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IntroductionIschemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.MethodsIn our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.ResultsBoth groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates’ temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).DiscussionOur results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other popular networks in many segmentation tasks for medical images. Due to the complex structure of the brain and the approximate grayscale of healthy tissue and lesions, lesion segmentation suffers from over-smooth boundaries or inaccurate segmentation. Existing methods, including the transformer, utilize stacked convolutional layers as the decoder to uniformly treat each pixel as a grid, which is convenient for feature computation. However, they often neglect the high-frequency features of the boundary and focus excessively on the region features. We propose an effective method for lesion boundary rendering called TransRender, which adaptively selects a series of important points to compute the boundary features in a point-based rendering way. The transformer-based method is selected to capture global information during the encoding stage. Several renders efficiently map the encoded features of different levels to the original spatial resolution by combining global and local features. Furthermore, the point-based function is employed to supervise the render module generating points, so that TransRender can continuously refine the uncertainty region. We conducted substantial experiments on different stroke lesion segmentation datasets to prove the efficiency of TransRender. Several evaluation metrics illustrate that our method can automatically segment the stroke lesion with relatively high accuracy and low calculation complexity.

ObjectiveBrain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.ApproachIn this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.Main resultsThe performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).SignificanceThe proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.

This research improves our understanding of entrepreneurial intention in the Chinese cultural context. Drawing on career construction theory, we received 408 valid surveys from rural Chinese dwellers and examined the relationships rural Chinese have among “face consciousness,” indecisiveness, career adaptability, and entrepreneurial intention. We found that those who are fearful of losing face are less likely to have entrepreneurial intentions, but one’s desire to gain face has no significant direct impact on entrepreneurial intention. In addition, face consciousness and an indecisive personality interact to influence career adaptability and, in turn, entrepreneurial intention. In sum, this study supports the career construction perspective on understanding the formation of entrepreneurial intention and offers theoretical, practical, and policy implications for entrepreneurial career counseling and training.

Although face recognition has received a lot of attention and development in recent years, it is one of the research hotspots due to the low efficiency of Single Sample Per Person (SSPP) information in face recognition. In order to solve this problem, this article proposes a face recognition method based on virtual sample generation and multi-scale feature extraction. First, in order to increase the training sample information, a new NMF-MSB virtual sample generation method is proposed by combining the Non-negative Matrix Factorization (NMF) reconstruction strategy with Mirror transform(M), Sliding window(S), and Bit plane(B) sample extension methods. Second, a feature extraction method (named WPD-HOG-P) based on Wavelet Packet Decomposition, Histograms of Oriented Gradients, and image Pyramid is proposed. The proposed WPD-HOG-P method is beneficial to multi-scale facial image feature extraction. Finally, based on the extracted WPD-HOG-P features, the recognition model is established by using a grid search optimization support vector machine. Experimental results on ORL and FERET data sets show that the proposed method has higher recognition rates and lower computational complexity than the benchmark methods.

BackgroundBladder carcinoma (BLCA) is a heterogeneous disease that makes it difficult to achieve proper individual treatment and predict prognosis. This study aimed to develop a risk score from a new perspective of pyroptosis and guide accurate treatment and prognosis prediction for BLCA.MethodsThe TCGA-BLCA cohort data were downloaded from The Cancer Genome Atlas database. Two external validation cohorts were collected from the Gene Expression Omnibus. Another independent validation cohort (the Xiangya cohort) was recruited from our hospital. The least absolute shrinkage and selector operation (LASSO) algorithm and Cox regression models were used to establish the pyroptosis risk score. Thereafter, we correlated the pyroptosis risk score with prognosis, tumor microenvironment (TME) immune hallmarks, and multiple treatments, including anticancer immunotherapy, chemotherapy, radiotherapy, and targeted therapy.ResultsThe pyroptosis risk score was an independent prognostic predictor of BLCA. We found that the activities of multiple steps of the anticancer immune response cycle, such as the release of cancer cell antigens, CD8 T cell recruitment, and NK cell recruitment, were significantly higher in the high-risk score group than in the low-risk score group. In addition, the infiltration levels of the corresponding tumor-infiltrating immune cells (TIICs), such as CD8 T cells and NK cells, were positively correlated with the pyroptosis risk score. Thus, BLCA with a high-risk score may be associated with inflamed phenotypes. Simultaneously, the expression of multiple immune checkpoints (such as PD-L1, CTLA-4, and PD-1) and enrichment scores of gene signatures positively correlated with immunotherapy response were positively correlated with the pyroptosis risk score. Therefore, patients with a high pyroptosis risk score may be more sensitive to immunotherapy. In addition, patients with high pyroptosis risk scores may be more sensitive to chemotherapeutic drugs, such as cisplatin, docetaxel, and paclitaxel. In addition, the pyroptosis risk score accurately predicted the molecular subtypes of BLCA, which were cross-validated in several independent systems.ConclusionsThis study developed and validated a robust pyroptosis risk score that can predict the clinical outcomes and TME immune phenotypes of BLCA. In summary, the pyroptosis risk score helps drive precision therapy in patients with BLCA.