A new study has developed a sophisticated spatial modeling framework that provides an unprecedented, fine-resolution view of heat-related health risks. 

In the pollination evolution of angiosperms, oil flowers represent a highly unique group. Approximately 2000 species across 11 families secrete lipids (rather than traditional nectar) through specialized oil glands as floral rewards. Oil-offering plants rely heavily on specialized oil-collecting bees for pollination, with females using specialized brushes on their legs to collect lipids for nest-building and larval provisioning. Among them, Macropis (Melittidae) is a classic genus of oil-collecting bees known for their strict oligolecty on the genus Lysimachia (Primulaceae). However, for decades, studies in Europe and North America generally assumed that Macropis bees primarily associate with oil-secreting loosestrifes, where males also patrol for mates. Prior to this work, specialized visits by Macropis bees—especially males—to non-oil, nectar-bearing Lysimachia species had not been documented.

This study develops a novel “Panning for Gold” framework that integrates Koopman analysis with clustering algorithms to identify representative water quality dynamic data for wastewater treatment plants (WWTPs). Using a full-scale Anaerobic-Anoxic-Oxic process in southern China as a case study, the research demonstrates that machine learning models can maintain predictive accuracy with 43.59% less training data, establishing a low-data-requirement paradigm for efficient water system management.

Researchers developed a palladium-engineered nickel phosphide catalyst for selective bio-oil upgrading. In anisole tests at 200 ℃, NiPdP achieved near-complete conversion and 97.28% selectivity to cyclohexane; with m-cresol at 250 ℃, selectivity to methylcyclohexane reached 95.72%. Palladium altered catalyst formation, lattice strain and electronic structure, helping suppress unwanted carbon–carbon bond cleavage, sintering and coke formation while supporting reuse over 10 cycles.

The present study investigated the disease burden and associated productivity losses attributable to T2DM among adults, while also forecasting the disease burden through 2050. The study adopted a quantitative approach and drew upon datasets obtained from the Global Burden of Disease (GBD) 2021 database, the World Bank, the United Nations, and the International Labor Organization. Projections based on the BAPC model indicate that the ASDR will rise to 2243.12 per 100,000 population by 2050. The estimated annual productivity loss attributable to T2DM in the region exceeds 220 billion U.S. dollar (USD). Comprehensive and sustainable interventions, including health policies, educational initiatives, and social protection measures, are needed to reduce disparities associated with T2DM.

We are pleased to present a curated collection of our recent cover articles, featuring breakthroughs ranging from enzyme–chemical interaction prediction, genome-scale modeling and novel biosensor development to metabolic engineering of industrially relevant microbial hosts and next-generation gene-editing tools. These studies highlight the field's rapid evolution from fundamental tool discovery to translational industrial applications.

Researchers from the Dalian Institute of Chemical Physics with collaborators revealed that active motifs can be generated insitu through the reconstruction of NiO surface during POM, identifying the atomic-scale origin of catalytic activity.

Published chemical literature has accumulated a wealth of reaction data, but this data comes from diverse sources, is unevenly distributed, and is clearly biased towards highly selective "successful experiments." Can such literature data be used for reliable machine learning modeling and further guide new catalytic reactions? Professor Luo Sanzhong's team and collaborators at Tsinghua University recently reported OrgAIcat, a machine learning framework for asymmetric organocatalysis. The research team manually compiled over 22,000 data points on Aldol and Michael addition reactions from 844 papers to construct the iSynth dataset. Based on this, the team developed R-SPOC, a reaction descriptor that requires no quantum chemical calculations, for predicting enantioselectivity and screening catalysts. When OrgAIcat was combined with Bayesian optimization, a challenging Aldol reaction was improved from 41% ee to 96% ee, with the entire optimization process requiring only 12 experiments. These results demonstrate that after rigorous compilation, proper characterization, and sufficient validation, historical literature data can still be used to guide experiments, despite its heterogeneity and publication bias. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

Antibiotic resistance is increasingly recognized as a major public health and environmental challenge, yet the risks are not evenly distributed across aquatic environments. A new study published in Biocontaminant finds that river-type drinking water sources generally carry higher risks from antibiotics, antibiotic resistance genes, and resistant bacteria than reservoir-type sources, highlighting the importance of considering water source type when designing monitoring and pollution-control strategies.

Excess nitrogen entering rivers, lakes, and groundwater is a major environmental challenge worldwide. Fertilizers, livestock waste, domestic sewage, atmospheric deposition, and other diffuse sources can all contribute to nitrogen pollution, yet determining exactly where the nitrogen comes from and how it moves through a watershed remains difficult.