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Zhen Li

Researcher at Wuhan University

Publications -  3347
Citations -  95191

Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

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Journal ArticleDOI

Overcoming Radioresistance in Tumor Therapy by Alleviating Hypoxia and Using the HIF-1 Inhibitor.

TL;DR: The excellent efficacy of radiotherapy achieved by using a new type of yolk-shell Cu2-xSe@PtSe (CSP) nanosensitizer functionalized with the HIF-1α inhibitor acriflavine (ACF) is reported.
Journal ArticleDOI

Fabrication of cross-linked fluorescent polymer nanoparticles and their cell imaging applications

TL;DR: Biocompatibility evaluation and cell uptake behaviour of the nanoparticles were further investigated to explore their potential biomedical applications and the demonstrated excellent biocompatible made them promising for cell imaging.
Journal ArticleDOI

Functional Disubstituted Polyacetylenes and Soluble Cross-Linked Polyenes: Effects of Pendant Groups or Side Chains on Liquid Crystallinity and Light Emission of Poly(1-phenyl-1-undecyne)s

TL;DR: A group of new poly(1-phenyl-1-undecyne)s with different mesogenic and chromophoric pendant groups or side chains were successfully synthesized and the structural variations were found to greatly affect the mesomorphic and luminescent properties of the polymers as discussed by the authors.
Proceedings ArticleDOI

A new blind robust image watermarking scheme in SVD-DCT composite domain

TL;DR: Experimental results show that the proposed watermarking method performs better than state-of-the-art SVD-based methods, and is comparable with the state- of-art wavelet-based robust image water marking method.
Posted Content

Graph-RISE: Graph-Regularized Image Semantic Embedding

TL;DR: A large-scale neural graph learning framework that allows embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels, Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking.