A semiconductor–metal synergistic interface design via in situ engineering of a Bi/BiOCl heterostructure on Zn anodes was presented. This dual–functional heterointerface enables unprecedented electrochemical performance, including: (i) stable cycling for 2500 h at 10 mA cm^–2 in symmetric cells; (ii) 1000 cycles at 10 A g^–1 for the Zn@Bi/BiOCl//dibenzo[b,i]thianthrene–5,7,12,14–tetraone (DTT) full battery, and 15,000 cycles at room temperature and 7500 cycles at –20 °C for the Zn@Bi/BiOCl//activated carbon (AC) hybrid ion capacitor (HIC), outperforming most reported AZIBs. This breakthrough originates from a dual–functional synergy: Bi nanoparticles serve as zincophilic nucleation guides to expedite homogeneous Zn²⁺ deposition, while the BiOCl semiconductor establishes a built–in electric field with Zn to redistribute interfacial ion/charge flux and elevate the hydrogen evolution barrier. This coordinated regulation simultaneously inhibits Zn dendrite formation, HER, and Zn corrosion, imparting promising applications for Zn anodes in AZIBs. Our work not only resolves the long–standing interfacial instability of Zn anodes but also pioneers a semiconductor–metal heterojunction strategy, offering a universal platform for designing dendrite–free metal batteries operable under extreme thermal and rate conditions.

This review explores how viral infections remodel the host cell’s cytoskeleton and membrane systems to form viral replication factories, facilitating viral replication and assembly. These factories come in various shapes, such as viroplasms, spherules, double-membrane vesicles, and tubes, often derived from host organelles like the endoplasmic reticulum and Golgi apparatus.

In recent years, digital agricultural technology extension services (DATES), leveraging Internet platforms such as WeChat official accounts and mobile applications, have gained popularity, providing a new pathway for agricultural technology dissemination. This service overcomes the temporal and spatial limitations of traditional agricultural technology extension, enabling farmers to conveniently access planting knowledge. Then, can DATES effectively encourage farmers to adopt OMF and contribute to the green transformation of agriculture? Professor Minjuan Zhao from the College of Economics and Management, Northwest A&F University, and her team addressed this question through a survey of farmers in major apple - producing areas in China. The related research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024590).

The POINT platform (http://point.gene.ac/) integrates multi-omics biological networks, advanced network topology analysis, deep learning prediction algorithms, and a comprehensive biomedical knowledge graph. It provides a powerful tool to overcome current bottlenecks in network pharmacology and advance the field.

Dynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing con ditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforce ment learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCEalgorithm.Experimentalcomparisonswithstate-of-the-artbaselinesdemonstratethatSTFRLachievessuperior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.

A research team led by Professor Yumei Zhang from the Academy of Global Food Economics and Policy (AGFEP) at China Agricultural University, in collaboration with Dr. Issa Ouedraogo from the Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), has conducted a study that reveals the structural characteristics and emission reduction potential of agrifood system carbon emissions in China and Africa. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025609).

Investigating structural and hydroxyl group effects in electrooxidation of alcohols to value-added products by solid-acid electrocatalysts is essential for upgrading biomass alcohols. Herein, we report efficient electrocatalytic oxidations of saturated alcohols (C₁-C₆) to selectively form formate using NiCo hydroxide (NiCo–OH) derived NiCo₂O₄ solid-acid electrocatalysts with balanced Lewis acid (LASs) and Brønsted acid sites (BASs). Thermal treatment transforms BASs-rich (89.6%) NiCo–OH into NiCo₂O₄ with nearly equal distribution of LASs (53.1%) and BASs (46.9%) which synergistically promote adsorption and activation of OH⁻ and alcohol molecules for enhanced oxidation activity. In contrast, BASs-enriched NiCo–OH facilitates formation of higher valence metal sites, beneficial for water oxidation. The combined experimental studies and theoretical calculation imply the oxidation ability of C₁-C₆ alcohols increases as increased number of hydroxyl groups and decreased HOMO–LUMO gaps: methanol (C₁) < ethylene glycol (C₂) < glycerol (C₃) < meso-erythritol (C₄) < xylitol (C₅) < sorbitol (C₆), while the formate selectivity shows the opposite trend from 100 to 80%. This study unveils synergistic roles of LASs and BASs, as well as hydroxyl group effect in electro-upgrading of alcohols using solid-acid electrocatalysts.

In a review published in Molecular Biomedicine, a team of Chinese scientists summarizes the pivotal role of N⁶-methyladenosine (m⁶A)—the most abundant chemical modification in eukaryotic mRNA—in cancer biology. The authors describe how m⁶A regulators (writers, erasers, and readers) influence tumor progression, metastasis, treatment resistance, and the tumor microenvironment. They also discuss emerging therapeutic strategies, including small-molecule inhibitors, RNA-based editing technologies, and combination therapies, highlighting m⁶A's potential as a diagnostic and prognostic biomarker and a target for precision oncology.

Lithium–oxygen (Li–O₂) batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage, utilizing ambient air as an energy source, eliminating the need for costly cathode materials, and offering the highest theoretical energy density (~ 3.5 kWh kg^–1) among discussed candidates. Contributing to the poor cycle life of currently reported Li–O₂ cells is singlet oxygen (¹O₂) formation, inducing parasitic reactions, degrading key components, and severely deteriorating cell performance. Here, we harness the chirality-induced spin selectivity effect of chiral cobalt oxide nanosheets (Co₃O₄ NSs) as cathode materials to suppress ¹O₂ in Li–O₂ batteries for the first time. Operando photoluminescence spectroscopy reveals a 3.7-fold and 3.23-fold reduction in ¹O₂ during discharge and charge, respectively, compared to conventional carbon paper-based cells, consistent with differential electrochemical mass spectrometry results, which indicate a near-theoretical charge-to-O₂ ratio (2.04 e⁻/O₂). Density functional theory calculations demonstrate that chirality induces a peak shift near the Fermi level, enhancing Co 3d–O 2p hybridization, stabilizing reaction intermediates, and lowering activation barriers for Li₂O₂ formation and decomposition. These findings establish a new strategy for improving the stability and energy efficiency of sustainable Li–O₂ batteries, abridging the current gap to commercialization.

Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.