dataesr/scientific-paragraphs-categorization huggingface dataset:
[. 100 ] The PV values have been derived using [. 101 ]the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis since 2000, with a 1 • × 1 • resolution, and interpolated along the aircraft trajectories. As for other ancillary data developed for the IAGOS [. 102 ]database (www. iagos. org), the up-to-date better vertical resolution of the operational analysis since 2000 was clearly more relevant [. 103 ]e. g. for the representativeness of vertical transport processes[. 104 ], thus for the precision of the ancillary data itself. This has been [. 105 ]demonstrated particularly for the coupling system (SOFT-IO) combining the FLEXPART Lagrangian particle dispersion model and emissions inventories to provide systematic [. 106 ]origins of CO mixing ratio anomalies observed by IAGOS throughout the troposphere (see  Sauvage et al.
[. 118  ][. 119 ][. 120 ]Based on these monthly anomalies, the trends are computed using non-parametric [. 121 ]Mann-Kendall analysis combined with [. 122 ]Theil-Sen slope estimate  (Sen, 1968). Calculations are performed with the OpenAir R package dedicated to atmospheric sciences  (Carslaw and Ropkins, 2012). [. 123 ]Theil-Sen slope estimate corresponds to the median of the slopes defined by every couple of points. [. 124 ]In this paper, all trend uncertainties are given at a 95% confidence interval (i. e. with a statistical significance above 2 σ). The linear regression is based on the hypothesis that every point is independent of its neighbours. As a consequence, a positive (negative) autocorrelation can lead to an overestimation (underestimation) of the regression slope. In the aim of reducing such kinds of bias, the method implemented in the OpenAir package applies a correcting factor to one half of the confidence interval depending on the autocorrelation.
More details are available in the Openair Manual (https://cran. r-project. org/web/packages/openair/openair. pdf). account only if the amount of available data points in a month exceeds 300, and if the first and last measurements are separated by a 7 days-period at least. These selected thresholds limit the influence of short-lived events which are not representative of the whole month. The boundaries of each region are chosen as a compromise between a high level of sampling and a good representativity of the expected impact of local surface emissions and meteorological conditions. 119 removed: The mean seasonal cycles are computed from 1995 to 2013, without taking into account the incomplete years in order to avoid biases between two seasons. In this study, the so-called incomplete years are defined as years with less than 7 available months and less than 3 sampled seasons.
409 ]Northeast Asia  (-3. 81 [-6. 75; -1. 37 ] ppb. yr -1 ), about a factor of 2 higher than the trends observed in [. 410 ]East United-States, North Atlantic, Europe and Siberia (about -1. 9 ppb. yr -1 ), while no significant trend is found in both [. 411 ]the Western Mediterranean basin and Middle East. [   The insignificant trends are represented by hatched areas. The coloured labels correspond to the regions defined in Fig. 1. In the framework of the European research infrastructure IAGOS, in situ measurements of O 3 and CO are performed in the UTLS since 1994 and 2002, respectively. Thanks to its high frequency of measurements over such long periods, IAGOS provides the most representative in situ data set in the UTLS. In the present study, we used the IAGOS data available until 2013 to establish a new semi-global climatology and to investigate the seasonal variability and trends in both O 3 (19 years of data) and CO (12 years of data).
Our study does not support this second option since all mean O 3 trends in the LS were found to be statistically insignificant over the studied period, although the insignificance of our trends may be due to the interannual variability. Finally, attributing the trends of O 3 in the UT to one leading process remains difficult and requires further investigations thanks to global models. The IAGOS data set has highlighted significant inter-regional and interannual variabilities, which can provide helpful information for shorter and/or localized measurement campaigns. Its good vertical accuracy in the UTLS makes it a useful dataset for evaluating the ability of current chemistry-transport and chemistry-climate models to reproduce the characteristics of the chemical composition in this layer, including climatologies and trends.
respectively. Thanks to its high frequency of measurements over long periods, the IAGOS database provides representative sets of data in the UTLS. In the present study, we examine more precisely the seasonal cycles and the (bi-) decadal trends of these trace gases, above eight subcontinental-scale regions of the northern extratropics. These regions cover a wide range of longitudes excluding so far the Pacific Ocean. For information, this latter region has been visited by IAGOS equipped aircraft since mid-2012. A first analysis of these data has been presented by  Clark et al. (2015). One of the objectives of the present study was to highlight the regional variability in the UTLS in terms of horizontal composition distributions and trends. It is complementary with other analyses usually dealing with zonal means, and/or focusing on the vertical gradients of species.
g. Neu et al. , 2014a). The latter is not inconsistent with our results in the LS, as the absence of a significant trend of O 3 in this layer may be due to a strong IAV. It is also consistent with the All ozone and carbon monoxide data [. 469 ]used in this study were obtained from the existing IAGOS database, freely available on the IAGOS website (http://www. iagos. org) and via the AERIS website (http://www. aeris-data. fr).
analysis commonly named TOAR, makes use of the [. 87 ]MOZAIC-IAGOS database as a seamless one. The period of the present study spreads from the start of MOZAIC program [. 88 ]until December 2013, since the data from the following years are not [. 89 ]fully calibrated yet. 2. 2 Methodology 2. 2. 1 Definition [. 90 ]for the upper troposphere and [. 91 ]the lower stratosphere During each flight in extratropical latitudes, both tropospheric and stratospheric air masses are encountered. Their different.
(Tereszchuk et al. , 2013)June in the TPL too, and in the LS to a lesser extent. High summertimeCO [. 241]mixing ratios are also observed over eastern Asia by MLS observations at 215 hPa(Huang et al. , 2016), slightly above the TPL. The  [. 242 ]upper-tropospheric CO in Siberia shows a leveling off from February [. 243 ]until June and peaks in July, like the [. 244 ]95 th percentile. This last feature is consistent with [. 245 ]the Global Fire Emissions Database (GFED3: van der Werf et al. , 2010) and the Global Fire Assimilation System (GFASv1. 0:Kaiser et al. , 2012) inventories for CO emissions from Asian boreal forest fires [. 246 ](Andelaet al. , 2013). Since the peak is noticeable in the TPL too, it [. 247 ]suggests a non-negligible impact of pyroconvection(Tereszchuk et al. , 2013).
394 ]the Western Mediterranean basin and Siberia, the changes for [. 395 ]East United-States, North Atlantic and Europe were small compared to the confidence intervals (0. 1%. yr -1 or less). With the sampling periods of [. 396 ]Northeast Asia, these in [. 386 ] 311 removed: WMed 312 removed: trend 313 removed: calculation of the trends 314 removed: (2000 -2009) and 315 removed: 1994-1997 316 removed: IAV 317 removed: WNAm, Eur and Sib 318 removed: 95th 319 removed: seasons 320 removed: 95%-confidence 321 removed: datasets 322 removed: a 323 removed: upper tropospheric 324 removed: NAt precision, we use the values reported in this table to comment the following figures. The relative trends (in %. yr -1 ) are also reported in Tabs. D3 and D4 in order to facilitate the comparison with other [.
Hi-C Hi-C was performed essentially as previously described  (Rao et al. , 2014). Briefly, for each template roughly 10 million cells were crosslinked using 2% formaldehyde. In nucleus restriction was performed using MboI. Restriction overhangs were filled in with biotinylated nucleotides followed by blunt-end ligation. A streptavidin pull-down was performed to enrich for ligated sequences. Pulled-down DNA was end-repaired and an A-overhang was added. Sequencing adapters were ligated to the DNA samples to create sequencing libraries. Libraries were sequenced on a HiSeq 2500 (paired-end 100bp) and on a HiSeq X (paired-end 150bp). We created two templates for wild-type and the three mutant cell lines to control for differences in template generation. Raw sequence data was mapped and processed using HiC-Pro v2.
4C libraries were sequenced on a HiSeq 2500 (single end 65). Reads for every sample were demultiplexed and trimmed based on the reading primer adding GATC to the start of the sequence. Trimmed reads were mapped to the genome using bwa bwasw  (Li and Durbin, 2010). Reads that did not start at a DpnII restriction site were filtered out as they probably do not represent ligation events. Non-unique fragments and fragments created by the digestion of two DpnII sites (''blind'' fragments) were also filtered out. Peak calling was performed using peakC (https://github. com/deWitLab/peakC). The main assumption in our 4C analysis is that the contact frequency decreases monotonically with the distance. We use monotonic regression to model the background contact frequency. Because the background contact frequency can differ between the region upstream and downstream of the viewpoint we model these independently.
Cells were mounted onto glass slides using Prolong antifade gold (Invitrogen). Imaging for chromatin-bound cohesin quantifications was performed using a DeltaVision Elite system (Applied Precision) equipped with 63x/1. 42 oil lens. Z stacks of the whole cell were deconvoluted using SoftWorx (Applied Precision) and quantification of the mean intensity of SCC1 in the DAPI region was performed using an in house written ImageJ macro. Immunofluorescence images were taken on a confocal laser-scanning microscope (Leica), equipped with HCX Plan-Apochromat 63x/1. 32 oil lens using LAS-AF Software. Brightness levels of SCC1 images were equally adjusted. Chromosome spreads were performed as described  (Elbatsh et al. , 2016). Spreads with 3 or more chromosomes that lost their centromeric constriction were quantified as harboring a railroad phenotype.
RNA-seq Total RNA from cultured cells was extracted using TRIzol reagent (Invitrogen). Strand-specific libraries were generated using the Tru-Seq PolyA Stranded mRNA sample preparation kit (iIlumina). In brief, polyadenylated RNA was purified using oligo-dT beads. Following purification, the RNA was fragmented, random-primed and reserve transcribed using SuperScript II Reverse Transcriptase (Invitrogen). The generated cDNA was 3 0 end-adenylated and ligated to Illumina Paired-end sequencing adapters and amplified by PCR using HiSeq SR Cluster Kit v4 cBot (Illumina). Libraries were analyzed on a 2100 Bioanalyzer (Agilent) and subsequently sequenced on a HiSeq2000 (Illumina). We performed RNaseq alignment using TopHat 2. 1. 1  (Kim et al. , 2013). Differentially expressed genes were called with DEseq2  (Love et al.
Chromatin precipitation was performed overnight using antibody-bound (CTCF 5 ml, SMC1 10 ml, Igg 10 mL per IP) proteinA coupled DynaBeads (Invitrogen). Elution and decrosslinking was performed overnight at 65 C in EB pH 8,0 (50mM Tris-HCl; 10mM EDTA; 1% SDS). Samples were treated with Proteinase K and RNaseA for 2 hr. DNA was isolated using phenol extraction and ethanol precipitation. Library preparation was done using a KAPA Library preparation kit using the manufacturer's protocol. Products were sequenced using an Illumina HiSeq 2500 (single end 65bp). Reads were mapped to hg19 using Bowtie2. 1. 0 (Langmead and Salzberg, 2012)  with default settings. Regular peak calling was performed using MACS2 (v2. 1. 1) (Feng et al. , 2012)  using IgG as control. Venn diagrams were generated using the R package ven-nEuler.
Compartment scoring To segregate A and B compartments we performed eigenvalue decomposition of the Hi-C correlation matrix  (Lieberman-Aiden et al. , 2009). We used the Homer package (v4. 8) to perform PCA analysis. The resolution was set to 100kb and the window size to 200kb. In order to identify genomic regions that robustly switch from A to B or vice versa we calculated the following threshold: we determined the range of the 15-85 percentile in the wild-type dataset and divided this by 2. We selected those regions as switchers where the absolute difference between the eigenvectors exceded this threshold and where the eigenvector switched from positive to negative or from negative to positive between the two conditions.
Libraries were quantified on an Agilent Bioanalyzer and sequenced using an Illumina HiSeq2000 machine. DamID profile analyses were performed as described previously  (Pindyurin et al. , 2016). Briefly, reads from Lamin-Dam and Damonly were processed by removing the adaptor sequences: GGTCGCGGCCGAG or CTAATACGACTCACTATAGGGCAGCG TGGTCGCGGCCGAG using cutadapt  (Martin, 2011). Then, the reads were mapped against the human reference genome hg19 using bowtie2  (Langmead and Salzberg, 2012)  with default parameters (except for-local and -k 3). We discarded reads that mapped to multiple locations. Then, we calculated the coverage of reads per GATC fragment using the program htseq-count  (Anders et al. , 2015). GATC fragments were obtained by digesting in-silico the reference genome at every GATC motif with in-house scripts.
Aggregate Peak Analysis To appreciate the genome-wide effects of deleting one or more factors on the 3D genome confirmation we performed aggregate peak analysis  (Krijger et al. , 2016; Rao et al. , 2014). In this analysis we calculate the average contact frequency over a given set of 2D genomic coordinates, such as loops or TAD corners. For a given 2D genomic coordinate we select a square region flanking the coordinate 100kb upstream and 100kb downstream. As a result we get a 210kb by 210kb, or 21x21 submatrix considering we use a 10kb resolution. We average all the submatrices, which results in an average contact profile over a given set of 2D coordinates (e. g. , loops). In our analyses we make a distinction between primary loops and extended loops. Primary loops are the loops that are called in the wild-type dataset (see above) and are formed between a 5 0 loop anchor and a 3 0 loop anchor.
ACKNOWLEDGMENTS We are grateful to the  Medema laboratory  and  Ahmed Elbatsh  for helpful discussions,  Kim Nasmyth  for encouragement,  Wouter de Laat  for support,  Max van Osch  for technical assistance,  Bram van den Broek ,  Suzan Stelloo , and  Sandra de Vries  for technical advice,  Tao Chen  for antibodies, and the  NKI Genomics Core and Digital Microscopy  facilities for assistance. This work was funded by grants from the  Dutch Cancer Society (KWF)  ( NKI 2010-4706 ,  NKI 2011-5103 , and  NKI 2015-7657  to B. D. R. ), the  European Research Council  ( ERC Starting Grant   637587 HAP-PHEN  to E. d. W. and  ERC Advanced  Grant  293662  to B. v. S. ), the  Cancer Genomics Center  (CGC. nl to T. R. B. ), and an  EMBO Long Term Fellowship  (to J. O. Y. -C. ). T. R. B. is a co-founder of  Haplogen and Scenic Biotech.
Developmental and epileptic encephalopathy (DEE) is a group of conditions characterized by the co-occurrence of epilepsy and intellectual disability (ID), typically with developmental plateauing or regression associated with frequent epileptiform activity. The cause of DEE remains unknown in the majority of cases. We performed whole-genome sequencing (WGS) in 197 individuals with unexplained DEE and pharmaco-resistant seizures and in their unaffected parents. We focused our attention on de novo mutations (DNMs) and identified candidate genes containing such variants. We sought to identify additional subjects with DNMs in these genes by performing targeted sequencing in another series of individuals with DEE and by mining various sequencing datasets. We also performed meta-analyses to document enrichment of DNMs in candidate genes by leveraging our WGS dataset with those of several DEE and ID series.
WGS WGS was performed at the McGill University and Genome Quebec Innovation Center as part of the Illumina Genome Network (IGN) according to the IGN standard procedure. In brief, genomic DNA extracted from blood samples was subjected to an additional cleaning step with the ZR-96 DNA Clean & Concentrator-5 Kit (Zymo) and then used for generating sequencing libraries with the TruSeq DNA PCR-Free Library Preparation Kit according to the manufacturer's procedure. Sequencing was done either on the HiSeq 2000 (100 bp paired-end reads; one genome per three lanes) or on the HiSeq 2500 (125 bp paired-end reads; one genome per two lanes) such that a minimum final coverage of 303 was attained after data processing.
WGS Data Processing, Variant Calling, and Analyses The Illumina sequencing reads were generated with bcl2fastq v. 1. 8. 4. Trimmomatic v. 0. 32 was used to remove bad-quality reads and to trim the read edges with a lower quality. The filtered reads were aligned to reference Homo sapiens assembly b37 (GRCh37) with BWA-mem v. 0. 7. 10 for the creation of a binary alignment map (BAM) file. Read-set BAM files from different sequencing lanes for the same sample were merged into a single global BAM file with Picard v. 1. 123. Regions containing multiple base mismatches were realigned locally with Picard. Once local regions were realigned, Picard was also used to recalculate the coordinates of read mates and to mark duplicates for removal. Individual base-quality values were recalibrated with the Genome Analysis Toolkit (GATK) v.
3. 3-0. Genotypes were called with the GATK HaplotypeCaller, and all variant calls were merged and recalibrated in three different sensitivity tranches with GATK according to its recommended best practices. All variant sites were annotated with a custom version of ANNOVAR. 13 Only variants with positions covered at R103 and supported by at least four variant reads constituting R25% of the total reads for each called position were considered. Rare variants included those present with a minor allele frequency (MAF) of %0. 005 in 1000 Genomes, GoNL, ExAC Browser v. 0. 3, or the NHLBI Exome Sequencing Project Exome Variant Server (EVS) or %2% in the unaffected parents from the entire trio dataset. Variant segregation (in child-parent trios) was analyzed with an in-house script. We identified putative DNMs by excluding those present in the genomes of the parents and those with a MAF R 0.
CNV Analyses CNVs were identified by two algorithms: Lumpy, whose calls integrate multiple breakpoint signals, and PopSV, whose calls rely on deviation from normalized read depths across samples. 15, 16 efault parameters were used unless otherwise specified. For PopSV, 5 kb bin scans of the genome were used. We filtered CNV calls to exclude those with a size < 1 kb and with a quality-value (PopSV) or evidence-set (Lumpy) score % 0. 1%. CNVs falling in regions of segmental duplications were also excluded. To identify de novo CNVs, we excluded those present in any of the parents' samples from the entire dataset, those in population controls from 1000 Genomes, and those from the CNV map of high-quality datasets of common variants. 17 De novo CNVs called by both Lumpy and PopSV were prioritized for validation.
,  19  except the ratio of probe to genomic DNA was adjusted to 2,000:1, a 10-fold increase from the previously reported ratio. We performed sequencing on an Illumina HiSeq 2500 to generate 100 bp paired-end reads. Raw read mapping and processing were performed as previously described. 12 Private variants (absent from SNP public databases: ExAC Browser v. 0. 3, EVS, and 1000 Genomes) predicted to affect the protein sequence (missense variants, nonsense variants, indels, and CSSs) were validated by Sanger sequencing in the proband and the parents.
Gene-Specific DNM Enrichment We used the DenovolyzeR open-access program to assess whether a specific gene is enriched in DNMs in subjects with DEE, GDD, and/or ID. 20  This R package program is based on gene-specific mutation rates. 9 DNM gene-specific p values calculated by DenovolyzeR for loss-of-function (LoF) variants (nonsense variants, CSSs, and frameshift indels) and functional variants (missense and LoF variants) were further corrected for multiple testing (Bonferroni correction) on the basis of the 19,618 genes with available mutation rates on which DenovolyzeR based its calculation and the number of tests (two; for LoF and functional categories; i. e. , 2 3 19,618 ¼ 39,236). A corrected p value (c. p value) < 0. 05 was considered statistically significant. To increase statistical power, we performed a meta-analysis to combine DNMs identified herein with those previously reported from trio whole-exome sequencing (WES) done on other DEE cohorts.
NTRK2 Our strategy involving trio WGS and targeted sequencing led to the identification of three DEE individuals carrying de novo predicted-damaging missense variants in NTRK2 (GenBank: NM_006180. 4); these included an individual with the c. 2159C>T (p. Thr720Ile) variant and two unrelated individuals with the same c. 1301A>G (p. Tyr434Cys) variant. In addition, we identified two other individuals with the de novo p. Tyr434Cys missense variant through clinical WES. In total, we identified four individuals with the p. Tyr434Cys missense variant. All subjects with this missense variant had severe GDD or ID and optic nerve hypoplasia with visual impairment, and three had significant feeding impairment (Table  2  and Supplemental Note). Three of them presented with epileptic spasms in the first few months of life and subsequently developed intractable seizures of various types in association with multifocal epileptic activity on electroencephalography (EEG), whereas the remaining individual had startle-like myoclonic events at 12 hr of life and developed, at 5 years of age, focal seizures that caused impaired awareness and occasionally evolved into bilateral tonic-clonic seizures.
Clustering analysis using the Denovonear algorithm indicated that the presence of the p. Tyr434Cys variant in four individuals with similar phenotypes is statistically significant (p ¼ 0. 0001). The subject with the p. Thr720Ile missense variant had moderate to severe ID, ASD, and intractable generalized tonic-clonic and focal seizures with impaired awareness starting at the age of 2. 5 years. Unlike the individuals with the p. Tyr434Cys missense variant, she had hyperphagia and early-onset obesity from the age of 3 years. Interestingly, Yeo et al. reported that an individual carrying the de novo c. 2165A>G (p. Tyr722Cys) variant, affecting an amino acid residue adjacent to Thr720, presented with a phenotype similar to that of our subject; characteristics of this phenotype included excessive weight gain, moderate ID, language delay, autistic features, hypotonia, and seizures.
GABRB2 Our WGS and MIP screens identified three DEE individuals carrying DNMs in GABRB2 (GenBank: NM_021911. 2), including the c. 911C>T (p. Ala304Val) variant in one subject and the recurrent c. 730T>C (p. Tyr244His) variant in two subjects. Two other individuals with DNMs in GABRB2, one with the c. 830T>C (p. Leu277Ser) variant and another with the c. 373G>A (p. Asp125Asn) variant, were identified by the DDD study. 10 We also identified from WES and targeted gene-panel sequencing six individuals with de novo missense variants in GABRB2, including one with the same c. 830T>C (p. Leu277Ser) variant found in the DDD subject, one with c. 851C>A (p. Thr284Lys), one with c. 878G>C (p. Arg293Pro), one with a missense c. 908A>G (p. Lys303Arg) variant adjacent to c. 911C>T (p. Ala304Val) (which was identified in our MIP screen), one with c.