Effect of polydextrose on intestinal microbes and immune functions in pigs

Dietary fibre has been proposed to decrease risk for colon cancer by altering the composition of intestinal microbes or their activity. In the present study, the changes in intestinal microbiota and its activity, and immunological characteristics, such as cyclo-oxygenase (COX)-2 gene expression in mucosa, in pigs fed with a high-energy-density diet, with and without supplementation of a soluble fibre (polydextrose; PDX) (30 g/d) were assessed in different intestinal compartments. PDX was gradually fermented throughout the intestine, and was still present in the distal colon. Irrespective of the diet throughout the intestine, of the four microbial groups determined by fluorescent in situ hybridisation, lactobacilli were found to be dominating, followed by clostridia and Bacteroides. Bifidobacteria represented a minority of the total intestinal microbiota. The numbers of bacteria increased approximately ten-fold from the distal small intestine to the distal colon. Concomitantly, also concentrations of SCFA and biogenic amines increased in the large intestine. In contrast, concentrations of luminal IgA decreased distally but the expression of mucosal COX-2 had a tendency to increase in the mucosa towards the distal colon. Addition of PDX to the diet significantly changed the fermentation endproducts, especially in the distal colon, whereas effects on bacteria] composition were rather minor. There was a reduction in concentrations of SCFA and tryptamine, and an increase in concentrations of spermidine in the colon upon PDX supplementation. Furthermore, PDX tended to decrease the expression of mucosal COX-2, therefore possibly reducing the risk of developing colon cancer-promoting conditions in the distal intestine.

The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions

In this review we evaluate the cognitive and neural effects of positive and negative mood on executive function. Mild manipulations of negative mood appear to have little effect on cognitive control processes, whereas positive mood impairs aspects of updating, planning and switching. These cognitive effects may be linked to neurochemistry: with positive mood effects mediated by dopamine while negative mood effects may be mediated by serotonin levels. Current evidence on the effects of mood on regional brain activity during executive functions, indicates that the prefrontal cortex is a recurrent site of integration between mood and cognition. We conclude that there is a disparity between the importance of this topic and awareness of how mood affects, executive functions in the brain. Most behavioural and neuroimaging studies of executive function in normal samples do not explore the potential role of variations in mood, yet the evidence we outline indicates that even mild fluctuations in mood can have a significant influence on neural activation and cognition. (c) 2006 Elsevier Ltd. All rights reserved.

Right hemisphere language functions and schizophrenia: the forgotten hemisphere?

This review highlights the importance of right hemisphere language functions for successful social communication and advances the hypothesis that the core deficit in psychosis is a failure of segregation of right from left hemisphere functions. Lesion studies of stroke patients and dichotic listening and functional imaging studies of healthy people have shown that some language functions are mediated by the right hemisphere rather than the left. These functions include discourse planning/comprehension, understanding humour, sarcasm, metaphors and indirect requests, and the generation/comprehension of emotional prosody. Behavioural evidence indicates that patients with typical schizophrenic illnesses perform poorly on tests of these functions, and aspects of these functions are disturbed in schizo-affective and affective psychoses. The higher order language functions mediated by the right hemisphere are essential to an accurate understanding of someone's communicative intent, and the deficits displayed by patients with schizophrenia may make a significant contribution to their social interaction deficits. We outline a bi-hemispheric theory of the neural basis of language that emphasizes the role of the sapiens-specific cerebral torque in determining the four-chambered nature of the human brain in relation to the origins of language and the symptoms of schizophrenia. Future studies of abnormal lateralization of left hemisphere language functions need to take account of the consequences of a failure of lateralization of language functions to the right as well as the left hemisphere.

Optimization of apodization functions in terahertz transient spectrometry

A quadratic programming optimization procedure for designing asymmetric apodization windows tailored to the shape of time-domain sample waveforms recorded using a terahertz transient spectrometer is proposed. By artificially degrading the waveforms, the performance of the designed window in both the time and the frequency domains is compared with that of conventional rectangular, triangular (Mertz), and Hamming windows. Examples of window optimization assuming Gaussian functions as the building elements of the apodization window are provided. The formulation is sufficiently general to accommodate other basis functions. (C) 2007 Optical Society of America

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

MARCKS functions as a novel growth/tumour suppressor in cells of melanocyte origin

Protein kinase C (PKC) plays a pivotal role in modulating the growth of melanocytic cells in culture. We have shown previously that a major physiological substrate of PKC, the 80 kDa myristoylated alanine-rich C-kinase substrate (MARCKS), can be phosphorylated in quiescent, non-tumorigenic melanocytes exposed transiently to a biologically active phorbol ester, but cannot be phosphorylated in phorbol ester-treated, syngeneic malignant melanoma cells. Despite its ubiquitous distribution, the function of MARCKS in cell growth and transformation remains to be demonstrated clearly. We report here that MARCKS mRNA and protein levels are down-regulated significantly in the spontaneously derived murine B16 melanoma cell line compared with syngeneic normal Mel-ab melanocytes. In contrast, the tumourigenic v-Ha-ras-transfonned melan-ocytic line, LTR Ras 2, showed a high basal level of MARCKS phosphorylation which was not enhanced by treatment of cells with phorbol ester. Furthermore, protein levels of MARCKS in LTR Ras 2 cells were similar to those expressed in Mel-ab melanocytes. However, in four out of six murine tumour cell lines investigated, levels of MARCKS protein were barely detectable. Transfection of B16 cells with a plasmid containing the MARCKS cDNA in the sense orientation produced two neomycin-resistant clones displaying reduced proliferative capacity and decreased anchorage-independent growth compared with control cells. In contrast, transfection with the antisense MARCKS construct produced many colonies which displayed enhanced growth and transforming potential compared with control cells. Thus, MARCKS appears to act as a novel growth suppressor in the spontaneous transformation of cells of melanocyte origin and may play a more general role in the tumour progression of other carcinomas.

Efficient on the fly calculation of time correlation functions in computer simulations

Time correlation functions yield profound information about the dynamics of a physical system and hence are frequently calculated in computer simulations. For systems whose dynamics span a wide range of time, currently used methods require significant computer time and memory. In this paper, we discuss the multiple-tau correlator method for the efficient calculation of accurate time correlation functions on the fly during computer simulations. The multiple-tau correlator is efficacious in terms of computational requirements and can be tuned to the desired level of accuracy. Further, we derive estimates for the error arising from the use of the multiple-tau correlator and extend it for use in the calculation of mean-square particle displacements and dynamic structure factors. The method described here, in hardware implementation, is routinely used in light scattering experiments but has not yet found widespread use in computer simulations.

Differential effects of overexpression of ERalpha and ERbeta in MCF10A immortalised, non-transformed human breast epithelail cells

Background: Cellular effects of oestrogen are mediated by two intracellular receptors ERα and ERβ. However, to compare responses mediated through these two receptors, experimental models are needed where ERα and ERβ are individually stably overexpressed in the same cell type. Methods: We compared the effects of stable overexpression of ERα and ERβ in the MCF10A cell line, which is an immortalised but non-transformed breast epithelial cell line without high endogenous ER expression. Results: Clones of MCF10A cells were characterised which stably overexpressed ERα (10A-ERα2, 10A-ERα13) or which stably overexpressed ERβ (10A-ERβ12, 10A-ERβ15). Overexpression of either ERα or ERβ allowed induction of an oestrogen-regulated ERE-LUC reporter gene by oestradiol which was not found in the untransfected cells. Oestradiol also increased proliferation of 10A-ERα13 and 10A-ERβ12 cells, but not untransfected cells, by 1.3-fold over 7 days. The phytoestrogen, genistein, which is reported to bind more strongly to ERβ than to ERα, could induce luciferase gene expression from an ERE-LUC reporter gene at concentrations of 10−6 M and 10−5 M but only in the clones overexpressing ERβ and not in those overexpressing ERα. Clone 10A-ERβ12 also yielded growth stimulation with 10-6 M genistein. Finally, the overexpression of ERα, but not ERβ, gave rise to increased growth in semi-solid methocel suspension culture in the presence of 70 nM oestradiol, suggesting that overexpression of ERα, but not ERβ, produces characteristics of a transformed phenotype. Conclusions: This provides a model system to compare effects of oestradiol with other oestrogenic ligands in cells stably overexpressing individually ERα or ERβ.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.