dataesr/scientific-paragraphs-categorization huggingface dataset:
Compound selection and toxicity Compounds were sourced from Calibr (California Institute for Biomedical Research, La Jolla, California, USA) or from MMV except for IFN-α (IF007 #3308269), IFN-λ (SRP3060), ebselen (E3520), dalbavancin (SML-2378) and nanchangmycin (SML2251 #0000040357) which were purchased from Sigma Aldrich (Buchs SG, Switzerland), while IFN-β (AF-300-02B) was sourced from Peprotech (LubioScience GmbH, Zürich, Switzerland). Compounds were selected using available data from primary screens using various cell lines (Vero E6, HeLa-ACE2, Calu-3) and organoids and were obtained blinded as 10 mM stock solution in dimethyl sulfoxide (DMSO). Compound toxicity (n=104) was evaluated by assessing the release of lactate dehydrogenase (LDH, Assay Kit-WST CK12-20, Dojindo EU, Munich, Germany) from damaged cells at 48 and 72h post treatment.
Compound-containing cell culture media was replenished every 24h. Trans-epithelial electrical resistance (TEER) was monitored at 24, 48, and 72h, while ciliary beating frequency (CBF) was assessed at 72h by capturing 256 images at a high frequency rate (125 frames per second) at room temperature (RT). CBF was calculated using Cilia-X software (Epithelix Sàrl, Geneva, Switzerland).
Virus propagation SARS-CoV-2 (reference strain: BetaCoV/France/IDF0571/2020, EPI_ISL_406596) used at the primary testing site was acquired from EVAg (Emerging Viral Diseases Medical Faculty, Marseille, France) and propagated on Vero E6 cells (provided by Prof. Dr. Volker Thiel, University of Bern, Bern, Switzerland) in Minimum Essential Medium (MEM; Seraglob, Schaffhausen, Switzerland) supplemented with 2% Fetal Bovine Serum (FBS; Seraglob) at 37°C, 5% CO2 and >85% relative humidity (rH) for 3 days prior to harvest. The SARS-CoV-2 strain used at the second testing site was isolated from a patient in a French clinical cohort of patients with COVID-19 (NCT04262921) in January 2020 at the Department of Infectious and Tropical Diseases, Bichat Claude Bernard Hospital, Paris (BetaCoV/France/IDF0571/2020, EPI_ISL_411218)  (29).
Acknowledgements We thank  Monalisa Chatterji ,  Robert Jordan  and  Betsy Russell  from the  Bill and Melinda Gates Foundation  for advice and discussions. We thank  Mitchell Hull  and  Kelli Kuhen  from  CALIBR  for providing the ReFrame library and validation plates. This work was supported by funding to  Medicines for Malaria Venture  from the  UK Foreign and Commonwealth Development Office , and the  Bill and Melinda Gates Foundation  ( INV-016776 ).
Study Design and Recruitment This multicenter uncontrolled pragmatic trial (NCT05221697) was conducted with 3 home aid facilities participating in the study, located in 3 French cities: Marseille, Versailles, and Dinan. To be eligible, participants should be aged ≥75 years and living at home, receive the help of a social worker from these facilities, have seen their general practitioner within the last 12 months and had a mild or moderate level of dependency according to French national dependency tool, the AGGIR scale  [19]. Written consent was obtained before inclusion in the study. Screening and enrollment started on July 1, 2020, and data were collected from September 1, 2020, to August 31, 2021. Participants' family caregivers and general practitioners received information about the participation of their proxy or patient in the study, as well as the patient's nurse if the patient received nursing care at home.
For each of the 23 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level and displayed it on a web-based secure medical device called PRESAGE CARE, which is CE marked. A simplified diagram of the processing system and the description of data processing system can be found in Multimedia Appendix 2. Particularly, when the algorithm predicted a high-risk level, an alert was displayed in the form of a notification on the screen to the coordinating nurse of the health care network center of the district (Dispositif d'Appui à la Coordination of the Agence Régionale de Santé). This risk notification was accompanied by information about recent changes in the patients' functional status, identified from the HAs' records, to assist the coordinating nurse in interacting with family caregiver and other health professionals.
Statistical Analysis Continuous variables were described by means and SD, or medians and IQRs, if not normally distributed; categorical variables were described by relative frequencies. The 2-tailed t test or Wilcoxon test was used to compare quantitative variables and the chi-square or Fisher exact test was used for qualitative variables. Sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios were estimated for each one of the alert visits (by an index test) in relation to an ED visit, that was considered the target condition of the reference standard. P values <. 05 were considered statistically significant. Statistics were conducted using Stata software (version 16; StataCorp LLC).
Acknowledgments The authors would like to thank the managers and the staff of the following home aid facilities as well as the care managers for their contribution to the study:  Adar Provence ,  Arcade Assistance ,  Association Le Connétable ,  Centre Communale d'Actions Sociales de la ville du Chesnay-Rocquencourt, Communauté Professionnels de Territoire de Santé Marseille Itinéraire Santé ,  Intervenant Libéraux et Hospitaliers Unis pour le Patient, Pôle Autonomie Territorial Grand Versailles , and  Plateforme Territoriale d'Appui Marseillaise. This work is funded by the  Agence Régionale de Santé Provence Alpes Cotes d'Azur  and the  Agence du Numérique en Santé. They did not play any role in the study except for the participation of  Agence Régionale de Santé Provence Alpes Cotes d'Azur  in the discussion about the ATHI.
6 Several studies have demonstrated that reovirus has the potential to kill MM cells through apoptosis or autophagy. 7, 8 However, in a Phase 1 clinical trial performed on MM patients (NCT01533194  9  ), the anticipated oncolysis was less than optimal. Thus, approaches enhancing the oncolytic capacity of reovirus, for instance by combination therapies,  10, 11  are much needed. Metabolic reprogramming can increase the susceptibility of cancer cells to OV-driven oncolysis. 12 Viruses hijack host cell metabolism to support their own growth. Exploiting this strategy, metabolic reprogramming has been used to enhance viral replication and oncolysis. 12, 13 Recently, we found that an infection-induced block of pyruvate flux into mitochondria via inhibitory phosphorylation of pyruvate dehydrogenase (PDH), protected breast cancer cells from reovirus-induced oncolysis.
Reovirus + FK866 combination induces autophagy-dependent cell death in KMS12 cells One potential consequence of decreased mitochondrial function is the upregulation of autophagy. 51 Of note, increased autophagic flux has already been linked to both FK866 treatment and reovirus infection in MM cells. 21, 52 We found that several autophagy proteins, e. g. , Beclin 1, were changed following the treatment with reovirus + FK866 in our proteomics dataset (Figures  S6A  and S6B ). To precisely understand the role of autophagy during reovirus + FK866 combination, we measured autophagic flux by immunoblot analysis of LC3B and STQM1/p62 proteins 24 h after inhibition with chloroquine in KMS12 cells (Figure  S6C ). As shown (Figures  S6D  and S6E ), the LC3BI-to-LC3BII conversion as well as the levels of p62 were significantly higher following reovirus + FK866 combination compared with monotherapies.
These data suggested that reovirus + FK866 treatment promotes autophagic flux in MM cells. To test whether this increase in autophagy contributed to KMS12 cell death following reovirus + FK866 treatment, we measured cell viability in the presence of chloroquine. Supporting the role of autophagy in our model, chloroquine prevented the synergistic effect of combination therapy without affecting the sensitivity to monotherapies (Figure  4G ). These data suggest that reovirus + FK866 therapy leads to autophagydependent cell death in KMS12 cells.
Flow cytometry Cell death and glucose uptake were determined with 7AAD/annex-inV [eBioscience #00-6993-50/#88-8005-74] and 2-NBDG [Thermo #N13195], respectively, as previously described. 14 For reovirus infectivity detection, cancer cells were permeabilized and incubated with a rat anti-reovirus antibody (generated in-house) as previously described. 14 Data acquisition and analysis were completed with BD-FACSCantoII and FCS Express (De Novo), respectively.
Statistics Statistical analysis was done using ANOVA or Kaplan-Meier survival analysis coupled with log rank test (both with 95% confidence interval); p values of <0. 05 were considered to be statistically significant. Correlation factors were calculated from datasets that were sampled from Gaussian distribution using Pearson correlation coefficients measuring two-tailed probability. Panther analysis of biological pathways was described previously. 100 For original data, please contact shashi. gujar@dal. ca.
ACKNOWLEDGMENTS This work was supported by the  Dalhousie Medical Research Foundation (DMRF) ,  Canadian Institutes of Health Research (CIHR) ,  Cancer Research Society (CRS) , and  Beatrice Hunter Cancer Research Institute (BHCRI)  to S. G. B. E. K. , J. P. M. , and M. G. are funded by  BHCRI 's  Cancer Research Training Program  (CRTP). B. E. K is also funded by  Dalhousie Medical Research Foundation (DMRF) /Infection, Immunity, Inflammation & Vaccinology (I3V). P. K. is funded by  Nova Scotia Graduate Scholarship (NSGS). We would like to thank  Drs. Alejandro Cohen  ( Dalhousie University ) and  Sylvere Durand  ( Gustave Roussy ) for their expertise with metabolomics and mass spectrometry. The graphical abstract was created with  BioRender. com. Finally , we thank the  Dalhousie University animal care facility  and staff for general maintenance of all animals used in this study.
(  1 ) of  Hennebelle et al. 2020). A sink particle approach is used to follow the formation and evolution of the protostar  (Bate et al. 1995; Bleuler & Teyssier 2014). The code benefits from the adaptive mesh refinement (AMR) technique to adapt the resolution. In these simulations, the refinement criterion is the local Jeans mass, for which we impose that the Jeans length is resolved by at least 20 cells. The structure of the data is a fully threaded ocTree. The setup of the simulations is the same as that of  Hennebelle et al. (2016)  and  Masson et al. (2016) , with the only difference being that it includes sink particles and covers a wider range of initial conditions. All the model cores have an initial mass of 1 M ⊙ , and the magnetic field is initially uniform and aligned with the z direction.
Temperatures and dust alignment with POLARIS We post-processed each one of the snapshots described in Sect. 2. 1 with the POLARIS code  (Reissl et al. 2016). The photon propagation is based on a Monte Carlo scheme and, along with the local values of the density and the magnetic field, it is used to compute the dust temperature and the grain alignment efficiency on a cell-by-cell basis. As the gas temperature computed by RAMSES using an EoS is the result of an approximation and not the result of an actual radiative transfer, we recompute the gas temperature as well. In addition to the gas and dust heating, the radiation field induces a radiation torque on dust grains, spinning them up to suprathermal rotation velocities, at which they can align with the magnetic field  (Barnett 1915; Dolginov & Mitrofanov 1976; Lazarian & Hoang 2007a; Hoang & Lazarian 2014).
Acknowledgements. We thank  M. Küffmeier  for constructive comments to improve the readability of this paper. This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (MagneticYSOS project , Grant Agreement No  679937 ). A83 , page  18  of 36 V. Valdivia et al. : Protostellar B-fields traced with dust polarization.
Data Collection Data were collected from electronic medical files using DxCare software (Le Plessis Robinson, Dedalus France, version 779). Demographic data, comorbidities (hypertension, obesity (body mass index > 30 kg/m 2 ), diabetes, malignancy, and immunodeficiency), and clinical, microbiological, and radiological parameters were recorded. Immunocompromised patients included patients with malignancy or non-malignant immunodeficiency (HIV infection, systemic disease treated with long-term steroids (>30 days) or any immunosuppressive drug, or solid-organ transplantation). On admission, viral and bacterial infections were systematically screened using pharyngeal multiplex PCR, urinary antigen tests, blood cultures, and sputum cultures (where possible). During hospitalization, if clinical deterioration was evident, blood cultures, urine cultures, and tracheal aspiration or bronchoalveolar lavage were performed.
Replication of SOCS3m-height association We observed a robust replication of the SOCS3m-height association in cross-sectional analyses for all three LMIC cohorts in mid-childhood aged between 5 and 9 years, with consistent direction of effects and P values ranging from 0. 047 to 3 × 10 -14 across these different sized cohorts (Table  3 ). On average, a 1% change in methylation was associated with 0. 23, 0. 15, and 0. 08 cm increase in height, equivalent to 0. 054, 0. 029, 0. 021 SD in the MPC, PMMST, and ENID cohorts, respectively. Similar to the discovery cohort, methylation at these CpGs was correlated in all the replication cohorts (Supplementary Fig. 1 ). Sensitivity analyses did not identify any effect of cell count variation on the SOCS3m-height association in any of the replication cohorts (Supplementary Data 4).
We carried out an additional association analysis of early-childhood effects in Gambian ENID participants for which height measurements and methylation data were available at 2 years (mean age: 2 years, mean HAZ: -1. 33, n = 238). This analysis was restricted to cg18181703 as this is the only CpG covered by the Illumina 450 K array. We found a significant association with an increase of 0. 14 cm per 1% increase in methylation (P = 1 × 10 -3 ; see Table  3 ). Previous analyses of several of the cohorts studied here have reported associations between DNAm and maternal nutritional interventions  18  and season of conception (SoC)  18. Sensitivity analyses confirmed that these cohort-specific exposures did not confound the observed SOCS3m-height associations (Supplementary Data 2 and 5). To investigate whether the SOCS3m-height association observed in LMIC cohorts could be replicated in children from a HIC, we used data from the ALSPAC cohort (n = 863, UK).
SOCS3m-height association in LMIC Methylation is influenced by genetic variation, notably in cis 23 , and it is therefore possible that single nucleotide polymorphisms (SNPs) may confound methylation-height associations at SOCS3. Since we had limited power to detect genome-wide methylation quantitative trail loci (mQTL) in our datasets, we performed a cis-mQTL analysis in all the LMIC cohorts, restricted to SNPs within 1 Mb of the three SOCS3 CpGs. No significant cis-mQTLs were identified for the three SOCS3 CpGs (summarized in Supplementary Data 6). Next, to increase the power to detect cis-mQTLs, we performed meta-analysis for each CpG using the summary statistics from all four LMIC cohorts (Supplementary Data 7). Again, no significant mQTLs were identified, suggesting that SOCS3m is not influenced by cis-acting genetic variation.
Proportion of height variance explained by SOCS3m and height PRS We next investigated the proportion of height variance explained (R 2 ) by mean SOCS3m (across the three CpGs) and by height PRS in all cohorts using a single analysis of variance (ANOVA) model (see "Methods"). Similar amounts of height variance were explained by SOCS3m and height PRS in each LMIC cohort (Supplementary Data 9). EWAS analysis was conducted using multiple linear regression models with a predefined FDR threshold of 0. 05. The Benjamini-Hochberg method was used for multiple testing correction. We also investigated associations between SOCS3m measured at 5 years and height across all available time points from 5 to 21 years in this cohort. The SOCS3m-height association was observed across all the time points, although there was some evidence that the strength of the effect decreased with age (Supplementary Data 11).
Since height is strongly correlated across all time points, we performed an additional conditional height analysis, with height at each time point adjusted for all preceding height values, giving a measure of relative height gain at each time point. There was no significant association between SOCS3m at 5 years and height gained from 5 to 9. 5 years or from 9. 5 to 13. 5 years. However, we found a significant inverse association between late childhood and adulthood, where increased SOCS3 methylation at 5 years was associated with decreased height gain from 13. 5 to 21 years (Supplementary Data 12).
Links between SOCS3m and BMI SOCS3m has previously been linked to adult BMI  29. We attempted to replicate this finding in one of our datasets (MPC) where crosssectional data on both children and adults were available. In line with previous findings  30  , we observed that SOCS3m was negatively correlated with BMI at 21 years. However, this effect was reversed in childhood (Supplementary Data 21). We performed sensitivity analyses and confirmed that the SOCS3m-BMI association at 21 years persisted after adjustment for height. However, no association was apparent at 5 years after adjustment for child height (Supplementary Data 21).
In our discovery analysis, we found on an average 0. 25 cm increase in height per 1% increase SOCS3m. This observed effect size at a single epigenetic locus is substantial when compared to the effect sizes of height-associated SNPs. The effect of SOCS3m in Indian cohorts (0. 2-0. 3 cm per 1% increase in methylation) was comparatively larger than that observed in the Gambian cohorts which might reflect differences in environmental exposures. Existing evidence suggests that the relative genetic contribution to stature increases with age, while environmental factors have their greatest effect during early childhood  7. In the MPC, which has height data from birth to young adulthood, the association between SOCS3m and height was positive throughout (Supplementary Data 11) but the effect size diminished with age.
SOCS3 has two cytokine-inducible isoforms (long and short) with varying expression levels (Supplementary Fig. 5b ), half-lives and stability  58. As enhancers are known to regulate isoform expression and DNAm can influence enhancer activity, variable levels of methylation at this region might influence preferential expression of SOCS3 isoforms. Further studies are needed to investigate whether SOCS3m leads to isoform-specific SOCS3 expression implicated in differential inflammatory responses. We note that this study was limited to loci on the Illumina EPIC and 450 K arrays and that a genome-wide analysis would improve the ability to detect epigenetic factors associated with child height. Other limitations include a lack of available methylation data at birth so that we were unable to investigate cross-sectional associations with birth length or perinatal exposures.
DNA was extracted from white blood cells and genome-scale methylation profiles were obtained using the Illumina Human Methylation 450 K Bead Chips (450 K array) as described in a previous publication  66. A followup study measured child heights and generated DNA methylation data using the Illumina EPIC array in a subset of ENID children at 5-7 years of age  67, 68. In the current study, we have used child height at 2 years and 6 years for association studies. Avon longitudinal study of parents and children. Pregnant women resident in Avon, UK with expected dates of delivery between April 1, 1991 and December 31, 1992 were invited to take part in the ALSPAC study  22. The initial number of pregnancies enrolled was 14,541. Of these initial pregnancies, there were a total of 14,676 foetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year of age.
Methylation data was generated in a subset of motherchild pairs using 450 K Illumina arrays  69. Methylation data used in this study was generated using blood samples collected when children were ~7 years old. Child height was measured to the last complete mm using the Harpenden Stadiometer at clinic visits attended when children were ~7 years old. Maternal and partners ethnic group were obtained by questionnaires administered to mothers during pregnancy. Non-white individuals were removed from the dataset (n < 5). A total of 925 children had methylation data and were included in the dataset used in this study. Children with missing height (n = 46) or genetic data (n = 16) were removed from analyses. The study website (http://www. bristol. ac. uk/alspac/researchers/our-data/) contains details of all the data that is available through a fully searchable data dictionary and variable search tool.
Pre-processing of child height measures Height measures for each cohort were first assessed for normality, and outliers were removed before generating residuals. As the height of an individual varies with age and sex, we first generated height residuals adjusted within each cohort for the child's age and sex by linear regression. The residuals were centered to have mean 0 and variance 1. Child stunting, defined as standing height-for-age Z-score (HAZ) more than two standard deviations below the WHO growth reference mean was computed using the "addWGSR'" function from the R package "zscorer" (version 0. 3. 1).
The converted DNA were then processed either on the EPIC array (Illumina Inc. , San Diego, USA-MMNP, PMMST and ENID 6-year cohorts), or on the 450 K array (ENID 2 year and ALSPAC). Arrays were scanned on an Illumina iScan and the first pass quality review was carried out using GenomeStudio. Raw. idat files were imported into R and processed using the R Bioconductor package "meffil" with default parameters  72. CpG probes identified as crossreactive  73  or mapping to X/Y chromosomes were removed. Finally, the data from all cohorts were independently normalized using Functional Normalization as a part of the meffil pipeline  74. For MPC, genomic DNA from 5-year-old children (n = 561) were randomized on the EPIC chip using the Optimal Sample Assignment Tool (OSAT) R package and DNAm data was generated on the EPIC array using the above-mentioned protocol.
On QC analysis, no sex mismatches were identified. One sex detection outlier (>5 SDs from the mean), and six outliers with predicted median methylation of >3 SDs from the regression line were excluded. Finally, probes with detection P values > 0. 01 (n = 2985) and/or bead numbers <3 in more than 10% of the samples were excluded. Following meffil default parameters, functional normalization was carried out using the first 20 PCs derived from the control probes and the final dataset comprised 862,933 probes and 554 samples.
Generation of high-throughput genotype data and imputation Genome-wide SNP data from the Illumina Infinium Global Screening Array-24 v1. 0 Beadchip (GSA array; Illumina, California, USA) was available for MMNP, PMMST and MPC. Detailed information on data processing has been reported previously  18. Briefly, quality control involved the removal of samples with genotyping call rate <95% and SNPs with Hardy-Weinberg equilibrium (HWE) P value > 5 × 10 -6. For imputation, array-derived genotypes were pre-phased using SHAPEITv2  75  and imputation was carried out using IMPUTE2 software (version 2. 3. 2)  76  using the 1000 Genomes phase 3 reference panel. SNPs with a minor allele frequency (MAF) < 0. 10 and an IMPUTE2 "info" metric <0. 9 were excluded, the latter to ensure maximum confidence in imputation quality.