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Background: Mesenchymal stromal cell (MSC)-derived exosomes (MSC-Exo) have been recognized for their significant role in regulating macrophage polarization, a process crucial to the pathogenesis of abdominal aortic aneurysm (AAA). However, the therapeutic effects of MSC-Exo on AAA remain largely unexplored. Therefore, this study aimed to investigate the functional and mechanistic aspects of MSC-Exo in the progression of AAA. Methods: The MSC-derived exosomes were characterized using Transmission Electron Microscopy, Nanoparticle Tracking Analysis, and Western blotting. An experimental mouse model of AAA was established through the administration of angiotensin II (Ang II) in male apoe−/− mice and calcium chloride (CaCl2) in male C57/B6 mice, with subsequent tail vein injection of exosomes to evaluate their efficacy against AAA. Macrophage polarization was assessed using immunofluorescence staining and WB analysis. Mechanistic analysis was performed using 4D Label-free Proteomics analysis. Results: We found that intravenous administration of MSC-Exo induced M2 polarization of macrophages within an inflammatory environment, effectively impeding AAA development in Ang II or CaCl2-induced AAA model. The therapeutic efficacy of MSC-Exo treatment was dependent on the presence of macrophages. Mechanistically, MSC-Exo suppressed the levels of cluster of differentiation 74 (CD74), modulating macrophage polarization through the TSC2-mTOR-AKT pathway. These findings highlight the potential of MSC-Exo as a therapeutic strategy for AAA by modulating macrophage polarization. [ABSTRACT FROM AUTHOR]

We present a Ge/MoS2 van der Waals heterojunction photodetector with a short active region constructed using a transfer process. The device exhibits broadband, self-powered, superior device performance within the visible to infrared wavelength (500–1700 nm) operated in a photovoltaic mode. Intriguingly, a sharp increased gain of 10 556 (93) with a varied breakdown voltage of −8.02 V (−6.25 V) under the 700 nm (1550 nm) laser illumination was observed, which was interpreted as the synergistic effect of both soft and avalanche breakdown behavior. These results imply disposable high-sensitivity broadband light-detection potentials with a simple Ge/MoS2 heterojunction, exempting it from the complex and strict construction requirement of conventional avalanche photodetectors. [ABSTRACT FROM AUTHOR]

The optimized design of a new high-temperature superconducting rotating pole machine is presented. Its main structural feature is the use of a double stator core which separates the synchronous machine pole shoe from the pole body to rotate separately as the machine rotor, allowing the superconducting coil to operate in a stationary state. The inner stator core, the stationary dewar and the rotor core together form the excitation system of the machine. The excitation coil windings adopt a rectangular cross-section, with flux divertor strategically placed between the high-temperature superconducting coils. This configuration aims to modulate the background magnetic field, specifically reducing the perpendicular magnetic field component. This mitigation minimizes the impact of ambient magnetic fields on the superconducting coil's current carrying capacity, ensuring an optimized magnetic field environment for its operation. Through the integration of these modifications, the technical and economic parameters of the enhanced high-temperature superconducting machine have been significantly improved. The optimization of design, coupled with detailed calculations of the 3D electromagnetic field, was achieved utilizing the commercial software Ansys EM module. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of information technology, the volume of image data has grown exponentially. However, these datasets typically contain a large amount of redundant information. To extract effective features and reduce redundancy from images, a representation learning method based on the Vision Transformer (ViT) has been proposed, and to our best knowledge, Transformer was first applied to zero-shot learning (ZSL). The method adopts a symmetric encoder–decoder structure, where the encoder incorporates Multi-Head Self-Attention (MSA) mechanism of ViT to reduce the dimensionality of image features, eliminate redundant information, and decrease computational burden. Consequently, it effectively extracts features, and the decoder is utilized for reconstructing image data. We evaluated the representation learning capability of the proposed method in various tasks, including data visualization, image reconstruction, face recognition, and ZSL. By comparing with state-of-the-art representation learning methods, the outstanding results obtained validate the effectiveness of this method in the field of representation learning. [ABSTRACT FROM AUTHOR]

In order to cope with the grid fluctuation caused by large‐scale wind power connected to the grid, the wind thermal power generation system has been proposed and extensively studied. The wind thermal power generation system uses a wind turbine to drive a heat generation device to heat the heat storage medium, which is further exchanged to drive a turbine to generate electricity. A superconducting magnetic eddy current heater (SMH) is proposed for the characteristics of wind thermal power generation system, which uses non‐resistive, large current‐carrying superconducting coils for excitation, and has high output efficiency and power density. The working principle of magnetic eddy current heating is analysed, and the structure of SMH with no ferromagnetic material and two heating screens inside and outside is proposed according to the characteristics of SMH. An analytical model of the SMH is established, and the influence of the structure and materials of the SMH on the magnetic field distribution is analysed. Based on this, a 22‐pole SMH was designed and analysed for output power at 10–20 rpm. A three‐dimensional magnetism‐stress combined analysis model of SMH is established, and the strain and stress characteristics of SMH are simulated under the condition of maximum output power, which verifies the feasibility of the mechanical properties of existing superconducting materials for application in SMH. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval methods based on hashing have gained significant attention in both academic and industrial research. Deep learning techniques have played a crucial role in advancing supervised cross-modal hashing methods, leading to significant practical improvements. Despite these achievements, current deep cross-modal hashing still encounters some underexplored limitations. Specifically, most of the available deep hashing usually utilizes pair-wise or triplet-wise strategies to promote the separation of the inter-classes by calculating the relative similarities between samples, weakening the compactness of intra-class data from different modalities, which could generate ambiguous neighborhoods. In this article, the Deep Neighborhood-aware Proxy Hashing (DNPH) framework is proposed to learn a discriminative embedding space with the original neighborhood relation preserved. By introducing learnable shared category proxies, the neighborhood-aware proxy loss is proposed to project the heterogeneous data into a unified common embedding, in which the sample is pulled closer to the corresponding category proxy and is pushed away from other proxies, capturing small within-class scatter and big between-class scatter. To enhance the quality of the obtained binary codes, the uniform distribution constraint is developed to make each hash bit independently obey the discrete uniform distribution. In addition, the discrimination loss is designed to preserve modality-specific semantic information of samples. Extensive experiments are performed on three benchmark datasets to prove that our proposed DNPH framework achieves comparable or even better performance compared with the state-of-the-art cross-modal retrieval applications. The corresponding code implementation of our DNPH framework is as follows: https://github.com/QinLab-WFU/OUR-DNPH. [ABSTRACT FROM AUTHOR]