Researchers at the University of Tokyo clarified how membrane lipid composition determines the surface charge of extracellular vesicles (EVs). They show that differences between exosomes and membrane-derived EVs arise from phospholipid asymmetry, particularly phosphatidylserine distribution. The study proposes zeta potential as a key indicator for EV classification and quality control, offering a foundation for standardization and rational design of EV-based therapeutics. This work was conducted as part of a JST COI-NEXT program, led by Innovation Center of NanoMedicine (iCONM).

A research team has developed a Gaussian Splatting processing platform that supports end-to-end processing from data acquisition to multi-platform rendering. Their framework provides a solid foundation for the large-scale adoption and future research of Gaussian Splatting technology.

Florida’s coral reefs are under siege from fast-spreading diseases like Stony Coral Tissue Loss Disease, yet their hidden structural impacts remain poorly understood. FAU researchers used advanced micro-CT imaging and deep learning to analyze coral skeletons in 3D, revealing subtle changes in porosity, density and thickness with 98% accuracy. This innovative approach offers a powerful new tool to rapidly assess reef health and better guide conservation strategies in the face of escalating environmental threats.

Researchers at EPFL have developed a deep-learning framework that dramatically improves vehicle re-identification in large-scale drone traffic monitoring. By combining visual features with traffic-based travel-time predictions grounded in shockwave theory, the system can reliably match vehicles that appear nearly identical from a bird’s-eye view. Tested on one of the largest multi-UAV traffic datasets, the approach boosts ReID accuracy by 36.8 percent, enabling more robust urban vehicle tracking.

Microplastic pollution is a growing environmental concern due to its widespread presence in oceans, rivers, and other water systems. Now, scientists from Shinshu University have introduced an eco-friendly water purification method using the natural slimy mucilage of nameko mushrooms, a common food in Japan. The results show that this natural material can effectively remove more than 90% of plastic particles from water in a very short time, offering a promising sustainable solution to plastic pollution.

Autonomous driving requires real-time interaction between vehicles and infrastructure to ensure safety and efficiency. However, current V2X system focused on simulation environments or constrained testbeds, overlooking critical aspects, including the scalability of the autonomous driving environment, as well as infrastructures, vehicles, and control data exchange. To address these issues, this study proposes mOS, a modular edge-intelligent framework validated through real-world intersection deployments and metaverse-based mixed-reality simulations. Built on a containerized and extensible architecture, mOS enables dynamic coordination among vehicles, infrastructure, and virtual entities. Experimental results demonstrate enhanced safety, responsiveness, and scalability while overcoming the limitations of conventional J2735-based V2X systems. Operating over commercial 5G with acceptable latency, the framework provides a cost-effective and practical platform for next-generation intelligent transportation systems.

A research paper by scientists from Chinese Academy of Sciences proposed a hybrid frequency–phase–space encoding method, integrated with high-density electroencephalogram (EEG) recordings, to develop high-speed BCI systems.

The new research paper, published on Mar. 26 in the journal Cyborg and Bionic Systems, developed a groundbreaking visual brain-computer interface (BCI) that sets a new benchmark for noninvasive BCI communication speed.

Understanding traffic scenes under adverse conditions such as rain, fog, night-time illumination, and motion blur remains a major challenge for intelligent transportation systems. Researchers at Tsinghua University propose TrafficPerceiver, a multimodal large language model framework that unifies traffic scene understanding and target-oriented segmentation under natural language instructions. By incorporating reinforcement learning based on group-relative policy optimization, the framework improves robustness and interpretability in complex real-world traffic environments.

6G’s space-ground integrated networks need reliable simulation tools for LEO satellite mega-constellations. A new study in Engineering unveils UltraStar, a high-fidelity simulator that models dynamic satellite topologies, real communication conditions and boosts efficiency via parallel computing. It’s validated across network layers and paves the way for 6G space-ground network development.