A novel potassium deficiency-induced stimulon in Anabaena torulosa

Potassium deficiency enhanced the synthesis of fifteen proteins in the nitrogen-fixing cyanobacteriumAnabaena torulosa and of nine proteins inEscherichia coli. These were termed potassium deficiency-induced proteins or PDPs and constitute hitherto unknown potassium deficiency-induced stimulons. Potassium deficiency also enhanced the synthesis of certain osmotic stress-induced proteins. Addition of K+ repressed the synthesis of a majority of the osmotic stress-induced proteins and of PDPs in these bacteria. These proteins contrast with the dinitrogenase reductase of A. torulosa and the glycine betaine-binding protein of E. coli, both of which were osmo-induced to a higher level in potassium-supplemented conditions. The data demonstrate the occurrence of novel potassium deficiency-induced stimulons and a wider role of K+ in regulation of gene expression and stress responses in bacteria

Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithm

Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98

A retrospective analysis of bone tumors and tumor like lesions: a hospital based study of 76 cases

Background: Globally Bone tumors constitute 0.5% of the total World Cancer Incidence. In addition to benign and malignant bone tumors there are a number of nonneoplastic lesions that present in a manner similar to neoplastic conditions. Relevant demographic features such as age, sex and skeletal site are important to come to a conclusive diagnosis. The present study aims to show the prevalence and demography of bone tumors and tumor like lesions.Methods: A total of 76 cases of Bone Tumors and Tumor like Lesions were studied. They were reviewed and analyzed for age, gender, site of tumor and histologic types. Classification was done according to WHO histologic Classification of Bone Tumors.Results: There were 49 cases of primary bone tumors and tumor Like lesions with a median age of 22 years and 27 cases of metastatic bone tumors with a median age of 56 years. Males are more commonly affected. Osteosarcomas and Chondrosarcomas are the most common primary malignant Bone Tumors.Conclusions: Metastatic bone tumors constitute the highest number of bone tumors occurring at an older age group. Maximum numbers of bone tumors are found in the age range 11-20 years and all are primary bone tumor and tumor like lesions

Computational binding mechanism of Mycobacterium tuberculosis UDP-NAG enolpyruvyl transferase (MurA) with inhibitors fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic aci

Worldwide, tuberculosis (TB) remains the most frequent and important infectious disease causing morbidity and death. One-third of the world's population is infected with Mycobacterium tuberculosis (Mtb), the etiologic agent of TB. In this context, TB is in the top three, with malaria and HIV being the leading causes of death from a single infectious agent, and about two million deaths are attributable to TB annually. The bacterial enzyme MurA catalyzes the transfer of enolpyruvate from phosphoenolpyruvate (PEP) to uridine diphospho-N-acetylglucosamine (UNAG), which is the first committed step of bacterial cell wall biosynthesis. In this work, 3D structural model of Mtb-MurA enzyme has been developed, for the first time, by homology modeling and molecular dynamics simulation techniques. The model provided clear insight in its structure features, i.e. substrate binding pocket, and common docking site. Multiple sequence alignment and 3D structure model provided the putative substrate binding pocket of Mtb-MurA with respect to E.coli MurA. This analysis was helpful in identifying the binding sites and molecular function of the MurA homologue. Molecular docking study was performed on this 3D structural model, using different classes of inhibitors like fosfomycin, cyclic disulfide analog RWJ-3981, pyrazolopyrimidine analog RWJ-110192, purine analog RWJ-140998, 5-sulfonoxy-anthranilic acid derivatives T6361, T6362 and the results showed that the 5-sulfonoxyanthranilic acid derivatives is showed best interaction compared with other inhibitor, taking in to this we also design a new efficient analogs of T6361 and T6362 which are showed even better interaction with Mtb-MurA than the parental5-sulfonoxy-anthranilic acid derivatives. Further the comparative molecular electrostatic potential and cavity depth analysis of Mtb-MurA suggested several important differences in its substrate and inhibitor binding pocket. Such differences could be exploited in the future for designing of a more specific inhibitor for Mtb-MurA enzym