dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Tested for Estimating Empty Body and Carcass Chemical Compositions In vivo methods. The day before slaughter, an US image was recorded on the left side of each animal, located at onethird of the ischium-sacrum distance  (Realini et al. , 2001; Schröder and Staufenbiel, 2006). A Logiq e R7 US machine equipped with a 9L-RS Linear-Transducer (3. 0-9. 0 MHz; GE Healthcare, Glattbrugg, Switzerland) was used. The thickness of the subcutaneous adipose tissue was further estimated as the average thickness measurement between the maximum and minimum thickness of the tissue recorded on the image without the skin  (Realini et al. , 2001) , which was analyzed using the ImageJ software (1. 52r version, National Institute of Health, USA). The thickness of muscle was not treated because of the difficulties to locate precisely the lower muscle fascia for 11 images on 22.
Around 0. 5 g were maintained in a physiological saline solution at 39 °C. Adipose samples were then cut in small cubes, washed with saline solution, and fixed in a 4% osmium tetroxide solution within 2 h following sampling. One week later, ATs were mechanically disaggregated and washed with distilled water. Adipose cells were trapped by filtration on a 0. 45 μm-pore filter  (Robelin, 1981)  before being resuspended in a 0. 01% Triton X100 solution and measured on a BX53 microscope (Olympus Schweiz AG, Wallisellen, Switzerland). The diameter of at least 200 adipose cells was determined using ImageJ software. Post mortem methods. The day after slaughter, the 10th to 12th rib section was removed from the left half-carcass. The limit of the section was defined by a line parallel to the spine axis passing through the point of the ilium at the 11th rib and reaching straightly the 5th intercostal space (6 and 7 ribs intercostal space).
Equations with better estimations than those with BW or carcass weight alone were calibrated and validated based on the main dataset with a leave-one-outcross-validation, which was performed with the R package "caret"  (Kuhn, 2021). The results include the determination coefficient (R 2 ) and the root mean square error of prediction (RMSEP). Data from 3D imaging analyses (n = 8 cows) were treated by partial least square (PLS) regression with a leave-oneout-cross-validation method (R package "pls";  Mevik et al. , 2020)  in order to analyze the relationship between in vivo body measurements, EB, carcass chemical composition, and 3D variables. The PLS regression allows to take into account the strong correlation between predictive variables (Supplementary Table  S1 ) and the large number of predictive variables (n = 10, when having access to a limited number of observations: i.
e. , 8 cows). The predictive 3D variables were standardized thanks to the "Scale" option of the function "plsr", and were integrated in Latent variables to explain the outcome variable. The presented RMSEP corresponded to the adjusted RMSEP calculated by the package "pls," and the R 2 is the variance explained by the model. Models were compared with each other based on the value of R 2 , RMSEP, and the residual coefficient of variation (rCV; ratio of RMSEP to the mean of the dependent variable).
Acknowledgments We thank  Y. Aeby  and the team of the  Posieux Experimental Barn of Agroscope  for diligent feeding, milking, and management of cows and calves;  B. Egger ,  F. Sansonnens  and  G. Maikoff  for slaughter and empty body mincing procedures;  S. Dubois  and all the feed chemistry unit of  Agroscope (Posieux, Switzerland ) for empty body and carcass chemical analyses. C. X. acknowledges the financial support of a Ph. D. studentship provided by  Agrocampus-Ouest (Rennes, France)  and  Institut national de recherche pour l'agriculture , l'alimentation et l'environnement (INRAE; department of Animal Physiology and Livestock Systems; Paris, France). We thank the program  Hubert Curien (PHC) Germaine de Staël between France and Switzerland  for funding awarded to support mobility of partners along the project  2021-20  CompoMeat  3D.
This error is slightly lower than the one reported in present study, presumably due to the difference in predicted composition [chemical in the present study and tissue in López-Campos et al. (2018)], statistical methods [multiple linear regression in the present study and PLS regression in López-Campos et al. (2018)], validation methods [leave-one-out-cross-validation in the present study and external validation dataset in López- Campos et al. (2018)], and DXA devices. Although the half- carcass DXA scan is the most accurate method to estimate the carcass' composition, it remains time consuming when dealing with large pieces and requires an expensive device. Water, kg M1 + body weight 6 7. 7 0. 9993 3. 1 4 9. 2 0. 9856 6. 2 M1 6 9. 8 0. 9997 3. 9 4 10. 1 0. 9721 6. 8 Lipids, kg M1 + body weight 6 12.
Introduction In Thailand, pork is one of the most consumed meats nationwide. In 2018, pork consumption was estimated at 10 kg per capita  (FAO, 2016). Pig farming is hence an important business in the country. In total, 9,886,897 pig heads were notified in the database of the Department of Livestock Development (DLD) in 2015. These pigs were reared in 191,289 households, of which 95% were considered smallholders raising only 50 pigs or less  (Department of livestock development, 2016). With limited budgets and resources, the biosecurity level in these farms is relatively low. Infectious diseases may attack the farms easily. Evidently, outbreaks of several infectious diseases were previously recorded in the Thai pig population. For instance, different lineages of porcine reproductive and respiratory syndrome viruses (PRRSV) have been separately introduced into Thai pig production systems on different occasions and remained cocirculated ever since  (Tun et al.
Network analysis at the subdistrict level We used official electronic records of pig movements in Thailand, from January 1 to December 31, 2021, to crosssectionally illustrate how pigs were distributed across the country. Briefly, Thai DLD has designed an electronic system to capture all movements of livestock species including pigs. An online form must be filled out once animals are translocated between subdistricts and provinces. The subdistricts (a smaller administrative subunit of the province) of origin and destination are recorded in each transaction. These movement data are centrally stored in the DLD database. We filtered the pig movements and used them in our analysis. In this study, we excluded all links toward slaughterhouses as these were dead-end. A directed weighted one-mode network was constructed.
A node represents a subdistrict, in which the pigs were moved in and out. In this nationwide pig movement network, a total of 484,483 pig movement activities were identified throughout the year 2021. After excluding slaughterhouses, we had 104,535 remaining trade activities for further analysis. Ultimately, our network contained 2704 nodes (unique subdistricts) with 11,688 unique links (Fig. S1  of Supplementary Material 1). No missing data were found in our dataset as these were included in the obligatory fields of our electronic recording system. Additionally, the data are mandatorily requested following our legal requirements for the permission of animal movements. The node was weighted by the aggregated number of movements in and out of the subdistrict within the year 2021. Degree centrality was calculated for both subdistrict and provincial levels with the formula D W i = ∑ j∈Π(i) W ij , where D W i denotes the weighted degree of node i , W is the frequency of pig move- ment between node i and node j , and node j is a contact member of node i ( Π(i) ).
Network analysis at the provincial level To ease the visualization, we aggregated the pig movement network from the level of subdistrict to the provincial level. Weighted degree centrality was also aggregated accordingly. Additionally, we mapped the community at this level after the largest network community was detected. All network analyses were performed with the functions equipped in package "igraph" version 1. 2. 7  (Csardi and Nepusz, 2006)  in R version 4. 1. 2 (R core team, Vienna, Austria). All network parameters used in our study are defined in Table  1.
The PCR products were sent to Genoscreen (Lille, France) for genotyping. All microsatellite loci were visually peak called, using GS500Liz size standard (Applied Biosystems), and GeneMarker software. Only 16 loci were subsequently used for analysis as allelic dropout of two loci (C131 and Sh8) occurred in at least 20% of the samples (Supplementary Tab. S1). Errors due to large allele dropout or stutter bands and evidence of the presence of null alleles at each locus were checked using Micro-checker, version 2. 2. 3  [43].
9. 4  [28]. Genetic diversity was compared between sites and between cox1/ITS2 genetic profiles. Expected heterozygosity (He), number of alleles (A), allelic richness (Ar), and the inbreeding coefficient (F IS ) of each microsatellite locus were computed per sampling site, using FSTAT version 2. 9. 4  [28]. He and Ar were compared between the populations using the pairwise Friedman rank test, followed by the Nemeyi multiple comparison test implemented in the R studio PCMP plus package  [36].
The length of the burn-in period was 250,000 with the number of MCMC replicates after burn-in at 1,000,000 and K from 1 to 8 using an admixture model. The log likelihood for each K was averaged over three runs with the CorrSieve package in R (Vienna, Austria) and the delta K-values were then computed to determine the most likely number among the K values tested  [21]. For the most likely number of genetic clusters, an additional 10 runs were computed with the same initial parameters as those described. The probability of each miracidium belonging to each cluster was averaged over the 10 runs and graphically represented using CLUMPP, version 1. 1. 2 [23]  and DISTRUCT, version 1. 1  [52]. Analysis of molecular variance (AMOVA) was performed to evaluate the partitioning of the overall genetic variance to the hierarchical levels at infrapopulation "within miracidia", "among miracidia within individual children", "between miracidia from individual children within each site" and "between sites" using ARLEQUIN, version 3.
Genetic diversity Of the 2164 miracidia from which cox1/ITS2 profiles were obtained, 1966 (90. 8%) also produced reliable allele data for at least 10 microsatellite loci for subsequent analyses. We found a significant genotypic disequilibrium (adjusted p-value at 5% level < 0. 001) for 120 pairwise locus combinations across the whole dataset. Genetic diversity indices (He, A, Ar and F IS ) and the probability of deviation from Hardy-Weinberg equilibrium (P HWE ) for each microsatellite locus by sampling site are shown in Table  2. Most loci showed a deviation from Hardy-Weinberg equilibrium. All loci were highly polymorphic and the number of alleles and the allelic richness ranged from 7 to 17 and 7. 0 to 15. 8, respectively per locus in the whole dataset. Inter-population comparisons showed that heterozygosity (He) was relatively stable (Friedman test, v 2 = 1.
Acknowledgements. We are grateful to the district health and village authorities for their support, to  Dr. Jan Hattendorf  for statistical support, and to the children and their parents/guardians for their commitment throughout the study. This work was supported financially by the  Agence National de Recherche (ANR) , which supports the  HySWARM  project ( ANR-18-CE35-0001 ) within the framework of the " Laboratoire d'Excellence (LABEX)" TULIP program  ( ANR-10-LABX-41 ), and by the  Swiss National Science Foundation  (grant no. 31003A_170113  to JU, JZ and OB). EKA is a recipient of a  Swiss Government Excellence Scholarship (ESKAS , grant no. 2017-0746 ) for which he is deeply grateful.
Previous studies in laying hens have investigated resilience indicators that were derived from body weight  [18] , egg weight  [19] , or eggshell color  [20]. Because these traits were monitored only at specific time points, the number of records was very small in these studies (3 to 8 records during the production cycle per hen). However, breeders record the number of eggs laid daily, at the individual level in nucleus farms or at a group cage level in recurrent test farms. Resilience indicators that are derived from daily data on egg production have not been investigated but may hold potential to implement selection on resilience in breeding programs for layers. The breeding goal of poultry breeding companies is to genetically improve multiple traits in commercial hens, often a 3-or 4-way cross.
The final dataset included records on 12,852 groups of WA crossbreds (6154 records on cages of 6 hens and 6698 records on cages of 7 hens) distributed among four CA farms and six NL farms and 17 batches, and records on 3898 groups of BD crossbreds (2428 records on cages of 7 hens and 1470 records on cages of 8 hens) distributed among one CA farm and four NL farms and nine batches. For crossbred RT, 71 and 29% of the sires used in the WA and BD lines, respectively, had offspring in both countries (CA and NL). The purebred and crossbred populations consisted mostly of half-sib families with, on average, 50 offspring per sire in purebreds and 70 offspring per sire in crossbreds. The pedigree for each recorded individual was traced back between 4 and 16 generations (paternal only for crossbreds).
Egg production recording Eggs were collected several times a week (with the interval between two collections ranging from 1 to 4 days). Therefore, the raw data consisted of dates and number of eggs collected. Egg production was recorded individually for purebreds and as a pooled average for a cage for crossbreds, i. e. , the total number of eggs produced during a particular period was divided by the average number of hens alive in the cage during that period. Observations for individual purebred hens with two eggs per day were not discarded from the analyses, since the first egg could have been laid after collection time on the day before and the second egg could have been laid before collection time on the day of recording. Observations of three or more eggs per day were discarded from the analyses, because this is not biologically possible, even with different collection times in successive days.
Resilience indicator traits based on egg production The longitudinal egg production data enabled the computation of the types of resilience indicators based on deviations from the average performance of a contemporary group that had been used for other species in other studies  [6, 16, 18]. The average weekly number of eggs of the batch was calculated as the total number of eggs divided by the number of hens alive in the batch and used to calculate deviations of individual weekly EP records from the batch average. Resilience indicator traits evaluated were the natural logarithm of the variance (LNVAR) of an individual's or group's deviations, the skewness of the distribution of the deviations (SKEW), and the lag-one autocorrelation (AUTO-R) of the deviations. These three traits are complementary  [6] : a higher LNVAR is an indication of poor resilience of an animal to a perturbation, while resilient animals show a smaller range of deviations from the expected performance and therefore a smaller LNVAR; a more negative SKEW means that the animal shows more negative than positive deviations or that the animal experiences severe drops from the expected performance, which is an indication of poor resilience; and the closer AUTO-R is to 1, the more two subsequent deviations are alike, which is interpreted as a slower recovery from the perturbation and thus an indication of poor resilience.
Observations for the resilience traits were discarded if they were outside the mean plus or minus four times the standard deviation within populations (combination of lines and purebreds/crossbreds separately). However, only the observation for the particular resilience indicator trait was discarded, i. e. , observations for all other resilience indicators and egg production were kept. This resulted in the removal of about 4 and 8% of the raw data for each resilience indicator trait in the WA and BD lines, respectively. Data handling and graphs were performed in base R  [25]. Resilience indicator traits were calculated in R, i. e. LNVAR with the log and var functions, AUTO-R with the acf function, and SKEW with the skewness function of the e1071 package  [26].
Because purebred EP is recorded in individual cages and crossbred EP is recorded in group cages, social interactions may increase phenotypic variation for crossbred EP. Differences in heritability estimates between individual and group records have also been reported in the literature  [28, 29, 37]. These differences can be explained by differences in the estimation of variance components, either because of incomplete information on the relationships between individuals due to the use of group information instead of individual information (in purebreds the complete pedigree is known while in purebred only the sire branch is known), or modelling choices (e. g. taking social interactions into account or not). Yet, when using individual data to simulate group records, estimates of the variance components based on individual and group records are the same  [30].
, 2019; Ezanno et al. , 2020). Although contacts between individuals and contributions from the environment are critical for pathogen transmission at farm level, additional contact structures -such as animal movements and indirect contacts through transportation or geographical/social proximity between actors -were highlighted as pivotal in the study of transmission of animal diseases at a large geographical scale  (Büttner et al. , 2016; Galvis et al. , 2021). Social network analysis has the potential to shed light on contact structures, which, joined to epidemiological data, provided insights into the role of specific actors in the transmission process for livestock infections  (Hardstaff et al. , 2015; Salines et al. , 2017; Schulz et al. , 2017). Metapopulation models of epidemics were also developed to study the role of animal movements on infection spread based on either real movements or summaries derived from movement datasets  (Beaunée et al.
Data Data were extracted from the national swine movement database BDPORC  (Rautureau et al. , 2012; Salines et al. , 2017). Briefly, notification of all shipments of live animals is compulsory in France since 2010, allowing for tracing of contacts between swine production units. The origin, the destination, the date, the number of animals that were exchanged and the truck identification number are collected at every shipment. Farm specific data are also available, allowing for geolocation and informing on the herd types and capacities (total number of animals). The data used hereafter cover the period from 2017 to 2019, over which two types of networks were designed, namely the animal introduction model (AIM) and the transit model (TM) (Figure  1 )  [22]. While the AIM approach considers effective contacts through the movements of live animals between production sites, the transit model accounts for complex rounds consisting of successive loadings and unloadings in different sites.
The order of visited farms during the rounds was informed by the database, allowing for reproducing the exact contact tracing. TM links all herds that were visited by the same truck during a round. In the AIM, movements to slaughterhouses and rendering plants were neglected as they were considered as epidemiological endpoints  (Rautureau et al. , 2012). However, if several herds were visited to collect slaughter-aged animals during the same round and using the same truck, they were fully considered in the TM due to the possibility of viral transmission from one herd to the other.
Within-herd transmission dynamics A stochastic Susceptible-Exposed-Infected-Removed (SEIR) model was used to represent the dynamics of infection in every infected herd. The initial number of susceptible animals was determined from the total pig capacity provided in the database, under the assumption of homogenous mixing. Running the simulations over a relatively short period of time (c. f. Simulations subsection described later), no birthdeath process was considered except for infection-related mortality. Therefore, for agents with a high lethality rate such as ASF virus, the within-herd population decreases as the infection progresses, thereby affecting the transmission process, due to the frequency dependent formulation of the force of infection. At each time step  (day), the number of exposed animals in herd , E j () , is updated using a binomial process acting on the number of susceptibles   () with probability   (𝑡) = 1 - 𝑒𝑥𝑝 (-𝛽 𝐼 𝑗 (𝑡) 𝑁 𝑗 (𝑡) ) , where  denotes the transmission rate and   () and   () , the numbers of infectious and total number of animals in herd  at time.
Animal introduction model (AIM) A reaction-diffusion approach was considered to represent the spread of the agent between farms. For each recorded shipment sourced from an infected farm, the transferred individuals are randomly drawn from the total population of the farm (herd size) according to the number of animals actually moved recorded in the database, and regardless of their infection status. They are then integrated into the target population (replacing former susceptible animals to keep the population size constant) and replaced by susceptible individuals in the source population. A susceptible farm was considered infected when at least one infectious individual was introduced, initiating the within-herd transmission process.
Outcomes For each scenario and seeded introduction at each herd category, the number of simulations where secondary infections occurred was recorded. For those simulations, the number of secondary infected herds -along with the transmission route and the distance to the source of infection -were registered. Where applicable, the delay between infection and detection, otherwise stated as the 'at risk' period, for each infected and detected herd was also analyzed. The model was developed in R software, using the igraph package to represent the time-respective networks, rgeos, rgdal and sf to deal with spatial features and shiny for user-friendly implementation interface. The model is available on GitHub, along with a data sample for testing (https://github. com/mandraud/EpiNet/tree/main).
When applying the PBK model in a "reverse dosimetry" fashion, the PBK models are used to infer external equivalent exposure regimes that would cause blood, plasma, or target tissue concentrations equal to bioactive concentrations identified by in vitro assays  (Louisse et al. 2017; Rotroff et al. 2010; Tan et al. 2007; Yoon et al. 2012). Historically, PBK models were developed for specific chemicals and for purposes other than QIVIVE. A so-called top-down approach was used by designing the model structure and fitting its parameters to reproduce specific in vivo experimental animal or human kinetic studies  (Bessems et al. 2014 ). These PBK models were designed to allow extrapolation from data collected in certain situations by estimating chemical-specific parameters and then using knowledge of physiology to make predictions for other scenarios without kinetic data (for example in different species or exposures by other routes)  (Andersen 1995; Chiu et al.