Hyperspectral image (HSI) denoising is a fundamental task in a plethora of HSI applications. Global low-rank property is widely adopted to exploit the spectral-spatial information of HSIs, providing satisfactory denoising results. In this paper, instead of adopting the global low-rank property, we propose to adopt a local low rankness for HSI denoising. We develop an HSI denoising method via local low-rank and sparse representation, under an alternative minimization framework. In addition, the weighted nuclear norm is used to enhance the sparsity on singular values. The experiments on widely used hyperspectral datasets demonstrate that the proposed method outperforms several state-of-the-art methods visually and quantitatively. INDEX TERMS Hyperspectral image denoising, local low rankness, sparse representation, weighted nuclear norm.
Pan-sharpening is used to fuse multispectral images and panchromatic images to produce a multispectral image with high spatial resolution. In this paper, we design a new iterative method based on framelet for pan-sharpening. The proposed model takes advantage of the upsampled multispectral image and a linear relation between the panchromatic image and the latent high-resolution multispectral image. Since the sparsity of the pan-sharpened image under a B-spline framelet transform is assumed, we regularize the model by penalizing l 1 norm of a framelet based term. The model is solved by a designed algorithm based on alternating direction method of multipliers (ADMM). For better performance, we propose an iterative strategy to pick up more spectral and spatial details. Experiments on four datasets demonstrate that the proposed method outperforms several existing pan-sharpening methods.
Background: Tumor immune cell infiltration is closely associated with the occurrence and development of tumors. Collagen triple helix repeats containing 1 (CTHRC1), a regulator of collagen expression and cell migration, is involved in the metastasis and invasion of tumors. However, the role of CTHRC1 in breast cancer remains unclear. This study aimed to investigate the prognostic value of CTHRC1, and further explore its association with immune infiltration in breast cancer. Methods: CTHRC1 expression pattern and prognostic value were analyzed using ONCOMINE, PrognoScan, GEPIA, and Kaplan-Meier Plotter databases. We then detected CTHRC1 mRNA levels in breast cancer tissues and paired normal breast tissues by Q-PCR. Subsequently, the University of California Santa Cruz (UCSC) database was used to determine the methylation status of CTHRC1. Furthermore, CTHRC1 mutations were investigated using the Catalogue of Somatic mutations in Cancer (COSMIC) and cBioPortal databases. We also assessed the correlation between CTHRC1 expression and immune cell infiltration using TIMER. In addition, The relationship of CTHRC1 expression with the immune marker sets of various immune cells was evaluated using GEPIA and TIMER. Results: CTHRC1 was highly expressed in a variety of tumors, including breast cancer. Elevated CTHRC1 expression was related to a poor prognosis. Notably, CTHRC1 expression was significantly associated with macrophage infiltration, especially the immune infiltration gene marker set of M2. Copy number variations, DNA mutations and methylation states might be potential mechanisms for regulating CTHRC1 expression. Protein digestion and absorption, human papillomavirus infection, ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways were identified as the potential CTHRC1-driven signaling pathways. Conclusion: These findings suggest that CTHRC1 could be a promising immune-related biomarker for the treatment of breast cancer patients.
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