dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotation of Complete Genomes For gene annotation, Geneious software (v5. 6. 5, Biomatters ApS, Aarhus, Denmark) was used. The tprK gene showed intra-strain variability in all samples and the corresponding nucleotides positions were denoted as "N. " Raw data were deposited with NCBI under BioProject number: PRJNA508872 and the complete genome sequences can be found under the following Accession Numbers: CP034921, CP034920, CP034919, CP034918, CP034972, CP034917, CP034916, CP034915, CP034914, CP034913, CP034912, CP035104, and CP035193.
3D Structure Prediction The 3D structures of TP0858 and TP0865 were generated using the SWISS-MODEL server  (Waterhouse et al. , 2018)  using as templates the Protein Data Bank entries 3DWO and 3BS0, respectively. HHblits was used to find suitable template models  (Remmert et al. , 2011). The orientation of proteins with respect to the outer membrane corresponded to that predicted in the OPM database 1  (Lomize et al. , 2012). The TprC model was built using the TMBpro server  (Randall et al. , 2008)  according to  Kumar et al. (2018). The antigenic peptides were predicted by "Predicted Antigenic Peptides tool 2. " Predictions were based on a table that reflects the occurrence of amino acid residues in experimentally known segmental epitopes. Segments were only reported if they had a minimum size of 8 residues.
Acknowledgements We thank  Dr X. Carpentier  ( Nice Hospital ),  Dr Ch. Chappard  ( Lariboisière Hospital ),  Prof. P. Conort  ( La Pitié-Salpetrière Hospital ),  Dr P. Dorfmüller  ( La Pitié-Salpetrière Hospital ),  Dr E. Estève  ( Tenon Hospital ),  Prof. D. Hannouche  ( Lariboisière Hospital ),  Dr J. P. Haymann  ( Tenon Hospital ),  Prof. P. Jungers  ( Necker Hospital ),  Prof. B. Knebelman  ( Necker Hospital ),  Dr E. A. Korng  ( Lariboisière Hospital ),  Dr E. Letavernier  ( Tenon Hospital ),  Prof. F. Liote  ( Lariboisière Hospital ),  Dr M. Livrozet  ( Tenon Hospital ),  Prof. M. Mathonnet  ( Limoges Hospital ),  Prof. P. Meria  ( Saint-Louis Hospital ),  Dr C. Nguyen  ( Lariboisière Hospital ),  Dr J. Rode  ( Tenon Hospital ),  Prof. P. Ronco  ( Tenon Hospital ),  Dr I. Tostivint  ( La Pitié-Salpetrière Hospital ),  Prof.
Matzen  ( CEMHTI ),  Dr C. Mocuta  ( SOLEIL Synchrotron ),  Dr R. Papoular  ( CEA ),  Dr P. Reboul , ( UMR 7561 ),  Dr M. Réfrégiers  ( SOLEIL Synchrotron ),  Dr S. Reguer  ( SOLEIL Synchrotron ),  Dr S. Rouzière  ( LPS ),  Dr J. -P. Samama  ( SOLEIL Synchrotron ),  Dr C. Sandt  ( SOLEIL Synchrotron ),  Dr A. Somogi  ( SOLEIL Synchrotron ),  Dr D. Thiaudière  ( SOLEIL Synchrotron ),  Dr E. Véron  ( CEMHTI ) and  Dr R. Weil  (LPS). This work was supported by the  Physics and Chemistry Institutes of CNRS  and by contracts  ANR-09-BLAN-0120-02 ,  ANR-12-BS08-0022 ,  ANR13JSV10010-01 , convergence  UPMC CVG1205  and  CORDDIM-2013-COD130042. The authors are grateful to the  SOLEIL Synchrotron Facility  and the  Leon Brillouin laboratory  for beam time allocation.
We also allowed for those who attended the seminar to receive text messages if they so indicated. House officers, pharmacy interns and students were excluded from the study as they were undergoing supervised training at the time. Only consenting HCPs were recruited into the study after stratification into the various professional cadres. The seminar was an hour-long presentation delivered to the HCPs at specially organized meetings. It was in two parts: first, the scope and aims of pharmacovigilance were outlined using the World Health Organization (WHO) documents on pharmacoviglance. 1, 11, 24 The definitions of the different key items of ADRs,  25, 26  the historical aspect of ADRs, and relevant history of pharmacovigilance in Nigeria was described. The number of reports presently in the Nigerian database with the system organ classification and pharmacological classification was made known.
Statistical analysis Sample size. To calculate sample size for this randomized study and to get the required sample size per s with a power of 1-β, (80%) and to detect a difference of d, the sample size (n) we estimated the sample size for individual randomized study comparing two proportions using Epi Info™ version 7 software (Centers for Disease Control). 35 he proportion of those who had used the national form to ever report an ADR was about 26% in a previous study,  9  and we hoped that the intervention would improve the prevalence by 40% at a power of 80% and a 95% confidence interval. The estimated sample size for each of the studies was 178 HCPs and cumulatively, 356. A 15% nonresponse rate was anticipated, and this increased the sample size to 410 persons (205 per arm). Data analysis.
The study was analysed descriptively using frequencies and proportions. In defining an ADR, the key elements (noxious and unintended) had to be present to be regarded as a correct answer. Partially correct answers may contain one or the other and an incorrect answer need not contain any of the key elements or related synonyms. The various answers from open-ended question were synthesized thematically, and similar answers merged (multiple responses were accepted). The chi-square test was used to assess categorical variables and the significance value set at 0. 05. SPSS (IBM SPSS Statistics for Windows, Version 21. 0, Armonk, NY: IBM Corp) version 21 was used for the analysis of the study.
goeBURST Analysis of Methicillin-Susceptible S. Aureus (MSSA) Distribution In order to determine the distribution of STs of toxinogenic MSSA strains isolated from different sources, MLST dataset was performed using goeBURST software  [52]  implemented by the PHYLOVIZ program  [53]  available at http://phyloviz. net. Related ST is inserted in the software to provide an unrooted tree-based representation of the relationship of the founding genotype, reflected in the appearance of STs differing only in one housekeeping gene sequence from the founder genotype-single locus variants (SLVs). Further diversification of those SLVs results in the appearance of variations of.
Acknowledgments: We thank  Sarah Kabani  for her editing assistance. A. P. , A. S. , C. D. -R. and J. -P. L. belong to the  FHU INCh (Federation Hospitalo Universitaire Infections Chroniques), Aviesan. We thank the  Nîmes University hospital  for its structural, human and financial support through the award obtained by our team during the internal call for tenders « Thématiques phares ».
Population and sample collection Two hundred and seventy women having had one or more pregnancies were included in the study. None of these women had any history of transfusion. Maternal blood samples and umbilical cord samples were collected at delivery  [1]. Patient data (age, number of children, pregnancies and miscarriages) are given in Table  1. The study was designed in collaboration with the Besançon Cord Blood Bank (Etablissement Français du Sang) and carried out in accordance with the recommendations of Besançon Hospital's Ethics Committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Besançon Hospital's Ethics Committee.
HLA-G analysis and HLA typing Maternal DNA was sequenced for HLA-G gene from position -1983 to +3447 by Next Generation Sequencing (NGS). PCR fragments were sequenced using an NGS platform (MiSeq, Illumina, The Netherlands)  [1]. HLA-G NGS data were analyzed using PolyPheMe (Xegen, France). HLA-G allelic assignment at 8 digits was based on the HLA sequences listed in the official IMGT/HLA database 3. 28. 0 [4]. All polymorphic variations from position -1983 to -1 in 5'URR and from +2540 to +3447 in 3'UTR, i. e. 37 SNPs, 1 base deletion, 1 base insertion and a 14bp insertion were used for haplotype estimation using PHASE. HLA-G haplotype identification numbers were coded according to  [1]. Allelic and haplotype frequencies were estimated using an EM algorithm implemented in the Gene [Rate] computer tools  [5].
Statistical analysis Missing data led to the exclusion of the concerned sample from further analyses. No multiple imputation was used. Analysis were performed using GRAPH PAD Prism 5 software (CA, www. graphpad. com), SPSS 24. 0 software (SPSS Inc. , Chicago) and SAS 9. 4 (SAS Institute Inc. Cary, USA). Biological and genetic data frequencies between anti-HLA class I immunized women, anti-HLA class II immunized women and non-immunized women were compared with Chi-square tests. The presence of anti-HLA class I antibodies and anti-HLA class II antibodies were independently tested in univariate analyses according to biological data (women's age, number of pregnancies, number of children delivered, number of miscarriages, sex of the latest child) and according to genetic data (HLA-G alleles and HLA-G haplotypes, IL-6 -174 C/G genotype, HLA-A and HLA-B antigen, latest child/paternal HLA-A and HLA-B antigen mismatch).
Review Figure  1  The percentage of TUEs that were entered in the WADA's ADAMS database prospectively and retroactively. It also compares TUEs for all substances with glucocorticoids only. ADAMS, Anti-Doping Administration and Management System; TUEs, Therapeutic Use Exemptions. Figure  2  The duration of granted GC TUEs from the ADAMS database for the period 2012-2016. ADAMS, Anti-Doping Administration and Management System; GC, glucocorticoid; TUEs, Therapeutic Use Exemptions. With good administration systems, professional sports are able to implement regulatory controls similar to the successful 'needle' and 'athlete health' policies of the IOC, UCI and a number of other international sport federations. Physician control is easier in the setting of team sports, particularly with professional teams where there are contracted team physicians and auditing of healthcare practices.
2% (WADA data, confirmed by the IOC). Importantly, when granted, a TUE for a GC directly relates to individual medical needs and generally is narrowly limited in dose, frequency and duration of use. Anecdotal stories of cheating with GC TUEs are mostly reported in cycling and have been less common in the last 5 years. This may be due to a better understanding by athletes of the medication's benefit-risk relationship, the more stringent application of the rules and investigative follow-up of AAFs. Due to the value of systemic GCs in the treatment of many medical conditions common in athletes (eg, exacerbations of asthma, sinusitis, musculoskeletal injuries, acute allergic reactions and inflammatory bowel disease (IBD)), this class of prohibited substances consistently makes up the largest share of granted TUEs, according to recent statistics collected from WADA's Anti-Doping Administration and Management System (ADAMS) database (see figure  1 ).
Solid-State Fourier Transform Infrared Spectroscopy (FTIR) A Spectrum One FTIR spectrometer (PerkinElmer, Connecticut, USA) was utilized to obtain FTIR data  [1, 9]. The following parameters of the analysis, accumulating 10 scans in total, were employed: 450-4000 cm -1 was spectral range, 4 cm -1 was resolution, while the scan speed was 0. 2 cm/s. A sample concentration of 1% (w/w) was obtained, diluting the powdered sample with KBr and making disks suitable for FTIR by applying pressure of approximately 10 bar for 1 min. Deconvolution of the FTIR spectra was conducted to facilitate their comparison. OriginPro 7. 5 software was used to subtract the baseline and carry out Gaussian peak fitting on the spectra. In each case, seven overlapping peaks were detected in the region under examination, whose combined area and shape were similar to those of the original bands  [1].
Modulated Temperature Differential Scanning Calorimetry (MTDSC) The T g s of the ASDs were detected by MTDSC using a Q200 DSC instrument and TA Instruments DSC Refrigerated Cooling System (TA Instruments, New Castle, Delaware). Samples of 3-4 mg were heated in aluminum pans with sealed aluminum lids. Nitrogen was used as the purge gas at a flow rate of 20 mL/min. Samples were heated from 0 • C to 110-185 • C at 2 • C/min, with an amplitude of ± 0. 318 • C and a modulation period of 60 s. Results were analyzed with the Universal Analysis 2000 software (TA Instruments). The midpoint of the transition was taken as the T g. Sapphire was used to calibrate the heat capacity, while indium was used for the calibration of enthalpy and temperature. All measurements were carried out in triplicate.
Thermogravimetric Analysis (TGA) TGA was done using a Mettler TG50 measuring module coupled to a Mettler Toledo MT5 balance (Schwerzenbach, Switzerland)  [1]. The heating rate employed was 10 • C/min and samples  (8-10 mg)  were loaded into open aluminum pans. 2. 2. 9. Dynamic Vapor Sorption (DVS) and Mathematical Modeling Using Young-Nelson Equations DVS studies were performed using an Advantage-1 automated gravimetric vapor sorption analyzer (Surface Measurement Systems Ltd. , London, UK) at 25. 0 ± 0. 1 • C, between 0 and 90% RH, in steps of 10% RH, as described previously  [9]. The complete sorption and desorption profile is shown as an isotherm. PXRD analysis was performed on all samples following DVS to identify any solid-state transformations. In order to determine how water uptake occurs in the ASDs, the experimental sorption and desorption data were fitted to equations using the Young-Nelson model, as described previously  [22, 23] : M s = A(β + θ) + B(θ)RH, (3) M d = A(β + θ) + B(θ)RH max.
a The muscle groups evaluated were knee extensors, knee flexors, and abductors in both legs. b The 6-MWD at baseline represents data from the analysis of the prespecified subgroup, which included 29 patients who were able to walk 350 m or more in 6 minutes and 18 patients who were not able to walk 350 m in 6 minutes; data are based on MFM D1 subscores (P for interaction =. 67). c Corticosteroid treatment represents data from the analysis of the post hoc subgroup, which included 33 patients who received corticosteroid treatment before randomization and 14 patients who did not; data are based on MFM D1 subscores (P for interaction =. 20). Prespecified subgroup analyses indicated that 13 patients in the stable subgroup who received combination therapy had a smaller, statistically significant decrease in their MFM D1 subscores after 26 weeks compared with 16 patients in the stable subgroup who received placebo (the mean between-group difference in MFM D1 subscores was 6.
| PET-CT imaging Synthesis and radiolabeling of the 18 F-florbetaben probe were performed as previously described  (Barthel et al. , 2011)  and was provided by the cyclotron and radiochemistry facility at Paris Diderot University. Mice were anesthetized with 2% isoflurane in 2 L/min oxygen, injected with 18 F-florbetaben (10 MBq) via the tail vein, and positioned in the prone position. PET images were then acquired 90 min postinjection over 45 min in 3D list mode on a Siemens Inveon hybrid PET-CT System (axial field-of-view of 12. 7 cm with a bore diameter of 12 cm, approximately 1. 4 mm full width at half-maximum spatial resolution; Siemens Healthcare, Erlangen, Germany). During the scan, a heating pad prevented hypothermia. PET images were reconstructed using Pmod software (Pmod Technologies, Zurich, Switzerland), OSEM3D, 12 iterations, matrix: 256 × 256.
Attenuation and scatter corrections were applied. The PET images were coregistered with the MRI template. Cortical and cerebellum volumes of interest (VOI) were generated. Global cortex to cerebellum SUVr was calculated, and means were compared between groups using a Mann-Whitney test. For three or more groups, Tukey's or Dunn's post hoc tests were used to assess differences between specific groups. Statistical analysis was completed using Prism 7. 03 (GraphPad Software). All values are represented as the means ± SEM. No data were excluded from analysis.
ACKNOWLEDGMENTS This work was supported by a grant from  Agence Nationale de la Recherche :  ANR   MALZ 2013 Neurobio PKR. CONF LICT OF I NTEREST Dr. Tible has received a grant from  Fondation Philippe Chatrier. Dr. Mouton Liger ,  Dr. Schmitt ,  Dr. Giralt ,  Dr. Farid ,  Ms Thomasseau ,  Dr. Gourmaud , and  Pr. Paquet  reported no biomedical financial interests or potential conflicts of interest. Dr. Rondi Reig  has received a grant from  Agence Nationale de la Recherche :  MALZ 2013. Dr. Meurs  has received a grant from  Agence Nationale de la Recherche :  MALZ 2013. Dr. Girault  has received a grant from  Agence Nationale de la Recherche :  MALZ 2013  and  Inserm. Pr. Hugon  has received a grant from  Agence Nationale de la Recherche :  MALZ 2013  and  Inserm.
Funding: This work was supported by Fondation de France Grant number 00066525, a France Parkinson Grant and an IDEX Emergence Grant number OPE-2018-410 (B. D. ). M. L. A and M. T. are recipients of a MSER fellowship (France). The LABEX Brain, the University of Bordeaux, and the Centre National de la Recherche Scientifique provided infrastructural support.
PocketMatch, SiteEngine, eF-site, MultiBind and ProBis  (29)  are available as web servers (Supplementary Table  S1 ). Most of the above approaches compare or align binding sites only. ProBis web server is the only one able to search for a binding site on the entire surface of proteins based on local structural alignments. ProBis web server requires a query structure of a protein-ligand complex. The user can select a query binding site which is compared to entries in the non-redundant PDB (nr-PDB) or to a user-supplied list of PDB identifiers. Molecular docking approaches can be also used to identify protein target of a ligand and consequently help the detection of off-target protein. Thus, IdTarget web server was developed to predict possible binding targets of a small chemical molecule via a divide-and conquer docking approach  (30).
It requires an input ligand file for the target screening. The user can choose to perform the search of potential binding targets among two predefined datasets of PDB identifiers or a user-supplied list of PDB identifiers. Recently, PatchSearch  (31)  was developed to search for structurally conserved binding sites on the entire surface of a protein in order to help for the detection of potential off-target protein. It uses a quasi-clique approach which avoids a too stringent distance conservation between atoms and hence takes into account flexibility of binding sites. A quasi-clique is a dense subgraph. Our approach is similar to those used for dense subgraph or community detection in graph clustering  (32) (33) (34). Cliques in correspondence graph involves the conservation of all internal distances between protein and patch surfaces.
Based on Euclidean distance matrix properties, a well-chosen set of conserved internal distances is sufficient to ensure that all distances are equals or almost equals. Hence the complete node connection condition can be relaxed. In PatchSearch, a quasiclique is a correspondence subgraph including a clique with added nodes ensuring a spatial similarity. It is computed with a greedy algorithm which starts from cliques and at each step add nodes with at least four connections with the current quasi-clique and maximizing a 3D similarity score. The quasi-clique approach compared to a classical clique technique allows for fast detection of larger and new patches on protein surfaces which could potentially provide new yet unpredicted off-target binding sites. PatchSearch has been validated against a number of widely used datasets  (35) (36) (37) (38) (39) (40)  (Supplementary Table  S2 ) with a large diversity of ligands.
First, PatchSearch ability to recognize proteins binding the same ligand has been assessed on four datasets (Kahraman, Homogeneous, Gunasekaran and Milletti). Second, it was applied to three drugs (ursodeoxycholic acid, prazosin and naproxen) from Drugs/sc-PDB dataset and to three drugs (sunitinib, imatinib and sorafenib) used in cancer therapy from the Multiple Target Ligand Database. The aim of these experiments was to prove that PatchSearch manages to identify true offtarget proteins. The main motivation of the present development of the PatchSearch web server is to provide the user with an automated workflow (Figure  1 ) to identify among a selection of proteins, which of them have a region sharing structural similarities with a ligand binding site. Through an energy minimization by steepest descent algorithm, the ligand conformation is locally optimized and scored within the binding site by smina program (41) using Vinardo scoring function  (42).
Patches and surfaces A patch is a small piece of protein surface given by a set of solvent accessible atoms. Here, a patch is constituted by the solvent accessible atoms that have a distance smaller than 5 Å to the ligand. Each atom of the patch is assigned a label corresponding to its type: N, O, S, C and Ca (for carbons ␣). Aromatic rings are replaced by one or two pseudoatoms corresponding to one centroid for phenylalanine or tyrosine and two centroids for tryptophan). In this study, we are only interested in patch representing binding sites with ligands. The solvent accessibility is calculated using the program NACCESS (Hubbard, S. J. & Thornton, J. M. (1993), http://www. bioinf. manchester. ac. uk/naccess/) and all atoms with relative accessibility over 1% are conserved. This very small threshold allows to extract the largest surface involved in the interaction with the ligand.
WEB SERVER The PatchSearch web server allows a local non-sequential searching for similar regions, called patches, on the entire protein surfaces, without any knowledge on binding site localizations or preliminary binding site detections. The service is fully embedded in the Mobyle framework  (44). It embeds simple yet powerful data management features that allow the user to reproduce analyses. It gives to the users the possibility to create registered accounts, which allows user data and jobs to be maintained and managed across multiple work sessions and therefore to reuse and share data and results.
Input There are two main inputs. First, the user must provide a PDB identifier containing at least one protein-ligand complex. The ligand, for which similar binding sites have to be detected, can be selected in the list of non-protein molecule in the PDB file. The selected ligand, the patch around the ligand and the protein surface can be visualized by NGL viewer  (45). If the user knows the residue number and the chain of the ligand of interest, he can directly fill the fields in the form. Second, the user must define a collection of structures in which the search will be performed. This can be done either on the form of a collection of proteins defined as a list of PDB identifiers provided by the user. At present, this list cannot exceed 5000 proteins. Alternatively, five predefined lists are available.
They correspond to subsets of the PDB with a minimal sequence length of 50 residues. These subsets are created using the Advanced Search Interface in the Protein Data Bank server. Three lists correspond to human proteins at different sequence identities (30%: 7922 structures; 50%: 9118 structures; 70%: 9836 structures), a list of 14 191 eukaryotic proteins with 30% of sequence identity and a list of 14 650 prokaryotic proteins with 30% of sequence identity.
Output The server returns patches similar to the query identified among the collection of proteins. Pairings between the query atoms and the atoms in the targeted protein surface are detailed and patches are scored by the relative Binet-Cauchy score. Similar patches are output to a table with the PDB identifier, the number of patch atoms, alignment length (the number of retrieved atoms), the RMSD between the query patch and the retrieved patch, the relative Binet-Cauchy score and docking values resulting from the Smina rescoring calculations. All the alignment results, as well the estimated affinity and the binding mode of the ligand for each targeted protein are downloadable in separated files.