A research team led by Prof. WAN Yinhua from the Institute of Process Engineering has developed a machine learning framework to analysis virus filtration processes in therapeutic protein purification. The new method enables intelligent identification of critical parameters affecting virus retention efficiency and provides predictive guidance for process optimization.
A research team led by Prof. WANG Shuqiang from the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences introduced a Prior-Guided Adversarial Learning with Hypergraph (PALH) model for predicting abnormal connections in Alzheimer's disease.
A research team led by Dr. HAN Wenbiao from the Shanghai Astronomical Observatory found compelling evidence that a recent binary black hole merger, known as GW190814, likely occurred in the gravitational field of a third compact object, possibly a supermassive black hole.
A research team from the Xinjiang Astronomical Observatory (XAO) of Chinese Academy of Sciences used the IRAM 30-meter telescope in Spain to observe a large number of molecular clouds at the Galactic edge with a Galactocentric distance of 14-22 kiloparsecs.
Research teams led by Prof. ZHU Zhengjiang and Prof. CHEN Yiyun at Shanghai Institute of Organic Chemistry developed a subcellular photocatalytic labeling strategy that enables organelle-selective lipid analysis by mass spectrometry and the quantitative profiling of lipid transport between organelles.
A collaborative research team from the Xinjiang Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences, along with researchers from the Lawrence Livermore National Laboratory in the U.S. and the International Iberian Nanotechnology Laboratory in Portugal, has revealed at the atomic scale how factors such as size, defect density, and approach pathways influence crystal growth through coalescence.
In a collaborative research effort, Prof. LAI Ren from the Kunming Institute of Zoology of the Chinese Academy of Sciences, together with Prof. NI Heyu from the University of Toronto, has uncovered a novel link between gut microbiota-derived palmitic acid and increased thrombosis risk in cardiovascular disease.
A study led by Prof. LI Hong from the Shanghai Institute of Nutrition and Health, and Assoc. Prof. HU Bo from the Zhongshan Hospital, Fudan University, reported a scalable, data-driven computational framework for designing combinatorial immunotherapies, offering hope for patients with poor responses to current immunotherapies.
A new study led by Dr. DUO Jia from the Xinjiang Institute of Ecology and Geography (XIEG) of the Chinese Academy of Sciences unveils a novel and environmentally friendly approach to remediating saline-alkali soils using cotton straw, a by-product of agricultural practices.
A research team led by Prof. LI Yuqiang from the Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences has uncovered the mechanisms behind the formation of self-organized Turing patterns in biocrusts. This self-organizing ability of biocrusts has significant implications for ecosystem functions and the resilience of dryland ecosystems.
A recent study offers the first city-level analysis of urban sustainability trends across over 7,000 urban centers in the Belt and Road Initiative region. Using multi-temporal Earth observation data, the study assesses two indicators tied to Sustainable Development Goal 11—Land Use Efficiency and population-weighted PM2.5 concentrations—from 2000 to 2020. It delivers a spatiotemporal breakdown of how urban expansion and environmental exposure intersect with sustainability.
A research team led by Prof. CHENG Tianhai from the Aerospace Information Research Institute of the Chinese Academy of Sciences has made a breakthrough by developing a high-resolution satellite remote sensing method to quantify global methane emissions from landfills.
A research team from the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences has developed a new method to enhance the efficiency of dynamics modeling for industrial robots, tackling long-standing bottlenecks in real-time torque computation.
A research team led by Prof. SUN Youwen from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed two innovative artificial intelligence systems to enhance the safety and efficiency of fusion energy experiments.
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