This study evaluates virtual 3D scanned prosections in gross anatomy education. Twenty-nine medical students were divided into physical or virtual teaching groups. Both groups showed significant post-test score improvements, with no significant difference between them. While students found 3D scans effective for learning and exam preparation, they preferred dissection for lab experience. Results indicate virtual 3D scans are a valuable supplementary tool but not a replacement for traditional dissection in medical education. 

The POINT platform (http://point.gene.ac/) integrates multi-omics biological networks, advanced network topology analysis, deep learning prediction algorithms, and a comprehensive biomedical knowledge graph. It provides a powerful tool to overcome current bottlenecks in network pharmacology and advance the field.

Dynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing con ditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforce ment learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCEalgorithm.Experimentalcomparisonswithstate-of-the-artbaselinesdemonstratethatSTFRLachievessuperior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.

Researchers from the Hong Kong University of Science and Technology (HKUST) and Tongji University have developed FerroAI, a deep learning model that can produce phase diagrams for ferroelectric materials in just 20 seconds. Using FerroAI, the team discovered a novel ferroelectric material with an exceptionally high dielectric constant of 11,051, representing a significant advancement in AI-driven ferroelectric materials research. Their findings, published in the prestigious journal npj Computational Materials, offer an innovative approach to accelerating the design and discovery of functional materials.

Christina Wang, MD, and Lynda Polgreen, MD, MS, investigators at The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, have been awarded $9 million from the California Institute for Regenerative Medicine (CIRM) to establish the South Los Angeles Community Center of Excellence for Regenerative Medicine (SoLA-CCERM) at The Lundquist Institute. SoLA-CCERM is a pioneering initiative designed to expand access to cutting-edge regenerative therapies and create a robust pipeline of skilled healthcare professionals serving Los Angeles County’s most diverse and underserved communities.