dataesr/scientific-paragraphs-categorization huggingface dataset:
(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) in vivo data (CF3), in vivo adverse effects for limited test duration (CF4) and in vivo adverse effects from assays covering more extensive parts of the life cycle of the organism (CF5). The CF is not intended to be a testing strategy nor align directly to AOPs, but it provides a guide to test methods that can populate AOPs related to endocrine disruption. In addition, the European Food Safety Authority (EFSA)/European Chemicals Agency (ECHA) guidance on the identification of EDCs in the EU pesticides regulations requires the identification of an endocrine disrupting-related adversity and mechanism  (ECHA, 2018 and EPA, 2018). The key characteristics of EDCs recently described in a consensus paper from leading experts in the field can be used to identify and classify a chemical as EDC  (La Merrill et al.
, 2020). Both the mechanism and the adversity are ideally connected by an AOP. CF could also guide the identification and choice of methods for IATAs. AOP-based IATAs help to combine and establish in vitro methods that are predictive of endocrine-related adversities and may therefore make additional animal testing for some modalities unnecessary  (OECD, 2019). Finally, the JRC has recently published its Endocrine Active Substances Information System (EASIS, https://easis. jrc. ec. europa. eu/), which contains information on the endocrine activity of chemicals as well as adverse effects that may be linked to certain endocrine activities. AOPs were used to identify mechanistic effects that are involved in the endocrine activity. In combination with AOP knowledge, these data help interested parties to get a picture of a substance's potential to be an EDC.
EASIS currently contains data on over 600 chemicals collected from around 10 000 study entries covering in vitro and in vivo assays in different species, including some human data. EASIS is a JRC-run installation of IUCLID 6, the software explicitly designed to manage scientific data on chemicals in a regulatory context, for example under the EU Biocides and EU REACH regulations. Parties familiar with the IUCLID software immediately feel comfortable when using EASIS, which improves its usability. Like all IUCLID instances, EASIS uses the OECD Harmonized Templates (OHTs) to facilitate the reuse and exchange of the data. It is actually the first IUCLID installation that makes full use of a special template (OHT 201) dedicated to reporting mechanistic data derived from non-animal methods, mostly from the published scientific literature.
This fulfils one of the main requirements of the European Commission's Chemical Strategy for Sustainability, which calls for the increased uptake of non-animal methods and the better use of academic data  (European Commission, 2020). Another application where prioritization is relevant includes hazard identification for substitute chemicals. However, this is particularly challenging because of the typically scarce availability of data for such substitutes. For example, a literature review of 52 novel flame retardants (nFRs) used as substitutes for the restricted brominated flame retardants such as polybrominated diphenyl ethers (PBDEs), showed that hazard data for nFRs are very limited  (Bajard et al. , 2019). Nine out of 52 nFRs were prioritized based on evidence for hazards, and the biological effects reported in peer-reviewed literature and databases were mapped onto AOP knowledge.
Knowledge from the AOP-Wiki provided additional supporting evidence highlighting the health outcomes of highest concern (namely hepatotoxicity, neurotoxicity, and reproductive toxicity) and major data gaps (e. g. , insufficient information on MIEs)  (Bajard et al. , 2019). In a follow-up study, the AOP knowledge was also used to design a testing strategy for screening the effects of nFRs on hepatic steatosis  (Negi et al. , 2021). The approach refined the prioritization to four nFRs and helped to identify a potential mechanism for this endpoint  (Negi et al. , 2021). For bisphenol F (BPF) and bisphenol S (BPS), alternatives to the well-known EDC bisphenol A (BPA), an artificial intelligence computational tool, the AOP-helpFinder (http://aop-h elpfinder. u-paris-sciences. fr/index. php), was used to automatically decipher connections between data on stressors and the biological events reported in the literature  (Carvaillo et al.
, 2019; Rugard et al. , 2019). This approach optimized the identification of dispersed data available and allowed to predict the main health outcomes associated with BPA substitutes in terms of obesity and metabolic disruption (e. g. for BPS) and thyroid cancer (e. g. for BPF)  (Carvaillo et al. , 2019; Rugard et al. , 2019). Associations between exposure to BPS and metabolic disorders were indeed reported in several, but not all, epidemiological studies examining this endpoint  (Beausoleil et al. , 2022). These examples illustrate how the stepwise application of AOPs can aid in organizing and simplifying a complicated issue, thereby assisting the regulatory process. The AOP framework has been particularly valuable for the screening, prioritization and hazard identification of chemicals, especially those with limited toxicity data.
, 2018; Terron et al. , 2018)  (https:// aopwiki. org/aops/3). Substantial data link the insecticide rotenone to this AOP, but any stressor perturbing the KEs of this AOP can be potentially connected to Parkinson's disease, as was shown for deguelin  (OECD, 2020a). This AOP therefore increases the biological plausibility of human associations and may guide the identification and implementation of BoEs in future studies.
, 2017)   The following paragraphs and Table  4  provide suggestions and describe examples and tools for establishing links between chemicalspecific data from different sources (e. g. , peer-reviewed literature, reports from agencies, databases) and the knowledge on AOPs (MIEs/KEs) which is recorded in the AOP-Wiki. Test methods as core elements for connecting chemicals to AOP knowledge. Information on methods used to measure the KEs of an AOP is essential when connecting toxicological data to AOP content. By bridging the gap between chemical data and AOP knowledge via the introduction of test method information, the picture gets complete. AOP knowledge highlights the necessity to explore certain mechanistic effects (KEs) by describing how these are linked to an adverse outcome. Test method information captures how the mechanistic effects were actually explored.
e. the template OHT 201) and increasingly also with the test method database developers. The connection between KEs and mechanistic effects reported in OHT 201 is already well under way  (Ives et al. , 2017) , with the ontologies currently being refined and further expanded. Development of systematic ontologies based on AOPs is expected to have direct impacts on the accessibility of highly fragmented mechanistic evidence and its application in risk evaluations using computational methods  (Whaley et al. , 2020). Tools to assist AOP users in linking chemical data with existing KEs. Establishing the connections between the effects of a chemical reported in the literature and databases and the corresponding KE in the AOP-Wiki, can be challenging and time-consuming. In addition, insufficient machine readability of the AOP-Wiki content, and the lack of harmonized ontology terms used to characterize KEs (see above) further complicate the process of linking chemical stressors with AOP-Wiki.
Funding and acknowledgments This research was co-funded from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  733032  ( HBM4EU ), and grant agreement No  857560  ( CETOCOEN Excellence ), and from the  European Union's Horizon Europe  project  PARC  [grant agreement No  101057014 ]. This publication reflects only the authors' view and does not necessarily reflect those of the  European Union. The European Union is not responsible for any use that may be made of the information it contains. Additional support was provided by the  Ministry of Education , Youth and Sports by the  RECETOX Research Infrastructure  [No. LM2018121 ] and OP  RDE  project  CETOCOEN Excellence [CZ. 02. 1. 01/0. 0/0. 0/17_043/0009632 ]. This research was supported by  Japan Agency for Medical Research and Development (AMED)  Grant  Numbers JP21mk0101216  and  JP22mk0101216  ,  JSPS KAKENHI  Grant Number  21K12133.
Hens and housing Hens and data came from the nucleus of the Novogen breeding company (Plédran, France). The two pure lines studied were from the breeds Rhode Island Red (RIR, brown eggshell) and White Leghorn (WL, white eggshell). Both were selected for the number of eggs, egg quality, BW, and behaviours such as reduced feather pecking. The population included hens from three batches hatched from 2018 to 2020 (Table  1 ). Hens were raised together from hatching to 17 weeks of age on the floor in a barn. They were then raised in floor housing composed of several pens, one per line, until 64 weeks of age. Roosters were raised with the hens (approximately one for every 10 hens). The floor pens were equipped with individual electronic nests (20 cm wide Â 40 cm long Â 27 cm high) developed by Novogen.
Acquisition of egg production and nesting behaviour The individual electronic nests' recorded egg production and nesting behaviour using radio-frequency identification. Hens were individually identified by a transponder tagged on one leg. The system recorded which nest the hen chose and when it entered and exited it. Oviposition time, and thus egg production, was recorded by an egg sensor located behind each nest. Data were collected daily from 24 to 64 weeks of age after the hens had adapted to the floor housing for about 7 weeks. For the 2018-2019 batch of RIR, data after 58 weeks of age were excluded due to an infection that strongly decreased production. In total, 279 582 and 350 940 nest visits with oviposition were kept for analyses for RIR and WL, respectively.
Acquisition of BW and egg quality A scale was used to measure BW at 50 weeks of age. Eggs were collected on two consecutive days at about 55 weeks of age. To measure egg quality at the individual scale, an egg-collection system behind each nest stored eggs by oviposition time, which enabled us to know which hen had laid which egg. Eggs were excluded if their order of laying could not be accurately determined (15 and 32 eggs for RIR and WL, respectively) or they could not be attributed to a specific hen (e. g. when the nest did not detect a hen's transponder correctly; 29 and 106 eggs for RIR and WL, respectively). Data for 2 112 and 2 468 eggs for RIR and WL, respectively, were kept for analyses. Egg quality was measured by the Zootests company (Ploufragan, France). Egg-quality traits were named according to the Animal Trait Ontology for Livestock  (ATOL, 2022).
ESshape and AH were not recorded for the 2018-2019 batch. To identify outliers, egg-quality traits were adjusted for significant environmental effects (P < 0. 05): egg-laying date, waiting time between laying and measurement (for EW and AH only), and the permanent environmental effect of the hen. Then, measurements were considered outliers and removed when adjusted data exceeded four phenotypic SDs from the mean (<0. 7% of data for all traits).
Second, we fitted another Yolo-v2 CNN to detect the head of the sow. Only 860 images For each image, the two Yolo models provided three informative items: (i) a bounding box around the sow to determine the center of gravity of her body, (ii) a bounding box around her head to determine the center of gravity of her head, and (iii) the posture of the sow. The two centers of gravity for the head and the body were initially in pixels. An image registration technique was used to convert the location into spatial coordinates  (34). It was then possible to calculate the distance between the sow head and the trough or the feeder to detect drinking and eating activities, respectively. While standing, a sow not drinking or eating was classified as doing something other. The MATLAB software (R2020b) was used to train the two CNNs and run the image analysis.
Statistical methods The statistical analyses were carried out using R software [version R 4. 1. 0 (35) ]. The database contained more than 8 million observations, with one observation every 5 s for each of the 11 days and for each of the 43 sows. Postures lasting only one observation were considered false predictions and were replaced by the posture detected at the previous observation. We extracted five behavioral traits from the image analysis computed on a daily basis. They were the proportion of time spent in each posture and the proportion of time spent in each activity while standing, which corresponded to the postural budget and standing-activity budget, respectively. We also computed the total number of postural changes per day (PCAll) to depict sow restlessness. We calculated the number of postural changes at risk of crushing for the piglet (PCRiskCrush).
Cluster analysis To identify the sow activity patterns, we applied a clustering analysis on the postural budget and the standing-activity budget in three periods. To take into account the compositional aspect of budget data, i. e. , the sum of proportions equal to 100%, an isometric log-ratio (ILR) transformation was first applied to the two budgets  (40). Then, k-means clustering approaches were applied to identify different posture patterns and different standing-activity patterns. The klm3d package was used  (41)  to take into account the longitudinal aspect of the data in the before-farrowing and after-farrowing periods, as those periods included several days each. The classic k-means was used to analyze clusters on the day of farrowing. The optimal number of clusters was defined based on the Elbow method  (42).
Survival analysis In the analysis of piglet survival, we defined the deaths of live-born piglets as events. When piglets were alive at weaning, the data were censored. We used a Kaplan Meier (KM) approach to estimate survival probabilities for each piglet genetic type. Then, the Cox proportional hazard models were enabled to estimate the influence of sow behavior on piglet survival, following the methodology described hereafter. The R Survival package was used  (43). In the first step, each of the five behavioral items (postural pattern, standing-activity pattern, PCall, PCRiskCrush, and PCStopNurse) were considered one by one as an explanatory variable and for this, analyses were run separately in each of the three periods. The aim of this first step was to determine which variables to be included in the model with which coding, i.
Image-based predictions and behavior database For some days and sows, video recordings were not analyzed due to video recording problems. The percentage of days with missing observations was low (7. 5% of the global database). The precision of the prediction of the CNNs was 31% for posture SI, 88% for ST, 9% for K, 95% for SL, 96% for LLU, and 78% for LL (Figure  2 ) and did not differ between breeds (result not shown). As they were highly confounded, the K and S postures were grouped together for the analyses. Descriptive statistics for daily postural changes are given in Table  2. The number of PCAll per day was on average 266 ± 171. The number of PCStopNurse and PCRiskCrush per day was 67 ± 56 and 51 ± 35 on average. Values for each type of postural change did not differ significantly between days.
Yet, Weary et al. (38)  and  Damm et al. (20)  did not find any effect of the frequency of postural changes on piglet survival the day of farrowing. We did not test the interaction of postural changes with breed due to the small size of our dataset. It could be relevant if the two sow genotypes have different abilities to control body movements  (36) , which is assumed, considering the slow motion of MS sows. The daily weight gain of each piglet was 45 g lower in sows that were more restless, i. e. , that used more postural changes to presumably limit access to the udder than the others. We assumed that in restless sows, colostrum intake, which greatly determines early growth  (74, 75) , is lower, since we know from the literature that they tend to be less involved in the nursing activity  (3, 39).
Tri-axial accelerometers quantify inertial forces along three orthogonal axes  [16, 17]. Attached to an animal, they record acceleration that is the result of both static or gravitational acceleration reflecting the posture of the animal relative to the earth's gravitational field, and dynamic or specific acceleration resulting from changes in speed due to movement of the animal  [18] [19] [20]  and vibrations due to effects of tag attachment  [21, 22]. The resulting datasets are large (especially at high sampling frequencies) and complex and commonly, machine learning tools are used to classify the accelerometer data into discrete behaviors, using predictor variables that quantify characteristics of the accelerometer traces  [14, 23, 24]. Supervised machine learning algorithms are trained by linking behavioral observations to simultaneously recorded accelerometer data, thereby creating a labeled data set, in order to distinguish the observed behaviors based on characteristic differences in the accelerometer traces, allowing for the quantification of model performance  [11, 14, 25].
Data collection To study moose behavior, we fitted 12 individuals of subspecies A. a. gigas in Alaska (all female) and two individuals of subspecies A. a. alces in Norway (one female, one male) with Vectronic Vertex Plus accelerometer-GPS neck collars (Vectronic Aerospace GmBH; Berlin, Germany), which recorded tri-axial accelerometer data at 32 Hz with a sensor range of ± 4 g and a resolution of 8 bit (Additional file 1: Table  S1 ). Accelerometer data were recorded continuously, and accelerometer time stamps were synchronized with GPS time during GPS fixes (every 15 min in collars in Alaska, every 60 min in collars in Norway). We conducted behavioral observations on individual collared moose and distinguished 21 mutually exclusive behaviors, including multiple foraging, locomotor, grooming and inactive behaviors, expanding on Herberg  [38]  (Additional file 1: Table  S2 ).
Alaska Twelve captive female moose at the Kenai Moose Research Center (Alaska Department of Fish and Game, Alaska) were collared without anesthesia three times for data collection over the course of 3 years. Deployment periods were October 1-11 2020, May 7-November 23 2021, and March 24-July 14 2022. Collars were fitted with a 6-cm gap between the collar and the neck to allow for seasonal changes in neck diameter. The moose were kept in two large (2. 6 km 2 ) enclosures with varying terrain and vegetation consisting of boreal and black spruce forest, meadows, bogs and lakes  [57]. Supplemental feed was provided from January through April. Supplemental water was provided in one enclosure during June and early July when warm, dry conditions depleted the natural water supplies from wetlands, and in October and November when natural water sources were frozen prior to adequate snow fall.
Each animal was observed for at least six hours per observation day during daylight hours. During the observations, moose were followed on-foot by one of five observers, who logged time-stamped behaviors to the nearest second using GPS time on a tablet running ArcGIS QuickCapture software (Esri, Redlands, CA, USA) and connected to a handheld GPS unit (Bad Elf GPS Pro, Bad Elf, West Hartford, CT, USA).
Filming each day was opportunistic and depended on the activity level of the moose, visibility of the moose from the perimeter of the enclosure, and available daylight. Filming took place in approximately 1-h intervals, and the camera was briefly switched off between intervals. At the start and end of each filming interval, the video was synchronized with GPS time by filming the screen of a handheld GPS unit (GPSMAP 64s, Garmin, Southampton, U. K. ). Each filming interval focused on one moose, unless both moose were in close proximity to each other. Collars were removed on December 4, 2020 (Mattis, male) and December 9, 2020 (Idun, female) following anesthesia with etorphine and xylazine  [58]. Using the software BORIS v. 7. 9. 22  [59] , the videos were then transcribed by a single observer with experience in the data collection on Alaskan moose to ensure comparability between the data sets from the two locations.
Data preparation Behavioral data Observation data from Alaska were downloaded from ArcGIS QuickCapture and checked manually. Duplicated entries were removed (e. g. the same button was pressed repeatedly by accident). Within observations, time periods with nonsensical behavioral sequences were excluded from the analysis (e. g. lying followed by running, without any recording of the moose standing up in between). Observations with many errors were entirely excluded from the analysis. Transcribed observation data from Norway were exported from BORIS for further analysis. All behavioral data were imported into R Studio  [60]  v. 2022. 7. 2. 576 running R  [61]  v. 4. 2. 2 for subsequent analysis.
Accelerometer data The accelerometer data were downloaded from the collars using Vectronic GPS Plus X software v. 10. 7. 2 (Alaska) or v. 10. 7. 1. (Norway), extracted using Vectronic MotionData Monitor software v. 1. 2. 0 and imported into R Studio  [60]. Inspection of the data revealed a delay in the date switching of the timestamps after midnight each day. We therefore excluded the first 20 s after midnight for all observations. Inspection of the data also revealed gaps in the accelerometer data of each collar (< 1 min) that occurred at least once per 24-h period, due to rebooting of the unit, as well as inconsistencies in the values of consecutive seconds of time stamps assigned during GPS time synchronization during GPS fixes. Because of these data gaps and the inconsistencies in time stamps assigned during GPS fixes, we summarized the 32-Hz raw accelerometer data in intervals, rather than correcting each individual time stamp, which also facilitated the temporal matching of the behavioral data with the accelerometer data intervals.
Based on a preliminary analysis of the data with interval lengths varying from 1 to 10 s, we summarized the accelerometer data in 3-s intervals to maintain a high temporal resolution of individual behaviors (the shortest mean duration of a behavior in our ethogram was two seconds, Additional file 1: Table  S2 ) while maximizing classification performance (i. e. maximizing recall and precision for the largest number of behaviors). Inspection of the data revealed that one collar (Individual: Minnie) recorded at 8 Hz, while the remaining accelerometers recorded at 32 Hz. However, because we summarized our data into intervals, this data was included in the analysis. Opportunistic video recordings revealed that two accelerometer axes were reversed in the collars from Norway compared to Alaska. The data from Norway were adjusted to standardize axis orientation across all collars (Fig.
1 ). From the raw accelerometer data, we calculated variables that were frequently used in other studies  [14, 23, 25]  and did not require continuous time series, to accommodate the aforementioned gaps and inconsistencies in the data. We then summarized the variables in each 3-s interval (Table  1 ). Most variables described the distribution of raw accelerometer values within each 3-s interval on each axis (X-Z). In addition, pitch (corresponding to vertical neck orientation) (Eq. 1) and Minimum Specific Acceleration (MSA) (Eq. 2) were calculated from the raw accelerometer data in each interval. We also included individual metrics which are easy to record in the field: Subspecies, sex, body length, girth and season. Such metrics could improve the generalizability of our model to individuals not seen during model training  [25].