dataesr/scientific-paragraphs-categorization huggingface dataset:
Predictiveness Curve Molecular docking is a valuable method often used to elucidate a mechanism of action or to predict the nature of interactions established between a ligand and a target protein. It can also be used as a screening tool to filter a database according to docking scores. In a virtual screening protocol using molecular docking, the ranked list of compounds according to the docking scores is generated. Then, a fraction of the top scoring compounds (1%, 5%, 10%. ) is tested experimentally depending on the budget and experimental facilities. For this type of protocol, defining a docking score threshold is not necessarily a priority. In our study, we preferred to rationally select an optimal docking threshold rather than selecting an arbitrary fraction of the top scoring compounds.
Interestingly, our SBLB pharmacophores applied to the external validation data yielded very good sensitivities and lower specificities. This is similar to the results obtained by Réau et al. [27]  with pharmacophores models generated using the NR-DBIND AR set. This study also suggests that pharmacophores are only suited for data filtering as long as the compounds belong to the same chemical space as the molecules used to build the model. The SALI map of all the databases (the EPA training database and the EADB and NR-DBIND ERα external validation sets) in Figure  2  shows that our data fit this requirement and supports the use of pharmacophores for this study. The lower sensitivities obtained with the EPA database compared to those obtained with the external validation sets may be explained by the imbalance in the number of B and NB that exists in the EPA database (223 B and 2219 NB) compared to the validation sets which present lower proportions of inactive data.
Studies of the latter category are the most abundant, and present high AUC values around 0. 8 with good overall sensitivities and specificities. These good prediction performances are not surprising since classification and QSAR models are known for their ability to well predict structural analogs. However, these methods can suffer from overfitting bias which can lead to lower performances if applied on a different dataset as they will be unable to predict completely new/different molecules  [58]. Moreover, outliers are frequently discarded in this kind of study, but these compounds may introduce a category of yet unrepresented compounds. Nevertheless, these LB methods perform better than our LB pharmacophores and should be investigated for future integration in the protocol. Some sources of bias that may have affected the performances should be taken into consideration.
Annotation errors of biological assays are possible, and compounds identified with binding assays may bind on different ERα binding sites. Furthermore, the compounds of the EPA database are mostly compounds suspected to be toxic and not therapeutic compounds. Even if our models were validated with external sets dedicated to therapeutic compounds, it is important to enrich databases with more compounds relative to both therapeutic and toxicological explorations according to the purpose of the study  [59, 60]. Previous studies  [27, 49, 55, 61]  suggested that the pharmacological profile of the ligands should be considered to better discriminate agonist from antagonist compounds. Endocrine disrupting chemicals act in several ways including agonism and antagonism  [17]  and it is important to be able to retrieve ERα B regardless of their pharmacological profiles.
It should be noted that these docking studies used various ERα structures, and especially the 1a52 we chose to discard because of its artifactual position of the helix 12  [4]. Studies of the latter category are the most abundant, and present high AUC values around 0. 8 with good overall sensitivities and specificities. These good prediction performances are not surprising since classification and QSAR models are known for their ability to well predict structural analogs. However, these methods can suffer from overfitting bias which can lead to lower performances if applied on a different dataset as they will be unable to predict completely new/different molecules  [58]. Moreover, outliers are frequently discarded in this kind of study, but these compounds may introduce a category of yet unrepresented compounds.
Nevertheless, these LB methods perform better than our LB pharmacophores and should be investigated for future integration in the protocol. Some sources of bias that may have affected the performances should be taken into consideration. Annotation errors of biological assays are possible, and compounds identified with binding assays may bind on different ERα binding sites. Furthermore, the compounds of the EPA database are mostly compounds suspected to be toxic and not therapeutic compounds. Even if our models were validated with external sets dedicated to therapeutic compounds, it is important to enrich databases with more compounds relative to both therapeutic and toxicological explorations according to the purpose of the study  [59, 60]. Previous studies  [27, 49, 55, 61]  suggested that the pharmacological profile of the ligands should be considered to better discriminate agonist from antagonist compounds.
Endocrine disrupting chemicals act in several ways including agonism and antagonism  [17]  and it is important to be able to retrieve ERα B regardless of their pharmacological profiles. The structure identified to be optimal for the docking study is in an agonist-bound conformation. We thus verified that our protocol was not biased towards agonist ligands and that we were also able to identify ERα B with different pharmacological profiles. We compared the distribution of pharmacological profiles within the starting database, i. e. , the EPA database, as well as within the hits obtained for each screening protocol (see Figure  7 ). In the EPA database, the pharmacological profile annotation was achieved using agonist and antagonist experimental assays. Among the 223 compounds, 58 are agonist (26%), 50 antagonist (22.
4%), and 66 agonist-antagonist (29. 6%) compounds. No pharmacological profile annotation was available for 49 molecules (22%). Interestingly, the relative proportion of each pharmacological profile observed in the initial EPA database was maintained among the hits of both consensus and hierarchical protocols. This highlights the fact that the screening protocol presented herein is able to identify ERα B, regardless of their pharmacological profile and is thus not overfitted towards any pharmacological profile. conformation. We thus verified that our protocol was not biased towards agonist ligands and that we were also able to identify ERα B with different pharmacological profiles. We compared the distribution of pharmacological profiles within the starting database, i. e. , the EPA database, as well as within the hits obtained for each screening protocol (see Figure  7 ).
In the EPA database, the pharmacological profile annotation was achieved using agonist and antagonist experimental assays. Among the 223 compounds, 58 are agonist (26%), 50 antagonist (22. 4%), and 66 agonist-antagonist (29. 6%) compounds. No pharmacological profile annotation was available for 49 molecules (22%). Interestingly, the relative proportion of each pharmacological profile observed in the initial EPA database was maintained among the hits of both consensus and hierarchical protocols. This highlights the fact that the screening protocol presented herein is able to identify ERα B, regardless of their pharmacological profile and is thus not overfitted towards any pharmacological profile. Finally, it is important to mention that both sensitivity and specificity are valuable for assessing the screening quality.
EPA Dataset Compounds and biological data used to build the training dataset were extracted from the United States Environmental Protection Agency (EPA). Chemical compounds and their associated biological data were downloaded from the DSSTox dashboard in February 2019. The platform has been removed since then and compounds can now be found under the Comptox dashboard  [62]. This dashboard gathers high throughput screening data of a large and structurally diverse chemical library of compounds sus-pected to be of risk for humankind and for the environment against a wide spectrum of biological targets involved in toxicity pathways  [63]. Compounds included in training dataset were obtained by filtering the DSSTox/Comptox database to only keep compounds that have undergone binding assays on ERα receptor.
• NR-DBIND The NR-DBIND (Nuclear Receptors DataBase Including Negative Data) is a noncommercial manually curated benchmarking database that provides affinity data for small molecules that were experimentally tested against 28 nuclear receptors  [64]. For this study, a filter was applied to extract compounds tested against ERα. All compounds were directly downloaded from the website (http://nr-dbind. drugdesign. fr/, accessed on 20 November 2019) in SMILES format and annotated by their CAS names. Binding compounds were selected to form the active subset and the non-binding molecules constituted the inactive subset.
• EADB The Estrogenic Activity Database (EADB) developed by the NCTR (National center for toxicological research) assembles a large number of estrogenic activities data from various sources  [56, 65, 66]. It contains 18. 114 estrogenic-activity data points collected for 8212 chemicals tested in 1284 binding assays, reporter-gene assays, cell-proliferation assays, and in vivo assays in 11 different species. The database has been directly downloaded from the website (https://www. fda. gov/science-research/bioinformatics-tools/ estrogenic-activity-database-eadb, accessed on 25 November 2019) and filtered to only keep data relative to human ERα.
Molecule Curation and Preparation The same molecule curation and preparation protocol was applied for the EPA database, the NR-DBIND, and the EADB validation sets. SMILES were standardized using Standardizer from the ChemAxon suite [67] and salts and fragments were removed together with duplicates and small molecules containing less than 5 atoms. Conformations were generated using i-Con  [16] , the conformer generation tool of LigandScout  [68] , with BEST settings except for the maximum number of conformations per molecule that was set to 25. Compounds containing certain metal atoms (e. g. , Pb or Hg) were removed from the docking collection mainly because the software used were unable to process these molecules. Finally, molecules were converted into the appropriate format for the different software at use, i.
e. , pdbqt for docking with smina, mol2 for PLANTS, and Surflex_dock and ldb for pharmacophore model generations. In order to assess the accuracy of the data, 15 constitutional, physiochemical, and molecular descriptors were computed for each molecule of the three databases, namely, molecular weight (MW), ClogP, ClogS, number of HBond-Acceptors (H-Acc), number of HBond-Donors (H-Don), Total Surface Area (TSA), Relative Polar Surface Area (RPSA), Shape Index, Molecular flexibility (Mol_Flex), Molecular Complexity (Mol_Comp), number of Electronegative atoms (Elect_atom), number of Stereo Centers (Stereo_cent), number of rotatable bonds (rotat_bond), number of aromatic rings (aromatic_rings), and number of aromatic atoms (aromatic_atom). Descriptors were computed using the DataWarrior software  [26].
For the docking procedure, the structures were directly downloaded from the NR-DBIND database  [64]  since they are already enumerated, annotated, and cleaned. Format conversion from PDB to the appropriate docking format was done accordingly to the requirements of the software, i. e. , PDB were converted into mol2 format using the software chimera  [70] , into pdbqt with the prepare_receptor4. py python script available with the MGLTool  [18]. In order to generate the structure-based pharmacophores, structures were directly downloaded from the RCSB website  [71]  via the LigandScout graphical interface.
Docking 4. 3. 1. Protocol Docking is a structure-based virtual screening method that aims at predicting the pose of a ligand inside a protein  [17]. All docking calculations were performed with 3 different software with free academic licenses, i. e. , smina  [72] , PLANTS  [73] , and Surflexdock  [74]. The same binding site was used with the 3 software that was delimited using the co-crystallized ligands. For each software, 5 docking runs were performed. Smina is a fork of AutoDock Vina  [28]  that is designed for scoring function development and minimization workflows  [72]. It relies on the same sampling algorithm as vina, the latter being the succession of stochastic mutations steps, but integrates several scoring functions. For this study, we relied on 4 scoring functions already implemented within smina, i.
• Single structure docking and ensemble docking In the single structure docking approach, docking performance for each PDB structure was evaluated individually by calculating the area under the ROC curve (AUC). AUC values were computed with python using the scikitlearn library  [78]  and the package sklearn metrics. In the ensemble docking approach, docking performances of all the possible ensembles of 2, 3, 4, 5, 6, and 7 structures were computed. In this approach, each ligand was sequentially docked into several protein structures. The results were post processed to keep only, for each ligand, the best docking score among all structures. All ligands are then ranked according to these new scores and the corresponding AUC are computed. Python version 3. 8. 1 was used to prepare data and analyze the results.
The active compounds of the training set were clustered using the i-cluster  [79]  tool provided with LigandScout software and pharmacophores were generated for each of the resulting clusters. Default parameters of the I-cluster tool were used, i. e. , cluster_dis = 0. 4 with average method, except for the maximum number of conformations set to 3. In order to derive a LB-pharmacophore dedicated to a particular cluster of compounds, Ligand-Scout operates in several steps: (1) conformations of the ERα ligands included in the cluster are generated using the ICON algorithm; (2) molecules are ranked according to their flexibility and the best alignments; (3) for each compound, the generated conformations are used to create intermediate pharmacophores that are ranked using several scoring functions; (4) common features are aligned to all the conformations of the next molecule and so on until all the molecules are processed  [80].
Pipelines Construction Two different ways of combining pharmacophore models and docking were explored, the consensus and the hierarchical protocols. The first protocol consists in the analysis of the union of the results belonging to each model. Each molecule predicted as active by docking or pharmacophore will be predicted as active compound by the consensus protocol. The remaining compounds will be predicted as inactive. The second approach used a hierarchical protocol in which the database undergoes a sequence of screening methods. Two possible sequences exist: [pharmacophore-docking] or [docking-pharmacophore].
Conclusions In the present work, we present a pipeline designed for the prediction of potential EDCs acting through the binding to ERα. Optimized protocols for docking studies and SB and LB pharmacophore models' generation were evaluated together with the best approach to combine them. Both combination approaches that were investigated here, i. e. , consensus protocol and the hierarchical protocol, yielded good results. However, we recommend favoring the consensus protocol for toxicological studies and the hierarchical protocol for the identification of therapeutic compounds. Results were validated using two external datasets. Using our pipeline, we show that combining several in silico methods can enhance the prediction performances for compounds binding to ERα. Additional methods should be evaluated and implemented in this pipeline such as classification models.
scoring thresholds. Scoring Threshold (TH) Performances EPA Estrogenic Activity DataBase (EADB) Nuclear Receptors DataBase Including Negative Data (NR-DBIND) TH = -7 Se 0. 79 0. 48 0. 93 Sp 0. 55 0. 58 0. 03 PPV 176/2442 63/232 513/732 Se 0. 46 0. 77 0. 99 TH = -6 Sp 0. 78 0. 198 0. 001 PPV 103/2442 101/232 553/732 2. 3. Pharmacophore Modeling 2. 3. 1. LB Pharmacophore Models.
Hi-C sequencing Hi-C data was generated using the Arima-HiC kit (Ref. Image analyses and base calling were performed using the Illumina NovaSeq Control Software and Real-Time Analysis component (v3. 4. 4). Demultiplexing was performed using Illumina's conversion software (bcl2fastq v2. 20). The quality of the raw data and potential contaminants was assessed using FastQC (v0. 14. 0)  59  from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer).
Genome annotation The cavefish whole genome assembly was annotated using a pipeline adapted from previous studies  65, 66. In brief, RepeatModeler, RepeatProteinMask, and RepeatMasker (open-4. 0. 7, http://www. repeatmasker. org/) were first used to scan the genome and mask out repeats. Then protein-coding genes were annotated by collecting gene evidence from homology alignment, RNA-seq mapping, and ab initio prediction. For homology alignment, 464,144 protein sequences collected from NCBI were aligned to the assembly using Genewise 67 and Exonerate respectively. RNA-seq data were mapped to the assembly in two independent parallel steps. First Hisat 68 was used to align RNA-seq reads and then StringTie 69 was used to predict the gene models; in the other step reads were first assembled into transcript sequences using Trinity  70  and then PASA  71  was used to map the transcripts to the assembly and model the gene structures.
Genes with query and subject coverage both >90% were considered as being of good quality. To characterize the A chromosome content of the B chromosome, sequences of the B chromosome assembly were aligned to the sequences of the 25 A chromosomes with minimap2  64 (v2. 11)  and the best match of each contig fragment was retained. Overlapping matches were manually filtered considering match lengths and similarities (cigarline and edit distance) in order to build the best non-overlapping matching list. Karyoplots were then plotted using the R package karyoploteR  75  (https://bernatgel. github. io/karyoploter_tutorial/). The median, minimum, and maximum sizes of the 628 B best matches on A chromosomes were respectively 1,087 bp, 44 bp and 41,908 bp.
Three-dimensional protein modelling Three-dimensional models for the proprotein of A. mexicanus A-Gdf6b and B-Gdf6b were obtained by homology modeling. The amino acid sequences of full-length A-Gdf6b and B-Gdf6b were submitted to automated homology modeling using the hm_build macro of the software package YASARA  94. Modeling includes alignment of the two target sequences against sequences from Uniprot via PSI-Blast to build a position-specific scoring function matrix/profile, which is then used to search the PDB structure data bank for suitable modeling targets. Five templates were identified with sufficiently high scores, i. e. PDB entries 4YCG (proprotein complex of GDF2 (alternative naming BMP9))  95  , 5HLY (proprotein complex of Activin A)  96  , 6Z3J (the mature C-terminal growth factor domain of GDF5 in complex with repulsive guidance molecule B)  97  as well as 6Z3M (which is like 6Z3J but includes neogenin in complex with GDF5 and the repulsive guidance molecule B)  97  , and 3QB4 (which is only the C-terminal growth factor domain of GDF5)  98.
Several initial models were built on the basis of these template structures, missing sequence elements were modeled in an automated procedure through YASARA on the basis of an indexed PDB structure database. By this scheme 13 models were built, three only covered the C-terminal growth factor domain comprising residues 293 to 398, while 10 models covered the proprotein complex consisting of residues 3 to 398 (models on the basis of the 5HLY entry) and of residues 66 to 398 (models on the basis of template 4YCG). These models were individually refined by a short molecular dynamics simulation in explicit water to optimize hydrogen bonding and protein packing. Due to the overall low Z-score of the individual models, YASARA used the various models to form a hybrid homology model combining the elements with the highest-scoring factors into a single 3D model.
Knockout of Pachón A. mexicanus gdf6b Pachón cave A. mexicanus inactivated for gdf6b were generated using the CRISPR/Cas9 method. Guide RNAs (sgRNAs) targeting two sites located in exon 2 of gdf6b were designed using ZiFiT software  99   S3A , Data S1E) flanking the sgRNAs target sites leading to a ~470 bp deletion on the exon 2 of the gdf6b genes (Figure  S10 ). The genetic sex of the mutants was determined by specific primers (P5-P6) on the gdf6b promoter with gap/indel variation between A-gdf6b and B-gdf6b (see STAR methods above and Figure  S3A ).
ACKNOWLEDGEMENTS We thank  Victor Simon ,  Stéphane Père ,  Krystel Saroul ,  Pierre-Lo Sudan  and  Amélie Patinote  for taking care and handling cavefish, and  Manon Thomas  and the  LPGP TEFOR  infrastructure platform for acquisition of the RNAScope confocal images. This project was supported by funds from the  " Agence Nationale de la Recherche " ( ANR/DFG ,  PhyloSex  project,  2014-2016 ) to Y. G and M. S. S. R was supported by grants from an  Equipe FRM  ( Fondation pour la Recherche Médicale ,  DEQ20150331745 ) and  MITI CNRS  ( Mission pour les Initiatives Transverses et Interdisciplinaires ). J. H. P was supported by a  NIH  grant ( R35 GM139635 ). Sequencing was supported by  France Génomique  as part of an " Investissement d'avenir" program  managed by  ANR  (contract  ANR-10-INBS-09 ) and by the  GET-PACBIO program  ( « Programme opérationnel FEDER -FSE MIDI-PYRENEES ET GARONNE 2014-2020 » ).
The CytoEvol platform at ISEM was supported by the  Labex CeMEB. J. B, T. P and A. S were supported by  RVO :  67985904  of  IAPG  CAS, Liběchov. T. P was supported by the projects of the  Czech Ministry of Education  ( SVV 260571/2021 ). S. S was supported by the  Russian Foundation for Basic Research (RFBR)  ( 18-34-00638 ). B. I PhD fellowship was supported by the  Doctoral School of Ecology, Geosciences, Agronomy, Nutrition of the University of Rennes 1  and  INRAE. We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie (Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12) for providing help, computing and storage resources. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study Design and Inclusion Criteria We analyzed individual patient-level data included in the Highly Effective Reperfusion Evaluated in Multiple Endovas-cular Stroke (HERMES) trials collaboration. The collaboration includes all randomized clinical trials of stent retriever thrombectomy devices (entirely or predominantly) vs medical therapy in patients with anterior circulation LVO within 12 hours of last known well time and for which a peerreviewed, complete primary results article was published by August 1, 2020. [9] [10] [11] [12] [13]  These trials were approved by the respective local ethics boards and enrolled patients using prospective (6 trials) or prospective and deferred (1 trial) written informed consent. The details of the HERMES collaboration initiation and the method of the meta-analysis search strategy and data gathering have been previously reported.