Identification of fungus-growing termite-associated halogenated-PKS maduralactomycin a as a potential inhibitor of MurF protein of multidrug-resistant Acinetobacter baumannii.

Multidrug-resistant Acinetobacter baumannii infections have become a major public health concern globally. Inhibition of its essential MurF protein has been proposed as a potential target for broad-spectrum drugs. This study aimed to evaluate the potential of a novel ecological niche of 374 fungus-growing termite associated Natural Products (NPs). The molecular docking and computational pharmacokinetics screened four compounds, i.e., Termstrin B, Fridamycin A, Maduralactomycin A, and Natalenamide C, as potential compounds that have higher binding affinities and favourable protein-ligand interactions. The compound Maduralactomycin A induced more stability based on its lowest average RMSD value (2.31 Å) and low standard deviation (0.35) supported by the consistent flexibility and ß-factor during the protein's time-dependent motion. While hydrogen bond analysis indicated that Termstrin B has formed the strongest intra-protein interaction, solvent accessibility was in good agreement with Maduralactomycin A compactness. Maduralactomycin A has the strongest binding energy among all the compounds (-348.48 kcal/mol) followed by Termstrin B (-321.19 kcal/mol). Since these findings suggest Maduralactomycin A and Termstrin B as promising candidates for inhibition of MurF protein, the favourable binding energies of Maduralactomycin A make it a more important compound to warrant further investigation. However, experimental validation using animal models and clinical trials is recommended before reaching any final conclusions.

Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment.

Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In some practical scenarios of systems, there are certain interruptions in addition to these faults. These interruptions may occur in the form of noise. However it is expected that the decision about the sensor data is difficult in the presence of noise. This is because the noise adversely affects the communication amongst sensors and the processing unit. More complexity is expected when there are faults and noise simultaneously. To deal with this problem, in addition to existing fault detection and isolation schemes, the Kalman filter is employed. Here, a generic discussion is provided, which is equally applicable to other situations. This work addresses various faults in the presence of noise for decentralized tracking systems. Local single faults and multiple faults in the presence of noise are the core issues addressed in this paper. The proposed work is comprised of a general scenario for a decentralized tracking system followed by a case study of a target tracking scenario with and without noise. The presented schemes are also tested for different types of faults. The proposed work presents effective tracking in the presence of noise and faults. The results obtained demonstrate the acceptable performance of the scheme of this work.