In vitro fermentation by human fecal microflora of wheat arabinoxylans

The fermentation of three arabinoxylan (AX) fractions from wheat by the human fecal microflora was investigated in vitro. Three AX fractions, with average molecular masses of 354, 278, and 66 kDa, were incorporated into miniature-scale batch cultures (with inulin as a positive prebiotic control) with feces from three healthy donors, aged 23-29. Microflora changes were monitored by the culture-independent technique, fluorescent in situ hybridization, and short chain fatty acid (SCFA) and lactic acid production were measured by high-performance liquid chromatography. Total cell numbers increased significantly in all treated cultures, and the fermentation of AX was associated with a proliferation of the bifidobacteria, lactobacilli, and eubacteria groups. Smaller but statistically significant increases in bacteroides and clostridia groups were also observed. All AX fractions had comparable bifidogenic impacts on the microflora at 5 and 12 h, but the 66 kDa AX was particularly selective for lactobacilli. Eubacteria increased significantly on all AX fractions, particularly on 66 kDa AX. As previously reported, inulin gave a selective increase in bifidobacteria. All supplemented cultures showed significant rises in total SCFA production, with a particularly high proportion of butyric acid being produced from AX fermentation. The prebiotic effect, that is, the selectivity of AX for bifidobacteria and lactobacilli groups, increased as the molecular mass of the AX decreased. This suggests that molecular mass may influence the fermentation of AX in the colon.

Effect of fecal water on an in vitro model of colonic mucosal barrier function

Fecal water (FW) has been shown to exert, in cultured cells, cytotoxic and genotoxic effects that have implications for colorectal cancer (CRC) risk. We have investigated a further biological activity of FW, namely, the ability to affect gap junctions in CACO2 cell monolayers as an index of mucosal barrier function, which is known to be disrupted in cancer. FW samples fi-om healthy, free-living, European subjects that were divided into two broad age groups, adult (40 +/- 9.7 yr; n = 53) and elderly (76 +/- 7.5 yr; n = 55) were tested for effects on gap junction using the transepithelial resistance (TER) assay. Overall, treatment of CACO2 cells with FW samples fi-om adults increased TER (+ 4 %), whereas FW from elderly subjects decreased TER (-5%); the difference between the two groups was significant (P < 0.05). We also measured several components of FW potentially associated with modulation of TER, namely, short-chain fatty acid (SCFA) and ammonia. SCFAs (propionic, acetic, and n-butyric) were significantly lower in the elderly population (-30%, -35%, and -21%, respectively, all P pound 0.01). We consider that FW modulation of in vitro epithelial barrier function is a potentially useful noninvasive biomarker, but it requires further validation to establish its relationship to CRC risk.

Effects of probiotic or prebiotic supplemented milk formulas on fecal microbiota composition of infants

The aim of the study was to evaluate whether supplementation of milk-formulas with prebiotic fructooligosaccharides or a probiotic, Lactobacillus johnsonii La1 (La1), could modulate the composition of the fecal microbiota of formula-fed infants, compared to breastfed (BF) infants. Ninety infants close to 4 months of age were randomized into one of three groups to be blindly assigned to receive for 13 weeks: a) an infant formula (Control), b) the same formula with fructo-oligosaccharides (Prebio), or c) with La1 (Probio). At the end of this period, all infants received the control formula for 2 additional weeks. Twenty-six infants, breastfed throughout the study, were recruited to form group BF. Fecal samples were obtained upon enrolment and after 7 and 15 weeks. Bacterial populations were assessed with classical culture techniques and fluorescent in situ hybridisation (FISH). Seventy-six infants completed the study. On enrolment, higher counts of Bifidobacterium and Lactobacillus and lower counts of enterobacteria were observed in BF compared to the formula-fed infants; these differences tended to disappear at weeks 7 and 15. No major differences for Clostridium, Bacteroides or Enterococcus were observed between the groups or along the follow up. Probio increased fecal Lactobacillus counts (P<0.001); 88% of the infants in this group excreted live La1 in their stools at week 7 but only 17% at week 15. Increased Bifidobacterium counts were observed at week 7 in the 3 formula groups, similar to BF infants. These results confirm the presence of higher counts of bifidobacteria and lactobacilli in the microbiota of BF infants compared to formula-fed infants before dietary diversification, and that La1 survives in the infant digestive tract.

Recovery of norbixin from a raw extraction solution of annatto pigments using colloidal gas aphrons (CGAs)

Annatto dyes are widely used in food and are finding increasing interest also for their application in the pharmaceutical and cosmetics industry. Bixin is the main pigment extracted from annatto seeds and accounts for 80% of the carotenoids in the outer coat of the seeds; norbixin being the water-soluble form of the bixin. Typically annatto dyes are extracted from the seeds by mechanical means or solutions of alkali, edible oil or organic solvents, or a combination of the two depending on the desired final product. In this work CGAs are investigated as an alternative separation method for the recovery of norbixin from a raw extraction solution of annatto pigments in KOH. A volume of CGAs generated from a cationic surfactant (CTAB) solution is mixed with a volume of annatto solution and when the mixture is allowed to settle it separates into the top aphron phase and the bottom liquid phase. Potassium norbixinate presented in the annatto solution will interact with the surfactant in the aphron phase, which results in the effective separation of norbixin. Recovery= 94% was achieved at a CTAB to norbixin molar ratio of 3.3. In addition a mechanism of separation is proposed here based on the separation results with the cationic surfactant and an anionic surfactant (bis-2-ethyl hexyl sulfosuccinate, AOT) and measurements of surfactant to norbixin ratio in the aphron phase; electrostatic interactions between the surfactant and norbixin molecules result in the fort-nation of a coloured complex and effective separation of norbixin. (c) 2005 Elsevier B.V. All rights reserved.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

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.

Video activity extraction and reporting with incremental unsupervised learning

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterises the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France.

Perturbation, extraction and refinement of invariant pairs for matrix polynomials

Generalizing the notion of an eigenvector, invariant subspaces are frequently used in the context of linear eigenvalue problems, leading to conceptually elegant and numerically stable formulations in applications that require the computation of several eigenvalues and/or eigenvectors. Similar benefits can be expected for polynomial eigenvalue problems, for which the concept of an invariant subspace needs to be replaced by the concept of an invariant pair. Little has been known so far about numerical aspects of such invariant pairs. The aim of this paper is to fill this gap. The behavior of invariant pairs under perturbations of the matrix polynomial is studied and a first-order perturbation expansion is given. From a computational point of view, we investigate how to best extract invariant pairs from a linearization of the matrix polynomial. Moreover, we describe efficient refinement procedures directly based on the polynomial formulation. Numerical experiments with matrix polynomials from a number of applications demonstrate the effectiveness of our extraction and refinement procedures.

Extraction of Tschebysheff design data for the low pass dielectric multilayer

The extraction of design data for the lowpass dielectric multilayer according to Tschebysheff performance is described. The extraction proceeds initially by analogy with electric-circuit design, and can then be given numerical refinement which is also described. Agreement with the Tschebysheff desideratum is satisfactory. The multilayers extracted by this procedure are of fractional thickness, symmetric with regard to their central layers.