A new study has revealed how pear trees use a hidden layer of RNA communication to defend themselves against fungal infection. Scientists discovered that a long non-coding RNA named MSTRG.32189 forms a regulatory network with PcmiR399b and PcUBC24 to control phosphate levels and disease resistance. Acting like a molecular decoy, MSTRG.32189 fine-tunes the plant’s internal balance between nutrition and immunity. When its expression decreases during infection, the pear’s defense system activates, boosting resistance through increased phosphate accumulation and reactive oxygen species (ROS) signaling. This finding offers a new window into how plants coordinate nutrient metabolism with immune protection.

A new study has assembled the first high-quality chromosome-level genome of Acer pentaphyllum, one of the world’s most endangered and ornamental maples. The findings reveal how the species’ genome adapted to the harsh dry-hot valleys of western China through expansions and positive selection of genes related to photosynthesis, plant hormone signaling, and pathogen defense. Population genomic analyses across 28 wild populations uncovered low genetic diversity, high inbreeding, and accumulation of harmful mutations, especially at range edges. These results provide essential genetic insights for guiding conservation planning and genetic rescue of this critically endangered species.

A new study led by Nigerian researchers suggests a simple ingredient could make diesel engines cleaner: water. By blending precise amounts of water with diesel fuel, scientists have shown that emissions of several harmful pollutants can be dramatically reduced without major engine modifications.

A new study led by researchers at Tianjin University reveals that particles from shipping exhausts are contributing more to global warming than previously thought. The study identifies “brown carbon,” a light‑absorbing component of ship emissions, as a hidden driver of heat retention in the atmosphere.

Researchers from the University of Melbourne have discovered a sustainable new use for lignite, also known as brown coal, showing how it can clean up toxic heavy metals from wastewater and then be reused as a light‑driven catalyst for breaking down pollutants.

A research team from the Harbin Institute of Technology has developed a new analytical method to accurately calculate the performance of high-torque permanent magnet motors. This approach successfully accounts for the armature magnetic field, which often ignored in traditional methods and leads to significant calculation errors. The new model demonstrates high precision in calculating critical performances like air-gap flux density, back EMF, and output torque, providing a reliable tool for optimizing high-performance motor design.

Artificial intelligence (AI) technology is revolutionizing antimicrobial drug development. In response to increasingly severe antimicrobial resistance challenges, AI can efficiently predict pathogen evolutionary trends, identify potential drug targets, and accelerate compound design and optimization, thereby significantly shortening the development timeline for antimicrobial agents. This correspondence focuses on the applications of AI in phenotype-driven target identification and validation, rational molecular design, and lead compound optimization for antimicrobial drug development, while highlighting current limitations and providing perspectives on future directions.

A research team from Sichuan University, in collaboration with Southeast University and the Hong Kong University of Science and Technology (Guangzhou), has proposed a novel dual three-phase four-level space vector pulse width modulation (DTP-FL SVPWM) strategy to improves the efficiency and precision of high-power motor drives. The proposed strategy is designed for the developed dual three-phase open-winding permanent magnet synchronous motors (DTP-OW-PMSM). This technology achieves high modulation precision and low switching frequency. On this basis, the current harmonics caused by the DC-link voltage deviations are reduced by compensating for the duty ratio.

Researchers at National Institute of Technology Puducherry develop a centroid-synthesized virtual voltage vector method for induction motor drives, cutting switching losses while maintaining dynamic performance.

This research proposes a novel disturbance suppression method for hybrid magnetic bearings (HMB). By constructing a cascaded reduced-order extended state observer, the proposed approach achieves precise estimation and feedforward compensation of motor startup-induced disturbances in the magnetic bearing system, thereby significantly enhancing rotor levitation stability.