Exploring the role of PRDX4 in the development of uterine corpus endometrial carcinoma.

Peroxicedoxin 4 (PRDX4), a member of the peroxicedoxins (PRDXs), has been reported in many cancer-related studies, but its role in uterine corpus endometrial carcinoma (UCEC) is not fully understood. In the present study, we found that PRDX4 was highly expressed in UCEC tissues and cell lines through the combination of bioinformatics analysis and experiments, and elevated PRDX4 levels were associated with poor prognosis. Knockdown of PRDX4 significantly blocked the proliferation and migration of the UCEC cell line Ishikawa and reduced degree of cell confluence. These findings highlight the oncogenic role of PRDX4 in UCEC. In addition, genes that interact with PRDX4 in UCEC were MT-ATP8, PBK, and PDIA6, and we speculated that these genes interacted with each other to promote disease progression in UCEC. Thus, PRDX4 is a potential diagnostic biomarker for UCEC, and targeting PRDX4 may be a potential therapeutic strategy for patients with UCEC.

Restoration of Spatially Variant Blurred Images with Wide-Field Telescope Based on Deep Learning.

The wide-field telescope is a research hotspot in the field of aerospace. Increasing the field of view of the telescope can expand the observation range and enhance the observation ability. However, a wide field will cause some spatially variant optical aberrations, which makes it difficult to obtain stellar information accurately from astronomical images. Therefore, we propose a network for restoring wide-field astronomical images by correcting optical aberrations, called ASANet. Based on the encoder-decoder structure, ASANet improves the original feature extraction module, adds skip connection, and adds a self-attention module. With these methods, we enhanced the capability to focus on the image globally and retain the shallow features in the original image to the maximum extent. At the same time, we created a new dataset of astronomical aberration images as the input of ASANet. Finally, we carried out some experiments to prove that the structure of ASANet is meaningful from two aspects of the image restoration effect and quality evaluation index. According to the experimental results, compared with other deblur networks, the PSNR and SSIM of ASANet are improved by about 0.5 and 0.02 db, respectively.

Optimization of the Winding Layer Structure of High-Pressure Composite Overwrapped Pressure Vessels.

The large thickness COPV is designed by netting theory and the finite element simulation method, but the actual performance is low and the cylinder performance still cannot be improved after increasing the thickness of the composite winding layer. This paper analyzes the reasons for this and puts forward a feasible solution: without changing the thickness of the winding layer, the performance of COPV can be effectively increased by increasing the proportion of annular winding fiber. This method has been verified by tests and is supported by theory.

Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network.

Ground-based telescopes are often affected by vignetting, stray light and detector nonuniformity when acquiring space images. This paper presents a space image nonuniform correction method using the conditional generative adversarial network (CGAN). Firstly, we create a dataset for training by introducing the physical vignetting model and by designing the simulation polynomial to realize the nonuniform background. Secondly, we develop a robust conditional generative adversarial network (CGAN) for learning the nonuniform background, in which we improve the network structure of the generator. The experimental results include a simulated dataset and authentic space images. The proposed method can effectively remove the nonuniform background of space images, achieve the Mean Square Error (MSE) of 4.56 in the simulation dataset, and improve the target's signal-to-noise ratio (SNR) by 43.87% in the real image correction.

Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation.

In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.