Researchers have developed a programmable genome evolution platform called RAMPAGE that rapidly generates beneficial mutations in the industrial yeast Pichia pastoris. The technology enabled the evolution of strains that tolerate extremely high methanol concentrations, grow at elevated temperatures, and overcome growth defects caused by glycoengineering. The platform could accelerate the development of more robust microbial cell factories for sustainable biomanufacturing.

Two-wheeled vehicles naturally lean during turns, making it challenging for rider-assistance systems to tell if the rider is making a planned turn or losing balance. In a recent study, scientists from Shibaura Institute of Technology in Japan created a machine-learning-based steer-by-wire bicycle that can differentiate between these situations. This innovation could make electric bicycles and motorcycles safer without disrupting the riders’ intended movements.

POSTECH and Incheon National University develop mussel adhesive protein to achieve “sustained immunity,” offering a technology that could ease the burden of repeat vaccination.

Periodontal (gum) disease is one of the most prevalent diseases worldwide, caused by the bacterium Porphyromonas gingivalis (P. gingivalis). Published in Communications Biology, a joint study including the Okinawa Institute of Science and Technology (OIST) provides new insights into how this bacterium causes plaque formation. Using cryo-electron (cryo-EM) microscopy, they reveal the 3D structure of Mfa pili, an arm-like filament which enables the bacteria to stick to host tissues and other microbes. 

Catalysts play a vital role in the chemical reactions used to produce fertilizers, fuels, pharmaceuticals, and hydrogen, to name a few. AI-driven catalyst discovery is revolutionizing the field of materials science. But AI is only as reliable as the data it learns from. Therefore, researchers from Tohoku University have introduced an AI-powered digital catalysis platform that serves as a one-stop source for experimental data, theoretical calculations, and scientific literature.

CHANGWON, South Korea — Korea Institute of Materials Science (KIMS), led by President Chul-jin Choi, announced that a research team led by Kyung Mun Min and Seonghwan Choi of the Materials Processing Research Division has developed a new analysis model capable of predicting the anisotropic mechanical behavior of sheet metals within seconds using only microstructural information of metallic materials. The technology is expected to dramatically reduce the time and cost required for designing forming processes for metallic materials used in automobiles and batteries, as it enables fast and accurate prediction of how sheet metals stretch and deform without complex repetitive experiments.

Sub-headline: NUAA researchers introduce the GPDG framework,tackling cross-domain adaptation via environment-to-function meta-mapping.

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation learning