dataesr/scientific-paragraphs-categorization huggingface dataset:
• For bone marrow, there was poor agreement between comet and TGR for each of the statistics -there was even some evidence of disagreement based on the lower confidence limits -and the sensitivity was low. Chemicals for which overall calls of positive (+) or negative (-) could be made for liver, GI tract and bone marrow are listed in Table  6. There were overlapping data sets in liver for 35 chemicals, in GI tract for 19 chemicals, and in bone marrow for 15 chemicals. As expected, the number of chemicals with positive test results in TGR and/or comet exceeds the number of chemicals with negative test results, particularly for liver and GI tract. Whilst many concordant results were found across different species, routes of administration, treatment times and sampling times, which might be expected from a database containing so many recognized genotoxicants with broad-ranging activity, such factors may also contribute to the observed discordant results.
Impact of data curation on the database Detailed results of the data curation are presented within the TGR and comet databases in Supplementary Tables  1  and 2 , respectively. These tables include all the parameters captured during the initial data checking exercises, the outcome of the experiment in each tissue sampled, and the classifications made during the data curation. The comet database has 471 lines of data, with each line reporting results for one chemical in a single study of male or female test animals. During curation of the database, studies were excluded from the analysis for a variety of reasons (Table  1 ). In a few cases, comet data described in an abstract could not be confirmed based on reading the entire paper. More often deficiencies in assay conduct or reporting led to such low confidence in the validity of the results that they were excluded.
The curated comet database included 358 lines of data for the 91 chemicals. Data for 26 of the chemicals consisted of only a single study. Removing almost a quarter of the data from the analysis had minimal impact on the number of chemicals analysed. This was because many of the studies removed for poor quality were of extremely potent alkylating agents like cyclophosphamide or similarly potent mutagens, and the remaining excluded studies were for chemicals for which there were other, higher quality studies in the database. Analysing results from multiple studies for a chemical in the database was not straightforward. For example, two lines of data could report results from two experimental groups from the same experiment (e. g. 90-day repeat dose protocol compared to 3-day exposure protocol both in the same species) or from different laboratories using the same or different species.
The comet data were fairly evenly divided between rats (174/358, 49%) and mice with a few studies in hamsters, guinea pigs, etc. The most common routes of administration were oral/gavage (52%) and intraperitoneal injection (38%). Less common routes of administration included drinking water/feed, dermal application, inhalation, intravenous injection, or intratracheal instillation of particles. Liver was sampled in the majority of studies (68%); other commonly sampled tissues included stomach (44%), bone marrow (40%), kidney (39%), lung (35%), bladder (30%), brain (30%), colon (27%), and blood (24%), with <4% of studies conducted in other tissues. After curation, which led to the exclusion of certain poor quality studies, approximately 90 lines of data remained from studies conducted by Sasaki and colleagues published between 1996 and 2003 (see Supplementary Table  3  for references).
The TGR database has 525 lines of data, each line generally reporting results for one chemical in a single group of test animals. Similar to comet data, during curation of the database, studies were removed from the analysis for a variety of reasons (see Table  1 ). The curated database included 404 lines of data for the 91 chemicals. As for the comet database, removal of those data from the TGR database had minimal impact on the number of chemicals analysed and for the same reasons as above: there were many studies of potent alkylating agents (various nitrosamines, ethyl-and methyl methanesulfonate) and other potent genotoxicants, or of less potent chemicals all of which were also represented in the database by higher quality studies. As mentioned, a large fraction of the studies was conducted before protocols were standardised by publication of the OECD Test Guideline, and many of these consisted of < 28 days of dosing.
Negative results from such studies were interpreted as less reliable. Data were limited to only a single study for 26 of the chemicals, the same number as for the comet database. Unlike the comet data, the database consists mostly of data generated from mice, as there were relatively little rat data (74/405, 18%) and transgenic guinea pigs, hamsters, etc. , are not commercially available. Routes of administration were more variable than for comet. The most common routes of administration were intraperitoneal injection (39%) with a relatively lower proportion by oral/gavage (24%), and less so in drinking water (10%), and feed (17%). Liver was sampled in the majority of studies (58%). Other tissues were sampled less frequently for TGR mutations than for comets. Listed in the same order as for comet in the preceding paragraph they include stomach (8%), bone marrow (17%), kidney (13%), lung (19%), bladder (4%), brain (4%), and colon (12%).
Analysis of overall TGR and comet responses in liver, stomach, colon and bone marrow after database curation Throughout the curation process, many calls switched either from equivocal to positive or negative, or vice versa. There were no instances in which a chemical that was judged as positive or negative using the WoE approach had the call reversed (i. e. became negative or positive, respectively) after data curation. Where there were multiple studies for the same compound, the TGR study results were frequently in agreement, and therefore there was only a single overall call of equivocal in the curated TGR data set across all tissues. Conversely, there was less agreement between different studies for the same chemical using the comet assay, which resulted in 50 overall equivocal calls in the curated comet data set across all tissues (see Supplementary Table  2  for details).
• For bone marrow, there was very poor or no agreement between comet and TGR for each of the statistics -there was even some evidence of disagreement -and the sensitivity of the comet assay in detecting TGR-positive responses in this tissue was low. A list of chemicals for which overall calls of positive (+) or negative (-) could be made for liver, stomach/colon, and bone marrow are listed in Table  10. Due to attrition of lower quality data, the total number of chemical comparisons was lower post data curation (than after WoE evaluation), particularly when analysing stomach and colon as separate tissues. Thus, in liver there were overall + or -calls for 33 chemicals, in stomach for 5 chemicals, in colon for 8 chemicals, and in bone marrow for 10 chemicals. However, these numbers generally increased when E calls were counted as positive or negative (Tables  7 vs 8 ).
Similar to the WoE approach, the number of chemicals with positive test results in TGR and/or comet exceeds the number of chemicals with negative test results for liver, stomach and colon; however, there were an equal number of positives and negatives for bone marrow. It should be noted that induction of comet damage in bone marrow was detected least frequently in a compilation of comet data with over 200 rodent carcinogens, almost all of which were tested in 8 tissues including liver, stomach, colon and bone marrow  [33]. This apparent insensitivity in bone marrow may be due to the nature of this tissue as a haematopoietic tissue with a high cell turnover rate. DNA damage may cause delayed cell cycle (particularly for high dose levels), whereas a stable mutation may not. Hence, the analysis of bone marrow data may be impacted by the inability of the standard comet assay to efficiently detect DNA damage in that tissue.
• In contrast, the opposite was observed when equivocal calls were included in the statistical analyses for the stomach. These statistical results showed better agreement when equivocal results were considered negative, which is contrary to the expectation stated above. This result could be a consequence of the very small stomach data set (Tables  7  and 8 ). • "Bracketing" the results by counting all equivocal calls as either positive or negative suggests that resolving each equivocal call into a positive or negative call would not likely change the conclusions drawn about results in liver, but could have led to a conclusion of some level of agreement between the two assays in bone marrow, or to a conclusion of lack of agreement between the two assays in stomach.
Discussion and conclusions The key question that drove the construction of this database was whether it is necessary to follow up a chemical that has been shown to be mutagenic in vitro with an in vivo gene mutation assay (TGR test), or whether it might be equally acceptable to conduct an in vivo comet assay. The decision on which of these two approaches to use is related to the ultimate purpose of the in vivo follow-up test. Accordingly, if the intention is to confirm in vitro gene mutation activity in terms of genotoxicity in general, then the comet assay is an acceptable choice; however, if the intention is to confirm specifically that an in vitro gene mutagen induces in vivo gene mutations per se, then the TGR assay is the more appropriate test. In general, from the analyses performed herein using either WoE evaluation of all the publications or evaluation of a subset of a priori curated data, the comet assay appears to yield similar results to the TGR assay in liver.
The WoE analysis of aggregate GI tract data also showed statistical agreement for the comet and TGR assay outcomes. However, while analysing the curated stomach and colon data separately with the same statistical approaches showed a similar outcome, the confidence intervals were wider, thus reducing the reliability of the conclusions for those tissues. For bone marrow, neither the WoE nor data curation exercises showed sufficient agreement between the two assays. There were a number of results considered to be equivocal (E), i. e. not clearly positive or negative. There is no general agreement as to how, or whether, such results should be included in an evaluation of test performances. The data curation results were analysed without the "E" calls, and then separately with "E" calls included with positives and then with "E" calls included as negatives, to bracket a range of probable measures of agreement if results for these chemicals could be definitively resolved as either positive or negative.
Further analyses and additional experiments are required to identify the factors responsible for this variability. This database was assembled with chemicals that had been tested in either TGR or comet assays in vivo. While most of the chemicals that had data across both assays in a specific tissue were positive in each of the two assays, the workgroup did not analyse combinations of results from multiple tissues, particularly tissues other than liver, stomach, colon and bone marrow, into a whole animal "overall call" for each individual assay. Thus, the only comparisons in this analysis are between comet and TGR results for the same chemical in the same tissue. There was a high prevalence of positive calls for chemicals with results in both TGR and comet assays for liver and GI tract. This pattern was less pronounced for bone marrow.
This high prevalence of positive calls was absent or less obvious when reviewing all the data (not just from chemicals with both TGR and comet results) from an individual tissue in an individual assay (i. e. positive and negative results were more evenly distributed among all 73 chemicals with liver comet data as opposed to the 35 chemicals with liver data in both comet and TGR assays). As a consequence, whilst it is possible to reach meaningful conclusions regarding the agreement of positive results (i. e. the sensitivity of the comet assay relative to the TGR assay) in liver and GI tract, it is not possible to reach meaningful conclusions on the agreement between the two endpoints for negative results. To achieve this goal, more chemicals with negative results in both the TGR and comet assays would be required.
It is also possible that our database is biased in favour of potent genotoxicants, which tend to be more commonly used in published studies than found during regulatory testing. Potent genotoxicants are frequently screened out early in product development and thus our results may be less predictive of the outcome of less potent genotoxicants that are more commonly submitted for in vivo genotoxicity testing in a regulatory environment. Unfortunately, the proprietary data for such compounds is rarely published so it is difficult to establish the extent to which this bias would affect the choice of test. The overall calls for the comet assay for two chemicals are worthy of specific comment since the overall conclusions reached in the above analyses were different from those reported in the JaCVAM trial [10]: • 2-acetylaminofluorene (2-AAF) was judged as negative in the liver and stomach comet assays by the JaCVAM-organized international validation study [10].
• For the TGR assay, while there are a few examples in the literature where toxicity in the form of induced cell proliferation allows DNA adducts, which would otherwise not be fixed as gene mutations (for the given dose and study design), to become full-fledged gene mutations, this is not regarded as a widespread problem with the assay. Rather, if anything, it increases the sensitivity of the assay to detect genotoxic agents (see  [34, 35] ). It should be noted that there is no requirement in the OECD test guideline to examine cytotoxicity in the tissues being examined, and as such, it is rarely conducted. A vast majority of the TGR studies in our database were conducted over the duration of 1-month, and de novo tissue proliferation could be a confounding factor. However, to the best of our knowledge, during 1-month of daily dose administration, observing tissue regeneration in liver or GI tract is a rare event, and this observation was not detailed in the discussion section of the papers included in the database.
In order to simplify the analyses based on the total publications in the data entry spreadsheets, equivocal and inconclusive results were excluded from the WoE analyses and only positive and negative overall calls were analysed. Analysis of TGR and comet results could be carried out for tissues other than those reported here by further interrogating the results in the final 91 chemical database, as well as the results tabulated in the data entry spreadsheets. Further analysis of how TGR and comet assays respond to in vitro gene mutagens and mutagenic (genotoxic) carcinogens is ongoing, and will be reported elsewhere  [36]. The outcome of those analyses may allow more specific recommendations on follow-up testing for regulatory purposes. Our database may also provide opportunities for additional analyses, such as reproducibility of comet and TGR data across laboratories, concordance of results across species, difference in results due to route of administration, and investigating the degree of agreement in responses across tissues (or combinations of tissues) other than those analysed here.
Data curation, by removing studies of lower quality, will make such comparisons more reliable and less subject to distortion due to results from repeatedly testing the same potent genotoxicants. Such analyses may provide crucial insight into the choice of which assays to conduct to provide the most robust evaluation of genotoxicity in rodents. The general conclusion that can be drawn from the analyses of this database is that the comet assay can detect potent genotoxicants that would otherwise be detected using the TGR assay in the liver, and GI tract; however, confidence in this GI tract association is reduced if separate regions are considered. In contrast, the BM data suggests that one cannot expect the two assays to give the same result in every tissue. Assay concordance for genotoxicants which induce weak responses is less certain since our database contained fewer such chemicals.
• Poor to no agreement between assays in bone marrow Literature quality of all of the available comet and transgenic rodent assay data in the literature. A total of 91 chemicals with data in the same tissue(s) across both assays were included in the final database. Assignment of quality scores for comet and TGR papers using pre-determined criteria. Lists of chemicals with overall positive/negative results in both TGR and comet assays (WoE approach)  Comparison of TGR and comet assay results using the data curation approach     Lists of chemicals with overall positive /negative calls in both TGR and comet assays (data curation approach).
Acknowledgments The authors would like to thank  Mugimane Manjanatha ,  Robert Heflich ,  Nan Mei ,  Wei Ding ,  Yan Li ,  Julia Kenny ,  Rosalie Elespuru ,  Jan van Benthem ,  Maik Schuler  and  Anne Doherty  for their efforts in checking large amounts of published data. We would also like to thank  HESI , and in particular  Jenifer Tanir ,  Lauren Peel  and  Stan Parrish , for supporting the efforts of the working group members, and for providing the facilities for exchange of ideas and opinions.
Over three months, behavioral observations were performed once per week by one observer and with the help of one technician: the observer indicated the beginning and ending of each behavior (tail movement and ear movements), and the technician noted the frequencies and durations of each behavior with the help of a stopwatch on a data collection sheet. The interaction with the enrichment materials was scored as a "yes" or "no" for each individual during the two-minute observation. More specifically, focal samplings of each pen were performed every Wednesday morning for 12 weeks, from 9 a. m. to 1 p. m. , using a direct observation data collection sheet. The behaviors were described as follows: tail movement, ear movement and interaction with the enrichment. Tail movement was defined as tail swinging in any direction, but mostly from side to side (lateral tail movements)  [9, 10] ; ear movement could be defined as any ear movement or ear posture change, including one or two ears (i.
Statistical Analysis Data analysis was performed using SAS 9. 4 software (copyright (c) 2002-2012 by SAS Institute Inc. , Cary, NC, USA). The significance threshold was fixed at 5%. The experimental unit was the pen: thus, N = 6. First, differences between the three types of enrichment materials in relation to the three variables (tail movement frequency and duration and ear movement frequency) were analyzed over all 12 weeks. Before analysis, the assumption of the normality of the model residuals was verified with the UNIVARIATE procedure using residual diagnostics plots; homogeneity of variances of the data was verified with the GLM procedure using Levene's test (using the HOVTEST = LEVENE option in the MEANS statement). As these conditions were met, a General Linear Mixed Model (including the room as a random effect) was carried out using the MIXED PROCEDURE, with multiple comparisons being performed using the LSMEANS statement in the MIXED procedure with the option ADJUST = TUKEY.
These results do not show that chains are equivalent to other enrichment materials such as straw, as it has already been shown that they are not as adequate as other enrichment materials  [29]. One limitation of this study was the sampling time: pigs were observed only 2 min per animal per week, because of practical reasons. Accordingly, we analyzed the data by recording if a pig interacted or did not interact with the enrichment (scored as high or low use) but not the amount of time spent interacting with it. Additionally, we did not confirm that in that case pig enrichment did produce positive emotions to pigs by using other indicators than tail and ear movements: this could also be a limitation. It may have been better to, firstly, corroborate that animals were experiencing positive emotions when interacting with the enrichment using other indicators, and secondly, measure tail and ear movements to obtain more information about these potential indicators.
Acknowledgments: We would like to extend our sincere thanks to all the members of IRTA for giving us the opportunity to perform this study in their facilities and for having the opportunity to work together for some months. In particular, we would like to thank all the team of the  Animal Welfare Program  and all the members of the  Center for the Control and Evaluation of Pigs (Centre de Control i Avaluació de Porcí, Monells ). Additionally, we would like to thank the interns  Alícia Rodríguez  and  Roger Vidal  for helping us perform the observations. We would also like to thank  Céline Lafont-Lecuelle  and  Sana Arroub  for helping us perform the statistical analysis. Finally, we would like to thank all the members of IRSEA's  Directory Board  and IRSEA's Research and  Education Board  for giving us the opportunity to develop this project.
Experimental Design and Dosing Table  1  summarizes the protocol of the experiments and the plasma and urine datasets further considered for TK analyses. A first experiment (Exp. 1) was performed in 8 male piglets age 42 d (20:8 ± 1:6 kg BW) to investigate oral BPA TK. At a 7-d interval, piglets were administered BPA by intravenous route (IV) and by gavage at respective doses of 21:9 lmol=kg (5 mg=kg), and 438 lmol=kg (100 mg=kg), using a two-treatment, twosequence, two-period crossover design. BPA data obtained after IV BPA administrations had already been used to evaluate human BPA clearance using an allometric approach  (Collet et al. 2015). A second experiment (Exp. 2) designed to assess the elimination of BPA and BPAG in urine involved IV administration of BPA at a dose of 21:9 lmol=kg in 3 male piglets age 38 d (17:1 ± 0:46 kg BW).
Chromatographic data were monitored by Targetlynx ® software (Waters Corporation). Blanks and qualitycontrol samples were used to monitor potential contamination during analysis and the accuracy and precision of the method. The mean intra-and interday coefficients of variation for three concentration levels of BPA and BPAG and of BPS and BPSG were lower than 15%, respectively. The LOQ values for each molecule in plasma and urine are given for each experiment in the Table  S2.
Noncompartmental Analysis Plasma concentration-time profiles of BPA, BPAG, BPS, and BPSG were first analyzed according to a noncompartmental approach. The plasma analytes concentration-time data from individual piglets were used to derive the maximum concentration (Cmax), and the time to maximum concentration (Tmax). The area under the plasma concentration-time curves from dosing time to the time of the last measurable concentration (AUC 0-t last ) were calculated using the linear trapezoidal rule (Equation  1 ). AUC 0-tlast = X tlast 0 C i -1 + C i 2 × t i -t i-1 ð Þ (1) where C i is the concentration obtained at the time i (t i ). The area under the plasma concentration time curve from t = 0 to infinity (AUC 0-inf ) was obtained using Equation  2. AUC 0-inf = AUC 0-t last + AUC t last-inf (2) where AUC t last-inf is the area extrapolated from the last observation to infinity, calculated according to Equation  3 : AUC tlast-inf = C last k z (3) where C last is the last observed quantifiable plasma concentration, and k z is the slope of the terminal phase, as estimated by linear regression using the best fit option of Phoenix ®.