A review paper by scientists at Peking University Third Hospital investigates underlying determinants of low translational success of central nervous system drugs and therapeutic devices, reviews the historical and technical bottlenecks that lead to the neglect of ECS research, and emphasizes its transformative potential in reshaping therapeutic strategies.

The review paper, published on Mar 4, 2026 in the journal Cyborg and Bionic Systems.

A new study reviews how machine learning (ML) is being used to help communities recover critical infrastructure after natural hazards such as earthquakes, floods, and hurricanes. The research synthesizes global studies and shows that ML can support recovery by characterizing recovery trends, predicting recovery times, and optimizing recovery schedules. The authors also identify key challenges, such as limited data availability, and outline future directions for building more resilient infrastructure systems using ML.

Neuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.

The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.