dataesr/scientific-paragraphs-categorization huggingface dataset:
The ABF program showed decreased performances compared to the shuttle program, but an effect of the excretion peak on performances could not be assessed. Indeed, other factors such as preventive use of antibiotics (Supplemental material) and incidence of necrotic enteritis (data not shown) in the flocks were different between treatments and these variables are reported to negatively affect growth performances  (Gaucher et al. , 2015). Environmental temperature and relative humidity were recorded continuously in all flocks during the study (data not shown). These parameters were uni-form between paired flocks, i. e. , temperature and relative humidity did not show differences for flocks raised at the same time for a same age on each participating farm. These similarities may be due to the identical houses management under the same external weather.
Brucella genotyping confirmed that the strain infecting the dairy herd and the ibex was the same, and closely related to the one observed in domestic livestock in 1999 during the last outbreak reported in domestic cattle in the area  (Mick et al. , 2014). It was thus hypothesized that a probable spillover occurred from domestic animals to ibex in the 90's and a spillback from ibex to domestic cattle in 2011  (ANSES, 2015). Alpine ibex were reintroduced in the Bargy Massif during two release events in 1974 and 1976 comprising a total of 14 released individuals  (Gauthier and Villaret, 1990). Since the discovery of the outbreak in 2012, the Alpine ibex population of the Bargy Massif has been continuously monitored by Capture-Mark-Recapture (CMR). Because there were too few physical recaptures to estimate the population size, data on visual recaptures (capture-mark-resight) were used instead.
, 2009) , and in order to account for random/cluster effects in our dataset, we used a mixed model with glmer from the lme4 R package, with the spatial unit and the year fitted as random effects  (Bates et al. , 2015). The binomial generalized linear mixed model expresses the log of the odds of the probability of a bacterial culture being positive as a linear function of the explanatory variables. For each explanatory variable, the odds ratio (OR) was therefore expressed as the exponential of the associated model coefficient  (Dohoo et al. , 2009). Three models were adjusted to analyze the probability of a bacterial culture being positive in (i) any organ, (ii) organs in the pelvic area, or (iii) urogenital organs. According to its capture or culling location, each individual was assigned to one of the five socio-spatial units structuring the population previously identified by  Marchand et al.
Collected Data Bacterial cultures were performed on 88 seropositive individuals slaughtered between October 2012 and June 2017: 56 females aged from 2 to 14 (pregnant: 11, non-pregnant: 42, unknown: 3) and 32 males aged from 2 to 15. The sampling was heterogeneous among spatial units (Units 1-2: 6, Unit 3: 41, Unit 4: 23, Unit 5: 17, unknown: 1) and over time  (2012: 11, 2013: 20, 2014: 30, 2015: 16, 2016: 5, 2017: 6). Among the 88 individuals, 81 were necropsied at the LDAV73, whereas only genital swab and blood sampling was performed on the seven remaining animals. Most individuals were captured or culled in spring during the last third of gestation (43 females, 28 males), and a few were culled or captured in autumn before the mating period (13 females, 4 males). Among the 88 seropositive ibex, one was negative with RBT and CFT (<20 UI/mL) but was positive on LFIA, iELISA and cELISA; one was negative in RBT but positive on LFIA, CFT, iELISA, and cELISA; one was negative in iELISA but positive on LFIA, RBT, CFT, and cELISA; and six individuals culled in 2015 were only tested by LFIA because of small blood samples; all six LFIA tests were positive.
The 79 remaining ibex were positive to all tests performed and did not show inconsistency between tests. Overall, CFT titer decreased with age in both males and females (data not shown). A total of 516 bacteriological results were obtained from these 88 animals. The number of analyzed organs per individual ranged from 1 to 15 (mean: 5. 9, 95% CI: [5. 3-6. 4]-Figure 2A ). In 2012-2013, the number of analyzed samples per individual was higher  (7. 8 [6. 8-8. 8] ) than during the following years (4. 8 [4. 2-5. 4]). Out of these 516 bacterial cultures, 173 tissues/samples, were positive (1-9 positive tissues/samples per individual, 2. 0 on average [1. 4-2. 5]). The number of analyzed organs and the number of positive results are shown in detail in Table  2. Positive abscesses and positive samples from other organs came from the spleen (n = 6), udder (5), other lymph nodes (5), lungs (4), nuchal ligament (2), C3/C4 cervical vertebrae (1), cotyledons (1), and mesentery (1).
DISCUSSION This study analyzes the largest dataset of bacteriological investigations on Alpine ibex infected by Brucella ever collected to date. Using semi-quantitative measurements of bacterial load in a large variety of organs allowed us to infer the pattern of bacteria shedding in infected animals as well as its variation among individuals, and to explore the level of active infection in seropositive individuals. In summary, the probability of bacteria presence decreased with ibex age, in both males and females. For a given age, females with highest CFT titers exhibited the highest bacteria presence and load, a trend not observed in males. The average bacterial load was significantly lower in females than in males. Furthermore, in females, pathogen loads were lower in urogenital organs than in other organs, whereas no differences were observed between organ categories in males.
It should be noted, however, that six bacterial cultures were performed on four entirely seronegative individuals from the same area, and that 17 bacterial cultures were performed on three found-dead animals on which serological tests could not be performed. All 23 bacterial cultures were negative (data not shown). To detect active infection, we chose bacterial culture, the gold standard for brucellosis diagnosis  [Alton et al. , 1988; World Organization for Animal Health (OIE), 2016]. We targeted organs that were described as the preferred tissues for bacteria isolation, i. e. , retropharyngeal, supramammary, iliac and inguinal lymph nodes, as well as genital organs and vaginal secretions  (Marín et al. , 1996; European Commission, 2001; Godfroid et al. , 2013). Because of obvious limitations in terms of time and costs, we were not able to necropsy all seropositive animals culled since the discovery of the outbreak, or perform bacterial cultures in all relevant organs from all individuals.
, 2007). In the present case, removing old animals might have contributed to limited efficacy of the mass culling performed in 2013 (focused on animals aged more than 5 years old) since older animals were not necessarily the ones shedding the most bacteria. In the Yellowstone area, test-and-cull strategies were indeed most successful at reducing seroprevalences in bison when targeting young seropositive females  (Ebinger et al. , 2011). Modeling approaches, including the different kinds of data (on Brucella epidemiology and ibex demography) that have been collected in the field from 2012 onwards, will now be used to infer disease management principles that may be relevant in this and other cases.
ACKNOWLEDGMENTS The authors would like to thank the field technicians and researchers from the  French Hunting and Wildlife Agency  ( Office National de la Chasse et de la Faune Sauvage, ONCFS ) who performed ibex captures, monitoring and management and provided transportation of dead animals to the laboratory. We also thank the local veterinary services, the local veterinarians and the  Préfecture of Haute Savoie  who supported the field work, and the departmental laboratory that performed all necropsies and bacterial examinations. Finally, we would like to thank  Ms. Mirat  for her English grammar and language checking of our manuscript.
Cosmetics Europe, the European Trade Association for the cosmetics and personal care industry, is conducting a multi-phase program to develop regulatory accepted, animal-free testing strategies enabling the cosmetics industry to conduct safety assessments. Based on a systematic evaluation of test methods for skin sensitization, five non-animal test methods (DPRA (Direct Peptide Reactivity Assay), KeratinoSens TM , h-CLAT (human cell line activation test), U-SENS TM , SENS-IS) were selected for inclusion in a comprehensive database of 128 substances. Existing data were compiled and completed with newly generated data, the latter amounting to one-third of all data. The database was complemented with human and local lymph node assay (LLNA) reference data, physicochemical properties and use categories, and thoroughly curated.
Focused on the availability of human data, the substance selection resulted nevertheless resulted in a high diversity of chemistries in terms of physico-chemical property ranges and use categories. Predictivities of skin sensitization potential and potency, where applicable, were calculated for the LLNA as compared to human data and for the individual test methods compared to both human and LLNA reference data. In addition, various aspects of applicability of the test methods were analyzed. Due to its high level of curation, comprehensiveness, and completeness, we propose our database as a point of reference for the evaluation and development of testing strategies, as done for example in the associated work of Kleinstreuer et al. We encourage the community to use it to meet the challenge of conducting skin sensitization safety assessment without generating new animal data.
2014) , potentially reducing the uncertainty introduced by the use of animal data in human risk assessment. The results of the second phase of the program are summarized here and in  Kleinstreuer et al. (2018). In this work, we present a database compiled of existing and newly generated data of the test methods together with LLNA and human reference data for 128 substances. We describe the database in detail and make use of it to evaluate the individual test methods' predictivity and aspects of applicability in a harmonized way. Kleinstreuer et al. (2018)  complement these evaluations by assessing the twelve defined approaches and IATA for skin sensitization that have been proposed to the OECD as case studies (OECD 2016c;  Ezendam et al. 2016). In particular, the predictive performance of six of these approaches using the information held within our database was analyzed.
In the third phase of Cosmetic Europe's program, the database will be expanded with substance of high relevance for the cosmetic industry, such as hair dyes and UV filters, whereas in the fourth phase risk assessment approaches exclusively based on non-animal data will be developed. With our work presented here, we aim to advance the understanding of strengths and weaknesses of test methods and defined approaches and inform the assessments of variability, applicability domains, and uncertainties. Such knowledge is indispensable for developing a comprehensive and modern safety assessment approach for skin sensitization.
Substance selection The primary substance selection criterion for inclusion in our database was the availability of human data and associated potency categorization according to  Basketter et al. (2014). Another pre-requirement was the availability of LLNA data, preferably accompanied by information on the maximum dose tested and the solvent/vehicle used. In addition, the substances needed to be commercially available in a sufficient quantity for testing. Metals and metal-containing substances were excluded because of their very limited relevance for the development of new cosmetic ingredients. Of the 131 substances in  Basketter et al. (2014) , 98 fulfilled the selection criteria. Using the same selection criteria, further 30 eligible fragrances were identified in collaboration with the Research Institute for Fragrance Materials (RIFM), resulting in a total of 128 selected substances.
Description of the selected substances Substances were assigned to primary use categories (fragrances, preservatives, actives (e. g. acrylates), surfactants, dyes, pharmaceuticals, UV filters, other cosmetic use, or no cosmetic use) according to their main historical or actual use in cosmetic products, where applicable. In addition, chemical categories data were retrieved from the CPCat (Chemical and Product Categories) Database of the U. S. EPA (http://actor. epa. gov/cpcat; accessed on January 3, 2017) to more broadly describe the use categories covered  (Dionisio et al. 2015). Substances were assigned to one or several use categories as defined in the Aggregated Computational Toxicology Resource (ACToR) database. Where possible (i. e. excluding the natural extracts), the molecular structures of the substances were expressed in Daylight SMILES format (see Supplemental Table  1 ).
Data on six physicochemical properties related to skin penetration and skin protein binding, i. e. octanol-water partition coefficient (LogP), water solubility (LogS), melting point (MP), boiling point (BP), vapor pressure (LogVP) and molecular weight (MW) were collated. If experimental measurements were not available in the Estimation Program Interface (EPI) Suite Data, a publicly available data source (http://esc. syrres. com/ interkow/EPiSuiteData. htm; accessed January 3, 2017), these properties were predicted using quantitative structure-property relationship (QSPR) models  (Zang et al. 2017). Mechanistic domains of chemical reactions were extracted in a sequential manner from  Roberts et al. (2007a Roberts et al. ( , 2007b) )  and  Urbisch et al. (2015). For substances not included in any of these three references or with multiple assigned domains, the protein binding alerts for skin sensitization in the OECD QSAR toolbox 4.
1 (freely available at www. qsartoolbox. org) and the skin sensitization reactivity domains of Toxtree v2. 6. 13 (freely available at www. toxtree. sourceforge. net) were obtained  (Patlewicz et al. 2008). In cases when either the OECD QSAR toolbox or Toxtree predicted a domain and the other model did not, the predicted domain was used. In addition, it is indicated if a substance has been reported to require (metabolic) activation in the skin (pro-hapten) or external activation (pre-hapten) by  Patlewicz et al. (2016)  or by  Urbisch et al. (2015). Structurally, the substances were characterized by investigating the occurrences of important structural or functional groups by inspection (data not shown, but presented in the Supplemental Table  1 , worksheet 4). Non-animal test method data was collated from the literature and completed with newly generated data, the latter amounting to approximately one-third of all data.
Human Human data and the resulting human potency category for 98 substances were available from  Basketter et al. (2014)  and for additional 30 fragrances from  Api et al. (2017). The human evidence comprised of data from human maximization tests (HMT) and human repeat insult patch tests (HRIPT) and diagnostic patch test (DPT) data. In addition, use information, especially annual volumes, but also typical maximum use levels, has been used for some fragrances. The relevant information was collected by exploiting various sources, such as standard contact dermatitis textbooks and the dermatological literature. Whilst  Basketter et al. (2014)  describe criteria for translation of the available evidence into six potency categories,  Api et al. (2017)  provide a comprehensive table that summarizes how the human evidence is used to assign substances to the six potency categories, with categories 5 and 6 being basically non-sensitizers and categories 1-4 being sensitizers ranging from extreme to weak.
In brief, a preliminary category is determined based on no observed effect levels (NOEL) from HRIPT or HMT, accounting for information on dose-response. This preliminary category is subsequently refined by DPT data accounting for the amount and severity of the clinical data and for some fragrances by the use information, primarily by relating it to the frequency of positive DPT  (Api et al. 2017). Through this procedure, direct human induction (HRIPT) and pre-clinical induction data (LLNA) have been combined with human clinical data, building on an approach of translating clinical data into a frequency of contact allergy in a general population  (Thyssen et al. 2009 ). The impact of the modifications becomes evident when correlating the HRIPT NOEL of the 128 substances, where available in  Basketter et al.
LLNA Existing LLNA data were available for all 128 substances. These data were collected from the literature, the NICEATM LLNA database (which includes data from studies from 1989 to 2010), included in NTP's integrated chemical environment  (Bell et al. 2017 ) and from the proprietary database of the Research Institute for Fragrance Materials (RIFM). The LLNA studies were conducted according to the OECD test guideline 429 'Skin sensitization: local lymph node assay' (OECD 2010) or similar protocols. For inclusion of positive/sensitizing results, the LLNA EC3 value (i. e. estimated concentration [by interpolation] of a substance expected to produce a stimulation index of 3, the threshold value for a substance to be considered a sensitizer in the LLNA), highest dose tested and vehicle identity were required.
e. the interpolated concentration inducing a 1. 5-, 2and 3-fold response as compared to vehicle control, respectively. In addition, the test concentration with the highest induction is defined as the lmax. Cytotoxicity was determined by the MTT assay and expressed as IC50, i. e. the concentration inducing 50% of the maximum cytotoxicity. KeratinoSens TM data were available for all 128 substances and were produced and interpreted according to procedure described in the OECD test guideline 442 D (OECD 2015 b). Data for 27 of these substances were newly generated, while data on 99 substances were obtained from the literature, mainly  Natsch et al. (2015). In addition, the test developer provided data on two substances through personal communication.
h-CLAT The h-CLAT quantifies changes in the cell surface expression of the co-stimulatory molecules CD86 and CD54 in THP-1 cells after 24 h exposure to a test substance  (Ashikaga et al. 2006). For dose finding, the CV75 value, i. e. a concentration showing 75% THP-1 cell survival (25% cytotoxicity), was obtained by cytotoxicity tests using propidium iodide staining. Anchored around the CV75, eight concentrations were tested in one experiment consisting of at least two independent runs. After 24 h of exposure, the expression levels of CD86 and CD54, and cell viability were analyzed using flow cytometry. The relative fluorescence intensity (RFI) of CD86 and CD54 for positive control cells and substance-treated cells were calculated. The data presented were obtained and interpreted in accordance with the OECD test guideline 422E, which essentially describes the test protocol used since 2010  (Ashikaga et al.
U-SENS TM data were available for 105 substances, which were obtained and interpreted in accordance with the OECD test guideline 442E (OECD 2017). Five substances were newly tested. The data for the remaining 100 substances, for most of which data have been published earlier using a different prediction model  (Piroird et al. 2015) , were re-interpreted (using the actual prediction model) and provided by the test developer through personal communication.
The prediction model, which was developed to predict the LLNA, was based on the number of overexpressed genes in the SENS-IS and REDOX group and the test substance concentration. To be considered as a skin sensitizer, a test substance had to induce at least a 1. 25-fold increase in the expression of at least seven genes in either the SENS-IS or REDOX group. The lowest concentration meeting these requirements determined the potency prediction: 100% or 50% ¼ weak sensitizer, 10% ¼ moderate sensitizer, 1% ¼ strong sensitizer, and 0. 1% ¼ extreme sensitizer. If none of the test concentrations met these criteria, the substance was considered negative. Relative gene expression data are provided in Supplemental Table 1 (worksheet 4). SENS-IS data are presented for 126 substances, comprising of new test data for 73 substances and published data on 53 substances, obtained mainly from  Cottrez et al.
The database The following information was entered in the database (Supplemental Table  1 ). Substances were identified by substance name, CAS number, SMILES, and primary use category. For each individual LLNA study, the EC3 was recorded together with vehicle, maximum dose tested, and reference (see Supplemental Table  1 , worksheet 2). In addition, the modified median EC3 and the human potency category of each substance are captured. For the non-animal test methods, the following information was entered into the database and referenced: DPRA: C-and K-peptide depletion (in %) KeratinoSens TM : EC1. 5; EC3; IC50; lmax (all in mM; for natural extracts in mg/mL) h-CLAT: CD86 EC150; CD54 EC200, CV75 (all in mg/mL) U-SENS TM : CD86 EC150; CV70 (all in mg/mL) SENS-IS: potency prediction, vehicle and gene expression data.
Note that the variables recorded for the DPRA, KeratinoSens TM , and h-CLAT allow ready application of the standard prediction models of the respective test methods  (OECD 2015a (OECD , 2015b (OECD , 2017)). Moreover, the database includes for each substance: data on six physicochemical properties related to skin penetration and skin protein binding, mechanistic reaction domain classification(s) and an indication if the substance is a pre-or pro-hapten. Primary data extraction involved a review of the studies, identification of relevant results, and data entry into the database. Quality assurance (of extraction) was conducted by another individual that reviewed the same studies and confirmed that the appropriate data had been entered into the database. Any differences between the two reviewers were discussed to obtain consensus.
Data analysis The predictions were calculated using the standard prediction models of the test methods for both the human and the LLNA reference dataset. The skin sensitization potential predictivity of the individual test methods was described using the standard parameters of specificity, i. e. the proportion of the non-sensitizing substances predicted as non-sensitizers, sensitivity, i. e. the proportion of the sensitizing substances predicted as sensitizers, accuracy (or concordance), i. e. the proportion of the correctly predicted substances, and balanced accuracy, i. e. the mean of specificity and sensitivity. The predictivity of skin sensitization potency classes of the SENS-IS was described for both reference datasets by contingency tables and accuracy. Correlations of physico-chemical properties were evaluated using Pearson's correlation coefficient.
Description of the database The database contains 128 substances, which were identified by substance name and CAS number (Supplemental Table  1 ). With the exception of six natural extracts (Jasmine absolute [grandiflorum], Jasmine absolute [sambac], oakmoss, treemoss, tea leaf absolute, and ylang-ylang), the included substances were well-defined single substances. Three substances were considered as solutions (glutaraldehyde [act. 50%], formaldehyde [act. 37%] and glyoxal [act. 40%]). With the exception of data missing for 2-hexylidene cyclopentanone (no DPRA h-CLAT and SENS-IS data) and for menthadiene-7-methyl formate (no SENS-IS data), which was at the time of testing not commercially available, and data on 23 substances for the U-SENS TM , failing quality control primarily due to deviations from the standard protocol, the data are complete.