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.

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.