Classification and transfer learning of sleep spindles based on convolutional neural networks.

Background: Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science. Methods: This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers. Results: The classification accuracy for the healthy and insomnia subjects' spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects' spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes. Discussion: These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.

A comprehensive physiological and -Omic analysis of trypsin-mediated protection of green pepper fruits from chilling injury.

Chilling injury (CI) in green pepper fruits during low-temperature storage causes a significant decline in quality. The present study utilized physiological, transcriptomic, and metabolomic analyses to idneitfy the mechanisms by which trypsin mitigates CI in green peppers stored at 4 °C for 8 days, followed by 3 days of shelf life. Results indicated that the trypsin treatment significantly reduced electrolyte leakage and the CI index in peppers, effectively extending their shelf life and preserving postharvest quality. After 4 days of storage, comparative -omic analyses identified 2514 differentially expressed genes (DEGs) and 397 differentially abundant metabolites (DAMs) between trypsin-treated and control peppers. The trypsin treatment induced changes in sugar metabolism, modulating the expression of HK, SUS, INV, and GLGC, which affected the abundance of metabolites such as CDP-glucose and α-D-p-glucose. Trypsin also enhanced carotenoid metabolism, altering the abundance of rhodopinal glucoside, 1'-hydroxyl-γ-carotene glucoside, and farnesyl 1-PP, and influencing the expression of PDS, CRTH, CRTB, and LUT5. Notably, the trypsin treatment activated the mitogen-activated protein kinase (MAPK) pathway that plays an integral role in the signal transduction of abiotic stress. Differential expression of FLS2, ELF18, PTO, PR1, PTI5, WPKY, MEKK1, and MPK6 genes in the MAPK pathway was observed, which was correlated with CI mitigation in green peppers during cold storage. In conclusion, trypsin is an effective treatment for reducing CI in green peppers during cold storage. The present study provides valuable insights into its physiological and molecular impact on green pepper fruit.