African swine fever virus (ASFV) is a large enveloped DNA virus that causes a highly pathogenic hemorrhagic disease in both domestic pigs and wild boars. The ASFV genome contains a double-stranded DNA encoding more than 150 proteins. The ASFV possesses only one protease pS273R, which is important for virion assembly and host immune evasion. Therefore, the specific monoclonal antibody (mAb) against pS273R is useful for ASFV research. Here, we generated two specific anti-pS273R mAbs named as 2F3 and 3C2, both of which were successfully applied for ELISA, Western blotting and Immunofluorescence assays. Further, we showed that both 2F3 and 3C2 mAbs recognize a new epitope of N terminal 1-25 amino acids of pS273R protein, which is highly conserved across different ASFV strains including all genotypes I and II strains. Based on the recognized epitope, an indirect ELISA was established and was effective in detecting antibody during ASFV infection. To conclude, the specific pS273R mAbs and corresponding epitope identified will strongly promote ASFV serological diagnosis and vaccine research.

Using UAV-based multispectral images for quickly and accurately monitoring chlorophyll content is critical for field management and yield estimation. However, the lower model accuracy and the poor robustness of the estimation models are still preventing the widespread application of UAV-based multispectral images. We carried out two field trials at various experimental sites to further enhance the precision and applicability of the model used to estimate the chlorophyll content of potato plants. Firstly, the texture features and vegetation indices derived from multispectral images were screened using the Pearson correlation coefficient method, and Normalized difference red edge (NDRE) performed the best over the two growth periods. Secondly, principal component analysis (PCA) was applied to recombine five bands of multispectral images, and third PCA results (PCA3) was selected to combined with NDRE according to the construction principle of NDRE, and the newly constructed parameter was named improved NDRE (INDRE). Finally, INDRE was used to establish a chlorophyll content estimation model of potato plants, and compared with some traditional parameters. The results demonstrated that the INDRE had the maximum accuracy (R2 = 0.7865, RMSE = 2.1378), and corresponding R2 increased by 0.1481 and RMSE decreased by 1.2994 than NDRE. Additionally, the model was validated using independent data from Experiment 2, and INDRE considerably increased estimation accuracy compared to other factors. In conclusions, the INDRE suggested in this study significantly enhances the accuracy and applicability of the chlorophyll content inversion model and can serve as an additional reference for fertilization management.