Researchers engineered cultured mammalian cells to produce human milk oligosaccharides (HMOs), complex sugars naturally found in human milk. By introducing α-lactalbumin and modifying glycosyltransferase expression, they were able to alter the HMO profiles produced by the cells. Cell-derived HMOs also supported the growth of beneficial gut bacteria, highlighting the potential of this system for studying HMO biosynthesis and future applications in infant nutrition.

A localized study in Tiaty, Kenya, shows how climate variability and land-use change are reshaping pastoral and agro-pastoral livelihoods. Using satellite imagery, climate records, agroecological information, and socioeconomic data, researchers found shifts in vegetation, rainfall seasonality, livestock choices, and settlement patterns. The findings highlight the need for locally tailored adaptation strategies that support resilient livestock systems, sustainable rangeland management, and climate-smart farming.

A new study analyzed data from multiple independent sources, ranging from satellite remote sensing-based machine learning products to state-of-the-art Earth System Models, to reveal how climate change shapes the Northern Eurasian carbon sink. The study confirms that Northern Eurasia accounts for nearly one-third of the global terrestrial carbon sink. It also shows that, despite warming faster than most regions worldwide, the northern high latitudes contribute little to strengthening the Northern Eurasian carbon sink because of their low baseline carbon uptake.

Professor Sungjin Ahn's team in the School of Computing has developed NEO (Neural Theorizer), an AI model that learns executable theories of how the world works from observation alone, rather than simply predicting what happens next

Selected for an oral presentation at ICML 2026 — the top 0.7% of 23,918 submissions — and for the Best Paper Award at the award of Compositional Learning Workshop

Like a child learning how the world works, the model explains unfamiliar phenomena by recombining rules it has already learned, pointing toward a potential foundation for intelligent robots and autonomous agents

A new study published in Big Earth Data proposes an operational framework that integrates multiple machine learning and deep learning models with high-resolution Sentinel-2 data to estimate agricultural drought conditions in irrigated and non-irrigated maize fields in Eastern Austria. Results from 2018–2023 show that deep learning models, particularly the Deep Neural Network (DNN), achieved the best performance, while non-irrigated fields exhibited higher prediction accuracy due to clearer drought signals, demonstrating the value of Sentinel-2–based deep learning for operational drought monitoring.

Findings from a new study comparing the effects of different beverages on mouth acidity suggest that unsweetened sparkling water may be better for your teeth than sugar-sweetened soda.

Litchi fruit harbors beneficial surface microbes that change predictably during storage. A 9‑day study of litchi peel microbiota identified multiple key bacterial genera linked to storage progression. A random forest model based on these biomarkers accurately predicted fruit freshness. Among the isolates, Gluconobacter sp. Lc45 strongly inhibited two major postharvest pathogens (Peronophythora litchii and Colletotrichum gloeosporioides), offering the potential of native microbiota for freshness monitoring and eco‑friendly disease control.

Researchers have developed a solid-state electrolyte design strategy that enables rapid lithium-ion transport while using substantially less lithium. By forming connected interfacial networks inside oxychloride electrolytes, the strategy combines high ionic conductivity with tunable properties, including high-voltage stability. The concept was validated in long-life, high-rate all-solid-state batteries and may provide a new route for designing a family of lean-lithium solid electrolytes.

A recent study published in National Science Review developed an open-access interactive map of ARGs in Chinese soils. It revealed that the relative abundance of total ARGs and Rank I ARGs in croplands declined by 52.6% and 77.0%, respectively, after 2016, coinciding with the implementation of China’s National Action Plan to Contain Antimicrobial Resistance (2016–2020). The study further highlights chemical pollutants and agricultural activities as important factors for future environmental AMR management.