The molecular basis of HIV capsid assembly - five years of progress

The assembly of HIV is relatively poorly investigated when compared with the process of virus entry. Yet a detailed understanding of the mechanism of assembly is fundamental to our knowledge of the complete life cycle of this virus and also has the potential to inform the development of new antiviral strategies. The repeated multiple interaction of the basic structural unit, Gag, might first appear to be little more than concentration dependent self-assembly but the precise mechanisms emerging for HIV are far from simple. Gag interacts not only with itself but also with host cell lipids and proteins in an ordered and stepwise manner. It binds both the genomic RNA and the virus envelope protein and must do this at an appropriate time and place within the infected cell. The assembled virus particle must successfully release from the cell surface and, whilst being robust enough for transmission between hosts, must nonetheless be primed for rapid disassembly when infection occurs. Our current understanding of these processes and the domains of Gag involved at each stage is the subject of this review. Copyright (C) 2004 John Wiley Sons, Ltd.

Analysis of shade and temperature effects on assimilate partitioning in the everbearing strawberry 'Everest' as the basis for optimised long-season fruit production

Growth patterns and cropping were evaluated over the season for the everbearing strawberry 'Everest' at a range of temperatures (15-27degreesC) in two light environments (ambient and 50% shade). The highest yield was recorded for unshaded plants grown at 23degreesC, but the optimum temperature for vegetative growth was 15degreesC. With increasing temperature fruit number increased, but fruit weight decreased. Fruit weight was also significantly reduced by shade, and although 'Everest' showed a degree of shade tolerance in vegetative growth, yield was consistently reduced by shade. Shade also reduced the number of crowns developed by the plants over the course of the season, emphasising that crown number was ultimately the limiting factor for yield potential. We conclude that, in contrast to Junebearers which partition more assimilates to fruit at temperatures around 15degreesC (Le Miere et al., 1998), optimised cropping in the everbearer 'Everest' is achieved at the significantly higher temperature of 23degreesC. These findings have significance for commercial production, in which protection tends to reduce light levels but increase average temperature throughout the season.

The role of drama in the teaching of speaking and listening as the basis for social capital

This paper explores how the concept of 'social capital' relates to the teaching of speaking and listening. The argument draws on Bourdieu's notion that a common language is an illusion but posits that an understanding of the grammar of speech can be productive in the development of both an understanding of what constitutes effective speech and the development of competence in speaking. It is argued that applying structuralist notions of written grammar is an inadequate approach to understanding speech acts or enhancing the creative use of speech. An analysis is made of how typical features of speech relate to dramatic dialogue and how the meaning of what is said is contingent upon aural and visual signifiers. On this basis a competent speaker is seen as being one who produces expressions appropriate for a range of situations by intentionally employing such signifiers. The paper draws on research into the way drama teachers make explicit reference to and use of semiotics and dramatic effectiveness in order to improve students' performance and by so doing empower them to increase their social capital. Ultimately, it is concluded that helping students identify, analyse and employ the aural, visual and verbal grammar of spoken English is not an adjunct to the subject of drama, but an intrinsic part of understanding the art form. What is called for is a re-appraisal by drama teachers of their own understanding of concepts relating to speech acts in order to enhance this area of their work.

Comparison of small and large deformation rheological properties of wheat dough and gluten

The rheological properties of dough and gluten are important for end-use quality of flour but there is a lack of knowledge of the relationships between fundamental and empirical tests and how they relate to flour composition and gluten quality. Dough and gluten from six breadmaking wheat qualities were subjected to a range of rheological tests. Fundamental (small-deformation) rheological characterizations (dynamic oscillatory shear and creep recovery) were performed on gluten to avoid the nonlinear influence of the starch component, whereas large deformation tests were conducted on both dough and gluten. A number of variables from the various curves were considered and subjected to a principal component analysis (PCA) to get an overview of relationships between the various variables. The first component represented variability in protein quality, associated with elasticity and tenacity in large deformation (large positive loadings for resistance to extension and initial slope of dough and gluten extension curves recorded by the SMS/Kieffer dough and gluten extensibility rig, and the tenacity and strain hardening index of dough measured by the Dobraszczyk/Roberts dough inflation system), the elastic character of the hydrated gluten proteins (large positive loading for elastic modulus [G'], large negative loadings for tan delta and steady state compliance [J(e)(0)]), the presence of high molecular weight glutenin subunits (HMW-GS) 5+10 vs. 2+12, and a size distribution of glutenin polymers shifted toward the high-end range. The second principal component was associated with flour protein content. Certain rheological data were influenced by protein content in addition to protein quality (area under dough extension curves and dough inflation curves [W]). The approach made it possible to bridge the gap between fundamental rheological properties, empirical measurements of physical properties, protein composition, and size distribution. The interpretation of this study gave indications of the molecular basis for differences in breadmaking performance.

The neural basis of maternal responsiveness to infants: an fMRI study

Using fMRI, we examined the neural correlates of maternal responsiveness. Ten healthy mothers viewed alternating blocks of video: (i) 40 s of their own infant; (ii) 20 s of a neutral video; (iii) 40 s of an unknown infant and (iv) 20 s of neutral video, repeated 4 times. Predominant BOLD signal change to the contrast of infants minus neutral stimulus occurred in bilateral visual processing regions BA minus neutral stimulus occurred in bilateral visual processing regions (BA 38), left amygdala and visual cortex (BA 19), and to the unknown infant minus own infant contrast in bilateral orbitofrontal cortex (BA 10,47) and medial prefrontal cortex (BA 8). These findings suggest that amygdala and temporal pole may be key sites in mediating a mother's response to her infant and reaffirms their importance in face emotion processing and social behaviour.

Decoding emotional prosody in Parkinson's disease and its potential neuropsychological basis

Parkinson's disease patients may have difficulty decoding prosodic emotion cues. These data suggest that the basal ganglia are involved, but may reflect dorsolateral prefrontal cortex dysfunction. An auditory emotional n-back task and cognitive n-back task were administered to 33 patients and 33 older adult controls, as were an auditory emotional Stroop task and cognitive Stroop task. No deficit was observed on the emotion decoding tasks; this did not alter with increased frontal lobe load. However, on the cognitive tasks, patients performed worse than older adult controls, suggesting that cognitive deficits may be more prominent. The impact of frontal lobe dysfunction on prosodic emotion cue decoding may only become apparent once frontal lobe pathology rises above a threshold.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Grey-box radial basis function modelling: the art of incorporating prior knowledge

A basic principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.