{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYGkqV6cnTcC"
      },
      "outputs": [],
      "source": [
        "# Copyright 2025 Google LLC\n",
        "\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "\n",
        "#      https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctPS-bphuog2"
      },
      "source": [
        "# Cloud AI AlphaGenome Notebook\n",
        "<table align=\"left\">\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb\">\n",
        "      <img src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2alphagenome%2Fcloudai_alphagenome_vai_quickstart.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb\">\n",
        "      <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/alphagenome/cloudai_alphagenome_vai_quickstart.ipynb\">\n",
        "      <img width=\"32px\"src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cabQQ79c5T-r"
      },
      "source": [
        "# Overview\n",
        "\n",
        "AlphaGenome on Google Cloud Platform is a project to offer Google DeepMind's cutting-edge AlphaGenome model as a commercial-grade \"Model as a Service\" (MaaS) on Google Cloud Platform (GCP).\n",
        "\n",
        "\n",
        "This will be the exclusive commercial platform for AlphaGenome, and is being launched in conjunction with the publication of a paper in Nature and the release of a non-commercial version of the model.\n",
        "\n",
        "\n",
        "References:\n",
        "*  <a href=\"https://github.com/google-deepmind/alphagenome\">Google Deepmind's AlphaGenome Github</a>\n",
        "* <a href=\"https://www.nature.com/articles/d41586-025-01998-w\">DeepMind’s new AlphaGenome AI tackles the ‘dark matter’ in our DNA (Nature journal)</a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oHoxBFtPnD7"
      },
      "source": [
        "# Disclaimer\n",
        "- This is an experimental release.\n",
        "- Check frequently for updated content.\n",
        "- Check the followings: 'Intialize variables' section"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUmKwF5ZMMS-"
      },
      "source": [
        "## Authenticate (option 1) your notebook environment (Colab only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F4kFRwdlPbqT"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "    from google.colab import auth\n",
        "\n",
        "    auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzJ_ldY-uwzg"
      },
      "source": [
        "## Intialize variables\n",
        "### Gather Vertex AI URL from your admin / your contact @ google\n",
        "### Gather service account from your admin / your contact @ google"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rwCxbxg0Jnoa"
      },
      "outputs": [],
      "source": [
        "gcp_project = \"\"  # @param {type:\"string\"}\n",
        "vertex_ai_url = \"\"  # @param {type:\"string\"}\n",
        "service_account_key = \"\"  # @param {type:\"string\"}\n",
        "# Keep Service account and token empty when using the key\n",
        "service_account = \"\"  # @param {type:\"string\"}\n",
        "token = \"\"  # @param {type:\"string\"}\n",
        "code_whl = \"alphagenome-0.4.2.6-py3-none-any.whl\"\n",
        "file_path = f\"gs://alphagenome-whl/{code_whl}\"\n",
        "\n",
        "service_account = service_account or None\n",
        "token = token or None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1902445c3133"
      },
      "source": [
        "## Authenticate (option 2) using your service account key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1ed156e7c578"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "!gcloud auth activate-service-account --key-file={service_account_key}\n",
        "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = service_account_key"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6J0tpPXMXEs"
      },
      "source": [
        "## Install AlphaGenome for Vertex AI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5tMaDT9UpVpr"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downlaoding the wheel file: gs://alphagenome-whl/alphagenome-0.4.2-py3-none-any.whl\n",
            "Copying gs://alphagenome-whl/alphagenome-0.4.2-py3-none-any.whl to file://./alphagenome-0.4.2-py3-none-any.whl\n",
            "  Completed files 1/1 | 178.7kiB/178.7kiB                                      \n"
          ]
        }
      ],
      "source": [
        "if service_account and service_account != \"\":\n",
        "    print(f\"Service Account is set to '{service_account}'. Proceeding with the copy...\")\n",
        "    !gcloud config set auth/impersonate_service_account $service_account\n",
        "print(f\"Downlaoding the wheel file: {file_path}\")\n",
        "!gcloud storage cp {file_path} . --billing-project={gcp_project}\n",
        "\n",
        "# to unset impersonate_service_account\n",
        "# !gcloud config unset auth/impersonate_service_account"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PBvSSZ53ptUq"
      },
      "outputs": [],
      "source": [
        "# @markdown Run this cell to install AlphaGenome.\n",
        "from IPython.display import clear_output\n",
        "\n",
        "! pip install $code_whl\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naobZsTzMmAU"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-LHW4cz3nD1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "AlphaGenome package version: 0.4.2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/google/home/dpani/dev/ag-nbv1/vertex-ai-samples/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from alphagenome.data import gene_annotation, genome\n",
        "from alphagenome.data import transcript as transcript_utils\n",
        "from alphagenome.data.genome import Interval\n",
        "from alphagenome.interpretation import ism\n",
        "from alphagenome.models import (dna_client, dna_client_http, interval_scorers,\n",
        "                                variant_scorers)\n",
        "from alphagenome.visualization import plot_components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kw7d6ztaNNcG"
      },
      "source": [
        "## Predict outputs for a DNA sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eg86dA5pzqFO"
      },
      "source": [
        "AlphaGenome is a model that makes predictions from DNA sequences. Let's load it up:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AjpSs0Y0zXUD"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Creating HttpDnaClient...\n",
            "HttpDnaClient created.\n"
          ]
        }
      ],
      "source": [
        "print(\"Creating HttpDnaClient...\")\n",
        "dna_model = dna_client_http.create_http_client(\n",
        "    vertex_ai_url=vertex_ai_url,\n",
        "    service_account=service_account,\n",
        "    token=token,\n",
        ")\n",
        "print(\"HttpDnaClient created.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zva6-7GO0j1u"
      },
      "source": [
        "The model can make predictions for the following output types:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f4yYT7zZzTNm"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['ATAC',\n",
              " 'CAGE',\n",
              " 'DNASE',\n",
              " 'RNA_SEQ',\n",
              " 'CHIP_HISTONE',\n",
              " 'CHIP_TF',\n",
              " 'SPLICE_SITES',\n",
              " 'SPLICE_SITE_USAGE',\n",
              " 'SPLICE_JUNCTIONS',\n",
              " 'CONTACT_MAPS',\n",
              " 'PROCAP']"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "[output.name for output in dna_client.OutputType]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpFi65J3kYVt"
      },
      "source": [
        "AlphaGenome predicts multiple 'tracks' per output type, covering a wide variety\n",
        "of tissues and cell-types. However, predictions can be made efficiently for\n",
        "subsets of interest.\n",
        "\n",
        "Here is how to make DNase-seq predictions (as specified by `OutputType`) in a\n",
        "subset of tracks corresponding to lung tissue (as specified by `ontology_terms`)\n",
        "for a DNA sequence of length 1Mb:\n",
        "\n",
        "*Note: We use ontology terms from standardized biological sources like UBERON\n",
        "(for anatomy) and the Cell Ontology (CL) to provide consistent and widely\n",
        "recognized classifications for tissue and cell types.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Neq8yfM9yFv8"
      },
      "outputs": [],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence=\"GATTACA\".center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, \"N\"\n",
        "    ),  # Pad to valid sequence length.\n",
        "    requested_outputs=[\n",
        "        dna_client.OutputType.CAGE,\n",
        "        dna_client.OutputType.DNASE,\n",
        "    ],\n",
        "    ontology_terms=[\n",
        "        \"UBERON:0002048\",  # Lung.\n",
        "        # 'UBERON:0000955',  # Brain.\n",
        "    ],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "meJvrjB71uNC"
      },
      "source": [
        "The output object contains predictions for all the different requested output types (in this case, only output type DNASE). Predictions for genomic tracks are stored inside a TrackData object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ts9SILFP1xbh"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "alphagenome.data.track_data.TrackData"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dnase = output.dnase\n",
        "type(dnase)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EV4x7l0G1494"
      },
      "source": [
        "`TrackData` objects have the following components:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmXvTQWK166n"
      },
      "source": [
        "<a href=\"https://services.google.com/fh/files/misc/trackdata.png\"><img src=\"https://services.google.com/fh/files/misc/trackdata.png\" alt=\"trackdata\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PoIJ1tK2CAO"
      },
      "source": [
        "The predictions of shape `(sequence_length, num_tracks)` are stored in\n",
        "`.values`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G0vN0-7I2FCF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1048576, 1)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[0.00683594],\n",
              "       [0.00750732],\n",
              "       [0.00704956],\n",
              "       ...,\n",
              "       [0.00704956],\n",
              "       [0.00772095],\n",
              "       [0.00793457]], dtype=bfloat16)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(dnase.values.shape)\n",
        "\n",
        "dnase.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viz8sSNH2Ogf"
      },
      "source": [
        "And the corresponding metadata describing each of the tracks is stored in\n",
        "`.metadata`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9j1ZPSCb2SDH"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"dnase\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"UBERON:0002048 DNase-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"STRAND_UNSTRANDED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ontologyTerm\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assay\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"DNase-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataSource\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"encode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"endedness\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"ENDEDNESS_PAIRED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geneticallyModified\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonzeroMean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.42750496,\n        \"max\": 0.42750496,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.42750496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-700988cb-0e22-4546-a237-65732de8007e\" 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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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>ontologyTerm</th>\n",
              "      <th>biosample</th>\n",
              "      <th>assay</th>\n",
              "      <th>dataSource</th>\n",
              "      <th>endedness</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>UBERON:0002048 DNase-seq</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lun...</td>\n",
              "      <td>DNase-seq</td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_PAIRED</td>\n",
              "      <td>False</td>\n",
              "      <td>0.427505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  <style>\n",
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              "    }\n",
              "  </style>\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-700988cb-0e22-4546-a237-65732de8007e');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                       name             strand  \\\n",
              "0  UBERON:0002048 DNase-seq  STRAND_UNSTRANDED   \n",
              "\n",
              "                                        ontologyTerm  \\\n",
              "0  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "\n",
              "                                           biosample      assay dataSource  \\\n",
              "0  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lun...  DNase-seq     encode   \n",
              "\n",
              "          endedness  geneticallyModified  nonzeroMean  \n",
              "0  ENDEDNESS_PAIRED                False     0.427505  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dnase.metadata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzUGUVEs2bj3"
      },
      "source": [
        "In this case, there is only one output track, so the track metadata returns only\n",
        "1 row.\n",
        "\n",
        "The track metadata is especially useful when requesting predictions for multiple\n",
        "tissues or cell-types, and when dealing with stranded assays (which are assays\n",
        "with separate readouts for the two DNA strands, such as CAGE and RNA-seq):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rb9iddb62iIn"
      },
      "outputs": [],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence=\"GATTACA\".center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, \"N\"\n",
        "    ),  # Pad to valid sequence length.\n",
        "    requested_outputs=[\n",
        "        dna_client.OutputType.CAGE,\n",
        "        dna_client.OutputType.DNASE,\n",
        "    ],\n",
        "    ontology_terms=[\n",
        "        \"UBERON:0002048\",  # Lung.\n",
        "        \"UBERON:0000955\",  # Brain.\n",
        "    ],\n",
        ")\n",
        "\n",
        "print(f\"DNASE predictions shape: {output.dnase.values.shape}\")\n",
        "print(f\"CAGE predictions shape: {output.cage.values.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHBi3Yv_2lrA"
      },
      "source": [
        "Notice that in this example, we requested predictions for 2 assays and 2 ontology terms simultaneously.\n",
        "\n",
        "The CAGE track metadata describes the strand and tissue of each of the 4 predicted tracks (2 per DNA strand):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TBQKgkeI2p4G"
      },
      "outputs": [
        {
          "data": {
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              "type": "dataframe"
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              "    <div>\n",
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              "\n",
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              "          '<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",
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              "\n",
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              "\n",
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              "  }\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
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              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                   name           strand  \\\n",
              "0  hCAGE UBERON:0000955  STRAND_POSITIVE   \n",
              "1  hCAGE UBERON:0002048  STRAND_POSITIVE   \n",
              "2  hCAGE UBERON:0000955  STRAND_NEGATIVE   \n",
              "3  hCAGE UBERON:0002048  STRAND_NEGATIVE   \n",
              "\n",
              "                                        ontologyTerm  \\\n",
              "0  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "1  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "2  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "3  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "\n",
              "                                           biosample  assay dataSource  \\\n",
              "0  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'bra...  hCAGE     fantom   \n",
              "1  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lung'}  hCAGE     fantom   \n",
              "2  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'bra...  hCAGE     fantom   \n",
              "3  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lung'}  hCAGE     fantom   \n",
              "\n",
              "   nonzeroMean  \n",
              "0    28.432245  \n",
              "1    30.655853  \n",
              "2    28.432245  \n",
              "3    30.655853  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output.cage.metadata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29lZ-Z4-2wPy"
      },
      "source": [
        "See the\n",
        "[output metadata documentation](https://www.alphagenomedocs.com/exploring_model_metadata.html)\n",
        "for more information on the output types and output shapes. For the mapping\n",
        "between tissue names (e.g. 'brain' -> 'UBERON:0000955') and ontology terms, see\n",
        "this [tutorial](tissue_ontology_mapping.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2vaBz9Tx3cT"
      },
      "source": [
        "## Predict outputs for a genome interval (reference genome)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZuGzeStkYVt"
      },
      "source": [
        "For convenience, you can also directly make predictions for a human reference\n",
        "genome sequence specified by a **genomic interval**. For example, let's predict\n",
        "RNA-seq for tissue 'Right liver lobe' in a 1MB region of Chromosome 19 around\n",
        "the gene *CYP2B6*, which encodes an enzyme involved in drug metabolism, and is\n",
        "primarily expressed in the liver.\n",
        "\n",
        "We first load up a GTF file containing gene and transcript locations as\n",
        "annotated by GENCODE (more information on GTF format\n",
        "[here](https://www.gencodegenes.org/pages/data_format.html)):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QR1dageWyEeV"
      },
      "outputs": [],
      "source": [
        "# The GTF file contains information on the location of all trancripts.\n",
        "# Note that we use genome assembly hg38 for human.\n",
        "gtf = pd.read_feather(\n",
        "    \"https://storage.googleapis.com/alphagenome/reference/gencode/\"\n",
        "    \"hg38/gencode.v46.annotation.gtf.gz.feather\"\n",
        ")\n",
        "\n",
        "# Set up transcript extractors using the information in the GTF file.\n",
        "# Mane select transcripts consists of of one curated transcript per locus.\n",
        "gtf_transcripts = gene_annotation.filter_protein_coding(gtf)\n",
        "gtf_transcripts = gene_annotation.filter_to_mane_select_transcript(gtf_transcripts)\n",
        "transcript_extractor = transcript_utils.TranscriptExtractor(gtf_transcripts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8M3kaS04XMn"
      },
      "source": [
        "And then fetch the gene's location as a `genome.Interval` object by passing\n",
        "either its `gene_symbol` (HGNC naming convention) or ENSEMBL `gene_id`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "In3IlnGj4agW"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Interval(chromosome='chr19', start=40991281, end=41018398, strand='+', name='CYP2B6')"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Ucomment after fixing strand\n",
        "\n",
        "interval = gene_annotation.get_gene_interval(gtf, gene_symbol=\"CYP2B6\")\n",
        "interval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SS2kb8Rf5bv1"
      },
      "outputs": [],
      "source": [
        "interval = Interval(\n",
        "    chromosome=\"chr19\", start=40991281, end=41018398, strand=\".\", name=\"CYP2B6\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fLrfgyvkYVt"
      },
      "source": [
        "We can resize it to a length compatible with the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VV5k05MykYVt"
      },
      "outputs": [],
      "source": [
        "sample_interval = interval.resize(dna_client.SEQUENCE_LENGTH_1MB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pu1A32KJkYVt"
      },
      "source": [
        "The `.resize()` method adjusts the interval to the specified width by expanding\n",
        "(or contracting) around its original center. Note that\n",
        "`dna_model.predict_interval()` interprets this resizing as an expansion of the\n",
        "actual genomic sequence rather than padding tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3TdgcNRskYVt"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1048576"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_interval.width"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hI5hi6DckYVu"
      },
      "source": [
        "See the\n",
        "[essential commands documentation](https://www.alphagenomedocs.com/colabs/essential_commands.html)\n",
        "for more handy commands like `resize`.\n",
        "\n",
        "Note that AlphaGenome supports the following input sequence lengths:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "22gZecZTkYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['SEQUENCE_LENGTH_2KB', 'SEQUENCE_LENGTH_16KB', 'SEQUENCE_LENGTH_100KB', 'SEQUENCE_LENGTH_500KB', 'SEQUENCE_LENGTH_1MB'])"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dna_client.SUPPORTED_SEQUENCE_LENGTHS.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRk3W7yjkYVu"
      },
      "source": [
        "We can now make predictions using our interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ME7NH0B-kYVu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"predict_interval\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr19\",\n",
            "          \"start\": \"40480552\",\n",
            "          \"end\": \"41529128\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"requestedOutputs\": [\n",
            "          \"OUTPUT_TYPE_RNA_SEQ\"\n",
            "        ],\n",
            "        \"ontology_terms\": [\n",
            "          {\n",
            "            \"ontology_type\": \"ONTOLOGY_TYPE_UBERON\",\n",
            "            \"id\": 1114\n",
            "          }\n",
            "        ],\n",
            "        \"modelVersion\": \"FOLD_0\"\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(1048576, 3)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output = dna_model.predict_interval(\n",
        "    interval=sample_interval,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=[\"UBERON:0001114\"],\n",
        ")  # Right liver lobe.\n",
        "\n",
        "output.rna_seq.values.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZD6iGfrkYVu"
      },
      "source": [
        "In general, you can have multiple tracks for a given ontology term. In this\n",
        "case, we have 3 RNA-seq tracks for the tissue \"Right liver lobe\".\n",
        "\n",
        "Let's visualise these predictions. It's helpful visualise gene transcripts\n",
        "alongside the predicted tracks, so we extract them here:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zU61GXrekYVu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracted 28 transcripts in this interval.\n"
          ]
        }
      ],
      "source": [
        "transcripts = transcript_extractor.extract(sample_interval)\n",
        "print(f\"Extracted {len(transcripts)} transcripts in this interval.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hUwvkl_kYVu"
      },
      "source": [
        "We also provide a\n",
        "[visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html)\n",
        "that integrates nicely with `TrackData` and other objects returned by the model\n",
        "API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MjJsLGnGkYVu"
      },
      "outputs": [
        {
          "data": {
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uf1QsFbf9LKfj2N9//w0gI4h6+vTpOHjwICwsLNCyZUuVwQ36+vo4e/YsPDw8YG9vX2RdYvCc8fMrztu3sbExevXqlet5KqhtSBO3xeKyPnN7X+VzyO3xJeu2aGxsjPbt2yvkMzQ0xKxZs8T7QqpMnjwZxsbG0NPTQ8+ePWFhYYEhQ4bkuTyZ/NyT0kRr167Fb7/9Bjc3tyLtEoO/V3mT3TnMgwcP4ODgoFY5kZGR+PHHH+Hn54f+/fvnqg6Ojo6IioqSS9uwYYPC/j58+HCFcf39/dG+fXtYWFjA0tISbm5uCAgIyHZ6xsbGCAsLQ4cOHWBnZ1ekXWJwm6XihAEQRERERaBHjx548eIFDhw4gHbt2uHkyZOoV68eAgMDxTyyi1dDQ0MsWbIEixcvRseOHXM1nZUrVyI8PBw3btzAwYMHcf/+fQwYMEBp3smTJ+PVq1fw9/dXGLZ27Vq8e/dOafAEaZ5Zs2YhKCgIbm5uMDU1LerqEOVK5je9CzJvdmRvTn38+BGDBw9Gs2bNcPHiRZw7dw41atRAx44d8fHjR7lxzpw5g/DwcISHh2PTpk1YuHAh/Pz8CqQ+JM/FxQXh4eG4dOkSPD09MWjQIPTo0QMAEBcXhz179ogPzAHAw8MDUqlUoZzsfjPT0tLQr18/zJkzB9WqVVOrPuHh4bh8+TLc3NzQvn37bN/cKc5ysx/NmDED6enpmDJlitIgEdmNPwMDAwwdOhT/+9//MGLECIV827dvx5w5cxASEoJSpUqJ6ffu3cPly5fRt29fABkPP9zd3eXWZ3p6OlJSUrBmzRq4ubmhcePG2LFjBx48eIATJ07kZtaJPpvitJ8BOR/H0tPTAQDTpk1Djx49UL9+fQQEBEAikSgNMMts2rRp2LVrFzp27AgTExO155s0V3HevsPDw7FmzZrcz9RXrDitT3XuqxRnWbdF2XWDMoMHD4aVlRWWLFmisryJEyciPDxcbMVs5cqVsLe3z3N5BXFPSlO5ubnhwYMHmDdvHh/oapjszmFyc/wqX7486tWrh2XLliE6OjpXdRAEQWG76d+/v8L+PnfuXLk8aWlp2Lx5s8K1a2BgoHjulZ2FCxfC398frVq1grm5ea7qTPQlUv3aChERERUqfX19uLq6wtXVFTNmzMCQIUMwa9YseHl5Aci4ePXy8oKxsTFKly6dp4suGxsb8YLXwcEB7969Q9++fTF//nyFC2Fzc3P4+vpizpw56NSpk9yw48eP48KFC9DT05NLb9CgAfr374/Nmzfnum7q1F1dzs7OePPmDQAgNjYWFhYW4rDM37MbllVsbKxa5atTr+zGyVy2OunZ1V2WllMdk5KSEB8fD0NDQxgaGuZ6WT98+FBp+VmXtbL5UGf5ZR4n8zIEIM6f7H/6+lStWhUSiQR3795VmUf2gDoyMhJNmzZVGB4ZGYnq1auL5cXHx+PFixcoW7asXL7k5GQ8evQIbm5uADJuvkZFReHChQviW7Dbt2+HhYUF9u/fjz59+ojjVq5cWbzp4OzsjEuXLmHBggXiTV1l+5K6x4qs+3pOxzJ1yrSyskJERITSfMWdkZGR+Jvm7++P2rVrQyqVYvDgwdi+fTs+ffok15WFIAhIT0/H/fv35YIZsvvNLFmyJMLCwnD9+nWx+dP09HQIggBtbW0cPnxYfCMuc30AYNOmTTAzM8Ovv/6K+fPnq/WbkvXYmdNvQmFSZ5+TkT3cUNVCRv/+/TFt2jQYGBigTJkySlvFCAoKwpAhQ7Bz5060adNGbphUKkVqaqrcvioIAvT09LBu3Tq51lxk+zgAWFtbo2TJknj69CmA//a/zJydncW/RbWs6etVnPYzIOfjmLL9TE9PD3Z2dnL7mbJ9KfN5qJGRkVrnoTmdwyv7TeW+XHwU9+07N5Rdp2SV+Tooc74v5RqmuK3PnO6r5ETV77+y61llx57Mw3JL2bb4zz//KM2rra2NBQsWwMvLS2VT/CVLloS9vT3s7e2xc+dO1KxZEw0aNJA7VuemvOzuSZmamioN7o2Li4OWlpbS7lHyStm+puocWtX9H3V+RzJPIzU1FbGxsdDW1oa1tXWOv1XZTSunecv6N/PxgnIvu3OYatWqqXXsAjJafzx69ChcXV3h4uKCEydOKG01UpnIyEhUrlxZLs3MzExhf88aoHfo0CE8f/4c7u7uculpaWk4duwYXF1dAaj+/fn06RMSEhJgZGQEfX39PJ1jqUrLTNn0ed5FxREDIIiIiIqJ6tWrY9++feJ32cVrQdLS0gIAhTeVZUaNGoU1a9Zg9erVculr1qyRa/rvxYsXcHNzQ3BwcJ77tM9JTEyMWvkkEkmhPrTLS/myGwM5jaeq7MKcp5EjR2L37t0ICwtDvXr1cjVubuuVl/zAf8utsNctaSZZM5Dr16/H6NGjFW6uxcXFoW3btrC0tMSKFSsUAiAOHDggvs0DAD179sTkyZOxYsUKrFixQi7vzz//jA8fPmDgwIEAgA8fPqBEiRJyN/9k33N6I0NLS0s89ua2ZYrC2Be+1P2rRIkSmDp1KsaNG4d+/fpBKpVi/PjxCjfBR44cCX9/fyxevFhlWZl/M01NTXHr1i254Rs2bMDx48exa9cuhRtcmUkkEpQoUUJc/7n9bSjqdaXOPqfuG0bKbvxltmPHDnh7eyMoKEjhDcPU1FRs2bIFK1asQNu2beWGde3aFTt27MDw4cPRrFkzABmtRZQvXx5ARn/lr1+/RsWKFZXuf7JlnHlZ820/+pyKy36mStbjWP369aGnp4d79+7h22+/BQCkpKQgKioKFStWBKD8WDdq1CgEBwfj0qVLaNiwoVrTVqduWaeV3Tl2QbUOReor7tt3bqj7e6zsd+VLUdzXZ9b7KjnJbj1lt/6KYt326tULy5Ytw5w5c3LMa2trC3d3d/j6+mL//v15Ki+7e1IODg4ICgpCUlKS3Isy165dQ+XKlaGjo6PGHKmnsJdz1nW5detWjBo1Cn5+fhg2bFihT/dLPl4UB5nPYfr164c+ffpg//796NKli1w+QRCQkJAAMzMzMc3CwgJHjx5F27Zt0bJlS5w4cULhpYms7t69i9DQ0Dy1oCvrujFrl0ELFiyAVCoVAyCUbSdDhgzBwYMHcfr0adSqVSvX01ZXbs67iIoaAyCIiIg+szdv3qBXr17w9vZGrVq1YGJigrCwMCxdulThBDy/4uLiEBMTg/T0dDx48ABz585FtWrV4OTkpDS/vr4+5syZAx8fH7n0ChUqyH03NjYGAFSpUkV8uEDF2+vXr/Hdd9/BysoKT548gb6+flFXiSjP1q9fj2bNmuGbb77B3LlzUatWLaSmpuLIkSPw8/NDZGQkfvnlF/Tp0wfDhg3Djz/+CFNTUxw7dgwTJ05Ez549xf6DK1SogKVLl2LChAnQ19fHgAEDoKOjg/3792Pq1KmYP38+atSoAQBwdXXFxIkT4ePjg1GjRiE9PR2LFy+GtrY2XFxc5Or48uVLfPr0CUlJSbh8+TJ+++039OzZ87Mvq69Rr169MHHiRKxfvx7Xrl3Dtm3bFPqp7tu3L+bOnYv58+eLbyVm95tZokQJcTuQKVWqFPT19RXSk5KSxCC+2NhYrFu3DomJiejcuXMhznXhUmefy6/t27fD09MTq1evRqNGjcRlaGBgADMzMxw8eBCxsbEYPHiw3I1JIKMJbKlUiuHDh6NatWro0qULxowZg40bN8LU1BS+vr5wdHRU2E+JipPisJ/J5HQcMzU1xfDhwzFr1izY2tqiYsWKWLZsGYCMY3BWb9++xXfffQdTU1M8ffqU56FfoeK0fWcnMTFRroWgx48fIzw8HJaWlgrXxF+z4rA+1b2vEhMTg5iYGHG93rp1CyYmJhq7PhcvXiy2TpeTMWPGoEaNGggLC0ODBg3yXV5m/fv3x9y5czFw4EBMmjQJZmZmOH36NFatWoWlS5fK5U1LS0N4eLhcmp6eHpycnIrVPpeeng53d3dcuHABYWFhqFKlymedPhWM7M5hWrRogb1796Jv376YPn062rZtC2tra9y6dQsrV67EqFGj0LVrV7nyzM3NceTIEbi5uaFly5Y4efKkGASRmpoqXj++efMGJ0+exPz581GnTh1MnDgxV/V+9eoVfv/9dxw4cEDh+nLgwIHo1q0b3r59C0tLS7lhL1++xLfffovy5cvj6dOnYvedRARAICIios/q06dPwpQpU4R69eoJZmZmgqGhoeDg4CBMnz5d+PDhgyAIglCxYkVh5cqVOZZ14sQJAYAQGxurMAyA+JFIJEKZMmUEd3d34dGjR2KegIAAwczMTG681NRUoXr16gIA4cSJE0qn+/jxYwGAcP36dTXnmopSUlKSYGlpKSxcuLCoq0JUYF68eCH4+PgIFStWFHR1dYVy5coJ33//vdxx6/Tp04Kbm5tgamoq6OrqCs7OzsLy5cuF1NRUhfL27dsnfPfdd4KRkZF47NyxY4dCvsOHDwvNmjUTzMzMBAsLC6FVq1bChQsXxOGy47Lso62tLVSuXFmYMGGCkJiYWCjL4mvm6ekpdOnSRSF90aJFgra2tlCpUiWl40VHRwslSpQQ9u/fLwiCer+ZWc2aNUuoXbu2Qn0yl2ViYiI0bNhQ2LVrV57nsbhQZ58ThIxluXfvXoXxW7RoIYwZM0Zl+S1atJBbdrKPp6enIAiC0KlTJ6FDhw5Kx7106ZIAQLhx44YgCIIQHx8veHt7C+bm5oKlpaXQrVs34enTp3mZbaLPqqj3M0FQ/ziWnJwsjB8/XihVqpRgYmIitGnTRrh9+7bCNFNSUgQrKythzpw5uVkU9AUqLtu3svMGmaznccrKoAxFvT7Vua8iCBnna8rKCQgIyP9CyAdV22Lmezyq7ve0bdtWYR5U3UNyc3MT2rdvr1B2XsvL7N69e0K3bt2EsmXLCkZGRkLt2rWFX3/9VUhPTxfzBAQEKF3+VapUkatTcdjn6tWrJ3Tq1ElIS0v77NOmgqHOOUxaWprg5+cnNGzYUDA0NBRMTU2F+vXrC6tXrxaPHcqu8+Lj44UmTZoI9vb2wj///CN3bNHS0hIsLS2Fb7/9Vli5cqXw6dMnuXFVHe8y35Ndvny5YG5uLiQnJyvkS0pKEszNzYXVq1fLpb9//16wsLAQli9fnoelRfTlkwgC234jIiIi+tIlJiaKLXcQUfbevn2L1q1bw9TUFH/99RcMDQ2LukpEREQai+ehRERU3PG3ijQRt1si1RgAQURERERElMWbN2/E5n1bt25d1NUhIiIiIiIiIiIiNTAAgoiIiIiIiIiIiIiIiIiIiDReiaKuABEREREREREREREREREREVF+MQCCiIiIiDSal5cXunbtCgCIioqCRCLJ9hMYGIiTJ0/KpRkYGMDZ2RkbN25UKFuWR1dXF/b29pg7dy5SU1MV6vHw4UOYmJjA3Nz8M8w1aTLZNpvTtjp79myFbdrKygpt27bF9evXxfJatmyJsWPHyk1j9erV0NPTQ1BQEAAgISEB06ZNg6OjI/T19WFjY4M2bdpgz549YKOAeZP5+JD5065dOzHP9evX4e7ujjJlykBPTw8VK1ZEp06d8Pvvv4vLXbaOw8PDlU4nMDBQoVwAiIuLg0QiwcmTJ8VyBg8ejMqVK8PAwABVqlTBrFmzkJycXCjzr0liYmIwatQo2NnZQU9PD7a2tujcuTPmzJkDHR0dnD17Vi7/+/fvYWdnhwkTJgDI2Mdk61dfXx/Vq1fHhg0bxPx79uyBq6srrK2tYWpqiiZNmuDQoUNK63LhwgVoaWmhY8eOCsNk61rZ5+XLlwW4RIhI02jKcQwARo8ejfr160NPTw916tRRGH7v3j24uLigdOnS0NfXh52dHaZPn46UlJQ8Lh2iz0/VfvDnn39CV1cX165dk0tfsWIFSpYsiZiYGADy17D09VDn+k9m9+7daNWqFSwsLGBgYAAHBwd4e3vLXQeqOnfctGmT0uHGxsaoX78+9uzZI1evPXv2oG3btrCyssr2uoSKh8zXoTo6OihdujRcXV3h7++P9PR0MV+lSpUgkUjEewKZOTs7i/fHslq0aBG0tLSwbNkypdNXdU5y7NgxAMDbt28xatQoODg4wMDAABUqVMDo0aMRHx9fMAuASE0MgCAiIiKiL4atrS2io6PFz/jx4+Hs7CyX5u7uLua/d+8eoqOjcefOHfzwww8YMWKEeNEm065dO0RHR+PBgwcYP348Zs+erXAhmJKSgr59++K77777LPNJX4bM2+WqVatgamoqlyZ7aAEAR48eRXR0NA4dOoTExES0b98ecXFxSsudNWsWpk6div3796NPnz6Ii4tD06ZNsWXLFvj6+uLatWs4ffo03N3dMWnSJN6IyAfZ8SHzZ8eOHQCA/fv3o3HjxkhMTMTmzZsRGRmJ0NBQdOvWDdOnT8/VctfW1sbRo0dx4sQJlXnu3r2L9PR0/PLLL4iIiMDKlSvx888/Y+rUqfmeT00WFRWF+vXr4/jx41i2bBlu3bqF0NBQuLi4YMeOHRg1ahS8vLzw/v17cZxJkybBwMAA8+fPF9OGDh0q/l707t0bPj4+4ro+ffo0XF1d8eeff+Lq1atwcXFB586d5W5Qy0ilUowaNQqnT5/Gixcv5Ia5u7srbE9ubm5o0aIFSpUqVUhLiIiKO006jsl4e3vLnXNnpqOjg4EDB+Lw4cO4d+8eVq1ahV9//RWzZs3Kz2Ii+qxU7QcdOnTAwIEDMXDgQCQlJQEA7ty5g+nTp2P9+vWwsbEpqipTMaDu9d/kyZPh7u6OOnXq4MCBA7h37x62b98OOzs7+Pr6ypWZtYzo6Gj0799f6fDr16/Dzc0NvXv3xr1798Q879+/x7fffoslS5Z8ngVB+Sa7Do2KisJff/0FFxcXjBkzBp06dZJ7YcfW1hYBAQFy4168eBExMTEwMjJSWra/vz8mTZoEf39/hWHZnZP4+PgAAF68eIEXL15g+fLluH37NgIDAxEaGorBgwcX4BIgUoNARERERKTBPD09hS5duigdNmvWLKF27doK6SdOnBAACLGxsXLpVapUEZYuXZpt2a6urkLjxo3l0iZNmiR4eHgIAQEBgpmZWR7mgr4myrYrVdvO48ePBQDC9evXxbRz584JAITQ0FBBEAShRYsWwpgxY4T09HThxx9/FMzNzYVz586J+UeMGCEYGRkJz58/Vyj/3bt3QkpKSoHM19cmu2NPYmKiYGVlJXTr1k3l+Onp6YIgKF/Hmcm2jaFDhwrffPONmB4bGysAEE6cOKFyGkuXLhUqV66c47x8ydq3by+UK1dOSExMVBgWGxsrfPz4UXBychJ8fHwEQRCE48ePC7q6ukJYWJiYT7aPZVa1alWhT58+KqdbvXp1Yc6cOXJp7969E4yNjYW7d+8K7u7uwoIFC7Kt+8uXLwUdHR1hy5YtOc0mEX3BNPU4puo8XJn//e9/wrfffqtWXqKiltN+kJCQIFSsWFGYPHmykJKSIjRo0EDo1auXXJ7sziPp66Dq+u/ChQsCAGH16tVKx5NdQ2RXRnbD09LSBB0dHSEkJEQhf07XJVQ8qDp+HDt2TAAg/Prrr4IgCELFihWFKVOmCHp6esLTp0/FfEOHDhVGjRolmJmZCQEBAXJlnDx5UihXrpyQnJwslC1bVu6+giDkfE6iSkhIiKCrq8t7D/RZsQUIIiIiIvrqCYKA0NBQPH36FI0aNco2r4GBgVyT8sePH8fOnTuxfv16tacnkUiU/p9dvtwMy5xHWT5V6aQZDAwMAEBuO0xNTYWHhwd27dqFU6dOoWnTpgCA9PR0BAUFoX///ihbtqxCWcbGxtDW1gag3jaVE1VlqLu9fikOHz6MN2/eYNKkSSrz5HZ+Z8+ejVu3bmHXrl1qjxMfHw9LS8t8T1tTvX37FqGhofDx8VH6hpO5uTn09fWxZcsWbNy4Efv374e3tzemTp2K+vXrZ1t21t+CzNLT0/Hu3TuFZR8SEgJHR0c4ODjAw8MD/v7+2XZBs2XLFhgaGqJnz54Kw/L7+0BEmkHTj2PqePjwIUJDQ9GiRQuFYbLjWX7OL4gKWk77gYmJCfz9/bFixQr0798fz549g5+f32evp7r7R077WUFNR9U43I/l7dixA8bGxhg5cqTS4flZXmlpadi8eTMAoF69enkupyDkdj64neSsVatWqF27tlwXJ6VLl4abm5u43j98+IDg4GB4e3srLUMqlaJv377Q0dFB3759IZVKxWHqnJOoEh8fD1NTU/HeA9HnwAAIIiIiIvpqlS9fHsbGxtDV1UXHjh0xa9YsNG/eXGleQRBw9OhRHDp0CK1atQIAvHnzBl5eXggMDISpqennrDp9peLi4jBv3jwYGxvjm2++EdN//fVX7Nq1CydOnECtWrXE9NevXyM2NhaOjo5FUd0v3sGDB2FsbCz3WbhwIe7fvw8AcHBwEPNeuXJFLt/BgwdzNa2yZctizJgxmDZtmlyzpqo8fPgQa9euxQ8//JC7mfqCPHz4EIIg5Lj9N2jQAL6+vujevTusrKwwbdo0lXnT0tKwdetW3Lx5U/wtyGr58uVITExE79695dKlUik8PDwAZDRbGx8fj1OnTqmcllQqRb9+/cSgJyL6+mj6cSw7TZs2hb6+PqpWrYrvvvsOc+fOzVM5RJ+bOvtBq1at0LNnT4SEhGDNmjWwsrIqiqqSBrp//z7s7OzkHhT/9NNPctcRmbvSi4+PlxuWtZuVzMN1dXUxYsQIbNy4EVWqVPls80Sfj6OjI6KiouTSvL29ERgYCEEQsGvXLlSpUgV16tRRGDchIQG7du0Sj28eHh4ICQlBYmIiAPXPSbJ6/fo15s2bh2HDhuVpnojyigEQRERERPTVOnPmDMLDwxEeHo5NmzZh4cKFCm/nyB5w6uvro3379nB3d8fs2bMBZPSl3K9fP5VBE5qCb1MUf02bNoWxsTEsLCxw48YNBAcHo3Tp0uLwb7/9FsbGxpgxY4bcw/H8vpX5uWla6yQuLi7iMUT2GT58uNK8tWrVEvO8f/9erSCGrCZPnoxXr14p7Y81s+fPn6Ndu3bo1asXhg4dmuvpZKZJ6yOr3Gz/M2bMQHp6OqZMmaL0zaQNGzbA2NgYBgYGGDp0KP73v/9hxIgRCvm2b9+OOXPmICQkBKVKlRLT7927h8uXL6Nv374AAG1tbbi7u8u9VZXZhQsXEBkZyb5yib5ymnwcy0lwcDCuXbuG7du3448//sDy5cvzVA7R56TufvD8+XOEhobC0NAQZ86cKYqqahTZNYAmn3cWJm9vb4SHh+OXX37B+/fvFVocyXwtcv78eblxMw+/fv06Fi5ciOHDh+P3339Xe/rFZb0Ul3oUZ4IgKCynjh07IjExEadPn4a/v7/K1h927NiBKlWqoHbt2gCAOnXqoGLFiggODhbLzq2EhAR07NgR1atXF++jAZp33U+aie2NEBEREdFXq3LlymIzfc7Ozrh06RIWLFggdzPYxcUFfn5+0NXVRdmyZeVuKB8/fhwHDhwQb9gKgoD09HRoa2tj48aN8Pb2hrOzM968eaMwbWdnZzx8+FDufwsLC8TGxqrMBwAWFhZyw2Rlx8bGyg3LWkbmfMrSqXgLDg5G9erVYWVlpbRpyZo1a2LFihVo06YN3N3dERwcDG1tbVhbW8Pc3Bx3795Vazq52SZUbXPZbfNZ02Xbo6ptt7gzMjKCvb29QnrVqlUBZNwkb9y4MQBAT09Pad7cMDc3h6+vL+bMmYNOnTopzfPixQu4uLigadOm2Lhxo9ywzOsgp3Wd9VikiapWrQqJRKLW9i87tqtqlrV///6YNm0aDAwMUKZMGZQoofg+SVBQEIYMGYKdO3eiTZs2csOkUilSU1PluqIRBAF6enpYt24dzMzM5PJv2rQJderUUWjCPutvx5s3b8R9MbvfASLSTJp8HMuJra0tAKB69epIS0vDsGHDMH78eGhpaSn8Xsn+yo55REVF3f1g6NChqF+/PqZNmwZXV1f07NlTaTcvqijb3vPyG6/uuX3W/Swzdc7Xsxsv67iydF6LKle1alWcPXsWKSkp0NHRAZBxDWBubo5//vlHIX+JEiWyvcbIOrxWrVo4fPgwlixZgs6dO6tdr6znnbmlbDtStd3wfDbvIiMjUblyZbk0bW1tDBgwALNmzcKlS5ewd+9epeNKpVJERETInUekp6fD398fgwcPztU5CQC8e/cO7dq1g4mJCfbu3Stuz5llvrbJepyQfZf9b2VlhYiICLWmTQQwAIKIiIiISKSlpYWPHz/Kpal6wAlkvKGblpYmft+/fz+WLFmC8+fPo1y5cgCg9AJNIpGI6Zn/zy5fboZlzqOsDlnTGXlf/Nna2ubYTGmdOnVw7NgxtGnTBr1790ZwcDB0dHTQp08f/Pbbb5g1a5bczVoASExMhL6+vniTI783FFRtl7nZXr8Ebdu2haWlJZYsWaLyBlNejRo1CmvWrMHq1asVhj1//hwuLi6oX78+AgICFB5uqXPcyUqT14ulpSXc3Nywfv16jB49WqGv2ri4uGz7qs3MzMws25vLO3bsgLe3N4KCgtCxY0e5YampqdiyZQtWrFiBtm3byg3r2rUrduzYIddySGJiIkJCQrBo0SKF6aj720FEXwZNPY7lVnp6OlJSUpCeng4tLS2FY1125xdEn4u6+8GmTZtw9uxZ3Lp1CxUrVsSIESPg7e2NmzdvKuzDqhTEQz51z/dy2s8KajqqxuF+LK9v375Yu3YtNmzYgDFjxhTKNJTd98hJQT94zu12w+0kZ8ePH8etW7fwv//9T2GYt7c3li9fDnd3d6UBJrdu3UJYWBhOnjwJS0tLMf3t27do2bIl7t69C0dHR7XPSRISEuDm5gY9PT0cOHAA+vr6SuvMgAYqTAyAICIiIiKNFx8fj/DwcLk0dfpZffnyJT59+oSkpCRcvnwZv/32G3r27Kn2dJ2cnOS+h4WFoUSJEqhRo4baZRQHmtZNAqlWu3ZtHD9+HK1bt0bv3r0REhKCBQsW4OTJk2jUqBEWLFiABg0aQEdHB2fOnMGiRYtw5coVtR+eFDZN2xaTkpIQExMjl6atrY2SJUti06ZNcHd3R8eOHTF69GhUrVoViYmJCA0NBZBx4zGze/fuKZQvexsvM319fcyZMwc+Pj5y6c+fP0fLli1RsWJFLF++HK9evRKHZe0LODc0bZ1ktX79ejRr1gzffPMN5s6di1q1aiE1NRVHjhyBn58fIiMj8z2N7du3w9PTE6tXr0ajRo3EbcLAwABmZmY4ePAgYmNjMXjwYIU3pHv06AGpVCr34DA4OBipqali/7tE9HXTtOPYw4cPkZiYiJiYGHz8+FE8R69evTp0dXWxbds26OjooGbNmtDT00NYWBh8fX3h7u6u9O1QouJCnf2gffv2GDduHJYvX46KFSsCAJYsWYK//voLU6ZMwdq1a8VxVF3DylpH+Zpo+vlmQWrSpAnGjx+P8ePH48mTJ+jevTtsbW0RHR0NqVQKiUSitAUfVQRBEI/pHz9+xJEjR3Do0CHMnDlTzPP27Vs8ffoUL168APDfdYmNjQ1sbGyKzfopLvUoDmTXoWlpafj3338RGhqKRYsWoVOnThg4cKBCficnJ7x+/RqGhoZKy5NKpfjmm2+Udu/asGFDSKVSLFu2TK1zkoSEBLRt2xYfPnzA1q1bkZCQgISEBACAtbU1tLS0uC7ps2AABBERERFpvJMnT6Ju3bpyaYMHD0b58uWzHc/BwQFAxgNLW1tb/PDDD3L9EhaWzBd72V345XVYTnl4sfllq1mzphgE0atXL4SEhODixYtYvHgx5s+fjydPnsDCwgI1a9bEsmXLxBu4BbFd5Geb08TtMjQ0FGXKlJFLc3BwwN27d9GtWzecP38eS5YswcCBA/H27VuYmZmhQYMGCAoKUujCok+fPgrlP3v2TOl0PT09sWLFCty5c0dMO3LkCB4+fIiHDx8qHPuyLltNXNZ5ZWdnh2vXrmHBggUYP348oqOjYW1tjfr168PPz69AprFx40akpqbCx8dHLjDF09MTgYGBkEqlaNOmjdLm4Xv06IGlS5fi5s2bqFWrFoCMG5Ddu3fPNjApv78PRKQ5NO04NmTIEJw6dUocLjtHf/z4MSpVqgRtbW0sWbIE9+/fhyAIqFixIn788Uelb6zKjmc8p6XiQJ39YNCgQWjSpAmGDRsmDjM0NERgYCBatmwp1xWGqmvYTZs2FUh91d0/ctrPCmo6qsbhfqxo+fLl+Oabb+Dn5wd/f398+PABpUuXRvPmzXHhwgWYmpqqXVZCQoJ4vaKnp4eKFSti7ty5mDx5spjnwIEDGDRokPhddl0ya9asQrs/ktv1zu1Enuw6VFtbGxYWFqhduzbWrFkDT09PlQEyql4SSk5OxtatW+W2icx69OiBFStWYOHChWqdk1y7dg2XLl0CAIWWp2TnAkSfg0TgkYOIiIiIiIiIiIiIiIiIiIg0nPpt5RAREREREREREREREREREREVUwyAICIiIiIiIiIiIiIiIiIiIo3HAAgiIiIiIiIiIiIiIiIiIiLSeAyAICIiIiIiIiIiIiIiIiIiIo3HAAgiIiKir9SrV68wYsQIVKhQAXp6erCxsYGbmxvOnTsHAKhUqRJWrVqlMN7s2bNRp04dld8B4MyZMzA3N8fYsWMhCAJmz54NiUQifszMzPDdd9/h1KlTcuNVqlRJzGNkZIR69eph586d4vCIiAj06NFDzKesflQwvLy8IJFIsHjxYrn0ffv2QSKRAAACAwNhbm6udHyJRIJ9+/YBAKKioiCRSKClpYXnz5/L5YuOjoa2tjYkEgmioqLk8ss+urq6sLe3x/z58yEIgjiusm2PCp46xwqJRIKLFy/KjTd27Fi0bNlS/J75OKClpQVbW1sMGzYMb9++lRsvp+MAAHz8+BGWlpYoWbIkkpKSCmfGiYiIiIhIo8TExGDUqFGws7ODnp4ebG1t0blzZxw7dkzMc/36dfTq1QulS5eGvr4+qlatiqFDh+L+/fsAFK9HTUxM4OzsDB8fHzx48EBhmsnJyVi6dClq164NQ0NDlCxZEs2aNUNAQABSUlIAAO/evcPYsWNRsWJFGBgYoGnTprhy5YpcOZmnmfmzbNmyQlxixZPsfkTWT7t27fDixQtYWFhgzZo1cuNcunQJOjo6OHz4MAAgLS0NK1euRM2aNaGvrw8LCwu0b99evI6VSUtLw+LFi+Ho6AgDAwNYWlqiUaNG2LRpk1w+dbatli1bKtR5+PDhhbSU8i675QvIX5MbGhqiZs2aCsvj5MmTkEgkiIuLE9MEQcDGjRvRqFEjGBsbw9zcHA0aNMCqVavw4cMHAKrv48j2u/DwcIVhb968Qfny5RWmR5QdBkAQERERfaV69OiB69evY/Pmzbh//z4OHDiAli1b4s2bN/kq948//oCbmxvGjRuHVatWiQ/LnZ2dER0djejoaFy4cAFVq1ZFp06dEB8fLzf+3LlzER0djevXr6Nhw4Zwd3fH+fPnAQAfPnyAnZ0dFi9eDBsbm3zVk3Kmr6+PJUuWIDY2tkDKK1euHLZs2SKXtnnzZpQrV05p/qNHjyI6OhoPHjzAnDlzsGDBAvj7+xdIXUh96hwr9PX1MXny5BzLkh0Hnj59ioCAAISGhmLEiBEK+bI7DgDA7t274ezsDEdHRzHQhoiIiIiIvl5RUVGoX78+jh8/jmXLluHWrVsIDQ2Fi4sLfHx8AAAHDx5E48aNkZSUhG3btiEyMhJbt26FmZkZZsyYIVee7Hr0xo0bWLhwISIjI1G7dm25B97Jyclwc3PD4sWLMWzYMJw/fx6XL1+Gj48P1q5di4iICADAkCFDcOTIEfz222+4desW2rZtizZt2si9ICC7XyL7+Pv7QyKRoEePHp9h6RU/7dq1U1gmO3bsQNmyZbF27Vr4+vqKASkfP36Ep6cnhgwZgrZt20IQBPTp0wdz587FmDFjEBkZiZMnT8LW1hYtW7aUu4acM2cOVq5ciXnz5uHOnTs4ceIEhg0bJvegXZ1tS2bo0KFydV66dOnnWFy5pmr5ysiuyW/fvg0PDw8MHToUf/31V7ZlDhgwAGPHjkWXLl1w4sQJhIeHY8aMGdi/f78YmJIXgwcPRq1atfI8Pn2dtIu6AkRERET0+cXFxeHMmTM4efIkWrRoAQCoWLEivvnmm3yVu337dgwaNAgrVqzAjz/+KDdMW1tbDFqwsbHB3LlzERAQgPv376Nhw4ZiPhMTE9jY2MDGxgbr16/H1q1b8fvvv6Np06Zo2LChmHfKlCn5qivlrE2bNnj48CEWLVpUIBftnp6eCAgIgK+vr5gWEBAAT09PzJs3TyG/lZWVuM1UrFgRAQEBuHbtGgYPHpzvupB61D1WDBs2DD///DP+/PNPdOjQQWV5mY8D5cqVQ69evRAQEKCQL7vjAABIpVJ4eHhAEARIpVK4u7sX1CwTEREREZEGGjlyJCQSCS5fvgwjIyMx3dnZGd7e3vjw4QMGDRqEDh06YO/eveLwypUro1GjRgpvlme+HrWzs0Pnzp3RunVrDB48GI8ePYKWlhZWrVqF06dPIywsDHXr1hXHtbOzQ69evZCcnIyPHz9i9+7d2L9/P5o3bw4g4y3433//HX5+fpg/fz4AKLzksX//fri4uMDOzq5Al5OmkLU+qIyHhwf27NkDLy8vnDlzBr6+vkhJSRFbywgJCcGuXbtw4MABdO7cWRxv48aNePPmDYYMGQJXV1cYGRnhwIEDGDlyJHr16iXmq127ttz0ctq2MjM0NNSIF3ayW77Af9fkADB58mQsXboUR44cQfv27ZXmDwkJwbZt27Bv3z506dJFTK9UqRK+//57JCQk5Kmefn5+iIuLw8yZM3MMwCDKjC1AEBEREX2FjI2NYWxsjH379hVY8/Hr16/HoEGD4O/vrxD8kFVSUhICAgJgbm4OBwcHlfm0tbWho6OD5OTkPNdL1gJF1v8pZ1paWli4cCHWrl2Lf/75J9/lff/994iNjcXZs2cBAGfPnkVsbKzcDQlVwsLCcPXqVTRq1CjX0+V6zzt1jxWVK1fG8OHD4evri/T0dLXKjoqKwqFDh6Crq5ttvqzHgUePHuHChQvo3bs3evfujTNnzuDJkyfqz5QaeNwgIiIiItIcb9++RWhoKHx8fOQeUMuYm5vj0KFDeP36NSZNmqS0DFXdO8qUKFECY8aMwZMnT3D16lUAwLZt29CmTRu54AcZHR0dGBkZITU1FWlpadDX15cbbmBgIF4bZ/Xvv//ijz/+kAv+V+e6RFmeL/V65ueff8aDBw/Qv39/rFu3DgEBATA2NgaQ8XJOtWrVlN5rGD9+PN68eYMjR44AyAg8OX78OF69eqV0OupsW5lt27YNJUuWRI0aNeDr6yt2/aCu4ra+0tPTsXv3bsTGxmZ77b5t2zY4ODjIBT/IyLrCza07d+5g7ty52LJlC0qU4ONsyh1uMURERERfIW1tbQQGBmLz5s0wNzdHs2bNMHXqVNy8eVMu3+TJk8UHoLLPwoULFcqLjIzEjz/+CD8/P/Tv31/pNG/duiWWYWBggOXLl2PHjh0wNTVVmj85ORmLFi1CfHw8WrVqlf+ZzqPidvH5uXXr1g116tTBrFmz8l2Wjo4OPDw8xG4s/P394eHhAR0dHaX5mzZtCmNjY+jq6qJhw4bo3bs3Bg4cqPb0ZH1WUt6pe6wAgOnTp+Px48fYtm2byvJkxwEDAwNUrlwZERER2Xadoew44O/vj/bt28PCwgKWlpZwc3NT2ooEERERERF9HR4+fAhBEODo6Kgyj6y7hOzy5EQ2blRUlFhmTuWZmJigSZMmmDdvHl68eIG0tDRs3boVFy5cQHR0tNJxNm/eDBMTE3Tv3j3Pdc2O7Fq5OF8vHzx4MNv7UaVKlcK8efMQFBSEYcOGia1rAMD9+/fh5OSktFxZ+v379wEAP/30E169egUbGxvUqlULw4cPl2tpQJ1tS6Zfv37YunUrTpw4AV9fX/z222/w8PDI0/wXtpyWr+x+oJ6eHnr27AkLCwsMGTJEZXkPHjzI9gWnzDLfH5R9nJ2d5fIkJSWhb9++WLZsGSpUqKCyrOK8DVPRYgAEERER0VeqR48eePHiBQ4cOIB27drh5MmTqFevHgIDA8U8EydORHh4uNxn+PDhCmWVL18e9erVw7Jly1RewDs4OIhlXL16FSNGjECvXr0QFhYml092kWVoaIglS5Zg8eLF6NixY4HOO+XOkiVLsHnzZkRGRua7LG9vb+zcuRMxMTHYuXOnQnORmQUHByM8PBw3btxASEgI9u/fz65PioA6xwoAsLa2xoQJEzBz5kyVrbbIjgNXrlzB5MmT4ebmhlGjRinkU3UcSEtLw+bNm+VuInl4eCAwMFDtlieIiIiIiOjLIghCgeRRtwzZQ1d1y/ztt98gCALKlSsHPT09rFmzBn379lX5Vru/vz/69++v0GrE18TFxSXb+1FpaWkIDAyEoaEhLl68iNTUVLnx1V031atXx+3bt3Hx4kV4e3vj5cuX6Ny5s/iwPzfbzbBhw+Dm5oaaNWuif//+2LJlC/bu3YtHjx6pXcbnktPyld0PPH78OBo1aoSVK1fC3t5eZXm5WU6Z7w/KPn/++adcHl9fXzg5ORXbABIq/rSLugJEREREVHT09fXh6uoKV1dXzJgxA0OGDMGsWbPg5eUFAChZsqTCBY6lpaVCOSYmJjh69ChcXV3h4uKCEydOoEyZMnJ5dHV15cqqW7cu9u3bh1WrVmHr1q1i+sSJE+Hl5QVjY2OULl06V9Hczs7OePjwofjdwsJCTH/z5o1cHtkwmdjYWDFN9n9sbKza0/6SNW/eHG5ubvD19RW3DQAwNTXF+/fvkZ6eLnfjRtZ3qrImDmvWrAlHR0f07dsXTk5OqFGjBsLDw5VO19bWVtxmnJyc8OjRI8yYMQOzZ8/O9Y2gzNtA5nUtoyxNnbyZt5XM+ZSNZ2VlhYiIiFzVu7jI6VghM27cOGzYsAEbNmxQWk7m44AsqGHOnDmYN2+eXD5Vx4FDhw7h+fPncHd3l8uflpaGY8eOwdXVVW5dq6JsHWalTjlERERERFT0qlatColEgrt376rMU61aNQDA3bt30aRJkzxNR/ZSQOXKlcUys5umTJUqVXDq1Cm8f/8eCQkJKFOmDNzd3WFnZ6eQ98yZM7h37x6Cg4MVhqlzjaIsjyZe2xgZGWX7wH358uX4+++/ERYWhhYtWmDhwoWYOXMmgIz1ouoFDlm6bHsAMro3adiwIRo2bIixY8di69atGDBgAKZNm6bWtqWKrAvPhw8fokqVKuJ6UHU9KrsHpWp9FeQ9hZyWr+x+oL29PXbu3ImaNWuiQYMGqF69utL86u4LgOL9QSCj9cnMjh8/jlu3bmHXrl0A/guwKFmyJKZNm4Y5c+YotBpBlBkDIIiIiIhIVL16dezbty9P41pYWODo0aNo27YtWrZsiRMnTqBs2bLZjqOlpYWPHz/KpSkLulCXsgtBiUQipmf+X11sTi/D4sWLUadOHbkmDR0cHJCamorw8HDUq1dPTL927RoA+RsKmXl7e2PkyJHw8/PLVR20tLSQmpqK5OTkXAdAaGrgQXGl6lhhbGwsBql8//33OZYzffp0tGrVCiNGjJA7Xqg6DkilUvTp0wfTpk2TS1+wYAGkUilcXV0LZF1nPW4QEREREVHxJesab/369Rg9ejSMjIzkhsfFxaFt27YoWbIkli5dir179yqUERcXB3Nzc5XTSE9Px5o1a1C5cmXUrVsXQEaXB1OnTsX169fFNJmUlBQkJyfL1cXIyAhGRkaIjY3FoUOHsHTpUoXpSKVS1K9fH7Vr11YYltO1jrJ7HqrSNFlERARmzZqF7du3w8nJCX5+fujbty+6du2KWrVqoU+fPujXrx9+//13dO7cWW7cFStWwMrKCq6urirLlz3kf//+PSpXrpzjtqVqu5G97CF7QUida9W83LcqbLa2tnB3d4evry/279+vNE+/fv3Qp08f7N+/H126dJEbJggCEhISlL4ko8ru3bvl7hdeuXIF3t7eOHPmDKpUqQIgY3lq+rZMhYddYBARERF9hd68eYNWrVph69atuHnzJh4/foydO3di6dKlChcquWFubo4jR47AwsICLVu2xIsXL8RhqampiImJQUxMDB48eID58+fjzp07uZpecnKy2DxecnIynj9/jvDwcLlWHwpaQTST+SWQNeG4Zs0aMc3Z2Rlt27aFt7c3jh07hsePHyM0NBQjR46Eu7s7ypUrp7SsoUOH4tWrV9n2HwlkbKcxMTH4559/8Ndff2H16tVwcXGBqampmOfjx48KTSfKmpcUBIHrL5/ycqwYNmwYzMzMsH379hzLb9KkCWrVqiXX16gqr169wu+//w5PT0/UqFFD7jNw4EDs27cPb9++zfU8EhERERGR5lu/fj3S0tLwzTffYPfu3Xjw4AEiIyOxZs0aNGnSBEZGRti0aRP++OMPfP/99zh69CiioqIQFhaGSZMmKXT3Kbse/fvvv3HgwAG0adMGly9fhlQqhZaWFgBg7NixaNasGVq3bo3169fjxo0b+PvvvxESEoLGjRvjwYMHADJasgsNDcXjx49x5MgRuLi4wNHREYMGDZKbZkJCAnbu3JnjtXJ+ya6Vi/P1clJSkngPSfZ5/fo1UlNT4enpie7du6N79+4AMrpt7NGjB7y8vJCamoo+ffqgW7du8PT0hFQqRVRUFG7evIkffvgBBw4cwKZNm8RAhp49e2LlypW4dOkSnjx5gpMnT8LHxwfVqlWDo6MjgJy3LQB49OgR5s2bh6tXryIqKgoHDhzAwIED0bx5c9SqVatoFmI2VC1fVcaMGYPff/9doRtbmd69e8Pd3R19+/bFwoULERYWhidPnuDgwYNo06YNTpw4kav6ValSRe6aX9bqipOTE0qVKiXmK87bMBUttgBBRERE9BUyNjYW+/B79OgRUlJSYGtri6FDh2Lq1Kn5KtvMzAyHDx9Gu3bt0KJFC5w8eRJARmS2LOrd0NAQVapUgZ+fHwYOHKh22S9evJB7q2L58uVYvny53HSyynwxxAuj/Jk7d65CM5zBwcGYNWsWfvjhB7x48QLly5dHt27dMGPGDJXlaGtro2TJkjlOr02bNgAyWn4oU6YMOnTogAULFsjluX//vsKbNq1bt8bRo0fF71zveZeXY4WOjg7mzZuHfv36qTWN//3vf/Dy8sLkyZNha2urMt+WLVtgZGSE1q1bKwxr3bo1DAwMsHXrVowePVq9mcsGjxtERERERJrFzs4O165dw4IFCzB+/HhER0fD2toa9evXF1sf7NKlC86fP49FixahX79+SEhIgK2tLVq1aoX58+fLlSe7HjU0NETFihXh4uKCjRs3yrVUp6enhyNHjmDlypX45ZdfMGHCBBgaGsLJyQmjR49GjRo1AADx8fHw9fXFP//8A0tLS/To0QMLFiyAjo6O3DSDgoIgCAL69u2rMH/qXJcoy6Op1zOhoaEKXas6ODigX79+eP78OQ4fPiw3bP369XB2dha7wggJCcGqVauwcuVKjBw5Evr6+mjSpAlOnjyJZs2aieO5ublhx44dWLRoEeLj42FjY4NWrVph9uzZYrcM6mxburq6OHr0KFatWoX379/D1tYWPXr0wPTp03M1359rfalavqq6sahevTratm2LmTNn4s8//1QYLpFIsH37dmzcuBH+/v5YsGABtLW1UbVqVQwcOBBubm6FMh9EqkgETT36EREREREREREREREREREREf0/doFBREREREREREREREREREREGo8BEERERERERERERERERERERKTxGABBREREREREREREREREREREGo8BEERERERERERERERERERERKTxGABBRERERESkgS5cuAAtLS107NhRYVhycjKWLl2K2rVrw9DQECVLlkSzZs0QEBCAlJQUSCSSbD+zZ89GVFQUJBIJtLS08Pz5c7nyo6Ojoa2tDYlEgqioKIXpu7m5QUtLC1euXFEY5uXlJU5HV1cX9vb2mDt3LlJTUwts2RAREREREVH+ZL52k0gksLKyQrt27XDz5k0xz7Vr1+Dq6gpzc3NYWVlh2LBhSExMFIfLritlHxMTEzg7O8PHxwcPHjyQm96ePXvg6uoKa2trmJqaokmTJjh06NBnm9/PRZ3l+v3336NChQrQ19dHmTJlMGDAALx48UKuHEEQ8Ouvv6JJkyYwNTWFsbExnJ2dMWbMGDx8+FDMN3v2bEgkEgwfPlxu/PDwcPGaXpYnuw8ApKWlYcaMGahcuTIMDAxQpUoVzJs3D4IgFOISI8o9BkAQERERERFpIKlUilGjRuH06dNyN0KSk5Ph5uaGxYsXY9iwYTh//jwuX74MHx8frF27FhEREYiOjhY/q1atgqmpqVzahAkTxPLKlSuHLVu2yE178+bNKFeunNJ6PX36FOfPn8ePP/4If39/pXnatWuH6OhoPHjwAOPHj8fs2bOxbNmyAlgqREREREREVFBk127R0dE4duwYtLW10alTJwDAixcv0KZNG9jb2+PSpUsIDQ1FREQEvLy8FMo5evQooqOjcePGDSxcuBCRkZGoXbs2jh07JuY5ffo0XF1d8eeff+Lq1atwcXFB586dcf369c81u59NdssVAFxcXBASEoJ79+5h9+7dePToEXr27CkOFwQB/fr1w+jRo9GhQwccPnwYd+7cgVQqhb6+PubPny83PX19fUilUoWgE5kJEybI3RMoX7485s6dK5cGAEuWLIGfnx/WrVuHyMhILFmyBEuXLsXatWsLYSkR5Z1EYFgOERERERGRRklMTESZMmUQFhaGWbNmoVatWpg6dSoAYOnSpfD19UVYWBjq1q0rN15KSgqSk5NhZGQkpgUGBmLs2LGIi4uTyxsVFYXKlStj+vTpCA4Oxv3798VhDg4OcHd3x7x58/D48WNUqlRJHDZnzhzcvXsXs2bNQuPGjREdHQ0DAwNxuJeXF+Li4rBv3z4xrW3btnj37h0uXLhQAEuHiIiIiIiI8kvZtdvZs2fx3Xff4eXLl9i7dy9mzJiB6OholCiR8b71rVu3UKtWLTx48AD29vbideX169dRp04dsZz09HS0bt0ajx8/xqNHj6ClpaW0Ds7OznB3d8fMmTMLc1Y/q5yWq7W1tcI4Bw4cQNeuXZGUlAQdHR0EBQWhb9++2L9/P77//nuF/IIgiK02zJ49G/v27UOpUqVgbm6OkJAQABktQNStW1fhmh4AKlWqhLFjx2Ls2LFy6Z06dULp0qUhlUrFtB49esDAwABbt27N4xIhKnhsAYKIiIiIiEjDhISEwNHREQ4ODvDw8IC/v7/Y5OS2bdvQpk0bheAHANDR0ZELflDH999/j9jYWJw9exZAxo2Z2NhYdO7cWSGvIAgICAiAh4cHHB0dYW9vj127duU4DQMDAyQnJyuky27YyP6qktNwIiIiIiIiyp/ExERs3boV9vb2sLKyQlJSEnR1dcXgBwBi8Lvs+lGVEiVKYMyYMXjy5AmuXr2qNE96ejrevXsHS0tLMe1LvPbLulyzevv2LbZt24amTZtCR0cHALBjxw44ODgoDX4AlC+nxYsXY/fu3QgLC8tzXZs2bYpjx46JL0jcuHEDZ8+eRfv27eWm/SWuJ9IsDIAgIiIiIiLSMFKpFB4eHgAyms6Mj4/HqVOnAAAPHjyAo6NjgU1LR0dHDLIAAH9/f3h4eIg3XjI7evQoPnz4ADc3NwCAh4eH3JshWQmCgKNHj+LQoUNo1aqVmJ6XGya8wUJERERERFSwDh48CGNjYxgbG8PExAQHDhxAcHAwSpQogVatWiEmJgbLli1DcnIyYmNjMWXKFAAQu0zIjuy6NSoqSunw5cuXIzExEb179y6w+SkusluuMpMnT4aRkRGsrKzw9OlT7N+/Xxx2//59ODg4yJU5duxYsczy5csrTLNevXro3bs3Jk+enOd6T5kyBX369IGjoyN0dHRQt25djB07Fv3791fIy2t0KkoMgCAiIiIiItIg9+7dw+XLl9G3b18AgLa2Ntzd3cVAg8Lo5dDb2xs7d+5ETEwMdu7cCW9vb6X5/P394e7uDm1tbQBA3759ce7cOTx69Egun+xmj76+Ptq3bw93d3fMnj27wOtNREREREREeefi4oLw8HCEh4fj8uXLcHNzQ/v27fHkyRM4Oztj8+bNWLFiBQwNDWFjY4PKlSujdOnScg/yVZFduyp7UL59+3bMmTMHISEhKFWqVIHPV1HLbrnKTJw4EdevX8fhw4ehpaWFgQMHZnu9P23aNISHh2PmzJlITExUmmf+/Pk4c+YMDh8+nKd6h4SEYNu2bdi+fTuuXbuGzZs3Y/ny5di8eXOeyiMqLNpFXQEiIiIiIiJSn1QqRWpqKsqWLSumCYIAPT09rFu3DtWqVcPdu3cLdJo1a9aEo6Mj+vbtCycnJ9SoUQPh4eFyed6+fYu9e/ciJSUFfn5+YnpaWhr8/f2xYMECMc3FxQV+fn7Q1dVF2bJlxYAJZZydncW/Dx8+hIWFRYHOGxERERERESlnZGQEe3t78fumTZtgZmaGX3/9FfPnz0e/fv3Qr18//PvvvzAyMoJEIsFPP/0EOzu7HMuOjIwEAFSuXFkuPSgoCEOGDMHOnTvRpk0bhfGcnZ3x5s0b8XtsbGy214mxsbGwt7dHREREjnX6XHJargBQsmRJlCxZEtWqVYOTkxNsbW1x8eJFNGnSBFWrVsW9e/fkyrS2toa1tXW2ASNVqlTB0KFDMWXKlGxba1Rl4sSJYisQQMa9gidPnmDRokXw9PTMdXlEhYUBEERERERERBoiNTUVW7ZswYoVK9C2bVu5YV27dsWOHTvQr18/TJ06FdevX0fdunXl8qSkpCA5ORlGRka5nra3tzdGjhwpF9yQ2bZt21C+fHns27dPLv3w4cNYsWIF5s6dCy0tLQCKN3uyyvwmUEREhPhXFYlEUigtXxAREREREdF/JBIJSpQogY8fP8qlly5dGkBGq4D6+vpwdXXNtpz09HSsWbMGlStXlrtu3bFjB7y9vREUFISOHTsqHbc4BTIUFFXLVSY9PR0AkJSUBCCjtcV+/fph//796NKlS66mNXPmTFSpUgVBQUG5rueHDx8UWvfQ0tIS65cZr9GpKDEAgoiIiIiISEMcPHgQsbGxGDx4MMzMzOSG9ejRA1KpFGfPnsUff/yB1q1bY968efj2229hYmKCsLAwLFmyBFKpFHXq1Mn1tIcOHYpevXrB3Nxc6XCpVIqePXuiRo0acum2trbw9fVFaGioyhtYREREREREVPwkJSUhJiYGQEZLCuvWrUNiYiI6d+4MAFi3bh2aNm0KY2NjHDlyBBMnTsTixYsVrhvfvHmDmJgYfPjwAbdv38aqVatw+fJl/PHHH2Kg/Pbt2+Hp6YnVq1ejUaNG4nQNDAwUrn81XXbL9dKlS7hy5Qq+/fZbWFhY4NGjR5gxYwaqVKmCJk2aAAD69OmDPXv2oE+fPvD19YWbmxtKly6NJ0+eIDg4WFymypQuXRrjxo3DsmXLcl3vzp07Y8GCBahQoQKcnZ1x/fp1/PTTTyq7ySQqKjl3wkNERERERETFglQqRZs2bZTe/OnRowfCwsJw7949HDlyBJMmTcIvv/yCxo0bo2HDhlizZg1Gjx6tEKCgLm1tbZQsWVJpdxVXr17FjRs30KNHD4VhZmZmaN26dZ6a15S9MZLTmyN8s4SIiIiIiKjghYaGokyZMihTpgwaNWqEK1euYOfOnWjZsiUA4PLly3B1dUXNmjWxceNG/PLLLxg9erRCOW3atEGZMmVQs2ZNTJkyBU5OTrh58yZcXFzEPBs3bkRqaip8fHzEaZYpUwZjxowR83wp137ZLVdDQ0Ps2bMHrVu3hoODAwYPHoxatWrh1KlT0NPTA5DRYkRwcDBWrVqFP//8U8zr7e0NW1tbnD17NtvpT5gwAcbGxrmu99q1a9GzZ0+MHDkSTk5OmDBhAn744QfMmzdPzCMIwheznkhzSQRuhURERERERERERERERERERKTh2AIEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREdFXy8vLCxKJBMOHD1cY5uPjA4lEAi8vLzFv165dFfJduHABWlpa6NixYyHXloiIiIiIqHiRSCTZfmbPno2oqChIJBJoaWnh+fPncuNHR0dDW1sbEokEUVFRACDmV/a5ePEiACAwMBASiQROTk4Kddq5cyckEgkqVaokl75+/Xo4OTnBwMAADg4O2LJli9zwiIgI9OjRA5UqVYJEIsGqVasKbDkR0efDAAgiIiIiIiL6qtna2iIoKAgfP34U0z59+oTt27ejQoUKOY4vlUoxatQonD59Gi9evCjMqhIRERERERUr0dHR4mfVqlUwNTWVS5swYYKYt1y5cgpBB5s3b0a5cuWUln306FG5sqKjo1G/fn1xuJGREV6+fIkLFy7IjSeVShWu5fz8/ODr64vZs2cjIiICc+bMgY+PD37//Xcxz4cPH2BnZ4fFixfDxsYmz8uEiIoWAyCIiIiIiIjoq1avXj3Y2tpiz549YtqePXtQoUIF1K1bN9txExMTERwcjBEjRqBjx44IDAws5NoSEREREREVHzY2NuLHzMwMEolELs3Y2FjM6+npiYCAALnxAwIC4OnpqbRsKysrubJsbGygo6MjDtfW1ka/fv3g7+8vpv3zzz84efIk+vXrJ1fWb7/9hh9++AHu7u6ws7NDnz59MGzYMCxZskTM07BhQyxbtgx9+vSBnp5evpYLERUdBkAQERERERHRV8/b21vuRpy/vz8GDRqU43ghISFwdHSEg4MDPDw84O/vD0EQCrOqREREREREGun7779HbGwszp49CwA4e/YsYmNj0blz5zyX6e3tjZCQEHz48AFARtcY7dq1Q+nSpeXyJSUlQV9fXy7NwMAAly9fRkpKSp6nT0TFDwMgiIiIiIiI6Kvn4eGBs2fP4smTJ3jy5AnOnTsHDw+PHMeTSqVivnbt2iE+Ph6nTp0q7OoSERERERFpHB0dHTFwHMgIPPfw8JBr1SGzpk2bwtjYWO6TVd26dWFnZ4ddu3ZBEAQEBgbC29tbIZ+bmxs2bdqEq1evQhAEhIWFYdOmTUhJScHr168LdkaJqEhpF3UFiIiIiIiIiIqatbW12IWFIAjo2LEjSpYsme049+7dw+XLl7F3714AGc2vuru7QyqVomXLlp+h1kRERERERJrF29sbTZs2xcKFC7Fz505cuHABqampSvMGBwfDyclJrTIDAgJQoUIFvH//Hh06dMC6devk8syYMQMxMTFo3LgxBEFA6dKl4enpiaVLl6JECfXeF5dIJGq3+JebvERUsBgAQURERERERISMm2Y//vgjAGD9+vU55pdKpUhNTUXZsmXFNEEQoKenh3Xr1sHMzAzOzs548+YNYmNjYW9vj4iIiEKrPxERERERUXFXs2ZNODo6om/fvnByckKNGjUQHh6uNK+trS3s7e1zLLN///6YNGkSZs+ejQEDBkBbW/Hxp4GBAfz9/fHLL7/g33//RZkyZbBx40aYmJjA2tpa7fpnvsazsLBQmic2Nlbt8oio4DEAgoiIiIiIiAgZXVgkJydDIpHAzc0t27ypqanYsmULVqxYgbZt28oN69q1K3bs2IHhw4cz4IGIiIiIiCgLb29vjBw5En5+fgVSnqWlJb7//nuEhITg559/zjavjo4OypcvDwAICgpCp06d1G4BAoDa13gSiUTtMomoYDEAgoiIiIiIiAiAlpYWIiMjxf+zc/DgQcTGxmLw4MEwMzOTG9ajRw9IpVIMHz680OpKRERERESkqYYOHYpevXrB3Nw823xv3rxBTEyMXJq5uTn09fUV8gYGBmLDhg2wsrJSWtb9+/dx+fJlNGrUCLGxsfjpp59w+/ZtbN68WcyTnJyMO3fuiP8/f/4c4eHhMDY2hr29fa66tGD3F0RFR/2QJiIiIiIiIqIvnKmpKUxNTXPMJ5VK0aZNG4XgByAjACIsLAw3b94sjCoSERERERFpNG1tbZQsWVJpVxWZtWnTBmXKlJH77Nu3T2leAwMDlcEPAJCWloYVK1agdu3acHV1xadPn3D+/HlUqlRJzPPixQvUrVsXdevWRXR0NJYvX466detiyJAheZlNIioiEoEhSERERERERERERERERERERKTh2AIEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERERERERERERERERERaTwGQBAREREREREREREREREREZHGYwAEERERFTuCICAhIQGCIBR1VYiIiIiIiIiIiIiISEMwAIKIiIiKnXfv3sHMzAzv3r0r6qoQEdEXzLVEL7iW6IV+FYcXdVWIiIiIiIiIlLp9NlK8fiWinDEAgoiIiIiIiL5qr569KeoqEBERERERERFRAWAABBERERERERERUR6sH+2PaZ0WIj09vairQkRERERERGAABBERERUCPz8/1KpVC6ampjA1NUWTJk3w119/FXW1iIjoC3fnwj0s6LsSL5+9LuqqENFXYt+6v3D5z+u4e+lBUVeFiIiIiL5UEklR14BIo2gXdQWIiIjoy1O+fHksXrwYVatWhSAI2Lx5M7p06YLr16/D2dm5qKtHRERfqDHNpgMA4l7GY9mx2UVbGSL6qqSmpBV1FYiIiIiIiAhsAYKIiIgKQefOndGhQwdUrVoV1apVw4IFC2BsbIyLFy8WddWIiOgrEPP4pcph6enpSIx7/xlrQ0Sfw4d3H5GakprvchLevsvTeIIg5HvaRERERERElH8MgCAiIqJClZaWhqCgILx//x5NmjRRmicpKQkJCQlyHyIiorySlFB9qTupzVx0s/TCs3vPP2ONiKgwJbx9hy5mA+FVbXS+ytn10+/oUdIbe9f8mfuRlcQ/MCiCiIiIiAqChF1gEOUKAyCIiIioUNy6dQvGxsbQ09PD8OHDsXfvXlSvXl1p3kWLFsHMzEz82NrafubaEhHRl0RSQvXNoRsnIwAAhwNPfqbaEFFhu33mLgDg3yev8lXOLxO2AAA2jA3I9bjp6ely38/tu4zeNkNw7ditfNWJiIiIiIiIcocBEERERFQoHBwcEB4ejkuXLmHEiBHw9PTEnTt3lOb19fVFfHy8+Hn27Nlnri0REX1J+HYM0VemGO7ys7svQ9yrBEx2nVvUVSEiIiIiDceWxYhyR7uoK0BERERfJl1dXdjb2wMA6tevjytXrmD16tX45ZdfFPLq6elBT0/vc1eRiIi+UOrEP/AGEhEVJB5SiIiICtbD8MdITU6F4zdVi7oqRESkYRgAQURERJ9Feno6kpKSiroaRET0FYj++2WOefiwkujL8TlbfXmf8AGJse9RuqK1/AAeVIiIiApMWloaRtSbBADY+zYQxuZGRVwjIiLSJAyAICIiogLn6+uL9u3bo0KFCnj37h22b9+OkydP4tChQ0VdNSIi+gqkpablmOfp3X9UDkv+lAxdfd2CrBIRfSG6Ww1Celo6tkVtQKkK/wVBsFUZIiKigpOanCr+n/DmHQMgiHiuSZQrJYq6AkRERPTlefnyJQYOHAgHBwe0bt0aV65cwaFDh+Dq6lrUVSMiIgIAJH1IVpq+ZuSv6GjYH49uRH3eChFRnn3OFiDS09IBALfP3v1s0yQiIvrayH5vAfWCm4m+Jgy8JcoZAyCIiIiowEmlUkRFRSEpKQkvX77E0aNHGfxARETFSvSjGKXpv/98GACwbf6uz1kdIsqHwop/eP3iLdaP9sfTu88VhmW978z70ERERAUnLfW/AIhLf1wrwpoQEZEmYgAEERERERERfXViol5lO5wPM4loYd9V2LfuL/g0nKwwLD09Xe4738QjIiIqOJ/efxL//5j4KZucREREihgAQURERERERJRF5n6HiaiYK6QmIO6HPQIAfHqfpDgwS7xD9KN/C6UOREREX6Ot83aL//O8nEhecQ28ZXc1VJwwAIKIiIiIiIgoi4sHryIq4llRV6PIxL9OQPKn5KKuBlGupSSnFFhZJbRU3zbL2gLEpT+vFth0iYiIvnZXD4eL/8f+G190FSEqJgo75iE1JRWLB6zBX9Jjao/zNiYWs3ssw7WjN7FiiB+6mnvi9Yu3hVhLIvUxAIKIiIiIvhhpaYw2J6KCI526rairUCTexsSiZ6nB6Gs7vKirQqQWSaYWIB5c/bvAylXa8sP/K6Yv3hEREX0R0tL+CzQ8tu10EdaEqPgpjBYgzu+/gmPbzuCnoT+rPc66UVKc23sZk9vOQ6j/cXz6kIQD60MLvG5EecEACCIiIiL6IhzYcAhdTAci4vy9oq4KEX0hPiV+QmjACcT+G1fUVfmsbp2OBAAkvHlXxDUhUk/Sh/8CFd68iC2wcrO7uSykp+Pu5QcFNi0iIiL6T1rqfwEQ33SoV4Q1Ifo6pCRl39XMu9hEhAacwPv492La6+eKrT0cCjxZ0FUjyhMGQBARERHRF2Htj5uQ9DEZSwauLeqqENEXIvxEBFYM3oDxLWcVdVWIKDuZWoAwNDX4LJMUBGBU46ni97J2Np9lukRERF+DtJT/HsZalDIrwpoQFQ+F0epDZplOp5Wa1/snrBi8AYs81mSqk2K+t9EFF4xMlB8MgCAiIiIiIiLKxrN7L4q6CkSUDSH9v7dES1Uo+dmnCQDOzRw+y3SJiIi+BumZusBIT8/5we/HxI9I/pRcmFUiKj4KIxYihwiI68duAQAu/XGtECZOVPAYAEFEREQFatGiRWjYsCFMTExQqlQpdO3aFffusUsC+nwKOyqeiIiIihch04MRSYnPc6sr6+kGTz+IiIgKTuYuMLR1tLLNm/QxCd+bDkSPkt6FXS0iyuTlk1dFXQUilRgAQURERAXq1KlT8PHxwcWLF3HkyBGkpKSgbdu2eP/+fc4jExERUZFLS00r6ioQ5UrmN0NLlMih/d58OL79jPh/1hYgiIiIqOCkZuoCo1m3b7LN++JhDADg04ckvhBBlEeSnPrAyOLDu494GxNXOJUhKgDaRV0BIiIi+rKEhobKfQ8MDESpUqVw9epVNG/evIhqRUREROp6F8ugRdIsmYMRJIUYAJG5z+Os+wkfuBARERWczC1A6OrrZpu3hPZ/LUSkp6XjxaMYWJQ2h7G5UaHVj6go5fe8UxAELB+8AYbGBvBZk9FySi7jH/Dq2et81YGosLEFCCKiHHh5eaFr165FXQ0ijRUfHw8AsLS0LOKa0FeDDyCIiPJFR0+nqKtAlCuZf/pz+/ZaXgUt3qu6EkRERJQvmVskyym4UUvrv8dcf998Am+nsehdZmih1Y2oKBRksG3M45c4HHgS+9b9hdfP3xRYuQDQsk+zAi2PKK8YAEEapWXLlhg7dqxCemBgIMzNzcXvs2fPhkQiET9mZmb47rvvcOrUKbnxKlWqJJdP9lm8eDEAICoqSi7d0tISLVq0wJkzZ5DV27dvMXbsWFSsWBG6urooW7YsvL298fTpU7l8Xl5ectOQ2bdvn1o3atavX49KlSpBX18fjRo1wuXLl+WGf/r0CT4+PrCysoKxsTF69OiBf//9Vy7P06dP0bFjRxgaGqJUqVKYOHEiUlP/a1YsOjoa/fr1Q7Vq1VCiRAmlyzwiIgI9evQQl+GqVasU8pw+fRqdO3dG2bJlIZFIsG/fvmznbfjw4SrLykzVdpCTvI6Xk5MnT8ptJ9bW1ujQoQNu3bolly8v697R0RF6enqIiYkp8HoXlFOnTqFVq1awtLSEoaEhqlatCk9PTyQnJ4vzrOpTqVIlABnrRpamr6+PatWqYdGiRUpP7C5cuAAtLS107NhRYZhsny1VqhTevXsnN6xOnTqYPXu2+D3zNPX09FCuXDl07twZe/bsydX8Zz3WNGvWDMePH5fL8+zZM3h7e6Ns2bLQ1dVFxYoVMWbMGLx5I3+C+fjxY/Tr1w9ly5aFvr4+ypcvjy5duuDu3bty09u3bx8CAwOzXbYSiQRRUVGYPXs26tSpAwAYNWoUnJyclM7H06dPoaWlhQMHDijMV+ZPUFBQrpZPeno6xo4di2bNmqFGjRoq8yUlJSEhIUHuQ0REREXjftijoq4CUa4YGOt/9ml+TPwk953xD0RERIUjp3vmJTIFQFwJDQcApCSlFGaViDTau9hE8f+t83YDyH0QcfTfL5Wm6+iy4wEqHhgAQV8sZ2dnREdHIzo6GhcuXEDVqlXRqVMn8U1kmblz54r5ZJ9Ro0bJ5Tl69Ciio6Nx+vRplC1bFp06dZILKnj79i0aN26Mo0eP4ueff8bDhw8RFBSEhw8fomHDhvj777/lytPX18eSJUsQGxubq3kKDg7GuHHjMGvWLFy7dg21a9eGm5sbXr7878fmf//7H37//Xfs3LkTp06dwosXL9C9e3dxeFpaGjp27Ijk5GScP38emzdvRmBgIGbOnCnmSUpKgrW1NaZPn47atWsrrcuHDx9gZ2eHxYsXw8bGRmme9+/fo3bt2li/fn2O87Z3715cvHgRZcuWVXdxFDv37t1DdHQ0Dh06hKSkJHE5Z5abdX/27Fl8/PgRPXv2xObNmwur2vly584dtGvXDg0aNMDp06dx69YtrF27Frq6ukhLS8Pq1avl9i0ACAgIEL9fuXJFLGvo0KGIjo7GvXv34Ovri5kzZ+Lnn39WmKZUKsWoUaNw+vRpvHjxQmm93r17h+XLl+dYf9k0Hz16hN27d6N69ero06cPhg0blqvlIJunc+fOoWTJkujUqZO43//9999o0KABHjx4gB07duDhw4f4+eefcezYMTRp0gRv374FAKSkpMDV1RXx8fHYs2cP7t27h+DgYNSsWRNxcXEK03R3d5dbtk2aNBHnR/axtbWVG2fw4MG4e/cuzp8/r1CerJuKDh06KMxX5k9uW0Px8fHB7du3cwycWLRoEczMzMRP1roTERHR53N+/+WcMxEVI1bl/mtprEQBdYGR27fs2AUGERFR4cjpuaxWpi4w0lLSsskJJCel4J/7yu8nEmmK/J53Jmbqyu3a0ZsZ/+QyAOLZ3ef5qgNRYWMABH2xtLW1YWNjAxsbG1SvXh1z585FYmIi7t+/L5fPxMREzCf7GBnJ9w9mZWUFGxsb1KhRA1OnTkVCQgIuXbokDp82bRpevHiBo0ePon379qhQoQKaN2+OQ4cOQUdHBz4+PnLltWnTBjY2Nli0aFGu5umnn37C0KFDMWjQIFSvXh0///wzDA0N4e/vDyCjmXmpVIqffvoJrVq1Qv369REQEIDz58/j4sWLAIDDhw/jzp072Lp1K+rUqYP27dtj3rx5WL9+vfiwvlKlSli9ejUGDhwIMzMzpXVp2LAhli1bhj59+kBPT09pnvbt22P+/Pno1q1btvP1/PlzjBo1Ctu2bYOOTvbN7Xp5eeHUqVNYvXq13FvuQEZLBN988w309PRQpkwZTJkyRWzZQtV4aWlpGDx4MCpXrgwDAwM4ODhg9erV2dZBlVKlSsHGxgb16tXD2LFj8ezZM7k394HcrXupVIp+/fphwIAB4jrOTmxsLPr37w9ra2sYGBigatWqCAgIEIc/e/YMvXv3hrm5OSwtLdGlSxdx2QEZwTHjxo2Dubk5rKysMGnSJHh6emb7wPvw4cOwsbHB0qVLUaNGDVSpUgXt2rXDr7/+CgMDA5iZmcntWwBgbm4ufre2thbLMjQ0hI2NDSpWrIhBgwahVq1aOHLkiNz0EhMTERwcjBEjRqBjx44IDAxUWq9Ro0bhp59+kgsOUkY2zfLly6Nx48ZYsmQJfvnlF/z66684evRotuNmJpunGjVqwM/PDx8/fhTr7uPjA11dXRw+fBgtWrRAhQoV0L59exw9ehTPnz/HtGnTAGS0qvLo0SNs2LABjRs3RsWKFdGsWTPMnz8fjRs3VpimgYGB3LLV1dUV50f20dLSkhunTp06qFevnsL2JAgCAgMD4enpCW3t/6J0M68r2UdfX/2363788UccPHgQJ06cQPny5bPN6+vri/j4ePHz7NkztadDREREBSvzTWQiUk/C63c5ZyIiIqICl7kFiOsnbmWTE5jgMguDHMfg0p/XCrtaRAWrAGNtM7eQEv33v9nkVI3XjFTcMQCCvgpJSUkICAiAubk5HBwc8lzOx48fsWXLFgCArq4ugIzm3YOCgtC/f3+FlhAMDAwwcuRIHDp0SHzLGwC0tLSwcOFCrF27Fv/884/K6UkkEvEBb3JyMq5evYo2bdqIw0uUKIE2bdrgwoULAICrV68iJSVFLo+joyMqVKgg5rlw4QJq1qyJ0qVLi3nc3NyQkJCAiIiIvCyWfElPT8eAAQMwceJEODs755h/9erVCm+629ra4vnz5+jQoQMaNmyIGzduwM/PD1KpFPPnz892vPT0dJQvXx47d+7EnTt3MHPmTEydOhUhISF5nqf4+HjxbXfZdiKj7rp/9+4ddu7cCQ8PD7FVAGVdr2Q2Y8YM3LlzB3/99RciIyPh5+eHkiVLAshoXcDNzQ0mJiY4c+YMzp07B2NjY7Rr104MfFmxYgUCAwPh7++Ps2fP4u3bt9i7V75fW1m3CzI2NjZi6ygFRRAEnDlzBnfv3lVYfiEhIXB0dISDgwM8PDzg7++vNOK1b9++sLe3x9y5c3M9fU9PT1hYWOS6KwwZAwMDABn77Nu3b3Ho0CGMHDlSTJexsbFB//79ERwcDEEQYG1tjRIlSmDXrl1IS8s+Wj0/Bg8ejJCQELx//1+k78mTJ/H48WN4e3sXyDQEQcCPP/6IvXv34vjx46hcuXKO4+jp6cHU1FTuQ0RERAXP73+BGNlwMpI/JavMo52p2VK+1U4aoRC2U0EQUPM75d3HAUAFp3Jy34OX7S/wOhAREVHOP/OZAyA+vvuUTU4g8uIDAMBfm9R/8YnoS5OclKqQlssGIFCtYRWl6VG3nypNJ/rcGABBX6xbt27B2NgYxsbGMDAwwPLly7Fjxw6Fh2qTJ08W88k+WR80N23aFMbGxjAyMsLy5ctRv359tG7dGgDw6tUrxMXFwclJ+Y0RJycnCIKAhw8fyqV369YNderUwaxZs1TOg4ODg9gCw+vXr5GWliYXuAAApUuXRkxMDAAgJiYGurq6MDc3zzaPsjJkwz63JUuWQFtbG6NHj1Yrv5mZmcKb7lpaWtiwYQNsbW2xbt06ODo6omvXrpgzZw5WrFiB9PR0lePp6Ohgzpw5aNCgASpXroz+/ftj0KBBeQqAKF++PIyNjWFubo7t27fj+++/h6Ojo0I+ddZ9UFAQqlatCmdnZ2hpaaFPnz6QSqXZTv/p06eoW7cuGjRogEqVKqFNmzbo3LkzgIzuU9LT07Fp0ybUrFkTTk5OCAgIwNOnT3Hy5EkAwKpVq+Dr64vu3bvDyckJP//8s0ILIGZmZnJBRL169ULfvn3RokULlClTBt26dcO6deuQkJCg7mITbdiwAcbGxtDT00Pz5s2Rnp6usF1IpVJ4eHgAANq1a4f4+HicOnVKoSyJRILFixdj48aNePQod31YlyhRAtWqVZNrHUNdHz58wPTp06GlpYUWLVrgwYMHEAQh2+NDbGwsXr16hXLlymHNmjWYOXMmLCws0KpVK8ybN0+hC5386tevH1JSUrBz504xLSAgAN9++y2qVasml7dv374Kx8enT3M+ifXx8cHWrVuxfft2mJiYICYmBjExMfj48WOBzguRKnxWR0SkaH6fnzC98yLsWf0HHlz9G+f3X1GZV1vnv7d5ZP0oExVnhRGoIwgCJNl0p+HS51u572kpijeSiYiIqPBl7v4qNVm93+PUlDS8T/iAsMM3kJaa9xeR0lLTCvVFJiJl8nvqm5JNMLy69Ax0laY/uPY432UTFQQGQNAXy8HBAeHh4QgPD8fVq1cxYsQI9OrVC2FhYXL5Jk6cKOaTfRo0aCCXJzg4GNevX8fu3bthb2+PwMBAha4a8nLDZcmSJdi8eTMiIyOVDr97926O3UdosqtXr2L16tUKrQrkRWRkJJo0aSJXTrNmzZCYmJhtSwsAsH79etSvXx/W1tYwNjbGxo0b1XrIm9WZM2dw9epVBAYGolq1avj5559V5s1p3fv7+4sP+gHAw8MDO3fuxLt3Gc2qtm/fXnwgLWs5Y8SIEQgKCkKdOnUwadIknD9/Xhz/xo0bePjwIUxMTMTxLC0t8enTJzx69Ajx8fGIjo5Go0aNxHG0tbUV9oVu3brJdeuhpaWFgIAA/PPPP1i6dCnKlSuHhQsXwtnZGdHR0blYekD//v0RHh6Oc+fOoX379pg2bRqaNm0qDr937x4uX76Mvn37ivVzd3dXGRji5uaGb7/9FjNmzMhVPYD/v9mZi21SFihgYmKC3bt3QyqVolatWnLlqcPHxwcxMTHYtm0bmjRpgp07d8LZ2VmhK5D8MDc3R/fu3cVuMBISErB7924MHjxYIe/KlSsVjo9ly5bNcRp+fn6Ij49Hy5YtUaZMGfETHBxcYPNBRERE6kv+lIxTIRdw6Y//mvrN7lxHW+e/FiD4Bg99tXJ5iZ/57VMiIiIqGim5CICY4DIbvu3mY8vsvLUEnJaaBq9qozCi3iQIgoC4V/G4cSqCLahRsZe5BYgm32fc/8/t8xldFQEQ5tZs1ZeKB+2csxAVH6ampoiPj1dIj4uLU3hTXVdXF/b29uL3unXrYt++fVi1ahW2bt0qppcsWVIunzK2traoWrUqqlatitTUVHTr1g23b9+Gnp4erK2tYW5urvJBdmRkJCQSidJpNG/eHG5ubvD19YWXl1e2dShZsiS0tLTw77/yfTL9+++/YtcbNjY2SE5ORlxcnFwrEFnzXL58WaEM2bDP6cyZM3j58iUqVKggpqWlpWH8+PFYtWpVnt7Az62goCBMmDABK1asQJMmTWBiYoJly5bh0qVLuS6rcuXKYjcrL1++hLu7u8quIbJb93fu3MHFixdx+fJlTJ48WUxPS0tDUFAQhg4dik2bNolv08uCcdq3b48nT57gzz//xJEjR9C6dWv4+Phg+fLlSExMRP369bFt2zaFulhbW+d6XrMqV64cBgwYgAEDBmDevHliAMicOXPULsPMzEzcT0JCQmBvb4/GjRuLXbpIpVKkpqbKPYAXBAF6enpYt26dwjEAABYvXowmTZpg4sSJatcjLS0NDx48QMOGDdUeZ+XKlWjTpg3MzMzklqe9vT0kEgkiIyOVBjNFRkbCwsJCbhwTExN07twZnTt3xvz58+Hm5ob58+fD1dVV7frkZPDgwWjdujUePnyIEydOQEtLC7169VLIZ2Njk+PxURle6BERUVHg749q6emKy6ZENn22yneBUShVIipQRbGdZj3m9Br//eevBBER0VdGEATERL1EmcqllQ7P2ppDYtx7SCSAkZmRXPrVwzfE/7cv3INB8/vmui7Rf/+LmKhX4nR7lR4CAJgU+CNcB7bIdXlEasvnyW9KUor4f+bW/3JDVcCE87eKLWITFQWGp5NGcXBwwLVr1xTSr127ptB0uzJaWlr5boK9Z8+e0NbWxoYNGwBkNJffu3dvbN++XaELiY8fP2LDhg1wc3ODpaWl0vIWL16M33//HRcuXMh2urq6uqhfvz6OHTsmpqWnp+PYsWNo0qQJAKB+/frQ0dGRy3Pv3j08ffpUzNOkSRPcunULL1++FPMcOXIEpqamqF69ei6WRP4NGDAAN2/eVHi7fOLEiTh06JDK8XR1dRWaFnNycsKFCxfkbkKdO3cOJiYmKF++vMrxzp07h6ZNm2LkyJGoW7cu7O3tc91lgjI+Pj64ffs29u7dqzKPqnUvlUrRvHlz3LhxQ27ZjBs3TmztoFy5crC3t4e9vT0qVqwojmttbQ1PT09s3boVq1atwsaNGwEA9erVw4MHD1CqVClxPNnHzMwMZmZmKFOmjFzgR2pqKq5evZrrebewsECZMmXw/v37XI8rY2xsjDFjxmDChAkQBAGpqanYsmULVqxYIbdMbty4gbJly2LHjh1Ky/nmm2/QvXt3TJkyRe1pb968GbGxsejRo4fa48gCBbIGk1hZWcHV1RUbNmxQOPbIWnpwd3dXecIokUjg6OiYr2WpjIuLCypXroyAgAAEBASgT58+MDIyynlEIiKiYowBEKopWzZa2qpvB9Ro9t9NKy5X0jQFtcnmuO1nGWxiZVIwEyYiIiI5mX+T22r1xsAqP2L9GH+ledPT0sX/U5JT0M3SC10tvAqlm4rMrU1kDjhe6rUu2/H+ffIKv076DS+fvS7wOtGXqyCvy5I+JCkm5rIFCFX1EZQE3xMVBQZAkEYZMWIE7t+/j9GjR+PmzZu4d+8efvrpJ+zYsQPjx4+Xy5uamir2O//gwQPMnz8fd+7cQZcuXeTyvXv3Tswn+yQkJKisg0QiwejRo7F48WJ8+PABALBw4ULY2NjA1dUVf/31F549e4bTp0/Dzc0NKSkpWL9+vcryatasif79+2PNmjUKwxwdHeUeoI8bNw6//vqr2HXCiBEj8P79ewwaNAhAxhv0gwcPxrhx43DixAlcvXoVgwYNQpMmTdC4cWMAQNu2bVG9enUMGDAAN27cwKFDhzB9+nT4+PhAT09PnJbsAXNiYiJevXqF8PBw3LlzRxyenJws5klOTsbz588RHh6Ohw8finkSExPFPADw+PFjhIeHi91LWFlZoUaNGnIfHR0d2NjYwMHBQeUyq1SpEi5duoSoqCi8fv0a6enpGDlyJJ49e4ZRo0bh7t272L9/P2bNmoVx48ahRIkSKserWrUqwsLCcOjQIdy/fx8zZszAlSuq+0NWl6GhIYYOHYpZs2apPBlQtu5TUlLw22+/oW/fvgrLZsiQIbh06RIiIiKUljdz5kzs378fDx8+REREBA4ePAgnJycAGd1LlCxZEl26dMGZM2fw+PFjnDx5EqNHjxa7CBkzZgwWL16Mffv24e7duxg5ciTi4uLkprF37144Ov53Q/yXX37BiBEjcPjwYTx69AgRERGYPHkyIiIi0Llz5/wsQvzwww+4f/8+du/ejYMHDyI2NhaDBw9WWC49evRQ2Q0GACxYsADHjx/HvXv3FIZ9+PABMTEx+Oeff3Dx4kVMnjwZw4cPx4gRI+Di4pKv+susW7cOSUlJcHNzw+nTp/Hs2TOEhobC1dUV5cqVw4IFCwBk7HNdunTBrl27cOfOHTx8+BBSqRT+/v4Kx638kkgk8Pb2hp+fHy5cuKC0+wsgo3WdrMfHgg7GICIiKih8UJ8NJcsmczcXWZWqULIwa0NU4Apj/8+pzKzD89mrIxEREeXCvrV/KU3P3AJE/Ot34v+3z95Vlj1f4l7+11J15sCLnEztsAAhyw9gWoeFBV4nInUc2XJKIS3X57IqzpXT09XfF4gKEwMgSKPY2dnh9OnTuHv3Ltq0aYNGjRohJCQEO3fuRLt27eTyRkREiP3O16lTByEhIfDz88PAgQPl8s2cOVOuj/oyZcpg0qRJ2dbD09MTKSkpWLcuI5rTysoKFy9ehIuLC3744QdUqVIFvXv3RpUqVXDlyhXY2dllW97cuXOV/jDcu3dPrssPd3d3LF++HDNnzkSdOnUQHh6O0NBQlC79X5NfK1euRKdOndCjRw80b94cNjY22LNnjzhcS0sLBw8ehJaWFpo0aQIPDw8MHDgQc+fOlZt23bp1UbduXVy9ehXbt29H3bp10aFDB3H4ixcvxDzR0dFYvnw56tatiyFDhoh5wsLCxDxARgBH3bp1MXPmzGyXR04mTJgALS0tVK9eHdbW1nj69CnKlSuHP//8E5cvX0bt2rUxfPhwDB48GNOnT892vB9++AHdu3eHu7s7GjVqhDdv3mDkyJH5qp/Mjz/+iMjISOzcuVNlnqzr/sCBA3jz5o3S7hKcnJzg5OSk8mG/rq4ufH19UatWLTRv3hxaWloICgoCkBGQcfr0aVSoUAHdu3eHk5MTBg8ejE+fPsHUNKNfrvHjx2PAgAHw9PQUuwPJWo/4+Hi5QIJvvvkGiYmJGD58OJydndGiRQtcvHgR+/btQ4sW+WvqzdLSEgMHDsTs2bMhlUrFLiay6tGjB8LCwnDz5k2l5VSrVg3e3t749OmTwrBff/0VZcqUQZUqVdC9e3fcuXMHwcHBYgsvBUEWZGNnZyceF4YNGwYXFxdcuHBBbB2mfPnyqFSpEubMmYNGjRqhXr16WL16NebMmYNp06YVWH1kvLy8EB8fD2dnZzRq1EhpnkGDBikcH9euXVvgdSEqaJkfSBzffgauJXrh1M7sW1oiIvraZNcChNyDXQaWkCYolACInIYLWb4XeBWIiIgol9JS/7vPmrlp/5un7sjlq1TDNt/TsixjIf5/48Rttcd7GvkcABAV8axAHxZ/fP8JgTOD8PD64wIrk4qn/Ab/vngYk+3w/JSfm2AgosIkEfiaDBERqeDl5YW4uDjs27evqKtCX5mEhASYmZkhPj5eDNIhyolriV4AMt5c3hblJ5cGAEfSVQekEX3tZPvKodRgsfUsTZOWmoZ2un3E76r2+czHhcxk+XMarok+vPuILmYDFdIPvt8KPQM9hfTgpfuxacpWAEC9NjWx5HD+ApiJCtudC/cwpllG8PvWxxtQuqJ1DmMol3n//+Pjdvi2my8+MDmSvlNueP/pPbBt/m7x+3jpSLQb5KK0LE0+fhARERWFLuYD8SEhozvZ1efmo3qTjNaClf2+xv4bh95lhgIATK1MkPAmo+WHvW8D0c3SCwAwcHZvDJjZSxy/ar3KeHDtsUJZufEk8h8Mcf4fAKBF7yY4FZLx4kVZextsvq/6BaKs1xubH6xF2So2uZ5+Vr9O3oqQZfsB8NzjSxR+4jYmtp4DQPV1nLpGNfbF3csZLXl/270RZu2agHP7LmN292UAgMNpIQpdNmfd96IinmFozXEKZTdsVwcL/yz4l/mIcksz72wREREREanw8ulrpKak5pwxBzdORmBur+WF0lcoUXGS/ClZ/N9N271Im6zcOHELbp6+k3NGJRjbr5qqZfOX9LjS9Mx9wl47eqtQ6kRUkApl98+xCYhCmCYREREBAIzMDPM0XuZ7AXLPb7P8bhf0ucPF36+K/5tYGOVqXM+qowqkDo9uRIn/sxsCyk6XH9srpGUOeFDn2lpVnvR0niRT8cAACCIiIhUWLlwIY2NjpZ/27RVPFImo+Ahesl8hTXYjJOzwDQytNQ7vEz5kW8aEVrNxZvclzOu1ojCqSFRsZL1BEXboRpHUo4e1N3au+B3jW87K0/iaEABx/sAV/CU99vknrGLZJH9MVkhLTUnFb3P5xhhRTscUTTjmEBERfW3SU5U/+Nc31pf7XhC/40Km6yhbx3KZys530XnyPu69+P+rZ2+KphJUaDJvs/ndxkytTMT/szT0oDCtbCqkPJnBN1RMaBd1BYiIqPgKDAws6ioUqeHDh6N3795KhxkYGHzm2hBRblz84yr6T+8hlza6yVRsCFsK33bzAQBdzT3VahaS0ev0pct6cyPm8csiqYesqdov2ayuSwEANZtXR/mqZYq4Nsr7Zz0UcKIIakKUP4URjJBjAxAMgCAiIio8efyZTVNyfgsA1erbqRwna1P/6jqy5ZT4v0GmAAtl59jqEgQBSwauhYmlMXxWe+dqXFmXBoB8K39EasnbbqAgP9s/UUFiAAQREZEKlpaWsLS0LOpqEFEBydy/Z3Y2TdkKx0ZVxe9mmSLjib5EQpYgn8zdH2gSZa0ZFFexMXGfNQBC1XPaj4mfFNL+ffKqkGtDVAjk3oj7PIEJjH8gIiIqPHl92z0tVXkXllnPD4QCOHeIfvyv+L+uga74f34eAD+79wLHtp0BAIxcNSjPwRkf3ime5xOpLe8NQPAlIio22AUGERERFbjTp0+jc+fOKFu2LCQSCfbt21fUVaKvzN1LD/DwumLAQ9CSfdmO99ucnQheuh9zeiwX0z6+540D+rJl7icXAD6pEQDx8f2nYvf288aJvxV1FdSm6sbs53Yv7JFCmo6eThHUhKj4ye5Byf8nZP+diIiICo2hqfKWWVUFHxTGtUvdVjXF/y1tzP+rQz66AMgcjF4Qdf6Y+BH/3H+R73KoeMnvtlEQ3WmoqkPWFyyIigoDIIiIiKjAvX//HrVr18b69euLuir0FRtRf5JCmtR3m9K814/fwu9+h7BlTojCsFMhFwq8bkTFyb0r8g/Bc2pJ4eWz1/jeZAB82y9QGPbpQ5LSGyHqBFXk19Gtpwt9GppK1c2ptCzBLwADIEgzFUrsgSDIvXWZ9WFGcQsCIyIi+pLk9DtbLpetqRXGz3bpitbi/xWcyov/p6XmPQAiJfm/8/P8nGvIAkE8q47CIMcxWNBvFd7Hv89zefTly3zeq862pypPfgKAiAoSAyCIiIiowLVv3x7z589Ht27diroqRDlKTUnFpDZzscZnk8o8vICjL1nKpxS57zkFKxz//yZZrx6+IZd+9/IDdDb2wMT/Y+++45uo3ziAf9Jd6IBCoewNZSggG2WITAVlyJIpOECGgCCCyBJZooCylNHCj71lyZZNGYUySimbMlpW927G74+SI5edNGmS9vPm1RfJ3ffunlwuyY3nnu8H00TDD64+hk5efbFkVJAFotXNyYmHt7roOjklzdKsROHqxp4yiQAtBR7k6hUhcjEYIiIiEjGmawjRb7WeH25zu5nQNQ9DXWA4OWkuLzMj+5gsOe5NksL2+XvNjkdZcS7uWQIA4NjG05gzYJHZ8yM7k8MdUW3bvKkJELqwAgTZC54hIiIiIpvLyMhAYmKi6I8otxjT3/3B1cdzIRIi23ge9VL0/NjG03rb3wnT7F4GAEY0nggAuHIsXDT818+zqwHt+GOfxjQpCSl4+TTWcKl5I0i0nEgk/bz9vDSGOTnzNAE5HmtUY1Cfp0Z/xsyAICIiyhWW+J23duUm1fkb6u4uoGJxjWFd/QYiLTlNNJ+/vze/iz9tMZzdddHs+RFp0PGR0pZkT2QLPLNBRERENjdr1iz4+voKf2XKlLF1SJSPGFPuPWT3hVyIhMg21CucxL/Qn4SWnmKZ7izuXX2IzoUHonfprzGp0yxxTAbumtLG0IlGWxP3s5rLF051LE7bemYiCTmi3PhMKVgNioiIKNcY+mk3tWiD1XcVVBdgxsIy0jIRfuYWPAq6ax0vk8nw9G6M0fOT8SJ0nmPJbVjbvrPqZ8qYZena/7518a7W4US5jQkQREREZHMTJkxAQkKC8Pfo0SNbh0T5iDHl3nmTJ+VlLq7iz0D7Qa30tm/csb5Flvt1nbHC4/P7LovGmZPMkKnWlYe9yfWkByOWrW09ayuHasvYiWzGQAUIfi6IiIisyMK/s1ZPZFTZh5aZkcwNALIsKe5fjdI6bt6gJRhQZQT2rzpq3Ly07OcXKVnYrLjI/lhlN9TEDAjuC5O9YwIEERER2Zy7uzt8fHxEf0S5xZi7nZPjUwy2IXJUGWmZoufvdW2kt72zi/UPI+29moM57DMBQvPkrLY+ibMy7Du5hEhXlZMczVKhVrlFIwFCvb14gOpnKS9+pzmaxFdJOLbpNDLTMw03JiIimzO076wtaVf//MxfljFU41HfZzCWTCrHg3DxDUEyWfY+xOH/nQAA/G/6Fp3Tl6/1ppqqtv18IqL8hAkQRERERJSvGXOu4+rxG9YPhMhG1C8GmXvCzpJiY+JzNH3luhUsE4gl2X61atBaAcJJ8zRB9L1nuREOkUVYKtlIfT6GnqtzVqmus/X3PRaJiQy7ef42BtcchQv7xZWFvm8zHb/0XoBVE9fbKDIiIjKbA9xprrpfoF41yljSLCkadqgrHpYpFT3Xl1RZs2mg3nYSiQQKhQKH157AvasPzYqR8i5REo9RFSCsGQ1RzjEBgoiIiCwuOTkZYWFhCAsLAwDcv38fYWFhiIrSXsqPyN7p6oeTKC+IU0s2MPdO5ZrvVrNANNlio+NzNP1bzapbJhALMuYkUnpqBq4cCxfeg/TUDCz4+i9cOxmRw2VrH679xKhmuwfhj3O0fCJry40KK4aSw9TvRHVxdRYeH1573CoxUfb32JVj4UhPzQAATPxwJqIinmDihzNF7e6GPQAA/LfxdG6HSEREZjD4025yBYjcu1prbncbCS8S4esvroiqngBhLGmW9mO6E1vOYk7/P0XdEZKDUK1MZoXt2cQeMJgBQXaPCRBERERkcRcvXkTdunVRt2525vqYMWNQt25dTJ482caREWlhxEFbzXcDDbYhclT/LN4vem4oAULXR8bUMrT6uHm65Wh6e+yPVPVOMF3hTe36K8a2mooNs3YAAJaOCsLe5YcxpkUOfz9N6AJDWwWI1MTUnC2fyMqs8ZlXn6dc/WKGgYoQqgkQD66Ly1mTcaRZUhxcfQzPHr7Q2eZ/07dgbKupmNHzdwBAWlKa3nnKzeyXnYiIHJy1jw8Uhvf1DVk7Y5tGXod6d4XGHnPpqgDxS+8F5gVHeZY9HjsTWQITIIiIiMjiWrZsCYVCofEXHBxs69CIzGLuHRxEjsjci0M5OXHiUUBcZWXxyJVmzwuwbDKGxaitn7hn8Ti145zoomrowSsAgF1LspNS9q04YqFF60iAyNK8o8zJSXPdPbzBChCUD6l9bNQrQBj6ylPtAoPMs2PhPvz6+WIMrDpCZ5t/FmV/X57bewmA4e9/c8uSExGR7Wj7zTV1dz83r/GaezwVGx2n8cISXiYZPwOVF6k1qV3Ci92kh8ldYGhvU6iYr8VCIsoJJkAQERERUb5mzIEd7xak/ERbVQBLU//cVaxTXvQ8IuR2juZvjwkQotesUKBHiS8xrds8bJi5Q6Nt3LOEXIlJ63utZd1tm78nF6IhMp81zuVrVoDQX/FBnbMLT7nlVNix6wDEZbxn9/8Dvw5aDAAIPxOJ5PgU0TRZBkqFZ2Vk8eIPEZEDiH9u2f1hR+gCI3ticZxzBywyaza5cUxHNmTB7VnrsbNRCRDah1v6s0tkLh6NERERERG99k7rt7QOlzEBgvIwVzfxXcqGusDQdabDlHMwhkrJ50W6XmLw5I1WWd6Zfy5g3/LDettIjawAQZRfqZ4QVr+YYehCStFSflaJKT95cjtGeCyXyxH/IgFH1p7EweBjSHyVhFHvTTJ5nmnJ6Wjr3AM3Qm5ZMlQiIsplxiQ8q/5W6/vdtkTytFqus0XmAwAvHr00az7aKr2p0zgmI4My0jLw66DFOPPPBVuHYjPKzwsTSsneMQGCiIiIiPI10TGbjhMfrABBeZmru6voeW6UB1e/Iyk/nDwRvUaV75rSVUtYZXlTuszF/K//QtTNJzpPwmq7M0zCBAhyRFb4DjFUAUK9iwx1hQMKWTagfOjJ7Wjh8cUDV+Ds4iw8T0/NyNG8v236Y46mJyIi6/qgTzPLzjA3u8DIQWKBesKlr7+PWfPRVQHCt6i38DgtKc2seednW3/fg4PBxzCly9xcX7a1jpmV8xUl/upZlB0WWyTSigkQREREREQGyLIM3BFP5MB6jPvEMjMy4YSMelKR+qQNOtTNUSgurs6GG+UyhVz7bWH12tS26nKfR73UebJMW7UPbXfAffxNO4vHRWRJ4i5mLDVPtecmdoGh3p5yJjk+RVSh5uyuixptUhJSNIYREZFjKlOtlPBY62+uiVdh9f1uW/rCck72Ae6EPRA9j4p4YvxyVV6Hrv3897o2Fp53LjwQbZy64+qJG6YHmk+9ePTKZsvOSM0UHlsjF0L1I6X3M6FsmA9uYiDHxgQIIiIiIsrXjDnZcfP8nVyIhMg2UhNTLTIfU04calRVUZv2wr+XLRGSXdFVgtfaFRe2L9ij8+SUthOj2rrAqFq/ksXjIrJ7ap8bU7vAUK+uQ6ZTXYdxMfGityTm/nON9pkZhst9ExGRY7B0UkJuVLkTlpWDCpKLR64SPQ+oUEz03Nj1ItVxE0eFt8pqDPuu5RQjoyP1/UFtUhJTsXrKJpzZdQHPHr6w2LL/GrvaYvOyRPIC8x/I3jEBgoiIiIjoNUv0/UnkaDbP22VSe0ucjFS/8G7xu67s8WyMSkxZKhfpnJyse1h+YX+YiV1gaMajq4Qukb3IjY+8RkUIA8ssZGbJanqj5/dvKhQt+0580t9SyXtEjiL6/jP8NXYNMjOybB0KUa4wtD9v8qF7Lh4fWDLZQpopTu4z9pyFrgoQ7p5uFokrv1LdjK6fvol7Vx9qtFkxfi3W/rwVUzrPRd8K30Ams0xF0ad3n6nEYfntWSIuAWGwnV0ecxOpYAIEEREREeVvPGgjynWGusDIi1RPhP45fIXw2NoVIPSRaz0xqtkuKTY5F6Ihsi/qJ3U1+vPOD19cNubk/Oa0paubi+g9SdGSAJGTO27J/sS/SMDzRy9tHYbd6F9pOLb+vhsfeX6GxFdJtg7H4aUmpeHUjnPISMuwdSikQ1TEY4vOz9oXa0XV3oyoEmCsZt0aG26khbYECIAXrXNKdf2NbvYTvq4zVqONepciESG3rR6XRagcCBrTAwaRvWMCBBEREREREeUqXSfkLMUez+up9gX8POrNBR1nZ+selrt5uOo80amtNK62ChArflhr8bjI/sTGxOH8v5ehUCiQFJeMrEzHucvYGifzNSo+5GLpbMomU/mO+nhYe9G41MQ0jfaWvOBEtpOemoF7Vx+ie/Ev0KfcUF7sh2YCVjf/QTaKJO+YN2gxpnWbhyXfBmkdL82S4si6k0hP1UyQUCgUuH4qAikJKVaNcf+qo5jUaRbSUtKtuhx7dfHAFeGxtt95U6s3Wvv4QNzdneXm61vU+IpSqsuVa6v0JuH+TI5peXP3LT8sev7sgbjbi7P/XLBqSDn2+jXZc1UVInMwAYKIiIiI8jV7O2bjHRnkqEzZdDUrQOT97V7j7vHXrF0BouknDXS+OdpL41o1HJ0yM7Kw5bfdSIpjtQlb6VnyK/z40UysmboZXYt8jg89PtO53dodqyRAqFeAUOgdbyiE1CTNC/Z51ay+C9HGqXuO57Nh9g7hsbOLs1oFiDRRhQjKG2RSGTp59RXdUTvt03nCY4VCIZQSD9kTijZO3RF9/5nGfPKaf1ccsXUIec7JbecAAPtWHMGyMcGiShDXT0Wgg3tvzO73Bzp59QUAXDx4BX8MWwGZVIZlY1ZjdPPJ6Fx4oDCujVN3tHHqjtM7z+c4tocRj3FyWwh++2Ipzu29hL++W5Pjedob5fqWyWS4fz1K67HAR1+1tuxCrZ4B8eahTCrD0Q2nAAAvn8bi0uGrZu8HqO+vZ6Ybl6CqbT8/5sELi3bPkR9p24zmf/2X6Hnt92uKnpva5aRxceReRRN1b7rA0D29IyVSU97FIwUiIiIiotdy68Jf5IU7eBD+SDQsLTkNbZy6o61zD6QmpeHulQdIS0nHw4jH6FfxG7Rx6o64Z/HZbVPSsX/VUaH8c/S9ZzijcleBXC7H5aPXkBibhKibT/AqOg4KhQIXD15BbEwcZDIZJnSYgej7z5CZkaX1zibKP1xcnUXP05PT9Z6w0Hmiw4STMBplyl9P61vUGwDgX7qI0fPSHor9ndjTVZpdW8UFS0pPyUCKljulASAtOV04YZ+SkILnUS8w/6u/tLa1to88P8Pf49aga5HPrbqcqJtPcOnINb1ttv6+G/9tPG3VOOyN6sXqtT9vFR7fvxYlavd13bFo49Qd8wYtwfBGP6CNU3e7+LylJr25O1YZT3pqhvAbd/30TZzcFoLMjCyz4/282kjR86wMqY6W2b/psTHxomGf+PY3a7mORiaT4ej67Is+Kyeuz9G8RN+bau9bamIqnGzYhRBZR3u3XhrDrh6/gbbOPfD41lO0de6B9q690MapO376eDaA7K4h8jqfIt4aw37sONMGkeRN2xbsRceCfYXno5tP1mgzof0M7F56AD92nIXtC/dqjFOa2vXXHMfzRc3RmN79N+H53r8PYXzb6SbNIy0lHXMHLkL0Pf0JQjKZDOf2XcL10zdx5/J9jfGPb0dj3/LDuHT4qtbpfx20GG2cuuPc3lDR8DO7LuDw2hNo49Qd637ZJgxfNiYYbZy6o2PBvlg9ZRNm9/0DX739Hdo690Abp+5YOPRvoa22Cg/aEkwUCgV2LTmgNb7tC968V0/vPsOTO9HC83tXHorabpi1A2d3X8Qvny1A/IsEnN55HleOhePcvksYUGU4ts3fg499+uH+9ShIs6Qa+xPq3anM6pOdDNi79NcY3/ZnfOLbX9jvfnonRmu82oT9d130PPFVEs7tuyQ8f3jjEc7uvqgxnTRL+36K6jrOL2RSGX7pPR87//w3x/OKefBcY1jDD+uKnjtZ6fiuoG8Bq8xXeSJMIuoCw3AfGPraJMdZt0IOkTEkCns4UiUiIiJSkZiYCF9fX/wx6m94uHpAJpVBJpNDLpNDJs3+Xy6TI/5FAs7tvYRWn72HYmWKYteSA0hNSkPP7z+BxMkJESG3EBsTj8CGleFd2Asyqez1PGT4d+VRKBQK9BrfWftVb4UCCoUCCrkCcvmbx6r/y+UK4PX/CrkcCkV26d3E2GSc+ecCnJyd0GPcJ2/mpVCbt+L1AYPK4+znbw4khOlej0tPzUDkhbsoUbEYKr5VDtIsGaSZUuxcJD6Qa9mzKYqV9X8Tn47lyF/HLYpRIX59CgUgV8gRdvQ6ipbyg1ehgqhct4KwLFmWTMhor9OqFsKOvjlAb92vOYqUKIwH4Y9wbm/2QXr37zoJ76NMKoNMKsPNC3eEExBvt6iBq8ff9JnYvHsTPI96CZ8iXji/7zIAoGgpP7Tu1wIAsFHl7kBr6jH2Y9F7LZfLhe1D23Zw71oUbl28CwDo9UMXYT45jbfXD11E8+g1vjM2ztkJAChXozQaffiO1jsM/MsUwYtHr3TOt0y1kni3S6McxZYrHOTwxZEOs87tvaSRkGOqj75sjb1qpT8BoHLdChonUz/o2wxH1p40OM/2n78PT29P0fei6vfUm++o7GFn/jmPhJdvymRXeacCKrxdDgeDjwHI3sYDG1fBodXHzXqNTs5OaDugJWQymej3SCaV4XHkUzy88VhYFzKZHBf2X8arp3FGzfujL1sjK0sqxAoAH3/TTnQy95Nh7cW/S/Ls5e8P+k9o027g+5DJZJBmyXDMyhfw2w5sCQkkb35CJRJIJG+eSySS18N0P88elv1c/YRkpyFthbu7Dd5pLyo5rLtteko6jqwzvO0Z0qZ/C2RlZOHYpjM62zTv3gRFS/qJfnOV8an/3qo/Vv3NBgAFsscd23gGWRlZeKf1W4BEgkuHsi9GlKxUHA3a10Vmehb+Xan/LuGOX7eBTCrH1RM38OR2tN62llLn/ZooXq4YgDevRfUzre3zrb4ukD1Y5/5MyJ5QPRGYp17b2gg9eMVwQxN8/E07g22MLeltdOlvY5tZaLnJCSmi77I2A1oI+5zK91q076RlH1v5v3LfUan9oFbYv+qocS8IQJGShfV+D/cY+7HR87JnjrTPYciW33abNV35mmUQ2LCyxnBDq0YB3Q30rld9o/RMZ+48Y2PicdlA8p5Sg/Z1UKl2eY1lihat5XdTdbxC/ETneHE7/eO1Ld+c8dpiE42H5mtTjU857PD/TkCbd7s0xOkd4ovsXUZ+iB1/7NPavvPwDhrH4nVa1UJgwypGJ9crFIA0UwppphTR959pfPcpffxNO3h6eQjnAO5eeYDM9CzUbFote7/K6c2+mOpnqdf4zirLEm9om+b+o7Ec5TmMVzFxon32j75qA+/CBUX74KrL6fn9J5BIJHhw4xFCdot/l3uN76x1OJmv7cCWcHJyMul3UR9nF2e81SwQ/mWKan6Gjf18G/vdYsQ41YcKLfPVF5P698mF/WGi1/pB32YoVNRHOK+jem5MoeN/uSK73aE1mseSfgGF0Lpvc0icJFDIFVrPx7Tu11zvd5LmY/UHQOSFuxpJTf6li+DF4zfneao3roKIkNsay1eq+8FbRv2elKtRWji+NVfHr9sI529l8jfnch9HPsXtS/c12vf8/pMcLc+WTO2Sx644aOyDZ35msA0TIIiIiMjuKBMgWuITuEhcbR0OERERERHlASUrFcfTu3m/ywQiIiJ9ChXzxcYnf6G9q2blHSIie3dIvsVgGyZAEBERkd1RJkAkJCTAx8fH1uEQEREREREREREREZEDYAIEERER2R2FQoGkpCR4e3s7dhkxIiIiIiIiIiIiIiLKNUyAICIiIiIiIiIiIiIiIiIiIofnZOsAiIiIiIiIiIiIiIiIiIiIiHKKCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERERERERERERERERERA6PCRBERERERERERERERERERETk8JgAQURERHZHoVAgMTERCoXC1qEQEREREREREREREZGDYAIEERER2Z2kpCT4+voiKSnJ1qEQERFRPvIsLgnvDJ2Pd4bOx95zEbYOh4iIiIiIiIhMxAQIIiIiIiIiIiIAGVky4fFPwfttGAkRERERERERmYMJEEREREREREREREREREREROTwmABBRERERERERARAIrF1BERERERERESUE0yAICIiIiIiIiICwPwHIiIiIiIiIsfGBAgiIiIiIiIiIiIiIiIiIiJyeEyAICIiIiIiIiIiIiIiIiIiIofHBAgiIiIiIiIiIgAZWVJbh0BEREREREREOcAECCIiIrKoWbNmoUGDBvD29kaxYsXQuXNnREZG2josIiIiIoP+ORNu6xCIiIiIiIiIKAeYAEFEREQWdfz4cQwbNgwhISE4dOgQsrKy0LZtW6SkpNg6NCIiIiK9pDK5rUMgIiIiIiIiohxwsXUARERElLfs379f9Dw4OBjFihVDaGgomjdvbqOoiIiIiAwr6OFm6xCIiIiIiIiIKAeYAEFERERWlZCQAADw8/PT2SYjIwMZGRnC88TERKvHRURERKTOyUli6xCIiIiIiIiIKAfYBQYRERFZjVwux6hRo/Duu++iVq1aOtvNmjULvr6+wl+ZMmVyMUoiIiIiIiIiIiIiIsoLmABBREREVjNs2DBcv34dGzdu1NtuwoQJSEhIEP4ePXqUSxESERERveEkYQUIIiIiIiIiIkfGLjCIiIjIKoYPH449e/bgxIkTKF26tN627u7ucHd3z6XIiIiIiLSTMAGCiIiIiIiIyKExAYKIiIgsSqFQYMSIEdixYweOHTuGChUq2DokIiIiIqMw/4GIiIiIiIjIsTEBgoiIiCxq2LBhWL9+Pf755x94e3sjJiYGAODr6wtPT08bR0dERESkXZZUBoXC1lEQERERERERUU5IFAoe3hMREZHl6CodHRQUhIEDBxo1j8TERPj6+iIhIQE+Pj4WjI6IiIhI09YTVzBzw1GN4ZeWjrZBNERERERERERkLlaAICIiIotibiURERE5Gm3JD0RERERERETkeJxsHQARERERERERERERERERERFRTrECBBERERERERHlGwqFAlKZHM5OTrh2PxrzthyzdUhEREREREREZCFMgCAiIiIiIiKifGP4oh24fOcJivoUxOOXCbYOh4iIiIiIiIgsiAkQRERERERERJRvnL3xEADyZPLD6ev34ebqggbVytg6FCIiIiIiIiKbYAIEEREREREREZGDi0tKxYjFOwEAFxePgpOTxLYBEREREREREdmAk60DICIiIiIiIiKinDl/85HwWKaQ2zASIiIiIiIiItthAgQR0WsDBw5E586dbR0GERERERGRybJkMuGxQq6wYSREREREREREtsMECLJLLVu2xKhRozSGBwcHo1ChQsLzqVOnQiKRCH++vr5o1qwZjh8/LpqufPnyonbKv9mzZwMAHjx4IBru5+eHFi1a4OTJkxoxxMbGYtSoUShXrhzc3NxQsmRJDBo0CFFRUaJ2AwcOFC1DaefOnZBIDJciXbx4McqXLw8PDw80atQI58+fF41PT0/HsGHDUKRIEXh5eaFbt2549uyZqE1UVBQ++ugjFChQAMWKFcO4ceMglUqF8dHR0fjss89QtWpVODk5aV3n4eHh6Natm7AOFyxYoNHmxIkT6NSpE0qWLAmJRIKdO3fqfW1DhgzROS9VurYDQ8ydzpBjx46JthN/f398+OGHuHbtmqidOe99YGAg3N3dERMTY/G4LeX48eNo1aoV/Pz8UKBAAVSpUgUDBgxAZmam8Jp1/ZUvXx5A9nujHObh4YGqVati1qxZUCg0T9CePXsWzs7O+OijjzTGKT+zxYoVQ1JSkmhcnTp1MHXqVOG56jLd3d1RqlQpdOrUCdu3bzfp9StjfvjwoWh4586dMXDgQOG5rnXRvn170XSXL19Gz549UaJECbi7u6NcuXLo2LEjdu/erXV9tGvXDs7Ozrhw4YJoHej7Cw4OFrbb+Ph4bNu2Dc7Oznjy5InW11ilShWMGTNGY72p/g0ZMsSk9UZERERE1peWkQW5yj4k8x+IiIiIiIgov2ICBDm8mjVrIjo6GtHR0Th79iyqVKmCjh07IiEhQdRu+vTpQjvl34gRI0RtDh8+jOjoaJw4cQIlS5ZEx44dRUkFsbGxaNy4MQ4fPoxly5bhzp072LhxI+7cuYMGDRrg3r17ovl5eHhgzpw5iIuLM+k1bdq0CWPGjMGUKVNw6dIl1K5dG+3atcPz58+FNqNHj8bu3buxZcsWHD9+HE+fPkXXrl2F8TKZDB999BEyMzNx5swZrF69GsHBwZg8ebLQJiMjA/7+/pg0aRJq166tNZbU1FRUrFgRs2fPRkBAgNY2KSkpqF27NhYvXmzwte3YsQMhISEoWbKksavD7kRGRiI6OhoHDhxARkaGsJ5VmfLenzp1Cmlpafj000+xevVqa4WdIzdu3ED79u1Rv359nDhxAteuXcOff/4JNzc3yGQyLFy4UPTZAoCgoCDhufKiPQB8+eWXiI6ORmRkJCZMmIDJkydj2bJlGstcuXIlRowYgRMnTuDp06da40pKSsK8efMMxq9c5t27d7Ft2zbUqFEDvXr1wldffWXSepBIJKLPkC7t27fX+L7ZsGGDMP6ff/5B48aNkZycjNWrVyMiIgL79+9Hly5dMGnSJI3vr6ioKJw5cwbDhw/HqlWrAABlypQRzf+7774TfR9GR0ejZ8+eovl8/PHHKFKkiNbt7MSJE7hz5w4GDx6ssd5U/+bOnWvSOiMiIiIi69pz7gbeHbUIG/8LE4ZpS6glIiIiIiKyJ2kZWbYOgfIoJkCQw3NxcUFAQAACAgJQo0YNTJ8+HcnJybh165aonbe3t9BO+VewYEFRmyJFiiAgIAC1atXCxIkTkZiYiHPnzgnjf/zxRzx9+hSHDx9Ghw4dULZsWTRv3hwHDhyAq6srhg0bJppf69atERAQgFmzZpn0mn7//Xd8+eWX+Pzzz1GjRg0sW7YMBQoUEC58JiQkYOXKlfj999/RqlUr1KtXD0FBQThz5gxCQkIAAAcPHsSNGzewdu1a1KlTBx06dMDPP/+MxYsXCxfry5cvj4ULF6J///7w9fXVGkuDBg3w66+/olevXnB3d9fapkOHDpgxYwa6dOmi93U9efIEI0aMwLp16+Dq6qq37cCBA3H8+HEsXLhQuPP8wYMHALIrETRs2BDu7u4oUaIEfvjhB6Gyha7pZDIZBg8ejAoVKsDT0xPVqlXDwoUL9cagS7FixRAQEIB33nkHo0aNwqNHj3Dz5k1RG1Pe+5UrV+Kzzz5Dv379hPdYn7i4OPTp0wf+/v7w9PRElSpVEBQUJIx/9OgRevTogUKFCsHPzw+ffPKJsO6A7OSYMWPGoFChQihSpAi+//57DBgwQG/3HwcPHkRAQADmzp2LWrVqoVKlSmjfvj2WL18OT09P+Pr6ij5bAFCoUCHhub+/vzCvAgUKICAgAOXKlcPnn3+Ot99+G4cOHRItLzk5GZs2bcLQoUPx0UcfITg4WGtcI0aMwO+//y5KDtJGuczSpUujcePGmDNnDv766y8sX74chw8f1jutquHDh2Pt2rW4fv263nbu7u4a3zeFCxcGkJ0wNHjwYHz00UfYu3cv2rZti4oVK6J69eoYPHgwrly5ovF5DAoKQseOHTF06FBs2LABaWlpcHZ2Fs3fy8tL9H0YEBAAT09P0XxcXV3Rr18/retz1apVaNSoEWrWrKmx3lT/fHx8jF5fRERERPbmhxV7bR2CxU0OPgAAuPnozT6xnAkQRERERERkx05fv493Ry3C4n9O2zoUyoOYAEF5SkZGBoKCglCoUCFUq1bN7PmkpaVhzZo1AAA3NzcAgFwux8aNG9GnTx+NSgienp745ptvcODAAcTGxgrDnZ2dMXPmTPz55594/PixzuUpS9UDQGZmJkJDQ9G6dWthvJOTE1q3bo2zZ88CAEJDQ5GVlSVqExgYiLJlywptzp49i7feegvFixcX2rRr1w6JiYkIDw83Z7XkiFwuR79+/TBu3DjRBVZdFi5ciCZNmojuQC9TpgyePHmCDz/8EA0aNMCVK1ewdOlSrFy5EjNmzNA7nVwuR+nSpbFlyxbcuHEDkydPxsSJE7F582azX1NCQgI2btwI4M12omTse5+UlIQtW7agb9++aNOmDRISErR2vaLqp59+wo0bN/Dvv/8iIiICS5cuRdGiRQEAWVlZaNeuHby9vXHy5EmcPn0aXl5eaN++vZD48ttvvyE4OBirVq3CqVOnEBsbix07doiWERwcLOquIyAgQKiOYikKhQInT57EzZs3Ndbf5s2bERgYiGrVqqFv375YtWqV1rvYevfujcqVK2P69OkmL3/AgAEoXLiwSV1hvPvuu+jYsSN++OEHk5endPDgQbx69Qrff/+9zjaq616hUCAoKAh9+/ZFYGAgKleujK1bt5q9/MGDB+P27dui9zI5ORlbt24VVX8gIiIiymvik9NwMPSW4YZ5ABMgiIiIiIjIns3dfAwAsHL/ef0NiczABAhyeNeuXYOXlxe8vLzg6emJefPmYcOGDRp3KY8fP15op/xTv9DctGlTeHl5oWDBgpg3bx7q1auHDz74AADw4sULxMfHo3r16lrjqF69OhQKBe7cuSMa3qVLF9SpUwdTpkzR+RqqVasm3PH98uVLyGQyUeICABQvXhwxMTEAgJiYGLi5uaFQoUJ622ibh3JcbpszZw5cXFwwcuRIo9r7+vrCzc1NdAe6s7MzlixZgjJlymDRokUIDAxE586dMW3aNPz222+Qy+U6p3N1dcW0adNQv359VKhQAX369MHnn39uVgJE6dKl4eXlhUKFCmH9+vX4+OOPERgYqNHOmPd+48aNqFKlCmrWrAlnZ2f06tULK1eu1Lv8qKgo1K1bF/Xr10f58uXRunVrdOrUCUB29ylyuRwrVqzAW2+9herVqyMoKAhRUVE4duwYAGDBggWYMGECunbtiurVq2PZsmUaFQd8fX1FSUTdu3dH79690aJFC5QoUQJdunTBokWLkJiYaOxqEyxZsgReXl5wd3dH8+bNIZfLNbaLlStXom/fvgCyu5NISEjA8ePHNeYlkUgwe/Zs/P3337h7965JcTg5OaFq1aqi6hjGmDVrFvbv3683UWXPnj0a3zczZ84EAKE6jer6vXDhgqjtnj17hHGHDx9Gamoq2rVrBwDo27evwW1Enxo1aqBx48aiaiObN2+GQqFAr169RG2V75Xq37p168xeNhEREZEtSWVyW4dgUXefvkTXadq70FPImQBBRERERET2y0nlJkAiS2MCBDm8atWqISwsDGFhYQgNDcXQoUPRvXt3XLx4UdRu3LhxQjvlX/369UVtNm3ahMuXL2Pbtm2oXLkygoODNbpqMKcv1Tlz5mD16tWIiIjQOv7mzZsGu49wZKGhoVi4cKFGVQFzREREoEmTJqL5vPvuu0hOTtZbaQEAFi9ejHr16sHf3x9eXl74+++/ERUVZXIMJ0+eRGhoKIKDg1G1alUsW7ZMZ1tD7/2qVauEC/1A9sXtLVu2ICkpCUB29yLKC8/KyhlDhw7Fxo0bUadOHXz//fc4c+aMMP2VK1dw584deHt7C9P5+fkhPT0dd+/eRUJCAqKjo9GoUSNhGhcXF43PQpcuXUTdejg7OyMoKAiPHz/G3LlzUapUKcycORM1a9ZEdHS0CWsP6NOnD8LCwnD69Gl06NABP/74I5o2bSqMj4yMxPnz59G7d28hvp49e+q86N+uXTu89957+Omnn0yKA8j+PJu6TdaoUQP9+/fXWwXi/fff1/i+GTJkiM72b7/9ttAuJSVF6NIFyN5GevbsCRcXFwDZVS9Onz5tcsKHqkGDBmHr1q3CdrZq1Sp0794d3t7eonbK90r17+OPPzZ7uURERES2lNeqIkxc9S8exMRqHXc07I7W4URERERERPZA4sQECLIeJkCQXfLx8UFCQoLG8Pj4eI071d3c3FC5cmVUrlwZdevWxezZs1GqVCksWLBA1K5o0aJCO+Wfp6enqE2ZMmVQpUoVdOnSBTNnzkSXLl2QkZEBAPD390ehQoV0XsiOiIiARCJB5cqVNcY1b94c7dq1w4QJEwy+9qJFi8LZ2RnPnj0TDX/27JnQ9UZAQAAyMzMRHx+vt422eSjH5aaTJ0/i+fPnKFu2LFxcXODi4oKHDx/iu+++Q/ny5XMlho0bN2Ls2LEYPHgwDh48iLCwMHz++edCtxCmqFChAqpVq4YBAwbgiy++QM+ePXW21ffe37hxAyEhIfj++++F9dK4cWOkpqYKXWusWLFCuPC8b98+ANlJEQ8fPsTo0aPx9OlTfPDBBxg7diyA7K4M6tWrp3HR+tatW/jss89Mfq3qSpUqhX79+mHRokUIDw9Henq63gQQbXx9fVG5cmU0aNAAmzdvxqJFi3D48GFh/MqVKyGVSlGyZElhvSxduhTbtm3T+r0AALNnzxYSmIwlk8lw+/ZtVKhQwaT4AWDatGm4dOkSdu7cqXV8wYIFNb5v/Pz8AABVqlQBkJ3ooeTu7i60U6XsnmTJkiXCuihVqhSkUqmogoOplJUeNm/ejNu3b+P06dNau79Qvleqf+pJEkRERESOQibPWxUgbj95qXPc9LWHcjESIiIiIiIi0zD9gayJCRBkl6pVq4ZLly5pDL906RKqVq1qcHpnZ2ekpaXlKIZPP/0ULi4uWLJkCYDscvk9evTA+vXrNbqQSEtLw5IlS9CuXTvhIqe62bNnY/fu3Th79qze5bq5uaFevXo4cuSIMEwul+PIkSNo0qQJAKBevXpwdXUVtYmMjERUVJTQpkmTJrh27RqeP38utDl06BB8fHxQo0YNE9ZEzvXr1w9Xr14VXZAvWbIkxo0bhwMHDuiczs3NDTKZTDSsevXqOHv2rKgSx+nTp+Ht7Y3SpUvrnO706dNo2rQpvvnmG9StWxeVK1fO0R30SsOGDcP169exY8cOnW10vfcrV65E8+bNceXKFdG6GTNmjFDtoFSpUsKF53LlygnT+vv7Y8CAAVi7di0WLFiAv//+GwDwzjvv4Pbt2yhWrJjGhWtfX1/4+vqiRIkSOHfunDAvqVSK0NBQk1974cKFUaJECaSkpJg8rZKXlxe+/fZbjB07FgqFAlKpFGvWrMFvv/0mWidXrlxByZIlsWHDBq3zadiwIbp27aq3KoO61atXIy4uDt26dTM57jJlymD48OGYOHGixrZmSNu2beHn54c5c+YYbLtu3TqULl1aYxv57bffEBwcbPKylby9vdG9e3esWrUKQUFBqFq1Kpo1a2bWvHQ5ceIEOnXqhJIlS0IikehMFiEiIiLKLbcevbB1CBazbI/+40oiIiIiIiJ7dl9HNTsiS2ACBNmloUOH4tatWxg5ciSuXr2KyMhI/P7779iwYQO+++47UVupVIqYmBjExMTg9u3bmDFjBm7cuIFPPvlE1C4pKUlop/xLTEzUGYNEIsHIkSMxe/ZspKamAgBmzpyJgIAAtGnTBv/++y8ePXqEEydOoF27dsjKysLixYt1zu+tt95Cnz598Mcff2iMCwwMFF1AHzNmDJYvXy50nTB06FCkpKTg888/B5B9V/bgwYMxZswY/PfffwgNDcXnn3+OJk2aoHHjxgCyL7LWqFED/fr1w5UrV3DgwAFMmjQJw4YNg7u7u7As5QXV5ORkvHjxAmFhYbhx44YwPjMzU2iTmZmJJ0+eICwsDHfuvCmpmpycLLQBgPv37yMsLEzoXqJIkSKoVauW6M/V1RUBAQGoVq2aznVWvnx5nDt3Dg8ePMDLly8hl8vxzTff4NGjRxgxYgRu3ryJf/75B1OmTMGYMWPg5OSkc7oqVarg4sWLOHDgAG7duoWffvoJFy5c0LlsYxUoUABffvklpkyZorN7FG3vfVZWFv73v/+hd+/eGuvmiy++wLlz5xAeHq51fpMnT8Y///yDO3fuIDw8HHv27EH16tUBZHdZULRoUXzyySc4efIk7t+/j2PHjmHkyJFCFyHffvstZs+ejZ07d+LmzZv45ptvNKqJ7NixA4GBgcLzv/76C0OHDsXBgwdx9+5dhIeHY/z48QgPD0enTp1ysgrx9ddf49atW9i2bRv27NmDuLg4DB48WGO9dOvWTWc3GADwyy+/4OjRo6LKCkqpqamIiYnB48ePERISgvHjx2PIkCEYOnQo3n//fbPinjBhAp4+fSqqXqGUkZGh8X3z8mX2HXpeXl5YsWIF9u7di48++ggHDhzAvXv3cPXqVcydOxdAdhIXkJ0k8+mnn2qsi8GDB+Ply5fYv3+/WbEDwODBg3HmzBksW7YMgwYN0tpGud5U/+Li4oyaf0pKCmrXrq33e5GIiIgoNz19pfv4z1HcevwC/zscir/3htg6FCIiIiIiIiK7xAQIsksVK1bEiRMncPPmTbRu3RqNGjXC5s2bsWXLFrRv317UNjw8HCVKlECJEiVQp04dbN68GUuXLkX//v1F7SZPniy0U/59//33euMYMGAAsrKysGjRIgDZF/JDQkLw/vvv4+uvv0alSpXQo0cPVKpUCRcuXEDFihX1zm/69OmQaym7GhkZKSrt37NnT8ybNw+TJ09GnTp1EBYWhv3796N48eJCm/nz56Njx47o1q0bmjdvjoCAAGzfvl0Y7+zsjD179sDZ2RlNmjRB37590b9/f0yfPl207Lp166Ju3boIDQ3F+vXrUbduXXz44YfC+KdPnwptoqOjMW/ePNStWxdffPGF0ObixYtCGyA7gaNu3bqYPHmy3vVhyNixY+Hs7IwaNWrA398fUVFRKFWqFPbt24fz58+jdu3aGDJkCAYPHoxJkybpne7rr79G165d0bNnTzRq1AivXr3CN998k6P4lIYPH46IiAhs2bJFZxv1937Xrl149eoVunTpotG2evXqqF69us6L/W5ubpgwYQLefvttNG/eHM7OzkKXGQUKFMCJEydQtmxZdO3aFdWrV8fgwYORnp4OHx8fAMB3332Hfv36YcCAAWjSpAm8vb014khISBAlEjRs2BDJyckYMmQIatasiRYtWiAkJAQ7d+5EixYtjF9ZWvj5+aF///6YOnUqVq5cidatW2t0dQMA3bp1w8WLF3H16lWt86latSoGDRqE9PR0jXHLly9HiRIlUKlSJXTt2hU3btzApk2bhAov5sY9fvx4rcvbv3+/xvfNe++9J4zv0qULzpw5gwIFCqB///6oVq0aWrVqhaNHj2Ljxo3o2LEjQkNDceXKFa0VKnx9ffHBBx/oTQgx5L333kO1atWQmJio8X2ppFxvqn+9e/c2av4dOnTAjBkztG7jRERERLYg15Gw7Eh6/bIW87edsHUYRERERERERHZLotB1yzIREeUbAwcORHx8PLspIKuQSCTYsWMHOnfubPQ0iYmJ8PX1RUJCgpC8Q0RERJQT/zscanLywKWlo60UjXneGTrf6Lb2FjsREREREZGS6rGNtmOXuKRUFPYukJshUR7CChBERERkcxkZGUhMTBT9EREREVlS1VJFbR0CERERERERGbDl+BV88P1f7PqPzMYECCIiyvdmzpwJLy8vrX8dOnSwdXj5wqxZs+Dr6yv8lSlTxtYhERERUR7j5upi6xCIiIiIiIg0/BS8HzPWHbZ1GHZj1sajAIBle87aOBJyVDz6JyIiBAcH2zoEmxoyZAh69OihdZynp2cuR5M/TZgwAWPGjBGeJyYmMgmCiIiILIo9gBIRERERkb15FpeEveciAADfdW8BTzdXG0dE5PiYAEFERPmen58f/Pz8bB1Gvubu7g53d3dbh0FERER5GNMfiIiIiIjI3qjmacvlef+o5fjVuwa7tnBxcoJULs+liCgvYgIEERERWVxycjLu3LkjPL9//z7CwsLg5+eHsmXL2jAyIiIiyq/yw8lEIiIiIiJyLE5OEuGxPB9c9B+9dJfhRhLDTYj0YQIEERERWdzFixfx/vvvC8+V3VsMGDAg33e5QkRERLYhZxcYRERERERkZ5wkb672y5i0DQCQyt4kgizccRJyuQKjuzW3YUTkaJgAQURERBbXsmVL9rNNREREdoX7JkREREREZG+cVSpAXLn3FE1rloers7MNI7Ivqw9eBAB83q4BCnl52jgachROtg6AiIiIiIiIiMjaeDcVERERERHZG4lKBYjRS3fhty3HbRiNbW07eRUHLkZqHadaFYLIEFaAICIiIiIiIqI8z9ErQDx6EW/rEIiIiIiIyMJUu8AAgM3Hr+CHXq1Ew3aeuQ4XZyd0bFQjN0OzOLmepPSnrxLxy/ojOsc7+vEc5S4mQBAREREREZFdi01MxeU7T9C8dkWWAiWzOfIJs7TMLHwyOcjWYRARERERORzlcYBELdHAXhgKKzElHdP/dwgA0LpuVXi4Oe6lXZlCdxWHhJQ0vdM67tEc2QK7wCAiIiIiIiK71nf2eoxbvgdrDobaOhRyYIW9C9g6BLMlpqTbOgQiIiIiIoeTmSXFp9PX4Pvle2wdiobzN6Nw6/ELg+0ypFLhcWaWVE9L+6evAoS+cUSmYgIEERERERER2Z30TCmypDIAQExcEgDgvyt3bBkSOTg3F8etHsLKJ0REREREprtw6zHux8TiyGX7OpZ8+ioBQxZuQ69f1sJQoTpnyZtLuTK57goKjuBVYorOcTIDCRCOXNGPcp/j1kkhIiIiIiKiPCkjS4qm3/4JAAhdMkoYzjtCKCcc+YSZizPvXyEiIiIiMpW9HgM8fZUoPH74PE5vW9UuMgwlCdi7n4IP6BxnKLlDbqfvJdknHkETERERERGRXdly/IrwuMfP/7NhJET2wU67KyYiIiIiIjOoVnjLMNCthep1f6nMsStAvEzQXQHC0A0PzH8gUzABgoiIiIiIKBe9SEhGlkxm6zDsWnxKuvD4bvQrrY+JTMXzZURERERE+Yu9VYBQVjlQrfCWKdV/fkC18oHUwc8l6KtsZ7h7D/t6L8m+MQGCiIiIiIgol9x4GIN2PyxHo+F/4MbDGFuHY7f2nY/QOjxLKkNaRlYuR0PWJpPL0Xz0YizZdcbWoRARERERUR5iL90mHLgYiXeGzkeDYQuxav95uLqoVIDINFQB4s1ryHLwChD6ElLs5b2ivIEJEERERERERLnk++V7hcd9Z2/gxXwt0jKyEBObpHN8fEpaLkZDueGnoP1ITs/Ein/PWXU59nb3FxERERERWY9crsDopbtsHQYAYMLKfcLjRf+cFlVCSDeUAKHy2NG7wEhOy9A5TsYuMMiCmABBRERERESUS56+ShQ913fwr/QgJjZfJUqsO3pJ73hD/YKS4zkQGpkry+EJMyIiIiKi/ONVUoqtQ9DJSSIRHhvsAkOed7rAeJmYqnOcoYR1VoggUzABgoiIiIiIyEakKn1cajvY33c+Al2nrUaHicsBACERD7F8X0ievpPdUDcI6neFJKWm42BopMG7Zsh+qW7OTHAhIiIiIiJLcHF2Fj23p+NoJ6c3l2cNJTWoXvjPkjp2BYha5QN0jpPJ9b82O3r7yAEwAYKIiIiIiMhGCrq7AchOdHh/7FKE3n4sGj8paD8AIDE1u1LEN39sx9LdZ3Ew9BYAYM2hi9h68mouRmx76ietuk1fgx9W7EPz0YttFBFZkqGTXjnDM2ZEREREROa48+Ql0jItU5nwRXyyReZjiEItudqeKgg4vSkAgSwDFSBUj4EdvQLE9QcxOscZToa3n/eP7B8TIIiIiIiIiLTIzbtDJgXtR2JqhtH9k05YuQ9BB85jwfaTmLn+COKSdJeRzGvUL5C/TMguayq16oVzsiYPNxfhsaP3aUtERERElNecvn4fPWb8D+9+u8jkacev2It3hs5HRNQzAECnSSvRbsJyjFn25tg3NT0Ta49cwtNXCRrTv0xIwf4LN6FQKJCUloFT1+8jy8gkAJlCfGxhR/kPIlkGjoHkogSIvHu8xGqAZElMgCAiIiIiojxLoVCg3Q9/o8uUYJ3j3xk6H+8MnS+6m+VZXBLqfbMA4/7ebZE4op7HY+2RS5rL1wzI6Hn+ufO08Hja/w5pjP/i9814Z+h8PH2VKBp+4to9vDN0PuZtOQYAuBf9CrvPhttVOVB9eFIk98nkcqSkZ+LpqwQ8t8LdWh5ursJjayayXLuv+24jIiIiIiLSlJiSjhGLd5o9/aHX1Qv7zFoPAHjy+vj02JW7QpvFu87g963H0XHSKo3p2/7wNyau+hf1vlmAFmOWYOTinRjx5w4A2ckAyuN5bdSPHe31mHffuQi941XDNpQsYS+S0zJw9sZDjYSN9vWr6ZxGPWFFnZ2+fWSnXAw3ISIiIiIickx7Qm7gRUIKgBQcvnQL3y/fCwC4tHQ0AODhszihbYvRS3B+8bcAgP5zNgAAjly+I5rf4xfxuHznCTo2roF1Ry8jSyrF+7UrI+zuU3zcpCacnCSITUxFSkYmyvgXEqbrPCVIa3yWOgFz4to9jWGXbj8BAHSctFJ4vQAwask/AID1Ry9jbPeW+HT6GgCAm6sL2uk5GWEv7KlsaX5w/OpdjcokFxePgpNqzdYcuPEwBvHJacJza97RNGfTf1abtzWlZWZpJDIREREREeWGlmOXWn0ZG/67bFL785GPAIiPg5NS0+FdwAMAkJKeiQLurhrHjvZ6LBn5+IXe8Y7UBYZcrsCqA+exZNcZAMA3nZriiw8bCeOdnXXfl2/oZgd7ff/IPjEBgoiIiIiI8qRncUmYsuag8FyZ/ABkn0CQSCSig2/VO8/jkt5ckFX18eTsRIYLkY+w5/VdGspKDDFxSRjSsQlaj/8LALBnxiCULOKrN0aNw3eJ7ovK1q58cO1+tM0TIIxJCNF30uPPnacwovN7lgwp39PWLUtqRia8PN1NnteAuRtx7X40AODC4m/h7OSEvrM3iNpY44RelkyGD8Yus/h8c0N6ptSsUsNERERERHmdb0EP4bHk9bH03nMR+Cl4P9rUq4qRaseGtrqAru04N/jgRaOnd6QuMOoPWyB6vv3UNVECRJZU9/GezMA5D3ut4EH2iQkQRERERERkddoOVLUduyo0UwI02mlroz5IoVCgw8QVOuNJy8hCAQ830bAu79YSHn/VsbFwx4I2e7SUqPx7bwgGt28oPF9/9DKGdmoKmb4TFCYcwMclp+ofn5QKJyfTejncfTZceGzoZIMxFAoFFIo375HwWPFmG1CoDRemA3Dq2n2Dy5DJ5EjPlCI9M0vj5EnQgQtMgHhN9TOnupmpfn50DVc+fPQiXuu8D1++jU+a1BROMurS8rsl8C3oiX+mf46k1HQh+QEAjl6+gzb1qmpMk54p1Tk/hUIBqVwOqUwOmSz7f6lMLnTRUbKIL2RyOVLTM9FuwnK9sdkD5ecFyF7/m46FYcW+c4hPSbdtYFag3B5T0jPh6e4KZxO/q8ixmHJy2tTz2Fr3AWw4f5ObmxiQqb/M+e/1Wnf+pqxQ09e9aU5eu4fJqw/g1PxhcHV1hrPECRIJDP4WU/6m6zOib3vV9b1gzjSvR+qYn+5pUtIz8fhlAqqXLQ4XPXeLk3HSM6XwcMu+FCiXK0SV3BQKBeSv/xRyBeQKzWH2zEVtn1KhUOCn4P0AsrveGNCmvnh8Lrwe1WNi5f/pGVka7bafumbU/ORyhSjpQSq1rwQIuVwBBRTIlMpwUktlypi4JNFzXQkQLxKSkZGl+1gwv1P/ztR5LA/N72ttx/q65uvi7Gyxao/qy1H/XCjPEynPBdWvVkaU1JRTEgVTZoiIiMjOJCYmwtfXF81G/A4Xd883I7jbQhagbSsy5uK81q1P23RGzN/YGDQOTIxIDjB6/hZMPnDUj6arizPkcrnowr+XhxtkCgXSVE5QeHm6Cy8yOT1T7zydnSQ5TiRQlupUKBTIyLLM3fBuLs7CPA3F5+Hm8iYxQTg4VdmG1A9aYX/bQCGVg2bV0HQnBahRbadreh3DxecWDC/P9Gn1n9zITcr1nH2CFJDL5dmP5dknwJR0fS58C3ogQcvFfuV8FcDrJAcZZDKFqEpLbvEt6KGWqADg9Ukahcr/Oj8zWhKCcus9U548EpKPVF+DyusQtVF9rvKalNOrt1d/3ertDXF3dTbqtVj7wqLJp8Yc/KK9vX1nE5H5JBLASSKBRCKBk0QCJ6fX/yuHqV3o1Eb/hW09dM1P7ySWm0b/NXfdI82YnUVjMDeRID9/dxfy8tQ90sIrxtKr2dKX35RJT28uJkJYB9r2T9N1XFB2kkjMrobg4uQET3dX8brScZwEZCe0KLm6OIsugHt7ukMBIDktQzTMyUkCuTw7CSNFxzG4u6uzWcfLyv17Zdja9u8B8b68oWNia3w+9Z1bKGTBi8TGUH3tqocBqu+bPj4F3IV1aOw0uii3GXGAasfIolHGj9N1PK5lERacj/bjflsq6OEGF2cnje8Zbd8xuo5/c/LZ8HBzEb7rJMj+3pMAwOv/JRLg2G/fGJwPEyCIiIjI7igTIBISEuDj42PrcIiIiIiIiIiIiIiIyAEwAYKIiIjsjkKhQFJSEry9vVnSk4iIiIiIiIiIiIiIjMIECCIiIiIiIiIiIiIiIiIiInJ4TrYOgIiIiIiIiIiIiIiIiIiIiCinmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREREREREREREREREREDo8JEEREREREREREREREREREROTwmABBREREOk2dOhUSiUT0FxgYqHeaLVu2IDAwEB4eHnjrrbewb98+k5erUCiQmJgIhUJhbuhERERERERERERERJTPuNg6ACIiIrJvNWvWxOHDh4XnLi66dx/OnDmD3r17Y9asWejYsSPWr1+Pzp0749KlS6hVq5bRy0xKSoKvry8SEhLg4+OTo/iJiIhMtTUqBAtv/osMeZYw7Hz7mTaMiIiIiIiIiIiIjMEKEERERKSXi4sLAgIChL+iRYvqbLtw4UK0b98e48aNQ/Xq1fHzzz/jnXfewaJFi3IxYiIiopyZe2OXKPmBiIiIiIiIiIgcAxMgiIiISK/bt2+jZMmSqFixIvr06YOoqCidbc+ePYvWrVuLhrVr1w5nz57Vu4yMjAwkJiaK/oiIiIiIHFW6LAv7n4YhPjPF1qEQERERERHlK0yAICIiIp0aNWqE4OBg7N+/H0uXLsX9+/fRrFkzJCUlaW0fExOD4sWLi4YVL14cMTExepcza9Ys+Pr6Cn9lypSx2GsgIiIiIsptiyL3Y/LVzfjmwkpbh0JERERERJSvMAGCiIiIdOrQoQO6d++Ot99+G+3atcO+ffsQHx+PzZs3W3Q5EyZMQEJCgvD36NEji86fiIiIiCg3HY65CgC4k6Q/EZiIiIiIiIgsy8XWARAREZHjKFSoEKpWrYo7d+5oHR8QEIBnz56Jhj179gwBAQF65+vu7g53d3eLxUlEREREZFsSWwdARERERESUL7ECBBERERktOTkZd+/eRYkSJbSOb9KkCY4cOSIadujQITRp0iQ3wiMiIiIiIiIiIiIionyMCRBERESk09ixY3H8+HE8ePAAZ86cQZcuXeDs7IzevXsDAPr3748JEyYI7b/99lvs378fv/32G27evImpU6fi4sWLGD58uK1eAhERERERERERERER5RPsAoOIiIh0evz4MXr37o1Xr17B398f7733HkJCQuDv7w8AiIqKgpPTm3zKpk2bYv369Zg0aRImTpyIKlWqYOfOnahVq5atXgIREREREREREREREeUTTIAgIiIinTZu3Kh3/LFjxzSGde/eHd27d7dSRERERERE9k8ikdg6BCIiIiIionyJXWAQERERERFRvnTh1V0su30IMoXc1qEQEREREREREZEFsAIEERERERER5UvDLqwEAAR4FELnMg1sHA0R5SXJWWm2DoGIiIiIiChfYgIEERERERER5WuPU1/ZOgTKJdfjH+F5egJ8XQugXpGKtg6H8gi5Qg4nibjIaoZcaqNoiIiIiIhyn0whh7OEHQ+QfWACBBEREREREeVrj1NjbR2CXXqSGgt3Z1cUdfe2dSgWkZiVhkEhS4Xn698dicreATaMiPKCSVc24kbCY6x/91t4OLvaOhwiIiLKA8LjHyFDnoV3/JiwS46h6YGfIFXIsLDeQDTxr2rrcIjAVBwiIiIiIiLK144+u27rEOxOQmYqupyYhw//m2XrUCwmNiNJ9PxOUoyNIqG85GD0VTxOjcXpFzdtHQoRERHlAVlyKT4PWYoh51fgeXqCrcMhMopUIQMAfBsabNtAiF5jAgQRERERERHla28XKmvrEOzOw5SXtg7B4iSQiJ4roLBRJJQXcWsiIiIiS7iZ+FR4HJeZYsNIiIgcFxMgiIiIiIiIKF+r5FXc1iHYnbyYHCCRiBMgUqQZouepas+JTHH2xS1bh0BERER5wMo7R4XH6gm8RERkHCZAEBERERERUZ4Q+uoefgzbgFdqXR0YIs+DF/tzKikrzdYhWJyTWgLE3Bu7cPHVXQDAmnsn0PLwNByMvmKL0CgPKFfQ39YhEBERUR5w5uWbpMoMeZYNIyEiclxMgCAiIiIiIqI8YeiFFTgUcw2Trmw0abp0GU8sqguNvSc8VijyboLIklsHAQCLbu0HAEy/ts2W4ZADK+rubesQiIiIKI/xcHa1dQhERA6JCRBERERERESUp4TG3jep/QHe9a9BppALj28nRdswEss5//KOwTaqr5uIiIiIyJbcJC62DoGIyCExAYKIiIiIiIiIRMoWLCo8luWRChBJ0nSNYTcSHmPspf8Jz/NytQuyLoWernRepifmYiRERESUV+jbvyAiIt2YAEFEREREREREIgEehYTHThKJ7QKxMjkUOPE8QvScyBz6tpy5EbtyLQ4iIiLKO7hnSvYuKSsN95Kf2ToMIg2sn0NERERERER5Sn2/imZPG/LytgUjcVx+7l7C42vxUajmU9KG0RA5AD3VQ049j8zFQIiIiCivkLM6Gdm5T47/imQtlfaIbI0VIIiIiIiIiChPcZLoPtRVKBSQymU6x4+8GGSNkBzOP48uCI/zyonXu0nG35mkbxsh0kb9U1LItYDwWKrg9kRERESmS5dl2joEIr2Y/ED2igkQlKe1bNkSo0aNsnUYZhs4cCA6d+5s6zAAOP66JCIiIqL8Q1+HDROvbMCH/81CchZP1OjzMiNJeBydFmfDSCwnzcgTyIeir6LpwZ8w4/p2K0dEju5h8gvhsXof3TV8S+d2OERERJTHHHl23dYhEBE5JJMSIHRdAA0ODkahQoWE51OnToVEIhH+fH190axZMxw/flw0Xfny5UXtlH+zZ88GADx48EA03M/PDy1atMDJkyc1YoiNjcWoUaNQrlw5uLm5oWTJkhg0aBCioqJE7QYOHChahtLOnTshMaJf08WLF6N8+fLw8PBAo0aNcP78edH49PR0DBs2DEWKFIGXlxe6deuGZ8/Ed5lERUXho48+QoECBVCsWDGMGzcOUqlUGB8dHY3PPvsMVatWhZOTk9Z1Hh4ejm7dugnrcMGCBRptTpw4gU6dOqFkyZKQSCTYuXOn3tc2ZMgQnfMyVfny5S0yH0PUtz1127dvx88//2z1OOxNWloa/Pz8ULRoUWRkZNg6HKOofm84OzujTJky+OqrrxAbGytqp9zmQ0JCRMNHjRqFli1basz38ePHcHNzQ61atawZfo4tX74ctWvXhpeXFwoVKoS6deti1qxZAHR/Vyr/Bg4cCACiYT4+PmjQoAH++ecfrcubNWsWnJ2d8euvv2qMCw4OhkQiQfv27UXD4+PjIZFIcOzYMWGY6jILFiyIKlWqYODAgQgNDTX6tev7HKt+dyl/E4oVK4akpCRRuzp16mDq1KnC8/v37+Ozzz5DyZIl4eHhgdKlS+OTTz7BzZs3hden7+/BgwdGb5OA4c+csdut6jJdXFxQtGhRNG/eHAsWLNCYb8uWLbXGPmTIENH6y+n7Q0RE5Jh0H9sdibmO+KxUHOXJRJ3kCjlOvrgpPD//6q4No7EcIw75AQA/XtkIANj1+KIVoyFH9XvEXgw6uxSZcikW3z4gDM+QZdkwKiIiIsqLmherbusQiIgcktUqQNSsWRPR0dGIjo7G2bNnUaVKFXTs2BEJCQmidtOnTxfaKf9GjBghanP48GFER0fjxIkTKFmyJDp27ChKKoiNjUXjxo1x+PBhLFu2DHfu3MHGjRtx584dNGjQAPfu3RPNz8PDA3PmzEFcnGl3sWzatAljxozBlClTcOnSJdSuXRvt2rXD8+fPhTajR4/G7t27sWXLFhw/fhxPnz5F165dhfEymQwfffQRMjMzcebMGaxevRrBwcGYPHmy0CYjIwP+/v6YNGkSateurTWW1NRUVKxYEbNnz0ZAQIDWNikpKahduzYWL15s8LXt2LEDISEhKFkyb/Xr6ufnB29vb6suIyvL/k5ybNu2DTVr1kRgYKDBxBd7ovzeiIqKQlBQEPbv34+hQ4dqtPPw8MD48eONmmdwcDB69OiBxMREnDt3ztIhW8SqVaswatQojBw5EmFhYTh9+jS+//57JCcnAwAuXLggfD9u27YNABAZGSkMW7hwoTCvoKAgREdH4+LFi3j33Xfx6aef4tq1a1qX+f3332PVqlVaY3JxccHhw4fx33//GYxfuczw8HAsXrwYycnJaNSoEdasWWPO6jAoKSkJ8+bN0zk+KysLbdq0QUJCArZv347IyEhs2rQJb731FuLj49GzZ0/Rb06TJk3w5ZdfioaVKVMGgPHbpDGfOWO3W9Vl/vfff+jevTtmzZqFpk2baiR+qMcdHR2NuXPnitrk9vtDRERkD4y50J1XunWwhuPPI0TPbydF2ygSy3LSWxuEyDgbH57G9YRHOPYsHMee3RCGb4kK0TMVERERkelcJM62DoGIyCFZLQHCxcUFAQEBCAgIQI0aNTB9+nQkJyfj1q1bonbe3t5CO+VfwYIFRW2KFCmCgIAA1KpVCxMnTtS4kPnjjz/i6dOnOHz4MDp06ICyZcuiefPmOHDgAFxdXTFs2DDR/Fq3bo2AgADh7mpj/f777/jyyy/x+eefo0aNGli2bBkKFCggXEBMSEjAypUr8fvvv6NVq1aoV68egoKCcObMGeGu34MHD+LGjRtYu3Yt6tSpgw4dOuDnn3/G4sWLkZmZXY6zfPnyWLhwIfr37w9fX1+tsTRo0AC//vorevXqBXd3d61tOnTogBkzZqBLly56X9eTJ08wYsQIrFu3Dq6urgbXQ1JSEvr06YOCBQuiRIkSmD9/vqg6SMuWLfHw4UOMHj1auPNY6dSpU2jWrBk8PT1RpkwZjBw5EikpKQCANWvWwMvLC7dv3xbaf/PNNwgMDERqaqrBuLRRjWvixIlo1KiRRpvatWtj+vTpwvMVK1agevXq8PDwQGBgIJYsWSKMU96BvmnTJrRo0QIeHh5Yt26d1mVLJBIsXboUHTp0gKenJypWrIitW7eK2ly7dg2tWrWCp6cnihQpgq+++kq46K1uzZo1KFKkiMZd4J07d0a/fv1Ew1auXIm+ffuib9++WLlype4V9Jqyq41p06bB398fPj4+GDJkiLBN6np96hd6CxUqhODgYABAZmYmhg8fjhIlSsDDwwPlypUz+JlTfm+UKlUKrVu3Rvfu3XHo0CGNdl999RVCQkKwb98+vfNTKBQICgpCv3798Nlnnxm1Lq5cuYL3338f3t7e8PHxQb169XDx4ps7v/RtwwDw/PlzdOrUCZ6enqhQoQLWrVtnsCLKrl270KNHDwwePBiVK1dGzZo10bt3b/zyyy8AAH9/f+H70c/PDwBQrFgxYZjq90ShQoUQEBCAqlWr4ueff4ZUKtVIYjh+/DjS0tIwffp0JCYm4syZMxoxFSxYEIMGDcIPP/xgcJ0pl1m+fHm0bdsWW7duRZ8+fTB8+HCTE82MMWLECPz++++i5DNV4eHhuHv3LpYsWYLGjRujXLlyePfddzFjxgw0btwYnp6eot8cNzc3FChQQDTM2Tn74MLYbdKYz5yx261ymSVLlsRbb72FESNG4Pjx47h+/TrmzJkjaqsed0BAAHx8fERtcvv9ISIisgcSXujOkfjMFMONHBK3C7KcTJlU9Dw2U3wsH5dnP0dERESUW3Y8Om+4ERERabBaAoSqjIwMBAUFoVChQqhWrZrZ80lLSxPuWHVzcwMAyOVybNy4EX369NGohODp6YlvvvkGBw4cEJUsd3Z2xsyZM/Hnn3/i8ePHOpcnkUhEF3NDQ0PRunVrYbyTkxNat26Ns2fPAgBCQ0ORlZUlahMYGIiyZcsKbc6ePYu33noLxYsXF9q0a9cOiYmJCA8PN2e15IhcLke/fv0wbtw41KxZ06hpxowZg9OnT2PXrl04dOgQTp48iUuXLgnjt2/fjtKlS4uqewDA3bt30b59e3Tr1g1Xr17Fpk2bcOrUKQwfPhwA0L9/f3z44Yfo06cPpFIp9u7dixUrVmDdunUoUKBAjl9rnz59cP78edy9+6Z8a3h4OK5evYrPPvsMALBu3TpMnjwZv/zyCyIiIjBz5kz89NNPWL16tWheP/zwA7799ltERESgXbt2Opf5008/oVu3brhy5Qr69OmDXr16ISIi+26qlJQUtGvXDoULF8aFCxewZcsWHD58WFgf6rp37w6ZTIZdu3YJw54/f469e/di0KBBwrC7d+/i7Nmz6NGjB3r06IGTJ0/i4cOHBtfPkSNHEBERgWPHjmHDhg3Yvn07pk2bZnA6Xf744w/s2rULmzdvRmRkpJAIYKwHDx7gwIEDwmddVYUKFTBkyBBMmDABcrlc5zz+++8/pKamonXr1ujbty82btwoSlbQpk+fPihdujQuXLiA0NBQ/PDDD0JikKFtGMhOJnn06BH+++8/bN26FUuWLNG4UD9w4EBRtwcBAQEICQkx6n0yllQqFS7Eq6/DlStXonfv3nB1dUXv3r11XrCfOnUqrl27ppG4Y4zRo0cjKSlJa7JATvXu3RuVK1cWJS6p8vf3h5OTE7Zu3QqZTGax5eraJo39zBm73WoTGBiIDh06YPt2y/RDbc33h4iIyFwyhRyDQ5Zh6tUtOZ5XsjTdYBu5wrTf4/zkXvIzw42I8qBnafFouH8ivj73NwAgNiMZ3U78hlV3NSvjSdW+Q1ydXETPX2Vqv7mBiIiIyFgX8khXdEREuc1qCRDXrl2Dl5cXvLy84OnpiXnz5mHDhg0ad6aOHz9eaKf8O3nypKhN06ZN4eXlhYIFC2LevHmoV68ePvjgAwDAixcvEB8fj+rVtfeFVL16dSgUCty5c0c0vEuXLqhTpw6mTJmi8zVUq1ZNuLP65cuXkMlkosQFAChevDhiYmIAADExMXBzc9Poy169jbZ5KMfltjlz5sDFxQUjR440qn1SUhJWr16NefPm4YMPPkCtWrUQFBQkusjo5+cHZ2dnUXUPAJg1axb69OmDUaNGoUqVKmjatCn++OMPrFmzBunp2Sco//rrL0RHR2PkyJEYPHgwpk6dinr16lnktdasWRO1a9fG+vXrhWHr1q1Do0aNULlyZQDAlClT8Ntvv6Fr166oUKECunbtitGjR+Ovv/4SzWvUqFFCmxIlSuhcZvfu3fHFF18Id+PXr18ff/75JwBg/fr1SE9Px5o1a1CrVi20atUKixYtwv/+9z9RFy9Knp6e+OyzzxAUFCQMW7t2LcqWLSu6mL5q1Sp06NABhQsXhp+fH9q1ayeaRhc3NzesWrUKNWvWxEcffYTp06fjjz/+MPlCrVJUVBSqVKmC9957D+XKlcN7772H3r17651G+b2hrJ4QHh6us8uASZMm4f79+zorcADZF/p79eoFZ2dn1KpVCxUrVsSWLfpPqEdFRaF169YIDAxElSpV0L17d6ErGkPb8K1bt/Dvv/9i+fLlaNy4MerVq4eVK1ciLS1NtIwSJUqgbNmywvMpU6agUKFCKF++PKpVq4aBAwdi8+bNZq373r17w8vLC+7u7hg9ejTKly+PHj16COMTExOxdetW9O3bFwDQt29fbN68WWvlkZIlS+Lbb7/Fjz/+CKlUqjFen8DAQADZSQOWJpFIMHv2bPz999+ihCalUqVK4Y8//sDkyZNRuHBhtGrVCj///LNGd0jGMGabNOUzZ8x2q0tgYKDG+lyyZInGb6gx8zbl/Zk1axYaNGgAb29vFCtWDJ07d0ZkZKTeaYKDg4UKQMo/Dw8Pg8siskcxafGYd2M3L5QS5YLw+Me4Fh+FfU8v53he1+KjDLZJk+mudpbf/fP4ouFGDsiYrlEof+t0PLs7uctxDwAAK+8exaPUV1h2Oztx+Hn6m25d1ROtepVrKnquurn5uYkrnRIREREZQwF220f2S8FuJcmOWS0Bolq1aggLC0NYWBhCQ0MxdOhQdO/eXVRKHgDGjRsntFP+1a9fX9Rm06ZNuHz5MrZt24bKlSsjODhYo6sGcz5oc+bMwerVq4U78tXdvHnTYPcRjiw0NBQLFy4ULlQZ4969e8jKykLDhg2FYb6+vkZV9rhy5QqCg4NFF+ratWsHuVyO+/fvAwAKFy6MlStXYunSpahUqZJR5fdN0adPHyEBQqFQYMOGDejTpw+A7IoMd+/exeDBg0UxzpgxQ+Miq/o2qkuTJk00niu3t4iICNSuXVvU5cu7774LuVyu8+Lil19+iYMHD+LJkycAsi8yDhw4UHj/ZDIZVq9eLVzcBrIvcAcHBxu8mF67dm1RpY0mTZogOTkZjx49Muq1qhs4cCDCwsJQrVo1jBw5EgcPHjQ4jfJ748KFCxg/fjzatWuHESNGaG3r7++PsWPHYvLkyVq76oiPj8f27ds11oVqtQPV93nIkCEAsiucfPHFF2jdujVmz54teu8NbcMRERFwcXERJe0EBgZqJEXNmjVLqGYDZCdEnD17FteuXcO3334LqVSKAQMGoH379iYnQcyfPx9hYWH4999/UaNGDaxYsULoNgMANmzYgEqVKglJHXXq1EG5cuWwadMmrfMbP348Xrx4IXT1Yyzld7Kx3y2mateuHd577z389NNPWscPGzYMMTExWLduHZo0aYItW7agZs2aJlc8MLRNmvqZM7Td6qNQKDTWZ58+fTR+Qz/++GOj5gUY9/4cP34cw4YNQ0hICA4dOoSsrCy0bdvWYDUVHx8foQpQdHS0RSucEOWmj4/Pxeaos2h8YJKtQyHK8xKyzOv2zlxB947l6vIcSZ3C5W0dglWwaxQy1fHnb84XKRQKdDz2pku6FXeOiNoWdBF3T1rSs7DwuEVx4ypuEhEREaniBWayZ4tvHbB1CEQ6mZQA4ePjg4SEBI3h8fHxoj7ogey7yStXrozKlSujbt26mD17NkqVKoUFCxaI2hUtWlRop/zz9PQUtSlTpgyqVKmCLl26YObMmejSpQsyMjIAZF9MKlSokM4khoiICEgkEuEOf1XNmzdHu3btMGHCBIOvvWjRonB2dta4M//Zs2dChYOAgABkZmYiPj5ebxtt81COy00nT57E8+fPUbZsWbi4uMDFxQUPHz7Ed999Z1JXBcZKTk7G119/LbpQd+XKFdy+fRuVKlUS2p04cQLOzs6Ijo42eIHNVL1790ZkZCQuXbqEM2fO4NGjR+jZs6cQHwAsX75cFOP169cREhIimo9q0kJuqlu3LmrXro01a9YgNDQU4eHhGDhwoDD+wIEDePLkCXr27Cm8p7169cLDhw9x5MgR3TM2g0Qi0dgBy8rKEh6/8847uH//Pn7++WekpaWhR48e+PTTT/XOU/m9UatWLcyePRvOzs56u+EYM2YM0tLSsGTJEo1xygobjRo1EtbF+PHjcerUKdy6dQsARO+zsjuFqVOnIjw8HB999BGOHj2KGjVqYMeOHQCM34bNVatWLXzzzTdYu3YtDh06hEOHDuH48eMmzSMgIACVK1dG27ZtERQUhJ49e4q64Fi5ciXCw8OFdeLi4oIbN27oTHAoVKgQJkyYgGnTpiE11fiLEsrv5AoVKhhs6+Pjg5SUFI2EAeV3qfrvi9Ls2bOFBDltvL290alTJ/zyyy+4cuUKmjVrhhkzZhj9GgDD26Q5nzl9260+ERERGuvT19dX4zfU29vbqHkBxr0/+/fvx8CBA4UqOsHBwYiKikJoaKje6SQSiVAFKCAgQKP6ERERkTrVbhdyo+pKFe/cPf5yJA2LaB4/5wW8g45MpVrxQX37STVQRaao+5v98ogE3d2vEhEREenCvVeyZ2vun7B1CEQ6mZQAUa1aNVy6dElj+KVLl1C1alWD0zs7O2uUgjfVp59+ChcXF+HCkZOTE3r06IH169drdCGhvMDUrl070R3QqmbPno3du3fj7Nmzepfr5uaGevXqiS5oyeVyHDlyRLjLv169enB1dRW1iYyMRFRUlNCmSZMmuHbtmuiC5KFDh+Dj44MaNWqYsCZyrl+/frh69aroYm7JkiUxbtw4HDigPXOrYsWKcHV1xYULF4RhCQkJwgVlJTc3N1G3GED2BfEbN25oXKyrXLky3NzcAABnzpzBnDlzsHv3bnh5eWH48OEWfc2lS5dGixYtsG7dOqxbtw5t2rRBsWLFAGR3RVKyZEncu3dPIz5jLhJqo544ERISInTXUr16dVy5ckWU5HH69Gk4OTnprajxxRdfIDg4GEFBQWjdujXKlCkjjFN2+aB+R3ivXr1ElQ+0uXLliujzGRISAi8vL9H8Vfn7+yM6Olp4fvv2bY0L5D4+PujZsyeWL1+OTZs2Ydu2bYiNjdUbh6pJkyZh3rx5ePr0qdbxXl5e+Omnn/DLL78gKSlJNG7lypX47rvvNJIVmjVrJlzsV32PldsBAFStWhWjR4/GwYMH0bVrV6E7A0PbcGBgIKRSqejCcGRkpEZSlDGU3wc5SQJq2LAh6tWrh19++QVAdncOFy9exLFjx0Tr5dixYzh79ixu3rypdT4jRoyAk5MTFi5caPSyFyxYAB8fH7Ru3dpg22rVqkEqlSIsLEw0XPl7o+v3pWHDhujatatRlWIkEgkCAwNznFSlvk2a85nTt93qcvPmTezfvx/dunXLUfxKprw/6pSJkLp+V5WSk5NRrlw5lClTBp988gnCw8P1ts/IyEBiYqLoj4iI8hcPpzdV/jLlpnW/ZY66hc3bx88Pwl6X/1eq4q272z+i/EJu4h2YqhVHbiZqP6YlIiIi0ocJvERE5nExpfHQoUOxaNEijBw5El988QXc3d2xd+9ebNiwAbt37xa1lUqlQkJCUlISNm3ahBs3bmj0nZ6UlKSRuFCgQAH4+PhojUEikWDkyJGYOnUqvv76axQoUAAzZ87EkSNH0KZNG8ydOxe1atXC/fv3MWnSJGRlZWHx4sU6X9Nbb72FPn364I8//tAYFxgYiFmzZgndYIwZMwYDBgxA/fr10bBhQyxYsAApKSn4/PPPAWTfiTt48GCMGTMGfn5+8PHxwYgRI9CkSRM0btwYANC2bVvUqFED/fr1w9y5cxETE4NJkyZh2LBhcHd/Uy5ReSEwOTkZL168QFhYGNzc3ISLopmZmbhx44bw+MmTJwgLC4OXl5dQ7SI5ORl37twR5nn//n2EhYXBz88PZcuWRZEiRVCkSBHRa3Z1dUVAQIDOC/De3t4YMGAAxo0bBz8/PxQrVgxTpkyBk5OTqJR6+fLlceLECfTq1Qvu7u4oWrQoxo8fj8aNG2P48OH44osvULBgQdy4cQOHDh3CokWLkJSUhH79+mHkyJHo0KEDSpcujQYNGqBTp056KwfIZDKNC6fu7u5CooG6Pn36YMqUKcjMzMT8+fNF46ZNm4aRI0fC19cX7du3R0ZGBi5evIi4uDiMGTNGZwy6bNmyBfXr18d7772HdevW4fz588JFUWUcAwYMwNSpU/HixQuMGDEC/fr103un9GeffYaxY8di+fLlom4UXrx4gd27d2PXrl2oVauWaJr+/fujS5cuiI2N1XnRMjMzE4MHD8akSZPw4MEDTJkyBcOHD4eTk/Y8qVatWmHRokVo0qQJZDIZxo8fL+qa5vfff0eJEiVQt25dODk5YcuWLQgICNDoDkKfJk2a4O2338bMmTOxaNEirW2++uorzJ8/H+vXr0ejRo0AZH9+Ll26hHXr1iEwMFDUvnfv3pg+fTpmzJgBFxfxV2BaWhrGjRuHTz/9FBUqVMDjx49x4cIF4aKzoW24WrVqaN++Pb7++mssXboULi4uGDVqlEZVmwkTJuDJkyfC+zd06FCULFkSrVq1QunSpREdHY0ZM2bA399foxsVU40aNQpdunTB999/j5UrV6Jhw4Zo3ry5RrsGDRpg5cqV+PXXXzXGeXh4YNq0aRg2bJjWZcTHxyMmJgYZGRm4desW/vrrL+zcuRNr1qwx6v2uWbMm2rZti0GDBuG3335DxYoVERkZiVGjRqFnz54oVaqUzml/+eUX1KxZU/RehoWFYcqUKejXrx9q1KgBNzc3HD9+HKtWrdL4DTKV6jY5ZcoUsz9z2rZbJeXvp1wux6tXr3Ds2DHMmDEDderUwbhx40RtU1NTNX5D3d3dUbjwm3K7OX1/VMnlcowaNQrvvvuuxmtWVa1aNaxatQpvv/02EhISMG/ePDRt2hTh4eEoXbq01mlmzZqlt+ILERHlfe7Ob/YlU6QZ8HB2M3tePq6ehhuRTi/SxYmInUsb1wWgvfN1LWC4ETmsX2/sgpuTC74N/NAq8zf1AoS1ugOknHuWFg9/Dx84SazWMzAREZFZstQSwV9mGHfzFBERiZm0p1+xYkWcOHECN2/eROvWrdGoUSNs3rwZW7ZsQfv27UVtw8PDUaJECZQoUQJ16tTB5s2bsXTpUvTv31/UbvLkyUI75d/333+vN44BAwYgKytLuCBapEgRhISE4P3338fXX3+NSpUqoUePHqhUqRIuXLiAihUr6p3f9OnTtfbVHhkZKeryo2fPnpg3bx4mT56MOnXqICwsDPv37xddrJ4/fz46duyIbt26oXnz5ggICMD27duF8c7OztizZw+cnZ3RpEkT9O3bF/379xfK7yvVrVsXdevWRWhoKNavX4+6deviww/fHMQ/ffpUaBMdHY158+ahbt26+OKLL4Q2Fy9eFNoA2QkcdevWxeTJk/WuD0N+//13NGnSBB07dkTr1q3x7rvvonr16vDw8BDaTJ8+HQ8ePEClSpXg7+8PAHj77bdx/Phx3Lp1C82aNRNiKVmyJADg22+/RcGCBTFz5kwA2ckpM2fOxNdff40nT57ojCc5OVl4ncq/Tp066Wz/6aef4tWrV0hNTUXnzp1F47744gusWLECQUFBeOutt9CiRQsEBwebXQFi2rRp2LhxI95++22sWbMGGzZsEJJYChQogAMHDiA2NhYNGjTAp59+ig8++EDnhX4lX19fdOvWDV5eXqL416xZg4IFC+KDDz7QmOaDDz6Ap6cn1q5dq3O+H3zwAapUqYLmzZujZ8+e+PjjjzF16lSd7X/77TeUKVMGzZo1E5IyChR4c0LR29sbc+fORf369dGgQQM8ePAA+/bt05lQocvo0aOxYsUKPHr0SOt4V1dX/Pzzz0hPTxeGrVy5EjVq1NBIfgCALl264Pnz59i3b5/GOGdnZ7x69Qr9+/dH1apV0aNHD3To0EG4KGtoGwaAoKAglCxZEi1atEDXrl3x1VdfiapLAEB0dDSioqKE561bt0ZISAi6d++OqlWrolu3bvDw8MCRI0c0kpRM1b59e1SoUAG//PIL1q5dq7OCQLdu3bBmzRpRNyaqBgwYoPO79PPPP0eJEiUQGBiIoUOHwsvLC+fPn8dnn31mdJybNm1CixYt8PXXX6NmzZoYOXIkPvnkE6xYsULvdFWrVsWgQYNE73/p0qVRvnx5TJs2DY0aNcI777yDhQsXYtq0afjxxx+NjkkX5Ta5ZMkSsz9z2rZbJeXvZ9myZdGyZUts3rwZEyZMwMmTJ+Hl5SVqu3z5co3f0N69e4vaWOL9URo2bBiuX7+OjRs36m3XpEkT9O/fH3Xq1EGLFi2wfft2+Pv746+//tI5zYQJE5CQkCD86frME+WmFGmGrUMgyle8XN8cT6hfgDeVCy9q5Yj6hdu8ciE3J0k1ZN+epMZiS1QI1j04hYRM47vuM4WpFSCc8sjnJq+ZcnULOh2fi8YHJtk6FCIiIg17nmhWYCeyB8lZ6djz5BKSszTPZxPZI4lCYeIRHJGalJQUlCpVCr/99hsGDx5s63DshkQiwY4dOzSSLCzhgw8+QM2aNbVWLjHHwIEDER8fj507d1pkfiRWvnx5jBo1CqNGjbJ1KERmGz58OP755x+cOHHCrKSw7t27w8XFBRs2bDCqfWJiInx9fZGQkKCzKhSRtYXHP8LnIUuF5+fbz7RhNER535SrW/Dv08sAgN0tx6O4h6/J82i4fyIAoKi7N/a9P0Fvmy8qtcKKu0eF4crPuHK8uvz0HfD52SUIT3gsPB9bvRN6lMtZZTJ78HvEXmx8eNqkafLT++7I9j25jKnXtgAADrSaiMJuXgam0E7183++/UzR8xNtpqH5oSk6px1XvRO6q3xOVL/TavmWwaomQ82KiSxL/T0mIiKyJ0tuHUDwveOiYfy9InswOnQ1Tr+IROOiVfBH/c+hUCjQ6ID2mw25zZI94G0xZLLLly9jw4YNuHv3Li5duoQ+ffoAAD755BMbR5b3xcXFYceOHTh27JjO7giIiCxJoVBg+PDh2LFjB44ePWpW8oNMJsO1a9dQogT7DyfHEpuZYusQiPIV5YVCAEjMytkd3CxrnjMSiO9czyv3TSigWfmR8h717ddc6tu9qZ8D1ShKFdDeFSURERGRqsSsNNFzLxcPHS2JctfpF5EAgJCXtwEAGXLtlaSJ7AXPCpFZ5s2bh9q1a6N169ZISUnByZMnUbRoUVuHlefVrVsXAwcOxJw5c1CtWjVbh0NklA4dOsDLy0vrn7LLG7Jfw4YNw9q1a7F+/Xp4e3sjJiYGMTExSEt7c0DWv39/TJjw5i7b6dOn4+DBg7h37x4uXbqEvn374uHDh6JumogcwflXt20dAlG+FZGguws8YxRgVwcWJUfeSIAwtQsDchyqvU1YKgECAJoXqy48NvQ5UB/LLjCIiIjIVK5OzqLneSURmfKeJ6lxtg6BSC8XWwdAjqdu3boIDQ21dRh2zxo7Jw8ePLD4PAEgODjYKvOlbNZ63xzFihUrRBfLVfn58U4oe7d0aXb5/5YtW4qGBwUFYeDAgQCAqKgoODm9yamMi4vDl19+iZiYGBQuXBj16tXDmTNnUKNGjdwKm8hkJ55HIDLxKb6o1Ap7nlzCz9e34e1CZW0dFlG+VaZAkRxN37hoFQtFQkDeSRzIK6+D9LNU4oECCtG8FCYmAlkyEYOIiIjyB1cn8SW7MgVzdlxEZC1M9iV7xwQIIiKyqlKlStk6BMoBY5K5jh07Jno+f/58zJ8/30oREVnH2Ev/AwDU8C2Nn69vAwBcjY+yZUhE+dqM69uxrfl3Zk/PC485I1E7mWXqhV975eiVLBQKBWQKOVzU7gwkoKSn7ROr1T8nqieFS3gWyuVoiIiIyN5dir2PC6/uYHClVsL+nYeTq6jNBwFv2SI0yofSpJlocXgqKnkVx4b3vjXY3jMHVRelchmPacjq2AUGEREREeVrUrlMeHzu5R0bRkJESo9SX+VoevUL+GQa9bWXVyonOHoJ4ZEXg/HRsdlIl2XaOhS74+HsariRiRQAcrLJqCZiuTtZPj4iIiJybEPOL8fKu/9hx6PzwrCSnoVFbayxH/48PQH3kp9ZfL7k2ILuHQMA3DVy2zD3mPtpahxaHZmOeTd2mzU9kbGYAEFERERE+Vp0WrzwuIp3gO0CISKLMeZUDJMkdMuUS0XPc6MCxB83/0Xw3WNWXYajV4A49+o24jJTcOHVXVuHYtfs5V1W/dzklSoqjo7JQ0REZI/W3j8pPC7m6SsaJ1fILb68jsfmoNephXiRnsjfRhIUdHE3qb25yeXB944hXZaFzVFnzZqeyFhMgCAiIiKifGXd/ZNYcuuA8NzT5U3ZviRpmtZpStlBaW0iotyQLsvEzcSnomHWrpzwIPk51j44iSW3D1p1WdY4gWwLt5KiMSlsI+4nP7d1KHbJUtuQQqGwWOIC0x/sQ2xGiq1DICIiQqZcir1PLgnPo9PjsefJJaRIMzTayqy4//pD2Ho0PzQVC27us9oyyHGUKmDaeS9z95OvxD0wazoiU7nYOgAiIiIioty0MPJfAMDHpeujdIEionFPU+O0TvMkLdbqcRHlZ6pd0ZBtRSQ80Rhm7coJGx6cFh7LFHK4SKzTH2xe6crjr9uHAQCnX0Siqk8JfFjqHdTzq4BSnn74NWIXvFw88E3VdjaOMnepJj3Y7F3Ws2BH734lr3Bi5R8iIrIDP1/bhgPRV0TDpl/bipAXt/Bx6fqi4ZbeD1fdJ7kWHwUAWP/gFEYFfmjR5ZDjMXUvydxjq3RZllnTEZmKCRBERERElG+o3j2h7aBLVwa7n5uX1WIiImD3k9Acz0P1ZJ7E5NM3pI+1L97ueHxBeGzNZIu81g1BiiwDl+Me4PLru6i8XDyQLE0HAHxVuTVcnKyTSGKPrPHOWnJ72fTwDL6q0tpi8yPz6Kr0RURElJvUkx+UDsZc1UyAsHAFCGtWlCD9otPicOr5TXQqXR8ezq62Dgc/X9uG6wmPsPHdb83qHtLcfeXo9HizpiMyFbvAICIiIqJ84eyLW2h5aJrw/FD0VY02WTruQucdg2RJvBNY06zwnTmeR167uG0r2taitStAlC1Q9M2yrHhSNq9UgNBFmfxAVnqfTZytavMkvjd24eKre7YOgYiISK90ufhGDZmF91/Pvrxt0fmR8T45/it+jdiNyVc32ToUpEkzsftJKO4nP8c/jy8CMP0mAkscW2mrPkhkKUyAICIiIqJ84dvQYGSonEwIuncs+4HKQdu/T8O0Tpu3L5lRbvrk2Fw0OvAjYtLibR2KXWtStKrJ04hOwBhx7iY/pzX9F3MdR2KuGd3e2kk7df3KC48tfZJXlTWTK8i2VBOgLJUMpYBp2z6TsOxfplxq6xCIiIj0Gnvpf6Lnlt5//e7SGovOj0x37NkNs6eVymUWqeKhem4sItG8JARzjxHf8w8UHn99/m9MvboFGewWg6yACRBERERElOc8SH6OrNcnuZOz0o2+2FzMw0frcAUvmpGFKMs99jg137aB2IlFkfvx641dGsOLunvnaL7sAkO3x6mvMD5sPSaEbcCL9ESjprF2BQgnlffLqhUgeIE6X7BUDo0lE386lKxrsXmReS7F3sfiWwdsHQYREeVh/z0Lx6iLwYjNSLbYPC2RHPw8PQHrH5xCchYrUtmLh8kvAAAHo6/g/Ms7Rk0jlcvQ8dgc9Dg5P8f7qao3D2jrHtaoeZh5bFW7cDnRsvc9vYx1D06ZNS8ifZgAQURERER5yqHoq+hxagFGha4GALQ6Mh0fH5+r0c7dyUVjWCXv4lrnmdfLplPuYzcY2Sdw1tw/gS1RIRoVH8z5zOXFu6/TpJn4MuQvXIq9b7F5dj3xm/B4wc29Wlporkdrfweq3nVkzQoQjvy5MzX2/NYdhjXeWwVMqwClr20h1wI5jIZyasj55bYOgYiI8qCeJ+ej4f6JkMplGH95Hc68vIU/Iv+12Pxzmhwcn5mCjsfmYMHNfZh6bYuFoqKc6n5qPp6kxmLSlU0YfnGVUdM8SYtDbGYyHqW+QpZCe/etxlLdrpT70abeQmDuMaKHk6vGsGW3D5k1LyJ9mABBRERERHnKtqhzAIALr+7i7ItbOtsp1P4HmOhAuSdDLsXUqzwBpeTp7CZ6bs7dJHnx09vi8FRciX9otQt35Qr6G9XOUheXIxOf4pyWfodVkx6sWQHCmskV1mbqZ2KulsoqeZlC9Ng+3mfVz419RERERESWdj8l+07+TQ/PCMNiMy1XAUKOnO0btz36i/D4xPOInIZDJsiSS/FMTzXShKxU4bExx1uvMpJMaq+PzAJ7p+buc+c0eYPIWEyAICIiIqI8xVWlssO3ocEWmScvXJA17Ht62aHvSFeXLsvC7PCdeJD8XDT8TlIMPjg8HUdjrouG9z3zp/BYqnYSJFWaYfLyVdelMXev2GM3GTFp8fjh8jrcTYrJ8bziM1Nx6vlNUR+x6bJMUZu6fhWMmpcluo6QK+Tod2YRRlwMwpPUWNE41XfCEn3a6mIvF8bNYep3xaXYe1aKxD6pvreWe5ctN6eXGYl56vvenknlMlyOvc++pImIyOpUL26/VLk4nWLGsYwulk7gHVa1nUXnZ48Ss9Iw8/oOXIl7aNM4ep1aiE7H5yIy8anW8a5OzsJj9eNhbdbePyk8zunNOzK5SgUI5T6vxLTjY3NjkMqZAEG5gwkQRERERJSn3E9+Zva0ui68OfJFM7Jvjnj3Q7osEw33T0TD/RNFw5sfmoLtj86jx6kFouGfnf4DSdJ0/BC2XhgmU8hxTyVRIkvtJMjx5zdMvhCu+jm1x+QGdbEZyQiLeyAa9vHxuTj6LBy9T/+R4/m3PToDYy6twZJbB4Vh6x+cFrVxMnI9WeLCbar0TfLFtfgo4fGOR+dxKylaeG7NBAhHrvJjahJKbGaKlSKxT6K31kLvsyU3l8Mx19DowI+WmyEJsuRSnH91R+i/+u87R/D1+eWYdm2rjSMjIiJrMXQB9Xr8I7xMT9Q6LvTVPYy4EIRHKa+QJZfiStxDs/c/dz6+IDwu6u4tPH6S+sqs+Wlj6epoPq6eZk2nUCjQ+sjPGH95nUXjsYY/Iv/FzscX8OW5v2yyfJlCjpPPI/Do9XagmrigixdnvoEAADyZSURBVHqCuDaZcqnwOKcJ6qqVRbQd6xlz/McKEGTvNDs+JiIiIiJyUOHxj/A8Q/uJDnXKAzrRXaM6DvIc+aIZ2Q9t25dMLreLtPRMuRQXX91F3cIV4Oniprftmnsncrw89ZOM2u54eZaWgJIFChs9z3QHu9u384lfkS7LwqL6g9CwaGWrLed/909gRLX2ALIvwhqi7evOEhUgVKtPeKuceJ0VvlPUzpi7n8zlyMls/B0yxPLrR/H6n+pzQ+0pd8kVcnx57m/cSHiMsgWKYmvzMdjwOtHrcMw1zERvG0dIRESWNjhkGa7FR2Fs9U7oUa6JMHzK1c1IzErDl5U/wKCQpQCA8+1nIlMuhVyhgIezKwBg6IUVAIB+Z/6ETCFHhlyK0gX8sL35WJNjKeRWUHjs5+6Vk5elkyn7gLcSo4Uqe+fbz9TaxtzE5i1RIUjMSsN/z8LNmj433VerSJjbZl7fgd1PQoXnaWpV+JRUu4Es5FYQP7xOLplV5zNItFRjUE38yel+p0yhuY9r6k0ErABB9s4OTrUREREREeWcQqHA569PdBjV3shhynkT5dQrLX3Byl7feZEuy8T5V3e0ngzIjRMEC27uxajQ1RhxcZXBtmULFs3x8tTvZFKvAAFkJwg8T08wep7zb+7NcVy5SZmwcfpFZK4t845a1xrGJjZY4uL7M5X30ttF951n2rYFS7FmdQlrs6ffIXs8aSkXJSpQfjEvYjduJDwGAESlvgQAZMgdKxmOiPKO5Kx0u/yNzGuUlcTmRewWhskUcvz7NAynX0Ri+6PzwnCFQoH3Dk5G80NTRHfPA0CqLBMZr4c9NuLue21q+ZYRHns6uwuPAzyNT+I2xJT9V9UuBp/pOI4ydz/pwqu7Zk4JnH1xC39G7rd4NQtdVKvNAcCRmGs4YkQiuKWoJj8AwInnEVrbqSYcJGal4eizcBx9Fo7ND89q7UZFdR8nx11gKFSTKTQZk2BhjQoQCoVCqDaZKZfiaMx1JEvTzVoOERMgiIiIiChPmH9zn0ntlQdrCi3DdLUlyolXKv3CKin73vzh8noMv7AKf90+LBp/KPoqmh78Cfufhlk1tq1R5wAAV+OjDPZZa4nPg3pftrpOFnc8Nsfoef6rso4c6TOrWn7UmrSdPDV2PSksEGPIy9vC43S59rugAOsmQNhTEoGpLFGFwxIeJr9A04M/YX6EfSUcKbTcxZbjeZocg0UWSyZQ/nYZo0yBIhrDHPk7gbLfP10l7sl+JWal4WHKS1uHYXHP0xPQ6sh0ND34k61DyVNi0uJxKfa+wXaq3+dhsQ+Ex6qVxY4/u2HR2AC1u+hV9rWN7WbOGOZe6M6USbUON2c/afzldTj+3Pz1921oMP53/wQWqJyzyVCr3idXyBHy8jbiLdyNW4o0AxPCNmBC2AYsjjxg0XnnlGoChJNKxYffbu7B8AsrRW3TZVkIf530CVgiAUKlCwwz953NrwCh+9jyQPQV4fH3l9bih7D1aHV4ulnLIWICBBERERHlCRsfnjbcSIW2gzWdXWDYyYUncmyJWWkaw5QXv8+8vAUA2PooRDT+xysbAQCTr24GAByNuY4/Iv+16t0zhk4w6DrRUcrTz+hlqMev784mc+7aN+oza7nzkkbb8zgUP1/blut3ByoUCvQ4OV/LCG2DtH035jwG1e1/+IVVOr9vrdkFxsXYe1abt7XZy4Xaia+/kzY8PI34zFSTqrRYk0LH45zNU2E3653e2P80DAeeXjHcUI2zRPMUKPfv7NvT1DgkZ+m+6/PXiF348Nhs/K2WPEr2rfWRn9H95O+4m/TM1qFYlGrSrqN1y2bPPj4+F0POLxeq/eiierygrAgEiPcJoqyQeKO63yxTeayt+wJzmXvc5+JkuUt/6t1emLt/tPHhGQDAlodn0ezQFJx8flMYt+/JZYy8GIS+ZxaZH+hryu5OAHFy9er7x3M8b1UKhcKoY9XuZRtrXWeq24/6MXa42jZ/WSMRKGf7MHJR8o622EybhymkCu3JOYD4dSrPkRCZiwkQRERERJQvabvIp+tEOC+AkCUkaUmAUK+EYMgPYeux9v5JrL1/ylJhaTDYz72OmAu5FTB6GRoVIPScOPrl+nbMCt+JNKnuqgHqTO2/NLdMv74Nu5+EouuJ34RhzhJniy9H/T3KkEvxKPWVZjsjT5xZIinhvWKBoudX1UrTKlmiAsS1+Cg03D8R+5+GISEzFRmyLGTJpQ7dBUZuXKjd/zQMn536A7EZyXiWnqA1ueF2UrTwuO3RGeh4bA7iM1OtHpshVkkKU1vl6qWzDU5AFnEk5hrGXvofYjOSkSHLwuSrm/HT1U1atzttlZaUnLRcjMqtUtxkumfpCeh84le0OqI7KVNZAWTF3aO5FRblkGoC6NRrW2wYiXWpdzlGOXc9/pHe8br2aVUv0NbwLW3RmABx4oXqb4olj0SUXSbKFHLcTHiid3/249L1hceZOvapLXFuI6f71L++7r7ku0trhGHHXleYsERyrY/rm+NSS1SyUyVTyLHp4RncSoxGowM/osmBSVpvdNA2nT7pMv3Huu4qSR3GzM+UeJRbhKnbrb5jSX3bmb4KEBkG97eJjMcECCIiIiLK3wxkvgO8pOGoFAoFup/8Hb/e2GXrUABAazlP9Ysvxl643/f0EoDsEyWGL8pZliUuxGpWgNB90XvPk0vY8eg8Whyeipd6Lm6pcndyyVF81haTHi881nZRLqfUT0bprG5j5AnQ7Y/O57jMeDF3H9Hzi7Ha+xGONfI9VnUrMVp0Z+DgkGUAsiuntDk6A80OTcG7ByebPF97kCrNwOPUV1h2+6DVlzX56mbcSY7BiItB6HRsDjoem2PUydUZ17dZPTZDRIkzFkpaVKh9kjr8N8tAe83pKWduJUZjQtgGnHgegSW3D4q2x5i0OI32+tb4k1TN9qYmIVLueJz6CiMvrBKe3+XFZADZVcA2PThj6zByJE3lAl9lrwAbRmJZWWr74t9eDLJRJHmLatJDTFq83ra6v8+tU5VBmLvKclX3q520VB0yl3K+y24dQv+zi/Fn5H6dbVWPgRQ69uEs8ctnjcTc6zqSowHgSWoszr+6AwAIi3uAsLgHeuelmmxl6d/6vU8u4beIPeh75k9h2LSr+hO6tkSFaE34nxC2QXhsqPKFh1oCRE67wJDr2HaVjEmU0bevq28b0Zdcn9vnNihvs++zQkREREREVqZ6WJbTg0iyL40O/AgAeJjyEt8Gfgg3G18UL+RWUGOYuXdu3Et+job7JwrPT7aZpnFXSKo0A3GZKShVwPiuKYxhic+JKV1gqPrwv1k4336maFhCZirWPTiZ45hsxZJ9BCupv0cyE+580vXufnhsNkLazTD7hK76SbDbidovaI0PW6/xHuujUCiEE5CHWk2CrwmVSOxd0wM/WbVLEF1UqzzcTXqGqj4l9LY/8TzC2iEZpHq3mKV+yU39rlP/JPPies71V7kYcDj6KkYFfig813aCXF8FCG9XD2RkiMvSO3JVmLxMtUoSAPQ+/Yfwu3D6RSSW3DqIyt555+K5sX4IWw8gu2/4nPwe25Jqom95L38bRmKYsrS9i5PhSl3rH4i7YkyS6u66hXTLTvqMRWXv4nCSOGFQyFJhXGP/KnqnzWmyr7lU97Fl1qoA8Xq+yu4b1j84Jfo9VCW6qK2ruuXr4e8Xr4l2JWoL3y0mxSSXW/zW6lgtNwsodTkxT2PY5vdGobxXMQBARMIT+Lt7o6hHdsK1rsocynHausUy1ozr2zWGxWfpr4ZW0MVd6z79zcQnRi/X3Ul8rJ/TYwRxBYjsbcLUJCF9ny+FQqHzg6Cv4p+uBIjYjGQAgJ+7l/EBUr7neHtKREREREQWpHpHeZYNLjTlNrlCjtMvIhEW9yBfZde/9/ru72W3D2H341DRuH+fXsb5l3esHkPpAkU0hlnqpNz3l9dpDOt75k90OTFPo4/luMxk3Ex4c7IlRZphkRhMIVM7IWfKRahnamVR2xydgeB74v5c7fHOa10nZq1xAcPoChBQ4GpclOh7MC4zWed8Gx+YZHQMGbIsNNw/EQ33T4RCoUBEgvgEX2Xv4kbPSx9ltQcAGBSyFC9yWKnCVBmyLDy0QJ/SL9MT0XD/REy6shFnX9zC6NDVuZr8cCj6qtbhz1SqlZjjWVo8Gu6fiBV3jgAQ35FnSVmiBAhLVoAwb15zwv/Bv08vWySO/Ez14s37xWuJvsu03Ump727Faj4lNYap37VNtqe+z6LUcP9E/PcsHKNDV+N2UnS++3ypf3dq+z0ef3kdgu8ey6WIzKO6v1fJyzL7AdbS6MCPaHrwJ6Oqjy2+dSAXIsrb4jKT0fLwNPQ986ewfdfzqyiM91C7+KtO17GEtbsQUz2WU4gqQFguBULb8eJTLVWNAPHr1Vnd8vVwF4kTCrq4mxWTLRJ01fU4tQAA8CjlFQacXYwPj80WxqluD+rbRk73Rd8pXEFjmI+rp95pOpWqp7fbB3PkvAsMzfVgchcYeva7mh78Cd+cX6F1nDkVINr/NxPt/5uZr85h2atTz28a1e2LPWAFCCIiIiJyKFlyKVxV7uS/lfjUYLlAXWLS4kV3lug6gMutgyzl8o3JvL+X/AwlPQvDw9kNex6H4nDMNcx7p59wl1Lw3WN4nBaLXY8vws+tIDa+Nxptj84QzaNL6QZIl2fhWnwUtjcfa/kXZGdmhe/EjkfnAQAdStaBi5MzYtLiMeV1yUrVKgoKhQJ/Ru5HY/8qaFiksjCP6/GPcDPxCT4t2xgv0hNRwMXd6BNH2g701U9c6Hvn9Z1gOPvylsawx6mxAIBDMVdRybuNMLzd0Td3159vPxP3k5/rWaomS/SZrj4PXX3UapMu1d8/qr2ITHyKfq+/mwJ9Sum8w8fZyBOkCoXC6Lty1LcrXXeijw5dbdT8VDXcP9GoCg3zXvftCwAvMhI1+vpOk2VZpA/i6wlvyiM/Sn2Fj1ROflqCvvX+LD0BnY7NAQBMrNkFncs0MGneCZmp8HL1gLPESThpezD6Kg7qSEYwVao0AzKFHN56TspK5TKkSDPw45WNWsdnyKWIy0zGL9d34FZitNY2ALD8zhF8UakVbiVFI+TlbTQrFggvFw90Oj4XAPD3nSMoXaAIJl/dDHcnF5xsOz1nL06NNX6nFVCYvI2qVubJz9bcO4FbSU8xo3avHM2nU6l62P0kO2mxgIub6DKWthP/2u4K/OX6dvzz+KLwvGuZhtj+el+A/Uzbn96nF+ocN15Lsmd+oZ78CWj/vvnvWTjOvbqDpQ2/yI2wTCaH5h3HtiSVy+Di5KzxW7//aZjwWFl9LDkrHRdj7+E9/2pIyEpFh/9m4dtqHdCnQjON+VYoaN/VLUxx8dVdlPcqhqLu3lrHp8sycepFJN7zDxSV6E+RZsDD2RUXXt1FdZ9SQoUuuSL7Er1cIReO6XX9dr5fvCZCY+8BeFPdSXeXlfov9luL6vxVE7xVq50kZaUhUy5FAWd3tDg81eRlaPu9m3RlI1Y2HiJUKVFuw6J4dLz232/uBQAcirmGT8s2Fi3H2MoI+u7gN5ebk4tZ+3ORSU+Fx3KFHE4SJ7UKEOL1IFXIYV7aR7ZK3sVxKe6+aNj7xWvqneZu8jMLJI2o3UCQw4QKbduHQvTY8GfH0Pt1MfYePjn+K7Y1GyOqpqNv+0mXZekcBwBxmSko7uErGhYW9wBVvUuggJkJPWS8MaFrcOrFTQAwqWqjIa0OT0eyNB3N/APxW73+FpuvRGHtXwEiIiIiEyUmJsLX1xc7Ik/D06sgFFBArlBA/vpEtPC/Qi4aFp0Wj7tJz9C5TH1IIMGDlBeQQIISnoXg5uQCmUKOTLkUmXIpwuIeIk2WgbqFK0CuUAjLUKgtQ6aQY8Xdo0JsZQsUxdCqbbLjed1eppAL08hV4pIrFIhJj8eN+Mf4pEx9uDq5CAekymkB5Qn2N31NKxQK0WNlG+W8t0aFoHmxGqjoXQyy18Nkr/9SpBl4lZGEQJ9ScH19gKGcVvWxAgCUr1N1ma/HqT9+lpaA9Q9OoUPJuihbsChKF/BDllwGqUIGqVyG2Tf+0fpeDq/aHn7uXniY/EIo2TipVldIIEGKNF04+FbVqEgVnHt1W3ju7uSCRkWrQAIJjj+/IQz/vsbHkMplWudhDbPq9IZE5VSGtr6+L7y6i+2PzsPPzQtfV2kNZ4kT5K+3I+W2suVhCKJSNe8WruFbWtSHvS4jq3XAH5H/Cs9/qd1LdOFqYs0u2P0kFNf09KGpy8SaXYTXAohP6AgnnXTcWa4xXqXZm21d7bniTQtdy1AoNIfpW67y/79uH9b2EgW9y72LkgUK41VGMoLvHROGz67zGWQKOf57Fo7DMdcAADPr9IZCoUCWXKZxERcACrkW0Fr2ckiVNnCROGd/RiFHdFo8dqlchFGaWLMLZobvEJ5Pf7uH8J03/dpWYXh1n1KI0FMmc2iVtqLvg6DXr6uwW0GMqNYB0tef2bk3duldNwDwXWBHHIq5iqvxUfi4dH34uHqisFtBZMllWP/glJDx/32Nj+EscYJULsOvKhe8J9bsgiy5FInSNIPvhTlq+ZZBi+I1AOi+625c9U7Znz/I33wOFQosuX3QrGV6OLsiXZYFX9cCSNBT5nRM4EeQKuSiz6k5mhatijNqiS0titVAy+I1IFXIIZXLEJeZjL/vHEEBZzd8Wbl19neyQmaVda7N0CptUdTdGzKFHFKFDDK5HC8zkoTve0voVKoeqngHvP4Nl2GpyvvXr0Jz/O/+CYsty5Bvq3WA9PXn68Kru8JJeXXDqrbDmReRuBz3AL3KvYunabGo71cREYlPbXLXcgmPQuhboZkQe6o0Q7RvYyt1CpdHp1L14Oni9nq/Trn/8/rx6/0Q1f2+i7H3cDjmGgZWbIFyBf2Fz/bluPv49/UFq4EVWyLAw1e4C1I5L+W+mULtf6lChuWvK1RY27S3u7/Z51Pd54J4X0wZu9BGZd8we59OgYWvv2PG1/gEc17vh019qzs8nfXfJQto7sPIoYBULkOGXIptUSG4mfgUYwI/enNBUKE+vdpvstq4BTf3AQB6lmuCmr5ljNr/VH3NcigARXapf2vyd/fBiwzdVWPUk9cqewWgf8XmervU0refKH6uNl5fGWmNfSD943M6vcbcFPrfb33TGjrtrD79rPCdetsbY8pbn8LT2U3YrpXffYlZqVh97zgGVGyBEp6FNeJT3z8V7Y+qjFPfr1VOoIDmOO3z12wPlfba9qlN/SxMeetTuEgMd91gDEsmKoTG3hMSkt4pXAGflmss+v7XdsyvPI6SKxS4GvcQt5KikSxNx8uMJEyu1Q1uzi7C+6J6PK/6+6HtWP+3COPX6Wfl38P6B6cAZCc33E95obd9+YL+GFKljej7Xdc5B23DVfepVc2q01t0HkL93IPquQnlPGr5lsFnFd7TuZ5vJDzGtkfnjFoPP9T4BF6uHtn7ffLsfT/Vz+y0t7tDKpfjeUaCxr7oxJpdkCrLEH4fLOXLyh/A28UDSdJ0g7/lZQsUxYel6mLZ7UNax6seO8gU2ccP6scZJT0Lo1mxQLhKXBCR+ETnvqClDavaziqVRva2/EFr8nCZAkXQuUwDSOVyZMmlOvcbJ9XqCjcnF2Gbkr9eb+rnyVQ/b+8Xr4n/noULz3+p3QtyhQI/Xd0kDJv+do/X88jeTn++vk3r8t8pXAGpsgzcTMxOguhetjGKexTColv7db7m94vXRKvitSB/fYyoHrvqZ2rvk0u4pdI13LS3uws3T6ivr3RZFpKkaRhRtb3Oz7CplOtBrlDgWXqCaNut5VsGPco1AWDeeb4HKc+xNerNZ7+ZfyBOvr6wDWQfP0S/rgTXu9y78PfwyfHxbaBPSbQvWQe7Hl/EPRNvwlAaUqUNirn7IEMuFfZ/lWbW6S08NiZZSrWJrt86fftQ4v0E7Y2M3WcyLl7j5isOxYhpVB4q9zcGVmyJaj4lhHNL2r7zv6veEU6QvNmfUT2vDcXr1SB+rrovpByu/p08q05vvd2EKsNtFVBLZxslJkAQERGR3VEmQLyzdQycCzKDl4gov5BAgi5lGqBNwNsYekF7yUwiU+1uOR7+7t7Y9ThUlGxERERERJQf7WrxPYq4e+Hd111FUu4q4OyG7c3H4lp8FN71rwYXJ2eHryS2pMFgfHNhpdHtZ9bpjevxj4QEMyJTGFOBggkQREREZHeUCRAJCQnw8fGxdThEJsnIyMCsWbMwYcIEuLszgYeI7Ae/n4jIXvH7iYjsEb+biP7f3p2HVVWtfwD/HqbDEYFAJoc4iCOYoKmQmCJJopJKeh0pj+a18odTJrcsFcckxKGbqGmPqKXZtbScwhAhb4jiADkRpmkWhtrVVBwA4f394cPOLYdJOSr0/TzPeR72Wmuv/a6191kez1l7bXpccXwiKh8nQBAREdFjR0Rw7do12NraVvp550SPC07gIaLHFccnInpccXwioscRxyYielxxfCIqX9kPrSMiIiJ6RDQaDT+8ExERERERERERERFRlZg96gCIiIiIiIiIiIiIiIiIiIiIHhQnQBAREREREREREREREREREVGNxwkQRERERETVSKvVIioqClqt9lGHQkSkwvGJiB5XHJ+I6HHEsYmIHlccn4jKpxERedRBEBERERERERERERERERERET0IrgBBRERERERERERERERERERENR4nQBAREREREREREREREREREVGNxwkQREREREREREREREREREREVONxAgQRERERERERERERERERERHVeJwAQURERERUSdHR0dBoNJgwYUK55TZs2ICWLVvC2toarVu3xvbt20uVycrKQp8+fWBvbw8bGxt06NABZ8+eNVHkRFSbVdfYlJeXhzFjxqBRo0bQ6XTw9vbGsmXLTBg5EdV2lRmfjh07hv79+8PDwwMajQaLFi0yWi4uLg4eHh6wtraGv78/0tPTTRM0Ef0tVNf4NHfuXHTo0AG2trZwcXFBWFgYsrOzTRc4EdVq1fnZqSp1EtU2nABBRERERFQJ+/fvx0cffQQfH59yy+3ZswdDhgzByJEjkZGRgbCwMISFheHo0aNKmVOnTuHZZ59Fy5YtkZKSgsOHD2Pq1KmwtrY2dTOIqJapzrFp4sSJSEhIwKeffoqsrCxMmDABY8aMwebNm03dDCKqhSo7Pt24cQOenp6Ijo6Gm5ub0TKff/45Jk6ciKioKBw6dAi+vr4ICQnBhQsXTBE6EdVy1Tk+fffdd4iIiMDevXuRmJiIwsJCdO/eHdevXzdF6ERUi1Xn2FTVOolqG06AICIiIiKqQF5eHsLDw7FixQo4ODiUW/aDDz5Ajx49EBkZCS8vL8yaNQtPP/00Fi9erJR599130atXL8TExKBt27Zo0qQJ+vTpAxcXF1M3hYhqkeoem/bs2QODwYCuXbvCw8MDr776Knx9fXmXNRFVWVXGpw4dOmDevHkYPHgwtFqt0TILFizAqFGjMGLECGV1mjp16mDlypWmCJ+IarHqHp8SEhIwfPhwtGrVCr6+vli1ahXOnj2LgwcPmiJ8IqqlqntsqmqdRLUNJ0AQEREREVUgIiICoaGhCA4OrrBsWlpaqXIhISFIS0sDABQXF2Pbtm1o3rw5QkJC4OLiAn9/f3z11VemCJ2IarHqHJsAICAgAJs3b0ZOTg5EBMnJyThx4gS6d+9e7bETUe1WlfGpIgUFBTh48KCqLjMzMwQHB6vGMCKiyqjO8cmYK1euAAAcHR1NUj8R1U6mGJtMPd4RPc4sHnUARERERESPs/Xr1+PQoUPYv39/pcrn5ubC1dVVlebq6orc3FwAwIULF5CXl4fo6GjMnj0b77//PhISEtCvXz8kJycjMDCw2ttARLVPdY9NAPDhhx/i1VdfRaNGjWBhYQEzMzOsWLECXbp0qdbYiah2q+r4VJE//vgDRUVFRsewH3/8sVqOQUR/D9U9Pt2ruLgYEyZMQKdOnfDUU0+Z5BhEVPuYYmwy9XhH9LjjBAgiIiIiojL8+uuvGD9+PBITE2FtbV0tdRYXFwMA+vbtizfeeAMA0KZNG+zZswfLli3jBAgiqpApxibgzgSIvXv3YvPmzdDr9di9ezciIiLQoEED3jVERJViqvGJiOhBPYzxKSIiAkePHsX3339vkvqJqPYxxdjEz2NEnABBRERERFSmgwcP4sKFC3j66aeVtKKiIuzevRuLFy9Gfn4+zM3NVfu4ubnh/PnzqrTz58/Dzc0NAODk5AQLCwt4e3urynh5efGLMiKqFFOMTTdv3sQ777yDTZs2ITQ0FADg4+ODzMxMxMbGcgIEEVXK/YxPFXFycoK5uXm5YxgRUUVMMT7dbcyYMdi6dSt2796NRo0aVUfIRPQ3YIqxydTjHVFNwAkQRERERERl6NatG44cOaJKGzFiBFq2bIm33nrL6H8YO3bsiKSkJEyYMEFJS0xMRMeOHQEAVlZW6NChA7Kzs1X7nThxAnq9vvobQUS1jinGpsLCQhQWFsLMzEy1n7m5ubJyDRFRRe5nfKqIlZUV2rVrh6SkJISFhQG4s6JWUlISxowZUx1hE9HfgCnGJwAQEYwdOxabNm1CSkoKGjduXB3hEtHfhCnGJlONd0Q1CSdAEBERERGVwdbWttSzW21sbFCvXj0lfdiwYWjYsCHmzp0LABg/fjwCAwMxf/58hIaGYv369Thw4ACWL1+u1BEZGYlBgwahS5cuCAoKQkJCArZs2YKUlJSH1jYiqrlMMTbZ2dkhMDAQkZGR0Ol00Ov1+O6777BmzRosWLDg4TaQiGqs+xmfCgoKcPz4ceXvnJwcZGZmom7dumjatCkAYOLEiTAYDGjfvj38/PywaNEiXL9+HSNGjHiIrSOimsxU41NERATWrVuHr7/+Gra2tsjNzQUA2NvbQ6fTPazmEVENZYqxqTJ1EtV2nABBRERERPQAzp49q7pjOiAgAOvWrcOUKVPwzjvvoFmzZvjqq69U/8l88cUXsWzZMsydOxfjxo1DixYt8OWXX+LZZ599FE0golrofsam9evXY/LkyQgPD8elS5eg1+sxZ84cvP7664+iCURUS907Pp07dw5t27ZVtmNjYxEbG4vAwEBlcuigQYNw8eJFTJs2Dbm5uWjTpg0SEhLg6ur6sMMnolrsfsanpUuXAgC6du2qqis+Ph7Dhw83dchE9DdwP2MT0d+dRkTkUQdBRERERERERERERERERERE9CDMKi5CRERERERERERERERERERE9HjjBAgiIiIiIiIiIiIiIiIiIiKq8TgBgoiIiIiIiIiIiIiIiIiIiGo8ToAgIiIiIiIiIiIiIiIiIiKiGo8TIIiIiIiIiIiIiIiIiIiIiKjG4wQIIiIiIiIiIiIiIiIiIiIiqvE4AYKIiIiIiIiIiIiIiIiIiIhqPE6AICIiIiIiIiJ6hDQaDb766qtHHQbdh65du2LChAnlllm1ahWeeOKJhxLP3VJSUqDRaPDnn38+9GNXJ1O+Pzw8PLBo0SKT1E1ERERE9Heze/du9O7dGw0aNLivz/HTp0+HRqMp9bKxsalSPZwAQURERERERERkIrm5uRg7diw8PT2h1Wrx5JNPonfv3khKSnrUoVE12LhxI2bNmqVsG/tBfdCgQThx4sRDjgwICAjA77//Dnt7+0rvM3z4cISFhZkuqFri1q1bGD58OFq3bg0LC4sy+ywuLg5eXl7Q6XRo0aIF1qxZU+ljiAh69uxp9Ivjs2fPIjQ0FHXq1IGLiwsiIyNx+/Zto/WkpqbCwsICbdq0UaUXFRVh6tSpaNy4MXQ6HZo0aYJZs2ZBRFQxTJs2DfXr14dOp0NwcDB++uknVT0eHh6lvqCOjo5W8s+cOWP0S+y9e/cqZVasWIHOnTvDwcEBDg4OCA4ORnp6erX01bhx49CuXTtotdpSfVCd8Z0/fx7Dhw9HgwYNUKdOHfTo0aNUXy1fvhxdu3aFnZ2d0clJZ86cwciRI1XnJCoqCgUFBapyO3bswDPPPANbW1s4Ozujf//+OHPmTKX7i4iIiMhUrl+/Dl9fX8TFxd3X/pMmTcLvv/+uenl7e2PAgAFVqocTIIiIiIiIiIiITODMmTNo164ddu3ahXnz5uHIkSNISEhAUFAQIiIi7rteESnzx056uBwdHWFra1tuGZ1OBxcXl4cU0V+srKzg5uYGjUbz0I997w+2tUlhYSGKioqg0+kwbtw4BAcHGy23dOlSTJ48GdOnT8exY8cwY8YMREREYMuWLZU6zqJFi4yeu6KiIoSGhqKgoAB79uzB6tWrsWrVKkybNq1U2T///BPDhg1Dt27dSuW9//77WLp0KRYvXoysrCy8//77iImJwYcffqiUiYmJwb///W8sW7YM+/btg42NDUJCQnDr1i1VXTNnzlR9ST127NhSx9u5c6eqTLt27ZS8lJQUDBkyBMnJyUhLS8OTTz6J7t27Iycn54H6qsQrr7yCQYMGlVvHg8QnIggLC8PPP/+Mr7/+GhkZGdDr9QgODsb169eVem7cuIEePXrgnXfeMRrDjz/+iOLiYnz00Uc4duwYFi5ciGXLlqnKnz59Gn379sVzzz2HzMxM7NixA3/88Qf69etXqb4iIiIiMqWePXti9uzZePHFF43m5+fnY9KkSWjYsCFsbGzg7++PlJQUJb9u3bpwc3NTXufPn8fx48cxcuTIqgUiRERERERERERU7Xr27CkNGzaUvLy8UnmXL19W/gYgK1askLCwMNHpdNK0aVP5+uuvlfzk5GQBINu3b5enn35aLC0tJTk5WW7duiVjx44VZ2dn0Wq10qlTJ0lPTy+1X0JCgrRp00asra0lKChIzp8/L9u3b5eWLVuKra2tDBkyRK5fv67sV1G9ly5dkqFDh4qTk5NYW1tL06ZNZeXKlUr+4cOHJSgoSKytrcXR0VFGjRol165dU/INBoP07dtX5syZIy4uLmJvby8zZsyQwsJCmTRpkjg4OEjDhg1VdYqInD17VgYMGCD29vbi4OAgffr0kdOnT5fZ/yXt37p1q7Ru3Vq0Wq34+/vLkSNHVOW++OIL8fb2FisrK9Hr9RIbG6vKj4uLk6ZNm4pWqxUXFxfp37+/khcYGCjjx49X/gageomIxMfHi729varOJUuWiKenp1haWkrz5s1lzZo1qvyKronKKGl/ybVWEkdCQoK0bNlSbGxsJCQkRM6dOyciIlFRUaXiT05OFpGK+77knM6ePVvq168vHh4eMnnyZPHz8ysVl4+Pj8yYMUNERNLT0yU4OFjq1asndnZ20qVLFzl48GCpvti0aVOV2n63zZs3S/v27UWr1Uq9evUkLCxMydPr9TJnzhwZMWKE1K1bV5588kn56KOPlPzTp08LAFm/fr106dJFtFqtxMfHq+ovafu9OnbsKJMmTVKlTZw4UTp16lRhzBkZGdKwYUP5/fffS7V/+/btYmZmJrm5uUra0qVLxc7OTvLz81X1DBo0SKZMmSJRUVHi6+urygsNDZVXXnlFldavXz8JDw8XEZHi4mJxc3OTefPmKfl//vmnaLVa+eyzz5Q0vV4vCxcuLLMtJX2YkZFRYbtL3L59W2xtbWX16tUVli2vr+5mrA+qK77s7GwBIEePHlXKFBUVibOzs6xYsaLU/ve+N8sTExMjjRs3VrY3bNggFhYWUlRUpKRt3rxZNBqNFBQUVLoNRERERKZm7LPZP//5TwkICJDdu3fLyZMnZd68eaLVauXEiRNG6xgzZow0b968ysfmChBERERERERERNXs0qVLSEhIQEREhNHnlT7xxBOq7RkzZmDgwIE4fPgwevXqhfDwcFy6dElV5u2330Z0dDSysrLg4+ODf/3rX/jyyy+xevVqHDp0CE2bNkVISEip/aZPn47Fixdjz549+PXXXzFw4EAsWrQI69atw7Zt2/Dtt9+q7vquqN6pU6fi+PHj+Oabb5CVlYWlS5fCyckJwJ0lT0NCQuDg4ID9+/djw4YN2LlzJ8aMGaOKadeuXTh37hx2796NBQsWICoqCi+88AIcHBywb98+vP7663jttdfw22+/Abhz131ISAhsbW3x3//+F6mpqahbty569OhR4WoDkZGRmD9/Pvbv3w9nZ2f07t0bhYWFAICDBw9i4MCBGDx4MI4cOYLp06dj6tSpWLVqFQDgwIEDGDduHGbOnIns7GwkJCSgS5cuRo+zceNGNGrUSHU3vDGbNm3C+PHj8eabb+Lo0aN47bXXMGLECCQnJ6vKVXRNeHh4YPr06eW2/V43btxAbGwsPvnkE+zevRtnz57FpEmTANxZbnbgwIHo0aOHEn9AQECl+z4pKQnZ2dlITEzE1q1bER4ejvT0dJw6dUopc+zYMRw+fBhDhw4FAFy7dg0GgwHff/899u7di2bNmqFXr164du1amW1o1aoV6tatW+arZ8+eStlt27bhxRdfRK9evZCRkYGkpCT4+fmp6ps/fz7at2+PjIwM/N///R9Gjx6N7OxsVZm3334b48ePR1ZWFkJCQirV1/n5+bC2tlal6XQ6pKenK9dfSkoKNBqN6vEFN27cwNChQxEXFwc3N7dS9aalpaF169ZwdXVV0kJCQnD16lUcO3ZMSYuPj8fPP/+MqKgoo/EFBAQgKSlJeTzLDz/8gO+//17pv9OnTyM3N1e1woW9vT38/f2Rlpamqis6Ohr16tVD27ZtMW/ePKMr1PTp0wcuLi549tlnsXnzZqMx3d0HhYWFcHR0rLBceX1VFQ8SX35+PgCozreZmRm0Wi2+//77B4rrypUrqn5o164dzMzMEB8fj6KiIly5cgWffPIJgoODYWlpCeCvx3rcfTclERER0aN29uxZxMfHY8OGDejcuTOaNGmCSZMm4dlnn0V8fHyp8rdu3cLatWurvvoDwBUgiIiIiIiIiIiq2759+wSAbNy4scKyAGTKlCnKdl5engCQb775RkT+ulv4q6++UpWxtLSUtWvXKmkFBQXSoEEDiYmJUe23c+dOpczcuXMFgJw6dUpJe+211yQkJKTS9fbu3VtGjBhhtC3Lly8XBwcH1aoX27ZtU92xbjAYRK/Xq+5gbtGihXTu3FnZvn37ttjY2Ch3mn/yySfSokULKS4uVsrk5+eLTqeTHTt2GI2lpP3r169X0v73v/+JTqeTzz//XEREhg4dKs8//7xqv8jISPH29hYRkS+//FLs7Ozk6tWrRo9x9woQIsbvhr93BYiAgAAZNWqUqsyAAQOkV69eynZF14SIyHPPPScffvih0bjubv/dK0AAkJMnTypl4uLixNXVVdk2tppBZfreYDCIq6trqRUIfH19ZebMmcr25MmTxd/fv8yYi4qKxNbWVrZs2aLqi7vvHDtz5oz89NNPZb5+++03pWzHjh2VFQ2M0ev18tJLLynbxcXF4uLiIkuXLhWRv1YHWLRoUZl1lLUCxOTJk8XNzU0OHDggxcXFsn//fnF1dRUAyqob+/btkxYtWqhifvXVV2XkyJFltn/UqFHSvXt31bGuX7+urBIjInLixAlxcXGR7OxsETG++kFRUZG89dZbotFoxMLCQjQajbz33ntKfmpqqirWEgMGDJCBAwcq2/Pnz5fk5GT54YcfZOnSpfLEE0/IG2+8oeRfvHhR5s+fL3v37pX09HTlmOWtaDJ69Gjx9PSUmzdvllmmMn11t7JWgKiO+AoKCsTd3V0GDBggly5dkvz8fImOjhYApc6VSOVXgPjpp5/Ezs5Oli9frkpPSUkRFxcXMTc3FwDSsWNHVV2//fabtGjRQvbt21du/URERESmdO9ns61btwoAsbGxUb0sLCxUny9LrFu3TiwsLFQrn1WWRdWnTBARERERERERUXnufN9TeT4+PsrfNjY2sLOzw4ULF1Rl2rdvr/x96tQpFBYWolOnTkqapaUl/Pz8kJWVVWbdrq6uqFOnDjw9PVVp6enpla539OjR6N+/Pw4dOoTu3bsjLCwMAQEBAICsrCz4+vqqVr3o1KkTiouLkZ2drdy13qpVK5iZ/bUwqaurK5566ill29zcHPXq1VP64IcffsDJkydha2uratutW7dUKwwY07FjR+VvR0dHtGjRQmlLVlYW+vbtqyrfqVMnLFq0CEVFRXj++eeh1+vh6emJHj16oEePHnjxxRdRp06dco9ZnqysLLz66quljvnBBx+o0iq6JpKSkqp87Dp16qBJkybKdv369UtdZ/eqbN+3bt0aVlZWqjLh4eFYuXIlpk6dChHBZ599hokTJyr558+fx5QpU5CSkoILFy6gqKgIN27cwNmzZ8uMR6/XV6qtAJCZmYlRo0aVW+buftZoNHBzcyv3vVdZU6dORW5uLp555hmICFxdXWEwGBATE6Nc+35+fvjxxx+VfTZv3oxdu3YhIyOjyscrUVRUhKFDh2LGjBlo3rx5meX+85//YO3atVi3bh1atWqFzMxMTJgwAQ0aNIDBYKj08e4+nz4+PrCyssJrr72GuXPnQqvVwsnJSVWmQ4cOOHfuHObNm4c+ffqUqi86Ohrr169HSkqKsqLCe++9h/fee08pc/z4cWRmZj5wXwGolvgsLS2xceNGjBw5Eo6OjjA3N0dwcDB69uxZ5X8LSuTk5KBHjx4YMGCA6hrOzc3FqFGjYDAYMGTIEFy7dg3Tpk3DP/7xDyQmJkKj0aBhw4aq64qIiIjocZCXlwdzc3McPHgQ5ubmqry6deuWKv/xxx/jhRdeUK18VlmcAEFEREREREREVM2aNWsGjUZT6R+hSpYuL6HRaFBcXKxKM/YojarWrdFoKnWs8vTs2RO//PILtm/fjsTERHTr1g0RERGIjY29r5gqE1deXh7atWuHtWvXlqrL2dm50setKltbWxw6dAgpKSn49ttvMW3aNEyfPh379+8v9RiT6vag56mydVb0A21l+97Y9TlkyBC89dZbOHToEG7evIlff/0VgwYNUvINBgP+97//4YMPPoBer4dWq0XHjh3LfaxJq1at8Msvv5SZ37lzZ3zzzTcA7jxyoiKmeu/pdDqsXLkSH330Ec6fP4/69etj+fLlsLW1LfOa3bVrF06dOlXq2urfvz86d+6MlJQUuLm5KROWSpw/fx4A4ObmhmvXruHAgQPIyMhQHj1TXFwMEYGFhQW+/fZbPPfcc4iMjMTbb7+NwYMHA7gzgeWXX37B3LlzYTAYlEdKlMR+97HatGlTZrv9/f1x+/ZtnDlzBi1atCizTGJiYqn02NhYREdHY+fOnaqJKa+//joGDhyobDdo0AALFiyosK/uV1XjA+48miIzMxNXrlxBQUEBnJ2d4e/vf1+TZ86dO4egoCAEBARg+fLlqry4uDjY29sjJiZGSfv000/x5JNPYt++fXjmmWeqfDwiIiKih6Ft27YoKirChQsX0Llz53LLnj59GsnJyRU+mqwsnABBRERERERERFTNHB0dERISgri4OIwbN67UD6h//vnnA/2A3qRJE1hZWSE1NVW5I76wsBD79+/HhAkTTF6vs7MzDAYDDAYDOnfujMjISMTGxsLLywurVq3C9evXlTanpqbCzMyszB9DK+Ppp5/G559/DhcXF9jZ2VVp371798Ld3R0AcPnyZZw4cQJeXl4AAC8vL6SmpqrKp6amonnz5spdSRYWFggODkZwcDCioqLwxBNPYNeuXejXr1+pY1lZWaGoqKjceEqOefdd9qmpqfD29q5Su0zBWPwP0veNGjVCYGAg1q5di5s3b+L555+Hi4uLkp+amoolS5agV69eAIBff/0Vf/zxR7l1bt++HYWFhWXm3z3pwcfHB0lJSRgxYkSV4q5OlpaWaNSoEQBg/fr1eOGFF1Srn9zt7bffxj//+U9VWuvWrbFw4UL07t0bwJ0VTebMmYMLFy4ofZmYmAg7Ozt4e3vD0tISR44cUdWxZMkS7Nq1C1988QUaN24MALhx40apOMzNzZXJH40bN4abmxuSkpKUCQ9Xr17Fvn37MHr06DLbm5mZCTMzM9V5Nlbm7kkVABATE4M5c+Zgx44dpSYNODo6wtHRscp9db+qGt/d7O3tAQA//fQTDhw4gFmzZlXp2Dk5OQgKCkK7du0QHx9f6hyVdd4APPAEKSIiIqIHlZeXh5MnTyrbp0+fRmZmJhwdHdG8eXOEh4dj2LBhmD9/Ptq2bYuLFy8iKSkJPj4+CA0NVfZbuXIl6tevj549e95XHJwAQURERERERERkAnFxcejUqRP8/Pwwc+ZM+Pj44Pbt20hMTMTSpUtLPaqiKmxsbDB69GhERkbC0dER7u7uiImJwY0bNzBy5EiT1jtt2jS0a9cOrVq1Qn5+PrZu3apMKAgPD0dUVBQMBgOmT5+OixcvYuzYsXj55Zfva+nSEuHh4Zg3bx769u2LmTNnolGjRvjll1+wceNG/Otf/1J+YDZm5syZqFevHlxdXfHuu+/CyckJYWFhAIA333wTHTp0wKxZszBo0CCkpaVh8eLFWLJkCQBg69at+Pnnn9GlSxc4ODhg+/btKC4uLnMyh4eHB3bv3o3Bgwcry//fKzIyEgMHDkTbtm0RHByMLVu2YOPGjdi5c+d990918fDwwI4dO5CdnY169erB3t7+gfoe+OuaKCgowMKFC1V5zZo1wyeffIL27dvj6tWriIyMrHDVhqo8AiMqKgrdunVDkyZNMHjwYNy+fRvbt2/HW2+9Vek6ynL8+HEUFBTg0qVLuHbtGjIzMwFAmSxw4sQJpKenw9/fH5cvX8aCBQtw9OhRrF69WqkjPT0dw4YNQ1JSEho2bAg3Nzdl5YW7ubu7KxMXunfvDm9vb7z88suIiYlBbm4upkyZgoiICGi1WgBQPU4GAFxcXGBtba1K7927N+bMmQN3d3e0atUKGRkZWLBgAV555RUAd1bCmDBhAmbPno1mzZqhcePGmDp1Kho0aKC8f9LS0rBv3z4EBQXB1tYWaWlpeOONN/DSSy/BwcEBALB69WpYWVmhbdu2AICNGzdi5cqV+Pjjj5VY3n//fUybNg3r1q2Dh4cHcnNzAdxZCtnYcsgAKtVXAHDy5Enk5eUhNzcXN2/eVM6Tt7c3rKysqi2+DRs2wNnZGe7u7jhy5AjGjx+PsLAwdO/eXaknNzcXubm5yo8CR44cga2tLdzd3eHo6IicnBx07doVer0esbGxuHjxoqq9ABAaGoqFCxdi5syZyiMw3nnnHej1eqUNOTk56NatG9asWQM/Pz8AwOTJk5GTk4M1a9YY7U8iIiKi6nDgwAEEBQUp2yWPGjMYDFi1ahXi4+Mxe/ZsvPnmm8jJyYGTkxOeeeYZvPDCC8o+xcXFWLVqFYYPH17qURmVJkREREREREREZBLnzp2TiIgI0ev1YmVlJQ0bNpQ+ffpIcnKyUgaAbNq0SbWfvb29xMfHi4hIcnKyAJDLly+ryty8eVPGjh0rTk5OotVqpVOnTpKenq7kG9svPj5e7O3tVfVERUWJr69vpeudNWuWeHl5iU6nE0dHR+nbt6/8/PPPSv7hw4clKChIrK2txdHRUUaNGiXXrl1T8g0Gg/Tt21cVQ2BgoIwfP16VptfrZeHChcr277//LsOGDVPi8vT0lFGjRsmVK1fEmJL2b9myRVq1aiVWVlbi5+cnP/zwg6rcF198Id7e3mJpaSnu7u4yb948Je+///2vBAYGioODg+h0OvHx8ZHPP/+8zLjT0tLEx8dHtFqtlHztZqzPlyxZIp6enmJpaSnNmzeXNWvWqPIruiZK+icqKspo2+9uf8n5NxbHpk2b5O6vBy9cuCDPP/+81K1bVwAo12lFfW/snJa4fPmyaLVaqVOnjuo6EBE5dOiQtG/fXqytraVZs2ayYcOGUufdWF9UxZdffilt2rQRKysrcXJykn79+il59x5LRMTX11fp19OnTwsAycjIKFWvXq8XAKVeJY4fPy5t2rQRnU4ndnZ20rdvX/nxxx9VdZSco9OnT5cZv7H2nzlzRnr27Ck6nU6cnJzkzTfflMLCwjLruPc9LiJy9epVGT9+vLi7u4u1tbV4enrKu+++K/n5+UqZ4uJimTp1qri6uopWq5Vu3bpJdna2kn/w4EHx9/cXe3t7sba2Fi8vL3nvvffk1q1bSplVq1aJl5eX1KlTR+zs7MTPz082bNhQqb4s7/qubF8FBgYarbukz6srvg8++EAaNWqkjCNTpkxR9aXInfNgrJ6S93V8fLzR/Hu/wv/ss8+kbdu2YmNjI87OztKnTx/JyspS8kuu27v/nTEYDBIYGFil/iQiIiKqqTQiFTzoj4iIiIiIiIiIqIZJSUlBUFAQLl++/ECPG3kc3bhxA/Xq1cM333yDrl27PupwiIiIiIiIHhvGHzhHREREREREREREj6Xk5GQ899xznPxARERERER0D06AICIiIiIiIiIiqkFCQ0Oxbdu2Rx0GERERERHRY4ePwCAiIiIiIiIiIiIiIiIiIqIajytAEBERERERERERERERERERUY3HCRBERERERERERERERERERERU43ECBBEREREREREREREREREREdV4nABBRERERERERERERERERERENR4nQBAREREREREREREREREREVGNxwkQREREREREREREREREREREVONxAgQRERERERERERERERERERHVeJwAQURERERERERERERERERERDUeJ0AQERERERERERERERERERFRjff/XCzTVNGFtwwAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 2000x440 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    components=[\n",
        "        plot_components.TranscriptAnnotation(transcripts),\n",
        "        plot_components.Tracks(output.rna_seq),\n",
        "    ],\n",
        "    interval=output.rna_seq.interval,\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBtGbT0skYVu"
      },
      "source": [
        "This plot visualises the 3 predicted RNA-seq tracks and also marks the location\n",
        "of the MANE select transcript per gene in the 1MB region.\n",
        "\n",
        "We can zoom in to the middle of the plot by resizing the interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8_0AvHbkYVu"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x400 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    components=[\n",
        "        plot_components.TranscriptAnnotation(transcripts, fig_height=0.1),\n",
        "        plot_components.Tracks(output.rna_seq),\n",
        "    ],\n",
        "    interval=output.rna_seq.interval.resize(2**15),\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o5l2yUGkYVu"
      },
      "source": [
        "You can see here that predicted RNA-seq values are nicely aligned with the\n",
        "location of exons, and that the predictions are stranded – the predicted values\n",
        "are much higher for the positive strand, where the gene is located. We see that\n",
        "the *CYP2B6* gene is on the positive strand since the arrows in the transcript\n",
        "go from left to right.\n",
        "\n",
        "For more detail on the visualization library, please refer to the\n",
        "[visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html)\n",
        "and\n",
        "[library documentation](https://www.alphagenomedocs.com/api/visualization.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ylhAGnpyHjO"
      },
      "source": [
        "## Predict variant effects"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktZ0PqhQkYVu"
      },
      "source": [
        "We can predict the effect of a variant on a specific output type and tissue by\n",
        "making predictions for the reference (REF) and alternative (ALT) allele\n",
        "sequences.\n",
        "\n",
        "We specify the variant by defining a `genome.Variant` object. The specific\n",
        "variant below is a known variant affecting gene expression in colon tissue:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YaxavVV_kYVu"
      },
      "outputs": [],
      "source": [
        "variant = genome.Variant(\n",
        "    chromosome=\"chr22\",\n",
        "    position=36201698,\n",
        "    reference_bases=\"A\",  # Can differ from the true reference genome base.\n",
        "    alternate_bases=\"C\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HE2_iU6akYVu"
      },
      "source": [
        "Next, we define the interval over which to make the REF and ALT predictions. A\n",
        "quick way to get a `genome.Interval` from a `genome.Variant` is by calling\n",
        "`.reference_interval`, which we can resize to a model-compatible sequence\n",
        "length:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0jxThai2kYVu"
      },
      "outputs": [],
      "source": [
        "interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oupoz0bGkYVu"
      },
      "source": [
        "We then use `predict_variant` to get the REF and ALT RNA-seq predictions in the\n",
        "interval for \"Colon - Transverse\" tissue (`UBERON:0001157`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TZbfcADGkYVu"
      },
      "outputs": [],
      "source": [
        "variant_output = dna_model.predict_variant(\n",
        "    interval=interval,\n",
        "    variant=variant,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=[\"UBERON:0001157\"],\n",
        ")  # Colon - Transverse."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzYgHwSAkYVu"
      },
      "source": [
        "We can plot the predicted REF and ALT values as a single plot and zoom in on the\n",
        "affected gene to better visualise the variant's effect on gene expression:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_OTUiWNjkYVu"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 2000x500 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "transcripts = transcript_extractor.extract(interval)\n",
        "\n",
        "plot_components.plot(\n",
        "    [\n",
        "        plot_components.TranscriptAnnotation(transcripts),\n",
        "        plot_components.OverlaidTracks(\n",
        "            tdata={\n",
        "                \"REF\": variant_output.reference.rna_seq,\n",
        "                \"ALT\": variant_output.alternate.rna_seq,\n",
        "            },\n",
        "            colors={\"REF\": \"dimgrey\", \"ALT\": \"red\"},\n",
        "        ),\n",
        "    ],\n",
        "    interval=variant_output.reference.rna_seq.interval.resize(2**15),\n",
        "    # Annotate the location of the variant as a vertical line.\n",
        "    annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)],\n",
        ")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSz201LRkYVu"
      },
      "source": [
        "We see that the ALT allele (base 'C' at position 36201698) is associated with\n",
        "both lower expression and an exon skipping event in the *APOL4* gene on the\n",
        "negative strand. Note that we can ignore the uppermost line plot which shows a\n",
        "very minimal predicted amount of expression on the positive DNA strand (check\n",
        "the y axis scales). It is possible to adjust the y axes limits, see\n",
        "[visualization basics](https://www.alphagenomedocs.com/visualization_library_basics.html#visualization-library-basics)\n",
        "and\n",
        "[library documentation](https://www.alphagenomedocs.com/api/visualization.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1-4NWIwyPum"
      },
      "source": [
        "## Scoring the effect of a genetic variant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fTtdkpM-Ycm"
      },
      "source": [
        "Scoring the effect of a genetic variant involves making predictions for the REF\n",
        "and ALT sequences and aggregating the track signal. This is implemented in\n",
        "`score_variant`, which uses specific `variant_scorer` configs for aggregation.\n",
        "\n",
        "We provide a set of recommended variant scoring configurations as a dictionary\n",
        "(`variant_scorers.RECOMMENDED_VARIANT_SCORERS`), covering all output types,\n",
        "which we have assessed for their performance at domain-specific tasks. See the\n",
        "[variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html)\n",
        "for more information. Here is a quick demo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Aj_T3sE8kYVu"
      },
      "outputs": [],
      "source": [
        "variant_scorer = variant_scorers.RECOMMENDED_VARIANT_SCORERS[\"RNA_SEQ\"]\n",
        "\n",
        "variant_scores = dna_model.score_variant(\n",
        "    interval=interval, variant=variant, variant_scorers=[variant_scorer]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncUCGp0JkYVu"
      },
      "source": [
        "The returned `variant_scores` is a list of length 1 because we only specified 1\n",
        "scorer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xI2aR0OtkYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(variant_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oow5vOLVkYVu"
      },
      "source": [
        "The actual scores per variant are in `AnnData` format, which is a way of\n",
        "annotating data (the numerical scores) with additional information about the\n",
        "rows and columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WCy_9aQ2kYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AnnData object with n_obs × n_vars = 37 × 667\n",
              "    obs: 'gene_id', 'strand', 'gene_name', 'gene_type'\n",
              "    var: 'name', 'strand', 'Assay title', 'ontology_curie', 'biosample_name', 'biosample_type', 'biosample_life_stage', 'gtex_tissue', 'data_source', 'endedness', 'genetically_modified', 'nonzero_mean'\n",
              "    uns: 'interval', 'variant', 'variant_scorer'\n",
              "    layers: 'quantiles'"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "variant_scores = variant_scores[0]\n",
        "variant_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2URPdQGtkYVu"
      },
      "source": [
        "`AnnData` objects have the following components:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssPveqJvkYVu"
      },
      "source": [
        "<a href=\"https://services.google.com/fh/files/misc/anndata.png\"><img src=\"https://services.google.com/fh/files/misc/anndata.png\" alt=\"anndata\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1u94SdOkYVu"
      },
      "source": [
        "We have a variant effect score for each of the 37 genes in the interval and each\n",
        "of the 667 `RNA_SEQ` tracks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eyv_SHT_kYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(37, 667)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "variant_scores.X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3cT3a6FkYVu"
      },
      "source": [
        "We can access information on the 37 genes using `.obs`. Here are just first 5\n",
        "genes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQBAsm2skYVu"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"variant_scores\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"gene_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"ENSG00000100336.18\",\n          \"ENSG00000100348.10\",\n          \"ENSG00000100342.22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"+\",\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gene_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"APOL4\",\n          \"TXN2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gene_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"protein_coding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
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              "      <td>ENSG00000100320.24</td>\n",
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              "\n",
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            ],
            "text/plain": [
              "              gene_id strand gene_name       gene_type\n",
              "0  ENSG00000100320.24      -    RBFOX2  protein_coding\n",
              "1  ENSG00000100336.18      -     APOL4  protein_coding\n",
              "2  ENSG00000100342.22      +     APOL1  protein_coding\n",
              "3  ENSG00000100345.23      -      MYH9  protein_coding\n",
              "4  ENSG00000100348.10      -      TXN2  protein_coding"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "variant_scores.obs.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6mVhremkYVu"
      },
      "source": [
        "Note that if you are using a variant scorer that is not gene-specific (i.e., a\n",
        "`variant_scorers.CenterMaskScorer`), then `variant_scores.X` would have shape\n",
        "`(1, 667)` and there will be no gene metadata available since there is no\n",
        "concept of genes in this scenario.\n",
        "\n",
        "The description of each track is accessed using `.var` (this is the same\n",
        "dataframe as the output metadata, but is included alongside the variant scores\n",
        "for convenience):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_VRdJMakYVu"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"variant_scores\",\n  \"rows\": 667,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 371,\n        \"samples\": [\n          \"UBERON:0001873 gtex Brain_Caudate_basal_ganglia polyA plus RNA-seq\",\n          \"CL:0000792 total RNA-seq\",\n          \"CL:0000223 polyA plus RNA-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"+\",\n          \"-\",\n          \".\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Assay title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"total RNA-seq\",\n          \"polyA plus RNA-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ontology_curie\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 285,\n        \"samples\": [\n          \"CL:0000182\",\n          \"NTR:0000524\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 285,\n        \"samples\": [\n          \"hepatocyte\",\n          \"fibroblast of skin of scalp\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"primary_cell\",\n          \"tissue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_life_stage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"adult,unknown\",\n          \"adult\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gtex_tissue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"Spleen\",\n          \"Testis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"gtex\",\n          \"encode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"endedness\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"single\",\n          \"paired\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genetically_modified\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonzero_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1649971323318487,\n        \"min\": 0.03440511226654053,\n        \"max\": 1.3723630905151367,\n        \"num_unique_values\": 396,\n        \"samples\": [\n          0.189997598528862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
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              "\n",
              "  <div id=\"df-5b49e0ec-87ba-46c4-9aac-207f0ad1a94b\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>Assay title</th>\n",
              "      <th>ontology_curie</th>\n",
              "      <th>biosample_name</th>\n",
              "      <th>biosample_type</th>\n",
              "      <th>biosample_life_stage</th>\n",
              "      <th>gtex_tissue</th>\n",
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              "      <th>endedness</th>\n",
              "      <th>genetically_modified</th>\n",
              "      <th>nonzero_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL:0000047 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000047</td>\n",
              "      <td>neuronal stem cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.143617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CL:0000062 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000062</td>\n",
              "      <td>osteoblast</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.094144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CL:0000084 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.124296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CL:0000084 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.100934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CL:0000115 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000115</td>\n",
              "      <td>endothelial cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.135553</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>UBERON:0018115 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018115</td>\n",
              "      <td>left renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.268222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>663</th>\n",
              "      <td>UBERON:0018116 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018116</td>\n",
              "      <td>right renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.258522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>UBERON:0018117 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018117</td>\n",
              "      <td>left renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.215190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>665</th>\n",
              "      <td>UBERON:0018118 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018118</td>\n",
              "      <td>right renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.365676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0036149</td>\n",
              "      <td>suprapubic skin</td>\n",
              "      <td>tissue</td>\n",
              "      <td>adult</td>\n",
              "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
              "      <td>gtex</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.045404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>667 rows × 12 columns</p>\n",
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              "\n",
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            ],
            "text/plain": [
              "                                                  name strand  \\\n",
              "0                        CL:0000047 polyA plus RNA-seq      +   \n",
              "1                             CL:0000062 total RNA-seq      +   \n",
              "2                        CL:0000084 polyA plus RNA-seq      +   \n",
              "3                             CL:0000084 total RNA-seq      +   \n",
              "4                             CL:0000115 total RNA-seq      +   \n",
              "..                                                 ...    ...   \n",
              "662                  UBERON:0018115 polyA plus RNA-seq      .   \n",
              "663                  UBERON:0018116 polyA plus RNA-seq      .   \n",
              "664                  UBERON:0018117 polyA plus RNA-seq      .   \n",
              "665                  UBERON:0018118 polyA plus RNA-seq      .   \n",
              "666  UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...      .   \n",
              "\n",
              "            Assay title  ontology_curie                   biosample_name  \\\n",
              "0    polyA plus RNA-seq      CL:0000047               neuronal stem cell   \n",
              "1         total RNA-seq      CL:0000062                       osteoblast   \n",
              "2    polyA plus RNA-seq      CL:0000084                           T-cell   \n",
              "3         total RNA-seq      CL:0000084                           T-cell   \n",
              "4         total RNA-seq      CL:0000115                 endothelial cell   \n",
              "..                  ...             ...                              ...   \n",
              "662  polyA plus RNA-seq  UBERON:0018115                left renal pelvis   \n",
              "663  polyA plus RNA-seq  UBERON:0018116               right renal pelvis   \n",
              "664  polyA plus RNA-seq  UBERON:0018117   left renal cortex interstitium   \n",
              "665  polyA plus RNA-seq  UBERON:0018118  right renal cortex interstitium   \n",
              "666  polyA plus RNA-seq  UBERON:0036149                  suprapubic skin   \n",
              "\n",
              "                    biosample_type biosample_life_stage  \\\n",
              "0    in_vitro_differentiated_cells            embryonic   \n",
              "1                     primary_cell                adult   \n",
              "2                     primary_cell                adult   \n",
              "3                     primary_cell                adult   \n",
              "4    in_vitro_differentiated_cells                adult   \n",
              "..                             ...                  ...   \n",
              "662                         tissue            embryonic   \n",
              "663                         tissue            embryonic   \n",
              "664                         tissue            embryonic   \n",
              "665                         tissue            embryonic   \n",
              "666                         tissue                adult   \n",
              "\n",
              "                         gtex_tissue data_source endedness  \\\n",
              "0                                         encode    paired   \n",
              "1                                         encode    paired   \n",
              "2                                         encode    paired   \n",
              "3                                         encode    single   \n",
              "4                                         encode    single   \n",
              "..                               ...         ...       ...   \n",
              "662                                       encode    single   \n",
              "663                                       encode    single   \n",
              "664                                       encode    single   \n",
              "665                                       encode    single   \n",
              "666  Skin_Not_Sun_Exposed_Suprapubic        gtex    paired   \n",
              "\n",
              "     genetically_modified  nonzero_mean  \n",
              "0                   False      0.143617  \n",
              "1                   False      0.094144  \n",
              "2                   False      0.124296  \n",
              "3                   False      0.100934  \n",
              "4                   False      0.135553  \n",
              "..                    ...           ...  \n",
              "662                 False      0.268222  \n",
              "663                 False      0.258522  \n",
              "664                 False      0.215190  \n",
              "665                 False      0.365676  \n",
              "666                 False      0.045404  \n",
              "\n",
              "[667 rows x 12 columns]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "variant_scores.var"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrbvEe4xkYVu"
      },
      "source": [
        "Some handy additional metadata can be found in `.uns`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n3GGvosdkYVu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Interval: chr22:35677410-36725986:.\n",
            "Variant: chr22:36201698:A>C\n",
            "Variant scorer: GeneMaskLFCScorer(requested_output=RNA_SEQ)\n"
          ]
        }
      ],
      "source": [
        "print(f'Interval: {variant_scores.uns[\"interval\"]}')\n",
        "print(f'Variant: {variant_scores.uns[\"variant\"]}')\n",
        "print(f'Variant scorer: {variant_scores.uns[\"variant_scorer\"]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0WK-6IpkYVu"
      },
      "source": [
        "We recommend interacting with variant scores by flattening `AnnData` objects\n",
        "using `tidy_scores`, which produces a dataframe with each row being a single\n",
        "score for each combination of (variant, gene, scorer, ontology). It optionally\n",
        "excludes stranded tracks which do not match the gene’s strand for gene-specific\n",
        "scorer.\n",
        "\n",
        "The `raw_score` column contains the same values as stored in `variant_scores.X`.\n",
        "The `quantile_score` column is the rank of the `raw_score` in the distribution\n",
        "of scores for a background set of common variants, represented as a quantile\n",
        "probability. This allows for direct comparison across variant scoring strategies\n",
        "that yield scores on different scales. See\n",
        "[FAQs](https://www.alphagenomedocs.com/faqs.html#what-is-the-difference-between-a-quantile-score-and-raw-score)\n",
        "for further details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cVG5ApfkkYVu"
      },
      "outputs": [],
      "source": [
        "# Uncomment after fixing columns\n",
        "# variant_scorers.tidy_scores([variant_scores], match_gene_strand=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQXsJenMyf1x"
      },
      "source": [
        "## Highlighting important regions with *in silico* mutagenesis\n",
        "\n",
        "To highlight which regions in a DNA sequence are functionally important for a\n",
        "final variant prediction, we can perform an **in silico mutagenesis** (ISM)\n",
        "analysis by scoring all possible single nucleotide variants in a specific\n",
        "interval.\n",
        "\n",
        "Here is a visual overview of this process:\n",
        "\n",
        "<a href=\"https://services.google.com/fh/files/misc/ism_green_v2.png\"><img src=\"https://services.google.com/fh/files/misc/ism_green_v2.png\" alt=\"ISM\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O93kx8lckYVu"
      },
      "source": [
        "We define an `ism_interval`, which is a relatively small region of DNA that we\n",
        "want to systematically mutate. We also define the `sequence_interval`, which is\n",
        "the contextual interval the model will use when making predictions for each\n",
        "variant."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Xhk_TsHkYVu"
      },
      "outputs": [],
      "source": [
        "# 16KB DNA sequence to use as context when making predictions.\n",
        "sequence_interval = genome.Interval(\"chr20\", 3_753_000, 3_753_400)\n",
        "sequence_interval = sequence_interval.resize(dna_client.SEQUENCE_LENGTH_16KB)\n",
        "\n",
        "# Mutate all bases in the central 256-base region of the sequence_interval.\n",
        "ism_interval = sequence_interval.resize(256)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9LSDA0QkYVu"
      },
      "source": [
        "Next, we define the scorer we want to use to score each of the ISM variants.\n",
        "Here, we use a center mask scorer on predicted `DNASE` values, which will score\n",
        "each variant's effect on DNA accessibility in the 500bp vicinity. See the\n",
        "[variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html)\n",
        "for more information on variant scoring."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twYn3L9qkYVu"
      },
      "outputs": [],
      "source": [
        "dnase_variant_scorer = variant_scorers.CenterMaskScorer(\n",
        "    requested_output=dna_client.OutputType.DNASE,\n",
        "    width=501,\n",
        "    aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbQxCvutkYVu"
      },
      "source": [
        "Finally, we can use `score_variants` (notice the plural s) to score all\n",
        "variants.\n",
        "\n",
        "Note that this operation is quite expensive. For speed reasons, we recommend\n",
        "using shorter input sequences for the contextual `sequence_interval` and\n",
        "narrower `ism_interval` regions to mutate if possible."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WMuA2NQXkYVu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753072\",\n",
            "          \"end\": \"3753082\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753082\",\n",
            "          \"end\": \"3753092\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753092\",\n",
            "          \"end\": \"3753102\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753102\",\n",
            "          \"end\": \"3753112\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753112\",\n",
            "          \"end\": \"3753122\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
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            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Variant: {'chromosome': 'chr20', 'position': 3753073, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753073, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753073, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753074, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753074, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753074, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753075, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753075, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753075, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753076, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753076, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753076, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753077, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753077, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753077, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753078, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753078, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753078, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753079, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753079, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753079, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753080, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753080, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753080, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753081, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753081, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753081, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753082, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753082, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753082, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753122\",\n",
            "          \"end\": \"3753132\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753083, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753083, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753083, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753084, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753084, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753084, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753085, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753085, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753085, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753086, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753086, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753086, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753087, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753087, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753087, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753088, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753088, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753088, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753089, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753089, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753089, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753090, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753090, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753090, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753091, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753091, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753091, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753092, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753092, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753092, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753132\",\n",
            "          \"end\": \"3753142\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753103, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753103, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753103, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753104, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753104, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753104, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753105, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753105, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753105, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753106, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753106, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753106, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753107, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753107, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753107, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753108, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753108, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753108, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753109, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753109, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753109, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753110, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753110, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753110, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753111, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753111, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753111, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753112, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753112, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753112, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753142\",\n",
            "          \"end\": \"3753152\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753093, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753093, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753093, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753094, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753094, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753094, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753095, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753095, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753095, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753096, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753096, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753096, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753097, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753097, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753097, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753098, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753098, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753098, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753099, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753099, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753099, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753100, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753100, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753100, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753101, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753101, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753101, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753102, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753102, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753102, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753152\",\n",
            "          \"end\": \"3753162\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753113, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753113, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753113, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753114, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753114, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753114, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753115, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753115, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753115, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753116, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753116, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753116, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753117, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753117, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753117, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753118, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753118, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753118, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753119, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753119, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753119, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753120, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753120, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753120, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753121, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753121, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753121, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753122, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753122, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753122, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753162\",\n",
            "          \"end\": \"3753172\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753123, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753123, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753123, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753124, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753124, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753124, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753125, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753125, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753125, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753126, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753126, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753126, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753127, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753127, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753127, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753128, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753128, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753128, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753129, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753129, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753129, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753130, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753130, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753130, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753131, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753131, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753131, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753132, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753132, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753132, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753172\",\n",
            "          \"end\": \"3753182\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753133, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753133, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753133, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753134, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753134, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753134, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753135, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753135, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753135, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753136, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753136, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753136, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753137, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753137, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753137, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753138, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753138, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753138, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753139, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753139, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753139, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753140, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753140, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753140, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753141, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753141, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753141, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753142, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753142, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753142, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753182\",\n",
            "          \"end\": \"3753192\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753143, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753143, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753143, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753144, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753144, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753144, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753145, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753145, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753145, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753146, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753146, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753146, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753147, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753147, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753147, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753148, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753148, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753148, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753149, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753149, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753149, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753150, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753150, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753150, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753151, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753151, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753151, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753152, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753152, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753152, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753192\",\n",
            "          \"end\": \"3753202\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753153, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753153, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753153, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753154, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753154, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753154, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753155, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753155, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753155, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753156, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753156, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753156, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753157, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753157, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753157, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753158, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753158, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753158, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753159, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753159, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753159, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753160, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753160, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753160, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753161, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753161, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753161, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753162, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753162, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753162, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753202\",\n",
            "          \"end\": \"3753212\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753163, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753163, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753163, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753164, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753164, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753164, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753165, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753165, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753165, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753166, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753166, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753166, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753167, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753167, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753167, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753168, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753168, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753168, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753169, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753169, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753169, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753170, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753170, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753170, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753171, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753171, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753171, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753172, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753172, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753172, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753212\",\n",
            "          \"end\": \"3753222\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753173, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753173, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753173, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753174, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753174, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753174, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753175, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753175, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753175, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753176, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753176, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753176, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753177, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753177, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753177, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753178, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753178, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753178, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753179, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753179, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753179, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753180, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753180, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753180, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753181, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753181, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753181, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753182, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753182, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753182, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753222\",\n",
            "          \"end\": \"3753232\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753183, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753183, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753183, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753184, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753184, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753184, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753185, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753185, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753185, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753186, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753186, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753186, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753187, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753187, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753187, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753188, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753188, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753188, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753189, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753189, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753189, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753190, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753190, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753190, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753191, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753191, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753191, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753192, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753192, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753192, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753232\",\n",
            "          \"end\": \"3753242\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753193, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753193, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753193, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753194, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753194, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753194, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753195, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753195, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753195, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753196, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753196, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753196, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753197, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753197, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753197, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753198, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753198, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753198, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753199, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753199, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753199, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753200, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753200, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753200, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753201, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753201, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753201, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753202, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753202, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753202, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753242\",\n",
            "          \"end\": \"3753252\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753203, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753203, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753203, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753204, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753204, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753204, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753205, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753205, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753205, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753206, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753206, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753206, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753207, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753207, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753207, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753208, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753208, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753208, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753209, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753209, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753209, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753210, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753210, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753210, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753211, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753211, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753211, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753212, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753212, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753212, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753252\",\n",
            "          \"end\": \"3753262\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753213, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753213, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753213, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753214, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753214, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753214, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753215, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753215, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753215, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753216, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753216, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753216, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753217, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753217, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753217, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753218, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753218, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753218, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753219, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753219, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753219, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753220, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753220, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753220, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753221, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753221, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753221, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753222, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753222, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753222, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753262\",\n",
            "          \"end\": \"3753272\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753223, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753223, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753223, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753224, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753224, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753224, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753225, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753225, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753225, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753226, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753226, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753226, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753227, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753227, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753227, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753228, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753228, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753228, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753229, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753229, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753229, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753230, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753230, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753230, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753231, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753231, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753231, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753232, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753232, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753232, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753272\",\n",
            "          \"end\": \"3753282\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753233, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753233, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753233, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753234, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753234, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753234, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753235, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753235, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753235, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753236, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753236, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753236, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753237, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753237, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753237, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753238, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753238, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753238, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753239, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753239, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753239, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753240, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753240, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753240, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753241, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753241, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753241, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753242, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753242, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753242, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753282\",\n",
            "          \"end\": \"3753292\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753243, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753243, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753243, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753244, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753244, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753244, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753245, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753245, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753245, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753246, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753246, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753246, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753247, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753247, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753247, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753248, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753248, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753248, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753249, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753249, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753249, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753250, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753250, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753250, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753251, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753251, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753251, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753252, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753252, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753252, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753292\",\n",
            "          \"end\": \"3753302\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753253, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753253, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753253, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753254, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753254, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753254, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753255, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753255, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753255, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753256, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753256, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753256, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753257, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753257, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753257, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753258, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753258, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753258, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753259, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753259, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753259, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753260, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753260, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753260, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753261, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753261, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753261, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753262, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753262, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753262, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753302\",\n",
            "          \"end\": \"3753312\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753263, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753263, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753263, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753264, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753264, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753264, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753265, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753265, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753265, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753266, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753266, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753266, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753267, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753267, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753267, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753268, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753268, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753268, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753269, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753269, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753269, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753270, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753270, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753270, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753271, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753271, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753271, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753272, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753272, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753272, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753312\",\n",
            "          \"end\": \"3753322\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753273, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753273, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753273, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753274, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753274, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753274, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753275, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753275, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753275, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753276, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753276, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753276, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753277, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753277, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753277, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753278, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753278, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753278, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753279, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753279, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753279, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753280, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753280, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753280, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753281, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753281, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753281, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753282, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753282, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753282, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_ism_variant\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3745008\",\n",
            "          \"end\": \"3761392\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"ismInterval\": {\n",
            "          \"chromosome\": \"chr20\",\n",
            "          \"start\": \"3753322\",\n",
            "          \"end\": \"3753328\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"variantScorers\": [\n",
            "          {\n",
            "            \"centerMask\": {\n",
            "              \"width\": \"501\",\n",
            "              \"aggregationType\": \"AGGREGATION_TYPE_DIFF_MEAN\",\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_DNASE\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753283, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753283, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753283, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753284, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753284, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753284, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753285, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753285, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753285, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753286, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753286, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753286, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753287, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753287, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753287, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753288, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753288, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753288, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753289, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753289, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753289, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753290, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753290, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753290, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753291, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753291, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753291, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753292, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753292, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753292, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753293, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753293, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753293, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753294, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753294, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753294, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753295, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753295, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753295, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753296, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753296, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753296, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753297, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753297, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753297, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753298, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753298, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753298, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753299, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753299, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753299, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753300, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753300, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753300, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753301, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753301, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753301, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753302, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753302, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753302, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753303, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753303, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753303, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753304, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753304, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753304, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753305, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753305, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753305, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753306, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753306, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753306, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753307, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753307, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753307, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753308, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753308, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753308, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753309, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753309, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753309, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753310, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753310, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753310, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753311, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753311, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753311, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753312, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753312, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753312, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753313, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753313, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753313, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753314, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753314, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753314, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753315, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753315, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753315, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753316, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753316, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753316, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753317, 'reference_bases': 'C', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753317, 'reference_bases': 'C', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753317, 'reference_bases': 'C', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753318, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753318, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753318, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753319, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753319, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753319, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753320, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753320, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753320, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753321, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753321, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753321, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753322, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753322, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753322, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753323, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753323, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753323, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753324, 'reference_bases': 'A', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753324, 'reference_bases': 'A', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753324, 'reference_bases': 'A', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753325, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753325, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753325, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753326, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753326, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753326, 'reference_bases': 'T', 'alternate_bases': 'G'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753327, 'reference_bases': 'G', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753327, 'reference_bases': 'G', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753327, 'reference_bases': 'G', 'alternate_bases': 'T'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753328, 'reference_bases': 'T', 'alternate_bases': 'A'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753328, 'reference_bases': 'T', 'alternate_bases': 'C'}\n",
            "Variant: {'chromosome': 'chr20', 'position': 3753328, 'reference_bases': 'T', 'alternate_bases': 'G'}\n"
          ]
        }
      ],
      "source": [
        "variant_scores = dna_model.score_ism_variants(\n",
        "    interval=sequence_interval,\n",
        "    ism_interval=ism_interval,\n",
        "    variant_scorers=[dnase_variant_scorer],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3iJ9E-ckYVu"
      },
      "source": [
        "The length of the returned `variant_scores` is 768, since we scored 768 variants\n",
        "(256 positions * 3 alternative bases per position):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NrRgRYF1kYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(variant_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e2WoG6pkYVu"
      },
      "source": [
        "Each variant has scores of shape `(1, 305)`, reflecting the fact that we are not\n",
        "using a gene-centric scorer and that there are 305 `DNASE` tracks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f-raKWmbkYVu"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1, 305)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Index into first variant and first scorer.\n",
        "variant_scores[0][0].X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyDoTBdAkYVu"
      },
      "source": [
        "To understand which positions are most influential in the predictions, we can\n",
        "visualize these scores using a sequence logo. This requires summarizing the\n",
        "scores into a single scalar value per variant.\n",
        "\n",
        "As an example, let's extract the DNASE score for just the K562 cell line, a\n",
        "widely used experimental model. Alternatively, you could average across multiple\n",
        "tissues to obtain a single scalar value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jK9UF4PZkYVu"
      },
      "outputs": [],
      "source": [
        "def extract_k562(adata):\n",
        "  values = adata.X[:, adata.var['ontology_curie'] == 'EFO:0002067']\n",
        "  assert values.size == 1\n",
        "  return values.flatten()[0]\n",
        "\n",
        "\n",
        "ism_result = ism.ism_matrix(\n",
        "    [extract_k562(x[0]) for x in variant_scores],\n",
        "    variants=[v[0].uns['variant'] for v in variant_scores],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CxEM6lqMYpBV"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(256, 4)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ism_result.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYtOmG4xkYVu"
      },
      "source": [
        "The shape of `ism_result` is `(256, 4`) since we have 1 score per position per\n",
        "each of the 4 DNA bases.\n",
        "\n",
        "Note that in this case, our call to `ism.ism_matrix()` had the argument\n",
        "`multiply_by_sequence` set to 'True', so the output array contains non-zero\n",
        "values only for the bases corresponding to the reference sequence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ONduc-kwkYVv"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(256, 4)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ism_result.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj0VMFwCkYVv"
      },
      "source": [
        "Finally, we plot the contribution scores as a sequence logo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0X9fwKw6kYVv"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3500x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    [\n",
        "        plot_components.SeqLogo(\n",
        "            scores=ism_result,\n",
        "            scores_interval=ism_interval,\n",
        "            ylabel=\"ISM K562 DNase\",\n",
        "        )\n",
        "    ],\n",
        "    interval=ism_interval,\n",
        "    fig_width=35,\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qkuuj1p7kYVv"
      },
      "source": [
        "This plot shows that the sequence between positions ~225 to ~240 has the\n",
        "strongest effect on predicted nearby DNAse in K562 cells.\n",
        "\n",
        "These contribution scores can be used to systematically discover motifs\n",
        "important for different modalities and cell types, find the transcription\n",
        "factors binding those motifs and map motif instances across the genome. Here are\n",
        "a few tools you can use to do this: -\n",
        "[tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite/) -\n",
        "[tangermeme](https://github.com/jmschrei/tangermeme) -\n",
        "[tomtom](https://meme-suite.org/meme/tools/tomtom)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-q-aXjAZ3ix"
      },
      "source": [
        "## Score Interval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjXr_bsxZ7WD"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"score_interval\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr19\",\n",
            "          \"start\": \"1048576\",\n",
            "          \"end\": \"2097152\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\",\n",
            "        \"intervalScorers\": [\n",
            "          {\n",
            "            \"geneMask\": {\n",
            "              \"requestedOutput\": \"OUTPUT_TYPE_RNA_SEQ\",\n",
            "              \"width\": \"200001\",\n",
            "              \"aggregationType\": \"INTERVAL_AGGREGATION_TYPE_MEAN\"\n",
            "            }\n",
            "          }\n",
            "        ]\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "interval = Interval(\n",
        "    chromosome=\"chr19\", start=2**20, end=2**20 + 2**20, strand=\".\"\n",
        ")\n",
        "\n",
        "scores = dna_model.score_interval(\n",
        "    interval,\n",
        "    interval_scorers=[\n",
        "        interval_scorers.GeneMaskScorer(\n",
        "            requested_output=dna_client.OutputType.RNA_SEQ,\n",
        "            width=200_001,\n",
        "            aggregation_type=interval_scorers.IntervalAggregationType.MEAN,\n",
        "        )\n",
        "    ],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y6U6SGiFaBlE"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AnnData object with n_obs × n_vars = 14 × 667\n",
              "    obs: 'geneId', 'strand', 'name', 'type'\n",
              "    var: 'name', 'strand', 'ontologyTerm', 'biosample', 'assay', 'gtexTissue', 'dataSource', 'endedness', 'geneticallyModified', 'nonzeroMean'\n",
              "    uns: 'interval', 'interval_scorer'"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SO9ViUOxaN6p"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"scores[0]\",\n  \"rows\": 667,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 371,\n        \"samples\": [\n          \"UBERON:0001873 gtex Brain_Caudate_basal_ganglia polyA plus RNA-seq\",\n          \"CL:0000792 total RNA-seq\",\n          \"CL:0000223 polyA plus RNA-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"STRAND_POSITIVE\",\n          \"STRAND_NEGATIVE\",\n          \"STRAND_UNSTRANDED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ontologyTerm\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"assay\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"total RNA-seq\",\n          \"polyA plus RNA-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gtexTissue\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"Spleen\",\n          \"Testis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataSource\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"gtex\",\n          \"encode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"endedness\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ENDEDNESS_SINGLE\",\n          \"ENDEDNESS_PAIRED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geneticallyModified\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonzeroMean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16499713265942026,\n        \"min\": 0.034405112,\n        \"max\": 1.3723631,\n        \"num_unique_values\": 396,\n        \"samples\": [\n          0.1899976\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8d71507c-5210-49cb-b1c1-fd1952ad5ed9\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>ontologyTerm</th>\n",
              "      <th>biosample</th>\n",
              "      <th>assay</th>\n",
              "      <th>gtexTissue</th>\n",
              "      <th>dataSource</th>\n",
              "      <th>endedness</th>\n",
              "      <th>geneticallyModified</th>\n",
              "      <th>nonzeroMean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL:0000047 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_POSITIVE</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '47'}</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIAT...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_PAIRED</td>\n",
              "      <td>False</td>\n",
              "      <td>0.143617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CL:0000062 total RNA-seq</td>\n",
              "      <td>STRAND_POSITIVE</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '62'}</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_PAIRED</td>\n",
              "      <td>False</td>\n",
              "      <td>0.094144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CL:0000084 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_POSITIVE</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '84'}</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_PAIRED</td>\n",
              "      <td>False</td>\n",
              "      <td>0.124296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CL:0000084 total RNA-seq</td>\n",
              "      <td>STRAND_POSITIVE</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '84'}</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.100934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CL:0000115 total RNA-seq</td>\n",
              "      <td>STRAND_POSITIVE</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '115'}</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIAT...</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.135553</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>UBERON:0018115 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lef...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.268222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>663</th>\n",
              "      <td>UBERON:0018116 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'rig...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.258522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>UBERON:0018117 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lef...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.215190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>665</th>\n",
              "      <td>UBERON:0018118 polyA plus RNA-seq</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'rig...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>ENDEDNESS_SINGLE</td>\n",
              "      <td>False</td>\n",
              "      <td>0.365676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...</td>\n",
              "      <td>STRAND_UNSTRANDED</td>\n",
              "      <td>{'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...</td>\n",
              "      <td>{'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'sup...</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
              "      <td>gtex</td>\n",
              "      <td>ENDEDNESS_PAIRED</td>\n",
              "      <td>False</td>\n",
              "      <td>0.045404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>667 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d71507c-5210-49cb-b1c1-fd1952ad5ed9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8d71507c-5210-49cb-b1c1-fd1952ad5ed9 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-8d71507c-5210-49cb-b1c1-fd1952ad5ed9');\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",
              "\n",
              "\n",
              "    <div id=\"df-d20af698-1917-4f52-b42c-114d729bbce6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d20af698-1917-4f52-b42c-114d729bbce6')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-d20af698-1917-4f52-b42c-114d729bbce6 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                  name             strand  \\\n",
              "0                        CL:0000047 polyA plus RNA-seq    STRAND_POSITIVE   \n",
              "1                             CL:0000062 total RNA-seq    STRAND_POSITIVE   \n",
              "2                        CL:0000084 polyA plus RNA-seq    STRAND_POSITIVE   \n",
              "3                             CL:0000084 total RNA-seq    STRAND_POSITIVE   \n",
              "4                             CL:0000115 total RNA-seq    STRAND_POSITIVE   \n",
              "..                                                 ...                ...   \n",
              "662                  UBERON:0018115 polyA plus RNA-seq  STRAND_UNSTRANDED   \n",
              "663                  UBERON:0018116 polyA plus RNA-seq  STRAND_UNSTRANDED   \n",
              "664                  UBERON:0018117 polyA plus RNA-seq  STRAND_UNSTRANDED   \n",
              "665                  UBERON:0018118 polyA plus RNA-seq  STRAND_UNSTRANDED   \n",
              "666  UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...  STRAND_UNSTRANDED   \n",
              "\n",
              "                                          ontologyTerm  \\\n",
              "0     {'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '47'}   \n",
              "1     {'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '62'}   \n",
              "2     {'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '84'}   \n",
              "3     {'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '84'}   \n",
              "4    {'ontologyType': 'ONTOLOGY_TYPE_CL', 'id': '115'}   \n",
              "..                                                 ...   \n",
              "662  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "663  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "664  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "665  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "666  {'ontologyType': 'ONTOLOGY_TYPE_UBERON', 'id':...   \n",
              "\n",
              "                                             biosample               assay  \\\n",
              "0    {'type': 'BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIAT...  polyA plus RNA-seq   \n",
              "1    {'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...       total RNA-seq   \n",
              "2    {'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...  polyA plus RNA-seq   \n",
              "3    {'type': 'BIOSAMPLE_TYPE_PRIMARY_CELL', 'name'...       total RNA-seq   \n",
              "4    {'type': 'BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIAT...       total RNA-seq   \n",
              "..                                                 ...                 ...   \n",
              "662  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lef...  polyA plus RNA-seq   \n",
              "663  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'rig...  polyA plus RNA-seq   \n",
              "664  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'lef...  polyA plus RNA-seq   \n",
              "665  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'rig...  polyA plus RNA-seq   \n",
              "666  {'type': 'BIOSAMPLE_TYPE_TISSUE', 'name': 'sup...  polyA plus RNA-seq   \n",
              "\n",
              "                          gtexTissue dataSource         endedness  \\\n",
              "0                                        encode  ENDEDNESS_PAIRED   \n",
              "1                                        encode  ENDEDNESS_PAIRED   \n",
              "2                                        encode  ENDEDNESS_PAIRED   \n",
              "3                                        encode  ENDEDNESS_SINGLE   \n",
              "4                                        encode  ENDEDNESS_SINGLE   \n",
              "..                               ...        ...               ...   \n",
              "662                                      encode  ENDEDNESS_SINGLE   \n",
              "663                                      encode  ENDEDNESS_SINGLE   \n",
              "664                                      encode  ENDEDNESS_SINGLE   \n",
              "665                                      encode  ENDEDNESS_SINGLE   \n",
              "666  Skin_Not_Sun_Exposed_Suprapubic       gtex  ENDEDNESS_PAIRED   \n",
              "\n",
              "     geneticallyModified  nonzeroMean  \n",
              "0                  False     0.143617  \n",
              "1                  False     0.094144  \n",
              "2                  False     0.124296  \n",
              "3                  False     0.100934  \n",
              "4                  False     0.135553  \n",
              "..                   ...          ...  \n",
              "662                False     0.268222  \n",
              "663                False     0.258522  \n",
              "664                False     0.215190  \n",
              "665                False     0.365676  \n",
              "666                False     0.045404  \n",
              "\n",
              "[667 rows x 10 columns]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores[0].var"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57j3JFxrZN4A"
      },
      "source": [
        "## Get Metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGmiW_DxZR2h"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"get_metadata\",\n",
            "      \"data\": {\n",
            "        \"organism\": \"ORGANISM_HOMO_SAPIENS\"\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>Assay title</th>\n",
              "      <th>ontology_curie</th>\n",
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              "      <th>biosample_life_stage</th>\n",
              "      <th>gtex_tissue</th>\n",
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              "      <th>genetically_modified</th>\n",
              "      <th>nonzero_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL:0000047 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:47</td>\n",
              "      <td>neuronal stem cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.143617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CL:0000062 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:62</td>\n",
              "      <td>osteoblast</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.094144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CL:0000084 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:84</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.124296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CL:0000084 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:84</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.100934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CL:0000115 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:115</td>\n",
              "      <td>endothelial cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.135553</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>UBERON:0018115 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:18115</td>\n",
              "      <td>left renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.268222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>663</th>\n",
              "      <td>UBERON:0018116 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:18116</td>\n",
              "      <td>right renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.258522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>UBERON:0018117 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:18117</td>\n",
              "      <td>left renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.215190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>665</th>\n",
              "      <td>UBERON:0018118 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:18118</td>\n",
              "      <td>right renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.365676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:36149</td>\n",
              "      <td>suprapubic skin</td>\n",
              "      <td>tissue</td>\n",
              "      <td>adult</td>\n",
              "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
              "      <td>gtex</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.045404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>667 rows × 12 columns</p>\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('rna_seq_metadata_df');\n",
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              "\n",
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            ],
            "text/plain": [
              "                                                  name strand  \\\n",
              "0                        CL:0000047 polyA plus RNA-seq      +   \n",
              "1                             CL:0000062 total RNA-seq      +   \n",
              "2                        CL:0000084 polyA plus RNA-seq      +   \n",
              "3                             CL:0000084 total RNA-seq      +   \n",
              "4                             CL:0000115 total RNA-seq      +   \n",
              "..                                                 ...    ...   \n",
              "662                  UBERON:0018115 polyA plus RNA-seq      .   \n",
              "663                  UBERON:0018116 polyA plus RNA-seq      .   \n",
              "664                  UBERON:0018117 polyA plus RNA-seq      .   \n",
              "665                  UBERON:0018118 polyA plus RNA-seq      .   \n",
              "666  UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...      .   \n",
              "\n",
              "            Assay title ontology_curie                   biosample_name  \\\n",
              "0    polyA plus RNA-seq          CL:47               neuronal stem cell   \n",
              "1         total RNA-seq          CL:62                       osteoblast   \n",
              "2    polyA plus RNA-seq          CL:84                           T-cell   \n",
              "3         total RNA-seq          CL:84                           T-cell   \n",
              "4         total RNA-seq         CL:115                 endothelial cell   \n",
              "..                  ...            ...                              ...   \n",
              "662  polyA plus RNA-seq   UBERON:18115                left renal pelvis   \n",
              "663  polyA plus RNA-seq   UBERON:18116               right renal pelvis   \n",
              "664  polyA plus RNA-seq   UBERON:18117   left renal cortex interstitium   \n",
              "665  polyA plus RNA-seq   UBERON:18118  right renal cortex interstitium   \n",
              "666  polyA plus RNA-seq   UBERON:36149                  suprapubic skin   \n",
              "\n",
              "                    biosample_type biosample_life_stage  \\\n",
              "0    in_vitro_differentiated_cells            embryonic   \n",
              "1                     primary_cell                adult   \n",
              "2                     primary_cell                adult   \n",
              "3                     primary_cell                adult   \n",
              "4    in_vitro_differentiated_cells                adult   \n",
              "..                             ...                  ...   \n",
              "662                         tissue            embryonic   \n",
              "663                         tissue            embryonic   \n",
              "664                         tissue            embryonic   \n",
              "665                         tissue            embryonic   \n",
              "666                         tissue                adult   \n",
              "\n",
              "                         gtex_tissue data_source endedness  \\\n",
              "0                                         encode    paired   \n",
              "1                                         encode    paired   \n",
              "2                                         encode    paired   \n",
              "3                                         encode    single   \n",
              "4                                         encode    single   \n",
              "..                               ...         ...       ...   \n",
              "662                                       encode    single   \n",
              "663                                       encode    single   \n",
              "664                                       encode    single   \n",
              "665                                       encode    single   \n",
              "666  Skin_Not_Sun_Exposed_Suprapubic        gtex    paired   \n",
              "\n",
              "     genetically_modified  nonzero_mean  \n",
              "0                   False      0.143617  \n",
              "1                   False      0.094144  \n",
              "2                   False      0.124296  \n",
              "3                   False      0.100934  \n",
              "4                   False      0.135553  \n",
              "..                    ...           ...  \n",
              "662                 False      0.268222  \n",
              "663                 False      0.258522  \n",
              "664                 False      0.215190  \n",
              "665                 False      0.365676  \n",
              "666                 False      0.045404  \n",
              "\n",
              "[667 rows x 12 columns]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output_metadata = dna_model.output_metadata()\n",
        "\n",
        "# Accessing a specific output type, for instance, RNA_SEQ:\n",
        "rna_seq_metadata_df = output_metadata.rna_seq\n",
        "\n",
        "rna_seq_metadata_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5n0fCd-Cy1-A"
      },
      "source": [
        "## Make mouse predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-E-kEitg6ru"
      },
      "source": [
        "So far, this notebook has focused on predictions for human\n",
        "(`Organism.HOMO_SAPIENS`). To generate predictions for mouse, specify the\n",
        "organism as `Organism.MUS_MUSCULUS` instead. Please note that the supported\n",
        "ontology terms differ between species.\n",
        "\n",
        "The following example demonstrates how to call `predict_sequence` for mouse\n",
        "predictions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QjFVRG1QLVba"
      },
      "outputs": [],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence=\"GATTACA\".center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, \"N\"\n",
        "    ),  # Pad to valid sequence length.\n",
        "    organism=dna_client.Organism.MUS_MUSCULUS,\n",
        "    requested_outputs=[dna_client.OutputType.DNASE],\n",
        "    ontology_terms=[\"UBERON:0002048\"],  # Lung.\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwpupBP1hPgh"
      },
      "source": [
        "And here is an example of calling `predict_interval` for a mouse genomic\n",
        "interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKOIZAtMLrEu"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Request body: {\n",
            "  \"instances\": [\n",
            "    {\n",
            "      \"request_type\": \"predict_interval\",\n",
            "      \"data\": {\n",
            "        \"interval\": {\n",
            "          \"chromosome\": \"chr1\",\n",
            "          \"start\": \"2475713\",\n",
            "          \"end\": \"3524289\",\n",
            "          \"strand\": \"STRAND_UNSTRANDED\"\n",
            "        },\n",
            "        \"organism\": \"ORGANISM_MUS_MUSCULUS\",\n",
            "        \"requestedOutputs\": [\n",
            "          \"OUTPUT_TYPE_RNA_SEQ\"\n",
            "        ],\n",
            "        \"ontology_terms\": [\n",
            "          {\n",
            "            \"ontology_type\": \"ONTOLOGY_TYPE_UBERON\",\n",
            "            \"id\": 2048\n",
            "          }\n",
            "        ],\n",
            "        \"modelVersion\": \"FOLD_0\"\n",
            "      }\n",
            "    }\n",
            "  ]\n",
            "}\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(1048576, 3)"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "interval = genome.Interval(\"chr1\", 3_000_000, 3_000_001).resize(\n",
        "    dna_client.SEQUENCE_LENGTH_1MB\n",
        ")\n",
        "\n",
        "output = dna_model.predict_interval(\n",
        "    interval=interval,\n",
        "    organism=dna_client.Organism.MUS_MUSCULUS,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=[\"UBERON:0002048\"],  # Lung.\n",
        ")\n",
        "\n",
        "output.rna_seq.values.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1isDYNjkYVv"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "That's it for the quick start guide. To dive in further, check out our\n",
        "[other tutorials](https://www.alphagenomedocs.com/tutorials/index.html)."
      ]
    }
  ],
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