Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regularizer and to set the regularization parameters. To address these issues, in this paper we introduce a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution. Specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels. A measure of 3D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic sub-problems, as well as a stopping criterion. Experimental results on both simulated and real-world data with ground-truth demonstrate the superiority of the proposed method.Comment: IEEE Trans. Geosci. Remote sens. Manuscript submitted June 30, 202

Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability

Hyperspectral and multispectral image fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquisition conditions. This paper presents a general imaging model that considers inter-image variability of data from heterogeneous sources and flexible image priors. The fusion problem is stated as an optimization problem in the maximum a posteriori framework. We introduce an original image fusion method that, on the one hand, solves the optimization problem accounting for inter-image variability with an iteratively reweighted scheme and, on the other hand, that leverages light-weight CNN-based networks to learn realistic image priors from data. In addition, we propose a zero-shot strategy to directly learn the image-specific prior of the latent images in an unsupervised manner. The performance of the algorithm is illustrated with real data subject to inter-image variability.Comment: IEEE Trans. Geosci. Remote sens., to be published. Manuscript submitted August 23, 2022; revised Dec. 15, 2022, and Mar. 13, 2023; and accepted Apr. 07, 202

Identification of disulfidptosis related subtypes, characterization of tumor microenvironment infiltration, and development of DRG prognostic prediction model in RCC, in which MSH3 is a key gene during disulfidptosis

Disulfidptosis is a newly discovered mode of cell death induced by disulfide stress. However, the prognostic value of disulfidptosis-related genes (DRGs) in renal cell carcinoma (RCC) remains to be further elucidated. In this study, consistent cluster analysis was used to classify 571 RCC samples into three DRG-related subtypes based on changes in DRGs expression. Through univariate regression analysis and LASSO-Cox regression analysis of differentially expressed genes (DEGs) among three subtypes, we constructed and validated a DRG risk score to predict the prognosis of patients with RCC, while also identifying three gene subtypes. Analysis of DRG risk score, clinical characteristics, tumor microenvironment (TME), somatic cell mutations, and immunotherapy sensitivity revealed significant correlations between them. A series of studies have shown that MSH3 can be a potential biomarker of RCC, and its low expression is associated with poor prognosis in patients with RCC. Last but not least, overexpression of MSH3 promotes cell death in two RCC cell lines under glucose starvation conditions, indicating that MSH3 is a key gene in the process of cell disulfidptosis. In summary, we identify potential mechanism of RCC progression through DRGs -related tumor microenvironment remodeling. In addition, this study has successfully established a new disulfidptosis-related genes prediction model and discovered a key gene MSH3. They may be new prognostic biomarkers for RCC patients, provide new insights for the treatment of RCC patients, and may inspire new methods for the diagnosis and treatment of RCC patients

Identify stakeholders' understandings of life cycle assessment results on wastewater related issues

To facilitate decision-making processes in waste management, it is important to not only evaluate environmental impacts, but also to measure how stakeholders form opinions and make choices based one valuation results. Life cycle assessments (LCAs) have been widely used to evaluate environmental impacts; however, LCAs cannot be used to measure how people make judgments based on evaluation results. As such, in this study, we combined LCA with conjoint analysis, an economic method that allows individuals to consider all factors and demonstrate their preferences simultaneously. We used this combined method in a case study on wastewater treatment, and obtained two major types of estimation results: (1) the relative importance of each impact category of LCA, and (2) the overall preferences of respondents for each alternative. This study also highlighted some issues regarding the combination of methodologies, such as the selection of impact categories in LCA, the conversion of impact categories into understandable attributes for conjoint analysis, and weaknesses in conjoint analysis that need to be addressed and corrected in future studies

Overexpression of Pleomorphic Adenoma Gene-Like 2 Is a Novel Poor Prognostic Marker of Prostate Cancer.

Pleomorphic adenoma gene like-2 (PLAGL2) is a member of the PLAG gene family. Previous studies have revealed that overexpression of PLAGL2 is associated with many human cancers. However, it has been reported that PLAGL2 also plays as a tumor suppressor. The precise role of PLAGL2 in prostate cancer (PCa) is still unknown. The aim of this study was to investigate the expression and prognostic value of PLAGL2 in PCa. Data from microarray datasets demonstrated that the DNA copy number and mRNA level of PLAGL2 were significantly increased in PCa compared with normal prostate. qRT-PCR and western blot analysis from paired PCa samples and prostate cell lines confirmed upregulated mRNA and protein expression levels in PCa. Immunohistochemistry analysis showed that staining of PLAGL2 in PCa tissues was significantly higher than that in benign prostatic hyperplasia (BPH) tissues. In addition, the high expression of PLAGL2 was only involved in preoperative PSA, but was not related to age, Gleason score, seminal vesicle invasion, surgical margin status, clinical stage and positive lymph node metastasis. Moreover, our results showed that PLAGL2 was an independent prognostic factor for biochemical recurrence (BCR)-free survival and overall survival (OS) of PCa patients, and overexpressed PLAGL2 was related to early development of BCR and poor OS. In conclusion, our findings suggest that PLAGL2 is overexpressed in PCa. The increased expression of PLAGL2 correlates to PCa progression following radical prostatectomy and may serve as a novel poor prognostic marker for PCa

Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation

International audienceHyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical model and deep prior information. Specifically, a novel, yet effective two-stream fusion network is designed to serve as a regularizer for the fusion problem. This fusion problem is formulated as an optimization problem whose solution can be obtained by solving a Sylvester equation. Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and the learned prior to reconstruct the final HR HSI. Experimental results on both simulated and real data demonstrate the superiority of the proposed method over other state-of-the-art methods on both quantitative and qualitative comparisons

Study on Tribological Properties and Mechanisms of Different Morphology WS2 as Lubricant Additives

In the present work, the relationship curve of the coefficient of friction (COF) with varying loads of different morphology WS2 lubricating additives in the friction process at various sliding speeds was studied. On this basis, wear marks and elements on the wear surfaces after friction were analyzed, and then the anti-wear and mechanism effects of WS2 of different forms in the lubrication process were discussed. Meanwhile, the Stribeck curve was used to study the lubrication state of the lubricating oil in the friction process. It was revealed that the COF of lubricating oil containing lamellar WS2 decreased by 29.35% at optimum condition and the minimum COF was concentrated at around 100 N. The COF of lubricating oil containing spherical WS2 decreased by 30.24% and the minimum coefficient was concentrated at 120 N. The extreme pressure property of spherical WS2 was better than that of lamellar WS2, and the wear resistance of spherical WS2 was more stable when the load was over 80 N. The different morphology of WS2 additives can play anti-wear and anti-friction roles within a wide range of sliding speeds