Tomato bacterial wilt, caused by Ralstonia solanacearum, is a persistent and destructive disease that leads to serious yield losses worldwide. 

Polyploid plants often undergo extensive genomic restructuring, which can generate new traits and enhance adaptability. 

In a remarkable leap forward for green chemistry, researchers at the School of Life and Environmental Science, Shaoxing University, China, have developed an innovative method to efficiently adsorb and reduce Au(III) ions to gold particles using cost-effective chitosan-functionalized cellulose nanofibers. This groundbreaking study, titled "Efficient Adsorption and Reduction of Au(III) to Gold Particles Using Cost-Effective Chitosan Functionalized Cellulose Nanofiber," offers a sustainable and eco-friendly solution for gold recovery, led by Prof. Baowei Hu.

A new genomic study provides a breakthrough in understanding cold tolerance in pomegranates, a major hurdle for cultivating prized soft-seeded varieties.

Fruit ripening in climacteric species is tightly controlled by ethylene, yet the regulatory influence of melatonin during this process has remained unclear. 

Simultaneous localization and mapping (SLAM) is widely used in autonomous driving, augmented reality, and embodied intelligence. In real-world settings, sensor measurements often suffer from substantial clutter (false alarms) and missed detections, which complicate SLAM data association. This complexity manifests as uncertainty in associating observations to landmarks, the possibility of erroneous associations between clutter and landmarks, and the potential absence of landmark observations. Random Finite Set (RFS) theory offers a Bayesian estimation framework well suited to SLAM with uncertain data association and an unknown, time-varying number of landmarks, and has spurred extensive research on RFS-based SLAM methods. Particle-filter-based Probability Hypothesis Density (PHD)-SLAM can effectively estimate the joint probability density of the pose and the map under clutter and missed detections, yielding robust SLAM performance. However, improving the estimation accuracy of particle-filter PHD-SLAM typically requires increasing the number of particles, which rapidly scales the computational cost.