dataesr/scientific-paragraphs-categorization huggingface dataset:
Final model development Final models were developed using the full internal data set with the external dataset of 858 studies across 240 chemicals characterizing the overall model performance and uncertainty. The final RF models were also developed using a large number of trees (ntree = 2500) for a robust evaluation of descriptor importance. Model performance (i. e. , external test set R 2 and RMSE) and the top five additional descriptors with their relative rank amongst all descriptors are summarized in Table  3. Variable importance plots are also available (Supplementary Data, Fig. 1 ). All models have significantly increased R 2 , 32-43%, as compared to the baseline performance metric (R 2 = 11-16%) established by developing a model only using in vivo covariates. The performance metrics were also highly comparable between the cross-validated training models and the final models.
e. , 'burst' activity,  Judson et al. 2016) , and estrogen receptor activity. Additionally, ToxCast descriptors provided a metabolic context with xenobiotic metabolism induction as a highly important descriptor. The preliminary addition of kinetic (httk) descriptors for a relatively small number of chemicals (322 chemicals in the internal dataset and 74 in the external dataset) showed marginal variable importance in combination with all other input descriptors. The underlying httk data and models have and continue to be expanded, improved and evaluated for chemical-specific reliability. For the purpose of this study, only publicly available and unfiltered data was used in an attempt to allow for immediate public use as well as to maintain as much chemical overlap as possible. The relationship between modeled kinetic descriptors and effect levels requires further exploration.
Discussion At face value, this work demonstrated the marginal predictivity of regression-based, random forest models of systemic effect levels using a collection of descriptors. However, this work also demonstrated the importance of accounting for study-level covariates within the modeling process and estimating performance expectation bounds to assess model utility. The modeled dataset comprised of 4379 studies 1247 chemicals curated from three different database resources: ToxRefDB, HESS-DB, and COSMOS. Effect levels (log 10 mg/kg/day) were randomly divided into an internal (3476 studies of 998 chemicals) and external dataset (903 studies of 249 chemicals) with no single chemical in both the internal and external set. The databases cover a diverse set of chemicals spanning pesticides, industrial chemicals, cosmetics, and pharmaceuticals and are generally underrepresented by highly toxic compounds (e.
g. , dioxin) to avoid over-weighting of extreme values. Chemical, biological, and kinetic descriptors were applied if available. A novel step in this analysis was the use of study-level covariates in the model development process. The internal dataset was used to develop a suite of models with fivefold cross-validation and 5X bootstrapping to test overall model performance and stability. The full internal dataset was then used for final model development. The results were very similar to the cross-validation models demonstrating the robustness of the modeling approach and that overfitting was unlikely to have a significant impact on future performance. The baseline and benchmark models provided lower and upper performance bounds with RMSE estimates of 0. 85 and 0. 5 log 10 mg/kg/ day, respectively, equating to approximately 15 and 70% variance explained (R 2 ).
The final consensus model, including chemical, biological, and kinetic descriptors, had an RMSE of 0. 69 log 10 mg/kg/day explaining 43% of the study-level effect level variance. Performance metrics of regression models are often reported as RMSE and R 2. However, these metrics do not provide the full context of model performance, as they do not take into account underlying expectations and upper performance bounds for how good a model could be given the underlying data. Effect levels, in particular, are associated with a large amount of variability due to study design and interpretation of the observations that form the basis for assigned NOAEL, LOAEL, NEL, or LEL values  (Leisenring and Ryan 1992). Effect levels extracted from various data sources also carry with them a level of 'unexplainable' variability, as the source of the variability may not be captured in the primary reports, study summaries, or a computable format in database.
This methodology enabled accounting for variability in the study type and other study-level covariates. Although not directly comparable, the chemical-level predictions from the EPA challenge  (Novotarskyi et al. 2016; USEPA 2013)  resulted in final models that explained roughly 30% of the variance, while the chemical-level predictions from the current model explain nearly 50% of the variance in the external validation dataset. There is a long history of attempting to develop QSAR and other predictive models of NOAEL, LOAEL, LD 50 , and other effect levels  (Hisaki et al. 2015; Mumtaz et al. 1995; Pizzo and Benfenati 2016; Rupp et al. 2010; Toropov et al. 2015; Veselinovic et al. 2016) , and the summary performance statistics for these published models vary widely. Additionally, external evaluation of these models has often shown that the models either only apply to a very specific chemical domain or were originally reported with overoptimistic performance statistics  (Pizzo and Benfenati 2016).
A limitation of the current model is the available bioactivity descriptors from ToxCast/Tox21 represent a finite biological space currently covered in the ToxCast/Tox21 HTS program. ToxCast and Tox21 research programs continue to expand biological and technological space, including investigation into the feasibility and utility of high throughput transcriptomics and assays with increased metabolic capacity. Additionally, the current model does not take into account kinetics for the full chemical library. To ensure the model will have a broad domain of chemical applicability, future directions will include incorporation of additional reverse toxicokinetic data  (Rotroff et al. 2010; Wetmore et al. 2012)  to enhance the kinetic context; transcriptomics  (Paules 2014 ) and additional HTS assays to provide broader biological coverage; and, expansion of the chemical space included in the internal validation set.
Each model expanded the descriptor set beyond the original eight study-level covariates with an additional 12 physchem, 554 PaDEL, 119 ToxPrint, 49 ToxCast, and 6 httk descriptors. The baseline model was developed using only the study-level covariates (i. e. , in vivo covariates only) and resulted in a median R 2 of 18% across all cross-validated and bootstrapped datasets and chemical sets. The three primary model sets improved over the baseline performance with a median R 2 of 35% demonstrating model stability. However, the addition of biological and kinetic descriptors did not significantly improve model performance. Adding biological or kinetic descriptors alone without chemical descriptors does improve model performance over baseline (data not shown). The benchmark model was developed using study-level covariates and chemical-level effect level (i.
Methods Three clinically sound saddle horses (mean (SD): 8 (1) years, 590 (18) kg) were used. After trimming, the right hind hoof of each horse was equipped with a dynamometric horseshoe (DHS), composed of 4 triaxial piezoelectric force sensors (9251A, Kistler) sandwiched between two aluminium plates  (Chateau et al. , 2013). A non-instrumented horseshoe with matching height and weight was attached to the left hind, as well as to both fore hooves. The hoof reaction force was calculated as the sum of forces applied on each sensor and was expressed in a local reference frame with the vertical (Z) axis perpendicular to the solar plane of the shoe, pointing downward. The DHS wires were connected an analogue-to-digital converter (NI-USB 6218) plugged in a computer remotely controlled (Wi-Fi). Data were acquired at 7.
8 kHz. The horse's speed was measured and recorded by a global positioning system (GPS, Racelogic RLVBSS 100), the antenna of which was glued to the horse's croup. The complete acquisition system was placed in saddle bags. The horses were lunged on a 5-m radius circle, on a sand&fiber mix surface, at trot then canter, alternatively at both hands. The equipped right hind limb was inside the circle at the right hand, and outside the circle at the left hand. For each trial, force and speed data were recorded on 10 successive strides. For each horse, 4 trials were selected for each condition. The stance phase was delimited (100 N vertical force threshold). Customised programmes developed in Matlab (MathWorks) were used to determine the peak vertical force and vertical impulse of each stance. Linear mixed-effects regression models were used in order to compare left and right circle conditions at each gait, taking into account repeated measurements per horse within each trial and adjusted for speed (SAS 9.
Acknowledgements The  Région Basse-Normandie ,  FEDER ,  Fonds Eperon  and  IFCE  for financial support; the  Haras du Pin (IFCE) ,  Garde Républicaine de Paris , and  Centre Sportif d'Equitation Militaire de Fontainebleau , for loaning the horses; the firm  Toubin & Clément  for providing the experimental site and surface.
Due to lack of quantitative data, the BIOHAZ Panel decided to conduct a qualitative evaluation of the risk. To assist in the formulation of the Opinion, the published information available was reviewed, including relevant scientific literature, data from national surveillance and monitoring programmes and the information collected through a Questionnaire sent by the EC to the European Union (EU) Member States (MSs). The evidence was collected and assessed by expert opinion, and related conclusions were formulated in a qualitative way. The assessment was structured as follows: 1) current farming practices in the EU countries in relation to treatment of dairy cows with antimicrobials and the use of the milk potentially containing antimicrobial residues available on dairy farms (colostrum and post-colostral milk from cows treated at the beginning of the dry period as well as to non-marketable milk from cows treated during lactation, waste milk) as feed for calves; 2) antimicrobial residues present in milk and colostrum from treated cows; 3) antimicrobial-resistant bacteria present in colostrum and milk from treated cows; 4) description of experimental studies providing direct evidence of the effect of feeding waste milk containing antimicrobial residues to calves by analysing data collected: a) on farms where waste milk was routinely fed; b) on farms where waste milk was fed to a group of calves compared to a control group; and c) by carrying out experimental studies where milk with known concentrations of antimicrobials was fed to calves in comparison with a control group.
Depending on the data available, for both residues and AMR, the Opinion will focus on the most commonly used antimicrobials for treatment of dairy cows. This mandate is relevant from the public health point of view. However, the Opinion does not do a full farm-to-fork risk assessment on AMR development, and stops at the level of the development and the excretion of resistant bacteria from calves fed with either colostrum or milk potentially contaminated with antimicrobial residues or antimicrobial-resistant bacteria. The impact on human health of the practices indicated in the ToRs is not covered by the Opinion. ToR 3: 'Propose possible options to mitigate the risk for the development of AMR derived from such practices if relevant'. Different mitigation options will be considered, and the advantages and disadvantages of each option will be analysed.
European Medicines Agency (EMA) Maximum Antimicrobial Residue Limits (MRL) assessment of antimicrobials Data on antimicrobial concentrations found in milk after antimicrobial administration in lactating cows obtained from the EMA MRL assessment reports were reviewed by the members of the WG. The extracted data, when appropriate, were used to assess the potential contamination of milk and to estimate the concentration of antimicrobial residues in milk after antimicrobial treatment in dairy cows.
The ESVAC project provided a harmonised approach to the collection and presentation of data on antimicrobial sales in European countries. The main limitation is that the data are not presented by animal species and it is not possible to make a specific extraction of the data concerning lactating cows. However, the ESVAC report presents specific monitoring of sales of intramammaries for all species combined which can be extrapolated to bovine species as the main target for these products. Data on sales of antimicrobials at national level were also obtained from scientific literature or from national monitoring programmes. The data extracted from the ESVAC reports, national monitoring programmes and scientific literature were used in the present Opinion to define the antimicrobials most commonly used in dairy cows in order to identify which substances display higher risk in terms of exposure but also to assess the extent of different types of treatments.
This was used to support the expert review in these areas. Relevant data from a number of official EU publications and Agencies, reports from the EMA, EFSA and specific reports from the MSs were used as appropriate. Links for such reports are given in the reference section. Finally, considerable use was made of ongoing citation input by WG members and information about relevant publications provided by members of the EFSA Biological Hazards (BIOHAZ) Panel. For the area of 'Antimicrobials used in EU for treatment of dairy cows' searches were conducted in PubMed using the keywords: Antibiotic AND Antimicrobial AND Usage AND Dairy cattle. The search included publications from 2012 till now. The references retrieved were screened for studies of interest. For the area of 'Antimicrobial residues concentration found in colostrum' searches were conducted in PubMed using the keywords: Antibiotic OR Antimicrobial AND residue AND colostrum; dry cow therapy AND residue.
• Possible management and mitigation options to reduce the risk for the development of AMR derived from feeding calf practices (Section 3. 6). For each of these subtopics the evidence was collected, judged on the basis of expert knowledge and the summarising remarks conclusions were expressed in a qualitative way. Results collected in Sections 3. 1, 3. 2, and 3. 3, delivered indirect evidence for the effect on the faecal antimicrobial-resistant bacteria when calves were fed contaminated milk and/or colostrum. The studies in Section 3. 4 provided direct evidence of this effect by analysing data collected 1) on farms where waste milk was routinely fed, 2) on farms where waste milk or colostrum was fed to a group of calves compared to a control group and 3) by carrying out experimental studies where milk spiked with known concentrations of antimicrobials was fed to calves in comparison with a control group.
Summary of answers and raw data from the MSs addressing the Commission Questionnaire mentioned in the Mandate As stated in Section 2. 1. 1, the EC sent a questionnaire (SANCO/WT/jd/2014/2646653; Appendix B) concerning 'Feeding of calves with the milk (colostrum) containing residues of antimicrobials from lactating cows after their treatment by antimicrobials' to the MSs in September 2014. Twenty-four MS replied to the questionnaire (Austria -AT, Belgium -BE, Bulgaria -BG, Cyprus -CY, the Czech Republic -CZ, Denmark -DK, Estonia -EE, Spain -ES, Finland -FI, France -FR, Croatia -HR, Hungary -HU, Ireland -IE, Italy -IT, Lithuania -LT, Luxembourg -LU, Latvia -LV, Malta -MT, the Netherlands -NL, Poland -PL, Sweden -SE, Slovenia -SI, Slovakia SK and the United Kingdom -UK). Four MS (Germany -DE, Greece -EL, Portugal -PT and Romania -RO) did not provide any response.
Injectables As represented in Table  C. 1 (Appendix C), there is a substantial lack of data on the use of injectable antimicrobials in dairy cows in the EU and information was only available from six MSs (BE, CZ, DK, the NL, SE and the UK). Also, the data available are not directly comparable because categorisation of antimicrobials and different units of measurements are used in the different datasets. Also, in some of the data sets, dairy cows are not separated from other cattle categories. Two of the data sets  (Brunton et al. , 2012; Stevens et al. , 2016)  are from surveys in a limited number of herds and should not be considered representative for the national situation. From the available data, it appears that penicillins are by far the most commonly used group in SE and DK (76-84%), and this antimicrobial class is also used to a large extent in BE, the CZ and the NL (27-32%) but not very often in the UK (6%).
3. 2. Antimicrobial residues in colostrum and milk of cows treated during lactation with antimicrobials and milked during the withdrawal period In this section, the main information related to veterinary drug residues will be reviewed; first to clearly define, according to the European guidelines, what are the concepts of MRL and withdrawal period, and second, to extract the main scientific information available related to the compounds and concentrations of antimicrobial residues found in milk or colostrum. It is important to note that in the present report mainly the publically available data were assessed. Data related to milk kinetic depletion presented during the veterinary medicine marketing authorisation procedure are not available due to confidentiality concerns.
Antimicrobial concentration found in milk Some information related to antimicrobial concentrations found in milk after treatment could be obtained from the scientific published literature and from MRLs assessment reports. When available, these data are useful to estimate the residual antimicrobial activity and then to estimate the potential selection pressure of the contaminated milk on the pathogenic and commensal flora. The concentration of antimicrobial residues found in milk is dependent on the nature of the antimicrobials, the route of administration, the administered dose and the duration of the treatment.
Data extracted from public available MRL summary reports Information related to concentration of antimicrobials residue in milk of dairy cows after antimicrobial treatment were extracted from the EMA MRL summary reports, public available on the EMA website. 5 The information extracted for lactating cows is presented below. • Aminoglycosides: -Neomycin After oral administration, the residues of neomycin in milk were low just after the antimicrobial treatment. Following an intramammary infusion of 330 mg of lincomycin hydrochloride and 100 mg of neomycin sulfate, the level of neomycin residues in milk and in udder tissues were measured by HPLC (limit of quantification, LOQ = 100 lg/kg) per quarter for three successive milking in 16 healthy lactating cows. When udder quarters were analysed independently, the mean concentration of neomycin residue during the 1st, 2nd and 3rd infusion reached 22,200, 29,900 and 28,000 lg/L, respectively.
Data extracted from publically available MRL summary reports Information related to concentration of antimicrobials residue in the colostrum of dairy cows after antimicrobial treatment were extracted from the EMA MRL summary reports, publically available on EMA website and are presented below. For dry cows: • Cephalosporins -Cefalonium Concerning dry cow therapy, milk residues studies were conducted with cefalonium. The results were then compared in dairy cows with a short versus a longer dry period. After intramammary infusion of a solution of 250 mg per quarter of radiolabelled cefalexin in seven dry cows which calved either from 11 to 17 or from 29 to 37 days after treatment, the concentration of radioactive residues were measured. In the 1st milking after calving, the concentration of total radiolabelled residue was 666-6544 lg/kg.
statistical analysis Results were analyzed using GraphPad Prism software (GraphPad Software, San Diego, CA, USA). Normality distribution was evaluated using the D' Agostino-Pearson omnibus normality test followed by one-way ANOVA with Bonferroni Multiple Comparison test or a student's T test was used. Results were considered to be significantly different when p < 0. 05 (*), 0. 01 (**), or 0. 001 (***).
Statistical analyses Data were processed by ANOVA using the MIXED procedure of SAS v9. 3 (SAS Institute Inc. , Cary, NC, USA). For each digestive segment, the statistical model included horse, diet, sampling time and diet × sampling time. Horse was considered as random effect. A repeated-measures ANOVA was performed to compare profiles of fermentation parameters (VFA concentrations, proportions and pH) using the repeated time option of SAS v9. 3. All statistical procedures were performed separately for the caecum, colon and faeces with horse as experimental unit. Least-squares means were compared using a Tukey's test, and effects declared significant at P < 0. 05. Volatile fatty acid concentrations and pH were also analyzed using a principal component analysis (PCA) performed on the covariance matrix of the samples (caecum, right ventral colon and faeces).
Our results appeared to be in disagreement as no effect of concentrate feeding sequence was observed on pH and VFA concentrations. In the present study, pH and VFA postprandial responses to diet changes were also compared between different hindgut segments. Taking into account all sampling times, we found no difference regarding pH values and VFA concentrations between the caecum, right ventral colon and faeces. This was not in accordance with previous data reporting that microbial fermentation parameters were different between caecum, right dorsal colon and faeces (Dougal et al. , 2012). The dissimilarities of the results could be due to differences in experimental procedures; whereas horses were euthanized for collecting a single digestive sample at the end point of the experimental trial, we collected five samples of digestive contents on alive horses from each intestinal compartment and from faeces.
When pH and VFA postprandial responses were studied in each digestive segment separately, the response differed according to the hindgut location. To our knowledge, this postprandial response had never been compared between the caecum, right ventral colon and faeces of the horse within the same trial. In the caecum, the pH decrease and the VFA concentrations increase observed from 4 to 8 h after the morning meal were consistent with data previously reported in the literature  (Moore-Colyer et al. , 2000; Medina et al. , 2002; Zeyner et al. , 2004; Jensen et al. , 2012). Similarly to the caecum, pH values decreased and VFA concentrations increased in the colon within the range of 4 to 8 h after the morning meal. In their recent chapter,  Merritt and Julliand (2013)  summarized that the transit rate of digesta through the small intestine of the horse is very rapid for liquids and in average between 4 and 7 h for particulate matter.
For all days of blood sampling, plasma cortisol concentrations were determined by a radioimmunoassay method  (Boissy and Bouissou, 1994)  using an antibody produced by Cognié and Poulin (INRA, Tours, France). Limit of detection was 0. 02 ng/ml and within-and between-assay CV values were 6. 5% and 13%, respectively, for low controls (4 ng/ml) and 11% and 13%, respectively, for high controls (32 ng/ml). Statistical analysis SAS software (version SAS 9. 4; SASTM Institute, Cary, NC, USA) was used to analyze the data. The homogeneity of variance was verified. The normal distribution for the residuals of all values was checked on a Q-Q plot. Latencies to approach the different bucket locations in the judgment test were non-normally distributed (i. e. standardized residuals did not meet the assumption of normality on a Q-Q plot) and so could not be analyzed with traditional parametric statistics.