dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptome analyses Quantitative transcriptome analyses were primarily performed using kallisto v0. 43. 1  [95]  , which quantifies transcripts by pseudo-aligning raw sequencing reads to a transcriptome reference. The transcriptome reference was derived from the reference FASTA and annotation GFF3 data using gffread (https://github. com/gpertea/gffread). Raw sequencing reads from each of the life-stages were pseudo-aligned to the reference using kallisto quant (parameters: --bias --bootstrap-samples 100 --fusion), before the differential expression between pairwise combinations of life-stages was determined using sleuth  [96]. For each transcript, differential expression between life-stages was tested using a Wald test, for which a q-value less than 0. 05 was deemed significant. To explore coordinated gene expression throughout the life-stages, we used clust v1.
8. 4  [97]  to automatically define and group transcripts by expression level profiles. Transcripts per million (TPM) counts per life-stage were generated from the kallisto analysis described above and used as input to clust. To explore putative regulatory sequences that may be shared by co-expressed genes, we searched in each group of clustered transcripts for conserved motifs in the 100 bp region immediately upstream of the start codon for each transcript using DREME  [98]  , after which motif-containing sequences were identified using FIMO  [99]  (MEME v5. 0. 4). To perform gene set analyses, subsets of H. contortus genes together with associated C. elegans orthologs were obtained using WormBase ParaSite BioMart  [15]. Functional enrichment analyses for both species were performed using g:Profiler (version e97_eg44_p13_d22abce), with a g:SCS multiple testing correction method applying a significance threshold of 0.
05  [100]. OrthoFinder v2. 2. 7  [101]  was used to determine orthologous relationships between protein sequences inferred from the H. contortus V1 and V4, the McMaster H. contortus , as well as H. placei and C. elegans protein-coding gene annotations. Trans -splicing: Characterisation of splice leader usage and distribution We characterised the number and distribution of genes for which mapped RNAseq transcripts contained the canonical H. contortus SL1 (GGTTTAATTACCCAAGTTTGAG) and SL2 (GGTTTTAACCCAGTATCTCAAG) sequences. For each splice leader sequence, cutadapt v1. 13  [102]  was used to detect (allowing up to 10% divergence in sequence match [--error-rate = 0. 1] and minimum match length of 10 bp [--overlap=10]) and subsequently trim the leader sequences from raw RNAseq reads from each of the life stages, after which all trimmed reads were collated and mapped to the genome using hisat2 v2.
1. 0 [103]. The relative coverage of the sequence between the trimmed splice leader site and (i) the start codon (using a window 200 bp upstream and 50 bp downstream of the start codon) and (ii) internal exons (excluding the 1st exon), was determined using bedtools coverage. Weblogo  [104]  was used to visualise sequence conservation in the genomic sequence surrounding the splice leader site, and in the splice leader sequences themselves. The spliced leader sequence splice sites for all life stages were combined into a single dataset for plotting and quantitative analyses. Cis -splicing: Differential isoform usage using Leafcutter To analyse the extent of differential spliced isoform usage between life-stages, we used leafcutter  [105]  (see http://davidaknowles. github. io/leafcutter/ for installation and scripts described below) to quantify the proportion of reads that span introns and then subsequently quantify differential intron usage across samples.
Population genetic analyses To understand how genetic variation was distributed throughout the chromosomes, we collated sequencing data from our recent global analysis  [25]  together with in-house archival sequencing data to form the most complete sample cohort of H. contortus whole-genome data to date. Reanalysis of the global cohort data was necessary, as the previous analysis was performed using the V3 assembly. In total, 338 samples from 46 populations in 17 countries were analysed; the samples together with geolocation, mitochondrial phylogeny describing genetic connectivity between samples, and ENA references to individual sequencing datasets are available to explore using Microreact  [106]  and can be visualised here: https://microreact. org/project/hcontortus_global_diversity. Raw sequencing data were mapped to the V4 reference using bwa mem  [84]  (parameters: -Y and -M options were used to use soft clipping for supplementary alignments, and to mark shorter hits as secondary, respectively), followed by identification of duplicate reads using Picard (v2.
5. 0; https://github. com/broadinstitute/picard ). Samples for which multiple sequencing lanes were generated were mapped independently and subsequently combined into a single dataset using samtools merge. Mapping statistics were generated using samtools (v1. 3) flagstat and bamtools (v 2. 3. 0) stats , and compared using MultiQC v1. 3  [107]. Genetic variation within and between samples was determined using GATK (v 4. 0. 3. 0). GVCFs were first created using HaplotypeCaller ; to improve efficiency, this process was performed in parallel for each chromosome for each sample separately, and subsequently combined by chromosome using CombineGVCFs. Variants were determined per chromosome using GenotypeGVCFs , after which the final raw variants were combined using vcftools concat. A hard filter was applied using GATK VariantFiltration to mark variants using the following criteria: QD < 2.
0, FS > 60. 0, MQ < 40. 0, MQRankSum < -12. 5, and ReadPosRankSum < -8. 0. Nucleotide diversity and F-stats within and between populations was determined using vcftools v0. 1. 14 [108] -- window-pi and --weir-fst-pop , respectively, using a window size of 100 kbp. To analyse mitochondrial DNA diversity, bcftools view was used to select single nucleotide variants with a read depth > 10, homozygous variant, and with minor allele frequency > 0. 01. To generate the phylogeny, variants were used to generate a consensus mitochondrial genome per sample, which were aligned using mafft v7. 205  [109]  , and from which maximum likelihood phylogenies were generated using iqtree v1. 6. 5  [110]  (parameters: -alrt 1000 -bb 1000 -nt 1 ) using automated ModelFinding  [111]. Principal component analysis (PCA) of the mitochondrial DNA variation was also performed using the poppr  [112]  package in R.
contortus and H. placei were identified using Orthofinder as described above, from which amino acid sequence alignments per orthologous pair were obtained. Codon alignments were generated using PAL2NAL v14  [115]. codeml was run using m1a and m2a models to examine nearly neutral and positive selection scenarios, from which the log-likelihood (lnL) of each test were compared using a likelihood ratio test. The D test statistic was calculated, where D = 2*(lnL m2a -lnL m1a ), and compared against a chi-square distribution using df = 2 and a significance threshold of 0. 05. The resulting gene set was filtered to only include genes with d S < 2, d S > 0. 02, and d N / d S >1. To calculate d N / d S from the global H. contortus dataset, genome-wide variation was filtered using vcftools (parameter: --maf 0.
https://github. com/PacificBiosciences/IsoSeq3 ), which consisted of four steps: (i) the generation of circular consensus sequencing (CCS) reads from raw subreads ( ccs subreads. bam subreads. ccs. bam --noPolish --minPasses 1 ), (ii) the removal of library primers, and reorientation of reads ( lima subreads. ccs. bam primers. fa subreads. demux. bam --isoseq --no-pbi ), (iii) clustering of transcripts, removal of concatemers and trimming of polyA tails ( isoseq3 cluster subreads. demux. primer_5p--primer_3p. bam subreads. isoseq3_unpolished. bam ), and finally, (iv) polishing of the transcripts to generate a high quality set of isoforms ( isoseq3 polish merged_subreads. isoseq3_unpolished. bam merged_subreads. bam merged_subreads. isoseq3_polished. bam ).
Acknowledgements We would like to acknowledge members of the  BUG  Consortium, and  Parasite Genomics  group at the  Wellcome Sanger Institute , for insightful discussions over the course of this project. We also thank  Pathogen Informatics  and  DNA Pipelines (WSI)  for their support and expertise, and the  Biosciences Division, Moredun Research Institute , for expert care and assistance with animals.
Funding Work described here was supported by  Wellcome 's core funding of the  Wellcome Sanger Institute  (grant  WT206194 ), a  Wellcome Trust Project Grant  to JSG (grant  067811 ), and the  Biotechnology and Biological Sciences Research Council (BBSRC)  grants ( BB/M003949/1 ,  BB/P024610/1  and  BB/K020048/1 ). JDN was supported by a  Graduate Mobility Award  from  McGill University.
Availability of data and materials The raw sequence data is available under the ENA accession PRJEB506, with reference to specific sequencing libraries described throughout the text, and/or in Table  S3  of Laing et al. 2013. RNAseq data is available from the ENA study ID PRJEB1360. The genome assembly has Code and workflows used to analyse data are available at https://github. com/stephenrdoyle/hcontortus_genome.
( https://github. com/PacificBiosciences/IsoSeq3 ), which consisted of four steps: (i) the generation of circular consensus sequencing (CCS) reads from raw subreads ( ccs subreads. bam subreads. ccs. bam --noPolish --minPasses 1 ), (ii) the removal of library primers, and reorientation of reads ( lima subreads. ccs. bam primers. fa subreads. demux. bam --isoseq --no-pbi ), (iii) clustering of transcripts, removal of concatemers and trimming of polyA tails ( isoseq3 cluster subreads. demux. primer_5p--primer_3p. bam subreads. isoseq3_unpolished. bam ), and finally, (iv) polishing of the transcripts to generate a high quality set of isoforms ( isoseq3 polish merged_subreads. isoseq3_unpolished. bam merged_subreads. bam merged_subreads. isoseq3_polished. bam ).
PRJNA205202. WBPS11. genomic. fa3. New Zealand[31]: ENA accession GCA_007637855. 2: GCA_007637855. 2_ASM763785v2_genomic. fna 4. C. elegans WBcel235: caenorhabditis_elegans. PRJNA13758. WBPS9. genomic_softmasked. fa.
1and GFF 2 annotations from Braker and PASA as ab initio and transcript evidence, respectively, and protein sequences from C. elegans and H. contortus V1 genome assemblies (obtained from Wormbase ParaSite 9 caenorhabditis_elegans. PRJNA13758. WBPS9. protein. fa. gz and haemonchus_contortus. PRJEB506. WBPS9. protein. fa. gz, respectively), as well as 110 curated genes; each protein dataset was mapped to the genome using exonerate v2. 2. 0 (parameters: --model protein2genome --percent 50 --showtargetgff ; [91] ) followed by GFF extraction using Exonerate_to_evm_gff3. pl from EvidenceModeller. The weighting was applied as follows: PROTEIN (hc_v1 * curated models) = 2; PROTEIN (Ce) = 1;.
One of the most debated topics in pig production is the need to study, understand and change the production system in order to improve nutrient efficiency, becoming more environmentally friendly. The nitrogen excretion has highly deleterious effects on the environment, and it is necessary to develop tools that help to reduce the excretion of this compound without compromising productivity. Therefore, two models were generated to estimate the efficiency of weight gain in relation to excreted nitrogen in postweaning piglets. Data for testing these models were obtained from previous master and PhD studies carried out at the Federal University of Rio Grande do Sul, Animal Science Laboratory using piglets in the post-weaning phase with results for performance and digestibility. The database that was constructed was composed of raw data from 10 studies carried out between 2000 and 2016, on a total of 726 piglets weaned at ages between 17 and 28 days, and to which 62 different treatments were applied.
, 2011) , lower heat production  (Barea et al. , 2010)  and better use of nutrients. Since growth rate and feed intake are related to the productive efficiency, more efficient animals will consequently be less polluting. The analysis of six genetic lines of pigs showed that from 1969 to 2004 there was a 19% improvement in N retention, resulting in 20% less excretion  (Knap and Bishop, 2000). Similarly,  Shirali et al. (2012)  and  Morel and Wood (2005)  showed that N excretion was lower in lean pigs or in pigs with a greater tendency for meat deposition. Meta-analysis is a useful tool for grouping data and quantifying results from studies where the focus of the research on a particular topic was only secondary, or even nonexistent  (Sauvant et al. , 2008). Therefore, it is possible to make new inferences on a distinct topic based on a database of information from previous studies.
Material and methods A database was constructed in an electronic spreadsheet using data from previous studies selected on the basis of the following criteria. To be included, studies must have: (i) been carried out at the Federal University of Rio Grande do Sul (UFRGS) Animal Science Laboratory, basis of published theses and dissertations; (ii) used piglets in the post-weaning phase (up to 30 days after weaning); (iii) presented performance results; and (iv) presented digestibility results. The first criterion was established to allow a database of raw data collected under comparable conditions. After the selection step, detailed information about the studies was included in the database, including bibliographic aspects (trial and year), experimental characteristics (ingredients and nutritional composition of diets, sex, age of animals and duration of the trial) and results (body weight, weight gain, feed intake, feed conversion, energy, fat, protein and dry matter digestibility coefficients, CP, lysine, energy, calcium and phosphorus intake, urinary nitrogen, fecal nitrogen (FN) and retained nitrogen (RN)).
In each column a descriptive characteristic or result was inserted, and in each line the characteristics and results of an experimental unit (cage with one pig in seven studies or mean of two pigs in three studies) were described, totaling 144 columns and 491 rows. Some variables were calculated from tabulated data to increase the total number of variables, such as lysine intake, feed conversion and the relationship between weight gain and excreted nitrogen, which was considered as the main response variable in this study. The methodologies described in the literature were used for the elaboration of the database and for the coding of the data  (Sauvant et al. , 2005; Lovatto et al. , 2007). Codes were used as moderating variables in the analysis to account for variability among the compiled trials (e.
g. , effect of study). The database was built from the responses observed in 10 studies carried out between 2000 and 2016. The database was composed of data for a total of 726 animals (218 females and 508 barrows), and the sum of the treatments of the individual studies results in 62. All piglets came from commercial genetic lines and were weaned between 17 and 28 days of age. The piglets' average ± SD body weight at the beginning of the pre-starter phases (1 to 14 days post-weaning) was 5. 05 ± 2. 90 kg and 13. 38 ± 4. 50 kg at the end of the starter phase (15 to 28 days post-weaning). They were individually housed in metabolic cages and kept in thermal comfort during the experimental periods. The main ingredients used in the diets, their average compositions and other descriptive statistics related to the feeds used are presented in Table  1.
Discussion The equations generated in this study make it possible to extend the exploration of data obtained in digestibility studies. Besides analyzing the efficiency in the use of a nutrient or ingredient, it is also possible to analyze the results in order to increase environmental sustainability. All predictive variables were obtained only from digestibility studies and, consequently, the number of variables is limited in the database. For that reason, some variables were calculated and all variables with r<0. 07 were considered as predictors. This is a feature that allows the models to be applied in the future, since the equations are simple to interpret and include variables that are often used in digestibility studies, such as 'Fecal Nitrogen', 'Urinary Nitrogen' or 'Retained Nitrogen'.
Another limitation of database was that some variables, as ingredients, were not equally distributed among experiments, which reduced their potential as predictors. Cardinal, Vieira, Warpechowski, Ziegelmann, Montagne, Andretta and Ribeiro Previous research demonstrated that nutrition and feeding practices have a direct influence on the environmental impact generated by pig production chain  (Cherubini et al. , 2015; McAuliffe et al. , 2016). The effect of increased N intake on performance variables and nitrogen excretion is well documented in the literature. However, the relationships among these variables have not previously been investigated to understand the main factors that affect this relationship. From an economic and environmental point of view, it is desirable that BWG is maximized while N excretion is minimized  (Pomar et al.
, 2017). In general, the means of feed intake and weight gain observed in the database constructed in this study were within the expected range for the evaluated range of piglet weights. The good performance observed was likely related to the high nutrient digestibility coefficients of the feed used, which reached mean values >80%. This was due to the quality of the diets, which were formulated with ingredients of high digestibility and associated with good animal health status. As expected, model I had a higher coefficient of determination than model II (R 2 =0. 9013 v. 0. 8271), due to the better adjustment of predicted values by information on N retention. This result is coherent with the importance of the self-explanatory RN variable within model I. Although model II presents a lower R 2 , this model may also be an alternative to be used in studies that measure urinary N and fecal N.
The relation between predicted and observed BWG/EN is not totally linear (Figure  1 ). The models fit better for the BWG/ EN ranges where there are more observations in the database for the models to be calibrated, and therefore it is possible to observe that higher values of BWG/EN make the equations less precise. It may be associated with different factors, such the ingredients' digestibility, that decrease along the feeding phases, or the age of the pigs, because of the decrease in nitrogen retention efficiency. Also, equation 2 has high variability than equation 1 with higher BWG/EN values. Since the main difference between the equations 1 and 2 is FN and nitrogen retained, the greater variability indicates that the nitrogen retained can be more accurate that the first one. Future studies should be focused to better understand these patterns to improve the capacity of the models to predict high values of BWG/EN.
The ability of weaned piglets to preferentially deposit protein over fat demonstrates the efficiency of growth performance and N excretion in these animals. The average nitrogen retention rate (retention/intake) observed in the studies that composed our database was 75%, showing that the efficiency of the use of this nutrient was high, and it is in accordance with values of nitrogen retention found by  Straarup et al. (2006). In contrast, when pigs were evaluated in the grower-finishing phase, when they were fed on cereal-based diets, only 35% of the ingested nitrogen was retained  (Dourmad et al. , 1999 and 2009). Shirali et al. (2012)  revealed that nitrogen excretion is changing throughout the entire growing period. Greatest nitrogen retention rate (32%; nitrogen retention/NI), least nitrogen excretion and greatest retention were found during early growth from 60 to 90 kg.
17 1. 32 17. 50 24. 00 Total lysin (%) 491 1. 44 0. 11 1. 13 1. 71 Ca 6 (%) 491 0. 73 0. 07 0. 60 0. 93 P 7 (%) 491 0. 48 0. 02 0. 43 0. 55 Fat (%) 391 6. 51 2. 94 1. 17 11. 15 n = Number of rows of database with the ingredient; SD = standard deviation; Min = minimum; Max = maximum. 1 Nuklospray E50 ® (Sloten do Brasil Ltda, Santos, São Paulo, Brasil). 2 Betaglucano (Betamune, Biorigin -São Paulo, Brasil). 3 Promosoy 90 ® (Central Soya Inc. , Chicago, Ill). 4 Metabolizable energy. 5 Crude protein. 6 Calcium. 7 Phosphorus.
Other deleterious behaviours To the best of our knowledge, only  Ursinus et al. (2014)  examined the relationship between litter size during lactation and the level of tail chewing and biting later on. Animals were housed in single-sex pens and only pens of females were observed. They showed that females expressing a relatively high level of tail chewing and biting were more likely to originate from larger litters than females with a relatively low level of tail chewing and biting. However, this result was dependent on the rearing environment, since it occurred only when pigs were housed in an environment enriched with jute sacks during lactation and after weaning. Therefore, non-aggressive biting behaviour may be increased in piglets originating from large litters in specific environments, but more data are needed to examine this relationship.
The method of standardization was based on the use of abamectin as internal standard, which was added to the samples just before the extraction procedure. The internal standard working solution concentration was 10 ng ml -1. All of the solutions were stored in a freezer at -20 °C. Lc/eSi + -MS/MS instrumentation and settings. We applied the method proposed by Ortiz et al. 35  for the quantitative determination of ivermectin. Briefly, ivermectin was analysed on an Agilent 1100 HPLC system, which was coupled to an Ion Trap MS analyser (Esquire 6000, Bruker Daltonics, Bremen, Germany) equipped with an electrospray ionization source (ESI). The system was controlled with the Agilent ChemStation (version A. 06. 01, Agilent Technologies) and Bruker Daltonics Esquire control (version 6. 08, Bruker Daltonics) software packages.
The data were processed with Data Analysis software (version 3. 2, Bruker Daltonics). A C 18 Kinetex analytical column (75 mm × 3. 0 mm, 3. 0 μm, Phenomenex, Torrence, CA, USA) was used and it was operated at 40 °C. The mobile phase solvent A was a solution of 0. 1% (v/v) formic acid in water, and solvent B was 0. 1% (v/v) formic acid in acetonitrile. The column flow rate was 0. 25 ml min -1 , the injection volume was 1 μl and the gradient elution timetable was as follows: 0-10 min, 50% A-50% B; 10-15 min, 100% B; 15-20 min 50% A-50% B. The MS/MS conditions were initially optimized by injecting standards at concentration of 1 μg/ml of ivermectin and internal standard solution. The optimal MS/MS sensitivity was obtained using electrospray in positive ionization (ESI + ) and for quantitative purposes, the instrument was operated in multiple reaction monitoring (MRM) mode, scanning from 50 to 2000 m/z.
For each fraction, the kinetic parameters (k s and k e ) were calculated in one run by applying a piecewise linear regression model after log transformation of the data from both the uptake and elimination phases, simultaneously. The breakpoint was 12 days coinciding with the time when the beetles have stopped eating dung containing ivermectin. Least squares regression fitting was used to obtain confidence intervals for slope parameters (95% CI) and to quantify the goodness-of-fit of the regression (R 2 ). To discard any possible negative effects caused by ivermectin, the beetle body weight, lipid content and survival under laboratory conditions were compared between treated and control individuals by means of t tests. All data were analysed using the software GraphPad Prism (v8, San Diego, USA).