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Liqing Zhang

Researcher at Virginia Tech

Publications -  131
Citations -  4628

Liqing Zhang is an academic researcher from Virginia Tech. The author has contributed to research in topics: Gene & Genome. The author has an hindex of 30, co-authored 120 publications receiving 3566 citations. Previous affiliations of Liqing Zhang include University of Chicago & University of California, Irvine.

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Effects of Dairy Manure-Based Amendments and Soil Texture on Lettuce- and Radish-Associated Microbiota and Resistomes.

TL;DR: A controlled, integrated, and replicated greenhouse study revealed that multiple preharvest factors, including antibiotic use during manure collection, composting, biological soil amendment, and soil type, influence vegetable-borne resistomes, and radishes, a root vegetable, carried a greater load of ARGs and species richness than lettuce, a leafy vegetable.
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Parallel Evolution of Genome Streamlining and Cellular Bioenergetics across the Marine Radiation of a Bacterial Phylum.

TL;DR: In this article, the authors track the genome evolution of the globally abundant marine bacterial phylum Marinimicrobia across its diversification into modern marine environments and demonstrate that extant lineages are partitioned between epipelagic and mesopelagic habitats and that these habitat preferences are associated with fundamental differences in genomic organization, cellular bioenergetics, and metabolic modalities.
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Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet

TL;DR: The results indicate that the increment of diversity (ID) is a simple and efficient prediction method for mitochondrial proteins of malaria parasite and performs better than existing methods.
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Herbicide injury induces DNA methylome alterations in Arabidopsis

TL;DR: It is demonstrated that plants respond to herbicide stress through changes in methylation patterns that are, in general, dose-sensitive and, at least partially, stress-specific.
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Data Analytics for Environmental Science and Engineering Research.

TL;DR: In this article, the authors provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications and present current applications of ML algorithms within the ESE domain using three representative case studies.