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To overcome limitations in the generalizability and efficiency of current AAV vectors, in this current study, we constructed an AAV variant library by the insertion of random heptapeptide sequences in the receptor-binding domain of the AAV9 capsid gene. We then applied a recently developed organ-on-a-chip in vitro model of the human blood–brain barrier (BBB) to iteratively enrich for variants that efficiently cross the BBB and transduce astrocyte cells. Through multiple rounds of screening, we obtained two candidate AAV variants, AAV-M6 and AAV-M8, which showed significantly higher BBB penetration efficiency than AAV9 or AAV-PHP.eB. Quantitative PCR (qPCR) assay showed that AAV-M6 could accumulate to a 5 times higher titer, while AAV-M8 reached a 3 times higher titer, than AAV-PHP.eB in the neural chamber of the model. The transduction assay further verified that the AAV-M6 candidate vector was able to infect HA-1800 cells after crossing the BBB, suggesting it could potentially transduce brain parenchymal cells after crossing the hCMEC/D3 layer at higher efficiency than AAV-PHP.eB. Molecular simulations suggested that the human receptor proteins, LY6D and M6PR, could bind the AAV-M6 heptapeptide insertion with high affinity. This study provides two promising candidate AAV vectors and demonstrates the use of this in vitro BBB model for scalable, high-throughput screening of gene therapies. These tools can drive investigations of the mechanisms underlying BBB permeability and the cell-type specificity of virus vectors. [ABSTRACT FROM AUTHOR]

Hydrogels are one of the most commonly used materials in our daily lives, which possess crosslinked three-dimensional network structures and are capable of absorbing large amounts of fluid. Due to their outstanding properties, such as flexibility, tunability, and biocompatibility, hydrogels have been widely employed in biomedical research and clinics, especially in on-demand drug release. However, traditional hydrogels face various limitations, e.g., the delivery of hydrophobic drugs due to their highly hydrophilic interior environment. Therefore, micelle-containing hydrogels have been designed and developed, which possess both hydrophilic and hydrophobic microenvironments and enable the storage of diverse cargos. Based on the functionalities of micelles, these hydrogels can be classified into micelle-doped and chemically/physically crosslinked types, which were reported to be responsive to varied stimuli, including temperature, pH, irradiation, electrical signal, magnetic field, etc. Here, we summarize the research advances of micelle-containing hydrogels and provide perspectives on their applications in the biomedical field based on the recent studies from our own lab and others. [ABSTRACT FROM AUTHOR]

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment's probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification. [ABSTRACT FROM AUTHOR]

The material network in the chemical process has the characteristics of a complex network. Once the fault occurs, it will spread and lead to cascading failures, causing the chemical production process to not proceed normally. Previous studies of cascading failures have been limited by the size and characteristics of the network, which prevented the definition of the fault propagation capacity of the edge based on the actual situation. Studies on the effects of network topology and certain risk factors on the amount of fault propagation still need to be completed. Because of the above shortcomings, this study constructs a chemical material network cascading failure invulnerability analysis model that considers the failure propagation ability. Through the study of the chemical material network topology structure and the analysis of various flammable and explosive chemicals, the paper defines the fault propagation coefficient, which affects the load propagation quantity on the connecting edge. Then, based on the load‐capacity nonlinear cascading failure model, the chemical material network's invulnerability was analyzed by adjusting the model parameters. The effectiveness of the model is verified through case analysis. Compared to the existing model, the model in this paper effectively improves the invulnerability of the material network and can reduce the phenomenon of large‐scale failure of the material network due to the failure propagation capability is not considered. [ABSTRACT FROM AUTHOR]