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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]

EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system. [ABSTRACT FROM AUTHOR]

Salmonella spp. are health-threatening foodborne pathogens. The increasingly common spread of antibiotic-resistant Salmonella spp. is a major public healthcare issue worldwide. In this study, we wished to explore (1) antibiotic or polypeptide combinations to inhibit multidrug-resistant Salmonella bredeney and (2) the regulation of cross-resistance and collateral sensitivity of antibiotics and polypeptides. We undertook a study to select antibiotic combinations. Then, we promoted drug-resistant strains of S. bredeney after 15 types of antibiotic treatment. From each evolving population, the S. bredeney strain was exposed to a particular single drug. Then, we analyzed how the evolved S. bredeney strains acquired resistance or susceptibility to other drugs. A total of 105 combinations were tested against S. bredeney following the protocols of CLSI-2016 and EUCAST-2017. The synergistic interactions between drug pairings were diverse. Notably, polypeptides were more likely to be linked to synergistic combinations: 56% (19/34) of the synergistic pairings were relevant to polypeptides. Simultaneously, macrolides demonstrated antagonism toward polypeptides. The latter were more frequently related to collateral sensitivity than the other drugs because the other 13 drugs sensitized S. bredeney to polypeptides. In an experimental evolution involving 15 drugs, single drug-evolved strains were examined against the other 14 drugs, and the results were compared with the minimal inhibitory concentration of the ancestral strain. Single drug-evolved S. bredeney strains could alter the sensitivity to other drugs, and S. bredeney evolution against antibiotics could sensitize it to polypeptides. [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]

An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Yang, Tian and Ju.)