using_enformer_celltyping.ipynb

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   "source": [
    "# Using Enformer Celltyping\n",
    "\n",
    "This workbook steps through the different use cases for the trained, Enformer Celltyping model.\n",
    "\n",
    "To begin, download all necessary reference files for Enformer Celltyping by running the following\n",
    "in the command line from within the downloaded repo - `EnformerCelltyping/`:\n",
    "\n",
    "```\n",
    "conda env create -f ./environment/enformer_celltyping.yml &&\\\n",
    "make renv &&\\ \n",
    "make pyanalyenv &&\\\n",
    "conda activate EnformerCelltyping &&\\\n",
    "pip install -e . &&\\\n",
    "python bin/download_Enformer_Celltyping_dependencies.py\n",
    "```\n",
    "\n",
    "This will download:\n",
    "  1. The saved weights for the trained Enformer Celltyping model (`./EnformerCelltyping`)\n",
    "  2. The hg19/GRCh37 reference genome split by nucleotide, saved as a bigWig file (`./data/dna/`)\n",
    "  3. The average chromatin accessibility signal for the 104 [EpiMap](http://compbio.mit.edu/epimap/) training cell types (`./data/model_ref`)\n",
    "  4. The [ENCODE Blacklist](https://www.nature.com/articles/s41598-019-45839-z) regions (`./data/model_ref/`)\n",
    "  5. The trained [enformer model](https://www.nature.com/articles/s41592-021-01252-x) from Tensorflow Hub (`./data/enformer_model/`)\n",
    "  6. BigWig Microglia chromatin accessibility signal at 128 base-pair resolution (`./data/demo/`)\n",
    "  \n",
    "Some notes on the usage of these in the model:\n",
    "  * **Enformer Celltyping model** - The model after training regeme outlined in [full_training_recreation.ipynb](https://github.com/neurogenomics/EnformerCelltyping/blob/master/full_training_recreation.ipynb) and in our manuscript.\n",
    "  * **hg19 genome reference** - Needed to make any predictions by the model, pass the DNA from that genomic region.\n",
    "  * **average chromatin accessibility** - Used as input to the model along with the cell type specific chromatin accessibility.\n",
    "  * **ENCODE Blacklist** - These regions can be excluded from predictions.\n",
    "  * **Trained enformer model** - This model is processed to remove final layers to enable transfer learning in Enformer Celltyping.\n",
    "  * **Microglia chromatin accessibility signal** - data taken from [Nott et al. 2019](https://www.science.org/doi/10.1126/science.aay0793?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed) which has been processed as discribed in section 1.1 so it is ready for input to Enformer Celltyping. We will use this to demo Enformer Celltyping's functionality\n"
   ]
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   "source": [
    "## 1. Predicting with Enformer Celltyping\n",
    "\n",
    "The first thing we can do with Enformer Celltyping is predict the cell type-specific histone marks for a particular\n",
    "region of the genome. To make a prediction of the histone mark signal for your cell type/tissue of interest, you need the corresponding chromatin accessibility (ATAC-Seq data).\n",
    "\n",
    "\n",
    "### 1.1 Processing your cell type-specific chromatin accessibility data\n",
    "\n",
    "The data needs to be the -log10 p-value signal for every base-pair, in bigWig format. This is an output after peak calling but **note** that this is not the default output type. The easiest way to get this data is to run the [ENCODE ATAC Pipeline](https://github.com/ENCODE-DCC/atac-seq-pipeline) with the raw FASTQ files from your study. Make sure to use the hg19 reference genome as this is the reference genome for which Enformer Celltyping predicts. Also *Note*, you need to subsample reads to get the same read depth used in the training data (50 million reads for ATAC data). You can do this using the ENCODE pipeline by updating the json file, adding this line:\n",
    "\n",
    "```\n",
    "\"atac.subsample_reads\": 50000000,\n",
    "```\n",
    "\n",
    "Similarly, the predictions of CHIP-seq histone mark peaks are made at a sequencing depth of 30 million reads. So if using ENNCODES CHIP pipeline to compare the model predictions to the true signal, subsample to this level with `\"chip.subsample_reads\": 30000000,` added to the json. \n",
    "\n",
    "Once run, the bigWig file with the pooled -log10 p-value should be in the following folder: \n",
    "\n",
    "```\n",
    "./atac/<run-id>/call-macs2_signal_track_pooled/execution/rep.pooled.pval.signal.bigwig\n",
    "```\n",
    "\n",
    "Now, this file will be averaged to 25 base-pairs whereas the model takes 128 base-pair averaged chromatin accessibility. To update the file to 128 base-pair averaging, activate the `r_bioc` (`conda activate r_bioc` - this was created with the `mke env` call earlier) and run:\n",
    "\n",
    "```\n",
    "Rscript ./bin/avg_bigwig_tracks.R path/to/bigwig/file 128\n",
    "```\n",
    "\n",
    "This will create the 128 base-pair averaged bigWig file in the same location as the original with `_128` appended to the name. Now you are ready to pass your data to Enformer Celltyping and get some predictions."
   ]
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   "source": [
    "### 1.2 Predicting histone mark signals\n",
    "\n",
    "To show how to predict there is a chromatin accessibility (ATAC-Seq) bigWig file, averaged at 128 base-pairs for Microglia sourced from [Brain cell type–specific enhancer–promoter interactome maps and disease-risk association](https://www.science.org/doi/10.1126/science.aay0793?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed). This will be used for demostration purposes.\n"
   ]
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f332db34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-08 14:35:05.085197: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
     ]
    }
   ],
   "source": [
    "#first let's sort our data loader\n",
    "\n",
    "#use the following:\n",
    "#PROJECT_PATH - path to the EnformerCelltyping repo to get relative paths\n",
    "from EnformerCelltyping.constants import PROJECT_PATH\n",
    "\n",
    "#path to the cell type-specific chromatin accessibility bigWig\n",
    "chrom_access_pth = str(PROJECT_PATH /'data/demo/Nott19_Microglia_128.bigWig')\n",
    "#give the name of the cell\n",
    "cell = 'Nott19_Microglia'\n",
    "\n",
    "#import the data generator which will take care of any preprocessing\n",
    "from EnformerCelltyping.utils import generate_sample\n",
    "\n",
    "#data generator class for samples\n",
    "data_generator = generate_sample(\n",
    "    cells = {cell:chrom_access_pth}, #should be a dict\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6936cd49",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-08 14:40:19.269038: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
      "2024-02-08 14:40:19.405289: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:19.406990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:00:06.0 name: NVIDIA A100 80GB PCIe computeCapability: 8.0\n",
      "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.76TiB/s\n",
      "2024-02-08 14:40:19.407011: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
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      "2024-02-08 14:40:19.946412: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n",
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      "2024-02-08 14:40:20.088183: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2024-02-08 14:40:20.088495: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:20.090391: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:20.093249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2024-02-08 14:40:20.094105: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-08 14:40:20.102565: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:20.104128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
      "pciBusID: 0000:00:06.0 name: NVIDIA A100 80GB PCIe computeCapability: 8.0\n",
      "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 79.15GiB deviceMemoryBandwidth: 1.76TiB/s\n",
      "2024-02-08 14:40:20.104261: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:20.105908: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:20.107431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
      "2024-02-08 14:40:20.108747: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
      "2024-02-08 14:40:21.502887: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2024-02-08 14:40:21.502937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 \n",
      "2024-02-08 14:40:21.502946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N \n",
      "2024-02-08 14:40:21.503466: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:21.506251: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:21.507555: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2024-02-08 14:40:21.508581: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 78936 MB memory) -> physical GPU (device: 0, name: NVIDIA A100 80GB PCIe, pci bus id: 0000:00:06.0, compute capability: 8.0)\n",
      "2024-02-08 14:40:29.126441: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
      "2024-02-08 14:40:30.524667: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8201\n",
      "2024-02-08 14:40:32.841755: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-02-08 14:40:32.841817: W tensorflow/stream_executor/gpu/asm_compiler.cc:56] Couldn't invoke ptxas --version\n",
      "2024-02-08 14:40:32.842885: E tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-02-08 14:40:32.842982: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] Internal: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n",
      "2024-02-08 14:40:32.867653: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
      "2024-02-08 14:40:34.801597: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
      "2024-02-08 14:40:34.853710: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "2024-02-08 14:40:36.242624: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "2024-02-08 14:40:43.483631: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 6593986560 exceeds 10% of free system memory.\n"
     ]
    }
   ],
   "source": [
    "#load Enformer celltyping model\n",
    "import tensorflow as tf\n",
    "from EnformerCelltyping.enf_celltyping import Enformer_Celltyping\n",
    "\n",
    "#load histone marks from constants so the ordering of predictions is known\n",
    "from EnformerCelltyping.constants import PRED_HIST_MARKS\n",
    "\n",
    "hist_marks = PRED_HIST_MARKS\n",
    "\n",
    "model = Enformer_Celltyping(enf_path=str(PROJECT_PATH / \"data/enformer_model\"),#path to enformer model\n",
    "                            use_prebuilt_model=True,\n",
    "                            enf_celltyping_pth = str(PROJECT_PATH /'EnformerCelltyping'/'enformer_celltyping_weights')\n",
    "                           )\n",
    "#compile loaded model\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),\n",
    "              loss=tf.keras.losses.mean_squared_error,\n",
    "              #mse for each hist mark\n",
    "              metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5c5f9d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"EnformerCelltyping\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "dnaInput (InputLayer)           [(None, 896, 1536)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense1_quant (Dense)            (None, 896, 1536)    2360832     dnaInput[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "ChromAccessLclInput (InputLayer [(None, 1562)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense1_dna (Dense)              (None, 896, 1536)    2360832     dnaInput[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_quant (Dropout)        (None, 896, 1536)    0           dense1_quant[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf.clip_by_value (TFOpLambda)   (None, 1562)         0           ChromAccessLclInput[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_dna (Dropout)          (None, 896, 1536)    0           dense1_dna[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_finalpoint_quant (Ba (None, 896, 1536)    6144        dropout1_quant[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.add (TFOpLambda)        (None, 1562)         0           tf.clip_by_value[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "ChromAccessGblInput (InputLayer [(None, 14592)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_finalpoint (BatchNor (None, 896, 1536)    6144        dropout1_dna[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_4 (TFOpLambda) (None, 896, 1536)    0           batchnorm1_finalpoint_quant[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "128bp_embedding (Embedding)     (None, 1562, 25)     39050       tf.math.add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "500bp_embedding (Embedding)     (None, 1562, 40)     15640       tf.math.add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "5kbp_embedding (Embedding)      (None, 1562, 45)     1800        tf.math.add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "gbl_embedding (Embedding)       (None, 14592, 25)    364800      ChromAccessGblInput[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply (TFOpLambda)   (None, 896, 1536)    0           batchnorm1_finalpoint[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_2 (TFOpLambda)  (None, 896, 1536)    0           tf.math.multiply_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "flatten128 (Flatten)            (None, 39050)        0           128bp_embedding[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten500 (Flatten)            (None, 62480)        0           500bp_embedding[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten5k (Flatten)             (None, 70290)        0           5kbp_embedding[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "flattenGbl (Flatten)            (None, 364800)       0           gbl_embedding[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid (TFOpLambda)    (None, 896, 1536)    0           tf.math.multiply[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_5 (TFOpLambda) (None, 896, 1536)    0           tf.math.sigmoid_2[0][0]          \n",
      "                                                                 batchnorm1_finalpoint_quant[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "cat_embed (Concatenate)         (None, 536620)       0           flatten128[0][0]                 \n",
      "                                                                 flatten500[0][0]                 \n",
      "                                                                 flatten5k[0][0]                  \n",
      "                                                                 flattenGbl[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_1 (TFOpLambda) (None, 896, 1536)    0           tf.math.sigmoid[0][0]            \n",
      "                                                                 batchnorm1_finalpoint[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_finalpoint_quant (Conv1 (None, 896, 3072)    4721664     tf.math.multiply_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "dense_combn1_celltype (Dense)   (None, 3072)         1648499712  cat_embed[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_finalpoint (Conv1D)     (None, 896, 3072)    4721664     tf.math.multiply_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_finalpoint_quant (Drop (None, 896, 3072)    0           conv1d1_finalpoint_quant[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_celltype (Dropout)     (None, 3072)         0           dense_combn1_celltype[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_finalpoint (Dropout)   (None, 896, 3072)    0           conv1d1_finalpoint[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_6 (TFOpLambda) (None, 896, 3072)    0           dropout1_finalpoint_quant[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "dense_combn2_celltype (Dense)   (None, 3072)         9440256     dropout1_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_2 (TFOpLambda) (None, 896, 3072)    0           dropout1_finalpoint[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_3 (TFOpLambda)  (None, 896, 3072)    0           tf.math.multiply_6[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "dropout2_celltype (Dropout)     (None, 3072)         0           dense_combn2_celltype[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_1 (TFOpLambda)  (None, 896, 3072)    0           tf.math.multiply_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_7 (TFOpLambda) (None, 896, 3072)    0           tf.math.sigmoid_3[0][0]          \n",
      "                                                                 dropout1_finalpoint_quant[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_1 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_2 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_3 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_4 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_5 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_chan_celltype_6 (Dense)   (None, 896)          2753408     dropout2_celltype[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_3 (TFOpLambda) (None, 896, 3072)    0           tf.math.sigmoid_1[0][0]          \n",
      "                                                                 dropout1_finalpoint[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_1 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_2 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_3 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_4 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_5 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "quant_out_chan_6 (Dense)        (None, 896, 10)      30730       tf.math.multiply_7[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims (TFOpLambda)     (None, 896, 1)       0           dense_chan_celltype_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_1 (TFOpLambda)   (None, 896, 1)       0           dense_chan_celltype_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_2 (TFOpLambda)   (None, 896, 1)       0           dense_chan_celltype_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_3 (TFOpLambda)   (None, 896, 1)       0           dense_chan_celltype_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_4 (TFOpLambda)   (None, 896, 1)       0           dense_chan_celltype_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_5 (TFOpLambda)   (None, 896, 1)       0           dense_chan_celltype_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "avg (Dense)                     (None, 896, 6)       18438       tf.math.multiply_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_6 (TFOpLambda)   (None, 896, 1, 10)   0           quant_out_chan_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_7 (TFOpLambda)   (None, 896, 1, 10)   0           quant_out_chan_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_8 (TFOpLambda)   (None, 896, 1, 10)   0           quant_out_chan_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_9 (TFOpLambda)   (None, 896, 1, 10)   0           quant_out_chan_4[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_10 (TFOpLambda)  (None, 896, 1, 10)   0           quant_out_chan_5[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_11 (TFOpLambda)  (None, 896, 1, 10)   0           quant_out_chan_6[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "delta (Concatenate)             (None, 896, 6)       0           tf.expand_dims[0][0]             \n",
      "                                                                 tf.expand_dims_1[0][0]           \n",
      "                                                                 tf.expand_dims_2[0][0]           \n",
      "                                                                 tf.expand_dims_3[0][0]           \n",
      "                                                                 tf.expand_dims_4[0][0]           \n",
      "                                                                 tf.expand_dims_5[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_12 (TFOpLambda)  (None, 896, 6, 1)    0           avg[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "quant (Concatenate)             (None, 896, 6, 10)   0           tf.expand_dims_6[0][0]           \n",
      "                                                                 tf.expand_dims_7[0][0]           \n",
      "                                                                 tf.expand_dims_8[0][0]           \n",
      "                                                                 tf.expand_dims_9[0][0]           \n",
      "                                                                 tf.expand_dims_10[0][0]          \n",
      "                                                                 tf.expand_dims_11[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_13 (TFOpLambda)  (None, 896, 6, 1)    0           delta[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "cat_avg_delta (Concatenate)     (None, 896, 6, 12)   0           tf.expand_dims_12[0][0]          \n",
      "                                                                 quant[0][0]                      \n",
      "                                                                 tf.expand_dims_13[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem (Slici (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem_1 (Sli (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem_2 (Sli (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem_3 (Sli (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem_4 (Sli (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf.__operators__.getitem_5 (Sli (None, 896, 12)      0           cat_avg_delta[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_0 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_1 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_2 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem_2[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_3 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_4 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem_4[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batchnorm1_chan_5 (BatchNormali (None, 896, 12)      48          tf.__operators__.getitem_5[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_8 (TFOpLambda) (None, 896, 12)      0           batchnorm1_chan_0[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_12 (TFOpLambda (None, 896, 12)      0           batchnorm1_chan_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_16 (TFOpLambda (None, 896, 12)      0           batchnorm1_chan_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_20 (TFOpLambda (None, 896, 12)      0           batchnorm1_chan_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_24 (TFOpLambda (None, 896, 12)      0           batchnorm1_chan_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_28 (TFOpLambda (None, 896, 12)      0           batchnorm1_chan_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_4 (TFOpLambda)  (None, 896, 12)      0           tf.math.multiply_8[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_6 (TFOpLambda)  (None, 896, 12)      0           tf.math.multiply_12[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_8 (TFOpLambda)  (None, 896, 12)      0           tf.math.multiply_16[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_10 (TFOpLambda) (None, 896, 12)      0           tf.math.multiply_20[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_12 (TFOpLambda) (None, 896, 12)      0           tf.math.multiply_24[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_14 (TFOpLambda) (None, 896, 12)      0           tf.math.multiply_28[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_9 (TFOpLambda) (None, 896, 12)      0           tf.math.sigmoid_4[0][0]          \n",
      "                                                                 batchnorm1_chan_0[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_13 (TFOpLambda (None, 896, 12)      0           tf.math.sigmoid_6[0][0]          \n",
      "                                                                 batchnorm1_chan_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_17 (TFOpLambda (None, 896, 12)      0           tf.math.sigmoid_8[0][0]          \n",
      "                                                                 batchnorm1_chan_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_21 (TFOpLambda (None, 896, 12)      0           tf.math.sigmoid_10[0][0]         \n",
      "                                                                 batchnorm1_chan_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_25 (TFOpLambda (None, 896, 12)      0           tf.math.sigmoid_12[0][0]         \n",
      "                                                                 batchnorm1_chan_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_29 (TFOpLambda (None, 896, 12)      0           tf.math.sigmoid_14[0][0]         \n",
      "                                                                 batchnorm1_chan_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_0 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_9[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_1 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_13[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_2 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_17[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_3 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_21[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_4 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_25[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv1d1_chan_5 (Conv1D)         (None, 896, 96)      1248        tf.math.multiply_29[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_0 (Dropout)       (None, 896, 96)      0           conv1d1_chan_0[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_1 (Dropout)       (None, 896, 96)      0           conv1d1_chan_1[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_2 (Dropout)       (None, 896, 96)      0           conv1d1_chan_2[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_3 (Dropout)       (None, 896, 96)      0           conv1d1_chan_3[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_4 (Dropout)       (None, 896, 96)      0           conv1d1_chan_4[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout1_chan_5 (Dropout)       (None, 896, 96)      0           conv1d1_chan_5[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_10 (TFOpLambda (None, 896, 96)      0           dropout1_chan_0[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_14 (TFOpLambda (None, 896, 96)      0           dropout1_chan_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_18 (TFOpLambda (None, 896, 96)      0           dropout1_chan_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_22 (TFOpLambda (None, 896, 96)      0           dropout1_chan_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_26 (TFOpLambda (None, 896, 96)      0           dropout1_chan_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_30 (TFOpLambda (None, 896, 96)      0           dropout1_chan_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_5 (TFOpLambda)  (None, 896, 96)      0           tf.math.multiply_10[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_7 (TFOpLambda)  (None, 896, 96)      0           tf.math.multiply_14[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_9 (TFOpLambda)  (None, 896, 96)      0           tf.math.multiply_18[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_11 (TFOpLambda) (None, 896, 96)      0           tf.math.multiply_22[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_13 (TFOpLambda) (None, 896, 96)      0           tf.math.multiply_26[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.sigmoid_15 (TFOpLambda) (None, 896, 96)      0           tf.math.multiply_30[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_11 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_5[0][0]          \n",
      "                                                                 dropout1_chan_0[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_15 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_7[0][0]          \n",
      "                                                                 dropout1_chan_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_19 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_9[0][0]          \n",
      "                                                                 dropout1_chan_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_23 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_11[0][0]         \n",
      "                                                                 dropout1_chan_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_27 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_13[0][0]         \n",
      "                                                                 dropout1_chan_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf.math.multiply_31 (TFOpLambda (None, 896, 96)      0           tf.math.sigmoid_15[0][0]         \n",
      "                                                                 dropout1_chan_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_0 (Flatten)        (None, 86016)        0           tf.math.multiply_11[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_1 (Flatten)        (None, 86016)        0           tf.math.multiply_15[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_2 (Flatten)        (None, 86016)        0           tf.math.multiply_19[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_3 (Flatten)        (None, 86016)        0           tf.math.multiply_23[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_4 (Flatten)        (None, 86016)        0           tf.math.multiply_27[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "flatten_chan_5 (Flatten)        (None, 86016)        0           tf.math.multiply_31[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel0 (Dense)  (None, 896)          77071232    flatten_chan_0[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel1 (Dense)  (None, 896)          77071232    flatten_chan_1[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel2 (Dense)  (None, 896)          77071232    flatten_chan_2[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel3 (Dense)  (None, 896)          77071232    flatten_chan_3[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel4 (Dense)  (None, 896)          77071232    flatten_chan_4[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "avg_delta_out_channel5 (Dense)  (None, 896)          77071232    flatten_chan_5[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_14 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel0[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_15 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_16 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_17 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_18 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel4[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf.expand_dims_19 (TFOpLambda)  (None, 896, 1)       0           avg_delta_out_channel5[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "cat_combn_avg_delta (Concatenat (None, 896, 6)       0           tf.expand_dims_14[0][0]          \n",
      "                                                                 tf.expand_dims_15[0][0]          \n",
      "                                                                 tf.expand_dims_16[0][0]          \n",
      "                                                                 tf.expand_dims_17[0][0]          \n",
      "                                                                 tf.expand_dims_18[0][0]          \n",
      "                                                                 tf.expand_dims_19[0][0]          \n",
      "==================================================================================================\n",
      "Total params: 2,151,696,972\n",
      "Trainable params: 462,435,024\n",
      "Non-trainable params: 1,689,261,948\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7d9655",
   "metadata": {},
   "source": [
    "Now let's predict\n",
    "\n",
    "Let's predict at the TSS of [Iba1/AIF-1](https://grch37.ensembl.org/Homo_sapiens/Gene/Summary?db=core;g=ENSG00000204472;r=6:31582961-31584798) Gene\n",
    "\n",
    "This is a microglia marker gene\n",
    "\n",
    "Found at Chromosome 6: 31,582,961-31,584,798 hg19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa3dd69d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X {'dna': TensorShape([1, 196608, 4]), 'chrom_access_lcl': TensorShape([1, 1562]), 'chrom_access_gbl': TensorShape([1, 14592])}\n"
     ]
    }
   ],
   "source": [
    "#load data for a specific genomic position\n",
    "tss = 31_582_961\n",
    "dna_window = 196_608\n",
    "chro='chr6'\n",
    "#we want the TSS centered in the prediction\n",
    "#pos is the start of the DNA window\n",
    "strt = tss-(dna_window//2)\n",
    "end = tss+(dna_window//2)\n",
    "X = data_generator.load(pos=strt,\n",
    "                           chro=chro,cell=cell)\n",
    "print(\"X\",{key:item.shape for key, item in X.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62892b65",
   "metadata": {},
   "source": [
    "So you can see the data loader has returneda dictionary of tensors containing the DNA sequence for the region (one-hot encoded) (`dna`) along with the local and global chromatin accessibility data for the cell type. The local chromatin accessibility (`chrom_access_lcl`) is the chromatin accessiblity signal for the same region as the DNA whereas the global chromatin accessibility is the chromatin accessibility from the transcriptional start site (3k base-pairs) for all known marker genes taken from [PangloaDB](https://panglaodb.se/) (`chrom_access_gbl`). Both these signals correspond to the arc-sin transformed chromatin accessibility data for the cell type of interest. Enformer Celltyping uses arc-sin transformed signal as a method to deal with differing sequencing depths and other batch effects across studies. The global chromatin accessibility signal is averaged at 250 base-pairs whereas the local chromatin accessibility signal is at 128 base-pairs.\n",
    "\n",
    "So the model uses the DNA sequence to highlight DNA interactions based on sequence to predict an average histone mark signal. The chromatin accessibility data is jointly used to derive the cell type-specific histone mark signal based on this average. In this manner, the cell type specificity of the model is derived from chromatin accessibility. We include the gobal and local signal so the model can have an understanding of the cell type (global) and the region in which it is trying to predict (local).\n",
    "\n",
    "Now we can pass this data to our model to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ba02cc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-08 14:41:25.166464: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2024-02-08 14:41:25.212113: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2245780000 Hz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 896, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's predict\n",
    "pred = model.predict(X)\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a1ab9e",
   "metadata": {},
   "source": [
    "So the shape of the output is (1,896,6) matrix where 1 is the number of samples we predicted, 896 is the length of the genomic region we predicted for (where each 1 corresponds to 128 base-pairs) and 6 is the six separate histone marks we predicted for. Let's plot the prediction to view the signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0d0452b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get order of histone marks in predictions\n",
    "from EnformerCelltyping.constants import PRED_HIST_MARKS\n",
    "#make plotting easier\n",
    "from EnformerCelltyping.utils import plot_signal\n",
    "    \n",
    "#make dict with histone mark prediction and name\n",
    "#get the prediction window, Enformer Celltyping pridicts in a funnel shape\n",
    "#where it won't predict for every base-pair of the input length\n",
    "#get bp size of DNA input\n",
    "from EnformerCelltyping.constants import WINDOW_SIZE_DNA\n",
    "#use buffer to deal with funnel\n",
    "buffer = (WINDOW_SIZE_DNA - pred.shape[1]*128)//2\n",
    "#create interval dict for plotting\n",
    "interval = {'start':strt+buffer,'end':end-buffer,\n",
    "            'chro':chro,'cell':cell}\n",
    "#now plot\n",
    "plot_signal({i:pred[0,:,ind] for ind,i in enumerate(PRED_HIST_MARKS)},\n",
    "            interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604e3e5e",
   "metadata": {},
   "source": [
    "### Predicting entire genome (CHR 1-22)\n",
    "\n",
    "You may want to predict the entire genome and save the result as a bigWig file (a separate one for each histone mark). This can be done on the command line. See below for the command to use with the EnformerCelltyping conda env to make a bigwig for each histone mark for the microglia from [Nott et al., 2019](https://www.science.org/doi/10.1126/science.aay0793?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed):\n",
    "\n",
    "```\n",
    "python ./bin/predict_genome.py -c Nott19_Microglia -p data/demo/Nott19_Microglia_128.bigWig -o ./model_results/predictions/\n",
    "```\n",
    "\n",
    "This will predict on a batch of 4 positions at a time, if you have available RAM, increase this batch size by using the -b input paramter which will increase the speed of predictions. Conversely, if you hit memory issues, drop this number.\n",
    "\n",
    "**Note** this should only really be run with a GPU as it takes **33 hours with a GPU**. The script can be adapted to run in parallel. Also make sure to process your ATAC-seq data as described in section 1.1 before running the above on it.\n",
    "\n",
    "**Second Note** - if you want to predict in more than one cell type/tissue, I advise precomputing ands saving the DNA embedding by passign it through enformer, this will massively speed up genome-wide predictions for your cell type of interest so pays off if you (eventually) want to look at more than 1 cell type. This does require a substantial amount of disk space (**~240 GB**). To use this approach, first precomute the DNA embeddings with:\n",
    "\n",
    "```\n",
    "python ./bin/precompute_dna_embeddings.py \n",
    "```\n",
    "\n",
    "Then use these embeddings when predicting in your cell type of interest with:\n",
    "\n",
    "```\n",
    "python ./bin/predict_genome_precomp.py -c Nott19_Microglia -p data/demo/Nott19_Microglia_128.bigWig -o ./model_results/predictions/\n",
    "```\n",
    "\n",
    "You can change the DNA Embedding directory with the `-d` parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b27f6f5",
   "metadata": {},
   "source": [
    "## 2. Testing Enformer Celltyping's receptive field\n",
    "\n",
    "Although Enformer Celltyping takes in ~200k of DNA to make it's predictions, it is not guaranteed that a model will pay attention to the full width of the input length (this is especially true for convolutional models as the input size increases). \n",
    "\n",
    "We can test Enformer Celltyping's receptive field by passing it random DNA sequences and then permuting the base-pair at different positions to check if the change has any effect on the model's outputted prediction. By using many random sequences of DNA and many random changes of base-pairs, at many locations of the input window, we can get a good idea of how much a position will contribute to the prediction. We can then aggregate this contribution and plot it against the input position that's permutation caused the contribution. That's exactly what the function below does so let's run it on Enformer Celltyping.\n",
    "\n",
    "This approach is similar in approach to occlusion methods in image analysis where models predictions are linked back to regions of the input image that drove it by systematically excluding groups of pixels of the image and checking the difference in the prediction. The difference is here, we test if a change in certain genomic positions cause any change in the prediciton for the centre of the predicted window, testing how far away genetic variants can be picked up in the models prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b988a67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:54<00:00,  2.74s/it]\n"
     ]
    }
   ],
   "source": [
    "from EnformerCelltyping.utils import measure_receptive_field\n",
    "import numpy as np\n",
    "\n",
    "# Set random seeds so same data used for both\n",
    "np.random.seed(101)\n",
    "\n",
    "#increase the number of iterations and number of positions tested to increase accuracy\n",
    "N_iter= 4\n",
    "N_pos= 20\n",
    "\n",
    "ec_data,ec_mut_eff_size = measure_receptive_field(model,N_iter= N_iter,N_pos= N_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f01d27d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_252005/323053159.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f0e502d44f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now let's plot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "col = \"#F5CDB4\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "plt.rc('axes', titlesize=15, labelsize=14)\n",
    "label_format = '{:,.0f}'\n",
    "\n",
    "ax.plot(ec_data.keys(),ec_mut_eff_size,\n",
    "        linewidth=2.5,\n",
    "        color=col)\n",
    "ax.set_title(f\"Enformer Celltyping receptive field\")\n",
    "ticks_loc = ax.get_xticks().tolist()\n",
    "ax.set_xticklabels(\n",
    "    [label_format.format(x) for x in ticks_loc],\n",
    "    rotation=45,\n",
    "    fontsize=12,\n",
    "    ha=\"right\"\n",
    ")\n",
    "ax.set_xlabel('Target sequence (bp)', fontsize=16)\n",
    "ax.set_ylabel('Mutation Aggregate Effect Size', fontsize=16)\n",
    "ax.axhline(y=0, color='grey', linestyle='dotted')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4533f0b",
   "metadata": {},
   "source": [
    "We can see even at this low number of iterations, that there is a noticable effect of permuting a base-pair at the edge of the ~200k input (look at position 0). You can replicate the receptive field in our manuscript by increasing the `N_iter` and `N_pos` values but note this will take some time to run."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68c7de6",
   "metadata": {},
   "source": [
    "Now let's test the receptive field based on the local chromatin accessibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12f771cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:53<00:00,  2.68s/it]\n"
     ]
    }
   ],
   "source": [
    "from EnformerCelltyping.utils import measure_receptive_field_CA, smooth\n",
    "import numpy as np\n",
    "\n",
    "# Set random seeds so same data used for both\n",
    "np.random.seed(101)\n",
    "\n",
    "#increase the number of iterations and number of positions tested to increase accuracy\n",
    "N_iter= 4\n",
    "N_pos= 20\n",
    "\n",
    "ec_data_ca,ec_mut_eff_size_ca = measure_receptive_field_CA(model,N_iter= N_iter,N_pos= N_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa647bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_252005/359336786.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mutation Aggregate Effect Size')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lJJnu3bubDRs22Mbu3LnTfPXVV67bf/zxh0lJSTGZmZlmyZIltrHPP/+8kWT69Onj2paXl2cSExNNmzZtzM6dO73m6X4M50VRnz59bD9XY4w5cOCA+eabb2zbKroYycjIMD/99JNr+759+8zw4cN9BmerGnxyKi94sm7dOiPJHHnkkV737d692yQlJZn69eubgwcP+tzfU3nBp5tvvtl1wWhMyfutSZMmJj4+3rz++uu2sQsXLjQJCQmmefPmJj8/37Xd+RqXZK6++mqvi0Hnz0uSGTp0qNm1a5fXvocddpipV6+e+eKLL1z3bd682XVh+Pvvv9uOWZngk+fz4PmzcFqxYoXPi/qPPvrIJCQkmPbt23u9h5yvobL4el0UFxe7Ahq33HKL7Zivv/66iYmJMenp6bZAh/tzOGjQIFdw2xhjfvzxRxMXF2dSUlLMvn37fD4XNRV8cgaS3AMu77zzjiuI4Km8n8mpp55qJJmxY8fafjd9+eWXJiUlxcTGxnr9vJxzOOyww8xff/1V5vnS09Ntr7Xs7GzTuHFjY1mW6dy5s5k8ebLr70BxcbG56KKLjCRz55132o43b948I8n07NnT9hgcDofry5T/+7//8zmHpKQks3jxYh/Ppm/r1693Bf4932MVKSwsdP1NfOSRR2yvuf/973+mfv36JjU11WzdutW1vTLBJ2PK/t3odP755xtJ5p133vG6z/nFw4MPPmjb7vx5tmjRwvY7YNu2bbbH466i3/dNmzY1P//8s2v79u3bzWGHHWYkmc8++8y2z7HHHmskmWuuucb2ueCFF15wzY3gExC9CD4BgJ+cQYePPvqo0vs6P9xdeOGFXvctXbrU5wdW5wfTbt26eV3AGWPM4MGDjSQze/Zsr/tWrFhhLMsy6enptote5zw8g0jGGPPoo48aSSYzM9Ps3r3bdt+ePXuMZVllXrz68vTTTxtJ5tFHH7Vtr2rwqTLPYUXuuOMOI8lce+21tu09evQwkrwyVpwXTj169PAKuHnKz883derUMZZlmR9//LHCuVx77bVGknnqqad83n/mmWcaSWbp0qXGGGO2bt3qypyoyPfff+/zcZaloouRW2+91WufX375xViWZTIyMmxBnpoIPhljzLBhw4wkW1DMGGMee+wxI5VkzPnLV/Bp8+bNZsaMGa7MtLlz5xpjjHn44YddQRFfJk2aZCSZN99807XN+Rpv2LChyc3N9drHeREbGxtrCywbU5LN0bBhQ58X9sYYc9111/l8vqsj+FSeCy64wEjyypYMJPi0aNEiI8m0bdvWZxBh5MiRRpIrE86Y0ucwJibGrFu3zmuf0047zefr76233jIdO3Y0Y8eO9fOR2gNJ5f1z/73q3CcjI8MreGyMcQXbPJ/7sn4mv//+u+t4nr+/jSnNNpowYYJtu3NunoFTz/P5eq05j9m+fXuv7Jzly5f7/J3szPZZu3at1/Hy8vJMkyZNTJ06dWy/X51zuOqqq3zOsSzfffedkWSaNGlSqf2MKck0k2TOO+88n/fPnDnTK/gT7ODTkiVLjCRz6qmn2rYXFBSYJk2amISEBFtQ1ZjSn+d///tfr+MtWLDASDIdOnSwba/o9/0zzzzjdawHH3zQ63eGM9hXr149r+w1Y0o/sxB8AqIXPZ8AwE8mCP1BTjjhBK9tHTp0kFTSP8WX0047zdZnRSrp3/Pdd9/Jsiydf/75Xvt0795dPXr00L59+7R8+XKv+48//nivbc4Gtr179/bqpZGZman69euXOcevvvpK9957r/7xj3/o4osv1vjx4/X6669LktavX+9zn0AF8hyW5aWXXpIkjR071rbdeXvu3Lm27Z9++qkk6fLLL1dMTPl/Qn/66Sft2bNHRx11lF/N5xcuXChJOuOMM3ze7+xH4uxv06hRI7Vo0UIffvihHnjgAf39999lHrtTp05KTU3Vs88+q6efftqrL1VljRkzxmtb586ddcQRRygnJ6fcXi/V5corr5QkPf3007btztuXXXZZpY/p3pesefPm+r//+z8VFBTo1ltv1QUXXCCp9Ofm7JHjyfPn5u64444rt8lvmzZtdNhhh9m2xcTEqHXr1pJ8v4/bt28vqfLvhUDl5+fr3Xff1W233aYrrrhC48eP1/jx47Vy5UpJwXn/f/nll5Kk0aNHKzY21ut+5/vVOc5dmzZtXL8f3JX1O+Oss87S2rVr9cILL1R6no0bN9a4cePK/JeQkOC1T+/evVWvXj2/51cWZ9+fk08+2WcvpPKeI8uydNppp5V7/PL+ZgwdOlRxcfYFtH29Drdt26bly5erc+fO6tixo9fxkpKS1Lt3b+3Zs8fn6+b0008vd46eqvI3uyrv62AZPHiwunTpogULFmjz5s2u7e+//762bNmis846q8yFOXz9jj7xxBNVt25d/frrr9q+fbvf8/D3b+4333wjqeQ16LkYiySdc845fp8TQGSKq3gIAEAqWbVr3bp1lfrQ5qlFixZe29LS0iSVXMT54mv54507d8rhcLiaTPvSpk0bLV++3GdQwtcy9s4Pi2UtcZ+amqodO3bYtu3du1cjR44sd5Wgffv2lXlfIAJ5Dn356quv9Mcff6hTp07q1auX7b7zzz9fN910k9544w3NmjXL9Rw7m0w7L6zKU5mxklwNtps0aVLuOPefwfPPP68xY8boxhtv1I033qi2bdtq8ODBOv/8820XDBkZGXr66ad1xRVX6IorrtCECRPUsWNHDRs2TBdddJH69u3r1xydnMEPT23atNGyZcvKDYRVl7POOkuNGzfWyy+/rAcffFCpqan64YcftGLFCg0cOLDCZsO+jBgxQk2aNJFlWUpOTtZhhx2m008/3RYQcv7cjjnmmHKP5fnekXy/t92V914s637nfZV5LwRq5cqVOv300302w3cKxvvf+Xoqq2G6c7uv152v3xdSYL8zKtKpUye/G+s7BWt+VXmOGjVqpMTExHKPX9m/Gb5eh85m82vWrPH6QsXTjh07vAJUFb1fPDkDM7t371ZRUZHPwGVZnK/p0aNHa/To0eXOszpdccUVmjRpkubMmaM77rhDUmlA/fLLL/e5T926dZWenu7zvtatW2v37t36+++/1bBhQ7/m4O/fXOdrq2XLlj6PU9mfH4DIQ/AJAPx05JFH6uuvv9b//vc/XXjhhQEdo6IP3L6UFVzy93i+xpS3X2XmeNNNN2nRokUaPHiwpkyZom7duqlOnTqKjY3VJ598ohEjRgR9RalAnkNfXnzxRUnSrl27fK5yFB8fr5ycHL333ns699xzA56Dv2OLiopkWZYuuuiicsd17drV9f/Dhw/Xb7/9pg8++EAfffSRlixZoueff17PP/+8zj33XM2bN8819rzzztNxxx2nd999V5988omWLFmiJ554Qk888YRuuOEG3X///X4/prIE+2ddGfHx8br44ot133336bXXXtPFF1+sZ555RlLZF2kVufnmmzV06NByxxQVFUkq+Va/vCwmX8Gp8t7bUsWvnWC9F4qLiyu9jzFG5557rjZs2KArr7xSV155pdq1a6e0tDRZlqVbb71V06ZNC+prIpDnI1jPUXUJ9vzKOp5zu6/7K3odlnfciu5z53yvNG3a1Gc2jbv69et7bfNnnu7atWunzMxM7d27V6tXr1aPHj383tc515NOOkmNGjUqc1ynTp0qNafKGjdunG655RbNmTNHt99+u/766y99/PHHateunYYPH17p4wXyfqzsa7Ss8aH8+wCgdiD4BAB+OuWUUzRr1iy9/vrruv/++73KDGpS/fr1lZCQoC1btigvL0/JycleY5zfMjdt2rTa5vH2228rNjZW7733njIzM233/fHHH9V23qrKz893lQVu27ZN27ZtK3Psiy++6Ao+Ob/R/e233yo8R2XGSiXfLv/+++969NFHlZGR4dc+UklW0/nnn+8qv/zuu+90zjnn6LXXXtP48eN10kknucY2bNhQl112mS677DIZY/Txxx9r9OjReuCBBzR+/Hh16dLFr3Nu3LhR3bt399qelZUlSWrWrJnf8w+mK664QtOnT9fTTz+tc845R6+++qoyMzOrtdyjRYsWWrdunW6//fZKXdzWtPj4eJ/L2EulWXqVsXbtWq1du1a9e/fWE0884XV/MN//ztfTn3/+6fP+mvhdV9tV9Bw5M3lC+Rw5M2iaNGlS6QyxQMTExGjEiBF67bXX9PLLL1fq/emc65VXXlnpcr9gqlOnjkaPHq3nnntOCxcu1Lfffqvi4mJddtllZQZ5du/erX379vnMfnL+jq6O14HzmM5zeArk9wyAyELPJwDw04knnqiuXbvqr7/+0tSpU8sdm5OTo9WrV1fbXOLj49W3b18ZY/TKK6943b9q1SotX75c6enpOuKII6ptHrt371Z6erpX4EmSXnvtNZ/7OPueFBYWVtu8KvLBBx9o9+7d6tOnj0zJ4hte/3JycpSUlKSPP/7YVVpx3HHHSZKeeeaZCr/FdfbO+t///qelS5dWOCfnsd95550qPba+ffu6+rs4++74YlmWTjzxRJ1yyimSSl4z/nLPqHJau3atli1bpvT09GoJwsTHx1f4mmnbtq1OOOEEffvtt7rjjju0b98+XXjhhT6Ds8ESrJ9bdWvatKl27typXbt2ed33ySef+NynvPfq7t27Jfkuydm9e7erZ05ljlmWQYMGSSp53TkzUtw5e7M5x0UjZ/bmhx9+qD179njdXxueoxYtWqhjx45asWJFmUGyYJs8ebIsy9Kjjz6qNWvWlDvW2bNIqpn3tb/vhQkTJkiSnnrqKc2ZM0dxcXEaP358ufv4+h398ccfa/fu3Tr88MPLzeYKVP/+/SVJ8+fP14EDB7zuf+ONN4J+TgDhheATAPjJsizNnTtXSUlJuvvuu3XLLbcoNzfXNsYYo/fee0+9e/eu1kakknTNNddIku666y5blsG+fft09dVXyxijCRMm+GxyGywdOnTQnj17vD7oPvzww/r888997uP8hn7dunXVNq+KOEvuzjvvvDLHpKen6+STT1ZBQYFeffVVSdLIkSPVoUMHLV++XDfffLPXRcOyZcv0119/SSq5sLjuuutkjNGll17q9a3vrl279PXXX7tuX3/99UpOTtZ1112n999/32s+u3bt0uOPP668vDxJJd8uP/fcc14f8vPz813PvbPHxs8//6y33npLBQUFtrG7d+/W999/bxvrj//85z/6+eefXbdzc3N1zTXXyBijSy65pML+MYFo1qyZtm7d6vPC2p2z8fjMmTMlBdZovDImTJighg0b6t///reeffZZr6Bkbm6uXnjhBdfrIlSGDBkiSfrXv/7l2maM0bRp02wX3e7Ke68edthhiomJ0aJFi2zNoQ8ePKgrr7zSZ5CromOWZejQoerevbv+/PNP3Xnnnbbn+J133tFbb72ltLS0Ci/I/fH222+rU6dOFZa/1jbt2rXTKaecon379unaa6+1vde//fZbPfHEE4qNjdU///nPEM5Suv3221VUVKRRo0b5DHj//vvvmjNnTtDOd8wxx+jGG29UXl6ehg8frvnz53uN2bt3r+666y4NGzbMte3ss8929fCaPn261+9Oh8Oht956q9wAf0X8fS/07dtXRxxxhN566y1lZWXp1FNPrTBzacqUKbZebDt27NCNN94oSdX2Gjj88MM1bNgw7dq1S7feeqvtffrSSy9pyZIl1XJeAOGDsjsAqIQjjzxSn376qUaNGqX77rtPjz76qPr166fGjRtr7969+umnn7R161YlJSWV2XQzWM4++2xdccUV+u9//6tu3bpp+PDhSklJ0eLFi7V9+3b17dtX99xzT7XO4ZZbbtGFF16oMWPGaNasWWrRooWWL1+utWvX6rrrrtPDDz/stc9pp52mKVOm6Prrr9fChQtdTWGnT5/us89HsO3atUsLFixQTEyMVy8nT2PGjNFbb72luXPn6uqrr1ZcXJzefPNNHX/88br//vs1d+5c9e/fX4WFhVq3bp3WrFmjzz//3JUNcuutt+rnn3/WO++8o8MPP1yDBg1SgwYNlJWVpf/9738aPXq0BgwYIKnkg/vcuXN14YUX6vTTT1fHjh3VuXNnGWO0ceNG/fLLL3I4HDr//POVnJysXbt26eKLL9ZVV12l3r17q0WLFsrNzdU333yj7du36+ijj9bIkSMllZQljRo1SpmZmerdu7eaNGmiPXv26Msvv1ROTo7OOuusSjUdv/DCC3XMMcdo+PDhyszM1BdffKEtW7aoa9eu1faaO/300/XYY4/pqKOOUv/+/ZWUlKSOHTvqhhtusI077bTT1Lx5c23evFl9+vTRkUceWS3zcapbt67efvttnX766brkkkt0zz33qFu3bkpMTFRWVpbWrFmj3Nxc/fzzz2U2l64Jzgb6M2fO1OLFi9W+fXutXLlSmzZt0j//+U89/vjjXvucfvrpev75510N7J0Zjs8884waNWqkSy+9VE8//bSOOOIIDR8+XMnJyfryyy9VVFSk8ePH+yytOv3007VkyRIde+yxGjZsmFJTU9WgQQPdd999Zc7dsiy99NJLGjZsmP7973/r7bff1pFHHqmsrCx9/fXXiouL05w5cyps1u+PvXv3at26dQEda+3ateUGwE444QSfq5MGy1NPPaVBgwbphRde0JIlS9SvXz9t375dixcvVlFRkR588MGQl4ZeeOGFWrlype6//34deeSR6tmzp9q2baucnBxt3LhRa9eu1RFHHKFLLrkkaOecNm2a4uLiNG3aNJ1yyilq3bq1evbsqeTkZP3111/6/vvv5XA4dPjhh7v2iYuL09tvv60RI0bo5ptv1iOPPKIePXooIyNDmzZt0tq1a7Vnzx69/fbbPkuQ/XHCCScoKSlJDz/8sFatWqVmzZrJsizdcMMNXs3WJ0yY4AoaVdTDrlWrVurRo4e6du2qY489VvHx8Vq0aJH27NmjYcOG6eqrrw5ovv544okn1L9/fz3yyCP6+OOP1bNnT23YsEHfffed6/dMdX4hBqB2I/MJACppwIAB+u233zRjxgz16dNHK1as0Guvvaavv/5abdq00V133aX169fr2GOPrfa5PPXUU3rhhRfUs2dPLVmyRO+//74aNWqkqVOnatGiReU2QA6GCy64QB9++KH69u2rZcuWacGCBWrWrJkWLVpUZp+MXr16ae7cueratas++eQTzZ49W7Nnzw76qnhlefXVV+VwODRo0KAyVxNzOvXUU5Wenq7vv/9ev/76qySpW7duWrZsma6//nqlpqbq/fff15IlS5SYmOjV98cZrJozZ4769OmjH374Qe+8846ys7M1atQoVzmF08iRI7V8+XJNmDBBBQUFWrBggRYvXqz8/HxdcMEF+uCDD1wBgPbt22vGjBkaOnSosrKy9NZbb7leg48++qgWL17s+pDft29f3XvvverVq5fWrVun119/XT/99JN69Oih559/vswSybI89thjmjZtmjZu3Kh3331XlmXpqquu0pdffumzBDMYpk2bpquvvlqFhYWaN2+eZs+erQ8//NBrXGxsrAYPHiwp8EbjlTVgwACtXLnSlb22aNEiffLJJ8rJydGpp56qefPm+d1Pq7p07dpVixYt0tChQ/Xrr79q4cKFat++vb799lv16dPH5z4jR47Uww8/rBYtWuj99993vVednnjiCT344INq27atPvvsM3355Zc67rjj9NNPP5W5IuLEiRN1++23Ky0tTW+++aZmz57tyiwsT/fu3fW///1Pl19+ufbv36833nhD69at05lnnqmvv/66VizjvnXrVlfDf1//fvjhh2o9f/PmzfXjjz/q+uuvV1xcnN566y0tXbpUxx57rD7++GNNnjy5Ws/vr+nTp+uzzz7T6aefrr/++kvvvPOOfv75Z6WkpOiGG24IauaTVBK8vPfee7VixQpdddVVSklJ0WeffaY33nhDv//+u0aMGKG5c+d6lcp36tRJy5Yt0913361GjRrpq6++0ocffqjt27dr8ODBevbZZ13leYFo1qyZ3n33XfXt21dfffWV5syZo9mzZys7O9trrPPzRIsWLTRixIgKH+8bb7yhSZMmaeXKla6/G7fddps+/PDDau1X2bFjR33//fc6++yztWXLFr377ruSSkrdnWV5NfElE4DayTIsPQAAACLEgQMH1KxZMxUVFenvv/8uc8lxAAgX//73v3Xbbbfprrvu0t13313mOMuy1Lp1a1vJXW3xj3/8Q08++aReffVVjR49OtTTARACZD4BAICIMWvWLO3du1fjxo0j8AQg7OXk5Og///mPEhISdMUVV4R6OuU6ePCgz8bub775pp555hllZma6FrkAEH3o+QQAAMLazp07ddNNN2nLli1asGCB0tPTdcstt4R6WgAQsGeffVZLlizRF198oezsbF133XWuJuW11Z49e9SlSxd16tRJhx9+uOLj47VmzRqtWbNGMTExevzxx5WWlhbqaQIIETKfAABAWNu3b59mz56thQsX6qijjtIHH3xQYT8vAKjNlixZoueff1779+/XNddco2nTpoV6ShXKzMzU9ddfr4SEBH311Vd67733tHPnTp1xxhlasmRJtTbcB1D70fMJAAAAAAAA1YbMJwAAAAAAAFQbgk8AAAAAAACoNjQcr0bFxcWuZZ4tywr1dAAAAAAAAILCGKN9+/apWbNmiokpP7eJ4FM1+vvvv9WyZctQTwMAAAAAAKBabNq0SS1atCh3DMGnapSeni6p5AeRkZER4tkAAAAAAAAER05Ojlq2bOmKfZSH4FM1cpbaZWRkEHwCAAAAAAARx582QzQcBwAAAAAAQLUh+AQAAAAAAIBqQ/AJAAAAAAAA1YbgEwAAAAAAAKoNwScAAAAAAABUG4JPAAAAAAAAqDYEnwAAAAAAAFBt4kI9AQAAAAAAwkVhYaEKCwtDPQ0gqOLi4hQXV30hIoJPAAAAAABU4MCBA9qxY4dyc3NDPRWgWqSmpqpBgwZKSUkJ+rEJPgEAAAAAUA6Hw6FNmzYpPj5eTZs2VWJioizLCvW0gKAwxig/P1+7du3Spk2b1LZtWyUkJAT1HASfAAAAAAAox7Zt2xQbG6vWrVsrNjY21NMBgi45OVnp6en6888/tW3bNrVo0SKox6fhOAAAAAAAZTDG6MCBA8rMzCTwhIgWGxurzMxMHThwQMaYoB6b4BMAAAAAAGUoKChQUVGRkpOTQz0VoNolJyerqKhIBQUFQT0uwScAAAAAAMpQXFwsSWQ9ISo4X+fO132wEHwCAAAAAKACNBhHNKiu1znBJwAAAAAAAFQbgk8AAAAAAACoNgSfAAAAAAAAIszdd98ty7J09913h3oqBJ8AAAAAAEDVtGnTRpZlybIsXX/99eWOfeSRR1xj6aUVHQg+AQAAAACAoHn55ZdVVFRU5v1z586twdmgNiD4BAAAAAAAgqJjx47asmWLPv30U5/3r1u3Tj/99JM6duxYwzNDKBF8AgAAAAAAQXHhhRdKKju76cUXX5QkjR07tsbmhNAj+AQAAAAAAIJiyJAhatmypd5++23l5uba7jPG6KWXXlJycrJGjhxZ5jGMMXr11Vd1/PHHq379+kpMTFS7du00ceJEbdmyxec+Cxcu1NVXX60jjjhC9erVU1JSktq3b69//OMfysrK8rlPYWGhHnnkER199NFKT09XYmKimjVrpv79++uuu+7Snj17XGM3bNggy7LUpk2bMuddVg8r9+1vvvmmBg8erDp16siyLG3YsME1bteuXbrtttvUrVs3paamKj09XX379tXTTz+t4uLiMh/D/fffr06dOikpKUnNmzfX5Zdfrq1bt5Y5z1Ag+AQAAAAAAILCsixdcMEFys3N1dtvv22776uvvtKGDRt05plnKj093ef+BQUFGj16tM477zx9+umnSkpKUufOnbV161Y99thjOuqoo/Trr7967XfSSSfp8ccf15YtW9S6dWsdfvjh2rp1q5588kkdddRR+uWXX7z2GTNmjCZNmqQff/xRjRs31hFHHKG4uDj98MMPmjJlii0wFAzTp0/X2WefrV9//VUdOnRQw4YNXfetXr1aPXr00L///W+tX79ebdq0UePGjfXDDz/oiiuu0OjRo2WMsR2vqKhII0eO1E033aR169apdevWatiwoZ599lkdffTR2rlzZ1DnXxUEnwAAAAAAQNA4S+qcJXZO/pTc3XnnnXr99dfVs2dP/fzzz9q8ebOWLVumHTt26J///Keys7N1wQUXeO33+OOP66+//tLWrVv1888/a+XKldq+fbumTp2qnTt36qqrrrKNX7p0qd588021bNlSv/zyi3777Tf98MMPysrK0q5du/T000+rfv36VX0qvB7bf//7X2VnZ+uHH37Q33//rRYtWig3N1dnnHGGNm/erIkTJ2r79u1avXq1fvvtN61atUpdu3bVG2+8occff9x2vFmzZun9999X3bp19eWXX2rdunVatmyZfvvtN2VkZOipp54K6vyrIi7UEwAAAAAAIByZwkKZA7kVD6zFrJRUWXHBDQ106dJFPXv21Geffabs7Gw1bdpU+fn5ev3119WoUSMdf/zxPsvntm/frocfflgZGRl677331KJFC9d9ycnJeuyxx/Tjjz/qxx9/1JdffqlBgwa57r/iiiu8jpecnKxbb71VCxYs0OLFi7V582Y1b95ckrR+/XpJ0tlnn63OnTvb9svIyNBll10WlOfC3YQJE3T55Ze7bscdet6feOIJ/f777zrrrLP0yCOP2Pbp0qWLXn75ZR155JF66KGHXEE0Y4wefPBBSdK9996rgQMHuvZp06aNnn/+efXq1SvojyFQBJ8AAAAAAAiAOZCrotUrQj2NKont2kNWRmbQjzt27FhNnjxZr7zyiiZPnqwPPvhAe/bs0bXXXusKuniaP3++8vPzdfrpp9sCT04xMTE69dRT9eOPP2rJkiW24JMk/fTTT3rjjTf0yy+/aO/evSoqKpJUGmhasWKFK/jUsmVLSdJnn32mXbt2qV69ekF77GW56KKLfG5/6623JKnMgFePHj3Upk0b/fHHH/rrr7/UokULrVmzRllZWUpKStL48eO99jnqqKPUt29ffffdd0Gbf1UQfAIAAEDUKsraoOJtWxTToJFi27QL9XQAIGKcd955uuGGG/Tiiy9q8uTJrpI752p4vqxcuVKS9N1339kyedw5G2lv3rzZtc0Yo6uvvtqrLM3Trl27XP/fr18/HXPMMfr+++/VsmVLHX/88Ro8eLCGDBmio446ymfj8KryzLBycj7uO++8U//+9799jtmxY4ekksfdokULV9+r1q1bKyUlpczzEXwCAAAAQsjkHVDx5k2SpOLszYpp2EhWalqIZwUAkaFJkyY67rjj9PHHH+uLL77QggUL1KlTJ/Xu3bvMffbu3StJ2rRpkzZt2lTu8fPy8lz//+KLL+rxxx9XamqqHnjgAR1//PFq3ry5kpOTJZUEvF566SUVFBS49omJidGCBQt0zz33aO7cuXr33Xf17rvvSioJ6Nx9990+M4qqIjU11ed25+NeunRphcdwPu79+/dLkq1puafGjRtXdorVhuATAAAAopLJO2C/fSCX4BOASrFSUhXbtUeop1ElVorvgEgwjB07Vh9//LHGjh0rh8NRbqNxSUpLK/kdfNttt+nee+/1+zwvvfSSJOnBBx/UhAkTvO4vK5BVt25dzZw5Uw8//LCWL1+uL774Qu+8844+//xzXXzxxUpLS9PZZ58tSa5MKM8V55xycwPv/ZWWlqY9e/Zo/fr1Ouyww/zeRyrpk1WWbdu2BTynYCP4BAAAgKhk8vPttw/mlTESAHyz4uKqpV9SpDjrrLOUlpamrKwsWZblc5U6d126dJEkrVq1qlLn2bBhgySpf//+XvcVFBRozZo15e5vWZaOPPJIHXnkkZo4caJuueUW3XfffXr66addwSdn1lJZwZ7ffvutUnN216VLF33zzTdatWqV38GnDh06SJKysrJ04MABn6V3FT3umhQT6gkAAAAAIeHwCD55BKMAAFWTkpKi66+/Xscee6wmTJig1q1blzv+lFNOUUJCgubPn+9qEu4PZ3mdsx+Uu2effbbc7CBf+vbtK0n6+++/Xdvq16+vzMxM5eXlafXq1V77PPPMM5U6h7uRI0dKkh599NEyM6s8derUSS1btlReXp5eeOEFr/uXLVumb7/9NuA5BRvBJwAAAEQlr2DTwYOhmQgARLC7775bn376qZ544okKxzZr1kyTJk1SQUGBRowYocWLF9vuN8bohx9+0D/+8Q/98ccfru3O5uS33367LdD00Ucf6YYbblBSUpLXuV566SX961//cmVNOe3cuVOPPvqopJIV45wsy9KIESMkSZMnT3b1XJKk559/XnPmzKnw8ZVlwoQJateunT7//HNdcMEFys7Ott2/f/9+vfbaa5o8ebJrW0xMjOv2bbfdpm+++cZ138aNGzVu3DjFx8cHPKdgI/gEAACA6OSV+UTwCQBCberUqbrwwgv1559/atiwYWratKmOOeYYHXnkkcrMzNQxxxyjJ598Ug6Hw7XPjTfeqHr16un7779X69at1bNnT7Vt21YnnXSSevXqpVGjRnmdZ/v27brzzjvVtm1btWjRQkcffbS6d++uZs2aadGiRWrevLn+9a9/2fa55557lJaWpk8++URNmjRRr1691KxZM40fP14PPvhgwI85LS1NH374odq2batXXnlFLVq0UJcuXdS3b1917NhRderU0ejRo20BJkm65pprdPLJJ2vXrl0aMGCAOnfurJ49e+qwww7Tnj17fPa/ChWCTwAAAIhKXplPDodMcVFoJgMAkCTFxcXpxRdf1IcffqgzzzxTkvTzzz8rOztbHTp00NVXX63Fixe7eh5JUqtWrfTtt99q5MiRSkhI0Nq1a5WUlKR77rlHH330keLivNtdjxo1StOnT9fxxx+v2NhYrVy5UtnZ2erWrZvuvfderVq1Sq1atbLt06lTJ33xxRc68cQTFRMTo3Xr1qlt27Z6//33deWVV1bpcXfq1EnLly/Xfffdpz59+mjz5s1atmyZHA6HhgwZohkzZujVV1+17RMbG6t33nlH06ZNU4cOHfTHH39o69atGjdunH744QfVr1+/SnMKJsv4W1CISsvJyVFmZqb27t2rjIyMUE8HAAAAhxhTrMLvv5Y8PgnHHdFLlo+mrQCi18GDB/Xnn3+qbdu2Psu3gEhSmdd7ZWIeZD4BAAAg+jgcXoEnidI7AACqA8EnAAAARJ2yVrYj+AQAQPARfAIAAED0cfgOPrHiHQAAwUfwCQAAAFGHzCcAAGoOwScAAABEn7Iynwg+AQAQdASfAAAAEHVMGcEnk39QLAYNAEBwEXwCAABA9Cmj7E6FRVJhYc3OBQCACEfwCQAAAFHHlvmUmGi/j9I7AACCiuATAAAAooopKpIKSrObrIxM+wCCTwAABBXBJwAAAEQXj35PMR7BJ3OQ4BMAAMFE8AkAAABRxXj2e0pKluLjS2+T+QQAQFARfAIAAEB08ch8shITZSUluW6T+QQAQHARfAIAAEBUsTUbtyTFJ0iJbsEnMp8AAAgqgk8AAACIKrayu/gEWTExstyCT3LkyxhT8xMDACBCEXwCAABAdHHLfLISE0v+61Z2p2LjVZoHAAACR/AJAAAAUcWW+ZRQEnxyL7uT6PsEAAiOxYsXy7IsDR06NNRTCSmCTwAAAIgaxpiKM58kVrwDgABkZWVp8uTJ6tatm1JTU5WcnKxWrVqpf//+uuGGG/Txxx+HeopBN3PmTN19993as2dPqKdSq8WFegIAAABAjSkqlIqKS287M58SEiTLkg71eqLpOABUzqJFi3TmmWdq3759io2NVcuWLdWoUSPt2rVL3333nb799ls9++yz2rFjR6inGlQzZ87Uxo0bNX78eNWpUyfU06m1CD4BAAAgeuTbezm5Mp+sGCkxUTpUbkfZHQD4LycnR6NHj9a+fft0yimnaNasWWrdurXr/j179ujdd9/Va6+9FsJZIpQouwMAAEDUMJ6NxJ2ZT5J9xTsynwDAb/Pnz9eOHTuUkZGh1157zRZ4kqQ6depo3Lhx+vDDD0M0Q4QawScAAABEDVNG5pNk7/tE2R0A+O+PP/6QJHXo0EEpKSl+79emTRtZlqUNGzZoyZIlOu6441SnTh3Vq1dPZ511ltavX+8a+95772nQoEHKyMhQ3bp1dd555+nvv/8u89irV6/W2LFj1aJFCyUkJKhx48YaNWqUvvvuuzL3McZo7ty5GjJkiOrUqaPk5GR16tRJN910k3bt2mUb+9xzz8myLG3cuFGS1LZtW1mW5fq3ePFir+MXFxfrkUceUbdu3ZSUlKTGjRvr0ksv1fbt2/1+zsIVwScAAABED/fMpxhLiosvve2e+eQokCkqqrl5AUAYy8jIkCStX78+oMbbb7/9to499litXLlS7du3l8Ph0DvvvKMhQ4Zoy5Ytevjhh3XGGWdow4YNateunfLy8vTqq69q+PDhOuijTPq9995Tr169NHfuXOXm5uqII46QMUZvvfWWBgwYoKefftprH2OMLrzwQo0dO1ZffPGF6tevry5duujPP//U/fffr6OOOsoVZJOkxo0ba8CAAUo89CVG7969NWDAANe/zMxMr3OMHTtWkyZNksPh0GGHHaZdu3Zpzpw5GjZsmPI9vhyJNASfAAAAEDVsmU8JibIsy3XTVnYnefWHAgD4dsIJJygmJkZ79+7VcccdpzfffFN79+71e/+bbrpJ999/v7Kzs7V06VL99ddf6tu3r7Kzs3XZZZfp9ttv10svvaRNmzZp2bJlWr9+vdq1a6d169bp2WeftR3r77//1tixY5Wfn69rr71WW7du1Y8//qgtW7Zo6tSpKi4u1lVXXaUVK1bY9ps1a5Zefvllpaen65NPPtHvv/+upUuXauPGjRowYIA2btyo888/3zX+pJNO0ldffaUmTZpIkl5//XV99dVXrn89e/a0Hf+bb77R4sWL9f333+vXX3/VqlWrtHr1arVo0UKrV6/2ehyRhuATAAAAoofD4fpf95I7SVKSPfhk8vNqYkYAEPY6dOigf/3rX5KkpUuX6uyzz1bdunXVqVMnXXzxxZo3b165mT0nn3yyJk+erJiYkhBFnTp1dM8990iSPvzwQ11++eW2wE/Lli114403SpI++ugj27Eef/xx5eTk6Mgjj9TMmTOVkJAgSYqJidGtt96qk08+WQUFBZoxY4ZrH2OM7r//fknSlClTdPzxx7vua9KkiebNm6eEhAR9//33WrRoUUDPUUFBgR577DEdffTRrm0dOnRwPY4FCxYEdNxwwWp3AAAAiBq2Xk4J9uCTZ+YTK94BqEieI19bdod3v54mdRsq2eP3YSBuvfVW9evXTzNmzNCnn34qh8OhdevWad26dXruuefUqlUrPf/88xo6dKjXvpdeeqnXtiOPPLLc+52ZRe6lcJL0ySefSJKuvvpqn/O89tprNX/+fNc4SVqzZo02bdqkpKQkXX755V77NG/eXKNGjdIrr7yiTz75RMOHD/d57PLUrVtXI0eO9Nrep08fn48j0hB8AgAAQFQwxkgFbplPnhdbcXFSbIxUVFxym6bjACqwZfd2Pf7Ry6GeRpX888Tz1bZxi6Aca9iwYRo2bJjy8vL0008/6fvvv9f8+fO1ePFiZWVl6eSTT9b//vc/derUybZf+/btvY7VsGFDv+7fv3+/bfuvv/4qSerSpYvPOXbt2lWStHXrVuXk5CgjI8O1T6tWrZSamlrufs6xleXrMUhSo0aNJHk/jkhD2R0AAACiQ4FDKjaum55ld5Zl2ZqOs+IdAAQmOTlZgwYN0v/93/9p0aJF+uKLL5Samqq8vDw9+OCDXuN9rZDn3pOvvPuNMbbtziCOM6jjqXHjxq7/37dvn1/7uO/n3KeyygpqOUsNPR9HpCH4BAAAgKhgPPuNePZ8kmS59X2i7A4AgmPgwIH65z//KUn64YcfqvVcaWlpkqRt27b5vH/r1q2u/09PT/drH/f9nPugcsKi7O7xxx/XAw88oOzsbHXt2lUzZ87UoEGDyhy/ZMkSTZ48WatXr1azZs1044036sorr3Tdv3r1at15552uzvUPP/ywJk2aZDvGtGnT9NZbb2nt2rVKTk5W//79NX36dHXs2LG6HiYAAACqk8MefPIqu1NJ3yfXd8/5B2WMsX37DgDumtRtqH+eeH7FA2uxJnUbVjwoCNq1aydJcrgt/FAdOnTooB9//FG//PKL+vXr53X/6tWrJZVkMmVkZLj2kaSsrCzt37/fFYzytZ9zrBN/I/xT64NP8+bN06RJk/T4449rwIABeuqpp3TSSSfpl19+UatWrbzG//nnnzr55JN1+eWXa+7cufr666/1z3/+Uw0bNtSoUaMkSQcOHFC7du10zjnn6LrrrvN53iVLluiqq65Snz59VFhYqNtuu00nnHCCfvnllzLT5QAAAFB7eWU++Wqw6950vKhYKiyU4uOrd2IAwlZyQmLQ+iWFsx07dqh+/frlBmK++eYbSdLhhx9erXMZMWKEfvzxR/3nP//x2aj80UcfdY1z6ty5s1q1aqWsrCw988wzXskpf//9t958802v/aSSEkNJystjhdTy1Pqyu4ceekiXXnqpLrvsMnXu3FkzZ85Uy5Yt9cQTT/gc/+STT6pVq1aaOXOmOnfurMsuu0yXXHKJbRnFPn366IEHHtCYMWOU6CPdWipZrnH8+PHq2rWrjjjiCD377LPKysrS0qVLq+VxAgAAoJq5Zz7FxcqK8/4e1r3sTqLvEwD4Y+7cuTryyCP19NNPa+fOnbb79uzZozvvvFNz586VJF188cXVOpd//OMfysjI0LJly3Tddde5Mq2Ki4t1//3368MPP1R8fLyuv/561z6WZemGG26QJN1111367LPPXPdt3bpVY8aMkcPhUN++fTVs2DDb+ZwZXUuWLKnWxxXuanXwyeFwaOnSpTrhhBNs20844QRX1NTTt99+6zV+xIgR+umnn1RQUBDwXPbu3StJqlevXsDHAAAAQOgY9+BTGcuKW4nJ9g30fQKAClmWpRUrVuiKK65QgwYN1K5dOx1zzDHq0KGDGjdurH/9618yxuj//u//dNZZZ1XrXJo1a6YXX3xRCQkJmjlzppo0aaKjjz5aTZs21U033aSYmBj95z//UY8ePWz7XXXVVTr//POVk5Oj4447Tocffrh69eqlVq1a6csvv1SrVq300ksveZ1v9OjRkkqCXt27d9fQoUM1dOhQLVu2rFofZ7ip1WV3O3bsUFFRka0bvVRSm7llyxaf+2zZssXn+MLCQu3YsUNNmzat9DyMMZo8ebIGDhyobt26lTkuPz9f+W7p3Dk5OZU+FwAAAKqJ2+c0X/2eJHk1ISfzCQAq9s9//lM9evTQggUL9PXXX+uvv/7SsmXLFBcXp9atW6tfv366/PLLNXDgwBqZz+mnn66lS5fqvvvu06JFi7Rs2TLVqVNHZ511lm644QafvaAsy9LcuXN14okn6umnn9by5cu1adMmtW7dWmeeeaZuuukm1a9f32u/sWPHavfu3Zo9e7bWr1+vVatWSSrJ+EKpWh18cvKsG62o8aOv8b62++vqq6/WihUr9NVXX5U7btq0abrnnnsCOgcAAACql3vmk1VG6wUrNlZKiJcchzLmCT4BQIXi4+M1bNgwr5K0imzYsKHc+53X8r60adOm3Pu7devmKvXzl2VZGjt2rMaOHVup/SZOnKiJEyf6vG/o0KFVehyRolaX3TVo0ECxsbFeWU7btm3zym5yatKkic/xcXFxPqOUFbnmmmv03nvv6fPPP1eLFuU3krvlllu0d+9e179NmzZV+nwAAAAIPlNcXBpQksosu5NKVrxz7UfZHQAAVVarg08JCQnq1auXFi5caNu+cOFC9e/f3+c+/fr18xr/ySefqHfv3oqvxEolxhhdffXVeuutt7Ro0SK1bdu2wn0SExOVkZFh+wcAAIBawGFf6a6szCdJthXvKLsDAKDqan3Z3eTJkzV27Fj17t1b/fr103//+19lZWXpyiuvlFSSbbR582a98MILkqQrr7xS//nPfzR58mRdfvnl+vbbbzV79my98sorrmM6HA798ssvrv/fvHmzli1bprS0NB122GGSSpqNvfzyy3r33XeVnp7uyqbKzMx0LaUIAACA8GA8gk/lZj4lJclVAJGfL2OKZVm1+jtbAABqtVoffBo9erR27typKVOmKDs7W926ddP8+fPVunVrSVJ2draysrJc49u2bav58+fruuuu06xZs9SsWTM9+uijGjVqlGvM33//rZ49e7puz5gxQzNmzNCQIUO0ePFiSdITTzwhqaQ+092zzz6r8ePHV8+DBQAAQPXI9z/zyb3sTsZIDoctGwoAAFSOZaKhs1WI5OTkKDMzU3v37qUEDwAAIISKNmepOGuj63bcMf1lxcT6HFu8d4+Kflnpuh3bpbtiMutU9xQB1FIHDx7Un3/+qbZt2yopiUA0IltlXu+ViXmQPwwAAIDI5575FB9fZuBJKim7s+9L3ycAAKqC4BMAAAAinnE4XP9fbrNxqaQflGWV7suKdwAAVAnBJwAAAEQ826p15TQblyTLsiS3ABUr3gEAUDUEnwAAABD53Fa7qzDzSR5Nxwk+AQBQJQSfAAAAENFMUaFUWFS6oYLMJ8ne94myOwCSxFpdiAbV9Ton+AQAAIDIlu+w3fQn80numU8FBTJFRWWPBRDRYmNLFigoKCgI8UyA6ud8nTtf98FC8AkAAAARzTg8MpcqmfkkidI7IIrFx8crMTFRe/fuJfsJEc0Yo7179yoxMVHx8fFBPXZcUI8GAAAA1Db5+bablc58UknTcSslNZizAhBGGjRooM2bN+uvv/5SZmam4uPjSxYnACKAMUYFBQXau3ev9u/fr+bNmwf9HASfAAAAENGMW7NxWZYUn1DhPpZn8Im+T0BUy8jIkCTt2LFDmzdvDvFsgOqRmJio5s2bu17vwUTwCQAAABHNuGc+JST4l60QFyfFxZY2KqfsDoh6GRkZysjIUEFBgYroA4cIExsbG/RSO3cEnwAAABDZ3DKf/Cq5k2RZlqzEJJnCXEklZXcAIJX0gKrOi3QgEtFwHAAAABHNVnbnR7NxF7fSO8ruAAAIHMEnAAAARCxjjJTvcN22KhF8svV9yj/IKlcAAASI4BMAAAAiV2GBVFxcetvPsjtJUpJb8KmouORYAACg0gg+AQAAIHI5HLabAWc+idI7AAACRfAJAAAAEcu20p38bzheMtYefGLFOwAAAkPwCQAAABHLa5W6yjQcT7KPZcU7AAACQ/AJAAAAkct9pbuYGCkuzu9drZhYKSGhdANldwAABITgEwAAACKWce/5lJgoy7Iqtb976R2ZTwAABIbgEwAAACKXW8+nyjQbd0ki+AQAQFURfAIAAEDEMg63gFElmo072RqU5+fLmOIgzAoAgOhC8AkAAAARyZhiya3sLpDMJysp2e2AkvIdZY4FAAC+EXwCAABAZHIUlASMDrECyHzyzJai9A4AgMoj+AQAAICIZNxXupOkQDKfEpPtGwg+AQBQaQSfAAAAEJny7cGngDKfEhKkmNIV8sxBgk8AAFQWwScAAABEpKBkPlmWrfSOsjsAACqP4BMAAAAik3vwKS5OVmxsQIexEpNKbxB8AgCg0gg+AQAAICIZt7K7gEruXPuWBp8ouwMAoPIIPgEAACAyuWcpBVBy5+Ke+VRQIFNUFPixAACIQgSfAAAAEJGMw+H6/yplPiUl2TeQ/QQAQKUQfAIAAEDEMcVFUkFB6YYqZD7Zej6JpuMAAFQWwScAAABEnnyH7aYVrLI7EXwCAKCyCD4BAAAg4hj3le4kKTEh8IPFxUlxbivlEXwCAKBSCD4BAAAg8ngEnzxL5yrDsixWvAMAoAoIPgEAACDimHy34JMlKb4KmU+S5NZ0nLI7AAAqh+ATAAAAIo975lN8gqyYqn3stWVO5R+UMaZKxwMAIJoQfAIAAEDEcc98shKr0GzcyT34VFRsX0kPAACUi+ATAAAAIo5xuJXGVWWlu0OsJFa8AwAgUASfAAAAEFGMMZJ75lNCFfs9yUfDcoJPAAD4jeATAAAAIktRUUlpnFMVVrorPYY9e4rMJwAA/EfwCQAAAJHFfaU7SVYwyu5iYiW3DCpzkOATAAD+IvgEAACAiGIc9uCTZ9ZSoGx9n8h8AgDAbwSfAAAAEFE8g0/ByHySZCvfI/MJAAD/EXwCAABAZHEvu4uxpPj4oBzW1nTckS9TXFz2YAAA4ELwCQAAABHFlvmUkCjLsoJyXFvZnZHkWd4HAAB8IvgEAACAyOKW+RS0kjvJa9U8Su8AAPAPwScAAABEFFvmU5CajUseZXcSTccBAPATwScAAABEDGOMrRwuqJlPCQklPaSc5yL4BACAXwg+AQAAIHIUFEjFpvR2MDOfLMu+4h3BJwAA/ELwCQAAABHDeDQBtxISgnp8W+kdPZ8AAPALwScAAABEjnzP4FNSGQMD477iHZlPAAD4h+ATAAAAIoZn5lMwy+5KjucWzCoolCkqDO7xAQCIQASfAAAAEDncM59iY6TY2KAe3mvFu4P5vgcCAAAXgk8AAACIGLbMp8TEkibhQeRedidRegcAgD8IPgEAACByuAWfgt3vSZK97E4EnwAA8AfBJwAAAEQMk+8efAruSneSZMXFSXFupXwEnwAAqBDBJwAAAEQEU1wsFThKN3j2ZwoS975P5iDBJwAAKkLwCQAAAJGhwCGZ0ptWsFe6c3Lr+2Ty86rnHAAARBCCTwAAAIgI7iV3kqRqKLuTPFa8O5gvY0zZgwEAAMEnAAAARAiP4FO1NByX7OV8xcVSQUH1nAcAgAhB8AkAAAARwTg8Mp8SqynzKYkV7wAAqAyCTwAAAIgM7sGn+DhZMbFlj60Cy7OROU3HAQAoF8EnAAAARAT3nk9WQjU1G5dKyu4s9/MSfAIAoDwEnwAAABAZ3DOfqmulO0lWTIytmTnBJwAAykfwCQAAABHBveeTV2lckNmOT/AJAIByEXwCAABA2DNFRVJBYemG6iy7k2wr3hl6PgEAUC6CTwAAAAh/HivdWQnVs9Kd6/juK9458mWKi6v1fAAAhDOCTwAAAAh77s3GJdkyk6qDrezOyCv4BQAAShF8AgAAQPjzynyqubI7idI7AADKQ/AJAAAAYc+W+WRJSoiv1vPZyu4kmo4DAFAOgk8AAAAIe+4r3SkhQZZVzR9z4xOkGKv0/ASfAAAoE8EnAAAAhD+3zCcroXr7PUmSZVmseAcAgJ8IPgEAACDs2TKfEqt3pTsnW9NxMp8AACgTwScAAACENWOMR+ZTNTcbd57Hre8TZXcAAJSN4BMAAADCW2GhVFxceruGgk+2Fe8KCmUKC2vmvAAAhBmCTwAAAAhv7iV3kqzEGsp8SmTFOwAA/EHwCQAAAGHN5HsGn6q/4bhkL7srmQfBJwAAfCH4BAAAgLBmPDKflFAzDcflEeRixTsAAHwj+AQAAIDw5h58iomR4uJr5LRWXJwUH1e6gcwnAAB8IvgEAACAsGYru0tMkGVZNXZu9xI/yu4AAPCN4BMAAADCm1vmk1VTK905uQefKLsDAMAngk8AAAAIa7bMpxoOPtmam+fnyxhTo+cHACAcEHwCAABA2DLG2DOfEms488l9xbviYqnAUbPnBwAgDBB8AgAAQPgqcEhuyUY1XXZnea54l59fxkgAAKIXwScAAACELa9gTw1nPnkGn3Qwr0bPDwBAOCD4BAAAgPDlsAefrISkMgZWk8REyW1xPTKfAADwRvAJAAAAYcs78ymhRs9vxcRICaXnNGQ+AQDgheATAAAAwpd75lNcrKzYuBqfgpWYXHqDzCcAALwQfAIAAEDYsmU+1XCzcRe3PlMmn8wnAAA8EXwCAABA+HLLfLJquNm467xJbplPDodMcXFI5gEAQG1F8AkAAABhyz3zyQpR5pMt6GVE6R0AAB4IPgEAACAsmeJiqaCgdEOIMp/knvkkyeQfDM08AACopQg+AQAAIDw57BlGtSLzSQSfAADwRPAJAIAwZ4qLVbxzu4pz9oR6KkCNMp7lbaHKfIpPkGLcPlYfJPgEAIC7ml+LFgAABFXxxj9UvCW75EbHLoqpVz+0EwJqSm3JfLIsKSlROlCy0h2ZTwAA2JH5BABAmCveub30/7dvCeFMgJplPIJPClHwSZKsxKTSGwSfAACwIfgEAEAYM0VFUkFh6e3c/SGcDVDD3MvuEuJlxYTuo6178MlQdgcAgA3BJwAAwplnhkW+Q8bhCM1cgBrm3vMpVCV3Lu6ZT4WFMoWFZY8FACDKBCX4tGnTJr388st64IEHNGXKFNt9BQUFclTxQ/Djjz+utm3bKikpSb169dKXX35Z7vglS5aoV69eSkpKUrt27fTkk0/a7l+9erVGjRqlNm3ayLIszZw5MyjnBQCgpvnqLUP2E6KFrewuVM3GD7GSkuwbKL0DAMClSsGnHTt2aPTo0Wrbtq3Gjh2rm2++Wffcc49tzMUXX6zk5GQtXbo0oHPMmzdPkyZN0m233aaff/5ZgwYN0kknnaSsrCyf4//880+dfPLJGjRokH7++Wfdeuutmjhxot58803XmAMHDqhdu3a677771KRJk6CcFwCAUPBV3kPwCVHDUXsyn2w9n0TpHQAA7gIOPu3bt09DhgzR66+/rubNm2v8+PFq3ry517jLLrtMxhi99dZbAZ3noYce0qWXXqrLLrtMnTt31syZM9WyZUs98cQTPsc/+eSTatWqlWbOnKnOnTvrsssu0yWXXKIZM2a4xvTp00cPPPCAxowZo8QyviWr7HkBAAgJz6XmJZn9+0IwEaBmmcJCqbCodENtKrsTK94BAOAu4ODT/fffrzVr1mjUqFFau3atZs+erdatW3uNGzx4sJKTk/X5559X+hwOh0NLly7VCSecYNt+wgkn6JtvvvG5z7fffus1fsSIEfrpp59UUFBQbeeVpPz8fOXk5Nj+AQBQnSi7Q9TyWOnOCnXZXVycFB9XuoHgEwAALgEHn9544w0lJibqmWeeUXJyctkniInRYYcdFlC52o4dO1RUVKTGjRvbtjdu3FhbtvheSnrLli0+xxcWFmrHjh3Vdl5JmjZtmjIzM13/WrZs6df5AAAImK8LXIfDewl6IMJ4vcZDnfkkVrwDAKAsAQefNmzYoA4dOigzM7PCsSkpKX4HfnyxLMt22xjjta2i8b62B/u8t9xyi/bu3ev6t2nTpkqdDwCAyiqrtIfsJ0S8/NqV+STJVnpH2R0AAKXiKh7iW1JSkvbt86+nRHZ2tl9BKk8NGjRQbGysV7bRtm3bvLKSnJo0aeJzfFxcnOrXr19t55WkxMTEMntIAQAQbF49b9zv279fquvf3z0gHNkyn2IsKT4hdJM5xEpKknHeyD9Y4ReXAABEi4Azn7p27apNmzZp48aN5Y5btmyZsrKy1KtXr0qfIyEhQb169dLChQtt2xcuXKj+/fv73Kdfv35e4z/55BP17t1b8fHx1XZeAABqXDmZFWQ+IdIZ98yn+IRaEeSxrXhXbKQCR+gmAwBALRJw8OnCCy9UUVGRrrjiCh04cMDnmN27d+vSSy+VZVm66KKLAjrP5MmT9cwzz2jOnDlas2aNrrvuOmVlZenKK6+UVFLq5n7sK6+8Uhs3btTkyZO1Zs0azZkzR7Nnz9b//d//ucY4HA4tW7ZMy5Ytk8Ph0ObNm7Vs2TL99ttvfp8XAIBQ8yrrSUkpvS93n6vsHIhIbplPtaLkTvJe8Y6+TwAASKpC2d3ll1+uV155RQsXLlT37t11zjnnaOvWrZKkOXPmaNWqVZo7d6527NihE044QWPGjAnoPKNHj9bOnTs1ZcoUZWdnq1u3bpo/f75rZb3s7GxbM/O2bdtq/vz5uu666zRr1iw1a9ZMjz76qEaNGuUa8/fff6tnz56u2zNmzNCMGTM0ZMgQLV682K/zAgAQasaj501MvQYqPnDob6KjoCTrohY0YQaqg+31X0te51aSPfhUkp1Y+dYTAABEGstU4WvRffv26YorrtC8efNkWZatsbfz/88991zNnj1bqampwZlxGMnJyVFmZqb27t2rjIyMUE8HABBhijb8ruLsv0tuxMUptmMXFa1e4bo/tmMXxdSj7xMijzFGhT98XVLaJimmeQvFtmob4llJpri4ZF6HPl3HtGyl2BZ8cQkAiEyViXkEnPkkSenp6XrllVd066236u2339bKlSu1d+9epaWlqUuXLjrrrLMC6vUEAAAq5l7SYyUmykpNkyy5LnxN7j6J4BMiUWGBK/AkqfZkPsXElMzlUFYWZXcAAJSoUvDJqXv37urevXswDgUAAPxk6/mUlCQrNlZKTpYO5JXcv5+m44hQHiWntabnkw6teOecXzmLAgAAEE0CbjgOAABCxxhju7C1Ekp6zVip6aVjcvfTdBwRyTg8gk+1JPNJkq3puNeiAAAARKmAg08xMTGKjY3V1VdfraKionLHDho0SHFxQUmyAgAAklRYKBUVl95Ocgaf0kq3FRRIDpZ6R+TxbLZfW8ruJMlyX/HO4ZApLi57MAAAUaJKmU/GGD3xxBM67rjjtHPnzgrHAgCA4PDMqHCWHVlp6fZxuftqbE5AjXHPfIqNkWrRl5y24JMRpXcAAKiKwadOnTrpyCOP1JIlS9SnTx+tXLkyWPMCAADl8Qo+Hcp8SkktaTp+CH2fEIlsmU8JibIsq+zBNS0pyXaT0jsAAKoYfGrQoIG++uorjR49Whs2bFD//v31xhtvBGtuAACgDF6raDmDT7GxUnJK6bhcgk+IQG6ZT7Wq35M8Mp/EincAAEhBaDienJysV155RVOnTlVeXp5Gjx6tO++8MxhzAwAAZXHPpoiPLwk6HeLe98nk7qP0HRHHlvlUi1a6kyTFx0sxbh+xyXwCACB4q93dcsstevfdd5Wenq6pU6fqzDPPVG5ubrAODwAA3LhffHsuM29vOl5o748DhDljiqWC0kb6nq//ULMsS0oqnRNldwAABDH4JEmnnHKKvv/+ex1++OF67733dMwxx+j3338P5ikAAIA8Lmg9yny8mo7T9wmRxOEoaeR9SG0ru5MkKzG59AZldwAABDf4JEkdO3bUDz/8oJNOOkm//PKLjj76aP3xxx/BPg0AAFHLGCO5Zz55NDj2ajrOineIILaSO0mqjcEnt/ckmU8AAFRD8EmSMjIy9MEHH+iGG27Q7t27lZ2dXR2nAQAgOhU4pOJi103PBsdeTcfJfEIk8SgjrW1ld5Ls2YiFRTKFBaGbCwAAtUBcoDveddddatWqVZn3W5al6dOnq2fPnnrqqacCPQ0AAPDglfnh4+LbSkuXOXCgZHzufhljatdy9ECAwiLzySMgrPx8KS4+NJMBAKAWqFLwyR9jxozRmDFjAj0NAADw5FHG43Whq5Km40ZbS24UFpZc/CZ5jwPCjnvmU3ycbaXH2sLzPWkOHrQvBAAAQJSplrI7AABQfbx6yPjKfPK40KXvEyKFcQs+1cZm45Jsq91J9H0CAMCvzKesrCxJUnx8vJo2bWrbVhnllekBAAA/ua+elZAgK8Y788NKTZUsSzIly4KZ/ful+g1raoZA9XEvu6uN/Z4kWbFxUnycVFBYsoHgEwAgyvkVfGrTpo0sy1KnTp20evVq2zZ/WZalwsLCwGYJAABc3Hve+Cq5kyQrJlZWSopMbm7JPrk0HUdkCIvMJ5W8N01ByfvOHCT4BACIbn4Fn1q1aiXLslxZT+7bAABAzbKV8JSX+ZGaJrmCT/toOo6wZ4qKSrOJpFrZbNwlKUk6tNIkZXcAgGjnV/Bpw4YNfm0DAADVyxhja7hsldNE3EpLl9nmbDpeVFL6k5Rc3VMEqo/DvtKdVUvL7qRDmU/OG/kHCf4CAKIaDccBAAgnjnyp2HVJW2bZneSj6fh+Su8Q3txLTiXV6swn23uz2EgOR+gmAwBAiBF8AgAgjHivdFdO8CklVYopzbRgxTuEvTDKfPJ8b5r8vBBNBACA0POr7M5fO3fu1H//+1/98MMPcjgcOvzwwzVu3Dj17NkzmKcBACB65ft/8W3FxMhKpuk4Iodxzx6yJMUnhGwuFfEqifXM2gIAIIr4nfn07bff6uijj9b48eN93r9q1Sp169ZNt99+u959910tWLBAjz32mPr06aNZs2YFa74AAEQ1W+aTpQrLjqy09NJ9c/eX9IwCwpTt9R+fICumFifxJySWvEcPYcU7AEA08/sv9oIFC7R06VIdc8wxXvcVFxfrvPPO09atW2VZls455xzddNNNGjhwoIqLi3Xddddp1apVQZ04AADRyHYBm5BY4cW3re9TYZHEBTDCmXuz/dpccqeSzEP34DAr3gEAopnfwacvv/xSkjRq1Civ+xYsWKDVq1fLsiy99tprmjdvnqZNm6YlS5boiiuuUGFhoZ588sngzRoAgGiV737xXXa/J9cYz6bj9H1CGDPuPZ9qcbNxJ1vpHcEnAEAU8zv4tHHjRrVu3VqNGjXyuu+DDz6QJB199NEaOXKk7b67775bcXFxWrJkSRWnCgAAbNkT/mR+eDYdZ8U7hCljjEfwtfYHn9ybjlN2BwCIZn4Hn7Zt2+Yz8CSV9IOyLEunnXaa131NmjRRy5YtlZWVFfgsAQCATHGxvezIs6GxD1ZMTMmqd85j0HQc4aqoUCoqLr0dbplPDodMcVHoJgMAQAj5HXwqLi7Wrl27vLbn5eXpl19+kST17dvX574NGzZUPit8AABQNY58ya1fuD9ld5K99M7k7qPpOMJTvsN2Mxwyn7zeo3weBgBEKb+DT87spR07dti2L1myRIWFhYqNjdXRRx/tc9/du3erYcOGVZspAABRzqthsZ8X3+4r3qmoWDqYF8RZATXDODxe/2GQ+SSP4BNNxwEA0crv4NPQoUNVUFCgW2+91bWtqKhIDz74oCzL0sCBA5WWlua13/79+/X777+refPmwZkxAADRyiNrwkpM9ms3r6bj9H1CGDKer/8wCD55lsbS9wkAEK38Dj5NmjRJ8fHxmj17trp06aLRo0erS5cuWrRokSRp4sSJPvf74IMPVFxcrGOOOSY4MwYAIErZLlxjLCkhwb8dk1PsTcdZ8Q7hyH2luxhLio8P3Vz8FRcvxbp93CbzCQAQpfwOPnXu3FmzZ89WQkKC1q5dq9dff13r16+XMUZXXHGFzjzzTJ/7Pf7447IsSyeddFKw5gwAQFSylewkJMqyrLIHu6HpOCKBLfOpEq//ULIsixXvAACQFFeZwRdccIEGDhyoV199VX/88YcyMjJ04okn6thjj/U5fvv27erVq5d69+6tYcOGBWXCAABELdsy8/41G3eNT0t3lduZ3P0yxoTFxTvg4ihtOB4OzcadrMQkmQMHSm6Q+QQAiFKVCj5JUuvWrXXTTTf5NbZhw4Z6+OGHKz0pAADgzT3zqbIX37a+T0XFUl6elJISrKkB1c4z8y9cWElJrkUqTf5BAr8AgKjkd9kdAAAIHVNcZMv8UFIlM59S02236fuEcGKMkQrcMp/CKPhkW/GusEgqLAzdXAAACBGCTwAAhAOvle4qF3xSSrIUU/pnn75PCCsFDqnYuG6GW9mdDaV3AIAoRPAJAIAwYDwvWCvb88mKkZXq1nR8P8EnhA/jEXxVOAWfPLIUvd7LAABEAYJPAACEAc+L70pnPsne98kc2C9jiqs8L6BGODxe/+FadidWvAMARCeCTwAAhAP3C9aYGCk+vtKHsNLc+j45m44DYcC49zuTwqvheGys/f1K5hMAIAoRfAIAIAzYSnUSEwNaLcu24p3o+4Qw4v76j4uVFVfpBZtDyr30jrI7AEA0IvgEAEA4cLtgDaTkTpKU7NF0fD8r3iE8GPeyuzDKenJxe89SdgcAiEYBB5+++OILLV++3K+xK1as0BdffBHoqQAAiHruPZ88Gxj7q6TpuFvfJzKfEC7cX/9hGHyyBYwd+TLGlD0YAIAIFHDwaejQoZo4caJfY6+99loNHz480FMBABDVTFGRVFBQuqEKF99WmnvwKZem4wgL7plPVhitdOdkCxgXG8mzhxUAABGuSmV3lfnWhm94AAAIkEePmEAznySPvk/FxdIBmo6jdjPFxUELvoaM54p3+bzvAADRpUZ6Pu3cuVPJyck1cSoAACKOV4PiQHs+SbJS0223TS59n1DLORyS23eYYZn55PmedSsjBAAgGvi9VEhOTo727Nlj25afn69NmzaVmdWUl5enJUuWaNWqVTriiCOqNFEAAKKVZ4PigBuOSyVNx2NjpKKScjv6PqG2Mw6P4GtYZj4lSJZcQTRzkMwnAEB08Tv49PDDD2vKlCm2bT/99JPatGnj1/6XXnpppSYGAAAOcc+SiI2RqrDMvGVZslLTZHJyJLHiHcKAR5ZQWGY+WTFSYqJ0sOSxGDKfAABRxu9Pr3Xq1FGrVq1ct7OyspSQkKAmTZr4HG9ZlpKTk9WuXTuNHj1aF154YdVnCwBAFLKV3SUmybKsKh3PFnw6kCtTXCwrpkYq8YFKM57NuRMSQjORKrISk2UOBZ/kkc0IAECk8zv4dO211+raa6913Y6JiVGfPn30xRdfVMvEAADAIW7BpyqV3DmPYWs6bqS8A5L7NqA2cQ++xsfLiokN3VyqwEpMdLWu8urjBgBAhAs4b//ZZ59V48aNgzkXAADgg/uFalVWunMdI82z6fh+e0AKqEXcM5/CseTOJclt8R2HQ6a4KGwDaQAAVFbAwadx48YFcx4AAMAHU1goFRaVbghC5pOSPJqO798nNfJdRg+Emi1LKBybjR/iFTg7mC+lpIRmMgAA1LCAGzwsW7ZMl1xyiV555ZVyx7388su65JJLtHLlykBPBQBA9Mr3XOmu6hffJU3HS7OfWPEOtZqjtDl3WGc+eQSOTT4r3gEAokfAwaennnpKzz//vK0JuS+tW7fWc889p//+97+BngoAgKjl2RsmGD2fJHvfJ2fTcaC2MUUemX/hnPmU5Bl8YsU7AED0CDj4tGTJEmVkZGjAgAHljhswYIAyMzP1+eefB3oqAACiltcFarCCT2k+mo4DtU2+faW7sM58iosvKXd1YsU7AEAUCTj4tGnTJrVr186vsW3bttVff/0V6KkAAIhe7plPcbGy4gJu12jj2WDc7N8XlOMCwWQcHgGacM58sixb8JgV7wAA0STg4FNMTIwcDkfFAyUVFBSoqKio4oEAAMDGuGVHBKvkTlJJ0/G40pW26PuEWskRQZlP8ii9I/gEAIgiAQef2rdvr7Vr1yo7O7vccX///bfWrFmjtm3bBnoqAACili07Iil4waeSpuNufZ/IfEItZHv9W5YUnxC6yQSBewDZHDwoY0wIZwMAQM0JOPh02mmnqaioSJdffrkKCgp8jiksLNSECRNkjNHpp58e8CQBAIhGxhhbdoSVEMTMJ3k0Hc87QNNx1Dq2nmcJCSWla+HMPXuxqEgqLAzdXAAAqEEBB58mTZqkpk2basGCBTrqqKP0zDPPaOXKldq0aZNWrlypp59+WkcddZQ+/PBDNWnSRNdff30w5w0AQOQrLJSK3AJCQcx8kiQrNb30RrGROZAb1OMDVeZWdhfuJXeSrxXvKL0DAESHgLuW1q1bV/Pnz9fpp5+u1atXa8KECV5jjDFq3bq13n33XdWtW7dKEwUAINp4XpgG++LbtuKdDvV9SksvYzRQ82wNx8O42biTV9+2/IO85wAAUSHgzCdJOuKII7Rq1SpNnz5d/fv3V926dRUbG6s6depowIABmjFjhlauXKkePXoEa74AAEQPr+BTcDOflJhkazou+j6hFikpO3XLfIqA4JM83sPuCwoAABDJqrxec3p6um644QbdcMMNwZgPAAA4xOvCNMjBp5Km4+kye/eUnI8V71CbFBZK7n3IIqHsLjZWio+XnP1SKbsDAESJKmU+AQCAauR+YRofX3LhGmS2puMHDsgUFwX9HEBAHPm2mxGR+SR73ycynwAA0aLKmU+S9PXXX2vJkiXavHmz8vLyNGfOHNd9GzZskMPhUIcOHYJxKgAAoob7Sl/V1WzZ1vfJGJkDB2TRgwa1gG2lO0VGw3FJJRmM+0pKXGk4DgCIFlUKPv3222+64IIL9NNPP0kqqc23LMsWfLr//vv11FNPafHixRo0aFDVZgsAQBSxXZgGu9/TIbYV7ySZ/ftogIxawXhkPkVCw3GppHebcd7Iz3d9fgYAIJIFXHa3detWDRkyRD/++KN69+6tu+++W4cddpjXuPHjx8sYozfffLNKEwUAIJqUNFt2y3xKqp7gkxITpbjS76Lo+4Rawz34GhNje52GM9t72Riv8kIAACJRwMGnf//738rOztZVV12l7777TnfccYcaN27sNe7oo49Wenq6vvnmmypNFACAqFLg8Gi2XE2ZT5Zl7/vEineoJYyjdKU7JSZGTnYQK94BAKJQwMGnDz74QKmpqZoxY0aFHwbatWunTZs2BXoqAACiTk32u7H1fcqj6ThqCffMvwgpuZN8ZDHS9wkAEAUCDj5t3rxZhx9+uBL9+DCcmJio3bt3B3oqAACij8cFqVVNmU+SR98nI5nc3Go7F+Av43DveRY5wSclJEpuX9zSdBwAEA0CDj6lpaVp+/btfo3NyspS/fr1Az0VAABRx+uCtKYyn0TfJ4SeMUZyK7uzEhJCOJvgsizL9n6m7A4AEA0CDj717NlTf//9t1auXFnuuCVLlmjLli3q27dvoKcCACD6uF+QJiTIiomtvnMlJErxbk3H6fuEUHM4VLokXPVm/oWC7fGQ+QQAiAIBB58uvfRSGWN0ySWXKDs72+eY33//XZdccoksy9Lll18e8CQBAIg27j2fqvvCu6TpeGnpHZlPCDXjuQJcBPV8kux9n8h8AgBEg4DXrB0zZozeeustvfHGG+rSpYtGjBihrKwsSdKdd96pVatWaf78+XI4HBo7dqxOPPHEoE0aAIBIZyu7q4F+N1ZqmsyeQ/0Z8w7IFBXJiq3GbCugPJ4N9yMs+GRb8a6ggPcbACDiBRx8kqSXX35Z7du318yZM/Xaa6+5tk+dOlXGGCUkJOjGG2/U1KlTqzxRAACiRUm/m5rLfJI8+j4ZyRzIlZWeUe3nBXzxynyKpIbj8vGezs+XUlJCMxkAAGpAlYJPcXFxmjZtmq6//nrNnz9fK1eu1N69e5WWlqYuXbrolFNOUdOmTYM1VwAAooPDIRWXNrzxWpq9GthWvNOhvk8EnxAq7sGnuLjIywryeE+b/DxZBJ8AABHMr+DTlClT1KpVK40fP97n/Q0aNNBFF10UzHkBABC1TH6efUNNNFtOSJDi46WCgpI50PcJIWTveRZZWU+Sd+YTfZ8AAJHOr4bjd999t+bMmWPbFhsbqyFDhlTLpAAAiGqe/W5qoueTZclKLS29M7mseIcQcu95Fmn9niQpLk5yz+ZixTsAQITzK/gUExOjgkPfhDoZY0p6UgAAgKCyNRu3VGMX37a+T3l5MkVFNXJewJNxOFz/H5GZT5Zl62NlCD4BACKcX8GnJk2aaN26ddq/nxR8AACqm60EJyFRVoxff66rzNb3yVB6h9AwxUWu8k9JkZn5JMlKSnb9P2V3AIBI51fPp5NOOkmzZ89Wly5dNGDAACUnl/yxXL9+vS655BK/TmRZlmbPnh34TAEAiBb5NbvSnetc7plPOhR8ysissfMDkqR8h+2mFanBp8REuWoI8g/KGFOSEQUAQATyK/g0ffp0rVmzRt98843mzZvn2r5161Y999xzfp2I4BMAAP6xleDUZMlRfIKUEC85DjUd30/fJ9Q847D3PFNiQmgmUt3cA8tFxVJhQcl7EACACORX8KlevXr66quvtHLlSq1du1YHDhzQxRdfrA4dOuiWW26p7jkCABA1THGxbZl5K6kGM58sS1ZquoxjV8lcKLtDKDg8G+7X3HugJnm+t01+viyCTwCACOVX8Mmpe/fu6t69uyTp4osvVqNGjTRu3LhqmRgAAFHJkS+5redR0xfeVmqazO6S4JMO5skUFsqKq9THBaBKjPtqj5YiNhvISky2bziYJ6Wl+x4MAECY8+vT5CWXXKIOHTro5ptvdm179tln1alTp2qbGAAA0chr1asaXunL1vfJSObAflkZdWp0Dohy7plP8Qk11nC/xnm8t21BNwAAIoxff82fe+45zZ8/37bt4osv1o033lgtkwIAIGrle5YcJZcxsHpYqR5Nx1npFjXM2BruR2azcUmyYmNLeqw5eQaeAQCIIH4Fn+Li4pTv49sYY4yP0QAAIFC2JddjLCmhZkuOrIRE2znp+4SaZhxu74EIXenOyT24bHvvAwAQYfwKPrVo0UK//PKL/v777+qeDwAAUc1WdpeQGJKl192zn0wuK96hhuU7XP9r1XDwtca5ZXZ5ldwCABBB/Or5NHLkSD300EPq2LGjunXrpuTkkm9pVq5cqeHDh/t1Isuy9NlnnwU+UwAAooGt5Cg0q3xZaemlTcfzDtJ0HDXGFBZKRUWlGyJ0pTsnKympdH2B/IMypliWFaE9rgAAUc2vT5L/+te/lJ2drXnz5un77793bd+7d68WL17s14lC8c0tAADhxj37IVT9brz6PuXul5VZJyRzQZTx7HkW8WV3bsE1I8nhiPiAGwAgOvkVfEpOTtZLL72kWbNmaf369Tpw4ICGDRum7t2769FHH63uOQIAEBVMcVHJxadTUogyn3wEn0TwCTXAODx6jEZww3FJXu9xc/BgyDIeAQCoTpXKoa9Tp4769Onjup2ZmakhQ4YEfVIAAEQlr5XuQhR8SkiQEhNcvXfo+4Qa44jizCeJFe8AABEr4AYOf/75p5JC9I0sAACRyKvhcAgzIKzUNJn8kr5PZj8r3qFmGPcAbIwlxceHbjI1ISGx5HEWl3R+YsU7AECkCjj41Lp162DOAwCAqGe8Mp9Cl/VhpabL7DrUdPzgQZnCAllxER4IQMjZyu4SEiK+Z6hlWSUBqENBJ1a8AwBEKr+X0xg+fLgmTZrk875HH31Ub775Zpn7nnzyyapfv36lJwcAQFRxv/CMiZHiQ7fMvJXmo+8TUN3cgk9WQnRk2NtK7wg+AQAilN/Bp8WLF+t///ufz/smTZqkRx55pMx99+3bpz179lR6cgAARBNbyU1iYkizPryajlN6hxpgy/6L9Gbjh1hubSwouwMARCq/g08AAKCauWU9hHrFKys+wXbxT+YTqpsxxiPzKTqCT7bebgUFMkVFoZsLAADVhOATAAC1hHvWh1ULFvVwL71jxTtUu4ICV+NtSVGZ+SSJ0jsAQEQKi+DT448/rrZt2yopKUm9evXSl19+We74JUuWqFevXkpKSlK7du305JNPeo1588031aVLFyUmJqpLly56++23bfcXFhbq9ttvV9u2bZWcnKx27dppypQpKi4uDupjAwBAUkm2Q0FB6YZakPVhK707mC/jPj8gyGzNxiVZCaHreVajPLIcaToOAIhEtT74NG/ePE2aNEm33Xabfv75Zw0aNEgnnXSSsrKyfI7/888/dfLJJ2vQoEH6+eefdeutt2rixIm2hujffvutRo8erbFjx2r58uUaO3aszj33XH3//feuMdOnT9eTTz6p//znP1qzZo3uv/9+PfDAA3rssceq/TEDAKKQxwVnrch8Sk233ab0DtXKc7XHaGw4Lvo+AQAiU60PPj300EO69NJLddlll6lz586aOXOmWrZsqSeeeMLn+CeffFKtWrXSzJkz1blzZ1122WW65JJLNGPGDNeYmTNn6vjjj9ctt9yiTp066ZZbbtGxxx6rmTNnusZ8++23OuOMM3TKKaeoTZs2Ovvss3XCCSfop59+qu6HDACIQl7ZDiHu+ST5aDpO8AnVyDPzKVrK7hQXJ8XFlt4m8wkAEIFqdfDJ4XBo6dKlOuGEE2zbTzjhBH3zzTc+9/n222+9xo8YMUI//fSTCg6VC5Q1xv2YAwcO1GeffaZff/1VkrR8+XJ99dVXOvnkk6v8uAAA8OSZ7RDqhuOSZMXHezQdp+8TqpF75lNsjBQbW/bYCGJZlu39TuYTACASxVVm8MqVKzV8+PCA7gvEjh07VFRUpMaNG9u2N27cWFu2bPG5z5YtW3yOLyws1I4dO9S0adMyx7gf86abbtLevXvVqVMnxcbGqqioSFOnTtV5551X5nzz8/OV7/bBKScnx+/HCgCIcp4X3nGV+hNdbay0dFcjdLOfzCdUH1vmU2KiLMsK3WRqWmKSlJsriZ5PAIDIVKlPtnv37tXixYsrfZ+kKn2A8NzXGFPu8XyN99xe0THnzZunuXPn6uWXX1bXrl21bNkyTZo0Sc2aNdO4ceN8nnfatGm65557/HtQAAC4sV1wJibVmgtvKzVNZueOkhv5JU3Hrfj40E4Kkckt+BQt/Z6crMQkudb5yz9Y4WddAADCjd/Bp7vuuqs65+FTgwYNFBsb65XltG3bNq/MJacmTZr4HB8XF6f69euXO8b9mDfccINuvvlmjRkzRpLUvXt3bdy4UdOmTSsz+HTLLbdo8uTJrts5OTlq2bKln48WABDV3IJPtaHkzsm779M+WXXqhWg2iGQm3z34FCUr3Tm5LzBQVCwVFkjxUfYcAAAiWq0OPiUkJKhXr15auHChzjrrLNf2hQsX6owzzvC5T79+/fT+++/btn3yySfq3bu34g99U9uvXz8tXLhQ1113nW1M//79XbcPHDigmBh7S6zY2FgVFxeXOd/ExEQlRktzTABAULlnPtWGle6crDQfTccJPiHITHGxVOAo3VCLArA1wdeKdxbBJwBABKkdDSXKMXnyZI0dO1a9e/dWv3799N///ldZWVm68sorJZVkG23evFkvvPCCJOnKK6/Uf/7zH02ePFmXX365vv32W82ePVuvvPKK65jXXnutBg8erOnTp+uMM87Qu+++q08//VRfffWVa8xpp52mqVOnqlWrVuratat+/vlnPfTQQ7rkkktq9gkAAEQ8U1goFRaVbqhFF95WXHxJVsahJsj0fUK1KHCotO5MsqLsyzyvgHP+QSk9IzSTAQCgGtT64NPo0aO1c+dOTZkyRdnZ2erWrZvmz5+v1q1bS5Kys7OVlZXlGt+2bVvNnz9f1113nWbNmqVmzZrp0Ucf1ahRo1xj+vfvr1dffVW333677rjjDrVv317z5s3TMccc4xrz2GOP6Y477tA///lPbdu2Tc2aNdOECRN055131tyDBwBEh3zPle5q14W3lZrmWoGLFe9QHdxL7iRJ0VZ25/Gep+k4ACDSWMbZjRtBl5OTo8zMTO3du1cZGXx7BQDwrXjXDhWtW+O6Hdejp1evpVAq2rxJxVkbXLfjeh9DSRCCqnjHNhWtX+e6HXdkb1nJySGcUc0rWPq95CgpPbQaNVZc+w4hnhEAAOWrTMwjptx7AQBAtfPK+qhFZXeSZKWl226bXErvEFze74HoC27a+j6R+QQAiDAEnwAACDX3C824WFlxtasq3mvFO/o+IdgcbsGn+DhZMbGhm0uouPV9cpa5AgAQKQg+AQAQYu4Xmp6rXtUGVlyc/cKYvk8IMvfMJyuhdvU8qym2Xm+O/JIVAAEAiBAEnwAACDFbc+FaGHyS7KV3ZD4h6Nwzn2pZw/2aYiW59bgysj8nAACEOYJPAACEkDHGVnZXGzOfJI/SO4dD5lBjZCAYjIPMJ1a8AwBEsqAFn4qLi7V9+3ZlZWUF65AAAES+wkKpyK28JikMgk+i9A7BY4qKpILC0g21NABb3axEj9X9PJuwAwAQxqocfJo/f76OP/54paenq0mTJmrXrp3t/qlTp+r888/X9u3bq3oqAAAijmd2g1VLS468g0+U3iFIPMrLrIToW+lOkpSQIMVYrpvmYF4IJwMAQHBVKfh044036rTTTtNnn32moqIixcfHl5QPuGnatKnmzZunt99+u0oTBQAgInkFn2pn1ocVFycll2Zm0PcJwWI8M3xq6XugulmWZSu983peAAAIYwEHn958803NmDFDzZo10wcffKDc3Fz16dPHa9xZZ50lSXrvvfcCnyUAABHKa0n1Wnzh7Z79ROYTgsYr86l2Zv/VBFvpHZlPAIAIEhfojrNmzZJlWXr99dfVt2/fMsfVrVtXbdu21fr16wM9FQAAkcv9wjs+XlZsbOjmUgErLV1mx6EyeodDxpEf1YECBIctw8eSlBAfsrmEmpWYKGcNAZlPAIBIEnDm088//6yWLVuWG3hyatiwoTZv3hzoqQAAiFjumU+1td+Tk5WaartN9hOCwX2lOyUkyLKieDHmJLfMp4KCkmbsAABEgID/uufn56tOnTp+jT1w4IBia/E3uQAAhIqt4XgtLrmTDpXdlfZDltnPincIArcMHyuhdr8HqptXANqzLBcA/GTy8lS4erkKVy+XyTsQ6ukAgQefWrZsqd9++00FBQXljtu7d6/Wrl2r9u3bB3oqAAAikjHGfuGdVLsvvK1Yj6bjZD4hCGyZT4lRutLdIZ4LDniuhgkA/iratEEmJ0cmJ0dFmzaGejpA4MGnESNGKC8vTw8//HC546ZMmaLCwkKdeuqpgZ4KAIDIVOCQiotLb9fyzCdJslLTXf9v9u/3WuUWqAxjjK3vWdT3EEsi+ASg6owxMjl7S2/vywnhbIASAQefbrrpJqWnp+vWW2/VDTfcoLVr17ruKy4u1ooVK3TJJZfo4YcfVoMGDXTttdcGZcIAAEQKz4bCtb3nk2Rf8U4FBZLDEbrJIPwVFkpFbgHYKA8+WXHxUpxbqwqCTwAC4XCU/I12u20qqFgCqlvAq901b95c7777rkaOHKmHHnpIDz30kOu++PiSVUqMMapXr57efvtt1a9fv+qzBQAgknhcWHqW3NRGtuCTJJO7LyyCZqilHOEXgK1uVmKSTGGuJPuCBADgL19l8SYvV1Z8nZqfDHBIlZYTGTJkiFatWqVJkyapdevWJel9h/41bdpUV199tZYvX67+/fsHa74AAEQMr5KaMLjw9mo6Tt8nVIHxCj7V/gBstXMrvaPsDkAgTK73giAmNzcEMwFKBZz55NS0aVM9+OCDevDBB5Wbm6u9e/cqLS1NGRkZwZgfAACRyz2rISFBVkztXxnWio2VklOkAyUr55j9BJ8QOM/SUyVEd8Nx6VDmk/NG/kEZY2RZVnm7AICNz7/NrHiHEAs4+JSVlaWkpCQ1atTItS01NVWpqaleY7dt26aDBw+qVatWgZ4OAICI437hHU4ZH1Zqmowz+JS7n4tjBM498ykmRoqLD91cagv33wVFxSV9WwjKAfCTMcZ32d0BMp8QWgGX3bVp00bnnHOOX2NHjx6tdu3aBXoqAAAikq2kJgxK7py8m47nlz0YKIct8ykxgSCmJIsV7wBUhWez8UPMgVxWqEVIVannU2VevLzQAQAo5bXEfDhlPqWl225TeoeAub8HonylOyev3wUEnwBUgq9+T5JKMin5fYIQqlLwyV85OTlKDKNvdAEAqHYOh1Rc+sWMZ7ZDbWalpNJ0HEFhy3wi+FQiMdH+/mLFOwCVUN7fZErvEErVGnzKz8/XJ598ohUrVqhNmzbVeSoAAMKKyc+zbwijL2lcTccPKfNbVqAcJdl/DtdtK4zeA9XJiomV4kt7PFF2B6AybNnISR7B7AM0HUfo+B18uueeexQbG+v6J0lff/21bZvnv5SUFJ100kkqKirSmDFjqu1BAAAQdvLDe4l5975PzqbjQKUUOCS31w1ld6VsmZAEnwD4ybPZuJWeaVvEgMwnhJLfq90ZY2wfLC3LqvCDZnJystq1a6fRo0fr5ptvDnyWAABEGFs2g6WwKzmy0tJltm8ruVFQWNK7J8wCaAgt4xGADafsv2qXmCQpRxJldwAqwZFvazZupaZJRUWu3yMEnxBKfmc+3X333SouLnb9M8Zo4MCBtm2e/3Jzc7Vy5UrdfvvtiovzO84FAEDEs11QJiTKiqmRNoxBY1vxTpLZT+kdKsljlUQrgeClky0T0pEvU1wcuskACBue/Z6stHRZKaVl8jqYJ1NUVMOzAkoEHBG666671KpVq2DOBQCA6JEfnivdOVmph5qOH0qCNrn7pfoNQzonhBfvzKcE3wOjkK3szqgkUJeUHLL5AAgPtn5P1qEFQtx668lIJu+A16q1QE2oUvAJAAAExlZ2F4blRlZMrKyUVJnckhR+2wdewB/umU9xsbJiyZJ38QhIm4MHZRF8AlABW+ZTckrJAiHumU+SdCBXIviEEAivHH8AACKAKS62XXjbshzCCU3HUQXGPfgUZj3PqpvX7wSajgOoQEmz8dISeFd5fFKy5FbaT98nhEqVv2L68ssv9dJLL2n58uXatWuXCtwanLmzLEu///57VU8HAED4c+S7ytWk8Cy7k0o+2BptLblRWFhygUx2BvxlKz0l+GQTnyDFWFJxyS8KQ/AJQEUc+SULgBziLK2zLEtWSoorQ5ngE0KlSsGnq666Sk8++aRf33RallWVUwEAEDG8LiTD9MLbs2eEyd1PaRD85t7zySLzycayrJLSu7w8Sax4B6BinuXvtoVBUlIlt+CTMYbrc9S4gMvu5s6dqyeeeEKdO3fWp59+qt69e8uyLK1fv16LFi3Sww8/rNatWys5OVlPPvmk/vjjj2DOGwCA8OXRaNlKDM+AjZWSWpKdcQh9n+AvU1xsWw48XAOw1cmWEUnmE4AK2Po9OZuNO2+6/b8KCu2/f4EaEnDw6ZlnnpFlWXr11Vc1fPhwJR760NC+fXsNHTpU1157rdavX69TTjlFEydO1Pbt24M2aQAAwpktiyHGkhLCc5UvKyZGVnJpI1P3XhNAuRweAVgyn7y4932i7A5ARWx/g53Nxg+xBZ9E6R1CI+Dg04oVK9SqVSt169ZNUmlZnXsJXlxcnJ5++mnFxsZq6tSpVZwqAACRwXYhmZAY1qnv7qV3NB2Hv4xH9h+ZTz64Zz4VFMoUFpY9FkBUK2k2Xpr5ZCu5k2R5rHhH8AmhEHDwKS8vT40aNXLdTk4uKRnYs2ePbVxmZqa6dOmib775JtBTAQAQWWyNlsOz2biT7QNuYRHlQfAPmU8V8vrdwHsLQFnKaDbuuh2fICXEu24TfEIoBBx8atKkiXbv3u263bRpU0nSL7/84jV2+/btysnJCfRUAABEFPfMp3Bf5cvz21Wzn9I7VMx4BJ9E8MmLe9mdROkdgLKV22zcuS3ZrfSO4BNCIODgU8eOHfX333+70usHDhwoY4ymT5+uArcGZi+++KKysrLUrl27qs8WAIAwZ4qLJIejdENSeGc+ybPpeC5Nx+EH97K7hHhZMQF/JI1ciZ7Bp/wyBgKIdrZ+T5ZkpaZ6jXHfZvIOyJjimpga4BLwX/pTTjlFBw4c0BdffCFJGjNmjJo2baoPP/xQHTt21DnnnKPBgwdr/PjxsixLV155ZdAmDQBA2PJa6S68g09WTIytkSnBJ/jDPZBCyZ1vVlycFBdXuuFgXugmA6BWs2U+JafIion1GmNrOl5spDyyKVGz4ioe4tu5556rnJwcxceX1I6mpaXpgw8+0Lnnnqvff/9dGzZsKDlBXJwmTZqka665JigTBgAgnHmVzoR58EkqSe93fvA1+0uajodzE3VUP1vZXZiXnlYnKzHR1WiczCcAvhhjZA64NRv36Pfk2u654l1erlcjcqA6BRx8aty4sW677Tbbtp49e2rdunX64YcftGHDBiUnJ6tv375q3LhxlScKAEAk8LyADPeeT9KhD7pbt5TcKCoqydBI5gMtyuEg88kvSclSbklvFpNP5hMAHzybjfvo9yRJSk6WLEmHFqU1ublS/YbVPz/gkICDT2WJiYlR37591bdv32AfGgCA8Oee+RQTI8UnhG4uQeLVdDx3vyyCTyiDKSwsWRnRieBTmazEROd1onQwn6xCAF48F/ooK/hkxcSWBKAOlASyTR5Nx1Gz6O4IAEANMgfdgk+JiZFxIZmcYm86zop3KI8j8rL/qk1Scun/FxdLBY6yxwKISrZei2U0G3fdnezeo5HgE2pWwJlPzkbj/oiNjVV6erpat26tzMzMQE8JAED4c8t8Cvdm407OpuOuvk98oEU5jEfwicynsnkG5kx+PmWKAGz8aTbuZKWmyuzcUXIjP1+msLBkcQOgBgT8Shs6dGhA39Z27dpVV155pf7xj39Exre9AABUgm2Vr6TICD5JJX2fSoNP+ygPQtkisO9ZdfEKUB/Mk9IzQjMZALWOMcaW+VRWs3HX/cmeTccPyOJ3CmpIwGV3gwcP1uDBgxUXFydjjJKSktS9e3f1799f3bt3V1JSkowxio+P16BBg9SrVy9lZGRo1apVuuaaa3TmmWequLg4mI8FAIBazRQVSQUFpRsiKIPB1mOiqFjKozkyfLNlPllWRPQ9qzaJSSUNgg9hxTsANvn5UqEfzcZd93uU5B0gUxk1J+Dg02effaY6deooNTVVzz77rHbt2qVly5bpyy+/1LJly7R7924999xzSktLU926dfXtt99q165deu2111SvXj198MEHmjNnTjAfCwAAtZt7s3FFWOaTV9Nx+j7BN1sAJSGBDLlyWDExUkJpcM54/A4BEN08/9ZWlPmkhEQptrQszxB8Qg0KOPj0wAMP6L333tP777+vcePGKdEjZTohIUEXXXSR3n33Xb377ruaPn26LMvS2WefrRdffFHGGL3wwgtVfgAAAIQLrwvHCOn5JOlQ0/HSjxW2BqiAO7fMJ0ruKmYlujUdP0jwCUApe7NxS1ZK+SvNWpYlK8Wt6TjBJ9SggINPzz//vDp06KABAwaUO27AgAHq2LGjLdB04oknqmnTplq1alWgpwcAIOx4lsxESsNx6VDT8VT3VXQIPsE3e+YTwacKuQXoyHwC4M692biVUn6zcfdxrv0P5MoYUy1zAzwFHHzasGGDUstZxtFdamqqsrKybNtatGihAwcOBHp6AADCj3vWQmyMFGErzLiX3pnc/XyghRdjDJlPlWQluWU+OfJl6JkKQN7NxlVBvycXt8wnFRZJDkdwJwaUIeDgU7169bRq1SplZ2eXOy47O1srV65U3bp1bdu3bNnitQ0AgEhmy1pITIq4Xje2XhNFxVIeXzLBQ2GBVOwWlCTzqUK2AJ2RLXgHIIrlH6xUs3HXuBSPFe8OkKmMmhFw8OnUU0+Vw+HQWWed5ZXV5LRp0yaNHDlShYWFOv30013bd+3apb/++ktt27YN9PQAAIQft+BTJJXcOVkpnk3H+UALD16lpwSfKuSe+STJ0PcJgLz/xlbYbNw5ziv4xBdFqBkB5/vfe++9+uijj/TDDz+oQ4cOGjp0qHr06KH09HTt379fK1as0Oeffy6Hw6HWrVtrypQprn1nz54tY4xOPPHEoDwIAADCgXvmUyStdOeSklzSdPxQWZDJ3S81bBziSaE2MR5ZOxaZTxXyCtDR9wmA7P2e/Gk27hoaFyclJkj5JeV2NB1HTQk4+NSwYUN98803mjBhgubPn69PPvlECxcudN3v7PNwyimn6Mknn1TDhg1d940bN07nnnuuGjVqVIWpAwAQPkxhYUlvBadIzHyyYmSlpsnsy5Ekmf37KtgD0caz6T5ld36IT5BiLFe5IplPACR75pO/zcZLx6fJ5O8qOQ7BJ9SQKnU6bd68uT744AP9+uuvWrhwodavX6/c3FylpqaqQ4cOOv7443X44Yd77UfQCQAQdTyyFSK13MhKcws+5ebKmGJZVsBV/og07plPEdh0vzpYllUSrM7Lk8SKdwCczcZLv+Dxt9+Ta3xKiszukuCT8vJkiotlxfC3GtUrKH/xO3TooA4dOgTjUAAARCTPC8ZI7PkkeXwALi6WDuRJfq6Oi8hny3xKSIy4pvvVxUpMkjkUfKLsDkBJs3G3bGo/+z052fo+GVMS3OZvNaoZ4U0AAGqAV7lRxAaf7B+AWUUHNm6ZT/R78p97jzgynwB4NRuvdOYTK96h5lU5+LRy5Updfvnl6tChg1JTUxXnkT791FNP6dZbb1VOTk5VTwUAQPhyv2CMiy1p+BmJkpNLyqkOoe8T3Nkajkdo6Wm1cA9WFxSW9JADELVsf1tjLK9gUoWSkkt6yTmPx4p3qAFVCj7NmjVLvXr10uzZs/Xbb78pLy/P1WjcKT8/X9OnT9f7779fpYkCABDO3JsER2rJnVTSn8b9G1jPb2cRvYwplhwO1+1I7XtWHbxWxyT7CYhqtmbjySmV7tdkxcSUBKCcx6PpOGpAwMGnzz//XBMnTlRKSooee+wxbdy4Uf379/cad+6558oYo7fffrtKEwUAIJzZSmUiOPgkySP4lCtTXBzC2aDWcDgkt+8oKbvzn2fAmtI7IHqVNBt3Cz5Vst+Taz/3v9UEn1ADAs75f/DBByVJL730kk455RRJ8tk0skmTJmrZsqV++eWXQE8FAEBYM8bYMhUiOfNJ8tF0PO+AVMl+FIg8xi3rSZJE8Ml/nsGngwSfgKjl0Wy8sv2eXPslp5R+H+BwyBQUyIqPr/r8gDIEnPn03XffqUmTJq7AU3maNm2qzZs3B3oqAADCW2GhVOSW/RPh5Uae38JGe+ld8Y5tKlj+kwp/WxfdWWBeKz5G9vsgmKy4OCne7TtjMp+AqGX2e/xNDTT45LG6nckj+wnVK+Dg0/79+9WkSRO/xjocDhUVFVU8EACACORZIuPVvyXSJHk0HY/i4FNxzl4V/bZOOpAns32birf8HeophYzXio9kPlWKe8YkmU9A9DK5VWw2fojXine5BJ9QvQIOPjVt2lS///57heMOHjyotWvXqnXr1oGeCgCA8OaV8RHZwaeSpuOl2U/RuuKdKSwsCTy59Tkq3pbttThL1HBf6S4+TlZsbOjmEo7cg09kPgFRq6rNxl3iE+wZlXmseIfqFXDwadiwYdq3b5/mzJlT7rhHHnlEBw8e1IgRIwI9FQAAYc0rSyHCg0+SdyPTaCw3K9rwu+SZ7ZN3UCZnT0jmE2rGLfhEs/HKswWt8w9GbxATiGLBajYuHfqiKLk0+4mm46huAQefbrrpJsXHx+uaa67RrFmztN+j9nTPnj2aMmWKbr/9dqWmpuq6666r8mQBAAhLtoyP+KjI+LDS3JuOm6j7RrV453aZ7dt837d1Sw3PppZwD8QRfKo0W7lusZEKHGUPBhCZDgan2Xjp/vbgE0FtVKeAg08dO3bUCy+8oOLiYk2cOFH16tXTTz/9JElq1aqVGjZsqHvuuUdxcXGaO3euWrZsGbRJAwAQTtwzn6KlybLnB+JoKr0zjnwV/fFb6QZLUkpK6f27dtqygKKFLfMpSt4HQeW54t3/s3ffUa6d5b34v+/e6tKoTC+nzOnNPm44DhibSwg2GLIwJXYopoaLCfdSTHKJwfxCKDGkECeXCySQwMXBDkkcAmTBjYGADbEB1+Pj09ucNr1IGnVp7/f3x9ZIe0szc2Y00qh9P2uddWbv2ZLeU2ZG+up5nre0qo6IWp5l3hOqED65iz+boOnczIBqquLwCQBuu+02/OpXv8Ktt94Km82GdDoNKSUuXLgARVHwqle9Cr/4xS/wmte8plrrJSIiajqW+Sxt0HIHID90vFjh1S5Dx6WU0E4eN3Y4zFOGNkLdvNV8EfSpiTqsrn6kpgHZ4t8JK59Wr2yjglSyPgshorqx/CxVBFDhsPGC0qHjbL2jGrJd+pLlXX755XjooYeQzWZx/PhxRCIR+Hw+7NixA263uxprJCIialpSSku7UcvvdJcnhIDw+SAjEQDtEz7p46OQkXDhWPh8UIY2AUIALpfRMgGj9U4Z3AghRJ1Wus5KKr1Y+VQBh9Ooost3xbDyiaj9WOY9ebyVDxs33Yfl/hMJoHNNd0m0pIrDpy1btsDv9+OJJ56Aw+GA3W7Hvn37qrk2IiKi5pfNAOZh2+1S+QSjHaAQPuWHjq/1iXIjk4k49HNniicUBer2XYU/s9I3AP1s/vPpNGR4FiLUVYeVrr+yNkNWPq2aUBTj7y0fOklWPhG1lbJh42tsuQNgzKA0vTHCyieqpYqfAU5OTsLpdMLhcFRzPURERC2ltDqhnSo+LE+MdQm08JNaqevInThq/DnzlOGtlnkaSk+v0SaR11aDx9v466CarDvesfKJqK2kklUdNl64Hw93vKP1UXH4tG3bNoTD4SouhYiIqAWVDO8U7VT5VLIFdCu33unnR4BEcUc/EeqE0ttvuUbYHRCd3YVjGZ61zgNrYTJj2plNALDzzcuKmNp2ZZqVT0TtpPRnaOnP2EoJ04YYSCWNGX1ENVBx+PSWt7wFp06dwq9+9atqroeIiKillIUL7VTx4XQBttYfOq5HwtDHLhZP2O1Qt+5YdJ6T0jdQPJCAPtkm1U/mrwO7o6XbL2vJEl5nMpDmll4iamkyVjJs3LxT3RpY5j5JQCYTS19MtAYV/+T/8Ic/jFtuuQWvec1r8J3vfMcYqEpERERWKdOLbocDQlGXvrbFCCEsbQEyNr/M1c1J5rLQTh0rDIEGAHXbDoglxhKIDj/gKW7Iok+Ot0WAYJ75xJa7yln+7iS4LTpRG6n2sHHzfVmw9Y5qpOKB4y9/+cshpcT09DRe97rXIRAIYMeOHfB6F9/uUQiBH//4xxUvlIiIqBmZZz61U8vdAuHtMA0dT0DqWksFcNqZU0C62FKm9PVDWWaIuBACSu8A9JHTxolMFnJuFqKre8nbtALLwHEOG6+cy7qTtEynLHPFiKg11WLYeIHLDShKYXMUzn2iWqk4fPrpT39qOQ6Hw3jiiSeWvL5tthImIiIysbTdtWHFh/CZniBLCZlIVG1ORb3p05OQ01PFE24XlM1bL3k7pacP+rmRwhN9fWIMSguHT1JKy3BsVj5VrvTvTqZY+UTUFlJJwDSLqZo/R4UQEB5Poa1PJth2R7VRcfj0k5/8pJrrICIiajlSSiDT7pVP1ndnZWweaIHwSaZT0M6cLJ4QgLp9t7Ft9SUImw2iuwdycsK4r0gYMpmEcLsvccsmpeUAzdRayMqnytkdlgoFtt0RtYeyYePVrHwCAI8XKIRPrHyi2qg4fHrJS15SzXUQERG1nkwG0IvDgISr/cInY+i4DcjlALTG0HEpJbSTxy1bXisbNkNZRaim9A1Ay4dPAKBPjkFdQdVUUzK1JQKsfFoLIQTgcgIJY6c7c1svEbWuWg0bXyA83uLowmwWMpNZcnYhUaW41QgREVGNlG2F3oYvukuHjqMFwid97CJkNFI4Fh0dUIY2rOo+hNcHYZqTqU9NQOqtub21zJRU57DyaU2E01Qhl0oufSERtYxaDRsv3qc1zGL1E9UCwyciIqJaKalKaMe2O8A690km4k0dssh4DPr5keIJVYG6fReEWN1TKiEElL6B4olsDnJmpjqLbDCl1TmC4dOamCvHWPlE1PqMYePF3WKr3nKH8h3vGD5RLVTcdvcbv/EbK75WVVV0dHRgeHgY119/PX7rt34LDpbxERFRi7MMGxdo24oP4TW1o0lAxuMQHf76LahCUteQO3nU0kqpDm+DcFU2q0l09wJnzxSGyOqTY1B6equy1oZi3ulOEYDdXr+1tALz/7dcDjKXg7BV/JSeiBpdKmmZm1eLTTuE3WF8b85mATB8otpY8253C7vYSSnLrlnsc3/1V3+FoaEh3H///ZwbRURELc2yE5XDWfUy+WZh2fEO+faBJgyf9HMjhVk7ACA6uyB6+iq+P6GqUHp6oY+PAQBkNAqZiJe9A93sLNU5Did3QF6jsplZ6RRgq34lBBE1Bsu8J9Sm8gnIz32KhI0Dhk9UA2va7e6xxx7DJz7xCWzevBlvectbsH//fnR0dGB+fh4HDx7EN7/5TYyMjOATn/gE9u/fjyNHjuAb3/gGDh48iFe/+tV4+umnsWPHjmr+eYiIiBpHur13uitwOAG7Dcjmh47H5i9xg8ajh+egj40WTzjsULfuWHOQovQOFMInANAnxqBu2b6m+2w4meLAcQ4bX7vS7yUylarZi1Eiqj9zy10tho0vMIdPMpmAlPqqW8qJllPx/6ZAIIBPf/rTeOMb34jDhw/j//v//j/ceuuteNnLXoZbb70VH//4x3Ho0CG88Y1vxCc/+Un09/fjwx/+MJ555hm8+c1vRjwex5/92Z9V889CRETUUCxtd238ort06Hiz7Xgns1lop45bzqnbdkJUoX1MeL2WFkR9ehJSa96ZWIuxDBxv09bTqioNn9KpJS4kqi2ZyyJ3+Dlkn3kCeni23stpWbUeNl64b9MmGNAlkOL3Fqquiv/n/tEf/RHsdju+/OUvw7ZEn7mqqvjSl74Eh8OBT3ziE8YDKgr++q//Gna7HT/60Y8qfXgiIqKGJnXdMutGuNq48gklc5+SiaYJWKSU0M6csFTvKP0DUIKdVXsMpa+/eJDTIGemqnbf9SaltFY+MXxaM2GzGZWECxg+UZ3oF85BRiJAKgXt9IlFx7DQ2hjDxk3hUw3mPRXum0PHqcYqDp8ee+wx7Nq1C65LPJl2uVzYtWsXHnvsscK5UCiEPXv2YGxsbJlbEhERNbFMGjA9D2/rtjuUzH2SzfOkVk5PWneh87ihbNpS1ccQXd2WMEGfaKHnR9mMZUA72+6qw/z9RLI6gepASgl9Zrp4Ip3hnKBaSJYMG69li63bbWyOktcsP6epeVQcPiWTSYyOjl76QgCjo6NIlfxgFEJwxzsiImpZZVugt/mL7tInzM0w90mmktDOnCyeUARs23dDqGpVH0coKpTu4uByGYtBb4K/n5WQpqonAG3/dVA15vCJlU9UB3I+aqlqBIzZeFRdlnlPqG34JBTVspsmwyeqtorDp8suuwyjo6P4yle+sux1X/3qV3Hx4kVcfvnllvOnTp1Cb28LbidMREQElLXCCKd7iQvbhMNpbOOc1+hzn6TUoZ08bnnHWdmwuWZP/C2tdwBkq1Q/lX4dsO2uKixtvOkU251o3cnZ6fJzCzulUdVYflbWcNj4AnPrHcMnqraKw6e77roLUkq8973vxZ133olf/OIXSCQSAIyqqF/+8pd473vfi/e+970QQuDDH/5w4bY///nPEYvF8Ou//utr/xMQERE1IEsrjCKANq/2bbah4/roBeOd/Tzh90MZ3FCzxxNuD0QgUHz8mSnIXK5mj7deyiqfGD5VhaWNV5dGeyPROilruVs4Px9pmnl+zcIy78nrq9mw8cJjmOc+pdKQWvP/HKLGsfik8BW47bbbcOzYMfzxH/8xvvKVrxQqoIQQhXdfpJQQQuCP//iP8YY3vKFw2+PHj+Ntb3sb3v72t69t9URERA3K0grjcEIIsfTFbUL4fJALbRn5oePVbmGrBj02D/38ueIJmwp1+66a/xsqfQPQIhHjQNOhT09C7R+s6WPWnPnrwKYaw7Jp7Up3vEulWFVG60bOR8pa7gAAuoSMRiBC1duQoZ2VDRuv5bynhccoGzqesOzISrQWa4pOP/7xj+Pxxx/Hbbfdhq6uLiMF13VIKdHV1YU3vvGN+MUvfoF77rnHcrt3vvOd+NrXvoaXvOQla1o8ERFRwzLNfGr3YeMLLDveNejQcalp0E4eA0xtTOrwtnX5NxShLsBRbE3UJ8aavp1KmnZ8ZNVT9ZTtnsm5T7SOpLnqScA6pDrCuU9Vs57DxhceoyR84hB5qqY1v/107bXX4sEHHwQAhMNhxONxeL1eBIPBtd41ERFR0zJXPnGHL8OiQ8cb7B1V/exp4wl/nujuhuhenxmVQlGg9PRDv3jeOJFIQM5HIfyB5W/YyMwhLMOn6nE4jRf8+WySO97RepFSQjfNexIdAQASMmq0KevhOTRePWtzKh823rHElVXkdAKqCuTbJxvxTSJqXlVtGg0GgxgaGmLwREREbU3qmrUlobRKoV05HA09dFyfm4E+MV484XRA3bJ9XVsmlb5+SxWB3uSDx80znxjCVo9QFEslGXe8o/VitNxlC8eiqxsiECpekEzy/2OVWIeNK4Cn9huXCCE4dJxqprYTy4iIiNqRqdoDYNvdAiEEhK/4zm3pu7r1JLMZaKdOWM6p23ZC2OxL3KI2hNMFESzOS5Gz05DZ7DK3aFxS162DsFn5VFWlO94RrYfSljulsxsiGLJeE2brXTXImHnekxdCrM9Ld+Ep7qgnE/Gmb/+mxlFx29073/nOFV+rqio6OjowPDyM66+/Htdcc02lD0tERNTwyt71ZfhUILxeyLlZ4yCZhNRyEGp9h1BLKY3gyRTyKANDUAKhZW5VO0pfP7SFvyNdQp+agFrDnfZqJpMptIUBrHyqOqcLgDGgnm13tB5Kd7kT/gDEQkWr3QZkjZ3R9MgclL6Bei2zJUgpIRPrO2y8wDz3KZev5Ob3b6qCip/tff3rXweAQin6Yolo6ecWjq+55hr83//7f7Fnz55KH56IiKhhybLKJz5pW2CufII02gqEP1i39QCAnBwvBmIA4PFA2bS5busRwU7A6QDSRtWQPjEGZWCo6XZMlJmSQISVT1UlnK5itpfJQOoahMJpO1Q7MhqxhPSis9v4XQiIQBBy2gimZCQMKfV1q9RpSXUYNl54rLId7+J8HkNVUXH49LWvfQ2nTp3C5z73OXi9Xtx6663Yv38/Ojo6MD8/j4MHD+Lf/u3fEI/H8b/+1/9Cf38/jhw5goceeghPPvkkXvrSl+KZZ57BwABTcSIiajHmyidFAeyO+q2lwZQNHY/HgTqGTzKZgHb2dPGEImDbvruuL+KFEFB6B6CfP2ucSKUgI+Gy1paGxxC2psp3vEsDbs/iFxNVgZwtb7lboARC0PLhE3IaZCwG0WAbSjSTugwbX3isRcInhDqXuJpo5SoOn2688Ubcdddd+I3f+A08+OCDiw4Z/8u//Evcfvvt+OIXv4hf/epX+L3f+z189rOfxete9zr86Ec/wp/92Z/h85///FrWT0RE1HAsLTBOZ9NVrNSScDiNweP5QdQyVr+5T1LXoZ08Znl3Wdm0BcLrXeZW60Pp7YN+4WyhbU2fHIPSZOGTedg4AOPfnaqnpJ1XplMQDJ+oRpZsuVs4DpTOfZptuN1Mm0k9ho0vEDabpfqWQ8epWiquhbznnnuQSqWWDJ4AwO/344EHHkAymcQ999wDAPB6vfj7v/97CCHw/e9/v9KHJyIialymyicOGy9nrn6q5453+sVz1oGugSCU/sG6rcdMOJwQnV2FYzk7A5lJL3OLBmSuALTb2RJWZaXfWzj3iWppqZa7wrHTCZgHVUfC67W0llSvYeOFx/SYfk4zfKIqqfh/8Y9//GPs27dvyeBpQSgUwr59+/Cf//mfhXNDQ0PYvXs3zp8/v6LH+uIXv4gtW7bA5XLhmmuuwc9+9rNlr3/kkUdwzTXXwOVyYevWrfjyl79cds1DDz2EvXv3wul0Yu/evfj2t79dds3Fixfxlre8BV1dXfB4PLjyyivx1FNPrWjNRETUvswzn9hqVE74TK13ySRkLrfua9Dno9Avmp6H2FRjd7sGqlJTek2jCSSgT07UbzEVMFc+8eugBux2QDU9leeOd1RDy7XcLTBv0iBj85C55typs97qOmx84TFNQSKSSWP3UqI1qjh8ikajmJ2dvfSFAGZnZxGNRi3nnCtsQ/jWt76FD37wg/jYxz6GZ555BjfccANe+cpX4ty5c4tef+bMGdxyyy244YYb8Mwzz+CjH/0o3v/+9+Ohhx4qXPP444/j9ttvxx133IEDBw7gjjvuwG233YZf/vKXhWvm5uZw/fXXw2634wc/+AEOHz6Mv/iLv7hk2EZERO1Naprl3WHudFeufO7T+lY/SS1ntNuZ9kpRt+5ouIBEBIKAaa6PPjnWVFteW3Z95LDxqhNCWL6/lO2ySVQlUurLttwVzptbgyWrnyqWTFiHjfvWb95T4THNc5+kNAagE61RxeHTjh07cObMGfz7v//7stf9+7//O06fPo2dO3dazp8+fRo9PT2XfJzPf/7zeNe73oXf/d3fxZ49e3Dfffdh48aN+NKXvrTo9V/+8pexadMm3HfffdizZw9+93d/F+985zvx53/+54Vr7rvvPrz85S/H3Xffjd27d+Puu+/Gy172Mtx3332Faz73uc9h48aN+NrXvoZf+7Vfw/DwMF72spdh27Ztl1wzERG1sZIXgGVDgalscOp6h0/ayGnA1KIkenqhdF36Ocl6E0JYtytPZ4w5Ks0iwwrAWrO03rHtjmpERqPWlrslvl8Kv9+YT5Snh+dqvrZWVPoz0dwCt14WHTpOtEYVh0/vfe97IaXEbbfdhs9+9rMYHx+3fH5iYgKf+9zn8Du/8zsQQuC9731v4XMHDhxAJBLB1VdfvexjZDIZPPXUU7jpppss52+66SY89thji97m8ccfL7v+5ptvxpNPPols/pvmUteY7/O73/0uXvCCF+C3f/u30dvbi6uuugpf+cpXll1vOp1GNBq1/CIiovZSVn3AyqcywuGwDJ9ez/BJn52GNLevOZ1Qhxv3jSWlpw9QipXi+sT4Mlc3DqlpQE4rnmDlU02Yw21WPlGtyJmp4oEAFNM8OjOhqEYAtXC7yFxTVWs2inoOGy9wuS0/exg+UTVUHD7deeedeMc73oFUKoWPfexjGBoaQm9vL7Zt24a+vj4MDg7iox/9KBKJBN75znfiPe95T+G2P/3pT/GSl7wEb33rW5d9jOnpaWiahr6+Psv5vr6+srBrwfj4+KLX53I5TOe3/1zqGvN9nj59Gl/60pewY8cO/Md//AfuvPNOvP/978c3vvGNJdd77733IhAIFH5t3Lhx2T8fERG1Hlm2vTzDp8WY5z6t1453MpOGdvqEaRGAun2XsbNPgxJ2u2WwrwzPNkfIUPZ1wPCpJszfX3IaZ+xQ1UmpQ5+dKRwLfxDCvvTOlZZd79IZtmtVoN7DxgFAKIoRQC2sieETVcGa/if/3d/9Hf7pn/4Jv/ZrvwbACIvOnDmDqakpCCHw67/+6/jnf/7nsoqhD3zgA/jJT36C17zmNSt6nNLZUFLKZedFLXZ96flL3aeu67j66qvxJ3/yJ7jqqqvwnve8B+9+97uXbPcDgLvvvhuRSKTwa6UD1YmIqIWYW19UBWjgYKOeLK13qVTNh45LKaGdOgFki4+jDG6A4g/U9HGrwdJ6J5uj+klmSgIyVj7VRFm4zdY7qrLSXe6UrvJB42aKee4TAD3C1rvVkFK3VD7VY95T4bG93PGOqmvNz4jf8IY34A1veANisRhOnjyJeDwOr9eL7du3w+dbW39qd3c3VFUtq3KanJwsq1xa0N/fv+j1NpsNXV1dy15jvs+BgQHs3bvXcs2ePXssg8tLOZ1OOPnOHhFRW7O86Ha6Gmr3tEYifOVDx0UgWLPH0yfGIE3zR4TXC2XD5po9XjWJDr+xhXkiAQDQJ8ehbNhkvDPdqEw73QGAYPhUE6Xhk0yn6vpilVqPnLHucieWaLkrcHuMtur89wAZngMGhmq4whaTTAKmneXqsdNd4bHdnuK+HJkMZDYLYbfXbT3U/Kr2rMXn8+HKK6/E9ddfjyuvvLIseJqcnFz1fTocDlxzzTX44Q9/aDn/wx/+EC960YsWvc0LX/jCsusffvhhvOAFL4A9/8Wy1DXm+7z++utx7NgxyzXHjx/H5s3N8USViIjqxDzImi13S1rPHe9kIgH97JniCUWBun13Y4c3JmWDx7NZyLmZpW/QACytgQKWGV9URa7S8Cm9xIVEq2e03Jl3uVu+5Q4wvl+Zd72T0Qikri1zCzIrGzZez/DJWzJ0PMnqJ1qbmj7rSqVSePDBB3HLLbdUPP/orrvuwle/+lX8/d//PY4cOYIPfehDOHfuHO68804ARqubeXbUnXfeibNnz+Kuu+7CkSNH8Pd///f4u7/7O/z+7/9+4ZoPfOADePjhh/G5z30OR48exec+9zn86Ec/wgc/+MHCNR/60Ifwi1/8An/yJ3+CkydP4oEHHsDf/u3f4n3ve19lfxlERNQWzC+6udPd0oTdAZiqhWs190nqOrSTRy3vJCubt0B4PDV5vFpRunuNNs68Rm+9s4QgDicrAGtEqCpgrkRIcb4OVY+MRKytypdouStcZ577pOvGbnm0IpafhaoCuOswbDyvfMe7RJ1WQq2iJoMo/vM//xP3338//vVf/xWxWOySM5qWc/vtt2NmZgaf/OQnMTY2hssuuwzf//73CxVIY2NjOHfuXOH6LVu24Pvf/z4+9KEP4f/8n/+DwcFB/PVf/zVe//rXF6550YtehH/8x3/EPffcg49//OPYtm0bvvWtb+G6664rXHPttdfi29/+Nu6++2588pOfxJYtW3DffffhzW9+c4V/K0RE1OpkLmfd4YuVT8sSXl8hpKhV5ZN+4SxkvPhurQiGrFVETULYbBBdPYWd+mQkDJlMQLgbNEQztd2x5a62hNMJmZ/Jw8onqiY5u8qWu4VLA0Gj4jHfsyUjc0DJLChanGXek8dXl2HjBXYHYLcVA0jOfaI1ErJK+18eOnQI999/Px544AFcvHgRgDHcc2BgAL/zO7+Dt7zlLbjqqquq8VBNIxqNIhAIIBKJwG/adpSIiFqTjMeQe+6ZwrG6aw+UzpW9U9yOtIvnoJ87Wzi2XfvrELbqzZPQo2Fohw8WXgDBboNt/9VNG4bosXloB58tHCuDQ1A3b63fgpaRffbJwi5XorsHth2767yi1pU7cRRyeso4cLtgv/La+i6IWoKUOnJP/bIQPIhAELa9l6/49rmDzxR3bfN4YL/imloss6VIqSP3q8cLlbrKwCDU4W11XVPu0HPG0HkAoqMDtsuurOt6qPGsJvNYU+XTxMQEHnjgAdx///04cOAAgOLOch0dHfiXf/kXvOxlL4PSJDMViIiI1sIy5wac+XQpZXOfYjHLrJC1kLkctJPHi8ETAHXrjqYNngBA8XVA9/kKL+j0qQkoGzdDKGqdV2YlpQRMFTjN/HfeDITTWfxvnk6vqeOAaEGlLXcLRDBUDJ8SCchMmt8LLiXROMPGi2vwFsInmYjz+wutyapToWQyiQceeACvfOUrsXHjRvz+7/8+nn32WdhsNtxyyy144IEHAABerxcvf/nLGTwREVHbKGt5Yfi0LOG17spVzdY7beSUNQDp7WuJKjTr4PGcdSeqRpHLWV5AgTsB15RwmWbC6LJsp0GiSpS33K02fOq03l8kXIVVtTaZaJxh44U1mFu7NR0oeZONaDVWXPn04x//uDDHKR4vpp4vetGL8KY3vQm33XYburqMPuA3velNNVswERFRwzI/KbOpELaajFZsGcJuN4KJKs990qenIKdMu+y6XHVvXagW0dUDnD1dmC2mT4xD6emr86pKZKwhLKsdaqwk3JPpJAQDP1oDqZfschcIGt+vV0H4fIBNLX6vCs813veqBlM+bLwBZvqVDR2PWwNvolVY8bPil7/85RBCQEqJffv24U1vehPe9KY3FQZ/ExERtTuZMu10x6qnFRE+09DxKux4J9NpaGdOmB4AULfvMnYFawFCVaF090IfHwMAyPkoZDxetiV2PZVWADIIqa2y7zUcOk5rJKOlLXc9q74PIRQIfxBydsa4z8gcW7YuoXzYeP3/rhbd8a5ziYuJLmHVPXGhUAivfe1r8drXvpbBExERkYll5hPDpxWxtN6l04VduyohpYR26phlx0FlaCOUjtba9EPpte7Wp0+O1Wkli5MllU9g5VNtOZzGzmJ5MpWs31qoJciZqeKBAERoZbvclVLMM/yyuZrtatoKpNStO7P66t9yBxhveMBVfD4jueMdrcGKw6f3ve996OrqwtzcHD7zmc9g3759uPrqq/H5z3++sLsdERFRuzKGLLPyabXKho6v4cWJPnbRGJK7cN8+H5ShTRXfX6MSXi+EKVDTpyYhNW2ZW6wzc+WNogBsP60poSiWgK9s9hzRKkhdhz43UzgWgdCqW+4Kty3ZQEJG5ta0tpbWgMPGF5irnxg+0VqsOHz63//7f2N0dBTf/e538frXvx5OpxPPPvss/uAP/gCbN2/GS1/6Unz1q19FOByu4XKJiIgaVC5nDONcwFajFSkPnyprvZPxOPTzI8UTqgJ1+27jhXkLUvr6iweaBjk9ufTF68xS+eR0NkTrSKsTpsoEpDgQmCono+E17XJnJpwuwF2cDyTDDJ+WUvqzr3RDjnoSHtPsqVSysd7soKayqmdkNpsNr371q/FP//RPGB8fx9/8zd/g+uuvh5QSjzzyCN7znvegv994MpTL5ZBMsuyXiIjagyzZAcbyYpCWJOx2a0l/BZVPUteQO3nU2OkrT928FcLdukNRRVc3YC9WFOkTY0b1XSMw7zLIlrv1Yaq0LP1eRLQaunkHTSEqbrlbYG69k/PzkLncMle3L8vPPlWxhHb1Zpn7JAGZTNRvMdTUKn470O/3493vfjceffRRnD59Gp/85Cexc+dOZPLbu87MzKCvrw9vf/vb8aMf/ahxnhARERHVQmn4xLa7FTNXP8nY6sMn/dxZIFF8Miw6OyF6+5e5RfMTimrZOUrG4w0zT0VmzLPPGD6tB0vYnclA6qxMoNWTul4YEA5UtstdKREImh5AGpVVVMYybNzbGMPGF5QOHQdb76hCValF37x5M+655x4cOXIEv/zlL/F7v/d76OrqQiwWw/3334+bb74ZGzdurMZDERERNSRZ2urC8GnFLK136TRkNrPi2+qROehjptmTdjvUrTsa6ol7rZQNHp+o/+BxKSWQKf77CYejjqtpH9zxjqpBRsNGC3neWlruFgh/EFCK349lJLzm+2w1UtfLwqeG4nIb8/vyOPeJKlX1QQjXXnstvvCFL2B0dBTf+c538LrXvQ52ux1jY/V/QkRERFQz5jk3druxQwytiPBZZ1ustIJHZrPQTh63nFO37YCwt0fgIdxuS1WBnJ6qf0tLJgOYit1ZAbhOSv6ey8JwohWodssdYOyWZtkggXOfyiUTlrbxRgufhBCWuU8ywbY7qkzNpnDabDb81m/9Fv75n/8ZExMT+Ju/+ZtaPRQREVHdmV/sCbYarUrZ0PEVtN5JKaGdOWmpslH6B6BU4cVSM1H6TNVPug59aqJ+i0HJsHHAsgsb1U7pjDnOfaLVqkXLXfG+TLvepVKQKc4FNit9w6WRho0XcMc7qoJ12QImEAjgd3/3d9fjoYiIiOrC8mKP1R6rImy2VQ8dl9OTkOZ36d1uKJu21GJ5DU2EOgFH8QWiPlnnweMl7V4cOL5ObHZjSPECVj7RKtWi5a5wX6ah4wCrn0o18rDxBZa5T9ksZGbl7fFEC1pz/2EiIqJ1JKW07vDF8GnVzK13pVtOl5LpFLSRU6YbC6jbd7Vlq6NQFCjm4eqJJOR8tG7rKat8YhXguhBCcMc7WhN9eqp4oAiIzipWkXq8gKmKSjJ8spCx4s+8Rhs2vsDcdgew+okqw/CJiIhorbJZQNeLxy6GT6tlHTqeWfJdVSkltJPHgFxxNy9l4yYovgZsU1gnSm8/YHqtUtfB4+bwyWZry0CwXiyhN8MnWgWp65BzJS13tuq03AH5mUGm6icZDUOaf2a2ManrliCn0eY9LSjd8Y7hE1WC4RMREdEalVYZcObT6pXNfVqi9U4fvQAZLVb2iA4/lMH23lFXOF0Qwc7CsZydXtWOgdUkzW133OluXZnnPsl0qr7tl9RUZCRsDfS7eqr+GIppcwRoOmSsfhWaDaVs2HhjvpEi7A5r9RrDJ6oAwyciIqK1KgufWPm0WisJn2Q8Bv382eIJVTXa7RqwRWG9KX2m1jtdQp+arM9CMmw/rRvz33dOs8zvIVqOPlPSchfqXPriComSuU9svTOUDRv3NWblE1BS/cTwiSrA8ImIiGiNyuarsPJp1YTNZhmyWjr3SWoacieOAqZqDnXLtrJdvtqVCHZa/t/pE/UZPC4ts8/4dbCeuOMdVaK85S5U1Za7wv3aHRBe045pDJ8AWOc9QVUAV+MNG19gDp9kMgEp2TpJq8PwiYiIaK3MO0s5HBAK59xUwlz9JGPWd4P1c2eAZHF7btHVBdHdu25ra3RCCGv1UypltNKsI6lrxvyzBdzpbl2VVZoxfKIVkJG5kpa76u1yV8oy9yker1t7cCMxVz4Jb0dDV/Kaw0Pokrtq0qoxfCIiIlojyZ3uqsLSepfJFHZO08Oz0MdNQ7QdDqhbdjT0k/R6UHr6ANPfiT65zoPHS4bEC4ZP66vke4/kC0NaAX1munigCIhQFXe5KyECpa134Zo9VjMoHzbuXebq+uPQcVorWzXuJBKJ4PTp04jFYsuWeN94443VeDgiIqKGYmlvYatRxUpnXch4DBAC2qnjlvPqtp0Q9uq3hTQ74XBCdHZB5l9MytkZyEx63UIgy7BxAHBy4Ph6EqpqDAReqD5j5RNdwuItd1V5ebgo0eE3Wss0o11Lj8xB6WnjCtYmGTZe4HYbO6vmlywTcaAGw+mpda3pu8ujjz6KP/zDP8Qvf/nLS14rhECOgw+JiKjFSCk5ZLlKhNdnfWIbm4c+OQFkiq1cysAglJLBtVSk9PVDW6hkkIA+OQF1w6b1efCMNXzi18L6Ey4XZD58YuUTXcp6ttwBgFAUCH8Qcm628PhSyratYm2mYeMAjJECLnehBZ6VT7RaFYdPP/nJT/CKV7wC2WwWTqcTw8PD6O3thaKwk4+IiNpIJmN955IDsCsmVJvlia0+MQZkTW9cedxQNg3XZ3FNQviDgNsFJI3gQZ8cgzK0AULU/vmZpfJJALCz8mndOV3AvDHAmAPH6VLWs+VugQiGCuETMllj1zRvY4cutWIdNq429LDxBcLjhWT4RBWqOHz6oz/6I2SzWbz5zW/Gfffdh66u2n+zIiIiajQynbSeYNvdmghfR+GJrSV4UgRs23dzmPslCCGg9A5AP3vGOJHOQIbn1uVFpaXyye6A4BuS6044XShE4Zl0W1eV0PLWu+VugRIIwbxHmh6eg9qu4ZNl2LivKb5WhcdbaO1GKg2p5Yw3johWoOJnBU8//TSCwSC+/vWvM3giIqL2lWarUTWJJV6EKBuHl/wcWSk9fYBiGjw+sT6Dx62D9xnC1oOl8lKX0E4eLbThEZmtd8tdgctleZNGRubW53EbTPmw8eb4+VY+dDxRp5VQM6o4fHI4HNi+fTtUle9AEhFR+7K0tghwe/k1WuwJuAgEoAwM1WE1zUnY7RCmIbAyPLcuLVjSXPnEr4O6KP36kdPTyD33NPTwbJ1WRI2qHi13QL460zS3T85HITVtmVu0qNJh4w0+72lBafgEtt7RKlQcPl133XUYGRlZdnc7IiKiVmcZ6utwstVojQpDxxfYVGN3uyZoR2gkSt9A8UAC+sR47R/UXPnk4LynehBenzEXzfzlkslAO3II2pmT7fkin8pIXbO23AXXp+XO/HgFuoSMRtbtsRuFZd4TmqfyCU6nMZ8qj3OfaDUqfob8R3/0R4hEIvjzP//zaq6HiIiouaS50101CVUtvgMvAHXLDv69VkD4OgCPp3CsT45D6voyt1gbmcsB5mCD/2Z1ow5thHrZlca26Cb6+BhyB5+BXvKil9qPDIetLXedPUtfXAPCH7QEpO3YemfZ6a5Jho0DRuWaMP1sYfhEq1FxxD04OIjPf/7z+PCHP4zHHnsM73rXu7Bt2zZ4vd4lb7Np0zpt9UtERLROLO1MnHNTFeqOXZCzM4DTBaXDX+/lNCUhBJS+AehnThknslnIuRlLO15VZUpmn7Htrq4UXwfE5VdBPz8CfWy0+IlkEtrzz0IObYQytImVmm1Kn5kqHigCItS5ro8vbDaIDj9kNGqsJzyHdhvk0ozDxhcIjxdyYVfNRJwbG9CKVRw+DQ8PQwgBKSW++93v4rvf/e6y1wshkMvllr2GiIiomUhdt7zotgz7pYoJRYXo7q33Mpqe0t0L/dwZQDMqnvSJMSg1Cp9kyeB9BrH1J1QV6vA2iGAntFPHgUzG+IQE9AvnIcNzULftslQxUOszWu6KM8DWu+Wu8LiBUCF8QjIJmU61TZVr2bDxJpn3VGCe+5TTjO8t/J5PK1Dxd5pNmzYx4SQiovaWSQOm0Yft8sSZmoOw2aB09UCfnAAAyEgEMpmAcNcgbGDlU8NSgiGIK66GduYU5HSx4kXGYsgdfAbKpmEo/YN8Xt8mZDhsaZGtVSB9KSIYAs6fLRzL8ByEeVZdK0vErcPGm2XeU175jndx7nBKK1Jx+DQyMlLFZRARETUfVntQo1P6BgrhE2BUP6nD26r+OJavBUUAdnvVH4MqJ2x22Hbshh7qgnbmJLDQjaDr0EdOQ87NGoP9+T2s5dW75W6B8PoAuw3IGv8X9cicdaOEFmaZ9wRAeDvqtJLKlFZLykQcqNP/I2oubPQmIiKqVMn29ax8okYjfB2Wlg59ahJSr/6OZ9Jc+eRwsIqmQSndPbBdcbV1tzEAMhJG7rmnjP8f3Mm6ZZXvctcJoa5/yx2QH1wdCBaOZSQMKWu3KUIjsYRPNhVospZ9YbMDzuKOphw6TivF8ImIiKhClmHjigDYakQNyFJNkMtBzkxX/0HMs88czfVCqt0IhxPq7n1QtmwDVNNLgZwG7eQxaCeOQmaz9Vsg1YwMzxVmwAGA0tVdx9UASsAUguY0yFhs6YtbiDTtONlsw8YXmFvvGD7RSq056k6n03jwwQfx8MMP4/jx45ifn0dHRwd27tyJm2++Gb/zO78DJ0t4iYioBcmUKXxyOJvyCSS1PtHVA5w9XdhaXZ8Yg9LTV9XHsLTd8XlfwxNCQO0fhBIIQTt1rLBzFQDImWnk5qNQt+2AEmQrTSvRzcFzHVvuFohASQVeeBZo8R1Opa5DJhOF42ab97RAeLyQc3PGQTIJqevcPZMuaU3h09NPP43f/u3fxsjISFmJ7tNPP41vfetb+NSnPoV/+qd/wtVXX72mhRIRETUc0wtuttxRoxKqCqWnD/rYKABAzs9DxmNVe9EjpSypfHIsczU1EuF2Q923H/rFC9AvnAMWns9nMtCOHILs64eyeSuEqtZ3obRmjdRyV1iD0wl4PEDCCGNkJAxsrOuSaq/Jh40vsAwdlxJIJgGvd+kbEGEN4dOFCxfw8pe/HHNzc+ju7sa73/1u7Nu3D319fZiYmMChQ4fw1a9+FadPn8bNN9+MZ599FkNDQ9VcOxERUV2Z2+44qJcamdLbXwifAECfHIe6ZXt17jybtbyYAoPYpiKEAnXDJijBEHInjxkvIvP0iXHokTDU7bugtHhFSqtrtJa7BUogBH0hfIrNQ+ayxkyhFlU2bNzXXMPGFyy64x3DJ7qEisOne++9F3Nzc3jd616H+++/H263u+yaj3/847jjjjvw0EMP4d5778UXvvCFNS2WiIioUUhdAzKZ4okmGxhK7UV4vBB+P2Q0CgDQpyagbBquSuWDZdg4WPnUrISvA7b9V0E/N2IJKpFKQTt0AHJwI5QNm9ha06SsLXcKRKirfosxEcEQMHbROJBG9ZPo6qnvomqobNh4s4b1Lrcx6zL/xgPnPtFKVPzT4wc/+AG8Xi++/vWvLxo8AYDL5cLXvvY1eL1efP/73694kURERA0nXfKCu1mfQFLbsAwe13TI6amlL16N0q8FDhxvWkJRoQ5vg7r3MstuVpCAfvE8tOefhUwklr4DakhlLXehUMO0Ugq/HzAFmnp4ro6rqb1WGDYOwAihXcUMgOETrUTF4dPo6Cj27NkDn2/5PlWfz4c9e/ZgbGys0ociIiJqOJad7oDmffeS2obo7AbsxUonfWKsbGZnJUornzhwvPkpgRBs+6+B6Om1nJfxOHIHn4Y2drEq/3dofci5kpa7zsapLBKKagRQeTIy17L/t1pl2PgC7nhHq1Vx+NTR0YGJiYkVXTsxMQEve0CJiKiVlFU+8QU3NTahKJZd7mQ8XjZ/pCLmrwVVARqkooLWRthssG3fBXXnbktoCV1CHzkN7cjB8hCeGpI+a6pyVJS673JXyrLrXTpjmTvWSmTZsPHmnPe0wDL3KZOBzGbrtxhqChWHT9dccw0uXLiAf/zHf1z2ugcffBDnz5/HC17wgkofioiIqOFYXnQpCmDnnBtqfJbWOxjVT2tlqXxyOpu2jYQWp3T1wLb/amM2j4mMRJB77mnoUxMtW6nSCqSmQc7NFo4bqeVugVLyf0uPtGbrXfmw8SavfCopLpFJVj/R8ioOn/7n//yfkFLibW97Gz784Q/jzJkzls+fOXMGd911F97xjndACIH3v//9a14sERFRo5ApU/jEF9zUJITLDREIFo7l9BRkbo3vVpvCJ857ak3C4YS6ex/UrduN6rYFOQ3ayePQThxh1UODKt/lrnFa7grcHsC0UYFs1blPpnlPTT1sPK98xzvOg6PlVRw+vepVr8JHPvIRZLNZ3Hfffdi+fTs8Hg82b94Mj8eD7du346/+6q+QyWTwh3/4h7jllluquW4iIqL6MlU+cdg4NRNL9ZOuQ5+aXNP9ybQ5fGIFYKsSQkDpGzCqoDqs7UJyZga5A09BNw21psagz5S03AUbq+UOMP5vmSvrZDRi7CjbYsyVT808bLzA7gBsppZczn2iS1jTXqn33nsvvvvd7+KFL3whhBBIpVI4f/48UqkUhBC4/vrr8b3vfQ+f+cxnqrVeIiKihmB5wc15T9RERKjTUmWgT1Y+eFzqOpDNFE8wiG15wuWGum8/lE2bAfOL52wW2tHD0E6fgNRaLzhoRlLTIMPmlrvOhmu5W2BpvdN1yGi0foupAalrlsqgZp/3BORDQw4dp1WwXfqS5b361a/Gq1/9asTjcZw8eRKxWAw+nw/bt2/nkHEiImpJUtMAc4sJX3BTExGKAqW3D/qF88aJRBJyPgLhD67+zrIZwJRbMYhtD0IoUIc2QQl2InfyKJAoDojWJ8ahR8JQt++C0uFf5l6o1mR4tqTlrruOq1me8AcBgcL3ExmZA0pmQTUzmUgAppC/2ec9LRAeD2Q0AsAIn6SUzV/RRTWz5vBpgdfrxRVXXFGtuyMiImpcJTs8CRfDJ2ouSm8/9IvnCy/09IlxKBWET7Jk10ew7a6tCK8Ptsuvgn7uLPTxi8UgMpWCdugA5OAGKBs2QyhraragCukz08WDBm25WyDsdgivDzJmtKbp4Tmom+u8qCqS5nlPML52WoFl7pOmG8+PXO76LYgaGn8SEBERrVLZ9uKsfKImI5wuywtROTsNaW6fW6mMNXziwPH2IxQV6vBWqHsuB8yVbxLQL16A9vyzbMepg2ZquVtg2VExkbDupNnkLDvdtcCw8YKyoeP8Wqelrajy6Rvf+AYAIBAI4DWveY3l3Gq89a1vXfVtiIiIGk1ptQcHjlMzUvoGoC1swa5L6JMTUIc2ruo+yiqfnKx8aldKIAix/2poI6cgTUPsZTyO3MFnoGwchjIwxJacddJMLXcLRLATWGgHBiAjYYievjquqIosw8Y7WubrYNEd7xq3wI7qbEXh09vf/nYIIbBr165C+LRwbjUYPhERUUtImSqfVMW62wtRkxDBkFGpkg+Q9MlxKIMbVvf8zlyZYLdBKI1dWUG1JWw22Lbvgt7ZBe30CSCbMz6hS+hnz0DOzULdvpOB/TqwtNypjd1yt0D4fEZVUM4YWK+H56C0QPhUPmy8NVruABjVdC5X4XkRK59oOSt6tvzWt74VQggMDAyUnSMiImo3MmMKn5wu/jykpiSEgNLXD/3cWeNEKmVUGqxiyK9l10cHh42TQenshvD5jZ3v5oqtXzIaQe7A01C3bIPo7uX3zhqRmmb5exfBxm+5A4xB9sIfhJydAWAMHW+FAdatOmx8gfB4IRk+0QqsKHz6+te/vqJzREREbcFU+cR38KmZKT390M+fK7ww0ifGrFueX4q58ok73ZGJcDig7toLOTUBbeRUsQVM06CdPA4xOwN163YIO1s1q02GZwHd3HLXU8fVrI4SDEHLh0/I5iDjMQhfR30XtUatOmx8gfB4CoEhUklIXWMVLC2KA8eJiIhWyVLtwZ3uqIkJhwOis6twLOdmVjXk13wtK5+olBACSm8/bPuvhujwWz4nZ2eQO/A09IUXrVQ1+sxU8UBVVlXNWG+la5WRuTqtpHqsw8ZtrTNsPM8y90kCMLUYEplVHD79xm/8Bj74wQ+u6NoPfehDeNnLXlbpQxERETUMmcsBuVzxRIs9iaT2o/QVxypAAvrkxIpuJzWtONMH4NcCLUm43FD37YeyaRhQTC1U2Sy0Y4eRO3UcUssteXtaOaPlrhjYNMMud2bC6QLc7sKxDDd/+GQdNu5r+jbCUuVDx9l6R4ureELqT3/6U+RyK/sh8eyzz+LRRx+t9KGIiIgaRzplORRsNaImJ/wB48VeMgkA0CfHoAxtgBCXeI+ypEJKONg+RUsTQkAd2gglGELu5DFLdYScnEAuGoa6bRcUf6COq2x+cq6k5a6zeVruFijBEPT89yM5Pw+Zy0E06cYexrDxYhjTavOeAAAuN6Aohf93DJ9oKevSdpfJZKA2UeJORES0FFkWPrHag5rbQmtUQTpjqZxYirn9FAArn2hFhNcH2+VXQRkcAswFIKk0tMPPQTt7BtIUntDq6LMlLXeh5mm5WyACweKBlJDRcL2WsmYyHjda0fJabd4TYPwMER5P4Viy7Y6WUPPwKZlM4vjx4+jq6rr0xURERA2OL7ipFSk9fZZ2KH1i7NI3Kqt8YhUgrYxQFKibt0Lde7l1UL0E9NELyB18xnjRTqtS3nLX1ZSDn4U/aPl+1Mytd5Z5TwCEt7mHpy/J1HrHyidayorrF7/zne/gO9/5juXciRMn8M53vnPJ2ySTSTzxxBOYnZ3FG97whspXSURE1CjMlU82tWlbAYjMhN0O0dUDOTUJIL/FeSoJ4XIveRtLECsAOOw1XiW1GsUfhNh/NbSzpyHNs8YSCeSefwbKhs1QBje03IycWpFzMyW73HXXcTWVE6oK0eGHjEQAAHokjOaL0Azlw8ZbM6QXHm+xwCubhcxk2IpNZVb8jPnZZ5/F17/+9cKxEAITExOWc0vZsWMH/uRP/qSS9RERETUUmSqGT2y5o1ai9A1Ay4dPxuDxcaibtix5vWVXPIfj0jOiiBYhbDbYtu2EHuqCdvoEkM0an9Al9HMjkOFZqNt2cWfRFdBnposHTbbLXSkRCBXCJ6RSlwzDG5WMzRc+bsVh4wvMbXeAUf3E8IlKrTh8uvXWWzE8PAwAkFLine98J3bu3Im777570euFEHC73di6dSuuvvrqlv1CIyKi9mKZ+cTwiVqI8HUAHk9hELQ+OQFlw2YIZYlQyVT5JBz8WqC1UTq7IDo6oJ0+ATk7Wzgvo1HknnsK6radULqab3j2epFaztKe1qwtdwuUYAj6uZHCsR6eg9rfXOGT1DUgWZx/tB7DxqWUiCRimIzM5H/NQkodIV8AXR1BdPoC6PQF4HG6q/r6fNEd75o4/KTaWHH4dMUVV+CKK64oHH/iE5/AFVdcgbe97W01WRgREVGjkVJa2u5Y+UStRAgBpW8A+plTxolsFnJ2BqJ78Rf8lsonJ9/hprUTdgfUnXshpyagjZwGNM34hKZDO3EMcDihdPjru8gGVbbLXZO23BV4vIDdXqiEk+E5oH+wzotanfJh49Wb96TpOmbm5zAZmcVkZAYT4RlMRWYwGZ1FOpu55O2dNjs6TWFU8eMgOn1+OOyr+54u7A7rvxfnPtEiKh5UMTIyUsVlEBERNYFcDtBMuzC16OwGal9Kdy/0c2cK/8/1yTEoi4RPUkrLwHEOG6dqEUJA9PZDBILQTh6HjOZbr6SEduIIxOVXQ9g5X6xUK7XcAfn/B8FQcQ5dNAyp60tXYjagsmHjFVQ+ZbIZTEaNgGkyPIuJfEXTzPwctDXsCpnOZTE2N4WxualFP+9zeUpCKePjLl8AAa8f6iL/DsLjhYyEjQOGT7QITkklIiJaIUvLHcAZJNRyhM1mBFAT4wAAGYlAJhMQbus8j7IgluETVZlwuqDuvRzayWOQ0/kXyOkMtFPHoe7ay5EeJkbLXbFVcT1a7rJaDrFkHPPJOHK6hg6XFx0eL5w2R9X+bZRAsDiHTtMhY1FjJ7wmYZ73BLtt2e+TsVSi2CoXnsFEvqIpHI+uaQ1uhxOqoiKWSlz64pL1xFIJnJsu3/lUEQIBrx+dvgC6OhaqpQIIZFMI5jLwqXYgmYSUOmcBkkVVwqdf/OIXOHDgAGZnZ5FdGBJYQgiBj3/849V4OCIiovooDZ/YdkctSOkbKIRPAKBPjEEd3ma9yNxyB0CwCpBqQAgBdesO5OIxIJkEYLSX6WMXoQ5uqPPqGofRclfs76p0NpY5UJpPxhFLJco/TsURS8aRWqK1y26zG0GU2/rLbzn2wefywKYuH5CVVm/J8BzQTOGTqfJJeH2QAOZikULAtDCTaSIyg0Q6uabHCng60BvoRG+gC72BLvQFjd99Lg+EEMhkM5iNRTEbC2M2FsHsfP73/K+VtOot0KXEXCyCuVgEp8bLP28XCkJ2JzojF9AV6CqroHLxzYq2tabw6dFHH8W73vUunD59etnrpJQMn4iIqOlZtpYHOHCcWpLw+iB8PsiY8cJJn5qAsmnYUkkhy8Infi3UmqbryOSyyOQyyOZy+Y+Lv7Lmj7UcpJTGLwCA6WMpISVQ+IyUkMifkxILnzE+xiL3k79CLnJd/vNlj1c4b7xwXfZ+Fm5ruh+h63Akk3ALFS5VhWvmAjyTm+DpCMJld8DtcMHlcBZ+d9odUNqoMkqfMbVOqSpEMFg4LAZKCcwnY4sGSrGUcbxUoLQa2Vw2H3CEL3mtx+leJJgqBlQdLi88TgdcqTSEEEb4tMwOnI0ip2mYCk9jbPI8ptJJTGWSmBrNYerpHyGbW7xQYyUUIdDVESqGTMEu9AW60BPohMu+fKDjsDvQH+pGf6h8FpiUEol0yhRMRTAbC2NmPoK5WBhz8eiqWvyyUsdkJonJsbPA2Nmyz3ucblMrX7FyqrMjgJA3cMlQkppXxeHT4cOH8cpXvhLZbBZvfvOb8cgjj+DChQv46Ec/ivPnz+PAgQM4cOAA3G433vve96Kjo3oD1oiIiOrCXPlkt0PwCRK1KKVvAFrshHGQ0yBnpiF6+gqfLwtiuaU29EI4VP6rNBjK5LLIZI0gKWM6zmqL3yaTy65pvktLmjm/5KcEAJfDCZfDBZfdCbfD+OVyuPK/O+G2O02hlbMQXrkdTjjtzkVn2jQKS6AUjyIychTz2QxiWhYxVSD+HyP5QCmBVDZ96Tusk0Q6iUQ6iYnw9LLXqRDw2ezoUO3omDwNv8dXDKhKKqvstvWbB5bKpAsDv82/ZubD+ZC1MnabHb3+zrKQqasjVJNgRggBr8sNr8uNjd0DZZ/XdR3RZAwz5mqpeaPyaWY+jGgytsi9Lm3h3/3CTHnZlAAQ8Hag0xdAKB9KmXfp6/D42ipYbjUVh0+f/exnkUql8NWvfhXveMc7cMMNN+DChQv41Kc+Vbjm4Ycfxrve9S78x3/8Bx5//PGqLJiIiKheZMq80x3Lxql1ia4e4OxpIGfsNqZPjEExhU+WtjtFAdbxBV+1JTNpnJ26iFQmfcngKJPL5Y8zheNiOKTV+49CeRLGv2syU3nw4rTZrWHVQnhlLx4vhFcuezG4Wrjepq7uZZY5UFqoRCpvfVv/QEkA8Lg8Rsjj8sDn9ubb6oof21S1sMbCr1Qc0YTxcTwVR+VRDKBBIpLLIJLLABeX77hx2R1lwVR5C6APXqcbygoCRikl5lPxfJucNWiKJFYXupTyOt2FcKnX31kImQJef0MFLIqiIOj1I+j1Y9sin89qOczlQ6mpI89hLjGP2WwaczKHuWwGyUxqkVstTgIIx+cRjs8DExfKPm9TVHT5QxgM9WAg1IuBzh4MhnrR4fZyDlwTqDh8+ulPf4pAIIC3ve1tS15z00034V//9V9x3XXX4ZOf/CT+9E//tNKHIyIiqjvLwHG2GVELE6oKpacP+tgoAEDOz0PGYxBeY7cmS+WTs3oDhtfT7HwYPzvyFH514jlk1tAK08gEAAgBAQHjn0gYu4jlzyuFzyP/b2hcJyAK5wEBpXBb03WltzFdb34MsdT9mB6j9LFL16pLiVQ2jVQmjWQyjpSWW1OYsRLpXBbpXBaRxPylL16ETbUVwyh7serK7XBCEUpJy1sjBErGcTFc8sLn9sDr9Ky5CkzTdcTTxRBtPmGEU8ZxrHA+moyvavbQYlLZDFLZDKaic8teJ4SAb+HvwPRn9nt8yGm5wiymqcjMmkJMAAjanejrGTRCpnw1U1+gC16X59I3bgJ21VaYNbU9noZc2HnR5YT9ql9DMpPKt/KZ2/ki+Ta/KHJabsWPldM1TISnMRGexjNnjhTOe12efCDVg4HOXgyGetEb6GILX4OpOHyanJzE3r17C4mxzWbcVTKZhNvtLlx37bXXYteuXfjXf/1Xhk9ERNS0pJSA6QU3Z9xQq1N6BwrhE5AfPL51h3FgejEmmmx47MjkRTx6+Ak8f+5EYc5QPdhVG+w2OxyFX7b87w7YCx/bYVftcNhN15mO7aoNDpuj5PZ22Gz2hqqcqCaZiCPz3DPI5LJI6RpSMofMxmGk7TYkM/mAKpMyPs6WHxvXpGrexpjTcphP5jCfXJ8t5wUAj92BDl9gyUDJ5zaOqxEorYaqKPC7ffC7fZe8NpPNYN5U7TWfjCN8/gzmY1HEtCzm9SxiQmA+GYcuK/83lFIW7r8aVEVBd0coHzB1oSsSRY9U0ONww9nZDduey6ryOI1OeLzF8CmVhtRycDtcGOpyYairr+x6XUrEknHMxMKFgGqhnW82FkEkMb+i79PxVAInxs7ihGnGlKoo6A10GYFUqBeDnb0YDPXA5/ZW7c9Lq1Nx+BQIBKBpxfLizs5OAMDZs2exe/duy7UOhwMjIyOVPhQREVH9ZbOA+cWKi+ETtTbh8UD4A5DRCABAn56EsnkLhGqzVj41Qfik6TqeP3ccjx5+EuemRi99AxiVK6WhTmkQZFfzH9vzodDCedNtSn/Z879aNRyqNeHxwr5tB5STx+FSbQCcwFwEtv1XQdhXNntMSomslssHU0Y4lSqEUylTiGUEVclCaJU/zqSRXUW1RqUKFUqWEKn4sTedgWdiEh02OzyqHY49+6CEumq+rlpy2B3osjvQ1REsnNOCfdDPnCoc2/ZfBenxIplOFiqmrG1/MUv7XyK98ravS3HaHYUWuYVqn95AJ7o6glDzmzJITUPuicewUJ63UDHaDoTHGuzIRAKiw7/k9YoQ8Ht88Ht82NJbvoNlTtMQjkcLVVOz8xGMh6cwNjdltOYtQ9N1jM0Z1wKHC+c73F6jZS/Ug8F8215PoLPw70e1U3H4tGnTJpw9W0wWL7/8cvzbv/0bvve971nCp5GRERw7dgyBQGBtKyUiIqojWfLklTOfqB0off3Q8uETNB1yegro7QcyxdaYRv5aSGXTeOLEQfzsyFOYi0UWvWZDVx9u3HstNvUMmkIl24rmwVB9KD190CNhyKlJ40QmA+3kMai7L1tRC6gQovBv7fdUFgzkNK0QVhWDKmt4VRZmZY3wKqdphZYvn7n1bZUVSrmjhyBd+Rf7NhUiEKroz9LolEAI5honPTwH1euD1+WB1+VBf6hn2dsbVWiJYjBlmqkVNbcAJmLI5We3+Vxe9AU7TQGT0Srn9/gu+X9MJuIw94W2c/iERBxYJny6FJuqotsfQre//P92Ip3E2NwURmcnC79PhKcL/4ZLMf7tz+D46JnCOVVR0R/sQn/IqI4a7OzFQKgXXpd7mXui1ao4fHrpS1+Kv/iLv8DIyAiGh4fxxje+EZ/+9KfxsY99DJFIBC984QsxMTGBz372s8hms7jllluquW4iIqL1VRY+sfKJWp/o7Absp4CsUeWhT4xBDXUCpjaIRmy7m4tF8V9Hn8Ivjx9YdPt4AWDvxu24cd+12NK7oSlnVrU7dct25OIxIJEAAMhwGProeahDm9bl8W2qCp/qga9Oc3tkLgcZKc41EqEuiFYNTF0uwOkstL7LyBwwtHHFN7epNoR8foR8y4cgMj9bDBBwr+H7moxZK3KEr33CJzidgKoAmhEXykTt2k49Tje29W/Ctv7i17ym65iOzhYDqblJjM1OInqJ9kpN13BxdhIXZyfxlOl8wOMzVUkZgVS3P9TQu2E2sorDp9e//vX49re/jZ///OcYHh7Grl278KlPfQof+9jHcO+99xauk1Ji69at+OxnP1uVBRMREdVDaeUTGrjag6hahKJA6emHPmrsOiTjcegzJduiN9DXwvnpMTx6+Ek8N3J00a3O7TY7rt12GV689xr0+DvrsEKqFqGqsO3Yg9zzzxRe6Ornz0L4/FACwfoubh3IuRlAL/4fV7q667ia2hJCQAmGoE+MAwDkfBRS0yCqPExaCAG3Y+1vLMm4aRc8u60pWpOrRQhhzH2aNwI4mQ+H14uqKOgLdqMv2I2rTOdjqYSpSmoSo7NTmIxMX3L2WyQRQyQRw1HTLos21Yb+YFdhjtTCTCkP35S8pIrDp+uuuw4nTpywnLv77rvx4he/GN/85jcxMjICt9uNF7/4xfjv//2/o6OjY82LJSIiqpuUKXxyOCA4G4DahNJXDJ8AQL943vJ5UYUXa2uh6zoOXziFRw89gTOT5VtzA4Df7cX1e67Br++8Ah4n2yhahfB4oG7ZDu3kceOEBLSTRyEuvxrCsbL5T83KEgK3cMvdAhEMAfnwCbqEjEYgQo0ZIJvDJ+HtaLvKSmv4FIOUsu5/Bz6XBzsGNmPHwObCuZymYapQJWUEUmNzk4illg/McloOF2YmcGFmwnI+6PUbFVKhXgzkZ0l1dQTZwm1Scfi0lBtuuAE33HBDte+WiIioriR3uqM2JVxuiGAQMhw2TmSz1guc9XmRn8lm8MSp5/Hzw09iej686DUDoR7cuO9aXDm8h1tutyilpw96NAI5mX8hmMka85/2rGz+UzNqq5a7POEPGv2y+WIvGZkDGjB8kpoGJIvhRTvNeyowz33KacaMwAaqkF1gU9V81VIPgH2F89FkDGP5IGp0bgpjs5OYjMxecnfFcDyKcDyKIxeKw/HtNjv6g90YDPVgoLMXg6Fe9Id61tTW2cwqDp++8Y1voK+vDzfffPMlr3344YcxPj6Ot771rZU+HBERUV1Z2u4a8EkUUS0pfQPQFsInM5sKoVb9vcxlRRLzeOzoM3j82LNIZhbfxWr30FbcuO9abO/f1LIBBBWpW7YhF5svzn+KhKFfPA91w/rMf1pv7dRyt0DYbBAdfshoFEB+6Hid17SYsmHj7TTvKU94rHPQZCLe0BtTlPK7ffAP+bBraEvhXE7LYSI8Y5kjNTo3hUQ6uex9ZXNZnJ8ew/npMcv5kC9gCaSGOnvRadrhsVVV/Gzh7W9/O2644YYVhU/33nsvHn30UYZPRETUlKSUQIaVT9S+RLATcDgsu9wBWNdZJqOzk3j08BN49syRRed02BQV12zbhxv2Xou+YHNvN0+rIxQVtp17kDtomv904SxER2vOf2q3lrsFIhAqhE9IJiHTqYb7eVw2bNzbfqNnSne8k4l4Q1aprYZNtWGoqw9DXX2Fc1JKRBOxskBqKjprPG9cxlwsgrlYBIfOnwQAbO3biPe+4o01/TM0gjW9VXWpv9TVXkdERNSQMhnLu8zC1VhPdolqTSgKlN4+6BdK5j3V+N1sXUocu3gajx5+EifHzi56jdflwYt2XYUX7boSPrd30Wuo9Qm3B+rWHdBOHDNOSEA7cRRif2vNf2rHlrsFIhgCzhe/D8jwHETfQB1XVM46bNxuhPZtRtjsRjt22nizopY73tWTEAIBbwcC3g7s3rC1cD6by2I8PI2xuSnLkPOk6U3MUoOdveux5Lpblzrp8fFxeL18MkBERM2JO90RAUrvgDFs3NxSUqPKp2wui6dPH8ajh5/EZGRm0Wv6Al24cd+1uGrrXtjXufWPGpPS3QsZjRR2RUM2C+3kUah7Lm+Z9svylrueOq5mfQmvz9g9LpsDAOiROSgNFz4VK5+E19cy/+9WS3i8kC0ePi3FbrNjY/cANnYX/29KKRGOz+cHm0/mg6lJTEfnIIH83KnWt+Kf1OfOncPIyIjlXCQSwaOPPrrkbZLJJB555BEcP34c1113XcWLJCIiqquS8KnRyvyJ1oNwOiFCnZCzs8WTVQ5iY8k4Hjv2DB479iziS+w4tGNgGDfuewF2DW5p2xd2tDRleCtkbB4ybrzglZEI9AtnoW4cru/CqkSfmSoe2FSIFmwrXIoQAiIQhJw22g5lJAwpdQjRGJVfxrDx4gygdpz3tEB4vJBz+Qq9ZBJS19umQm8xQgiEfH6EfH7s3bi9cD6TzWA8PI2QL1DH1a2fFYdPX/va1/DJT37Scu7555/HS1/60mVvt9By98EPfnD1qyMiImoAlsongXWdc0PUSJS+AWim8KlaQexEeBqPHn4CT586jJyulX1eVRRctWUvbtj7grZpT6DKCEWFumO3df7TxfMQHQEoweaejSRzWchIuHDcTi13C5RACFo+fEJOg4zFIDr89V1UnozHrJWh7bjTXZ5l7pOURijHTqgyDrsDm3oG672MdbPi8Gl4eBg33nhj4fiRRx6B3+/HlVdeuej1Qgi43W5s3boVt99+O1784hevebFERET1IFOm8MnhbLsn+0QLRCAEEQxBhucApwMiVPlgbyklToydxaOHn8Cxi2cWvcbjdOGFO6/Ci3ZfBb+nfV/I0eoY8592Qjtx1DghAe3kMYj9V9WsVXQ9yLlZa8tdd3u06piVDleX4VmgkcInk3YcNr5AuMuHjguGT21vxeHT2972NrztbW8rHCuKgssvvxw/+clParIwIiKihpHmTndEgPHmorprL5BMAG43hLL6zc5zWg7PnDmCRw8/ifG5qUWv6faHcMOeF+AF2y+Dw2Zf67KpDSndPZDzEejj+S3Os1loJ45B3XtZw7RprZY+XdJy5w/WbS31IpxOwOMBEkZbroyEgY31XdMCDhs3cbsBRRTC0nab+0SLq3g6409+8hMEAu3Rm0hERO3N0nbHYePU5oSiABW0k8RTSfzi+LP4r6NPYz65+AuRrX0b8ZJ912L3hm1QOM+J1kjZvAVyPlqc/xSNQD9/Duqm4fourAJlLXed3W1bhasEQtAXwqfYPGQ2C2Gvf0jNYeNFQlEAl7sYEjJ8IqwhfHrJS15SzXUQERE1JKnrgGl7XOFi5RPRakxFZvGzI0/iyZPPI6vlyj6vCAVXDO/CjfuuxYau/jqskFqVUFSoO/cg99wzgGbMEtNHz0P4/VCCnXVe3erI2Rljdk6e0tVdx9XUlwiGgLGLxoEEZDQMUedd/zhsvJzweCEZPpFJxeHTuXPnVn2bTZs2VfpwRERE9ZFJWweIsu2O6JKklDg9cR6PHnoCRy6cMn8JFbjsTvz6ritw/e6rEfQ2xswWaj3C5Ya6bQe04yXzny6/2mjhahL6zHTxwGZry5a7BcLvBxQF0PMD5cNzUOodPpUNG2/feU8LhMcLiXyraCbTMBVqVD8Vh0/Dw8OrKiUUQiCXK3+3i4iIqJFJ07wnAGy7I1qGpmt4buQYHjn0BC7OTix6TacvgBv2GvOcXHZ+PVHtKV09kP3m+U85aCePQt17eVPMf5LZ0pa79tvlzkwoKoQ/YGx8AEBG5iClrGubW/mwcVY+lQ4Yl8k4hD1Yn8VQQ6g4fNq0adOSX+DxeBzT+S0w7XY7BgfbZ/tAIiJqMeZ5T2DlE9FikpkUfnH8AP7ryFOIJGKLXrO5Zwg37nsBLtu4A0obv3Cm+lA2bzXmA8WM/58yGoV+/izUTVvqvLJLk3NsuSslAsFC+IR0xmh583jqth4OGy9XvuNdAmjjij1aQ/g0MjKy7Oej0Si+8pWv4FOf+hTe9KY34TOf+UylD0VERFQ3lmHjQgBNvE03UbXNzIfx8yNP4VcnnkMmly37vBACl2/aiRv3XYvNPXwzkupHKArUHbuRO/gMkMvPf7p4AaIjACXU2POfLC139vZuuVugBEPQz54pHOuROaj1DJ9ipmHjvvYeNl7gcAA2G7DQ/cS5T22v4vDpUvx+Pz784Q9j3759eNWrXoXdu3fjjjvuqNXDERER1YRMWXe64xNKanW6lJBSz/8uoUvd+F0vnp+en8PPjzyF58+dgJTlE52cNjt+bcd+vHjvC9Dp4+7I1BiM+U87oR07UjinnWrs+U9lLXeh9m65K3B7jHAjkwEAowpqYKguS5FaDkiZho2z5Q6A8eaD8HghoxEAHDpONQyfFrziFa/A5s2b8Vd/9VcMn4iIqPmYZj6x5Y5WSkqJWCqB6fk5zETDmI2FkdM06OZQR9cXCXrKg58lr9FLr1/8fha7XsIaJpmvX4ugtwMv3nMNfm3HFXCzSpAakNLZDTkwCH1s1DiRzUE7kZ//1IChTnnLXX0HazcKIQREMAQ5acyWk9EIpK5BKOq6r0XG4xw2vgTh8VjCp3rP5qL6qnn4BADBYBBHjx5dj4ciIiKqKnPbXaO+M071IaVENBnDTDRshEzzYUxH5zAzP4fp+TDS2Uy9l7huNnT14yX7rsXlm3dCrcOLP6LVUDZtgZyPFuc/zUehnx+BunlrnVdWTp+ZKh7YbRB+VhIuUIIhaPnwCboOGY1CBEPrvo6yYeM+Vj4tEB7T3CdNN+Zoutz1WxDVVc3Dp8nJSRw5cgTekmn3REREjU7qWqGkHwDgYuVTu9GlRDQRMwKl6Bym5+cwHQ0XAqbsInOO2oUAsHfjDty47wXY0ruB72ZT0xCKAnXnHuSee7o4/2n0IoQ/ACXUVefVFZW23ClsubMQ/qDxjShfdSQjc0A9wifTvCc47ICdw8YLPKVDx+MQDJ/aVs3Cp+npaTzxxBO45557kMlk8JrXvKZWD0VERFQbppY7gG13rUrXdUQS85jOVy6Zq5dm5sPIabk1P4YiBBw2O4RQoAiR/x3WY0WYPrfwsflYgRDCcr2AWPL2xd8Xu1/z7RWI/G3LHqNw++J5RShQVQWbe4bQ1RFc+z8AUR0Ipwvqtl3Qjh0unNNOHofYf1XDfK83Wu6Kx4ItdxbCbofw+goVbHp4Durm9V+HufJJeDsYxJuIkiHwMpEAGnu+P9VQxeGTqq6spFpKif7+fnz2s5+t9KGIiIjqwrLTHQA0yAsSWj1N1xGOR/PBUjhfwWSETDPzEWi6tubHUBUFnb4guv0hdHcE0eUPobsjhK6OIEK+AFRWLBA1FKWzC3JgCPrYReNEbmH+0/6GqDDSp0ta7gJsuSslgqFC+IREAjKThljHeXMcNr48odqMqvH85i0cOt7eKg6fFtvZxMzr9WLr1q145Stfid///d9Hd3d3pQ9FRERUH2WVT5z51Mg0XcNsLLLoDKbZWASarq/5MWyKiq5CsGQETV0dIXT7Qwh6OqA0wAtWIlo5ZdMwZCwKOW+0Tsn5eejnRqAO13f+k8xmIaPhwrHS2Q0h+P2llAh2AhfOF45lJAzR07dujy/jMWt1Guc9lREeT2HnYIZP7a3i8EmvwhM4IiKiRmapfFIE5zg0gJymYTYWLlYwRRfa4+YwF4usebc2ALCrtkK4ZARLQXTnAya/pwMKWyqIWoZQFKg7diP33DNAzmix1cfy85866zf/Sc5OW0ONTr6Rvxjh8wE2tTi7KzwHZT3Dp1jJsHFWPpURHi/k7KxxkErWbVdCqr912e1urb74xS/iz/7szzA2NoZ9+/bhvvvuww033LDk9Y888gjuuusuHDp0CIODg/hf/+t/4c4777Rc89BDD+HjH/84Tp06hW3btuEzn/kMXvva1y56f/feey8++tGP4gMf+ADuu+++av7RGtp8Mo6jB5+AqqqwhbpgszugKApsimr5XVVUqIv8XrxG5RN1ImpKlvDJ6eIch3WQ0zQk0knE00nMzocLc5gWZjCF49FLVl+vhMNmR1dHCD1+oy2ue+H3jhD8Hh//rYnaiHC6oG7fCe2oaf7TqWMQnqsh6rTRhD4zXTxgy92ShFAg/EHI2RkAxtBxKeW6fQ+37HTncKxry1+zsOx4JwEkEoCvo27rofpp+PDpW9/6Fj74wQ/ii1/8Iq6//nr8zd/8DV75ylfi8OHD2LRpU9n1Z86cwS233IJ3v/vd+Id/+Af813/9F37v934PPT09eP3rXw8AePzxx3H77bfjU5/6FF772tfi29/+Nm677Tb8/Oc/x3XXXWe5vyeeeAJ/+7d/i/3796/Ln7eRTMxM4J+O/Koq9yWEMIVTpoBKqLCpChRFhU0xfleFAlXNX7vwschfoxq3sYZdCx+XhmKLB2N21YagtwMBr5/zN4hoeali+NQoA2ibhZQSqWwa8VQSiXSqECgl8r/i+XPFX8Zxuoq7xzntjnzFUrDQGtfdEUKXP4gOl5cBExEVKKEuyMEh6KML8580aCeOQN13xbrPf5LZDFvuVkEJhqDlwydkc5DxGMQ6hRvWYeOselqMWGzHO4ZPbUnIarx9WEPXXXcdrr76anzpS18qnNuzZw9uvfVW3HvvvWXXf+QjH8F3v/tdHDlypHDuzjvvxIEDB/D4448DAG6//XZEo1H84Ac/KFzzile8AqFQCA8++GDhXCwWw9VXX40vfvGL+PSnP40rr7xyVZVP0WgUgUAAkUgEfr9/NX/shnD04BP4u6d/Uu9l1IwiBAKeDoR8AXT6AoXfFz4OeHyc3UHU5rJP/gLIGmGI0tcPdeuOOq+oPrJazhIQxU2BkREulQdKyUyqKi1wl+J2OPOBknXId7c/BK/TzYCJiFZM6jq0wwch56OFc8rAINThbeu6Dn1iDNrpk4Vjde9lUAKhdV1DM5HpFHJPP1E4VjZthjpUXqRQ9cfN5ZB74vHi427YBHVjHbbba3BSSuR+9RiQH9tTj68pqp3VZB4rrnxa6e52SxFCIJdb3VbFmUwGTz31FP7wD//Qcv6mm27CY489tuhtHn/8cdx0002WczfffDP+7u/+DtlsFna7HY8//jg+9KEPlV1TGiy9733vw6te9Sr85m/+Jj796U+vau2tQGvx4EWXEnPxKObiUZyeOF/2eUUoCHr96PT5ywKqkC8Av8fHdkKiFiY1rRA8AWiJne50KZHKpBBPJ5FMp/Ih0vJVSfF0CtkqViNVwuN0lwRLwfwuciF4Xe66ro2IWkdh/tPBp4HswvynUYiOAJSu9Zu5ZG25s0P42XK3HOF0AW43kDR2nZPhOWA9wqdEybwnDhtflBDCGDqen48lE4k6r4jqZcXh01oLpCq5/fT0NDRNQ1+fdWhcX18fxsfHF73N+Pj4otfncjlMT09jYGBgyWvM9/mP//iPePrpp/HEE09gpdLpNNKmnZGi0egyVze+nbuvwEc374AWnkV2chy5eWOnIF1KaJDQpPFLh4Tm8UD6A9Ddbmi6blyn68jpmul3Lf85rXDNwse6riFX8rvlWqlD0/LXSg05zfhd0xfOa6j2++u61DEbC2M2Fl7086qiIuQtr5xa+N3n9jKcImpm5nlPQN3mfqxENpfFqfHzmItHl2xri6dTSGZSVZmXtFaKUOBxuuF1uuBxuo2PXfmPHW548ueD3g50dYTgaYHgj4iag3A6oW7fBe3IocI57fRxCK8XYh3C7vKWuy623K2AEgxBXwif5uchczkIW20nzHDY+Cq4PUAhfOKOd+1qVV+RQgjs2rULd9xxB173utfBt07pbmnJ/KWGyC12fen55e7z/Pnz+MAHPoCHH34YrlW82Lj33nvxx3/8xyu+vtHZVRtCPj/g8wMbhiFTSegTY9AnJwq7gVjMp4AsoPQNQOnpg7Db13W9+kJIpWvQNB2azAdfmgZdFsOudDaDuVgUs7EIZmMRzOV/hRPzq3pRpumaMQh3Przo522KilAhjPKj0xdEyOcvtPZ5XR62gxA1MFkSPjVa5VNWy+HYxTM4MHIUh8+fRKZO1Ukuu6MQIJUFSs5ikORxuvLHbjjtDn7/I6KGpQQ7IYc2QL94wTiR06CdOLou85/k7Ix1l7uunpo+XqsQgSAwNmocSAkZDdd8h0AOG1854fVBTk0aB9ksZCYD4eAOwu1mxeHTX/7lX+Kb3/wmnnzySdxzzz2F3eHuuOMO/OZv/mZNZuN0d3dDVdWyKqfJycmyyqUF/f39i15vs9nQ1dW17DUL9/nUU09hcnIS11xzTeHzmqbh0UcfxRe+8AWk0+lF2xDvvvtu3HXXXYXjaDSKjRs3ruJP3NiEyw1181YoGzZDzkxBHx+FjJck16kU9LNnoJ8/C9HdA7V/cN3eBVAUBQoU2FUbUEHupekaIvF5Syg1G4vmf48gmphfVXVVTtcwFZ3FVHR20c8b4Z65WspvmTvl4awSorqSpkpWoDEGjuc0DSfGRvDsmaM4fP4EUtlM1e5bVVRLcGQNjEoDJSNkcjtdULldMhG1IGXjZsj5KGS+k0HGYtDPnoa6ZXtNH1efmSoe2O0QTTg3th6EPwgoAtCNZ+syPAesY/jEqqflCbfHciwTcYZPbWjF4dMHPvABfOADH8Dx48fxjW98Aw888AC++c1v4oEHHkBvby/e9KY34c1vfjOuvvrqqi3O4XDgmmuuwQ9/+EO89rWvLZz/4Q9/iNe85jWL3uaFL3whvve971nOPfzww3jBC14Ae74S54UvfCF++MMfWuY+Pfzww3jRi14EAHjZy16GgwcPWu7jHe94B3bv3o2PfOQjS86/cjqdcDpbP/EWqgrR2w/R0wcZmzdCqNnpwjd7AICuQ05OIDc5AdHRAaVvEKKre913C1kNVVHR2RFEZ0dw0c/nNA3heNRULRXNt+UZAVU0GVv0dkvJajlMRmYwGZlZ9PNOm91UOWX+3aiicjucDKeIasm00x1UBahx+f5SNF3DybFzODByFM+fO45kJn3J27gdziWqj5auRnLY7PyeQkSUJ0R+/tNzpvlP42MQ/gCUGlUjGS13kcIxW+5WTqgqRIcfMmL8/emRMGr51ojM5QozpgDOe7oU4S3f8Q5BDtFvN2va7e7nP/85vvGNb+Bf/uVfEA6HIYTA7t278da3vhVvetObqlL1861vfQt33HEHvvzlL+OFL3wh/vZv/xZf+cpXcOjQIWzevBl33303Ll68iG984xsAgDNnzuCyyy7De97zHrz73e/G448/jjvvvBMPPvggXv/61wMAHnvsMdx44434zGc+g9e85jX4zne+g3vuuQc///nPcd111y26jv/23/5b2+12txoyk4E+NQ59YgxIL/FOvN0OpbcfSt8ARAuGdFkth/Ai7Xyz+QqqWKq6/c0uuwMhXwBBrz8/J8UJt9MNj8MFt9MJj8MNt9NVeBHqdjhZoUC0CrnjhyFn8uGwxwP7Fdcsf4Mq0nQdpyfO48CZIzh47gQS6eSS19oUFbuGtuLKLbuxrX8TPE431AYO+omImokenoV29FCxFU5VYdt/VU3mP2kTY9Atu9xdDiUQrPrjtCrt4nno50YKx7arXlCzOV16JAztcLFYQd29F0qoqyaP1SrMOwiLnl7Ytu+q84qoGlaTeawpfFqQyWTwve99D/fffz/+3//7f8hmsxBC4M4778QXvvCFtd49vvjFL+JP//RPMTY2hssuuwx/+Zd/iRtvvBEA8Pa3vx0jIyP46U9/Wrj+kUcewYc+9CEcOnQIg4OD+MhHPoI777zTcp//8i//gnvuuQenT5/Gtm3b8JnPfAave93rllwDw6eVkVKHnJuFPj4GGQkvfpEARKgLSv8AhD/YNu+0Z3JZzJna+EoDqvgyLy6rxWmzw+10w+1wweN0wu1w5393FQKqxT52OZwcnk5tJ/fc04XWYhHqhG33vpo+nq7rODN5AQdGjuHg2WOIpZbeDUZVFOwc3IIrhndj38btcHHOBBFRzWjnRqBfLO6MLLxeqJddAVHlN/Vyh58rVO7Aboftmuva5nlyNch4DLnnnikcK1u2Qe0frMljaaMXoJ89Uzi2XXMd28guIXf4YOH1ofB6YdtfvY4pqp91D5/Mfvazn+GOO+7AuXPn8Ju/+Zt4+OGHq3n3TaUdwyczmUhAnxiFPjUJaNriF7ndxQHldWppaRTFQejh8oHo8QgSpcOP15EA4HKUhlUuuB0u68dOV77yqvi708bBwtScsk88XthcQRkYhDq8reqPoUuJc1OjODByFM+NHEU0uXSFpCIU7BjcjCuGd+OyTTvgdtR/BhURUTuQUod2+HlrS1z/QFXnP8lMBrmnf1mosKr2/bcDKSVyT/2yWF1TwzeOcieOQk7n53M5HLBfs3j3DBVpI6egLwyFVxTYfu2FbCttAavJPKryan9iYgIPPvgg7r//fjz77LOQUsLn8+HFL35xNe6empTweKBu2Q5l0zD0qUmjJS9R8k5+Mgl95DT08yNQevqMljyPd/E7bHFOuwP9oW70hxYfjpjMpC2VUgsD0SOJeSQzqfyvdE22UZf5x09m0phd3WgrKEIxqqhMgZTbUfKx6ZzH6Ybf44Xb4WJoRXUjcznrrp5VrCySUuL89DgOjBzBc2ePIRyfX/JaIQS292/KB0474V2Hbb6JiMjKmP+0y6iqyQcb+vgYREcASnd15j/ps9PWXe5qPCy7FQkhIIKhwq5qMhqG1PWazJyVpp/dnPe0Mpah7LpuzNYsGUROra3i8CmZTOLb3/427r//fvz4xz9GLpeDqqq46aabcMcdd+C1r30t3G4+SSZAqDao/YNQ+gYgoxHoE2PGgHJzRqLp0MfHioMc+wcgQl0NPaB8vbkdTrg7ezHY2bvkNbqUSGfTSKZTSGRSSKRThWCq8HHaCKkS6aRxPpNGMp2s6q5Z1jXpiKeTq24rtCkqOjw++N1edLh9CHh86HB74Xf74F/42OODx+lmSyBVX0mloXCtrcpISomLsxM4MHIUB0aOYS4WWfJaAWBr/0ZcMbwbl2/aCZ+7PQN5IqJGIhxOqNt3QTv6fOE5rHb6OITXB1GF1zxydrp4YLdD+ANrvs92pASC0PLhEzQdMhY1dsKrImPYePF5gvB2VPX+W9WiO94xfGorqwqfpJT40Y9+hH/4h3/At7/9bcTjcUgpcdVVV+GOO+7AG9/4RvT19dVqrdTkhBAQgSCUQBAyk4Y+MQZ9YrzwDtICGY1Ai0YAh8Noyevtg+A8kxVRhCi0wnWu8raariOVSSORSSKZTucDq2S+4qn4sRFiJfOhlRFyZXPZSz/AKuV0rVDptRxVUdCRD6iMYGrxsMrr8jCkohWTpeGTc/Xhk5QSY3NThZa66fnwstdv6d2A/cO7sH/zLvg9fBeViKjRKMEQ5NBG6Bfy8580HdqJI2ue/yQzJbvcdXWz+rtComQHNRmeA6odPsWtbQCWih5amsdjvMOWD29lIg7UaOdIakwrDp/+4A/+AA888ADGx8chpcTGjRvxP/7H/8Add9yBPXv21HKN1IKEwwl14zCUoU2Qs9NGNVQ0ar0ok4F+/iz0C+cgurqg9A1CdPj5w7hGVEWB1+WuqK0np+VMwZS10mqp3xc+1vQl5oGtkKbrCMfnl21dAoxgzucyqqX8bm++qsoIq8zVVD6XlzuFEWQ6bT2xivBpIjyNZ/OB02RkdtlrN3UP4Irh3dg/vBtBvnNKRNTwlA2bIeejhcHgMh6HPnIa6tYdFd9nWctdF1vuKiXsDgivt7BhiAzPAZu2VPUxGD5VRigq4HIDSaMbQiaquxM4Nb4Vh09/8Rd/ASEEdu3ahbe85S14yUteAiEE5ubm8Nhjj63oPl70ohdVvFBqTUJRILp7oXT3QsZjRjXU9CSg6cWLpIScnoY2PQ14PFD7ByG6eyHU6u4wQpWzqTZ0uG3oWGV7kJQSWS1XqKBKplOIpROYT8QRTcYQTcQQTcYxn/94LTsC6lIa95lcfmiVEAJepweBfAXVQjjVkQ+oitVVXqhV3uWGGoi58smmXnJDhKnIbL6l7ijGw9PLXruhq68QOHX62FZBRNRMhBBQt+9G7rmni/OfJsaNsRHdS49GWI6cMf3ccNghOvizYS1EMFQMn+JxyGwGwl69nejM857gcHCXu1UQHi8kw6e2teqZT8eOHcPHP/7xVT+QEAI58/BWohLC64O6dQeUTVugT01Anxi19FMDABIJaKdPAufOFAeUs1e4aQkh4LDZ4bDZEVhB1UdO0xBLxQuhlPF7LB9OLQRWccRTcVQ6dl1KiVgqjlgqDmBy2Wu9Ls8lZ1L5XB7YVRsr9pqMTJlmOSxR9TQzHy4ETqOzy/9fGQj14sotu7F/8y50+0PLXktERI1NOBxQd+yGduSgaf7Tifz8p9U9L5WZDOS8qeWuky13ayUCIeDihcKxDIcheioLBhcjY8U3MjlsfHWEx1sMW1NpSC0Hobb3juftZMX/0ps2beI3QloXwmaDOjAEpX8QMhKGPjEKOTdrHVCe06CPjUIfGzXmSPUPQoQ6+X+0xdlUFUGvH0Hv8tt4arqOWDKeD6biiCTyAVUyjvl8YBVNGAGTvobdAeOpBOKpBMbmppa9TgBGyGZ35MM243en3b7Ecf6c3Q5n/nOO/Ocsx6odClsEa8Iy88kUPs3FIjgwcgwHRo7iwsz4svfRF+zGFcO7ccXwLvQGumq1VCIiqgMlEITcsAn6+XPGCU1H7vgR2C67clXV+Wy5qz7R4QdUpdBJoUfmoFQpfJK5nLFL28JjsWV+VUp3NZeJhPHvRW1hxeHTyMhIDZdBVG5hu1QlGIJMp4yWvMlxIGutoJORMLRIGHA6iwPKq1haS81HVRQEvB2XrKbSdX3ZNr/ix3HoUl/2vpYjAaRzWaRrMJjdrtoswZZzIbxa8niRMMsUgi0EYO3cUiiltLTdRaHh0OEn8ezIUZybGl32tj3+UD5w2oP+EF9AEBG1MmVoE2Q0ChkJGycSCWgjp2DbtnPF92FtuXOw5a4KhKJA+IPGm9cw5j5JKavyJnXZvCdWPq2K8JRUBibiAMOntsEaN2oKwumCumkLlA2bIGemoY+PWkpeAQDpNPRzI9AvnIXo6oHSPwjFx3cjaGmKohhtcm4fhrD0Tp26lEikk8U2v2XCqrUOUF+trJZDVsutaR7WYlRFXaI6ywGX3QGXwwmX3Vn43Wl3wO0wfi98zu6Ey+FoviArl8N8OoXn52fwXGwGZ48vP8y+0xfElVt244rh3RgI9bACk4ioTRjzn3Yhd/BpIGO8wSQnJ6D7A1B6Lr0DuMykS1ruuvgzpEpEMFQIn5DNGiFHFQaDc9j4Gjldlqo0zn1qLwyfqKkIRYXo6YPS0wc9Nm+EUDNTgG6qV9Yl5NQktKlJ6D6f0ZLX1b2mLXCpvRk75Xngc3kwiKXLtqWUSGZSxflTyRgSqSQyuazlVzqbyX+cyR9nC8fpbHZNVVbVoukakhkNyUzq0hdfgl215QMphxFU5cMpa1jlsIRZ1usdcNocNX9CHkslcPDsMTx76hDOTI0uOzcs5PVj//BuXLllN4Y6+/higYioTQmHA+r2kvlPZ05CeDvKqzxK6LMzJS133Ha+WpRACOZnU3p4Dmo1wqeY6Q0pp4PdFqskhDDmPs0bf48ykajzimg9MXyipqX4OqBs3wW5eSv0yXHoE2NAyfboMhaDdvI4cPY0lJ5+Y0C5a+VbphOthhACHqcbHqd7TS1XOU0rCacy1gArm0U6/7lMNh9aFT6Xv80ixzmtPps+ZLUcsskc5pOVv7slhIDL7oCzJJgqVGGZqq2cjpLPmUItW8kcjkQ6iYNnj+PAyDGcGj+77AywgMdnBE7Du7Gxe4CBExERAViY/7QZ+vmzxglNR+7Epec/yRnTzEiHg7NvqsnlApzOwmsDGZkDhjau+W7NlU+c91QZa/gUq1pLJDU+hk/U9ITdDnVoI5TBDZDhWejjY5DhOetF2Rz00QvQxy5ABDuhDm+FcLnrs2CiS7CpKmyqCs8Su6xVStd1ZLSFwKo0wMofZxevyFo4TucySGfSSGXTSGXSNZljtRijqiyNZCYNrKFC26aohbDKrtoxEZ5ettKsQ7Vj//bLcMXWPdjcMwSFT46IiGgRytBGyPkIZDhsnEgkoJ05Cdv2XYteb7TcRYu37+Iud9UkhIASDEGfMDYHkfNRSE1b1TD4UjKXLRk2zpa7ipiHjuc0IJMxgkJqeQyfqGUIISBCXVBCXZDJJPSJUehTE8Y3tQUSkHOzyMVjsF1xDYSNXwLUPhRFgUsxKoCqRdd1pHMZpDKZQiBV/L14Lp3NIJlJI50t+Xz+ek1fn1bDnK4hlkogllq6zNtrd+IyTxCXd3Rh2N8J57UvWpe1ERFR8yrMf3ruGePFNAA5NQk9EFx0/pM+U7LLXSc3qag2EQwB+fAJuoSMRiBCnRXfH4eNV0dpO6pMxCEYPrUFvvKmliTcbqjD26BsHIacnoQ+MQYZN5VLZDLQJ8agVqH8lqidKYoCt8MFt6PyKi0pJXK6Vh5c5Y8XgquUObxa+Lwp5EpnMxWvweN04bJNO3HF8G5suq49TQAAZjZJREFUDkehRIwBsKyQJCKilRJ2B9Qdu6Edfq44/+n0SQivr3yL+dnSXe7Ycldtwh8EBAr/FjIyB6wlfIpx2Hg1lH0tJOJr+neh5sHwiVqaUFWIvgGI3n7I2Dy0E0eAtPECVR+7CGVgkIPIiepMCAG7aoPdbUOH23vpGyxBlxLpfCiVzqaRzKaRzldkFaquFiq08iFXh9uLfZt2YMfA5sKufNnJJ4t3WuXWRyIiam2KPwC5cRj6uRHjhL4w/+mqQssXW+7Wh7DZIDr8kFHj71oPz2Etz/otlU8cNl4xYbMDTkfhNRl3vGsfDJ+oLQghjB8+Axugj5w2Tmaz0CcnoPYP1ndxRFQVihBwO4xd9ColpbRsXCAYPhER0Sopgxsgo5HiDNJEEtqZk1C37YQQorzljrvc1YwIhArhE5JJyHSq4p/tHDZePcLthWT41HaUei+AaD0pvf2AvZi56qMXIBtgW3siahDZLGCeP8XdMYmIaJUW5j/BWayMkVOTkFMTxsczJS13PgYZtSKCIctx2aZEK1Q2bJzzntZEeE2V7skk5DrN/qT6YvhEbUWoKpT+oeKJdBpyenrpGxBRW5HplOWYAzCJiKgSwm6HumOPMXMoTztzCnp4li1360h4fdY3niMVhk+c91RVwjxmQUogmazfYmjdMHyitqP0DwBq8b++NnreaLUhIioLn1j5RERElVE6/FA2DhdP6Dq0o4ct17DlrraEEBCBYOFYRsIVdT2U7XTH8GlNFh06Ti2P4RO1HWGzQ+kbKJ5IJCDDs/VbEBE1jNLKJ7DyiYiI1kAZ3ABh3snL/Ianky1360EJmFrvclpZFdNKWIeNOzlsfK3cbsBU8SeTDJ/aAcMnakvKwBCgFL/h6Rcv1HE1RNQwTMPG4XBwN0wiIloTIQTUbTst858WKJ1suVsP5XOfVv+ms4zPF++PVU9rJhTFCKDyZJzhUztg+ERtSTicULp7C8dyPgo9GqnjioioEUjzMFG23BERURUU5z9Zgya23K0P4XACHk/hWEbCq7q9zGaBlGknXA4brwpz6x0rn9oDwydqW8rgBssQSH2U1U9E7c7SdseWOyIiqhKlww9l03DhWHi9bLlbR+bWOxmbNwKlFeK8p9qwzH1KZ4wdBamlMXyitiXcHojO7sKxnJtlySdRG5NSAhnTO5usfCIioipSBoag7twDZeNmqLv2suVuHVla7yQgo+EV37Y8fGJoWA0cOt5+GD5RW1MHN1iOtdHzdVoJEdVdJgPoxUGwwsXwiYiIqkcIAaWrG+qGTXyDY50Jvx9Qii999fDcim9rnvdkDBu3V3Npbas8fErUaSW0Xhg+UVsTvg7r9qszU5CpZP0WRER1w53uiIiIWpNQVAh/oHAsI3NGxfMKmHfH47ynKnI4AJtpYxdWPrU8hk/U9pShjcUDCehjF+u3GCKqn5Lwie9KExERtQ7zG85IZ4Dkpd9wltmsZSdcttxVjxACwlMM89h21/oYPlHbE/6A5V0MfXIcMpOp44qIqB6k6cklBAAHK5+IiIhahWKe+wRAj1y69Y7DxmtLmHchTMRXXI1GzYnhE7U9IYS1+kmX0MdZ/UTUbiwttw4nhMIfkURERC3D7TFavfLkCuY+MXyqLcvcJ00vq0Kn1sJn1kQARKgLcLsLx/rEGGQuV8cVEdG6S3OnOyIiolYlhLDseiejEUhdW/Y2HDZeYxw63lYYPhHB+GGkmqufchr0ibH6LYiI1p1l4DiHjRMREbUcS+udrkNGo8tez2HjtWVuuwM496nVMXwiyhNdPYCzWIqrj1285LshRNQapK4DGVY+ERERtTLhDxpzHfPkMnOfOGy89oRqA1zF51wMn1obwyeiPKEoUAY2FE9ks9CnJuu3ICJaP5kMYJpxKVwMn4iIiFqNsNstc5v0ZeY+WVruwHlPtVI6dJxaF8MnIhOltx+w2wrH+ugFSKnXcUVEtB5k6YBLtt0RERG1JPPcJyQSkKbKZzMOG18flqHjqSQ7T1oYwyciE6GqUPqHiidSKciZ6fotiIjWR0n4xLY7IiKi1iSCnZZjGQkvep0lfHJx2HitWMInCYBDx1sWwyeiEkrfAKAWvzSM6ie5zC2IqNlZKp+EABysfCIiImpFwucDbGrheKnWO8uwcc57qhlRuuNdkuFTq2L4RFRC2O1G+12ejMeXHUZIRM1Ppqw73Qkhlr6YiIiImpYQijF4PE9G5sreaC4fNs6Wu5pxuQGlGEuUtjtS62D4RLQIZWADoBRffOoXL9RxNURUc2nudEdERNQuFPPcp2yuLPDgsPH1I4QoGTrOyqdWxfCJaBHC6YTo7i0cy2gE+ny0jisiolqytN1x2DgREVFLswwdByBLWu84bHydubnjXTtg+ES0BHVwA2DqvNEvnq/fYoioZqSuA5lM4Vi4WPlERETUyoTTBbjdhePSERvmeU9wuThsvMYs4V42C2l6Xkatg+ET0RKE2wPR2VU4lnOzTOKJWhF3uiMiImo75tY7OT8PmcsVj01td6x6qj1hqnwCWP3Uqhg+ES1DGdxoOdZGOfuJqNXIkvAJDJ+IiIhanggEiwdSQkbDxofZDJA2VUQzfKo54eWOd+2A4RPRMhRfh+UHk5yeLH+hSkTNzTRsHDBmvhEREVFrE/6gZYOhhblPZfOefAyfak3YHYCptZE73rUmhk9El6AMbSgeSEBn9RNRS7EEyooA7I76LYaIiIjWhVBViA5/4ViPzEFKaZ33BFY+rRfhMVU/sfKpJTF8IroE4Q9a3vHQJyeMclwiagnWne5cEEIsfTERERG1DBEw7XqXSgPplLXqxuWCsHHY+HoQHvOOdwlIKeu4GqoFhk9ElyCEsM5+0nXoY6P1WxARVVeqGD5x2DgREVH7MA8dBwA9PMdh43Vi+bvWdSCVrN9iqCYYPhGtgOjssmzHqk+MWnbEIKLmJU0znzjviYiIqI14vNZZQ9OTHDZeJ9zxrvUxfCJaASEE1EHT7KecBn1yvH4LIqKqkJoGZLPFE6x8IiIiahtCCAhT9ZOcn7d+3tex3ktqXx4PYJp8wPCp9TB8Iloh0d0LOIqDiPWxC5C6VscVEdGalexeKVwMn4iIiNqJYtrZuhQrn9aPUFTAVew0YfjUehg+Ea2QUBQo5uqnTBZyarJ+CyKiNZMl4RMrn4iIiNqLKJn7VOByQdhs67uYNmfe8U4muONdq2H4RLQKSm8/YPohpI1e4E4MRE3MPO8J4MwnIiKidiPsDgivt/y8j1VP680cPiGVgtQ4Y7eVMHwiWgWhqlD6B4onUinI2en6LYiI1sZc+aQqALdTJiIiajuLVT8JL+c9rTdL+ARWP7Uahk9Eq6T0DxkvUvP0i+dZ/UQtT0oJfXoS2vgoZDLRMv/nLW13TheEEEtfTERERC1JBBYLn1j5tN6Ex7rjHTj3qaWwiZVolYTdDqW3H/rYKABAxuOQkTmIYGedV0ZUG1LXoZ04Cjk7AwDQAcDlhBLshAiGIPxBCFWt6xorliqGT4LznoiIiNqS6PAbby5revEcw6f153RZ/h04dLy1MHwiqoAyMAR9fAzIV3/ooxegMHyiFmQET0cgZ2etn0ilja+B8TFAEUYAFQxBCXVCmHYqaXTmmU/c6Y6IiKg9CUWB8Ach5/LPd9xuDhuvAyEEhMcLOT8PgG13rYZfUUQVEE4XRHdPYbc7GYlAn49C6fDXeWVE1SN1Ddrxo8UnYkvRJWR4DjI8B33kNOB2maqiAsbWuQ1I5nJAzjTI0sFh40RERO1K3bAJuWgYkBLq0KZ6L6dtWcOnGKSUHIvQIhg+EVVIHdyIXD58AgB99DyUXfvquCKi6pG6Bu3YEcjwXPGkqkDdugMyk4EMz0JGo4XqP4tkCnpyFBgbBRQFImCqimqk1jbzvCew8omIiKidCV8HbC/4dUDTIezcgKRuzEPHcxqQyQDcjbglMHwiqpDweCA6uwpzcOTsLGQiUT4oj6jJSE2DduwwZCRcPKkqUHfvg+IPGseDGyBzOchIGHp41gipMpnyO9N1yLlZyLlZ6GdOAR43lEAnRCgE0RGAUOq374UsDZ8aKRgjIiKidScUFWjQiu12UfpaSibiEAyfWgLDJ6I1UAY3QMuHTwCgjZ6HbfuuOq6IaG2M4OkQZCRSPKmqUPdcVtZWKmw2iK5uKF3dxu53iTj08JxRFTUfBRbbEC+RhJ64CIxdBFQFIhCCEgxBBDvX/YmFed4TAGPIJRERERHVjTBXPiE/dDzE2bqtgOET0RooHX7ogUDhhbqcnoLcuJkVFNSUpKZBO3oIMmoKnmwq1N3lwVMpIQTg9UH1+oChjZC5rFEVNZevispmy2+k6ZCzM8UA1+MxZkWFQhA+f+2rosyVTzaVg0WJiIiI6kzY7IDTAaSNinrueNc6+EybaI2UwY3QFqpEpIQ+dhHq8Lb6LopolWQuZwRP89HiSZsKdc/lUHwdq74/YbNDdPVA6eoxqqLisWJVVGx+iaqoBPREAhi9YIRBhaqoEEQNhoHLVDF8YmBMRERE1BiE2wvJ8KnlMHwiWiMRCEJ4vZBx4xujPjEOZWgjhN1R55URrYwRPD1f2FkEAGCzwbb3cgivb833L4QAfB1QfR3Ahk2Q2SxkZM6oiorMAdlc+Y1yGuTMNLSZaeM+vF6jNS/UCeHzQYi1V0VZZj4xfCIiIiJqCMLrLW56k0xC6npd54RSdTB8IlojIQSUoY3Qjh81Tug69PFRqBuH67ouopWQuRy0IwchY7HiSbsNtj3VCZ4WI+x2iO5eKN29kFJCxuaNiqjwnHUd5nXG40bAe/E8YLMZu+ctVEVVEPRKKS1tdxxkSURERNQYhNs090lKIJkEvN6lb0BNgeETURWIzm7A7QKSxotZfXwMyuAGCJVfYtS4ZDZrBE9xUzmz3Z4PntbnB7wQAqLDD3T4gY3DkJmMURW1sINeTiu/US4HOT0FbXoKEIDw+oyqqGAIwtdhVFpdSi4HaHrxmJVPRERERA1hsaHj6/XclGqHr4yJqkAIAXVwI7RTJ4wTuRz0iXGogxvquzCiJSwZPO3dX7bF7XoSDgdETx+Unj5IqUPOm6qi4ov0/EtAxmJGxdSFc4DdBhHsNKqiAiEIu33Rx7G03AEQLoZPRERERA3B7QaEMKqeAMgk5z61AoZPRFUiunuB82eBjDEcTx+7CKV/kP3J1HBkNoPc4YNAIlE86XAYM57c9QueSgmhQPgDgD8AbNoCmUlDhheqosKAtkhVVDYHOTUJbWrSqIrydRhhVKgT8HiLVVGl4RMrn4iIiIgaglAUI4DKP1dd9A1IajoMn4iqRCgKlIEh6GfPGCcyGcjpSYje/voujMhEZjLIHVkseNoP4XbXb2ErIBxOiN5+KL39kLoOOR+FDM9CD89Z/zwLJIzKqfl56OfPAna7MSsq1Fm+cwrDJyIiIqKGITxeyIXwiZVPLYHhE1EVKX390C+eK8yp0UbPQ/T0rWwGDUEm4pCplLGDoKrWezktR2bSRsVTMlk86cwHT67GDp5KCUWBCASBQBDqZqONrlAVFQlb5zktyGaLVVFmdhv/vxERERE1EOHxQmLKOEhnIHNZCNvi4xSoOTB8Iqoiodqg9A9Cv3DeOJFMQc5OQ3T11HdhTUAbuwh95LRx4HJC3bYLij9Q30W1kMWDJ2c+eGr+qh/hdEH0DUDpGzCqoqKRYlWU+c+8xG2JiIiIqHEsOnTcH6zPYqgqGD4RVZnSPwh99CKgG5UX+ugFiM5uVj8tQ58cLwZPAJBKQzv8HOTgBigbNnNu1hrJdBq5w88BKdOcI1c+eGrB4EUoCkQwBARDUAHIVBJ6eM4YXB6JFL42CzzcPYWIiIiokZSHTwmA4VNTY/hEVGXC7jDa78ZGAeR34oqEjRfDVEafnoR2+kT5JySgX7wAfW4Wtu27ub1qhWQ6lQ+e0sWTLlc+eHLWb2HrSLjcUPvdQP8gpK7lq6LmoEfCEDY71KGN9V4iEREREZk5HIBNLYwzQem8Tmo6DJ+IakAZGII+PlbYHlQfPQ+F4VMZfXYa2sljgCyeEz4fZCxWPJFIIPf8M1A2DkMZGGIF2SrIVNJotUubgie329jVztEewVMpoagQwU4g2AlOeSIiIiJqTEIICI8PMhoBgPLNYqjpsJeFqAaE0wXRXZzzJCMR6LH5Oq6o8ejhWWgnjlqCJ2VgEOplV0LdtRewmwYK6hL62TPQDh+ETKfK74zKGMHTcwyeiIiIiKgpCY+n8LFMxCGlXOZqanQMn4hqRB3cYDnWL56v00oajx4NQzt2BNCLP0CUvn4om7dCCAGlswu2/VdDhDott5PRCHIHnoY+NcEfPsuQyQRyh54D0pniSY/HaLVj8ERERERETcAy90nTAb4J3dQYPhHViPB4ITqL4YmcnTEG5bU5fT4K7eghy9Bn0dMLZct2S0udcDig7toLddsOQDV9q9I0aCePQzt+BDKbAVnJZMJotcuUBk+XQzgc9VsYEREREdFqLDZ0nJoWwyeiGlIGrYOMtdH2rn6S8Ri0o88b71zkia4uqNt2LDrLSQgBpbffqILq8Fvva3bGqIKam6n5upuFTMSNiidT8CS8XqPiyc7giYiIiIiah7ntDuDcp2bH8ImohpQOP4Q/UDiW01OQ5hk8bUQmEsgdOVjcsQKACIWgbt8NIZb/ViRcbqj79kPZNAwoppAqm4V29DC0U8chtVyNVt4cZDxuVDxls4VzwueDuudyCPP8LCIiIiKiJiBUG+Aqjoxg+NTcGD4R1ZgyZJr9JCX0sQv1W0ydyFTSCJ6yxYBIBAJQd+6BUFb2bUgIAXVoI2yXXQmUvAuiT04g99wz0Oej1Vx205DxGHJHnmPwREREREQtxTz3ieFTc2P4RFRjIhCC8Ba/aeqT45CmkKDVyXSqbAaR6PBD3bUPQln9ZvfC64Pt8iuhDA4B5k69VAraoQPQzp2BNM2TanUyHstXPJmCvY4OI3iy2eq4MiIiIiKitbEMHU8lIXVt6YupoTF8IqoxIYR19pOmQx8frd+C1pHMpI1gxNRqKLxeqLv3QairD54K96GoUDdvhbr3csBp2r1NAvrFC9Cef7Yt3hnRY/PIHX4OyJmCJ78f6p7LGDwRERERUdOzhE8SAIeONy2GT0TrQHR1AS5X4VifGIXUWju1l9ms0WqXMm2J6vFUtSJH8QeNYeQ9vdbHjseRO/gMtNELkFJW5bEajT4fhVY6Q8sfgLr7MqM/noiIiIioyYnSHe+SDJ+aFcMnonUghAJ10DT7KZuDPjlevwXVmMzljGAkkSyedLtgq8EMImGzwbZ9F9RdewC7KXTRJfSzZ6AdOQiZTi19B03ICJ6etwZPgcCaK8qIiIiIiBqKywWYZsTKeKyOi6G1YPhEtE5ETy/gKAYv+uiFlpxNJDUN2tHnIeOmtjenE7Y9+yEcjpo9rtLZDdv+ayBCndb1RCLIPfc09KmJlqiC0qNhI9jTzMFT0JihxeCJiIiIiFqIEAqE2104lmy7a1oMn4jWiVBUKP1DxROZDOT0ZP0WVANS16AdOwQ5P1886XDAtvdyCPNsphoRDgfUXXuhbt0OqKZvbzkN2snj0E4caeph73okDO3oIUArhpYiGIK6ey+DJyIiIiJqTdzxriUwfCJaR0rfAGArhgStNJNI6jq040cgI5HiSbvNaLVzuZe+YZUJIaD0DRizoDr81jXOzCB34CnoczPrtp5q0cNz5cFTqBPqrr0V7RpIRERERNQMLHOfsllI0y7a1DwYPhGtI2GzQekbLJ5IJiFnmy8IKSWlDu3kUci5ueJJm2oETx5PXdYkXG6o+/ZD2TQMKKL4iWwW2tHD0E6faJqh73p4FtqxQ4CpTVN0dkLduQdC4bdxIiIiImpdZUPHWf3UlPiqhWidKQODlqF5+uj5pq5+klJCO3UCcsYUoqmKseua11e/hcGoglKHNsJ22ZWAx1p9pU+MG7Og5qP1WdwK6XOz0I4dBvTi/xHR1QV1B4MnIiIiImp93PGuNfCVC9E6E3YHlN6+wrGMxSCj4fotaA2klNDPnIScMs2uUhSou/dBKWl5qyfh9cF2+VVQBoYAUxEUUilohw5AOzfSkMPf9dkZaMdLg6duqNt3M3giIiIiorYgHA7AtGO2PjkGmcvVcUVUCb56IaoDZWCDJQTRL16o32IqJKWEfvY09Inx4klFQN21B4o/WLd1LUUoKtThrVD3XA44TbvuSUC/eB7a88821O4Z+uw0tONHrMFTdw/UHbsYPBERERFRWxHBUPEgkYR24mhTd4+0I76CIaoD4XJBdPUUjmUkDD02v8wtGo9+/iz0sdHiCQGoO/ZACXbWb1EroASCsO2/BqKn13JexuPIHXwa2tjFuv8g02emjODJtA7R0wt1+y4IwW/bRERERNRe1E3DljeQZXgO+tnT9VsQrRpfxRDViTq40XKsj56v00pWT7t4HvpF03oFoG7fBaWzq36LWgVhs8G2fRfUnXsAu634CV1CHzkN7chByHSqLmvTpyehnTgKmPIv0dsHddtOCCGWviERERERUYsSDidsu/YBqml27tgo9ImxOq6KVoPhE1GdCK8XIlSsEpKzM00xPE8buwj93IjlnLp1B5Tu3sVv0MCUrm7Y9l8NEQpZzstIxBhGPjWxrlVQ+tQEtJPHLMGT0tcPdesOBk9ERERE1NaE1wd1+y7L+BLtzCnokXDd1kQrx/CJqI6UIVP1kwS00cae/aRPjkMfsZa3KsNbofT212lFayccTqi79kHdut3yTgpyGrSTx41+8my25uvQpyagnTpuDZ76B6Bs2c7giYiIiIgIgNLZDWXjcPGElNCOH26KN/HbHcMnojpSOvwQ/uKucHJ6EjKdruOKlqZPTUI7fcJyTtk0DHVgqE4rqh4hBJS+AaMKqqPD8jk5M21UQYVna/b4+uR4efA0MAhleBuDJyIiIiIiE2Vwg3V+a05D7tghyFzt3zCmyjF8IqozxTz7SZfQxy7WbzFL0GenoZ0qaQfbsBHq0Malb9SEhMsNdd9+KJs2A+bQJ5OBduQQtNMnIDWtqo+pT4xBO3WiPHjavJXBExERERFRCSGEMZbC9CY+kilox49A6nr9FkbLYvhEVGciGAI8nsKxPjm2Lm1eK6WHZ8sGYCsDg1A2bK7fompICAXq0CbYLr8S8Lgtn9MnxpE7+DT0+WhVHksbH4V2+qTlnDI4xOCJiIiIiGgZQlGg7twLuJyFczISgT5yqu47V9PiGD4R1ZkQwlpBpOkNs2uDHglDO3YE0IvfwJW+/rYIR4TXB9vlV0EZGLR+IpmCdugAtPMja3pnRRu7CP3MKcs5ZWgjlE1bWv7vloiIiIhorYTdDtuuywCbWjinT4xDHx+t46poKQyfiBqA6Oq2pPb6+MWqt3etlj4fhXbsEGAKWERPb1sNwBaKCnV4G9S9lwFOR/ETEtAvnId26ABkYvXDDbXRC+WD2zdshLJxc9v83RIRERERrZXweKDu2G3ZAU8/e7qm81qpMk0RPn3xi1/Eli1b4HK5cM011+BnP/vZstc/8sgjuOaaa+ByubB161Z8+ctfLrvmoYcewt69e+F0OrF37158+9vftnz+3nvvxbXXXouOjg709vbi1ltvxbFjx6r65yJaIIQCZWBD8UQ2B31yvG7rkfEYtKPPA5opeOrqhrptR1uGI0ogBNv+a6yDDQHIWAy5g89AG7u44vJe7eJ56GfPWO9/42aoG4fb8u+WiIiIiGgtlGAnlM1biyckoB0/CpmI129RVKbhw6dvfetb+OAHP4iPfexjeOaZZ3DDDTfgla98Jc6dO7fo9WfOnMEtt9yCG264Ac888ww++tGP4v3vfz8eeuihwjWPP/44br/9dtxxxx04cOAA7rjjDtx222345S9/WbjmkUcewfve9z784he/wA9/+EPkcjncdNNNiMf5H5hqQ+ntA+z2wrE+dqEuA/NkIoHckYNArlh5JUKdULfvghAN/y2jZoTNBtv2XVB37gbstuIndB36yGloR56/5E6F2oVz0M+NWM4pm4ahbthUgxUTEREREbUHpX8QSl9/8YSW3wEvm6nfoshCyAafxnXdddfh6quvxpe+9KXCuT179uDWW2/FvffeW3b9Rz7yEXz3u9/FkSNHCufuvPNOHDhwAI8//jgA4Pbbb0c0GsUPfvCDwjWveMUrEAqF8OCDDy66jqmpKfT29uKRRx7BjTfeuKK1R6NRBAIBRCIR+M2T+ImWoF08bwkn1O07ofT0rdvjy1QSuUPPAZniN2kRCEDdvQ9CUZe5ZXuRmTS0Uycgw3PWT9hUqFu2Q3T1WKqYpJTQL5yDfsEamiubt0Ad3AAiIiIiIlobqevQjh6CjIQL50SHH+reyyGU9n0TvZZWk3k09L9AJpPBU089hZtuusly/qabbsJjjz226G0ef/zxsutvvvlmPPnkk8jmdxBb6pql7hMAIpEIAKCzs3PJa9LpNKLRqOUX0WoofQOWgXna6Pl1261BplPIHT5oDZ46/FB3MXgqJRxOqLv3Qdm6HVBN30ZzGrQTx6CdOFrYsVBKCf382fLgaXgrgyciIiIioioxdsDbDbiLO1bL+Si00ye4A14DaOjwaXp6Gpqmoa/PWvnR19eH8fHF5+GMj48ven0ul8P09PSy1yx1n1JK3HXXXXjxi1+Myy67bMn13nvvvQgEAoVfGzduXPJaosUIm80IoBYkkpBzMzV/XJlJG8GTqW1MeL1GxZPK4GkxQgiofQOwXX41REeH5XNyZhq5556GHp6Ffn4E+sXzls8rW7ZBHRhaz+USEREREbU8YbPDtmsfYCuOyZBTk9BHL9RxVf9/e/cdHkW5tgH8nt1NNj0BQkghEAIkEDqEXhLwUKQJCAgIgsqHokiTo7ECFiQeESmCSBELcCh6OGKhKKCAooj0XkJNgJACgdTdfb4/cnbIkgRCWTabuX/X5SV5Z2bz7N6ZLc/OvENAKW8+Wd08Ca+I3HJi3qLWv3n8Tm5z1KhR2Lt3b7Gn5Fm98soruHLlivrf2bNnb7k+UVF0gSGA7sbfouW8fY9+krzc/DmesrNvDHp4QF+7HpQCT9pUNMXdHfo69aELrQoUfA7JzYX50AFYztu+0OmqVYc+MPgBV0lEREREpA2Kuzv0EbVt3ptbzpyCJfWyA6uiUt188vf3h16vL3RE0qVLlwoduWQVGBhY5PoGgwEVKlS45TpF3eYLL7yAb7/9Fps2bULlyrc+RcZoNMLHx8fmP6I7pbi6QhdwY7I8uXYNcvWKXX6XmEwwH9oPZGbdGHR3g6F2PSgFJj+nW1MUHfSVq0Bft4HNYb62KwH68BpsPBERERER2ZnO1w/6atVtxszHj0CuX3NQRVSqm0+urq5o0qQJNmzYYDO+YcMGtGrVqshtWrZsWWj99evXIzo6Gi7/+zBd3DoFb1NEMGrUKHzzzTfYuHEjqlWrdj/uElGJ6IJCgAIH0VgS7/9RdGI2wXx4P6TgFRyNRhhq14fi6nrff58W6Ly8YajfCLqgmxpMCqAPr2l7SiUREREREdmNrlKQ7ftysyX/Cni5t75CNdlHqW4+AcD48eOxYMECLFq0CIcOHcK4ceNw5swZPPvsswDyT3V74okn1PWfffZZnD59GuPHj8ehQ4ewaNEiLFy4EBMmTFDXGTNmDNavX4/4+HgcPnwY8fHx+OmnnzB27Fh1neeffx5fffUVli5dCm9vb1y4cAEXLlxAVlaBI0SI7ERxc4dSoaL6s6Sn39cuvVjMMB85CMnIuDHo6gpDVD0oRuN9+z1apOj00IdVh752XSheXoDRCH3NWjZHsxERERERkf3pqoZDKVfuxkBObv7nIIvZcUVplCJOMO37nDlz8P777yMpKQl169bF9OnT0a5dOwDAsGHDcOrUKWzevFld/5dffsG4ceNw4MABBAcH4+WXX1abVVarVq3C66+/jpMnT6J69ep499130adPH3V5cfM/ffbZZxg2bFiJ6r6Tyw4S3UyuX4dp79/qz0oFfxgiat/77Vos+U+46Wk3Bl1cYIiqD8XD455vn4iIiIiIqLQQkwmmA3uAzEx1TKngD33NWrecS5pu7056Hk7RfHJWbD7RvTId3g9J+1+TSAEMDaKhFDenUAmIWGA+dhiSUuAKegZ9fuPJ0+seqyUiIiIiIip9JCcbpn27gbw8dUxXORT60DCH1VQW3EnPo9SfdkekZbrg0Bs/CGBJuvtLhIoIzMeP2jae9Droa9Vl44mIiIiIiMosxegGfWSU7VXFz52F5fIlB1alLWw+EZViOh9fKN43OsiW5It3NUGeiMCScBxyObnAjeugr1UHOm8elUdERERERGWbztsH+vCaNmPmE0dhybjqoIq0hc0nolJOF1L5xg8WgSXp/B1tLyKwnD4Jy8ULBW5UgT6yNnQ+fvenSCIiIiIiolJOV7ESdCEFzi6xSP58uDnZjitKI9h8IirlFL/yQIGJwC0XkyCmvFtsYcty9jQsSYkFbhDQ16wNnV/5+1kmERERERFRqacLrQqlQoUbA3l5+Q0oM6+AZ09sPhGVcoqiQF9w7iezBZYLSSXa1nz+DCznzxa4MUBfIxK68hWK34iIiIiIiKiMUhQF+uqRUDw91TG5fh3m44fB67HZD5tPRE5A8fcHjEb1Z8uF87ftzJuTzsNy5rTNmD68JnT+AXapkYiIiIiIyBkoej30teoArq7qmKSmwnLmlOOKKuPYfCJyAoqigy64wNxPeSZYki8Wu77lYhIsp07ajOmqVYcuINBeJRIRERERETkNxdX4vyvg3WiLWBLP3fJzFt09Np+InIQuoBLg4qL+bEk8B7FYCq1nSb4Ic8Jx222rhEEfGGz3GomIiIiIiJyFzssb+hoRNmPmk8dguZrumILKMDafiJyEotNDF1SggZSTA0lJtlnHknIZ5hNHgQKnKusqh0Jf8IoOREREREREBADQVagIXWjVGwMWgfnoIUh2luOKKoPYfCJyIrpKwYBer/5sTjynTopnSUuF+fhh28ZTUAh0lavefDNERERERET0P7qQUCj+FW8M5JlgOnIAYjI5rqgyhs0nIieiGAzQVQq6MZCZCUlLheVKOsxHDwGWG50nXaVA6KpWg6IoDqiUiIiIiIjIOeRfAa8mFG/vG4OZWTAfOwyRwlOd0J1j84nIyeiCggHdjYaS+UwCzEcOAAXmf1IqBkBXrQYbT0RERERERCWg6PTQR0TZXGVc0tNgOZ3gwKrKDjafiJyM4mqErmKlGwNZWYC5QOOpgn9+156NJyIiIiIiohJTXF1hiKwD6AtcAS8pEeYLiQ6sqmxg84nICemCKwNF9JaUcuWhrxEJReGuTUREREREdKcUT0/oa9ay+bxlOXUClvQ0xxVVBvATKpETUtzcoVTwtx3z9YU+ohYUHXdrIiIiIiKiu6UrVwG6KtVuDAhgPnYIkpXpuKKcHD+lEjkpfeWqgIsBwP8aT5F1oOj0t9mKiIiIiIiIbkcXFAIloMB0JyYzTIcPQPLyHFeUEzM4ugAiujuKuwcMDZpAcnKgeHpxjiciIiIiIqL7RFEU6KvVgDk7G3L1Sv5gdjbMxw5BX6suzzi5Q3y0iJyY4uIKnZc3G09ERERERET3maLTQR9RG3BzU8fkyhVYEo5DRBxYmfNh84mIiIiIiIiIqAiKiwsMteoAhhtTnFguXYQl6bwDq3I+bD4RERERERERERVDcfeAvmZt2yvgnUmAJS3FcUU5GTafiIiIiIiIiIhuQedXDrqw6jcGBDAfOwy5ft1xRTkRNp+IiIiIiIiIiG5DHxgMXWDQjQGzBaYjByB5uY4rykmw+UREREREREREVAK6sHAofn43BnJyYD5yEGIxO6wmZ8DmExERERERERFRCSiKLn/+J3d3dUwyMmA+cYxXwLsFNp+IiIiIiIiIiEpIMRjyr4DnYlDH5HIyLIlnHVhV6cbmExERERERERHRHVDc3KGPqA3oblwCz3LmNCwplx1YVenF5hMRERERERER0R3S+fhBX62GzZj5+BHItQwHVVR6sflERERERERERHQXdAGB0AWH3BiwWGA6chCSm+O4okohNp+IiIiIiIiIiO6Srko1KOXL3xjIzc2/Ap6ZV8CzYvOJiIiIiIiIiOguKYoCfY1IwMNDHZNr12A+cYRXwPsfNp+IiIiIiIiIiO6BordeAc9FHZOUFFjOnXZgVaUHm09ERERERERERPdIMbpBHxllewW8c2dhSb7kwKpKBzafiIiIiIiIiIjuA523D/TVI2zGzCePwpJx1UEVlQ5sPhERERERERER3Sc6/wDoKofeGLBI/gTkOdmOK8rB2HwiIiIiIiIiIrqPdJWrQqngf2MgLw+mwwcgZpPjinIgNp+IiIiIiIiIiO4jRVGgrx4BxcvrxmBmJszHtHkFPDafiIiIiIiIiIjuM0Wvz5+A3NVVHZO0VFjOJDiwKsdg84mIiIiIiIiIyA4UVyMMteoA+hvtF0vieVguXXBgVQ8em09ERERERERERHaieHpBXz3SZsx88jgsV9IdU5ADsPlERERERERERGRHugr+0FUJuzEgAvOxQ5DsLIfV9CCx+UREREREREREZGe64MpQKgbcGMgz5V8Bz1T2r4DH5hMRERERERERkZ0pigJ9eE0o3j7qmM7bB9CV/dZM2b+HRERERERERESlgKLTQR9ZG3Bzgy4sHLrwmlA00HwyOLoAIiIiIiIiIiKtUFxcYWjQGIpO7+hSHpiy314jIiIiIiIiIipFtNR4Ath8IiIiIiIiIiIiO2LziYiIiIiIiIiI7IbNJyIiIiIiIiIishs2n4iIiIiIiIiIyG7YfCIiIiIiIiIiIrth84mIiIiIiIiIiOyGzSciIiIiIiIiIrIbNp+IiIiIiIiIiMhu2HwiIiIiIiIiIiK7YfOJiIiIiIiIiIjshs0nIiIiIiIiIiKyGzafiIiIiIiIiIjIbth8IiIiIiIiIiIiu2HziYiIiIiIiIiI7IbNJyIiIiIiIiIishs2n4iIiIiIiIiIyG7YfCIiIiIiIiIiIrth84mIiIiIiIiIiOyGzSciIiIiIiIiIrIbNp+IiIiIiIiIiMhu2HwiIiIiIiIiIiK7YfOJiIiIiIiIiIjshs0nIiIiIiIiIiKyGzafiIiIiIiIiIjIbth8IiIiIiIiIiIiu2HziYiIiIiIiIiI7Mbg6ALKMhEBAFy9etXBlRARERERERER3T/WXoe193ErbD7ZUUZGBgAgNDTUwZUQEREREREREd1/GRkZ8PX1veU6ipSkRUV3xWKxIDExEd7e3lAUxdHl3LGrV68iNDQUZ8+ehY+Pj6PLITtj3trCvLWDWWsL89YW5q0tzFs7mLW2OHPeIoKMjAwEBwdDp7v1rE488smOdDodKleu7Ogy7pmPj4/T7QR095i3tjBv7WDW2sK8tYV5awvz1g5mrS3Omvftjniy4oTjRERERERERERkN2w+ERERERERERGR3bD5RMUyGo2YOHEijEajo0uhB4B5awvz1g5mrS3MW1uYt7Ywb+1g1tqilbw54TgREREREREREdkNj3wiIiIiIiIiIiK7YfOJiIiIiIiIiIjshs0nIiIiIiIiIiKyGzafiIiIiIiIiIjIbth8IiIiIiIiIiIiu2HzScMsFoujSyAiIiIiIiKiMk4REXF0EWR/2dnZ2Lp1Ky5evIiKFSuiU6dOji6J7Cg7Oxu5ublwc3ODq6uro8shO2Pe2sK8tU1EoCiKo8ugB4R5awvz1hbmrR3MOp/B0QWQ/WVkZCA2NhZXr17FiRMn4OLigoiICLz33nto164dfHx8HF0i3UejRo3CwYMHcezYMdSvXx/Dhg1Dv379HF0W2Qnz1hbmrS2rV6/GhQsX4OrqilatWiEyMhKKosBisUCn48HrZQ3z1hbmrS3MWzuYdfF45FMZl5OTgw4dOsDT0xOTJ09GcHAwNm/ejLlz5+LgwYN47bXX8NRTT6FixYqOLpXug86dOyMhIQGPPPIIsrOzsWXLFuzduxejR4/G2LFjERYW5ugS6T5i3trCvLWla9euOHDgAK5fv4709HRUrlwZjzzyCD788EPo9XqYzWbo9XpHl0n3CfPWFuatLcxbO5j1bQiVadu3b5ewsDD5+eef1TGTySSXLl2S3r17i4uLi0ydOlXS09MdWCXdD4sWLZKwsDDZs2ePOnbkyBF58803xWAwyKBBg+T48eMOrJDuJ+atLcxbW5588kmpVauW/Pnnn3L27Fk5fPiwdO/eXby9vSU2NlZMJpOIiPp/cm7MW1uYt7Ywb+1g1ren7eO+NODSpUtITEyEp6cnAMBkMkGv16NixYpYuXIlevTogXfeeQc///wzAE5C7szOnTsHg8GAypUrQ/53QGNERATi4uIwffp0rFixAu+//z5ycnIcXCndD8xbW5i3dqSmpmLfvn0YOnQooqOjUblyZURGRuKrr77C+PHj8ccffyA2Nlb99lR4ALtTY97awry1hXlrB7MuGTafyrjq1atDp9Nh27ZtAACDwQARgYhAr9dj/vz5aNiwIV599VWYTCbNn4fqzLKyspCWlgY3NzcoigKTyQQAcHd3x4gRI/D2229j/vz5+Pzzzx1cKd0PzFtbmLd2ZGZmYv/+/TAajerkpGazGb6+vvjnP/+JV199Fbt378awYcM4gWkZwLy1hXlrC/PWDmZdMuw0lHE1a9bEI488YnN0U8E/dj8/P4wbNw6nTp3CqlWrHFUm3QPr0Wpdu3aFyWRCXFwcgPxGo9lsBgC4urriiSeeQLdu3RAfH48LFy44rF66N8xbW5i39vj6+iIqKgobNmxQs9Tr9bBYLPD09MTo0aPRt29frFu3DmvXrgUAzX6DWhYwb21h3trCvLWDWZcMm09lnIuLC9566y14e3vjjTfewPbt2wFA/eZcp9OhQ4cO0Ov1SEpKcnC1dDesR6vVrVsXsbGxWLFiBRYsWAAA6sR2ABAcHIx+/frh7NmzSE1NdVi9dG+Yt7Ywb+3x9vZGr169sHbtWqxbt07NWKfTwWKxwMfHB1OmTAEAbNiwAQA0+w1qWcC8tYV5awvz1g5mXTJsPmlAREQEVq1ahUOHDmHChAnYtGkTgPxvzgEgOTkZVatWRVBQkCPLpDuwZs0aLFq0CAsWLEBKSgpycnLg5+eH6dOnw2g0YvLkyerpN3q9Xj1FJyQkBL6+vuoTIjkH5q0tzFtbsrOzcfXqVeTm5qpjcXFx6Nq1K8aPH4/169er4zqdDmazGUFBQWjSpAn2798PQJvfnjor5q0tzFtbmLd2MOu7w+aTRjRt2hTr1q3D6dOnMWDAAMydOxcJCQnYs2cPPvjgA6Snp6NVq1aOLpNKoFu3bhg/fjzGjh2LMWPGoH79+vjXv/6FM2fOoFq1ali/fj3MZjNee+01TJo0CUB+ozEnJwc///wzypcvj/Llyzv2TlCJMW9tYd7aMmrUKHTt2hV16tRB7969sXLlSgD5Ry2/++67iIyMxODBg7FixQqkp6cDyG84Xrt2Dbm5uahdu7YDq6c7xby1hXlrC/PWDmZ9Dx7YdfWoVDh06JD07NlT9Hq9eHl5SdWqVaVatWqya9cuR5dGJTBs2DCJjIyUbdu2ybFjx2TXrl3SrVs3cXV1lX79+smRI0dEROTYsWPStm1b8fHxkYYNG8qTTz4pffr0ETc3N/n6668dfC+opJi3tjBvbenUqZPUrFlTJkyYIKNGjZIGDRqIoigyevRoOXv2rIiI7Ny5Ux566CFxdXWV5557TrZs2SI7d+6USZMmiYeHh2zcuNHB94JKinlrC/PWFuatHcz63rD5pEFZWVny559/yrx58+S7776Tc+fOObokKoFz585Jo0aNZPbs2YWWvfDCC+Lr6yu9e/eWEydOiIjIxYsXZeHChdK9e3dp3LixDBo0SNavXy8iIhaL5YHWTneOeWsL89aWRYsWSVhYmOzZs0cdO3LkiLz55ptiMBhk4MCBcvr0aRERuXz5sowdO1bKly8viqJIpUqVJDQ0VFatWiUizNsZMG9tYd7awry1g1nfOzafiJzEmTNnxNfXV6ZNm6aO5eXlqf8eP368uLu7y7hx4yQ9Pd1mW7PZLGazWUTyn+y0+oTnTJi3tjBvbXnrrbekRo0akpKSYpNXZmamzJo1SwwGgwwfPlyys7PVZQcOHJCff/5Zfv31Vzl27JiIMG9nwby1hXlrC/PWDmZ97wyOPu2PiErGaDTC19cXO3bsUCeos15uXa/XY9q0aUhMTMTChQvx9NNPw9fXFyaTCQaDATqdTt1Gi1dWcEbMW1uYt7ZkZWUhLS0Nbm5u6tVnDQYD3N3dMWLECFy7dg2vvvoqmjZtihEjRgAAoqKiEBUVZXM7zNs5MG9tYd7awry1g1nfO044TlSKWT9QiggCAgIwZswYrFixAsuXL4eiKBARm8utz549G+7u7vj0008B3LiiIaDtJzpnwby1hXlrj8ViAQB07doVJpMJcXFxAG40GgHA1dUVTzzxBLp164b4+HhcuHDBYfXSvWHe2sK8tYV5awezvn/YfCIqxa5evQrgxgfLLl26oEOHDhgyZAjWrl2rfkDV6fJ35XLlyiEsLAxJSUkOq5nuHvPWFuatPdYs69ati9jYWKxYsQILFiwAAJtGY3BwMPr164ezZ88iNTXVYfXSvWHe2sK8tYV5awezvn/YfCIqhcaOHYsOHTogMjISQ4YMwRdffAEg/9DNCRMmoF69eujVqxdWr14NRVHUD6+ZmZnw8fFBaGgogBtHVlDpxry1hXlry5o1a7Bo0SIsWLAAKSkpyMnJgZ+fH6ZPnw6j0YjJkyfj888/B5D/JtZkMgEAQkJC4Ovrq76pJefAvLWFeWsL89YOZm0nD3SGKSK6rY4dO0qtWrXk+eeflwkTJkjVqlXF3d1dhg8frq7z3XffSevWrUVRFImLi5OtW7fK7t27ZfLkyeLh4SGbN2924D2gO8G8tYV5a0vXrl2lRo0a4u3tLR4eHhIcHCxvv/22ejWcw4cPS1BQkISEhMjEiRPV7bKzs+WVV16RiIgIXpHWiTBvbWHe2sK8tYNZ2w+bT0SlyMKFC6Vy5cqye/duMZlMIiKyf/9+GTVqlCiKIn379lXX/fPPP2XChAmi1+vFw8NDAgMDpUqVKpq/hKczYd7awry1ZdiwYRIZGSnbtm2TY8eOya5du6Rbt27i6uoq/fr1kyNHjoiIyLFjx6Rt27bi4+MjDRs2lCeffFL69Okjbm5u8vXXXzv4XlBJMW9tYd7awry1g1nbF5tPRKXIG2+8IVFRUeoHU+vl05OSkuTtt98WvV4vQ4YMsdnm4MGDsnr1avnxxx/l8OHDIqLtS3g6E+atLcxbO86dOyeNGjWS2bNnF1r2wgsviK+vr/Tu3VtOnDghIiIXL16UhQsXSvfu3aVx48YyaNAgWb9+vYiw0egMmLe2MG9tYd7awaztj80nolLkrbfeEm9vb0lOThaRGx9ORfKf4F566SVRFEX+9a9/OapEuo+Yt7Ywb+04c+aM+Pr6yrRp09SxvLw89d/jx48Xd3d3GTdunKSnp9tsazab1b8NNhqdA/PWFuatLcxbO5i1/XHCcaJSpG7dusjNzcWnn36KzMxM6HQ6dVLhgIAAjBgxAi1btsTXX3+NjIwMTjjs5Ji3tjBv7TAajfD19cWOHTsg+V/02VySedq0aXjkkUewcOFCnDt3DgDUyUp1Op06yXzBCeep9GLe2sK8tYV5awezfgAefL+LiG6lY8eOEhAQIN9++63k5uaKSH4H3dpNX7FihSiKIvv373dkmXSfMG9tYd7aMW3aNNHpdLJs2TIRuXEIvvW0y8uXL0ulSpVk9OjRDquR7h/mrS3MW1uYt3Ywa/vikU9EpYS1q/7ZZ5+hUqVKGDduHDZu3IicnByb7rnRaET58uXh5ubmqFLpPmDe2sK8tad79+7o0KEDhgwZgrVr10JRFIgIdLr8t17lypVDWFgYkpKSHFwp3Q/MW1uYt7Ywb+1g1vbF5hNRKaHX6wEAISEhWLRoEYxGI4YPH47FixcjNTUVOp0OWVlZ2LFjBwICAvjh1Mkxb21h3toTERGBl156CfXq1UOvXr2wevVqm0PxMzMz4ePjg9DQUADgaZZOjnlrC/PWFuatHczazhx1yBWRlhWcaLg4hw8fllatWomrq6s0atRIRowYIb179xaj0SjffPPNA6iSHiTmXXZw/6aCE43+8MMP0rp1a1EUReLi4mTr1q2ye/dumTx5snh4eMjmzZsdWCndD8xbW5i3tjBv7WDW9qeIsF1H9KB8/fXXePTRR+9om/j4eOzYsQNHjx5F/fr1MWTIEHTu3BkiwsnsnIzFYlEP2y0O83Ze3L+1qbisCo7v2rULS5cuxfTp02E0GuHj4wNXV1d8+OGHePTRR5m3E8jMzISHh0exy5l32cT9Wxu4f2sP923HYPOJ6AGJiYnB1atXsXHjRpQrV+6265tMJhgMBvXn7OxsuLi4QK/Xq4d48gmv9Jo3bx7OnDmD7OxsxMbGon379vDy8oLZbFZPwSqIeTs37t/acvr0aeTk5MDHxweBgYHq+M1vRG/++dChQzh69CiMRiOqVauGyMhI5u0EhgwZgocffhgDBw68ZU7Mu2zg/q0t3L+1g/u247H5RPQAtG/fHomJifjmm29Qp04dm2U3P8Hd3JzIy8uDi4vLA6uV7l2nTp1w9OhR+Pj4ICkpCTk5OWjQoAGWLVuGypUr26zLvJ0f929teeKJJ/D333/j1KlT8PHxwTPPPIMePXqgcePGAGwzL8nRjlS6xcbG4sSJE9i0aRNq1Khxy3WZt/Pj/q0t3L+1g/t26cBHlcjOYmJikJSUhJUrVyIqKqrQ8oJPdMCNiYknTpyI48ePw8XFhZPZOZFx48bh9OnT+O677/DHH38gMTERL774Ik6cOIHmzZtj165dAJh3WcH9W1u6d++OHTt2YNy4cZg+fTp69OiByZMn4/nnn8d//vMfAFCvjANAffN68OBB9TaYt/Ow7t9r164t8oPpzVkyb+fG/VtbuH9rB/ftUuT+TR9FRDcbOHCg6PV6uXDhgjqWkJAgU6ZMkX79+skzzzwjq1atkszMTBG5MVHxDz/8IIqiSL169eTKlSsOqZ3unNlslk6dOsnIkSPFbDareZpMJlm1apXUrl1bgoKC5MSJE+q4CPN2Vty/tWX9+vVSo0YN2bhxo8348uXLJTAwUCIjI2XlypWFthszZoyEhITIli1bHlSpdB9069ZNatSoISdPnpTc3FwREUlPT5cjR47Ib7/9JmfPni1yO+btnLh/awv3b+3gvl268MgnIjs5ceIEzp49CwBYvHgxAODAgQNo2bIlFi5ciN27d+M///kPhg0bhjfffBNZWVlqp/3hhx/GmDFjkJSUhOTkZEfdBbpDWVlZOHr0KPLy8qDT6aDT6dTTrPr06YP33nsPLi4u6NmzJ9LT09WjYJi380lISOD+rTHJyck4e/YsQkJCANw4mq1///5YuHAhMjMzMWXKFGzZssVmuypVqiAxMRF//fXXA6+Z7s6kSZPwww8/oEePHihfvjxcXFywb98+9OrVC7GxsWjXrh3q1auHuXPnIiUlxWZb5u2cuH9rB/dvbeG+Xco4uvtFVJb99ddf0qNHD1EURcaMGSPh4eEyePBg2bVrl2RlZcnFixclJiZGPDw85KuvvhIRkZycHBHJv9xncHCwxMXFOfIu0B0aOnSohIeHy86dO9WxgkdAzZw5U/z8/CQ+Pl5ERP3GjXk7j3379omIyB9//MH9W0NWrlwpbm5usnbtWhHJ368LXpb5m2++ERcXFxk+fLjNMovFIs8995zUr19fLl686JDa6c5YLBZp06aNuLm5yezZs+X3338XX19f6dixo7z//vsya9Ys6dSpkxiNRpk5c6aI3HieZ97Oifu3dnD/1hbu26ULm09EdlDwSe3PP/+Ubt26iV6vl4EDB8qlS5ds1k1LS5OQkBB5/PHH1bGCp2NZT9Gi0mnWrFmSmpqq/vyf//xHFEWRF154weZ0LGumeXl50rx5c+nYsWOhZcy79OvUqZOEhYWp+/jOnTu5f5dx1qyvX78uVatWlS5duqjLTCaTzfP922+/LTqdTvbu3WtzG6tWrZI+ffpIWlraA6mZ7l7BBnGrVq1EURSpVKmSjBgxQpKTk9X1zpw5Iz179hQPDw85efKkzW0wb+dhbSpw/y77rl27pv6b+3fZx9fu0onNJ6L7aNGiRfLiiy/K0KFDZfr06er477//Ls8884z8+OOP6ljBJ73mzZtL69atC91ewXWo9GnVqpVUrlzZpskkIvLqq6+KXq+Xt99+2+bNTF5enojkn0dep04d9UOOFfMu3dq3by+KooiiKDJv3jx1fMeOHdy/NcBiscgnn3wiiqLI008/rY6bTCa1obh//37x8fGRRYsWiciND7YiIpcvX36wBdNdK7hvtmvXTiIjI+XIkSOFln/55ZeiKIp8//33hbZj3qXbqlWr1H9b91Pu32XXI488It27d5ecnBw1TxHu31rA1+7SxeDo0/6IyopevXrh4MGDcHNzQ3JyMpYtW4bdu3dj8eLFaNGiBUJCQhAaGgrA9hKeJ0+eRE5ODjp06ADA9lKfBS/RTqVLbGws0tLS8OOPP6JSpUo2y+Li4nDp0iVMmjQJ165dw5NPPonIyEgYDAbk5OQgMTERtWvXhk6nY95OIjY2FqmpqdiwYQOGDBmC9evXY8SIEQCA6OhoBAYGonLlygC4f5cF8+bNw5kzZ5CdnY3Y2Fi0b98eXl5e6NatG0aOHIm5c+fCxcUFc+fOVedus/Ly8kL58uUB5F8xx/r3UKFCBUfcFSqBdevW4ciRI8jMzES3bt1Qu3ZtGAz5b5F/+eUXrFy5EhEREQBs92EA8PHxQWBgIID8fZp5l34xMTG4evUqOnTogHLlyqnP17169cLevXu5f5cx//jHP7Bx40bUqFEDKSkpCAoKQk5ODoxGI/fvMoav3U7Aoa0vojKif//+UqdOHdm+fbvk5eVJRkaGPPbYY+Lj4yN//PGHzbrWOX5ERK5cuSJvvPGG+Pv7y++///6gy6a71LFjR4mIiJCDBw+q35qYTCbJzs5Wj2YymUzywgsviKIo0rZtW5k3b56sWbNG4uLixGg0queeU+kXGxsrERER6uHYEyZMEEVRbI50suL+7fw6duwoVatWlXr16om/v794e3tL69at5fTp0yIicvLkSfm///s/URRFHn30UTlw4ICYzWZJT0+Xd955R0JCQmT//v0OvhdUUv369ZM6depIuXLlxNXVVcLDw2XFihVisVjUo1WtCv589epVeeqpp6R58+Zy/vz5B1023SXr83lx+2hCQgL37zIkJiZGIiMjZfr06aIoikyePFldxv27bOFrt3Ng84noHn3xxRfSoEEDWbdunc34sWPHRFEU+eSTT4rcbuvWrWojoqhLfFLpNGjQIFEURdauXasegn3kyBF5/vnnpVmzZtKyZUsZOXKkZGdni4jIp59+KtHR0WIwGMTf31+ioqLkm2++ERGeduUM2rdvL+Hh4XLgwAF1bN26daIoiowYMUJMJpPN4dlW3L+d09ixYyUiIkL27dsnmZmZkpubK5MmTZKgoCAJDAyUv/76S0RETp8+Le+9954EBASIn5+fREVFSUxMjHh5eTFvJ9K7d2+JioqSdevWydGjR2X37t0SHh4uLVq0KPTBtODzdWJiokyePFlcXV1lzZo1D7psukvWU6z27Nlzy9df7t9lQ0xMjERERMjhw4fl9OnTUqtWLalVq1ahOZxEuH87O752Ow82n4juQV5enowdO1Y6d+4sKSkpNuPp6ekSFBQkEydOFBHb84e3bt0qVapUkfDwcPnvf/8rImxEOIO8vDyZNm2auLm5yaBBg+TatWuyd+9eKV++vNSrV08efvhhadasmSiKIg0bNpSsrCwREbl06ZKcOHFCjh07JklJSSKSnzczL93OnTsnQ4YMkYMHDxZaNnjwYPH395ezZ88WWsb92zmZzWbp1KmTjBw5Usxms81VKletWiW1a9eWwMBAOXbsmIjkT1577tw5efXVV+Wpp56S119/XbZs2SIizNsZfPjhhxIdHS2//vqrzfgXX3whiqLIL7/8UuR2y5cvl+7du0v58uXVDyvMu/QbOHCg6PV6mzkaExISZMqUKdKvXz955plnZOXKleqk1NevX+f+7cRatGghUVFRNkeyzJ07VxRFueUXgNy/nQ9fu50Lm09E9+jQoUPy22+/iUjhJ606derIuHHjilz29ddfy86dO9VlfMJzDrm5uTJ79mxxdXWVzp07S5UqVWTYsGHqN2mZmZkyffp00el0Nlc4I+dU3KTwS5YsEUVRZPz48UUeIfHtt99y/3Yy165dk7CwMBk+fLg6Zj2t1mKxyOrVq6VKlSoSFRV12yvfMO/SLTU1VVq3bi39+/eX69ev2yz7+++/iz2tNi0tTd555x3p37+/bNiwQUS4fzuD48ePS5s2bUSv18vUqVNFJH+C4cDAQKlevbrUrFlTAgICxMvLS1588cVCfxM3Y96l2w8//CDBwcGyb98+dcxisciRI0ekSpUq0rhxY5uLwVhx/3ZOfO12Lmw+EdmJ2WyWWrVqyTPPPKOO5ebmypIlS/jk5uTy8vJk1qxZ4urqKg8//LB6NJPVtWvX5KGHHpJ69epJRkaGg6oke8rLy5NmzZpJnTp11G/Kizr9jpzL0KFDJTw8XG0ciojNt6gzZ84UPz8/iY+PV8es+LzuXObOnSs///yzzZjZbJbz58+LwWCwuRpaQWlpaZKamioi/GDqTP7++2/p0aOHKIoiY8aMkfDwcBk8eLDs2rVLsrKy5OLFixITEyMeHh7y5Zdfigj3b2eWnp5e5PioUaPExcVFPdKlYMYi+XM9cf92HtZ8+NrtPHSOnvCcqCyS/MYujEajesWc7OxsTJ8+HYMHD8bmzZsdWyDdE4PBgBEjRuCzzz7D4MGD1SuhiAgAwNPTE1WrVkVGRoY6RmWH2WyGwWDA6NGjcfDgQcyfPx8A1CsmkXMpuI/26tULCQkJWLx4MS5evAggP1ez2Qy9Xo+RI0ciMjISP/30EwDYXC2HVy90DmazGQDw7LPPokOHDjb563Q6+Pr6QqfT4erVqzbbbNmyBdevX4efnx/KlSsHID9z5l66WfNu1KgR3nrrLXTt2hWzZ89G8+bN8eGHH6Jhw4Zwc3NDQEAAVq9ejXLlymHt2rUAuH87I5PJBADw9fW1GbdYLACA8ePHw8fHB7NmzQKAQlc88/b25v7tRKz5dOvWja/dToLvlInswNp8MhgM6hvbDz74AK+88gr+/e9/o3379g6ukO6Vq6sr+vXrh0GDBgHIf4NrfQG7cOECTp48iZiYGLi7uzuyTLID65uWZs2aISAgAF9//TXS0tLYaHQyP/30E7KysqAoivoBtVevXnjllVcwZ84czJ8/H5cvXwaQn7nJZILBYECLFi2QmJiI3NxcR5ZPd8iat16vt9lXb/7gkZubCxGBq6srACAvLw/x8fEYPHgwTp069SBLpntQMG/r/t2wYUNMmjQJw4cPxxNPPIGKFSsCuNGA9vPzQ+XKlZmzE7LmXfB9d0HWL4cqVqyItm3b4rvvvsOvv/4KAHztdjKfffYZJkyYgGHDhmHGjBkAgH79+uGf//wnX7udAJtPRHag0+mg0+mQk5ODjIwMzJ49G2+++SaWLl2K/v37q80pcm4uLi4AoH6zAgDp6en49NNPsXPnTgwdOlQ98o3Knpo1a+KVV17Btm3b8Pfff/PbMyfSo0cPDBw4EMuWLUNOTo7NB9S4uDg8/fTTmDRpEj744AMcOXIEQP4Rjzk5OUhMTETt2rWh0+n4PO4kbs5bUZQiszObzcjJyYFOp1Of36dNm4bXX38dU6dORZ06dR506XQXbrV/R0dH4/XXX0eXLl0A5B8RY33uPnnyJHJyctCuXTsAbEo4i5Lu3wDg5eWFf/7znzCZTNi0aRMAHvniTHr16oX33nsP69evx7p16/DSSy9h6NChAIBJkybxtdsZPMhz/Ii0xGw2S7t27cTNzU0URZGlS5eKCM8hL8s2bNggzz77rHh4eMiKFStEhOeSl3W//PKL+Pj4qJOTUukXFxcnnp6eUqFCBQkODpZFixZJdna2zTqpqakybtw4URRF2rZtK/PmzZM1a9ZIXFycGI1GWbt2rYOqpzt1q7yLen6+cuWKuLq6yuzZsyU+Pl4URZHly5cXuz6VLrfK++b5fXJzc9V/X7lyRd544w3x9/eX33///YHWTHfvTvdvi8UiV65cke7du4uXl5fN1fCodOvfv7/UqVNHtm/fLnl5eZKRkSGPPfaYeHt7y44dO0REJCUlha/dpRybT0R2kpubK7GxsaIoiqxZs0ZE2HgqyxITE6Vt27bSpEkT+e6770SEeWvF8ePHRYQfTJ3B2rVrJSQkREaOHCn79u2TevXqSaVKlYpsQImIfPbZZxIdHS0Gg0H8/f0lKirqlpfpptKlJHnfnOPVq1clICBAmjZtKoqiyLJly9T1mHnpdjd5i4hs3bpV/XC6cuXKB1023aW7zVtEZNq0aaIois0V8aj0+uKLL6RBgwaybt06m/Fjx46Joigyd+5cm3G+dpdePB+EyE5cXFwwc+ZMJCcn20xqysN7y6agoCAsW7YMmZmZqFmzJvPWABGBoiioXr26o0uhEvLz84ObmxueffZZ1K1bFz/88AM6d+6MuLg4AMCgQYNgNBrVOSKGDRuGnj17Ij09HRaLBV5eXggMDOQh+06ipHlbLBbodDpYLBakpqYiOTkZycnJWL16NXr27Mnncydxp3kDwLZt2zBo0CAYDAasWLFCzZtZl353k7c12zFjxqB9+/aoW7euI+8ClYDJZMLff/+NwMBAREdH24xXrFgRgYGBuHDhgjrG1+7STRGmQGR3fONKRFQ6XLlyBb6+vsjLy4OLiwsSExPRsWNHXL58GVOnTsXAgQPh5uamzs3Hqxg6t5LmDUD9kPruu++iXr16bDw5oTvJ29qI+Pbbb1G5cmU0btyYeTuZu8n75uYim42l3+HDh5GWloaWLVsWyqtu3bro1KkTPvzww0LLCjYeqXRgGkQPAC/XSkRUOlgvwe3i4gIRQXBwMDZs2AB/f3/ExcVh2bJlMJlMSE9Px/Tp05GQkODgiulelDTvtLQ0fPjhh0hISMBrr73GxpOTupu8e/bsicaNGwPg+zVncyd5W5/Pb86XeZd+tWrVQsuWLQGgUHPJbDYjMzNTXWYymbB06VIAYOOpFOJpd0RERKRJ1m/Bg4ODsX79enTu3BmvvPIKMjIycPz4ccyePRuNGjVCtWrVHF0q3Qd3mjc/lDq3kuTdsGFD7t9lBPPWFuvRyUajUb2ydHZ2NmbOnIm4uDgEBQWhffv2Dq6SbsbT7oiIiEjTzGYz9Ho9Ll68iIceeggHDx4EAKxcuRKPPvqog6uj+415awvz1hbmrQ0WiwUWiwUtWrRA8+bN8fHHH+Odd97BxIkTsWzZMvTv39/RJVIReCwaERERaZperwcAeHp6onnz5gCANWvW4NFHH1W/XaWyg3lrC/PWFuatDTqdDjqdDjk5OcjIyMDs2bPx5ptvYunSpejfvz+zLqXYfCIiIiLNS01Nxcsvv4zFixdjxYoV6NatG+f8KcOYt7Ywb21h3tpRvnx5rFy5EqNHj8aSJUvw2GOPMetSjHM+ERERkeZZLBZs3boVS5cuRd++ffnmtYxj3trCvLWFeWuD2WxWj3769ttv0b17d2ZdynHOJyIiIiIA169fh6enJ9+8agTz1hbmrS3MWxv27duH5ORkdOjQgVk7ATafiIiIiJB/9Ry+adUO5q0tzFtbmLe2sPHkHNh8IiIiIiIiIiIiu+GE40REREREREREZDdsPhERERERERERkd2w+URERERERERERHbD5hMREREREREREdkNm09ERERERERERGQ3bD4RERERkWZlZ2ejatWqiIqKgsViUcdPnToFRVEQFhb2QOowm82IiIhAeHg4cnNzH8jvJCIielDYfCIiItIoRVHu+L/Y2FhHl33PNm/ejEmTJmHz5s2OLoVKgVmzZuHMmTN4/fXXodM57q2xXq/HK6+8goSEBMydO9dhdRAREdmDIiLi6CKIiIjowWvTpk2hsStXrmD//v0AgNatWxda3qhRI8yaNcvutdnTpEmTMHnyZEycOBGTJk1ydDnkQFevXkVYWBjKlSuHo0ePQq/Xq8tOnTqFatWqoWrVqjh16tQDqcdkMiE8PBxZWVk4deoUPD09H8jvJSIisjeDowsgIiIix9i6dWuhsc2bN6N9+/bFLicqS5YsWYK0tDSMHj3apvHkKAaDAYMGDUJ8fDz+/e9/4+mnn3Z0SURERPcFT7sjIiIiIk2aP38+AGDgwIEOruSGAQMGAAAWLFjg4EqIiIjuHzafiIiIqEROnjyJ+Ph4xMbGIjQ0FEajERUrVkSXLl3w/fffF7nN5s2b1bmiTCYT3n//fdSrVw8eHh6FJnJesWIFWrRoAU9PT/j7+6Nnz57YtWuXzW0UJTU1Fa+99hrq1q0LT09PeHt7o0WLFpg/f77NBNJA/jxXkydPBgBMnjzZZj6rYcOGlehxuH79Ot566y3Ur18fnp6ecHNzQ2hoKGJjYzF16lTk5eUV2iYzMxPx8fGIjo6Gj48PPDw80LBhQ/zrX/9CTk5Okb9HRLBgwQI0bNgQ7u7uCAgIwIABA3D8+HEsXry4yJonTZoERVGKPZ2wuO2szp07h9GjRyMiIgLu7u7w8/ND+/btsWrVqiLXj42NhaIo2Lx5Mw4fPox+/frB398f7u7uaNKkCVasWFHs4wgAGzZsQJ8+fRAcHAyj0Yjg4GC0b98eH3/8cZGPy+HDh/HUU08hLCwMRqMRFSpUQLdu3bBx48Zb/p6iHD16FLt27UL16tURGRl5y3VFBLNmzVL/dgMCAjBkyBCcOXOmyPWtf1MAsHTpUjRr1gxeXl4oX748evXqpZ7aWpSGDRsiJCQE27dvL/b2iYiInI4QERER/c+mTZsEgBT1FuHpp58WAOLl5SURERESHR0tQUFB6vpTp04t9vbatWsn3bp1EwBSvXp1adKkidSpU0dd76233lJvJzg4WKKjo8Xb21vc3Nzk3XffFQASExNT6Pb3798vISEhAkBcXV0lKipKqlevLoqiCADp27evWCwWdf3WrVtLaGioAJDQ0FBp3bq1+t+7775728cnLy9PWrRoIQBEp9NJZGSkREdHS3BwsOh0OgEgaWlpNtucO3dOoqKiBIAYDAapUaOG1K5dWwwGgwCQNm3aSGZmZqHfNXLkSPUxCQsLk8aNG4vRaBQ/Pz959dVXBYAMHTrUZpuJEycKAJk4cWKR9X/22WdFbicisnnzZvH19RUA4u7uLvXq1VMfKwDy4osvFtomJiZGAMgHH3wgXl5e4u3tLU2aNJGKFSuq23355ZdF1vL888+r61SoUEGio6OlatWq6uOYkJBgs/7y5cvF1dVVAIi3t7c0bNhQAgMDBYAoiiIzZ84s8vcUZ/78+QJABg4cWOTyhIQEASBVq1ZVs6hSpYo0adJE3NzcBIBUrFhRDh8+XGhb6/2Kj48XABIYGKj+TVsf3y1bthRbW+/evW/52BERETkbNp+IiIhIdavm0w8//CDbt2+3aeaIiPz6668SFBQker1ejh8/XuTt6fV6CQgIkN9++01dlpWVJSIif/zxh+h0OlEURebOnave/vXr12XIkCHi4uJSZPPp2rVrUr16dQEgo0ePlitXrqjLDhw4IHXq1BEAMnv2bJvtbteguZVVq1YJAGnQoIGcPXvWZtmlS5fko48+kuvXr6tjZrNZWrVqJQBkwIABcuHCBXXZ2bNnpW3btgJAJkyYYHNb//3vfwWAGI1G+frrr21+R2xsrPqY3K/m0/nz56V8+fKiKIpMmTJFsrOz1WXbtm1TG3xr1qyx2c7afHJxcZFRo0apmVosFnn55ZfVZqLJZLLZ7qOPPhIA4uHhIV9++aWYzWZ1WUpKikybNk0uXbqkju3Zs0eMRqO4ubnJp59+arP+t99+Kz4+PqLX62X37t1F3u+iPPnkk2rjrCjW5pPBYBAXFxdZtmyZuuzy5cvyj3/8QwBIs2bNCu0T1n3IxcVFpk2bptZ7/fp1efzxx9WmVlFNRxGRKVOmCAB55plnSnx/iIiISjM2n4iIiEh1q+bTrSxYsEAAFDp6qODtFWyiFDRgwAABIMOHDy+0LDc3V2rUqFFk82nmzJkCQHr37l3k7e7Zs0cURZHw8HCb8XtpPr333nsCQGbMmFGi9b/99lsBIE2bNpW8vLxCyxMTE8XLy0u8vLxsGhFt2rQRAPLPf/6z0DZJSUnqEUD3q/k0fvx4ASDjxo0rcrs1a9YIAOnQoYPNuLX51KBBA5uGkEh+dtYjk/7++291PDMzUypUqCAA5Isvvijy992sT58+t3zcZ82aJQDkqaeeKtHtiYh06NBBAMiSJUuKXG5tPlmbmze7ePGiegTUxo0bbZZZt+vZs2eh7XJyctTHZdGiRUX+bmtOXbp0KfH9ISIiKs045xMRERGVWHJyMmbMmIFBgwbhH//4B9q0aYM2bdrgo48+AgDs2bOnyO18fX3xyCOPFLnsp59+AgA8+eSThZa5uLhg8ODBRW73zTffAACGDx9e5PL69esjLCwMJ0+exLlz5255v0oqNDQUAPD9998jMzPztutbaxw2bBgMhsIXGQ4KCkLTpk1x7do17Ny5EwBw7do1/PbbbwCAkSNHFtomMDAQffr0uev7cKs6i3ssu3TpAldXV/z2228wmUyFlj/11FPQ6WzfVrq4uKBBgwYA8ucLs9q2bRtSUlIQHByMxx9//La15ebm4ocffoBery92rqqePXsCAH755Zfb3p7V5cuXAQDly5e/7brPP/98obGAgAD07dsXALBu3boSb+fq6qo+zsVtZ60pOTn5trURERE5g8LvgoiIiIiKsH79evTv3x9Xrlwpdp3U1NQix2vWrFnkpezT0tLUJkD9+vWL3La48X379gEA3nzzTUyZMqXIday3ff78eVSuXLnYukuqV69eCAsLw/r16xEcHIwuXbqgbdu2iI2NRZ06dYqtce7cuVi6dGmRt3n06FG1RgA4fvw4LBYL3NzcUK1atSK3qV279j3fF6tr167h1KlTAIARI0bcct3s7GykpKSgUqVKNuPVq1cvcv2AgAD1d1gdOnQIANCsWbNCDauiHD16FNnZ2XB1dUXXrl2LXEdEANx4DEsiOzsbAGA0Gm+5nouLC2rUqFHkMmsO1gyLW36n27m7uwMAsrKyblkbERGRs2DziYiIiG4rPT0dAwYMwJUrV/DEE0/gueeeQ2RkJHx8fKDT6fDTTz+hY8eORV7pDQA8PT2LHL9+/TqA/KuDeXl5FbmOt7d3kePWJpj1iKFbuV8f4j09PbFlyxa8+eabWLVqFZYvX47ly5cDAKKiohAfH4/u3bsXqvFWVze7uUZro8bf37/YdW9u/tyLgs3Ebdu23Xb9oh7L4vK1NpeszSEAuHr1KgDAz8/vjurLzc29bX3WhlJJWI8uSk9Pv+V6FSpUKLZJZs0hIyOjyOXW5tudbmdt4t7qb4CIiMiZ8LQ7IiIiuq0ff/wRaWlpaNmyJRYvXozmzZvDz89P/VB+9uzZu7pda9NCRNRG1M2K+4BubVYdO3YMkj+PZbH/xcbG3lV9RalcuTIWLVqE1NRUbN++HVOnTkV0dDQOHjyIXr164Y8//ihU44YNG25bo/WUMus21qO2inLp0qUixxVFAWDb7CmoqMe4YNMvNzf3tnWGhYUV/+CUgLWZeLumz831hYSE3La24u53UayNoeKO1rNKSUmBxWIpcpk1h+IapMWdNne77aw1VaxY8Za1EREROQs2n4iIiOi2rKdltWzZUm1wFFTcXE+3U65cOfXojr179xa5jvXUtZtFRUUBKNlRRQUVVf/dMBgMaN68OV5++WXs2LEDAwYMgNlsxqJFi+6pxho1akCn0yE7O1t93G9mPXXtZtZmXnFNj+PHjxca8/X1RXBwMADgwIEDJa7zbllPT9yxY0exTZ2CatasCRcXFyQlJd22UXQnGjZsCKD4x9IqLy8PJ06cKHKZdduIiIhbLr/T7Q4ePAgAaNy48S1rIyIichZsPhEREdFtWeeguXjxYqFlKSkpWLhw4V3fdseOHQEAixcvLrTMZDJhyZIlRW5nnXR75syZd3TEi73m02nRogUAIDExUR2z1jhv3rwSnxLm5eWFli1bAgA++eSTQssvXryoThB+s/DwcAD5jZ2bXb9+Hf/+97+L3M5ap3XieHtq3bo1/P39cf78eSxbtuy263t4eKBz586wWCyYOXPmfaujTZs2AIC//vrrtuvOmTOn0FhycjJWrlwJAOjUqVOJt8vNzVX3l+K2+/PPPwEAbdu2vW1tREREzoDNJyIiIrot64fgFStWqFenA4CkpCQ8+uijRV4BraTGjh0LRVGwYMECzJ8/Xx3PysrC//3f/yEhIaHI7Z555hmEh4dj06ZNePzxx5GUlGSz/Nq1a1ixYgXGjx9vM25t0BR35bZbmT59Oj766KNCTbgzZ85gwYIFAGyPVunduzdatGiBw4cPo0ePHoWOPMrJycH333+Pp556ymZ8woQJAIAZM2Zg9erV6vjly5fx+OOPF3vEUPv27eHm5oa//voLn376qTqenp6OYcOGISUlpcjtXn75ZZQvXx6ff/45xo8fX+iUuNTUVCxatAjvvPNOkdvfCTc3N7zxxhsA8jNctmyZTfMwLS0N06dPtzl66+2334bRaMQ777yDqVOnFmocJiUlYcaMGUU264rTqlUreHp64q+//rplY9BgMGDOnDlqownIfzwGDx6M7OxsREdHo3379kVu+/3332PGjBnq/bP+TScmJiI0NBQDBgwotM21a9ewZ88e+Pn5oVmzZiW+P0RERKWaEBEREf3Ppk2bBIAU9Rahb9++6rIaNWpIw4YNxWAwiLe3t3z00UcCQGJiYoq8vZvHbzZ58mT1tkNCQqRp06bi4+MjRqNR3n33XQEgHTp0KLTdoUOHpFq1agJAdDqd1K5dW5o3by4RERGi1+sFgDRv3txmmytXrki5cuUEgAQFBUnr1q0lJiZG3nvvvds+PmPGjFHrDAsLk2bNmkmtWrXU31W3bl1JT0+32SYxMVEaNWpk89g1b95coqKixNXVVQBIpUqVCv2uESNGqNtUq1ZNmjRpIm5ubuLn5yevvvqqAJChQ4cW2u7tt9+2eSybNGki7u7uUqlSJZk0aVKx223dulX8/f0FgLi4uEi9evWkefPmEh4eLoqiCAB57LHHbLaJiYkRALJp06YiH6+hQ4cKAPnss89sxi0Wi4wcOVKt09/fX5o2bSphYWHqY5mQkGCzzTfffCMeHh4CQNzc3KRhw4bSrFkzCQ0NVW/n5ZdfLrKO4jz99NMCQFatWlVoWUJCggCQqlWrqrVWrVpVoqOjxd3dXQBIhQoV5ODBg4W2tdYTHx8vACQwMFD9m7bW/8svvxRZ01dffSUA5Lnnnruj+0JERFSa8cgnIiIiKpElS5bgjTfeQFhYGE6fPo0LFy6gb9++2LFjBxo0aHBPt/3mm29i+fLlaNasGVJTU3H8+HG0adMGW7duVW+7qMmZa9WqhT179mDq1Klo2rQpzp8/j927dyM3NxcxMTH44IMPCp1q5uPjg/Xr1+Phhx9GTk4Ofv/9d/zyyy84fPjwbet89tlnMWnSJLRr1w55eXnYvXs30tLS0LRpU8yaNQt//vknfH19bbYJCgrC77//jjlz5qBdu3ZISUnBrl27kJGRgWbNmmHy5MnYtGlTod/1ySefYN68eahfvz4SExNx5swZ9OzZEzt27EDNmjWLrfH111/Hxx9/jKioKCQnJ+Ps2bPo27cv/vrrL1StWrXY7Vq3bo2DBw/itddeQ1RUFBISErB3717odDp06dIFc+bMwYwZM277GJWEoiiYM2cOvv/+e3Tv3h2KomDPnj3Iy8tDTEwM5syZo85DZdW7d28cPHgQY8aMQVhYGI4cOYKDBw/Cw8MDvXv3xueff464uLg7qmPEiBEAUOypnVYff/wxZsyYAW9vb+zfvx+enp54/PHHsXPnTtSuXbvY7V566SUsWbIEoaGhOHDgABRFQc+ePfHHH3+gXbt2RW5jPRVx+PDhd3RfiIiISjNF5A4mSSAiIiJ6wKZNm4YJEyZgzJgxD2ROImewePFiPPnkkxg6dGiRc2VRyXXq1AmbNm3CsWPH7vlKfla3u+pgcY4fP45atWqhc+fO+P777+9LLURERKUBj3wiIiKiUstsNuOLL74AkH9kDtH9Fh8fD7PZjClTpji6FLz77rsQEUydOtXRpRAREd1XbD4RERGRwy1cuBBbtmyxGUtNTcWwYcOwd+9eBAcHo0ePHg6qjsqyRo0aYf78+ahWrVqxE7k/CGazGTVq1MCCBQtQr149h9VBRERkDwZHF0BERES0ZcsWDB8+HF5eXqhevTpEBIcOHUJeXh48PDzw5Zdfws3NzdFlUhn19NNPO7oE6PV6vPbaa44ug4iIyC7YfCIiIiKHGzp0KPLy8rB9+3acOHECubm5CA4OxkMPPYSXXnoJkZGRji6RiIiIiO4SJxwnIiIiIiIiIiK74ZxPRERERERERERkN2w+ERERERERERGR3bD5REREREREREREdsPmExERERERERER2Q2bT0REREREREREZDdsPhERERERERERkd2w+URERERERERERHbD5hMREREREREREdkNm09ERERERERERGQ3/w/Jv1Uohc3A0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1400x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "extended_palette = [\"#9A8822\",\"#F5CDB4\",\"#F8AFA8\",\n",
    "                    \"#FDDDA0\",\"#74A089\",\"#85D4E3\",\n",
    "                    #added extra to make 7\n",
    "                    '#78A2CC']\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(14, 9), \n",
    "                       sharey=False, sharex=True)\n",
    "\n",
    "#change col enf\n",
    "colour_ind = 2\n",
    "indices = list(ec_data_ca.keys())\n",
    "ax.plot(indices,ec_mut_eff_size_ca,alpha=0.7,\n",
    "        linewidth=2.5,\n",
    "        color=extended_palette[colour_ind],\n",
    "        label='Measured')\n",
    "ax.plot(indices,\n",
    "        smooth(ec_mut_eff_size_ca,.95),\n",
    "        linewidth=2.5,\n",
    "        color=extended_palette[colour_ind+2],\n",
    "        label='Smooth')\n",
    "ax.legend(fontsize=16)\n",
    "\n",
    "ax.set_title(f\"Chromatin Accessibility Permutation: Enformer Celltyping\")\n",
    "ticks_loc = ax.get_xticks().tolist()\n",
    "ax.set_xticklabels(\n",
    "    [label_format.format(x) for x in ticks_loc],\n",
    "    rotation=45,\n",
    "    fontsize=12,\n",
    "    ha=\"right\"\n",
    ")\n",
    "ax.set_xlabel('Target sequence (bp)', fontsize=16)\n",
    "ax.set_ylabel('Mutation Aggregate Effect Size', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53bba88c",
   "metadata": {},
   "source": [
    "At such low number of iterations, the relationship between distance and effect is not visible. See our manuscript for the result of a lot higher number of iterations and positions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac797ce",
   "metadata": {},
   "source": [
    "## 3. Visualising Enformer Celltyping's cell type embedding\n",
    "\n",
    "Enformer Celltyping takes in a global chromatin accessibility signal to have a global representation of a cell type. This information is passed to embedding layers which learns a latent representation of each cell type. This approach is similar to that used in [Avocado](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-01977-6).\n",
    "\n",
    "As such we can take the weights of the layer to visualise this latent representation and plot it in a 2D space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c57ed0",
   "metadata": {},
   "source": [
    "### NOTE\n",
    "This section requires chromatin accessibility data from the 104 training cell types used for the trained Enformer Celltyping model. If you would like to rerun this yourself, see [full_training_recreation.ipynb](https://github.com/neurogenomics/EnformerCelltyping/blob/master/full_training_recreation.ipynb) for instructions for how to download and preprocess the necessary data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e9b5508f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from EnformerCelltyping.constants import (WINDOW_SIZE_GBL_CA,\n",
    "                                          WINDOW_SIZE_LCL_CA,\n",
    "                                          ENF_CHANNELS,\n",
    "                                          ENF_PRED_POS)\n",
    "\n",
    "n_gbl_factors=25\n",
    "\n",
    "#Now let's get the index of the weights from the embedding layer\n",
    "embed_GBLw = model.load_embedding('gbl_embedding')\n",
    "\n",
    "#now upload these weights to separate layers and get result for passing in DNA from diff cell types\n",
    "inputs = dict(\n",
    "        dna= tf.keras.layers.Input(\n",
    "            shape=(ENF_PRED_POS,ENF_CHANNELS),\n",
    "            name='dnaInput'\n",
    "        ),\n",
    "        #local chrom access embed\n",
    "        chrom_access_lcl= tf.keras.layers.Input(\n",
    "            shape=(WINDOW_SIZE_LCL_CA//128,),\n",
    "            name='ChromAccessLclInput'\n",
    "        ),\n",
    "        #global chrom access embed\n",
    "        chrom_access_gbl= tf.keras.layers.Input(\n",
    "            shape=(WINDOW_SIZE_GBL_CA//250,),\n",
    "            name='ChromAccessGblInput'\n",
    "        )\n",
    "    )\n",
    "\n",
    "#Embed global\n",
    "embedGbl = tf.keras.layers.Embedding(WINDOW_SIZE_GBL_CA//250,\n",
    "                                     n_gbl_factors,\n",
    "                                     input_length=1,\n",
    "                                     name=\"gbl_embedding\")(inputs['chrom_access_gbl'])\n",
    "#flatten\n",
    "embedFlat = tf.keras.layers.Flatten(name=\"flattenGBL\")(embedGbl)\n",
    "\n",
    "#make model\n",
    "mod_embed = tf.keras.Model(inputs, embedFlat, name = \"EmbeddedWeights\")\n",
    "\n",
    "#Update layer weights from model\n",
    "mod_embed.layers[1].set_weights(embed_GBLw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a01a915",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now use a data loader to pass all cell types through the model\n",
    "#chosen position doesn't matter since it is only the global CA signal we want\n",
    "\n",
    "#import ids for 104 training and valid samples\n",
    "from EnformerCelltyping.constants import SAMPLE_NAMES\n",
    "\n",
    "#data generator class for samples\n",
    "data_generator = generate_sample(\n",
    "    cells = SAMPLE_NAMES, #we pass the ID since train/valid samples are referenced in the constants file\n",
    "    return_y=False #only need CA data\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b9d4dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 104/104 [06:39<00:00,  3.84s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "vals_embed = []\n",
    "\n",
    "#any chro and postion is fine since we only want global signal\n",
    "chro='chr6'\n",
    "pos=10_000_000\n",
    "#loop through each train/valid cell type\n",
    "for ind, cell_i in enumerate(tqdm(SAMPLE_NAMES)):\n",
    "    X = data_generator.load(pos=pos,\n",
    "                           chro=chro,cell=cell_i)\n",
    "    if ind ==0:\n",
    "        #get enf pred for DNA to pass to Enformer Celltyping Embedding layer\n",
    "        enf_pred_dna = model.enf.predict_on_batch(X['dna'])\n",
    "    X['dna'] = enf_pred_dna\n",
    "    #get emebedding\n",
    "    vals_embed.append(np.reshape(mod_embed.predict(X),[-1]))\n",
    "vals_embed = np.array(vals_embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af13b584",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now let's inspect\n",
    "#first get metdata for cells\n",
    "from EnformerCelltyping.constants import METADATA_PATH, CELLS\n",
    "\n",
    "cell_info = pd.read_csv(METADATA_PATH/'cell_info.csv')\n",
    "#get names for EpiMap ID's\n",
    "train_valid_sample_names = [CELLS[x] for x in SAMPLE_NAMES]\n",
    "\n",
    "#Let's visualise with UMAP, need to install it (and scikit-learn):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1d648490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: umap-learn in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (0.5.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (1.19.5)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (1.2.1)\n",
      "Requirement already satisfied: tqdm in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (4.64.1)\n",
      "Requirement already satisfied: pynndescent>=0.5 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (0.5.8)\n",
      "Requirement already satisfied: numba>=0.49 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (0.56.4)\n",
      "Requirement already satisfied: scipy>=1.0 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from umap-learn) (1.10.0)\n",
      "Requirement already satisfied: setuptools in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from numba>=0.49->umap-learn) (65.6.3)\n",
      "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from numba>=0.49->umap-learn) (0.39.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from pynndescent>=0.5->umap-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f63d0ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages/tqdm/auto.py:22: 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": [
    "#umap only including tissues\n",
    "#\n",
    "grouping = np.array(cell_info[cell_info['BSSID'].isin(SAMPLE_NAMES)]['Group'])\n",
    "cell_type = np.array(cell_info[cell_info['BSSID'].isin(SAMPLE_NAMES)]['Type'])\n",
    "\n",
    "vals_embed_tissue = vals_embed.copy()\n",
    "#keep tissue and remove where just one tissue type\n",
    "rmv = list(set(np.where(cell_type!='tissue')[0].tolist()+\n",
    "               np.where((grouping=='Spleen')|(grouping=='Other')|\n",
    "                        (grouping=='Adipose')|(grouping=='Pancreas')|\n",
    "                        (grouping=='Lung'))[0].tolist()))\n",
    "keep = np.where((cell_type=='tissue')&(grouping!='Spleen')&\n",
    "                (grouping!='Other')&(grouping!='Adipose')&\n",
    "                (grouping!='Pancreas')&(grouping!='Lung'))[0].tolist()\n",
    "vals_embed_tissue = np.delete(vals_embed_tissue,rmv,axis=0)\n",
    "\n",
    "import umap\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "reducer = umap.UMAP(random_state=11)\n",
    "#transform data\n",
    "scaled_X = StandardScaler().fit_transform(vals_embed_tissue)\n",
    "#embed\n",
    "embedding_X = reducer.fit_transform(scaled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caa9a0bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1300x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot umap\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#only colur the tissue as we know these should be similar,\n",
    "#harder to say for cell lines and cancer\n",
    "grouping_tiss = grouping[keep]\n",
    "\n",
    "grouping_unique_tiss = list(set(grouping_tiss))\n",
    "colors_ = [i for i in sns.color_palette(\"tab10\")]\n",
    "colors_.pop(7)\n",
    "sns.set(font_scale = 1.2)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(13, 8))\n",
    "for g in grouping_unique_tiss:\n",
    "    ix = np.where(grouping_tiss == g)\n",
    "    ax.scatter(embedding_X[ix, 0], embedding_X[ix, 1], \n",
    "               c = colors_[grouping_unique_tiss.index(g)], \n",
    "               label = g, s=20)\n",
    "ax.legend(prop={'size': 13}, loc=(1.02,0))\n",
    "ax.set_xlabel('UMAP 1', fontsize = 13)\n",
    "ax.set_ylabel('UMAP 2', fontsize = 13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b81949",
   "metadata": {},
   "source": [
    "## 4. Predicting SNP effects - in silico mutagenesis\n",
    "\n",
    "A common task of deep learning models that predict epigenetic signatures or gene expression from DNA sequence is _in silico_ mutagenesis or estimating the effect of a genetic variant of interest on a prediction [ref](https://academic.oup.com/bioinformatics/article-abstract/38/14/3557/6604724?redirectedFrom=fulltext).\n",
    "\n",
    "Enformer Celltyping can also be used for this task and, taking advantage of it's attention layers and large DNA input window (~200k base-pairs) can predict the effect of mutations in distal enhancers.\n",
    "\n",
    "The idea is simple, pass DNA sequence with and without the genetic variant to the model and then compare the change in prediction:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f574fb",
   "metadata": {},
   "source": [
    "![_in silico_ mutagenesis](./in_silico_mut.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "21fd5424",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages/seaborn/axisgrid.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "/home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages/seaborn/axisgrid.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "/home/aemurphy/anaconda3/envs/EnformerCelltyping/lib/python3.9/site-packages/seaborn/axisgrid.py:118: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  self._figure.tight_layout(*args, **kwargs)\n",
      "/home/aemurphy/RDS/project/celltypeai/live/Projects/EnformerCelltyping/EnformerCelltyping/utils.py:3008: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  axes.set_yticklabels(['{:,.1f}'.format(x) + 'M' for x in axes.get_yticks()/1_000_000])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f112446fc40>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import functions to make predicting SNP effect easier\n",
    "from EnformerCelltyping.utils import (create_ref_alt_DNA_window,\n",
    "                                      plot_snp_dna_window,\n",
    "                                      predict_snp_effect_sldp\n",
    "                                      )\n",
    "\n",
    "dna_strt, snp_pos = create_ref_alt_DNA_window(chro = 'chr6', \n",
    "                                              pos = 10_000_001)\n",
    "#can plot if we want:\n",
    "plot_snp_dna_window(dna_strt,snp_pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235e9ada",
   "metadata": {},
   "source": [
    "`create_ref_alt_DNA_window` calculates the start positions and relative SNP psoitions needed to pass to Enformer Celltyping to predict the effect of a SNP. Note that there are three separate predictions needed to do this. This is contrary to other approaches (like [Enformer](https://www.nature.com/articles/s41592-021-01252-x)) who put the SNP position at the center of the input window only (same as Prediction 1 above). \n",
    "\n",
    "By moving the input window relative to the SNP position, we can see the full effect of the SNP across the ~200k base-pairs of the window. This shows the distal effect the SNP as well as the local effect. If we were to just use Prediction 1, we may as well have a model that only takes in 58% of the current input window or ~115k base-pairs rather than ~200k base-pairs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f76af715",
   "metadata": {},
   "source": [
    "Now let's predict the effect of the SNP across the full ~200k base-pairs, we have a function to help with this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "66ae958e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.10295194, -0.07486513,  0.04953669,  0.12222822,  0.10130289,\n",
       "       -0.1174833 ], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predict the effective difference of the alternative allele\n",
    "\n",
    "#Microglia from Nott et al., 2019\n",
    "cell = 'Nott19_Microglia'\n",
    "\n",
    "#use the following:\n",
    "#PROJECT_PATH - path to the EnformerCelltyping repo to get relative paths\n",
    "from EnformerCelltyping.constants import PROJECT_PATH\n",
    "\n",
    "#path to the cell type-specific chromatin accessibility bigWig\n",
    "chrom_access_pth = str(PROJECT_PATH /'data/demo/Nott19_Microglia_128.bigWig')\n",
    "#give the name of the cell\n",
    "cell = 'Nott19_Microglia'\n",
    "\n",
    "#import the data generator which will take care of any preprocessing\n",
    "from EnformerCelltyping.utils import generate_sample\n",
    "\n",
    "#data generator class for samples\n",
    "data_generator = generate_sample(\n",
    "    cells = {cell:chrom_access_pth}, #should be a dict\n",
    "    #get the reverse complement and random shift so difference can be aggreated across these\n",
    "    #too to make the prediction more robust\n",
    "    reverse_complement = True, \n",
    "    rand_seq_shift = True,\n",
    "    rtn_rand_seq_shift_amt = True #need to return this values so the same value can be used for ref and alt\n",
    "    )\n",
    "#Pick SNP related to AD pathology - rs34487851\n",
    "# Associated with neuropathologic traits of Alzheimer's disease -\n",
    "# https://pubmed.ncbi.nlm.nih.gov/29458411/\n",
    "# Is in ECRG4 which encodes a peptide hormone that is involved in NFT formation, \n",
    "# age-related senescence of precursor cells in the CNS and activation of microglia - \n",
    "# https://pubmed.ncbi.nlm.nih.gov/33815092/\n",
    "\n",
    "#pred across full 200k - pass in SNP position and chromosome\n",
    "dna_strt, snp_pos = create_ref_alt_DNA_window(chro = 'chr2', \n",
    "                                              pos = 106_642_554)\n",
    "#aggregate effect\n",
    "agg_eff = predict_snp_effect_sldp(model=model,\n",
    "                                  alt='A',cell = cell, \n",
    "                                  chro='chr2',\n",
    "                                  dna_strt=dna_strt, snp_pos=snp_pos,\n",
    "                                  data_generator = data_generator,\n",
    "                                  effect_mode = 'sum')\n",
    "agg_eff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8096a797",
   "metadata": {},
   "source": [
    "This is the sum aggreated effect of the SNP across the full 200k base-pairs for each of the six histone marks. We can also plot the signal with and without the SNP to understand where the effect is most prominent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d33ab36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot prediction where SNP centered\n",
    "#don't need reverse compliment or random perm\n",
    "data_generator.reverse_complement = False\n",
    "data_generator.rand_seq_shift = False\n",
    "data_generator.rtn_rand_seq_shift_amt = False\n",
    "\n",
    "X_ref = data_generator.load(pos=dna_strt[1],\n",
    "                            chro='chr2',cell=cell)\n",
    "\n",
    "X_alt = data_generator.load(pos=dna_strt[1],\n",
    "                            chro='chr2',cell=cell,\n",
    "                            snp_base='A',snp_pos=snp_pos[1])\n",
    "pred_ref = model.predict(X_ref)\n",
    "pred_alt = model.predict(X_alt)\n",
    "pred_diff = pred_ref - pred_alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "88333076",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from EnformerCelltyping.constants import PRED_HIST_MARKS\n",
    "from EnformerCelltyping.utils import plot_signal\n",
    "    \n",
    "#make dict with histone mark prediction and name\n",
    "#get the prediction window, Enformer Celltyping pridicts in a funnel shape\n",
    "#where it won't predict for every base-pair of the input length\n",
    "#get bp size of DNA input\n",
    "from EnformerCelltyping.constants import WINDOW_SIZE_DNA\n",
    "#use buffer to deal with funnel\n",
    "buffer = (WINDOW_SIZE_DNA - pred_diff.shape[1]*128)//2\n",
    "#create interval dict for plotting\n",
    "interval = {'start':dna_strt[1]+buffer,'end':dna_strt[1]+WINDOW_SIZE_DNA-buffer,\n",
    "            'chro':'chr7','cell':cell}\n",
    "#now plot\n",
    "plot_signal({i:pred_diff[0,:,ind] for ind,i in enumerate(PRED_HIST_MARKS)},\n",
    "            interval,min_y=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bb39d2",
   "metadata": {},
   "source": [
    "We can see H3K27ac (amoung other marks) shows a decrease in binding around the SNP site. However, overall, the effect sizes of the change is small (see y-axis)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3eda6b",
   "metadata": {},
   "source": [
    "We used _in silico_ mutagenesis predictions from Enformer Celltyping along with [SLDP](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202062/) to understand the genome-wide, directional effect of genetic variants that relate to a certain phenotype on histone mark binding. The code to run the analysis once the genetic variant effect predicitons have been made (like above) can be found here: `./bin/run_sldp.py`. See our manuscript for more details and our use cases."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "EnformerCelltyping",
   "language": "python",
   "name": "enformercelltyping"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}