dataesr/scientific-paragraphs-categorization huggingface dataset:
MR imaging measurements and blinded reading Averaged results for DIPJ space thickness, as well as individual cartilage thickness of the middle and distal phalanges and cross-sectional area measurements are summarized in Tables 2 and 3 and Figs  5  and 6. Complete datasets of frontal and sagittal measurements are available in S1 and S2 Tables. The mean cartilage measurements were significantly thinner and the areas significantly less in the weight-bearing DIPJ (p < 0. 0001 and p = 0. 0001) and distal phalanx (p < 0. 0001) cartilage in both the sagittal and frontal planes, compared with non-weight-bearing cartilage measurements. Given a pixel size of 0. 332 mm, percentages of error associated to the averaged linear thickness measurements ranged from 6% for DIPJ space to 39% for the individual cartilage of the distal phalanx (mean 18%) on the frontal images, and from 6% for DIPJ space to 83% for the individual cartilage of the distal phalanx (mean 21%).
Data Collection At arrival, bulls were immediately weighed and fitted in the front right leg with an activity monitoring device (Fedometer [FEDO] system; ENGS, Rosh Pina, Israel). The device consists of an accelerometer that measured daily steps counts (n/day), lying bouts (n/day), and lying time (min/day). An electromagnetic field-generating antenna was located along the feed bunk to detect bulls which front leg was less than 30 ± 2 cm from the feed bunk to allow the calculation of the number of visits (n/day) and its duration (min/day) at the feed bunk. Data collected was transmitted wirelessly every 6 min to a computer with a system-specific software (Eco-herd-software; ENGS, Israel). The system has been described in detail and validated for feedlot cattle  [12]. Bulls ID, birth date, body weight, and group entry date were recorded for each animal.
Statistical Analyses All statistical analyses were performed with SAS (v. 9. 4; SAS Institute Inc. , Cary, NC, USA). For the normal behavior, only bulls that had no treatment during all the study were used. A descriptive analysis was conducted for the entire period, and univariate analysis (ANOVA) was performed to identify differences by age for each behavioral index. Significance was declared at p < 0. 05 and trends reported at 0. 05 ≤ p < 0. 10. A matched pair design was used to assign bulls to the healthy group for comparisons. For each sick bull, we identified three healthy bulls that were on the same date, the same group, and similar age (±4 d) and weight at entry (±17 kg). For sick bulls that were treated multiple times, only the first treatment event was considered. Day 0 was defined as the day of sickness diagnosis, and the database contained records from day -10 to day +10.
Once the database was generated, a multivariate linear mixed model was built to describe differences for each behavioral index between sick and healthy bulls. The model included bulls health status (healthy, sick), season (summer, autumn, winter, and spring), age at entry, days around the treatment event (from -10 to +10), and the interaction between days and health status as fixed effects. The animal was considered a random effect. When an interaction was significant, the SLICE option of SAS was used to evaluate these differences. For the development and validation of the model, 70% of the original dataset was used to develop the prediction model, and the remaining 30% was used to measure the performance of the model. To build the prediction model, multivariate logistic regression was performed on the days before the first treatment event to identify variables that were associated with health status.
The Sp was defined as the proportion of healthy bulls that were correctly diagnosed. The FPR was defined as the proportion of calves that were diagnosed incorrectly sick. The FNR was defined as the proportion of calves that were diagnosed incorrectly healthy. Once all prediction models were generated on the days before the first treatment event, the one with the highest area under the curve (AUC) was chosen to evaluate its performance in the validation dataset (30% remaining from the original database). The model was applied on the same day as the validation dataset to obtain the FPR and FNR.
Predictive Models and Validation The match-pair design is a useful tool to assign healthy bulls to control for environmental and site-specific factors affecting behavior  [14]. All prediction models for the days before the first treatment event had an AUC > 0. 75. Table  2  shows the outcomes of the diagnostic test characteristic resulting from all the prediction models of the training database from the days before the diagnostic of sick bulls. The best model with the highest AUC value (0. 88) was found for day -9. This model at the cut-off point chosen from the highest combination of Se (79%) and Sp (81%) had 81%, 47%, and 6% accuracy, FPR, and FNR, respectively. The predictive model of day -9 included attendance at the feed bunk, lying bouts, and lying time as behavior indexes remaining significant after the backward stepwise procedure.
Predictive Models and Validation To our knowledge, our study is the first to develop a model to predict diseases in bulls with a formal validation using an independent dataset. Because we considered it important to select the earliest date for an early treatment, the analysis was conducted on individual days. According to our results, the prediction model developed using the training database on day -9 had a Se and an Sp of 79% and 81%, respectively. The Se and the Sp were similar to the results found in previous studies predicting different diseases. For example, Marchesini et al. [11]  reported that Se and Sp of the prediction model from days 1 to 3 before the clinical diagnostic for the BRD and lameness in beef cattle were 81% and 95%, respectively. Quimby et al. [9]  used feeding behavior to predict BRD in newly received calves and found a Se of 78% and an Sp of 79% of the model at day 6 before the visual detection of the diseases.
Wolfger et al. [10]  also used feeding behavior to predict BRD and reported a Se of 82% and a Sp of 78% on day 5 before the observation of clinical signs. The prediction model included time of attendance to the feed bunk, lying bouts, and lying time as behavioral predictors. Surprisingly, measures of activity (step counts), in spite of the clear differences between healthy and sick bulls (Figure  2 ), did not provide additional information to the model. The application of the prediction model at day -9 in the independent dataset (validation data) resulted in an acceptable overall accuracy (82%). However, the low specificity (42%) resulted in a high FPR (50%). The FPR represents the percentage of animals that would be treated without any need. However, the high FPR should be interpreted in the context of current preventive practices.
Of the countries without a formal control program for paratuberculosis, 76% were in South and Central America, Asia and Africa while 20% were in Europe. Control programs were justified most commonly on animal health grounds, but protecting market access and public health were other factors. Prevalence reduction was the major objective in most countries, but Norway and Sweden aimed to eradicate the disease, so surveillance and response were their major objectives. Government funding was involved in about two thirds of countries, but operations tended to be funded by farmers and their organizations and not by government alone. The majority of countries (60%) had voluntary control programs. Generally, programs were supported by incentives for joining, financial compensation and/or penalties for non-participation.
Responses to open questions were manually paraphrased using consistent terms developed following assessment of all responses, summarised into groups and tabulated for analysis. The relationship between herd-level prevalence estimates and log transformed herd size was determined using ordinal logistic regression. Number of farms and herd sizes were compared between countries with and without a paratuberculosis control program using Wilcoxon two sample tests. Statistical analyses were conducted using SAS (© 2002-2012 by SAS Institute Inc. , USA), whereas graphs were produced using R Version 3. 5. 1  [149]  and Excel. Proportions were compared using a two-sample two-sided binomial test in Genstat (VSN International).
Financial assistance to farmers At country level, full or partial financial support, assistance or compensation to farmers for one or more operational aspects of the control program was provided in one or more species between 2012 and 2018 in 12 of the 22 countries (55%). Due to program review processes, this may have begun or ceased during this period, for example in Australia financial support was available to beef farmers until 2016, then ceased. Among the 12 countries in which financial support was provided, the components covered included the cost of testing (11 countries), the cost of culling infected livestock (seven countries), and the value of culled livestock (eight countries). All three components were covered in four countries, two were covered in six countries and one was covered in two countries.
As 77% of countries aimed to reduce prevalence, there tended to be emphasis on culling clinical and subclinical cases. In contrast, where paratuberculosis was absent (Sweden), the primary activity was surveillance, and eradication measures would be undertaken there if paratuberculosis was detected. Sweden's program may be considered a surveillance program. Only 2 of 22 countries (Norway and Sweden) had sufficiently low (or zero) prevalence that they could aim to eradicate the disease. There was government funding of program leadership in about two thirds of countries, but actual operations tended to be funded by farmers and not by government alone. The majority of countries (60%) had voluntary control programs, and overall programs were supported by incentives for joining, plus financial compensation and/or penalties for non-participation.
Acknowledgements The authors would like to thank the following people who contributed to this study through provision of data, advice or information:  Julio Cèsar Jimenez Arias ,  Ori Koren ,  Subir Singh ,  Kaylene Larking ,  Dag Lindheim ,  Gareth Bath ,  Sanely Lourenço da Costa Caliman. We acknowledge with sadness the passing in 2018 of  Colin Wakelin , veterinarian, who had intended to contribute to this study for Fiji.
Session 3 lasted for 3 days, 1 day with only the reward bowls filled and 2 days with both types of bowls filled. The tests were performed from 0845 to 1215 h and from 1315 to 1645 h, 5 days a week, with two groups being tested on the morning and the other two on the afternoon. The passage order of each group changed daily according to a Latin square design. In return, animals passage order within a group was determined by individuals' willingness to go to the test arena, to avoid stress due to sorting animals within the waiting pen. Two video cameras positioned over each entrance door recorded animals' behaviour, which was encoded using 'The Observer TM software (XT12 version 12. 5, Noldus, The Netherlands)'. The observed behaviours (feeding and sniffing at the bowls, walking, idling, exploring and number of laps executed) were used to calculate the times spent feeding and walking, the walking speed to get the reward and intake rates of each feed.
Statistical analyses All data were analysed using the SAS software (SAS Enterprise Guide, v5. 1) and the mixed procedure. The normality of residuals was checked and we proceeded to variable transformation when needed (square root transformation for FEC; arcsine transformation for SF exp preference ratio in the conditioning trial). We expressed intake data relative to metabolic weight (BW 0. 75 ) to account for differences in BW between animal species. The individual animal was the experimental unit. Data relative to animals' BW, FEC and PCV were analysed for the effects of animal species, parasitic status (except for FEC with only P animals considered), day and their interactions. We considered the animal as the random factor and used the repeated statement with the autoregressive covariance structure to account for the measurements being made on the same animals on different dates.
Acknowledgements The authors acknowledge the financial support of the  STReP  project from  INRA  metaprogram focused on the integrated management of animals' health (GISA), the  French Agency for Research (ANRt)  for the CIFRE PhD thesis 2013/1480 of  E. Gaudin , and the  Phase INRA department and Auvergne region  for the PhD thesis of M. Costes-Thiré. The authors also particularly acknowledge the help of  Mickaël Bernard ,  Aline Cercy  and  Bruno Viallard  for taking care of the animals, and  MG2MIX  for providing the SF pellets.
e. , non-T cells) CD3 + T cells, CD4 + , TCR-γδ, and CD8 + T cells were identified by gating, as shown in Supplementary Figure  S1. All of these T-cell subsets and CD3 -cells were analyzed for proliferation by dilution of a proliferation dye. Data from one representative animal are shown in Supplementary Figure  S2. Figure  2  summarizes the data of experiments with PBMCs from seven different pigs. Live lymphocytes (Figure  2a ), CD3 -non-T cells (Supplementary Figure  S2a ), and all major T-cell subsets (Supplementary Figure  S2b-e ) showed similar reductions on proliferation at DON concentrations higher than 0. 4 µM. A significant reduction in proliferation was observed for live lymphocytes (p < 0. 01), CD3 -non-T cells (p < 0. 05), CD3 + T cells (p < 0. 01), and CD4 + T cells (p < 0. 05) (Figure  2b-e   Next to the proliferation of T-cell subsets, we also aimed to investigate whether DON influences the composition of T-cell subsets that are driven into death by its presence in the context of ConA stimulation.
Statistical analyses Data, except litter size and mortality rate, were analysed by ANOVA using the MIXED procedure of SAS (SAS Institute Inc. , Cary, NC, USA), with the sow as experimental unit. All models included at least the effect of the system (C or E) and the replicate (1, 2 or 3). For traits measured several times, analyses were done separately for each time-point. Cortisol concentrations and total cell numbers in mammary secretions were transformed by square root and Natural logarithm, respectively, before analysis. For body condition and reproductive performance, the model also included the effect of parity (primiparous or multiparous) and the system × parity interaction. The GENMOD procedure was used with a Poisson distribution and a Log link function for numbers of piglets and with a binomial distribution and a Logit link function for mortality rates, in models that included the effects of system, parity, replicate and the interactions.
Importantly, there were no differences in the main causes of piglet mortality between systems, and the herd was free from any specific swine disease. Effects of the environment during gestation on sow cortisol Present data showed that, at DG35 and DG105, sows from the C system displayed a greater salivary cortisol concentration than E sows, as previously observed  (Merlot et al. , 2017). The higher cortisol level in the C environment could be explained by higher social stress and frustration due to an Mortality 'at birth' included stillborn piglets and piglets that died within 12 h, relatively to total born piglets. Early, late and 12 h pp to weaning mortality included piglets that died during the respective periods relatively to born alive piglets. Overall mortality included stillborn piglets and piglets born alive that died before weaning, relatively to total born piglets.
Acknowledgements The authors thank  C. Calvar  and  B. Le Bris  ( CRAB, France ),  F. Robert ,  A. Hamard  and  T. Pinard  ( Deltavit CCPA group ) for their involvement, the staff of Crécom ( B. Le Guilcher ,  P. Lirzin ,  D. Lesaicherre ,  M. Gautier ) for animal care and data collection, and the staff of  UMR1348   PEGASE  ( N. Bonhomme ,  R. Comte ,  S. Daré ,  S. Philau ,  C. Guérin ,  S. Jaguelin ,  M. Lefèbvre ,  F. Mayeur ,  S. Tacher ,  F. Thomas , and  C. Tréfeu ) for sample collection and biological analyses. Research has received funding from the  EU   FP7   Prohealth  project (no. 613574 ).
The same is likely true for mice. However, laboratory mice we use for most biomedical studies are bred in ultra-hygienic environments and are kept free of specific pathogens  [24]. The immune system of a human adult is probably more closely replicated by mice caught in the wild or purchased in pet stores  [22]. Indeed, mice with diverse environmental exposures have more mature immune responses, similar to human adults, whereas laboratory mice have immature or neonate-like immune systems. Moreover, in mouse models the genomic response to endotoxemia, trauma and burns was reported to only poorly mimic that of healthy volunteers and/or patients  [25]. It is noteworthy in this context, however, that using the same database other authors came to exactly the opposite conclusion, i. e. , that "mouse models greatly mimic human inflammatory diseases"  [26].
Data analysis WA). New covariates were built from the raw data using R (R Core Team, 2017). The effectiveness of both treatment regimens was evaluated on the clinical healing of bDD lesions and the reduced occurrence of bDD lesions through a survival analysis with a hind foot as the statistical unit. Survival analyses were carried out using the Frailtypack package in R  (Rondeau et al. , 2017). Nested survival models  (Rondeau et al. , 2012)  were applied including a nested random effect for cows grouped in farms to adjust for clustering within observations, thereby feet in the models were considered as independent observations clustered at the farm level and subclustered at the cow level. Factors considered as potential effect modifiers of the healing and the occurrence of bDD lesions at the herd, cow and feet levels were included as covariates in the models (Table  1 ).
Effect on Occurrence At the start of the trial, 852 hind feet (41%) out of 2,072 hind feet (1,036 cows) were free of active bDD lesions during two consecutive visits. Then, 109 feet were excluded from the dataset because the interval between visits was longer than 45 days. Finally, 743 hind feet of 468 cows from 10 herds were included in the analysis (Figure  2 , total initial feet without lesion). Only 275 cows shared the same bDD-free score in both hind feet at the start of the trial. Significant baseline differences were found between the feet allocation groups for cow feet hygiene, initial prevalence, proportion of heifers, herd size and farm feet hygiene covariates (Table  3 ). Among all of the feet included in the analyses, 161 (21%) experienced a bDD lesion during the trial period. Inactive lesions (142) were more prone to occur than active lesions (19).
Effect on Healing Initially, 1,107 hind feet (53%) out of 2,072 hind feet (1,036 cows) were affected with active or inactive bDD lesions. However, of these feet, 300 were excluded from the dataset because the interval between their visits was longer than 45 days. Therefore, 807 hind feet of 508 cows from 10 herds were included in the analysis (Figure  2 , total initial feet with lesion). Among these cows, 299 had bDD lesions on both hind feet at the start of the trial. Significant baseline differences were found between the feet allocation groups for cow feet hygiene, preventive hoof-trimming, initial prevalence, proportion of heifers and herd size (Table  3 ). Among all of the feet included in the analyses, 186 (23%) achieved the healing either of an active lesion (74) or an inactive lesion (112).
1, unc-38, unc-63 and acr-8  29  ) from H. contortus was also expressed. Expression of recombinant nAChRs was achieved by microinjection of cRNAs into the oocyte cytoplasm and two-electrode voltage-clamp recordings were carried out as previously described  29. Acetylcholine was applied first to check for the presence of functional nAChRs and for current normalization in presence of lupanine or alkaloid extracts. Antagonist modulation of acetylcholine-elicited current was determined by pre-application of lupanine or alkaloid extracts for 10 seconds, followed by their co-application in the presence of acetylcholine. Data were collected and analysed using pCLAMP 10. 4 package (Molecular Devices, UK). In vivo testing. To establish the palatability and the in vivo anthelmintic effect of lupin seeds, 48 female Romane ewe and 48 Alpine dairy goats were allocated to four experimental groups each corresponding to possible meal composition and infection status combinations (Lup-Inf: lupin-fed and infected; Lup-Ninf: lupin-fed and not infected; Conc-Inf: concentrate-fed and infected; Conc-Ninf: concentrate-fed and not infected).
5  56. Model diagnostics was run to check for residual normality and detect putative outliers that were discarded accordingly. Post-hoc comparisons accounting for multiple comparisons between tested effect levels were performed using a Tukey's test implemented with the multcomp package v1. 4-8  57. Dose-response data were modelled as a log-logistic regression with two parameters, i. e. the curve slope and the associated median excitatory (EC50) or inhibitory (IC50) concentration using the drc package v3. 0-1  58. To account for differences in maximal response values, a third parameter was added for electrophysiology data modelling. Linear regression of FEC and haematocrit upon fixed effects (experimental group, day post infection and their interaction) and individual fit as a random effect, was implemented with the lme function of the R v3.
Acknowledgements This project has received the grateful support of the  Centre-Val de Loire region (Lupinema project , grant number  2014-00091669 ). Alkaloid-rich seeds were provided by the  INRA CRB Protéagineux  and nematodes were maintained by the  BRC4Env Animal Parasitic Nematodes collection , the environmental resources pillar of the AgroBRC-RARe research infrastructure. Authors are grateful to  N. Harzic  for providing commercial lupin seeds and to  Dr. P. Driguez  for improving the English language in this manuscript.
The authors concluded that neural network models can be used to simplify data extraction from complex models and be used in instances where the use of the full model is difficult or impossible. Another grey-box model application was proposed by  Hauschild et al. (2012) , who combined black-box (i. e. , empirical) and "knowledge-based" (i. e. , mechanistic) model components to estimate daily amino acid requirements in individual growing-finishing pigs. The empirical component of this model estimated daily feed intake, body weight, and daily gain based on individual information collected in real time. Based on these daily estimations, the mechanistic model component predicted the concentration of amino acids required to meet the daily growth needs. The principles behind this model approach have been described  (Hauschild et al.
all-smart-pigs. com/), and Feed -a-Gene (www. feed-a-gene. eu). An increasing concern is the adaptability and training required by farmers to use PLF systems. Some authors  (Van Hertem et al. , 2017)  believe that use of appropriate data visualization tools can facilitate the farmer acceptance and adoption of PLF applications. These authors tested and evaluated PLF systems on ten fattening pig farms and five broiler farms. Data of production, climate and behaviour was continuously measured, analyzed daily and made available on a webbased tool. Nearly 50% of the farmers took the training, but only 28% of the trained farmers actively used the tool. According to the authors, the success of the training seemed to be dependent on the complexity of the system installed on the farm (e. g. , environmental sensors) and the training/education of the end user.