dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Canu 1. 5 Changes to Canu relative to v1. 4 are documented in the v1. 5 release notes (available at https://github. com/ marbl/canu/releases/tag/v1. 5). Briefly, one major change was a switch in the alignment algorithm used to generate consensus for the corrected reads and final contigs. This improved the corrected reads by slightly increasing their identity and splitting fewer reads. Canu also started using the raw read overlaps to estimate corrected read lengths and used that information to inform its selection of reads for correction. As with all such analysis packages, bugs continue to be identified and corrected. The first release of v1. 5, for example, included a bug that erroneously removed heterozygous edges such that in the type of subgraphs shown in Fig. 4a , the results presented would potentially correspond to lower bounds of complexity.
The specific base-calling version ID was reported as: The raw reads were recalled using Albacore 1. 1. 0, which implements a time-domain correction (transducer) for homopolymeric regions in the called sequence. This is also available in the open-source Scrappie (https://github. com/nanoporetech/scrappie) base caller: for x in $(ls -d [CFED]_??????); do echo ${x}; read_fast5_basecaller. py -t 6 -i ${x} -s called_${x} -o fastq -c r94_45 0bps_linear. cfg; done Illumina reads for the WTSI genome assembly were retrieved from the Sequence Read Archive (accession ERR063640). These reads were processed into k-mer counts using Jellyfish v2. 2. 6  [32] , and the resulting histogram used in GenomeScope (see below): jellyfish count -C -bf-size 20G -t 6 -s 2G -m 21 <(zcat ERR063640_1P. fastq. gz) <(zcat ERR063640_2P.
Transcript filtering Our RNA-seq data is publicly available from the European Nucleotide Archive of the European Bioinformatics Institute (https://www. ebi. ac. uk/ena/data/view/ERS1809079), where details of the samples can be found. The estimated numbers of mapped RNA-seq reads (as predicted by Salmon  [34] ) were used to filter transcripts, because the genome-guided assembly was based on RNA-seq expression. Salmon uses a pseudoalignment approach and was chosen to correct for reads mapping to the same sequence, but on different isoforms and/or genes rather than alternative alignment-based approaches that are designed around the expectation of a relatively complete and nonrepetitive genome. Since this analysis was to estimate the expression distribution, however, the specific mapping strategy is not expected to influence greatly the outcome.
Whole-genome amplification and assembly DNA extracted from a single adult worm was amplified using a Qiagen Midi RepliG kit. Raw reads and called FASTQ files were obtained from ONT (called by workflow "1D RNN Basecalling 450 bps FastQ v1. 121") following sequencing. Reads were filtered to exclude those from contaminating DNA using OneCodex (http://onecodex. com). Reads that mapped to any DNA in the OneCodex database were excluded from the read set: To define the region of raw nanopore sequences for adapter exclusion, the >10 k reads were mapped to 50 M reads that had been generated by WTSI and that had been used for the existing WTSI assembly: An alternative, less-stringent overlap was also attempted (with trimReadsCoverage = 2), but resulted in a less complete assembly. The results of an analysis surrounding the different assemblies suggested that the default Canu assembly process of correction, trimming, then assembly produced the best outcome, namely the WGA assembly presented in Table  1.
Acknowledgements We thank  Rick Maizel  for a discussion, and  Simon Mayes  and colleagues at  ONT  for their assistance. This study utilized the computational resources of the Biowulf system at the  National Institutes of Health, Bethesda, MD  (https://hpc. nih. gov/systems/). The  Cloud Infrastructure for Microbial Bioinformatics (CLIMB)  service in the U. K. and the  Genomics Research Centre server at Queensland University of Technology (QUT)  were used to strip out event data and facilitate data transfer.
Uncertainty due to sampling duration Given that 90-s sampling duration and time-averaging for velocity measurements was found to be adequate for the reference velocities across the whole cross section (see Figure  3a ), the standard uncertainty for each measurement location was computed by comparing reference velocities with the actual mean velocities computed from incremental time series of 10 to 30, 40, 60, and 90s. The differences between the such-obtained velocities at the 209 point locations were then averaged for the reference area to estimate one representative uncertainty associated with the sampling duration. The procedure was subsequently used for all sampling durations. In the next analysis step, the sampling duration standard uncertainties associated with various velocity acquisition times (acting as uncorrelated independent sources) were used in a uncertainty propagation software, QMSys, to obtain the impact of this elemental source on the discharge total uncertainty.
QMSys is a commercially-available software developed by Qualisyst Inc. (www. qsyst. com) that implements the guidelines specified by  [7]  for the propagation of the elemental uncertainties to the final experimental results. Fig. 3b  displays the variation of the uncertainty in discharge for sampling durations of the point velocity measurements from 10 to 90s. The plotted results displays an uncertainty range from 1. 1 to 0. 6% for the sampling durations between 10 and 90s, respectively. As expected, the plot shows that the increase of the sample duration for the ADV measurements gradually improves the quality of the measurements commensurate with the duration length. It is worth pointing out that the conventional 40s for sampling velocity at a point as prescribed by conventional guidelines  [8]  still produces an uncertainty of 0.
Plant materials, experimental designs, growth performance, and samplings Analyses were performed on samples collected in three independent experiments conducted in different environments with distinct genotypes. These experiments had initially been set up to characterize the ecophysiological response to water availability and pedoclimatic conditions at the genotype level. A comparative analysis was therefore carried out by merging the ecophysiological data already published (Table  1 ;  Fichot et al. , 2010 Fichot et al. , , 2011;; Toillon et al. , 2013a, b; Bizet et al. , 2015; Lafon-Placette et al. , 2018)  and the epigenomic analyses performed in the framework of the present study (Fig. 1 ). Experiment 1 consisted of a field drought experiment conducted in a common garden. The experiment was installed in spring 2006 at the INRA Research Station of Forest Genetics in Orléans in central France (Loiret, 47°46ʹN, 1°52ʹE, 110 m above sea level) on poor loamy sand.
Methyl DNA immunoprecipitation and microarray analysis Custom microarray probes designed with eArray software (https:// earray. chem. agilent. com/earray/; Agilent Technologies, Massy, France) using Populus trichocarpa genome v2  (Tuskan et al. , 2006 ; http://www. phytozome. net/poplar. php) have been described by Lafon-Placette et al. (2018) (GEO accession number GSE46624). For each poplar gene model (over 42 000 models), we designed five probes: one probe bound to the 0. 5 kb upstream from the start codon (annotated 'PROMOTER'), one probe bound to the 0. 5 kb downstream from the stop codon, and three probes bound within the body of the gene (located between the transcription start site and transcription termination site, and annotated 'BODY',). Probes were annotated 'TE' if they were identified in a transposable element (TE) described by the RepeatMasker annotation of the P.
DMR identification and bioinformatics analyses DNA methylation data obtained with the MeDIP approach were used to detect DMRs between favorable and unfavorable conditions for each experiment. For experiment 1, we compared the Eco 28 genotype in favorable well-watered (ORL WW ) and unfavorable water-deficit (ORL WD ) conditions. For experiment 2, we compared the Dorskamp genotype in favorable growing conditions at the ECH site and in unfavorable conditions at SCV or GMN. Identifying DMRs within each experiment allowed us to compare favorable and unfavorable conditions for each experiment without taking into account differences among experiments for the growing design. To identify DMRs, we applied a mixed model to normalized and filtered signal probes mapped on to the poplar reference genome (ftp:// plantgenie.
org/Data/PopGenIE/Populus_trichocarpa/v3. 0) and estimated the false discovery rate (FDR) for each set of biological conditions, following Lafon-Placette et al. (2018)  (see Supplementary Method S1). Briefly, we first estimated the reference mean from 20% of the probes selected at random, for each chromosome under each growth condition and for each experiment. This became our reference mean, or base level of methylation, for a given chromosome in a given biological sample. We used mixed models based on the bimodal empirical distributions for this estimated reference mean  (McLachlan and Peel, 2000). We used the R statistical software mixtool package  (Benaglia et al. , 2009)  for subsequent analyses. We first constructed 50 kb analysis windows (~7550 windows) based on the remaining 80% of the probes, following Lafon-Placette et al.
This FDR was estimated with the R package fdrtool  (Strimmer, 2008). The results were ranked according to decision rule scores based on the FDR. We defined three levels: not significantly different from the mean reference level of methylation (0), significantly lower levels of methylation than the reference mean (-1), and significantly higher levels of methylation than the reference mean (+1). DMR was defined as a window for which different decision rule scores were obtained for favorable and unfavorable growth conditions for each experiment (0 and +1, 0 and -1, or +1 and -1). To assess Gene Ontology (GO) term enrichment for the genes inside DMRs, we used the corresponding best BLAST hit for each v3. 0 poplar model gene from the best v3. 0 BLAST hits with Arabidopsis TAIR10. AgriGO (http://bioinfo.
Statistical analyses Statistical analyses were performed with Rstudio statistical software (R Core Team, 2015; http://www. rstudio. com/). Means are expressed with their standard error. Differences between groups were evaluated by ANOVA (one-way ANOVA test, general linear model procedure, twoway ANOVA test, genotype and treatment effect). Tukey rank tests were also performed post hoc, considering only the effect of genotype in a given set of conditions. Relationships between global DNA methylation and ecophysiological parameters were analyzed for normality by carrying out Shapiro-Wilk tests. Linear correlation analyses were then carried out to determine Pearson's coefficient (r) and Spearman's coefficient (rho). Significant effects of the methylation pattern on the distribution of DMRs were evaluated with the chi-squared (χ 2 ) test.
Acknowledgements CLP and ALLG received PhD grants from the  Région Centre and the Ministère de la Recherche et Enseignement Supérieur. This work was funded by the  ANR France , within the project  BIOENERGIE SYLVABIOM  ( ANR-08-BIOE-0006 ). We acknowledge  Prof. Phillipe Gallusci  ( Université INRA Bordeaux, France ) and  Dr Farshad Roodbarkelari  ( Institute for Biology III, University of Freiburg, Germany ) for their help in improving the final version of the manuscript.
The pavlovian fear conditioning. Polymodal operant chambers (Coulbourn Instruments, Allentown, PA, USA) were used. Each chamber (18. 5 × 18 × 21. 5 cm) consisted of aluminum side walls and Plexiglas rear and front (the door) walls. A loudspeaker and a bright light constituted the sources of the cues during conditioning and cue-testing. The general activity of animals was recorded through the infrared cell placed at the ceiling of the chambers and was directly recorded on a PC computer using the Graphic State software (Coulbourn). For conditioning, mice were allowed to acclimate for 4 min, then a light/tone (10 kHz) CS was presented for 20 s and co-terminated by a mild (1 s, 0. 4 mA) footshock (US). Mice were returned to their home cages 2 min later. Testing was performed 24 h following conditioning session.
A Student-Neumann-Keuls post hoc analysis was used to compare data between each two groups. Additional one group t-test or paired t-test were used to compare performance to the chance level or between two conditions in the same group (e. g. familiar vs novel congener). Qualitative parameters (e. g. clinical observations, susceptibility to seizures) were analyzed using 2 test. The level of significance was set at P < 0. 05.
DNA bisulfate sequencing DNA was isolated using the Cell Culture DNA Midi Kit from QIAGEN (Cat No 13343) or GenElute™ Mammalian Genomic DNA Miniprep Kit (Cat No. G1N350, Sigma). BS-seq was performed as described previously  (Lister et al. , 2008). FASTQ sequence files generate for BS-seq were aligned to Mus musculus NCBIM37 genome assembly (mm9) using rmapbs-pe (https://github. com/smithlabcode/ rmap) allowing a maximum of 10 mismatches for efficient mapping of reads containing bisulfite converted unmethylated cytosines. 5mC level were directly determined using MethPipe  (24). In short, both mates of the paired-end sequencing were mapped separately by RMAPBS. Reads mapping equally well on multiple positions on the genome were excluded from further analysis. Mates mapping within a maximum distance of 500 bp were merged and other reads were excluded from further analysis.
Peak calling Peak calling was performed for H3K4me1/2, H3K27ac/me3, H3K36me3 and H3K9me3 as follows. The 25% of the mappable genome with the lowest nonzero coverage was estimated as the background level. Next, regions displaying a signal higher than two times the background for at least 300 kb were considered as peaks. Peaks called from multiple samples of the same histone mark were than merged. Merged peaks and normalized tags counts are available in GEO. The code used for peak calling has been deposited in github https://github. com/iaconogi/Histone-mark-analysis.
Data normalization and DEseq2 Significant changes in expression or histone marks were calculated as follows. Initially, the uniquely mapped tags were counted in a given annotation list. Annotations used were (i) refseq mm9 genes for RNA-seq (exon data only), (ii) refseq mm9 gene bodies for H3K36me3, (iii) refseq mm9 gene promoters (TSS ± 2 kb) for H3K9me2/3, H3K27me3, H3K4me1 and DNA methylation. (iv) Previously called peaks for H3K4me3, H3K27ac. Next, tags counts were normalized to account for library size (geometric mean) and Pvalues were calculated in DEseq2, which estimates the likelihood of a change by means of a negative binomial distribution.
GEO (gene expression omnibus data) Data deposited in GEO, ID GSE89010. Elaborated data uploaded in GEO includes: D-scores for P30 Ehmt1 and Ehmt2 epigenomes, developmental Ehmt1 clusters, P30 Ehmt1 deregulated genes clusters, Ehmt1 vs Ehmt2 gene sets, Raw reads counts for RNAseq samples, Peak calls and raw reads counts for H3K4me1, H3K4me3, H3K36me3, H3K27ac, H3K9me3, H3K27me3.
Increased H3K9 di-and tri-methylation in the adult hippocampus of Ehmt1 +/-mice To gain insight into the epigenetic changes associated with haploinsufficiency of Ehmt1 or Ehmt2 in mice, a comprehensive epigenetic dataset (seven histone marks, DNA methylation and RNAseq) encompassing 200 biological samples of multiple brain areas and developmental stages was generated according to the BLUEPRINT/IHEC standard (25) (Figure  1A ). All ChIP-seq profiles show an excellent enrichment of the signal over the background (Figure  1B ). We initially focused on the hippocampus, a crucial brain structure for a multitude of cognitive functions. A genome wide assessment of the epigenome of Ehmt1 +/-(5) and wild type adult (P30) hippocampus revealed that heterozygous mice display extensive increase in H3K9me2 and H3K9me3, with more restricted changes of the other histone marks (Figure  1C ).
Interestingly, H3K9me3 appeared markedly higher in Ehmt1 +/-hippocampi in every developmental stage tested, starting already from postnatal day 1 (Supplementary Figure  S3A  and B ), indicating that aberrant H3K9me3 is deposited before birth at embryonic stage. Given the remarkable extent of H3K9me3 up-regulation in multiple brain areas and developmental stages (conserved up-regulation in 51 Mb Supplementary Figure  S1B  and C ) and the minimal (0. 02 Mb) down-regulation, we assessed whether the expression of any of the known H3K9 (de)methyltransferases (namely Suv39h1/2, SetDB1/2, Prdm2, Kdm4A-D) or (de)acetyltransferases are deregulated in Ehmt1 +/-brain. Intriguingly, none of these regulators appeared deregulated in any brain area or developmental stage of our dataset (Supplementary Figure  S3C ).
Gene Ontology and Tissue-Specific Expression Enrichment Analyses We analyzed protein-coding genes overlapped by one or more candidate windows. GO and tissue of expression enrichment analyses were performed using GOWINDA (Kofler and Schlo ¨tterer 2012), which corrects for gene length-related biases and/or gene clustering. GO accession terms were downloaded from the GO Consortium website. We ran analyses in mode: gene (which assumes that all SNPs in a gene are completely linked) and performed 100,000 simulations for FDR (false discovery rate) estimation (significance at FDR 0. 05). A minimum of three genes in the enriched category was required. For tissue-specific expression analysis, we used Illumina BodyMap 2. 0  (Derrien, Johnson, et al. 2012)  expression data for 16 tissues, and considered genes   S1 , Supplementary Material online.
SNP Annotations Functional annotations for SNPs were obtained from ENSEMBL-based annotations on the 1000 G data and SNPs were categorized as intergenic, genic, exonic, regulatory, synonymous, and nonsynonymous. For each candidate window, we sum the number of SNPs in each category, and then across candidate windows. To compare with noncandidate windows, we performed 1,000 resamplings of the number of candidate windows from the set of all background (i. e. , scanned) windows. Empirical P values were obtained by comparing proportions of SNPs in each category to those obtained in 1,000 resamplings (fig. 5  and supplementary table  S5 , Supplementary Material online). Because we considered the sum across windows, and counted each SNP only once, results should be insensitive to window length (as candidate windows were merged).
Splicing Variants, Mono-Allelic Expression, Immune-Related Genes To test whether candidate genes have a larger number of transcripts than controls, while controlling for gene length, we: 1) divided candidate genes into quantiles of gene length; 2) sampled a set of genes (matching candidate genes in quantile bin; 3) calculated the mean and median number of transcripts for each set; 4) repeated this process 1,000 times and calculated the empirical one-tailed P value. The number of transcripts per gene was obtained from Ensembl Biomart. For this analysis, to reduce complexity, we used the set of candidate genes shared by populations within a continent (265 or 1594 for outlier and significant sets, respectively). To test for enrichment for genes with mono-allelic expression (MAE), we quantified the proportions of candidate genes with MAE or biallelic expression in  Savova et al.
(2016)  and compared these proportions to those in all genes analyzed (one-tailed Fisher's test). The same procedure was adopted to test for enrichment of immune-related genes among our sets, using a list of 386 keywords from the Comprehensive List of Immune Related Genes (https://immport. niaid. nih. gov/ immportWeb/queryref/immportgene/immportGeneList. do; last accessed July 2016). Gene Cards was used to obtain basic functional information about genes. The GWAS catalog  (Welter et al. 2014 ) was used to search for genome-wide phenotype associations included in the discussion (we only report "strong associations," i. e. , when there is at least one SNP with P < 10 À8 ). All statistical analyses and figures were performed in R (Development Core Team R 2009) (scripts available on https://github.
Acknowledgments We would like to dedicate this manuscript to  Scott Williamson , in memoriam, for playing a fundamental role in the conception of  NCD. We also thank  Warren Kretszchmar  for analyses on the properties of related statistics not included here, and  Eric Green  for his support of that work. We thank  Michael DeGiorgio  for assistance with BALLET,  Felix Key  with 1000 Genomes data sets,  Michael Dannemann  with the implementation of expression analyses, St ephane Peyr egne for comments on the manuscript,  David Reher , members of the  Evolutionary Genetics Group (São Paulo) ,  Alex Cagan  and Svante P€ aabo for helpful comments. We also thank two reviewers for helping improve this manuscript. This work was supported by  Fundac¸ão de Amparo a Pesquisa do Estado de São Paulo  (grant numbers  11/12500-2  and  12/ 19563-2  to B.
We worked in the field mainly using digital topographic maps (1:5,000) and high-resolution coloured aerial photographs (1:18,000) from GRAFCAN (Public Company of Cartography of the Canary Islands) (www. grafcan. es) and the LIDAR DEM (reclassified to 5 m resolution) from the National Geographic Institute (IGN) (available at: http:// centrodedescargas. cnig. es/CentroDescargas/index. jsp). A spatial database was designed and implemented with different data sets and layers containing all information obtained for this study. These data sets comprise volcanic vents, eruptive fissures, and all those observations related to geomorphology and morphometry, geochemistry, and petrology as well as 14 C ages  (Aulinas et al. , 2010a (Aulinas et al. , , 2010b;; Kissel et al. , 2015a Kissel et al. , , 2015b;; Rodriguez-Gonzalez, 2009; Rodriguez-Gonzalez, Fernandez-Turiel, Perez-Torrado, Gimeno, & Aulinas, 2010; Rodriguez-Gonzalez et al.
Software Georeferencing and digitization of the Holocene eruptions were originally performed using TNTmips 6. 9 but for this work we used TNTmips 2015, ESRI ArcGIS 10. 5 and QGIS 2. 18. 4. The 3D images on the map were produced using the ArcScene module and Qgis2threejs plugin in QGIS 2. 18. 4. The topographic profiles were produced using the profile tool for TNTmips 2015, the GeoMap application for bathymetric maps, and Microsoft Excel 2016. The final design was carried out using Adobe Illustrator CC.
Acknowledgements The research carried out to develop this map, including field work, radiometric dating, geomorphology, petrology and geochemistry, was funded by the  Spanish Ministry of Education and Science  through the project  CGL2004-04039BTE  and the  Canarian Government  through the projects  PB96-0243  and  PI2002/148. We especially acknowledge  Dr. G. Beconyté ,  Dr. J. C. Carracedo , and  Dr. N. I. Deligne  for their critical reading and useful suggestions on the manuscript and map.
Adolescent MR Imaging Data Acquisition and Processing Each adolescent underwent an hour-long MR imaging protocol using a 3T MR750 MR imaging scanner (GE Healthcare, Milwaukee, Wisconsin) and a 32-channel Nova Medical head coil (Siemens, Erlangen, Germany). The scan included a standard T1weighted sequence and a spin-echo echo-planar DTI sequence (TR ϭ 7. 5 seconds, minimum TE, FOV ϭ 25. 6 cm with a 128 ϫ 128 matrix, slice thickness ϭ 2 mm). Diffusion-sensitizing gradients were applied at a b-value of 1000 s/mm 2 along 30 noncollinear directions. An array spatial sensitivity encoding technique acceleration factor was set to 2, resulting in a sequence scan time of 4 minutes. Preprocessing was performed using the FMRIB Software Library (FSL 5. 0. 8; http://www. fmrib. ox. ac. uk/fsl)  34  and Matlab (MathWorks, Natick, Massachusetts).