Salt stress has become a major problem in the tomato planting process, of which peroxidase (POD) activity is an important parameter reflecting the antioxidant capacity of plants. In order to explore the dynamic changes of catalase in leaves under different concentrations of NaCl stress, it is necessary to establish a rapid detection technology for changes of POD activity in micro-areas of leaves. In this study, a total of 139 microscopic images were obtained under different concentrations of salt stress (0 g/L, 1 g/L, 2 g/L, 3 g/L) in the spectral range of 400–1000 nm. Regions of interest were extracted according to the reflectance of the samples, and the model was established by combining POD activity. Various spectral pre-treatment combined with partial least-squares regression models was compared to original spectrum combined with partial least-squares regression model. The characteristic wavelength was extracted by four methods, and partial least-squares regression (PLSR) and principal component regression (PCR) were established according to the characteristic wavelength. The results show that multiple scattering correction (MSC) is optimized as the pre-treatment method. The partial least-squares regression model based on the interval variable iterative space contraction method is the best, and the coefficient of determination and root mean square error of prediction set (RMSEP) are 0.66 and 18.94 U/g·min, respectively. The results show that it is feasible to detect the peroxidase activity in tomato leaves by micro-hyperspectral imaging combined with stoichiometry.

With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. We used the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and the presence or absence of covariance between maternal and direct genetic effects to distinguish between the six animal models. The variance components and genetic parameters of 13,607 weight records from Angus cattle were estimated using the Average Information Restricted Maximum Likelihood (AI-REML) method. The best estimated model was selected based on the Akaike Information Criterion (AIC) and Likelihood Ratio Test (LRT). The results of this study revealed that, in addition to individual genetic effects, maternal genetic effects had a significant impact on unbiased and accurate genetic parameter estimates of body weight in Angus cattle. The total heritability estimated with the best model for body weight at birth (BW0), 3 months (BW3), 6 months (BW6), 12 months (BW12), and 18 months (BW18) was 0.215 ± 0.007, 0.340 ± 0.021, 0.239 ± 0.035, 0.362 ± 0.044, and 0.225 ± 0.048, respectively. The maternal heritability ranges from 0.017~0.438 and significantly affects Angus cattle throughout their growth and development stages, with the effect decreasing with increasing age. Positive correlations were observed between body weights at different months of age, ranging from 0.061 to 0.828. BW6 has a high positive genetic correlation with later age weight, and BW6 is a good predictor of later age weight. Thus, it is possible to optimize breeding programs and accelerate genetic progress by selecting for higher 6-month-old live weights for early Angus selection. In addition, our results emphasize the importance of considering maternal effects in genetic evaluation to improve the efficiency and accuracy of selection programs and thereby contribute to sustainable genetic improvement in beef cattle.

The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein (PAG) family genes and immune-related genes such as UL16-binding protein 21 (ULBP21), CD1b molecule (CD1B), and tumor necrosis factor ligand superfamily member 11 (TNFSF11). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies.