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Jiayin Zhang on Muck Rack

Jiayin Zhang

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Articles

Exploring dynamic changes in metabolic profiles of Artemisia argyi leaves across different growth stages via plant metabolomics

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By Yue Zhong, Jiayin Zhang, Jialei Liu, Yuguang Zheng, Dan Zhang, Lei Wang, Long Guo, Yiyang Ren
| Frontiers
Abstract Introduction: Artemisia argyi Levl. et Vant is a traditional Chinese medicinal herb with considerable medicinal value. A. argyi Levl. et Vant. leaves (AAL) have exhibited diverse biological activities that are tightly associated with their chemical components. During growth, the dynamic accumulation trends of bioactive metabolites were varied along with AAL developing.
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Tension between spatial pattern and hydrological adaptability in early Yangtze River Basin settlements

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By Jiayin Zhang, Fang Wang, Ying Dong, Changping Zhang, Xie Hu, Chao Ning, Jianli Chen, Jianbao Liu, Yiyong Chen, Jianguo Liu, Qin Fang, Shaoyi Mao
| Nature Verified
Abstract Early settlements faced hydrological opportunities and constraints that informed transitions to urban living. Along the way, imbalances between urban expansion and infrastructural development and hydrological systems could threaten the sustainability of growing settlements. Here using archeological data and spatial modeling, we examine 2,106 early settlements dating from 8.5 ka BP to 2.72 ka BP in the middle reaches of the Yangtze River Basin.
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A deep learning system for non-invasive breast cancer diagnosis with multimodal data

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By Yonghao Li, Jiadong Zhang, Haoyuan Chen, Long Yang, Yu Xie, Qi Xu, Jun Li, Ling Dai, Zhenlu Yang, Yingnan Luo, Jue Zhao, Yu Yin, Shijie Wang, YUAN ZHANG, Zhuolin Li, Zhen Li, Rui Zheng, Jianping Wu, Zhihui Shi, Jun Liu, Jiayin Zhang, Rongpin Wang, Zhongxiang Ding, Jing Ke, Zhenhui Li, Dinggang Shen, Pinxiong Li, Zheren Li, Ruoxia Shen
| Nature Verified
Abstract Early and accurate diagnosis of breast cancer is critical for minimizing needle biopsies and enhancing patient outcomes and requires effective integration of multimodal information. In this article, we introduce a breast cancer intelligent non-invasive diagnosis system (BINDS) to integrate multimodal medical imaging data for breast cancer risk assessment and subtype classification.
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