dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it should be noted that for the pig1 dataset, the dam path coefficient estimated in the transmissibility model was higher than 0. 50 (0. 53), but the transmissibility variance was still slightly higher than the genetic variance due to the sire path coefficient of transmission being lower than 0. 5 (0. 46). Finally, our results suggest that phenomena other than genetic sources of inheritance explain the phenotypic resemblance for RFI observed between relatives, with a higher transmission from the dam's side than from the sire's side. It is likely that one of these non-genetic inherited effects is the gut microbiota. Indeed, it has been reported in pigs and rabbits that the gut microbiota affects RFI. Microbiota might be crucial in improving FE in herbivores, because gut microbes hydrolyse the plant fibres that mammalian digestive enzymes cannot degrade  (Dehority, 1991; Van Soest, Robertson, & Lewis, 1991) , and which represent up to 70% of their energy intake  (Flint, Bayer, Rincon, Lamed, & White, 2008).
Statistical analysis Graphpad Prism software (GraphPad Software v7, CA, USA) was used to perform statistical analyses on the antibody titre. Antibody titres were analysed using two-way ANOVA with Tukey's correction for multiple comparisons. The time to ulceration data were displayed as a Kaplan-Meier plot and statistical significance was determined using a Log-Rank (Mantel-Cox) test. For all tests *p < 0. 05, **p < 0. 01 and ***p < 0. 001 and **** p < 0. 0001 were considered statistically significant.
For our identification process, N is taken equal to one thousand which ensures a good accuracy for the objective calculation (since it is one point every 2 meters for the longest race as the Bocop calculation) while keeping a relative low time cost. The points are evenly distributed between s 1 and s N. The algorithm used here is a particle swarm optimization method  [20]  available in the pyswarm library in Python 3, part of the family of the heuristic optimization methods  [21]. The main advantage of such a method is its good ability to explore the design space and its ease of use and implementation. A swarm of designs {p i } is tested, that is the optimal control problem is solved for these parameters using Bocop, and its objective value is calculated. The performance influences the speed and the direction of the particles inside the design space for the next iteration.
Data analyses Preliminary calculations. As proposed by O'Toole et al. (1998) and  Veissier et al. (1999) , we calculated areas under the curves (AUCs) to describe the integrated cortisol responses according to the equation: AUC ¼ X nÀ1 t ¼1 1=2 C t þ C t þ1 ð Þ Δt where C t is the cortisol concentration at time t; n is the total number of samplings and Δt is the time interval between successive samplings. In Experiment 1, we estimated the proportion of plasma cortisol found in saliva by calculating two ratios: the average ratio between AUCs of salivary and plasmatic cortisol (18 pairs), and the average ratio between pairs of salivary and plasmatic cortisol obtained at each sampling time (220 pairs available out of the 234 theoretical ones, due to few missing samples). Statistical analyses.
We analysed the data with the SAS package (version Enterprise Guide 5. 1; SAS Institute Inc. , Cary, NJ, USA). In Experiment 1, we calculated Pearson's correlation coefficients between salivary and plasmatic concentrations in cortisol at each point in time or considering only the AUC. We used Pearson's coefficient because the relation between the two concentrations was linear. We then expressed the ratio between the salivary and plasmatic concentrations in cortisol as a function of the plasmatic concentration. For this purpose, we created 22 classes of 10 paired data of salivary and plasmatic concentrations and calculated the standard error (SE) of the ratio between the 2 concentrations. We calculated Spearman correlations between ratio of the salivary and plasmatic concentrations in cortisol or its SE on the one hand, and the plasmatic concentration in cortisol on the other hand.
Acknowledgements We are grateful to the  INRA experimental facility Herbipole  ( UE1414 ) for taking care of the animals, to  Pierre Amblard  and  Gilles Toporenko  for their help in equipping the sheep with catheters and taking samples and to  Henri Cassagne  for his contribution to the cortisol analyses. We thank  AJE  ( American Journal Experts, Durham, NC, USA ) for English editing. Saliva or plasma to assess cortisolaemia in sheep.
Then we evaluated the emotional value of the stimuli through isolation/reunion tests. We hypothesized that being reunited with a stimulus would attenuate the stress of social isolation and promote positive behaviors, and even more that the stimulus has a positive emotional value for piglets. We used behavioural but also bioacoustics data known to be relevant in comparing emotional states of pigs  (Friel et al. 2019; Leliveld et al. 2017; Villain et al. 2020). Additionally, we tested if the level of attraction toward the stimuli could predict vocal expression in presence of each stimuli.
Behavioural observations and analyses The tests were video recorded by a camera (Bosh, Box 960H-CDD) and a recorder (Noldus Mpeg recorder 2. 1. , The Netherlands), linked to a LCD monitor  (DELL 48 model 1907FPc)  which allowed us to visualize the experimental room from an adjacent room. The location of the piglets was monitored directly during the tests and the other behaviors later from videos, both using the software The Observer XT 14. 0 (Noldus®, The Netherlands). All behaviors recorded are indicated in The piglet is considered in a virtual zone when its forelegs and head are in the zone.
Behavioural analyses The tests were video recorded (camera Bosh, Box 960H-CDD) and a recorder (Noldus Mpeg recorder 2. 1. , The Netherlands), linked to a LCD monitor  (DELL 48 model 1907FPc)  which allowed us to visualize the test from an adjacent room. The location of the piglets was monitored directly during the tests and the other behaviors later from videos, both using the software The Observer XT 14. 0 (Noldus®, The Netherlands). All behaviors used are indicated in table 1 and correspond to numbers : 1, 8-10, 14. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted June 29, 2020. ; https://doi. org/10. 1101/2020. 06. 29. 177162 doi: bioRxiv preprint Object and human contact for piglets 3.
5. 3 Acoustic monitoring and analyses Vocalizations produced during the Isolation/Reunion test were recorded with a C314 microphone (AKG, Austria) placed in the center of the room and at one-meter-high, connected to a MD661MK2 recorder (Marantz, USA). The vocal types were then manually annotated (grunt, squeak, bark, scream and mixed calls), after visual inspection of spectrograms on Praat® software, by an expert. Only grunts were subsequently acoustically analyzed as they represented the most frequent call type that constituted a dataset of 5766 calls. A spectro-temporal analysis was performed with custom-written codes using the Seewave R package  (Sueur, Aubin, and Simonis 2008)  implemented in R (R Core Team 2015). After a 0. 2-8 kHz bandpass filtering ('fir' function), a standardized grunt was detected when the amplitude crossed a 5% amplitude threshold ('timer' function) to measure the duration.
For human, HproxPC1 explained 63% of data variability and for the object, OproxPC1 explained 47%. For further analyses, the score toward each stimulus was matched accordingly to the type of reunion the piglet was experiencing (Human vs. Object): when reunited with the human, the behavioural proximity score toward the human (HproxPC1) was used, whereas when reunited with the object, the behavioural proximity scores toward the object (OproxPC1) was used.
To increase interpretability, all continuous variables (interval index and behavioural proximity scores) were scaled, so the Z score is presented every time  (Schielzeth 2010) , the individual was put as random factor to take into account multiple tests of the same piglet. On this full model, a model selection was performed with the 'dredge' function of the 'MuMIn' R package  (Bartoń 2016) , which compares all possible models built using subsets of the initial explanatory variables of the full model, including null model. Models were compared using Akaike Information Criteria corrected for small sample size (AICc). Significant models were selected when delta AICc was below two  (Burnham and Anderson 2002) , the weight of remaining explanatory variables was evaluated by calculating the presence or absence of the term in the remaining models ('importance' function).
All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted June 29, 2020. ; https://doi. org/10. 1101/2020. 06. 29. 177162 doi: bioRxiv preprint Anovas were computed on models to test for significant effects of explanatory variables ('car' R package), effect were considered significant when the p value was below 0. 05. Model estimates and pairwise post hoc tests were computed using Tukey correction for multiple testing ('lsmeans' R package  (Lenth 2016) , (models 1-3)). A complete report of statistics is available as supplementary material (supplementary tables S4-S6). For the model selection (model 4), the analysis does not give p values but rather a subset of significant models and weight of predictors. A model averaging step  ('model.
Molecular analysis and drug susceptibility testing Total DNA was extracted using GenoLyse v2. 0 (Hain Lifescience, Nehren, Germany). Isolates were assigned to the M. abscessus complex using the multiplex GenoType Mycobacterium CM assay (Hain Lifescience Nehren, Germany), and subspecies were identified with the GenoType NTM-DR Kit (Hain Lifescience, Nehren, Germany), the index test, according to the manufacturer's recommendations. All isolates were also characterized by multilocus sequence typing (MLST), based on eight housekeeping genes (argH, cya, glpK, gnd, murC, pgm, pta, and purH), as previously described  [10]. M. abscessus complex subspecies, allele and sequence type were identified using the M. abscessus MLST database (http://www. pasteur. fr/recherche/genopole/ PF8/mlst/Myco-abscessus).
Statistical Analysis Statistical analysis was carried out using SAS 9. 4 software (SAS Institute Inc. , Cary, NC). The parameters included in the statistical analysis were the following: the chick weight, BF weight, BF volume, BBR, and HLR. For each time point, the mean values of the MHUSA group (n 5 10) were compared with those of the control group (n 5 10). For each time point, assumptions of the normality and homogeneity of variances were verified, using the univariate procedure for normality and the t-test procedure for homogeneity of variances. If the conditions were verified, a Student's t-test was performed. If normality was not verified, the nonparametric alternative of the Wilcoxon test was preferred and computed using the npar1way procedure. If normality was verified but variances were heterogeneous, the Welch test was used, using the t-test procedure.
Process To answer these questions, we conducted an online survey of potential cultured meat consumers in France and Germany. The survey, administered by Ipsos in December 2019, used Computer-Assisted Web Interviews (CAWI) to ask questions about participants' current diets as well as their attitudes towards cultured meat and other meat alternatives. As well as consumer analysis within each country based on preferences and demographic data, this design allows us to directly compare responses between each country. The data for this study was collected before the involvement of any authors connected with academic institutions. As such, formal ethical approval of this study by an IRB was not sought or granted. However, as with all data collected by Ipsos, the process adhered entirely to the ESOMAR guidelines on ethical online research  [36].
Statistical Analyses In order to answer the research questions, we used a variety of statistical techniques. Our first question about conventional meat consumption in France and Germany can be answered using descriptive data primarily. As well as the percentages of participants identifying with each diet, we reported the percentages intending to reduce their meat consumption in the future, and the reasons given by those reducing or intending to reduce their meat consumption. In order to test statistically for differences in the rates of strict vegetarianism and veganism between Germany and France, we also recoded the dietary variable to binary 'vegetarian' and 'vegan' variables for use in 2 × 2 chi square analyses. Differences in the proportion of vegetarians and vegans between the two countries would be considered significant if p < 0.
05. Our second research question about overall cultured meat acceptance in France and Germany is similarly answered using descriptive data primarily. We reported the percentage of respondents in each country answering on the positive side, the negative side, or the centre of the 5-point Likert scales for a variety of questions about cultured meat. This includes purchase intent, willingness to try, and familiarity. We also reported the percentages of respondents with a preference for cultured meat or other alternative proteins (plant-based meat and insects) in each country. Our third research question about differences in cultured meat acceptance between Germany and France was addressed using a series of independent t-tests. We compared responses to five different questions about cultured meat acceptance.
, 2017 , Lesmerises, Johnson, & St-Laurent, 2018 , for Cape buffalo see  Bennitt et al. , 2018)  as it provides detailed information on when and where individuals are in the same subgroup but does not provide information on subgroup size and composition. We used GPS tracking data to quantify the time that pairs of Cape buffalo (dyads) spent in the same subgroup and the temporal dynamics of fission and fusion events. We explored seasonal changes in fission-fusion dynamics. Building on our knowledge of the species' ecology and on consistent results from previous studies, we also specifically tested the predictions that (a) fission and fusion events would occur more during the periods when Cape buffalos are more active, that is, early in the morning and late afternoon, because conflicts of interest in the upcoming activities or directions would be higher  (Cornélis et al.
| Capture and collaring Between 2008 and 2016, we tracked 47 adult female Cape buffalo across the three study areas (GNP: n = 12, HNP: n = 20, KNP: n = 15) using GPS collars. We focused on adult females, as adult males are known to leave subgroups and groups more often  (Prins, 1996; Sinclair, 1977). All animals were captured by authorized personnel using established techniques  (la Grange, 2006)  and were observed returning to their subgroups after collaring operations. All field operations were conducted in accordance with the legal and permit requirements of the countries in which they were carried out. The data acquisition periods extended from October 2008 to May 2011 in GNP, from April 2010 to January 2016 in HNP, and from June 2010 to July 2015 in KNP. Duration of the tracking varied between 19 and 1,013 days (median = 486) across individuals, and GPS loggers were scheduled to acquire locations at synchronous times (the top of the hour) every hour.
We computed fix success rate within each season within each year for each individual, and we retained GPS data from 38 collared individuals for which the success rate was higher than 90%. Using this threshold, all selected individuals had at least one location per day within a single season (median = 24 locations/day). These individuals consisted of 10 groups: 2 in GNP, 4 in HNP, and 4 in KNP. The number of collared cows in each group varied between 1 and 7 (GNP groups, n 1 = 4, n 2 = 6; HNP groups, n 1 = 7, n 2 = 6, n 3 = 1, n 4 = 1; KNP groups, n 1 = 5, n 2 = 2, n 3 = 5, n 4 = 1). Of these, 31 females were tracked in both wet and dry seasons (GNP: n = 10, HNP: n = 11, KNP: n = 10) and 7 in only one season (HNP: n = 4, KNP: n = 3). This global dataset was used for a preliminary analysis (see section Definition of association and fission-fusion events below), before some data selection processing for subsequent analyses (see Statistical analyses section).
| Estimation of home ranges and home range overlaps To estimate home range overlap (HRO) between individuals, we considered seasonal home ranges (HR) as the 90% utilization distribution during the dry and wet seasons for each year. Utilization distributions were computed using the movement-based kernel density estimation method (MKDE,  Benhamou & Cornélis, 2010 ) implemented in the "adehabitatHR" package  (Calenge, 2007 ) for R v. 3. 3. 2 (R Development Core Team, 2016). Home range overlap between individuals was estimated using the Bhattacharyya's affinity index  (Benhamou, Valeix, Chamaillé-Jammes, Macdonald, & Loveridge, 2014). The index accounts for variation in the intensity of HR use and can take values from 0 (no overlap) to 1 (identical space use).
However, it is worth noting that the constraints associated with this technology (e. g. , deployment costs) have limited the number of individuals to be monitored simultaneously within the same group. Fission and fusion events involving noncollared animals have not been recorded, but it is unlikely that the behavior of those animals was highly different from our collared Cape buffalo and that the biases related to sample size were heterogeneous across sites and seasons. Consequently, the differences observed across sites and seasons should remain valid. Despite limitations, GPS tracking provides new information about whether local environmental conditions affect where fission and fusion events occur. In particular, our GPS data allowed us to test the predictions that Cape buffalo Our wet season data suggested that grasslands are more likely to be associated with fusion than fission events.
These criteria were used in an effort to enrich our study sample with confirmatory or proof-of-concept experiments  2, 25, 26  while minimizing the inclusion of reports of exploratory experiments with as yet unclear relevance to human disorders. All articles in Stroke were independently selected and data extracted using standardized case report forms by 2 reviewers, including via review of all articles' supplementary material. Discrepancies were resolved by consensus or by an independent adjudicator. Our group has reported 94. 5% interrater agreement for study inclusion before resolution using this approach in a previous review of >28 000 articles. 12 ll studies published in Nature Medicine and Science TM between 2009 and 2017 were similarly reviewed, but the study selection process differed slightly for these journals as described in methods in the online-only Data Supplement.
Data extraction was performed by 2 independent assessors as was done for Stroke. The starting year of 2009 for data collection was selected because the inaugural issue of Science TM was published in that year. We previously examined the reported methodological quality of articles published in an additional 4 leading cardiovascular journals between July 2006 and June 2016 using before-after comparisons. 12 wo of these journals (Circulation and Circulation Research) publish on all aspects of cardiovascular medicine, allocate a substantial amount of space to preclinical research that is of similar reported methodological quality,  12  have among the highest journal impact factors in the cardiovascular field, and did not explicitly and publicly implement initiatives to improve the reporting or methodology of their articles until October 2017.
17 Limited journal-and diseasespecific data from those before-after comparisons have been reported previously. 12, 14, 27 The data set from these journals was extended to December 2017, expanded in scope (see below), and combined into a single control group to increase statistical power. Study selection and data extraction for these control journals were performed as was done for Stroke. Diseases or disease systems of interest in each journal are listed in methods in the online-only Data Supplement. Studies on sex-specific diseases (for instance, prostate or ovarian cancer) were prospectively identified during data collection.
Data Extraction The date of publication, disease system studied, and animal model(s) used were collected for all articles. Disease systems studied and animal models used in <2% of reports in all journals were included in "other" categories. Data on randomization, blinding (concealed allocation or blinded outcome assessment), sample size/power estimations, and the sex of the animals used were also collected. These data have been identified as minimum reporting requirements by the National Institutes of Health. 2, 13 A distinction was made between the reporting of any given SDE and its reported use such that cases in which studies reported not randomizing animals to treatment groups or not using blinding, for instance, were captured. Similarly, studies that reported using a specific SDE, but its description revealed that it had not been properly executed, were captured (eg, studies that reported being adequately powered because group comparisons yielded statistically significant results).
The reporting and use of SDEs are, therefore, described separately. These elements were deemed to have been reported on or used if done for any experiment or animal model within an article. Because of the small number of Science TM studies published in 2009 (the first issue was published in October of that year), studies from 2009 to 2010 were combined for that journal for all analyses. Similarly, studies published in control journals between July and December 2006 were included with articles published in 2007. Given the volume of articles screened, journal-specific stylistic features (page layouts, article organization, color schemes, and appearance of figures), and the variability and volume of supplementary materials, de-identification of articles was not performed and reviewers were not blinded to journal or date of publication during screening or data extraction, although they were blinded to scores from other reviewers.
Data on sex reporting and sex of animals used were similarly but separately evaluated and excluded studies on sex-specific disorders. For temporal analyses, Stroke was examined in 12-month intervals whereas Nature Medicine, Science TM, and control journals were examined in 6-month intervals to render sample sizes similar per time point between journals. The influence of journal initiatives was also estimated by comparing reporting and methodological quality measures before versus after their implementation in each journal and via simple and multivariable modified Poisson regression models with robust error variance (sandwich estimation) to generate journal-specific crude and adjusted relative risks (RRs) with 95% CIs. Covariate category inclusion in multivariable models was prespecified and included relevant disease system studied and animal model used.
Within these covariate categories, specific disease system and animal model covariates were selected via backward elimination using an inclusion criterion of P<0. 20 to ensure adequate events per predictor variable. All analyses were performed using SAS 9. 4 (SAS Institute Inc, Cary, NC) using a 2-tailed α level of 0. 05 to define statistical significance. In Stroke and in control journals, ≥93. 0% of studies focused on cardiovascular diseases and ≥85. 5% used mice or rats. In Nature Medicine and Science TM, the most commonly studied disease systems were oncology, infectious diseases, neurology, endocrinology, cardiovascular, and "other," with 71. 3% and 85. 6% using mice in each journal, respectively. Psychiatry, anesthesia, obstetrics/gynecology, ophthalmology, and toxicology were each studied in <2% of studies in all journals and were therefore included in the "other" category (Table  I  in the online-only Data Supplement).