Fermentation, isolation, purification and characterization of an antitubercular antibiotic from Streptomyces luridus MTCC 4402.

Tuberculosis, caused by Mycobacterium tuberculosis, continues to be a serious public health problem around the world, and it urges the need for development of new antitubercular drugs. An antibiotic producing strain, Streptomyces luridus (MTCC 4402) was earlier isolated from soil by our group. In this work, the phylogenic status was established by 16S rRNA gene sequence analysis. The strain was found to be active against clinically resistant strains. The culture was grown in shake flasks in a medium containing cornsteep liquor, glucose, CaCO(3), soyabean meal and starch. Antibiotic production reached maximum at the end of 72 h. and fermentation profile was obtained. The active compound was extracted into ethyl acetate and was subjected to activity guided purification by column chromatography using silica gel, TLC and HPLC methods. The pure compound eluted at 16.7 min. by gradient elution was subjected to (1)H, (13)C NMR and mass spectral analyses. The acquired data was compared with that of natural products' data base and found to be a known antibiotic, spiramycin. The purified compound was studied for mutagenic, cytotoxicity, antitubercular activities. It was non mutagenic at the concentration of 1000 µg/mL, non cytotoxic and active as antitubercular agent at a concentration of 64 mg/mL and was comparable to rifampicin.

Single layer neural networks for linear system identification using gradient descent technique.

Recently, some researchers have focused on the applications of neural networks for the system identification problems. In this letter we describe how to use the gradient descent (GD) technique with single layer neural networks to identify the parameters of a linear dynamical system whose states and derivatives of state are given. It is shown that the use of the GD technique for the purpose of system identification of a linear time invariant dynamical system is simpler and less expensive in implementation because it involves less hardware than the technique using the Hopfield network as discussed by Chu. The circuit is considered to be faster and is recommended for online computation because of the parallel nature of its architecture and the possibility of the use of analog circuit components. A mathematical formulation of the technique is presented and the simulation results of the network are included.