Research on identification method of peanut pests and diseases based on lightweight LSCDNet model.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, are pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with an accuracy, precision, recall, and F1 score of 96.67%, 98.05%, 95.56%, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59M. When compared to established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and Xception, LSCDNet outperformed with accuracy gains of 2.65%, 4.87%, 8.71%, 5.04%, 6.32%, and 8.2% respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application, achieving an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

[Regulating effect of ORF128 on NF-κB signaling pathway of HEK293T cells and its mechanism].

Objective To elucidate the regulating effect of orf virus (ORFV) encoded ORF128 on NF-κB signaling pathway during the infection of HEK293T cells with ORFV and the underlying mechanism. Methods The ORF128 DNA sequences from ORFV/QH02/2010 strain were constructed into eukaryotic expression vectors pCMV-tag2B and pEGFP-N1. During viral infection of cells, the level of ORF128 mRNA was detected by reverse transcription PCR and the subcellular localization of ORF128 protein by laser confocal microscopy. A dual luciferase reporter assay system was used to analyze the regulating effect of ORF128 on NF-κB signaling pathway, and Western blot analysis to detect the nuclear translocation of NF-κBp65 and the phosphorylation of IκBα protein (p-IκBα). Results ORF128 protein was expressed at the early stage and localized in the cell nuclear during ORFV infection, and it inhibited the expression of NF-κB reporter luciferase activity. The expression of the protein blocked the nuclear translocation of NF-κBp65 and the degradation of p-IκBα. Conclusion ORF128 inhibits the activation of NF-κB signaling pathway by blocking the nuclear translocation of NF-κBp65 and the degradation of p-IκBα.