Herbal cosmetics are cosmetics derived from plants. One example is bakuchiol. Bakuchiol is a meroterpene phenol from the seeds of the Psoralea corylifolia plant. Bakuchiol can be derived from Psoralea grandulosa, Pimelea drupaceae, Ulmus davidiana, Otholobium pubescens, and Piper longum. Bakuchiol has antioxidant, anti-inflammatory, and anti-aging activities such as retinol. Bakuchiol will replace retinol with much better safety. Bakuchiol and retinoids share similar cellular pathways, such as the regulation of retinoic acid, the expression of collagen, and extracellular matrix synthesis enzymes. Bakuchiol has many benefits for the skin, so cosmetics and skin therapy can use bakuchiol. This review aims to provide information about the activity of bakuchiol, which has benefits for facial skin, so that this review can be a reference in making cosmetics containing bakuchiol. Bakuchiol has many benefits for skincare such as anti-aging, anti-hyperpigmentation, anti-oxidant, anti-inflammatory, anti-acne, and anti-melanogenesis. Products containing bakuchiol on the market are cleansers, serum, nanoemulsion, and cream.

Human beatboxing is a vocal art making use of speech organs to produce vocal drum sounds and imitate musical instruments. Beatbox sound classification is a current challenge that can be used for automatic database annotation and music-information retrieval. In this study, a large-vocabulary humanbeatbox sound recognition system was developed with an adaptation of Kaldi toolbox, a widely-used tool for automatic speech recognition. The corpus consisted of eighty boxemes, which were recorded repeatedly by two beatboxers. The sounds were annotated and transcribed to the system by means of a beatbox specific morphographic writing system (Vocal Grammatics). The image processing techniques plays vital role on image Acquisition, image pre-processing, Clustering, Segmentation and Classification techniques with different kind of images such as Fruits, Medical, Vehicle and Digital text images etc. In this study the various images to remove unwanted noise and performs enhancement techniques such as contrast limited adaptive histogram equalization, Laplacian and Harr filtering, unsharp masking, sharpening, high boost filtering and color models then the Clustering algorithms are useful for data logically and extract pattern analysis, grouping, decision-making, and machine-learning techniques and Segment the regions using binary, K-means and OTSU segmentation algorithm. It Classifying the images with the help of SVM and K-Nearest Neighbour(KNN) Classifier to produce good results for those images.

The growing worldwide cancer incidence, coupled to the increasing occurrence of multidrug cancer resistance, requires a continuous effort towards the identification of new leads for cancer management. In this work, two C-scorpionate complexes, [FeCl2(κ3-Tpm)] (1) and [Co(κ3-TpmOH)2] (2), (Tpm = hydrotris(pyrazol-1-yl)methane and TpmOH = 2,2,2-tris(pyrazol-1-yl)ethanol), are studied as potential scaffolds for future anti-cancer drug development. Their cytotoxicity and cell migration inhibitory activity are analyzed, and an untargeted metabolomics approach is em-ployed to elucidate the biological processes significantly affected by these two complexes, using two tumoral cell lines (B16 and HCT116) and a non-tumoral cell line (HaCaT). While [FeCl2(κ3-Tpm)] did not display a significant cytotoxicity, [Co(κ3-TpmOH)2] was particularly cy-totoxic against the B16 cell line. While [Co(κ3-TpmOH)2] significantly inhibited cell migration in all tested cell lines, [FeCl2(κ3-Tpm)] displayed a mixed activity. From a metabolomics perspective, exposure to [FeCl2(κ3-Tpm)] is associated with changes in various metabolic pathways involving tyrosine, where iron-dependent enzymes are particularly relevant. On the other hand, [Co(κ3-TpmOH)2] is associated with dysregulation of cell adhesion and membrane structural pathways, suggesting its antiproliferative and anti-migration properties can be due to changes in the overall cellular adhesion mechanisms.

Encephalitis is a condition with a variety of etiologies, clinical presentations and degrees of severity. The causes of these disorders include both neuroinfections and autoimmune diseases in which host antibodies are pathologically directed against self antigens. In autoimmune encephalitis, autoantibodies are expressed in the central nervous system. The incidence of this disease is approximately 4% of all reported cases of encephalitis. Autoimmune encephalitis can be induced by antibodies against neuronal surface antigens such as N-methyl-D-aspartate-activated glutamate receptors (NMDAR), ⍺-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPAR) or gangliosides GQ1b, DPPX, CASPR2, LGI1, as well as by antibodies against neuronal intracellular antigens. The paper presents a number of both mental and neurological symptoms of autoimmune encephalitis. Moreover, the coexistence of psychoses, neoplastic diseases and the methods of diagnosing autoimmune encephalitis are discussed. Attention was also drawn to the fact that early diagnosis, as well as early initiation of targeted treatment, increases the chance of a successful course of the therapeutic process.

Objective Image Quality Assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by MOS/DMOS scores. One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of the rectangular pixel grid.The authors in this paper have been proposed three modifications of SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from the algorithm for computing the MDSI index. The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained on many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for high-quality digitization of content from pages and whiteboards to sharable online material. But capturing this information is not always easy, nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts, by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.

It has remained a hard nut for years to segment sonar images, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds to be calculated. To realize this goal, it follows the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, segment the region into the background region, buffer region and target regions. After filtering, reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts. Finally, remove the remaining noise from images by means of morphological image processing. The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar image quickly and effectively, with no problem of incomplete image target shape. Thus, the stable and feasible method is testified.

Work reviews the new isolated isolated cembranoid diterpene derivatives from species belonging to the family Alcyoniidae, which comprises the genera Sarcophyton, Sinularia, and Lobophytum as well as their biological properties, during 2016–2017. The compilation permitted to conclude that much more new cembranoid diterpenes were found in the soft corals of the genus Sarcophyton sp. (33 new compounds) than in those belonging to the genera Lobophytum (17) or Sinularia (8). Several methods have been used for identifying these new compounds, after extraction with organic solvents and fractionation. The fractions obtained, in some cases, were followed by TLC, and again subjected to chromatographic procedures, including semi-preparative HPLC. Beyond the chemical composition, the biological properties were also evaluated, namely anti-microbial against several Gram-positive and Gram-negative bacteria and fungi, anti-inflammatory and anti-tumoral against several types of cancer cells. Although the biological activities detected in almost all samples, they were not outstanding ones.

Digital images have become an important carrier for people to access information in the information age. However, with the development of the technology, digital images are vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics and commonly used datasets for forgery detection are presented. The performance, advantages and disadvantages of existing methods are also compared and analyzed according to different types of detection. In active forensics, robust image watermarking techniques are overviewed, the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are given to provide useful suggestions for people in image forensics and related research fields.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Curcumin is the main secondary metabolites of Curcuma longa and other Curcuma spp, and has been reported to have some potential in preventing and treating some physiological disorders. This study investigated the effect curcumin in inhibiting high-fat diet and streptozotocin (STZ)-induced hyperglycemia and hyperlipidemia in rats. Twenty-six male Sprague-Dawley (SD) rats (170-190 g) were randomly divided into a standard food pellet diet group (Control group), a high-fat diet and streptozotocin group (HF+STZ group), and a high-fat diet combined with curcumin and STZ group (HF+ Cur +STZ group). Compared with the HF+STZ group, the HF+Cur+STZ group exhibited significantly reduced fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (AST) and aspartate transaminase (ALT) levels, and liver coefficients; in the livers of these rats, the expression of malondialdehyde (MDA) and Bax was downregulated, whereas that of superoxide dismutase (SOD) and Bcl-2 was upregulated. Moreover, the liver histology of these rats was improved and resembled that of the control rats. These results suggest that curcumin prevents high-fat diet and STZ-induced hyperglycemia and hyperlipidemia, mainly via anti-oxidant and anti-apoptotic mechanisms in the liver.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

(1) In order to study the influence of chaotic system on wheat image encryption, a method to find the optimal chaotic system encryption based on wheat image is proposed. (2) 10 different chaotic system schemes are combined to encrypt wheat images by 13 common chaotic maps, respectively. The best chaotic system scheme is obtained by considering the anti-attack capability of these encryption schemes, which analyzes 8 commonly used image encryption performance evaluation indexes. (3) The experimental results show that the new four-dimensional chaotic system has the best encryption effect, which is suitable for wheat image encryption. (4) The proposed scheme of wheat image encryption based on chaotic system provides a reference for other crop image encryption.

Inotodiol, a lanostane-type triterpenoid, and many phytochemicals from Chaga mushroom have been investigated for various allergic diseases. However, antiaging and anti-inflammatory activities of inotodiol under different types of oxidative stress and impact of inotodiol on collagen and hyaluronan synthesis are not sufficiently studied. Lanostane triterpenoids-rich concentrate, which contained 10% inotodiol as major (the inotodiol concentrate), was prepared from Chaga and compared with pure inotodiol in terms of anti-inflammatory activities on a human keratinocyte cell line, HaCaT cells, under various stimulation such as stimulation with ultraviolet (UV) B or tumor necrosis factor (TNF)-α. In stimulation with TNF-α, interleukin (IL)-1β, IL-6, and IL-8 genes were significantly repressed by 0.44~4.0 μg/mL of pure inotodiol. UVB irradiation induced overexpressions of pro-inflammatory cytokines, but those genes were significantly suppressed by pure inotodiol or inotodiol concentrate. Moreover, pure inotodiol/inotodiol concentrate could also modulate synthesis of collagen and hyaluronic acid by controlling COL1A2 and HAS2/3 expression, which implies a crucial role of pure inotodiol/inotodiol concentrate in prevention of skin aging. These results enlighten anti-inflammatory and anti-aging effect of pure inotodiol/inotodiol concentrate, and it is highly conceivable that pure inotodiol and inotodiol concentrate could be promising natural bioactive substances to be incorporated in therapeutic and beautifying applications.

Antibiotic failure is one of the most worrying health problems worldwide. Nowadays we are facing an international crisis where several issues are involved: new antibiotics are not being discovered any longer, resistance mechanisms become spread in nearly every clinical isolate of bacteria and the appearance of recurrent infections caused by persistent bacteria complicates the overcoming of infections. In this context, it has been explored new anti-infectious strategies against MDR and persistent bacteria as well as the rescue of FDA-approved compounds (drug repurposing). Among the highlighted new anti-infectious strategies we find anti-microbial peptides, anti-virulence compounds, phage therapy and new molecules. On the other hand, as drugs of repurposing that have been described, we have anti-inflammatory compounds, anti-psychotics, anti-helmintic drugs, anti-cancerous and statins.

Counterfeiting and theft have always been problems that incur high costs and results in considerable losses for the international markets. In this research paper, we will address the issue of counterfeiting while using RFID technology in retailer systems or other industries by presenting a new anti-counterfeiting and anti-theft system for the retailer market. This system will address the two above mentioned issues and provide a solution that can save the retailer systems millions of dollars yearly. This proposed system will achieve the objective of preventing or minimising the counterfeiting and theft of tagged products. At the same time, it will provide a strong indication for suspiciously sold or obtained items. Furthermore, we conducted a security analysis to prove the correctness of our protocol on the basis of the strand spaces.

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV-Python software is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, monetary velocity fields, and vortices.

Abstract: Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only takes out the shallow feature and the deep feature by transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN.

Maslinic acid (MA) is a natural triterpene from Olea europaea whose pharmacological functions have been showed. The objective of this study was to examine MA effect on cell viability (by MTT assay), reactive oxygen species (ROS levels, by flow cytometry) and key anti-oxidant enzyme activities (by spectrophotometry) in murine skin melanoma (B16F10) cells compared to healthy cells (A10). MA induced cytotoxic effects in cancer cells (IC50 42 µM) whereas no effect was found in A10 cells treated with MA (up to 210 µM). In order to produce a stress situation in cells, 0.15 mM of H2O2 were added. Under stressful conditions, MA protected both cell lines against oxidative damage, decreasing intracellular ROS, being higher in B16F10 than in A10 cells. The treatment with H2O2 and without MA produced different responses in anti-oxidant enzymes activities depending on cell line. In A10 cells, all enzymes were up-regulated, but in B16F10 cells only superoxide dismutase, glutathione S-transferase and glutathione peroxidase increased their activities. MA restored the enzyme activities to similar levels than control group in both cell lines, highlighting that in A10 cells the highest MA doses induced values lower than control. Overall, these findings demonstrate the great anti-oxidant capacity of MA.

Image denoising has been one of the important problems in the field of computer vision, and it has a wide range of practical value in many applications, such as medical image processing, image enhancement, and computational photography. Traditional image denoising methods are usually based on hand-designed features and filters, but these methods perform poorly under complex noise and image structures. In recent years, the rapid development of neural network technology has revolutionized the image-denoising task. This paper introduces the knowledge about neural networks and image denoising, explores the impact of neural networks on image denoising, and how is it possible to denoise images by neural networks. It also summarises other image-denoising methods and finally points out the challenges and problems faced by image-denoising at present. Some possible new development directions are proposed to provide new solutions for image-denoising researchers and to promote the development of the field.

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

Immune-mediated necrotizing myopathy (IMNM) is a rare and severe disease that corresponds to a specific entity of idiopathic inflammatory myopathy. Patients with IMNM suffer from proximal muscle weakness, and present high levels of creatine kinase and necrotic myofibers. Anti-Signal Recognition Particle (SRP) and anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase autoantibodies (HMGCR) have recently been identified in two thirds of patients with IMNM and are used as a hallmark of the disease. In this review, we provide a detailed description of these antibodies and the tests used to detect them in the serum of patients. Based on in vitro studies and mouse models of IMNM, we will discuss the role of autoantibodies in the pathogenesis of the disease. Finally, in the light of the latest knowledge, we will conclude with a review of recent therapeutic approaches.

Three new polyketides (penidihydrocitrinins A–C, 1–3) and 14 known compounds (4–17) were isolated from the deep-sea-derived Penicillium citrinum W17. Their structures were elucidated by comprehensive analyses of 1D and 2D NMR, HRESIMS, and ECD calculations. Compounds 1–17 were evaluated for their anti-inflammatory and anti-osteoporotic bioactivities. All isolates exhibited significant inhibitory effects on microglial activation in LPS-stimulated BV-2 cells in a dose-response manner. Notably, compound 14 displayed the strongest effect with the IC50 value of 4.7 µM. Moreover, compounds 6, 7, and 8 not only suppressed osteoclastogenesis, but also promoted osteoblast mineralization.

Green products have excellent potential for discovering and producing new medicinal products. In recent years, there is a growing interest towards the green synthesis of metal nanoparticles, particularly from plants sources. This eco-friendly approach was used in the recent research to biosynthesize zinc oxide nanoparticles (ZnO NPs) from Cissus antractica plant. The present study aims to examine the cell cytotoxicity, cell death, and pathways of apoptosis in lung cancer cells (A549) and inflammation properties treated with biosynthesized ZnO NPs. Additionally, synthe-sized ZnO nanoparticles were characterized using UV–vis spectroscopy, X-ray diffraction (XRD), Transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FT-IR), and Energy-dispersive X-ray spectroscopy (EDS). The XRD and TEM analyses exhibited 31 nm as the size of synthesized nanoparticles, the pure crystal form of ZnO NPs, and the shape of the NPs as nearly spherical and hexagonal quartzite. The MTT assay used for cytotoxicity evaluation de-picted the significant toxic effect of CA-ZnONPs against the A549 lung cancer cells at concentra-tions up to 20 µg/mL than non-cancerous cells. Furthermore, we observed an elevated ROS level in cancer cells. These NPs also demonstrated dose-dependent ability to suppress colony for-mation and cell migration, which increases their potential as a lung cancer treatment. Further-more, The NPs formulation's ability to induce apoptosis in cancer cells was further supported by Hoechst and propidium iodide dye. Further, CA ZnO NPs increased the gene expression of BAX, Cyto-c, Caspase 3 and 9, but decreased the expression of Bcl-2, indicating that the nanoformulation triggered mitochondrial-mediated apoptosis. Besides, this study showed that NPs suppressed in-flammatory responses by subsequently decreasing reactive oxygen species (ROS) generation, downregulating the expression of the pro-inflammatory cytokines TNF-α, iNOS, COX-2, IL-6, IL-8, and NO. Moreover, the study's findings imply that Cissus antractica are a potential source with biosynthesize nanoparticles that can might be further investigated for the progress of an-ti-inflammatory and anti-cancer drugs with further study.

Acemella oleracea is known as toothache plant belongs to the family of Asteraceae. It is treated as a medicinal remedy like tuberculosis, illness, cough, rheumatism, and illness. This study was concentrated on the antioxidant, cytotoxic, antimicrobial & thrombolytic activities of methanol extract of Acmella oleracea leaves using in vitro model. The antioxidant activity was estimated as trolox equivalent antioxidant capacity utilizing the DPPH and reducing power tests. The plant extract was tested for its cytotoxic action using a brine shrimp lethality bioassay, thrombolytic activity using clot disruption, and antibacterial activity using a disc diffusion assay technique against four distinct gram-positive and gram-negative bacteria. IC₅₀ value of standard ascorbic acid for DPPH was 7.8µg/ml and the IC50 of the methanol extract of Acmella oleracea leaves was 198.34µg/ml that was moderate effect at all compare with ascorbic acid. Thrombolytic assay of Streptokinase as a positive control showed 88.49% where the extract shows 18.69% lytic activity shows the assay. In this study, the sample of LC₅₀ results of cytotoxicity assay was 1.431 µg/mL which can be treated as less activity. Moreover, the extracts showed low to moderate antibacterial activity against both gram-negative and gram-positive bacterial stains (zone of inhibition-10-26 mm). Vibrio Mimicus bacteria stains exhibited the highest level of activity, with a range of 23 for the diameter of the growth inhibition zone. The current review shows leaf extracts of Acmella oleracea may be used as a source of antioxidant and thrombolytic activity, as well as a significant source of antibacterial and anticancer substances. Further research is required to evaluate in-vivo the pharmacological activity of Acmella oleracea leaves in order to identify the essential metabolites and potential mechanisms.

Methotrexate (MTX) is a potent drug for the treatment of various diseases globally amidst being a chemotherapeutic and immunosuppressant agent. However, hepatotoxicity induced by MTX could be life-threatening if left untreated. Folate supplementation is concurrently applied to reduce the adverse effects of MTX, albeit efficacy compromise. Therefore, there is the need to understand the process for the prevention and treatment strategies for MTX induced hepatotoxicity (MIH). In recent times, preliminary preclinical and clinical findings indicate the potential of natural phytobioactive compounds for MIH prevention and treatment. This mini review therefore summarizes proposed mechanisms of MIH and recent advances in the prevention and treatment prospects of natural phytobioactive compounds on MIH.

The use of plant extracts in the synthesis of metal nanoparticles is a very attractive approach in the field of green synthesis. To benefit from the potential synergy between the biological activities of the Moringa oleifera leaves extract and metallic bismuth, our study aimed at synthesizing bismuth nanoparticles using a hydroalcoholic extract of M. oleifera leaves as a means of green synthesis that yields nontoxic products and reduces the production of wasteful material. To this end, the M. oleifera leaves extract was treated with a bismuth nitrate pentahydrate solution. A color change from light brown to dark brown indicates the synthesis of bismuth nanoparticles. The total phenolic content in the M. oleifera leaves extract used was 23.0 ± 0.3 mg gallic acid equivalent/g of dried M. oleifera leaves powder. Antioxidant property of MO synthesised bismuth Nanoparticles was evaluated and in line with the extract used in the synthesis of NPs. The physical properties of the synthesized bismuth nanoparticles were characterized using UV-Vis spectrophotometer, FT-IR spectrometer, TEM, SEM, and XRD. The synthesized bismuth nanoparticles have a size in the range of 40.4-57.8 nm with amorphous morphology. Using DPPH and phosphomolybdate assays, our findings revealed that the M. oleifera leaves extract and the synthesized bismuth nanoparticles possess antioxidant properties. Using resazurin microtiter assay, we also demonstrate that the M. oleifera leaves extract and the synthesized bismuth nanoparticles exert potent anti-bacterial activity against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus and Enterococcus faecalis, similarly to the inhibition exerted by Moringa extract, especially against Enterococcus faecalis (MIC values for the extract: 500, 250, 250, and 250 µg/mL; MIC values for the bismuth nanoparticles: 500, 500, 500, and 250 µg/mL, respectively). Similarly, the M. oleifera leaves extract and the synthesized bismuth nanoparticles display relatively stronger anti-fungal activity against Aspergillus niger, Aspergillus flavus, Candida albicans, and Candida glabrata (MIC values for the extract: 62.5, 62.5, 125, and 250 µg/mL; MIC values for the bismuth nanoparticles: 250, 250, 62.5, and 62.5 µg/mL, respectively). Thus, the hydroalcoholic extract of M. oleifera leaves was successfully used in the synthesis of bismuth nanoparticles, showing a positive antioxidant, anti-bacterial, and anti-fungal activity. Therefore, the synthesized bismuth nanoparticles can potentially be employed in the alleviation of symptoms associated with oxidative stress and in the topic treatment of Candida infections.

The synthesis of metal nanoparticles using plant extracts is a very promising method in green synthesis. The medicinal value of Moringa oleifera leaves and the anti-microbial activity of metallic copper were combined in the present study to synthesize copper nanoparticles having a desirable added-value inorganic material. The use of a hydroalcoholic extract of M. oleifera leaves for the green synthesis of copper nanoparticles is an attractive method as it leads to the production of harmless chemicals and reduces waste. The total phenolic content in the M. oleifera leaves extract was 23.0 ± 0.3 mg gallic acid equivalent/g of dried M. oleifera leaves powder. The M. oleifera leaves extract was treated with a copper sulphate solution. A color change from brown to black indicates the formation of copper nanoparticles. Characterization of the synthesized copper nanoparticles was performed using UV-Vis spectrophotometer, FT-IR spectrometer, TEM, SEM, and XRD. The synthesized copper nanoparticles have an amorphous nature and particle size of 35.8-49.2 nm. We demonstrate that the M. oleifera leaves extract and the synthesized copper nanoparticles display considerable antioxidant activity. Moreover, the M. oleifera leaves extract and the synthesized copper nanoparticles exert potent anti-bacterial activity against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Enterococcus faecalis (MIC values for the extract: 500, 250, 250, and 250 μg/mL; MIC values for the cooper nanoparticles: 500, 500, 500, and 250 μg/mL, respectively). Similarly, the M. oleifera leaves extract and the synthesized copper nanoparticles exert relatively more potent anti-fungal activity against Aspergillus niger, Aspergillus flavus, Candida albicans, and Candida glabrata (MIC values for the extract: 62.5, 62.5, 125, and 250 μg/mL; MIC values for the cooper nanoparticles: 125, 125, 62.5, and 31.2 μg/mL, respectively). Our study reveals that the green synthesis of copper nanoparticles using a hydroalcoholic extract of M. oleifera leaves was successful. In addition, the synthesized copper nanoparticles can be potentially employed in the treatment of various microbial infections due to their potent antioxidant, anti-bacterial, and anti-fungal activities.

The anti-money laundering (AML) process has failed both in identifying suspicious cases in due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud approach, this article presents the main aspects related to the development of a multi-agent system that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained in a data mining process. A set of rules, obtained through data mining over a real database, in conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make up the agents' knowledge base. The cases for which the system was unable to suggest a decision are flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts who proved the suspicion of several cases, including some that had not been identified by the systems in execution.

Several vaccines against COVID -19 have been developed and licenced to enhance the immune response against SARS-CoV-2. Similarly, infection with SARS-CoV-2 before vaccination has been shown to provide significant protection against severe infection and hospitalisation. The aim of this study was to investigate the effect of three doses of Sinopharm vaccine and SARS-CoV-2 infection on the specific immune response in 103 volunteers, measuring neutralizing antibodies, anti-S1 IgG, anti-RBD IgM, anti-N IgM, anti-N IgG antibodies and interferon γ. Our results showed that the presence of cardiovascular diseases increased the level of anti-N-IgG antibodies, while endocrinological diseases decreased the level of neutralizing antibodies and anti-N-IgG antibodies, suggesting that these diseases alter the effect of vaccine immunity. In addition, there was a significant decrease in anti-S1 IgG levels 6 months and in anti-N IgG levels 18 months post-infection, while neutralizing antibody and interferon γ levels were constant at 3, 6 and 18 months post-infection. Therefore, our results confirm the importance of hybrid immunity as the strongest and most durable compared to exclusively natural or vaccine-induced immunity. Significant positive correlations were found between humoral and cellular immunity markers: neutralizing antibodies, anti-S1 IgG, anti-N IgG and interferon γ, indicating a unique coordinated response specific to COVID-19.

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness [1]. Currently, a popular research area related to artificial neural networks is adversarial attacks. The effect of adversarial attacks on the image is not highly perceptible to the human eye, also it drastically reduces the neural network accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. The approach proposed in this paper can improve the image recognition accuracy in the presence of high-frequency distortions, in particular, caused by adversarial attacks. The proposed technique makes it possible to measure up the logic of artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

Parallax processing and structure preservation has long been important and challenging tasks in image stitching. In this paper, an image stitching method based on sliding camera to eliminate perspective deformation and asymmetric optical flow to solve parallax is proposed. By maintaining the viewpoint of two input images in the mosaic non-overlapping area and creating a virtual camera by interpolation in the overlapping area, the viewpoint is gradually transformed from one to another image view-point so as to complete the smooth transition of the two image viewpoints and reduce perspective deformation. Two coarsely aligned warped images are generated with the help of a global projection plane. After that, the optical flow propagation and gradient descent method are used to quickly calculate the bidirectional asymmetric optical flow between the two warped images, and the optical flow-based method is used to further align the two warped images to reduce parallax. In the image blending, the softmax function and registration error are used to adjust the width of the blending area, further eliminating ghosting and reduce parallax. Finally, the comparative experiments show that our method can effectively eliminate perspective deformation, reduce misalignment, and save running time.

Femur length (FL) is a biometric measurement of ultrasound images used to identify the growth patterns and abnormalities of the foetus. Automatic segmentation of FL can improve the efficiency and convenience of inexperienced users. The femur region in a low-cost ultrasound image is a small area with low contrast intensity compared with the non-femur region. This study proposed a fully automated framework for segmenting and measuring the FL in a portable ultrasound image. We utilized statistical modeling, employing Gaussian mixture functions for thresholding to extract the femur area from the background. We also used the localizing region-based active contour method to segment the femur region accurately. We also proposed a refinement step in this framework to obtain a more accurate femur area in the ultrasound image. Because the length and position of the femur are the main factors affecting foetal biometric measurement accuracy, we not only evaluated segmentation results using region-based methods, such as sensitivity, specificity, and dice similarity. We also applied the FL measurement approach to evaluate the angle and length accuracy relative to the ground truth. In terms of region-based segmentation performance, the proposed method provided the best average performance with a sensitivity of 0.78, specificity of 0.87, and dice similarity of 0.81. For FL measurement performance, the proposed framework achieved an average angle accuracy of 0.99, distance accuracy of 0.88, and length accuracy of 0.93.

An algorithm for the treatment of images affected by both blurring and salt&pepper noise is proposed with a cost only proportional to the number of pixels. The methodology uses a discretization scheme for the Laplace operator multiplied by a suitable nonlinear term depending on the gradient. Even if this approach resembles a diffusion type algorithm, only one step of the procedure is applied, leading to significant time savings. The procedure is successfully tested on some standard black&white natural images.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

Abstract: Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. P articular ly , devices of less than 100,000 pixels are still widely used, and the environment for image processing is very bitter . Since the sewage conduit image s covered in this study ha ve a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolution of the sewe r conduit images are very low in Korea, this problem of low resolution was selected as the subject of study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Desp ite the very low resolution, the cracked image s showed 96.4% accuracy of detection, and the non cracked image s showed 94.5% accuracy . Moreover, the analysis was excellent in quality , also . It is believed that the findings of this study can be effectively u sed for crack detection with low resolution images.

Recent studies on the image of the Turin Shroud (TS) lead to think it could have been formed through a not well-identified mechanism of energy radiation. In order to remove some lacunas about this imaging process, a reverse engineering method has been applied to it, arriving to exclude some possible mechanisms. The image formation of a human face wrapped on a cloth by using an ad-hoc developed software has been simulated. The results of different kinds of the radiation depending from different parameters have been simulated, each one connected with accredited hypotheses. On the basis of the comparison among the different images produced by the software and the TS Face, some useful information both about the kind of radiation and the cloth wrapping conditions have been obtained. The effect of image distortion of a cloth wrapped around a face has been discussed too by defining the best laws of radiation and of their attenuation with distance. A Lambertian law is not compatible with the TS image. A vertical radiation shows a problem in reproducing the requested resolution. A radiation perpendicular to the emitting surface, like that produced by an electric field appears promising to explain the TS Face.

It is one of very important and basic problem in compute vision field that recovering depth information of objects from two-dimensional images. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the best appropriate a pair of straight line segments are chose and reduce the computation, the theory of convex hull and the knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

Across different cultures around the globe, human beings have historically depended largely on medicinal plants for managing diseases that have hitherto threatened their optimal health, survival, and longevity. Evidently, the health-derived benefits of medicinal plants can be strongly attributed to the presence of secondary metabolites, particularly polyphenols. The health-promoting effects of Sorghum bicolor supplement Jobelyn® (SBSJ) —a unique supplement derived from the leaf sheaths of a West African variety of Sorghum bicolor (L.) Moench—have also been ascribed to its high levels of polyphenols. This review seeks to gather and synthesize findings from various experimental and clinical studies on the health benefits of SBSJ in arthritis, cancer, chronic viral infections, stroke, anaemia, and aging. SBSJ has been reported to contain potent bioactive polyphenolic compounds with polyvalent biological activities, including antioxidant, anti-inflammatory, immunomodulatory, chemopreventive, and neuroprotective activities. Moreover, the probable benefits of SBSJ in chronic viral infections (e.g., HIV/AIDS and COVID-19) have been attributed to its potent anti-inflammatory and immunomodulatory activities. As this supplement is increasingly becoming one of the fastest-selling herbal medicines in Nigeria, there is a need for more robust studies (including clinical trials) in order to replicate and validate the prior insights gleaned from experimental studies.

This paper describes an image processing method that makes use of image parts instead of neural parts. Neural networks excel at image or pattern recognition and they do this by constructing complex networks of weighted values that can cover the complexity of the pattern data. These features however are integrated holistically into the network, which means that they can be difficult to use in an individual sense. A different method might scan individual images and use a more local method to try to recognise the features in it. This paper suggests such a method, where a trick during the scan process can not only recognise separate image parts, as features, but it can also produce an overlap between the parts. It is therefore able to produce image parts with real meaning and also place them into a positional context. Tests show that it can be quite accurate, on some handwritten digit datasets, but not as accurate as a neural network, for example. The fact that it offers an explainable interface could make it interesting however. It also fits well with an earlier cognitive model, and an ensemble-hierarchy structure in particular.

The article describes a classification solution for amber stones. The problem of classifying amber is known for a long time among jewelers and artisans of amber art. Existing solutions can classify amber pieces according to color, but a need to classify by shape and texture is not satisfied up to now. The proposed solution is capable of classifying the gemstones according to a shape. Amber can be considered as a specific object since the form is difficult to define unambiguously. Data for amber experiments was gathered from amber art craftsmen. In the proposed solution amber form can be classified into 10 different classes (7 classes chosen during the experiment).

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.

The landscape image is the bridge of communication between people and forests, and the cut point of the supply-side reform of forest tourism products. The research collected 140 copies in total of forest landscape image drawings from non-art-major graduate students by randomly sampling during April and May, 2018, and constructed the landscape image conceptual model of forest by utilizing the landscape image sketching technique. The results showed that (1) In regard to linguistic knowledge, the natural landscape elements for instance, herbaceous plants, terrains, creatures, water and sky, and the broad-leaf forest objectively reflected not only the real forest landscape and the local native vegetation, but the variation of forest species with little attention. (2) On the perspective of spatial view, the sideways view indicated that graduate students preferred to watch forests at a moderate distance externally and few looked at forests internally. (3) In the view of self-orientation, the objective landscape indicated that graduate students preferred to demonstrate forest landscapes, they did not realize to interact with the environment. (4) On the aspect of social meaning, the scenic view and forest structure stated that graduate students preferred rural forest landscapes, not significantly for other special interests for forest. In conclusions, (1) the forest is thought to be a feature of people's life world and of rural scenes around homes, not an objective perception of the forest. (2) The forest is regarded as an important habitat for animals and a limited resource for people's life, production and recreation needs, into which people will go only to meet such needs. (3) The natural values of forests, like the ecology and aesthetics, etc. get more attention, while the social values of forests, like the life, production and culture receives rather low attention.

Hydrogenated water has emerged as a novel approach in the field of health and wellness. It is be-lieved to have potent antioxidant properties that can neutralize harmful free radicals in the hu-man body. It has also been shown to be beneficial in mitigating oxidative stress-induced damage through its anti-inflammatory and anti-apoptotic pathways. We aim to conduct a systematic re-view to evaluate the potential benefits of hydrogenated water. The review protocol was uploaded on PROSPERO. After the initial search criteria, the articles were reviewed by two blinded inves-tigators, and a total of 25 articles were included in the systematic review. The potential benefits of hydrogenated water on various aspects of health, including exercise capacity, physical endurance, liver function, cardiovascular disease, mental health, COVID-19, oxidative stress, and anti-aging research, are a subject of growing interest and ongoing research. Although preliminary results in clinical trials and studies are encouraging, further research with larger sample sizes and rigor-ous methodologies is needed to substantiate these findings. Current research needs to fully ex-plain the mechanisms behind the potential benefits of hydrogenated water. Continued scientific exploration will provide valuable insights into the potential of hydrogenated water as an adjunc-tive therapeutic approach in the future.

A healthy cornea is a nonvascularized, transparent tissue. It is nourished by diffusion through the tear film, aqueous fluid and neurotrophins supplied by the corneal innervation. The cornea is devoid of both blood and lymphatic vessels, which makes it an immunologi-cally privileged tissue. Corneal neovascularization develops due to an imbalance between angiogenic and anti-angiogenic factors and can lead to a significant decrease in visual acui-ty. It may cause scarring, lipid keratopathy, and corneal edema. The resulting pathological vascularization of the cornea reduces its immune privilege and significantly worsens the prognosis after subsequent penetrating keratoplasty, increasing the risk of rejection of the transplanted flap. Neovascularization is favored by many disease processes, including auto-immune diseases, viral inflammations, mainly herpes and varicella-zoster, and blepharitis [1]. There are several options to treat corneal neovascularization, both pharmacological and surgical. Angiostatic treatment involves inhibiting the growth of new vessels by suppressing inflammatory responses. VEGF inhibitors, steroids, cyclosporine A, and amniotic membrane grafts secured to the cornea work this way. VEGF inhibitors are the agents with the direct and most potent angiostatic effect. Angioregressive treatment, on the other hand, results in the regression of already existing pathological vessels. This therapy is also based on VEGF inhibitors, which, however, have the most potent effect on newly formed and small vessels but are less effective in reducing large-caliber and mature vessels. A more effective method for such vessels is ablation, which involves surgical closure. The most commonly used an-gio-occlusive method is needle diathermy and argon or yellow laser therapy. In advanced corneal neovascularization, corneal transplant surgery may be necessary. It is best performed after pretreatment with anti-VEGF preparations, often continued after corneal transplanta-tion to reduce the risk of transplant rejection [2]. This paper discusses the pathogenesis of corneal neovascularization and the current and future trends in its treatment.

Quinoa, known as the "golden grain" for its high nutritional value, is a significant source of essential nutrients, including proteins, minerals, vitamins, polyphenols, phytosterols, and flavonoids. However, the biological functions of quinoa polysaccharides remain understudied. In this research, two crude quinoa polysaccharides extracts (Q-40 and Q-60) were obtained extracting by 40% and 60% alcohol. The purity of Q-40 and Q-60 was 58.29% and 62.15%, with protein contents of 8.27% and 9.60%, respectively. Monosaccharide analysis revealed that Q-40 contained glucose (Glc), galacturonic acid (GalA), and arabinose (Ara) in a molar ratio of 0.967:0.027:0.006. Q-60 was composed of xylose (xyl), arabinose (Ara), galactose, and galacturonic acid (GalA) with a molar ratio of 0.889:0.036:0.034:0.020. The average molecular weight of Q-40 ranged from 47484 to 626488 Da, while Q-60 showed a range of 10025 to 47990 Da. Rheological experiments showed that Q-40 exhibited higher viscosity, while Q-60 demonstrated more elastic properties. Remarkably, Q-60 showed potent antioxidant abilities, with scavenging rates of 98.49% for DPPH and 57.5% for ABTS. Antibacterial experiments using the microdilution method revealed that Q-40 inhibited the growth of Methicillin-resistant Staphylococcus aureus (MRSA) and E. coli, while Q-60 specifically inhibited MRSA. At lower concentrations, both polysaccharides inhibited MDA cell proliferation, but at higher concentrations, they promoted proliferation. Similar proliferation-promoting effects were observed in HepG2 cells. The research provides important information in application of quinoa in food and functional food industries.

Rhizophora mangle L. is a well-known medicinal plant found in mangroves worldwide used to treatment diabetes. This study evaluated the chemical composition of the acetonic extract from Rhizophora mangle barks (AERM), by HPLC-PDA and FIA-ESI-IT-MS/MS and the effects on high-fat diet induced obesity in mice and its mechanism of action by gene expression of inflammatory markers (Pparg, Ppara, Srebf1, Cd36, Tnf, Ccl2, Lep, Il10, Il6, Fasn, 18s). High-fat diet fed mice during 12 weeks was used as model of obesity and associated alterations. The results were very satisfactory, the extract, rich in polyphenolic compounds, flavonoids and phenolic acids, displayed intense antioxidant activity in vitro (608 µmol Trolox/g), and showed excellent activity against non-alcoholic fatty liver disease (NAFLD) and reverse insulin resistance in a model of diet-induced obesity. We can registered a modulatory effect of AERM in liver PPAR-γ mRNA expression associated to an important inhibition of CD36 mRNA expression suggesting that AERM induces the down regulation of CD36 mRNA via PPAR-gamma inhibition. These results support the traditional knowledge about the use of R. mangle for the treatment of type 2 diabetes and reveal the potential of AERM for the treatment of NAFLD and management of obesity and comorbidities.

Breast cancer is a leading cause of death among women. Conventional screening methods, such as mammography, and ultrasound diagnosis are expensive and have significant limitations. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening system that has a potential to be a low cost and reliable breast cancer screening tool. It is based on modal analysis of the breast mass, and stereographic 3D image analysis to detect the stiffer abnormal tissues. However, camera sensor noise, especially Gaussian noise is a major source of Optical Flow (OF) error in this approach to tumor detection. This work studies the performance of different conventional filters, including the standard Gaussian filter tool to remove this noise and produce more robust screening results. A radical approach, Multiple Frame Noise Removal (MFNR) is proposed, for use in this type of medical image processing instead of a Gaussian filter or other typical image noise removal tools. Its a multiple frame noise removal method where Probability Density Function (PDF) of noise is extracted from the multiple images by characterizing the same pixel positions in multiple images. The noise becomes deterministic, and hence easily removed. The proposed algorithm was applied to a data set from 10 phantom breast tests with a prototype DIET system, and 10 in-vivo samples from healthy women. Comparisons were made to an optimal Gaussian filter form that is commonly used. Reductions in OF error using these digitally imaged data sets was used to compare performance. Refinement of the images for medical applications requires higher PSNR, which was successfully achieved by using MFNR algorithm. In this study, the algorithm was used to improve the imaging results of a DIET system. The conventional wisdom that states that noise removal and detail preservation are contrasting effects is

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

Recently, the mapping industry has been focusing on the possibility of large-scale mapping from unmanned aerial vehicles (UAVs) owing to advantages such as easy operation and cost reduction. In order to produce large-scale maps from UAV images, it is important to obtain precise orientation parameters. For this, various techniques have been developed and are included in most of the commercial UAV image processing software. For mapping, it is equally important to select images that can cover a region of interest (ROI) with the fewest possible images. Otherwise, to map the ROI, one may have to handle too many images, and commercial software does not provide information needed to select images, nor does it explicitly explain how to select images for mapping. For these reasons, stereo mapping of UAV images in particular is time consuming and costly. In order to solve these problems, this study proposes a method to select images intelligently. We can select a minimum number of image pairs to cover the ROI with the fewest possible images. We can also select optimal image pairs to cover the ROI with the most accurate stereo pairs. We group images by strips, and generate the initial image pairs. We then apply an intelligent scheme to iteratively select optimal image pairs from the start to the end of an image strip. According to the results of the experiment, the number of images selected is greatly reduced by applying the proposed optimal image–composition algorithm. The selected image pairs produce a dense 3D point cloud over the ROI without any holes. For stereoscopic plotting, the selected image pairs were map the ROI successfully on a digital photogrammetric workstation (DPW), and a digital map covering the ROI is generated. The proposed method should contribute to time and cost reductions in UAV mapping.

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

This article presents a method of segmenting images with gray levels that uses Onicescu's information energy calculated in the context of the neutrosophic theory. Starting from the information energy calculation for complete neutrosophic information, it is shown how to extend its calculation for incomplete and inconsistent neutrosophic information. The segmentation method is based on calculation of thresholds for separating the gray levels using the local maximum points of the Onicescu information energy.

Document deskewing is a fundamental problem in document image processing. While existing methods have limitations, such as Hough Line Transformation that can deskew images upside down, and Deep Learning models that require huge amounts of human labour and computational resources and still fail to deskew while taking care of orientation, OCR-based methods also struggle to read text when it is tilted. In this paper, we propose a novel, simple, cost-effective deep learning method for fixing the skew and orientation of documents. Our approach reduces the search space for the machine learning model to predict whether an image is upside down or not, avoiding the huge search space of predicting an angle between 0 and 360. We finetuned a MobileNetV2 model, which was pre-trained on imagenet, using only 200 images and achieve good results. This method is useful for automation-based tasks, such as data extraction using OCR technology, and can greatly reduce manual labour.

Our report presents the findings from a treatment program that involved plant-based diets (PBDs) and supplements for 1,750 elderly cardiology patients diagnosed with COVID-19 between April 2020 and June 2023. At the start of the program, there was no published data supporting the use of PBDs for COVID-19 patients. However, after 18 months, studies were released that indicated the effectiveness of PBDs in decreasing the incidence and severity of COVID-19. Our treatment protocol differed from these studies in several ways. We carefully controlled the quality, quantity, and processing method of the foods we provided, opting for raw instead of cooked vegetables. Additionally, we incorporated supplementation that complemented the nutrients lacking in a PBD and enhanced the anti-viral, anti-inflammatory, antioxidant, antithrombotic, and immunomodulatory properties of a PBD. Our patients recovered faster (12±1.4 days vs. 20.9±4.3 days), with lower severity (2% vs. 10-20%), hospitalizations (0 vs. 5-10%), and deaths (0 vs. 15-17%) compared to the general population. These favorable outcomes are particularly noteworthy given Indonesia's high COVID-19 morbidity and mortality rates, among the highest in Asia. The findings of this report provide valuable insights for practitioners in managing high-risk elderly COVID-19 patients, particularly in avoiding severity and mortality.

The goal of this study was to develop a rapid diagnostic kit for rheumatic disease and systemic lupus erythematosus (SLE) screening. A novel rapid vertical flow assay (VFA) was engineered and used to assay anti-nuclear (ANA) and anti-dsDNA (αDNA) autoantibodies from systemic lupus erythematosus (SLE) patients and healthy controls (HC). Observer scores and absolute signal intensities from the VFA were validated by ELISA. The rapid ten minute point of care VFA test engineered demonstrated a limit of detection of 0.5 IU/ml for ANA and αDNA autoantibodies in human plasma with inter-operator CV of 19% for ANA and 12% for αDNA. Storage stability was verified over a three-month period. When testing anti-dsDNA and ANA levels in 22 SLE and 18 HC serum samples, the duplex VFA revealed 95% sensitivity, 72% specificity and 84% accuracy in discriminating disease groups, comparable to the gold standard, ELISA. The rapid αDNA/ANA duplex VFA can potentially be used in primary care clinics for evaluating patients or at-risk subjects for rheumatic diseases and for planning follow-up testing. Given its low cost, ease and rapid turnaround, it can also be used to assess SLE prevalence estimates.

Neuromyelitis Optica Spectrum Disorder (NMOSD) is a rare immune-mediated relapsing-remitting disease of the central nervous system. The usage of rituximab, to prevent NMOSD relapses, is common in Sweden, but the knowledge on long-term efficacy and risk for severe infections are limited. We performed a retrospective cohort study with medical chart review of rituximab-treated NMOSD patients at the Karolinska University Hospital, Sweden, to assess the occurrence of relapses and severe infectious events (SIE). Furthermore, we collected data on CD19+ lymphocyte, and IgG levels during therapy. A total of 42 NMOSD patients; 24 with aquaporin -4 antibodies (AQP4+), eight with myelin oligodendrocyte glycoprotein antibodies (MOG+) and 10 with absence of antibodies (double-seronegative) were included. During a mean treatment time of 4.4 years, 37% AQP4+, 75% MOG+, and 60% double-seronegative patients experienced 1 relapse. SIEs were common with 46% of AQP4+, 25% of MOG+, and 40% of double-seronegative patients affected. Incomplete CD19+ lymphocyte suppression did not predict a relapse and there was no correlation between IgG levels and SIEs. In conclusion, our data demonstrated a high risk for both relapses and SIEs in rituximab-treated NMOSD patients highlighting the need for close clinical vigilance of disease activity and infections during treatment.

More than 15,000 prescriptions and over the counter drugs are available according to the US Food and Drug Administration website. Moreover, several herbal medicines and dietary supplements are readily available to add to the list of possible drugs, which can potentially cause adverse drug interactions. These are a pressing concern for all as they can interfere with many dental procedures. Additionally, the number of geriatric patients seen in routine dental practice has increased over time. This implies that there are more patients with multiple diseases and health conditions like hypertension, diabetes, problems associated with the cardiovascular, renal and gastrointestinal systems, arthritis, osteoporosis, etc. All these require patients to be on certain medications. Furthermore, advancement in the dental field has led to more complex dental procedures (implants, grafts) being carried out in a general dental practice. These advanced and slightly more invasive treatments require the use of certain drugs before, during and after the treatment like local anesthetics, vasoconstrictors, anxiolytics, analgesics and antibiotics. All of these can possibly interact with medications a patient is already taking and can also interfere with the current dental treatment and create complications. This article aims to provide necessary information about commonly encountered systemic diseases and associated treating medications, their mechanism of action, possible complications and their management. The classes of drugs discussed include anti-platelet agents, vitamin K antagonists, novel oral anticoagulants, bisphosphonates, disease-modifying anti-rheumatic drugs and oral contraceptives.

Abstract: This study reviewed aspects of the biology of two members of the glucosinolate family, namely sinigrin and glucoraphanin and their anti-tumour and anti-microbial properties. Sinigrin and glucoraphanin are converted by the β-sulphoglucosidase myrosinase or the gut microbiota into their bioactive forms, allyl isothiocyanate (AITC) and sulphoraphanin (SFN) which constitute part of a sophisticated defence system plants developed over several hundred million years of evolution to protect them from parasitic attack from aphids, ticks, bacteria or nematodes. Delivery of these components from consumption of cruciferous vegetables rich in the glucosinolates also delivers many other members of the glucosinolate family so the dietary AITCs and SFN do not act in isolation. In-vitro experiments with purified AITC and SFN have demonstrated their therapeutic utility as antimicrobials against a range of clinically important bacteria and fungi. AITC and SFN are as potent as Vancomycin in the treatment of bacteria listed by the World Health Organisation as antibiotic-resistant "priority pathogens" and also act as anti- cancer agents through the induction of phase II antioxidant enzymes which inactivate potential carcinogens. Glucosinolates may be useful in the treatment of biofilms formed on medical implants and catheters by problematic pathogenic bacteria such as Pseudomonas aeruginosa and Staphylococcus aureus and are potent antimicrobials against a range of clinically important bacteria and fungi. The glucosinolates have also been applied in the prevention of bacterial and fungal spoilage of food products in advanced atmospheric packaging technology which improves the shelf-life of these products.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.

The theory of fractional calculus extends the order of classical integer calculus from integer to non-integer. As a new engineering application tool, it has made many important research achievements in many fields, including image processing. This paper mainly studies the application of fractional calculus theory in image enhancement and denoising, including the basic theory of fractional calculus and its amplitude frequency characteristics, the application of fractional differential operator in image enhancement, and the application of fractional integral operator in image denoising. The experimental results show that the fractional calculus theory has more special advantages in image enhancement and denoising. Compared with the existing integer order image enhancement operators, the fractional differential operator can more effectively enhance the "weak edge" and "strong texture" details of the image. The fractional order integral image denoising operator can not only improve the signal-to-noise ratio of the image compared to traditional denoising methods, but also better preserve detailed information such as edges and textures of the image.

When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

During the few years, various algorithms have been developed to extract features from high-resolution satellite imagery. For the classification of these extracted features, several complex algorithms have been developed. But these algorithms do not possess critical refining stages of processing the data at the preliminary phase. Various satellite sensors have been launched such as LISS3, IKONOS, QUICKBIRD, and WORLDVIEW etc. Before classification and extraction of semantic data, imagery of the high resolution must be refined. The whole refinement process involves several steps of interaction with the data. These steps are pre-processing algorithms that are presented in this paper. Pre-processing steps involves Geometric correction, radiometric correction, Noise removal, Image enhancement etc. Due to these pre-processing algorithms, the accuracy of the data is increased. Various applications of these pre-processing of the data are in meteorology, hydrology, soil science, forest, physical planning etc. This paper also provides a brief description of the local maximum likelihood method, fuzzy method, stretch method and pre-processing methods, which are used before classifying and extracting features from the image.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

: Since the COVID-19 pandemic, people have become aware that materials (products) can transmit infection and have begun to focus on questions such as how bacteria and germs, the sources of in-fection, maintain their infectivity on material surfaces, and the extent to which materials can or cannot prevent this. and to what extent materials can and cannot prevent it. The SIAA, a non-profit organization of many Japanese companies, had created an entirely separate interna-tional standard for materials' antibacterial, antiviral, and antibiofilm properties (products). The authors have studied the academic basis for the operation of the standards and were deeply in-volved as researchers in establishing one of the standards. This paper describes the interna-tional standards and some representative examples of the authors' actual use of these standards and explains the current status and future problems.

Abstract: In the last decade, gypsogenin has widely grabbed the attention of medicinal chemists by virtue of its prominent anti-cancer potential. Despite its late identification, gypsogenin proved itself as a new an-ti-proliferative player battling for a front position among other classic pentacyclic triterpenes such as oleanolic acid, glycyrrhetinic acid, ursolic acid, betulinic acid, and celastrol. Herein, we present insights into the anti-cancer activity of gypsogenin and its semisynthetic derivatives and go further by introducing our perspective to judiciously guide the prospect rational design. The present article opens a new venue for better exploitation of gypsogenin chemical entity as a lead compound in cancer chemotherapy. To the best of our knowledge, this is the first review article exploring the anti-cancer activity of gypsogenin derivatives.

The synthesis of the presented benzo[d]thiazol-2-amine derivatives was performed by condensing-(4-chlorobenzylidene) benzo[d]thiazol-2-amine with a number of substituted phenols in the presence of potassium iodide and anhydrous potassium carbonate in dry acetone. IR spectroscopy, 1HNMRspectroscopy and spectroscopy methods were used to characterize the structural properties of newly synthesized derivatives. In order to observe the attributes of drug-like candidates of these derivatives, the number of their molecular properties was estimated. Benzo[d]thiazol-2-amine derivatives were molecularly docked with selective enzymes COX-1 and COX-2. Findings suggested that Compounds G3, G4, G6, G8 and G11 possess higher binding affinity than diclofenac sodium, when docking was performed with enzyme COX-1. Compounds G1, G3, G6, G8and G10 showed lower binding affinity than Indomethacin when docking performed with enzyme COX-2.In vitro evaluation of the COX-1 and COX-2 enzyme inhibitory activities were performed for synthesized compounds. Compounds 10 and 11 exhibited significant COX-1 and COX- enzyme inhibitory action with IC50 values of 5.0 and 10 µM, respectively. Using the hot plate method and the carrageenan-induced rat paw edema model, the synthesized compounds were screened for their biological activities, including analgesic and anti-inflammatory activities. The highest analgesic action was exhibited by derivative G11and the compound G10 showed the highest anti-inflammatory response.

Polyacetylene phytochemicals are emerging as potentially responsible for the chemoprotective effects of consuming apiaceous vegetables. There is some evidence suggesting that polyacetylenes impact carcinogenesis by influencing a wide variety of signalling pathways, which are important in regulating inflammation, apoptosis, cell cycle regulation, etc. Studies have shown a correlation between human dietary intake of polyacetylene-rich vegetables with reduced risk of inflammation and cancer. Polyacetylene supplementation can influence cell growth, gene expression, and immunological responses, and reduce tumour number in rat and mouse models. Cancer chemoprevention by dietary polyacetylenes involves several mechanisms, including effects on inflammatory cytokines, the NF-κB pathway, antioxidant response elements, UPR pathway, growth factor signalling, cell cycle progression, and apoptosis. This review summarises the published research on falcarinol-type polyacetylene compounds and their mechanisms of action regarding cancer chemoprevention and treatment, and also identifies some gaps in our current understanding of the health benefits of these polyacetylenes.

： Anti-MDA5 antibody bearing (anti-MDA5+)-dermatomyositis (DM) or polymyositis (PM) is notorious for causing rapidly progressive interstitial lung disease (RPILD) &/or cancers with high mortality rate. However, anti-MDA5 antibodies (Abs) are also found in other connective tissue diseases and their link with RPILD especially with regards to the mortality rate are unknown. We retrospectively recruited 71 patients bearing anti-MDA5-Abs in serum, stratified them in terms of a presence or absence of RPILD, and evaluated their clinical features, laboratory findings, associated myositis antibodies, concurrent connective tissue disease (CTD) as well as newly developed malignancies. Thirty-nine (55%) patients presented with DM/PM but 32 (45%) didn’t. Twenty-two of the former and 11 of the latter developed RPILD eventually, accounting for a total of 46% of all MDA5 bearing patients. On the other hand, 15 of all 71 (21.1%) patients had cancers. Among 32 patients who didn’t have DM/PM, 27 (38.0% of all 71) had other CTDs, indicating that only 5 (7.0% of 71) patients did not have CTDs. Senility (odds ratio [OR]=1.816, P=0.032), presence of anti-Ro-52 antibody (OR=1.676, P=0.018), elevated C-reactive protein (CRP, OR= 4.354, P<0.001) and carcinoembryonic antigen (CEA, OR=2.625, P=0.005) posed risks for RPILD. High lactose dehydrogenase (LDH, p=0.009), CRP (p=0.001), CEA (p=0.001), ferritin (p ≤ 0.001) and low albumin (p ≤ 0.001) were significantly associated with mortality. Anti-SAE antibodies were negatively correlated with RPILD as analyzed by univariate (OR=0.245, P=0.017) and multivariate (OR= 0.058, P=0.036) regressions, indicating that they may be a protective factor from RPILD (OR= 0.543, P=0.008) or fatality (OR= 0.707, P=0.012), which was also demonstrated in subgroup analyses. In contrast to various risk factors for RPILD or mortality, anti-SAE antibodies might be conversely a protective factor in anti-MDA5+ patients.

This research paper presents a deep learning approach to early detection of skin cancer using image augmentation techniques. The authors propose a two-stage image augmentation technique that involves the use of geometric augmentation and generative adversarial network (GAN) to classify skin lesions as either benign or malignant. This research utilized the public HAM10000 dataset to test the proposed model. Several pre-trained models of CNN were employed, namely Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19. Our approach achieved accuracy, precision, recall, and F1-score of 96.90%, 97.07%, 96.87%, 96.97%, respectively, which is higher than the performance achieved by other state-of-the-art methods. The paper also discusses the use of SHapley Additive exPlanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention showing state-of-the-art performance on various downstream tasks, though showing poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, exploiting an image-level approach by learning common features from two different augmented views. We strengthen MIM by learning long-range global patterns from contrastive loss. Our framework adopts simple encoder-decoder architecture, learning rich and general representation by following a simple process: 1) two different views are generated from an input image with random masking and by contrastive loss, we can learn semantic distance of the representations generated by an encoder. By applying a high mask ratio, 80%, it works as strong augmentation and alleviates the representation collapse problem. 2) With reconstruction loss, decoder learns to reconstruct an original image from the masked image. We assess our framework by several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieve 84.3% fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, exceeding previous studies and show promising results on other downstream tasks. Experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

Multimodal medical image fusion is a fundamental but challenging problem in the fields of brain science research and brain disease diagnosis, and it is challenging for sparse representation (SR)-based fusion to characterize activity level with single measurement and no loss of effective information. In this paper, the Kronecker-criterion-based SR framework is applied for medical image fusion with a patch-based activity level integrating salient features of multiple domains. Inspired by the formation process of vision system, the spatial saliency is characterized by textural contrast (TC), which is composed of luminance and orientation contrasts to promote more highlighted texture information to participate in the fusion process. As substitution of the conventional l1-norm-based sparse saliency, a metric of sum of sparse salient features (SSSF) is used for promoting more significant coefficients to participate in the composition of activity level measure. The designed activity level measure is verified to be more conducive to maintain the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verify the effectiveness of the proposed fusion method on both visual quality and objective assessment. Furthermore, the research work of this paper is helpful for further detection and segmentation of medical images.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

Many dedicated designs for real-time operations provide functionality on fixed-sized operators, but where speed, scalability, and flexibility are required, extensive research is demanded. Dedicated designs can provide real-time processing for many applications. This paper presents an FPGA-based design of a general image processor. The proposed design is based on a fixed-point representation of binary numbers. The proposed design provides a mechanism to manage matrices on-chip along with matrix arithmetic. The matrices are represented with simple identifiers and microinstruction that assist in the computation of many operations which are useful for solving complex problems. The design was successfully implemented and tested using VHDL language. The proposed design is an efficient architecture as a standalone processor with all embedding computational resources necessary for an embedded image processing application.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEI_ns, and AWEI_s) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEI_ns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEI_ns and AWEI_s are better than NDWI, MNDWI and MNDWI2. AWEI_ns works better than AWEI_s under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEI_ns.

Background: Antiphospholipid antibodies (aPL) are a laboratory criterion for the classification of antiphospholipid syndrome (APS), and are known to cause clinical symptoms: vascular thrombosis or obstetric complications. It is suggested that aPL may be associated with thromboembolism in severe COVID-19 cases. Aim: to combine clinical data with aPL findings at 4 time points (admission, worsening, discharge, 3-month follow-up) in patients hospitalized with COVID-19 pneumonia. Methods: current and past history of thrombosis and obstetric complications; aPL determined at 4 time points: anticardiolipin (aCL), anti-β2-glycoprotein I (anti- β2GPI) and antiphosphatidylserine/prothrombin (aPS/PT) of the IgM, IgG or IgA isotypes. Results: 111 patients with COVID-19 pneumonia were enrolled. During hospitalization, 7 patients died, 3 of them due to pulmonary artery thromboembolism (none was aPL positive). Only one of the five who developed pulmonary artery thrombosis was aPL positive. Of 9/101 patients with a history of thrombosis, 5 had arterial thrombosis and none was aPL positive at admission and follow-up; 4 had venous thrombosis and one was aPL positive at all time points (newly diagnosed APS). Of these 9/101 patients, 55.6% were transiently aPL positive at discharge only, compared to 26.1% without history of thrombosis (p=0.05). Conclusions: aPL were not associated with fatal outcomes and vascular thrombosis; aPL were transiently positive in more than half of patients with a history of thrombosis.

Background and Objectives: Age-related macular degeneration (AMD) is one of the major causes of central vision loss in the older age group and its dry form accounts for the majority of cases. Although several causes and mechanisms for the development and progression of AMD have previously been identified, the pathogenesis of this complex disease is still not entirely understood. As the inflammation and involvement of the immune system are strongly suggested to have a central role to promote the degenerative process and to stimulate the onset of complications, we aimed to analyze the prevalence of serum anti-retinal (ARA) and anti-endothelial cell antibodies (AECA) in patients with dry AMD and to determine their relationship with the clinical features of the disease, notably area of geographic atrophy (GA). Materials and Methods: We enrolled 41 patients with dry AMD and 50 sex- and age-matched healthy controls without AMD. The presence of ARA was determined by indirect immunofluorescence using monkey retina as an antigen substrate, while the presence of AECA was determined using cultivated human umbilical vein endothelial cells and primate skeletal muscle. Results: ARA were detected 36 (87.8%) AMD patients (titers range from 1:20 to 1:320) and in 16 (39.0%) (titers range from 1:10 to 1:40) of controls (p=0.0000). Twenty of the 41 patients (48.8%) were positive for AECA, while in a control group AECA were present only in 5 sera (10.0%). The titers of AECA in AMD patients ranged from 1:100 to 1:1000 and in the control group the AECA titers were 1:100 (p=0.0001). There were no significant correlations between the presence of AECA and disease activity. Conclusions: This study provides evidence for the possible involvement of an autoimmune process against retina and retinal vessel antigens in the pathogenesis of geographic atrophy associated with dry AMD.

The West-African variety of Sorghum bicolor leaf sheath (SBLS) Jobelyn® is a natural remedy, which has gained international recognition for its anti-anemic effect and energy boosting qualities in debilitating diseases. The widespread use of traditional medicine in the region usually confirms its safety, but not its efficacy or deep assessment of their pharmacological properties. The other major issue for herbal-based treatments is the lack of definite and complete information about the composition of the extracts. Despite limitations, efforts have been made in isolation and characterisation of active compounds in this specie of sorghum showing various subclasses of flavonoids including apigeninidin, a stable 3-deoxyanthocyanidin and potential fungal growth inhibitor, which accounts for 84% of the total extract. Non-clinical in vitro and in vivo studies support previous indications that this variety of Sorghum bicolor possesses several biologically active compounds with potent antioxidant, anti-inflammatory, anti-aging and neuro-protective properties. Clinical studies show that SBLS has the ability to boost hemoglobin concentrations in anemic conditions and most remarkably to increase CD4 count in HIV-positive patients. The multiple effects and high safety profiles of this extract may encourage its development as a therapeutic agent for the treatment of anemia, chronic inflammatory conditions or in the symptomatic management of HIV infections. This review describes the potential therapeutic aspects of SBLS extract and its potential benefits.

Despite extensive use of intravitreal anti-vascular endothelial growth factor (anti-VEGF) biologics for over a decade, neovascular age-related macular degeneration (nAMD) or choroidal neovascularization (CNV) continues to be a major cause of irreversible vision loss in developed countries. Many nAMD patients demonstrate persistent disease activity or experience declined responses over time despite anti-VEGF treatment. The underlying mechanisms of anti-VEGF resistance are poorly understood, and no effective treatment strategies are available to date. Here we review evidence from animal models and clinical studies that supports the roles of neovascular remodeling and arteriolar CNV formation in anti-VEGF resistance. Cholesterol dysregulation, inflammation, and ensuing macrophage activation are critically involved in arteriolar CNV formation and anti-VEGF resistance. Combination therapy by neutralizing VEGF and enhancing cholesterol removal from macrophages is a promising strategy to combat anti-VEGF resistance in CNV.

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Video lectures are becoming more popular and in demand as online classroom teaching is becoming more prevalent. Massive Open Online Courses (MOOCs), such as NPTEL, have been creating high-quality educational content that is freely accessible to students online. A large number of colleges across the country are now using NPTEL videos in their classrooms. So more video lectures are being recorded, maintained, and uploaded. These videos generally contain information about that video before the lecture begins. We generally observe that these educational videos have metadata containing five to six attributes: Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. It would be easy to maintain these videos if we could organize them according to their categories. The indexing of these videos based on this information is beneficial for students all around the world to efficiently utilise these videos. In this project, we are trying to get the metadata information mentioned above from the video lectures.

Pediatric interventional cardiology procedures are essential in diagnosing and treating congenital heart disease in children; however, they raise concerns about potential radiation exposure. Managing radiation doses and assessing image quality in angiographs becomes imperative for safe and effective interventions. This systematic review aims to comprehensively analyze the current understanding of physical image quality metrics relevant for characterizing X-ray systems used in fluoroscopy-guided pediatric cardiac interventional procedures, considering the main factors reported in the literature that influence this outcome. A search in Scopus and Web of Science, using relevant keywords and inclusion/exclusion criteria, yielded fourteen relevant articles published between 2000 and 2022. The physical image quality metrics reported were noise, signal-to-noise ratio, contrast, contrast-to-noise ratio, and high contrast spatial resolution. Various factors influencing image quality were investigated, such as polymethyl methacrylate thickness (often used to simulate water equivalent tissue thickness), operation mode, anti-scatter grid presence and tube voltage. Objective evaluations using these metrics ensure impartial assessments for main factors affecting image quality, improving in the characterization fluoroscopic X-ray systems, and aiding informed decisions to safeguard pediatric patients during procedures.

Neural networks were treated as black boxes for a long time. Previous works have unearthed what aspects of an image were important for convolutional layers at different positions in the network. This was done using deconvolutional networks. In this paper, we examine how well a convolutional neural network performs when those convolutional layers which are relatively unimportant for a particular image (i.e., the image does not produce one of the strongest activations) are skipped in the training, validating, and testing process.

This paper presented a feature vector using a different statistical texture analysis of brain tumor from MRI image. The statistical feature texture is computed using GLCM (Gray Level Co-occurrence Matrices) of Brain Nodule structure. For this paper, the brain nodule segmented using strips method to implemented marker watershed image segmentation based on PSO (Particle Swarm Optimization) and Fuzzy C-means clustering (FCM). Furthermore, the four angles 0^o, 45^o, 90^o and 135^o are calculated the segmented brain image in GLCM. The four angular directions are calculated using texture features are correlation, energy, contrast and homogeneity. The texture analysis is performed a different types of images using past years. So the algorithm proposed statistical texture features are calculated for iterative image segmentation. These results show that MRI image can be implemented in a system of brain cancer detection.

During the high-speed flight of a vehicle in the atmosphere, surface friction with the air generates aerodynamic heating. The aerodynamic heating phenomenon can create extremely high temperatures near the surface. These high temperatures impact material properties and the structure of the aircraft, so thermal deformation measurement is essential in aerospace engineering. This paper revisits high-temperature deformation measurement using the digital image correlation (DIC) technique under high-intensity blackbody radiation with a precise speckle pattern fabrication and a heat haze reduction method. The effect of speckle pattern on the DIC measurement has been thoroughly studied at room temperature, but high-temperature measurement studies have not reported such effects so far. We found that the commonly used methods to reduce the heat haze effect could produce incorrect results. Hence, we propose a new method to mitigate heat haze effects. An infrared radiation heater was employed to make an experimental setup that could heat a specimen up to 950 ℃. First, we mitigated image saturation using a short-wavelength bandpass filter with blue light illumination, a standard procedure for high-temperature DIC deformation measurement. Second, we studied how to determine the proper size of the speckle pattern under a high-temperature environment. Third, we devised a reduction method for the heat haze effect. As proof of the effectiveness of our developed experimental method, we successfully measured the deformation of stainless steel 304 specimens from 25 ℃ to 800 ℃. The results confirmed that this method can be applied to the research and development of thermal protection systems in the aerospace field.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

Artistic image completion is fundamental for the preservation and restoration of invaluable art paintings and it has experienced significant progress through the implementation of deep learning methodologies. Despite these advancements, challenges persist, even with sophisticated approaches like Generative Adversarial Networks (GANs), particularly in achieving optimal results for high-resolution paintings. Small-scale texture synthesis and the inference of missing information from distant contexts present persistent issues, leading to distortions in lines and unnatural colors, especially in art paintings with complicated structures and textures. Concurrently, patch-based image synthesis has evolved by incorporating global optimization on the image pyramid to enhance structural coherence and details. However, methods relying on gradient-based synthesis encounter obstacles related to directionality, inconsistency, and the heavy computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a groundbreaking approach, integrating Weighted Similarity-Confidence Laplacian Synthesis, to comprehensively address these challenges and advance the field of artistic image completion. This proposal addresses challenges not only in high-resolution artistic image completion but also makes a significant contribution to the broader field of patch-based synthesis by utilizing the Laplacian pyramid for enhanced edge-aware correspondence search. Experimental result confirms the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with complicated details and irregular missing regions.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.