dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sharing statement Merck Sharp & Dohme Corp. , a subsidiary of Merck & Co. , Inc. , Kenilworth, NJ, USA (MSD) is committed to providing qualified scientific researchers access to anonymized data and clinical study reports from the company's clinical trials for the purpose of conducting legitimate scientific research. MSD is also obligated to protect the rights and privacy of trial participants and, as such, has a procedure in place for evaluating and fulfilling requests for sharing company clinical trial data with qualified external scientific researchers. The MSD data sharing website (available at: http://engagezone. msd. com/ds_documentation. php) outlines the process and requirements for submitting a data request. Applications will be promptly assessed for completeness and policy compliance.
Andrey Meshcheryakov Grants or contracts from any entity: Sanofi, Astra-Zeneca, Merck Sharp & Dohme -My institution, me. Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Amgen, Bayer AG, BIOCAD, Bristol Myers Squibb, Eli Lilly, Merck, SERVIER, Takeda Pharmaceuticals, Eisai, AstraZeneca, Sanofi-Aventis -Honoraria for lectures, presentations, speakers bureaus. Support for attending meetings and/or travel: BIOCAD, SERVIER, Merck Sharp & Dohme, Sanofi-Aventis, Merck -Attending meetings and/or travel. Participation on a Data Safety Monitoring Board or Advisory Board: Amgen, Bayer AG, BIOCAD, Bristol Myers Squibb, Eli Lilly, Merck, SERVIER, Takeda Pharmaceuticals, Eisai, AstraZeneca, Sanofi-Aventis -Advisory Board. Adnan Khattak None declared.
Matteo S. Carlino Payment or honoraria for lectures, presentations, speakers bureauads, manuscript writing or educational events: Bristol Myers Squibb, MSD, Novartis -Honoraria for lectures, presentations, speakers bureaus. Participation on a Data Safety Monitoring Board or Advisory Board: Bristol Myers Squibb, MSD, Amgen, Novartis, Pierre Fabre, Roche, IDEAYA Biosciences, Sanofi, Merck Serono, Regeneron Pharmaceuticals, QBiotics, Nektar, Eisai -Advisory Board. Shahneen Sandhu Grants or contracts from any entity: Advanced Accelerators Applications (a Novartis company), Amgen, Merck Sharp & Dohme, Merck Serono, Genentech, AstraZeneca -Funding to the institution. Consulting fees: Amgen, Merck Sharp & Dohme, Merck Serono, AstraZeneca, Bristol Myer Squibb -Funding to the institution. James Larkin Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Eisai, Novartis, Incyte, Merck, touchIME, tou-chEXPERTS, Pfizer, Royal College of Physicians, Cambridge Healthcare research, Royal College of General Practitioners, VJOncology, Agence Unik, Bristol Myers Squibb -Honoraria for lectures, presentations.
Acknowledgements The authors are grateful to  Merck & Co. , Inc. , Kenilworth, NJ,  USA  for supporting the independent EORTC study  EORTC1325/KEYNOTE-054. We thank all the investigators who participated to this study, who have not been included among the co-author list of this publication (Supplementary List  A1 ). The authors warmly thank all EORTC Headquarters team members who have not been included among the co-author list of this publication, and who contributed to the study success ( S. Marreaud ,  S. Janssen ,  R. Louis ,  N. Elaut ,  L. Wijnen ,  N. Jha ,  S Rivrain  and  G. de Schaetzen ) as well as  Merck & Co. , Inc. , Kenilworth, NJ, USA team members ( C. Krepler ,  N. Ibrahim ,  V. Rivas ,  R. Kloss Silverman  and  S. Diede ).
The huge amounts of cancer genomic data generated is housed in open access biological repositories, such as the Gene Expression Omnibus (GEO) 1 , Array Express 2 , and Sequence Read Archive (SRA) 3. Such databases represent an important source of biological information that can be reanalyzed with different approaches and interests in mind. While the cost of high-throughput technologies has decreased significantly in recent times, sample size remains a limiting factor in cancer research, leading to the problem of class-imbalanced datasets that hampers correct statistical analysis of the data  [5]. Meta-analysis, which allows a measure of the combined effect of interest and offers greater precision than individual studies, may. CC-BY 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Systematic Review and Selection of Studies Following the inclusion criteria, we identified and carefully reviewed a total of 77 studies to avoid duplicates and ensure a focus on breast cancer, which finally provided 39 full-text articles. We retrieved raw DNA methylation data from five studies (Figure  1  -GSE78751, GSE52865, GSE141338, GSE101443, and GSE59901), which represents a total of 14 different comparisons between case and control groups including a total of 185 patients (150 cases and 35 controls) (Figure  2 ). Adjacent to tumor normal breast tissue was considered (paired samples) in all the studies unless GSE59901 where it is not indicated. The GSE101443 study (A) comprises four tumor samples and four healthy samples, which we included in the next step of the meta-analysis. For the GSE141338 study (B), only healthy and tumor samples were considered for the meta-analysis (41 cancer and 6 healthy samples).
For example, Figure  3b  shows how the luminal-A and luminal-B subtypes from the GSE52865 study share a total of 69 hypomethylated genes and 3 hypermethylated compared to control samples (see Figure  3a ). Of note, UpSet plots demonstrate a lack of significant genes across all studies. This data highlights the need for integrated strategies, such as meta-analyses, to increase the statistical power of any findings. For each of the comparisons described, we functionally profiled the list of all genes ranked by their differential MS. Table  1  shows the GSEA results for the Gene Ontology terms and KEGG pathways -the intersection of significant results failed to highlight common functions across the different studies. We integrated those results in the final functional meta-analysis in the hope of retrieving common functions affected in the different breast cancer studies.
A, B, C, D , and E report the distribution of samples for studies GSE101413  [19] , GSE141338  [21] , GSE52865  [22] , GSE59901, and GSE78751  [23]  respectively. Abbreviations: brc -no subtype specified, healthy -healthy breast tissue, basal -triple negative/basal-like subtype, her2 -her2-positive subtype, lumA --luminal A subtype, lumB -luminal B subtype, lumHer2 -luminal-Her2 undifferentiated, inVitro -in vitro samples, brca1 -BRCA1-mutated breast cancer, idc -invasive ductal carcinoma, ilb -invasive lobular carcinoma, and lymph -lymph node metastasis.
Systematic Review and Selection of Studies This review was conducted in October 2019, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines  [68]. Breast cancer methylation datasets were selected from GEO repository  [69] , using the following keywords: breast cancer, methylation, and Homo sapiens in studies published in English. The following exclusion criteria were applied: (i) studies conducted in organisms other than humans; (ii) sample size less than 10 in each experimental group; (iii) experimental design different from case-control and (iv) methylation profiling platform other than Infinium HumanMethylation450 BeadChip from Illumina. The study platform was chosen for its high resolution -around 450,000 methylation sites -and its wide acceptance in the field  [19].
Individual Epigenomic Analysis Differential methylation analysis for each comparison was carried out with the minfi R package. Briefly, permutation tests were performed to obtain the methylation scores of all analyzed regions on the Illumina BeadChip 450k. A total of 13 comparisons derived from the individual studies were performed in the form control vs cancer (where a positive methylation score indicates a higher methylation value on cancer samples than in the control condition). Comparisons were performed between paired samples for all the studies except for the GSE59901 study where the information Subsequently, methylation scores were annotated to gene level through the bumphunter R Package  [70]. In cases where more than one differentially methylated region (DMR) was reported for a gene, the one with the highest absolute value was used.
Then, a functional enrichment analysis was performed from the differential methylation results using Gene Set Analysis (GSA)  [71]  in which the genes were ordered according to their p-values and the sign of the contrast statistic. The GSA was then performed following the logistic regression model implemented in the mdgsa R package  [72] , as well as its corresponding functional annotation. The p-values were corrected for each function by false discovery rate (FDR)  [73]. The databases used for functional enrichment were the Gene Ontology (GO)  [74]  and the Kyoto PATHWAY Encyclopedia of Genes and Genomes (KEGG)  [75]. Significant functions were represented in the form of Upset plots  [76]  from which the number of functional elements specific and shared by each breast cancer subtypes can be observed -see Figure  5E -.
Web Tool The large volume of data and results generated in this study is freely available in the metafun-BC web tool (https://bioinfo. cipf. es/metafun-BC), which will allow users to review the results described in the manuscript and any other results of interest to researchers. The front-end was developed using the Bootstrap library. All graphics used in this tool were implemented with Plot. ly, except for the exploratory analysis cluster plot, which was generated with the ggplot2 R package. This easy-to-use resource is organized into five sections: (1) a quick summary of the results obtained with the analysis pipeline in each of the phases. Then, for each of the studies, the detailed results of (2) the exploratory analysis, (3) the differential expression, and (4) the functional characterization are shown.
case of the GSE59901 (D) and GSE78751 (E) studies, we discarded information regarding cell lines and lymph node metastasis (4 and 15 samples, respectively) and we considered only the breast tissues (28 tumor and 4 healthy samples for the GSE59901 study and 23 tumor and 4 healthy samples in the case of the GSE78751 study). Finally, we considered all samples from the GSE52865 (C) study (40 tumor and 17 healthy samples). Figure2presents a detailed configuration of the different datasets.
about the source of the control samples were missing. The comparisons are: Control vs TNBC (Triple Negative Breast Cancer) (GSE52865 and GSE141338), Control vs HER-2 subtype (GSE52865 and GSE141338), Control vs Lum-A subtype (Luminal A) (GSE52865 and GSE141338), Control vs Lum-B subtype (Luminal B) (GSE52865 and GSE141338), Control vs BRCA1 mutated (no subtype specified), Control vs Invasive Ductal Carcinoma (IDC), Control Vs Invasive Lobular Carcinoma (ILC) and Control vs BRCA (no molecular subtype specified).
Funding: This research was supported by and partially funded by the  Institute of Health Carlos III  (project  IMPaCT -Data, exp. IMP/00019 ), co-funded by the  European Union ,  European Regional Development Fund (ERDF , "A way to make Europe"),  PID2021-124430OA-I00  funded by  MCIN/ AEI  and  European Regional Development Fund (ERDF , "A way to make Europe").
The Slovak fieldwork was carried out in the newspaper SME between 2013 and 2016. Data were obtained from three rounds of interviews. Firstly, interviews with six former 'admins' (who worked at SME between 2004 and 2013) yielded accounts of how ex-admins remember the task being performed in the past and biographical narratives that help understand how administrative work fitted into journalistic trajectories and professional identities. Then, in 2013-2014, all five people who were employed in the job at that time were observed at work using a think-aloud protocol  (Ericsson and Simon 1993)  to understand both how they interacted with the technology and how they justified their decisions to approve or delete comments. Finally, this exercise was repeated with three new administrators in 2016, following a major staff turnover and an interface redesign, to test the influence of technological factors, managerial policies and workplace organisation on routine configuration and task performance.
Studying current trends in journalists' work thus enriches the sociological analysis of professions understood as systems of overlapping jurisdictions relating to the legitimate control of specific positions and particular kinds of socially-valued work. Finally, we wish to say a few words regarding the defamiliarising dialogical method used to retrospectively integrate our three studies and their datasets. The integration was hardly spontaneous owing to our different approaches, but the rounds of questioninganswering we absolved were helpful precisely because they revealed theoretical and methodological blind spots. Some questions, central in one study, drew blanks from one or both of the others and had to be excluded from the comparative analysis presented here. The process had its frustrations-sometimes resembling the old joke about a traveller who asks a local for directions and gets the response: "well, if you're trying to get there, I wouldn't start from here".
It acted as a rigorous test of the strength and limitations of specific research designs and a reminder that datasets come pre-formatted. A limitation of our method is that we did not re-interrogate each other's datasets. This would have been more time-consuming, and while it would have doubtlessly prompted fresh interpretations, it would not have overcome the (more significant) blind spots that become locked into a research project once basic theoretical and methodological choices have been established, interviews and observation conducted, etc. It nonetheless demonstrated the primacy of secondary things in a second sense: that datasets can offer insights which their authors had not anticipated when re-interrogated from the perspective of a critical friend in a collaborative dialogue around a shared 'boundary object'  (Star and Griesemer 1989) -in this case the boundary or edge of the journalistic field.
Reads were subsampled to 50Â coverage of the donor genome using seqtk version 1. 3-r106 (https://github. com/lh3/seqtk). Reads of recombinants were mapped to their corresponding donor genome (M2, 40288, or AYE) using bwa mem version 0. 7. 17-r1888 (64) and sorted using samtools version 1. 10. 3 (65). Donor (40288 or AYE) and recipient (M2) genome short reads were simulated using wgsim version 1. 10 (https://github. com/lh3/wgsim) and mapped to the donor genome (40288 or AYE) using the same parameters as for transformant sequencing reads. Reads were then tallied on the donor genome using bcftools version 1. 10, to keep only sites with read coverage above 25Â, where haplotype differs between donor and recipient and match donor haplotype. The variant calling and identification of converted markers were performed using twgt version 0.
1. 0. 5 (https://gitlab. com/bacterial-gbgc/twgt/-/tree/master). The resulting VCF file was then parsed using VariantAnnotation (66) using R version 3. 6. 6. With Tn2006, AbaR4, and AbaR1 being absent from the recipient genome, their insertions in the transformant genome were confirmed via read screening: their sequence together with 1 kb of flanking sequence was sketched using mash sketch and the presence of this sketch in sequencing reads was confirmed using mash screen version 2. 2. 2  (67). Data availability. Genome sequences are available within BioProject PRJNA741866. Raw sequencing reads are available in Sequence Read Archive (SRA) within PRJNA741866.
ACKNOWLEDGMENTS We warmly thank  Agnese Lupo  and  Marisa Haenni  ( ANSES, Lyon, France ) for sharing the clinical isolate 40288 and sequencing and antimicrobial susceptibility data. This work was supported by the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  and by the  RESPOND program  of the  Université de Lyon (UDL). A. -S. G. was also supported by  VetAgro Sup (Ph. D. funding).
Family history of myocardial infarction was considered when it occurred before the age of 60 years (mother) or 50 years (father). As blood samples were collected between 2000 and 2005 and death causes were available until 2014, the analysis concerned the occurrence of new CV events from 2005 to 2014. We used piecewise exponential Poisson regression, as the data were composed of discrete times of observation  (23). High blood pressure, dyslipidemia, diabetes, BMI, tobacco and alcohol intakes were accounted as timedependent covariates. Subjects who declared having RA but who were not reached by phone to con rm their diagnosis were excluded from the analyses. A sensitivity analysis was performed to compare subjects with available plasma sample and subjects without.
Acknowledgments We express our thanks to the Cohort team of the  Unit UMS 011 Paris University -Inserm -Versailles St-Quentin-Paris-Saclay University responsible  for the GAZEL database management. The GAZEL Cohort Study was funded by  EDF-GDF  and  INSERM , and received grants from the ' Cohortes Santé TGIR Program' ,  Agence nationale de la recherché (ANR)  and  Agence française de sécurité sanitaire de l'environnement et du travail (AFSSET) ".
Availability of data and materials The data support the ndings of this study are available from Cohortes team of the Unit UMS 011 Paris University -Inserm -Versailles St-Quentin-Paris-Saclay University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Cohortes team of the Unit UMS 011 Paris University -Inserm -Versailles St-Quentin-Paris-Saclay University responsible for the GAZEL database management.
Competing interests The authors declare no con icts of interest. Authors' contributions HG, PA, MB and MADA analyzed and interpreted the data and drafted the manuscript. GS performed the autoantibodies quanti cation, contributed to data interpretation and manuscript writing. GM and RSN performed the validation process of diagnosis. MZ and MG are responsible for the GAZEL database management and contributed to data interpretation and manuscript writing. All authors contributed, read and approved the nal version of the manuscript. Patients With Rheumatoid Arthritis: A Systematic Review and Meta-Analysis. Arthritis Care Res. 2020 Apr;72(4):561-76. Figures Figure 1.
Materials and Methods This paper is part of the International COVID-19 Sleep Study (ICOSS), and the research protocol and standardized survey questionnaire are described in detail elsewhere. 1 The project is a cross-sectional online survey including 14 countries: Austria, Brazil, Canada, Hong Kong (China), Province Jilin (China), Finland, France, Italy, Japan, Norway, Sweden, Poland, the UK and the USA. Data was obtained from May to August 2020. Participants received a link or QRcode to access the questionnaire. All subjects were at least 18 years old and agrees voluntarily to participate anonymously, which was considered as implied consent, as approved by the local Institutional Review Board in each country. Informed consent was obtained as every subject voluntarily agreed to participate in the survey Investigators from each country obtained ethical approval from the local ethics committee.
For the survey in Austria, the ethics commission of the Medical University of Vienna approved the survey and stated, that no further approval is needed as participation is voluntarily and anonymously and no clinical population is targeted. General data protection regulations were applied and the study complies with the Declaration of Helsinki. The questionnaire collected data on sociodemographic variables (eg, age, gender, marital status, education, work) and COVID-19 related information (ie, infection, treatment, confinement). To assess psychological variables, standardized and validated measures were included. Depressive symptoms were measured using the Patient Health Questionnaire -2 (PHQ-2, Kroenke et al, 2009  37  ) and the Generalized Anxiety Disorder -2 (GAD-2, Kroenke et al, 2009  37  ) was used to assess symptoms of anxiety.
For both scales, sum scores are calculated and the cut-off score is ≥3. The Abbreviated PTSD Checklist (PCL-2, Lang et al, 2012) assessed post-traumatic stress symptoms (PTSS) and consists of two items referring to repeated disturbing thoughts of stressful events in the past and feeling very upset when thinking about stressful events of the past, answered on a five-point scale (1 to 5). If the sum score is ≥4 it indicates PTSS. A single item assessed stress  39  and the WHO-5  40  measured overall psychological well-being. Quality of life (QoL) and health (QoH) data were measured through linear visual analogue scales. Questionnaires on sleep problems included items of the Basic Nordic Sleep Questionnaire,  41  the STOP Questionnaire (STOP, Chung et al, 2008  42  ), the Insomnia Severity Index (ISI; Morin et al, 2011  43  ) and a single question on REM sleep behavior disorder (RBD).
Statistical Analysis Statistical procedures were carried out using Stata/SE 16. 1. 46 Data from the USA were excluded, because collection method differed as participants received a monetary reward. Data from Poland was excluded due to too many missing values. Additionally, any participants with missing values on any of the variables were excluded. This led to the exclusion of 9100 respondents. Chi-square tests were calculated to detect differences in NMF between the pre-pandemic and pandemic period, and to investigate differences between individuals high and low in NMF on pandemic related and sociodemographic characteristics. The participants characteristics were described using frequencies (percentages). Logistic regressions were conducted to examine associations between high NMF during the pandemic (dependent variable) and PTSS, insomnia, sleep quality, stress, anxiety, RBD, OSA, depressive symptoms, sleep talking, sleep maintenance problems, early morning awakenings, sleep onset problems, use of sleep medicine/hypnotics, daytime sleepiness, fatigue, financial burden due to COVID-19, confinements, having had COVID-19, work situation during the pandemic that impacts how much everyday life changed, living alone, QoL, QoH and wellbeing (independent variables).
Spectropolarimetric data allow for simultaneous monitoring of stellar chromospheric log 𝑅 HK activity and the surface-averaged longitudinal magnetic field,   , giving the opportunity to probe the relationship between large-scale stellar magnetic fields and chromospheric manifestations of magnetism. We present log  HK and/or   measurements for 954 mid-F to mid-M stars derived from spectropolarimetric observations contained within the PolarBase database. Our magnetically active sample complements previous stellar activity surveys that focus on inactive planet-search targets. We find a positive correlation between mean log  HK and mean log |  |, but for G stars the relationship may undergo a change between log  HK ∼ -4. 4 and -4. 8. The mean log  HK shows a similar change with respect to the log  HK variability amplitude for intermediately-active G stars.