RESUMO
The efficient and safe management of air conditioner (AC), Piped Natural Gas (PNG) and water pipelines in large buildings is a major challenge for the safety of these buildings. In recent years, Linear Wireless Sensor Networks (LWSN) are being used extensively for monitoring of long natural gas, water, and oil pipelines. LWSNs can also be used for efficient and safe management of AC, PNG and water pipelines in large buildings. In this paper, a scheme for optimal placement of sensors and base stations in a linear fashion to monitor the various pipelines used in large buildings has been proposed. The proposed scheme utilizes the Lion Optimization Algorithm (LOA) and has been compared with three strategies, namely Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Greedy Approach with respect to throughput, lifetime and end-to-end delay. The simulation results show that the proposed scheme exhibits better performance in comparison to the other three considered techniques for all the three parameters. The most striking feature of the proposed approach is that optimization is more effective when the length of the pipeline is more as far as end-to-end delay is concerned. The lifetime of the network is significantly improved using the proposed approach, especially when the length of the pipeline is of medium size, which makes the proposed scheme suitable for energy efficient buildings.
RESUMO
CONTEXT: Acinetobacter baumannii, one of the critical ESKAPE pathogens, is a highly resilient, multi-drug-resistant, Gramnegative, rod-shaped, highly pathogenic bacteria. It is responsible for almost 1-2% of all hospital-borne infections in immunocompromised patients and causes community outbreaks. Because of its resilience and MDR characteristics, looking for new strategies to check the infections related to this pathogen becomes paramount. The enzymes involved in the peptidoglycan biosynthetic pathway are attractive and the most promising drug targets. They contribute to the formation of the bacterial envelope and help to maintain the rigidity and integrity of the cell. The MurI (glutamate racemase) is one of the crucial enzymes that aid in the formation of the pentapeptide responsible for the interlinkage of peptidoglycan chains. It converts L-glutamate to D-glutamate, which is required to synthesise the pentapeptide chain. METHODS: In this study, the MurI protein of A. baumannii (strain AYE) was modelled and subjected to high-throughput virtual screening against the enamine-HTSC library, taking UDP-MurNAc-Ala binding site as the targeted site. Four ligand molecules, Z1156941329 (N-(1-methyl-2-oxo-3,4-dihydroquinolin-6-yl)-1-phenyl-3,4-dihydro-1H-isoquinoline-2-carboxamide), Z1726360919 (1-[2-[3-(benzimidazol-1-ylmethyl)piperidin-1-yl]-2-oxo-1-phenylethyl]piperidin-2-one), Z1920314754 (N-[[3-(3-methylphenyl)phenyl]methyl]-8-oxo-2,7-diazaspiro[4.4]nonane-2-carboxamide) and Z3240755352 (4R)-4-(2,5-difluorophenyl)-1-(4-fluorophenyl)-1,3a,4,5,7,7a-hexahydro-6H-pyrazolo[3,4-b]pyridin-6-one), were identified to be the lead candidates based on Lipinski's rule of five, toxicity, ADME properties, estimated binding affinity and intermolecular interactions. The complexes of these ligands with the protein molecule were then subjected to MD simulations to scrutinise their dynamic behaviour, structural stability and effects on protein dynamics. The molecular mechanics/Poisson-Boltzmann surface area-based binding free energy analysis was also performed to compute the binding free energy of protein-ligand complexes, which offered the following values -23.32 ± 3.04 kcal/mol, -20.67 ± 2.91kcal/mol, -8.93 ± 2.90 kcal/mol and -26.73 ± 2.95 kcal/mol for MurI-Z1726360919, MurI-Z1156941329, MurI-Z3240755352 and MurI-Z3240755354 complexes respectively. Together, the results from various computational analyses utilised in this study proposed that Z1726360919, Z1920314754 and Z3240755352 could act as potential lead molecules to suppress the function of MurI protein from Acinetobacter baumannii.