dataesr/scientific-paragraphs-categorization huggingface dataset:
Generalization of the Consensus and Implementation in OPERA The two-step approach for combining predictions from the 139 submitted models resulted in a robust consensus model that covered the entire prediction set of 48,137 chemical structures. To make the model applicable for further screening of new chemicals, an additional step was required. A weighted-kNN modeling approach was implemented in OPERA (version 2. 5) to mimic the initial consensus predictions and generate new ones. This was achieved by training extended models based on the existing experimental data and predictions with high concordance. To facilitate the training process, the five end points were processed separately. Then, the WoE approach was similarly applied to the generated predictions to make a final consistent consensus call.
Additional Evaluation Using a Highly Curated Data Set Preparation of the curation data set and consistency analysis. Prior to combining the experimental data for chemicals with multiple entries from the data collected for the CATMoS project and the ECHA data set to produce a curated data set, a review of the different LD 50 values revealed a number of inconsistencies between the two data sets. This finding can be partly attributed to the variability of animal data, but in some cases, were also due to errors that may have been introduced and propagated during reporting, publishing, or data retrieval. To estimate the disagreement between replicate LD 50 studies per chemical, the different LD 50 values and binary/multiclass calls were compared using a representative from each of the two data sets (as described above).
As shown in Table  15 , the discordance was highest for the very toxic compounds, as represented by the Sn of the VT end point. This is due to the fact that 25 out of the 38 chemicals that are considered very toxic in the CATMoS data set are associated with an LD 50 > 50 mg=kg in the ECHA data set. Discordance between the Sn and Sp parameters was less apparent for the NT end point. However, a closer look at the confusion matrix generated during the calculation of these parameters revealed a disagreement on a total of 126 chemicals, with 109/126 classified as toxic in the CATMoS data but not in the ECHA data set. A similar level of disagreement was observed for U. S. EPA and GHS categorizations; the confusion matrices in Tables  13  and 14  showed that most of the discordant classifications differed by one category.
e. , data used to train the CATMoS models), as noted above. However, an examination of the LD 50 values revealed several chemicals that were associated with low LD 50 values in ECHA but were predicted as less toxic by CATMoS with a high concordance among the submitted models. A closer look at 28 of these chemicals with available source toxicity reports on the ECHA website revealed a number of reporting errors in the source database, such as unit conversion (grams vs. milligrams), typos, decimal misplacement ("," vs. ". ") and estimated doses using read-across, among others. Such findings highlighted how robust and highly concordant CATMoS predictions  are because they helped to identify where the compiled in vivo data had typographical errors. These types of errors most likely affect other chemicals in the data set and therefore affect the statistics in Tables 15-17 even further.
003. 300). However, in the experimental data underlying the CATMoS training set, the LD 50 of this chemical was only 11 mg=kg, which was also the value reported for the chemical on the U. S. EPA CompTox Chemicals Dashboard (https://comptox. epa. gov/dashboard/dsstoxdb/results?search= DTXSID1023996#toxicity-values). This chemical, although present in the training set and therefore seen by the fitting algorithms during the learning process, was consistently predicted differently by most models based on its structural features and was initially counted as an inaccurate prediction in the evaluation process. The fact that the other chemicals in the data set and the modeling algorithms predicted a value closer to the true value identified from the curated in vivo data set rather than the possibly erroneous value that made its way into the training set revealed that the size of the data set and approaches used could overcome outlier values and that predictions are robust.
Similar to most QSAR/QSPR models, the CATMoS can be applied only to single organic compounds. Mixtures of organic chemicals should be studied separately. However, to accommodate mixtures of multiple compounds, the GHS system provides an additivity rule to help classify chemicals that can use CATMoS predictions as input (http://www. unece. org/fileadmin/ DAM/trans/danger/publi/ghs/GHS_presentations/English/health_ env_e. pdf). Additionally, as most molecular descriptors are developed for small-and medium-size molecules, CATMoS and other QSAR models cannot process large biomolecules, long polymeric chains, and nanomaterials. To help identify and use the most adequate chemical structures for predictions and to avoid   unpredictable substances, OPERA users wishing to generate CATMoS predictions can use either of two input options: • Provide a text file with a list of chemical identifiers (CASRN, DTXSID, InChI) and let OPERA pull the correct QSAR-ready structure from its database of 830,000 highly curated DSSTox chemicals; or, • Provide their own structures but apply the embedded standardization workflow to generate QSAR-ready structures prior to curation.
A possible limiting aspect of the OPERA standalone application is that it must be installed locally, requiring access to the user's computer operating system and hardware to perform calculations. Users can, however, avoid this process and access predictions by visiting the Integrated Chemical Environment (ICE) dashboard of the NTP (https://ice. ntp. niehs. nih. gov/) and querying its internal database of predictions for the DSSTox chemicals. These predictions will also be made available on the U. S. EPA CompTox Chemicals Dashboard (https://comptox. epa. gov/dashboard/).
In addition to the initial prediction set, CATMoS was implemented in OPERA and used to screen the list of 837,000 chemical substances in the U. S. EPA's DSSTox database (underpinning the Dashboard application). These predictions are made available via the Integrated Chemical Environment dashboard (https://ice. ntp. niehs. nih. gov/) of the NTP and in the future via the U. S. EPA's CompTox Chemicals Dashboard (https://comptox. epa. gov/dashboard/). CATMoS is an example of how toxicological problems can be solved collaboratively using computational approaches. In fact, the resulting consensus models leverage the strengths and overcome the limitations of any single approach, proving to be as good as or better than animal data. Such successful collaborative projects support international collaboration, a legacy of free and open-source code and workflows, and increasing consideration and adoption from regulators interested in implementing NAMs.
Another point of unanimity related to the importance and value of the Tutorials published in PSP. Further, respondents provided a wealth of topic areas to serve as potential future Tutorials, such as PK-PD concepts and modeling approaches for novel drug modalities (e. g. , cell-and gene-based therapies), approaches integrating bioinformatics (omics) analyses with QSP modeling, guidelines for QSP model validation/qualification, model-based preclinical to clinical integration of large data sets/streams, and techniques connecting machine learning with pharmacometrics and QSP. The vital importance of an 'Open' approach to science and access to published data and mathematical models has become well-recognized. 9, 10 As an important step in maintaining reproducibility, rigor, and integrity in published pharmacometrics and systems pharmacology models, submission of relevant model code to supplement Original Research articles is required for publication in CPT:PSP.
PSP also plans to update the current journal scope to clarify that it welcomes translational and preclinical modeling submissions, understanding fully that earlier integration of quantitative tools are needed in drug discovery and early development. While big data and AI/ML are the "buzz" words, and these approaches are well positioned to rapidly increase with time, the integration of the core areas is still largely lacking and needs fostering. Increase of data is inevitable; however the quality of data of well-designed trials currently still outweighs big noisy 'real world' data. Agreement on a recognized standard of data entry and collection is needed e. g. electronic health records could be an initial step forward to improve the quality of big data. AI and ML together with big data may be useful in identifying early signals or help sort and search the large amount of knowledge using Natural Language Processing.
11 oremost was the paramount importance of Open Science, Open Data and Open Access to ensure re-usability, reproducibility, integrity and adaptability of models, tools and databases. This was considered the foundation upon which the journal could articulate a clear vision. Built on this core identity and the benefits to authors in being Gold Open Access, PSP will continue working towards being the first choice for articles on best practices, standards and how-to (tutorials, white papers) and be a preferred forum for discussions and community interactions (perspectives). Equally important is the continued emphasis on showcasing impactful examples (original research articles, case reports) of successful application of PMX/PKPD/PBPK/QSP to support decisionmaking, including novel therapeutic modalities (e.
g. CAR-T, AAV, siRNA). Here, the team sought to clarify that impactful examples do not automatically imply complex or novel modeling methods. A well-performed population pharmacokinetic analysis of a novel therapeutic leading to impactful decisions was seen as valuable to the community. Future areas of high potential that the team considered relatively untapped included the application of pharmacometric models for pivotal/registrational clinical outcome data, model-based probabilities of success to inform design and go/no-go decisions at all stages of drug development, machine learning and analyses of health databases and/ or other unstructured data sources (e. g. wearables), pharmacometric/pharmacoeconomic models that address important gaps in clinical evidence to inform reimbursement decisions and precision dosing at the bedside.
Pharmacometrics and Systems Pharmacology Journal Vision. CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP) welcomes contributions in Pharmacometrics and Systems Pharmacology and research bridging across related quantitative disciplines with the overall objective of improving global health on a foundation of open science, open data and open access. The journal aims to be a holistic source for impactful model-informed applications, educational endeavors, harmonization/best practices and new methods and tools. QSP, Quantitative Systems Pharmacology; MID3, model-informed drug discovery and development; AI, artificial intelligence; RWD, real world data Impacƞul models leading to decisions across the discovery, development, regulatory, reimbursement and uƟlizaƟon • Novel therapeuƟc modaliƟes • Integrated, mulƟ-disciplinary approaches to problem solving • PracƟcal tutorials to foster educaƟon and talent development • Broader disseminaƟon of innovaƟve conference workshops • Emphasis on translaƟonal modeling and emerging methods with access to code • White papers promoƟng greater consistency in MID3 pracƟce and applicaƟon • Diversity in publicaƟons to facilitate globally harmonized regulatory assessments • Cross-societal perspecƟves on best pracƟces • Advances in QSP model formulaƟon and verificaƟon • Machine learning, AI, RWD, informaƟcs, wearables, cross-funcƟonal databases, open-source soŌware • StaƟsƟcal pharmacometric advances F I G U R E 5 Country location of the (a) Members of the Editorial Leadership team, including Editor in Chief, Associate Editors, Editorial Board and Scientific Advisory Board in 2021 and (b) corresponding authors of all articles published in CPT:PSP.
ACKNOWLEDGEMENTS The authors acknowledge the following leaders for graciously sharing their perspectives in support of this editorial:  David D'Argenio ,  Paolo Denti ,  Jin Yan Jin ,  Kristin Karlsson ,  Mats Karlsson ,  Ron Keizer ,  Richard Lalonde ,  Peter Milligan ,  Joseph Standing ,  Piet van der Graaf and Sandra Visser. Karen Rowland Yeo 1 * Stefanie Hennig 2 * Sriram Krishnaswami 3 * Natasha Strydom 4 †.
F I G U R E 4 • 4 Pharmacometrics and Systems Pharmacology Journal Vision. CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP) welcomes contributions in Pharmacometrics and Systems Pharmacology and research bridging across related quantitative disciplines with the overall objective of improving global health on a foundation of open science, open data and open access. The journal aims to be a holistic source for impactful model-informed applications, educational endeavors, harmonization/best practices and new methods and tools. QSP, Quantitative Systems Pharmacology; MID3, model-informed drug discovery and development; AI, artificial intelligence; RWD, real world data Impacƞul models leading to decisions across the discovery, development, regulatory, reimbursement and uƟlizaƟon • Novel therapeuƟc modaliƟes • Integrated, mulƟ-disciplinary approaches to problem solving • PracƟcal tutorials to foster educaƟon and talent development • Broader disseminaƟon of innovaƟve conference workshops • Emphasis on translaƟonal modeling and emerging methods with access to code • White papers promoƟng greater consistency in MID3 pracƟce and applicaƟon • Diversity in publicaƟons to facilitate globally harmonized regulatory assessments • Cross-societal perspecƟves on best pracƟces • Advances in QSP model formulaƟon and verificaƟon • Machine learning, AI, RWD, informaƟcs, wearables, cross-funcƟonal databases, open-source soŌware • StaƟsƟcal pharmacometric advances F I G U R E 5 Country location of the (a) Members of the Editorial Leadership team, including Editor in Chief, Associate Editors, Editorial Board and Scientific Advisory Board in 2021 and (b) corresponding authors of all articles published in CPT:PSP.
Double exponential smoothing Precision feeding and management of growing-finishing pigs typically require mathematical models to forecast individual pig performance from past data. The current approaches, namely double exponential smoothing (DES) and dynamic linear regression are likely to have some limitations in their applicability since they: (1) assume that responses can be forecasted linearly, which only holds in the short-term, and (2) often take insufficient account of uncertainty and correlations in the estimated traits. We developed and evaluated alternative approaches to forecasting individual growth or intake responses based on nonlinear models (allometric, monomolecular, rational) and Bayesian methodology to fit models to the data and generate probabilistic forecasts. We applied these approaches to individual data from two distinct pig populations, to parameterise the models (fitting based on a training dataset) and forecast performance (forecast horizons: 1e30 d tested on a validation dataset).
Data Empirical data, at age t (d), on daily feed intake, DFI t (kg/d), and bodyweight, BW t (kg), of individual pigs from two distinct pig populations were obtained from two independent experiments conducted by: (1) INRAE at the UE3P unit (Pig Physiology and Phenotyping Experimental Facility, https://doi. org/10. 15454/1. 5573932732039927E12), Saint Gilles, France (Population A) and (  2 ) Topigs Norsvin at their commercial testing facilities (Population B). Data from these two populations (Population A: n ¼ 32 pigs; Population B: n ¼ 30 pigs) covered the growth period from a typical postenursery weight (approximately 35 kg) to a typical slaughter weight (approximately 110e120 kg). In each of the two experiments, pigs were kept in nearly commercial conditions (ad-libitum access to water, group housing, ambient room temperature of 20e24 C) and were offered two feeds successively, formulated to meet or exceed the expected population-average nutritional requirements prevailing at the time of the experiment  (National Research Council, 1998).
For each animal, an animal-specific total number of observations, N, was collected at animal-specific successive time points tðiÞ. We denote the time point of the last observation in the dataset as T ¼ tðNÞ ¼ tðnðTÞÞ. Most observations were daily, dt ¼ tði þ1Þ À tðiÞ ¼ 1d, but in rare instances the lags were greater than 1 d. In addition to the biological motivation above, the use of cumulative data was intended to reduce the effects of longitudinal random variation on the estimation and to lead to a better identification of a suitable mathematical representation of the feed intake-BW gain relationship. Next, we assumed that the relationship between CG t and CFI t has the general form: CG t ¼ f ðCFI t ; qÞ þ ε t ; (3) where f ðCFI t ; qÞ is a trend function of CFI t that depends upon a vector of unknown parametersq, and ε t is an 'error', or 'residual' term representing random deviations from the deterministic temporal trend.
Goodness of fit and model comparison To avoid ambiguity, we refer to a training dataset as past data that was utilised to estimate model parameters, while we refer to a validation dataset as subsequent data that was not utilised to estimate unknown model parameters  (Armstrong, 2001). Consequently, fitting refers to the process of estimating unknown model parameters from a training dataset, while forecasting refers to the prediction of data in a validation dataset. The accuracy of the models quantified in terms of fitting a training dataset (section 2. 5. 1) or in terms of forecasting tested on a validation dataset (section 2. 5. 2) are presented for every individual pig in each of the two populations. In addition, for illustration, fitted and forecasted growth responses are presented for four pigs selected from each of the two populations.
The four pigs per population were selected such that their observed growth trajectories contained: (a) no perturbation; (b) an early perturbation; (c) a mid perturbation; or (d) a late perturbation during the observation period. A perturbation is defined here as a short-lived, but evident deviation from the previous trend in CG t ; these perturbations were identified among the individual pigs by detecting clusters of data points for which CG t > CG tþ1 and calculating the magnitude of the downward deviation. Fitted and forecasted growth responses (in terms of the posterior medians and posterior ninety-five percent credible intervals (95% CrIs; the Bayesian equivalent of confidence intervals)) for the remaining pigs in each of the two populations are presented in the Supplementary Material.
Forecasting evaluation Here, we adopt common forecasting terminology from  Bergmeir and Benı ´tez (2012). Let the origin (T0) be last time point in a training dataset, from which forecasts of future growth responses are generated. Let the forecast horizon (H) be the time window, ranging between the first (T0 þ dt) and the last time point (T) in a validation dataset over which forecasts are made. We considered a typical dataset split  (Hyndman, 2015)  with the validation dataset consisting of the last 30 observations for each individual pig (approximately forty percent of all available data per pig). For most pigs all the lags between observations were 1 d and the maximum forecast horizon under consideration was H m ¼ 30d. Model testing was carried out on forecast horizons ranging from 1 d and H m ; in total, thirty different forecast horizons were considered.
To quantify the forecast accuracy of each model in each forecast horizon, we calculated the Bayesian equivalent of the standard absolute percentage error, MAPE Bayes , as an average of the model error over the posterior sample and over the data points in the forecast horizon: MAPE Bayes ¼ 1 H X i2½1;N:T0 < tðiÞ T0þH 1 N S X N S s¼1 Y tðiÞ À b Y tðiÞ ðq s Þ Y tðiÞ ð%Þ (11) where the notation is as in equation (  9 ), but b Y tðiÞ are forecasts rather than fitted values, and the sum over time points i is limited to the forecast horizon Hwithin the validation dataset; q s are the MCMC sample parameters. To rank the models forecast accuracy by the MAPE Bayes we followed the following procedure: (1) for each pig we ordered the MAPE Bayes values of the five models (from lowest to highest since values closer to zero indicate smaller predictive error) after averaging across the forecast horizons, H ¼ 1;…;30d; and (2) we counted how many times across all pigs each model scored 1st, 2nd, …, 5th.
Discussion The main contributions of this study in the context of precision feeding and management of pigs are twofold: (i) a comprehensive evaluation of existing and alternative nonlinear models, based on straightforward mathematical relationships that approximate the underlying growth trend; and (ii) implementation of an alternative, framework for parameter estimation accounting for uncertainty and correlations in the estimated traits, which is expected to generate more robust forecasts for any chosen model. We used two datasets to apply our methodology to address whether similar conclusions could be drawn from pig populations that differed substantially, especially in their growth and feed conversion efficiency characteristics.
Criteria for model selection: ability to fit vs ability to forecast On the basis of our statistical analysis of individual feed consumption and BW data from growing-finishing pigs, we identified clear differences in the overall classification of models. These differences depended on whether model accuracy was measured in terms of fitting or forecasting. Specifically, our findings suggest that a good fit to a training dataset does not necessarily translate into accurate forecasts of individual growth responses in a validation dataset. Accordingly, while all models under consideration seemed to fit the observed data well, the nonlinear models generated markedly more accurate forecasts of growth responses than the linear models across all forecast horizons. For every model, the overall forecast accuracy decreased with increasing forecast horizon, but this decrease was substantially greater for the linear models.
Forecasting ability under growth perturbations Our findings demonstrated that the timing of a perceived short-lived growth perturbation, caused e. g. by infrequent, temporary physiological and/or environmental disturbances  (Kyriazakis, 1997; Kyriazakis & Tolkamp, 2011)  could impact the ability to reliably forecast individual growth responses in different ways. Based solely on a subset of illustrative animals, whose growth trajectories were examined because they were perturbed, we found the following: when a single, shortlived growth perturbation occurred early in the training dataset (and away from the time point of the last known value, from which the forecasts were performed), all considered models were largely able to cope with this unexpected deviation from the trend. Contrastingly, when a perceived single, short-lived growth perturbation occurred late in the training dataset (and near the time point of the last known value, from which the forecasts were performed), the forecast accuracy of all models was reduced, but to different extents.
each individual pig, we assessed the goodness of fit of the models to a training dataset, through a Bayesian coefficient of determination, R 2 Bayes based on the variance of the errors of the fitted model (Gelman, Goodrich, Gabry, & Vehtari, 2019): Yare the Nobservations from the given animal and b Y are the fitted model values corresponding to a single parameter point in the MCMC sample. The median of R 2.