dataesr/scientific-paragraphs-categorization huggingface dataset:
4) following the manufacturer instructions. Per extracted tissue sample, relative qPCR was carried out in duplicate simultaneously using the CHIKV specific primers and FAM-Probe (as described for CHIKV quantification in plasma) and a Cy5 GAPDH-specific probe. For each qPCR, CHIKV, and GAPDH, a standard curve was included. All reactions were approximately 100% efficient; thus, each CHIKV sample was normalized using a GAPDH sample, and the relative copy number for each sample was calculated with the following equation: Relative copy number = 2 -(Cq CHIKV -Cq GAPDH). CHIKV RNA quantification data were expressed as the ratio of CHIKV RNA to cellular GAPDH RNA. LLOD depended on the amount of GAPDH RNA within the sample and on the quantity of CHIKV RNA  (35) (36) (37) (38) , which varied from one tissue to another, from 3 × 10 -7 for muscle to 6 × 10 -5 for joints.
To illustrate the level of detectable RNA in tissue, a calculation of the CHIKV genomic RNA/ng total RNA in each sample was performed. Total RNA was quantified per tissue sample using UV spectroscopy (absorbance at 260 and 280 nm). The calculation took into account the total RNA used in each amplification round. Per sample and assay, 500 ng of total RNA were used; thus, the GAPDH RNA amount/500 ng total RNA was determined, and the ratio of CHIKV RNA/ng total RNA was calculated accordingly. The samples from the standard curve were also normalized to 500 ng of total RNA (50 ng/μL, 10 μL per assay) from tissues per PCR; thus, the final CHIKV copies of the standard ranged from 1,730,000 to 207 copies. Statistics. Krustal-Wallis test was used for multiple comparisons. Repeated-measures 2-way ANOVA followed by Bonferroni's multiple-comparison test was used for time point analyses, and Deming regression was used for comparison of data from different neutralization assays.
The statistical analyses were performed with GraphPad Prism 9. 2 (GraphPad Software). Differences between groups were considered significant when P < 0. 05. Study approval. All procedures were validated and approved by the ethic committee from CEA no. 44 with decision no. A18-051 and validated by the French MENESR under the no. APAFIS 16755-2018091711094093v2 from  November 26, 2018.
Acknowledgments The work was, in part, funded by  CEPI and Horizon 2020. We thank  Stefan Fernandez  and colleagues at the  Department of Virology at AFRIMS  for sharing critical reagents to our study team. We thank  B. Delache ,  S. Langlois ,  P. Le Calvez ,  M. Potier ,  J. M. Robert ,  T. Prot , and  C. Dodan  for the NHP experiments;  M. Galpin-Lebreau ,  M. Lafosse , and  J. Morin  for the qPCR, cytokine measurements, and preparation of reagents;  M. Barendji ,  J. Dinh , and  E. Guyon  for the NHP sample processing;  S. Keyser  for the transports organization;  F. Ducancel ,  A. Pouget , and  Y. Gorin  for the logistics and safety management;  I. Mangeot  for her help with resources management; and  B. Targat , who contributed to data management. We thank  K. Lingnau  and  H. Stockinger  for their contribution to retrospective validation of qPCR and  O.
Zoihsl  for management of the phase I clinical study and acquisition of clinical samples. Furthermore, we thank  K. Moreau  and  M. Esnault  for analyses of NHP plasma samples by TCID 50. We thank  A. Klingler  for statistical support to determine the surrogate. The  Infectious Disease Models and Innovative Therapies (IDMIT)  research infrastructure is supported by the " Programme Investissements d'Avenir ," managed by the  ANR  under no. ANR-11-INBS-0008.
For all microscopic examinations, the sample temperature was maintained at 20 ± 1 • C, using a custommade heat block connected to a water-bath circulator and controlling the room temperature. Image Analysis. Quantitative image analysis was performed by using a custom code written in MATLAB. The center of mass of each droplet was detected through the binarization of bright-field images. To determine the radius R and diameter D = 2R of the droplets, the area of each droplet S was extracted and then calculated from R = S/π. The aspect ratio of the droplet with h > D was set to h/D = 1. The droplet speed V drop was calculated from the straight displacement of the droplet centroid from the initial position to the final position divided by the track duration. The migration speed of the droplets under 2D confinement was calculated from the displacement within 10 min.
ACKNOWLEDGMENTS. We thank  T. Q. P. Ueda  and  Y. Okada  for sharing materials and  J. Ikenouchi  for fruitful discussion. This work was supported by  Scientific Research (B)  Grants  20H01872  (to Y. T. M. ) and  19H03201  (to Y. S. );  Scientific Research on Innovative Areas "Molecular Engines " Grants  18H05427  (to Y. T. M. ) and  19H05393  (to M. M. );  Japan Society for the Promotion of Science Fellows  Grant  JP19J20035  (to R. S. );  Challenging Research (Exploratory)  Grants  21K18605  (to Y. T. M. ) and  20K21404  (to Y. S. ) from the  Ministry of Education, Culture, Sports, Science, and Technology, Japan ;  Human Frontier Science Program   Research Grant   RGP0037/2015  (to Y. T. M. );The  Hakubi Project of Kyoto University  (M. M. ); and  Japan Science and Technology Agency PRESTO, Japan  Grant  JPMJPR20ED  (to M.
to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints.
Second, the timeconsuming nature of manual dissection makes data gathering burdensome. Third, the digital representation of WM pathways from tractography (streamlines) makes the quantification and interpretation of reproducibility difficult and creates computational challenges. Our main goal is to assess the consistency of raters performing repeated virtual dissection tasks and to investigate variables that impact the results. The contributions of this study are threefold. First, we quantify intrarater and interrater reproducibility for raters performing the same dissection protocol on matched data. Second, we investigate the longitudinal relationship between reproducibility scores and protocol repetitions (i. e. , practice). Third, we included an online educational session that encompasses an introduction to the project, software tutorials, and a live protocol demonstration to examine its impact against previous work that included written instruction only (pdf document).
| Dissection protocol Our goal is to evaluate the capacity of raters to perform repeated virtual dissection tasks. These tasks are limited to ROIs "drawing" (i. e. , shape, size, and position) on provided data. The raters only had to open the software and load the preprocessed data (tractograms and maps), then follow instructions to identify anatomical landmarks as described. One of the limiting factors in the initial Tractostorm project was the use of only one bundle of interest. This was due to the initial complexity of the study design and the number of unknown variables. Using the same template and aiming for the same level of clarity, a dissection protocol was defined for each of the three bundles of interest (CC, AF, and PYT). The prior work helped with refining the project and allowed us to expand the number of bundles.
Some relationships between bundles are observable across HCP data sets (true at most/all of 20 timepoints). For example, the average length of the AF is systematically higher (143. 45 ± 13. 09) than both the CC (93. 24 ± 3. 43) and the PYT (126. 00 ± 5. 25) or the volume of the CC (94,827 ± 20,213 mm 3 ) is higher and more variable than both the AF (50,101 ± 8,403 mm 3 ) and PYT (52,282 ± 8,232 mm 3 ).
Modifications to protocols should trigger a reproducibility evaluation, and it should be targeted for a somewhat specific range of audiences, data sets, and populations. For example, this work was mainly designed for raters without neuroanatomy background on young/ healthy subjects from the HCP database. However, a silver lining is that some flexibility is possible when targeting the scope of a protocol. Rheault, De Benedictis, et al. (2020)  demonstrated that the experts and nonexperts group distinction (with and without formal anatomy background) had a minimal effect for spatial agreement in voxel representation (Dice score of voxels). Furthermore, TractEM  (Bayrak et al. , 2019)  showed that acquisition quality (angular/spatial resolution) did not have a major influence on the agreement (both Dice score of voxels and correlation of density map).
ACKNOWLEDGMENT • Leon Cai was supported by NIH/NIGMS, grant number 5T32GM007347. • Viljami Sairanen was supported the Orion Research Foundation sr, the Instrumentarium Science Foundation sr. , and the Emil Aaltonen Foundation. • Pietro Bontempi was supported by Verona Brain Research Foundation (high-field MRI for the study of peripheral nerve microstructure). • Guido Guberman was supported by the Vanier Canada Graduate Scholarship. • Maggie Roy was supported by The Mathematics of Information Technology and Complex Systems (MITACS). • Charles Poirier, Gabrielle Grenier, and Philippe Karan were supported by the scholarship from Natural Sciences and Engineering Research Council (NSERC) and Fonds de Recherche du Québec Nature and Technologies (FRQNT). • We thank the Universite de Sherbrooke institutional research chair in Neuroinformatics that supports Maxime Descoteaux and his team.
Standardized anatomical definitions are clearly needed. Both theoretical (with no regard for acquisition, local modeling and tractography reconstruction), and practical definitions are needed. The data generated by this work will be made available online (https://doi. org/10. 5281/zenodo. 5190145) in March 2022. Theoretical definitions must refer to conceptual landmarks and regions and convey the general shape, orientation, and terminations. They must aim to be distinct enough between bundles while encompassing a variety of practical definitions. Such definitions should refer to anatomical structures of the brain and not be tied to specific contrasts or processing. Work such as Catani & De F I G U R E 7 Reproducibility scores (gold standard) for all timepoints showing agreement with the group average.
Next, we conducted in silico analyses to annotate eQTLs and sQTLs with a variety of omics data in cattle, including DNA methylation, chromatin states, and chromatin conformation characteristics. Finally, we integrated gene expression with a large GWAS of 27,214 dairy bulls and 43 cattle traits via both transcriptome-wide association study (TWAS) and colocalization analyses to detect genes and variants associated with these economically important traits. We make the results freely and easily accessible to the research community through a web portal (http://cgtex. roslin. ed. ac. uk/). This Cattle Genotype-Tissue Expression (CattleGTEx) atlas as part of the Farm animal GTEx (FarmGTEx) project will serve as a primary reference for cattle genomics, breeding, adaptive evolution, veterinary medicine, and comparative genomics.
General characteristics of transcriptome across samples As expected, the number of expressed genes (Transcripts per Million, TPM > 0. 1) increased with the number of clean reads across samples. However, we observed a plateau at 50 million clean reads (Extended Data Figure  2a ) where we only detected ~60% of 27,607 Ensembl annotated genes. Only 61 genes were not expressed in any of the samples, and 33 of them (54. 10%) were located in unplaced scaffolds, with significantly shorter gene length, fewer exons, higher CG density, and lower sequence constraints than expressed genes (Extended Data Figure  2b-f ). Similarly, we detected more alternative splicing events with increasing numbers of clean reads across samples (Extended Data Figure  2g ). However, we did not detect splicing events for 874 genes in any sample, which also exhibited significantly shorter gene length, fewer exons, lower expression, and lower sequence constraints than spliced genes (Extended Data Figure  2h-k ).
Significant differences (denoted as *) were observed between 1 st vs. 2 nd (P = 2. 4×10 -3 ), 2 nd vs. of genes by leveraging information of junction reads (i. e. , reads spanning introns) that were obtained from the STAR alignment. The Leafcutter enables the identification of splicing events without relying on existing annotations that are typically incomplete, especially in the setting of large genes or individual-and/or population-specific isoforms  28. We first converted bam files from STAR alignment into junction files using the script "bam2junc. sh", and then performed intron clustering using the script "leafcutter_cluster. py" with default settings of 50 reads per cluster and a maximum intron length of 500 kb. We employed the "prepare_genotype_table. py" script in Leafcutter to calculate intron excision ratios and to remove introns used in less than 40% of individuals or with no variation.
Genotyping and imputation We called genotypes of known genomic variants in the 1000 Bull Genomes Projects 10 for 7,180 high-quality RNA-Seq samples individually, following the recommended best practices pipeline in Genome Analysis Toolkit, (GATK) (v4. 0. 8. 1)  29  with default settings. We filtered out low quality SNPs using --filter-expression "FS > 30. 0 & QD < 2. 0". We then imputed the filtered SNPs on autosomes to sequence level using Beagle (v5. 1)  30   15 ). Finally, we obtained 6,123 samples that were genotyped and imputed successfully. We filtered out variants with MAF < 0. 05 and dosage R-squared (DR 2 ) < 0. 8, resulting in 3,824,444 SNPs used for QTL mapping. To evaluate the accuracy of imputation, we called genotypes (~6 M SNPs) from WGS (average read depth > 10×) of Holstein (n = 4), Limousin (n = 3) and Angus (n = 5) animals, which had RNA-Seq data as well.
Bioinformatics analysis of WGBS data For WGBS data analysis, we first used FastQC (v0. 11. 2) and Trim Galore v0. 4. 0 (--max_n 15 --quality 20 --length 20 -e 0. 1) to determine read quality and to filter reads with low quality, respectively. We then mapped clean reads to the same reference genome (ARS-UCD1. 2) using Bismark software (v0. 14. 5)  34  with default parameters. After deduplication of reads, we extracted methylation levels of cytosines using the bismark_methylation_extractor (--ignore_r2 6) function. The coverages of all WGBS data were calculated using clean reads with an average of 27. 6-fold coverage (range: 5-47 ×). Ultimately, we kept CpG sites that were represented by at least five reads for subsequent analyses. We visualized sample clusters based on DNA methylation levels of shared CpGs using t-SNE approaches.
Identification of TAD and significant Hi-C contacts To find potential chromatin interactions between distant eVariants and target eGenes, we identified TADs and Hi-C contacts from Hi-C data from lung tissue in cattle that was retrieved from NCBI Sequence Read Archive (SRA) under accessions: SRR5753600, SRR5753603, and SRR5753606. We used Trim Galore (v0. 4. 0) to trim adapter sequences and low-quality reads (--max_n 15 --quality 20 --length 20 -e 0. 1), resulting in ~820 million clean reads. We then mapped clean reads to the reference genome (ARS-UCD1. 2) using BWA(v0. 7. 17)  35. We applied HiCExplorer v3. 4. 1  36  to build a Hi-C contact matrix with 10kb resolution and identified TAD with hicFindTAD. We kept TADs with FDR less than 0. 01 to link eQTLs to eGenes. We further employed HiC-Pro (v2.
Covariate analysis for QTL discovery To account for hidden batch effects and other technical/biological sources of transcriptome-wide variation in gene expression, we estimated latent covariates in each tissue using the Probabilistic Estimation of Expression Residuals (PEER v1. 3) method  41. In each tissue, we estimated 75 PEER factors first. The posterior variances of factor weights dramatically decreased and reached or nearly reached plains when 10 PEER factors were included (Extended Data Figure  6a ). Therefore, we used 10 PEER covariates to account for the effects of confounding variables on gene expression in all following QTL analyses. For instance, the variance of gene expression among samples in adipose captured by 9 out of 10 PEER factors were significantly (FDR < 0. 05) correlated with known technical and biological covariates like clean data size, mapping rate, project, breeds, sub-species, sex and age (Extended Data Figure  6b ).
cis-eQTL mapping We conducted cis-eQTL mapping for 23 distinct tissues with at least 40 individuals each, while adjusting for corresponding PEER factors and genotype PCs. Detailed information about these 23 distinct tissues is in Supplementary Table  4. As the majority of cis-eQTLs are shared across sub-species/breeds (Figure  3g ), we combined, adjusting for species/breed, all of the datasets from the same tissue to perform cis-eQTL mapping in order to increase the statistical power. We kept genes with TPM > 0. 1 in ≥ 20% samples in each tissue. Gene expression values of all samples in a given tissue were quantile normalized to the average empirical distribution and expression values for each gene then inverse normal transformed (INT) across samples. The cis-eQTL mapping was done using a linear regression model, implemented in FastQTL (v2.
Meta-analysis of cis-eQTLs of muscle samples from three sub-species Data from muscle samples were available from three sub-species: Bos indicus (n = 51), Bos taurus (n = 505), and their crosses (n = 108). To explore the similarity and variability of cis-eQTLs among sub-species, we conducted cis-eQTL mapping using muscle samples from each of the sub-species separately. We then conducted a meta-analysis to integrate cis-eQTL results from three sub-species using the METAL (v2020-05-05) tool  45. We obtained Z-scores (the sum of weighted effect sizes) of SNPs from the meta-analysis. Weights were proportional to the square-root of the number of individuals in each sub-species  45. We employed plink (v1. 90)  31  to test the SNP × breed interaction in muscle samples, and adjusted the p-values to FDR using Benjamini-Hochberg procedure.
Other downstream bioinformatics analysis We used Genomic Association Tester (GATv1. 3. 4)  55  1,000 permutations to estimate the functional enrichment of QTLs in particular genomic regions, e. g. , chromatin states and methylation elements. We considered enrichments with FDR < 0. 05 as significant. We used the R package, ClusterProfiler (v3. 0. 4) 56 , to annotate the function of genes based on the Gene Ontology database from Bioconductor (org. Bt. eg. db v3. 11. 4). We considered GO terms with FDR < 0. 05 as significant.
Yu, G. , Wang, L. G. , Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-7 (2012). 57. Liu, S. et al. A multi-tissue atlas of regulatory variants in cattle. Code resource at github website. https://github. com/shuliliu/cattleGTEx; DOI:.
Acknowledgements This work was supported in part by  AFRI grant  numbers  2016-67015-24886 ,  2019-67015-29321 , and  2021-67015-33409  from the  USDA National Institute of Food and Agriculture (NIFA) Animal Genome and Reproduction Programs  and  BARD  grant number  US-4997-17  from the  US-Israel Binational Agricultural Research and Development (BARD) Fund. L. Fang. was partially funded through  HDR-UK  award  HDR-9004  and the  Marie Skłodowska-Curie  grant agreement No [ 801215 ]. A. Tenesa  acknowledged funding from the  BBSRC  through programme grants  BBS/E/D/10002070  and  BBS/E/D/30002275 ,  MRC  research grant  MR/P015514/1 , and  HDR-UK  award  HDR-9004. O. Canela-Xandri  was supported by  MR/R025851/1. R. X. was supported by  Australian Research Council's Discovery Projects  ( DP200100499 ).
Data Availability All raw data analyzed in this study are publicly available for download without restrictions from SRA (https://www. ncbi. nlm. nih. gov/sra/) and BIGD (https://bigd. big. ac. cn/bioproject/) databases. Details of RNA-Seq, WGBS and WGS can be found in Supplementary Table  1 , 2 and 15, respectively. All processed data, the full summary statistics of QTL mapping are available at https://cgtex. roslin. ed. ac. uk/.
Quantification of gene expression We downloaded 11,642 RNA-Seq datasets (by June 24th, 2019) from SRA (n = 11,513, https://www. ncbi. nlm. nih. gov/sra/) and BIGD databases (n = 129, https://bigd. big. ac. cn/bioproject/) by searching the 'Organism' for 'Cattle' and the 'Strategy' for 'RNA seq'. We merged multiple datasets from single samples, yielding 8,536 unique RNA-Seq samples. We applied a stringent and uniform pipeline to filter and analyze all the data. Briefly, we first removed adaptors and low quality reads using Trimmomatic (v0. 39)  25  with parameters: adapters/TruSeq3-SE. fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. We filtered out samples with clean read counts ≤ 500K, resulting in 7,680 samples, and mapped clean reads to the ARS-UCD1. 2 cattle reference genome  17  using single or paired mapping modules of STAR (v2.
Quantification of alternative splicing We used Leafcutter (v0. 2. 9)  28  to identify and quantify variable alternative splicing events mapping, functional enrichment, TWAS and colocalization are available at both the web portal of CattleGTEx (https://cgtex. roslin. ed. ac. uk/) and the github website (https://github. com/shuliliu/cattleGTEx, DOI: 10. 5281/zenodo. 6510550)  57. Observed (-log 10 P) Observed (-log 10 P).
Yu, G. , Wang, L. G. , Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284-7 (2012). 57. Liu, S. et al. A multi-tissue atlas of regulatory variants in cattle. Code resource at github website. https://github. com/shuliliu/cattleGTEx; DOI: 5610. 5281/zenodo. 6510550; https://zenodo. org/record/6510550#. Ym-j8NrMKUk.
Petrochronology (zircon, rutile) U-Th-Pb petrochronology of zircon was primarily carried out to document the microchemical fingerprints (U-Pb dates, trace element and REE signatures) of individual zircon domains targeted for O-isotope analyses. U-Th-Pb data and REE compositions of zircon and rutile were analyzed by (LASS)-ICP-MS at the University of California -Santa Barbara on a Photon Machines Analyte 193 nm Excimer Laser with HelEx ablation cell, combined with a Nu Instruments HR plasma highresolution multi-collector ICP-MS (U-Th-Pb) and an Agilent 7700S quadrupole ICP-MS (REEs). Detailed analytical protocols are described in  Kylander-Clark et al. (2013)  and in supplement A1. Additional geochronology results for LJ eclogite zircon analyzed by SHRIMP-II (methods in  Whitney et al. , 2020)  are in Supplement C.
Summary of O-isotope data analysis (SIMS) results. Weighted mean δ 18 O values for individual garnet grains and distinct zircon domains are given for all four eclogite samples analyzed, with the error reported at 95% confidence; for dispersed analytical groups, dispersion is reported and 95% conf. region of the dispersion (1. 96 × ω). Full dataset can be found in TableE. + ω upper /-ω lower Wtd. mean δ 18 O ± 95% conf. of ω 2.