Mutations in the kissing-loop hairpin of human immunodeficiency virus type 1 reduce viral infectivity as well as genomic RNA packaging and dimerization

A stem-loop termed the kissing-loop hairpin is one of the most highly conserved structures within the leader of human immunodeficiency virus type 1 (HIV-1) and chimpanzee immunodeficiency virus genomic RNA. Because it plays a key role in the in vitro dimerization of short HIV-1 RNA transcripts (M. Laughrea and L. Jette, Biochemistry 35:1589-1598, 1996, and references therein; M. Laughrea and L. Jette, Biochemistry 35:9366-9374, 1996, and references therein) and because dimeric RNAs may be preferably encapsidated into the HIV-1 virus, alterations of the kissing-loop hairpin might affect the in vivo dimerization and encapsidation processes. Accordingly, substitution and deletion mutations were introduced into the kissing-loop hairpin of an infectious HIV-1 molecular clone in order to produce viruses by transfection methods. The infectivity of the resulting viruses was decreased by at least 99%, the amount of genomic RNA packaged per virus was decreased by 50 to 75%, and the proportion of dimeric genomic RNA was reduced from >80 to 40 to 50%, but the dissociation temperature of the genomic RNA was unchanged. There is evidence suggesting that the deletion mutations moderately inhibited CAp24 production but had no significant effect on RNA splicing. These results are consistent with the kissing-loop model of HIV-1 RNA dimerization. In fact, because intracellular viral RNAs are probably more concentrated in transfected cells than in cells infected by one virus and because the dimerization and encapsidation processes are concentration dependent, it is likely that much larger dimerization and encapsidation defects would have been manifested within cells infected by no more than one virus.

Home-range, diet and breeding of a Powerful Owl Ninox strenua in East Gippsland, Victoria

Between 2004 and 2008 the diet and breeding success of a pair of Powerful Owls Ninox strenua were studied near Lakes Entrance, Victoria. In early November 2006 the adult female Powerful Owl was captured and radio-tracked for a period of 7.5 months. During this time the Owl's location was recorded on 111 occasions, including 65 nocturnal locations over 29 nights. Her home-range was calculated as 1589 ha using the Minimum Convex Polygon (MCP) method, or 871 ha based on the 95% Adaptive Kernel method. The area of forested habitat within the MCP home-range was 896 ha (the remainder representing cleared land). Her activity was centred primarily on the nesting gully where two dependent juveniles roosted, but several long-distance foraging expeditions (including roosting) that occurred more than 2.5 km from the juveniles were recorded. Arboreal mammals and birds dominated the Owls' diet. Low prey availability is suggested as being responsible for the single successful breeding event recorded in four nesting seasons.

Medical image retrieval based on unclean image bags

Traditional content-based image retrieval (CBIR) scheme with assumption of independent individual images in large-scale collections suffers from poor retrieval performance. In medical applications, images usually exist in the form of image bags and each bag includes multiple relevant images of the same perceptual meaning. In this paper, based on these natural image bags, we explore a new scheme to improve the performance of medical image retrieval. It is feasible and efficient to search the bag-based medical image collection by providing a query bag. However, there is a critical problem of noisy images which may present in image bags and severely affect the retrieval performance. A new three-stage solution is proposed to perform the retrieval and handle the noisy images. In stage 1, in order to alleviate the influence of noisy images, we associate each image in the image bags with a relevance degree. In stage 2, a novel similarity aggregation method is proposed to incorporate image relevance and feature importance into the similarity computation process. In stage 3, we obtain the final image relevance in an adaptive way which can consider both image bag similarity and individual image similarity. The experiments demonstrate that the proposed approach can improve the image retrieval performance significantly.

A review of online learning in supervised neural networks

Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003–2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.