High glucose uptake by cancer cells is one of the most widely recognized features of tumor metabolism and provides an important biological basis for positron emission tomography/computed tomography (PET/CT) imaging. Although this phenotype is commonly attributed to the energetic and biosynthetic demands of rapid proliferation, glucose-derived carbon is also distributed among multiple downstream pathways.

A research team has uncovered a multilayered regulatory network that controls pear seed germination by integrating metabolomic, transcriptomic, and microRNA analyses. 

A research team has identified an optimal lipid range for granulated microdiets in largemouth bass larvae, showing that dietary fat can strongly shape growth, feed efficiency, nutrient retention, liver condition, and intestinal development.

Many vitreoretinal disorders, including retinal detachment (rhegmatogenous, exudative, or tractional), macular holes, proliferative vitreoretinopathy, diabetic retinopathy, and vitreous hemorrhage, often require vitrectomy with retinal repair, followed by intraocular tamponade to stabilize the retina and preserve ocular integrity. The tamponade agents most used in clinical practice are expansile gases and silicone oil. Although these materials can effectively support postoperative retinal reattachment, they remain limited in terms of long-term stability and biosafety. Complications such as cataract formation, elevated intraocular pressure, intraocular inflammation, and retinal injury associated with silicone oil emulsification may still occur. To address this challenge, Prof. Jianhai Yang and Prof. Wenguang Liu (Tianjin University), together with Prof. Xiaorong Li and Prof. Xiaoli Xing (Tianjin Medical University Eye Hospital) developed an injectable zwitterionic polymer hydrogel based on poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC).

Since the Last Glacial Maximum, the Earth’s climate system has undergone pronounced temperature and hydroclimatic changes. This long-term evolution provides an important context for understanding the natural background of modern climate change and the response of the climate system to external forcing. Over China, climate evolution exhibits substantial regional heterogeneity under the combined influence of the East Asian monsoon and the complex topography of the Tibetan Plateau. Using high-resolution dynamical downscaling simulations, Professor Liang Ning and colleagues at Nanjing Normal University systematically investigated millennial-scale climate evolution over China since the Last Glacial Maximum. The results show that the regional climate model provides a more detailed representation of the spatial heterogeneity of temperature and precipitation associated with complex terrain. In addition, the contrasting Holocene trends of summer cooling and winter warming over northern China, the Tibetan Plateau, and northwestern China provide a new seasonal perspective on the Holocene temperature conundrum between proxy reconstructions and climate-model simulations. The study was published in the 2026 Issue 8 of the Chinese and English editions of SCIENCE CHINA Earth Sciences.

Published in Engineering, this research from PolyU and Guangzhou Institute of Industrial Technology develops a ConvLSTM-Fire deep learning model to repair broken sensor data and reconstruct 2D building temperature fields. Paired with a ChatGPT-4o-based LLM self-driven agent, the hybrid system converts real-time fire thermal data into actionable emergency guidance. Quantitative tests prove full 2D temperature inputs greatly improve the accuracy of LLM firefighting decisions, supporting voice-aided on-site response for occupants and firefighters.

Published in Engineering, this study introduces HCGCDM, an LLM-powered cognitive diagnosis model built on heterogeneous concept graphs. Unlike traditional tools that only capture prerequisite knowledge links, it automatically mines three types of concept relations via RAG. Tailored graph encoders and adaptive weighting boost student proficiency assessment accuracy, with ablation and LLM comparison experiments verifying its effectiveness and the impact of LLM capability on diagnostic performance.

Published in Engineering, this report introduces LLM-IDA, a three-tier multi-agent LLM framework for automated industrial design. It divides design workflows into four functional modules to address LLM hallucination and poor robustness via closed-loop code generation and self-verification. Benchmarked against RAG with pass@10 metrics and ablation tests, the modular system cuts manual engineering overhead and supports cross-domain mechanical design optimization, with its current limits and future research directions covered.

Published in Engineering, this news report covers a comprehensive survey exploring how large language models tackle long-standing flaws in two mainstream autonomous driving frameworks: modularized pipelines and end-to-end neural networks. It details LLM applications across perception, prediction, planning and data construction, lists core deployment barriers like hallucination and latency, and unpacks ongoing debates over AGI and the future architecture of intelligent driving systems.

Traditional manual design of Earth Observation Satellite Scheduling Problem algorithms costs massive expert labor. This research introduces AgentAD, an LLM-powered five-agent collaborative framework that converts natural language scenario descriptions into executable Python scheduling codes via four sequential phases. Tests prove its self-evolved algorithms outperform mainstream human-designed heuristics, with memory stream and chain-of-coding mechanisms stabilizing iterative optimization for both basic and complex satellite task scenarios.