Natural image restoration based on multi-scale group sparsity residual constraints.

The Group Sparse Representation (GSR) model shows excellent potential in various image restoration tasks. In this study, we propose a novel Multi-Scale Group Sparse Residual Constraint Model (MS-GSRC) which can be applied to various inverse problems, including denoising, inpainting, and compressed sensing (CS). Our new method involves the following three steps: (1) finding similar patches with an overlapping scheme for the input degraded image using a multi-scale strategy, (2) performing a group sparse coding on these patches with low-rank constraints to get an initial representation vector, and (3) under the Bayesian maximum a posteriori (MAP) restoration framework, we adopt an alternating minimization scheme to solve the corresponding equation and reconstruct the target image finally. Simulation experiments demonstrate that our proposed model outperforms in terms of both objective image quality and subjective visual quality compared to several state-of-the-art methods.

Multi-Modal Image Fusion Based on Matrix Product State of Tensor.

Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily. The traditional tensor decomposition is an approximate decomposition method and has been applied to image fusion. In this way, the image details may be lost in the process of fusion image reconstruction. To preserve the fine information of the images, an image fusion method based on tensor matrix product decomposition is proposed to fuse multi-modal images in this article. First, each source image is initialized into a separate third-order tensor. Then, the tensor is decomposed into a matrix product form by using singular value decomposition (SVD), and the Sigmoid function is used to fuse the features extracted in the decomposition process. Finally, the fused image is reconstructed by multiplying all the fused tensor components. Since the algorithm is based on a series of singular value decomposition, a stable closed solution can be obtained and the calculation is also simple. The experimental results show that the fusion image quality obtained by this algorithm is superior to other algorithms in both objective evaluation metrics and subjective evaluation.

Integrating Constituents Absorbed into Blood, Network Pharmacology, and Quantitative Analysis to Reveal the Active Components in Rubus chingii var. suavissimus that Regulate Lipid Metabolism Disorder.

Rubus chingii var. suavissimus (S. K. Lee) L. T. Lu (RS)-a sweet plant also known as Tiancha distributed in the south of China where it is used as a beverage-recently gained extensive attention as adjuvant therapy of diabetes and hypertension. Although pharmacological studies indicate that RS has beneficial effects in regulating lipid metabolism disorder characteristics, the active chemicals responsible for this effect remains unclear. The present study aims to predict the effective substances of RS on regulating lipid metabolism disorder through the analysis of the chemical profile of RS, the absorbed prototype components in rat plasma, and network pharmacology. Also, a UPLC method able to quantify the screened potential effective chemicals of RS products was established. First, a total of 69 components-including diterpene, triterpenoids, flavonoids, polyphenols, and lignans-were systematically characterized in RS. Of those, 50 compounds were detected in the plasma of rats administered with RS extract. Through network pharmacology, 9 potential effective components, 71 target genes, and 20 pathways were predicted to be involved in RS-mediated regulation of lipid metabolism disorder. The quantitative analysis suggested that the contents of potential effective components varied among samples from different marketplaces. In conclusion, the presented results provide a chemical basis for further research of Rubus chingii var. suavissimus.

Development of flexible electrochemical impedance spectroscopy-based biosensing platform for rapid screening of SARS-CoV-2 inhibitors.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters the cells through the binding of its spike protein (S-protein) to the cell surface-expressing angiotensin-converting enzyme 2 (ACE2). Thus, inhibition of S-protein-ACE2 binding may impede SARS-CoV-2 cell entry and attenuate the progression of Coronavirus disease 2019 (COVID-19). In this study, an electrochemical impedance spectroscopy-based biosensing platform consisting of a recombinant ACE2-coated palladium nano-thin-film electrode as the core sensing element was fabricated for the screening of potential inhibitors against S-protein-ACE2 binding. The platform could detect interference of small analytes against S-protein-ACE2 binding at low analyte concentration and small volume (0.1 µg/mL and ~1 µL, estimated total analyte consumption < 4 pg) within 21 min. Thus, a few potential inhibitors of S-protein-ACE2 binding were identified. This includes (2S,3aS,6aS)-1-((S)-N-((S)-1-Carboxy-3-phenylpropyl)alanyl)tetrahydrocyclopenta[b] pyrrole-2-carboxylic acid (ramiprilat) and (2S,3aS,7aS)-1-[(2S)-2-[[(2S)-1-Carboxybutyl]amino]propanoyl]-2,3,3a,4,5,6,7,7a-octahydroindole-2-carboxylic acid (perindoprilat) that reduced the binding affinity of S-protein to ACE2 by 72% and 67%; and SARS-CoV-2 in vitro infectivity to the ACE2-expressing human oral cavity squamous carcinoma cells (OEC-M1) by 36.4 and 20.1%, respectively, compared to the PBS control. These findings demonstrated the usefulness of the developed biosensing platform for the rapid screening of modulators for S-protein-ACE2 binding.

[Rapid resolving algorithm of gas chromatographic overlapping peaks based on the asymptotic expansion of integration].

A novel algorithm, called asymptotic expansion of integration, is suggested to resolve gas chromatographic overlapping peaks. There are three steps for the algorithm. First, a valley peak or a shoulder peak is separated into two domains, and an integral equation on a subdivision and an algebraic equation on the overlapping peak domain are listed. Secondly, areas needed in two equations, are computed by a numerical integral method, then the integral equation is expended to an algebraic equation by the asymptotic formula of integration. At last, combing two equations with constraint equations of peak heights, we got a nonlinear algebraic set. The equation set can be solved rapidly by Gauss-Seidel iteration, and the maximum number of iterations is not more than 20 times. The simulation and experimental results showed that height and area errors of resolving peaks are quite small, the maximum error of area is less than 6.44%, and that of the height is about 6.80%. Because of the high accuracy and computational efficiency, the algorithm can be used in decomposition of gas chromatographic overlapping peaks and online real-time processing of general chromatographic overlapping peaks.

[Rapid determination of 10 volatile N-nitrosamines in sour meats by modified QuEChERS and gas chromatography- triple quadrupole mass spectrometry].

A rapid gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) method with modified QuEChERS method was developed for the determination of 10 volatile N-nitrosamines in sour meats. The samples were extracted with acetonitrile and eliminated the fat with n-hexane. The extracts in acetonitrile layer were purified by octadecyl silane (C18) and primary secondary amine (PSA) sorbents. The N-nitrosamines were determined on a DB-WAX column (30 m×0.25 mm×0.25 µm) by GC-MS/MS in selected reaction monitoring (SRM) mode. The external standard method was used to quantify the N-nitrosamines. The correlation coefficients (r) of the standard calibration curves were greater than 0.99 in the range of 1-100 µg/L. The average recoveries ranged from 79.8% to 115.3% with the relative standard deviations (RSDs) from 0.6% to 22.9% (n=6). The limits of detection (LODs, S/N=3) and limits of quantification (LOQs, S/N=10) were 0.04-0.3 µg/kg and 0.1-1 µg/kg, respectively. The developed method is simple, rapid, sensitive and efficient for the screening of the N-nitrosamines in sour meats.

Triple-amiRNA VEGFRs inhibition in pancreatic cancer improves the efficacy of chemotherapy through EMT regulation.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with dismal outcome. Both novel prognostic markers and therapeutic targets are needed to improve the overall outcome of patients. Although single or double VEGFRs have been studied in PDAC, little is known about the role of triple combination of VEGFRs (VEGFR1, 2, and 3) in prognosis and therapy. We determined VEGFRs protein expression in 241 pancreatic tissues by tissue microarray immunohistochemistry (TMA-IHC), and correlated with patients' clinical characteristics and overall survival. Subsequently, we inactivated VEGFRs expression using artificial microRNAs (amiRNAs) in vitro. Triple combination of amiRNAs to VEGFRs reduced cell proliferation, increased apoptosis, and reduced cell migration and invasion in pancreatic cancer cell lines. In the mouse xenograft pancreatic cancer model, triple VEGFRs silencing significantly reduced tumor growth, had synergistic effect with standard chemotherapy, and was associated with inhibition of epithelial mesenchymal transition (EMT). We conclude that triple combination of VEGFRs is a prognostic marker for PDAC, and inhibition of VEGFRs expression via amiRNA represents a novel targeted therapy in PDAC through regulating EMT.

Symmetry Analysis of Gait between Left and Right Limb Using Cross-Fuzzy Entropy.

The purpose of this paper is the investigation of gait symmetry problem by using cross-fuzzy entropy (C-FuzzyEn), which is a recently proposed cross entropy that has many merits as compared to the frequently used cross sample entropy (C-SampleEn). First, we used several simulation signals to test its performance regarding the relative consistency and dependence on data length. Second, the gait time series of the left and right stride interval were used to calculate the C-FuzzyEn values for gait symmetry analysis. Besides the statistical analysis, we also realized a support vector machine (SVM) classifier to perform the classification of normal and abnormal gaits. The gait dataset consists of 15 patients with Parkinson's disease (PD) and 16 control (CO) subjects. The results show that the C-FuzzyEn values of the PD patients' gait are significantly higher than that of the CO subjects with a p value of less than 10(-5), and the best classification performance evaluated by a leave-one-out (LOO) cross-validation method is an accuracy of 96.77%. Such encouraging results imply that the C-FuzzyEn-based gait symmetry measure appears as a suitable tool for analyzing abnormal gaits.

A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis.

Gait variability reflects important information for the maintenance of human beings' health. For pathological populations, changes in gait variability signal the presence of abnormal motor control strategies. Quantitative analysis of the altered gait variability in patients with amyotrophic lateral sclerosis (ALS) will be helpful for either diagnosing or monitoring pathological progression of the disease. Thus, we applied Teager energy operator, an energy measure that can highlight the deviations from moment to moment of a time series, to produce an instantaneous energy time series. Then, two important features were extracted to assess the variability of the new time series. First, the standard deviation statistics were used to measure the magnitude of the variability. Second, to quantify the temporal structural characteristics of the variability, the permutation entropy was applied as a tool from the nonlinear dynamics. In the classification experiments, the two proposed features were input to the support vector machine classifier, and the dataset consists of 12 ALS patients and 16 healthy control subjects. The experimental results showed that an area of 0.9643 under the receiver operating characteristic curve was achieved, and the classification accuracy evaluated by leave-one-out cross-validation method could reach 92.86 %.

MiR-200a enhances the migrations of A549 and SK-MES-1 cells by regulating the expression of TSPAN1.

MicroRNA-200a (miR-200a) has been reported to regulate tumour progression in several tumours; however, little is known about its role in non-small cell lung cancer cells (NSCLCs). Here, we found that miR-200a was up-regulated in A549 and SK-MES-1 cells compared with normal lung cells HELF. By a series of gain-of-function and loss-offunction studies, over-expression of miR-200a was indicated to enhance cells migration, and its knock-down inhibited migration of cells in NSCLC cell lines. Furthermore, miR-200a was identified to induce TSPAN1 expression which was related to migration. TSPAN1 was proved to induce migration, and so up-regulation of TSPAN1 by miR-200a may explain why over-expressing miR-200a promotes NSCLC cells migration.

Copy number variations of DNA repair genes and the age-related cataract: Jiangsu Eye Study.

PURPOSE: DNA damage is critical in the pathogenesis of age-related cataract (ARC). This study examined the association of copy number variations (CNVs) of DNA repair genes with susceptibility to ARC in the Han Chinese. METHODS: Study participants were from the population-based Jiangsu Eye Study, which includes 780 ARC patients and 525 controls. DNA was extracted from blood for copy number (CN) assays using RT-PCR. The Comet assay was to assess DNA damage in peripheral lymphocytes. RESULTS: Novel CNV was detected in WRN. Initial analyses found that CN = 3+ for WRN had an increased risk of ARC (odds ratio [OR] = 1.88, P = 0.02); CN = 1 for HSF4 had an increased risk of ARC (OR = 4.09, P = 0.004). CN = 3+ for WRN was associated with nuclear and posterior subcapsular cataract (OR = 2.06, P = 0.02; OR = 3.72, P = 0.02). CN = 1 for HSF4 was associated with nuclear and posterior subcapsular cataract (OR = 5.73, P = 0.001; OR = 6.80, P = 0.01). The combination WRN and HSF4 CNVs markedly increased the risk of ARC; the OR was increased from 2.63 by HSF4 alone to 6.80 by combined WRN and HSF4 CNVs. However, after multiple testing correction, only HSF4 CNV was associated with ARC overall and with nuclear and posterior subcapsular cataract as well. The DNA damage in lymphocytes from ARC patients was significantly higher when compared to normal controls. CONCLUSIONS: HSF4 and WRN CNVs might be involved in ARC pathogenesis in the Han Chinese. These findings suggest the importance of DNA repair in ARC susceptibility and distinct risk factors in ARC subtypes.