In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from the rodent medial prefrontal cortex [J. Henke et al., eLife10, e71612 (2021)] during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the \"past cells\" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the \"future cells\" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Mining and updating the post-marketing safety signals of esketamine nasal spray for better identification of adverse drug event (ADE) signals and medication monitoring during clinical use to ensure patient medication safety. Downloading data from the US Food and Drug Administration Adverse Event Reporting System from Q1 2019 to Q2 2023, the reporting odds ratio, proportional reporting ratio, Multi-item Gamma Poisson Shrinker, and Bayesian Confidence Propagation Neural Network methods of the disproportionality method were used to mine and analyze ADEs, and finally to screen for signals of ADEs with esketamine nasal spray as the primary suspected drug. The Preferred Terminology of the Medical Dictionary of Regulatory Activities (version 26.0) was used to standardize the description of ADEs and to attribute ADEs to the System Organ Classification. A total of 5132 ADEs reports of esketamine nasal spray as the primary suspected drug were obtained from the Food and Drug Administration Adverse Event Reporting System. The most frequently observed ADEs are dissociation, sedation, and hypertension, while some new rare signals have been detected, such as interstitial cystitis, substance abuse, and drug diversion. The present study identified significant new ADEs signals for esketamine nasal spray, which may provide a source for healthcare professionals to assess patients\' symptoms and risk identification.

BACKGROUND: To determine the relationship between mixed exposure to three types of endocrine-disrupting chemicals (EDCs), namely phenyl hydroxides, polycyclic aromatic hydrocarbons (PAHs), and phthalates (PAEs), and risk of arthritis.
METHODS: Participants were selected from National Health and Nutrition Examination Survey (NHANES). The relationships between the urinary concentrations of phenyl hydroxides, PAHs, and PAEs and the risk of arthritis were analyzed by generalized linear regression model. The mixed exposure to these EDCs and the risk of arthritis was analyzed by weighted quantile sums (WQSs) and Bayesian kernel machine regression (BKMR) model.
RESULTS: Our analysis showed that participants with urinary benzophenone-3 and methylparaben concentrations in the highest quartile (Q4) had an increased risk of arthritis compared with those in Q1. For each one-unit increase in the natural logarithm-converted urinary concentrations of 1-hydroxynapthalene and 2-hydroxynapthalene, the risk of arthritis increased by 5% and 8%, respectively. Chemical mixing index coefficients were significantly associated with risk of arthritis in both WQS positive- and negative-constraint models. In the BKMR model, there was a significant positive correlation between mixed exposure and the risk of arthritis.
CONCLUSIONS: Mixed exposure to phenyl hydroxides, PAHs, and PAEs increased the risk of arthritis, with exposure to PAHs being the key factor.

BACKGROUND: To inform global ambitions to end AIDS, evaluation of progress toward HIV incidence reduction requires robust methods to measure incidence. Although HIV diagnosis date in routine HIV/AIDS surveillance systems are often used as a surrogate marker for incidence, it can be misleading if acquisition of transmission occurred years before testing. Other information present in data such as antibody testing dates, avidity testing result, and CD4 counts can assist, but the degree of missing data is often prohibitive.
METHODS: We constructed a Bayesian statistical model to estimate the annual proportion of first ever HIV diagnoses in Scotland (period 2015-2019) that represent recent HIV infection (ie, occurring within the previous 3-4 months) and incident HIV infection (ie, infection within the previous 12 months), by synthesizing avidity testing results and surveillance data on the interval since last negative HIV test.
RESULTS: Over the 5-year analysis period, the model-estimated proportion of incident infection was 43.9% (95% CI: 40.9 to 47.0), and the proportion of recent HIV infection was 21.6% (95% CI: 19.1 to 24.1). Among the mode of HIV acquisition categories, the highest proportion of recent infection was estimated for people who inject drugs: 27.4% (95% CI: 20.4 to 34.4).
CONCLUSIONS: The Bayesian approach is appropriate for the high prevalence of missing data that can occur in routine surveillance data sets. The proposed model will aid countries in improving their understanding of the number of people who have recently acquired their infection, which is needed to progress toward the goal of HIV transmission elimination.

Prior distributions, which represent one\'s belief in the distributions of unknown parameters before observing the data, impact Bayesian inference in a critical and fundamental way. With the ability to incorporate external information from expert opinions or historical datasets, the priors, if specified appropriately, can improve the statistical efficiency of Bayesian inference. In survival analysis, based on the concept of unit information (UI) under parametric models, we propose the unit information Dirichlet process (UIDP) as a new class of nonparametric priors for the underlying distribution of time-to-event data. By deriving the Fisher information in terms of the differential of the cumulative hazard function, the UIDP prior is formulated to match its prior UI with the weighted average of UI in historical datasets and thus can utilize both parametric and nonparametric information provided by historical datasets. With a Markov chain Monte Carlo algorithm, simulations and real data analysis demonstrate that the UIDP prior can adaptively borrow historical information and improve statistical efficiency in survival analysis.

Sustainable management of transboundary fish stocks hinges on accurate delineation of population structure. Genetic analysis offers a powerful tool to identify potential subpopulations within a seemingly homogenous stock, facilitating the development of effective, coordinated management strategies across international borders. Along the West African coast, the Atlantic chub mackerel (Scomber colias) is a commercially important and ecologically significant species, yet little is known about its genetic population structure and connectivity. Currently, the stock is managed as a single unit in West African waters despite new research suggesting morphological and adaptive differences. Here, eight microsatellite loci were genotyped on 1,169 individuals distributed across 33 sampling sites from Morocco (27.39°N) to Namibia (22.21°S). Bayesian clustering analysis depicts one homogeneous population across the studied area with null overall differentiation (F ST = 0.0001ns), which suggests panmixia and aligns with the migratory potential of this species. This finding has significant implications for the effective conservation and management of S. colias within a wide scope of its distribution across West African waters from the South of Morocco to the North-Centre of Namibia and underscores the need for increased regional cooperation in fisheries management and conservation.

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.

BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.
METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.
RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.
CONCLUSIONS: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.

BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction.
METHODS: We explore four imputation methods to address missing data and ME: single imputation (SI), multiple imputation under missing at random (MI-MAR), multiple imputation with calibration procedure (MI-ME), and multiple imputation under missing not at random (MI-MNAR). We compare four machine-learning models (Decision Tree, Naive Bayes, Random Forest, and Xgboost) and three statistical models (logistic regression, stepwise logistic regression, and least absolute shrinkage and selection operator logistic regression) in myopia risk prediction. We apply these models to the Shanghai Jinshan Myopia Cohort Study and also conduct a simulation study to investigate the impact of missing mechanisms, the degree of ME, and the importance of predictors on model performance. Model performance is evaluated using the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).
RESULTS: Our findings indicate that in scenarios with missing data and ME, using MI-ME in combination with logistic regression yields the best prediction results. In scenarios without ME, employing MI-MAR to handle missing data outperforms SI regardless of the missing mechanisms. When ME has a greater impact on prediction than missing data, the relative advantage of MI-MAR diminishes, and MI-ME becomes more superior. Furthermore, our results demonstrate that statistical models exhibit better prediction performance than machine-learning models.
CONCLUSIONS: MI-ME emerges as a reliable method for handling missing data and ME in important predictors for early-onset myopia risk prediction.