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 Prof. ZHANG Shihua from the Academy of Mathematics and Systems Science has proposed a new computational tool, STAGATE, to decipher tissue substructures from spatial resolved transcriptomics. The model uses artificial intelligence technology to integrate spatial location information and gene expression profile of spatial spots. In this algorithm, a graph attention autoencoder is introduced, with a graph attention mechanism in the middle hidden layer, which can learn the heterogeneous similarities between neighboring spots adaptively.
A recent study by researchers from the Xinjiang Astronomical Observatory of the Chinese Academy of Sciences, in collaboration with the Shanghai Astronomical Observatory, Yunnan Observatories, and the University of Heidelberg, has identified four previously unknown primordial open cluster groups in the Milky Way.
An international team led by the National Astronomical Observatories has recently released new findings on dark energy detection. This discovery provides significant experimental support for the Quintom dark energy theory proposed by Prof. ZHANG Xinmin’s team from the Institute of High Energy Physics
A research team led by Eric H. Xu (XU Huaqiang) and WU Canrong from the Shanghai Institute of Materia Medica (SIMM) of the Chinese Academy of Sciences unveiled high-resolution structures of human DP1.
A research team led by Prof. LIU Zhongmin and Prof. YE Mao from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS), in collaboration with Prof. BAO Xiaojun and Prof. ZHU Haibo from Fuzhou University, proposed a theoretical model describing the migration-aggregation behavior of confined metal clusters within individual zeolite. The study was published in Nature.
A research team led by Prof. FU Qiaomei at the Institute of Vertebrate Paleontology and Paleoanthropology of the Chinese Academy of Sciences has analyzed data from 127 ancient humans, dating from 7,100 to 1,400 years ago. The results show that this region is pivotal to understanding the origin of both Tibetan and Austroasiatic (i.e., ethnic groups with a shared language group in South and Southeast Asia) population groups.
Researchers from the Wuhan Botanical Garden of the Chinese Academy of Sciences conducted studies and discovered the role of the FaNAC047-FaNAC058 module in regulating heat-induced leaf senescence in tall fescue.
A recent study led by Prof. CHEN Yaning from the Xinjiang Institute of Ecology and Geography (XIEG) of the Chinese Academy of Sciences has released the Tianshan Watershed Streamflow (TSWS) dataset (1901-2019). The dataset compiles daily streamflow data for 56 watersheds and monthly data for 89 watersheds in the Tianshan Mountains.
A recent study led by Prof. TAO Hui from the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences has revealed that natural environmental factors are the predominant drivers of desertification across Central Asia.
A recent study has introduced a novel data-driven model that distinguishes between human-induced and natural water consumption in croplands, providing valuable insights into the sustainability of arid lake ecosystems.
A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences has released a long-term, high-resolution dataset that tracks global cropland water-use efficiency from 2001 to 2020. The dataset provides annual WUE estimates for croplands worldwide at a one-kilometer spatial resolution. It is expected to be a valuable resource for promoting sustainable agricultural water management.
A research team at the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences has developed a novel degradable silicone-hydrogel coating, delivering impressive synergistic anti-biofouling performance for marine applications.
A research team led by Prof. DI Kaichang from the Aerospace Information Research Institute of the Chinese Academy of Sciences has developed an innovative method to enhance the study of lobate scarps—small reverse fault landforms thought to reflect ancient tectonic activity on the Moon.
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