Diet, immunity and functional foods

Functional foods (specific nutrient and/or food components) should beneficially affect one or more target functions in the body. The use of functional foods as a form of preventive medicine has been the subject of much research over the last two decades. It is well known that nutrition plays a vital role in chronic diseases, but it is only recently that data relating to the effects of specific nutrients or foods on the immune system have become available. This chapter aims to summarize the effects of some functional foods (e.g., prebiotics and micronutrients) on the immune system. It should be noted, however, that studies into the role of functional foods with regard to the human immune system are still in their infancy and a great deal of controversy surrounds the health claims attributed to some functional foods. Consequently, thorough studies are required in human and animal systems if we are to move towards developing a functional diet that provides maximal health benefits.

Soy-isoflavone-enriched foods and markers of lipid and glucose metabolism in postmenopausal women: interactions with genotype and equol production

Background: The hypocholesterolemic effects of soy foods are well established, and it has been suggested that isoflavones are responsible for this effect. However, beneficial effects of isolated isoflavones on lipid biomarkers of cardiovascular disease risk have not yet been shown. Objective: The objective was to investigate the effects of isolated soy isoflavones on metabolic biomarkers of cardiovascular disease risk, including plasma total, HDL, and LDL cholesterol; triacylglycerols; lipoprotein(a); the percentage of small dense LDL; glucose; nonesterified fatty acids; insulin; and the homeostasis model assessment of insulin resistance. Differences with respect to single nucleotide polymorphisms in selected genes [ie, estrogen receptor a (Xbal and PvuII), estrogen receptor beta (AluI), and estrogen receptor beta(cx) (Tsp5091), endothelial nitric oxide synthase (Glu298Asp), apolipoprotein E (Apo E2, E3, and E4), cholesteryl ester transfer protein (TaqIB), and leptin receptor (Gln223Arg)] and with respect to equol production were investigated. Design: Healthy postmenopausal women (n = 117) participated in a randomized, double-blind, placebo-controlled, crossover dietary intervention trial. Isoflavone-enriched (genistein-to-daidzein ratio of 2: 1; 50 mg/d) or placebo cereal bars were consumed for 8 wk, with a wash-out period of 8 wk before the crossover. Results: Isoflavones did not have a significant beneficial effect on plasma concentrations of lipids, glucose, or insulin. A significant difference between the responses of HDL cholesterol to isoflavones and to placebo was found with estrogen receptor 0(cx) Tsp5091 genotype AA, but not GG or GA. Conclusions: Isoflavone supplementation, when provided in the form and dose used in this study, had no effect on lipid or other metabolic biomarkers of cardiovascular disease risk in postmenopausal women but may increase HDL cholesterol in an estrogen receptor P gene-polymorphic subgroup.

Soy-isoflavone-enriched foods and inflammatory biomarkers of cardiovascular disease risk in postmenopausal women: interactions with genotype and equol production

Background: Dietary isoflavones are thought to be cardioprotective because of their structural similarity to estrogen. The reduction of concentrations of circulating inflammatory markers by estrogen may be one of the mechanisms by which premenopausal women are protected against cardiovascular disease. Objective: Our aim was to investigate the effects of isolated soy isoflavones on inflammatory biomarkers [von Willebrand factor, intracellular adhesion molecule 1, vascular cell adhesion molecule 1 (VCAM-1), E-selectin, monocyte chemoattractant protein 1, C-reactive protein (CRP), and endothelin 1 concentrations]. Differences with respect to single-nucleotide polymorphisms in selected genes [estrogen receptor alpha (XbaI and PvuII), estrogen receptor beta [ER beta (AluI) and ER beta[cx] (Tsp5091), endothelial nitric oxide synthase (Glu298Asp), apolipoprotein E (Apo E2, E3, and E4), and cholesteryl ester transfer protein (TaqIB)] and equol production were investigated. Design: One hundred seventeen healthy European postmenopausal women participated in this randomized, double-blind, placebo-controlled, crossover dietary intervention trial. Isoflavone-enriched (genistein-to-daidzein ratio of 2:1;50 mg/d) or placebo cereal bars were consumed for 8 wk, with a washout period of 8 wk between the crossover. Plasma inflammatory factors were measured at 0 and 8 wk of each study arm. Results: Isoflavones improved CRP concentrations [odds ratio (95% Cl) for CRP values >1 mg/L for isoflavone compared with placebo: 0.43 (0.27, 0.69)]; no significant effects of isoflavone treatment on other plasma inflammatory markers were observed. No significant differences in the response to isoflavones were observed according to subgroups of equol production. Differences in the VCAM-1 response to isoflavones and to placebo were found with ER beta AluI genotypes. Conclusion: Isoflavones have beneficial effects on CRP concentrations, but not on other inflammatory biomarkers of cardiovascular disease risk in postmenopausal women, and may improve VCAM-1 in an ER beta gene polymorphic subgroup.

Recent advances in the application of capillary electrophoresis for food analysis

This article reviews recent developments in the application of capillary electrophoresis (CE) for the analysis of foods and food components. CE has been applied to a number of important areas of food analysis and is fast becoming an established technique within food analytical and research laboratories. Papers are reviewed that were published during the two years to date following the previous review.

Reversing the emotional Stroop effect reveals that it is not what it seems: the role of fast and slow components

The relative contributions of slow and fast (online) components in a modified emotional Stroop task were evaluated. The slow component, neglected in previous research, was shown to lead to the prediction of a reversed emotional intrusion effect using pseudorandomly mixed negative and neutral stimuli. This prediction was supported in Experiments 1 and 2. In Experiments 3 and 4, a new paradigm was developed that allowed a more direct observation of the nature of disruptive effects from negative stimuli. The results provided a clear demonstration of the presence of the slow component. The fast component, which has generally been assumed to be the source of the interference, was shown, in fact, to have little or no role in the disruption.

Local, national and imported foods: a qualitative study

The UK government is currently attempting to encourage consumers to buy more locally produced food. It is hoped that this will provide economic, environmental and social benefits to local areas, leading to more sustainable patterns of consumption. This qualitative study looks at the views and behaviour of consumers towards local foods with a particular focus on the barriers that prevent greater uptake of local produce. In total, four focus groups (n = 33) were conducted. Content analysis identified six relevant themes in relation to local, national and imported foods. These were cost, lifestyle, food quality, consumer ethnocentrism, choice and farmers. Overall, although participants reported buying few local products currently, there was widespread enthusiasm across socio-economic groups for local foods, with participants perceiving them as being of a higher quality than imported foods. They also generally endorsed the idea of supporting local farmers and their own national economy. The main barriers preventing participants from buying more local products were price and inconvenience. The results are discussed in relation to developing future strategies for encouraging people to buy more local food products. (c) 2007 Elsevier Ltd. All rights reserved.

Verification and FPGA circuits of a block-2 fast path-based predictor

This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of size two for a lower hardware solution while maintaining similar input-output characteristic to the algorithm. The blocked solution, here referred to as B2P algorithm, is obtained using graph theory and retiming methods. Verification approaches were exercised to show that prediction performances obtained from the FPP and B2P algorithms differ within one mis-prediction per thousand instructions using a known framework for branch prediction evaluation. For a chosen FPGA device, circuits generated from the B2P algorithm showed average area savings of over 25% against circuits for the FPP algorithm with similar time performances thus making the proposed blocked predictor superior from a practical viewpoint.

FPGA organization for the fast path-based neural branch predictor

This paper develops cycle-level FPGA circuits of an organization for a fast path-based neural branch predictor Our results suggest that practical sizes of prediction tables are limited to around 32 KB to 64 KB in current FPGA technology due mainly to FPGA area of logic resources to maintain the tables. However the predictor scales well in terms of prediction speed. Table sizes alone should not be used as the only metric for hardware budget when comparing neural-based predictor to predictors of totally different organizations. This paper also gives early evidence to shift the attention on to the recovery from mis-prediction latency rather than on prediction latency as the most critical factor impacting accuracy of predictions for this class of branch predictors.

Clustering quality and topology preservation in fast learning SOMs

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

Fast and accurate SCFT calculations for periodic block-copolymer morphologies using the spectral method with Anderson mixing

We study the numerical efficiency of solving the self-consistent field theory (SCFT) for periodic block-copolymer morphologies by combining the spectral method with Anderson mixing. Using AB diblock-copolymer melts as an example, we demonstrate that this approach can be orders of magnitude faster than competing methods, permitting precise calculations with relatively little computational cost. Moreover, our results raise significant doubts that the gyroid (G) phase extends to infinite $\chi N$. With the increased precision, we are also able to resolve subtle free-energy differences, allowing us to investigate the layer stacking in the perforated-lamellar (PL) phase and the lattice arrangement of the close-packed spherical (S$_{cp}$) phase. Furthermore, our study sheds light on the existence of the newly discovered Fddd (O$^{70}$) morphology, showing that conformational asymmetry has a significant effect on its stability.