A research team led by Professor Yoshitaka Nishiyama of Saitama University has developed cyanobacterial strains that efficiently produce and secrete free fatty acids (FFAs), key precursors for jet and diesel fuels, using photosynthesis. By enhancing endogenous galactolipases and an RND-type efflux transporter, the team enabled the microalga Synechococcus elongatus PCC 7942 to release FFAs outside the cell, simplifying and reducing the energy-intensive steps of harvesting, drying, and extraction. Because the strains were developed using a self-cloning approach without retaining foreign genes, it offers advantages for future industrial use. The findings could help advance low-cost, carbon-neutral biofuel production from carbon dioxide and sunlight.

Kyoto, Japan -- Every year in the West Pacific, as summer ends and September rolls around, typhoons are not far behind. Typhoons are the most impactful extreme weather events affecting Japan and East Asia, and due to climate change, extremely strong typhoons are becoming more frequent. In order to adapt critical infrastructure to these massive storms and protect coastal areas, accurate accounting for their future impact is essential.

Assessing the disaster risks of a typhoon season involves quantitatively predicting storm intensity and frequency, relying heavily on the characteristics of regional meteorological fields and natural variability inherent in the climate system. Though varying typhoon characteristics are related to sea surface temperatures -- SST -- probabilistic evaluations that account for SST have been insufficient.

This motivated a team of researchers at KyotoU's Disaster Prevention Research Institute to analyze typhoon intensity through spatial SST patterns. They combined a slab-ocean model with the Global Atmospheric Climate Model developed by the Meteorological Research Institute of the Japan Meteorological Agency, resulting in a unique simulation that successfully represents atmosphere-ocean interactions on a global scale.

Proteins change shape as they function, and these changes are essential for processes such as drug interactions and cellular activity. Researchers from the Tokyo University of Science developed an AI-based method called DeepAFM that is trained on millions of simulated images representing different protein states, accurately identifying transitions between closed and open states in a protein called SecA. This approach highlights the growing potential of AI to solve complex challenges in biology and medicine.

The function of transposable elements (TEs), during the conversion of stem cells into specialized cell types, remains poorly understood. A study shows that TEs act as binding platforms for key proteins involved in brain development, with many becoming active during neuronal differentiation. The study traces the evolutionary origins of these regulatory elements and demonstrates their role in gene regulation. These findings indicate that TEs may have expanded and refined gene regulatory networks in the brain.