In food processing, the temperature is usually reduced to limit bacterial reproduction and maintain food safety. However, Staphylococcus aureus can adapt to low temperatures by controlling gene expression and protein activity, although its survival strategies normally vary between different strains. The present study investigated the molecular mechanisms of S. aureus with different survival strategies in response to low temperatures (4 °C). The survival curve showed that strain BA-26 was inactivated by 6.0 logCFU/mL after 4 weeks of low-temperature treatment, while strain BB-11 only decreased by 1.8 logCFU/mL. Intracellular nucleic acid leakage, transmission electron microscopy, and confocal laser scanning microscopy analyses revealed better cell membrane integrity of strain BB-11 than that of strain BA-26 after low-temperature treatment. Regarding oxidative stress, the superoxide dismutase activity and the reduced glutathione content in BB-11 were higher than those in BA-26; thus, BB-11 contained less malondialdehyde than BA-26. RNA-seq showed a significantly upregulated expression of the fatty acid biosynthesis in membrane gene (fabG) in BB-11 compared with BA-26 because of the damaged cell membrane. Then, catalase (katA), reduced glutathione (grxC), and peroxidase (ahpC) were found to be significantly upregulated in BB-11, leading to an increase in the oxidative stress response, but BA-26-related genes were downregulated. NADH dehydrogenase (nadE) and α-glucosidase (malA) were upregulated in the cold-tolerant strain BB-11 but were downregulated in the cold-sensitive strain BA-26, suggesting that energy metabolism might play a role in S. aureus under low-temperature stress. Furthermore, defense mechanisms, such as those involving asp23, greA, and yafY, played a pivotal role in the response of BB-11 to stress. The study provided a new perspective for understanding the survival mechanism of S. aureus at low temperatures.

Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a flat capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates.