dataesr/scientific-paragraphs-categorization huggingface dataset:
We study the relevance of experimental data on heavy-flavor [D 0 , J/ψ, B → J/ψ and Υ(1S ) mesons] production in proton-lead collisions at the LHC to improve our knowledge of the gluon-momentum distribution inside heavy nuclei. We observe that the nuclear effects encoded in both most recent global fits of nuclear parton densities at next-to-leading order (nCTEQ15 and EPPS16) provide a good overall description of the LHC data. We interpret this as a hint that these are the dominant ones. In turn, we perform a Bayesian-reweighting analysis for each particle data sample which shows that each of the existing heavy-quark(onium) data set clearly pointswith a minimal statistical significance of 7 σto a shadowed gluon distribution at small x in the lead. Moreover, our analysis corroborates the existence of gluon antishadowing.
Second, R pA is in general less sensitive to QCD corrections which may affect the normalization of the cross-section predictions. Third, some experimental uncertainties cancel in R pA and, at the LHC, R pPb is usually more precise than the corresponding pPb cross sections. To connect R pA and R A g , we will use the data-driven approach  [54] [55] [56]  where the parton-scattering-matrix elements squared |A| 2 are parametrized into empirical functions and determined from pp data assuming a 2 → 2 kinematics together with given proton PDF, where we choose CT14NLO  [1]. It was first motivated to bypass the complications inherent to our lack of understanding of the quarkonium-production mechanisms (see e. g. , Refs. [7, 57] ) whereas it suffices to evaluate the nPDF effects in R pA. Such an approach also applies to open HF hadrons  [54].
Hence, one obtains a modified probability distribution of the nPDFs like a fit would do. Data selection -Like for all global PDF fits, a data selection is in order to avoid HT corrections. In our case, it is also important to select a kinematical region where gluon fusion dominates and other effects are negligible. As such, we considered the HF production in pA collisions at LHC energies. In the quarkonium case, due to the large Lorentz boost at these energies, the heavy-quark pair remains almost pointlike all along its way through the nuclear matter. Therefore, breakup  [84, 85] , thought to be important at lower energies, is negligible at the LHC. We focused on J/ψ and Υ(1S ) to limit the contamination by possible comover effects  [33] [34] [35] [36] 86] , on the more fragile excited states [ψ(2S ), Υ(2S ), Υ(3S )].
43, and after our HF reweighting it becomes (2. 14, 2. 49, 3. 11) for D 0. With the same caveats as above, our reweighted nPDFs do not change the theory-data compatibility with the LHC W/Z data. The χ 2 /N data of the J/ψ PHENIX R dAu results  [96, 97]  with nCTEQ15 is (3. 58, 2. 55, 3. 12) and after our J/ψ reweighting becomes  (1. 81, 2. 38, 2. 77). This confirms the global coherence of the HF constraints. Tables of these χ 2 values can be found as Supplemental Material. Conclusion -In this Letter, we used, for the first time, experimental data for the inclusive HF [D 0 , J/ψ, B → J/ψ, Υ(1S )] production in pPb collisions at the LHC to improve our knowledge of the gluon density inside heavy nuclei. We compared the data with computations obtained in the standard LT factorization framework endowed with the two most recent globally fit nPDFs (nCTEQ15, EPPS16).
No other nuclear effects were included which are supposed to be of HT origin and hence suppressed as inverse powers of the hard scale. We found a good description of the LHC data with both nCTEQ15 and EPPS16 nPDFs validating our theoretical framework. By performing a Bayesian-reweighting analysis and studying the scale uncertainties, we demonstrated that the existing heavy quark(onium) data can significantly -and coherentlyreduce the uncertainty of the gluon density down to x 7 × 10 -6. For charm(onium), the gluons are shadowed with a statistical significance beyond 7 σ at µ F = 2 GeV and x = 10 -5. These data should thus be included in the next generation of global nPDF analyses. While our results cannot rule out that other HT CNM effects were effectively "absorbed" into seemingly universal LT nPDFs, the observed consistent description of both the D 0 and J/ψ data is far nontrivial since they may interact differently with the nuclear matter.
χ 2 numbers The χ 2 values before and after reweighting are displayed in Tab. II for {D 0 , J/ψ, B → J/ψ, Υ(1S )} production in pPb collisions at the LHC, together with total number of data points N data. As it is customary, these χ 2 values do not account for any theoretical uncertainties. Regardless of the scale choice (i. e. ξ), χ 2 /N data are around 1 after reweighting, while χ 2 /N data varies significantly with the original nPDFs. It is normal as our replicas are matching the R pPb vs P T , y data. We take the inclusive J/ψ PHENIX R dAu results as a postdiction, where the χ 2 numbers before and after reweighting are shown in Tab. III. The compatibility between the theoretical calculations and the PHENIX data is further improved with the reweighted nPDFs. We also have checked the global coherence of the HF constraints with the LHC W/Z and DIS NMC data.
ACKNOWLEDGMENTS We are grateful to  H. Paukkunen  for the comments on using EPPS16 grids, to  M. Cacciari  for the private FONLL code, and to  F. Arleo ,  E. G. Ferreiro , and  P. Zurita  for useful discussions. The work is supported by the  ILP Labex  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ) and the  COPIN-IN2P3  Agreement.
SPNs and MNs were targeted on the basis of their location in the transverse plane (see Fig  6A ) and subsequently identified by recording their antidromic action potentials in response to electrical ventral root stimulation of the same segment (see Fig 6A2  -6A3 ). A liquid junction potential of +12 mV was experimentally determined  [63]  and records were corrected for this potential. Series resistance was monitored throughout the experiments and was not compensated. Data were discarded if series resistance varied more than ±20% of the initial value. Motor activities were recorded extracellularly from various spinal cord ventral roots using glass suction electrodes. Recorded activity was amplified using custom-made amplifiers. The recordings were digitized using an analog-to-digital interface (Heka Elektronik, Germany) driven by Axograph software (Axograph, Australia).
Raw signals were high-pass filtered (50 Hz), rectified, and integrated before analysis. Locomotor burst parameters were computed using custom-made routines written in Matlab (Mathworks, France). Mean cycle periods were computed using an L2 ventral root as the reference because it invariably exhibited the best signal-to-noise ratio. Wavelet transform analyses  [64]  were performed using a Matlab wavelet coherence package provided by Aslak Grinsted (http://noc. ac. uk/using-science/crosswaveletwavelet-coherence). Of particular interest was the extraction of the common power, correlation, and phase relationship between two simultaneously acquired signals of the cross wavelet transform and wavelet coherence  [65, 66]. A detailed explanation of the wavelet-based methodology used in the present work has been previously reported  [32].
Statistics Statistical analyses of raw data were conducted using GraphPad Prism software. Because of some relatively small samples, nonparametric tests were used for all analyses performed. In the text, all data are expressed as means ± SEM. In the figures, the box plots display the distribution of data based on the minimum, first quartile, median, third quartile, and maximum. Asterisks in the figures indicate statistical significance (p < 0. 05). Wilcoxon or Mann-Whitney tests were used when applicable. The coupling between left and right activities was analyzed using circular statistics, using the Oriana Software (Kovach Computing Services).
Galaxy HiCExplorer is a web server that facilitates the study of the 3D conformation of chromatin by allowing Hi-C data processing, analysis and visualization. With the Galaxy HiCExplorer web server, users with little bioinformatic background can perform every step of the analysis in one workflow: mapping of the raw sequence data, creation of Hi-C contact matrices, quality assessment, correction of contact matrices and identification of topological associated domains (TADs) and A/B compartments. Users can create publication ready plots of the contact matrix, A/B compartments, and TADs on a selected genomic locus, along with additional information like gene tracks or ChIP-seq signals. Galaxy HiCExplorer is freely usable at: https://hicexplorer. usegalaxy.
This poses bioinformatic challenges for efficient processing of the data and subsequent analyses. Here, we introduce Galaxy HiCExplorer, a package that aims to make Hi-C data processing, analysis and visualization available to non-bioinformaticians. Our goal is to provide a software environment able to automate the whole workflow of Hi-C data analyses from raw read mapping, filtering and correction, to the computation of topological associated domains and A/B compartments, and finally to the visualization of contact matrices, along with various other genomic features and omics data. Moreover, Galaxy HiC-Explorer is easy to install, maintainable, stable and well documented. The availability of a docker container in conjunction with Bioconda (http://dx. doi. org/10. 1101/207092), eliminates the need for complex software and dependency installations.
Galaxy HiCExplorer is freely available at https://hicexplorer. usegalaxy. eu as well as a Docker container: https://github. com/deeptools/docker-galaxyhicexplorer. Galaxy HiCExplorer was designed to provide an easily accessible data-analysis environment such that W12 Nucleic Acids Research, 2018, Vol. 46, Web Server issue biomedical researchers can focus on critical research aspects instead of dealing with terminal-based applications that are not user-friendly. It smoothly integrates the HiCExplorer analysis toolset (8) into the Galaxy scientific analysis platform to provide web-based, easy-to-use and thoroughly tested workflows that provide pipelines for the most common Hi-C data processing steps. In contrast to other available Hi-C analysis software like HiCUP  (14) , HOMER  (15)  and TADbit  (16)  among others (see  (17, 18)  for a comprehensive list of tools), Galaxy HiC-Explorer provides a fully comprehensive analysis pipeline available to much broader community of researchers and is not restricted to a subset of important features.
HiC-Pro  (19)  is one of the few packages that offers a complete pipeline; however, its visualization tools are limited and it is only available as a command line tool. Similarly, Juicer (20) offers a command line tool processing pipeline while Juicebox (  21 ) only provides visualizations. Moreover, the integration of HiCExplorer into Galaxy offers the possibility to process and integrate other data types like ChIP-Seq or RNA-Seq into the analysis using the same interface. None of the aforementioned tools offer web server access except HiFive  (22). A strong advantage of HiCExplorer is that it can take multiple matrix data formats developed by different research groups as input. Thus, it is well integrated in the landscape of Hi-C data analysis algorithms, as Hi-C matrices can be produced by other tools and visualized with HiC-Explorer.
GALAXY HiCExplorer TOOLS AND WORKFLOWS Galaxy HiCExplorer provides a plethora of tools for processing, normalization, analysis, and visualization of Hi-C data (Figure  1A ). Apart from HiCExplorer, the https:// hicexplorer. usegalaxy. eu website and the Docker container also include the genome alignment tools BWA-MEM  (25)  and Bowtie2  (26) , as well as additional tools for text manipulation, data import and quality control. The inclusion of deepTools  (27)  further facilitates the integration of ChIPseq, RNA-seq, MNase-seq as well as other kind of datasets with Hi-C data. The analysis of Hi-C data can be divided into three steps: pre-processing (including quality control), analysis and visualization.
Pre-processing and quality control hicBuildMatrix. A contact matrix is the main data structure of Hi-C data analysis which is generated from the individual alignment of valid Hi-C paired-end reads. This tool filters out potentially erroneous reads, such as unmappable reads, self-ligated reads, dangling-ends, PCR duplicates or incomplete digestions  (4, 14)  and tabulates the results based on user defined bins (either based on restriction sites or on fixed size bins). Because building the Hi-C matrix is one of the most time consuming steps in the Hi-C workflow, we developed hicBuildMatrix to be multi-processing to significantly reduce running time. A comprehensive quality report is generated as an HTML file. This report includes a number of useful quality measures including: number of valid Hi-C read pairs and the number of filtered reads per category (unmappable and non-unique pairs, duplicates, dangling ends, self-circles, etc.
IMPLEMENTATION Galaxy HiCExplorer is implemented as a Docker container based on the web-based Galaxy scientific workflow platform  (32). HiCExplorer itself is implemented in Python, supporting version 2. 7, 3. 5 and 3. 6, and available as a Bioconda package (http://dx. doi. org/10. 1101/207092) and as BioContainer  (33). This guarantees a fixation of versions and therefore reproducibility of analysis. Galaxy wrappers for HiCExplorer are available at the Galaxy tool shed.
Installation and usage The Galaxy HiCExplorer web server can be used by visiting http://hicexplorer. usegalaxy. eu, or by installing it on a personal computer or locally (e. g. an institute intranet). For this, pre-configured Docker containers and conda packages are available. Galaxy For advanced users a detailed step-by-step tutorial for the analysis of Hi-C data from mouse embryonic stem-cells, as well as a comprehensive API documentation, is hosted at https://hicexplorer. readthedocs. org. The how-to describes how to set up the mapping of the reads. It suggests parameter settings for the creation of Hi-C contact matrices and describes the process of merging and threshold determination to remove poor bins prior to correction. The determination of TADs using the separation score is described in detail, including examples on visualization.
DISCUSSION Galaxy HiCExplorer gives researchers the opportunity to run their Hi-C data analysis in a user-friendly, web browser based environment. The highly configurable framework provided by Galaxy makes this web server extendable to the various needs of researchers. Especially in conjunction with software for other high-throughput analysis protocols like RNA-seq or ChIP-seq, Galaxy HiCExplorer serves as a powerful basis for flexible explorative biomedical research in a high-throughput sequencing data analysis environment. By combining all the necessary stages of pre-processing and visualization into a single tool, analysis not only becomes easier, but faster, highly reproducible, and more readily exchangeable. Biomedical researchers can focus their efforts on their data analysis without having to concern themselves with the particulars of managing various different software setups and configurations or learning to use command-line tools in an UNIX environment.
Cancer risk SNPs preferentially target cancer genes We expected that cancer-risk SNPs might be preferentially associated with genes known to be involved in cancer development and progression. We assembled a catalog of oncogenes and tumor suppressor genes ("cancer genes") using databases that included the Network Gene Cancer version 5. 0  [18]  and the COSMIC  [19]  census (see Methods and Supplementary Table  S7 ). We tested whether cancer-risk SNPs are more frequently associated with cancer genes than other SNPs based on the eQTL networks. We mapped cancer-risk SNPs to the giant connected component of each of the thirteen tissue-specific eQTL networks. We then compared the number of cancer genes targeted by cancer-risk SNPs and others SNPs, taking into account linkage disequilibrium and global degree distribution (the total number of genes to which they were associated; see Methods).
Representing eQTLs using a bipartite network in thirteen tissues, we find that SNPs and genes are organized into communities that reflect the genetic regulatory influence of SNPs on functionally related groups of genes, as demonstrated by GWAS annotation, gene ontology analyses, and enrichment of cancer-risk SNPs in the promoters of cancer genes. By mapping disease-risk SNPs to these networks, we can develop hypotheses about how these SNPs work individually and collectively to moderate risk and possibly enable disease development. It is well known that the power of eQTL studies to detect associations between genotype and gene expression level depends with the minor allele frequency  [25, 26]. In this study, we used data from thirteen tissues for which we had available matching RNA-seq and genotyping data in 200 or more samples; the sample sizes vary between 212 (HRV-heart left ventricle) and 378 samples (SKN-skin).
GTEx data preprocessing, filtering, and merging We downloaded NHGRI GTEx v 6. 0 imputed genotyping data and RNA-seq data (phs000424. v6. p1, 2015-10-05 release) from dbGaP (approved protocol #9112). The RNA-Seq data were preprocessed using Bioconductor R YARN package  [28, 29]  and normalized in a tissue-aware manner using smooth quantile normalization Bioconductor R qsmooth package  [30]. We identified and removed GTEx-11ILO due to potential sex misannotation. We also filtered out sex-chromosome and mitochondrial genes, retaining 29,242 genes. We excluded five sex-specific tissues (prostate, testis, uterus, vagina, and ovary) and grouped skin samples from the lower leg (sun exposed) and from the suprapubic region (sun unexposed) based on gene expression similarity. For our analysis we only considered tissues for which we had both RNA-seq and imputed genotyping data for at least 200 individuals.
eQTL mapping and bipartite network construction For eQTL analysis, we excluded SNPs from all analyses if they had a call rate under 0. 9 or a minor allele frequency lower than 5% in any tissue. A gene was considered expressed in a sample if its read count was greater than or equal to 6. Genes that were expressed in fewer than 10 of the samples in a tissue were removed for the eQTL analysis in that tissue. To correct for varying degrees of admixture in the African American subjects, we used the first three principal components of the genotyping data provided by the GTEx consortium and included these in our eQTL model. We used the R MatrixEQTL package  [7]  to calculate eQTLs with an additive linear model that included age, sex and ethnic background, as well as the first three genotype PCs, as covariates: Expression ∼ Genotype + Age + Sex + Ethnic Background + P C1 genet + P C2 genet + P C3 genet + We tested for association between gene expression levels and SNPs both in cis and trans, where we defined cis-SNPs as those within 1MB of the transcription start site of the gene based on mapping using Bioconductor R biomaRt package  [32].
Gene Ontology functional category enrichment We extracted the list of genes within each community in each tissue-specific network, and then used the R GOstat package  [40]  to perform a tissue-by-tissue analysis of the over-representation of Gene Ontology Biological Processes terms within each community enriched for cancer-risk SNPs. Our reference set consisted of all the genes present in the corresponding tissue-specific network. Communities were considered significantly enriched in a given category if the FDR-adjusted p-value was < 0. 05.
Cancer risk SNPs Analysis We compared the distribution of SNP core scores between cancer-associated SNPs from the NHGRI-EBI catalog and those not associated with traits or diseases for each tissue-specific network using a likelihood ratio test (LRT). In our setting, the LRT assess whether a linear model that includes cancer risk status (Eq. (  4 )) fits the observed data better than a linear model that doesn't include this variable (Eq. (  3 )). As the distribution of SNP core scores (Q ih ) is not uniform across communities, we added community identity as a covariate in the linear regression. In Eqns. 3 and 4, Q ih is the core score of SNP i in community h, n the number of communities in the tissue. I(GW AS = 1) is an indicator function equal to 1 if the SNP is associated with a higher risk to develop cancer in GWAS and equal to 0 if it's not associated with any trait or diseases.
Acknowledgments This work was supported by grants from the  US National Institutes of Health , including grants from the  National Heart, Lung, and Blood Institute  ( 5P01HL105339 ,  5R01HL111759 ,  5P01HL114501 ,  K25HL133599 ,  K25HL140186 ), the  National Cancer Institute  ( R35CA220523 ,  5P50CA127003 ,  1R35CA197449 ,  1U01CA190234 ,  5P30CA006516 ), and the  National Institute of Allergy and Infectious Disease  ( 5R01AI099204 ). Additional funding was provided through a grant from the  NVIDIA foundation. This work was conducted under dbGaP approved protocol # 9112.
• The biocomposite sample with dimensions of 4. 3 mm × 4. 6 mm × 6. 4 mm was examined to select smaller region of interest (ROI) for further high-resolution imaging. The sample was scanned with double-helix trajectory with projections per revolution set to 2880. Total amount of the acquired projections was 10,180. The volume had an isotropic voxel size of 2. 35 μm. AvizoTM software was applied for building 3D model of the sample and selection of the ROI for further investigation. Sample block of 1. 3 mm × 1. 3 mm × 2 mm was cut off using a scalpel according to the 3D model based defined ROI. High resolution μCT on HeliScan™.
High resolution Automated Slice & View stacking on Scios DualBeam™ microscope • Simultaneous slicing with FIB and SEM imaging was performed on FEI's Scios DualBeam™ microscope (Merignac, France). Auto Slice and View 4. 0 software was used to automatically collect 3D data from a user-defined volume of the sample by milling serial sections (slices) and then acquiring high resolution images of each slice. This software enabled study of the 3D structure and composition of samples at the nanometer scale.
• The biocomposite sample with dimensions of 4. 3 mm × 4. 6 mm × 6. 4 mm was examined to select smaller region of interest (ROI) for further high-resolution imaging. The sample was scanned with double-helix trajectory with projections per revolution set to 2880. Total amount of the acquired projections was 10,180. The volume had an isotropic voxel size of 2. 35 µm. AvizoTM software was applied for building 3D model of the sample and selection of the ROI for further investigation. Sample block of 1. 3 mm × 1. 3 mm × 2 mm was cut off using a scalpel according to the 3D model based defined ROI. High resolution µCT on HeliScan™.
To identify the interconnectivity further microstructural analyses and software were developed. Three-dimensional-correlative and multimodal imaging techniques and the software developed by Thermofisher Bordeaux was highly helpful in quantifying the highly porous biomaterial for tissue engineering applications. Hence, it is foreseen that both the fabrication process and the 3D-correlative and multimodal imaging alongside the software developed can be successfully employed for other porous materials for energy or biomedical applications.
This paper provides the first phase for a demonstration of the use of a model chain that may ultimately provide input to policy analysis (Fig. 1 ). The sites where the models were applied represent the LTER-Europe site network  (Haase et al. , 2018; Mollenhauer et al. , 2018) , covering a wide range of environmental conditions within several distinct biomes. By including (partly co-located) sites of the ICP Forests and ICP Integrated Monitoring programmes under the LRTAP Convention  (ICP Forests, 2018; ICP IM, 2018) , we were able to make use of high quality long-term data on ecosystem response. We also used data provided by the European Monitoring and Evaluation Programme (EMEP, 2018). To our knowledge, this is the first multi-site application of the VSD+ model chain at such a broad regional extent in Europe.