dataesr/scientific-paragraphs-categorization huggingface dataset:
Health Data A total of 39 animals were culled between calving and 50 DIM. Furthermore, data concerning HK and CE were missing for 126 and 449 animals, respectively. Missing HK data in a few herds were caused by a problem in having regular BHBA testing implemented. Missing CE data were caused by the impossibility of collecting these data in some herds (too complicated for research staff to restrain cows properly in the freestalls). Postcalving DHIA testing was performed within 50 DIM in 904 cows that were used for the evaluation of the incidence of post-calving IMI. Out of the 904, 169 were nulliparous and were used for computing incidence of NIMIh. Among remaining cows (n = 735), data about dry-off DHIA were missing for 79 cows; 445 cows had SCC <200,000 cells/mL at dry-off and were used for the calculation of prevalence of NIMIc; and 211 cows had SCC ≥200,000 cells/mL at dry-off and were included in the calculation of CIMI.
ACKNOWLEDGMENTS The authors acknowledge financial support from the  Fonds de recherche du Québec -Nature et technologies  ( Québec, QC, Canada ), the  Réseau Québécois en reproduction  ( Université de Montréal, St-Hyacinthe, QC, Canada ), the  Regroupement Op+lait (Université de Montréal) , and the  Fonds de recherche clinique Zoetis of the Bovine Ambulatory Clinic of the Université de Montréal. The authors thank  Jean-Philippe Pelletier  ( Université de Montréal, St-Hyacinthe, QC, Canada ),  Sarah Lescure  and  Léa Lanogue  ( École Nationale Vétérinaire de Toulouse, Toulouse, France ), as well as the veterinarians from the  Bovine Ambulatory Clinic of the Université de Montréal  for their technical help during data collection. The authors also express their gratitude to the participating dairy producers for their contribution to this study.
Data recording All imaging studies obtained were reviewed on an imaging software   1B. For MRI studies, all available sequences were evaluated. In these acquisitions, signal characteristics-compared to adjacent muscles-were evaluated. Ultrasonographic images and video clips, when available, were reviewed. Ultrasonographic images were evaluated for the size of the tumor (length, width and height), shape (rounded, oval, irregular), margination (distinct, vague outline), echogenicity (compared to adjacent organs, homogeneous/heterogeneous), location and relative topography (using surrounding organs), contact with adjacent organs, presence or absence of mass effect, number (uni-, bilateral, possible metastasis), vascularization as assessed on Doppler examination (intensity, blood flow pattern), and nature of other lesions if present.
Models on relative abundance of bacterial families were restricted to the commonest taxa (5 families with relative abundance > 10%), to avoid the risk of spurious results when using rare taxa, occurring in a small number of individuals. However, despite having a low relative abundance (1. 67%), we also included Lactobacillaceae in our analysis, because we had a priori reasons to think that Lactobacilli may play a role during H. polygyrus infection  (Walk et al. , 2010; Reynolds et al. , 2014; Su et al. , 2018). All linear mixed effect models were run using PROC MIXED in SAS and degrees of freedom were approximated using the Satterthwaite method. To infer the amount of variance accounted for the fixed effects included in the models we used two approaches. First, we used the method proposed by  (Nakagawa and Schielzeth, 2013) , which gives an estimate of the total amount of variation explained by fixed effects.
To this purpose, we simulated 100 datasets with the same number of mice and observations of the actual dataset. For each αand β-diversity metric, the simulated data were randomly drawn from a normal distribution with mean and standard deviation corresponding to the actual values. For each of these 100 datasets, we ran a mixed effects model as described above, and we checked whether the host strain Â infective dose interaction was statistically significant. We deemed that if none of these simulated datasets returned a P-value < 0. 05, this might be seen as an indication that the actual sample size was too low to detect a significant host strain Â infective dose interaction. The second aim was to identify the sample size that would have been needed to have 80% likelihood to get a statistically significant G Â E interaction, provided the strength of the interaction reported by the model on the actual data does not change with the sample size.
To this purpose, for each αand β-diversity metric, we randomly drawn 100 values from a uniform distribution bounded between AE1 standard deviation (computed on the actual data). These values were subsequently subtracted from the actual data, to obtain the simulated data. Therefore, these simulated data represent slight deviations from the actual data, ensuring that the magnitude of the interaction effect did not change when increasing the sample size. The sample size (number of individual mice and total number of observations) was increased up to 4 times the actual sample size (i. e. 216 mice and 668 observations). For each of the simulated dataset, we ran a mixed effects model as described above and computed the percentage of models with P-values below 0. 05 for the infective dose Â host strain interaction.
S1A ; Supplementary Table  S1 ). Host strain explained the largest fraction of variance (29. 2%), while infective dose and the interaction accounted for only 2. 2% and 9. 4% of total variance, respectively. Lack of support for the host strain Â infective dose interaction may stem from a small sample size and the associated low statistical power. To investigate this point, we ran linear mixed effects models on simulated datasets (see Section 2. 3. for details). In four out of 100 models run on simulated OTU richness (and in six models on simulated Shannon index), we found that the G Â E interaction was statistically significant (at the 0. 05 significance threshold), showing that our sample size was large enough to detect significant G Â E interactions, provided the differences in the reaction norms were large enough (e.
Feedback from the participants Following completion of the ASF Challenge, a series of internal workshops were organised to collectively gain knowledge on the approaches used by each team and most importantly to obtain feedback from the teams on several topics. An online feedback questionnaire was administered to the participants to investigate several questions related to the relevance of the data and documents provided before and during the challenge, the feasibility of the challenge in relation to the time available, the skills used or missing in the modelling teams, the organisation of the challenge itself, and the knowledge gained by teams during the ASF Challenge.
, 2022)  and Massey  (Han and Vignes, 2022)  teams defined a force of infection in both directions, from wild boar to pig farms and from pig farms to wild boar. Assuming no transmission from pigs to wild boar (three teams) implies that control measures targeting pig farms had, by definition, no impact on wild boar. Also, pig farms can get infected by other transmission pathways: proximity to other farms (CIRAD, UK and INRAE teams) and by animal trade movements between farms (all but WUR team). Due to the limited number of pig farm outbreaks and the dominant role of boar-to-farm transmission in disease introduction into pig farms, the power of data was considered insufficient by the WUR team for estimating a non-zero between-farm transmission kernel  (Boender et al. , 2014). Trade movements of pigs were generally modelled based on the historical data that were provided to teams, except the UK team who used historical data for observed period and fitted exponential random.
First, teams were asked for a textual description of their predictions, to ease qualitative comparisons. Second, teams were asked to provide the organisers with quantitative model predictions that should be "as precise as possible". Teams were free to choose the epidemiological unit, the spatial unit and the format that they desired for submitting their predictions. As a result, a large variety of ouput types was received, which made comparisons somewhat difficult. With regards to file format, most teams chose the csv format, also some teams also provided data in Rdata files. Some teams provided output means and confidence intervals, others provided full details of every stochastic repetition. Some provided aggregated outputs, others provided the exact time and/or location of case detection.
Spatial distribution of detected wild boar infections Three teams (INRAE, CIRAD, Massey) provided spatial predictions for the number of infected wild boar detected in a spatial location for phase 2 (days 80-110; Fig. 8 ). Those simulations were used to calculate the probability of detecting infected wild boar in specific tiles (hexagons in the INRAE and CIRAD models vs. squares in the Massey model). The INRAE model was the most sensitive here, probably due to the larger tile size. However, the INRAE model predicted the occurrence of a second cluster in the south-west of the area with a probability of 0. 5 (Fig. 8 ). Such a cluster did not occur in the selected synthetic data produced by model M0. Teams' models agreed that the virus had a high probability of escaping from the fenced zone by its south-west corner.
Funding This work was supported by the animal health division of  National Research Institute for Agriculture, Food  and the  Environment (INRAE) (France)  [ PPApred  project]. For intensive computing resources, we used the  Bioinformatics Core Facility of Nantes BiRD , member of  Biogenouest ,  Institut Français de Bioinformatique (IFB)  ( ANR-11-INBS-0013 ), and the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , http://doi. org/10. 15454/1. 5572390655343293E12 ).
Study design We conducted a cohort study between January 2020 and March 2021 in the Brittany region, which is a typical western European plain and the main milk-producing area in France. Our main objective was to compare the incidence of SHC between two groups of cows, those fed with an acidified diet (AcD) and those fed with a standard, i. e. , non-acidified, diet (StD). We designed our study by taking into account published data from studies under AcD conditions, in which the incidence of SHC was 40% in the AcD group  (McArt and Neves, 2019). Based on this, a sample size of 300 cows in each group (AcD, StD) should reveal a 12% difference in SHC incidence between the two groups with P < 0. 05. For practical reasons, we intended to enroll 20 cows from each farm. Thus, we aimed to include 30 farms in total, 15 in each group.
We selected a study population of dairy farms from a database provided by the private animal health company GDS Bretagne (France) which contains all dairy farms in Brittany (n = 9525). Only dairy farms with at least 60 cows were selected (in order to ensure that the dairy herd was the main means of production) and at least 90% of cows had to be Holstein, in order to avoid any breed-induced variability in hypocalcemia risk (N = 4238). Farms were then randomly selected using successive random samplings (without replacement) of 100 farms. To control for variability in hypocalcemia risk linked to milk yield among herds, the random selection was stratified by the median milk yield in the region (7500 kg/cow per year). The selected farms were called to ask for their consent to participate and were included in the study if they met the following criteria: 1) they participated in the official French milk recording scheme, to make it possible for us to obtain values of milk yield index; 2) they did not use grazed pasture to feed dry cows in late gestation, so that it would be easier to estimate feed intake, and 3) for members of the AcD group, they would supply anionic salts to cows during the last three weeks of gestation (the "close-up" period) to prevent hypocalcemia at calving.
Data collection Each farm included in the study was visited at least once before the experiment began (inclusion visit) by one of the three investigators (the first author and two veterinary students), in the presence of the farmer's private veterinarian. During the visit, we described in detail the diets to be fed to cows and heifers in the close-up period during the experiment (list of ingredients and corresponding amounts) and collected the composition sheets for all commercial mixes used (i. e. , concentrates and minerals) along with samples of forage for further analysis. Forage samples were transported to the laboratory (INRAE, St Gilles, France) at ambient temperature and stored at -20°C until analysis. The farmer was asked to alert the investigator if any ingredients changed during the study period, so that a new forage sample or composition sheet could be collected.
The blood sample was collected from the coccygeal vessels using 25-mm 18 gauge needles and 4 mL lithium heparin vacutainer tubes (Greiner Bio-One, Monroe, NC). Samples were transported to the veterinary clinic at ambient temperature and centrifuged at 1 500 × g for 15 min at ambient temperature; plasma was pipetted into two aliquots and stored at -20°C. At the end of the study period, plasma samples were transported to the laboratory (INARE, St Gilles, France) at -80°C in dry ice (CRYO-Distribution, Pléchatel, France) and stored at -20°C until analysis. Data on parity and the milk yield index of cows included in the study were collected from the official milk records in the French national genetic information system (SIG, Idele, France). We chose to use the milk yield index instead of the 305-day milk yield to characterize each cow's potential milk yield because we viewed the former as being highly predictive of effective milk yield  (Brochard et al.
Moreover, we wished this study to focus only on data that could be routinely obtained, and feedstuff analysis of vitamin D content is difficult and expensive under field conditions. Thus, only the vitamin D supplied by commercial mixes was considered in this study; this was calculated from the declared amounts fed and the composition data provided by feed manufacturers. Because weighing practices for the estimation of the amount of commercial mixes could influence mineral intake, farms were divided into two categories: 1) those that weighed less than 50% of commercial mixes, or 2) those that weighed 50% of commercial mixes or more.
Strategy of analysis The statistical unit was the cow. Subclinical hypocalcemia (SHC) was defined using three different cut-offs-plasma calcium below 2. 0, 2. 1, or 2. 2 mmol/l  (Reinhardt et al. , 2011; Venjakob et al. , 2017) -in order to compare the incidence observed here with previous studies that used different thresholds. Subclinical hypophosphatemia (SHP) was defined as levels of plasma inorganic phosphorus below 1. 3 mmol/l because this is the level at which detrimental immune effects become apparent  (Eisenberg et al. , 2014). The incidence of SHC and SHP was determined for each parity group and diet group (i. e. , AcD and StD). All other data were collected and presented for the whole study sample and by diet group (AcD and StD). Quantitative data are reported as mean ± standard deviation (µ ± SD).
Acknowledgments The authors thank  Maxime Pocher  and  Alexandre Gallais  ( ONIRIS ) for participating in the study;  Alexandre Gallais ,  Nadine Brisseau  (ONIRIS) and  Anne Lehebel  ( INRAE BIOEPAR ) for contributing to data analysis;  Maryline Lemarchand ,  Cećile Perrier , and  Arnaud Mottin  ( INRAE PEGASE ) for sample analysis and chemical analyses, and  Vincent Ballard  (CCPA),  Alexandre Budan  (Cargill),  Lionel Raulin  (TECHNA), and  Benoıt Astruc  ( VILOFOSS ) for their input on the study design.
Discussion The present study is the rst to compare lifestyles of owners and dogs according to the dog's diet in France. Moreover, there are very few data about the habits of French pet owners in terms of owner food choice for their dogs  (Colliard et al. , 2006). Last, but not least, this survey is focused on the Internet population, which is a highly active population as far as BARF and raw diets are concerned but still underresearched. Information collected in this study highlights differences between owners/dogs using NCD versus owners using CD. Owners using NCD lived more frequently outside of the metropole of Paris, had fewer children, and dewormed less than owners using CD. These results are consistent with the analysis by  Morgan et al. , (2017) , where pet owners who fed raw animal products were in majority without child (61%) and only 28% lived in an urban area.
Data transformation and analysis Data from Google form transferred into Microsoft Excel. Binary variables were created (living in French metropole area of Paris; neutered, female, gestation, purebred dog, sport activity with the dog, nogluten food (according the petfood references named by the owner), food reward, measuring food accuracy, walking each day, deworming at least every 6 months, walking more than 6 hours/walk without leash, age of 40 years and more). The household place was divided between "French metropole area of Paris" and "Rest of France" because of higher occurrence of the former. The age of owners was split at 40 years to compare with the results by  Morgan et al. , 2017  where 39 percent of Raw animal product feeders were 40 years of age or younger in United State population.
Statistical and data analyses Each experiment was analyzed separately using the MLM to test the respective effect of each experimental parameter and the E2 dose on the level of CXCL12 secretion. In experiment A, the effects of the cell line (reference = T47D(ATCC)), medium (reference = RPMI), serum (reference = chsFBS(Mer)) and E2 dose (reference = ethanol) were tested as the main fixed effects. Evaluated doses of E2 were 1. 0 10 -9 M, 1. 5 10 -10 M, 4. 4 10 -11 M, 2. 8 10 -11 M, 8. 8 10 -12 M, 2. 5 10 -12 M. To detect non-linearity in the E2 -CXCL12 secretion dose-response relationship and to estimate differential secretion at the specific E2 concentrations tested, the dose of E2 was considered a categorical variable. Moreover, the interactions among the cell line, dose of E2, medium, and serum were also evaluated in experiment A (data not shown).
In experiments B and C, biological replicates were performed and mixed effect models were implemented, in which the sample (experimental/biological) incurred a random effect, to consider the repeated measures for each sample. Moreover, the additional E2 dose of 2. 5 10 -12 M was also evaluated. In experiment B, the analysis included not only the same fixed factors as in experiment A but also the "number of cells per well" (20,000 vs 10,000 as a reference). In experiment C, the "number of cells per well" was not included; the "cell density before experiment" was considered instead (low vs high as a reference), and the medium effect was not taken into account. Interactions were also tested between the cell line and dose of E2 or between the cell line and serum (data not shown). Furthermore, to reveal potential differences in the effect of the E2 dose and experimental parameters on CXCL12 secretion among the tested cell lines, the same analyses were applied to each cell line separately.
Cell line source T47D(ATCC) cells were obtained directly from the original supplier, ATCC. Although T47D(IBT) and T47D(IRSET) cells were kindly provided by two laboratories (Czech Republic, France), however the origin of the cell lines was ATCC as well. The cell lines showed slightly different dynamics of growth during maintenance (data not shown) and different resistances to buffer rinsing (see below). The cell comparison under a phase-contrast microscope showed identical cell shapes and very similar cell sizes (Figure  S1 ). As shown in Figures  2, 3, 4,  and 5 , the cell lines demonstrated different levels of basal (ethanol control) and maximal cytokine secretion. The lowest secreted level of CXCL12 was detected in T47D(ATCC) (basal 32-48 pg/ml, maximal 85-196 pg/ml); T47D(IBT) basally secreted 44-102 pg/ml of CXCL12 and maximally 210-332 pg/ml; and the highest secretion was detected in T47D(IRSET) (basal 103-217 pg/ml, maximal 280-412 pg/ml) when 20,000 cells/well were seeded in treatment medium (Figures  4  and 5 ).
The material could increase basal secretion, but the maximal secretion was determined by the cell secretion ability. Therefore, the FI was not a convenient outcome to analyse the data obtained due to the variability of the ethanol controls in different conditions. The number of cells per well (10,000 cells/well in a 96-well plate) was optimized in a preceding study by  Habauzit et al. (2017)  with T47D(IRSET) cells that secreted more CXCL12 than other tested cells evaluated within this work. In this study, the 10,000 cells/well did not provide a sufficient dose-response curve, especially in T47D(ATCC), where the amount of the secreted cytokine was in some cases also near the detection limit of the ELISA. A marked effect of treatment medium was detected in all the experiments. DMEM significantly increased global CXCL12 secretion in all the tested cell lines compared to RPMI.
18, 21 From 2001 to 2004, a national network of volunteer community pharmacies was implemented on a national level to collect suspect prescriptions during two periods each year in May and November. 18, 21 In addition, from 2005 to the present, all other suspect prescriptions identified outside these periods have been recorded in the same way and included in the global analysis with data collected in May and November. Consequently, participating pharmacies do not ensure the representativeness of the around 21 000 community pharmacies in France. 2. The OPPIDUM ('Observation of illegal drugs and of the misuse of psychotropic medicines') survey collects information (drug name, modalities of use, ways of procurement) on drug use in individuals with SUD and consulting in addiction-specialised centres.
22 This survey is conducted annually over one month. The number of addiction centres participating in the OPPIDUM survey increased from 168 in 2013 to 259 in 2018. One-third of the ambulatory addiction centres that have included 77% of OPPIDUM individuals participated in the survey in 2015. 23  DRAMES ('Death with abuse of psychoactive medicines or substances') and DTA ('Death related to analgesics in a therapeutic context') national programmes analyse forensic data periodically transmitted by analytical toxicology laboratories in the context of abuse of psychoactive substances and patients treated for pain, respectively. 24  For a given case, each substance identified is subjected to a causality assessment, establishing the link between the substance and the cause of death. The strength of the causal connection is determined by a score, from high (level 1) to low (level 4).
The causal link is made on blood concentrations (or other matrices if no blood) quantification and relies on the analysis from toxicology experts and published references. In the DRAMES programme, 19 analytical toxicology laboratories participated in 2013 and 27 in 2018. Concerning DTA, there were 14 and 21 in 2013 and 2018, respectively. The included cases came from 80% of the French geographic counties. For each fatality case, the record includes the sex and age, circumstance of death, autopsy data and if the deceased person was a psychoactive substance abuser. Figure  1  provides an overview of the data sources used for each addictovigilance programme presented above in detail thanks to collaborations with partners on the interface with field data related to high-risk substance use and the potential related disorders.