Nationwide Duke-NUS study finds asthma-related emergency visits rise within two days of increases in PM2.5, underscoring the importance of early precautions during periods of poorer air quality.

To develop batteries that last longer on a single charge, more energy must be stored within the same size and weight.

A research team led by Dr. San Moon and Dr. Jungdon Suk at the Korea Research Institute of Chemical Technology (KRICT, President Seokmin Shin) has developed a dry thick-film cathode technology by incorporating graphitic carbon nitride (g-C3N4) as a cathode additive for the first time. The additive enhances electrolyte wettability, allowing the electrolyte to penetrate the electrode more effectively, while reducing the energy required for lithium ions to migrate into the cathode active material. The research was conducted in collaboration with domestic universities, including Seoul National University and Yonsei University, as well as Thermo Fisher Scientific Korea, a global scientific instrumentation company.

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.

Finding a few tumor cells hidden among billions of blood cells is one of the biggest challenges in liquid biopsy. Circulating tumor cells (CTCs), which break away from primary tumors and enter the bloodstream, can provide important clues about cancer progression and treatment response. However, their extremely low abundance—often only one to ten cells among a billion blood cells—makes them difficult to isolate and accurately identify.

In a new study published in Biomedical Analysis, researchers from Taiyuan University of Technology and Shanxi Bethune Hospital developed a new strategy that combines a microfluidic chip with artificial intelligence to address this challenge. The platform first separates tumor cells from surrounding blood cells according to their physical differences, then uses deep learning to identify the enriched cells from ordinary microscope images. This approach provides a new route toward simpler, less label-dependent CTC analysis.