The purpose of this study was to investigate the immobilization of denitrifying bacteria on microbial cellulose (MC) for biological denitrification. A novel denitrifying bacterium, Pseudomonas stutzeri, was immobilized in microbial cellulose and introduced into an up flow packed bed reactor in order to remove nitrate from synthetic influent. The MC presented the high biomass concentration throughout the experiment, achieving 3.4 mg biomass/g support. The efficiency of the system for denitrification was tested under different running conditions. Complete biological denitrification of the synthetic effluent was achieved at low hydraulic residence times, less than 4 h, and high nitrate concentration (200 mg NO 3 -N/L). The immobilization of the bacterium in MC increased the adsorption capacity, decreased the cell leakage from the beads, resulted in higher activity of the immobilized cells, and allowed better operational control.
In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.
Introduction: Fungal aerosols cause life-threatening infections in patients hospitalized in critical wards. Antiseptics and disinfectants have broad-spectrum antimicrobial activity against the living tissue and inert surfaces microorganisms; hence, they have an essential role in controlling and preventing nosocomial infections. This study aimed to evaluate in vitro antifungal activity of benzalkonium chloride (BAC), chlorhexidine digluconate (CHX), and sodium hypochlorite (SH) against isolated fungal aerosols from the hospital environment.
Materials and Methods: The susceptibility tests were performed on fungal aerosols isolated from various wards of Children’s Medical Center, based on broth microdilution antifungal susceptibility testing of filamentous fungi approved by the Clinical and Laboratory Standards Institute (CLSI) M38-A2 document. The isolates included Aspergillus (Aspergillus flavus (n = 14), Aspergillus niger complex (n = 12), Penicillium spp. (n = 14), and Cladosporium spp. (n = 14).
Results: The geometric means (GM) of the Minimum Inhibitory Concentrations (MICs) of the biocides across all isolates were as follows: BAC, 3.56 µg/ml, CHX, 9.45 µg/ml, and SH, 810.35 µg/ml. The highest range of MICs was found for SH (50-12800 µg/ml), while the lowest range was for BAC (1-16 µg/ml) against all fungal isolates. Generally, BAC showed the highest in vitro activity among disinfectants tested. The lowest MIC50 and MIC90 values were 4 and 8 µg/ml for BAC, followed by 16 and 32 µg/ml for CHX, and 800 and 6400 µg/ml for SH, respectively.
Conclusion: The findings showed that BAC was an effective disinfectant, which can prevent resistant species and fungal pathogens and be used an alternative to other disinfectants and antiseptics.
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