dataesr/scientific-paragraphs-categorization huggingface dataset:
At the end of the first cycle, the quality of the prediction was good enough to allow the discrimination of substituted from unsubstituted reads for the 40 to 100% BrdU samples. Reads were relabelled as substituted or unsubstituted based on these results, and a second training cycle was run. This allowed the separation of substituted from unsubstituted reads for the 20 and 30% BrdU samples. The final network (RepNano v2 architecture) was trained on this cleaner dataset of 400 reads per sample.
sh script on NanoForkSpeed GitHub repository). This step was carried out using a GPU-enhanced computer to allow fast processing. As the resulting BAM files could be quite big, a custom R  58  script (R version 4. 0. 3) named basecalling_sample. r first split BAM files into 50,000 read subfiles. The parsing was then performed in R with the mega_parsing homemade function. For each read, this function imported from the BAM file the read identity (read_id), the DNA strand, the chromosome name, the start position and length of the read, the sequence and the Mm and Ml fields which contain the position and output score of detected BrdUs, respectively, using Samtools (version 1. 10). The function then allocated a probability of being a BrdU at every position corresponding to a thymidine in the mapped sequence.
Fork detection and orientation were performed with custom R scripts using a piecewise linear simplification approach deriving from the original FORK-seq manuscript  13. Reads were first converted into a series of segments using the Ramer-Douglas-Peucker algorithm  59, 60  (Hausdorff distance epsilon = 0. 1, using DouglasPeuckerEpsilon function of the kmlShape R package). Only reads with 3 or more segments were kept as reads with less segments could not form a complete fork. Segments were classified into 4 categories using their slope and mean BrdU signal (B = flat segment with a background BrdU level, A = flat segment with a BrdU level above background, P = segment with a positive slope and N = segment with a negative slope). In order to set the background threshold, the distribution of the mean BrdU signal on 1 kb windows computed for the reads containing at least 3 segments of a given experiment was plotted; in every experiment, DNA replicated before the BrdU pulse (i.
Data were filtered using these parameters to group fork speeds according to the criteria presented in the figures of this manuscript. Detailed information for every sample sequenced in our study are presented in Supplementary Data 1. Computing fork velocity from reads of increasing minimal length resulted in a negligible rise in the median fork speed estimated by NFS, confirming that a 2 min BrdU pulse duration was adequately suited with respect to read length to detect short as well as long tracks in our experiments; the median length of reads with forks is indicated for each sample in Supplementary Data 1. Genomic map of fork speed. Forks detected in all BT1 biological replicates grown in standard conditions at 30 °C were converted into GenomicRanges using the GenomicRanges R package and reduced to their centre.
21. Oriented genes in Supplementary Fig. 9  are from the Saccharomyces_cerevisiae. R64-1-1. 104. gtf file from Ensembl (see "Computational resources" below for further details). Replication fork directionality (RFD) computation. RFD is calculated for a given position as the difference between the proportions of rightward-(R) and leftward-(L) moving forks (RFD = R -L). RFD profiles from MCM869 FORK-seq and OKseq data were computed as in ref. 13. BT1 RFD profile from NFS data (i. e. , X0 and X1 coordinates of oriented forks) was generated in a similar way with the sim-pleRFD function of the script helper_function. r (this function produces 4 bigwig files corresponding to the RFD and the total, leftward and rightward forks coverage) using forks from all BT1 biological replicates grown in standard conditions   3b  reporting Spearman's pairwise correlation coefficients was produced using the cor.
rfd function of the helper_function. r script and the ggcorrplot function of the ggcorrplot R package. The cor. rfd function rests on the base R cor function but works on a coverage type of data (RleList) and excludes positions where N/A are present in at least one of the RFD profiles for which the correlation is computed. Analysis of initiation and termination events. Initiation (ini) and termination (ter) event positions, defined as the midpoints between diverging and converging forks, respectively, were extracted from all BT1 biological replicates grown in standard conditions at 30 °C and processed as in ref. 13. Replication origins (ORIs) are from ref. 21. Distance of each ini or ter to the centre of the nearest ORI was computed using the distanceToNearest function of the GenomicRanges R package.
Empirical cumulative distribution functions were plotted using the Ecdf function of the Hmisc R package. Shuffled versions of ini and ter positions were produced as a control using the custom shuffleGRen function of the helper_function. r script, which randomizes GenomicRanges positions while conserving the number of events per chromosome. Simulation of BrdU incorporation during DNA replication. We simulated reads containing a BrdU signal mimicking an elongating fork as well as reads bearing multiple forks. Simulations were performed using Python scripts (Python version 3. 6). Simulation of BrdU level. We modelled intracellular BrdU level b(t) as a function of time t as (i) zero before the BrdU pulse; (ii) an ascending section modelled by an exponential increase AsðtÞ ¼ Mð1 À expðÀ t τ 1 ÞÞ where t is the time since the start of the pulse, M the saturating concentration of BrdU and τ 1 the characteristic time of BrdU intake; (iii) a decreasing section starting at the start of the chase at time t pulse (also corresponding to the duration of the pulse) that was modelled by a similar exponential decrease from the value attained at t ¼ t pulse : DsðtÞ ¼ Asðt pulse Þ þ ðm À Asðt pulse ÞÞð1 À exp(- tÀt pulse τ 2 )Þ, where m is the asymptotical BrdU concentration and τ 2 the characteristic time of BrdU outtake.
The weights w k are an estimate of the true fork speed distribution within the 44 classes. This procedure was repeated independently 5 times and the average of the 5 weight sets were used as the deconvolved true fork speed distribution. Finally, this discretised distribution was fitted using a Gaussian mixture with six components to create a continuous true fork speed distribution. To evaluate the deconvolution procedure, we generated a new set of 100,000 reads with multiple forks following the estimated true fork speed distribution and processed them with NFS. The obtained observed fork speed distribution closely resembled the experimental distribution of >125,000 fork velocities, validating our approach. Deconvolution was performed with custom Python scripts using a library from ref. 61.
The mode of the estimated true speed distribution in Fig. 3b  was determined using the mlv1 function of the modeest R package. The major peak of the distribution was fitted with a normal distribution with a mean equal to this mode. Standard deviation and weight of the normal distribution were then adjusted to best fit the peak. Fork speed versus replication timing. Replication timing (RT) data are from ref. 42. The liftOver tool (http://hgdownload. soe. ucsc. edu/goldenPath/sacCer1/ liftOver/) was used to convert genomic positions from sacCer1 to the sacCer3 version of the yeast genome. Data were normalized between 0 and 1 (start and end of S phase, respectively). The mean RT was computed for each fork, then fork speed versus RT was plotted as a 2D density plot with hexagonal bins using the geom_hex() function of the ggplot2 R package with default parameters.
The overlap was tested with the OverlapsAny function of the GenomicRanges R package (minimal overlap was one nucleotide). Yeast replication origins in Fig. 5  and Supplementary Figs. 3a, c , 8 and 11 are from ref. 21. RT data in Fig. 6d  are from ref. 42  (data accessible from NCBI's Gene Expression Omnibus repository, accession code GSM1036187, GSM1036187_T7107_normalised. wig. gz file). Oriented genes in Supplementary Fig. 9  are from the Saccharomyces_cerevisiae. R64-1-1. 104. gtf file downloaded from Ensembl (http://ftp. ensembl. org/pub/release-104/gtf/ saccharomyces_cerevisiae/). DNAscent v1 and v2 were downloaded from https:// github. com/MBoemo/DNAscent. c, Representative FACS analysis of S phase progression of BT1 cells. WT and BT1 cells were arrested in G1 with α-factor and released synchronously into S phase.
Acknowledgements The authors thank  E. Schwob  ( IGMM, Montpellier, France ) and  Sophie Loeillet  ( Institut Curie, Paris, France ) for kindly providing plasmids and strains, the  Genoscope (Evry, France)  for performing MinION and PromethION sequencing,  Michael Mitter  and  Vienna BioCenter Metabolomics facility  for mass spectrometry measurements as well as the IBENS IT platform and the  BioClust computing cluster (Labex Memolife ). The authors also thank  Stevenn Volant  for his comments regarding statistical analyses,  Erwan Sallard  for his participation in strain construction, all members of the O. H. laboratory for helpful discussions and  Nathalie Verin  for proofreading the manuscript. This work was supported by the  Ligue Nationale Contre le Cancer , the  Association pour la Recherche sur le Cancer , the  Agence Nationale de la Recherche  [ ANR-18-CE45-0002  to B.
A. and  ANR-19-CE12-0028  to O. H. ], the  Fondation pour la Recherche Médicale  [ FRM DEI201512344404  to O. H. ] and the  Cancéropôle Ile-de-France  [ PLBIO16-302  to O. H. ]. This work was partly supported by the  Genoscope , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) and France Génomique  [ ANR-10-INBS-09-08 ]. B. T. was supported by fellowships from the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Fondation pour la Recherche Médicale  [FRM FDT202106013030].
Data availability Nanopore sequencing data generated in this study have been deposited in the ENA database under accession code PRJEB50302. Source data are available at https://github. com/LacroixLaurent/NanoForkSpeed and https://doi. org/10. 5281/zenodo. 5958270. Yeast genomic feature coordinates used in this study originate from UCSC SGD_other track (https://genome. ucsc. edu/cgi-bin/hgTables), oriented genes are from Ensembl database (Saccharomyces_cerevisiae. R64-1-1. 104. gtf file available at http://ftp. ensembl. org/pub/ release-104/gtf/saccharomyces_cerevisiae/), replication origins are from ref. 21  and replication timing data are from ref. 42  (data accessible at NCBI GEO database, accession code GSM1036187). Source data are provided with this paper.
Code availability Megalodon-based BrdU basecaller, NFS software and associated R scripts can be accessed at https://github. com/LacroixLaurent/NanoForkSpeed and https://doi. org/10. 5281/ zenodo. 6535390. Custom R scripts for statistical analyses can be accessed at https://gitlab. pasteur. fr/gmillot/anova_contrasts/-/tree/v7. 2. 0. Python scripts are available at https:// github. com/organic-chemistry/simunano.
For details on sequencing and annotation see Supplementary Methods. This study (CMO-2018-4922) was approved by the Radboudumc Ethical Committee. Nine different noncoding aberrations in 9 of 32 individuals (28. 1%) were identified (Figure  1B ). Five different deep intronic single nucleotide variants in MSH2 (n ¼ 3), MLH1 (n ¼ 1), and PMS2 (n ¼ 1) that are likely to introduce novel splice sites based on in silico predictions were identified in 6 individuals. Additionally, 2 intronic Alu element insertions (>96% similarity to Alu elements) in introns 1 and 8 of MSH2 (of which the latter was in cis with a deepintronic single nucleotide variant), 1 1704-bp intronic deletion in intron 3 of MLH1, and 1 tandem duplication in the MLH1 promoter region were identified. None of the variants was previously reported in population databases (GnomAD-G, 1000G).
Variant Calling and Annotation For single nucleotide variant and small indel detection, mapped bam files were loaded into JSI SeqNext Software v5. 1. 0 Build 503 (JSI Medical Systems GmbH) to perform quality filtering and variant calling. Variant calling format (VCF) files containing distinct variants (!20% coverage per direction) were annotated using an in-house annotation pipeline and were filtered for GnomAD-G allele frequency < 0. 1% and in-house database frequency (>26,000 alleles) < 0. 1%. Coding variants and variants with a SpliceAI delta score > 0. 1 were included for (re)evaluation. Coding variants located upstream of EPCAM exon 8 were excluded. Noncoding variants were filtered for variant allele frequency ! 35% (correction for mapping difficulties in mononucleotide repeats) and selected if there was a SpliceAI delta score > 0.
Acknowledgments The authors thank all study participants. The authors thank  Neeltje Arts ,  Edris Askar ,  Anouk Bertram ,  Linske de Bruijn ,  Ronny Derks ,  Michael Kwint ,  Mieke Lutje-Berenbroek ,  Luke O'Gorman ,  Bruce Poppe ,  Robin de Putter ,  Hanneke Volleberg-Gorissen ,  Marcel Nelen , and  Lisenka Vissers  for their contributions to this project. Furthermore, the authors want to thank the  Radboudumc Genome Technology Center  for infrastructural and computational support. The ERN-GENTURIS Lynch-like Working Group (alphabetical order, grouped per hospital) includes  Stéphanie Baert-Desurmont , 1  Kathleen B. M. Claes ,  2 Kim de Leeneer , 2  Lisa Elze , 3 Simone van den Heuvel, 3 Rachel S. van der Post, 4 Yvonne van Twuijver, 3 Tjakko J. van Ham, 5  Anja Wagner , 5  Mirjam M. de Jong , 6  Edward M.
Funding This project received funding from the  Sacha Swarttouw-Hijmans Foundation. The  Solve-RD  project received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 779257. This research is supported (not financially) by the European Reference Network on Genetic Tumor Risk Syndromes (ERN GENTURIS)-Project  ID  No. 739547  and the  Dutch X-omics Initiative , which is partly funded by  NWO  ( 184. 034. 019 ). ERN GENTURIS is partly co-funded by the  European Union  within the framework of the  Third Health Program ERN-2016 with Framework Partnership Agreement   2017-2021.
Subjects The EDEN mother-child study is a prospective cohort designed to assess pre-and postnatal determinants of child health and development  (Heude et al. , 2016). In brief, 2002 pregnant women (<24-weeks' gestation) aged 18-44 years were recruited between 2003 and 2006 in two university hospitals located in Nancy and Poitiers, France. Exclusion criteria were multiple pregnancies, history of diabetes, illiteracy and any plan to move out of the region within the next 3 years. Approval for the study was obtained from the relevant ethics committee (ID 0270 of 12 December 2012) and the French Data Protection Authority (CNIL, ID 902,267 of 12 December 2012). Written consents were obtained from both parents. A total of 1138 children with data on diet and activity-related behaviors at 2 years and an estimated age at AR were included in the present study.
Scores for each pattern were calculated at the individual level by summing the observed standardized frequencies of consumption of food groups, weighted according to the principal component analysis loadings. The overall measure of sampling adequacy from the Kaiser-Meyer-Olkin was 0. 71. The Barlett's test of Sphericity confirmed the correlation matrix of the 27 dietary items included in the PCA was statistically different (p < 0. 001) from an identity matrix that contained zero correlations. The dataset was therefore deemed suitable for PCA as it met the assumptions of these tests.
Finally, this model was further adjusted for parental BMI, maternal age, maternal education, and birth weight, referred as the fully-adjusted model; we checked the absence of multicollinearity, assessed with the variance inflation factor criterion (VIF < 5) ()  (Rogerson, 2001). Analyses were conducted with multiple imputations to deal with missing data for maternal BMI (N = 21) and paternal BMI (n = 74). Missingness of each variable was<6. 5% (1. 8% for maternal BMI; 6. 5% for paternal BMI; and 0% for all other variables of interest). On the assumption that data were missing at random, 10 independent datasets were generated with the fully conditional specification method (MI procedure, FCS statement, NIMPUTE option; SAS software ("SAS [computer program]," 2013)) and pooled effect estimates were then calculated (MIANALYSE procedure; SAS software ("SAS [computer program]," 2013)).
Acknowledgements We are extremely grateful to all the families who took part in this study, the midwives and psychologists who recruited and followed them, and the whole EDEN team, including research scientists, engineers, technicians and managers and especially  Josiane Sahuquillo  and  Edith Lesieux  for their commitment and their role in the success of the study. We also acknowledge the commitment of the EDEN mother-child cohort study group, whose members are:  I. Annesi-Maesano ,  J. Y. Bernard ,  M. A. Charles ,  P. Dargent-Molina ,  B. de Lauzon-Guillain ,  P. Ducimetière ,  M. de Agostini ,  B. Foliguet ,  A. Forhan ,  X. Fritel ,  A. Germa ,  V. Goua ,  R. Hankard ,  B. Heude ,  M. Kaminski ,  B. Larroquey ,  N. Lelong ,  J. Lepeule ,  G. Magnin ,  L. Marchand ,  C. Nabet ,  F Pierre ,  R.
Funding The EDEN study was supported by  Foundation  for medical research (FRM),  National Agency for Research (ANR) ,  National Institute for Research in Public health (IRESP: TGIR cohorte santé 2008 program ),  French Ministry of Health (DGS) ,  French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A) , and  Human Nutrition National Research Programs, Paris-Sud University, Nestlé ,  French National Institute for Population Health Surveillance (InVS) ,  French National Institute for Health Education (INPES) , the  European Union   FP7  programmes (FP7/2007-2013, HELIX, ESCAPE, ENRIECO, Medall projects),  Diabetes National Research Program  (through a collaboration with the  French Association of Diabetic Patients (AFD)) ,  French Agency for Environmental Health Safety (now ANSES) ,  Mutuelle Générale de l'Education Nationale  a complementary health insurance (MGEN), French national agency for food security, French-speaking association for the study of diabetes and metabolism (ALFEDIAM).
(b) Capture and sampling Snakes were located by visual encounter surveys, an approach frequently used for sampling snakes  [36]. Surveys were guided by pre-existing knowledge of snake presence or prediction of suitable habitats to achieve sufficient sample sizes. Snakes were captured by hand, placed in individual cloth bags for temporary holding during processing and sampling, and released at their capture location. Sterile handling procedures, including frequent glove changes and decontamination of gear between each snake were followed during sample collection to avoid cross-contamination. Snakes were individually identified using photo-identification or marking. For each snake captured, location and morphometric data were collected including latitude/longitude, species, sex, snout-vent-length, tail length, and weight.
(e) Quantification of infection severity Infection severity was measured by calculating the surface area of each snake covered by lesions. Using the image processing program ImageJ  [40]  and the photos of the snakes taken in the field, we measured each lesion five times and recorded the mean length and width. Each surface area of a snake's lesion was calculated, and all surface areas were added to get a total lesion surface area. Using the morphometric measurements collected in the field, we also calculated each snake's total surface area from the snout to the tail tip. We then calculated the percentage of total surface area covered by lesions. We also quantified infection severity following a previously described SFD scoring system  [41]  using a combination of lesion type, location, number, and coverage.
(f) Statistical analyses Data were analyzed using Bayesian hierarchical models and we assessed statistical support for effects of genus, species, lesion presence/severity and pathogen clade using credible intervals that do not overlap zero. We fit all models (unless otherwise noted) using the No-U-Turn Sampler (NUTS), an extension of Hamiltonian Markov chain Monte Carlo (HMCMC). All Bayesian models were created in the Stan computational framework (http://mc-stan. org/) accessed with the "brms" package  [42]. To improve convergence and avoid over-fitting, we specified weakly informative priors (a normal distribution with mean of zero and standard deviation of 10), unless otherwise noted. Models were run with a total of 4 chains for 2000 iterations each, with a burn-in period of 1000 iterations per chain resulting in 4000 posterior samples, which, given the more efficient NUTS sampler, was sufficient to achieve adequate mixing and convergence.
All 𝑅 ̂ values were less than or equal to 1. 01, indicating model convergence. All statistical analyses were performed in R software version 4. 2. 0  [43]. To examine hotspots (i. e. , relative disease risk) of the pathogen O. ophidiicola across Europe, we used the Getis-Ord Gi * analysis with Local Indicators of Spatial Association (LISA) statistics in the open-source Geographic Information System QGIS (version 3. 22. 10). A grid map of 100 km x 100 km was created across the landscape to aggregate point data and look at significant spatial clustering of neighboring features (each square) using the resultant z-scores and p-values. A heatmap was created to visualize the O. ophidiicola infection risk zone using a kernel density estimation with a quartic kernel shape and kernel radius of 100 km in QGIS.
Acknowledgements We thank  Otto Aßmann ,  Florian Bacher ,  Jacek Błachuta ,  Philomin Briot ,  Jon Buldain ,  Maxime Chèvre ,  Pierre Cheveau ,  Sylvain Dubey ,  Anna Egerer ,  Lea Endrejat ,  Karin Ernst ,  Niklas Franzen ,  Inês Freitas ,  Georg Gassner ,  Davy Guinchard ,  Krisztián Harmos ,  Marietta Hengl ,  Piotr Hubal ,  Kacper Jurczyk ,  Yurii Kornilev ,  Roman Kurek ,  Nahla Lucchini ,  Adrian Neumann ,  Daniel Renner ,  Sandra Wallner , and  Bartłomiej Zając  for assistance in the field. Many thanks also to  Kate Langwig  for statistical insights, and to  Oleksandra Klynova  and  Megan Winzeler  for laboratory assistance.
Funding This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship  under Grant No. 480040. Additional funding to GB was provided by a  Virginia Tech Cunningham fellowship , and a  CeZAP (Center for Emerging, Zoonotic , and  Arthropod-borne Pathogens)  grant as part of the  Infectious Diseases (ID)   Interdisciplinary Graduate Education Program  (IGEP). FM-F is supported by  FCT -Fundação para a Ciência e a Tecnologia, Portugal  (contract ref. DL57/2016/CP1440/CT0010 ). Sampling in Poland was funded by the statutory funds of the  Institute of Nature Conservation, Polish Academy of Science. Sampling in Hungary was supported by  Duna-Ipoly National Park ,  Duna-Dráva National Park ,  Aggtelek National Park , and  LIFE HUNVIPHAB  project.
specific genotypes because the four genotype sample sizes (i. e. , I-A, I-B, II-D/E and II-F) were too small to discern plausible data patterns. The clade variable consisted of either clade I (sites where only snakes infected with clade I were detected) or both clade I and II (sites where snakes were infected with either clade I or clade II, as there were only few locations with detections of only clade II). Sites with no clade confirmation were assigned to clade I or clade I and II when they were less than 100-km from the nearest sample of known clade. All sites outside this 100 km radius that could not be assigned a clade were dropped from the final analysis. The final clade dataset used for this analysis included 21 sites and 787 observations. Prevalence by pathogen clade comparisons are only reported for four species (N.