dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses Biomarkers were dichotomized with respect to the median value to avoid the log-linearity assumption (thus avoiding inflation of the type-1 error rate associated with the estimation of the optimal cutoff value for predicting graft survival). Graft survival was the primary outcome and was defined as the time between the transplantation and the return to dialysis or preemptive retransplantation (deaths were censored). Survival curves were obtained using the Kaplan-Meier estimator. 37 he raw and KTFS-adjusted associations were calculated using Cox proportional hazards models. 38 The Holm procedure was used to correct for the multiplicity of the tests. The CD8 phenotypes with a corrected P value ,0. 05 were selected for further analyses. The discriminatory capacities were evaluated by the AUC for data up to 8 or 11 years posttransplant obtained via the inverse probability censoring weighted estimator.
39 The corresponding 95% confidence intervals (CIs) and P values related to the differences between AUC values were obtained by nonparametric bootstrap sampling (1000 iterations). All statistical analyses were performed using R version 3. 3. 2 or GraphPad Prism. The package ROCt version 0. 9 was used to generate the time-dependent ROC curves (www. labcom-risca. com/packages-r). The package nricens was used to calculate the net reclassification improvement. The package corrplot (https://github. com/taiyun/corrplot) was used to calculate and visualize the correlation between the CD8 cell-related populations. Mann-Whitney U tests, Kruskal-Wallis tests followed by Dunn post hoc tests, and paired Wilcoxon tests were used as appropriate, and the type of test used is included in the figure legends.
1%65. 1% TEMRA versus 8. 4%62. 6% EM; P,0. 001; Figure  3D ). Whereas most CD3 1 CD8 1 CD16 1 cells exhibited a TEMRA phenotype, a fraction of TEMRA CD8 expressed CD16 (11. 8%63. 9%; Figure  3E ). To validate the expression of CD16 by TEMRA CD8 and to gain additional insight into these CD8 subsets, we analyzed data from CyTOF performed on PBMCs from HV. 26  Via unsupervised clustering (Pheno-Graph; see the Methods section) and t-distributed stochastic correlation, and only significant correlations are shown (P,0. 01). (E) An EM CD8 T cell percentage of ,36% was associated with increased risk of graft failure. Patients considered at high risk (KTFS. 4. 17) were stratified according to the median EM (CD45RA 2 CCR7 2 ) CD8 T cell percentage 12 months post-transplant, and graft survival was assessed using the Kaplan-Meier estimator.
The number of patients at risk was calculated every year. neighbor embedding to visualize high-dimension data with single-cell resolution in two dimensions, we identified 23 clusters based on the expression of 24 markers (Supplemental Figure  7 ). Major CD8 subsets (naive, central memory, EM, and TEMRA) were identified according to the expected expression of CD45RA and CCR7 and were localized in neighboring locations on the t-distributed stochastic neighbor embedding map. Eight subsets of TEMRA CD8 were identified (clusters 5, 9, 10, 16, 17, 18, 22, and 23) with a shared phenotype (CD45RA 1 CCR7 2 CD25 2 CD62L 2 CCR6 2 GZMb 1 CD95 1 CD11a 1 CD57 1 ). Most TEMRA CD8 subsets were CCR5 1 CCR6 2 CCR4 int. The TEMRA subsets differed in their expression of costimulatory molecules (CD28 and CD27), IL-7 receptor a-chain (CD127), and inhibitory molecules (BTLA, CTLA4, and LAG3).
ACKNOWLEDGMENTS We would like to thank Dr. Bertram Bengsch and Dr. E. John Wherry for kindly sharing the CyTOF data and Samuel Granjeaud for his invaluable help with the R-based cytometric analysis. The Biological Resource Center of the Nantes University Hospital, F-44093, France (BRIF: BB-0033-00040) guarantees the quality of the biologic samples. Dr. Jacquemont and Ms.
FUNDING Dr. Yap is supported by a Fondation ProGreffe international fellowship grant. Dr. Jacquemont received a Société Française de Transplantation grant. This work was funded by ITMO Santé Publique grant A13053NS, supported by Agence Nationale de la Recherche (ANR; French National Research Agency) grant ANR-11-JSV1-0008-01, and supported in part by Réseau Thématique de Recherche et de Soins (RTRS) Fondation de Coopération Scientifique CENTAURE grants PAC8 and PAC9. This work was performed as a part of the IHU-CESTI project, which received French government financial support managed by the ANR via the "Investment into the Future" program ANR-10-IBHU-005. The IHU-CESTI project is also supported by the Nantes Metropolis and the Conseil Régional des Pays de la Loire. This work was also supported by the FP7 VISICORT project, which has received funding from the European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreement.
These events allowed for having in-depth discussions with interested aging people who accepted to visit a demonstration house, see a live demonstration of the system, and get their feedback. The demonstration house highlights the unobtrusiveness of the technological solution proposed for the potential participants. Interested people also observe sensor events in real-time and examples of real data over weeks and months indicating significant changes in health status. Potential participants asked for information about employed technological solutions, real benefits of adopting them at home, and possible risks. Nineteen participants accepted and have been equipped with the City4Age solution. An initial interview with included participants allowed for collecting some indications on their social and health profiles.
The objective is to be able to accurately determine the "habitual behaviors" of people by collecting data over time. Figure  3  showcases some deployed sensors in the Montpellier pilot site. For ethical reasons, the collected data will be available on request. All indoor and outdoor data are collected in the Montpellier pilot site local server. Data are completely anonymous and are referred uniquely by a unique participant identifier. The correspondence between the participant identifier and his identity is only accessible to the pilot site coordinator when an investigation or an intervention is needed. On the pilot site local server, collected data are used for the identification of Low Elementary Actions (LEAs) and measures. Low Elementary Actions are basic participants' actions which are inferred from received sensors' events (e.
g. , Start Moving, Stop Moving, Change Room, Visit Restaurant, etc. ). Measures are quantified data extracted from LEAs (e. g. , time in the bedroom/day, number of toilet visits/day, number of shops visits/week, etc. ). Later, this information is transferred to the City4Age repository and analytic algorithm where further treatments are performed in order to produce visualizations for the geriatrician. The complete architecture of the City4Age solution and the performed deployment and data analysis in the Montpellier pilot site is presented in Figure  4. We have identified relevant LEAs Based on internationally-validated geriatric references (e. g. , SEGA  [4] , MMSE  [5] , GDS  [6] , IADL  [7] , AGGIR  [8] , and others) and distinguished four main categories that can be monitored by our system: activities of daily living, mobility, social life, and nutrition as detailed in Table  4.
Technology Validation We have firstly validated the selected sensors and devices that have been used in the deployment. In the Montpellier pilot site, we have deployed a demonstration house as shown in Figure  7  and deployed outdoor sensors in the metropolis of Montpellier. All employed technologies, such as movement sensors, contact sensors, gateways, receivers, beacons, and Internet access points have been tested in this experimental house before being deployed. These experimentation allowed for identifying and fixing some technical problems in the technologies used. The validation of collected data in Montpellier pilot site started with the data collected from the demonstration house. This phase allowed for validating the daily activity recognition process based on collected row data and real observations.
Personal interviews with monitored older adults enable investigation of possible causes explaining observed changes; e. g. , detected changes in activity patterns correlate with personal feelings of slowness in physical movements and trembling in fingers related to Parkinson's, and detected changes in preparing meal duration correlate with forgetting recent events related to cognitive impairments. Regular review meetings allow for accurately investigating possible causes and investigating multi-dimensional correlations. Validating data collection with older adults, family members, and family doctors provides better correlation of detected changes in monitoring data with real medical health status. Professional caregivers collect electronic health records while visiting monitored participants a couple of times per day for medicine taking, toilet entry assistance, room cleaning, and nutritional services.
Introduction As part of the virtual BioHackathon 2020, we formed a working group that focused on the analysis of gene expression in the context of COVID-19. More specifically, we performed transcriptome analyses on published datasets in order to better understand the interaction between the human host and the SARS-CoV-2 virus. The ideas proposed during this hackathon were divided into five projects (Figure  1 ): 1. SARS-CoV-2 infection global analyses: Understanding how global gene expression in human cells responds to infection by the SARS-CoV-2 virus, including changes in gene regulatory networks. 2. Human-virus interaction analyses: Identification of human RNA-binding proteins that might be key in the interaction between human cells and the RNA genome of SARS-CoV-2. 3. Increased risk factors analyses: Investigating gene expression in other datasets with the goal of identifying commonalities and differences with the two previous analyses, focusing on specific genes.
, 2008). Conversely, Nucleolin (Nsr1p) binds to the 3' noncoding region of the tombusvirus RNA and is able to inhibit viral replication  (Jiang, Li, & Nagy, 2010). In this work, we analyzed not only the aforementioned transcriptomic dataset, but also two additional studies involving SARS and MERS, with GEO accessions GSE122876  (Yuan et al. , 2019)  and GSE56192. All datasets used were mapped to the GRCh38 assembly of the human genome (see Methods for details), as well as to a database of viral sequences  (Uphoff, Pommerenke, Denkmann, & Drexler, 2019). The overarching goal was to find specific features from SARS-CoV-2 which are less accentuated or absent in the other species. Since it is not yet known which specific human proteins bind to the SARS-CoV-2 genome or what role they play, we also analyzed the SARS-CoV-2 genome sequence to predict putative interactions with human RNA-binding proteins, and used the gene expression analysis we performed to identify instances of bidirectional regulation between human RNA-binding proteins and SARS-CoV-2.
Exploratory data analysis We used the first RNA-Seq dataset available of in vitro cells infected with SARS-CoV-2 (GSE147507), as mentioned in the previous section. We first performed a Principal Component Analysis (PCA) to bring out strong patterns in the data to analyze the overall effect of experimental covariates, and to identify unexpected outliers or batch effects. Figure  2  shows that there exist clear sources of variation in the data that need to be taken into account if cross-sample analyses were to be performed. Since our aim was to find specific SARS-CoV-2-infection regulated genes, we decided to perform the downstream analyses on pairwise comparisons controls vs. infected within each experiment (batch), maintaining the different cell lines separated, and comparing the results of the differential expression and functional analyses across experiments.
Differentially expressed genes We decided to focus on genes differentially expressed (DE) upon SARS-CoV-2 infection, whether detected in NHBE or in A549 cells. Figure  3  shows the number of genes DE detected from each of the methods used: DESeq2  (Love, Huber, & Anders, 2014) , edgeR  (Robinson, McCarthy, & Smyth, 2010)  and limma-voom  (Law, Chen, Shi, & Smyth, 2014) ; for details, see the Methods section. In order to select SARS-CoV-2 specific genes, we selected among these DE genes only those that were not present in the other RSV and H1N1 infected cells. The full list has been made publicly available through the Zenodo platform (see the "Data, GitHub repositories and reproducible workflows" section). The top 15 genes up-and down-regulated are given in Table  1  and Table 2.
Gene expression analysis These analyses included two other datasets that were not integrated in the previous pipeline, and that are further detailed in the datasets from the Methods section. Statistical analyses of the differential gene expression, transcript isoforms, functional terms, and signaling pathways yielded a large number of significant entities, even after applying multiple hypothesis correction (FDR-adjusted p-values or Bonferroni-adjusted p-values as detailed in Table  4 , Table  5 , Table  6 , and Table  7 ).
Integrative analysis Combining the results from the multiple pathway and functional enrichment analyses revealed 56 activated pathways and 185 inhibited pathways across the combined dataset. Of these, nine pathways were activated during SARS-CoV-2 infection, while four were inhibited. Six pathways were uniquely present during SARS-CoV-2 infection (e. g. "Complement and Coagulation Cascades", "TRAF6 mediated induction of NF-kB and MAP kinase", and "Cytokine Signaling in Immune system"). We also found two pathways that had opposite directionality during SARS-CoV-2 infection when compared to other viruses (Chemokine signaling, Plasminogen activating cascade). A similar analysis that focused on the enriched functional terms showed 279 annotated terms that were unique to SARS-CoV-2 infection (e.
The list of RBPs was found to be enriched for proteins involved in mRNA splicing, translational control, gene expression, deadenylation-dependent mRNA decay and formation of the HIV-1 elongation complex containing HIV-1 TAT (HNRNPA1, PABPN1, SRSF3, SRSF1, PTBP1). We next searched the DE gene sets generated by our previous analyses, as well as a protein expression dataset  (Bojkova et al. , 2020)  to test whether any of the putative virus-interacting proteins showed expression changes in response to viral infection. We saw evidence of weak (FC<1. 5) but significant (adjusted p-value < 0. 05) increase in protein expression 24 hours after infection for ELAVL1, PABPC1, PTBP1, and SRSF1. For ELAVL1, this was supported by a similar change in gene expression levels in infected NHBE cells. Interestingly, the PABPC1 and PABPC4 proteins, whose binding sites are enriched in the SARS-CoV-2 3'UTR, have been shown to interact with the N protein of SARS-CoV-2  (Gordon et al.
Integrated analysis with chronic obstructive pulmonary disorder We combined the virus results reported in Project 1, with those generated from an analysis of a subset of samples (20 cases vs. 20 controls) from human patients diagnosed with chronic obstructive pulmonary disorder (COPD; GSE57148). Briefly, the significant results consisted of: 1,724 DEGs, 66 isoforms, 16 pathways, and 0 functional terms. Comparing these results to those identified in the host response to virus infection revealed several shared pathways (e. g. Innate immune system, Nonsense-mediated decay). Even so, none of the significant COPD pathways were identical to those for SARS-CoV-2. Interestingly, there were a subset of pathways that were unique to COPD when compared against virus infection (e. g. PI3 kinase pathway, IL1-mediated signaling events, IL23-mediated signaling events).
Discussion Our analysis from Pipeline 1 regarding the dataset GSE147507 shows that SARS-CoV-2 elicits a transcriptional response in human cells with a total of 213 genes upregulated and 87 genes downregulated in NHBE cells. The response of A549 immortalized cells was milder as compared to primary cells, however we believe that the response from the primary epithelial cells can be seen as much closer to what happens in vivo. This is why we did not select only genes common to both cell types infected with SARS-CoV-2 for subsequent analyses, but rather a combination of them. Pipeline 2 detected a greater number of DE genes and isoforms, and of enriched functions as reported above, however, the results were for the most part comparable. In general, the transcriptional response observed in SARS-CoV-2 was similar to a general viral response seen with the other viruses tested, in accordance to what was reported in the original preprint  (Blanco-Melo et al.
Inspection of the top upregulated genes specifically in SARS-CoV-2 infected NHBE cells identified genes that are also induced during CO intoxication (SPRR2E, SPRR2D, SPRR2A) and malaria infection (VNN3, VNN1, MMP9 )  (Min-Oo et al. , 2007; Prato & Giribaldi, 2011; Zheng et al. , 2009). Interestingly, ongoing clinical trials are trying to assess the efficacy of hyperbaric oxygen chambers used in CO intoxication (https://clinicaltrials. gov/ct2/show/NCT04332081) or the antimalarial drug hydroxycloroquine (https://clinicaltrials. gov/ct2/show/NCT04332991) for the treatment of COVID-19. Several high-confidence down-regulated genes from (Table  2 ) were also of interest given their role in host immune response and cytokine signaling. CD86 is commonly associated with immune activation and antigen presentation  (Na-Ek et al.
Future work Although a lot has been accomplished during the virtual BioHackathon, many avenues can be pursued in the future and several of the proposed projects could be taken to completion. Below are listed some potential future lines of work based on the methodologies and workflows described here. * Projects 1, 2 and 3 would benefit from the inclusion of additional datasets as these become available. For this purpose, a thorough research of the literature and public databases should be performed on a regular basis. More specifically, for projects 1 and 2, the list of potentially interesting genes obtained could be further refined by including these, increasing the robustness of the final results. * A deeper analysis to interpret all the results generated here is required, especially with the help of experts in the fields of virology, immunology, molecular and cellular biology, and clinicians.
Datasets Raw sequencing data were downloaded from the Gene Expression Omnibus (GEO) database (see Table  12 ). These studies compare the transcriptional or proteomic response of different cell types to different viruses, with different time points and concentrations. Table  12. Transcriptomic datasets used in this work  (Robinson et al. , 2010) , Limma-voom  (Law et al. , 2014)  and DESeq2 were then used to obtain pairwise differential expression, applying a statistical cut-off of 0. 05 for the adjusted p-value, retaining only the set of genes which are identified by at least two methods across the condition of interest. The P-values obtained were adjusted using the Benjamini-Hochberg method. Finally, a comprehensive enrichment analysis was performed employing GeneAnalytics (Ben-Ari  Fuchs et al.
, 2016) , including Gene Ontology (GO) and pathway enrichment analyses. Within GeneAnalytics, p-values are calculated assuming an underlying binomial distribution and corrected for multiple comparison using False Discovery Rate (FDR). Reads were also aligned against the SARS-CoV-2 Wuhan-Hu-1 reference genome (NC_045512. 2) using HISAT2 v2. 2. 0. Thereafter, reads that aligned to both the human and virus reference genome with the same edit distance were discarded. At last, StringTie v2. 1. 1 was used for gene count generation for the viral reads. The highly reproducible, containerized pipeline was implemented in Nextflow based on the nf-core framework  (Ewels et al. , 2020)  to enable its reuse with additional datasets (see Project 5). A step for read filtration was included in the workflow, in which 0.
Differential gene expression and enrichment analyses with Pipeline 2 This analysis was an alternative approach to the pipeline described above, and consisted of processing and analyzing multiple publicly available datasets from the Sequence Read Archive (SRA)  (Leinonen, Sugawara, Shumway, & International Nucleotide Sequence Database Collaboration, 2011). The viruses included in the analysis include severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus, and respiratory syncytial virus (GSE147507); additionally, samples infected with middle east respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-1 (GSE56192), or MERS-CoV1 (GSE122876) were also included. The studies analyzed were limited to those performed in human-derived cells. The SRA files were transformed to fastq files prior to applying the ARMOR workflow  (Orjuela et al.
Regulation of gene expression Data from the GEO database series GSE147507 (April 7th 2020) were analyzed with the integrated system for motif activity response analysis  (Balwierz et al. , 2014)  considering transcription factors, epigenetic regulators and microRNAs (human genome version hg19). For every run replicates of similar experimental conditions were averaged. The scripts used for this analysis and the according results are available on GitHub (https://github. com/ajgruber/ SARSCoV2_Host_Cell_Response_Analysis).
Project 2: Analysis of SARS-CoV-2 interactions with human RNA binding-proteins The reference genome sequences for SARS-CoV-2, SARS-CoV-1 and RaTG13 were downloaded from NCBI (accession numbers NC_045512. 2, NC_004718. 3, MN996532 respectively). A list of human RNA-binding proteins and their experimentally determined PWMs was downloaded from the ATtRACT database  (Giudice, Sánchez-Cabo, Torroja, & Lara-Pezzi, 2016). The list was filtered to retain PWMs from competitive RNA-protein binding experiments, and to remove PWMs with high entropy. 102 RBPs with 205 experimentally generated PWMs were selected for analysis. The TFBSTools (version 1. 20. 0) library in R version 3. 5. 1 was used to scan the SARS-CoV-2 reference genome as well as all simulated nucleotide sequences for binding sites. To identify proteins whose binding sites were enriched in the genome, a background model was generated by simulating 5000 genomes of the same size and nucleotide composition as the reference genome.
Integrated analysis with chronic obstructive pulmonary disorder The RNA-sequencing reads from a COPD related dataset involving human clinical samples were downloaded from the GEO database (GSE57148). These reads were then analyzed using pipeline 2 from project 1 as described above  (Orjuela et al. , 2019). Differentially expressed genes from the case vs. control comparison were then subjected to the same signaling pathway and functional enrichment analyses produced from the second pipeline in Project 1. These results were combined and analyzed together to identify significant pathways and functions that were shared between COPD and SARS-CoV-2 infection.
Project 4: Identification of potential pharmacological treatments As a first proof-of-concept, we carried out a compound-gene association analysis using Ge-neAnalytics, which enables exploring relationships between compounds and gene networks to discover potential pharmacological treatments. More specifically, this tool retrieves information from GeneCards, which associates (human) genes with compounds and drugs. Furthemore, the compound-gene association is based on information pertaining to direct binding between the compound and the gene product (e. g. , enzyme, carrier, transporter), or the existence of a functional relationship (e. g. , pharmacogenomics). Similarly to the GO and pathway enrichment analysis within GeneAnalytics, the binomial distribution is used to test the null hypothesis that the queried genes are not over-represented within any compound in the data sources utilized.