New color-changing sensor detects alcohol with a smartphone snap
Peer-Reviewed Publication
Updates every hour. Last Updated: 18-Jun-2025 07:10 ET (18-Jun-2025 11:10 GMT/UTC)
Scientists developed a smartphone-compatible ethanol sensor using a metal–organic framework called Cu-MOF-74. The sensor visually detects ethanol concentrations across a wide range, with no electronics or lab tools required. This technology has promising applications in environmental monitoring, healthcare, industrial processes, and alcohol breath analysis.
Researchers in Japan have developed an AI model that objectively evaluates atopic dermatitis (AD) severity using smartphone images shared by patients on the country’s largest online AD platform. This technology could help patients monitor their condition more precisely at home and support timely treatment decisions.
Higher maternal selenium levels during pregnancy were associated with a lower risk of streptococcal infections in children, suggesting a potential protective effect.
The information age is built on mathematics. From finding the best route between two points, over predicting the future load on a national power grid or tomorrow's weather, to identifying ideal treatment options for diseases, algorithms share a common structure: they take input data, process it through a series of calculations, and deliver an output. Powering the ongoing AI revolution are increasingly sophisticated algorithms, often composed of millions of lines of code. And the more steps a model goes through before presenting a solution, the costlier it is in the number of physical computing units, time, and energy required.
Optimizing these mathematical models is at the heart of the work of the Machine Learning and Data Science Unit (MLDS) at the Okinawa Institute of Science and Technology (OIST). Led by Professor Makoto Yamada, the unit strives to unlock the full potential of machine learning (ML) and improve efficiency, optimizing not just data science but also education and the scholarly output within the unit through a distributed hierarchy.
A research team at The University of Osaka has identified a crucial brain region involved in motor learning during reaching movements. The parvocellular division of the red nucleus, a small but specialized structure in the midbrain, was found to generate and transmit “error signals” necessary for adapting hand movements. This discovery clarifies a long-standing question in neuroscience about how the brain detects and corrects motion inaccuracies, with potential applications in developing new rehabilitation methods.
In a recent study published in Current Biology, a research team led by Professor Takashi Ueda of the National Institute for Basic Biology and Associate Professor Masaru Fujimoto of the University of Tokyo has revealed the molecular steps that led to the emergence of this plant-specific vacuolar transport system. Their work shows that the acquisition of this pathway was driven by the stepwise neofunctionalization of a membrane fusion protein called VAMP7.
Strategically arranging histidine residues inside a protein cage is a promising approach to create artificial enzymes, reports researchers from Institute of Science Tokyo. The engineered protein cage mimics natural enzymes using simple amino acids without any metal cofactors, overcoming a major limitation in artificial enzyme design. Molecular simulations confirmed that the confined environment within the protein cage enhances catalytic efficiency, offering a new route to develop sustainable biocatalysts.
Heart failure often occurs alongside other chronic conditions in older adults, but their combined impact remains unclear. Japanese researchers have now analyzed data from over 1,100 patients with heart failure aged 65 and older, revealing that overlapping cardiovascular, kidney, and metabolic conditions are associated with lower physical function and worse prognosis. These findings highlight the importance of simpler screening tools to identify high-risk patients early and improve outcomes in older adults.