dataesr/scientific-paragraphs-categorization huggingface dataset:
The substances comprised of 58 fragrances, 16 preservatives, nine actives (e. g. acrylates), seven surfactants, seven dyes, six pharmaceuticals, one UV filter, eight substances with other cosmetic uses, and 16 substances without any known cosmetic use. In addition, the chemical and product categories (CPCat) from EPA were used to more broadly describe the use categories covered. Substances were assigned to one or more use categories as defined in the aggregated computational toxicology resource (ACToR) database (Table  1 ). Overall, 12 use categories were covered by the 128 substances, most of them well-populated. Most substances have multiple use categories, resulting in an average number of 4. 2 use categories per substance. For example, penicillin G is used only as a pharmaceutical, whereas isopropanol and phenol have the most use categories.
This broad coverage demonstrates the diversity of the included substances in terms of their uses. Consequently, the database and the results are not limited to cosmetic ingredients, but apply to a broad range of chemical uses. The included substances also covered all mechanistic domains of chemical reaction. Domains have been assigned to 122 substances, comprising of 38 Michael acceptors, 21 Schiff base electrophiles, eleven S N 2 electrohiles (including three substance with proposed S N 2-reaction at the S-atom), nine acyl transfer agents, two S N Ar electrophiles, and 41 substances without a domain (Supplemental Table  1 : worksheet 1). The chemical space covered by the database was also described in terms of physicochemical properties, either experimentally determined or estimated, excluding the natural extracts.
Concordance of reference results The results for the LLNA and human data are presented in Supplemental Table  1. The substances covered the six human potency categories with six substances in category 1, 24 in category 2, 29 in categories 3, 29 in category 4, 25 in category 5, and 15 in category 6. In the LLNA, 95 substances were sensitizing and 33 nonsensitizing. For 54. 7% of the substances, only one LLNA study was available. The median number of studies for the 58 substances, for which repeat studies were available, was 4. 5, with the maximum of 44 studies on isopropanol. The individual repeat LLNA EC3 for each substance (regardless of the vehicle used) are presented in Figure  3 , whereas studies that did not reach a three-fold stimulation index, i. e. are considered negative in the LLNA, are shown with an EC3 value of 100% for plotting purposes.
This dichotomization allowed us to compare the two reference data sets by means of a 2 Â 2 contingency table, resulting in an accuracy of 74% (95/128), with 20 non-sensitizers and 75 sensitizers predicted concordantly (Table  2A ). Of the discordant substances, 20 (16%) were predicted as sensitizers in the   LLNA, but classified as not sensitizing in humans, and 13 (10%) were sensitizing to humans, but not in the LLNA. Categorizing both human (categories 5 and 6 as non-sensitizers, categories 3 and 4 as lower potency sensitizers and categories 1 and 2 as higher potency sensitizers) and LLNA (non-sensitizers, modified median EC3 ! 1% as weak/moderate sensitizers and modified median EC3 < 1% as strong/ extreme sensitizers) in three groups resulted in an accuracy to 59% (76/128) (Table  2B ).
Predictivity of individual test methods We analyzed the predictivity of the test methods, most of which have been evaluated by others in detail, e. g. by  Urbisch et al. (2015) , using the parameters specificity, sensitivity, and (balanced) accuracy compared to human and to the LLNA using the same dichotomization of the reference standards as described above (Table  3 ). In the absence of humanspecific prediction models for the individual test methods, we applied the standard prediction models, which were developed for predicting LLNA results, also for the comparison with human data. For both references, specificity and sensitivity was almost equally balanced for the DPRA and the KeratinoSens TM , while the other three test methods had higher sensitivity, but lower specificity. Accuracy with human data ranged from 72% to 79%, being lowest for the DPRA, while the balanced accuracy was slightly lower (70% to 76%), being highest for the KeratinoSens TM.
Accuracy with LLNA data was lower for the DPRA and KeratinoSens TM as compared to the h-CLAT, U-SENS TM , and SENS-IS, while balanced accuracies were very similar across test methods. The SENS-IS test method has established a prediction model to assign substances to one of five potency classes. Compared to human categories, the SENS-IS predicted the   The predictivity of the LLNA compared to human data. Ã Two substances possibly co-eluted with cysteine, one substance commercially not available and one substance not testable. One substance commercially not available. # Two substances commercially not available and 21 substances excluded due to noncompliance with the protocol during testing. "Two substances commercially not available. ÃÃ Excluding six natural extracts. same potency for 51% (64/126) substances (Table  4 ).
DPRA Of the 127 commercially available substances, 124 could be tested in the DPRA. Two substances likely co-elutied with cysteine, so that no reliable peptide depletion data could be obtained, and one (Dextran) was not testable due to its physico-chemical properties. Interestingly, these three substances had relatively high molecular weights (> 300). Of the 21 human sensitizing pre-and pro-haptens, for which DPRA results were available, eight were predicted as non-sensitizers, seven being pro-or pre/pro-haptens according to  Patlewicz et al. (2016). Regarding the mechanistic reaction domains, no pattern deviating from the overall predictivity was observed, with the exception that the substances reacting by acyl transfer were correctly predicted as positive as compared to human data. Comparing the distributions of the physico-chemical properties between the four groups of correctly predicted human sensitizers and non-sensitizers, and the respective false negative and positive predictions, no significant differences were detected (Kruskal-Wallis test p values >.
Discussion Based on a systematic evaluation of available test methods for skin sensitization  (Reisinger et al. 2015) , we selected five non-animal test methods for the compilation of a comprehensive database of 128 substances. Existing and newly generated data for the test methods were obtained according to standard protocols. Across the five non-animal test methods, a total of 613 test results have been compiled. About a third of these (202) are unpublished results, the vast majority from new testing (179), but also from personal communications (23). The primary output data of the test methods were recorded, e. g. the depletion values of the synthetic peptides used in the DPRA. In addition, the predictions of skin sensitization potential and, if applicable, potency were reported for each test method according to the standard prediction models (as included in the respective OECD test guidelines or publications).
Our dataset has several advantages compared to other dataset, such as  Asturiol et al. (2016) ,  Urbisch et al. (2015) , or  Natsch et al. (2013) , which were compiled for different purposes. It includes a substantial set of newly generated data, systematically reviewed reference data (especially LLNA) and a level of harmonization that allows meaningful comparative assessments. In particular, the comprehensiveness and level of detail presented enables the use of our database for data-mining purposes, e. g. the evaluation of defined approaches for skin sensitization  (Kleinstreuer et al. 2018). The main selection criterion for the substances was the availability of both human and LLNA reference data. For many substances, several LLNA studies were identified (Supplemental Table  1 ). These were summarized by a modified median EC3.
While the general variability of the LLNA has been discussed in detail, e. g. by  Hoffmann (2015) ,  Roberts et al. (2016), and Dumont et al. (2016) , care needs to be taken when addressing individual substances, especially regarding the suitability of the solvent used and the concentrations tested. For example, xylene, which was tested once in the LLNA giving an EC3 of 95. 8% and was therefore considered for the purpose of this work as positive in the LLNA, was consistently negative in all non-animal test methods and is also not considered a human skin sensitizer. This difference leads to discordance when assessing the skin sensitization potential, but is of low-practical relevance for the assessment of skin sensitization potency, e. g. in the risk assessment of chemical ingredients. The substance selection process resulted in a high diversity of chemistry in the database in terms of ranges of physicochemical properties covered, mechanistic reaction domains and use categories.
While the majority of the included substances have specific cosmetic uses, other use categories are also well represented. Natural extracts pose specific challenges. Their composition is defined by natural material and extraction process, so that it cannot be defined unambiguously and is likely to vary over time. Consequently, comparison with existing data, which usually lack proper documentation of composition, is compromised. All data included in the database were extensively curated and are provided in Supplemental Table  1. We made use of the database to compare the two reference datasets -LLNA and human data. In dichotomizing the human data, potency categories 5 and 6 were grouped as negative, because we considered that the human evidence on skin sensitization for group 5 substances would justify no classification, e.
g. according to CLP. However, we acknowledge that this, like any dichotomization of a continuum, might create the need for discussion of specific cases. The overall accuracy of predictions of skin sensitization potential was 74% and thus lower than the 82% reported by  Urbisch et al. (2015). Potential reasons for this are differences in the respective substance samples and in the reference data used. When focusing on the prediction of potency, the accuracy between the reference datasets was 59% for three categories and 45% for five categories. As expected, this decrease is inversely dependent on the number of categories considered. However, this quantification provides for the first time a systematically derived basis for the assessment of potency predictions of individual test methods or testing strategies, such as defined approaches, for skin sensitization.
Also, classification loss functions weighing mispredictions, e. g. penalizing false negative predictions higher than false positives, are an informative tool for prediction model development and predictivity assessment, which is recommended for the validation of (quantitative) structure-activity relationship models  (OECD 2007) , but is rarely applied to non-animal test methods  (Hoffmann et al. 2008). However, for the more challenging aims of using the data of the individual test methods for skin sensitization and potency and risk assessment, individual test methods' prediction models should be abandoned and the focus placed on the primary data, as provided here (Supplemental Table  1 ). In summary, this database provides a wealth of high quality information that can be used for a range of purposes, but especially for advancing the understanding of strengths and weaknesses of test methods and testing strategies, including defined approaches, but possibly also IATA.
Here, we used it as a common basis to conduct a harmonized assessment of the various reference and non-animal test methods involved. In this way, we exclude uncertainties introduced by differences in substance sample used and by differences in reference values. As another example, we utilized the data to assess the predictive performance of six defined approaches for skin sensitization, resulting in an independent and harmonized evaluation  (Kleinstreuer et al. 2018). We propose our database as a point of reference for example for the evaluation and development of testing strategies and encourage the community to use it to meet the challenge of conducting skin sensitization safety assessment without generating new animal data. However, we are aware of aspects requiring further considerations.
First, the variability and uncertainties in all relevant information sources, i. e. in all data used in testing strategies and in the reference data used for assessment of test methods and testing strategies, should be identified, qualified, and if possible, quantified. While the variability of the LLNA has been well described  (Hoffmann 2015; Dumont et al. 2016; Dimitrov et al. 2016) , confidence in the human data has not yet been addressed systematically. Another issue related to human data is that it will be available for a limited number of substances only. Variability of non-animal test method results is usually investigated to some extent in validation studies, which should include an experimental evaluation of within-and between laboratory reproducibility. However, the impact of this variability has not yet been fully accounted for when assessing the predictive performance of individual test methods and testing strategies.
On the level of individual test methods, a first attempt to evaluate the effect of variability in the context of prediction thresholds and models has been published by  Leontaridou et al. (2017). On the level of testing strategies, the impact of variability of input data is even more complex to account for, as the propagation of variability and error is difficult to model and quantify. In addition, we are aware that inclusion of further substances in our database increases the power of statistical analyses. This, in turn, may allow more detailed analyses of uncertainties and applicability of individual test and testing strategies. Ultimately, an increased substance sample may enable developing statistically sound sub-group analyss, e. g. based on physicochemical properties of substances, for specific purposes, e.
g. identifying groups of substances, which can be predicted well or poorly. Therefore, we are continuing this work in the frame of Cosmetics Europe's program to achieve regulatory acceptance of animal free testing strategies that enable cosmetic industries to conduct skin sensitization safety assessments, which was initially described by  Reisinger et al. (2015). While some regulatory acceptance of non-animal test methods for the prediction of skin sensitization potential of chemicals has been achieved (EU 2016), the need of cosmetic industry to distill information on skin sensitization potency from these methods in order to facilitate human safety assessment remains a challenge. As a next step, we will complete the U-SENS TM dataset, add the PPRA method, and expand the database by approximately 40 substances, which are of high relevance for cosmetic industry.
With the enlarged database including two test methods (SENS-IS and PPRA), which have not yet been used in defined approaches and IATA for skin sensitization, we will explore optimizations of existing and the construction of new testing strategies to predict potency, maintaining the focus on human reference data where possible. This work will go hand-in-hand with our efforts to develop safety assessment approaches based on non-animal data only. Including information on bioavailability and metabolism, we intend to highlight opportunities and limitations of the use of the non-animal test method data presented here for safety assessment purposes through case studies.
Acknowledgements The authors thank  Eileen Phillips  ( ILS, USA ) for supporting the data curation,  Andreas Natsch  ( Givaudan, Switzerland ) for providing KeratinoSens TM data for some substances and  Dori Germolec  ( NIH, NIEHS, USA ) and  Nicola Gilmour  ( Unilever, UK ) for reviewing the manuscript. In addition, the authors gratefully acknowledge the constructive comments of the reviewers, which were very helpful in improving the manuscript.
Performance follow-up All horses were followed up for two years after injury. The criteria used were: time to race post injury (established as the number of months from the treatment date to the first post-injury race), the number of races, the number of places, victories, the earnings per start post-injury. The results of performances after treatment were compared with results the year before injury. Racing data was obtained through websites (http://www. letrot. com/fr/fiche-horse and http:// www. turf-fr. com/pack-pro). Re-injuries or end of careers were also recorded based on the information that the owners or the treating veterinarians gave us directly during the consultation or by phone call.
Funding: This work was financially supported by the  National Agency of Research (Agence Nationale de la Recherche ; project#  ANR-08-BIOT-0021 , France); url: http://www. agence-nationalerecherche. fr/Projet- ANR-08-BIOT-0021. The funder, National Agency of Research did not provide support in the form of salaries to any author. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. OTR3 provided support in the form of salaries for authors [FS and FC] and research material,.
The purpose of this review is to assess the existing knowledge on the effects of human activities and habits on the risk of fasciolosis. We focus on three groups of human activities that are mentioned in the literature as the main factors affecting fasciolosis: (i) irrigation-system management, (ii) livestock management, and (iii) human diet and hygienic habits. For each of these activities, we gathered available data to highlight knowledge gaps and perspectives and to identify solutions that may help mitigate the risk of fasciolosis transmission through preventive measures and control strategies.
Electron Microscopy Carbon film copper 300 mesh grids (Electron Microscopy Sciences) were glow-discharged at 25 mA for 25 s at 10 -1 mbar in a Bal-Tec SCD 005 Sputter Coater. Glow-discharged grids were applied to a 15 µL drop of purified periplasmic flagella for 5 min, washed in 3 drops of ultrapure water, and negative-stained with 2% phosphotungstic acid (PTA, adjusted to pH 7) (Electron Microscopy Sciences) for 2 min. Grids were wicked from behind with Whatman filter paper and allowed to dry horizontally for 20 min before imaging in a Philips TECNAI 12 BioTwin II electron microscope at 80 kV. Images were acquired on a Soft Imaging System Morada camera using iTEM image acquisition software. Diameter measurement of PFs was performed using Adobe Illustrator software.
CryoEM Data Collection and Image Analysis Purified wild-type or fcpB -flagella were applied to glowdischarged Quantifoil holey carbon grids, blotted and plungefrozen using a Vitrobot Mark III (FEI Company, Eindhoven, The Netherlands). Cryo-EM images were collected using a Tecnai F20 transmission electron microscope (FEI Company) operating at 200 kV. Micrographs were acquired at ∼52 KX magnification in SerialEM  (Mastronarde, 2005)  using an UltraScan 4000 (4 K by 4 K) CCD camera (Gatan, Inc. , Pleasanton, CA, USA) at the Yale School of Medicine, Center for Cellular and Molecular Imaging. Box segments of imaged filaments were manually selected using the boxer program from the EMAN software suite  (Tang et al. , 2007). CTF-correction was carried out on the selected particles using Gctf  (Zhang, 2016)  and then subjected to iterative reference-free 2D classification and alignment using RELION  (Scheres, 2012) , with N = 50 classes.
Motility Assays Bacteria were counted in a Petroff-Hausser counting chamber under dark-field microscopy. A total of 10 5 leptospires in 5 µL were inoculated onto soft 0. 5% agar EMJH plates and plates were incubated at 30 • C for 5 days. Plates were imaged in a Bio-Rad ChemiDoc XRS+ photo documentation instrument with white light transillumination using ImageLab 3. 0 software (Bio-Rad), and the diameter of the zone of spread for each colony was measured at the pixel level in Photoshop CS 5. 5 Standard (Adobe). Statistical analysis was performed using a one-way ANOVA with Bonferroni multiple-comparisons post-test (Prism 6. 3, GraphPad Software).
Dark-Field Video Microscopy Late-log phase cultures were diluted to 1:100 in 0. 1% sodium azide (Sigma) or 1:10 in 1% methyl-cellulose (MP Biomedicals) and 10 µL of the dilution was transferred to a slide. An 18 × 18 mm coverslip was applied, and the edges of the coverslip were sealed with nail polish. Samples were analyzed in an Axio Imager. M2 motorized dark-field microscope (Carl Zeiss). Videos were recorded on an AxioImagerM3 camera (Carl Zeiss) at 1,388 × 1,040 standard mono pixels in 16-bit gray levels using AxioVision 4. 8. 2 software (Carl Zeiss). Samples were focused 5-10 µm beneath the glass coverslip (z-position), and the field of view for each new video (xy-position) was systematically selected so as to be non-redundant. Videos for qualitative analysis were recorded at 100X (EC Plan-Neofluar 100x/1.
30 Oil) under oil immersion (Zeiss Immersol 518F) in 33 ms exposures at 125 ms intervals over 10 s (frame rate 8 fps; digital gain = 2). Digital high-speed videos for tracking were recorded at 200 ms intervals for up to 10 s (50 frames at 5 fps; digital gain = 1; sensitivity = 100%; image orientation = flipped vertically). All videos recorded were analyzed using the AxioVision Tracking Module. Seeds (search area = 30) were manually set on the start frame of each video, noise reduction was applied (noise reduction = 5), and automatic tracking was performed of each seed by the software algorithm. Inclusion criteria for tracking consisted of all leptospires whose search area was entirely within the field of view at the start frame and in the plane of focus at the start frame. Chains of two or more leptospires (for example, dividing cells) were excluded from tracking, as were leptospires whose tracks jumped to nearby static objects (for example, debris or imperfections on the surface of the glass slide) and could not be followed by the tracking algorithm.
Statistical Analysis and Software Statistical analyses were performed in Prism 6. 3 (GraphPad Software). Drawings were composed in Illustrator CS 5. 5 (Adobe), graphs were generated in Prism 6. 3, and tables and figures were assembled in InDesign CS 5. 5 (Adobe). Gel and blot images were captured and exported using ImageLab 3. 0 (Bio-Rad). Dark-field micrographs and videos were captured using AxioVision 4. 8. 2 (Carl Zeiss) and tracking analysis was performed using the Tracking module available for AxioVision from Zeiss.
Complementation of such LEPBIa1597 -mutant with the respective wild-type gene from L. biflexa or with the orthologous gene from L. interrogans (data not shown), restored the morphology (Figure  2A ) and motility (Video 3) comparable with the wild-type Patoc strain. FcpB Is Required for Hooked-End Morphology, Coiled PF, and Normal Motility The LEPBIa1597 -mutant presented an atypical straight cell morphology under dark field microscope, lacking the characteristic hook-shaped extremities; even though bent ends could be observed, they were not as pronounced as the spiral-shaped ends of wild-type strains (Figure  2A ). To further investigate the function of the protein encoded by LEPBIa1597, flagella were purified and observed by electron microscopy. Wild-type flagella typically form a strongly supercoiled filament structure characteristic of Leptospira  Li et al.