dataesr/scientific-paragraphs-categorization huggingface dataset:
Another short-term objective is to integrate most IOGA 4 MET's stations into the IGS and SONEL (the French dedicated GNSS data center for GLOSS) networks. For this purpose, real-time transmission will be implemented at all Eparses locations (using TAAF's satellite communication system) and all stations will be upgraded to include the European and Chinese (BeiDou) GNSS satellite constellations (allowing to both increase positioning performance and generate the multi-GNSS products required by IGS). The stations of Aldabra (ALBR), Sainte-Marie (MASM) and Diego-Suarez (DSUA), all used in this study, are among the first candidates. In the frame of the research project MAP-IO, a mobile GNSS station will also be installed permanently onboard the research vessel Marion Dufresne in late 2020. The RV Marion Dufresne, which cruises around 300 days per year in the Eparses Islands and Austral territories, will permit to collect meaningful observations in inaccessible areas of the SWIO as well as to further densify the spatial density of water vapor data collected in this oceanic basin.
Studies are also ongoing to evaluate the impact of GNSS data assimilation on AROME-IO's analysis and forecasts using the 3DVar assimilation scheme implemented in the research version of this operational model  (Bousquet et al. , 2020). GNSS data assimilation is expected to improve TC forecast in this basin through constraining more precisely the water vapor field in the model. We expect this development to improve, in particular, model forecasts in areas characterized by elevated and complex terrain like Madagascar or small volcanic islands such as Reunion and Mayotte. project "IOGA 4 MET-EI, " funded by the research consortium "Iles Eparses" 2017-2020, while NOSY was funded by La Reunion University through its internal research program OMNCG. IGN was funded by CNRS to coordinate geodetic operations for the Mayotte seismic-volcanic crisis.
ACKNOWLEDGMENTS We express their warmest thanks to TAAF administration,  Mrs. de Plucinski  ( Soanambo Hotel ),  Mr. Badourali  ( Kaleta Hotel ), and  Vidal-Lamouroux  ( Ambonara Mall ) for hosting our instruments in Eparses Islands,  Sainte-Marie ,  Fort-Dauphin  and  Nosy Be. All IOGA 4 MET GNSS stations, excepting GLOR which belongs to IPGP, are owned by  La Reunion University  and  CNRS. The IGS MAYG station is owned by  CNES. This is IPGP contribution 4158. IOGA 4 MET and IGS (MAYG) GNSS data used in this manuscript are available from IGN's RGP and IGS databases at ftp://rgpdata. ign. fr/pub/gnssmayotte and https://files. igscb. org,respectively. ERA5 data can be downloaded at https://cds. climate. copernicus. eu/cdsapp!320/dataset/reanalysis-era5-single-levels?tab=overview.
Human plasma We used a panel of 167 samples (Table  1 ) to validate our assay. Of these, 77 were COVID-19 negative and were collected in 2015 as described eralier  [13]. The remaining 90 samples were from consenting COVID-19 patients hospitalized in Montpellier University hospitals and included in the "COVIDOtheque cohort" (ClinicalTrials. gov Identifier: NCT04347850). The cohort received an institutional ethics committee approval (CPP Ile de France III, n°2020-A00935-34). They were collected between March, 26th and April, 25th, 2020 from RT-qPCR confirmed COVID-19 cases as described earlier  [14].
9 % for Nucleocapsid. We also combined the results of NC and Spike antigens to evaluate a sample status. By doing so, all the assay parameters (sensitivity, specificity, accuracy and the predictive values) were 100 % (Table  2 ). We next stratified the capacity of our novel assay to detect IgG directed against the spike and nucleocapsid by time after symptoms onset. We defined 3 categories: samples collected less than a week, between one week and two weeks and two weeks or more after symptoms onset. Table  3  summarizes these data and show that 50 % of patients seroconverted during the first week for both antigens, 77. 7 % and 83. 3 % between 1 and 2 weeks after symptoms onset on nucleocapsid and Spike, respectively. Two weeks or more after symptoms onset, 100 % of patients had seroconverted against both antigens.
Cross-reactions of COVID-19 convalescent samples with SARS-CoV1 and MERS-CoV antigens Although very diverse, some coronavirus proteins are conserved through the different clades while others, like Spike proteins, are quite species-specific  [18]. To estimate the level of antibody cross-reactions  induced by SARS-CoV2 in convalescent plasma, we also tested our positive control panel samples on the other highly pathogenic human coronaviruses, namely SARS-CoV1 and MERS-CoV recombinant proteins. Data from Table  5  summarize the results of this comparison. Of the 61 samples of presumably fully seroconverted COVID-19+ patients (i. e. two weeks after symptom onset) tested on the five antigens, 100 % cross-reacted with SARS-CoV1 Nucleocapsid protein and 45. 9 % also cross-reacted with SARS-CoV1 Spike protein.
Our data showed that for both antigens, the sensitivity was 100 % (Table  2 ). However, while the specificity of Spike antigen was also 100 %, that of the Nucleocapsid antigen was slightly lower (98. 7 %) because one sample from the pre-epidemic panel reacted weakly above the cut-off threshold with that antigen. This could reflect a non-specific binding or a cross-reaction with one of the mild coronaviruses circulating in France in 2015. Overall, the accuracy of both antigens was above 99 % (Table  2 ). Because a fully established IgG response in a natural infection normally covers all immunogenic antigens, we also analyzed the performance of our assay by combining the two antigens we tested. As expected, this resulted in a highly sensitive and specific assay with 100 % performance for all the parameters evaluated.
Acknowledgements This work was supported by  Institut de Recherche pour le Développement (IRD) , the  French Agence Nationale de la Recherche  (ANR; ZOOCOV grant) and  Montpellier University of Excellence (MUSE)  emergency response funding ( PANCOV-S grant ). Raisa Raulino  was supported by PhD grant from  INSERM  and  University of Montpellier  and  Antoine Nkuba  by a ARTS PhD grant from  IRD. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
W e announce the COVID-19 Disease Map (https://doi. org/10. 17881/covid19-disease-map), an effort to build a comprehensive, standardized knowledge repository of SARS-CoV-2 virus-host interaction mechanisms, guided by input from domain experts and based on published work. This knowledge, available in the vast body of existing literature  1, 2  and the fast-growing number of new SARS-CoV-2 publications, needs rigorous and efficient organization in both human and machine-readable formats. This endeavour is an open collaboration between clinical researchers, life scientists, pathway curators, computational biologists and data scientists. Currently, 162 contributors from 25 countries around the world are participating in the project, including partners from Reactome 3 , WikiPathways 4 , IMEx Consortium 5 , Pathway Commons 6 , DisGeNET 7 , ELIXIR  8  , and the Disease Maps Community  9.
With this effort, we aim for long-term community-based development of high-quality models and knowledge bases, linked to data repositories. The COVID-19 Disease Map will be a platform for visual exploration and computational analyses of molecular processes involved in SARS-CoV-2 entry, replication, and host-pathogen interactions, as well as immune response, host cell recovery and repair mechanisms. The map will support the research community and improve our understanding of this disease to facilitate the development of efficient diagnostics and therapies. By combining diagrammatic representation of COVID-19 mechanisms with underlying models, the map fulfils a dual role. First, it is a graphical, interactive representation of disease-relevant molecular mechanisms linking different knowledge bases.
Second, it is a computational resource of reviewed content for graph-based analyses  12  and disease modelling  13. Thus, it provides a platform for domain experts, such as clinicians, virologists, and immunologists, to collaborate with data scientists and computational biologists for a rigorous model building, accurate data interpretation and drug repositioning. It offers a shared mental map to understand gender, age, and other susceptibility features of the host, disease progression, defence mechanisms, and response to treatment. Finally, it can be used together with the maps of other human diseases to study comorbidities. In the construction of the COVID-19 Disease Map, we rely on multiple tools for curation and review the contributed content in a distributed, on-the-fly manner. Most importantly, already at this early stage, we involve practising physicians and clinical researchers to improve the scope and quality of the map.
We anticipate that the structure of the map will evolve as new knowledge about the disease is revealed. This distributed, multi-tool, multi-group approach is dictated by the urgency of the ongoing pandemic, by the high volume of new COVID-19-related publications, and by an impressive response from the research community. In this challenging situation, it is imperative that community-based approaches are used to develop high-quality models and data. To ensure a transparent view of the contributors and community resources, we rely on the support of FAIRDOMHub 14. All data and curation guidelines related to the COVID-19 Disease Map are available at https://fairdomhub. org/projects/190. We invite curators to join the project and contribute to building a solid foundation of COVID-19 molecular and cellular mechanisms using systems biology standards  [15] [16] [17].
Acknowledgements We would like to acknowledge the members of the  COVID-19 Disease Map Community. Their support, contributions and engagement make this project possible. All the members of the COVID-19 Disease Map Community can be found at https://fairdomhub. org/projects/190. We would also like to acknowledge the  FAIRDOMHub  project members, in particular  Martin Golebiewski ,  Carole Goble ,  Stuart Owen  and  Xiaoming Hu , for their support for COVID-19 Disease Map project on the FAIRDOMHub platform (https://fairdomhub. org/).
Data collection Clinical data Data will be collected using case report forms (CRFs) designed especially for the purpose of this project for patients and HCPs (online supplemental data). According to the operating modes of each participating hospital and their organisation in terms of clinical or epidemiological research, the CRF will be completed during a face-to-face interview and the use of medical records. The CRF has been pretested on a small number of patients and HCPs and adjusted accordingly. CRF will include demographic characteristics, underlying comorbidities, medical history, and clinical, biological and laboratory data on the SARS-CoV-2 infectious episode. In case the HCP is hospitalised, a patient's CRF including additional data regarding information on hospitalisation (ward specialty and type of room) and biological parameters (blood cell counts) will be completed.
Statistical analyses Data will be analysed using statistical methods to describe the characteristics of the healthcare setting and study participants, the percentage of COVID-19 of those presenting influenza-like illnesses, the percentage of patients with nosocomial COVID-19 among the confirmed cases. The primary criteria will be the proportion of patients and HCPs with confirmed nosocomial SARS-CoV-2 infection relative to all patients and HCPs with syndromes suggestive of SARS-CoV-2 infection during the study period. Secondary outcome criteria will be to (1) describe the delay of onset of suspected or confirmed nosocomial COVID-19 infection for hospitalised patients, (2) estimate the attack rate of confirmed COVID-19 cases in hospitalised patients according to their length of stay and (3) estimate the attack rate of confirmed COVID-19 cases among HCPs.
Data management and archiving Case report form All required information will be recorded on a paperbased CRF in a clear and legible manner, and justification must be provided for all missing data. Erroneous data noted in the CRF will be clearly crossed out and the correct data will be written next to the crossed-out information, accompanied by the initials of the investigator or authorised person who made the correction, the date and, if possible, a justification for the correction. To reduce data-entry errors by predefining plausibility checks and facilitate the rapid transfer of data, the electronic case report form (e-CRF) version of the study CRF will also be available. Transfer of the data to the coordinating centre in Lyon (France) will be performed via the e-CRF following approval by the French Commission nationale de Open access l'informatique et des libertés-National Commission for Data Protection.
Rights to access source documents Source documents are defined as any original documents, data and records in which data collected for a clinical trial are first recorded. Source documents will be kept by the investigator for 10 years or, if hospital medical records, by the hospital for 10 years. Each centre will have access to its own data during the study. To ensure quality control and auditing, the sponsor will be responsible for obtaining the agreement of all parties involved in the research to guarantee direct access to all places where the research is carried out and to source data, documents and reports. In accordance with current laws and regulations (articles L. 1121-3 and R. 5121-13 of the French Public Health Code), all documents and personal data required for monitoring, quality control or auditing will be available to the persons in charge of these activities.
Confidentiality The principal and associate investigators are required to respect professional confidentiality (articles 226-13 and 226-14 of the French Penal Code). In accordance with French laws regarding the confidentiality of study participant personal data (article L. 1121-3 of the French Public Health Code) and clinical/laboratory results obtained throughout the study (article R. 5121-13 of the French Public Health Code), individuals with direct access to the data will take all necessary precautions to ensure the confidentiality of the overall collected information. All personal data concerning study participants will remain strictly confidential. To respect privacy, all participant details will be anonymous for the purpose of database preparation. Study subjects will be coded as follows: AAXXXXZZYY.
DISCUSSION Human-to-human transmission of SARS-CoV-2, including nosocomial transmission, is now well documented. The risk of amplification of spread of the disease in healthcare facilities is strong in case of a lack of infection control measures. Early recognition of nosocomial COVID-19 infection in patients or HCPs is therefore essential for the setting up of immediate investigation and implementation of appropriate hygiene measures. Describing the signs and symptoms associated with nosocomial COVID-19 will enable comparisons with existing data on other nosocomial viral respiratory infections (ie, influenza, RSV, SARS-CoV and MERS-CoV). The prospective design and face-to-face interviews will make it possible to reduce recall bias and to collect accurate data. The expertise of the Lyon University-affiliated hospitals already strongly involved in the surveillance of nosocomial infections and the extensive experience of GABRIEL network countries in various research studies related to respiratory infections also strengthen this research project.
METHODS Study Locations and Participants This was a prospective, observational study (ClinicalTrials. gov; NCT03083158) of immune responses to hepatitis B virus (HBV) vaccine, with recruitment occurring at the Vaccine Evaluation Center (VEC), BC Children's Hospital, Vancouver, Canada in accordance with a local ethics committee-approved protocol (Ethics Ref: H17-00175). All initial sample processing was undertaken at the VEC laboratory. Other laboratory work occurred at various institutions in Vancouver, La Jolla, Boston, and Paris. Participants were healthy adults, aged 40-80 years, who were seronegative to HBV at the time of enrolment. Eligibility criteria are shown in recruited by e-mail, mail and telephone via an existing "permission to contact for research" database held at the VEC (Figure  1 ).
Known globin genes were removed corresponding to the f o l l o w i n g s i x E n s e m b l I D s : E N S G 0 0 0 0 0 2 0 6 1 7 2 , ENSG00000188536, ENSG00000244734, ENSG00000223609, ENSG00000213934, and ENSG00000196565. After filtering and globin removal, samples had a median library size of 4,842,497, with a minimum of 2,217,471 and maximum of 14,755,180. DESeq2 v1. 20. 0 (12) was used to identify differentially expressed (DE) genes. DE genes were identified using the Wald statistics test, followed by filtering for significance using a combined threshold of adjusted p-value ≤ 0. 05 and absolute fold-changes ≥ 1. 5. For pathway enrichment analysis, Sigora v2. 0. 1 (13) was used with the Reactome database at a hierarchy level of four. Correction for multiple comparisons was done using the Bonferroni method, filtering results based on a value of ≤0.
Correction of blood cell composition was performed to in whole blood samples using linear regression of counts from the complete blood count  (16). Batch effects between Sentrix IDs were observed using Principal Component Analysis (PCA) and ComBat (  17 ) was used to correct for for Sentrix ID. Data from technical replicates were also assessed. In order to improve computational efficiency and reduce multiple test correction, 101,864 non-variable CpGs were removed as previously described  (18). To analyze the data looking at the effect of vaccination, age or sex in the cohort, a linear mixed effects model was ran with DNAm value as outcome, the other factors as main effects with a covariate of the subject ID as a random effects.
Two micrograms per sample was injected into the EasynLC-1000 chromatography system (Thermo) with a 50-cm analytical column, packed in-house with C18, coupled to an Impact II Q-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany), with detailed parameters as previously described  (23). Data was analysed using MaxQuant (v1. 5. 5. 1) with default values and "Match Between Runs" activated, searched against the human Uniprot database (downloaded on 15 July, 2017). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (24) partner repository with the dataset identifier PXD020474. Plasma lipidomics samples were prepared using a variation of a published protocol  (25). Aliquots of 10 ml of plasma were mixed with 180 ml of ammonium bicarbonate (150 mM), followed by extraction with 790 ml of a 7:2 mixture of methyl tert-butyl ether and methanol, respectively.
Samples were spiked with 21 ml of internal standard (Table  4 ). Samples were shaken for 15 min at 4°C, and then underwent centrifugation at 3000 × g for 5 min, from which 100 ml of the organic portion was taken and dried on a 96-well plate; each was resuspended in 20 ml of ammonium acetate (7. 5 mM) in a 1:2:4 mixture of chloroform, methanol, and isopropanol, respectively. The samples were loaded using the TriVersa NanoMate (Advion, Ithaca, NY, USA) infusion robot and a 4-mm infusion chip, and analyzed by the Impact II Q-TOF mass spectrometer (Bruker Daltonics, Bremen, Germany) in positive and negative ionization mode. Raw data were processed using an in-house script. Batch correction was performed on log2-transformed data using ComBat from the "sva" R package  (26). A paired t-test was used to test for differentially abundant proteins at each postvaccination time point (Days 1, 3, 7, and 14) compared to the pre-vaccine time point (Day 0).
Immune Cell Phenotyping Fixed cells were stained per manufacturer recommendations with different labeled antibodies (Table  5 ) to identify specific cell populations. Flow cytometry data were acquired on a LSRII flow cytometer (BD Biosciences). Flow cytometry raw data were analyzed manually using Flowjo software (version 9. 9). In addition, immunophenotyping using automated gating was undertaken on the same samples. This has advantages over manual gating, including increased throughput and identification of specific cell populations with up to 50dimensional datasets  (27). Pre-processing to detect anomalous events obtained during data acquisition was done using the flowCut algorithm (https://rdrr. io/github/jmeskas/flowCut/ man/flowCut. htmL). In brief, this approach detects and removes events within time segments for which the fluorescence intensities deviate from the norm.
Amplified cDNAs from the eight 96-well plates were consolidated to two 384-well plates and purified with Ampure magnetic particles. A 10-fold diluted portion of each cDNAs was assessed for expression of the Beta-Actin housekeeping gene by qPCR for quality control of the amplified cDNAs. A cycle threshold (Ct) of ≤35 was used as a cutoff for the selection of 3,072 cDNAs for library preparation and sequencing. A Star liquid handling platform (Hamilton) was used to consolidate cDNAs selected for Illumina Nextera XT library  Single cell RNA-seq data was processed according to published methods  (30). Briefly, raw sequencing files were demultiplexing using Illumina barcodes, while sequencing primers and low-quality bases were removed using the Trimmomatic software package  (32). Trimmed reads were then aligned using HISAT  (33)  in two steps: first to a reference of ERCC sequences, and then to GRCh38 (Ensembl).
StringTie  (33)  was used to assemble the resulting alignments into transcript structures and gene expression values (TPM) estimated using GENCODE v25 annotation (Ensembl 87; 10-2016); HTSeq-count  (10)  was used to generate raw gene alignment counts. Quality control analysis was performed using sequencing and laboratory metrics, including average Phred score, read complexity, and sample concentration, to classify cell samples as pass or fail using a Random Forest quality control classification model as previously described  (34). Expression values for cell samples that passed quality control classification were fed into the Scater and SC3 algorithms for PCA, tdistributed stochastic neighbor embedding (tSNE) visualization, and cluster analysis  (35, 36). Unsupervised clustering was performed for the entire dataset, while additional supervised clustering guided by flow cytometry marker panels was performed to investigate within cell type variation.
Cell Mediated Immunity Cryopreserved PBMCs were thawed at 37°C and rested overnight in AIMV serum free medium (MAfisher, MA, USA). Viable cells were counted using an automated cell counter (Nexcelom, Lawrence, MA, USA). Up to 10 6 PBMCs were plated per well in 96-well plates and either left unstimulated or stimulated with either Staphylococcal enterotoxin B (positive control) or a HB peptide pool (AA labs, San Diego) to assess T cell activation in response to HBV re-stimulation. At 44-h post-stimulation, cells were detached from the wells using 20 mM EDTA (Invitrogen), washed in PBS, and stained with viability dye and antibodies specific to T cell activation markers (Table  6 ). At the end of the incubation, the stained cells were washed and flow data acquired on a LSRII flow cytometer (BD Biosciences).