Savory peptides present in Moromi obtained from soy sauce fermentation of yellow soybean

The presence of savory peptides in moromi has been investigated. Moromi was prepared by fermenting yellow soybean using Aspergillus oryzae as the starter at the first step (mold fermentation) and 20% brine solution at the next step (brine fermentation). The moromi was then ultrafiltered stepwise using membranes with MW cut-offs of 10,000, 3,000, and 500 Da, respectively. The fraction with MW < 500 Da was chromatographed using Sephadex G-25 SF to yield four fractions, 1-4. Analysis of soluble peptides, NaCl content, alpha-amino nitrogen, amino acid composition, peptide profile using CE coupled with DAD, taste profile and free glutamic acid content, were performed for each fraction. Fraction 2 contained a relatively high total glutamic acid content, but a relatively low free glutamic acid content and had the highest umami taste. This fraction also had more peptides containing non-aromatic amino acids than the other fractions. The peptides present in fraction 2 may play a role, at least in part, in its intense umami taste.

Asymmetric synthesis of arylalanines via asymmetric aza-darzens (ADZ) reaction

(R)-3-Arylalanines may be prepared in high enantiomeric purity from N-dpp imines by a four-step reaction sequence involving asymmetric aza-Darzens reaction, dephosphinylation, hydrogenolysis and hydrolysis. The amino acids thus obtained were of >95% enantiomeric purity.

Introducing assistive technology into the homes of older people: Some lessons from the Netherlands - A case study

The authors have just completed a study of assistive technology (AT) and how it can be introduced into the existing homes of older people. As part of this empirical study some collaboration with the Netherlands took place for two reasons. First to learn about the use of assistive technology from a country where its creative use was well established and second to learn how older people influence policy. Two Dutch consultants helped the team and a short visit was paid to Eindhoven. As a result of this collaboration new ways of using AT were found and comparisons made with the policy and legislative background. In addition lessons were drawn about the involvement of older people.

Formate-dependent growth and homoacetogenic fermentation by a bacterium from human feces: Description of Bryantella formatexigens gen. nov., sp nov.

Formate stimulates growth of a new bacterium from human feces. With high formate, it ferments glucose to acetate via the Wood-Ljungdahl pathway. The original isolate fermented vegetable cellulose and carboxymethylcellulose, but it lost this ability after storage at -76degreesC. 16S rRNA gene sequencing identifies it as a distinct line within the Clostridium coccoides supra-generic rRNA grouping. We propose naming it Bryantella formatexigens gen. nov., sp. nov.

Effect of Ginkgo biloba (EGb 761®) on differential gene expression

Supplementation of diets with plant extracts such as ginkgo biloba extract (EGb 761®) (definition see editorial) for health and prevention of degenerative diseases is popular. However, it is often difficult to analyse the biological activities of plant extracts due to their complex nature and the possible synergistic and/or antagonistic effects of their components. Genome-wide expression monitoring with high-density oligonucleotide arrays provides one way to examine the molecular targets of plant extracts and may prove a useful tool in evaluating their therapeutic claims. Here, we will briefly describe some of our work on the effect of EGb 761® on differential gene expression in relation to its potential anti-carcinogenic, photoprotective and neuromodulatory properties.

Potential for the development of prebiotic oligosaccharides from biomass

Waste biomass contains a multitude of complex carbohydrate molecules. These carbohydrates can be considered as a resource for the development of novel prebiotic oligosaccharides which may have better functionality than those currently established on the market. Enhanced persistence of the prebiotic effect along the colon, antipathogen effects, and more closely targeted prebiotics, might all be possible starting from plant polysaccharides. Of particular interest for the development of novel prebiotics are oligosaccharides from arabinoxylans and pectins. Oligosaccharides derived from the breakdown of both classes have received increased research attention recently. The development of prebiotics based upon biomass will demand the development of new manufacturing technologies.

Stress relaxation behavior of wheat dough, gluten, and gluten protein fractions

Relaxation behavior was measured for dough, gluten and gluten protein fractions obtained from the U.K. biscuitmaking flour, Riband, and the U.K. breadmaking flour, Hereward. The relaxation spectrum, in which relaxation times (tau) are related to polymer molecular size, for dough showed a broad molecular size distribution, with two relaxation processes: a major peak at short times and a second peak at times longer than 10 sec, which is thought to correspond to network structure, and which may be attributed to entanglements and physical cross-links of polymers. Relaxation spectra of glutens were similar to those for the corresponding doughs from both flours. Hereward gluten clearly showed a much more pronounced second peak in relaxation spectrum and higher relaxation modulus than Riband gluten at the same water content. In the gluten protein fractions, gliadin and acetic acid soluble glutenin only showed the first relaxation process, but gel protein clearly showed both the first and second relaxation processes. The results show that the relaxation properties of dough depend on its gluten protein and that gel protein is responsible for the network structure for dough and gluten.

Pectic oligosaccharides as prebiotics

Pectic oligosaccharides were observed to have bifidogenic prebiotic properties. Pectic oligosaccharides were also found to possess anti-adhesive properties for food pathogen toxins and they stimulated apoptosis of colon cancer cells. Orange peel albedo (white part) was a good source of pectic oligosaccharides with prebiotic properties. Microwave and autoclave extraction produced pectic oligosaccharides with higher degrees of polymerization than those produced with an ultrafiltration dead-end membrane enzyme reactor. We propose that these larger orange albedo pectic oligosaccharides may have greater persistence through the colon, making them excellent candidates for second generation prebiotic product development.

Extensional rheology and stability of gas cell walls in bread doughs at elevated temperatures in relation to breadmaking performance

The rheological properties of gas cell walls in bread doughs are considered to be important in relation to their stability and gas retention during proof and baking. Large deformation rheological properties of gas cell walls were measured using biaxial extension for a number of doughs of varying breadmaking quality at constant strain rate and elevated temperatures of 25-60degreesC. Strain hardening and failure strain of cell walls both decreased with temperature, with cell walls in good breadmaking doughs remaining stable and retaining their strain hardening properties at higher temperatures (60degreesC), while the cell walls of poor breadmaking doughs became unstable at lower temperatures (45-50degreesC) and had lower strain hardening. Strain hardening measured at 50degreesC gave good correlations with baking volume, with the best correlations achieved between rheological measurements and baking tests that used similar mixing conditions. As predicted by the considered failure criterion, a strain hardening value of I defines a region below which gas cell walls become unstable, and discriminates well between the baking quality of a range of commercial flour blends of varying quality. This indicates that the stability of gas cell walls during baking is strongly related to strain hardening properties, and that extensional rheological measurements can be used as indicators of baking quality.

Comparison of two methods of presenting risk information to patients about the side effects of medicines

Objective: To determine whether the use of verbal descriptors suggested by the European Union (EU) such as "common" (1-10% frequency) and "rare" (0.01-0.1%) effectively conveys the level of risk of side effects to people taking a medicine. Design: Randomised controlled study with unconcealed allocation. Participants: 120 adults taking simvastatin or atorvastatin after cardiac surgery or myocardial infarction. Setting: Cardiac rehabilitation clinics at two hospitals in Leeds, UK. Intervention: A written statement about one of the side effects of the medicine (either constipation or pancreatitis). Within each side effect condition half the patients were given the information in verbal form and half in numerical form (for constipation, "common" or 2.5%; for pancreatitis, "rare" or 0.04%). Main outcome measure: The estimated likelihood of the side effect occurring. Other outcome measures related to the perceived severity of the side effect, its risk to health, and its effect on decisions about whether to take the medicine. Results: The mean likelihood estimate given for the constipation side effect was 34.2% in the verbal group and 8.1% in the numerical group; for pancreatitis it was 18% in the verbal group and 2.1% in the numerical group. The verbal descriptors were associated with more negative perceptions of the medicine than their equivalent numerical descriptors. Conclusions: Patients want and need understandable information about medicines and their risks and benefits. This is essential if they are to become partners in medicine taking. The use of verbal descriptors to improve the level of information about side effect risk leads to overestimation of the level of harm and may lead patients to make inappropriate decisions about whether or not they take the medicine.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.