Z
Zhenyu Lu
Researcher at University of Utah
Publications - 52
Citations - 1679
Zhenyu Lu is an academic researcher from University of Utah. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 21, co-authored 42 publications receiving 1394 citations. Previous affiliations of Zhenyu Lu include Lanzhou University & Chinese Ministry of Education.
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Isoechinulin-type alkaloids, variecolorins A-L, from halotolerant Aspergillus variecolor.
TL;DR: In this article, 12 new compounds, variecolorins A−L (1, 12), together with eleven known analogues (13, 23), were isolated from the broth of a halotolerant fungus, Aspergillus variecolour.
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Citrinin Dimers from the Halotolerant Fungus Penicillium citrinum B-57
Zhenyu Lu,Zhenjian Lin,Wen-Liang Wang,Lin Du,Tianjiao Zhu,Yuchun Fang,Qianqun Gu,Weiming Zhu +7 more
TL;DR: From the ethyl acetate extract of P. citrinum B-57, two new citrinin dimers, pennicitrinone C and penicitrinol B, and 11 known related compounds were isolated and identified by spectroscopic and chemical methods and showed antioxidative activity against DPPH radicals.
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Cytotoxic Polyphenols from the Marine-Derived Fungus Penicillium expansum
Zhenyu Lu,Huajie Zhu,Peng Fu,Yi Wang,Zhihua Zhang,Hai-Peng Lin,Peipei Liu,Yibin Zhuang,Kui Hong,Weiming Zhu +9 more
TL;DR: Two new polyphenols containing both phenolic bisabolane sesquiterpenoid and diphenyl ether units and two known compounds, (S)-(+)-sydonic acid (5) and diorcinol (6), were isolated from the metabolites of the marine-derived fungus Penicillium expansum 091006 endogenous with the mangrove plant Excoecaria agallocha.
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Penicillenols from Penicillium sp. GQ-7, an Endophytic Fungus Associated with Aegiceras corniculatum
TL;DR: Six new tetramic acids derivatives, penicillenols A (1), A(2), B(1), B (2), C(1, and C(2) (1-6), together with citrinin, phenol A acid, Phenol A, and dihydrocitrinin were identified from Penicillium sp.
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Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data
TL;DR: This article proposed a hierarchical classification method to detect impervious surfaces through a fusion of optimized artificial immune networks (OPTINC) and decision trees at high spatial resolution, and evaluated the method using multi-sensor data (i.e., WorldView-2 and LiDAR data) to map impervious surface.