dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of variants in the WES panel We assessed the overall sequencing quality of individuals in the WES panel by computing distributions of global statistics, stratified by reported ancestry (Fig. 2b ). After quality control, the number of nonreference variants, mean heterozygosity, and average allele balance (fraction of non-reference reads at heterozygous sites) per individual approximately matched a Gaussian distribution within each ancestry. Concordance between genotypes from exome-sequence data and those from independent SNP arrays was above 99% for the vast majority of individuals, with non-reference concordance above 99. 5% for individuals genotyped on the (highest-quality) OMNI array. We also assessed bulk properties of indels within the WES panel. The length distribution of indels showed an excess of variants with lengths a multiple of three, as expected.
Evaluation of exome array sensitivity We assessed the overlap of variants present on the exome array with those observed in the WES panel. As the exome array primarily contains SNVs that are predicted to be protein altering, we focused on nonsense, essential splice site, and missense variants; only variants passing QC in both sequence and array data were included in the assessment. The fraction of variants in the WES panel on the exome array was highest for Europeans, at 81. 6%, and lowest in African-Americans, at 49. 0% (Fig. 3b ).
These statistics may prove useful for fine mapping or functional studies of T2D GWAS signals, in which enumeration of potential causal variants is required, or for 'reverse genetic' approaches, in which estimates of the phenotypic effects of variants with strong molecular effects are desired. For this usage, we advise investigators to query the T2D Knowledge Portal at www. type2diabetesgenetics. org as a first step, as its goal is to provide a simple and continuously updated means to query these and other association statistics. The portal is designed specifically for queries about individual variants or those within a single gene or genomic locus, as well as variant-or gene-level analyses for which investigators desire to adjust included variants, covariates, or individuals. Users should note that data from the T2D-GENES and GoT2D studies are included in, and thus should not be combined with data from, the Exome Aggregation Consortium (exac.
broadinstitute. org). Participants Genotyping Quality control Association analysis Resources Accession 2 Should investigators desire access to all association statistics, genome-wide, the files at EGA should be used (as the T2D Knowledge Portal does not support bulk download of association statistics). For single variant associations, a statistic in the largest available sample size should be used. Coding variant association statistics present in both the WES panel analysis and the exome chip analysis can be safely combined via meta-analysis, as can non-coding variant association statistics present in both the WGS panel analysis and the imputation-based analysis; statistics should not, however, be combined across the non-coding and coding variant analyses. Association results from the sequence or exome chip data should not need to be filtered, but it is advisable to filter results from the imputation-based analysis according to a threshold on imputation quality (e.
Figure2. Summary of key quality control metrics for WGS and WES panels. We computed several metrics to verify the sequencing accuracy of the study individuals. (a) Estimates of sensitivity of WGS panel. Shown is the fraction of variants, as a function of minor allele count in the WGS sequenced individuals, estimated as included in the WGS panel. Green circles show the total fraction of variants; green crosses show the fraction of variants for hypothetical variants with a T2D odds ratio of 5 (because T2D cases are overrepresented in our sample, the sensitivity to detect risk variants is increased). For comparison, shown are the fraction of variants that are included in the 1000G Phase 1 dataset (blue circles) or HapMap panel (red circles). (b) Distribution of minor alleles carried by individuals in the WES panel.
6 total neurons per larva from 44 larvae; Figure  4 ). The labeled newborn neurons already exhibited spontaneous Ca 2+ activity at 1 dpe (Figure  4 ; Table  S1 ). We examined the development of the Pearson's pairwise correlations between the Ca 2+ dynamics of the newborn neurons and the large functionally mature neuronal population from 1 to 4 dpe (Figure  5A ). For each dataset, significant correlations were calculated using surrogate shuffled datasets (Figure  5B ; STAR Methods). We observed that at 1 dpe, the mean of the significant correlations between newborn and functionally mature neurons was very low and significantly different from the mean correlation values between functionally mature tectal neurons (0. 06 ± 0. 003 for newborn neurons; 0. 13 ± 0. 01 for functionally mature neurons; p = 0.
Analysis of Ca 2+ imaging data Processing of the GCaMP5 fluorescence signal was performed as previously described  [21, 28, 44, 52]. Important steps are summarized below. The series of images during a given experiment were saved as TIFF stacks. Stacks were registered using the ImageJ plugin Template Matching  [53] , in combination with a custom-made algorithm in MATLAB to further smooth the registration. When the real-time Z axis drifting compensation failed, videos were systematically discarded. Movement artifacts were detected according to large deviations in the cross-correlation between successive frames. All frames with large deviations (z-score smaller than À2) were then manually inspected. Due to the agarose elasticity, the imaging plane almost invariantly returned to its original position, after observing movement artifacts.
Eventually, neuronal boundaries were obtained by watershed segmentation. Statistical significance of single-neuron calcium transients was calculated in an adaptive and unsupervised manner. In this framework, any event in the fluorescence time series data belongs to either a neuronal activity process, A, or an underlying noisy baseline, B. We devised a data-driven model to discriminate with a desired degree of confidence between these two sources. Biophysical constraints of the fluorescent calcium indicator were taken into account (GCaMP5G fluorescence decay time constant: 1. 44 s). Then, we applied a Bayesian odds ratio estimation framework. Non-significant portions of the DF/F traces were then set equal to 0 in all subsequent analysis.
From these patterns of spontaneous activity, we computed the temporal Pearson's linear correlation p for all neuronal pairs. For a pair of neuron i and j, p(i,j) was computed using significant DF/F values (see Analysis of Ca 2+ imaging data). p(i,j) is the covariance of Xi and Xj divided by the product of their standard deviations. Significant correlations were extracted by thresholding correlation values. The threshold was calculated based on a surrogate dataset where the spontaneous Ca 2+ -events times stamps were randomly permuted. We then calculated the 99. 9 th percentile value of the distribution of the correlations which was defined as the threshold for each dataset (Figure  2B ). The mean value of the threshold was 0. 059 ± 0. 003 (mean ± SEM) for n = 44 recordings. All correlations greater than this threshold were considered as significant.
01 cycles/ , 2 cycles/s) moving in eight different directions. The order of the different stimuli directions was randomly interleaved, and each direction was presented 8 times. The stimulus lasted for 5 s and the inter-stimulus interval was of 15 s. A Gaussian mask was overlaid on the stimulus to minimize border effects. The stimuli were centered at 140 relative to the larva's midline and the azimuthal plane was chosen according to the maximal neuronal response in the imaged optical plane. The inter-stimulus interval was 20 s (from onset to onset). Overall, we presented to the larvae a total of 64 drifting gratings per experiment. All visual stimuli were programmed using Psychtoolbox  [43]  for MATLAB and synchronized with ScanImage 3. 8 using a TTL pulse delivered by a MATLAB-controlled I/O board (ActiveWire, Inc, USA) or an Arduino Uno (Arduino, Italy).
ACKNOWLEDGMENTS We thank  A. Mizrahi  and  A. Marı ´n-Burgin  for comments on the manuscript;  P. Gongal  for editorial assistance;  L. Bally-Cuif ,  S. Dieudonn  e,  M. Kapsimali ,  S. Garel ,  B. Mathieu , and  M. Privat  for helpful discussions; and  A. Torres-Herraez ,  M. Haudrechy , and  M. Larroche  for help during preliminary experiments. We are also grateful to the  IBENS Imaging Facility. This work was supported by  ERC  grant  StG 243106  to G. S. ,  Boehringer-Ingelheim Fonds, Ministere de l'Enseignement Sup erieur et de la Recherche ,  Fondation ARC  dossier no. DOC20140601024 , and a  Labex MemoLife  grant to J. B. -W. ( ANR-10-LABX-54 MEMO LIFEANR-11-IDEX-0001-02 PSL* Research University ).
Behavioral assessment Actimetry. Spontaneous motor activity in mice and rats was recorded by an actimeter (Panlab, Barcelona, Spain) over 10 minutes  (Laloux et al. , 2012). This apparatus and the associated Actitrack software allowed distance travelled, speed and rearing behavior to be measured based on infrared beams obstructions. Drug-induced rotation test. To assess rotational asymmetry, contralateral rotations over 10 minutes were counted 30 minutes after rats were subcutaneously (s. c. ) injected with apomorphine (APOKINON®; 0. 5 mg/kg). Only nigro-striatal-lesioned animals performing >5 turns/min were included in the experimental cohorts as these have more than 80% depletion of striatal dopamine terminals  (Francardo et al. , 2011). Cylinder test. Rats performed the cylinder test  (Schallert et al.
Nigro-striatal tyrosine hydroxylase staining and analysis After treatment and behavioural analysis, mice and rats were sacrificed and 4% paraformaldehyde perfused brains were microscopically sectioned by cryostat for tyrosine A C C E P T E D M A N U S C R I P T 8 hydroxylase immunochemistry (1:1000; Chemicon International, CA, USA). SN and striatal terminal THr+ neurons were counted by stereological analysis software (Explora Nova, La Rochelle, France)(See supplemental material for details).
Statistical analysis All data were expressed as mean ± SEM, mostly as a percentage from the saline or vehicle control group. For all parameters, a one-way ANOVA was used to assess group effect, followed by LSD Fisher post hoc test. If data did not follow a Gaussian distribution, a Kruskal-Wallis variance analysis was performed, followed by Mann-Whitney post hoc test. The threshold for statistical significance was set to p<0. 05. All statistical analyses were performed with IBM SPSS Statistics (Version 20).
Data Collection The data were collected between November 4th and 18th, 2015, using an anonymous, self-completed, online questionnaire; the average completion time was 21 minutes. The questionnaire was first developed in English and translated by the research agency into the Portuguese, Spanish, and Polish languages. The translation and coherence with the English version were then assessed by native speakers from each of the 3 countries. In terms of content, the questionnaire consisted of 26 items including both open-ended questions, as well as 5-point and 10-point Likert-scales. Possible responses ranged from strong agreement, to neutral, to strong disagreement ( Table  1  ). The questionnaire aimed to evaluate understanding, relevance of daily use, and appreciation of a tool based on the UC scale; it also aimed to assess baseline hydration awareness among the participants.
Statistical Analysis All data were analyzed using JMP 10 and SAS 9. 2 statistical software. Items assessing positive or negative impressions with a 5-point Likert-scale were grouped into "top 2" (strong agreement/agreement), neutral, and "bottom 2" (disagreement/strong disagreement) responses, and expressed as percentages. Appreciation, assessed with a 10 point scale, was converted into a continuous score and expressed as a mean (standard deviation). A Wilcoxon test was used to compare the mean appreciation between tools. The comparison of mean appreciation before and after explanation of each tool was done using a Wilcoxon rankedsigned test. To most open-ended items, participants provided free text responses, which ranged from a few words to several paragraphs. Two researchers read each of these responses mul-tiple times and developed an index of codes and themes across questions.
Water levels were recorded by pressure sensors at a 15 -minute time-step in the Ragas karst conduit (Fig. 2 ). The Dardennes springs' discharge is the sum of the discharge to the Las river downstream from the dam, the withdrawal water discharge for water supply, the lake evaporation and the management balance of the lake water volume by the dam factory. This discharge was computed at a daily time-step, i. e. , the time-step of the lake and water supply data available. The mean daily rainfall was computed by taking the mean value of the three available raingauges, which integrates the spatiotemporal variations in rainfall over the recharge area.
Acknowledgements. The authors would like to thank the  KARST observatory network (SNO KARST) initiative  at the  INSU/CNRS , which aims to strengthen knowledge-sharing and promote cross-disciplinary research on karst systems at the national scale, for providing the KarstMod platform. We also thank  ''Météo-France " for the meteorological data. We also thank the two reviewers who helped to clarify a previous version of the paper. This work is part of the  DARDENNES  project funded by the  Agence de l'Eau (RMC) , the city of Toulon,  Veolia Eau ,  Cenote Company , and  Aix-Marseille University. Results will be available on www. karsteau. fr. The KarstMod platform is free and available at www. sokarst. org.
Data post-processing Collecting the density slices we then formed a full 3D density matrix M 0 (see cartoon for two particles in fig. 2-B ) which we post-processed with a homemade Python algorithm to extract the position x, y, z and the orientation θ 1 , θ 2 , θ 3 of each particle. Because of the large dimension of the density matrix we first resize it to M a 400 × 400 × 400 binarized matrix choosing a 60% density threshold (100% is the highest density, 0% the lowest). A virtual smaller binary matrix m(θ 1 , θ 2 , θ 3 ) is then formed. As presented in fig. 2-C  this matrix is null everywhere except for the pattern of an hexapod centered in the middle of the matrix and oriented along three axis: θ 1 , θ 2 , θ 3. Taking advantages of the particle symmetry, for all the possible families of direction with θ i going from 0 • to 90 • with 2 • steps (91125 families), we convoluted the binarized density matrix M with m(θ 1 , θ 2 , θ 3 ) to get a convolution matrix C(θ 1 , θ 2 , θ 3 ) of the same size as M.
Conclusions We presented an experimental method to investigate the local structure of aggregates made from non-convex particles. This method is based on X-ray scanning and on an efficient and 0. 1mm accurate homemade particle detection code using parallelized matrix convolutions. This permits to measure the position and orientation of different sized hexapods forming large piles in vibrated and nonvibrated configurations. This local scale information is in good agreement with the macroscopic stability measurement made by  [2]. Moreover, contact position statistics and local packing fraction variability provided interesting information about the effect of the pile preparation. However, these experimental results do not permit to detect the exact nature of the contact (point, line or plane) nor to measure the force.
Creation of the Dataset Genome sequences of known spounavirins and spouna-like viruses were retrieved from GenBank or (preferably) RefSeq databases based on literature data, ICTV and taxonomic classifications provided by the NCBI. Records representing genomes of candidate spounavirins were retrieved by searching the same databases with the tBLASTx algorithm using as a queries terminase and major capsid proteins of Bacillus phage SPO1, Staphylococcus phage Twort, Bacillus phage Bastille, Listeria phage A511, Enterococcus phage φEF24C, and Lactobacillus phage LP65 [type isolates of the original subfamily,  (Altschul et al. 1990; Brister et al. 2015) ]. Sequences were manually curated and preclustered using Cluster Analysis of Sequences (CLANS; E-value cut-off 1e-10) to confirm their spounaviral affiliation  (Frickey and Lupas 2004).
This search yielded a set of 93 complete virus genomes, which were used in the following analyses (Supplementary Table  1 ). The genomes were re-annotated using PROKKA with the settings --kingdom Viruses, --E-value 1e-6  (Seemann 2014). All original genome sequences are available from NCBI (accession number information listed in Supplementary Table  1 ) and the reannotated genomes from Github (github. com/evelienadri/herelleviridae).
Trees were visualized using Itol  (Letunic and Bork 2007). Additionally, pairwise comparisons of the nucleotide sequences using VICTOR, a Genome-BLAST Distance Phylogeny (GBDP) method, were conducted under settings recommended for prokaryotic viruses  (Meier-Kolthoff et al. 2014; Meier-Kolthoff and Göker 2017). The resulting intergenomic distances (including 100 replicates each) were used to infer a balanced minimum evolution tree with branch support via FASTME including subtree pruning and regrafting post-processing  (Lefort et al. 2015)  for each of the formulas D0, D4, and D6, respectively. Trees were visualized with FigTree  (Rambaud 2007). Taxon demarcations at the species, genus and family rank were estimated with the OPTSIL program  (Göker et al. 2009) , the recommended clustering thresholds (Meier-Kolthoff and Göker 2017), and an F value (fraction of links required for cluster fusion) of 0.
Proteome-based Analyses The Phage Proteomic Tree was constructed as described previously  (Rohwer and Edwards 2002 ) and detailed at https://github. com/linsalrob/PhageProteomicTree/tree/master/spounavirus. Briefly, the protein sequences were extracted and clustered using BLASTp. These clusters were refined by Smith-Waterman alignment using CLUSTALW version 2  (Larkin et al. 2007). Alignments were scored using open-source PROTDIST from the phylogeny inference package (PHYLIP)  (Felsenstein 1989 ). Alignment scores were averaged and weighted as described previously  (Rohwer and Edwards 2002)  resulting in the final tree. Orthologous protein clusters (OPCs) were constructed using GET_HOMOLOGUES software, which utilizes several independent clustering methods (Contreras-Moreira and Vinuesa 2013).
To capture as many evolutionary relationships as possible, a greedy COGtriangles algorithm was applied with a 50% sequence identity threshold, 50% coverage threshold, and an E-value cut-off equal to 1e-10 (  Kristensen et al. 2010). The results were converted into an orthologue matrix with the "compare_clusters" script (part of the GET_HOMOLOGUES suite)  (Felsenstein 1989 ). The OPCs defined above were used to compute the genomic fluidity for each pair of genomes. For two genomes i and j: Fluidity(i,j) = 𝑈𝑖+𝑈𝑗 𝑀𝑖+𝑀𝑗 with Ui being the number of genes of i not found in j and Mi being the number of genes in i  (Kislyuk et al. 2011). The resulting distance matrix was hierarchically clustered (complete linkage) using the hclust function of R (Development Core Team 2008). Trees were visualized using Itol  (Letunic and Bork 2007).
Multiple alignments were generated for each OPC using Clustal Omega  (Sievers et al. 2011 ). For each cluster, the amino acid identity between all protein pairs inside a cluster was determined using multiple alignment. For all genome pairs, the AAI (Konstantinidis and Tiedje 2005) was then computed and transformed into distance using the formula: Distance = 100-𝐴𝐴𝐼 100 The resulting distance matrix was clustered and visualized as described above. OPCs and multiple alignments for each cluster were used to determine a distance similar to the distance used to generate the Phage Proteomic Tree. To estimate protein distances, in this case, the distance (dist) function of the seqinR package  (Charif et al. 2005 )was preferred to PROTDIST of the PHYLIP package  (Felsenstein 1989)  as the resulting distances are between 0 and 1.
: Python scripts are available on request, manuscript in preparation). GOAT first identified protein-coding genes in the 93 spounavirin and spouna-like virus genomes using Prodigal V2. 6. 3 in anonymous mode  (Hyatt et al. 2010) , and assigned them to the latest pVOGs  (Grazziotin et al. 2017) ). pVOG alignments (9,518) were downloaded (http://dmkbrain. ecn. uiowa. edu/pVOGs/) and converted to profiles of hidden Markov models (HMM) using HMMbuild (HMMer 3. 1b2,  (Finn et al. 2011)). Proteins were assigned to pVOGs using HMMsearch (E-value <10-2) and used to generate a synteny profile of every genome. GOAT accounted for gene replacements and distant homology by using an all-vs-all similarity matrix between pVOG pairs based on HMM-HMM similarity (HH-suite 2. 0. 16) (Söding et al. 2005) ). Distant HHsearch similarity scores between protein families were calculated as the average of reciprocal hits and used as substitution scores in the gene order alignment.
Prokka re-annotated genomes were used to create pan-, core-, and accessory genomes of all selected spounavirins and spouna-like viruses  (Seemann 2014 ). The annotations were analyzed using Roary  (Page et al. 2015)  with a 50% length BLASTp identity threshold for homologous genes. Roary functions as follows: CD-HIT  (Fu et al. 2012 ) was used to precluster protein sequences and perform an all-vs-all comparison of protein sequences with BLASTp to identify orthologs and paralogs within the genomes. Markov cluster algorithm (MCL)  (Enright et al. 2002)  was then used to cluster the genomes based on the presence and absence of the accessory genes. The gene presence-absence output table from Roary was then imported into R and pairwise shared gene contents were calculated for each combination of genomes using a custom R-script (available from github.
Single Protein Phylogenies Based on the OPC and pVOG analyses, we chose nine well-annotated protein clusters present in all 93 spounavirins and spouna-like viruses. Selected clusters included: DNA helicases, major capsid proteins, tail sheath proteins, two different groups of baseplate proteins, and four clusters with no known function. The members of these clusters were aligned using Clustal Omega with default parameters  (Sievers et al. 2011). Resulting alignments were analyzed with ProtTest 3. 4  (Darriba et al. 2011)  to determine a suitable protein evolution model (only variations of models compatible with downstream software like JTT  (Jones et al. 1992 ) and WAG  (Whelan and Goldman 2001)  were considered). Estimated models were used to generate phylograms with FastTree 2. 1. 7 (Price et al.
These results were also confirmed with the Virus Classification and Tree Building Online Resource (VICTOR), a genome-BLAST distance phylogeny (GBDP) method (Supplementary Fig. 2 ) (Meier-Kolthoff and Göker 2017) and the Dice score (Supplementary Fig. 3 ), a tBLASTx-based measure that compares whole genome sequences at the amino acid level  (Mizuno et al. 2013 ). The patterns coalesced at a higher taxonomic level when the genomes were analyzed using tBLASTx (Supplementary Fig. 4 ). a The species listed here are representing the 93 genome dataset on which all analyses have been performed. Species names ratified in 2017 and later are not included. Phage isolates at the subspecies or strain level are indicated between brackets. Non-ICTV-ratified taxa are indicated between quotation marks. Clustering was performed using nucleotide similarities (BLASTn) or B) translated nucleotide similarities (tBLASTx,).