Antimicrobial ZnO Nanoparticle–Doped Polyvinyl Alcohol/Pluronic Blends as Active Food Packaging Films

Today, plastic waste has been highlighted as one of the greatest threats to the environment. These environmental concerns and the increased necessity for safe food packaging have inspired scientists to focus on the development of active biodegradable materials. Herein, a novel poly(vinyl alcohol)/pluronic/ZnO nanocomposite film (PVA/PLUR/ZnO) is introduced as an active packaging material with enhanced antimicrobial activity. Gamma irradiation is used as a “green” route to prepare ZnO nanoparticles via a polymer pyrolysis method. The as-prepared ecofriendly ZnO nanoparticles are characterized and incorporated into the PVA/PLUR matrix in different concentrations. Transmission electron microscopy and dynamic light scattering measurements prove that ZnO nanoparticles have a mean particle size of 30 nm with a spherical-like morphology. Morphological and structural characterization confirm the successful incorporation of ZnO into the PVA/PLUR matrix, which in turn enhances the thermal and barrier properties of PVA/PLUR/ZnO nanocomposite films. On the other hand, the opacity of blends is increased. The PVA/PLUR/ZnO composites exhibit broad-spectrum antimicrobial activity against Gram-positive, Gram-negative bacterial pathogens, and fungi, and the activity increases with increasing concentrations of ZnO nanoparticles. These results introduce PVA/PLUR/ZnO films as effective antimicrobial materials for active food-packaging applications.     

Biosynthesis of Silver Chloride Nanoparticles by Rhizospheric Bacteria and Their Antibacterial Activity against Phytopathogenic Bacterium Ralstonia solanacearum

Ralstonia solanacearum is the most destructive pathogen, causing bacterial wilt disease of eggplant. The present study aimed to develop green synthesis and characterization of silver chloride nanoparticles (AgCl-NPs) by using a native bacterial strain and subsequent evaluation of their antibacterial activity against R. solanacearum. Here, a total of 10 bacterial strains were selected for the biosynthesis of AgCl-NPs. Among them, the highest yield occurred in the synthesis of AgCl-NPs using a cell-free aqueous filtrate of strain IMA13. Ultrastructural observation revealed that the AgCl-NPs were spherical and oval with smooth surfaces and 5–35 nm sizes. XRD analysis studies revealed that these particles contained face-centered cubic crystallites of metallic Ag and AgCl. Moreover, FTIR analysis showed the presence of capping proteins, carbohydrates, lipids, and lipopeptide compounds and crystalline structure of AgCl-NPs. On the basis of phylogenetic analysis using a combination of six gene sequences (16S, gyrA, rpoB, purH, polC, and groEL), we identified strain IMA13 as Bacillus mojavensis. Three kinds of lipopeptide compounds, namely, bacillomycin D, iturin, and fengycin, forming cell-free supernatant produced by strain IAM13, were identified by MALDI-TOF mass spectrometry. Biogenic AgCl-NPs showed substantial antibacterial activity against R. solanacearum at a concentration of 20 µg/mL⁻¹. Motility assays showed that the AgCl-NPs significantly inhibited the swarming and swimming motility (61.4 and 55.8%) against R. solanacearum. Moreover, SEM and TEM analysis showed that direct interaction of AgCl-NPs with bacterial cells caused rupture of cell wall and cytoplasmic membranes, as well as leakage of nucleic acid materials, which ultimately resulted in the death of R. solanacearum. Overall, these findings will help in developing a promising nanopesticide against phytopathogen plant disease management.     

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.     

Flavonoid profiles of immature and mature fruit tissues of Citrus grandis Osbeck (Dangyuja) and overall contribution to the antioxidant effect

Citrus fruits are a valuable functional food and their peel is used in East Asian folk medicine. In this study, the polar components of the fruit tissues of Citrus grandis Osbeck were analyzed using high‐performance liquid chromatography–tandem mass spectrometry and compared with reported data. Among the 13 characterized compounds, eight flavonoids and one coumarin were identified for the first time in fruit tissues. The total amount of the identified components was the largest for the immature fruit peel, followed by mature fruit peel, mature fruit flesh, and immature fruit flesh. Naringin (2) and neohesperidin (3) were particularly rich in all samples. The antioxidant activity of the flavonoids extracted from fruit tissues increased in a dose‐dependent manner. The activity of the fruit peels was significantly higher than that of the fruit flesh. Copyright © 2014 John Wiley & Sons, Ltd.     

A combination of solid‐phase extraction and dispersive solid‐phase extraction effectively reduces the matrix interference in liquid chromatography–ultraviolet detection during pyraclostrobin analysis in perilla leaves

Perilla leaves contain many interfering substances; thus, it is difficult to protect the analytes during identification and integration. Furthermore, increasing the amount of sample to lower the detection limit worsens the situation. To overcome this problem, we established a new method using a combination of solid‐phase extraction and dispersive solid‐phase extraction to analyze pyraclostrobin in perilla leaves by liquid chromatography with ultraviolet absorbance detection. The target compound was quantitated by external calibration with a good determination coefficient (R² = 0.997). The method was validated (in triplicate) with three fortification levels, and 79.06– 89.10% of the target compound was recovered with a relative standard deviation <4. The limits of detection and quantification were 0.0033 and 0.01 mg/kg, respectively. The method was successfully applied to field samples collected from two different areas at Gwangju and Muan. The decline in the resiudue concentrations was best ascribed to a first‐order kinetic model with half‐lives of 5.7 and 4.6 days. The variation between the patterns was attributed to humidity. Copyright © 2015 John Wiley & Sons, Ltd.     

Lie‐group method for solving the problem of fission product behavior in nuclear fuel

Calculation of the gas atom concentration is an important feature of all physical models of fission gas release. We apply Lie‐group method for determining symmetry reductions to the diffusion equation describing the fission gas release from nuclear fuel. The resulting nonlinear ordinary differential equation is solved numerically using nonlinear finite difference method. Effects of the dimensionless group constant, the time, and the grain radius on the concentration diffusion function have been studied, and the results are plotted. It is found that the concentration of gas atoms increases as the dimensionless group constant, the power index, and the time increase, and it decreases with increase of the grain radius. Copyright © 2013 John Wiley & Sons, Ltd.