dataesr/scientific-paragraphs-categorization huggingface dataset:
TCR residues are renumbered using a standardized procedure  (53, 54). The initial models generated by the web server were further refined in four rounds using a protocol adapted from Bobisse et al. (28). Briefly, the CDR loops were refined by pairs using Modeller software version 9. 25  (55, 56) : CDR loops 1/2 at round 1, 1/3 at round 2, 1/2 at round 3, and 1/3 at round 4. Only the residues in coil conformations in the initial models were refined. In each round, 500 models were generated, and the best model based on the Modeller internal DOPE score was selected and used as an input for the next round. At the end of the four rounds, a representative model was chosen on the basis of the consensus of unweighted contacts as follows: Contacts between residues at the TCR-pMHC interface (defined by a distance lower than 5 Å between heavy atoms) were counted in the set of 4*25 models with best DOPE scores of each refinement round.
Washing was performed two times to avoid nonspecific dextramer staining. CMV pp65 NLVPMVATV was used as a positive control. For TIL analysis, a dextramer complexed to a non-natural irrelevant peptide (ALIAPVHAV) was used as a negative control. All samples were analyzed on an LSRFortessa (BD Biosciences, FR, EU) with conserved settings throughout the entire study. Data were analyzed using FlowJo software (Tree Star v10. 4, NJ, USA).
Acknowledgments: We would like to thank  B. Vincent ,  C. Smith , and  J. Garness  for sharing data and fruitful exchanges. We also thank  G. Ichim  ( Centre de Recherche en Cancérologie de Lyon ) for help with the setup of the Nanolive experiments and  M. Ouzounova  for help in establishing organoid models. We thank  B. Manship  for manuscript editing. Funding: This work was financially supported by the  PRT-K  grant for research in translational medicine for oncology, the  Ligue Nationale contre le Cancer ,  CLARA  project  ERV-BREAST CVPPRCAN00080 ,.
Participants We carried out a longitudinal, retrospective, single-center observational study between May 2015 and March 2020 in the Diabetology, Endocrinology and Nutrition Department of the University Hospitals of Strasbourg, France. Adult patients aged 18 years or older with T1D managed by SAP with PLGM (Minimed 640G system, Medtronic) were included in the study. For the participants, the time of study participation was between 6 months and 3 years. Pregnant patients and those with missing data or insufficient sensor use (<50 %) were excluded from the study. The study was approved by local ethics committee and each patient has provided signed written informed consent.
Metabolic outcomes Metabolic parameters were collected for patients in both group: HbA1c measured by high-performance liquid chromatography (HPLC) was the primary endpoint. The secondary endpoints were the parameters of CGM (time-in-range [TIR], i. e. , time spent in the glucose target range of 70-180 mg/dL [3. 9-10 mmol/L]; time-below-range [TBR], i. e. , time spent below blood glucose levels of 70 mg/dL [3. 9 mmol/L]; timeabove-range [TAR], i. e. , time spent above blood glucose levels of 180 mg/dL [10 mmol/L]), coefficient of variability and the percentage of sensor wear. Severe hypoglycemia was defined as the occurrence of hypoglycemia requiring external assistance with hospitalization or the injection of glucagon by a third party. Data were collected at the beginning of the study, then at 6 months, 1 year, 2 years, and 3 years of Minimed 640G system use.
Statistical analysis The characteristics of the total population and HbA1c were expressed as mean ± standard deviation (SD). CGM data were expressed as median [25th-75th percentile]. Comparisons of CGM data, HbA1c, and Enlite sensor adherence between the different groups were performed using the Student's test. The threshold for statistical significance (p-value) was set at 5 %. All statistical analyses were performed with the XLSTAT software.
Similarly, in their 12-month retrospective study, Tsunemi et al. [13]  observed a significant increase in TAR > 180 mg/dL along with a significant reduction in TBR < 50 mg/dL and a non-significant decrease in TBR < 70 mg/dL after 3 months of follow-up. According to the authors, this increase in TAR was related to hyperglycemia observed after the predictive suspension of insulin delivery by the pump and to the inappropriate consumption of carbohydrate substances by some patients in order to correct hypoglycemia. Regarding TIR in our patients, it should be noted that we did not find any increase as described by Battelino et al. ,  [17]  considering the significant decrease in HbA1c. The lack of significant improvement in TIR and TAR could be related to the fact that each 15-day period CGM data were only collected every 6 months during the follow-up.
Data Collection Participants completed an online survey using online software (Eval&Go) (Evalandgo SAS, Montpellier, France) between 1 September 2020 and 31 December 2020. The survey, developed by Chaire Prevent'Horizon, was composed of three different sets of questions: (1) demographics and morphology, (2) physical and sedentary activity behavior, and (3) domestic and familial activities. Demographic and morphological characteristics included age, sex, residential status (three categories: lives alone, as a couple, with others), and body mass index (BMI). Additionally, occupational status (regrouped into six categories: retired persons, civil servants, intellectual professions/managers, clerks, service workers, other) and annual taxable household income (regrouped into five categories: <EUR 20,000; EUR 20,000-30,000; EUR 30,000-50,000; EUR 50,000 and above; non-responses) were captured.
Consequently, the explanatory factors of experiencing difficulties could differ between the general population (containing retired and unemployed persons) and the employed population. The comparison of both regressions allows us to identify potential discriminant effects of covariates for the employed population that could support the work design change (such as working from a home office) during the lockdown. All analyses were performed using Stata V. 16. 1 software (StataCorp, College Station, TX, USA) and significance was set at p < 0. 10.
Statistical Analysis The mortality was recorded, and data were represented using Kaplan-Meier survival curves  [25]. The protection elicited by vaccines was determined by comparing the survival percentage of vaccinated and unvaccinated groups using a Log-rank test. Further, the Relative Proportion Survival (RPS) was calculated as RPS (%) = (1-A/B) * 100 where A and B are the mortalities at the end of challenges in vaccinated and unvaccinated fish, respectively. Additionally, differences in the pathological symptoms of P. salmonis infection between different treatments were analyzed using a non-parametric Chi-square test. Finally, significant differences in ELISA tests were compared using the Student's two-tailed t-test, p < 0. 05. All statistical analyses were performed using R Core Team (RStudio, Vienna, Austria).
Funding: This research study was funded by  CONICYT-Chile  through the project  FONDECYT  No. 1140772  awarded to J. A. G. and P. C. Furthermore, J. A. G. was supported by the  Cooperative Research Program Fellowships of OECD-PCI 2015-CONICYT. C. F. was supported by  PUCV  and  CONICYT-Chile  through a  Postdoctoral fellowship  ( Proyecto VRIEA-PUCV Postdoctorado and FONDECYT  No. 3170744 ). D. T. was supported by  ANID-Chile  through a  Postdoctoral fellowship  (Fondecyt No. 3210502 ). B. M. -L. was supported by the  Postdoctoral program  from the  National Research and Development Agency of Chile  ( ANID-Chile No. 74200139 ). Institutional Review Board Statement: The animal study protocol was approved by the  Bioethics Committee of the Pontificia Universidad Católica de Valparaíso  and the  Comisión Nacional de Investigación Científica y Tecnológica de Chile  ( FONDECYT No.
Single-variant analysis We first performed an exome-wide association study using single variants with a MAF (minor allele frequency) higher than 0. 1% and an allele count of 6 or more in at least one cohort, with the same additive model and covariates used in the COVID-19 HGI GWAS  [8]. Analyses were performed separately by each cohort and each ancestry using Firth regression as applied in the Regenie software  [18]. Firth regression is a penalized likelihood regression method that provides unbiased effect estimates even in highly unbalanced case-control analyses  [19]. The summary statistics were then meta-analyzed with a fixed effect inverse-variance weighted model within each ancestry, and then with a DerSimonian-Laird random effect model across ancestries. The previously described Neanderthal chromosome 3 locus associated with COVID-19 outcomes  [8, 20]  was also found in all three phenotypes (Figs  3  and S1 ), with lead variants in the CXCR6 gene for the severe COVID-19 phenotype (rs13059238), and in FYCO1 in the hospitalisation phenotype (rs13069079), and for the LIMD1 gene in the susceptibility phenotype (rs141045534).
Cohort inclusion criteria and genetic sequencing Any cohort with access to genetic sequencing data and the associated patient level phenotypes were allowed in this study. Specifically, both whole-genome and whole-exome sequencing was allowed, and there were no limitations in the platform used. There were no minimal number of cases or controls necessary for inclusion. However, the first step of Regenie, which was used to perform all tests (see below), uses a polygenic risk score which implicitly requires that a certain sample size threshold be reached (which depends on the phenotype and the observed genetic variation). Hence, cohorts were included if they were able to perform this step. All cohorts obtained approval from their respective institutional review boards, and informed consent was obtained from all participants.
Variant calling and quality control Variant calling was performed locally by each cohort, with the pre-requisite that variants should not be joint-called separately between cases and controls. Quality control was also performed individually by each cohort according to individual needs. However, a general quality control framework was made available using the Hail software  [54]. This included variant normalization and left alignment to a reference genome, removal of samples with call rate less than 97% or mean depth less than 20. Genotypes were set to unknown if they had genotype quality less than 20, depth less than 10, or poor allele balance (more than 0. 1 for homozygous reference calls, less than 0. 9 for homozygous alternative calls, and either below 0. 25 or above 0. 75 for heterozygous calls.
Single variant association tests We performed single variant association tests using a GWAS additive model framework, with the following covariates: age, age 2 , sex, age � sex, age 2� sex, 10 genetic principal components obtained from common genetic variants (MAF>1%). Each cohort performed their analyses separately for each genetic ancestry, but also restricted their variants to those with MAF>0. 1% and MAC>6. Summary statistics were then meta-analyzed using a fixed effect model within each ancestry and using a DerSimonian-Laird random effect model across ancestries with the Metal package  [55]  and its random effect extension  [56]. Lastly, given that multiple technologies were used for sequencing, and that whole-exome sequencing can provide variant calls of worse quality in its off-target regions  [57] , we used the UKB, GHS, and Penn Medicine whole-exome sequencing variants as our "reference panel" for whole-exome sequencing.
Gene burden tests The following analyses generally followed the methods used by recent literature on large-scale whole-exome sequencing  [22]  and the COVID-19 HGI  [8]. The burden tests were performed by pooling variants in three different variant sets (called masks), as described in recent UK Biobank whole-exome sequencing papers by Backman et al. [22]  and Kosmicki et al. [17]. : "M1" which included loss of functions as defined by high impact variants in the Ensembl database  [23]  (i. e. transcript ablation, splice acceptor variant, splice donor variant, stop gained, frameshift variant, stop lost, start lost, transcript amplification), "M3" which included all variants in M1 as well as moderate impact indels and any missense variants that was predicted to be deleterious based on all of the in-silico pathogenicity prediction scores used, and "M4" which included all variants in M3 as well as all missense variants that were predicted to be deleterious in at least one of the in-silico pathogenicity prediction scores used.
For in-silico prediction, we used the following five tools: SIFT  [58] , LRT  [59] , MutationTaster  [60] , PolyPhen2  [61]  with the HDIV database, and PolyPhen2 with the HVAR database. Protein coding variants were collapsed on canonical gene transcripts. Once variants were collapsed into genes in each participant, for each mask, genes were given a score of 0 if the participant had no variants in the mask, a score of 1 if the participant had one or more heterozygous variant in this mask, and a score of 2 if the participant had one or more homozygous variant in this mask. These scores were used as regressors in logistic regression models for the three COVID-19 outcomes above. These regressions were also adjusted for age, age 2 , sex, age � sex, age 2� sex, 10 genetic principal components obtained from common genetic variants (MAF>1%), and 20 genetic principal components obtained from rare genetic variants (MAF<1%).
The Regenie software  [18]  was used to perform all burden tests, and generate the scores above. Regenie uses Firth penalized likelihood to adjust for rare or unbalanced events, providing unbiased effect estimates. All analyses were performed separately for each of six genetic ancestries (African, Admixed American, East Asian, European, Middle Eastern, and South Asian). Summary statistics were meta-analyzed as for the single variant analysis. Participant assignment to genetic ancestry was done locally by each cohort, more details on the methods can be found in the S3 Table. Lastly, we used ACAT  [35]  to meta-analyze p-values across masks, within each phenotype separately. ACAT is not affected by lack of independence between tests. These values were used to draw Manhattan and QQ plots in.
supported by a  FRQS Merite Clinical Research Scholarship. Support from  Calcul Quebec and Compute Canada  is acknowledged. GBL is supported by  FRQS  and  CIHR  fellowships. The Columbia COVID-19 Biobank is supported by the  Vagelos College of Physicians & Surgeons Office for Research, Precision Medicine Resource, and Biomedical Informatics Resource of the Columbia University Irving Institute for Clinical and Translational Research (CTSA). Columbia CTSA is funded by the  National Center for Advancing Translational Sciences  ( UL1TR001873 ). The  Columbia University  COVID-19 Biobank was supported by  Columbia University  and the  National Center for Advancing Translational Sciences  (NCATS),  National Institutes of Health (NIH) , through grant no. UL1TR001873. DeCOI NGS  was supported by funding from the  German Research Foundation  (DFG;  INST 217/1011-1 ) and were performed at the DFG-funded  NGS Competence Center Tu ¨bingen  ( INST 37/1049-1) -Project-ID 286/2020B01 -428994620  and the  DFG-funded West German Genome Center  ( INST 216/981-1 ) -Project Number  407493903.
For FhOGID: P-YB is supported by the  Swiss National Science Foundation  ( 31CA30_196036 ,  33IC30_179636  and  314730_192616 ), the  Leenaards Foundation , the  Santos-Suarez Foundation  as well as grants allocated by Carigest. CR is supported by the  Swiss National Science Foundation  ( 31CA30_196036 ,  31003A_176097 , and  310030_204285 ). The  GEN-COVID Multicenter Study (Italy)  was funded by the  MIUR  project " Dipartimenti di Eccellenza 2018-2020 " to  Department of Medical Biotechnologies University of Siena, Italy  (Italian D. L. n. 18 March 17, 2020), private donors for COVID-19 research, " Bando Ricerca COVID-19 Toscana " project to  Azienda Ospedaliero-Universitaria Senese , charity fund 2020 from  Intesa San Paolo dedicated  to the ( CU_FRB640001_01_30_10 ) and  Thailand Research Fund  ( DPG6180001 ).
Further, PC is supported by  Ratchadapiseksompotch Fund, Faculty of Medicine,Chulalongkorn University, Bangkok, Thailand , Grant number  RA(PO) 003/63. VN is supported by  Ratchadapiseksompotch Fund, Faculty of Medicine,Chulalongkorn University, Bangkok, Thailand , Grant number  RA(PO) 001/63. NH is supported by  The e-ASIA   Joint Research Program  (e-ASIA JRP) as administered by the  National Science and Technology Development Agency. VS is supported by  Health Systems Research Institute  ( 64-132 ) and the  Ratchadapisek Sompoch Endowment Fund, Chulalongkorn University  ( 764002-HE01 ), Bangkok, Thailand. Interval was funded by the  NHS Blood and Transplant , the  National Institute for Health Research , the  UK Medical Research Council , and the  British Heart Foundation  Japan COVID-19 Task Force acknowledges the contribution of  Japan Agency for Medical Research and Development (AMED)  and  Japan Science and Technology Agency (JST).
2020/ABM/COVID19/0001. The  Qatar Genome Program  and  Qatar Biobank are both Research, Development & Innovation entities within Qatar Foundation for Education, Science and Community Development. The Saudi COVID-19 acknowledges the  Saudi Ministry of Health and King Abdulaziz City for Science and Technology (KACST). The Swedish Biobank received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  824110  ( EASI-Genomics ). Sequencing was performed by the  National Genomics Infrastructure SNP&SEQ facility , which is supported by  Science for Life Laboratory , the  Swedish Research Council , and the  Knut and Alice Wallenberg Foundation. UCLA acknowledges OCRC, Microsoft COVID Compute Funding, Illumina  in-kind donation. We.
Funding: Genome sequencing of  Biobanque Que  ´bec COVID-19 was funded by the  CanCOGeN HostSeq  project, with contribution from  Fonds de Recherche Quebec Sante(FRQS) ,  Genome Quebec , and the  Public Health Agency of Canada. The Richards group is supported by the  Canadian Institutes of Health Research (CIHR) , the  Lady Davis Institute of the Jewish General Hospital , the  Canadian Foundation for Innovation , the  NIH, Cancer Research UK , and  FRQS. The Richards research group is supported by the  Canadian Institutes of Health Research  (CIHR:  365825 ;  409511 ,  100558 ,  169303 ), the  McGill Interdisciplinary Initiative in Infection and Immunity  ( MI4 ), the  Lady Davis Institute of the Jewish General Hospital , the  Jewish General Hospital Foundation , the  Canadian Foundation for Innovation , the  NIH Foundation ,  Cancer Research UK ,  Genome Quebec , the  Public Health Agency of Canada, McGill University, Cancer Research UK  [grant number  C18281/A29019 ] and the  Fonds de Recherche Quebec Sante(FRQS).
He is the CEO of 5 Prime Sciences Inc (www. 5primesciences. com). DeCOI: Oliver Witzke has received research grants for clinical studies, speaker's fees, honoraria and travel expenses from Amgen, Alexion, Astellas, Basilea, Biotest, Bristol- Myers Squibb, Correvio, Chiesi, Gilead, Hexal, Janssen, Dr. F. Ko ¨hler Chemie, MSD, Novartis, Roche, Pfizer, Sanofi, Takeda, TEVA and UCB. Kerstin U. Ludwig is co-founder and holds equity in the LAMPseq Diagnostics GmbH. UP serves as ad hoc advisor for Sanofi-Pasteur, BioNtech and Sobi and is member of the SAB of Leukocare. Christoph D. Spinner reports grants, personal fees from AstraZeneca, personal fees and non-financial support from BBraun Melsungen, grants, personal fees and non-financial support from Gilead Sciences, grants and personal fees from Janssen- Cilag, personal fees from Eli Lilly, personal fees from Formycon, personal fees from Roche, other from Apeiron, grants and personal fees from MSD, grants from Cepheid, personal fees from GSK, personal fees from Molecular partners, other from Eli Lilly,.
Numerical validation 3. 2. 1. Model description To evaluate the proposed experimental set-up, a finite element model of the four-point bending test was built using the commercial Finite-Element code Abaqus™ v. 6-14 (Dassault Systèmes). This simulation assumes a non-deformable test device, so only the components shown in Fig. 1  are modelled. As buckling is one of the main issues for sandwich beams, the assumption of symmetry was avoided, especially in the middle area of the specimen, to capture all possible buckling modes. Thus, the specimen is fully modelled. The model contains skins modelled as linear shell elements with four nodes, four integration points in the plane and three integration points in the thickness of each ply of the laminate. The two core components (Nomex ® and mixed filled resin insert) are each modelled in a specific way: i) the mixed filled resin insert in the areas outside the loading rollers is modelled by linear volume elements with 8 nodes and 8 integration points; ii) the Nomex ® , present along the entire specimen length, has all its cells modelled.
Acknowledgements We would to thank the  French ANRT  for partial funding of the PhD grants of  A. Launay  (#  2017/0421 ) and  P. -Y. Mechin  (#  2012/1621 ). Encouraging discussions with  Dr. P. Davies  (Ifremer),  Prof. C. Baley  ( UBS ) and  Dr. D. Cartié  ( Coriolis Composites ) are also acknowledged. The experimental contributions of  F. Portanguen ,  H. Bellegou ,  A. Le Palabe  and  K. Henry  ( UBS ) are finally acknowledged.
Western Blot Cells were collected in RIPA buffer (Sigma, R0278) for western blot analysis. Proteins were separated by SDS-PAGE (Mini-Protean TGX Precast Gels; Bio-Rad, Marnesla-Coquette, France, 456-1083) and transferred to PVDF membranes (Bio-Rad, 170-4156). Blots were incubated with primary antibodies with 5% nonfat dry milk in TBS-0. 1% Tween 20 overnight at 4 • C. HRP-linked secondary antibodies were used for 1 h at room temperature and antigen-antibody complexes were visualized by enhanced chemiluminescence (GE Healthcare, RPN2209; Luminol-based Enhanced Chemiluminescent (ECL) Western blotting detection reagents); quantifications were assessed using the ImageQuant LAS4000 imager software (GE Healthcare, Buc, France).
Fluorescence Microscopy DUGV-infected and non-infected (Mock) GFP-LC3 HeLa cells were fixed for 20 min in 4% PFA after 48 h and permeabilized with PBS (Life Technologies, 10010-015)-0. 5% Triton X-100 (Sigma, 3273372500) for 5 min. Cells were analyzed using confocal microscopy (LSM710 Zeiss) and the ImageJ software. The number of GFP+ dots was numerated from one single plan section per cell.
Anti-human PCSK9 pre-coated 96-well microplate was washed 4 times with washing buffer. Fifty µL of assay buffer was added per well prior to loading 50 µL of standard or diluted samples in duplicates on the plate. No blocking step was required. The plate was incubated at room temperature for 2 hours, then washed 4 times before addition of 100 µL of anti-human PCSK9 detection antibody. A 1hour incubation followed. After 4 more washes, avidin-HRP solution was added to each well and allowed to incubate for 30 minutes. The plate was washed 6 times preceding addition of the substrate solution followed by a 15-minutes incubation in the dark. The HRP chromogenic reaction was quenched with a stop solution and OD was recorded at 450nm. Statistical analysis. Data are presented in tables as means with standard deviations and as medians with interquartile ranges.