dataesr/scientific-paragraphs-categorization huggingface dataset:
Scoring of the immunohistochemical stainings was performed by a medical doctor specialist in breast cancer pathology (DL), two certified veterinary pathologists (JA, FN), and a resident in veterinary pathology (ED), with no knowledge of either the clinical outcome or clinicopathologic data. ERα, PR and Ki-67 were scored based on the number of positive nuclei among at least 500 neoplastic cells (manual image analysis using the Image J software, Research Service Branch, National Institute of Health, Bethesda, Maryland, USA). ERα and PR were considered positive if nuclear staining was observed in at least 10% of the neoplastic cells, as previously reported for canine mammary carcinomas  [38] [39] [40] , and human breast cancer  [41] , as the threshold proposed for ER and PR positivity of feline mammary carcinomas (Allred score equal of higher than 3)  [42]  was not significantly prognostic in this cohort.
Acknowledgments The authors thank  Dr. Mélanie Pohu ,  Dr. Floriane Morio , and  Dr. Clotilde de Brito , who helped in collecting the clinical and follow-up data, as well as the veterinary pathologists ( Dr. Jean-Loïc Le Net ,  Dr. Virginie Théau ,  Dr. Pierre Lagourette ,  Dr. Olivier Albaric  and  Dr. Sophie Labrut ) who performed the initial diagnoses of feline mammary carcinomas, and the technicians in histopathology ( Mr. Bernard Fernandez ,  Mrs. Florence Lezin , and  Mrs. Catherine Guéreaud ) who made the slides. Finally, we thank the referring veterinarians and the owners of the cats included in this study, who gave us the clinical and follow-up data.
Motivation: Adverse outcome pathway (AOP) is a toxicological concept proposed to provide a mechanistic representation of biological perturbation over different layers of biological organization. Although AOPs are by definition chemical-agnostic, many chemical stressors can putatively interfere with one or several AOPs and such information would be relevant for regulatory decision-making. Results: With the recent development of AOPs networks aiming to facilitate the identification of interactions among AOPs, we developed a stressor-AOP network (sAOP). Using the 'cytotoxitiy burst' (CTB) approach, we mapped bioactive compounds from the ToxCast data to a list of AOPs reported in AOP-Wiki database. With this analysis, a variety of relevant connections between chemicals and AOP components can be identified suggesting multiple effects not observed in the simplified 'one-biological perturbation to one-adverse outcome' model.
Materials and methods To develop the sAOP server, we made use of the ToxCast program  (Dix et al. , 2007). This program builds large collections of in vitro assay data on a diverse set of chemicals. A filtering step using the cytotoxic-associated burst (CTB) was applied, as it was demonstrated that chemicals activate assays at concentration levels also observed for cytotoxicity or cell stress in ToxCast  (Judson et al. , 2016). The remaining chemicals showing activities on one of the targets in ToxCast were then mapped to AOPs, collected from AOPwiki (version as of March 2018), a knowledgebase structure of AOP  (Villeneuve et al. 2014) , resulting to a stressor-AOP network between 4960 chemicals, 369 proteins, 1089 KEs and 207 AOPs. More detailed information is found in the Supplementary Information.
Results Users can query the sAOP database by 'chemicals', 'assay', 'protein', 'Key Event' and 'AOP'. Some parameters can be included in the search such as 'AC50 score', 'z-score' or 'degree of separation' to facilitate the visualization of the network. For example, the search for 'phenanthrene', a polycyclic aromatic hydrocarbon, with a degree of separation of 4, results to a network represented in Figure  1 , i. e. 13 assays which show activity on three proteins (ESR1, ESR2 and NR1I2) linked to 6 MIEs, 16 KEs and 2 AOs. More examples are provided in the Supplementary Information.
Acknowledgement We would like to acknowledge  Catherine Bjerre Collin 's help on the revision of English. Funding This work was supported by the  European Union 's  Horizon 2020 program  [ 681002 , EUtoxRisk], the  Novo Nordisk Foundation  under [NNF14CC0001], the  University of Paris Descartes-USPC , the university of Paris Diderot and INSERM.
Patients This monocentric retrospective cohort study was conducted in the liver ICU (19 beds) at Henri Mondor University Hospital (a 900-bed, tertiary care institution for adults), between January 2005 and December 2015. In compliance with French law and regulations, the need for informed consent was waived. The Hospital's Ethics Committee nevertheless approved the study, and the database was officially registered with the French Data Protection Authority (Commission Nationale Informatique et Liberté) (no. 1699340). All patients included in this study were > 18 years of age, hospitalised in the liver ICU and had histologically proven or clinically diagnosed cirrhosis (e. g. portal hypertension with ascites, oesophageal varices or encephalopathy). For the purposes of this study, cirrhotic patients with inherited severe immunodeficiency, allogenic stem cells or who had undergone organ transplantation, were excluded.
Positive Aspergillus patients characteristics Finally, 60 patients fulfilled the study criteria (positive culture of Aspergillus sp. ) and were included in this monocentric retrospective analyse. With the increase in the number of patients hospitalised in liver ICU between 2005 and 2015, the occurrence of IPA and/or respiratory tract colonisation by Aspergillus in cirrhotic patients has increased in the past 10 years (Fig. 1 ). The demographic features, risk factors and outcome data of the 60 patients included are summarised in Table  2. Overall, 85% of the patients were male and their median age was 55. 4 ± 12. 6 years. Their cirrhosis was alcohol related (42%) or viral related (38%). The most common primary diagnoses at ICU admission were sepsis (n = 24, 40%), acute variceal bleeding (n = 17, 28%) or hepatic encephalopathy (n = 12, 20%).
Phenotypes and genotypes Data were provided by a collective French Landrace pig program led by the breeding companies NUCLEUS (Le Rheu, France) and AXIOM (Azay-sur-Indre, France). Animal Care and Use Committee approval was not requested for this study because the data used were routinely collected data according to standard practices in breeding herds. Performances recorded in their selection herds and test stations are centralized to perform common genetic evaluations. The main selection objective of the collective program is the improvement of reproductive criteria. However, improving growth and feed efficiency traits without impairing carcass composition and meat quality is also considered in the breeding goal  [23]. The Landrace breed is renowned for its high prolificacy, excellent maternal instinct, and milking abilities.
It has also good rusticity, fattening performance, and meat quality and carcass composition characteristics  [24]. Data on age at 100 kg (AGE), backfat depth (BD) at 140 days, and average piglet weight at birth within litter (APWL) were analyzed. Performances were recorded in breeding herds at around 100 kg live weight for AGE and BD. Backfat depth was measured ultrasonically at the shoulder, last rib and hip joint on each side of the animal (4 cm from the middorsal spine line) and an average BD was calculated for each animal using these six measurements. AGE    , with r equal to 1. 05 and 1. 125 for males and females, respectively; BD was adjusted to 100 kg as: BD = averageBD + r * (100 -w test ) , with r equal to 0. 1 and 0. 12 for males and females, respectively. APWL was considered as a sow trait and was the average of individual birth weights of piglets.
It was recorded for each litter, with an average of 2. 7 parities per sow. Only Landrace animals with genotypes were included in the analysis. Average performance and number of animals and records used for analyses of the three traits are in Table  1. Pedigrees were extracted from the collective French Landrace pig program and included all available ancestors of the genotyped animals in the datasets. Three generations were traced back from the pedigree for each trait, resulting in 5534, 6541, and 3331 animals for AGE, BD and APWL, respectively. Two medium-density panels were used for genotyping: the porcine SNP60 Illumina BeadChip (Illumina, San Diego, CA) and the GeneSeek Genomic Profiler HD 80 k (GeneSeek, Licoln, NE), on 98% and 2% of the genotyped animals, respectively. SNPs with a call rate lower than 0.
The task of maximizing additive gain or total genetic gain of progeny was addressed via linear programming  [32]  using the R  [33]  lpsolve package  [34]. Two constraints were set for the optimization: (1) each boar could be mated to up to 15 sows and (2) each sow could not be mated to more than one boar. In this setting, optimization was done for both boars and sows. The total number of sows was always equal to the number of selected matings (600), but the number of boars and their contribution to the progeny could differ depending on the mate allocation strategy. As a result, the objective function for the linear program, for instance for the total genetic value g ij , was defined as: with constraint x i1 + x i2 + x i3 + • • • + x is = 15(i = 1, 2,. , nb) for boar i and: x 1j + x 2j + x 3j + • • • + x bj ≤ 1 j = 1, 2,.
MultiLocus Sequence Typing (MLST) The genetic relationship between the T. equigenitalis strains was investigated using the MLST where a scheme was specifically defined for the Taylorella genus  [19]. Clusters of related strains and individual unlinked sequence types (STs) found in the https://pubmlst. org/taylorella/ database plus the T. equigenitalis strains isolated in this study are displayed as a single eBURST diagram by setting the group definition to zero of seven shared loci using goeBURST algorithm  [20]  implemented in PHYLOViZ program  [21].
Discussion The present study reports clinical, bacteriological and molecular evidence of the clinical transmission of T. equigenitalis in a mare inseminated with frozen-thawed stallion semen cryopreserved over a six-year period. Antimicrobials are added to semen cryodiluents for their bacteriostatic and/or bactericidal properties. The presence of T. equigenitalis DNA in the semen alone does not confirm viable T. equigenitalis organisms were present in the semen used to inseminate the mare. However, in this case, the presence of T. equigenitalis in bacterial culture supported evidence of its transmission. The MLST lineage ST-46 of both strains is known to circulate (or to have circulated) in Germany but not in France, according to the https://pubmlst. org/taylorella/ database, which corresponds to the geographical location of the semen collection centre (Fig.
Acknowledgements We are very grateful to  Marine Collet  and  Anne-Sophie Jouanny  from  LABOCEA22  ( France ) who helped isolate T. equigenitalis from the mare, and  Hervé Morvan , head of the  Veterinary Bacteriology department of LABOCEA22 (France). We would also like to thank  Delphine Libby-Claybrough , professional translator and native English speaker, for her editorial assistance.
Acknowledgements. C. Martínez-Ortiz-de-Montellano  acknowl- edges receiving a scholarship from  CONACYT, Mexico  to undertake her PhD studies. The financial help of the  Institut National Polytechnique de Toulouse (INPT)  and the  Marie Curie Programme  " Healthy Hay" project as well as the collaboration project between Mexico and France  ( CONACYT PCP 229330 ) is much appreciated. We also thank the  IEPAC  project (FEDER Convention No. 31439 -Programme INTERREG IV CARAIBES 2007-2013 -No. 41000140 ) for financial support. The authors would like to thank  Rodrigo Carrillo-Peraza ,  Dr. Bruno Payre  and  Dr. Fotini Manolaraki  for their help during the experiment, and offer special thanks to  Dr. Ian Beveridge  and  Dr. Wieslaw Kozek  for their remarks about this work. The authors wish to report that there are no competing interests affecting the outcome of this work.
, 2015)  or mobile  (Kuzuhara et al. , 2015)  device. Other authors  (Negretti et al. , 2008; Buranakarl et al. , 2012; Guo et al. , 2017; Pezzuolo et al. , 2018)  have also developed and used 3D imaging technologies for a wider variety of livestock. In most cases, animals were measured under laboratory conditions (heavy equipment and landmarks on animals to guide computer measurements), which provided images of the entire body of restrained animals or portions of the body of unrestrained animals. Equipment can often be adapted to farm conditions and recent literature describes devices used to estimate body weight (BW) of pigs in barns  (Pezzuolo et al. , 2018; Wang et al. , 2018)  and broilers in houses  (Mortensen et al. , 2016). Inexpensive and portable equipment based on the Microsoft Kinect® v1 sensor have often been used; however, authors concluded that most methods needed additional technical development to acquire and extract data automatically.
Materials and methods 2. 1. Animals: Data were obtained from the INRA-UMR PEGASE experimental dairy station at Méjusseaume, Le Rheu, in western France (48°11' N; 1°71' W; elevation 35 m). The study involved 64 Holstein dairy cows with a mean BW of 673 kg (standard deviation (SD) = 65 kg) and parity ranging from 1-5. After each milking (twice a day), cows were individually and automatically weighed at a static weighing station (DeLaval France, Elancourt, France) at the milking parlor's exit. Mean BCS, based on the French scoring scale of 0-5  (Bazin et al. , 1984) , was 2. 05 (± 0. 25). One month, after weighing, cows were scanned with the Morpho3D device (section 2. 3). Data were collected from March-June 2018, yielding 289 3D images. However, due to abnormal behavior of the cows or excessive light in May (which generated artifacts on images), cloud points were considered of too low quality to be used for 3D images reconstruction (Fig.
Undesired objects were deleted during a cleaning process using Metrux2α® software (3D Ouest, Lannion, France). This step ensured that the point cloud is a sampling of a smooth surface on which surface normal vectors can be estimated. Finally, surface normals were estimated from the point cloud, and a screened Poisson surface-reconstruction algorithm was applied to build a triangulated mesh  (Kazhdan and Hoppe, 2013)  using Meshlab® open-source software  (Cignoni et al. , 2008).
5c ). Given the need to restrain some cows, we assessed the ability to estimate total volume and area from images of animals cut off at shoulder level. Only high-quality images (i. e. with the head scanned correctly) were selected. Two volumes and areas were determined on from the images: total volume and area, calculated using the automatic algorithm, and truncated volume and area, measured by placing one plane at the tip of the shoulder blades (similar to that used by  Minagawa (1994) ) and another at the rump (Fig. 6 ). The two planes were placed using 3D image-processing software Metrux2α® after placing three points on the image (Fig. 5 ). The Metrux2α® was used to estimate the volume and area between the two planes and to measure six morphological traits from the images: heart girth, chest depth, wither height, hip width, backside width and ischial width.
Estimation of body weight Cow BW was measured twice a day. BW was also predicted using several regression models based on morphological traits, volume and area calculated with the Morpho3D device. Predictions of these models were then compared to the BW measured with the weighing system. From the measurements performed on complete images, a correlation matrix of Pearson coefficients was calculated to identify relationships between traits. Volume, area and morphological traits were measured or/ calculated from 3D images, while other traits, such as BCS and BW, were available in the database of the herd management system. Several linear regression models to predict BW from traits measured or calculated from the 3D images were tested. The models were developed using the Akaike information criterion (AIC) with "backward elimination" variable selection.
This method begins with all variables and then removes one or more variables at each iteration until it obtains the minimum value of the AIC, defined as 2 × (n-2) × log(L), where n is the number of variables in the model and L is the maximum probability of the likelihood function. Each final model was cross-validated to estimate its prediction error. Cross-validation was preferred due to the small size of the dataset, which precluded separating it into sufficiently large calibration and validation datasets. The dataset was thus randomly divided into 2 groups, one of them containing 90%of datasets. This large group was used to develop BW models, which were then validated on data from the remaining group. Ten iterations of each model were crossvalidated 100 times using different random draws to obtain a large number of variations and calculate the root mean square error of prediction (RMSEP).
To assess the interest of developing this tool to render measurements automatic or semi-automatic, inter-operator reproducibility of plane placement was considered high (CVs for volume and area measurement = 1. 00% and 1. 80%, respectively). According to  Fischer et al. (2015) , a method is repeatable and reproducible when its CV is less than 3-5%. Unfortunately, repeatability and reproducibility tests are not widely available in the literature, even though they are essential for determining the relevance of tools and methods  (Marinello et al. , 2015). It is therefore difficult to compare the method we developed to other tools. BW 0. 46 ). When we applied these models to the recorded BW in our database, some Errors resulting from the image-reconstruction process or low-quality images may occur with the Morpho3D device.
Acknowledgments The authors wish to thank everyone involved in the  Morpho3D  project, especially technicians at the  Méjusseaume experimental station , who took excellent care of the animals. The  Morpho3D  project is supported by the  National Fund CASDAR , which supports innovation in agriculture (RFP " Recherche Technologique " 2015, no. 005 ), special funds from the  INRA Animal Physiology  and  Livestock Systems division for innovative projects and the collaborative ANR -APIS-GENE  project  DEFFILAIT.
To address this conundrum, we obtained mitochondrial and nuclear data from modern and ancient Near Eastern and European pigs. Our analyses indicate that, aside from a coat color gene, most Near Eastern ancestry in the genomes of European domestic pigs disappeared over 3,000 y as a result of interbreeding with local wild boars. This implies that pigs were not domesticated independently in Europe, yet the first 2,500 y of human-mediated selection applied by Near Eastern Neolithic farmers played little role in the development of modern European pigs. almost certainly represents populations originally domesticated in Anatolia and the Near East. In the case of cattle and pigs, however, the widespread distribution of their wild progenitors across most of Eurasia complicates the classification of archaeological specimens as wild or domestic, and leaves open the possibility that these taxa were also independently domesticated in Europe.
To do so, we obtained mitochondrial (including PCR data [n = 230] and next-generation sequencing (NGS) data [n = 327]) and nuclear data, including 2 highcoverage (>10-fold), 7 medium-coverage (1-to 10-fold), and 54 low-coverage (<1-fold) genomes from an assessment of >500 archeological pig remains (Dataset S1). Our dataset (including publicly available sequences) spans the past 14,000 y and includes a total of 2,099 samples from the Near East and Europe, including samples from contexts that precede and follow the origins of pig domestication.
Altogether, this confirms that Near Eastern farmers brought domestic pigs possessing an mt-Y1 signature into Europe during the Neolithic expansion  (21, 28). Our analysis of mtDNA data from 2,099 samples (557 newly generated data), including 1,318 ancient samples (262 of wild boars, 592 of domestic pigs, and 464 of unknown status) and 781 modern samples (467 of wild boars and 314 of domestic pigs), demonstrates that the first appearance of the mt-Y1 haplotype in our continental European dataset was ∼8,000 y ago in Neolithic Bulgarian pigs (Kovačevo: Kov18, Kov21), and its terminal appearance in a Neolithic context was ∼5,100 y ago in a Polish sample (AA134, _ Zegotki 2). The few pigs possessing an mt-Y1 signature from post-Neolithic contexts were found mostly on islands beyond mainland Europe in southwestern Greece (4,350 to 3,250 y BP: MM495, MM486, MM303), in Crete (3,100 y BP), in Sardinia (∼3,750 y BP)  (29) , near Naples (∼800 y BP: VM_CM01, VM_CM02, VM_CM03), and in Corsica (modern noncommercial pigs)  (21) , as well as in Tuscany (∼800 y BP: VM_TM01) (Fig.
Of these, only the Herxheim sample (KD033), with ∼54% Near Eastern ancestry, possessed the Near Eastern mt-Y1 haplotype (Dataset S1), and also had substantially more Near Eastern ancestry than any of the ancient or modern European wild boar (Fig. 2B ). This is supported by outgroup f3-statistics analysis, which indicates that KD033 shares more drift with Near Eastern wild boar than any other ancient or modern pig genome (SI Appendix, Fig. S17 ), as well as significant D-statistics of the form D (outgroup, Near Eastern wild boar; European wild boar: KD033) (Z << 3; SI Appendix, Fig. S18 ). These results indicate that European wild boars were being incorporated into domestic populations relatively soon after the latter were introduced from the Near East. To obtain a more precise temporal and geographic resolution of the disappearance of Near Eastern genomic signatures in Europe, we performed additional analyses of 54 low-coverage ancient genomes (<1-fold) that possessed sufficient data (>5,000 SNPs covered from a panel of ∼12 million SNPs; SI Appendix) to be confidently projected onto a principal component analysis (PCA) alongside both modern and (high-and medium-coverage) ancient genomes.
We analyzed these data together with those of Asian wild and domestic pigs. In this analysis, principal component 1 (PC1) separated European and Asian pigs, while PC2 separated Near Eastern and European pigs (SI Appendix, Fig. S14 ). After removing Asian pigs, PC1 separated modern European domestic pigs from all other samples, while PC2 separated European from Near Eastern pigs (Fig. 2C ). The separation between European domestic pigs and all other samples on PC1 is most likely the result of admixture between Asian and European breeds following breed improvement programs in the 19th century  (35, 36)  (SI Appendix, Figs. S15  and S16 ). The PCA revealed 2 groups of ancient European pigs (including 25 previously identified as domestic using a combination of morphometric and contextual data and 10 with unknown status) (Fig.
2C ). The first group consisted of 8 domestic pigs that are closer to Near Eastern wild boars and ancient Near Eastern domestic pigs (Fig. 2C ). In all, this group comprised Neolithic pigs from contexts dating from 7,650 to 6,100 y BP, including the following: Madzhari, Northern Macedonia (∼7,650 y BP: BLT022, BLT023); Herxheim, Germany (7,100 y BP: KD033, KD032); Mȃgura, Romania (7,100 y BP: BLT010); Pločnik, Serbia (∼6,650 y BP: AA212); Vinča Belo Brdo, Serbia (∼6,500 y BP: BLT014); and Cȃscioarele, Romania (∼6,000 y BP: AA072). Interestingly, 7 of these samples also possessed the Near Eastern mt-Y1 haplogroup (AA212 is unknown) (Dataset S1). We also identified 3 samples from Buran-Kaya, Crimea (∼7,000 y BP: AA380, AA480, AA483) that also cluster close to Near Eastern wild boars, although they each possess the mt-Y2 haplotype and so are thought to be local wild boars (Dataset S1).