Development and application of the physiologically-based toxicokinetic (PBTK) model for ochratoxin A (OTA) in rats and humans.

Ochratoxin A (OTA) is a common fungal toxin frequently detected in food and human plasma samples. Currently, the physiologically based toxicokinetic (PBTK) model plays an active role in dose translation and can improve and enhance the risk assessment of toxins. In this study, the PBTK model of OTA in rats and humans was established based on knowledge of OTA-specific absorption, distribution, metabolism, and excretion (ADME) in order to better explain the disposition of OTA in humans and the discrepancies with other species. The models were calibrated and optimized using the available kinetic and toxicokinetic (TK) data, and independent test datasets were used for model evaluation. Subsequently, sensitivity analyses and population simulations were performed to characterize the extent to which variations in physiological and specific chemical parameters affected the model output. Finally, the constructed models were used for dose extrapolation of OTA, including the rat-to-human dose adjustment factor (DAF) and the human exposure conversion factor (ECF). The results showed that the unbound fraction (Fup) of OTA in plasma of rat and human was 0.02-0.04% and 0.13-4.21%, respectively. In vitro experiments, the maximum enzyme velocity (Vmax) and Michaelis-Menten constant (Km) of OTA in rat and human liver microsomes were 3.86 and 78.17 µg/g min-1, 0.46 and 4.108 µg/mL, respectively. The predicted results of the model were in good agreement with the observed data, and the models in rats and humans were verified. The PBTK model derived a DAF of 0.1081 between rats and humans, whereas the ECF was 2.03. The established PBTK model can be used to estimate short- or long-term OTA exposure levels in rats and humans, with the capacity for dose translation of OTA to provide the underlying data for risk assessment of OTA.

Extrapolation of cytotoxic masked effects in planar in vitro assays.

The masking of specific effects in in vitro assays by cytotoxicity is a commonly known phenomenon. This may result in a partial or complete loss of effect signals. For common in vitro assays, approaches for identifying and quantifying cytotoxic masking are partly available. However, a quantification of cytotoxicity-affected signals is not possible. As an alternative, planar bioassays that combine high-performance thin layer chromatography with in vitro assays, such as the planar yeast estrogen screen (p-YES), might allow for a quantification of cytotoxically affected signals. Affected signals form a typical ring structure with a supressed or completely lacking centre that results in a double peak chromatogram. This study investigates whether these double peaks can be used for fitting a peak function to extrapolate the theoretical, unaffected signals. The precision of the modelling was evaluated for four individual peak functions, using 42 ideal, undistorted peaks from estrogenic model compounds in the p-YES. Modelled ED50-values from bisphenol A (BPA) experiments with cytotoxically disturbed signals were 13 times higher than for the apparent data without compensation for cytotoxicity (320 ± 63 ng versus 24 ± 17 ng). This finding has a high relevance for the modelling of mixture effects according to concentration addition that requires unaffected, complete dose-response relationships. Finally, we applied the approach to results of a p-YES assay on leachate samples of an elastomer material used in water engineering. In summary, the fitting approach enables the quantitative evaluation of cytotoxically affected signals in planar in vitro assays and also has applications for other fields of chemical analysis like distorted chromatography signals.

Projection of Interspecific Competition (PIC) Matrices: A Conceptual Framework for Inclusion in Population Risk Assessments.

Accounting for intraspecific and interspecific competition when assessing the effects of chemical and nonchemical stressors is an important uncertainty in ecological risk assessments. We developed novel projection of interspecific competition (PIC) matrices that allow for analysis of population dynamics of two or more species exposed to a given stressor(s) that compete for shared resources within a landscape. We demonstrate the application of PIC matrices to investigate the population dynamics of two hypothetical fish species that compete with one another and have differences in net reproductive rate and intrinsic rate of population increase. Population status predictions were made under scenarios that included exposure to a chemical stressor that reduced fecundity for one or both species. The results of our simulations demonstrated that measures obtained from the life table and Leslie matrix of an organism, including net reproductive rate and intrinsic rate of increase, can result in erroneous conclusions of population status and viability in the absence of a consideration of resource limitation and interspecific competition. This modeling approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects due to stressors to possible outcomes within an ecosystem. In addition, PIC matrices could be combined with adverse outcome pathways to allow for ecosystem projection based on taxonomic conservation of molecular targets of chemicals to predict the likelihood of relative cross-species susceptibility. Overall, the present study shows how PIC matrices can integrate effects across the life cycles of multiple species, provide a linkage between endpoints observed in individual and population-level responses, and project outcomes at the community level for multiple generations for multiple species that compete for limited resources. Environ Toxicol Chem 2024;00:1-17. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Atypical Time to Contact Estimation in Young Adults with Autism Spectrum Disorder.

Individuals with Autism Spectrum Disorder (ASD) present atypical sensory processing in the perception of moving stimuli and biological motion. The present study aims to explore the performance of young adults with ASD in a time to contact (TTC) estimation task involving social and non-social stimuli. TTC estimation involves extrapolating the trajectory of a moving target concealed by an occluder, based on the visible portion of its path, to predict the target's arrival time at a specific position. Sixteen participants with a diagnosis of level-1 ASD (M = 19.2 years, SE = 0.54 years; 3 F, 13 M) and sixteen participants with TD (M = 22.3 years, SE = 0.44 years; 3 F, 13 M) took part in the study and underwent a TTC estimation task. The task presented two object types (a car and a point-light walker), different object speeds, occluder lengths, motion directions and motion congruency. For the car object, a larger overestimation of TTC emerged for ASDs than for TDs, whereas no difference between ASDs and TDs emerged for the point-light walker. ASDs exhibited a larger TTC overestimation for the car object than for the point-light walker, whereas no difference between object types emerged for TDs. Our results indicated an atypical TTC estimation process in young adults with ASD. Given its importance in daily life, future studies should further explore this skill. Significant effects that emerged from the analysis are discussed.

Quantitative Cross-Species comparison of serum albumin binding of per- and polyfluoroalkyl substances from five structural classes.

Per- and polyfluoroalkyl substances (PFAS) are a class of over 8,000 chemicals, many of which are persistent, bioaccumulative, and toxic to humans, livestock, and wildlife. Serum protein binding affinity is instrumental in understanding PFAS toxicity, yet experimental binding data is limited to only a few PFAS congeners. Previously, we demonstrated the usefulness of a high-throughput, in vitro differential scanning fluorimetry assay for determination of relative binding affinities of human serum albumin for 24 PFAS congeners from 6 chemical classes. In the current study, we used this assay to comparatively examine differences in human, bovine, porcine, and rat serum albumin binding of 8 structurally informative PFAS congeners from 5 chemical classes. With the exception of the fluorotelomer alcohol 1H, 1H, 2H, 2H-perfluorooctanol (6:2 FTOH), each PFAS congener bound by human serum albumin was also bound by bovine, porcine, and rat serum albumin. The critical role of the charged functional headgroup in albumin binding was supported by the inability of albumin of each species tested to bind 6:2 FTOH. Significant interspecies differences in serum albumin binding affinities were identified for each of the bound PFAS congeners. Relative to human albumin, perfluoroalkyl carboxylic and sulfonic acids were bound with greater affinity by porcine and rat serum albumin, and the perfluoroalkyl ether acid congener bound with lower affinity to porcine and bovine serum albumin. These comparative affinity data for PFAS binding by serum albumin from human, experimental model and livestock species reduce critical interspecies uncertainty and improve accuracy of predictive bioaccumulation and toxicity assessments for PFAS.

Extrapolating animal consciousness.

I argue that the question of animal consciousness is an extrapolation problem and, as such, is best tackled by deploying currently accepted methodology for validating experimental models of a phenomenon of interest. This methodology relies on an assessment of similarities and dissimilarities between experimental models, the partial replication of findings across complementary models, and evidence from the successes and failures of explanations, technologies and medical applications developed by extrapolating and aggregating findings from multiple models. Crucially important, this methodology does not require a commitment to any particular theory or construct of consciousness, thus avoiding theory-biased reinterpretations of empirical findings rampant in the literature.

A simulation study of extrapolation uncertainty in exposure assessment - Use of pilot study results for site investigation.

Accurate characterization of soil contaminant concentrations is often crucial for assessing risks to human and ecological health. However, fine-scale assessments of large tracts of land can be cost prohibitive due to the number of samples needed. One solution to this problem is to extrapolate sampling results from one area to another unsampled area. In the absence of a validated extrapolation methodology, regulatory agencies have employed policy-based techniques for large sites, but the likelihood of decision errors resulting from these extrapolations is largely unexplored. This study describes the results of a simulation study aimed at guiding environmental sampling for sites where extrapolation concepts are of interest. The objective of this study is to provide practical recommendations to regulatory agencies for extrapolating sampling results on large tracts of land while minimizing errors that are detrimental to human health. A variety of site investigation scenarios representative of environmental conditions and sampling schemes were tested using adaptive sampling when collecting discrete samples or applying incremental sampling methodology (ISM). These simulations address extrapolation uncertainty in cases where a Pilot Study might result in either false noncompliance or false compliance conclusions. A wide range of plausible scenarios were used that reflect the variety of heterogeneity seen at large sites. This simulation study demonstrates that ISM can be reliably applied in a Pilot Study for purposes of extrapolating the outcome to a large area site because it decreases the likelihood of false non-compliance errors while also providing reliable estimates of true compliance across unsampled areas. The results demonstrate how errors depend on the magnitude of the 95% upper confidence limit for the mean concentration (95UCL) relative to the applicable action level, and that error rates are highest when the 95UCL is within 10%-40% of the action level. The false compliance rate can be reduced to less than 5% when 30% or more of the site is characterized with ISM. False compliance error rates using ISM are insensitive to the fraction of the decision units (DUs) that are characterized with three replicates (with a minimum of 10 percent), so long as 95UCLs are calculated for the DUs with one replicate using the average coefficient of variation from the three replicate DUs.

SurvInt: a simple tool to obtain precise parametric survival extrapolations.

BACKGROUND: Economic evaluation of emerging health technologies is mandated by agencies such as the National Institute for Health and Care Excellence (NICE) to ensure their cost is proportional to their benefit. To avoid bias, NICE stipulate that the benefit of a treatment is assessed across the lifetime of the patient population, which can be many decades. Unfortunately, follow-up from a clinical trial will not usually cover the required period and the observed follow-up will require extrapolation. For survival data this is often done by selecting a preferred model from a set of candidate parametric models. This approach is limited in that the choice of model is restricted to those originally fitted. What if none of the models are consistent with clinical prediction or external data? METHOD/RESULTS: This paper introduces SurvInt, a tool that estimates the parameters of common parametric survival models which interpolate key survival time co-ordinates specified by the user, which could come from external trials, real world data or expert clinical opinion. This is achieved by solving simultaneous equations based on the survival functions of the parametric models. The application of SurvInt is shown through two examples where traditional parametric modelling did not produce models that were consistent with external data or clinical opinion. Additional features include model averaging, mixture cure models, background mortality, piecewise modelling, restricted mean survival time estimation and probabilistic sensitivity analysis. CONCLUSIONS: SurvInt allows precise parametric survival models to be estimated and carried forward into economic models. It provides access to extrapolations that are consistent with multiple data sources such as observed data and clinical predictions, opening the door to precise exploration of regions of uncertainty/disagreement. SurvInt could avoid the need for post-hoc adjustments for complications such as treatment switching, which are often applied to obtain a plausible survival model but at the cost of introducing additional uncertainty. Phase III clinical trials are not designed with extrapolation in mind, and so it is sensible to consider alternative approaches to predict future survival that incorporate external information.

Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.

Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking.

Background: Calibrated electromyography (EMG)-driven musculoskeletal models can provide great insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unclear. Methods: This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces. Results: On average, SynX produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15,r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N,r value 0.53 vs. 0.07) compared to SO. SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values. Conclusions: These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

The generalized sigmoidal quantile function.

In this note we introduce a new smooth nonparametric quantile function estimator based on a newly defined generalized expectile function and termed the sigmoidal quantile function estimator. We also introduce a hybrid quantile function estimator, which combines the optimal properties of the classic kernel quantile function estimator with our new generalized sigmoidal quantile function estimator. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. This property of extrapolation is illustrated in order to improve standard bootstrap smoothing resampling methods.

Challenges with Estimating Long-Term Overall Survival in Extensive Stage Small-Cell Lung Cancer: A Validation-Based Case Study.

Objective: The study aimed to explore methods and highlight the challenges of extrapolating the overall survival (OS) of immunotherapy-based treatment in first-line extensive stage small-cell lung cancer (ES-SCLC). Methods: Standard parametric survival models, spline models, landmark models, mixture and non-mixture cure models, and Markov models were fitted to 2-year data of the CASPIAN Phase 3 randomised trial of PD-L1 inhibitor durvalumab added to platinum-based chemotherapy (NCT03043872). Extrapolations were compared with updated 3-year data from the same trial and the plausibility of long-term estimates assessed. Results: All models used provided a reasonable fit to the observed Kaplan-Meier (K-M) survival data. The model which provided the best fit to the updated CASPIAN data was the mixture cure model. In contrast, the landmark analysis provided the least accurate fit to model survival. Estimated mean OS differed substantially across models and ranged from (in years) 1.41 (landmark model) to 4.81 (mixture cure model) for durvalumab plus etoposide and platinum and from 1.01 (landmark model) to 2.00 (mixture cure model) for etoposide and platinum. Conclusion: While most models may provide a good fit to K-M data, it is crucial to assess beyond the statistical goodness-of-fit and consider the clinical plausibility of the long-term predictions. The more complex cure models demonstrated the best predictive ability at 3 years, potentially providing a better representation of the underlying method of action of immunotherapy; however, consideration of the models' clinical plausibility and cure assumptions need further research and validation. Our findings underscore the significance of adopting a clinical perspective when selecting the most appropriate approach to model long-term survival, particularly when considering the use of more complex models.

Pediatric Extrapolation Approach for U.S. Food and Drug Administration Approval of Brexpiprazole in Patients Aged 13 to 17 Years with Schizophrenia.

A pharmacokinetic (PK) bridging approach was successfully employed to support the dosing regimen and approval of brexpiprazole in pediatric patients aged 13-17 years with schizophrenia. Brexpiprazole was approved in 2015 for the treatment of schizophrenia and the adjunctive treatment of major depressive disorder in adults based on efficacy and safety data from clinical trials. On January 13, 2020, the US Food and Drug Administration issued a general advice letter to sponsors highlighting the acceptance of efficacy extrapolation of certain atypical antipsychotics from adult patients to pediatric patients considering the similarity in disease and exposure-response relationships. Brexpiprazole is the first atypical antipsychotic approved in pediatrics using this approach. The PK data available from pediatric patients aged 13-17 years have shown high variability due to the limited number of PK evaluable subjects, which limits a robust estimation of differences between adult and pediatric patients. The PK model-based approach was thus utilized to evaluate the appropriateness of the dosing regimen by comparing PK exposures in pediatric patients aged 13-17 years with exposures achieved in adults at the approved doses. In addition to exposure matching, safety data from a long-term open-label clinical study in pediatric patients informed the safety profile in pediatric patients. This report illustrates the potential of leveraging previously collected efficacy, safety, and PK data in adult patients to make a regulatory decision in pediatric patients for the indication of schizophrenia.

Outcomes in Adult Inflammatory Bowel Disease Clinical Trials: Assessment of Similarity Among Participants with Adolescent-Onset and Adult-Onset Disease.

BACKGROUND AND AIMS: Most pediatric IBD studies are performed after medications are approved in adults and the majority of participants in these studies are adolescents. We hypothesized that adolescent-onset IBD is not fundamentally different than adult-onset IBD. If this is correct, the value of delaying access to novel drugs in adolescents becomes questioned. METHODS: Data from 11 randomized, double-blind, placebo-controlled adult phase 2 and 3 trials of 4 biologics were analyzed. Participants were categorized as having adolescent- or adult-onset disease (diagnosed 12 to <18, or ≥18 years). Multivariable modelling explored the association between age at diagnosis and response to treatment after adjustment for disease duration, extent, and severity at baseline. Data from dose arms were pooled to evaluate similarity of therapeutic response between adolescent- and adult-onset IBD within the same trial (not between doses or across trials). Ratios of odds ratios between the two groups were evaluated. RESULTS: Data from 6,283 study participants (2,575 with Crohn's disease [CD], 3,708 with ulcerative colitis [UC]) were evaluated. Of 2,575 study participants with CD, 325 were 12-<18 years old at diagnosis; 836 participants (32.4%) received placebo. Of 3,708 participants with UC, 221 were 12-<18 years old at diagnosis; 1,212 (33%) were receiving placebo. The majority of the ratios of ORs were within two-fold, suggesting that responses in adolescent and adult-onset participants are generally similar. CONCLUSION: Data presented lend support for extrapolating efficacy of biologics from adults to adolescents with IBD, which would facilitate earlier labeling and patient access.

Fish Physiologically Based Toxicokinetic Modeling Approach for In Vitro-In Vivo and Cross-Species Extrapolation of Endocrine-Disrupting Chemicals in Risk Assessment.

High-throughput in vitro assays combined with in vitro-in vivo extrapolation (IVIVE) leverage in vitro responses to predict the corresponding in vivo exposures and thresholds of concern. The integrated approach is also expected to offer the potential for efficient tools to provide estimates of chemical toxicity to various wildlife species instead of animal testing. However, developing fish physiologically based toxicokinetic (PBTK) models for IVIVE in ecological applications is challenging, especially for plausible estimation of an internal effective dose, such as fish equivalent concentration (FEC). Here, a fish PBTK model linked with the IVIVE approach was established, with parameter optimization of chemical unbound fraction, pH-dependent ionization and hepatic clearance, and integration of temperature effect and growth dilution. The fish PBTK-IVIVE approach provides not only a more precise estimation of tissue-specific concentrations but also a reasonable approximation of FEC targeting the estrogenic potency of endocrine-disrupting chemicals. Both predictions were compared with in vivo data and were accurate for most indissociable/dissociable chemicals. Furthermore, the model can help determine cross-species variability and sensitivity among the five fish species. Using the available IVIVE-derived FEC with target pathways is helpful to develop predicted no-effect concentration for chemicals with similar mode of action and support screening-level ecological risk assessment.

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry.

BACKGROUND: In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework. AIM: To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks. METHODS: From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales). RESULTS: For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models. CONCLUSIONS: For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models. HIGHLIGHTS: For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

Methods for Spatial Extrapolation of Methane Measurements in Constructing Regional Estimates from Sample Populations.

Methane emission estimates for oil and gas facilities are typically based on estimates at a subpopulation of facilities, and these emission estimates are then extrapolated to a larger region or basin. Basin-level emission estimates are then frequently compared with basin-level observations. Methane emissions from oil and gas systems are inherently variable and intermittent, which make it difficult to determine whether a sample population is sufficiently large to be representative of a larger region. This work develops a framework for extrapolation of emission estimates using the case study of an operator in the Green River Basin. This work also identifies a new metric, the capture ratio, which quantifies the extent to which sources are represented in the sample population, based on the skewness of emissions for each source. There is a strong correlation between the capture ratio and extrapolation error, which suggests that understanding source-level emissions distributions can mitigate error when sample populations are selected and extrapolating measurements. The framework and results from this work can inform the selection and extrapolation of site measurements when developing methane emission inventories and establishing uncertainty bounds to assess whether inventory estimates are consistent with independent large spatial-scale observations.