32 research outputs found

    Novel inter-subunit contacts in Barley Stripe Mosaic Virus revealed by Cryo-Electron Microscopy

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    Barley stripe mosaic virus (BSMV, genus Hordeivirus) is a rod-shaped single-stranded RNA virus similar to viruses of the structurally characterized and well-studied genus Tobamovirus. Here we report the first high-resolution structure of BSMV at 4.1 Ă… obtained by cryo-electron microscopy. We discovered that BSMV forms two types of virion that differ in the number of coat protein (CP) subunits per turn and interactions between the CP subunits. While BSMV and tobacco mosaic virus CP subunits have a similar fold and interact with RNA using conserved residues, the axial contacts between the CP of these two viral groups are considerably different. BSMV CP subunits lack substantial axial contacts and are held together by a previously unobserved lateral contact formed at the virion surface via an interacting loop, which protrudes from the CP hydrophobic core to the adjacent CP subunit. These data provide an insight into diversity in structural organization of helical viruses

    Electrochemically synthesized polymers in molecular imprinting for chemical sensing

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    This critical review describes a class of polymers prepared by electrochemical polymerization that employs the concept of molecular imprinting for chemical sensing. The principal focus is on both conducting and nonconducting polymers prepared by electropolymerization of electroactive functional monomers, such as pristine and derivatized pyrrole, aminophenylboronic acid, thiophene, porphyrin, aniline, phenylenediamine, phenol, and thiophenol. A critical evaluation of the literature on electrosynthesized molecularly imprinted polymers (MIPs) applied as recognition elements of chemical sensors is presented. The aim of this review is to highlight recent achievements in analytical applications of these MIPs, including present strategies of determination of different analytes as well as identification and solutions for problems encountered

    A knowledge-intensive genetic algorithm for supervised learning

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    Abstract. Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The fullmemory approach developed here uses the same nigh-level descriptive language that is used in rule-based systems. This allows for an easy utilization of inference rules of the well-known inductive learning methodology, which replace the traditional domain-independent operators and make the search task-specific. Moreover, a closer relationship between the underlying task and the processing mechanisms provides a setting for an application of more powerful task-specific heuristics. Initial results obtained with a prototype implementation for the simplest case of single concepts indicate that genetic algorithms can be effectively used to process nigh-level concepts and incorporate task-specific knowledge. The method of abstracting the genetic algorithm to the problem level, described here for the supervised inductive learning, can be also extended to other domains and tasks, since it provides a framework for combining recently popular genetic algorithm methods with traditional problem-solving methodologies. Moreover, in this particular case, it provides a very powerful tool enabling study of the widely accepted but not so well understood inductive learning methodology
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