dataesr/scientific-paragraphs-categorization huggingface dataset:
2011 ) and the samples from Bosque del Apache. These results may suggest undetected species or accelerated molecular evolution  (Fourdrilis et al. , 2016; Pinceel et al. , 2005; Thomaz et al. , 1996). In North America,  Burch (1982)  recognized 22 species of small, mud-dwelling lymnaeids, which he grouped into the genus Fossaria with two subgenera, Fossaria (s. s. ) with 11 species and Bakerilymnaea with 11 (see Table  S5  for species names). Johnson et al. (2013)  transferred these species to the genus Galba, but otherwise retained the Burch system. Included in the present analysis were topotypic samples of obrussa from Philadelphia and parva from Cincinnati, both of which we here show to be indistinguishable from topotypic humilis, collected at Owego, New York. Remigio (2002)  contributed to Genbank a 16S sequence from a Canadian population he identified as Fossaria obrussa, also grouping with cluster III (humilis).
We suggest that obrussa, parva, and the seven other uniquely North American specific nomina listed above ascribed by  Burch (1982)  to the subgenus Fossaria are junior synonyms of G. humilis, setting aside American populations of G. truncatula as distinct. In addition to his obrussa sequence,  Remigio (2002)  contributed a 16S sequence from Oklahoma to Genbank which he labelled "Fossaria bulimoides". This sequence grouped with cluster VI (cubensis/viator) in our analysis. We suggest that all 12 specific nomina ascribed by  Burch (1982)  to the Fossaria subgenus Bakerilymnaea (Table  S5 ) including bulimoides (Lea 1841), are junior synonyms of G. cubensis/viator.
Acknowledgments We would like to express our gratitude to  Nicolás Bonel  for useful comments on earlier drafts of the manuscript and  Harry G. Lee  for advice and assistance on the taxonomy. We thank  Jimena Guerrero ,  Björn Stelbrink  and  Thomas Wilke  for suggestions on phylogenetic analyses and for  Graham R. Jones ,  Patricio Maturana Russel  and  Remco R. Bouckaert  for assistance in running STACEY and Multi-threaded nested sampling. We thank the reviewers  Pável Matos-Maraví  and  Christelle Fraïsse  and the recommender from  PCI in Evolutionary Biology Fabien Condamine  for their thoughtful comments and suggestions. Fellowships granted by  Erasmus Mundus PRECIOSA  and Méditerranée Infection supported research stays of PA at the  Institute de Recherche pour le Développement, MIVEGEC (Montpellier, France ).
AV was supported by a grant from  IRD (BEST)  and ML by a doctoral fellowship from  University of Montpellier  and a post-doctoral grant from  Labex CeMeb. This study was financially supported by  IRD ,  CNRS ,  ECOS-SUD (A16B02 ) and  Malacological Society of London. Version 3 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100089).
We used 7 GPS GSM and 7 GPS Iridium collars. Data were uploaded daily to a server through either GSM or iridium satellite transmission, depending on the type of collar used. Prior to deployment, the accuracy of the GPS was tested in a stationary position, under different vegetation covers with the program that was planned to be set when deployed on pigs. The maximum margin of error on the GPS locations provided by the collars was assessed to be 5 m and did not differ between the two transmission systems. Collars were programmed to take one location every 30 min and were deployed for 2 weeks on each selected pig. We considered this schedule as the best compromise between precision, saving of battery power (some of the collars were to be deployed again or had already been deployed) and duration of the monitoring (i.
Home Range, Daily Distance, and Activity Pattern To maximize the precision of the estimate, each home range was generated from all the available locations for each animal  (26, 27). We obtained the home range and the core area using the fixed kernel method (28) taking into account 90% and 50% of the locations respectively, as it is commonly used in ecological studies  (27, 29, 30). The smoothing parameter value was estimated by using the reference bandwidth method since the least-squares cross-validation method did not converge for most of the pigs  (31, 32). The home range was additionally estimated by using the minimum convex polygon, taking into account 100% of the locations (100% MCP) in order to determine the maximum area the pigs were able to cover. These treatments were performed using the adehabitat R package  (33).
The areas of the home ranges and core areas were then calculated by using QGis 2. 18  (34). The full day distance for each pig was calculated by connecting the consecutive locations belonging to the same day, from 1 am to midnight (i. e. , during 24 h). The first and last days of monitoring as well as the days with more than 10% of missing data (i. e. , at least 5 missed locations) were excluded from the analysis. This was followed by computing the minimum, maximum and mean distances for each animal. To determine the activity pattern, we split each full monitoring day into daytime (i. e. , from 7 a. m. to 7 p. m. ) (daily distance) and night time (i. e. , from 7 p. m. to 7 a. m. ) (nightly distance) and computed the minimum, maximum and mean distances for these 2 periods for each pig. The maximum and mean distance from the individual homestead were calculated.
Use of Habitat Once the pig tracking was completed and all the GPS locations retrieved, the GPS coordinates were plotted on a map and a landscape item was assigned to every location. This was done by going physically to the locations: for each location the GPS coordinates were entered in a handheld GPS unit (Garmin GPS Map 60Cx) enabling the operator to reach the locations uploaded from the pig's collar. The corresponding landscape was recorded according to one of these 5 categories: 1) homestead, 2) crop, 3) grassland (including also bush and forest), 4) waterpoint (river, borehole, pond, swamp or spring) and 5) road. When "homestead" was assigned, it was noted whether it was the one to which the pig belonged, or another one. The number of "other homesteads" was recorded. For each pig, we used the same dataset as for the full day distances (see paragraph 2.
3) i. e. , where days with more than 10% of missing data were excluded. We calculated the use of each habitat item by the ratio: number of locations falling into the habitat item i to the total number of locations n. As previous studies carried out in the same study sites pointed out that waterpoints and crops may be items at risk for interactions between domestic and wild pigs  (22, 24) , we focused on these two items by checking if their use was different between day and night and between the rainy and the dry seasons. We kept the definition of daytime and night time and used the same tests and software as for the activity pattern (see paragraph Home Range, Daily Distance, and Activity Pattern).
Data Collected Fourteen pigs were collared between March 2016 and April 2017. Among these 14, two lost their collars 2 days after deployment and two reported for only 1 or 2 days. For these reasons, data from these 4 pigs were excluded from the analysis. Out of the 10 remaining pigs, five were females and five were males. Six were tracked during the dry season and four during the rainy season (Table  1  and Supplementary Material). Their age ranged between 5 and 9 months. The data regarding the use of habitat could not be collected for one pig (ID 10) within the time frame of the study. As a consequence, all the analysis regarding the use of habitat was performed on nine pigs. The mean duration of the tracking was 10. 7 days (range: 5-17 days). The deployment had to be shortened for several pigs, due to either a fast growth of the body size, leading to remove the collar earlier than planned to prevent injury (4 pigs), or a stop in the reporting (2 pigs) or the tethering of the pig (1 pig; despite the fact that the pig's owner agreed to take part in the study and leave his pig roaming freely, he had to tether the pig because it damaged lots of crops).
ACKNOWLEDGMENTS We are grateful to Alfred Ojok who was the local facilitator and made that work possible. We acknowledge all the pig owners who allowed us to fit their pigs with GPS equipment. We thank  Henrik Rasmussen , for assisting us with the GPS equipment functioning and for having designed the harnesses. We warmly thank  Raymond Ssekadde  who drove us and took part into the field work, and the team in Lutuk who help with the logistics. Finally, we thank  Gordon Yofesi Mwesigwa  and  Edward Rubongoya  for helping us with the mapping of the study site.
Acknowledgments: We are grateful to  A. Lazou  for his valuable help with the literature search and management and to all the facility managers and staff who accepted to host our research projects The  GIS CCS (Cerveau-Comportement-Société)  and  NAM (Nutrition-Alimentation-Métabolisme ) gave us the opportunity to discuss the current issues in a multidisciplinary context. This paper has largely benefitted from our experiences through continuous education, over 20 years, of professional and non-professional horse persons in horse behavior and welfare especially in the context of the university degree " DU Ethologie du cheval ". Our own thinking and research have been largely expanded thanks to the exchanges with all these field persons who shared their experiences and difficulties, whether being vets, therapists, coaches, riding school teachers or caretakers, farriers, competition riders, outdoor leisure riders or mere admirers of horses.
Joint Research Centre, 2017), that can be highlighted through modelling of underlying mechanisms of action and delineation of AOPs. In some cases, the MIE is known and endpoints can be experimentally observed. For example, in the thyroid hormone system, inhibition of thyroid peroxidase (involved in thyroid hormones (TH) production) has been associated with the formation of periventricular heterotopia, symptomatic disruption of thyroid-dependent neuronal migration  (Ramhøj et al. , 2021) , yet not currently adequately investigated by available test guidelines  (Kortenkamp et al. , 2020). AOP development within the EURION cluster is ongoing for different modalities, particularly, those related to androgen, thyroid hormone, and retinoid signalling. An AOP working group (WG) has been set up to support and facilitate AOPrelated activities within the EURION projects and to bring together AOP-structured information and data across projects.
One approach may be to develop search strategies for prototypical stressors or model compounds chosen from reliable resources like EURL ECVAM (European Union Reference Laboratory, European Centre for the Validation of Alternative Methods), for the methods/models validation  (Sund and Deceuninck, 2021). Another approach to support the development of adequate search strategies would be to evaluate a priori the chemical applicability domain of specific KEs. A proof of concept used the case of 'PPARγ dysregulation' to characterize the chemical domain of PPARγ full agonists  (Al Sharif et al. , 2017). Approaches for chemical screening to capture MIEs relevant to human health have been proposed for a multitude of human relevant targets by the Danish database (qsar. food. dtu. dk/), for hepatic steatosis  (Gadaleta et al.
FUNDING This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements Nos. 825712 (OBERON), 825759 (ENDpoiNTs), 825489 (GOLIATH), 825753 (ERGO), 825762 (EDCMET), and  825161  (ATHENA) all part of the EURION cluster.
6) and bubbled with room air. After dissection, the preparations were allowed to equilibrate for 15 min under an initial tension of 1. 5 g. Different concentrations of ACh were then added to the preparation (1, 3, 10, 30 and 100 µM), and the maximum contraction was observed before washing and subsequent application of the next concentration of acetylcholine. Responses for each concentration were expressed in grams of tension (g), produced by each individual flap preparation. The effect of carvacrol (300 µM) on the acetylcholine dose-response plots was determined. Contractions were monitored on a PC using a BioSmart interface and eLAB software (ElUnit, Belgrade, Serbia). The system allows real-time recording, display and analysis of experimental data. Sigmoid dose-response curves for each individual flap preparation at each concentration of the antagonist were described by the Hill equation.
Even for dairy cattle welfare experts, it can be a daunting task to make decisions about overall welfare status by integrating the scores of the various measures in such a way that the outcome reflects the range of what can be expected among real farms and allows realistic differentiation between these farms. Expert welfare scoring of herds was, therefore, based on a large database of WQ data that reflect a wide range of dairy herd types in Europe and thereby ensuring a substantial but realistic spread in observed values.
At the core of Steps 2 and 3 is the WI. Based upon Burow et al. (25) , the WI was constructed from perceived severity of welfare problems ("severity score") and observed prevalence of these welfare problems. The severity scores for the various welfare measures were determined in Step 2 by asking the experts to score how severely each of the selected welfare problems (that are quantified by the selected measures) impairs the welfare of an animal. The following formula forms the basis to integrate data on selected welfare measures into one score: Welfare index score = 1 nm × nm m=1 Sm × rPm Here, n represents "number, " m refers to "measure, " S represents the "severity score, " which ranged from 0 to 100, and rP refers to "relative prevalence, " which is calculated as prevalence per herd/prevalence at 97.
5th percentile of that measure among all herds in the EU database. In the proposed formula, rP rather than absolute prevalence was used so each herd covered the same possible spectrum for each measure. Prevalence of the 97. 5th percentile was set as the maximum for each measure score, to prevent an extreme prevalence value of single measures from having a disproportionately large influence on the score. Therefore, herds with values equal to or higher than the 97. 5th percentile were automatically given the maximum measure score. This allowed for a uniform method to determine thresholds for the different compensation-reduction methods (CRMs) that were tested. To achieve a score on a scale of 0 (very poor welfare)-100 (excellent welfare) and to test various CRMs, the formula was complemented as follows: Welfare index score = 100 - 100 Smax × nm m=1 Sm × rPm×Cm Here, Cm is the "compensation-reduction factor" for measure m (value between 1 and Cmax), and Smax is the sum of the products of Sm and the maximal compensation-reduction factor (Smax = nm m=1 Sm × Cmax).
Step 3: Calculating WI and Testing Coherence With Expert Opinion For checking correspondence between expert scores and aggregated WIs, in the subsequent part of the second survey, the 14 selected experts were presented with a graph showing the observed prevalence distribution of all selected welfare measures for 491 European herds that had been assessed using the WQ protocol (Figure  1 ). To reflect the current range present in Europe across various herding systems, existing WQ datasets were collated from seven European research institutes and included data from 10 countries [Macedonia, The Netherlands, France, Belgium, Scotland, Denmark, Romania, Northern Ireland, Spain, and Austria, more details in de Graaf et al. (20) ]. In the graph, six "focus herds" were highlighted per expert (example: Figure  1 ; data shown in Table  1 ).
These focus herds were fictitious but were based upon real herd data from the European dataset. In total, 12 focus herds were created to fit the following descriptions: (1) two herds that scored high in prevalence, taking the European dataset as a reference (indicating poor welfare) on all measures; (2) two herds that scored low (indicating good welfare) on all measures; (3) two herds that scored medium on one-half of the measures and high on the other half; (4) two herds that scored the other half of the measures medium and the other half high;  (5)  two herds that scored medium on all measures except for one (high for somatic cell count > 400,000), and (6) two herds that scored medium on all measures but high for one (high for severe lameness). High scoring measures in a Highest scores belonged to the top 5% of herds in the European dataset (n = 491), medium between 40 and 60%, and lowest scores were from the lowest 5% of herds.
The model with the lowest mean rank was selected as the model (i. e. , type of CRM) that provided the best fit with the opinion of the experts. Statistical analyses were performed using the program R 3. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria). Both linear and quadratic models were used to test correspondence between expert scoring and the integrated scores to determine if adding a CRM to the WI formula generated a better fit for varying thresholds and values of C. The Agreement Interval package was used to calculate the measures of agreement.
Whiskers: data within 1. 5 × the interquartile range. Higher scores imply better welfare. QBA, Qualitative Behavior Assessments. Highest scores belonged to the top 5% of herds in the European dataset (n = 491), medium between 40 and 60%, and lowest scores were from the lowest 5% of herds. 1 "% of too lean cows," "SCC > 400,000," and "number of hairless patches" were high, 2 "% of cows with lesions," "% of cows with severe lameness," and "% of mortality" were high. to cope with the large number of measures included. However, an unintended consequence of the large number of measures and the method used to integrate them is that sensitivity of the overall welfare category to changes in individual welfare measures partly depends on the number of measures integrated into the criterion and principle scores  (18, 21).
These thresholds are only indicative, given the limited number of herds and experts that they are based on. Two main inputs were used in the current study: expert opinion and a European database of selected welfare measures' prevalence. As expert opinion was vital in the current study, we used stringent criteria to select experts. While this limited the number of experts who could participate, it also ensured adequate knowledge about dairy cattle welfare and the welfare measures concerned. Still, it would be relevant to test whether outcomes (in terms of selected welfare measures, severity scores, and correspondence with expert opinion) would be similar with a different composition or type of experts. Similarly, it would be interesting as well to test whether another setting [e. g. , a workshop to achieve consensus like in Rodenburg et al.
It could be verified in a follow-up study whether consumers and other stakeholders accept or refute the authority and outcome of these scientific expert judgements. If this would reveal important discrepancies, we would face the dilemma of increasing social acceptance either by adapting the protocol to better accord with stakeholder opinion or by better clarifying and explaining the decisions, outcomes, and credibility of the scientific experts to the stakeholders. The second important, and rather unique, input used in the current study was the database containing prevalence data on the selected measures of 491 European dairy herds. This dataset allowed selected experts to benchmark results of the focus farms based on a wide range of data, which supported them in allocating welfare scores that can realistically be attained on commercial farms in Europe.
). In preparing this overview, we conducted searches of the PubMed-Medline database and of the non-specific Google search engine using the following keywords: cosmetics AND endocrine disruptors, cosmetic AND cancer, sunscreens AND toxicity, sunscreens AND endocrine disruptor, preservatives AND endocrine disruptor, essential oil AND toxicity, essential oils AND endocrine disruptor, cosmetic AND coral, sunscreen AND coral, plastic AND endocrine disruptor, phthalate AND endocrine disruptor, bisphenol AND endocrine disruptor; then, for each ingredient cited in the article: name AND endocrine disruptor, name AND toxicity, name AND contact dermatitis. European labels and regulatory texts were examined for each group of ingredients (CL).
Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous lesions as well as lesions induced by exposure to test materials. Relevant infectious and parasitic lesions are included as well. A widely accepted and utilized international harmonization of nomenclature for lesions in laboratory animals will provide a common language among regulatory and scientific research organizations in different countries and increase and enrich international exchanges of information among toxicologists and pathologists.
Common Artifacts Certain incidental changes are commonly misidentified as neuropathologic lesions by inexperienced researchers. The changes listed here are the most common of such findingsdark neurons and white matter vacuolation. Identified artifacts should be not reported in the pathology dataset. and should be used for the rabbit as appropriate. Generally, very few spontaneous or treatment induced microscopic changes are reported in the female reproductive tract of rabbits. Female rabbits become sexually mature at 5 months of age, and so rabbits on routine tox studies are fully mature.
Acknowledgements: The authors wish to express their thanks to the INHAND GESC, and the BSTP, ESTP, JSTP and STP membership for comprehensive reviews, excellent comments and helpful edits. We also thank Dr Rupert Kellner for manuscript review and Ms Beth Mahler, Ms. Emily Singletary, and Ms. Maureen Puccini from EPL Inc. , for image editing. Photographs used in this document were either provided from the coauthors or are as acknowledged in the figure legends.
In total, 92 hair samples were analyzed, with a number of samples ranging from 4 to 6 per subject. After collection, hair samples were stored in aluminum paper at -80 °C in the lab freezer until analysis. The date of sampling, participant code and length of the strand were written both on the aluminum paper and on a paper form. A mark was also applied on the aluminum paper to indicate which part of the strand was the closest to the scalp and the foil was folded to immobilize the strand of hair until analysis. Population characteristics were previously detailed in  Faÿs et al. (2020)  and presented in supplemental data (Supplemental Data, Table  S1 ). This study was approved by the National Committee of Ethic and Research of Luxembourg (CNER, approval number 201601/ 04).
The MS/MS parameters used for detection are detailed in the Supplemental Data S4. The results from the method validation for hair determination are detailed in the Table  S3  (Supplemental Data). 2. 6. Data analysis 2. 6. 1. Intraclass coefficient and variability Intraclass correlation coefficient (ICC) was first used by  Shrout and Fleiss (1979). Briefly, ICC represents the portion of the total variability explained by difference between individuals, assuming that only between-individuals variability (BV) and within-individuals variability (WV) composed the total variance. BV and WV were here calculated using "Excel". Formulas used for ICC calculation are detailed in  Faÿs et al. (2020). All the samples (for both urine and hair) collected over the 6 months-follow up were used for the calculation of the variability and ICC.