dataesr/scientific-paragraphs-categorization huggingface dataset:
These models can be used to compare kinetics of substances within a cumulative assessment group, which includes the substances for which an assessment of combined exposures is warranted, and therefore help identifying the drivers of internal exposure or toxicity in a mixture. The use of kinetic modeling in the higher tier of risk assessment of multiple substances has been recently recommended in order to improve hazard identification and characterization  [1]. Chemical risk assessment increasingly calls for PBTK models when available  [2] [3] [4] [5] , and, therefore, a need for tools available to regulators and stakeholders is also rising  [6]. Over the past years, many tools have been developed for various applications including in a regulatory context  [7]. For example, MERLIN-Expo simulates the fate of substances in environmental systems and in the human body  [8, 9] ; httk provides a generic PBPK model and a large in vitro database as an R package  [10].
Exposure can occur through the dermal route, ingestion or inhalation. The absorbed substances can be excreted to urine, exhaled through the lung, or metabolized in the liver. External compartments, including urine and gut lumen, are not modelled. The model is coded as a set of ordinary differential equations. There is one such equation per time-dependent chemical quantity of the model (so-called state variable). The model predicts, as a function of time, for given oral, dermal and/or inhalation exposures, the quantities in organs and the corresponding concentrations as a function of time. The model equations are provided in Supplementary Information (SI), section 1. 1. The model, written in GNU MCSim  [19] , can be downloaded from ZENODO with the DOI 10. 5281/zenodo. 3553690 together with an example of code for running it under R  [20].
A-QSAR models Partition coefficients between blood and tissues and between air and blood, unbound fraction in blood, dermal absorption rate, and renal elimination rate were estimated with the QSAR models used in the Induschem tool  [27] , based on the (log) octanol:water partition coefficient, volatility, and tissue composition. Hepatic clearance was estimated by using the whole-body primary biotransformation rate constant (kM) model for fish, as implemented in the BCFBAF QSAR model by the USEPA. This model is based on an evaluated database kM estimates in fish for ~700 chemicals with satisfactory predictive performance  [28] [29] [30]. The allometric scaling from fish (10 gr, 15°C) to rat (200 gr, 37°C) or humans (70 kg, 37°C) and the resulting estimate of rat and human hepatic clearance has not been validated Substance-specific parameters obtained in humans in parameterization A are reported in Table  3.
At concentration levels below 1µM, the main metabolic pathway is hydroxylation (see section 3. 2 in SI). Clearance was therefore estimated as the hydroxylation Vmax: Km ratio, assuming that the internal concentrations were sufficiently low compared to the Km for the kinetics to be approximated as first-order (see summary on in vitro data in section 3. 3 of SI). Cyproconazole, thiacloprid, and clothianidin were also tested but no kinetic constants could be determined from the in vitro experiments. The clearance values used for the remaining substances were therefore obtained from in vitro screening tests reported in the literature, in  [35]  (cyproconazole, thiacloprid, clothianidin, and also triadimefon) and in  [36]  (valproic acid). Unbound fractions in plasma were based on the screening in vitro tests reported in  [37]  (flutamide), by TNO and reported in R package httk  [26]  (valproic acid), and in  [35]  (other substances).
Model verification The internal concentrations predicted by the PBTK model were compared to in vivo data on kinetics for each of the 9 chemicals. Area Under the Curve (AUC) and maximal concentration (Cmax) of kinetics were also computed and compared to in vivo data. In vivo data on kinetics was collected from the literature in humans and rats (Table  6  hereafter, Table  23  in section 6. 1 in SI). Human in vivo data on internal kinetics was only available for flutamide, valproic acid and imazalil in blood, plasma or urine. With parameterization C, where some parameters specific to flutamide, valproic acid, imazalil, thiacloprid, or clothianidin were calibrated to fit in vivo data, prediction quality is assessed using data that served for calibration and additional data, used as validation datasets  [44] [45] [46] [47] [48] [49] [50] [51] , when available.
For imazalil, thiacloprid and clothianidin, no additional data was available: quality of prediction could not be assessed on data which had not been used to calibrate the model. Quality of prediction for valproic acid and flutamide was assessed on all data together and also on the additional data. 2. 5 -250 mg/kg 168  [56]  (unpublished data),  [57]  Cyproconazole* -1 Organs, excreta Oral IV (1) 0. 5 -130 mg/kg 168  [56]  (unpublished data) Linuron - - -- Dienestrol - - -- *: Only measured as total radioactivity, not parent substance. Data on flutamide and valproic acid were collected in both species. For imazalil, as data was only found in humans, only the human parameterization of the PBTK model was checked. Conversely, for thiacloprid, clothianidin, cyproconazole, and triadimefon, data was only available in rat, and so only the rat parameterization was checked.
The data collected in rats on thiacloprid, clothianidin, and cyproconazole was expressed as equivalents of administered dose and not as levels of the parent substance, as it was obtained using autoradiography. Metabolism was the main elimination pathway for thiacloprid and cyproconazole  [55, 56]  and occurs to a lower extent for clothianidin  [56]. Body weight was set to the value reported in the studies if specified, or with a default 57 kg for women and 75 kg for male if gender was specified, or to a default 70 kg. Levels were often quantified in plasma rather than blood, therefore blood concentrations predicted by the PBTK model were converted to plasmatic concentrations when necessary, using a default value of 1 in parameterization A and the substance-specific blood to plasma ratio (RBP)  [26]  in parameterizations B and C (see Table  4  and Table  5  for humans, and section 2 of SI for rats).
With parameterization C, in the cases where validation datasets where available, i. e. for valproic acid in humans, and for flutamide and valproic acid in rats, all predictions were within a 10-fold interval, including the validation datasets, which were generally less well predicted in parameterizations A and B. For flutamide in humans, with parameterization C, predictions quality was similar in both validation and calibration datasets: 2 out of 5 data points in the validation dataset were within a 10-fold interval compared to 6. 25% overall. With the rat data -excluding radioactivity data -parameterization A and B provided overall similar but not very good predictions: respectively 37% and 34% of the predictions were within a 10-fold factor. With parameterization C, 70% were within a 10-fold factor.
The timecourse of plasmatic levels (including the AUC and the Cmax) is better predicted in parameterization C. According to Figure  4  and Figure  5 , flutamide concentrations in the dataset from Radwanski et al. [52]  (humans) and in Zuo et al. [44]  (rats) were better predicted with parameterization B than with parameterization A due to in vitro hepatic clearances which were over 2 orders of magnitude higher than the QSAR estimates. A sensitivity analysis of blood concentrations after repeated ingestion of flutamide every 8 hours confirmed the importance of hepatic clearance, by showing that with parameterization B, hepatic clearance, unbound fraction in blood and, to a lesser extent, absorbed fraction were by far the most influent parameters (see SI section 8). Predicted triadimefon concentrations in rats with parameterization B were not significantly improved compared to parameterization A, although the in vitro hepatic clearance was also higher than the QSAR estimate.
The estimate of metabolic clearance using ab initio (QSAR) predictions is notably uncertain as it involves multiple assumptions, that a) fish whole body clearance is mainly due to hepatic clearance, b) the fish hepatic clearance is an indication of (similar) rat and/or human hepatic metabolization, and c) that this can even be extrapolated quantitatively (applying allometric scaling). One obvious way to improve on the generic QSAR model estimations is to use a similar model specific for human whole body clearance  [59]  which has only recently become publicly available in a software implementation (QSARINS package, http://www. qsar. it/). Metabolic clearances were in some cases underestimated when based on in vitro data, either due to limitations of the in vitro assays or because metabolism may occur at sites other than the liver.
Estimates of oral absorption and total clearance parameters were very uncertain and could be improved if input from in vivo data was available. Overall, the use of default values illustrates how risk assessment tools must compromise between data availability and model complexity  [7, 60] : calibrating parameters is timeconsuming, and requires large amounts of in vivo data for the model to be predictive in doseto-dose extrapolation. Under the perspective of providing PBTK models for a large number of substances in lower-tier calculations, ab initio models that rely upon existing databases or simple QSAR models are more feasible. Their use is increasingly encouraged; many databases of physico-chemical parameters and in vitro-determined ADME properties are available and have been recently described  [61].
Data from several sources could be combined in a meta-analysis before being used as input in PBTK parameterization. This approach can also provide indications on the uncertainty and variability of the parameters. Recent examples of application show how the httk package can be used to analyses in vitro toxicity databases  [62]. On the other hand, it is a bonus for higher-tier calculations if more complex models are also available. For cypermethrin, for example, precise simulations require diffusion-limited models  [63] , which is a complexification of all the flow-limited model implemented in MCRA: simpler models will imply sacrifices on precision and accuracy of the predictions. More complex kinetic models can be specified and linked to MCRA. The PBTK model can be used to compare bioaccumulation and kinetics of substances belonging to the same cumulative assessment group.
[40,41] acute reference dose (ARfD), which is then provided as an input to the PBTK model once per day. The EuroMix generic PBTK model, version 6 (see https://doi. org/10. 5281/zenodo. 3553689)wasimplemented as a set of ordinary differential equations (ODE) written in C and compiled into a dynamically linked library (DLL). The R package deSolve[40,41]is used to load the DLL into R. Compiled code has the benefit of increased simulation speed. This may be a significant gain especially when the model is applied multiple times which is current practice in MCRA. Through the use of a forcing function time series, external input variables (i. e. the administered doses) are fed into the model and the solver will interpolate between the different timesteps. (Example code written in R is available on request).
A new spectral database has been developed for the bioMerieux Vitek system which allows the identification of Brucella at the species level  [15]. Detection of Brucella DNA in clinical samples by PCR is a powerful tool when culture is not conclusive. Many assays are based on detection of the Brucella specific IS711 sequence, which is found in multiple copies in the genomes of all Brucella. PCR was effectively employed to rapidly detect Brucella DNA in the blood of six suspected cases which all subsequently met confirmed case definitions, and multiplex assays can expedite the confirmation and speciation of Brucella isolated by culture and rapidly identify the species and biovar  [16]. The atypical strains present a major problem for the diagnostic laboratory. Phenotypically, many do not look like Brucella (rapid growth, motile, abnormal metabolic profiles) and many do not produce the persoamine based O antigen meaning that they will not be agglutinated by typing serum, and serological reactions to standard tests based on Brucella LPS will be negative.
Clinical and Biological Data Over the study period the cases were identified using the database of the Program for Medicalization of Information System (PMSI) looking for confirmed cases of leptospirosis in patients hospitalized in the Intensive Care Units of our Hospital. Data were collected retrospectively through the computerized medical files of the patients using the hospital medical software: DXlab ® (v4. 23. 30, MEDASYS, Le Plessis-Robinson, France) and Millenium ® (v2015. 01. 19, CERNER, Paris-La défense, France). The data analyzed were age and sex of the patients, clinical and biological symptoms suggestive of leptospirosis and date of events (year, month). Results from molecular biology and serological testing, performed by EIA completed with MAT, were collected and reported for each patient in addition to the outcome of the disease and antimicrobial therapy used.
Epidemiological Data Over a seven-year period (January 2011 to January 2018) the PMSI database of Tours University Hospital identified 16 cases of confirmed leptospirosis in adult patients hospitalized in the ICU. Our laboratory received 75 samples of serum, urine and CSF from 34 patients hospitalized in the ICU to test for leptospirosis with a molecular technique. We performed our in-house PCR assay targeting lfb1 on these samples. Among the 34 patients, 15 (44%) were found positive for leptospirosis involving 33 (44%) of the 75 samples tested. Only one patient showed negative PCR results but positive serological results. The median age at diagnosis was 56 years (Table  1 ). Sex ratio was noticeably in favor of male with 14 out of 16 patients (87%). The majority of cases (13/16; 81%) occurred over the summer period from May to September including a peak of 6 cases in July.
FTPI-) (Hajian-Tilaki, 2013). Finally, Cohen's kappa coefficients (κ) were computed to show the level of agreement between the evaluated tests and the reference RID assay at the selected optimal cutoffs. The precision and repeatability of the RID assay were assessed by intra-assay CV, based on 10 repeated measures for 5 independent serum samples with IgG concentrations ranging from 2 to 30 g/L. Intra-assay coefficients of variation were between 4. 8% and 8. 2% (mean 6. 2%), and RID was deemed to be repeatable enough for the purpose of the study. Statistical analyses were performed in R software (version 3. 5. 0) with ROC curves fitted with the pROC package  (Robin et al. , 2011). Descriptive statistics for tests results are shown in Table  1. The mean serum RID-IgG concentration was 16. 26 g/L (SD ± 8.
, 2015). To exploit the recorded data, the software Rumiwatch® converter  (version 36, ITIN & Hoch, Liestal, Switzerland) , previously validated for posture and locomotion behaviour in cattle housed indoors, was used  (Alsaaod et al. , 2015). At the age of 10 and 11 months, animals of E1 and E2, respectively, were subjected individually to a Novel object test (NOT). The test arena (Figure  1 ) had a size of 4 x 4 meters and was bordered by 2-mhigh, solid barriers that ensured visual separation from conspecifics. In E1, the arena was erected in the feeding area of the rearing pen, containing a solid floor. In E2, the test arena was erected in the outdoor area, which contained an earth floor with woodchips. All animals of one replicate were tested in the afternoons of two consecutive days, using a design balanced for rearing group.
Statistical analysis General activity. Various interventions (feeding, straw distribution, cleaning, control of accelerometers) took place between 8-11 AM and 4-5 PM and these periods were therefore excluded from the statistical analysis. Consequently, analysed periods started at 11:00 AM and ended at 8:00 AM the following day (total of 20h). Duration of each activity was summed and expressed in minutes per animal and per day. Number of stand-ups were also summed and expressed per animal and per day. Only complete 20h days without missing data were included in the statistical analysis. Reactivity tests. For statistical analysis of NOT, ranked data were used. Animals that did not touch the object were removed from the analyses of phases 3 and 4. For the FCT, for each animal, the number of each behaviour/animal/minute and the percentage of time for each of the vicinities relative to the food (number of observations where the animal was in a certain vicinity divided by the number of total observations) were calculated for each of the three sessions.
Acknowledgements The authors thank  Alain Boissy  and  Marie-Madeleine Mialon  from the research group CARAIBE at the  French National Institute for Agricultural Research  and  Prof. Susanne Ulbrich  from the  Animal Physiology group at the Institute of Agricultural Sciences of ETH Zurich (CH)  for scientific support, the statistical consulting service at  ETH Zurich  and  Juerg Huesler  from the  Institute of Mathematical Statistics  and  Actuarial Science at the University of Bern (CH)  for statistical advice, and  Yvo Aeby  and the members of his team from  Agroscope Posieux (CH)  for animal care and involvement in behaviour tests. This study was financially supported by the  Foundation Sur-la-Croix, Basel (CH).
abscessus. Moreover, the methyl group of the ring B participates to the antimycobacterial effect since the activity of FMD-15 was lower than the one of PIPD1. The donor inductive effect of methyl group may impact the inhibitory effect. With respect to ring A, the removal of the trifluoromethyl group (FMD-32) clearly impacted on the biological activity as compared to the removal of the chlorine atom (FMD-33). Although the dataset of compounds tested is limited, important SAR elements have been unravelled and subsequently utilized for guiding the design of the series II compounds (Table  2 ). From these data, one can deduce that the aromatic moieties are key to the biological activity. The nature of the aromatic substituent was modulated to determine if steric effect influences the activity.
Molecular Modelling Comparative modelling of MAB_4508 was performed using Modeller  [35]  and the crystal structure (2. 8 Å, PDB ID: 6AJJ) of a mycolic acid transporter from M. smegmatis (Sequence identity: 64. 9 %) as template. Quality of the model was assessed using MolProbity. [36] Subsequent energy minimization of the global model was performed using AMBER forcefield in order to remove putative steric clashes between side chains. Simulation of the binging modes of the target and the designed compounds was performed using GOLD docking program. [37] GOLD is based on a genetic algorithm and considered the ligand as flexible, while side chains of most residues are kept rigid. For the search procedure, a sphere of 10 Å was centred on the X, Y and Z coordinates of our identified binding centre.
Total run time was 5. 0 min. Compounds were detected with a triple quadrupole Quantiva mass spectrometer (Thermofisher) equipped with a heated electrospray ionization source. Nitrogen (N2-45 nitrogen generator, VWR International, Fontenay sous bois, France) was employed as sheath and auxiliary gas. The HESI source was set in positive ionization with the following parameters: sheath gas flow rate: 45; aux. gas flow rate: 10; sweep gas: 1; spray voltage: 2. 4 kV; ion transfer tube temp. : 300 °C; vaporizer temp. : 100 °C. Data acquisition was performed in the multiple reaction monitoring mode with the following ions (m/ z) monitored for each compound were (parent ion!product ion (collision energy (V))): PIPD1: 384. 3!105. 4 (26); 384. 3!261. 9 (19)  and 384. 3!278. 0 (20); penfluridol (IS): 524.
1!183. 0 (55) and 524. 1!291. 9 (36) and their retention times were 2. 2 and 4. 2 min, respectively. Chromatographic data acquisition and processing were performed using the Chromeleon v6. 80 (Thermo Scientific Dionex) and Xcalibur v3. 0. 63 (Thermofisher) softwares. A calibration curve including a zero and seven calibration standards was performed with each series of experiments. Quantitation was achieved by plotting the peak area ratios of PIPD1 to the internal standard versus concentration followed by mean least squares linear regression. A 1/x weighing factor was applied to the linear regression to correct for data heteroscedasticity. The lower limit of quantification (LLOQ) was 0. 1 ng/ml for plasma samples and 0. 1 pg/ mg for tissue samples, the upper limit of quantification (ULOQ) was 5000 ng/mL and 5000 pg/mg, respectively.
Data was analysed using the nonlinear mixed effect modelling software program Monolix v4. 4. 0 (Lixoft, Orsay, France). [34] A twocompartment open model was fitted and the parameters were estimated by computing the maximum likelihood estimator of the parameters without any approximation of the model (no linearization) using the stochastic approximation expectation maximization algorithm combined to a Markov Chain Monte Carlo procedure.
Populations We used two datasets. The first dataset contained information on 11,971 purebred rabbits born in 2016 and 2017 from six maternal lines: two lines from each of the following breeding companies Eurolap, Hycole, and Hypharm (Table  1 ). These lines have been operated as closed populations since their establishment between 1980 and 1985 (except for one line established in 1997). In these lines, maternal line traits, such as prolificacy, fertility, functional longevity, number of teats, direct and maternal effects on weaning weight, and homogeneity of weight at birth are continuously improved. For this study, we considered only two major traits that are under selection across all lines, i. e. number of kits born alive (NBA) and weaning weight (WW). The second dataset included data from an experimental population of crossbred rabbits that were submitted to an experimental infection trial with P.
All the other rabbits were euthanized at 14 days post-inoculation and their bodies were examined for signs of pasteurellosis. A brief timeline of this experiment is shown in Fig. 1. Of the 964 inoculated crossbred rabbits, 844 remained alive until the end of the experiment and were euthanized on day 14 post-inoculation. Among the 120 rabbits that died or were euthanized prior to the end of the experiment, 109 rabbits were confirmed to have died of pasteurellosis, thus the data on these were included in the analysis. In total, 953 crossbred rabbits were used for analysis.
On average, each litter included 3. 21 rabbits (from 1 to 10). Fixed effects were considered significant and included in the final model if the P-value was less than 0. 05. To test the significance of the random effects, log likelihood values obtained from ASReml were used to perform a likelihood ratio test in the statistical software R  [19]  and included if the resulting P-value was less than 0. 05. The final models used to estimate heritability and correlations contained only significant fixed and random effects. y = Xβ + Zu + Vm + Wl + Op + Sb + ε,.
Acknowledgements We acknowledge  Alain Fadeau  ( Laboratoire de Touraine; Parçay-Meslay, F-37210 ) for his rigorous bacteriological analysis of numerous infected samples, and  Emilie Chambellon  ( CIRM-BP ), who prepared and checked the standardized inoculum. We also acknowledge all the participants in this project for the quality of their work, especially the teams at the Pôle d'expérimentation cunicole toulousain (PECTOUL),  Plateforme d'Infectiologie Expérimentale (PFIE)  and the  Eurolap, Hycole and Hypharm breeding companies.