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Zhu Yu

As seen in: Wiley Online Library, Frontiers, MDPI, Cell Press, BeSoccer, Optica, Texas Medical Center, Journal of Intellectual Capital, National Business Daily, Coinlive

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Articles

Correction: Biomimetic metal-organic framework nanoparticles for synergistic combining of SDT-chemotherapy induce pyroptosis in gastric cancer

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By Zhu Yu, Wenlong Cao, Chuangye Han, Zhen Wang, Yue Qiu Verified, Jiancheng Wang, Mengda Wei, Junfu Wang, Siwen Zhang, Shutian Mo, Junqiang Chen, Senfeng Liu
| Frontiers
CORRECTION article Volume 13 - 2025 | https://doi.org/10.3389/fbioe.2025.1644914 There was a mistake in Figure 5 as published. Figure 5G, the statistical chart of Figure 5C, was inadvertently misplaced during the final formatting process. The corrected Figure 5 appears below. Figure 5. In vitro ROS/hypoxia assay and IF. ROS production was detected by fluorescence of DCFH-DA in AGS cells (A) (100×). Intracellular hypoxia imaging using Image-iT™ Green Hypoxia Reagent as the syndicator (B) (200×).
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A Fed rate cut is almost a certainty; this meeting is more like a political stress test.

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By Zhu Yu, Jinshi Data
| Coinlive Verified
Author: Zhu Yu, Jinshi Data This week's Federal Reserve policy meeting is expected to be the most controversial in years, more like a stress test for financial markets to observe how the debate on US monetary policy will unfold in 2026. As the meeting approaches, the market is almost certain that the Fed will cut interest rates, even though the Federal Open Market Committee (FOMC), which is responsible for setting policy, is experiencing its most divided period in years.
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Supervising radar depth completion using the monocular depth large model

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By Jiacheng Ying, Zhu Yu, Zili Zhou, Fuyi Zhang
| Optica Verified
In recent years, radar depth completion has made significant advances in developing backbone networks and high-quality datasets. However, less attention has been paid to optimizing the supervision manner. In this work, we propose a novel supervision method, to the best of our knowledge, using a relative-to-metric conversion (R2MC) module to leverage the generalization capability of the monocular depth large model (MDLM).
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