A call for both distinguished global experts and rising early - career stars to join a systematic, multi - dimensional platform for biodiversity research.

A new perspective published in Biological Diversity links plant functional traits to ecological resilience, offering a more precise way to forecast how ecosystems will respond to climate change.

Researchers synthesize clinical and molecular evidence showing how the "Starflower" fights inflammation, metabolic disorders, and neurological decline.

A groundbreaking review in Biological Diversity proposes a proactive, AI - integrated approach to stop biological invasions before they devastate ecosystems and economies.

Cryptocurrencies, like Bitcoin, are often too volatile for everyday use. Stablecoins, digital coins linked to steady assets like the US dollar, make using crypto for daily payments more practical. Prof. Min DAI, Chair Professor in Applied Statistics and Financial Mathematics at the Department of Applied Mathematics, and Director of The Research Centre for Blockchain Technology at The Hong Kong Polytechnic University, presents a robust stablecoin design using option pricing theory and smart contracts on Ethereum. The dual-class structure delivers fixed-income and stablecoin options, effectively separates speculation from real usage and demonstrates resilience to extreme market event. 

This study introduces the Multidimensional Antiviral Antibody Database (MAAD), a comprehensive and standardized platform integrating sequence, structure, and functional data for antibodies targeting three high-impact RNA virus families. MAAD serves not only as a curated data repository but also as an interactive analytical toolbox designed to support rational antibody engineering, structure-based vaccine design, and AI-driven antibody discovery.

A review paper by scientists from Tianjin University presented light on brain-on-a-chip interfaces (BoCIs)—a groundbreaking technology that fuses lab-grown biological neural networks with electronic systems to enable bidirectional information exchange.

The new research paper, published on Jun. 17 in the journal Cyborg and Bionic Systems, presented a systematic categorization and detailed characterization of Brain-on-a-Chip Interfaces (BoCIs). It discusses the interaction methods employed in lab-grown brain models, followed by an exploration of hybrid intelligence research based on BoCIs.

Review re-maps multi-view learning into four supervised scenarios and three granular sub-tiers, delivering the first unified blueprint for researchers to navigate classification, clustering, incomplete views and hybrid techniques.

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification accuracy for streaming irregular multivariate time series.

Graph neural networks are increasingly used to model molecules, yet many of them remain difficult to interpret. MolUNet++ is a new framework that not only improves prediction accuracy across multiple molecular tasks, but also highlights the specific substructures that drive those predictions. By learning molecular features at different levels of detail, MolUNet++ helps bridge the gap between model performance and chemical interpretability.