An international team led by Prof. John Speakman from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences derived a predictive model by combining classical statistics and machine learning for total energy expenditure, providing a more objective way to assess the validity of food intake records.
A new technology developed by the Single-Cell Center at Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences, in collaboration with several institutions, is poised to transform the discovery of microbial species and enzymes with specific in situ metabolic functions.
A research team led by Prof. LIANG Xinmiao from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences, and collaborators, identified an intestinal bacterium that can reduce dietary sugar intake, opening up new avenues for the therapies of obesity and metabolic diseases.
A research team led by Prof. CHEN Chunying from the National Center for Nanoscience and Technology of the Chinese Academy of Sciences designed a wireless photothermal DBS nanosystem, Au@TRPV1@β-syn nanoparticles. This system achieves precise modulation of degenerated neurons by directly stimulating the endogenous expression of the thermosensitive TRPV1 receptor in neurons.
A research team from the South China Botanical Garden of the Chinese Academy of Sciences discovered that wind speed influences plant hydraulics, even when controlling for other climatic factors such as moisture index, temperature, and vapor pressure deficit.
Researchers from the Wuhan Botanical Garden of the Chinese Academy of Sciences achieved the first high-quality chromosomal-level genome assembly of celeriac using whole-genome sequencing. They also constructed a comprehensive genomic variation map by re-sequencing 177 celery samples.
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