Mycosynthesis of silver nanoparticles using marine fungi and their antimicrobial activity against pathogenic microorganisms.

OBJECTIVES: At the present time, there is a persistent need to get rid of environmental contaminants by eco-friendly, sustainable, and economical technologies. Uncontrolled disposal practices of domestic and industrial solid and liquid wastes led to water pollution which has negative impacts on public health, environment, and socio-economic development. Several water-borne diseases are spreading man to man by microorganisms such as pathogenic bacteria. For the protection of water bodies, all wastewater from various sources should be managed and remediated properly. Myco-remediation is a form of bioremediation in which fungi are used to get rid of contaminants. Fungi are attractive agents for the biosynthesis of nanoparticles especially silver nanoparticles (AgNPs) which are considered one of the most widely utilized nanoparticles because of their unique characteristics such as antibacterial, antiviral, antifungal, and anti-inflammatory properties. METHODS: This study uses silver nitrate and supernatants of four marine fungi; Penicillium simplicissimum, Aspergillus terreus, Aspergillus japonicus, and Aspergillus oryzae for extracellular biosynthesis of silver nanoparticles and to evaluate its activity against different pathogenic microorganisms. These nanoparticles may subsequently be applied for the treatment or nano-bioremediation of microbial contaminants in water bodies and improve water quality. RESULTS: Silver nanoparticles were synthesized and the results revealed that spherical and well-dispersed nanoparticles of different sizes were formed with sizes ranging between 3.8 and 23 nm. Characterization results approved the existence of stable nanocrystalline elemental silver. Antibacterial activity results revealed that AgNPs can be used as a powerful antimicrobial agent for several pathogenic bacteria, yeast, and fungi. Among the biosynthesized NPs mediated by the four marine fungi, AgNPs mediated by A. japonicus (5 mM) had the highest antibacterial activity, while AgNPs mediated by Penicillium simplicissmum (8 mM) had the highest antifungal activity. CONCLUSION: Marine fungi can biosynthesize stable AgNPs that exhibit potent antimicrobial activity against a variety of pathogens.

Investigation on the use of ensemble learning and big data in crop identification.

The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in modern agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed a weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Built upon Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. The proposed ensemble approach boosted accuracy by 6.5%, 1.9%, 4.4%, 4.9%, 4.7% in precision, recall, F-score, Overall Accuracy (OA), and Matthews correlation coefficient (MCC) metrics respectively. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting.

Mapping the distribution and tree canopy cover of Jacaranda mimosifolia and Platanus × acerifolia in Johannesburg's urban forest.

This study investigated the distribution and the tree canopy cover (TCC) of the two most prominent street trees (Jacaranda mimosifolia and Platanus × acerifolia) in Johannesburg, using the multispectral SPOT 6 satellite data and field survey GPS points. The importance of the spectral bands (Blue, Green, Red and NIR) and the NDVI index in discriminating between the tree species was quantified using five separability indices (Divergence, Bhattacharyya, Transformed Divergence, Jeffries-Matusita and M-statistic). The visual comparison of the Blue band and the NDVI histograms between the two species and other vegetation type showed the lowest feature overlap, suggesting the highest separability between paired classes. This was further supported by the highest Divergence value for the Blue band (3.68) and NDVI index (2.48) followed by the M-statistic (0.8 and 0.73, respectively) indicating good to moderate separability between the two species, respectively. The results were also consistent with the RF classification where the Blue band and NDVI index were the most important variables for the discrimination between the two species with an overall accuracy of 88% (kappa = 8). The TCC of J. mimosifolia and P. × acerifolia constituted 38% of the total vegetation cover in the city. These findings not only would help prioritize the increase of targeted vegetation cover in low cover areas, but will also provide a valuable information for assessment and protection of vulnerable species such as P. × acerifolia from the threat of the polyphagous shot hole borer, Euwallacea fornicatus in Johannesburg.