dataesr/scientific-paragraphs-categorization huggingface dataset:
lingua from Atlantic cod and whiting were added to the GenBank database. On the cox1 gene, no significant similarity was found with the available sequences of the other parasites causing black spot diseases (Apophallus, Haplorchis, Stellantchasmus). Likewise, the percentage of similarity (4 to 96%) and of query cover (10 to 70%) was lower for other parasites inducing black spot disease than for Cryptocotyle. Phylogenetic trees on the cox1 gene and ITS region showed differences for C. dominicana and outgroup phylogenetic positions. This result can be attributed to the evolutionary history of these selected regions. For instance, the selected cox1 region is a short fragment compared with the ITS region sequence. In both trees, phylogenetic comparisons within the Cryptocotyle genus revealed high sequence similarity for both parasite origins with C.
Acknowledgements The authors are indebted to  J. P. Jeppesen ,  Øresund aquarium (Helsingør, Denmark)  for supplying cod for our studies. We also thank all the staff of the  Laboratory of Aquatic Pathobiology of the University of Copenhagen  for technical help in this study and  M. Leblond  who fished the whiting samples. In addition, we would like to thank  G. Karadjian  for his help with the phylogenetic analyses. The authors would like to thank  D. Pottratz  from  Anses  and  C. Engel-Gautier  from  Coup de Puce Expansion  for English language editing.
| Association study in the G 0 population A regression-based association study was performed between FEC measured at first and second infection in the G 0 population to evaluate the segregation of the targeted QTL and their effect in the G 0 population. At each polymorphic marker (n = 898, Table  S1 ), phenotypic data were regressed upon fixed effects (sex, feeding lot and lamb suckling modality, i. e. artificial or not), a polygenic effect estimated from all SNPs (accounting for population structure) and the marker effect as already implemented elsewhere  (Legarra et al. , 2015). SNP substitution estimates and associated standard errors were used to derive P-values from a Student's t-test statistic. QTL heritability was estimated by aggregating the ratios of additive genetic variance to phenotypic variance estimated as part of the REML procedure run for each SNP.
| Response to selection To estimate phenotypic divergence in FEC between sheep lines, individual H. contortus infection data were pre-corrected for fixed environmental effects, that is sex, milking mode (artificial or not) and generation (accounting for year effect), and an individual random effect, using the nlme package v3. 1-140  (Pinheiro et al. , 2019). Individual random effects were standardized to unselected G0 mean and standard deviation. Responses to selection for FEC at first and second infection or across infections were evaluated within each generation and each line by regressing individual random effects from H. contortus-infected offspring upon their respective midparent values, computed as the average value of each lamb's sire and dam  (Falconer & Mackay, 1996). This regression coefficient provides an estimate for realized heritability  (Falconer & Mackay, 1996)  and was used to establish the asymmetry of response between resistant and susceptible lines.
To estimate the expected genetic gain across infection, we computed the mean genetic gain between first and second infection. We considered pedigree-based breeding values (eBV) as they were available across generations (geBVs were only available for G0 and G1 individuals) and were strongly correlated with geBV. eBVs were estimated from recorded phenotypes in G0, G1 and G2 individuals using a mixed model including fixed environmental effects and a random individual effect estimated from the pedigree relationship matrix (encompassing 1559 individuals) as implemented in the AIReml software (http://nce. ads. uga. edu/wiki/doku. php?id=readme. aireml). Genetic gain within each line (R line ) was expressed in genetic standard deviation (σ g ) and was estimated as the cross-product between the overall selection intensity i (average of selection intensities in males, i m , and in females, i f ) and the selection accuracy h  (Falconer & Mackay, 1996)  as: R line = 0.
For both G1 and G2 generations, total expected divergence was derived as the sum of expected responses from resistant and susceptible lines. To estimate putative trade-offs between resistance to GIN and lamb weight, measured body weights were modelled using a mixed model with repeated measures, including fixed effects (litter size, sex, generation, day postinfection, and an effect aggregating line and corresponding generation), and a random effect accounting for interindividual variations. To account for differences in body weight at the beginning of the trial, weight data measured before any infection took place was fitted as a covariate.
Behaviour data were considered as normally distributed (Shapiro-Wilk's test ranging between 0. 91 and 0. 98). Physical contact records were however skewed towards 0 and were thus binary encoded, that is 0 or 1 in absence or presence of contact with their mates or with the operator, and modelled using a logistic regression framework. To test for the effect of behaviour treatment, recorded behaviour data were regressed upon sheep line, sex and day and their interactions, and a random effect accounting for inter-individual variation. Regression models were built following stepwise variable selection procedure that aims to find the model with minimal Akaike Information Criterion (AIC). Bleating records in phase 1 of the test were also corrected for their initial value before chronic stress treatment took place to correct for the increased bleating in susceptible lambs from the stress group.
To estimate the density of sett clusters in suitable habitat by study site (D C. Distance ; Table  3 ), we used stratumspecific detection probabilities. The effective strip width was based on the best model, and encounter rates were calculated for each study site. We derived confidence intervals from a non-parametric bootstrap method, resampling transects within each study site to estimate bootstrap variances of the encounter rates (with n = 999). We then derived the density of sett clusters in each site (D C , Table  3 ) by multiplying each D C. Distance by the proportion of suitable habitat in the corresponding study site. All analyses were conducted in DISTANCE software 7. 3  [55]. A DISTANCE project containing the data and analyses is available from the authors.
Determinants of spatial variation in density In order to identify which ecological factors influenced spatial variation in density, we performed a PCA using the FactomineR package  [56]  operating in R software  [57]. We included in the PCA adult density (D Ad ), each intrinsic component used in Eq. 1 (n = 7), and extrinsic factors linked with local environmental characteristics (n = 10). The environmental variables included were Suit. area, Edge density (m/ha), VRM (°), Soil texture and depth (indices), Earthworm abundance (ind. /m 2 ), percentage of Pasture, Maize, Urbanised area in the study site, and Road density (km/km 2 ), defined in Additional file 4. PCA allowed us to identify a reduced set of variables, both intrinsic and extrinsic, likely to be associated with D Ad ; we further explicitly tested for the association using non-parametric Spearman's rank correlation tests (R package pspearman,  [58] ).
Acknowledgements We are very grateful to all the people who participated in the fieldwork at the study sites, in particular the students:  T. Beshers ,  A. Blain ,  C. De Moncuit ,  E. Harwood ,  M. Lemaire ,  T. Ludemann ,  S. Malevre ,  C. Pacot ,  P. Perrin ,  J. M. Poincelet ; their field supervisors:  M. Bouchez-Zacria ,  F. Boué ,  A. Courcoul ,  J. M. Demerson ,  L. Legras , and  C. Richomme  ( French Agency for Food, Environmental and Occupational Health and Safety; ANSES) ,  C. Peboscq  ( Departmental Hunting Association in Pyrénées-Atlantiques ;  FDC64 ),  C. Langumier  (National Forestry Office; ONF),  A. M. Griffon-Picard  ( Departmental Direction  for the Protection of Populations in Seine-Maritime; DDPP),  S. Depraz  ( GDS 24 ), and our colleagues  M. Albaret ,  S. Brandt ,  H.
Data Information retrieved included signalment (breed, sex and age), weight, body condition score (BCS), initial and follow-up biochemical values, affected kidneys, operative and anesthetic times, intraoperative urine culture results, bacterial species isolated, duration, administration of antibiotic treatment including prophylactic parenteral administration of antimicrobial in the immediate proximity of surgery, or antimicrobial gave postoperatively, antimicrobial type used, preoperative and postoperative antimicrobial route of administration, time between surgery and discharge. At the end of the follow-up period, the number of animals with persistent ITU and cause of death were noted.
Three studies reported that cats treated with SUB device had a  88 to 94% survival to discharge rate  [1, 9]  and a 1 year survival of 83%  [7]. In the present study, survival to discharge rate was 100%. Three cats died during the followup period, at 262, 448 and 1231 days. In previous studies, postoperative positive urine culture, obtained from the SUB port, was reported from 21 to 25% of the cases  [1, 6, 7]. Our findings were slightly higher compared to those reported as we identified 35. 7% of positive bacterial urine culture during followups. All of the urine samples were obtained from the SUB port in order to avoid contamination. Bacteriuria resolved in 60% of the cases with appropriate antibiotic treatment which is consistent with the previously reported data of 50 to 78% of cats  [1, 2].
In Japan, in multi-cat households, each cat was independently allocated, treated and evaluated for efficacy and tolerance. Most study activities (flea infestation evaluations, treatments, and physical examinations) were performed at the veterinary practices, except day-to-day owner observations. The study activity schedules are summarized in Table  2. The flea efficacy assessment was based on comparison of the number of live fleas found before treatment to the number of live fleas found after treatment, at the scheduled visits. In Australia, blinding was not applicable as no control group was used; otherwise, all personnel collecting efficacy, tolerance or flea-infestation cutaneous signs data were blinded to treatment. Owners were blinded to treatment in the EU and Japan. In the USA, the owners were not blinded as they administered the treatments.
Acknowledgments: The authors would like to thank the SBEA members and all the care staff for their daily work and commitment to animal welfare improvement. Additionally, they would like to thank all the members of the IRSEA's board of directors and the IRSEA's research and education board for the opportunity to carry out this project. Finally, the authors would like to thank the anonymous reviewers for their helpful feedback and suggested revisions, as well as the editors for their professional and timely communication.
fattening  18, 19. Reducing transport distances of weaned calves before fattening could therefore have direct and indirect repercussion on their survival and performance. In this study, we aimed to minimize the total distance travelled by weaned beef calves during their transfer from cow-calf producer to fattener. To this end, we developed an algorithm for assigning calves to sorting centres based on the total distance travelled. The algorithm was constrained by the following two restrictions: (1) not to change the composition of the batches or their destination, and (2) always to respect the maximum capacity of each centre, i. e. the maximum number of calves they can host at the same time. This algorithm was tested on a dataset of 136,892 weaned calf transfers managed by Terrena Production Bovine, a French organisation of beef producers that operates 13 different sorting centres between 2010 and 2018.
Results The algorithm reduces the longest transfer distances. We computed the distance travelled by each weaned calf according to the itineraries defined by the algorithm (optimised distances), and compared them with the distances computed according to the itineraries indicated in the Terrena Production Bovine dataset (historical distances). The distribution of these transfer distances are shown in Fig. 1. The distributions of the optimised and historical distances were clearly bimodal, with a "short-transfer" peak around 90 km for both distributions and a "long-transfer" peak around 420 km for the historical distances, and 310 km for the optimised distances. We considered a cut-off between the two peaks that was at the second third for both distributions. We have defined short and long transfers as those respectively below and above this threshold (at 294 km for historical distances and 245 km for optimised distances).
Only 19. 2% of the optimised ratios exceeded this threshold, and the third quartile of their distributions was 1. 39. The impact of the optimisation was also visible by comparing the historical and optimised ratios separately for each of the 13 centres managed by Terrena Production Bovine (Fig. 2b, d ). For each centre, the optimised ratios were more closely gathered around one than the historical ratios. The distribution of weaned calves between the centres was also modified by the algorithm (Fig. 2c, e ). More than 40% of the transfers still went through the largest centre (numbered 13), but the rest were distributed more evenly among all other centres. In particular, centre 12, with the second-highest number of transfers according to the dataset, was almost never assigned by the algorithm.
Discussion Our algorithm successfully identified potential improvements in the assignment of the weaned calves of the dataset provided by Terrena Production Bovine to sorting centre, according to their transport distances. Its impact overwhelmingly visible on long transfers suggest that the transfers most affected were also those with the greatest potential for improvement. This hypothesis is confirmed by looking at the ratios of transport distances relative to their minimum, rather than the absolute values, for which the impact of the algorithm is retained. As its code is freely available, this algorithm could easily be included in a set of decision-support tools used by any cooperative seeking to reduce the transport distances of the livestock it manages. Although a test of the algorithm on a historical dataset is presented here, it can also be used routinely to assign calves to centres.
In addition, it could also be used to assess the relevance of the sorting centres for improving transport distances. Indeed, the number of calves assigned to each Terrena Production Bovine centre differed substantially from the actual number recorded in the dataset. This result sheds light on the question of the location and maximum capacity of the centres. In particular, the algorithm highlighted a single centre (the 12), whose location was inadequate to limit transport distances, despite being the second-most used overall. This centre was no longer used in the most recent years of the dataset, for reasons independent from the distance travelled. Conversely, several centres were more used by the algorithm, and therefore considered more useful in terms of transport distance, although their maximum capacities were lower.
Numerous criteria can guide the choice in the location of sorting centres, including historical, economical, logistical or linked to pre-existing employment pool. Yet, our algorithm provides additional information on transport distance, which could also be taken into account, along with the other criteria, for centre management. For example, running the algorithm without a given centre would provide information on its role in reducing distances travelled by weaned calves. Conversely, running the algorithm with a potential new centre would allow the associated impact on transport distance to be assessed. Table  1. Distribution of the transfers of weaned calves depending on the first centre they went through according to the database. First column: the sorting centre considered. Second column: the proportion of all the transfers that went through a given centre.
9  for a French example of transport distance on stress levels). Unfortunately, information on the quantitative impact of transport distance on cattle welfare and health at European spatial scales is still scarce, and further observational studies would be needed to characterise the impact of our algorithm on welfare and health more precisely. A statistical study on a subset of the dataset presented here showed an overall negative impact of the transport distance on the performance of young bulls during fattening, estimated at -11 g/day per 120 km travelled over an average fattening period of 313. 4 days  13. These results were obtained on calves managed by Terrena Production Bovine, using the same sorting centres and over the same range of transfer distances. On the basis of these results, we could extrapolate an expected gain of 7 g/day of ADG for long distance transfers on average.
To assess the impact of these rules, we identified the proportion of calves that were not assigned to their first choice because other calves had priority over them on the same day (see Supplementary Fig. S1 ). The results showed that 92. 6% of calves were assigned to their first choice, and only 1. 2% were not assigned to their first or second choice. We also tested removing any capacity limitation by running the algorithm on the dataset from Terrena Production Bovine without any maximum capacity (see Supplementary Fig. S2 ). The results show little impact on transport distances: only 11. 2% of the transfers were reduced without maximum capacity and 4. 8% of them decrease by more than 25 km. Overall, these results confirm that the way in which the algorithm handled this constraint had only a marginal impact on the distances travelled, and thus on our conclusions.
Secondly, the algorithm relied on a computation of distances as the crow flies rather than the driven distances, as the latter were not available for the transfers listed in the dataset were not available. However, we showed that the road network in the area studied was dense enough so that the distances we computed were reliable approximations (see. Supplementary Figure  S3 ). Indeed, they reliably corresponded to 77. 3% of the road distances, which means that the improvement in distances following optimization were expected to be conserved. This correspondence is valid for our study area in France, but it should not be assumed that it is the case everywhere, particularly given the differences between the road networks of various countries. Moreover, computing the actual distances travelled by transport trucks would only make sense if the algorithm reconstructed their exact routes, including the possibility that they visit several farms in a single trip.
Currently, bodyweight homogeneity among the animals is a major criterion and is usually measured on arrival at the centre. Yet, the relevance of this criterion is being questioned  13, 23. Batch composition based on other criteria evaluated before calves are transported is increasingly taken into account by cooperatives such as Terrena Production Bovine, and would facilitate the use of the algorithm presented in this study. Even then, sorting centres would remain essential, as the location where animals are grouped in batches before they are sent to a fattener. Finally, the algorithm only modifies the centre by which a weaned calf goes through. However, it also ensures that the weaned calves only go through a single sorting centre, as it also minimizes the total distance of the transfer. Although this practice tends to be rarer among cooperatives such as Terrena Production Bovine, we estimated that at least 8% of the weaned calves of the database went at least through two sorting centres.
This proportion necessarily drops to 0 according to the transfers generated by the algorithm. Direct modification of other characteristics of the transfers could improve them further. Firstly, transport distances could be reduced by eliminating transit through a centre altogether when possible. Direct transfer of animals from cow-calf producer to fattener would by definition reduce the distance travelled to a minimum. They represent only 5% of the transfers included in the dataset from Terrena Production Bovine, but their share could increase, especially if the design of batches no longer depends on bodyweight measurements at sorting centres. Secondly, an algorithm could also aim to match cow-calf producers and fatteners according to their location. Yet, improvement could be limited by other criteria to be taken into account, such as breed, weight and age.
Methods Presentation of the dataset. The dataset presented in this study was provided by Terrena Production Bovine, a beef producer organization located in Western France. The data concerns 136,892 weaned calves managed by the organization between 2010 and 2018, and grouped with other calves into one of the 9701 batches sent to fattening farms over the period. The dataset includes the following information: their breed, the cow-calf producer they come from, the batch they were assigned to, the fattener they were sent to, their date of transfer from the cow-calf producer and their date of transfer to the fattener. In total, the dataset lists 3675 different cow-calf producers and 1028 different fatteners. Over the all period, the cooperative operated 13 different centres through which 95% of the weaned calves (129,756 animals in 9383 batches) transited.
The first sorting centre each of them was sent to is indicated in the dataset. The sorting centres were numbered for confidentiality reasons, from 1 to 13 by increasing order of number of calves managed. Hence, centre 1 received the least amount of calves of all centres, while centre 13 received the most. The location of the sorting centres is presented in Fig. 3 , together with the locations of the cow-calf producers and fatteners. The remaining 5% of the calves (7136 animals in 318 batches) did not go through a sorting centre, but were sent directly from a cow-calf producer to a fattener. We computed the distances travelled by the weaned calves in the dataset based on the location of each holding. They were all associated with a set of coordinates (latitude, longitude) according to the INSEE code of the commune (a French administrative subdivision) they were in.
These coordinates were used to compute distances between two holdings, using the spherical law of cosines. Hence, the distance between holding A and holding B was computed as follows: with lat A and lat B the respective latitudes of A and B, lon A and lon B their respective longitudes, and R the Earth's radius in km. Hence, we computed distances as the crow flies, which were systematically smaller than the actual driving distances. However, the distances computed corresponded to a consistent proportion of the driving distances in the area covered by the dataset (see Supplementary Fig. S3 ). Therefore, comparisons between distances computed relative to one another were good approximations of comparisons between the corresponding driving distances. The transport distance of each calf was computed as the sum of all the distances travelled along its complete itinerary.