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Forest carbon stock is an important indicator reflecting a forest ecosystem's structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion. [ABSTRACT FROM AUTHOR]

To describe visual field outcomes in the Primary Tube Versus Trabeculectomy (PTVT) Study. Cohort analysis. A total of 155 eyes (155 subjects) randomly assigned to treatment with tube shunt surgery (n = 84) or trabeculectomy with mitomycin C (n = 71). The PTVT Study was a multicenter randomized clinical trial comparing the safety and efficacy of trabeculectomy and tube shunt surgery in eyes without previous intraocular surgery. Subjects underwent standard automated perimetry (SAP) at baseline and annually for 5 years. Standard automated perimetry tests were deemed reliable if the false-positive rate was ≤ 15%. Tests were excluded if visual acuity was ≤ 20/400 or loss of ≥ 2 Snellen lines from baseline because of a nonglaucomatous etiology. Linear mixed-effects models were used to compare rates of change in SAP mean deviation (MD) between the 2 groups. Intraocular pressure (IOP) control was assessed by percentage of visits with IOP < 18 mmHg and mean IOP. Rate of change in SAP MD during follow-up. A total of 730 SAP tests were evaluated (average of 4.7 tests per eye). The average SAP MD at baseline was –12.8 ± 8.3 decibels (dB) in the tube group and –12.0 ± 8.4 dB in the trabeculectomy group (P = 0.57). The mean rate of change in SAP MD was –0.32 ± 0.39 dB/year in the trabeculectomy group and –0.47 ± 0.43 dB/year in the tube group (P = 0.23). Eyes with mean IOP 14 to 17.5 mmHg had significantly faster rates of SAP MD loss compared with eyes with mean IOP < 14 mmHg (–0.59 ± 0.13 vs. –0.27 ± 0.08 dB/year; P = 0.012), and eyes with only 50% to 75% of visits with IOP < 18 mmHg had faster rates than those with 100% of visits with IOP < 18 mmHg (–0.90 ± 0.16 vs. –0.29 ± 0.08 dB/year; P < 0.001). Multivariable analysis identified older age and worse IOP control as risk factors for faster progression in both treatment groups. No statistically significant difference in mean rates of visual field change was observed between trabeculectomy and tube shunt surgery in the PTVT Study. Worse IOP control was significantly associated with faster rates of SAP MD loss during follow-up. Older patients were also at risk for faster progression. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. [ABSTRACT FROM AUTHOR]

Longitudinal studies with binary or ordinal responses are widely encountered in various disciplines, where the primary focus is on the temporal evolution of the probability of each response category. Traditional approaches build from the generalized mixed effects modeling framework. Even amplified with nonparametric priors placed on the fixed or random effects, such models are restrictive due to the implied assumptions on the marginal expectation and covariance structure of the responses. We tackle the problem from a functional data analysis perspective, treating the observations for each subject as realizations from subject-specific stochastic processes at the measured times. We develop the methodology focusing initially on binary responses, for which we assume the stochastic processes have Binomial marginal distributions. Leveraging the logits representation, we model the discrete space processes through continuous space processes. We utilize a hierarchical framework to model the mean and covariance kernel of the continuous space processes nonparametrically and simultaneously through a Gaussian process prior and an Inverse-Wishart process prior, respectively. The prior structure results in flexible inference for the evolution and correlation of binary responses, while allowing for borrowing of strength across all subjects. The modeling approach can be naturally extended to ordinal responses. Here, the continuation-ratio logits factorization of the multinomial distribution is key for efficient modeling and inference, including a practical way of dealing with unbalanced longitudinal data. The methodology is illustrated with synthetic data examples and an analysis of college students’ mental health status data. [ABSTRACT FROM AUTHOR]

Introduction: Gene regulatory networks (GRNs) are characterized by their dynamism, meaning that the regulatory interactions which constitute these networks evolve with time. Identifying when changes in the GRN architecture occur can inform our understanding of fundamental biological processes, such as disease manifestation, development, and evolution. However, it is usually not possible to know a priori when a change in the network architecture will occur. Furthermore, an architectural shift may alter the underlying noise characteristics, such as the process noise covariance. Methods: We develop a fully Bayesian hierarchical model to address the following: a) sudden changes in the network architecture; b) unknown process noise covariance which may change along with the network structure; and c) unknown measurement noise covariance. We exploit the use of conjugate priors to develop an analytically tractable inference scheme using Bayesian sequential Monte Carlo (SMC) with a local Gibbs sampler. Results: Our Bayesian learning algorithm effectively estimates time-varying gene expression levels and architectural model indicators under varying noise conditions. It accurately captures sudden changes in network architecture and accounts for time-evolving process and measurement noise characteristics. Our algorithm performs well even under high noise conditions. By incorporating conjugate priors, we achieve analytical tractability, enabling robust inference despite the inherent complexities of the system. Furthermore, our method outperforms the standard particle filter in all test scenarios. Discussion: The results underscore our method's efficacy in capturing architectural changes in GRNs. Its ability to adapt to a range of time-evolving noise conditions emphasizes its practical relevance for real-world biological data, where noise presents a significant challenge. Overall, our method provides a powerful tool for studying the dynamics of GRNs and has the potential to advance our understanding of fundamental biological processes. [ABSTRACT FROM AUTHOR]

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

Aim: At fine scales (<10 km in spatial resolution), only a few studies have evaluated the relationships between abiotic factors and the horizontal occurrence of vascular epiphytes. At fine scales, variation in abiotic variables can be attributed to topographic heterogeneity. For example, air humidity and wind speed are likely to vary along ridge–valley gradients, and differences in slope aspect cause variation in light intensity as well as temperature gradient along an elevation. We tested the hypothesis that the horizontal epiphyte occurrence at fine scales is influenced more by topographic gradients related to abiotic factors than by host tree size and species. We also assessed air humidity variation along the ridge–valley gradient at our study site as a way to explore its possible correlation with the epiphyte occurrence. Location: A coniferous/broadleaf evergreen mixed forest in a humid temperate zone of Kochi, southwest Japan. Methods: We surveyed epiphyte occurrence on 310 trees and recorded host tree size and species within a 52‐ha watershed with an elevational range of 378–777 m a.s.l. Epiphyte occurrence was modeled using three topographic variables (ridge–valley gradient, slope aspect differences and elevational gradient), host tree size and species. The effect sizes of each variable were then compared. Air humidity was measured on the valley bottom, slope and ridge throughout the year. Results: Epiphyte occurrence probability increased toward the valley bottom and on larger host trees; it also varied among host tree species. Slope aspect differences and elevational gradient were less effective. The ridge–valley gradient had a greater effect than host tree size on epiphyte occurrence. Air humidity was highest at the valley bottom and lowest on the ridge. Conclusions: Horizontal epiphyte occurrence was driven more by topographic gradient than by host tree size and species in a small watershed covered by a warm‐temperate broadleaf forest. This finding suggests the importance of forests at valley bottoms to support epiphyte occurrence. [ABSTRACT FROM AUTHOR]

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population’s exposure. Software for implement is provided in the R package nbRegQF [ABSTRACT FROM AUTHOR]

Abstract Background Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. Methods Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. Results In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. Conclusion Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint.

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Background: Surrogate endpoints, such as those of interest in chronic kidney disease (CKD), are often evaluated using Bayesian meta-regression. Trials used for the analysis can evaluate a variety of interventions for different sub-classifications of disease, which can introduce two additional goals in the analysis. The first is to infer the quality of the surrogate within specific trial subgroups defined by disease or intervention classes. The second is to generate more targeted subgroup-specific predictions of treatment effects on the clinical endpoint. Methods: Using real data from a collection of CKD trials and a simulation study, we contrasted surrogate endpoint evaluations under different hierarchical Bayesian approaches. Each approach we considered induces different assumptions regarding the relatedness (exchangeability) of trials within and between subgroups. These include partial-pooling approaches, which allow subgroup-specific meta-regressions and, yet, facilitate data adaptive information sharing across subgroups to potentially improve inferential precision. Because partial-pooling models come with additional parameters relative to a standard approach assuming one meta-regression for the entire set of studies, we performed analyses to understand the impact of the parameterization and priors with the overall goals of comparing precision in estimates of subgroup-specific meta-regression parameters and predictive performance. Results: In the analyses considered, partial-pooling approaches to surrogate endpoint evaluation improved accuracy of estimation of subgroup-specific meta-regression parameters relative to fitting separate models within subgroups. A random rather than fixed effects approach led to reduced bias in estimation of meta-regression parameters and in prediction in subgroups where the surrogate was strong. Finally, we found that subgroup-specific meta-regression posteriors were robust to use of constrained priors under the partial-pooling approach, and that use of constrained priors could facilitate more precise prediction for clinical effects in trials of a subgroup not available for the initial surrogacy evaluation. Conclusion: Partial-pooling modeling strategies should be considered for surrogate endpoint evaluation on collections of heterogeneous studies. Fitting these models comes with additional complexity related to choosing priors. Constrained priors should be considered when using partial-pooling models when the goal is to predict the treatment effect on the clinical endpoint. [ABSTRACT FROM AUTHOR]

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared. [ABSTRACT FROM AUTHOR]

In mixed‐stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non‐overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad‐scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad‐scale or regional baselines. We demonstrate the utility of our integrated multistage model using a synthesized data set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North Bering Sea of Alaska. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high‐resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost‐effective than updating range‐wide baselines with all regionally important markers. [ABSTRACT FROM AUTHOR]

This article considers one-off actions for a football coach who is asking for a specific outcome from a play. This will be in the form of a minimum gain in yards, usually in order to gain a first down. Using a random utility model approach we propose the play to be called is the one which maximizes the probability of the desired outcome. We specifically focus on pass plays, which requires the modeling of outcomes in terms of yards gained, for which we use the family of generalized gamma distributions. The data and results relate to the Fall 2021 Presbyterian College football team, in which we leverage specific information pertaining to the offensive playbook. [ABSTRACT FROM AUTHOR]

When using Bayesian hierarchical modeling, a popular approach for Item Response Theory (IRT) models, researchers typically face a tradeoff between the precision and accuracy of the item parameter estimates. Given the pooling principle and variance-dependent shrinkage, the expected behavior of Bayesian hierarchical IRT models is to deliver more precise but biased item parameter estimates, compared to those obtained in nonhierarchical models. Previous research, however, points out the possibility that, in the context of the two-parameter logistic IRT model, the aforementioned tradeoff has not to be made. With a comprehensive simulation study, we provide an in-depth investigation into this possibility. The results show a superior performance, in terms of bias, RMSE and precision, of the hierarchical specifications compared to the nonhierarchical counterpart. Under certain conditions, the bias in the item parameter estimates is independent of the bias in the variance components. Moreover, we provide a bias correction procedure for item discrimination parameter estimates. In sum, we show that IRT models create a unique situation where the Bayesian hierarchical approach indeed yields parameter estimates that are not only more precise, but also more accurate, compared to nonhierarchical approaches. We discuss this beneficial behavior from both theoretical and applied point of views. [ABSTRACT FROM AUTHOR]

A hierarchical Bayesian method is proposed that can be used to fit multiple psychometric functions (PFs) simultaneously across conditions and subjects. The method incorporates the generalized linear model and allows easy reparameterization of the parameters of the PFs, for example, to constrain parameter values across conditions or to code for experimental effects (e.g., main effects and interactions in a factorial design). Simulations indicate that fitting PFs for multiple conditions and observers simultaneously using the hierarchical structure effectively eliminates bias and improves precision in parameter estimates relative to fitting PFs individually in each condition. The method is further validated by analyzing human psychophysical data obtained in an experiment investigating the effect of attention on correspondence matching in an ambiguous long-range motion display. The method converges successfully, even for experiments that use a low number of trials per subject, without the need for fine-tuning by the user and while using the default essentially uninformative priors. The latter may make the method more acceptable to those critical of applying informative priors. The method is implemented in the freely downloadable Palamedes Toolbox, which also includes routines that graphically display the fitted psychometric functions alongside the data, and derive and display posterior distributions of parameters, summary statistics, and diagnostic measures. Overall, these features make hierarchical Bayesian modeling of PFs easily available to researchers who wish to use Bayesian statistics but lack the expertise to implement these methods themselves. [ABSTRACT FROM AUTHOR]

People's cooperativeness depends on many factors, such as their motives, cognition, experiences, and the situation they are in. To date, it is unclear how these factors interact and shape the decision to cooperate. We present a computational account of cooperation that not only provides insights for the design of effective incentive structures but also redefines neglected social-cognitive characteristics associated with attention-deficit hyperactivity disorder (ADHD). Leveraging game theory, we demonstrate that the source and magnitude of conflict between different motives affected the speed and frequency of cooperation. Integrating eye-tracking to measure motivation-based information processing during decision-making shows that participants' visual fixations on the gains of cooperation rather than its costs and risks predicted their cooperativeness on a trial-by-trial basis. Using Bayesian hierarchical modeling, we find that a situation's prosociality and participants' past experience each bias the decision-making process distinctively. ADHD characteristics explain individual differences in responsiveness across contexts, highlighting the clinical importance of experimentally studying reactivity in social interactions. We demonstrate how the use of eye-tracking and computational modeling can be used to experimentally investigate social-cognitive characteristics in clinical populations. We also discuss possible underlying neural mechanisms to be investigated in future studies. [ABSTRACT FROM AUTHOR]

Hierarchical Bayesian modeling is beneficial when complex models with many parameters of the same type, such as item response theory (IRT) models, are to be estimated with sparse data. Recently, Koenig et al. (Applied Psychological Measurement, 44, 311–326, 2020) illustrated in an optimized hierarchical Bayesian two-parameter logistic model (OH2PL) how to avoid bias due to unintended shrinkage or degeneracies of the posterior, and how to benefit from this approach in small samples. The generalizability of their findings, however, is limited because they investigated only a single specification of the hyperprior structure. Consequently, in a comprehensive simulation study, we investigated the robustness of the performance of the novel OH2PL in several specifications of their hyperpriors under a broad range of data conditions. We show that the novel OH2PL in the half-Cauchy or Exponential configuration yields unbiased (in terms of bias) model parameter estimates in small samples of N = 50. Moreover, it outperforms (especially in terms of the RMSE of the item discrimination parameters) marginal maximum likelihood (MML) estimation and its nonhierarchical counterpart. This further corroborates the possibility that hierarchical Bayesian IRT models behave differently than general hierarchical Bayesian models. We discuss these results regarding the applicability of complex IRT models in small-scale situations typical in psychological research, and illustrate the extended applicability of the 2PL IRT model with an empirical example. [ABSTRACT FROM AUTHOR]

Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomized survey (reference) sample by increasing the effective sample size. Estimation of a population quantity derived from a convenience sample will typically result in bias since the distribution of variables of interest in the convenience sample is different from the population distribution. A recent set of approaches estimates inclusion probabilities for convenience sample units by specifying reference sample-weighted pseudo likelihoods. This paper introduces a novel approach that derives the propensity score for the observed sample as a function of inclusion probabilities for the reference and convenience samples as our main result. Our approach allows specification of a likelihood directly for the observed sample as opposed to the approximate or pseudo likelihood. We construct a Bayesian hierarchical formulation that simultaneously estimates sample propensity scores and the convenience sample inclusion probabilities. We use a Monte Carlo simulation study to compare our likelihood based results with the pseudo likelihood based approaches considered in the literature. [ABSTRACT FROM AUTHOR]

Introduction: Gene regulatory networks (GRNs) are characterized by their dynamism, meaning that the regulatory interactions which constitute these networks evolve with time. Identifying when changes in the GRN architecture occur can inform our understanding of fundamental biological processes, such as disease manifestation, development, and evolution. However, it is usually not possible to know a priori when a change in the network architecture will occur. Furthermore, an architectural shift may alter the underlying noise characteristics, such as the process noise covariance.Methods: We develop a fully Bayesian hierarchical model to address the following: a) sudden changes in the network architecture; b) unknown process noise covariance which may change along with the network structure; and c) unknown measurement noise covariance. We exploit the use of conjugate priors to develop an analytically tractable inference scheme using Bayesian sequential Monte Carlo (SMC) with a local Gibbs sampler.Results: Our Bayesian learning algorithm effectively estimates time-varying gene expression levels and architectural model indicators under varying noise conditions. It accurately captures sudden changes in network architecture and accounts for time-evolving process and measurement noise characteristics. Our algorithm performs well even under high noise conditions. By incorporating conjugate priors, we achieve analytical tractability, enabling robust inference despite the inherent complexities of the system. Furthermore, our method outperforms the standard particle filter in all test scenarios.Discussion: The results underscore our method’s efficacy in capturing architectural changes in GRNs. Its ability to adapt to a range of time-evolving noise conditions emphasizes its practical relevance for real-world biological data, where noise presents a significant challenge. Overall, our method provides a powerful tool for studying the dynamics of GRNs and has the potential to advance our understanding of fundamental biological processes.

Purpose: To compare how linear mixed models (LMMs) using Gaussian, Student t, and log-gamma (LG) random effect distributions estimate rates of structural loss in a glaucomatous population using OCT and to compare model performance to ordinary least squares (OLS) regression. Design: Retrospective cohort study. Subjects: Patients in the Bascom Palmer Glaucoma Repository (BPGR). Methods: Eyes with ≥ 5 reliable peripapillary retinal nerve fiber layer (RNFL) OCT tests over ≥ 2 years were identified from the BPGR. Retinal nerve fiber layer thickness values from each reliable test (signal strength ≥ 7/10) and associated time points were collected. Data were modeled using OLS regression as well as LMMs using different random effect distributions. Predictive modeling involved constructing LMMs with (n – 1) tests to predict the RNFL thickness of subsequent tests. A total of 1200 simulated eyes of different baseline RNFL thickness values and progression rates were developed to evaluate the likelihood of declared progression and predicted rates. Main Outcome Measures: Model fit assessed by Watanabe–Akaike information criterion (WAIC) and mean absolute error (MAE) when predicting future RNFL thickness values; log-rank test and median time to progression with simulated eyes. Results: A total of 35 862 OCT scans from 5766 eyes of 3491 subjects were included. The mean follow-up period was 7.0 ± 2.3 years, with an average of 6.2 ± 1.4 tests per eye. The Student t model produced the lowest WAIC. In predictive models, all LMMs demonstrated a significant reduction in MAE when estimating future RNFL thickness values compared with OLS (P < 0.001). Gaussian and Student t models were similar and significantly better than the LG model in estimating future RNFL thickness values (P < 0.001). Simulated eyes confirmed LMM performance in declaring progression sooner than OLS regression among moderate and fast progressors (P

Abstract In mixed‐stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non‐overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad‐scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad‐scale or regional baselines. We demonstrate the utility of our integrated multistage model using a synthesized data set made up of Chinook salmon, Oncorhynchus tshawytscha, from the North Bering Sea of Alaska. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high‐resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost‐effective than updating range‐wide baselines with all regionally important markers.

Rigorous evaluation of surrogate endpoints is performed in a trial‐level analysis in which the strength of the association between treatment effects on the clinical and surrogate endpoints is quantified across a collection of previously conducted trials. To reduce bias in measures of the performance of the surrogate, the statistical model must account for the sampling error in each trial's estimated treatment effects and their potential correlation. Unfortunately, these within‐study correlations can be difficult to obtain, especially for meta‐analysis of published trial results where individual patient data is not available. As such, these terms are frequently partially or completely missing in the analysis. We show that improper handling of these missing terms can meaningfully alter the perceived quality of the surrogate and we introduce novel strategies to handle the missingness. [ABSTRACT FROM AUTHOR]

Incomplete data are unavoidable and pose serious complications in statistical analyses of epidemiological research. However, many researchers still ignore these complications and proceed with complete case analysis. In this paper, we propose modern procedures to handle incomplete spatially correlated data. We illustrate these procedures with an application to spatially-referenced HIV data from Ghana. The main goal was to identify significant risk factors of HIV prevalence in Ghana via a Bayesian spatial hierarchical modeling approach. Results revealed substantial differences in parameter estimates and highest posterior density intervals widths obtained using our approach and that of complete case analysis. Based on these findings, our approach provides more accurate parameter estimates, reduces the chances of committing a type I error, and has higher predictive efficiency. In addition, results from these findings identified higher-risk regions that require targeted interventions by government to help reduce the spread of HIV in Ghana. [ABSTRACT FROM AUTHOR]

The seminal Bradley-Terry model exhibits transitivity, that is, the property that the probabilities of player A beating B and B beating C give the probability of A beating C, with these probabilities determined by a skill parameter for each player. Such transitive models do not account for different strategies of play between each pair of players, which gives rise to intransitivity. Various intransitive parametric models have been proposed but they lack the flexibility to cover the different strategies across n players, with the O (n 2) values of intransitivity modeled using O (n) parameters, while they are not parsimonious when the intransitivity is simple. We overcome their lack of adaptability by allocating each pair of players to one of a random number of K intransitivity levels, each level representing a different strategy. Our novel approach for the skill parameters involves having the n players allocated to a random number of A < n distinct skill levels, to improve efficiency and avoid false rankings. Although we may have to estimate up to O (n 2) unknown parameters for (A , K) we anticipate that in many practical contexts A + K < n . Our semiparametric model, which gives the Bradley-Terry model when (A = n − 1 , K = 0) , is shown to have an improved fit relative to the Bradley-Terry, and the existing intransitivity models, in out-of-sample testing when applied to simulated and American League baseball data. for the article areavailable online. [ABSTRACT FROM AUTHOR]

Government statistical agencies compose a population statistic for a given domain using a sample of units nested in that domain. Subsequent modeling of these domain survey estimates is often used to "borrow strength" across a dependence structure among the domains to improve estimation accuracy and efficiency. This paper focuses on models jointly defined for sample-based point estimates along with their sample-based estimates of variances. Bias may be present in the sample-based (observed) variances due to small sample sizes or the estimation procedure. We propose a new formulation that extends existing joint model formulations to allow for a multiplicative bias in observed variances. Our approach capitalizes on the unbiasedness property of point estimates. We utilize a nonparametric mixture construction that allows the data to discover distinct bias regimes. As a consequence of the better variance estimation, domain point estimates are more robustly estimated under a joint model for the domain point estimates and their associated variances. We compare the performances of alternative models in application to estimates of total employment from the Current Employment Statistics survey conducted by the US Bureau of Labor Statistics, and in simulations. [ABSTRACT FROM AUTHOR]

Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging‐related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real‐world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade‐off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects. [ABSTRACT FROM AUTHOR]

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer's property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer's land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer's field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research. [ABSTRACT FROM AUTHOR]

When producing creative ideas (i.e., ideas that are original and useful) two main processes occur: ideation, where people brainstorm ideas, and evaluation, where they decide if the ideas are creative or not. While much is known about the ideation phase, the cognitive processes involved in creativity evaluation are less clear. In this article, we present a novel modeling approach for the evaluation phase of creativity. We apply the drift diffusion model (DDM) to the Creative-or-Not task (CON-task) to study the cognitive basis of evaluation and to examine individual differences in the extent to which people take originality and utility into account when evaluating creative ideas. The CON-task is a timed decision-making task where participants indicate whether they find uses for certain objects creative or not (e.g., using a book as a buoy). The different uses vary on the two creativity dimensions "originality" and "utility." In two studies (n = 293, 17,806 trials; n = 152, 9,291 trials), we found that stimulus originality was strongly related to participants' drift rates but found only weak evidence for an association between stimulus utility and the drift rate. However, participants differed substantially in the effects of originality and utility. Furthermore, the implicit weights assigned to originality and utility on the CON-task were associated with self-reported importance ratings of originality and utility and with divergent thinking performance in the Alternative Uses task (AUT). This research provides a cognitive modeling approach to creativity evaluation and underlines the importance of communicating rating criteria in divergent thinking tasks to ensure a fair assessment of creative ability. [ABSTRACT FROM AUTHOR]

Disease surveillance data are important for monitoring disease burden and occurrence, and for informing a wide range of efforts to improve population health. Surveillance for infectious diseases may be conducted passively, relying on reports from healthcare facilities, or actively, involving surveys of the population at risk. Passive surveillance typically provides wide spatial coverage, but is subject to biases arising from differences in care-seeking behavior, diagnostic practices, and under-reporting. Active surveillance minimizes these biases, but is typically constrained to small areas and subpopulations due to resource limitations. Methods based on linkage of individual records between passive and active surveillance datasets provide a means to estimate and correct for the biases of each system, leveraging the size and coverage of passive surveillance and the quality of data in active surveillance. We develop a spatial Bayesian hierarchical model for bias-correcting data from both systems to yield an improved estimate of disease measures after adjusting for under-ascertainment. We apply the framework to data from a passive and an active surveillance system for pulmonary tuberculosis (PTB) in Sichuan, China, and estimate the average sensitivity of the active surveillance system at 70% (95% credible interval: 62%, 78%), and the passive system at 30% (95% CI: 24%, 35%). Passive surveillance sensitivity exhibited considerable spatial variability, and was positively associated with a site's gross domestic product per capita. Bias-corrected estimates of county-level PTB prevalence in the province in 2010 identified regions in the southeast with the highest PTB burden, yielding different geographic priorities than previous reports.

Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors.

This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13–90% accuracy improvement, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least.

Many surveys have suggested that U.S. public library usage correlates with socioeconomic status and race; persons identifying as Non-Hispanic White, highly-educated, and affluent claim to use library services at a rate greater than those not belonging to these groups. These findings suggests that the dominant model of library service—a brick and mortar localized service point—interfaces poorly with marginalized and disenfranchised persons. This paper combines Public Library Survey and American Community Survey data for 49 U.S. states to explore the relationship between socioeconomic variables and public library usage. Using Bayesian hierarchical modeling, this paper finds corroboration for these surveys at the “community-level,” i.e., communities with higher socioeconomic status and more Non-Hispanic Whiteness are associated with greater levels of library activity. It also finds, using propensity score matching, that local funding is strongly associated with library activity, underscoring the importance of funding advocacy. The analysis suggests that U.S. library administrators who aim to reach marginalized and disenfranchised persons might want to consider service models alternate to the dominant brick and mortar model.

In many large-scale inverse problems, such as computed tomography and image deblurring, characterization of sharp edges in the solution is desired. Within the Bayesian approach to inverse problems, edge-preservation is often achieved using Markov random field priors based on heavy-tailed distributions. Another strategy, popular in sparse statistical modeling, is the application of hierarchical shrinkage priors. An advantage of this formulation lies in expressing the prior as a conditionally Gaussian distribution depending on global and local hyperparameters which are endowed with heavy-tailed hyperpriors. In this work, we revisit the shrinkage horseshoe prior and introduce its formulation for edge-preserving settings. We discuss a Gibbs sampling framework to solve the resulting hierarchical formulation of the Bayesian inverse problem. In particular, one of the conditional distributions is high-dimensional Gaussian, and the rest are derived in closed form by using a scale mixture representation of the heavy-tailed hyperpriors. Applications from imaging science show that our computational procedure is able to compute sharp edge-preserving posterior point estimates with reduced uncertainty. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non‐rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women. [ABSTRACT FROM AUTHOR]

Why are we so slow in choosing the lesser of 2 evils? We considered whether such slowing relates to uncertainty about the value of these options, which arises from the tendency to avoid them during learning, and whether such slowing relates to frontosubthalamic inhibitory control mechanisms. In total, 49 participants performed a reinforcement-learning task and a stop-signal task while fMRI was recorded. A reinforcement-learning model was used to quantify learning strategies. Individual differences in lose-lose slowing related to information uncertainty due to sampling, and independently, to less efficient response inhibition in the stop-signal task. Neuroimaging analysis revealed an analogous dissociation: subthalamic nucleus (STN) BOLD activity related to variability in stopping latencies, whereas weaker frontosubthalamic connectivity related to slowing and information sampling. Across tasks, fast inhibitors increased STN activity for successfully canceled responses in the stop task, but decreased activity for lose-lose choices. These data support the notion that fronto-STN communication implements a rapid but transient brake on response execution, and that slowing due to decision uncertainty could result from an inefficient release of this "hold your horses" mechanism.

Abstract Background Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. Results We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. Conclusions We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

As a social species, ready exchange with peers is a pivotal asset - our “social capital”. Yet, single-person households have come to pervade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in ∼40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Both positive effects, equating to greater gray matter volume associated with living alone, and negative effects, which can be interpreted as greater gray matter associations with not living alone, were found across the cortex and subcortical structures Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centered on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural pattern of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The persistent trend for solitary living will require new answers from public-health decision makers. Significance statement: Living alone has profound consequences for mental and physical health. Despite this, there has been a rapid increase in single-person households worldwide, with the long-term consequences yet unknown. In the largest study of its kind, we investigate how the objective lack of everyday social interaction, through living alone, manifests in the brain. Our population neuroscience approach uncovered a gray matter signature that converged on the 'default network', alongside targeted subcortical, sex and demographic profiling analyses. The human urge for social relationships is highlighted by the evolving COVID-19 pandemic. Better understanding of how social isolation relates to the brain will influence health and social policy decision-making of pandemic planning, as well as social interventions in light of global shifts in houseful structures.

Consideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark‐resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic‐breeding shorebird of conservation concern, the red knot (Calidris canutus rufa), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs (Limulus polyphemus), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring. [ABSTRACT FROM AUTHOR]

Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model. We generalize stacking to Bayesian hierarchical stacking. The model weights are varying as a function of data, partially-pooled, and inferred using Bayesian inference. We further incorporate discrete and continuous inputs, other structured priors, and time series and longitudinal data. To verify the performance gain of the proposed method, we derive theory bounds, and demonstrate on several applied problems. [ABSTRACT FROM AUTHOR]

The state and dynamics of river chemistry are influenced by both anthropogenic and natural catchment characteristics. However, understanding key controls on catchment mean concentrations and export patterns comprehensively across a wide range of climate zones is still lacking, as most of this research is focused on temperate regions. In this study, we investigate the catchment controls on mean concentrations and export patterns (concentration–discharge relationship, C–Q slope) of river chemistry, using a long‐term data set of up to 507 sites spanning five climate zones (i.e., arid, Mediterranean, temperate, subtropical, tropical) across the Australian continent. We use Bayesian model averaging (BMA) and hierarchical modeling (BHM) approaches to predict the mean concentrations and export patterns and compare the relative importance of 26 catchment characteristics (e.g., topography, climate, land use, land cover, soil properties and hydrology). Our results demonstrate that mean concentrations result from the interaction of catchment indicators and anthropogenic factors (i.e., land use, topography and soil), while export patterns are influenced by topography. We also found that incorporating the effects of climate zones in a BHM framework improved the predictability of both mean concentrations and C–Q slopes, suggesting the importance of climatic controls on hydrological and biogeochemical processes. Our study provides insights into the contrasting effects of catchment controls across different climate zones. Investigating those controls can inform sustainable water quality management strategies that consider the potential changes in river chemistry state and export behavior. Plain Language Summary: Riverine water quality can change markedly between locations. Understanding the underlying causes of these differences is of great importance for water quality management. In this study, we analyze water quality monitoring data from 507 catchments across the Australian continent, spanning five major climate zones, using a statistical modeling approach. We identify the key catchment characteristics influencing water quality differences between rivers and develop models that predict these differences. We find that land use, topography and soil properties have the highest impact on water quality, while topography tends to control the patterns of solute export. This work provides data‐driven evidence for catchment managers, which can help them develop effective water quality management strategies. Key Points: Consideration of climate zones in a hierarchical modeling structure improves the predictability of both mean concentrations and C–Q slopesLand use, topography and soil are the most influential factors for mean concentrations, while topography controls export patternsThe influence of catchment controls on mean concentrations/C–Q slopes varies across climates zones [ABSTRACT FROM AUTHOR]

A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator bacteria (FIB) and pathogenic bacteria persist similarly in surface waters, but median virus and protozoa persistence metrics may be 2–3 times greater than FIB in similar conditions. The two-parameter model underpinning the approach was used to identify drivers of these differences. Virus decay rates were shown to taper off more quickly than FIB, whereas protozoa were associated with longer initial periods of minimal decay. Despite the low accuracy of the hierarchical model compared to models fit to individual datasets, this approach addresses a critical gap for water management decision-making as site-specific and pathogen-specific persistence data are uncommon in water monitoring practices. • Indicators and pathogens persist longer at colder temperatures. • Decay of viruses and protozoa may be more than 3x slower than that of FIB. • A generalizable persistence model can support surface water management. [ABSTRACT FROM AUTHOR]

Abstract Consideration of the full annual cycle population dynamics can provide useful insight for conservation efforts, but collecting data needed to estimate demographic parameters is often logistically difficult. For species that breed in remote areas, monitoring is often conducted during migratory stopover or at nonbreeding sites, and the recruitment rate of new breeding adults can be difficult to estimate directly. Here, we present an integrated population model that uses mark‐resight and count data to estimate survival probability, population growth rate, and recruitment rate for an Arctic‐breeding shorebird of conservation concern, the red knot (Calidris canutus rufa), from data collected during spring stopover in Delaware Bay, USA, from 2005 to 2018. At this site, red knots feed primarily on the eggs of spawning horseshoe crabs (Limulus polyphemus), a legally harvested species. We used this model to estimate the relationship between horseshoe crab abundance and red knot demographics, which informed a recent revision to the framework used to establish horseshoe crab harvest regulations. Our analysis indicates that the red knot population was most likely stable from 2005 to 2018 (average λ = 1.03, 95% credible interval [CRI]: 0.961, 1.15) despite low recruitment rates (average ρ = 0.088, 95% CRI: 0.012, 0.18). Adult survival probability was positively associated with horseshoe crab abundance in the same year (β = 0.35, 95% CRI: 0.09, 0.63), but we found no effect of horseshoe crab abundance two years previously on recruitment of new adults (β = −0.08, 95% CRI: −0.41, 0.38). Our approach demonstrates the utility of integrated population models for understanding population dynamics, even when data are only available from migratory stopover monitoring.

Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy in a sanitary environment are more susceptible to hepatitis A because they do not have the opportunity to acquire natural immunity. In Korea, hepatitis A is prevalent because of the distribution of uncooked seafood, especially during hot and humid summers. In general, the transmission of hepatitis A is known to be dynamically affected by socioeconomic, environmental, and weather-related factors and is heterogeneous in time and space. In this study, we aimed to investigate the spatio-temporal variation of hepatitis A and the effects of socioeconomic and weather-related factors in Korea using a flexible spatio-temporal model. We propose a Bayesian Poisson regression model coupled with spatio-temporal variability to estimate the effects of risk factors. We used weekly hepatitis A incidence data across 250 districts in Korea from 2016 to 2019. We found spatial and temporal autocorrelations of hepatitis A indicating that the spatial distribution of hepatitis A varied dynamically over time. From the estimation results, we noticed that the districts with large proportions of males and foreigners correspond to higher incidences. The average temperature was positively correlated with the incidence, which is in agreement with other studies showing that the incidences in Korea are noticeable in spring and summer due to the increased outdoor activity and intake of stale seafood. To the best of our knowledge, this study is the first to suggest a spatio-temporal model for hepatitis A across the entirety of Korean. The proposed model could be useful for predicting, preventing, and controlling the spread of hepatitis A.

Abstract There has been a steady rise in the use of dormant propagules to study biotic responses to environmental change over time. This is particularly important for organisms that strongly mediate ecosystem processes, as changes in their traits over time can provide a unique snapshot into the structure and function of ecosystems from decades to millennia in the past. Understanding sources of bias and variation is a challenge in the field of resurrection ecology, including those that arise because often‐used measurements like seed germination success are imperfect indicators of propagule viability. Using a Bayesian statistical framework, we evaluated sources of variability and tested for zero‐inflation and overdispersion in data from 13 germination trials of soil‐stored seeds of Schoenoplectus americanus, an ecosystem engineer in coastal salt marshes in the Chesapeake Bay. We hypothesized that these two model structures align with an ecological understanding of dormancy and revival: zero‐inflation could arise due to failed germinations resulting from inviability or failed attempts to break dormancy, and overdispersion could arise by failing to measure important seed traits. A model that accounted for overdispersion, but not zero‐inflation, was the best fit to our data. Tetrazolium viability tests corroborated this result: most seeds that failed to germinate did so because they were inviable, not because experimental methods failed to break their dormancy. Seed viability declined exponentially with seed age and was mediated by seed provenance and experimental conditions. Our results provide a framework for accounting for and explaining variability when estimating propagule viability from soil‐stored natural archives which is a key aspect of using dormant propagules in eco‐evolutionary studies.

Early detection of dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities. [ABSTRACT FROM AUTHOR]

Until recently, there has been a focus on exploring the influence of average blood pressure (BP) on risk of mortality. We go beyond average BP to also investigate mortality risk with respect to variation in BP over 2 timescales-short-term variation among multiple measures at 1 visit, and medium-term variation among the measures at 2 visits several months apart. We present an application of Bayesian hierarchical modeling to the problem of estimating the effect of BP variability on all-cause and cardiovascular mortality. We use data from the Third National Health and Nutrition Examination Survey linked with up to 27 years of mortality follow-up. We find that medium-term systolic BP variability had a very significant predictive value for all-cause mortality in addition to mortality from cardiovascular disease, cerebrovascular disease and heart-attacks combined, approximately 1/3 as large as the well-established impact of mean systolic BP. Medium-term diastolic variability had an additional, although smaller, predictive effect. Short-term variability, in contrast, had little or no measurable predictive value. The medium-term variability effect persisted when controlling for Framingham Risk Score., Competing Interests: Declaration of competing interest Dr. Steinsaltz reports financial support was provided by Biotechnology and Biological Sciences Research Council. The remaining authors have no competing interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer’s property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer’s land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer’s field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research.

Background: Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. Results: We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. Conclusions: We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach. [ABSTRACT FROM AUTHOR]