dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Individual MFDs are expressed in mf per mL. The following MFD D0 were considered as MFD thresholds in the analysis: (1) 8000 mf per mL, corresponding to a 10-fold increase in the risk of a marked adverse event  8  and used in therapeutic guidance;  28  (2) 10 000 mf per mL, which is the threshold proposed by experts at a meeting organised by the Mectizan Donation Program in 1995 and included in the 2003 classification of L loa-related adverse events;  18  (3) 20 000 mf per mL, which is the threshold used by the LoaScope point-of-care device, which measures MFD within 3 min of blood sampling; 10 (4) 30 000 mf per mL, corresponding to a 1000-fold increase in the risk of a marked adverse event,  8  the threshold above which ivermectin is contraindicated in therapeutic guidance;  28  (5) 50 000 mf per mL, corresponding to the lowest MFD D0 among the eight cases of L loa-related serious adverse events for which the MFD D0 had been measured.
Considering that clinical manifestations of concern usually occur at day 3-4, and thus that the investigation of potential serious adverse events cannot start earlier, the first timepoint was set at day 3-4 after treatment (MFD D3-4 ). We used the arithmetic mean of the values for individuals who had their MFD measured on both day 3 and day 4. Given the significant decrease in MFD between day 3-4 and day 7, a second timepoint was set around day 7 (from day 5 to 10 [MFD D5-10 ]). MFD D5-10 could be the value of a single MFD measured between day 5 and day 10 or the average of several measurements made between day 5 and day 10, if available. Analyses on data collected at days 5-10 were performed because, in remote locations, serious adverse events might be investigated during this interval of time.
We used the first MFD measurement available between 14 and 180 days (MFD D14-180 ) of the 167 individuals already included in the first part of the study (ie, who had MFD D3-4 or MFD D5-10 available), but also of 271 other individuals who had MFD D0 and MFD D14-180 measured but no MFD data between day 3 and 10. 16  As the Mectizan Donation Program classification also mentions MFD values measured 1 month and 6 months after treatment, additional analyses were done using data collected after day 10. All analyses were performed using R, version 4. 1. 0, and Stata, version 17. 0.
Although PPV and NPV data depend on the prevalence of each class of MFD, the overdispersion parameter of the distribution of MFD was not found to depend on the endemicity level,  29  and similar reasoning can be conducted using the sensitivity and specificity data provided herein. For programmatic purposes, thresholds with the greatest accuracy can be used to obtain reliable estimates of the number of attributable cases overall and monitor the incidence of these effects. Analysis of the MFD data collected between 14 and 180 days after treatment confirms the generally good prediction accuracy and adds that thresholds might retain utility even for MFD measurements obtained much later during follow-up of programmes. This study has some limitations. The study population was included as part of trials.
However, the fact that some were community trials reduces the risk of selection bias. Most importantly, there were few serious neurological adverse events, which are to be attributed, with a risk of classification bias. Nevertheless, it is probable that there is a continuum between the different types of serious adverse events, limiting this issue. Finally, all data were collected in Cameroon, where most of the L loa-related post-ivermectin adverse events have been reported (with the Democratic Republic of the Congo), and certainly permits generalisation to central Africa. The predictive performance was not improved by taking into account the age and sex of the participants (data not shown). Nevertheless, we cannot exclude that some other factors could help improve the classification performance.
-TGGCATTGATCTGGTTCATC-3 Rs16944 (-511) 5 -GTTTAGGAATCTTCCCACTT-3 5 -AGAAGCTTCCACCAATACTC-3 Rs1143627 (-31) 5 -AGCACCTAGTTGTAAGGAAG-3 5 -CCTCAGCAACACTCCTAT-3 IL-1RN VNTR  [15]  5 -TCCTGGTCTGCAGGTAA-3   3 ) including all the variables of the study was generated using the "crosstable" function, and the differences' significance was analyzed through a t-test and χ2 test for age and other categorical variables, respectively. "Chi2loop" R-package version 1. 1. 2 was used to perform iteration chi-square testing between the categorical variables from the cohort dataset, available on https://github. com/ cdesterke/chi2loop (accessed on 21 September 2022). Data must be converted to a data frame before running the following functions. Then, the "cltest" function was applied to perform chi-square iterations between the overall categorical variables, each represented by a character column.
The output was filtered and visualized with an NLP plot (NLP: negative log10 of chi-square test p-values) through the "nlpplot" function, where only significant associations were represented. The output was also visualized with variable community detection with a Louvain classification algorithm network through the "chinet" function to detect the variables' clusters. To determine the eventual association type between polymorphisms and the clinical and biological features of H. pylori infection, a Mosaic plot  [16]  was performed through the "mosaic" function available in the "vcd" package. At last, and based on the iterative chi-square test, the linked variables to the polymorphisms' cluster were selected, and a multiple correspondence analysis (MCA)  [17]  was built with FactoMineR R-package version 2.
Material and methods In total 1,686 MAC isolates (1,271 M. avium, 415 M. intracellulare) and 1,014 MAB isolates from 12 laboratories collected between 2010 and 2022 were included. Identification of species and inducible macrolide resistance (MAB) was performed according to routine procedures by each participating laboratory, which was by line probe assays (GenoType Mycobacterium CM and NTM-DR, Hain Lifescience, Germany) in the majority of cases. The Sensititre TM SLOMYCOI and RAPMYCOI assays were performed according to the instructions for use  (17)  which are in turn based on CLSI protocol M24-A2  (11). Further details of culture, species determination and BMD are described in the Supplementary file 1. Data are presented as aggregated distributions based on all available MIC data from all laboratories.
These truncations will unfortunately remain with the implementation of new versions of BMD plates, currently recommended for research use only (SLOMYCO2 and RAPMYCO2). On the other hand, clofazimine is included in both updated commercial plates, where future studies for defining ECOFFs for this drug are warranted (2). On-scale QC data are essential to assuring the reproducibility of MICs and the validity of AST methods used in clinical routine. There has been low essential and categorical agreement for MAB of 47-76% for clarithromycin and amikacin  (19, 20)  and the slow uptake of standardized QC testing for mycobacteria was recently discussed  (21). Considering MAB and other rapidly growing mycobacteria (RGM), current guidelines recommend QC M. peregrinum ATCC 700686. However, recommended QC ranges are broad, usually spanning over four MIC concentrations and without a lower defined range for several drugs including the essential drugs clarithromycin and amikacin  (11).
a b s t r a c t The relational database SOWELL was created to better understand the behaviour and individual responses of gestating sows facing different short-term events induced: a competitive situation for feed, hot and cold thermal conditions, a sound event, an enrichment (straw, ropes and bags available) and an impoverishment (no straw, no objects) of the pen. The data were collected on 102 crossbred sows equipped with activity sensors, group-housed in video-recorded pens (16-18 sows per pen), with access to automatons. Feeding and drinking behaviours were extracted from the electronic feeders and drinkers' recordings. Social behaviours, physical activities and locations in the pen were recorded thanks to manual video analysis labelling at the individual scale. Accelerometer fixed on the sows' ears also recorded individual physical activities.
The physical activity was also determined at a group scale by automatic video analysis using deep learning techniques. BWs, back fat thickness, and body condition (cleanliness, body damages) were recorded weekly during the whole gestation. Last gestation room data regarding environmental conditions (temperature, humidity, noise level) were recorded using automatic sensors. The database can fulfil different research purposes, namely sows' nutrition for example to better calculate the energy requirements regarding environmental factors, or also on welfare or health during gestation by providing indicators. Ó 2023 The Author(s). Published by Elsevier B. V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.
Value of the data This data set presents a huge diversity of data collected on gestating sows at an individual scale: feeding and drinking behaviours, social behaviours, physical activities, reproductive performances, health and welfare status, and environmental conditions. High number of individual data, mostly time series, can be used by researchers for machine learning approaches or data mining tasks requiring large datasets. Different short-term events can be useful for scientists to better understand sow's behaviour and individual variability of response to these different types of events. Finally, access to the database is made public to contribute to future research in gestating sows' behaviour, health, nutrition or welfare fields.
Data description The dataset is composed of 18 tables structured as a relational database, using SQL standard (Table  1 ). These tables are organised into three groups (Fig. 1 ): -The tables named Farm, Sow, Pen, Group, Exp_sow, Event are linked to the sow's and experiment characteristics (in red, Fig. 1 ) -The tables named Water_measure, Feed_measure, Sensor, Sound_measure, Air_temperature_measure, Activity_measure con-tain data collected by automatons or sensors at different time scales (in green, Fig. 1 ) -The tables named Farrowing, Health_sow, Performances_sow, Sow_temperature, Activity_video_measure, Video_analysis contain other collected data (in blue, Fig. 1 ). More precisely, Farm, Sow, Pen, Group, Exp_sow, Event and Sensor tables gather all information regarding, respectively: farms (country, number of sows per band and number of sow per feeder), sows (birth date and farm), gestation rooms (farm), groups (beginning and end date, gestation room), sows during an experiment (sow, artificial insemination date and boar identification dose, body condition at insemination, parity), events (date and kind), and sensors or automatons used (type, room of gestation).
Data acquisition and pretreatment Feeding and drinking behaviours were automatically recorded using automatons (electronic feeders and drinkers). Ambient tem- 2 Foreign keys are used to link data between tables of the database. perature and humidity were recorded thanks to sensors fixed in the gestation room at 1. 8 m high from the ground (Lascar Electronics, United Kingdom, precision ± 0. 45 °C), and the noise level by a sensor near one of the drinkers (Velleman Group, Belgium). Manual video analysis was carried out by trained observers to monitor individual social behaviour, localisation, occupation, and physical activity on defined short periods of 5-36 h. Sows were also equipped with an ear tag accelerometer (RF-Track, Rennes, France) continuously recording their physical activities. Physical activities were also automatically analysed at a group scale from video recordings by a convolutional neural network algorithm (Dilepix, Rennes, France;  Durand et al.
, 2022). Data extracted from automatons and sensors were preprocessed to filter the outliers and aggregated daily sensors' files to feed a relational database. Row data from the accelerometers were not included in the database when the cumulated duration per hour was not equal to 60 minutes, and in case of duplicated rows. All the identification codes of automatons or sensors were linked to the experiment number of each sow. Every Monday morning during the gestation, sows were led to a nearby room in order to measure their BW (Schippers, The Netherlands, precision ± 0. 5 kg), and their back fat thickness using an ultrasound portable device (Imago, ECM, France). Body conditions, including skin lesions and potential health problems, were also simultaneously recorded based on the Welfare Quality Ò Assessment  (Dalmau et al.
Acknowledgements The authors would like to thank  C. Guérin ,  P. Touanel ,  L. Derouet ,  A. Julienne ,  C. Orsini ,  J. Abarnou ,  M. Misrach ,  C. Deroiné ,  M. Couasnon , and all the staff from the experimental farm helping during the experimental period, for collecting the data and analysing the video.
Dog Population The study population was 88 adult (>1 year), purebred Labradors, born in one breeding kennel (Centre d'Etude de Sélection et d'Elevage pour Chiens Guides d'Aveugles et Autres Handicapés, CESECAH; Lezoux, France). At the time of examination, they were either active, retired or breeding assistance dogs and they are housed in volunteer families. Age, sex and neuter status were recorded and weights at birth were extracted from the kennel database.
Statistical Analysis The information collected was transferred into an Excel spreadsheet (Microsoft Corporation, Redmond, Washington, DC, USA). Then, statistical analysis and graphical representations were performed using R software, version 4. 2. 1  [33]  and the following packages: corrplot  [34] , emmeans  [35] , FSA  [36] , ggplot2  [37] , lme4  [38] , performance  [39]  and rcompanion  [40]. Statistical significance was defined as p value < 0. 05 and statistical uncertainty was assessed by calculating the 95% confidence intervals (95% CI). After a description of the population studied, the relationship between BCS and SFT for each point of measurement was tested with the Kruskal-Wallis rank sum test followed by a Dunn test with Benjamini-Hochberg correction. Then, the interplay between birth weight and SFT values was investigated by fitting a linear mixed regression model with SFT as response variable.
Statistical Analysis The information collected was transferred into an Excel spreadsheet (Microsoft Corporation, Redmond, Washington, DC, USA). Then, statistical analysis and graphical representations were performed using R software, version 4. 2. 1  [33]  and the following packages: corrplot  [34] , emmeans  [35] , FSA  [36] , ggplot2  [37] , lme4  [38] , performance  [39]  and rcompanion  [40]. Statistical significance was defined as p value < 0. 05 and statistical uncertainty was assessed by calculating the 95% confidence intervals (95% CI). After a description of the population studied (Table  S1 ), the relationship between BCS and SFT for each point of measurement was tested with the Kruskal-Wallis rank sum test followed by a Dunn test with Benjamini-Hochberg correction. Then, the interplay between birth weight and SFT values was investigated by fitting a linear mixed regression model with SFT as response variable.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/vetsci10030208/s1; Table  S1 : Dataset. Institutional Review Board Statement: Dogs were not subjected to any invasive act requiring prior ethics approval. Informed Consent Statement: All the owners were informed prior to bringing their pet for evaluation that their participation was voluntary. It was explained to them that by bringing their dog to the examination session, owners were given their informed consent for the scientific use of the data.
Conceptualization: S. C. , A. G. and A. M. (Amélie Mugnier); data curation: A. G. and A. M. (Amélie Mugnier); formal analysis: S. C. , A. G. and A. M. (Amélie Mugnier); funding acquisition: S. C. , A. G. and A. M. (Amélie Mugnier); investigation: S. C. , A. G. , A. M. (Amélie Mugnier) and F. C. ; methodology: A. G. , A. M. (Amélie Mugnier) and S. C. ; project administration: A. M. (Amélie Mugnier); resources: A. M. (Anthony Morin), M. D. , S. C. and A. G. ; supervision: S. C. and A. G. ; visualization: F. C. and A. M. (Amélie Mugnier); writing-original draft: S. C. , A. G. and A. M. (Amélie Mugnier); writing-review and editing: all authors. All authors have read and agreed to the published version of the manuscript. Funding: This study was partially funded by a research grant from Société Centrale Canine (Aubervilliers, France) and AGRIA (Paris, France).
Author Contributions: Conceptualization: S. C. , A. G. and A. M. (Amélie Mugnier); data curation: A. G. and A. M. (Amélie Mugnier); formal analysis: S. C. , A. G. and A. M. (Amélie Mugnier); funding acquisition: S. C. , A. G. and A. M. (Amélie Mugnier); investigation: S. C. , A. G. , A. M. (Amélie Mugnier) and F. C. ; methodology: A. G. , A. M. (Amélie Mugnier) and S. C. ; project administration: A. M. (Amélie Mugnier); resources: A. M. (Anthony Morin), M. D. , S. C. and A. G. ; supervision: S. C. and A. G. ; visualization: F. C. and A. M. (Amélie Mugnier); writing-original draft: S. C. , A. G. and A. M. (Amélie Mugnier); writing-review and editing: all authors. All authors have read and agreed to the published version of the manuscript. Funding: This study was partially funded by a research grant from Société Centrale Canine (Aubervilliers, France) and AGRIA (Paris, France).
Acknowledgements The authors thank  Raphaël Comte ,  Dorian Grivault ,  Mélinda Oiry  and  Camille Robert  for their technical assistance in the laboratory, with a special thanks to  Colette Mustière  and  Françoise Thomas  for developing the method for 5-HT analysis. The authors gratefully acknowledge the personnel from the 'Porganic' experimental organic station, led by  Stéphane Ferchaud , for taking excellent care of the animals and for their help in field measures, with a special thanks to  Tony Terrasson  for managing video material and recordings. This study was part of the  POWER  project (Power to strengthen welfare and resilience in organic pig production) supported by the  ERA-NET CORE (Coordination of European Translational Research in Organic Food and Farming Systems) Organic Cofund.
Experimental design and subjects Forty female ponies were distributed into four groups: High parasite shedders treated with pyrantel to remove adult cyathostomin while leaving developing stages (HIGH TRT, n = 10), high parasite shedders non-treated (HIGH CTL; positive control, n = 10), low parasite shedders treated with pyrantel (LOW TRT; intrinsic pyrantel effect, n = 10), and low parasite shedders non-treated (LOW CTL; negative control, n = 10). For each individual, nemabiome and microbiota data were produced over a 42 day-trial to quantify interspecies interactions and resilience after anthelmintic treatment. Within each group, a subset of six ponies was selected to analyze the whole blood transcriptomic profiles. Horses were indoors on a uniform diet, with equal nutrient levels, feeding frequency, and dietary type (Figure  1 ; Table  S1 ).
Overview of the interconnected triad (nemabiome, gut microbiota and host) before treatment Following prokaryotic data processing ($1. 6$10 8 16S rRNA sequences; 30,793 G 6 391. 3 sequences per sample), 7 545 Amplicon Sequence Variants (ASV) were identified. Of these, 7,142 ASVs (95. 5%) were present in all groups, and only 30 were exclusively detected in the highshedding individuals (Figure  S1A ). Rarefaction curves nearly reached saturation for the ASVs noticed in all groups, indicating that our sequence inventory covered most of the taxa in these samples (Figure  S1B ). They encompassed 11 phyla, 87 families, and 179 genera, matching previous studies in horses  [53] [54] [55] [56] [57] [58]  Figure  1. Experimental design The experimental cohort comprised 40 female Welsh ponies divided into four subgroups.
17, 18, 20, 21  As with the study cohort, the microbiota profiles could be distinguished based on the horse's egg parasite shedding, measured via parasite FEC (pairwise PerM-ANOVA on a Bray-Curtis dissimilarity matrix; adj p = 0. 017, R 2 = 0. 0158) and showed similar dispersion (betadisper, 0. 23 G 0. 015; adj p = 0. 094). Using sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), we found that the set of 51 genera that showed temporal variation between low and high shedders, in combination, were able to accurately detect the level of cyathostomin egg excretion in the orthogonal dataset (AUC = 0. 8736, p = 7. 202e À10 , minimal error of 11. 94%; Figures  4A  and 4B ). Among them, Clostridia clades capable of producing butyrate and metabolizing complex polysaccharides were the best discriminants between high and low shedders (Figure  4C ).
Animals Forty female Welsh ponies (average age 4. 77 G 2. 05 years) were selected from an experimental herd of 107 ponies at Nouzilly (France), 120 regularly monitored for FEC during the grazing season since 2013. The selection of the 40 ponies was based on the FEC records database consisting of 703 observations of the whole herd at the time of selection. Every individual pony had been recorded at least three times over a minimum of two years (seven observations per pony on average across the entire herd). FEC data were log-transformed to correct for overdispersion and fitted to a linear mixed model accounting for environmental fixed effects (month of sampling, year of selection, time since last treatment, age at sampling), as previously described. 17, 121  The individual was considered a random variable to account for the intrinsic pony susceptibility against cyathostomin infection.
ITS-2 barcode data preprocessing and inferences on community composition Amplicon sequencing data were quality-trimmed using cutadapt 129 (v. 1. 14) (with options-pair-filter anyno-indels-m 50 -max-n 1-q 15-n 2) and subsequently handled with the Divisive Amplicon Denoising Algorithm (DADA2) algorithm 107 implemented in the R (v. 4. 0. 2) software version. Errors were learned, and the reads denoised, tolerating a single error for each of the forward and reverse reads, with further filtering of reads shorter than 200 bp and setting the band size parameter to 32 as recommended for ITS-2 amplicons before additional chimaera removal. The amplicon sequence variant (ASV) taxonomy assignment was subsequently performed using the IDTAXA algorithm 109 as implemented in the DECIPHER(v. 2. 18. 1) R package, enabling a 50% bootstrap cutoff and using the public nematode ITS-2 database v1.
3 (https://www. nemabiome. ca/its2-database. html, last accessed, February 3 rd , 2022). The ASV count table was handled with the phyloseq 110 package (v. 1. 34). A total of 345 ASVs were identified, of which 106 with less than 50 occurrences were deemed contaminant and removed, and 16 were not assigned to any genus or species. Nemabiome samples showing less than 30 counts (n = 11 and the negative control) were not considered further. ASVs were subsequently aggregated at the species level using the tax_glom() function of the phyloseq package and removing unassigned ASVs. This left 14 strongylid species and three genus-wise ASVs for 52 samples. As a result of pyrantel treatment, 16 samples were missing due to low or no parasite egg excretion in the treated groups, e. g. , FEC below 200 eggs/g.