A research team led by Kyushu University have developed a new water-soluble and metal-free molecule that could be detected by both fluorescence imaging and utilized as an MRI contrast agent. The team modified the luminescent organic radical tris(2,4,6-trichlorophenyl) methyl (TTM) to make it water-soluble. The new molecule produced deep-red fluorescence in water and enhanced the contrast of MRI phantom images by approximately 2.5 to 4.5 times stronger than that of previously reported water-soluble TTM radicals.

Development of a compact current comparator capable of evaluating both AC and DC current ratios using the same setup

Using an integrated New Approach Methodologies (NAMs) framework combining bioinformatics, 3D modeling, machine learning, and in vitro assays, this study analyzed 107 ecological factors and estrogen receptor alpha (ERα) diversity across 169 mammalian species. Key discoveries include an expanded ligand-binding pocket in omnivores, a cetacean-specific substitution, and brain-to-body mass ratio as a sensitivity predictor. These findings provide essential molecular mechanisms for predicting wildlife chemical sensitivity.

A tiny device that can be reprogrammed using a laser could lead to adaptable devices for computing, imaging and telecommunications.

A new AI tool can identify aging patterns in blood stem cells by analyzing DAPI-stained nuclear images and chromatin architecture. It may accelerate the discovery and testing of cellular rejuvenation strategies.

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