dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We acknowledge the  NOAA/CLASS data centre  for the IASI Level 1c spectra and  EUMETSAT  for the Level 2 data. IASI is a joint mission of  EUMETSAT  and the  Centre National d'Études Spatiales (CNES, France). The ozone sonde data were provided by the  Global Monitoring Division of NOAA  (www. esrl. noaa. gov/gmd)and by the  World Ozone and Ultraviolet Data Centre  (www. woudc. org). Part of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. We acknowledge  NASA  support under the grant  NNX11AE19G. We thank  Gaëlle Dufour  ( LISA, France ) for discussions. Edited by:  M. Weber.
Microbial community analyses 454 pyrosequencing of the V4 variable region of the 16 S rRNA gene To analyse bacterial composition, we subjected extracted genomic DNA to PCR-amplification of the V4 hypervariable region of the 16S rRNA gene. On the basis of our analysis done using PrimerProspector software  [16] , the V4 primer pairs used in this study were expected to amplify almost 100% of the Archaea and Bacteria domains. The 5' ends of the forward primer V4F_517_17 (5′-GC CAGCAGCCGCGGTAA-3′)  [17]  and the reverse primer V4R_805_19 (5′-GACTACCAGGGTATCTAAT-3′)  [18]  were tagged with specific sequences for pyrosequencing as follows: 5′-CCATCTCATCCCTGCGTGTCTCCGACTC AG-{MID}-{GCCAGCAGCCGCGGTAA}-3′ and 5′ CCT ATCCCCTGTGTGCCTTGGCAGTCTCAG-{GACTACC AGGGTATCTAAT}-3′. Tag pyrosequencing was performed using multiplex identifiers (MIDs) (Roche Diagnostics) of 10 bases, which were specified upstream of the forward primer sequence (V4F_517_17).
16S rRNA sequence data analysis A total of 1. 47 million sequence reads from 96 samples were analysed using the default settings in the Quantitative Insights Into Microbial Ecology (QIIME) package of software tools  [19]. The 16S rRNA sequences were qualityfiltered and demultiplexed. These reads had an average length of 290 bp. Using the pick-otus protocol, we classified the sequence reads into OTUs on the basis of sequence similarity. Sequence reads were then clustered against the February 2011 release of the Greengenes 97% reference dataset (http://greengenes. secondgenome. com) [20, 21]. Taxonomy was assigned using the Basic Local Alignment Search Tool (BLAST)  [22]. The representative sequences of all OTUs were then aligned to the Greengenes reference alignment using PyNAST  [18] , and this alignment was used to construct a phylogenetic tree using FastTree  [23]  within QIIME.
Acknowledgements We thank  Ricardo Gonzalo ,  Francisca Gallego ,  Rosa Arjona  and  Rosario M. Prieto  from the  Unit of High Technology, Vall d'Hebron Research Institute , for technical assistance. This work was performed as a part of the PhD research of  Ms. Alba Santiago  and  Ms. Suchita Panda , students of the  Universitat Autònoma de Barcelona (UAB). This study was partially funded by unrestricted grants from the  Fondo de Investigacion Sanitaria  ( PI10/00902 ,  CP13/00181 ) and in part by  HENUFOOD  ( CEN-20101016 ) and by the  European Community 's  Seventh Framework Programme  (FP7/2007-2013):  International Human Microbiome Standards (IHMS) , grant agreement  HEALTH. 2010. 2. 1. 1-2. CIBERehd is funded by the  Instituto de Salud Carlos III.
1A, right ). These results were validated by high MAP1LC3B expression in TN tumors in another independent cohort of BC patients, treated at Marseille  35  (Fig. 1B  and Table  S1  for clinical features of this cohort). Furthermore, we combined the Curie cohort dataset with another independent data set, named the Stockholm cohort  36  (henceforth the combined set is referred to as Curie/ Stockholm cohorts), to increase the statistical power of the study, in particular with respect to the TN BC patients (see the Materials and Methods section for details on normalization and procedure). We confirmed the accumulation of MAP1LC3B mRNA in the TN subtype in this new combined dataset (Fig. 1C ). We next evaluated if MAP1LC3B mRNA could be a prognostic factor for BC. To do so, we performed analyses based on log-rank test.
Further assessment of the prognostic value of MAP1LC3B in each BC subtype showed that high MAP1LC3B expression was indicative of both OS and DFS in the case of TN (Fig. 1E ), but not in LumA, LumB, and HER2 BC subtypes (Fig. S1 ). We validated that MAP1LC3B correlated with the survival of TN BC patients in 2 other independent cohorts, from The Cancer Genome Atlas (TCGA) cohort (Fig. 1F ) and Marseille (Fig. 1G ) (expression data from Marseille and TCGA cohorts are described in ref. 35 and https://tcga-data. nci. nih. gov/tcga/, respectively; see also Table  S1  for clinical features of the cohorts). These observations also revealed that the prognostic value of MAP1LC3B in TN patients was independent of the transcriptomic platform used (Affymetrix for Curie, Stockholm and Marseille cohorts, and Agilent for TCGA cohort).
2A , black arrows in high-magnification insert). We next validated the number of autophagosomes, i. e. , LC3B-positive dots, using an automated quantification method (Fig. 2C-E , See also the Materials and Methods section for details). Using EBSS and bafilomycin A 1 (BafA1) treatments that induce autophagosome formation and accumulation, respectively, we first assessed that the automated quantification of the staining was specific for autophagic vacuoles (Fig. S3A  and B ). As expected, we did see an increase in the number of autophagosomes upon EBSS and BafA1 treatments, as compared to control conditions (Fig. S3A  and B ), results that were highly significantly correlated with autophagosome quantification using ImageJ software (r D 0. 996, P-value D 0. 003 by Pearson's test). In tumors, as observed with the manual histological scoring, LC3B staining quantified by the automated method was mainly restricted to the epithelial compartment, with a negligible signal in stroma (Fig.
Breast microarray data sets and gene expression profiling In a first step, we combined breast tumor micro-array data sets from the Institut Curie (Curie) and the Karolinska Institute Stockholm (Stockholm), in order to increase the number of TN BC patients, and further enhance the power of analysis. The Curie microarray dataset is freely accessible in the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo), under accession number GSE45827. Samples were hybridized on Affymetrix U133 plus 2. 0 arrays. Data were first normalized using the GC-RMA algorithm (R version 2. 14. 1; http://cran. r-project. org). A linear mixed model was then applied using the nlme package to estimate fixed effects related to array batches and hybridization series. Following GC-RMA normalization, only genes with a log2 intensity greater than 4 in at least 5% of all samples were considered for the analysis.
This filter reduced the number of probesets from 54,673 to 29,873. The Stockholm data set was retrieved from the Gene Expression Omnibus under accession number GSE1456. These 2 datasets, Curie and Stockholm, were combined using 27,482 probesets in common and the intensity levels were standardized (mean equal to 0 and standard deviation equal to 1) in order to avoid batch effect. Each sample was classified into a 'high' or 'low' sub-group according to MAP1LC3B mRNA levels in every tumor subtype. The Marseille data set was retrieved from Gene Expression Omnibus under accession number GSE21653. The TCGA transcriptome dataset is publicly available from The Cancer Genome Atlas website (https://tcga-data. nci. nih. gov/tcga/). The gene expression data set for BC cell lines (Affymetrix Human Genome U133 Plus 2.
0 Array) comes from the Gene Expression Omnibus under accession number GSE36133. qRT-PCR Human RNAs were analyzed for MAP1LC3B expression using real-time reverse transcription-PCR (RT-PCR). The RT-PCR protocol using the SYBR Green Master Mix kit (Applied Biosystems, 4367659) on the ABI Prism 7900 Sequence Detection System (Perkin-Elmer Applied Biosystems, Foster City, CA) is described in detail in ref. 69. The primer pairs for the MAP1LC3B gene were selected to be unique when compared with the sequences of the other MAP1LC3 family genes. Two different sets of primers were used for real-time RT-PCR amplification of MAP1LC3B (forward and reverse primers): (1) 5 0 -CCGCACCTTCGAACAAAGAGTA- 3 0 and 5 0 -TGCTTCTCACCCTTGTATCGTTCTA-3 0 ; (2) 5 0 -CGCGTCGCGAGTCACCTGA-3 0 and 5 0 -TGGCGATAGC-CACTTCCCTTGT-3 0.
S2D ). To identify the foreground and the background inside the images, the standard Otsu's method for image binarization has been used. A global threshold was chosen to minimize the intraclass variance of the thresholded black and white pixels. The foreground/background mask defines the region area for the rest of the analysis. -Color deconvolution for IHC analysis: The color deconvolution allows transforming natural RGB images into channels containing only 1 marker. In this study, the color deconvolution gives 1 hematoxylin channel (blue) and 1 DAB channel (brown). The algorithm used is the ImageJ module "Color deconvolution 1. 5" with "H DAB" parameters. -Nuclear segmentation: Nuclear segmentation was performed using the blue channel of IF images and using the blue channel after color deconvolution for IHC.
Data are represented in arbitrary units (a. u. ). The number of living cells was determined after extraction from Matrigel. The Matrigel was dissolved in phosphate-buffered saline (PBS; Life technologies, Gibco Ò 10010-015) C 5 mM EDTA C protease inhibitors (Roche, 14044500) for 30 min at 4 C. The cells were centrifuged in PBS C 0. 5% BSA (Euromedex, 4-100-811-C)-coated tubes and washed twice. The pellet fraction was resuspended in Trypsin-EDTA (GE healthcare, L11-4) and trypan blue-negative cells were considered as alive and counted using a Vi-cell apparatus (Millipore). For siRNA transfection experiments, the cells were transfected prior to 3D plating. Briefly, 2 £ 10 5 cells per well were transfected, the day after plating, with 10 nM of the siRNA complex and DharmaFECT one transfection reagent (Dharmacon, T-2001) following the supplier's protocol recommendations.
For investigating the chloroquine effect on invasion, MDA231 cells were treated with 50 mM of chloroquine during 16 h before seeding. After 6 h, the remaining cells on the upper side of the Transwell device were removed. Invading cells at the bottom side of the Transwell device were fixed in paraformaldehyde 4% (Electron Miscroscopy Sciences, 15710) for 20 min, permeabilized with 0. 5% IGEPAL CA-630 (Sigma, I-3021), 0. 5%, for 5 min and stained with 1 mg/ml DAPI for 30 min. Invaded cells were then counted under a microscope with a UV camera (x10 magnification, Zeiss Axioplan microscope, cool SNAP HQ 2 ). Each condition was done in duplicate per experiment and the results are the sum of the number of cells counted in 8 different fields per condition. Data are from means C/¡ sem of at least 3 independent experiments.
After electrophoresis, the proteins were transferred to a 0. 2-mM nitrocellulose transfer membrane (Whatman,10 401 396). Membranes were then blotted overnight at 4 C with the appropriate primary antibodies. Specific binding of antibodies was detected using appropriate conjugated secondary antibodies, and was visualized by enhanced chemiluminescence detection (Western Lightning Ò Plus-ECL, PerkinElmer, NEL-105001EA). Densitometric analyses of immunoblots were performed using the Multi Gauge software (FujiFilm).
Quantifications of the number of LC3 dots per cell were performed on at least 50 cells with a minimum of 8 cells per picture using ImageJ software. Particle number was quantified with the "analyze particle" function on thresholded sections. To evaluate the number of autophagosomes and autolysosomes and to analyze the autophagic flux, MDA231 cells stably expressing the tandem probe RFP-GFP-LC3B were used. RFP-GFP-positive puncta (yellow puncta) are autophagosomes before lysosomal fusion, and RFP-only positive puncta (red puncta) are autolysosomes; GFP fluorescence is quenched at low pH. 72.
Acknowledgments We are grateful to  P. Codogno  for critical reading of the manuscript, as well as to  M. Cardon  and  Maria-Carla Parrini  for advices on several aspects of this work, namely image analysis and invasion assay, respectively. We also thank  Lisa Maranghoni ,  F. Assayag , and  A. Thuleau  from the  Preclinical investigation laboratory at the Institut Curie  for expert technical assistance in establishing mouse models,  E. Martel ,  M. Richardson  and  A. Nicolas  from the experimental pathology platform at the  Institut Curie  for help and advice,  L. Sengmanivong  and the  Nikon Imaging Center at the Institut Curie  for technical assistance with microscopy and  L. Cerf  and  V. Racine  from  FluoFarma Ò. We  acknowledge  O. Mariani  from  CRB Institut Curie  for her help in preparing the mRNA and protein samples from patient tumors and  D.
The sc-PDB database (available at http://bioinfopharma. u-strasbg. fr/scPDB/) is a comprehensive and up-to-date selection of ligandable binding sites of the Protein Data Bank. Sites are defined from complexes between a protein and a pharmacological ligand. The database provides the all-atom description of the protein, its ligand, their binding site and their binding mode. Currently, the sc-PDB archive registers 9283 binding sites from 3678 unique proteins and 5608 unique ligands. The sc-PDB database was publicly launched in 2004 with the aim of providing structure files suitable for computational approaches to drug design, such as docking. During the last 10 years we have improved and standardized the processes for (i) identifying binding sites, (ii) correcting structures, (iii) annotating protein function and ligand properties and (iv) characterizing their binding mode.
INTRODUCTION The 3D structures of macromolecules, as collected by the Worldwide Protein Data Bank (PDB) organization (http:// wwpdb. org, (1)), offer wealth of information for computeraided approaches to drug design. During the last 30 years, the steady increase of the PDB archive (2) has prompted the development of 3D methods for hit identification by virtual screening of chemical libraries, de novo ligand design and hit to lead. Many success stories have been reported in the literature  (3). Besides, some proteins have never been efficiently modulated by chemical compounds despite intense efforts in medicinal chemistry. The concept of ligandability has thus been suggested to qualify the ability of a protein to bind with high affinity a small molecular weight com-pound  (4, 5). Recent studies demonstrated that simple geometric and physico-chemical descriptors of protein cavities (principally size, shape and polarity) are sufficient to predict structural ligandability  (6) (7) (8) (9).
The sc-PDB is a specialized structure database focused on ligand binding site in ligandable proteins  (10). We have selected in the PDB all proteins in complex with a small synthetic or natural ligand (140 Da < MW < 800 Da), provided this ligand was well buried and biologically relevant and since 2013 provided the binding site was predicted ligandable according to a machine leaning-based model. The different stages of database design process are detailed in the online documentation and summarized in Figure  1. The first publicly available version of sc-PDB has been released in 2004. The database has been annually updated with regular additions of new features (See Supplementary Table  S1  for a summary of changes since the database creation). Not only the quality and the precision of data improved over the 10 years, but new tools have allowed global analysis of data.
sc-PDB CONTENT The sc-PDB data are directly compatible with computational methods, such as docking, molecular mechanics and electrostatic calculations. Unlike the PDB, which generally does not represent hydrogen atoms nor defines ionization state of titratable groups, the sc-PDB provides an allatom model of molecules: (i) hydrogen atoms are added to amino acids considering that arginine and lysine are positively charged and aspartic and glutamic acids are negatively charged, (ii) hydrogen atoms are added to other residues according to ionized templates built from HET group dictionary (12), (iii) the intermolecular H-bonding network is optimized using the BioSolveIT Hydescorer program  (13). The overall processing of an original PDB entry yields atomic data for a single ligand, the protein chain(s) surrounding this ligand and its binding site (i.
e. all protein D400 Nucleic Acids Research, 2015, Vol. 43, Database issue residues with at least one heavy atom closer than 6. 5 Å to any ligand heavy atom). Of note, protein and binding site contain standard amino acids, and may include cofactor(s), metallic ion(s) and covalently bound residue(s), such as carbohydrate. Last, each sc-PDB entry is characterized with functional and chemical annotations. The current sc-PDB release contains 9283 entries, representing 3678 different UniProt  (14)  proteins and 5608 different HET ligands. The data set is non-redundant: although about 10% of ligands and almost half of proteins are present more than once in the database, each sc-PDB ligand/protein complex is unique. Less than 5% of proteins are encountered more than 10 times in the database, yet some of them have a very high copy number.
The three most frequent proteins are HIV protease (248 entries), cyclindependent kinase 2 (180 entries) and beta-secretase 1 (155 entries). More statistics are given in Supplementary Figure  S1 , and at http://cheminfo. u-strasbg. fr/scPDB/ABOUT. The total size of compressed database is 1. 5 GB. Its downloadable content is summarized in Table  1.
Similarity scores Text All charts and pictures of sc-PDB website are downloadable in png format. The complete database is downloadable as a compressed archive from the database homepage. protein. As mentioned in the Introduction, we have previously addressed this issue by analyzing the multiple binding sites in a given protein  (11). The sc-PDB clusters of binding sites can reveal differences in location, size, composition or 3D structure. For example, clustering the sc-PDB sites of adenylosuccinate synthetase yields three clusters; two of them that have similar structures and compositions except guanosine diphosphate (GDP) and Mg 2+ cofactors; the third one is localized in a different region in the protein (Supplementary Figure  S3 ). Other high quality databases derived from the PDB also facilitate the comparison of the binding sites across a protein family  (21) (22) (23).
The sc-PDB database is, however, the only meta-database enabling to search the PDB using user-defined queries mixing protein, ligand, binding site and binding mode properties. For ex-ample, a single query in the sc-PDB enables the selection of all protein-ligand complexes for which (i) the target is a protein kinase, (ii) the ligand is a fragment with a molecular weight between 150 and 300, (iii) the binding site comprises at least one bound water molecule, (iv) the ligand is neutral and contacts its target by one aromatic face-to-face interaction. Local structural similarity between non-homologous proteins can account for the promiscuity of a ligand, and thus can help explaining the side effects of a drug or suggest its repositioning toward a novel target and therapeutical indication  (24, 25).
The sc-PDB database now enables the identification of similar sites in distinct proteins using a pre-computed all-against-all comparison with the in-house developed Shaper algorithm  (8). The sc-PDB website allows to query the matrix of scores for any given sc-PDB site. It displays the distribution of scores and lists the entries whose similarity score is higher than a given threshold (default value is 0. 44). For example, the binding site for phosphomethylphosphonic acid-guanylate ester in Escherichia coli adenylosuccinate synthetase (PDB ID: 1HOP, HET: CGP) shares significant 3D similarity with a single site in sc-PDB, that of GTP in a murine homologous protein (Supplementary Figure  S4 ).
Using this approach we recently demonstrated that the protein-ligand binding mode is generally conserved within a family of homologous protein even though bound ligands are dissimilar. The sc-PDB database now enables the identification of similar 3D pattern in distinct complexes; the all-against-all comparison of sc-PDB complexes was computed using the program Grim  (26). The sc-PDB website allows to query the matrix of scores for any given sc-PDB ligand/protein binding mode. It displays the distribution of scores and lists the entries whose similarity score is higher than the threshold selected on the distribution (default value is 0. 65). For example, the binding mode of phosphomethylphosphonic acid-guanylate ester to E. coli adenylosuccinate synthetase (PDB ID: 1HOP, HET: CGP) shares significant similarity with 25 complexes in sc-PDB, representing 19 different proteins bound to GDP, GTP or close analogs.
A new interface The main architecture of database has not been changed, but the sc-PDB website has been completely re-designed to enhance interactivity. For every entry, the user can navigate in the main menu and directly switch views in the same window focusing on either a simple description of the entry, or a full characterization of the ligand or its binding site. Only searches for similar binding sites or binding modes open a new window with the rank-ordered list of sc-PDB hits corresponding to the query. At almost all sections of the web interface, molecules (protein, ligand, binding site), interaction pattern, tabulated results (hit lists, protein-ligand interactions) and charts (ligand and binding site properties, distribution of similar binding sites or binding modes) can be downloaded in the relevant file format (mol2, xlsx, csv, tsv, png, jpg, svg, pdf).
Figure3. Search the sc-PDB for similar binding modes. Screenshots display the distribution of values for a given query binding mode (top left), the ranked list of similar entries (bottom left) and the 3D alignment of a selected hit with the query complex (top right). The closer view (bottom right) better shows aligned interaction points. The 3D structure of the query is colored in yellow (PDB ID: 1hop, HET: GCP), the selected hit in green (PDB ID: 1rj9, HET: GCP). Interaction pseudo-atoms are colored by interaction type (green, hydrophobic; blue, H-bond with ligand acceptor; red, H-bond with ligand donor; brown, metal chelation).
FUNDING Centre National de la Recherche Scientifique (CNRS) ;  University of Strasbourg. LABEX  ANR-10-LABX-0034  Medalis; French government managed by ' Agence Nationale de la Recherche ' under ' Programme d'investisssement d'avenir'. Funding for open access charge:  LABEX ANR-10-LABX-0034  Medalis.
txt one or list of PDB ID matching Non-bonded interactions between Binary string search criteria sc-PDB ligand and its binding site For each residue in site is marked the presence or absence of interaction with ligand (hydrophobic contact, aromatic bond, H-bond, ionic bond, metal-ion bond) C-clusterID. tar. gz Clusters of binding sites The archive classifies all the Atomic data sc-PDB entries of a UNIPROT protein. It is organized into directories, one for each cluster of sites. Each cluster contains protein. mol2, site. mol2 and ligand. mol2 files of all individual PDB entries, which have been 3D-aligned to the site at cluster center Alignment. tar. gz Pair of PDB ID, 3D-aligned for The archive describes the protein, Atomic data optimizing site similarity (1) or site, ligand and cavity (1) or binding mode similarity (2) non-bonded interaction (2) files for the reference entry (original coordinates) and the compared entry (fitted coordinates) scPDB results list of entries matching search Annotation and 2D structure of Text criteria ligands in csv, xlsx or sdf formats PDBID distribution.