Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.

Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This research proposes an innovative deep reinforcement learning-based (DRL) cooperative control framework, including signal and speed modules, to dynamically adapt signal timing and CAV velocities for traffic safety and efficiency optimization. Among the DRL-based signal modules, a traffic state prediction model is merged with the current state to augment characteristics and the agent-learning process.

HI-Net: A novel histopathologic image segmentation model for metastatic breast cancer via lightweight dataset construction.

Since 2020, breast cancer has remained the most prevalent cancer worldwide and the World Health Organisation projects significant increases by 2040, with new cases expected to exceed 3 million annually (a 40% increase) and deaths to surpass 1 million (a 50% increase), highlighting the urgent need for advancements in detection and treatment. Current detection of metastasis is highly dependent on labour-intensive and error-prone pathological examination of large-scale biotissue. Given the high-resolution (100,000 × 100,000 gigapixels) but limited quantity of open-source pathological slide datasets, existing deep learning models face preprocessing challenges.

RUNX1-PDIA5 Axis Promotes Malignant Progression of Glioblastoma by Regulating CCAR1 Protein Expression.

PDIA5 is responsible for modification of disulfide bonds of proteins. However, its impact on the malignant progression of glioblastoma multiforme (GBM) remains unknown. We analyzed the expression and prognostic significance of PDIA5 in cohorts of GBM and clinical samples.