Polyoxometalates (POMs) have broad applicability and significant potential in electrocatalysis and photocatalysis. However, the practical application of pure POMs is significantly constrained by their decomposition in polar media (such as neutral and alkaline solutions). The modification of POMs with metal-calixarene clusters is beneficial for fabricating functional hybrid materials with the combined merits of the two components. Four new thiacalixarene-functionalized polyoxometalate clusters were synthesized by researchers at School of Petrochemical Engineering, Liaoning Petrochemical University, China. These four clusters were characterized by Keggin-type PM₄Mo₈ motifs, which confer redox properties similar to those of PMo₁₂O₄₀³⁻ (PMo₁₂) while providing superior structural stability and electrocatalytic reduction of IO₃⁻. The substitution of four metal ions in PMo₁₂, along with the capping TC4A ligand and VO unit, significantly modulated visible-light absorption, enhancing photothermal conversion in the solid state and organic solutions.

Obesity-related metabolic disorders are driven by insulin resistance and inflammation, yet therapeutic efficacy of endogenous lipid mediators is limited by poor bioavailability and enzymatic degradation. Self-assembled vesicles combining inorganic clusters and lipid mediators improve glucose control, reduce inflammation, and promote weight loss in preclinical study.

In a paper published in Mycology, an international team of scientists mainly reported the isolation and characterization of two new pairs of (±)-penithrones A (1) and B (2), and a chlorinated derivative (±)-penithrone C (3), along with their biogenetic precursors (4–6), from the mangrove-derived fungus Penicillium hispanicum LA032. The study demonstrated significant cytotoxic activity of compounds 1 and 2 against multiple cancer cell lines (IC₅₀ = 5.09–9.47 μmol/L), and identified MAPK10 as a potential molecular target through integrated network pharmacology and molecular docking approaches, providing new insights into fungal-derived anticancer agents.

In the context of global aging, aging has become a key risk factor for chronic diseases and death. It remains uncertain which of the several promising DNA methylation (DNAm)-based algorithms best captures the true state of biological aging. Recently, the team led by Li Xiangwei from Shanghai Jiao Tong University School of Medicine, in collaboration with the team led by Tong Tianlang from Hainan International Medical Center published an article titled "Associations of twelve DNA methylation signatures of aging with mortality" on hLife, bringing new breakthroughs to the field of aging and mortality risk assessment. Based on long-term and population-based cohorts, the team found that GrimAge2 has significant advantages over other DNAm signatures of aging in predicting mortality risk and is expected to become a powerful tool for mortality risk assessment.

As urban drone logistics becomes a practical reality, balancing economic cost, ground safety risk, and noise impact poses a systemic challenge. A recent study proposes a novel approach to design urban low-altitude logistics networks, incorporating noise constraints into a multi-objective optimization framework. By combining a layered network model with a dual-population co-evolutionary algorithm, the research provides a new direction for the low-altitude logistics infrastructure of future cities.

A recent study investigates the contrasting patterns of symbiotic nitrogen fixation (SNF) and asymbiotic nitrogen fixation (ANF) along altitudinal gradients in subtropical forests. The research found that SNF rates declined with increasing altitude due to higher soil nitrogen availability and lower air temperatures, while ANF rates showed a hump-shaped pattern, influenced by soil properties at lower altitudes and climatic factors at higher altitudes. The study underscores the importance of distinguishing between SNF and ANF in ecological studies and Earth system models, providing valuable insights for improving global BNF estimates and refining model predictions.

The Vortex Particle Method (VPM) is a meshless vortex flow simulation approach gaining traction for its efficient simulation of unsteady vortex wakes evolution. However, traditional VPM has huge challenge on accurately simulating complex flows due to its poor numerical stability. Recently, a team of aviation researchers led by Min Chang from Northwestern Polytechnical University in China have developed a Stability-enhanced VPM (SEVPM). These advancements enable stable, high-fidelity simulations of complex flows. The researchers demonstrated that their SEVPM can accurately and stably simulate high Reynolds number flows and shear turbulence. The researchers plan to further validate and refine the Stability-enhanced VPM by applying it to more complex and realistic flow scenarios.

Aircraft conceptual design is a highly complex process involving multidisciplinary trade-offs and creative thinking. Recent advances in generative artificial intelligence (AI) provide promising opportunities to automate and augment this process. A new study, recently published in the Chinese Journal of Aeronautics, presents an AI-driven framework capable of generating aircraft configuration schemes based on design requirements, integrating aerodynamic knowledge and system constraints. This research fills a key gap in intelligent design methodology, offering a new tool to revolutionize the early stages of aircraft development.

High-resolution flow field data are critical for accurately evaluating the aerodynamic performance of aircraft. However, acquiring such data through large-scale numerical simulations or wind tunnel experiments is highly resource-intensive. Flow field super-resolution techniques offer an efficient alternative by reconstructing high-resolution data from low-resolution inputs. While existing super-resolution methods can recover the global structure of the flow, they often struggle to capture fine local details, especially shock waves. To address this limitation, this research proposes the FlowViT-Diff framework that integrates Vision Transformers (ViT) with an enhanced denoising diffusion probabilistic model to simultaneously capture global coherence and local flow features with high fidelity.