Facile and Eco-Friendly Fabrication of Silver Nanoparticles Using Nyctanthes arbor-tristis Leaf Extract to Study Antibiofilm and Anticancer Properties against Candida albicans

The synthesis of silver nanoparticles has been gaining more attention in recent years due to their small size and high stability. For this study, silver nanoparticles were biosynthesized from leaf extract of the medicinal plant (N. arbor-tristis). Vitally, the shrub with tremendous medicinal usage was diversely observed in South Asia and South East Asia. The synthesized silver nanoparticles were characterized by color visualization, ultraviolet-visible spectrophotometry (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), field emission-scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and dynamic light scattering (DLS) technique. A sharp peak at 427 nm for biosynthesized nanoparticles was obtained using UV-Vis, which represents surface plasmon resonance. Thus, characterization techniques showed the green synthesis of AgNPs leads to the fabrication of spherical shape particles with a size of 67 nm. Furthermore, AgNPs were subjected to antibiofilm studies against Candida albicans and it was observed that 0.5 μg mL−1 of AgNPs significantly reduced 50% of biofilm formation. These biosynthesized nanoparticles also showed a considerable reduction in viability of HeLa cells at 0.5 μg mL−1. The morphological changes induced by AgNPs were observed by AO/EB staining. The toxic effect of AgNPs was studied using brine shrimp as a model system. Therefore, it is envisaged that further investigation with these AgNPs can replace toxic chemicals, assist in the development of biomedical implants that can prevent biofilm formation, and avoid infections due to C. albicans.

Improved interactive color visualization approach for hyperspectral images

Hyperspectral images (HSIs) have become increasingly prominent as they can maintain the subtle spectral differences of the imaged objects. Designing approaches and tools for analyzing HSIs presents a unique set of challenges due to their high-dimensional characteristics. An improved color visualization approach is proposed in this article to achieve communication between users and HSIs in the field of remote sensing. Under the real-time interactive control and color visualization, this approach can help users intuitively obtain the rich information hidden in original HSIs. Using the dimensionality reduction (DR) method based on band selection, high-dimensional HSIs are reduced to low-dimensional images. Through drop-down boxes, users can freely specify images that participate in the combination of RGB channels of the output image. Users can then interactively and independently set the fusion coefficient of each image within an interface based on concentric circles. At the same time, the output image will be calculated and visualized in real time, and the information it reflects will also be different. In this approach, channel combination and fusion coefficient setting are two independent processes, which allows users to interact more flexibly according to their needs. Furthermore, this approach is also applicable for interactive visualization of other types of multi-layer data.

PROFIL DAN ANALISIS AKTIVITAS ANTIOKSIDAN DALAM EKSTRAK AIR MENIRAN YANG DIKERINGKAN DENGAN METODE YANG BERBEDA Profile and Analysis of Antioxidant Activity in Dried Meniran Water Extracts using Different Methods

Meniran (Phyllanthus niruri Linn.) is a medicinal plant that can reduce obesity status, hypoglycemic, hypotensive, and have antioxidant activity. Meniran has been long used as a medicinal plant, but its utilization in a form of water infusion is still rare. This research is a continuation study which aims to identify the biomolecules that have antioxidant activity in water infusion of meniran. The making of water infusion of meniran requires drying process, which can be done by room temperature drying and sun drying. Phenol and fl avonoid compounds in meniran are assumed to have antioxidant activity. Both compounds are easily oxidized and isomerized due to sun exposure. It is assumed that the drying method will aff ect the presence of phenol and fl avonoid compounds and its antioxidant activity. The study began with the process of sun drying and room temperature without direct sun exposure. Then the extraction process used water soxhlet by soxhlet extraction method. The extract was then tested qualitatively using the DPPH IC50 method. The results of the qualitative analysis with meniran color visualization are positive containing fl avonoids and phenols. The results of quantitative analysis of meniran which are dried by sun drying; fl avonoids 0.90% w/w and 1.65% w/w phenols, in samples stored at room temperature: 2.00% w/w fl avonoids and phenol 56.16% w/w. The antioxidant activity of IC50-DPHH in extract concentrations (2, 4, 6, 8, 10, 15, 20 ppm) of dried meniran at room temperature 18.48 ppm, sun drying cannot be determined.

A Multiclass Detection System for Android Malicious Apps Based on Color Image Features

The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied. If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images. In this paper, a method of color visualization for Android Apps is proposed and implemented. Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families. In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features. Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results. We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization. According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.

An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery

Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability (δ) in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and δ , which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).