Kyoto, Japan -- How and why we experience consciousness is a question that has long plagued philosophers and scientists alike. We have come to understand that when awake our brains organize neural information for perception, yet we completely lose this organization under anesthesia. Why this happens is a longstanding mystery in neuroscience, as simple changes in the activity levels of specific brain regions fail to explain this disappearance of consciousness.

Theories have hypothesized that conscious perception arises from two core pillars: the brain's ability to encode external features, and its ability to transmit information across different cortical regions. While the former has been well-supported, direct physical evidence of the latter has remained elusive. A team of researchers at Kyoto University set out to bridge this critical gap between neural signals and information dynamics.

The team developed arrays for high-density custom electrocorticography -- or ECoG -- that covered most of the cerebral cortex of a rat's right hemisphere. This allowed them to extract traveling waves, physical phenomena in which electrical activity propagates across the brain like ripples in a pond. The scientists then analyzed these signals in simulations with transfer entropy, a mathematical metric that quantifies the directional flow of information. This approach allowed the team to investigate how tightly the neural signals and informational flows are linked.

An Osaka Metropolitan University-led research team conducted a within-subject crossover study, which examined electroencephalographic source activity and heart rate variability in 29 community-dwelling elderly women during flower arrangement tasks using fresh and artificial flowers.

A Ramsey-based diamond quantum magnetometer developed by researchers at Science Tokyo enables highly sensitive biomagnetic measurements without compromising thermal safety. Using a low-power laser, a light-trapping diamond waveguide, and a compact microwave antenna, the temperature rise in the sensor is just 13 K with a sensor-to-sample distance of 2 mm, enabling detection of picotesla-level (10^-12 T) magnetic fields. The sensor enables safe, close-proximity biomagnetic measurements for future magnetocardiography and magnetoencephalography applications.

Photothermal therapy is a cancer treatment option lacking the negative side effects associated with chemotherapy. Near-infrared laser light selectively heats and destroys cancer cells. However, two issues have stopped this less invasive treatment from becoming mainstream. Scientists have long sought to find a way to prevent nanoparticle clearance - where the immune system clears the nanoparticles carrying therapeutic agents before they can reach tumors - and better laser efficacy so it can reach deeper tumors. Now, a research team has overcome these challenges.

Organic light-emitting diodes (OLEDs) have become a standard in modern devices with incredible contrast and sleek designs. While initially an expensive luxury, OLEDs are gradually becoming more financially accessible as the technology improves. Now, researchers at the Institute of Transformative Bio-Molecules (WPI-ITbM) at Nagoya University and the Institute for Advanced Study at Kyushu University have combined quantum chemistry with machine learning to identify new materials for blue OLEDs to incorporate in next-generation ultra-high-definition displays. Their research was published in Angewandte Chemie International Edition on July 21, 2026.

In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.

Researchers developed a diffusion-based artificial intelligence system that learns ship navigation directly from real-world voyage data rather than relying on optimization algorithms. Trained on a vessel in Japan's busy and geographically complex Seto Inland Sea, the AI learned to balance multiple objectives simultaneously, including collision avoidance, efficient routing, and speed control. The AI also learned local navigation practices, such as staying within designated traffic lanes, without being explicitly programmed to do so. The study demonstrates that diffusion-based AI can capture human-like navigation behavior from real-world data, offering a promising new approach to autonomous ship navigation in complex maritime environments.

We examine whether penalties for long ground delays with passengers kept on board (i.e., lengthy tarmac delays) deter airline violations or are simply treated as a cost of doing business. Using severe-weather cases as a quasi-natural experiment, we compare penalized and non-penalized airlines with difference-in-differences methods. We find modest and inconsistent improvements. Deterrence is weaker when penalties are small relative to airlines’ net incomes, suggesting that some carriers may view them as routine business costs.