Scientists have unveiled the magnesium-bound crystal structure of Akkermansia muciniphila sialidase Amuc_1547, a key enzyme enabling gut bacteria to degrade protective mucins. The study reveals a unique metal-binding pocket, a carbohydrate-binding domain, and an unconventional catalytic mechanism. These findings advance understanding of gut microbiota adaptation and offer potential pathways for therapies targeting metabolic diseases linked to mucin metabolism.

Workshop Name: The 2nd International Workshop on Data-driven AI(DATAI 2025)

As lithium-ion batteries (LIBs) continue to power electric vehicles and energy storage systems, their long-term health remains a critical challenge. A groundbreaking new method combines deep learning with physical modeling to deliver rapid, accurate degradation diagnosis at the electrode level. This innovative approach requires only 11 data points from a charging cycle, achieving reliable predictions in just 2.5 minutes. By simplifying the data needed and eliminating the need for specialized equipment, this technique offers a faster, more accessible solution to battery health monitoring—paving the way for safer, more efficient battery management in large-scale applications.

Workshop Name：1st Large Language Models for Spatial-rich Data Management (LLM+Spatial)

This study introduces a deep-learning system for rapid, automated detection and classification of tiny calcium deposits (microcalcifications) in mammograms to aid early breast cancer diagnosis. Leveraging a multi-center dataset of 4,810 biopsy-confirmed mammograms, our pipeline uses a Faster RCNN model with a feature-pyramid backbone to detect and classify microcalcifications—the pipeline requires no hand-tuned rules and provides both the overall cancer risk and highlighted lesion regions in seconds per image. On unseen test data, it achieved overall classification accuracy of 72% for discriminating between benign and malignant breasts and 78% sensitivity of malignant breast cancer prediction, marking a significant step toward AI-assisted, cost-effective breast-cancer screening that can run on standard radiology workstations.