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Plant viruses are tiny pathogenic obligate parasites that cause significant damage to global crop production. They exploit and manipulate the cellular components of host plants to ensure their own survival. In response, plants activate multiple defense signaling pathways, such as gene silencing and plant hormone signaling, to hinder virus propagation. Growing evidence suggests that the regulation of protein homeostasis plays a vital role in the ongoing battle between plants and viruses. The ubiquitin-proteasome-degradation system (UPS) and autophagy, as two major protein-degradation pathways, are widely utilized by plants and viruses in their arms race. One the one hand, these pathways act as essential components of plant's antiviral defense system by facilitating the degradation of viral proteins; on the other hand, viruses exploit the UPS and autophagy to create a favorable intracellular environment for viral infection. This review aims to provide a comprehensive summary of the events involved in protein homeostasis regulation during viral infection in plants. Gaining knowledge in this area will enhance our understanding of the complex interplay between plants and viruses. [ABSTRACT FROM AUTHOR]

To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is one of the major staple foods for global consumption. A major roadblock to global rice production is persistent loss of crops caused by plant diseases, including rice blast, sheath blight, bacterial blight, and particularly various vector-borne rice viral diseases. Since the late 19th century, 19 species of rice viruses have been recorded in rice-producing areas worldwide and cause varying degrees of damage on the rice production. Among them, southern rice black-streaked dwarf virus (SRBSDV) and rice black-streaked dwarf virus (RBSDV) in Asia, rice yellow mottle virus (RYMV) in Africa, and rice stripe necrosis virus (RSNV) in America currently pose serious threats to rice yields. This review systematizes the emergence and damage of rice viral diseases, the symptomatology and transmission biology of rice viruses, the arm races between viruses and rice plants as well as their insect vectors, and the strategies for the prevention and control of rice viral diseases. [ABSTRACT FROM AUTHOR]

The advances in developing more accurate and fast smoke detection algorithms increase the need for computation in smoke detection, which demands the involvement of personal computers or workstations. Better detection results require a more complex network structure of the smoke detection algorithms and higher hardware configuration, which disqualify them as lightweight portable smoke detection for high detection efficiency. To solve this challenge, this paper designs a lightweight portable remote smoke front-end perception platform based on the Raspberry Pi under Linux operating system. The platform has four modules including a source video input module, a target detection module, a display module, and an alarm module. The training images from the public data sets will be used to train a cascade classifier characterized by Local Binary Pattern (LBP) using the Adaboost algorithm in OpenCV. Then the classifier will be used to detect the smoke target in the following video stream and the detected results will be dynamically displayed in the display module in real-time. If smoke is detected, warning messages will be sent to users by the alarm module in the platform for real-time monitoring and warning on the scene. Case studies showed that the developed system platform has strong robustness under the test datasets with high detection accuracy. As the designed platform is portable without the involvement of a personal computer and can efficiently detect smoke in real-time, it provides a potential affordable lightweight smoke detection option for forest fire monitoring in practice. [ABSTRACT FROM AUTHOR]

Plants use RNA silencing as a defense against viruses. In response, viruses encode various RNA silencing suppressors to counteract the antiviral silencing. Here, we identified p22 as a silencing suppressor of cucurbit chlorotic yellows crinivirus and showed that p22 interacts with CsSKP1LB1, a Cucumis sativus ortholog of S-phase kinase-associated protein 1 (SKP1). The F-box-like motif of p22 was identified through sequence analysis and found to be necessary for the interaction using a yeast two-hybrid assay. The involvement of the F-box-like motif in p22 silencing suppressor activity was determined. Proteomics analysis of Nicotiana benthamiana leaves expressing p22, and its F-box-like motif deletion mutant showed 228 differentially expressed proteins and five enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways: ABC transporters, sesquiterpenoid and triterpenoid biosynthesis, ubiquitin-mediated proteolysis, riboflavin metabolism, and cysteine and methionine metabolism. Collectively, our results demonstrate the interaction between p22 and CsSKP1LB1 and show that the deletion of F-box-like motif inhibits p22 silencing suppressor activity. The possible pathways regulated by the p22 through the F-box-like motif were identified using proteomics analysis. [ABSTRACT FROM AUTHOR]