Graphs are widely used to represent complex relationships in everyday applications such as social networks, bioinformatics, and recommendation systems, where they model how people or things (nodes) are connected through interactions (edges). Subgraph matching—the task of finding a smaller pattern, or query subgraph, within a larger graph—is crucial for detecting fraud, recognizing patterns, and performing semantic searches. However, current research on streaming subgraph, a similar task where timing is important, matching faces major challenges in scalability and latency, including difficulties in handling large graphs, low cache efficiency, limited query result reuse, and slow indexing performance. To address these issues, Liuyi Chen et al. presented a new framework that leverages a subgraph index based on graph embeddings, enabling effective caching and reuse of query results while demonstrating robustness and consistency across varying batch sizes and datasets. Their work was published in Intelligent Computing, a Science Partner Journal, under the title “Accelerating Streaming Subgraph Matching via Vector Databases”.

This article describes a new method (VS-FPM) for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin (H&E) images from phase images recovered using Fourier ptychographic microscopy (FPM). 

Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability directly affect subsequent quantitative analysis and clinical decision-making. Based on a systematic analysis of feature utilization in U-shaped architectures, Prof. Zhao Qi’s team at Liaoning University of Science and Technology and Prof. Shuai Jianwei’s team at the Wenzhou Institute of the University of Chinese Academy of Sciences have developed an E-shaped medical image segmentation framework that differs from conventional symmetric decoders.