To address the challenge of high computational cost on transformer-based network for change detection (CD) with high-resolution remote sensing images, a neighborhood attention transformer (NAT) based on multi-scale feature fusion method (NATCD) is proposed. Initially, to effectively extract neighborhood features while reducing model complexity, a hierarchical NAT encoder is constructed for multi-scale feature extraction of bi-temporal remote sensing images. Secondly, to associate multi-scale features and alleviate the issue of poor inter-neighborhood feature correlation caused by neighborhood attention operations, a feature fusion decoder is constructed for predicting binary change maps. Experiment on LEVIR-CD and WHU-CD datasets show that NATCD achieves a better performance with a significantly computational cost than previous methods.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Addressing the challenge of multi-class change detection with a small sample size, a change vector analysis with adaptive weight Gaussian mixture model (CVA-AWGMM) is proposed in this article. Initially, in order to avoid errors caused by geometric distortion and imprecise alignment, we employed a neighborhood search approach when calculating the different map. Instead of direct pixel-to-pixel comparisons, we compensated corresponding pixels in the images before and after the change by finding the minimum difference within a specified pixel range. Following this, we employ the fundamental framework of CVA to extract magnitude and angle features from the change vectors, facilitating the identification of both changed and unchanged regions through threshold segmentation. Based on the above binary change detection result, a semi-supervised Gaussian mixture model is used for further change class differentiation. Recognizing the inherent challenges of effectively training classifier model with a small sample size, the clustering features in a large number of unlabeled samples and the supervised information from a few labeled samples are simultaneously utilized to co-construct the objective function of the model. Meanwhile, considering the complementary properties of magnitude and angle features, the labeled samples are used to adaptively weight the features of both to further improve the accuracy of the method. Experiments were conducted on two public data sets and one self-made data set, and the results demonstrate that the proposed CVA-AWGMM outperforms several typical methods.