Published by Wiley for the South China Botanical Garden, Chinese Academy of Sciences, the peer‑reviewed open‑access journal Biological Diversity is calling for papers for its new special issue that decodes biodiversity via evolutionary mechanisms. Moving beyond single‑region or single‑taxon limitations, this issue encourages integrated research covering speciation, genomic adaptation, reticulate evolution, community assembly, eco‑evolutionary feedbacks, biogeography and evolutionary‑informed conservation, and accepts theoretical modelling, experiments, field surveys, comparative analyses and methodological innovations. Guest‑edited by Professor Pat Heslop‑Harrison, the special issue waives article processing charges (APCs). Manuscripts should be designated for this special issue during online submission, with a submission deadline of 31 August 2027.

In the era of artificial intelligence and machine learning, all-optical computing has garnered renewed attention for its intrinsic advantages in processing speed, energy efficiency, and parallelism. Scientists have developed a new class of metasurface optical differentiators capable of simultaneously performing arbitrary-order optical differentiation and high-resolution imaging in a standalone single-layer configuration. The technique provides a realistic path towards realization of ultracompact all-optical computing devices with potential applications in biological imaging, advanced information processing, and beyond.

Optical tweezers manipulate microparticles via optical forces and torques. Researchers from China and Singapore demonstrate that particle morphology and rotation angle critically govern optical torques and forces under paraxial trapping system. Non-spherical particles may exhibit positive torques, negative torques, or rotational freezing accompanied by stable lateral drift. Originating mainly from geometric asymmetry, this mechanism completes a missing element in optical manipulation and enables shape-based optical sorting.

Forests are a critical frontline in the fight against climate change, but the complex details of their carbon storage have long been hidden in a blur of overlapping canopies. 

With the continuous advancement of deep-space exploration missions, the potential threat of microorganisms in the closed environment of space stations to astronauts' health has attracted increasing attention. Fusobacterium nucleatum, as an opportunistic pathogen colonizing the oral cavity and mucosal sites, is closely associated with various diseases including periodontal disease and colorectal cancer. Under extreme space conditions such as microgravity and radiation, the growth, virulence, drug resistance, and metabolic characteristics of microorganisms may undergo significant alterations, thereby increasing the risk of infection for astronauts. However, current research on the effects of microgravity on anaerobic bacteria remains extremely limited, and the phenotypic changes and molecular regulatory mechanisms of F. nucleatum under simulated microgravity conditions have not yet been elucidated, making it difficult to provide a scientific basis for the prevention and control of anaerobic bacterial contamination and infection risk assessment in space stations.

With the exponential increase in the number of objects in low Earth orbit (LEO), collision risks have risen correspondingly, making high-precision orbit prediction a core requirement for ensuring satellite safety and space environment management. Two-Line Elements (TLEs) are the only publicly available orbital data source and are widely used in conjunction with the Simplified General Perturbations 4 (SGP4) model for LEO object orbit prediction. However, LEO objects are significantly affected by complex time-varying perturbations such as atmospheric drag, causing model prediction errors to grow rapidly over time—short-term errors can reach hundreds to thousands of meters, while medium- and long-term prediction errors may exceed 10 km. Although traditional dynamical models can achieve relatively high accuracy through numerical integration, they incur substantial computational costs and suffer from limited atmospheric density modeling accuracy (only about 5%–15%). Analytical methods, while computationally efficient, struggle to handle complex perturbations. In recent years, bidirectional long short-term memory (BiLSTM) networks have demonstrated strong capabilities in time-series prediction; nevertheless, an effective approach for systematically modeling and compensating for TLE-based prediction errors using solely publicly available TLE data has remained lacking.

Gastric cancer remains a major global health challenge, and therapeutic resistance continues to limit the effectiveness of existing HER2-targeted treatments. A research team from Jiangsu University has developed a bispecific cellular nanovesicle platform that simultaneously targets CD16-positive natural killer (NK) cells and HER2-positive gastric cancer cells. The engineered nanovesicles enhance NK cell recruitment, activation, cytokine secretion, and tumor-killing activity, providing a promising strategy for next-generation cancer immunotherapy.