dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations details and Statistical analyses. Stochastic simulations numerically solve Eq. (  1 ) with parameters in Table  1  at daily time step and with initial 10,000 individuals over a 30 year period. Unless stated otherwise, all the parameters are kept the same in all simulations except for the contact-transmission β value that is varied via R 0 using Eq. (  5 ). 1000 simulations were used for statistical analysis. The average numbers of each compartment in Eq. (  1 ) were recorded every 30 days to generate a monthly data series. All simulations were performed using the MATLAB R2016b and statistical analyses were performed using R version 4. 0. 2. (9) p i = a i (1 -1/R 0 ) k i , (10)                q 1,s = IA 2 IA 2 + EA 2 = φ ea [1 -φ ia2 ] 1 -φ ea φ ia2 q 2,s = IA 1 IA 1 + RA 1 = 1 -φ ia1 1 -φ ia1 φ ra1 q 3,s = IA 3 IA 3 + RA 3 = 1 -φ ia3 1 -φ ia3 φ ra3.
Study Design and Location Serum, saliva, and faecal samples were collected between May 2017 and January 2018, and all samples were obtained from naturally infected horses. In this study, strategic deworming management was defined as a periodically repeated anthelmintic treatment applied to all or the majority of horses on a farm carried out without prior testing to identify parasitic infections. Selective deworming was defined as an anthelmintic treatment on individual horses according to a predetermined diagnosis, most commonly based on FEC. Each farm was asked to respond to a questionnaire with questions about the current parasite control management as well as pasture and hygiene management (Supplementary Questionnaire 1)  (85). Data regarding eggs of Anoplocephalidae and antibodies against equine tapeworms in serum and/or saliva in the same sample set have been published previously  (85).
The subsequent melting curve analysis consisted of a rise in temperature from 68 • C-95 • C in 0. 5 • C increments for 5 s with fluorescence monitoring in each step. All samples positive for S. vulgaris according to amplification and melting curves were also analysed by gel electrophoresis on 2. 0% agarose gels to control the size of the PCR products. Finally, PCR products were purified using the DNA Clean & Concentrator R -5 purification kit (Zymo Research, Freiburg, Germany) and sent to LGC Genomics (Berlin, Germany) for Sanger sequencing. The determined DNA sequences were compared to the NCBI database using Blastn  (92).
Real-Time PCR and High-Resolution-Melt PCR Analysis for Species Differentiation of Strongylus edentatus, Strongylus equinus, and Strongylus asini In order to identify samples positive for the three other members of the genus Strongylus, a single PCR suitable for amplifying partial S. edentatus, S. equinus, and S. asini ITS-2 fragments from Equus spp. derived samples was developed as a real-time PCR followed by a high-resolution-melt PCR analysis. Under the optimised PCR conditions, the primer pair that was used did not amplify S. vulgaris DNA from a plasmid containing the S. vulgaris ITS-2 region as insert. Plasmids for S. equinus and S. asini to be used as positive controls were ordered from Shanghai ShineGene Molecular Bio-Technologies, Inc. and contained the ITS-2 according to the GenBank accession number X77808.
Subsequently, an HRM analysis was performed by raising the temperature from 65-98 • C in 0. 1 • C increments with each step lasting for 10 s each and fluorescence read at each step. Melting curves of PCR products were assigned to a large strongyle species using the Precision Melt Analysis Software 1. 3 (Bio-Rad). All positive samples were validated by Sanger sequencing of the PCR product as described above.
Pyrosequencing of Isotype 1 β-Tubulin Polymorphisms A partial S. vulgaris isotype-1 β-tubulin gene sequence was obtained using primers obtained from isotype-1 β-tubulin gene fragments in the S. vulgaris genome database (WormBase ParaSite PRJEB531)  (93, 94). One pyrosequencing assay for the analysis of the BZ resistance associated amino acid exchange F167Y and a second for E198A and F200Y of the isotype-1 βtubulin gene was designed based on the S. vulgaris isotype βtubulin isotype 1 sequences using the Pyromark Assay Design Software version 2. 0 (Qiagen, Hilden). Plasmid DNAs containing the PCR amplicons with the susceptible genotypes were obtained by PCR using DNA of adult S. vulgaris specimens stored in the parasite archive of the Institute for Parasitology and Tropical Veterinary Medicine at -80 • C.
Study Population and Collection of Field Samples Faeces and serum samples were collected from 484 domestic horses from 48 yards. The study was planned to include horses that had not received anthelmintic treatment for at least 6 weeks. However, a shorter period of time elapsed between the last deworming and sampling was subsequently reported for 13 horses. For one horse, no conclusive information was available regarding the last deworming. Table  2  shows a modified version of Table  2  from Jürgenschellert et al. (85)  containing general data of the study population relevant for the data presented here. The median time between the last anthelmintic treatment and sampling was 14. 4 weeks. Access to pasture was available for 96. 7% of the horses (with at least seasonal access). Most of the farms followed a strategic deworming approach, therefore 91.
Detection of Strongylus vulgaris DNA by Real-Time PCR The PCR specific for S. vulgaris was positive for four samples (1. 3% of the horses for which nematode DNA was amplifiable; 95% CI. 5-3. 3%) (Figure  2 ). Positive samples were confirmed by sequencing purified PCR products followed by comparison with  GenBank entries. After removing low quality sequence regions at the beginning and the end of the chromatograms, Blastn searches against GenBank revealed 99-100% identity to the GenBank entries KT2506171. 1 and KT250621. 1. On the farm level, 3 out of 48 farms (6. 3%, 95% CI 2. 1-16. 8%) were positive for S. vulgaris.
Correspondence Between PCR and ELISA Data The ODR values for the rSvSXP ELISA (54) of the horses tested positive for S. vulgaris in the qPCR ranged from 16. 9 to 35. 6% (Table  4 ). Focusing on the three farms where S. vulgaris was detected by real-time PCR, the seroprevalences at farm level were 10% (95% CI 1. 79-40. 42), 12. 5% (95% CI 2. 24-47. 09), and 70% (95% CI 39. 68-89. 22) -independently of the cut-off value used. Table  4  provides details of the FEC of the horses that were tested positive for S. edentatus or S. vulgaris by real-time PCR. One horse (ID 06. 10) tested positive for both large strongyle species that were identified in this study. In the faecal analysis for these horses, strongyle type eggs were found in at least one but not always both of the methods in each case. In the majority of Strongylus spp.
MATERIALS AND METHODS A comprehensive literature search was conducted for the creation of a data set using the PUBMED (https: / / www. ncbi. nlm. nih. gov/ pubmed/ ), Agricola (https: / / agricola. nal. usda. gov/ ), CABI (https: / / www. cabi. org/ publishing -products/ animal -science -database/ ), and Google Scholar (https: / / scholar. google. com/ ) databases. The number of articles that appeared for each key word was noted for all databases. For PubMed, the following key words were used: "heat treated colostrum" (48), "heat treated colostrum and calves" (20), "heat treatment colostrum" (125), "heat treatment colostrum and calves" (46), "pasteurized colostrum" (62), "passive transfer in calves" (328), and "passive immunization and calves" (228). For Agricola, we used the follow-ing key words: "heat treated colostrum" (34), "heat treated colostrum and calves" (20), "heat treatment colostrum" (70), "heat treatment colostrum and calves" (27), "pasteurized colostrum" (34), "passive transfer in calves"  (190) , "passive immunization and calves" (357).
Inclusion and Exclusion Criteria To be included, studies needed have a control group of calves fed fresh or frozen colostrum and a treated group fed heat-treated colostrum (Figure  1 ). They also had to be peer-reviewed articles and written in English. At least 1 of the 3 following criteria needed to be present: (1) assessment of the colostrum IgG for calves of the 2 groups, (2) assessment of the serum or plasma IgG for calves of the 2 groups, or (3) assessment of the STP for calves of the 2 groups. The raw data for studies included in the meta-analysis for colostrum IgG, serum IgG, and STP are presented in Tables  1, 2 , and 3, respectively. The references of all selected publications were screened to identify potential new relevant publications. All publications were manually screened to identify duplicates.
Data Extraction The 21 selected publications were thoroughly studied by 2 authors, and data were extracted and implemented in the data set. When available, the information retained included (1) heating temperature applied to colostrum during the heating process, (2) duration of heat treatment applied to colostrum, (3) the sample sizes for the 2 groups of each trial, (4) the method for IgG analysis [ELISA, radial immunodiffusion (RID), turbidimetric immunoassay (TIA)], (5) the mean colostrum IgG concentration (and its variance, SD or SEM), (  6 ) the blood sampling time after colostrum feeding, (7) the mean serum IgG and STP concentrations  (and Malik et al. :  META-ANALYSIS ON HEAT-TREATED COLOSTRUM their variances, SD or SEM), (8) the techniques carried out to analyze the colostrum IgG, serum IgG (RID, ELISA, TIA), and STP (Brix refractometer), and (9) sample size.
All the responses of interest were standardized to the same unit. For missing data related to IgG or STP means or variance, emails were sent to corresponding authors of the respective studies. For those who had not replied after 7 d, a second reminder email was sent to collect missing information. For the studies that were found to have multiple treated and one control group comparison, the data from these experiments were extracted, and calculations were carried out using RevMan 5. 4 (The Cochrane Centre, The Cochrane Collaboration). Only 2 studies were found to have more than 1 treated group and 1 control;  Hesami et al. (2021)  had 1 control and 3 heat-treated colostrum groups, and  Kryzer et al. (2015)  had 1 control and 2 heat-treated colostrum groups.
Serum IgG For serum IgG concentration, 16 studies and 27 trials met the inclusion criteria (Figure  4 ), and raw data are provided in Table  2. In the HT subgroup, there were 4 studies in 5 trials and 398 and 346 observations for heat-treated and raw or frozen colostrum, respectively. In the LT subgroup, 12 studies and 22 trials were included, and the numbers of observations were 947 and 932 for heat-treated and raw or frozen colostrum, respectively. The heterogeneity of the serum IgG concentration in the HT colostrum subgroup was high (I 2 = 81%, τ 2 = 13. 42, Q statistic: χ 2 = 21. 0). The heterogeneity for serum IgG concentration in the LT subgroup was low (I 2 = 38%, τ 2 = 2. 20, Q statistic: χ 2 = 33. 9). The overall (HT and LT) heterogeneity was high (I 2 = 73%, τ 2 = 9. 00, Q statistic: χ 2 = 96.
Phenotypes. Animals were misspelled weighed at the beginning (when the animals reached approximately 35 kg) and at the end of the test period (when they reached 110 kg). Average daily gain (ADG, kg/day) was calculated as the body weight gained during the test period divided by the duration of the period. Rib back fat thickness (BFT, mm) was measured on carcass with the Capteur Gras Maigre method  19. At the slaughterhouse, carcasses were chilled in a cooling room at 4 °C for 24 h. Ultimate pH of the semimembranosus dorsi muscle (PHS, pH units) was measured using a Xerolyt electrode (Mettler-Toledo, Australia) and a Sydel pH meter (Sydel, France) at 24 h post-mortem. Further details regarding complete data collection and experimental design can be found in Tusell et al. 10. Genotypes. Animals were genotyped using the Illumina Porcine SNP60 Bead Chip (Illumina, Inc.
, San Diego). Single nucleotide polymorphisms (SNPs) with a call rate lower than 0. 90 and a minor allele frequency lower than 0. 05 were removed. For the remaining SNPs. Missing genotypes were imputed using a naïve method that sampled genotypes with probability weights of the allele frequencies at each locus. The missing genotypes were then replaced with these sampled genotypes. Animals that presented Mendelian inconsistencies with their parents were discarded. After quality control, 46,816 SNPs were included in the analyses. Due to separate data edition, the number of animals with records and number of SNPs retained for the analyses slightly differed for each trait. Summary statistics of the three analyzed phenotypes in the two populations are presented in Table  1. SNP annotation. Chromosome information of SNPs (i.
e. the map file containing SNP-ID and RS-Numbers) was downloaded from the Animal Genome Database (https:// www. anima lgeno me. org/ repos itory/ pig/). The SNPs that did not have RS number or a non-unique RS number were discarded leading to 39,727 SNPs available for the analysis. The physical positions of the SNPs obtained through Ensembl database (https:// useast. ensem bl. org/ Sus_ scrofa/ Tools/ VEP) for pig (Sus Scrofa, Assembly: Sscrofa11. 1, accession date: December 2018) allowed to locate each SNP into a genic or an intergenic region. SNPs were classified in three categories. Genic region category (Genic, 19,672 SNPs) encompassed the SNPs annotated into the following categories: introns (15,824 SNPs), synonymous (321 SNPs), upstream the gene Table  1. Summary statistics of the purebred (PB) and crossbred (CB) phenotype data.
Univariate models were initially implemented using BGLR software (https:// github. com/ gdlc/ BGLR-R)  21  , and the multiple-trait models were later run with GIBBS2f90 software (http:// nce. ads. uga. edu/ wiki/ doku. php? id= start)  22. For each proposed model, single chains of 200,000 iterations were run by discarding the first 20,000 iterations. The burn-in was determined by visual inspection of the chains and by the procedures of Raftery and Lewis 23 and Geweke  24. Samples of the parameters of interest were saved every 10 rounds.
In chickens, Abdollahi-Arpanahi et al. 16  concluded as well that both genic and non-genic regions contributed to phenotypic variation to body weight, ultrasound measurement of breast muscle and hen house egg production. Regulatory functions such as promoter, enhancer or transcription factor binding sites are located the upstream and downstream regions of genes, and Gusev et al. 26  reported that this part of the genome is responsible for genetic variation in 11 human diseases. However, also in humans, Yang et al. 18  indicated that genic regions described more genetic variation than intergenic regions for height and body mass index. Given the precision of the pig genome annotation and quantity of data available in our study, the hypothesis of different shares of variance for these categories could not be further tested in our dataset.
In this study, as a limitation, we used the commercial SNP panel for pigs that was the only one available to us. So, we supposed that the SNPs from genic and non-genic regions from a 60 K commercial SNP chip explain the same amount of genetic variance. However, with availability of the whole genome sequence data it might be a clearer result would be achievable. Estimated heritabilities were within the range of the previously reported values in Piétrain breed and other pig breeds  27, 28. Heritabilities for the three analyzed traits were higher in CB than in PB populations, however the difference was never significant. This suggests that the gamete effect of the PB population could not be the same when mated within the PB than mated to gamete of another population to produce the crossbreds.
Dominance ratio remained of equal magnitude across populations and represented about 7 to 14% of phenotypic variance depending on the trait, suggesting that, for example, assortative matings could slightly enhance both purebred and crossbred performances. In a previous study using the same dataset, different contributions of ratios of dominance deviation variance with respect to the total phenotypic variance were reported across a wide variety of traits related to growth and feed efficiency, carcass composition, meat quality, behavior, boar taint and puberty (ratios ranging from 6 to 18% across 22 traits, Tusell et al. 10. Our results in some cases were slightly different from the previous study which could be due to the inclusion of the inbreeding effect in the model or related to using the different software's algorithm  6, 29.
Obtained predictive abilities for ADG and PHS were slightly higher than those obtained in a previous study that used same datasets and slightly different models  30. This could be because in the previous study, CV folds were split according to the sires to keep all records of the offspring of a sire assigned to the same fold. This precludes an overestimation of the model predictive ability due to strong family relationships between training and testing sets. However, the data split used in the present study does not preclude making fair comparisons between models because both models were evaluated using exactly the same CV split. Our results are in accordance with Do et al. 25. In that study, predictive accuracy and bias of feed intake, daily feed intake, ADG, BFT in pigs obtained with a model with specific genomic regions did not significantly differ from those models that used randomized SNP groups  25.
83 ) and none of the highest posterior density interval at 95% included values below 0. 50. This indicates that genetic interaction between the Piétrain and Large White breeds for those traits is small in this dataset, which suggests that the genetic progress attained in the purebreds can mostly be completely transferred to the crossbreds as a genetic correlated response. This could be because PB and CB animals of this study were raised in the same environmental conditions, which is not the practice in pig breeding schemes where purebred lines are raised in highly sanitized environments and crossbred animals in commercial farms. Using the same dataset in a previous study  33  , genetic correlation between purebred and crossbred was estimated to be of 0. 50 for ADG and 0. 94 for PHS. Dugué et al.
Samples and Data Collection Farm visits were performed every 14 d, by the herd veterinarian and an animal health technician. During the visits, animals within 35 d (±7) of subsequent predicted calving date were enrolled in the study. On the day of enrollment and every 14 d after, a blood sample was collected from each cow enrolled in the study for the subsequent evaluation of serum NEFA concentration. Blood samples were collected from the coccygeal vessels in tubes without anticoagulant  (Vacutainer, Becton Dickinson and Company). Within 4 h of sampling, tubes were centrifuged at 1,750 × g for 10 min at 20°C, and the serum was harvested and stored at -20°C. All samples were labeled with a unique identification number that included the date of sampling. Once cows calved, these samples were reorganized to determine the real number of days before calving at which they were sampled.
All cows enrolled in the study were followed until 50 DIM (Figure  1 ). Data concerning postpartum diseases were collected from computerized record systems. Hy-Nicola et al. : NONESTERIFIED FATTY ACIDS AND DISEASE perketonemia, AD, and postpartum reproductive tract diseases (RTD) were always diagnosed by veterinarians during the postpartum period. Hyperketonemia was diagnosed during the first herd health visit after calving, between 1 and 14 DIM. On that occasion a blood sample was obtained from the coccygeal vessels and immediately analyzed with a Precision Xtra portable device (Abbott Diabetes Care). The BHBA concentration in the first 14 DIM was used to define cases of HK, with a validated threshold of ≥1. 4 mmol/L  (Iwersen et al. , 2009). A diagnosis of AD was made when a highpitched sound was heard in the paralumbar fossa or in the last intercostal spaces on the left or the right side and was confirmed during surgical resolution.
, 2014). New subclinical IMI were also computed for nulliparous cows by including animals that had ≥200,000 cell/mL at their first test after calving (NIMIh;  Fauteux et al. , 2014). Cows that had their last test before dry-off with SCC ≥200,000 cells/mL and SCC <200,000 cells/mL at their first test after calving were considered to be cured (CIMI;  Fauteux et al. , 2014). Data concerning CUL during the first 50 DIM were also collected.
Statistical Analysis Statistical analyses were performed using a freeware statistical software package R v. 3. 4. 1 (R Foundation for Statistical Computing). Descriptive statistics were computed. Each disease was included only once for each cow (lactational incidence). Lactational incidence of diseases were reported as frequency (%), and NEFA serum concentrations were reported as median (minimum, maximum). To quantify the relationships between prepartum serum NEFA concentration and subsequent disease development, generalized linear mixed models with logit link ("glmer" using the "bobyqa" optimizer) were computed using the lme4 package  (Wang et al. , 2022). Step 1. The associations between NEFA concentrations (continuous variable) and diseases or culling were investigated. An individual multivariable model was computed for each disease (RP, HK, AD, MET, CM, PVD, CE, IMI, NIMIc, NIMIh, CIMI), and CUL, which were considered as the dependent variable of each model.
Another dependent variable (dummy variable) was created for having at least one disease in the first 30 DIM (AL1D) within the following: RP, HK, AD, CM, and MET. Serum NEFA concentration was included as a numerical independent variable, and parity (first lactation vs. subsequent lactation), season (January-March; April-June; July-September; October-December), and week of sampling (wk 1: 1-7 d, wk 2: 8-14 d before calving) were forced in the models as possible confounding factors. Herd was included in the models as a random variable to account for herd clustering. All models were offered the 2-way interaction term NEFA concentration × week of sampling. Significance was declared at a P-value <0. 05. Step 2. To identify the optimal thresholds for prepartum serum NEFA concentration, a receiver operating characteristic (ROC) analysis was performed using the "cutpointr" function of the "cutpointr" package  (Thiele and Hirschfeld, 2021).
Selection of the optimal threshold was based on the highest Youden's J statistic index (to maximize the sum of sensitivity and specificity). The performance of the threshold was determined based on its area under the ROC curve (AUC), where it ranged from 0. 50 to 1. 00; AUC of 0. 50 was considered non-informative, 0. 5 to 0. 7 was considered poor, 0. 7 to 0. 8 was considered acceptable, 0. 8 to 0. 9 was considered excellent, and greater than 0. 9 was considered outstanding  (Mandrekar, 2010; Hosmer et al. , 2013). Sensitivity, specificity, and AUC confidence interval were estimated with the bootstrap procedure (1,000 interactions) using the "boot_ci" function of the "cutpointr" package  (Thiele and Hirschfeld, 2021). Step 3. To identify the optimal threshold for prepartum NEFA concentration accounting for the possible effect of parity, season, week of sampling, and herd clustering, mixed logistic regression models were computed with serum NEFA concentration as a categorical variable (high or low).