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Caixia Gao

Researcher at Chinese Academy of Sciences

Publications -  177
Citations -  18934

Caixia Gao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Genome editing & CRISPR. The author has an hindex of 46, co-authored 143 publications receiving 12339 citations.

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Journal ArticleDOI

Automatic boat detection based on diffusion and radiation characterization of boat lights during night for VIIRS DNB imaging data.

TL;DR: In this paper , a two-step threshold detection algorithm based on the point spread and the radiative characteristics of nightlight point sources is proposed, so that the interference from adjacent pixels could be reduced as much as possible and a reasonable threshold could be determined.
Patent

Generation and application of herbicide-resistant gene

Caixia Gao, +1 more
TL;DR: In this article, a method for creating new herbicide-resistant plants by a plant base editing technology and screening endogenous gene mutation sites capable of enabling the plants to have herbicide resistance was proposed.
Patent

Method for conducting site-specific modification on entire plant via gene transient expression

TL;DR: In this article, a method for site-directed modification of whole plant through gene transient expression is presented, which is independent of the genotype and recipient, and can be applied to various varieties of various species; T1 mutants can be obtained directly and the mutation can be stable inherited; more importantly, the mutant plant as obtained is free of exogenous genes, and thus have higher bio-safety.
Journal ArticleDOI

Preface to the special topic on genome editing research in China

TL;DR: This special topic of National Science Review presents three timely review articles and three insightful perspectives, along with three interesting research highlights and a valuable interview, on the state-of-the-art nature of genome editing research in China.
Posted ContentDOI

Discovery of deaminase functions by structure-based protein clustering

TL;DR: The authors used AlphaFold2 to predict and subsequently cluster an entire protein family based on predicted structure similarities, and applied these new deaminases to the development of new cytosine base editors with distinct features.