dataesr/scientific-paragraphs-categorization huggingface dataset:
The veterinarian noted whether the cow displayed signs of dyspnoea, signs of tremor, and other miscellaneous signs, and then collected blood samples into Na2-EDTA-coated vacutainer tubes by puncture of the coccygeal vein. The cow was then released from the headlock. The blood samples were centrifuged at 3000 g for 20 min at 4 °C, and the plasma was collected and frozen at -20 °C to determine cortisol, pro-inflammatory cytokines IL-1β, IL-6, and TNF-α, and the haptoglobin inflammatory parameter. Plasma cortisol concentration was determined by enzyme assay  38. Concentrations of pro-inflammatory cytokines were determined using a custom bovine MilliPlex xMAP cytokine assay (Merck Millipore, France)  39. Data was recorded on a MagPix flow cytometer using Xponent software (Luminex, Austin, TX).
Continuous monitoring of cow activities and position by collar-attached accelerometers. The data from the accelerometers was processed using the FARMLIFE software (ITK/NewMedria, Chateaubourg, France) to provide information on animal activity and position. The accelerometer measures changes of inclination, and lateral and vertical accelerations, from which the behaviour and the posture of the animals are derived by the ITK/ NewMedria Company. Every 5 min, the cow was classified as ingesting feed or ruminating or showing no motion (without activity) or in another activity according to the state in which it had spent most of it time over the previous five minutes. The position of the cow, i. e. standing up vs. lying down, was also recorded. The sensors are able to record eating and rumination time with a precision of 89-90%  41  and from 83% for the position to 90% for no activity  42.
Statistical analyses. For the cows in the CHALLENGE situation, we modelled time-course changes in cortisol, cytokine and haptoglobin levels, RT, HR, RMR and RR using linear mixed-effects models with time (T + 3 hpi, T + 6 hpi, T + 12 hpi, T + 24 hpi vs. T-1 h) for cortisol, cytokines, RT, HR, RMR and RR and with T + 12 hpi, T + 24 hpi vs. T-1 h for haptoglobin, with age (old vs. young cows) and their interaction (age x Time) as fixed effects and 'cow' as random effect. The T-1 h time-point was time before LPS injection (i. e. the control level). Cortisol, IL-6, and TNF-α concentrations were log-transformed to satisfy conditions of normal distribution and homogeneity of residuals. Behavioural data recorded through direct observations in the barn and the feeding motivation test were analyzed as qualitative paired series using McNemar tests.
Discriminant analysis identifies linear combinations of variables that best discriminate groups. The analysis was run on the behavioural data that had showed significant changes in univariate analyses (McNemar test; see above) and on the data from the continuous monitoring of cow activities and position, which was limited to the three hours before each timepoint in order to match it to data from direct observations. All analyses were performed using R software version 4. 0. 5  44. The lmer function from the lme4 package was used for the linear mixed effect models  45  and the emmeans package was used to calculate least square means. The threshold for significance of a fixed effect was set at a t-value ≥|2| and a Tukey-adjusted p value ≤ 0. 05  46. The normal distribution and homogeneity of residuals were visually verified using plots of residuals and quantile-quantile plots of residuals and random effects.
For the McNemar test, a difference was considered significant at p ≤ 0. 05 and as a trend when at 0. 05 < p ≤ 0. 10. The ade4 package was used to perform the factorial discriminant analysis. To interpret the discriminant components of the factorial discriminant analysis, we focused on variables with a correlation of absolute value with the axis >|0. 4|. Only significant results and tendencies are reported in the "Results" section. Results from direct behavioural observations in the barn and the feeding motivation test are reported as number of cows. The results of clinical measurements, analyses of blood samples, and continuous monitoring of cow activities and position are reported as least square means ± Standard Error (SE).
Results Cows reaching an endpoint and final dataset. Two cows reached endpoints. One young cow suffered a hip dislocation between the CONTROL and CHALLENGE situations and was therefore euthanized; all data from this cow were excluded from the dataset. One old cow displayed downer cow syndrome at 24 hpi, with sternal recumbency, apathy, subnormal RT (37. 7 °C), ruminal atony and anorexia. It was given an oral ruminatoric administered (TRANSITONYL, Ceva Santé Animale, Libourne, France) added to water and pumped into the rumen, and it lifted up after 48 h and achieved totally recovery after 15 days. The data from this cow were kept in the final dataset. One young cow was excluded from the dataset due to no sensor data records in the CONTROL situation. The final dataset therefore contained 12 cows: 6 old cows and 6 young cows.
Acknowledgements This work is part of the  LongHealth  project that received financial support from  INRAE  ( GISA [Gestion Intégrée de la Santé Animale] metaprogram ). Sensors for the project were supplied by  ITK/NewMedria. The authors thank  Valerie Gelin  and all the staff at the  Bressonvilliers  farms for their many years of excellent husbandry of the cloned cows,  Charline Pontlevoy  for her courteous and motivational assistance with clinical and behavioural observations,  Christine Ravel  for running the cortisol analyses,  Hervé Chandèze  for his advice on heart rate recording,  Eric Delval  for maintenance and advice on video-recording procedures,  Rodolphe Robcis  for his help at the experimental station, and  Noémie Devriendt  for patiently retranscribing all the data. The authors also acknowledge the help they received for statistical analysis through the  VetAgro Sup  biostatistics training chaired by  Marie-Laure Delignette-Muller ,  Karine Chalvet-Monfray  and  Emmanuelle Gilot-Fromont  and for drafting this article through the UMRH Scientific Writing Workshop chaired by  Isabelle Veissier  and  Isabelle Ortigues-Marty.
Guidelines for Corynebacterium in humans recommend the use of beta-lactams or macrolides. Yet, while chronic human carriers can be treated with azithromycin or rifampicin, the use of the latter is forbidden in the veterinary field. Recent data suggest the use of spiramycin in cases of animal infection by C. ulcerans. A 10-day course of the combination of spiramycin and metronidazole was successful in clearing C. ulcerans from a dog, and a 6-day course of the same antibiotic combination was successful in cats  (18, 29). As spiramycin only exists in a combination with metronidazole, and as metronidazole has side effects (neurotoxicity and effect on the microbiota), azithromycin seems to be a better choice for treatment. The efficiency of the antibiotic treatment should be verified ideally by 2 samples, taken 5 to 7 days following completion of the antibiotic course.
Clinical data (age, sex, and clinical symptoms) were collected using information communicated on the order form. Sterile swabs with liquid Amies culture medium were used to sample the affected animals and were sent to the laboratory under cooled conditions. There, swabs were plated on solid culture media (Columbia agar supplemented with 5% sheep blood and colistin plus nalidixic acid [CNA]) under an extractor hood. Solid culture media were incubated at 35°C under a 5% enriched CO 2 atmosphere for 24 to 48 h. Isolates belonging to the Corynebacterium diphtheriae species complex form dry colonies of a dark yellow color on CNA culture medium. The identity of the suspected colonies was confirmed by matrixassisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS; Bruker Daltonics, Germany).
Note that this method identifies C. diphtheriae and C. ulcerans but does not discriminate well other members of the CdSC from these two species. Antibiotic susceptibility testing was performed by disk diffusion on Mueller-Hinton culture medium supplemented with 5% blood following the instructions of the French AFNOR standard NF U47-107 (https:// www. boutique. afnor. org/fr-fr/norme/nf-u47107/methodes-danalyse-en-sante-animale-guide-de-realisation -des-antibiogrammes-/fa170310/40286). Diameters of the inhibition zone were read and interpreted using software tool SIRWeb (i2A, France). As there are no veterinary breakpoints, the 2013 guidelines from the human CASFM (https://www. sfm-microbiologie. org/wp-content/uploads/2020/07/CASFM_2013. pdf) were applied. The antibiotic susceptibility testing included antibiotics commonly used in veterinary clinics, including beta-lactams (amoxicillin), tetracyclines (doxycycline and tetracycline), aminoglycosides (gentamicin), macrolides (erythromycin, azithromycin, and spiramycin), and others (trimethoprim-sulfamethoxazole).
ACKNOWLEDGMENTS We thank  Stéphanie Gilles  from the bacteriology department of  Cerba Vet  for her support and  Annick Carmi-Leroy ,  Annie Landier  and  Nathalie Armatys  ( Institut Pasteur ) for technical help. This work was supported financially by the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ). This work used computational and storage services provided by the  IT department at Institut Pasteur. The National Reference Center for Corynebacteria of the diphtheriae complex is supported by  Institut Pasteur and Santé publique France (Public Health France ). Initial microbiological analyses and logistics for referring isolates to the National Reference Laboratory were supported financially by Cerba Vet.
M. H. was supported financially by the  Ph. D. grant "Codes4strains" from the  European Joint Program One Health , which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 773830. We declare that 2 authors (K. M. and G. R. ) are employees of  Cerba Vet , which performs diagnostic testing on a commercial basis. K. M. and S. B. conceived, designed, and coordinated the study. G. R. and E. B. performed the microbiological cultures of the isolates and their biochemical and molecular characterizations. M. H. analyzed the sequence data and created the figures. G. A. and K. M. reviewed the clinical source data of the isolates. K. M. and S. B. wrote the initial version of the manuscript. All authors provided input to, read, and approved the final manuscript.
This guidance, initiated and led by the Joint Research Centre (JRC), primarily targets users who implement in vitro methods. GIVIMPs aims to reduce uncertainties in cell-and tissue-based in vitro method application but also provides guidance for in vitro method developers with the following objectives: 1) detailed update on good practices for in vitro method application; 2) help ensure the standard operating procedures (SOPs) are well-designed, robust, well-defined, and can be carried out in a Good Laboratory Practice (GLP) environment; 3) descriptions of the key aspects that may impact the reliability and relevance of the in vitro data; and 4) descriptions of the importance of reporting criteria, applying good experiment design, establishing acceptance criteria, and performance standards based on scientific evidence from the generated in vitro datasets.
EpiSkin™ skin irritation test method The SOP is published on Database on Alternative Methods (DB-ALM) as protocol n °131 (http://cidportal. jrc. ec. europa. eu/ftp/ public/JRC-OpenData/EURL-ECVAM/datasets/DBALM/LATEST/ online/DBALM_docs/). Briefly, the tested chemicals, 10 μL of liquids or 10 mg of solids, were applied on the surface of the epidermis with triplicate tissues for 15 min of treatment at room temperature, followed by 42 h of post-incubation at 37 °C, 5% CO 2 , and 95% humidity. Cell viability was determined by the MTT assay and measured optical density (OD) at 570 nm and expressed as a percentage normalized to the negative control set at 100%.
squaredGLMM function of the MuMIn package  (Barton, 2016). We used p-value only for interpretation of the F-test conducted in order to test prediction 1 of a relationship between FCMs and immunity. This was necessary only when interactions between FCMs and age, year quality, sectors, body weight, or sex were retained by the model selection procedure and interaction parameters did not overlap 0. In this case, the marginal effect of each variable is not estimated by the model and must be tested separately. For each parameter in each population, the normality of residuals was tested (Shapiro-Wilk test) and visually assessed. All analyses were performed using R version 3. 5. 1 (R Development Core Team 2018) and using the lmer function (lme4 package,  Bates et al. , 2014)  and the dredge function in order to generate a model selection table of models (MuMIn package,  Barton, 2016).
The onset of lactation and the subsequent habituation to the milking routine is a stressful period, particularly for primiparous (PRI) cows. The objective of this study was to describe the dynamics of milking unit kick-off (KO) behavior in PRI cows during the first 3 mo of lactation, considering multiparous (MUL) cows as a reference for comparison. In addition, the potential associations between KO and milk yield and mastitis presentation were investigated. A total of 869 cows (PRI = 199; MUL = 670) on a dairy farm in northern CO were included in the analysis. Cows calving between August and November 2020 were enrolled from 3 DIM until 90 DIM. Participants were milked 3×/day in a 60-unit rotary parlor and data from each milking session were downloaded from parlor management software. Milking unit kick-off was used as a proxy for habituation to the milking procedure.
Kick-off events were reported by the milking system and defined as an abrupt interruption in the milk flow during the milking process. Cow KO events were recorded for each milking session. Subsequently, occurrence of KO was analyzed by grouping the 3 consecutive milking sessions in each day and categorized as yes or no, indicating whether or not an individual kicked at least once in a given day. Data were analyzed by repeated measures ANOVA and logistic regression, including parity category, calving season, occurrence of dystocia, and their potential interactions in the models. Least squares means for daily proportions of KO were calculated considering the number of cows with KO events per day in the PRI and MUL categories. Subsequently, odds ratios for the occurrence of KO in PRI versus MUL were calculated at multiple periods of time.
Behavioral and milk production data were collected from an organic-certified dairy farm in northern Colorado using a parlor management software (DelPro Farm Manager software; DeLaval International AB, Tumba, Sweden). A total of 869 Holstein cows  (199 PRI and 670 MUL)  that calved between August and November 2020 were enrolled in the study at 3 DIM and monitored until 90 DIM. As all the data used in this study were collected from on-farm software, no Colorado State University Institutional Animal Care and Use Committee approval was required. Study cows were milked 3 times per day in a 60-unit rotary parlor (DeLaval International AB). The main variable of interest was milking unit kick-off (categorized as yes or no), monitored with the electronic on-farm milk meters (DeLaval model MM27BC). The meter measures milk yield, milk flow, and milking duration by use of infrared light technology.
Kick-off occurrence, detected through these measurements, is reported and stored in the parlor management software, where the records become available for transferring into Excel (Microsoft Corp. ) spreadsheets. Milking unit kick-off was identified by the milking system as an abrupt interruption in the milk flow during the milking process and considered in this study as a proxy for habituation to the milking routine. Data pertaining to individual cows, such as parity, calving date, and calving ease score, were exported from PCDART herd management software (Dairy Records Management Systems). Reports were then generated and downloaded via a remote server once weekly for the duration of the study, which lasted until the last cow enrolled had completed her first 90 DIM. Data exploration and descriptive analyses for daily average milk yield and daily average milk flow were performed using PROC MEANS and PROC GLM (SAS 9.
05; interaction terms remained in the models at P-value ≤ 0. 10. The study group consisted of 199 PRI and 670 MUL cows. A total of 4 study cows (1 PRI, 3 MUL) left the milking herd before completion of the monitoring period, but data collected from these cows before leaving the herd were still included in the analysis. Figure  1  provides daily milk yield (kg/d) and average milk flow (kg/min) during the study period for the participant cows separated by parity category. Though not directly related to the main goal of this study, data were consistent with other research on the relationship between parity and milk production, with daily yield and flow rate being higher in MUL cows than PRI  (Erb and Martin, 1980; Firk et al. , 2002; Lee and Kim, 2006; Ben Meir et al. , 2019). Average (SE) number of days with a KO event were 11.
GPS location data Our main source of location data is Movebank 3   [12] , an online repository for animal movement data. The location data in Movebank is presented as latitude/longitude pairs along with UTC timestamps and is grouped into trajectories (deployments) and associated with (occasionally missing) metadata such as a taxon name, sex, and date of birth. We used the Movebank API to retrieve data from GPS sensors for all 269 studies that were available  4  for download under a Creative Commons 5  license (CC0, CC BY and CC BY-NC), obtaining 13 577 trajectories comprising a total of 197 million observations (location events). We subsampled the trajectories (splitting them into segments when necessary) so that observations occur at midnight and at noon (according to local mean time) with a tolerance of ±3 h and so that the time difference between consecutive observations is 9 to 15 h.
We discarded trajectory segments shorter than 120 observations, leaving us with 1440 trajectories from 98 different studies. See Table  4  in the appendix for the full list of studies and their licenses. We added unpublished data from 4 more studies, collected by one of us (S. C-J). These are GPS data from plains zebras and African elephants, collected in Hwange National Park (Zimbabwe), and GPS data from plains zebras and blue wildebeest, collected in Hluhluwe-iMfolozi Park (South Africa). After subsampling and filtering as in the case of the Movebank data, we obtained 73 2) each individual appears in exactly one split. Table  1  details the amounts of    extends, to some degree, to similarity between species, we display in Fig. 2  the PCA (principal component analysis) projections of species embeddings, labeled by higher taxonomic ranks.
e. ξ 1. n along with the timestamp for the next observation, t n+1. The candidate selection module then encodes each candidate x (i) n+1 and employs an attention mechanism to compute a probability distribution over the candidates. The model is described in detail in Section 3. 1, followed by our choice of input representation in Section 3. 2. ϕ in 1 ϕ in 2 ϕ in N -1 • • • trajectory encoder causally masked Transformer h 1 h 2 h N -1 candidate selection module softmax i q ⊤ n k (i) n / √ d sel ϕ cand,(i) 1 ϕ cand,(i) 2 ϕ cand,(i) N -1 k (i) 1 k (i) 2 k (i) N -1 q 1 q 2 q N -1 Figure  3 : The high-level architecture of MoveFormer. The input to the trajectory encoder is a sequence of embedding vectors ϕ in 1. N -1 , each corresponding to a different data point (location-timestamp pair) in the trajectory.
Location In the raw data, each location x n is represented as a GPS coordinate pair (latitude, longitude). We represent it as a geodetic normal vector (n-vector) ν(x n ) ∈ R 3. Additionally, we encode the position relative to the previous location x n-1 as a movement vector µ(x n-1 , x n ) ∈ R 2 , obtained by computing the bearing and distance from x n-1 to x n and converting them to cartesian coordinates. We apply scaling to make the overall root-mean-square (RMS) of the norms of movement vectors computed on the training dataset equal to 1.
Candidate sampling We sample each candidate location x (i) n+1 as follows: • we estimate the current bearing β of the animal from the positions x n and x n-1 ; • we independently sample a turning angle θ ∼ P (θ) and a log-distance log d ∼ P (log d); β ′ ← β + θ; • we compute x (i) n+1 by moving x n according to β ′ and d. P (θ) and p(log d) are estimated on the training set as follows: • We collect all turning angles from the training set and compute the quantiles (estimated using linear interpolation) at 101 equally spaced points 0 = q 0 , q 1 ,. , q 100 = 1. We use them to construct the quantile function of P (θ) as a piecewise linear function with knots at q 0 , q 1 ,. , q 100. • We collect the natural logarithms of all non-zero distances between consecutive points in the dataset; we construct the quantile function of P (log d) analogously.
Implementation details and hyperparameters Our implementation of MoveFormer, available as open source software, 7  is written in Python using the PyTorch framework 8  and the x-transformers 9  package. The code for efficient geospatial variable loading relies on the rasterio 10  library and is released as a separate package, gps2var. 11 he trajectory encoder is a 6-layer Transformer with 8 attention heads per layer and a feature dimension of 128. The candidate encoder is a fully-connected neural network with one hidden layer of size 256 and a GELU activation  [173]. The candidate selection module has d sel = 128. The total number of parameters of the model is around 2. 6 million -several orders of magnitude smaller than current state-of-the-art Transformer language models, for instance, but appropriate for the limited-size dataset that we are working with.
Candidate feature importance While the parameters of step-selection models fitted by conditional logistic regressions or point-process models are directly interpretable  [4] , deep learning models are known as "black boxes" that require special techniques to be interpreted post-hoc. A simple but popular technique  [175, 176]  is based on testing the model on a dataset with the values of a given feature randomly permuted. While aware of the caveats related to using this technique with correlated features  [177] , we employ it here to demonstrate the possibility of interpretation, and leave more advanced techniques for future work.
Specifically, we study how individual candidate features (components of ϕ cand,(i) n ) influence the selection of candidates. We pick a feature (or a group of features), and for every observation in the dataset, we randomly shuffle the feature's values among the K candidates (in contrast to Fisher et al. [176] , who shuffle values across the entire dataset). The aim is to make the feature completely uninformative while maintaining its values plausible in the given context. We evaluate the model on both the permuted and the original dataset, and use the difference in performance as a measure of the importance of the selected feature.
Our results suggest that for most datasets, predictions are improved when integrating the information from about a few days to two or three weeks before the movement to be predicted. Why this is the case, and why these results are broadly consistent between species, with possibly significant within-species variability, remains to be investigated further as it was beyond the goal of this methodological work. We note that, possibly, these results are affected by our choice to alternate sampling at midnight and at noon and to limit the length of trajectories to 500 locations, restricting the receptive field of our model to about 250 days. This may have weakened or excluded the influence of migration, which commonly leads to seasonal back-and-forth movement patterns and that, when accounted for, could help improve predictions about future movements.