Monoclonal antibodies in the study of architecture of plant viruses

Monoclonal antibodies have been used as probes to study the architecture of several plant viruses over the past decade. These studies complement the information obtained through X-ray crystallography and help in delineating epitopes on the surface of the virus. The monoclonal antibodies that recognize distinct epitopes also aid in unravelling the mechanisms of assembly/disassembly of virus particles. Group-specific and strain-specific monoclonal antibodies are widely used in the classification of viruses. The significant developments made in this emerging area are reviewed here with specific examples.

Making consistent contacts to graphene: effect of architecture and growth induced defects

The effects of contact architecture, graphene defect density and metal-semiconductor work function difference on the resistivity of metal-graphene contacts have been investigated. An architecture with metal on the bottom of graphene is found to yield resistivities that are lower, by a factor of four, and most consistent as compared to metal on top of graphene. Growth defects in graphene film were found to further reduce resistivity by a factor of two. Using a combination of method and metal used, the contact resistivity of graphene has been decreased by a factor of 10 to 1200. +/-. 250 Omega mu m using palladium as the contact metal. While the improved consistency is due to the metal being able to contact uncontaminated graphene in the metal on the bottom architecture, lower contact resistivities observed on defective graphene with the same metal are attributed to the increased number of modes of quantum transport in the channel.

The PE and PPE proteins of Mycobacterium tuberculosis

India already has earned the dubious distinction of being one of the countries with the highest incidence of tuberculosis (TB). The conventional control measures have had little impact on the relentless march of the TB epidemic. Potential solutions to this problem include the development of new drugs and an effective TB vaccine. In this perspective, identification of the mycobacterial components that have important role(s) in the establishment of the infection assumes crucial importance. Mycobacterium tuberculosis is an intracellular pathogen and it resides inside the macrophage, which is considered to be the most important component of the immune system. M. tuberculosis possesses two highly polymorphic sets of genes called the PE and PPE families. These unique families of proteins account for about 10% of the mycobacterial genome and have drawn considerable interest from different schools of M. tuberculosis researchers across the globe. In this review, we discuss the importance of these proteins in the regulation of dendritic cell and macrophage immune-effector functions, as well as the relevance of these proteins in the clinical manifestation of TB. This information may be helpful to better understand the immunological importance of PE/PPE proteins and their roles in mycobacterial virulence. (C) 2011 Elsevier Ltd. All rights reserved.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.