dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the editor and three referees for excellent guidance in the revision of the paper, and participants at the Duke workshop on animal welfare, as well as  Richard Bradley ,  Mark Budolfson ,  Paula Casal ,  Sara Constantino ,  Yann Kervinio ,  Nicolas Treich  for helpful comments. Christy Leppanen  was supported by the  Nancy Gore Hunger Professorship in Environmental Studies at the University of Tennessee. This work was assisted through participation in the DySoC/NIMBioS Investigative Workshop: Extending the Theory of Sustainability, December 5-7, 2018, at the  National Institute for Mathematical and Biological Synthesis , supported by the  National Science Foundation  through  NSF  Award # DBI-1300426 , with additional support from The  University of Tennessee, Knoxville.
For cultured bacteria concentrations, logarithmic transformations (log10) were performed on colony counts before the statistical analysis. Analysis of variance was performed using the PROC MIXED of the SAS software (SAS Inst. , Inc. , Cary, NC, USA). All variables were analysed by a statistical model including fixed (period, diet, segment (caecum, RV colon and faeces)) and random (horse) effects. The model components were the overall mean, the effect of horse, the effect of period, the effect of segment, the effect of diet, the effect of the interaction between diet and segment, and the random error. For fungi and protozoa ANOVA was performed with one diet as a point of comparison and to compare the three segments. Pair-wise t-tests were used to separate the main effect means. Values are presented as least square means with the pooled standard error of the mean (SEM).
The measurements were performed during a behavioural test commonly used to estimate the sociability of individual sheep in genetic selection  [8, 19]. First, we compared the estimate of the sheep position with the radar and standard video tracking and infrared cells. Second, using the radar data we identified new behavioural estimators that could be used for large-scale behavioural phenotyping. Third we showed that the radar system can also operate for long-distance tracking, in the field, by adjusting radar emission frequency and radiated electromagnetic power.
Data Collection We measured the displacement of the focal sheep in phases 2 and 3 of the arena test (phase 1 is the initiation phase) using three automated tracking systems: (1) infrared sensors, (2) a video camera, and (3) a millimetre-wave FMCW radar. During the measurement, an experimenter also recorded the number of high-pitched bleats by the focal sheep, a proxy of sociability  [8]. A proximity score was computed as the time spent in each virtual area weighted according to the virtual area delimited by the infrared receptor in such a way that a high score indicated high proximity to conspecifics  [20]. Crossing rate measured the number of virtual areas crossed during arena test phases 2 and 3. Sets of 2 infrared cells were placed at the height of the sheep's body and every meter along the arena test to define 7 virtual areas of 1 m.
Data Collection We measured the displacement of the focal sheep in phases 2 and 3 of the arena test (phase 1 is the initiation phase) using three automated tracking systems: (1) infrared sensors, (2) a video camera, and (3) a millimetre-wave FMCW radar. During the measurement, an experimenter also recorded the number of high-pitched bleats by the focal sheep, a proxy of sociability  [8]. A proximity score was computed as the time spent in each virtual area weighted according to the virtual area delimited by the infrared receptor in such a way that a high score indicated high proximity to conspecifics  [20]. Crossing rate measured the number of virtual areas crossed during arena test phases 2 and 3.
Video and Radar Tracking We compared the efficiency of the radar system and standard video tracking for monitoring the 2D movements of the sheep. For the video tracking, we placed a camera on one end of the arena (opposite to entrance side, Figure  1B ). The camera was elevated 2 m above ground in order to film the entire arena, producing black and white images of size 720 p × 576 pixels every 25 ms. Sheep movements were tracked in 2D. For image processing, we applied a detection algorithm using the state-of-the-art image object detector tiny-YOLO V3 (You Only Look Once) network, which is a version of the YOLO model adapted for faster processing allowing 244 images of 0. 17 mega pixels (416 × 416 pixels) per second (on a TITAN X graphics card)  [23]. This Convolutional Neural Network (CNN) was pre-trained on the PASCAL Visual Object Classes Challenge dataset  [24].
Processing of radar data included two main steps. First, we extracted the position of the animal. Next, we computed behavioural parameters to characterize the movement of the animal. We extracted the distance of the focal sheep to the radar and its direction in the horizontal plane of the scene. To mitigate the electromagnetic clutter, we estimated the mean value and standard deviation of the radar signal in absence of the sheep and we derived the signal, denoted by D, from the signal S delivered by the radar in presence of the animal, as follows: D(t, r, θ) = S(t, r, θ) -mean(r, θ) std(r, θ) Sensors 2021, 21, 8140 5 of 18 where r is the radar-to-sheep separation distance, mean is the time-averaged radar signal at the range r and angular position θ, std is the time-standard deviation of the radar signal.
Extraction of New Behavioural Parameters Form the Radar Data We used the radar estimated 2D trajectories to extract new behavioural parameters characterizing sheep movements using three approaches. We statistically identified broad classes of behaviour using Gaussian Mixture Models (GMM). First, we divided the trajectories into time windows of 1 s for each sheep and for each experimental phase. Next, we extracted movement parameters from each window: average speed, sinuosity (total displacement over distance between the first position and the last) and total displacement distance. Then, because the social stimuli (i. e. , the three conspecifics) were located at one end of the corridor, we split the speed vector into two components: along the two lateral walls of the corridor and across the two longitudinal walls.
Finally, to derive behavioural classes we performed a GMM on the extracted movement parameters for each lamb  [28]. The number of classes (i. e. , the number of Gaussians to be used) was determined by comparing models using 1 to 15 classes. We selected the model with the lowest Akaike score, which represents the model with the features best explaining the parameter under consideration  [29]. The GMM was performed using the Python package scikit-learn  [30]. We estimated the rate of time spent in each movement classes for the two phases. Table  1. Technical characteristics of the FMCW radar used for indoor tracking  [22]  and outdoor tracking  [31].
• Classification of behavioural types; To improve the interpretation of the sheep behaviour in the corridor, we reduced our four movement features (proportion of fast movements, changes in longitudinal and transversal movements, space coverage) for phases 2 and 3, using a Principal Component Analysis (PCA). The PCA was performed using the R package FactoMineR  [39]. We explored afterward whether our new automated estimators could be used to replace estimators recorded manually using a General Linear Model (GLM, using the R package stats) approach.
Radar Tracking Is Faster and More Accurate Than Video Tracking To test the efficiency of the radar tracking system, we compared the data obtained from the infrared cells, the video and the radar. This efficiency was estimated by comparing the proximity score estimated using the infrared cell, video and radar detection but also by using the crossing rate estimated by the infrared cell and the mean speed along the longitudinal axis estimated by the video and radar detection. We analysed data from 58 individuals (29 males, 29 females). Both data collected by the radar and the video enabled to capture information given by infrared cells with high fidelity. Proximity scores and crossing rates obtained from infrared cells were positively correlated with data obtained from the radar (Pearson correlation; proximity: r = 0.
Sheep Behavioural Phenotype We explored whether the new movement features extracted from the radar data could capture information from behavioural traits measured manually by the experimenter in the arena test. We focused on docility and sociability. We ran a PCA based on the eight behavioural measures extracted from the radar data in phase 2 and phase 3: proportion of fast movements (class 1) out of all movements (class 1 + class 2), longitudinal movements (wavelets Y), transversal movements (wavelets X) and space coverage (heatmaps). We retained two PCs using the Kaiser-Guttman criterion  [40]. PC1 explained 30. 65% of the variance and PC2 explained 19. 31% of the variance (Table  6 ). The eigenvalues associated to the 3 first components are: 2. 8928914, 1. 7375911, 0. 9738257. PC1 was positively associated with all behavioural variables (Figure  5A ).
1). However, the acr-8 indel was present in the levamisole susceptible parental MHco3(ISE) strain (59. 05%) and was present at only a slightly higher frequency in the resistant MHco18(UGA) strain (63. 55%). Thus, these data argue that the acr-8 indel is a poor marker of levamisole resistance. We did, however, identify a non-synonymous variant (Ser1 68Thr) in acr-8 (Figure  4B ; blue line) that was strongly correlated with resistance across multiple datasets. In the X-QTL analyses, Ser168Thr increased to a high frequency after drug selection in the F2 generation (Figure  4D ; position 31,521,884; genomewide CMH: p = 1. 6 3 10 À15 ). It was also found at a high frequency in the US field population with the highest levamisole drug resistance phenotype (Farm 7; freq Ser168Thr = 0. 64). This association was supported by global diversity data of H.
Mapped reads are colored to reflect the degree to which they have been clipped to allow correct mapping in the presence of the deletion, i. e. , reads that have not been clipped are blue, whereas reads that are moderate to highly clipped are colored red to yellow, respectively. (D) Comparison of Ser168Thr variant frequency between pre-and post-levamisole treatment (red) and time-matched untreated controls (green). Each line represents a biological replicate (n = 3). (E) Structure of the pentameric Cys-loop acetylcholine receptor of Torpedo marmorata (Protein DataBase ID: 4AQ9), one of the few species from which the receptor's structure has been resolved  (Unwin and Fujiyoshi, 2012). The Trp[Ser/Thr]Tyr motif is highly conserved among the clade V nematodes (Figure  S6 ) and the distantly related alpha subunit of T.
Finally, this was the only region in the genome where increased levels of ivermectin resistance (i. e. , EC 50 ) were associated with a loss of genetic diversity in moderately or highly resistant field populations relative to susceptible populations (Figure  5C ), consistent with a selective sweep in response to ivermectin-mediated selection. The main chromosome 5 QTL contained 25 genes and included an expansion of protein kinases (8 of 21 genes present in the genome with the InterPro identifier IPR015897), some of which had the highest statistical association with resistance; for example, HCON_00155240 (intronic position 37,336,132, p = 3. 3 3 10 À13 ; position 37,335,944, p = 1. 2 3 10 À12 ) and HCON_00155270 (intronic position 37,343,439, p = 1. 0e-10). However, these protein kinases are novel leads with no previous association with drug resistance, and a lack of functional orthologs and observed gene expansion made it difficult to further infer and test a role for these genes in ivermectin resistance.
Sampling and dose-response of parasites from US farms To complement the selection experiments derived from the genetic cross and X-QTL analyses, we sampled pools of L 3 from nine farm populations in the US. These samples were collected as part of routine anthelmintic resistance screening and phenotyped for ivermectin, levamisole and thiabendazole resistance using the DrenchRite assay described above  (Howell et al. , 2008). See Table  S2  for EC 50 data for the three drug classes. These farms have applied different management strategies and drug exposure histories, and thus the worms will have been exposed to different drug selection pressure(s). While we do not have complete detail of the management history of these populations, we selected populations for comparative analysis from a larger collection of farms based on these DrenchRite EC 50 data, from which an ivermectin susceptible, three moderately resistant and five highly resistant farm populations to ivermectin were chosen (see Figures  S2  and S3 ).
Mapping and variant calling Raw sequence data were first assessed for quality using Fastqc (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and visualised using MultiQC  (Ewels et al. , 2016). Reads were first trimmed using trimmomatic (version 0. 32)  (Bolger et al. , 2014)  before they were mapped to the H. contortus V4 reference genome  (Doyle et al. , 2020)  (available here: https://parasite. wormbase. org/ Haemonchus_contortus_prjeb506/Info/Index/) using BWA-MEM (bwa: version 0. 7. 12-r1039;  (Li, 2013 )) (parameters: -Y -C -M). Duplicate reads were marked using Picard (version 2. 5. 0; https://github. com/broadinstitute/picard), after which mapped paired reads were extracted using samtools (version 1. 3) view (-f 14). Finally, indel realignment was performed using GATK (version 3.
7. 0) IndelRealigner  (McKenna et al. , 2010). Genetic variation was determined using samtools mpileup (parameters: -F 0. 25 -d 500 -b bamlist), which was performed for each experimental group of samples separately (i. e. each drug treatment group including all replicates). To determine the predicted functional consequence of individual variants, mpileup files were processed using bcftools (version 1. 9; parameters: call -vm -Oz), after which annotation of the variant call format (VCF) file was performed using SNPeff  (Cingolani et al. , 2012) (version 4. 3r; parameters: -no-intergenic -no-downstream -no-upstream). The current annotation GFF used to build the annotation database is available at WormBase ParaSite (release 17; https://parasite. wormbase. org/index. html)  (Doyle, 2022).
Resource Between-sample measures of genetic diversity were determined using Popoolation2 (version popoolation2_1201)  (Kofler et al. , 2011) ; briefly, a synchronised file was first generated from each mpileup (popoolation2 mpileup2sync. jar -min-qual 20). Pairwise estimates of genetic differentiation were first determined by F ST calculated in 5 kb windows throughout the genome (popoolation2 fst-sliding. pl -pool-size 200 -window-size 1000 -step-size 500 -min-count 4 -min-coverage 50 -max-coverage 2%), and secondly using a Fisher's exact test on individual variants (popoolation2 fst-sliding. pl -window-size 5000 -step-size 5000 -min-count 4 -mincoverage 30 -max-coverage 2%). To determine the statistical significance of the genetic differences between groups, the binomial distribution of the F ST data was used to calculate a Z score, from which a p value could be derived.
This was defined in two ways: (i) per individual and (ii) per sampled population. The code describing this analysis is available here: https:// github. com/stephenrdoyle/hcontortus_xqtl/blob/master/03_code/hcontortus_xqtl. workbook. haplotypeblocks. md. The cross was established with 100 male and 100 female parasites. The F2 generation of the cross were the first recombinants produced between the two parental strains; $5000 F2 L3 were used to establish the generation undergoing drug treatment. The F3 generation sampled before and after drug treatment were also recombinant; in total, 400 F3 L3 (200 pre-drug treatment and 200 post-drug treatment) were sampled. From the H. contortus genetic map  (Doyle et al. , 2018) , we expect 0. 69 crossovers per chromosome on average (for the autosomes). Using the number of crossovers together with total recombinant progeny sampled, we estimate that $37,260 recombination events occurred; per individual, this equates to 93.
r-project. org/web/packages/ggmsa/vignettes/ggmsa. html) R package. Finally, we explored the putative protein structure of ACR-8, again with particular reference to the position of the candidate levamisole-resistance associated variant Ser168Thr, using the I-TASSER (Iterative Threading ASSEmbly Refinement) webserver (https://zhanglab. dcmb. med. umich. edu/I-TASSER/)  (Yang et al. , 2015)  and compared predictions against known molecular structures at the Protein DataBank.
ACKNOWLEDGMENTS This work was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  [ BB/M003949 ], the  Scottish Government's Rural and Environment Science and Analytical Services (RESAS) division , a  University of Glasgow James Herriot Scholarship , the  Wellcome Trust  ( 208390/Z/17/ Z ,  206194 , and  216614/Z/19/Z ), and  UKRI  ( MR/T020733/1 ). We would like to acknowledge members of the  BUG Consortium and Parasite Genomics group  at the  Wellcome Sanger Institute  for insightful discussions throughout this project. We also thank  Pathogen Informatics and DNA Pipelines (WSI)  for their support and expertise and the  Biosciences Division at the Moredun Research Institute  for expert care and animal assistance. For the purpose of Open Access, the authors have applied a CC BY public copyright License to any Author Accepted Manuscript version arising from this submission.
Sequence Processing and Data Analysis Bioinformatics analysis was performed using the Find Rapidly operational taxonomic unit (OTU) with Galaxy Solution (FROGS pipeline). Sequences were filtered, chimeric DNA sequences were removed using VSEARCH, and reads were clustered into operational taxonomic unit (OTUs) with Swarm. Resulting OTUs that were not present in at least 2 samples or whose abundance was <5 × 10 -5 , were then aligned to the SILVA 123 database [20,21] using basic local alignment search tool (BLAST). Relative abundances of the different families and genera were calculated and expressed as the percent of the total number of sequences. The within-sample (α) diversity was estimated using Shannon and InvSimpson indexes. The between-samples (β) diversity was estimated by computing unweighted or weighted UniFrac distances and was visualized with principal coordinate analysis (PCoA).
Clostridioides difficile Quantification Quantitative PCR (qPCR) was performed to measure Clostridioides difficile, formerly Clostridium difficile  [24] , in fecal DNA extracts as described by Bandelj et al. [25]. Oligonucleotide primers CDIFF16S-F (5'-TTGAGCGATTTACTTCGGTAAAGA-3') and CDIFF16S-R (5'-TGTACTGGCTCACCTTTGATATTCA-3'), and CDIFF16S TaqMan probe (FAM-CCACGCGTTACTCACCCGTCCG) (MGW, Eurofins Genomics, Ebersberg, Germany) specific for the C. difficile 16S rRNA gene (accession number: AB548672) were used for the qPCR assay  [26]. The qPCR mixture (final volume 25 µL) was composed of FastStart Universal Probe Master Mix 2× (12. 5 µL) (Merck KGaA, Darmstadt, Germany), primers (900 nM), probe (200 nM), and DNA (1 µL). Twenty nanograms of bovine serum albumin (BSA) were added to the mixture to reduce the effect of inhibitors.
Statistical Analysis Data were statistically analyzed using the MIXED procedure in SAS v 9. 3 (SAS Inst, Inc. , Cary, NC, USA) with a model including supplementation, day, and the interaction between supplementation and day as fixed effects. Individual horse values were included as intercepts and as a random effect to avoid baseline differences because of the longitudinal design of the trial. Days were considered as repeated measures with the horse as subject. In case of significant day × supplementation interactions, means have been separated using the pdiff option in the least squares means statement. The significance threshold was set at p < 0. 05, and trends were considered at p < 0. 10.
Acknowledgments: We would like to thank the technical staff of Lab To Field ( Agathe Martin ,  Manon Guy-Coquille ,  Ludivine Gadrey  and  Charly Poillot ) for taking care of horses during the experimental trial and contributing to the sampling. We are grateful to the technical staff of  AgroSup Dijon (Alicia Froidurot, Marie-Claire De Vos Franzin and Emmanuel Jacotot ) for their contribution to the laboratory analyses. We acknowledged our two master's degree interns ( Camille Delfortrie  and  Marion Roman ) for their involvement in the project. We thank the  INRAE GenoToul Platform (Toulouse, France ; 512 http://bioinfo. genotoul. fr)for high-throughput sequencing.
To confirm the results obtained, this experiment was designed with 3 replicates, each replicate has like 8 sample/ treatment. The insecticide doses were chosen based on literature data of LD (500 mg/kg) and considering the "no observed adverse effect level" (NOAEL) which for PER is 5 mg/kg  (WHO, 1996; IRIS, 1998). The treatment was performed daily for 90 days. After 90 days of PER administration, the rats were sacrificed by decapitation. To relieve pain and minimize harm and distress, the animals were anesthetized with an anaesthetic agent "ketamine hydrochloride (30 mg/kg bw)". Plasma was obtained after centrifugation of collected blood (350×g, 10 min, 4 • C) and then stored at -20 • C for further processing. The abdominal aorta was rapidly removed from the renal artery level to the bifurcation of the iliac arteries, as well as, the liver, and weighed.
The plasmatic and aortic lipidic parameters (i. e. , TC and LDL-C), together with plasmatic and hepatic HDL-C, were estimated enzymatically using an analytical kit (Boehringer, Ingelheim am Rhein, Germany). Data analyses were calculated as mmol/L and nmol/mg in plasma and tissue, respectively. The concentrations of apo B-100 and ox-LDL were measured in the LDL fraction using enzyme-linked immunosorbent assay (ELISA) kits following the manufacturer's protocol (Uscn Life Science Inc. , Wuhan, China) and expressed as ng/μg LDL protein.
Soluble membranes were then incubated using the specific antibody. The molecular weights of the proteins were evaluated using Chemiluminescent (Sigma, St. Louis, MO). Finally, the obtained films were scanned and proteins quantified by densitometry and normalized to GAPDH using Image J software NIH  (Hachani et al. , 2012). The mean values of the protein markers/GAPDH ratio were compared between groups.
59% and 25. 19%, respectively, Fig. 3. Impact of PER-1 and PER-2 on the expression of hepatic and aortic native LDL receptors (LDL-R) as well as aortic scavenger receptors (CD36) analyzed by Western blot (A) and densitometric quantification using Image J (B). GAPDH was used as an equal loading control. Data are presented as mean ± SD of three independent experiments (n = 3). ¥ p < 0. 05 significant differences compared to controls. Fig. 3Brelative  to the control group.
Acknowledgments The authors extend their appreciation to the  Deanship of Scientific Research at King Saud University  for funding this work through the research group RG-254. We are also grateful to the facilities and the support provided by the director and technicians of the  Microcirculation Study Laboratory  ( EA 3509 ),  Faculty of Medicine Lariboisière St-Louis, Paris VII University, Paris , and to  Hafedh Trabelsi ,  Jihen Jeffel , and  Rached Raddadi , technicians at  Anatomopathology Laboratory, Gafsa, Tunisia , for their assistance with the histological studies.