MendelR is a fully automated R package specifically developed for Mendelian randomization (MR) studies, designed to address the technical challenges of causal inference in biomedical research. As a powerful causal inference method, Mendelian randomization can effectively reduce confounding bias in observational studies. However, its complex data processing workflow, diverse analytical approaches, and stringent hypothesis-testing requirements often deter researchers. MendelR integrates the latest eQTL and pQTL databases, along with multi-omics resources, to provide a one-stop solution covering data acquisition, preprocessing, and instrumental variable selection, through to various MR analysis methods, thereby significantly lowering the technical barriers to research. This toolkit supports not only standard two-sample MR analysis but also encompasses advanced applications, including mediation analysis, multivariate MR, drug target analysis, and multi-omics integration. It also incorporates rigorous sensitivity analyses and multiple-testing correction mechanisms to ensure the reliability of research findings. With its user-friendly interface, fully automated workflow, and diverse visualization features, MendelR offers an efficient and accurate MR analysis platform for researchers at all levels, from beginners to seasoned professionals, thereby advancing the in-depth development of causal relationship research in the biomedical field.

Researchers at Zhejiang University have developed AtomDiT, a machine learning framework that builds a digital twin of dislocations from a single high-resolution electron micrograph. The twin simultaneously reveals dislocation type and position, local stress, and chemical fields. Demonstrated across metals and alloys, including dual-phase systems and hydrogen-segregated grain boundaries, AtomDiT uncovers how stress and chemistry govern dislocation motion, offering a new route to understanding crystal deformation and damage.

The rise of “smart” vaping devices that combine nicotine or cannabis with cryptocurrency rewards, gamification and artificial intelligence is raising new public health concerns. In a New England Journal of Medicine perspective, MUSC Hollings Cancer Center researchers Amanda Palmer, Ph.D., Tracy Smith, Ph.D., and Benjamin Toll, Ph.D., warn that these products may reinforce addictive behaviors by rewarding users for vaping rather than quitting. The researchers are particularly concerned about their potential appeal to adolescents, young adults and people experiencing financial hardship, as well as regulatory gaps that have struggled to keep pace with rapidly evolving vaping technology. They are calling for greater scrutiny of these products before they become more widespread.

With the growing demand for in-situ exploration of complex extreme terrains such as polar craters in lunar exploration missions, quadruped robots have demonstrated unique advantages owing to their efficient jumping and locomotion capabilities in weak gravitational fields. However, lunar gravity is only one-sixth of Earth's, which substantially prolongs the flight phase of underactuated jumping. Incorrect takeoff actions on rugged terrain generate rotational torques that cause the robot to continuously rotate in mid-air, with the angular momentum accumulating over time and leading to catastrophic consequences, thus imposing significant uncertainty on landing control. Conventional optimal control methods rely on offline preplanned trajectories and are unable to cope with the variability of unknown rugged terrains; existing deep reinforcement learning models lack effective utilization of temporal causal relationships, relying solely on current states for decision-making and struggling to predict the long-term consequences of actions. Therefore, how to leverage historical sequence information to model temporal dependencies between actions, enabling the robot to possess forward-looking prediction and adaptive adjustment capabilities during the jumping process, has become a key technical challenge for jumping control on complex lunar terrains.