{ "cells": [ { "cell_type": "markdown", "id": "590bc2f5-6e17-4c58-9816-907ab4d74a9a", "metadata": {}, "source": [ "### What is BPNet?\n", "Author: Jacob Schreiber \\\n", "\n", "\n", "#### Introduction\n", "\n", "BPNet is a deep learning model trained to predict readouts from genomics experiments using nucleotide sequence [[paper]](https://www.nature.com/articles/s41588-021-00782-6). BPNet's innovation over other models of the time is that it makes predictions at basepair resolution (hence, the name) and decomposes the prediction task into that of making predictions for the total log counts in a region (the strength of the signal) and a probability distribution over the exact positions (the shape of the signal). It turns out that learning how to predict the shape of the signal resulted in models that achieved state-of-the-art accuracy because the exact positioning of reads in the profile contains quite a bit of information about the more subtle aspects of the cis-regulatory code. BPNet models were original introduced in the context of making predictions for transcription factor (TF) binding as measured by ChIP-nexus experiments but can, in principle, be trained for readouts of any sequencing-based experiment, although performance will vary. When the readouts are stranded, such as for TF binding or transcription initiation, BPNet makes predictions for both strands, though the signal being stranded is not a requirement for using BPNet.\n", "\n", "The architecture of a BPNet model consists of an initial convolutional layer that is quite wide compared to the expected size of most motifs, followed by eight dilated residual convolutions that aggregate information across the input sequence, followed by outputs for the profile and counts predictions. The profile prediction proceeds by applying another wide convolution to the output of the last dilated residual convolution. The count prediction proceeds by averaging the representations across all positions and then using a single linear layer to predict a single value per example. As a final detail: a control track can be passed into the model by concatenating the per-position mapped counts to the feature representations before applying the last convolution or linear layers.\n", "\n", "" ] }, { "cell_type": "markdown", "id": "74b7c1d2-ad39-4d65-a50a-74f464bd2151", "metadata": {}, "source": [ "An important conceptual note is that BPNet is usually trained on the readouts from one experiment at a time, in contrast to large models such as Enformer, Borzoi, or AlphaGenome, which are trained on readouts from thousands of experiments simultaneously. Although training on a single experiment may yield lower predictive accuracy in some settings, limiting the focus of the model in this manner provides two important benefits. First, these models can be trained extremely quickly, allowing the pipeline from experimental data to machine learning analysis results to be quite fast. Second, because the model only ever sees data from one experiment, it cannot learn spurious correlations *between* tasks. This does not mean the BPNet models cannot learn spurious correlations of any sort, just that the model cannot connect TF binding sites learned for one task to all other tasks simply because machine learning models have a had time *not* connecting features to outputs once they have been identified." ] }, { "cell_type": "markdown", "id": "23f85027-17c5-4bb7-9d9d-fd24afddb2fd", "metadata": {}, "source": [ "#### Using bpnet-lite\n", "\n", "##### Loading and Wrapping Models\n", "\n", "Now that we conceptually understand what a BPNet model is, let's quickly take a look at some of the things you can do with bpnet-lite. We will talk much more about each of these topics in other tutorials, so please take a look at those if you want to know more about any of these topics.\n", "\n", "First, we can load trained models easily! Here, we are loading a model that has been trained with bpnet-lite (see the BPNet pipeline tutorial for the exact details on how we trained this model). Note that we can also load models that have trained using the official BPNet repository, even though it is implemented in TensorFlow. See the tutorial for more details on how to do that." ] }, { "cell_type": "code", "execution_count": 1, "id": "c6667beb-3850-43a0-bf35-6439a0369241", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BPNet(\n", " (iconv): Conv1d(4, 64, kernel_size=(21,), stride=(1,), padding=(10,))\n", " (irelu): ReLU()\n", " (rconvs): ModuleList(\n", " (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))\n", " (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))\n", " (2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(8,), dilation=(8,))\n", " (3): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(16,), dilation=(16,))\n", " (4): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(32,), dilation=(32,))\n", " (5): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(64,), dilation=(64,))\n", " (6): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(128,), dilation=(128,))\n", " (7): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(256,), dilation=(256,))\n", " )\n", " (rrelus): ModuleList(\n", " (0-7): 8 x ReLU()\n", " )\n", " (fconv): Conv1d(66, 2, kernel_size=(75,), stride=(1,), padding=(37,))\n", " (linear): Linear(in_features=65, out_features=1, bias=True)\n", ")" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import torch\n", "\n", "model = torch.load(\"quick-jun.torch\", weights_only=False)\n", "model" ] }, { "cell_type": "markdown", "id": "8e310dd8-c509-425c-9049-04ec4f7e9cf8", "metadata": {}, "source": [ "You can see that the trained BPNet model from bpnet-lite is just a PyTorch module with no complicated wrappers or functions or I/O. This minimalism makes it easy to use these models in your personal development environments. Specifically, you can use these models in the same manner you can use any other PyTorch package and so they plug into any other package that expect PyTorch modules. For example, if you use PyTorch Lightning to train your models, you can easily just pass in these BPNet models without modification. Alternatively, if you use tangermeme to use your genomics models after trained, you can pass these models directly into those functions as we will see below." ] }, { "cell_type": "markdown", "id": "3a96c2c9-e884-47b4-9f5d-b8d4406a74bf", "metadata": {}, "source": [ "To begin, we can use these BPNet models to make predictions for the profile and log counts of any sequence. However, because BPNet is trained using a control track as input to factorize out the influence of mapping bias, we need to pass in a control track here. Usually, an actual control track is used when training and evaluating the models on experimental data because the artifacts are observed in the experimental data and an all-zeroes control track is used at inference time to factorize out the influence of mapping artifacts that are not biologically interesting. This means that, for most downstream applications of BPNet, we want to pass in an all-zeroes track." ] }, { "cell_type": "code", "execution_count": 2, "id": "a1e7bcf7-3715-49ae-bad4-fb435400024f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[tensor([[[-0.0901, -0.5976, -0.3234, ..., 1.4522, 1.0508, 1.1012],\n", " [-0.3575, -0.5376, -1.4749, ..., -0.2960, -0.4365, -0.2700]]]),\n", " tensor([[0.5266]])]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tangermeme.predict import predict\n", "from tangermeme.utils import random_one_hot\n", "\n", "X = random_one_hot((1, 4, 2114), random_state=0)\n", "X_ctl = torch.zeros((1, 2, 2114))\n", "\n", "predict(model, X, args=(X_ctl,))" ] }, { "cell_type": "markdown", "id": "6dce32f9-6b72-42fa-ba14-6b1692623852", "metadata": {}, "source": [ "The output from these models is a tuple of two tensors with the first being the profile predictions and the second being the log count predictions. The profile predictions are broken down into predictions from each of the two strands and are in logit space, meaning that they have not been normalized to sum to 1 (or logsumexp to 0, since they're in log space). When you use these profile predictions, you will need to softmax them yourself if you need that property.\n", "\n", "Passing in an all-zeroes control track can be annoying, so we have added in `ControlWrapper` which is a wrapper module that automatically generates an all-zeroes control track based on the dimensions of your input. To the user, it looks and operates exactly the same as the original model but now you do not need to pass in a control track to use it." ] }, { "cell_type": "code", "execution_count": 3, "id": "1d84d82b-b635-464c-8696-5e002110cfca", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ControlWrapper(\n", " (model): BPNet(\n", " (iconv): Conv1d(4, 64, kernel_size=(21,), stride=(1,), padding=(10,))\n", " (irelu): ReLU()\n", " (rconvs): ModuleList(\n", " (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))\n", " (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))\n", " (2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(8,), dilation=(8,))\n", " (3): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(16,), dilation=(16,))\n", " (4): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(32,), dilation=(32,))\n", " (5): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(64,), dilation=(64,))\n", " (6): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(128,), dilation=(128,))\n", " (7): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(256,), dilation=(256,))\n", " )\n", " (rrelus): ModuleList(\n", " (0-7): 8 x ReLU()\n", " )\n", " (fconv): Conv1d(66, 2, kernel_size=(75,), stride=(1,), padding=(37,))\n", " (linear): Linear(in_features=65, out_features=1, bias=True)\n", " )\n", ")" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bpnetlite.bpnet import ControlWrapper\n", "\n", "model = torch.load(\"quick-jun.torch\", weights_only=False)\n", "model = ControlWrapper(model)\n", "model" ] }, { "cell_type": "markdown", "id": "a1ab6cac-41b2-43ea-bf88-3c724cec4802", "metadata": {}, "source": [ "Note that this wrapped model can still be used in all downstream packages just as easily as the original model. It can also be saved to and loaded from disk without hassle. Personally, I do not usually save them because I want my code to be explicit about how base models are modified or wrapped to do analyses, but that is a matter of personal taste." ] }, { "cell_type": "code", "execution_count": 4, "id": "6ad036c4-c0af-4d3b-9bce-8ddd935fa54e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[tensor([[[-0.0901, -0.5976, -0.3234, ..., 1.4522, 1.0508, 1.1012],\n", " [-0.3575, -0.5376, -1.4749, ..., -0.2960, -0.4365, -0.2700]]]),\n", " tensor([[0.5266]])]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict(model, X)" ] }, { "cell_type": "markdown", "id": "45536616-e552-41f2-8489-8bfceb149c8f", "metadata": {}, "source": [ "Once again, we get the profile and counts output and the values are the exact same as before. The only difference is we now do not need to pass in the optional arguments." ] }, { "cell_type": "markdown", "id": "6dbaba5a-a10e-4443-85b1-feaea66d8065", "metadata": {}, "source": [ "##### Inspecting Loci\n", "\n", "Now that we know how to use the BPNet models, we can use them to take a look at a real JUN peak to get a sense for the kinds of insights BPNet can add to your work and how to use bpnet-lite to achieve those. Let's load up the peak set corresponding to the JUN experiment that this model was trained on. Note that the tangermeme function here can operate on BED files that are hosted remotely, so we do not even need to download and permanently store them." ] }, { "cell_type": "code", "execution_count": 5, "id": "02206746-09aa-41ca-8764-03df2b9d305f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading Loci: 100%|████████████████████████████████████████████████████████████| 18218/18218 [00:00<00:00, 59916.98it/s]\n" ] }, { "data": { "text/plain": [ "torch.Size([18214, 4, 2114])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tangermeme.io import extract_loci\n", "\n", "X = extract_loci(\n", " \"https://www.encodeproject.org/files/ENCFF331IUK/@@download/ENCFF331IUK.bed.gz\", \n", " \"../../../../common/hg38.fa\", \n", " verbose=True)\n", "\n", "X.shape" ] }, { "cell_type": "markdown", "id": "f1508120-d553-4305-81c1-cb3b3f1edd7d", "metadata": {}, "source": [ "Despite loading the regions remotely it only takes roughly ~1s to load and one-hot encode the ~18k peaks in preparation for our later usage.\n", "\n", "In the same way that we made predictions for a single randomly generated region before, we can make predictions for the entire set of peaks using the same function. Internally, `predict` handles the batching of data, conversion to a uniform datatype, and moving batches of data (and the model) to the correct device." ] }, { "cell_type": "code", "execution_count": 6, "id": "3b8b8b36-cb69-4a3b-a5d3-9ec5afcc82db", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████| 570/570 [00:00<00:00, 621.18it/s]\n" ] }, { "data": { "text/plain": [ "(torch.Size([18214, 2, 1000]), torch.Size([18214, 1]))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profiles, counts = predict(model, X, verbose=True)\n", "profiles = torch.softmax(profiles, dim=-1)\n", "\n", "idx = counts.argmax()\n", "\n", "profiles.shape, counts.shape" ] }, { "cell_type": "markdown", "id": "8118084b-8a37-44be-ad60-3bdee2a6fea1", "metadata": {}, "source": [ "Just like before, we are getting profile predictions at basepair resolution across both strands and log count predictions. Note that the profile predictions are the logits, which are the raw predicted values before a softmax normalization. This means that they do not sum (or log-sum-exp) to any particular value. These values can be more useful in a variety of settings than the raw predictions, but if you want predicted probabilities all you need to do is softmax across the last dimension.\n", "\n", "Let's take a look at the region with the strongest predicted binding." ] }, { "cell_type": "code", "execution_count": 7, "id": "8083b301-b740-4d55-b381-3c0bc01b4956", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn; seaborn.set_style('whitegrid')\n", "\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(profiles[idx][0], label='+ strand', c='0.3')\n", "plt.plot(profiles[idx][1], label='- strand', c='0.7')\n", "plt.title(\"Predicted JUN Binding\")\n", "plt.xlabel(\"Genomic Coordinate\")\n", "plt.ylabel(\"Predicted Probabilities\")\n", "plt.legend()\n", "seaborn.despine(bottom=True, left=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "60c83071-14f5-4245-bfb1-5bd2c7cae45b", "metadata": {}, "source": [ "This pattern is what you would expect for most TFs, with a peak for the positive strand coming first and a peak for the negative strand coming after. This is because BPNet is trained to predict only the read starts and not the entire length of the fragment. This choice was made because the length of the fragment is based on non-biological factors and the reads may even terminate before seeing the relevant motif. Another relevant issue is that if the entire length of the fragment were used, the pileup at any particular position would depend strongly on the length of any particular fragment, necessitating that each fragment be normalized by length.\n", "\n", "Looking closer at the predictions, one can see something more interesting than most readouts: a cyclic pattern of spikes in both the positive and negative strands. Is this just noise in the predictions, or is there something more interesting happening here? \n", "\n", "We can use feature attribution methods such as DeepLIFT/SHAP to explore the sequence drivers of these predictions. However, these methods work best when they are explaining a single value as the output instead of an entire profile and so it may be initially easier to focus on explaining the log count predictions.\n", "\n", "As a convenience, bpnet-lite provides a wrapper that will take the pair of outputs from a BPNet model and wrap them to only return the first one. This can be stacked with the control wrapper to yield a model that only takes in nucleotide sequence and only outputs log counts." ] }, { "cell_type": "code", "execution_count": 8, "id": "d8e6a9fd-cdc1-476e-a9c3-1b2b46b21f82", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████| 570/570 [00:00<00:00, 694.72it/s]\n" ] }, { "data": { "text/plain": [ "(tensor([[0.8294],\n", " [0.6294],\n", " [0.8181],\n", " ...,\n", " [1.7862],\n", " [1.7810],\n", " [2.4474]]),\n", " tensor([[0.8294],\n", " [0.6294],\n", " [0.8181],\n", " ...,\n", " [1.7862],\n", " [1.7810],\n", " [2.4474]]))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bpnetlite.bpnet import CountWrapper\n", "\n", "model = torch.load(\"quick-jun.torch\", weights_only=False)\n", "model = ControlWrapper(model)\n", "count_model = CountWrapper(model)\n", "\n", "predict(count_model, X, verbose=True), counts" ] }, { "cell_type": "markdown", "id": "91141231-8001-49b9-a7c0-fe41462e1f46", "metadata": {}, "source": [ "As an aside, there also exists a wrapper for the profiles. This does more than just pull out the profile predictions, though. It also transforms them in a manner described in the original BPNet paper by taking the dot product between the predicted logits and the softmaxed logits to return a single value per example that is solely based on the profile." ] }, { "cell_type": "code", "execution_count": 9, "id": "cea6476b-4fbc-4414-ab50-b2e1cd9d1ee0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████| 570/570 [00:00<00:00, 673.61it/s]\n" ] }, { "data": { "text/plain": [ "tensor([[1.3220],\n", " [1.9733],\n", " [1.4456],\n", " ...,\n", " [1.7456],\n", " [2.5753],\n", " [4.5717]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bpnetlite.bpnet import ProfileWrapper\n", "\n", "profile_model = ProfileWrapper(model)\n", "\n", "predict(profile_model, X, verbose=True)" ] }, { "cell_type": "markdown", "id": "53ccf504-ec2a-417f-97f9-9a75352e79c0", "metadata": {}, "source": [ "Returning to the counts attributions, though, we can now easily run DeepLIFT/SHAP on the chosen example through the associated tangermeme function." ] }, { "cell_type": "code", "execution_count": 10, "id": "fc607ca5-9adc-4902-a953-82e77a6feecc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([1, 4, 2114])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tangermeme.deep_lift_shap import deep_lift_shap\n", "\n", "X_attr = deep_lift_shap(count_model, X[idx:idx+1])\n", "X_attr.shape" ] }, { "cell_type": "markdown", "id": "2a307444-fbff-4233-8ac6-0271cc56585f", "metadata": {}, "source": [ "And we can now visualize these attributions through a logo plot that is centered on a region of interest." ] }, { "cell_type": "code", "execution_count": 11, "id": "5a878d77-672e-40cb-ae8a-e9a53bbf62e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tangermeme.plot import plot_logo\n", "\n", "plt.figure(figsize=(12, 2))\n", "plt.title(\"JUN Binding Sites\")\n", "plt.ylabel(\"Attributions\")\n", "plt.xlabel(\"Genomic Coordinate\")\n", "plot_logo(X_attr[0, :, 1057-105:1057+170])\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9eea63f8-a491-4c65-a808-c4056ca74356", "metadata": {}, "source": [ "Looks like the reason that this region had such strong binding of JUN, and the reason that there seemed to be several spikes in the predicted profiles, is that this is a region that has many AP-1 motifs in a row. There is interesting biology here that we will not go into, but you can see how with just a few lines of code of bpnet-lite + tangermeme we can go from a trained BPNet model, to finding a region that seems like there is more to explore, to getting an explanation as to why that region might be interesting to explore." ] }, { "cell_type": "markdown", "id": "f7b4bc69-3f7a-4a24-a179-498a39e7b7d7", "metadata": {}, "source": [ "##### Variant Effect Prediction\n", "\n", "A common goal for sequence-based prediction models is to predict the influence that variants have on phenotypes of interest. Usually, these are variants that have been identified as common (or rare!) in actual human populations, or among groups of people with particular diseases. In this setting, we usually make predictions for a region before and after incorporating the variants and aggregate the difference in predictions into a score that can be used to identify the variants driving molecular phenotypes from those that may only be happening by chance.\n", "\n", "Once again, we can use tangermeme + bpnet-lite to quickly predict the influence of variants. Here, we will consider only substitutions, but one can also consider insertions or deletions. First, we will consider a substitution that involves knocking out a key nucleotide in the left-most AP-1 motif above. Making the variant effect predictions is just as simple as creating a sparse tensor of variants where the three columns correspond to example index, position index within the example, and which character index should be present at that position. If the chosen character is the same as the original character there will be no effect. For visual impact, we will focus on the softmaxed profile predictions." ] }, { "cell_type": "code", "execution_count": 12, "id": "36285d1c-9b74-4d7b-8af3-2b355be579c5", "metadata": {}, "outputs": [], "source": [ "from tangermeme.variant_effect import substitution_effect\n", "\n", "variants = [\n", " [0, 960, 2],\n", "]\n", "\n", "before, after = substitution_effect(model, X[idx:idx+1], variants)\n", "before = torch.softmax(before[0], dim=-1)\n", "after = torch.softmax(after[0], dim=-1)" ] }, { "cell_type": "code", "execution_count": 13, "id": "3733e143-2573-4b42-a4ae-051876f4a474", "metadata": {}, "outputs": [ { "data": { "image/png": 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Vq1ejrKwMderUwcCBA+1O0XrhhRewc+dO/PTTT7h9+zbKysrg5+eHNm3aYM6cOYIVs4wrhy1YsABFRUXgOM4iu8jc4MGDkZOTg40bN2LLli0IDw/HhAkTcOvWLavLrdvTpUsXfP311/jyyy8xdepUBAUFYdiwYVCr1VVukxBCagrDcRx3tztBCCGEEEIIIYQQQqqOavAQQgghhBBCCCGE3OMowEMIIYQQQgghhBByj6MADyGEEEIIIYQQQsg9jgI8hBBCCCGEEEIIIfc4CvAQQgghhBBCCCGE3OMowEMIIYQQQgghhBByj6MADyGEEEIIIYQQQsg9jgI8tQDLsvjvv//Asuzd7so9g66ZdHTNpKNrJh1dM+nomj046GctHV0z6eiaSUPXSzq6ZtLRNSN3CgV4agGO46DVasFx3N3uyj2Drpl0dM2ko2smHV0z6eiaPTjoZy0dXTPp6JpJQ9dLOrpm0tE1I3cKBXgIIYQQQgghhBBC7nEU4CGEEEIIIYQQQgi5x1GAhxBCCCGEEEIIIeQeRwEeQogkZTeuQJ2Zcbe7QQghhBByX9OXlqDo/H9Ut4UQ4jDJAZ4jR47URD8IIfcAbUEejg3rjcRBXe92VwghhBBC7mtJEwYhedzTyP7z97vdFULIPUJygGfcuHHo3bs3lixZgowMuotPyIOk7Oa1u90FQgghhJAHQtnVSwCArL2/3OWeEELuFZIDPP/88w9GjhyJvXv3olevXhg7dix27twJjUZTE/0jhNQiDMPc7S4QQgghhNxxBSePIidh/905OY2/CCEOkhzg8fX1xciRI7Ft2zZs2bIFjRo1wqxZs9CtWzd8+umnOH/+fE30kxBSG8gqBxg0H5wQQgghD4r/pgzF2XcnQH07/W53hRBCrKpWkeUWLVpg/PjxGD58OEpLS7FlyxYMGjQIzz//PFJSUpzVR0JILcEwvD8ZFOAhhBBCyAOAf1NLk5t95ztAGTyEEAdVKcCj1Wqxa9cujB8/Hj179kRCQgI+/PBDHDx4EPv370dISAhee+01Z/eVEHK38QcYLHv3+kEIIYQQcqfwxjyM7C4sQkwBHkKIgxRSD/jkk0+wY8cOAMBTTz2Ft956C02bNjW97u7ujjfeeAM9e/Z0Xi8JIbUDw5+ixYKGG4QQQgi533F6XeWTuxDgYWjERQhxkOQAz6VLl/DBBx+gT58+UKlUovsEBwdjzZo11e4cIaS24Wfw0BQtQgghhNz/OF1lgIeRye/4+bMP/I6ytGtwC21wx89NCLm3SA5Bv/zyy+jXr59FcEen0+Ho0aMAAIVCgfbt2zunh4SQWoO/ihbH6u9iTwghhBBC7oz8pCOVT+7GFC0AZ96ZcFfOSwi5t0j+CzVy5EgUFBRYbC8qKsLIkSOd0ilCSC0lE07RIoQQQgi5n5XfSsfZd8abnt+VGjwAyq5euivnJYTcWyT/heI4TnAX3yg/Px9ubm5O6RQhpLaiKVqEEEIIqd20hfm48OmbyD9+qNptqW/ddEKPCCHkznC4Bs/LL78MwDBF49133xVM0dLr9bhw4QLatGnj/B4SQmoPwRQtyuAhhBBCSO1z9dv/IXPXNmTu2oZuCanVaotRmH1d4ugGFyGk9nI4wOPl5QXAkMHj4eEBV1dX02tKpRKxsbEYMmSI83tICKk1BNl7NEWLEEIIIbWQOsN5WTeMQil4XtUbXKxGDZnKxRldIoQQqxwO8MTHxwMAQkNDMWbMGLi7u9dYpwghtRQVWSaEEEJIbSd33kpXMqVwYZnyslJ4SGwjY/t6XPrfB2jx2RIE9ujrtL4RQoi5Kq2iRcEdQgjV4CGEEEJIbcQwziuEbD5FqyDfcrEZey797wMAwLkPXnZKnwghxBqHMniefvppfP/99/Dx8cHAgQNFiywbbdu2zWmdI4TUMrx557SKFiGEEEJqJZn17yrVZeNrUA0fTAgh9jkU4OnVq5epqHLv3r1rtEOEkNqL4xcWpCLLhBBCCKmFcvPyndeYeVFlKrJMCKnFHArwGFfQMn9MCHnA8DN4aIoWIYQQQmqhkrIyyXVyrOHMAjrVmfzFgDJ4CCE1y3kTVAkhDwB+Bg8VWSaEEEJI7cPInPgVhzJ2CCH3EIcyeB566CGbdXf4EhMTq9UhQkgtxhvjUA0eQgghhNRKTiyyLBj8AGBQjYAP1eAhhNQwhwI806dPr+l+EELuBVSDhxBCCCG1nVMzeMye0xR1Qkgt5vAqWoQQwglW0aIBDiGEEEJqIScFeDiOg1pdbratGje4KIOHEFLDHArwFBcXw9PT0/TYFuN+hJD7EWXwEEIIIaSWc0IgheM4nJw8BMWXUyy2VxnFdwghNczhGjwJCQkICAhAu3btROvxcBwHhmFw7tw5p3eSEFJLCFbRoiLLhBCDdevWYcWKFcjKykJkZCSmT5+Odu3aWd0/MTERs2fPRkpKCoKDgzFu3DgMGzZMsM/u3buxcOFCXL9+HfXr18e0adPw6KOPml7v2bMn0tLSLNp+/vnn8dFHHwEA3n33XWzbtk3wekxMDH788cfqvF1CSC3HyOTVbkOTdQtFp5MstnP6qo9/aBUtQkhNcyjAs3r1avj4+AAA1qxZU6MdIoTUYoIaPDRFixAC7Ny5E/Hx8fjoo4/Qtm1bbNy4EePHj8dvv/2GevXqWex/48YNTJgwAUOGDMHcuXNx4sQJzJw5E/7+/ujbty8AICkpCdOmTcNrr72G3r17Y9++fZg6dSrWr1+PmJgYAMDmzZuh533RSklJwejRo9GvXz/B+bp164b4+HjTc6VSWROXgRBSmzhjipaVTB2OMpjJfYzjOOh0OsHnK7n75HI5FAqFQwtfORTgad++vehjQsiDi1bRIoQAwKpVqzB48GAMGTIEADBjxgwkJCRgw4YNeOONNyz237hxI0JCQjBjxgwAQEREBE6dOoWVK1eaAjyrV69G586dMXHiRNM+iYmJWL16NebPnw8A8Pf3F7S7bNky1K9f32KcolKpEBQU5Nw3TQip1fjLpLNqNWQuLpLbsDYVq3pTtBzP4FFnZlT9PIRUgUajQUZGBkpLS+92V4gId3d3hISEQKVS2dzPoQCPuYKCAmzevBmpqalgGAYREREYNGgQfH19q9IcIeQewdEqWoQQHo1GgzNnzmDChAmC7V26dEFSkuXUBgBITk5Gly5dBNu6deuGLVu2QKvVQqlUIjk5GaNGjbLYZ/Xq1Vb78csvv2D06NEWd7cSExPRqVMneHt746GHHsK0adMQEBDg8HtkWdZpReWNd0Tpzqjj6JpJR9cM4HjLpKd+HY/Gr31gdV9r10uv04ruz1Yju0HPsg4fmzioq+XxteRnSv/GpHP2NZPLqz8NkY9lWVy5cgVyuRz16tWDSqVyKFuE1DyO46DRaJCVlYUrV64gMjISMhtZipIDPImJiZg8eTK8vLwQFRUFAFi7di2+/vprLF26lDJ8CLmv8VfRogAPIQ+6vLw86PV6i4BJYGAgsrKyRI/Jzs5GYGCgYFtAQAB0Oh3y8vIQHByM7OxsizYDAgKstrlv3z4UFRVZrPrZvXt39OvXD/Xq1cPNmzexcOFCvPjii9i6davdO2BGp0+fhlYr/kWvqk6dOuXU9h4EdM2ke5CvWblaDbeKx7e2rkVhjyftHmN+vdjsW6L73bhxHYXJyVXqF6tnkVzFYwFU69ia8CD/G6sqZ12zuLg4p7RjpNFowLIswsPD4e7u7tS2SfW5ublBqVTi2rVr0Gg0cHV1tbqv5ADPrFmz0L9/f3z88cemyKFer8fMmTMxa9Ys7Nixo+o9J4TUbryb2JyeAjyEEAPzu3zGhRek7G++XUqbW7ZsQffu3VGnTh3B9v79+5seN23aFFFRUejZsycOHDiAPn362HhHlaKiopyawXPq1ClER0c7/e7r/YqumXR0zYBcDw/B89jYWKv7WrteZTevQiwPMTQkBE1stCfmkIN9sXaM1GNrGv0bk+5euWa2MkPI3eXoz0ZygOf69ev46quvBP8w5XI5Ro0ahe3bt0ttjhByDykvK6t8Qhk8hDzw/Pz8IJfLkZ2dLdiek5NjkaVjJJbdk5ubC4VCYZrqHRgYaNFmbm6uaJtpaWk4dOgQFi1aZLe/wcHBqFevHq5evWp3X6OaGOzK5fJaPcCvjeiaSfcgXzOZ2ft25DqYXy9rv/kMw1T5unJM9abW1Laf54P8b6yq6JqRmiZ51NKyZUtcvnzZYvvly5fRokULp3SKEFI7FRQUmB7TKhKEEJVKhVatWuHgwYOC7YcOHUKbNm1Ej4mNjcWhQ8J70wkJCYiKijKtcBUbG2vRZkJCgmibW7duRUBAAB5++GG7/c3Ly0NGRgaCg4Pt7ksIuYcx1Q/MWlsOncY/hJDazKG/fufPnzf9N3LkSHz22WdYsWIFjh07hmPHjmHFihX4/PPP8eKLL9Z0fwkhd5FgcoSTpiwQQu5to0ePxubNm02LL3z++efIyMjA0KFDAQDz5s3D22+/bdp/6NChSE9PR3x8PFJTU7F582Zs2bIFY8aMMe0zcuRIHDx4EMuWLUNqaiqWLVuGw4cPW4wzWJbF1q1bMXDgQCgUwqTkkpISzJkzB0lJSbh58yaOHDmCyZMnw8/PD717967BK0IIudsYJ2Te1UyAh4G2IA831y+HJjuzGu0QQpzh5s2baNasGc6dO+fwMVu3bkW7du3uej+scWiK1sCBA8EwjGAO+ty5cy32e+ONNwTz3Qkh9xd++QtrAx9CyIOlf//+yMvLw5IlS5CZmYmmTZti2bJlCA0NBQBkZWUhI6Nyud/w8HAsW7YM8fHxWLduHYKDgzFjxgzTEukA0LZtW8yfPx9ffvklvvrqK4SHh2PBggWIiYkRnPvQoUNIT0/H4MGDLfoll8tx8eJFbN++HUVFRQgKCkKHDh2wYMECeHp61tDVIITUCk4J8OjEt1dzivr5j6ci/2gCbu/ehrjVOw1t6nTQq8ug8PCqVtuEPKgyMjKwaNEi/P3338jPz0dQUBB69eqFKVOmwM/Pz+pxISEhSEhIsLmPuf79+6NHjx7O6HaNcCjAs3///pruByHkXsAL8lKKMiHEaPjw4Rg+fLjoa7Nnz7bY1r59e2zbts1mm/369UO/fv1s7tO1a1dcuHBB9DVXV1esWLHC5vGEkPuTMzJ4YG2cU53xDwPkH00AAJSmVv7t+u+1F1CScg4P/XQASh/Hv2gSQoAbN27gueeeQ8OGDTF//nyEhYUhJSUFc+fOxT///INNmzaZavzxaTQaqFQqBAUFSTqfq6urzVWs7jaH/vqFhoY6/B8h5MFQePrE3e4CIYQQQoglWfWL2FrN4HHyDS59aQkKTx6FvrQYhafF1u0i5O7hOA4ajeaO/id15cqZM2dCqVRi5cqVaN++PerVq4cePXpg1apVuH37NhYsWAAA6NmzJ5YsWYJ3330XcXFx+OCDD0SnRu3fvx99+vRB69atMWLECGzbtg3NmjVDYWEhAMspWosWLcKAAQOwfft29OzZE3FxcZg2bRqKi4tN+/z9998YNmwY2rVrhw4dOmDixIm4fv16dX40VkleRcvo0qVLSE9Ph1arFWzv1atXtTtFCKmleH9wr3+3AA1GvXwXO0MIIYQQUklbkAcwDBgnrFKkM/uOY1SdAI/511aOZVGSet70XFZRaJ6Q2oDjOKxatQo3bty4o+cNDw/H6NGjwTCM3X3z8/ORkJCAadOmWWTVBAUF4cknn8Tvv/+Ojz/+GACwYsUKvPTSS5g8ebJoezdv3sRrr72GESNGYMiQITh37hzmzJljtx/Xr1/H/v378c0336CwsBBTp07F8uXLMW3aNABAWVkZRo8ejaZNm6KsrAwLFy7ElClT8PPPPzt9tU7JAZ4bN25gypQpuHjxoqAuj/EH4IzCQISQ2smRP7SEEEIIIXcaq1bj38cNd9WZXgOr3V5ZcZHo9urW4OHL3LUVSr9A03N9aYnT2ibkQXDt2jVwHIeIiAjR1yMiIlBQUIDc3FwAQMeOHTF27FjT6zdv3hTsv3HjRjRq1AjvvPMOAKBx48a4ePEivvnmG5v94DgO8fHxphp/Tz31FA4fPmwK8PDrDALA559/jk6dOuHSpUto2rSphHdsn+QAz2effYawsDB8//336NWrFzZv3oy8vDzMmTPHdCGkWLduHVasWIGsrCxERkZi+vTpNqtSJyYmYvbs2UhJSUFwcDDGjRuHYcOGmV5PSUnBV199hTNnziAtLQ3vvfceRo0aVe3zEkJAK2cRQgghpFYqv51mesxYmV4lBauriSlajGHFiorxVM4/+1D3qedMr557fwq6JaRWo31CnIdhGIwePdpixk5NUyqVTrupbJ6MEhUVZXP/K1euWOzTunVru+cJDQ0VLOAQHByMnJwc0/Pr169j4cKFSE5ORl5enqlfGRkZTg/wSM4HSkpKwquvvgp/f3/IZDIwDIN27drh9ddfx6effiqprZ07dyI+Ph6TJ0/G9u3bERcXh/HjxyM9PV10/xs3bmDChAmIi4vD9u3bMWnSJHz22WfYvXu3aZ+ysjKEhYXhjTfesFowSep5CSGEEEIIIbWXvrSy3gX/qyGHqn1RtLZaaMHKL6vUnrE3MPviyrF084zUXgzDQKVS3dH/pAR36tevD4ZhcOnSJdHXL1++DB8fH9MqWW5ubjbb4zjO4vyO1ARSKCzzZvjHTZo0Cfn5+fj000/x008/4ccffwSAGgmeSQ7wsCwLDw8PAICfnx8yMzMBGKJWV65ckdTWqlWrMHjwYAwZMgQRERGYMWMG6tatiw0bNojuv3HjRoSEhGDGjBmIiIjAkCFDMGjQIKxcudK0T+vWrfHOO+/g8ccfh0qlcsp5CSEGNEOLEEIIIbWRYHoTbxoVA85qsMYWq0WWy0slt1XZF4BhKr9+cSyLvMMHqtweIQ86Pz8/dOnSBevXr0d5ebngtaysLPz666947LHHHA4aNW7cGKdOnRJsO336dLX6mJeXh9TUVEyePBmdOnUyTRurKZKnaEVGRuLChQsIDw9HTEwMvvvuOyiVSvz4448IDw93uB2NRoMzZ85gwoQJgu1dunRBUpJ4Bfnk5GR06dJFsK1bt27YsmULtFotlA4UJqvKea1hWVZylW8x+ooPHX0VPnweVHTNpHPGNWP1wrRknVbrnKVIayn6dyYdXTPpnH3N5E4oLkoIIfea8oJ802OOZQV5O6xOK/lvI2dlila1cBwgkwEVf+5zD+4X383KNLD0betQ7+nhzu8XIfewDz74AEOHDsXYsWMxdepUwTLpderUMdXBccRzzz2H77//HnPnzsUzzzyDc+fOYdu2bQCqXovUx8cHvr6+2LRpE4KCgpCeno558+ZVqS1HSA7wTJ48GWVlZQCAqVOnYuLEiRg+fDh8fX1NS5A5Ii8vD3q9HgEBAYLtgYGByMrKEj0mOzsbgYGBgm0BAQHQ6XTIy8tDcHBwjZzXmtOnTzs1rco8Wkjso2smXXWuWdH16+APj5ITj4BxtZ3qeD+gf2fS0TWTzlnXLC4uzintEELIvaQkt7LeBatnBeMVTqcFXFwtD7LBWg0eqfg3gxnO8CXR1u1hjmVxa8ePoq+lzvuQAjyEmGnYsCG2bNmCxYsXY9q0acjPz0dgYCB69+6NKVOmwNfX1+G2wsPDsXDhQsyZMwdr1qxBbGwsJk2ahI8//tjq7CB7ZDIZFixYgE8//RRPPPEEGjVqhPfffx8jRoyoUnv2SA7wdOvWzfQ4PDwcO3fuRH5+Pnx8fKoU1RKb42arHWtz4qSeW+p5xURFRTktg+fUqVOIjo6mO68OomsmnTOu2bXcW0jjPW8VGQFVgP3A6r2K/p1JR9dMOrpmhBBSfZqiwsonrDAjUq9WQ+HhJak9jnVSJqrguwIHzs73DY7V49IXM5xzbkIeEKGhoYiPj7e5zx9//GGxLSwsDBcuXBBs69WrF3r16mV6vnTpUtStWxcuLi4AgEGDBmHQoEGm11955RW88sorgjZGjRolWOipc+fO2Llzp2Af/nnF+lFVkgM8fBkZGWAYBnXr1pV8rJ+fH+RyObKzswXbc3JyLLJ0jMSybHJzc6FQKByOzFXlvNY4e816uVxOg3uJ6JpJV51rZjEm0agfiOtP/86ko2smHV0zQgipOm1JZZFl8xuweo1acnt6Z2Xp86ZbMRwLnV4Pm3/p9bZX6bq5fhnCnp9gcx9CSNWtW7cO0dHR8PPzw/Hjx7FixQoMH37vZM5JjlDodDp8+eWXiIuLQ8+ePfHII48gLi4OCxYskDRdSaVSoVWrVjh48KBg+6FDh9CmTRvRY2JjY3Ho0CHBtoSEBERFRTlUf6eq5yWEGJiv9MDe4WUTCSGEEELE6MrLKp+Y1bDRqqUHeFidc8Y4HK/gs4zjbE7PAqwXdza6smSOE3pFCLHm2rVreOmll9C/f38sXboUo0ePtsjQqc0kZ/DMmjUL+/btw1tvvYXY2FgAhuLHixcvRl5eHmbNmuVwW6NHj8bbb7+NqKgotGnTBps2bUJGRgaGDh0KAJg3bx5u376NL774AgAwdOhQrFu3DvHx8Xj22WeRlJSELVu2CIoUaTQapKammh7fvn0b586dg7u7Oxo0aODQeQkh1giHJZxWc5f6QQghhBBSiZ9xYz69SqcuN9/dLmfV4IHZzTG7AR4rBZYJIXfG9OnTMX369LvdjSqTHOD57bffMH/+fPTo0cO0rXnz5ggJCcHrr78uKcDTv39/5OXlYcmSJcjMzETTpk2xbNkyhIaGAjAsbZaRkWHaPzw8HMuWLUN8fDzWrVuH4OBgzJgxA3379jXtk5mZiYEDB5qer1y5EitXrkT79u2xdu1ah85LCBFnPuigDB5CCCGE1AaCaVhm4xV9FTJ4nLWKlmXOjp0aPHYyeAghxBbJAR4XFxeEhYVZbA8LC3N4mhTf8OHDrc5pmz17tsW29u3bm5YqE+NogSJb5yWEOIYyeAghhBBSG+g1vDGJWQ0eXRVq8LDOCrRIzMhxWnFnQsgDSXINnueffx5LliyBhvdHVKPRYOnSpXjhhRec2jlCSO1iXrSwtmXwOOtuGyGEEELuLYIxSW3K4OGEfWHsrMDL6SnAQwipOocyeF5++WXB80OHDqF79+5o3rw5AOD8+fPQarXo1KmT83tICKk9uNpbg0eTm43jw/sg8OF+iHzn87vdHUIIIYTcQZxeZ5r8ZD6lvCqraLEaJ41xzGrwMJztjB5ORwEeQkjVORTg8fLyEjzn17wBgJCQEOf1iBBSa1lm8NSeAE/Gth+gKyrArV83UYCHEEIIuQOys7ORmZmJFi1agGFs15apaaxWU7n8uFkQpUpTtJw0xjEPNtkL8ICmaBFCqsGhAE98fHxN94MQci/gzIssV2/wc/Xb/yHv2EG0XvgD5O4e1WqLkcvt70QIIYQQp/n6668BACNGjEDjxo3val/4U6osF4WQPt3KaTexzMZOMrtTtGi6OSGk6iTX4DHKzc3FsWPHcPz4ceTm5jqzT4SQ2spsTMJVswbPjbVLUXzuP2T8vKFa7QAAI5dcM54QQgghTnD79u273QVwOt6YxCyIotdJH684axq61GXPWarBQ0it0KxZM+zbt+9ud0MyyQGe0tJSvPfee+jatSteeOEFDB8+HN26dcP06dNRVlZWE30khNQSlnfEnDP4KU+7Xu02KMBDCCGE3Dl6XiDCzc3tLvbEQBDgMRuvVGVlKudl8NjO2LE4r8TizhzLQq8ul3QMIfeLSZMmYdSoUaKvJSUloVmzZjhz5kyV2k5ISED37t2r0TtLI0aMwGeffebUNs1JDvDMnj0bR48exdKlS3Hs2DEcO3YMS5YswdGjR0WXNSeE3E/Miyw7ZxUtdWZGtdtgFBTgIYQQQu6UkpIS02OlUnkXe1KBHxgxC+iwEgsX60qLoTmw0xm9kp7BIzHAc+q1F3C4byy0hfmSjiPkfvDMM8/g33//RVpamsVrW7ZsQYsWLdCqVStJbRpXCw8KCoJKpXJKP+8kyQGe3bt347PPPkOPHj3g6ekJT09P9OjRA5988gl2795dE30khNQCuuIi3J47XbDNWXe3tHk51W6DMngIIYSQO4cf4GElBjGsOX/+PLKysqp0rKB2jfmqnxIzeLL3/1alPohhJZ6blTidrCDpCDidFrmH/pR0HCGO4DgO+rLSO/qf+aIutjz88MMICAjAtm3bBNvLysqwc+dO9O7dG6+//jq6d++OmJgYPPnkk9ixY4dg3xEjRmDWrFmIj49Hhw4dMGbMGACWU7Tmzp2Lvn37IiYmBr169cKXX34JLe9m96JFizBgwABs374dPXv2RFxcHKZNm4bi4mIAwLvvvovExESsWbMGzZo1Q7NmzXDz5k3JPxN7JH8jKi8vR2BgoMX2gIAAlJdTeiAh96vrqxdbbHNWBg9nb0UJB1CRZUIIIeTO0fEyTXQSs07ELF68GDk5hhs+H330UVU6ZHposeqnxP45c0zB6aWNcapSLwgAZCqXKh1HiDUcx+G/l55F4akTd/S83tFxaL1kk0Mr8ykUCgwYMABbt27FlClTTMfs2rULWq0WQ4YMwY4dOzB+/Hh4enriwIEDePvttxEeHo6YmBhTO9u2bcOwYcOwYcMGqwEmDw8PxMfHIzg4GBcvXsQHH3wADw8PjB8/3rTP9evXsX//fnzzzTcoLCzE1KlTsXz5ckybNg0zZszA1atXERkZiVdffRUA4O/vX51LJUpyBk9sbCy++uorqNWVyw2Wl5dj8eLFiI2NdWbfCCG1SOmVFIttd2t+uhhGUZkeLjUdmhBCCCHS8L8E6Z1QGDgnJwfe+VlQlZdWrT/8DB6LGjzSxgWME4MlUrOHpKz4xX9fFOAhNcN+kOVuGzx4MNLS0nDkyBHTti1btqBPnz6oU6cOxo4dixYtWiA8PBwjRoxA165dsWvXLkEbDRo0wNtvv43GjRsjIiJC9DwvvfQS2rZti7CwMPTs2ROjR4/G77//LtiH4zjEx8ejadOmaNeuHZ566ikcPnwYAODl5QWlUglXV1cEBQUhKCgI8hq4QS05g2f69OkYP348unfvjubNm4NhGJw7dw4uLi5YsWKF0ztICKkdyjMsUwidtsKEMwI8vD+QnE7r1MEZIYQQQoT4n91VyeDhOA6bNm2CQqHAM888A6/CHLQ7avyyNEd6hwRTtMwCPBKXHndmsKQmp2ixmsob7jIXV0nnIcQehmHQeskmsOV3diElmaubQ9k7RhEREWjTpg22bNmCjh074vr16zh27BhWrlwJvV6PZcuWYefOncjMzIRGo4FGo7EoDB8VFWX3PLt27cLq1atx/fp1lJaWQqfTwdPTU7BPaGioYFtwcLApM/FOkRzgadasGfbs2YNffvkFly9fBsdxePzxx/Hkk0/C1ZX+sBByt2m1WqxYsQINGjTAY4895rR2dcWFFtuccccOgJMyeCr/nLE6Ld3JIoQQQu6QqowHsrKycOHCBQDAgAED4JdbzQUX+H2wqMEjLYNHZqdoNMdxDn8BlTpFS8p0Mv4Xb9k9WAyW1H4Mw0Du5n63u2HXM888g08++QQfffQRtmzZgnr16qFTp0747rvv8P3332P69Olo1qwZ3Nzc8Pnnnwtq5wD2VwJMTk7G66+/jldeeQVdu3aFl5cXfvvtN6xatUqwn0Jk0Rdn3MiWQlKAR6vVol+/fvj222/x7LPP1lSfCCHVcPbsWdy+fRu3b992aoAHIoM3p9XdckoGT+WfM2fVBiKEEEKIuOpO0crPzzc91mq1kFX3phH/+Gouk86xdsYlHAc4GuCReG57NXi0vCnpekFmRe2fSkNITXnsscfw2Wef4ddff8X27dsxZMgQMAyD48ePo1evXhgwYAAAQ0H4q1evWp2GZc2JEydQr149TJ482bQtPT1dcj+VSqXTitJbI6kGj1KphEajkZQyRQi5s2rqj4bY3a+qFgK0bNwJAR5Z5Z+z8zOnVbs9Qojj1q1bh549eyI6OhqDBg3CsWPHbO6fmJiIQYMGITo6Gr169cKGDRss9tm9ezf69++PqKgo9O/fH3v37hW8vmjRItMqFMb/unTpItiH4zgsWrQIXbt2RevWrTFixAikpFjWEyOESFfdKVoFBQWmx1qtFjJeIKQqd7wZfiDFYoqWxACPyJQurYKXISNhrGU3WGTGXgYP/1uYcOrMnc0SIKQ28fDwQP/+/bFgwQJkZmZi0KBBAID69evj0KFDOHHiBFJTU/Hhhx8iOztbcvv169dHRkYGfvvtN1y/fh1r1qwRrLDlqNDQUJw8eRI3b95Ebm5ujXxvk1xkecSIEVi+fLlTquUTQu4dYoMdvZP+DjhjFS1+ACo/8Z87ng5JyINq586diI+Px+TJk7F9+3bExcVh/PjxVu9s3bhxAxMmTEBcXBy2b9+OSZMm4bPPPsPu3btN+yQlJWHatGkYMGAAfv75ZwwYMABTp07FyZMnBW1FRkYiISHB9N+vv/4qeH358uVYtWoVPvzwQ2zevBmBgYEYPXq0aclSQkjVVTeDR6OprOPnjACPIKhjFlSRHuARyVp29ah8XUIwhZVY/8fuFC3+decVpKYFJsiD7plnnkFBQQE6d+6MevXqATAURm7ZsiXGjh2LESNGIDAwEL1795bcdu/evfHiiy9i1qxZGDBgAJKSkgTZPI4aM2YM5HI5Hn/8cXTq1KlKWUD2SK7Bc/LkSRw+fBgJCQmmeWx8ixdbLqVMCLlzaiqwIZ7B46RArzP6bN6GXg+IzIMlhDjXqlWrMHjwYAwZMgQAMGPGDCQkJGDDhg144403LPbfuHEjQkJCMGPGDACG4oinTp3CypUr0bdvXwDA6tWr0blzZ0ycONG0T2JiIlavXo358+eb2pLL5QgKChLtF8dxWLNmDSZNmoQ+ffoAAObMmYPOnTtjx44dGDp0qPMuAiEPoOoGePjHa7VayHlt6HQ6qKTWlOGPU6qbwSM2vuHPYOCdS1tYAKW3j41uSazBYycgxPCGOyxvqnxpSQl8JZ2JkPtLmzZtTHW9jHx9fbFkyRKbx61du1Z0u3lbb7/9Nt5++23BtlGjRpkev/LKK3jllVcsXufv06hRI2zatMlmf6pL8rcfb29v0wCMEPLgqMkMHmdkFZtnAbF6HeQU4CGkRmk0Gpw5cwYTJkwQbO/SpQuSkpJEj0lOTraYStWtWzds2bIFWq0WSqUSycnJggGRcZ/Vq1cLtl27dg1du3aFSqVCTEwMXn/9dYSHhwMAbt68iaysLHTt2tW0v0qlwkMPPYSkpCSHAzwsyzotcG78Euy0AvUPALpm0t2pa8ZvX6vVSj4ff3+1Wg0ZqxM8l7x8MP/3VGRMYK1/YtfrVoblXXWOF+DR6/Tg5HrcWLMEN1Z8iSbvzkbwY4PE25dYF1CvsbdCKWfqq7asMoMnPe0m6tyh3xP6vZTO2desJpbXJvcHyd9+4uPja6IfhJAapNPpql87S2QVCGd9SDljipbFihlaLUBLhhJSo/Ly8qDX6xEQECDYHhgYiKysLNFjsrOzERgYKNgWEBAAnU6HvLw8BAcHIzs726LNgIAAQZutW7fGnDlz0LBhQ+Tk5GDp0qUYOnQoduzYAT8/P9O+Yn2TkhJ9+vRpi9U2quvUqVNObe9BQNdMupq+ZpmZmabHWVlZSE5OlnQ8//fw/PnzUGkqs1FOJiXBzcND7DDreJkyep1wfJKbk2u3f/zrlXnjOjzNXud41W9OnkwGo3JB6YovAQCX5s5Aekhj0XbLrkir+5V247rNL2gMxyE5ORlcSRH0l86ZtmfdzpT8M6gu+r2UzlnXLC4uzintkPuPwwEelmWxcuVK7Nu3DzqdDp07d8aUKVPg4kJLERNSm5jfad63bx8OHjyIsWPHVq/dGs3gcf4ULc5ZBaAJIXaZB5DtLSEstr/5dntt9ujRQ/B6bGwsHn30UWzfvh2jR4+2ey5HRUVFOTWD59SpU4iOjqa7rw6iaybdnbpmKSkpSExMBGDI8I+NjZV0fEFBAS5evAgAaNCgATJzb5leax7ZBH7BdSS19xfvZpHc7Pfe19vLav/Erlfysb9Rar4jr83WUVGQu3vgUMVzmVxhtf3bjA6pEt5H3Tp1IFYC9nR0N0Sd+gcAhxb16uD4EOGKxqH1QtBK4s+gquj3Ujq6ZuROcTjAs3z5cnz55Zfo1KkTXFxcsGrVKuTl5eGTTz6pyf4RQqrp4MGDAID9+/cjKirKqW3bLQToKCd8eTKf4y46f54Q4lR+fn6Qy+UWK1Lk5ORYZOkYiWX35ObmQqFQwNfX17SPeZu5ublW2wQAd3d3NG3aFFevXgUAU22e7OxsBAcHO9Q3MTKZ5PUo7JLL5TTAl4iumXQ1fc34vxscx1XrXHq9HjJegIbT6Zw6RYu7norcv3YjqGd/q4fzr5dS5PeeP0WLkTHC/slkVvsr9S+ItWLJHGNoieGA/H//snhdcRd+R+j3Ujq6ZqSmOfw3Z+vWrXj//fexcuVKLF26FF9//TW2b99OK9UQco/Q2J3TLZ1TplY5qZ2b168LnrOUwUNIjVOpVGjVqpUpkGx06NAhtGnTRvSY2NhYHDp0SLAtISEBUVFRUCqVpn3M20xISLDaJmD4G5eammoK7ISFhSEoKEjQjkajwdGjR222QwhxTHWXSeffmDEfo2jV5ea728UIAjzC7yds0iGc//AVFJ13bHqM6A0sQZFlzuwl61+ppK5uZT3AYzg/A040q1pW3an4hKDmFmsh1efoz8bhAE96ejoeeeQR0/Nu3bqB4zjB/FtCSO1VEwEe8wFOlZsRqe8jVUlJieA5TdEi5M4YPXo0Nm/ejM2bNyM1NRWff/45MjIyTEWM582bJ1h1YujQoUhPT0d8fDxSU1OxefNmbNmyBWPGjDHtM3LkSBw8eBDLli1Damoqli1bhsOHD+PFF1807TNnzhwkJibixo0bOHnyJF599VUUFxfj6aefBmCYmjVy5Eh8++232Lt3Ly5evIj33nsPrq6ueOKJJ+7Q1SHk/uXUVbTMxih6jboKHeKvoiU+Prn9x07HmhIJoCh4q3qVpV1D4WleIXm58wI8rNVryQvgiOzjrJtu5MFkvMFSWmoxOZHUEsafjfFnZY3DU7S0Wi1cXSsLljIMA6VSWTNfGgkhVWYtuuvsIqEVJ3NKM+oq3KkzJzO7ccXWxPslhFjo378/8vLysGTJEmRmZqJp06ZYtmwZQkNDARiKr2ZkZJj2Dw8Px7JlyxAfH49169YhODgYM2bMEKzQ2bZtW8yfPx9ffvklvvrqK4SHh2PBggWIiYkx7XPr1i28/vrryM/Ph5+fH2JjY/Hjjz+azgsA48ePh1qtxsyZM1FQUICYmBisXLkSnp7m5VMJIVI5M4PHPMCjU0sP8DA2pmgZZaxfhoYvvgSFh5ftvondJOJlyJwcN1Dw3FYGj9Rl0jlWPMDDnyIm2j+J5yGETy6Xw9fX15S84e7uXv0FWohTcByH0tJSZGZmwtfX1+4UP0mraH355Zdwc3MzPddqtVi6dCm8vCr/SL733nsSu0wIuRNqZopW1QM8/GOt361ynFwmA78VqsFDyJ0zfPhwDB8+XPS12bNnW2xr3749tm3bZrPNfv36oV+/flZfX7Bggd1+MQyDV155Ba+88ordfQkh0vA/x4uLi6t1vE6jBv8ri/2lwi3xAzyKshKr++UdO4SgHn2tvg5YGUOYB3H4YyAbX7isBWyssTYm4gd4OJGbWFIzhQgxV7duXQCgGTq1lK+vr+lnZIvDAZ6HHnoIV65cEWxr06YNbty4YXpOUT5Cahf+4KlGsu2qkw7M6xvnhClaFivl0BQtQgghpMbwxxg5OTm4dOkSmjRp4vDxLMvCtawYHMNAq9EIAjza8jK7q/GZY3hjEoW6zOp+JTlZCLLXt4ogS4FPEHwKKorC2yy4br2fkmvwWAnw+Pj58/axDEBRgIdUF8MwCAkJQXBwcM1k/pMqUyqVDhfndjjAs3bt2ip3iBBy91Vlfrxd1ZmixRuIOGPeuHlxQQrwEEIIITXHPIv3zz//lBTg4dTl6JywFRwYFD/UAa6810qOH8K/X7yDyHc+R2D3Pg61xzg4JsnPsp+dYJwCxfKDOjaCTWobN9GkZilbC9S0jo1F0R8/AQCur1rk8HGESEUrfd3bnL/2JyGk1pA671uq6gRmnF0M0PyPGdXgIYQQQmqOeYDHXuFPc8ztm4b/BwcuN0vwWtnW76EryMO56ZMd64uE8Y7GgSKyxiwajjcti2EYWAsh6WwEcaxl5FhjLSDE2PnCLXUqGCHk/iSpBg8hpPbjD7hqfKnDamXw2C+GKIXMrMqyWPoyIYQQQqqnvLwcZWWWU6CkBnhkWbdMj9mc29XqE2vlM59lGMjMl0x3YGEHYw0eQQYPGHAMI5opxNnI7pGcWWNlTCSzG+ChDB5CCGXwEHJfq80BHo5/H8wJ3WTM5r+ztMIfIYQQ4nTz58/HV199hby8PMF2qQEe8AohMznVK+qqt5K1KxZ40Zc7EOCpCBjxM3hsTdGyKMDMb6si8FLs4Wv3vIZzV2biXGvQ0vRYJrd9X54f4Lm5fhnOvjeRFpwg5AFEGTyE3GecncFTnHJWdK43UM27RYJjnRDhMXuvVVmBgxBCCCG2GYuvmi++wg/wnDhxAnK5HDExMdYb4k0p4srtT5uyxVogwxCgEY5VWI0jAZ6KKVr8rBmGgaGYssQMHmNGDgNwYGB9opfx3JXvRTBFTOF4gOfKkjkAgOx/9iLokcdsHkcIub9QgIeQ+5gzAjwnJw2xms7saEFDMYIaPE7op3mwSefAAI4QQgghVaNWqwXPFRUBiJKSEvz6668AgFatWpm2m+NnqnBmbUmlt5apIpJZwzpwLmPAyMXdo7IpBuDE4zs2s3uM4xMODDgGYOwMefjBKn7gqCpTtDgt3ewi5EHjUIDn/PnzDjfYvHnzKneGEOJczgjw2JqrzlUn88bJ08fMizbrqzlYJIQQQoh1GrNMWWMGTzlvCpTNcQg/g6eaN2WsLawgllnDahwI8Oj1honfggweGawuh+5IDR4bGUCC/fnBKl67jEx6gIexM62LEHL/cei3fuDAgYbK8RwHxtb8UwDnzp1zSscIIVXDsSyanT2MQu9AFBQUVGzkbM8drypWWpCG0+mQuWc7XOqGwTUkjPdCdTKBONz84Vtok48Iu0bzzgkhhJAaYy2Dx9GbS/yABFPNTBNrRZY5mWUGD+dIgIc1BHjMAyTWpmI5UmSZqyjSbO/emLUpWvYyeCCyipa9aV2EkPuPQ7/1+/fvNz0+d+4c5syZg7FjxyI2NhYAkJycjFWrVuGtt96qkU4SQhzHnj2B0LQUhKal4JtvvkH41TNocPUMTjzU1/knk7j6VcbP65G6YKbF9upM9co78jeufjvXYrte4rKkhBBCCLGN5QVlzDN4jAEe/j42gz38z+nqBnhs1uAx2+ZQgMfwHgQBEhtBHIVWDW1BHpQ+fiJtVbxPxvQ/tvGnrgmmaNleG4eruOnGn/rGKCQWviaE3PMcCvCEhoaaHr/22mt4//330aNHD9O25s2bIyQkBAsXLkTv3r2d30tCiMO4kiLB88iU4wCApuePABju5JNJ270gKdG55wdQfPGM6HbK4CGEEEKcy1aAxxjMcTjAw8s4qW4Gj14nPkVLrAYPZ2U6l2Cfir4JMngYxmqQR1Vein8fb4euBy5YZM2YMngYQw0eu+fmZfCEhIYCl5IqTm8nwFNx043lXUsZZfAQ8sCRvEz6xYsXERYWZrE9LCwMly5dckqnCCHVYGUspbA2+KnWuaqxipagnapn8GiyxZdW1VtJ1yaEEEJI1djKjjUGdhxezdOJU7Q4K/3iZCIRFa2EDB5706LM6MVqCVW0pVQoHZouz38vKhdX02P7GTyG8+iLK2/0qXWUzUzIg0ZygCciIgJLliwRzLvVaDRYsmQJIiIinNo5Qoh0/MLHTc9V1qWR67SCu2rOOZdEVgY21Zmipcm+LbqdpSlahBBCiFOZB3gYVg+VugyA9AwewVSiambdWs3g4RUm1ssM2SwuaVdQcsnOAjIVfZOZTdGyVWsHABiRKVgsP4PHgSlaHO+98ANMMjtFlo2BpDPvjDdtSktPt3s+Qsj9RXLe3syZMzFp0iT06NHDtGLW+fPnwTAMvv32W6d3kBAiDX8sFXbzgumxogYCPHBWe9UI8FhbDYMCPIQQQohzmY8j2hzfA9/8LPzb6SnpU7R4WcBytnoBHuuraFXey9YpFJBrDOc5895EtP/pLxtdq+iboMiycRUs60SXKuevouVIBk9BnuH/IVw5y95CN8bzFF84bdomE8tgIoTc1yQHeFq3bo39+/fjl19+weXLl8FxHPr3748nnngC7u7uNdFHQogE1oZScn0NBHikBmasjDPkei2Kzv8Hr+atpffByoDH2ooahBBCCKka8wwe3/wsAEBIRqppjFGVIsvyan5mW5uiBd4qWqxCBVRMoSrPvGWnQcspWgzsr0jKiaxkxQ/wODRqOnui4nwAeAEaRmRFMHvnljlS1JkQcl+pUuUtNzc3PPfcc07pwLp167BixQpkZWUhMjIS06dPR7t27azun5iYiNmzZyMlJQXBwcEYN24chg0bJthn9+7dWLhwIa5fv4769etj2rRpePTRR02vL1q0CIsXLxYcExgYiIMHDzrlPRFyV1kZTCn0ursf4LEhedzT6JaQ6rT2qMgyIYQQ4lzWxhEylq1CkeXK/WTVDPBYrbvHD/AoK1eUYu2NX0yraAlXobI36hELNFUGeGQOZfDwCYI69oosi/xsKIGHkAeP5Bo8ALB9+3YMGzYMXbt2RVpaGgDg+++/x759+yS1s3PnTsTHx2Py5MnYvn074uLiMH78eKRbmS9648YNTJgwAXFxcdi+fTsmTZqEzz77DLt37zbtk5SUhGnTpmHAgAH4+eefMWDAAEydOhUnT54UtBUZGYmEhATTf7/++qvEq0BI9f3zzz9ITk52eH+bA6UKrI3hR20O8FRFyeULyDt8QPS16k7RUmdmIGPzGnBqkYKJhBBCyAPIWpFlmV5nGqPw93F0Fa3qZvBYnaLFC5BwSpfKF+xl4lT0W1BkmbN/nOi4yLRMumNTtPj4K2cxdqI1XFmpxTaZxPMRQu59kgM869evx+zZs9G9e3cUFhaavjB6e3tj9erVktpatWoVBg8ejCFDhiAiIgIzZsxA3bp1sWHDBtH9N27ciJCQEMyYMQMREREYMmQIBg0ahJUrV5r2Wb16NTp37oyJEyciIiICEydORMeOHS36JpfLERQUZPrP399f4pUgpHoyMzPxxx9/4Oeff3Zof41Gg8WLF+OXX36xvaPNbGgn16Vx1ipaVXRiZH+rr1lN13bQf68Ox5VFn0KzeaX9nQkhhJAHgGAcwQtmyFg9kpKSsGrVKhQWFvJ2cSyDRy4yvUgK61O0eAEaVWWAx+7tqYq+yXg1eAwTrGwHTNRlZZZ9E0zRkhrg4U3RspPBo9u92ebxhJAHg+QpWj/88AM+/fRT9O7dG8uWLTNtj4qKwpw5cxxuR6PR4MyZM5gwYYJge5cuXZCUlCR6THJyMrp06SLY1q1bN2zZsgVarRZKpRLJyckYNWqUxT7mAZ5r166ha9euUKlUiImJweuvv47w8HCH+w8YsiEcyaiwx/hh6fQv3/ex++GaFRcXmx7rdDq7H8JnzpxBbm4ucnNz8fjjj1vdj2VZq5FbnU7n1GvGcZyk9uz9tjizbzqdtlrtld+8BgDQJx5AbuI/8G/fzVldu6/dD7+bd5qzr5lc4rK+hBDiKH4msIxXGFnG6qHVanH9+nXB+Obq1ato2bIlFAqRrxxOzCq2WnePP7ZSOZ7BY6rBY9Zvzl68ROTGl9Qiy3zCKVrSgzXVWaWUEHJvkhzguXnzJlq0aGGxXaVSoUwkam1NXl4e9Ho9AgICBNsDAwORlZUlekx2djYCAwMF2wICAqDT6ZCXl4fg4GBkZ2dbtBkQECBos3Xr1pgzZw4aNmyInJwcLF26FEOHDsWOHTvg5+fn8Hs4ffo0tFZSQqvi1KlTTmvrQXEvX7OcnBzT46SkJMjsFM+7efOm6bGtaV35BQUItPKaVqt16jXTabWSppip8/Jsvs5vi83PAcpKIQuRFng1ys3JkdQ3W86/NRbuC390SlsPinv5d/NucdY1i4uLc0o7hBBiTm+lMDI/kKDj1cDbtm0brly5ggEDBlg25sQsYKt19/irULm4Vj52tAaPYBUtYQZPnm8dyFg9fAqzKw+zVYNHVoUaPPwMHgcK6rBajfi5CSEPDMkBnrCwMJw7dw6hoaGC7X///TeaNGkiuQPmWQscx9nMZBDb33y7vTZ79OgheD02NhaPPvootm/fjtGjRzvc96ioKKdl8Jw6dQrR0dF059VB98M1u3LlCg4fPgwAiI6OhpJX/C87OxtlZWWCrDJ+barY2FjTY/N/3/l/7rR6Tp1OJ7hmBcmJKDh2EOGjXrG4S2V0yMZ7UMjlgr7Yc8TO6/y2DvVoCgCI2/w3XILqSu6br7ePpL7Za7s6bT1I7offzTuNrhkh5F4hCPDohBk8Ri4uLog8nwhWJkdq0zgkJyeLB3icmVHswCpajKubaH9FGado8cZm5jV4rjZuDffSAkGAhxPJJDJtY2TgpGbhCPa3f+yNH74VnpsCPIQ8cCQHeMaOHYtZs2ZBozFEiP/77z/s2LEDy5Ytw6effupwO35+fpDL5cjOzhZsz8nJscjSMRLL7snNzYVCoYCvr69pH/M2c3NzrbYJAO7u7mjatCmuXr3qcP8B2M24kEoul9PgXqJ7+ZqZ//vhv49vvvkGADBt2jR4e3sDAE6cOCE4lmEYlJeXY+nSpWjQoAEGDRpkeNFG0FGr1Qqu2ZnXXgAAqAKCEPrMi1V4F5zF9dcWFqDwZCL8OvaATKkSvFZcUgo3iFP6BYj+LMuvpMC9bqjIEdL7Vh336r+zu+Ve/t28W+iaEUJqO/4ULX4GDz9gwuXnIvzGeQDAlYhYsFb+rjF3IIOHfwNMJiGDp7LIsqJyejknzOAxBGuEQRe9SD9MwSeZzGJ/uwQZPDJcbhyDxpdPWt09a4+wrqPY0ukAkHv4T+Qd+QeNprxrMVYjhNzbJEcoBg8ejJdffhlz585FWVkZ3njjDWzcuBHTp0+3WRfEnEqlQqtWrSyWJj906BDatGkjekxsbCwOHRLeV09ISEBUVJQp+yE2NtaizYSEBKttAoZ6QKmpqQgKCnK4/4RUF3+QZK32Rp6VKU3G9Of//vsPhYWFgqkd1j7M+ceZK029YLGtJPUCTr48zGpbhpNZDpBOvzEKZ9+bhOvfL7Z9rBmlr3ih8yoXS6a7VoQQQohTCTJ4eDV4/HNvQak2lGrQlRbzjnCsyHJ1WQvwyHhTlviBDI3SVWz3ShXBJxkvu5mDWQ0ehrF4d6JjFivLpOtl9gP65kWWs4Lr29y/7MYVYX+sXOMzb41D+ubVuPXLRrt9IITcW6qUgvLss8/izz//xKFDh3Dw4EH89ddfGDJkiOR2Ro8ejc2bN2Pz5s1ITU3F559/joyMDAwdOhQAMG/ePLz99tum/YcOHYr09HTEx8cjNTUVmzdvxpYtWzBmzBjTPiNHjsTBgwexbNkypKamYtmyZTh8+DBefLEyO2HOnDlITEzEjRs3cPLkSbz66qsoLi7G008/XZXLQUiV8AdJ/Mf8wM+6detw6dIli+XNjYGa0tLKJTGNWXXQWx8w6UuKcHLsANxYu1SwndWoLfY9/fooFCYn2n4TvAAPx7JI/XIWis/9BwDI3L3d9rHmfbNSzyp9yxqU30oXfc1m16jILyGEEOJU/PGKeRZM5MVjAABtebnVfQSquXIWn7WbW4y2cnwjkytw7KHHAAAqbTnK0q7Z6JtlkWUGnCBAwwEWNXXEij2b6vLIGMEULb1c2kQKmUqFEi8/ZNoJ8gjYyZIqv5UmqQ+EkNpPcoBn5MiRpuUP/f39TQWNi4uLMXLkSElt9e/fH++99x6WLFmCAQMG4NixY1i2bJmpvk9WVhYyMjJM+4eHh2PZsmVITEzEgAEDsGTJEsyYMQN9+/Y17dO2bVvMnz8fW7duxYABA7Bt2zYsWLAAMTExpn1u3bqF119/HY899hhefvllKJVK/PjjjxZ1hQipSfxsGr1ej//++w8nT54UDJ60Wi3WrVtnkeEjFuAxrlphK4PHPSkBJZfO4eq3/xNs16vLLfbV5GTafxO8gVte4j9I31y5Wh3jwJ0pvrzcXNHt+UcTkDTmScE2VqdFyWXLrCNh16p3Z1AnceBFCCGE3O/U6sqACWN288lFbRiTCOvbiAd49Hr9HcngYXg3sBi5DMFtHjI9L0u7br3BivGNTKHkbRJO0er16KMWi7OIF1k2BnjkwgweR8YZusqbX3I3dwDAtUbR9o+rUPz7ZrA6GwvCsLTKFiH3G8nfYBITE0VXjlKr1Th+/LjkDgwfPhzDhw8XfW327NkW29q3b49t27bZbLNfv37o16+f1dcXLFggrZOE1AB+gKesrMz077pBgwYW+1oL8JiydmAI8Pj7+4sW+DPiykpFt7MiAR6H8FfNKC4UviZjkPrlTJRev4KW8d9A7uJqc/UI/lx88+LlusJ8wfMLs15H9h/Wi0kD1S8sqFOooLBxLQkhhJAHDX/cYT5ByRiwkPE+z8UyePbu3YsjR44gxokr0VpbJl0Y4FHgqacH4Z8Ni+BVlIecjHRw/+yFV2wHywONU7R4QRhGGN+BUuWC0LBw8M9sHmgqvXoJXEnFlDWzVbQcCvDwA2oVNYSkFGrWXbmI6ysWouHEN0VfZ52YRUUIqR0cDvCcP3/e9PjSpUuCYscsy+Kff/5BnTp1nNs7Qu5j/ADP2bNnTY9//vlnm/vyn/MHWkVFRYYHNgIbrJXBlDMCPOZF+hiZHOmb1wAAbu/4CfUGj3C4LXsra9gL7gDVD/BolS5wVYsHxAghhJAHET+Dx7wOn15uyHbhZ/aIBXiM9TTtLlUOx6cxWZuWnRfWBEGXTxvOJ5fDy8sL8oqsnMxl/8Otglz4d+0NDJkgPNA0RasyG5kDZ7ZsuUywShcgzOApOHkM/015rvJFsyLLrCPvjT/FTCbDsGHD8MuyJfaP47n16yarAZ6s27chfQ1kQkht5nCAZ+DAgWAYBgzDCOrZGLm6uuL99993aucIuZ/xs3L4hcHFVnOzlsHDz6YzTtGyFRzheGm6Wft/Mz1m1ZY1eBzCG5wxZgMVjte33MN/2g3w8Ad61Z1eZWikam2UZ9xExrYfoNA7784iIfe7devWYcWKFcjKykJkZCSmT5+Odu3aWd0/MTERs2fPRkpKCoKDgzFu3DgMGyYs6r57924sXLgQ169fR/369TFt2jQ8+uijpte//fZb7NmzB5cvX4arqyvatGmDN998E40bNzbt8+6771pk/cbExODHH3900jsn5MEimKJlHuCpqFfjyBQtw/H2P6fleh1K8vPg4etncz9rAZ7soPDKAE/F1HGmIijDFhimhucm7IO7eYCn4r3J+WMbQ9Ed01OZXC4I+Jj3I+uPHYLXGLMpWqzC/upVLK+ekVKpNCwII3GpdW2++BR4ACgvK5PUFiGk9nM4wLN//35wHIfevXvjp59+gr9/5Yo3SqUSAQHiSxwT8qDiOA7FxcXw8vISfd3ailZi/vvvP9FjzadoAbZr8MjLSkyPz3/0qulx0ZkkZP+1G4E9+oodZh1vcHf+49cEL+l55yq+eMZuU/yBYta+X6X1Q6xrVZxXfnb6ZJSknLW6nDshRGjnzp2Ij4/HRx99hLZt22Ljxo0YP348fvvtN9SrV89i/xs3bmDChAkYMmQI5s6dixMnTmDmzJnw9/c31dRLSkrCtGnT8Nprr6F3797Yt28fpk6divXr15tq6iUmJmL48OGIjo6GXq/HggULMHbsWPz2229wd3c3na9bt26Ij483PTeuukkIkU4wRct8OnVFBg8/wMNUfBZnZGQgJCTEsJHjIGN1DmXwAEDe9St2AzzW6sw0bd4cOPK7oS8VgR2OcaAEaUXwiTH7bsOfHsXI5RbBlvz9vwKXziJ06BjL2JbZFC3OLPO53MXdInOY5Y2lXFxc4O7ujrh27YDDv9h/D1bwx2TmU+IJIfc+hwM8xgLE/KlahBDr/vrrL/z111/o168fOnSwnN8tJcDzxx9/CJ7/9NNPGD9+vGiAx9YUrYDb1leMODfjJXRLSHW4TwaGgQGrUYMtF94F4t8x0lup/cNnnMuvzrqFi5+9bWdvR7pWtQyekpSzFts0Spfq9oaQ+9aqVaswePBg02qaM2bMQEJCAjZs2IA33njDYv+NGzciJCQEM2bMAABERETg1KlTWLlypSnAs3r1anTu3BkTJ0407ZOYmIjVq1dj/vz5AIAVK1YI2o2Pj0enTp1w5swZPPRQZSFVlUpluOtNCKk2YQaP8HPWOOVIzsti6frPZpxq3R3Lli3D5MmTERwcjGbn/kVoWopo+xzDWAR+Cm5cQ1jrtjb7ZW1adnCdusgx9rciw8iRzCFjYEomCPAIV9GSyeSmoJFR/q6tyAfg3ijScjwmkwvr56gqxxZH2/dHQE46GqcmCw7Rl5eZVsQx3khv3KQJbJSHFsVqNbi86HPIXFyQtuE73ltyXqFrQkjtILnI8rfffouAgAA888wzgu2bN29Gbm4uJkyYYOVIQh4sf/31FwBg165dogEefnBGqqKiIuzcuRNarRYyvRasXImyijTb6i4Pri3Ig9LH9p0yk4pBmPq27WXM2bJScCwLm0nFFW3ZCwY5erdJ7DroiotQnnYNns2iHGrDdJzSfho1IQ8ijUaDM2fOWHz2d+nSBUlJSaLHJCcno0uXLoJt3bp1w5YtW6DVaqFUKpGcnIxRo0ZZ7LN69WpYY6xD5uPjI9iemJiITp06wdvbGw899BCmTZtmWgHUESzLOu0ut3G6rfm0W2IdXTPpavKa2ZqiZSQzyySO/u9v/PFoQ2zYsAEvv/yy1eAOALAyOeRmBZOL02/afS9iNQbL3DyFARVGBr1eD5mVgsz8cxh/5wXHc2bLpDPmZaYraQryoDPPKpIxguMLI1vDLfMm8gJCUOQTiMDsm5bvixfgMfVP4hQtAMg7fhgZW9dabOdYtkr/Tuj3UjpnXzOaOUOskRzg2bRpE/73v/9ZbI+MjMS0adMowEOIg8rLq1jYuEJ+fj68Uk4i5vgfOBPVFZpGjQwvVGNFhIzt63Hpfx+g7sDnbe5nWkiiYgBUfivNbttseZnNO0WMyJKkoud2NDAmcq7jI/pBk3ULUV+ugV+7LiIHWekbLSNKiKi8vDzo9XqLgElgYKBgMQa+7OxsBAYGCrYFBARAp9MhLy8PwcHByM7OtmgzICDAapscxyE+Ph5xcXFo2rSpaXv37t3Rr18/1KtXDzdv3sTChQvx4osvYuvWrVCpHAvcnj59WnT10Oo4deqUU9t7ENA1k64mrllOTo7psXmAR6FVw7MwBzJWPIBSWFiI5ORkm+2LTZ/Kupxi97jsrEx4mm1LiusD5tYtuFY8zy8qQnJyMvRWsn3418u4Iumt25kwTvjUabVgeLeqrt24gfK8PIhNxL9+5TJyr1wR9Emt0ULJu2ZqRo5D3QabAjbmy84DwK3IWIRd/A+36zQ0XYPCa1ftfoEr8vKHV1FlJvWl/5JF9ysvK7N7bW2h30vpnHXN4uLinNIOuf9IDvBkZWWJpjr7+/tbHXgRQoR0Oh2OHz9erTbkcjnqn0wAALQ6nYAL7boZXqjG6lGX/vcBAODW9vV29mQAcKYAj7Ygz27b+rJSwEagxHgfzFoNoQufvglVQDDqj3rZ7rkM7VieS5N1CwCQ89ceiwAPx7I4+654gFrG0R0qQmyxKDTKcRbb7O1vvl1Km7NmzcLFixexfr3wb1f//v1Nj5s2bYqoqCj07NkTBw4cQJ8+fWy8o0pRUVFOzeA5deoUoqOj6e6rg+iaSVeT18z45dTV1dUiwBN28yLCbl5EdkCo6LEMwyA2NhaHzLZzKhfTcuacXA6YxYfctGrExsba7NexQ/vAv/1T7uIOxj8I4fXrw/jtxD84GLGxsfjDSg0e/vX6s+JvTWhYGIwjHIVCAZZ3PRs1boyswlyILVMR6OeLYrN6X67u7ob3V8Hbxxso4q2SJTJ+U0a2wj89noVW6YKnK65BukqOq6LvwOCvR4aixZlDggBPWGAALovs66pS2r22Yuj3Ujq6ZuROkRzgCQkJwYkTJxAeHi7Yfvz4cQQHBzutY4TcT7RaLVJSUuDi4oLQ0FCcPn26Su3Uu3kRpR4+yPerg7S0NNRz84BXkcZ0DsB2kWVn4RiA4Xh37xwIKunLSkTnvXNgwIADU9GUteLImbsMK+GEPT/esT5KDHSVpF5A7qE/RV9ztBAkIQ8aPz8/yOVyZGdnC7bn5ORYZOkYiWX35ObmQqFQwNfX17SPeZu5ubmibX7yySf4448/8MMPP6Bu3bo2+xscHIx69eqJrlZojUzmQEFWieRyOQ3wJaJrJl1NXDPj9BIXFxertWwCc6xn9Yr9PnGu7pUBHpllf9n8XLvvw+Izn2EwZswY5J08ZtqkULlWtCP+mS64XhXvTc4P0nCcYFl0hVJlVqOnUtq1axbLyMvkCuh5wSVPTy9TgOehhx6Ce8F14LqwDqBMJoNW5Wrqn+G8tjOdjcvVC/rzq5WVA3W6av0bod9L6eiakZomOcDzzDPP4PPPP4dOp0PHjh0BAIcPH8bcuXMxZswYp3eQkPvBrl27cOLECQBAnTp10KpVK8lt+OTdRvNz/wIA/nh0JABAx1tiU2usXXNH50NXZN1Ymc8OAGoXN7ioyyoyeCwHg6yMgZzlwHCGOheZu7baPGPZjat2e2SYPiYtwGPrPcipCCEholQqFVq1aoWDBw8KljA/dOgQevXqJXpMbGws/vxTGExNSEhAVFSUaYWr2NhYHDx4UFCHJyEhAW3atDE95zgOn3zyCfbu3Yu1a9da3HgSk5eXh4yMDLohRUgVGW8mubi4wNYS6GI4jgMrMg5g3NyBQkOeDD/DReviBqW6DFyh/Sxhi89wmRyBgYEoUFS2J3NwWmZFZw19Mws48QNQjExmUWTZSA4OWvObQ3LhKloRTZqgPKgewsPD0a5dO/y9d7NFOz179kRGRoaglqNMLvz6lhHSGCEZvPwchoHSRbg4hDrFymqmVEOHkPuO5ADP+PHjUVBQgJkzZwr+yI8bN8602gUhRIhfbPT27duCLymOcisrttgmmKtelG/4/2pM0XKccIoWp7d+Tp1cCReUQV9aCkYku8jwHljIWD0SujWxe2Zr06j47TEcK5gOps66hdOvj7LbtjVi8+IJIQajR4/G22+/jaioKLRp0wabNm1CRkYGhg4dCgCYN28ebt++jS+++AIAMHToUKxbtw7x8fF49tlnkZSUhC1btmDevHmmNkeOHIkXXngBy5YtQ69evbB//34cPnxYMAVr5syZ2LFjB5YsWQIPDw9TVpCXlxdcXV1RUlKCxYsXo0+fPggKCkJaWhoWLFgAPz8/9O7d+w5eIULuHzqdDuA4+OTdRqmNGyM2jzfDuHlUPuEFUHSePlCqy8AUFwj25zjD+IMfXLFYWMFY14bXntyBBRP0ZaXIPfQn5BUZRcJl0jmAN+6SiSyTbnqN1Vtk8DCMcH+ZXI6nn366snWd1mIxCl9fX0yZMkXYtkX2BwOWYSCrON9rr72GI9PEi9xbuANZ34SQO0tygIdhGLz11lt46aWXkJqaCldXVzRs2NDhYoWEPAiuXRMuR65QKARFOo2PAzOvQ6dQId/f9rQCQDhNiGFZcDKZsJBhUaHh/+9kMMJYZNnGylfGZVN1pcWiWTWcxNUg+Muvi3aJYSpiT5Xnur5qEUqvWF+1wx6xwBQhxKB///7Iy8vDkiVLkJmZiaZNm2LZsmUIDTXU4cjKykJGRoZp//DwcCxbtgzx8fFYt24dgoODMWPGDNMS6QDQtm1bzJ8/H19++SW++uorhIeHY8GCBYiJiTHts2HDBgDAiBEjBP2Jj4/HoEGDIJfLcfHiRWzfvh1FRUUICgpChw4dsGDBAnh6mpdjJYQ4QqfTmZY5L3UTKy8srt7Ni7hVv7logEfm7gnjJzbHz07x9gNybsG1MBe60mIo3A2/t2feGA119m20XfmraelzawEemaKyPZmqYuqSyLRr7T+7gdhYpMx9H1l7fjZ9QRIEUzgAvOcyucJqBg+r0ViMeRizDB7zYzmRayOG/54MDRn/x/C+vL29HV5p605M6yeE3FmSAzxGHh4eaN26tTP7Qsh94/vvvxc8N59zXlhYCNeyIrQ+eQBA5ZQrW1qcrSxLKNdroZO5CJYSdS3IMaQ+36EaPOAAZVE+bqz9BnnZ1gussxV3u7TqctHgk/TFPu31raJF3rk4s6VKS0UCUhq19VXNGI6zWzSWkAfZ8OHDMXz4cNHXZs+ebbGtffv22LZtm802+/Xrh379+ll9/cKFCzaPd3V1xYoVK2zuQwiRRqvVmpY5dy8rcvi45uf+tRHg8TAFeOSqyqlFjI+/6fH1VYvReMq74DgOeYn/AACKL52FV/PW4FjWegYPL2DEb9vifW1eAbzyDrL2/CxshpcBZAg+8W62ya1P0WK1Wsvi7HKFIAPIopC8+bLqVjBmGTweHsKANcMwYKwUkjZSq1zhoimHPPsWcv/9C/4dezh0bkJI7edQgOfll1/G7Nmz4enpiZdftr2CzeLFi53SMULuJ2q1cI2F3NxcuJbzggwcK/jQ9yrIhlKrRm5gxUoU5kuR6rTQKYUBHt+CTENm0B0J8MhhnFZ19du5dvY1DGC05WrRAI/MyfO/jdPW+AUXGbO07KzMTIvj8nlLv4rS6wHzu2aEEELIA0QsQOMol7JiU5FmPhkvQKHy9QN3+4bhCe+zO23DcpRdu2TIjDHiAFatxolRj4O7cUXQJieSwWMrwGMNP4OnLLQhXDKum54zMrnVQAqn1ViUKGJkwila5oEaRzN4zINKKhcXfgKPIXBk54ZUamQcWp45CEVBLs68OQZtVvwMl5BwlN+8Co+mLSFTCAs1c3q9RX8JIbWTQ0tDeHl5CR7b+o8QYl9qaqogaCM3G/A8lLgTsUn74V5imHduvnSmvOIuDz84otSUGwZOd2CKFivlQ94Y4NGUiwafODt3maQyZfDwUqNlDgzq7N05s7g7SAghhDxg+NPNpWp58i8rGTyV3x9c6lUWS+fUZcgaVLlyZu6hP5F/7KDpuU6nQ37SYZSZBXcAQOPpA0AYDJFXoZwEI5cjNSIWWUFhKGzxkCDIIVcoAJl4IIXVOjBFyyyIovfydahPcrPjwBhWJBWws/qf+fT4ktTzOP5CHyRPGISDDzcXjHnSt6zF4cfaovBMskP9I4TcXQ7djo6Pjxd9TAixTaFVQ6cUDy7wlxeVsTroYTk3vOOhn3Epsi3SQyOF7eoMd7D4GTxyna5iilb1Ajxlrp5wK7cs6MzHSlg2WFaxKo5WLZ7BI7UGjz3G9gRLplpk3oic086dM1anhcxF+t0/Qggh5H6g1+urteqSV2EO9GYZzQAg52fw+AfBOGGaKytF9JODcWvrctH28vJyockRThEv9vBFiacPcjv0MbQtlsEjUoPHGplMhmuNDSUpGioUgsAJI5NZz+DRaWGewmOewSMzW+68uNOjyL2dgXppl2z2yWJamNg4ys7Ns+C6IcBpXn9lcmhzs03PdUUFUPoapsilLvgYAJAy+13Erd1ls11CyN3n3FvnhBCT+ldPo/uBTQi5KV7cl5+1I9frUSfjMupkXDasvMDTJOWEIJADAHHHdsO9pMAU6AEAhV4LvV5f7YJ5egemIbEyxzN4jPPfdWqNMOhSwfkBnoo/axyHzL2/IHFwN2QkHrJ5TPrWH5C3ba3NfdhqpKUTQggh9zqdTgdZFVbO4tNrhQEelmEgd3c3PVcF1TE95tRlaNy4MS5FthVti9Oz0JSVCbbl+dfFmdY9ENAwAoDZKlp2btKkbfjOYht/ildAQIAgoMPI5VYzZTitRrCap2l//vFmmThauRLnW3ZGuas7bJGbBYbEblrZqxnYoFEjwfOMW7cFz43TxWxNdyeE1E4OZfAMHDjQ4eKi9oomEvKgaJJyAgDQ4txhZIRFWrwu11emOSs15Wh1OgEAcKjr0xb7KrQai20dDwkLAcp1WrAsC4Z1/M6UGL3cfOBgibMT4OHAgKm4c2VMZ9Zp1VaWSXdy4WLeFK0LM6cZHt9OF+yiOvEPksYOQPRXPwAckDr/I7vN6jRq0NCGEELIg0qv10POigR4ZHIH6/9x0JULFzTgGBnkvICGi39gZbMaNeRyOQLDGwAVYyo+VqcFaza92tPLCw899BAefvhhQxu8AI3CSka10bVvvrDYxsjk6NmzJ27evIlHH30Uf21bZXpNrlBYXSiC0+ksxjeGAE/lc/OaQGL1icSIFnY2H0vZybTmF58GAL1ZVlPab5tRf8iLuMQbH+mqN7wkhNwhDgV4evfubXqsVquxfv16NGnSBLGxsQCAkydPIiUlBc8//3yNdJKQ+xE/K6fhlVOmxy7qMot92yX+7kB7WqesosXPCrJGSgaPcZDB6nTi08ecncFT0TfOTqCr+MJpHO4bi0ZT3nOoXX016g4QQggh9zq9Xm+RUQwAjEoFrtxy7GKJERZJhmHKt9zVzfTcxcfX9DigbggAIDgkBKIjE60GrNlns4e3N3r17296LuOvolWFadYyhQLdunXjbeBP0ZILptsL6LSAWYYOI5eD4Q1NlLz3DQBdunTBxYsXYW99UZlZcEb0JrydsZVMYTaO0wgzq24un4e07xcZMpEq5BYUQFuYD6W3r822CSF3l8OraBnNmDEDI0aMwNSpUwX7fPXVV8jIyHBq5wi5n/EHSUFZN0yP445azm8WvWNWQeflC0VRPhQ6nVOKLItlC5ljJMxfhzHgYiXAY1EYsJpY4906a4MuM1e+dqyuGOvAdSGEEEJquyNHjuCvv/7CqFGjEBwc7PBxer1edOVLmVIJvQMBHgYcdGqzDB6ZXFDfztXHD81nLcLNdd+i+bufG46Ti39d0avVYM3aY8xuQAkyeFxcK07q+BjGcuUoXpFkmcxipSwTtdqiPo95QEhhFnCqX78+3nrrLRw6aHs2hExkKr35WMrezAvzNsrWLrJs02zc45F5E//2j0PM0h/h0TLWZvuEkLtHcg2eXbt2YeDAgRbbn3rqKezZs8cZfSLknlVQUIDExEQEZN20uR/Dsoi8eMwp52Qr7gDJWZ0hy0QksMGBQVLXgbjYtJ3dgMqNyDZ2zyl3MHgCoDKDR6+3soqW7f5cr9/C8XMB4IwDQSlBKAfoKMBDCCHkHsdxHHbt2oWysjIcOXIEnITPSqsZPI7WZuE4i5slHl7eguXQXX39EdSzP9qs+BluYQ0BwGLJbiOdRg19iXBRCPPpS9VdRQvm75dfJFkut1r3UFZcYFGQmjFbWUvh6mpxnLu77fo7gKHws0WfJN4rMw+ESXHjh2+rfCwhpOZJDvC4urri+PHjFtuPHz8OF1phhjzgVq1ahd9//x0xyX8Itst1WtS7eRGP6PLRK7oF6makOu2cnKJywKIrLbGyUhVQ5heMmw1aghMZBGh5gydtcD0UeAda7iQF7xzGu1+sTicedLE7KJE2amGN78XJy8Wbp4ETQggh95ri4sqAiFqtxty5c3HokO2FCIxYlhXNKJapHBv/MwDOnzkt2Kbw8ATL+5xXiUz/MV9tykivLjeMe/jnMB9n8AIwxuLEjo4q8n2D4RoSLtjGryUod3UDZxYAMt5Ek5UUWgaHyksFYxOFg9fNnFiAh7OzapY58wLPUrDOrp1ICHEqh6Zo8b344ov4+OOPcebMGcTExAAw1ODZsmULpkyZ4vQOElKbaTQaqHh3hAoKCkT3a538J/zybgHnAO7PX9D96ReQdfawU/rA8VKX9RrxpcgBBuVmhQ35UiPj0PzcvwAAlasbdHbuxhWGR0J//SIKfIMRmia+SpiJcYpWWbFokWXG2VO0jEWipWQZOYBq8BBCCLnXFRYWmh6fOXMGALB371507tzZ7rF6vR5ykRUlHQ3wAMDlixcRwHvu+1AXePv5wrhAt4K3ZLqpfWtTtDRq6NVlglGEviBXsA+/HJ9cwipQqU3a4FqjaHQxL5TMC9rIZDKL1UGvNopCoyunINNpwZQKs4tQViKY0aWwumqp7XGRxfQrhgFnnrlkIzMrOzAUAQ6smGr1+Jwc02NNbjZSPnkdIQOGIajXE1VukxDiPJJ/uydMmICwsDCsWbMGO3bsAAA0btwY8fHx6M8rakbI/e7AgQP466+/0KNHD3h5eVkN7gAwBHd45DonBgsYBqxMBhnLQldeLlrwT2Yn2MEvmqxydbW/spWLGxI7DwAABwI8hkGH5rdNqNL9Iqnxn4o7dC6Xz1XlbFZRBg8hhJB7HT/AI5Ver4dSa3mzSObiavHcvDaOkfmNnsAe/aAKrFwa3bLmDcBYCUawGg30arXgy8ztyFjBPp4hobgZ1hQcI0Mnb5+KrfanpV1v0AoNGzaEn5+fsC+88ZtcLgfHm4aV3KYX8vxDUP/aWchZPVQ5wrEfW1IsWIXUWoAnKyIaYacOItc/xGr//ot5GK1PHjD0CSIZPFYCPP/FPIw8/xC0sBI0c4RGV/mery2ZjYIT/6LgxL8U4CGklqjSb3f//v0pmEPuaWq1YelN63dPhDQaDTIyMhAeHm5Kjf3rr78E/y/FrV83ST7GKoYxDBhYFqzVDB7b+AMDV3cP2IuqmN8pstk9e/O87QSTJFfSsbMMqpFG6QKVVm13P5ZhIOM46KtQg4djWfHlTAkhhJC7oNoBHo3l56bcrbJujKZJK/T8bisSHm5msZ9OoYSMN0bxahEDn5iHwMjlaPHp11D6i08Pt1Y7R69WCwoBF3oHoOPTzwr2cXFxQfvPFkMmkzk85gOAqW+8AW9vb4vtHG8VMJlMZqgvWCE3MBQNGzYE+5cMcpGMZU3nPuB+/sH03Foh5NtN2+CWqzeKvfwwwEr/soPrVz5hGItl0a3VVsoOro+OHTtCpqx6nUJGJkPhyaNQr1oEzc2rVW6HEFIzqvTNo7CwED/99BPmz5+P/Px8AIY0z9u3bzuzb4TUiIyMDHzxxRdYtGgRtA5mZfz000/4/vvvceLECaf2xb1RpFPaMU7T0qvLqxTgqVe/cqDQtHkLuxk89qZw8YndjTN3tlVnXG4cI/paUJDjK3wAhuVaxeT61xU8V7t6ONSeMbtJL5KWbkvKnPeQ+HQXaAutZ3YRQgghd5K1AM/MmTPxyy+/2DyWZVmoRDJ4+AEejpFZzbhR6LSQ6w3jLr2rB2K++ck0Rgh8uB98WrcTPU5s1SgA0GvV4HjjOIWbBxo3bmyxX0REBBo1amTlXVliGQZeXl7iL5rd7OEXWY6Li8MzzzwjyNIBgALvQBzoORxlXn6AA0Gm0LAwFPoGgbWRZdOzZ8/KPkDsxpt4AOfll19Gnz59rE57U7u4iW4XkMlw+tXh0Cf/C132Lfv7E0LuKMkBnvPnz6Nv375Yvnw5VqxYgaKiIgCG+bvz5s1zegcJcbZLly6BZVkUFhaaApRGCQkJ+Pbbb1FaWgoASEtLw/r163Hp0iUAwLFjhpWvpKw6YUujKe9J2l/j6SO6nasYIOk1GtEpWvbUC6ssIuju5WUqRCgm178u8ho0Nz2/EW55l07AgQDPrXpNcDVCPMCjtNEXMYyVWgD5fnWRybvjxTpYkNCY3cRKnFZ369cfocnJROae7ZKOI4QQQmqKcdwuJikpCVeuXMFff/0FnchNDUMGjyHAw5+WxQ/wwE7Wbkzyn4a2fPwdugEEWF9FS//renAaXsDJ0cVe7AzhOEZmfZlx8wAPL4PniSeegIeHh0WAJzWyLVi5HEql0qHixo8//jg6deqEyZMnW92nW7dupseMWJFl1vJNHm3fHwEBAWAYxuo1TW77qN3+2fsZE0LuLskBntmzZ+Ppp5/Gnj17BMVlu3fvbvryS0htxs80+/XXXwXFh/fv349bt26ZVpRYu3YtUlIqa8y4VixpaS3zxyfvNlr997fDfVH5BdjfqQIHQCdyZ4XjOKDiTkx+dmaVMnj46c9ylQpyK3eYrjaMQnJcHwTWrZwXntK8g2CfC2bPq7MUp6EBaUV45Fb2ZxkZzkR3Nz1XgsWlyLZ222N5wTO+0mupOD/rdZSlXbN5vK3i1oQQQsidZG+K1po1a3DgwAHs2bPH4jX+FC2Vf5Bpu2fTVqbH1rJ3LNqS8NluLRgBjoMqpXJVLn3brg62WBn8uNjsIcvz2RhHMWY3e8yLLAOVN92MWJkckZGR6Nq1K+BAgMfd3R19+vRBcLCDGcwMI3IzTRjgyQyuD1Xjyhty1rKinho23P7pHAzMEULuDskBnlOnTmHo0KEW2+vUqYOsrCyndIqQmnTrVmU66Y0bNzBnzhzMnDkTp06dMm03FkxWq4VzzY0BHrE7WwAQd2w36ty+6nBflL7+Du/LMYzVonnGKVrHjxwxLEcuET/Ao1Aqra40wVQMGMLCwvDss89iwoQJgtfzfYOQZp7Rc4cHAspS8buTDSIiBCnMMkYGndz+QEtfUdOHn8GT9uMqHB/eB1l7fkbqos/BsSyKU86Z7uTxM7xu3b4NjmWRn3QEuhLrd04JIYSQmsZfJt2Wo0ePWtyg0Ov1pilaLK8WT3CfykoxYvVy4tbvtdimk5AIbSur2Fi0+WZ4M3SfNNXxRis0GWmZJcPYSPHJbma4MZQVVJH5LFJrh5MJgydPDBiA559/Hu7u7mAkZiU7hGEss2pEAk+jR482PbZ2Iy+obl3R7cLziX99TPliBjTZmfaPJ4TUKMkBHhcXF9EPhytXrsDf3/Evq4TcDRqNBjm85R35tm7danpskXXBsXAtK8aFCxfAcRx0Oh2UmjKEXzuL0Ovn0fnUAbRN/F1SXxS+/hYFBc3TegWv2brbVRHgkbH6Kk3R4gd05EqV9cLAFWMeDw8PtGjRAiEhIRg4cKDpZa1IgWN7d3rSm7fDsGHD8OSTT4rvIDGDhwupL7pd4eKKadOm8XZk0by1+LQwPmMNHlarxZl3J+LUtBdx+atPTa/nJexD1t5fkDT6CVz63weGffmrhzAy5CTsw6lXnsexYb0lT/UihBBCnMXR2oMAkJeXZ3pcUFCArKws0xQtl7qhptdkvKlRcrMMFbWXL9zrW9bFsQyLWCdzICgS0bMfPD0tl1i3p0ePHrgZZmeqOU9WRDQSOzyO060NGcGyKEMGUL4vL9vGbNwiuGnmJT7VvjoYsWXSzZZo9/L2hodHZe1BfgYPf8q6woEl762N6279shFn3psIwFATMv/4IbBVWKCCEFI9kgM8vXr1wtdffy34gEhPT8e8efPQp08fp3aOEGezNfecT2b2Qdn0/FF0TtiKoNvXkJaWBp1Oh44HtyPy4jE0u5AI11vX4VtgP4PN5/lJaD5zIeLW7UGb736GTKkyBXk8IluC5c9jt+wVGLEMHoYBV/FB7aIuFd+Hv7vIyzLeh7VcpbI6rUqhkKNVq1aIiooybYuJ4QVJOFguKWqjSODhzgOR06gVmjZtirZtxadLSV0lXd28Dc617IQzUcJUbZlCIVwRg+Og8rJcIcOCcYpWSTFyE/Yh/2iCxS4XPnkDgGF1NFarga6kcmDFbl2FrOP/AgC0udkoOn/K4nhCCCHkTrCWgSwmOzsbgKG48pdffok///wTqorMncYvv4eAbo+i1RffQca7uSM3+/LvamVcw4isxmWNWIAnOzBU8FzuQGDCxGwc5PPCSw4fOmDgQGgCQ9D/yacMffPxw4Gew3CiXV/TPuYZQHJeAEzfuQ+yAsNwtlUXx/trj0gGT0FUB7N9hONa/jVleT8zhQN1jKzWJwJQfO4/6NXluDj7PZx6bQSuLF9gtz1CiHNJDvC88847yM3NRefOnaFWqzFixAj06dMHHh4ewrvjhNQSV69exYoVK3D16lWsXLnSoWP0ej04joNnUS6ik/9A2M0LAIDGl5JQXl4ObXk5lFXIxGA8vRHU6wm4N4iAa916AIDWX61DnSeeRcvPloBTuVo91pDBYyV4UxFEaXH2MNzLxINYEyZMQHR0tOhr/ACPQuUCyMX/NNStUwfPPPOMxQDOiAGH559/XrjNRlZSmYc3POzdcZOYwVPAcsgIjUSRlzCjUGbWDgMHAzwVg6L81V85dP6iC2egLswXbMvestr0+L9JQ6AvLXGoLUIIIaS6ioqK8Oeff6KwsFA0g8fHRzyrZOvWrcjIyEBubi4AQKbXQc4aAkTujZqiZfw38O/8CGS8aVneHsIVKt3cxQM8Co3j9enMp43fqtsQrp7Cz2+Fi/XxkyXhWKpPv34OHxkeHo533nkH7doZVvyKiopCYEgoOnTsaNrH/EYbP/jUtWcvXO76JBo/M0JCfx1gNi7zaNsJB3oOq9xgNgaSyfkBnsobcdam6PMxdn52ZbfSkL3XsCJb2vrldtsjhDiXY5XQeDw9PbFhwwYcPnwYZ8+eBcuyaNWqFTp37lwT/SOk2tauXQuWZbF69WrR16dNm4bk5GT8+eefCMy8joDsNJTXqwedToeYpP1wUZeZ9vUoLURZWRlc9I4Fdxq++gGufvWJ6bleZN6ye8MmaPpuPADYCfDA+soPDhTtCw4ORoMGDXCiRQc0P/ev4DX+XGy5UgnGyvxqe6uHeXp4IjAwEBwqM2+spfJmBYUjNDRUMMXL3LGHHkOsg/Gdcy07o8AnEI+374iUazegN7sm5gEeQwaP/VRpVUXAjHOwfk7GscPwb9Pe5j63/9iJek8Mcag9QgghpKo0Gg3mz58PwLCKqFiA58UXX8S5c+ewd69lrZzjx4+blhhX6AzTbTgwkHtU3pxheEEBV/MbJxVjgNZfb0LhqeO4+s0XAGCa6uUI/rSv8y06osvLb+Hm4s+E+7hKCfAI2cpIEcPP8lapVJarXZlNlednxXh5eeGNN96QfE6bRDJ4unbtariL/8eGyn14+HWN+DWDFCqVYAwnys54KO/q5cqugcM/3SMBlkXLOcsR0KWnjSMJIc4gKYNHp9OhZcuWuHjxIjp16oSxY8di/PjxFNwhtYJGo8HevXtx8+ZNAIaUYuN/AhyHhmkpaJR9A9HR0fD29kaLFi0AAK1PHkBoWgq8kxOg1WoFwR2j0otnoC0Rzm12G/CCaJ8CH3lM8Jy194Fu8w6U9SLLUlKT08OaotDbfPWuyn7JVSrAag0ee1URDa9n12kAACjy9BMN8Fxs9hDOtuqCcePGITAw0OJ1o0LfIKuvmSt190Kppy9CQ0Px2muvYdiLo0T7VvmUg4sDGTyqMuvZNmWuHhbbci+ehbqwwGabt3Jy7Z6XEEIIqa7jx4+bHqenp4vu4+LigoiICKvHX7hgyGKWV0zv0isUggAF/7HSz5A9qwwwfH4HdDZ8ofeJaYfwFyaa9lNppUzRqgwgDXz3Y7SMiQVjdkNM4Wq5yuhdYzZWMs8ucmpwx9AiysObAKisp+Ph4YGevXtbPSe/Bg+nqBynyWQysNbGgMb9i20HePJTLwg3VIzDz74zHidffQFXVyy0eTwhpHokZfAoFArUq1fP8gszIbXA4cOHcejQIRw6dAjjxo3D9u3bUVJi+eU87MZ5NL5wFADgXpiBEz+vgL68FC2iKudDu96+AY1GvDBcVupFlHPC6Kjc00t0X4WLC+oOGIZbPxvuoLD2lgznDWLqDRkF37Ydcfa9SYa2GOuFBj18fGA+VGr4v+/hX6curiz6DPXHvCZ4TaOqHAgVevkjhPe5r1Bar8FjN8BT8fqNdr2Qd+kUMoMboIvIX5n0Bi0wdNjzli+YCQkJAefeAPjnd5S5esKt3PrqHwzHwcPDA15eXpDJZKYVrSq7Zh7gAVx9fO32wRbWzQMoN/wbK3f1gGt5CdRp16Apsh3gcXQZWUIIIaQ6SktL7e6jVCqhsPG5ZFxlVF6RvcyJLKhgpPAx1OFr89125CUmIPhRKwsoSCDjBSCUFZk6MrNaMfbqD/KZFyR2NpleWOdI2vQxx7EMAxnHgWvSCt4RzXG2qBj5fnXRw9gP/vs0z+DhB3jMVv3iGBkAG9/1SsXHYsbMn/xfNlg9tPDEYRSeOIyGY1+zug8hpHok/4WbPHky5s2bh/z8/BroDiFVs2fPHhw4cMD0/LvvvkN2djbKyiwzcELSL5kel6aeR0nqeZSnXUfI7soPJBaAxkqdlPRLKTh++JBgm8JVfI65TKFEg3GVtakaNGhg833wU4wbjHkNAd0eNT1ntBp4elieR6lUwj3UcuUohZs7PBpFImr+9/COaiN4jT8MOtahv+CDX6FUVjmDxxhEee7F0WDbP4Lnxo6HTGRe2bvvTUdkZKTNtoCKJT3r1cehLk/jSOenbO7b59FHMXXqVNOAxtXdHX89PNRG3zm4ORDguV6/hdXXOLfKDJ6iUMMqIfrCfKjtFfOWUFySEEIIqSqVyLLlwZnXEZyTDnAcArJugisqsBngMVJUBC7czRZT4POJMawq5RJUF3UffwYyswzjhhPfBAA0mPCGw++BH5owLsMuMwuaaPSOr8vlMeYNqFVuyO012LQtpVk7h4+3R1kuDKopRH4GznCi70gcb9cXkY8NQLeHH0GL4eMxatqbptf5WTsysz4JMnrMMq3ldq6lvFx8fFzmXpEVnX3Lbt9v/74VrJrGQoTUBMm3kdeuXYtr166hW7duqFevHtzNiqdt27bNaZ0jxFGHDx+2+ppMr4NfbgZyA0Lhoi6FV1Ge1X2NGK1GMM2m7ZqdSPoqHtyxf+BTkGm6i2Ukt7ZKhFIJJW9ww9gpzKxwczfdMxHN8hBJ6w0IDETwI/2RZ3bHxFqhvEGDBiEl+Q/T8wkTJ0FTUHlNZDKZ6LQqnVwJTYdeNvtvDB2FhIRgwoQJAID0nNsWe1kbSP7b6Sl0PPyL6blSqURkZCT++ccLSqUSJ+L6oO3xPaLHMuAE7bq6uqJRi5bAAWPXzAM8jM0MniuNWqMsMhoejZog4dwZtD2626KANcOrQeAdWh9IPQWmvBSanEyr7QKArsz+HVVCCCGkusrLhbVuVOWliDp5AABwtWEUGl49jTNvj0Ozr9bbbUteMYZxEalfF/fDbqizbsEzsqXNNsJemITAno/DtV64g+8AghWgZBUFgTnetmsNWqJdr8csDrOm05Dnkd7lYdSpU8e0rdELk3B412+IPJ+IzLoN0M3x3tmUFRQOpZUs7+p66a13UFBQgOBgwxLt3bt3t9hHJ1dCoddCE9pI+AJvjKhz9wRyK8dq5quAmZNbGcvqfQOB0kKH+n7xs7eQc+USWr70tkP7E0IcJznA06tXrxqYO0qIY7RaLRiGEfwbVPPuACg15dApVKb0W4ZhEHnhKELTUpAVGIag7JsWbfq06QBNXg7KrlZm9jDqcvy1+3c0AKBXqODRuBk869ZDEYB6aZcs2lC4e4BlZJCZFdZjFEpBX9lyy4wiPpW7B4xDMWOAR+HpDV2x4QNT5RuAMgjP7+IXAJ+IphZtefv7W2wDgJYtWyKNF5AKCQlBgbs7NnZ9GnqZAt0YBoxZBk9WUBhOt34YHf3Ma/cYqFWucNGUo6xhc4vX9Go1zMNF1v6GeDZuCvZfQ8qxUXh4OMaPHw9fX19s27YNJef+hYfIAEIe1tDiHMOHD8c/Sz80PG9oyBg617IzmqQcw9WO/dDTw/oKXu3e+wxhYWG4ePEiBg1+BnuWBcJ98zeCfWSelYNc16A64ACoivJRtP4b2KKjVbQIIYTcAcZMZg8PD5SUlMA3v/KLfMOrpwEAxedPIW/vLwDHIfj2NdTNSMXZqK7QmU3Fkldk8MjdLOvPuTdsAveGTez2h2EYuIlkHdviHVYfaaGRYGUydK64MePWpDl0f/+GXP8QPLPiJ7hKKLLMMAxCQ4XLrHt7e2Pap/E4evQoOjRsKKl/5vL86sIv7xYyg+tDOWoaXBxYerwqXFxcTMEda7i3vsDFbevRcciLgu0efv441bo7AAbR/Qeg6OtPcDO8ueTA1rnHR6PFb6sAAPLgECC9ssDy6ejuCIqIRJ3tK0SPzVn/LY6e/w/qq5cQ1O9pNJ34ptWFOQghjpMc4HnllVdqoh+EWMVxHJKTk+Hl5YV169YBgOmD+dChQ8jNzYV/dhpC0lNR5/ZVAEBS295oFeCD+i5y3EpLAQCL4I7Cxw/erdqg1RfLwel0ODH2KZRWFIZT6LS4ff0aGgBgK1Jr/VvGoGjHJtE++gbXQZpcAZlOWLfHGMio038w8o8fhn9X2xkwPsF1eAEeQ72dqAWrkTL3fTSa/A5c64Xh4mdvI2z4RLDqcmT8vB6NJr8DpVngJeyFSXCpU8/qeWRmGTQ+Pj54cuQYuLlV1ObhBXhuhjXF5SZtEBAcjK5du4q2l9jxSfgUZCGs/SMWr+W26Q7VtcvwKcy2+d4Bw0oe+35bC7/829Dy6xHVM7yXwYMH488180zbT8T1QYmHD2Qci4kNGou2ebDrILiWl+CpNh0AAO1fegN7du/G88OHC+enm2nZsiX0ej0YhoGLiwv6TJiCrUf/QYNrZ0z7KLx9TY/d69SDvbCNVukCpVYNvZ1AHyGEEGJLaWmpIVjiZru4cElJCfyz0xDB+eAY54a6vC/gfJfnzgB/faPuBzbhj94jTJnDTS4cRf3r5wAATA1NObJGpVLhka9WQyaTQV4RAIgdNhqHvAPQsUs3ScEdW2QymVMWjqk7bSb+W78C7ca9itiOnZzQs6rr+cRTKO/dx+IayeVyjPxiERiGgaurK5LqhqJjY/FxlDXZDVpg5LQ38dcfm+FeVoTgIaNQnHzQ9PqkJSvBsiwOWQnwAED5CUMGfub6ZchcvwzNvlyL4Ha0eA8h1eFwgKesrAxffPEF9u3bB51Oh86dO2PGjBnwt5IlQEhVlJWVQSaTQalU4ujRo1CpVJDJZPjlF8O0HVXFHOK0tLTKgzgOsUn7Be20ObEPAGBrFnD7Lf+YVmZgFArELv0JV3/4FulrvoZbeTHaH/kNgCGDBwCC23fDNSttufn4gpPLAZ34602nfwGOZS0yY8x5NWwC4701Y3DIq0VrtF1ZOW0pZkllkCmoZ3/TY9eoOJSnnEGHzX9DZSXTxqigcz/Ib6TiRv0Wprs1LVvy0qr9K1evutiiI3r16mU1uAMAz4wag6SkJPTu08fitQ6PP4U1Oflwu30DrU7/g5SmD1m9Q+Tu7o7cvs+h6PBeZDZsJRhsAoZpV4XN2sDzxAEU+ASi1ZODERYWBhcXF/hZqQkw8tVpyMzMROOKgUtsbCxiYmKsZhFpVK640LyDRR/d3d0R3jgC4AV4+GnXniGhdgM8Gm8/KHNuUYCHEEKIw65du4bvv/8ezZs3xzPPPIOcnBzs+nQ6oFBi+NzFpqBHQUEBvLwMn0u5yYnYO+cdFPvUQey5fwEATcKbw7sox+Hz9ty3FmeiuqLIO8AU3AEAeQ0VDbaFP50KANzc3NDrmefueD8c0b5nb7Tu3NVpgafqstYPT8/KLOZ27SprEOX5h8AvNwOF3gEo9AlEoXcgvOo3hAurR/Dujab9FB6eUCqVqDtnBYry89GmR0/8w2ufYRjTv01HXZg6AupZi6ErzEdB6gV4hDWAtqQYLUa/TDNICHGQwwGer776Ctu2bcOTTz4JFxcX7NixAx9//DG++uqranVg3bp1WLFiBbKyshAZGYnp06cL/siYS0xMxOzZs5GSkoLg4GCMGzcOw4YNE+yze/duLFy4ENevX0f9+vUxbdo0PProo4J9pJ6X1Lzy8nIs/d8XKFNrAFdXuOZlgWNk8AkJRbsjv8GjOA9yJ63gVqf/YMjNltSUu3ug3sBhSF/ztXB7RQFllzohom0FPNwPnpEtoXRzB3jLqhtXkjCyF9wBAJ+2HR3qv5h2X28Aqy6H3N0yddpcs87dsO1WFrx9LOfRA4B3r6dw8fR/yA0IwYgRI9CoUSPR/YyaNm2Kpk0tp4kBhmyrd999F1lZWdi1axeeePhhm209PXIU/m7YGI926CB+rlEv4W9GhbpxHfEEbwlQa+rVq2fKADKyNkjIDG6AwJdnYGhsG9HX+bzn/YDS44dMMT3fBo1hWW1IiKkTBuTcApebBY7jwGrUd2WgfKelH0mAf9OWcPWr/g2B8rxc3D55DPV7PApWrQar01apvgHHsji5eA7yD+6HKqwhIse9BhmrR/rxRDR9aghcfIW/v0lL50Hh7o7oFyeD4zgwDAN9eTkykxLhVicEMoaBZ1gDyJRKaEuKcX7dd2CUKrQaNRl6dTk4jQYKL+/7doBaWz/LOY7D4sWLsWnTJhQWFiImJgYffvihQ0XeCXEm498N823G/5fJZCgvL8fPa1ah8GgCGg8ajq49e2HnLz+jdPta9Lx2FpoDrlix71eE3byARkW5AICVk0Yiov/TSD1xFH5njyJo0Eh0HToCrqv+BwBonn7FdL7wG+dF++YV1QZFp5NEX2t1OgEFPkGCbXUHDBPdl1SqLcGdqmg16yscXzQbrUZPQWhUDNzc3ODi4oLMq5dxgRfgMY5rY9rG2WwvK7YrgpITRF9Lb9AS9a6dFWy7+uHLpsfGyocJh/9CYPdH4eLjB7mbO/xbxsAr1FDHieM4lKTfRPHtdNRt2wHakiIoXNzAKBS4nXQUCg8PBDS1XRuKkPuJwwGevXv34rPPPsPjjz8OAHjqqacwbNgw6PV6ydFZo507dyI+Ph4fffQR2rZti40bN2L8+PH47bffLL6QAcCNGzcwYcIEDBkyBHPnzsWJEycwc+ZM+Pv7o2/fvgCApKQkTJs2Da+99hp69+6Nffv2YerUqVi/fj1iYmKqdN7aiNXpIFMooCkthcqs0HXOlUtQFxdB6ekF//CGgqUQ9Xo9OM5QjFan0eDGyeOQK5RI2bkVTZ54Bpd/3oSmg56Hb3gDyFUukCsUyE+/iYt/7kVwTBzY0hJc/2cfPMIaotXjT8Pb1xdlZWXQarU4f+gfaLQauDAMvEPqQSaXQ+bqgRsnEtH28QEoLCqCr68vMjMzkfj9N6jXtj3CW7ZGfn4eUg4nIOvSBcSe/gdKs+Jtuf4h8C50/I5TvWdGIn3zGoQ9PwEBPfoCHAevljEovXoJrnVDkZOwD/6dzXNDDFx8LL+ABjUzfCgwDAOfTo+g4PCfptci341H3SeeBQBEjHkFqfM/hmerNvBuEY3Q58Y63Gcj9/qN0WruCii8vCUfy8jlDgV3ACA6OhpeXl6oW7eu6OtxDz2EgjGvoWOjRqbMl+pgGAbBwcEYOXKk3X19fHzw5JPWl1WNbdMWYeGznJY9eDO8Geqmp4J77VN07/YwgoKCrO/MC9LFdOiEnEYR2HvqJGSubujSsDEu2DmXS3AIcBaQ/3cECd0qaxXog0Lg2a0vQrv1gmdIGDKO/IOgFtHQs3rIFEqkHzoAN/9AgGMR3K4TvMJtB9xsUefn4vyGlShMOQv/uM5oOuA5FFxLhat/IDxDwnD75HEUp12HvrQEuuIihPV6DN7hDZGybQPSF84C5+MHec5tsHI5/MdMQ+6Rf8CWlQIlhZCHNYLSwwtFKldkKhlc2/0LvOo3Qu6iWUgF4D32ddTr/DDK83Ihd3FB7oWz8G0YAchkuLDkCzDp16Bo2xl1uz+KJn2fwumN38OzXhgCmjTH8fj34NG4GYovnoHq7HFc570njbcflEX50LcyfLFntVpwWelgOA6yBk3gEhIO74YRKEi9gAZ9noLS0wsXJxlWT5ED0Kddwfkjlb/X/373P+giWkLu4w9OowZ3LQXKAsPfoMT0myj7/SfIWPFVRnQqVyg0lUVNE1ZUTinUNGiKbst+wuW9vyEr8R9Ar4fmdjqavfQ2IL93vwzU5s/y5cuXY9WqVZg9ezYaNmyIpUuXYvTo0di1a5fg7jVxHm1FUV+ZUikYH5aVFMPV3cMU5BALeFQHx3EoyLwNr4BAyBUKaMpKUXArHQENGoPVag2fkQoFOI5D9tXLuLB9I7wbRcIrNBwqb19oiovg4uWNU9/Mg0+bDtBq1Mj6ey9kMhl8rpyFvlNvtJvyNnzD6qMs6zYK0q5D6eqGtL/3QhlUF3VjH4J7QBBOfjsfJQn74PvEswh9uC+S58+C26kj0KtcIevwMDxbxiKkR18kLvsSLol/mmrKFfnXhZ7Vo15+FuoBwGf/Ys/XwQjMryzar9KWo8U54aISzc/9C5z7F6YKeEs/wV/HE6C0ca1kbu5g5HLoiw1fn2O+3gRtfi6ODBC/yeRTkGV6HPPtFni3ipX2wyH3lIjYtohY8aPF9oBw4Uqw8s4iN9lenAr92kXQPzXCtGnAwpUoyMvFv0vnI7BdZ2RsXAH/1FMAgOfW/Yri4mJk3b6No3PeR8jZRPFOnUtC9rnKIOR1ALro9vCLfQg5u3+GKtNQhuFMUAgU2bfBurhC0bYLcGgvtC5u6L7zGDJPJ0OmcoGbrx9Unl5QuLpB4epm+jukLSvFlb2/QVOYj/BH+kGuUMDdPxAypRKsXg+wLHTlZVB5eaMk8xbKC/Lh1zACJdmZuPbH7/AKbwiVtw/cAoLAaTQ4/d1CaHOzUa//IMhd3NDwkb5SfgyEVBnDcXbWPa4QFRWF/fv3C1IkW7dujd27dyMkRDyzwZ4hQ4agZcuWmDlzpmnbY489ht69e+ONNyyXT5w7dy7++OMP/P7776ZtH374IS5cuIBNmwzTVqZOnYri4mJ89913pn3Gjh0LHx8fzJ8/v0rnrWk3TiTi3HuToCorgrZZLBhGBi7tKjiVCigrhVtxPliZHKxcDnVQKJjiArgX5kIvV5gK3qndveEiUniWZRiUB9aDojAXqorsEp1cCU1QCGSFeXAttb2cs06hhMLOyk/GCv13W8vZ38KrVRuo/ALAatRglKoqDR4LTh5F5q5tcAtvCFajQcjTw6GsyMbR5GSh6PwpKDy9oAqsIygUyLEsii+chkfjZpDVUEG96tDr9UhOTkZsbGyVg7L3o4KCAmTcvIFmLVtZ/Hsxv2Z56Tdx9MXHwbWKQ98vV1q0tfOZnnDJvQ2VphwapQsKY7vCq1ETlCUdhmvDSNRt1wn58W9aHCeVplFzIC8b0KjBubhClZcFjU8AOJULZMUFYN29AA8vuF431J/SurhBqS4DB8bu6hi1kVblCqWm3P6O9wCWERYRNyrs1Af9Zi++J383a+tnOcdx6NatG0aOHGla1U+j0aBz58548803MXToUOdeCDvUJSU4uvR/yM/MhIeLCqy6HGxJMVzDG0JXVABdThYUfgFQevtCm58LdcYNyFUuAKsHo1DCtVEkwHEov3EVnFYDmbsnwBq+dCj8g6ArzAPAQKZUgQMH3a2bkHn5ACwLyGTgGBm40mK4NIiAuiAfMnU5GBdXw114mQxyd09oMq4bbiC5uILT66EMCIZrnXoounAauHoRbiWG1SVZhoG6XkPI64ZBf+Ui3CpW4GEZGTgGkLMs9DI5GHDQunmCAwPXimP1MsO/cU4mg8bTB+752SgOCAHcPYC8bHA+/mDKSg1ThfOyoCguBCeXg/PyBVNSBL3KFYxeB/gGAJpyyMrLwAGQlxbBRW14rHH1gFJdChnHQe3uBUV5KeSsHuV164MtKoB7SYHIT8jBn2OzWChSTkFuJchbG7k3bobGU97F2RkvgS0vg3dse7gE1kHWvl/hUjcU7Tf/DY7jTDcegvsORMmVFJRcPCNoh39T635C4yPHnT/6L9IvpyIgvAG0Kle0adNGcM1YlsWt9DTUrRdqtc7hqe2bkP+/6dDL5Hj474uC1/7pGlGj/TfHgYHWzQMynRZyrRrm3xpYhoFe5Qql2jlT6wsbNke/1b/QvzNSoxzO4NHr9VAqhfcD5HI5dDorRUfs0Gg0OHPmjGnQZdSlSxckJYmniSYnJ6NLly6Cbd26dcOWLVug1WqhVCqRnJyMUaNGWeyzevXqKp/XGpZl4WB8zKYL3y8xDZrk506I7iNj9ZCxeih4qbbG4A4A0eAOAMg4Du5ZaYJtCr0WilvXRfc3Zy+4Y2zvTmgydyUCY9sj92QiLs6cBvngMYgbMR5sfg7AMFAFGFYS0Ov1gFwBrorTuTyj2sIzqq1gm15vGMjJff3h27GHxXYj96atwIlsrw2MfaqNfbubPD09Edm8BViRfy/m18y7Tgi6/3oECoVC9Dp2WbHt/+3deVhU59k/8O+ZjUWQHRdEXAgkAsoSNIq+bo27BqNpNXGpNlHb2FQTNWpjqoihsVHzizYNtG82k5hYTEzT6GujVqPgGheUuOKGAsrAACPbbM/vD+TIyKCMIDD6/VyXl8w5z5zznJuZuQ/3nOc5qCi9CWNZGcyGSnQIfaJWm6vBj+P07Im1brluVGugMhpqnVzYorlY4zL7W0Vap+IaV7mVlwIFt2egqj4xqVncsUgSzCoN1Mbbd6G7X+Ve/jB7+wFGI9yunL33E+x0t+JOeavWMLfvBLdzGTA4uUBIEsxBoVBdOAWNsep5Fa7uNovZxaFR0Fw+C5eKqtmTyjz9ELrs/yHr6y9g0l6HMJuBijK4XD5b6w55tui6hMOlTXuYr1+FpbU31KePwvnWtku828K1WAuVuXbONGhcIJxdG+292ZQnji05l1+9ehX5+flW84dpNBrExsbi6NGj9S7wNFauP/XdPyE2f4paA2OPp0OF2ydk4tbPd56giZ9/AgDU9fXBndPe1nkFR1Ym6pqW19Z1ZCagVnuFEHC5dhG4dvGO5RZUf8xUF0CcSq3PT+TCiMUMVVHV5PtuBblA9UfYzaKq/wvvmEXvzvdwse2J+yUAThW3Z0RzqvE853qe+9Qkbm1T3t6ZY/f93JqMzq5Q35pXsCb3Ec+hIicbxmP75WWqp8dCkZcNw4nDEJKEwHmJaNfvF8j4NAWVqf8L9ByAgGHxyH57IUwqDSytveCcexkI7oae762HqlXVMNbwtV+gYNf/oc2YCVCo1VD7+KHd+F/Lnz1+T4+B/vQJdJ67FEoXVxSm/xenF80EAHj2+h94DxzxUJ5D8Pyo/h6LjsVj0bEwm804ceKEzZi1adceQog64/nEqHE4XmlAm4ioWm1cZi1G/rZvELV0DTRePsg+sBcKSUJR4hwAVedKCrMZRld3KA0VEACMAV2gCAmD2LcDon0QWp2uygF15f6aJAhoym/WuV4hBBT3Wdyx9SW5VHbTIXM9OZZ6F3iEEFi4cCE0NWbONxgMWLp0qdUM/uvWravX9nQ6HcxmM3x8rCeD9fX1RX5+vs3naLVa+Pr6Wi3z8fGByWSCTqeDv78/tFptrW36+PjI27yf/dbl5MmTMBobXtxQxf0Ceu11SIZKmD28AUmC5NsGSo0zjNo8SJ4+UHr6wFKqB4QAnF2AslKofP0BISAsFph+PgpNziWY1U6AUgnE9INbRDRKfvgWKCqAcHaFVFEO0coNMJsBtQYwGqBsF1g1N0SbdpDUTjCcOASFf/uq23srFXBu3xHqNgFVQ4YqK6Dw8AKUKhiu56D02AEYr16Ce9RTgEsrqL19oFCpofDwQsG/voS5MB8wmyC5uMJSVAB1UQFMPm2gycuGycsPQqWCkBRQmIwQ7h5QuHnAUlIIhVIFZecQuHXoBFXbDig/dRwKT2/kaNyQ8/PPgNoNrol/l38HsuycBv8uHgUnTpxo7i44nPuNmfbYMZvLff/8vygvLkJR+k6Iwnw4PREJn+heKMz4CYbzP8Nj4AiU5V6DwskZKpUKZedPwTUiBobrOag4lQFFSSEUGieIogLAZIRUUQ5L58cBXT7g7ApoNJDyrkJdpEWlT1soHgsDzmVCUaaHOaQ7PH4xGq63rkorOnUCwmKGKCmC4dCPkCQJ0hNRsGjz4DFoFIr/bxNQUQannv3hE/0USs6fRunurVAEdoHl+jX4jJ0Mnxpz4FgsFpTnXUPJj/+ButNjMF7JglNoODwjYlCUeQyGKxdg0eZBKiuFIrAzLDot4OQChYcXLMU6+I2ZgMI922HRFwFKVdWVB/piOIVGoPLsScBsgjKwCxRKFdzCo+Hp5g7lrSGnzipVVf9vXYlVXpAPc6ke3h27oOx6LtStPWAqL8fNU8fh2rEL2gV2svq9uKLqb0zPZ62HEhpu6lGpvQ6XgCDoDu6B2tMbbqHhMBbrICmUsJhNkBRKBHjXPbm5663+lJ7NhKW8FE6BnVGZfQnecYPh6uQETzTeezMm5u7zITSmlpzLq/+31SYnp/75orFyvSmgC0pi+gN5V6vyuIsrAAlScSGEqxvg4Q1RooNUWQFIgEVSQlmmh9A4Qbh5QKHNA5RKmP0DIDm7AiYDUFQAyVAJoXEGnJwBo6HqcWvPqu16+gJOTpCEgKgoB9QaSDdyIDx9oHBpBYvFXHVeYTJBKtEBZiNgsUB4+1edJ5TqId0sAbx8AUMFUFkBIUmAQgnJUAlIgHB2haKiHBAWWNy9oPBtA0mhgNLHDxZ9CSxGI5T+7aAKCIJUqkdZxkFIJhPcYvuh9PghiOwsCGcXKLz8YJYkKLR5kIwGQOMEqUQHZXkpDAGdIXn5Qu3fHjBUwKC9UdW31l7QBHUFDBXQtAmApbIChoIb0LQNAFQamG+WoPLCGajbB8JcrIO5rBQouwm3Hj0hubaCwtUNkiRB5eUDQ84VVFw6D5WTMwza6/B6+hkoawwpq9QVQPf1p8DNEghnFyjDYiCp1fCK6YMKXSEqzmXCVHADHnGDUXbtCpxcW0Fyaw3XNu1gMZuh/eFbSGoNWj3eHWWnM+AVNxgCQHlONpy9faHb9g2cw6NhDouCSggob5agolAL47XLcO7ZD5JCCafL54DWXij08UfhxcuQ+vwCysAu0AQFQydJcFv5qfx6s1y/BsnLDyfPZVm/EHsOhi7v1nuzzzAU5eQBObeKaaMmQRopcOLMrUK9qxdc3q26qs4gSThx+l4DkR0bz4/sd98xC+6G6+WVuH7HeZL0RCT8n4jEtaISoKgE8K+6a64q8R+wGCrh4XOXYfQjngMAmAyVMN68CW9vH1jMZhQd2gvDtcsQJiMkdw+4hoRDUqlgrqiAWV8Ms74YMBmh6RwC907BKPjxP3BuHwjJ1Q3CbELZySMQxYWAyQinqN6o3LcT8GkDt6cGwHD9Goz5efD+n6FVV0I6OaPocDrUvm3g1qkrFEolTEYj9CePojL7AnyHPOOQuZ4cS70LPGPHjq21bMyYMQ3ugK0J5+42rKauCepqLq/PNu3dry3h4eGN8q2eOSICJ4KfQERERONXY0fFN+72ZFHAsJF1r47t2Xi76l/71tvV3xw8kJg9pBgz+z3wmPUfYP04MrLx93Ev0TWvVnu19vphI6wfR0YC4+u+8sFsNuOEQoGnVrxbO2bR0bafdKcHclvZyNs/Dhp8/5uJjW1YNwY/XWvRw/DebMm5vK591Vdj5XoAMPd6yuF/1w32wq9v/zxhcp3NqjXd+6Met2YeeJfPjqdr30XSSs0/xkbWPH++9Xk3wPbcgFZsfYba+CPPbDbjBPBov87s8DB8Bjc1h4qZvYWQO/P8s3cMS/y19ZWj9dpfbKxjxYwcWr0LPElJSY26Yy8vLyiVSmi11pfYFhQU1Ppmr5qtbwQLCwuhUqng6ekpt7lzm4WFhfI272e/dalrbOn9UiqVfMPbiTGzH2NmP8bMfoyZ/RwxZi05l1dPmK7VauHv71+vvtnS2LkecMzfdXNjzOzHmNmH8bIfY2Y/xowetMY/a6knjUaDsLAwpKWlWS1PT09HVJTtWxRHRkYiPT3datnevXsRHh4uzw8UGRlZa5t79+6Vt3k/+yUiIqLaWnIu79ChA/z8/KzaGAwGHDp0iPmeiIiIHkrNVuABgGnTpiE1NRWpqanIysrCW2+9hdzcXHniw1WrVmHBggVy+wkTJiAnJwdJSUnIyspCamoqNm3ahOnTp8ttpkyZgrS0NKSkpCArKwspKSnYt28fpk6dWu/9EhERUf201FwuSRKmTJmC5ORk/PDDDzh79iwWLVoEZ2dnjBo1qomiQ0RERNR06j1E60EYMWIEdDod3n//fdy4cQMhISFISUlBQEDVhFr5+fnIzc2V2wcGBiIlJQVJSUn4/PPP4e/vjz/+8Y8YOnSo3CY6OhqrV6/Gu+++i/feew+BgYFYs2YNevToUe/9EhERUf205Fz+0ksvobKyEsuWLUNxcTF69OiBDz/8EG5ubk0QGSIiIqKmJYnGmjmQ7pvZbMaxY8cQGRnJMZn1xJjZjzGzH2NmP8bMfozZo4O/a/sxZvZjzOzDeNmPMbMfY0ZNhQUeIiIiIiIiIiIH16xz8BARERERERERUcOxwENERERERERE5OBY4CEiIiIiIiIicnAs8BAREREREREROTgWeIiIiIiIiIiIHBwLPEREREREREREDo4FHiIiIiIiIiIiB8cCDxERERERERGRg2OBh4iIiIiIiIjIwbHAQ0RERERERETk4FjgaQE+//xzDBo0CBEREXj22Wdx+PDh5u5Ss0hOTsa4ceMQFRWF3r1743e/+x0uXLhg1UYIgbVr16Jv377o3r07Jk+ejHPnzlm1MRgMWL58OXr16oXIyEjMmjULeXl5TXkozSY5ORmhoaFYsWKFvIwxq+369euYN28eevXqhR49euCZZ57ByZMn5fWMmTWTyYQ1a9Zg0KBB6N69OwYPHox169bBYrHIbR71mB06dAizZs1C3759ERoaiu3bt1utb6z4FBcXY/78+YiJiUFMTAzmz5+PkpKSB3581HDM9VWY6xuOub5+mOvtw1x/b8z15BAENavvv/9ehIWFiY0bN4rz58+LxMREERkZKa5du9bcXWty06dPF5s2bRJnz54Vp06dEjNmzBADBgwQpaWlcpvk5GQRFRUltm3bJs6cOSPmzJkj4uLihF6vl9u8+eabol+/fiItLU1kZmaKyZMnizFjxgiTydQch9Vkjh8/LgYOHChGjx4tEhMT5eWMmbWioiIxcOBAsXDhQnH8+HGRnZ0t0tPTxeXLl+U2jJm1999/X/Ts2VP897//FdnZ2WLr1q0iMjJSfPzxx3KbRz1mu3btEqtXrxbbtm0TISEh4ocffrBa31jx+c1vfiNGjRoljhw5Io4cOSJGjRolZs6c2WTHSfeHuf425vqGYa6vH+Z6+zHX3xtzPTkCFnia2fjx48Wbb75ptWzYsGHinXfeaaYetRwFBQUiJCREHDx4UAghhMViEXFxcSI5OVluU1lZKWJiYsSGDRuEEEKUlJSIsLAw8f3338tt8vLyxOOPPy5+/PHHpj2AJnTz5k0xZMgQkZaWJiZNmiSf9DFmtf3lL38REydOrHM9Y1bbjBkzxKJFi6yWzZ49W8ybN08IwZjd6c6TvsaKz/nz50VISIg4duyY3Obo0aMiJCREZGVlPejDogZgrq8bc339MdfXH3O9/Zjr7cNcTy0Vh2g1I4PBgMzMTPTt29dqeVxcHI4ePdpMvWo59Ho9AMDDwwMAcPXqVeTn51vFS6PRIDY2Vo7XyZMnYTQaERcXJ7dp06YNHnvssYc6pgkJCejfvz/69OljtZwxq23nzp0IDw/HK6+8gt69eyM+Ph4bN26U1zNmtcXExGD//v24ePEiAOD06dP46aef0L9/fwCM2b00VnyOHj0Kd3d39OjRQ24TGRkJd3f3hz6Gjoy5/u6Y6+uPub7+mOvtx1zfMMz11FKomrsDjzKdTgez2QwfHx+r5b6+vsjPz2+mXrUMQggkJSUhJiYGISEhACDHxFa8cnJyAABarRZqtVo+UazZRqvVNkHPm97333+Pn3/+GampqbXWMWa1ZWdnY8OGDZg2bRpmzZqFjIwMJCYmQqPRID4+njGz4aWXXoJer8fw4cOhVCphNpsxd+5cjBo1CgBfZ/fSWPHRarW1tlG93Yc9ho6Mub5uzPX1x1xvH+Z6+zHXNwxzPbUULPC0AJIkWT0WQtRa9qhJSEjA2bNn8cUXX9RaZyte91KfNo4oNzcXK1aswIcffggnJ6c62zFmtwkhEB4ejldffRUA0K1bN5w/fx4bNmxAfHy83I4xu23Lli3417/+hVWrViE4OBinTp1CUlIS/P39MXbsWLkdY3Z3Dyo+zBmOgbm+Nub6+mGutx9zvf2Y6xsHcz01Nw7RakZeXl5QKpW1qrEFBQXw9fVtpl41v+XLl2Pnzp345JNP0LZtW3m5n58fANw1Xr6+vjAajSguLq6zzcMkMzMTBQUFePbZZ9GtWzd069YNBw8exPr169GtWzf5mBmz2/z8/NC1a1erZV26dJG/XeHrrLaVK1dixowZGDlyJEJDQxEfH4+pU6ciOTkZAGN2L40VH19fXxQUFNTafmFhoc1v+6hlYK63jbm+/pjr7cdcbz/m+oZhrqeWggWeZqTRaBAWFoa0tDSr5enp6YiKimqmXjUfIQQSEhLwn//8B5988gkCAwOt1nfo0AF+fn5W8TIYDDh06JAcr/DwcKjVaqs2N27cwLlz5x7KmD711FP47rvvsHnzZvlfeHg4Ro8ejc2bNyMwMJAxu0N0dLQ8vrzapUuXEBAQAICvM1sqKipqfWukVCrlb5wYs7trrPhERUVBr9cjIyNDbnP8+HHo9fqHPoaOjLneGnO9/Zjr7cdcbz/m+oZhrqeWgkO0mtm0adOwYMEChIeHIyoqCl999RVyc3MxYcKE5u5ak1u2bBn+/e9/4/3330erVq3ksazu7u5wdnaGJEmYMmUKkpOT0alTJwQFBSE5ORnOzs7y+GB3d3eMGzcOb7/9Nry8vODh4YG3334bISEhtSYlfBi4ubnJ8xZUc3V1haenp7ycMbM2depUTJw4ER988AGGDx+OjIwMbNy4EQkJCQDA15kNAwcOxAcffID27dvLl21/9NFHGDduHADGDABKS0tx5coV+fHVq1dx6tQpeHh4oH379o0Sn65du6Jfv35444035NfrkiVLMHDgQHTp0qXpD5rqjbn+NuZ6+zHX24+53n7M9ffGXE8O4cHfqIvu5bPPPhMDBw4UYWFhYuzYsfKtQh81ISEhNv9t2rRJbmOxWMR7770n4uLiRHh4uHjhhRfEmTNnrLZTUVEhEhISRM+ePUX37t3FzJkzRU5OTlMfTrOpeetUIRgzW3bu3ClGjRolwsPDxbBhw8RXX31ltZ4xs6bX60ViYqIYMGCAiIiIEIMHDxarV68WlZWVcptHPWb79++3+fn1+uuvCyEaLz46nU689tprIioqSkRFRYnXXntNFBcXN9lx0v1jrq/CXN84mOvvjbnePsz198ZcT45AEuIRmvWKiIiIiIiIiOghxDl4iIiIiIiIiIgcHAs8REREREREREQOjgUeIiIiIiIiIiIHxwIPEREREREREZGDY4GHiIiIiIiIiMjBscBDREREREREROTgWOAhIiIiIiIiInJwLPAQERERERERETk4FniI6IGbPHkyVqxY0dzdsOnAgQMIDQ1FSUnJXdsNGjQIH3/8cdN0ioiIiIiIyE6SEEI0dyeIqG75+flITk7G7t27kZeXB3d3dwQFBWHMmDGIj4+Hi4tLc3fxnoqKiqBSqeDm5nZfz588eTIOHjwIAFCr1QgICMDYsWPx0ksvQalUNqhvBoMBxcXF8PX1hSRJ+Prrr/HWW2/h8OHDVu0KCwvh4uLiEPEmIiIiIqJHj6q5O0BEdcvOzsbEiRPh7u6OuXPnIjQ0FCaTCZcuXcKmTZvg7++PwYMHN3c378nT07PB2/jlL3+JV155BZWVldi1axcSExOhUCgwY8aMBm1Xo9HAz8/vnu28vb0btB8iIiIiIqIHiUO0iFqwpUuXQqlUYtOmTRgxYgS6du2K0NBQDB06FCkpKRg0aJDcVq/XY8mSJejduzeio6MxZcoUnD59Wl6/du1aPPPMM9i8eTMGDRqEmJgYzJ07Fzdv3pTbGAwGJCYmonfv3oiIiMDEiRORkZEhr68ezrRnzx7Ex8eje/fumDJlCgoKCrB7924MHz4c0dHRePXVV1FeXi4/784hWgaDAStXrkT//v0RHh6OIUOG4J///OddY+Hs7Aw/Pz906NABkyZNQu/evbFjxw4AQHFxMRYsWIDY2Fj06NEDL774Ii5duiQ/99q1a5g1axZiY2MRGRmJkSNHYvfu3VbHVFJSggMHDmDRokXQ6/UIDQ1FaGgo1q5dC6D2EK2cnBz89re/RVRUFKKjo/GHP/wBWq3WrngTERERERE1FhZ4iFoonU6HtLQ0vPDCC3B1dbXZRpIkAIAQAjNmzEB+fj5SUlLw9ddfIywsDFOnTkVRUZHc/sqVK9ixYwc++OADJCcn49ChQ/j73/8ur1+5ciW2bduGP//5z/jmm28QFBSEF1980WobALBu3TosWbIEX375JfLy8jBnzhx8+umnWLVqFVJSUpCWlob169fXeWwLFizAli1b8MYbb2Dr1q1YtmwZWrVqZVd8nJycYDQaAQALFy7EyZMn8be//Q1fffWVHI/q9QkJCTAYDPjss8/w3XffYd68eTZjGhUVhcWLF8PNzQ179+7F3r17MX369FrthBB4+eWXUVxcjPXr1+Ojjz5CdnY25s6da9XuXvEmIiIiIiJqLByiRdRCXblyBUIIdO7c2Wp5r169YDAYAADPP/885s+fj/379+Ps2bPYt28fNBoNAOD111/H9u3bsW3bNvzqV78CUFWYSEpKkufCGTNmDPbt24e5c+eirKwMX375JZKSktC/f38AwPLly5GWlobU1FS8+OKLch/mzJmDmJgYAMD48eOxatUqbN++HYGBgQCAoUOH4sCBAzaHT128eBFbt27FRx99hD59+gCA/Lz6sFgscvFl6tSpuHTpEnbu3IkNGzYgOjoaAPDOO+9gwIAB2L59O4YPH46cnBwMHToUoaGhd92fRqOBu7s7JEm667Ct9PR0nDlzBjt27EC7du0AVBXHRo4ciYyMDHTv3h3A3eNNRERERETUmFjgIWrhqq/SqZaamgqLxYJ58+bJhZ7MzEyUlZWhV69eVm0rKipw5coV+XFAQIDVRMf+/v4oKCgAUFVQMhqNcpEEqJrQuHv37sjKyrLabnWhBAB8fHzg4uJiVTTx9fXFiRMnbB7PqVOnoFQqERsbW6/jr7ZhwwakpqbKV+WMGTMGs2fPRnp6OlQqFXr06CG39fLyQufOneV+T5kyBUuXLsXevXvRp08fDBkyBI8//rhd+68pKysLbdu2lYs7ABAcHIzWrVvjwoULcoHnbvEmIiIiIiJqTCzwELVQHTt2hCRJuHDhgtXy6kKKs7OzvMxiscDPz8/msCh3d3f5Z5Wq9lv+zhvp3VlQEkLUWlZzO5Ik1dquJEmwWCw2j6tmv+0xevRozJo1CxqNBv7+/vLds+q6EWDNfj/33HPo27cvdu3ahbS0NKSkpOD111/H5MmT76svtmJiqy/1iTcREREREVFj4Bw8RC2Ul5cX4uLi8Nlnn6GsrOyubcPCwqDVaqFUKhEUFGT1r753f+rYsSPUajV++ukneZnRaMTJkyfRtWvXBh1LTSEhIbBYLDh06JBdz3Nzc0NQUBDatWtndWv04OBgmEwmHD9+XF6m0+lw6dIlq363a9cOEydOxLp16zBt2jRs3LjR5n7UajXMZvNd+xIcHIzc3Fzk5ubKy86fPw+9Xt+osSIiIiIiIqovFniIWrA//elPMJvNGDduHLZs2YKsrCxcuHAB3377LS5cuCAXOvr06YPIyEi8/PLL2LNnD65evYojR45gzZo1dQ6VupOrqysmTpyIlStX4scff8T58+exZMkSVFRUYPz48Y12TB06dMDYsWOxePFibN++HdnZ2Thw4AC2bNlyX9vr1KkTBg8ejCVLluDw4cM4ffo05s+fjzZt2si3kF+xYgX27NmD7OxsZGZmYv/+/XUWYgICAlBWVoZ9+/ahsLDQ6m5g1fr06YPQ0FDMmzcPmZmZyMjIwIIFC9CzZ09ERETc13EQERERERE1BIdoEbVgHTt2xDfffIPk5GSsWrUK169fh1qtRnBwMKZPn47nn38eQNWQqJSUFLz77rtYvHgxdDodfH198eSTT8LX17fe+5s3bx6EEFiwYAFKS0sRHh6Of/zjH/Dw8GjU41q6dClWr16NpUuXoqioCO3bt8fMmTPve3tJSUlYsWIFZs2aBaPRiCeffBIpKSlQq9UAqoawJSQkIC8vD25ubujXrx8WLVpkc1vR0dGYMGEC5syZg6KiIsyePRu///3vrdpIkoS//vWvWL58OSZNmgRJktCvXz8sWbLkvo+BiIiIiIioISTBCSGIiIiIiIiIiBwah2gRERERERERETk4FniIiIiIiIiIiBwcCzxERERERERERA6OBR4iIiIiIiIiIgfHAg8RERERERERkYNjgYeIiIiIiIiIyMGxwENERERERERE5OBY4CEiIiIiIiIicnAs8BAREREREREROTgWeIiIiIiIiIiIHBwLPEREREREREREDu7/A4+En4VuO+5uAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 2))\n", "plt.subplot(121)\n", "plt.title(\"Plus Strand\")\n", "plt.plot(before[0, 0], c='0.5')\n", "plt.plot(after[0, 0], c='#C23B22')\n", "plt.xlabel(\"Genomic Position\")\n", "plt.ylabel(\"Predicted Probability\")\n", "\n", "seaborn.despine(bottom=True, left=True)\n", "\n", "\n", "plt.subplot(122)\n", "plt.title(\"Minus Strand\")\n", "plt.plot(before[0, 1], c='0.5', label=\"Original\")\n", "plt.plot(after[0, 1], c='#C23B22', label=\"Variant\")\n", "seaborn.despine(bottom=True, left=True)\n", "plt.legend(loc=(1.05, 0.5))\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "c101b19f-f0cc-4b14-843f-27872f350985", "metadata": {}, "source": [ "Knocking out the left-most AP-1 motif seems to have the expected change in profile predictions, with the left-most predicted peak being knocked out. The rest of the peaks seem to be pretty similar to before. \n", "\n", "Sometimes, when considering a list of variants, you want to calculate the effect of each one individually and sometimes you want to calculate the influence of all variants incorporated at the same time. Here, you can do this by having multiple variants in your sparse tensor correspond to the same example. As a demonstration, we will now also consider a variant that knocks out binding in the second AP-1 site from the left." ] }, { "cell_type": "code", "execution_count": 14, "id": "7097d231-e62f-4b3c-9c01-0fa23d3c48b0", "metadata": {}, "outputs": [], "source": [ "from tangermeme.variant_effect import substitution_effect\n", "\n", "variants = [\n", " [0, 960, 2],\n", " [0, 1009, 0]\n", "]\n", "\n", "before, after = substitution_effect(model, X[idx:idx+1], variants)\n", "before = torch.softmax(before[0], dim=-1)\n", "after = torch.softmax(after[0], dim=-1)" ] }, { "cell_type": "code", "execution_count": 15, "id": "80a1886b-47ef-4a14-93d5-593b3e533f19", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 2))\n", "plt.subplot(121)\n", "plt.title(\"Plus Strand\")\n", "plt.plot(before[0, 0], c='0.5')\n", "plt.plot(after[0, 0], c='#C23B22')\n", "seaborn.despine(bottom=True, left=True)\n", "\n", "plt.subplot(122)\n", "plt.title(\"Minus Strand\")\n", "plt.plot(before[0, 1], c='0.5')\n", "plt.plot(after[0, 1], c='#C23B22')\n", "seaborn.despine(bottom=True, left=True)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "7cf18af4-4f37-4be2-a5b4-0dcdbeb11a93", "metadata": {}, "source": [ "Again, we get the expected result, with even more peaks from the left hand side being knocked out while the remainder of the predicted signal is less effected.\n", "\n", "With BPNet models there are four different variant effect scores one can consider, based on differences in (1) log count prediction, (2) profile prediction, (3) log count attributions, and (4) profile attributions. Each one may be relevant in different ways, with profile predictions or attribution-based differences potentially being able to pick up on more subtle effects where some binding sites are eliminated but others remain, keeping general predicted binding high." ] }, { "cell_type": "markdown", "id": "1ce73e14-7c5d-4928-ac79-e5c86641f277", "metadata": {}, "source": [ "##### Design\n", "\n", "As a final note, BPNet models can be used for design. Several design methods have been implemented in tangermeme but potentially the most widely used one right now is greedy substitution, where each potential mutation is considered at each round and the one that drives predictions towards the desired goal the most is kept. As a simple demonstration we will try to increase model predictions as much as possible using only ten iterations of this greedy approach. Here, we do not need to specify a specific target for the model to meet; rather, we want model predictions to go as high as possible." ] }, { "cell_type": "code", "execution_count": 16, "id": "471b0577-2a6f-4cf9-8683-e1a967616f9d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0 -- Loss: -0.5266, Improvement: N/A, Idx: N/A, Time (s): 0s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 1 -- Loss: -0.7301, Improvement: 0.2036, Motif Idx: 0, Pos Idx: 1458, Time (s): 0.4441\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.63it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 2 -- Loss: -0.8852, Improvement: 0.1551, Motif Idx: 2, Pos Idx: 472, Time (s): 0.4169\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.72it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 3 -- Loss: -1.022, Improvement: 0.1368, Motif Idx: 0, Pos Idx: 1584, Time (s): 0.4139\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.72it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 4 -- Loss: -1.136, Improvement: 0.1135, Motif Idx: 0, Pos Idx: 704, Time (s): 0.4132\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.75it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 5 -- Loss: -1.361, Improvement: 0.2255, Motif Idx: 3, Pos Idx: 697, Time (s): 0.412\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.75it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 6 -- Loss: -1.422, Improvement: 0.06125, Motif Idx: 3, Pos Idx: 1546, Time (s): 0.4114\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.74it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 7 -- Loss: -1.48, Improvement: 0.0578, Motif Idx: 2, Pos Idx: 951, Time (s): 0.4117\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.66it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 8 -- Loss: -1.533, Improvement: 0.05234, Motif Idx: 2, Pos Idx: 470, Time (s): 0.4152\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.76it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 9 -- Loss: -1.581, Improvement: 0.04863, Motif Idx: 0, Pos Idx: 477, Time (s): 0.4109\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.76it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration 10 -- Loss: -1.63, Improvement: 0.04928, Motif Idx: 2, Pos Idx: 691, Time (s): 0.4106\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from tangermeme.design import greedy_substitution\n", "\n", "X = random_one_hot((1, 4, 2114), random_state=0)\n", "X_hat = greedy_substitution(count_model, X, max_iter=10, verbose=True)" ] }, { "cell_type": "markdown", "id": "23696b4c-e8f1-482a-a226-1612ee182b8b", "metadata": {}, "source": [ "Because the BPNet model is quite small, this design task finishes pretty quickly.\n", "\n", "We can now look at model predictions and confirm that they have increased." ] }, { "cell_type": "code", "execution_count": 17, "id": "33a20e97-d34b-45e2-9a32-3bc99f11d64a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(tensor([[0.5266]]), tensor([[1.6305]]))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict(count_model, X), predict(count_model, X_hat)" ] }, { "cell_type": "markdown", "id": "77dba57f-239d-4523-a341-c1a70399da33", "metadata": {}, "source": [ "Now, we can use attribution methods to identify what sorts of motifs were added in to cause this increase in prediction." ] }, { "cell_type": "code", "execution_count": 18, "id": "b79bcc1c-f17d-456d-87b6-b6675776fd6a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_attr0 = deep_lift_shap(count_model, X)\n", "X_attr1 = deep_lift_shap(count_model, X_hat)\n", "\n", "\n", "s, e = 1425, 1600\n", "\n", "plt.figure(figsize=(10, 3))\n", "plt.subplot(211)\n", "plt.title(\"Original Sequence\")\n", "plot_logo(X_attr0[0, :, s:e])\n", "plt.ylim(-0.01, 0.035)\n", "plt.grid(False)\n", "\n", "plt.subplot(212)\n", "plt.title(\"Edited Sequence\")\n", "plot_logo(X_attr1[0, :, s:e])\n", "plt.ylim(-0.01, 0.035)\n", "plt.grid(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "b0b4a372-50f4-481a-8d7d-f60e3e152c24", "metadata": {}, "source": [ "Looks like we are mostly seeing AP-1 motifs, with one happening by chance on the right-hand side and increasing in affinity after some minor editing, and a second site being added around 120bp in. It looks like there is also a CEBP motif being added in between 20 and 40. Note that because these are attributions, it is not only the case that the motif sequence is there, but that the model is using this sequence to drive model predictions higher. This means that the model we are using seems to recognize CEBP motifs as well as the more classic AP-1 motifs when considering JUN binding." ] }, { "cell_type": "markdown", "id": "d66334be-ec2c-4b95-adca-b59a22c7f58a", "metadata": {}, "source": [ "#### Conclusions\n", "\n", "In this introduction, we described what a BPNet model was, showed how one can use bpnet-lite to load and use these models, and highlighted some of the common operations that one may wish to do with a BPNet model. An important note is that this introduction is not comprehensive: it is meant only to show off a few of the features and get you started with these models. Please see the rest of the documentation for more thorough descriptions of the other operations that one can do with them." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }