dataesr/scientific-paragraphs-categorization huggingface dataset:
Laboratory procedures and datasets DNA from ethanol preserved tissues was extracted using either a standard phenol/chloroform technique or the Wizard Genomic DNA extraction kit (Promega). Amplification and sequencing of a panel of 74 nuclear, mostly 3'UTR markers, followed  Zieliński et al. (2014). This procedure resulted in physically phased sequences of both haplotypes for most markers in most individuals. We used four different datasets for analyses (Table  S2 ) i) dataset 1 with sequences of all 74 loci and all 128 individuals, ii) dataset 2 with sequences of all 74 loci but with 118 individuals (excluded 9 admixed individuals and one of two newts from locality Kapakli), iii) dataset 3 with sequences of all 128 individuals but 70 loci (excluded 4 loci with > 10% missing sequences), iv) dataset 4 consisting of SNPs extracted from 71 loci (excluded loci with >10% missing data at nucleotide positions) and 120 individuals (including outgroup).
Species delimitation Species delimitation was based on identifying distinct genetic groups from haplotype frequency data and validating the genetic clusters using multispecies coalescent-based and genealogical sorting methods. Sequence alignments (dataset 1) were converted into input for STRUCTURE v. 2. 3. 4  (Pritchard et al. , 2000)  by coding each haplotype as a unique integer in a custom Python script. If nucleotide sequences contained over 10 Ns or gaps, they were coded as missing data, if they had less than 10 Ns or gaps, then columns with missing data were removed from the alignment. This procedure ensured that haplotype identity was based on nucleotide differences between sequences (and not on e. g. indels or low-quality data) and provided a conservative estimate of the number of haplotypes per locus.
STRUCTURE was run under the admixture model (α = 1. 0) with independent allele frequencies among populations (λ = 1. 0) for K from 1 to 12, iterated 10 times each. The Markov chain in each analysis was set to 1 million burnin steps; a further 2 million steps were used for parameter value estimation. STRUCTURE HARVESTER  (Earl and vonHoldt, 2012)  and CLUMPAK  (Kopelman et al. , 2015)  were used process the STRUCTURE output and to evaluate the most probable number of clusters (K) by examining logPr(Data). We validated the genetic clusters identified by STRUCTURE by applying joint Bayesian species delimitation and species tree estimation using the program BPP v3. 1  (Yang, 2015). This approach is based on the multispecies coalescent and compares different models of species delimitation and species phylogeny in a Bayesian framework, accounting for incomplete lineage sorting and gene tree-species tree conflict  (Rannala and Yang, 2013; Yang and Rannala, 2010, 2014).
Input alignments were constructed from dataset 2 by taking 10 sequences per marker for each of the 9 STRUCTURE-delimited operational taxonomic units (hereafter OTUs). This sampling strategy was based on the minimum number of sequences available per taxon (imposed by 5 specimens of L. v. meridionalis), with random subsampling of 10 sequences (without replacement) in all other taxa. Admixed individuals (as defined by STRUCTURE) were excluded from this analysis. We explored a wide variety of priors on the population size parameters (θs) and divergence time at the root of the species tree (τ0) (see Extended Methods in the Supplementary Material). Next, we estimated gene trees in MrBayes 3. 2  (Ronquist et al. , 2012)  and used them as input in Bayesian concordance analysis and an assessment of genealogical sorting (see below).
We applied dataset 3; outgroups consisted of a single, randomly chosen haplotype per locus of a Lissotriton helveticus individual, or if unavailable due to lack of amplification, a single haplotype from either L. italicus (for 3 markers) or L. boscai (for 9 markers) from  Zieliński et al. (2014). Nucleotide substitution models for each marker were determined in jModeltest 2. 1. 4 (Darriba et al. , 2012)  under the Bayesian information criterion. Due to the low phylogenetic information content of many of the markers, we did not partition the alignments into codon positions for those containing coding regions. We implemented two runs each with four chains for 3 million generations with trees and parameters sampled every 3000 generations. Convergence was assessed by comparing the -lnL values between runs and by examining parameter values for a subset of markers in Tracer  (Rambaut and Drummond, 2007).
Phylogeny estimation Our first phylogenetic approach involved using individuals as taxonomic units and concatenating the sequences of all loci. This procedure necessitates the selection of a single haplotype per individual per marker and becomes non-trivial if that individual is heterozygous at multiple loci, because the choice of which haplotype to concatenate into the matrix may influence both the topology and branch support of resultant trees  (Weisrock et al. , 2012). For each individual in dataset 2, we randomly merged haplotypes from each gene into two concatenated sequences (i. e. , each individual was represented by two entries of 36, 918 bp consisting of 74 concatenated markers). We then pooled haplotypes for each candidate species and randomly selected half of the haplotypes from each group for analysis.
, 2007; Larget et al. , 2010)  to estimate levels of concordance in reconstructed topologies among the posterior distributions of gene trees generated in MrBayes. Because the number of unique tips per gene tree was prohibitive (see Extended Methods), we pruned the gene trees in the MrBayes posterior distributions by randomly selecting a single tip from each OTU using an R script from  Weisrock et al. (2012) , modified to accommodate our dataset and subsampling requirements. Admixed individuals were also pruned from gene trees. We tested a range of priors and parameter values for BUCKy (see Extended Methods). We pruned the original gene trees to 10 OTUs (9 candidate species plus outgroup) and repeated the subsampling and pruning routine 50 times, using BUCKy to calculate the primary concordance trees for each replicate.
We extracted and sorted biallelic SNPs at each of the 74 markers according to minor allele frequency (MAF). Five SNPs with the highest MAF, i. e. most informative at the level of the entire dataset, were retrieved per marker, under the condition that they contained <10% missing data at a nucleotide position; these positions were then converted to TreeMix format using a custom Python script. A total of 71 out of 74 markers met our criteria, resulting in a matrix of 355 SNPs (dataset 4). TreeMix was invoked with -k 5 to account for linkage disequilibrium for SNPs from the same marker. In consecutive analyses, we added from one to three migration edges to allow for gene flow in the tree. We also ran TreeMix with an alternative dataset using only 1 SNP per marker with the highest MAF.
Gene flow among OTUs We first summarized the pattern of shared variation in our dataset by counting the numbers of shared haplotypes between OTUs in a custom Python script using dataset 2. We also used this dataset to calculate dxy values among the newt OTUs in DnaSP v5  (Librado and Rozas, 2009). We estimated gene flow between all pairs of OTUs (36 pairwise comparisons) in an Approximate Bayesian Computation (ABC) framework. We followed  Zieliński et al. (2016)  in removing all fully coding markers from analyses, excluding all missing data by removing loci or individuals in pairwise comparisons and excluding all admixed individuals; final datasets included 60-66 loci with between 5 and 24 sequences per taxon (Table  S3 ). The information present in the data was summarized for each pairwise comparison with a set of 9 summary statistics (Table  S3  and Extended Methods).
Coalescent simulations were done in fastsimcoal2. 01 (Excoffier et al. , 2013) , and the ABC analysis was conducted within the ABCtoolbox  (Wegmann et al. , 2010) ; detailed prior and parameter values are given in the Extended Methods. A total of 10 6 datasets were simulated under each demographic model from which we retained 1% (10 4 ) best simulations and computed the marginal likelihood of the observed and retained datasets under the generalized linear model  (Leuenberger and Wegmann, 2010). To control the type I error for multiple comparisons, we used a Bonferroni correction and removed models with the nominal P<0. 0125. The best fitting model was then selected via Bayes factors. We estimated the power to distinguish between the four models within the ABC framework by randomly picking 1000 pseudo-observed datasets from all simulations generated for each model and checking how often the ABC procedure correctly predicted the true model (the one that produced the dataset).
Each pseudo-observed dataset was treated as the observed data and used to calculate marginal densities of all compared models; Bayes factors were used to select the best model. Because identical prior distributions were used for all pairwise comparisons, we conducted a single power analysis using populations with the fewest sequences (L. kosswigi -L. v. meridionalis). This can be considered the minimum estimate as all other comparisons (with larger sample sizes) will have more power in distinguishing between models. Next, we formally tested whether the sharing of mtDNA haplotypes among Lissotriton groups could be accounted for by ILS (incomplete lineage sorting) or is due to hybridization. We assumed that the partially resolved nuclear phylogeny based on the concordant signal from concatenated/BUCKy/TreeMix analyses (further referred to as the species tree) reflects true relationships among the evolutionary lineages within L.
2 ). For L. montandoni two scenarios were evaluated, allowing introgression from either L. v. ampelensis or N L. v. vulgaris. For each evolutionary lineage we randomly sampled one sequence from each locality for which mtDNA sequences were available  (Babik et al. , 2005; Pabijan et al. , 2015; Zieliński et al. , 2013). These sequences were used to calculate parameters (times of divergence and mutation-scaled effective population sizes of extant and ancestral taxa) of competing species trees in BPP v3. 1. A thousand gene trees were simulated for each species tree with MCcoal. Sequences were simulated along gene trees using SeqGen  (Rambaut and Grass, 1997)  assuming the HKA + gamma model of sequence evolution with parameter values estimated in MEGA6  (Tamura et al. , 2013). For each scenario mean and minimum uncorrected (p) distances between evolutionary lineages were calculated for 1000 simulated datasets and compared to the observed distances to obtain P values for individual comparisons.
Results The largest dataset 1 contained 128 individuals and 74 loci with an average of 72. 1 ± 20. 29 haplotypes/locus (range 29-117) and 1. 9 ± 3. 87 % missing data per locus. Markers were of similar length (498. 9 ± 6. 79 bp) and highly polymorphic with an average of 91. 8 ± 34. 56 segregating sites per marker, one third of these (29. 1 ± 9. 52) were singleton polymorphisms (Table  S4 ).
However, species tree estimation in BPP under the multispecies coalescent consistently recovered a sister group relationship between L. montandoni and L. v. ampelensis (Table  1 ). Likewise, analyses using *BEAST  (Heled and Drummond, 2010) , which experienced overall convergence issues, produced similar results (data not shown). Given the strong evidence for recent gene flow among these two taxa (Fig. 4 ;  Zieliński et al. , 2016) , we consider this relationship as highly unlikely. Coalescent-based species tree methods are sensitive to violations of the assumption of no gene flow, particularly between non-sister taxa  (Leaché et al. , 2013; Solís-Lemus and Ané, 2016) , and are thus unsuited to study the relationships within the L. vulgaris complex. Given the increasing number of studies reporting reticulate evolution, there is an urgent need for progress in methods co-estimating phylogeny and gene flow in multispecies and multilocus datasets; phylogenetic multilocus network methods (Solís-Lemus and  Ané, 2016; Yu et al.
Acknowledgements We thank  Marcin Piwczyński  and  Ben Wielstra  for insightful comments on the manuscript. Piotr Grodek ,  Marcin Liana  and  Krystyna Nadachowska-Brzyska  offered invaluable help in the field. Pim Arntzen ,  Konstantinos Sotiropoulos  and  Ben Wielstra  generously shared samples and locality information. The work was funded by the  Polish National Science Centre  ( 2012/04/A/NZ8/00662  to W. B) and the  Jagiellonian University  ( DS/WBiNoZ/INoS/762/15 ).
Objective measurement: force plate analysis Ground reaction forces were measured with kinetic analysis using biomechanical force platforms (Kistler, Winterthur, Switzerland) at the Laboratory of Human Motion Analysis at the University of Liège. At study start (T0 = inclusion +14 days of transition diet), the limb most affected by lameness, as shown by peak vertical force (PVF) gait analysis and orthopedic examination, and with radiographic OA lesions was defined as "the most affected limb" and followed throughout the study. Dogs were acclimated to the force plate before data collection. Data for the right and left sides were collected from separate passes across the plate. The limb most affected by lameness, as shown by PVF gait analysis and orthopedic examination, and with radiographic OA lesions was considered as the most affected limb and its contralateral limb was considered as less affected limb (compared to its contralateral most affected limb).
Ground reaction forces were recorded at T0 and after 3 months of diet (T3). The owner trotted dogs across the force plate, and an investigator observed and filmed each pass to confirm foot strikes and gait. A test was considered valid when only one limb landed on the force plate at a time, while the dog was trotted across it at a velocity of 1. 8 to 2. 2 m/s and acceleration-deceleration variation of ±0. 5 m/s 2. The dog's forward velocity was measured with a 3D sensor (Charnwood Dynamics, Rothley, United Kingdom) placed on the back of the dog. Five valid tests were obtained for each limb. PVF, braking and propulsive peak forces, vertical impulse, braking and propulsive impulses, and loading and unloading rates were measured and analyzed using the software CODAmotion (V6. 78. 1) (Charnwood Dynamics).
Dog was defined as a random term in mixed models. PS and PI were assessed using generalized linear model. Diet, defined as the main effect, was investigated on Δ T3-T0. Generalized linear model or mixed model were analyzed using SAS 9. 3. According to data features and residuals distribution of each model, the outcomes were previously rank transformed or not. Homoscedasticity was checked on residuals with a white test at a level of 1%. All data were expressed as mean ± standard deviation. The analysis was two-sided. A pvalue ≤0. 05 was considered as statistically significant.
However, PVF is often recognized as the most appropriate measure for assessing the effects of therapeutic modalities in OA dogs and is frequently used in clinical trials  [26, 28, 29]. PVF was increased by a diet supplemented with fish oil omega-3 fatty acids  [26, 30] , with type II collagen  [24]  and with green lipped mussel  [31]  but not by an extract of curcuma  [9]. PVF presents some weaknesses, however. It is less reliable when used in owner's dogs. The fact that data for the most and less affected limbs were mostly collected from separate runs could also bring a bias. Minimal body weight of included dogs was 10 kg. It may have been better to start with higher body weight of dogs (e. g. 20 kg). Historically the weight limitation was related to the ability for the force plate to record a single stride (the size of the plate was too big to have only one leg striking the plate at the same time).
To our knowledge, this is the first time that serum OA biomarkers were measured to assess the effects of a diet supplemented with CCOT mixture in OA dogs. Collagen degradation is one of the main features of cartilage breakdown during OA. Coll2-1 and Coll2-1 NO 2 serum concentrations were correlated with the macroscopic and histological changes in dogs with OA induced by transection of the anterior cruciate ligament  [34]. We failed to observe an effect of CCOT on Coll2-1 and Coll2-1 NO 2 serum levels. This may be explained by the higher heterogeneity of biomarker values in our population than in a model of surgically induced OA dogs. No age effect was observed (data not shown) on the outcomes of interest but the study was not designed for this purpose. Our study presented some limitations such as its short duration and the number of dogs included.
Availability of data and materials Data will not be shared because this current research was conducted without explicit consent for data sharing from the participants. Consent for publication of raw data not obtained and dataset could in theory pose a threat to confidentiality. It was not possible to obtain consent due to a lack of specific requirements in the informed consent obtained from the participants. Research was conducted before data sharing policies were established. Publication of dataset constituted a risk to confidentiality especially because dogs' owners responded to an anonymous questionnaire. Royal Canin was consulted for approval of publication.
Data and methodology 2. 1 Data The work is conducted from the Analysis Balance of Road Traffic Injury Accidents (BAAC), which is a French national basis established by the order forces, from the minutes made as soon as an accident occurs and involving at least one victim. This database includes detailed information on the characteristics and the general circumstances of the accident occurrence (time, places, vehicles and persons involved, etc. ). The BAAC files are coupled to GIS software (Geographic Information System) named CONCERTO, this software allows the statistical and cartographic treatment of data. The duration of the study is spread over three years from 2012 to 2014, this period has recorded, according to the BAAC files, 175440 accidents (all modes) distributed throughout the territory of the Metropolitan France.
Methodology To deeply analyze the accidents of the tram per communes, and to avoid the comparisons between raw data, we tried to calculate the average density of accidents and victims per km of tram line for each class of commune. Indeed, we managed to have the layout of tram lines in all the networks of tram in the territory of Metropolitan France, after that we use a communal map of France, Next we could calculate the length of tram networks line by each commune by using MAP-info software, we gathered these communes by size, relating to the population of each commune by using the official statistics of the National Institute of Statistics and Economic Studies (INSEE) of 2013. For accidentology data, CONCERTO enabled us to have the number of accidents and victims of tram in each commune. After having obtained for each commune the tram accidents data, the layout length of tram lines and the population data, we could calculate the average density of tram accidents and the serious victims for each class of commune.
Background: Exponentially increasing numbers of NGS-based epigenomic datasets in public repositories like GEO constitute an enormous source of information that is invaluable for integrative and comparative studies of gene regulatory mechanisms. One of today's challenges for such studies is to identify functionally informative local and global patterns of chromatin states in order to describe the regulatory impact of the epigenome in normal cell physiology and in case of pathological aberrations. Critically, the most preferred Chromatin ImmunoPrecipitation-Sequencing (ChIP-Seq) is inherently prone to significant variability between assays, which poses significant challenge on comparative studies. One challenge concerns data normalization to adjust sequencing depth variation. Results: Currently existing tools either apply linear scaling corrections and/or are restricted to specific genomic regions, which can be prone to biases.
Multiple factors like antibody efficacy, sequencing library accuracy and depth have a direct impact on data quality and thus on any downstream analysis. Therefore, it is imperative to evaluate the quality of data prior to comparative studies (see for example, www. ngs-qc. org) [1]. However, even high quality datasets generally exhibit significant technology/user-derived signal amplitude differences, which require normalization prior to comparative analysis. While significant computational efforts have been made in the past for single ChIP-seq data analysis, sophisticated computational and experimental methods to correct technical variability among multi-sample ChIPseq analyses is acquiring importance recently. Initially, a simple linear normalization approach was widely used, where the counts were represented relative to the total number of reads to correct the throughput differences.
However, such linear normalization, as used by RNAseq-based tools like DESeq  [2]  and EdgeR  [3]  does not address the inherent differences in signal-to-noise ratios among samples. As there can be a significant disproportionate fraction of reads falling in background regions, the normalization of the total number of reads can lead to biased results for certain regions and for the entire global profile. This proved linear normalization to be unsuitable for ChIP-seq. Further, Taslim et al. , proposed a two-step non-linear approach, based on a locally weighted regression (LOESS) method to correct such differences among ChIP-seq data  [4]. LOESS's restriction to pairwise normalization led us to develop Polyphemus  [5] , a multi-profile normalization approach for RNA polymerase II (RNA PolII) datasets based on quantile correction, a method widely used in microarray studies  [6].
The genome-wide normalization procedure applied by Epimetheus enables optimal processing of datasets from different enrichment patterns, including broad/sharp histone modification or PolII-seq profiles, chromatin accessibility profiles generated by FAIRE-seq  [9]  and ATAC-seq  [10] , DNase-seq  [11]  and DNA methylome profiles generated by MeDIPseq or related approaches  [12, 13]. Furthermore, users have the possibility to exclude specific genomic regions like, for example, repetitive elements or any other genomic locations for which artefactual enrichments might be expected.
Methods The basic assumption underlying quantile normalization is the presence of a common read-count distribution in the compared datasets. In cases where the compared enrichment events comprise factors that are implicated in house-keeping events, it is reasonable to assume that the distribution of the read counts for a given target will be similar across cell types  [7]. As for gene expression analysis (RNA-seq and microarrays) or RNA polymerase II enrichment (Polyphemus  [5] ), where quantile has been widely used, histone modifications are expected to occur at both house-keeping and cell/tissue-specifically regulated genes. With this assumption, we apply genome-wide quantile normalization on multiple samples for each chromatin modification. Subsequently Z-score scaling is used, such that each dataset is represented relative to its mean of distribution, which renders different target histone data comparable.