POSTECH Unveils World’s First Dynamic Shape-Morphing Smartphone sized OLED Panel with Built-In Speaker — All While Maintaining Ultra-Thin Flexibility.

The National Institute of Information and Communications Technology (NICT) has revealed, through fMRI-based brain activity analysis, that multiple regions in the human cerebral cortex flexibly represent numerical quantity. This finding comes from research by HAYASHI Masamichi (Researcher (Tenure-Track)) at Center for Information and Neural Networks (CiNet), part of NICT’s Advanced ICT Research Institute, in collaboration with the University of Tokyo’s graduate student KIDO Teruaki (NICT cooperative visiting researcher), and Prof. YOTSUMOTO Yuko.

Although certain brain areas are known to respond to numerical quantity, this study expands that understanding by showing that some regions respond to relative quantity (e.g., “extra-small,” “small,” “large,” and “extra-large”) rather than absolute quantity (i.e., specific quantity). Moreover, these context-dependent, relative representations become more pronounced along the pathway from the parietal to the frontal lobe.

These results highlight the flexible nature of numerical quantity processing in the brain, and they are expected to advance our understanding of how the brain handles other types of “magnitude” concepts, including time and size.

This work was published in the journal “Nature Communications” on January 6, 2025.

Crashes on electric scooters are mostly due to the behaviour of the riders, with one-handed steering and riding in a group being some of the largest risk factors. The researchers are also concerned about riders who deliberately crash or cause dangerous situations when riding, a phenomenon that seems to be specific to electric scooters. This is shown by a study from Chalmers University of Technology in Sweden, which for the first time examines the causes behind crashes with electric scooters from naturalistic data within an urban environment.

Hosted by Duke-NUS, the International Women in Science, Technology, Engineering and Mathematics & Medicine (STEMM) Symposium draws over 350 attendees from healthcare, academia and public sectors

Symposium sparks cross-sector dialogue in leadership, mentorship and shaping a more equitable STEMM ecosystem in Singapore

Keynote by Professor Tan Chorh Chuan spotlights inclusive strategies and structural support to retain and grow women in science and medicine

This study achieved CRISPR/Cas9-mediated genome editing in grass carp (Ctenopharyngodon idella), China's most economically vital freshwater fish. Targeting the tyrb gene critical for pigmentation were performed. Mutations confirmed by sequencing caused a golden phenotype with melanophore reduction. Using multiple gRNAs enhanced mutation rates (88.9%) and triggered large DNA deletions. This successful gene editing in grass carp enables rapid creation of golden germplasm, addressing industry demands for improved aquaculture varieties.

Austrian researchers have successfully converted a by-product of wood gasification into high-quality activated carbon. The material, known for its ability to capture micropollutants, was produced by physical activation using hot gases that create fine pores. Their method increased the adsorption capacity by 11 times, outperforming commercial alternatives. With growing demand in water treatment, this discovery offers a renewable alternative to fossil-based activated carbon.

For new research, published in the journal Ocean and Coastal Management, ten fishermen working around islands in the Aegean Sea were asked to pinpoint areas where they believed seagrass beds could be found along the coastlines of their respective islands. The maps they produced were then compared with satellite data of the same regions, with analysis showing an average accuracy of 78% – and a high of 92%. Researchers from the University of Plymouth and the Archipelagos Institute of Marine Conservation say the results are a clear demonstration of the value of tapping into local knowledge, and how doing so can be a low-cost means of generating environmental data without compromising the high accuracy needed for the data to be still valuable for policy use.