ms1, a novel stress-responsive, muscle-specific gene that is up-regulated in the early stages of pressure overload-induced left ventricular hypertrophy

We have identified and characterised a cDNA encoding a novel gene, designated myocyte stress 1 (ms1), that is up-regulated within 1 h in the left ventricle following the application of pressure overload by aortic banding in the rat. The deduced ms1 protein of 317 amino acids contains several putative functional motifs, including a region that is evolutionarily conserved. Distribution analysis indicates that rat ms1 mRNA expression is predominantly expressed in striated muscle and progressively increases in the left ventricle from embryo to adulthood. These findings suggest that rust may be important in striated muscle biology and the development of pressure-induced left ventricular hypertrophy. (C) 2002 Published by Elsevier Science B.V. on behalf of the Federation of European Biochemical Societies.

Application of molecular biological methods for studying probiotics and the gut flora

Increasingly, the microbiological scientific community is relying on molecular biology to define the complexity of the gut flora and to distinguish one organism from the next. This is particularly pertinent in the field of probiotics, and probiotic therapy, where identifying probiotics from the commensal flora is often warranted. Current techniques, including genetic fingerprinting, gene sequencing, oligonucleotide probes and specific primer selection, discriminate closely related bacteria with varying degrees of success. Additional molecular methods being employed to determine the constituents of complex microbiota in this area of research are community analysis, denaturing gradient gel electrophoresis (DGGE)/temperature gradient gel electrophoresis (TGGE), fluorescent in situ hybridisation (FISH) and probe grids. Certain approaches enable specific aetiological agents to be monitored, whereas others allow the effects of dietary intervention on bacterial populations to be studied. Other approaches demonstrate diversity, but may not always enable quantification of the population. At the heart of current molecular methods is sequence information gathered from culturable organisms. However, the diversity and novelty identified when applying these methods to the gut microflora demonstrates how little is known about this ecosystem. Of greater concern is the inherent bias associated with some molecular methods. As we understand more of the complexity and dynamics of this diverse microbiota we will be in a position to develop more robust molecular-based technologies to examine it. In addition to identification of the microbiota and discrimination of probiotic strains from commensal organisms, the future of molecular biology in the field of probiotics and the gut flora will, no doubt, stretch to investigations of functionality and activity of the microflora, and/or specific fractions. The quest will be to demonstrate the roles of probiotic strains in vivo and not simply their presence or absence.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.

Markers of intestinally-derived lipoproteins: application to studies of altered diet and meal fatty acid compositions

BACKGROUND AND AIM: The atherogenic potential of dietary derived lipids, chylomicrons (CM) and their remnants (CMr) is now becoming more widely recognised. To investigate factors effecting levels of CM and CMr and their importance in coronary heart disease risk it is essential to use a specific method of quantification. Two studies were carried out to investigate: (i) effects of increased daily intake of long chain n-3 polyunsaturated fatty acid (LC n-3 PUFA), and (ii) effects of increasing meal monounsaturated fatty acid (MUFA) content on the postprandial response of intestinally-derived lipoproteins. The contribution of the intestinally-derived lipoproteins to total lipaemia was assessed by triacylglycerol-rich lipoprotein (TRL) apolipoprotein B-48 (apo B-48) and retinyl ester (RE) concentrations. METHODS AND RESULTS: In a randomised controlled crossover trial (placebo vs LC n-3 PUFA) a mean daily intake of 1.4 g/day of LC n-3 PUFA failed to reduce fasting and postprandial triacylglycerol (TAG) response in 9 healthy male volunteers. Although the pattern and nature of the apo B-48 response was consistent with the TAG response following the two diets, the postprandial RE response differed on the LC n-3 PUFA diet with a lower early RE response and a delayed and more marked increase in RE in the late postprandial period compared with the control diet, but the differences did not reach levels of statistical significance. In the meal study there was no effect of MUFA/SFA content on the total lipaemic response to the meals nor on the contribution of intestinally derived lipoproteins evaluated as TAG, apo B-48 and RE responses in the TRL fraction. In both studies, the RE and apo B-48 measurements provided broadly similar information with respect to lack of effects of dietary or meal fatty acid composition and the presence of single or multiple peak responses. However the apo B-48 and RE measurements differed with respect to the timing of their peak response times, with a delayed RE peak, relalive to apo B-48, of approximately 2-3 hours for the LC n-3 PUFA diet (p = 0.002) study and 1-1.5 hours for the meal MUFA/SFA study. CONCLUSIONS: It was concluded that there are limitations of using RE as a specific CM marker, apo B-48 quantitation was found to be a more appropriate method for CM and CMr quantitation. However it was still considered of value to measure RE as it provided additional information regarding the incorporation of other constituents into the CM particle.

A moving-mesh finite element method and its application to the numerical solution of phase-change problems

A distributed Lagrangian moving-mesh finite element method is applied to problems involving changes of phase. The algorithm uses a distributed conservation principle to determine nodal mesh velocities, which are then used to move the nodes. The nodal values are obtained from an ALE (Arbitrary Lagrangian-Eulerian) equation, which represents a generalization of the original algorithm presented in Applied Numerical Mathematics, 54:450--469 (2005). Having described the details of the generalized algorithm it is validated on two test cases from the original paper and is then applied to one-phase and, for the first time, two-phase Stefan problems in one and two space dimensions, paying particular attention to the implementation of the interface boundary conditions. Results are presented to demonstrate the accuracy and the effectiveness of the method, including comparisons against analytical solutions where available.

The application of affine projection to the development of a siren noise cancellation system

Emergency vehicles use high-amplitude sirens to warn pedestrians and other road users of their presence. Unfortunately, the siren noise enters the vehicle and corrupts the intelligibility of two-way radio voice com-munications from the emergency vehicle to a control room. Often the siren has to be turned off to enable the control room to hear what is being said which subsequently endangers people's lives. A digital signal processing (DSP) based system for the cancellation of siren noise embedded within speech is presented. The system has been tested with the least mean square (LMS), normalised least mean square (NLMS) and affine projection algorithm (APA) using recordings from three common types of sirens (two-tone, wail and yelp) from actual test vehicles. It was found that the APA with a projection order of 2 gives comparably improved cancellation over the LMS and NLMS with only a moderate increase in algorithm complexity and code size. Therefore, this siren noise cancellation system using the APA offers an improvement in cancellation achieved by previous systems. The removal of the siren noise improves the response time for the emergency vehicle and thus the system can contribute to saving lives. The system also allows voice communication to take place even when the siren is on and as such the vehicle offers less risk of danger when moving at high speeds in heavy traffic.

The underlying return generating factors for REIT returns: an application of independent component analysis

Multi-factor approaches to analysis of real estate returns have, since the pioneering work of Chan, Hendershott and Sanders (1990), emphasised a macro-variables approach in preference to the latent factor approach that formed the original basis of the arbitrage pricing theory. With increasing use of high frequency data and trading strategies and with a growing emphasis on the risks of extreme events, the macro-variable procedure has some deficiencies. This paper explores a third way, with the use of an alternative to the standard principal components approach – independent components analysis (ICA). ICA seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the kurtosis characteristics of REIT returns, offering possibilities for the development of risk management strategies that are sensitive to extreme events and tail distributions.

Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Functional oligosaccharides: application and manufacture.

Oligosaccharides are attracting increasing interest as prebiotic functional food ingredients. They can be extracted or obtained by enzymatic hydrolysis from a variety of biomass sources or synthesized from simple oligosaccharides by enzymatic transfer reactions. The major prebiotic oligosaccharides on the market are inulin, fructo-oligosaccharides, and galacto-oligosaccharides. They have been evaluated using a range of in vitro and in vivo methods, although there is a need for more large-scale human trials using modern microbiological methods. Prebiotics are being studied for their effects on gut health and well being and specific clinical conditions, including colon cancer, inflammatory bowel disease (IBD), acute infections, and mineral absorption. Developing understanding of the functional ecology of the human gut is influencing current thinking on what a prebiotic might achieve and is providing new targets for prebiotic intervention.

Data assimilation for state and parameter estimation: application to morphodynamic modelling

Data assimilation is predominantly used for state estimation; combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts we need good estimates of both the current system state and the model parameters. This paper presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three dimensional variational(3D-Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from SAR satellite imagery and a ship-borne survey is shown to significantly improve the predictive capability of the model over a 2 year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this paper considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D-Var data assimilation algorithm for joint state-parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation-based state-parameter estimation in coastal morphodynamic modelling.

On discrimination algorithms for ill-posed problems with an application to magnetic tomography

In this paper we explore classification techniques for ill-posed problems. Two classes are linearly separable in some Hilbert space X if they can be separated by a hyperplane. We investigate stable separability, i.e. the case where we have a positive distance between two separating hyperplanes. When the data in the space Y is generated by a compact operator A applied to the system states ∈ X, we will show that in general we do not obtain stable separability in Y even if the problem in X is stably separable. In particular, we show this for the case where a nonlinear classification is generated from a non-convergent family of linear classes in X. We apply our results to the problem of quality control of fuel cells where we classify fuel cells according to their efficiency. We can potentially classify a fuel cell using either some external measured magnetic field or some internal current. However we cannot measure the current directly since we cannot access the fuel cell in operation. The first possibility is to apply discrimination techniques directly to the measured magnetic fields. The second approach first reconstructs currents and then carries out the classification on the current distributions. We show that both approaches need regularization and that the regularized classifications are not equivalent in general. Finally, we investigate a widely used linear classification algorithm Fisher's linear discriminant with respect to its ill-posedness when applied to data generated via a compact integral operator. We show that the method cannot stay stable when the number of measurement points becomes large.

Application of the BRAFO-tiered approach for benefit-risk assessment to case studies on natural foods

There is evidence that consumption of fish, especially oily fish, has substantial beneficial effects on health. In particular an inverse relationship of oily fish intake to coronary heart disease incidence has been established. These beneficial effects are ascribed to fish oil components including long chain ω-3 polyunsaturated fatty acids. On the other hand it should be noted that oily fish also contains hazardous substances such as dioxins, PCBs and methylmercury. Soy consumption has been associated with potential beneficial and adverse effects. The claimed benefits include reduced risk of cardiovascular disease; osteoporosis, breast and prostate cancer whereas potential adverse effects include impaired thyroid function, disruption of sex hormone levels, changes in reproductive function and increased breast cancer risk The two cases of natural foods highlight the need to consider both risks and benefits in order to establish the net health impact associated to the consumption of specific food products. Within the Sixth Framework programme of the European Commission, the BRAFO project was funded to develop a framework that allows for the quantitative comparison of human health risks and benefits in relation to foods and food compounds. This paper describes the application of the developed framework to two natural foods, farmed salmon and soy protein. We conclude that the BRAFO methodology is highly applicable to natural foods. It will help the benefit-risk managers in selecting the appropriate dietary recommendations for the population.

Specific detection and localization of microsporidian parasites in invertebrate hosts using in situ hybridization

We designed FISH-probes for two distinct microsporidian clades and demonstrated their application in detecting respectively Nosema/Vairimorpha and Dictyoceola species. We applied them to study the vertical transmission of two microsporidia infecting the amphipod Gammarus duebeni

Immunopharmacology - antibodies for specific modulation of proteins involved in neuronal function

The application of antibodies to living neurones has the potential to modulate function of specific proteins by virtue of their high specificity. This specificity has proven effective in determining the involvement of many proteins in neuronal function where specific agonists and antagonists do not exist, e.g. ion channel subunits. We discuss studies where antibodies modulate functions of voltage gated sodium, voltage gated potassium, voltage gated calcium hyperpolarisation activated cyclic nucleotide (HCN gated) and transient receptor potential (TRP) channels. Ligand gated channels studied in this way include nicotinic acetylcholine receptors, purinoceptors and GABA receptors. Antibodies have also helped reveal the involvement of different intracellular proteins in neuronal functions including G-proteins as well as other proteins involved in trafficking, phosphoinositide signalling and neurotransmitter release. Some suggestions for control experiments are made to help validate the method. We conclude that antibodies can be extremely valuable in determining the functions of specific proteins in living neurones in neuroscience research.