Background: The microbial production of substances that have the potency to suppress the growth of other microorganisms is probably one of the prevalent defense strategy developed in nature, microorganisms produce a variable bunch of microbial defense systems, which include antibiotics, metabolic by-products, lytic agents, bacteriocins and others. Objective: The purpose of the present study was to isolate and identify Enterococcus faecium isolates then detecting its ability of carrying the gene responsible for enterocin production in this species. Materials and methods: Out of 50 samples from different sources (food and clinical sources) were collected for the Enterococcus faecium isolation, and the isolated bacteria Enterococcus faecium (37) isolates were detected for their harboring of Enterocin A gene (entA), using conventional PCR technique. Results: The identification revealed that 37(74%) isolates were considered as Enterococcus faecium, 20 isolates (54.05%) out of food samples (10 samples were collected from dairies, 7 from vegetables and 3 from fish samples), and 17 isolates 45.9% out of clinical samples (11 from stool and 6 from urine source). Genotypic Detection done by the amplification of the enterocin coding gene (ent A), and the results revealed that all the isolates were harboring that gene despite of the phonotypical differences, that they amplified entA gene and the PCR product size (362 bp) was detected using agarose gel electrophoresis. Conclusions: This study indicates the presence of Enterococcus spp. in food and clinical sources and the ability of these bacteria to produce antibacterial substances which is active against closely related clinical isolates.
S Khalifa E, N Adil A, AS Mazin M…, 2008
In the present work, a density functional theory (DFT) calculation to simulate reduced graphene oxide (rGO) hybrid with zinc oxide (ZnO) nanoparticle's sensitivity to NO2 gas is performed. In comparison with the experiment, DFT calculations give acceptable results to available bond lengths, lattice parameters, X-ray photoelectron spectroscopy (XPS), energy gaps, Gibbs free energy, enthalpy, entropy, etc. to ZnO, rGO, and ZnO/rGO hybrid. ZnO and rGO show n-type and p-type semiconductor behavior, respectively. The formed p-n heterojunction between rGO and ZnO is of the staggering gap type. Results show that rGO increases the sensitivity of ZnO to NO2 gas as they form a hybrid. ZnO/rGO hybrid has a higher number of vacancies that can b
... Show MoreThe present research was conducted to reduce the sulfur content of Iraqi heavy naphtha by adsorption using different metals oxides over Y-Zeolite. The Y-Zeolite was synthesized by a sol-gel technique. The average size of zeolite was 92.39 nm, surface area 558 m2/g, and pore volume 0.231 cm3/g. The metals of nickel, zinc, and copper were dispersed by an impregnation method to prepare Ni/HY, Zn/HY, Cu/HY, and Ni + Zn /HY catalysts for desulfurization. The adsorptive desulfurization was carried out in a batch mode at different operating conditions such as mixing time (10,15,30,60, and 600 min) and catalyst dosage (0.2,0.4,0.6,0.8,1, and 1.2 g). The most of the sulfur compounds were removed at 10 min for all catalyst types. The maximum
... Show MoreCarbon dioxide geo-sequestration (CGS) into sediments in the form of (gas) hydrates is one proposed method for reducing anthropogenic carbon dioxide emissions to the atmosphere and, thus reducing global warming and climate change. However, there is a serious lack of understanding of how such CO2 hydrate forms and exists in sediments. We thus imaged CO2 hydrate distribution in sandstone, and investigated the hydrate morphology and cluster characteristics via x-ray micro-computed tomography in 3D in-situ. A substantial amount of gas hydrate (∼17% saturation) was observed, and the stochastically distributed hydrate clusters followed power-law relations with respect to their size distributions and surface area-volume relationships. The layer-
... Show MoreAutomated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health
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