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Ziying Li

As seen in: Nature, Wiley Online Library, Cambridge University Press & Assessment, Journal of Automated Methods and Management in Chemistry, Frontiers, MDPI, SAGE Publishing, Taylor & Francis Online, ACS Publications, Royal Society of Chemistry and
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Articles

Effects of magneto-mechanical coupling on enhanced magnetic memory signals of defective coiled tubing

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By Tianyu Huang, Xu Luo, Yu Li, Wenbo Jiang, Ziying Li
| Nature Verified
Abstract Coiled tubing (CT) is subjected to complex magneto-mechanical loading conditions during service, making it susceptible to defects such as cracks, pits, and perforations that seriously threaten operational safety.
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Chromosome-level genome assembly of the Shiqi pigeon ( Columba livia )

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By Wei Zhang, Yanlin Zhang, Shiqi Zheng, Ziying Li, Wuhao Chen, Yunyan Huang, Guolong Zhang, Yitian Chen, Zhiyong Qiu, Zhengsheng Li, Xiaofei Xu Verified, Yiwei Chen, Jie Peng, Yayan Liang, Haopeng Lu, Shuangyan Mo, Guohua Tan, Zhongyong Gou, Zewu Wang, Yuanhui Lie
| Nature Verified
Abstract The Shiqi pigeon (Columba livia), an economically important meat breed originating from Zhongshan, China, serves as a valuable model for investigating the genetic evolution of this local breed and the evolutionary dynamics of birds. Here, we present a chromosome-level genome assembly of the Shiqi pigeon generated using PacBio HiFi reads (assembled with Hifiasm), and Hi-C data (scaffolded with ALLHiC).
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Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS

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By Hongyi Chen, Yang Gu, Guangwei Zhang, Yan Zhang, Xiaohui Duan, Ziying Li, Jiaqi Lin, Xiaoling Yi, Mansheng Chen, Tianqi Yang, Xuanqi Huang, Guoqiong Zeng, Huijun Hu, Riyu Han, Hao Wu, Peiyuan Lai, Changdong Wang, Yunfang Yu, Tao Yu, Mohsen Guizani, Leqi Zheng
| Nature Verified
Abstract Sepsis-Induced Acute Respiratory Distress Syndrome (SI-ARDS) presents significant diagnostic and prognostic challenges due to its complex clinical manifestations and high mortality rate. We developed a deep Knowledge-Driven multi-Modal Fusion (KDMF) framework for the accurate diagnosis and prognosis of SI-ARDS. The model leverages multi-modal data, including CT images, CT reports, and laboratory indicators, alongside a disease-specific knowledge graph.
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