Origin and evolution of 3'UTR of flaviviruses: long direct repeats as a basis for the formation of secondary structures and their significance for virus transmission

The 3' untranslated regions (3'UTRs) of flaviviruses are reviewed and analyzed in relation to short sequences conserved as direct repeats (DRs). Previously, alignments of the 3'UTRs have been constructed for three of the four recognized flavivirus groups, namely mosquito-borne, tick-borne, and nonclassified flaviviruses (MBFV, TBFV, and NCFV, respectively). This revealed (1) six long repeat sequences (LRSs) in the 3'UTR and open-reading frame (ORF) of the TBFV, (2) duplication of the 3'UTR of the NCFV by intramolecular recombination, and (3) the possibility of a common origin for all DRs within the MBFV. We have now extended this analysis and review it in the context of all previous published analyses. This has been achieved by constructing a robust alignment between all flaviviruses using the published DRs and secondary RNA structures as "anchors" to reveal additional homologies along the 3'UTR. This approach identified nucleotide regions within the MBFV, NKV (no-known vector viruses), and NCFV 3'UTRs that are homologous to different LRSs in the TBFV 3'UTR and ORF. The analysis revealed that some of the DRs and secondary RNA structures described individually within each flavivirus group share common evolutionary origins. The 3'UTR of flaviviruses, and possibly the ORF, therefore probably evolved through multiple duplication of an RNA domain, homologous to the LRS previously identified only in the TBFV. The short DRs in all virus groups appear to represent the evolutionary remnants of these domains rather than resulting from new duplications. The relevance of these flavivirus DRs to evolution, diversity, 3'UTR enhancer function, and virus transmission is reviewed.

Mechanisms and function of flower and inflorescence reversion

Flower and inflorescence reversion involve a switch from floral development back to vegetative development, thus rendering flowering a phase in an ongoing growth pattern rather than a terminal act of the meristem. Although it can be considered an unusual event, reversion raises questions about the nature and function of flowering. It is linked to environmental conditions and is most often a response to conditions opposite to those that induce flowering. Research on molecular genetic mechanisms underlying plant development over the last 15 years has pinpointed some of the key genes involved in the transition to flowering and flower development. Such investigations have also uncovered mutations which reduce floral maintenance or alter the balance between vegetative and floral features of the plant. How this information contributes to an understanding of floral reversion is assessed here. One issue that arises is whether floral commitment (defined as the ability to continue flowering when inductive conditions no longer exist) is a developmental switch affecting the whole plant or is a mechanism which assigns autonomy to individual meristems. A related question is whether floral or vegetative development is the underlying default pathway of the plant. This review begins by considering how studies of flowering in Arabidopsis thaliana have aided understanding of mechanisms of floral maintenance. Arabidopsis has not been found to revert to leaf production in any of the conditions or genetic backgrounds analysed to date. A clear-cut reversion to leaf production has, however, been described in Impatiens balsamina. It is proposed that a single gene controls whether Impatiens reverts or can maintain flowering when inductive conditions are removed, and it is inferred that this gene functions to control the synthesis or transport of a leaf-generated signal. But it is also argued that the susceptibility of Impatiens to reversion is a consequence of the meristem-based mechanisms controlling development of the flower in this species. Thus, in Impatiens, a leaf-derived signal is critical for completion of flowering and can be considered to be the basis of a plant-wide floral commitment that is achieved without accompanying meristem autonomy. The evidence, derived from in vitro and other studies, that similar mechanisms operate in other species is assessed. It is concluded that most species (including Arabidopsis) are less prone to reversion because signals from the leaf are less ephemeral, and the pathways driving flower development have a high level of redundancy that generates meristem autonomy even when leaf-derived signals are weak. This gives stability to the flowering process, even where its initiation is dependent on environmental cues. On this interpretation, Impatiens reversion appears as an anomaly resulting from an unusual combination of leaf signalling and meristem regulation. Nevertheless, it is shown that the ability to revert can serve a function in the life history strategy (perenniality) or reproductive habit (pseudovivipary) of many plants. In these instances reversion has been assimilated into regular plant development and plays a crucial role there.

Green tea (Camellia sinensis) catechins and vascular function

The health benefits of green tea (Camellia sinensis) catechins are becoming increasingly recognised. Amongst the proposed benefits are the maintenance of endothelial function and vascular homeostasis and an associated reduction in atherogenesis and CVD risk. The mounting evidence for the influential effect of green tea catechins on vascular function from epidemiological, human intervention and animal studies is subject to review together with exploration of the potential mechanistic pathways involved. Epigallocatechin-3-gallate, one of the most abundant and widely studied catechin found in green tea, will be prominent in the present review. Since there is a substantial inconsistency in the published data with regards to the impact of green tea catechins on vascular function, evaluation and interpretation of the inter- and intra-study variability is included. In conclusion, a positive effect of green tea catechins on vascular function is becoming apparent. Further studies in animal and cell models using physiological concentrations of catechins and their metabolites are warranted in order to gain some insight into the physiology and molecular basis of the observed beneficial effects.

pH-mediated interactions between poly(acrylic acid) and methylcellulose in the formation of ultrathin multilayered hydrogels and spherical nanoparticles

Poly(acrylic acid) forms insoluble hydrogen-bonded interpolymer complexes with methylcellulose in aqueous solutions under acidic conditions. In this work the reaction heats and binding constants were determined for the complexation between poly(acrylic acid) and methylcellulose by isothermal titration calorimetry at different pH and findings are correlated with the aggregation processes occurring in this system. The principal contribution to the complexation heat results from primary polycomplex particle aggregation. Transmission electron microscopy of nanoparticles produced at pH 1.4 and 2.4 demonstrated that they are spherical and dense structures. The nanoparticles ranged from 80 to 200 nm, whereas particles formed at pH 3.2 were 20-30 nm and were stabilized against aggregation by a network of uncomplexed macromolecules. For the first time, multilayered materials were developed on the basis of hydrogen-bonded complexes of poly(acrylic acid) and methylcellulose using layer-by-layer deposition on a glass surface. The thickness of these films was a linear function of the number of deposition cycles. The materials were subsequently cross-linked by thermal treatment, resulting in ultrathin hydrogels which detached from the glass substrate upon swelling. The swelling capacity of ultrathin hydrogels differed from the swelling of the thicker films of a similar chemical composition.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

The evolution of innovation capability in multinational enterprise subsidiaries: dual network embeddedness and the divergence of subsidiary specialisation in Taiwan

This paper examines how innovation-related capabilities for production, design and marketing develop at the subsidiary level within multinational enterprises (MNEs). We focus on how subsidiary autonomy and changing opportunities to access internal (MNE) and external (host country) sources of capability contribute in a combined way to the accumulation of specialist capabilities in five Taiwan-based MNE subsidiaries in the semiconductor industry. Longitudinal analysis shows how the accumulation process is subject to discontinuities, as functional divisions are (re)opened and closed during the lifetime of the subsidiary. A composite set of innovation output measures also shows significant variations in within-function levels of capability across our sample. We conclude that subsidiary specialisation and unique subsidiary-specific advantages have evolved in a way that is strongly influenced by the above factors.

A model-based PID controller for Hammerstein systems using B-spline neural networks

In this paper, a new model-based proportional–integral–derivative (PID) tuning and controller approach is introduced for Hammerstein systems that are identified on the basis of the observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The control signal is composed of a PID controller, together with a correction term. Both the parameters in the PID controller and the correction term are optimized on the basis of minimizing the multistep ahead prediction errors. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on B-spline neural networks and the associated Jacobian matrix are calculated using the de Boor algorithms, including both the functional and derivative recursions. Numerical examples are utilized to demonstrate the efficacy of the proposed approaches.