dataesr/scientific-paragraphs-categorization huggingface dataset:
For this, coverage of each scaffold was estimated by mapping reads back to the scaffolds using bwa mem v0. 7. 15  (Li, 2013)  and coverage calculated with BBTools v73. 82  (Bushnell, 2014). Additionally, for annotation, scaffolds were blasted using ncbi-blast v2. 7. 1+ blastn with options -outfmt '6 qseqid staxids bitscore evalue std sscinames sskingdoms stitle' -max_target_seqs 10 -max_hsps 1 -evalue 1e-25 against the nt database v 2016-06. Scaffolds without hits to metazoans were filtered out from the assemblies using a custom script (see https://github. com/AsexGenomeEvol/HD_Oppiella: contamination_filtration. py). Next, scaffolds were sorted by decreasing length, scaffold headers renamed and scaffolds shorter than 500 bp removed, resulting in the final assemblies (v03). The assemblies were checked for quality and completeness by calculating standard genome statistics and by checking presence, fragmentation and duplication of arthropod core genes using CEGMA v2.
The Trinity assembled mRNA-seq sequences (described below) were used as transcript evidence. The resulting gene models from iteration 1 were then used to retrain Augustus as well as SNAP v2013. 11. 29  (Korf, 2004 ) and a second iteration was performed. Following, predicted protein coding genes were functionally annotated using Blast2GO v5. 5. 1  (Conesa et al. , 2005)  with default parameters against the NCBI non-redundant arthropods protein database and the Drosophila melanogaster (drosoph) database v 2018-10. The MAKER configuration files are available at https://github. com/AsexGenomeEvol/HD_Oppiella. For the MAKER annotation, RNAseq reads were quality trimmed with Trimmomatic v0. 36 with options adapters. fa:2:30:12:1:true LEADING:3 TRAILING:3 MAXINFO:40:0. 4 MINLEN:80. For generating genome-guided transcriptome assemblies, trimmed reads were first mapped against the genomes using STAR v2.
Paired-end sequencing with a read length of 100 bp was performed on a HiSeq2000 platform at the GTF (Genomics Technology Facility Lausanne, Switzerland). Data processing for haplotype divergence inference was done using the high 20 performance computing cluster of the Gesellschaft für Wissenschaftliche Datenverarbeitung Goettingen, Germany (GWDG). RNAseq reads were adapter-and quality-trimmed using TrimGalore v0. 6. 5 with default options (Phred quality threshold 20; adapter auto-detection;  Martin, 2011; Krueger, 2012). Contaminating sequences were removed using kraken2 (--paired; --db minikraken2_v2;  Wood & Salzberg, 2014)  followed by mapping paired-end reads of each individual simultaneously against their respective reference genome, scaffolds flagged as contaminating sequences assembled together with the respective mite reference genomes (identified as described above), the human reference genome GRCh38.
p12 (GenBank assembly accession:   S7  ). Only oribatid-mite-exclusive reads, i. e. , read pairs that mapped best and unambiguously against the mite reference genomes were kept for further processing and mapped to the reference genomes using STAR v2. 7. 3a with standard parameters. All commands are available under https://github. com/AsexGenomeEvol/HD_Oppiella: mapping. U.
Haplotype phasing For phasing, variants were filtered as described above (except only sites completely missing any genotype information, i. e. all individuals missing a genotype, were excluded). Phasing of haplotypes was done per individual using phASER v1. 1. 1 with minimum mapping quality of reads set to 30, minimum base quality set to 20 and bottom cut-off to quantile for alignment score set to 0 in paired-end mode for each individual, separately, utilising heterozygous variants with minimum coverage of 10 for each individual  (Castel et al. , 2016). Haplotypes with < 10 unique reads mapping were removed from the output data. Output data from phASER were converted into haplotype sequences for the corresponding positions in the reference genome using a custom script (available under https://github.
Haplotypes were labeled according to their divergence from the reference genome (haplotype A being closer to the reference genome, haplotype B being more diverged). For this, positions including degenerate bases were deleted using trimAl  (Capella-Gutiérrez et al. , 2009)  and the pairwise distance of each haplotype to the reference genome was calculated using snp-dist (Seeman, 2020). Only thus modified phaseable regions >= 100bp were included for downstream analyses. All commands and two scripts used are available under https://github. com/AsexGenomeEvol/HD_Oppiella: phasing. U, refgenomedist. U, extract. pl and convert_fasta. py. To identify putative paralogs in the phased regions, these regions were tested for double coverage compared to the genomic baseline. Reads used to assemble genomes were mapped back to single copy genes and duplicated genes identified by BUSCO (see above), and additionally to the phased regions and to scaffolds from which the phased regions were derived (but that were masked in the phased regions), using bowtie2 v2.
Topology testing To enable testing whether alignments of phaseable regions are better explained by a topology separating the haplotypes (as expected under asexuality) as compared to a topology separating populations (expected under sexual reproduction) or vice versa , a constrained tree search was done. Two constrained Maximum Likelihood (ML) trees, one complying with a fixed haplotype-separating topology (asex-tree), the other with a fixed population-separating topology (sex-tree) were calculated for each phased region using iqtree v1. 6. 10 with 1000 bootstrap replicates and model-testing included  (Nguyen et al. , 2015). For O. nova, we restricted the analysis to the seven individuals representing the two divergent lineages (see Results; Figure  2a  ). The O. nova asex-trees were constrained to separate the haplotypes A & B at its base, lineages I & II per haplotype and finally the populations per lineage and haplotype (for the constraining tree, see https://github.
com/AsexGenomeEvol/HD_Oppiella: Onasex. tre). The sex-trees were constrained to separate the lineages I & II at its base and the populations per lineage (no haplotype separation; for the constraining tree, see https://github. com/AsexGenomeEvol/HD_Oppiella: Onsex. tre). For O. subpectinata the asex-trees were constrained to separate the haplotypes A & B at its base and the populations per haplotype (to provide an unrooted tree including a trichotomy the haplotypes B of the most divergent population SA were separated from haplotypes B of the other two populations; for the constraining tree, see https://github. com/AsexGenomeEvol/HD_Oppiella: Osasex. tre). The sex-trees were constrained to separate exclusively the populations (no haplotype separation; for the constraining tree, see https://github.
com/AsexGenomeEvol/HD_Oppiella: Ossex. tre). Next, the distance between the two resulting trees and an unrestricted best fitting ML tree was estimated according to  Kuhner & Felsenstein, 1994  with the dist. topo function implemented in the R package ape  (Paradis & Schliep, 2019). To enable the comparison between distances of different phaseable regions, the topological distances of the best fitting ML tree to the haplotype-separating tree and to the population-separating tree were combined as Δ dist. HD-tree -dist. sex-tree value for each phaseable region. To test if the haplotype-separating tree was a significantly better fit to the alignment than the population-separating tree, trees were compared using RELL approximation with 10,000 RELL replicates and an approximately unbiased (AU) test with iqtree  (Kishino et al.
Acknowledgements We like to thank  Juergen Brandt  for adjusting figures to the needs of people with colour vision deficiency and  Christoph Bleidorn  for advice regarding topology testing. This study was supported by core funding of S. S. , by  DFG Emmy Noether  fellowship  BA 5800/3-1  to  J. B. ,  Swiss SNF  grant  CRSII3_160723  to T. S. , M. R. R. and N. G. , and  Swiss SNF  grant  PP00P3_170627  to T. S.
al in prep https://github. com/YoannAnselmetti/Pop-Con). The fold excesses of shared heterozygous SNPs over HWE were estimated by running Pop-Con with the option -fold for a range of parameters and comparing the specific genotype profiles for an indication of excess (shown as part of expected SFS). All commands used are available under https://github. com/AsexGenomeEvol/HD_Oppiella: heterozygosity. U, Fis. U and SFS. U.
Input variables for SMM We focused our analysis on the growing season starting in calendar year 2000 (and potentially finishing in calendar year 2001). SMM was forced by the short-wave downwelling radiation (rsds in MJ m -2 d -1 ) and the daily mean temperature (tas, in • C) from the AgMERRA weather dataset  (Ruane et al. , 2015). SMM also needs the beginning and end of the maize growing season, and we used the planting day (t p ) and the timing of maturity (t m ), respectively, both being provided in the output of each GGCM. Despite the fact that all GGCMs are forced by the same growing season in harmnon, some GGCMs allow flexibility in regards to t p and t m prescribed as input  (Müller et al. , 2017) , as suggested by the GGCMI protocol: "crop variety parameters (e. g. , required B. Ringeval et al.
Code and data availability. The scripts that were the basis of this study have been made available at https://doi. org/10. 15454/9EIJWU (Ringeval, 2020). They encompass three Python scripts and shell scripts and the directories to use these Python scripts. The first Python script (called SIM. Py) encompasses SMM equations and performs SMM simulations for different combinations of parameters and for each GGCM growing season. The second Python script (ReadMulti-param_WriteOPTIM. py) performs the SMM calibration against each GGCM output. The third Python script (ReadPlotOPTIM. py) performs the main plots. GGCM inputs and outputs required to force or calibrate SMM are given in  Müller et al. (2019). Supplement. The supplement related to this article is available online at: https://doi. org/10.
The DeWorm3 project is a large multi-country community cluster randomized trial designed to determine the feasibility of interrupting the transmission of STH using community-wide delivery of MDA with anthelmintics over multiple rounds  [10]. Here, we present baseline prevalence data and discuss the prevailing epidemiological patterns in the three study sites prior to the initiation of expanded community-wide MDA delivered through the DeWorm3 trial. Using baseline prevalence and intensity data from each study community, we describe the distribution of infections within each age group, estimate the number of heavy intensity infections as a proxy for morbidity, and calculate the estimated force of infection, or rate of new STH infections, within each geography.
Baseline census In order to enumerate the entire population eligible for MDA within DeWorm3, a baseline census was conducted in October-December 2017 in India, November-December 2017 in Malawi and in January-February 2018 in Benin. Heads of household or other adult decision-makers consented on behalf of their households to participate in the baseline census, which consisted of a combination of a questionnaire delivered to the household head plus observation of housing conditions by data collectors. Data were recorded electronically using Android phones with SurveyCTO software (Dobility, Inc; Cambridge, MA, USA and Ahmedabad, India). The baseline census enumerated all members of each household, including those considered migratory (having spent the majority of nights elsewhere in the past six months), and collected demographic data and information on occupation, assets, and access to WASH.
The geospatial location of each dwelling was also collected and mapped using ArcGIS (ESRI, Redlands, CA, USA) and overlaid on satellite imagery using SASPlanet software (https://www. openhub. net/p/sasplanet) and QGIS (https://www. qgis. org). Each household was categorized based on surrounding population density. Population density per square kilometer was estimated by constructing a one km 2 buffer around each household in ArcGIS and summing to total number of individuals that fell within each household's buffer. For households near study area boundaries, areas of the buffer that fell outside of the study area were removed and the population density was calculated by the number of individuals falling within the buffer divided by the area of the buffer. Data on the construction material of the dwelling were recorded based on the data collector's observations.
Statistical analysis Initial data management was conducted and descriptive statistics generated using Stata 14. 0 (Stata Corp, College Station, Texas). The estimation of key epidemiological parameters was performed using R 3. 6. 1  [14]. Study site, household and individual characteristics were summarized using numbers and percentages or medians and interquartile ranges (IQR), as appropriate. Baseline prevalence and intensity of infection were weighted to the age distribution and different cluster sizes in the baseline census, to account for the stratified sampling approach. Cluster-specific ageweighted prevalences were calculated based on age distribution in each cluster, excluding infants (who were not eligible to be sampled and therefore had no prevalence data) and those with missing ages.
Details of the likelihood, model assumptions and method of estimation can be found in S1 Text. Most morbidity due to STH occurs in individuals with heavy intensity infections  [2]. To estimate the burden of morbidity within the DeWorm3 study sites at baseline, we derived the relationship of the mean egg count from individual egg count data (for both hookworm and A. lumbricoides), assuming a negative binomial distribution of the egg counts (the aggregation parameter was also fitted). We then combined the fitted relationship with demographic data from the baseline census of each of the sites to estimate the total egg count output produced by the population, stratified into 5 year age categories. The total egg count in an age category serves as a proxy for the total worm burden (the mean worm burden of each individual times the number of individuals in the age category).
Finally, the census data obtained in DeWorm3 relied heavily on self-report, which may have led to reporting bias. The DeWorm3 trial is designed to assess the feasibility of interrupting the transmission of STH across a range of epidemiologic transmission settings. The study has demonstrated significant social, demographic, environmental and geographic differences between the three countries participating in this multi-country community cluster randomized trial. These differences provide important baseline data used to determine the age specific intensity of transmission (the FOI) at each site. In addition, each site provides important comparative data to both assess the impact of the MDA in the trial sites and to further our understanding of STH epidemiology in settings where elimination of STH may be feasible.
Acknowledgments We acknowledge the dedication and effort of the all members of the communities participating in the DeWorm3 study as well as the  DeWorm3 teams in Benin, India and Malawi. We also acknowledge the members of the DeWorm3 Data Safety and Monitoring Committee;  Richard Hayes ,  Harriet Mpairwe ,  Purushothaman Jambulingam ,  Jimmy Whitworth  and the  DeWorm3 Scientific Advisory Group ;  Dean Jamison ,  Zulfiqar Bhutta  and  Nilanthi de Silva. We would also like to acknowledge  Antonio Montressor ,  Wilfred Batcho ,  Achille Massougbodji ,  Dorothee Gazard ,  Manfred Accrombessi ,  Rajeshkumar Rajendiran ,  Chinnaduraipandi Paulsamy ,  Sobana Devavaram ,  Samuel Paul Gideon Martin ,  Noel Joyce Mary Hillari ,  Naveen Kumar Sekar ,  Dhanalakshmi Manoharan ,  Selvi Laxmanan ,  Dasthagir Basha ,  Alvin Blessings Chisambi ,  Zac Kamwendo ,  Alfred Mbwinja ,  Lyson Samikwa ,  Leanne Doran ,  Victoria Carter ,  Steven Williams ,  Nils Pilotte ,  Katherine Thomas ,  Barbra Richardson ,  Alyson Shumays ,  Simon Brooker ,  Kelli Jehle  and  Christy Hanson.
Sequence processing Primer sequences were removed with cutadapt version 1. 8  (38). Fastq files were processed with DADA2 version 1. 6. 0 (39), using the following parameters: truncLen=c(190, 200), maxN=0, maxEE=c(1,1), truncQ=5 for gyrB reads. ITS1 reads were processed with the same parameters, except that reads were not trimmed to a fixed length and that trunQ was set to 2. Chimeric sequences were identified and removed with the removeBimeraDenovo function of DADA2. Taxonomic affiliations of amplicon sequence variants (ASV) were performed with a naive Bayesian classifier  (40)  implemented in DADA2. ASVs derived from gyrB reads were classified with an in-house gyrB database (train_set_gyrB_v4. fa. gz) available upon request. ASVs derived from ITS1 reads were classified with the UNITE v7.
Microbial community analyses Analyses of diversity were conducted with the R package Phyloseq version 1. 26. 0  (42). Since the primer set gyrB_aF64/gyrB_aR553 can sometimes co-amplify parE, a paralog of gyrB, the gyrB taxonomic training data also contained parE sequences. Hence, ASV affiliated to parE or unclassified at the phylum-level were removed. Sequences were aligned with DECIPHER 2. 14. 0 (43) and neighbor-joining phylogenetic trees were constructed with Phangorn 2. 5. 5  (44). Identification of sequence contaminant was assessed with decontam version 1. 4  (45)  using the "prevalence" method at a threshold of 0. 1. Fungal ASVs unassigned at the phylum level were removed. For the ITS1 dataset, we created a hybrid-gene phylogenetic tree (i. e. ghost tree)  (46)  based the 18S rRNA gene sequence database (Silva 132) and ITS database (UNITE v8) in order to perform phylogenetic analyses.
Data were normalized based on sequencing depth. For soil analyses, data were rarefied at 30 000 and 25 000 reads per sample for gyrB and ITS1, respectively. For plant (root and stem) analyses, data were rarefied at 1 000 reads per sample for gyrB and ITS1 (Fig. S1 ), a validated choice to keep an optimal number of reliable samples. Faith's phylogenetic diversity was calculated on gyrB and ITS1 datasets with the R package picante version 1. 8  (47). Differences in alpha-diversity estimators were assessed with Analysis of Variance. Differences were considered as significant at a p-value < 0. 05. Changes in microbial community composition were assessed on log 10 +1 transformed values with Bray-Curtis (BC) index. Principal coordinate analysis (PCoA) was used for ordination of BC index. Homogeneity of group dispersions was assessed with betadisper function of vegan 2.
Transmission analyses A prevalence matrix of common ASVs between seeds, soils, roots and stems was constructed on non-rarefied data to have the most exhaustive presence-absence analysis of all taxa. Owing to the microbial variability between individual seeds, an ASV was recorded as present if detected in at least one of the 3 seed sample repetitions. Visualization of bacterial and fungal ASVs shared between biological habitats or specific to one was assessed with the R package UpSetR 1. 4. 0  (52). The datasets supporting the conclusions of this article are available in the European Nucleotide Archive under the accession number PRJEB41004.
Acknowledgements: We thank  Muriel Bahut  from the  ANAN platform (SFR QuaSav ) for amplicon sequencing and  Laurent Philippot  for discussion. This project named RhizoSeed was funded by  INRAE  ( Metaomics and Ecosystems Metaprogram, Plant Health division ) and  Région Bretagne. We want to apologize to authors whose relevant work was not included in this article due to space constraints.
Network Based Models Intermolecular Amino Acid Network The toxin interfaces were modelled by a network of the amino acids that belong to different chains and had at least one pair of atoms within a 5 A ˚distance, so-called hotspots. [24] The amino acid network (AAN) of the whole pentamer was built first and the intermolecular amino acid network was generated after by selecting only intermolecular interactions. The networks were created using the Python Biograph module, available at https://github. com/rodogi/biographs, or directly with the script available at https://github. com/lorpac/ network_vmd. Briefly this was done using the atomic coordinates of the toxin structure (PDB: 1EEI) freely available from the RCSB website (https://www. rcsb. org/) and computing intermolecular atomic distances between amino acids that belong to different chains (nodes).
Network Generation and Representation All networks were generated by manipulating the amino acid network of the toxin using the Python package NetworkX. [46] wo-dimensional network representations were produced using the Gephi software  [48]  and three-dimensional network representations were produced using the Python scripts available at https:// github. com/lorpac/network_vmd and visualised with the VMD software. [49] For simplicity, the toxin pentamer having a central symmetry, only links between nodes involved in the interfaces between chain E and D and between chain E and F are represented.
1 g of DNA fragmented with an E220 focused ultrasonicator (Covaris), with an average insert size of 400 bp, using a HyperPrep kit (KAPA Biosystems). The final library (10 pM) was sequenced to 20 million reads using MiSeq 600-cycle v3 reagents (Illumina). Previously reported short-read sequencing data were used for the type strain NRRL B-4156  (5). Long-read sequencing libraries for the type strain and the clinical isolate were prepared using a HyperPrep kit and 1D sequencing kit and were sequenced on the MinION platform with an SQK-LSK108 SpotON flow cell (Oxford Nanopore Technologies, UK). Base calling and trimming using Albacore v2. 3. 1 (https://github. com/dvera/ albacore) and Porechop v0. 2. 3 (https://github. com/rrwick/Porechop) resulted in 130,472 and 559,846 reads for the type strain and clinical isolate, respectively.
We assembled long-read genomes using Canu v1. 7 (  6 ) and then corrected them with short-read sequences using Pilon v1. 22  (7)  and Bowtie 2 v2. 3. 4. 3  (8). All software programs were run using default settings. We obtained 6 contigs, with an N 50 of 2. 26 Mbp, for the clinical isolate and 1 continuous 6,612,442-bp contig for the type strain. The type strain assembly is likely the entire chromosome. Optical mapping of the clinical isolate DNA on a Bionano Genomics Saphyr system, following direct labeling and staining protocol revision A (Bionano Genomics), resulted in a continuous 7. 6-Mbp linear chromosome with overlapping ends, with 70ϫ effective coverage. Three fragmented contigs from the corrected long-read assembly were aligned to the optical map, yielding a 6,744,575-bp hybrid scaffold, using Bionano Access v1.
In Nantes, low-cost sensors were installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM 10 concentrations. This work aims to use the large amount of observations provided by the sensors for air quality mapping at the urban scale in order to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations. A preprocessing is applied to the raw sensor dataset to remove the unreliable observations based on an outlier detection technique and to compensate for the measurement drift by adjusting the estimated daily variation of the underlying background PM 10 concentrations with the reference stations. Then, data fusion is performed by combining the preprocessed fixed and mobile lowcost sensor observations and the 2016 annual average of the ADMS-Urban outputs.