dataesr/scientific-paragraphs-categorization huggingface dataset:
Coverage comparison between the SWISS-MODEL repository and AlphaFold2 databases The SMR and AF2 databases were accessed on 24 July 2021. Reference proteomes for 11 species shared between AF2 and SMR were downloaded from the Uniprot release 2021_03. Only structures corresponding to entries from the reference proteomes were used for the analysis. Numpy 58 , Pandas  59  , Prody  60  and Matplotlib  61  Python libraries were used for the analysis and the visualization. Structure counters for protein domains were extracted from the corresponding InterPro entries  62. Code and data are available online (https://github. com/aozalevsky/ alphafold2_vs_swissmodel/).
Comparison between human RoseTTAFold Pfam domain and AlphaFold2 structures We used the 17,006 human proteins that were defined as the principal isoform for their corresponding gene according to APPRIS and whose sequences were the same in ENSEMBL and Uniprot. We used Pfamscan to identify PFAM domains in the 17,006 protein sequences. The database of PFAM-A models was downloaded on 29 June 2021 and created on 19 March 2021. We kept only those PFAM domains identified with an e-value below 1 × 10 -8. AF2 models for human proteins were downloaded on 23 July 2021 from https://alphafold. ebi. ac. uk. We extracted the sequences and compared them with the ENSEMBL protein sequences used for the structural analysis. For comparison purposes, all the analyses and results presented here are based on the subset of 17,006 protein sequences for which the ENSEMBL and Alpha-Fold protein sequences were identical.
We also extracted pLDDT values for each residue from the AlphaFold models, as these are stored as if they were the B-factor of the protein coordinates file. The RoseTTAFold models were downloaded from the EBI website on 27 July 2021, and the r. m. s. d. between models from both methods was calculated using the function struct. aln from the R package bio3d. All statistical analyses were done using R 4. 0. 2. Graphical plots were created with the packages ggplot2 (ref. 63 ), patchwork and reshape2. Molecular graphics and analyses performed with UCSF Chimera  64  , developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco.
Disorder prediction Benchmarking data for ordered and disordered protein regions were taken from the benchmark set of IUPred2 (ref. 22 ) and were filtered for proteins for which AF2-predicted structures are available in the Alpha-Fold database. Relative SASA was calculated by determining the absolute SASA using DSSP and then comparing it to the SASA calculated in a GGXGG conformation. Receiver operating characteristic (ROC) curves plotting the true positive rate as the function of the false positive rate were calculated on a per-residue level. Area under the ROC curves are single-number measures of the overall predictive performance in the range of 0. 5 (for random predictions) to 1. 0 (for perfect predictions).
Exploration of structural space covered by the AlphaFold database compared with the Protein Data Bank We use the 365,198 proteins from the current AlphaFold database (AF) and 104,323 proteins from PDB in 2016 (until CASP12) with a 100% sequence identity threshold, removing duplicates. Owing to the presence of low-confidence regions in the AF proteins, we first performed trimming to split each AF protein into smaller high-confidence units as follows: a one-dimensional Gaussian filter with a standard deviation σ of 5 is applied to the sequence of pLDDT scores extracted from the Cα atoms. The resulting scores are used to split the protein into continuous segments of residues with smoothed pLDDT scores > 70. Segments with fewer than 50 residues are discarded. This removed 68,890 AF proteins with too few high-confidence residues for accurate structural comparison.
Pocket and structural motif prediction We downloaded structures from the AlphaFold Protein Structure Database  76  except for analyses of the LBSp dataset  37. In the latter case, we used locally modeled structures, as many LBSp structures are from species not included in the public database. We detected pockets and calculated overlap metrics (F-score, Matthew's correlation coefficient (MCC)) using AutoSite  77  from ADFRsuite version 1. 0, and OpenBabel 78 was used to prepare PDBQT input for AutoSite (obabel -h -xr-partialcharge gasteiger). For enzyme activity predictions, we selected AlphaFold models without corresponding entries in SWISS-MODEL 2021-11-30, and kept 921 structures with mean pLDDT ≥ 70 and 100-500 residues. We considered 170 proteins as having known enzymatic activity if there was an EC number and/or a catalytic activity annotation in the corresponding UniProt records.
Oligomerization state prediction To test the ability of AF2 to predict the oligomeric state of homo-oligomeric assemblies, we downloaded the dataset from Ponstingl et al. 42. Since the PDB files were not provided, the dataset was filtered to entries for which the oligomeric state was in agreement with PISA annotation. Because AF2's training was done only on single chains, we reasoned that examples, even if they overlap with the training set, could be used to evaluate AF2's oligomeric state prediction capabilities. For each PDB entry, the sequence of chain A was extracted, and a multiple sequence alignment was generated using the automated MMseqs2 webserver through ColabFold. For homo-oligomeric prediction, each MSA was copied, padded with gaps to the total length reflecting the number of copies in the assembly and concatenated.
Fold-and-dock prediction of heterodimeric protein complexes We used 219 heterodimeric complexes from Dockground benchmark 4 (ref. 79 ). This set contains unbound forms of heterodimeric protein chains, which share at least 97% sequence identity with the bound forms. The dataset consists of 54% eukaryotic proteins, 38% bacterial proteins and 8% from mixed kingdoms, for example one bacterial protein interacting with one eukaryotic protein. To evaluate performance, one model for each pair was generated with AF2 (using default parameters, except that model_2 was used, providing a complementary set of results to those derived in ref. 20  ). To enable docking, we changed only the residue number so that both chains are treated as a long chain with a 200-residue gap, as in ref. 80. We compared AF2 predictions with models docked with GRAMM.
After energy minimization and equilibration, the model was inspected and, where necessary, interactively remodeled; where reference model restraints clearly disagreed with the model, they were selectively released. Where the AF2 models included previously-unmodeled residues supported by the density, they were merged into the working model. The final model was refined with phenix. real_space_refine  52 using settings defined by the 'isolde write phenixRsr' command. For the recapitulation of 7OGG, the AF2 predictions for its two chains were fetched in ChimeraX, as above. The rigid core of each chain was extracted using a community clustering approach based on the PAE matrix; source code for this is available at https://github. com/tristanic/ pae_to_domains. After setting B-factors to a constant value of 50, these were used to generate a fresh molecular replacement (MR) solution using PHASER  51.
In a second and third round of interactive rebuilding in ISOLDE (during which the distance and torsion restraints were fully released) interspersed with phenix. refine, a small number of residues deleted in the first step were re-introduced. In both the above cases, the final coordinates have been shared with the original authors. https://doi. org/10. 1038/s41594-022-00849-w Pocket predictions: https://github. com/jurgjn/af2_pockets. Protein complexes: https://gitlab. com/ElofssonLab/FoldDock, https://colab. research. google. com/github/sokrypton/ColabFold/blob/main/ AlphaFold2. ipynb. Model building: https://github. com/tristanic/ pae_to_domains.
Acknowledgements B. M. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 842490  (MIMIC). A. Stein received funding from the  Lundbeck Foundation  ( R272-2017-4528 ) and  Novo Nordisk Foundation  ( NNF18OC0033950 ). D. B. A. received funding from the  National Health and Medical Research Council of Australia  ( GNT1174405 ) and the  Victorian Government's Operational Infrastructure Support Program. K. L. -L. received funding from the  Novo Nordisk Foundation  ( NNF18OC0033950 ). D. E. V. P. received funding from an  Oracle Research Grant. A. O. Z. received funding from the  Russian Science Foundation (RSF)   20-14-00121. A. E. ,  A. Shenoy , G. P. , P. Bryant and W. Z. received funding from the  Swedish Research Council for Natural Science , grant No.
ACKNOWLEDGEMENTS We first would like to thank  M. Masella  and  P. Guilbaud  for fruitful discussions. We acknowledge support by the  French government  through the  Program "Investissement d'avenir " (Grants  LABEX CaPPA/ANR-11-LABX-0005-01  and  I-SITE ULNE/ANR16-IDEX-0004  ULNE), as well as by the  Ministry of Higher Education and Research, Hauts de France council and European Regional Development Fund (ERDF)  through the  Contrat de Projets État Region (CPER CLIMIBIO). We further acknowledge the " Groupement de recherche" GDR 2035 SolvATE. In addition, this work was granted access to the  HPC resources of CINES/IDRIS/TGCC  under the allocation  2019-2020  ( A0070801859 ) made by  GENCI. We also acknowledge the  CEA  for the Ph. D. grant given to A. F.
3 700 4. 82 1000000 0. 3 700 4. 82 1000000 3 ) NCT3 NCT3 NCT3 C C C C O O O O HA N C O HA N O HA N 4. 25 500000 3. 23 1476 4. 75 500000 4. 50 304000 4. 20 500000 3. 23 1476 CT3 CT2 CT1 CT2-N HN 4. 30 1000000 HA 5. 10 900000 7. 00 75000 5. 20 500000 N O C 0. 3 0. 5 0. 3 0. 3 0. 4 0. 5 -0. 21 -0. 14 -0. 07 -0. 02 0. 10 0. 3 0. 07 0. 3 0. 5 0. 5 -0. 34 -0. 42 0. 32 700 4. 16 1000000 0 7. 50 75000 700 5. 30 1000000 500 4. 82 1000000 1000 5. 30 1000000 0 7. 50 2. 0 2. 0 2. 0 2. 0 0. 0 75000 0. 0 700 5. 30 800000 0 4. 80 1000000 0 8. 00 50000 1200 5. 10 1000000 1. 3 1. 3 1. 0 HA HA 3. 65 1000 0. 5 0 8. 00 50000 HA N 5. 50 350000 0. 5 0 8. 00 50000 N N 4. 70 9000000 0. 3 700 5. 60 500000.
Explanatory variables. We selected an initial set of the following nine bioclimatic variables covering relevant temperature and precipitation conditions: annual mean temperature (BIO1), temperature seasonality (BIO4), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation seasonality (BIO15), precipitation of driest quarter (BIO17), and precipitation of warmest quarter (BIO18). Bioclimatic variables were obtained from CHELSA (v1. 2) at a resolution of 30 arc seconds (about 1 km) averaged across the years 1979-2013  99, 100. To analyze the impact of activity period and time of sampling in the respective sampling year, we extracted data on the start and length of the vegetation period for each site as a proxy for the time of flower availability.
Acknowledgements We thank the referees for valuable and insightful comments which helped improve the clarity of the manuscript. The authors acknowledge the support of the  EU COST-Action   FA1307  Sustainable pollination in Europe-joint research on bees and other pollinators (Super-B) for providing the platform to initiate this project, as well as internal funders from each country and institute for financing the sample collection and processing costs. We also would like to thank the technicians and students at the collecting-processing labs and the assaying labs for their invaluable help and competence. RJP thanks  Kerstin Gößel  and  Anja Manigk  for technical help. JRM would like to thank the excellent technical support provided by  MSc student Sofie Tinggren, BMA student Nellie Svedin and BMA Emilia Semberg  during the course of this project.
54 Å) radiation and a step size of 0. 02 •. DiffracEVA and TOPAS software were used for the phase identification and quantification of the raw materials' composition, respectively. The amorphous content of the raw slag was determined by applying the method described in Srodon et al. [20]  where 10 wt% of analytical grade zincite is added as an internal standard during sample preparation. The ATR-FTIR measurements were made using Bruker Alpha-P spectrometer with diamond crystal. Each measurement (average of 3 runs per sample) consisted of 32 scans with a resolution of 4 cm -1 and a range from 380 to 4000 cm -1. The raw results were expressed in transmittance and were then converted to absorbance for the analyses. The measurements presented in Fig. 2  demonstrate the complex spectra.
Acknowledgements J. Astoveza  is grateful for receiving funding as an early-stage researcher of the  SOCRATES  Project under the  European Union Framework Program for Research and Innovation Horizon 2020  Grant Agreement No. 721385  ( EU MSCA-ETN SOCRATES ; project website: htt p://etn-socrates. eu). The authors would also like to acknowledge  Tobias Hertel  and  Jeroen Soete  of  KU Leuven  for their valuable assistance in performing the ATR-FTIR and XCT, respectively; and  Vincent Hallet  of  KU Leuven  for his review of the paper.
We performed a one-to-one procedure (macro ONETOMANY) to match age groups based on their propensity score. Next, we analysed this paired population adopting the generalized linear model with the binomial distribution. Baseline characteristics were compared between groups before and after the matching procedure exploiting the standardized average differences. All analyses were conducted in the thorough case analysis. A p-value less than 0. 05 was considered signi cant; all p values were two-tailed. Statistical analysis was performed with SAS software V9. 4 (SAS institute, NC, Cary) and R software, version 4. 0. 5 (the R Foundation for Statistical Computing).
Patients are assessed by dieticians and receive nutrition therapy if necessary. The rate of serum albumin level was described in the literature to detect undernutrition: low albumin are associated with an risk of readmissions, mortality, length of stay, and mortality [63]  [10]. This data was not available. As the undernutrition, obesity also presented risks. Giannadakis et al  [15]  found that obesity increases the duration of the surgery. In addition to being a risk factor for LSS  [24] , it rises postoperative complications in spine surgery  [4]  especially for spinal infections. Beside obesity, other risk factors for wound infections have been described in the literature: blood loss, reoperation, diabetes, duration of the surgery and length of stay at hospital  [55]. Age over 80 was not found to be a risk factor of wound infection in our study.
Funding: This work was financially supported by  LA/P/0045/2020  ( ALiCE) ,  UIDB/00511/2020  and  UIDP/00511/2020  (LEPABE), funded by national funds through FCT/MCTES (PIDDAC); Project  POCI-01-0145-FEDER-028715  ( MicroDelivery-Development of controlled delivery functional systems by microencapsulation of natural  and active compounds with therapeutic, nutritional and technological interest), funded by  FEDER  funds through  COMPETE2020-Programa Operacional Competitividade e Internacionalização (POCI)  and by national funds (PIDDAC) through  FCT/MCTES.
These results indicate that a major fraction of PDGFRa + subepithelial stromal cells generated from LTBR + progenitors develop before weaning age, and low numbers of LTBR + stromal progenitors are still present in adult intestine. To confirm this hypothesis, we generated a direct LTBR GFP reporter model (Figure  1F ). Expression pattern of GFP was consistent with reported LTbR expression  (Lu and Browning, 2014; Macho-Fernandez et al. , 2015; Figures   S1I-S1K ). In the intestinal lamina propria, we detected around 1% of PDGFRa + stromal cells expressing GFP close to the intestinal epithelial layer  (Figures 1F and 1G) , showing that a small fraction of PDGFRa + LTBR + stromal progenitors are still present in adult intestine. Analysis of available single-cell datasets of the small intestine confirmed Ltbr expression in PDGFRa hi stromal cells (Figure  1H ;  McCarthy et al.
Data and code availability d The raw data of bulk RNA-seq generated by this study have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GEO: GSE200365. Accession numbers of each dataset are listed in the key resources table. d This paper does not report original code. d Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.
Cells were fixed and permeabilized according to the manufacturer's instructions. Retinoic Acid production by dendritic cells was assessed using the Aldefluor kit according to manufacturer's instructions. Cell doublets were excluded during analysis. Cells were analyzed with Fortessa (BD Biosciences) and Flowjo software (Tristar) and isolated with FacsARIA III (BD Biosciences). The immune populations were gated as follow: CD19 + cells were gated on CD45 + cells, Th2 and Th17 were gated on CD45 + CD3 + CD4 + cells, CD8 + T cells were gated on CD45 + CD3 + cells, ILC2 and ILC3 were gated on CD45 + CD19 -CD3 -IL-7R + cells, CD103 + CD11b -DCs and CX 3 CR1 + mononuclear phagocytes were gated on CD45 + CD19 -CD3 -CD11c high MHC-II high cells, SiglecF + eosinophils, Ly6G high neutrophils and Ly6C high monocytes were gated on CD45 + CD19 -CD3 -CD11c -/low MHC-II -/low CD11b + cells.
RNA Isolation and qRT-PCR We FACS sorted cells, or collected epithelial cells directly into vials containing lysis buffer complemented with 1% ß-mercaptoethanol. We isolated total RNA using the Single Cell RNA Purification Kit (Norgen) according to the manufacturer's instructions. We assessed the quality of total RNA using the 2100 Bioanalyzer system (Agilent Technologies) and the quantity using Qbit RNA HS kit (ThermoFischer). Total RNA was transcribed into cDNAs using SuperScript IV reverse transcriptase (Invitrogen). We performed qRT-PCR using RT 2 -qPCR primer sets (Bio-Rad Laboratories) and SYBR-Green master mix (Bio-Rad Laboratories), on a PTC-200 thermocycler equipped with a Chromo4 detector (Bio-Rad Laboratories), and analyzed data using Opticon Monitor software (Bio-Rad Laboratories).
RNA sequencing Librairies were prepared from total mRNA using the SMARTerÒ Stranded Total RNA-Seq Kit v2-Pico Input Mammalian (Takara Bio) according to the manufacturer's instruction. Library quality and quantity were assessed using the 2100 Bioanalyzer system (Agilent Technologies) and the Qbit dsDNA HS kit (ThermoFischer). Sequencing was performed using Illumina NextSeq 500/550 High Output kit v2. 5. The RNA-seq analysis was performed with Sequana 0. 11. 0 (https://github. com/sequana/sequana_rnaseq) built on top of Snakemake 6. 1. 1 (Koster and Rahmann, 2012). Briefly, reads were trimmed from adapters using cutadapt 3. 4 then mapped to the genome assembly GRCm38 from Ensembl using STAR 2. 7. 3a. FeatureCounts 2. 0. 1 was used to produce the count matrix, assigning reads to features using corresponding annotation GRCm38_92 from Ensembl with strand-specificity information.
Quality control statistics were summarized using MultiQC 1. 10. 1  (Ewels et al. , 2016). Clustering of transcriptomic profiles were assessed using a Principal Component Analysis (PCA). Differential expression testing was conducted using DESeq2 library 1. 24. 0  (Love et al. , 2014)  and SARTOOLS scripts  (Varet et al. , 2016). The normalization and dispersion estimation were performed with DESeq2 using the default parameters; statistical tests for differential expression were performed applying the independent filtering algorithm. A generalized linear model was set in order to test for the differential expression between conditions. Raw p-values were adjusted for multiple testing according to the Benjamini and Hochberg procedure and genes with an adjusted p-value lower than 0. 05 were considered differentially expressed.
(F) Immunofluorescence analysis of GFP, CD31, and PDGFRa in intestinal sections from LTBR GFP mice. (G) FACS plot of CD45 À CD31 À stromal cells expressing GFP in the small intestine of LTBR GFP mice or nontransgenic littermate. One representative plot of 3 individual experiments is shown. (H) Expression of Ltbr in intestinal PDGFRa + stromal cells, analyzed from single-cell dataset (GSE130681) (McCarthy et al. , 2020). In (C), (D), and (F), representative images of 3 independent experiments are shown. *p < 0. 05, **p < 0. 01, ****p < 0. 0001, as determined by unpaired Student's t test. ns (not significant). Scale bars, 20 mm. Dapi stains nuclei. See also Figure S1.
ACKNOWLEDGMENTS We thank all members of the  Stroma, Inflammation & Tissue Repair Unit , as well as members from  Microenvironment & Immunity Unit , for discussions; the  Mouse Genetics Engineering Platform , the  Cytometry Platform , and the  ICM Institute  for excellent technical support; and  Jeffrey Browning  for the kind gift of LTbR-Ig. J. -M. J. , I. S. , and A. B. were funded by fellowships from  Le Ministere de l'Enseignement Superieur et de la Recherche and Fondation pour la Recherche Medicale (FRM). This work has received funding from the  Institut Pasteur, INSERM  and the  European Union 's  Horizon 2020 research and innovation program  ( ERC Consolidator  grant No. 648428 ) to L. P.
The inclusion criteria were adult patients 18 years of age and above who had ever been diagnosed by a neurologist to have had an ischemic or hemorrhagic stroke. Patients with a history of stroke symptoms without medical diagnosis or prior hospitalization were excluded. Included patients were interviewed by well-trained healthcare professionals, either face-to-face or through a phone call. The direct caregiver was interviewed on behalf of the patient in cases involving patients with communication disabilities. The interview started with an introduction to explain the objectives of the study and the importance of its outcomes on the QOL of stroke survivors. A well-structured questionnaire in Arabic, the native language of Lebanon, was developed and utilized for data collection that took place between October 2021 and June 2022.
Statistical Analysis Data were analyzed using IBM Statistical Package for Social Sciences (IBM SPSS) version 26. 0. The sociodemographic, socioeconomic, and clinical characteristics were evaluated by descriptive statistics. Continuous variables were described by their means (±standard deviation, SD), and categorical variables were described by their frequencies and percentages. Factor analysis with principal component analysis (PCA) was run to validate the LMAS-14 with rotated matrix. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity were ensured to be adequate. Factors retained in the final scale had Eigen values greater than one. The score of the LMAS-14 was computed from the extracted factors. The correlation of each item of the scale with the whole scale was determined by Pearson correlation.
In addition, participants were recruited from community pharmacies where patients are presumably going to obtain or attempt to obtain medications. Thus, the study did not capture patients who decide not to take medications, or those who have difficulties having their prescriptions delivered to the pharmacy. Prospective research to follow-up hospitalized stroke patients is still recommended to obtain less biased data, although it is believed that such prospective research may still carry a risk of clinical information bias due to possible positive or negative long-term clinical changes that could occur to the different dimensions of HRQOL and thus influence medication adherence. Moreover, the data collection in the current study involved two different methods of collection (face-to-face and phone calls).