CgCFEM1 Is Required for the Full Virulence of Colletotrichum gloeosporioides.

Colletotrichum gloeosporioides is widely distributed and causes anthracnose on many crops, resulting in serious economic losses. Common fungal extracellular membrane (CFEM) domain proteins have been implicated in virulence and their interaction with the host plant, but their roles in C. gloeosporioides are still unknown. In this study, a CFEM-containing protein of C. gloeosporioides was identified and named as CgCFEM1. The expression levels of CgCFEM1 were found to be markedly higher in appressoria, and this elevated expression was particularly pronounced during the initial stages of infection in the rubber tree. Absence of CgCFEM1 resulted in impaired pathogenicity, accompanied by notable perturbations in spore morphogenesis, conidiation, appressorium development and primary invasion. During the process of appressorium development, the absence of CgCFEM1 enhanced the mitotic activity in both conidia and germ tubes, as well as compromised conidia autophagy. Rapamycin was found to basically restore the appressorium formation, and the activity of target of rapamycin (TOR) kinase was significantly induced in the CgCFEM1 knockout mutant (∆CgCFEM1). Furthermore, CgCFEM1 was proved to suppress chitin-triggered reactive oxygen species (ROS) accumulation and change the expression patterns of defense-related genes. Collectively, we identified a fungal effector CgCFEM1 that contributed to pathogenicity by regulating TOR-mediated conidia and appressorium morphogenesis of C. gloeosporioides and inhibiting the defense responses of the rubber tree.

An Adaptive Human Posture Detection Algorithm Based on Generative Adversarial Network.

Human posture equipment technology has advanced significantly thanks to advances in deep learning and machine vision. Even the most advanced models may not be able to predict all body joints accurately. This paper proposes an adaptive generative adversarial network to improve the human posture detection algorithm in order to address this issue. GAN is used in the algorithm to detect human posture improvement. The algorithm uses OpenPose to detect and connect keypoints and then generates heat maps in the GAN system model. During the training process, the confidence evaluation mechanism is added to the system model. The generator predicts posture, while the resolver refines human joints over time. And, by using normalization technologies in the confidence evaluation mechanism, the generator can pay more attention to the prominent body joints, improving the algorithm's body detection accuracy of nodes. In MPII, LSP, and FLIC datasets, the proposed algorithm has shown to have a good detection effect. Its positioning accuracy is about 95.37 percent, and it can accurately locate the joints of the entire body. Several other algorithms are outperformed by this one. The algorithm described in this article has the best simultaneous runtime in the LSP dataset.