Accelerated ovarian failure results in brain alterations related to Alzheimer's disease that are not recovered by high-intensity interval training in mice.

Introduction: The menopausal decline in ovarian estrogen production is thought to increase the risk of Alzheimer's disease; however, this link requires further investigation. The chronological development of this connection is not well defined because of the lack of animal models that recapitulate the time course of menopause. This study characterized the cognitive and neuronal effects of the 4-vinylcyclohexene diepoxide (VCD) model of ovarian failure in female mice and assessed whether high-intensity interval training (HIIT) would attenuate impairments.

Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.

Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalking risks. In this study, we propose a risk-aware deep reinforcement learning (DRL) approach for AVs to make decisions safely and efficiently in jaywalker-vehicle interactions.

Effect of moderate-intensity aquatic treadmill exercise on cognitive function and cerebral blood flow for healthy older adults.

To compare the effect of moderate-intensity aquatic treadmill exercise (ATM) on cerebral blood flow (CBF) and cognitive function in healthy older adults to that of moderate-intensity land-based treadmill exercise (LTM). This randomized controlled trial study was conducted between May 2023 and Oct 2023. Twenty-eight participants aged 60-80 were randomly assigned to either ATM group (N = 14) or LTM group (N = 14).

A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model.

To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. The RF algorithm, demonstrating the best performance, was selected and integrated into an interpretable machine learning-based method (i.

Correcting for endogeneity of crash type in crash injury severity at highway ramp areas.

Crash type, a key contributory factor of crash injury severity level, is typically included in crash severity models as an explanatory variable. However, certain unobserved factors could influence both the crash type and crash injury severity simultaneously. As such, there could exist an endogenous effect of crash type on crash injury severity.