dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Table  1. S. gives a description of the 53 chemicals selected from the ACuteTox databank. Data in this table were derived from the ACuteTox database and from  [39] , with compound use applications (pharmaceutical drugs, biocides, industrial chemicals), acute oral toxicity categories (assigned to the LD 50 values according to the EU CLP regulation), production or absence of metabolites in humans, basal cytotoxicity and/or organ specific toxicity, and possible mechanisms involved in human acute toxic effects. As previously shown in  [39] , it appears that cytotoxicity is an important determinant of acute systemic toxicity, and that there is no clear relationship between specific mechanisms of target organ toxicity and the four categories of acute toxicity. Table  1  gives a summary of all the collected data used for linear regression analyses: rat and mouse mean oral LD 50 values from three datasets ("Mean of rat oral LD 50 from Hoffman et al", "Mean of rat oral LD 50 from Kinsner et al" and "Mean of mouse oral LD 50 from Hoffman et al",  [26, 42] ), human LC 50 and mouse 3T3 (fibroblasts) NRU EC 50 values collected from Sjöström et al.
Evaluation of human predictivity value of LUCS assay data The relevance of cell-based test data for acute human toxicity has been inferred previously by the MEIC study. LUCS acute toxicity EC 50 (logM) data generated from our previous study  [41]  and human acute LC 50 (logM) data from the ACuteTox programme  [40]  were compared. Analysis of correlation by linear regression was possible for the 53 chemicals common in the two databases, and resulted in a determination coefficient (R 2 ) value of 0. 691 with a regression line slope = 0. 965, close to normal value (Fig. 1 ). This result indicated that LUCS assays predicted human oral acute toxicity to 69 %. In addition, according to compound classification (pharmaceutical drugs, industrials, and biocides/pesticides, Table  S1 ), the chemicals with higher human to in vitro differences seem to belong to specific categories.
Comparison of LUCS versus rodent data to predict human acute toxicity In order to evaluate which tests predict human toxicity the best, we then compared the different R 2 values obtained for regressions of LUCS with human LC 50 versus rodent data with human LC 50 , calculated for each of the three rodent LD 50 datasets (Table  2 ). Only chemicals with shared data were retained for analyses. All linear regression analyses are depicted in Fig. 2. Using a first rat dataset (from  [42] ) we identified 37 substances with both LUCS and rat LD 50 data, and found for linear regression analysis of LUCS with human acute toxicity an R 2 = 0. 670 (regression line slope = 0. 96), and for regression of rat with human acute toxicity an R 2 = 0. 504 (regression line slope = 0. 97). Using a second rat dataset (from  [26] ), we identified 35 substances with both LUCS and rat LD 50 data, and found for regression of LUCS with human acute toxicity an R 2 = 0.
695 (regression line slope = 1. 04) and for regression of rat with human acute toxicity an R 2 = 0. 579 (regression line slope = 1. 14). Eventually, from the mouse dataset  [26] , we identified 30 substances with both LUCS and mouse data, and found for regression of LUCS with human acute toxicity an R 2 = 0. 753 (regression line slope = 1. 14) and for regression of mouse with human acute toxicity an R 2 = 0. 537 (regression line slope = 1. 28). The R 2 s for regression of LUCS with human data were always higher than the R 2 s for regression of rodent with human data, suggesting that the LUCS test predicts human acute toxicity better than standard in vivo animal tests do: 67 % versus 50. 4 % or 69. 5 % versus 57. 9 % for rat data, and 75. 3 % versus 53. 7 % for mouse data. LUCS-based regression line slopes were closer to normal value than corresponding animal-based ones.
Furthermore, the R 2 value obtained for human versus LUCS data (R 2 = 0. 691, regression line slope = 0. 96, n = 53) was similar to the These results suggest that the LUCS test is more effective than rodent acute oral toxicity test methods in predicting human acute oral toxicity. This is in line with the MEIC programme which studied the relevance of cell-based assays and rodent oral acute toxicity LD 50 for predicting acute human toxicity  [30] [31] [32] 43]. MEIC study showed that rat LD 50 values predicted human data (LC, human acute lethal oral concentrations) to 60. 7 % (R 2 = 0. 607, slope 0. 83, n = 50) and that mouse LD 50 values predicted human data to 65. 3 % (R 2 = 0. 653, slope 0. 90, n = 50). The same set of 50 substances were tested in 61 different in vitro assays (among which, 20 human cell assays, 21 non-human mammalian cell assays, 18 ecotoxicological tests, and 2 cell-free systems).
An alternative method was developed by replacing the photoinduced ROS production by addition of H 2 O 2 in the culture medium. This new protocol was called C-LUCS for chemically-induced LUCS. L-LUCS and C-LUCS procedures were tested on HepG2 cells using a set of 20 compounds from AcuteTox database. Figs. 4 and 5 depict dose-response curves obtained with L-LUCS and C-LUCS protocols, respectively. The list of the 20 compounds and the corresponding EC 50 values obtained using the different protocols are given in Table  3. Linear regression analysis of the results obtained from the two protocols gave a slope = 1. 046, close to normal, and an R 2 = 0. 992 (Fig. 6 ) demonstrating the ability of the C-LUCS protocol to be used in the L-LUCS test's homeostasis/toxicity application fields. This result also makes C-LUCS protocol an easy option as a fast tool for acute toxicity screening in any existing HTS platform.
Some differences appear between EC 50 s calculated from  [41]  and the present study. They are due to an optimization of LUCS protocol. SYTO dye was used as photo-inducer in the former study. It was replaced by TO in the present study because the latter gives a better signal amplitude in terms of fluorescence variations when comparing normal and toxic conditions. Linear regression analysis of SYTO vs TO LUCS data gives an R 2 of 0. 766, increasing to 0. 929 when 5-fluorouracil (compound 17), which showed a very atypical dose-response profile and high SD value in the SYTO study, is removed from the analysis.
Conclusions This study shows that data generated with the in vitro cell-based LUCS test resulted in a better prediction of human acute toxicity data than the in vivo rodent oral acute toxicity tests do. Meigs et al. [44]  have already shown that animal tests are costly, time-consuming, unethical, and give misleading results. They have discussed the important positive aspects of alternative methods for industry (more effective and rapid discovery of new entities, increased satisfaction of the end users with regards to the 3Rs, manufacturing of better and safer products). By creating a large toxicological database from ECHA's data (aproximately 10,000 chemicals), Luechtefeld et al. [45]  could assess the reproducibility of the animal tests used and showed that in vivo OECD tests were not strongly reproducible  [46].
When cells are damaged after a treatment with toxic compounds (alternatively after light application in the case of L- LUCS or H 2 O 2 treatment in the case of C-LUCS), dysfunction of Overall, LUCS test method measures accurate signals of cell homeostasis on a high throughput format with a live cell readout that keeps cells operational for parallel or further analyses (multiplexing). In addition, the LUCS test presents the benefit of adaptability to any cell lines (assuming they contain DNA), including iPSCs. Ratio mode of analysis also allows quantitative data that remain to a certain extent independent of the number of cells present in the well. This work also aimed to show that the LUCS can be optimized towards a universal protocol adapted to end users equipped with fluorescence readers or high throughput platforms.
Acknowledgements The authors thank various foundations that helped financing the  LUCS  project and its positioning with respect to regulatory applications through the  "VALITOX program ", namely: Comité Scientifique PROANIMA (coordinator), SNDA, SPA, FBB, 30 Millions d'Amis, LSCV and  Fondation Bourdon. We also thank  André Menache  and  Andrew Rabeneck  for helpful proofreading.
, 2010). It consisted of principal component analysis (PCA) followed by hierarchical clustering (HC). In the PCA, parameters were considered as illustrative Cadéro, Aubry, Dourmad, Salaün and Garcia-Launay variables, and variables were considered as active variables, which were then subjected to HC. The PCA and HC were performed using the PCA and HCPC functions of the FactoMineR package  (Lê et al. , 2008)  of R software (R Development Core Team, 2016). In a first step, PCA of all 96 scenarios was performed using 10 active variables (Supplementary Material Figure  S1 ). This first PCA revealed that population health status strongly separated the scenarios on the first axis and that the population with impaired health had higher variability among scenarios than the healthy one. Therefore, a second PCA (Supplementary Material Figures  S2  and S3 ) and HC of the 48 scenarios for each population (healthy v.
Consequently, since all environmental impacts were calculated according to LCA, EU impact was degraded by impaired health status through the increase of emissions and resources needed for feed production. The variability in model variables is similar to that observed in the French fattening pig unit database  (IFIP, 2016)  for FCR (2. 76 ± 0. 08 v. 2. 88 ± 0. 28 kg feed/kg gain), Lean (60. 2 ± 0. 7 v. 60. 3 ± 0. 9%), %ASW (91. 4 ± 9. 8 v. 86. 0 ± 6. 8%) and Rev (127. 10 ± 5. 80 v. 125. 00 ± 11. 90 €/pig). Differences are observed, however, between predicted and observed values for certain variables, such as SW (114. 9 ± 4. 2 v. 120. 9 ± 4. 1 kg) and GM (6. 40 ± 5. 30 v. 16. 60 ± 15. 80 €/pig), which can be explained by the greater diversity in batch management and economic context in the French database (e.
g. batch interval, pig genotype, health status and label). In addition, 50% of model scenarios represented impaired health status, while results of the French database are rarely associated with health status. Furthermore, the DP feeding and individual scale of application simulated do not currently exist on commercial farms and thus do not exist in the French database. Variability in performance among scenarios is due mainly to the health status of the population, followed by the batch interval and the feed sequence plan. In simulations, health status is represented by growth, FI potential and mortality rate. In a previous sensitivity analysis, the last two factors had the largest influence on model variables  (Cadero et al. , 2018b). With reduced FI and growth potential, pigs with impaired health have higher FCR and lower SW than healthy pigs for a given fattening duration, which results in lower revenue and gross margin per pig, which are also influenced by mortality.
Acknowledgements This work was conducted as part of  A. Cadero 's thesis. This study was supported by  CIFRE Ph. D. funding from the  ANRT , with co-funding from the  MOGADOR  project of the  CASDAR program  ' Targeted research and innovation ' (2014 to 2017) of the  French Ministry for Agriculture and Food. The authors would like to thank  E. Lopez , who performed the first virtual experiment using the pig-fattening unit model during her M. Sc. internship at  INRA.
The selling price of live realizer was equal to 53% of the price of animals that reach the optimal slaughter weight (590 kg)  [13, 37]. The loss of Quality Grade, from Choice to Select or Standard for example, induces about a 10% price penalty  [46]. We used this value to calculate the impact of loss of Quality Grade due to disease incidence. Additional costs and cost adjustments. We calculated the daily production costs (sum of feed and daily operating costs) for a healthy animal. We calculated the costs of treatment and labor costs for a sick animal, and multiplied the values by the number of sick animals under each scenario and incidence (low, moderate, high) rate. For each clinical disease, we set an average day of death or realizer slaughter according to published data  [12, 36] , and calculated the number of days for which a steer should have been fed until slaughter (Table  3 ).
96% and 1. 17% of the industry gross revenue, depending on the dataset. In a meta-analysis comparing conventional and nonconventional beef production, Wileman et al. [23]  also reported that metaphylaxis enabled higher growth performance (0. 11 kg/d of additional ADG) for the cattle treated. It is noteworthy that the existence of compensatory gain may moderate the ADG losses experienced during the clinical phase of disease, which are unlikely to be sustained through slaughter. We did not account for potential compensatory gains in our model. Removing any kind of AMU led to negative revenue with moderate and high disease incidence under the assumptions of our model. When incidence rates were low, the net revenues were $27 and $12 per animal entering the feedlot, in the No-PC and No-PCT scenarios, respectively.
08; AOAC International, 2006). Chloride was analysed using a kit (kit Chlorures; Biolabo). The right femurs were broken and then defatted using diethyl ether, dried at 103°C for 18 h, and finally ashed in a muffle furnace at 600°C for 16 h. The left femurs were scanned using a clinical computed tomography machine (Somatom Définition AS128; Siemens) with scan parameters set at 140 kV tube voltage and 500 mAs current. The image acquisition mode was 32 cm × 512 pixels matrix size with a slice thickness of 0⋅4 mm and a resolution of 625 μm. The images were converted into Digital Imaging and Communication in Medicine (DICOM) format for analysis using the SINGO. VIA software. Bone mineral density was estimated and given in Hounsfield units (HU) by the scanner software and later converted into g/cm 3 using a phantom (Electron Density Phantom Model 062M).
Cortical area (cm 2 ) = π × (diaphysis width (cm)  2  medullary cavity (cm) 2 ). Determination of the sample size was based on a power calculation using the estimated mean and standard deviation for the ATTD of P (26⋅2 (SD 5⋅5) %) and blood pH (7⋅4963 (SD 0⋅017)) observed in previous studies  (19, 20). Consequently, we estimated that in order to detect a change in the ATTD of P of approximately nine points, at a significance level of 0⋅05, with a power of 80 % in a two-sided t test, six animals per group should be used for the digestive trial. For blood analyses, four animals per group are necessary to detect a change of blood pH of 0⋅03 units. All data were analysed using the MIXED procedure from SAS (SAS Institute Inc. ) as appropriate for a randomised complete block design after the normality of the variables had been checked.
Acknowledgements The authors are grateful to  E. Labussière  for his scientific expertise and advice,  M. Couty  and  S. Urvoix  for their help during blood and slaughtered sampling and laboratory analyses,  S. Daré  for her technical support concerning blood gas analyses,  Y. Jaguelin-Peyraud  and  C. Perrier  for their help and laboratory expertise,  F. Le Gouevec ,  J. George ,  A. Chauvin ,  M. Genissel ,  G. Guillemois ,  H. Demay ,  R. Janvier ,  M. Alix  and  V. Piedvache  for their help and animal care throughout the entire experiment. The present study was funded by  MiXscience, Terres UNIVIA and Terres INOVIA (FLUMINE ,  32 000 580 ). M. B. contributed to all aspects of the research including definition of the design, data collection, statistical analyses, interpretation of results and manuscript drafting.
Five parameters are used to describe the pig profile: initial body weight, the two parameters in the equation for feed intake, average protein deposition between 70 days of age and 110 kg body weight (BW), and the shape parameter of the Gompertz function that describes the precocity of protein deposition. In the model, we use a database, which includes 1000 male and 1000 female pig profiles (characterized by individual growth and feed intake potentials). To create a batch of pigs, by default, the model selects randomly from this database the same number of males and females to generate the appropriate variability within the pig herd. The model can be run either with all pigs characterized by the same average pig profile or with individual pig profiles taken at random within a set of 1000 male and 1000 female pig profiles  (Brossard et al.
We used mean feed and pork prices over 2 years  (2016 and 2017). The years 2016-2017 are medium situation for pork price (1. 314 €/kg carcass whereas in 2019 it moved from 1. 1 to 1. 7 €/kg), and low feed price situation compared to 2014 (Notes de conjoncture  Aliment IFIP, 2016; Marché du Porc Breton, 2017). Further description of inputs common to all case studies is available in Table  S3  in Supplementary Material. Definition of different case studies. All feeding strategies were based on feed A and feed B, which were formulated on a least-cost basis to contain 9. 75 MJ NE/kg and to meet 110% and 90% of the average SID lysine requirement of female pigs at 30 and 120 kg BW, respectively. This approach resulted in 1. 05 and 0. 47 g SID lysine/MJ NE in feed A and B, respectively (diet composition and economic data for feeds A and B are shown in Table  S5  in Supplementary material).
Statistical analysis Data for BW, DM intake and haptoglobin concentration in plasma were analysed for each day of measurement with piglet as the experimental unit. Data for the average daily gain (ADG) and DM intake were also analysed per period (suckling and post-weaning periods). Statistical analysis was performed using ANOVA for all the parameters using the MIXED procedure of SAS 9. 4 (SAS Institute Inc. Cary, NC, USA) including the experimental group as fixed effect and the litter of origin as random effect. The Shapiro-Wilk's normality test was performed on the residuals for each trait. Differences with a P-value under 0. 05 are considered significant and with a P-value under 0. 10 are considered as trend. Retrospective power calculations were performed for growth and feed intake. With β = 0.
Similarly, systematic changes in ontogenetic allometry are also expected between sexes due to the different roles supposedly played by horns in male and female bovids  (Darwin, 1871; Roberts, 1996). To test these hypotheses, we collected data on horn length and body mass growth in males and females of bovid species. We obtained data from 22 populations of bovids encompassing 19 extant species ranging from 4. 6 (Madoqua kirkii) to 717 kg (Syncerus caffer; Table  1 ). For each species and population, we estimated sex-specific ontogenetic allometries and tested whether these allometries changed in response to variation in body mass, and whether such a change differed between sexes.
| Data collection We conducted a literature survey using the keywords "horn" or "weapon" in combination with the genus names of the 137 extant  Note: For each sex, population, and species, parameters (±SE) were estimated from a segmented regression model. These parameters are as follows: the intercept (α, in log(cm)), the slope before the threshold mass (β 1 ), the slope after the threshold mass (β 2 ), the absolute threshold mass (T BM in %, and its 95% confidence interval)). We also report the adjusted R 2 of the model, the total number of data points per age class used to measure the ontogenetic allometry (N dots), and the number of individual measures available (N ind). Thresholds reported in bold are statistically different from the maximum body mass in the dataset. The sex-and population-specific maximum adult body mass (BM max ) have been obtained from the original study except in M.
kirkii (collected from  Bro-Jørgensen, 2007). From BM max and T BM , we calculated the proportional threshold mass (Tp) as T BM /BM max. Birth mass (BM 0 ) has been collected in the original study when available and from AnAge  (Tacutu et al. , 2012)  in other cases (except for M. kirkii,  Hutchison, 1970)  and O. g. musimon (M. G, unpubl. data)). From BM max and BM 0 , we estimated the proportion of the total body mass range that was covered by the data (Range BM). a 2 individuals have been measured repeatedly all along their growth. We also included in our analysis unpublished data provided by the French Hunting and Wildlife Agency (ONCFS) for the Alpine ibex (Capra ibex) and the Mediterranean mouflon (Ovis gmelini musimon × Ovis sp. ). These data correspond to individual data for horn length and body mass obtained from long-term population monitoring  (Garel et al.
SE In total, we collected data on horn length and body mass for 19 bovid species (19 species for males with two species including two different populations, and 11 species for females with three species including two different populations). This yielded a total of 35 ontogenetic allometries (Table  1 ). Data come from wild or semiwild (unfed) populations except for males of M. kirkii (Table  1 ) for which data were obtained from two calves captured in the wild and raised in captivity hand-fed. Horn length is generally measured from the tip to the base of the horn using a flexible ruler placed along the external curvature of the horn (see Table  S1 ). Although this method may slightly differ among studies, these differences are not expected to affect the results because allometry measures the proportional change in horn size for a proportional change in body mass.
, 2018 , see also Appendix S1). Because body mass is a function of the volume of the organism and horn length is a linear measurement, isometry between horn length and body mass corresponds to an allometric slope of 1/3 on a log-log scale. The precision of the parameter estimates for ontogenetic allometry depends on the range of body mass over which measurements are performed. Shorter is the range, lower is the precision, particularly for the threshold. Differences in the range of body mass used to estimate ontogenetic allometry may generate heterogeneity among species or bias the analysis if the range is correlated with the size of the species. We thus estimated the range of body mass for each data set by collecting sex-and population-specific adult and neonate body mass either from the same studies as the ones providing the data on ontogenetic allometry or from other sources including the database AnAge  (Tacutu et al.
| Analyzing variation in ontogenetic allometry We first assessed patterns of variation in ontogenetic allometries by estimating variation and covariation in α, β 1 , and Tp. Because these three parameters were strongly correlated (see Results), we ran a normed principal component analysis (PCA, package "ade4,"  Dray & Dufour, 2007 ) and used the scores of the first principal component (PC1) as a "shape index": a measure of variation in ontogenetic allometry between horn length and body mass across bovids. To test the effects of body mass and sex on ontogenetic allometry, we ran mixed effect linear model using the function "lmer" (package "lme4,"  Bates et al. , 2015) , where PC1 scores were the response variable, sex-and sex-specific mean adult body mass and their interaction were predictor variables, and species were fitted as random effect to account for the fact that species were sometimes represented by several populations or/and by males and females.
We also included the range of body mass covered by each data set (in percent) as predictor variable to correct for the possible bias in the parameter estimates of the ontogenetic allometry generated by a small range of data. We used the inverse of the variance in the ontogenetic slope β 1 (i. e. , squared SE of β 1 ) as a weighting factor to account for variation in the precision of the estimates  (Burnham, 1987). We were not able to perform an analysis controlling for phylogenetic relationship among species due to the small number of species considered (i. e. , 19 species). Indeed, analyzing less than 20-25 species prevents a robust estimation of phylogenetic inertia (see e.