dataesr/scientific-paragraphs-categorization huggingface dataset:
ASH individuals were recruited at the School of Public Health, University of Hong Kong, and were included in a community-based sero-epidemiological COVID-19 study (research protocol number JTW 2020. 02). Inclusion for the study described here was restricted to nominally healthy ASH individuals (30 men and 41 women) aged between 19 and 65 years of age and seronegative for SARS-CoV-2. Samples were collected at the Red Cross Blood Transfusion Service (Hong Kong) where the PBMC fraction was isolated and frozen. Target sample sizes were determined to ensure >80% power for the detection of eQTLs with R2 higher than 0. 2, at a P < 5 × 10 -9 threshold. In this study, we refer to individuals of Central African (AFB), West European (EUB) and East Asian (ASH) ancestries to describe individuals who are genetically similar (that is, lowest F ST values) to populations from West-Central Africa, Western Europe and East Asia, using the 1000 Genomes (1KG) Project 56 data as a reference (Supplementary Fig.
We then used the 1KG Project Phase 3 data 56 as a reference for merging the ASH genotyping data with that of AFB and EUB individuals and detecting SNPs misaligned between the three genotype datasets. Before merging, we removed SNPs that (1) were absent from either the Omni5 or 1KG datasets (n = 469,535); (2) were transversions (n = 138,410); (3) had incompatible alleles between datasets, before and after allele flipping (n = 1,250); and (4) had allele frequency differences of more than 20% between the AFB and Luhya from Webuye, Kenya (LWK) and Yoruba from Ibadan, Nigeria (YRI), or between the EUB and Utah residents with Northern and Western European ancestries (CEU) and British individuals from England and Scotland (GBR), or between the ASH and Southern Han Chinese (CHS) (n = 777). Once the data had been merged, we performed principal component analysis (PCA) using PLINK (v.
Haplotype phasing and imputation After merging genotypes from AFB, EUB and ASH donors, we filtered genotypes for duplicates with bcftools norm --rm-dup all (v. 1. 16)  58 nd lifted all genotypes over to the human genome assembly GRCh38 with GATK's (v. 4. 1. 2. 0) LiftoverVcf using the RECOVER_SWAPPED_ALT_ REF=TRUE option  59. We then filtered out duplicated variants again before phasing genotypes with SHAPEIT4 (v. 4. 2. 1)  60  and imputing missing variants with Beagle5. 1 (v. 18May20. d20)  61 , treating each chromosome separately. For both phasing and imputation, we used the genotypes of 2,504 unrelated individuals from the 1KG Project Phase 3 data as a reference (downloaded from http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502 and lifted over to GRCh38) and downloaded genetic maps from the GitHub pages of the associated software (that is, SHAPEIT4 for phasing and Beagle5.
In vitro peripheral blood mononuclear cell stimulation We performed scRNA-seq analysis of SARS-CoV-2-, IAV-and mockstimulated (referred to as the non-stimulated condition) PBMCs from healthy donors (80 AFB, 80 EUB and 71 ASH) in 16 experimental runs. We first performed a kinetic experiment (run 1) on samples from 4 AFB and 4 EUB individuals stimulated for 0, 6 and 24 h to validate our in vitro model across different timepoints (Supplementary Note 1, Supplementary Fig. 2  and Supplementary Table  2 ). The 6 h timepoint was identified as the optimal timepoint for the analysis (Supplementary Note 1). We then processed the rest of the cohort, over runs 2 to 15. Finally, we reprocessed some samples (run 16) to assess the technical variability in our setting and to increase in silico cell counts (see the 'scRNA-seq library preparation and data processing' section).
scRNA-seq library preparation and data processing We generated scRNA-seq cDNA libraries using the Chromium Controller (10x Genomics) according to the manufacturer's instructions for the Chromium Single Cell 3′ Library and Gel Bead Kits (v. 3. 1). Library quality and concentration were assessed using the Agilent 2100 Bioanalyzer and a Qubit fluorometer (Thermo Fisher Scientific). The final products were processed for high-throughput sequencing on a HiSeqX platform (Illumina). Paired-end sequencing reads from each of the 133 scRNA-seq cDNA libraries (13 libraries from the kinetic experiment and 120 from the population-level study) were independently mapped onto the concatenated human (GRCh38), SARS-CoV-2 (hCoV-19/France/GE1973/2020) and IAV (A/Puerto Rico/8/1934(H1N1)) genome sequences using the STARsolo aligner (v.
2. 7. 8a)  64  (Supplementary Fig. 3b ). We obtained a mean of 10,785 cell-containing droplets per library, and each droplet was assigned to its sample of origin with Demuxlet (v. 0. 1)  65  , based on the genotyping data available for each individual. Singlet/doublet calls were compared with the output of Freemuxlet (v. 0. 1)  65  to ensure good agreement (Supplementary Fig. 3c-e ). We loaded feature-barcode matrices for all cell-containing droplets identified as singlets by Demuxlet in each scRNA-seq library onto a SingleCellExperiment (v. 1. 14. 1) object  66. Data from barcodes associated with low-quality or dying cells were removed with a hard threshold-based filtering strategy based on three metrics: cells with fewer than 1,500 total unique molecular identifier (UMI) counts, 500 detected features or a mitochondrial gene content exceeding 20% were removed from each sequencing library (Supplementary Fig.
3f ). We also discarded samples from nine ASH donors from whom fewer than 500 cells were obtained in at least one condition (Supplementary Fig. 3g ). We then log-normalized raw UMI counts with a unit pseudocount and library size factors (that is, the number of reads associated with each barcode) were calculated with quickClusters and computeSum-Factors from the scran package (v. 1. 20. 1)  67. We then calculated the mean and variance of log-transformed counts for each gene and broke the variance down into a biological and a technical component with the fitTrendPoisson and modelGeneVarByPoisson functions of scran. This approach assumes that technical noise is Poisson-distributed and simulates Poisson-distributed data to derive the mean-variance relationship expected in the absence of biological variation.
We then bound all 133 separate preprocessed feature-barcode matrices into a single merged SingleCellExperiment object, log-normalized UMI counts according to the scaled size factors and selected genes with mean log-expression values over 0. 01 or a biological variance compartment exceeding 0. 001 (Supplementary Fig. 3j ). On the basis of this set of highly variable genes and the variance decomposition, we then performed PCA on the whole dataset using denoisePCA, and then used Harmony (v. 0. 1. 0) on the PCs to adjust for library effects  69.
This made it possible to define unbiased markers (|log 2 FC| ≠ 0, FDR < 0. 01) for each cluster (Supplementary Fig. 4d ). We then used these expression profiles of these genes to assign each cluster manually to one of 22 different cell types (Supplementary Fig. 4e ), which, for some analyses, were collapsed into five major immune lineages. This step was performed in parallel by three investigators to consolidate consensus assignments. We also used cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) data, generated for a subset of cells (2% of the whole dataset), to validate our assignments and redefine clusters presenting ambiguous transcriptional profiles (for example, memory-like NK cells; Supplementary Fig. 4f ). By calling cell types from high-resolution, homogeneous clusters, assigned independently for each lineage and stimulation condition (that is, non-stimulated, SARS-CoV-2, and IAV), we were able to preserve much of the diversity in our dataset, while avoiding potential confounding effects due to the stimulation conditions.
4g, h ). For clusters of mixed identity AB, we built a training dataset from 10,000 observations sampled from the set of cells called as A or B, preserving the corresponding frequencies of these cells in the whole dataset. We then used a model trained on these data to predict the specific cellular identities within the mixed cluster. Cell abundance from each immune lineage/cell type was compared between non-stimulated and SARS-CoV-2-/IAV-stimulated conditions, using Wilcoxon's signed-rank test matching on the individual. FDR was calculated across all conditions and lineages with the Benjamini-Hochberg procedure (p. adjust function with the 'fdr' method). Viral stimulation had a moderate effect on the estimated cell proportions and, although significant differences were detected, the total number of cells per cell type was generally consistent across conditions (Supplementary Note 2 and Supplementary Table  3e ).
Pseudobulk estimation, normalization and batch correction Individual variation in gene expression was quantified at two resolutions: five major immune lineages and 22 cell types. We aggregated raw UMI counts from all high-quality single-cell transcriptomes (n = 1,047,824) into bulk expression estimates by summing gene expression values across all cells assigned to the same lineage/cell type and sample (that is, individual and stimulation conditions) using the aggregateAcrossCells function of scuttle (v. 1. 2. 1)  74. We then normalized the raw aggregated UMI counts by library size, generating 3,330 lineage-wise (222 donors × 3 sets of conditions × 5 lineages) and 14,652 cell type-wise (666 samples × 22 cell types) pseudobulk counts-per-million (CPM) values, for all genes in our dataset. CPM values were then log 2 -transformed, with an offset of 1 to prevent nonfinite values and to stabilize variation for weakly expressed genes.
Genes with a mean CPM < 1 across all conditions and lineages/cell types were considered to be non-expressed and were discarded from further analyses, leading to a final set of 12,667 genes at the lineage level (12,672 genes when increasing granularity to 22 cell types), including 12 viral transcripts. To quantify the experimental variation induced by the experimental run, library preparation and sequencing, and to remove unwanted batch effects, we first used the lmer function of the lme4 package (v. 1. 1-27. 1)  75 to fit a linear model of the following form in each stimulation condition and for each lineage/cell type: α ε log(1 + CPM ) = +IID + LIB + RUN + FLOW + ( 1 ) i i i i i i where CPM i is the gene expression in sample i (that is, one replicate of a given individual and set of experimental conditions); α is the intercept; captures the effect of the sequencing flow cell; and ε i captures residual variation between samples.
We then ran the carscore function from care R package (v. 1. 1. 11)  76 on each model, setting λ = 0 (that is, no shrinkage), to obtain the CAR score associated with each parameter. In brief, care decorrelates a set of predictors using a Mahalanobis whitening transformation and computes CAR scores as marginal correlations between these decorrelated predictors and the response variable. This enables direct estimation of the contribution of each predictor to the variance of the response variable as the square of its CAR score. The variance explained by cellular identity (lineage) and stimulation is then computed as: ∑ ∑∑ β γ Var = CAR( ) and Var = CAR( ) (3.
Pathway enrichment analyses We performed functional assessments of the effects of cellular composition variability on differences in gene expression between donors in the basal state and in response to each virus, using the fgsea R package (v. 1. 18. 1)  83  and the default options. This made it possible to perform a gene set enrichment analysis with population differences in each lineage ranked by the magnitude of the effect of ancestry on the expression or response of the gene before (β r ) and after (β a ) adjustment for differences in cellular composition.
Specifically, for each immune lineage/cell type and condition, surrogate variables were obtained using the sva function from the sva R package (v. 3. 40. 0)  85  with option method=‛two-steps', providing all other covariates as known confounders (mod argument). The number of surrogate variables to use in each lineage/cell type and condition was determined automatically based on the results from num. sv function with method=‛be'  85. For each gene, immune lineage/cell type and stimulation condition, Z-values (that is, the effect size of each eQTL divided by the standard error of effect size) were then calculated, and the fine mapping of eQTLs was performed using SuSiE (v. 0. 11. 42)  86  (susie_rss function of the susieR R package), with a default value of up to 10 independent eQTLs per gene.
01 for all s S ∈ and α ∑ >0. 95 s S s ∈. Only eQTLs with a log-Bayes factor (lbf) > 3 were considered for further analyses. For each lineage and set of stimulation conditions, each eQTL identified by SuSiE was assigned an eQTL evidence score, defined as the absolute Z-value of association between the eQTL index SNP and the associated gene. We then used a pooled permutation strategy to define the genome-wide number of significant eQTLs (that is, eQTL × gene combinations) expected under the null hypothesis, for different thresholds of the eQTL evidence score. We repeated the eQTL mapping procedure on the dataset after randomly permuting genotype labels within each population. We then counted, for each possible evidence score threshold T, the number of eQTLs identified in the observed and permuted data.
ebi. ac. uk/pub/databases/spot/eQTL/sumstats/GTEx/ ge/GTEx_ge_lung. all. tsv. gz). We next considered the index SNP from each eQTL, focusing on the subset of genes with median TPM > 10 in the lung and eQTLs with MAF > 5% in the GTEx dataset. We considered any eQTL with (1) P < 0. 01 in the lung and (2) the same effect direction between lung and the lineage/cell type/condition in which it is the most significant in our study as replicated. As a comparison, we evaluated the amount of eQTLs that would be replicated when selecting SNPs at random, matching for MAF in GTEx (bins of 5%) and the distance between the eQTL index SNPs and the nearest gene (that is, bins of 0-1, 1-5, 5-10, 10-20, 20-50 and 50-100 kb), and computed the fold-enrichment in replicated eQTLs as the ratio between the observed and expected number of replicated eQTLs.
Mediation analyses For all popDEGs and popDRGs, we evaluated the proportion of the difference in expression or response to viral stimulation between populations attributable to either genetic factors (that is, eQTLs) or cellular composition, using the mediate function of the mediation R package (v. 4. 5. 0)  80. Mediation analysis made it possible to separate the differences in expression/response between populations that were mediated by genetics (that is, differences in allele frequency of a given eQTL between populations, ζ g ), or cellular composition (that is, differ- ence in cell type proportions between populations, ζ c ) from those occurring independently of the eQTL/cell type considered (independent or direct effect δ). It was then possible to estimate the respective proportion of population differences mediated by genetics τ g and cellular composition τ c as τ = corresponding to the total differences in expression/response between populations.
9 and P Denisova < 0. 5 as Neanderthal-introgressed haplotypes and those containing alleles with P Denisova ≥ 0. 9 and P Neanderthal < 0. 5 as Denisovan-introgressed haplotypes. For the S′-based calling of introgressed regions, we considered all biallelic SNPs with an allele frequency of <1% in the YRI population to be Eurasian-specific alleles. We then ran the Sprime (v. 07Dec18. 5e2; https://github. com/browning-lab/sprime) separately for the CEU and CHS populations to identify and score putatively introgressed regions containing a high density of Eurasian-specific alleles. Putatively introgressed regions with a S′ score of >150,000 were considered to be introgressed. This cut-off score has been shown to provide a good trade-off between power and accuracy based on simulations of introgression under realistic demographic scenarios  98.
To avoid biases related to an increased power for the detection of eQTLs that segregate at higher frequencies in European genomes, (that is, n EUB = 80 and n ASH = 62), we considered a set of n = 10,276 eQTLs mapped on a downsampled dataset composed of the same number of individuals from each population (EUB and ASH) within each cell type and condition. This downsampled set of eQTLs was highly concordant with the original eQTL mapping (that is, >95% sharing at the lineage level). Within each cell type/stimulation condition, we considered the set of all (r)eQTLs for which the index SNP displayed at least a marginal association (Student's t-test, P < 0. 01) with gene expression. For each population and (r)eQTL set, we then grouped (r)eQTLs in high LD (r 2 > 0. 8), retaining a single representative per group, and counted the total number of (r)eQTLs for which the index SNP was in LD (r 2 > 0.
Transcriptome-wide association tests Using the summary statistics from the COVID-19 Host Genetics Initiative  8  , we applied the S-PrediXcan framework (v. 0. 6. 11)  99 to leverage our genotype-expression dataset and identify associations between genotypes and COVID-19 traits that could be mediated by the regulation of gene expression. These analyses were conducted separately in each of the 5 lineages or 22 cell types and the 3 experimental conditions of our setting. To perform these transcriptome-wide association tests (across the 12,655 human genes of our final dataset), we proceeded in two steps. First, we used the pseudobulk gene expression levels detected in each cell type/lineage and condition, together with the associated genotypes, from each of the 222 donors to build reference transcriptome datasets.
Acknowledgements We thank all of the members of the  Human Evolutionary Genetics Laboratory ,  B. -A. Olin  in particular, and  M. Van der Wijst ,  D. de Vries  and  L. Franke  for discussions;  L. Speidel  for help in running Relate and interpreting the results; and  A. Coen  and  F. Clément  for assistance with sample collection. The  Human Evolutionary Genetics Laboratory  is supported by the  Institut Pasteur , the  Collège de France , the  Centre Nationale de la Recherche Scientifique (CNRS) , the  Agence Nationale de la Recherche (ANR)  grants  COVID-19-POPCELL  ( ANR-21-CO14-0003-01 ),  POPCELL-REG  ( ANR-22-CE12-0030-01 ) and  COVIFERON  ( ANR-21-RHUS-0008 ), the  EU   HORIZON-HLTH-2021-DISEASE-04-07  grant  UNDINE  (no. 101057100 ), the  French Government 's  Investissement d'Avenir program ,  Laboratoires d'Excellence.
( ANR-10-LABX-62-IBEID ) and ' Milieu Intérieur ' ( ANR-10-LABX-69-01 ), the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20180339214 ), the (no. 00106080 ). K. L. , J. T. K. W. and M. P. are supported by the  Health and Medical Research Fund Commissioned Research on the Novel Coronavirus Disease, Hong Kong SAR  ( COVID190126 ); S. A. V. by the  Theme-based Research Scheme of the Research Grants Council of the Hong Kong SAR  ( T11-705/21 -N, SAV:  T11-712/19-N ); and M. P. , R. B. and D. D. by  InnoHK, an initiative of the Innovation and Technology Commission , the  Government of the Hong Kong SAR. Figure 1a and Supplementary Fig. 3a were created using BioRender.
Data availability The scRNA-seq data generated and analysed in this study have been deposited in the Institut Pasteur data repository, OWEY, which is available online (https://doi. org/10. 48802/owey. e4qn-9190). The genome-wide genotyping data generated or used in this study have been deposited in OWEY and can be accessed online (https://doi. org/10. 48802/owey. pyk2-5w22). In accordance with the General Data Protection Regulation (GDPR) in force in the European Union, the aforementioned data can be accessed only from the institutional data repository after authorization by the relevant Data Access Committee (DAC). The DAC ensures that data access and use is authorized for academic research relating to the variability of the human immune response, as defined in the informed consent signed by research participants.
COVID-19 GWAS summary statistics used in the present study can be downloaded from https://www. covid19hg. org/results/r7. Human (1000G data, low (phase 3) and high (NYGC) coverage), archaic (Vindija and Denisova) and ancestral (EPO6) genomes used can be retrieved from http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/release/20130502 (1000G phase 3), https://www. internationalgenome. org/data-portal/ data-collection/30x-grch38 (1000G high coverage), http://cdna. eva. mpg. de/neandertal/Vindija/ (archaic) and http://ftp. ensembl. org/ pub/release-71/emf/ensembl-compara/epo_6_primate/ (EPO6). Uniformly processed summary statistics from GTEx lung tissue were downloaded from http://ftp. ebi. ac. uk/pub/databases/spot/eQTL/sumstats/ (GTEx/lung/ge/all: study_id: QTS000015, dataset_id: QTD000271, file: QTD000271.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy The single-cell RNA sequencing data generated and analyzed in this study have been deposited in the Institut Pasteur data repository, OWEY, which can be accessed at: https://doi. org/10. 48802/owey. e4qn-9190. The genome-wide genotyping data generated or used in this study have been deposited in OWEY and can be accessed at https://doi. org/10. 48802/owey. pyk2-5w22. Data access and use is restricted to academic research related to the variability of the human immune response.
COVID-19 GWAS summary statistics used in the present study can be downloaded from https://www. covid19hg. org/results/r7. Human (1000G data, low [phase 3] and high [NYGC] coverage), archaic (Vindija and Denisova) and ancestral (EPO6) genomes used can be retrieved from ftp://ftp/1000genomes. ebi. ac. uk/ vol1/ftp/release20130502 (1000G phase 3), https://www. internationalgenome. org/data-portal/data-collection/30x-grch38 (1000G high coverage), http:// cdna. eva. mpg. de/neandertal/Vindija/ (archaic) and ftp://ftp. ensembl. org/pub/release-71/emf/ensembl-compara/epo_6_primate/ (EPO6), respectively. Uniformly processed summary statistics from GTEx lung tissue were downloaded from http://ftp. ebi. ac. uk/pub/databases/spot/eQTL/sumstats/ (GTEx/lung/ge/all: study_id: QTS000015, dataset_id: QTD000271, file: QTD000271.
We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with first-principles predictions.