In vitro three-stage continuous fermentation of gluco-oligosaccharides produced by Gluconobacter oxydans NCIMB 4943 by the human colonic microflora

Gluco-oligosaccharides produced by Gluconobacter oxydans NCIMB 4943 from maltodextrin as the source, were evaluated for their fermentability by the human colonic microflora. The selectivity of growth of desirable bacteria in the human colon was studied in a three-stage continuous model of the human large intestine. Populations of bacteria, and their fluctuations as a response to the fermentation, were enumerated using fluorescent in situ hybridization (FISH). The gluco-oligosaccharides resulted in increases in numbers of bifidobacteria and the Lactobacillus/Enterococcus group in all 3 vessels of the system, representing the proximal, transverse and distal colonic areas. The prebiotic indices of the glucooligosaccharides were 2.29, 4.23 and 2.74 in V1, V2 and V3 respectively.

Polydextrose, lactitol, and fructo-oligosaccharide fermentation by colonic bacteria in a three-stage continuous culture system

In vitro fermentations were carried out by using a model of the human colon to simulate microbial activities of lower gut bacteria. Bacterial populations (and their metabolic products) were evaluated under the effects of various fermentable substrates. Carbohydrates tested were polydextrose, lactitol, and fructo-oligosaccharide (FOS). Bacterial groups of interest were evaluated by fluorescence in situ hybridization as well as by species-specific PCR to determine bifidobacterial species and percent-G+C profiling of the bacterial communities present. Short-chain fatty acids (SCFA) produced during the fermentations were also evaluated. Polydextrose had a stimulatory effect upon colonic bifidobacteria at concentrations of 1 and 2% (using a single and pooled human fecal inoculum, respectively). The bifidogenic effect was sustained throughout all three vessels of the in vitro system (P = 0.01 seen in vessel 3), as corroborated by the bacterial community profile revealed by %G+C analysis. This substrate supported a wide variety of bifidobacteria and was the only substrate where Bifidobacterium infantis was detected. The fermentation of lactitol had a deleterious effect on both bifidobacterial and bacteroides populations (P = 0.01) and decreased total cell numbers. SCFA production was stimulated, however, particularly butyrate (beneficial for host colonocytes). FOS also had a stimulatory effect upon bifidobacterial and lactobacilli populations that used a single inoculum (P = 0.01 for all vessels) as well as a bifidogenic effect in vessels 2 and 3 (P = 0.01) when a pooled inoculum was used. A decrease in bifidobacteria throughout the model was reflected in the percent-G+C profiles.

Predicting the thermal inactivation of bacteria in a solid matrix: simulation studies on the relative effects of microbial thermal resistance parameters and process conditions

A combined mathematical model for predicting heat penetration and microbial inactivation in a solid body heated by conduction was tested experimentally by inoculating agar cylinders with Salmonella typhimurium or Enterococcus faecium and heating in a water bath. Regions of growth where bacteria had survived after heating were measured by image analysis and compared with model predictions. Visualisation of the regions of growth was improved by incorporating chromogenic metabolic indicators into the agar. Preliminary tests established that the model performed satisfactorily with both test organisms and with cylinders of different diameter. The model was then used in simulation studies in which the parameters D, z, inoculum size, cylinder diameter and heating temperature were systematically varied. These simulations showed that the biological variables D, z and inoculum size had a relatively small effect on the time needed to eliminate bacteria at the cylinder axis in comparison with the physical variables heating temperature and cylinder diameter, which had a much greater relative effect. (c) 2005 Elsevier B.V All rights reserved.

The absorption of vitamin E is influenced by the amount of fat in a meal and the food matrix

Vitamin E absorption requires the presence of fat; however, limited information exists on the influence of fat quantity on optimal absorption. In the present study we compared the absorption of stable-isotope-labelled vitamin E following meals of varying fat content and source. In a randomised four-way cross-over study, eight healthy individuals consumed a capsule containing 150 mg H-2-labelled RRR-alpha-tocopheryl acetate with a test meal of toast with butter (17.5 g fat), cereal with full-fat milk (17.5 g fat), cereal with semi-skimmed milk (2.7 g fat) and water (0g fat). Blood was taken at 0, 0.5, 1, 1.5, 2, 3, 6 and 9 h following ingestion, chylomicrons were isolated, and H-2-labelled alpha-tocopherol was analysed in the chylomicron and plasma samples. There was a significant time (P<0.001) and treatment effect (P<0.001) in H-2-labelled alpha-tocopherol concentration in both chylomicrons and plasma between the test meals. H-2-labelled alpha-tocopherol concentration was significantly greater with the higher-fat toast and butter meal compared with the low-fat cereal meal or water (P< 0.001), and a trend towards greater concentration compared with the high-fat cereal meal (P= 0.065). There was significantly greater H-2-labelled α-tocopherol concentration with the high-fat cereal meal compared with the low-fat cereal meal (P< 0.05). The H-2-labelled alpha-tocopherol concentration following either the low-fat cereal meal or water was low. These results demonstrate that both the amount of fat and the food matrix influence vitamin E absorption. These factors should be considered by consumers and for future vitamin E intervention studies.

Absorption of isoflavones in humans: effects of food matrix and processing

If soy isoflavones are to be effective in preventing or treating a range of diseases, they must be bioavailable, and thus understanding factors which may alter their bioavailability needs to be elucidated. However, to date there is little information on whether the pharmacokinetic profile following ingestion of a defined dose is influenced by the food matrix in which the isoflavone is given or by the processing method used. Three different foods (cookies, chocolate bars and juice) were prepared, and their isoflavone contents were determined. We compared the urinary and serum concentrations of daidzein, genistein and equol following the consumption of three different foods, each of which contained 50 mg of isoflavones. After the technological processing of the different test foods, differences in aglycone levels were observed. The plasma levels of the isoflavone precursor daidzein were not altered by food matrix. Urinary daidzein recovery was similar for all three foods ingested with total urinary output of 33-34% of ingested dose. Peak genistein concentrations were attained in serum earlier following consumption of a liquid matrix rather than a solid matrix, although there was a lower total urinary recovery of genistein following ingestion of juice than that of the two other foods. (c) 2006 Elsevier Inc. All rights reserved.

A two-stage continuous culture system to study the effect of supplemental alpha-lactalbumin and glycomacropeptide on mixed cultures of human gut bacteria challenged with enteropathogenic Escherichia coli and Salmonella serotype Typhimurium

Aims: Certain milk factors may promote the growth of a gastrointestinal microflora predominated by bifidobacteria and may aid in overcoming enteric infections. This may explain why breast-fed infants experience fewer intestinal infections than their formula-fed counterparts. The effect of formula supplementation with two such factors was investigated in this study. Methods and Results: Infant faecal specimens were used to ferment formulae supplemented with glycomacropeptide (GMP) and alpha-lactalbumin (alpha-la) in a two-stage compound continuous culture model. At steady state, all fermenter vessels were inoculated with 5 ml of 0.1 M phosphate-buffered saline (pH 7.2) containing 10(8) CFU ml(-1) of either enteropathogenic Escherichia coli 2348/69 (O127:H6) or Salmonella serotype Typhimurium (DSMZ 5569). Bacteriology was determined by independent fluorescence in situ hybridization. Vessels that contained breast milk (BM), as well as alpha-la and GMP supplemented formula had stable total counts of bifidobacteria while lactobacilli increased significantly only in vessels with breast milk. Bacteroides, clostridia and E. coli decreased significantly in all three groups prior to pathogen addition. Escherichia coli counts decreased in vessels containing BM and alpha-la while Salmonella decreased significantly in all vessels containing BM, alpha-la and GMP. Acetate was the predominant acid. Significance and Impact of the Study: Supplementation of infant formulae with appropriate milk proteins may be useful in mimicking the beneficial bacteriological effects of breast milk.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors. (C) 2007 Elsevier B.V. All rights reserved.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.