dataesr/scientific-paragraphs-categorization huggingface dataset:
For each condition, the proteomes of five animals were analyzed independently and technical duplicates were recorded for each animal. In total, almost 3 million MS/MS spectra (2,980,656) were recorded, with an average of 74,516 MS/MS spectra per run. We analysed these spectra against the protein database of Paralichthys olivaceus, a flatfish species closely related to P. flesus with a fully sequenced genome  (Shao et al. , 2015) , and we could assign a total of 853,691 spectra to peptide sequences (Supplementary data, Table  S1 ). On average, about 21,000 MS/MS spectra per run could be interpreted, i. e. an attribution rate of 28%, which is quite usual for a nonmodel organism and the use of a heterologous protein database. In total, no less than 3,761 proteins could be identified, including 2,511 certified by at least 2 different peptides (Table  S2 ), on which the comparative statistical analysis was carried out using the PatternLab software.
Functional analysis of deregulated proteins Functional analysis makes it possible to visualize the main modifications and orientations of the metabolism under the different experimental conditions. To this end, the various protein subgroups were re-annotated using Blast2GO and EggNOG  (Conesa et al. , 2005; Powell et al. , 2014)  softwares, followed by a careful manual re-examination. Results presented in Figure  4  show the number of proteins assigned to the different Clusters of Orthologous Group (COG) categories. They show that the main categories accumulated in the liver from fish caged in Rouen were related to the transport and metabolism of lipids (I), amino acids (E) and carbohydrates (G), as well as energy production and conversion (C). Concerning the Fosse Nord site, a single category clearly dominated the subgroups of accumulated proteins, i.
Beyond these general changes, we have seen an increase in many proteins clearly associated with the detoxification mechanisms of xenobiotics, both Phase I and Phase II mechanisms. The increase in these proteins was mainly observed in the fish encaged in Rouen, in strong agreement with the pollution data we acquired for this site. The caging technique allows transplanting a homogeneous population of fish into different environments, thus reducing the risks of bias due to their genetic variability, while retaining most of the complexity of the natural environment; as such, it is a very attractive technique in an environmental assessment context. Thus, caging techniques have been used widely for field exposures of fish to chemical contaminants , but only rare studies have evaluated the physiological consequence of caging fish  (Oikari, 2006).
Therefore, our study emphasized the fact that in complex estuarine ecosystems, numerous environmental parameters could become stressors for caged fish such as limited access to nutritional resources, and physico-chemical parameters of the micro-habitat around the cage, which do not necessarily represent the overall heterogeneity of the estuarine environment. *For these KEGG enrichment analyses, the protein sequences of the different datasets (up -and downaccumulated proteins in the livers of fish caged in Rouen and Fosse Nord (compared to fish caged in Canche), and fish caged in Canche (compared to fish stayed in natura)) were submitted to STRING v11. 0  (Szklarczyk et al. , 2019) , and analyzed against the genome of the closest available pleuronectiform flatfish functionally annotated genome, ie.
Spreads from ovaries were immunostained with anti-H2Aub (green) and anti-SYCP3 (red) antibodies. Quantitative analysis of anti-H2Aub intensity in cells in leptotene and (c) in zygotene (d) stages. b Representative images of H2Aub in somatic cells. The Barr body is shown with an arrow. Images from 4 control and 5 treatment biological replicates were used for analysis, and the normalized fluorescence intensities in somatic cells (e) are provided. The immunofluorescence was performed as described in Methods section using antibodies against H2Aub and SYCP3; the images were obtained using microscope using the fixed exposure time. The images were analyzed using ImageJ software and normalized fluorescence was calculated and the averaged value ± SEM were compared, **p < 0. 01, t test, the bar represents 20 µm.
c The quantitative analysis of follicle numbers. The follicles were counted manually using NDP. view2 software by two independent researchers and were categorized according to the classification defined in Methods sections. We counted follicles in 4 biological replicates for control and treated group. The total numbers were compared, n = 4 for each group, *p < 0. 05, t test, AT, atretic follicles, AN, normal antral follicles, CL, corpus luteum.
The genome-wide analysis of H3K4me3 revealed changes in genes important for regulating imprinting and pluripotency To reveal the regions that have altered epigenetic marks, we performed chromatin immunoprecipitation against the H3K4me3 antibody followed by high-throughput sequencing using Illumina technology (ChIP-seq, details in Methods section). The analysis of sequencing reads revealed that 151 H3K4me3 peaks were differential between control and treated groups (FC > 1. 5, FDR < 0. 1) (Additional file 1: Table  S1 ). For example, H3K4me3 in Glo1 gene that is overexpressed in many cancers  [39] [40] [41] [42]  has increased (Fig. 10a ), whereas in Dynlt1a has both decreased and increased occupancies (Fig. 10b ) in treated ovary. Next, we used ChEA (ChIP Enrichment analysis) to compare our identified differential regions with published datasets  [43].
After washing in PBS-T, the sections were incubated with appropriate fluorescent secondary antibody (1:100) for 1 h in a humidified chamber at room temperature. The sections were counterstained with 0. 001% (v/v) 4,6-diamidino-2-phenylindole dihydrochloride (DAPI) and mounted using Vectashield solution. The images were taken using an AxioImager microscope equipped with an AxioCam MRc5 camera and AxioVision software version 4. 8. 2 (Zeiss, Le Pecq, France) with a 5X or 40X objective lens. We analyzed intensity of IF only in antral follicles. For the analysis, we chose oocytes with visibly similar size nucleus. We analyzed minimum 4 follicles per replicate and quantitated signal intensity in oocyte and surrounding granulosa cells separately using ImageJ v1. 52n. We subtracted background using the regions with no cells.
The mean intensity for H3K4me3 or H4Ac signals was counted, normalized to oocyte marker MSY2 and presented as normalized mean fluorescence compared to control. For γH2AX analysis, we analyzed sections with 5X magnification. We considered the follicle as impaired if the number of γH2AX-positive cells exceeded 5. We counted on average at least 4 sections for each biological replicate and presented the data as a number of "damaged" follicles per section.
The quantification cycle (Cq) values were calculated with Bio-Rad CFX Manager 3. 1. Cq values of Hprt and Dazl (embryonic gonad) or Rpl37a (adult ovaries) cDNA were used for normalization. The data were analyzed and are presented as mean values of Fold Change (FC) compared to control ± SEM, *p < 0. 05, **, P < 0. 01, *** p < 0. 001, t test.
Functional annotation of ChIP-seq data Functional annotation was performed using GREAT version 3. 0. 0 or EnrichR with default parameters  [74]. We used ChEA to functionally annotate differential peaks. ChEA is a database of genes potentially regulated by specific transcription factors, where the data were extracted manually from different ChIP-seq experiments for each transcription factor. Motif finding was performed using MEME-ChIP  [75]  with the default parameters. Identified motifs were compared with known motifs using TomTom  [76]  with the default parameters.
Acknowledgements The authors express their gratitude to  Severine Mazaud-Guittot  for her help with ovarian section staining and classification of follicles. We are thankful for H2P2 platform of Rennes for assistance with tissue preparation for histology. Sequencing was performed by the GenomEast platform, a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 ). We also would like to thank the  French Institute of Bioinformatics (IFB ,  ANR-11-INBS-0013 ) for providing storage and computing resources on its national life science Cloud.
Conventional fingerprinting methods, however, normally perform a training using a site survey only once in the beginning and thereafter only if the environment changes significantly, which are not resistant to dynamic environment changes. Continuously updating the fingerprint database is a new way to enhance the achievable positioning accuracy. For that purpose, a Differential Wi-Fi (DWi-Fi) scheme by analogy to DGNSS is applied. It is a network calibration method based on reference stations realized by low-cost Raspberry Pi units which is able to derive the correction parameters in real-time. In this way, the measured RSS values at the user's side are corrected, the fingerprinting database is continuously updated, and thus an adaption to the possible changes in the dynamics of the environment is achieved.
This is usually expressed as the probability of a user being exposed to an error larger than alert limits without warning.  Time To first Fix (TTFF): a measure of a receiver's performance covering the time between activation and output of position within the required accuracy bounds. Activation means subtly different things depending on the status of the data the receiver has access to.  Robustness: the ability of systems or system elements to withstand a level of interference and/or jamming without significant degradation or loss of performance.  Authentication: the ability of the system to assure that they are utilising signals and/or data from trustworthy source (e. g. GNSS constellation), and thus protecting sensitive applications from spoofing threats. If these key requirements and performance parameters are applied, for instance, for Wi-Fi or UWB positioning a similar statement or meaning can be formulated.
In other words, the percentage of time where a RSS from one APi is higher than a RSS from a second APj was investigated. From all the measured RSS data unfortunately not all Access Points recorded at once. So among those seven units, only four could be used to perform lateration, which are AP2, AP4, AP5 and AP6. For 2D lateration four APs provide at least some redundancy. Fig. 3  shows a visualization of the RSS value distributions. The two horizontal axes in the diagrams show either the AP number or the reference points RPs with their Point ID. The absolute measured RSS values from up to 10 scans at each RP are shown on the vertical axis  [16]. As it can be seen from the plots, the absolute RSS values of the seven APs vary significantly in dependence on the respective location of the user.
Furthermore, the noise of the RSS numbers is higher. Thus, the consistency of the ranking order was further investigated more in detail. It is defined as follows: equality between the RSS APi => APj and RSS APj => APi. Equal consistency means in other words that if a signal coming from APi to APj is the same in reverse from APj to APi. It was also verified if the RSSs were distributed according to a Gaussian law to check some properties on them like computing trust intervals. Fig. 5  shows as an example the consistency of the RSS values sequence between AP2 and AP4. Consistency could never been verified in the whole data set. There is always a significant difference between the RSS APi => APj and RSS APj => APi. In the case shown in Fig. 5  for the consistency between AP2 and AP4 a relatively constant difference of around 11 dBm occurred which can be seen as a significant bias.
Wi-Fi Radio Map Interpolation and DWi-Fi In analyses of the Wi-Fi measurements the next step is the creation of maps of RSS distribution for the whole test site. These maps are reffered to as radio maps. In our study, the concept of radio map estimation is based on DWi-Fi, so using two data files, one for RSS APi => APj and one for RSS APj => APi to determine one distance. The method of Voronoï Tessellation, based on geometrical theory, allows to conduct the estimation of all APs. For that purpose an interpolation method has to be applied to estimate the RSS for each point on the map. For this interpolation several methods exist, but it is necessary to achieve a good balance between the accuracy of the algorithm and its speed or efficiency. Thus, mainly two options shall be applied, i. e. :  Linear interpolation: it is quite simple, but not so accurate.
11 Unfortunately, Variants of Uncertain Significance (VUS) currently outnumber all other categories in the ClinVar database and their number is rapidly growing. 12 For SCN5A, 698 variants are listed as VUS or have conflicting interpretations in ClinVar,  13  and a total of 1,712 SCN5A variants have been observed in at least one individual to date in the literature or in the gnomAD database, a majority of which are classified as VUS's. 5, 14 As genomic medicine becomes commonplace, the problem of variant classification will continue to grow unless accurate, highthroughput variant annotation strategies are developed. Deep mutational scanning (DMS) is a massively parallel method for evaluating variant effects. In a DMS, a pool of variants is created, and a selection for the gene product's activity is applied.
32 Single integrant cells, which became mCherry+, BFP-following plasmid integration, were isolated using fluorescence activated cell sorting. After cell recovery from sorting, a pre-drug sample was collected. Then, the triple drug treatment was applied, dead cells were removed by repeated media changes, and a post-drug sample was collected. For both pre-and post-drug samples, barcodes were sequenced on an Illumina MiSeq sequencer. Six independent replicates were collected, and data analyzed as described below.
To test the drug assay performance in transient expression, HEK293T cells were transfected with wildtype or mutant SCN5A pIRES2-EGFP plasmids (Clontech) using FuGENE 6 (Promega) on day 0. On day 1, cells were passaged into multiple replicates in 6-or 12-well plates. On day 2, drugs were added for 5 hours after which media changes were performed to remove the drugs. On day 3, dead cells were non-adherent and were removed by multiple media changes, and GFP fluorescence was assessed by analytical flow cytometry. Flow cytometry data were collected on a BD LSR Fortessa with a 488 nm laser and 525/50 nm filter for GFP. For each sample, single cells were identified by Forward and Side Scatter measurements. Highly GFP+ cells were identified, defined as cells with GFP fluorescence greater than 100X the mean background fluorescence of untransfected control cells.
Sodium currents were recorded using standard protocols as previously described. 51 Peak and late currents were measured from a 200 ms depolarization from -120 mV to -30 mV. Peak current density was calculated as the peak current (pA) divided by the capacitance (pF). Late current was divided by peak current to determine a late current percentage. 4-7 replicate cells were analyzed per mutation. S2. *Patient counts are taken from a curation of the SCN5A variant literature. 5 Patch clamp data are from the literature or this study (Figure  4 , Table  S2 ). For patient counts a representative reference was chosen. Figure Legends.
Acknowledgements This research was funded by  F32 HL137385  (AMG),  K99 HL135442 (BMK) ,  P50 GM115305  (DR),  1R01GM109110  (DMF), and an  American Cancer Society   Postdoctoral Fellowship   PF-15-221-01  (KAM). The authors thank the Vanderbilt VANTAGE genomics core for high-throughput sequencing.
| Competition experiment We tested for the effect of the number of neighbors per pot (0 or 4) on the number of tillers, vegetative biomass, fitness, and height of the focal plant, using generalized linear mixed models (GLMMs) with genotype as a random-effect factor and the number of plants per pot as a fixed-effect factor. Initial seed mass data of focal plants were Z-standardized and were included as a covariate. Vegetative biomass and plant height were analyzed using a linear model with a normally distributed error. The number of tillers and plant fitness were analyzed using a Poisson error distribution with a log link function. Significance of fixed effects was assessed by computing 95% confidence intervals on the model parameters from the likelihood profile  (Bates, Machler, Bolker, & Walker, 2015).
We thus assumed that a genotype had the same effect on yield whatever its position in the pot. Random effects in u were assumed to be normally distributed with mean zero and variance σ 2. For each pot, initial seed mass was calculated as the sum of individual initial seed mass over the five plants. Seed mass data and data of genetic similarity measured as the proportion of shared alleles between the focal plant and its neighbors were Z-standardized. To account for potential environmental effects occurring at a finer scale within blocks, we partitioned the random error ε into an uncorrelated error ε uncor by including pot as a random factor and a spatially correlated environment error ε cor that models the effect of shared environmental conditions by adjacent pots on a grid ("AR1 two-dimensional spatial models")  (Gilmour, Gogel, Cullis, Thompson, & Butler, 2009).
southgreeen. fr/), and by the project  ARCAD-FEDER  ( Fonds Européen de Développement Régional ) cofunded by the  European Union  and the  Région Languedoc-Roussillon. NOR  acknowledges funding from the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme  ( AgreenSkills fellowship , grant agreement n°  267196 ) and the  CeMEB LabEx/University of Montpellier  ( ANR-10-LABEX-04-01 ).
In Rhynchosporium commune, 39 genomic regions with signatures of positive selection were detected across the 27 genome scaffolds  [23]. Among genes associated with these selective sweeps were a multiantimicrobial extrusion multidrug transporter and enzymes involved in degradation of plant cell walls. To gain further insight into positive selective pressure in plant pathogens, we examined the genome of the fungus Sclerotinia sclerotiorum for evidence of selective sweeps. The genome sequence of S. sclerotiorum was recently sequenced to completion using PacBio technology and has been annotated using extensive RNA sequencing data and manual curation, which makes it an attractive resource for genome scale analyses of selection  [24]. S. sclerotiorum is a host generalist that infects more than 600 species of plant  [25].
Fungal isolates In this study, the reference isolate 1980 (NCBI Bioproject ID: PRNJA348385) was used as a reference for genotype calling in most analyses, and as a result is not explicitly included as a sample. For the whole genome alignment described, the reference isolate was explicitly included since this analysis requires no designated reference strain. Two originally selected isolates from a panel of 27 re-sequenced strains were dropped as they were likely not S. sclerotiorum. Thus, a total of 26 isolates were analysed, including the reference isolate, for which a continuous chromosome sequence is available (NCBI Bioproject ID: PRJNA348385)  [24]. These also included isolates from Australia (x 12), Europe (x 5), North America (x 6) and Africa (x 2). The names of the isolates, host of origin (if known), location, year collected, accession numbers (where applicable) and names of isolate stock collections (where applicable) are summarised in S1 Table.
0 fluorometer (Life Technologies) and assessed by gel electrophoresis for quality control. Sequencing was performed by the Australian Genome Research Facility using the Illumina Hiseq platform with 2 x 250 bp paired end sequencing. All genomic DNA sequencing reads generated for this study have been deposited to the GenBank Sequence Read Archive under the bioproject numbers PRJNA449247 (for isolates CULm and CULa) and PRJNA516948 (for all other isolates including non-S. sclerotiorum samples).
HSP60 phylogeny to assign isolates to the species Sclerotinia sclerotiorum To assess whether isolates were the species S. sclerotiorum, a phylogenetic tree was built using previously published heat shock protein 60 (HSP60) gene sequences  [34]  in conjunction with HSP60 gene sequences derived from de novo assemblies. GenBank accessions for previously published HSP60 sequences are in S2 Table. HSP60 sequences were extracted from de novo assembled contigs (de novo assembly of fungal isolate genomes is detailed in a later section 'Quality filtering and de novo assembly of fungal isolate genomes'). Exonerate version 2. 2. 0  [35]  was used to align the previously published HSP60 gene sequence for S. sclerotiorum to each de novo assembly. The commands '-bestn 1-mode est2genome' were used to identify the single best alignment for each genome.
The coordinates of these alignments were extracted to produce browser extensible data (BED) formatted files containing strand information. Bedtools version 2. 25. 0  [36]  was then used with the commands 'getfasta -s' in conjunction with these files and the de novo assemblies to extract regions aligning to the published HSP60 sequence. The option '-s' was used to take into account strand information of the alignments. The isolate specific and previously published HSP60 sequences were aligned using ClustalW version 2. 1  [37]  with default settings. The appropriate nucleotide substitution model was chosen using JModelTest version 2. 1. 10 (v20160303)  [38]  with the settings '-s 11 -f -i -g 4 -AIC -BIC -AICc -DT -p -a'. The Akaike Information Criterion (AIC) test was used to determine which nucleotide substitution model to use.
Variant calling and quality filtering Following alignment, the Genome Analysis Toolkit (GATK) version 3. 7-0-gcfedb67  [45]  was used to call variants between Illumina reads and the reference. The module 'HaplotypeCaller' with the setting '-ploidy 1' was used to call initial variants between reads and the reference assembly. Then, the module 'SelectVariants' was used to select only SNPs. The module 'Var-iantFiltration' was then used to quality filter these SNPs based on the values 'QD < 2. 0', 'AF < 1. 0', 'FS > 60. 0', 'MQ < 40. 0', 'MQRankSum < -12. 5' and 'ReadPosRankSum < -8. 0'; the filter flag 'snp_filter' was applied to the resulting variant call format (VCF) file. Following this, the latter two procedures were followed for InDel polymorphisms derived from the initial HaplotypeCaller step.
For the 'SelectVariants' step considering InDels, the filtering parameters 'QD < 2. 0', 'AF < 1. 0', 'FS > 200. 0', 'ReadPosRankSum < -20. 0' were used. The filter tag 'indel_filter' was applied to the resulting VCF file. The two VCF files and the original VCF file produced by HaplotypeCaller were then merged using the 'CombineVariants' module. The settings for this module were '-genotypeMergeOptions PRIORITIZE -priority a,b'. The modules 'BaseRecalibrator' and 'PrintReads' were then used in conjunction with the merged VCF to recalibrate the BAM alignment. The merged VCF contained higher confidence SNPs and InDels that had passed the quality filtering steps specified in the previous paragraph. HaplotypeCaller was subsequently used, with the settings '-ploidy 1' and '-emitRefConfidence GVCF' to generate a genomic VCF (GVCF) file using the recalibrated BAM.
Principal component and population structure analysis of genotypic variation and analysis of linkage disequilibrium decay The filtered VCF file containing information from all isolates produced in the previous section 'Variant calling and quality filtering' was subjected to a principal component analysis (PCA) using the R Bioconductor package 'SNPRelate' version 1. 4. 2. First, SNPs in strong linkage disequilibrium were filtered using PLINK version 1. 90b4. 1  [50]. To do this, the VCF file was converted into a PED file using VCFTools version 0. 1. 15 [51]. This file was then filtered with PLINK using the settings '-indep-pairwise 50 5 0. 5'. The coordinates of the resulting SNPs were extracted from the PED file using Awk and then passed to VCFTools to use as a filter with the '-positions' argument.