Search

With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.

An efficient carbon dioxide reduction reaction (CO2RR), which reduces CO2 to low-carbon fuels and high-value chemicals, is a promising approach for realizing the goal of carbon neutrality, for which effective but low-cost catalysts are critically important. Recently, many inorganic perovskite-based materials with tunable chemical compositions have been applied in the electrochemical CO2RR, which exhibited advanced catalytic performance. Therefore, a timely review of this progress, which has not been reported to date, is imperative. Herein, the physicochemical characteristics, fabrication methods and applications of inorganic perovskites and their derivatives in electrochemical CO2RR are systematically reviewed, with emphasis on the structural evolution and product selectivity of these electrocatalysts. What is more, the current challenges and future directions of perovskite-based materials regarding efficient CO2RR are proposed, to shed light on the further development of this prospective research area.

Emerging as a new class of advanced functional materials with hierarchical architectures and redox characters, organic–inorganic hybrid materials (OIHs) have been well developed and widely applied in various energy conversion reactions recently. In this review, we focus on the applications and structure–performance relationship of OIHs for electrochemical water oxidation. The general principles of water oxidation will be presented first, followed by the progresses on the applications of OIHs that are classified as metal organic frameworks (MOFs) and their derivates, covalent organic framework (COF)-based hybrids and other OIHs. The roles of organic counterparts on catalytic active centers will be fully discussed and highlighted with typical examples. Finally, the challenges and perspectives assessing this promising hybrid material as an electrocatalyst will be provided.

Ship classification, as an important problem in the field of computer vision, has been the focus of research for various algorithms over the past few decades. In particular, convolutional neural networks (CNNs) have become one of the most popular models for ship classification tasks, especially using deep learning methods. Currently, several classical methods have used single-scale features to tackle ship classification, without paying much attention to the impact of multiscale features. Therefore, this paper proposes a multiscale feature fusion ship classification method based on evidence theory. In this method, multiple scales of features were utilized to fuse the feature maps of three different sizes (40 × 40 × 256, 20 × 20 × 512, and 10 × 10 × 1024), which were used to perform ship classification tasks separately. Finally, the multiscales-based classification results were treated as pieces of evidence and fused at the decision level using evidence theory to obtain the final classification result. Experimental results demonstrate that, compared to classical classification networks, this method can effectively improve classification accuracy.

Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.

Recent studies of alternative probabilistic transformation (PT) in Dempster–Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief function for decision-making problems. In the process of such a transformation, how to precisely evaluate the closeness between the original basic belief assignments (BBAs) and transformed BBAs is important. In this paper, a new aggregation measure is proposed by comprehensively considering the interval distance between BBAs and also the sequence inside the BBAs. Relying on this new measure, we propose a novel multi-objective evolutionary-based probabilistic transformation (MOEPT) thanks to global optimizing capabilities inspired by a genetic algorithm (GA). From the perspective of mathematical theory, convergence analysis of EPT is employed to prove the rationality of the GA used here. Finally, various scenarios in evidence reasoning are presented to evaluate the robustness of EPT.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

ObjectiveStreptococcus pneumoniae (S.pn) is a common respiratory pathogen and a frequent cause of acute otitis media (AOM) in children. However, little is known about the immunometabolism during AOM. This study was to assess the presence of glucose metabolic reprogramming during AOM and its underlying mechanism affecting inflammatory response and middle ear injury.MethodsThe levels of glycolytic metabolism were evaluated by measuring the expression of glycolysis-related genes and the production of metabolites. HE stain, immunofluorescence, immunohistochemistry, enzyme-linked immunosorbent assay (ELISA) and Western blot were performed to measure the effect of glucose metabolic reprogramming on inflammatory response, pneumococcal clearance, hypoxia-inducible factor 1 alpha (HIF-1α) expression and cytokine secretion during AOM, respectively.ResultsThe analysis of microarray revealed an increase of the expression of glycolysis-related genes during S.pn–induced AOM, which was verified by real-time PCR. Increased glycolysis promoted the production of IL-1β and TNF-α and facilitated the clearance of S.pn by enhancing phagocytosis and killing capability of neutrophils, but also aggravated the middle ear injury. Furthermore, these pathogenic effects could be reversed after glycolytic inhibitor 2DG treatment. Additionally, HIF-1α was observed to involve in glycolytic metabolism during AOM.ConclusionS.pn infection induced increased glycolysis conversion during AOM, which promoted inflammatory responses and bacterial clearance, but also aggravated tissue damage.

Acute otitis media (AOM) is one of the most common infectious diseases primarily caused by Streptococcus pneumoniae (S.pn) among children. Progranulin (PGRN) is a multifunctional growth factor widely expressed in various tissues and cells. Studies have confirmed that PGRN is involved in the development of a variety of inflammatory diseases. We found that the expression of PGRN increased significantly in the middle ear of wild mice with AOM. However, its physiological functions in AOM still remain unknown. To examine the role of PGRN during AOM, we established an acute otitis media model in both C57BL/6 wild mice and PGRN-deficient (PGRN−/−) mice via transbullar injection with S.pn clinical strain serotype 19F. Interestingly, we observed dual results: on one hand, macrophage recruitment notably increased in PGRN−/− mice compared with WT mice; on the other hand, the overall bacterial clearance was surprisingly dampened in PGRN−/− mice. The enhanced recruitment of macrophages was associated with increased production of chemokine (C-C motif) ligand 2 (CCL2), while the decreased bacterial clearance was associated with impaired endocytosis capacity of macrophages. The scavenging ability of bacteria in PGRN−/− mice was recovered with administration of recombinant PGRN. These results suggested a novel dual role of PGRN in affecting the activities of macrophages.

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users' soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time.