dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of regulatory elements We used ChromHMM to jointly learn chromatin states across samples and segment the genome of each sample  17. ChromHMM implements a multivariate Hidden Markov Model aiming to summarize the combinatorial interactions between multiple chromatin datasets. Bam files from the five histone modifications profiled were binarized into 200 bp density maps. Each bin was discretized in two levels, 0 or 1, depending on their enrichment computed by comparing immunoprecipitated (IP) versus background noise (input) signal within each bin and using a Poisson distribution. Binarization was performed using the BinarizeBam function of the ChromHMM software  17. A common model across species was learned with the LearnModel ChromHMM function for the concatenated genomes of all samples but O1 (orangutan sample 1) due to its anomalous epigenetic profiles (Supplementary Fig.
76 ). Several models were trained with a number of chromatin states ranging from 8 to 20. To evaluate the different n-state models, for every sample, the overlap and neighborhood enrichments of each state in a series of functional annotations were explored. A 16-state model was selected for further analysis based on the resolution provided by the defined chromatin states, which capture the most significant interactions between histone marks and the state enrichments in function-annotated datasets (Supplementary Fig. 2 ). The genomic coordinates of regulatory elements (RE) were defined for each sample by merging all consecutive 200 bp bins excluding elongating (E1 and E2), repressed heterochromatin (E16) and low signal (E15) chromatin states. Species regulatory elements were defined as the union of sample regulatory elements.
LDA is a technique developed to transform the features into a lower-dimensional space, which maximizes the ratio of between-class variance to the within-class variance, thereby granting maximum class separation. We performed LDA analysis using the lda function in the R package MASS (version 7. 3-47)  54. The predictor variables were the background-noise normalized IP signals from the five different histone modifications profiled and chromatin accessibility signal at species regulatory elements. The categorical variable to be predicted based on the underlying enrichments was the chromatin-state based annotation. The regulatory state at the species level was determined based on the regulatory state in each of the biological replicates. Thus, the regulatory state of a regulatory element with different epigenetic states in the two replicates (ambiguous), could be aP or aE, when both samples of a given species were annotated as either P or E but differ in their activity; P/E, when a regulatory element was classified as P in one biological replicate and E in the other one; and P/Non-RE or E/Non-RE, when the regulatory elements was so only in one replicate (Supplementary Fig.
This protocol is intended to detect biologically relevant co-dependences out of the set of significant partial correlations and as a result, this approach filters out many significant partial correlations with very low explanatory power. In fact, all the partial correlations recovered in any of the analyses performed showed very low P-values (Benjamini-Hochberg's correction  62  , P < 1. 8 x 10 -22 ). In our case, given the relatively small amount of data, we focused on recovering those partial correlations that are likely to be relevant in any species. For these analyses, we used a modified version of the R code provided by the authors (http://spcn. molgen. mpg. de/code/sparse_pcor. R/) to perform 5-fold cross-validation analyses separati by species instead of the original 10-fold cross-validation protocol suitable for larger datasets.
Our dataset of regulatory components shows a quite unbalanced contribution of the components to the architectures, with intragenic enhancers being the most abundant type of component and promoter-interacting and enhancer-interacting enhancers being the least abundant (Supplementary Fig. 33 ). These differences could be at least partially related to our inability to recover some of the chromatin interaction-mediated regulatory associations. More importantly, this imbalance, if not real, could affect the ability of our partial correlation networks to reflect the contribution of those components less represented in our datasets. To explore this point, we recovered the subset of genes (an average of 1068 genes per sample) with full architectures (those with at least one element in every type of component) and repeated all the Sparse Partial Correlation Analyses explained above with this dataset of genes.
Differential gene expression analyses We identified genes with differential expression levels across species using the iDEGES/edgeR pipeline in the R package TCC (version 1. 12. 1)  64, 65 at an FDR of 0. 1 and testing all species pairwise comparisons. Then, we determined the patterns of differential expression, species and direction of the gene expression change, using a two-step approach. For every gene, the Q-values obtained in species pairwise comparisons were ordered from smallest to largest. Different expression labels were then assigned to each species according to the ordered Q-values. Once all species had an assigned label, the average normalized expression values between groups were compared to determine the directionality of the change. We separate differentially expressed genes into two categories: genes with species-specific expression changes and gene with non-species-specific expression changes.
Over-representation of Gene Ontology (GO) terms was performed using the WebGestaltR function from the R package WebGestaltR (version 0. 4. 3) 66 with minNum = 25 and remaining default options. This function controls the false discovery rate (FDR) by applying Benjamini-Hochberg procedure (default threshold FDR = 0. 05)  62, 67. Previous analyses have shown that recent enhancers tend to occur in the same genes that already have highly conserved enhancers 9. To control for the particular background of each component, we built different background gene sets including the set of human genes associated with at least one-to-one orthologous regulatory regions of each type of component, hence we have specific and different backgrounds for genic promoters, intragenic enhancers and promoter-interacting enhancers.
To represent and compare enriched GO terms between componentstate combinations, we performed a clustering of all significantly enriched GO terms using REVIGO  68. We associated each GO term with the proportion of genes from each component-state combination that overlapped that GO term. In the case of GO terms enriched in more than one gene set, we chose the highest proportion of genes. We used this list as input for REVIGO. Given that REVIGO only reports the clustering of approximately 350 GO terms and our input list was larger than that, we used the R package GofuncR (version 1. 8. 0)  69  to retrieve the parent GO terms of the remaining unassigned GO terms and add them to the corresponding group as defined by REVIGO. REVIGO group names were manually assigned, taking into account the most representative parent term (Supplementary Table  19 ).
For each component-state combination we followed a two-step approach to remove consistently low-expressed genes across tissues. For that we first assigned a value of 0 to all genes with a median expression level below 0. 1 TPM and then we excluded from the analyses those genes that had an accumulated expression value in all tissues below 0. 1xNumber of tissues (n = 29 tissues). For each component-state combination, differences in median expression across tissues were assessed with the Friedman test using the friedman. test function as implemented in R  55. We used the Wilcoxon-Nemenyi-McDonald-Thompson test implemented in the pWNMT function of the R package NSM3 (version 1. 14)  70  to assess whether expression levels were significantly different for all pairwise tissue combinations. Then, we made use of the rank-biserial correlation to calculate the effect sizes for all statistically significant pairwise tests with the wilcoxonPairedRC function of the R package rcompanion (version 2.
99. 0)  73. The calcTau function from this package provides a tau value for each gene and also a tau expression fraction for each tissue (which also ranges from 0 to 1) that indicates the specificity of a given gene for that tissue. After calculating τ values, we compared their distributions between gene datasets with the Kruskall- Glass rank biserial correlation coefficient was used to compute the effect sizes associated with all statistically significant pairwise comparisons using the wilcoxonRG function from the R package rcompanion version 2. 3. 25  57  (P < 0. 05).
Association of genes containing intragenic enhancers with signals of positive selection in humans We built a database of human genomic regions with previously detected signals of positive selection in humans  [27] [28] [29]  and selective sweeps in modern compared to archaic humans  30. BEDtools 74 was used to assign these regions to both protein-coding and non-coding genes following similar criteria to those used for building the gene regulatory architectures (Methods' section Classification of regulatory elements in different types of components of gene regulatory architectures). We assigned these regions to a protein-coding gene if they were located within the gene or up to 5 Kb upstream of its TSS. Then, we made use of available interaction data for the cell line GM12878 (HiC 21 , HiChIP-H3K27ac 22 and ChIA-PET  23  ) to assign positively selected regions to their interacting protein-coding genes.
Analyses of the density of human-fixed single nucleotide changes (hSNCs) in intragenic enhancers with weak enhancer states In order to study the distribution of human-fixed changes in a specific type of regulatory element, we first generated a dataset with human-specific changes. We used sequencing data from a diversity panel of 27 orangutans, 42 gorillas, 11 bonobos and 61 chimpanzees  [75] [76] [77]  , as well as 19 modern humans from the 1000 genomes project 78 , all mapped to the human reference assembly hg19. We applied a basic filtering for each site in each individual (sequencing coverage >3 and <100), and kept sites where at least half of the individuals in a given species had sufficient data. Furthermore, at least 90% of the kept individuals at a given site in a given species had to share the same allele, otherwise the site was labeled as polymorphic in the population.
Indels and triallelic sites were removed, and only biallelic sites were kept. We used data from a macaque diversity panel  79  , applying the same filters described above. The allele at monomorphic sites was added using bedtools getfasta 74 from the macaque reference genome rheMac8. Since this panel uses the macaque reference genome, we performed a liftover to hg19 using the R package rtracklayer  80  and merged the data with the great ape diversity panel. Lineage-specific changes were retrieved as polymorphisms with sufficient information. Hence, human-specific changes (hSNCs) were defined as positions where each species carry only or mostly one allele within their respective population, the majority of individuals in each population have a genotype call at sufficient coverage, and the human allele differs from the allele in the other populations.
Figure. 1 Overview of the study design and data generated. a, One human and eight non-human primate lymphoblastoid cell lines (LCLs) were cultured to perform a variety of high-throughput techniques including whole genome sequencing (WGS), whole genome bisulfite sequencing (WGBS), chromatin-accessibility sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) targeting five different histone modifications (H3K27me3, H3K4me1, H3K27ac, H3K4me3 and H3K36me3) and transcriptome sequencing (RNA-seq). We integrated previously published datasets from an extensively profiled human LCL (GM12878) to balance the number of human samples (Supplementary Methods). b, Number of sequencing reads generated per sample and experiment. Striped lines indicate data retrieved from previously published experiments 81,82.
Acknowledgments: R. G. -P. was supported by a fellowship from  MICINN  ( FPU13/01823 ). P. E. -C. was supported by a  Formació de Personal Investigador fellowship  from  Generalitat de Catalunya  ( FI_B00122 ). M. K. was supported by a  Deutsche Forschungsgemeinschaft (DFG)  fellowship ( KU 3467/1-1 ) and the  Postdoctoral Junior Leader Fellowship Programme  from  "la Caixa" Banking Foundation  ( LCF/BQ/PR19/11700002 ). D. J. was supported by a  Juan de la Cierva fellowship  ( FJCI2016-29558 ) from  MICINN. T. M-B. is supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 864203 ),  BFU2017-86471-P  ( MINECO/FEDER, UE) ,  "Unidad de Excelencia María de Maeztu ", funded by the  AEI  ( CEX2018-000792-M ),  Howard Hughes International Early Career, Obra Social "La Caixa " and  Secretaria d'Universitats i Recerca and CERCA Programme del Departament d'Economia i Coneixement de la Generalitat de Catalunya  ( GRC 2017 SGR 880 ).
1 1 Figure. 1 Overview of the study design and data generated. a, One human and eight non-human primate lymphoblastoid cell lines (LCLs) were cultured to perform a variety of high-throughput techniques including whole genome sequencing (WGS), whole genome bisulfite sequencing (WGBS), chromatin-accessibility sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) targeting five different histone modifications (H3K27me3, H3K4me1, H3K27ac, H3K4me3 and H3K36me3) and transcriptome sequencing (RNA-seq). We integrated previously published datasets from an extensively profiled human LCL (GM12878) to balance the number of human samples (Supplementary Methods). b, Number of sequencing reads generated per sample and experiment. Striped lines indicate data retrieved from previously published experiments 81,82.
FUNDING The Postic's lab ( U1016-Institut Cochin ) is supported by grants from the  ChroME Network (Marie Curie Skłodowska Action   H2020-MSCA-ITN-2015-675610 ), the  Foundation for the Medical Research (FRM)  ( DEQ20150331744 ) and the  ANR-15-CE14-0026-Hepatokind.
Analysis details This analysis uses 470 μb -1 of Pb+Pb data at √ s NN = 5. 02 TeV collected by ATLAS  [6]. The measurements are performed using the inner detector, the forward calorimeters (FCal), and the zero-degree calorimeters. To increase the number of recorded events from very central Pb+Pb collisions, a dedicated L1 trigger was used to select events requiring the total transverse energy (ΣE T ) in the FCal to be more than multiple threshold values. The c n {4} for n = 1, 2, 3, 4 are calculated using the Q-cumulant method for charged particles in |η| < 2. 5. The method calculates 2k-particle azimuthal correlations {2k} n , and 2k-particle cumulants, c n {2k}, for the n th -order flow harmonics. The two-and four-particle correlations in one event are obtained as: {2} n = e in(φ1-φ2) , {4} n = e in(φ1+φ2-φ3-φ4) (1) where " " denotes a single-event average over all pairs or quadruplets, respectively.
Acknowledgements This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. The experiment was performed within the  LASERLAB  project (FBR) No  PALS001773. Participation of OR and MS in this research was partially supported by the  Czech Republic MSMT  targeted support of Large Infrastructures, ELI  Beamlines  Project  LQ1606  of the  National Programme of Sustainability II , and  Prague Asterix Laser System  Project  LM2015083. This work was partially supported by the project  ELITAS (ELI Tools for Advanced Simulation )  CZ. 02. 1.
Genomic DNA was isolated from bacterial cells using a microbial DNA kit (reference number 740235; Macherey-Nagel, Hoerdt, France). The 16S RNA gene was amplified with PCR using 20 ng genomic DNA, 0. 2 mM deoxynucleoside triphosphates (dNTPs), 50 nM each com1 (5=CAGCAGCCGCGGTAA TAC) and com2 (5=CCGTCAATTCCTTTGAGTTT) primers, and 2. 5 U Taq polymerase (Invitrogen, France). The amplified DNA product was sequenced using Sanger sequencing at Biofidal, Vaulx-en-Velin, France. Sequence comparison using a BLASTN search with the NCBI database showed that the strain belongs to a group of the genus Pseudomonas. The Pseudomonas sp. strain ST1 genome bank was made using a Nextera XT DNA library prep kit and protocol (Illumina, Évry, France) and sequenced using Illumina MiSeq technology with a paired-end 2 ϫ 300-bp run (Biofidal).
done with the MicroScope platform version 3. 10. 0 (9, 10) using the Rapid Annotations using Subsystems Technology (RAST)  (11)  and PATRIC  (12)  Web servers and Prokka software  (13). The genome of Pseudomonas sp. strain ST1 has 6,070,031 bp assembled in 318 contigs. The genome has 6,019 predicted genes. The public version of the Pseudomonas sp. strain ST1 genome sequence at GenBank was annotated using the Prokaryotic Genome Annotation Pipeline (PGAP)  (14). The version described in this paper is the first version.
ACKNOWLEDGMENTS This work was supported by the  Split-Dalmatia County  grant for the project "Agents, transmitters and bio-control of olive knot disease in the olive groves of the  Split-Dalmatia County " and by the  Unity Through Knowledge Fund , collaboration grant 2017 (contract number  10/17 ) within the  Research Cooperability Program. M. R. is a Ph. D. fellow supported by a  French government  scholarship in 2019/2020 announced by the  Ministry of Science and Education of Republic of Croatia, Agency for Mobility and EU Programmes and Embassy of the French Republic  to the  Republic of Croatia. The LABGeM (CEA/Genoscope  and  CNRS UMR8030 ), the  France Génomique , and  French Bioinformatics Institute National Infrastructures  (funded as part of an  Investissement d'Avenir program  managed by the  Agence Nationale pour la Recherche , contracts  ANR-10-INBS-09  and  ANR-11-INBS-0013 ) are acknowledged for support within the MicroScope annotation platform.
Context. Detailed chemical abundances of Galactic stars are needed in order to improve our knowledge of the formation and evolution of our galaxy, the Milky Way. Aims. We took advantage of the GIANO archive spectra to select a sample of Galactic disc stars in order to derive their chemical inventory and to compare the abundances we derived from these infrared spectra to the chemical pattern derived from optical spectra. Methods. We analysed high-quality spectra of 40 stars observed with GIANO. We derived the stellar parameters from the photometry and the Gaia data-release 2 (DR2) parallax; the chemical abundances were derived with the code MyGIsFOS. For a subsample of stars we compared the chemical pattern derived from the GIANO spectra with the abundances derived from optical spectra. We derived P abundances for all 40 stars, increasing the number of Galactic stars for which phosphorus abundance is known.
Observations Stars were observed with GIANO, the high-resolution (R≃50 000) IR (950-2450 nm) spectrometer of the TNG  (Origlia et al. , 2014). The instrument was designed for direct feeding of light at a dedicated focus of the TNG. In 2012 the spectrometer was provisionally positioned on the rotating building and fed via a pair of IR (ZBLAN) fibres connected to another focal station  (Tozzi et al. , 2014). In 2016 it was eventually moved to the originally foreseen configuration where it can also be used for simultaneous observations with HARPS-N  (Tozzi et al. , 2016). The spectra presented here were collected in March 2015, when the spectrometer was still in its provisional configuration and fed via fibres. The raw data were downloaded from the public archive of the TNG. The stars were observed by nodding on fibre, that is, target and sky were taken in pairs and alternatively acquired on the two fibers (A,B) for an optimal subtraction of the detector noise and background.
1, used in previous investigations  (Caffau et al. , 2011 (Caffau et al. , , 2016)). For a subsample of nine stars, we compared the abundances derived from the IR GIANO spectra and SOPHIE optical spectra. For eight chemical abundances common to both surveys agreement was found to be very good. Nevertheless, the line-toline scatter in the IR spectra is larger than in the optical spectra, probably due to a better knowledge on atomic data in the latter range and this is probably related to the longer history of use of optical ranges. Both the good agreement on the abundances derived from IR and optical spectra and the good agreement with the literature are indicators that the abundances we derive from GIANO spectra are reliable. For the star HD 98736, we analysed only < 5 d a angular distance ′′ between the X-ray source and the star four orders of GIANO (47-50) which correspond to the highresolution mode of the H-band for MOONS.
ICP-AES The emission intensities of Mg (279. 55 nm), Li (670. 78 nm), Y and Al were monitored as a function of time with a data acquisition rate of 1 point per second. The instantaneous elemental concentrations, C M , were calculated from the emission intensities using standard ICP-AES calibration techniques. The potential of the test sample monitored by a Gamry Reference 600 potentiostat simultaneously. Dissolution rates of metal n M were calculated from the downstream concentration as: n M = f C M (Eqn. 1) where f is the flow rate of the electrolyte through the cell. The dissolution rates may also be expressed as equivalent current and current densities by application of Faraday's law to Eqn. 1. A test solution of 0. 01M NaCl with pH 6. 8 passed through the cell with a flow rate of 3 ml/min.
The aim of this study was to identify relevant biomarkers for the prognosis of glioma considering current molecular changes such as IDH mutation and 1p19q deletion. Gene expression profiling was performed using the TaqMan Low Density Array and hierarchical clustering using 96 selected genes in 64 patients with newly diagnosed glioma. The expression dataset was validated on a large independent cohort from The Cancer Genome Atlas (TCGA) database. A differential expression panel of 26 genes discriminated two prognostic groups regardless of grade and molecular groups of tumors: Patients having a poor prognosis with a median overall survival (OS) of 23. 0 ± 9. 6 months (group A) and patients having a good prognosis with a median OS of 115. 0 ± 6. 6 months (group B) (p = 0. 007). Hierarchical clustering of the glioma TCGA cohort supported the prognostic value of these 26 genes (p < 0.
Clinical and Histological Characteristics The analysis was performed in radiotherapy-and/or chemotherapy-naive glioma surgical specimens. Sixty-four patients (26 women and 38 men; median age: 47. 5 years; range: 22-81) were included. The main clinical and histological data from this treatment-naive population are shown in Supplementary Table  S1. The population comprised patients with gliomas ranging from grade II to grade IV (grade II, n = 18; grade III, n = 25; grade IV, n = 21). Thirty-six tumors exhibited the IDH mutation and 28 were IDH wild-type; 14 tumors had 1p19q deletion.