dataesr/scientific-paragraphs-categorization huggingface dataset:
For a given protein entry, initial data are processed from external portals and enriched by using state-of-the-art bioinformatics methods. As structural information is extremely useful to understand protein function and predict the impact of mutations, a strong effort has been put on the prediction of protein structures with a special treatment for membrane proteins. Browsing the database is available through text search for reference gene names or protein identifiers, through pre-defined queries or via hyperlinks. The RESPIRE database provides valuable information and unique annotations that should be useful to a wide audience of biologists, clinicians and structural biologists.
Indeed, many other cellular processes also occur in the RBC including ion and metabolites exchange with plasma, which is ensured by specialized membrane protein complexes. These membrane complexes may also carry specific epitopes (characterizing the so-called "blood groups"), which are vital for blood transfusion. Many attempts have been made to assemble and organize the existing knowledge on genes and proteins important in RBC in open/free dedicated databases (Table  1 ). In these databases, gene or protein-driven, sometimes genetic, clinical or genomic information is provided. It is however difficult to gather a cell-centric view of the RBC protein content in different conditions, for instance how variants of a given protein will be linked to new RBC group antigens definitions  [15, 16]  or alternatively how mutations can be related to diseases from diverse origins  [17].
Consequently, in order to obtain a more comprehensive view of the human RBC content and its implications in physiological and pathological conditions  [18, 19] , we have set up a dedicated database that aims at gathering in a one-stop window crucial information related to RBC. This database is called the Repository of enriched structures of proteins involved in the red blood cell environment (RESPIRE). The red blood cell protein content was extracted from review of the erythrocyte content performed by Goodman and co-workers  [24]  and completed with very recent proteomics analyses  [25, 26]  when list of protein identifiers were available. We have also added and curated this list with the proteins involved in regular red blood cell antigens definition. Proteins in Goodman's list that were not identified in subsequent studies were not included in the present version, even though they were submitted to the whole treatment process.
The corresponding data are available upon request. Data from external databases were processed for direct display in RESPIRE to limit the burden of gathering different information from various sources. More importantly, besides collecting scattered data from diverse sources, the originality of RESPIRE lies in the additional information it brings. This enrichment is obtained by applying cuttingedge bioinformatics methods resulting in enriched sequence annotation but also original structural data availability. At different steps, resulting data, e. g. multiple sequence alignments, can be downloaded for further processing and use. As it is now well admitted that 3D structure is an unavoidable link between sequence and function  [27] , we chose to provide to the user with as much structural information as possible using current data available or through state-of-the-art structural bioinformatics prediction methods.
Database name and URL Description Reference BloodSpot, http://servers. binf. ku. dk/bloodspot/ Gene expression in mice and human hematopoiesis in normal and pathological conditions  [20]  Erythrogene, http://www. erythrogene. com/ Genetic variation of the 36 blood group antigens extracted from the 1000 genomes project  [21]  HbVar, http://globin. cse. psu. edu/hbvar Database linking haemoglobin genomic mutations with thalassemia and hemoglobinopathies  [22]  Red Blood Cell Collection, http://rbcc. hegelab. org/ Compendium of proteins detected in red blood cells for which their presence is qualified by a confidence index  [5]  The Human RhesusBase, http:// rhesusbase. info/ This database integrates a very up-to-date knowledge on the rhesus locus and its consequence to the RH antigen D expression and phenotype  [23]  (Note that Respire Database is cited as related resources in this database) https://doi.
org/10. 1371/journal. pone. 0211043. t001 RBC membrane proteins play an important role in transfusion and in blood physiology, we paid a specific attention to membrane proteins, particularly to their 3D modelling. In these regards, a main difficulty arose from the distinction between "membrane-associated" protein and fully embedded membrane proteins. The corresponding annotation was mainly retrieved from uniprot or gene ontology databases. Tools to predict membrane protein topology were also used  [28]. Results can be provided upon request. These predictions were also useful to build the 3D models. Each protein model can be manipulated and visualized in 3D within RESPIRE without any expert knowledge. Altogether, this makes RESPIRE a unique resource in RBC knowledge. The database content is updated monthly, it is possible to follow only one protein entry by subscribing to its RSS feed, otherwise a more general report is indicated in the database history tab.
The definitive list in the database consists of 2475 unique proteins. Out of these proteins, according to their UNIPROT annotation, 384 are membrane proteins with a single-pass or a multiple-pass membrane domain (545 in GO annotation), 1190 are cytoplasmic proteins, and 647 proteins are found in the nucleus. Some proteins are found in multiple or smaller compartments, a more complex sub-cellular location decomposition from gene ontology annotation is available on the statistics tab in RESPIRE. A representative indication of data processing for a given protein entry is now described. The incorporation of a protein of interest into the database was initially performed using its UNIPROT identifier from the curated list of proteins. The UNIPROT  [29]  xml file was retrieved and then processed using biopython  [35]  to extract reference data altogether with identifiers for NCBI's Reference Sequence  [36] , PFAM  [37] , and the Human Gene Nomenclature Committee  [30]  if they exist.
This property has led to identifying so-called protein superfamilies. Hence, we searched for homologous sequences having an atomic 3D structure available (called "templates" in the following) in Protein Data Bank (PDB,  [49] ) using Blast software or HHblits, a highly sensitive similarity search method based on hidden Markov models  [50]. Default recommended parameters were used in both cases. Results of search were classified into four categories that guided the choice of appropriate tools to establish 3D models. The categories detailed in Table  2  are based on (i) the coverage on the target sequence (% cov), which is defined as the percentage of amino acids aligned between the template and the target sequences, (ii) the sequence identity (%id) between the target and the template sequence once aligned and (iii) the number of experimental structures needed to obtain a complete model.
Thresholds were adapted to account for the differences in sequence characteristics between soluble and membrane proteins due to the different environment in which they are embedded. For comparative modelling  [51] , the thresholds were %id >35 and %cov >70 for soluble proteins, while lower thresholds are applied, %id>25 and %cov>60 for membrane proteins  [52]. When the structural coverage was lower, a threading approach (alternatively called fold recognition) was considered to detect far putative homologous template. When no template could be used to predict the protein structure (no structural coverage), ab initio fragment-based assembly method was applied. For soluble proteins or single membrane-spanning proteins, MODELLER software  [53, 54]  was used for the comparative modelling category, while MEDELLER was used for multiple membrane-spanning proteins  [55].
For the threading category, I-TASSER  [56]  was used without distinction between soluble and membrane proteins. For ab initio category, Rosetta  [57]  and Rosetta membrane  [58]  were primarily used. For model production, each method was executed with default options. The best model produced was determined for every software by using its internal scoring function: objective function for MODELLER, cscore for I-TASSER, lowest energy for ROSETTA. Only the best model from each method according to their internal scoring function is displayed in RESPIRE. This model evaluation shall be enriched with independent scoring functions  [59]. Each model can be downloaded for visualization in PyMol  [60]. Depending on the protein modelling method, the prediction can take up to one week to be performed.
Description, implementation and architecture of the RESPIRE database The database is stored in MySQL version 5. 5. 32 and tables where created using the ORM implementation as available in the DJANGO framework. Data analysis, parsing and import were performed with in-house routines based on bioperl  [61]  or biopython  [35]. The project development was managed using the SCM git and REDMINE. The web server is powered by DJANGO in WSGI mode under Apache 2. 4 in Ubuntu 14. 04 LTS, the responsive design is obtained using Bootstrap (http://getbootstrap. com/), jQuery (http://jquery. com/) and BioJS  [62]. Interactive structure visualization is offered to the user using JSMol  [63]. Interactive sequence-to-structure mapping is performed using JSAV  [64]. The database is architected around 10 main tables filled in into two steps (see above).
Database statistics The database contains 2475 proteins for an average sequence length of 380 amino acids. On average, each protein has 4 sequence variants, and binds to more than 30 other proteins (not all incorporated in RESPIRE). Half of proteins have (partial) experimental structural data and more than a quarter of proteins have a model produced exclusively for RESPIRE. More than 1800 diseases are linked to RBC proteins from 3100 OMIM entries. Nearly 8500 gene ontology functions divided in three classes allow to regroup proteins (biological process, in a cellular component or possessing a particular molecular function). More than 380 proteins are annotated as integral or single-pass transmembrane proteins. These statistics are regularly updated and presented in detail on the "Statistics" tab in RESPIRE, with dedicated links to each category to restrict the protein search.
These interactions can be directly mapped on the structure by clicking on the dedicated checkbox. If no positions are described in the UNIPROT upstream entry, no list is provided to the user. RBC proteins may interact with many proteins to fulfil their function; these interactions are assembled from various tissues in BioGrid  [39]. To integrate these interactions into a functional network, the Interactions tab shows a dynamic responsive graph containing the direct binding partners available only in the RBC, limited to the first 50 members of the network for performance issues (Fig  1E). Many inferences between proteins are derived automatically from data-driven knowledge associations as performed by the Gene Ontology Consortium  [65, 66]. This information is regrouped under the Gene Ontology (GO) frame, which allows browsing the database content alternatively by clicking on a GO category.
The data presented in our database mainly present the content of the latest stages of the RBC differentiation. In order to get a broader overview of the protein expression levels during the RBC maturation process, an interactive diagram is available under the Expression tab (Fig 1H). These expression data were carefully determined by our partners of the GR-Ex consortium  [12]. Many entries in the database come from external databases, it is therefore important to track the changes appearing in the protein card. The History tab references all the modification of the protein entry in the database. It is also possible to register to the protein feed using tools such as Feedly (https://feedly. com) or Reeder (https://reederapp. com/) to be informed rapidly when a modification happened (Fig  1H).
Structural annotation and model prediction In comparison with other databases dedicated to RBC proteins, an important interest of our database relies on the important information brought by the 3D structure and the possibility of visualizing experimental structures or models produced specifically for RESPIRE. When available, experimental PDB structures  [43]  are presented, but most of the time, these structures are missing for the human species. We therefore computed the existing experimental structural coverage to qualify the category for modelling protein structures (see methods). The proteins belonging (i) to the comparative modelling category were modelled using Modeller for soluble proteins  [53, 54]  or Medeller for membrane proteins  [55] ; (ii) to the threading category were modelled using I-TASSER  [56, 69] ; (iii) to the ab initio category were modelled with ROSETTA suite  [57].
The membrane content could be considered within ROSETTA using a dedicated protocol  [58]. This focus on membrane proteins is particularly important since some of them have a high therapeutic interest but they are much less characterized experimentally than globular proteins. To ease a better comprehension of these categories and to provide an estimation of the model quality, two progress bars are displayed in the Structure tab. This condensed view allows also interactively to display and retrieve a PDB file or the model produced for RESPIRE. When the user clicks on a pdb entry, its title and reference are indicated, when a model is selected, the details of its prediction are provided (Fig  2 ).
Querying the database There are many ways to query features in the database. The general principle is to provide many hyperlinks redirecting within RESPIRE when possible or to the upstream annotation otherwise. For some annotations, an additional link to an icon directly allows the opening of the upstream reference in a new window. Complex queries are also available in a dedicated menu for specific categories, namely proteins with a model (RESPIRE), proteins at the cell membrane (as defined in UNIPROT), protein defining blood group antigens (ISBT) or proteins associated to a disease (OMIM) ( For more complex queries, a more advanced form allows the combination of criteria. In this form, selecting (a) Cell Localization "Single-pass or Multiple pass membrane proteins (Uniprot)", (b) Protein name "transport" and (c) Protein Size (AA) "200-800", there will be 14 answers in RESPIRE related to a protein containing the "transport" keyword in its name without models in the RBC membrane.
This complexity of membrane definition including localizations and/or organisations is assembled differently in different databases, giving different results for close queries. For instance, for membrane proteins, 384 proteins are membranous in RESPIRE if the "single-pass or multiple-pass membrane" annotation of UNIPROT is considered while in Gene Ontology, the "plasma membrane" annotation (GO:0005886) leads to 545 entries. The possibility to Clicking on the links will retrieve all proteins pertaining to a given category. (A) Query results concerning the "membrane" annotation in UNIPROT. 384 proteins are annotated with this membranous localisation. By clicking on a number or letter, the user can see all the protein names starting with this item, with an indication of an existing structural when the (3D) keyword is appended to the name, proteins starting with letter E are displayed.
Contact and expert annotation RESPIRE is a combination of upstream data references and of specifically produced protein models. Our main objective is to propose a high quality of service with regularly updated information. Importantly, any expert in RBC is welcomed to contribute in RESPIRE evolution and update. In this perspective, a contact form with pre-defined subjects is available if a user is willing to further enrich a given protein entry or report problems. The "Collaboration" subject is dedicated to more complex demands such as the incorporation of new data in the database, the additions of links to or the processing of other reference databases. The "Feature Request" is mostly for database updates on specific subjects like the addition of pre-defined queries, demands for updating a given protein model.
Conclusion and future directions The importance of RBCs in vital processes has driven the extensive characterization of protein abundance and expression level using large-scale studies. Up to now, it is difficult to assemble protein information linking these experiments and reference databases. To address these needs, we have set up a new database called RESPIRE devoted to red blood cell proteins, starting from a list of proteins available from the literature. The RESPIRE database combines sequence, structure and functional annotations altogether with original data obtained using up-to-date bioinformatics methods  [17, [56] [57] [58] [59] [60] 69, [71] [72]  in particular 3D models supported or not by experimental data  [73]. These predicted models should be considered carefully, but can serve as tools to design further experimental validations.
As new structural information is available regularly, low-quality models will be regularly improved. In the future, we shall continue to enrich this database with experimental results as they become available in the literature but also with manual curation involving biologist members of the research consortium GR-Ex. This curation effort is expected to focus on inherited disease such as sickle-cell disease or Diamond-Blackfan anemia, or to focus on infectious diseases such as malaria. We will expose convenient access to these data to ease cross-referencing in more general-purpose databases. The database is freely accessible, with an unrestricted access to data for download and external analysis. The evolution of the database is detailed per protein in a dedicated tab, and globally in a dedicated page.
The results of these queries can be retrieved in Comma Separated Value (CSV) format for further processing off-line in any spreadsheet editor (Fig 3B). It is also possible to browse the database by clicking on hypertext links provided at various places, like for instance clicking on a given gene ontology category under the Genetics tab to retrieve all proteins involved in a specific process (Fig 3C). It is also possible to use the "search as you type" box to search by UNI-PROT accession id or protein name (Fig 3D).
This work was funded by:  Institut National de la Santeet la Recherche Medicale , Recurrent funding;  UniversiteParis Diderot, Sorbonne Paris Cite, Recurrent funding ;  Institut National de la Transfusion Sanguine , Recurrent funding;  Conseil Regional Ile de France  ( SESAME 2009 );  French National Research Agency  [ ANR-11-IDEX-0005-02 ],  Program "Investissements d'avenir" for the Laboratory of Excellence GR-Ex  who granted  S. Teletchea  [ ANR-11-LABX-0051 ]; and  University La Reunion , Recurrent funding.
Lung Histological Examination and Colonization Analysis Fixed lungs were embedded in paraffin, sections of four mm thickness were realized and tissues were stained with hematoxylin-phloxin saffron (HPS). The morphology of tumor cells and adjacent tissue were then analyzed. After a two-dimensional lung surface reconstruction (NIS-Element software, Nikon), lung metastases areas were quantified with NIS-Analysis software (Nikon).
Colony Forming Assay Twenty-four hours post-irradiation, 60 spheroids/condition were collected and centrifuged. Supernatants were removed and spheroids were dissociated with 500 ml collagenase IV (0. 2%; Sigma) during 30 min at 37 C. Cells were re-suspended, counted and seeded in 6 well plates with a 2 mL final volume of complete culture medium. SK-MEL3 cells were seeded at 7200 cells/well, SK-MEL28 at 1200 cells/well, and B16BL6 and B16F10 at 400 cells/well. After 18 days of incubation for SK-MEL3, 10 days for SK-MEL28 and 8 days for both B16F10 and B16BL6 cells, medium was removed and colonies were rinsed once with PBS. Colonies were then fixed by methanol absolute during 3 min then revealed with 0. 5% crystal violet solution. The counting of colonies was realized using the ImageJ software.
Acknowledgments We are grateful to  Dr. M. Bonnet  ( UMR 1071 ,  Clermont-Ferrand ) for her expertise concerning RT-qPCR analyses. We also thank Master's students,  Raphael Benalouane  and  Zenthao Shao , for their participation to this project. We want to acknowledge the anatomopathology department of the  Centre Jean Perrin  and the  CICS  for the immunofluorescence and flow cytometry studies.
1 year and ,5 years. Anthropometric data were measured at baseline and after 4 weeks, 8 weeks, and 12 weeks of treatment. Although 18 women were enrolled in the study, 4 withdrew from the study and 2 did not return to their planned study visit, resulting in 12 women completing the study. All volunteers gave their informed written consent to participate in the study. The Tulane University's Institutional Review Board reviewed and approved the protocols. Investigational New Drug exemption was granted by the FDA to use CE/BZA in women with history of hysterectomy.
F. Data Analysis Measures of insulin secretion, elimination, and sensitivity were derived by mathematical modeling analysis of the IVGTT glucose and insulin concentration profiles using the minimal model (MINMOD Millennium, Pasadena, CA, version 6. 02) as previously described  [30]. Four major independent parameters were derived from the analysis by the minimal model: the insulin sensitivity (S I ) index, the acute insulin response to glucose (AIRg), the disposition index (DI), and glucose effectiveness (S G ). Homeostatic model assessment (HOMA)-B and HOMA-IR were derived from the HOMA model as described by Matthews et al. [31]. HOMA-b cell function [HOMA-B 5 20 3 fasting insulin/(fasting glucose/18-3. 5)] and HOMA-IR [HOMA-IR 5 fasting glucose 3 fasting insulin/405]. HOMA2-b and HOMA2-IR (using fasting C-peptide and glucose) were calculated with a HOMA2 calculator software (version 2.
068), which utilizes C-peptide (Table  3 ; Fig. 2 ). There was no difference between the two groups in IR assessed by HOMA-IR or HOMA2-IR. Glucose homeostasis was further assessed using an IVGTT followed by mathematical modeling using the MINMOD Millennium software. Unlike results obtained by HOMA-B, and HOMA2-B, the AIRg was not different between the CE/ BZA arm and the placebo [CE/BZA vs placebo: 189 (231 to 444) vs 225 (247 to 29), P 5 0. 273] (Table  3 ). However, unlike results obtained with HOMA-IR and HOMA2-IR, S I increased in the placebo arm. No changes were seen between groups in DI, S G , the basal insulin concentration, the removal rate of insulin from the interstitial space, the movement of circulating insulin to the interstitial space, glucose at time zero, and S G at zero insulin.
Acknowledments Financial Support: The study was supported by an investigator-initiated award from  Pfizer, Inc  to F. M. J. F. M. J. was also supported by  National Institutes of Health (NIH)  awards  DK074970  and  DK107444  and a  Department of Veterans Affairs Merit Review Award  (# BX003725 ). D. L. was supported by  NIH  Award  K12HD043451  from the  Eunice Kennedy Shriver National Institute of Child Health & Human Development of the Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Scholar  and  U54 GM104940  from the  National Institute of General Medical Sciences , which funds the  Louisiana Clinical and Translational Science Center. K. M. U. was supported by the  Department of Veteran Affairs. Clinical Trial Information: ClinicalTrials. govno. NCT02237079 (registered 11 September 2014).