Bayesian clustering of farm types using the mixtures model

A Bayesian method of classifying observations that are assumed to come from a number of distinct subpopulations is outlined. The method is illustrated with simulated data and applied to the classification of farms according to their level and variability of income. The resultant classification shows a greater diversity of technical charactersitics within farm types than is conventionally the case. The range of mean farm income between groups in the new classification is wider than that of the conventional method and the variability of income within groups is narrower. Results show that the highest income group in 2000 included large specialist dairy farmers and pig and poultry producers, whilst in 2001 it included large and small specialist dairy farms and large mixed dairy and arable farms. In both years the lowest income group is dominated by non-milk producing livestock farms.

Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes

Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk .

Fas ligand-induced apoptosis is regulated by nitric oxide through the inhibition of fas receptor clustering and the nitrosylation of protein kinase Cepsilon

Apoptosis induced by the death-inducing ligand FasL (CD95L) is a major mechanism of cell death. Trophoblast cells express the Fas receptor yet survive in an environment that is rich in the ligand. We report that basal nitric oxide (NO) production is responsible for the resistance of trophoblasts to FasL-induced apoptosis. In this study we demonstrate that basal NO production resulted in the inhibition of receptor clustering following ligand binding. In addition NO also protected cells through the selective nitrosylation, and inhibition, of protein kinase Cepsilon (PKCepsilon) but not PKCalpha. In the absence of NO production PKCepsilon interacted with, and phosphorylated, the anti-apoptotic protein cFLIP. The interaction is predominantly with the short form of cFLIP and its phosphorylation reduces its recruitment to the death-inducing signaling complex (DISC) that is formed following binding of a death-inducing ligand to its receptor. Inhibition of cFLIP recruitment to the DISC leads to increased activation of caspase 8 and subsequently to apoptosis. Inhibition of PKCepsilon using siRNA significantly reversed the sensitivity to apoptosis induced by inhibition of NO synthesis suggesting that NO-mediated inhibition of PKCepsilon plays an important role in the regulation of Fas-induced apoptosis.

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particulary in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process. Genet Epidemiol 25:106-114, 2003. (C) 2003 Wiley-Liss, Inc.

Complete filling of zeolite frameworks with polyalkynes formed in situ by transition-metal ion catalysts

We report the use of transition-metal-exchanged zeolites as media for the catalytic formation and encapsulation of both polyethyne and polypropyne, and computer modeling studies on the composites so formed. Alkyne gas was absorbed into the pores of zeolite Y (Faujasite) exchanged with transition-metal cations [Fe(II), Co(II), Cu(II), Ni(II), and Zn(II)]. Ni(II) and Zn(II) were found to be the most efficient for the production of poly-ynes. These cations were also found to be effective in polymer generation when exchanged in zeolites mordenite and beta. The resulting powdered samples were characterized by FTIR, Raman, diffuse reflectance electronic spectroscopy, TEM, and elemental analysis, revealing, nearly complete loading of the zeolite channels for the majority of the samples. Based on the experimental carbon content, we have derived the percentage of channel filling, and the proportion of the channels containing a single polymer chain for mordenite. Experimentally, the channels for Y are close to complete filling for polyethyne (PE) and polypropyne (PP), and this is also true for polyethyne in mordenite. Computer modeling studies using Cerius2 show that the channels of mordenite can only accept a single polymer chain of PP, in which case these channels are also completely filled.

In vitro fermentation of mixed linkage glucooligosaccharides produced by Gluconobacter oxydans NCIMB 4943 by the human colonic microflora

The aim of this study was to develop selectively fermented (prebiotic) carbohydrate molecules which would also result in the generation of butyric acid. Glucooligosaccharides produced by Gluconobacter oxydans NCIMB 4943 from various types of maltodextrins were evaluated for their fermentation by mixed cultures of human colonic microflora. The selectivity of growth of desirable bacteria (bifidobacteria, lactobacilli) was studied in stirred pH-controlled (6.8) batch cultures. Bacterial populations were enumerated using fluorescent in situ hybridization (FISH). Gluco-oligosaccharides resulted in significantly (P<0.05) increased numbers of bifidobacteria and lactobacilli within 24 hours. Bacteroides, clostridial and eubacterial populations were slightly decreased at 48 h. There was very little difference in selectivity between the maltodextrin substrates and the products, although maltodextrin displayed a slightly less selective fermentation than the gluco-oligosaccharide products, also stimulating the growth of bacteroides, clostridia and eubacteria. Gluco-oligosaccharides, produced from G19 maltodextrin, resulted in the best prebiotic effect with the highest prebiotic index (PI) of 5.90 at 48 hours. Acetate, propionate and butyrate were all produced from glucooligosaccharides, derived from G19 maltodextrin, at 48 hours but no lactate or formate were detected.

Clustering techniques applied to multiple-models structures

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Dimension and coverage of multiple-models structures using clustering techniques

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Clustering quality and topology preservation in fast learning SOMs

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.

Automatic artefact removal from event-related potentials via clustering

This paper outlines a method for automatic artefact removal from multichannel recordings of event-related potentials (ERPs). The proposed method is based on, firstly, separation of the ERP recordings into independent components using the method of temporal decorrelation source separation (TDSEP). Secondly, the novel lagged auto-mutual information clustering (LAMIC) algorithm is used to cluster the estimated components, together with ocular reference signals, into clusters corresponding to cerebral and non-cerebral activity. Thirdly, the components in the cluster which contains the ocular reference signals are discarded. The remaining components are then recombined to reconstruct the clean ERPs.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Effect of molecule branching and glycosidic linkage on the degradation of polydextrose by gut microbiota.

Polydextrose is a randomly linked complex glucose oligomer that is widely used as a sugar replacer, bulking agent, dietary fiber and prebiotic. Polydextrose is poorly utilized by the host and, during gastrointestinal transit, it is slowly degraded by intestinal microbes, although it is not known which parts of the complex molecule are preferred by the microbes. The microbial degradation of polydextrose was assessed by using a simulated model of colonic fermentation. The degradation products and their glycosidic linkages were measured by combined gas chromatography and mass spectrometry, and compared to those of intact polydextrose. Fermentation resulted in an increase in the relative abundance of non-branched molecules with a concomitant decrease in single-branched glucose molecules and a reduced total number of branching points. A detailed analysis showed a preponderance of 1,6 pyranose linkages. The results of this study demonstrate how intestinal microbes selectively degrade polydextrose, and provide an insight into the preferences of gut microbiota in the presence of different glycosidic linkages.