dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; Viazzi et al. , 2014)  or sows' posture  (Nasirahmadi et al. , 2015; Leonard, 2019). Moreover, diet formulation options should be added in the model and available "in real time". All these adjustments will require the farms to be equipped with automated feeding equipment, devices including the full DSS and sensors (weighing scales, cameras, accelerometers, hydro-thermometers, etc. ) that will continuously collect information to increment real-time databases. These changes will also allow to improve the management of sows across their successive physiological stages and over their whole lifetime.
ACKNOWLEDGMENTS Many thanks to  Chandra Logie ,  Craig Pennycook , and  Craig Johnstone  from the  University of Edinburgh  for supporting this project. The authors' gratefully acknowledge  Hallmarq  for support in MR scanning of the specimen for this study and IMV-imaging for sponsoring Claudia's internship/residency in equine diagnostic imaging. Histological processing and interpretation was part fund by  National Research Foundation (NRF) South Africa.
4 of cow height (carpus), was thinner than 15 cm (neck/shoulder/back and carpus) and when the height of the feeding step was 0. 04 to 0. 1 of cow height (neck/shoulder/back) and the length of the feeding step was <0. 2 of cow length (carpus). A headlock articulation nut positioned between 0. 62 and 0. 78 of cow height significantly reduced skin injuries on the neck/shoulder/back. Here, by combining the diversity of on-farm self-locking barriers and their respective dimensions, we were able to refine the International Commission of Agricultural and Biosystems Engineering recommendations for self-locking barrier design and to propose new ones. This information now needs to be confirmed on other datasets, but can already help farmers and dairy industry stakeholders improve the design of self-locking barriers to improve dairy cow welfare.
Statistical analyses The analysis included a total of 3801 cows with access to at least one self-locking barrier. As each cow can come across one to three types of self-locking barriers in its pen, we linked each cow to all self-locking barriers in its pen and obtained 5137 'cow-self-locking barrier' dyads. There were 85 farms with one type of self-locking barrier, 37 farms with two types, and 5 farms with three types of self-locking barrier. First, for each cow, we built Boolean data to express the presence v. absence of skin injuries on each body part. Then, based on the dimensions recorded, we modelled the shape of each self-locking barrier in space (Supplementary Figure  S1 ). This information was used to graphically check the consistency of on-farm data recorded. Because the type and dimensions of self-locking barriers can vary within farm, we assumed that the effect of a given type of self-locking barrier was proportional to its proportion on the farm.
The third step consisted of simplification of each of the full models by bidirectional stepwise elimination according to the AIC  (Akaike, 1973). The simplification was stopped once no factor that would decrease the AIC value could be added or removed  (Burnham et al. , 2011). We also only kept factors that are significant enough (i. e. P < 0. 05). For qualitative factors, we considered that at least one modality (except 'missing data') needed to be significant with a P-value <0. 05. The fourth step consisted of assessing model robustness and efficiency using leave-one-out cross-validation (e. g. Refaeilzadeh et al. , 2016). The cross-validation consisted of removing each cow from the dataset before calculating the coefficients of the model. Then, using the new coefficients, we predicted the probability of skin injury for the missing cow.
Two types of cross-validation were performed: by removing a whole farm, or by removing single cows. We plotted the receiver operating characteristic (ROC) curve that illustrates the diagnostic ability of a model as its discrimination threshold varies. It corresponds to the true-positive (i. e. cows predicted with injuries and that were observed with injuries) rate against the falsepositive (i. e. cows predicted with injuries and that were observed with no injuries) rate at various threshold settings. We used the area under the curve (AUC) index to compare the different models. The AUC index ranged from 0 to 1, where 1 represents a perfect model and 0. 5 corresponds to a random model  (Dohoo et al. , 2009). All calculations were performed with R software v3. 4. 4 (R Core Team, 2018). The speelglm package  (Enea, 2017)  was used to calculate logistic regressions.
The MASS package  (Venables and Ripley, 2002) , vcd package  (Meyer et al. , 2017)  and wCorr package  (Bailey and Emad, 2017)  were used to calculate correlations and simplify the models. The ROCR package  (Sing et al. , 2005)  was used to calculate ROC curves and AUC indexes. The foreach package (Microsoft Corporation and Weston, 2017a) and doParallel package (Microsoft Corporation and Weston, 2017b) were used to shorten calculation time for model assessments and to calculate optimal quantile thresholds. Calculations were performed at the Mésocentre Clermont Auvergne supercomputer facilities. The sample of farms was surveyed in 2010 and reflects the diversity of dairy systems in France.
Prevalence of skin injuries In our dataset, 14. 0% of the 3801 cows had skin injuries on the carpus and 29. 0% on the neck/shoulder/back. There were 13. 4% and 0. 34% of missing data for observations of the carpus and neck/shoulder/back, respectively. These results are reported and discussed elsewhere (de Boyer des Roches et al. , 2019).
Bottom rail position The risks of skin injuries on the neck/shoulder/back and carpus reduced when the bottom rail was positioned on the food side relative to the separation wall. This is a novel finding that, if confirmed by other studies, merits a new recommendation. As previously shown on the same dataset (de Boyer des Roches et al. , 2019), the CIGR recommendation for bottom rail height (0. 4 of cow height relative to the walking alley, that is, 57 cm for an average 144-cm-high cow) was associated with increased risk of skin injury. We, therefore, suggest refining the current recommendation, and we recommend positioning the bottom rail on the food side, at <0. 39 of cow height relative to the walking alley and <0. 25 of cow height relative to the feeding floor.
Performances of the models This study was performed on a big dataset of 3801 cows from 131 farms. This large dataset allowed us to construct models combining multiple factors. The models gave better predictions of risks of skin injuries on the neck/shoulder/back than on the carpus. Our results, therefore, suggest that self-locking barrier design is a better predictor of skin injuries on the neck/shoulder/back than on the carpus. This might be explained by the influence of other risk factors not studied here. For instance, the cubicle floor surface is known to impact the prevalence of skin injuries on the legs, and the cubicle neck rail position is known to impact the prevalence of skin injuries on the neck/shoulder/back (see European Food Safety Authority (EFSA) (2009) for a review). Further studies focusing on the links between cubicle design and legs and neck/shoulder/back problems might help refine the existing recommendations.
By considering only elements that were present in >5% of the dataset, we were able to avoid potential overinterpretation. The data used in the present study is 10 years old; however, they are still relevant. First, they still cover the main farm types of France  (Agreste 2010 and 2019). Second, French cow dimensions might have slightly changed; however, by considering factors expressed in relation to cow dimensions and by taking into account all types of self-locking barriers present within the farm, we increased the genericity of the models. However, this study was performed in France during winter, and the models would benefit from larger studies in other countries and/or conditions to confirm our findings. Even allowing for these limitations, the results still give sound pointers where to refine the existing recommendations and where to propose new ones.
Acknowledgements This work received financial support from  Danone Research-ESTIVE Centre  and the  French government's IDEX-ISITE  initiative  16-IDEX-0001. We thank the  'Groupements de Défense Sanitaire'  for providing lists of eligible farms, and all the farmers who willingly cooperated in this survey. We also thank the  Mésocentre Clermont Auvergne  for providing access to the supercomputer facilities to compute the various models. We are particularly grateful to  Eric Delval (INRAE) ,  Christophe Mallet (INRAE) ,  Maud Coignard  (ONIRIS) and  Remi Debauchez  ( ISARA ) for their help with data collection; Jean-Yves Audiart (ONIRIS),  Didier Billon  (ONIRIS) and  Eric Delval (INRAE)  for their help with data input; and  Jos Noordhuizen  ( VetAgro Sup ),  Raphaël Guatteo  (ONIRIS),  Nathalie Bareille  (ONIRIS) and  Claire Agabriel  ( VetAgro Sup ) for their valuable advice on survey design.
DHPLC and sequencing The KIT Exon 9 PCR amplicon was run on an automated denaturing high-performance liquid chromotography (DHPLC) apparatus (WAVE DHPLC system, ADS Biotec Limited) equipped with a proprietary column (DNASep, Transgenenomic). Elutions were carried out with a mixture of Buffer A (0. 1 mol/L triethylammonium acetate-TEAA) and Buffer B (0. 1 mol/L TEAA, 25% acetonitrile). The amplicons were run at two different partiallydenaturing temperatures of 56. 3 °C and 57. 8 °C used for the DHPLC screening of the entire exon 9, including the p. Asp508Ile mutation as previously reported  [25]. The data analysis was carried out using Navigator software, (ADS Biotec Limited). The PCR products were then purified using ExoSAP-IT PCR Product Clean-Up kit, and forward and reverse direct sequenced using Big-Dye terminator chemistry, additionally purified with Centri-Sep columns (Life Technologies, Monza, Italy) and electrophoresed on an ABI Prism 310 automated sequencer.
After transfection, the HEK293 cells were cultured for another 8 h and were then serum starved for 16 h. The HEK293 cells were then additionally cultured with masitinib in order to study the tyrosine kinase inhibition (ChemScene; 0-10 μM) for 90 min. The cells were then lysed, and the aliquots underwent Western blotting using the following antibodies: polyclonal rabbit anti-human CD117 antibody (Dako) or monoclonal rabbit anti-human phosho-c-Kit (Tyr703) antibody (Cell Signaling), followed by horseradish peroxidase-conjugated donkey anti-rabbit immunoglobulin G whole antibody (GE Healthcare). The immunoreactive bands were visualised using an enhanced chemiluminescence system (GE Healthcare) and the LAS-4000 (Fujifilm). The band intensities were semi-quantified using ImageQuant TL software (Fujifilm), and signal levels for phosphorylated KIT were normalised to the levels of KIT expression.
Funding The study was supported by  RFO grants  from the  University of Bologna. MET and CD are affiliated at commercial laboratories carrying out veterinary testing. As such, these laboratories provided indirect financial support in the form of authors' salaries and/or research materials. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
Calculations and Statistics Feed intake of each pig was determined from the daily weighing of feed offer and refusal. Then, average daily feed intake (ADFI in g/d or in g/d/ kg 0. 60 ), average daily gain (ADG in g/d), and the feed-conversion ratio (F:G in kg of feed/kg of gain) were calculated for the total duration of the experiment. These data were analyzed in a general linear model using the GLM procedure of SAS (SAS Inst. Inc. , Cary, NC), with the fixed effects of experimental group, replicate, and their interaction. Within each period, performance data were split into three time intervals: days -3 to 0, days 0 to 5, and days 5 to 12. In P1, data from the TTT, THT, and TTH groups were pooled into a single group called "T," while data from the HTT and HHH groups were pooled into a single group called "H.
" In P2, data from the TTT, TTH, and HTT groups were pooled into a single group called "TT&HT," while the THT and HHH groups were renamed "TH" and "HH," respectively. In P3, data from the TTT, HTT, and THT groups were pooled into a single group called "TTT&HTT&THT. " These data were subjected to a repeated MIXED procedure of SAS, with the fixed effects of experimental group, replicate, time interval, and their interactions. Blood parameters were analyzed with a similar model. ST and RR were first averaged (from the measurements taken at 0900 and 1600 h) and, then, in each period, the effect of the duration of the HS challenge on these thermoregulatory traits was analyzed using a MIXED model, with the fixed effects of experimental group, replicate, time interval, and their interactions. Continual measurements of internal body temperature and ADFI were averaged daily per pig for each of the three periods.
Acknowledgements The authors acknowledge the  Brazilian Program Science Without Borders -CSF/CNPq , for funding and supporting the project, the staff of  INRA-UERT  and  INRA -La Fage , and  Boris Cluzel , for their valuable assistance in field. The authors also thank the editor and the reviewers for insightful contributions to the paper. Priscilla Regina Tamioso ,  0000-0003-3141-9720.
The imaging assessments were performed again, confirming the extensive lesions of the initial assessment (Fig. 3 ). Bacterial DNA in the grains isolated from the patient was extracted using bio-robot EZ1 and then subjected to a conventional PCR, targeting the 16S-rDNA gene with fD1 (fwd: AGAGTTTGATCCTGGCTCAG) and rp2 (rvs: ACGGCTACCTTGTTACGACTT) primers  [9]. Obtained bilateral sequences were edited, aligned using BLAST, and identified as Actinomadura madurae, based on ≥99% identity with GenBank sequence (NR026343)  and then deposited in GenBank with accession number NZ148818. In order to evaluate intraspecific variability, phylogenetic relationships, and polymorphisms within Actinomadura species, an inferred phylogenetic tree of A. madurae (identified in this study) together with GenBank sequences, was constructed based on the Neighbor-Joining method with bootstrap values, determined by 1000 replicates.
Nevertheless, some reports of autochthonous cases from Italy  [23]  and France  [24]  have been also documented, suggesting the local occurrence of actinomycetoma in Europe. Diagnosis of the mycetoma and identification of etiological agents is a challenging issue, especially in non-endemic areas. Microbiological culture and histopathological analysis are the diagnostic tools used for the identification of causal agent(s)  [7]  but they possess some drawbacks, including failure in culture, difficulties in the direct examination or invasive biopsy  [25]. Molecular analysis of bacterial species isolated from our patient revealed A. madurae as the etiologic agent with ≥99% identity with GenBank sequences. Therefore, molecular analysis can be considered as a reliable tool, asserting accurate identification of causal agents  [26].
After adapting to a commercial adult maintenance dry diet (Veterinary™ HPM Adult Large and Medium, Virbac, Carros, France) for four weeks, the dogs were equally divided into two groups, randomly, matched by age and sex. In the control group, eight beagles were fed to maintain a lean body weight and body condition score (BCS 4-5/9) for 83 weeks  [24]. Part of the dataset from these eight beagles were published by Liu et al  [25, 26]. The body condition scoring system based on 9-points used in this study is a validated method to semi-quantitively assess the body composition of dogs base on visual and palpable characteristics  [24]. The system ranges from 1 which stands for cachectic to 9 which stands for severely obese. A BCS of 4 or 5 represents an ideal body composition  [24]. Individual maintenance energy requirements, based on the National Research Council requirement of 552 kJ/kg 0.
A bolus was repeated if a large movement artifact was present. The recording from the second bolus of the left kidney was used for further evaluation  [32]. An example of a recording can be seen in S1 Video. The CEUS clips were analyzed using specialized computer software (VueBox, Bracco Suisse, Switzerland) for objective quantitative analysis by first author. The quantification protocol was previously described by Liu et al  [25]. Briefly, three region-of-interests (ROI) were manually drawn, keeping the area and depth for each location approximately the same for every dog at all time points (Fig 2 ). The software generated time-intensity curves and determined mean pixel intensities for each ROI. Intensity-related parameters representing blood volume and time-related parameters representing blood velocity were determined from the time-intensity curve.
For the CEUS parameters, kidney depth was accounted for in the mixed models (fixed effect). Dog and kidney side were included as random effects. When the interaction was significant, a post hoc comparison for each time point was conducted and a Bonferroni correction was consistently applied to account for multiple testing. The results are presented as mean ± SD for body characteristics, sBP, serum leptin, serum creatinine, BUN, mGFR, UPC, sCysC and the urinary biomarkers; and for the CEUS parameters of the cortex and medulla of both groups or median (minimum-maximum) for BCS. All statistical analyses were conducted in R version 3. 4. 4. In all mixed models, the default variance-covariance structure of the lme4-package was used.
Acknowledgments The authors want to thank  Bracco Suisse SA (Geneva, Switzerland)  for their scientific support through the use of VueBox 1. The authors thank  Elke Lecoqc ,  Kristel Demeyere ,  Siegrid De Baere ,  Jelle Lambrecht ,  Pieter Defauw  and  Agnès Andre  for assistance with analytical work; colleagues of the  Laboratory of Animal Nutrition , the  Department of Veterinary Medical Imaging  and the  Small Animal Department  for assistance during sample collections; and  Daniel Tensy ,  Amy Deluycker ,  Liesbeth Timmermans  and  Isolde Tack  for excellent care of the animals.
The pooled prevalence of lameness in British dairy cattle was estimated at 29. 5% (95% CI 26. 7-32. 4%) whilst all-cause lameness incidence rate indicated 30. 9 cases of lameness per 100 cow-years (95% CI 24. 5-37. 9). The pooled cause-specific lameness incidence rate per 100 cow-years was 66. 1 (95% CI 24. 1-128. 8) for white line disease, 53. 2 (95% CI 20. 5-101. 2) for sole ulcer, 53. 6 (95% CI 19. 2-105. 34) for digital dermatitis, with 51. 9 (95% CI 9. 3-129. 2) attributable to other lameness-related lesions. Heterogeneity levels remained high. Sixty-nine papers contributed to a chronological overview of lameness data source. Although the AHDB Dairy mobility scoring system (MSS) was launched in the UK in 2008 and adopted shortly after by the British Dairy sector as the standard tool for assessing lameness, other methods are used depending on the investigator.
MATERIALS AND METHODS A systematic review to identify papers reporting frequency of lameness in British dairy cattle was conducted in six electronic scientific literature databases-Agricola, Cab Direct, Cochrane Library, PubMed, Scopus, and Web of Science (all databases) on the 4th of January 2020. The systematic review protocol was developed based on the Cochrane guidelines  (38) , and the PRISMA-P (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement  (39) , with specific modifications for a systematic review reporting measures of disease frequency, as recommended by the Joanna Briggs Institute  (40). The search was limited to peer reviewed articles, published since 1823 in English. The population search terms were (dairy AND cattle) AND (UK OR Britain OR British OR kingdom).
The outcome search terms were (lameness AND (prevalence OR incidence). The following code was used for all six databases considered: (dairy AND (cattle OR cow * ) AND (UK OR British OR Britain OR kingdom) AND (lame * OR locomotion) AND (incidence OR prevalence). The search through Scopus was limited to abstract, title and keywords. A synthesis of the diagnostic protocols used was also conducted, with the objective of identifying temporal patterns in the use of different methodologies and to determine if any diagnostic protocol has been used consistently over time. EndNote X9 (Thompson Reuters) bibliographic software was used to manage citations. Duplicate entries were identified, using the automatic function in EndNote and manually during the screening process, by considering the author, the year of publication, the article title, and the volume, issue, and page numbers of the source.
Only studies published in peer-reviewed journals were included, with no date restriction. Language of publication was restricted to English. Papers that reported data from previous publications were excluded as to have only one entry per data collection exercise. If the study met all the inclusion criteria but didn't provide data on the number of lame cows and/or study population the corresponding author was contacted via email in an effort to retrieve the missing information and for clarification. If the corresponding author was not available, one of the co-authors was contacted. If the author(s) did not reply or could not provide the information requested the paper was excluded from the meta-analysis. In addition to the references identified through the systematic review, five other papers were identified  (41) (42) (43) (44) (45)  following a backward search (also known as chain search) on the papers admitted for full-text screening, and added to the database (Figure  1 ).
The backward search involves identifying references cited in an article that may be relevant for the study in question  (46). For the chronological analysis of the use of different lameness detection and classification methods, all studies that underwent full-text screening were considered. Papers with information on the lameness data source were included, regardless of them meeting inclusion criteria regarding lameness data availability. Papers that reported the same original research were considered duplicates and were removed (Figure  1 ). The usage of lameness detection and classification methods through time was analyzed by means of a histogram.
Database Management and Parameters Microsoft Excel  (48)  was used to create a database with the data extracted from the papers. Most variables contained a substantial number of categories, or range in values. This would result in a high number of strata with small number of papers when conducting the analysis, at the expense of statistical power. In order to solve this problem new binary variables were created based on the values extracted from the papers (Supplementary Table  2 ). Papers reported lameness for different study units depending on their study population: heifers, cows, lactations and culled cows. Papers reporting lameness per lactation were included in the category "cow, " assuming that each lactation represents a dairy cow. Sample size was used to create a new binary variable, and to explore the potential effect of smaller sample sizes on lameness estimates.