Advanced Scientific Instruments (ASI), a new interdisciplinary open-access journal, has published its inaugural issue. The journal is dedicated to the art and science of instrumentation—from fundamental principles and system architecture to advanced applications. It is published by Science China Press and KeAi, under the auspices of the Chinese Academy of Sciences.

Seamless integration between electronics and the human body is the goal, and Ga-LMs are key to this transformation. Essential properties,such as, fluidity, conductivity, and biocompatibility, enable Ga-LMs to form stretchable, self-healing circuits, paving the way for advanced wearables, soft robotics, and medical implants that promise to redefine human-machine interaction.

In the macro world, building a robot is straightforward: you connect motors to joints and follow the laws of physics. But at the nanoscale—where machines are a billion times smaller—how to build a robot? Scientists are now engineering DNA to perform complex tasks at the nanoscale, building machines that move, grip, and even process information. In a new review published in SmartBot, Professor Lifeng Zhou of Peking University, along with Academician Jian S. Dai from Southern University of Science and Technology, takes us through the cutting-edge world of DNA nanorobots and explores the transition from static DNA structures to dynamic, programmable machines.

Animal studies have shown that some cartilage cells can transition to a bone-like phenotype, challenging the belief that bone cells arise solely from stem cells in the bone marrow and growth plate. However, the molecular mechanisms driving this process remain unclear. Researchers have now developed in vitro and in vivo models of bone formation that enable tracking of cartilage-to-bone transition, providing new insights into the mechanisms and signaling pathways involved in cartilage-derived bone formation.

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.