[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Objective: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals. Methods: The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder. Results: Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage. Conclusion: Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.

Self-compatibility in 'Zaohong' Japanese apricot is associated with the loss of function of pollen S genes.

While most Japanese apricot (Prunus mume Sieb. et Zucc.) cultivars display typical S-RNase-based gametophytic self-incompatibility, some self-compatible (SC) cultivars have also been identified. In this study, we confirmed SC of 'Zaohong' through replicated self-pollination tests. Cross-pollination tests showed that SC of 'Zaohong' was caused by a loss of pollen function, so we determined that the S-genotype of 'Zaohong' was S 2 S 15 . Sequence analysis of the S-haplotypes of 'Zaohong' showed no mutations which were likely to alter gene function. Furthermore, expression analysis based on RT-PCR of S-locus genes revealed no differences at the transcript level when compared with 'Xiyeqing', a self-incompatible cultivar with the same S haplotypes. In addition, except for S-locus genes, a new type of F-box gene encoding a previously uncharacterised protein with high sequence similarity (61.03-64.65 %) to Prunus SFB genes was identified. Putative structural regions of PmF-box genes have been described, corresponding to regions in PmSFB alleles, but with some sequence variations. These results suggest that SC in 'Zaohong' occurs in pollen, and that other factors outside the S-locus, including PmF-box genes, might be associated with the loss of function of pollen S genes.