Search

Nonnegative matrix factorization (NMF) and its numerous variants have been extensively studied and used in hyperspectral unmixing (HU). With the aid of the designed deep structure, deep NMF-based methods demonstrate advantages in exploring the hierarchical features of complex data. However, a noise corruption problem commonly exists in hyperspectral data and severely degrades the unmixing performance of deep NMF-based methods when applied to HU. In this study, we propose an ℓ 2 , 1 norm-based robust deep nonnegative matrix factorization ( ℓ 2 , 1 -RDNMF) for HU, which incorporates an ℓ 2 , 1 norm into the two stages of the deep structure to achieve robustness. The multiplicative updating rules of ℓ 2 , 1 -RDNMF are efficiently learned and provided. The efficiency of the presented method is verified in experiments using both synthetic and genuine data. [ABSTRACT FROM AUTHOR]

The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms. The noise formulation existing in HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. We formulated a close-to-reality noise model for hyperspectral data and provide a calibration approach for the noise parameters. On the basis of the calibrated noise model, synthetic data were generated and used for training a KST-based denoising network. The noisy hyperspectral data were firstly denoised by the trained denoising network and were then used to perform the unmixing process. A variety of unmixing algorithms can be integrated into our method to improve the accuracy of unmixing in noisy situations. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims at estimating a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. The performance of HU is often seriously degraded by various kinds of noise existing in hyperspectral images (HSIs). Most of existing robust HU methods are based on the assumption that noise or outlier only exists in one kind of formulation, e.g., band noise or pixel noise. However, in real-world applications, HSIs are unavoidably corrupted by noisy bands and noisy pixels simultaneously, which require robust HU in both the spatial dimension and spectral dimension. Meanwhile, the sparsity of abundances is an inherent property of HSIs and different regions in an HSI may possess various sparsity levels across locations. This article proposes a correntropy-based spatial-spectral robust sparsity-regularized unmixing model to achieve 2-D robustness and adaptive weighted sparsity constraint for abundances simultaneously. The updated rules of the proposed model are efficient to be implemented and carried out by a half-quadratic technique. The experimental results obtained by both synthetic and real hyperspectral data demonstrate the superiority of the proposed method compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims to estimate a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. Nonnegative matrix factorization (NMF) and its various robust extensions have been widely applied to HU. Most existing robust NMF methods consider that noises only exist in one kind of formulation. However, the hyperspectral images (HSIs) are unavoidably corrupted by noisy bands and noisy pixels simultaneously in the real applications. This paper proposes a novel spectral–spatial robust NMF model by incorporating $\ell _{2,1}$ norm and $\ell _{1,2}$ norm, which achieves robustness to band noise and pixel noise simultaneously. The Huber’s M-estimator is integrated into the proposed model to achieve better assignations of weights for each pixel and band with various noise intensities, which avoids the singularity problem and effectively improves the unmixing performance. The elegant updating rules of the proposed spectral–spatial robust model are also efficiently learned and provided. Experiments are conducted on both synthetic and real hyperspectral data sets. The experimental results demonstrate the effectiveness of the proposed methods in unmixing performance. [ABSTRACT FROM AUTHOR]

Kernel nonnegative matrix factorization (KNMF) is an extension of NMF designed to capture nonlinear dependence features in data matrix through kernel functions. In KNMF, the size of the kernel matrices is closely associated with the input data matrix, of which the calculation consumes a large amount of memory and computing resource. When applied on large-scale hyperspectral data, KNMF often meets the bottleneck of memory and may cause the overflow of memory. And when dealing with dynamically acquired data, KNMF requires recomputation of the whole data set when newly acquired data arrived, which produces huge memory and computing resource requirements. To reduce the usage of memory and improve the computational efficiency when applying KNMF on large scale and dynamic hyperspectral data, we extend KNMF by introducing partition matrix theory and considering the relationships among dividing blocks. The decomposition results of hyperspectral data are derived from much smaller scale matrices containing the formerly achieved results and the newly data blocks incrementally. In this paper, we propose an incremental KNMF (IKNMF) to reduce the computing requirements for large-scale data in hyperspectral unmixing. An improved IKNMF (IIKNMF) is also proposed to further improve the abundance results of IKNMF. Experiments are conducted on both synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed methods can effectively save memory resources without degrading the unmixing performance and the proposed IIKNMF can achieve better abundance results than IKNMF and KNMF. [ABSTRACT FROM AUTHOR]

Background: Lymph node metastasis is a poor prognostic factor for lung cancer. However, the risk of lymph node metastasis has not yet been clarified. This study was conducted to analyze the predictive factors for lymph node metastasis in patients with clinical-stage IA3 lung adenocarcinoma. Methods: We retrospectively analyzed all surgical patients with clinical stage IA3 lung adenocarcinoma admitted to our hospital from January 2017 to January 2022. Three hundred and thirty-four patients underwent lobectomy combined with systematic lymph node dissection. Univariate and multivariate logistic regression analyses were used to predict the risk factors of lymph node metastasis. Results: Of the 334 patients eligible for this study, the overall lymph node metastasis rate was 15.3%. There were 45 cases with N1 metastasis, 11 cases with N2 metastasis, and five cases with both N1 and N2 metastasis. The lymph node metastasis rate was 18.1% in patients with a consolidation tumor ratio (CTR) of >0.75, 57.9% in those with >5 ng/mL carcinoembryonic antigen (CEA), and 18.0% in those with a maximum standardized uptake value of >5. Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) for CTR and CEA was 0.790 [95% confidence interval (CI): 0.727–0.853, P < 0.001] and 0.682 (95% CI: 0.591–0.773, P < 0.001), respectively. According to multivariate regression analysis, CEA (>5 ng/mL) [odds ratio (OR) = 3.05, P = 0.016] and CTR (>0.75) (OR = 2.75, P = 0.025) were significantly correlated with lymph node metastasis of clinical stage IA3 lung adenocarcinoma. Conclusions: CEA (>5 ng/mL) and CTR (>0.75) are two important predictors of lymph node metastasis in patients with clinical stage IA3 lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

Summary: Leucine‐rich repeat‐receptor‐like kinase (LRR‐RLK) is a large subfamily of plant RLKs; however, its role in cold tolerance is still unknown. A novel cold tolerance LRR‐RLK gene (MtCTLK1) in Medicago truncatula was identified using the transgenic lines overexpressing MtCTLK1 (MtCTLK1‐OE) and mtctlk1 lines with Tnt1 retrotransposon insertion. Compared with the wild‐type, MtCTLK1‐OE lines had increased cold tolerance and mtctlk1 showed decreased cold tolerance. The impaired cold tolerance in mtctlk1 could be complemented by the transgenic expression of MtCTLK1 or its homolog MfCTLK1 from Medicago falcata. Antioxidant enzyme activities and proline accumulation as well as transcript levels of the associated genes were increased in response to cold, with higher levels in MtCTLK1‐OE or lower levels in mtctlk1 lines as compared with wild type. C‐Repeat‐Binding Factors (CBFs) and CBF‐dependent cold‐responsive genes were also induced in response to cold, and higher transcript levels of CBFs and CBF‐dependent cold‐responsive genes were observed in MtCTLK1‐OE lines whereas lower levels in mtctlk1 mutants. The results validate the role of MtCTLK1 or MfCTLK1 in the regulation of cold tolerance through the CBF pathway, antioxidant defense system and proline accumulation. It also provides a valuable gene for the molecular breeding program to improve cold tolerance in crops. Significance Statement: A novel cold tolerance leucine‐rich repeat‐receptor‐like kinase was identified from Medicago truncatula. It confers cold tolerance through regulation on the C‐repeat‐binding factor cold response pathway, antioxidant defense, and proline accumulation. [ABSTRACT FROM AUTHOR]

This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution to the optimization problems. Subsequently, we propose to solve the PNLS problems based on the Gauss–Newton method. Compared to the existing nonnegative matrix factorization (NMF)-based algorithms for UNSU, the well-designed PNLS-based algorithms have faster convergence speed and better unmixing accuracy. To verify the performance of the proposed algorithms, the PNLS-based algorithms and other state-of-the-art algorithms are applied to synthetic data generated by the Fan model and the generalized bilinear model (GBM), as well as real hyperspectral data. The results demonstrate the superiority of the PNLS-based algorithms. [ABSTRACT FROM AUTHOR]

Loss of ARID1A, a subunit of the SWI/SNF chromatin remodeling complex, contributes to malignant progression in multiple cancers including non-small cell lung cancer (NSCLC). In the search for key genes mediating the aggressive phenotype caused by ARID1A loss, we analyzed 3 Gene Expression Omnibus (GEO) datasets that contain RNA sequencing data from ARID1A-depleted cancer cells. PLAU was identified as a common gene that was induced in different cancer cells upon ARID1A depletion. Overexpression of PLAU positively modulated NSCLC cell growth, colony formation, cisplatin resistance, and survival under serum deprivation. Moreover, enforced expression of PLAU enhanced tumorigenesis of NSCLC cells in nude mice. Mechanistically, PLAU interacted with TM4SF1 to promote the activation of Akt signaling. TM4SF1-overexpressing NSCLC cells resembled those with PLAU overepxression. Knockdown of TM4SF1 inhibited the growth and survival and increased cisplatin sensitivity in NSCLC cells. The interaction between PLAU and TM4SF1 led to the activation of Akt signaling that endowed ARID1A-depleted NSCLC cells with aggressive properties. In addition, treatment with anti-TM4SF1 neutralizing antibody reduced the growth, cisplatin resistance, and tumorigenesis of ARID1A-depleted NSCLC cells. Taken together, PLAU serves as a target gene of ARID1A and promotes NSCLC growth, survival, and cisplatin resistance by stabilizing TM4SF1. Targeting TM4SF1 may be a promising therapeutic strategy for ARID1A-mutated NSCLC. [ABSTRACT FROM AUTHOR]