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Background: Lung adenocarcinoma (LUAD) is a fatal disease with metabolic abnormalities. The dysregulation of S100 calcium‐binding protein A2 (S100A2), a member of the S100 protein family, is connected to the development of various cancers. The impact of S100A2 on the LUAD occurrence and metastasis, however, has not yet been reported. The functional mechanism of S100A2 on LUAD cell metastasis was examined in this article. Methods: The expression of TFAP2A and S100A2 in LUAD tissues and cells was analyzed by bioinformatics and qRT‐PCR, respectively. The enrichment pathway analysis was performed on S100A2. Bioinformatics analysis determined the binding relationship between TFAP2A and S100A2, and their interaction was validated through dual‐luciferase and chromatin immunoprecipitation experiments. Cell viability was determined using cell counting kit‐8 (CCK‐8). A transwell assay was performed to analyze the invasion and migration of cells. Immunofluorescence was conducted to obtain vimentin and E‐cadherin expression, and a western blot was used to detect the expression of MMP‐2, MMP‐9, GLS, and GLUD1. The kits measured the NADPH/NADP ratio, glutathione (GSH)/glutathione disulfide (GSSG) levels, and the contents of glutamine, α‐KG, and glutamate. Results: S100A2 was upregulated in LUAD tissues and cells, and S100A2 mediated glutamine metabolism to induce LUAD metastasis. Additionally, the transcriptional regulator TFAP2A was discovered upstream of S100A2, and TFAP2A expression was upregulated in LUAD, which indicated that TFAP2A promoted the S100A2 expression. The rescue experiment found that upregulation of S100A2 could reverse the inhibitory effects of silencing TFAP2A on glutamine metabolism and cell metastasis. Conclusion: In conclusion, by regulating glutamine metabolism, the TFAP2A/S100A2 axis facilitated LUAD metastasis. This suggested that targeting S100A2 could be beneficial for LUAD treatment. [ABSTRACT FROM AUTHOR]

Most existing super-resolution (SR) imaging systems, inspired by the bionic compound eye, utilize image registration and reconstruction algorithms to overcome the angular resolution limitations of individual imaging systems. This article introduces a multi-aperture multi-focal-length imaging system and a multi-focal-length image super-resolution algorithm, mimicking the foveal imaging of the human eye. Experimental results demonstrate that with the proposed imaging system and an SR imaging algorithm inspired by the human visual system, the proposed method can enhance the spatial resolution of the foveal region by up to 4 × compared to the original acquired image. These findings validate the effectiveness of the proposed imaging system and computational imaging algorithm in enhancing image texture and spatial resolution.

Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has more outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing more targets hidden in uniform ground fog than state-of-the-art technologies. In addition, this work provides a method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.

The current advancements in image processing have led to significant progress in polarization defogging methods. However, most existing approaches are not suitable for scenes with targets exhibiting a high degree of polarization (DOP), as they rely on the assumption that the detected polarization information solely originates from the airlight. In this paper, a dual-polarization defogging method connecting frequency division and blind separation of polarization information is proposed. To extract the polarization component of direct transmission light from the detected polarized signal, blind separation of overlapped polarized information is performed in the low-frequency domain based on visual perception. Subsequently, after estimating airlight, a high-quality defogging image can be restored. Extensive experiments conducted on real-world scenes and comparative tests confirm the superior performance of our proposed method compared to other competitive methods, particularly in reconstructing objects with high DOP. This work provides a quantitative approach for estimating the contributions of polarization light from different sources and further expands the application range of polarimetric defogging imaging.

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.