dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, clone T cells at a 10/1 E:T ratio, if not indicated otherwise, were added to each well, and data acquisition lasted for the next 24 hours. For 7AAD experiments, target cells were incubated with specific Tcell clones during 4 hours. For the positive control, the cells were incubated with 1 μmol/L of corresponding peptide. The cells were then stained with 7AAD final concentration (0. 5 μg/mL; cat: 559763 from BD) and antibodies (CD45BV711, CD8FITC). Cells were then washed and analyzed in an LSR Fortessa cytometer.
Single-Cell RNA-seq Processing Singlecell expression was analyzed using the Cell Ranger single cell Software Suite (v3. 0. 2, 10x Genomics; ref. 50)  to perform quality control, sample demultiplexing, barcode processing, and singlecell 5′ gene counting. Sequencing reads were aligned to the GRCh38 human reference genome. Further analysis was performed in R (v3. 5. 1) using the Seurat package (v3. 1. 1; 51). Cells were then filtered out when expressing fewer than 500 genes for UM1 and fewer than 1,000 genes for UM2/UM3 because this sample was of lower quality. Cells were also filtered out when expressing more than 10% mitochondrial genes, indicative of potential cell death or stress. Samples were then filtered for contaminating cells using classic markers. Notably, CD19 was used to remove B cells, MAFB was used to remove myeloid cells, and CD3D/E/G and CD8A/B were used as positive controls.
Altogether, 3,441 cells were kept for UM1 and 3,231 were kept for UM2 and UM3. For each sample, the genecellbarcode matrix of the samples was then normalized to a total of 1 × 10 4 molecules. TotalSeq val ues were normalized according to the CLR method implemented in Seurat. The top 2,000 variable features were identified using the "vst" method from Seurat. For UM2/UM3 samples, the doublets were removed, leveraging the TotalSeq information. Since the TotalSeq features were bimodal, we first binarized the TotalSeq features. The expression threshold was defined as 1 for UM2A2:26 and 1. 2 for the rest of the specificities. Cells were then labeled cells as doublets if they were expressing more than 1 TotalSeq above the expression threshold. In all, 490 cells out of the 3,213 (15%) were excluded after removing TotalSeq doublets.
Dimension Reduction and Unsupervised Clustering The top 30 principal components (PC) were computed and UMAP was performed using the top 30 PCs of the normalized matrix. Clusters were identified using the FindNeighbors and FindClusters func tion in Seurat with a resolution parameter of 0. 4 for UM1 and 0. 35 for UM2/UM3 and using the first 30 PCs. To choose the optimal number of clusters and prevent overclustering, clustree analysis was performed using the clustree package  (52). Unique clusterspecific genes were identified by running the Seurat FindAllMarkers function using Wilcoxon test.
Single-Cell TCR-Seq Data Processing and Analysis TCRseq data for each sample was processed using Cell Ranger soft ware with the command "cellranger vdj" using the human reference genome GRCh38. Because of dropouts, both TCRα and TCRβ are not always sequenced in a given T cell. Thus, as a T cell can express up to two TCRα chains and one TCRβ chain, it is easy to artificially split true Tcell clones into two different clonotypes. To the contrary, incompletely sequenced doublets can mistakenly lead to the creation of artifactual clo notypes. Because our data sets encompassed very large clonal expansions and the dropout and the number of cells loaded on the chip was high for patient UM2/3 data sets, we manually curated all the recurrent clono types to exclude doublets and merge clonotypes. We merged clonotypes using the same TCRα or TCRβ chains.
Barcoding and incorporation of the sequencing primers for Pairedend Illumina sequencing was per formed with PE1_CS1 forward primer and PE2_barcode_CS2 reverse primer (Fluidigm) at 400 nmol/L using Platinium Taq DNA Poly merase High Fidelity (Thermo Fisher Scientific). PCR products were sequenced using Miseq V3 PE300 kit (Illumina). The data have been deposited in the GEO database under accession number GSE169610.
Acknowledgments We acknowledge the help of  C. Guerin ,  S. Grondin , and  A. Viguier  from the flow cytometry core at  Institut Curie. We thank  Professor Caroline Robert  for insightful comments. We thank the  ICGex NGS platform of the Institut Curie  ( S. Lameiras ,  S. Baulande ,  M. Bohec ) for technical help with singlecell RNAseq experiments. ICGex is supported by the grants  ANR10EQPX03  ( Equipex)  and  ANR10INBS0908  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  Canceropole Ile-de-France , and by the  SiRIC Curie program. The  Laboratoire de Spectrométrie de Masse Protéomique  is supported by " Région Ile-de-France " and  Fondation pour la Recherche Médicale  grants (D. Loew). This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
667787  ( UM Cure 2020  project). P. Gueguen  has received a fellowship from the  Ligue Nationale Contre le Cancer  and  F. Lucibello  from  Institut Curie. This work was supported by the  Institut National de la Santé et de la Recherche Médicale, Institut Curie ,  ANR ,  ARC fondation, INCA, Ligue Nationale Contre le Cancer  ( M. H. Stern 's team is " Equipe Labellisée par la Ligue Contre le Cancer ") and  Labex DCBIOL ,  Cancéropole Ile de France émergence, SIRIC. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received April 28, 2020; revised January 29, 2021; accepted March 31, 2021; published first April 2, 2021.
Removal of mitochondrial/contaminant DNA To identify scaffolds that contained mitochondrial cytochrome oxidase subunit 1 (COI) DNA, we used BLAT v. 35  (Kent 2002; 2012)  using a reference sequence from Pachyrhynchus smaragdinus (Supplemental Information file "P79_coI. fasta") to query our scaffolds. Once identified, these scaffolds were removed. We also used blast  (Camacho et al. 2008)  with the nt database and default settings to identify contaminant (non-arthropod or undetermined) sequences and then removed these from the final assembly. These represented only a handful of sequences.
This process used the bam file from HISAT2 and results from a BUSCO search as 'seeding' genes to make the resulting gff. In addition, we used the PASA pipeline  (Campbell et al. 2006; Haas 2008 ) which used our rnaSPAdes transcripts aligned to the genome assembly with BLAT  (Kent 2002)  and gmap  (Wu and Watanabe 2005). Lastly, we used EVidenceModeler  (Haas et al. 2008 ) to evaluate our different annotations using the developers' recommended weights for each assembly type to produce the final gene model gff.
2010 , Mesquita et al. 2015)  as well as Hi-C data generated by the DNA Zoo Consortium and assembled using 3D-DNA  (Dudchenko et al. , 2017)  and Juicebox Assembly Tools  (Dudchenko et al. , 2018). Next, we identified the BUSCO v. 2 loci, (1658 Insecta gene set) and extracted their coordinates for the single and fragmented loci. We then compared the coordinates of Pachyrhynchus sulphureomaculatus to the other Coleoptera genomes. Following, we calculated the number of loci found in P. sulphureomaculatus chromosomes and those in the other Coleoptera and calculated the percent conserved within a chromosome. To visualize the shared synteny, we plotted the different pairs using the R package RIdeogram  (Hao et al. 2020). To help visualize the relationship between the different taxa, we generated an ultraconserved elements (UCE) dataset between the taxa using the PHYLUCE pipeline (Faircloth 2012).
We used the loci to help reconstruct a concatenated phylogeny in RAxML  (Stamatakis 2014)  and calculate branch lengths to render the tree ultrametric. We dated the tree using dates (95% highest posterior density interval HPD) from  McKenna et al. (2019)  using the R package ape v. 5. 4 'makeChronosCalib' function  (Paradis and Schliep 2019 ) (see Supplemental Information file "Insecta_Claibrations_table" for dates). Next, we investigated whether the observed synteny was distinctive within Coleoptera relative to other orders of insects, such as Lepidoptera, in which high levels of synteny between taxa have been recorded  (Hill et al. 2019 , Ahola et al. 2014). We used all insect genomes (with some exceptions) available from NCBI that were marked as "chromosome" level. (See Supporting Information for a complete list.
) We tried to sample evenly across insect orders. For example, we excluded the many Drosophila genomes as they are all phylogenetically close relatives, and this would cause over-representation (i. e. we want patterns of chromosomal evolution across Diptera, not just Drosophila). Instead, we sampled individual species across the phylogenetic breadth of the genus. In addition, we also gathered genomes from the literature. (see Supplemental Information file "NCBI_all_taxa_genomes_list". ) Next, we identified all BUSCO version 5-beta loci that were single copy and calculated the gene order conservation (GOC) score (see https://m. ensembl. org/) using a custom script (Supplemental Information files "1make_scaff_order_busco_tsv. sh", and "2busco_GOC. sh"). First, we ordered the BUSCO v5beta genes by scaffold and position and then identified two genes upstream and downstream from a particular gene.
We used custom scripts to identify a 70% complete matrix and used mafft with 1000 iterations and the "localpair" settings to align the sequences. Next, we used trimAI  (Capella-Gutierrez et al. 2009)  with "automated1" settings to remove ambiguously aligned positions. RAxML-ng with the LG+G8+F site rate substitution model was used to reconstruct the phylogeny for our exemplar taxa across Insecta. We calibrated our tree using the same methods for the beetle tree (above), and calibration points can be found in the Supplemental Information file "Insecta_Claibrations_table", from Misof et al 2014,  Obbard et al. 2012   and Mckenna et al. 2019). This calibration was done to help visualize the data as the subsequent Mantel test did not require an ultrametric tree. Lastly, to test if pairwise phylogenetic distance covaries with pairwise synteny values, we conducted a Mantel test.
Acknowledgments We would like to thank the  Ruth Tawan -tawan,  Ceso II of the Philippines' Department of Environment and Natural Resources Region XI  for help with the Gratuitous and export permits. We would also like to thank the  University of Mindanao  for the mobility support, and  Milton N. Medina  and  Chrestine Torrejos of U. M. for help collecting specimens. We would like to thank  Zane Colaric  of  B. C. M. , for the help loading the QC library runs. We would also like to thank  Sarah Crews  of C. A. S. for help with the manuscript text. We were funded in part through  NSF :DEB award number  1856402  made to MHVD.
Root System Architecture Uniformity in size and shape of cassava roots is an important breeding objective. Routine assessment of storage root size and shape have relied on visual scores that are both subjective, time consuming, labor-intensive and destructive. Implementation of non-invasive approaches include image analysis of roots to provide unbiased quantitative data on important root traits. High throughput three-dimensional imaging and non-destructive methods such as ground penetrating radar (GPR) have recently been developed and used for quantifying cassava storage roots and their parameters for estimation of size and shape under field condition  (Delgado et al. , 2017 (Delgado et al. , , 2019)). Kengkanna et al. (2019)  developed cassava shovelomics to evaluate root architecture traits.
, 2017; Garcia-Oliveira et al. , 2020). The detected QTLs were not consistent across studies. Different mapping populations were used in the case of experimental populations. There might be some variations in CBSD response. Furthermore, QTLs tend to be population specific in bi-parental populations. Likewise, different environmental conditions, population size (106-1986 samples), and the subjectivity of the scoring system used for data collection could have affected QTL detection and localization. The use of different versions of the cassava genome assembly makes it challenging to compare some of these results. Therefore, these QTLs should be mapped onto the most recent version of the cassava reference genome. Cassava Green Mite and Related Traits.
However, the designation of linkage groups/chromosomes are sometimes different, making it challenging to align QTL detected in different studies  (Garcia-Oliveira et al. , 2020). Synchronization of cassava QTL information and the development of a cassava QTL repository is needed. A cassava QTL database that contains QTL information systematically aligned to the cassava reference genome, as well as information about germplasm and genetic material used in the QTL studies, will be a useful resource for both cassava geneticists and breeders  (Ni et al. , 2009; Yonemaru et al. , 2010; Said et al. , 2015). A benefit envisioned from the identification of molecular markers tightly linked to traits of interest is their deployment in breeding as an indirect selection method to accelerate the rate of genetic gain.
However, refined strategies should be adopted for complex traits. Non-additive genetic variation prevails for low heritability cassava traits and should be accounted for. Therefore, models that capture non-additive effects should be applied  (Wolfe et al. , 2016). GS, like any other approaches, is facing various challenges. The GS models predict poorly across populations, and consequently, the strategy requires continuous re-calibration with every breeding cycle  (Wolfe et al. , 2017). Allele frequency changes, introgression of new alleles, SNP-QTL linkage disequilibrium association, lack of relatedness between germplasm and population structure, as well as genotype-by-environment interaction effects, compromise the efficiency of GS. To overcome some of these limitations, dual-purpose population development and variety development pipelines have been applied to ensure training data are closely related to the new clones and for continuously updating the training model  (Santantonio et al.
, 2020). Image recognition has been evaluated for high accuracy disease detection and mobile, as well as web-based tools developed for cassava disease scoring and monitoring 7. An application programming interface has been implemented within Cassavabase in order to process cassava root images. Breeders can upload cassava root necrosis sectional image captured during harvest and the result will be returned back to Cassavabase. IITA and NARS partners have digitized all processes; data are uploaded, processed, and accessed within Cassavabase to minimize human errors. CRISPR/Cas9-based genome-editing is mainly driven by IITA, in partnership with a handful of national research organizations. The difficulty in acquiring laboratory supplies, as well as the need for constant and sufficient level of funding, constitute a bottleneck for the implementation of gene-editing technology by NARS.
DMC MCOL 1684 × THAI 1 98 SSRs Six (6) QTLs on four different linkage groups explaining Kizito et al. , F 2 (n = 199) 14 to 40% of the PVE. Cyanogenic Two (2) QTLs on two different LGs explaining 7% and glycoside (CN) 20% of the PVE, respectively. Anthracnose TME 117 (♀) × TMS 53 RAPDs Using bulk segregant analysis, two markers (OPAF2 Akinbo et al. , 2007 92/0326 (♂) and OPF06) flanking the CAD1 genes were identified F 1 (n = 60) that explained 33. 7 and 27. 4% of the phenotypic variation. Fresh foliage TMS 30572 (♀) × CM 122 SSRs Three (3) QTLs for fresh foliar detected on three linkage Okogbenin et al. , 2177-2 (♂) groups. The PVE ranged from 5. 5 to 31. 1%. F 2 (n = 268) Harvest index Three (3) QTLs associated with harvest index on three LGs explaining 2. 10-6. 67% of the phenotypic variation.
METHODS Data Availability The authors declare that the data supporting the findings of the study are available within the article, its Supplementary Material, and on request from the corresponding author. Genome-wide summary-level statistics are available to download from GWAS Catalog with study accession GCST90032674 (ftp://ftp. ebi. ac. uk /pub/databases/gwas/summary_statistics/ GCST90032001-GCST90033000/GCST90032674/). The FINRISK data are available from the THL Biobank (https://thl. fi/en/web/thl-bioba nk/for-resea rchers) based on a written application and following the relevant Finnish legislation. For the FinnDiane study and the twin samples, individual-level data cannot be shared because of restrictions in patient and participant consent. FinnGen Data Freeze 5-summary level data are publicly available at the FinnGen website (https://www.
FinnGen Study The Finngen study population used in the study was independent from the GWAS meta-analysis. Finngen is a growing project with samples from multiple biobanks and cohort studies: Megastroke is an international consortium of 29 studies, including 67,162 cases and 454,450 controls  38. Megastroke is included in the Cerebrovascular Disease Knowledge Portal (CDKP, https://cd. hugeamp. org/), along with 22 datasets, of which 18 datasets have GWAS information available. The portal has data on 48 disease traits, all connected to cerebrovascular diseases.
Genotyping and genotype imputation The FinnDiane cohort was genotyped in three batches using Illumina Human CoreExome chips (v12. 1. 0, v12. 1. 1 and v24. 1. 0 correspondingly). The FINRISK genotype data has been created using multiple genotyping platforms. Parts of the FR97 and FR92 cohorts were genotyped in the same.
12wnloaded from http://ahajournals. org by on April 15, 2022 for Cardiovascular Research (to Dr Salomaa). The FinnDiane study was supported by grants from Folkhälsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och Hälsa Foundation, Helsinki University Central Hospital Research Funds, the Novo Nordisk Foundation (NNFOC0013659/PROTON), European Foundation for the Study of Diabetes Young Investigator Research Award funds, and the Academy of Finland (Nos. 275614 and 316664). Genotyping of the FinnDiane genome-wide association study (GWAS) data was funded by the Juvenile Diabetes Research Foundation within the Diabetic Nephropathy Collaborative Research Initiative (grant 17-2013-7), with GWAS quality control and imputation performed at the University of Virginia. Phenotype and genotype data collection in the twin cohort has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007 (No.
201413), the National Institute of Alcohol Abuse and Alcoholism (Nos. AA-12502, AA-00145, AA15416, and K02AA018755), and the National Heart, Lung, and Blood Institute (HL104125). GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. The funding of the FinnGen project consists of 2 grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and support from 11 industry partners: AbbVie, AstraZeneca, Biogen, Celgene, Genentech (part of Roche), GSK, Janssen, Maze Therapeutics, MSD, Pfizer, and Sanofi.
The MEGASTROKE project received funding from sources specified at http:// www. megas troke. org/acknowledgments. html. DisclosuresDr Groop has received investigator-initiated research grants from Eli Lilly and Roche; is an advisory board member for AbbVie, AstraZeneca, Boehringer Ingelheim, Cebix, Eli Lilly, Janssen, Medscape, Merck Sharp & Dohme, Novartis, Novo Nordisk, and Sanofi; and has received lecture fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Elo Water, Genzyme, Merck Sharp & Dohme, Medscape, Novo Nordisk, and Sanofi. Dr Salomaa has received honoraria for consultations from Novo Nordisk and Sanofi. He also has an ongoing research collaboration with Bayer Ltd (all unrelated to the present study). The remaining authors have no disclosures to report. The subset of FINRISK -92 with LPS measures available was a CVD case-cohort sample12, and thus is not a random population sample.
P. d. ); LipSTIC LabEx, Dijon, France (J. -P. P. d. B. , L. L. );  Department of Public Health Solutions  (A. L. , A. S. H. , V. S. ) and  Genomics and Biomarkers Unit, Department of Health  ( M. Perola ),  Finnish Institute for Health and Welfare, Helsinki, Finland and University Bourgogne Franche-Comté, Dijon, France  (L. L. );  University Hospital, Hôpital du Bocage, Dijon, France  (L. L. );  Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia  (P. -H. G. ). Acknowledgments  I. Toppila ,  Dr Leskelä ,  Dr Kopra ,  Dr Salminen ,  Dr Sandholm ,  Dr Pietiäinen ,  Dr Salomaa ,  Dr Kaprio ,  Dr Groop ,  Dr Perola ,  Dr Lehto , and  Dr Pussinen  designed the study and interpreted the results. I. Toppila  and  Dr Leskelä  drafted the manuscript. I.
Other Experts/ Non-Voting Members 12wnloaded from http://ahajournals. org by on April 15, 2022 for Cardiovascular Research (to Dr Salomaa). The FinnDiane study was supported by grants from Folkhälsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och Hälsa Foundation, Helsinki University Central Hospital Research Funds, the Novo Nordisk Foundation (NNFOC0013659/PROTON), European Foundation for the Study of Diabetes Young Investigator Research Award funds, and the Academy of Finland (Nos. 275614 and 316664). Genotyping of the FinnDiane genome-wide association study (GWAS) data was funded by the Juvenile Diabetes Research Foundation within the Diabetic Nephropathy Collaborative Research Initiative (grant 17-2013-7), with GWAS quality control and imputation performed at the University of Virginia.
Phenotype and genotype data collection in the twin cohort has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007 (No. 201413), the National Institute of Alcohol Abuse and Alcoholism (Nos. AA-12502, AA-00145, AA15416, and K02AA018755), and the National Heart, Lung, and Blood Institute (HL104125). GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. The funding of the FinnGen project consists of 2 grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and support from 11 industry partners: AbbVie, AstraZeneca, Biogen, Celgene, Genentech (part of Roche), GSK, Janssen, Maze Therapeutics, MSD, Pfizer, and Sanofi.
Objectives. To map the current evidence on surgical antibiotic prophylaxis (SAP) administration and identify knowledge gaps in the literature available in this field. Methods. The PubMed, Cochrane Library, Epistemonikos, and Health Systems Evidence databases were searched from January 2015 to March 2020 for systematic reviews published in English, French, Portuguese, and Spanish. Results. Eighty-three systematic reviews were included, the quality of the reviews was assessed using AMSTAR 2, and data were extracted for all primary outcomes. Perioperative antibiotic administration, the use of first generation cephalosporins, and surgical site infection (SSI) were the most commonly reported for timing of antibiotic administration, drug class, and primary outcome, respectively. Findings showed that, overall, SAP may reduce SSIs compared with a placebo or with no SAP.