Xiaonan Li

Lys63-linked polyubiquitination of RIG-I is essential in antiviral immune defense, yet the molecular mechanism that negatively regulates this critical step is poorly understood. Here, we report that USP21 acts as a novel negative regulator in antiviral responses through its ability to bind to and deubiquitinate RIG-I. Overexpression of USP21 inhibited RNA virus-induced RIG-I polyubiquitination and RIG-I-mediated interferon (IFN) signaling, whereas deletion of USP21 resulted in elevated RIG-I polyubiquitination, IRF3 phosphorylation, IFN-α/β production, and antiviral responses in MEFs in response to RNA virus infection. USP21 also restricted antiviral responses in peritoneal macrophages (PMs) and bone marrow-derived dendritic cells (BMDCs). USP21-deficient mice spontaneously developed splenomegaly and were more resistant to VSV infection with elevated production of IFNs. Chimeric mice with USP21-deficient hematopoietic cells developed virus-induced splenomegaly and were more resistant to VSV infection. Functional comparison of three deubiquitinases (USP21, A20, and CYLD) demonstrated that USP21 acts as a bona fide RIG-I deubiquitinase to down-regulate antiviral response independent of the A20 ubiquitin-editing complex. Our studies identify a previously unrecognized role for USP21 in the negative regulation of antiviral response through deubiquitinating RIG-I.

As an important research topic in recent years, semantic segmentation has been widely applied to image understanding problems in various fields. With the successful application of deep learning methods in machine vision, the superior performance has been transferred to agricultural image processing by combining them with traditional methods. Semantic segmentation methods have revolutionized the development of agricultural automation and are commonly used for crop cover and type analysis, pest and disease identification, etc. We first give a review of the recent advances in traditional and deep learning methods for semantic segmentation of agricultural images according to different segmentation principles. Then we introduce the traditional methods that can effectively utilize the original image information and the powerful performance of deep learning-based methods. Finally, we outline their applications in agricultural image segmentation. In our literature, we identify the challenges in agricultural image segmentation and summarize the innovative developments that address these challenges. The robustness of the existing segmentation methods for processing complex images still needs to be improved urgently, and their generalization abilities are also insufficient. In particular, the limited number of labeled samples is a roadblock to new developed deep learning methods for their training and evaluation. To this, segmentation methods that augment the dataset or incorporate multimodal information enable deep learning methods to further improve the segmentation capabilities. This review provides a reference for the application of image semantic segmentation in the field of agricultural informatization.

The rapid spread of clubroot disease, which is caused by Plasmodiophora brassicae, threatens Brassicaceae crop production worldwide. Breeding plants that have broad-spectrum disease resistance is one of the best ways to prevent clubroot. In the present study, eight Chinese cabbage germplasm were screened using published CR loci-/gene linked markers. A clubroot resistant gene Crr3 potential carrier ‘85-74’ was detected which linked to marker BRSTS61, however, ‘85-74’ shows different responses to local pathogens ‘LAB-19’, ‘LNND-2’, and ‘LAB-10’ from ‘CR-73’ which harbors Crr3. we used a next-generation sequencing-based bulked segregant analysis approach combined with genetic mapping to detect clubroot resistance (CR) genes in an F2 segregant population generated from a cross between the Chinese cabbage inbred lines ‘85-74’ (clubroot resistant) and ‘BJN3-1’ (clubroot susceptible). The ‘85-74’ line showed resistance to a local pathogen ‘LAB-19’ which was identified as race 4; a genetic analysis revealed that the resistance was conferred by a single dominant gene. The clubroot resistance gene which we named CRd was mapped to a 60 kb (1 cM) region between markers yau389 and yau376 on chromosome A03. CRd is located upstream of Crr3 was confirmed based on the physical positions of Crr3 linked markers. The identification of CRd linked markers can be applied to marker-assisted selection in the breeding of new CR cultivars of Chinese cabbage and other Brassica crops.