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Background: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance. Method: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model. Result: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively. Conclusion: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects. [ABSTRACT FROM AUTHOR]

The information propagation on the social network has been an important research topic, with a focus on the significant influence of individual behaviors. The marginal utility behavior can affect the process of information propagation. But most previous research ignore it. Besides, the process can also be influenced by limited-contact capacity, which increase the complexity of networks. In this paper, the marginal utility behavior model on the single-layer network with limited-contact capacity is proposed first. Then the edge-based compartmental (EBC) method is used to explore the novel information propagation mechanism. Through experiments, it was found that when individuals show an increasing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a discontinuous increase pattern by weakening behavior. However, the final spreading scope shows no outbreak by strengthening behavior. In contrast, when individuals show a diminishing marginal utility behavior, with the propagation probability increasing, the final spreading scope shows a continuous increase pattern by strengthening. Nevertheless, the final spreading scope shows a discontinuous increasing by weakening. What's more, the limited-contact capacity and the degree distribution heterogeneity can also change the information propagation pattern. Besides, the experimental results are in agreement with the theoretical results. [ABSTRACT FROM AUTHOR]

LI-F type peptides are a family of cyclic lipodepsipeptide antibiotics isolated from Paenibacillus polymyxa and display potent activities against positive bacteria including methicillin-resistant S. aureus (MRSA). In this study, we investigated the mechanism of action of LI-F type peptide AMP-jsa9 against a MRSA (S. aureus CICC10790), which is resistant to ciprofloxacin, gentamicin, kanamycin, chloramphenicol, methicillin, and tetracycline. It was found that AMP-jsa9 mainly targets the cell membrane of MRSA and is able to inhibit biofilm formation through killing planktonic bacteria cells. Moreover, AMP-jsa9 can bind to DNA in vitro, which represents another pathway for the action on MRSA. Furthermore, in vivo treatment of scalded mice with AMP-jsa9 resulted in inhibiting MRSA infections and healing of the scalded wound. In addition, it was demonstrated that AMP-jsa9 can effectively inhibit MRSA infections in scalded murine epidermis and that inflammatory cytokines including IL-8, IL-6, tumor necrosis factor alpha (TNF-α), and monocyte chemotactic factor-1 (MCP-1) were reduced; moreover, both protein and gene expression levels of vascular endothelial growth factor (VEGF) and endothelial nitric oxide synthase (e-NOS) were enhanced, which promote neovascularization and proliferation of new granulation tissue. [ABSTRACT FROM AUTHOR]