During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the ...removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.
During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications ...are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.
In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in ...both spatial and spectral dimensions for hyperspectral image (HSI) classification. First, the original HSI is reduced to three principal components in the spectral domain using principal component analysis (PCA). Then, a fast and efficient segmentation algorithm named simple linear iterative clustering is utilized to segment the principal components into a certain number of superpixels. By setting different numbers of superpixels, a set of multiscale homogenous regional features is extracted. Based on those extracted superpixels and their first-order adjacent superpixels, EMAPs with multimodal features are extracted and embedded into the multiple kernel framework to generate different spatial and spectral kernels. Finally, a PCA-based kernel learning algorithm is used to learn an optimal kernel that contains multiscale and multimodal information. The experimental results on two well-known datasets validate the effectiveness and efficiency of the proposed method compared with several state-of-the-art HSI classifiers.
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•Molecular mechanisms of PAZ on T2DM was firstly investigated using network pharmacology;•PAZ is beneficial for ameliorating T2DM by reducing oxidative stress and anti-insulin ...resistance;•AMPK/PI3K/AKT signaling is related to the mechanisms of PAZ.
Type 2 diabetes mellitus (T2DM), the main type of diabetes, is a common chronic metabolic disease and a serious health concern worldwide. The aim of this study was to investigate the anti-diabetic effects and underlying mechanisms of pungent substances in Zanthoxylum armatum DC (ZA) fruit (PAZ), which are both edible and medicinal. We firstly investigated the possible molecular mechanisms of the PAZ on T2DM using network pharmacology and online databases. Furthermore, the predicted results were explored by Glucosamine induced AML12 experiments in vitro. After PAZ intervention, ROS levels decreased, MDA levels of oxidative stress indicators decreased, and SOD, GSH, and CAT levels increased compared to the model group. Thus, the pungent agents from the fruits of Z. armatum are beneficial for ameliorating type 2 diabetes mellitus via regulation of AMPK/PI3K/AKT signaling and reducing oxidative stress levels and exerting an anti-insulin resistance effect.
The effects of video game experience on cognitive functioning have been extensively studied; however, differences in inhibitory control between different types of video game players at high and low ...perceptual loads have yet to be explored. This study aimed to examine the inhibitory control of two types of video game players using distracting stimuli with different perceptual loads. Therefore, 35 action video game (ACT) players and 35 strategy video game (STG) players were recruited via a video game questionnaire and were studied using flanker tasks with different perceptual loads. The results showed that ACT players could attend to the distribution of distracting stimuli and inhibit them better than STG players could. Furthermore, players with ACT game experience obtained better task performance when the perceptual load was high. The current findings highlight the relevance of inhibitory control in high perceptual load conditions to the game genre and discuss the reasons for this phenomenon.
Abstract
Background
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia and insulin resistance. Mung bean sprouts are traditionally considered a “folk” ...hypoglycemic food and their pharmacological effects and underlying mechanisms warrant further investigation.
Purpose
This study aimed to investigate the anti-diabetic effects of the exosomes-like nanoparticles in mung bean sprouts (MELNs) and explore the related molecular mechanisms.
Results
MELNs were isolated using a differential centrifugation-polyethylene glycol (PEG) method, and the identification of MELNs were confirmed by PAGE gel electrophoresis, agarose gel electrophoresis, thin-layer chromatography (TLC), and transmission electron microscopy (TEM). In the high-fat diet/streptozotocin (HFD/STZ) mouse model, MELNs ameliorated the progression of T2DM by increasing oral glucose tolerance test (OGTT) and insulin tolerance test (ITT) results, decreasing the fasting blood glucose level, and reducing the serum triglycerides (TG) and total cholesterol (TC). Histopathological examinations indicated MELNs diminished inflammatory infiltration of hepatocytes and amplified the area of islet B cells. In addition, MELNs decreased the oxidative stress levels in liver tissue and had good biocompatibility. In vitro experiments verified that MELNs improved the viability of glucosamine (GlcN) induced insulin-resistant hepatocytes. Furthermore, this study also revealed that MELNs upregulated GLUT4 & Nrf2 and down-regulated GSK-3β via activating the PI3K/Akt signaling pathway, promoting the production of antioxidant enzymes, such as HO-1 and SOD, to reduce oxidative stress.
Conclusion
MELNs mitigated the progression of type 2 diabetes in HFD/STZ mouse model. The underlying molecular mechanism is related to PI3K/Akt/GLUT4/GSK-3β signaling pathway.
This paper intended to study the mechanism and active ingredients of ACB anti-epilepsy. The antiepileptic activity of ACB validated in PTZ kindled rats, ACB could increase the seizure latency and ...reduce seizure duration, attenuate spatial learning and memory deficits, improve hippocampus neuronal damage and regulate unbalanced neurotransmitters. Furthermore, network pharmacology and molecular docking analysis predicted four potential active compounds, in addition, PI3K/Akt signal pathway may be the main signal pathway of ACB anti-epilepsy. In vitro, ACB greatly increased the vitality and reduced apoptosis of PC12 cells exposed to H2O2. Additionally, ACB elevated Bcl-2 and downregulated C-caspase-3 and Bax proteins expression. Importantly, ACB improved the phosphorylation of PI3K and Akt in H2O2-stimulated PC12 cells, and stimulated the nuclear transfer of Nrf2. These findings indicated that ACB has effective on antiepileptic by activating of the PI3K/Akt/Nrf2 pathway to reduce oxidative stress and neuronal cell apoptosis.
With the rapid advancement of spectrometers, the imaging range of the electromagnetic spectrum starts growing narrower. The reduction of electromagnetic wave energy received in a single wavelength ...range leads more complex noise into the generated hyperspectral image (HSI), thus causing a severe cripple in the accuracy of subsequent applications. The requirement for the HSI mixed denoising algorithm's accuracy is further lifted. To address this challenge, in this letter, we propose a novel difference continuity-regularized nonlocal tensor subspace low-rank learning (named DNTSLR) method for HSI mixed denoising. Technically, the original high-dimensional HSI data was first projected into a low-dimensional subspace spanned by a spectral difference continuous basis instead of an orthogonal basis, so the data continuity of the restored HSI spectrum and tensor low-rankness was guaranteed. Then, a cube matching strategy was employed to stack the nonlocal tensor patches from the projected coefficient tensor, and a shrinkage algorithm was used to approximate the low-rank coefficient tensor. Eventually, the subspace low-rank learning algorithm was designed to alternately separate the noise tensor and restore the latent clean low-rank HSI tensor. Extensive experiments on multiple open datasets validate that the proposed method realizes the state-of-the-art denoising accuracy for HSI.
Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral image (HSI) low-level vision applications, but previous studies often failed to comprehensively exploit the ...low-rank nature of HSI along different modes in low-dimensional subspace, and unsurprisingly handled only one specific task. To address these challenges, in this paper, we figured out that in addition to the spatial correlation, the spectral dependency of HSI also implicitly exists in the coefficient tensor of its subspace, this crucial dependency that was not fully utilized by previous studies yet can be effectively exploited in a cascaded manner. This led us to propose a unified subspace low-rank learning regime with a new tensor cascaded rank minimization, named STCR, to fully couple the low-rankness of HSI in different domains for various low-level vision tasks. Technically, the high-dimensional HSI was first projected into a low-dimensional tensor subspace, then a novel tensor low-cascaded-rank decomposition was designed to collapse the constructed tensor into three core tensors in succession to more thoroughly exploit the correlations in spatial, nonlocal, and spectral modes of the coefficient tensor. Next, difference continuity-regularization was introduced to learn a basis that more closely approximates the HSI's endmembers. The proposed regime realizes a comprehensive delineation of the self-portrait of HSI tensor. Extensive evaluations conducted with dozens of state-of-the-art (SOTA) baselines on eight datasets verified that the proposed regime is highly effective and robust to typical HSI low-level vision tasks, including denoising, compressive sensing reconstruction, inpainting, and destriping. The source code of our method is released at https://github.com/CX-He/STCR.git .
Typical high-level vision tasks in hyperspectral image (HSI) processing, such as target detection, often suffer from insufficient information inherent in real-world sampled data. Super-resolution, a ...powerful tool in HSI low-level vision, is expected to enhance the accuracy of detection results by computationally providing the high-resolution HSI (HR-HSI) with additional information. However, existing solutions for HSI super-resolution and target detection have always been implemented independently. This conventionally adopted paradigm overlooks the interconnectedness between low-level and high-level visions, inevitably introducing additional errors, redundancies, and inefficiencies. To address this challenge, in this study, we put our efforts into exploring the uncharted continent of hyperspectral remote sensing, that is, realizing the mutual guidance and joint optimization of HSI super-resolution and target detection concurrently within a unified framework. Technically, we first construct different spectral bases to span the target and background subspaces of the underlying HR-HSI. Then, we look in-depth at the intrinsic properties of the HSI tensor, henceforth jointly optimizing both tasks by innovatively developing a novel low-cubic-rank tensor approximation model with a unique constrained energy minimization (CEM) loss. While we have developed efficient algorithms to optimize the proposed model, we also put into place a refinement procedure for spectral bases, aimed at further enhancing the spectral fidelity of the fused results and the compact representation of the target subspace. Finally, empirical studies conducted on synthetic and real-world datasets substantiate that compared with state-of-the-art solutions, the proposed method delivers highly competitive and practical performance in terms of both tasks. Source codes are available at https://github.com/CX-He/HySRTD.git .
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