As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionising the modelling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.

A research paper by scientists at University of Electro-Communications explored the long-term clinical outcomes of multi-degree-of-freedom myoelectric prosthetic hands.

The research paper, published on Mar 18, 2025 in the journal Cyborg and Bionic Systems.

Chronic pain is frequently accompanied by anxiety and depression. 

Comprehensive review reveals breakthrough modification strategies bringing non-noble metal catalysts closer to replacing platinum

Comprehensive review reveals roadmap for wearable electronics powered by multifunctional, shape-shifting energy storage devices

Mini-review highlights breakthroughs in photo-electrochemical systems that transform greenhouse gas into methanol, ethanol, and other valuable chemicals

We aimed to evaluate the mutual influences between gut microbiota and insomnia. We conducted Mendelian randomisation (MR) analysis using genome-wide association studies datasets on insomnia (N=386 533), gut microbiota data from the MiBioGen alliance (N=18 340) and the Dutch Microbiome Project (N=8208). The inverse variance weighted (IVW) technique was selected as the primary approach. Then, Cochrane’s Q, Mendelian randomization-Egger (MR-Egger) and MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO) tests were used to detect heterogeneity and pleiotropy. The leave-one-out method was used to test the stability of the MR results. In addition, we performed the Steiger test to thoroughly verify the causation.

Whatever challenges life throws, mothers often know best as they guide offspring through the risky stages of early development.