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Xueying Li

As seen in: Nature, Wiley Online Library, Elsevier, Frontiers, MDPI, Taylor & Francis Online, ACS Publications, BMC Cancer, Science Magazine, BioRxiv and
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Articles

Multilayer soil moisture depletion intensifies drought impacts on global ecosystems

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By Xihui Gu, Ruijie Wang, Deliang Chen, Chiyuan Miao, Sijia Luo, Yansong Guan, Sixiang Wang, Liangwei Li, Jianfeng Li, Shengzhi Huang, Xueying Li, Jiefeng Wu, Xiang Zhang, Lunche Wang, Tianshun Gu, Chuyu Luo
| Nature Verified
Abstract When soil moisture across all layers of the entire profile is depleted, ecosystems face the highest risk because they lose all vertical buffering capacity against drought. However, these vertically compound soil moisture droughts, in which every soil layer suffers simultaneous water deficits, have been largely ignored because previous monitoring and risk assessments relied on entire-profile mean soil moisture.
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Nanoengineering Interfacial Reconstruction in Cu 2 O@SiO 2 Catalysts to Tune C–C Coupling and Deep Hydrogenation in CO 2 Electroreduction Click to copy article link Article link copied!

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By Wancai Shi, Yuxin Chen, Xueying Li
| ACS Publications
Abstract Click to copy section linkSection link copied! Electrochemical CO2 reduction offers a promising route for converting CO2 into value-added chemicals and fuels; however, switching selectivity between C2+ and CH4 pathways by controlling interfacial reconstruction remains challenging. Herein, we report a simple and effective interface engineering strategy to address this issue.
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E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis

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By Yu Ban, Yipeng Zhang, Xueying Li
| Optica Verified
In our previous study, a home-built handheld OCT system was used to collect OCT images in vocal cord leukoplakia. First, 383 valid OCT images were collected from 12 patients with leukoplakia (including low-risk, high-risk, and malignant types). The best overall accuracy and recall were 92.59% and 93.25% for low-risk, high-risk, and malignant classification, by random forest (RF) model using 5-fold validation.
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