Background: Acute necrotizing encephalopathy (ANE) is a devastating neurologic condition that can arise following a variety of systemic infections, including influenza and SARS-Cov-2. The clinical features of COVID-19-associated ANE in pediatric patients based on multi-case data have not yet been described and remain obscure. We reviewed 12 pediatric patients to better describe the clinical features of ANE with COVID-19.Methods: We retrospectively collected and summarized the clinical features of ANE in children with COVID-19. Clinical data were collected from 12 children, including their general status, clinical symptoms, laboratory tests, and neuroimaging features.Results: Among the subjects, 10 were over 5 years old and they accounted for 83.33%. A large percentage of those affected (66.67%) were females. The major manifestations included fever (100%), impaired consciousness (100%), and convulsions (75%). We determined that increased interleukin (IL)-6 and IL-10, and tumor necrosis factor-α and interferon gamma were not predictive of severe ANE and mortality in children with COVID-19 in this study. All children presented with abnormal neuroimaging with multiple and symmetrically distributed lesions, involving the thalamus, basal ganglia, cerebellum, and brain hemispheres. Eight of the 12 children died, resulting in a mortality rate of 66.67%, and 75% of these children were females. Importantly, we found the timely administration of mannitol after an acute onset of convulsions or disturbance of consciousness may be decreased the high mortality induced by ANE children with COVID-19.Conclusion: COVID-19 associated with ANE in children is characterized by sudden symptom onset, rapid disease progression, and high mortality.

Background: Recording the calibration data of a brain–computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target. Methods: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors. Results: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain–computer interfaces.

A potential limitation of motor imagery (MI) based brain-computer interface (BCI) (MI-BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust feature extraction and classification. Moreover, due to the non-stationarities in EEG signals, the offline training model has poor adaptability and classification ability in cross-session or sample-wise online testing. Methods: To address the problems, we propose a model updating scheme with adaptive and fast operation. Based on the Common Spatial Pattern (CSP), we propose an online and fast generalized eigendecomposition method by Recursive Least Squares updates of the CSP filter coefficients (RLS-CSP), which allows incremental training for CSP spatial filters. Additionally, we present an Incremental Self-training Classification algorithm based on Density Clustering (ISCDC) to select high-confidence samples to update spatial filters and classifier, and classify at the same time. Results: We conducted extensive experiments to validate the efficiency of the proposed adaptive CSP and classifier on the BCI III_IVa and BCI III_V data sets. Experimental results demonstrate that RLS-CSP outperforms significantly in a small sample setting (SSS), and ISCDC has great adaptability in cross-session and non-stationary EEG signals. The results indicate that our proposed methods are feasible to improve the real-time performance of online BCI system.

The advent of 5G technology and new energy radio communication systems heralds a significant shift in the landscape of automated driving. This paper focuses on the integration of 5G and broadcast services in the realm of new energy automatic assisted driving, emphasizing the importance of reliable, energy-efficient communication in the large-scale IoV. The enhanced capabilities of 5G enable improved vehicle battery endurance while safeguarding user data privacy and road traffic safety. We introduce RIS relay reflection as a novel approach to optimize non-line-of-sight links, presenting a RIS-assisted communication model tailored for 5G-enhanced large-scale IoV. The paper evaluates the trustworthiness of RIS relays using user behavior data, proposing a reliable and energy-efficient communication scheme that incorporates RIS security relay assistance. This scheme ensures the selection of trustworthy relays, synergizing the beam direction of transmitters and RISs for optimal 5G broadcast service delivery and OTA updates. Our approach promises to revolutionize communication in large-scale IoV systems, paving the way for a more connected and efficient future in automated vehicle networks.

In the above article [1] , we had mixed up the definition of the symbol $X_{c}(n)$ in formula (7) and formula (8) . This affected only the descriptions of formula (7) and formula (8) , and had no influence on the discussion and conclusions in the article. The correct definition and illustration are as follows: