dataesr/scientific-paragraphs-categorization huggingface dataset:
Each commercial exchange of animals was assumed to take place directly from one herd to another, neglecting intermediaries. This means that markets and sorting centres were not considered for this study. They differ from herds in that they tend to concentrate a large number of animals, but for a limited period of time (less than a day for markets, a few days for sorting centres). Two types of demographic events were considered: (iv) births had only a destination, corresponding to the herd where the animal was born, and (v) deaths had only an origin, corresponding to the herd where the animal died. The dataset was represented as a network with herds and internal movements corresponding to the vertices and edges, respectively. This network was (i) dynamic, i. e. movements were characterized by the date at which they occurred, (ii) weighted, i.
Epidemiological model: within and between-herd dynamics and infection settings The model developed aimed to simulate pathogen transmission within herds, and infection spread between herds through cattle movements. A full description of the model is available in Supplementary material 1. The model is stochastic in discrete time -each time-step corresponding to a day of 2014 -and in discrete space -by integrating the network of herds and movements described above. Commercial exchanges and demography were data-based: movement m was characterized by its origin O m , its destination D m , its date according to the dataset T * m and the date at which it was simulated T m. By default, movements were simulated according to the dataset, i. e. T m = T * m. Within-herd dynamics were based on a SIRS model with three parameters: the infection rate β, the recovery rate γ -therefore the average infection duration was 1/γ -and the rate of return to susceptibility δ.
At each time-step t, herd h was characterized by its number of susceptible, infected and recovered individuals, noted respectively S h (t), I h (t) and R h (t). The total herd size N h (t) was defined as the sum of these three values and infection prevalence as P h (t) = I h (t)/N h (t). Each simulated infection began with an initial outbreak in a metapopulation without infection, i. e. with only susceptible individuals. At t = t I , the date of the outbreak, 10% of all herds in the metapopulation were infected, by replacing 1 susceptible individual with 1 infected individual in each of the herds. The probability of a herd being part of this 10% was proportional to the number of imports in the herd according to the 2014 dataset. The rationale was that herds receiving the most individuals from herds outside of the metapopulation were the most likely to introduce a new infection.
Simulations Simulations were performed on the dataset between 01/01/2014 (defined as t = 0) and 01/01/2015 (t = 365). Different epidemiological settings were explored by manipulating the SIRS model parameters (β, γ and δ) and infection type (epidemic or endemic). Two clustering analyses were performed on the preliminary simulations to define six epidemiological settings (Supplementary material 3): weak, moderate and strong epidemic settings and weak, moderate and strong endemic settings (Fig. S2 ). The effectiveness of the algorithm was tested by running simulations with 3 × 3 × 3 × 2 combinations of the algorithm parameters, respectively (i) the number of prevalence classes c (2, 3 or 4 classes), (ii) the update period q (1, 28 or 91 days), the maximum delay ∆ M AX (1, 3 or 7 days) and (iv) the prohibition of movements at risk (yes or no).
The first principal component of the PCA was also used to assess the distribution of the simulations depending on the algorithm parameters. The network-related outcomes were based on an static view of the network aggregating all the internal movements performed during the simulation, from t = 0 to t = 365. Therefore, they took into account the rewiring performed by the algorithm, and the potential removal of problematic movements if movements at risks were completely prohibited. The outcomes recorded for the modified networks were compared to the same metrics for the original network defined by the 2014 dataset. The strongly connected components -from which n SCC and max SCC were computed -corresponded to groups of vertices linked to each other by a directed path. The percentiles of the distributions of ind h and outd h of all herds in the static network were used to assess the in-degree and out-degree distributions, respectively.
g. among seed exchange networks, which face similar infection risks  (Jeger et al. , 2007 , Pautasso et al. , 2010). While the need for controlled movements makes this method more relevant to agricultural systems, the spatial and temporal scales considered can also be adapted depending on the context. Indeed, the daily time-step and the region level were used here as they correspond to the BDNI data structure, but are not necessary for the algorithm to work. The usefulness of our rewiring method could therefore extend beyond cattle concerns, even though the effectiveness of the algorithm in other contexts remains to be tested. algo/. The dataset used in the study is an extraction from the French national bovine identification database (BDNI), which is confidential, and therefore cannot be provided publicly.
Acknowledgements The authors acknowledge the financial support of the  CeraTIS -Corse  project, within the framework of ECOPHYTO 2019, "Leviers territoriaux pour réduire l'utilisation et les risques aux produits phytopharmaceutiques ( 2020 -2023 )". YD is also co-funded by the  European Union: Agricultural Fund for Rural Development (EAFRD) , by the  Departmental council of La Réunion  and by the  Regional council of la Réunion. YD acknowledges the support of the  DST/NRF SARChI Chair ,  South Africa M3B2 in Mathematical Models and Methods in Biosciences and Bioengineering at the University of Pretoria ,  South Africa  (grant  82770 ).
Morphometry All bees were frozen after the behavioural assays and stored at -18°C. An experimenter blind to bee identity measured their fresh body weight (±0. 001g) (precision balance ME103T, Mettler-Toledo Gmbh, Greifensee, Switzerland) and took measures (±0. 01 mm) under a Nikon SMZ 745T dissecting microscope (objective x0. 67) with a Toupcam camera model U3CMOS coupled to the ToupView software. We measured the head length, head width, wing length and femur length (Fig. 4A )  (Monchanin et al. , 2021a).
Statistics We analysed the data using R Studio v. 1. 2. 5033  (RStudio Team, 2019). All data are available in Dataset S1. For the metal analysis, we conducted a PCA (package FactoMineR;  (Lê et al. , 2008) ) on the levels of the seven selected metals  (Szentgyörgyi et al. , 2017). Linear mixed-effects models (LMM) (package lmerTest;  (Kuznetsova et al. , 2017) ) were run on the pollution index (PC1) and arsenic content considering site as fixed effect, and hive as random factors. For the conditioning trials, we performed generalized linear mixed-effects models (GLMM) (package lme4;  (Bates et al. , 2015) ), fitted with binomial family, with hive and conditioning date as random factors and site as fixed effect. Using GLMM, we evaluated whether site location would impact the percentage of initial responses to antennal stimulation, spontaneous responses at the first conditioning trial, conditioned responses at the last conditioning trial and responses to each odour during memory test, as well as the proportion of individual response patterns during retrieval.
GLMMs were followed by ANOVAs to test the overall significance of fixed categorical variables using the Anova function (package car;  (Fox and Weisberg, 2019). Acquisition scores were transformed as in  (Smithson and Verkuilen, 2006) , using the formula ((x * (n -1) + 0. 5) / n), to avoid zeros and ones in the normalized variables. They were then compared with GLMM (package Template Model Builder;  (Brooks et al. , 2017) ), and fixed categorical variables significance was tested using Anova. glmmTMB function of that package. For the morphometric analyses, we conducted a PCA on the five parameters measured, and clusters were compared with permutational multivariate analysis of variance (PERMANOVA) (package Vegan;  (Oksanen et al. , 2019). In addition, head width and length measures were collapsed into the first component of a PCA, which was used as a proxy of the head size.
Acknowledgements We thank  Laurent Drouin  and  Julien Denoy , for letting us work on their hives and  Renaud Lebrun  for the help with micro-CT. 3D data acquisitions were performed using the micro-CT facilities of the Montpellier Ressources Imageries platform member of the national infrastructure France-BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»), and of the  Labex CEMEB  ( ANR-10-LABX-0004 ) and  NUMEV  ( ANR-10-LABX-0020 ). We thank  Jérôme Silvestre  and  Amélie Brocherieux  ( Laboratoire Ecologie Fonctionelle et Environnement, Toulouse ) and  Camille Duquenoy  ( Géosciences Environnement Toulouse and Service ICP-MS OMP Toulouse ) for help with trace element measurements in bees. CM received funding by a PhD fellowship from the  French Ministry of Higher Education, Research and Innovation.
ABB was funded by a  Future Fellowship  from  Australian Research Council  ( FT140100452 ) and the  Eldon  and  Anne Foote Trust. AE and ML were funded by the  CNRS  and grants of the  Agence Nationale de la Recherche  (project  POLLINET   ANR-16-CE02-0002-01 , project  3DNaviBee   ANR-19-CE37-0024 , project  BEE-MOVE ANR-20-ERC8-0004-01 ), the  European Regional Development Fund (project ECONECT) , the  Occitanian Regional Council (project DIAGNOSE)  and the  Agence de la Transition Ecologique  (project  LOTAPIS ). While writing, ML received support from the  European Commission  ( ERC Cog   BEE-MOVE GA101002644 ).
Acknowledgments We thank  Erwan Grandgirard  and the  Imaging Platform of IGBMC , and the  Salbreux and Riveline  groups for help and discussions. Q. V. and G. S. are supported by the  University of Geneva. L. L. and T. G. are supported by  HFSP  and by the  University of Strasbourg  and by  la Fondation pour la Recherche Médicale. D. R. acknowledges the  Interdisciplinary Thematic Institute IMCBio , part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , which was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), and by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program. O. P. and D. R. thank funding from  SNF Sinergia. D. R. and A. H. acknowledge the  Research Grant  from  Human Frontier Science Program.
The cluster A grouped 20 strains including the strain CFBP 7706 that did not give any signal with the X. hortorum pv. carotae Meng's test. This cluster was supported by a 100% bootstrap value and grouped 18 strains having 100% identical MLSA sequences while the 2 others (CFBP 7760 and CFBP 7762) differed by only one nucleotide on the gyrB gene fragment (A <-> C bp n°3612/4189 of the final concatenate dataset). The cluster B grouped 6 strains and the sequences retrieved from the genome sequence of the strain M081, with a 100% identical sequence for the 7 housekeeping gene fragments and a bootstrap value of 100. These 26 X. hortorum pv. carotae strains were genetically distant from the other 18 strains, 17 of which tested negative with the X. hortorum pv. carotae Meng's test and one (strain CFBP 7868) giving an amplification using the X.
All 122 X. hortorum pv. carotae strains were genotyped at all eight VNTR loci (Table  1 ). The MLVA-8 scheme was sufficient to reveal diversity in our dataset as it accurately discriminated the haplotypes in our strain collection, as the haplotype accumulation curve revealed that more than 95% of the haplotypes were detected with a set of seven markers (Figure  3 ). The MLVA 8-VNTR scheme allowed to observe a large diversity among the strains as they were typed in 106 haplotypes, with a discriminatory power of 0. 9955, allowing further analyses. Diversity was also observed at each locus, with allele number per locus ranging from 5 to 14 and low Simpson's diversity index ranging from 0. 13 to 0. 40 (Table  2 ). DAPC analysis performed on the MLVA dataset allowed to group the strains into two clusters (Figure  4 ).
i. water-soaked lesions that evolved into necrotic spots, then dry and brittle, fitting initial description  (Kendrick, 1934). Moreover, the strain CFBP 7900 was phenotypically characterized by standard biochemical tests and metabolic profiling on Biolog GEN III microplates  (Morinière et al. , 2020). All these data are consistent with the first description of the holopathotype made by Kendrick  (Kendrick, 1934). To date (04-03-2023) only three genomes of X. hortorum pv. carotae are publicly available in the National Center for Biotechnology Information (NCBI). Two of them are from the strain CFBP 7900 (= M081) and the third from a strain named MAFF 301101 isolated in Japan (BioSAmple: SAMN02470773, SAMEA6962650 and SAMN16925265)  (Kimbrel et al. , 2011; Dia et al. , 2020). Only the first one deposited in 2013 and published by  (Kimbrel et al.
Acknowledgements We thanks Lindsey du Toit (Washington State University, USA),Olivier Pruvost (CIRAD), Isabelle Serandat (GEVES), Valérie Olivier (Anses), Perrine David (HM-Clause), Margaret Asma (Bejo), Benoit Meriaux (FNAMS) for strains donations. We also thank CIRM-CFBP (Beaucouzé, INRAE, France http://www6. inra. fr/cirm_eng/CFBP -Plant-Associated-Bacteria) for strain preservation and supply.
, Van Vaerenbergh, J. , Van Malderghem, C. , Blom, J. , Smits, T. H. M. , Cottyn, B. , et al. (2021). Xanthomonas hydrangeae sp. nov. , a novel plant pathogen isolated from Hydrangea arborescens. International Journal of Systematic and Evolutionary Microbiology 71. doi: 10. 1099/ijsem. 0. 005163. Dowson, W. J. (1939). On the systematic position and generic names of the Gram-negative. PAMDB, bacterial plant pathogens. On the systematic position and generic names of the Gram-a multilocus sequence typing and analysis database and website for plant-associated negative bacterial plant pathogens. Available at: microbes. Phytopathology 100, 208-215. doi: 10. 1094/PHYTO-100-3-0208. https://www. cabdirect. org/cabdirect/abstract/19391101451 [Accessed March 15, 2023]. Barnhoorn (2014). Validate the analytic specificity of four new qPCR assays developed to be used in direct PCR to detect Xanthomonashortorumcarotae, a pathogen in carrots.
Acknowledgments The authors would like to thank the  ANR SUPERGENE  ( ANR-18-CE02-0019 ) for funding the PhD of LM, and the  Emergence program  from the  Paris city council  for supporting the team of VL. This work was also supported by the  Chair "Modélisation Mathématique et Biodiversité " of  VEOLIA-Ecole Polytechnique -MNHN-F. X. LM would like to thank  Thomas Aubier  for helpfull discussions. The authors would like to thank  Judith Mank ,  Andy Gardner  and the two anonymous reviewers for the relevant comments on previous versions of the article.
URL https://github. com/timflutre/rutilstimflutre). The broad-sense heritability at the whole design level (H2) was derived from the variance components of eqn1 and calculated in equation 2 (eqn2): ‫ܪ‬ ଶ ൌ ߪ ଶ ீ ൬ߪ ଶ ீ ߪ ଶ ீൈா ݊ ௩௧ ߪ ଶ ఌ ݊. ௩௧ ൈ ݊ ௩௧ ൰ with genotype (G) variance (ߪ²) at the numerator. Random variance components involving environment (E) were divided by the number of environments (n environments ). Residual variance was divided by the number of environments multiplied by the average number of replicates per environment (n rep. environment ).
Acknowledgements. The authors thank  Philippe Chartier ,  Cédric Duranton  and  Christian Gauthier  ( Invenio ),  Luis Miranda  and  José A. Gómez-Mora  ( IFAPA ) for strawberry culture and phenotyping. The authors thank  Drs. Isidore Diouf  and  Iñaki Garcia de Cortazar Atauri  for fruitful discussions. Authors contributions KO chose the cross 'Candonga' × ' Senga Sengana ' and produced the 1 st generation of individuals; BD and members of the  GoodBerry  project conceived and designed the experiments. AleP and BD analyzed and interpreted the data; BD and AleP wrote the manuscript and CR contributed to the writing of the manuscript. All authors discussed the results and commented on the manuscript.
g. , PVMC = 0. 040 ± 0. 003, DMC = 0. 090 ± 0. 009, DSDD = 0. 100 ± 0. 005, DS = 0. 150 ± 0. 007, DNRV = 0. 020 ± 0. 002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e. g. , PVMC = 0. 070 [range: 0. 020, 0. 110], DMC = 0. 460 [range: 0. 050, 0. 680], DSDD = 0. 180 [range: 0. 010, 0. 340], DS = 0. 19 [range: 0. 070, 0. 430], DNRV = 0. 120 [range: 0. 030, 0. 450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0. 59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves.
INTRODUCTION Precision livestock farming has increased the availability of on-farm data collected from livestock operations  (Baker et al. , 2022; Neethirajan and Kemp, 2021; Groher et al. , 2020). Information from various sensors can be captured, stored in large databases, and analyzed to determine individual differences among animals for breeding purposes, as well as for management decisions  (Brito et al. , 2020; Kleen and Guatteo, 2023; Montes et al. , 2023). Multiple disciplines in the field of animal sciences have begun using high-throughput phenotyping platforms to investigate health and production efficiency in livestock, presenting large gains in the dairy cattle industry  (Berckmans, 2017). Most of this data has been used for real-time adaptation of management, nutrition, and breeding to increase the productive efficiency and sustainability of the dairy industry.
Some examples of precision livestock farming technology in dairy cattle are automatic milking robots (e. g. ,  Pedrosa et al. , 2023)  that capture information on productivity per milking, as well as sensors that capture movement, laying time, and rumination  (Deming et al. , 2013; John et al. , 2016; Pavlovic et al. , 2021; Solano et al. , 2022). All these data sources provide longitudinal phenotypic information that can be used to accelerate genetic and genomic selection for novel breeding goals  (Brito et al. , 2020). Longitudinal calf data from automated milk feeders (AMF) have been previously used to determine patterns in consumption, drinking speed, and number of visits, and then identify possible illness. For instance,  Borderas et al. (2009)  reported that calves considered ill (respiratory or enteric disease) drank 2.
Phenotypic Data Summary The data were collected between 2015 and 2021 on a large commercial dairy cattle farm in North Central Indiana. Purebred Holstein calves were reared since birth in group housing barns with 223-m 2 pens, split into 2 separate 111. 5-m 2 sections containing roughly 60 animals at a time. Crossbred terminal calves and male calves were removed from the dataset because they were not retained in the herd or used for breeding purposes, leaving a total of 14,749 calves in the dataset, before data editing. The calves were reared in 4 separate barns, with 4 pens in each barn, and each Förster-Technik AMF feeding 2 pens (Förster-Technik, Engen, Germany; www. foerster -technik. com/ ), with 2 stalls in each pen section available for feeding. Calves were removed from their dam at d 0 and fed colostrum before being relocated to their rearing pen for 58.
Deriving Calf Traits Data were collected from multiple information sources throughout the farm, which were pooled into the Precision Animal Science Data Ecosystem at Purdue University (West Lafayette, IN) for subsequent analyses. The datasets for this study combined Förster-Technik calf feeding information, weather station, health records, and identification information from the Dairy Comp software (Valley Agricultural Software, Tulare, CA). Two separate datasets were generated based on datatype, either per-visit (dataset 1; n = 17,073 calves) or daily records (dataset 2; n = 25,153 calves) information. The dataset 1 file contained individual recordings of calf feeding behavior at each visit, and the dataset 2 file contained daily calf feeding records. The dataset 2 information was available from 2015 to 2021, whereas the dataset 1 information was only available starting in the summer of 2019 until 2021.
Each dataset was used to generate novel phenotypes for evaluating calfhealth, feeding behavior, and performance. For dataset 1, the main phenotypes recorded were per-visit milk consumption (PVMC) in liters and drinking duration per visit (DDPV) in minutes. For the PVMC trait, all zero values were removed to better account for variability in true recordings. The variance of total milk consumption (TCV) in liters squared and variance of total drinking duration (TDV) per visit in minutes squared were calculated. We also derived feeding interval (FI) by averaging the amount of time between rewarded visits in the AMF, starting the feeding day at midnight. We used rewarded visits to remove any bias from misreported visits. The main traits evaluated were daily milk consumption (DMC) in liters; daily sum of drinking duration (DSDD) in minutes; daily average of drinking speed (DS) in liters/minute, where DS = DMC/DSDD; daily number of rewarded visits (DNRV); and total number of visits per day (TNV).
Data-Editing Procedures To edit dataset 2, individuals with missing birthweights and those that were above 4 standard deviations (SD) for birth weight were removed (9 individuals), as well as negative consumption and duration observations (12,865 records), observations that occurred twice on the same day (116 records), observations with zero rewarded visits (1,479 records with no milk consumption), observations with more than 6 h of total daily drinking duration, which would allow for 15 min of consumption per hour to occur (1,149 records), observations with more than 144 visits or once every 10 minutes in a 24-h period (6 records), and outliers were removed based on a z score = x x i x - σ , where x i is the observed phenotype of calf i, x is the phenotypic mean, and σ x is the SD of the trait for daily consumption, daily drinking speed, daily duration, and daily rewarded visits (12,218 records).
Values above 50 unrewarded visits were recoded as 50. Individuals with less than 5 observation days and contemporary groups with less than 5 individuals were removed (1 individual). Observations with entitlements less than 0 and more than 24 were filtered out, with entitlements >24 being recoded as 24, and dams with missing parity information were fixed by taking the maximum parity and adding one, or by adding one if no previous parity existed (220 obser-vations). Parities were labeled as 1, 2, and those that were ≥3 were recoded as 3+. The final file for dataset 2 comprised 566,156 observations from 10,076 individuals. The descriptive statistics for each trait are presented in Table  1. To clean dataset 1, individuals that did not have unique identification (426 individuals), observations above 5 L, per-visit duration above 25 min, calves with less than 5 observation days, and contemporary groups with less than 5 individuals were removed  (7,939 individuals, 2,466,717 observations).