Apophis will make an extremely close flyby of Earth on April 13, 2029, at a distance of approximately 31,029 km—an event unprecedented for an asteroid of this size—providing a valuable natural experiment for studying tidal interactions, spin-state variations, and surface material migration between the asteroid and Earth. A large number of ground-based observations and space missions are expected to participate, but most observations will be concentrated from Earth's perspective, leaving observational blind spots as Apophis rapidly recedes from Earth's view after the flyby. LUMIO (Lunar Meteoroid Impacts Observer) is a 12U CubeSat of the European Space Agency, planned to be deployed in a quasi-halo orbit around the Earth–Moon L2 point; its primary mission is to observe meteoroid impact flashes on the lunar farside, with a launch expected in 2028. However, three critical questions remain unanswered: whether LUMIO-Cam—a camera designed for observing impact flashes—can detect Apophis as a sub-pixel target, whether it can resolve fluctuations in its light curve, and whether it can observe for a sufficiently long duration to obtain complete spin-period information.

With the widespread application of space traveling wave tube amplifiers (TWTAs) as core final-stage amplification devices in satellite communication systems, their operational reliability directly affects satellite signal transmission capability, and any failure may even lead to service interruption. Currently, fault statistics for space TWTAs are recorded in text form, encompassing a large number of fault phenomena, causes, and corresponding solutions. However, as the volume of fault events continues to grow, traditional analysis methods relying on manual statistics and empirical judgment suffer from low efficiency and lack of knowledge reasoning capabilities, making it difficult to comprehensively analyze multi-dimensional fault information. Although artificial intelligence technologies, particularly knowledge graphs (KGs) and large language models (LLMs), have demonstrated substantial potential in fault diagnosis, research on effectively integrating these two approaches and applying them to the specific fault diagnosis of space TWTAs remains unexplored. Therefore, how to construct an intelligent diagnosis framework capable of automatically understanding natural language fault descriptions and performing knowledge reasoning has become a key challenge in improving the troubleshooting efficiency and decision support capabilities for space TWTAs.

Antibiotic resistance is a growing global threat, yet how the collection of antibiotic resistance genes in the human gut—the antibiotic resistome—changes during aging has been poorly understood. Researchers at Westlake University have developed LARGE (https://github.com/hehang0618/LARGE), a large language model-based tool that detects and classifies antibiotic resistance genes with species-level resolution. Trained on 64,317 sequences, LARGE outperformed widely used tools such as DeepARG, RGI, PLM-ARG, and ARGNet, and identified more novel resistance genes than any comparator. Applying LARGE to more than 2000 gut metagenomes from three Chinese cohorts, the team found that resistance genes in 31 bacterial species changed significantly with age, and these changes were associated with 13 age-related chronic diseases, including chronic kidney disease. LARGE offers an online platform (https://large.lab.westlake.edu.cn), and all annotated data have been made publicly available.

Candidemia is a major cause of hospital-acquired bloodstream infections, with high mortality and substantial healthcare costs. However, delayed diagnosis, limited susceptibility testing, antifungal resistance, and the complexity of treatment continue to challenge effective patient management. This review examines recent advances in candidemia epidemiology, diagnosis, and therapy. It also discusses emerging approaches, including artificial intelligence-assisted diagnosis, vaccine-based prevention, and synergistic therapies, that could reshape candidemia management and improve outcomes.

Heat accumulation and severe light scattering limit high-power NIR pc-LEDs. Herein, Cr³⁺-doped MgNb₂O₆ transparent glass-ceramics were fabricated by co-melting and controlled crystallization, achieving single-phase precipitation. The material exhibits broadband NIR emission from 600 to 1300 nm (FWHM = 210 nm), ~50% transmittance, and retains 67.5% of its initial luminescence at 373 K. An integrated NIR LED delivers 240.9 mW output power and 6.3% power conversion efficiency, showing promising potential for night-vision illumination, food testing, biomedical diagnosis, and anti-counterfeiting.

China's 2025 climate: Distinct warm‑wet conditions and multiple new climate records

Recently, Food Science of Animal Products (FSAP) has been officially included in the Emerging Sources Citation Index (ESCI) by Web of Science, a leading global provider of scientific and scholarly research information. All articles published by FSAP since 2023 will be included in ESCI. Its first impact factor will be released in 2027.

New comprehensive survey catalogues 21 ant species across seven South China Sea islands, revealing human activity as primary driver for exotic ant establishment amid climate-vulnerable coral island ecosystems.

In a paper published in Science China Chemistry, a research team led by Professors Atsuhiro Osuka and Jianxin Song at Hunan Normal University developed a concise synthetic method for the single, double, triple, and quadruple installations of directly fused hemiporphycenes at the periphery of Ni(II) porphyrin. They subjected the Ni(II) complexes to demetallation under acidic conditions, which furnished the corresponding free-base compounds, and efficiently converted these to the corresponding Zn(II) congeners. The resulting hemiporphycene-fused porphyrins displayed nonplanar distorted structures, panchromatic absorptions with red-shifted and enhanced Q bands, and multiple reversible redox waves.