Rock weathering is an important process regulating atmospheric CO₂ concentrations and long-term climate evolution, yet how enhanced erosion affects rock weathering and its carbon effects remains poorly understood. Focusing on the Lancang River basin on the southeastern margin of the Tibetan Plateau, the authors quantitatively reveal the differential responses of silicate, carbonate, and sulfide weathering to changes in erosion. The results show that, as erosion intensifies, silicate weathering rates decrease while sulfide oxidation rates increase, causing weathering in the basin to tend toward a net carbon source. The related findings have now been published in Science China Earth Sciences.

An international team of researchers led by Dr. Meng Li at Shenzhen University has conducted a comprehensive survey of fungal communities across seven representative Chinese mangrove wetlands. Using high-throughput sequencing of 300 sediment samples, the team discovered that over one-third of fungal diversity remains unclassified at the phylum level, making mangrove sediments a major reservoir of uncharacterized fungal life. The study reveals that fungal community assembly is governed by a hierarchical framework operating across three scales: macro-climatic filtering by temperature and dispersal limitation, local edaphic filtering by salinity and nutrient stoichiometry, and micro-scale biotic interactions through cross-kingdom co-occurrence networks. These findings, published in Mycology, provide a theoretical foundation for understanding microbial community stability in coastal ecosystems under global change.

A new study published in Advances in Atmospheric Sciences finds that tropical cyclone activity and coastal hazard along East Asia have been shifting steadily northward.

First report of whole virome profiling of Naemorhedus griseus, identifying diverse viruses across multiple organ tissues. Identification and characterization of multiple mammalian viruses: including parvoviruses, picobirnaviruses, phenuivirus. Furthermore, this study provides insights into genetic diversity of Naemorhedus griseus-borne viruses and their cross-species transmission dynamics.

The F1 hybrid between Black king crayfish and wild-type crayfish exhibited a chimeric variant. Its internal organs contained an irregular mixture of cells with two distinct genetic components that derived from the two parents respectively; however, this chimerism was not reflected in its external body coloration and patterning, which were symmetrical.

The nightly closure or downward folding of leaves, a process known as sleep movement or nyctinasty, is a widespread phenomenon in legumes that helps optimize photosynthesis, conserve water, and reduce pathogen susceptibility. Although the physical process has been well characterized, the molecular regulation of nyctinasty remains poorly understood. Here, we combined temporal transcriptome profiling, photoperiod manipulation, and tissue-specific gene expression analysis to dissect the regulatory network in peanut (Arachis hypogaea) during nyctinastic leaf movement. Under normal light/dark cycles, leaves displayed distinct nyctinastic behavior, which was suppressed under continuous light, underscoring the importance of photoperiod for this process. Transcriptome analysis identified conserved light- and circadian-related genes exhibiting rhythmic expression patterns. Key candidates included light-responsive SWEET and aquaporin genes with distinct tissue-specific expression patterns, along with a pulvinus-enriched MYB transcription factor gene. Promoter-binding and reporter assays supported transcriptional links between circadian/light-signaling components and these candidate downstream genes. Together, these findings support a working model in which photoreceptors and the circadian clock coordinate the expression of transporter and regulatory genes, which may contribute to nyctinastic leaf movement in peanut. These findings provide a molecular framework for future functional investigations of nyctinasty in legumes.

The research team from National University of Defense Technology systematically described the memory efficiency issues in the training process of large language models (LLMs) with Transformer architecture for scientific fields. As the scale of models continues to expand, memory requirements have become a key bottleneck restricting their applications in scientific fields such as biology, medicine, chemistry, and meteorology. The article systematically reviewed memory-efficient pre-training technologies at the algorithm level, system level, and software-hardware collaborative optimization, and combined with the AlphaFold 2 case, demonstrated how customized memory optimization methods can reduce storage overhead while maintaining prediction accuracy.