The blood–brain barrier (BBB) controls chemical access to the brain and maintains fluid homeostasis, but in vitro models accurately simulating the physiological characteristics of the BBB are lacking. Here, we describe a simple and reproducible biomimetic chip-based model of the human BBB. In this bilayer co-culture, astrocytes and brain microvascular endothelial cells (BMECs) are respectively seeded in upper and lower chambers separated by a semi-permeable membrane, with fluid shear force provided by a precision tilt shaker. Evaluation of barrier crossing by fluorescein sodium, 40 kDa or 70 kDa FITC-dextran, or adeno-associated virus (AAV) particles demonstrates that this bilayer model provides similar or greater barrier function than Transwell assays. Comparison of AAV serotypes indicated that AAV-PHP.eB can cross the human BBB in vitro, and at higher efficiency than AAV9. Additionally, RNAi knockdown and virus capsid protein binding assays show that AAV-PHP.eB delivery is facilitated by receptor protein lymphocyte antigen-6E (LY6E) in humans. This in vitro model system uses a miniaturized chip to enable high-throughput investigations of AAV crossing efficiency in the BBB, and provides strong initial evidence that human LY6E mediates AAV-PHP.eB crossing the BBB.

Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality–individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods.

The Traveling Salesman Problem (TSP), a classic combinatorial optimization problem, has been extensively studied for many years. Recently, the Multi-solution Traveling Salesman Problem (MSTSP) has received increasing attention and achieved significant progress. Compared to the traditional TSP, MSTSP can identify more optimal solutions, providing more satisfactory solutions in urgent situations or according to user preferences. However, research on MSTSP remains relatively limited. Most existing algorithms primarily focus on the traditional TSP and pay little attention to the search for multiple solutions. As a result, there are very few algorithms specifically designed for MSTSP. This paper proposes a hierarchical genetic algorithm of “preprocessing -approximate search -precise search”. In the preprocessing stage, simple evolution of the initial population is performed to enhance the reliability of critical edges. In the approximate search, individual generation strategy is designed to achieve global rapid convergence. A new environment selection mechanism is designed for the deletion and retention of multimodal solutions. In the precise search, individual clustering and regeneration strategy, and a local search strategy based on jump operator are designed to find more multimodal solutions. The hierarchical approach, which progresses sequentially, can discover more multimodal solutions while also achieving the global optimal solution. To validate the proposed algorithm’s performance, comprehensive experiments are conducted on multi-solution optimization test suite, and compared it with the latest algorithms. Experimental results show that the proposed algorithm outperforms compared algorithms.