From 2e59c9e0d8e897603ad0243b1281adeacafacb0e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 10 Nov 2023 00:07:53 -0700 Subject: [PATCH 1/2] Bump pyarrow from 13.0.0 to 14.0.1 in /requirements (#22) Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1. - [Commits](https://github.com/apache/arrow/compare/go/v13.0.0...apache-arrow-14.0.1) --- updated-dependencies: - dependency-name: pyarrow dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/dev_3.10.txt | 2 +- requirements/dev_3.11.txt | 2 +- requirements/dev_3.9.txt | 2 +- requirements/main_3.10.txt | 2 +- requirements/main_3.11.txt | 2 +- requirements/main_3.9.txt | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/requirements/dev_3.10.txt b/requirements/dev_3.10.txt index 30eed72..603c85b 100644 --- a/requirements/dev_3.10.txt +++ b/requirements/dev_3.10.txt @@ -566,7 +566,7 @@ ptyprocess==0.7.0 # terminado pure-eval==0.2.2 # via stack-data -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via diff --git a/requirements/dev_3.11.txt b/requirements/dev_3.11.txt index 61a0e1b..21d13ab 100644 --- a/requirements/dev_3.11.txt +++ b/requirements/dev_3.11.txt @@ -560,7 +560,7 @@ ptyprocess==0.7.0 # terminado pure-eval==0.2.2 # via stack-data -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via diff --git a/requirements/dev_3.9.txt b/requirements/dev_3.9.txt index 05429de..7559aa8 100644 --- a/requirements/dev_3.9.txt +++ b/requirements/dev_3.9.txt @@ -576,7 +576,7 @@ ptyprocess==0.7.0 # terminado pure-eval==0.2.2 # via stack-data -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via diff --git a/requirements/main_3.10.txt b/requirements/main_3.10.txt index b15014b..7d5d601 100644 --- a/requirements/main_3.10.txt +++ b/requirements/main_3.10.txt @@ -344,7 +344,7 @@ protobuf==4.24.4 # orbax-checkpoint psutil==5.9.6 # via lightning -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via diff --git a/requirements/main_3.11.txt b/requirements/main_3.11.txt index 585b680..d842965 100644 --- a/requirements/main_3.11.txt +++ b/requirements/main_3.11.txt @@ -340,7 +340,7 @@ protobuf==4.24.4 # orbax-checkpoint psutil==5.9.6 # via lightning -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via diff --git a/requirements/main_3.9.txt b/requirements/main_3.9.txt index 64f6d93..94c217a 100644 --- a/requirements/main_3.9.txt +++ b/requirements/main_3.9.txt @@ -348,7 +348,7 @@ protobuf==4.24.4 # orbax-checkpoint psutil==5.9.6 # via lightning -pyarrow==13.0.0 +pyarrow==14.0.1 # via efaar-benchmarking (pyproject.toml) pyasn1==0.5.0 # via From b190fa3532f8aa2aecd2543998495adc87b6b7f4 Mon Sep 17 00:00:00 2001 From: safiyecelik <77417129+safiyecelik@users.noreply.github.com> Date: Fri, 10 Nov 2023 15:55:23 -0700 Subject: [PATCH 2/2] Clean jump notebook (#21) * convert string to Path in reading benchmark data * simple jump notebook * fix perturbation column * separate filtering from data loading * fix median syntax * Bump pyarrow from 13.0.0 to 14.0.1 in /requirements (#22) (#23) Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1. - [Commits](https://github.com/apache/arrow/compare/go/v13.0.0...apache-arrow-14.0.1) --- updated-dependencies: - dependency-name: pyarrow dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * add no scaling option to align_on_controls * update docstring and delete the jump metadata files * convert read_parquet function to lambda function --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- efaar_benchmarking/benchmarking.py | 12 +- efaar_benchmarking/constants.py | 12 +- efaar_benchmarking/data_loading.py | 82 +- efaar_benchmarking/efaar.py | 167 ++- efaar_benchmarking/plotting.py | 1 - notebooks/data/crispr.csv.gz | Bin 64923 -> 0 bytes notebooks/data/plate.csv.gz | Bin 8803 -> 0 bytes notebooks/data/well.csv.gz | Bin 5399263 -> 0 bytes notebooks/jump_map_building.ipynb | 1568 +------------------------ notebooks/replogle_map_building.ipynb | 22 +- 10 files changed, 268 insertions(+), 1596 deletions(-) delete mode 100644 notebooks/data/crispr.csv.gz delete mode 100644 notebooks/data/plate.csv.gz delete mode 100644 notebooks/data/well.csv.gz diff --git a/efaar_benchmarking/benchmarking.py b/efaar_benchmarking/benchmarking.py index 6d3f7ab..6aec832 100644 --- a/efaar_benchmarking/benchmarking.py +++ b/efaar_benchmarking/benchmarking.py @@ -44,7 +44,7 @@ def pert_stats( def benchmark( map_data: Bunch, benchmark_sources: list = cst.BENCHMARK_SOURCES, - pert_label_col: str = cst.PERT_LABEL_COL, + pert_label_col: str = cst.REPLOGLE_PERT_LABEL_COL, recall_thr_pairs: list = cst.RECALL_PERC_THRS, filter_on_pert_prints: bool = False, pert_pval_thr: float = cst.PERT_SIG_PVAL_THR, @@ -78,15 +78,15 @@ def benchmark( """ if not len(benchmark_sources) > 0 and all([src in benchmark_data_dir for src in benchmark_sources]): - AssertionError("Invalid benchmark source(s) provided.") + ValueError("Invalid benchmark source(s) provided.") md = map_data.metadata idx = (md[cst.PERT_SIG_PVAL_COL] <= pert_pval_thr) if filter_on_pert_prints else [True] * len(md) features = map_data.features[idx].set_index(md[idx][pert_label_col]).rename_axis(index=None) del map_data if not len(features) == len(set(features.index)): - AssertionError("Duplicate perturbation labels in the map.") + ValueError("Duplicate perturbation labels in the map.") if not len(features) >= min_req_entity_cnt: - AssertionError("Not enough entities in the map for benchmarking.") + ValueError("Not enough entities in the map for benchmarking.") print(len(features), "perturbations exist in the map.") metrics_lst = [] @@ -98,7 +98,9 @@ def benchmark( random_seed_str = f"{rs1}_{rs2}" null_cossim = generate_null_cossims(features, n_null_samples, rs1, rs2) for s in benchmark_sources: - query_cossim = generate_query_cossims(features, get_benchmark_relationships(benchmark_data_dir, s)) + rels = get_benchmark_relationships(benchmark_data_dir, s) + print(len(rels), "relationships exist in the benchmark source.") + query_cossim = generate_query_cossims(features, rels) if len(query_cossim) > 0: metrics_lst.append( convert_metrics_to_df( diff --git a/efaar_benchmarking/constants.py b/efaar_benchmarking/constants.py index 454606f..951bf09 100644 --- a/efaar_benchmarking/constants.py +++ b/efaar_benchmarking/constants.py @@ -2,12 +2,16 @@ BENCHMARK_DATA_DIR = str(resources.files("efaar_benchmarking").joinpath("benchmark_annotations")) BENCHMARK_SOURCES = ["Reactome", "HuMAP", "CORUM", "SIGNOR", "StringDB"] -PERT_LABEL_COL = "gene" -CONTROL_PERT_LABEL = "non-targeting" -PERT_SIG_PVAL_COL = "gene_pvalue" -PERT_SIG_PVAL_THR = 0.01 RECALL_PERC_THRS = [(0.05, 0.95), (0.1, 0.9)] RANDOM_SEED = 42 RANDOM_COUNT = 3 N_NULL_SAMPLES = 5000 MIN_REQ_ENT_CNT = 20 +PERT_SIG_PVAL_COL = "gene_pvalue" +PERT_SIG_PVAL_THR = 0.01 +CONTROL_PERT_LABEL = "non-targeting" +REPLOGLE_PERT_LABEL_COL = "gene" +REPLOGLE_BATCH_COL = "gem_group" +JUMP_PERT_LABEL_COL = "Metadata_Symbol" +JUMP_PLATE_COL = "Metadata_Plate" +JUMP_BATCH_COL = "Metadata_Batch" diff --git a/efaar_benchmarking/data_loading.py b/efaar_benchmarking/data_loading.py index ee9d9e9..542a0f4 100644 --- a/efaar_benchmarking/data_loading.py +++ b/efaar_benchmarking/data_loading.py @@ -1,17 +1,95 @@ import os +import shutil +import tempfile +import zipfile +from concurrent.futures import ThreadPoolExecutor, as_completed import numpy as np +import pandas as pd import scanpy as sc import wget -def load_replogle(gene_type, data_type, data_path="data/"): +def load_cpg16_crispr(data_path: str = "data/") -> tuple[pd.DataFrame, pd.DataFrame]: """ - Load Replogle et al. 2020 single-cell RNA-seq data for K562 cells. + Load and return the JUMP-CP (cpg0016) CRISPR dataset. + The metadata is downloaded from here: + https://zenodo.org/records/7661296/files/jump-cellpainting/metadata-v0.5.0.zip?download=1 + The cellprofiler features are downloaded from here: + https://cellpainting-gallery.s3.amazonaws.com/index.html#cpg0016-jump/ + We read the metadata first, filter it to CRISPR plates, and download the features for these plates only. + + Parameters: + data_path (str): Path to the directory containing the dataset files. + + Returns: + tuple[pd.DataFrame, pd.DataFrame]: A tuple containing two DataFrames: + - features: A DataFrame containing the CRISPR dataset features. + - metadata: A DataFrame containing the CRISPR dataset metadata. + """ + metadata_source_path = "https://zenodo.org/records/7661296/files/jump-cellpainting/datasets-v0.5.0.zip?download=1" + plate_file_name = "plate.csv.gz" + well_file_name = "well.csv.gz" + crispr_file_name = "crispr.csv.gz" + plate_file_path = os.path.join(data_path, plate_file_name) + well_file_path = os.path.join(data_path, well_file_name) + crispr_file_path = os.path.join(data_path, crispr_file_name) + if not (os.path.exists(plate_file_path) and os.path.exists(well_file_path) and os.path.exists(crispr_file_path)): + path_to_zip_file = data_path + "tmp.zip" + wget.download(metadata_source_path, path_to_zip_file) + with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref: + for name in zip_ref.namelist(): + if name.endswith(plate_file_name) or name.endswith(well_file_name) or name.endswith(crispr_file_name): + with tempfile.TemporaryDirectory() as temp_dir: + zip_ref.extract(name, temp_dir) + shutil.move(os.path.join(temp_dir, name), os.path.join(data_path, os.path.basename(name))) + os.remove(path_to_zip_file) + + plates = pd.read_csv(plate_file_path) + crispr_plates = plates.query("Metadata_PlateType=='CRISPR'") + wells = pd.read_csv(well_file_path) + well_plate = wells.merge(crispr_plates, on=["Metadata_Source", "Metadata_Plate"]) + crispr = pd.read_csv(crispr_file_path) + metadata = well_plate.merge(crispr, on="Metadata_JCP2022") + + cp_feature_source_formatter = ( + "s3://cellpainting-gallery/cpg0016-jump/" + "{Metadata_Source}/workspace/profiles/" + "{Metadata_Batch}/{Metadata_Plate}/{Metadata_Plate}.parquet" + ) + + features_file_path = os.path.join(data_path, "cpg_features.parquet") + if not os.path.exists(features_file_path): + cripsr_plates = metadata[ + ["Metadata_Source", "Metadata_Batch", "Metadata_Plate", "Metadata_PlateType"] + ].drop_duplicates() + features = [] + with ThreadPoolExecutor(max_workers=10) as executer: + future_to_plate = { + executer.submit( + lambda path: pd.read_parquet(path, storage_options={"anon": True}), + cp_feature_source_formatter.format(**row.to_dict()), + ): cp_feature_source_formatter.format(**row.to_dict()) + for _, row in cripsr_plates.iterrows() + } + for future in as_completed(future_to_plate): + features.append(future.result()) + pd.concat(features).to_parquet(features_file_path) + features = pd.read_parquet(features_file_path).dropna(axis=1) + return features, metadata + + +def load_replogle(gene_type: str, data_type: str, data_path: str = "data/") -> sc.AnnData: + """ + Load Replogle et al. 2022 single-cell RNA-seq data for K562 cells published here: + https://pubmed.ncbi.nlm.nih.gov/35688146/ + Four types of K562 data and downloaded using the links at: + plus.figshare.com/articles/dataset/_Mapping_information-rich_genotype-phenotype_landscapes_with_genome-scale_Perturb-seq_Replogle_et_al_2022_processed_Perturb-seq_datasets/20029387 Parameters: gene_type (str): Type of genes to load. Must be either 'essential' or 'genome_wide'. data_type (str): Type of data to load. Must be either 'raw' or 'normalized'. + Normalized means Z-normalized by gemgroup. data_path (str): Path to the directory where the data will be downloaded and saved. Returns: diff --git a/efaar_benchmarking/efaar.py b/efaar_benchmarking/efaar.py index 75bbb3d..9f40ba2 100644 --- a/efaar_benchmarking/efaar.py +++ b/efaar_benchmarking/efaar.py @@ -1,75 +1,139 @@ +from typing import Optional + import numpy as np import pandas as pd import scanpy as sc from scvi.model import SCVI +from sklearn.covariance import EllipticEnvelope +from sklearn.decomposition import PCA +from sklearn.preprocessing import StandardScaler from sklearn.utils import Bunch import efaar_benchmarking.constants as cst -def embed_by_scvi(adata, batch_key="gem_group", n_latent=128, n_hidden=256): +def embed_by_scvi_anndata(adata, batch_col=cst.REPLOGLE_BATCH_COL, n_latent=128, n_hidden=256) -> np.ndarray: """ - Embeds the input AnnData object using scVI. + Embed the input AnnData object using scVI. - Parameters: - adata (anndata.AnnData): The AnnData object to be embedded. - batch_key (str): The batch key in the AnnData object. Default is "gem_group". - n_latent (int): The number of latent dimensions. Default is 128. - n_hidden (int): The number of hidden dimensions. Default is 256. + Args: + adata (anndata.AnnData): The AnnData object to be embedded. + batch_col (str): The batch key in the AnnData object. Default is "gem_group". + n_latent (int): The number of latent dimensions. Default is 128. + n_hidden (int): The number of hidden dimensions. Default is 256. Returns: - None + numpy.ndarray: Embedding of the input data using scVI. """ - SCVI.setup_anndata(adata, batch_key=batch_key) + SCVI.setup_anndata(adata, batch_key=batch_col) vae = SCVI(adata, n_hidden=n_hidden, n_latent=n_latent) vae.train(use_gpu=True) return vae.get_latent_representation() -def embed_by_pca(adata, n_latent=100): +def embed_by_pca_anndata(adata, n_latent=100) -> np.ndarray: """ - Embeds the input data using PCA. + Embed the input data using principal component analysis (PCA). + Note that the data is centered by the `pca` function prior to PCA transformation. - Parameters: - adata (AnnData): Annotated data matrix. - n_latent (int): Number of principal components to use. + Args: + adata (AnnData): Annotated data matrix. + n_latent (int): Number of principal components to use. Returns: - numpy.ndarray: Embedding of the input data using PCA. + numpy.ndarray: Embedding of the input data using PCA. """ sc.pp.pca(adata, n_comps=n_latent) return adata.obsm["X_pca"] -def align_by_centering(embeddings, metadata, control_key=cst.CONTROL_PERT_LABEL, pert_col=cst.PERT_LABEL_COL): +def embed_align_by_pca( + features: np.ndarray, + metadata: pd.DataFrame = None, + variance_or_ncomp=100, + plate_col: Optional[str] = None, +) -> np.ndarray: + """ + Embed the input data using principal component analysis (PCA). + Note that we explicitly center & scale the data by plate before and after calling `PCA`. + Centering and scaling is done by plate if `plate_col` is not None, and on the whole data otherwise. + Note that `PCA` transformer also does mean-centering on the whole data prior to the PCA operation. + Args: + features (np.ndarray): Features to transform + metadata (pd.DataFrame): Metadata. Defaults to None. + variance_or_ncomp (float, optional): Variance or number of components to keep after PCA. + Defaults to 100 (n_components). If between 0 and 1, select the number of components such that + the amount of variance that needs to be explained is greater than the percentage specified. + plate_col (str, optional): Column name for plate metadata. Defaults to None. + Returns: + np.ndarray: Transformed data using PCA. + """ + + def centerscale(features, metadata, plate_col): + if plate_col is None: + features = StandardScaler().fit_transform(features) + else: + if metadata is None: + raise ValueError("metadata must be provided if plate_col is not None") + unq_plates = metadata[plate_col].unique() + for plate in unq_plates: + ind = metadata[plate_col] == plate + features[ind, :] = StandardScaler().fit_transform(features[ind, :]) + return features + + features = centerscale(features, metadata, plate_col) + features = PCA(variance_or_ncomp).fit_transform(features) + features = centerscale(features, metadata, plate_col) + + return features + + +def align_on_controls( + embeddings: np.ndarray, + metadata: pd.DataFrame, + scale: bool = True, + pert_col: str = cst.REPLOGLE_PERT_LABEL_COL, + control_key: str = cst.CONTROL_PERT_LABEL, +) -> np.ndarray: """ - Applies the centerscale method to align embeddings based on the centering perturbations in the metadata. + Center the embeddings by the control perturbation units in the metadata. Args: embeddings (numpy.ndarray): The embeddings to be aligned. metadata (pandas.DataFrame): The metadata containing information about the embeddings. + scale (bool): Whether to scale the embeddings besides centering. Defaults to True. + pert_col (str, optional): The column in the metadata containing perturbation information. + Defaults to cst.REPLOGLE_PERT_LABEL_COL. control_key (str, optional): The key for non-targeting controls in the metadata. Defaults to cst.CONTROL_PERT_LABEL. - pert_col (str, optional): The column in the metadata containing perturbation information. - Defaults to cst.PERT_LABEL_COL. Returns: numpy.ndarray: The aligned embeddings. """ - ntc_idxs = np.where(metadata[pert_col].values == control_key)[0] - ntc_center = embeddings[ntc_idxs].mean(0) - return embeddings - ntc_center - - -def aggregate_by_mean(embeddings, metadata, control_key=cst.CONTROL_PERT_LABEL, pert_col=cst.PERT_LABEL_COL): + ss = StandardScaler() if scale else StandardScaler(with_std=False) + ss.fit(embeddings[metadata[pert_col].values == control_key]) + return ss.transform(embeddings) + + +def aggregate( + embeddings: np.ndarray, + metadata: pd.DataFrame, + pert_col: str = cst.REPLOGLE_PERT_LABEL_COL, + control_key: str = cst.CONTROL_PERT_LABEL, + method="mean", +) -> Bunch[pd.DataFrame, pd.DataFrame]: """ - Applies the mean aggregation to aggregate replicate embeddings for each perturbation. + Apply the mean or median aggregation to replicate embeddings for each perturbation. Args: embeddings (numpy.ndarray): The embeddings to be aggregated. metadata (pandas.DataFrame): The metadata containing information about the embeddings. - control_key (str, optional): The key for non-targeting controls in the metadata. Defaults to "non-targeting". - PERT_COL (str, optional): The column in the metadata containing perturbation information. Defaults to "gene". + pert_col (str, optional): The column in the metadata containing perturbation information. + Defaults to cst.REPLOGLE_PERT_LABEL_COL. + control_key (str, optional): The key for non-targeting controls in the metadata. + Defaults to cst.CONTROL_PERT_LABEL. + method (str, optional): The aggregation method to use. Must be either "mean" or "median". + Defaults to "mean". Returns: Bunch: A named tuple containing two pandas DataFrames: @@ -81,5 +145,50 @@ def aggregate_by_mean(embeddings, metadata, control_key=cst.CONTROL_PERT_LABEL, final_embeddings = np.zeros((len(unique_perts), embeddings.shape[1])) for i, pert in enumerate(unique_perts): idxs = np.where(metadata[pert_col].values == pert)[0] - final_embeddings[i, :] = embeddings[idxs, :].mean(0) + if method == "mean": + final_embeddings[i, :] = np.mean(embeddings[idxs, :], axis=0) + elif method == "median": + final_embeddings[i, :] = np.median(embeddings[idxs, :], axis=0) + else: + raise ValueError(f"Invalid aggregation method: {method}") return Bunch(features=pd.DataFrame(final_embeddings), metadata=pd.DataFrame.from_dict({pert_col: unique_perts})) + + +def filter_cpg16_crispr( + features: pd.DataFrame, + metadata: pd.DataFrame, + filter_by_intensity: bool = True, + filter_by_cell_count: bool = True, + drop_image_cols: bool = True, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """ + Filter the given features and metadata dataframes based on various criteria. + + Args: + features (pd.DataFrame): A dataframe containing the features to filter. + metadata (pd.DataFrame): A dataframe containing the metadata to filter. + filter_by_intensity (bool, optional): Whether to filter by intensity. Defaults to True. + filter_by_cell_count (bool, optional): Whether to filter by cell count. Defaults to True. + drop_image_cols (bool, optional): Whether to drop image columns. Defaults to True. + + Returns: + Tuple[pd.DataFrame, pd.DataFrame]: A tuple containing the filtered features and metadata dataframes. + """ + if filter_by_intensity: + features = features.loc[ + EllipticEnvelope(contamination=0.01, random_state=42).fit_predict( + features[[col for col in features.columns if "ImageQuality_MeanIntensity" in col]] + ) + == 1 + ] + if filter_by_cell_count: + mask = np.full(len(features), True) + for colname in ["Cytoplasm_Number_Object_Number", "Nuclei_Number_Object_Number"]: + mask = mask & (features[colname] >= 50) & (features[colname] <= 350) + features = features.loc[mask] + if drop_image_cols: + features = features.drop(columns=[col for col in features.columns if col.startswith("Image_")]) + + metadata_cols = metadata.columns + merged_data = metadata.merge(features, on=["Metadata_Source", "Metadata_Plate", "Metadata_Well"]) + return merged_data.drop(columns=metadata_cols), merged_data[metadata_cols] diff --git a/efaar_benchmarking/plotting.py b/efaar_benchmarking/plotting.py index ee04776..ea52167 100644 --- a/efaar_benchmarking/plotting.py +++ b/efaar_benchmarking/plotting.py @@ -49,7 +49,6 @@ def plot_recall(df): curr_ax.set_ylabel("Recall Value") curr_ax.set_xticks(range(len(x_values_orig))) curr_ax.set_xticklabels(x_values_orig, rotation=45) - curr_ax.set_ylim(0, 0.8) plt.tight_layout() plt.show() diff --git a/notebooks/data/crispr.csv.gz b/notebooks/data/crispr.csv.gz deleted file mode 100644 index ee54baa5a6f578bdfdd060d5206c12303acee6f5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 64923 zcmV)EK)}BriwFP!0000018n_Ek88QorU}mbxA+HWEppw4y34og7Ls-x_n0KIQQ3(C zy0d^n_MrcL+w)TUt9yZpER`CtFH|N1}w>wo#Rz4xoW|G515 z;kw;?{+Hu_`yc=9zkKfguV0`4zyHtw^Z)pN|3BV;{a@x){IC1Cu0Q_A|M4IH{r~&F z|8M`zQQy%2_>cejU;oel{@?!R|NcMzyYuqVf2{k9|5!h|ej0|=$GRNX=U4A^_!!Dk zr;l|7IvYQRuB#*Ln$Ywy4$C}ztk>J8llh}7@$+uI_1_mhHjYC6v2OLz_j8>CeRWQ{ zkB&U9IYs}`@ui379U0H7sH(^=eJaFI`lT)bpSzQ@;bUCYWo+nvPnnG$UHr)eP5Dh9 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