Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies.

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Effect of Catadioptric Component Postposition on Lens Focal Length and Imaging Surface in a Mirror Binocular System.

The binocular vision system is widely used in three-dimensional measurement, drone navigation, and many other fields. However, due to the high cost, large volume, and inconvenient operation of the two-camera system, it is difficult to meet the weight and load requirements of the UAV system. Therefore, the study of mirror binocular with single camera was carried out. Existing mirror binocular systems place the catadioptric components in front of the lens, which makes the volume of measurement system still large. In this paper, a catadioptric postposition system is designed, which places the prism behind the lens to achieve mirror binocular imaging. The influence of the post prism on the focal length and imaging surface of the optical system is analyzed. The feasibility of post-mirror binocular imaging are verified by experiments, and it is reasonable to compensate the focal length change by changing the back focal plane position. This research laid the foundation for the subsequent research on the 3D reconstruction of the novel mirror binocular system.

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework.

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.