In a leap forward for legume crop research, scientists have assembled a high-quality reference genome for 'D30', an ancient landrace of pigeonpea. 

With its ageing population, the prevalence of dementia in Hong Kong is expected to increase. Healthcare researchers from The Hong Kong Polytechnic University (PolyU) have developed an innovative mobile application-based cognitive remediation programme, Enhancing Memory in Daily Life (E-MinD Life), targeting those who experience memory complaints, mild cognitive impairment (MCI) and mild dementia. The programme, leveraging memory encoding strategies, provides early intervention to delay the functional deterioration associated with cognitive decline, supporting these individuals’ independent living and enhancing their quality of life.

Natural enzymes are highly efficient catalysts with strong substrate specificity, making them ideal for biomedical applications. However, they often face issues such as variability, high costs, challenging preparation processes, and difficulties in large-scale production. This has led to significant efforts in developing effective nanoenzymes and exploring their application potential. In recent years, carbon dots (CDs) have gained attention due to their strong fluorescence, excellent biocompatibility, and low cytotoxicity. Cationic CDs, which possess a positively charged surface, have shown the ability to mimic natural enzyme applications. The positive charge on the surfaces of these nanomaterials significantly influences their fluorescence, biological activity, and interactions with other biomolecules. Therefore, understanding how surface charge affects the performance of CDs is crucial for enhancing their usability. Considerable progress has been made in the design, synthesis, and mechanistic research of enzyme-like cationic CDs, as well as their advanced applications. This article reviews the latest research on the design structure, catalytic mechanisms, biosensing capabilities, and biomedical applications of enzyme-like cationic CDs. First, we review the synthesis strategies for cationic CDs and how surface charge influences their physical and chemical properties. Next, we highlight various applications of these cationic CDs, demonstrating their use in areas such as detection, biomedical applications (including antibacterial agents, gene carriers, and therapeutic agents), catalysis, and more. Finally, we discuss the challenges and obstacles faced in the development of cationic CDs and look forward to exploring new applications in the future.

Researchers from Sun Yat-sen University’s Shenzhen Campus, led by WenYuan Yang and Gege Jianga, have developed a decentralized federated learning framework, DFUN-KDF, to enhance UAV network efficiency. By leveraging federated knowledge distillation, it reduces data transmission by up to 99% while addressing model heterogeneity. A robust filtering mechanism ensures stability by eliminating faulty or malicious data. DFUN-KDF outperforms traditional methods in communication energy efficiency, adaptability, and resilience to node failures and attacks. This scalable solution offers significant potential for large-scale UAV deployments in urban management and logistics.

Accurate and efficient traffic speed prediction is crucial for improving road safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than the one under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Unfortunately, most of the state-of-the-art (SOTA) models do not differentiate the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.

Can metal-based nanoparticles generated by lasers help build smarter, more immersive electronics? In the latest issue of International Journal of Extreme Manufacturing, Jun-Gyu Choi and collaborators from Ajou University and Samsung Electronics present how laser ablation in liquids enables scalable, surfactant-free nanoparticle synthesis tailored for artificial sensory and neuromorphic devices. Their work marks a breakthrough in bridging material science and intelligent electronics, paving the way for high-performance, flexible, and human-like interfaces in the next wave of extended reality technologies.

A 20% increased risk of all-cause dementia was observed among individuals with regular opioid use compared to non-users.

Gaining insights into the complex pathways and key cell populations involved in immune dysregulation can aid the development of therapeutic approaches to treat polytrauma, which is associated with poor patient outcomes. In a new study, researchers from the USA have utilized advanced genetic analysis tools and techniques to reveal the cellular and molecular processes involved in polytrauma-induced immune dysregulation. Their findings advance our current knowledge on polytrauma and indicate actionable targets to treat immune dysregulation.

Increasing numerical studies showed that the simplest Hubbard model on the square lattice with strong repulsion may not exhibit high-temperature superconductivity (SC). It is desired to look for other possible microscopic mechanism beyond the simplest Hubbard model to realize d-wave high-temperature SC. This study proposed that the interplay between the Su-Schrieffer-Heeger electron–phonon coupling (EPC) and the Hubbard repulsion can induce robust d-wave high-temperature SC. Using state-of-the-art density-matrix renormalization group simulations, the researchers shows that d-wave SC emerges in the Su-Schrieffer-Heeger-Hubbard model with strong Hubbard interaction and moderate EPC, paving a possible new route in understanding and looking for high-temperature SC in quantum materials.