A joint research team from the Institute of Geochemistry of the Chinese Academy of Sciences (IGCAS) and Shandong University has for the first time identified crystalline hematite (α-Fe₂O₃) and maghemite (γ-Fe₂O₃) formed by a major impact event in lunar soil samples retrieved by China's Chang'e-6 mission from the South Pole–Aitken (SPA) Basin. 

A recent study investigates the role of sonic vibrations in improving pollination efficiency and fruit size in tomatoes. 

Tea bud size is closely tied to both yield and processing suitability in tea production. 

In bustling megacities worldwide, smart trains and railways are emerging as heroes in the fight against gridlocked traffic and choking pollution. Powered by cutting-edge tech like IoT and digital connectivity, these systems promise faster, greener, and more personalized travel—think seamless real-time updates and automated operations. Yet, this digital evolution brings a dark side: heightened vulnerability to cyberattacks that could derail operations, compromise passenger safety, or expose sensitive data. As railways go "smart," the need for robust cybersecurity isn't just a tech add-on—it's essential to unlock the full potential of a connected world without inviting chaos. This study dives into the cybersecurity maze of smart railways, mapping out threats, vulnerabilities, and defenses to ensure these vital infrastructures remain resilient in an increasingly hostile digital landscape.

Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.

Accurate measurements of dual parameters of phase retardance and retardance axis of birefringent materials are of fundamental importance to their fabrication and applications. However, current techniques typically exhibit limited versatility, suffering from high complexity, insufficient accuracy, and low efficiency. In this study, an anisotropic laser feedback polarization effect is proposed and demonstrated for birefringence measurement, featuring simultaneous dual-parameter demodulation, unified polarization modulation-analysis architecture, high detection sensitivity, user-friendly operation, and versatile functionality. Importantly, such system can be self-calibrated with its own physical phenomena to reduce the installation derivation. To showcase the powerful effectiveness, we perform the static birefringence, dynamic birefringence variation, and spatial birefringence distribution, which remarkably exhibits the standard deviation of 0.0453° and 0.0939° for phase retardance and retardance axis azimuth, with the limit allowable sample transmittance around 10^-5. This work demonstrates comprehensive applicability across diverse birefringence scenarios, extending the application of anisotropic laser feedback polarization effect, while establishing a novel strategy for birefringence measurement.

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.

Therapeutic cancer vaccines have experienced a remarkable resurgence over the past decade, representing a paradigm shift in oncology toward harnessing the immune system's intrinsic ability to combat malignancies. These innovative immunotherapeutic approaches are fundamentally designed to enhance specific stages of the cancer-immunity cycle, a conceptual framework that describes the sequential events required to mount effective anti-tumor immune responses. By strategically targeting critical phases including antigen release, presentation, T-cell priming, trafficking, infiltration, and cytotoxic execution, cancer vaccines aim to overcome immune resistance mechanisms and promote durable tumor regression.