Recent advances on the possible neuroprotective activities of Epstein-Barr virus oncogene BARF1 protein in chronic inflammatory disorders of central nervous system

Multiple sclerosis and neurodegenerative diseases in which cells of the central nervous system (CNS) are lost or damaged are rapidly increasing in frequency, and there is neither effective treatment nor cure to impede or arrest their destructive course. The Epstein-Barr virus is a human gamma-herpesvirus that infects more than 90% of the human population worldwide and persisting for the lifetime of the host. It is associated with numerous epithelial cancers, principally undifferentiated nasopharyngeal carcinoma and gastric carcinoma. Individuals with a history of symptomatic primary EBV infection, called infectious mononucleosis, carry a moderately higher risk of developing multiple sclerosis (MS). It is not known how EBV infection potentially promotes autoimmunity and central nervous system (CNS) tissue damage in MS. Recently it has been found that EBV isolates from different geographic regions have highly conserved BARF1 epitopes. BARF1 protein has the neuroprotective and mitogenic activity, thus may be useful to combat and overcome neurodegenerative disease. BARF1 protein therapy can potentially be used to enhance the neuroprotective activities by combinational treatment with anti-inflammatory antagonists and neuroprotectors in neural disorders.

A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

A multiple population genetic algorithm and its application in fuzzy controller

Genetic Algorithm is an important optimization technique, though its application in Fuzzy system is usually limited by problems like local optimal and premature convergence. With an aim to improve the performance of simple Genetic Algorithm, we propose a multi-population genetic algorithm MP-GA which uses two populations collaborating with each other, and apply it to fuzzy controller design to optimize its control rules. The simulation results of Inverted Pendulum demonstrate the effectiveness of this proposed method.

Many routes lead to Rome : potential causes for the multi-route migration system of Red Knots, Calidris canutus islandica

Migrants, such as birds or representatives of other taxa, usually make use of several stopover sites to cover the distance between their site of origin and destination. Potentially, multiple routes exist, but often little is known about the causes and consequences of alternative migration routes. Apart from their geographical distribution, the suitability of potential sites might play an important role in the animals’ decisions for a particular itinerary. We used an optimal-migration model to test three nonmutually exclusive hypotheses leading to variations in the spring migration routes of a subspecies of Red Knot, Calidris canutus islandica, which migrates from wintering grounds in Western Europe to breeding grounds in Greenland and the Canadian Arctic: the breeding location hypothesis, the energy budget hypothesis, and the predation risk hypothesis. Varying only breeding location, the model predicted that birds breeding in the Canadian Arctic and on West Greenland stop over on Iceland, whereas birds breeding in East and Northeast Greenland migrate via northern Norway, a prediction that is supported by empirical findings. Energy budgets on stopover sites had a strong influence on the choice of route and staging times. Varying foraging-intensity and mass-dependent predation risk prompted the birds to use less risky sites, if possible. The effect of simultaneous changes in the energy budget and predation risk strongly depended on the site where these occurred. Our findings provide potential explanations for the observations that C. canutus islandica uses a diverse array of migration routes. Scrutinizing the three alternative driving forces for the choice of migratory routes awaits further, specific data collection in rapidly developing fields of research (e.g., predation risk assessment, GPS tracking). Generally, the type of modeling presented here may not only highlight alternative explanations, but also direct follow-up empirical research.

Empirical investigation of critical success factors for implementing business intelligence systems in multiple engineering asset management organisations

Engineering asset management organisations (EAMOs) are increasingly motivated to implement business intelligence (BI) systems in response to dispersed information environments and compliance requirements. However, the implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet, so far, there are few defined critical success factors (CSFs) to which management can refer. Drawing on the CSFs framework derived from a previous Delphi study, a multiple-case design was used to examine how these CSFs could be implemented by five EAMOs. The case studies substantiate the construct and applicability of the CSFs framework. These CSFs are: committed management support and sponsorship, a clear vision and well-established business case, business-centric championship and balanced team composition, a business-driven and iterative develop ment approach, user-oriented change management, a business-driven, scalable and flexible technical framework, and sustainable data quality and integrity. More significantly, the study further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.