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In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

Gastric adenocarcinoma carries a poor prognosis, in part due to the late stage of diagnosis. Risk factors include Helicobacter pylori infection, family history of gastric cancer—in particular, hereditary diffuse gastric cancer and pernicious anaemia. The stages in the progression to cancer include chronic gastritis, gastric atrophy (GA), gastric intestinal metaplasia (GIM) and dysplasia. The key to early detection of cancer and improved survival is to non-invasively identify those at risk before endoscopy. However, although biomarkers may help in the detection of patients with chronic atrophic gastritis, there is insufficient evidence to support their use for population screening. High-quality endoscopy with full mucosal visualisation is an important part of improving early detection. Image-enhanced endoscopy combined with biopsy sampling for histopathology is the best approach to detect and accurately risk-stratify GA and GIM. Biopsies following the Sydney protocol from the antrum, incisura, lesser and greater curvature allow both diagnostic confirmation and risk stratification for progression to cancer. Ideally biopsies should be directed to areas of GA or GIM visualised by high-quality endoscopy. There is insufficient evidence to support screening in a low-risk population (undergoing routine diagnostic oesophagogastroduodenoscopy) such as the UK, but endoscopic surveillance every 3 years should be offered to patients with extensive GA or GIM. Endoscopic mucosal resection or endoscopic submucosal dissection of visible gastric dysplasia and early cancer has been shown to be efficacious with a high success rate and low rate of recurrence, providing that specific quality criteria are met.

BackgroundEarly diagnosis of gastric cancer is difficult in China due to the lack of a valid method for endoscopic screening. Early gastric cancer, especially flat gastric cancer, lacks specific endoscopic features. Many cases appear to be similar to ordinary gastritis cases under normal white light endoscopy, which can lead to misdiagnosis.AimsIn order to find a new method to improve detection rate of early gastric cancer in China, we designed a trial to validate linked color imaging (LCI) for screening of early gastric cancer in a high-risk population, as compared to white light imaging (WLI).MethodSubjects were randomly allocated to either the LCI + WLI or WLI group and then subjected to gastroscopy and all endoscopies were made after special preparation. All endoscopists had knowledge of this experiment. The main indicator was the rate of detection of gastric neoplastic lesions. The difference in the detection rate between the two groups is reported.ResultsThe detection rate was 4.31% in the WLI group and 8.01% in the LCI + WLI group. This is a difference of 3.70% with a P value < 0.001 and an OR (95% CI) of 1.934 (1.362, 2.746). The lower limit of the 95% CI was greater than 0, and the superiority margin was 1%.ConclusionThe detection rate of gastric neoplastic lesions was higher in the LCI + WLI group than in the WLI group, LCI might be an effective method for screening early gastric cancer.

During the last few decades, whole-cell biosensors have attracted increasing attention for their enormous potential in monitoring bioavailable heavy metal contaminations in the ecosystem. Visual and measurable output signals by employing natural pigments have been demonstrated to offer another potential choice to indicate the existence of bioavailable heavy metals in recent years. The biosynthesis of the blue pigment indigoidine has been achieved in E. coli following heterologous expression of both BpsA (a single-module non-ribosomal peptide synthetase) and PcpS (a PPTase to activate apo-BpsA). Moreover, we demonstrated herein the development of the indigoidine-based whole-cell biosensors to detect bioavailable Hg(II) and Pb(II) in water samples by employing metal-responsive transcriptional regulator MerR and PbrR as the sensory elements, and the indigoidine biosynthesis gene cluster as a reporter element. The resulting indigoidine-based biosensors presented a good selectivity and high sensitivity to target metal ions. High concentration of target metal exposure could be clearly recognized by the naked eye due to the color change by the secretion of indigoidine, and quantified by measuring the absorbance of the culture supernatants at 600 nm. Dose–response relationships existed between the exposure concentrations of target heavy metals and the production of indigoidine. Although fairly good linear relationships were obtained in a relatively limited concentration range of the concentrations of heavy metal ions, these findings suggest that genetically controlled indigoidine biosynthesis triggered by the MerR family transcriptional regulator can enable a sensitive, visual, and qualitative whole-cell biosensor for bioindicating the presence of bioaccessible heavy metal in environmental water samples.Key points• Biosynthesis pathway of indigoidine reconstructed in a high copy number plasmid in E. coli.• Visual and colorimetric detection of Hg(II) and Pb(II) by manipulation of indigoidine biosynthesis through MerR family metalloregulator.•Enhanced detection sensitivity toward Hg(II) and Pb(II) achieved using novel pigment-based whole-cell biosensors.

Detecting clouds, snow, and lakes in remote sensing images is vital due to their propensity to obscure underlying surface information and hinder data extraction. In this study, we utilize Sentinel-2 images to implement a two-stage random forest (RF) algorithm for image labeling and delve into the factors influencing neural network performance across six aspects: model architecture, encoder, learning rate adjustment strategy, loss function, input image size, and different band combinations. Our findings indicate the Feature Pyramid Network (FPN) achieved the highest MIoU of 87.14%. The multi-head self-attention mechanism was less effective compared to convolutional methods for feature extraction with small datasets. Incorporating residual connections into convolutional blocks notably enhanced performance. Additionally, employing false-color images (bands 12-3-2) yielded a 4.86% improvement in MIoU compared to true-color images (bands 4-3-2). Notably, variations in model architecture, encoder structure, and input band combination had a substantial impact on performance, with parameter variations resulting in MIoU differences exceeding 5%. These results provide a reference for high-precision segmentation of clouds, snow, and lakes and offer valuable insights for applying deep learning techniques to the high-precision extraction of information from remote sensing images, thereby advancing research in deep neural networks for semantic segmentation.

In this study, a successfully rapid, simple approach was applied for biosynthesis of silver nanoparticles AgNPs using for the first time the mixed leaves extract of Olea europaea subsp. europaea var. sylvestris and Pistacia lentiscus from natural association aimed to enhance their antimicrobial potential. The plant extract acts both as reducing and capping agents. When the aqueous extract was added to AgNO3 solution, the color was changed from pale to yellow to brown indicating the reduction of Ag ions and synthesis of silver nanoparticles (AgNPs) without any solvent or hazardous reagents. The green synthesized AgNPs were characterized by UV–Vis spectrophotometer, FTIR spectrum and the X-ray crystallography. The AgNPs showed superior antioxidant activity measured by DPPH, Ferric Antioxidant Reducing Power (FRAP) as well as the total antioxidant activity methods. Moreover, the analysis of phytochemical constituents including flavonoids, tannins, alkaloids and total polyphenols contents mentioned the most richness of the silver nanoparticles compared to plant extract. The new synthesized AgNPs demonstrated the bactericidal and fungicidal effects against all the tested bacterial and fungal strains and found to limit the spore germination of filamentous fungi. AgNPs also gave an anti-biofilm activity and synergistic effect with the conventional antibiotic’s drugs. Here we firstly describe the silver nanoparticles effect on virulence factors of Candida species by reduction of enzymes like proteinase and phospholipase, inhibition of morphogenesis of Candida albicans cells. This natural product, acquiring these properties, should be promoted to be used in pharmaceutical and medical industries in future.

Molecular interactions are key to many chemical and biological processes like protein function. In many signaling processes they occur in sub-cellular areas displaying nanoscale organizations and involving molecular assemblies. The nanometric dimensions and the dynamic nature of the interactions make their investigations complex in live cells. While super-resolution fluorescence microscopies offer live-cell molecular imaging with sub-wavelength resolutions, they lack specificity for distinguishing interacting molecule populations. Here we combine super-resolution microscopy and single-molecule Förster Resonance Energy Transfer (FRET) to identify dimers of receptors induced by ligand binding and provide super-resolved images of their membrane distribution in live cells. By developing a two-color universal-Point-Accumulation-In-the-Nanoscale-Topography (uPAINT) method, dimers of epidermal growth factor receptors (EGFR) activated by EGF are studied at ultra-high densities, revealing preferential cell-edge sub-localization. This methodology which is specifically devoted to the study of molecules in interaction, may find other applications in biological systems where understanding of molecular organization is crucial.

Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallow convolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment.

The objective was to evaluate the effects of wet-aging, rosemary-enhancement, and modified atmospheric packaging on the color of dark-cutting beef during simulated retail display. No-roll dark-cutting strip loins ( = 12; pH > 6.0) were selected from a commercial packing plant within 3 d postharvest. Using a balanced incomplete block design, dark-cutting loins were sectioned in half, and assigned to 1 of 3 aging periods: 7, 14, or 21 d. After respective aging, each aged section was divided into 3 equal parts, and randomly assigned to 1 of 3 enhancement treatments: nonenhanced dark-cutting, dark-cutter enhanced with 0.1% rosemary, and dark-cutter enhanced with 0.2% rosemary. Following enhancement, steaks were randomly assigned to 1 of 3 packaging treatments: high-oxygen modified atmospheric packaging (HiOx-MAP; 80% O and 20% CO), carbon monoxide modified atmospheric packaging (CO-MAP; 0.4% CO, 69.6% N, and 30% CO), and polyvinyl chloride overwrap (PVC; 20% O). Instrumental and visual color measurements were recorded during 5 d simulated retail display. Lipid oxidation was determined utilizing the thiobarbituric acid reactive substances (TBARS) method. There was a significant packaging × enhancement × display time interaction for values and chroma ( 0.001). On d 0 of display, dark-cutting steaks enhanced with 0.1% and 0.2% rosemary and packaged in HiOx-MAP had greater ( 0.001) values and chroma than other dark-cutting packaging/enhancement treatments. A significant packaging × enhancement × display time interaction resulted for values ( 0.001). Dark-cutting steaks enhanced with 0.2% rosemary and packaged in HiOx-MAP was lighter ( 0.001; greater values) than other dark-cutting treatments on d 5 of display. There were no differences ( 0.34) in discoloration scores on d 5 among different dark-cutting treatments when steaks were packaged in HiOx- and CO-MAP. There was an aging period × enhancement × packaging interaction ( < 0.0033) for lipid oxidation. On d 0 of display, there were no differences ( 0.54) in TBARS values between different aging periods and enhancement treatments. Dark-cutting steaks enhanced with 0.2% rosemary had lower ( 0.001) TBARS values than 0.1% rosemary on d 5 when aged for 21 d and in HiOx-MAP. The results suggest that rosemary enhancement with CO- or HiOx-MAP has the potential to improve the surface color of dark-cutting beef.

Written information on the surface of cultural relics can record important historical events. Due to the influence of natural and human factors, the surface of cultural relics fades and the words are difficult to identify. Take advantage of the hyperspectral data image and spectral unity and wide spectral range, a cultural relics surface handwriting enhancement method based on manifold learning and mixed pixel decomposition was proposed. First, the minimum noise fraction (MNF) transformation was carried out on the hyperspectral image, and then the top 10 bands were selected for inverse MNF transformation to reduce noise of the hyperspectral image. Then, the reconstructed image was dimensionally reduced by locally linear embedding (LLE) to obtain a gray image with the maximum amount of information. At the same time, the spectral features of the handwriting and background area in the reconstructed image were analysed. The automatic target generation process (ATGP) was adopted for endmember extraction on the reconstructed image to identify the endmember of handwriting. The abundance map of handwriting area was obtained by the fully constrained least squares (FCLS). Finally, the gray image and the abundance map of the handwriting region were weighted together to obtain the handwriting enhanced image. The true color image was synthesized from the reconstructed image, Then the true color image and the handwriting enhancement image were fused to obtain the handwritting fusion image. The hyperspectral image of a faded text in Shuozhou City, Shanxi Province, China, was used as an example for verification, and the results showed that the method can effectively enhance the text on the surface of the artifacts.