Effects of combined high-pressure and heat treatment on the textural properties of soya gels

sSPI, 7S, and 11S globulin at 12% (w/v) protein concentration, at neutral pH, did not form gels when heat-treated (90 degreesC, 15 min) or when high pressure-treated (300-700 MPa), except for the I IS, which formed a gel when heat-treated. The combination of heat and pressure (that is heating the solutions in a water bath and then pressure-treating at room temperature or the reverse sequence), led to differences: when heat-treatment was before high-pressure treatment, only the I IS fraction formed a self-standing gel; however, when the solutions were pressurised before heat treatment, all the proteins formed self-standing gels. The textural and water-holding properties were measured on the gels formed with the three different soy proteins. (C) 2002 Elsevier Science Ltd. All rights reserved.

Synthesis of isomaltooligosaccharides and oligodextrans by the combined use of dextransucrase and dextranase

Batch syntheses of isomaltooligosaccharides (IMO) from sucrose, using the enzymes dextransucrase and dextranase were performed with the aim of understanding the reaction mechanism and the parameters which affect product characteristics and molecular size. Both activities described for dextransucrase (dextran formation and acceptor reaction) achieved synthesis whilst the hydrolytic activity of dextranase regulated the product molecular size and acceptor availability. Depending on the reaction conditions, the product oligosaccharide mixtures contained mainly sugars (up to 36%) with degrees of polymerization (DP) varying between 10 and 60 together with lower concentrations of both lower and higher molecular weight sugars. Alterations in substrate and dextranase concentrations (50-400 mg ml(-1) and 2.5-46 U ml(-1), respectively) affected the molecular weight of IMO, the reaction rate and the formation of leucrose. This permitted manipulation of the product characteristics. It was found that higher substrate and dextranase concentrations gave rise to products with lower molecular sizes and a dextransucrase:dextranase ratio of 1: 1 or 1:2 appeared to produce a polymer with a molecular weight which is desirable for prebiotic use. (C) 2004 Elsevier Inc. All rights reserved.

Synthesis of isomaltooligosaccharides and oligodextrans in a recycle membrane bioreactor by the combined use of dextransucrase and dextranase

A recycle ultrafiltration membrane reactor was used to develop a continuous synthesis process for the production of isomaltooligosaccharides (IMO) from sucrose, using the enzymes dextransucrase and dextranase. A variety of membranes were tested and the parameters affecting reactor stability, productivity, and product molecular weight distribution were investigated. Enzyme inactivation in the reactor was reduced with the use of a non-ionic surfactant but its use had severe adverse effects on the membrane pore size and porosity. During continuous isomaltooligosaccharide synthesis, dextransucrase inactivation was shown to occur as a result of the dextranase activity and it was dependent mainly on the substrate availability in the reactor and the hydrolytic activity of dextranase. Substrate and dextranase concentrations (50-200 mg/mL(-1) and 10-30 U/mL(-1), respectively) affected permeate fluxes, reactor productivity, and product average molecular weight. The oligodextrans and isomaltooligosaccharides formed had molecular weights lower than in batch synthesis reactions but they largely consisted of oligosaccharides with a degree of polymerization (DP) greater than 5, depending on the synthesis conditions. No significant rejection of the sugars formed was shown by the membranes and permeate flux was dependent on tangential flow velocity. (C) 2004 Wiley Periodicals, Inc.

A combined Bayesian Markovian approach for behaviour recognition

Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.

Analysis of the dominant enterprise information assurance mechanisms

The purpose of this study is to analyse current data continuity mechanisms employed by the target group of businesses and to identify any inadequacies in the mechanisms as a whole. The questionnaire responses indicate that 47% of respondents do perceive backup methodologies as important, with a total of 70% of respondents having some backup methodology already in place. Businesses in Moulton Park perceive the loss of data to have a significant effect upon their business’ ability to function. Only 14% of respondents indicated that loss of data on computer systems would not affect their business at all, with 54% of respondents indicating that there would be either a “major effect” (or greater) on their ability to operate. Respondents that have experienced data loss were more likely to have backup methodologies in place (53%) than respondents that had not experienced data loss (18%). Although the number of respondents clearly affected the quality and conclusiveness of the results returned, the level of backup methodologies in place appears to be proportional to the company size. Further investigation is recommended into the subject in order to validate the information gleaned from the small number of respondents.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF 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 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.

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.

A combined score test for binary and ordinal endpoints from clinical trials

There is growing interest, especially for trials in stroke, in combining multiple endpoints in a single clinical evaluation of an experimental treatment. The endpoints might be repeated evaluations of the same characteristic or alternative measures of progress on different scales. Often they will be binary or ordinal, and those are the cases studied here. In this paper we take a direct approach to combining the univariate score statistics for comparing treatments with respect to each endpoint. The correlations between the score statistics are derived and used to allow a valid combined score test to be applied. A sample size formula is deduced and application in sequential designs is discussed. The method is compared with an alternative approach based on generalized estimating equations in an illustrative analysis and replicated simulations, and the advantages and disadvantages of the two approaches are discussed.

Combined neutron and X-ray diffraction studies of DNA in crystals and solutions

Recent developments in instrumentation and facilities for sample preparation have resulted in sharply increased interest in the application of neutron diffraction. Of particular interest are combined approaches in which neutron methods are used in parallel with X-ray techniques. Two distinct examples are given. The first is a single-crystal study of an A-DNA structure formed by the oligonucleotide d(AGGGGCCCCT)2, showing evidence of unusual base protonation that is not visible by X-ray crystallography. The second is a solution scattering study of the interaction of a bisacridine derivative with the human telomeric sequence d(AGGGTTAGGGTTAGGGTTAGGG) and illustrates the differing effects of NaCl and KCl on this interaction.