dataesr/scientific-paragraphs-categorization huggingface dataset:
After aleatory delineation of regions of analysis (ROA) localized in surface epithelium of the whole biopsy (138 for CD3 (3 ROA/sample) and 50 for CD8 (2 ROA/sample)), we used CaloPix Software (TRIBVN Healthcare) and its Cell Recognition plugin with machine learning (pixel classification with human labelling) to create an automated count of the IEL and epithelial cells (EC). Additionally, an expert pathologist (JB) manually counted IEL and EC in the same ROA. Prism V9 Software was used for statistical analysis. Results: We observed a high correlation coefficient between manual and automated count of CD3+ or CD8+ IEL with respectively r=0. 9227 (R 2 = 0. 8513) and r=0. 9305 (R 2 =0. 8659). EC count correlation coefficient from same ROA is respectively r=0,9225 (R 2 =0. 8511) and r=0. 9191 (R 2 =0,8447).
The Visium GEX slides incorporate ~5000 molecularly barcoded, spatially encoded capture spots in which tissue sections were placed and imaged. Following incubation with the mRNA and antibody panels, tissues were permeabilized and representative probes were captured. Libraries were generated and then sequenced on an Illumina NovaSeq at a depth of ~50,000 reads per spot. Using the Space Ranger analysis pipeline, the resulting whole transcriptome gene expression and proteomic profiles were mapped onto H&E-stained tissue images where the reads were aligned, clustering performed, and mRNA and protein expression analyzed. Additional analyses and data visualizations were performed on the Loupe Browser desktop software. Results: We captured spatial patterns of mRNA and protein and overlaid the information back to H&E-stained images with regional annotations.
This is not very convenient when iterating on the system design and if the actual turbine does not exist yet. In this work, a meanline model for radial inflow turbine operating with real gas has been implemented in MODELICA language for the first time to the authors' best knowledge. This turbine model has been coupled with the available CSP sCO2 Brayton cycle model in MODELON ThermalPower library. A parametric study on a virtual test bench show the turbine performances for various rotational speed and inlet guide vane angle. The study focus then on the performance prediction of on and off design of the system with a 8 MW turbine obtained with a simple preliminary design calculation. The investigations showed that for the varying load conditions in the solar plant system, an improved operating point has been identified by adjusting the rotational speed and the inlet guide vane angle.
Also, thermal and mechanical component stress which is often limiting load gradients and therefore affecting plant behavior can easily be captured using dynamic power plant models  [14]. The most commonly used application for dynamics system models however is to improve the interaction of dynamic processes and controls, e. g. for startup  [15] , providing ancillary services  [16]  and failure mode  [17]  scenarios. The economic relevance of optimizing dynamic operation is shown in  [18]. In this paper, a meanline model predicting the full performance of any radial turbine is implemented in MODELICA language inside MODELON IMPACT platform and coupled with MODELON libraries VaporCycle and ThermalPower. VaporCycle provides thermodynamic properties of any fluids, organic Rankine cycles and vapor compression cycles (Brayton) components and systems models.
Conclusions and perspectives In this paper, a meanline model for radial inflow turbine operating with real gas has been implemented in MODELICA language for the first time to the authors' best knowledge. This turbine model has been coupled with a Brayton super critical CO2 cycle integrated to a concentrated solar power plant with thermal energy storage. The number of equations for the full system model is nearly not affected by the replacement of the Stodola simple turbine model with the proposed meanline approach. The operation of the CSP plant has been simulated for various conditions with the adaptation of the turbine rotational speed and inlet guide vane angle. The results demonstrated the compatibility of the designed turbine with the CSP plant and the variation of turbine efficiency and cycle power for varying and transient conditions.
1a-b ). Leveraging a newly built large multi-breed Pig Genomics Reference Panel (PGRP) consisting of 1,602 WGS samples (1,093 publicly available and 509 newly generated) (Fig. S3  and Table  S2 ), we imputed genotypes of RNA-Seq samples to the WGS level with an imputation accuracy of over 95% across multiple validation strategies (Extended Data Fig. 1c-h ). We also predicted missing breed information for 3,684 (52. 6%) public RNA-Seq samples using their imputed genotypes (Extended Data Fig. 1i-m , and Table  S1 ). Sample clustering based on genotypes overlapping both datasets demonstrated that the population structure of the RNA-Seq samples was similar to the PGRP (Fig. 1c ). After removing duplicated RNA-Seq samples based on their genetic relatedness (identity by state, IBS > 0. 9) derived from imputed genotypes, we retained a total of 5,457 samples representing 34 tissues, cell types, or organ systems (all referred to as 'tissues' hereafter), with at least 40 samples per tissue, for subsequent analysis (Fig.
1d , Extended Data Fig. 2a-e ). The 34 tissues originated from embryos and all three germ layers, and represent all major lineages in the pig body (Fig. 1e ). The number of detected molecular phenotypes in each of 34 tissues are summarized in Table  S3. We further analyzed 270 public pig multi-omics datasets, including 245 whole genome bisulfite sequence (WGBS) (Fig. S4-5  and Table  S4 -6), 20 single-cell RNA-seq (Fig. S6  and Table  S7 ) and five Hi-C samples (Table  S8-9 ), for the following integrative analysis.
3c-e ). For instance, MYL2, that plays a key role in heart development and function, showed heart/muscle-specific gene expression and promoter activity (Extended Data Fig. 3d ), as did MYOG in muscle and APOH in liver (Fig. S7d ). In addition, tissue-specific genes exhibited distinct patterns of evolutionary DNA sequence constraints across tissues (Fig. S7e ), in agreement with the hypothesis of tissue-driven evolution  18. To assign function to pig genes, we employed the principle of 'guilt by association' by performing a gene co-expression analysis in each of the 34 tissues (Fig. S8a ). In total, we detected 5,309 co-expression modules across tissues, and assigned 25,023 genes to at least one module (Fig. S8b-c , Table  S10 ). Among them, 13,266 (42. 57%) genes had no functional annotation in the Gene Ontology (GO) database (Extended Data Fig.
2e ). We applied four distinct strategies to validate the cis-eQTL. First, we observed that the summary statistics of cis-eQTL derived from the linear regression model in TensorQTL  19  had a strong correlation (an average Pearson's r of 0. 91 across tissues) with those from a linear mixed model (Extended Data Fig. 6a ). Second, we performed an internal validation in 18 tissues with over 80 samples by randomly dividing samples into two equal groups, and then conducting cis-eQTL mapping separately in both subgroups. This approach yielded a high replication rate (measured by π1) for cis-eQTL, with an average π1 of 0. 92 (range 0. 80 -1. 00) and an average of 0. 56 (range 0. 36 -0. 89) for Pearson's r between effect sizes across tissues (Fig. 2f ). Third, we found that 92%, 74%, 73%, and 69% of cis-eQTL in blood, liver, duodenum, and muscle, respectively, were replicated in independent datasets, but the replication rates depended on the sample size of validation populations (Extended Data Fig.
Red dots represent genes with significant (FDR < 0. 05) TWAS signals in both species. In brief, red is for promoter, yellow for enhancers, blue for open chromatin, and grey for repressed regions. (e) Enrichment of tissue-specific genes for two active chromatin states across 11 tissues, which have both chromatin states and gene expression data. The dots represent enrichments from matching tissues. TssA is for active TSS (promoter), and EnhA for active enhancers. (f) The comparison of genes with and without functional annotation (referred to as "annotated genes" and "unannotated genes", respectively) in the gene co-expression modules at different biological layers. The gene co-repression analysis was conducted using five complementary methods, including WGCNA, ICA, PEER, MEGENA and CEMiTool.
"All" shows the pooled results from the five methods. The functional gene annotation was based on the Gene Ontology database (version 2022-01-18). The plots from top to bottom include gene counts, expression level, PhastCons score from 100 vertebrate genomes, the proportion of orthologous genes in humans, and TAU values. Significant differences between annotated and unannotated genes are obtained using a two-sided t-test. ** means P < 0. 01. (g) The proportion of unannotated genes in each gene co-expression modules across 34 tissues. (h) An example of gene co-expression module in pituitary, which includes 59 unannotated and 42 annotated genes, respectively. The functional annotated genes are significantly (P = 8×10 -3 ) enriched in neuron apoptotic processes. The grey edges between genes represent Pearson's correlations of expression across all 53 samples in pituitary.
Single-cell RNA-Seq and cell-type deconvolution analysis We obtained 13 raw single-cell RNA-Seq data of five brain and eight lung samples from the CNGB Sequence Archive (CNSA) of the China National GeneBank DataBase (CNGBdb) under accessions: CNP0000686 and CNP0001486, respectively. We also obtained the processed single-cell RNA-Seq data of seven PBMC samples from USDA Ag Data Commons  74.
Identification of cell types For five brain regions (frontal lobe: FL, parietal lobe: PL, temporal lobe: TL, occipital lobe: OL, and hypothalamus: HT), we used the Cell Ranger Single Cell Software Suite (v3. 0. 2) (https://support. 10xgenomics. com/single-cell-gene-expression/software/pipelines/latest/what-is-cellranger) to process and analyze the raw 10x Genomics sequencing data. We used the cellranger mkgtf tool to keep all gene types in the gene annotation. gtf file (Ensembl v100) and the cellranger mkref tool to build a pig custom reference using both the generated GTF and Sscrofa11. 1 (v100) genome assembly. We generated the raw count matrices using the cellranger count tool and then performed all subsequent analyses using the Seurat (v3. 2. 3) R package  75. Overall, we obtained 2,465, 1,312, 4,974, 3,756, and 1,462 cells in FL, HT, OL, PL and TL, respectively, with the following criteria: 1) cells with gene counts over 200; and 2) cells with mitochondrial counts less than 5% of the total counts.
4. We then applied the UMAP algorithm to visualize the cell clustering. We utilized Azimuth (v0. 4. 0)  76  with a human motor cortex reference to label the cell clusters and assigned all cells to eight known brain cell types. For lung tissues, we employed the DNBelab C Series scRNA analysis software to process the raw sequencing data generated from the DNBelab C4 sequencing platform  77  , including creating a reference genome database and producing the gene expression matrix. We then applied the same pipeline and standards as described above to perform the following single-cell analysis. Collectively, 1,744, 966, 240, 1,317, 1,749, 10,000, 10,000, and 1,596 cells passed the quality control across eight samples. We assigned 35 cell types according to the human lung reference v1  78  , implemented in Azimuth (v0.
Cell type deconvolution Analysis We selected four, five, and ten major cell types from brain, lung, and PBMC, respectively, and then applied CIBERSORTx  79  to estimate the fraction of these cell types in bulk RNA-Seq samples from seven tissues, including brain, frontal cortex, hypothalamus, lung, blood, liver, and spleen. We extracted 150 cells from each cell cluster using the subset function, implemented in Seurat (v3. 0. 2)  75  , to create a signature matrix using the CIBERSORTx online tool by the custom option with default different K values in the admixture analysis, we decided to choose K = 5 for further analysis. We identified samples with more than 10% ancestry proportion from ≥ 2 ancestries as crossbreeds, while the remaining samples were considered as pure breeds. For crossbreeds and other pure breeds, we trained a machine learning model (implemented in RandomForest R package  86  ) using samples with known breed labels to predict their breed information.
Covariates for molQTL mapping For molQTL mapping within each of the 34 tissues, we only considered SNPs with MAF ≥ 5% and minor allele count (MAC) ≥ 6, resulting in an average of 2,705,637 SNPs (ranging from 1,815,729 in synovial membrane to 3,004,852 in muscle). We then computed genotype PCs based on the filtered SNPs within each of the tissues using SNPRelate (v1. 26. 0)  87. We used the top five and ten genotype PCs to account for the population structure among samples in tissues with < 200 and ≥ 200 samples, respectively (Extended Data Fig. 2f ). To account for technical confounders among RNA-Seq samples (e. g. , hidden batch effects and other technical or biological factors), we used the Probabilistic Estimation of Expression Residuals (PEER) method, implemented in peer R package  60  , to estimate a set of latent covariates within each of the 34 tissues based on gene expression matrices.
External validation of cis-eQTL To further validate the identified cis-eQTL, we examined two external datasets: 1) 179 animals with both 50K SNP array and blood RNA-Seq data at two time points in a composite population of ¼ Duroc, ¼ Yorkshire and ½ Landrace; 2) 100 Duroc pigs with both WGS and RNA-Seq data from muscle, liver, and duodenum. For the first validation dataset, we analyzed all the RNA-Seq data and imputed RNA-Seq genotypes to whole-genome sequence level based on the PGRP, using the same pipeline as described above. We also imputed genotypes from the 50K SNP array to sequence level. We then conducted the cis-eQTL mapping at each of two time points separately based on genotypes imputed from RNA-Seq and those imputed from 50K SNP array using the same pipeline as above, while considering the first five genotype PCs and ten PEER factors as covariates.
To quantify the validation rate between the validation and discovery dataset, we calculated the π1 statistic for the top cis-eQTL of genes identified in the validation dataset with those identified for blood in the PigGTEx discovery population. To further quantify the correlation of effect sizes of cis-eQTL between the discovery and validation datasets, we performed a meta-analysis of cis-eQTL using a multivariate adaptive shrinkage (MashR) method  95. Following the same pipeline as described for the human GTEx 7 , we used z-scores (i. e. , slope/slope_se from TensorQTL) of the top cis-eQTL of genes as input and fitted the mash model using 1,000,000 random SNP-gene pairs being tested as null input. We obtained the estimated effect size (i. e. , the posterior mean) of the cis-eQTL from the mash function and computed Spearman's correlation of those estimates between the discovery and validation datasets.
For the second validation dataset, we called genotypes directly from the WGS data but only considered 1,678,951 SNPs that overlapping those of the PigGTEx discovery dataset for cis-eQTL mapping. Using the same approach as described above, we performed cis-eQTL mapping for each of these three tissues using TensorQTL, while considering the first five genotype PCs and ten PEER factors as covariates. We then computed the π1 statistic to quantify the validation rates of cis-eQTL between the discovery and validation datasets.
05 in at least one tissue. For METASOFT (v2. 0. 1), we used summary statistics from TensorQTL (v1. 0. 3) (i. e. , slope and slope_se) of molQTL across all tissues. We estimated the meta-analytic effect size using a fixed effect model and calculated M-values (posterior probabilities) using the MCMC method. We considered a molQTL active in a tissue when it had an M-value > 0. 7. To evaluate the similarity of tissue-clustering patterns across different data types (i. e. , PCG expression, splicing quantifications, exon expression, lncRNA expression, enhancer expression, cis-eQTL, cis-sQTL, cis-lncQTL, cis-eeQTL, and cis-enQTL), we performed k-means clustering using the kmeans function in the stats R package (v4. 0. 2), in which k (i. e. , the number of clusters) was allowed to range from 2 to 20 and the maximum number of iterations was 1,000,000.
QTL mapping for DNA methylation (meQTL) in muscle We conducted QTL mapping for methylation level at each CpG locus with a coverage of at least five reads in muscle, where we had 101 WGBS samples. We called SNPs from the WGBS data using BisulfiteGenotyper in the Bis-SNP package (v1. 0. 1, and dbSNP build 150) with parameters: -nt 4 -stand_call_conf 10 -mmq 30 -mbq 17 -out_modes EMIT_ALL_SITES  107. After quality control using PLINK2  83  (MAF ≥ 0. 05, DR 2 ≥ 0. 8, and Hardy Weinberg equilibrium, HWE ≥ 1e-5), 7,410,484 SNPs remained for subsequent analysis. For methylation, we excluded CpG sites with missing rate ≥ 10% across samples. To improve the efficiency of QTL mapping, we only considered CpG loci with a deviation of methylation levels > 0. 3 and a standard deviation > 0. 1, yielding 18,023,521 CpG sites on 18 autosomes.
2 public)  114  databases. The TFBS models were represented as position weight matrices (PWM) and were derived from published collections of experimentally defined eukaryotic TFBS. We used the vertebrate PWM and kept only a candidate regulatory variant if the gene and the transcription factor, both impacted by the cis-eQTL, were both expressed in the same tissue. described in  10. Briefly, for each tissue, we kept SNPs with the most significant nominal P-value for a gene and then scaled P-values to a comparable level (lambda = 10) across 34 tissues. We selected the minimum P-value of each SNP in the 34 tissues as the background set, from which we extracted Pvalues for SNPs that overlapped with significant GWAS loci. Second, we applied QTLEnrich (v2)  7  to quantify the enrichment degree between significant molQTL and GWAS loci.
cis-molQTL-GWAS colocalization To identify shared genetic variants between the molecular phenotypes and complex traits, we performed colocalization analysis of molQTL and GWAS loci using fastENLOC (v1. 0)  27. Briefly, we obtained the probabilistic annotation of molQTL from the DAP-G (v1. 0. 0) and then used the summarize_dap2enloc. pl script to generate the annotation file of multi-tissue molQTLs. We generated approximate LD blocks across the entire genome based on the PGRP using PLINK (v1. 90), as described previously  102. We applied the TORUS tool to generate the posterior inclusion probability (PIP) of each LD block based on GWAS z-scores  110  , followed by the colocalization analysis with fastENLOC (v1. 0). We obtained the regional colocalization probability (RCP) of each LDindependent genomic region using a natural Bayesian hierarchical model  116  and considered a gene with RCP > 0.
Statistics & Reproducibility No statistical method was used to predetermine the sample size. The details of data exclusions for each specific analysis are available in the Methods section. For all the boxplots, the horizontal lines inside the boxes show the medians. Box bounds show the lower quartile (Q1, the 25 th percentile) and the upper quartile (Q3, the 75 th percentile). Whiskers are minima (Q1 - 1. 5 × IQR) and maxima (Q3 + 1. 5 × IQR), where IQR is the interquartile range (Q3-Q1). Outliers are shown in the boxplots unless otherwise stated. The experiments were not randomized, as all the datasets are publicly available and from observational studies. The Investigators were not blinded to allocation during experiments and outcome assessment, as the data are not from controlled randomized studies.
(b) Replication rates (π1) of blood cis-eQTL between the PigGTEx discovery population (n = 386, Discovery) and the external datasets (n = 179). For π1 calculation, rows are discovery populations, and columns are replication populations. The external datasets include whole-blood-cell RNA-Seq data and SNP Chip array (Chip) from 179 animals at two developmental stages (T1 and T2). The prefix "RNA" and "Chip" indicate imputed genotypes from RNA-Seq and SNP array, respectively. (c) Spearman's correlation (ρ) of effect size (z-scores) for blood cis-eQTL among the same populations above. (d) Replication rates (π1) of PigGTEx cis-eQTL in external validation datasets of three tissues, including muscle (nPigGTEx = 1,321, nexternal = 100), liver (nPigGTEx = 501, nexternal = 100) and Extended Data Figure 7.
Acknowledgements  Z. Zhang  ( SCAU ) acknowledges funding from the  National Natural Science Foundation of China  ( 32022078 ), the  Local Innovative and Research Teams Project of Guangdong Province  ( 2019BT02N630 ), and supporting from  National Supercomputer Center in Guangzhou China. Y. Chen ,  Z. Zhang  ( SCAU ),  Jiaqi Li ,  X. Liu ,  X. Ding , and  S. Zhao  acknowledge funding from the  China Agriculture Research System  ( CARS-35 ). L. Fang. acknowledges funding from  HDR-UK  award  HDR-9004  and the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  801215. G. E. L. , was supported by  USDA NIFA AFRI  grant numbers  2019-67015-29321  and  2021-67015-33409  and the appropriated project  8042-31000-112-00-D , " Accelerating Genetic Improvement of Ruminants Through Enhanced Genome Assembly, Annotation, and Selection " of the  USDA Agricultural Research Service (ARS).
This research used resources provided by the  SCINet  project of the  USDA ARS  project number  0500-00093-001-00 -D. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the  USDA. The  USDA  is an equal opportunity provider and employer. A. Tenesa acknowledges 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. P. Navarro  and  C. Haley  were supported by the  Medical Research Council  ( United Kingdom , grant number  MC_UU_00007/10 ). O. Canela-Xandri  was supported by  MR/R025851/1. M. Ballester  and  D. Crespo-Piazuelo  belonged to a Consolidated Research Group AGAUR, ref.
2017SGR-1719 , and  D. Crespo-Piazuelo  was supported by the  GENE-SWitCH  project (https://www. gene-switch. eu),which is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the grant agreement n°  817998. R. X. was supported by  Australian Research Council's Discovery Projects  ( DP200100499 ). L. M. was supported in part by  AFRI grant  numbers  2020-67015-31398  and  2021-67015-33409  from the  United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA). B. N. K and G. A. R. were supported by appropriated project  3040-31000-099-000-D , " Identifying Genomic Solutions to Improve Efficiency of Swine Production " of the  Agricultural Research Service (ARS)  of the  United States Department of Agriculture (USDA). A. K.
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, WGS, WGBS, single-cell RNA-Seq and Hi-C datasets can be found in Supplementary Table  1 , 2, 4, 5 and 6, respectively. All processed data and the full summary statistics of molQTL mapping are available at http://piggtex. farmgtex. org/).
Code Availability All the computational scripts and codes for RNA-Seq, WGS, WGBS, single-cell RNA-Seq and Hi-C datasets analyses, as well as the respective quality control, molecular phenotype normalization, genotype imputation, molQTL mapping, functional enrichment, colocalization, SMR and TWAS are available at the FarmGTEx GitHub website (https://github. com/FarmGTEx/PigGTEx-Pipeline-v0).