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The adaptive optics system (AOS) incorporating a modulated pyramid wavefront sensor (PWFS) represents a key area of exploration in current research aimed at enhancing the capabilities of future extremely large astronomical telescopes. However, incorporating a conventional modulated PWFS into an AOS presents several challenges, including complex optical pathways, intricate alignment procedures, and issues related to deviation in the exit pupil image. This paper introduces a novel discrete modulated operation method for the pyramid wavefront sensor, while the sensor concept has been already presented in authors’ last paper, termed the DM-PWFS. The DM-PWFS integrates the modulation mirror with the pyramidal prism, reducing the number of conjugate planes in the system and significantly simplifying the optical pathways and alignment processes. Moreover, the M-PWFS replaces the traditional method of incident light modulation by modulating the center point of pyramidal prism, ensuring that the primary optical axis remains stationary during modulation and effectively eliminating the impact of pupil image deviation. The theoretical model and geometrical optical analysis explain the wavefront sensing principle of the DM-PWFS. Simulation analysis discusses the system parameters and dynamic performance of the DM-PWFS. Experimental results demonstrate that an AOS equipped with the DM-PWFS successfully corrects wavefront distortion, achieving a residual wavefront Root Mean Square (RMS) value of approximately 0.02λ and a focal plane image Strehl Ratio value of around 0.83.

Underwater imaging is a crucial and challenging problem. The backscattering caused by particles in turbid water can severely degrade the image. In this paper, A novel image dehazing method is devised, which combines optics with image processing technology. This method can directly remove part of the backscattering through polarization technology, and then quickly solve the dehazing image through our algorithm. Experimental results show that our method is valid and robust for targets of diverse materials, grayscale images and color images in diverse scattering environments. Furthermore, our method takes only 2 percent of the time of the fast dark channel prior (DCP) method when processing 1 K resolution images, which meets the requirements of real-time.

Image registration is an important process for applications in various fields, such as remote sensing and medical imaging; thus, its accuracy significantly affects the efficacy as well as efficiency of those applications. Phase correlation algorithm (PCA) and normalized cross correlation-pyramid (NCCP) algorithm are the state-of-the-art frequency domain and spatial domain methods for image registration, respectively. However, these algorithms have some limitations. In particular, the registration speed of PCA needs to be improved, while the NCCP algorithm leads to errors if the image to be registered is partially occluded. Thus, to overcome these limitations, we propose a pyramid PCA that combines both algorithms. To verify the performance of our proposed algorithm, its results are compared with those obtained using the traditional PCA and NCCP algorithm. Our simulation results for partially occluded images indicate that the proposed algorithm outperforms the NCCP algorithm in terms of accuracy; in addition, it outperforms PCA in terms of speed. Furthermore, to test the feasibility of the proposed algorithm for real-time applications, a panoramic target detection system was set up, and the results obtained using the system proved that our method for image registration was both feasible and effective.

Resistance to targeted drugs is now a challenging clinical problem in the treatment of non-small cell lung cancer (NSCLC). So far, there are no approved targeted therapeutic drugs for patients with disease progression after the third-generation epidermal growth factor receptor-tyrosine kinase inhibitor osimertinib resistance (OR). Super-enhancers (SEs) are large clusters of transcriptional enhancers that drive gene expression. In this study, we aimed to explore the potential pathogenic SEs and their driven genes in OR NSCLC. OR cell line was established by exposure of H1975 cells to incremental dosing of osimertinib. RNA-sequencing and H3K27ac ChIP-sequencing were used to identify the differential expressed genes (DEGs) and SEs in parental and resistant cells. Gene ontology analysis for the OR-specific SEs-associated genes showed that histone citrullination, protein citrullination, and peptidyl-arginine modification are the top three biological processes, and the DEGs involved in these biological processes, including peptidyl arginine deiminase 1 (PADI1), PADI2, and PADI3. Realtime-PCR and western blot detections confirmed these genes were highly expressed in OR cells. SE inhibitor decreases their expression, ensuring that SEs regulate their transcriptional expressions. The PADI inhibitor inhibited OR cells’ proliferation, invasion, and colony formation. This study demonstrates that SE-driven PADI family genes are potential biomarkers and targets for OR NSCLC.

Abnormal fibroblast differentiation into myofibroblast is a crucial pathological mechanism of pulmonary fibrosis (PF). Super-enhancers, a newly discovered cluster of regulatory elements, are regarded as the regulators of cell identity. We speculate that abnormal activation of super-enhancers must be involved in the pathological process of PF. This study aims to identify potential pathogenic super-enhancer-driven genes in PF. Differentially expressed genes (DEGs) in PF mouse lungs were identified from a GEO dataset (GDS1492). We collected super-enhancers and their associated genes in human lung fibroblasts and mouse embryonic fibroblasts from SEA version 3.0, a network database that provides comprehensive information on super-enhancers. We crosslinked upregulated DEGs and super-enhancer-associated genes in fibroblasts to predict potential super-enhancer-driven pathogenic genes in PF. A total of 25 genes formed an overlap, and the protein-protein interaction network of these genes was constructed by the STRING database. An interaction network of transcription factors (TFs), super-enhancers, and associated genes was constructed using the Cytoscape software. Gene enrichment analyses, including KEGG pathway and GO analysis, were performed for these genes. Latent transforming growth factor beta (TGF-β) binding protein 2 (LTBP2), one of the predicted super-enhancer-driven pathogenic genes, was used to verify the predicted network’s accuracy. LTBP2 was upregulated in the lungs of the bleomycin-induced PF mouse model and TGF-β1-stimulated mouse and human fibroblasts. Myc is one of the TFs binding to the LTBP2 super-enhancer. Knockout of super-enhancer sequences with a CRISPR/Cas9 plasmid or inhibition of Myc all decreased TGF-β1-induced LTBP2 expression in NIH/3 T3 cells. Identifying and interfering super-enhancers might be a new way to explore possible therapeutic methods for PF.

Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its limitations in modeling sample relations. Graph convolution network (GCN) has been introduced to HSI classification due to its demonstrated ability in processing sample relations. Introducing GCN into HSI classification, the key issue is how to transform HSI, a typical euclidean data, into non-euclidean data. To address this problem, we propose a supervised framework called the Global Random Graph Convolution Network (GR-GCN). A novel method of constructing the graph is adopted for the network, where the graph is built by randomly sampling from the labeled data of each class. Using this technique, the size of the constructed graph is small, which can save computing resources, and we can obtain an enormous quantity of graphs, which also solves the problem of insufficient samples. Besides, the random combination of samples can make the generated graph more diverse and make the network more robust. We also use a neural network with trainable parameters, instead of artificial rules, to determine the adjacency matrix. An adjacency matrix obtained by a neural network is more flexible and stable, and it can better represent the relationship between nodes in a graph. We perform experiments on three benchmark datasets, and the results demonstrate that the GR-GCN performance is competitive with that of current state-of-the-art methods.

We describe a sequential operation approach for a three-sided pyramid wavefront sensor (PWFS) based on a micromirror array with hexagonal geometry. The geometrical optics analysis and numerical simulations show that a linear relationship exists between the signals calculated by the sensing data and the local tilt of the wavefront. We discuss the selection of parameter index that is used in our experiment and build a closed-loop system with this wavefront sensor and a 21-element deformable mirror as a wavefront corrector. The experimental results confirm the feasibility of using a nonmodulated sequential three-sided PWFS in adaptive optics systems.

In this study, Zernike polynomials and optical fiber field theory are applied to build a mathematical model of coupling efficiency (CE) and spatial mode of aberrations. The theory built in this paper can be used to quickly calculate the CE affected by a single aberration as well as the aberrations caused by atmospheric turbulence. The aberrations are classified based on Zernike polynomials and the effects of aberrations on CE of different types and different spatial frequencies are analyzed. The influence of the effects of AO system residual errors is also analyzed. Adaptive optics (AO) equipment is applied to build a system on which the proposed theory was tested; the experimental results validate the theoretical analysis.