV9fH1u2bEHv3r1Fa+Sxbt06eHp6IisrC3p6egCA1NRUKCkpYfny5fDw8GCiw6tc5eTkoF+/fti3bx9q164tM0v04MED9OjRA3v37oVEIhGtVRq8ffsWBgYGAIAnT55g27ZtyMjIQLdu3ZjN6pUGkZGRMvbP2tq6dB9IBGZmZl+9RxCEQrOy/5YGDRrAw8MDbm5uRab7+flhzZo1uHXrligdgK/940lERAS6dOmC1NRUCIKAJk2aICAgAD169EBOTo70/bKqt3jUi3mkpqbin3/+QUJCAgRBgJmZGdq3bw9dXV3mWjwYM2YMwsPD0a9fPxw5cgQxMTHo2LEjMjMz4eXlhbZt2zLT4lkHF0dUVBQaNWqE7Oxspvn+SPa2NCjvtJUhrK2t4eLighkzZiAkJAQuLi6wsbFBSEgIatasWdqPV04Btm3bhmPHjkFFRQUTJ06Era0tTp48iSlTpuDevXsYPHgwtmzZIlrnt99+g6enp0Iq16LgWbk/ffoUe/fuldHq3bs30+WmvMsVr4GQtLQ0LFu2DKGhoTKNI2dnZ6bfy61bt9CtWzc8efIE5ubm2LNnDzp16oS0tDRIJBKkpaXhzz//RI8ePZjoFSQzMxPBwcFIS0uDo6NjsYMlYsnKykJmZia0tbWZ5ZmTk4M7d+6gfv36AIDNmzfj8+fP0nQlJSWMHj36u+rMa2ho4N69ezAxMSkyPTExEZaWlsjIyBCtxdv+FQfLwbh27drB0NAQc+bMgb+/P9asWYPq1atj/vz5GDx4MJOlpaXB77//jnHjxuH9+/cy1/X09LB582b069ePiY6bmxt8fX2ho6PDJL/iMDU1hZ+fH9q3b49Hjx6hVq1amDBhAtasWaMQPZ51sDwU1WnLD2t7+9tvv5Xovnnz5onWMjMz+2oZFQSB/RLM0p3oKyc/2tra0hgm2dnZpKysTKdPny6VZ7l+/Tp16dKlVLS/lUuXLtHhw4dlrgUFBVH16tXJ0NCQRowYwWx51YoVK0hFRYUaN25MmpqapKmpSYsWLSIDAwOaP38+vX79mokOUeFAyqUJj827eaSlpTHJpyyVK1Z8+vSJGjduTGpqatSjRw+aMWMGTZ8+nbp3706qqqrUvHlzmfh3YujUqRN17dqVzp07R+7u7vTzzz/TsGHDKDs7m7Kzs2nMmDFka2vLRMvT05MmTJggPf/06RM1bNiQVFRUSE9Pj7S0tJjEujt06FAhxx/e3t6kpqZGSkpK5OjoKI2bJZY//viD2rRpIz3X1taW8TSrra1N27dvZ6LFC54OE3jbv61bt5KzszP1799fGmvsxIkTZG1tTRoaGsWG9fg3GBgY0O3bt4ko19ZJJBIKCQlhknd+eNaL169fJ2VlZRo6dChFRkZSZmYmZWRk0PXr12nw4MGkoqJCkZGRTLR4fhfKysr07Nkz6bmGhgbTsAz/Bl51cGRkJLPlpbzsrbW1tdzDxsaGNDU1mf1Pa9askXt4eHiQhoaGQpZtl3fayhC8AqPmwWv/HC86depES5culZ5HR0eTsrIy/frrr7Rq1SoyNjYmLy8vJlqWlpbSeCqnTp0iQRCoXbt20vXgLCn4XZQGL168oHHjxnFxN5yRkUErV64kIyMjJvnxLlfyYDkQsmbNGjIyMipyD1RMTAwZGRkx23NpYGAgbaB/+PCBBEGQCYAdExNDenp6TLTq1q1LYWFh0nN/f3/S19enhIQEysnJIVdXVyae7uzt7Wn9+vXS8wsXLpBEIiFvb2/at28fWVpa0qRJk0TrEBG1b99eZr9Lwe9v06ZNZGdnx0SLKHdgws/Pj7p06SL16titWzcKCgqinJwcJho8PRLytH88B+OKsksPHjxgln8ePOtFV1dXcnZ2lpveu3dvGjZsGBMtnt+FRCKhV69eSc/zDwTygmcdTMS208bT3hbFzZs3qWPHjqSiokLu7u4K03n79i15eHiQmpoatWnTRiEhSco7bWWIr8VDyjtYEBgYSIIgkIGBAQmCQIaGhrRz507S0dEhV1fXUhtFEoOxsbFMY3LWrFnUqlUr6XlISAhZWVkx0dLQ0JCJp6KqqiodlWWNIAgyFYai4LXZmih3BmXWrFnUpEkTatGiBf31119ElNtIr1y5MlWpUoUWL17MRItnueI1ENKmTRuZSrAga9eulZndEQPPmHA6OjoyDVcXFxcaMWKE9PzmzZtUuXJl0TqGhoZ048YN6fmkSZNkfptDhw5RrVq1ROsQEf38888yswsF39/du3eZzUrl5ORQly5dSBAEsra2JhcXF+rXrx81aNCABEGgX375hYkOT4cJvOwfEd/BOIlEQg8fPpS+Ox0dHYqKiqLU1FSZQyw860Vzc3P6559/5Kb/888/ZG5uzkSL53chCAI5OTlJHbYpKytThw4dpOd5h1h41sEF67+Cx5o1a5jZdZ72Nj+PHj2igQMHkrKyMvXt25fu37/PXIOIKD09nby9vUlPT48aNmxYpDMyVpR32soQPL0h5XljIyIKDg4mQRCoUaNG9PDhQyb5lwZqamoyQTVbtWpFCxculJ7Hx8eTtrY2Ey2eszeCIFD9+vXJxsam2EMso0ePpqpVq9KUKVOobt26JJFIqHPnzmRvb898OeHMmTNJV1eXevfuTcbGxqSsrEwjR44kCwsLCgwMZLa0j4hfueI5EFKpUiXp0qqiuHXrFlWqVImJVsHGUcFRZpadNj09PZmKtXr16tJGNFFuGWYx0lwwiG3Tpk1p2bJl0vOEhARmwd3V1NRk7OqrV69kPPM+ePCASTwuotxBDx0dHTp58mShtBMnTpCOjg4FBQUx0eLlkZCX/SPiPxiX/73JOxcLz3pRS0ur2ODQiYmJzMqVIAhUoUIF0tfXL/Zggaura4kOsfCsg3m2N3naW6Jcr6zjxo0jVVVVcnBwoCtXrjDLOz9ZWVm0adMmMjY2purVq9OOHTuYrWaQR3mctjIEz3hIcXFx0g3Bzs7OUFJSgo+Pz3ft8MTIyAjx8fGoVq0aPn/+jBs3bmDBggXS9A8fPkBFRYWZ3vbt26UbaLOyshAYGIhKlSrJ3DNhwgQmWh07dmTqHKEoDh06hICAALRv3x5jxoxBrVq1YGFhoZDN1iEhIQgMDETPnj0RFRUFGxsbvH//Hnfu3IGyMluzxKtcrV69GosXL5ZxeLJ69WrcvHmTeblKSUmRenIsCgMDA6SmpjLTc3V1hZqaGoBcxyCjRo2ClpYWAODTp0/MdCwtLREeHo7Jkyfjzp07ePz4Mezt7aXpiYmJMDIyEq1TpUoVxMTEwMTEBB8/fkRUVBRWr14tTX/79i2zTf9GRka4d++e9BswNDSUSY+JiYGxsTETrd27d2PWrFky7ywPBwcHzJgxA3/88QeGDBkiWqtnz57o2LEjF4cJPOwfkPttq6urS89VVVUL/V6s4BUbjWe9mJ6eLvP+CqKmpobMzEwmWgCwYMECqedhRRIQEKBwDYBvHcyzvcnL3qalpWHlypXw8fFBrVq1EB4ejg4dOojOtyhCQkIwZ84cpKamYtasWRg9ejRUVVUVopWfcu+R/1EkEgmSkpLw008/AQB0dHQQFRWFGjVqlPKTfTvu7u64desWli1bhv379yMoKAjPnz+XFqQ//vgDa9aswdWrV0VrVa9evUSeg8S61gYK/1aKQkVFBYmJiahSpQoAQFNTE1euXGEeZgLIrbzj4uJQtWpVAIC6ujouXbr0Xbv/1dHRQXR0NMzMzJCTkwM1NTUcP36cqTvoPJSUlJCUlCS3Qfny5UtUqVKFiecvecHCC8KiYbNv3z70798frVu3xp07d9C0aVOZAMbTp09HfHw8QkJCROlMnz4dBw4cwKxZs3D48GFERETg0aNHUFJSAgBs3boVO3bswPnz50XpALle7u7du4cLFy4USiMitGrVCpaWlvD39xetZWxsjCNHjsgtRzdv3kTnzp2RlJQkWosXvOxfnpa3t7e0gzh9+nRMnTpVYYNxPOBZL0okEgQFBcntSKWkpGDYsGFM7BLP76Iodu/eje7du0sHr1jAsw7mCS97a2xsjA8fPmD8+PHo37+/3DZagwYNROkAud+fhoYG+vfvX2woCx8fH9Fa+SmfaSvj6OrqIjIyUiGdqaNHj0qNa05ODk6cOIHbt2/L3NO9e3fmuorC29sbvXr1Qtu2baGtrY2goCCZkQ9/f39moy4JCQlM8ikJvFw/5+TkyIy4KikpMa2Q8vPlyxeZ30ZFRYXLiGkeiihXaWlp0vclkUigrq7ONHRBfogI7dq1kzsrmZWVxUyL1ygzAPTu3RuHDx/GoUOH0KFDB4wfP14mXVNTE2PGjBGt4+XlhefPn2PChAkwNjbG77//Lm1AALkNsm7duonWAYDZs2ejUaNGsLW1haenJywsLCAIAmJjY7Fy5Urcu3cPO3bsYKL17t27YmcijYyMkJycLFrHyckJu3fvlpbZRYsWYezYsahQoQKA3JHz1q1b4+7du6K1eLq+NzExwbZt26TnxsbG2LlzZ6HnUVSnrUuXLti+fTsqV67MLE+e9SIADB06tNh0Vr9naYdEcHd3h62tLdM6hGcdnMfevXulIWoEQYC5uTkGDBgAZ2dnZhq87O2rV68AAMuXL8eKFSuQf05KEAQQEQRBYDJo0KZNm6+69FfEN1o+01bGUdQMWEliArH6uHmTmpoKbW1tGaMA5DZotLW1uUxhs4TXiKJEIkHnzp2ly+DCw8Ph4OBQqNIIDQ1lojVy5EjpkogNGzZg0KBBhTpurEep8lBEuSo4yty/f3+sWbOmUCOaxUBI/uVNxeHl5SVaqxzxXLlyBa6uroiNjZVW5EQES0tLBAQEwNbWlokOrxlYJSUlvHjxQmqTCg6CsJzpLe0ZFZ4ocsVLeb3IFkXVIbzq4JycHPTv3x979+6FhYUFLC0tQUSIjY3Fw4cP0adPH+zevbvUO8f/hsTExBLdZ2pqquAnURzlM23/UXiuZ+aNvBmbihUrMtM4efIkxo0bh0uXLhWaGk9NTUXLli2xadMmtGnTRrRWfHy8wvZV5GfIkCEyBnrQoEEK02rTpg3u3bsnPW/ZsmWhpaTfU2WRR8FRZnd3d5lzVgMhPDtjvXr1KtF9LBoSPyrNmjXD3bt3cfPmTen+L3Nzc9jY2DDVISKZ/YcFYbX/sOBYryLHfnnZvx8dHvUiT37ENgzPOnjNmjU4fvw4Dhw4gK5du8qkHThwAMOGDYOvry88PDwU9gysKc3O2Js3byAIQrF7zVlQ3mkr4wwaNKjY9bLfipubG3x9faGjo8M87/8Ca9aswYgRI4r8bfT09ODu7o7Vq1cz6bTJm1XR09ND7dq1MWjQICab9AMDA0XnUVJOnz7NTasoFFGueDYi5syZAwcHB7Rs2bLYjf8s4Lls9UfHxsaGeUctP19bmgaAiRMSnvCyfwBKvExVUe/Q1NSUqbMs3owZMwbLly+X/h47d+5Ez549pecpKSkYMGAADh8+LFrLwcGhyOt538XYsWOZLU8v6KAGAP7++2/8/PPP0vMHDx7A3NxclA7POjgwMBArVqwo1GEDcleDLF++HGvWrPmuOm15XL16tcgln02aNGGqk5KSgtmzZyM4OFi67FxfXx8uLi7w9vaWLhdnSfnyyDLEvHnzMG/ePLn7VB4/fozhw4fjn3/+Ea1VcHlLOf8OU1NTHDlyBFZWVkWmx8bGokOHDnj8+LForZ49exZ5PSUlBXfu3IGKigrOnTsnepkGz2+iRo0auHr1qsJHpQB+5YrnQEjNmjURHx8PVVVV2Nrawt7eHg4ODmjevPl3t8zpR+e3334r0X3z5s1T8JOwo+AyzPxOeAC2yyN52T8gd3matrY2lJWV5c4eCoKAd+/eidY6ceIE2rVrJzc9JycHixcvxpw5c0Rr8YLnstlJkyYVeT0lJQXXr1/Ho0ePcP78eSbOrSwtLREUFCR3GbOPjw/mzp2LtLQ0UTo862ANDQ3cu3cPJiYmRaYnJibC0tISGRkZCn8WlkybNg0rV66EtrY2atSoASLCo0ePkJ6eDk9PTyxbtoyJzrt379CiRQs8e/YMAwcOhJWVFYgIMTEx2LVrF6pVq4aIiAjo6+sz0ZOi0IAC5fwrqlWrRtbW1hQdHV0obcuWLaSjo0OdOnViolUwzlg5/w41NTWZIMAFefDgAZN4Ul8jPT2dnJ2dqU+fPqLz4vlN8NTiVa4kEgnXMvX06VPasWMHubm5UY0aNUgQBNLU1KR27dqRt7c3XbhwgYlOXFycwmPP/MhYW1vLPWxsbEhTU5NZPCRefC3YsJOTE5f/iaX9IyKqU6cOGRgY0MSJEykqKopJnvJQUVGhsWPHUlpaWqG0W7duUaNGjahKlSoKfQbWfC1+KcuYjl9jzJgx1LlzZyZ5jR07llRVVWnGjBkyMUQfPHhArVq1okqVKjEJes2zXtTX1y/2G4+OjmYW544XgYGBpK6uTuvWrZP5nT5//ky+vr6krq7OLE7lxIkTqV69epSUlFQo7cWLF1S/fn3y8PBgopWf8k5bGSI1NZUGDx5MampqtHjxYsrOzqbExERq164d6enp0bZt25hpFQyYW86/o0aNGhQaGio3fd++fWRmZsblWa5evUrVqlUTnc+P2mnjVa5KeyDk8ePHFBQURMOGDSNdXV1SUlJikm/Bzmjfvn2LrKjK+XfcvHmTOnbsSCoqKuTu7l7aj/Ov4BVsuCSwsn95XLp0iUaOHEl6enrUuHFj2rhxI6WmpjLLP7+OpaUl1axZk86fP09ERNnZ2bRw4UJSVVWlgQMH0rt375jrKpKy1GmLiooiY2NjZvmdOHGCTE1NqV69enT16lXy8fEhDQ0N6tGjBzN7yLMOcXJyolGjRslNd3d3JycnJy7PwoqmTZuSj4+P3PRVq1ZR06ZNmWiZmprSkSNH5Kb//fffZGpqykQrP+XLI8sgYWFhcHd3h7GxMeLj49GiRQts27aNqftwiUQCPT29rzp7YLEM5Edk/PjxOH36NK5evVporXtGRgaaNWsGe3t7rF27VuHP8ujRI1hbW+P9+/ei8vlajJ08WHg/lEgkOHny5Fc3wbOIp5KHosuVRCLBy5cvS8VpQlxcHE6fPo2TJ0/i9OnTSE1NRYsWLZgspS5rMR0dHBxgb2+PKVOmMA/inJ8dO3agVatWzAOjx8fHY+7cuQgODkavXr3g7e0tei/MfxlW9q8gGRkZ2Lt3LwICAnDlyhX06NED/v7+cp28fAuZmZmYMWMGNm7ciJEjR+LSpUt49uwZNm/ejF9++YWZztc4e/YsGjZsKHr/6tdsBcvlkV/jwYMHaNasGZMQF3l8+PABgwYNwsGDB6GpqYktW7ZgwIABzPLnWQdHRETAzs4OPXr0gKenp9R7ZExMDFatWoWwsDCcOnUKrVq1Eq1VEljYWy0tLdy6dUtu3fTo0SPUr19f9DJWoHCs2YI8ffoUtWrVYhpMHih3RFImsbW1Rf369XHixAloaWlh2rRpCon3tGDBgnInA9/InDlzEBoaCgsLC4wbNw61a9eGIAiIiYnBhg0bkJ2djdmzZ3N5loiICGYNy5LE2GFV4bZr167IfSOs46nkwaNc5cXgKg4WAyHx8fE4deoUTp06Je2ktWrVCm3btsW4cePQtGlTuXv4vndMTU1x8uRJbN++vcQunr8FV1dXqKioYOTIkVi3bp3o/N68eYMFCxZg69at+N///oeIiAg0bdqUwZP+t2Fp//KjoaGBIUOGoHr16vDy8sKePXuwfv16pp02dXV1rF69Gq9evcLGjRuhpaWFq1evwtLSkplGSbCzs4O+vj5mzZqFKVOmiMpr3rx50sGUz58/Y9GiRdJ2Rnp6uuhnLSnHjh2DhYUF0zx3796NU6dOwdbWFtevX8fx48fRrVs3pvuYedXBLVu2RHBwMEaOHIl9+/bJpOnr62P37t3cOmwAG3urpKSEz58/y03/8uVLoZAX30qlSpWQkJAgt9MWHx+vkD37P2at/h2ze/dujBs3DtbW1oiJiYGfnx86d+6MUaNGYenSpdDQ0GCm5eLiUu6I5BsxMjJCREQERo8ejZkzZ0o7H4IgoGPHjti4cWOxQW7/DdHR0UVeT01NxdWrV7F48WJ4e3sz0eIZ9+by5cvcZqV4lSteAyE1a9aEiYkJxowZgwkTJqBRo0bMKqOCCIJQqCNamuEY8oJ9f/z4UaE6OTk5SEhIwNGjR0Xlk5aWhpUrV8LHxwe1atVCeHg402DG+QkPD2cWFLw4eIaB4Gn/8nj27BmCgoIQEBCAtLQ0DBo0CJs2bWLuVCAuLg6urq548OABNm/ejMDAQLRt2xabN2+W64BFEcTHxyM+Pl70t16SUC4sPCoDuW7piyLvu/Dz82PmjfHZs2dwc3PD1atXsXbtWri6uiIqKgpDhw5F3bp14efnB0dHRyZaPOvgnj17omPHjjh69Kg0FImFhQU6dOig0FUMRcHC3jZu3Bh//PEHFi5cWGT6zp070ahRo2/OPz+dOnXC7Nmz8c8//xRy/vXp0yfMnTsXnTp1YqIlA/MFl+V8M7179yZtbW1au3atzPWIiAiysLAgc3NzioiIYKLF22nCj8y7d+/oypUrdPnyZYXsQRAEgSQSCQmCUOj46aefaPny5Ux0eH4TPNfu8ypXPP+nvn37krGxMVWoUIG6detGK1eupOvXryvEYcjXnE7kHd8TT5484aZlZGREmpqaNH36dIqMjKSoqKgiDxaoqqqSm5sbffjwgUl+8ii4d01VVZV69+6tkD1tvOwfEVFwcDB16tRJulcpLCyMsrKymOWfn3Xr1pGWlhb16tVLur88Ozubli5dSurq6t/lnjaeFPU9CIJAurq61KxZMwoJCWGmVaFCBerYsWMhu/H582eaPXs2qaioFLs/rKSUt8vEER4eTkpKSjR16lSZfYYvXrwgT09PUlZWpvDwcCZaT548ISMjIzIxMaFly5ZRWFgYhYWF0ZIlS6hatWr0008/0ePHj5lo5ad8T1sZolWrVggKCkKtWrUKpWVmZmL69OnYtGlTsdO/JaXg2vNyyi7yloDp6emhQoUKSEtLw/Xr10WPYPL8Jnhq8SpXpRFGIzY2VrpE8syZM8jMzMT//vc/tG3bFnZ2dkyW3w0bNqxE9+XNgIkhIyMDJ06ckMYOmjlzpkxAaCUlJSxcuFB0bLoKFSpg3bp1GDx4sKh8SoJEIpH+nbf0t+A5qyVPUVFRGDZsGJKTk6WzNjxQ5D5HXvYPyP2tTExMMHDgwGJXSkyYMEG0loGBAdauXYuBAwcWSrtz5w6GDh2KFy9e4NmzZ6K1iuPFixdYtGgR1q9fr1CdPK5evcplWXBycjLCw8OZxNTbtGkTRo8eLTf92rVrcHV1xe3bt0Xp8KwXL1++jHfv3qFz587Sazt27ICXlxfS0tLQo0cPrFu3julyYB6sW7cOnp6eyMrKkq56SU1NhZKSEpYvX8407lx8fDzGjBmDY8eOyay2cnR0xPr164tsc4ilvNNWhsjJyZGp4Ivi7NmzTCqnrKws3L17F+bm5oWWhqWnp+Phw4eoV6/eV5+nnNInKioKjRo1Et3oGzZsGNauXcslzpi9vT3++usvhQSfLAivclUWBkLu3r2LXbt2Yd26dUhLS0NWVlapPcu3sGXLFhw8eBDh4eEAcjsDdevWldqo2NhYTJs2TW6MppKyceNGzJgxA46Ojti6datC4wWWdN+dqakpE72srCx4e3tj6dKlGDt2LGbPnl1ofyPrwPKl6ZyGlf0DgOrVq3916a8gCIWW/H0LL168QOXKleWmZ2dnY/HixZg7d65orbt37+LUqVNQUVFB3759UaFCBbx58waLFi3CZKqGxgAAxNdJREFU5s2bYWZmhrt374rWyePjx49QUlKSaVtERkZi7ty5OHz4MBdHJCy/i5Lw+fNn0TEyedbBnTt3hp2dHaZPnw4AuHXrFho1agRXV1dYWVlhxYoVcHd3x/z580VrSSSSEpUrVvXV06dPsXfvXpkln71791aIbwggd4AgT6tWrVpfdbAmCuZzd+V8FwQEBFDjxo2LXPqRlZVFjRs3pp07d5bCk5Xzb4mMjGTiRjkxMbHIIyUlhcFTyvLu3Ttau3Ztka60U1JS5KaVU5ikpCTas2cPjRo1imrXrk2CIJC6ujrZ2dmV9qP9a1q3bi0TSqOgy/CdO3dS8+bNmWg9evSI7O3tycjIiMLCwpjkWZY4evQoKSkpkUQikR55Sw1ZU/B34gkr+/ejEh4eTqqqqtLlgzVr1qSTJ09SpUqVyM7OjtlyMaLcJWMtW7YkiURCKioqNGnSJEpLS6PBgweTsrIy9e7dm9kWj6/xPX4Xz549oylTpsitFz09PZmFFzA2NqarV69Kz2fNmkWtWrWSnoeEhJCVlRUTrf3798s9pk2bRhoaGlzi2v4IlDsi+Y+yfft2eHp6Fum8QElJCdOmTcP69esxaNCgUni6ckqD4kaZDQ0NMW3aNEyePJmJ1oYNGxAVFYXx48cXStPT08O5c+fw/v17bh44WeDg4FDkdT09PdSuXRtjx45lNtK3d+9e6bLIe/fuQVlZGc2aNUPfvn1hb2+Pli1bfnfLWgDg/v37Mh7f1NXVZWZJmzVrhrFjxzLRMjMzw8mTJ7F+/Xr07t0bVlZWhWakbty4IVpHniONgrAMbxEaGorRo0ejTZs2Rc60lfPfYdGiRRg1ahQWLVqErVu3wtPTE6NGjcK+ffuYOQXJY8aMGfj48SN8fX2xb98++Pr64syZM2jYsCHu378PMzMzpno/GqtXr8b79++LnAnX09PDhw8f4OPjg2XLlonWSk5OllkCfObMGRnHGU2bNsWTJ09E6wAoMnxFbGwsZs6cifDwcAwcOFCu85B/gzznNAVhETKBpzOm/JRb8v8o9+/fR/PmzeWmN23aFDExMRyfqJzS5ubNm0VeT0lJwZUrV7Bo0SJoampi1KhRorX+/PNPrFq1Sm66u7s7PD09v6tOW8OGDYu8npKSgsOHD2P9+vU4f/48rK2tRWsNHDgQTZo0Qc+ePWFvb49WrVox9SxbWqSmpsp0MF6/fi2TnpOTI7PHTSyJiYnYt28fKlasiF9++UUhnRtra+tCe9kKwmpPW0pKCsaMGYMDBw5g0aJFmDhxoug8i6Jg4ygnJwcnTpwotKeHReOIJzt27CjRfSz2SfEiJiYGQUFB0NbWxoQJEzBt2jSsWbOGeYcNAE6dOoWQkBC0atUKzs7OqFKlCvr06YMZM2Yw1/oROXz4MLZs2SI3fciQIRgxYgSTTpuRkRHi4+NRrVo1fP78GTdu3MCCBQuk6R8+fICKioponYI8f/4cXl5eCAoKQseOHREZGYl69eoxybtHjx5fvYeVrdXV1S0VL8rlnbb/KGlpacUGI/3w4QPXmCrfG2fPni3RfSwqxq+NHsXHx4vWAOR3OgCgbdu2qFy5MlauXMmk0xYXF1dsQGFzc3PExcWJ1uHJ6tWri00fO3YsZs2ahcOHD4vWSk5OhpaWluh8yhpVq1bF7du3Ubt27SLTo6Oj5cbF+bds27YNU6ZMQfv27XH79m2FhZ9gVT5LQp06dWBiYoLr16/LfYevX78W/b8W1Thyd3eXOWfVOOJl/wAU28kVBEG6T/R76rS9f/9eundYWVkZGhoazOOX5ZGUlCSNmWdsbAwNDQ2FBQlfu3ZtsemKduCiCBISEmBiYiI3vWrVqkhISGCi1alTJ8yYMQPLli3D/v37oampidatW0vTo6OjmcY/TE1NxeLFi7Fu3TpYW1vjxIkTMnosyMnJYZpfcbAKJ/FvKe+0/UcxNzdHRESE3CU558+fL7ZR/V/Hzs5Oblre6AurjbUlHT1SNEXF3PlWlJSU8Pz5c7kV1PPnz384Jzju7u7o2LEjk7zyOmzPnj3Dvn37cP/+fQiCAHNzc/Tu3Rs///wzEx3eODk5Yd68eejSpUshD5EZGRlYsGABunTpIlqnU6dOuHLlCtavX6/wBjgrByMlYcyYMZg5c2ahZe9EhL///hvbt2/HoUOHRM9W8mwc8bR/ycnJRV5/8eIFFixYAH9/f2bxuHhy9+5dJCUlAcj9Fu7du4e0tDSZe1gtz83/7UkkEtGeXuXxtUEyAMV2gMoiGhoaxXbcEhISmK2o8Pb2Rq9evdC2bVtoa2sjKChIxpGKv78/s3iSy5cvx7Jly2BsbIzdu3crrCPv5uYGX19fLo5cSsNbNFDuPfI/y/Lly7F8+XKcPHmykLGOiopCu3btMG3aNEybNq2UnrBsk5qaWuT19PR0+Pr6Yu3atahRo4ZoF8BlievXr6Nnz554/Pix6Lzs7e1ha2uLpUuXFpk+ffp0XLlyBadOnRKtVRLOnj2Lhg0bKjQw9oMHD9CsWTO5DcN/y8aNGzF58mR8/vwZenp6ICK8f/8eqqqq8PHxwZgxY5jo8OTly5ewtraGqqoqxo0bBwsLCwiCgNjYWKxfvx5ZWVm4efOm6MD1jo6OCAgIYDZrJ4bQ0FDMnz+/xHvf/g2PHj2Cv78/goKC8PHjR3Tp0gW9e/cWHbiZZ+OoNPnw4QOWLVsGX19f1K1bF0uWLIG9vX1pP9a/Is9zX1FNPdYhJyQSCerVqyddZhwdHQ1LS8tCXhVZ7BUta7i5ucHe3l5UGJEuXbqgSpUq2LZtW5Hpv/76K54/f85ktUYeqamp0NbWLjTQ8+7dO2hra4v2iAnkfhcaGhpo3759kX4U8hC7/4tnR6q0vEWXz7T9R5k0aRL+/vtvNG7cGO3bt4elpSUEQUBMTAyOHz+OVq1aiXar/SNTsHGfk5MDf39/LFiwABKJBBs2bMDQoUNL6enY8/nzZyxfvrzYfZD/hnHjxsHFxQVVq1bF6NGjpYY8OzsbGzduxOrVq7Fr1y4mWiXBzs4O+vr6mDVrFqZMmaIQjWPHjjFblnTo0CFMmDABHh4emDJlitR1+IsXL7BixQpMnDgR1atXh5OTExM9XhgZGSEiIgKjR4/GjBkzCsW+2bhxo+gOGwD8888/2Lt3L/bv348vX76gffv2GDlypOh85bFt2zYcO3YMKioqmDhxImxtbXHy5ElMmTIF9+7dYxovLjMzE3/++Se2b9+OS5cuwdHRES9evGC6dyQoKAhLly79YTttnz9/xvr167F48WJUqlQJAQEBcHZ25v4cEokEdnZ2WLFiBRo3bvxNefBcnuvl5SVzrqgZlbLIo0ePcOrUKaxcuRJRUVHflIenpyccHR2hp6eHqVOnSm3dy5cvsXz5cgQGBuLYsWMsH1vuQCVLt/VDhgzhshrovzAHVT7T9h/my5cv0sbxgwcPQESwsLDAgAED4OHhwWSE5b9AaGgoZs2ahdevX2PmzJkYP368Qjz37d27F7t375ZZCjdgwABmjQl53pBSU1Nx+/ZtKCsr49y5c8xiMc2ePRtLliyBjo4OatSoAUEQEBcXh48fP2Lq1KlyZ+EUQWJiIuLj43H06FEsWbLkm/KQt/cmNTUVV69ehZ+fHwIDA9GnTx8xjwogd49h69at4e3tXWT6nDlzcO7cOZw5c0a0Vmnx7t07PHz4EAD72Ddbt27FqFGjYG5uDnV1ddy+fRvTpk375t++OFauXIlZs2ahQYMGUudOs2fPho+PD8aPH4+xY8eiUqVKTLTGjBmDPXv2oHbt2hg0aBBcXFxgYGAAFRUVREVFoU6dOkx0SmOUWdH2D8ht9O3YsQPz5s1DVlYWvLy8MHz48GJnBxRJYGAgEhMTcezYMVy4cKFUnqEsk5WVhdWrVxf5XUycOFEhjjS+xr179+TuJy0JW7ZswcSJE/Hlyxeps4vU1FSoqKhg9erVxQb5/q8jkUjw8uVLhe1PLqgVFBT01dU5rJ0xlXfayinnGzlz5gymT5+OW7duYeLEiZg+fbpCltfl5OSgf//+2Lt3LywsLGBpaQkiQmxsLB4+fIg+ffpg9+7dokeyhg0bVuR1XV1dWFpaYuDAgcyD8l65cgV//PEHHj58KDNo0KxZM6Y6PJC3B09HRweWlpbw9PRk0mEDcn+Tq1evym0c3Lt3D02aNMGHDx+Y6P1o1K9fHz169JC6mQ4MDMT48eMV8r6srKwwdepUuLm54fTp03BwcICDgwP+/PNP5sHllZWVMX36dMyYMUNmFkwRnTZejSNe9g/I3dcVFxeH8ePHw8PDA5qamkXex9oOKpKSLmdX9P6v5ORk/P777/Dz80NkZKTo/DIyMuDo6IiLFy+iffv2sLKykn4XeauFjh07prA9dYrk2bNnCAkJkakXnZ2dy8RybrGkpKTg4cOHEAQBNWvWZGoDJRIJ9PT0vmoL3r17x0Tra7BadiyTZ3mn7b/JlStX0LhxY+kIYt669jw+ffqEsLAw9O3bt7QesUzj5OSEEydOYNiwYZg/fz6MjY0VpuXj44NFixYhKCgIXbt2lUk7cOAAhg0bhrlz58LDw0Nhz1BO2UJbWxvR0dFyZz0fPXqEBg0a4OPHj5yfTBxubm5fvUcQBPj5+YnS0dLSwq1bt6TvLzs7GxoaGnj8+DHzsqypqYnY2Fhpo1hNTQ1nz56Fra0tUx0A2LVrFwICAnDx4kV06dIFgwcPRqdOnaChocG808arccTT/uVviBX1v7Hc/1US/vzzT9EziflnCfMvN85/TZH/0/Hjx+Hn54f9+/ejUqVK6NWrF3x9fUXnO2/ePAQFBSE8PLzIffndu3eX1s9iycnJwZ07d1C/fn0AwObNm/H582dpupKSEkaPHv3DOc9iSUJCAsaOHYujR4/KfIedOnXC+vXrUb16ddEaEokEa9as+ergOYutK6W1p6280/YfpeCGTV1dXURGRkobMS9fvkSVKlW4VU7fGxKJBMrKytDS0iq24cKi0dKgQQN4eHjIbdD6+flhzZo1uHXrliidV69eFWuAsrKycOPGDSazYMuXL8f48eOlnrDyGrF5y0o/fPiA6dOnY+PGjaK1zMzMvtq4zFua+b1ga2sLFxcXuftOfXx8EBwcjMuXL3N+MnEU5yAjOzsbx48fx6dPn0TbpaIqXB0dHURFRTFb/itPS1E6+UlISEBAQAACAwORnp6Od+/eITg4mNlSQp6NI172D0CJlxO3bdtWtBaQa1Pv3bsHFRUVmf2uYWFhmDdvHmJjY0V7+lRWVkbVqlXh6uqKbt26yY1FWFzIl3/L48ePERAQgICAAHz8+BHJyckICQlB7969mWlYWFhgyZIlcvPcu3cvZs+ejfv374vW2rVrF7Zs2SL9PnR0dFChQgXpu3zz5g3WrFmD4cOHi9IZM2YMli9fDm1tbQDAzp070bNnT+l5SkoKBgwYwNQRCQ+ePHmCpk2bQkVFBWPGjJHOisbExGDTpk3IysrC1atXRc8k8uxIlXuPLIcrX2tIvHz5EpUrV+bq2vl7IigoqET3sWi0aGho4N69e3KXryQmJsLS0hIZGRmidAoaISsrKxw9elSqy7Ijz3PQoLhR3YSEBGzZsoVJR8DJyQm7d++WNmQXLVqEsWPHSpd/vH37Fq1bt8bdu3dF6QC539/o0aOxcuVKjBw5Utp4yMrKwpYtWzB16lRs3LgRrq6uorXKAmFhYZg1axaeP38uXf4nBolEAm9vb2ljCMj1WDp16lSZ/WUTJkwQpVOUVlE6rLQKQkQ4evQo/P39ceDAAelMx9diXH0Nno0jXvaPN3fv3kXXrl2RmJgIINdpx6ZNm9C3b19ERUXh119/xcSJE1GtWjVROklJSQgKCkJgYCCSk5MxaNAgDB8+HFZWViz+DRlCQkKwfft2XLhwAU5OThg0aBA6d+4MLS0tpjO9AKCuro4HDx7IfT9PnjyBubk5MjMzRWs5OjrCzc0N/fv3B1C4vbR582YEBweL9nb8ow6mu7m5IS4uDkePHi0ylEunTp1Qq1Yt0Sso/gveI0Hl/CcRBIFevnwpPdfW1qa4uDjpeVJSEkkkktJ4tHIKoK+vT1FRUXLTo6OjSV9fX7ROSb4JQRBE65RUS5Hf39u3b8nDw4PU1NSoTZs2dPHiRdF5SiQSmf9JR0dHof/TlClTSBAE0tXVJRsbG7KxsSFdXV2SSCTk4eHBTKc0OX/+PLVq1Yo0NTVp2rRp9O7dOyb5mpqaUvXq1Ys9zMzMvjut4nj79i2tXr2aGjZsKDqvgt+6IuFl/3jTrVs3cnBwoPDwcHJxcSFBEMjc3JwWLFhA79+/V4jmuXPnyM3NjXR0dMjW1pa2bt1K2dnZzPJXUlKimTNnFnp+ZWVlunPnDjMdIiJDQ0O6du2a3PQrV66QoaEhE62ff/6ZIiMjpecF66u7d+9yq4O/x3ZZ5cqV6dy5c3LTz5w5Q5UrVxatU/D9KRJXV1eFldPiKO+0/Uf5UY0Db9LT0yksLIxWrFhBK1eupAMHDlB6ejpTDScnJxo1apTcdHd3d3JychKtw/ObKK3vLz09nby9vUlPT48aNmxIhw4dYpZ3afxPFy9epAkTJlDnzp2pc+fONHHiRCYd0NLm9u3b1LVrV1JWViY3Nzd68uRJaT/SD8G1a9eoS5cuovPh2TjiZf94Y2RkRNevXyciouTkZBIEgbZu3cpFOykpiezt7UkikdDbt2+Z5TtixAjS09Ojli1b0qZNm6SDLIrotPXt25d69eolN71Xr17Up08fJlpqamr08OFD6fmrV69kOrsPHjwgVVVV0To/artMVVW1WBv+5MkTJu/vy5cvFBUVVWQbLC0tjaKiopgNUjx79oymTJlCqamphdJSUlLI09OTkpKSmGjlp7zT9h9FEAQ6deoURUVFUVRUFGlpadGhQ4ek5ydOnPgujQNPwsLCyNDQkARBkDkMDQ3pwIEDzHQuXLhAKioq1KdPH7p8+TKlpqZSSkoKXbx4kZydnUlFRYXOnz8vWudH7rRlZWXRpk2byNjYmKpXr047duygnJwcZvkT/bgVLk8eP35Mrq6upKysTD169KC7d++W9iN9dxw7dow8PT1p5syZ0u8vJiaGfvnlF5JIJNSxY8dSfsJ/By/7xxtBEGQadVpaWnTv3j2Fal64cIGGDx9Ourq61LRpU9q0aRPTmTai3IGxwMBAatOmDampqVH37t1JSUmJbt26xVTnzp07pK2tTba2thQcHCxtu+zevZuaNWtG2tradPv2bSZaJiYmxQ7wHThwgExMTETr/Kh1SPXq1enIkSNy0//++28yNTUVrRMQEECNGzemrKysQmlZWVnUuHFj2rlzp2gdIiJPT08aMWKE3HR3d3eaNm0aE638lHfa/qMIgkASiaRQhyP/9e/ROPAiryHRu3dvioiIoOTkZEpOTqYLFy5Qr169SFVVlSIiIpjphYaGUqVKlUgikcgcBgYG9OeffzLRkEgk9PDhQ2mjSEdHh6Kioig1NZVSU1Pp/v37TDttixYtIl9fX/L19SV1dXWaO3eu9Nzb25uZVnBwMJmbm9NPP/1Ea9asoU+fPjHJtyASiYRevXolPdfW1qZHjx5Jz1lWuGlpaTRmzBiqUqUKGRoaUv/+/en169dM8i5NNDQ0SFNTk6ZPn05hYWFyDxZ8+fKFli9fTjY2NqSlpUXa2tpkY2NDK1asoM+fPzPRICLq3LkzpaSkSM+9vb0pOTlZev7mzRuysrJiohUYGEiCIJCBgYF0AGnnzp2ko6NDrq6uzBvOvOBh/3hT0F7o6OjI2AtWPH/+nJYuXUq1a9emn376iSZNmsSsM/M17t+/TzNmzKAqVaqQrq4u9e/fn/bt28cs/4sXL1KdOnWk7ZW8touVlRVduHCBmc6wYcOoZcuWRabl5ORQixYtaNiwYaJ1BEEgd3d3mjRpEk2aNIlUVVXJzc1Neu7u7v5dtssmTpxI9evXl/ne83j58iU1aNCAJk6cKFqnVatWtHv3brnpwcHB1Lp1a9E6RER16tQpdsnnhQsXqE6dOky08lPuiOQ/St7m569hamqq4Cf5PnFyckK1atWwZcuWItPd3d3x5MkTpl6e0tPTcfToUTx48ABArvesDh06yI0l9G+RSCRFuoIueM5iE3T16tVLFFcpPj5etJZEIoGGhgb69+9fbHwlHx8f0TqdO3eWesAMDw+Hg4MDtLS0AOSG0Thy5AiT95fnaGTgwIFQV1fH7t27YWdnh71794rOuzThFfuGZ4wnns4FrK2t4eLighkzZiAkJAQuLi6wsbFBSEgIatasKTr/0kTR9q8kEBFev37NxPlAwbAJKSkp0NXVLVQGxHogVlVVRZUqVTB06FB0795dbsDpgm7zWZKTk4NDhw7Bz88Pf//9t2iPmAWJjIyUeom0sLCAtbU10/zj4uLQqFEjabxNCwsLCIKA2NhYrFy5Evfu3cP169dRq1YtUTp2dnYlqhfFOjzhTXJyMmxtbZGUlIRBgwbB0tISQK4znl27dsHY2BiXLl1CxYoVRen89NNPuHLlitzwAfHx8WjWrBlev34tSgfIDRsTExMj10HS48ePYWVlhbS0NNFa+SnvtJVTJMnJyQgPD8eQIUNK+1HKJPr6+jh79qw0bktBoqOj0bZtWyQnJ3N+sm+Ht7trXpSkIhQEASdPnhSlIy84eUECAgJE6QBAzZo1sWjRIri4uADIjbvYqlUrZGZmysRlKqdoeMZ4KomnXladNh0dHURHR8PMzAw5OTlQU1PD8ePHv7syW1poamoiMTFRGjS8U6dOCAgIQOXKlQGw/a14eSAuKvZcwWYfr9hzGRkZWL9+PaZOnaowjaysLGRmZsp4hmXBlStX4OrqitjYWJn3aGlpiYCAAIXEXfyRSE5OxqxZsxAcHIyUlBQAQIUKFdC3b18sWrQIBgYGojW0tLRw8eJFuQMQ0dHRaNGiBZOOVKVKlRAaGoo2bdoUmX727Fn06tULb968Ea0lA/O5u3J+CCIjI7/LaXheqKurU0JCgtz0hIQE0tDQYKJ16dIlOnz4sMy1oKAgql69OhkaGtKIESMoMzOTiVZZ4fHjx0yWm/yoqKio0NOnT2Wuqaur0+PHj0vpib4vzM3Ni11WFxISQubm5ky0ytJe0e8RnvaPpwddXiQkJJToYMXr16/p4MGDdPToUeneos+fP9OaNWvI2NiYDAwMmOgcOnSIduzYIXPN29ub1NTUSElJiRwdHZl5m83PjRs3KDg4mIKDg+nGjRvM8y+Ohw8fkr29PVdN1uTk5NDLly/p5cuXzPeVN2zYkDZt2iQ3fcOGDUy85xLlOkj69ddf5aYPHz6cOnfuzEQrP0VHWSynnHKKxcLCAidPnpQ7u3LixAnRSyXymD9/Puzs7NC5c2cAwK1btzB8+HC4urrCysoKK1asQJUqVZjMCpQV3r17h6CgIPj7+zPP+82bNxAEgcnIXmmRnZ0NVVVVmWvKysrIysoqpSdiw4EDB0p0X/fu3UXpPH78uNgg8c2bN8fjx49FaeQhCEKhmd6SLIH6Vo4ePSqNFZiTk4MTJ07g9u3bMveIfX8l4ezZs2jYsOFXA3B/jbJm/1j+dnv37sX+/fvx5csXtG/fHiNHjmSWdx48tzhERESgS5cuSE1NhSAIaNKkCQICAtCjRw/k5ORg9uzZcoOk/1tWrlwpE1g7IiIC8+bNw2+//QYrKyvMnj0bCxcuFL3svSA2NjawsbFhmmdJ+fjxY4lXxJRVBEFQWGyzAQMGYM6cOWjZsmWRKyjmzZuHadOmMdHy9PSEo6Mj9PT0MHXqVBgZGQHInY1fvnw5AgMDcezYMSZa+SnvtJVTzjfg6uoKT09PGBkZwcnJSSbt0KFDmDZtGmbPns1EKzIyEgsXLpSe79mzB7a2tti2bRsAoFq1avDy8hLdaCm4p60oBEH4LjsGKSkpmD17NoKDg6VLVvX19eHi4gJvb29pAGwxlKQxIgiC6ACiQO6yHFdXV+n+OQDIzMzEqFGjpHvoACA0NFS0Fk969Ojx1XtYLOXS1dXFq1ev5AbmTUpKKnb/47+h4G9V8Hdivb+n4HI6d3d3mXNeS+Hs7Oygr6+PWbNmYcqUKd+cDy/79//Yu/O4GvP3f+CvcyoqLbJNzIiGSsLExxZDdmUwqYwtpjJjp6ipbKVRWafsxtLKTIqQhpElsjR2LZQUyVq2tCfp/v3h1/mqzqm473OfOq7n4+HxmHPeZ+7r3el0znlv18W3nTt3YtasWdDT04OysjIiIiKQkZGBVatWcRqnrpMPks7mfIrly5dj5MiRWLZsGQICArBhwwaMHj0aK1aswNSpUzkd8N66dQt//PGH6PaBAwcwfPhw0eeusrIyHBwcOBm0/f7773V6nLu7O+tY8qh79+51+t3fuHGDVZyFCxfi33//xf/+9z8MGzYMnTp1gkAgQEpKiuis8sKFC1nFqDB48GBs3boVDg4O8PPzg4aGBgQCAXJzc6GkpITNmzdjyJAhnMT6GJ1pI2IlJCSgR48evHy4N0Tl5eWYMGECIiIiYGBgAENDQwAfDtampaXBwsIC+/fvr1NihdooKysjLS1N9AXz+++/h5mZGZYtWwYAePDgAbp27Yr8/HxWcSIjIyW2xcXFYfPmzWAYBsXFxazi1AWXr7/Xr1/DxMQET548wZQpU0RJJ1JSUvD333+jbdu2iIuLg5aWFqs448aNk9j2/v17nDp1Cm/fvuXkZ+Lz/Jw8mjBhAsrKyhARESG23crKCgoKCggPD2cdy9bWtk5fWOTtd5WZmYmMjAxER0ezGojw9f4HfEgak5WVJTrTpqGhgYSEBOjq6gLg9kxb165dYWFhIRqQBgUFYf78+Zz8HB+TNBnHfJRoiqvJuBYtWiA2NhZGRkYoKiqCuro69u3bh/Hjx7O+dlUqKipITU0VDTZ79+4Na2tr0UpKZmYmOnfuzMn5pZpW1gQCAVJTU1FSUiL170sN9XuZp6dnnR7n4eHBOta7d+/g5+eHv//+G2lpaWAYBvr6+pg8eTIcHR2r7VBh68mTJwgPD0d6eroolrW1Nb755htO44hwvuGSNAgVqdUl/XNxcaEzbXWwb98+5scff2QMDQ0ZQ0ND5scff6wx5ezn0NHRYWJjYxmGYZi3b98yKioqzKlTp0TtiYmJjJaWFqcxK6SkpDAWFhaMgoICM23aNCYzM1Mqcari8kylg4MD06VLF7GFLp89e8Z07dqVcXR05CSWOIcPH2Y6d+7MNG3alFm1apXU4nwpCgsLWV+DzxpPhB0+3/8EAgHTtGlTRktLi9HS0mIEAgGjqakput20aVPO3pdUVVUrnZcrKytjlJSUmGfPnnFy/Qrx8fFi/928eZNxdXVlVFRUmJYtW3ISS9yZwLS0NE6uXdW3334rqv2Vn5/PNGrUqFK9vuvXrzMtWrSQSuwKN2/eZEaOHMkoKSkxM2fOlGoshmm4uQYyMzM5rwf4uW7evMlLnMTERE7KGFRF2yO/UH5+frU+hovtEvJuwoQJmDBhglRjmJmZwc3NDWvWrMHhw4ehqqqKAQMGiNoTExM5T+f99OlTeHh4IDg4GCNHjkR8fDy6dOnC2fUtLS1rbK/ILsWFw4cPY8eOHaI95x/T1tbG2rVrMWvWrDr9TXyKixcvwtXVFTdv3sS8efPg5ubGejXvS1ZSUoKtW7di3bp1yMrKYnWtzp074+TJk5g+fTomTpxYLRtcdHQ0jIyMuOh2tZT/fKtaYoAPz549g7e3N7Zs2cL6Wny+//G52llcXFwpw6GCggIaN26MoqIiTuN899131e47deoU3NzccPfuXbi4uMDZ2ZmTWAKBAPn5+VBWVhat5BUVFSEvL6/S47jYemxtbQ1HR0csWbIEx44dg7a2Nvr27Stqv3btGgwMDFjHEScjIwPLly9HWFgYLC0tcfv2bejp6bG+bm3bCLl+bfBFV1dXpu+Bubm5+Ouvv7B7924kJCRIbaUyLy8PoaGh8Pf3x7Vr16RSRoMGbV8oLupfEclu3LgBd3d3/PPPP6yv5eXlBUtLS5iamkJNTQ3BwcGVlvgDAgIwYsQI1nGAD29uPj4+2Lx5M4yNjXH69OlKX5C4UltyAk1NTc7KTTx79qzGL+BdunRhPQj42O3bt+Hm5objx49j2rRp2Ldvn/S2SsiZ0tJSeHp64sSJE1BSUoKLiwssLCwQGBiIpUuXQiAQwMHBgZNYffv2xe3bt6Ve44mR8QkEacVPTk7GmTNnoKSkhJ9++glNmzbFy5cv4e3tjT///FO0pZAtPt//dHV10a9fPygq8vPVaPfu3ZUGbmVlZQgKCkKLFi1E9y1YsICzeNevX4ebmxvOnz+PX375BceOHeP0izTz/7eHfXz7462FDIe1Pj08PPD06VMsWLAA2tra2Lt3b6VyJ6GhoRgzZgzrOB97+fIlPD09sXPnTnz//feIi4tDr169OLt+Xc70NkSyeg+MiYmBv78/Dh06hHbt2sHKyoqTM+VVxcbGwt/fHxERESgpKcFvv/2Gv//+m7NkdJVwvnZHyBfixIkTjLOzM7N48WLRNpeUlBTmxx9/ZIRCITNy5EhO471580aUQvljr169Yt6+fcv6+mvWrGGaNWvGdO7cmTl8+DDr69UXbdq0Yc6fPy+x/dy5c0ybNm1Yx3n48CFja2vLKCoqMhYWFkxycjLra35pFi9ezGhoaDBWVlaMtrY2o6ioyMyYMYPR19dngoKCmNLSUs5i5ebmit2y8/79eyY3N5ezOFW3jPFNGmn/o6KimEaNGjECgYARCARMhw4dmJiYGKZFixbMoEGDmKioKE7jMYz03/8YhmGEQiFvv6t27dox7du3r/Gfrq4uJ7HS0tKYn376iVFQUGAmTZoktTIQZ8+erdO/hqagoIBZsWIFo6GhwfTo0YOJjo6WdZcaFD7fAx89esSsXLmS0dXVZVq1asXMmzePUVRUZG7fvs1pnKdPnzLe3t5Mhw4dGG1tbWbhwoXM1atXpRLrY5SI5As1atQohIaGilY8vL29MXfuXFEWvVevXmHAgAFITk6WYS/rr+DgYNjZ2aFZs2Z4/fo1WrRoAV9fX8yZMwdWVlZwcnLidDshH4RCIVRUVDBs2LAaCzTzlZHw+fPnnMwCT58+Henp6Th58mS1Q8hv377FyJEj0aFDB9YzcKqqqhAIBJg/fz769esn8XF8pFtvqDp27Ih169Zh3LhxSEhIQPfu3TFhwgTs2bOH09WPQ4cOwdXVFfHx8VBVVa3UVlRUhO7du2P9+vWczNQLhUIEBwfXurosrdfF7NmzsXLlykqrN2yZmJigd+/e8Pb2xs6dO+Hs7Aw9PT3s2rVLYrHZhqBqIXR5MGfOHPj7+2Pw4MFYvXo15yvJXwJtbW3k5+dj/vz5mDRpksQtjGy3w9X2mVdWVoYbN27UWK6kPhIKhfDy8qq14DnbVeVRo0bhwoULGD16NKZMmQIzMzMoKChASUkJCQkJ6Ny5M6vrf0xZWRnjx4+HjY0Nhg8fLko6J41YH6NB2xeq6jmLquceuMySJY+MjY0xceJEuLm5ITw8HBMnTkT37t0RHh7O+fkyvvCZ5U5VVRWZmZmiLG1mZmYIDAxE69atAXD7+nv8+DF69uyJxo0bY+7cuejUqROAD9u7tm3bhrdv3+LatWsS07/XVV0yhfKVbr2haty4Me7duyfaTqqsrIxLly5x/kVzxIgR+Omnn/DLL7+IbQ8ICEBYWBiio6NZx+LzdeHu7g53d3eJA9yHDx9i+vTpOHnyJKs4TZs2xZUrV6Cvr4+ysjIoKysjKipKVEutoRIKhcjOzha9L8kDoVAIZWVl0fueJGzTrQMfXn9ubm6iiZCcnBy5OMf78d+wQCCotN2v4jYXf8NVv5cZGhoiOjpalF+goX4vEwqF+Oabb2qcDBYIBLh//z6rOIqKiliwYAFmz55d6YyhNAZSBgYGKC0txeTJkzF16lTR35e0B210pu0LVXWsTmP3T3Pv3j1RAhJra2soKCjA19e3wQ7YgA8pp/lSUlJS6TV38eLFaqUEuHpNfvPNN/jvv/8wZ84cLF68WHRdgUCA4cOHY8uWLawHbMCHMhCEnXfv3lVaDVVSUmJdnFmcW7duYdu2bRLbBw4cKEopzwW+Vm+CgoIQFRWFkJAQdO3atVJbxYpY//79WcfJy8sT7cpQVFSEiopKpbNMDdny5currb5WxUXtL752u3CRRr2uvL29MW/ePNHz165dO96T4EgDXzkAqn7mPX78uFophob6Xe3atWtSfw88f/48AgIC0LNnT3Tq1AlTp06VWqK41NRUXLx4Ef7+/ujVqxf09fVhY2MDAJzWI6yKBm2EfIbCwkJRcdyKmUwuvviT/8PlG5+uri7+/fdf5OTkIC0tDcCHrXjNmjXjLAbhhru7u+hLX2lpKby8vKoN3Nh+ac7JyamxLtW7d+9ERdjZkuYHeFW3bt3CvHnz0KtXL3h4eMDV1RWPHz+Gvb09rl27Bl9fX4mri58qOTlZlMCHYRikpqZWq4kljexp0paUlFRjLSeufp/R0dGVCquvWbMGkyZNEg3aysrKkJqayjoOn4M2eZ0Mbteunay7IMLn+wlX+OqziYkJTExMsHHjRuzbtw8BAQFYtGgRysvLcfLkSbRt2xbq6uqcxevfvz/69++PTZs2ITQ0FAEBAXj//j3mzJmDyZMnw8LCgvNVexq0faEEAkG1P6SG+GYgS9HR0aIvk+Xl5Th9+jRu3bpV6TF0fql+0dLSanDnAb4kAwcOrPRFtV+/ftW2zHDxPtW+fXtcu3ZN4paxa9eucfZFjc8vrhoaGggJCYGVlRVmzpyJsLAwZGRkwMTEBElJSZxOLA0dOrTSzzZ69GgA3G4Xk4VDhw7xsioqrwMceZSYmFinxzXESQo+8P3aVlVVhb29Pezt7ZGamgp/f3+sXr0abm5uGD58OI4cOcJpPDU1Nfz666/49ddfkZKSAn9/fyxbtgxz5szBu3fvOI1Fg7YvFMMwsLW1RePGjQF82K42a9Ys0erRxzOARLyff/650u2ZM2dWut1Qv7TwoeqkgbhJBK7UVhOuAl8JVohkZ8+e5SWOpaUlli5diuHDh1er35eVlYVly5aJtrqw9fPPP0NFRYWTa9VVnz590LVrV5w+fRpNmjSBi4sLpwO2+lYy5ty5c/juu+9Yb6WliUt2xNVpKygokEqdtroICQlB//79WR9bMDY2rnaWrSouPu9re/6qPo8NhYeHR61JSKTFwMAAa9euxapVqxAVFSX1WoyGhoZYv349Vq9ezck26qooEckXys7Ork6P47PYKPlyCIVCaGpqir4kvXnzBhoaGqID3wzDIC8vj5NBL58JVkjDkJ+fDxMTEzx8+BA2NjYwMDCAQCBASkoK/vrrL7Rt2xaXLl3iZCsN38W1Q0NDMW/ePBgbG2Pbtm3w9/fHxo0bMWvWLKxevZr3ASQfhEIhtLS0sGTJEjg5ObG6Tk3nD1+9eoU9e/bA0dHxs2NUUFBQQFZWlmj7lLq6OhITE0X17Rpi0gmhUFjpvbZi4FH1Nl8/k1AohJKSEmbMmIHNmzd/9nUyMzPr9Di2q/P17fnjSlpaGtzd3bFjx45qA/bc3FzMnj0bXl5eUj/7GBERAU9PzzqvnNZFQUEBFBQUKr2vxsfHw93dHUePHuX8d0UrbV8o+oLKjr29PTZu3Mjp/ugvCZ+vPz4TrBB2OnfujAsXLojOGs6YMQPe3t6iL7bPnz9H+/btUVRUxCqOuro6Ll68iMWLFyMsLEx0fk1LSws2Njbw8fHh7G+bz3lRa2trREdHw8fHB/PnzwcArF27FuPGjYOtrS3+/fdfBAcHw8TEhFWchw8f1ulxFVnvpC0jIwMZGRmss30GBgZWW61jGAYnTpyAv78/IiMjoaGhwcmgTR53u5w5c0bWXaikvLwcDx48YP264OtMW317/riyfv16tG3bVuwKq6amJtq2bYt169Zh+/btrGPt2rULJ06cgJKSEhwcHNCnTx/ExMTAyckJqampmDZtGusYwIckMRMmTMClS5egoKCAefPmwcvLC7NmzUJoaCh+/PFHXLhwgZNYH6OVNkI+A9+z5+Tz0e+q4ai60iGuFEnr1q05zdTJMAxevnwJhmHQsmVLzrfI8ZlGvn///ggODkbHjh2rtZWUlMDV1RXbt29HaWkpqzgfp+7+OBvrx/c1xBWBjz148AABAQEICgrCkydPMGXKFEybNg2DBw+uMXV5XdFuF3YeP34sKg0iawcPHsSKFSs4XcGRJwYGBti7dy969eoltv369euYPHky68Q769evx5IlS9CtWzekpKQAAJYuXQpfX1/Mnz8fc+fO5axepY2NDZKSkvDrr78iIiIC586dg7GxMb777jssX75ctGLONVppI+Qz0FxHw1Gffle6uroYMmQIfv/9d3z99dey7k69J+53x/WgSiAQiAZUsbGxKCwshImJCaf1pfT19Wvt9+vXr1nHOX/+vMS6cMrKyti4cSOsrKxYxxEIBPjmm29ga2uLMWPGcFr4/FM9e/YM3t7e2LJlC+trvX37FgcPHsTu3bsRFxcHc3Nz+Pr6YtKkSXBzc+O09hJfg7GQkJA6PY6rFQi+dOnSBZs3b8bUqVN5iVfbCg5f/WiIHj58WOOkaYsWLfDo0SPWcfz9/fHnn3/C3t4eZ8+exZAhQxATE4P09HRRVlaunDlzBuHh4ejfvz+sra3Rpk0bjB8/Hm5ubpzGqYoGbYR8Jjq0Tj7Vzz//jMzMTAwcOBD37t2TdXe+WOvWrUNBQQE8PT0BfBgcmpub48SJEwCAVq1a4fTp0zAyMuIknqenp1TqzVVVl0LeAwcOZB3n8ePHCA4ORlBQEP7880/Y2Nhg+vTpMDQ0ZH1tcZKTk3HmzBkoKSnhp59+QtOmTfHy5Ut4e3vjzz//5GxW++uvv0bnzp1hY2ODAwcOiAbukyZN4uT6suDg4CCxTSAQoLCwEGVlZZwM2qqeyZIUs6ZyG3Xl4+ODuXPn4vDhw9i5cyeaN2/O+pqSVF3BiYyMlNoKjjzS1NTEvXv3JG4zTU9P5yQ5TWZmJoYNGwYAGDRoEJSUlODt7c35gA34kLCqIsGNtrY2VFRU8OOPP3IepyoatBHymfiaPSfsfVyeQRI+yjOsWLFC6jEaMr5KkYSGhsLV1VV0+8CBAzh37hzOnz8PQ0NDTJs2DZ6enggPD+ck3sSJE+Vqe662tjZcXV3h6uqKCxcuIDAwEH369EHnzp0xffp0TJ8+vU4DyLr4559/YGVlJUqdvXbtWuzatQs//fQTunTpgv3794vKDbD1/v170WuQiy2Q9YGkeoPPnj2Dp6cnAgICMHz4cE5iHTp0SGJbXFwcNm/ezNnOhzlz5sDc3BzTp0+HkZERdu7cKbX3cD5XcOTRwIEDsXnzZgwZMkRs+6ZNmzBgwADWcUpKSqCsrCy63ahRI6luS//4PaKiXq+00Zk2Qj6DUCjEhg0bah0IVC0LQPhXly+PDf38jbwQCoXo0qWLaLtdYmIiOnXqJCp2XFZWhtu3b7P+XWlpaSEuLk60MmRnZ4eysjLs2bMHAHDp0iWMHz+eky07X8qZyuzsbEyaNAmxsbF48eIFZ4XrTUxM0Lt3b3h7e2Pnzp1wdnaGnp4edu3axcmq4cdKSkoQEREBf39/XLp0Cebm5rCxscGECRMQHx/P6fZIWcnPz8eaNWuwceNGGBkZYdWqVRg8eLDU4t25cweLFy9GVFQUpkyZgpUrV3KeoGbLli1YuHAhDA0Nq23VvXHjBuvrq6qq4s6dO6J+N27cGOfOnUOfPn1YX/tLcPPmTZiYmGD06NFwcXGBgYEBgA+vjbVr1+Lo0aOIi4tDjx49WMURCoXw8vISlRdwdXXFb7/9Vm0VdMGCBaziVMSq6bOqAhevv4/RoI2Qz1BbamhSf/D1u/r999/r9Dh3d3ep9qMhq9iuWBsPDw9WcdTU1JCYmChKcNKpUyc4ODhg9uzZAD6cwTAwMEBxcTGrOEDNr7+cnBzs3bsX/v7+iI+PZx1LFuLi4hAQEID9+/fDwMAA9vb2mDFjBmcrbU2bNsWVK1egr6+PsrIyKCsrIyoqCubm5pxcX5J79+4hMDAQwcHBePLkCSZNmgRbW1sMGTKkQa7ClZaWYsuWLfDx8UGLFi3g5eUFa2trqcV7+vQpPDw8EBwcjJEjR2LVqlXo0qUL53EyMzNha2uL5ORkzJgxo9qgje17BVD9b1hdXR0JCQlST1EvT/755x/Y29vj1atXle5v3rw5du/ezckqafv27eu0Pff+/fusY/H1WVUVbY8k5DPQeTbp+/333zFo0CDWs+l8/a5q2hokEAiQmpqKkpISGrTVwM7ODt988w1nX/gl6dixI86dO4dvv/0WDx8+xN27d2Fqaipqf/z4MWdnZMRlujx16hT8/f1x+PBhtGjRos4F4OuLZ8+eISQkBIGBgcjJycGUKVMQFxfH2RnAj+Xl5Ym2oCkqKkJFRQX6+vqcxwE+JO2YMGECGjdujA4dOsDLywu///47oqOj4e/vj9GjR0NdXR0vX76USnxpYBgGISEhcHd3R1lZGXx8fDB9+nSpDTxzc3Ph4+ODzZs3w9jYGKdPn+Zk65s4u3btgpOTE4YNG4Zbt25JdSvc7t27RSs4ZWVlCAoKksoKTm3s7e0xePDgBpf4ZPTo0cjMzMTx48eRnp4OhmGgr6+PESNGQFVVlZMYDx484OQ6dcH1YKyuaKWNkM9AK23Sp6uri+zsbAwdOhRRUVGffR1Z/67i4+Ph5uaGmJgY2Nvb488//5RJPxoCvrYS7tixA05OTqI6O02bNsXFixdF7V5eXrh8+TKr111VDx8+RGBgIAIDA1FQUICcnByEh4dzks2Rb40aNUKbNm3w888/Y+zYsVBSUhL7uG7durGOJRQKERMTI9pu2a9fP4SHh1dL985FrNpefy9evMCePXuwaNEi1rH40q1bN9y7dw/z58+Ho6OjxC/IXCSCWLt2LdasWQNtbW34+PhINTGDmZkZrly5gg0bNkg98yWfKzi1GTRoEDIzM6GhoYGEhASpx5O28vJyHD16VDSJ1VDExMRg4MCBvGfOpUEbIZ+hrKwMycnJ0NPTg4qKSqW2oqIipKeno0uXLlJfMZB3JSUliI2NxciRIz/7GnZ2dti0aRPvhdAzMjKwfPlyhIWFwdLSEl5eXtDT0+O1Dw0NnwNsf39//PPPP9DW1oaHhwe0tbVFbXPmzMHw4cMxbtw41nHCw8Oxe/duXLx4EaNGjYKNjQ3Mzc3RpEkTJCQkNMhzUh+/r1V8ma36VYKrc6IVGQkllX/gsiacrCd4pEHc7+pjXD9/KioqGDZsWI0reQcPHmQda/jw4QgMDKw3tdr4lpqaKjob1hClpaUhICAAwcHByMnJwciRI1kP2nR0dHDz5k3RLoktW7Zg2rRpnExIVFV1gqdv376IiIiQeikfGrQR8hmCgoKwZcsWXL58udqH0/v379GnTx84OjrCxsZGRj0kFZ4+fQpfX1+4u7tXe/POzc2Fl5cXnJ2d8dVXX3ES7+XLl/D09MTOnTvx/fffY/Xq1RKLipLK5PFLs6KiIlxcXLB48eJKEwdKSkoNdtCWmZlZp8dJSvFdX2PxWQidL7GxsXV63Mfbgz+Xra1tnbajc1Wjbv/+/Th8+DDevXuHYcOGYcaMGZxcl0hHcXExwsPDRYl+3r9/Dz8/P9jb24u2nrJR9fNDQ0MD8fHxUjl7KKtzjnSmjZDPsHv3bjg7O4udTVRQUICLiwu2bNlCg7bPdPDgQaxYsQKJiYmsr+Xn54e8vDyxs22amprIz8+Hr68v1qxZwypOYWEh1q9fD19fX3Ts2BFRUVEYMWIEq2t+iT4+OyIJV2dHnjx5goiICNy9excCgQD6+vqwtLTkdLbU3t4e27ZtQ2xsLKZOnYoJEyZwWrhbFrgYINXHWMCHgUfjxo1rfAwXK0V84WIwVldBQUG8xdq5cydmzZoFPT09KCsrIyIiAhkZGVi1ahXnsUaNGoXQ0FBRtmhvb2/MnTtXdNby1atXGDBgAJKTk1nFKS8vx+3bt9G1a1cAwJ9//onS0lJRu4KCAmbPnt3gdvBcuXIFu3fvRlhYGPT19WFjY4P9+/fjm2++wbBhwzgZsIkjj2tStNJGyGdo1aoVrly5gvbt24ttz8jIQO/evfHixQt+O9aA7Nq1CydOnICSkhIcHBzQp08fxMTEwMnJCampqZg6dSp27NjBOo6RkRF27NiB77//Xmx7XFwcfv31V9y+fZtVHG1tbeTn52P+/PmYNGmSxBlnLs7eyCuhUIhvvvmmxq1VXJ0d2bZtGxYtWoTS0lJoamqCYRjk5eWhUaNG8PX1xZw5c1jHqFAxwxwQEIDLly9j5MiROHr0KOLj46WSUU/aHj58WKfHcZHanc9YQqEQP/30U7Ut71VxtVLEJz4mKPjUtWtXWFhYYOXKlQA+DBjnz5+P/Px8zmNV3QpXdQUnOzsbbdq0Yb3F9O+//8aOHTtEq6Pq6upo2rSp6NzUy5cvsWHDBkyfPp1VHL4pKipi/vz5mDVrVqUtnVzvNuBz9UtBQQFZWVmiVfmKM4a6urqcx/oYDdoI+QxNmjTBf//9J/ELeGJiIkxMTFBYWMhzzxqG9evXY8mSJejWrRtSUlIAAEuXLoWvry/mz5+PuXPnVsvM9bmaNGmClJQUiV/qHj58CENDQ9a/q6pnRz5+a+X67I284mt75NGjR/Hjjz/C0dERTk5OaN26NYAPWRHXrVuHzZs3IzIyEqNGjeI8dsVZjpCQEBQUFOCHH36AtbV1g8ogWXHOrKqK1zjw4TVfVlbGOtbHA/iKv6mPY9OZttrxNUFR19cwFyuVTZo0QVJSkugL+fv376GiooKHDx9WOp/KhdoGA1wN2oYPHw57e3tMmjRJbJw///wTYWFhOHPmDKs4fBsxYgQuXbqEMWPGYOrUqRg5ciQEAoFUBm3yXqeNtkcS8hn09PQQFxcncdB24cIFSjpRA39/f/z555+wt7fH2bNnMWTIEMTExCA9PV205YQrKioqePDggcRB24MHD2qdWa+LjIwM1tcg/Fi7di3c3Nzg5eVV6f7WrVvD19cXqqqqWLNmjVQGbXp6eli1ahW8vb1FWdMmTZqEt2/fch5LWm7evCn2foZhsG/fPmzatImzLU8CgQDffPMNbG1tMWbMGKlma5PHUi5Hjx7FggULJE5QODg4oH379py81iu2D/KhuLi40mtMQUEBjRs3RlFREW994FpKSkqNAxhTU1MsWbKExx5x48SJE3j06BECAwMxe/ZsFBcXY8KECQC4/ZvT0dHBrl27RLe1tbWxZ8+eSo8RCAScDNqqpvyXZqbUj9FKGyGfYe3atVi7di1iYmKqDdwSEhIwdOhQuLi4wMXFRUY9rN9UVVVx584d0UCqcePGOHfuHPr06cN5rB9++AFt2rSp9Gb+sV9++QVPnz7FsWPHOI9NPg1fKx0aGhq4evWqxOxrqamp6Nmzp1S2Wonz/PnzBr+6c+rUKbi5ueHu3btYtGgRnJ2dORm4ZWVlITg4GEFBQcjJyYGNjQ2mT58OQ0NDDnpdmTyutJmammLAgAHVJigqLFu2DOfPn69zwpL6ouqqCiB+ZYWLL+hVt8Kpq6sjMTFRtBWOq5U2ZWVl3L59Gx06dADwocRE8+bNRbs40tPTYWRk1KAmeMQ5efIkAgICcPjwYbRt2xbW1tawsrLC//73P1l3rf5jCCGfrLS0lBk0aBCjqKjImJmZMY6OjszChQsZMzMzRlFRkTE1NWVKS0tl3c16SyAQMNnZ2aLbampqzL1796QSKyYmhlFQUGCcnJyYrKws0f1ZWVnMokWLGAUFBeb06dOcxbty5QqzcOFC5ocffmBGjx7NLFy4kLl69Spn15dngwYNYh49eiT1OE2aNKnx9Xbv3j2mSZMmnMS6du0aM2jQICY3N7da25s3b5hBgwYxCQkJnMSShWvXrjHDhg1jGjduzMydO7fS3zXXzp8/z9jb2zPq6upMnz59mJ07dzLv37/n7Ppnz55l3r17x9n16gN1dXXmzp07Etvv3LnDqKmp8dgjbrRr145p3759jf90dXU5iSUQCJhRo0Yx48aNY8aNG8coKioyI0aMEN0eNWoUIxQKWcfR0dFhjh49KrH9yJEjjI6ODus49cXr16+ZTZs2McbGxpw8f3w6ffq0TN4raKWNkM/07t07+Pn54e+//0ZaWhoYhoG+vj4mT54MR0fHanubyf/hc+858KGYsoODA969ewcNDQ0IBALk5uZCSUkJfn5+mD17NidxXFxcsH79eqipqeHbb78FwzC4f/8+ioqK4OzszDpDpbzjq7h2nz59MHHiRCxcuFBsu6+vL8LCwnD58mXWsSZPngxDQ0MsX75cbLu3tzdSUlKwd+9e1rH4lJ6ejqVLlyIiIgI//fQTvLy8pJ7uukJ2djYmTZqE2NhYvHjxQlR4m63Lly/j9evXMDc3F90XEhICDw8PFBYWwsLCAps3b641u2R9oqamhsTERIm/m/v376Nbt24oKCjguWcNB1+lDOzt7ZGamoqLFy9Wa2MYBv3790enTp0QEBDAKk59tHbtWtY7k76EOm200kYI4R2fs6QVHj9+zPj6+jJz5sxhZs+ezfj5+XG6qhMUFMQoKyszmzdvrrTKWlpaymzcuJFRVlZmgoODOYsnj6quwEpLUFAQo6KiwmzdurXSbOm7d++YLVu2MCoqKkxgYCAnsb799tsaV9ISExM5f61L2+zZs5lGjRoxI0eOZG7evMlb3IsXLzLTp09nNDQ0mF69ejHbt2/ndKXNzMyMWb16teh2YmIio6ioyPzyyy/MH3/8wWhrazMeHh6cxeND7969GV9fX4ntf/zxB9O7d28ee0QkSU9PZzQ0NJjevXsz4eHhTHx8PJOQkMCEhYUxvXr1YjQ0NJi0tDRZd/OzvHv3jrl16xaTmppa6f7Dhw8z3333HdOoUSPWMap+fqirq0ttBw+fu4U+RoM2Qj7D5cuXmbKyMtHt8vLySu0lJSVMWFgY390inyExMZFxcHBgfZ1evXrV+uWoV69erOPIM4FAwKSnpzO5ubk1/uOCk5MTIxAIGA0NDaZ79+5M9+7dGQ0NDUYoFDKOjo6cxGAYhmncuDFz//59ie33799nlJWVOYvHB4FAwKioqIieN0n/uPD06VNm9erVjIGBAdOqVStm4cKFzK1btzi5dlXa2tqVtjIvWbKE6d+/v+h2eHg4Y2hoKJXY0sLnBAXf3r17x6xdu5bp3r0706RJE0ZNTY3p3r07s27dOk6PJwiFQl4mkxjmw3cLQ0NDRiAQMEKhkBEKhYxAIGAMDQ2ZS5cu8dIHrt2+fZvR1dUV/Tzjxo1jsrKymIEDBzKampqMk5MT8/DhQ9Zx+BxIyWrQRtsjCfkMfNVtIdKRl5eH0NBQ+Pv749q1a+jWrRvi4+NZXbNqCuqq7t+/j65du1IZiBpISiVfgeG4bMKlS5cQGhqKtLQ0AIC+vj4mTpyIvn37cnJ9AGjbti127doFMzMzse3//vsvZsyYgUePHnEWU9o8PT3r9LiqGdY+R6NGjdCmTRv8/PPPGDt2LJSUlMQ+jov6h8rKykhLS0Pbtm0BAN9//z3MzMywbNkyAB8yzXbt2pW3BDVccXZ2hq+vL9TV1UVJLu7du4eCggIsWLAAfn5+Mu7hpysuLsbw4cPx33//YdiwYTA0NATDMLhz5w5OnTqF/v3748SJE1BWVmYdSxYJam7evCl6X9LT00P37t15i821sWPHorCwEAsXLsRff/2FsLAwdOzYETY2Nli4cCHU1dU5iUN12gghYtWlbkvr1q1RXl4uy27WWyEhIXV63LRp0ziNGxsbC39/f0RERKCkpAS//fYbfvnlF3Ts2JH1tTU0NHDlyhV06tRJbHtqaip69eqFvLw81rHklVAoRERERK1nlExNTXnqEXt2dnZIT0/H+fPnq7UxDIOBAweiY8eODbJgMx+q1j8EgKpfW7gayLdr1w579uzBwIEDUVpaiqZNmyIqKgpDhw4FACQlJcHU1BSvX79mHYtvfExQ8Mnd3R3BwcGIiooSm8F57NixsLOzw4oVK1jHksesonzS1tbGsWPH0KNHD7x58wbNmjXDjh078Ouvv3Ia50uo00aDNkI+A1/FNuWVUCiEmpoaFBUVq30BqyAQCDj5cvTs2TMEBgYiICAAhYWFmDRpEiZPngwTExNOC3sOHjwY33//PVauXCm2fdmyZbhw4QLOnj3LSTx5VF++HB08eBArVqxAYmIi62vdu3cP//vf/2BgYAAnJycYGBhAIBAgJSUFf/zxB+7evYtr165xMnEgjzIzM+v0uHbt2rGONXPmTCQlJWHNmjU4fPgwgoOD8fTpU9EXsb/++gsbNmzA1atXWcci7Ojr62PVqlWwsrIS275//34sXboUd+/eZR1LKBQiODi41jp0Y8eOZRXn999/r9Pj3N3dWcXhm1AoxLNnz/DVV18B+JAc58aNG9DX1+c0Tvv27WtNGCMQCHD//n3WsfjcbfAxKq5NCOGdoaEhsrOzYWNjA3t7e062Nkmiq6uL8ePHY+vWrRg+fHilmXsuOTk5wcLCAm/fvoWTk5PoAyorKwt//PEHNmzYgEOHDkkl9pekrKyMkwLLu3btwokTJ6CkpAQHBwf06dMHMTExcHJyQmpqKqZOncpBb4EOHTrg1KlTsLW1xcSJEyutFnXu3BknT56kAVsNuBiM1ZWXlxcsLS1hamoKNTU1BAcHV5o5DwgIwIgRI3jrDxfqOvEgzfdgaXj48CF69+4tsb1v3754+PAhZ/F+/vnnGtu5WO2t6fNBIBAgNTUVJSUlDW7QJhAIKn3uCoVCiduc2Xjw4AHn15SE68FYXdGgjZDPlJycjKysLAAQ7aWvSJv88uVLWXat3rt9+zYuX76MgIAA0faw6dOnY8qUKZyn523Xrh0uXLgAHR0dtGvXTuL2RbZGjx4NPz8/ODs7448//hDNyubm5kJBQQHr1q3D6NGjpRJbXrRr1w4KCgpi25KTk7F792789ddfyM7OZhVn/fr1WLJkCbp164aUlBRERkZi6dKl8PX1xfz58zF37txqW2rY6NmzJ27duoX4+PhK5UGMjY05iyGv6vrFW0dHh3Wsli1b4vz588jNzYWamlq11+L+/fs5KRjOJ2NjYwgEAok7GgDutpfySUNDA8+fPxedP6wqKyuL088SPnYA3Lx5U+z98fHxcHNzw61btzjfUsiHive7igmrgoICdO/evdoEakPcdsw32h5JyGeoSJgg7s+n4v6G+EEoC8XFxdi/fz8CAwNx5coVWFhYICAggNNaSBcvXoS/vz/2798PfX192NjYwMXFBYmJiTA0NOQsDgA8fvwY+/fvr3R2xMrKSuKXCyJZQUEB9u3bB39/f1y9ehV9+/aFlZWVxPpqdWVoaIjffvsN9vb2OHv2LIYMGYIhQ4bgwIEDaNq0KTedr0VZWRlKSkoa3CCAb5KS01S8xwIf3nPLysqkEj80NBRjx45FkyZNpHJ9aeNzeymfJkyYgLKyMkRERIhtt7KygoKCAsLDw1nH4qt+ZFUZGRlYvnw5wsLCYGlpCS8vL+jp6fHaBy4EBwfX6XG1rWbWZtOmTXV6HBdn2rp3716n2n10po2QekBePwhl6dy5c/Dw8MC5c+fw8uVLaGlpcR6joKAAoaGhCAgIwOXLl2FqaorJkyfDwsJClAWKyN6FCxewe/duREREQFdXF8nJyYiNjUX//v05ub6qqiru3LkjWp1p3Lgxzp07hz59+nBy/Y8dO3YMr169qrTd0tvbGytXrkRZWRmGDBmCsLAwqbze5UFCQoLY+xmGwb59+7Bp0yaoqanh+fPnUolfNTNwQ/P777/D2dkZqqqqsu4Kp5KTk9GnTx8YGRlh0aJFoh0UycnJ8PPzQ3JyMi5dugQjIyPWsfg+a/vy5Ut4enpi586d+P7777F69Wr06tWLl9gNWV0yNzb0M200aCNECnJychAVFcV59kN58+TJEwQHByMwMBCFhYWiM27S2sL4sZSUFPj7+2PPnj14/fo13r17x+p6R44cqdPj2B5Wl2dr165FQEAACgoKMGnSJNjY2OC7776DkpISp0lj+EwNPWTIEFhZWWHu3LkAgLi4OAwYMAC///47DA0NsXTpUpibm8PX15fz2NIiq+yvFU6dOgU3NzfcvXsXixYtgrOzs9RWLKX52uCDrFaJ+HDp0iVMnz4dKSkplc6KdurUCbt370a/fv04iWNnZ4dNmzZxlppeksLCQqxfvx6+vr7o2LEjVq1a1eDOUIoTEBCAKVOmcLp75ktFgzZCpCAhIQE9evSg7ZEShIeHIzAwELGxsRg5ciTs7Ozwww8/SDzPJE1lZWXw9fWFi4sLq+vUJcEJbZmtmaKiIlxdXfH7779Xei1IY9DGV2roVq1aITo6WlRnadGiRUhOTsbx48cBfFiJc3BwEG2nbQhqWhUUCAQoLCxEWVkZ56/169evw83NDefPn8cvv/wCd3d3qQ9GGvqgrb5kZJWm+Ph4UZZIaZwV5Wvgq62tjfz8fMyfPx+TJk2SuP2uoSWNqfr8tWnTBnFxcWjfvr1sO8aRly9f4sGDBxAIBGjfvj2aN28utVg0aCNECmjQVjOhUAgdHR1MmTJFlGVRHC6+NFcoKCiAgoICVFRURPfFx8fD3d0dR48epd9VPeDj44OgoCCUlJRg0qRJmDp1Krp06cL5oI3P1NAqKipITU0VbcXs3bs3rK2tRZMEmZmZ6Ny5s1wUXX/27Bk8PT0REBCAIUOGiAambKWnp2Pp0qWIiIjATz/9BC8vL94GURcuXECvXr0a7CqBUChEdna2XG7/zsvLg5qaWrUJs/LychQUFHCWiISvgW/VmoQffz1vyGfl+dzZUF5ejqCgIBw8eFA0kNLV1YW1tTWmTp1ap3NodXX79m3Mnj0bFy9erHS/qakptm/fDgMDA85iVaDskYQQ3uno6EAgEODvv/+W+BiBQMDJoO3x48eYMGECLl26BAUFBcybNw9eXl6YNWsWQkND8eOPP+LChQus49jb22Pjxo1S30Ijz5YsWYIlS5YgNjYWAQEB6Nu3Lzp06ACGYZCTk8NZHD5TQ7dp0wYpKSnQ0dFBQUEBEhIS4OfnJ2p/9epVgz9vlJ+fjzVr1mDjxo0wMjJCdHQ0Bg8ezMm158yZA39/fwwePBjXrl3jLeNmxey5iooKCgoKGuygDQCGDh1aa5kMrhMmSNuhQ4fg6uqK+Pj4an8/JSUl6NWrF9avX48xY8ZwEo/LL/uSZGRkSD2GPGMYBmPHjsWxY8fw3XffoWvXrmAYBikpKbC1tcXBgwdx+PBhTmJlZWXB1NQULVu2hK+vLzp16gSGYZCcnIxdu3ZhwIABuHXrFucDfVppI0QKaKWt/rCxsUFSUhJ+/fVXRERE4Ny5czA2NsZ3332H5cuX1+nwcl3I89kRWcnPz8dff/2FwMBAXL9+XbRKtWjRIll3rc5cXV1x5MgRLFmyBMeOHUNcXBzu378v2v65c+dOhISEcDJxwLfS0lJs2bIFPj4+aNGiBby8vGBtbc1pDKFQCGVl5VrPuXI16JDF7Lk0CYVCODk51XrmT1Z1pz7XiBEj8NNPP+GXX34R2x4QEICwsDBER0ezjiUUCqGpqVnrwI1S1ounoKCArKws0WqvhoYGEhISOPvsrRAYGAgHBwdERkZWmzSKiYmBhYUFtmzZwslZW1dXV5w6dQoXL16EsrJypbbi4mJ8//33GDFiBFatWsU61sdo0EbIZ6gtteyTJ0+wfv16GrTVA19//TXCw8PRv39/ZGVloU2bNvDx8YGbmxuncb6EsyOylJSUBH9/f/z999+sMwXGxMRg3rx5uHTpUrUtVLm5uejXrx+2b9+OgQMHsooDAEVFRZg5cyb++ecfaGtrY+fOnRgwYICoffDgwTAzM4OrqyvrWHxhGAYhISFwd3dHWVkZPDw8MH36dKmcSeUzS1tWVha6dOmCli1bYtasWdVmz1+9eiWV2XNpktf3pTZt2uDcuXMSC9Onp6dj4MCBePr0KetYQqEQGzZsENXelIRtynp5LYReddD75s0baGhocF6nbcSIERgyZIjEz3YfHx/ExsZyMpDv0aMH3Nzc8NNPP4lt37dvH9auXUsp/wmpD+o6Q0TbHcTjM/ucgoICnjx5Am1tbQBAkyZNcO3aNc7rs8nz2ZH65N27d1BSUmJ1jbFjx2Lw4MES671t2rQJZ86cwaFDh1jFkVfdunXDvXv3MH/+fDg6Okrc3sllcWM+yGr2XJrkdQeAiooKbt68KXEFNiUlBT169EBxcTHrWHyeaZPHQuh81WnT1tbG8ePHJW6hvnnzJszNzZGVlcUqDgA0bdoU165dq3HSoGfPnnjz5g3rWB+jQRshhHd8Zp+rujVDXV0diYmJnG/NoC007NVlBezPP/+stFL1Odq1a4fjx49LHLjfuXMHI0aMwMOHD1nFkVdVEyZU1VATJshq9lya5HWlraJcho2Njdj2PXv2wNvbG3fu3GEdi6+BL9V/ZadRo0bIzMxE69atxbY/ffoUurq6ePv2LetYtb0msrOz8fXXX6OsrIx1rI9RIhJCCO8kJZX4OPvc8OHDOYnFMEylg/jFxcUYM2YMGjVqVOlxXHwR8/T0rHULDZFsw4YN+PXXX8Wu0GhqamLmzJnw9fVlPWjLzs6ucbVOUVERL168YBVDnp05c0bWXZCK+/fvo0ePHhLbe/bsyUlGUT5lZGTI5eq/paUlli5diuHDh1fLQJyVlYVly5ZJHNB9qprWNnJycrB37174+/sjPj6eVRx5HYzxVaft/fv3NSbcUVBQ4HQQlZ+fX21FvkJeXl6Nr5vPRYM2Qj7DqFGjEBoaKvqC7u3tjblz56Jp06YAPmSEGzBgAJKTk2XYy4ZDmtnnqp51+fHHHzm5rjgTJ06UuxltPiUkJGDNmjUS20eMGIH169ezjvP1118jKSlJ4taWxMREibO15ENSDnmUn59f45ZOdXV1FBQU8Ngj9iSdCdTU1ISBgQFsbGykVphcmtzc3BAZGQk9PT3Y2NjAwMAAAoEAKSkp+Ouvv9C2bVvOzi2Xl5dXu+/UqVPw9/fH4cOH0aJFC1haWnISqyYHDx7EihUr6nz2rb749ddfMXr0aKnXaWMYBra2thIHh1yssH0cS19fv8Z2aWQcpUEbIZ8hOjq60hvAmjVrMGnSJNGgraysDKmpqTLqXcNRNftcYGAg59nn+MqKxkdKaHnH1wrYqFGj4O7uDnNzc7Fnlzw8PDB69GjWceTdkydPEBERgbt370IgEEBfXx+Wlpb4+uuvZd21zyaL2XNpkrSrISMjA3/99RdWrlyJ8+fPN7ji4erq6rh48SIWL16MsLAw0c+ppaUFGxsb+Pj4cF5+5eHDhwgMDERgYCAKCgqQk5OD8PBwWFlZcRZj165dOHHiBJSUlODg4IA+ffogJiYGTk5OSE1NxdSpUzmLxZeqfzP5+fliB8Js1eVMHBfn5AEZ7jZgCCGfTCAQMNnZ2aLbampqzL1790S3s7KyGKFQKIuuNQjl5eVMUFAQo6Ojw7Rp04bZsWMHU1ZWJpVYp0+fZt69eyeVa3+s6muCfLpvv/2WOXjwoMT2iIgIRldXl3WcrKwspk2bNkzbtm2ZNWvWMIcPH2YiIyOZ1atXM23btmXatGnDZGVlsY4jz7Zu3co0btyYEQgETNOmTRlNTU1GIBAwjRs3ZrZu3Srr7n0WgUDACIVCif8q2uVFUVERY21tzYwfP17WXWGlvLycef78OZOdnc2Ul5dzfv2wsDBm+PDhjKqqKmNtbc0cPnyYefv2LaOoqMjcvn2bszjr1q1jlJSUmP/973+Mqqoqo6qqynh7ezPNmzdnVqxYwbx48YKzWHyq7fsSqTtaaSOE8O67776rln2usLCw2uO4yD43fPjwSgeG+/bti4iICM5XA0pLS5GcnAx1dXWoqKhUaisqKkJ6ejq6dOlSLc0x+T98rYB99dVXiIuLw+zZs7F48WLRTLBAIMDIkSOxbdu2audkPhefmVL5cvToUSxYsACOjo5wcnISbSV99uwZ1q1bBwcHB7Rv3x6jRo1iHYvP509ez+pJoqKiAldXV1629kmTQCAQnduLjY1FYWEhTExMakx49SkmT54MFxcXREREcL569zF/f3/8+eefsLe3x9mzZzFkyBDExMQgPT1dtIunIRIIBJV2olS9TeqOskcS8hlqy0iYnZ2NNm3aNLjsaXzhM/tc1exp6urqSEhI4Hw7UFBQELZs2YLLly9Xq1f1/v179OnTB46OjpwdjpdH2dnZ6NGjBxQUFDBv3rxK51S2bt2K9+/f48aNG5wNqIAP28fS09PBMAz09PQ4+6JXgc9MqXwxNTXFgAED4OXlJbZ92bJlOH/+PGJjY1nHksfnrz65f/8+jI2NkZeXJ+uufJJ169ahoKBAdGaPYRiYm5vjxIkTAIBWrVrh9OnTMDIyYh1rxowZCA8Ph5GREaZOnYoJEyZAS0sLSkpKSEhIQOfOnVnHAABVVVXcuXMHOjo6AIDGjRvj3Llz6NOnDyfXlxW+6rR9CWiljZDPwFQ58FpSUoJZs2ahSZMmALg98CqP5HFGe/fu3XB2dhZbYFhBQQEuLi7YsmULDdpqwOcKWAUtLS306tWL02t+jM9MqXy5efMmdu7cKbF96tSp2LhxIyex+Hz+6jpwaWj152oSFxeHDh06yLobnyw0NLRSQfoDBw7g3LlzOH/+PAwNDTFt2jR4enoiPDycdaydO3di48aNCA8PR0BAABwdHTFy5EgwDMPp2aySkpJKOwwaNWokF5k/AwMDZd0FuUGDNkI+Q9UDr+K+iDek7U584zP7HF9bM+7evYu+fftKbO/VqxdSUlI4jytv2rVrh2PHjkl1Bayu28EOHjzIWcwK0syUypfy8vIaE8YoKSlJLWGHNJ+/pk2b1vjewOUOAL5IyjSYm5uLq1evwsfHR+KKaX2WkZGBbt26iW4fO3YMVlZW6N+/P4APq73jx4/nLJ6Kigp+/vln/Pzzz0hLS0NAQACuXbuG/v3744cffoC1tTUn20x3794tyuZZVlaGoKAgtGjRotJjFixYwDoOn9gWzSb/hwZthHwGmjlqOJgqddqKioqkUqetsLCwxpn6/Px8FBUVsYrxJfl4BSwzMxPPnj1Dp06dODkTKItaenxkSuWLkZERIiMjsXDhQrHthw8f5mRb2sf4eP7kcQeAsbExBAKB2EF0y5Yt4erqilmzZsmgZ+y8e/euUmr3//77Dw4ODqLbbdq0wcuXL6USW09PD6tWrYK3tzeOHj0Kf39/TJo0ifUOGx0dHezatUt0W1tbG3v27Kn0GIFA0OAGbXyJiorCmDFjZN0NqaJBGyFErvFVp01PTw9xcXGVZn8/duHCBejp6UkltrwIDg5GTk4OHB0dRffNmDED/v7+AAADAwNER0ejbdu2rOLwOenCMAxCQkLg7u6OsrIy+Pj4YPr06WK30TYUc+bMwezZs9G4cWPMmDFDNCFSVlaGHTt2YNmyZdi2bRsnsfh8/mrbAVBYWIjr169zHleaMjIyxN6vqamJpk2borCwEOfOncPAgQN57hk7HTt2xLlz5/Dtt9/i4cOHuHv3bqXf3+PHj9G8eXOp9kEoFGLMmDEYM2YMnj9/zvp6Dx48YN+pekhLS6tOu1vYnmmztraGjY0NNm7cKPXag/b29rU+RiAQiD67uEKJSAghhANr167F2rVrERMTU23glpCQgKFDh8LFxQUuLi4y6mH9Z2JighkzZsDOzg4AcPz4cYwZMwZBQUEwNDTEvHnz0LlzZ+zevVvGPa27bt26VcuUKk5DOyfl7OwMX19fqKuri85E3bt3DwUFBViwYAH8/Pw4iVOfnr+EhAT06NGjQW2PrE1D/Zl27NgBJycnTJgwAZcuXULTpk1x8eJFUbuXlxcuX76MqKgo1rGuX78OZ2dnREZGVnud5ebmwsLCAhs3bpQ4YfelCw4OrtPj2G6jTEhIgJ2dHXJychAUFCTVYxjjxo2T2Pb+/XucOnUKb9++5fzvigZthBC5FhMTg4EDB4pWA6Tl3bt3GDFiBC5cuIBhw4ahU6dOosyHp06dQv/+/XHy5MkazwJ96Zo3b46zZ8+ia9euAIDZs2fj+fPniIiIAACcPXsWdnZ2ElcP6qous6QAEBAQwCoOwG+mVL5dunQJoaGhSEtLAwDo6+tj4sSJNZ7t/FT16flrqAOcmjTkn8nf3x///PMPtLW14eHhAW1tbVHbnDlzMHz48Bq/XNfV5MmTYWhoiOXLl4tt9/b2RkpKCvbu3csqzqhRoxAaGiravu3t7Y25c+eK0v2/evUKAwYMQHJyMqs48qysrAxeXl5YvXo15s6di6VLl1b77JfmBE9kZCSWLFmCp0+fwtXVFW5ubpxenwZthJB6h2EYvHjxQpSmnw0FBQVe6rQBHwZufn5++Pvvv5GWlgaGYaCvr4/JkyfD0dGx2jk6UpmqqipSUlLQrl07AB/q+dnb24vOqjx8+BAGBgYoLi5mFUcoFKJdu3bo3r17jQkzDh06xCoOgDqnveczOU9DUp+ev4Y8wJFEHn8mrnXo0AGHDh2SuJKWlJSEH3/8Effv32cVp+pnlYaGBuLj40XlaRp6KSGGYXD9+nU8ePAAAoEAurq66N69u1QSg504cQKjRo2q9P4uzQmeixcvwtXVFTdv3sS8efPg5ubGefkYgM60EUJkQFVVFZmZmaJ0xmZmZggMDBQV6X3+/DlnH05Vv5Tfvn1baiUZlJSUatwCGR8fD2NjY6nElgft2rXD9evX0a5dO7x8+RK3b9/G999/L2rPysriJInIrFmzsG/fPty/fx/29vawsbFBs2bNWF9XHHkcjEnKSFgVF9vF5PH5I9x58uQJIiIicPfuXQgEAujr68PS0pLTSbknT57UWFRbTU0Nz549Yx2n6meVPK2pnDlzBtOnT0dmZmalUi66uroICAjg9EzlwYMHMXv2bAwcOFDsShuXbt++DTc3Nxw/fhzTpk3Dvn378M0330gtHg3aCCG8KykpqfSBdPHixWqrJ/LygZWbm4u//voLu3fvRkJCQoOdJeXDtGnTMHfuXNy+fRsxMTHo1KkT/ve//4na4+Li0KVLF9Zxtm3bBj8/Pxw8eBABAQFYvHgxfvjhB0yfPh0jRoyQyswvH18u+VJTRsIKXM9o8/H8HTlypMZ2tttyZUEef6YK27Ztw6JFi1BaWgpNTU0wDIO8vDz89ttv8PX1xZw5cziJ07JlS6SmpkJXV1ds+507d6ql5Sf/Jz09HaNHj0afPn3g5+eHTp06gWEYJCcnY9OmTRg1ahQSExNFK4qf682bN5gzZw6OHDkCb2/vStlEufbo0SO4u7tj7969GD16NBITE2FoaCi1eBVoeyQhhHdCoRBZWVmibSDq6upISEiQyjYQBQUFZGVliVb1NDQ0kJCQIPEDmCsxMTHw9/fHoUOH0K5dO1hZWcHKygrdu3eXatyGrLy8HB4eHqJzKr6+vpU+CMePHw8zMzNMnz6d07iZmZkICgpCSEgI3r17h+TkZE6zj0n6ctmoUSNOv1zyJTMzs06Pq9jmyhZfz19dykk0tPOH8vgzAcDRo0fx448/wtHREU5OTqJdGs+ePcO6deuwefNmREZGYtSoUaxj2dnZIT09HefPn6/WxjAMBg4ciI4dO7LOSlv1s0pdXR2JiYmiz6qGuj1y3rx5SElJwenTp6u1MQyDYcOGoXPnzti8eTOrOG3atIGOjg6Cg4NhYGAg9jEvXrzgpGC5qqoqBAIB5s+fj379+kl83NixY1nHqoQhhBCeCQQCJjs7W3RbTU2NuXfvnuh2VlYWIxQKOYvVtWtXpnv37kz37t0ZBQUFxsjISHS74h8XHj16xKxcuZLR1dVlWrVqxcybN49RVFRkbt++zcn1iXRkZmYynp6ejK6uLvP1118z+fn5nF37n3/+YRQUFBgnJyfm6dOnovufPn3KLFy4kFFUVGSOHj3KWTw+eHp6MoWFhbzEksfnj7A3cOBAZunSpRLbly5dygwcOJCTWOnp6YympibTu3dvJiwsjImPj2cSEhKYffv2Mb169WI0NTWZtLQ01nEEAgEzatQoZty4ccy4ceMYRUVFZsSIEaLbo0aN4uxzkU9GRkbMkSNHJLYfOXKEMTIyYh1n5cqVTFlZWbX7y8vLmaNHjzLjxo1jGjVqxDoOw3z4XdX2Txq/K1ppI4TwrrbVLy5nFD09Pev0uKr13D7VqFGjcOHCBYwePRpTpkyBmZkZFBQUoKSkhISEBHTu3JnV9Qm33r59K9oeWfF7s7Ozg5mZGScFvCuYmppiwIAB8PLyEtu+bNkynD9/vs4JN+qDqgkTpEkenz/CnoaGBq5evSpxRSU1NRU9e/ZEfn4+J/GuXbsGW1tbJCcni7ZPMwyDzp07IzAwEL169WIdw9bWtk5bs/msM8kFDQ0NJCYmon379mLbMzIy0K1bN85+VxXu37+PgIAABAcHo6CgAD/88AOsrKw4ySgqKzRoI4TwTigUQlNTU/QB9ebNG2hoaIi+LDP/f/tTQ9oGoqioiAULFmD27NmVimjToK3+mTNnDvbt2wcdHR3Y2dnBxsZGaoV4+f5yyYeq25ulSRbP3/79+xEaGio6P6enp4fJkyfD2tqasxh8k7efSU1NrcZzUPfv30e3bt1QUFDAadz4+PhKmYEpsVTtanu/4HKStqSkBAcOHMDu3btx6dIlDB8+HP/++y/i4+M5OQ8ta5SIhBDCu4Y2U1gX58+fR0BAAHr27IlOnTph6tSpmDBhgqy7RcT4888/oaOjA11dXcTGxkpcpTl48CDrWOXl5TXW5lNSUmqQSXekkaxFHD6fv/LyckyaNAn79++Hvr6+KGHC7du3MWHCBIwfPx6hoaG8/exckMefCQCMjIwQGRmJhQsXim0/fPgwjIyMOI9rbGwsGqiVlZWhoKCAs/OvfK5g8y05ORlZWVli216+fMlJjIrJOAMDA9jY2CAiIgLNmzeHkpISp7snPsb3ZAgN2gghvNPV1UW/fv2kXvAaQJ3rwNy4cYNVHBMTE5iYmGDjxo3Yt28fAgICsGjRIpSXl+PkyZNo27ZtjWmjCX+mTZvG25dUWX25lLahQ4fW+vfL9m8K4Pf527BhA06dOoUjR45g9OjRldqOHDkCOzs7bNy4EY6OjpzE44M8/kzAhy/os2fPRuPGjTFjxgzRa7GsrAw7duzAsmXLsG3bNk5iHTt2DK9evcLUqVNF93l7e2PlypUoKyvDkCFDEBYWxrouV0OcvKmroUOHiv35KrLQcvF+vHPnTlFBa2l/1spqMoS2RxJCeMfnjCJfZ9rESU1Nhb+/P/bs2YM3b95g+PDhtabgJvIlODgYs2fPxvr168V+ufztt9+wbds22Nrayrajn0AoFMLJyanWFQYu/qb4fP66desGR0dH2Nvbi2339/fHhg0bkJSUxDoWX+TxZ6rg7OwMX19fqKuro0OHDgCAe/fuoaCgAAsWLICfnx8ncYYMGQIrKyvMnTsXwIfSIwMGDMDvv/8OQ0NDLF26FObm5vD19WUVh89tx3ziK9vs33//jcDAQPz333/44YcfMHXqVJiZmUFFRYXzIwq+vr7w9vZGcHCwxMmQ5cuXcz4ZQoM2Qgjv5PXDSZL3798jKioKgYGBiIyMlHV36q3ff/+9To9zd3eXck+4xdeXS77w/ffL1/OnoqKC1NRU6OjoiG3PzMxEp06dqtWUrM/k8Wf62KVLlxAaGoq0tDQAgL6+PiZOnIi+fftyFqNVq1aIjo4WlWtZtGgRkpOTcfz4cQAfVuIcHBxEffhcQqEQwcHB0NTUrPFxnKeRlzMPHjxAYGAggoKCUFRUhNevXyMsLIzTLYuymgyhQRshhHdCoRDZ2dmc1Ev5FC9fvsSDBw8gEAjQvn17qSWfECciIgKenp5ITEzkLWZDU1MNO4FAgNTUVJSUlDSoBDUV+PhyyRdZnL3h4/lr1qwZzp49i27duoltT0pKgqmpKV6/fs1ZTGmTx5+Jb1UHvr1794a1tTVcXFwAfBj4du7cGYWFhaziyGtNPVlhGAbR0dEICAjAkSNH0KJFC1haWmLTpk2sry2ryRA600YIkYnly5dDVVW1xsew3W5S4fbt25g9ezYuXrxY6X5TU1Ns375dYma6T7Vr1y6cOHECSkpKcHBwQJ8+fRATEwMnJyekpqZi2rRpnMSRVzdv3hR7f3x8PNzc3HDr1i38+uuvPPeKG3379m2QAzRxZDHXy8fzZ2Jigu3bt2P79u1i27du3QoTExOp9oFr8vgz1cXBgwexYsUKTibJ2rRpg5SUFOjo6KCgoAAJCQmVVndfvXpV62dZXX1JO1CkTSAQwMzMDGZmZnj9+jVCQkIQFBTEybVVVFTw5s0biYO2vLw8qKiocBLrYzRoI4TIRFJSEho1aiSxnasDvFlZWTA1NUXLli3h6+srOjCcnJyMXbt2YcCAAbh16xbrD8r169djyZIl6NatG1JSUhAZGYmlS5fC19cX8+fPx9y5c9GiRQtOfqYvRUZGBpYvX46wsDBYWlri9u3blcopNAR1/dIoaSWkPsrIyOBtlZzP52/p0qUYNGgQXr16BWdnZ9F7RUpKCv744w9ERkbizJkzrOPwSR5/pgq1TZJ9nDiEDWtrazg6OmLJkiU4duwYtLW1K00gXLt2jZOJv4aWwbMhadasGQYMGIBTp05xcj2ZTYZwXq6bEEJqIRAImOzsbF5iubi4MD169GCKi4urtRUVFTE9evRg3NzcWMfp1KkT4+/vzzAMw5w5c4YRCATM0KFDmZycHNbX/tK8ePGCmTdvHtOoUSNmyJAhzJUrV2Tdpc8mEAgYoVDICAQCif+EQqGsu/lJ7OzsxP5zdHRktm/fzuTn53MWi+/n7+DBg0yLFi0YoVBY6V/z5s2ZAwcOcBaHT/L4M61bt45RUlJi/ve//zGqqqqMqqoq4+3tzTRv3pxZsWIF8+LFC85iFRYWMjY2NkzTpk2ZTp06MefOnavUPmjQIGb16tWs4/D5uSivTpw4wTg7OzOLFy9m7t27xzAMw6SkpDA//vgjIxQKmZEjR3IS5+LFi4ySkhIzfvx45vLly0xubi7z5s0b5r///mOsra0ZJSUl5sKFC5zE+hidaSOE8I7PMzE9evSAm5sbfvrpJ7Ht+/btw9q1a1mnJ1dVVcWdO3dE2yUaN26Mc+fOoU+fPqyu+yUpLCzE+vXr4evri44dO2LVqlUYMWIE53HOnTtXp8cNHDiQdSy+Mqfxady4cWLvf/PmDW7fvg0lJSWcP39eYuHjTyGL56+oqAjR0dGVzs+NGDGCsy1wsiBvP5OhoSF+++032Nvb4+zZsxgyZAiGDBmCAwcOoGnTprLu3mexs7PDpk2bvrjSMCUlJdiyZQucnZ1ZXSc4OBh2dnZo1qwZXr9+jRYtWsDX1xdz5syBlZUVnJycOC2wfejQIcyYMaPaeVAtLS3s2LEDVlZWnMWqQIM2Qgjvass+9+rVK+zZs4eTdLlNmzbFtWvX0LFjR7Ht6enp6NmzJ968ecMqTtWfSV1dHQkJCZx8cf1SaGtrIz8/H/Pnz8ekSZMkbhdiuxWupgP/FTEFAgHKyspYxQE+ZMR0dnZusF+OP1VxcbGoDl54eDjr631pzx+pG3mcJJPn4tovX77E5cuXoaSkhKFDh0JBQQHv3r3Dtm3bsGrVKpSVlbEusm1sbIyJEyfCzc0N4eHhmDhxIrp3747w8HBR1lmu8T0ZQoM2QgjvgoODMXHiRDRu3Fh0H8MwOHHiBPz9/REZGQkNDQ28ePGCdazaPgizs7Px9ddfs/6CLhQK4eXlJapd5erqit9++63aObYFCxawiiPPPh5MVRRdrXqbi+xpubm5Yu8vKirCxo0bsWnTJnz77be4desWqziAfH8Rk+TatWuwtLTEw4cPWV+Lz+fv8uXLeP36NczNzUX3hYSEwMPDA4WFhbCwsMDmzZsrvW/Vd/L4MwHyOUkmr6Vw4uLi8MMPPyA3NxcCgQA9e/ZEYGAgLCwsUF5eLkqdz3ago66ujsTEROjq6qK8vByNGzfGqVOnYGpqytFP8mmePHmCr7/+mtNrUiISQgjvfv75Z9F/P3jwAAEBAQgKCsKTJ08wZcoUHD16FIMHD+YsXn5+PpSVlcW25eXlcZINT0dHB7t27RLd1tbWxp49eyo9RiAQ0KCtBhkZGbzEqVoHqby8HAEBAfD09IRQKMTWrVsrvUbZ+BLnRZs1a8Z65boCn8/fihUrMGjQINEAJykpCdOnT4etrS0MDQ2xbt06tGnTBitWrOCtT2zJ489UYffu3aJJsrKyMgQFBTX4STJ5TEayfPlyjBw5EsuWLUNAQAA2bNiA0aNHY8WKFZg6dSpnP3NhYSGaNGkC4MMAWFlZGW3btuXk2p8iKysL3t7e2L17N+cp/2mljRDCu7dv3+LgwYPYvXs34uLiYG5ujsmTJ2PSpElISEhA586dOYslFApr/FDgavWGNEwHDx7EkiVL8OLFCyxevBjz58/ndNVBVjUJZWnv3r34448/JJZw+BR8Pn+tW7dGVFQUevbsCeBD5sXY2FhcuHABALB//354eHggOTlZ6n3hijz+TADQvn37Wr/sCwQC3L9/n6cesScUCqGpqVnrz9XQauq1aNECsbGxMDIyQlFREdTV1bFv3z6MHz+e0zhVi5NPmjQJGzZswFdffVXpcVwUJ3/z5g3mzp0ryl7q5uaGefPmYcWKFVi/fj2MjIywaNEiTJo0iXWsj9FKGyGEd19//TU6d+4MGxsbHDhwAFpaWgDA+RscgAabzvpLdvXqVYSGhuLu3bsQCATQ09PD5MmTRV88uRAbGwtXV1ckJSXBwcEBrq6u1VbguDJ06FAoKtb8ccs2EQ6fJKXhz83NxdWrV+Hj4wMvLy/O4vH1/OXk5FT6ghcbGwszMzPR7V69euHRo0es4/BJHn8m4MMODXnk6ekptfchWXn9+rVo0kVVVRWqqqro3r27VGJV3SExc+bMSre5mqBdsmQJzp07h59//hnHjx/HwoULcfz4cZSUlODff/+V2pZMGrQRQnj3/v17CAQCCAQCKCgoSDUWX/vZdXR0cPPmTTRv3hwAsGXLFkybNg0aGhq8xJcXLi4uWL9+PdTU1PDtt9+CYRicPXsWGzduhLOzM9asWcM6xqhRo3D69GnY2dnh8OHD0NbW5qDnko0cOVK0jUseGBsbVztzWKFly5ZwdXXFrFmzOIvH1/P31VdfISMjA23btkVpaSlu3LgBT09PUXt+fj6UlJSk3g8uyePPxLeQkJA6PW7atGmsY02cOFHuzrQJBALREYWKnS1FRUXIy8ur9Di2n5Xl5eWs/v9PcfToUQQGBmLYsGGYM2cOOnbsCH19fWzYsEG6gTkvIkAIIbUoLi5m9u7dywwePJhRUVFhLC0tmYMHDzJKSkrM7du3Zd29z1K1xo66urqoTgypm6CgIEZZWZnZvHkzU1paKrq/tLSU2bhxI6OsrMwEBwezjiMQCBglJSWmadOmjJaWlsR/XJDH2ksPHjwQ+6+iJmFBQQETGxvLSSw+n78ZM2YwJiYmzLlz55hFixYxzZs3Z96+fStq37t3L9OzZ09e+sIVefyZGIZhTp8+zRgaGjK5ubnV2t68ecN07tyZs9dg06ZNJf7T0tJiGjVqxEmtQKFQKHfvFQzzf7UWK/5Jut2QKCoqMk+ePBHdVlFRYZKSkqQfV7pDQkIIqU5ZWRlTpkzBlClTcO/ePQQGBmLBggUoKyuDt7c3bG1tMWTIEKmvwkkTQ8eFP9nWrVvh4+ODefPmVbpfSUlJ9PqoWMFkIzAwkNX//ynkMbFAbTXR0tPTMXjwYE62IfH5/Hl5ecHS0hKmpqZQU1NDcHAwGjVqJGoPCAiQSt1AaZLHnwkANmzYgF9//VXs6oympiZmzpwJPz8/Tmot5uTkiL3/2bNn8PT0REBAAIYPH846Tk2fGTk5Odi7dy/8/f0RHx/POhafZHlEQUNDA/Hx8ZxnFS0vL6+0Qq2goCBKgiJNlIiEEMK7kJAQTJgwoVLCh/LyckRHR8Pf3x9RUVFQV1dnXbeFT/KYgppvTZo0QVJSksTn7P79++jatSsKCwt57tnnk9c03jVJSEhAjx49OBm0yeL5y83NhZqaWrVJo9evX0NNTa3SoKehkLefqV27djh+/DgMDQ3Ftt+5cwcjRozgpOxEVfn5+VizZg02btwIIyMjrFq1itNsxx87deoU/P39cfjwYbRo0QKWlpbYuHGjVGLJI2l9DguFQpibm4u+w0RFRWHIkCHVBm4HDx7kNC6ttBFCeGdnZwczM7NKX8Qq3gTNzc3x4sWLaunyGwJ5TEHNJwUFBZSWlkpsf/fuHaerr8XFxTh58qQo4Ym+vj6GDRsGFRUVzmJkZGR8UZkjuSaL509SIohmzZrx2g8uydvPlJ2dXeNZPEVFRU7qfH6stLQUW7ZsgY+PD1q0aIHAwEBYW1tzGgMAHj58iMDAQAQGBqKgoAA5OTkIDw+HlZUV57H4EB4eDgsLC9HEwIMHD9C2bVvRe3lRURG2bNkCFxcXWXbzk0ybNq3SLgAbGxte4tJKGyGEd/K4+iCPKaj5NnjwYHz//fdYuXKl2PZly5bhwoULOHv2LOtYR44cwS+//FJtNbdFixbw9/fHmDFjWMcAAHt7e7H3a2pqwsDAADY2NnKVpATgdqXtS3z+SO06dOiA9evXY9y4cWLbDx48CGdnZ07ebxmGQUhICNzd3VFWVgYPDw9Mnz6d8+374eHh2L17Ny5evIhRo0bBxsYG5ubmaNKkCeelcPikoKCAZ8+eiT7vq25ZzM7ORps2baRSdmf27NlYuXJltcnThopW2gghMsHXWRVJX/o+JhAI4O/vzyqOvKag5pOTkxMsLCzw9u1bODk5iVKVZ2Vl4Y8//sCGDRtw6NAh1nHi4uJgbW2NsWPHwsnJSbTFKjk5GX/88Qesra1x9uxZmJiYsI4l6TxMRkYG/vrrL6xcuRLnz59vUNtojxw5UmM7l0XS5fH5I+yNGjUK7u7uMDc3h7KycqW24uJieHh4YPTo0ZzE+u6773Dv3j3Mnz8fjo6OUFVVFbtFm232w8mTJ8PFxQURERFQV1dnda36pOrakLTWitzd3eHu7l6pPMj27dtF//3w4UNMnz4dJ0+eZB2r6kCUL7TSRgjhXdX94JJwsR9c0kws8KH0wKlTp/D27Vsqrl1PbN68Gc7OzigrKxNt6crNzYWCggLWrl0LR0dH1jFGjRqFtm3bYseOHWLbZ86ciUePHuHYsWOsY9WkuLhYtM0mPDxcqrG4JBQKa30MHwXrG+rzR9jLzs5Gjx49oKCggHnz5sHAwAACgQApKSnYunUr3r9/jxs3blQrrPw5Pn69i5tsZP5/Gnu2r/cZM2YgPDwcRkZGmDp1KiZMmAAtLS0oKSk16JW22s57c7XSpqOjg+bNmyMkJARdu3at1LZz5044Ozujf//++Pfff1nFAWS3W4gGbYQQ3gmFQvz000+1nh2SZpa/yMhILFmyBE+fPoWrqyvc3NxYXY/qtHHn8ePH2L9/P9LS0gAA+vr6sLKyQtu2bTm5vpaWFs6dO1ftg71CYmIiTE1NJa7ycOnatWuwtLSUSsKELwE9f1+uzMxMzJ49G9HR0aLVG4FAgJEjR2Lbtm1o3749J3FiY2Pr9DguaoIWFxcjPDwcAQEBuHz5MkaOHImjR48iPj4eXbp0YX19WeBr0JaXl4d58+YhPDwcHh4ecHV1xePHj2Fvb49r165h/fr1+OWXX1j/PAAN2gghXxBZnmm7ePEiXF1dcfPmTcybNw9ubm7Q0tJifd2qP5O0Ug0T9lRUVHDnzh2JqeszMzNhaGiIoqIiqffl/v37MDY2rlZoltQNPX8kJycH6enpYBgGenp6nLyf1wdpaWkICAhASEgICgoK8MMPP8Da2hqWlpay7tonEQqFCA4OFu2cmDRpEjZs2CBaBX3z5g3s7Ow4W5mPjIzEzJkzoa2tjYyMDJiYmGDXrl2cTfoB1X8mScaOHctZTIDOtBFCZEAWtatu374NNzc3HD9+HNOmTcO+ffvwzTffSC0ezYd9utrOSlVg+0Gor6+PmJgY2NnZiW0/ffo0OnbsyCpGXcXFxaFDhw68xOLa/v37ERoaKsq+qaenh8mTJ0slo54kDfn5I9zQ0tJCr169pB7nyZMniIiIqJRt1tLSEl9//bVU4unp6WHVqlXw9vbG0aNH4e/vj0mTJuHt27dSiSdNP//8c6XbM2fOrHSby+8Effr0QdeuXXH69Gk0adIELi4unA7YKlT9maqSxhZxGrQRQnjH54Dm0aNHcHd3x969ezF69GgkJiZKrO1DZMvCwqLWx3DxQWhrawtnZ2d89dVXGDVqVKW2o0ePwsXFBUuXLmUVo0JiYqLY+3Nzc3H16lX4+PjAy8uLk1h8KS8vx6RJk7B//37o6+ujU6dOYBgGt2/fxoQJEzB+/HiEhoZy8kVMHp8/wl5dV5u4qpO1bds2LFq0CKWlpdDU1ATDMMjLy8Nvv/0GX19fzJkzh5M44giFQowZMwZjxozB8+fPpRZHWsrLy3mLFRoainnz5sHY2BgpKSnw9/eHubk5Zs2ahdWrV3NazkUWu4Vo0EYI4d2ZM2d4qw9UcUDdyckJ/fr1Q1pamuis1Me42MZAddrY4evD3cHBAXFxcRg9ejQMDAwqZY9MS0uDhYUFHBwcOIllbGwMgUAgdqKiZcuWcHV1xaxZsziJxZcNGzbg1KlTOHLkSLUMfUeOHIGdnR02btzISdIYeXz+CHu1bUvj0tGjR7FgwQI4OjrCyckJrVu3BgA8e/YM69atg4ODA9q3b19tAuhTXb9+Hc7OzoiMjKx2Fjo3NxcWFhbYuHGjXJXK4ZK1tTWio6Ph4+OD+fPnAwDWrl2LcePGwdbWFv/++y+Cg4M5yQosi91CAJ1pI4TIwOXLl/H69WuYm5uL7gsJCYGHhwcKCwthYWGBzZs315pdsi74ynRHddrYs7e3x8aNG3lLdx0WFiba3gd82DY5ceJETJw4kbMYmZmZYu/X1NRE06ZNUVhYiOvXr2PgwIGcxZS2bt26wdHRUWI5DX9/f2zYsAFJSUmsY8nj80caFlNTUwwYMEDiiu6yZctw/vz5OicskWTy5MkwNDTE8uXLxbZ7e3sjJSUFe/fuZRVHVl69eiVK1PXo0SPs2rULxcXFGDNmDCd/v/3790dwcLDYre0lJSVwdXXF9u3bUVpayjoWJSIhhHwxzM3NMWjQILi6ugIAkpKS0KNHD9ja2sLQ0BDr1q3DzJkzsWLFCtl2lPBKVrVvZInLQtR8UVFRQWpqKnR0dMS2Z2ZmolOnTiguLpZ6Xxri80caFg0NDVy9ehUGBgZi21NTU9GzZ0/k5+ezitOhQwccOnQI3bp1E9uelJSEH3/8scFN/CUlJWHMmDF49OgR9PT0sG/fPpiZmaGwsBBCoRCFhYU4cOBAnbbH16S8vLzWSdpz585xMkC0s7PDpk2beK+nR9sjCSG8i4+Px8qVK0W39+3bhz59+mDXrl0AgLZt28LDw4MGbV+Y+jKHeOPGDbi7u+Off/6RdVfqJRUVFbx580bioC0vL4/TsyOEVCVplbeqgIAA1rHKy8uhpKQksV1JSYmT964nT57UOAhQU1PDs2fPWMfhm4uLC7p27Yq9e/eKzpaPGjUKu3fvBgDMnz8fq1evZj1oq8uuGq5W5D09PZGTk1OtLIympqZUt+7SoI0QwrucnJxKRU9jY2NhZmYmut2rVy88evSI05jSznS3adOmOj2OzrTVjK+zAidPnsSJEyegpKSEX375Bd9++y3u3LkDNzc3REVFYfjw4bz0oyEyMTHB9u3bsX37drHtW7du5eTcCCGSBAUFoV27dujevbvUJ3uMjIwQGRmJhQsXim0/fPgwjIyMWMdp2bIlUlNToaurK7b9zp071c5INwRXr15FTEwMunXrBmNjY+zcuRNz5swRDbLmz5+Pvn37yriXn6am4xAtW7aEi4sLFi1axHlcGrQRQnj31VdfISMjA23btkVpaSlu3LgBT09PUXt+fn6NM5ufgq9Md35+frU+RiAQ0KCtFvr6+rX+Ll6/fs0qRnBwMOzs7NCsWTO8fv0au3fvFmWAs7KyQkJCQoMtZMuHpUuXYtCgQXj16hWcnZ1Ff1MpKSn4448/EBkZiTNnzsi6m0SOzZo1C/v27cP9+/dhb28PGxsbqSW3mjNnDmbPno3GjRtjxowZUFT88NW5rKwMO3bswLJly7Bt2zbWcYYNGwZvb+9KE5gVGIaBj48Phg0bxjoO316/fg1tbW0AH1YLmzRpUul3paWlxXprKd9u3rwp9v43b97gypUr8Pb2hqqqKudJkuhMGyGEdzNnzkRSUhLWrFmDw4cPIzg4GE+fPkWjRo0AAH/99Rc2bNiAq1evso7l6+sLb29vBAcHS8x0t3z5ck4y3RF2hEIhNmzYUOv2ktrq49TG2NgYEydOhJubG8LDwzFx4kR0794d4eHhnNf8qq32XEZGBhYtWtTgzmQdOnQIM2bMqDaA1tLSwo4dO2BlZcVJHHl9/gh7b9++xcGDBxEQEIC4uDj88MMPmD59OkaMGMH5ir2zszN8fX2hrq4ueo+4d+8eCgoKsGDBgjpN2tXm3r17+N///gcDAwM4OTmJMh9XTIbcvXsX165d462GJFeEQiGys7PRsmVLAIC6ujoSExNFK4rZ2dlo06aNXP0N7927F+vXr0d8fDyn16VBGyGEdy9evIClpSUuXrwINTU1BAcHY9y4caL2oUOHom/fvvD29mYdi89Md4QdvjJyffyloby8HI0bN8apU6dgamrKeSy+spfKQlFREaKjo0UlNPT19TFixAioqqpyFkOenz/CnczMTAQFBSEkJATv3r1DcnKyqPwKVy5duoTQ0NBKr/eJEydyurXv2rVrsLW1RXJysmjgyTAMOnfujMDAQF6KiHNNKBTC3NxclA06KioKQ4YMQZMmTQB8GHwfP35crv6G79+/D2NjY+Tl5XF6XRq0EUJkJjc3F2pqalBQUKh0/+vXr6GmpiZaeWODz0x35eXlCAoKwsGDB/HgwQMIBALo6urC2toaU6dOlVltl4aCr+yRVQeH6urqSEhIwLfffivVuIQQ6Xj48CGCgoIQFBSE0tJS3Llzh/NBG5/i4+ORlpYGhmGgr68PY2NjWXfps9nZ2dXpcYGBgVLuCX+uX7+OcePG4eHDh5xel860EUJk5uNtcKGhoRg7dmy1/e5s8ZXpjmEYjB07FseOHcN3332Hrl27is752Nra4uDBgzh8+DDrOPKMzznE6Oho0euvvLwcp0+fxq1btyo9houC6/KIzzqLhEjy8fbICxcuYPTo0diyZQvMzMzqtEJbV4mJiXV6nKRU/Z/D2NhYNFArKytDQUFBgx2EytNgrC5KS0uxdu1aqSRXoZU2Qki9oKGhgfj4eM5XO3744Qfo6OhIzHQ3a9YsPHr0CEePHmUVJzAwEA4ODoiMjMTgwYMrtcXExMDCwgJbtmzBtGnTWMUh7Mliy520s5fySRZ1FuXp+SPszZkzB/v27YOOjg7s7OxgY2MjKtzMNaFQCIFAUOOkEhfvF8eOHcOrV68wdepU0X3e3t5YuXIlysrKMGTIEISFhUFLS4tVHMKepaWl2Ptzc3Nx69YtKCoq4vz585x/n6FBGyGkXpDWFrW4uDgMGjQIFhYWNWa669+/P6s4I0aMwJAhQ+Dm5ia23cfHB7GxsYiOjmYVR54NGTJE7P2ampowMDDA3Llz0bZtW557xY6k7KV37txBeno6Z9lL+dS6dWtERUWhZ8+eAD5kk4yNjcWFCxcAfBhgeXh4IDk5mXUseXz+CHtCoRA6Ojro3r17jb/7gwcPso6VmZlZp8e1a9eOVZwhQ4bAysoKc+fOBfDhs2vAgAH4/fffYWhoiKVLl8Lc3By+vr6s4vBN0gCnKi5+V3yRtOVTQ0MDnTp1wpQpU6ChocF5XNoeSQiRa/369UNYWBhmzJiBiIiISm1aWloIDQ1lPWADPmyhWbt2rcR2c3PzOtdy+1J99913Yu9/8+YNjh07hi1btuDChQusz3fY29tj48aNNRay5cqGDRtw6tQpHDlyRGL20o0bNzao7KV81lmUx+ePsDdt2jTeBurBwcFwdnbmNMGOOLdu3cIff/whun3gwAEMHz4cS5cuBQAoKyvDwcGhwQ3apFlsWlZkteWTVtoIIfXChQsX0KtXL6mdg5F2prtGjRohMzMTrVu3Ftv+9OlT6Orq4u3bt5zE+xLNnTsXGRkZOHbsGKvr8JXwBJDP7KXt2rXDnj17MHDgQJSWlqJp06aIiorC0KFDAXzYLmlqasq6nh4gn88faVj4er+omjSrd+/esLa2houLC4APK36dO3dGYWGhVPtBavf8+fMaXw9lZWW4ceMGevfuzWlc7k5qEkLIZ3j58iWuXbsGFRUVFBQUSC2Oqqoqxo0bBxcXF7i4uMDCwkI0YHvy5Anr679//15UdFUcBQUFlJWVsY7zJZs5c6bEoqafgs+5yrS0tBoL4g4bNgzp6em89YcLZmZmcHNzw/nz57F48WKoqqpiwIABovbExETO6t3J4/NHGha+3i/atGmDlJQUAEBBQQESEhIq7QJ59eqV1Ff7pOH+/fu8vufyoXXr1nj+/LnotqGhYaVMka9evYKJiQnncWl7JCFEJm7fvo3Zs2fj4sWLle43NTXF9u3bYWBgIPU+ZGVlwdvbG7t372ad8p9hGNja2kpcKaQVNvZUVFRQUlLCybX42lrFV/ZSPnl5ecHS0hKmpqaiOosfl+cICAjAiBEjOIklj88faXj4eL+wtraGo6MjlixZgmPHjkFbW7tSBsJr167x8rnINT09vUorlRMmTMCmTZsqbbFuaKoOQh8/flxtUlYaA1UatBFCeJeVlQVTU1O0bNkSvr6+ouQCycnJ2LVrFwYMGIBbt25xsh3lzZs3mDt3Lk6cOAElJSW4ublh3rx5WLFiBdavXw8jIyMEBASwjvPzzz/X+hjKHMnOiRMnoK+vz8m19PX1a/0ixsX2PhMTE2zfvl1i9tKtW7dKZUZWmlq2bInz589LrLO4f/9+ztKTy+PzRxqeoUOH1riTAgBu3LjBKoaHhweePn2KBQsWQFtbG3v37q30txUaGooxY8awiiELVQcvx44dw6pVq2TUG/5IY6BPgzZCCO/8/PzQrl07XLx4EcrKyqL7zczMMHv2bHz//ffw8/Pj5I19yZIlOHfuHH7++WccP34cCxcuxPHjx1FSUoJ///0XpqamrGMAX14tGmk4cuSI2Ptzc3Nx9epV+Pv7IygoiJNYnp6evByQX7p0KQYNGoRXr17VmL20IZL0/HFZZ1Genz/ScIwcOVLqddJUVVWxZ88eie30OidgCCGEZ927d2fCwsIktoeGhjLdu3fnJJaOjg5z8uRJhmEY5t69e4xAIGAcHBw4uTbhlkAgEPtPQ0OD6d27NxMeHs5ZnOzsbE6uVRcHDx5kWrRowQiFwkr/mjdvzhw4cIC3fjRU9PwRWeL7/ULeCIVC5vnz56LbampqzP3792XYI/aEQiGTnp7O5ObmMm/evGHU1dWZhIQEJjc3l8nNzWXu3r3LCIVCzuNS9khCCO+aNm2Ka9euoWPHjmLb09PT0bNnT7x584Z1LCUlJWRmZqJNmzYAPsxmXrlyBV26dGF9bcKvnJwcREVFsd5mymf2yArSzl4q7+j5Ix87d+5cnR43cOBA1rFk8X4hT4RCIczNzUXnvaOiojBkyBA0adKk0uMaUp22ioLrFRiGEXubbcH1qmjQRgjhXW0fgtnZ2fj66685ybaooKCArKwstGzZEsCHIt6JiYnQ1dVlfW3Cr4SEBPTo0YP1B6FQKERWVhZ9CSOkgRIKJSc/r/jyLBAIOPkMofcLdiQVoq6qIR0xiI2NrdPjuDp+UYHOtBFCZCI/P7/SebaP5eXlcZZ5iamS1bGkpASzZs1q0LN8hJ3S0lIkJydDXV29WubBoqIipKeno0uXLjV+Mayry5cv4/Xr1zA3NxfdFxISAg8PDxQWFsLCwgKbN2+WWn3Cho6ePyJOTk6O2PuLioqwceNGbNq0Cd9++y0nsTIyMkSTfuTTNaTBWF1xPRirKxq0EUJ4xzBMjVkAq241YGPatGmVrmVjY8PJdauKiopqkJm9vkR79+7Fli1bcPny5WptjRs3hr29PRwdHTl5raxYsQKDBg0SDTqSkpIwffp02NrawtDQEOvWrUObNm2wYsUK1rHkET1/RJyqSXDKy8sREBAAT09PCIVCbN26tU4ZfevC09NTYh8MDAxgY2Mj9SQlhAC0PZIQIgOy2logTY0bN4aNjQ02btxIH+BSwtX2yO+//x7z5s3DxIkTxbaHh4djy5YtdT43U5PWrVsjKioKPXv2BPAhG2JsbCwuXLgA4EN6fA8PDyQnJ7OOJY/o+SO1OXjwIJYsWYIXL15g8eLFmD9/Pqcrr+PGjRN7/5s3b3D79m0oKSnh/PnzrFf2QkJC6vQ4Kh0je1XPtInD1fbcStekQRshRJ7xdYg8ISEBdnZ2yMnJQVBQUIMacNYXmzZtqrH9yZMnWL9+PetBW6tWrXDlyhW0b99ebHtGRgZ69+6NFy9esIoDAMrKykhLS0Pbtm0BfBgwmpmZYdmyZQCABw8eoGvXrsjPz2cdSx7R80ckiY2NhaurK5KSkuDg4ABXV1deynh8rLi4WLSbIzw8nNW1tLS0JLYJBAIUFhairKyM8+QW5NNFRkZKbIuLi8PmzZvBMAyKi4s5jUvbIwkhvMvLy6vT4zQ0NFjH4mte6rvvvsOVK1fg5eWFkSNHYu7cuVi6dGm1gqxc/Ezyys/Pr9bH6OjosI5TWFhY42swPz8fRUVFrOMAwFdffYWMjAy0bdsWpaWluHHjRqXtVvn5+VBSUuIkljyi54+IM2rUKJw+fRp2dnY4fPgwtLW1ZdIPFRUVuLq6wtLSkvW1JJ3Te/bsGTw9PREQEIDhw4ezjkPY+/HHH6vdd+fOHSxevBhRUVGYMmUKVq5cyXlcGrQRQnjXtGnTGrcWSCtdrrQpKipixYoV6NevH0aNGoUNGzaI2hrqz8SnjIwMXuLo6ekhLi4O3bp1E9t+4cIF6OnpcRLLzMwMbm5uWLNmDQ4fPgxVVVUMGDBA1J6YmIgOHTpwEkse0fNHxDl+/DgUFRURFhZW4wrX69evpd6XZs2acVKepqr8/HysWbMGGzduhJGREaKjozF48GDO4xB2nj59Cg8PDwQHB2PkyJGIj4+XWkkhGrQRQnh35swZXuNFR0fXum1m7NixnMQ6ePAgZs+ejYEDB4pdaSOyN3nyZCxbtgz9+vWrNnBLSEiAu7s7XFxcOInl5eUFS0tLmJqaQk1NDcHBwWjUqJGoPSAgACNGjOAkljyi54+IU58yEsbFxXE6cVBaWootW7bAx8cHLVq0QGBgIKytrTm7PuFGbm4ufHx8sHnzZhgbG+P06dOVJpSkgc60EULqncLCQly/fp2Twqh1SdvOxQrYmzdvMGfOHBw5cgTe3t5wcHBgdb0vVVlZGfz8/BAaGoq7d+9CIBBAT08PkydPhoODAydb4d69e4cRI0bgwoULGDZsGDp16gSBQICUlBScOnUK/fv3x8mTJznddpebmws1NTUoKChUuv/169dQU1OrNBAh1dHzR2QlMTFR7P25ubm4evUqfHx84OXlhVmzZrGKwzAMQkJC4O7ujrKyMnh4eGD69OnVXvNE9tauXYs1a9ZAW1sbPj4+YrdLSgMN2ggh9Q5XWQIB/gqjtmnTBjo6OggODoaBgYHYx7x48YLq/dSguLgYw4cPx3///Ydhw4bB0NAQDMPgzp07osHUiRMnJNb3+xTv3r2Dn58f/v77b6SlpYnKUEyePBmOjo40CCCkASguLsbJkydFEzz6+voYNmxYtfqLbFRkChT3dblly5ZwdnbGb7/9xjpOt27dcO/ePcyfPx+Ojo5QVVUV+zg6Fy17QqEQKioqGDZsWI2Daq7rv9KgjRBS73A5aOMre6SXlxcWL15c7Q2cYRj8+++/2L17N44ePYq3b99KtR8Nmbu7O4KDgxEVFSV22+LYsWNhZ2dHNbkIIThy5Ah++eUXvHz5stL9LVq0gL+/P2d1MzMzM8Xer6mpiaZNm3K2M+TjXSHiznzTuej6w9bWtk61ZLnexkuDNkJIvdMQV9qqun//PgICAhAcHIyCggL88MMPsLKykljzhwD6+vpYtWoVrKysxLbv378fS5cuxd27d1nFuXLlCv73v/+JBthVi7m/ffsWkZGR+Omnn1jFIYRIR1xcHAYNGoSxY8fCyckJhoaGAIDk5GT88ccf+Oeff3D27FmYmJhIvS9cfV7JY/1Swi0atBFC6h0uB212dnbYtGkT1NXVOehZzUpKSnDgwAHs3r0bly5dwvDhw/Hvv/9KNZuUPKlak6uqR48eQU9PDyUlJaziVF191dDQQHx8vKg4bnZ2Ntq0aUMz2oTUU6NGjULbtm2xY8cOse0zZ87Eo0ePcOzYMan3hcvPK0JqQmnNCCG8O3LkSI3tXKZ+9/T0RE5OTrUaOJqampwWYp0zZw727dsHAwMD2NjYICIiAs2bN4eSklKdkqGQD4On58+fSxy0ZWVlSaV2n7i5S5rPJKT++u+//7BmzRqJ7XPnzm2wK1JPnjxBREREpXN6lpaW+Prrr2XdNSJjNGgjhPDOwsKi1sfUZb94XbRv317itVq2bAkXFxcsWrSIdZydO3fC1dUVbm5uvKzqyaPBgwfDx8cHERERYttXr16NQYMG8dIXrl5/hBDulZSU1DiBo6mp2SDPD2/btg2LFi1CaWkpNDU1wTAM8vLy8Ntvv8HX1xdz5syRdReJDNGgjRDCu/Lyct5i3bx5U+z9b968wZUrV+Dt7Q1VVVXW6ZpDQkIQGBiI1q1b44cffsDUqVNhZmbG6ppfGg8PD/Tp0wd9+/bFokWL0KlTJwAfzqn4+fkhOTkZly5dknEvCSGypq+vj5iYGNjZ2YltP336NDp27MhJLL52hhw9ehQLFiyAo6MjnJyc0Lp1awDAs2fPsG7dOjg4OKB9+/YYNWoUJ/FIw0Nn2gghX7S9e/di/fr1iI+P5+R6Dx48QGBgIIKCglBUVITXr18jLCyMiqPW0aVLlzB9+nSkpKSIVrsYhkGnTp2we/du9OvXj3UMoVCImJgYNGvWDADQr18/hIeH45tvvgEAvHz5EsOHD6czKoTUU35+fvDy8sKePXuqDWKOHj2Kn3/+GUuXLsXChQtZx+Kr1qepqSkGDBgALy8vse3Lli3D+fPn65ywhMgfGrQRQmRm//79Yoso8znAuX//PoyNjZGXl8fpdRmGQXR0NAICAnDkyBG0aNEClpaW2LRpE6dx5FV8fLwoS6S+vj6MjY05u3ZNdZcq7qfU2oTUX+Xl5ZgwYQIiIiJgYGBQKXtkWloaLCwssH///gZ1nlhDQwNXr16VWOczNTUVPXv2RH5+Ps89I/UFDdoIIbwrLy/HpEmTsH//fujr66NTp06iIsrp6ekYP348QkNDeTlXdP36dYwbNw4PHz6UWozXr18jJCQEQUFBnK3ofUnKyspQUlICNTU1Tq4nqe5SVe3ateMkHiFEOsLCwkQTf8CHCZ6JEydi4sSJMu7Zp1NTU0NiYqIoi21V9+/fR7du3VBQUMBzz0h9QWfaCCG827BhA06dOoUjR45g9OjRldqOHDkCOzs7bNy4EY6OjlLtR2lpKdauXYu+fftKNU6zZs0wYMAAnDp1SqpxGrpjx47h1atXmDp1qug+b29vrFy5EmVlZRgyZAjCwsKgpaXFKk5tg7GcnBxERUVh2rRprOIQQqRrwoQJmDBhAi+xpL0zxMjICJGRkRK3dB4+fBhGRkacxCINE620EUJ4161bNzg6OsLe3l5su7+/PzZs2ICkpCTWsSwtLcXen5ubi1u3bkFRURHnz5+XOLv5KU6ePIkTJ05ASUkJv/zyC7799lvcuXMHbm5uiIqKwvDhw3H8+HHWceTVkCFDYGVlhblz5wL4UEB3wIAB+P3332FoaIilS5fC3Nwcvr6+Uu0H1V0ipGG7ceMG3N3d8c8//7C+Fl87Q4KDgzF79mysX78eM2bMgKLih3WVsrIy7NixA7/99hu2bdsGW1tb1j8TaZho0EYI4Z2KigpSU1Oho6Mjtj0zMxOdOnVCcXEx61iSsotpaGigU6dOmDJlCie1v4KDg2FnZ4dmzZrh9evXaNGihShFs5WVFZycnKjAdi1atWqF6OhodO/eHQCwaNEiJCcniwa6x44dg4ODA9LS0qTaDxq0EVL/8TVJ5uvrC29vbwQHB0vcGbJ8+XJOdoY4OzvD19cX6urq6NChAwDg3r17KCgowIIFC+Dn58c6Bmm4aNBGCOFds2bNcPbsWXTr1k1se1JSEkxNTfH69Wuee/b5jI2NMXHiRLi5uSE8PBwTJ05E9+7dER4eLvrwJTWrOpjv3bs3rK2t4eLiAuDDYL5z584oLCyUaj9o0EZI/cbnJBmfO0OADxl0Q0NDRZNTFef0pL2Nn9R/NGgjhPDuhx9+gI6ODrZv3y62fdasWXj06BGOHj3KOtbz58/RqlUrie1lZWW4ceMGevfuzSqOuro6EhMToauri/LycjRu3BinTp2Cqakpq+t+STp06IBt27Zh5MiRKCgoQPPmzRETE4P+/fsD+LDlaeTIkXjx4oVU+0GDNkLqNz4nyfjcGUJITSgRCSGEd0uXLsWgQYPw6tUrODs7i84IpKSk4I8//kBkZCTOnDnDSazWrVvj2bNnooGboaEhoqOjRR/Ar169gomJCesv6IWFhWjSpAmADynllZWV0bZtW3ad/8JYW1vD0dERS5YswbFjx6CtrV1pdvnatWsS02F/itrKLjx58oR1DEKI9Ny7d0+UgMTa2hoKCgrw9fWVyq4GFRUVvHnzRuKgLS8vDyoqKqzjJCYm1ulxknaoEPlHgzZCCO/69euHsLAwzJgxAxEREZXatLS0EBoaKlpdYavqZoLHjx+jrKysxsd8rujoaGhqagL4cHj99OnTuHXrVqXHjB07lpNY8sjDwwNPnz7FggULoK2tjb1790JBQUHUHhoaijFjxrCOU5dzIZK+oBFCZI/PSTITExNs375d4s6QrVu3wsTEhHUcY2NjifUjK1D9yC8bbY8khMhMUVERoqOjK+3dHzFiBFRVVTmLIRQKkZWVJVppU1dXR0JCgihbZHZ2Ntq0acP6g7AuRVzpA5cQQtgTCoUIDg4WTZJNmjQJGzZswFdffVXpcVxMksXFxWHQoEGwsLCocWcI24lGqh9JakODNkKIXONr0EYIIYQffE+SHTp0CDNmzKiWHEtLSws7duyAlZUV6xi///47nJ2dOZ20JPKl9lc9IYRw7PLly/j3338r3RcSEgJdXV20atUKM2bMwNu3bzmJJRAIkJ+fj7y8POTm5kIgEKCgoAB5eXmif+TLMmrUKOTm5opue3t7482bN6Lbr169QufOnWXQM0JIXZSXl9f6j8uJuHHjxiEzMxMHDhzAqlWrsGrVKkRERODhw4ecDNgAwNPTEwUFBZxci8gnWmkjhPDO3NwcgwYNgqurK4APKf579OgBW1tbGBoaYt26dZg5cyZWrFjBOpZQKKxU9JRhGLG3pbHSpqGhgfj4eE4KdxPuKCgoVEpOU/X3RKuvhNRv9vb22LhxI9TV1WXdFc5U3RVCSFWUiIQQwrv4+HisXLlSdHvfvn3o06cPdu3aBQBo27YtPDw8OBm0cZWF8nPQnFj9VPX3Qr8nQhqW4OBgrF69mpdB2+XLl/H69WuYm5uL7gsJCYGHhwcKCwthYWGBzZs3o3HjxqxjfTyhSEhVNGgjhPAuJyen0oHx2NhYmJmZiW736tULjx494iQW1UkjhBD5wudEy4oVKzBo0CDRoC0pKQnTp0+vtDOkTZs2nEwyDh06FIqKNX81v3HjBus4pGGiQRshhHdfffUVMjIy0LZtW5SWluLGjRvw9PQUtefn50NJSUmGPeSGjY0NNDQ0ZN0NuRISEoL+/fuzqsckEAiqzWjTDDchDQtff7N87gwZOXIk1NTUWF+HyCcatBFCeGdmZgY3NzesWbMGhw8fhqqqKgYMGCBqT0xM5KxIatUzbeIIBIJqtds+lbu7O9zd3SvNkn5c1+fhw4eYPn06Tp48ySrOl87W1hZKSkqYMWMGNm/e/FnXYBgGtra2ou1MJSUlmDVrlqjuE1dJcAgh0qOvr1/re3vVbI+fg8+dIb/99hudaSMS0aCNEMI7Ly8vWFpawtTUFGpqaggODkajRo1E7QEBARgxYgQnsQ4dOiSxLS4uDps3b+Zkq01QUBCioqIQEhKCrl27VmrbuXMnnJ2dOSsY/iUrLy/HgwcPEB0d/dnX+PnnnyvdtrGxqfaYadOmffb1CSHS5+npKarTJk187Qyh1X5SG8oeSQiRmdzcXKipqUFBQaHS/a9fv4aamlqlgRyX7ty5g8WLFyMqKgpTpkzBypUroaOjw+qaeXl5mDdvHsLDw+Hh4QFXV1c8fvwY9vb2uHbtGtavX49ffvmFo59APj1+/BjffPONrLtBCKnn+My0OHPmTCQlJYl2hgQHB+Pp06eiz6e//voLGzZswNWrV1nFoeyRpDZUp40QIjOamprVBmwA0KxZM6kM2J4+fYpff/0V3bp1Q1lZGeLj4xEcHMx6wAZ8SBsfEhKCsLAwbNy4ET169EDXrl2hqKiIpKQkGrDVQZcuXbBnzx5Zd4MQUs/xuSrl5eUFBQUFmJqaYteuXdi1a5dUdoZkZGSgZcuWrK9D5BettBFC5F5ubi58fHywefNmGBsbY82aNZXO0HEpKysLU6dOxenTp9GkSRNERkZiyJAhUoklb7Zt2wY3NzcMHz4cO3fuRPPmzWXdJUJIPSSLVSlp7wyxt7cXe7+mpiYMDAxgY2NDSUq+cLTSRgiRa2vXrsW3336Lf/75B6GhoYiLi5PagC00NBRGRkYoLy9HSkoKZs+eDXNzczg4OKC4uFgqMeXJnDlzkJCQgJycHBgZGeHIkSOy7hIhpB4qLS1FVlaW2PfVoqIiJCYmory8nNOY0t4ZkpOTI/ZffHw83N3dYWBggPv377OOQxouWmkjhMg1oVAIFRUVDBs2TOwHboWDBw+yimNtbY3o6Gj4+Phg/vz5ovv/++8/2NragmEYBAcHw8TEhFWcL8WWLVuwcOFCGBoaVqtbRHWKCPmyBQUFYcuWLbh8+XK19/X379+jT58+cHR0FJtkqCEqLi7GtGnTIBAIEB4eLuvuEBmh7JGEELlW8UEnbc+ePcPNmzfRsWPHSvebmJggISEBrq6uMDU1RWlpqdT70tBlZmYiIiICzZo1w48//lhrsVlCyJdl9+7dcHZ2FjsRp6CgABcXF2zZskVuBm0qKipwdXWFpaWlrLtCZIhW2gghhAPl5eUQCmvecX7u3DkMHDiQpx41TLt27YKTkxOGDRuGHTt20MF8Qkg1rVq1wpUrV9C+fXux7RkZGejduzdevHjBb8ek6P79+zA2NkZeXp6su0JkhKYvCSGEA7UN2ADQgK0WZmZmuHLlCrZs2UJ10gghEhUWFtY4eMnPz0dRURGPPZK+uLg4dOjQQdbdIDJEgzZCCCH1wvv375GYmEi12gghNdLT00NcXBy6desmtv3ChQvQ09PjuVfsJCYmir0/NzcXV69ehY+PD7y8vHjuFalPaHskIYSQemP//v04fPgw3r17h2HDhmHGjBmy7hIhpJ5Zu3Yt1q5di5iYmGoDt4SEBAwdOhQuLi5wcXGRUQ8/nVAohEAggLiv5S1btoSzszN+++03GfSM1Bc0aCOEEFIv7Ny5E7NmzYKenh6UlZVx69YtuLi4YNWqVbLuGiGkHnn37h1GjBiBCxcuYNiwYejUqRMEAgFSUlJw6tQp9O/fHydPnoSSkpKsu1pnmZmZYu/X1NRE06ZNUVhYiOvXr9M2+y8YDdoIIYTUC127doWFhQVWrlwJ4ENa7/nz5yM/P1/GPSOE1Dfv3r2Dn58f/v77b6SlpYFhGOjr62Py5MlwdHTkpHZafZKQkIAePXrg/fv3su4KkREatBFCCKkXmjRpgqSkJHz77bcAPpxxU1FRwcOHD6GtrS3j3hFCiOzQoI3Unu6MEEII4UFxcTHU1NREtxUUFNC4cWO5ywJHCGHnypUrlQYvVdcf3r59S0Woidyh7JGEEELqjd27d1cauJWVlSEoKAgtWrQQ3bdgwQJZdI0QUk+YmJjg2bNnaNWqFYAP577i4+NFq/Rv3rzBpEmT8NNPP8mym4RwigZthBBC6gUdHR3s2rWr0n3a2trYs2eP6LZAIKBBGyFfuKora+JO+jS00z9HjhypsT0jI4OnnpD6igZthBBC6oUHDx7IuguEEDkhEAhk3YVPYmFhUetjGtrPRLhFgzZCCCGEEEJkqLy8XNZdIPUcDdoIIYTUG2VlZfDz80NoaCju3r0LgUAAPT09TJ48GQ4ODg2q7hIhRHqSk5ORlZUF4MNWyDt37qCgoAAA8PLlS1l2jRCpoJT/hBBC6oXi4mIMHz4c//33H4YNGwZDQ0PRl7GKgrknTpyAsrKyrLtKCJEhoVAIgUAg9txaxf0CgaBBpsffv3+/2Ekra2trWXeNyBittBFCCKkXVq1ahUePHuHmzZvo1q1bpbaEhASMHTsWq1evxooVK2TTQUJIvSCPSTnKy8sxadIk7N+/H/r6+ujUqRMYhsHt27cxYcIEjB8/HqGhoXSu7QtGK22EEELqBX19faxatQpWVlZi2/fv34+lS5fi7t27PPeMENKQ5OTkICoqCtOmTZN1V+rM19cX3t7eCA4OxujRoyu1HTlyBHZ2dli+fDkcHR1l00EiczRoI4QQUi8oKysjLS0Nbdu2Fdv+6NEj6OnpoaSkhOeeEUIakoSEBPTo0aNBbY/s1q0bHB0dYW9vL7bd398fGzZsQFJSEs89I/WFUNYdIIQQQgBAQ0MDz58/l9ielZUFDQ0NHntECCH8SEtLw7BhwyS2Dxs2DOnp6Tz2iNQ3NGgjhBBSLwwePBg+Pj4S21evXo1Bgwbx1yFCCOGJiooK3rx5I7E9Ly8PKioq/HWI1DuUiIQQQki94OHhgT59+qBv375YtGgROnXqBOBDam8/Pz8kJyfj0qVLMu4lIYRwz8TEBNu3b8f27dvFtm/duhUmJiY894rUJzRoI4QQUi907twZJ0+exPTp0zFx4kRRljSGYdCpUydER0fDyMhIxr0khMjapk2bamx/8uQJTz3hztKlSzFo0CC8evUKzs7OouyRKSkp+OOPPxAZGYkzZ87IuptEhigRCSGEkHonPj5elCVSX18fxsbGsu0QIaTe0NXVrdPjGlppgEOHDmHGjBl4/fp1pfu1tLSwY8cOiZl1yZeBBm2EEELqjby8PKipqUEorHzkury8HAUFBZSIhBAi14qKihAdHY20tDQAHyatRowYAVVVVRn3jMgaDdoIIYTUC4cOHYKrqyvi4+OrfUEpKipC9+7dsX79eowZM0ZGPSSEEEJkg7JHEkIIqRe2b98OFxcXsTPKqqqqcHV1xZYtW2TQM0JIfTJq1Cjk5uaKbnt7e1fKvPjq1St07txZBj37fJcvX8a///5b6b6QkBDo6uqiVatWmDFjBt6+fSuj3pH6gAZthBBC6oVbt27VmNJ/4MCBVFiWEILo6OhKA5g1a9ZUOgdWVlaG1NRUWXTts61YsQKJiYmi20lJSZg+fTqGDRsGNzc3REVFYdWqVTLsIZE1GrQRQgipF3JyclBWViax/d27d8jJyeGxR4SQ+qjqyR55OOkTHx+PoUOHim7v27cPffr0wa5du7Bo0SJs2rQJ4eHhMuwhkTUatBFCCKkX2rdvj2vXrklsv3btGtq1a8djjwghhB85OTn46quvRLdjY2NhZmYmut2rVy88evRIFl0j9QQN2gghhNQLlpaWWLp0KbKzs6u1ZWVlYdmyZZTymhACgUAgquP48X0N2VdffSUqUVBaWoobN25UKqadn58PJSUlWXWP1ANUXJsQQki94ObmhsjISOjp6cHGxgYGBgYQCARISUnBX3/9hbZt28LNzU3W3SSEyBjDMLC1tUXjxo0BACUlJZg1axaaNGkCAA0yYYeZmRnc3NywZs0aHD58GKqqqhgwYICoPTExER06dJBhD4msUcp/Qggh9UZubi4WL16MsLAw0fk1LS0tTJgwAT4+PmjatKlsO0gIkTk7O7s6PS4wMFDKPeHOixcvYGlpiYsXL0JNTQ3BwcEYN26cqH3o0KHo27cvvL29ZdhLIks0aCOEEFLvMAyDly9fgmEYtGzZssFvfSKEkLrIzc2FmpoaFBQUKt3/+vVrqKmpoVGjRjLqGZE1GrQRQgip12JjY1FYWAgTExNoaWnJujuEEEII7+hMGyGEkHph3bp1KCgogKenJ4APq23m5uY4ceIEAKBVq1Y4ffo0jIyMZNlNQgghhHeUPZIQQki9EBoais6dO4tuHzhwAOfOncP58+fx8uVL9OzZUzSgI4QQQr4ktD2SEEJIvaClpYW4uDgYGhoC+JBsoKysDHv27AEAXLp0CePHj6daRYQQQr44tNJGCCGkXnj37p0ohTcA/Pfff+jXr5/odps2bfDy5UtZdI0QQgiRKRq0EUIIqRc6duyIc+fOAQAePnyIu3fvwtTUVNT++PFjNG/eXFbdI4QQQmSGEpEQQgipF2bPno158+bh/PnzuHTpEkxMTCqdcYuJiUH37t1l2ENCCCFENmjQRgghpF6YOXMmFBUV8c8//2DgwIHw8PCo1P706VPY29vLqHeEEEKI7FAiEkIIIYQQQgipx2iljRBCSL3y5MkTRERE4O7duxAIBNDX14elpSW+/vprWXeNEEIIkQlaaSOEEFJvbNu2DYsWLUJpaSk0NTXBMAzy8vLQqFEj+Pr6Ys6cObLuIiGEEMI7yh5JCCGkXjh69CgWLFiAefPm4cmTJ8jJycGbN2/w5MkTzJkzBw4ODjh27Jisu0kIIYTwjlbaCCGE1AumpqYYMGAAvLy8xLYvW7YM58+fR2xsLM89I4QQQmSLBm2EEELqBQ0NDVy9ehUGBgZi21NTU9GzZ0/k5+fz3DNCCCFEtmh7JCGEkHqhvLwcSkpKEtuVlJRA84yEEEK+RDRoI4QQUi8YGRkhMjJSYvvhw4dhZGTEY48IIYSQ+oFS/hNCCKkX5syZg9mzZ6Nx48aYMWMGFBU/fESVlZVhx44dWLZsGbZt2ybjXhJCCCH8ozNthBBC6g1nZ2f4+vpCXV0dHTp0AADcu3cPBQUFWLBgAfz8/GTcQ0IIIYR/NGgjhBBSr1y6dAmhoaFIS0sDAOjr62PixIno27evjHtGCCGEyAYN2gghhBBCCCGkHqNEJIQQQhqEgwcPolu3brLuBiGEEMI7GrQRQgipN3bt2oXx48dj8uTJuHz5MgAgJiYG3bt3h42NDUxMTGTcQ0IIIYR/tD2SEEJIvbB+/XosWbIE3bp1Q0pKCgBg6dKl8PX1xfz58zF37ly0aNFCxr0khBBC+Ecp/wkhhNQL/v7++PPPP2Fvb4+zZ89iyJAhiImJQXp6Opo2bSrr7hFCCCEyQytthBBC6gVVVVXcuXMHOjo6AIDGjRvj3Llz6NOnj4x7RgghhMgWnWkjhBBSL5SUlEBZWVl0u1GjRmjZsqUMe0QIIYTUD7Q9khBCSL2xe/duqKmpAQDKysoQFBRU7RzbggULZNE1QgghRGZoeyQhhJB6oX379hAIBDU+RiAQ4P79+zz1iBBCCKkfaNBGCCGEEEIIIfUYnWkjhBBCCCGEkHqMBm2EEELqhZiYGHTu3Bl5eXnV2nJzc2FkZIRz587JoGeEEEKIbNGgjRBCSL2wYcMG/Prrr9DQ0KjWpqmpiZkzZ8LPz08GPSOEEEJkiwZthBBC6oWEhASYmZlJbB8xYgSuX7/OY48IIYSQ+oEGbYQQQuqF7OxsKCkpSWxXVFTEixcveOwRIYQQUj/QoI0QQki98PXXXyMpKUlie2JiIlq3bs1jjwghhJD6gQZthBBC6oVRo0bB3d0dJSUl1dqKi4vh4eGB0aNHy6BnhBBCiGxRnTZCCCH1QnZ2Nnr06AEFBQXMmzcPBgYGEAgESElJwdatW/9fe/cflWV9/3H8dd03yggVUQN1B0UT/LH0iEfLuRR35sTDRplZY24gMLOlom4prrIt2zqrnZFlW21molulsUWeZi0H5G/8kRhigYiK5mmgc2AZEAL39f3DL/fxDlRcel8f9fk45z6e6/p87ut+Xff1h7zvz+f6XGpubtbevXsVHh7udFQAAPyKog0AYIxjx47pwQcf1IYNG9Ty35NlWYqLi9MLL7ygyMhIZwMCAOAAijYAgHFqamp06NAh2batqKgohYaGOh0JAADHULQBAAAAgMECnA4AAIAkTZkypV39cnJyrnISAADMQtEGADBCSEiI0xEAADAS0yMBAAAAwGA8pw0AAAAADMb0SACAEdLS0trVb+XKlVc5CQAAZmF6JADACC6XS3379lVMTIwu9l/Tm2++6cdUAAA4j6INAGCEWbNmae3aterTp4/S0tL04x//WN26dXM6FgAAjqNoAwAYo6GhQTk5OVq5cqUKCgr0ve99Tz/5yU80ceJEWZbldDwAABxB0QYAMNKxY8e0atUq/eUvf1FjY6NKSkrUqVMnp2MBAOB3rB4JADCSZVmyLEu2bcvj8TgdBwAAx1C0AQCM0dDQoDVr1ui73/2uBg4cqP379+sPf/iDPv74Y0bZAAA3LJb8BwAY4fyFSFJTU7V27Vp1797d6VgAADiOe9oAAEZwuVzq06ePYmJiLrroSE5Ojh9TAQDgPEbaAABGSE5OZoVIAADawEgbAAAAABiMhUgAAAAAwGAUbQAAAABgMIo2AAAAADAYRRsAAAAAGIyiDQAAAAAMxpL/AAAjbNmypV39xo0bd5WTAABgFpb8BwAYweW68OSPlue3WZalpqYmf0UCAMAIjLQBAIxQU1PT5v66ujo999xzWrZsmfr37+/nVAAAOI+iDQBghJCQEJ9tj8ejlStXasmSJXK5XPrjH/+o6dOnO5QOAADnULQBAIyTk5OjRx55RP/5z3/08MMPKz09XYGBgU7HAgDAEaweCQAwxubNmzV69GglJSVpypQpOnLkiBYsWEDBBgC4oTHSBgAwQnx8vPLz85Wamqp169apZ8+eTkcCAMAIrB4JADCCy+VSQECAgoODvatFtqW6utqPqQAAcB4jbQAAI2RlZTkdAQAAIzHSBgAAAAAGY6QNAGCU+vp65ebm6uDBg7IsS9HR0ZowYYKCgoKcjgYAgCMo2gAAxnjrrbc0Y8YMnTp1ymd/jx499PLLLyshIcGhZAAAOIcl/wEARigoKNDUqVM1btw4bd++XdXV1aqurta2bds0duxYTZ06VTt27HA6JgAAfsc9bQAAI8THxysiIkJ//vOf22x/4IEHdPz4cb3zzjt+TgYAgLMo2gAARggNDdWWLVs0dOjQNtuLi4sVGxurmpoaPycDAMBZTI8EABjhiy++UJcuXS7YHhISooaGBj8mAgDADBRtAAAjREdH67333rtge35+vgYMGODHRAAAmIGiDQBghJSUFC1YsKDNe9befvttZWRkKDU11YFkAAA4i3vaAABG8Hg8+sEPfqA33nhDAwcO1ODBgyVJJSUlKi8v1+TJk/W3v/1NLhe/NwIAbiwUbQAAo7z++utas2aNDh48KOnctMnExEQlJiY6nAwAAGdQtAEAAACAwZhjAgC4Juzdu1ff//73nY4BAIDfUbQBAIyRm5urhQsX6pFHHtGRI0ckSQcOHNDkyZM1atQoNTU1OZwQAAD/Y3okAMAIq1evVmpqqrp166bq6mr16NFDzzzzjGbNmqV77rlHDz30kG699VanYwIA4HcUbQAAIwwfPlyJiYn6xS9+oezsbCUmJiomJkbZ2dm65ZZbnI4HAIBjKNoAAEbo3LmziouL1a9fP3k8HgUGBiovL0+xsbFORwMAwFHc0wYAMEJtba2Cg4MlSS6XS1/72tcUERHhcCoAAJwX4HQAAABabNiwQSEhIZLOPWw7Pz9fH374oU+fO++804loAAA4humRAAAjuFyXnvxhWZaam5v9kAYAAHNQtAEAAACAwbinDQBghLS0NJ05c8bpGAAAGIeRNgCAEdxutyorKxUWFuZ0FAAAjMJIGwDACPyGCABA2yjaAADGsCzL6QgAABiH6ZEAACO4XC6FhIRcsnCrrq72UyIAAMzAc9oAAMZYsmSJ9zltAADgHEbaAABGcLlcqqqqYiESAAC+hHvaAABG4H42AADaRtEGADACEz8AAGgbRRsAwAhnz55VVVWV6uvrW7XV1dWpuLhYHo/HgWQAADiLog0AYIRXXnlFaWlp6tixY6u2wMBApaWl6bXXXnMgGQAAzqJoAwAYYcWKFVqwYIHcbnerNrfbrYyMDC1fvtyBZAAAOIuiDQBghIMHD2r06NEXbB81apRKS0v9mAgAADNQtAEAjFBbW6vPPvvsgu1nzpxRXV2dHxMBAGAGijYAgBGioqJUUFBwwfZt27YpKirKj4kAADADRRsAwAjTpk3T4sWLVVxc3Kpt3759+uUvf6lp06Y5kAwAAGdZNg/GAQAYoLGxURMnTtS2bds0YcIEDRo0SJZlqbS0VHl5efrWt76l3NxcdejQwemoAAD4FUUbAMAYjY2NWrp0qV577TWVl5fLtm1FR0dr2rRpmj9/fpuPAwAA4HpH0QYAAAAABuOeNgCAEXbv3q3m5mbv9pd/U2xoaFB2dra/YwEA4DhG2gAARnC73aqsrFRYWJgkqUuXLioqKlL//v0lSSdOnFDv3r19CjsAAG4EjLQBAIzw5d8Q2/pNkd8ZAQA3Ioo2AMA1w7IspyMAAOB3FG0AAAAAYLAApwMAANCipKREVVVVks5NhTxw4IA+//xzSdKpU6ecjAYAgGNYiAQAYASXyyXLstq8b61lv2VZLEQCALjhMNIGADBCRUWF0xEAADASI20AgGtCTU2N/vGPfyg5OdnpKAAA+BVFGwDgmrBv3z6NGDGC6ZEAgBsOq0cCAAAAgMEo2gAAAADAYBRtAAAAAGAwVo8EABhh2bJlF23/5JNP/JQEAACzsBAJAMAI/fr1a1c/Hg0AALjRULQBAAAAgMG4pw0AAAAADEbRBgAwQnx8vD799FPv9pNPPqnTp097t//73/9qyJAhDiQDAMBZTI8EABjB7XarsrJSYWFhkqQuXbqoqKhI/fv3lySdOHFCvXv35uHaAIAbDiNtAAAjfPk3RH5TBADgHIo2AAAAADAYRRsAwAiWZcmyrFb7AAC40fFwbQCAEWzbVkpKigIDAyVJX3zxhX76058qODhYktTQ0OBkPAAAHMNCJAAAI6SmprarX1ZW1lVOAgCAWSjaAAAAAMBg3NMGAAAAAAajaAMAAAAAg1G0AQAAAIDBKNoAAAAAwGAUbQAAAABgMIo2AMB1JSUlRZMnT3Y6xlcSGRmpZ5991u+fu2nTJlmWpdOnT3+l44wfP17z58+/IpkAABRtAIDLlJKSIsuyWr0mTZrkdDRJ0nPPPadVq1Y5HUOSZFmW1q1bd0WPGRkZ2eb33/IaP378Ff28/0VOTo5+/etfOx0DAK4bAU4HAABceyZNmtTqIdeBgYEOpTmnublZlmUpJCTE0RxX2/vvv6/m5mZJUkFBge655x6VlZWpS5cukqSOHTs6GU+S1K1bN6cjAMB1hZE2AMBlCwwMVM+ePX1eoaGhks5NsevYsaO2bt3q7Z+ZmakePXqosrJS0rnpc3PmzNGcOXPUtWtXde/eXYsXL5Zt2973nD17VhkZGfr617+u4OBg3X777dq0aZO3fdWqVeratavWr1+vIUOGKDAwUMeOHWs1PXL8+PFKT0/X/PnzFRoaqvDwcC1fvly1tbVKTU1V586ddcstt+if//ynzzmWlJQoPj5enTp1Unh4uJKSknTq1Cmf486dO1cZGRnq1q2bevbsqccff9zbHhkZKUm6++67ZVmWd/vw4cO66667FB4erk6dOmnUqFHKy8tr93d/8803e7/zluIoLCzMu+/AgQMaN26cgoKCFBERoblz56q2ttb7/oaGBmVkZCgiIkKBgYGKiorSyy+/7PMZhYWFGjlypG666SaNGTNGZWVl3rbHH39cw4cP11//+ldFRkYqJCREiYmJOnPmjM93c/70yJMnTyohIUFBQUHq16+fXn31VZ8poEePHpVlWSoqKvK+5/Tp07Isy+eaX+qaAMD1iqINAHBFtfzBnpSUpE8//VT79u3To48+qpdeekm9evXy9lu9erUCAgK0a9cuLVu2TEuXLtWKFSu87ampqdq+fbvWrl2r4uJi3XvvvZo0aZLKy8u9ferq6vTb3/5WK1as0EcffaSwsLA2M61evVo9evTQ7t27lZ6ergcffFD33nuvxowZo7179youLk5JSUmqq6uTJFVWVio2NlbDhw/Xnj179O677+rEiRO67777Wh03ODhYu3bt0u9+9zs98cQTys3NlXRuREySsrKyVFlZ6d3+/PPPFR8fr7y8PH3wwQeKi4tTQkKCPv7446/83e/fv19xcXGaMmWKiouL9frrr2vbtm2aM2eOt09ycrLWrl2rZcuWqbS0VH/605/UqVMnn+M8+uijyszM1J49exQQEKC0tDSf9sOHD2vdunVav3691q9fr82bN+upp566YK6UlBQdPXpU7733nv7+97/rhRde0MmTJy/r3Np7TQDgumQDAHAZpk+fbrvdbjs4ONjn9cQTT3j7NDQ02DExMfZ9991nf+Mb37BnzJjhc4zY2Fh78ODBtsfj8e5btGiRPXjwYNu2bfvQoUO2ZVn2J5984vO+73znO/bDDz9s27ZtZ2Vl2ZLsoqKiVvnuuusun8+64447vNtNTU12cHCwnZSU5N1XWVlpS7J37Nhh27ZtP/bYY/bEiRN9jnv8+HFbkl1WVtbmcW3btkeNGmUvWrTIuy3JfvPNN9v4Fn0NGTLEfv75573bffv2tZcuXXrJ923cuNGWZNfU1Ni2bdtJSUn2zJkzffps3brVdrlcdn19vV1WVmZLsnNzcy96vLy8PO++t99+25Zk19fX27Zt27/61a/sm266yf7ss8+8fRYuXGjffvvt3u3Y2Fh73rx5tm3b3s/cuXOnt720tNSW5D3HiooKW5L9wQcfePvU1NTYkuyNGzfatt2+awIA1yvuaQMAXLZvf/vbevHFF332nX8fU8eOHfXKK69o2LBh6tu3b5srIY4ePVqWZXm3v/nNbyozM1PNzc3au3evbNtWdHS0z3saGhrUvXt3n88ZNmzYJfOe38ftdqt79+4aOnSod194eLgkeUd/CgsLtXHjxlYjUNK5UaaWXF/+7F69el1yBKm2tlZLlizR+vXr9e9//1tNTU2qr6+/IiNthYWFOnTokF599VXvPtu25fF4VFFRof3798vtdis2Nvaixzn/vFpGR0+ePKk+ffpIOjf1s3Pnzj59LnTepaWlCggI0MiRI737Bg0apK5du172ubXnmgDA9YiiDQBw2YKDgzVgwICL9ikoKJAkVVdXq7q6WsHBwe0+vsfjkdvtVmFhodxut0/b+X+0BwUF+RR+F9KhQwefbcuyfPa1HMPj8Xj/TUhI0NNPP93qWOdP8WzruC3HuJCFCxdqw4YN+v3vf68BAwYoKChIU6dO1dmzZy95Hpfi8Xj0wAMPaO7cua3a+vTpo0OHDrXrOBf7br7c3tLnQudt//99ihe7Ti6Xy6evJDU2Nvr0ae81AYDrEUUbAOCKO3z4sH72s5/ppZdeUnZ2tpKTk5Wfn+/941ySdu7c6fOenTt3KioqSm63WzExMWpubtbJkyc1duxYf8fXiBEj9MYbbygyMlIBAf/7f5UdOnTwrvTYYuvWrUpJSdHdd98t6dw9bkePHv0qcb1GjBihjz766IIF9dChQ+XxeLR582ZNmDDhinzmpQwePFhNTU3as2ePbrvtNklSWVmZz7Pgbr75Zknn7luLiYmRJJ9FSaQrd00A4FrEQiQAgMvW0NCgqqoqn1fLKn7Nzc1KSkrSxIkTlZqaqqysLH344YfKzMz0Ocbx48f185//XGVlZVqzZo2ef/55zZs3T5IUHR2tH/3oR0pOTlZOTo4qKir0/vvv6+mnn9Y777xz1c9v9uzZqq6u1g9/+EPt3r1bR44c0b/+9S+lpaW1KsIuJjIyUvn5+aqqqlJNTY0kacCAAcrJyVFRUZH27dunadOmXXJ0rr0WLVqkHTt2aPbs2SoqKlJ5ebneeustpaene/NMnz5daWlpWrdunSoqKrRp0yZlZ2dfkc9vy8CBAzVp0iTdf//92rVrlwoLCzVjxgwFBQV5+wQFBWn06NF66qmnVFJSoi1btmjx4sU+x7lS1wQArkUUbQCAy/buu++qV69ePq877rhDkvTkk0/q6NGjWr58uSSpZ8+eWrFihRYvXuwzepKcnKz6+nrddtttmj17ttLT0zVz5kxve1ZWlpKTk/XQQw9p4MCBuvPOO7Vr1y5FRERc9fPr3bu3tm/frubmZsXFxenWW2/VvHnzFBIS4jNaeCmZmZnKzc1VRESEdwRp6dKlCg0N1ZgxY5SQkKC4uDiNGDHiiuQeNmyYNm/erPLyco0dO1YxMTF67LHHfKYPvvjii5o6dapmzZqlQYMG6f777/d5JMDVkJWVpYiICMXGxmrKlCmaOXNmq5U+V65cqcbGRo0cOVLz5s3Tb37zG5/2K3VNAOBaZNnnTyAHAMAPxo8fr+HDh7e5QAluDJGRkZo/f77P89wAAG3jpykAAAAAMBhFGwAAAAAYjOmRAAAAAGAwRtoAAAAAwGAUbQAAAABgMIo2AAAAADAYRRsAAAAAGIyiDQAAAAAMRtEGAAAAAAajaAMAAAAAg1G0AQAAAIDB/g/G9I2BAKgKEgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "experimental_tech.plot(kind='bar', color='purple')\n",
    "plt.xlabel('Experimental Technique')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Experimental Techniques')\n",
    "plt.grid(axis='y')\n",
    "plt.savefig('Images/Experimental_Distribution(3).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "residueCount_x\n",
       "6614      2716\n",
       "313236    1356\n",
       "198       1251\n",
       "271656    1176\n",
       "306       1021\n",
       "          ... \n",
       "1838         1\n",
       "1931         1\n",
       "3825         1\n",
       "3450         1\n",
       "2305         1\n",
       "Name: count, Length: 3780, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at residue count(the amount of amino acids in a protein sequence)\n",
    "residue_counts = protein_classification['residueCount_x'].value_counts() # Hist\n",
    "residue_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(residue_counts, bins=30, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Residue Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Residue Counts')\n",
    "plt.grid(True)\n",
    "plt.savefig('Images/Residue_Distribution(4).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This could poteintially be used for further subsetting using only the top 5 residue counts for further modelling  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resolution\n",
       "2.00     16691\n",
       "2.50     12705\n",
       "2.30     12638\n",
       "1.90     12590\n",
       "2.20     12146\n",
       "         ...  \n",
       "0.66         1\n",
       "9.90         1\n",
       "0.69         1\n",
       "22.20        1\n",
       "0.74         1\n",
       "Name: count, Length: 596, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at value counts of another potential input feature(resolution indicates how distngushable atoms are from one another in a molecule)\n",
    "resolution = protein_classification['resolution'].value_counts() #Hist\n",
    "resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram with different colors\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(resolution, bins=10, color=[ 'green'], edgecolor='black')\n",
    "plt.xlabel('Resolution')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Resolution')\n",
    "plt.grid(True)\n",
    "plt.savefig('Images/Resolution_Distribution(5).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This could potentially be used as an input feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "structureMolecularWeight\n",
       "34852576.00    1356\n",
       "30226128.00    1176\n",
       "18059512.00     862\n",
       "36720.62        514\n",
       "33813.86        365\n",
       "               ... \n",
       "4030.67           1\n",
       "35142.99          1\n",
       "11713.88          1\n",
       "43525.67          1\n",
       "18112.80          1\n",
       "Name: count, Length: 118620, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall Molecular weight value counts\n",
    "molecular_weight = protein_classification['structureMolecularWeight'].value_counts() # Hist\n",
    "molecular_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(molecular_weight, bins=30, color='salmon', edgecolor='black')\n",
    "plt.xlabel('Molecular Weight')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Molecular Weight')\n",
    "plt.grid(True)\n",
    "plt.savefig('Images/Molecular_Weight_Distribution(6).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above features 'experimentalTechnique', 'resolution', and 'structureMolecularWeight' can potentially be used as input features for protein classification independent of 'residueCount_x' ansd sequence. More analysis needs to be done on feature importance before constructing another model using the aforementioned input features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification\n",
      "HYDROLASE         46336\n",
      "TRANSFERASE       36424\n",
      "OXIDOREDUCTASE    34321\n",
      "IMMUNE SYSTEM     15615\n",
      "LYASE             11682\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Using The 5 most frequent proteins in 'classification' column \n",
    "# Get the top 5 most frequent proteins\n",
    "top_proteins = protein_classification['classification'].value_counts().head(5).index.tolist()\n",
    "\n",
    "# Subset the DataFrame to include only instances corresponding to the top 5 proteins\n",
    "protein_classification_top5 = protein_classification[protein_classification['classification'].isin(top_proteins)]\n",
    "\n",
    "# Confirm the subsetted DataFrame\n",
    "print(protein_classification_top5['classification'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144378, 18)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_classification_top5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For the modeling process I decided to utilized KNN, Decision Tree, Random Forest, and Decision Tres as my classification  models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Dummy Model\n",
    "# Convert categorical data to numerical data\n",
    "encoder = LabelEncoder()\n",
    "protein_classification_top5.loc[:,'sequence'] = encoder.fit_transform(protein_classification_top5['sequence'])\n",
    "\n",
    "# Select only relevant features\n",
    "X = protein_classification_top5[['sequence', 'residueCount_x']]\n",
    "y = protein_classification_top5['classification']\n",
    "\n",
    "# Split the dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=5)\n",
    "\n",
    "# Train the Dummy model\n",
    "classifier = DummyClassifier()\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = classifier.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "# Create ConfusionMatrixDisplay\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classifier.classes_)\n",
    "\n",
    "# Plot Confusion Matrix\n",
    "disp.plot(cmap=plt.cm.Blues,  xticks_rotation='vertical')\n",
    "plt.title('Confusion Matrix for Dummy Model')\n",
    "plt.savefig('Images/Dummy_Confusion_Matrix(12).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32005817980329687"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. KNN Model\n",
    "# Train the KNN model\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = knn.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluating Model (confusion matrix)\n",
    "cm = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=np.unique(y_val))\n",
    "disp.plot(cmap='Blues', xticks_rotation='vertical')\n",
    "plt.title('Confusion Matrix(KNN)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.savefig('Images/KNN_Confusion_Matrix(7).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     HYDROLASE       0.77      0.82      0.80      9242\n",
      " IMMUNE SYSTEM       0.78      0.74      0.76      3050\n",
      "         LYASE       0.75      0.75      0.75      2347\n",
      "OXIDOREDUCTASE       0.81      0.80      0.81      6889\n",
      "   TRANSFERASE       0.79      0.75      0.77      7348\n",
      "\n",
      "      accuracy                           0.79     28876\n",
      "     macro avg       0.78      0.77      0.78     28876\n",
      "  weighted avg       0.79      0.79      0.79     28876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating Model (classification report)\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8683074365663889"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 2. Decision Tree\n",
    "# Train the Decision Tree model\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = dt.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm2, display_labels=np.unique(y_val))\n",
    "disp.plot(cmap='Reds', xticks_rotation='vertical')\n",
    "plt.title('Confusion Matrix (Decision Tree)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.savefig('Images/DT_Confusion_Matrix(8).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     HYDROLASE       0.91      0.91      0.91      9242\n",
      " IMMUNE SYSTEM       0.91      0.89      0.90      3050\n",
      "         LYASE       0.88      0.91      0.90      2347\n",
      "OXIDOREDUCTASE       0.92      0.93      0.93      6889\n",
      "   TRANSFERASE       0.91      0.90      0.90      7348\n",
      "\n",
      "      accuracy                           0.91     28876\n",
      "     macro avg       0.91      0.91      0.91     28876\n",
      "  weighted avg       0.91      0.91      0.91     28876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating Model (classification report)\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9985223835799876"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 3. Random Forest\n",
    "# Train the Random Forest model\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = rf.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm3 = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm3, display_labels=np.unique(y_val))\n",
    "disp.plot(cmap='Greens', xticks_rotation='vertical')\n",
    "plt.title('Confusion Matrix (Random Forest)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.savefig('Images/RF_Confusion_Matrix(9).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     HYDROLASE       0.90      0.92      0.91      9242\n",
      " IMMUNE SYSTEM       0.93      0.89      0.91      3050\n",
      "         LYASE       0.93      0.91      0.92      2347\n",
      "OXIDOREDUCTASE       0.93      0.93      0.93      6889\n",
      "   TRANSFERASE       0.91      0.90      0.90      7348\n",
      "\n",
      "      accuracy                           0.91     28876\n",
      "     macro avg       0.92      0.91      0.91     28876\n",
      "  weighted avg       0.92      0.91      0.91     28876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating Model (classification report)\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9985108397017062"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 4. Extra Trees\n",
    "# Train the Extra Trees model\n",
    "et = ExtraTreesClassifier()\n",
    "et.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = et.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm4 = confusion_matrix(y_val, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm4, display_labels=np.unique(y_val))\n",
    "disp.plot(cmap='Purples', xticks_rotation='vertical')\n",
    "plt.title('Confusion Matrix (Extra Trees)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.savefig('Images/ET_Confusion_Matrix(10).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     HYDROLASE       0.91      0.93      0.92      9242\n",
      " IMMUNE SYSTEM       0.93      0.90      0.92      3050\n",
      "         LYASE       0.95      0.91      0.93      2347\n",
      "OXIDOREDUCTASE       0.94      0.93      0.94      6889\n",
      "   TRANSFERASE       0.91      0.91      0.91      7348\n",
      "\n",
      "      accuracy                           0.92     28876\n",
      "     macro avg       0.93      0.92      0.92     28876\n",
      "  weighted avg       0.92      0.92      0.92     28876\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating Model (classification report)\n",
    "print(classification_report(y_val, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9985223835799876"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The model with the best overall scores was the extra trees model\n",
    "* This model is untuned and based on the testing score above we can see that the model is over fit\n",
    "* Gridsearch will be used for further tuning to get a better fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'bootstrap': False, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 150}\n",
      "Best Score: 0.9010805164156197\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid you want to search\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'bootstrap': [True, False]\n",
    "}\n",
    "\n",
    "# Initialize the Extra Trees classifier\n",
    "extra_trees = ExtraTreesClassifier(random_state=42)\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=extra_trees, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Perform the grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "# Print the best parameters and best score\n",
    "print(\"Best Parameters:\", best_params)\n",
    "print(\"Best Score:\", best_score)\n",
    "\n",
    "# Make predictions on the validation set using the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "y_val_pred = best_model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'bootstrap': True, 'max_depth': 40, 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 125}\n",
      "Best Score: 0.9006534006478599\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid you want to search\n",
    "param_grid = {\n",
    "    'n_estimators': [70, 100, 125],\n",
    "    'max_depth': [ 10, 20, 40],\n",
    "    'min_samples_split': [2, 3, 4],\n",
    "    'min_samples_leaf': [1, 2, 3],\n",
    "    'bootstrap': [True]\n",
    "}\n",
    "\n",
    "# Initialize the Extra Trees classifier\n",
    "extra_trees = ExtraTreesClassifier(random_state=42)\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=extra_trees, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Perform the grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "# Print the best parameters and best score\n",
    "print(\"Best Parameters:\", best_params)\n",
    "print(\"Best Score:\", best_score)\n",
    "\n",
    "# Make predictions on the validation set using the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "y_val_pred = best_model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'bootstrap': True, 'max_depth': 50, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Best Score: 0.901138251131556\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid you want to search\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 250],\n",
    "    'max_depth': [40, 50, 60],\n",
    "    'min_samples_split': [2, 3, 5,],\n",
    "    'min_samples_leaf': [1, 2, 3],\n",
    "    'bootstrap': [True]\n",
    "}\n",
    "\n",
    "# Initialize the Extra Trees classifier\n",
    "extra_trees = ExtraTreesClassifier(random_state=42)\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=extra_trees, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Perform the grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "# Print the best parameters and best score\n",
    "print(\"Best Parameters:\", best_params)\n",
    "print(\"Best Score:\", best_score)\n",
    "\n",
    "# Make predictions on the validation set using the best model\n",
    "best_model = grid_search.best_estimator_\n",
    "y_val_pred = best_model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.9205914946668514\n"
     ]
    }
   ],
   "source": [
    "# Get the best estimator from the grid search\n",
    "best_estimator = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions on the validation set using the best estimator\n",
    "y_val_pred = best_estimator.predict(X_val)\n",
    "\n",
    "# Evaluate the predictions against the true labels\n",
    "validation_accuracy = accuracy_score(y_val, y_val_pred)\n",
    "print(\"Validation Accuracy:\", validation_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions on test data\n",
    "y_pred = best_estimator.predict(X_test)\n",
    "\n",
    "# Generate confusion matrix\n",
    "cmf = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cmf, display_labels=np.unique(y_test))\n",
    "disp.plot(cmap='viridis', xticks_rotation='vertical')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.savefig('Images/Final_Confusion_Matrix(11).png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9225308214434133\n"
     ]
    }
   ],
   "source": [
    "# Get the best estimator from the grid search\n",
    "best_estimator = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions on the validation set using the best estimator\n",
    "y_val_pred = best_estimator.predict(X_test)\n",
    "\n",
    "# Evaluate the predictions against the true labels\n",
    "test_accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Test Accuracy:\", test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Evaluation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metric used for evalution was accuracy. This metric assesses the efficacy of my model by looking at the amount of data points that were predicted correctly. In the final confusion matrix after tuning my model and comparing the validation and test sets. The final accuracy between both was about 92%. Despite this high accuracy there were a fair amount of data points that were not predicted correctly. The incorrect predictions that stand out were the 358 predictions where hydrolase was incorrectly predicted as transferase as well as the 218 instances of hydrolase being predicted as oxidireductase. There were aslo 184 instances of Hydrolases being predicted ad Immunoglobulins, 354 instances of transferases predicted as hydrolases, 156 instances of oxioreductases predicted as hydrolases, 122 instance of oxioreductases predicted as transferases and finally 166 instances of transferases being predicted as oxioreductase. Despite the high amount of correct predictions I wouldn't feel comfortable deploying such a model in an industry setting. Science is a field highly dependent on precision and accuracy and if there is no other way to further weed out the incorrect predictions I would not feel comfotable using this model.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Conduct comparative analysis between sequences to see if similarities between amino acid sequences could lead to incorrect predictions.\n",
    "\n",
    "2) Use other input features to see if an effective prediction model can still be built without sequence or residue data.\n",
    "\n",
    "3) Create and unsupervised neural net to analyze sequence data."
   ]
  }
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