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 team led by Prof. MAO Jirong from the Yunnan Observatories of the Chinese Academy of Sciences, in collaboration with international researchers, has recently published a study confirming the presence of a standing shock in low-angular-momentum black hole accretion modes.
A research team led by Prof. JIAO Chengliang at the Yunnan Observatories of the Chinese Academy of Sciences, along with collaborators, has introduced a self-consistent model that addresses long-unresolved theoretical gaps in the study of self-gravitating spherical accretion.
Researchers from the Xinjiang Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences have explored combining cellulose with inorganic rigid fibers, and have developed environmentally friendly and multifunctional air filtration materials.
A team led by Prof. TAN Weihong, Prof. HAN Da, and Prof. GUO Pei from Hangzhou Institute of Medicine of the Chinese Academy of Sciences determined the tertiary structure of a DNA aptamer-ATP 1:1 binding complex, revealed the recognition mechanism, and engineered an optimized DNA aptamer with a submicromolar KD for ATP binding, which exhibited the highest affinity reported for ATP-binding DNA aptamers to date.
Researchers led by Prof. YANG Weicai and Prof. LI Hongju from the Institute of Genetics and Developmental Biology identified two motor proteins, HUG1 and HUG2, which ensure sperm cells successfully reach the egg for fertilization during pollen development.
A research team led by Prof. LI Wenjun from the Xinjiang Institute of Ecology and Geography has made a breakthrough discovery in sustainable agriculture. Their new study reports that a desert bacterium, Nocardiopsis alba B57, can simultaneously fight harmful fungi and promote plant growth, offering a powerful alternative to chemical fungicides, marking a crucial step forward for ecological safety and sustainable agriculture in arid lands.
The uplift and outward growth of Asian's three great plateaus is a major driver of changes in Asian landscape and biodiversity, according to a new study led by Prof. WANG Wei from the Institute of Botany of the Chinese Academy of Sciences.
Researchers from Xishuangbanna Tropical Botanical Garden of the Chinese Academy of Sciences revealed a novel mechanism by which the plant hormone jasmonate controls seed size. They discovered that the jasmonate signaling pathway acts as a negative regulator of seed size in the model plant Arabidopsis thaliana.
A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences has developed a new method for evaluating urban sustainability, leveraging high-resolution data from the SDGSAT-1 satellite.
A new study led by researchers from the Aerospace Information Research Institute of the Chinese Academy of Sciences has for the first time mapped the long-term, large-scale transport pathways of PM2.5 pollution across China spanning from 2000 to 2021, providing scientific support for refining national air quality management strategies.
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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