Zhonghua 11 and Taichung 65 exhibit distinct physiological and molecular responses to brassinosteroids despite their close japonica relationship.

A near-isogenic line, d61-zh11, was developed to transfer the classical brassinosteroid (BR)-insensitive mutation into a modern functional genomics background.

d61-zh11 provides a standardized reference mutant for the reproducible and system-level dissection of BR signaling in rice.

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.

Scientists analyzed phytoliths, silica microfossils from plants, collected from archaeological pits dug in Cloggs Cave, a special place of the GunaiKurnai First Nations people. Almost all (up to 97%) of the phytoliths came from grasses of a range of species. Up to 18% of the phytoliths were melted or burnt, which means that they were purposively carried in by people. The authors concluded that the Old Ancestors selectively brought in grasses for burning. Approximately 2,000 years ago, grasses were burnt around a small standing stone used to perform magic. Ethnographers and oral traditions have reported that such rituals using ash from burnt plants took place across GunaiKurnai Country during the 19th century.

Researchers systematically evaluated the rationality of eight traditional inert powders to suppress accumulated nano-aluminum dust layers generated by additive manufacturing, revealing severe hidden fire risks of conventional inerting methods and offering theoretical guidance for dust explosion prevention in 3D printing metal processing. The study, published in Defence Technology, tested ignition sensitivity and flame spread characteristics of nano-aluminum dust mixed with three categories of inertants, and clarified distinct combustion-promoting mechanisms at low-to-medium mass fractions.

A physics- and spatially informed diffusion model was developed to achieve rapid Monte Carlo–consistent dose prediction for CyberKnife radiotherapy. The model integrates finite-size pencil beam dose distributions, planning CT images, and signed distance maps to incorporate physical, anatomical, and spatial prior information. PSIDMViT improved agreement with Monte Carlo reference doses across head-and-neck, lung, and liver cancer cases. The model reduced the time required for Monte Carlo–consistent dose estimation from approximately one hour to about 18 minutes, supporting more efficient high-precision radiotherapy planning and verification.

This study develops a novel “Hydrology–Environment–Ecology” framework that integrates aquatic ecological risk (AER) with landscape ecological risk (LER) across lake basin boundaries. Using Chaohu Lake—one of China's largest freshwater lakes—as a case study, the research reveals that both aquatic and terrestrial ecological risks increased significantly from 2000 to 2019, with habitat quality identified as the dominant driver of aquatic risk. The framework identifies an 8-km riparian buffer zone as the critical threshold for integrated conservation, and uncovers human activity as the dominant factor elevating landscape ecological risk through both direct and indirect pathways. The study provides a transferable paradigm for cross-scale lake ecosystem management.