dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We acknowledge the support of the  International Space Science Institute in Beijing , through the working team "Snow reanalyses over the  Himalaya-Tibetan Plateau region  and the monsoons" over the years 2016-2018 (team leaders:  Yvan Orsolini  and  Gianpaolo Balsamo ). Emanuel Dutra  was supported by the  Portuguese Science Foundation (FCT)  under project  IF/00817/2015. Joaquín Muñoz Sabater  is acknowledged for valuable comments on ERA5L testing. Yvan Orsolini also acknowledges partial support from the  Working Group on Subseasonal to Interdecadal Prediction (WGSIP)  (https://www. wcrp-climate. org/modelling-wgsip, last access:  20 August 2019 ) of the  World Climate Research Programme (WCRP). We are also grateful to  Shinya Kobayashi ,  Yayoi Harada ,  Rolf Reichle  and  Sujay Kumar  for help with the reanalyses.
Financial support. This research has been supported by the  International Space Science Institute in Beijing , through the working team "Snow reanalyses over the  Himalaya-Tibetan Plateau region  and the monsoons", the  Portuguese Science Foundation (FCT)  under project  IF/00817/2015 , and the  Working Group on Subseasonal to Interdecadal Prediction (WGSIP)  of the  World Climate Research Programme (WCRP). Review statement. This paper was edited by  Ross Brown  and reviewed by two anonymous referees.
Data availability. All reanalyses data are publicly available: atmospheric reanalyses at https://s-rip. ees. hokudai. ac. jp/resources/ links. html (last access: 20 August 2019), and ERA5-land at https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5land?tab=overview (last access: 20 August 2019). The station data have been made available through the National Meteorological Information Center (NMIC), China Meteorological Administration (http://data. cma. cn/en/?r=site/index, last access: 20 August 2019). IMS snow cover is publicly available from NOAA (https://nsidc. org/data/G02156, last access: 20 August 2019; National Ice Center, 2008), and the long-term MW satellite data are available from the National Tibetan Plateau Data Center (http://data. tpdc. ac. cn/en/data/df40346a-0202-4ed2-bb07-b65dfcda9368/, last access: 20 August 2019).
CD86, encoding a ligand of CTLA4, exhibited robust expression in microglia, as is often seen in antigen presenting cells, but the other ligands were seldom expressed in any of the immune cells in our dataset. We next examined additional components of checkpoint signaling pathways, including MHC I expression and expression of ligands that interact with immune checkpoint receptors other than PD1/CTLA4 (Supplementary Fig. 7a, b ). Robust expression of MHC class I genes (HLA-A, HLA-B, and HLA-C) was seen in PA tumor and immune cells, suggesting that antigen presentation is intact. Expression of checkpoint receptors and ligands varied between tumors and cell types, but in general the highest levels of expression in microglia were seen for ICOSLG (ligand of ICOS receptor), CD276 (B7-H3), TNFRSF14 (ligand of BTLA), and LGALS9 (ligand of TIM3).
We sought to determine which PA cells were proliferative and which were senescent within the transcriptional population structure that we identified within PA. To do so, we examined whether senescence  36  or cell cycling  37  gene programme were correlated with either the MAPK signaling or mature glia gene programme among cancer cells in our dataset (Fig. 8a, b ). We found that the MAPK and senescence programme were the most strongly correlated pair in this analysis (Spearman ρ = 0. 24, Bonferroniadjusted q < 10 -6 ). The MAPK gene programme score was also significantly correlated with BRAF expression (ρ = 0. 14, q = 0. 008) and with the cell cycling score (ρ = 0. 12, q = 0. 04). Cells expressing high levels of MAPK signaling and low AC-like or OC-like scores were enriched for cells expressing markers of the cell cycle (Fig.
To test this hypothesis, we examined single cell transcriptomes of mouse neural stem cells (mNSCs) that we engineered to express KIAA1549-BRAF fusion  38. In parallel, we examined mNSCs expressing BRAF-V600E, another common BRAF alteration found in pediatric gliomas  38  , or vector only control. Single cell RNA-seq data were generated for 487 mNSCs after quality filtering (n = 170 vector control mNSCs, n = 154 KIAA1549-BRAF mNSCs, and n = 163 BRAF-V600E mNSCs). mNSCs expressing each construct clustered separately from each other (Fig. 9a ). The top differentially expressed genes for mNSCs expressing each construct were identified (Fig. 9b ). Comparison of differentially expressed gene lists in the C2 MSigDB database revealed that mNSCs expressing oncogenic BRAF constructs has upregulation of genes in high-CpG-density promoters containing histone H3 dimethylation at K4 and trimethylation at K27 (H3K27M) in brain  39.
See Supplementary Data 7 for oligonucleotide sequences. This oligonucleotide as added at 13 µM final concentration during 3' primer addition step, at the same time as addition of the Smart CDS Primer IIA. The concentration and size distribution for each cDNA library was evaluated on an Agilent TapeStation 2200 using D5k screentapes. Sequencing libraries from samples with more than 0. 3 ng of cDNA greater than 400 bp were prepared using a ¼ Nextera XT reaction  47. Up to 384 single cell sequencing libraries were pooled and sequenced with pairedend reads using a high output kit on Illumina NextSeq500 or Illumina HiSeq2500 instruments. Sequenced reads were aligned to the UCSC hg19 reference genome assembly using STAR v2. 5. 1b  48 and gene expression was quantified using RSEM v1. 2. 28  49  as part of the VIPER snakemake pipeline  50.
Transcripts reflecting discordant mapping of reads between experiments were manually removed for downstream analyses. KIAA1549-BRAF gene fusions were identified in the RNAseq data using STAR-Fusion v0. 5. 4  51. The BRAF spike-in workflow was tested for systematic differences introduced into the data by examining mouse neural stem cells processed with and without the BRAF oligonucleotide spike-in (n = 6 for each). The resulting transcriptomic data from these cells clustered together based on principal component analysis and hierarchical clustering (Supplementary Fig. 13 ), indicating that data is comparable between protocols. Detection of BRAF fusion from single cell cDNA libraries. qPCR was performed on remaining cDNA libraries after sequencing if any material was available following NGS.
Clinical detection of KIAA1549-BRAF fusion breakpoints were determined by OncoPanel targeted sequencing. Taqman qPCR probes targeting the most common BRAF fusion breakpoints 15-9, 16-9, or 16-11 were used  49  , see Supplementary Data 7 for sequences. cDNA from BT827 and BT618 had been exhausted for RNA-seq analysis and was not available to perform qPCR. BT646 contained a complex rearrangement involving multiple breakpoints that linked KIAA1549 to BRAF that was not amenable to this qPCR approach. Data quality control, dimension reduction, and clustering. All processed data was analyzed in R v3. 4. 3. Data were initialized using functions in the Seurat package  52  with default parameters, unless specified otherwise, as follows. Transcripts per million data normalized for each cell by the total expression, then multiplied by a scale factor of 10,000, and then log transformed using the Log-Normalize function.
NMF was performed using R package NNMF  53. Rare A2B5+ cells that clustered with the immune cells were inspected for expression of PA cancer cell differential genes and for KIAA1549-BRAF expression, and fourteen discordant cells without expression of these markers were removed from the A2B5+, BRAF+ group. Rare A2B5+ cells expressing immune genes were removed from cancer cell analyses by filtering out cells expressing >1. 5 log-normalized GZMB reads. Gene set enrichment analysis was performed using the javaGSEA desktop application  54. Data visualization. Bar plots, PCA plots, volcano plots, lineage score plots, and violin plots were generated using ggplot2  55. Heat maps were generated using the pheatmap function on the scaled data matrix. T-SNE plots were generated using the FeaturePlot and TSNEPlot functions in Seurat with default settings.
Inferral of CNVs from single cell RNA-seq data. Large-scale chromosomal CNVs were inferred from expression data as using the inferCNV package  1. Expression for each gene was averaged over 100-gene windows. Averaged expression intensity was then compared to all other cancer cells as a reference. Estimation of contribution of cell types to bulk expression. To estimate contribution of different cell subtypes to bulk sequencing profiles, differential gene lists were first derived for all cancer cell compartments found in the single cell RNA-seq data (tumor, microglia, T cell, and macrophage) using Wilcoxon rank sum t-tests. Genes with FDR < 0. 05 (Bonferroni correction) were used for each differential gene list. For each bulk expression profile, enrichment scores for each compartment were calculated by dividing the average z-scored expression of all genes in that compartment's differential gene list, divided by the average expression of all genes.
Gene signatures for microglia and macrophage subtypes from a single cell RNA-seq atlas of mouse microglia  25  were the list of upregulated genes for a given subtype (>1. 5 fold change and FDR q-value < 0. 05 using Wilcoxon rank sum test and Bonferroni correction). Cluster 7a could not be analyzed in this way since only one gene was upregulated in that gene list, and that gene did not have a human ortholog that was expressed in our scRNA-seq dataset. Each microglia/macrophage signature enrichment scores were normalized to the mean enrichment score for the same signature in the tumor clusters. BioMart was used to convert mouse gene names to human ortholog gene names for comparisons to mouse gene lists  56. Gene programme were derived from the top or bottom 50 genes in principal component variables.
Tumor subpopulations were defined as cancer cells with enrichment scores of >2. 5 for a given gene programme. Cell cycle and senescence scores were determined by testing for enrichment of cell cycle  37  and for senescence  36  gene sets from the C2 curated gene set in MSigDB (http://software. broadinstitute. org/gsea/ msigdb/index. jsp). For heat maps comparing normal brain gene signature enrichment between different tumor types, 500 cancer cells of each type were randomly sampled and the mean enrichment scores for those cells was calculated. RNA in situ staining. RNA in situ staining was performed on freshly cut unstained formalin-fixed, paraffin embedded slides 4 using probes RNAscope Hs-APOD (cat no. 445171) and RNAscope Hs-JUN (cat no. 470541) (ACD Biotech). Fluorescence immunohistochemistry.
A multiplexed F-IHC was performed  57. The antibody panel consisted of Olig2 (R&D System, Goat polyclonal, AF2418) with CY5. 5, GFAP (Cell Signaling Techology, Mouse monoclonal, Clone GA5, #3670) with FITC, and Ki-67 (Dako, Mouse monoclonal, Clone MIB-1, M7240) with CY3. Whole slide Images were acquired from stained slides using a Perkin Elmer Vectra 3 imaging system (PerkinElmer, Inc. ), and InForm software (version 3. 4. 3, PerkinElmer) was used to unmix the signals. Twenty high power fields (20×) were analyzed utilizing Halo Image Analysis platform (Indica Labs) for each tumor. The thresholds for the markers were set, respectively, based on the staining intensity, by cross reviewing 20 images. Cells with the intensity above the setting threshold were defined as positive. Bulk tumor profiling.
The plot_cell_trajectory function was used to plot the cell trajectory, with color_by and markers calls used to specify the parameter to account for coloring of the cells. Mouse neural stem cell scRNA-seq. mNSCs expressing vector control, KIAA1549-BRAF, or BRAF-V600E were generated using pBabeNeo retroviral vectors and cultured in growth factor-containing media  38. Cells were trypsinized and subjected to scRNA-seq as described for human cells above, with the following modifications. BRAF oligonucleotide spike-in and qPCR was not performed for mouse scRNA-seq. Transcriptomes were aligned to UCSC mouse mm10 assembly. Data were generated for 576 mNSCs before quality filtering. Cells with <3000 genes or >3% mitochondrial genes were removed for data filtering. Genes expressed in <10 cells were removed for data filtering.
Data availability The human RNA sequencing data has been deposited in the dbGaP repository under the accession number phs001854. v1. p1. Expression data and metadata for tumor of origin, cluster, and t-SNE coordinates for single cells have been uploaded to the Broad Institute Single Cell Portal under accession number SCP271. The mouse neural stem cell RNA sequencing data, expression matrix, and metadata has been deposited to the Single Cell Portal under the accession number SCP468. The source data underlying Fig. 6a -j are provided as a Source Data file. The datasets for H3K27M mutant pediatric midline gliomas  4  , oligodendrogliomas  2  , and intermediate grade astrocytomas  3  referenced in the study are available from the Single Cell Portal with accession numbers SCP147, SCP12, and SCP50, respectively.
19timated proportion of tumor and immune cells in 151 pediatric low grade gliomas19, based on relative expression of gene signatures for tumor and immune cell types derived from single cell RNA-seq analysis. Center line shows median, hinges show 1st through 3rd quartiles, and whiskers extend to 1. 5 times the interquartile range or the value fathest from hinge, whichever is less. eEnrichment of gene signatures derived from comparison between bulk expression profiles of supratentorial and with infratentorial tumors19, among cell types in the single-cell dataset. P-values are for Wilcoxon rank sum test. f Expression of immune checkpoint ligand genes CD86, CD80, CD274 encoding PD-L1, and PDCD1LG2 encoding PD-L2 in tumor and immune compartments of six PA tumors analyzed with scRNA-seq. Center line shows median, hinges show 1st through 3rd quartiles, and whiskers extend to minimum and maximum data points.
Acknowledgements We would like to thank and acknowledge the  Dana-Farber PLGA programme, Boston Children's Hospital Division of Neuro-pathology , staff of the  Dana-Farber Cancer Institute Flow Cytometry Core , and members of the  Bandopadhayay and Beroukhim Laboratories  for their helpful discussions. We would like to thank  Eric Smith  for assistance with illustrations. We acknowledge the following funding sources: The  Pediatric Low-Grade Astrocytoma Fund  at the  Pediatric Brain Tumor Foundation  (R. S. , L. G. , D. H. K. , M. W. K. , K. L. L. , C. S. , P. B. , R. B. ),  Stop and Shop Pediatric Brain Tumor Program  (P. B. , M. W. K. ), a  Conquer Cancer Foundation  and  Strike 3 Foundation ASCO   Young Investigator Award  (Z. J. R. ), a  Michael Mosier Defeat DIPG and ChadTough Foundation  Fellowship (Z.
19timated proportion of tumor and immune cells in 151 pediatric low grade gliomas19, based on relative expression of gene signatures for tumor and immune cell types derived from single cell RNA-seq analysis. Center line shows median, hinges show 1st through 3rd quartiles, and whiskers extend to 1. 5 times the interquartile range or the value fathest from hinge, whichever is less. eEnrichment of gene signatures derived from comparison between bulk expression profiles of supratentorial and with infratentorial tumors19, among cell types in the single-cell dataset. P-values are for Wilcoxon rank sum test. f Expression of immune checkpoint ligand genes CD86, CD80, CD274 encoding PD-L1, and PDCD1LG2 encoding PD-L2 in tumor and immune compartments of six PA tumors analyzed with scRNA-seq. Center line shows median, hinges show 1st through 3rd quartiles, and whiskers extend to minimum and maximum data points Bulk PLGG expression (n = 151) DA Histology Histology DNT GG Age NOS GPM6B Location OD PA MT1X APOD MT3 Age SERPINA3 SPARCL1 STMN1 MT2A RAMP1 Infant Child Expression COL9A1 C1orf61 COL9A3 Adolescent 2 z-score MMP17 CCL3 HLA-DRA HLA-DRB1 CD74 CSF1R C3 Supratentorial Infratentorial Location C1QB CCL2 0 LCK IL32 IFITM1 CD52 GZMA z score Expression CCL5 CD3D PTPRCAP DUSP2 8 GZMK S100A9 S100A8 SOD2 4 G0S2 IFITM2 S100A12 FCN1 0 S100P CXCL2 -4 Estimated percent of tissue Fig.
It is possible to define the type of intervention planned, the associated risk of infection (and therefore the necessity or not of ABP), the time of prescription before surgery and any allergic antecedents which may modify the choice of the selected antibiotic molecule. Since 1992, the SFAR has established and regularly updated recommendations for the prescription of ABP in surgery and interventional medicine for adult patients. The latest recommendations were established in 2010 and are being updated based on literature data published since then. An exhaustive bibliographic search of the relevant articles was carried out on the available databases: the French-based health assessment (http://bfes. anaes. fr/HTML/ index. html); the National Guideline Clearinghouse American (Http://www. guidelines.
gov); the Lemanissier library in France (http://www. bmlweb. org/ consensus. html); the Cochrane Library (http://www. cochrane. org/index0. htm); the PUBMED database (http://www. pubmed. org). The keywords used were: antibioprophylaxis, antibiotic prophylaxis, surgery, interventional radiology, postoperative infection, antibiotic prophylaxis, perioperative antibiotics, surgery, interventional radiology, postoperative infection.
Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github. com/rondolab/HOPS.
Indeed, several versions of this analysis have been performed for different lists of traits  [2, 3, 8, 9]. However, the results of these analyses are highly dependent on the exact list of traits used, and traits of interest to researchers previously tend to involve only a small number of phenotypes and/or be heavily biased towards a small set of disease-relevant biological systems and processes. Due to these limitations, it is unknown to what extent horizontal pleiotropy affects genetic variation in the human genome at the genome-wide level. The proliferation of data sources like large-scale biobanks and metabolomics data that include a wide array of phenotypes in one dataset, combined with the growing public availability of genome-wide association studies (GWASs) summary statistic data, especially for extremely large meta-analyses, has allowed the development of methods that use these summary statistics to gain insight into human biology and particularly into the genetic architecture of complex traits and diseases  [10].
A quantitative score for pleiotropy We have developed a method to measure horizontal pleiotropy using summary statistics data from GWASs on multiple traits. Our method relies on applying a statistical whitening procedure to a set of input variant-trait associations, which removes correlations between traits caused by vertical pleiotropy and normalizes effect sizes across all traits. Using the decorrelated association Zscores, we measure two related but distinct components of pleiotropy: the total magnitude of effect on whitened traits ("magnitude" score, denoted P m ) and the total Fig. 1  Schematic of different types of pleiotropy. Previous studies distinguish between vertical pleiotropy, where effects on one trait are mediated through effects on another trait, and horizontal pleiotropy, where effects on multiple traits are independent number of whitened traits affected by a variant ("number of traits" score, denoted P n ).
Both scores are then scaled by the number of traits and multiplied by 100, so that the final score represents the value as it would be measured in a dataset of 100 traits. This two-component quantitative pleiotropy score allows us to measure both the magnitude (pleiotropy magnitude score P m ) and quantity (pleiotropy number of traits score P n ) of horizontal pleiotropy for all SNVs in the human genome. In principle, these are distinct quantities: the magnitude score P m measures the total pleiotropic effect size of a variant across all traits, while the number of traits score P n measures the number of distinct pleiotropic effects a variant has. A variant with a high P m score and a low P n score has a large effect spread over a small number of traits; a variant with a low P m score and a high P n score has only a minor effect overall, but that effect is spread out across a large number of traits; and a variant with high scores on both components has a large effect that is spread across a large number of traits.
Pleiotropic loci replicate in independent GWAS datasets As replication datasets, we used two additional sources of GWAS summary statistics to calculate our LDcorrected pleiotropy score ( P LD m and P LD n ): previously published GWASs and meta-analyses for 73 human complex traits and diseases, which we collected and curated manually from the literature (Methods, Additional file 1: Table  S3 )  [24] ; and a previously published study of 430 blood metabolites measured in 7824 European adults  [25]. For all variants covered by the UK Biobank, we were able to compute our pleiotropy score independently using these two datasets (Fig. 7 ). In the traits and diseases dataset, we observed that 57% of P LD m loci and 38% of P LD n loci replicated, while in the blood metabolites dataset, we observed that 17% of P LD m loci and 12% of P LD n loci replicated, compared to 5% of P LD m loci and 6% of P LD n loci expected by chance according to a permutation-based null model.
Pleiotropy is correlated with specific complex traits and diseases To characterize the phenotypic associations of these loci, we used our replication dataset of published GWAS summary statistics for 73 human quantitative traits and diseases, plus nine additional traits we excluded from our replication dataset for a total of 82 (Methods). We are not able to compute directly the degree of pleiotropy exhibited by these traits, since our definition of horizontal pleiotropy applies only to individual variants and does not apply to traits. However, we can identify traits whose GWAS variant associations are correlated to our pleiotropy score, which in some sense represents the traits that contribute most to our signal of pervasive horizontal pleiotropy. Figure  6c  shows the correlations between our LD-corrected pleiotropy score ( P LD m and P LD n ) and the association statistics for these 82 traits and diseases.
First, it is somewhat more difficult to tell which specific traits are driving a signal of pleiotropy at a particular locus. Our whitened traits are combinations of real observed traits and do not necessarily correspond to any specific biological traits of interest. However, it is relatively easy to inspect the input GWAS summary statistics for a particular variant of interest to see which traits it is associated with. Furthermore, since pleiotropic loci are by definition associated with a large cross section of traits, this kind of inspection is not likely We grouped variants by (i) molecular function as annotated by Ensembl, (ii) predicted chromatin state as annotated by the NIH Roadmap Epigenomics Project, (iii) transcriptional effects as annotated by the NIH Genotype-Tissue Expression (GTex) Project, and (iv) effects on model organism phenotypes as annotated by the International Mouse Phenotyping Consortium (IMPC) and Saccharomyces Cerevisiae Morphological Database (SCMD).