Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm

The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal. The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank. By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex nonsmooth optimization problem having a patchwise data fidelity and a generalized nonlocal low-rank regularization term. To solve this optimization problem, we propose the PARM algorithm, which has a proximal alternating scheme with a reweighted approximation of its subproblem. A theoretical analysis of the proposed PARM algorithm is conducted to guarantee its global convergence to a critical point. Numerical experiments demonstrate that the proposed method for multiplicative noise removal significantly outperforms existing methods such as the benchmark SAR-BM3D method in terms of the visual quality of the denoised images, and the PSNR (the peak-signal-to-noise ratio) and SSIM (the structural similarity index measure) values

Multiplicative Noise Removal: Nonlocal Low-Rank Model and It\u27s Proximal Alternating Reweighted Minimization Algorithm

The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal. The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank. By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex non-smooth optimization problem having a patchwise data fidelity and a generalized nonlocal low-rank regularization term. To solve this optimization problem, we propose the PARM algorithm, which has a proximal alternating scheme with a reweighted approximation of its subproblem. A theoretical analysis of the proposed PARM algorithm is conducted to guarantee its global convergence to a critical point. Numerical experiments demonstrate that the proposed method for multiplicative noise removal significantly outperforms existing methods, such as the benchmark SAR-BM3D method, in terms of the visual quality of the denoised images, and of the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) values

Diffusion of False Information During Public Crises: Analysis Based on the Cellular Automaton Method

The progress of false information diffusion in the public crisis is harmful to the society. When the public crisis occurs, the public respond in different ways and the public also want to tell others what they think right. But what they think is right is not recognized by the government. Thus the false information forms and it begins to diffuse. As the false information spreads, the harm to society magnifies gradually. Particularly in network society, false information diffusion can easily cause secondary hazards and accelerate public crises to a devastating degree. Thus intervening and controlling the false information diffusion is an important aspect of the public crisis management. From the perspective of the social network theory, this study analyzes the progress of false information diffusion in terms of different public crisis management strategies and presents the result of false information diffusion through simulation on cellular automaton of different public crisis management strategies. In simulations on cellular automaton, interventions are also carried to control false information diffusion and alternatives are proposed to help reduce public crises. This study also extends the theory of false information management, which is significant for the government to improve the ability to evaluate the false information and carry out interventions effectively to control the false information when it begins to diffuse

Identification of a novel O-conotoxin reveals an unusual and potent inhibitor of the human α9α10 nicotinic acetylcholine receptor

Conotoxins are a pool of disulfide-rich peptide neurotoxins produced by cone snails for predation and defense. They are a rich reservoir of novel ligands for ion channels, neurotransmitter receptors and transporters in the nervous system. In this study, we identified a novel conotoxin component, O-conotoxin GeXXVIIA, from the venom of Conus generalis. The native form of this component is a disulfide-linked homodimer of a 5-Cys-containing peptide. Surprisingly, our electrophysiological studies showed that, in comparison to the folded monomers, the linear peptide of this toxin had the highest inhibitory activity at the human α9α10 nicotinic acetylcholine receptor (nAChR), with an IC50 of 16.2 ± 1.4 nM. The activities of the N-terminal and C-terminal halves of the linear toxin are markedly reduced compared with the full-length toxin, suggesting that the intact sequence is required to potently inhibit the hα9α10 nAChR. α9α10 nAChRs are expressed not only in the nervous system, but also in a variety of non-neuronal cells, such as cochlear hair cells, keratinocytes, epithelial and immune cells. A potent inhibitor of human α9α10 nAChRs, such as GeXXVIIA, would facilitate unraveling the functions of this nAChR subtype. Furthermore, this unusual nAChR inhibitor may lead to the development of novel α9α10 nAChR-targeting drugs

A novel lid-covering peptide inhibitor of nicotinic acetylcholine receptors derived from αd-conotoxin GeXXA

Nicotinic acetylcholine receptors (nAChRs) play a fundamental role in nervous signal transmission, therefore various antagonists and agonists are highly desired to explore the structure and function of nAChRs. Recently, a novel dimeric αD-conotoxin GeXXA was identified to inhibit nAChRs by binding at the top surface of the receptors, and the monomeric C-terminal domain (CTD) of αD-GeXXA retains some inhibitory activity. In this study, the internal dimeric N-terminal domain (NTD) of this conopeptide was further investigated. We first developed a regio-selective protection strategy to chemically prepare the anti-parallel dimeric NTD, and found that the isolated NTD part of GeXXA possesses the nAChR-inhibitory activity, the subtype-dependence of which implies a preferred binding of NTD to the β subunits of nAChR. Deletion of the NTD N-terminal residues did not affect the activity of NTD, indicating that the N-terminus is not involved in the interaction with nAChRs. By optimizing the sequence of NTD, we obtained a fully active single-chain cyclic NTD, based on which 4 Arg residues were found to interact with nAChRs. These results demonstrate that the NTD part of αD-GeXXA is a lid-covering nAChR inhibitor, displaying a novel inhibitory mechanism distinct from other allosteric ligands of nAChRs

Metagenomics reveals the abundance and accumulation trend of antibiotic resistance gene profile under long-term no tillage in a rainfed agroecosystem

Widespread soil resistance can seriously endanger sustainable food production and soil health. Conservation tillage is a promising practice for improving soil structure and health. However, the impact of long-term no-tillage on the presence of antibiotic resistance genes in agricultural soils remains unexplored. Based on the long-term (>11 yr) tillage experimental fields that include both conservation tillage practices [no tillage (ZT)] and conventional tillage practices [plough tillage (PT)], we investigated the accumulation trend of antibiotic resistance genes (ARGs) in farmland soils under long-term no-tillage conditions. We aimed to provide a scientific basis for formulating agricultural production strategies to promote ecological environment safety and human health. In comparison to PT, ZT led to a considerable reduction in the relative abundance of both antibiotic resistance genes and antibiotic target gene families in the soil. Furthermore, the abundance of all ARGs were considerably lower in the ZT soil. The classification of drug resistance showed that ZT substantially decreased the relative abundance of Ethambutol (59.97%), β-lactams (44.87%), Fosfomycin (35.82%), Sulfonamides (34.64%), Polymyxins (33.67%), MLSB (32.78%), Chloramphenicol (28.57%), Multi-drug resistance (26.22%), Efflux pump (23.46%), Aminoglycosides (16.79%), Trimethoprim (13.21%), Isoniazid (11.34%), Fluoroquinolone (6.21%) resistance genes, compared to PT soil. In addition, the abundance of the bacterial phyla Proteobacteria, Actinobacteria, Acidobacteria, and Gemmatimonadetes decreased considerably. The Mantel test indicated that long-term ZT practices substantially increased the abundance of beneficial microbial flora and inhibited the enrichment of ARGs in soil by improving soil microbial diversity, metabolic activity, increasing SOC, TN, and available Zn, and decreasing pH. Overall, long-term no-tillage practices inhibit the accumulation of antibiotic resistance genes in farmland soil, which is a promising agricultural management measure to reduce the accumulation risk of soil ARGs

Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013-2017 in Zhejiang Province, China.

Preterm birth (PTB) can give rise to significant neonatal morbidity and mortality, as well as children's long-term health defects. Many studies have illustrated the associations between ambient air pollution exposure during gestational periods and PTB risks, but most of them only focused on one single air pollutant, such as PM2.5. In this population-based environmental-epidemiology study, we recruited 6275 pregnant mothers in Zhejiang Province, China, and evaluated their gestational exposures to various air pollutants during 2013-2017. Time-to-event logistic regressions were performed to estimate risk associations after adjusting all confounders, and Quasi-AQI model and PCA-GLM analysis were applied to resolve the collinearity issues in multi-pollutant regression models. It was found that gestational exposure to ambient air pollutants was significantly associated with the occurrence of PTB, and SO2 was the largest contributor with a proportion of 29.4%. Three new variables, prime factor (a combination of PM2.5, PM10, SO2, and NO2), carbon factor (CO), and ozone factor (O3), were generated by PCA integration, contributing 63.4%, 17.1%, and 19.5% to PTB risks, respectively. The first and third trimester was the most crucial exposure window, suggesting the pregnant mothers better to avoid severe air pollution exposures during these sensitive periods