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Summary Phytoplasmas are specialized phloem‐limited bacteria that cause diseases on various crops, resulting in significant agricultural losses. This research focuses on the jujube witches' broom (JWB) phytoplasma and investigates the host‐manipulating activity of the effector SJP39.We found that SJP39 directly interacts with the plant transcription factor bHLH87 in the nuclei. SJP39 stabilizes the bHLH87 homologs inArabidopsis thalianaand jujube, leading to growth defects in the plants.Transcriptomic analysis indicates that SJP39 affects the gibberellin (GA) pathway in jujube. We further demonstrate that ZjbHLH87 regulates GA signaling as a negative regulator, and SJP39 enhances this regulation.The research offers important insights into the pathogenesis of JWB disease and identified SJP39 as a virulence factor that can contribute to the growth defects caused by JWB phytoplasma infection. These findings open new opportunities to manage JWB and other phytoplasma diseases. 

Clone detection techniques have been explored for decades. Recently, deep learning techniques has been adopted to improve the code representation capability, and improve the state-of-the-art in code clone detection. These approaches usually require a transformation from AST to binary tree to incorporate syntactical information, which introduces overheads. Moreover, these approaches conduct term-embedding, which requires large training datasets. In this paper, we introduce a tree embedding technique to conduct clone detection. Our approach first conducts tree embedding to obtain a node vector for each intermediate node in the AST, which captures the structure information of ASTs. Then we compose a tree vector from its involving node vectors using a lightweight method. Lastly Euclidean distances between tree vectors are measured to determine code clones. We implement our approach in a tool called TECCD and conduct an evaluation using the BigCloneBench (BCB) and 7 other large scale Java projects. The results show that our approach achieves good accuracy and recall and outperforms existing approaches.