accelerating_metagenomic_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dcolinmorgan/grph/blob/main/accelerating_metagenomic_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accelerating metagenomic analysis with [Graphistry](graphistry.com), Part II, focusing on viral tracing over time\n",
        "\n",
        "## [viral calling pipeline here](https://github.com/dcolinmorgan/viral_snake)"
      ],
      "metadata": {
        "id": "ozrca88hza85"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using GPU-accelerated UMAP + DBScan analysis & visualization, metagenomic samples can be compared faster and much more easily explored.\n",
        "\n",
        "*   Task: Analyze metagenomic samples for similarity\n",
        "*   Data: time series samples\n",
        "**   563 samples collected from 84 donors, producing 4 dense long-term time series (up to 1 sample every other day during 18 months)\n",
        "*   [data](https://www.ebi.ac.uk/ena/browser/view/PRJNA544527)\n",
        "*   [metadata](https://static-content.springer.com/esm/art%3A10.1038%2Fs41591-019-0559-3/MediaObjects/41591_2019_559_MOESM3_ESM.xlsx)\n",
        "*   [paper](https://sci-hub.se/10.1038/s41591-019-0559-3)\n",
        "\n",
        "\n",
        "**Insight/ Result:**\n",
        "\n",
        "43s to umap and dbscan vs 2342s here\n",
        "over **50X** faster for a single run, and since [the reference paper for this analysis](https://journals.asm.org/doi/full/10.1128/msystems.00118-23) runs this analysis 12x per dataset (here we only have 1 dataset), we could expect to save nearly the entire 8hrs for this dataset, taking less than 10 minutes in total\n",
        "\n",
        "(See also: [CPU baseline](https://github.com/dcolinmorgan/grph/blob/main/generic_metagenomic_demo.ipynb))"
      ],
      "metadata": {
        "id": "tLRHg2VEzoYy"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjnS_PCWaClg"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JTzHVRgEcDUV"
      },
      "outputs": [],
      "source": [
        "!pip install --extra-index-url=https://pypi.nvidia.com cuml-cu11 cudf-cu11 cugraph-cu11 pylibraft_cu11 raft_dask_cu11 dask_cudf_cu11 pylibcugraph_cu11 pylibraft_cu11\n",
        "import cuml,cudf\n",
        "print(cuml.__version__)\n",
        "\n",
        "!pip install -U --force git+https://github.com/graphistry/pygraphistry.git@feat/gpu-featurization\n",
        "!pip install -U git+https://github.com/graphistry/cu-cat.git@DT4\n",
        "# !pip install dirty_cat\n",
        "\n",
        "!pip install Biopython\n",
        "\n",
        "!nvidia-smi\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ],
      "metadata": {
        "id": "Ob5QrPuiAf-Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# import /configure\n",
        "\n",
        "get a free api-key at https://www.graphistry.com/\n"
      ],
      "metadata": {
        "id": "HK_8_7UB0mhx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import graphistry\n",
        "from time import time\n",
        "import cuml,cudf\n",
        "print(cuml.__version__)\n",
        "\n",
        "\n",
        "graphistry.register(api=3,protocol=\"https\", server=\"hub.graphistry.com\", username='dcolinmorgan', password='***') ## key id, secret key\n",
        "graphistry.__version__"
      ],
      "metadata": {
        "id": "i42QrQ_ejC4h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "d8625b8a-5b15-4468-c0a7-c2c68b08959c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23.06.00\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.29.1+5.g777afd4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bio-ml dataset\n",
        "\n",
        "\n",
        "1.   [3 subjects x 10 time points](\n",
        "https://www.ebi.ac.uk/ena/browser/view/PRJNA544527)\n",
        "\n",
        "2.  [metadata](\n",
        "https://static-content.springer.com/esm/art%3A10.1038%2Fs41591-019-0559-3/MediaObjects/41591_2019_559_MOESM3_ESM.xlsx)\n",
        "\n",
        "3.   !wget https://raw.githubusercontent.com/dcolinmorgan/grph/main/ftp_PRJNA544527.txt\n"
      ],
      "metadata": {
        "id": "_Y1VlCGy4_FR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget -nc ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR922/006/SRR9224006/SRR9224006_1.fastq.gz\n",
        "# !wget -nc ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR922/006/SRR9224006/SRR9224006_2.fastq.gz"
      ],
      "metadata": {
        "id": "CBN6Z_77Sduq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! gunzip SRR9224006_1.fastq.gz\n",
        "# ! gunzip SRR9224006_2.fastq.gz"
      ],
      "metadata": {
        "id": "UjbF1ZfwULLt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !head /content/SRR9224006_1.fastq"
      ],
      "metadata": {
        "id": "ljJnoNeLSnWq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from Bio import SeqIO\n",
        "# import glob,os\n",
        "# import pandas as pd\n",
        "# B=pd.DataFrame()\n",
        "# for i in glob.glob('/content/*.fastq'):\n",
        "#     # j=os.path.basename(i)\n",
        "#     fasta_sequences = SeqIO.parse(open(i),'fastq')\n",
        "#     identifiers = []\n",
        "#     sequences = []\n",
        "#     for fasta in fasta_sequences:\n",
        "#         name, sequence = fasta.id, str(fasta.seq)\n",
        "#         identifiers.append(name)\n",
        "#         sequences.append(sequence)\n",
        "\n",
        "#     A=pd.DataFrame([identifiers,sequences]).T\n",
        "#     A.columns=['ID','seq']\n",
        "#     A.dropna(inplace=True)\n",
        "#     B=B.append(A)\n",
        "#     # A['ID']#=A.ID.str.split('-')[0:1]\n",
        "# # B['ID']=B['ID'].str.split('-').str[0]+'_'+B['ID'].str.split('-').str[1]#.cat()\n",
        "# B['ID']=B.ID.str.split('_length').str[0]\n",
        "# B.index=B.ID"
      ],
      "metadata": {
        "id": "UWDGFCWxSpv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# install [HUMAnN 3](https://huttenhower.sph.harvard.edu/humann), a method for efficiently and accurately profiling the abundance of microbial metabolic pathways and other molecular functions from metagenomic or metatranscriptomic sequencing data."
      ],
      "metadata": {
        "id": "fDvymRFjHPNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install humann --no-binary :all:\n",
        "# !pip install metaphlan"
      ],
      "metadata": {
        "id": "yIeeDXPBHN6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### !humann_databases --download utility_mapping full /path/to/databases --update-config yes\n",
        "\n",
        "# !humann_test\n",
        "\n",
        "# !wget https://github.com/biobakery/humann/raw/master/examples/demo.fastq.gz\n",
        "# !humann -i demo.fastq.gz -o sample_results"
      ],
      "metadata": {
        "id": "TnYmqPJ-bHPa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### takes very long for running all samples\n",
        " (1day+ run on cluster)"
      ],
      "metadata": {
        "id": "gtOG0QeoUoX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# mkdir assemble epi_sam_out mpa4_out\n",
        "# # !humann -i /content/All_MAGs/Sample_101_S75_bin_1.fa -o test_out\n",
        "# %%bash\n",
        "# seq=$(ls /content/*.fastq | cut -d / -f2| cut -d _ -f1)\n",
        "\n",
        "# for i in $(eval \"echo \"$seq\" | cut -d _ -f1\")\n",
        "\n",
        "# do\n",
        "# metaphlan /content/${i}.fa --nproc 40 --input_type fasta -o /content/assemble/${i}/h4_out.txt -t rel_ab_w_read_stats\n",
        "# done"
      ],
      "metadata": {
        "id": "XAtq7G_waxLd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# umap and dbscan\n",
        "\n",
        "idea for metagenomic analysis based on [Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems\n",
        "](https://journals.asm.org/doi/full/10.1128/msystems.00118-23)\n",
        "\n",
        "(analyze all samples run on cluster)\n",
        "\n",
        "also this [paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x?ref=https://codemonkey.link#Sec7) and [method](https://github.com/marbl/Mash/blob/master/INSTALL.txt)\n",
        "\n"
      ],
      "metadata": {
        "id": "dHbwBIEn6Wxr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sqlalchemy.util.compat import dataclass_fields\n",
        "!wget https://github.com/dcolinmorgan/grph/raw/main/PRJNA544527_mpa4out.txt\n",
        "data=pd.read_csv('/content/PRJNA544527_mpa4out.txt',sep='\\t',skiprows=1,index_col=0)\n",
        "data.index=data.reset_index().clade_name.str.split('|',expand=True)[6]\n",
        "data=data.reset_index().dropna(axis=0)\n",
        "data.index=data[6]\n",
        "data=data.drop(columns=6)\n",
        "\n",
        "!wget https://raw.githubusercontent.com/dcolinmorgan/grph/main/PRJNA544527-meta_inf.txt\n",
        "meta=pd.read_csv('/content/PRJNA544527-meta_inf.txt',sep='\\t',header=None)\n",
        "\n",
        "mm=pd.merge(data.T,meta[[3,5]],left_index=True,right_on=3)\n",
        "\n",
        "mm['id']=mm[5].str.split('-').str[0]\n",
        "mm['time']=mm[5].str.split('_').str[0].str.split('-').str[1]\n",
        "\n",
        "!wget https://static-content.springer.com/esm/art%3A10.1038%2Fs41591-019-0559-3/MediaObjects/41591_2019_559_MOESM3_ESM.xlsx\n",
        "metaa=pd.read_excel('/content/41591_2019_559_MOESM3_ESM.xlsx',sheet_name='SupTable2',skiprows=3)\n",
        "metaa=metaa[['Donor','Age','Sex','BMI']]\n",
        "\n",
        "Full_table=pd.merge(mm,metaa,left_on='id',right_on='Donor')\n",
        "Full_table=Full_table.drop(columns=[3,\t5,\t'id'])\n",
        "# Full_table.time=pd.to_datetime(Full_table.time,unit='d')\n",
        "# Full_table.time=Full_table.time.values.astype('datetime64[M]')\n",
        "\n",
        "data2=Full_table.melt(id_vars=['time','Donor','Age','Sex','BMI'])\n",
        "# data2.to_csv('PRJNA544527_mpa4_annot_table.txt',sep='\\t')\n",
        "\n",
        "data2=data2.rename(columns={'variable':'species'})\n",
        "data2=data2.sort_values(by=['Donor','time','value'])\n",
        "# final df stored here also\n",
        "# !wget https://raw.githubusercontent.com/dcolinmorgan/grph/main/PRJNA544527_mpa4_annot_table.txt\n",
        "# data2=pd.read_csv('PRJNA544527_mpa4_annot_table.txt',sep='\\t',index_col=0)"
      ],
      "metadata": {
        "id": "RoIBLY3-670T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !cp /content/PRJNA544527_mpa4_annot_table.txt /content/drive/MyDrive/graphistry/metagen_demo/"
      ],
      "metadata": {
        "id": "V8_uh77ijR_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(data2.species)+'_'+(data2.Donor)"
      ],
      "metadata": {
        "id": "3FvFtKukoI7E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1489e3be-582c-4481-f792-563451b0cdac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2678            s__Bacteroides_clarus_aa\n",
              "5378      s__Bacteroides_intestinalis_aa\n",
              "9158           s__Ruminococcus_bromii_aa\n",
              "12938              s__GGB6601_SGB9333_aa\n",
              "13478              s__GGB3256_SGB4303_aa\n",
              "                       ...              \n",
              "613383            s__GGB51647_SGB4348_dl\n",
              "287223       s__Phocaeicola_coprocola_dl\n",
              "300183       s__Phocaeicola_coprocola_dl\n",
              "178143        s__Phocaeicola_plebeius_dl\n",
              "219183        s__Phocaeicola_plebeius_dl\n",
              "Length: 1310040, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data2.index=data2.species+'_'+data2.Donor\n",
        "data2[data2['value']>1]"
      ],
      "metadata": {
        "id": "4savEas1jZyX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c06127b4-51ad-46f1-9ac6-c7ccb7a04899"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        time Donor  Age   Sex   BMI                          species     value\n",
              "39938   0154    aa   29  Male  24.1           s__Desulfovibrio_piger   1.00422\n",
              "958478  0154    aa   29  Male  24.1           s__Desulfovibrio_piger   1.00422\n",
              "11318   0154    aa   29  Male  24.1      s__Odoribacter_splanchnicus   1.12785\n",
              "77198   0154    aa   29  Male  24.1      s__Odoribacter_splanchnicus   1.12785\n",
              "73418   0154    aa   29  Male  24.1  s__Faecalibacterium_prausnitzii   1.14483\n",
              "...      ...   ...  ...   ...   ...                              ...       ...\n",
              "613383  0006    dl   32  Male  26.1              s__GGB51647_SGB4348   5.42813\n",
              "287223  0006    dl   32  Male  26.1         s__Phocaeicola_coprocola  24.16670\n",
              "300183  0006    dl   32  Male  26.1         s__Phocaeicola_coprocola  24.16670\n",
              "178143  0006    dl   32  Male  26.1          s__Phocaeicola_plebeius  28.49330\n",
              "219183  0006    dl   32  Male  26.1          s__Phocaeicola_plebeius  28.49330\n",
              "\n",
              "[17881 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8e5c46f0-6ee8-491e-92eb-381f78f9a344\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>Donor</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BMI</th>\n",
              "      <th>species</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>39938</th>\n",
              "      <td>0154</td>\n",
              "      <td>aa</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.1</td>\n",
              "      <td>s__Desulfovibrio_piger</td>\n",
              "      <td>1.00422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>958478</th>\n",
              "      <td>0154</td>\n",
              "      <td>aa</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.1</td>\n",
              "      <td>s__Desulfovibrio_piger</td>\n",
              "      <td>1.00422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11318</th>\n",
              "      <td>0154</td>\n",
              "      <td>aa</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.1</td>\n",
              "      <td>s__Odoribacter_splanchnicus</td>\n",
              "      <td>1.12785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77198</th>\n",
              "      <td>0154</td>\n",
              "      <td>aa</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.1</td>\n",
              "      <td>s__Odoribacter_splanchnicus</td>\n",
              "      <td>1.12785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73418</th>\n",
              "      <td>0154</td>\n",
              "      <td>aa</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.1</td>\n",
              "      <td>s__Faecalibacterium_prausnitzii</td>\n",
              "      <td>1.14483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613383</th>\n",
              "      <td>0006</td>\n",
              "      <td>dl</td>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.1</td>\n",
              "      <td>s__GGB51647_SGB4348</td>\n",
              "      <td>5.42813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287223</th>\n",
              "      <td>0006</td>\n",
              "      <td>dl</td>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.1</td>\n",
              "      <td>s__Phocaeicola_coprocola</td>\n",
              "      <td>24.16670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300183</th>\n",
              "      <td>0006</td>\n",
              "      <td>dl</td>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.1</td>\n",
              "      <td>s__Phocaeicola_coprocola</td>\n",
              "      <td>24.16670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178143</th>\n",
              "      <td>0006</td>\n",
              "      <td>dl</td>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.1</td>\n",
              "      <td>s__Phocaeicola_plebeius</td>\n",
              "      <td>28.49330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219183</th>\n",
              "      <td>0006</td>\n",
              "      <td>dl</td>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.1</td>\n",
              "      <td>s__Phocaeicola_plebeius</td>\n",
              "      <td>28.49330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17881 rows × 7 columns</p>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e5c46f0-6ee8-491e-92eb-381f78f9a344');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "rmGXS_aNYbwo",
        "outputId": "2bd15d17-7f10-4ee9-f6e3-a97a02f788aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                 SRR9224004  SRR9224005  SRR9224006  \\\n",
              "6                                                                     \n",
              "s__Eubacterium_rectale              9.66295     9.81136    14.82533   \n",
              "s__Bacteroides_uniformis            8.78315     8.85121     7.13981   \n",
              "s__Phocaeicola_vulgatus             7.87163     1.84120    27.58737   \n",
              "s__Phocaeicola_massiliensis         6.83574     1.71725     9.28006   \n",
              "s__Bacteroides_clarus               6.50124     7.00490     2.93553   \n",
              "...                                     ...         ...         ...   \n",
              "s__Bacteroides_mediterraneensis     0.00000     0.00000     0.00000   \n",
              "s__GGB3343_SGB4423                  0.00000     0.00000     0.00000   \n",
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              "\n",
              "                                 SRR9224007  SRR9224008  SRR9224009  \\\n",
              "6                                                                     \n",
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              "s__Phocaeicola_vulgatus            17.43556    23.91699    23.66567   \n",
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              "s__Bacteroides_clarus               0.00021     0.00267     0.00000   \n",
              "...                                     ...         ...         ...   \n",
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              "\n",
              "                                 SRR9224010  SRR9224011  SRR9224012  \\\n",
              "6                                                                     \n",
              "s__Eubacterium_rectale             17.09289     2.44399     0.32817   \n",
              "s__Bacteroides_uniformis            5.49703    28.62590    20.36611   \n",
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              "...                                     ...         ...         ...   \n",
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              "s__GGB3343_SGB4423                  0.00000     0.00000     0.00000   \n",
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              "\n",
              "                                 SRR9224013  ...  SRR9224554  SRR9224555  \\\n",
              "6                                            ...                           \n",
              "s__Eubacterium_rectale              1.38337  ...     0.17943     0.23359   \n",
              "s__Bacteroides_uniformis            9.53160  ...     8.03565    11.50562   \n",
              "s__Phocaeicola_vulgatus             1.96795  ...     4.43756     3.70480   \n",
              "s__Phocaeicola_massiliensis         0.00000  ...     9.41414     2.44829   \n",
              "s__Bacteroides_clarus               0.00000  ...     0.00000     0.00000   \n",
              "...                                     ...  ...         ...         ...   \n",
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              "\n",
              "                                 SRR9224556  SRR9224557  SRR9224558  \\\n",
              "6                                                                     \n",
              "s__Eubacterium_rectale              2.89280     2.23934     0.00000   \n",
              "s__Bacteroides_uniformis           12.07718    20.28629     8.80642   \n",
              "s__Phocaeicola_vulgatus             5.93137     3.94097     9.08411   \n",
              "s__Phocaeicola_massiliensis         0.72865     0.64923     8.89782   \n",
              "s__Bacteroides_clarus               0.00000     0.00000     0.00000   \n",
              "...                                     ...         ...         ...   \n",
              "s__Bacteroides_mediterraneensis    13.77536    15.74308     0.00000   \n",
              "s__GGB3343_SGB4423                  2.03774     1.21952     0.00000   \n",
              "s__GGB3139_SGB4152                  0.17281     0.02003     0.00000   \n",
              "s__Blautia_SGB14221                 0.01479     0.07402     0.00000   \n",
              "s__GGB48165_SGB66168                0.00100     0.00000     0.00000   \n",
              "\n",
              "                                 SRR9224560  SRR9224562  SRR9224563  \\\n",
              "6                                                                     \n",
              "s__Eubacterium_rectale             16.99579    12.38639    10.36104   \n",
              "s__Bacteroides_uniformis            4.65190    10.48361    10.76942   \n",
              "s__Phocaeicola_vulgatus            21.78286    23.46065    20.67648   \n",
              "s__Phocaeicola_massiliensis         5.34949     6.79459     7.27276   \n",
              "s__Bacteroides_clarus               1.33849     2.35695     2.23865   \n",
              "...                                     ...         ...         ...   \n",
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              "s__GGB3343_SGB4423                  0.00000     0.00000     0.00000   \n",
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              "\n",
              "                                 SRR9224564  SRR9224566  \n",
              "6                                                        \n",
              "s__Eubacterium_rectale             11.34234    14.66425  \n",
              "s__Bacteroides_uniformis           13.86282    11.01886  \n",
              "s__Phocaeicola_vulgatus            18.94887    14.10248  \n",
              "s__Phocaeicola_massiliensis         7.26365     7.28567  \n",
              "s__Bacteroides_clarus               2.16099     3.47488  \n",
              "...                                     ...         ...  \n",
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              "      <td>9.41414</td>\n",
              "      <td>2.44829</td>\n",
              "      <td>0.72865</td>\n",
              "      <td>0.64923</td>\n",
              "      <td>8.89782</td>\n",
              "      <td>5.34949</td>\n",
              "      <td>6.79459</td>\n",
              "      <td>7.27276</td>\n",
              "      <td>7.26365</td>\n",
              "      <td>7.28567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__Bacteroides_clarus</th>\n",
              "      <td>6.50124</td>\n",
              "      <td>7.00490</td>\n",
              "      <td>2.93553</td>\n",
              "      <td>0.00021</td>\n",
              "      <td>0.00267</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00066</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.33849</td>\n",
              "      <td>2.35695</td>\n",
              "      <td>2.23865</td>\n",
              "      <td>2.16099</td>\n",
              "      <td>3.47488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__Bacteroides_mediterraneensis</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>13.77536</td>\n",
              "      <td>15.74308</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__GGB3343_SGB4423</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>2.03774</td>\n",
              "      <td>1.21952</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__GGB3139_SGB4152</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.17281</td>\n",
              "      <td>0.02003</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__Blautia_SGB14221</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.01479</td>\n",
              "      <td>0.07402</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s__GGB48165_SGB66168</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00100</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2426 rows × 540 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0c02b81-0db9-4aa3-be4e-a098535fc5d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5d7e7aa0-2769-41b8-9930-2140da15189b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d7e7aa0-2769-41b8-9930-2140da15189b')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5d7e7aa0-2769-41b8-9930-2140da15189b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0c02b81-0db9-4aa3-be4e-a098535fc5d4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0c02b81-0db9-4aa3-be4e-a098535fc5d4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## UMAP by species\n"
      ],
      "metadata": {
        "id": "WRYYF1brbdgz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Zoo17Ui9zxh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34620642-2850-4117-eecb-eb6f1a52d287"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:! Failed featurize speedup attempt. Continuing without memoization speedups.WARNING:root:! Failed umap speedup attempt. Continuing without memoization speedups.WARNING:graphistry.umap_utils:* Ignoring target column of shape (3834, 0) in UMAP fit, as it is not one dimensional"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "16.825986862182617\n"
          ]
        }
      ],
      "source": [
        "data=pd.read_csv('/content/PRJNA544527_mpa4out.txt',sep='\\t',skiprows=1,index_col=0)\n",
        "\n",
        "g = graphistry.nodes(cudf.from_pandas(data))\n",
        "\n",
        "t=time()\n",
        "g2=g.featurize(feature_engine='cu_cat',memoize=False)\n",
        "g3=g2.umap(dbscan=True,engine='cuml')\n",
        "print(\"\\n\"+str(time()-t))\n",
        "\n",
        "emb2=g3._node_embedding\n",
        "# emb2['time']=data2.time\n",
        "# emb2['_n']=data2.species\n",
        "# emb2['index']=data2.species\n",
        "g22=graphistry.nodes(emb2.reset_index(),'index').edges(g3._edges,'_src_implicit','_dst_implicit').bind(point_x=\"x\",point_y=\"y\").settings(url_params={\"play\":0})\n",
        "##440s"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.index"
      ],
      "metadata": {
        "id": "qbFo2HcyxdKj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "A=emb2.reset_index()['clade_name'].to_pandas().str.split('|').str[6]#.str.split('|').str[0]#+'_'+data2.Donor\n",
        "emb2.index=A"
      ],
      "metadata": {
        "id": "nJxYK9FaiY3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "B=g3._edges\n",
        "B['_src_implicit'] = B['_src_implicit'].replace(A, regex=True)#apply(lambda x: [A[i] for i in x])\n",
        "B['_dst_implicit'] = B['_dst_implicit'].replace(A, regex=True)"
      ],
      "metadata": {
        "id": "VaW0W3ssaBX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## add this regex replacement rather than merge"
      ],
      "metadata": {
        "id": "I1CS8SMN68wQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# g3._edges[g3._edges['_src_implicit']!=g3._edges['_dst_implicit']]\n",
        "g22=graphistry.nodes(emb2.reset_index(),'clade_name').edges(g3._edges.dropna(),'_src_implicit','_dst_implicit').bind(point_x=\"x\",point_y=\"y\").settings(url_params={\"play\":0})\n",
        "\n",
        "g22.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "kjSdlnmeaOuL",
        "outputId": "2cfe51de-585d-4180-dec6-c2cb18649b37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"0ef7d983-f8fa-4a79-a1d8-09ffbe2411df\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=2fd244143b4040b2b90774b3e8f63991&type=arrow&viztoken=697aa8ab-b7a6-411b-a23a-e394503e18fd&usertag=73072404-pygraphistry-0.29.1+5.g777afd4&splashAfter=1690361605&info=true&play=0\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#0ef7d983-f8fa-4a79-a1d8-09ffbe2411df\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## UMAP for patient by time stamp"
      ],
      "metadata": {
        "id": "UZt6aGWnbS9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data2=data2[data2.value>0]\n",
        "data2=data2.reset_index(drop = True)\n",
        "data2=data2.drop_duplicates()\n",
        "\n",
        "data2[\"Label\"] = (\n",
        "    data2.groupby(\"Donor\")\n",
        "    .apply(lambda x: x.groupby(\"time\", sort=False).ngroup() + 1)\n",
        "    .values\n",
        ")\n",
        "\n",
        "cc=pd.unique(data2[data2.Label<5].Donor)\n",
        "data2=data2.loc[ data2.Donor.isin(cc), : ]\n",
        "data2=data2[data2.Label<5]\n",
        "\n",
        "data2[\"rank\"] = data2.groupby(\"Donor\")[\"value\"].rank(method=\"dense\", ascending=False)\n",
        "data2=data2[data2['rank']<10.0]\n"
      ],
      "metadata": {
        "id": "SJvc8pOsi3Dv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data2['id_time']=data2['Donor']+'_'+data2['Label'].apply(str)"
      ],
      "metadata": {
        "id": "dkWIAyP2oKHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data3=data2[['id_time','species','value']]"
      ],
      "metadata": {
        "id": "W4TCI3-YolwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = data3.pivot_table(index=['id_time'],columns='species')\n",
        "df3=df2.fillna(0).reset_index()\n",
        "df4=df3.droplevel(0, axis=1)\n",
        "df4.index=df4.iloc[:,0]\n",
        "df4=df4.loc[:, df4.columns.str.startswith('s__')]\n",
        "\n",
        "g = graphistry.nodes(cudf.from_pandas(df4))\n",
        "\n",
        "t=time()\n",
        "g2=g.featurize(feature_engine='cu_cat',memoize=False)\n",
        "g3=g2.umap(dbscan=True,engine='cuml')\n",
        "print(\"\\n\"+str(time()-t))\n",
        "\n",
        "emb2=g3._node_embedding\n",
        "# emb2['time']=data2.time\n",
        "# emb2['_n']=data2.species\n",
        "# emb2['index']=data2.species\n",
        "# g22=graphistry.nodes(emb2.reset_index(),'id_time').edges(g3._edges,'_src_implicit','_dst_implicit').bind(point_x=\"x\",point_y=\"y\").settings(url_params={\"play\":0})\n",
        "\n",
        "\n",
        "A=emb2.reset_index().iloc[:,0].to_pandas() #emb2.reset_index()['id_time'].to_pandas()#.str.split('|').str[6]#.str.split('|').str[0]#+'_'+data2.Donor\n",
        "emb2.index=A\n",
        "\n",
        "B=g3._edges\n",
        "B['_src_implicit'] = B['_src_implicit'].replace(A, regex=True)#apply(lambda x: [A[i] for i in x])\n",
        "B['_dst_implicit'] = B['_dst_implicit'].replace(A, regex=True)\n",
        "\n",
        "emb2['id_time']=emb2.index\n",
        "g22=graphistry.nodes(emb2.reset_index(),'id_time').edges(g3._edges.dropna(),'_src_implicit','_dst_implicit').bind(point_x=\"x\",point_y=\"y\").settings(url_params={\"play\":0})\n",
        "\n",
        "g22.plot()"
      ],
      "metadata": {
        "id": "aiw8WeENpRHT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "ece2f7ac-9888-433b-b34f-bdb73fd2e15f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:! Failed featurize speedup attempt. Continuing without memoization speedups.WARNING:root:! Failed umap speedup attempt. Continuing without memoization speedups.WARNING:graphistry.umap_utils:* Ignoring target column of shape (169, 0) in UMAP fit, as it is not one dimensional"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "0.2730567455291748\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"57c572f9-e240-42ab-9fa6-ffca3890d71f\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4c2aa987b2f64b54815157265f782ea8&type=arrow&viztoken=e39b06fd-19d5-4dcc-a904-c4964227d751&usertag=73072404-pygraphistry-0.29.1+5.g777afd4&splashAfter=1690361607&info=true&play=0\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#57c572f9-e240-42ab-9fa6-ffca3890d71f\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[per the Materials and Methods of our inspirational paper,](https://journals.asm.org/doi/full/10.1128/msystems.00118-23#sec-4)from these (too) many clusters, one can perform cluster-based functional enrichment analysis, cluster-based taxonomoical enrichment analysis and/or identify of ecologically conserved genes and differentially abundant genes"
      ],
      "metadata": {
        "id": "uyUBzVXFAik6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random\n",
        "data2['genus']=data2['species'].str.split('_').str[2]\n",
        "\n",
        "chars = '0123456789ABCDEF'\n",
        "palette_colors=['#'+''.join(random.sample(chars,6)) for i in range(len(pd.unique(data2.genus)))]\n",
        "palette_dict = {genus: color for genus, color in zip(pd.unique(data2.genus), palette_colors)}\n"
      ],
      "metadata": {
        "id": "zW6wQAODED6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "ax = sns.histplot(data2[data2['Sex']=='Male'], x='Donor', hue='genus', weights='value',\n",
        "             multiple='stack', palette=palette_dict, shrink=0.8)\n",
        "ax.set_ylabel('abundance')\n",
        "\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "ax.set_title('Male')\n",
        "\n",
        "legend = ax.get_legend()\n",
        "legend.set_bbox_to_anchor((1, 1))\n"
      ],
      "metadata": {
        "id": "l_SEv5MTrT6C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 992
        },
        "outputId": "c46686af-0980-4846-9185-3736186db991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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lSnz11Vd5WZqIiIiIiIiIiOSBIrHhuIiIiIiIiIjcXWxsLHFxcQU2ntFoxMfHp8DGK4p8fX0ZNmwYw4YNu2Mbg8HAxo0b6dy5c4HVVdwofBIREREREREp4mJjYwmsHkhSclKBjeno4MjhI4dzHUCFhISwZs0a83sPDw8aNmzI3LlzqVWrVp7UlJswKC/t3r0bJyenAhmrJFP4JCIiIiIiIlLExcXFkZScxNvNZlDVzS/nC/6lY/GnGRQ1gbi4uHua/RQcHEx4eDiA+YFgnTp1IjY2Nr9KvS+pqanY2trm2K5s2bIFUE3JZ1XYBYiIiIiIiIhI7lR186N2mcB8f91vwGVnZ4enpyeenp7UqVOHsWPH8vvvv3Pp0iUAxowZQ9WqVXF0dKRy5cpMmjSJtLQ0iz6+/PJLGjZsiL29PUajkS5dugA3H1Z29uxZhg8fjsFgwGAwmK/56aefaN68OQ4ODlSsWJEhQ4Zw48YN83lfX1+mTZtG3759cXV1ZeDAgQB88sknPPzww9jZ2eHr68uCBQssavH19eWNN94wvz9+/DgtWrTA3t6eGjVqsG3btmyfwe+//06PHj1wd3fHw8ODp556ijNnzpjPR0ZG0qhRI5ycnHB3d6dZs2acPXv2vj7v4kLhk4iIiIiIiIjkucTERN5//338/f0pU6YMAC4uLkRERHDo0CEWL17MO++8w6JFi8zXbN68mS5dutChQwf27dvH9u3badSoEQCffvopDz30EFOnTuX8+fOcP38egJMnTxIcHEy3bt04cOAAGzZs4KeffiI0NNSinvnz51O7dm327dvHpEmT2Lt3Lz169KBXr14cPHiQsLAwJk2aRERExG3vJzMzk65du2Jra8uuXbtYsWIFY8aMsWiTlpZGUFAQLi4u/Pjjj0RFReHs7ExwcDCpqamkp6fTuXNnWrZsyYEDB4iOjmbgwIEWQVpJpGV3IiIiIiIiIpInNm3ahLOzMwA3btzAy8uLTZs2YWV1c+7LxIkTzW19fX0ZOXIk69evZ/To0QDMmDGDXr16MWXKFHO72rVrAzf3kLK2tsbFxQVPT0/z+VmzZtGnTx/zPlABAQEsWbKEli1bsnz5cuzt7QF47LHHeO2118zX9enThzZt2jBp0iQAqlatyqFDh5g3bx4hISHZ7u3bb7/lyJEjfPPNN3h7ewMwc+ZM2rdvb26zYcMGMjMzWblypTlQCg8Px93dncjISBo0aEB8fDydOnWiSpUqAAQGBt7rx1zsaOaTiIiIiIiIiOSJ1q1bExMTQ0xMDL/88gtBQUG0b9/evKxsw4YNNGvWDE9PT5ydnZk4caLFflAxMTG0adPmnsbcv38/ERERODs7m19BQUFkZmZy+vRpc7sGDRpYXHf48GGaNWtmcaxZs2YcP36cjIyMbOMcPnyYihUrmoMngCZNmmSr5cSJE7i4uJhr8fDwICUlhZMnT+Lh4UFISAhBQUE88cQTLF682DyDqyTTzCcRERERERERyRNOTk74+/ub369cuRI3NzfeeecdOnbsSJ8+fZgyZQpBQUG4ubmxfv16i32WHBwc7nnMxMREBg0axJAhQ7Kd+/tm6QXx1LrExETq16/P2rVrs527tXl5eHg4Q4YMYcuWLWzYsIGJEyeybds2GjdunO/1FRaFTyIiIiIiIiKSLwwGA1ZWViQnJ7Nz504qVarEhAkTzOf/udF2rVq12L59O88///xt+7O1tc02K6levXocOnTIIvTKjcDAQKKioiyORUVFUbVqVaytrW/b/vfff+f8+fN4eXkB8PPPP2erZcOGDZQrVw5XV9c7jl23bl3q1q3LuHHjaNKkCevWrSvR4ZOW3YmIiIiIiIhInjCZTFy4cIELFy5w+PBhBg8eTGJiIk888QQBAQHExsayfv16Tp48yZIlS9i4caPF9a+//joffPABr7/+OocPH+bgwYPMmTPHfN7X15cdO3bwxx9/EBcXB9x8gt7OnTsJDQ0lJiaG48eP8/nnn2fbcPyfXnvtNbZv3860adM4duwYa9asYdmyZYwcOfK27du2bUvVqlXp168f+/fv58cff7QI0uDmPlJGo5GnnnqKH3/8kdOnTxMZGcmQIUM4d+4cp0+fZty4cURHR3P27Fm2bt3K8ePHS/y+T5r5JCIiIiIiIlJMHIs/nXOjQhxny5Yt5llBLi4uVK9enY8++ohWrVoBMHz4cEJDQzGZTHTs2JFJkyYRFhZmvr5Vq1Z89NFHTJs2jdmzZ+Pq6kqLFi3M56dOncqgQYOoUqUKJpOJrKwsatWqxQ8//MCECRNo3rw5WVlZVKlShZ49e9611nr16vHhhx8yefJkpk2bhpeXF1OnTr3tZuMAVlZWbNy4kf79+9OoUSN8fX1ZsmQJwcHB5jaOjo7s2LGDMWPG0LVrV65fv06FChVo06YNrq6uJCcnc+TIEdasWcPly5fx8vLi1VdfZdCgQff1eRcXhqysrKzCLqKwJSQk4ObmRnx8/F2nxYkUN1evXsXDw4NTPX7A3S7nr+1rpgQqf9iSK1euULp06QKoUERERETkwXS370NTUlI4ffo0fn5+5ie1xcbGElg9kKTkpAKr0dHBkcNHDlvsmyRyy+2+Tu9EM59EREREREREijgfHx8OHzlsXmpWEIxGo4InyRMKn0RERERERESKAR8fH4VBUixpw3EREREREREREck3Cp9ERERERERERCTfKHwSEREREREREZF8o/BJRERERERERETyjcInERERERERERHJNwqfREREREREREQk3yh8EhERERERERGRfGNT2AWIiIiIiIiISM5iY2OJi4srsPGMRiM+Pj551l9kZCStW7fm6tWruLu7ExERwbBhw7h27dp99+nr68uwYcMYNmxYntUpeU/hk4iIiIiIiEgRFxsbS/Xq1UlOTi6wMR0cHDhy5Mg9B1DR0dE8+uijBAcHs3nz5ju269mzJx06dMhVn3cKqnbv3o2Tk9M91ScFT+GTiIiIiIiISBEXFxdHcnIyPZ7oQ9ky5fN9vEuX/+LDL9cSFxd3z+HTqlWrGDx4MKtWreLPP//E29v7tu0cHBxwcHD4V3WWLVv2X10vBUN7PomIiIiIiIgUE2XLlKeC50P5/rrfgCsxMZENGzbw8ssv07FjRyIiIu7YNiIiAnd3d/P7/fv307p1a1xcXHB1daV+/frs2bOHyMhInn/+eeLj4zEYDBgMBsLCwoCby+7eeOMNcx8Gg4Hly5fTvn17HBwcqFy5Mh9//LHFuL///js9evTA3d0dDw8PnnrqKc6cOWM+HxkZSaNGjXBycsLd3Z1mzZpx9uzZ+/o85CaFTyIiIiIiIiKSJz788EOqV69OtWrVePbZZ1m9ejVZWVm5urZPnz489NBD7N69m7179zJ27FhKlSpF06ZNeeONN3B1deX8+fOcP3+ekSNH3rGfSZMm0a1bN/bv30+fPn3o1asXhw8fBiAtLY2goCBcXFz48ccfiYqKwtnZmeDgYFJTU0lPT6dz5860bNmSAwcOEB0dzcCBAzEYDHny+TyotOxORERERERERPLEqlWrePbZZwEIDg4mPj6eH374gVatWuV4bWxsLKNGjaJ69eoABAQEmM+5ublhMBjw9PTMsZ/u3bvz4osvAjBt2jS2bdvG0qVLeeutt9iwYQOZmZmsXLnSHCiFh4fj7u5OZGQkDRo0ID4+nk6dOlGlShUAAgMD7+kzkOw080lERERERERE/rWjR4/yyy+/0Lt3bwBsbGzo2bMnq1atytX1I0aM4MUXX6Rt27bMnj2bkydP3lcdTZo0yfb+1syn/fv3c+LECVxcXHB2dsbZ2RkPDw9SUlI4efIkHh4ehISEEBQUxBNPPMHixYs5f/78fdUh/0fhk4iIiIiIiIj8a6tWrSI9PR1vb29sbGywsbFh+fLlfPLJJ8THx+d4fVhYGP/73//o2LEj3333HTVq1GDjxo15WmNiYiL169cnJibG4nXs2DGeeeYZ4OZMqOjoaJo2bcqGDRuoWrUqP//8c57W8aBR+CQiIiIiIiIi/0p6ejrvvvsuCxYssAh19u/fj7e3Nx988EGu+qlatSrDhw9n69atdO3alfDwcABsbW3JyMjIVR//DIp+/vln89K5evXqcfz4ccqVK4e/v7/Fy83NzXxN3bp1GTduHDt37uQ///kP69aty9XYcnsKn0RERERERETkX9m0aRNXr16lf//+/Oc//7F4devWLceld8nJyYSGhhIZGcnZs2eJiopi9+7d5tDI19eXxMREtm/fTlxcHElJSXfs66OPPmL16tUcO3aM119/nV9++YXQ0FDg5qbmRqORp556ih9//JHTp08TGRnJkCFDOHfuHKdPn2bcuHFER0dz9uxZtm7dyvHjx7Xv07+kDcdFREREREREiolLl/8qkuOsWrWKtm3bWsweuqVbt27MnTuXAwcO3PF6a2trLl++TN++ffnrr78wGo107dqVKVOmANC0aVNeeuklevbsyeXLl3n99dcJCwu7bV9Tpkxh/fr1vPLKK3h5efHBBx9Qo0YNABwdHdmxYwdjxoyha9euXL9+nQoVKtCmTRtcXV1JTk7myJEjrFmzhsuXL+Pl5cWrr77KoEGD7unzEEuGrNw+87AES0hIwM3Njfj4eFxdXQu7HJE8c/XqVTw8PDjV4wfc7XL+2r5mSqDyhy25cuUKpUuXLoAKRUREREQeTHf7PjQlJYXTp0/j5+eHvb09cPNJcNWrVyc5ObnAanRwcODIkSP4+PgU2Jj/lsFgYOPGjXTu3LmwSynxbvd1eiea+SQiIiIiIiJSxPn4+HDkyBHi4uIKbEyj0VisgicpuhQ+iYiIiIiIiBQDPj4+CoOkWFL4JCIiIiIiIiIlgnYWKpr0tDsREREREREREck3Cp9ERERERERERCTfKHwSEREREREREZF8o/BJRERERERERETyjcInERERERERERHJNwqfREREREREREQk39gUdgEiIiIiIiIikrPY2Fji4uIKbDyj0YiPj0+e9BUREcGwYcO4du1anvSXX/K6zuJy3/lN4ZOIiIiIiIhIERcbG0tg9eokJScX2JiODg4cPnIk1wFUSEgIa9asAaBUqVL4+PjQt29fxo8fn59l5qmePXvSoUOHwi6jxFH4JCIiIiIiIlLExcXFkZSczNvdOlDNWCbfxzsad5lBn3xFXFzcPc1+Cg4OJjw8HJPJxFdffcWrr75KqVKl8PLyysdq846DgwMODg6FXUaJoz2fRERERERERIqJasYy1PYun++v+w247Ozs8PT0pFKlSrz88su0bduWL774wnz+m2++ITAwEGdnZ4KDgzl//rz5XGZmJlOnTuWhhx7Czs6OOnXqsGXLFov+z507R+/evfHw8MDJyYkGDRqwa9cuAE6ePMlTTz1F+fLlcXZ2pmHDhnz77bcW15tMJkaOHEmFChVwcnLikUceITIy0nw+IiICd3d3i2u+/PJLGjZsiL29PUajkS5dupjPXb16lb59+1K6dGkcHR1p3749x48fv+Pnk5saSyKFTyIiIiIiIiKSLxwcHEhNTQUgKSmJ+fPn895777Fjxw5iY2MZOXKkue3ixYtZsGAB8+fP58CBAwQFBfHkk0+aw5zExERatmzJH3/8wRdffMH+/fsZPXo0mZmZ5vMdOnRg+/bt7Nu3j+DgYJ544gliY2PNY4SGhhIdHc369es5cOAA3bt3Jzg4+I6B0ebNm+nSpQsdOnRg3759bN++nUaNGpnPh4SEsGfPHr744guio6PJysqiQ4cOpKWl3ba/3NRYEmnZnYiIiIiIiIjkqaysLLZv384333zD4MGDAUhLS2PFihVUqVIFuBkETZ061XzN/PnzGTNmDL169QJgzpw5fP/997zxxhu8+eabrFu3jkuXLrF79248PDwA8Pf3N19fu3ZtateubX4/bdo0Nm7cyBdffEFoaCixsbGEh4cTGxuLt7c3ACNHjmTLli2Eh4czc+bMbPcxY8YMevXqxZQpUyzGATh+/DhffPEFUVFRNG3aFIC1a9dSsWJFPvvsM7p3756tv5xqLKkUPomIiIiIiIhInti0aRPOzs6kpaWRmZnJM888Q1hYGB999BGOjo7m4AnAy8uLixcvApCQkMCff/5Js2bNLPpr1qwZ+/fvByAmJoa6deuag6d/SkxMJCwsjM2bN3P+/HnS09NJTk42zyo6ePAgGRkZVK1a1eI6k8lEmTK3X2YYExPDgAEDbnvu8OHD2NjY8Mgjj5iPlSlThmrVqnH48OH7qrGkUvgkIiIiIiIiInmidevWLF++HFtbW7y9vbGx+b/YoVSpUhZtDQYDWVlZue47p43AR44cybZt25g/fz7+/v44ODjw9NNPm5f9JSYmYm1tzd69e7G2tra41tnZ+b7GvFc51VhSac8nEREREREREckTTk5O+Pv74+PjYxE85cTV1RVvb2+ioqIsjkdFRVGjRg0AatWqRUxMDFeuXLltH1FRUYSEhNClSxdq1qyJp6cnZ86cMZ+vW7cuGRkZXLx4EX9/f4uXp6fnbfusVasW27dvv+25wMBA0tPTzRueA1y+fJmjR4+aa77XGksqhU8iIiIiIiIiUuhGjRrFnDlz2LBhA0ePHmXs2LHExMQwdOhQAHr37o2npyedO3cmKiqKU6dO8cknnxAdHQ1AQEAAn376KTExMezfv59nnnnGvBk5QNWqVenTpw99+/bl008/5fTp0/zyyy/MmjWLzZs337am119/nQ8++IDXX3+dw4cPc/DgQebMmWMe76mnnmLAgAH89NNP7N+/n2effZYKFSrw1FNP3ba/nGosqbTsTkRERERERKSYOBp3uUSN83dDhgwhPj6e1157jYsXL1KjRg2++OILAgICALC1tWXr1q289tprdOjQgfT0dGrUqMGbb74JwMKFC3nhhRdo2rQpRqORMWPGkJCQYDFGeHg406dP57XXXuOPP/7AaDTSuHFjOnXqdNuaWrVqxUcffcS0adOYPXs2rq6utGjRwqK/oUOH0qlTJ1JTU2nRogVfffVVtiWGt+SmxpLIkHUvCyxLqISEBNzc3IiPj8fV1bWwyxHJM1evXsXDw4NTPX7A3S7nr+1rpgQqf9iSK1euULp06QKoUERERETkwXS370NTUlI4ffo0fn5+2NvbAxAbG0tg9eokJScXWI2ODg4cPnIEHx+fAhtTio/bfZ3eiWY+iYiIiIiIiBRxPj4+HD5yhLi4uAIb02g0KniSPKHwSURERERERKQY8PHxURgkxZI2HBcRERERERERkXyj8ElERERERERERPKNwicREREREREREck3Cp9ERERERERERCTfKHwSEREREREREZF8o/BJRERERERERETyjcInERERERERERHJNzaFOfiOHTuYN28ee/fu5fz582zcuJHOnTubzxsMhtteN3fuXEaNGgWAr68vZ8+etTg/a9Ysxo4dm291i4iIiIiIiBS02NhY4uLiCmw8o9GIj49PgY13v8LCwvjss8+IiYkp7FLkDgo1fLpx4wa1a9fmhRdeoGvXrtnOnz9/3uL9119/Tf/+/enWrZvF8alTpzJgwADzexcXl/wpWERERERERKQQxMbGEhhYjaSklAIb09HRnsOHj+Y6gAoJCWHNmjUA2NjY8NBDD9G9e3emTp2Kvb19vtU5cuRIBg8enG/9y79XqOFT+/btad++/R3Pe3p6Wrz//PPPad26NZUrV7Y47uLikq2tiIiIiIiISEkRFxdHUlIKy0bVwt/HKd/HOxF7g9B5B4iLi7un2U/BwcGEh4eTlpbG3r176devHwaDgTlz5uRbrc7Ozjg7O+db//LvFZs9n/766y82b95M//79s52bPXs2ZcqUoW7dusybN4/09PS79mUymUhISLB4iYiIiIiIiBR1/j5O1PJ3y/fX/QZcdnZ2eHp6UrFiRTp37kzbtm3Ztm0bcHPbnDfeeMOifZ06dQgLCzO/NxgMvP3223Tq1AlHR0cCAwOJjo7mxIkTtGrVCicnJ5o2bcrJkyfN14SFhVGnTh3z+5CQEDp37sz8+fPx8vKiTJkyvPrqq6SlpZnbXL16lb59+1K6dGkcHR1p3749x48ft6gtKiqKVq1a4ejoSOnSpQkKCuLq1avAzVxhyJAhlCtXDnt7ex599FF2795tcf3//vc/OnXqhKurKy4uLjRv3tyi7tWrV/Pwww9jZ2eHl5cXoaGhAJw5cwaDwWCxjPDatWsYDAYiIyPN9ffp04eyZcvi4OBAQEAA4eHhuftNKgTFJnxas2YNLi4u2ZbnDRkyhPXr1/P9998zaNAgZs6cyejRo+/a16xZs3BzczO/KlasmJ+li4iIiIiIiDxwfvvtN3bu3Imtre09XTdt2jT69u1LTEwM1atX55lnnmHQoEGMGzeOPXv2kJWVZQ5q7uT777/n5MmTfP/996xZs4aIiAgiIiLM50NCQtizZw9ffPEF0dHRZGVl0aFDB3NAFRMTQ5s2bahRowbR0dH89NNPPPHEE2RkZAAwevRoPvnkE9asWcOvv/6Kv78/QUFBXLlyBYA//viDFi1aYGdnx3fffcfevXt54YUXzJNlli9fzquvvsrAgQM5ePAgX3zxBf7+/rn+jCZNmsShQ4f4+uuvOXz4MMuXL8doNN7Lx1ygCnXZ3b1YvXo1ffr0ybZOdMSIEeZf16pVC1tbWwYNGsSsWbOws7O7bV/jxo2zuC4hIUEBlIiIiIiIiMi/tGnTJpydnUlPT8dkMmFlZcWyZcvuqY/nn3+eHj16ADBmzBiaNGnCpEmTCAoKAmDo0KE8//zzd+2jdOnSLFu2DGtra6pXr07Hjh3Zvn07AwYM4Pjx43zxxRdERUXRtGlTANauXUvFihX57LPP6N69O3PnzqVBgwa89dZb5j4ffvhh4Ob+1cuXLyciIsK8ldA777zDtm3bWLVqFaNGjeLNN9/Ezc2N9evXU6pUKQCqVq1q7mv69Om89tprDB061HysYcOGuf6MYmNjqVu3Lg0aNABuzioryopF+PTjjz9y9OhRNmzYkGPbRx55hPT0dM6cOUO1atVu28bOzu6OwZSIiIiIiIiI3J/WrVuzfPlybty4waJFi7Cxscn20LCc1KpVy/zr8uXLA1CzZk2LYykpKSQkJODq6nrbPh5++GGsra3N7728vDh48CAAhw8fxsbGhkceecR8vkyZMlSrVo3Dhw8DN2c+de/e/bZ9nzx5krS0NJo1a2Y+VqpUKRo1amRxffPmzc3B099dvHiRP//8kzZt2tz9g7iLl19+mW7duvHrr7/Srl07OnfubA7SiqJisexu1apV1K9fn9q1a+fYNiYmBisrK8qVK1cAlYmIiIiIiIjILU5OTvj7+1O7dm1Wr17Nrl27WLVqFQBWVlZkZWVZtP/7Pky3/D2wMRgMdzyWmZl5xzr+GfoYDIa7tv8nBweHXLe91+tz6tvK6mZU8/fP6p+fU/v27Tl79izDhw83B1kjR478FxXnr0Kd+ZSYmMiJEyfM70+fPk1MTAweHh7m3fQTEhL46KOPWLBgQbbro6Oj2bVrF61bt8bFxYXo6GiGDx/Os88+S+nSpQvsPkREpHg5d+4ccXFxuW5vNBp56KGH8rEiERERkZLHysqK8ePHM2LECJ555hnKli3L+fPnzecTEhI4ffp0gdcVGBhIeno6u3btMs8Wunz5MkePHqVGjRrAzdlX27dvZ8qUKdmur1KlCra2tkRFRVGpUiXgZji0e/duhg0bZr5+zZo1pKWlZQvCXFxc8PX1Zfv27bRu3Tpb/2XLlgXg/Pnz1K1bF8Bi8/G/t+vXrx/9+vWjefPmjBo1ivnz59/fh5LPCjV82rNnj8UHfWsfpn79+pk3Alu/fj1ZWVn07t072/V2dnasX7+esLAwTCYTfn5+DB8+3GI/JxERkb9LSkqisp8faTk8GfXvStnYcC0+HkdHx3ysTERERKTk6d69u3kPpMcee4yIiAieeOIJ3N3dmTx5ssXSuIISEBDAU089xYABA3j77bdxcXFh7NixVKhQgaeeegq4uVd0zZo1eeWVV3jppZewtbXl+++/p3v37hiNRl5++WVGjRplnjwzd+5ckpKS6N+/PwChoaEsXbqUXr16MW7cONzc3Pj5559p1KgR1apVIywsjJdeeoly5crRvn17rl+/TlRUFIMHD8bBwYHGjRsze/Zs/Pz8uHjxIhMnTrS4h8mTJ1O/fn0efvhhTCYTmzZtIjAwsMA/y9wq1PCpVatW2abc/dPAgQMZOHDgbc/Vq1ePn3/+OT9KExGREspkMpGWns7m53vgkov9/66bTHQM/xCTyaTwSURERArdidgbxWocGxsbQkNDmTt3LsePH+f06dN06tQJNzc3pk2bVigznwDCw8MZOnQonTp1IjU1lRYtWvDVV19ZbA6+detWxo8fT6NGjXBwcOCRRx4xT4yZPXs2mZmZPPfcc1y/fp0GDRrwzTffmFdhlSlThu+++45Ro0bRsmVLrK2tqVOnjnmfqH79+pGSksKiRYsYOXIkRqORp59+2lzf6tWr6d+/P/Xr16datWrMnTuXdu3amc/b2toybtw4zpw5g4ODA82bN2f9+vUF9fHdM0NWTunPAyAhIQE3Nzfi4+PvuFmZSHF09epVPDw8ONXjB9ztcv7avmZKoPKHLbly5YqWrkqJdevPxemxobg72OfY/lpyCn6zl+nPhYiIiOSpu30fmpKSwunTp/Hz8zM/8T02NpbAwGokJaUUWI2OjvYcPnzUvC2OyN/d7uv0TorF0+5EREREREREHmQ+Pj4cPnz0nvat/LeMRqOCJ8kTCp9EREREREREigEfHx+FQVIsKXwSkQdWUlISJpMpV23t7Oy034+IiIiIiMh9UPgkIg+kpKQkKvv48dfli7lqX75MOU7FnlYAJSIiIiIico8UPonIA8lkMvHX5Yvs67wJN1uXu7aNT71O3c866WlnIiIiIiIi90Hhk4g80NxsXXL1JEARERERERG5P1aFXYCIiIiIiIiIiJRcCp9ERERERERERCTfaNmdiNzWvTwJDvQ0OBEREREREbk9hU8ikk1SUhKBAdVz/SQ40NPgRERERETyW2xsLHFxcQU2ntFoxMfHp8DGi4yMpHXr1ly9ehV3d/cCG/ffiIiIYNiwYVy7dq2wSynSFD6JSDapqam5fhIc6GlwIiIiIiL5LTY2luqB1UlOSi6wMR0cHThy+EiuA6iQkBDWrFmT7XhQUBBbtmzJ6/Luy5kzZ/Dz82Pfvn3UqVPnX/fXs2dPOnTo8O8LK+EUPonIHelJcCIiIiIiRUNcXBzJScl0HP8IZXzy///ol2MT2DxzF3Fxcfc0+yk4OJjw8HCLY3Z2dnldXpGQlpaGg4MDDg4OhV1KkacNx0VERERERESKiTI+rnhW9cj31/0GXHZ2dnh6elq8SpcuzZkzZzAYDMTExJjbXrt2DYPBQGRkpEUfUVFR1KpVC3t7exo3bsxvv/1mPnf58mV69+5NhQoVcHR0pGbNmnzwwQcW12dmZjJ37lz8/f2xs7PDx8eHGTNmAODn5wdA3bp1MRgMtGrVynzdypUrCQwMxN7enurVq/PWW2+Zz92qf8OGDbRs2RJ7e3vWrl1LRESExRLBkJAQOnfubFHPsGHDLMZp1aoVgwcPZtiwYZQuXZry5cvzzjvvcOPGDZ5//nlcXFzw9/fn66+/vodPvmhT+CQiIiIiIiIiRcaoUaNYsGABu3fvpmzZsjzxxBOkpaUBkJKSQv369dm8eTO//fYbAwcO5LnnnuOXX34xXz9u3Dhmz57NpEmTOHToEOvWraN8+fIA5nbffvst58+f59NPPwVg7dq1TJ48mRkzZnD48GFmzpzJpEmTsi0jHDt2LEOHDuXw4cMEBQXd9z2uWbMGo9HIL7/8wuDBg3n55Zfp3r07TZs25ddff6Vdu3Y899xzJCUl3fcYRYnCJxERERERERHJE5s2bcLZ2dniNXPmzHvq4/XXX+fxxx+nZs2arFmzhr/++ouNGzcCUKFCBUaOHEmdOnWoXLkygwcPJjg4mA8//BCA69evs3jxYubOnUu/fv2oUqUKjz76KC+++CIAZcuWBaBMmTJ4enri4eFhHnPBggV07doVPz8/unbtyvDhw3n77bctahs2bJi5jZeX131/TrVr12bixIkEBAQwbtw47O3tMRqNDBgwgICAACZPnszly5c5cODAfY9RlGjPJxERERERERHJE61bt2b58uUWxzw8PEhISMh1H02aNLG4tlq1ahw+fBiAjIwMZs6cyYcffsgff/xBamqqxYOPDh8+jMlkok2bNrke78aNG5w8eZL+/fszYMAA8/H09HTc3Nws2jZo0CDX/d5NrVq1zL+2tramTJky1KxZ03zs1kytixdz/wTyokzhk4iIPJAuJ94gPT09x3bxKaYCqEZERESkZHBycsLf3z/b8cTERACysrLMx24tpbsX8+bNY/HixbzxxhvUrFkTJycnhg0bRmpqKsB9bf59q7Z33nmHRx55xOKctbW1xXsnJ6e79mVlZWVxj3D7+yxVqpTFe4PBYHHMYDAAN/evKgkUPomIyAMlIyODUtZWNFgWnnPj/6+UtRUZGRn5WJWIiIhIyXZrudv58+epW7cugMXm43/3888/m5+wd/XqVY4dO0ZgYCBwczPyp556imeffRa4Gc4cO3aMGjVqABAQEICDgwPbt283L7X7O1tbWwCL/9uVL18eb29vTp06RZ8+ff71ff59g/Rb9/nPsOlBo/BJREQeKNbW1qRlZLJ1WRNcHHP+T8D1pDTahUZn+6mXiIiIiGRnMpm4cOGCxTEbGxuMRiONGzdm9uzZ+Pn5cfHiRSZOnHjbPqZOnUqZMmUoX748EyZMwGg0mp8gFxAQwMcff8zOnTspXbo0Cxcu5K+//jKHT/b29owZM4bRo0dja2tLs2bNuHTpEv/73//o378/5cqVw8HBgS1btvDQQw9hb2+Pm5sbU6ZMYciQIbi5uREcHIzJZGLPnj1cvXqVESNG5Pr+H3vsMebNm8e7775LkyZNeP/99/ntt9/MgduDSuGTiIg8kB4q54i7S87h07Xr9z4dXERERCS/XI7N/d5JhTHOli1bsm3EXa1aNY4cOcLq1avp378/9evXp1q1asydO5d27dpl62P27NkMHTqU48ePU6dOHb788kvzjKWJEydy6tQpgoKCcHR0ZODAgXTu3Jn4+Hjz9ZMmTcLGxobJkyfz559/4uXlxUsvvQTcDMKWLFnC1KlTmTx5Ms2bNycyMpIXX3wRR0dH5s2bx6hRo3BycqJmzZoMGzbsnu4/KCiISZMmMXr0aFJSUnjhhRfo27cvBw8evMdPsmQxZP1zMeIDKCEhATc3N+Lj43F1dS3sckTyzNWrV/Hw8ODnDp/gWso5x/YJaYk0/qobp06donLlypzq8QPudjn/mbhmSqDyhy25cuUKpUuXzovS892tzyY391gc70/u7Nbv/fZl9XF1yvlnMAk30mkTule//yIiIpKn7vZ9aEpKCqdPn8bPzw97e3sAYmNjqR5YneSk5AKr0cHRgSOHj5iXwIn83e2+Tu9EM59ESrCMjAxsDDY0/qpbrq+xMdhobxsp0TIyMrCxhjahe3N9jY01+nMhIiIihcrHx4cjh48QFxdXYGMajUYFT5InFD6JlGDW1takZ6Wz+fkeuNjZ5dj+uslEx/APtbeNlGjW1takZ8Ar7aZiZ+OYY3tTehJvbZ2sPxciIiJS6Hx8fBQGSbGk8EnkAVCjfDncHe4+DRLgWnJKAVQjUjRUKf8fnOxccmx3w3S9AKoRERERESm5rAq7ABERERERERERKbkUPomIiIiIiIiISL5R+CQiIiIiIiIiIvlG4ZOIiIiIiIiIiOQbhU8iIiIiIiIiIpJvFD6JiIiIiIiIiEi+sSnsAkREREREREQkZ7GxscTFxRXYeEajER8fn3u+7sKFC8yaNYvNmzdz7tw53Nzc8Pf359lnn6Vfv344OjoCsG/fPmbPns2OHTu4cuUKnp6e1KxZk0GDBtGpUycMBgNnzpzBz8/P3HepUqXw8fEhJCSECRMmYDAYAIiIiOD555+3qMPOzo6UlBTz+08//ZQVK1awd+9erly5wr59+6hTp062+qOjo5kwYQK7du3C2tqaOnXq8M033+Dg4GDRzmQy8cgjj7B//36LvsLCwpgyZUq2fh0dHblx48Y9f54lgcInERERERERkSIuNjaW6tWqk5ySXGBjOtg7cOTokXsKoE6dOkWzZs1wd3dn5syZ1KxZEzs7Ow4ePMh///tfKlSowJNPPsnnn39Ojx49aNu2LWvWrMHf3x+TycTOnTuZOHEizZs3x93d3dzvt99+y8MPP4zJZOKnn37ixRdfxMvLi/79+5vbuLq6cvToUfP7W8HULTdu3ODRRx+lR48eDBgw4Lb1R0dHExwczLhx41i6dCk2Njbs378fK6vsC8dGjx6Nt7c3+/fvtzg+cuRIXnrpJYtjbdq0oWHDhrn+HEsahU8iIiIiIiIiRVxcXBzJKcm0/U9HPJzK5Pt4V25c5tvfNhMXF3dP4dMrr7yCjY0Ne/bswcnJyXy8cuXKPPXUU2RlZXHjxg369+9Px44d+fTTTy2uDwwMpH///mRlZVkcL1OmDJ6engBUqlSJ8PBwfv31V4vwyWAwmNvcznPPPQfAmTNn7thm+PDhDBkyhLFjx5qPVatWLVu7r7/+mq1bt/LJJ5/w9ddfW5xzdnbG2dnZ/H7//v0cOnSIFStW3HHckk7hk4iIiIiIiEgx4eFUhrKu5Qu7jNu6fPkyW7duZebMmRbB098ZDAa2bt3K5cuXGT169B37+uespb/bs2cPe/fupW/fvhbHExMTqVSpEpmZmdSrV4+ZM2fy8MMP57r+ixcvsmvXLvr06UPTpk05efIk1atXZ8aMGTz66KPmdn/99RcDBgzgs88+My8hvJuVK1dStWpVmjdvnutaShptOC4iIiIiIiIi/9qJEyfIysrKNlPIaDSaZwONGTOGY8eOAZYzinbv3m1u4+zszKZNmyz6aNq0Kc7Oztja2tKwYUN69OhhET5Vq1aN1atX8/nnn/P++++TmZlJ06ZNOXfuXK7rP3XqFHBzz6YBAwawZcsW6tWrR5s2bTh+/DgAWVlZhISE8NJLL9GgQYMc+0xJSWHt2rUWM7QeRJr5JCIiIiIiIiL55pdffiEzM5M+ffpgMplu26ZWrVrExMQAEBAQQHp6usX5DRs2EBgYSFpaGr/99huDBw+mdOnSzJ49G4AmTZrQpEkTc/umTZsSGBjI22+/zbRp03JVZ2ZmJgCDBg0yb15et25dtm/fzurVq5k1axZLly7l+vXrjBs3Lld9bty4kevXr9OvX79ctS+pNPNJRERERERERP41f39/DAaDxabfcHO/J39/f/PT4gICAgAs2tnZ2eHv74+/v/9t+65YsSL+/v4EBgbSvXt3hg0bxoIFCyyeZvd3pUqVom7dupw4cSLX9Xt5eQFQo0YNi+OBgYHExsYC8N133xEdHY2dnR02Njbmehs0aHDbgGnlypV06tSJ8uWL5lLJgqLwSURERERERET+tTJlyvD444+zbNkybty4ccd27dq1w8PDgzlz5tz3WNbW1qSnp5Oamnrb8xkZGRw8eNAcKOWGr68v3t7e2cKzY8eOUalSJQCWLFnC/v37iYmJISYmhq+++gq4OTNrxowZFtedPn2a77///oFfcgdadiciIiIiIiIieeStt96iWbNmNGjQgLCwMGrVqoWVlRW7d+/myJEj1K9fH2dnZ1auXEnPnj3p2LEjQ4YMISAggMTERLZs2QLcDJf+7vLly1y4cIH09HQOHjzI4sWLad26Na6urgBMnTqVxo0b4+/vz7Vr15g3bx5nz57lxRdfNPdx5coVYmNj+fPPP4H/m3nl6emJp6cnBoOBUaNG8frrr1O7dm3q1KnDmjVrOHLkCB9//DFAtif/3XqqXZUqVXjooYcszq1evRovLy/at2+fVx9vsaXwSURERERERKSYuHLjcpEep0qVKuzbt4+ZM2cybtw4zp07h52dHTVq1GDkyJG88sorAHTp0oWdO3cyZ84c+vbty5UrV3Bzc6NBgwasX7+eTp06WfTbtm1b4GYo5eXlRYcOHSxmGl29epUBAwZw4cIFSpcuTf369dm5c6fFErovvvjCvJcTQK9evQB4/fXXCQsLA2DYsGGkpKQwfPhwrly5Qu3atdm2bRtVqlS5p88hMzOTiIgIQkJCsgVpDyKFTyIiIiIiJUBSUtIdN/K9HTs7u1w9IlxEigaj0YiDvQPf/ra5wMZ0sHfAaDTe83VeXl4sXbqUpUuX3rVdgwYN+Oijj+7axtfXl6ysrBzHXLRoEYsWLbprm5CQEEJCQnLsa+zYsYwdOzbHdnerz8rKit9//z1XfTwIFD6JiIiIiBRzSUlJVPbx46/LF3N9Tfky5TgVe1oBlEgx4ePjw5GjR4iLiyuwMY1GY7ZlZiL3Q+GTlBj38tM+/aRPREREShKTycRfly+yr/Mm3Gxdcmwfn3qdup91wmQy6f9EIsWIj4+PwiAplhQ+SYlwrz/t00/6REREpCRys3XB3c61sMsQERGxoPBJSoR7+WmfftInIiIiIiIiUnAUPkmJop/2iYiIiIiIiBQtCp9EREREREqIy8lXSc9Iz7FdfOr1AqhGRETkJoVPIiIiIiLFXEZGBqWsbGj4ZedcX1PKyoaMjIz8K0pEROT/U/gkIiIiIlLMWVtbk5aZTujzI7C3c8ixfYopmWXhC7G2ti6A6kRE5EGn8ElEREREpIQo7eaBg33OD1RJTkkqgGpERERusirsAkREREREREREihpfX1/eeOMN83uDwcBnn31WaPUUZ5r5JCIiIiIiIlIMxMbGEhcXV2DjGY1GfHx8ct0+JCSENWvWAFCqVCl8fHzo27cv48ePx8am8OIHX19fhg0bxrBhwwqthgedwicRERERERGRIi42Npbq1QNJTi64ZbMODo4cOXL4ngKo4OBgwsPDMZlMfPXVV7z66quUKlWKcePGWbRLTU3F1tY2r0uWIkrhk4iIiIiIiEgRFxcXR3JyEs+3GouXe+7DoPt1/los4ZGziYuLu6fwyc7ODk9PTwBefvllNm7cyBdffMHRo0e5du0aDRs25M0338TOzo7Tp0/z+++/89prr7F161asrKxo3rw5ixcvxtfXl61bt/Lkk09y4cIF3N3dzWMMHTqUgwcP8t133wHwySefMHnyZE6cOIGXlxeDBw/mtddeA6BVq1acPXuW4cOHM3z4cACysrIA+Omnnxg3bhx79uzBaDTSpUsXZs2ahZOTU67udcyYMWzcuJFz587h6elJnz59mDx5MqVKlcr15/WgUPgkIiIiIiIiUkx4ufvgYwwo7DJyzcHBgcuXLwOwfft2XF1d2bZtGwBpaWkEBQXRpEkTfvzxR2xsbJg+fTrBwcEcOHCANm3a4O7uzieffEL//v0ByMjIYMOGDcyYMQOAvXv30qNHD8LCwujZsyc7d+7klVdeoUyZMoSEhPDpp59Su3ZtBg4cyIABA8x1nTx5kuDgYKZPn87q1au5dOkSoaGhhIaGEh4enqt7c3FxISIiAm9vbw4ePMiAAQNwcXFh9OjRefkRlggKn0REREREREQkT2VlZbF9+3a++eYbBg8ezKVLl3BycmLlypXm5Xbvv/8+mZmZrFy5EoPBAEB4eDju7u5ERkbSrl07evXqxbp168zh0/bt27l27RrdunUDYOHChbRp04ZJkyYBULVqVQ4dOsS8efMICQnBw8MDa2trXFxczDOyAGbNmkWfPn3M+0AFBASwZMkSWrZsyfLly7G3t8/xHidOnGj+ta+vLyNHjmT9+vUKn25DT7sTERERERERkTyxadMmnJ2dsbe3p3379vTs2ZOwsDAAatasabHP0/79+zlx4gQuLi44Ozvj7OyMh4cHKSkpnDx5EoA+ffoQGRnJn3/+CcDatWvp2LGjeRne4cOHadasmUUNzZo14/jx42RkZNyxzv379xMREWEe19nZmaCgIDIzMzl9+nSu7nXDhg00a9YMT09PnJ2dmThxIrGxsbn9qB4omvkkIiIiIiIiInmidevWLF++HFtbW7y9vS2ecvfPvZQSExOpX78+a9euzdZP2bJlAWjYsCFVqlRh/fr15j2kIiIi/nWdiYmJDBo0iCFDhmQ7l5s9rqKjo+nTpw9TpkwhKCgINzc31q9fz4IFC/51bSWRwicRERERERERyRNOTk74+/vnqm29evXYsGED5cqVw9XV9Y7t+vTpw9q1a3nooYewsrKiY8eO5nOBgYFERUVZtI+KiqJq1apYW1sDYGtrm20WVL169Th06FCua/2nnTt3UqlSJSZMmGA+dvbs2fvq60GgZXciIiIiIiIiUuD69OmD0Wjkqaee4scff+T06dNERkYyZMgQzp07Z9Hu119/ZcaMGTz99NPY2dmZz7322mts376dadOmcezYMdasWcOyZcsYOXKkuY2vry87duzgjz/+IC4uDrj5pLqdO3cSGhpKTEwMx48f5/PPPyc0NDRXtQcEBBAbG8v69es5efIkS5YsYePGjXn0yZQ8mvkkIiIiIiL35fLlyyQkJOSqraurK2XKlMnnikRKvvPXCmZPoYIYx9HRkR07djBmzBi6du3K9evXqVChAm3atLGYCeXv70+jRo345ZdfeOONNyz6qFevHh9++CGTJ09m2rRpeHl5MXXqVEJCQsxtpk6dyqBBg6hSpQomk4msrCxq1arFDz/8wIQJE2jevDlZWVlUqVKFnj175qr2J598kuHDhxMaGorJZKJjx45MmjTJvL+VWCrU8GnHjh3MmzePvXv3cv78eTZu3Ejnzp3N50NCQlizZo3FNUFBQWzZssX8/sqVKwwePJgvv/wSKysrunXrxuLFi3F2di6o2xAREREReeAkJSVRsaIPyclJuWrv4OBIXNwlHB0d87kykZLJaDTi4OBIeOTsAhvTwcERo9GY6/Z324vpTuc8PT2zfd9/O7t27brjuW7dupmffnc7jRs3Zv/+/dmON2zYkK1bt97xujNnzli8z8rKsng/d+5c5s6da3Hs1tPzxFKhhk83btygdu3avPDCC3Tt2vW2bYKDgwkPDze///v0Org5/e78+fNs27aNtLQ0nn/+eQYOHMi6devytXYpmi4nXyU9I/2ubeJTrxdQNSIiIiIll8lkIjk5idDnR2Bv53DXtimmZJaFL8RkMil8ErlPPj4+HDly2LxsrCAYjcZcbb4tkpNCDZ/at29P+/bt79rGzs4OT0/P2547fPgwW7ZsYffu3TRo0ACApUuX0qFDB+bPn4+3t3ee1yxFU0ZGBqWsbGj4ZedctS9lZXPXx26KiIiISO6UdvPAwf7ugVJySu5mR4nI3fn4+CgMkmKpyO/5FBkZSbly5ShdujSPPfYY06dPN68Vj46Oxt3d3Rw8AbRt2xYrKyt27dpFly5dCqtsKWDW1takZabf00/ebj35QERERERERETyT5EOn4KDg+natSt+fn6cPHmS8ePH0759e6Kjo7G2tubChQuUK1fO4hobGxs8PDy4cOHCHfs1mUyYTCbz+9xukihFn37yJiIiIiIiIlK0FOnwqVevXuZf16xZk1q1alGlShUiIyNp06bNffc7a9YspkyZkhclioiIiIiIiIjIXVgVdgH3onLlyhiNRk6cOAHc3BX/4sWLFm3S09O5cuXKHfeJAhg3bhzx8fHm1++//56vdYuIiIiIiIiIPKiKVfh07tw5Ll++jJeXFwBNmjTh2rVr7N2719zmu+++IzMzk0ceeeSO/djZ2eHq6mrxEhERERERERGRvFeoy+4SExPNs5gATp8+TUxMDB4eHnh4eDBlyhS6deuGp6cnJ0+eZPTo0fj7+xMUFARAYGAgwcHBDBgwgBUrVpCWlkZoaCi9evXSk+5ERERE5IGTmHid9PS0HNulpKQUQDUiIiI3FWr4tGfPHlq3bm1+P2LECAD69evH8uXLOXDgAGvWrOHatWt4e3vTrl07pk2bhp2dnfmatWvXEhoaSps2bbCysqJbt24sWbKkwO9FRERERKSwZGRkYG1lzaKVc3J9jbWVNRkZGflYlYiIyE2FGj61atWKrKysO57/5ptvcuzDw8ODdevW5WVZIiIiIiLFirW1NRmZGex46Tlc7e1ybJ+QYqLFivewtrYugOpERG46c+YMfn5+7Nu3jzp16hAZGUnr1q25evUq7u7uhVaXr68vw4YNY9iwYQAYDAY2btxI586dC62mkqZIP+1O5F7lZqq5ppmLiIhISVXR3Q13B/sc211L1v+HRIqj2NhY4uLiCmw8o9GIj49PrtuHhISwZs0aAGxsbHjooYfo3r07U6dOxd7enooVK3L+/HmMRmN+lSxFlMInKRHudaq5ppmLiIiIiEhxEhsbS2BgdZKSkgtsTEdHBw4fPnJPAVRwcDDh4eGkpaWxd+9e+vXrh8FgYM6cOVhbW9/1yfRScil8khLhXqaaa5q5iIiIiIgUN3FxcSQlJfP2vP5UrZL/Ac6xkxcYNGoVcXFx9xQ+2dnZmQOmihUr0rZtW7Zt28acOXOyLbv7p6SkJLp160ZCQgKbN2/G3d2dlStXsmDBAk6fPo2vry9DhgzhlVdeAf5vGd8nn3zC0qVL2bVrFwEBAaxYsYImTZqY+/3pp58YN24ce/bswWg00qVLF2bNmoWTk1Ou7mnMmDFs3LiRc+fO4enpSZ8+fZg8eTKlSpUyt/nyyy+ZOnUqBw8exNnZmebNm7Nx40YATCYTEyZM4IMPPuDatWv85z//Yc6cObRq1QqAy5cvExoayo4dO7h69SpVqlRh/Pjx9O7d29x/ZmYmc+bM4b///S8XLlygatWqTJo0iaeffjrXvzeFSeGTlCi5mWquaeYiIiIiIlJcVa3iSe2HKxV2Gbny22+/sXPnTipVyrnea9eu0bFjR5ydndm2bRuOjo6sXbuWyZMns2zZMurWrcu+ffsYMGAATk5O9OvXz3zthAkTmD9/PgEBAUyYMIHevXtz4sQJbGxsOHnyJMHBwUyfPp3Vq1dz6dIlQkNDCQ0NJTw8PFf34eLiQkREBN7e3hw8eJABAwbg4uLC6NGjAdi8eTNdunRhwoQJvPvuu6SmpvLVV1+Zrw8NDeXQoUOsX78eb29vNm7cSHBwMAcPHiQgIICUlBTq16/PmDFjcHV1ZfPmzTz33HNUqVKFRo0aATBr1izef/99VqxYQUBAADt27ODZZ5+lbNmytGzZ8l5+WwqFwicRERERERERyRObNm3C2dmZ9PR0TCYTVlZWLFu27K7XXLhwgZ49exIQEMC6deuwtbUF4PXXX2fBggV07doVAD8/Pw4dOsTbb79tET6NHDmSjh07AjBlyhQefvhhTpw4QfXq1Zk1axZ9+vQxbyYeEBDAkiVLaNmyJcuXL8fePud98iZOnGj+ta+vLyNHjmT9+vXm8GnGjBn06tWLKVOmmNvVrl0buLlcMjw8nNjYWLy9vc31btmyhfDwcGbOnEmFChUYOXKk+drBgwfzzTff8OGHH9KoUSNMJhMzZ87k22+/Nc/oqly5Mj/99BNvv/22wicREREREREReXC0bt2a5cuXc+PGDRYtWoSNjQ3dunW76zWPP/44jRo1YsOGDebtUW7cuMHJkyfp378/AwYMMLdNT0/Hzc3N4vpatWqZf+3l5QXAxYsXqV69Ovv37+fAgQOsXbvW3CYrK4vMzExOnz5NYGBgjve0YcMGlixZwsmTJ0lMTCQ9PR1XV1fz+ZiYGIsa/+7gwYNkZGRQtWpVi+Mmk4kyZcoAN/cwnjlzJh9++CF//PEHqampmEwmHB0dAThx4gRJSUk8/vjjFn2kpqZSt27dHOsvChQ+iYiIiIiIiEiecHJywt/fH4DVq1dTu3ZtVq1aRf/+/e94TceOHfnkk084dOgQNWvWBCAxMRGAd955h0ceecSi/T/37/373ksGgwG4uUfSrX4GDRrEkCFDso2bm72soqOj6dOnD1OmTCEoKAg3NzfWr1/PggULzG0cHBzueH1iYiLW1tbs3bs3W93Ozs4AzJs3j8WLF/PGG29Qs2ZNnJycGDZsGKmpqeY+4ObyvgoVKlj0YWd39z2PiwqFTyIiIiIiIiKS56ysrBg/fjwjRozgmWeeuWO72bNn4+zsTJs2bYiMjKRGjRqUL18eb29vTp06RZ8+fe67hnr16nHo0CFzIHavbu1ZNWHCBPOxs2fPWrSpVasW27dv5/nnn892fd26dcnIyODixYs0b978tmNERUXx1FNP8eyzzwI3g7Njx45Ro0YNAGrUqIGdnR2xsbHFYond7Sh8EhEREREREZF80b17d0aNGsWbb7551yezzZ8/n4yMDB577DEiIyOpXr06U6ZMYciQIbi5uREcHIzJZGLPnj1cvXqVESNG5Gr8MWPG0LhxY0JDQ3nxxRdxcnLi0KFDbNu2Lce9qODmHlGxsbGsX7+ehg0bsnnzZvNT7G55/fXXadOmDVWqVKFXr16kp6fz1VdfMWbMGKpWrUqfPn3o27cvCxYsoG7duly6dInt27dTq1YtOnbsSEBAAB9//DE7d+6kdOnSLFy4kL/++sscPrm4uDBy5EiGDx9OZmYmjz76KPHx8URFReHq6mqx/1VRpfBJREREREREpJg4dvJCsRrHxsaG0NBQ5s6dS/v27e/adtGiRRYB1IsvvoijoyPz5s1j1KhRODk5UbNmTfPm4blRq1YtfvjhByZMmEDz5s3JysqiSpUq9OzZM1fXP/nkkwwfPpzQ0FBMJhMdO3Zk0qRJhIWFmdu0atWKjz76iGnTpjF79mxcXV1p0aKF+Xx4eDjTp0/ntdde448//sBoNNK4cWM6deoE3NzQ/NSpUwQFBeHo6MjAgQPp3Lkz8fHx5j6mTZtG2bJlmTVrFqdOncLd3Z169eoxfvz4XH8WhUnhk4iIiIiIiEgRZzQacXR0YNCoVQU2pqOjA0ajMdftIyIibnt87NixjB07Fri52fctrVq1sngPsGTJEpYsWWJ+/8wzz9xxyZ6vr2+2693d3bMda9iwIVu3br1j3WfOnLF4/8/r586dy9y5cy2O/TMA69q1q/mpfP9UqlQppkyZYvE0vL/z8PDgs88+u2N9cHMvq6FDhzJ06NC7tiuqFD6JiIiIiIiIFHE+Pj4cPnyEuLi4AhvTaDTmalNukZwofBIREREREREpBnx8fBQGSbFkVdgFiIiIiIiIiIhIyaXwSURERERERERE8o3CJxERERERERERyTcKn0REREREREREJN8ofBIRERERERERkXyjp92JiIiI5EJSUhImkylXbe3s7HB0dMznikREih/9XSryYFL4JPniXv5RgeL5D4v+4RQReXAkJSVR2cePvy5fzFX78mXKcSr2tP7uFxH5G/1dKvLgUvgkee5e/1GB4vcPi/7hFBF5sJhMJv66fJF9nTfhZuty17bxqdep+1knTCaT/t6XB0Ji4nXS09Pu2iYlJaWAqpGiTH+Xijy4FD5JnruXf1SgeP7Don84RUQeTG62LrjbuRZ2GSJFQkZGBtZW1ixaOSdX7a2trMnIyMjnqqQ40N+l9y82Npa4uLgCG89oNOLj43PP1124cIFZs2axefNmzp07h5ubG/7+/jz77LP069fP/H3Rvn37mD17Njt27ODKlSt4enpSs2ZNBg0aRKdOnTAYDJw5cwY/Pz9z36VKlcLHx4eQkBAmTJiAwWAA4NNPP2XmzJmcOHGCtLQ0AgICeO2113juuefM14aFhbF+/Xp+//13bG1tqV+/PjNmzOCRRx4xt7ly5QqDBw/myy+/xMrKim7durF48WKcnZ3Nbb755htef/11/ve//2Fvb0+LFi1YsGABvr6+AERGRtK6detsn8v58+fx9PS858+zJFD4JPnmQfhH5UG4RxF5sGhJsYjklrW1NRmZGex46Tlc7e3u2jYhxUSLFe9hbW1dQNWJlDyxsbFUDwwkOSmpwMZ0cHTkyOHD9xRAnTp1imbNmuHu7s7MmTOpWbMmdnZ2HDx4kP/+979UqFCBJ598ks8//5wePXrQtm1b1qxZg7+/PyaTiZ07dzJx4kSaN2+Ou7u7ud9vv/2Whx9+GJPJxE8//cSLL76Il5cX/fv3B8DDw4MJEyZQvXp1bG1t2bRpE88//zzlypUjKCgIgKpVq7Js2TIqV65McnIyixYtol27dpw4cYKyZcsC0KdPH86fP8+2bdtIS0vj+eefZ+DAgaxbtw6A06dP89RTTzFixAjWrl1LfHw8w4cPp2vXrvz6668Wn8XRo0dxdf2/7xfLlSt3X78PJYHCJxEREQG0pFhE7k9FdzfcHezv2uZaspbdifxbcXFxJCcl0TNiGuUC/XK+4F+6ePg0G0ImERcXd0/h0yuvvIKNjQ179uzBycnJfLxy5co89dRTZGVlcePGDfr370/Hjh359NNPLa4PDAykf//+ZGVlWRwvU6aMedZQpUqVCA8P59dffzWHT61atbJoP3ToUNasWcNPP/1kDp+eeeYZizYLFy5k1apVHDhwgDZt2nD48GG2bNnC7t27adCgAQBLly6lQ4cOzJ8/H29vb/bu3UtGRgbTp0/HysoKgJEjR/LUU0+RlpZGqVKlzP2XK1fOIkB7kCl8EhEREUBLikWk4GiWpcj9KxfoR4W61Qu7jNu6fPkyW7duZebMmRbB098ZDAa2bt3K5cuXGT169B37urWc7nb27NnD3r176du3723PZ2Vl8d1333H06FHmzLn90uDU1FT++9//4ubmRu3atQGIjo7G3d3dHDwBtG3bFisrK3bt2kWXLl2oX78+VlZWhIeHExISQmJiIu+99x5t27a1CJ4A6tSpg8lk4j//+Q9hYWE0a9bsjvdU0il8EhEREQtaUiwi+UmzLEVKrhMnTpCVlUW1atUsjhuNRvODB1599VU8PDwALNrt3r3bYp+k9evX06lTJ/P7pk2bYmVlRWpqKmlpaQwcODBb+BQfH0+FChUwmUxYW1vz1ltv8fjjj1u02bRpE7169SIpKQkvLy+2bduG0WgEbu5V9c+lcTY2Nnh4eHDhwgUA/Pz82Lp1Kz169GDQoEFkZGTQpEkTvvrqK/M1Xl5erFixggYNGmAymVi5ciWtWrVi165d1KtX794+1BJC4ZOIiIiIiBQYzbIUefD88ssvZGZm0qdPnzvOeqxVqxYxMTEABAQEkJ6ebnF+w4YNBAYGkpaWxm+//cbgwYMpXbo0s2fPNrdxcXEhJiaGxMREtm/fzogRI6hcubLFkrzWrVsTExNDXFwc77zzDj169GDXrl253o/pwoULDBgwgH79+tG7d2+uX7/O5MmTefrpp9m2bRsGg4Fq1apZBGtNmzbl5MmTLFq0iPfeey+Xn1rJovBJREREREQKnGZZipQ8/v7+GAwGjh49anG8cuXKADg4OAA3wyW4uSF348aNgZtLbP39/e/Yd8WKFc3nAwMDOXnyJJMmTSIsLAx7+5v7zllZWZnb1KlTh8OHDzNr1iyL8MnJyQl/f3/8/f1p3LgxAQEBrFq1inHjxuHp6cnFi5azMtPT081P4gN48803cXNzY+7cueY277//PhUrVmTXrl3m+/mnRo0a8dNPP93l0yvZrAq7ABEREREREREp/sqUKcPjjz/OsmXLuHHjxh3btWvXDg8Pjzvux5Qb1tbWpKenk5qaesc2mZmZOe4v9/c2TZo04dq1a+zdu9d8/rvvviMzM5NHHnkEuLl0+NZG43+v5VZfdxITE4OXl9fdb6oE08wnERERsXA5+SrpGel3bROfer2Aqslb97LJMWijYxERkXv11ltv0axZMxo0aEBYWBi1atXCysqK3bt3c+TIEerXr4+zszMrV66kZ8+edOzYkSFDhhAQEEBiYiJbtmwB/i/QueXy5ctcuHCB9PR0Dh48yOLFi2ndujWurjdnUM6aNYsGDRpQpUoVTCYTX331Fe+99x7Lly8H4MaNG8yYMYMnn3wSLy8v4uLiePPNN/njjz/o3r07cHNGVXBwMAMGDGDFihWkpaURGhpKr1698Pb2BqBjx44sWrSIqVOnmpfdjR8/nkqVKlG3bl0A3njjDfz8/Hj44YdJSUlh5cqVfPfdd2zdurVAfg+KIoVPIiIiAkBGRgalrGxo+GXnXLUvZWVDRkZG/haVh+51k2P4v42ORYqL3y9eIt7OLsd2CfcQwopI0XLxcMH8u3S/41SpUoV9+/Yxc+ZMxo0bx7lz57Czs6NGjRqMHDmSV155BYAuXbqwc+dO5syZQ9++fbly5Qpubm40aNAg22bjcPOpc3AzlPLy8qJDhw7MmDHDfP7GjRu88sornDt3DgcHB6pXr877779Pz549zdcdOXKENWvWEBcXR5kyZWjYsCE//vgjDz/8sLmftWvXEhoaSps2bbCysqJbt24sWbLEfP6xxx5j3bp1zJ07l7lz5+Lo6EiTJk3YsmWLeVlhamoqr732Gn/88QeOjo7UqlWLb7/91mJD9QeNwicREREBbv6nLC0zndDnR2Bv53DXtimmZJaFL8z2U8mi7F42OQbLjY5FirqMjAxsDAZarN6Q62tsDIZiFSCLPOiMRiMOjo5sCJlUYGM6ODqanwR3L7y8vFi6dClLly69a7sGDRrw0Ucf3bWNr68vWVlZOY45ffp0pk+ffsfz9vb2fPrppzn24+Hhwbp16+7aplevXvTq1euO50ePHs3o0aNzHOtBovBJRERELJR288DB/u5LzZJTkgqomrynTY6lJLK2tiY9K4sXWo/Dzubu4TGAKT2Z1d/PKlYBssiDzsfHhyOHDxMXF1dgYxqNRnx8fApsPCm5FD6JiIiIiJQQDz/UECe7nGf23TAVz33bRB50Pj4+CoOkWNLT7kREREREREREJN8ofBIRERERERERkXyj8ElERERERERERPKNwicREREREREREck3Cp9ERERERERERCTfKHwSEREREREREZF8o/BJRERERERERETyjcInEREREREREXmghYSE0Llz51y3j4yMxGAwcO3aNQAiIiJwd3fPl9pKApvCLkBEREREREREchYbG0tcXFyBjWc0GvHx8cl1+5CQENasWWN+7+HhQcOGDZk7dy61atXKjxLzzOLFi8nKyirsMkoshU8iIiIiIiIiRVxsbCyB1aqTlJJcYGM62jtw+OiRewqggoODCQ8PB+DChQtMnDiRTp06ERsbe181pKamYmtre1/X3gs3N7d8H+NBpvBJREREREREpIiLi4sjKSWZqQ/3x9fRK9/HO5N0nsn/W0VcXNw9hU92dnZ4enoC4OnpydixY2nevDmXLl2ibNmyjBkzho0bN3Lu3Dk8PT3p06cPkydPplSpUgCEhYXx2WefERoayowZMzh79iyZmZkYDAbeeecdNm/ezDfffEOFChVYsGABTz75pHns3377jVGjRvHjjz/i5OREu3btWLRoEUajEYCPP/6YKVOmcOLECRwdHalbty6ff/45Tk5OhISEcO3aNT777DMAMjMzmTNnDv/973+5cOECVatWZdKkSTz99NO5+hxOnjzJiBEj+Pnnn7lx4waBgYHMmjWLtm3b5vqzLEkUPomIiIiIiIgUE76OXlR3rVTYZeRKYmIi77//Pv7+/pQpUwYAFxcXIiIi8Pb25uDBgwwYMAAXFxdGjx5tvu7EiRN88sknfPrpp1hbW5uPT5kyhblz5zJv3jyWLl1Knz59OHv2LB4eHly7do3HHnuMF198kUWLFpGcnMyYMWPo0aMH3333HefPn6d3797MnTuXLl26cP36dX788cc7LrWbNWsW77//PitWrCAgIIAdO3bw7LPPUrZsWVq2bJmre+/QoQMzZszAzs6Od999lyeeeIKjR4/eU5hXUih8EhERERERyUNJSUmYTKZct7ezs8PR0TEfKxIpOJs2bcLZ2RmAGzdu4OXlxaZNm7Cyuvm8s4kTJ5rb+vr6MnLkSNavX28RPqWmpvLuu+9StmxZi75DQkLo3bs3ADNnzmTJkiX88ssvBAcHs2zZMurWrcvMmTPN7VevXk3FihU5duwYiYmJpKen07VrVypVuhne1axZ87b3YDKZmDlzJt9++y1NmjQBoHLlyvz000+8/fbbuQqfateuTe3atc3vp02bxsaNG/niiy8IDQ3N8fqSRuGTiIiIiIhIHklKSqKyjx9/Xb6Y62vKlynHqdjTCqDu4ty5c7neaNtoNPLQQw/lc0VyJ61bt2b58uUAXL16lbfeeov27dvzyy+/UKlSJTZs2MCSJUs4efKkORBydXW16KNSpUrZgifAYtNyJycnXF1duXjx5p+1/fv38/3335uDr787efIk7dq1o02bNtSsWZOgoCDatWvH008/TenSpbO1P3HiBElJSTz++OMWx1NTU6lbt26uPofExETCwsLYvHkz58+fJz09neTk5Pve+6q4U/gkIiIiIiKSR0wmE39dvsi+zptws3XJsX186nXqftYJk8mk8OkOkpKSqOznR1p6eq7al7Kx4Vp8vD7PQuLk5IS/v7/5/cqVK3Fzc+Odd96hY8eO9OnThylTphAUFISbmxvr169nwYIF2fq4nVv7Qt1iMBjIzMwEboY9TzzxBHPmzMl2nZeXF9bW1mzbto2dO3eydetWli5dyoQJE9i1axd+fn4W7RMTEwHYvHkzFSpUsDhnZ2eXq89h5MiRbNu2jfnz5+Pv74+DgwNPP/00qampubq+pFH4JCIiIiIiksfcbF1wt3PNuaHkyGQykZaezubne+CSwzf+100mOoZ/qDCvCDEYDFhZWZGcnMzOnTupVKkSEyZMMJ8/e/ZsnoxTr149PvnkE3x9fbGxuX3UYTAYaNasGc2aNWPy5MlUqlSJjRs3MmLECIt2NWrUwM7OjtjY2FwtsbudqKgoQkJC6NKlC3Az0Dpz5sx99VUSKHwSERERERGRIq9G+XK4O9jftc215JQCqkbuxGQyceHCBeDmsrtly5aZZyUlJCQQGxvL+vXradiwIZs3b2bjxo15Mu6rr77KO++8Q+/evRk9ejQeHh6cOHGC9evXs3LlSvbs2cP27dtp164d5cqVY9euXVy6dInAwMBsfbm4uDBy5EiGDx9OZmYmjz76KPHx8URFReHq6kq/fv1yrCcgIIBPP/2UJ554AoPBwKRJk8yztB5ECp9EREREREREiokzSeeL9DhbtmzBy8sLuBniVK9enY8++ohWrVoBMHz4cEJDQzGZTHTs2JFJkyYRFhb2r+v19vYmKiqKMWPG0K5dO0wmE5UqVSI4OBgrKytcXV3ZsWMHb7zxBgkJCVSqVIkFCxbQvn372/Y3bdo0ypYty6xZszh16hTu7u7Uq1eP8ePH56qehQsX8sILL9C0aVOMRiNjxowhISHhX99ncaXwSURERETkLu7lyWV6apmI5Bej0YijvQOT/7eqwMZ0tHfAaDTmun1ERAQRERF3bTN37lzmzp1rcWzYsGHmX4eFhd02jMrKysp27Nq1axbvb802up3AwEC2bNlyx7r+WbfBYGDo0KEMHTr0tu1btWplUVNISAghISHm976+vnz33XcW17z66qt3HL+kU/gkJcrvFy8Rn8M68IR7eOytiIiIPNju9cllemqZiOQXHx8fDh89kuun/uUFo9GIj49PgY0nJZfCJykRMjIysDEYaLF6Q67a2xgMZGRk5HNVIiLFU2LiddLT0+7aJiVFe2rIg+Fenlymp5aJSH7z8fFRGCTFksInKRGsra1Jz8rihdbjsLNxuGtbU3oyq7+fhbW1dQFVJyJSPGRkZGBtZc2ildkfUXw71lbWCvLlgaEnl4mIiNw/hU9Sojz8UEOc7O7+U8kbpusFVI2ISPFibW1NRmYGO156Dlf7HJYwp5hoseI9BfkiIiIikiOFTyIiImKhorubHmUtIiIiInlG4ZOIiIhIPtKT0kRERORBp/BJRPKcvtESEblJT0oTERERUfgkInlM32iJiPwfPSlNREREROGTiOQxfaMlIpKdnpQmIiWNZrqLyL1Q+CT55mJiHCmmnDekTUhLLIBqpKDpG63iTf+hFBERkTu535nu8u/FxsYSFxdXYOMZjUZ8fHzyrL/IyEhat27N1atXcXd3z3b+zJkz+Pn5sW/fPurUqZNn4xZVYWFhfPbZZ8TExBR2KfmuUMOnHTt2MG/ePPbu3cv58+fZuHEjnTt3BiAtLY2JEyfy1VdfcerUKdzc3Gjbti2zZ8/G29vb3Ievry9nz5616HfWrFmMHTu2IG9F/iYjIwMbgw2Nv+qW62tsDDZkZGTkY1UikltaOikiIvLvXU6+SnpGeo7t4lOvF0A1eet+Z7rLvxMbG0v16oEkJycV2JgODo4cOXL4ngOo6OhoHn30UYKDg9m8eXM+VVf8jRw5ksGDBxd2GQWiUMOnGzduULt2bV544QW6du1qcS4pKYlff/2VSZMmUbt2ba5evcrQoUN58skn2bNnj0XbqVOnMmDAAPN7F5e7/wUo+cva2pr0rHQ2P98DFzu7HNtfN5noGP4h1tbWBVCdiORESydFRETuX0ZGBqWsbGj4ZedcX1PKqnj+IFYz3QtWXFwcyclJDAwKw9vDN9/H+/PKGf77TRhxcXH3HD6tWrWKwYMHs2rVKv7880+LCSQFLS0tjVKlShXa+Hfj7OyMs7NzYZdRIAo1fGrfvj3t27e/7Tk3Nze2bdtmcWzZsmU0atSI2NhYiy9+FxcXPD0987VWuXc1ypfD3cE+x3bXknNemiciBU//oRQREbl31tbWpGWmE/r8COztHHJsn2JKZln4Qv0gVnLN28MX33LVC7uMO0pMTGTDhg3s2bOHCxcuEBERwfjx42/bNikpiW7dupGQkHDbGVIZGRkMGDCAnTt3snXrVnx8fPj888+ZMmUKhw4dwtvbm379+jFhwgRsbG7GGwaDgbfeeouvv/6a7du3M2rUKAA+++wzhgwZQlhYGFeuXKFv374sXbqUBQsWsHDhQjIzMxk6dCgTJkwwj79w4ULCw8M5deoUHh4ePPHEE8ydO9ccGEVERDBs2DA2bNjAsGHD+P3333n00UcJDw/Hy8sLuLnUcPTo0fzvf/+jVKlSPPzww6xbt45KlSplW3a3e/duxo8fz759+0hLS6NOnTosWrSIevXq5dnvT2GxKuwC7kV8fDwGgyHb2tDZs2dTpkwZ6taty7x580hPv/v0VpPJREJCgsVLREREREQkr5R288DDvUyOr9JuHoVdqkie+vDDD6levTrVqlXj2WefZfXq1WRlZWVrd+3aNR5//HEyMzPZtm1btu/zTSYT3bt3JyYmhh9//BEfHx9+/PFH+vbty9ChQzl06BBvv/02ERERzJgxw+LasLAwunTpwsGDB3nhhRcAOHnyJF9//TVbtmzhgw8+YNWqVXTs2JFz587xww8/MGfOHCZOnMiuXbvM/VhZWbFkyRL+97//sWbNGr777jtGjx5tMVZSUhLz58/nvffeY8eOHcTGxjJy5EgA0tPT6dy5My1btuTAgQNER0czcOBADAbDbT+769ev069fP3766Sd+/vlnAgIC6NChA9evF7/luf9UbDYcT0lJYcyYMfTu3RtX1//7SfyQIUOoV68eHh4e7Ny5k3HjxnH+/HkWLlx4x75mzZrFlClTCqJsERERERERkQfGqlWrePbZZwEIDg4mPj6eH374gVatWpnbXLhwgZ49exIQEMC6deuwtbW16CMxMZGOHTtiMpn4/vvvcXNzA2DKlCmMHTuWfv36AVC5cmWmTZvG6NGjef31183XP/PMMzz//PMWfWZmZrJ69WpcXFyoUaMGrVu35ujRo3z11VdYWVlRrVo15syZw/fff88jjzwCwLBhw8zX+/r6Mn36dF566SXeeust8/G0tDRWrFhBlSpVAAgNDWXq1KkAJCQkEB8fT6dOncznAwMD7/jZPfbYYxbv//vf/+Lu7s4PP/xAp06d7nhdcVAswqe0tDR69OhBVlYWy5cvtzg3YsQI869r1aqFra0tgwYNYtasWdjdYb+hcePGWVyXkJBAxYoV86d4ERERERERkQfA0aNH+eWXX9i4cSMANjY29OzZk1WrVlmET48//jiNGjViw4YNt11y2rt3bx566CG+++47HBz+b/nq/v37iYqKspjplJGRQUpKCklJSeY9SBs0aJCtT19fX4v9ocuXL4+1tTVWVlYWxy5e/L+H7nz77bfMmjWLI0eOkJCQQHp6eraxHB0dzcESgJeXl7kPDw8PQkJCCAoK4vHHH6dt27b06NHDvCTvn/766y8mTpxIZGQkFy9eJCMjg6SkJGJjY2/bvjgp8svubgVPZ8+eZdu2bRaznm7nkUceIT09nTNnztyxjZ2dHa6urhYvkaIuKSmJq1ev5uqVlFRwT8AQERERERGBm7Oe0tPT8fb2xsbGBhsbG5YvX84nn3xCfHy8uV3Hjh3ZsWMHhw4dum0/HTp0MC9T+7vExESmTJlCTEyM+XXw4EGOHz+Ovf3/7Tfs5OSUrc9/bjpuMBhueywzMxOAM2fO0KlTJ2rVqsUnn3zC3r17efPNNwFITU29a79/X2YYHh5OdHQ0TZs2ZcOGDVStWpWff/75tvfdr18/YmJiWLx4MTt37iQmJoYyZcpYjFdcFemZT7eCp+PHj/P9999TpkyZHK+JiYnBysqKcuXKFUCFIgUjKSmJyj5+/HX5Ys6NgfJlynEq9nQ+VyUiIiIiInJTeno67777LgsWLKBdu3YW5zp37swHH3xA9eo3N0qfPXs2zs7OtGnThsjISGrUqGHR/uWXX+Y///kPTz75JJs3b6Zly5YA1KtXj6NHj+Lv75/v97N3714yMzNZsGCBeXbUhx9+eF991a1bl7p16zJu3DiaNGnCunXraNy4cbZ2UVFRvPXWW3To0AGA33//nbi4uPu/iSKkUMOnxMRETpw4YX5/+vRpYmJi8PDwwMvLi6effppff/2VTZs2kZGRwYULF4CbU9dsbW2Jjo5m165dtG7dGhcXF6Kjoxk+fDjPPvsspUuXLqzbEslzJpOJvy5fZF/nTbjZuty1bXzqdep+1gmTyVRA1YmIiIiIyINu06ZNXL16lf79+5v3aLqlW7durFq1innz5pmPzZ8/n4yMDB577DEiIyPNwdQtgwcPJiMjg06dOvH111/z6KOPMnnyZDp16oSPjw9PP/00VlZW7N+/n99++43p06fn6f34+/uTlpbG0qVLeeKJJ4iKimLFihX31Mfp06f573//y5NPPom3tzdHjx7l+PHj9O3b97btAwICeO+992jQoAEJCQmMGjXKYtlhcVao4dOePXto3bq1+f2tfZj69etHWFgYX3zxBQB16tSxuO7777+nVatW2NnZsX79esLCwjCZTPj5+TF8+HCL/ZxEShI3Wxfc7bRMVERE8l5SUlKuf3BhZ2dn3utCREQK1p9XzhTJcVatWkXbtm2zBU9wM3yaO3cuBw4csDi+aNEiiwDqnxuPDxs2jMzMTDp06MCWLVsICgpi06ZNTJ06lTlz5lCqVCmqV6/Oiy++eM/3l5PatWuzcOFC5syZw7hx42jRogWzZs26Y3B0O46Ojhw5coQ1a9Zw+fJlvLy8ePXVVxk0aNBt269atYqBAwdSr149KlasyMyZM81PzivuCjV8atWq1W0fuXjL3c7BzSl3d1orKSLFj77xeXBdvnyZhISEXLV1dXXN1TJsEcm9+13erb+HRUQKjtFoxMHBkf9+E1ZgYzo4OGI0GnPV9ssvv7zjuUaNGpm/vx8yZIjFuSVLlrBkyRLz+3/mACNGjLCYYBIUFERQUNAdx7pdjhAWFkZYWJjFsYiIiGztIiMjLd4PHz6c4cOHWxx77rnnzL8OCQkhJCTE4nznzp3NNZQvX968+frt/LOuunXrsnv3bos2Tz/99B2vL06K9J5PIvLg0Dc+D66kpCQqVvQhOTl3G+U7ODgSF3dJv/cieeh+l3frz6GISMHx8fHhyJHDBboHkNFoxMfHp8DGk5JL4ZOIFAn6xid/FIfZZCaTieTkJEKfH4G93d3XtKeYklkWvlC/9yL5RMu7RUSKNh8fH4VBUiwpfBKRIkXf+OSd4vaUxNJuHjjY3z1QSk7J3ewoEREREREpOhQ+iYiUUHpKooiIiIiIFAUKn0RESjjNJhMRERERkcKk8ElERET+teKwv5iIiIiIFA6FTyIiIvKv6GmVIiIiInI3Cp9ERETkX9HTKkVERETkbhQ+iYiISJ7Q/mIiD6bfL14i3s7urm0S9EALEZEHmsInERERERG5ZxkZGdgYDLRYvSFX7W0MBjIyMrC2ts7nykRKrtjYWOLi4gpsPKPRiI+PT4GNl59CQkK4du0an332GQCtWrWiTp06vPHGGwD4+voybNgwhg0bVmg13iuDwcDGjRvp3LlzYZeSI4VPIiIiIiJyz6ytrUnPyuKF1uOws3G4a1tTejKrv5+l4EnkX4iNjSWweiBJyUkFNqajgyOHjxzOdQAVEhLCmjVrsh0/fvw4/v7+eV3ev/Lpp59SqlSpAh3zn4HXv3X+/HlKly6dJ33lN4VPIiIiIiJy3x5+qCFOdnff7+2G6XoBVVN0JCZeJz09Lcd2KSkpBVCNlARxcXEkJScxqtsbVCyb/0HO75dOMO+TYcTFxd3T7Kfg4GDCw8MtjpUtWzavy/vXPDw8CruE+5aamoqtrS2enp6FXUquWRV2ASIiIiIiIiVFRkYG1lbWLFo5h1nLpuT4WrRyDtZW1mRkZBR26VJMVCzrj7/3f/L9db8Bl52dHZ6enhavxYsXU7NmTZycnKhYsSKvvPIKiYmJFtf99NNPNG/eHAcHBypWrMiQIUO4ceOG+bzJZGLMmDFUrFgROzs7/P39WbVqFXDzz13//v3x8/PDwcGBatWqsXjx4rvW2apVq2xL7K5fv07v3r1xcnKiQoUKvPnmmxbnFy5cmON9REVF0apVKxwdHSldujRBQUFcvXqVkJAQfvjhBxYvXozBYMBgMHDmzBkAfvvtN9q3b4+zszPly5fnueees1he2apVK0JDQxk2bBhGo5GgoCDg5rK7W8sIIyMjMRgMXLt2zXxdTEyMxTgRERG4u7uzadMmqlWrhqOjI08//TRJSUmsWbMGX19fSpcuzZAhQ/L87yTNfBIREREL2jxYROT+WVtbk5GZwY6XnsPV/u5/lwIkpJhoseI9LUmUEs3KyoolS5bg5+fHqVOneOWVVxg9ejRvvfUWACdPniQ4OJjp06ezevVqLl26RGhoKKGhoeZZVH379iU6OpolS5ZQu3ZtTp8+bQ5oMjMzeeihh/joo48oU6YMO3fuZODAgXh5edGjR49c1zlv3jzGjx/PlClT+Oabbxg6dChVq1bl8ccfz9V9xMTE0KZNG1544QUWL16MjY0N33//PRkZGSxevJhjx47xn//8h6lTpwI3Z4Rdu3aNxx57jBdffJFFixaRnJzMmDFj6NGjB9999525tjVr1vDyyy8TFRX1r34vkpKSWLJkCevXr+f69et07dqVLl264O7uzldffcWpU6fo1q0bzZo1o2fPnv9qrL9T+CQiIiKANg8WEclLFd3dcHewz7HdtWQtu5OSZdOmTTg7O5vft2/fno8++sj83tfXl+nTp/PSSy+ZQ5tZs2bRp08f80ykgIAAlixZQsuWLVm+fDmxsbF8+OGHbNu2jbZt2wJQuXJlc5+lSpViypQp5vd+fn5ER0fz4Ycf3lP41KxZM8aOHQtA1apViYqKYtGiRebw6e8zpW53H3PnzqVBgwbm9wAPP/yw+de2trY4OjpaLJdbtmwZdevWZebMmeZjq1evpmLFihw7doyqVauaP5O5c+fm+l7uJC0tjeXLl1OlShUAnn76ad577z3++usvnJ2dqVGjBq1bt+b7779X+CQiIiJ5T5sHi4iIyL/VunVrli9fbn7v5OTEt99+y6xZszhy5AgJCQmkp6eTkpJCUlISjo6O7N+/nwMHDrB27VrzdVlZWWRmZnL69GkOHjyItbU1LVu2vOO4b775JqtXryY2Npbk5GRSU1OpU6fOPdXepEmTbO//vjl4TvcRExND9+7d72nM/fv38/3331sEdrecPHnSHD7Vr1//nvq9E0dHR3PwBFC+fHl8fX0txi9fvjwXL17Mk/FuUfgkIiIiFrR5sIiI5KeLiXGkmO4+4yshLfGu56XocnJysniy3ZkzZ+jUqRMvv/wyM2bMwMPDg59++on+/fuTmpqKo6MjiYmJDBo0iCFDhmTrz8fHhxMnTtx1zPXr1zNy5EgWLFhAkyZNcHFxYd68eezatSvP7is39+HgcPcf3t1OYmIiTzzxBHPmzMl2zsvLy/xrJyenu/ZjZXVzS++srCzzsbS07A89+OcT/gwGw22PZWZm5lz8Pbjv8OnkyZOEh4dz8uRJFi9eTLly5fj666/x8fGxmFYmIiIiIiJSHF2+fJmEhIRctXV1daVMmTL5XFHxdnN5tw2Nv+qWq/Y2Bhst7y4B9u7dS2ZmJgsWLDAHJB9++KFFm3r16nHo0CGL0OrvatasSWZmJj/88IN52d3fRUVF0bRpU1555RXzsZMnT95zrT///HO294GBgbm+j1q1arF9+3aLJYB/Z2trm20j73r16vHJJ5/g6+uLjc39zw+69UTB8+fPU7p0aeDmHlRFxX3d2Q8//ED79u1p1qwZO3bsYMaMGZQrV479+/ezatUqPv7447yuU0REREREpMAkJSVRsaIPyclJuWrv4OBIXNylfK6qeLu5vDudzc/3wCWHB1tcN5noGP6hgqcSwN/fn7S0NJYuXcoTTzxBVFQUK1assGgzZswYGjduTGhoKC+++CJOTk4cOnSIbdu2sWzZMnx9fenXrx8vvPCCecPxs2fPcvHiRXr06EFAQADvvvsu33zzDX5+frz33nvs3r0bPz+/e6o1KiqKuXPn0rlzZ7Zt28ZHH33E5s2bc30f48aNo2bNmrzyyiu89NJL2Nra8v3339O9e3eMRiO+vr7s2rWLM2fO4OzsjIeHB6+++irvvPMOvXv3ZvTo0Xh4eHDixAnWr1/PypUrc/1nwN/fn4oVKxIWFsaMGTM4duwYCxYsuKf7z0/3FT6NHTuW6dOnM2LECFxc/m9a/mOPPcayZcvyrDgREREREZHCYDKZSE5OIvT5Edjb3X0pTYopmWXhCzHpSaC5UqN8uRw3Y9dG7Hf2+6W7L0ErauPUrl2bhQsXMmfOHMaNG0eLFi2YNWsWffv2NbepVasWP/zwAxMmTKB58+ZkZWVRpUoViw2vly9fzvjx43nllVe4fPkyPj4+jB8/HoBBgwaxb98+evbsicFgoHfv3rzyyit8/fXX91Tra6+9xp49e5gyZQqurq4sXLiQoKCgXN9H1apV2bp1K+PHj6dRo0Y4ODjwyCOP0Lt3bwBGjhxJv379qFGjBsnJyZw+fRpfX1+ioqIYM2YM7dq1w2QyUalSJYKDg80zrHKjVKlSfPDBB7z88svUqlWLhg0bMn369Hvegyq/3Ff4dPDgQdatW5fteLly5cyPOhQRERERESnuSrt54GDveNc2ySm5mx1VFCUlJeU6NLOz+3/s3XlYlGX//vH3zAADiIKICxrjhgv6dUstlXLJNTXTTC1Nxa3FLTXTzI0y91xSU5/S0BbTFrXSMpcyn9TUXEpLzJ1Hcwlc2QYY+P3hz6kJgQEBWc7XcXgczH1f9319ZkQZzrkWM56e6b8WknP8/Pzw9PBk9ucjcq1PTw9P/Pz8nG6/YsWKOx4fOXIkI0eOdDjWu3dvh8cNGzZk8+bNad7b3d2duXPnMnfu3FTnzGYzYWFhhIWFORyfPn16mrVt377d4fGZM2fS7Ps2Z55Hs2bN2Llz5x2vr1q1Krt37051vEqVKqxduzbNfv9d623/XN8Jbu3W9+uvv6bZJiQkhJCQEIfzoaGhhIaGOhxL6+/xbmQpfPLx8eHChQuphrAdPHiQcuXKZUthIiIiIiIiknNiY2OpZKnIpSjndrUqXaIUpyJO53BVkhaLxcLR8KO5OuDDz88Pi8WSa/1JwZWl8Ompp55i7NixfPrpp/ZV0Hfu3Mno0aMdhpyJiIiIiIhI3mS1WrkUdZmDnTfg7Zb+LqfXE25Sb31HTS28xywWi8IgyZeyFD5NmzaNIUOGEBAQgM1mo0aNGthsNnr27MmECROyu0YRERERERHJId5uRfExF7vXZYhIAZal8MnNzY13332XSZMmcfjwYaKjo6lXrx5VqlTJ7vpEREREslVU3FWSbEkZtruecDMXqhEREREp+LIUPt0WEBBAQEBAdtUiIiIikmNsNhuuRhcaftXZ6WtcjS7YbDZt9S0iIiJyF7IUPnXt2pUHHniAsWPHOhyfNWsW+/bt49NPP82W4kRERESyi8lkIjE5yalt0+HvrdMVPInkf9rRTUTk3spS+LRjx45UW/EBPProo8yZM+duaxIRERHJMc5smw75e+t0EfmbdnQTEbn3shQ+RUdH4+bmluq4q6srN27cuOuiREQkf9InyyIiktdoRzcRkXsvS+FTrVq1WLNmDZMmTXI4vnr1amrUqJEthYmISP6iT5ZFRCQv045uIiL3TpbCp4kTJ/LEE09w8uRJHnnkEQC2bdvGxx9/rPWeREQKKX2yLCIiIpKzIiIiiIyMzLX+/Pz8sFgsudZfepo3b07dunWZP3/+vS4lXfmlztyWpfDpscceY/369UybNo3PPvsMDw8PateuzdatW2nWrFl21ygiIvmIPlnOXufOnXP6Taafnx/33XdfDlckIiIi90JERATVqwcRF5d7axJ6eHgSHn7U6QAqJCSEa9eusX79+pwtLA9bu3Ytrq6u97qMPCdL4RNAhw4d6NChQ3bWIiIiIv8QGxtLpYoVSUxKcqq9q4sL165f11paIiIiBVBkZCRxcbGEPP06ZUpVyPH+Ll4+w4qPJxEZGZlnRj/lB76+vumeT0hIuOMa2gVdlsMnuPWiXb58meTkZIfj+sYUERG5e1arlcSkJDb2605RszndtjetVjqEfYLValX4JCIiUoCVKVUBy33V73UZmTZ37lzCwsI4deoUvr6+PPbYY8yaNQsvLy97m507dzJ+/Hj27t2L2WzmgQceYPXq1RQvXhyA5ORkxowZw7Jly3Bzc+P5558nNDTUfr3BYODdd99l48aNfPvtt5QrV445c+bQqVMne5sffviBl19+mV9++QVfX1/69u3LG2+8gYvLrXjks88+47XXXuPEiRN4enpSr149vvjiC4oUKWIf2VWvXj0WLVqE1WqlZ8+eLFiwwB4o/XvaXYUKFRgwYADHjx9n/fr1PPHEE6xYsYKxY8eybt06zp07R5kyZejVqxeTJk0qsKOmshQ+HT9+nP79+7Nr1y6H4ykpKRgMBmw2W7YUJyIiIlCjdCl8PNzTbXMtLj6XqhERERHJPKPRyIIFC6hYsSKnTp1i8ODBjBkzhsWLFwNw6NAhWrZsSf/+/XnrrbdwcXHh+++/d8gXVq5cyahRo9izZw+7d+8mJCSE4OBgWrdubW/z2muvMWvWLGbPns3ChQvp1asXZ8+exdfXl/Pnz9O+fXtCQkJ4//33CQ8PZ9CgQbi7uxMaGsqFCxd4+umnmTVrFl26dOHmzZv897//JSUlxX7/bdu24e7uzvbt2zlz5gz9+vWjRIkSTJ06Nc3n/uabbzJp0iQmT55sP1a0aFFWrFhB2bJlOXz4MIMGDaJo0aKMGTMmO1/2PCNL4VNISAguLi5s2LABf39/DAZDdtclUmBpK3oRERERESlsRowYYf+6QoUKvPHGGzz//PP28GnWrFk0aNDA/higZs2aDveoXbu2PcCpUqUKixYtYtu2bQ7hU0hICE8//TQA06ZNY8GCBezdu5d27dqxePFiAgICWLRoEQaDgerVq/Pnn38yduxYJk2axIULF0hKSuKJJ56gfPnyANSqVcuhBjc3N9577z08PT2pWbMmr7/+Oi+//DJTpkzBaDTe8bk/8sgjvPTSSw7HJkyY4PB6jB49mtWrVyt8+qdDhw6xf/9+qlfPf0P9RO4lbUUvIiIiIiKF0datW5k+fTrh4eHcuHGDpKQk4uPjiY2NxdPTk0OHDtGtW7d071G7dm2Hx/7+/ly+fDnNNkWKFKFYsWL2NkePHqVx48YOA2iCg4OJjo7m3Llz1KlTh5YtW1KrVi3atm1LmzZtePLJJ+3T/gDq1KnjMECgcePGREdH87///c8eWP1bgwYNUh1bs2YNCxYs4OTJk0RHR5OUlESxYgV3054shU81atTI1e0dRQoKbUUvIiIiIiKFzZkzZ+jYsSMvvPACU6dOxdfXlx9//JEBAwaQkJCAp6cnHh4eGd7n3+shGQyGVGtQO9MmLSaTiS1btrBr1y42b97MwoULGT9+PHv27KFixYpO3eNOihQp4vB49+7d9OrVi9dee422bdvi7e3N6tWrmTNnTpb7yOuyFD7NnDmTMWPGMG3aNGrVqpXqL7cgp3Ui2UFb0YukFh19k6SkxHTbxMdrXaOCJjNTkUHTkUVERPKj/fv3k5yczJw5c+xT0z755BOHNrVr12bbtm289tprOVZHUFAQn3/+uX29ari1yHnRokW57777gFthVXBwMMHBwUyaNIny5cuzbt06Ro0aBcAvv/xCXFycPSz76aef8PLyIiAgwOk6du3aRfny5Rk/frz92NmzZ7PraeZJWQqfWrVqBUDLli0djmvBcRERySybzYbJaGLesplOtTcZTfo5U0Bkdioy/D0dWQGUiIhI3nT9+nUOHTrkcMzPz4/ExEQWLlzIY489xs6dO1m6dKlDm3HjxlGrVi0GDx7M888/j5ubG99//z3dunXDz88vW2obPHgw8+fPZ9iwYQwdOpRjx44xefJkRo0ahdFoZM+ePWzbto02bdpQqlQp9uzZw19//UVQUJD9HgkJCQwYMIAJEyZw5swZJk+ezNChQ9Nc7+lOqlSpQkREBKtXr6Zhw4Zs3LiRdevWZctzzKuyFD59//332V2H5DAtci0ieZXJZMKWbGPH870p5m5Ot+2NeCtNl36AyWTKpeokJ2VmKjI4TkfWzykRESmsLl4+k6f72b59O/Xq1XM4NmDAAObOncvMmTMZN24cTZs2Zfr06fTp08fepmrVqmzevJlXX32VBx54AA8PDx588EH74uHZoVy5cnz99de8/PLL1KlTB19fX3uQBLdmce3YsYP58+dz48YNypcvz5w5c3j00Uft92jZsiVVqlShadOmWK1Wnn76aUJDQzNVR6dOnRg5ciRDhw7FarXSoUMHJk6cmOn75CdZCp+aNWuW3XVIDsrqItd6Yy8iuSnAxxsfD/d021yL07S7gkhTkUVERDLm5+eHh4cnKz6elGt9enh4ZmrU0YoVK1ixYkWa50eOHOnwuHfv3g6PmzVrxs6dO+947fbt21MdW79+vcPjlJSUVG2uXbuWqo+9e/fesY+goCA2bdp0x3P/9Nprr6U5PfDfdZ45c+aO7WbNmsWsWbMcjv1zR8CCJkvh022xsbFERESQkJDgcPzfK9DLvZXVRa4VPomIiIiIiOQNFouF8PCjubr5l5+fHxaLJdf6k4IrS+HTX3/9Rb9+/fjmm2/ueF5rceRN+mRZRETEucXtQQvci4hI3mOxWBQGSb6UpfBpxIgRXLt2jT179tC8eXPWrVvHpUuXeOONNwr01oAiIiKSf2V2cXv4e4F7rTMmIiIi6U0plPRlKXz67rvv+OKLL2jQoAFGo5Hy5cvTunVrihUrxvTp0+nQoUN21ykiIiJyVzKzuD1ogXsRERGR7JKl8CkmJoZSpUoBULx4cf766y+qVq1KrVq1OHDgQLYWKCIiIpKdnFncHrTAvYiIiEh2yVL4VK1aNY4dO0aFChWoU6cO//nPf6hQoQJLly7F398/u2sUERHJE2JjY7FarU63N5vN2rxBRERERAq9LIVPL774IhcuXABg8uTJtGvXjo8++gg3NzfNgRQRkQIpNjaWoCrVuRR12elrSpcoxamI0wqgRERERKRQy1L49Mwzz9i/rl+/PmfPniU8PByLxYKfn1+2FSciIpJXJCQkcCnqMgc7b8DbrWiG7a8n3KTe+o5YrVaFTyIiIiJSqGUpfPo3T09P7r///uy4lYiISJ7m7VYUH3Oxe12GiIiIiEi+4XT4NGrUKKdvOnfu3CwVIyIiIiIiIiJ3FhERQWRkZK715+fnh8ViybX+sspgMLBu3To6d+58r0vJNhUqVGDEiBGMGDEizTb56Xk7HT4dPHjQ4fGBAwdISkqiWrVqAPzxxx+YTCbq16+fvRWKiIiIiIiIFHIREREEVa9ObFxcrvXp6eHB0f+/xI4zQkJCWLlyJQCurq5YLBb69OnDq6++iotLtky8umecCYOy0759+yhSpEiu9JUbnP7b//777+1fz507l6JFi7Jy5UqKFy8OwNWrV+nXrx8PP/xw9lcpIpKOqKgobty44VTbYsWKUaJEiRyuSEREREQke0VGRhIbF8eEkB6UL1Mqx/s7e/Eyb6xYQ2RkZKZGP7Vr146wsDCsVitff/01Q4YMwdXVlXHjxmWqf5vNhsFgwGg0Zrb0PC0hIQE3N7cM25UsWTIXqsk9WfpbnDNnDtOnT7cHTwDFixfnjTfeYM6cOdlWnIhIRmJjYwkIsFCpUiWn/gQEWIiNjb3XZYuIiIiIZEn5MqWoaimX43+yGnCZzWbKlClD+fLleeGFF2jVqhVffvklc+fOpVatWhQpUoSAgAAGDx5MdHS0/boVK1bg4+PDl19+SY0aNTCbzURERLBv3z5at26Nn58f3t7eNGvWjAMHDqTq98KFCzz66KN4eHhQqVIlPvvsM4fzY8eOpWrVqnh6elKpUiUmTpxIYmKiQ5uvvvqKhg0b4u7ujp+fH126dAGgefPmnD17lpEjR2IwGDAYDPZrfvzxRx5++GE8PDwICAhg+PDhxMTE2M9XqFCBKVOm0KdPH4oVK8azzz4LwOeff07NmjUxm81UqFAhVZZSoUIF5s+fb398/PhxmjZtiru7OzVq1GDLli2pXoP//e9/dO/eHR8fH3x9fXn88cc5c+aM/fz27dt54IEHKFKkCD4+PgQHB3P27Nm0/iqzVZbGvd24cYO//vor1fG//vqLmzdv3nVRIiLOslqtxMXFMrTfKNzNHum2jbfGsShsLlarNZeqExEREREp3Dw8PIiKisJoNLJgwQIqVqzIqVOnGDx4MGPGjGHx4sX2trGxscycOZNly5ZRokQJSpUqxalTp+jbty8LFy4kJSWFOXPm0L59e44fP07Ron/vQDxx4kRmzJjBW2+9xQcffMBTTz3F4cOHCQoKAqBo0aKsWLGCsmXLcvjwYQYNGkTRokUZM2YMABs3bqRLly6MHz+e999/n4SEBL7++msA1q5dS506dXj22WcZNGiQvc+TJ0/Srl073njjDd577z3++usvhg4dytChQwkLC7O3e/PNN5k0aRKTJ08GYP/+/XTv3p3Q0FB69OjBrl27GDx4MCVKlCAkJCTVa5icnMwTTzxB6dKl2bNnD9evX081/S8xMZG2bdvSuHFj/vvf/+Li4sIbb7xBu3bt+PXXXzEajXTu3JlBgwbx8ccfk5CQwN69ex2CtJyUpfCpS5cu9OvXjzlz5vDAAw8AsGfPHl5++WWeeOKJbC1QRMQZxb198XBPfzv7uHiNeBIRERERyQ0pKSls27aNb7/9lmHDhjmEJRUqVOCNN97g+eefdwifEhMTWbx4MXXq1LEfe+SRRxzu+8477+Dj48MPP/xAx44d7ce7devGwIEDAZgyZQpbtmxh4cKF9vtPmDDBof/Ro0ezevVqe/g0depUnnrqKV577TV7u9t1+Pr6YjKZKFq0KGXKlLGfnz59Or169bI/typVqrBgwQKaNWvGkiVLcHd3tz+Hl156yX5dr169aNmyJRMnTgSgatWq/P7778yePfuO4dPWrVsJDw/n22+/pWzZsgBMmzaNRx991N5mzZo1JCcns2zZMnugFBYWho+PD9u3b6dBgwZcv36djh07UrlyZQB7MJcbshQ+LV26lNGjR9OzZ0/7MDUXFxcGDBjA7Nmzs7VAEREREREREckfNmzYgJeXF4mJiSQnJ9OzZ09CQ0PZunUr06dPJzw8nBs3bpCUlER8fDyxsbF4et76ENnNzY3atWs73O/SpUtMmDCB7du3c/nyZWw2G7GxsURERDi0a9y4carHhw4dsj9es2YNCxYs4OTJk0RHR5OUlESxYsXs5w8dOuQwqskZv/zyC7/++isfffSR/VhKSgrJycmcPn3aHu40aNDA4bqjR4/y+OOPOxwLDg5m/vz52Gw2TCZTqvYBAQH24OlOz/eXX37hxIkTDqPBAOLj4zl58iRt2rQhJCSEtm3b0rp1a1q1akX37t3x9/fP1HPOqiyFT56enixevJjZs2dz8uRJACpXrlygVmIXERERERERkcxp0aIFS5Yswc3NjbJly+Li4sKZM2fo2LEjL7zwAlOnTsXX15cff/yRAQMGkJCQYA+fPDw8Uk0D69u3L1FRUbz11luUL18es9lM48aNSUhIcLqm3bt306tXL1577TXatm2Lt7c3q1evdlhnycMj/SU87iQ6OprnnnuO4cOHpzr3z0XacyMriY6Opn79+g5B2G23Fy8PCwtj+PDhbNq0iTVr1jBhwgS2bNlCo0aNcry+u9rrsEiRIqlSSREREREREcm82NjYTK1NaTab7b+0i+QVRYoUITAw0OHY/v37SU5OZs6cOfbd6z755BOn7rdz504WL15M+/btgVuLakdGRqZq99NPP9GnTx+Hx/Xq1QNg165dlC9fnvHjx9vP/3uh7dq1a7Nt2zb69et3xzrc3Nyw2WwOx+6//35+//33VM83I0FBQezcudPh2M6dO6latWqqUU+32//vf//jwoUL9pFKP/30U6pa1qxZQ6lSpRxGdP1bvXr1qFevHuPGjaNx48asWrUq74ZPMTExzJgxg23btnH58mWSk5Mdzp86dSpbihMREREREbmXoqNvkpSUmG6b+Pj4u+4nNjaWoCrVuRR12elrSpcoxamI0wqgJM8LDAwkMTGRhQsX8thjj7Fz506WLl3q1LVVqlThgw8+oEGDBty4cYOXX375jqOUPv30Uxo0aMBDDz3ERx99xN69e1m+fLn9HhEREaxevZqGDRuyceNG1q1b53D95MmTadmyJZUrV+app54iKSmJr7/+mrFjxwK31onasWMHTz31FGazGT8/P8aOHUujRo0YOnQoAwcOpEiRIvz+++9s2bKFRYsWpfmcXnrpJRo2bMiUKVPo0aMHu3fvZtGiRQ7rX/1Tq1atqFq1Kn379mX27NncuHHDIUiDW+tIzZ49m8cff5zXX3+d++67j7Nnz7J27VrGjBlDYmIi77zzDp06daJs2bIcO3aM48ePOwR2OSlL4dPAgQP54Ycf6N27N/7+/rm2OrpIXhMVd5UkW1K6ba4naAdIERERkfzGZrNhMpqYt2ymU+1NRtMd12pxVkJCApeiLnOw8wa83Ypm2P56wk3qre+I1WpV+FTInL3ofECZV/qpU6cOc+fOZebMmYwbN46mTZsyffp0p4KP5cuX8+yzz3L//fcTEBDAtGnTGD16dKp2r732GqtXr2bw4MH4+/vz8ccfU6NGDQA6derEyJEjGTp0KFarlQ4dOjBx4kRCQ0Pt1zdv3pxPP/2UKVOmMGPGDIoVK0bTpk3t519//XWee+45KleujNVqJSUlhdq1a/PDDz8wfvx4Hn74YVJSUqhcuTI9evRI9zndf//9fPLJJ0yaNIkpU6bg7+/P66+/fsfFxgGMRiPr1q1jwIABPPDAA1SoUIEFCxbQrl07extPT0927NjB2LFjeeKJJ7h58yblypWjZcuWFCtWjLi4OMLDw1m5ciVRUVH4+/szZMgQnnvuuQz/DrJDlsKnb775ho0bNxIcHJzd9YjkCzabDVejCw2/6uxUe1ejy129GRERERGR3GUymbAl29jxfG+KuZvTbXsj3krTpR9ky3s9b7ei+JjTnjIjhZefnx+eHh68sWJNrvXp6eGBn5+f0+1XrFiR5rmRI0cycuRIh2O9e/e2fx0SEnLH8KVevXrs27fP4diTTz7p8DglJQWAwYMHp9n/rFmzmDVrlsOxf+7AB/DEE0/wxBNP3PH6Ro0a8csvv6Q63rBhQzZv3pxmv2fOnLnj8a5du9K1a1enr6tatSr//e9/HY7dft63lSlThpUrV97xfsWKFUs12is3ZSl8Kl68OL6+vnfd+Y4dO5g9ezb79+/nwoULrFu3js6dO9vPp6SkMHnyZN59912uXbtGcHAwS5YsoUqVKvY2V65cYdiwYXz11VcYjUa6du3KW2+9hZeX113XJ5IWk8lEYnISQ/uNwt2c/sJ08dY4FoXNVfAkIiIikg8F+Hjj4+GebptrcXc/7U4kIxaLhaPh4Xdc7yin+Pn5OSycLZJVWQqfpkyZwqRJk1i5cuVdDfGMiYmhTp069O/f/47p4qxZs1iwYAErV66kYsWKTJw4kbZt2/L777/j7n7rB0CvXr24cOECW7ZsITExkX79+vHss8+yatWqLNcl4qzi3r54uKf/byAuPjaXqhERERERkYLMYrEoDJJ8KUvh05w5czh58iSlS5emQoUKuLq6Opw/cOCAU/d59NFHefTRR+94LiUlhfnz5zNhwgQef/xxAN5//31Kly7N+vXreeqppzh69CibNm1i3759NGjQAICFCxfSvn173nzzTcqWLZuVpyciIiIiIiIiItkkS+HTP6fG5ZTTp09z8eJFWrVqZT/m7e3Ngw8+yO7du3nqqafYvXs3Pj4+9uAJbq0CbzQa2bNnD126dLnjva1Wq8MWpjdu3Mi5JyIiIiIiIiIiUohlKXyaPHlydteRysWLFwEoXbq0w/HSpUvbz128eJFSpUo5nHdxccHX19fe5k6mT5/Oa6+9ls0Vi4iIiGSf2NhYhw/L0mM2m7XblYiIiORZWQqf8rtx48YxatQo++MbN24QEBBwDysSERER+VtsbCyVLBW5FOXcNtelS5TiVMRpBVAiIiKSJ2UpfDIajRgMhjTP22y2LBd0W5kyZQC4dOkS/v7+9uOXLl2ibt269jaXLzu+KUtKSuLKlSv26+/EbDZjNqe/XaqIiIjIvWK1WrkUdZmDnTfg7VY03bbXE25Sb31HrFarwicRERHJk7IUPq1bt87hcWJiIgcPHmTlypXZNp2tYsWKlClThm3bttnDphs3brBnzx5eeOEFABo3bsy1a9fYv38/9evXB+C7774jOTmZBx98MFvqEJH8ITr6JklJiem2iY/XNsgikr94uxXFx1zsXpchIiIicleyFD7d3n3un5588klq1qzJmjVrGDBggFP3iY6O5sSJE/bHp0+f5tChQ/j6+mKxWBgxYgRvvPEGVapUoWLFikycOJGyZcvaFzwPCgqiXbt2DBo0iKVLl5KYmMjQoUN56qmntNOdSCFhs9kwGU3MWzbTqfYmo+nWNSZTDleWfTKz7gto7RcREREREclbsnXNp0aNGvHss8863f7nn3+mRYsW9se312Hq27cvK1asYMyYMcTExPDss89y7do1HnroITZt2oS7u7v9mo8++oihQ4fSsmVLjEYjXbt2ZcGCBdn3pEQkTzOZTNiSbex4vjfF3NOfTnsj3krTpR/ku+ApM+u+wN9rv4iIiIhIwRIREUFkZGSu9efn54fFYsn2+xoMBtatW2cfWJJbtm/fTosWLbh69So+Pj53bLNixQpGjBjBtWvXAAgNDWX9+vUcOnQo1+osiLItfIqLi2PBggWUK1fO6WuaN29OSkpKmucNBgOvv/46r7/+epptfH19WbVqVaZqFZGCJ8DHGx8P93TbXIvLf9PuMrPuCziu/SIiIiIiBUdERARB1aoTGx+Xa316untw9Fh4pgOoixcvMnXqVDZu3Mj58+cpVaoUdevWZcSIEbRs2TJba8xMONSkSRMuXLiAt7e30/cfPXo0w4YNu4sKBbIYPhUvXtxhwfGUlBRu3ryJp6cnH374YbYVJyIit2jdFxEREZHCLTIyktj4OCbUbU15r+I53t/Z6Ku8cWgLkZGRmQqfzpw5Q3BwMD4+PsyePZtatWqRmJjIt99+y5AhQwgPD8/BqtOWmJiIm5tbupuT3YmXlxdeXl45VFXhkaXwaf78+Q6PjUYjJUuW5MEHH6R48Zz/RyAiIgXP/y7/xfUMdiK9oRFdIiIiUsiV9ypOVe9S97qMNA0ePBiDwcDevXspUqSI/XjNmjXp37//Ha85fPgwL774Irt378bT05OuXbsyd+5ce+izfft2xowZw2+//Yarqys1a9Zk1apVfP/99/ZNz24PkAkLCyMkJASDwcDixYv55ptv2LZtGy+//DLNmzdPNe1uxYoVTJo0icjISNq2bctDDz3kUNu/R1bt27ePV199lYMHD5KYmEjdunWZN28e999/v/2aa9euMXbsWNavX8/169cJDAxkxowZdOzYEYAff/yRcePG8fPPP+Pn50eXLl2YPn26/fX64IMPeOuttzh27BhFihThkUceYf78+ZQq9fff+5EjR3j55Zf573//S5EiRWjTpg3z5s3Dz88vq391OSpL4VPfvn2zuw4RESmkbDYbLgYDTd9b41R7F4MBm82Ww1WJiLO0KYKIiNx25coVNm3axNSpUx2Cp9vutM5STEwMbdu2pXHjxuzbt4/Lly8zcOBAhg4dyooVK0hKSqJz584MGjSIjz/+mISEBPbu3YvBYKBHjx4cOXKETZs2sXXrVgCHKXWhoaHMmDGD+fPn4+LiwqlTpxz63rNnDwMGDGD69Ol07tyZTZs2MXny5HSf482bN+nbty8LFy4kJSWFOXPm0L59e44fP07RokVJTk7m0Ucf5ebNm3z44YdUrlyZ33//3b7u7MmTJ2nXrh1vvPEG7733Hn/99RdDhw5l6NChhIWFAbdGaU2ZMoVq1apx+fJlRo0aRUhICF9//TVwK9x65JFHGDhwIPPmzSMuLo6xY8fSvXt3vvvuO+f/wnJRltd8unr1KsuXL+fo0aMA1KhRg379+uHr65ttxYmISMFnMplISkmhf4txmF080m1rTYrjve+n56tF40UKsrvZFEEBlIhIwXPixAlSUlKoXr2609esWrWK+Ph43n//fXtgtWjRIh577DFmzpyJq6sr169fp2PHjlSuXBm4tfP9bV5eXri4uNxxOl3Pnj3p16+f/fG/w6e33nqLdu3aMWbMGACqVq3Krl272LRpU5r1PvLIIw6P33nnHXx8fPjhhx/o2LEjW7duZe/evRw9epSqVasCUKlSJXv76dOn06tXL0aMGAFAlSpVWLBgAc2aNWPJkiW4u7s7jBCrVKkSCxYsoGHDhkRHR+Pl5cWiRYuoV68e06ZNs7d77733CAgI4I8//rD3m5dkKXzasWMHjz32GN7e3jRo0ACABQsW8Prrr/PVV1/RtGnTbC1SCg99eipSeNW8ryFFzOkvqh5jvZlL1YiIM+5mUwT9/BYRKXjS21AsLUePHqVOnToOI6WCg4NJTk7m2LFjNG3alJCQENq2bUvr1q1p1aoV3bt3x9/fP8N7384r0uu7S5cuDscaN26cbvh06dIlJkyYwPbt27l8+TI2m43Y2FgiIiIAOHToEPfdd1+aAdAvv/zCr7/+ykcffWQ/lpKSQnJyMqdPnyYoKIj9+/cTGhrKL7/8wtWrV0lOTgZuLTpfo0YNfvnlF77//vs7rkV18uTJghM+DRkyhB49erBkyRL7p882m43BgwczZMgQDh8+nK1FSuEQGxtLUJXq2lJeREQkn9GmCCIiArdG8RgMhmxfVDwsLIzhw4ezadMm1qxZw4QJE9iyZQuNGjVK97o7Tf27W3379iUqKoq33nqL8uXLYzabady4MQkJCQB4eKQ/kj86OprnnnuO4cOHpzpnsVjs0xDbtm3LRx99RMmSJYmIiKBt27b2PqKjo+0jw/7NmVDuXshS+HTixAk+++wzh2kPJpOJUaNG8f7772dbcVK4JCQkaEt5ERERERGRfMrX15e2bdvy9ttvM3z48FThz7Vr11Kt+xQUFMSKFSuIiYmxt9+5cydGo5Fq1arZ29WrV4969eoxbtw4GjduzKpVq2jUqBFubm5ZXg80KCiIPXv2OBz76aef0r1m586dLF68mPbt2wPwv//9j8jISPv52rVrc+7cuTSnv91///38/vvvBAYG3vH+hw8fJioqihkzZhAQEADAzz//nOoen3/+ORUqVMDFJcurKeUqY1Yuuv/+++1rPf3T7eFyInfj9qenGf1xJqASERERERGR3PP2229js9l44IEH+Pzzzzl+/DhHjx5lwYIFNG7cOFX7Xr164e7uTt++fTly5Ajff/89w4YNo3fv3pQuXZrTp08zbtw4du/ezdmzZ9m8eTPHjx+3r/tUoUIFTp8+zaFDh4iMjMzU4ITbo6nefPNNjh8/zqJFi9Kdcge3Rnd98MEHHD16lD179tCrVy+H0U7NmjWjadOmdO3alS1btnD69Gm++eYb+33Hjh3Lrl27GDp0KIcOHeL48eN88cUXDB06FLg1+snNzY2FCxdy6tQpvvzyS6ZMmeJQw5AhQ7hy5QpPP/00+/bt4+TJk3z77bf069cvz27M43RE9uuvv9q/Hj58OC+++CInTpywD3P76aefePvtt5kxY0b2VykiIiIiBYrWeRQRyZqz0VfzdD+VKlXiwIEDTJ06lZdeeokLFy5QsmRJ6tevz5IlS1K19/T05Ntvv+XFF1+kYcOGeHp60rVrV+bOnWs/Hx4ezsqVK4mKisLf358hQ4bw3HPPAdC1a1fWrl1LixYtuHbtGmFhYYSEhDhVa6NGjXj33XeZPHkykyZNolWrVkyYMCFV2PNPy5cv59lnn+X+++8nICCAadOmMXr0aIc2n3/+OaNHj+bpp58mJiaGwMBAe1ZSu3ZtfvjhB8aPH8/DDz9MSkoKlStXpkePHgCULFmSFStW8Oqrr7JgwQLuv/9+3nzzTTp16mS/f9myZdm5cydjx46lTZs2WK1WypcvT7t27TAaszTGKMc5HT7VrVsXg8HgsIDY7RXh/6lnz572F01ERERE5N/uZp1HBVAiUlj5+fnh6e7BG4e25Fqfnu4e+Pn5Zfo6f39/Fi1axKJFi+54/t8Lk9eqVYvvvvvujm1Lly7NunXr0uzLbDbz2WefZdgHQPPmzVMd79+/v8PucgAvvfSS/evQ0FBCQ0Ptj+vVq8e+ffsc2j/55JMOj319fXnvvffSrLlhw4Zs3rw5zfNPP/00Tz/9tMOxf9ddpUoV1q5dm+Y98hqnw6fTp7Wos4iIiIjcvbtZ51Hhk4gUVhaLhaPHwh3WF8ppfn5+WCyWXOtPCi6nw6fy5cunOvb7778TERFhX3EdwGAw3LGtiIiIiMg/aZc8EZHMsVgsCoMkX8rSsuinTp2iS5cuHD582GEqnsFgAMizC1yJiIiIiIiIiEjuytJKVC+++CIVK1bk8uXLeHp6cuTIEXbs2EGDBg3Yvn17NpcoIiIiIiIiIiL5VZZGPu3evZvvvvsOPz8/jEYjJpOJhx56iOnTpzN8+HAOHjyY3XWKiIiIiIiIiEg+lKXwyWazUbTorcUh/fz8+PPPP6lWrRrly5fn2LFj2Vqg3FtZ2QZZREREREREROS2LIVP//d//8cvv/xCxYoVefDBB5k1axZubm688847VKpUKbtrlHskNjaWSpaKmd4Gef8vB3KwKhERyasuR0cSb41Pt82NxOhcqkYKssx8OGY2m7Nlhzx9f4uIiGRdlsKnCRMmEBMTA8Drr79Ox44defjhhylRogRr1qzJ1gLl3rFarVnaBvmfux+KiEjBZ7PZcDG40Ojrrk61dzG4YLPZMJlMOVyZFESZ/XCsdIlSnIo4neUAKqvf3yIiIvK3LIVPbdu2tX8dGBhIeHg4V65coXjx4vYd76Tg0DbIIiKSHpPJRFJKEhv7dadoBtOvb1qtdAj7RMGTZFlmPhy7/cGY1WrNcvik728REZG7l6Xw6U58fX2z61YiIiKSD9UoXQofD/d021yLS3/akoizcvvDMX1/i0heEBERQWRkZK715+fnh8ViybX+pODKtvBJRERERERERHJGREQEQdWrExsXl2t9enp4cDQ8PNMB1MWLF5k+fTobN27k3LlzeHt7ExgYyDPPPEPfvn3to1EPHjzIjBkz2LFjB1euXKFMmTLUqlWL5557jo4dO2IwGDhz5gwVK1a039vV1RWLxUJISAjjx4+3z75q3rw5P/zwQ6pa2rdvz8aNGwG4dOkSY8eOZfPmzVy7do2mTZuycOFCqlSp4nDN7t27GT9+PHv27MFkMlG3bl2+/fZbPDw8ADhw4ABjx45l3759mEwmunbtyty5c/Hy8gJgxYoV9OvX746vzaVLlyhVqlSmXs+CQOGTiIiIiIiISB4XGRlJbFwcrz3YjQrFSuZ4f2du/MXkPZ8SGRmZqfDp1KlTBAcH4+Pjw7Rp06hVqxZms5nDhw/zzjvvUK5cOTp16sQXX3xB9+7dadWqFStXriQwMBCr1cquXbuYMGECDz/8MD4+Pvb7bt26lZo1a2K1Wvnxxx8ZOHAg/v7+DBgwAIC1a9c6rD8cFRVFnTp16NatGwApKSl07twZV1dXvvjiC4oVK8bcuXNp1aoVv//+O0WKFAFuBU/t2rVj3LhxLFy4EBcXF3755ReMRiMAf/75J61ataJHjx4sWrSIGzduMGLECEJCQvjss88A6NGjB+3atXN4XUJCQoiPjy+UwRMofBIRERERERHJNyoUK0l133L3uow0DR48GBcXF37++Wd7oANQqVIlHn/8cVJSUoiJiWHAgAF06NCBtWvXOlwfFBTEgAEDSElJcTheokQJypQpA0D58uUJCwvjwIED9vDp30sBrV69Gk9PT3v4dPz4cX766SeOHDlCzZo1AViyZAllypTh448/ZuDAgQCMHDmS4cOH88orr9jvVa1aNfvXGzZswNXVlbffftseSC1dupTatWtz4sQJAgMD8fDwsI+SAvjrr7/47rvvWL58eRZe0YLBeK8LEBEREREREZH8Lyoqis2bNzNkyBCH4OmfDAYDmzdvJioqijFjxqR5r/Q2M/v555/Zv38/Dz74YJptli9fzlNPPWWvw2q1AuDu/vf6fUajEbPZzI8//gjA5cuX2bNnD6VKlaJJkyaULl2aZs2a2c/fvo+bm5s9eALsQdM/2/3T+++/j6enJ08++WSa9RZ0Cp9ERERERERE5K6dOHGClJQUh5FCcGvhci8vL7y8vBg7dix//PEH4DiiaN++ffY2Xl5ebNiwweEeTZo0wcvLCzc3Nxo2bEj37t3p06fPHevYu3cvR44csY9mAqhevToWi4Vx48Zx9epVEhISmDlzJufOnePChQvArSmDAKGhoQwaNIhNmzZx//3307JlS44fPw7AI488wsWLF5k9ezYJCQlcvXrVPkrq9n3+bfny5fTs2dNhNFRho/BJRERERERERHLM3r17OXTokH3NpjupXbs2hw4d4tChQ8TExJCUlORwfs2aNRw6dIhffvmFTz75hC+++MJhatw/LV++nFq1avHAAw/Yj7m6urJ27Vr++OMPfH198fT05Pvvv+fRRx+1j2JKTk4G4LnnnqNfv37Uq1ePefPmUa1aNd577z0AatasycqVK5kzZw6enp6UKVOGihUrUrp0aYfRULft3r2bo0eP2qcHFlZa80lERERERERE7lpgYCAGg4Fjx445HK9UqRLw9/S027vLHTt2jEaNGgFgNpsJDAxM894BAQH280FBQZw8eZKJEycSGhrqMJUuJiaG1atX8/rrr6e6R/369Tl06BDXr18nISGBkiVL8uCDD9KgQQMA/P39AahRo4bDdUFBQURERNgf9+zZk549e3Lp0iWKFCmCwWBg7ty59uf5T8uWLaNu3brUr18/zedWGGjkk2QoKu4qkbFXMvwTFXf1XpcqIiIiIiIi90iJEiVo3bo1ixYtIiYmJs12bdq0wdfXl5kzZ2a5L5PJRFJSksMOdwCffvopVquVZ555Js1rvb29KVmyJMePH+fnn3/m8ccfB6BChQqULVs2VXj2xx9/UL58+VT3KV26NF5eXqxZswZ3d3dat27tcD46OppPPvmk0I96Ao18knTYbDZcjS40/Kqz09e4Gl2w2Ww5V5SIiIgUGFFxV0myJWXY7nrCzVyoRkREssPixYsJDg6mQYMGhIaGUrt2bYxGI/v27SM8PJz69evj5eXFsmXL6NGjBx06dGD48OFUqVKF6OhoNm3aBNwKl/4pKiqKixcvkpSUxOHDh3nrrbdo0aIFxYoVc2i3fPlyOnfuTIkSJVLV9umnn1KyZEksFguHDx/mxRdfpHPnzrRp0wa4tcj5yy+/zOTJk6lTpw5169Zl5cqVhIeH89lnn9nvs2jRIvsaVFu2bOHll19mxowZ+Pj4OPS3Zs0akpKS0g3CCguFT5Imk8lEYnISQ/uNwt2c8cJo8dY4FoXNTfWfhIiIiMg/3c0HXHqfISKF3Zkbf+XpfipXrszBgweZNm0a48aN49y5c5jNZmrUqMHo0aMZPHgwAF26dGHXrl3MnDmTPn36cOXKFby9vWnQoAGrV6+mY8eODvdt1aoVcOv3VH9/f9q3b8/UqVMd2hw7dowff/yRzZs337G2CxcuMGrUKC5duoS/vz99+vRh4sSJDm1GjBhBfHw8I0eO5MqVK9SpU4ctW7ZQuXJle5u9e/cyefJkoqOjqV69Ov/5z3/o3bt3qv6WL1/OE088kSqUKowUPkmGinv74uHumWG7uPjYXKhGRERE8jt9wCUiknl+fn54engwec+nudanp4cHfn5+mb7O39+fhQsXsnDhwnTbNWjQgE8/Tf/5VKhQgZSUFKf6rVatWrpthw8fzvDhwzO8zyuvvJLmYuYA77//vlP17Nq1y6l2hYHCJxERERG5J/QBl4iI8ywWC0fDw4mMjMy1Pv38/LBYLLnWnxRcCp9ERERERERE8gGLxaIwSPIlhU8iInJHl6MjibfGp9vmRmJ0LlUjIiIiIiL5lcInERFxYLPZcDG40Ojrrk61dzFoEWAREREREUmbwicREXFgMplISkliY7/uFDWb021702qlQ9gnCp5ERKTAioqOISkpKd021+OtuVSNiEj+pPBJRETuqEbpUvh4uKfb5lpc+tPyRERE8iubzYaryUiDRWFOtXc1GTUSWEQkDQqfRERERERE/sVkMpFoS2bzosYU9XRNt+3N2ETaDN2t4ElEJA0Kn0RERERERNJwXylPfIqmHz5du5mYS9WIiORPxntdgIiIiIiIiIiIFFwa+SQiIiIiIiKSD0RERBAZGZlr/fn5+WGxWLL9vgaDgXXr1tG5c+dsv3d6tm/fTosWLbh69So+Pj53bLNixQpGjBjBtWvXAAgNDWX9+vUcOnQo1+osiBQ+iYiIiIjkgNjYWKxW53ZBM5vNeHp65nBFIpKfRUREUL16deLi4nKtTw8PD8LDwzMdQF28eJGpU6eyceNGzp8/T6lSpahbty4jRoygZcuW2VpjZsKhJk2acOHCBby9vZ2+/+jRoxk2bNhdVCig8Cnf0ZsYERERkbwvNjaWSpaKXIq67FT70iVKcSritN67iUiaIiMjiYuL48HHX6BYibI53t+NqD/Z88USIiMjMxU+nTlzhuDgYHx8fJg9eza1atUiMTGRb7/9liFDhhAeHp6DVactMTERNzc3ypQpk6nrvLy88PLyyqGqCg+FT/lIVt/EiIiIiEjuslqtXIq6zMHOG/B2K5pu2+sJN6m3viNWq1Xhk4hkqFiJshT3r3ivy0jT4MGDMRgM7N27lyJFitiP16xZk/79+9/xmsOHD/Piiy+ye/duPD096dq1K3PnzrWHPtu3b2fMmDH89ttvuLq6UrNmTVatWsX333/Pa6+9BtyaygcQFhZGSEgIBoOBxYsX880337Bt2zZefvllmjdvnmra3YoVK5g0aRKRkZG0bduWhx56yKG2f4+sat68OXXr1mX+/Pn2Np07d8bHx4cVK1YAUKFCBQYOHMgff/zB2rVrKVGiBAsXLqRx48YMHDiQbdu2UalSJd577z0aNGhwty95vqDwKR/J6psYEREREbk3vN2K4mMudq/LEBHJFVeuXGHTpk1MnTrVIXi67U7rLMXExNC2bVsaN27Mvn37uHz5MgMHDmTo0KGsWLGCpKQkOnfuzKBBg/j4449JSEhg7969GAwGevTowZEjR9i0aRNbt24FcJhSFxoayowZM5g/fz4uLi6cOnXKoe89e/YwYMAApk+fTufOndm0aROTJ0/Oltdi3rx5TJs2jYkTJzJv3jx69+5NkyZN6N+/P7Nnz2bs2LH06dOH3377zR6cFWQKn/IhvYkRERGRgiA6+iZJSRlvUR8fH58L1YiIyN06ceIEKSkpVK9e3elrVq1aRXx8PO+//749sFq0aBGPPfYYM2fOxNXVlevXr9OxY0cqV64MQFBQkP16Ly8vXFxc7jidrmfPnvTr18/++N/h01tvvUW7du0YM2YMAFWrVmXXrl1s2rTJ+Sedhvbt2/Pcc88BMGnSJJYsWULDhg3p1q0bAGPHjqVx48ZcunQp01MB8yOFT4VIVNxVkmxJ6ba5nnAzl6oRERGRwspms2Eympi3bKbT15iMplvXmUw5WJmIiNyNlJSUTF9z9OhR6tSp4zBSKjg4mOTkZI4dO0bTpk0JCQmhbdu2tG7dmlatWtG9e3f8/f0zvHdGU9qOHj1Kly5dHI41btw4W8Kn2rVr278uXbo0ALVq1Up17PLlywqfpGCw2Wy4Gl1o+FVnp9q7Gl305k5ERKQQuhwdSbw1/VFGNxKj77ofk8mELdnGjud7U8zdnGH7G/FWmi79QO9NRETyuCpVqmAwGLJ9UfGwsDCGDx/Opk2bWLNmDRMmTGDLli00atQo3evuNPXvbhmNxlQhW2Ji6lG8rq6u9q9vT6u707Hk5ORsrzEvUvhUCJhMJhKTkxjabxTuZo9028Zb41gUNldv7kRERAoRm82Gi8GFRl93daq9iyF7PqgK8PHGx8M9w3bX4jTtTkQkP/D19aVt27a8/fbbDB8+PFX4c+3atVTrPgUFBbFixQpiYmLs7Xfu3InRaKRatWr2dvXq1aNevXqMGzeOxo0bs2rVKho1aoSbmxs2my1L9QYFBbFnzx6HYz/99FO615QsWZILFy7YH9tsNo4cOUKLFi2yVENhofCpECnu7YuHe/o7qMTFx+ZSNSIikpNiY2Od3nTCbDZrh61CzmQykZSSxMZ+3SlqTn8k0k2rlQ5hn+iDKhERuaO3336b4OBgHnjgAV5//XVq165NUlISW7ZsYcmSJRw9etShfa9evZg8eTJ9+/YlNDSUv/76i2HDhtG7d29Kly7N6dOneeedd+jUqRNly5bl2LFjHD9+nD59+gC3dpY7ffo0hw4d4r777qNo0aKYM/hZdtvw4cMJDg7mzTff5PHHH+fbb7/NcMrdI488wqhRo9i4cSOVK1dm7ty5XLt2LUuvVWGi8ElERKSAiY2NpZKlIpeiLjvVvnSJUpyKOJ3DVUl+UKN0qQxHImkUkojIvXUj6s883U+lSpU4cOAAU6dO5aWXXuLChQuULFmS+vXrs2TJklTtPT09+fbbb3nxxRdp2LAhnp6edO3alblz59rPh4eHs3LlSqKiovD392fIkCH2xby7du3K2rVradGiBdeuXSMsLIyQkBCnam3UqBHvvvsukydPZtKkSbRq1YoJEyYwZcqUNK/p378/v/zyC3369MHFxYWRI0dq1JMTFD6JiIgUMFarlUtRlznYeQPebkXTbXs94Sb11nd0epSUiIiI3Bt+fn54eHiw54vUAU5O8fDwwM/PL9PX+fv7s2jRIhYtWnTH8/9eM6lWrVp89913d2xbunRp1q1bl2ZfZrOZzz77LMM+AJo3b57qeP/+/enfv7/DsZdeesn+dWhoKKGhofbHrq6uLF68mMWLF6dZ05kzZzKsp0KFCllaoD2/UvgkIiJSQHm7FcXHXOxelyEiIiLZwGKxEB4eTmRkZK716efnh8ViybX+pOBS+CQiIiIiIiKSD1gsFoVBki8pfJJCLyoqihs3bjjVtlixYpQoUSKHKxIRERGRvOKvqJtY49P/telGTFIuVSMikj8pfJJCLTY2loAAC3Fxzu3y5+HhSWTkXzlclYiIiIjcazabDRcTNHthr1PtXUy3rtFOkCIiqSl8kkLNarUSFxfL0H6jcDd7pNs23hrHorC5WpRXtIW9iIhIIWAymUiyQecGPXA1pb9te6LNyvqf1yh4EhFJg8InEaC4ty8e7ukHBHHxzo2OkoItq1vYK4ASERHJn0p4lcLd1T3dNvGJ8blUjYhI/qTwSUQkE7K6hb3CJ7kbUdExJCWlv57I9XiNyhQRERGRvEnhk4hIFmgLe8kNNpsNV5ORBovCnGrvajJqvRERERERyXMUPomIiORRJpOJRFsymxc1pqina7ptb8Ym0mbobgVPIiIiIpLnKHwSERHJ4+4r5YlP0fTDp2s3E3OpGhEREblXIiIiiIyMzLX+/Pz8sFgsudafFFx5PnyqUKECZ8+eTXV88ODBvP322zRv3pwffvjB4dxzzz3H0qVLc6tEERERERERkRwVERFBUPUgYuNybyMkTw9PjoYfdTqACgkJYeXKlXf8nXzIkCEsXryYvn37smLFihyoVvKyPB8+7du3D5vNZn985MgRWrduTbdu3ezHBg0axOuvv25/rIV9JbdER98kKSn90Qbx8dr9RG6JjY3FanV+UWizOf1tnUVERESk8IiMjCQ2LpbXHllMheJVc7y/M1f/YPJ3g4mMjMzU6KeAgABWr17NvHnz8PDwAG79TrRq1SqNoirE8nz4VLJkSYfHM2bMoHLlyjRr1sx+zNPTkzJlyuR2aVKI2Ww2TEYT85bNdKq9yWjSIsCFXGxsLJUsFbkUddnpa0qXKMX+Xw7kYFX5X2YCPbPZrA8nREREJN+rULwq1UvWvtdlpOn+++/n5MmTrF27ll69egGwdu1aLBYLFStWtLdLTk5m5syZvPPOO1y8eJGqVasyceJEnnzySXubL7/8kpdeeon//e9/NG7cmJCQEEJCQrh69So+Pj5ERUUxdOhQduzYwdWrV6lcuTKvvvoqTz/9tP0ezZs3p1atWphMJlauXImbmxtvvPEGPXv2ZOjQoXz22WeULl2ahQsX8uijj9qv++GHH3j55Zf55Zdf8PX1pW/fvrzxxhu4uLjY71u7dm3c3d1ZtmwZbm5uPP/884SGhtrvMXfuXMLCwjh16hS+vr489thjzJo1Cy8vLwDOnj3L0KFD+fHHH0lISKBChQrMnj2b9u3b58jfzb2U58Onf0pISODDDz9k1KhRGAwG+/GPPvqIDz/8kDJlyvDYY48xceJE/YKRjZwZ3QOFa4SPyWTClmxjx/O9Keae/uiUG/FWmi79QMFTIWe1WrkUdZmDnTfg7VY0w/bXE25Sb31HEhIScqG6/CmzgV7pEqU4FXE6h6uSrLgcHUm8NeOfITcSo3OhGhEREblb/fv3JywszB4+vffee/Tr14/t27fb20yfPp0PP/yQpUuXUqVKFXbs2MEzzzxDyZIladasGadPn+bJJ5/kxRdfZODAgRw8eJDRo0c79BMfH0/9+vUZO3YsxYoVY+PGjfTu3ZvKlSvzwAMP2NutXLmSMWPGsHfvXtasWcMLL7zAunXr6NKlC6+++irz5s2jd+/eRERE4Onpyfnz52nfvj0hISG8//77hIeHM2jQINzd3R3CpZUrVzJq1Cj27NnD7t27CQkJITg4mNatWwNgNBpZsGABFStW5NSpUwwePJgxY8awePFi4NZUxISEBHbs2EGRIkX4/fff7cFUQZOvwqf169dz7do1QkJC7Md69uxJ+fLlKVu2LL/++itjx47l2LFjrF27Ns37WK1Wh0/Kb9y4kZNl51uZHd0Df4/wKSwCfLzx8XBPt821uMITyuVHzvzSm52/8Hq7FcXHXCzb7leYZSbQux3mZWbao+Q8m82Gi8GFRl93dfoaF4NLofo5IyIikh8988wzjBs3zr5+886dO1m9erU9fLJarUybNo2tW7fSuHFjACpVqsSPP/7If/7zH5o1a8Z//vMfqlWrxuzZswGoVq0aR44cYerUqfZ+ypUr5xBIDRs2jG+//ZZPPvnEIXyqU6cOEyZMAGDcuHHMmDEDPz8/Bg0aBMCkSZNYsmQJv/76K40aNWLx4sUEBASwaNEiDAYD1atX588//2Ts2LFMmjQJo9EIQO3atZk8eTIAVapUYdGiRWzbts0ePo0YMcJeQ4UKFXjjjTd4/vnn7eFTREQEXbt2pVatWvbXoKDKV+HT8uXLefTRRylbtqz92LPPPmv/ulatWvj7+9OyZUtOnjxJ5cqV73if6dOn89prr+V4vfldZkb3gEb4SP6S2V96b//Cq+/vvEeBXv5lMplISkliY7/uFHVijbObVisdwj7Rv0MREZE8rmTJknTo0IEVK1aQkpJChw4d8PPzs58/ceIEsbGx9pDmtoSEBOrVqwfAsWPHaNiwocP5fwZKcOs9/bRp0/jkk084f/48CQkJWK3WVDOhatf+e5qiyWSiRIkS9sAHoHTp0gBcvnxrRP3Ro0dp3Lixw4yr4OBgoqOjOXfunH3tqn/eF8Df399+D4CtW7cyffp0wsPDuXHjBklJScTHxxMbG4unpyfDhw/nhRdeYPPmzbRq1YquXbumumdBkW/Cp7Nnz7J169Z0RzQBPPjgg8Ctb+a0wqdx48YxatQo++MbN24QEBCQfcUWMM6M7gGN8MkNUXFXSbIlpdvmesLNXKomf8vML736hVckZ9UoXUo/Z0RERAqY/v37M3ToUADefvtth3PR0bdmFmzcuJFy5co5nMvMpjuzZ8/mrbfeYv78+dSqVYsiRYowYsSIVEtXuLq6Ojw2GAwOx26HTMnJyU73ndZ9b9/jzJkzdOzYkRdeeIGpU6fi6+vLjz/+yIABA0hISMDT05OBAwfStm1bNm7cyObNm5k+fTpz5sxh2LBhmaojP8g34VNYWBilSpWiQ4cO6bY7dOgQcCtxTIvZbNYuUpKv2Gw2XI0uNPyqs1PtXY0apeMsZ37p1S+8IiIihVdMXAxJiel/+GdN0rRukX9r164dCQkJGAwG2rZt63CuRo0amM1mIiIiHDYT+6dq1arx9ddfOxzbt2+fw+OdO3fy+OOP88wzzwC3wqM//viDGjVq3FXtQUFBfP7556SkpNiDqZ07d1K0aFHuu+8+p+6xf/9+kpOTmTNnjn2a3ieffJKqXUBAAM8//zzPP/8848aN491331X4dK8kJycTFhZG37597SvLA5w8eZJVq1bRvn17SpQowa+//srIkSNp2rRpgR2qJoWTyWQiMTmJof1G4W72SLdtvDWORWFzFTyJiIiI3AWbzYbRYGT1nvecam80GPXhn8g/mEwmjh49av/6n4oWLcro0aMZOXIkycnJPPTQQ1y/fp2dO3dSrFgx+vbty3PPPcfcuXMZO3YsAwYM4NChQ6xYsQL4e6RSlSpV+Oyzz9i1axfFixdn7ty5XLp06a7Dp8GDBzN//nyGDRvG0KFDOXbsGJMnT2bUqFH2ICkjgYGBJCYmsnDhQh577DF27tzJ0qVLHdqMGDGCRx99lKpVq3L16lW+//57goKC7qr2vCpfhE9bt24lIiKC/v37Oxx3c3Nj69atzJ8/n5iYGAICAujatat9ITGRgqa4ty8e7unv5BgXH5tL1YiIiIjkH5ldvsBkMpGcksz4Lktwdy2S7nXxiTFMXfeCgifJFWeu/pFv+ilWLO11OadMmULJkiWZPn06p06dwsfHh/vvv59XX30VgIoVK/LZZ5/x0ksv8dZbb9G4cWPGjx/PCy+8YJ/JNGHCBE6dOkXbtm3x9PTk2WefpXPnzly/fv2u6i5Xrhxff/01L7/8MnXq1MHX15cBAwZkKmuoU6cOc+fOZebMmYwbN46mTZsyffp0+vTpY29js9kYMmQI586do1ixYrRr14558+bdVe15Vb4In9q0aUNKSkqq4wEBAfzwww/3oCIREREREckP7nb5Al+v0hQxp7+raoxVa25KzvPz88PTw5PJ3w3OtT49PTwdFgrPyO2RSWlZv369/WuDwcCLL77Iiy++mGb7Tp060alTJ/vjqVOnct999+HufmvZDF9fX4d73sntHfb+6cyZM6mO/TtzaNasGXv37s3Uff9dy8iRIxk5cqTDsd69e9u/XrhwYZr3L2jyRfgkIiIiIiKSFVq+QAoKi8XC0fCjREZG5lqffn5+9p3d7oXFixfTsGFDSpQowc6dO5k9e7Z9EXPJXxQ+iYiIiIhIgVfQly+IjY3FanVu4XOz2ZxqK3rJHywWyz0Ng3Lb8ePHeeONN7hy5QoWi4WXXnqJcePG3euyJAsUPomIiIiIiORjsbGxVLJU5FLUZafaly5RilMRp3O4KpG7N2/evAK7BlJho/BJREQkF+gTaRGRu6f/S+/MarVyKeoyBztvwNst/fWprifcpN76jk6/jiIi2UHhk4iISA7TJ9IiIndP/5dmzNutKD7mtHcXExG5VxQ+iYiI5DB9Ip39MjP6AbBvySwi+Zf+LxURyb8UPomIiOQSfSKdPWJjYwmqUt3p0Q9wawTE/l8O5GBVIpJb9H+piEj+o/BJRERE8pWEhASnRz/A3yMgEhIScqE6EREREfk3hU+FSHT0TZKSEtNtEx8fn0vViIiI3B2NfhARERHJHxQ+FQI2mw2T0cS8ZTOdam8ymm5dYzLlcGUiIiIiIiLirIiICCIjI3OtPz8/PywWS671JwWXwqdCwGQyYUu2seP53hRzT3/B1RvxVpou/UDBk4iIiIiISB4SERFB9erViIvLvdkqHh7uhIcfy3QAdfHiRaZPn87GjRs5d+4c3t7eBAYG8swzz9C3b188PT0BOHjwIDNmzGDHjh1cuXKFMmXKUKtWLZ577jk6duyIwWDgzJkzVKxY0X5vV1dXLBYLISEhjB8/HoPBAEBiYiLTp09n5cqVnD9/nmrVqjFz5kzatWtnv7ZChQqcPXs2Vb2DBw/m7bffBqB58+b88MMPDuefe+45li5dan+8b98+XnnlFfbv34/BYOCBBx5g1qxZ1KlTJ1OvU2Gi8KkQCfDxxsfDPd0213LxPzIREREpGC5HRxJvzfg9xI3E6FyoRkSkYIqMjCQuLp4nW91PSV+vHO/vryvRfLb1AJGRkZkKn06dOkVwcDA+Pj5MmzaNWrVqYTabOXz4MO+88w7lypWjU6dOfPHFF3Tv3p1WrVqxcuVKAgMDsVqt7Nq1iwkTJvDwww/j4+Njv+/WrVupWbMmVquVH3/8kYEDB+Lv78+AAQMAmDBhAh9++CHvvvsu1atX59tvv6VLly7s2rWLevXqAbdCI5vNZr/nkSNHaN26Nd26dXN4DoMGDeL111+3P74dlgFER0fTrl07OnXqxOLFi0lKSmLy5Mm0bduW//3vf7i6umbqdS4sFD6JiIiISJbYbDZcDC40+rqr09e4GFwc3viLiEjmlPT1omxJn3tdRpoGDx6Mi4sLP//8M0WKFLEfr1SpEo8//jgpKSnExMQwYMAAOnTowNq1ax2uDwoKYsCAAaSkpDgcL1GiBGXKlAGgfPnyhIWFceDAAXv49MEHHzB+/Hjat28PwAsvvMDWrVuZM2cOH374IQAlS5Z0uOeMGTOoXLkyzZo1czju6elp7+vfwsPDuXLlCq+//joBAQEATJ48mdq1a3P27FkCAwMz9XoVFgqfRERERCRLTCYTSSlJbOzXnaLm9Kf2A9y0WukQ9omm94uIFFBRUVFs3ryZadOmOQRP/2QwGNi8eTNRUVGMGTMmzXvdnk53Jz///DP79++nT58+9mNWqxV3d8eZPh4eHvz44493vEdCQgIffvgho0aNStXXRx99xIcffkiZMmV47LHHmDhxon30U7Vq1ShRogTLly/n1VdfxWazsXz5coKCgqhQoUKaNRd2Cp9ERERE5K7UKF0qw6n9oOn9IiIF3YkTJ0hJSaFatWoOx/38/Ow7qw8ZMgRfX18Ah3b79u2jRYsW9serV6+mY8eO9sdNmjTBaDSSkJBAYmIizz77rEP41LZtW+bOnUvTpk2pXLky27ZtY+3atWmOtl2/fj3Xrl0jJCTE4XjPnj0pX748ZcuW5ddff2Xs2LEcO3bMPkKraNGibN++nc6dOzNlyhQAqlSpwrfffouLiyKWtOiVEREREREREZEcs3fvXpKTk+nVqxdWq/WObWrXrs2hQ4eAW2FOUlKSw/k1a9YQFBREYmIiR44cYdiwYRQvXpwZM2YA8NZbbzFo0CCqV6+OwWCgcuXK9OvXj/fee++O/S1fvpxHH32UsmXLOhx/9tln7V/XqlULf39/WrZsycmTJ6lcuTJxcXEMGDCA4OBgPv74Y2w2G2+++SYdOnRg3759eHh4ZPVlKtAUPomIiIiIiIjIXQsMDMRgMHDs2DGH45UqVQKwBzNVqlQB4NixYzRq1AgAs9mc7npJAQEB9vNBQUGcPHmSiRMnEhoairu7OyVLlmT9+vXEx8cTFRVF2bJleeWVV+x9/9PZs2fZunVrqvWm7uTBBx8Ebo3qqly5MqtWreLMmTPs3r0bo9EIwKpVqyhevDhffPEFTz31VIb3LIyM97oAEREREREREcn/SpQoQevWrVm0aBExMTFptmvTpg2+vr7MnDkzy32ZTCaSkpJISEhwOO7u7k65cuVISkri888/5/HHH091bVhYGKVKlaJDhw4Z9nN7NJa/vz8AsbGxGI1Gh3Wibj9OTk7O8vMp6BQ+iYiIiIiIiEi2WLx4MUlJSTRo0IA1a9Zw9OhRjh07xocffkh4eDgmkwkvLy+WLVvGxo0b6dChA99++y2nTp3i119/ZdasWQCpNqeIiori4sWLnDt3jm+++Ya33nqLFi1aUKxYMQD27NnD2rVrOXXqFP/9739p164dycnJqRY1T05OJiwsjL59+6Zao+nkyZNMmTKF/fv3c+bMGb788kv69OlD06ZNqV27NgCtW7fm6tWrDBkyhKNHj/Lbb7/Rr18/XFxcHNasEkeadicFyrWbUVjj7zyH+La4xOhcqkZERERERCR7/XUld36fyWo/lStX5uDBg0ybNo1x48Zx7tw5zGYzNWrUYPTo0QwePBiALl26sGvXLmbOnEmfPn24cuUK3t7eNGjQINVi4wCtWrUCboVS/v7+tG/fnqlTp9rPx8fHM2HCBE6dOoWXlxft27fngw8+wMfHx+E+W7duJSIigv79+6eq3c3Nja1btzJ//nxiYmIICAiga9euTJgwwd6mevXqfPXVV7z22ms0btwYo9FIvXr12LRpk310lKSm8EkKBJvNhtFgYsr6QU61NxpM2Gw2bfUsIvnChcvXiIlJ/0f2jZikdM9L/hQVd5UkW/p/t9cTbuZSNSIici/5+fnh4eHOZ1sP5FqfHh7u+Pn5Zfo6f39/Fi5cyMKFC9Nt16BBAz799NN021SoUIGUlJQM+2zWrBm///57hu3atGmT5v0CAgL44YcfMrxH69atad26dYbt5G8Kn6RAMJlMJKfYeHbrO5iLFUm3rfVGDO+0elbBk4jkeTabDRcTtBy636n2LiYUrBcQNpsNV6MLDb/q7FR7V6NLmltJi4hIwWCxWAgPP0ZkZGSu9enn54fFYsm1/qTgUvgkBUqZWoF4Fi+WbpvYqzdyqZq06ZNsEXGGyWQiyQaD27yO2cUz3bbWpFgWb56k4KmAMJlMJCYnMbTfKNzN6W/ZHG+NY1HYXP3dS5ZFRUVx44bz74+KFStGiRIlcrCi9Ol9lBRmFotFYZDkSwqfRHJRVj/J1i8UIoVb5dL/RxFz0XTbxFj1i1ZBVNzbFw/39IPHuPjYXKpGCqLY2FiqVKlKXJzz30ceHp5ERv6Vg1Xdmd5HiYjkXwqfRHKRPskWkdx0OTqSeGt8um1uaBMGkUItISGBuLhYp96bwN/vT6zW9Dd4yQl6HyUikn8pfJIMXbp+g7j49H95AbhhTciFagoGfZItIjnJZrPhYnCh0dddnWrvYtDoAJHCzpn3JpA33p/ofZSISP6j8EnSZLPZcDEaaLTkfaevcTEatOCp3BPOhKQKSKWwMJlMJKUksbFfd4qazem2vWm10iHsEwVPIjlE6xOJiIgofJJ0mEwmkpJTeGpeC8xFXDNsb41JZPXI7/ULjOSqzIaktwNSfZ9KYVCjdCl8PNzTbXMtLuORrSKSedqxUERE5G8KnyRDpSr54F7ULcN28Tc1qkRyX2ZCUgWkIlIYxMbGOr0ej9lsxtMz46lWknlan0hERORvCp9EpEBwJiRVQCoiBV1sbCyVLBW5FHXZqfalS5TiVMTpHK6qcNP6RCIiIgqfRERERAoMq9XKpajLHOy8AW+3oum2vZ5wk3rrO96TXctERCRrIiIiiIyMzLX+/Pz8sFgsudafFFwKn0REREQKGG+3oviYi+Vaf9oZV0Qk50VERFA9qDpxsXG51qeHpwfhR8MzHUBdvHiR6dOns3HjRs6dO4e3tzeBgYE888wz9O3b1z7l++DBg8yYMYMdO3Zw5coVypQpQ61atXjuuefo2LEjBoOBM2fOULFiRfu9XV1dsVgshISEMH78eAwGAwC//fYbkyZNYv/+/Zw9e5Z58+YxYsSIVLWdP3+esWPH8s033xAbG0tgYCBhYWE0aNAAgNDQUFavXs3//vc/3NzcqF+/PlOnTuXBBx8E4MyZM0yZMoXvvvuOixcvUrZsWZ555hnGjx+Pm1vGy9UUVgqfRERERCRLtDOuSPZzZodE0C6JhVFkZCRxsXE8PPNJvCuVyvH+rp+6zH/HfkZkZGSmwqdTp04RHByMj48P06ZNo1atWpjNZg4fPsw777xDuXLl6NSpE1988QXdu3enVatWrFy5ksDAQKxWK7t27WLChAk8/PDD+Pj42O+7detWatasidVq5ccff2TgwIH4+/szYMAA4P9PPa9UiW7dujFy5Mg71nb16lWCg4Np0aIF33zzDSVLluT48eMUL17c3qZq1aosWrSISpUqERcXx7x582jTpg0nTpygZMmShIeHk5yczH/+8x8CAwM5cuQIgwYNIiYmhjfffDNrL3YhoPBJRNL0v6g/ue5yI8N2N5Kic6EaERHJa7Qzrkj2yewOifD3Lon6N1W4eFcqRYkaZe91GWkaPHgwLi4u/PzzzxQpUsR+vFKlSjz++OOkpKQQExPDgAED6NChA2vXrnW4PigoiAEDBpCSkuJwvESJEpQpUwaA8uXLExYWxoEDB+zhU8OGDWnYsCEAr7zyyh1rmzlzJgEBAYSFhdmP/XNUFUDPnj0dHs+dO5fly5fz66+/0rJlS9q1a0e7du0cntexY8dYsmSJwqd0KHwSkVRsNhsumGi27Wmnr3HBpDc/IiKFlHbGFbl7mdkhEbRLouRNUVFRbN68mWnTpjkET/9kMBjYvHkzUVFRjBkzJs173Z5Odyc///wz+/fvp0+fPpmq78svv6Rt27Z069aNH374gXLlyjF48GAGDRp0x/YJCQm88847eHt7U6dOnTTve/36dXx9fTNVS2Gj8ElEUjGZTCRho+OERrh5ZPzfREJcEhve+ElvfkQy4MxUCk2jEMk5sbGxTi+wbjab7WuSiOQmZ3ZIBO2SKHnTiRMnSElJoVq1ag7H/fz8iP//awMOGTLEHtT8s92+ffto0aKF/fHq1avp2LGj/XGTJk0wGo0kJCSQmJjIs88+m+nw6dSpUyxZsoRRo0bx6quvsm/fPoYPH46bmxt9+/a1t9uwYQNPPfUUsbGx+Pv7s2XLFvz8/NJ8zgsXLtSopwwofBKRNFVq6K9PskWyQWanUmgahUj2i42NpZKlIpeiLjvVvnSJUpyKOJ3DVeUd0dE3SUpKzLBdvBMLy4uI/NvevXtJTk6mV69eaX4IULt2bQ4dOgRAlSpVSEpy/MBuzZo1BAUFkZiYyJEjRxg2bBjFixdnxowZTteRnJxMgwYNmDZtGgD16tXjyJEjLF261CF8atGiBYcOHSIyMpJ3332X7t27s2fPHkqVclxr6/z587Rr145u3bqlOXpKblH4JCIiksMyM5VC0yhEcobVauVS1GUOdt6At1vRdNteT7hJvfUdnR4llZ/ZbDZMRhPzls10+hqTUVPtReTOAgMDMRgMHDt2zOF4pUqVAPDwuPU+qEqVKgAcO3aMRo0aAbdGnAYGBqZ574CAAPv5oKAgTp48ycSJEwkNDcXd3d2p+vz9/alRo4bDsaCgID7//HOHY0WKFCEwMJDAwEAaNWpElSpVWL58OePGjbO3+fPPP2nRogVNmjThnXfecar/wkzhk4iISC5xZiqFplGI5Cxvt6L4mIvd6zLyDJPJhC3Zxo7ne1PM3Zxh+xvxVpou/UDBk4jcUYkSJWjdujWLFi1i2LBhaa771KZNG3x9fZk5cybr1q3LUl8mk4mkpCQSEhKcDp+Cg4NTBWN//PEH5cuXT/e65ORkhw8kzp8/T4sWLahfvz5hYWEYjcbMP4FCRuGTiOQpl6MjibemP6T/RqJ218sMrTMkIiIZCfDxxscj41/ersVp2p2IpG/x4sUEBwfToEEDQkNDqV27NkajkX379hEeHk79+vXx8vJi2bJl9OjRgw4dOjB8+HCqVKlCdHQ0mzZtAkgVckdFRXHx4kWSkpI4fPgwb731Fi1atKBYsVsfKCQkJPD777/bvz5//jyHDh3Cy8vLPmJq5MiRNGnShGnTptG9e3f27t3LO++8Yx+5FBMTw9SpU+nUqRP+/v5ERkby9ttvc/78ebp16wbcCp6aN29O+fLlefPNN/nrr7/sNd7ejU9SU/gkGYq5Fpdqvu2dWKMzXidAJC02mw0XgwuNvu7qVHsXg9bEyYjWGcoZzgSkoJBUREREcsb1U86tXXev+qlcuTIHDx5k2rRpjBs3jnPnzmE2m6lRowajR49m8ODBAHTp0oVdu3Yxc+ZM+vTpw5UrV/D29qZBgwapFhsHaNWqFXArlPL396d9+/ZMnTrVfv7PP/+kXr169sdvvvkmb775Js2aNWP79u0ANGzYkHXr1jFu3Dhef/11KlasyPz58+nVq5f93uHh4axcuZLIyEhKlChBw4YN+e9//0vNmjUB2LJlCydOnODEiRPcd999DjWmpKRk6TUrDBQ+SZpsNhtGFyPL+25y+hqjixGbzZaDVUlBZTKZSEpJYmO/7hQ1pz/s/6bVSoewTxSSZEDrDGWvzAakoJBUREREso+fnx8enh78d+xnudanh6dHmru8pcff35+FCxeycOHCdNs1aNCATz/9NN02FSpUcCrUcbZdx44dUwVbt7m7u7N27dp0rw8JCSEkJCTDfsSRwidJk8lkIjkpme6N+uBmyngNgASblU9+el+/ZMldqVG6VIbD/jXkP3O0zlD2yExACgpJRUREJHtZLBbCj4YTGRmZa336+flhsVhyrT8puBQ+SYaKunvj7prxGgDxiQoERKTgcyYgBYWkIiKSf2h9yPzDYrEoDJJ8SeGTCBAdfZOkpPTXrIqP1y+SIiIiIlJwaH1IEcktCp+kULPZbJiMJuYtm+lUe5PRpB+4IiIiIlIgaH1IEcktCp/yIQ2LzT4mkwlbso0dz/emmHv6a7jciLfSdOkH+oErIiIiOSo2Nhar1epUW7PZjKdn+uv6iWRE60OKSE5T+JSPaFhszgnw8dYi1yIiInLPxcbGUslSkUtRzm1xXrpEKU5FnM7hqkRERO6Owqd8RMNiRURE8qfL0ZHEW9P/EONGYnQuVSN5mdVq5VLUZQ523oC3W9F0215PuEm99R2dHiUlIiJyryh8yoc0LLbw0sLoIiL5i81mw8XgQqOvuzrV3sWgUctyi7dbUXzMxe51GSIiItlC4ZNIPqCF0UVE8ieTyURSShIb+3WnqDn9tQVvWq10CPtE/3eLiIhIgaPwSSQf0MLoIiL5W43SpbS2oIiI3LWIiAgiIyNzrT8/Pz8sFkuu9ScFl8InkXxEC6OLiPzNmXWUQGspiTjj0vUbxDkxdf+GNSEXqincnFlmAbTUQmEUERFBtepBxMfl3hIr7h6eHAs/mukA6uLFi0yfPp2NGzdy7tw5vL29CQwM5JlnnqFv3772XToPHjzIjBkz2LFjB1euXKFMmTLUqlWL5557jo4dO2IwGDhz5gwVK1a039vV1RWLxUJISAjjx4/HYDAA8NtvvzFp0iT279/P2bNnmTdvHiNGjHCoa/r06axdu5bw8HA8PDxo0qQJM2fOpFq1ag7tdu/ezfjx49mzZw8mk4m6devy7bff4uHx99rLGzdu5PXXX+fXX3/F3d2dZs2asX79+ky9ToWJwieRe0BrN4mIZF1m11GCv9dSEhFHNpsNF6OBRkved/oaF6NB0/tzQGaXWQAttVDYREZGEh8XS91+E/AqUz7H+4u+eJZDYW8QGRmZqfDp1KlTBAcH4+Pjw7Rp06hVqxZms5nDhw/zzjvvUK5cOTp16sQXX3xB9+7dadWqFStXriQwMBCr1cquXbuYMGECDz/8MD4+Pvb7bt26lZo1a2K1Wvnxxx8ZOHAg/v7+DBgwAPj/u4VWqkS3bt0YOXLkHWv74YcfGDJkCA0bNiQpKYlXX32VNm3a8Pvvv1OkSBHgVvDUrl07xo0bx8KFC3FxceGXX37BaDTa7/P5558zaNAgpk2bxiOPPEJSUhJHjhzJwqtceCh8EslFWrtJROTuZWYdJdBaSiLpMZlMJCWn8NS8FpiLuGbY3hqTyOqR3+vfUw7IzDILoKUWCjOvMuXxtlTLuOE9MnjwYFxcXPj555/tgQ5ApUqVePzxx0lJSSEmJoYBAwbQoUMH1q5d63B9UFAQAwYMICUlxeF4iRIlKFOmDADly5cnLCyMAwcO2MOnhg0b0rBhQwBeeeWVO9a2adMmh8crVqygVKlS7N+/n6ZNmwIwcuRIhg8f7nCPf46MSkpK4sUXX2T27Nn2vgFq1Kjh3AtUSCl8EslFWrtJ7gWNtJOCypl1lEDTkXNDxKlzuBXJ+G1lQkxSLlQjWVGqkg/uRd0ybBd/U9PucpozyyyA/m+TvCkqKorNmzczbdo0h+DpnwwGA5s3byYqKooxY8akea/b0+nu5Oeff2b//v306dPnruq9fv06AL6+vgBcvnyZPXv20KtXL5o0acLJkyepXr06U6dO5aGHHgLgwIEDnD9/HqPRSL169bh48SJ169Zl9uzZ/N///d9d1VOQKXySDMXExZCUmPGbRWuSNReqKRi0dpPkBo20E5GcZrPZMBhh/ch9Tl9jMKIpkCIiBdSJEydISUlJtYaSn5+f/cPOIUOG2MOef7bbt28fLVq0sD9evXo1HTt2tD9u0qQJRqORhIQEEhMTefbZZ+8qfEpOTmbEiBEEBwfbQ6NTp04BEBoayptvvkndunV5//33admyJUeOHKFKlSoObebOnUuFChWYM2cOzZs3548//rA/N3Gk8EnSZLPZMBqMrN7zntPXGA1GvaEUySM00k5EcprJZCIlGfq2fBk3V48M2yckxrFy22z9XyMiUsjs3buX5ORkevXqhdV650ELtWvX5tChQwBUqVKFpCTHARBr1qwhKCiIxMREjhw5wrBhwyhevDgzZszIUk1DhgzhyJEj/Pjjj/ZjycnJADz33HP069cPgHr16rFt2zbee+89pk+fbm8zfvx4una9tf5kWFgY9913H59++inPPfdcluop6BQ+SZpMJhPJKcmM77IEd9c7D5n8p/jEGKaue0FvKEXyGI20E5Gc1v6BZyjq4Z1hu5tx11m5bXYuVJT/RcVdJcmW/sjz6wk3c6mavCWr08k1DV0k5wUGBmIwGDh27JjD8UqVKgHYd4urUqUKAMeOHaNRo0YAmM1mAgMD07x3QECA/XxQUBAnT55k4sSJhIaG4u6e8VTVfxo6dCgbNmxgx44d3Hffffbj/v7+QOr1m4KCgoiIiEizjdlsplKlSvY2kprCJ8mQr1dpipiLZtguxlo43wCJiIjkFP2yXPjYbDZcjS40/KqzU+1djS6FZsp0VqeT3/5a09BFcl6JEiVo3bo1ixYtYtiwYWmu+9SmTRt8fX2ZOXMm69aty1JfJpOJpKQkEhISnA6fUlJSGDZsGOvWrWP79u1UrFjR4XyFChUoW7ZsqvDsjz/+4NFHHwWgfv36mM1mjh07Zl8HKjExkTNnzlC+fM7vQphfKXwSERERyWO0ZlvhZTKZSExOYmi/Ubib05/KGG+NY1HY3Hz79345OpJ4a/rh6Y3EaPvXdzOdXNPQRXLP4sWLCQ4OpkGDBoSGhlK7dm2MRiP79u0jPDyc+vXr4+XlxbJly+jRowcdOnRg+PDhVKlShejoaPuOdP/+dxgVFcXFixdJSkri8OHDvPXWW7Ro0YJixYoBkJCQwO+//27/+vz58xw6dAgvLy/7iKkhQ4awatUqvvjiC4oWLcrFixcB8Pb2xsPDA4PBwMsvv8zkyZOpU6cOdevWZeXKlYSHh/PZZ58BUKxYMZ5//nkmT55MQEAA5cuXZ/bsW6N6u3XrlvMvcD6l8ElEREQkj9GabVLc2xcPd89028TFx+ZSNdnLZrPhYnCh0dddnWrvYnAc3ZXV6eSahi4FRfTFs3m6n8qVK3Pw4EGmTZvGuHHjOHfuHGazmRo1ajB69GgGDx4MQJcuXdi1axczZ86kT58+XLlyBW9vbxo0aJBqsXGAVq1aAbd+Rvr7+9O+fXumTp1qP//nn39Sr149++M333yTN998k2bNmrF9+3YAlixZAkDz5s0d7h0WFkZISAgAI0aMID4+npEjR3LlyhXq1KnDli1bqFy5sr397NmzcXFxoXfv3sTFxfHggw/y3XffUbx48Sy9ZoWBwicRERGRPEq/LEtBZDKZSEpJYmO/7hQ1px+u3rRa6RD2icJVEW7tGOfu4cmhsDdyrU93D0/8/PwyfZ2/vz8LFy5k4cKF6bZr0KABn376abptKlSoQEpKSoZ9OtPOmfsAvPLKK7zyyitpnnd1dbWHW+KcPB0+hYaG8tprrzkcq1atGuHh4cCtNQ5eeuklVq9ejdVqpW3btixevJjSpUvfi3JFRERERMRJNUqXUrgqkgkWi4Vj4UeJjIzMtT79/PywWCy51p8UXHk6fAKoWbMmW7dutT92cfm75JEjR7Jx40Y+/fRTvL29GTp0KE888QQ7d+68F6WKiIiIiIiI5BiLxaIwSPKlPB8+ubi4UKZMmVTHr1+/zvLly1m1ahWPPPIIcGueZlBQED/99JN9u0YREREREREREbl3jPe6gIwcP36csmXLUqlSJXr16kVERAQA+/fvJzEx0b7oGED16tWxWCzs3r073XtarVZu3Ljh8EdERERERERERLJfnh759OCDD7JixQqqVavGhQsXeO2113j44Yc5cuQIFy9exM3NDR8fH4drSpcubd8uMS3Tp09PtZaUiIiIiEhhtfu/ezB5ZPy5tC0uOReqERGRgiZPh0+PPvqo/evatWvz4IMPUr58eT755BM8PDyyfN9x48YxatQo++MbN24QEBBwV7WKiIiIiOQ3NpsNgxH2vfmn09cYjLeu0w50IiLirDwdPv2bj48PVatW5cSJE7Ru3ZqEhASuXbvmMPrp0qVLd1wj6p/MZjPmDLZ1FREREREp6EwmEynJ4GEqhsGQ8cinlJRk4mw3FDyJiEim5KvwKTo6mpMnT9K7d2/q16+Pq6sr27Zto2vXrgAcO3aMiIgIGjdufI8rFRERERHJP/o0n467a5EM28UnxvCfbUNyoSIRESlI8nT4NHr0aB577DHKly/Pn3/+yeTJkzGZTDz99NN4e3szYMAARo0aha+vL8WKFWPYsGE0btxYO93lc1FxV0myJWXY7nrCzVyoRkRERERERETuRp4On86dO8fTTz9NVFQUJUuW5KGHHuKnn36iZMmSAMybNw+j0UjXrl2xWq20bduWxYsX3+OqJatsNhuuRhcaftXZ6WtcjS5ac0BERDLl0vUbxMXHZ9juhjUhF6oRERFxXkREBJGRkbnWn5+fHxaLJdf6ywkVKlRgxIgRjBgxwqn2zZs3p27dusyfPz9L19+2fft2WrRowdWrV1NtlFYY5enwafXq1emed3d35+233+btt9/OpYokJ5lMJhKTkxjabxTu5owXlI+3xrEobK6CJxERcYrNZsPFaKDRkvedvsbFaNCHHCIikidEREQQVL06sXFxudanp4cHR8PDnQ6gQkJCWLlypf2xr68vDRs2ZNasWdSuXdupe6xYsYIRI0Zw7dq1rJScyr59+yhSJONpxbetXbsWV1fXu+63SZMmXLhwAW9v77u+V0GQp8MnKZyKe/vi4e6ZYbu4+NhcqEZERAoKk8lEUnIKT81rgblIxm8qrTGJrB75vYInERHJEyIjI4mNi2PuU10JLFUyx/s7cfkvRq3+nMjIyEyNfmrXrh1hYWEAXLx4kQkTJtCxY0ciIiJyqtQ7SkhIwM3NzT5zylm+vr533XdiYiJubm4ZboZWmCh8EhERkUKlVCUf3Iu6Zdgu/qam3YmISN4TWKok/1eu7L0uI01ms9keupQpU4ZXXnmFhx9+mL/++ovffvst1VS0Q4cOUa9ePU6fPs2ZM2fo168fAAaDAYDJkydjNBr55JNPOHLkiENfdevW5bHHHmPKlCmEhIRw7do1GjZsyNtvv43ZbOb06dMO0+Z69uyJzWZjzZo19nskJibi7+/P3Llz6dOnT6ppdwA3b97k6aef5ssvv8THx4dXX32VIUP+3nzBYDCwePFivvnmG7Zt28bLL79M8+bNUz3Xzz//nEmTJnHixAn8/f0ZNmwYL730Unb/FeRJGe+nKiIiIiIiIiKSSdHR0Xz44YcEBgZSokSJDNs3adKE+fPnU6xYMS5cuMCFCxcYPXo0/fv35+jRo+zbt8/e9uDBg/z666/2sApg27ZtHDt2jC1btrBhw4ZU9+/VqxdfffUV0dHR9mPffvstsbGxdOnSJc26Zs+eTZ06dTh48CCvvPIKL774Ilu2bHFoExoaSpcuXTh8+DD9+/dPdY/9+/fTvXt3nnrqKQ4fPkxoaCgTJ05kxYoVGb4uBYFGPomIZIEzuzJqR0YRERERKWw2bNiAl5cXADExMfj7+7NhwwaMxozHvri5ueHt7Y3BYHCYsubl5UXbtm0JCwujYcOGAISFhdGsWTMqVapkb1ekSBGWLVuGm9udRzi3bduWIkWKsG7dOnr37g3AqlWr6NSpE0WLFk2zruDgYF555RUAqlatys6dO5k3bx6tW7e2t+nZs6dDEHbq1CmHe8ydO5eWLVsyceJE+31+//13Zs+eTUhISIavTX6n8ElEJBMyuyujdmQUERHJXv+7/BfXzeZ029ywWnOpGhH5txYtWrBkyRIArl69yuLFi3n00UfZu3fvXd130KBB9O/fn7lz52I0Glm1ahXz5s1zaFOrVq00gycAFxcXunfvzkcffUTv3r2JiYnhiy++yHCzs8aNG6d6/M9peQANGjRI9x5Hjx7l8ccfdzgWHBzM/PnzC8XvCwqfREQyITO7MmpHxtyhUWgi997J08fwMHtl2C7OGp1hG5G02Gw2XAwGmr63JuPGgItBu1WK3AtFihQhMDDQ/njZsmV4e3vz7rvv0qZNGwBSUlLs5xMTE52672OPPYbZbGbdunW4ubmRmJjIk08+marvjPTq1YtmzZpx+fJltmzZgoeHB+3atXOqhvRkZke9wkjhk4hIFjizK6N2ZMxZGoUmcu/ZbDaMBhOvru7u9DVGgwmbzZaDVUlBZTKZSEpJoX+LcZhd0v8AyJoUx3vfT9f/+SJ5gMFgwGg0EhcXZ9957sKFCxQvXhy4teD4P7m5ud3x54SLiwt9+/YlLCwMNzc3nnrqKTw80v+/4E6aNGlCQEAAa9as4ZtvvqFbt264uqa/C+5PP/2U6nFQUFCm+g0KCmLnzp0Ox3bu3EnVqlULxf9VCp9EJE0Rp87hViTj/yYSYtIfdSKSEzQKTeTeM5lMJKfY+L/nm+JiTv+NO0CSNZEjS3fo36LclZr3NaSIOe21WQBirBrxKnKvWK1WLl68CNyadrdo0SKio6N57LHHCAwMJCAggNDQUKZOncoff/zBnDlzHK6vUKEC0dHRbNu2jTp16uDp6Ymn560PfQcOHGgPff4d5GRGz549Wbp0KX/88Qfff/99hu137tzJrFmz6Ny5M1u2bOHTTz9l48aNmerzpZdeomHDhkyZMoUePXqwe/duFi1axOLFi7P6NPIVhU8ikorNZsNghPUj92Xc+P8zGNGoErknNApN5N77vz4PY/bO+NNn6/U4jizdkQsViYgUXCcu/5Wn+9m0aRP+/v4AFC1alOrVq/Ppp5/SvHlzAD7++GNeeOEFateuTcOGDXnjjTfo1q2b/fomTZrw/PPP06NHD6Kiopg8eTKhoaEAVKlShSZNmnDlyhUefPDBLD+3Xr16MXXqVMqXL09wcHCG7V966SV+/vlnXnvtNYoVK8bcuXNp27Ztpvq8//77+eSTT5g0aRJTpkzB39+f119/vVAsNg4Kn0TkDkwmEynJ0Lfly7i5ZvzLREJiHCu3zVbwJCIiIiKSQ/z8/PD08GDU6s9zrU9PDw/8/Pycbr9ixQpWrFiRbpvg4GB+/fVXh2P/XAMKYMmSJfZFy//d7s8//2Tw4MF37PtOzpw5k+pYUFBQqj5v2759e4bX36muf2vevHmq4127dqVr164Z3q8gUvgkImlq/8AzFPXwzrDdzbjrrNw2OxcqEhEREZG0XI6OJN4an26bG4la+D+/slgsHA0PJzIyMtf69PPzw2Kx5Fp/6fnrr79YvXo1Fy9epF+/fve6HMkkhU+S50RH3yQpKeMdD+Lj0//BKpKTnPk+1feoiIiI5IZbOwG60Ohr50ZUuBi0CUd+ZbFY8kwYlNtKlSqFn58f77zzjn2xcsk/FD5JnmGz2TAZTcxbNtPpa0xGk35wSq7K7PepvkdFREQkp93aCTCJjf26U9RsTrftTauVDmGfOLw30Ydqkh+kNU1O8geFT5JnmEwmbMk2djzfm2Lu6f/QBLgRb6Xp0g/0S73kqsx8n+p7VEQk74mKu0qSLf1dWq8naKc0yZ9qlC6Fj4d7um2uxf0dIulDNRHJLQqfJM8J8PHO8IcmOP7gFMltznyf6ntURCTvsNlsuBpdaPhVZ6fauxo1LUkKvrv9UE1hrog4S+GTiIiIiBR4JpOJxOQkhvYbhbs5/Z1c461xLAqbq+BJCo3MfqimMFdEMkvhk4iIiIgUGsW9ffFw90y3TVx8bC5VI5I/KcwVkcxS+CQiIiIiIiKZpjBXRJyl8ElERLLV5ehI4q3pr3d1IzE6l6oREREREZF7TeGTiOQIBRCFj81mw8XgQqOvuzrV3sWg9R9EREREMiMiIoLIyMhc68/Pzw+LxZJr/UnBpfBJRLKVAojCy2QykZSSxMZ+3SlqTn/HnJtWKx3CPil0f+/R0TdJSkpMt018vHZJFBERkdQiIiKoXr06cXFxudanh4cH4eHhmQ6gLl68yPTp09m4cSPnzp3D29ubwMBAnnnmGfr27Yun563pmgcPHmTGjBns2LGDK1euUKZMGWrVqsVzzz1Hx44dMRgMnDlzhooVK9rv7erqisViISQkhPHjx2MwGAD47bffmDRpEvv37+fs2bPMmzePESNGONS1Y8cOZs+ezf79+7lw4QLr1q2jc+fO9vOJiYlMmDCBr7/+mlOnTuHt7U2rVq2YMWMGZcuWtbfr1KkThw4d4vLlyxQvXpxWrVoxc+ZMhzbiSOGTiGQrBRBSo3SpTO2YUxjYbDZMRhPzls10qr3JaFIoKyIiBdbvp05R1M0t3TY3ExJyqZr8IzIykri4OAY+9Tz+pcrleH8XLp9n2eqlREZGZip8OnXqFMHBwfj4+DBt2jRq1aqF2Wzm8OHDvPPOO5QrV45OnTrxxRdf0L17d1q1asXKlSsJDAzEarWya9cuJkyYwMMPP4yPj4/9vlu3bqVmzZpYrVZ+/PFHBg4ciL+/PwMGDAAgNjaWSpUq0a1bN0aOHHnH2mJiYqhTpw79+/fniSeeSHU+NjaWAwcOMHHiROrUqcPVq1d58cUX6dSpEz///LO9XYsWLXj11Vfx9/fn/PnzjB49mieffJJdu3Y5/ToVNgqfRCRHKIAQ+ZvJZMKWbGPH870p5p5+KHsj3krTpR8oeBIRkQLn1gh5Ax0++dqp9i4Ggz6MuQP/UuUoX67CvS4jTYMHD8bFxYWff/6ZIkWK2I9XqlSJxx9/nJSUFGJiYhgwYAAdOnRg7dq1DtcHBQUxYMAAUlJSHI6XKFGCMmXKAFC+fHnCwsI4cOCAPXxq2LAhDRs2BOCVV165Y22PPvoojz76aJq1e3t7s2XLFodjixYt4oEHHiAiIsIewv0z3CpfvjyvvPIKnTt3JjExEVdX13Rfn8JK4ZOIiEguCfDxVigrIiKF1q0R8ik80XgQrq7p/zxMTIxn7e53FTzlM1FRUWzevJlp06Y5BE//ZDAY2Lx5M1FRUYwZMybNe92eTncnP//8M/v376dPnz53XXNGrl+/jsFgcBiF9U9Xrlzho48+okmTJgqe0qHwSUQkH4iKu0qSLSnDdtcTbuZCNSIiIpKRS9dvEOfEOn43rIVvelmP5kMp6uGdbpubcddZu/vdXKpIssuJEydISUmhWrVqDsf9/Pzs61oOGTIEX19fAId2+/bto0WLFvbHq1evpmPHjvbHTZo0wWg0kpCQQGJiIs8++2yOh0/x8fGMHTuWp59+mmLFijmcGzt2LIsWLSI2NpZGjRqxYcOGHK0lv1P4JCKSizIbItlsNlyNLjT8qrPTfbgatYi7SE5x5t+wQmCRws1ms+FiNNBoyftOX+NivDW9TKSg2rt3L8nJyfTq1Qur1XrHNrVr1+bQoUMAVKlShaQkx5+3a9asISgoiMTERI4cOcKwYcMoXrw4M2bMyJGaExMT6d69OykpKSxZsiTV+ZdffpkBAwZw9uxZXnvtNfr06cOGDRvSHbFVmCl8EhHJBVkNkQASk5MY2m8U7maPDK+Jt8axKGyugieRbJbZf8P3OgRWSCZy75hMJpKSU3hqXgvMRTKegmONSWT1yO/1s1sKhMDAQAwGA8eOHXM4XqlSJeDW7nlwK1wCOHbsGI0aNQLAbDYTGBiY5r0DAgLs54OCgjh58iQTJ04kNDQUd/f0p3Fm1u3g6ezZs3z33XepRj3BrdFcfn5+VK1alaCgIAICAvjpp59o3LhxttZSUCh8yoe0VbdI/mMyme4qRCru7YuHu2eG18XFx951rVJw7Dn0C14ZrD0QnZj+zxO5JTP/hu9lCJzfQjKRgqxUJR/ci6a/oxtA/M3CN+1OCq4SJUrQunVrFi1axLBhw9Jc96lNmzb4+voyc+ZM1q1bl6W+TCYTSUlJJCQkZGv4dDt4On78ON9//z0lSpTI8Jrk5GSANEd1icKnfEVbdYvkfwqRJDfc3k3oqU3/daq9dhNynjP/hu/lv9/8EpKJiEjBtXjxYoKDg2nQoAGhoaHUrl0bo9HIvn37CA8Pp379+nh5ebFs2TJ69OhBhw4dGD58OFWqVCE6OppNmzYBpPr5FBUVxcWLF0lKSuLw4cO89dZbtGjRwj4qKSEhgd9//93+9fnz5zl06BBeXl72EVPR0dGcOHHCfs/Tp09z6NAhfH19sVgsJCYm8uSTT3LgwAE2bNiAzWbj4sWLAPj6+uLm5saePXvYt28fDz30EMWLF7ePwKpcubJGPaVD4VM+oq26RUTEGbd3EyrZ0IIxg58DyTYbf+2LyJc/L378eT9erhm/lYlOzHidtYImr4dkIiKSdRcun8/T/VSuXJmDBw8ybdo0xo0bx7lz5zCbzdSoUYPRo0czePBgALp06cKuXbuYOXMmffr04cqVK3h7e9OgQYNUi40DtGrVCrj1Psff35/27dszdepU+/k///yTevXq2R+/+eabvPnmmzRr1ozt27cDt3bJ++ei5qNGjQKgb9++rFixgvPnz/Pll18CULduXYf+v//+e5o3b46npydr165l8uTJxMTE4O/vT7t27ZgwYQJmc/q/pxdmCp/yIW3VLSIizmj5Vm/M3umPfrFej2N1k6nptslrbo/s6r11t9PX3B7dJTkj7mq0U5spJN7Q+xMRkazy8/PDw8ODZauX5lqfHh4e+Pn5Zfo6f39/Fi5cyMKFC9Nt16BBAz799NN021SoUIGUlJQM+3SmXfPmzdNt48w9atWqxXfffZdhPeJI4ZOIiIjkK7dHdhUpWwSDMeMdZVKSU4j5MyZfju7K62w2G0ZXE190eMvpa4yuJgWBIiJZYLFYCA8PJzIyMtf69PPzw2Kx5Fp/UnApfBIRyUXObBgA2jRAxBnBox7GtUjGi/kmxiSwefSmXKio8DGZTCQn2njo1WW4untl2D4xPpofpw1UECgikkUWi0VhkORLCp9ERHJBZjcMgL83DRARyes8ff1xLVI0w3aJMTdzoRoRERHJaxQ+iYjkgsxsGADaNEBERERERAoOhU8iIrnImQ0DQJsGiIiIiIhIwaHwSUREREQkj3FmjUCtDygiIvmFwicRERERkTwis2sE3l4fUNO0RUQkL1P4JCIiIiKSR2RmjUCtDygiIvmFwicRERERkTzGmTUCtT6giIjkFwqfRERERERERPKBiIgIIiMjc60/Pz8/LBZLrvWXF2zfvp0WLVpw9epVfHx87nU5TlmxYgUjRozg2rVr97qUNCl8EikELl2/QZwTi5LesCbkQjUiIiIiIpJZERERVKtejfhcHPXo7uHOsfBjmQ6gLl68yNSpU9m4cSPnz5+nVKlS1K1blxEjRtCyZcscqjb/OHPmDBUrVuTgwYPUrVv3ru/Xo0cP2rdvf/eF5SCFTyIFmM1mw8VooNGS952+xsVowGaz5WBVIiIiIiKSWZGRkcTHxVO33/14lfHK8f6iL0ZzKOwAkZGRmQqfzpw5Q3BwMD4+PsyePZtatWqRmJjIt99+y5AhQwgPD890LQkJCbi5uWX6usIgMTERDw8PPDw87nUp6TLe6wJEJOeYTCaSklN4al4L+r7TJsM/T81rQVJyihYuFRERERHJo7zKeOFt8cnxP1kNuAYPHozBYGDv3r107dqVqlWrUrNmTUaNGsVPP/0E3BrF9fjjj+Pl5UWxYsXo3r07ly5dst8jNDSUunXrsmzZMipWrIi7uzvvv/8+JUqUwGq1OvTXuXNnevfu7XDdBx98QIUKFfD29uapp57i5s2b9vbJyclMnz6dihUr4uHhQZ06dfjss89SPY+dO3dSu3Zt3N3dadSoEUeOHLGfi4qK4umnn6ZcuXJ4enpSq1YtPv74Y4frk5OTmTVrFoGBgZjNZiwWC1OnTgWgYsWKANSrVw+DwUDz5s3t1y1btoygoCDc3d2pXr06ixcvtp87c+YMBoOBNWvW0KxZM9zd3fnoo49YsWKFwxTBkJAQOnfu7FDPiBEjHPpp3rw5w4YNY8SIERQvXpzSpUvz7rvvEhMTQ79+/ShatCiBgYF88803qV6brFD4JFIIlKrkQ+nA4hn+KVXJ516XKiIiIiL5RHT0TW5GX0/3T3T0zYxvJAXGlStX2LRpE0OGDKFIkSKpzvv4+JCcnMzjjz/OlStX+OGHH9iyZQunTp2iR48eDm1PnDjB559/ztq1azl06BDdunXDZrPx5Zdf2ttcvnyZjRs30r9/f/uxkydPsn79ejZs2MCGDRv44YcfmDFjhv389OnTef/991m6dCm//fYbI0eO5JlnnuGHH35w6P/ll19mzpw57Nu3j5IlS/LYY4+RmJgIQHx8PPXr12fjxo0cOXKEZ599lt69e7N371779ePGjWPGjBlMnDiR33//nVWrVlG6dGkAe7utW7dy4cIF1q5dC8BHH33EpEmTmDp1KkePHmXatGlMnDiRlStXOtT2yiuv8OKLL3L06FHatm3r/F/Qv6xcuRI/Pz/27t3LsGHDeOGFF+jWrRtNmjThwIEDtGnTht69exMbG5vlPm7TtDsREZECKu5qNEm2pHTbJN7QbllSuERH3yQpKTHdNvFOrJMoklPyw1qdNpsNk9HEvGUznWpvMppuXaPR9QXeiRMnSElJoXr16mm22bZtG4cPH+b06dMEBAQA8P7771OzZk327dtHw4YNgVtT7d5//31Klixpv7Znz56EhYXRrVs3AD788EMsFovDiJ7k5GRW/D/2zjusiezr498k9CKoWFCpgoqKimLBgnXtvSsu4qKo2LCuXdQV+65t7QV7772wlrUrNlwQCyp2RVGpAZLz/sGb+RES2eCwKQAA9fhJREFUkoCKoOfzPD6PTO6Zc3NnMjP3O+ecGxwMc3NzAMCvv/6KkJAQzJgxA1KpFEFBQTh16hQ8PDwAAI6Ojjh//jxWrFiB+vXrC/uZMmUKfvnlFwDpIk2pUqWwd+9edO3aFSVLlsSoUaOEtkOGDMHx48exY8cO1KhRA3FxcVi4cCGWLFmC3r17AwBKly6NunXrAoDwnQoXLozixYsr+Zw/fz46duwIID1CKjw8HCtWrBD2A6RHMSnafAmVK1fGxIkTAfxPLLOyskK/fv0AAJMnT8ayZctw584d1KpV64t8sfjEMAyTD9BlsgTwhIlJRyaTQawvwf5WC3VqL9bnSQHz48OTZSav8yW1OnP7PJVIJJDJZTg34FcUMDLU2PZzshSeyzfyb+kngYi0tomIiICNjY0gPAFA+fLlYWlpiYiICEF8srOzUxKeAKBfv36oXr06Xrx4gZIlSyI4OBg+Pj4QiURCG3t7e0F4AgBra2u8ffsWQLo4lpiYKIhKClJSUuDm5qa0TSFOAUChQoVQtmxZREREAEj/vQYFBWHHjh148eIFUlJSIJVKYWJiInxHqVSareLqCQkJePToEXx9fQXxBwDS0tJgYWGh1Nbd3V3n/WqiUqVKwv8lEgkKFy4MV1dXYZsiUksxfl8Ci08MwzB5mOxOlgCeMDHpDw/yVBnqjl8NfSPN9RpSk+NxPqgvny/MDw9Plpm8TsZanYam+lrbSxNSsW346e96ntpYWsDS2Ehjm4+5uDIb8/1xdnaGSCTKUVHxzKhL23Nzc0PlypWxYcMGNG3aFP/99x8OHz6s1EZfX/n3IxKJIJfLAQDx8fEAgMOHD6NkyZJK7QwNNd8bMjJ37lwsXLgQCxYsgKurK0xNTREQEICUlPSIxJwU/1b0bdWqVahZs6bSZ5l/5+rGJiNisVhFCFSkDGZE3Vhl3KYQ9RTj9yWw+MQwDJOHyc5kCeAJE6OMSSFr6Juaa2yTmsC1OJifC54sM3mdoo6WMDLXvqpXctz3S7tjmKwoVKgQmjVrhr///htDhw5VEUk+fvwIFxcXPHv2DM+ePROin8LDw/Hx40eUL19eq4++fftiwYIFePHiBZo0aaIUQaWN8uXLw9DQENHR0Uopduq4fPmysMpfbGws7t+/DxcXFwDpxcjbtWuHXr16AUgXZ+7fvy/039nZGcbGxggJCUHfvn1V9q1YuS/jKuPFihVDiRIlEBUVBS8vL52/kzqKFCmiVCAdAG7duqUiNuUmLD4xDJMln+I+QJaquV4MAMQnf86F3vzc6DJZAnjCxDDfGq4XxDAMw3xv4l/H52k/f//9N+rUqYMaNWpg2rRpqFSpEtLS0nDy5EksW7YM4eHhcHV1hZeXFxYsWIC0tDT4+/ujfv36OqWT9ezZE6NGjcKqVauwYYPuaaoAYG5ujlGjRmH48OGQy+WoW7cuPn36hAsXLqBAgQJKdZWmTZuGwoULo1ixYpgwYQKsrKyEFeScnZ2xa9cuXLx4EQULFsSff/6JN2/eCOKTkZERfv/9d4wZMwYGBgaoU6cO3r17h//++w++vr4oWrQojI2NcezYMZQqVQpGRkawsLDA1KlTMXToUFhYWKB58+aQSqW4fv06YmNjMWLECJ2/Z6NGjTB37lxs2LABHh4e2LRpE+7evauSWpibsPjEMIwKMpkMehJ9+C1pqLONnkSfU70Yhvlh4XpBDMNkl+io5zAw1T7dSknQ/qKPYQDAysoKRsZGuLXuRq75NDI2gpWVVbZsHB0dcePGDcyYMQMjR47Eq1evUKRIEVSrVg3Lli2DSCTC/v37MWTIEHh6ekIsFqN58+ZYvHixTvu3sLBAp06dcPjwYUEMyg7Tp09HkSJFMHPmTERFRcHS0hJVq1bF+PHjldrNmjULw4YNw4MHD1ClShUcPHhQiFiaOHEioqKi0KxZM5iYmMDPzw/t27fHp0+fBPtJkyZBT08PkydPxsuXL2FtbY0BAwYAAPT09LBo0SJMmzYNkydPRr169XDmzBn07dsXJiYmmDt3LkaPHg1TU1O4uroiICAgW9+xWbNmmDRpEsaMGYPk5GT89ttv8Pb2RlhYWLbH62vB4hPDMCpIJBKkyVIxLmAjjLXUiwGApOR4zFzwK0+yGIb5YeF6QQzD6IpMJoNIDOwbfk1nG5FYOf2GYdRha2uLyHuRiImJyTWfVlZWQupZdrC2tsaSJUuwZMkStZ/b2tpi//79WdoHBgYiMDAwy89fvHgBLy8vlTpN6uwCAgKUxBuRSIRhw4Zh2LBhavfdoEEDoV5S69at1bYpVKgQ9u3bl2X/gPS6SxMmTMCECRPUft63b1+1KXk9e/ZEz5491drY29urLeru4+MDHx8fpW1Tp07F1KlTs+zfmTNnVLY9efJEZZsuReR1gcUnhmGypHBBa5iYFNDaLjGR0+4Yhvk54HpBDMNoQyKRgORA78ajYaCvvehwSmoS1ofMZcGa0QlbW9sciUE/CrGxsThz5gzOnDmDpUuXfu/uMNmAxSeGYRiGYRiGYZivTMsavWBubKG1XVzSJ6wPmZsLPWKY/I+bmxtiY2Mxe/ZslC1b9nt3h8kGLD4xDMMwDMMwDMMwDJPnUZcWxuQPxN+7AwzDMAzDMAzDMAzDMMyPC4tPDMMwDMMwDMMwDMMwzDeDxSeGYRiGYRiGYRiGYRjmm8E1nxiGYRiGYRjmGxEfH4e0tFSNbZKTeYVEhmEY5seGxSeGYRiGYRiG+crIZDJIxBL8tXq2Tu0lYkm6jUTyjXvGMAzDMLkPi08MwzAMwzAM85WRSCSQyWU4N+BXFDAy1Nj2c7IUnss3svDEMAzD/LCw+MQwDMMwDMMw3wgbSwtYGhtpbPMxidPuGIbRjejoaMTExOSaPysrK9ja2mbb7vXr15g5cyYOHz6M58+fw8LCAk5OTujVqxd69+4NExMTAMDNmzcxa9YsnDt3Dh8+fEDx4sXh6uqK/v37o3Xr1hCJRHjy5AkcHByEfevr68PW1hY+Pj6YMGECRCIRAOC///7D5MmTERoaiqdPn+Kvv/5CQECAUr8CAwMxdepUpW1ly5bFvXv3hL8bNGiAs2fPKrXp378/li9fLvwdHR2NgQMH4vTp0zAzM0Pv3r0xc+ZM6OmxxJIVeXpkZs6ciT179uDevXswNjZG7dq1MXv2bJQtW1Zoo8uJwTAMwzAMwzAMwzD5mejoaLiUK4fEpKRc82libIyIe/eyJUBFRUWhTp06sLS0RFBQEFxdXWFoaIiwsDCsXLkSJUuWRNu2bbF//3507doVTZo0wfr16+Hk5ASpVIqLFy9i4sSJqFevHiwtLYX9njp1ChUqVIBUKsX58+fRt29fWFtbw9fXFwCQmJgIR0dHdOnSBcOHD8+yfxUqVMCpU6eEv9UJRv369cO0adP+Nw7/L5YB6WnVrVq1QvHixXHx4kW8evUK3t7e0NfXR1BQkM7j9LORp8Wns2fPYtCgQahevTrS0tIwfvx4NG3aFOHh4TA1NRXaaToxGIZhGIZhGIZhfgbexycgLS1NY5tPydJc6g3ztYmJiUFiUhI2dK6NckUsvrm/e+8+wXvXRcTExGRLfPL394eenh6uX7+uNG93dHREu3btQERISEiAr68vWrVqhT179ijZu7i4wNfXF0SktL1w4cIoXrw4AMDOzg7r1q3DjRs3BPGpevXqqF69OgBg7NixWfZPT09P2E9WmJiYZNnmxIkTCA8Px6lTp1CsWDFUqVIF06dPx++//47AwEAYGBho3PfPSp4Wn44dO6b0d3BwMIoWLYrQ0FB4enoK2zWdGAyjC8/evsMnQy31GKR8o2YYhmEYhmHyHjKZDPoSMdyXrNOpvb5EzAXu8zHliligaolC37sbann//j1OnDiBoKAgJeEpIyKRCCdOnMD79+8xZsyYLPelSKdTx/Xr1xEaGgpvb+9s9/HBgwcoUaIEjIyM4OHhgZkzZ6qIa5s3b8amTZtQvHhxtGnTBpMmTRKCXC5dugRXV1cUK1ZMaN+sWTMMHDgQ//33H9zc3LLdp5+BPC0+ZebTp08AgEKFlH9omk4MhtGETCaDnkgEz7XbdWqvJxLxjZph8hi8jDnDMAzzsyORSJAqk+PEEg+Ym+hrbBuXmIqmgy/x8yzzTXj48CGISKlUDpBeO0rxPDZo0CBhTp+x3bVr19CwYUPh723btqF169bC37Vr14ZYLEZKSgpSU1Ph5+eXbfGpZs2aCA4ORtmyZfHq1StMnToV9erVw927d2Fubg4A6NmzJ+zs7FCiRAncuXMHv//+OyIjI4UIrdevXysJTwCEv1+/fp2t/vxM5BvxSS6XIyAgAHXq1EHFihWF7dpODHVIpVJIM0SxfP78+Zv2ncm7SCQSpBHht4bjYKhnrLGtNC0Ja0/P5Bs1w+QReBlzJikuGWlpMq3tUpM0i5MMwzA/CqWKmsDSXLP49DGOr4lM7nP16lXI5XJ4eXkpzcUzUqlSJdy6dQsA4OzsrJJCun37dri4uCA1NRV3797FkCFDULBgQcyaNUvnfrRo0ULJX82aNWFnZ4cdO3YI6Xt+fn5CG1dXV1hbW6Nx48Z49OgRSpcurbMvRpl8Iz4NGjQId+/exfnz55W25+TEmDlzpkqFe+bnpkKp6jA1NNfYJkEal0u9YRhGF3gZ858XmUwGkUSEf6ee0dlGJEmPXGUYhmEY5tvh5OQEkUiEyMhIpe2Ojo4AAGPj9Bf+zs7OAIDIyEjUqlULAGBoaAgnJ6cs921jYyN87uLigkePHmHSpEkIDAyEkZHmVUWzwtLSEmXKlMHDhw+zbFOzZk0A6VFdpUuXRvHixXH16lWlNm/evAEALgekgXwhPg0ePBiHDh3CuXPnUKpUKY1tM58Y6hg3bhxGjBgh/P3582fY2Nh8vQ4zDMMwuQYvY/7zIZFIQDLCdf8WsDDU/IYfAD5JU+G+9CiLjwzDMAzzjSlcuDB++eUXLFmyBEOGDMmy7lPTpk1RqFAhzJ49G3v37s2RL4lEgrS0NKSkpORYfIqPj8ejR4/w66+/ZtlGEY1lbW0NAPDw8MCMGTPw9u1bFC1aFABw8uRJFChQAOXLl89RP34G8rT4REQYMmQI9u7dizNnzsDBwUGrTeYTQx2GhoYw1FJcmmEYhmGYvI2dpSkKGmu/n8cm8YIRDMMwPwIv339AvA7Rzsz3ZenSpahTpw7c3d0RGBiISpUqQSwW49q1a7h37x6qVasGMzMzrF69Gt26dUOrVq0wdOhQODs7Iz4+Xlh4LPNLo/fv3+P169dIS0tDWFgYFi5ciIYNG6JAgQIAgJSUFISHhwv/f/HiBW7dugUzMzMhYmrUqFFo06YN7Ozs8PLlS0yZMgUSiQQ9evQAADx69AhbtmxBy5YtUbhwYdy5cwfDhw+Hp6cnKlWqBCBdOCtfvjx+/fVXzJkzB69fv8bEiRMxaNAg1hk0kKfFp0GDBmHLli3Yv38/zM3NheJdFhYWMDY21unEYBiGYRiGYX483ifFIk2mZUn5FE6ZZ5gfAcUiQXVWbdGpvWKRoB+Ve+8+5Wk/pUuXxs2bNxEUFIRx48bh+fPnMDQ0RPny5TFq1Cj4+/sDADp06ICLFy9i9uzZ8Pb2xocPH2BhYQF3d3eVYuMA0KRJEwDpopS1tTVatmyJGTNmCJ+/fPlSaaW5efPmYd68eahfvz7OnDkDAHj+/Dl69OiB9+/fo0iRIqhbty4uX76MIkWKAAAMDAxw6tQpLFiwAAkJCbCxsUGnTp0wceJEYb8SiQSHDh3CwIED4eHhAVNTU/Tu3RvTpk3L0Xj9LORp8WnZsmUAgAYNGihtX7duHXx8fHQ6MRiGYRiGYZgfB5lMBn2xHqofbK9Te32xHi82wDD5HMUiQS2rdIC+xEBj21RZCo7c2vtD/uatrKxgYmwM710Xc82nibExrKyssm1nbW2NxYsXY/HixRrbubu7Y+fOnRrb2Nvbg4i0+tSl3bZt2zR+bmNjg7Nnz2r1ZWdnhyNHjmhtx/yPPC0+aTtxdD0xGIZhGIZhmLxHeFQUzA00TyQBIC4lRfi/RCJBqjwNg/uMgJGh5pVqk6VJWLLuzx9yEsowPyPWlqVgpK+5tk9y6o9b59HW1hYR9+4hJiYm13xaWVnB1tY21/wxPy55WnxiGIZhGIZhfjwUKTStduj+1liRRqMQkgpaFIKxkYlGm6TkxC/qJ8MwTF7D1taWxSAmX8LiE5PnePLqNQoYan8L+lmaorUNwzAMw/yMxMfHIS0tVWOb5OTvFx2gSKHp6NEP+lqiGAAgNTUZey6t4ggmhmEYhsmnsPjE5BkUb0Ebrt+ls03mt6AMwzDM90OXFKqM6VPM10cmk0EiluCv1bN1ai8RS77rfbRbg8EwN7bQ2i4u6RP2XFqVCz1iGIZhGOZbwOITk2dQvAVdOP1XmJpoj3xKSEzBsEkbWXhiGIb5zmQ3hYpfHHw7JBIJZHIZzg34FQV0WI7ccznfRxmGYRiG+faw+MTkOdo0qwpLC1Ot7T5+SsCwSRtzoUcMwzCMJrKTQsXpU7mDjaUFLI01H4uPST9uUV4mf3D+eijM9DVPR+JT03KpNwzDMMy3hMUnhmEYhmG+CrqkUHH6FMMwimjJX09d0qk9R0syDMPkf1h8YhgmT/E+PgFpaZrfcn5KluZSbxiGYRjm+xB6NxxmBlqiglLyZ1SQIlrStIQpRGKRxrYkJyS8TGDhiWEYJp/D4hPDMHkCmUwGfYkY7kvW6dReXyLmt6AMwzDMD4ciKqjzoX90ap+fo4LqjKgHfVPNdT5TE1JwYtSxXOoRwzAM861g8YlhmDyBRCJBqkyOE0s8YG6ir7FtXGIqmg6+lC8ftBmGYRhGE4qooOJObhBLND+qy2VpeP3wJt8PGYZhmDwPi08Mw+QpShU1gaW5ZvHpY1xqLvWGYRiGYb4PtdoOgIGx5gVYUpISsO/P/rnUI4Zh8gLR0dGIiYnJNX9WVlawtbXNtt3r168xc+ZMHD58GM+fP4eFhQWcnJzQq1cv9O7dGyYmJgCAmzdvYtasWTh37hw+fPiA4sWLw9XVFf3790fr1q0hEonw5MkTODg4CPvW19eHra0tfHx8MGHCBIhE6em7q1atwoYNG3D37l0AQLVq1RAUFIQaNWoItoq2mZkzZw5Gjx4NAGjbti1u3bqFt2/fomDBgmjSpAlmz56NEiVKAAACAwMxdepUlX2YmJggISEBANCgQQOcPXtWpU3Lli1x+PDhbI/njwCLTwzDMAzDMAzDMAyTx4mOjoZLuXJITErKNZ8mxsaIuHcvWwJUVFQU6tSpA0tLSwQFBcHV1RWGhoYICwvDypUrUbJkSbRt2xb79+9H165d0aRJE6xfvx5OTk6QSqW4ePEiJk6ciHr16sHS0lLY76lTp1ChQgVIpVKcP38effv2hbW1NXx9fQEAZ86cQY8ePVC7dm0YGRlh9uzZaNq0Kf777z+ULFkSAPDq1Sulvh49ehS+vr7o1KmTsK1hw4YYP348rK2t8eLFC4waNQqdO3fGxYsXAQCjRo3CgAEDlPbTuHFjVK9eXfh7z549SElJEf5+//49KleujC5duug8jj8aLD4xDMMwDMMwDMMwTB4nJiYGiUlJmNGkDxwKFf/m/h5/eI0Jp9YhJiYmW+KTv78/9PT0cP36dZia/i+C09HREe3atQMRISEhAb6+vmjVqhX27NmjZO/i4gJfX18QkdL2woULo3jx9O9tZ2eHdevW4caNG4L4tHnzZqX2q1evxu7duxESEgJvb28AEOwV7N+/Hw0bNoSjo6Owbfjw4cL/7ezsMHbsWLRv3x6pqanQ19eHmZkZzMzMhDa3b99GeHg4li9fLmwrVKiQkp9t27bBxMSExSeGYRiGYZifhUf3n8DARPsjUEpi/lxJjGGYn5fnr97j0yfN17c4vrblexwKFYdLkeynwuUG79+/x4kTJxAUFKQkPGVEJBLhxIkTeP/+PcaMGZPlvrJKkQOA69evIzQ0VBCV1JGYmIjU1FQVIUjBmzdvcPjwYaxfvz7LfXz48AGbN29G7dq1oa+vvjTI6tWrUaZMGdSrVy/L/axZswbdu3fPckx+Blh8YhiGYRjmp0Amk0EkBg6PvqmzjUiMfLuSGMMwPw8ymQx6YqDpsFs6tdfjaxvzjXj48CGICGXLllXabmVlheTkZADAoEGDBEEoY7tr166hYcOGwt/btm1D69athb9r164NsViMlJQUpKamws/PT6P49Pvvv6NEiRJo0qSJ2s/Xr18Pc3NzdOzYUa3tkiVLkJiYiFq1auHQoUNq95GcnIzNmzdj7NixWfbj6tWruHv3LtasWZNlm58BFp8YhmEYhvkpkEgkIDnQrXFdGGbx9jIj0tRUbA85z5Mz5qfgc1wspHrJWttJ03Kv1gyjOxKJBGlyYPX8vjAzNdTYNj5Bir4jV/O1jclVrl69CrlcDi8vL0ilUrVtKlWqhFu3bgEAnJ2dkZamHKW3fft2uLi4IDU1FXfv3sWQIUNQsGBBzJo1S2Vfs2bNwrZt23DmzBkYGRmp9bd27Vp4eXmp/Xz06NHw9fXF06dPMXXqVHh7e+PQoUMq0Vh79+5FXFwcevfuneV3X7NmDVxdXZUKn/+MsPjEMAzDMMxPxa8tGsH8/1fZ0URcYiK2h5zPhR4xzPdDJpNBBAm2Xp2os40IEo6ayaM0qlcBlhaa03o+fkrIpd4wPyNOTk4QiUSIjIxU2q6oqWRsbAwgXVwCgMjISNSqVQsAYGhoCCcnpyz3bWNjI3zu4uKCR48eYdKkSQgMDFQSkObNm4dZs2bh1KlTqFSpktp9/fvvv4iMjMT27dvVfm5lZQUrKyuUKVMGLi4usLGxweXLl+Hh4aHUbvXq1WjdujWKFSumdj8JCQnYtm0bpk2bluX3+llg8YlhGIbJ19yJvA9zA81RLHEpqbnUG4ZhmPyFRCIBQYaqHbdBYmCutb0sJQ439nRn4YlhGLUULlwYv/zyC5YsWYIhQ4ZkWeOoadOmKFSoEGbPno29e/fmyJdEIkFaWhpSUlIE8WnOnDmYMWMGjh8/Dnd39yxt16xZg2rVqqFy5cpa/cjlcgBQidh6/PgxTp8+jQMHDmRpu3PnTkilUvTq1UuXr/RDw+ITwzAMky+RyWTQE4nQbu8JndrriUT8pp5hmHxD+N3bkOhrTp+SpapPXckJZlbloG9kobVdavKnr+aTYZgfk6VLl6JOnTpwd3dHYGAgKlWqBLFYjGvXruHevXuoVq0azMzMsHr1anTr1g2tWrXC0KFD4ezsjPj4eBw7dgwAVJ7Z3r9/j9evXyMtLQ1hYWFYuHAhGjZsiAIFCgAAZs+ejcmTJ2PLli2wt7fH69evAUBldbrPnz9j586dmD9/vkrfr1y5gmvXrqFu3booWLCgEF1VunRplaintWvXwtraGi1atMhyLNasWYP27dujcOHCORvMHwgWnxiGYZh8iUQiQRoRWnm4w0Bf8+0sJTUNhy9dZ+GJYZg8j0wmA0Ri3D+xVDcDkZiFdYb5yXj84XWe9lO6dGncvHkTQUFBGDduHJ4/fw5DQ0OUL18eo0aNgr+/PwCgQ4cOuHjxImbPng1vb298+PABFhYWcHd3Vyk2DkAoHC6RSGBtbY2WLVtixowZwufLli1DSkoKOnfurGQ3ZcoUBAYGCn9v27YNRIQePXqo9N3ExAR79uzBlClTkJCQAGtrazRv3hwTJ06EoeH/XgjI5XIEBwfDx8cny+tvZGQkzp8/jxMndHtR+qPD4hPDMAyTrxnYqaXW+j1xiYk4fOl6LvWIYRhduX7zCoz1tS87nZT689SokUgkAMlhV9wdErHmR3WZPA1PX7OwzjA/C1ZWVjAxNsaEU+tyzaeJsTGsrKyybWdtbY3Fixdj8eLFGtu5u7tj586dGtvY29uDiLT6fPLkiU598/Pzg5+fn9rPXF1d8c8//2jdh1gsxrNnzzS2KVu2rE79/llg8YlhGIZhGIbJVWQyGcQiCeYeU//wrw6x6Ocqct2m3igYGWquwZQsjcOSnd1zqUcM8/U4d+E0jPQ1vzhKTk3Mpd7kH2xtbRFx7x5iYmJyzaeVlRVsbW1zzR/z48LiE8MwDMMwDJOrSCQSyEmGSuULQU8s1to+TS7HnfAPP43wxDA/Kgrh+e9zATq1/9lEZ12wtbVlMYjJl7D4xDAMwzDZIDwqCuYGBlrbxaWk5EJvGCZ/s3BqYxQw11xUGwA+x0nRsIvmtAyGYfI+CuHZylACMUQa28pBiJGy8MQwPwosPjEMwzCMDihW12u144jONrzCHqPg3NWrMNXT/NiVkJaWS71hGIb5vvz7a0tYGml+kfMxOQUuqw/mUo8YhvnWsPjEMMwPQXTUcxiYalnxLIEndkzOUayu19GjH/T1jbS2T01Nxp5Lq76r8JQU9xFpWgSN1KT4XOrNz4lCtOz9z1Wd2udXwfLF0yhIjDTXbwEAWTLXcGEYhmGYnxEWnxiGydfIZDKIxMC+4dd0ai8SI19O7Ji8Q7cGg2FubKG1XVzSJ+y5tCoXeqSKTCaDSKKHf6d66dReJNHj38U3QiFamjoUgEiiubYRyeRIePw5Xx0HmUwGiMT4b/EQ3Y1E4nS778SdyPswN9DX2CYuJTWXesMwTHZ49foF9CWaI6ZSZZz2zjB5ERafGIbJ10gkEpAc6N14NAz0jTW2TUlNwvqQuflqYscwOUEikYBkaTg0bCDMDTXX04mTStF64TL+XXxjWh/3haGl5muU9GMStpf5K5d69HWQSCQAydGxaj8YSLRHBKbIkrHnxveJCFREobXbe0Kn9vk1Co1hfkTSBWsRjtzbo6OF6LuK3AzDqMLiE8N8AS/ff0C8keaJ3edkaS715uemZY1eWqNR4pI+YX3IXKVtuhSP5sLRTH6lpKUlLEw0Cx6fEpNyqTdMTnjy6jUKGGq+Rn2Wfv9rVEOXDjA1NNfaLkEahz03vk9EoCIKrZWHOwz0taRpp6bh8KXrLDwxTB4h/bdI8Gs+GYYGmlN8pSmJWHlsGv9+GSaPweITw+QAxdvTOqu26NSe357mPbJbPJqP4bfn6fPHMNLX/ECZnMr1YpifA8U1quH6XTq152uU7gzs1BLmJpqvNXGJiTh86Xou9YhhGF1p7NZJp5eNK49Ny6UeMQyjKyw+MUwOULw9bVmlg05550du7eUJQR4jO8Wj80Lh6B8ZmUwGMcRYdHqkTu3FEPMkm/nhUVyjFk7/FaYmmu8zCYkpGDZpI/8mGIbBsxdv8OmT5ueaz/HJudQbhmG+hODgYAQEBODjx48AgMDAQOzbtw+3bt36rv3KKSw+McwXYG1ZCkZahIvkVL7B52V0KR79PQtH/wxIJBLIIUe/tk1hpKUIcHJKKlYdOMGTbAYAEBOXhLRU7TU9PiZ//7S0nNKmWVVYWphqbPPxUwKGTdqYSz1icov4pE9IlWlerVKakpBLvWHyOjKZDHoSMep3mKVTez0Jv8jJr0RHRyMmJibX/FlZWcHW1lbn9j4+Pli/fj0AQF9fH7a2tvD29sb48eOhp/dzyg+ZRaSflZ/z6DMMwzB5jnaetXRKhVl1QLdiwcyPi0wmg4FYDJfFh3W2MRB/3xXWGEZXZDIZxGI9BB/qr1N7sZhXq2T+P1pSJseYzotgZKBZsE5OScCcXUP5nMmHREdHo5xLOSTlYr1GYxNj3Iu4ly0Bqnnz5li3bh2kUimOHDmCQYMGQV9fH+PGjcuWb5lMBpFIBLFY82qxOSU1NRX6+ppffDJfDxafGIZhGIbJV0gkEqTI5djWdTzMDLSvsBafkozuO4J4osV8Ebm1QIVEIoFcnoYeHlNhqKUOnjQ1EVsvTeFzmxGo5lxfp4huJn8SExODpMQk1F3aFhZlrL65v0/3Y3De/wBiYmKyJT4ZGhqiePHiAICBAwdi7969OHDgAAwNDbFu3TpERUWhUKFCaNOmDebMmQMzMzMA/4sQ2rBhA8aOHYv79+/j4cOHePfuHcaPH4+bN28iNTUVVapUwV9//YWqVasKPkUiEZYuXYoDBw7gzJkzsLa2xpw5c9C5c2cAwJMnT+Dg4IBt27Zh6dKluHLlCpYvXw4fHx+sXr0a8+fPx+PHj2Fvb4+hQ4fC398fAFC7dm3Uq1cPs2fPFny9e/cOJUqUQEhICDw9PREbG4thw4bh4MGDkEqlqF+/PhYtWgRnZ2ecOXMGffr0EfoIAFOmTEFgYCCkUikmTJiArVu34uPHj6hYsSJmz56NBg0a6DTO165d0zoueQkWnxiGYRiGyZdYmxdCASPNb/gB4HMypyYx/+NjXDzS0jSns8Un/S9l/nstUFHAxApG+loiWFL53GaYnxGLMlYoXKn49+6GzhgbG+P9+/cQi8VYtGgRHBwcEBUVBX9/f4wZMwZLly4V2iYmJmL27NlYvXo1ChcujKJFiyIqKgq9e/fG4sWLQUSYP38+WrZsiQcPHsDc/H8rrU6aNAmzZs3CwoULsXHjRnTv3h1hYWFwcXER2owdOxbz58+Hm5sbjIyMsHnzZkyePBlLliyBm5sbbt68iX79+sHU1BS9e/eGl5cX5syZg1mzZgni0fbt21GiRAnUq1cPQHqq4YMHD3DgwAEUKFAAv//+O1q2bInw8HDUrl0bCxYswOTJkxEZGQkAgtg2ePBghIeHY9u2bShRogT27t2L5s2bIywsDM7OzlrHNS4uTqdxySuw+MQwDMMwDMP88KTXxJGg19Q/dWqvJ5EIItKXLFCRWxFTDMMweQ0iQkhICI4fP44hQ4YgICBA+Mze3h5//PEHBgwYoCQ+paamYunSpahcubKwrVGjRkr7XblyJSwtLXH27Fm0bt1a2N6lSxf07dsXADB9+nScPHkSixcvVtp/QEAAOnbsKPw9ZcoUzJ8/X9jm4OCA8PBwrFixAr1790bXrl0REBCA8+fPC2LTli1b0KNHD4hEIkF0unDhAmrXrg0A2Lx5M2xsbLBv3z506dIFFhYWEIlEQjQYkJ5CuW7dOkRHR6NEiRIAgFGjRuHYsWNYt24dgoKCtI6vruOSV2DxiWF+AqKjnsPAVPvPPSVB85tghmEYhskrxMfHIS0tVWOb5OT/RTCl18SRYXXdbjDT1ywGxaemoO/57UoiUnYXqPheEVMMwzDfm0OHDsHMzAypqamQy+Xo2bMnAgMDcerUKcycORP37t3D58+fkZaWhuTkZCQmJsLk/+t+GhgYoFKlSkr7e/PmDSZOnIgzZ87g7du3kMlkSExMRHR0tFI7Dw8Plb8zrwzn7u4u/D8hIQGPHj2Cr68v+vXrJ2xPS0uDhUX69b5IkSJo2rQpNm/ejHr16uHx48e4dOkSVqxYAQCIiIiAnp4eatasKdgXLlwYZcuWRURERJZjFBYWBplMhjJlyihtl0qlKFy4cJZ2GdF1XPIKLD4xzA+MTCaDSAzsG35NZxuRGFyUl2EYhsmzyGQySMQS/LV6tvbGACRiiZKoU9zEHOZaIpjivsJKtV8aMZXbJCd+0JqOCABpKZ9zoTcMw+RnGjZsiGXLlsHAwAAlSpSAnp4enjx5gtatW2PgwIGYMWMGChUqhPPnz8PX1xcpKSmC+GRsbCyktyno3bs33r9/j4ULF8LOzg6Ghobw8PBASg6iRk1N/5fOHB8fDwBYtWqVkngEQOl67OXlhaFDh2Lx4sXYsmULXF1d4erqmm3fGYmPj4dEIkFoaKjKtV+RlqeNrzkuuQGLTwzzAyORSEByoHfj0TDQN9baPiU1CetD5vJbV4ZhGCbPIpFIIJPLcG7AryhgZKix7edkKTyXb/yu97XsRkzlNukvqvRxdVMj7Y3/H5FYn6O0GIbJElNTUzg5OSltCw0NhVwux/z584XV63bs2KHT/i5cuIClS5eiZcuWAIBnz54hJiZGpd3ly5fh7e2t9Lebm1uW+y1WrBhKlCiBqKgoeHl5ZdmuXbt28PPzw7Fjx7BlyxYlHy4uLkhLS8OVK1eEtLv3798jMjIS5cuXB5AezZX55b6bmxtkMhnevn0rpPNlF13HJa/A4hPD/AS0rNFL64MvkP7wuz5kbi70iGFUeR/7USlFRh0JSV8ejcAwTN7h46dEpMm0R9vEx6u+xbWxtIClseaIoo98zdBK+ouqVMwZNhkmhtpfVCVKkzBm4TQWnpjvwrkLp2GkZRXI5NTEXOoNkx2cnJyQmpqKxYsXo02bNrhw4QKWL1+uk62zszM2btwId3d3fP78GaNHj4axser1aufOnXB3d0fdunWxefNmXL16FWvWrNG476lTp2Lo0KGwsLBA8+bNIZVKcf36dcTGxmLEiBEA0sW09u3bY9KkSYiIiECPHj2U+tauXTv069cPK1asgLm5OcaOHYuSJUuiXbt2ANLrW8XHxyMkJASVK1eGiYkJypQpAy8vL3h7ewsF0N+9e4eQkBBUqlQJrVq1+mrjkldg8ekn4n18gtZw6k/J0lzqDcMwTDrpKTRi9J6xSKf2ErE4375x58LDDJOOTCaDnp4YHfoe1tlGTy///vbzA0UsC8PMRPvqkfGJvMIek/vIZDKIRRL8fS5Ap/ZikeSHLiPx6X7uRLd8TT+VK1fGn3/+idmzZ2PcuHHw9PTEzJkzlaKIsmLNmjXw8/ND1apVYWNjg6CgIIwaNUql3dSpU7Ft2zb4+/vD2toaW7duFaKPsqJv374wMTHB3LlzMXr0aJiamsLV1VWpODqQnnrXsmVLeHp6wtbWVumzdevWYdiwYWjdujVSUlLg6emJI0eOQF9fHwBQu3ZtDBgwAN26dcP79+8xZcoUBAYGYt26dfjjjz8wcuRIvHjxAlZWVqhVq5bOxcJ1HZe8AotPPwEymQz6EjHcl6zTqb2+hB/uGEYbbz59RpKWKJ3P0q8nIuji72v7zC3SU2jkWN7QFyZa6qIkpiZjwOk1+e76xIWHGUYZiUSCtDQ5ltU6BFN97ctBJ6TGYeDl1vybYJifFIlEAjnJUACGEEGksS2B8JmkP+T1wsrKCsYmxjjvfyDXfBqbGMPKykrn9sHBwVl+Nnz4cAwfPlxp26+//ir838fHBz4+Pip2bm5uuHZNuYZt586dVdqVKFECJ06cUOvb3t4eRKT2s549e6Jnz55Z9hsAWrRokaV9wYIFsWHDBo32y5Ytw7Jly5S26evrY+rUqZg6dapam8zjERgYiMDAQOFvXcclr8Di00+ARCJBqkyOE0s8YG6ir7FtXGIqmg6+9ENerH8EclvwYFSRyWTQE4tQa5nmG4wCPfGXiQjZ9fc1fH4vSltao4CB5lDhzylJudSbr0t+KzzMMLlFMZOSMNfXIS089dNX8/nk1UuYSDRHICbK+F7KMHmRLXUH6rRgQOvzC3KnQ7mMra0t7kXcy9W6PlZWViqRPgyTE1h8+okoVdQEluaaxaePcZqXLGa+D7kteDBZI5FIkCYndP+rIQxNNf+epAmp2Db89Bcdh+z4+1o+mW9HXi88zDA/MjKZDGKIMDhMtwhEMfheqisv3zyHsZY6PElfsQ7P0+ePue4P89Nia2vLYhCTL2HxiWHyAbkteDDaKepoCSNzzW/Ok+O+3ptzXfx9bZ8caZf/eR8fh1SZ5pcKn7kgM/OTIJFIIAfBpfMQSAw0R07IUpIRsWsx30u1kC7oiTH/xFCd2ovxZaUdFP4WnR6ZK/4Yhsk/ZJUSx+QdWHximHxEbgsezNfne9Ruym6Ra460y/+k1/qToMn8JTq115dI+BgyPw02tZpD31RznanUhDhE7FqstO3Dx3eQahFrE6RxX9y//ES6oCfH0LazYGigORJJmpKIRQfGfnE0sBxy9GvbFEYGml/GJaekYtWBE/nyuhYd9RwGptqnaSkJqgsJnb94hqPCGIbJk7D4xDAMkwt8Se2mL/KZwyLXHGmXv0mv9SfD5GHTYWyoJRVGmohpCyfxMWSYLEhfkVMPA5c10am9RKz304m5dSq00CmleNGBsV/FXzvPWjA30Xxti0tMxKoD6gsP51VkMhlEYmDf8GvaG/8/IjGEZwWxSILFZ4fpZKdYDe5nOk8Zhvm+sPjEMAyTC3yP2k1fWuSaI+3yP4Uti8BUy9LpCXlg2fRb9+7BXF/77yIulesS6gqnzX490lfkTMPCAD+YGhtqbJuQJMWwBSt5Qs/kCIlEApIDvRuPhoG+5gU4ACAlNQnrQ+YK55ucZDCyMIVIrGU1ODkh+VMCn6cMw+QqLD4xDMPkIt+jdhMXuf66RL+IgomB9qXhE1N+rvSbnKCIzuuw75TONooIPUY9nDb77ShdqrhO0TYM86W0rNFL630bSL93rw+Zq7St65/DYWimWbiSxidhg+8fX9RHhmGY7MLiE8MAiIl9iwQDzW//k1K+f3QAk3fQpR6DuloMj+4/gYGJDnUcElVtme+LTCaDWCTB6A0ddLbhtAbNKKLzmlqXgb5YrLV9qlyOE6/u83hqgNNmfxwSkz4jLVXzvSAljcWu7HDq/HmY6Wm+B8en8f2XYRjmW8DiE/NTo5hMzjw0QKf2mSeSb969hoFEcxRLiozTGn4ksluPIWMtBpEYODz6ps6+Mtoy3x+JRAI5yTDc4w+txVyB9IKuf12ayBN7HRjq6glzLamhABCXmowTr+7nQo/yP5w2m39RPJtsvDhOp/YscmtHEWXZ7/wtndpnrIHIMAzDfB1YfGJ+ahSTyfmVBsNUonnikyBLxsg7S9JrP8hkAEQ49N9OHT0pP8Rkd/UxJu+QnXoMmWsxkBzo1rguDHWobSNNTcX2kPP84JsHaVmuKwoYWmpt91n6EX9dmvjtO8QweYC4hFik6VCTKyklPhd6k79RPJvcODIfFuaaa7Z9iktA1ZYjle4VL988h7EOAnnST7TimSLK0gimEEFLPSQQkonrIemKLjX7uF4fk5EnT57AwcEBN2/eRJUqVQAAFy5cwIABA3Dv3j20atUKAQEBaNiwIWJjY2Fpaal2P8HBwQgICMDHjx919i0SibB37160b9/+i79HbqBurPIzLD4xDIDKlk4ooK/5Ae9z6v/S7tIfSAh9Go2DoRYBQpqahHX/zBREq5yuPsbkLXSpx6CuFsOvLRpprRkCpNcN2R5y/ov6yDBM/uftu1gkS7UUDv+clEu9UUWxEtz4fV462yhWg2M0Y1eyKApamGlsE/vpf2KeTCaDGGLMPzFUZx9iiH+qY7G8w3GYGWi+d8enfILP3rq51KP8S3Zr9vEz7dcjOjoaMTExuebPysoKtra2Orf38fHB+vXrhb8LFSqE6tWrY86cOahUqRJsbGzw6tUrWFlZCW1GjBiBKlWq4OjRozAzM4OJiQlevXoFCwvttc/yGvb29ggICEBAQMAX70vdWOVnWHximC/A3bkhTI0KaGyTkPwZ6/6ZCeDLVx9jGIbJy3z4+A7SJM1CSYKUC7Hrgkwmg56eGLVaTdOpvZ6e+LtM7BQrwXVZsBYGpppf4gBASkICdgb8xve2b4BEIoEccgxtOwuGBtpfckhTErHowNh8eSzu3A2HsaHmCPIkXs3xm5Kdmn1cr+/rER0djXLlXJCUlHuRi8bGJrh3LyJbAlTz5s2xbt06AMDr168xceJEtG7dGtHR0ZBIJChevLhS+0ePHmHAgAEoVaqUsC1zm5+NlJQUGBgY/FDjwOITw3wHePUxJrtwofIfg8fRD2FiqDmSIVGa/9KSFNEvA5c10am9IvKFJyJZI5FIkJYmx/Kqo2Aq0RxhmyBLwoAb877reJoXKw4jM80vYwAgOf7zV/PJ6T7qqVOhhc4rpS06MDYXevT1SK+HJcL44F06tRdztM03R5eafVyv7+sRExODpKRE9Ks/CSUs7L65v5efnmLV2emIiYnJlvhkaGgoiCbFixfH2LFjUa9ePbx79w4JCQlCKpmlpSUcHBwAAL/99ht+++03rFu3Dvb29ippd8HBwZg8eTJiYmLQrFkz1K2rGqG4bNkyzJs3D8+ePYODgwMmTpyIX3/9VanNq1ev0KJFC5w5cwbW1taYM2cOOnfuLHz++++/Y+/evXj+/DmKFy8OLy8vTJ48GfoZ7jcHDx7EtGnTEBYWBjMzM9SrVw979+5FgwYN8PTpUwwfPhzDhw8HABARAOD8+fMYN24crl+/DisrK3To0AEzZ86E6f+/uLG3t4evry8ePHiAffv2oWPHjggMDFRKu1OXarhv3z506NBB8BMYGIh9+/Zh6NChCAwMxIcPH+Dt7Y3Fixdj/vz5+PPPPyGXyzFs2DBMmDBB52P6NWDxiWGYPMV/96JgZqL5ITE+Mf+mCHyMi0eaDivpxP9/9IiiwHlOCpXzw3beQVFAeOzmTjq1z1xAWJcaLt+zfosi+mVhgB9MjQ01tk1IkmLYgpV8fuqIk1mpbKWF/wxwus/PS3o9LIJN27KQGGg+nrIUGZ4diOTjzvyQlLCwg51V2e/dDZ2Ij4/Hpk2b4OTkhMKFCyMh4X/3LEVaWdmyZTFt2jR069YNFhYWuHLlitI+rly5Al9fX8ycORPt27fHsWPHMGXKFKU2e/fuxbBhw7BgwQI0adIEhw4dQp8+fVCqVCk0bNhQaDdp0iTMmjULCxcuxMaNG9G9e3eEhYXBxcUFAGBubo7g4GCUKFECYWFh6NevH8zNzTFmzBgAwOHDh9GhQwdMmDABGzZsQEpKCo4cSS+psmfPHlSuXBl+fn7o16+f4PPRo0do3rw5/vjjD6xduxbv3r3D4MGDMXjwYCFCDADmzZuHyZMnq3y37PLo0SMcPXoUx44dw6NHj9C5c2dERUWhTJkyOHv2LC5evIjffvsNTZo0Qc2aNb/IV3Zg8YlhmDxBeuQE0GXyY53aS/KZwCKTyaAnkaDX1D91ttHjQuU/DIoCwo1rloW+lmOTKpMh5EqkUCcuOzVcFPVbvtfxL12quNaaZnGJP0+RY+bbwOk+TO15LWFoqaXm5sckbD8QmUs9yv+c/PdfmOlpnhrG6/DyjGEA4NChQzAzS4/0TkhIgLW1NQ4dOgRxpmu2IgVPJBLBwsIiyxSzhQsXonnz5oIAVKZMGVy8eBHHjh0T2sybNw8+Pj7w9/cHkF5H6vLly5g3b56S+NSlSxf07dsXADB9+nScPHkSixcvxtKlSwEAEyf+b7EYe3t7jBo1Ctu2bRN8z5gxA927d8fUqVOFdpUrVwaQXt9KIpHA3Nxc6bvMnDkTXl5eQh0oZ2dnLFq0CPXr18eyZctgZJQePdioUSOMHDlSsHvy5InGcc4KuVyOtWvXwtzcHOXLl0fDhg0RGRmJI0eOQCwWo2zZspg9ezZOnz7N4hPDMD8f6ZETQAfPWjDQ13xpSklNw95zl/PVZEIikSBNJsPqut1gpq+5TgUAxKemoO/57cJ3/JJC5VyHJ+9Qs6IDjI201ClJTkHIlfQJU3ZquOTn+i3M1yc66jkMTLVcSxPy90SS030Y5stRRBL6XbitU3uOJGR0oWHDhli2bBkAIDY2FkuXLkWLFi1w9erVHO0vIiICHTp0UNrm4eGhJD5FRETAz89PqU2dOnWwcOFCFbvMf9+6dUv4e/v27Vi0aBEePXqE+Pj0jIUCBf6XVn7r1i2lqCZduH37Nu7cuYPNmzcL24gIcrkcjx8/FqKu3N3ds7XfrLC3t4e5ubnwd7FixSCRSJTEv2LFiuHt27dfxZ+usPjEMEyewrdtU50iJ/aeu5xLPfq6FDcx1zpZAtInTF8K1+H5cdClhkt+rN/CfH0Uqbr7hl/TqT2n6f54PHgUAWMDzbXlACApJf/Vl/te/HvxHAwkmu/dKbIvv29/DxSRhMYGEohEIo1tiQhJKXy9YLRjamoKJycn4e/Vq1fDwsICq1atEqKO8iKXLl2Cl5cXpk6dimbNmsHCwgLbtm3D/PnzhTbGxpqjLtURHx+P/v37Y+hQ1Uj2jLW0TLUs3CEWi4XaTgpS1dQ21M+ULSESidRuk8vlWvv+NWHxiWEY5geF6/AwzM+HRCIByYHejUfDQF/zA3JKahLWh8zl3/0PgqK23MQd3XW2UdSXY9SjSH3e92iBTu2/d+rzlxC6dSYszTW//PsYl4jyncbkUo+YHwmRSASxWIykpKQc2bu4uKjUgbp8+bJKmwsXLqB3797CtgsXLqB8+fIqdt7e3kp/u7m5AQAuXrwIOzs7pULcT58+VbKvVKkSQkJC0KdPH7V9NTAwULmuVq1aFeHh4UqCXE4oUqQI4uLikJCQIAhVGaO28josPjEMw/zgcB0ehvn5aFmjl07RcutD5uZSj5hvjaK2nH3jbpDoa37hAACyVCmehGzPl0JJbqFIfTYxgk5RQYnJch5PhgEglUrx+vVrAOlpd0uWLEF8fDzatGmTo/0NHToUderUwbx589CuXTscP35cKeUOAEaPHo2uXbvCzc0NTZo0wcGDB7Fnzx6cOqW8OMXOnTvh7u6OunXrYvPmzbh69SrWrFkDIL0WU3R0NLZt24bq1avj8OHD2Lt3r5L9lClT0LhxY5QuXRrdu3dHWloajhw5gt9//x1AesrbuXPn0L17dxgaGsLKygq///47atWqhcGDB6Nv374wNTVFeHg4Tp48iSVLlug8DjVr1oSJiQnGjx+PoUOH4sqVKwgODs7BiH4fWHxiGIZhGIZhmB+EMi28oW9qrrVdakIcnoRsz4Ue5X/mTmwBUxPN9foSElMwaOLRXOoR87Pz8tNT7Y2+o59jx47B2toaQPrqceXKlcPOnTvRoEGDHBXRrlWrFlatWoUpU6Zg8uTJaNKkCSZOnIjp06cLbdq3b4+FCxdi3rx5GDZsGBwcHLBu3To0aNBAaV9Tp07Ftm3b4O/vD2tra2zdulWIjmrbti2GDx+OwYMHQyqVolWrVpg0aRICAwMF+wYNGmDnzp2YPn06Zs2ahQIFCsDT01P4fNq0aejfvz9Kly4NqVQKIkKlSpVw9uxZTJgwAfXq1QMRoXTp0ujWrVu2xqFQoULYtGkTRo8ejVWrVqFx48YIDAxUqXWVV2HxiWEYhmEYhmEYhmHyOFZWVjA2NsGqs9O1N/5KGBubwMrKSuf2wcHBGqNx7O3tVeoWffz4UenvBg0aqLT57bff8Ntvvylty7gyHAAMHDgQAwcOzNK3Yp+KFfHUMWfOHMyZM0dpm2KVOgUdO3ZEx44d1drXqlULt2+rFvCvXr06Tpw4kaVfdaKcurFq37492rdvr7QtYwH0wMBAJbEMgNrjcebMmSz78q1g8YlhGIZhGIZhmHxD1H8PITHTHIkki0/Jpd4wTO5ha2uLe/ciEBMTk2s+rayslIpiM0xO+WHEp7///htz587F69evUblyZSxevBg1atT43t1iGIZh8igPHt2DkZ7mWlgAkJzG9bAYhmHyAjKZDJCIcK3DQd0MJKJ8WwCcYbLC1taWxSAmX/JDiE/bt2/HiBEjsHz5ctSsWRMLFixAs2bNEBkZiaJFi37v7jEMwzB5CMXqRcsvjtfZRrGCEcMwDPP9kEgkgIzQqG4l6OtrFpRSU2X45/wdFp4YhmHyCD+E+PTnn3+iX79+wnKHy5cvx+HDh7F27VqMHTv2O/eOYRiGyUsoVi9yrNoYEom+1vYyWSqiboTwBIZhGCaP0LF1XZiaGGlsk5CYjH/O38mlHjEMwzDayPfiU0pKCkJDQzFu3Dhhm1gsRpMmTXDp0iW1NlKpFFKpVPj706dPAIDPnz9/285+IYr+HfjnDEz0NB+6xLQ0JRsAOHj8PEwMNC8Tm5hCKnYh545DX6L5Bg8AqbJkAEBcXJzO/czYV4XdwSOnYGqsOY8fABKSUlT6eubAceiZae5rWnyyit3xS//ASKTZZzKp+gv59zgMtIxNikzV36mzR2Gob6zRTpqapGJ35ti/0DMSa7RLS5bnyF9Gn4pjcfrcCejr6XDs01S/Y07P05Onz8HIQLNdcoqqXU7HNLftTl6+CCORdhEjmdKjbBTHQpdxAdSPTU7HNKfnW06Pfcjlf2Ek1tJP+df7fkn6RSDW074cuTwt/X6hOBYhF4/DWGKq1S5JlqDi88Llq9CXaB7TVFnOfsPqzreLl87DwEDzd0xJkarY6XLdT1Vzbcvpscjp7+L8zaswlmi/XyTJUpTsvuT3lNPz+/bZG5CYaRY7ZfGpKnY5vT/l9JzJ6fe7c/Uy9Ay1p7GmSdPTWBXH4nrkFZiItdslyhNVfOpy3ijOmYx21y+cgVhf8/ktT/1653dOj8XV0Lsw1/JME6fmmUaX7wf87zsqjkXkw/swMtRulyz9es81Ny/fgthA8zVRnqJ6TXz46CmMDDX/npKlqr+nRy8jYaxnptEuKS1exe7ps7cwMtJyriWrnms5ff7Krl1Gn2+fvYKBlufoFDXP0E+ev4a5FkEvLlHN7yKHv8Nnn+LwMUnz7ylOze/pbfJnJKZJszIBAMSn/e++ltdfHCm+W+ai0gzzIyKifH6mv3z5EiVLlsTFixfh4eEhbB8zZgzOnj2LK1euqNgEBgZi6tSpudlNhmEYhmEYhmEYhlHh2bNnKFWqlNK25ORkPH78GA4ODjAy0i4MM8z3IDvnab6PfMoJ48aNw4gRI4S/5XI5Pnz4gMKFC0Mk0hwZlBf5/PkzbGxs8OzZMxQoUIDtvpNdfurrj26Xn/rKdnnHJ9vlHZ9sl3d8sl3e8cl2X9cuP/X1R7f7Xj7zAkSEuLg4lChR4nt3hWG+OflefLKysoJEIsGbN2+Utr958wbFixdXa2NoaAhDQ+U0BEtLy2/VxVyjQIECObrost3XtfsePtku7/hku69r9z18sl3e8cl2eccn2+Udn2z3de2+h0+2y1s+vzcWFhbfuwsMkytoTrLOBxgYGKBatWoICQkRtsnlcoSEhCil4TEMwzAMwzAMwzAMwzC5T76PfAKAESNGoHfv3nB3d0eNGjWwYMECJCQkCKvfMQzDMAzDMAzDMEx+Jzo6GjExMbnmz8rKCra2trnmj/lx+SHEp27duuHdu3eYPHkyXr9+jSpVquDYsWMoVqzY9+5armBoaIgpU6aopBKyXe7afQ+fbJd3fLLd17X7Hj7ZLu/4ZLu845Pt8o5Ptvu6dt/DJ9vlLZ/5kejoaJRzcUFSYmKu+TQ2McG9iIhsC1CvX7/GzJkzcfjwYTx//hwWFhZwcnJCr1690Lt3b5iYpK90evPmTcyaNQvnzp3Dhw8fULx4cbi6uqJ///5o3bo1RCIRnjx5AgcHB2Hf+vr6sLW1hY+PDyZMmKBUt/njx4+YMGEC9uzZgw8fPsDOzg4LFixAy5YthTYvXrzA77//jqNHjyIxMRFOTk5Yt24d3N3dAaTX4poyZQpWrVqFjx8/ok6dOli2bBmcnZ2FfcyYMQOHDx/GrVu3YGBggI8fP+ZkeH8q8v1qdwzDMAzDMAzDMAzzI6FuFbEbN26gWrVqaDJyMgqWsvvmfYh9/hSn5k9DaGgoqlatqrNdVFQU6tSpA0tLS0ydOhWurq4wNDREWFgYVq5cif79+6Nt27bYv38/unbtiiZNmmDYsGFwcnKCVCrFxYsXsWjRIpw9exaWlpaC+HTq1ClUqFABUqkU58+fR9++fbFkyRL4+voCAFJSUlCnTh0ULVoU48ePR8mSJfH06VNYWlqicuXK6d8pNhZubm5o2LAhBg4ciCJFiuDBgwcoXbo0SpcuDQCYPXs2Zs6cifXr18PBwQGTJk1CWFgYwsPDhWMxZcoUWFpa4vnz51izZs1PKz7xancMwzAMwzAMwzAM8wNSsJQdijiV/d7dyBJ/f3/o6enh+vXrMDU1FbY7OjqiXbt2ICIkJCTA19cXrVq1wp49e5TsXVxc4Ovri8xxMoULFxYWFbOzs8O6detw48YNQXxau3YtPnz4gIsXL0JfXx8AYG9vr7SP2bNnw8bGBuvWrRO2ZYyqIiIsWLAAEydORLt27QAAGzZsQLFixbBv3z50794dADB16lQAQHBwcE6H6acj3xccZxiGYRiGYRiGYRjm+/P+/XucOHECgwYNUhKeMiISiXDixAm8f/8eY8aMyXJfGdPpMnP9+nWEhoaiZs2awrYDBw7Aw8MDgwYNQrFixVCxYkUEBQVBJpMptXF3d0eXLl1QtGhRuLm5YdWqVcLnjx8/xuvXr9GkSRNhm4WFBWrWrIlLly7pNAaMelh8YhiGYRiGYRiGYRjmi3n48CGICGXLKkdmWVlZwczMDGZmZvj9999x//59AFBqd+3aNaGNmZkZDh06pLSP2rVrw8zMDAYGBqhevTq6du0Kb29v4fOoqCjs2rULMpkMR44cwaRJkzB//nz88ccfSm0U9ZuOHz+OgQMHYujQoVi/fj2A9FpVAFTqRxcrVkz4jMkZnHbHfDc2b96Mzp07/zQFAhmGYRiGYRiGYX5Grl69CrlcDi8vL0ilUrVtKlWqhFu3bgEAnJ2dkZaWpvT59u3b4eLigtTUVNy9exdDhgxBwYIFMWvWLACAXC5H0aJFsXLlSkgkElSrVg0vXrzA3LlzMWXKFKGNu7s7goKCAABubm64e/culi9fjt69e3+jb88AHPnE/D+xsbG56m/cuHEYPnz4D68ecz1/hs8BhmEYYPLkydi+ffv37sY348OHD9+7CwzzzUhNTc2RXVxcXI7sMgsOTP7CyckJIpEIkZGRStsdHR3h5OQEY2NjABBWjsvYztDQEE5OTnByclK7bxsbGzg5OcHFxQVdunRBQEAA5s+fj+TkZACAtbU1ypQpA4lEIti4uLjg9evXSElJEdqUL19eab8uLi6Ijo4GAKGm1Js3b5TavHnzRviMyRksPjFYt24dKlSogAcPHuSKvwcPHmDz5s1Ys2YN7Ozs8PjxY6U83LzEX3/9hRcvXuTItmvXrrh9+3a27YKDg/H06dMc+WTyBrt27cK5c+cApOeq54YA5efnJ7zB0ZXnz5/n2N+PLhx/TViAZPIap0+fzvFkMifMnDkTS5cuhZubW675zAnDhw/HvHnzsm1Xt25dLF269Bv0KG/y9u3bbF/X8tt1UDFJZdJ/v7t37872MfTx8cGWLVuy7W/FihUIDg5GYmJitm2ZvEHhwoXxyy+/YMmSJUhISMiyXdOmTVGoUCHMnj07x74kEgnS0tKE32ydOnXw8OFDyOVyoc39+/dhbW0NAwMDoU1mYez+/fuws0tfPdDBwQHFixdHSEiI8Pnnz59x5coVeHh45LivDItPeZqvcaPWZR+tWrVCsWLF0KVLFzx8+PCL/WjzaWRkBGtrazx+/Bhr165Fjx498OTJk2z7/NJoLW39/PTpEzZv3qzxoqkJAwMDFVVdG3FxcRg7diw6duz4RcKAguycQ9k5hpnJKnRWG7k5+VGgONe+xYMwEeHt27fw9/fHnDlzcPnyZQDfXoBasWIFdu7ciVatWulsM336dEyaNEl4U5Qdli5dCl9fX1y/fj3btj8DimMdHh6OtLQ0jcUysyLjQ1NOPs8JuTE5VPj4Fv3PTE59EJHQz5y+tVfsJy8SERGBxo0bY/z48bkSXZCamoqrV6+iV69eKFOmDK5fv56jFzO68iXjXrlyZTRu3DjbdnPmzMHo0aMBINvPDIr+Pn78ONt+vwc7d+6Er6+vzv19//69cB08evQodu/enW2fUVFR2bYBgLt37+LTp0/Ztjt37hxWr16drRej2X2G+l7Xh5xcF588eQJXV1eIRKJs2desWVNIYcrOc+KxY8cwa9Ys7N27F0lJSdnuL5M3WLp0KdLS0uDu7o7t27cjIiICkZGR2LRpE+7duweJRAIzMzOsXr0ahw8fRqtWrXD8+HFERUXhzp07mDNnDgAoRTAB6deU169f4/nz5zh69CgWLlyIhg0bokCBAgCAgQMH4sOHDxg2bBju37+Pw4cPIygoCIMGDRL2MXz4cFy+fBlBQUF4+PAhtmzZgpUrVwptRCIRAgIC8Mcff+DAgQMICwuDt7c3SpQogfbt2wv7iY6Oxq1btxAdHQ2ZTIZbt27h1q1biI+P/8ajm48hJs8il8uV/pbJZBrbKz5/+/YtPXv2TCcfCpvY2FiqVasWubq60v3797Pd14SEBEpKStK5/YQJE6hMmTIkEolo8eLFRKT6fTWxceNGsrOzo8jISJ3aZxyb169f6+wnNTWViIguXLhA0dHROtmkpaUp/b1kyRI6ffq01uOnIDo6mipWrEg1atTQ2aeCa9eu0Z49e+jkyZPCNl39Kr5rdvnnn39o8uTJ9PDhw2zZnT9/ntzc3OjNmzfZsjt58iT1798/WzYKtmzZQiVKlMiRbXa4efMmlS9fntq3b0/nz58XtmfnHM+INrtBgwZR8+bNiYjo+PHjtGvXLq37vHjxIv33339ERPTp06ds9eeff/4hGxsb8vLyouvXr2fLNr+g7neTneN34MABcnR0VDr+mlDsOzIyklJSUnSyefHiBd24cYOIiDZt2kSzZs3SuX8Kbt26RXfv3s22HRHR1atX6fjx4zmyW7Zsmc7XJgURERHZtiEi2rBhA504cYKItB9DuVwutAkJCaEuXbpk+zr84cMHSkhI0Pl3ldPrQkauXbtGZ8+e1dnnli1byMjIiMaMGaPz+abg0aNHFBERofNvPzk5mXx9falVq1Y0efJkMjY2pkuXLmXLZ0hICEmlUp3aKsbz9u3bFBISkm1fRERHjhyhadOm6XS+Zbzn//HHH9SjRw969epVtvxdu3aNSpcuTW/fvs12X3OTd+/eUeXKlenvv//WqX1sbCwVKVKE5syZQ1u3biWJREJ79uzJls9nz56RSCSiSZMm6Wwjl8spNjaWDAwMaNGiRdnyt2vXLhKJRGRoaKjTcVScb0lJSUq/5ax+14pnLcV58+DBAzp37hw9fvyYPn78SETan9sU+7579y5duXJFax/VcfPmTcFfVgQHB9PGjRuFv0+fPk3r16+nhIQEjXZjx46l1atXC38vX76cxowZQx8+fNDqb/PmzURE5OPjQ+XKlaMNGzZQYmKitq/zw5KUlETh4eFKc6zQ0FACQE1GTqYuf6355v+ajJxMACg0NDTb/X/58iUNHjyYHBwcSF9fn8zMzKhGjRo0d+5cpfPo2rVr1LlzZypatCjp6elR4cKFqVmzZrRt2zbhfH/8+DEBEP5JJBIqVaoU9evXT+XaefHiRapZsyYZGhqSo6MjzZgxQ2V+dvDgQapYsSIZGhpSuXLlaOXKlUqfy+VymjRpEhUrVowMDQ2pcePGKvPO3r17K/VJ8e/06dPZHqv8jLrzNCtYfMqjnD17lkaMGEFDhw6l5cuXC9sz35DWrVtHT548Ef7evXs3OTk5UalSpah8+fI0Z84cev78eZZ+FPu7e/cu7d27l0QiEXl6emZLgDp8+DA1bdqU3N3dqWHDhnTixAmKi4vT6G/nzp0kEonI3t6eVq1aJTww6/IQvnv3blqyZInWvmYemz179lCZMmWobNmy1KhRI4qKitLp+yUnJ5OzszOVL19eZ1EvI87OzmRnZ0cXLlzQeeL07NkzKleuXLYEqL1791KBAgXI1taWnJycqGvXrsJn2vyeOHGCvLy8qHPnzjR48GCtDwgKdu3aRWZmZjR9+nS6efMmEek+kXrw4AHZ2dlRzZo16d27dzrZEBENHz6c2rRpo3P7jLx9+5ZKly6t9JvKCsX3iI6OpujoaHrw4IFWG5lMJkzkLl++TE5OTtS9e3e6ePGiyn6z8nfr1i06fPgwbdu2TeuxV9isX7+eypcvT97e3iQWi+ngwYM62RGlP0x27dpV5wdYxbl04cIFcnR0pO7du+s8CVX4TU5OpuTkZJ1sMto9fPiQ7ty5IwgumT/Pqq/q2mj6TWRsu3v3bqWHCE3nt+Kzly9fUseOHWnp0qVZtlVnt2/fPipbtizNmjVLa/8SExOpatWq1KVLF5ozZw6JRCKVBydtPj99+kRWVlbUqlUrQYjU1TYpKYmqVatGffv2zZadXC6nvn37UtWqVYVzQJfrYnR0NBkaGtKFCxey5e/jx49ka2tLY8eO1bl/RETbt28nkUhEIpGILl++rLPPQ4cOUdOmTalixYrUpUsXOnz4sE5+idJ/+0eOHKETJ05kSxDevXs3FS5cmKZOnUqPHz/W6ivjfVgikdAff/yh88ujPXv2kLOzM1WuXJnMzc3J29tbp2vH69evqXTp0mRgYEAjR45U6o82IiMjSSQS0fTp07O8bgQFBdH48eOF77Znzx4yNTUVXnKNGzdO45hmPPbJycm0ceNGEolENHPmTI19zHjupqWlUUhICIlEIvL3989SuMjcVyKi/fv3k7u7u8o+tXHixAny9fWlCRMmZEsI3r17Ny1btoy2bdum83PNsWPHaMSIEeTl5UXv37/X2dfWrVvJ0NCQ9PT0aM2aNTrbZWTx4sVkZGREf/zxR7bsJk6cSPXr16enT5/q1H7Hjh0kFotp2rRpVLNmTaVnSHUozo3jx49T+/btqVmzZtSrV68sBd3ly5fTypUrKT4+XvBXpEgRsra2Jjs7O2rWrBndunWLiLI+DxQ+d+/eTXZ2djRjxgydnmcz7u/AgQNUsmRJWr58eZbP6/Hx8dSkSROqVauWcNx69epFJUuWpE2bNmUpCMXGxlKDBg3I09NTsAsICKAKFSrQ9OnTs3y+VPhzd3en3bt3ExGRt7f3Ty9AqZvUP336lIxNTNSKHt/qn7GJic6/I+bng8WnfM6ePXvIwsKCevXqRd27d6eKFStSv379hM8VN5DPnz9TsWLFqGrVqvTy5Uu6efMmFSxYkGbMmEEnTpwgf39/ql69OvXr149evHih0V+BAgVo9OjR1KZNG7KxsSFXV1edJtuHDh0iExMTCgwMpMuXL1P9+vWpVKlSdO3aNY12586dow0bNpC/vz+5urrSwoUL6fPnz0Sk+WH0999/pxIlStCff/5JgwYNIicnJ6pYsaJKXzOOzYsXL+j27dtUtGhR+uOPP2jt2rXk7u5OTk5OdPXqVa3fkSj9Ql+hQgWqXr26RkEgq4cFDw8PIQoiOwJU2bJltfqUy+WUkpJCnTt3po0bN9Lz589p06ZN5OLiQr/88ovWvu3bt48MDAzIz8+P+vTpQ87OzuTk5CQIJlkdjytXrpCVlZXS2y0iopiYGK3fLaOYULFiRapWrZrOb3w3btxITk5OFBMTk62IgdTUVEpISCAvLy/y8vLSqX/79+8nV1dXKlu2LBUrVowWLlyok/iwa9cuGjt2LFWoUIHEYjH98ssvShPYrPaxe/duKlKkCDVp0oRsbGyoYcOGOr+1bdOmDYlEIurcubOwTZdz7cyZM2RlZUU9evTQSUSSy+XC26OzZ8+So6Mjde3aVWcB6sCBA/TLL79Q48aNaeHChTr5I0ofUxsbG3JyciKxWExdu3alU6dOabUjIlq5ciUNGjSIxo8fr/TWXd34ZNwWGRlJBQoUoM6dO+t0/IjSx6RHjx7UoEEDQdDR5Tzdt28fGRsb09KlS3W69hIRhYWFUcmSJUkkEtG0adN06l9mLl++TCVLlqQuXbpkOwJqz549ZGlpSefOndPYTtEfxRv/hIQEKlmyJPn7++vs6/nz52Rra5stIUjhNzg4mGxsbIRJnbb2O3fuJLFYTJs2baJGjRrR9u3biUj772n//v1kYmJCQUFBtGHDBvLx8SFLS0u1kR4KAULxW9q9ezeZmJiQi4sLiUQiatKkCa1du1brd/z333/JwsKC1qxZI9xDNaH4DkeOHKE///yTbGxsSCQS0cSJE7VGQJ05c4bMzc1p1apVJJfL6cCBAyQSiWjr1q1a/d2+fVt44dS5c2cKCQkR2mg6X2fMmEGTJ08mc3NzEolENH78eLX9XLRoEYlEIpoxYwa9efOG3N3dad26dfTw4UPavn076evr08CBAyk2NlatH0UfTp48ScOHD6eIiAjasGEDicVimjFjhto+Zjwf5s2bR35+fvTx40cKCQkhsVhMAwYMUCtAKfo6c+ZM4Texfv16qlOnTpbjoI6TJ0+SiYkJtWnThmrWrEm2trY6CdBjxowhU1NTql69OhkaGlL9+vVp1apVGm2Sk5Np06ZNJBKJqFChQvTo0SON7TOKeZcvXyaRSERisZjmzZunEn2gKytWrCCxWKyTAKXwfeLECSpbtqwQDa7J944dO0gkEtHGjRspLS2NLC0tlaLIM6PYl+LF37Bhw2j58uXk6OhIjRo1UvtitH379lS6dGnatGkT3bp1i8qXL09LliyhR48e0aZNm6ht27bk5OREt2/fVutTcc6FhISQqakpLVu2TKtQnfFYEBGtXbuW5s6dS/r6+mRra0urVq3KUoB6+fIldenShTw9PWnHjh1EpDkiSeHnzZs31LlzZ2rQoAFt27aNiIgmT55MVapUoalTp2YpQCn81alTR7ju/vrrrz+1AJXVpP7p06cUGhqaa/9YeGI0weJTPubatWtkb28vRGZERERQkSJFyMDAQO2kUpGiVadOHdqxY4fSG0Wi9LdFVatWFSawmR+g3r59S05OTjRjxgwiSr+ZRkZGkpubm8YUPLlcTvHx8dSsWTOaOnUqEaWnGjg6OtLAgQPVticiunfvHl2+fFlJnPLz8yNXV1datGiRRgHqv//+o+LFi9OBAweEbVFRUeTm5kaVKlVS6WvGsdm/f79SyHZKSgrVq1ePHB0dVYSyjH29du2aMLF69uyZRgEq44PoyZMnae/evUrpT7Vr185SgMqpT6L0t0wfPnygrl27Unh4OBERSaVSOnjwIJUtW1ajAPXhwwdyd3dXepiTSqXUuHFjcnZ21ng8li9fTrVr1yai9Mnkrl27qF27duTi4pKlYJIxZFexzwcPHlDFihXJ3d09SwHq9u3b9ObNG0pISKDDhw9T6dKldY6Wyvzm8t9//yU9PT2l80gdhw8fJjMzM1q8eDFFRkbS7NmzSSQS0dSpUzVO0k6fPk0GBga0atUqCgkJoUOHDlHRokWpZcuWGgWMa9euUdGiRYXJw8WLF0kkEtHcuXM19jMtLY3evHlDJUuWpObNm1PVqlWV0q8yR/8oHphjYmKEh9awsDBydHSkLl26ZCkiZbR99+6dYHvnzh2ttgouXrxIBQsWpIEDB1Lfvn1JX1+fBg0apPWB8sKFC2Rubk4rV66k//77jy5evEju7u7UqlUrtaHNGcd28uTJZGpqSj169KDKlSuTi4sL9enTR23bjIwbN44GDx5MdnZ2ZGBgoLOAePr0aSpSpAiJRCLhYV1TeyKi9+/fU/369WnevHlElD7Ri4mJoeDgYAoLC1P7gJ+SkkKfPn2ikiVLUpEiRcjLy0vpWqbOn2KbIn1J8fe1a9eoWLFi1LlzZ6UIqKxSSGQyGcnlcnr79i21bduWxo8fL2zPilOnTtGcOXMoLCyMiNInbBUqVKCdO3eqba+u//Xq1aPp06cTUfp5n9V1NGM/idKvqx4eHrRixQrBNisUE9B169YREVH58uVpwYIFWbZX8ODBA3J3dxei3d68eUOlSpUiFxcXMjMzE97kK1AIEEFBQfTq1StydXWlFStW0Pv37+nevXvUuXNnql+/PgUHB6v1p/iugYGB1KpVK0pLSxO2aRPJDh8+TEZGRjR//nxaunQpjRkzhiQSCf3+++9qr22K/U6ePJl69+4tfF9nZ2elyLfM45oxfXH69Om0adMmevToEbm5uVH79u3pn3/+UWmbkWnTppGVlRUdPXqU9u3bRzNmzFDbT4XtqlWrSCwW0++//06//fabkhh39OhRMjAw0ChA7d69m4yNjWn69OnCb2nlypUaBSiidDGnSJEitHnzZiH9/NixY2oFqMx9VZzPq1atorp16xKR7pFPy5YtoyVLlhBR+vGYMGECmZuba4zsjYiIoCpVqtDly5dJLpfTkydPqGfPnlS/fn0h3SkzCkHu7t27tGvXLhKLxTRu3DiNqfqK7/nPP//Q9OnTadWqVYKo+8cff2gVoI4dOyb8BjOiEKBmz56t1u7x48cq0X89evSgSpUqaezvkydPyNvbW/i9xcXFkb29Pe3bt0+p3erVq5VSDu/evUsVKlQQyke8fPmSbGxsyMTEhCpWrCg8l2ZMJ+vTpw+5urrSrFmzyMvLSyma7/r169S6dWtq3769IAht2LBByWdqair5+fmpRJ3qEs07adIkKliwIG3atInWr19PLVq0oBIlSqgIUIp7DFH6s3eLFi2oZs2awnhkJQhlPK4XL16k+vXrk7u7uyDAT5w4Ua0AlZW//fv3a/T3M5CdST3DfC9YfMrHbNy4kfz8/IgoXdV2cHCgPn360OrVq8nIyEgpAkrBs2fPhLelLVu2VLmp//bbb1SlShW1/l6+fEn29vZCuLbiBhUREUHW1tbUpEkTioiIUGubmJhI1atXp/DwcHr79i1ZW1sLfSdKfyv+7t07YZ979+4le3t7KleuHBkbG9Ovv/4qCAj9+/cnV1dXWrJkSZZvca5fv04FCxYU+qO40YaFhVHBggWpYcOGwts4hc+MY5M52kUhQJUtW1aoCZG5ry4uLmRsbEw+Pj708uVLio6OFsSgjKHqGR9Kx44dSyVLliQ3NzcyNjYmLy8vIRpInQCVU58KmwoVKtAvv/xCJUqUUDpWUqmUDh06RBUrVqTq1aurHdN3796Rk5OT8EChuPknJiaSo6OjxlSVPXv2kL29PY0dO5YaNWpEbdq0oW7dutG0adNIJBKppEbFxsZS0aJFqX79+irjFhYWRsWKFaOWLVuq1OR68uQJWVtbU6lSpaho0aLUoUMHEolENGjQILp27ZoQraFu0rRmzRqqVKkS9e3bl54+fSpMRnx8fMjPz48SExPVPrC9fv2a2rdvT3PmzCGidCHT0dGRPD09SSKR0MSJE7O8wE6cOJE8PT2Vtt28eZOsrKzol19+ybL+yNq1a6lx48ZElB4R5uDgoPR7z/hAnVEIUjzAhYeHk0wmoxEjRlClSpWUBKiDBw8qRX3s3r2batasSY6OjtSmTRs6evQoPXr0SK2IdPjw4RzbKvqq4PTp04LQTZQefWFsbEwDBw7UeMOaM2cO1atXT2l///33H1WpUkVjFNvt27epefPmdPbsWSJKj4hcvXo1lS1bVmPUzaJFi8jS0pIuX75Md+/epTNnzpC1tbWKgCiTydSKeQ8ePCBHR0dq3ry5UnRlVpPXN2/ekKOjI61evZpSUlJo3LhxVKdOHbK0tCRzc3M6dOiQYK/wp0h7efToET18+JAcHR2pc+fOWqNOjx8/Tn5+fvTy5UulPl2/fp0sLCyoY8eOgkCUmWvXrqmkvc2cOZOKFCkiCMfqvmNCQgLVq1ePRCIRVa9enbZu3Urv3r2j3r1706BBg7JM4Tl48CD5+vrSmDFjaPfu3VStWjWaMGGCxu9HlJ5ynTndbdiwYWRnZ6c13XPWrFlKgk+zZs2UosqISKVOnVQqpffv39OQIUMoJiaGnj17RmXKlCE/Pz+KjIykevXqkZmZmRAhlFmAGDlyJHXr1k1pHB49ekTt27en5s2bq722KdJ2unTpQq1atRK2Zxz/iIgIlfupXC6nHj16UK9evZS2r1+/niQSCU2aNEnlt6iYlHbr1o0CAwNJJpNRyZIlyc/PT/C3du1atRFeu3fvJn19fQoKChJ+O1euXKGqVatS+/bts0xpTUpKovr16wvijILg4GCSSCQ0efJkSk5OVorqkMvltHnzZpJIJFSsWDGh5IDiGn/s2DEyNTWlXr16qdS6iYyMJAcHB7WpsgrBIygoSOV+cerUKXJwcFCq7aZoc/ToURKLxdS/f3969eqVSl83bdpEYrGYFi9eTCtXrqTmzZvT/fv36fbt2/TixQt68eIFnTt3TqWv4eHhdPv2berevbtSxNKLFy8EAUpdJFNQUBB16tSJunTpovQ7ePToETVv3pw6dOigYqNOkFOct5oEOYWt4tgrroOKsZw5c6ZwLVu6dKnSufP582eaMWOGEIWkQJEyOmTIEBKJRCoRUDdv3qQaNWpQlSpVKDg4WHiWiIiIoKpVqwqRNJn7fOrUKRo3bhwdOXJE6XMPDw+l6NwZM2aQi4sL1apVSxDGbt68SePGjSOi9OhMR0dH6t+/Pz169IhsbGyocePGFBoaqpJO1qtXL5JIJEov+RSsXLmS7Ozs6OXLl0IqmoeHh5IY16xZM+H5IPM5qXgOnDBhgtKLwBcvXlC5cuVUotV79OhBVlZWtGrVKpUXjtu3b6euXbuSh4cHmZiYkL29vXCsFILQpk2bVGpAjRgxgtq1a0c1atQgc3NzKl26tPCiQZ0A9aX+fmRYfGLyAyw+5XOuXr1KKSkp1Lx5c/L29iaidJHA2dlZrYhClC6yeHh4UMmSJenOnTtKn23fvp3KlSuXZTqUs7OzykQsKSmJGjZsSCKRiGrXrp1lpEfdunXJz8+PHB0dacCAAcIb9ZiYGGrRogVt2LCBiNInPJaWlrRixQqSSqV09OhRIUVI8YDg7+9PpUqVohUrVqh9oElJSSEbGxsaPXq00vbY2FiqUaMGWVhYkJubm7A9owBVs2ZNcnJyEt5KZkwDcXV1JTc3N+EHk7mvR44cIZFIRN26daNnz55RdHQ0ValShZycnFTqac2ePZusra2FGhiLFy8mkUhEbdu2VRKgMj+s5sTnjRs3qHDhwjR+/HgaPXo0OTg4kIeHh9KxkkqltHv3bqpevXqWIbNly5ZVKuCdkpJCcrmc2rdvryQmEv0vMuvKlSsUGxtL48aNo5o1a9KAAQMEUeXJkydUo0YNtSk8p0+fJhsbG6E4toKkpCT65ZdfSCQSUYMGDZQephITE+nly5d0584d2rp1K61YsYL09PTIwsKC7OzsyNTUlMqXL08dOnRQeQgLDw+nFStWUMWKFcnV1ZW6dOlCt2/fpoULF5KDg4PKBFzB+/fvaeHChRQdHU2vX7+mChUqCG8Zx4wZQyKRKMsogXHjxpGHh4ewX8VD/vbt28nQ0JBatGihNnVoyZIl5OPjQ4mJiVSqVCny8/NTSpGZN28e7dy5U0UIcnd3J2dnZ2rbti3t2LGDUlNTaeTIkYIA9fr1a3JwcCAfHx969OgR/ffff2Rubk5//PEHzZo1iwYMGEB6enoUHBwsiEg9evSgy5cvC7Z9+vTJtm3Gcb1y5Qpt2bJFECczcuTIETIyMqLBgwdn+UZzypQpVK1aNWGfiuvMqVOnSF9fX61A/vfff5OnpyfVrFlTSdD89OkTzZ8/n9zd3bOM7OzTpw/16NFDaZsirblZs2Y0d+5c4TjI5XIVQe7IkSMUFRWlVpDLLFh9/vyZEhMTKTAwkCwtLalAgQLUrl074U16kyZNqH79+mqPu5OTE7Vu3ZqOHDkijH+3bt2Eid748eNVIucOHTpEIpGIBg4cKERkKM6zHTt2kIGBAXXs2FGIolR8x/fv3wupnaNGjVKqK1a/fn0aMGCA0ouPjL+ptLQ0Cg4OpgYNGtD06dOpcOHCNGXKFAoICKBChQrR0aNHVWyI0iel3t7e5ObmRvXr1yd9fX0SiUTUo0cP6tChAy1fvpxWr16tdB1+9uwZtW3bVrhXzp8/X9jesGFDWrZsmeArcyRgQkKCSlSMt7e30j13+vTp5OXlJdwvTp48SQEBARQVFSVM3AICAqhTp06CaOPn50dFihQhW1tb+vjxo9KLB4VYYmJiovJi5datWyQSiVSK1p88eZJGjBhBUVFRtGLFCrKyslIRHWNiYmjUqFF06dIlYVwVglWjRo2Uov8UYzB48GCVa5vCV3R0NG3atImcnZ2pcOHCNHjwYKXj7ePjQ/7+/kqihiZBRyFAderUSaVWkSKl2tHRkSZOnCiMlUwmI5lMRl5eXkKqaeZ0ubt379L27dtJLBbTlClTVF7yHDhwgIoWLaqSDnfy5EkqU6aMUqRsxvuJIuUs8+9p7dq1VKFCBaXzJqPd2bNnSSKR0IABA4SJdsbUvrVr15JYLKYiRYpQiRIlyM7OjiwsLMjBwUGIasxYNmH37t1kampKzs7OZGFhofKC6OXLlzR58mSl6D2idEEnODiYRCIR2draCi+yMqZxiUQipft2TgU5bbaKaLLevXtTnz59yMTERPB78uRJGjVqFJ09e1YQoNavX69kP3fuXCpTpgxZWVkJLzBDQkIoMDCQpk6dSnPmzCFTU1Nq06YNTZ8+XXi+8PHxUenL7t27ydDQkGbMmCH8hhTniqenJ40aNYqISBhTxSIE9evXF8b30aNHJJPJqGfPntS9e3eSSqWUkpIiPNN4eHjQ06dPVdLJBg0aRKamprRgwQIlgfH27dtkY2MjvMBTpKI1aNBAiIzu06cPVa1aVbBR9Pndu3c0adIkOnfunFB3SZG+m5CQQKVLlxbE9YyTRDc3NypXrhytWbNGuA9fvnyZTExMaM2aNXTv3j168OABNWjQgKpXr0579+4lovTffdGiRYXvRJQuZBcsWJBCQ0MpJiaGXrx4Qb/88ouS+DZhwgSqXLkyTZs2TRDIc+rvR4fFJyY/wOJTPkMxoT9z5oywTZFupdj2/v176tWrF23cuFGI7lGXouXq6kpVq1almzdvCifAgAEDyM3NTXgwVtgp3jguWrSI3NzchId0BYMGDaJ//vlH6WEss+3q1aupVKlSKpE148ePp3LlytGTJ0/o06dP5OfnJ6TnRUVFUenSpalz585kaWlJrVq1Eh6uhg8frlRLQJG+pnjzMXPmTKpRo4aQnkKULk706tWL/v33XypZsiSNGzdO7dgoIoAU6WsZBSjFd9TUVwsLC2rbti09efKEnjx5Qh4eHkpFHl+8eEG9e/cW8tt3795NBQsWpEmTJpGlpSW1bdtWeGNep04dKl++PL158yZHPq9fv067du2iKVOmEFG6YHTt2jVycnKiunXrKokiKSkpwqRD3TFcvHgxValSReX4d+zYkYYMGSJM0jJGrhkaGtKwYcMoMjJS5a3dxIkTqVy5csKE/969e3T16lWhhtS///5LpUqVUhGghg0bRqdOnRIifBR2mevJJCQkUPPmzWnt2rX04cMHunr1Km3dulVpBYoTJ07Q7t27hRWuiNInD15eXlSoUCEaOHAgiUQiGjJkCKWmpqqIARmZNWsWNWrUSHjInTNnDpUrV46srKwE8SomJkaYaCrqWyhSrjJGtlWrVo2qVasmnG8Z7W7cuCGssDNq1CilifiAAQOobdu2ZG9vn6UQNHDgQNLX16clS5ZQWloajRw5kqpVq0ZTpkyh0NBQcnd3p0GDBtGECROEB2qi9HN+8eLFpK+vT6dOnaI7d+6QpaUl+fr6UnJy8hfZEpFQF6Zq1aokEomoXr16KisjHjt2jEQiEQ0fPlzpWCjG5urVqyQSiVTeXF+8eJHKli1LT548UZkAhYSEkK2tLUkkEpXUiYiICDIwMKCDBw+q2MlkMurVqxe1bdtW+Fshdi1atIgkEgmZmppSy5YtsyXI/fnnn2ojyEqXLk2dOnWioKAgunXrFm3evJni4uKEcVBcJ3UVD8uVK0e1a9emFi1aKBXojomJESY4ly9fJolEQv369VOahO/du5fq1KlDzs7O9OzZM6XoOsV18p9//iFPT08qV64ctWzZkq5cuULjxo2jjh07qkzoL126JKRWxcfHU4MGDSgwMJDevn1L/v7+5O/vTyKRiIoUKUKPHz9WOfYZj01ycjItWbKEChUqJEQK1atXj4oUKSJc06OiooTvGR4eToMHDyYbGxuqWbMmBQUFkYeHB/Xr109tNF+NGjWodOnS1L59e6WiyAMGDBCuVYoJqGJSqIgKmTZtmjBxTUlJoQYNGtCwYcOEfQwaNIhWrVpF79+/VyuWKNKRxowZo1JUtkyZMkrRZhl9Xr9+ncLCwqhp06bUokULQXRMSkqiyZMnU6lSpYRrjUJEevr0Kf39999UsmRJQRRV9GnOnDlUpkwZKlKkCL1580bF171796ht27bk6Ogo+IqPj6fx48eTtbU13bt3T+n4qxN0MgpWd+7cIQcHB+rZs6cQyTBv3jwaOHAgPX/+nKZOnUpOTk4qosDEiROpRYsWJBKJaNWqVRrT5f744w8VASrj/VDB3r17ycbGRuirIrWUKP2lSUREBO3YsUMQZRWf/f3331SuXDlBfFLcL2UyGW3bto3evXsnCFCzZs1S29eNGzeSRCIhX19fioqKoqioKHr+/Dk9fvxYKRX9zZs3VKVKFVq3bh39888/NGXKFBKJRCpp7s+ePaM//vhDOB6KMY2IiBCEufHjxyuJ/ZcuXaIyZcooCfI5FeR0sT106BDVrl2bWrZsKSxWsnv3bqGoeGhoKCUlJdHEiRNJLBYrCVDjxo2jtWvXCveH3bt3k4GBAc2aNUtI7Q8NDaXAwEAqVaoUNWrUiLp27UoikUgQuomyFsgU1/vu3bvTuHHjaN68eWRoaCi8VFGkhdWuXZu2bNki2NWqVUtpX4MGDaJz584J97uM6WQZxRRnZ2eaN28evXv3jmJjY2nUqFFkZ2dHb968UUlF8/DwoF27dtHdu3epVKlS1K1bN6W+jx07lsqWLUuvXr0S6i41atRIuKY1bdqUGjRooPJdu3XrRmXKlFGq97l8+XIqX7680nny/Plzqlu3LtnZ2QmlCwYMGKB0T588eTLVqVNH6Tf0/PlzqlGjBjk4OCil4Lm7uwvpiCtWrMiRvx8dFp+Y/ACLT/mIzKlWffr0oadPn9Lr16/Jzs6OhgwZQh8/fqSxY8dS9erVBfFCU4pWpUqVqEiRItSgQQMaMGAAFS1aVHhYzmw3aNAgOnv2LA0dOpQqV65Mffr0oa1bt9KAAQOocOHCSnWGMtv6+/vT+fPnyd/fn8qXL0+//vorBQUFkbe3N1lYWAgPFFKplHbs2EEPHz6k9+/fk5ubG/n6+hJR+iooIpGImjZtqpLKkDF9zcjIiAYNGkSnTp2igIAAIdJl7ty5VLduXapevTolJiZS06ZN6ZdfftE5fS3zm3ZNfd2yZQuJRCJq0aIFPX/+XKV+QFJSEu3Zs4diY2OF2l2KkO358+cLE+/4+HiSyWRUvHhxGj16dLZ9JicnCyv4/Prrr0ph/Irlmhs0aKA2Kier4z9s2DCqVKkSeXt70+rVq6l///5kbm4uPGhrisxSPAScOXOG/Pz8qFChQsKxz+jPyMiI+vbtSw8fPqRz586RnZ0d1a5dm5YtW0aDBg2iUqVKCSJk5n7+9ttvSqJk//79qV27dmoj5BTnTZUqVcjY2Jh69eqllK548OBBmjhxIhUtWpQcHR2FVJddu3YJ0Svt27cXQtN9fX2V6mZ17NiRpk2bJkxgMka9tG3bltauXUt//fUXGRsb07Zt24TaNN26dSN/f/8s7TZu3EjBwcFkZGREmzZtIqlUSi9evKCxY8eSlZUV/ffffzoJQQYGBrRv3z6KjY2l/v37k6enJ8XExFBoaCjVqFGD7OzsaNCgQUpj9vHjR/Lx8aHu3bsTUXqNpYxFr7Nrq5jAvHz5kjp37kxr1qyhd+/eUUhICBkZGVGvXr2Uri2HDx+m5cuXC5Ef6sZ0wYIFZGxsTFu2bCGpVEpSqZTGjx9PLi4uSpFNDx48EPb96NEjKl26NLVs2VJpAv/69WsqV66cUE+CKD3V8cWLFySXy+n06dMkEolU6vQsX75cEAEtLS2pU6dOOglyBQoUEFYGyywgBQUF0aBBg0hfX5/++usvYT/Pnz+niRMnUqFChWjXrl06CYAnT56ke/fuUadOnZTqNynG08HBgdq0aUMnT56kO3fukEQiIT8/PyHNbtKkSTRgwAClyDxF5KQiuk6RAnjhwgVyd3enBg0aULVq1UgkEgn1Z4jSJ/i9e/cWIlQ+fPggpKPt27ePkpOT6eLFi1SpUiUyMjISfqMZhaB27doJdZrkcjkdP36cKlWqRB8/fqSlS5fSjRs3BNFizJgx5OzsTFZWVlSvXj1hUvjp0ycaOHAg/frrryQSiQgAFS1aNEsR19/fnwwMDIR0r6VLl1K7du1o4sSJZGhoKAg2miI7Ro8eTY6OjrR06VIaMmQIWVtbK72o0JTKNHLkSLpx4wa9fPmSxo8fT1ZWVsLYZOVz9+7d1K5dO7K0tKQ6depQnTp1yMrKSq1IphCsWrduTS1btlSqwzdy5Ehavnw5xcfHZ+lrz5491LRpUypcuDDVq1eP6tevT9bW1ipp1kTqBR0FISEh9OrVKwoPDxfGRlE7acuWLfT06VO6du0atW7dmpo3by70MyEhgdq2bUu7du2iMWPGkJOTE9na2irVxFGgLTonI1FRUWRsbCzUL8tIQEAATZo0SW2dovDwcJJIJMKLIAXx8fHUpk0boV7YjBkzyMnJiezs7NSeM6tXryaxWJxljb9jx47RyJEj6bfffhMm5wkJCcJKl5kFKEVfx4wZQ1ZWVrRlyxZhnBXn2tChQ+nkyZMUFhZGLVq0oOrVqyuNU3YFuYxosg0JCaHo6GhKTU0VvktW59vnz58pMDCQxGIxNW7cmJo2bUoFCxYUfGr6HcpkMkpMTKShQ4dSkyZNyNjYWOXlZmaBLOMzxfTp00kkEpGRkZEgtmZOC3NychIiierWrUuenp507tw5CggIoBIlSgi/XU3pZD4+PmRkZEQODg7UtWtXcnNzo9DQ0CxT0ZydnWn58uW0fft2sre3p4oVK1LHjh2pffv2VLBgQaUoyIx1l3bt2kXXrl0jOzs7YTVkhY+ePXvSjRs3qHbt2tSkSRMiSq81VbZsWUEAVTxT3rlzh8zMzMjFxUUQ0TLua+bMmeTu7i4cW4XdqVOnyNTUlFxcXIQFB5o1a0YdO3bMkb+fBRafmPwAi0/5hKxS0bp160Z3796lBQsWCLVuihUrJjzcaRICFMvCN27cmEQiER07dkyYiGVl179/fzp9+jStXLmSqlSpQuXLlyc3NzdBQNBk6+fnRyEhIUKxTE9PT/Lx8VFZtltxMm7cuJE8PDyEG/LWrVupQYMGZGdnp5QWllX62m+//UZnzpyhLVu2CP46deokvL2pUaMGGRoa0ooVKyg5OTlbKXM57WtGFDfLmTNnUqtWrYRIg8WLF9Ovv/5KzZs3FyJCOnfuTIMGDaK0tLRs+3z69CnVqVNHKZVQQWhoKBUsWJBatGihtF3b8V+1ahW5u7uTm5sbNW7cWIgM0Ba51q5dO/r3338pMDCQunfvLkxks/Ln5eVFDx48oHv37pGnpye5urpSlSpVhPMtq99F9+7dheimv/76iypXrqwy/lmdNx07dlSKjHr9+jXZ2toKUUZZTUCXLVtG//77L4nFYurTpw+1bt2axGIxdejQQWXiOnPmTPL39ydjY2MaMmSIUFC4cuXKVLFiRRKJRNSuXbsso5aMjIyof//+NG/ePBKJRFS6dGlyc3Oj0qVLK03stAlBGVPGnj59qiTq3r59W4hey/j7JkqPVqxUqVKWN47s2ipWffvll1+UJt4XL14kQ0ND8vLyomfPnmlM7ctqTF1cXKhq1apkamqq9OZ5zJgxVK5cOWFivHfvXiH9rXbt2jR37lzau3cvVaxYkRwcHJQmaAo7T09PWrRoEc2bN4+MjIxo48aN9O7dO3r//j21bt2a1q5dS7du3SJHR0cyNzcna2trnQS5ffv2aRWQFP6uXbtGPXv2JAcHB2GsdRUAdUmzlEgktHnzZgoLC6PixYuTk5MTValShQoUKEAlS5bM8lgMGDCA9PX1lYr9bt26lUaMGEGGhobCNeP9+/cklUrp+fPndOTIEbKxsaEWLVrQX3/9RWvWrKHBgwfT8+fPhb527949SyHI0NCQZs6cSUTpE/rixYvThg0bqFSpUtSvXz96+PAh7dmzh4oXL0779u2j4OBgGjVqFOnp6SnVhPn48SNt376dqlWrRl26dCF3d3fy9/dXeyyWLFlCRkZGtHPnTiFN0dDQUEmo0TRxvXHjBg0cOJAcHByoWrVqSr9fTZNlhSigr69PvXr1UrHN7DOjUPDw4UPatWsXDR06lObPny+Ix1n527t3L7Vp04aKFClCHTp0oGbNmpG5ublS+lNWviIiImjbtm0UEBBAy5cvz3LlM02CzrBhw2jixInCmKmrnUSUvoJgmzZtyNzcXKjRWLFiRSIiWrBgATk6OpKzs3OOonMys2bNGtLX16fRo0dTWFgYhYeH05gxY8jS0jLL2pdE6SKXvr4+DR06lE6cOEFnzpyhpk2bUqVKlYRngkmTJgmRJVn1dePGjSQSiZQiu4mUV5rLKGgQpUd+z5kzhwwMDFSKcGc1pkTp55pEIiGRSEQDBgygdu3aCX1V9Cmngpw222HDhtGkSZOUXuBlPt8yv1g6evQoeXl5KYnl6uwy9j/jPhT3moxoEkf//fdfCggIEOqaEmWdFlajRg3asWMH3bx5kypWrEg2NjZK921N6WQKAWro0KEkEolo6dKlSv1UZ1u/fn2qX78+rV27lp48eUKjR48mb29vGjlypFL0Yea6S+XKlaPly5cL9TorVKggXAudnJyIKD1qSVH4/sGDB2RkZKS0WA9Ren3A+vXrU48ePdQ+C9+9e5f09PQoMDBQafvhw4eFBSoUz8EBAQFUt25dSkxMzLG/Hx0Wn5j8AItP+QBtqVY9evSga9eu0X///UeHDx8WBCRdUrSioqKEFC3FhToru06dOlGBAgWoe/fuwgTx8+fPSqteaLPt1q2b0uRS0yom06ZNo4oVKwq1D8aOHUuLFy9WitLJKn1t4sSJVKBAAerZs6fKCmZEREOGDCFjY2MaOnRolmOTVcpcTvuaGcXDTp8+fahu3br06dMnSkpKotatWwvfhyj9pt66dWuVukiafOqSSqjow82bN5UiV7JzDJOTk5XCnnWJXOvWrRtdu3ZNyN3X5M/CwoLatWsniH+xsbFCqps2oatt27b04sULOnjwIFWqVIk+fPggjLm2tMdOnToJD5EymYxCQ0OpePHiVKZMGY1RRGvXrqVNmzZRvXr1qGPHjrRt27YsRQRFRIaJiQlt3ryZQkNDae7cuTR37lzas2ePVjuFmBIWFkbr16+nY8eOqRVJtQlBlStXzrKw8p07d8jV1ZV8fHyUUo/8/PyoSZMmalNScmJ7+nT6qm/6+voqKwteunSJzMzMqE2bNvT8+XONEV0Zx3TLli1069YtWrx4MQUFBVHx4sU1ChCKdI0nT55Q6dKlhUUZzMzMhCi8Xbt2KdmNGTOGjIyMaOjQobR06VIyMDAgR0dHsrOzowoVKgg31mvXrlGFChXIxsZGZ0FOF+HQ29ub7ty5Q5s3b1a5Ruly3KVSqdYIuUWLFpG+vj6FhITQo0ePhPGMjIzUaqsQZhR1RBQpbIrfviJSysnJidq2bUvHjx+nmJgYGjduHJUvX54KFy5MFStWFIrP6upPUV/F2tqaNm7cSDdv3qRq1arRsGHDaMCAAfTnn38Kdp8/f6bFixeTqampSm2QrVu3krGxMR06dEjrsfDy8qJbt25Rr169VMQHTZEd58+fp0uXLlF8fLxKDSlt6Uh79+4lkUhEM2bMUEkBVudTcb89ffq0yipf6vxl9BUeHk7r16+nLl260IABA5Qm9dp8ZSU4ZUZXQSdz7aSM/bx//z7t37+fRo4cSbNnzxZeNg0cOJCqV6+ulF6YneiczMhkMtqxYwcVLFiQSpUqRU5OTlS2bFm1UV0ZkcvltG/fPrK1taWSJUtShQoVqGnTpkoLeMyZM4cWLVqU7UgidSvNTZkyRek5S1EzrlChQkoriKmrR5VRkNm7dy9JJBIKCgoS9pf5+S2nglx2bbVFSimuhZlFKU0C0pkzZzQuwKBJIBs9ejTNnj1bKZVYU1qYs7Mz7dy5k5KTk+nx48dKNVa1pZMp0sL9/PxUnm3V2T579ozq1q1LpUuXVirUnnFs1NVdatKkCXl6etK6devo6dOnNHLkSPLx8aFhw4YJ52r37t2pe/fulJqaSnK5nDZu3Ej6+vo0fvx4evz4McXGxtKkSZOod+/eWS4ORJS+6IO+vj6NGjWKrl69Sg8fPqSWLVsq1Sj78OEDBQYGKj1L5NTfjwyLT0x+gMWnfIAuqVatWrVSmXjqmqL17NkzpbdKutg1a9ZM7QOlrraZi3mr48aNG2RoaEh16tShxo0bU4ECBej27dtKbTSlrymiQurXry8ILrdu3aIhQ4aQvb09zZ49O0cpcznta1ZcunSJ9PX1qWLFiuTs7Eyurq4qPtXdSLPymZOV8DLyJcefSHM0WP369cnW1lZJAMupP12Ernbt2tHJkydVHtJ0SXts1KiR0jg1aNCAChQokOUE9LfffqOePXsSUfqKTwpBR5OIEBsbS3369BGiXjKSUzt1fImIdOPGDapYsSI5OjqSj48P9e/fnwoXLqwiaOhqW6hQISEdSd2qb23btlVZBe/cuXNUrFgxIdUyJ2OjTYBYuHAhGRkZ0fnz5yk8PJwcHR2pX79+tGnTJsGuf//+SnYKwcPc3JwOHjxI4eHhtH37dtq6daswMVOcB8nJydk+DpoEpHHjxlHVqlU1Cvi6+stOqmRmdBHJevbsqbQi68OHD9VGSunp6QnpeHfu3KHmzZuTSCSismXLChMeXfx1796dkpKSyN/fX4isVQhXBQsWVFkV7cOHD9S+fXvhZYTi+hsXF0eVK1emc+fOaRXzFAV91T1MaYsKmThxotpUL22pTHfv3s1SLNEWTTJ58mSSSqUqAkN2Bauc+lKHNkFHU+0kRT8V9wQFz58/p4CAALK0tKSjR4/mODonK168eEEXL16kS5cuqUTLaOLdu3f08OFDun//vnDsM0YTZTeSSFstq4xtk5KShBRybfWo5HI57dy5k+7evUvr168XVp9T91yUU0Euu7a6nG/qXvxpG9Os7BRkJZCZm5ur1DDTlhZWoUIF2rRpk4qPL0kn02ZbsWJFIe0v429RXd2lZ8+eCS8GMqeUf/jwgYYPHy6k+CuQy+W0ZcsWMjMzIwcHBypdujQVKlRIKQo0K3bt2kVFixalUqVKUalSpcjNzU3lWGQ+577E348Ki09MfoDFp3xCTtO7ctvuS20zc/HiRerVqxcNGjRI7YpoRLqlr2V8sD958iRFR0d/1X7q2tesCA0NpQkTJtDs2bOFG6zibVJ2fH7J6nsZ+Rpjk51osG91ntrY2Cit/pOR7Jw3T58+perVq9OWLVt0iibJjC5RKOrGJad26vgSEenOnTvk5ORENjY2NHPmTLXRhNpsraysaOjQoYKtplXfOnfurCJAZb5J5WRstAkQbdu2FQSN06dPk6OjI/Xs2ZOCg4OztHv//j21a9eOBg8erPLd1a3olt3joElAaty4sdZlpHX19yXnmjbbKlWqUEpKik51yAwNDZWWTF+2bJnKSoOa/E2YMIGqVKlCcrlcZbJy584dsre3p6pVq6pMan19fVXSj6dOnUoikUi4Vmo6Fo0aNdJ4LHISFfIlqUw58amriPS1vl9WaBN0sqqdFBcXR23bthUEzA8fPlBQUBA1bNhQOF5fs59fE3Xio6591XWlOU3nirYxVayouXr1ahKJRCqLjmQkp4Jcdmxzehy/5PhnRyDTlBbm6emZZVrYl6ST6WKbOfKdSHvdpfLlywsRV1FRUTRr1iyqUKFClvesx48f0/79+2nbtm1ZCtbqePHiBV29epVOnz4tnKu6vPzNqb8fkawm9U+fPqXQ0NBc+/e1Ux5Pnz5NAFSigzOybt06srCw+Kp+vza9e/emdu3aaWxjZ2enVNPzR4TFp3xGTtK7vofdl9pmJOPbGHXomr6Wld+v1U9d+qorutxwM/v8ktX3suJLxiYn0WBf+zzNaqJEpPt5o1iNSBElktMooty2y2pfORWRrl+/Tr/88ovSikq6cvz4cTI2NqYePXpka9W3jMWs1f2ucjI22gSI5s2bC74uXLggrMh3/fp1rXa6kt3j8CXCYXb85Uaapa6RUtpEtZyKcrdv36bKlSuTt7e3MH6fP3+m2rVrU79+/ZTa/vvvvyoiaE6PRU6jQnJrsvyl/r4k6iUnaKqdlPH+qajB9r36+SXo2tcvWWkuI1mNaeZo7PXr1+uUmvgtyelx/BrHX1eBLKdpYV+STpYTW13qLmU8n6KiolQW/fkWZDcKkVE/qX/69CmZmBgTgFz7Z2JinCMB6uLFiyQWi6lly5ZK23URnxITE7N9XgLI1cL0uohPb9++1fr8k9/JjvgkIiIC8125efMmPDw84O7uDiMjI1y7dg3//vsvKlWqlKfsvtQ2J1y+fBmenp4oW7YspFIpjIyMcOPGDejp6eWpfn4rUlJSsH//flStWhUFCxZEkyZNULVqVaxevRpbt26Fl5cXmjdvjlWrVqFYsWJaxwX48rG5dOkSli5dCgsLCwwcOBAVKlT4Jv6+pJ85OW9u3rwJb29vJCYmwtPTE4aGhti1axdOnTqFKlWq5Bk7dYSGhmLcuHHYvHkzihQpki3b5ORkGBkZZctGwaVLlzB06FDUrFkTlpaWkEqlmDt3LgDg8+fP2LBhA0aMGIGjR4+iaNGi8PT0RKdOnfD333/D0NAwy/3mZGzu3LkDb29vVK5cGcOHD0eVKlUQFxeH5s2bo0KFClixYgWICGKxGJcvX4aVlRWcnJy02q1cuVLn8cjucQgLC0PHjh0hlUrh7++PHj16wM7O7qv7+5JzTVfbO3fuoF27djAyMsLWrVuVPpswYQKOHDmCq1evQl9f/6v4U2fXq1cvxMbGwt3dHYaGhnj06BGuXLkCfX19yOVyAIBYLFZr/yXH4uXLl3j69ClEIhEcHBxQrFgxje3lcjl2796N/v37w9TUFEZGRpBIJNi6dSvc3Ny+us8v9Zfd75dTiAgHDhzA0KFDIZPJYGlpiZIlS+LQoUPQ19dHWlqaxmt4bvXza6Ctr/v27cPQoUPx77//ws7ODnK5HCKRCCKRCGfOnEHx4sURFhaGihUrwsXFJUs/2sZUJpNBIpF866+bLXJ6HHPj+BMRtm3bBj8/PxQpUgRisRixsbE4efIkqlat+tXtvsQ2ODgYfn5+GDZsGLp27YpChQph6NChqFSpEmbOnAkAWn9TzPcnOTkZjx8/hoODg/CsduPGDVSrVg3Tx9SBg02Bb96Hx88+Y9KcCwgNDdV6vmamb9++MDMzw5o1axAZGYkSJUoAAM6cOYOGDRsiNjYWlpaWX62vIpEIe/fuRfv27bNsk5KSAgMDg6/iz8fHBx8/fsS+ffu+yv7yK+rO0yz5hiIYkw1ymt6V23ZfapsTskpf00Zu9/Nb8bVTCYm+fGyyGw32Pc7TnJw3OY0iym07dXyvegDZqS104cIFpUL4msjJ2Ny4cYPKly9P1tbW1KZNG+rcubNSnYmszlttdtk517N7HL4k+iw7/r5GmuW3jLL6Gn0NCwsjJycnqlixIq1fvz5baR5EX34sssuXpDLlB385RV3tpOxEDv8IfGl6ZmZ4TL8uOU0L+5J0spzYaqq79DUi+plvj7qIktDQUAJAmxa3oNBjvb75v02LWxCAbNfeiouLIzMzM7p37x5169aNZsyYIXymiHw6dOgQubq6kqGhIdWsWVNp4Qt1aXdLly4lR0dH0tfXpzJlytCGDRuEz+zs7JSitezs7IiIaMqUKVS5cmVatWoV2dvbk0gkIqL0CLK2bduSqakpmZubU5cuXZTujQq75cuXU6lSpcjY2Ji6dOkilPQg+l/k09y5c6l48eJUqFAh8vf3V8ruyJx2N3/+fKpYsSKZmJhQqVKlaODAgUoLfeVHOO0un5LT9K7ctvtS2y8lJ+lrPwJfM5WQKPfH5nucpxn51hPQ3LbLS2irD5R51TddycnY5FSA+FLh4kvILeHwS841XW2/NJ3wS/t69epV8vPzE64Z6uruaIKLuuY9snsMfxS+ZS2rn3VMf0ZyWneJyRvkZ/FpzZo15O7uTkREBw8epNKlSystsAGAXFxc6MSJE3Tnzh1q3bo12dvbC/OazOLTnj17SF9fn/7++2+KjIyk+fPnk0QioX/++YeI0tPbANC6devo1atXwvPDlClTyNTUlJo3b043btyg27dvk0wmoypVqlDdunXp+vXrdPnyZapWrRrVr19f8Kewa9SoEd28eZPOnj1LTk5OwkJEROniU4ECBWjAgAEUERFBBw8eJBMTE1q5cqXQJrP49Ndff9E///xDjx8/ppCQECpbtiwNHDgwW2Ob1+C0O4b5AflRUgnzAzlNRcttu7xEWFgYvLy8UK1aNQQEBKBy5coAgP79+yMqKgr79u2Dqalptvebk7G5du0aVq9ejeXLl0MkEkEul2eZbvU17PITX3Ku6Wr7pemE2fWXGSL6YY8f8/PwNdIzGSYzeTHVkskaTWl3mxa3gItz4W/eh4gH79FryNFsp93VqVMHXbt2xbBhw5CWlgZra2vs3LkTDRo0ENLutm3bhm7dugEAPnz4gFKlSiE4OBhdu3ZFcHAwAgIC8PHjR2F/mcshdO3aFQkJCTh8+DAA9Wl3gYGBCAoKwosXL4QSBSdPnkSLFi3w+PFj2NjYAADCw8NRoUIFXL16FdWrV0dgYCD++OMPPH36FCVLlgQAHDt2DK1atcKLFy9QvHhx+Pj44MyZM3j06JHwu+ratSvEYjG2bdsGALC3t0dAQAACAgLUjtOuXbswYMAAxMTE6Dy2eY3spN1xoi/D5BPc3Nxw+vRpod7SwoULtdZbYnJGTifnuW2Xl3B1dcX69evh7e2Njh07CvV6du/ejVOnTuVIeAJyNjbVq1eHu7t7tgWInNrlJ77kXNPV1tXVFdu2bcO4cePg6+ub7Tpk2fWXGZFIJNT3Ypj8ilgsRpcuXVCnTp18U8uKyfuw8MTkBpGRkbh69Sr27t0LANDT00O3bt2wZs0aNGjQQGjn4eEh/L9QoUIoW7YsIiIi1O4zIiICfn5+Stvq1KmDhQsXau2PnZ2d0rNIREQEbGxsBOEJAMqXLw9LS0tERESgevXqAABbW1tBeFL0Vy6XIzIyEsWLFwcAVKhQQel3ZW1tjbCwsCz7curUKcycORP37t3D58+fkZaWhuTkZCQmJsLExETrd8nvsPjEMPkIDw8P1KxZUyg8yjB5CTc3N2zZsgUdO3ZESEgI/P39ERoamqOoly8lpwIECxdfh2rVquHAgQPfTVjl6yPzo1CiRAmhSC/DMEx+YM2aNUhLS1O6dhERDA0NsWTJklzvT05fgOpC5oVUFC8w1fHkyRO0bt0aAwcOxIwZM1CoUCGcP38evr6+SElJ+SnEJ366Zph8hlgs5okVk2dRRL2UK1cOvr6+30V4UpDT3wn/vr4OP0JEH8MwDMMwupOWloYNGzZg/vz5uHXrlvDv9u3bKFGiBLZu3Sq0vXz5svD/2NhY3L9/P8sVPF1cXHDhwgWlbRcuXED58uWFvxWreGrDxcUFz549w7Nnz4Rt4eHh+Pjxo9L+oqOj8fLlS6X+isVilC1bVqsPdYSGhkIul2P+/PmoVasWypQpo7T/nwGOfGIYhmG+Kt876oVhGIZhGIbJfQ4dOoTY2Fj4+vrCwsJC6bNOnTphzZo1mDt3LgBg2rRpKFy4MIoVK4YJEybAyspKqV5TRkaPHo2uXbvCzc0NTZo0wcGDB7Fnzx6cOnVKaGNvb4+QkBDUqVMHhoaGKFiwoNp9NWnSBK6urvDy8sKCBQuQlpYGf39/1K9fH+7u7kI7IyMj9O7dG/PmzcPnz58xdOhQdO3aVUi5yy5OTk5ITU3F4sWL0aZNG1y4cAHLly/P0b7yKyw+MQzDMF8dFp4YhmEYhmG+DY+ffc6TftasWYMmTZqoCE9Auvg0Z84c3LlzBwAwa9YsDBs2DA8ePECVKlVw8OBBGBgYqN1v+/btsXDhQsybNw/Dhg2Dg4MD1q1bp1RDav78+RgxYgRWrVqFkiVL4smTJ2r3JRKJsH//fgwZMgSenp4Qi8Vo3rw5Fi9erNTOyckJHTt2RMuWLfHhwwe0bt0aS5cuzdZ4ZKRy5cr4888/MXv2bIwbNw6enp6YOXMmvL29c7zP/Aavdvd/7d15WNVl/v/x5wFk86ioqBDDYfGQoIELmCllWbiklDn+xGnMbdAsza0xzVGTFhE0TcvUry1KOpNWWjaau5UplYhQWoAL2EkTmwNZyY6c3x9enumMUm7g9npcF9flue/7c7/vz+c61zVzvbo/9xERERERERG5hpzvV8QsFgthYaEUF5fU2jo8PT3IysrGZDLVWs2rLSEhgQ8++IDMzMyrvZRrnn7tTkREREREROQGYjKZyMrKxmq11lpNb2/vmyp4kpqj8ElERERERETkOmAymRQGyXVJr92JiIiIiIiIXEMu5nUmkavlYr6nTrW0JhERERERERERuQkpfBIRERERERERkRqj8ElEROQmM2TIEAwGAwaDgTp16tCsWTO6du3Km2++SVVV1dVenoiIiIjcYBQ+iYiI3IR69OjB8ePHOXLkCBs2bKBLly6MHTuW2NhYKisrr9q6bDbbVa0vIiIiIleewicREZGbkJubGz4+Pvj5+dGuXTv+8Y9/sHbtWjZs2MCyZcsAsFgs9O7dG6PRSP369YmLi+PEiRP2ORISEmjTpg3Lly8nMDCQBg0a8Je//IVff/3VPqasrIwxY8bQtGlT3N3dufPOO0lLS7P3f/LJJxgMBjZs2EBkZCRubm7s3Lmz1p6DiIiIiNQ8hU8iIiICwL333kvr1q1Zs2YNVVVV9O7dm8LCQj799FO2bNlCbm4u/fv3d7jm8OHDfPDBB6xbt45169bx6aefkpSUZO+fOHEiq1evJiUlhb1792I2m+nevTuFhYUO8zz99NMkJSWRlZVFRERErdyviIiIiNQOl6u9ABEREbl2hIaG8vXXX7Nt2zb27dtHXl4e/v7+ALz11lu0atWKtLQ02rdvD0BVVRXLli2jXr16AAwcOJBt27YxY8YMioqKWLRoEcuWLeP+++8H4LXXXmPLli288cYbPPXUU/a6zz33HF27dq3luxUREbm+WCwWrFZrrdXz9vbGZDLVWr3LNWTIEE6ePMkHH3wAwD333EObNm2YN29etdcEBgYybtw4xo0bVytrvFkpfBIRERE7m82GwWAgKysLf39/e/AE0LJlS7y8vMjKyrKHT4GBgfbgCcDX15cff/wROLMrqqKigujoaHt/nTp1uP3228nKynKoGxUVVZO3JSIict2zWCyEtQijuLS41mp6unuSlZN10QFUfn4+M2bMYP369Rw7doymTZvSpk0bxo0bx3333VdDqz3XmjVrqFOnzhWdc9myZYwbN46TJ09e0XlvdAqfRERExC4rK4ugoKALHv+//4fOYDBc0i/m1a1b96KvERERuZlYrVaKS4t5OnwuJqO5xutZTh0iad+TWK3Wiwqfjhw5QnR0NF5eXsyePZvw8HAqKirYtGkTo0aNIjs7uwZX7ahRo0a1Vkt+n858EhEREQC2b9/Ovn376Nu3L2FhYXz//fd8//339v5vv/2WkydP0rJlywuar3nz5ri6urJr1y57W0VFBWlpaRc8h4iIiDgyGc2E1L+txv8uNeAaOXIkBoOB3bt307dvX2699VZatWrFk08+yRdffAHA3LlzCQ8Pp27duvj7+zNy5EhOnTpln2PZsmV4eXmxadMmwsLCMBqN9l/qPev06dM8+eSTeHl50bhxYyZOnIjNZnNYyz333OPwOt2PP/7IAw88gIeHB0FBQfzzn/88Z/2/t7ZPPvmEoUOH8vPPP2MwGDAYDCQkJABn/gPc2df9zvLy8rL/kEt5eTlPPPEEvr6+uLu7ExAQwMyZMy/pGV+PFD6JiIjchMrKysjPz+fYsWPs3buXxMREevfuTWxsLIMGDSImJobw8HAGDBjA3r172b17N4MGDeLuu+++4Ffk6taty+OPP85TTz3Fxo0b+fbbbxk+fDjFxcXEx8fX8B2KiIhIbSssLGTjxo2MGjXqvLuavby8AHBycuLll1/mm2++ISUlhe3btzNx4kSHscXFxbz44ossX76cHTt2YLFYmDBhgr1/zpw5LFu2jDfffJOdO3dSWFjI+++//7vrGzJkCN9//z0ff/wx7733HgsXLrQfF3DW762tU6dOzJs3j/r163P8+HGOHz/usKbf8/LLL/Phhx/yzjvvkJOTwz//+U8CAwMv6NobgV67ExERuQlt3LgRX19fXFxcaNiwIa1bt+bll19m8ODBODmd+W9Ta9euZfTo0XTu3BknJyd69OjBK6+8clF1kpKSqKqqYuDAgfz6669ERUWxadMmGjZsWBO3JSIiIlfRoUOHsNlshIaG/u643+5GCgwM5IUXXuCxxx5j4cKF9vaKigoWL15M8+bNAXjiiSd47rnn7P3z5s1j8uTJ/PnPfwZg8eLFbNq0qdqaBw4cYMOGDezevdt+duUbb7xBWFjYBa/N1dWVBg0aYDAY8PHx+f2H8T8sFgshISHceeedGAwGAgICLur6653CJxERkZvMsmXL7FvAf4/JZGLt2rXV9ickJNi3mp/1v78W4+7uzssvv8zLL7983jnuueeec7bIi4iIyPXpQv83fevWrcycOZPs7Gx++eUXKisrKS0tpbi4GE9PTwA8PT3twRM4/qjJzz//zPHjx+nQoYO938XFhaioqGrXkJWVhYuLC5GRkfa20NBQ+26si1nbpRgyZAhdu3alRYsW9OjRg9jYWLp163bJ811v9NqdiIiIiIiIiFy2kJAQDAbD7x4qfuTIEWJjY4mIiGD16tWkp6fz6quvAmfORTrrfD9qUtP/wepC13Y+51tfRUWF/d/t2rUjLy+P559/npKSEuLi4vh//+//XfmbuEYpfBIRERERERGRy9aoUSO6d+/Oq6++SlFR0Tn9J0+eJD09naqqKubMmcMdd9zBrbfeyg8//HBRdRo0aICvry9ffvmlva2yspL09PRqrwkNDT1nTE5ODidPnrR/vpC1ubq6cvr06XPmb9KkicOB6AcPHqS4uNhhTP369enfvz+vvfYaq1atYvXq1RQWFl7wfV/PFD6JiIiIiIiIyBXx6quvcvr0aW6//XZWr17NwYMHycrK4uWXX6Zjx46YzWYqKip45ZVXyM3NZfny5SxevPii64wdO5akpCQ++OADsrOzGTlypEOQ9L/Ovu42YsQIvvzyS9LT0xk2bBgeHh72MReytsDAQE6dOsW2bduwWq32gOnee+9lwYIFZGRksGfPHh577DGH3Vtz587l7bffJjs7mwMHDvDuu+/i4+Nzzmt/Nyqd+SQiIiIiIiJynbCcOnRN1wkODmbv3r3MmDGDv//97xw/fpwmTZoQGRnJokWLaN26NXPnziU5OZnJkyfTuXNnZs6cyaBBgy6qztm5z/5Yyt/+9jf69OnDzz//XO01S5cuZdiwYdx99900a9aMF154gWnTptn7L2RtnTp14rHHHqN///4UFBQwffp0EhISmDNnDkOHDuWuu+7illtuYf78+Q67rOrVq8esWbM4ePAgzs7OtG/fno8++sj+Qy83OoNNp3yKiIiIiIiIXDNKS0vJy8sjKCgId3d34MyvpYW1CKO4tPgPrr5yPN09ycrJwmQy1VpNuX6c73taHe18EhEREREREbnGmUwmsnKysFqttVbT29tbwZNcEQqfRERERERERK4DJpNJYZBcl26OlwtFREREREREROSqUPgkIiIiIiIiIiI1RuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNUbhk4iIiIiIiIiI1BiXq70AEREREREREfljFosFq9Vaa/W8vb0xmUy1Vk9uXAqfRERERERERK5xFouF0NAwSkqKa62mh4cn2dlZFx1A5efnM3PmTNavX8/Ro0dp0KABZrOZRx55hMGDB+Pp6QlARkYGSUlJ7Nixg8LCQnx8fAgPD2fEiBHExsZiMBg4cuQIQUFB9rnr1KmDyWRiyJAhTJkyBYPBYO87efIkU6ZMYc2aNRQWFhIQEMC8efPo2bOnfcyxY8eYNGkSGzZsoLi4GLPZzNKlS4mKigLAZrMxffp0XnvtNU6ePEl0dDSLFi0iJCTEPseBAwd46qmn2LVrF+Xl5URERPD888/TpUuXS3rONwOFTyIiIiIiIiLXOKvVSklJMT2jJ9C4vn+N1yv45Xs+2vUiVqv1osKn3NxcoqOj8fLyIjExkfDwcNzc3Ni3bx9LlizBz8+PBx98kLVr1xIXF0dMTAwpKSmYzWbKyspITU1l6tSp3HXXXXh5ednn3bp1K61ataKsrIydO3cybNgwfH19iY+PB6C8vJyuXbvStGlT3nvvPfz8/Pjuu+8c5vjpp5+Ijo6mS5cubNiwgSZNmnDw4EEaNmxoHzNr1ixefvllUlJSCAoKYtq0aXTv3p1vv/0Wd3d3AGJjYwkJCWH79u14eHgwb948YmNjOXz4MD4+Ppf34G9QCp9ERERERERErhON6/vTrLH5ai+jWiNHjsTFxYU9e/ZQt25de3twcDC9e/fGZrNRVFREfHw8vXr1Ys2aNQ7Xh4WFER8fj81mc2hv3LixPdgJCAhg6dKl7N271x4+vfnmmxQWFpKamkqdOnUACAwMdJgjOTkZf39/li5dam/77a4qm83GvHnzmDp1Kr179wbgrbfeolmzZnzwwQf85S9/wWq1cvDgQd544w0iIiIASEpKYuHChezfv1/hUzV04LiIiIiIiIiIXLaCggI2b97MqFGjHIKn3zIYDGzevJmCggImTpxY7Vy/fZ3uf+3Zs4f09HQ6dOhgb/vwww/p2LEjo0aNolmzZtx2220kJiZy+vRphzFRUVH069ePpk2b0rZtW1577TV7f15eHvn5+cTExNjbGjRoQIcOHfj888+BMyFYixYteOuttygqKqKyspL/+7//o2nTpkRGRv7xQ7pJKXwSERERERERkct26NAhbDYbLVq0cGj39vbGaDRiNBqZNGkSBw4cAHAYl5aWZh9jNBpZt26dwxydOnXCaDTi6upK+/btiYuLY9CgQfb+3Nxc3nvvPU6fPs1HH33EtGnTmDNnDi+88ILDmLPnN23atInHH3+cMWPGkJKSApw5qwqgWbNmDrWbNWtm7zMYDGzdupWMjAzq1auHu7s7c+fOZePGjQ6v74kjvXYnIiIiIiIiIjVm9+7dVFVVMWDAAMrKys47JiIigszMTABCQkKorKx06F+1ahVhYWFUVFSwf/9+Ro8eTcOGDUlKSgKgqqqKpk2bsmTJEpydnYmMjOTYsWPMnj2b6dOn28dERUWRmJgIQNu2bdm/fz+LFy9m8ODBF3QvNpuNUaNG0bRpUz777DM8PDx4/fXXeeCBB0hLS8PX1/dSHtENTzufREREREREROSymc1mDAYDOTk5Du3BwcGYzWY8PDwA7L8c99txbm5umM1mzObzn2fl7++P2WwmLCyMfv36MW7cOObMmUNpaSkAvr6+3HrrrTg7O9uvCQsLIz8/n/LycvuYli1bOswbFhaGxWIBsJ/XdOLECYcxJ06csPdt376ddevWsXLlSqKjo2nXrh0LFy7Ew8PDvoNKzqXwSUREREREREQuW+PGjenatSsLFiygqKio2nHdunWjUaNGJCcnX3ItZ2dnKisr7cFSdHQ0hw4doqqqyj7mwIED+Pr64urqah/zv8HYgQMHCAgIAM4cPu7j48O2bdvs/b/88gtffvklHTt2BKC4uBgAJyfHOMXJycmhtjhS+CQiIiIiIiIiV8TChQuprKwkKiqKVatWkZWVRU5ODitWrCA7OxtnZ2eMRiOvv/4669evp1evXmzatInc3Fy+/vprZs2aBeCwgwnOHGaen5/P0aNH2bBhA/Pnz6dLly7Ur18fgMcff5zCwkLGjh3LgQMHWL9+PYmJiYwaNco+x/jx4/niiy9ITEzk0KFD/Otf/2LJkiX2MQaDgXHjxvHCCy/w4Ycfsm/fPgYNGsQtt9zCQw89BEDHjh1p2LAhgwcP5quvvuLAgQM89dRT5OXl0atXr1p4wtcnnfkkIiIiIiIicp0o+OX7a7pO8+bNycjIIDExkcmTJ3P06FHc3Nxo2bIlEyZMYOTIkQD06dOH1NRUkpOTGTRoEIWFhTRo0ICoqChWrlxJbGysw7xnf4HO2dkZX19fevbsyYwZM+z9/v7+bNq0ifHjxxMREYGfnx9jx45l0qRJ9jHt27fn/fffZ/LkyTz33HMEBQUxb948BgwYYB8zceJEioqKePTRRzl58iR33nknGzduxN3dHThzePrGjRuZMmUK9957LxUVFbRq1Yq1a9fSunXrS3pmNwODzWazXe1FiIiIiIiIiMgZpaWl5OXlERQUZA89LBYLoaFhlJQU19o6PDw8yc7OwmQy1VpNuX6c73taHe18EhEREREREbnGmUwmsrOzsFqttVbT29tbwZNcEQqfRERERERERK4DJpNJYZBcl3TguIiIiIiIiIiI1BiFTyIiIiIiIiIiUmMUPomIiIiIiIiISI1R+CQiIiIiIiIiIjVG4ZOIiIiIiIiIiNQYhU8iIiIiIiIiIlJjFD6JiIiIiIiIiEiNcbnaCxARERERERGRP2axWLBarbVWz9vbG5PJVGv15Mal8ElERERERETkGmexWAhtEUpJaUmt1fRw9yA7J/uiA6j8/HxmzpzJ+vXrOXr0KA0aNMBsNvPII48wePBgPD09AcjIyCApKYkdO3ZQWFiIj48P4eHhjBgxgtjYWAwGA0eOHCEoKMg+d506dTCZTAwZMoQpU6ZgMBgAqKioYObMmaSkpHDs2DFatGhBcnIyPXr0cFjbq6++yuzZs8nPz6d169a88sor3H777fb+e+65h08//dThmhEjRrB48WL7523btjFt2jT27dtH3bp1GTx4MDNmzMDFRRFLdfRkRERERERERK5xVquVktISukc8SqO6t9R4vcKiH9j09RKsVutFhU+5ublER0fj5eVFYmIi4eHhuLm5sW/fPpYsWYKfnx8PPvgga9euJS4ujpiYGFJSUjCbzZSVlZGamsrUqVO566678PLyss+7detWWrVqRVlZGTt37mTYsGH4+voSHx8PwNSpU1mxYgWvvfYaoaGhbNq0iT59+pCamkrbtm0BWLVqFU8++SSLFy+mQ4cOzJs3j+7du5OTk0PTpk3ttYYPH85zzz1n/3w2LAP46quv6NmzJ1OmTOGtt97i2LFjPPbYY5w+fZoXX3zxUh/3Dc9gs9lsV3sRIiIiIiIiInJGaWkpeXl5BAUF4e7uDsDevXuJjIzk4Y4JNG0QWONr+PHnI7z9eQLp6em0a9fugq/r0aMH33zzDdnZ2dStW/ecfpvNRnFxMQEBAXTu3Jk1a9acdx6bzeaw8ykjI4M2bdrY+2NiYmjRogWvvvoqALfccgtTpkxh1KhR9jF9+/bFw8ODFStWANChQwfat2/PggULAKiqqsLf35/Ro0fz9NNPA2d2PrVp04Z58+add13/+Mc/2LJlC2lpafa2f//738TFxfHjjz9Sr169C35W17vzfU+rowPHRUREREREROSyFRQUsHnzZkaNGnXe4AnAYDCwefNmCgoKmDhxYrVznX2d7nz27NlDeno6HTp0sLeVlZWdE4B4eHiwc+dOAMrLy0lPTycmJsbe7+TkRExMDJ9//rnDdf/85z/x9vbmtttuY/LkyRQXF/9hndLSUtLT06td881O4ZOIiIiIiIiIXLZDhw5hs9lo0aKFQ7u3tzdGoxGj0cikSZM4cOAAgMO4tLQ0+xij0ci6desc5ujUqRNGoxFXV1fat29PXFwcgwYNsvd3796duXPncvDgQaqqqtiyZQtr1qzh+PHjwJnXFk+fPk2zZs0c5m3WrBn5+fn2z3/9619ZsWIFH3/8MZMnT2b58uU88sgjDnVSU1N5++23OX36NMeOHbO/one2lpxLZz6JiIiIiIiISI3ZvXs3VVVVDBgwgLKysvOOiYiIIDMzE4CQkBAqKysd+letWkVYWBgVFRXs37+f0aNH07BhQ5KSkgCYP38+w4cPJzQ0FIPBQPPmzRk6dChvvvnmRa310Ucftf87PDwcX19f7rvvPg4fPkzz5s3p1q0bs2fP5rHHHmPgwIG4ubkxbdo0PvvsM5yctL+nOnoyIiIiIiIiInLZzGYzBoOBnJwch/bg4GDMZjMeHh7AmXAJcBjn5uaG2WzGbDafd25/f3/MZjNhYWH069ePcePGMWfOHEpLSwFo0qQJH3zwAUVFRXz33XdkZ2djNBoJDg4Gzuy+cnZ25sSJEw7znjhxAh8fn2rv6eyrfYcOHbK3Pfnkk5w8eRKLxYLVaqV37972+5TzU/gkIiIiIiIiIpetcePGdO3alQULFlBUVFTtuG7dutGoUSOSk5MvuZazszOVlZWUl5c7tLu7u+Pn50dlZSWrV6+2B0Ourq5ERkaybds2+9iqqiq2bdtGx44dq61zdjeWr6+vQ7vBYOCWW27Bw8ODt99+G39//4s6mP1mo9fuREREREREROSKWLhwIdHR0URFRZGQkEBERAROTk6kpaWRnZ1NZGQkRqOR119/nf79+9OrVy/GjBlDSEgIp06dYuPGjcCZcOm3CgoKyM/Pp7Kykn379jF//ny6dOlC/fr1Afjyyy85duwYbdq04dixYyQkJFBVVeVwqPmTTz7J4MGDiYqK4vbbb2fevHkUFRUxdOhQAA4fPsy//vUvevbsSePGjfn6668ZP348nTt3JiIiwj7P7Nmz6dGjB05OTqxZs4akpCTeeeedc9Ys/6XwSUREREREROQ6UVj0wzVdp3nz5mRkZJCYmMjkyZM5evQobm5utGzZkgkTJjBy5EgA+vTpQ2pqKsnJyQwaNIjCwkIaNGhAVFQUK1euJDY21mHes79S5+zsjK+vLz179mTGjBn2/tLSUqZOnUpubi5Go5GePXuyfPlyvLy87GP69+/Pf/7zH5555hny8/Np06YNGzdutB9C7urqytatW+2hlL+/P3379mXq1KkOa9mwYQMzZsygrKyM1q1bs3btWu6///5Lel43C4PNZrNd7UWIiIiIiIiIyBmlpaXk5eURFBSEu7s7ABaLhdAWoZSUltTaOjzcPcjOycZkMtVaTbl+nO97Wh3tfBIRERERERG5xplMJrJzsrFarbVW09vbW8GTXBEKn0RERERERESuAyaTSWGQXJf0a3ciIiIiIiIiIlJjFD6JiIiIiIiIiEiNUfgkIiIiIiIiIiI1RuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNcblai9ARERERERERP6YxWLBarXWWj1vb29MJlOt1ZMbl8InERERERERkWucxWIhLCyM4uLiWqvp6elJVlbWRQdQ+fn5zJw5k/Xr13P06FEaNGiA2WzmkUceYfDgwXh6egKQkZFBUlISO3bsoLCwEB8fH8LDwxkxYgSxsbEYDAaOHDlCUFCQfe46depgMpkYMmQIU6ZMwWAwAPDNN9/wzDPPkJ6eznfffcdLL73EuHHjHNaVkJDAs88+69DWokULsrOzz7kHm81Gz5492bhxI++//z4PPfSQvW/btm1MmzaNffv2UbduXQYPHsyMGTNwcVHEUh09GREREREREZFrnNVqpbi4mOXzxhFm/lON18s6dJSB4+ZhtVovKnzKzc0lOjoaLy8vEhMTCQ8Px83NjX379rFkyRL8/Px48MEHWbt2LXFxccTExJCSkoLZbKasrIzU1FSmTp3KXXfdhZeXl33erVu30qpVK8rKyti5cyfDhg3D19eX+Ph4AIqLiwkODqZfv36MHz++2vW1atWKrVu32j9XFxjNmzfPHmz91ldffUXPnj2ZMmUKb731FseOHeOxxx7j9OnTvPjiixf8nG42Cp9ERERERERErhNh5j/R7rbmV3sZ1Ro5ciQuLi7s2bOHunXr2tuDg4Pp3bs3NpuNoqIi4uPj6dWrF2vWrHG4PiwsjPj4eGw2m0N748aN8fHxASAgIIClS5eyd+9ee/jUvn172rdvD8DTTz9d7fpcXFzs81QnMzOTOXPmsGfPHnx9fR36Vq1aRUREBM888wwAZrOZWbNmERcXx/Tp06lXr97vzn2z0oHjIiIiIiIiInLZCgoK2Lx5M6NGjXIInn7LYDCwefNmCgoKmDhxYrVznW/X0Vl79uwhPT2dDh06XPQaDx48yC233EJwcDADBgzAYrE49BcXF/PXv/6VV1999bwhVVlZGe7u7g5tHh4elJaWkp6eftHruVkofBIRERERERGRy3bo0CFsNhstWrRwaPf29sZoNGI0Gpk0aRIHDhwAcBiXlpZmH2M0Glm3bp3DHJ06dcJoNOLq6kr79u2Ji4tj0KBBF7W+Dh06sGzZMjZu3MiiRYvIy8vjrrvu4tdff7WPGT9+PJ06daJ3797nnaN79+6kpqby9ttvc/r0aY4dO8Zzzz0HwPHjxy9qPTcTvXYnIiIiIiIiIjVm9+7dVFVVMWDAAMrKys47JiIigszMTABCQkKorKx06F+1ahVhYWFUVFSwf/9+Ro8eTcOGDUlKSrrgddx///0O9Tp06EBAQADvvPMO8fHxfPjhh2zfvp2MjIxq5+jWrRuzZ8/mscceY+DAgbi5uTFt2jQ+++wznJy0v6c6ejIiIiIiIiIictnMZjMGg4GcnByH9uDgYMxmMx4eHsCZcAlwGOfm5obZbMZsNp93bn9/f8xmM2FhYfTr149x48YxZ84cSktLL3m9Xl5e3HrrrRw6dAiA7du3c/jwYby8vHBxcbEfRt63b1/uuece+3VPPvkkJ0+exGKxYLVa7bukgoODL3ktNzqFTyIiIiIiIiJy2Ro3bkzXrl1ZsGABRUVF1Y7r1q0bjRo1Ijk5+ZJrOTs7U1lZSXl5+SXPcerUKQ4fPmw/VPzpp5/m66+/JjMz0/4H8NJLL7F06VKHaw0GA7fccgseHh68/fbb+Pv7065du0tey41Or92JiIiIiIiIyBWxcOFCoqOjiYqKIiEhgYiICJycnEhLSyM7O5vIyEiMRiOvv/46/fv3p1evXowZM4aQkBBOnTrFxo0bgTPh0m8VFBSQn59PZWUl+/btY/78+XTp0oX69esDUF5ezrfffmv/97Fjx8jMzMRoNNp3U02YMIEHHniAgIAAfvjhB6ZPn46zszMPP/wwAD4+Puc9ZNxkMhEUFGT/PHv2bHr06IGTkxNr1qwhKSmJd95555w1y38pfBIRERERERG5TmQdOnpN12nevDkZGRkkJiYyefJkjh49ipubGy1btmTChAmMHDkSgD59+pCamkpycjKDBg2isLCQBg0aEBUVxcqVK4mNjXWYNyYmBjgTSvn6+tKzZ09mzJhh7//hhx9o27at/fOLL77Iiy++yN13380nn3wCwNGjR3n44YcpKCigSZMm3HnnnXzxxRc0adLkou5xw4YNzJgxg7KyMlq3bs3atWsdzpOScxlsNpvtai9CRERERERERM4oLS0lLy+PoKAg3N3dAbBYLISFhVFcXFxr6/D09CQrKwuTyVRrNeX6cb7vaXW080lERERERETkGmcymcjKysJqtdZaTW9vbwVPckUofBIRERERERG5DphMJoVBcl3Sr92JiIiIiIiIiEiNUfgkIiIiIiIiIiI1RuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNUbhk4iIiIiIiIiI1BiXq70AEREREREREfljFosFq9Vaa/W8vb0xmUy1Vk9uXAqfRERERERERK5xFouFFqFhlJYU11pNdw9PcrKzLjqAys/PZ+bMmaxfv56jR4/SoEEDzGYzjzzyCIMHD8bT0xOAjIwMkpKS2LFjB4WFhfj4+BAeHs6IESOIjY3FYDBw5MgRgoKC7HPXqVMHk8nEkCFDmDJlCgaDAYCKigpmzpxJSkoKx44do0WLFiQnJ9OjRw/7tTt27GD27Nmkp6dz/Phx3n//fR566CGHtZ+d73/NmjWLp556yv55/fr1PPfcc3z99de4u7tz991388EHH1zUc7qZKHwSERERERERucZZrVZKS4pp1e0lPBuZa7xeceEhvtk8HqvVelHhU25uLtHR0Xh5eZGYmEh4eDhubm7s27ePJUuW4Ofnx4MPPsjatWuJi4sjJiaGlJQUzGYzZWVlpKamMnXqVO666y68vLzs827dupVWrVpRVlbGzp07GTZsGL6+vsTHxwMwdepUVqxYwWuvvUZoaCibNm2iT58+pKam0rZtWwCKiopo3bo1f/vb3/jzn/983vUfP37c4fOGDRuIj4+nb9++9rbVq1czfPhwEhMTuffee6msrGT//v0X/IxuRgabzWa72osQERERERERkTNKS0vJy8sjKCgId3d3APbu3UtkZCTt//Jv6je9rcbX8MuP+0lb+QDp6em0a9fugq/r0aMH33zzDdnZ2dStW/ecfpvNRnFxMQEBAXTu3Jk1a9acdx6bzeaw8ykjI4M2bdrY+2NiYmjRogWvvvoqALfccgtTpkxh1KhR9jF9+/bFw8ODFStWnDO/wWA4786n//XQQw/x66+/sm3bNgAqKysJDAzk2WeftQdfN6vzfU+rowPHRUREREREROSyFRQUsHnzZkaNGnXe4AnOhD6bN2+moKCAiRMnVjtXda+/AezZs4f09HQ6dOhgbysrKzsnAPHw8GDnzp0XeRf/deLECdavX+8QMu3du5djx47h5ORE27Zt8fX15f7779fOpz+g8ElERERERERELtuhQ4ew2Wy0aNHCod3b2xuj0YjRaGTSpEkcOHAAwGFcWlqafYzRaGTdunUOc3Tq1Amj0Yirqyvt27cnLi6OQYMG2fu7d+/O3LlzOXjwIFVVVWzZsoU1a9ac8xrdxUhJSaFevXoOr+jl5uYCkJCQwNSpU1m3bh0NGzbknnvuobCw8JJr3egUPomIiIiIiIhIjdm9ezeZmZn2M5vOJyIigszMTDIzMykqKqKystKhf9WqVWRmZvLVV1/xzjvvsHbtWp5++ml7//z58wkJCSE0NBRXV1eeeOIJhg4dipPTpcceb775JgMGDHDYUVVVVQXAlClT6Nu3L5GRkSxduhSDwcC77757ybVudAqfREREREREROSymc1mDAYDOTk5Du3BwcGYzWY8PDwACAkJAXAY5+bmhtlsxmw+/2Hq/v7+mM1mwsLC6NevH+PGjWPOnDmUlpYC0KRJEz744AOKior47rvvyM7Oxmg0EhwcfEn38tlnn5GTk8OwYcMc2n19fQFo2bKlw9qDg4OxWCyXVOtmoPBJRERERERERC5b48aN6dq1KwsWLKCoqKjacd26daNRo0YkJydfci1nZ2cqKyspLy93aHd3d8fPz4/KykpWr15N7969L2n+N954g8jISFq3bu3QHhkZiZubm0NwVlFRwZEjRwgICLikWjcDl6u9ABERERERERG5MSxcuJDo6GiioqJISEggIiICJycn0tLSyM7OJjIyEqPRyOuvv07//v3p1asXY8aMISQkhFOnTrFx40bgTLj0WwUFBeTn51NZWcm+ffuYP38+Xbp0oX79+gB8+eWXHDt2jDZt2nDs2DESEhKoqqpyONT81KlTHDp0yP45Ly+PzMxMGjVqhMlksrf/8ssvvPvuu8yZM+ec+6tfvz6PPfYY06dPx9/fn4CAAGbPng1Av379rtyDvMEofBIRERERERG5ThQXHvrjQVexTvPmzcnIyCAxMZHJkydz9OhR3NzcaNmyJRMmTGDkyJEA9OnTh9TUVJKTkxk0aBCFhYU0aNCAqKgoVq5cSWxsrMO8MTExwJlQytfXl549ezJjxgx7f2lpKVOnTiU3Nxej0UjPnj1Zvnw5Xl5e9jF79uyhS5cu9s9PPvkkAIMHD2bZsmX29pUrV2Kz2Xj44YfPe4+zZ8/GxcWFgQMHUlJSQocOHdi+fTsNGza8pGd2MzDYbDbb1V6EiIiIiIiIiJxRWlpKXl4eQUFB9sOuLRYLLULDKC0prrV1uHt4kpOd5bArSOSs831Pq6OdTyIiIiIiIiLXOJPJRE52FlartdZqent7K3iSK0Lhk4iIiIiIiMh1wGQyKQyS65J+7U5ERERERERERGqMwicREREREREREakxCp9ERERERERERKTGKHwSEREREREREZEao/BJRERERERERERqjMInERERERERERGpMQqfRERERERERESkxrhc7QWIiIiIiIiIyB+zWCxYrdZaq+ft7Y3JZKq1eoGBgYwbN45x48Zd1jz33HMPbdq0Yd68eVd0Xrl0Cp9ERERERERErnEWi4XQ0FBKSkpqraaHhwfZ2dkXHUB9//33TJ8+nY0bN2K1WvH19eWhhx7imWeeoXHjxjW02v9as2YNderUqfE6v/W/gZc4UvgkIiIiIiIico2zWq2UlJQw5i/D8GvqW+P1jv14nJdXvo7Var2o8Ck3N5eOHTty66238vbbbxMUFMQ333zDU089xYYNG/jiiy9o1KhRjay5vLwcV1fXGpu/Npy9hxuNznwSERERERERuU74NfUl2C+gxv8uNeAaNWoUrq6ubN68mbvvvhuTycT999/P1q1bOXbsGFOmTAHgxx9/5IEHHsDDw4OgoCD++c9/njOXxWKhd+/eGI1G6tevT1xcHCdOnLD3JyQk0KZNG15//XWCgoJwd3cHzuxC+t9X7H799Vcefvhh6tati5+fH6+++qpD/9y5cwkPD6du3br4+/szcuRITp065TBm165d3HPPPXh6etKwYUO6d+/OTz/9xJAhQ/j000+ZP38+BoMBg8HAkSNHANi/fz/3338/RqORZs2aMXDgQIdXJ++55x6eeOIJxo0bh7e3N927d7+k536tU/gkIiIiIiIiIpetsLCQTZs2MXLkSDw8PBz6fHx8GDBgAKtWrcJmszFkyBC+//57Pv74Y9577z0WLlzIjz/+aB9fVVVF7969KSws5NNPP2XLli3k5ubSv39/h3kPHTrE6tWrWbNmDZmZmdWubfbs2bRu3ZqMjAyefvppxo4dy5YtW+z9Tk5OvPzyy3zzzTekpKSwfft2Jk6caO/PzMzkvvvuo2XLlnz++efs3LmTBx54gNOnTzN//nw6duzI8OHDOX78OMePH8ff35+TJ09y77330rZtW/bs2cPGjRs5ceIEcXFxDmtLSUnB1dWVXbt2sXjx4kt59Nc8vXYnIiIiIiIiIpft4MGD2Gw2wsLCztsfFhbGTz/9RFpaGhs2bGD37t20b98egDfeeMPhum3btrFv3z7y8vLw9/cH4K233qJVq1akpaXZrysvL+ett96iSZMmv7u26Ohonn76aQBuvfVWdu3axUsvvUTXrl0BHHZKBQYG8sILL/DYY4+xcOFCAGbNmkVUVJT9M0CrVq3s/3Z1dcXT0xMfHx9724IFC2jbti2JiYn2tjfffBN/f38OHDjArbfeCkBISAizZs363fVf77TzSURERERERESuGJvN9rv9WVlZuLi4EBkZaW8LDQ3Fy8vLYYy/v789eAJo2bIlXl5eZGVl2dsCAgL+MHgC6Nix4zmffzvP1q1bue+++/Dz86NevXoMHDiQgoICiouLgf/ufLoYX331FR9//DFGo9H+FxoaCsDhw4ft4377HG5UCp9ERERERERE5LKZzWYMBoNDqPNbWVlZNGzY0CFkulx169a97DmOHDlCbGwsERERrF69mvT0dPuZUOXl5QDnvEZ4IU6dOsUDDzxAZmamw9/Bgwfp3LnzFb2Ha53CJxERERERERG5bI0bN6Zr164sXLiQkpISh778/Hz++c9/0r9/f0JDQ6msrCQ9Pd3en5OTw8mTJ+2fw8LC+P777/n+++/tbd9++y0nT56kZcuWF722L7744pzPZ1/zS09Pp6qqijlz5nDHHXdw66238sMPPziMj4iIYNu2bdXO7+rqyunTpx3a2rVrxzfffENgYCBms9nh72YInH5L4ZOIiIiIiIiIXBELFiygrKyM7t27s2PHDr7//ns2btxI165d8fPzY8aMGbRo0YIePXowYsQIvvzyS9LT0xk2bJjD7qKYmBjCw8MZMGAAe/fuZffu3QwaNIi7776bqKioi17Xrl27mDVrFgcOHODVV1/l3XffZezYscCZHVsVFRW88sor5Obmsnz58nMO/p48eTJpaWmMHDmSr7/+muzsbBYtWmT/5brAwEC+/PJLjhw5gtVqpaqqilGjRlFYWMjDDz9MWloahw8fZtOmTQwdOvScoOpGpwPHRURERERERK4Tx348fk3XCQkJYc+ePUyfPp24uDgKCwvx8fHhoYceYvr06TRq1AiApUuXMmzYMO6++26aNWvGCy+8wLRp0+zzGAwG1q5dy+jRo+ncuTNOTk706NGDV1555ZLW9fe//509e/bw7LPPUr9+febOnUv37t0BaN26NXPnziU5OZnJkyfTuXNnZs6cyaBBg+zX33rrrWzevJl//OMf3H777Xh4eNChQwcefvhhACZMmMDgwYNp2bIlJSUl5OXlERgYyK5du5g0aRLdunWjrKyMgIAAevTogZPTzbUXyGD7o5PARERERERERKTWlJaWkpeXR1BQEO7u7gBYLBZCQ0PPeZ2tJnl4eJCdnY3JZKq1mnL9ON/3tDra+SQiIiIiIiJyjTOZTGRnZ9tf86oN3t7eCp7kilD4JCIiIiIiInIdMJlMCoPkunRzvWQoIiIiIiIiIiK1SuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNUbhk4iIiIiIiIiI1BiFTyIiIiIiIiIiUmNcrvYCREREREREROSPWSwWrFZrrdXz9vbGZDLVWr1ryT333EObNm2YN28eAIGBgYwbN45x48Zd1XVdrxQ+iYiIiIiIiFzjLBYLoaGhlJSU1FpNDw8PsrOzLyqA+s9//sMzzzzD+vXrOXHiBA0bNqR169Y888wzREdH/+H1n3zyCV26dOGnn37Cy8vL3v6/YZBcXxQ+iYiIiIiIiFzjrFYrJSUlPDrwfnx9GtV4veP5hSxZvgGr1XpR4VPfvn0pLy8nJSWF4OBgTpw4wbZt2ygoKKjB1V648vJyXF1dr/YybjoKn0RERERERESuE74+jQj0b3a1l3FeJ0+e5LPPPuOTTz7h7rvvBiAgIIDbb78dgCNHjhAUFERGRgZt2rSxX9OwYUM+/vhjAgMD6dKlCwANGzYEYPDgwQB8+umnfPrpp8yfPx+AvLw8AgMD2b9/P0899RSfffYZdevWpVu3brz00kt4e3sDZ3ZM3Xbbbbi4uLBixQrCw8P5+OOP//C6PzJ37lyWLl1Kbm4ujRo14oEHHmDWrFkYjcYr8zBvMDpwXEREREREREQum9FoxGg08sEHH1BWVnbR1/v7+7N69WoAcnJyOH78OPPnz2f+/Pl07NiR4cOHc/z4cY4fP46/vz8nT57k3nvvpW3btuzZs4eNGzdy4sQJ4uLiHOZNSUnB1dWVXbt2sXjx4gu+7vc4OTnx8ssv880335CSksL27duZOHHiRd/zzUI7n0RERERERETksrm4uLBs2TKGDx/O4sWLadeuHXfffTd/+ctfiIiI+MPrnZ2dadTozCuFTZs2dTjzydXVFU9PT3x8fOxtCxYsoG3btiQmJtrb3nzzTfz9/Tlw4AC33norACEhIcyaNcs+5oUXXrig637Pbw8eDwwM5IUXXuCxxx5j4cKFf3jtzUg7n0RERERERETkiujbty8//PADH374IT169OCTTz6hXbt2LFu27IrX+uqrr/j444/tO66MRiOhoaEAHD582D4uMjLykq77PVu3buW+++7Dz8+PevXqMXDgQAoKCiguLr5Cd3dj0c4nEREREREREbli3N3d6dq1K127dmXatGkMGzaM6dOn89lnnwFgs9nsYysqKi65zqlTp3jggQdITk4+p8/X19f+77p1617SddU5cuQIsbGxPP7448yYMYNGjRqxc+dO4uPjKS8vx9PT8xLu5sam8ElEREREREREakzLli354IMPaNKkCQDHjx+nbdu2AGRmZjqMPftLdKdPnz6n/X/b2rVrx+rVqwkMDMTF5cLjjUu97qz09HSqqqqYM2cOTk5nXih75513Lnqem4leuxMRERERERGRy1ZQUMC9997LihUr+Prrr8nLy+Pdd99l1qxZ9O7dGw8PD+644w6SkpLIysri008/ZerUqQ5zBAQEYDAYWLduHf/5z384deoUcOZcpS+//JIjR45gtVqpqqpi1KhRFBYW8vDDD5OWlsbhw4fZtGkTQ4cOPSeo+q1Lve4ss9lMRUUFr7zyCrm5uSxfvpzFixdf3sO7wWnnk4iIiIiIiMh14nh+4TVbx2g00qFDB1566SUOHz5MRUUF/v7+DB8+nH/84x/AmYO94+PjiYyMpEWLFsyaNYtu3brZ5/Dz8+PZZ5/l6aefZujQoQwaNIhly5YxYcIEBg8eTMuWLSkpKSEvL4/AwEB27drFpEmT6NatG2VlZQQEBNCjRw/7jqTzueWWWy7purNat27N3LlzSU5OZvLkyXTu3JmZM2cyaNCgi35mNwuD7bcvW4qIiIiIiIjIVVVaWkpeXh5BQUG4u7sDYLFYCA0NpaSkpNbW4eHhQXZ2NiaTqdZqyvXjfN/T6mjnk4iIiIiIiMg1zmQykZ2djdVqrbWa3t7eCp7kilD4JCIiIiIiInIdMJlMCoPkuqQDx0VEREREREREpMYofBIRERERERERkRqj8ElERERERERERGqMwicREREREREREakxCp9ERERERERERKTGKHwSEREREREREZEao/BJRERERERERERqjMvVXoCIiIiIiIiI/DGLxYLVaq21et7e3phMplqrJzcuhU8iIiIiIiIi1ziLxUJYaBjFJcW1VtPTw5Os7KyLDqDy8/OZOXMm69ev5+jRozRo0ACz2cwjjzzC4MGD8fT0BCAjI4OkpCR27NhBYWEhPj4+hIeHM2LECGJjYzEYDBw5coSgoCD73HXq1MFkMjFkyBCmTJmCwWCw9508eZIpU6awZs0aCgsLCQgIYN68efTs2dM+5tixY0yaNIkNGzZQXFyM2Wxm6dKlREVFAbBmzRoWL15Meno6hYWFZGRk0KZNG/v1hYWFTJ8+nc2bN2OxWGjSpAkPPfQQzz//PA0aNLiUx3xTUPgkIiIiIiIico2zWq0UlxQzpsNM/lQ/uMbrHf0ll5e/nIzVar2o8Ck3N5fo6Gi8vLxITEwkPDwcNzc39u3bx5IlS/Dz8+PBBx9k7dq1xMXFERMTQ0pKCmazmbKyMlJTU5k6dSp33XUXXl5e9nm3bt1Kq1atKCsrY+fOnQwbNgxfX1/i4+MBKC8vp2vXrjRt2pT33nsPPz8/vvvuO4c5fvrpJ6Kjo+nSpQsbNmygSZMmHDx4kIYNG9rHFBUVceeddxIXF8fw4cPPub8ffviBH374gRdffJGWLVvy3Xff8dhjj/HDDz/w3nvvXfyDvkkofBIRERERERG5TvypfjDBjVpe7WVUa+TIkbi4uLBnzx7q1q1rbw8ODqZ3797YbDaKioqIj4+nV69erFmzxuH6sLAw4uPjsdlsDu2NGzfGx8cHgICAAJYuXcrevXvt4dObb75JYWEhqamp1KlTB4DAwECHOZKTk/H392fp0qX2tt/uqgIYOHAgAEeOHDnv/d12222sXr3a/rl58+bMmDGDRx55hMrKSlxcFLOcjw4cFxEREREREZHLVlBQwObNmxk1apRD8PRbBoOBzZs3U1BQwMSJE6ud67ev0/2vPXv2kJ6eTocOHextH374IR07dmTUqFE0a9aM2267jcTERE6fPu0wJioqin79+tG0aVPatm3La6+9dgl36ujnn3+mfv36Cp5+h8InEREREREREblshw4dwmaz0aJFC4d2b29vjEYjRqORSZMmceDAAQCHcWlpafYxRqORdevWOczRqVMnjEYjrq6utG/fnri4OAYNGmTvz83N5b333uP06dN89NFHTJs2jTlz5vDCCy84jFm0aBEhISFs2rSJxx9/nDFjxpCSknLJ92y1Wnn++ed59NFHL3mOm4FiORERERERERGpMbt376aqqooBAwZQVlZ23jERERFkZmYCEBISQmVlpUP/qlWrCAsLo6Kigv379zN69GgaNmxIUlISAFVVVTRt2pQlS5bg7OxMZGQkx44dY/bs2UyfPt0+JioqisTERADatm3L/v37Wbx4MYMHD77o+/rll1/o1asXLVu2JCEh4aKvv5kofBIRERERERGRy2Y2mzEYDOTk5Di0BwefOSDdw8MDOBMuAeTk5HDHHXcA4ObmhtlsrnZuf39/e39YWBiHDx9m2rRpJCQk4O7ujq+vL3Xq1MHZ2dl+TVhYGPn5+ZSXl+Pq6oqvry8tWzqelxUWFuZwhtOF+vXXX+nRowf16tXj/ffft58zJeen1+5ERERERERE5LI1btyYrl27smDBAoqKiqod161bNxo1akRycvIl13J2dqayspLy8nIAoqOjOXToEFVVVfYxBw4cwNfXF1dXV/uY/w3GDhw4QEBAwEXV/uWXX+jWrRuurq58+OGHuLu7X/J93CwUPomIiIiIiIjIFbFw4UIqKyuJiopi1apVZGVlkZOTw4oVK8jOzsbZ2Rmj0cjrr7/O+vXr6dWrF5s2bSI3N5evv/6aWbNmATjsYIIzh5nn5+dz9OhRNmzYwPz58+nSpQv169cH4PHHH6ewsJCxY8dy4MAB1q9fT2JiIqNGjbLPMX78eL744gsSExM5dOgQ//rXv1iyZInDmMLCQjIzM/n222+BM7uzMjMzyc/PB/4bPBUVFfHGG2/wyy+/kJ+fT35+vsPh5uJIr92JiIiIiIiIXCeO/pJ7Tddp3rw5GRkZJCYmMnnyZI4ePYqbmxstW7ZkwoQJjBw5EoA+ffqQmppKcnIygwYNorCwkAYNGhAVFcXKlSuJjY11mDcmJgY4E0r5+vrSs2dPZsyYYe/39/dn06ZNjB8/noiICPz8/Bg7diyTJk2yj2nfvj3vv/8+kydP5rnnniMoKIh58+YxYMAA+5gPP/yQoUOH2j//5S9/AWD69OkkJCSwd+9evvzyS4BzXhPMy8sjMDDwkp7bjc5gs9lsV3sRIiIiIiIiInJGaWkpeXl5BAUF2V/pslgshIWGUVxSXGvr8PTwJCs7C5PJVGs15fpxvu9pdbTzSUREREREROQaZzKZyMrOwmq11lpNb29vBU9yRSh8EhEREREREbkOmEwmhUFyXdKB4yIiIiIiIiIiUmMUPomIiIiIiIiISI1R+CQiIiIiIiIiIjVG4ZOIiIiIiIiIiNQYhU8iIiIiIiIiIlJjFD6JiIiIiIiIiEiNUfgkIiIiIiIiIiI1xuVqL0BERERERERE/pjFYsFqtdZaPW9vb0wmU63Vu1xDhgzh5MmTfPDBB1d87k8++YQuXbrw008/4eXldcXnv9EpfBIRERERERG5xlksFkJDW1BSUlprNT083MnOzrngAGrIkCGkpKQA4OLiwp/+9Cf69evHc889h7u7e00utcZ16tSJ48eP06BBg6u9lOuSwicRERERERGRa5zVaqWkpJRHB7Tjlmb1arzeDyd+Zck/92K1Wi9q91OPHj1YunQpFRUVpKenM3jwYAwGA8nJyTW42ppVUVGBq6srPj4+V3sp1y2d+SQiIiIiIiJynbilWT0C/+RV43+XGnC5ubnh4+ODv78/Dz30EDExMWzZsgWAsrIyxowZQ9OmTXF3d+fOO+8kLS3Nfu1PP/3EgAEDaNKkCR4eHoSEhLB06VJ7//fff09cXBxeXl40atSI3r17c+TIkXPW8Oyzz9KkSRPq16/PY489Rnl5ub0vMDCQefPmOYxv06YNCQkJ9s8Gg4FFixbx4IMPUrduXWbMmMEnn3yCwWDg5MmTABQUFPDwww/j5+eHp6cn4eHhvP3225f0zG4GCp9ERERERERE5Irbv38/qampuLq6AjBx4kRWr15NSkoKe/fuxWw20717dwoLCwGYNm0a3377LRs2bCArK4tFixbh7e0NnNl91L17d+rVq8dnn33Grl27MBqN9OjRwyFc2rZtG1lZWXzyySe8/fbbrFmzhmefffai156QkECfPn3Yt28ff/vb387pLy0tJTIykvXr17N//34effRRBg4cyO7duy/lUd3w9NqdiIiIiIiIiFwR69atw2g0UllZSVlZGU5OTixYsICioiIWLVrEsmXLuP/++wF47bXX2LJlC2+88QZPPfUUFouFtm3bEhUVBZzZpXTWqlWrqKqq4vXXX8dgMACwdOlSvLy8+OSTT+jWrRsArq6uvPnmm3h6etKqVSuee+45nnrqKZ5//nmcnC58/81f//pXhg4dav+cm5vr0O/n58eECRPsn0ePHs2mTZt45513uP322y/uod0EFD6JiIiIiIiIyBXRpUsXFi1aRFFRES+99BIuLi707duXr7/+moqKCqKjo+1j69Spw+23305WVhYAjz/+OH379mXv3r1069aNhx56iE6dOgHw1VdfcejQIerVc3wdsLS0lMOHD9s/t27dGk9PT/vnjh07curUKb7//nsCAgIu+D7OBmDVOX36NImJibzzzjscO3aM8vJyysrKHGrLfyl8EhEREREREZErom7dupjNZgDefPNNWrduzRtvvEH79u3/8Nr777+f7777jo8++ogtW7Zw3333MWrUKF588UVOnTpFZGQk//znP8+5rkmTJhe8PicnJ2w2m0NbRUXFee/j98yePZv58+czb948wsPDqVu3LuPGjXN4BVD+S2c+iYiIiIiIiMgV5+TkxD/+8Q+mTp1K8+bNcXV1ZdeuXfb+iooK0tLSaNmypb2tSZMmDB48mBUrVjBv3jyWLFkCQLt27Th48CBNmzbFbDY7/DVo0MB+/VdffUVJSYn98xdffIHRaMTf398+//Hjx+39v/zyC3l5eRd9b7t27aJ379488sgjtG7dmuDgYA4cOHDR89wsFD6JiIiIiIiISI3o168fzs7OLFq0iMcff5ynnnqKjRs38u233zJ8+HCKi4uJj48H4JlnnmHt2rUcOnSIb775hnXr1hEWFgbAgAED8Pb2pnfv3nz22Wfk5eXxySefMGbMGI4ePWqvV15eTnx8PN9++y0fffQR06dP54knnrCf93TvvfeyfPlyPvvsM/bt28fgwYNxdna+6PsKCQlhy5YtpKamkpWVxYgRIzhx4sQVeGI3Jr12JyIiIiIiInKd+OHEr9dVHRcXF5544glmzZpFXl4eVVVVDBw4kF9//ZWoqCg2bdpEw4YNgTOHhU+ePJkjR47g4eHBXXfdxcqVKwHw9PRkx44dTJo0iT//+c/8+uuv+Pn5cd9991G/fn17vfvuu4+QkBA6d+5MWVkZDz/8MAkJCfb+yZMnk5eXR2xsLA0aNOD555+/pJ1PU6dOJTc3l+7du+Pp6cmjjz7KQw89xM8//3x5D+wGZbD978uOIiIiIiIiInLVlJaWkpeXR1BQEO7u7gBYLBZCQ1tQUlJaa+vw8HAnOzsHk8lUazXl+nG+72l1tPNJRERERERE5BpnMpnIzs7BarXWWk1vb28FT3JFKHwSERERERERuQ6YTCaFQXJd0oHjIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNUbhk4iIiIiIiIiI1BiFTyIiIiIiIiIiUmMUPomIiIiIiIiISI1R+CQiIiIiIiIiIjXG5WovQERERERERET+mMViwWq11lo9b29vTCZTrdWTG5fCJxEREREREZFrnMViITQ0lJKSklqr6eHhQXZ29kUHUPn5+cycOZP169dz9OhRGjRogNls5pFHHmHw4MF4enoCkJGRQVJSEjt27KCwsBAfHx/Cw8MZMWIEsbGxGAwGjhw5QlBQkH3uOnXqYDKZGDJkCFOmTMFgMABQUVHBzJkzSUlJ4dixY7Ro0YLk5GR69OjhsLZXX32V2bNnk5+fT+vWrXnllVe4/fbbz7kHm81Gz5492bhxI++//z4PPfSQvS8tLY2nn36a9PR0DAYDt99+O7NmzaJ169YX9ZxuJgqfRERERERERK5xVquVkpISujzRDy+/JjVe7+Sx//DxgnexWq0XFT7l5uYSHR2Nl5cXiYmJhIeH4+bmxr59+1iyZAl+fn48+OCDrF27lri4OGJiYkhJScFsNlNWVkZqaipTp07lrrvuwsvLyz7v1q1badWqFWVlZezcuZNhw4bh6+tLfHw8AFOnTmXFihW89tprhIaGsmnTJvr06UNqaipt27YFYNWqVTz55JMsXryYDh06MG/ePLp3705OTg5NmzZ1uI958+bZg63fOnXqFD169ODBBx9k4cKFVFZWMn36dLp37873339PnTp1LuFp3/gMNpvNdrUXISIiIiIiIiJnlJaWkpeXR1BQEO7u7gDs3buXyMhI+swciXewX42vwZp7jPcnLyQ9PZ127dpd8HU9evTgm2++ITs7m7p1657Tb7PZKC4uJiAggM6dO7NmzZrzzmOz2Rx2PmVkZNCmTRt7f0xMDC1atODVV18F4JZbbmHKlCmMGjXKPqZv3754eHiwYsUKADp06ED79u1ZsGABAFVVVfj7+zN69Giefvpp+3WZmZnExsayZ88efH19HXY+7dmzh/bt22OxWPD39wdg3759REREcPDgQcxm8wU/q+vd+b6n1dGB4yIiIiIiIiJy2QoKCti8eTOjRo06b/AEYDAY2Lx5MwUFBUycOLHauc636+isPXv2kJ6eTocOHextZWVl5wQgHh4e7Ny5E4Dy8nLS09OJiYmx9zs5ORETE8Pnn39ubysuLuavf/0rr776Kj4+PufUbtGiBY0bN+aNN96gvLyckpIS3njjDcLCwggMDKx2zTc7hU8iIiIiIiIictkOHTqEzWajRYsWDu3e3t4YjUaMRiOTJk3iwIEDAA7j0tLS7GOMRiPr1q1zmKNTp04YjUZcXV1p3749cXFxDBo0yN7fvXt35s6dy8GDB6mqqmLLli2sWbOG48ePA2deWzx9+jTNmjVzmLdZs2bk5+fbP48fP55OnTrRu3fv895jvXr1+OSTT1ixYgUeHh4YjUY2btzIhg0bcHHRyUbVUfgkIiIiIiIiIjVm9+7dZGZm2s9sOp+IiAgyMzPJzMykqKiIyspKh/5Vq1aRmZnJV199xTvvvMPatWsdXpWbP38+ISEhhIaG4urqyhNPPMHQoUNxcrrw2OPDDz9k+/btzJs3r9oxJSUlxMfHEx0dzRdffMGuXbu47bbb6NWrV60eBn+9UfgkIiIiIiIiIpfNbDZjMBjIyclxaA8ODsZsNuPh4QFASEgIgMM4Nzc3zGZztWcm+fv7YzabCQsLo1+/fowbN445c+ZQWloKQJMmTfjggw8oKiriu+++Izs7G6PRSHBwMHBm95WzszMnTpxwmPfEiRP21+u2b9/O4cOH8fLywsXFxb6TqW/fvtxzzz0A/Otf/+LIkSMsXbqU9u3bc8cdd/Cvf/2LvLw81q5dezmP74am8ElERERERERELlvjxo3p2rUrCxYsoKioqNpx3bp1o1GjRiQnJ19yLWdnZyorKykvL3dod3d3x8/Pj8rKSlavXm1/fc7V1ZXIyEi2bdtmH1tVVcW2bdvo2LEjAE8//TRff/21fQdWZmYmAC+99BJLly4FzpwJ5eTk5HAm1dnPVVVVl3w/Nzq9kCgiIiIiIiIiV8TChQuJjo4mKiqKhIQEIiIicHJyIi0tjezsbCIjIzEajbz++uv079+fXr16MWbMGEJCQjh16hQbN24EzoRLv1VQUEB+fj6VlZXs27eP+fPn06VLF+rXrw/Al19+ybFjx2jTpg3Hjh0jISGBqqoqh0PNn3zySQYPHkxUVBS333478+bNo6ioiKFDhwLg4+Nz3kPGTSYTQUFBAHTt2pWnnnqKUaNGMXr0aKqqqkhKSsLFxYUuXbrUyDO9ESh8EhEREREREblOnDz2n2u6TvPmzcnIyCAxMZHJkydz9OhR3NzcaNmyJRMmTGDkyJEA9OnTh9TUVJKTkxk0aBCFhYU0aNCAqKgoVq5cSWxsrMO8Z3+lztnZGV9fX3r27MmMGTPs/aWlpUydOpXc3FyMRiM9e/Zk+fLleHl52cf079+f//znPzzzzDPk5+fTpk0bNm7ceM4h5L8nNDSUf//73zz77LN07NgRJycn2rZty8aNG/H19b2kZ3YzMNhsNtvVXoSIiIiIiIiInFFaWkpeXh5BQUG4u7sDYLFYCA0NrdVDrT08PMjOzsZkMtVaTbl+nO97Wh3tfBIRERERERG5xplMJrKzs7FarbVW09vbW8GTXBEKn0RERERERESuAyaTSWGQXJf0a3ciIiIiIiIiIlJjFD6JiIiIiIiIiEiNUfgkIiIiIiIiIiI1RuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNcblai9ARERERERERP6YxWLBarXWWj1vb29MJlOt1ZMbl8InERERERERkWucxWIhLCyU4uKSWqvp6elBVlb2RQdQ+fn5zJw5k/Xr13P06FEaNGiA2WzmkUceYfDgwXh6egKQkZFBUlISO3bsoLCwEB8fH8LDwxkxYgSxsbEYDAaOHDlCUFCQfe46depgMpkYMmQIU6ZMwWAwALBmzRoSExM5dOgQFRUVhISE8Pe//52BAwfar12zZg2LFy8mPT2dwsJCMjIyaNOmjb3/f2v91jvvvEO/fv0A7DV/6+233+Yvf/nLRT2nm4nCJxEREREREZFrnNVqpbi4hAWTBmM2+dR4vUOWfJ5ITsFqtV5U+JSbm0t0dDReXl4kJiYSHh6Om5sb+/btY8mSJfj5+fHggw+ydu1a4uLiiImJISUlBbPZTFlZGampqUydOpW77roLLy8v+7xbt26lVatWlJWVsXPnToYNG4avry/x8fEANGrUiClTphAaGoqrqyvr1q1j6NChNG3alO7duwNQVFTEnXfeSVxcHMOHDz9n7f7+/hw/ftyhbcmSJcyePZv777/foX3p0qX06NHD/vm3a5VzKXwSERERERERuU6YTT5EhPhf7WVUa+TIkbi4uLBnzx7q1q1rbw8ODqZ3797YbDaKioqIj4+nV69erFmzxuH6sLAw4uPjsdlsDu2NGzfGx+dM6BYQEMDSpUvZu3evPXy65557HMaPHTuWlJQUdu7caQ+fzu6COnLkyHnX7uzsbK9x1vvvv09cXBxGo9Gh3cvL65yxUj0dOC4iIiIiIiIil62goIDNmzczatQoh+DptwwGA5s3b6agoICJEydWO9f5Xm07a8+ePaSnp9OhQ4fz9ttsNrZt20ZOTg6dO3e+uJv4jfT0dDIzM+0B12+NGjUKb29vbr/9dt58881zwjJxpJ1PIiIiIiIiInLZDh06hM1mo0WLFg7t3t7elJaWAmdCm0aNGgE4jEtLS6NLly72zytXriQ2Ntb+uVOnTjg5OVFeXk5FRQWPPvoogwYNcqjz888/4+fnR1lZGc7OzixcuJCuXbte8v288cYbhIWF0alTJ4f25557jnvvvRdPT082b97MyJEjOXXqFGPGjLnkWjc6hU8iIiIiIiIiUmN2795NVVUVAwYMoKys7LxjIiIiyMzMBCAkJITKykqH/lWrVhEWFkZFRQX79+9n9OjRNGzYkKSkJPuYevXqkZmZyalTp9i2bRtPPvkkwcHB57ySdyFKSkr417/+xbRp087p+21b27ZtKSoqYvbs2QqffofCJxERERERERG5bGazGYPBQE5OjkN7cHAwAB4eHsCZcAkgJyeHO+64AwA3NzfMZnO1c/v7+9v7w8LCOHz4MNOmTSMhIQF3d3cAnJyc7GPatGlDVlYWM2fOvKTw6b333qO4uPic3VXn06FDB55//nnKyspwc3O76Fo3A535JCIiIiIiIiKXrXHjxnTt2pUFCxZQVFRU7bhu3brRqFEjkpOTL7mWs7MzlZWVlJeXVzumqqqq2p1Wf+SNN97gwQcfpEmTJn84NjMzk4YNGyp4+h3a+SQiIiIiIiIiV8TChQuJjo4mKiqKhIQEIiIicHJyIi0tjezsbCIjIzEajbz++uv079+fXr16MWbMGEJCQjh16hQbN24EzoRLv1VQUEB+fj6VlZXs27eP+fPn06VLF+rXrw/AzJkziYqKonnz5pSVlfHRRx+xfPlyFi1aZJ+jsLAQi8XCDz/8AGDfoeXj4+Pwy3WHDh1ix44dfPTRR+fc37///W9OnDjBHXfcgbu7O1u2bCExMZEJEyZc2Qd5g1H4JCIiIiIiInKdOGTJv6brNG/enIyMDBITE5k8eTJHjx7Fzc2Nli1bMmHCBEaOHAlAnz59SE1NJTk5mUGDBlFYWEiDBg2Iioo657BxgJiYGOBMKOXr60vPnj2ZMWOGvb+oqIiRI0dy9OhRPDw8CA0NZcWKFfTv398+5sMPP2To0KH2z3/5y18AmD59OgkJCfb2N998kz/96U9069btnPurU6cOr776KuPHj8dms2E2m5k7dy7Dhw+/pOd1szDY9HuAIiIiIiIiIteM0tJS8vLyCAoKsp9nZLFYCAsLpbi4pNbW4enpQVZWNiaTqdZqyvXjfN/T6mjnk4iIiIiIiMg1zmQykZWVjdVqrbWa3t7eCp7kilD4JCIiIiIiInIdMJlMCoPkuqRfuxMRERERERERkRqj8ElERERERERERGqMwicREREREREREakxCp9ERERERERERKTGKHwSEREREREREZEao/BJRERERERERERqjMInERERERERERGpMS5XewEiIiIiIiIi8scsFgtWq7XW6nl7e2MymWqtnty4FD6JiIiIiIiIXOMsFgthoaEUl5TUWk1PDw+ysrMvOoDKz89n5syZrF+/nqNHj9KgQQPMZjOPPPIIgwcPxtPTE4CMjAySkpLYsWMHhYWF+Pj4EB4ezogRI4iNjcVgMHDkyBGCgoLsc9epUweTycSQIUOYMmUKBoMBgDVr1pCYmMihQ4eoqKggJCSEv//97wwcONB+7dmx/2vWrFk89dRTDm1lZWV06NCBr776ioyMDNq0aWPvs9lszJkzhyVLlvDdd9/h7e3NyJEjmTJlykU9p5uJwicRERERERGRa5zVaqW4pIQFXdsT0rBejdc7+NOvPLElDavVelHhU25uLtHR0Xh5eZGYmEh4eDhubm7s27ePJUuW4Ofnx4MPPsjatWuJi4sjJiaGlJQUzGYzZWVlpKamMnXqVO666y68vLzs827dupVWrVpRVlbGzp07GTZsGL6+vsTHxwPQqFEjpkyZQmhoKK6urqxbt46hQ4fStGlTunfvDsDx48cd1rphwwbi4+Pp27fvOfcxceJEbrnlFr766qtz+saOHcvmzZt58cUXCQ8Pp7CwkMLCwgt+RjcjhU8iIiIiIiIi14mQhvWIaNrwai+jWiNHjsTFxYU9e/ZQt25de3twcDC9e/fGZrNRVFREfHw8vXr1Ys2aNQ7Xh4WFER8fj81mc2hv3LgxPj4+AAQEBLB06VL27t1rD5/uueceh/Fjx44lJSWFnTt32sOns9eftXbtWrp06UJwcLBD+4YNG9i8eTOrV69mw4YNDn1ZWVksWrSI/fv306JFCwCHnVlyfjpwXEREREREREQuW0FBAZs3b2bUqFEOwdNvGQwGNm/eTEFBARMnTqx2rupekQPYs2cP6enpdOjQ4bz9NpuNbdu2kZOTQ+fOnc875sSJE6xfv94eXv22ffjw4Sxfvtz+euBv/fvf/yY4OJh169YRFBREYGAgw4YN086nP6DwSUREREREREQu26FDh7DZbPYdQWd5e3tjNBoxGo1MmjSJAwcOADiMS0tLs48xGo2sW7fOYY5OnTphNBpxdXWlffv2xMXFMWjQIIcxP//8s31Mr169eOWVV+jatet515qSkkK9evX485//bG+z2WwMGTKExx57jKioqPNel5uby3fffce7777LW2+9xbJly0hPT+f//b//d+EP6iak1+5EREREREREpMbs3r2bqqoqBgwYQFlZ2XnHREREkJmZCUBISAiVlZUO/atWrSIsLIyKigr279/P6NGjadiwIUlJSfYx9erVIzMzk1OnTrFt2zaefPJJgoODz3klD+DNN99kwIABuLu729teeeUVfv31VyZPnlztvVRVVVFWVsZbb73FrbfeCsAbb7xBZGQkOTk55wRvcobCJxERERERERG5bGazGYPBQE5OjkP72TOVPDw8gDPhEkBOTg533HEHAG5ubpjN5mrn9vf3t/eHhYVx+PBhpk2bRkJCgj1AcnJyso9p06YNWVlZzJw585zw6bPPPiMnJ4dVq1Y5tG/fvp3PP/8cNzc3h/aoqCgGDBhASkoKvr6+uLi42IOns+uBM79IqPDp/PTanYiIiIiIiIhctsaNG9O1a1cWLFhAUVFRteO6detGo0aNSE5OvuRazs7OVFZWUl5eXu2Ys7uU/tfZnUqtW7d2aH/55Zf56quvyMzMJDMzk48++gg4s+tqxowZAERHR1NZWcnhw4ft1519jTAgIOCS7+dGp51PIiIiIiIiIteJgz/9ek3XWbhwIdHR0URFRZGQkEBERAROTk6kpaWRnZ1NZGQkRqOR119/nf79+9OrVy/GjBlDSEgIp06dYuPGjcCZcOm3CgoKyM/Pp7Kykn379jF//ny6dOlC/fr1AZg5cyZRUVE0b96csrIyPvroI5YvX86iRYsc5vnll1949913mTNnzjlrN5lMDp+NRiMAzZs3509/+hMAMTExtGvXjr/97W/MmzePqqoqRo0aRdeuXR12Q4kjhU8iIiIiIiIi1zhvb288PTx4YktardX09PDA29v7oq5p3rw5GRkZJCYmMnnyZI4ePYqbmxstW7ZkwoQJjBw5EoA+ffqQmppKcnIygwYNorCwkAYNGhAVFcXKlSuJjY11mDcmJgY4E0r5+vrSs2dP+24kgKKiIkaOHMnRo0fx8PAgNDSUFStW0L9/f4d5Vq5cic1m4+GHH76UR4KTkxP//ve/GT16NJ07d6Zu3brcf//95w2z5L8MNpvNdrUXISIiIiIiIiJnlJaWkpeXR1BQkMOB2BaLBavVWmvr8Pb2Pmc3kMhZ1X1Pz0c7n0RERERERESuAyaTSWGQXJd04LiIiIiIiIiIiNQYhU8iIiIiIiIiIlJjFD6JiIiIiIiIiEiNUfgkIiIiIiIiIiI1RuGTiIiIiIiIiIjUGIVPIiIiIiIiIiJSYxQ+iYiIiIiIiIhIjVH4JCIiIiIiIiIiNcblai9ARERERERERP6YxWLBarXWWj1vb29MJtNFX5efn8/MmTNZv349R48epUGDBpjNZh555BEGDx6Mp6cnABkZGSQlJbFjxw4KCwvx8fEhPDycESNGEBsbi8Fg4MiRIwQFBdnnrlOnDiaTiSFDhjBlyhQMBgMA33zzDc888wzp6el89913vPTSS4wbN67aNSYlJTF58mTGjh3LvHnz7O2HDx9mwoQJ7Ny5k7KyMnr06MErr7xCs2bN7GMCAwP57rvvHOabOXMmTz/99EU/q5uFwicRERERERGRa5zFYiGsRSjFpSW1VtPT3YOsnOyLCqByc3OJjo7Gy8uLxMREwsPDcXNzY9++fSxZsgQ/Pz8efPBB1q5dS1xcHDExMaSkpGA2mykrKyM1NZWpU6dy11134eXlZZ9369attGrVirKyMnbu3MmwYcPw9fUlPj4egOLiYoKDg+nXrx/jx4//3TWmpaXxf//3f0RERDi0FxUV0a1bN1q3bs327dsBmDZtGg888ABffPEFTk7/fXnsueeeY/jw4fbP9erVu+BndDNS+CQiIiIiIiJyjbNarRSXljA17AEC6jau8XrfFRXwQta/sVqtFxU+jRw5EhcXF/bs2UPdunXt7cHBwfTu3RubzUZRURHx8fH06tWLNWvWOFwfFhZGfHw8NpvNob1x48b4+PgAEBAQwNKlS9m7d689fGrfvj3t27cH+N0dSKdOnWLAgAG89tprvPDCCw59u3bt4siRI2RkZFC/fn0AUlJSaNiwIdu3bycmJsY+tl69evb1yB9T+CQiIiIiIiJynQio25hb612boUdBQQGbN28mMTHRIXj6LYPBwObNmykoKGDixInVznX2dbrz2bNnD+np6QwaNOii1zhq1Ch69epFTEzMOeFTWVkZBoMBNzc3e5u7uztOTk7s3LnTIXxKSkri+eefx2Qy8de//pXx48fj4qKIpTp6MiIiIiIiIiJy2Q4dOoTNZqNFixYO7d7e3pSWlgJnwp9GjRoBOIxLS0ujS5cu9s8rV64kNjbW/rlTp044OTlRXl5ORUUFjz766EWHTytXrmTv3r2kpaWdt/+OO+6gbt26TJo0icTERGw2G08//TSnT5/m+PHj9nFjxoyhXbt2NGrUiNTUVCZPnszx48eZO3fuRa3nZqLwSURERERERERqzO7du6mqqmLAgAGUlZWdd0xERASZmZkAhISEUFlZ6dC/atUqwsLCqKioYP/+/YwePZqGDRuSlJR0QWv4/vvvGTt2LFu2bMHd3f28Y5o0acK7777L448/zssvv4yTkxMPP/ww7dq1czjv6cknn3RYt6urKyNGjGDmzJkOu6bkvxQ+iYiIiIiIiMhlM5vNGAwGcnJyHNqDg4MB8PDwAM6ESwA5OTnccccdALi5uWE2m6ud29/f394fFhbG4cOHmTZtGgkJCdWGSb+Vnp7Ojz/+SLt27extp0+fZseOHSxYsICysjKcnZ3p1q0bhw8fxmq14uLigpeXFz4+PvZ7OJ8OHTpQWVnJkSNHztn1JWc4/fEQEREREREREZHf17hxY7p27cqCBQsoKiqqdly3bt1o1KgRycnJl1zL2dmZyspKysvLL2j8fffdx759+8jMzLT/RUVFMWDAADIzM3F2dnYY7+3tjZeXF9u3b+fHH3/kwQcfrHbuzMxMnJycaNq06SXfz41OO59ERERERERE5IpYuHAh0dHRREVFkZCQQEREBE5OTqSlpZGdnU1kZCRGo5HXX3+d/v3706tXL8aMGUNISAinTp1i48aNAOeEQQUFBeTn51NZWcm+ffuYP38+Xbp0sf8qXXl5Od9++63938eOHSMzMxOj0YjZbKZevXrcdtttDnPWrVuXxo0bO7QvXbqUsLAwmjRpwueff87YsWMZP368fUfT559/zpdffkmXLl2oV68en3/+OePHj+eRRx6hYcOGNfZcr3cKn0RERERERESuE98VFVzTdZo3b05GRgaJiYlMnjyZo0eP4ubmRsuWLZkwYQIjR44EoE+fPqSmppKcnMygQYMoLCykQYMGREVFnXPYOGD/pTlnZ2d8fX3p2bMnM2bMsPf/8MMPtG3b1v75xRdf5MUXX+Tuu+/mk08+ueD15+TkMHnyZAoLCwkMDGTKlCmMHz/e3u/m5sbKlStJSEigrKyMoKAgxo8f73AOlJzLYLPZbFd7ESIiIiIiIiJyRmlpKXl5eQQFBdnPM7JYLIS1CKW4tKTW1uHp7kFWTjYmk6nWasr143zf0+po55OIiIiIiIjINc5kMpGVk43Vaq21mt7e3gqe5IpQ+CQiIiIiIiJyHTCZTAqD5LqkX7sTEREREREREZEao/BJRERERERERERqjMInERERERERERGpMQqfRERERERERK5B+nF6uZZdzPdT4ZOIiIiIiIjINcTZ2RmA8vLyq7wSkeoVFxcDUKdOnT8cq1+7ExEREREREbmGuLi44OnpyX/+8x/q1KmDk5P2jci1w2azUVxczI8//oiXl5c9LP09Bpv28YmIiIiIiIhcU8rLy8nLy6OqqupqL0XkvLy8vPDx8cFgMPzhWIVPIiIiIiIiItegqqoqvXon16Q6depc0I6nsxQ+iYiIiIiIiIhIjdGLoyIiIiIiIiIiUmMUPomIiIiIiIiISI1R+CQiIiIiIiIiIjVG4ZOIiIiIiIiIiNQYhU8iIiIiIiIiIlJjFD6JiIiIiIiIiEiNUfgkIiIiIiIiIiI15v8Df1rH60Y0oGMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "ax = sns.histplot(data2[data2['Sex']=='Female'], x='Donor', hue='genus', weights='value',\n",
        "             multiple='stack', palette=palette_dict, shrink=0.8)\n",
        "ax.set_ylabel('abundance')\n",
        "\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
        "ax.set_title('Female')\n",
        "\n",
        "legend = ax.get_legend()\n",
        "legend.set_bbox_to_anchor((1, 1))\n"
      ],
      "metadata": {
        "id": "d1SZoCczrT81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "688b126e-9dc4-46e1-f846-af7c8737fc4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data2=data2[data2.value>0]\n",
        "data2=data2.reset_index(drop = True)\n",
        "data2=data2.drop_duplicates()\n",
        "\n",
        "data2[\"Label\"] = (\n",
        "    data2.groupby(\"Donor\")\n",
        "    .apply(lambda x: x.groupby(\"time\", sort=False).ngroup() + 1)\n",
        "    .values\n",
        ")\n",
        "\n",
        "cc=pd.unique(data2[data2.Label<5].Donor)\n",
        "data2=data2.loc[ data2.Donor.isin(cc), : ]\n",
        "data2=data2[data2.Label<5]\n",
        "\n",
        "data2[\"rank\"] = data2.groupby(\"Donor\")[\"value\"].rank(method=\"dense\", ascending=False)\n",
        "data2=data2[data2['rank']<10.0]\n",
        "\n",
        "\n",
        "grouped = data2.groupby('Donor')\n",
        "def get_next_clus(x): return x['species'].shift(-1)\n",
        "data2[\"next_species\"] = grouped.apply(\n",
        "     lambda x: get_next_clus(x)).reset_index(0, drop=True)\n",
        "grouped = data2.groupby('Donor')\n",
        "\n",
        "  # Working on the «nodes_dict\n",
        "import random\n",
        "data2_spec = list(data2.species.unique())\n",
        "chars = '0123456789ABCDEF'\n",
        "palette=[''+''.join(random.sample(chars,6)) for i in range(len(data2_spec))]\n",
        "output = dict()\n",
        "output.update({'nodes_dict': dict()})\n",
        "i = 0\n",
        "for Label in data2.Label.unique():\n",
        "    output['nodes_dict'].update(\n",
        "        {Label: dict()}\n",
        "    )\n",
        "\n",
        "    data2_spec_at_this_Label = data2[data2['Label'] ==\n",
        "                                   Label]['species'].unique()\n",
        "\n",
        "    Label_palette = []\n",
        "    for spec in data2_spec_at_this_Label:\n",
        "        Label_palette.append(palette[list(data2_spec).index(spec)])\n",
        "\n",
        "    output['nodes_dict'][Label].update(\n",
        "        {\n",
        "            'sources': list(data2_spec_at_this_Label),\n",
        "            'color': Label_palette,\n",
        "            'sources_index': list(range(i, i+len(data2_spec_at_this_Label)))\n",
        "        }\n",
        "    )\n",
        "    i += len(output['nodes_dict'][Label]['sources_index'])\n",
        "\n",
        "\n",
        "  # Working on the links_dict\n",
        "import random\n",
        "output.update({'links_dict': dict()})\n",
        "grouped = data2.groupby(['Donor', 'Label'])\n",
        "\n",
        "def update_source_target(user):\n",
        "    try:\n",
        "\n",
        "        source_index = output['nodes_dict'][user.name[1]]['sources_index'][output['nodes_dict']\n",
        "                                                                           [user.name[1]]['sources'].index(user['species'].values[0])]\n",
        "\n",
        "        target_index = output['nodes_dict'][user.name[1] + 1]['sources_index'][output['nodes_dict']\n",
        "                                                                               [user.name[1] + 1]['sources'].index(user['next_species'].values[0])]\n",
        "\n",
        "         # If this source is already in links_dict...\n",
        "        if source_index in output['links_dict']:\n",
        "            # ...and if this target is already associated to this source...\n",
        "            if target_index in output['links_dict'][source_index]:\n",
        "                # ...then we increment the count of users with this source/target pair by 1\n",
        "                output['links_dict'][source_index][target_index]['value'] += 1\n",
        "            # ...but if the target is not already associated to this source...\n",
        "            else:\n",
        "                # ...we create a new key for this target, for this source, and initiate it with 1 user and the time from source to target\n",
        "                output['links_dict'][source_index].update({target_index:\n",
        "                                                           dict(\n",
        "                                                               {'value': 1}\n",
        "                                                            )\n",
        "                                                           })\n",
        "        # ...but if this source isn't already available in the links_dict, we create its key and the key of this source's target, and we initiate it with 1 user and the time from source to target\n",
        "        else:\n",
        "            output['links_dict'].update({source_index: dict({target_index: dict(\n",
        "                {'value': 1})})})\n",
        "    except Exception as e:\n",
        "        pass\n",
        "\n",
        "grouped.apply(lambda user: update_source_target(user))"
      ],
      "metadata": {
        "id": "IxOeofOKBGho",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "ed04eb84-3121-40a1-e844-879f766952d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: []\n",
              "Index: []"
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0244524c-0817-4692-8e4e-52aeef361d7c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-285d965c-b562-420c-969c-f22e10cf76b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-285d965c-b562-420c-969c-f22e10cf76b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "targets = []\n",
        "sources = []\n",
        "values = []\n",
        "\n",
        "for source_key, source_value in output['links_dict'].items():\n",
        "    for target_key, target_value in output['links_dict'][source_key].items():\n",
        "        sources.append(source_key)\n",
        "        targets.append(target_key)\n",
        "        values.append(target_value['value'])\n",
        "\n",
        "labels = []\n",
        "colors = []\n",
        "\n",
        "for key, value in output['nodes_dict'].items():\n",
        "    labels = labels + list(output['nodes_dict'][key]['sources'])\n",
        "    colors = colors + list(output['nodes_dict'][key]['color'])\n",
        "for idx, color in enumerate(colors):\n",
        "    colors[idx] = \"#\" + str(color) + \"\""
      ],
      "metadata": {
        "id": "8kmmLqXSfum0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "import seaborn as se\n",
        "import matplotlib.pyplot as plt\n",
        "fig = go.Figure(data=[go.Sankey(\n",
        "    node=dict(\n",
        "        thickness=20,  # default is 20\n",
        "        line=dict(color=\"black\", width=0.5),\n",
        "        label=labels,\n",
        "        color=colors\n",
        "    ),\n",
        "    link=dict(\n",
        "        source=sources,\n",
        "        target=targets,\n",
        "        value=values,\n",
        "        hovertemplate='%{value} unique species episodes went from %{source.label} to %{target.label}.',\n",
        "    ))])\n",
        "\n",
        "fig.update_layout(autosize=True, title=dict(text=\"Movement of Top 10 Species During Time Series\", font_size=16), font=dict(size=12, family=\"Arial\"), plot_bgcolor='white')\n",
        "fig.update_layout(\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=800,)"
      ],
      "metadata": {
        "id": "vJhnSN6JfwSc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "outputId": "d7481710-f0ac-40d4-ced1-dad2b229d53a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('7c0fab9f-75bd-42dd-b7e6-a0c4ff3b9dd9');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
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              "        if (!display || display === 'none') {{\n",
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              "            observer.disconnect();\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chart_studio\n",
        "import chart_studio\n",
        "import chart_studio.plotly as py\n",
        "chart_studio.tools.set_credentials_file(username='dcolinmorgan', api_key='***')\n",
        "py.iplot(fig, sharing='public', filename='Meta-time-sankey')"
      ],
      "metadata": {
        "id": "qA9WsOMTfwU-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xlxboy9REcbx"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}