dataesr/scientific-paragraphs-categorization huggingface dataset:
76  The binding glycans may affect the interaction of S pro with the metal surfaces. S pro consists of 165 glycans per subunit. The obtained structures contain 72 990 atoms, and their total charge (at pH = 7) is -15e. S pro structures were solvated using the "gmx editconf" and "gmx solvate" modules of GROMACS  [77] [78] [79] [80]  in a cubic box, and then all the water molecules beyond 3 Å of a solvation shell were removed. The number of water molecules added to solvate glycosylated S pro were 83 809 and 67 041 for S up and S down , respectively. We also neutralized the systems by adding K and Cl ions at a concentration of 150 mM. The Avogadro software  81  was used to prepare the structure of Ag(100), Au(100), and Cu(100) slabs with dimensions of 30. 00 × 30. 00 × 1. 20 nm 3. Ag and Au slabs consist of 69 312 atoms in six layers and the Cu slab consists of 98 784 atoms in seven layers.
To study the wetting behavior of the metals, a water droplet with a diameter of about 0. 75 nm (6845 water molecules) was generated using VMD software. 82  The solvated and neutralized structures of S pro and also the water droplets (for wetting calculations) were placed on top of the metal slabs and their distance was set to approximately 5. 0 Å (Figures  1  and S1 ). As shown in these figures, the initial orientation of the spike was such that the long axis of the spike was perpendicular to the surface, mimicking the expected orientation for the spike of a virus approaching the surface. The total number of atoms for each system is mentioned in Table  1.
MD Simulation. All the MD simulations were done using the simulation package GROMACS version 2020. 5  [77] [78] [79] [80]  for S pro -metal and water-metal complexes. The CHARMM36 force field was employed in all the simulations. This force field considers the parameterization of carbohydrate derivatives, polysaccharides, and carbohydrate-protein interactions. 83  The TIP3P water model included in CHARMM36 is also used in our simulations. Most of the force fields, including CHARMM36, do not have parameters for all of the elemental metals we are studying and we must choose the level of accuracy that can be considered for Ag, Au, and Cu. Therefore, like some of the previously published MD simulation studies,  [84] [85] [86]  we prefer to model the metal surfaces at the classical level, using the Lennard-Jones (LJ) potential (Table  2 ).
The Berendsen thermostat has been widely employed in previous simulation works of proteins, showing good agreement with experiments. [92] [93] [94] [95]  During the simulations, a 1. 0 nm cutoff for LJ and Coulomb interactions was applied and the particle mesh Ewald method  96, 97  was used for long-range electrostatics. The LINCS method  98  was also used as a constraint algorithm. All the images were generated using VMD software. 82.
48 ± 0. 46, 2. 00 ± 0. 51, and 1. 14 ± 0. 30 nm for the adsorption onto the surfaces of Ag, Au, and Cu, respectively. Hence, it can be concluded that the structural changes of S up adsorbed onto the metal surfaces follow Au > Ag > Cu. Similar behavior has been seen for the adsorption of peptides to Cu and Au surfaces before. 104  Figure  6b  represents the tilt angle of the protein. The tilt angle between the major axis of S up and the z-axis (the axis perpendicular to the surface) was computed using the "gmx bundle" module of the GROMACS. This module analyzes bundles of axes and reads two index groups and the centers of mass of these groups define the tops and bottoms of the axes. As it is clear from this figure (and also Figures  3  and S3 ), the final equilibrium angles of S up are about 28.
■ DATA AND SOFTWARE AVAILABILITY Fully glycosylated structures of the S1 subunit of SARS-CoV-2 spike protein were taken from the CHARMM-GUI archive (https://www. charmm-gui. org/?doc=archive&lib=covid19). Specific open-access software from third parties was also used: GROMACS version 2019. 3 (https://www. manual. gromacs. org/documentation/2019. 3/download. html/), VMD1. 9 (http://www. ks. uiuc. edu/Research/vmd/) and Avogadro (https://www. avogadro. cc). Graphical abstract was created with http://www. BioRender. com/. Input and output files are available at https://github. com/soft-matter-theory-at-icmabcsic (and also from the corresponding author upon request).
■ ACKNOWLEDGMENTS This work was supported by Grant  PID2021-124297NB-C33  funded by  MCIN/AEI/10. 13039/501100011033  and, as appropriate, by " ERDF A way of making Europe ", by the " European Union " or by the " European Union   NextGener-ationEU/PRTR " and by the " Severo Ochoa" Program for Centers of Excellence in R&D  ( CEX2019-000917-S ) awarded to ICMAB. We thank the  Spanish national supercomputing network (BSC-RES)  for the award of computer time at the Minotauro supercomputer. M. S. is supported by the  European Union   Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie Action Individual Fellowship grant  agreement no. 101026158.
Genetic discovery from association analyses We performed a fixed-effect inverse-variance weighted (IVW) GWAS meta-analysis on 29 population-based cohorts or biobanks with incident stroke ascertainment and 25 clinic-based case-control studies, comprising up to 110,182 stroke patients and 1,503,898 controls (of which 45. 5% in longitudinal cohorts or biobanks), nearly doubling the number of cases in previous stroke GWAS (the GIGASTROKE initiative, Supplementary Table  1 , Extended Data Fig. 1 ). Genome-wide genotyping and imputation characteristics are described in Supplementary Table  2. The cohorts included individuals of European (EUR, 66. 7% of stroke patients), East-Asian (EAS, 24. 8%), African-American (AFR, 3. 7%), South-Asian (SAS, 3. 3%), and Hispanic (HIS, 1. 4%) ancestry. Analyses were performed for any stroke (AS: comprising ischemic stroke, ICH, and stroke of unknown or undetermined type), any ischemic stroke regardless of subtype (AIS, N=86,668), and ischemic stroke subtypes (LAS, N=9,219; CES, N=12,790; SVS, N=13,620).
Using public drug databases we curated drugs targeting those proteins in a direction compatible with a beneficial therapeutic effect against stroke based on MR estimates: such drugs were identified for PROC, VCAM1, F11, KLKB1, and GP1BA (Supplementary Table  28 ). Drugs targeting F11 (NCT04755283, NCT04304508, NCT03766581) and PROC (NCT02222714) are currently under investigation for stroke, and our results provided genetic support for this. Of note, F11 and KLKB1 are adjacent genes with a long range linkage disequilibrium pattern and complex co-regulation,  29  as illustrated here by the presence of a shared trans-pQTL in KNG1 (Supplementary Table  27 ). Additional studies are needed to disentangle causal associations and the most appropriate drug target in this region. 30, 31 To further validate the candidate drugs and estimate their potential side effects, we investigated whether the drug-target genes were associated with stroke-related phenotypes using a phenome-wide association study (PheWAS) approach.
32 We conducted PheWAS in Estonian Biobank (EstBB) for the pQTL variants and rare deleterious variants in PROC, VCAM1, F11, KLKB1, and GP1BA genes (Supplementary Table  29 ). Rs2289252, a cis-pQTL for F11, was associated with higher risk of venous thromboembolic disorders (p<5. 37×10 -  ), as previously described,  33  and showed suggestive association (p=4. 23×10 -3 ) with cerebral infarction (I63, Extended Data Fig. 8 ). Conversely, we observed no significant association with non-stroke-related phenotypes, suggesting the safety of targeting F11. Similar profiles were observed in UK Biobank and FinnGen (https://r5. finngen. fi/variant/4-186286227-C-T), with no significant associations with other disorders and no overlap of subthreshold signals with side-effects reported in clinical trials.
Polygenic risk prediction in the population We explored the risk prediction potential of stroke GWAS, alone and in combination with vascular risk trait GWAS, in Europeans and East-Asians, using ancestry-specific polygenic scores (PGS). PGS were based on ancestry-specific and cross-ancestry GWAS summary statistics. We first derived single PGS (sPGS) models from single stroke GWAS summary data (Supplementary Table  31 ). We then constructed integrative PGS (iPGS) models, which combined multiple GWAS summary data of different traits into a PGS using elastic-net logistic regression (Extended Data Fig. 9 -10). 6 The iPGS analysis used two datasets for each ancestry for model training and evaluation, respectively. Participants in the training and evaluation datasets did not overlap and were not included in the input GWAS summary data.
014). 6 The age-, sex-, and top 5 PCadjusted hazard ratio (HR) per standard deviation (SD) of the PGS was 1. 25 (95% confidence interval [CI], 1. 18-1. 32; P=8. 2×10 -14 ) for the GIGASTROKE-based iPGS model compared to 1. 19 (95%CI, 1. 12-1. 26; P=4. 2×10 -9 ) for the MEGASTROKE-based iPGS model (Fig. 4a ). For East-Asians, we derived the iPGS model using 577 prevalent AIS cases and 9,232 controls, and evaluated the model using 1,470 prevalent AIS cases and 40,459 controls, from Biobank Japan (BBJ). A base model including age, sex, and top 5 PCs showed an area under the curve (AUC) of 0. 634. The iPGS model was constructed by integrating 10 GIGASTROKE GWAS and 37 vascular risk trait GWAS (Extended Data Fig. 10 , Supplementary Table  34 ). The iPGS model for East-Asians showed an improvement in AUC (∆AUC) of 0.
FURIN is expressed in brain endothelial cells,  44  has previously been implicated in coronary artery disease,  45  and FURIN inhibition reduces vascular remodeling and atherosclerotic lesion progression in mice. 46 FURIN also plays a key role in SARS-CoV-2 infectivity,  47  and COVID-19 patients are at increased risk of AIS, especially LAS. 48 The FURIN locus was predominantly associated with LAS in our data (Supplementary Table  39 ). Our results provide genetic evidence for putative drug effects using three independent approaches, with converging results from two methods (gene enrichment analysis and pQTLbased MR) for drugs targeting F11 and KLKB1. F11 and F11a inhibitors (e. g. abelacimab, BAY 2433334, BMS-986177) are currently explored in phase-2 trials for primary or secondary stroke prevention (NCT04755283, NCT04304508, NCT03766581).
Additional evidence from pQTL-based MR suggested PROC, GP1BA, and VCAM1 as potential drug targets for stroke. A recombinant variant of human activated protein C (encoded by PROC) was found to be safe for the treatment of acute ischemic stroke following thrombolysis, mechanical thrombectomy or both in phase 1 and 2 trials (3K3A-APC, NCT02222714),  49, 50  and is poised for an upcoming phase 3 trial. 3K3A-APC is proposed as a neuroprotectant, with evidence for protection of white matter tracts and oligodendrocytes from ischemic injury in mice. 51 Anfibatide, a GPIbα antagonist, reduced blood-brain barrier disruption following ischemic stroke in mice  52  and is being tested as an antiplatelet drug in myocardial infarction (NCT01585259). While specific VCAM1 inhibitors are not available, probucol, a lipid lowering drug with pleiotropic effects including VCAM1 inhibition was tested for secondary prevention of atherosclerotic events in CAD patients (PROSPECTIVE, UMIN000003307).
Although the lack of suitable additional datasets precludes direct replication efforts, we provide validation of our findings in independent population studies and major clinical trials. The muted risk prediction in participants with previous stroke history possibly points to the impact of selection or index event biases and secondary prevention therapy. 57  conclusion, our results provide critical insight to inform future biological research into the pathogenesis of stroke and its subtypes, highlight potential drug targets for intervention, and provide genetic risk prediction tools across ancestries for targeted prevention. 58, 59 igures  American per-allele effect sizes (y axis) (middle); East-Asian per-allele effect sizes (x axis) are plotted against African-American per-allele effect sizes (y axis) (right).
Gene and pathway-based analyses We performed gene-based tests of common variant associations using the VEGAS2 software. 11 l variants in the gene or within 10kb on either side of a gene's transcription site were used to compute a gene-based p-value. We performed analyses using the '-top 10' parameter that tests enrichment of the top 10% variants assigned to a gene accounting for LD between variants and total number of variants within a gene. We used 1000 Genomes phase 3 continental reference samples European, East-Asian, African, South-Asian and South-American (for our Hispanic samples), to compute LD between variants for respective ancestry-specific gene-based analyses. We then meta-analyzed ancestry-specific gene-based results, using Stouffer's method for sample size weighted combination of P-values.
In individuals of European ancestry, we used summary statistics of the aforementioned GWAS. 8- 10,13-17  F r the analysis in East-Asians we used unpublished GWAS for SBP, DBP, LDL and HDL cholesterol, triglycerides, and BMI in up to 53,323 participants of the independent Tohoku Medical Megabank Project (Supplementary Appendix) We used cov-LDSC to compute genetic correlations between stroke and vascular risk traits, using European and East-Asian GWAS summary files and 1000Gp3v5 reference data of respective continental ancestries (considering the recommended subset of high quality HapMap3 SNPs only). For MR analyses, we constructed genetic instruments for each vascular risk trait based on genome-wide significant associations (p<5x10 -8 ) after clumping for LD at r 2 <0. 01 (based on European and East-Asian 1000G).
To detect putative causal regulatory variants in the credible sets, we conducted an in silico mutagenesis analysis using MENTR (Mutation Effect prediction on Non-coding RNA (ncRNA) TRanscription; https://github. com/koido/MENTR), a quantitative machine-learning framework that predicts the effect of genetic variants on transcription, including transcription of ncRNAs, in a tissue-or cell-type-dependent manner. 24, 25 The in silico mutations predicted to have strong effects are highly concordant with the observed effects of known variants in a cell-typedependent manner. Furthermore, MENTR does not use population datasets and therefore is less susceptible to LD-dependent association signals, allowing to pin-point prediction of causal variants on transcriptional changes. From 1,274 variants in the credible sets from the EUR and EAS fine-mapping, we searched FANTOM5 promoters and enhancers, obtained by cap analysis of gene expression (CAGE), within +/-100-kb from each variant.
8. In total, we used 277 lead variants for 218 drug-target proteins for MR. Weused the "TwoSampleMR" R package 57 for MR analysis. As post-MR quality controls, we performed (i) directionality check of causal relationships by Steiger filtering  58  and (ii) colocalization analysis for the proteins with FDR < 0. 05. To examine colocalization assuming multiple causal variants per locus, coloc  35  was applied to the decomposed signals by SuSiE  22  for the variants within 500 kb upstream and downstream of the lead variants (coloc + SuSiE). 59 If SuSiE did not converge after 10,000 iterations, coloc was used instead. Coloc + SuSiE and coloc were run with their respective default parameters. For the two pQTL studies without public summary statistics,  52, 56  we compared the R 2 between the lead variants of the pQTL study and the stroke GWAS.
PheWAS We conducted a phenome-wide association study (PheWAS),  60  in Estonian Biobank (EstBB) for the pQTL variants and rare deleterious variants in identified drug target genes using the R software (4. 0. 3). We tested the association between ICD10 main codes and genetic variants using logistic regression adjusting for sex, birth year and 10 genotype PCs. All ICD10 codes with number of cases<100 and all variants with MAF<0. 001 were removed from the analysis. We applied Bonferroni correction to select statistically significant associations (number of tested ICD main codes:1,034, number of tested SNPs:7 corrected p-value threshold:0. 05/1034*7=6. 91×10 -  ). Results were visualized using the PheWas library (https://github. com/PheWAS/PheWAS).
Polygenic risk prediction We constructed integrative polygenic scores (iPGS) models for stroke in Europeans and East-Asians (Extended Data Fig. 9 -10). For each ancestry, two independent datasets were used for model training and evaluation, respectively. We used as input summary statistics data of multiple GWAS for stroke outcomes and vascular risk traits in order to derive iPGS models. We denote the number of input GWAS as N. For each of the N GWAS summary data, 37 candidate single trait polygenic score (sPGS) models were generated using P+T, 61,62 LDpred,  63  and PRScs  64  algorithms with an ancestry-specific LD reference panel from the 1000 Genomes Project (Supplementary Methods). 65 The plink (v1. 90b6. 8), 66 LDpred (v. 1. 0. 11),  63  and PRScs. py (Jun 5, 2021)  64  programs were used to compute P+T, LDpred, and PRScs models, respectively.
Among the 37 candidate models, subsequently, the best sPGS model, which was defined as the model that showed a maximal improvement in AUC over a base model (age, sex, and top 5 PCs were included in the base model), was selected using the model training dataset. 62, 67 Then, N best sPGS models were selected from the N input GWASs. Each best sPGS was z-transformed (zero mean and unit SD) over the model training dataset, followed by elastic-net logistic regression  68  to model the associations between the N sPGS variables and IS with the adjustments for age, sex, and top 5 genetic PCs. Two regularization parameters (α and λ) were optimized using 10-fold cross-validation. Then, coefficients (weights) for the N sPGS models were determined by the elastic-net logistic regression with optimal regularization parameters, followed by the integration of N sPGS models into a single iPGS model according to the formula presented in a previous study.
67 The elastic-net regression was performed using the glmnet R package. 69  predictive ability of the iPGS model was estimated using the model evaluation dataset, where we evaluated the improvement in C-index for a prospective cohort dataset (Europeans) or AUC for a case-control dataset (East-Asians) over a base model that includes age, sex, and top 5 genetic PCs. We used EstBB data for the model training and evaluation of iPGS model in Europeans. The model training dataset was composed of 1,003 prevalent IS cases at baseline and 8,997 controls. The control subjects were randomly selected among EstBB participants who had no history of AS at baseline and who did not develop AS during follow-up. The remaining 102,099 EstBB subjects were used for the model evaluation (mean±SD age at baseline, 44.
0±15. 7 years; 37. 8% men). Among the subjects in the model evaluation dataset, 1,128 incident IS cases were observed during 4. 6±4. 8 years. To derive the European iPGS model, we incorporated 5 ancestry-specific and 5 cross-ancestry stroke GWAS (AS, AIS, LAS, SVS, and CES) from the GIGASTROKE project, and 14 GWASs of vascular risk traits from other groups (Extended Data Fig. 9 ). To avoid the overlap of subjects across datasets, the GWAS summary statistics for stroke outcomes were re-calculated for the iPGS analysis by excluding the EstBB from the meta-analysis of GIGASTROKE studies. To allow comparison with a previous European iPGS model based on the MEGASTROKE GWAS,  67  we selected the best sPGS model from 10 GWASs of vascular risk traits (T2D, SBP, DBP, TC, LDL-C, HDL-C, TG, BMI, height, and smoking)  15, [70] [71] [72] [73] [74]  using the model training dataset.
) were incorporated into the GIGASTROKE-based iPGS model as vascular risk traits. For the East-Asian iPGS model we used BBJ data for the model training and evaluation. The model training dataset was composed of 577 IS cases and 9,232 controls, whereas there were 1,470 IS cases and 40,459 controls in the model training dataset. The mean±SD of age at recruitment was 69. 2±10. 8 years old for cases and 66. 5±12. 5 for controls in the model evaluation dataset. The percentage of males was 70. 0% for cases and 53. 1% for controls. The two casecontrol datasets were not included in the meta-analysis of GIGASTROKE studies, and therefore, the overlap of subjects across datasets was avoided. To derive the East-Asian iPGS model, we incorporated 5 ancestry-specific and 5 cross-ancestry stroke GWAS (AS, AIS, LAS, SVS, and.
Individual studies performed a genome-wide association study (GWAS) using logistic regression (or cox regression in some longitudinal population-based cohorts) testing association of genotypes with five stroke phenotypes (AS, AIS, CES, LAS, and SVS) under an additive effect model, adjusting for age, sex, principal components of population stratification, and studyspecific covariates when needed (Supplementary Table  2 ). The R package EasyQC along with in-house custom harmonization scripts were used to perform the QC of individual GWAS summary results. Marker names and alleles were harmonized across studies. Meta-analyses were restricted to autosomal biallelic SNPs from the HRC panel. Duplicate markers were removed. Prior to meta-analysis we removed variants with extreme effect size values (log(OR)>5 or log(OR)<-5), minor allele frequency (MAF) <0.
Genomics-driven drug discovery We used three methodologies for in-depth genomics-driven drug discovery as described previously:  41  (i) an overlap enrichment analysis of disease-risk genes in drug-target genes in medication categories, (ii) negative correlation tests between genetically determined case-control gene expression profiles and compound-regulated gene expression profiles, and (iii) endophenotype Mendelian randomization (MR). The detail of the methods is described in the following sections. For the overlap enrichment analysis and the endophenotype MR nominated drug targets we curated drug candidates from four major drug databases, DrugBank,  42  Therapeutic Target Database (TTD),  43  PharmGKB,  44  and Open Target Platform. 45 As for the endophenotype MR, we curated drugs with opposite effects against the signs of the MR effect sizes.
Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and spatial accuracy of an SDM depend very strongly on the type of model used and the environmental data used as explanatory variables. In this article, we study a country-wide species distribution model based on very high resolution (VHR) (1 m) remote sensing images processed by a convolutional neural network. We demonstrate that this model can capture landscape and habitat information at very fine spatial scales while providing overall better predictive performance than conventional models.
e. , of the 2,048-dimensional feature vectors used as the input of the final species classifier). These low-dimensional representations are then exploited in three ways: (i) to visualize in geographic map form the spatial patterns of habitats and landscapes learned by the model, (ii) to visualize in graphical form the relationships between learned features, environment, and species traits, and (iii) to quantitatively verify these relationships using statistical tests. This interpretability study demonstrates that our CNN-SDM trained on VHR remote sensing data captures the landscape and habitat information at fine spatial scales while providing better overall predictive performance than conventional models. This offers the possibility to produce large-scale distribution maps for a large number of species simultaneously and at a spatial resolution rarely equalled.