Chemists routinely optimize reactions to maximize the yield of their desired products, but understanding why those reactions work can require laborious experiments that track reactions through time. Researchers at the University of Tokyo have developed a method to extract hidden information on reaction speeds from yield data obtained during reaction optimization using machine learning together with rate equations developed by chemists. Concentration-dependent yield analysis (CYAN) uses machine learning to expand the yield data from reaction optimization experiments, then uses this information to calculate how quickly the different steps of a reaction occur. This gives chemists a tool to understand, improve and design complex high-yield chemical reactions. 

Kyoto, Japan -- While silicon carbide -- SiC -- has long been promoted as the key to developing electronics suitable for extreme environments, the field has been stuck in the lab for some time. Now, a team of researchers focusing on junction field-effect transistors, or JFETs, have succeeded in developing a new SiC transistor structure that can operate at 600°C, as announced recently by Kyoto University.* One of the team's goals after their previous study was to scale up to wafer-level production, so that is what they have now accomplished.

Compared with the previous study, wafer-scale production refers to building much smaller components and involves developing a minuscule but more efficient SiC transistor. Progressing to the wafer-scale is essential for practical implementation, as the fabrication of integrated circuits requires a large number of very small transistors.

However, wafer-scale production is virtually impossible to accomplish in a university laboratory due to equipment constraints. This motivated the team to collaborate with Phenitec Semiconductor Corp, which operates a mass-production fabrication plant, making this next step in their research feasible.

- Professor Juyoung Lee's team at the Graduate School of Engineering Biology combined DNA elements recognized by bacteria and yeast and used the resulting system to control the production of a green pigment in both microbes

- The design reduces the need to redesign gene expression systems for each microbe and is expected to be useful in biofoundries, where production routes are designed and tested

From rhythmic activity in localized populations of cortical neurons to dengue hotspots that repeatedly reappear across Medellín, Colombia, localized oscillations shape collective dynamics in complex networked systems ranging from the brain to urban epidemics. However, existing explanations are often constrained by strict requirements on system dimensionality. This study reveals a more general mechanism for the emergence of localized oscillations, showing how collective dynamics can transcend these conventional constraints and broadening the theoretical framework for understanding localized collective behavior.

The rise of “smart” vaping devices that combine nicotine or cannabis with cryptocurrency rewards, gamification and artificial intelligence is raising new public health concerns. In a New England Journal of Medicine perspective, MUSC Hollings Cancer Center researchers Amanda Palmer, Ph.D., Tracy Smith, Ph.D., and Benjamin Toll, Ph.D., warn that these products may reinforce addictive behaviors by rewarding users for vaping rather than quitting. The researchers are particularly concerned about their potential appeal to adolescents, young adults and people experiencing financial hardship, as well as regulatory gaps that have struggled to keep pace with rapidly evolving vaping technology. They are calling for greater scrutiny of these products before they become more widespread.

Los Angeles, CA, September 28, 2026 - The Terasaki Institute for Biomedical Innovation has been awarded an NIH U01 research grant of approximately $6.9 million to develop a first-of-its-kind multimodal smart contact lens platform for monitoring post-traumatic stress disorder in real-world settings. The multi-institutional project brings together Principal Investigators Dr. Yangzhi Zhu of the Terasaki Institute, Dr. Nanthia Suthana of Duke University, and Dr. Ximin He of UCLA, combining complementary expertise in bioengineering, neuroscience, wearable biosensing, ocular bioelectronics, and clinical translational research. 

Wetlands are well known for filtering water, storing carbon, and supporting biodiversity. Yet much of their microbial genetic diversity remains unexplored, particularly in high-altitude ecosystems. A new study of the Napahai plateau wetland in Yunnan Province, China, has uncovered a distinctive pattern in DNA polymerase genes, suggesting that this isolated alpine wetland contains a genetic profile that differs from those found in marine, freshwater, hot spring, and agricultural environments.

Waterborne pathogens remain a major threat to public health, but many current monitoring methods are too slow or too narrowly focused to provide rapid warning of emerging biological risks. A new perspective published in Biocontaminant outlines how integrated biosensing systems could move aquatic pathogen surveillance from periodic single-target testing toward rapid, online monitoring of multiple pathogens at once.