dataesr/scientific-paragraphs-categorization huggingface dataset:
The search was translated and run in ABI/Inform (ProQuest), AGRICOLA (EBSCOhost), BIOSIS Citation Index(Clarivate Analytics), Business Source Complete (EBSCOhost), FSTA/Food Science and Technology Abstracts (Clarivate Analytics), Medline (PubMed), ProQuest Dissertations and Theses Global (ProQuest), VetMed Resource (CABI), and Web of Science Core Collection (Clarivate Analytics). Database searches were executed on August 14, 2019, without date or language restrictions, and updated on May 10, 2021 with no language restriction but a date restriction of August 2019 forward. Grey literature sources were searched between August 24, 2019 and September 24, 2019, and again from May 19-28, 2021 Publications and factsheets were manually searched in: Agriculture and Agri-Food Canada; Canadian Antimicrobial Resistance Surveillance System; Centers for Disease Control and Prevention (CDC) Antibiotic/Antimicrobial Resistance Reports and Publications; Environmental Working Group; European Commission; European Food Safety Authority; Food and Agriculture Organization (FAO) of the United Nations; FDA Antimicrobial Resistance Information; FDA Guidance Documents; Pew Charitable Trusts Antibiotic Resistance Project; USDA Economic Research Service; and World Health Organization (WHO).
Data Management and Statistical Methods Data were entered in Excel (Microsoft Corp. , Redmond, WA) and exported to R Statistical Software (R Core Team, 2018) for statistical analysis. Hair cortisol was log10-transformed to achieve normal distribution before calculation of Pearson's correlation coefficient for exact duplicates and duplicates of different dirtiness or colors. Plots of the correlation were generated using the package ggplot  (Wickham, 2016). The impact of remaining dirt, age, hair color, month, and methodological changes during the process was analyzed using a generalized additive model (GAM) with a log link function in the package gamm4  (Wood, 2004; Wood and Scheipl, 2017). In this analysis, one sample from each pair of exact duplicates was excluded from the analysis, but all other remaining samples were included.
A random effect of pen nested within-herd was included to account for the clustering of the observations. The model was reduced based on the change in R-squared after the removal of one variable at a time, and the final model was evaluated by residual plots. To explore and visualize the observations of the calves and HCC, a cluster analysis of mixed data was performed using the CluMix package in which distance between individual calves was based on Gower's general similarity coefficient and variables clustered based on similarity using measures of association  (Hummel et al. , 2017 (Hummel et al. , , 2019)). The associations between hair cortisol (log10-transformed) and welfare indicators were also analyzed using regularized regression using package glmnet  (Friedman et al. , 2010). For animals with multiple samples, only one sample (with the lowest CV value) was included.
These were treated as a separate category in the analysis. The remaining missing values were imputed using non-parametric random forest imputation in the package missForest before the elastic net regression  (Stekhoven and Bühlmann, 2012; Stekhoven, 2013). Two variables (lameness and conjunctivitis) were excluded due to little variance between the calves, and qualitative indicators with several levels were reduced by grouping if some levels contained very few observations and grouping was biologically plausible (Supplementary Table  S2 ). The counts of hairless patches and wounds or swellings were also condensed into categories (Supplementary Table  S2 ). For many behavioral observations, a high number of calves did not perform the behavior leading to a distribution with the high frequency of zero observations.
This was handled by adding a categorical variable describing if the behavior was observed and a quantitative variable with the number of times the behavior was observed  (Robertson et al. , 1994). The models were tuned and fitted using leave one out cross-validation using the package caret  (Kuhn, 2020) , and the models that generated the smallest mean squared errors were selected  (James et al. , 2013). The herd was included as a fixed effect to account for clustering.
ACKNOWLEDGMENTS The authors thank  Yongzhi Guo  ( Department of Clinical Sciences, Swedish University of Agricultural Sciences ) for technical assistance with running the analysis of hair cortisol. The authors also thank  Per Jensen ,  Ann-Sofie Sundman , and  Ann-Charlotte Svensson Holm  ( Department of Physics, Chemistry, and Biology, Linköping University ) for graciously providing advice and helpful tips regarding the protocol for hair cortisol analysis. We additionally want to thank the dairy producers participating in this study, the students  Linnea Andersson ,  Ellen Wik , and  Claire-Lise Sol  who assisted in collecting the samples, and the  Swedish Research Council Formas  for funding this project.
According to precision livestock farming principles, it is essential to apply feed intake forecasting processes to real time precision feeding strategies in order to improve the overall efficiency of the livestock feeding chain. Considering the lack of a mechanistic model that predicts daily feed intake in lactating sows, a novel approach combining an online forecasting procedure with an offline learning procedure is proposed. A database of 39,090 lactations, from 6 different farms and containing the first 20 daily feed intake records after farrowing, was used (1) to identify consistent sets of clusters and trajectory curves offline, and (2) to test 3 predictive functions of daily feed intake online. The homogeneity of the clusters resulting from the offline learning procedure was assessed according to Silhouette and Calinski-Harabasz scores.
Time-series definitions We begin by introducing the necessary definitions. Definition 1. A time-series T consists of a sequence of numerical vectors in successive order and equally spaced out over time: T = t 1 ,t 2 , ⋯,t n , with t i being a V-dimensional real-valued vector and n the length of the time-series T. The time-series T is univariate when V = 1, meaning that only one variable varies over time, otherwise T is multivariate. In this paper, we deal with univariate time-series, a sequence of numerical values over time. Definition 2. A dataset D is a set of time-series such that: D = {T 1 ,T 2 , ⋯, T m }, where m is the number of time-series in the dataset.
Time-series clustering In machine learning, clustering belongs to the class of unsupervised learning problems whose objective is to determine how the data is organized without any labeled examples. The objective of clustering is to partition the dataset into homogeneous groups of data, called clusters, where data points in the same cluster are the more similar to each other and dissimilar to data in other clusters. Clustering can be applied to time-series and is defined as follows  (Aghabozorgi et al. , 2015) : Definition 3. Given a time-series dataset D = {T 1 , T 2 , ⋯, T m }, time- series clustering consists in partitioning D into C = {C 1 , C 2 ,. , C k }, a set of k clusters, with D = ⋃ k i=1 C i , C i ∩ C j = ∅ for i ∕ = j. Homogeneous time-series are grouped together based on a certain similarity measure that maximizes inter-cluster distance and minimizes intra-cluster variance.
Time-series clustering algorithms Numerous approaches have been proposed to deal with time-series objects characterized by large data sizes and potentially high dimensionality. For whole time-series clustering (as opposed to subsequence clustering, for instance), clustering algorithms are generally classified into three groups, namely shape-based, feature-based, and model-based, depending on whether or not clustering is applied directly to raw data  (Warren Liao, 2005; Aghabozorgi et al. , 2015). Feature-based and model-based algorithms are not directly applicable to raw data and require time-series conversion. Feature-based algorithms work on vectors of features extracted from raw time-series, such as mean, variance, autocorrelation, etc. (Bandara et al. , 2020). This leads to dimensionality reduction, thus making it possible to cluster datasets that cannot fit into memory and time-series of unequal lengths.
A prototype is then extracted in order to summarize the feed intake trajectory curve (TC) followed by each cluster. This offline learning requires the availability of sufficient data to be able to extract consistent prototypes. The online forecasting (Fig. 2 ) consists in predicting the value f d+1,j of the time-series F j ,d ∈ [1,n] ∩ N. Forecasting uses both an assigned TC to sow j and f 1,j , f 2,j , ⋯, f d,j sow live data recorded since farrowing. The forecast f d+1,j may then be transmitted to any application that relies on daily feed intake forecasts at an individual level (e. g. precision feeding). The amount of feed really consumed, called f d+1,j , is finally recorded from the feeder by the end of the day in order to be used on the following days. Data preprocessing and offline and online methods are described in the following subsections.
Daily feed intake was recorded between April 2013 and June 2019 in 6 commercial farms where the farrowing crates were equipped with Gestal® system. The original database was composed of 78,863 lactations of variable lengths. Lactations shorter than 12 days or longer than 32 days were withdrawn, as they may represent abnormal events (e. g. early death of sow after farrowing, adoption of a second litter). It was assumed that occasional electronic anomalies occurred when encountering missing or negative daily feed intake records, and daily feed intake higher than 6 kg on first day after farrowing, greater than 15 kg on the second day or greater than 20 kg from day 3 on. It was also assumed that several electronic anomalies occurred while recording feed intake within a lactation when the cumulative feed intake during lactation was over 250 kg.
When one of these situations arose, the whole lactation was removed from the dataset. In addition, the last day of lactation showed a distinctive bimodal distribution, with some sows showing a huge drop in feed intake. Because this drop was likely related to specific feed allowance practices on weaning day (i. e. , feeding only half of the ration), this day was excluded from offline learning and online forecasting. The combination of these cleaning steps led to a clean database of 64,951 lactations. Data selection and data splitting. Most lactations last between 17 and 23 days as a result of biological variability in the duration of the gestational period and batch management at weaning  (Martel, 2008). Shorter lactations may be due to the occurrence of lactation problems, whereas longer lactations may be related to specific practices such as keeping sows longer to nurse fostered piglets.
In this study, for the selection of time-series, we used 20-day lactations as a compromise Fig. 2. Methodological approach for the daily prediction of sow feed intake during lactation, from individual sow live data and herd historical data. f d+1,j represents the forecasted value of feed intake for sow j on day d + 1. f d+1,j represents the amount of feed really consumed at the end of day d +1 for the sow j. Blue lines represent offline steps and red lines represent online forecasting tasks for feed intake time-series. between decreasing the number of available F j time-series, and increasing the lactation length to cover a wider range of practices (Fig. 3 ). This resulted in the selection of 39,090 20-day F j time-series. This dataset, called D, was then split at random into training and test sets according to an 80:20 ratio, and with respect to this ratio in each farm.
The training set of D was used for offline learning of feed intake trajectory curves (TCs), both per farm in the farm specific (FS) approach, and over all farms in the global (G) approach. The test set of D was used for the validation of the online forecasting simulation. Data description. The number of time-series available per farm, mean feed intake, and the standard deviation for each of the 6 farms in dataset D are presented in Table  1. Mean feed intake differed among farms. It was greatest in farm 2 and lowest in farm 1 and averaged 6. 05 kg (±1. 29). Over the period of 20 days, feed intake showed a gradual increase (Fig. 4 ). During the five first days of lactation, mean feed intake increased rapidly from 2 kg to 5 kg, and subsequently continued to increase more slowly, in line with the sows' appetite, reaching a plateau in the third week of lactation.
Smooth and farm specific feed intake trajectory curves were thus obtained within each farm, and for all farms together. There are very few studies available in the literature on variability in individual sow feed intake patterns during lactation in commercial swine herds, mainly because feed intake is very rarely recorded, except with the use of smart feeders in recent years  (Piñeiro et al. , 2019). In the study of  Koketsu et al. (1996) , daily sow feed intake was measured manually on a large number of sows, with data available for about 25,000 lactations from 30 commercial farms. Average lactation length (19 days) was similar to the present study and average feed intake (5. 2 kg/d) was 15% lower than in the present study. They identified six patterns of daily feed intake according to the amount of feed consumed, how quickly feed intake increased, and whether a transient drop in feed intake occured during lactation.
Three of these patterns presented similar trends to the two identified in the present study, with either a rapid or a gradual increase in feed intake over time. However, two of the patterns identified in  Koketsu et al. (1996) ' study, those with a rapid increase and a major or a minor transient drop in feed intake, were not identified in the present dataset, even when considering more than two clusters (results not presented). Dourmad et al. (1991) , who observed a drop in feed intake at about five days of lactation in lean sows with high appetite fed ad libitum from the day of farrowing, suggested that this was related to the occurrence of gastrointestinal disorders resulting from uncontrolled excessive feed intake at the beginning of lactation. It can be argued that in the present study the use of smart feeders, which make it posible to limit the risk of overconsumption, could have decreased the frequency of such a feeding pattern.
According to  Koketsu et al. (1994) , the "rapid" or "gradual" feeding patterns, which correspond to the two identified in the present study should be encouraged in order to optimize reproduction and lactation performance, and reduce the risk of reproductive failure after weaning. It is thus possible that with another database from farms with different feeding practices (e. g. with ad libitum feeding since farrowing) other feeding patterns, with a transient drop in feed intake, could be identified. Similar average feed intake patterns, as these identified in the present study, with a rapid or a more gradual increase in feed intake over lactation were also found by  Schinckel et al. (2010)  who compared three different breeds of sows using a generalized Michaelis-Menten functions to adjust the feed intake curves.
Acknowledgments The authors gratefully acknowledge  JYGA Technologies (Quebec, Canada ) and the farmers who provided the data used in this study, and  Thomas Dahmen  for his help at early stage of this study. This study formed part of a Ph. D. thesis in the # DigitAg  project ( ANR-16-CONV-0004 ), supported by the  French National Research Agency  in the " Investments for the Future" program ; and the  European Union 's  Horizon 2020 Research and Innovation program  (grant agreement No. 633531 ).
Conclusions: Our results show that an assessment of breeding program practices can be done based on the point of divergence of genetic and RMS trends between BLUP and ssGBLUP and based on the partitioning of the genetic trend into contributions from different selection pathways. However, it should be noted that genetic trends can diverge before the onset of genomic selection if superior animals are genotyped retroactively. For the pig population example, the results showed that genomic selection was effective in this population.
However, when the elite animals are genotyped retroactively or genotyping is done after selection, PBLUP and ssGBLUP can estimate different trends. The pig population that was used here has experienced both situations for some of the traits. Thus, we used three approaches to identify and investigate changes in breeding practices over time, in particular the use of genomic selection, namely: (1) based on differences in genetic trends estimated using PBLUP versus ssGBLUP, (2) based on differences in trends in RMS estimated using PBLUP versus ssGBLUP, and (3) based on partitioning the estimated genetic trends into different selection pathways as in  [14]. These approaches were applied to a real dataset from a purebred maternal pig line.
Data structure The phenotypes of the Landrace pigs used in this study were collected from 2012 to 2021 and included 228,427 records for birth weight (BW), 101,225 records for average daily gain from birth to the end of the nursery period (ADG) at 11 weeks of age, and 11,444 records for average daily feed intake during the finishing period (FEED) at 23 weeks of age. FEED was measured on males only. Descriptive statistics for each trait are in Table  1. Pedigree information was available for 282,035 animals, of which 32,856 were genotyped for 36,612 single nucleotide polymorphisms (SNPs) after quality control. The animals in the pedigree were the progeny of 809 sires and 14,674 dams. The number of pigs that had either both or one parent unknown was 2011 and 835, respectively. The ratio of genotyped pigs to all pigs born in each year ranged from 4% in 2015 to 18% in 2019.
The genomic relationship matrix ( G ) was constructed using the first method of VanRaden  [18] : where Z is a matrix of genotypes coded as 0, 1, and 2 for AA, AB, and BB, respectively, and then centered by subtracting twice the frequency of the major allele of SNP i (p i ) ( i = 1, …, 36,612). To avoid singularity problems, G was blended with A 22 as G = 0. 95 G + 0. 05 A  22. Solutions for the multi-trait PBLUP and ssGBLUP were obtained using the preconditioned conjugate gradient algorithm with iteration on data, as implemented in the BLUP90IOD2 program  [19]. The (co)variance components were the most recent estimates derived using PBLUP. The GEBV from ssGBLUP were set to the same base (i. e. , year 2015) as the mean EBV from PBLUP. To facilitate comparisons between traits, G(EBV) were divided by the square root of the additive genetic variance.
(6 ) RMS i = (G)EBV i -PA i , (7) a i = 1 2 a s(i) + 1 2 a d(i) + m i , (8) a = T m, (9) a = TP 1 T -1 a + TP 2 T -1 a = a 1 + a 2 , This procedure was implemented using the R package AlphaPart 0. 8. 1. [21] , using the GEBV obtained using ssGBLUP for all animals (i. e. , genotyped and non-genotyped) as the input. The contribution of each pathway for each birth year was expressed as a percentage by dividing the average GEBV of a partition by the average GEBV of the whole population.
Matrix assisted laser desorption ionization time-of-flight identification of colonies. The identification of the bacterial and fungal colonies was carried out using matrix-assisted laser desorption ionization time-of-flight mass spectrometry MALDI-TOF-MS as previously described  23, 24. For bacteria, each colony was deposited in duplicate onto a MALDI-TOF MSP 96 target plate (Bruker Daltonics, Leipzig, Germany), and 2 μL of matrix solution (saturated solution of alpha-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2. 5% trifluoroacetic acid) was added to each spot and allowed to dry for 5 min and then analysed by Microflex spectrometer (Bruker Daltonics) using the software MALDI BioTyper 3. 0 (Bruker Daltonics). For the identification of fungi, each colony was incubated in 1 mL of 70% ethanol for 10 min and then centrifuged at 1300g for 5 min.
The pellet was treated with 20 μL of acetonitrile and formic acid (v. v) at 100% and 70%, respectively. This mixture was then centrifuged at 1300g for 5 min, and 1. 5 μL of the supernatant was deposited on a MALDI-TOF-MS target and allowed to dry before 1. 5 μL of matrix was deposited on each spot, allowed to dry for 5 min and then analysed by par Microflex spectrometer (Bruker Daltonics) using the software MALDI BioTyper 3. 0 (Bruker Daltonics). Molecular identification and sequencing. All colonies that remained unidentified by MALDI-TOF-MS were subjected to molecular identification by sequencing the bacterial 16S rRNA  25  and the fungal ITS1, ITS2, β tubulin and TEF regions. The primers used in this study are reported in Table  S1. DNA extraction was performed using BioRobot EZ1 (Qiagen, Les Ullis, France) using the commercial EZ1 DNA Tissue Kit according to the manufacturer's instructions (Qiagen).
PCR was performed using Hotstar Taq polymerase according to the manufacturer's instructions (Qiagen) using a thermocycler (Applied Biosystem, Paris, France). PCR products were separated by electrophoresis on a 1. 5% agarose gel and stained with SYBR® safe (Thermo Fisher Scientific) before being visualized under an ultraviolet transilluminator. PCR products were then purified using a Millipore NucleoFast 96 PCR kit following the manufacturer's recommendations (Macherey-Nagel, Düren, Germany) and sequenced using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems) with an automatic sequencer ABI (Applied Biosystems). Sequences were assembled using the software ChromasPro 1. 7 (Technelysium Pty Ltd. , Tewantin, Australia) and blasted in the NCBI databank using default settings to identify bacterial species  26  and against the Mycobank database http://www.
xlsta t. com). Multivariate analysis was carried out on the set of microbes cultured from M. ulcerans positive and negative samples to reveal possible associations between some microbes and the positivity of M. ulcerans skin carriage. Multivariate analysis was performed via the Genmod procedure with the SAS 9. 4 statistical software using a negative binomial distribution, adjusted for the effect of time (quantitative variable) and by using generalized estimating equations to account for the non-independence of repeated measures to analyze the results of germination assays. The Wilcoxon signed rank test and Chi-squared test were used to compare the attraction effect of fungi by mycolactones and to compare M. ulcerans prevalence in the two geographical regions of Sindou and Tenkodogo, respectively.
Acknowledgements The authors acknowledge the technical help of  Nicholas Armstrong. This study was supported by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the  «Investissements d'avenir» (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d' Azur and European funding FEDER PRIMI.
Methods A systematic literature search was conducted to select available information according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. For this purpose, three databases, namely PubMed, Scopus and Clarivate Web of Science, were used to select papers according to search terms related to the occupational exposure to bisphenols. One search strategy was developed and adapted for each database. Searches were carried out in English and included the period ranging from 1 January 2000 to 27 March 2020. The search terms used for each database for bisphenol A, bisphenol S and bisphenol F were ("occupational" OR "worker*") AND ("biomonitoring" OR "biomarker*" OR "urine" OR "blood" OR "serum"). Items obtained from the databases led to 205 PubMed papers, 144 Scopus papers and 97 Web of Science papers for BPA.