Forests are a critical frontline in the fight against climate change, but the complex details of their carbon storage have long been hidden in a blur of overlapping canopies. 

With the continuous advancement of deep-space exploration missions, the potential threat of microorganisms in the closed environment of space stations to astronauts' health has attracted increasing attention. Fusobacterium nucleatum, as an opportunistic pathogen colonizing the oral cavity and mucosal sites, is closely associated with various diseases including periodontal disease and colorectal cancer. Under extreme space conditions such as microgravity and radiation, the growth, virulence, drug resistance, and metabolic characteristics of microorganisms may undergo significant alterations, thereby increasing the risk of infection for astronauts. However, current research on the effects of microgravity on anaerobic bacteria remains extremely limited, and the phenotypic changes and molecular regulatory mechanisms of F. nucleatum under simulated microgravity conditions have not yet been elucidated, making it difficult to provide a scientific basis for the prevention and control of anaerobic bacterial contamination and infection risk assessment in space stations.

With the exponential increase in the number of objects in low Earth orbit (LEO), collision risks have risen correspondingly, making high-precision orbit prediction a core requirement for ensuring satellite safety and space environment management. Two-Line Elements (TLEs) are the only publicly available orbital data source and are widely used in conjunction with the Simplified General Perturbations 4 (SGP4) model for LEO object orbit prediction. However, LEO objects are significantly affected by complex time-varying perturbations such as atmospheric drag, causing model prediction errors to grow rapidly over time—short-term errors can reach hundreds to thousands of meters, while medium- and long-term prediction errors may exceed 10 km. Although traditional dynamical models can achieve relatively high accuracy through numerical integration, they incur substantial computational costs and suffer from limited atmospheric density modeling accuracy (only about 5%–15%). Analytical methods, while computationally efficient, struggle to handle complex perturbations. In recent years, bidirectional long short-term memory (BiLSTM) networks have demonstrated strong capabilities in time-series prediction; nevertheless, an effective approach for systematically modeling and compensating for TLE-based prediction errors using solely publicly available TLE data has remained lacking.

Despite the surging global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and related liver fibrosis, effective treatments remain limited. While the traditional Chinese medicine Juanyu-Xiaozhi Formula (JYXZF) is used against MASLD, its bioactive components and mechanisms are poorly understood. This study aimed to investigate the therapeutic effects of JYXZF and elucidate its underlying mechanisms of action.

Forests store carbon and support biodiversity, but management that benefits one goal may not benefit the other. A study of 150 forest plots found that increasing management intensity reduced total and aboveground carbon while increasing belowground carbon. Aboveground insect diversity and biomass also increased, whereas belowground fauna showed no overall response. Relationships varied among arthropod groups, habitats and years, revealing no single optimal management strategy. The authors suggest combining generally lower-intensity management with a mosaic of forest types to better balance carbon storage, arthropod habitat, climate mitigation and biodiversity conservation.

Melon flesh thickness is the edible portion of the fruit and directly determines its commercial value and yield, yet the genes controlling this trait have long remained a mystery.