Host range expansion of Acinetobacter phage vB_Ab4_Hep4 driven by a spontaneous tail tubular mutation

Bacteriophages (phages) represent promising alternative treatments against multidrug-resistant Acinetobacter baumannii (MDRAB) infections. The application of phages as antibacterial agents is limited by their generally narrow host ranges, so changing or expanding the host ranges of phages is beneficial for phage therapy. Multiple studies have identified that phage tail fiber protein mediates the recognition and binding to the host as receptor binding protein in phage infection. However, the tail tubular-dependent host specificity of phages has not been studied well. In this study, we isolated and characterized a novel lytic phage, vB_Ab4_Hep4, specifically infecting MDRAB strains. Meanwhile, we identified a spontaneous mutant of the phage, vB_Ab4_Hep4-M, which revealed an expanded host range compared to the wild-type phage. A single mutation of G to C was detected in the gene encoding the phage tail tubular protein B and thus resulted in an aspartate to histidine change. We further demonstrated that the host range expansion of the phage mutant is driven by the spontaneous mutation of guanine to cytosine using expressed tail tubular protein B. Moreover, we established that the bacterial capsule is the receptor for phage Abp4 and Abp4-M by identifying mutant genes in phage-resistant strains. In conclusion, our study provided a detailed description of phage vB_Ab4_Hep4 and revealed the tail tubular-dependent host specificity in A. baumannii phages, which may provide new insights into extending the host ranges of phages by gene-modifying tail tubular proteins

Hyperspectral Image Classification Based on Transposed Convolutional Neural Network Transformer

Hyperspectral imaging is a technique that captures images of objects within a wide spectrum range, allowing for the acquisition of additional spectral information to reveal subtle variations and compositional components in the objects. Convolutional neural networks (CNNs) have shown remarkable feature extraction capabilities for HSI classification, but their ability to capture deep semantic features is limited. On the other hand, transformer models based on attention mechanisms excel at handling sequential data and have demonstrated great potential in various applications. Motivated by these two facts, this paper proposes a multiscale spectral–spatial transposed transformer (MSSTT) that captures the high-level semantic features of an HSI while preserving the spectral information as much as possible. The MSSTT consists of a spectral–spatial Inception module that extracts spectral and spatial features using multiscale convolutional kernels, and a spatial transpose Inception module that further enhances and extracts spatial information. A transformer model with a cosine attention mechanism is also included to extract deep semantic features, with the QKV matrix constrained to ensure the output remains within the activation range. Finally, the classification results are obtained by applying a linear layer to the learnable tokens. The experimental results from three public datasets show that the proposed MSSTT outperforms other deep learning methods in HSI classification. On the India Pines, Pavia University, and Salinas datasets, accuracies of 97.19%, 99.47%, and 99.90% were achieved, respectively, with a training set proportion of 5%

Properties Variation of Carbon Fiber Reinforced Composite for Marine Current Turbine in Seawater

Turbine blade which are generally made of composite is a core device among components of tidal current power generator that converts the flow of tidal current into a turning force. Recent years, damages of composite turbine blades have been reported due to reasons like seawater degradation, lake of strength, manufacture etc. In this paper, water absorption, tensile, bending, longitudinal transverse shearing properties of carbon fiber reinforced plastic (CRP) composite which would be applied to fabricate the marine current turbine blade has been investigated. Furthermore, the variations of properties with seawater immersion period were studied. The results indicated that the water absorption increased almost linearly at the beginning of immersion and then became stable. Tensile strength of specimen tended to decrease firstly and then recovered slightly. However, the longitudinal transverse shearing strength showed reverse variation trend comparing to tensile strength. And the bending property of specimens was depressed significantly. The properties variations in seawater shall be referenced to design and fabrication of composite marine current turbine blade

Properties Variation of Carbon Fiber Reinforced Composite for Marine Current Turbine in Seawater

Turbine blade which are generally made of composite is a core device among components of tidal current power generator that converts the flow of tidal current into a turning force. Recent years, damages of composite turbine blades have been reported due to reasons like seawater degradation, lake of strength, manufacture etc. In this paper, water absorption, tensile, bending, longitudinal transverse shearing properties of carbon fiber reinforced plastic (CRP) composite which would be applied to fabricate the marine current turbine blade has been investigated. Furthermore, the variations of properties with seawater immersion period were studied. The results indicated that the water absorption increased almost linearly at the beginning of immersion and then became stable. Tensile strength of specimen tended to decrease firstly and then recovered slightly. However, the longitudinal transverse shearing strength showed reverse variation trend comparing to tensile strength. And the bending property of specimens was depressed significantly. The properties variations in seawater shall be referenced to design and fabrication of composite marine current turbine blade